Fermipy_Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4139fdc5",
   "metadata": {},
   "source": [
    "Fermipy Tutorial\n",
    "\n",
    "I strongly suggest to cd to this folder and run the tutorial along. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fc738c6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "08267dab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: AstropyDeprecationWarning: astropy.extern.six will be removed in 4.0, use the six module directly if it is still needed [astropy.extern.six]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from fermipy.gtanalysis import GTAnalysis\n",
    "from fermipy.plotting import ROIPlotter, SEDPlotter\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from IPython.display import Image\n",
    "from astropy.io import fits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bec2b679",
   "metadata": {},
   "source": [
    "Before we start doing anything, we need to configure the entire analysis.\n",
    "\n",
    "The configuration should be written into a .yaml file. \n",
    "\n",
    "Here in this example, this file is named as config.yaml\n",
    "\n",
    "It is mostly important to get the configuration right before starting the processes. \n",
    "\n",
    "So we'll load the configuration file first, and have a look at each section of it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7a9e6d15",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 14:00:18 INFO    GTAnalysis.__init__(): \n",
      "--------------------------------------------------------------------------------\n",
      "fermipy version v1.0.1 \n",
      "ScienceTools version 2.0.8\n"
     ]
    }
   ],
   "source": [
    "#Loading the configuration file for the ROI. \n",
    "#For how to construct it, you can refer to the comments in the configuration file. \n",
    "gta = GTAnalysis('config.yaml')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7d3af17",
   "metadata": {},
   "source": [
    "From here you can start figuring out what we have configured for the analysis. Easiest way to do so is simply print out the configuration: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "898e3034",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_rootdir': '/data/users/ysong/Fermipy_Tutorial',\n",
       " '_outdir': '/data/users/ysong/Fermipy_Tutorial',\n",
       " '_loglevel': 20,\n",
       " '_config': {'logging': {'prefix': '', 'chatter': 3, 'verbosity': 3},\n",
       "  'fileio': {'outdir': '/data/users/ysong/Fermipy_Tutorial',\n",
       "   'scratchdir': '/scratch',\n",
       "   'workdir': '/data/users/ysong/Fermipy_Tutorial',\n",
       "   'logfile': '/data/users/ysong/Fermipy_Tutorial/fermipy.log',\n",
       "   'savefits': True,\n",
       "   'workdir_regex': ['\\\\.fits$|\\\\.fit$|\\\\.xml$|\\\\.npy$'],\n",
       "   'outdir_regex': ['\\\\.fits$|\\\\.fit$|\\\\.xml$|\\\\.npy$|\\\\.png$|\\\\.pdf$|\\\\.yaml$'],\n",
       "   'usescratch': False},\n",
       "  'optimizer': {'optimizer': 'NEWMINUIT',\n",
       "   'tol': 0.05,\n",
       "   'max_iter': 40,\n",
       "   'init_lambda': 0.0001,\n",
       "   'retries': 3,\n",
       "   'min_fit_quality': 2,\n",
       "   'verbosity': 0},\n",
       "  'binning': {'projtype': 'WCS',\n",
       "   'proj': 'AIT',\n",
       "   'coordsys': 'CEL',\n",
       "   'npix': None,\n",
       "   'roiwidth': 14.0,\n",
       "   'binsz': 0.1,\n",
       "   'binsperdec': 9.0,\n",
       "   'enumbins': None,\n",
       "   'hpx_ordering_scheme': 'RING',\n",
       "   'hpx_order': 10,\n",
       "   'hpx_ebin': True},\n",
       "  'selection': {'emin': 100.0,\n",
       "   'emax': 500000.0,\n",
       "   'logemin': None,\n",
       "   'logemax': None,\n",
       "   'tmin': 239160000,\n",
       "   'tmax': 644198000,\n",
       "   'zmax': 90.0,\n",
       "   'evclass': 128,\n",
       "   'evtype': 3,\n",
       "   'convtype': None,\n",
       "   'phasemin': None,\n",
       "   'phasemax': None,\n",
       "   'target': None,\n",
       "   'ra': 140.558425,\n",
       "   'dec': 6.639806,\n",
       "   'glat': None,\n",
       "   'glon': None,\n",
       "   'radius': None,\n",
       "   'filter': None,\n",
       "   'roicut': 'no'},\n",
       "  'model': {'src_radius': 20.0,\n",
       "   'src_roiwidth': None,\n",
       "   'src_radius_roi': None,\n",
       "   'isodiff': ['/data/users/ysong/fdata/iso_P8R3_SOURCE_V3_v1.txt'],\n",
       "   'galdiff': ['/data/users/ysong/fdata/gll_iem_v07.fits'],\n",
       "   'limbdiff': [],\n",
       "   'diffuse': [],\n",
       "   'diffuse_xml': [],\n",
       "   'sources': [],\n",
       "   'extdir': None,\n",
       "   'diffuse_dir': [],\n",
       "   'catalogs': ['/data/users/ysong/fdata/gll_psc_v27.fit'],\n",
       "   'merge_sources': True,\n",
       "   'assoc_xmatch_columns': ['3FGL_Name'],\n",
       "   'extract_diffuse': False},\n",
       "  'data': {'evfile': '/data/users/ysong/fdata/lat_source_zmax90_gti_30MeV_500GeV.fits',\n",
       "   'scfile': '/data/users/ysong/fdata/scfile_2021-06-04.fits',\n",
       "   'ltcube': '/data/users/ysong/fdata/lat_source_zmax90_ltcube_30MeV_500GeV.fits',\n",
       "   'cacheft1': True},\n",
       "  'ltcube': {'binsz': 1.0,\n",
       "   'phibins': 0,\n",
       "   'dcostheta': 0.025,\n",
       "   'use_local_ltcube': False},\n",
       "  'gtlike': {'irfs': 'P8R3_SOURCE_V3',\n",
       "   'edisp': True,\n",
       "   'edisp_bins': -1,\n",
       "   'edisp_disable': [],\n",
       "   'minbinsz': 0.05,\n",
       "   'rfactor': 2,\n",
       "   'convolve': True,\n",
       "   'resample': True,\n",
       "   'srcmap': None,\n",
       "   'bexpmap': None,\n",
       "   'bexpmap_roi': None,\n",
       "   'srcmap_base': None,\n",
       "   'bexpmap_base': None,\n",
       "   'bexpmap_roi_base': None,\n",
       "   'use_external_srcmap': False,\n",
       "   'use_scaled_srcmap': False,\n",
       "   'wmap': None,\n",
       "   'llscan_npts': 20,\n",
       "   'src_expscale': {},\n",
       "   'expscale': None},\n",
       "  'mc': {'seed': None},\n",
       "  'residmap': {'model': {},\n",
       "   'exclude': [],\n",
       "   'loge_bounds': [],\n",
       "   'make_plots': False,\n",
       "   'use_weights': False,\n",
       "   'write_fits': True,\n",
       "   'write_npy': True},\n",
       "  'tsmap': {'model': {},\n",
       "   'exclude': [],\n",
       "   'multithread': False,\n",
       "   'nthread': None,\n",
       "   'max_kernel_radius': 3.0,\n",
       "   'loge_bounds': [],\n",
       "   'make_plots': False,\n",
       "   'write_fits': True,\n",
       "   'write_npy': True},\n",
       "  'tscube': {'model': {},\n",
       "   'exclude': [],\n",
       "   'do_sed': True,\n",
       "   'nnorm': 10,\n",
       "   'norm_sigma': 5.0,\n",
       "   'cov_scale_bb': -1.0,\n",
       "   'cov_scale': -1.0,\n",
       "   'tol': 0.001,\n",
       "   'max_iter': 30,\n",
       "   'tol_type': 0,\n",
       "   'remake_test_source': False,\n",
       "   'st_scan_level': 0,\n",
       "   'init_lambda': 0.0},\n",
       "  'sourcefind': {'model': {},\n",
       "   'min_separation': 1.0,\n",
       "   'sqrt_ts_threshold': 5.0,\n",
       "   'max_iter': 5,\n",
       "   'sources_per_iter': 4,\n",
       "   'tsmap_fitter': 'tsmap',\n",
       "   'free_params': [],\n",
       "   'multithread': False,\n",
       "   'nthread': None},\n",
       "  'sed': {'bin_index': 2.0,\n",
       "   'use_local_index': False,\n",
       "   'free_background': False,\n",
       "   'free_radius': None,\n",
       "   'free_pars': [],\n",
       "   'ul_confidence': 0.95,\n",
       "   'cov_scale': 3.0,\n",
       "   'make_plots': False,\n",
       "   'write_fits': True,\n",
       "   'write_npy': True},\n",
       "  'lightcurve': {'outdir': None,\n",
       "   'use_local_ltcube': True,\n",
       "   'use_scaled_srcmap': False,\n",
       "   'save_bin_data': True,\n",
       "   'binsz': 86400.0,\n",
       "   'shape_ts_threshold': 16.0,\n",
       "   'nbins': None,\n",
       "   'time_bins': [],\n",
       "   'free_background': False,\n",
       "   'free_radius': None,\n",
       "   'free_sources': [],\n",
       "   'free_params': [],\n",
       "   'max_free_sources': 5,\n",
       "   'make_plots': False,\n",
       "   'write_fits': True,\n",
       "   'write_npy': True,\n",
       "   'multithread': False,\n",
       "   'nthread': None,\n",
       "   'systematic': 0.02},\n",
       "  'extension': {'spatial_model': 'RadialGaussian',\n",
       "   'width': [],\n",
       "   'fit_position': False,\n",
       "   'width_min': 0.01,\n",
       "   'width_max': 1.0,\n",
       "   'width_nstep': 21,\n",
       "   'free_background': False,\n",
       "   'fix_shape': False,\n",
       "   'free_radius': None,\n",
       "   'fit_ebin': False,\n",
       "   'update': False,\n",
       "   'save_model_map': False,\n",
       "   'sqrt_ts_threshold': None,\n",
       "   'psf_scale_fn': None,\n",
       "   'make_tsmap': True,\n",
       "   'tsmap_fitter': 'tsmap',\n",
       "   'make_plots': False,\n",
       "   'write_fits': True,\n",
       "   'write_npy': True},\n",
       "  'localize': {'nstep': 5,\n",
       "   'dtheta_max': 0.5,\n",
       "   'free_background': False,\n",
       "   'fix_shape': False,\n",
       "   'free_radius': None,\n",
       "   'update': True,\n",
       "   'tsmap_fitter': 'tsmap',\n",
       "   'make_plots': False,\n",
       "   'write_fits': True,\n",
       "   'write_npy': True},\n",
       "  'roiopt': {'npred_threshold': 1.0,\n",
       "   'npred_frac': 0.95,\n",
       "   'shape_ts_threshold': 25.0,\n",
       "   'max_free_sources': 5,\n",
       "   'skip': []},\n",
       "  'plotting': {'loge_bounds': [],\n",
       "   'catalogs': [],\n",
       "   'graticule_radii': [],\n",
       "   'format': 'png',\n",
       "   'cmap': 'magma',\n",
       "   'cmap_resid': 'RdBu_r',\n",
       "   'figsize': [8.0, 6.0],\n",
       "   'label_ts_threshold': 0.0,\n",
       "   'interactive': False},\n",
       "  'components': []},\n",
       " '_configdir': '/data/users/ysong/Fermipy_Tutorial',\n",
       " '_projtype': 'WCS',\n",
       " '_tmin': 239160000,\n",
       " '_tmax': 644198000,\n",
       " '_lck_params': {},\n",
       " '_logger_name': 'GTAnalysis',\n",
       " '_plotter': <fermipy.plotting.AnalysisPlotter at 0x7fa8f6a7c610>,\n",
       " '_like': None,\n",
       " '_components': [<fermipy.gtanalysis.GTBinnedAnalysis at 0x7fa9ecac1110>],\n",
       " '_roi': <fermipy.roi_model.ROIModel at 0x7fa9f566ac10>,\n",
       " '_files': {'ccube': '/data/users/ysong/Fermipy_Tutorial/ccube.fits'},\n",
       " '_ebin_edges': array([2.     , 2.11209, 2.22418, 2.33627, 2.44836, 2.56045, 2.67254,\n",
       "        2.78463, 2.89672, 3.00881, 3.1209 , 3.23299, 3.34508, 3.45717,\n",
       "        3.56926, 3.68135, 3.79344, 3.90553, 4.01762, 4.12971, 4.2418 ,\n",
       "        4.35389, 4.46598, 4.57807, 4.69016, 4.80225, 4.91434, 5.02643,\n",
       "        5.13852, 5.25061, 5.3627 , 5.47479, 5.58688, 5.69897]),\n",
       " '_enumbins': 33,\n",
       " '_loge_bounds': array([2.     , 5.69897]),\n",
       " '_roi_data': {'loglike': nan,\n",
       "  'npred': 0.0,\n",
       "  'npred_wt': 0.0,\n",
       "  'counts': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
       "  'counts_wt': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
       "  'model_counts': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
       "  'model_counts_wt': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
       "  'energies': array([1.00000000e+02, 1.29446407e+02, 1.67563723e+02, 2.16905218e+02,\n",
       "         2.80776011e+02, 3.63454458e+02, 4.70478736e+02, 6.09017819e+02,\n",
       "         7.88351685e+02, 1.02049293e+03, 1.32099143e+03, 1.70997594e+03,\n",
       "         2.21350241e+03, 2.86529934e+03, 3.70902704e+03, 4.80120224e+03,\n",
       "         6.21498379e+03, 8.04507320e+03, 1.04140582e+04, 1.34806241e+04,\n",
       "         1.74501836e+04, 2.25886356e+04, 2.92401772e+04, 3.78503587e+04,\n",
       "         4.89959294e+04, 6.34234701e+04, 8.20994032e+04, 1.06274727e+05,\n",
       "         1.37568816e+05, 1.78077890e+05, 2.30515429e+05, 2.98393941e+05,\n",
       "         3.86260235e+05, 4.99999995e+05]),\n",
       "  'log_energies': array([2.     , 2.11209, 2.22418, 2.33627, 2.44836, 2.56045, 2.67254,\n",
       "         2.78463, 2.89672, 3.00881, 3.1209 , 3.23299, 3.34508, 3.45717,\n",
       "         3.56926, 3.68135, 3.79344, 3.90553, 4.01762, 4.12971, 4.2418 ,\n",
       "         4.35389, 4.46598, 4.57807, 4.69016, 4.80225, 4.91434, 5.02643,\n",
       "         5.13852, 5.25061, 5.3627 , 5.47479, 5.58688, 5.69897]),\n",
       "  'loge_bounds': array([2.     , 5.69897]),\n",
       "  'components': [{'loglike': nan,\n",
       "    'npred': 0.0,\n",
       "    'npred_wt': 0.0,\n",
       "    'counts': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
       "    'counts_wt': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
       "    'model_counts': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
       "    'model_counts_wt': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
       "    'energies': array([1.00000000e+02, 1.29446407e+02, 1.67563723e+02, 2.16905218e+02,\n",
       "           2.80776012e+02, 3.63454458e+02, 4.70478737e+02, 6.09017821e+02,\n",
       "           7.88351687e+02, 1.02049293e+03, 1.32099143e+03, 1.70997595e+03,\n",
       "           2.21350242e+03, 2.86529935e+03, 3.70902706e+03, 4.80120226e+03,\n",
       "           6.21498382e+03, 8.04507324e+03, 1.04140582e+04, 1.34806242e+04,\n",
       "           1.74501837e+04, 2.25886358e+04, 2.92401774e+04, 3.78503590e+04,\n",
       "           4.89959297e+04, 6.34234706e+04, 8.20994038e+04, 1.06274728e+05,\n",
       "           1.37568817e+05, 1.78077891e+05, 2.30515432e+05, 2.98393944e+05,\n",
       "           3.86260238e+05, 5.00000000e+05]),\n",
       "    'log_energies': array([2.     , 2.11209, 2.22418, 2.33627, 2.44836, 2.56045, 2.67254,\n",
       "           2.78463, 2.89672, 3.00881, 3.1209 , 3.23299, 3.34508, 3.45717,\n",
       "           3.56926, 3.68135, 3.79344, 3.90553, 4.01762, 4.12971, 4.2418 ,\n",
       "           4.35389, 4.46598, 4.57807, 4.69016, 4.80225, 4.91434, 5.02643,\n",
       "           5.13852, 5.25061, 5.3627 , 5.47479, 5.58688, 5.69897]),\n",
       "    'src_expscale': {}}]},\n",
       " '_roiwidth': 14.0,\n",
       " '_binsz': 0.1,\n",
       " '_npix': (140, 140),\n",
       " '_geom': WcsGeom\n",
       " \n",
       " \taxes       : ['lon', 'lat', 'energy']\n",
       " \tshape      : (140, 140, 33)\n",
       " \tndim       : 3\n",
       " \tframe      : icrs\n",
       " \tprojection : AIT\n",
       " \tcenter     : 140.6 deg, 6.6 deg\n",
       " \twidth      : 14.0 deg x 14.0 deg}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gta.__dict__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65d43da5",
   "metadata": {},
   "source": [
    "As you can see, there are a lot of things going on. \n",
    "Since we want to see what configuration we have done for the ROI analysis, let's focus on that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0f70d3e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "roi_configuration=gta.__dict__['_config']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8e477ea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['logging', 'fileio', 'optimizer', 'binning', 'selection', 'model', 'data', 'ltcube', 'gtlike', 'mc', 'residmap', 'tsmap', 'tscube', 'sourcefind', 'sed', 'lightcurve', 'extension', 'localize', 'roiopt', 'plotting', 'components'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This is all the possible sections of the configuration that we might need to set up.\n",
    "#Some of these are very basic and need to be done anyway.\n",
    "#some of these are advanced analysis that we probably don't really need to do. \n",
    "#Each of these is the wrapper to use one or a few Fermi tools.\n",
    "roi_configuration.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1facbdce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'emin': 100.0,\n",
       " 'emax': 500000.0,\n",
       " 'logemin': None,\n",
       " 'logemax': None,\n",
       " 'tmin': 239160000,\n",
       " 'tmax': 644198000,\n",
       " 'zmax': 90.0,\n",
       " 'evclass': 128,\n",
       " 'evtype': 3,\n",
       " 'convtype': None,\n",
       " 'phasemin': None,\n",
       " 'phasemax': None,\n",
       " 'target': None,\n",
       " 'ra': 140.558425,\n",
       " 'dec': 6.639806,\n",
       " 'glat': None,\n",
       " 'glon': None,\n",
       " 'radius': None,\n",
       " 'filter': None,\n",
       " 'roicut': 'no'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Selection part is for gtselect & gtmktime together. \n",
    "#These parameters ought to look familiar :)\n",
    "#You need to do this if you start with raw even file and configure it like this would mean \n",
    "roi_configuration['selection']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daaf2004",
   "metadata": {},
   "source": [
    "Of course there are ways to construct the configuration as you go throughout the pipeline. \n",
    "\n",
    "Examples follow (I personally feel it is better to configure everything first.):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6f72e586",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/shared_data/anaconda3/envs/fermipy/lib/python3.7/site-packages/ipykernel_launcher.py:3: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "import yaml\n",
    "# Load a configuration\n",
    "config = yaml.load(open('config.yaml'))\n",
    "# Update a parameter and write a new configuration\n",
    "config['selection']['emin'] = 1000.\n",
    "yaml.dump(config, open('new_config.yaml','w'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c553ab",
   "metadata": {},
   "source": [
    "But of course be sure to update the configuration file before you start your analysis.\n",
    "\n",
    "Otherwise you need to start all over again!!\n",
    "\n",
    "Now all the configuration of the basic analysis you'll need can be found in the 'config.yaml' file. \n",
    "\n",
    "I tried to comment and notate it as much as possible, so let me know which ones are not clear. \n",
    "\n",
    "Once we are all set with the configuration, we can start setting up the analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8afda972",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 14:13:08 INFO    GTAnalysis.setup(): Running setup.\n",
      "2021-06-09 14:13:08 INFO    GTBinnedAnalysis.setup(): Running setup for component 00\n",
      "2021-06-09 14:13:08 INFO    GTBinnedAnalysis.run_gtapp(): Running gtselect.\n",
      "2021-06-09 14:13:08 INFO    GTBinnedAnalysis.run_gtapp(): time -p gtselect infile=/data/users/ysong/fdata/lat_source_zmax90_gti_30MeV_500GeV.fits outfile=/data/users/ysong/Fermipy_Tutorial/ft1_00.fits ra=140.558425 dec=6.639806 rad=10.399494936611667 tmin=239160000.0 tmax=644198000.0 emin=100.0 emax=500000.0 zmin=0.0 zmax=90.0 evclass=128 evtype=3 convtype=-1 phasemin=0.0 phasemax=1.0 evtable=\"EVENTS\" chatter=3 clobber=yes debug=no gui=no mode=\"ql\"\n",
      "2021-06-09 14:13:08 INFO    GTBinnedAnalysis.run_gtapp(): This is gtselect version HEAD\n",
      "2021-06-09 14:13:08 INFO    GTBinnedAnalysis.run_gtapp(): Applying filter string: ((EVENT_CLASS&o200) != o0) && ((EVENT_TYPE&o3) != o0) && angsep(RA,DEC,140.558425,6.639806) < 10.39949494 && 100 < ENERGY && ENERGY <= 500000 && 239160000 < TIME && TIME <= 644198000 && 0 < ZENITH_ANGLE && ZENITH_ANGLE <= 90 && gtifilter()\n",
      "2021-06-09 14:22:54 INFO    GTBinnedAnalysis.run_gtapp(): Done.\n",
      "2021-06-09 14:22:54 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtselect. Execution time: 585.92 s\n",
      "2021-06-09 14:22:54 INFO    GTBinnedAnalysis.setup(): Using external LT cube.\n",
      "/data/shared_data/anaconda3/envs/fermipy/lib/python3.7/site-packages/fermipy/irfs.py:51: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  log_ratio = np.log(x[xs1] / x[xs0])\n",
      "/data/shared_data/anaconda3/envs/fermipy/lib/python3.7/site-packages/fermipy/irfs.py:52: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return 0.5 * (y[ys0] * x[xs0] + y[ys1] * x[xs1]) * log_ratio\n",
      "2021-06-09 14:22:55 INFO    GTBinnedAnalysis.run_gtapp(): Running gtbin.\n",
      "2021-06-09 14:22:55 INFO    GTBinnedAnalysis.run_gtapp(): time -p gtbin evfile=/data/users/ysong/Fermipy_Tutorial/ft1_00.fits scfile=/data/users/ysong/fdata/scfile_2021-06-04.fits outfile=/data/users/ysong/Fermipy_Tutorial/ccube_00.fits algorithm=\"ccube\" ebinalg=\"LOG\" emin=100.0 emax=500000.0 enumbins=33 ebinfile=NONE tbinalg=\"LIN\" tbinfile=NONE nxpix=140 nypix=140 binsz=0.1 coordsys=\"CEL\" xref=140.558425 yref=6.639806 axisrot=0.0 rafield=\"RA\" decfield=\"DEC\" proj=\"AIT\" hpx_ordering_scheme=\"RING\" hpx_order=3 hpx_ebin=yes hpx_region=\"\" evtable=\"EVENTS\" sctable=\"SC_DATA\" efield=\"ENERGY\" tfield=\"TIME\" chatter=3 clobber=yes debug=no gui=no mode=\"ql\"\n",
      "2021-06-09 14:22:55 INFO    GTBinnedAnalysis.run_gtapp(): This is gtbin version HEAD\n",
      "2021-06-09 14:23:02 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtbin. Execution time: 6.98 s\n",
      "2021-06-09 14:23:02 INFO    GTBinnedAnalysis.run_gtapp(): Running gtexpcube2.\n",
      "2021-06-09 14:23:02 INFO    GTBinnedAnalysis.run_gtapp(): time -p gtexpcube2 infile=/data/users/ysong/fdata/lat_source_zmax90_ltcube_30MeV_500GeV.fits cmap=none outfile=/data/users/ysong/Fermipy_Tutorial/bexpmap_00.fits irfs=\"P8R3_SOURCE_V3\" evtype=3 edisp_bins=-1 nxpix=360 nypix=180 binsz=1.0 coordsys=\"CEL\" xref=0.0 yref=0.0 axisrot=0.0 proj=\"CAR\" ebinalg=\"LOG\" emin=100.0 emax=500000.0 enumbins=33 ebinfile=\"NONE\" hpx_ordering_scheme=\"RING\" hpx_order=6 bincalc=\"EDGE\" ignorephi=no thmax=180.0 thmin=0.0 table=\"EXPOSURE\" chatter=3 clobber=yes debug=no mode=\"ql\"\n",
      "2021-06-09 14:23:02 INFO    GTBinnedAnalysis.run_gtapp(): This is gtexpcube2 version HEAD\n",
      "2021-06-09 14:23:03 INFO    GTBinnedAnalysis.run_gtapp(): Using evtype=3 (i.e., FRONT/BACK irfs)\n",
      "2021-06-09 14:23:03 INFO    GTBinnedAnalysis.run_gtapp(): ResponseFunctions::load: IRF used: P8R3_SOURCE_V3\n",
      "2021-06-09 14:23:03 INFO    GTBinnedAnalysis.run_gtapp(): event_types:  0  1\n",
      "2021-06-09 14:23:12 INFO    GTBinnedAnalysis.run_gtapp(): Computing binned exposure map....................!\n",
      "2021-06-09 14:23:12 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtexpcube2. Execution time: 9.42 s\n",
      "2021-06-09 14:23:12 INFO    GTBinnedAnalysis.run_gtapp(): Running gtexpcube2.\n",
      "2021-06-09 14:23:12 INFO    GTBinnedAnalysis.run_gtapp(): time -p gtexpcube2 infile=/data/users/ysong/fdata/lat_source_zmax90_ltcube_30MeV_500GeV.fits cmap=none outfile=/data/users/ysong/Fermipy_Tutorial/bexpmap_roi_00.fits irfs=\"P8R3_SOURCE_V3\" evtype=3 edisp_bins=0 nxpix=140 nypix=140 binsz=0.1 coordsys=\"CEL\" xref=140.558425 yref=6.639806 axisrot=0.0 proj=\"CAR\" ebinalg=\"LOG\" emin=100.0 emax=500000.0 enumbins=33 ebinfile=\"NONE\" hpx_ordering_scheme=\"RING\" hpx_order=6 bincalc=\"EDGE\" ignorephi=no thmax=180.0 thmin=0.0 table=\"EXPOSURE\" chatter=3 clobber=yes debug=no mode=\"ql\"\n",
      "2021-06-09 14:23:12 INFO    GTBinnedAnalysis.run_gtapp(): This is gtexpcube2 version HEAD\n",
      "2021-06-09 14:23:12 INFO    GTBinnedAnalysis.run_gtapp(): Using evtype=3 (i.e., FRONT/BACK irfs)\n",
      "2021-06-09 14:23:12 INFO    GTBinnedAnalysis.run_gtapp(): ResponseFunctions::load: IRF used: P8R3_SOURCE_V3\n",
      "2021-06-09 14:23:12 INFO    GTBinnedAnalysis.run_gtapp(): event_types:  0  1\n",
      "2021-06-09 14:23:15 INFO    GTBinnedAnalysis.run_gtapp(): Computing binned exposure map....................!\n",
      "2021-06-09 14:23:15 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtexpcube2. Execution time: 3.11 s\n",
      "WARNING: FITSFixedWarning: RADECSYS= 'FK5 ' \n",
      "the RADECSYS keyword is deprecated, use RADESYSa. [astropy.wcs.wcs]\n",
      "2021-06-09 14:23:15 INFO    GTBinnedAnalysis.run_gtapp(): Running gtsrcmaps.\n",
      "2021-06-09 14:23:15 INFO    GTBinnedAnalysis.run_gtapp(): time -p gtsrcmaps scfile=/data/users/ysong/fdata/scfile_2021-06-04.fits sctable=\"SC_DATA\" expcube=/data/users/ysong/fdata/lat_source_zmax90_ltcube_30MeV_500GeV.fits cmap=/data/users/ysong/Fermipy_Tutorial/ccube_00.fits srcmdl=/data/users/ysong/Fermipy_Tutorial/srcmdl_00.xml bexpmap=/data/users/ysong/Fermipy_Tutorial/bexpmap_00.fits wmap=none outfile=/data/users/ysong/Fermipy_Tutorial/srcmap_00.fits irfs=\"P8R3_SOURCE_V3\" evtype=3 convol=yes resample=yes rfactor=2 minbinsz=0.05 ptsrc=yes psfcorr=yes emapbnds=no edisp_bins=-1 copyall=no chatter=3 clobber=yes debug=no gui=no mode=\"ql\"\n",
      "2021-06-09 14:23:15 INFO    GTBinnedAnalysis.run_gtapp(): This is gtsrcmaps version HEAD\n",
      "2021-06-09 14:23:15 INFO    GTBinnedAnalysis.run_gtapp(): Using evtype=3 (i.e., FRONT/BACK irfs)\n",
      "2021-06-09 14:23:15 INFO    GTBinnedAnalysis.run_gtapp(): ResponseFunctions::load: IRF used: P8R3_SOURCE_V3\n",
      "2021-06-09 14:23:15 INFO    GTBinnedAnalysis.run_gtapp(): event_types:  0  1\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 14:23:31 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 4FGL J0859.8+0053\n",
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      "2021-06-09 14:23:31 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 4FGL J0811.4+0146\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 14:23:31 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named 4FGL J0843.0-0853\n",
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      "2021-06-09 14:23:31 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named isodiff\n",
      "2021-06-09 14:23:31 INFO    GTBinnedAnalysis.run_gtapp(): Creating source named galdiff\n",
      "2021-06-09 14:23:32 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 4FGL J0804.5+0414 34....................!\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 14:24:53 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 4FGL J0917.3-0342 34....................!\n",
      "2021-06-09 14:24:54 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 4FGL J0918.9-0625 34....................!\n",
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      "2021-06-09 14:26:13 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 4FGL J1022.7-0112 34....................!\n",
      "2021-06-09 14:26:14 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 4FGL J1023.7+0038 34....................!\n",
      "2021-06-09 14:26:15 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 4FGL J1026.9+0608 34....................!\n",
      "2021-06-09 14:26:17 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 4FGL J1027.6+1828 34....................!\n",
      "2021-06-09 14:26:18 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 4FGL J1027.9+0252 34....................!\n",
      "2021-06-09 14:26:20 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 4FGL J1028.4-0234 34....................!\n",
      "2021-06-09 14:26:21 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 4FGL J1036.5+1231 34....................!\n",
      "2021-06-09 14:26:23 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 4FGL J1039.6+0535 34....................!\n",
      "2021-06-09 14:26:24 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for 4FGL J1040.5+0617 34....................!\n",
      "2021-06-09 14:27:32 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for galdiff 34....................!\n",
      "2021-06-09 14:28:25 INFO    GTBinnedAnalysis.run_gtapp(): Generating SourceMap for isodiff 34....................!\n",
      "2021-06-09 14:28:44 INFO    GTBinnedAnalysis.run_gtapp(): Finished gtsrcmaps. Execution time: 328.88 s\n",
      "2021-06-09 14:28:44 INFO    GTBinnedAnalysis.setup(): Finished setup for component 00\n",
      "2021-06-09 14:28:44 INFO    GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 00.\n",
      "2021-06-09 14:29:03 INFO    GTAnalysis.setup(): Initializing source properties\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 14:29:10 INFO    GTAnalysis.setup(): Finished setup.\n"
     ]
    }
   ],
   "source": [
    "matplotlib.interactive(True) ##This is needed for Fermipy to make all the plots. \n",
    "gta.setup() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cc7d1eb",
   "metadata": {},
   "source": [
    "Now as you can see in the output from the last cell, this step actually takes care of a lot of things in the analysis. \n",
    "\n",
    "1). It does gtselect with the stated criteria in your configuration. \n",
    "\n",
    "2). It should do gtmktime as well, but since I'm using an all-sky file that I pre-selected and filtered by GTIs, we're skipping this part. \n",
    "\n",
    "3). It normally does gtltcube too, but again I chose to use an exisitng file.\n",
    "3.1) Note here that if we don't mess around with the timing and zenith cut selection at all, we can use the same ltcube file for all the analysis. \n",
    "\n",
    "4). It does gtbin to create counts cube, gtexpcube2 twice to create an exposure map at the ROI and an all-sky exposure map.\n",
    "\n",
    "5). It does gtsrcmaps to create the source maps based on the model configurations we provided. \n",
    "\n",
    "Now up to this point the set-up should be done. Notice that it might take a long long time... \n",
    "\n",
    "I think we all are trying to detect new sources here, so let's add that new source into the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1f18602",
   "metadata": {},
   "outputs": [],
   "source": [
    "name='PSR J0922+0638'\n",
    "ra=140.558425\n",
    "dec=6.639806"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3b511d8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 14:29:53 INFO    GTAnalysis.add_source(): Adding source PSR J0922+0638\n"
     ]
    }
   ],
   "source": [
    "gta.add_source(name, #name of the source \n",
    "               { 'ra' : ra , 'dec' : dec, #location\n",
    "                'SpectrumType' : 'PLSuperExpCutoff', 'Index1':-1.5, 'Index2' : 1.0,'Scale' : 1000,'Prefactor':1e-9,'Cutoff':1e3,'SpatialModel' : 'PointSource' }) #and spectral parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fde42b8",
   "metadata": {},
   "source": [
    "Now of course everything is ready and we can start the fitting (gtlike) process.\n",
    "\n",
    "But let's actually free the parameters for the sources near the ROI center and the backgrounds. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ad31fcb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 14:31:50 INFO    GTAnalysis.free_source(): Freeing parameters for PSR J0922+0638        : ['Index1', 'Cutoff']\n",
      "2021-06-09 14:31:50 INFO    GTAnalysis.free_source(): Freeing parameters for 4FGL J0924.0+0534     : ['Prefactor', 'Index']\n",
      "2021-06-09 14:31:50 INFO    GTAnalysis.free_source(): Freeing parameters for 4FGL J0922.6+0434     : ['Prefactor', 'Index']\n",
      "2021-06-09 14:31:50 INFO    GTAnalysis.free_source(): Freeing parameters for 4FGL J0935.3+0901     : ['norm', 'alpha', 'beta']\n",
      "2021-06-09 14:31:50 INFO    GTAnalysis.free_source(): Freeing parameters for 4FGL J0914.4+0249     : ['Prefactor', 'Index']\n",
      "2021-06-09 14:31:50 INFO    GTAnalysis.free_source(): Freeing parameters for 4FGL J0932.7+1041     : ['Prefactor', 'Index']\n",
      "2021-06-09 14:31:50 INFO    GTAnalysis.free_source(): Freeing parameters for isodiff               : ['Normalization']\n",
      "2021-06-09 14:31:50 INFO    GTAnalysis.free_source(): Freeing parameters for galdiff               : ['Prefactor', 'Index']\n"
     ]
    }
   ],
   "source": [
    "gta.free_sources(distance=5) #this is freeing the parameters of ths sources within 5 degrees of the ROI center. \n",
    "gta.free_source('galdiff')\n",
    "gta.free_source('isodiff')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e52a2cb",
   "metadata": {},
   "source": [
    "Now the next part is a little arbitraty as it seems like optimization is not always needed before doing fitting.\n",
    "\n",
    "It doesn't hurt to do it though (except that it can be a little slow). So let's start with that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "66196765",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 14:33:14 INFO    GTAnalysis.optimize(): Starting\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Joint fit  ['PSR J0922+0638', 'galdiff', 'isodiff']\n",
      "Fitting shape 4FGL J0948.9+0022 TS:   5757.341\n",
      "Fitting shape galdiff TS:   5304.147\n",
      "Fitting shape isodiff TS:   4289.281\n",
      "Fitting shape 4FGL J0946.6+1016 TS:   3550.990\n",
      "Fitting shape 4FGL J0909.1+0121 TS:   3251.745\n",
      "Fitting shape 4FGL J0909.6+0159 TS:    868.042\n",
      "Fitting shape 4FGL J0922.6+0434 TS:    461.692\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/shared_data/anaconda3/envs/fermipy/lib/python3.7/site-packages/scipy/interpolate/fitpack2.py:279: UserWarning: \n",
      "The maximal number of iterations maxit (set to 20 by the program)\n",
      "allowed for finding a smoothing spline with fp=s has been reached: s\n",
      "too small.\n",
      "There is an approximation returned but the corresponding weighted sum\n",
      "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n",
      "  warnings.warn(message)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting shape 4FGL J0935.3+0901 TS:    339.093\n",
      "Fitting shape 4FGL J0930.9+0033 TS:    307.369\n",
      "Fitting shape 4FGL J0950.2+0615 TS:    118.574\n",
      "Fitting shape 4FGL J0946.2+0104 TS:     92.052\n",
      "Fitting shape 4FGL J0924.0+0534 TS:     87.952\n",
      "Fitting shape 4FGL J0932.7+1041 TS:     68.318\n",
      "Fitting shape 4FGL J0949.2+1749 TS:     60.619\n",
      "Fitting shape 4FGL J0914.4+0249 TS:     42.849\n",
      "Fitting shape 4FGL J0859.8+0053 TS:     33.799\n",
      "Fitting shape 4FGL J0947.9+1121 TS:     30.671\n",
      "Fitting shape 4FGL J0905.6+1358 TS:     28.051\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 14:36:03 INFO    GTAnalysis.optimize(): Finished\n",
      "2021-06-09 14:36:03 INFO    GTAnalysis.optimize(): LogLike: -185326.022169 Delta-LogLike: 581389.293168\n",
      "2021-06-09 14:36:03 INFO    GTAnalysis.optimize(): Execution time: 168.59 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'loglike0': -766715.3153369192,\n",
       " 'loglike1': -185326.0221688279,\n",
       " 'dloglike': 581389.2931680912,\n",
       " 'config': {'npred_threshold': 1.0,\n",
       "  'npred_frac': 0.95,\n",
       "  'shape_ts_threshold': 25.0,\n",
       "  'max_free_sources': 5,\n",
       "  'skip': [],\n",
       "  'optimizer': {'optimizer': 'NEWMINUIT',\n",
       "   'tol': 0.05,\n",
       "   'max_iter': 40,\n",
       "   'init_lambda': 0.0001,\n",
       "   'retries': 3,\n",
       "   'min_fit_quality': 2,\n",
       "   'verbosity': 0}}}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gta.optimize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d518aea",
   "metadata": {},
   "source": [
    "Now we can do gtlike finally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c5227144",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 14:36:03 INFO    GTAnalysis.fit(): Starting fit.\n",
      "2021-06-09 14:36:27 INFO    GTAnalysis.fit(): Fit returned successfully. Quality:   3 Status: 156\n",
      "2021-06-09 14:36:27 INFO    GTAnalysis.fit(): LogLike:  -185305.802 DeltaLogLike:       20.220 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'fit_quality': 3,\n",
       " 'fit_status': 156,\n",
       " 'fit_success': True,\n",
       " 'dloglike': 20.219984578812728,\n",
       " 'edm': 0.056120820583411786,\n",
       " 'loglike': -185305.8021842491,\n",
       " 'covariance': array([[ 4.76400931e-02, -4.80719517e-03, -1.37279358e-03,\n",
       "         -7.31625763e-04,  6.33237472e-04,  2.18541024e-04,\n",
       "          6.42278647e-04,  5.10342992e-04,  2.49228253e-04,\n",
       "          9.43107985e-04, -5.77399871e-04,  1.60741597e-08,\n",
       "          6.27757305e-04,  5.23800230e-04,  1.57993068e-04,\n",
       "          2.30772871e-05, -4.11165097e-04],\n",
       "        [-4.80719517e-03,  1.60313527e-02, -1.25403348e-03,\n",
       "         -7.92213169e-04,  4.31729627e-04,  1.98880947e-04,\n",
       "          2.92141180e-04,  3.80836207e-04, -1.45774513e-05,\n",
       "          9.81215783e-04, -5.95702217e-04,  8.14164327e-07,\n",
       "         -6.51238953e-04, -2.64095034e-03,  1.66387073e-04,\n",
       "         -5.30196663e-05, -2.78545027e-04],\n",
       "        [-1.37279358e-03, -1.25403348e-03,  8.62012350e-03,\n",
       "          7.29084643e-04, -5.09162315e-03, -2.22865406e-03,\n",
       "          2.50109993e-04,  2.25455602e-04,  1.35667982e-04,\n",
       "          2.37340356e-04, -1.31510843e-04,  2.08680691e-06,\n",
       "         -5.57516567e-03, -8.18649223e-03, -1.68522888e-05,\n",
       "         -6.72603188e-06, -3.35212969e-05],\n",
       "        [-7.31625763e-04, -7.92213169e-04,  7.29084643e-04,\n",
       "          2.70689409e-03, -1.18560539e-03, -7.14195625e-04,\n",
       "          7.08384863e-05,  1.07367537e-04,  3.96319533e-06,\n",
       "          1.62672110e-04, -9.23426311e-05,  2.06937415e-06,\n",
       "         -4.54676569e-03, -7.61843677e-03,  3.18909679e-05,\n",
       "         -1.49675328e-05, -4.71535010e-05],\n",
       "        [ 6.33237472e-04,  4.31729627e-04, -5.09162315e-03,\n",
       "         -1.18560539e-03,  1.07788987e-01,  1.35811482e-02,\n",
       "          3.59007222e-04,  1.81763792e-04,  2.63680904e-04,\n",
       "         -2.26134411e-04,  1.43758012e-04, -6.49244957e-06,\n",
       "          5.36464331e-04,  1.11927246e-02, -1.80766845e-04,\n",
       "          3.96129407e-05,  1.32615088e-04],\n",
       "        [ 2.18541024e-04,  1.98880947e-04, -2.22865406e-03,\n",
       "         -7.14195625e-04,  1.35811482e-02,  1.69817321e-02,\n",
       "          9.74371966e-05,  7.59237631e-05,  7.22276855e-05,\n",
       "         -8.70353600e-05,  5.88237138e-05, -2.30498030e-06,\n",
       "         -1.19744380e-03,  3.64431788e-03, -9.63318088e-05,\n",
       "         -6.32534745e-06,  1.14495980e-04],\n",
       "        [ 6.42278647e-04,  2.92141180e-04,  2.50109993e-04,\n",
       "          7.08384863e-05,  3.59007222e-04,  9.74371966e-05,\n",
       "          7.42583513e-02, -5.32612587e-05, -2.30725749e-03,\n",
       "         -1.88550242e-03,  7.80960595e-04, -1.86298797e-07,\n",
       "          6.06375367e-04,  1.17582579e-03,  8.62186125e-06,\n",
       "          5.06265039e-05, -1.71795985e-04],\n",
       "        [ 5.10342992e-04,  3.80836207e-04,  2.25455602e-04,\n",
       "          1.07367537e-04,  1.81763792e-04,  7.59237631e-05,\n",
       "         -5.32612587e-05,  2.29193044e-02, -1.59405553e-03,\n",
       "         -2.65571965e-03,  1.33030839e-03, -3.37882180e-07,\n",
       "          9.17025368e-04,  1.47282343e-03, -6.36709400e-06,\n",
       "         -7.15767106e-06, -6.42728598e-05],\n",
       "        [ 2.49228253e-04, -1.45774513e-05,  1.35667982e-04,\n",
       "          3.96319533e-06,  2.63680904e-04,  7.22276855e-05,\n",
       "         -2.30725749e-03, -1.59405553e-03,  3.27730994e-02,\n",
       "         -2.60520148e-03,  7.51514092e-03, -1.33878678e-06,\n",
       "          2.12987918e-03,  5.07635320e-03, -3.45124462e-04,\n",
       "         -8.90485406e-06,  3.85876483e-04],\n",
       "        [ 9.43107985e-04,  9.81215783e-04,  2.37340356e-04,\n",
       "          1.62672110e-04, -2.26134411e-04, -8.70353600e-05,\n",
       "         -1.88550242e-03, -2.65571965e-03, -2.60520148e-03,\n",
       "          8.49542315e-03, -2.96058891e-03,  1.59716692e-06,\n",
       "         -3.13090200e-03, -6.01767751e-03,  3.21227699e-04,\n",
       "         -2.33546534e-05, -4.96988078e-04],\n",
       "        [-5.77399871e-04, -5.95702217e-04, -1.31510843e-04,\n",
       "         -9.23426311e-05,  1.43758012e-04,  5.88237138e-05,\n",
       "          7.80960595e-04,  1.33030839e-03,  7.51514092e-03,\n",
       "         -2.96058891e-03,  5.27142225e-03, -1.47158678e-06,\n",
       "          2.69191462e-03,  5.43021251e-03, -2.75768479e-04,\n",
       "         -1.43738437e-06,  4.17896063e-04],\n",
       "        [ 1.60741597e-08,  8.14164327e-07,  2.08680691e-06,\n",
       "          2.06937415e-06, -6.49244957e-06, -2.30498030e-06,\n",
       "         -1.86298797e-07, -3.37882180e-07, -1.33878678e-06,\n",
       "          1.59716692e-06, -1.47158678e-06,  1.40340361e-07,\n",
       "         -1.67476567e-04, -4.89047026e-04, -8.43004504e-07,\n",
       "          8.98261182e-10,  1.13233833e-06],\n",
       "        [ 6.27757305e-04, -6.51238953e-04, -5.57516567e-03,\n",
       "         -4.54676569e-03,  5.36464331e-04, -1.19744380e-03,\n",
       "          6.06375367e-04,  9.17025368e-04,  2.12987918e-03,\n",
       "         -3.13090200e-03,  2.69191462e-03, -1.67476567e-04,\n",
       "          2.32796897e-01,  5.85811742e-01,  1.08256868e-03,\n",
       "         -1.18553012e-04, -1.50379066e-03],\n",
       "        [ 5.23800230e-04, -2.64095034e-03, -8.18649223e-03,\n",
       "         -7.61843677e-03,  1.11927246e-02,  3.64431788e-03,\n",
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       "         -6.01767751e-03,  5.43021251e-03, -4.89047026e-04,\n",
       "          5.85811742e-01,  1.84946098e+00,  2.58086961e-03,\n",
       "          5.67570299e-06, -3.64292833e-03],\n",
       "        [ 1.57993068e-04,  1.66387073e-04, -1.68522888e-05,\n",
       "          3.18909679e-05, -1.80766845e-04, -9.63318088e-05,\n",
       "          8.62186125e-06, -6.36709400e-06, -3.45124462e-04,\n",
       "          3.21227699e-04, -2.75768479e-04, -8.43004504e-07,\n",
       "          1.08256868e-03,  2.58086961e-03,  3.46418735e-04,\n",
       "          6.06620250e-05, -5.13849231e-04],\n",
       "        [ 2.30772871e-05, -5.30196663e-05, -6.72603188e-06,\n",
       "         -1.49675328e-05,  3.96129407e-05, -6.32534745e-06,\n",
       "          5.06265039e-05, -7.15767106e-06, -8.90485406e-06,\n",
       "         -2.33546534e-05, -1.43738437e-06,  8.98261182e-10,\n",
       "         -1.18553012e-04,  5.67570299e-06,  6.06620250e-05,\n",
       "          3.41262445e-05, -1.17487987e-04],\n",
       "        [-4.11165097e-04, -2.78545027e-04, -3.35212969e-05,\n",
       "         -4.71535010e-05,  1.32615088e-04,  1.14495980e-04,\n",
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       "         -1.50379066e-03, -3.64292833e-03, -5.13849231e-04,\n",
       "         -1.17487987e-04,  8.22535744e-04]]),\n",
       " 'correlation': array([[ 1.00000000e+00, -1.73948372e-01, -6.77426522e-02,\n",
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       "          4.68794614e-02, -3.64356464e-02,  1.96585139e-04,\n",
       "          5.96096924e-03,  1.76464276e-03,  3.88911734e-02,\n",
       "          1.80989843e-02, -6.56829350e-02],\n",
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       "         -1.20260039e-01,  1.03857908e-02,  1.20536091e-02,\n",
       "          8.46710690e-03,  1.98679213e-02, -6.35971651e-04,\n",
       "          8.40788828e-02, -6.48007793e-02,  1.71646792e-02,\n",
       "         -1.06602328e-02, -1.53374289e-02,  7.06047325e-02,\n",
       "         -7.16815943e-02, -7.67066355e-02],\n",
       "        [-6.77426522e-02, -1.06676142e-01,  1.00000000e+00,\n",
       "          1.50933476e-01, -1.67036859e-01, -1.84202247e-01,\n",
       "          9.88556846e-03,  1.60399707e-02,  8.07164147e-03,\n",
       "          2.77346275e-02, -1.95092653e-02,  5.99976482e-02,\n",
       "         -1.24455026e-01, -6.48363448e-02, -9.75217451e-03,\n",
       "         -1.24010349e-02, -1.25888680e-02],\n",
       "        [-6.44268736e-02, -1.20260039e-01,  1.50933476e-01,\n",
       "          1.00000000e+00, -6.94092480e-02, -1.05339332e-01,\n",
       "          4.99644360e-03,  1.36312801e-02,  4.20776124e-04,\n",
       "          3.39222741e-02, -2.44457131e-02,  1.06172512e-01,\n",
       "         -1.81124979e-01, -1.07673207e-01,  3.29330068e-02,\n",
       "         -4.92459294e-02, -3.16010070e-02],\n",
       "        [ 8.83676103e-03,  1.03857908e-02, -1.67036859e-01,\n",
       "         -6.94092480e-02,  1.00000000e+00,  3.17437551e-01,\n",
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       "         -7.47286183e-03,  6.03088380e-03, -5.27873786e-02,\n",
       "          3.38660904e-03,  2.50683664e-02, -2.95822292e-02,\n",
       "          2.06540698e-02,  1.40840757e-02],\n",
       "        [ 7.68343959e-03,  1.20536091e-02, -1.84202247e-01,\n",
       "         -1.05339332e-01,  3.17437551e-01,  1.00000000e+00,\n",
       "          2.74385487e-03,  3.84845150e-03,  3.06163877e-03,\n",
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       "         -1.90447622e-02,  2.05637669e-02, -3.97171463e-02,\n",
       "         -8.30900739e-03,  3.06352802e-02],\n",
       "        [ 1.07985273e-02,  8.46710690e-03,  9.88556846e-03,\n",
       "          4.99644360e-03,  4.01276141e-03,  2.74385487e-03,\n",
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       "         -7.50692255e-02,  3.94723122e-02, -1.82492955e-03,\n",
       "          4.61190450e-03,  3.17283686e-03,  1.69991690e-03,\n",
       "          3.18024836e-02, -2.19817856e-02],\n",
       "        [ 1.54445457e-02,  1.98679213e-02,  1.60399707e-02,\n",
       "          1.36312801e-02,  3.65695371e-03,  3.84845150e-03,\n",
       "         -1.29103513e-03,  1.00000000e+00, -5.81626214e-02,\n",
       "         -1.90322004e-01,  1.21028517e-01, -5.95762656e-03,\n",
       "          1.25542866e-02,  7.15364497e-03, -2.25964315e-03,\n",
       "         -8.09332110e-03, -1.48029925e-02],\n",
       "        [ 6.30742673e-03, -6.35971651e-04,  8.07164147e-03,\n",
       "          4.20776124e-04,  4.43642074e-03,  3.06163877e-03,\n",
       "         -4.67697078e-02, -5.81626214e-02,  1.00000000e+00,\n",
       "         -1.56131327e-01,  5.71760921e-01, -1.97406461e-02,\n",
       "          2.43841498e-02,  2.06191371e-02, -1.02427436e-01,\n",
       "         -8.42022700e-03,  7.43211061e-02],\n",
       "        [ 4.68794614e-02,  8.40788828e-02,  2.77346275e-02,\n",
       "          3.39222741e-02, -7.47286183e-03, -7.24623193e-03,\n",
       "         -7.50692255e-02, -1.90322004e-01, -1.56131327e-01,\n",
       "          1.00000000e+00, -4.42406702e-01,  4.62558329e-02,\n",
       "         -7.04025472e-02, -4.80080223e-02,  1.87249124e-01,\n",
       "         -4.33747186e-02, -1.88007889e-01],\n",
       "        [-3.64356464e-02, -6.48007793e-02, -1.95092653e-02,\n",
       "         -2.44457131e-02,  6.03088380e-03,  6.21723874e-03,\n",
       "          3.94723122e-02,  1.21028517e-01,  5.71760921e-01,\n",
       "         -4.42406702e-01,  1.00000000e+00, -5.41041627e-02,\n",
       "          7.68437916e-02,  5.49959029e-02, -2.04070409e-01,\n",
       "         -3.38894692e-03,  2.00690580e-01],\n",
       "        [ 1.96585139e-04,  1.71646792e-02,  5.99976482e-02,\n",
       "          1.06172512e-01, -5.27873786e-02, -4.72155368e-02,\n",
       "         -1.82492955e-03, -5.95762656e-03, -1.97406461e-02,\n",
       "          4.62558329e-02, -5.41041627e-02,  1.00000000e+00,\n",
       "         -9.26561311e-01, -9.59923760e-01, -1.20903233e-01,\n",
       "          4.10456454e-04,  1.05391970e-01],\n",
       "        [ 5.96096924e-03, -1.06602328e-02, -1.24455026e-01,\n",
       "         -1.81124979e-01,  3.38660904e-03, -1.90447622e-02,\n",
       "          4.61190450e-03,  1.25542866e-02,  2.43841498e-02,\n",
       "         -7.04025472e-02,  7.68437916e-02, -9.26561311e-01,\n",
       "          1.00000000e+00,  8.92785015e-01,  1.20549712e-01,\n",
       "         -4.20610150e-02, -1.08672936e-01],\n",
       "        [ 1.76464276e-03, -1.53374289e-02, -6.48363448e-02,\n",
       "         -1.07673207e-01,  2.50683664e-02,  2.05637669e-02,\n",
       "          3.17283686e-03,  7.15364497e-03,  2.06191371e-02,\n",
       "         -4.80080223e-02,  5.49959029e-02, -9.59923760e-01,\n",
       "          8.92785015e-01,  1.00000000e+00,  1.01963044e-01,\n",
       "          7.14418718e-04, -9.34008117e-02],\n",
       "        [ 3.88911734e-02,  7.06047325e-02, -9.75217451e-03,\n",
       "          3.29330068e-02, -2.95822292e-02, -3.97171463e-02,\n",
       "          1.69991690e-03, -2.25964315e-03, -1.02427436e-01,\n",
       "          1.87249124e-01, -2.04070409e-01, -1.20903233e-01,\n",
       "          1.20549712e-01,  1.01963044e-01,  1.00000000e+00,\n",
       "          5.57920051e-01, -9.62626106e-01],\n",
       "        [ 1.80989843e-02, -7.16815943e-02, -1.24010349e-02,\n",
       "         -4.92459294e-02,  2.06540698e-02, -8.30900739e-03,\n",
       "          3.18024836e-02, -8.09332110e-03, -8.42022700e-03,\n",
       "         -4.33747186e-02, -3.38894692e-03,  4.10456454e-04,\n",
       "         -4.20610150e-02,  7.14418718e-04,  5.57920051e-01,\n",
       "          1.00000000e+00, -7.01248305e-01],\n",
       "        [-6.56829350e-02, -7.67066355e-02, -1.25888680e-02,\n",
       "         -3.16010070e-02,  1.40840757e-02,  3.06352802e-02,\n",
       "         -2.19817856e-02, -1.48029925e-02,  7.43211061e-02,\n",
       "         -1.88007889e-01,  2.00690580e-01,  1.05391970e-01,\n",
       "         -1.08672936e-01, -9.34008117e-02, -9.62626106e-01,\n",
       "         -7.01248305e-01,  1.00000000e+00]]),\n",
       " 'values': array([1.32964543e+00, 2.47221364e+00, 1.38840440e+00, 2.36655569e+00,\n",
       "        1.72082191e+00, 1.86766776e+00, 1.46184882e+00, 2.02641692e+00,\n",
       "        2.13785713e+00, 2.37630575e+00, 1.98791183e-01, 5.00860477e-04,\n",
       "        2.12663027e+00, 1.49297962e+00, 1.01621541e+00, 1.11390670e-02,\n",
       "        1.04249381e+00]),\n",
       " 'errors': array([2.18266106e-01, 1.26614978e-01, 9.28446202e-02, 5.20278203e-02,\n",
       "        3.28312331e-01, 1.30313975e-01, 2.72503856e-01, 1.51391229e-01,\n",
       "        1.81033421e-01, 9.21706198e-02, 7.26045608e-02, 3.74620289e-04,\n",
       "        4.82490308e-01, 1.35994889e+00, 1.86123275e-02, 5.84176724e-03,\n",
       "        2.86798840e-02]),\n",
       " 'indices': array([168, 169, 186, 187, 189, 190, 204, 205, 207, 208, 209, 363, 364,\n",
       "        366, 368, 369, 371]),\n",
       " 'is_norm': array([ True, False,  True, False,  True, False,  True, False,  True,\n",
       "        False, False,  True, False, False,  True, False,  True]),\n",
       " 'src_names': ['4FGL J0914.4+0249',\n",
       "  '4FGL J0914.4+0249',\n",
       "  '4FGL J0922.6+0434',\n",
       "  '4FGL J0922.6+0434',\n",
       "  '4FGL J0924.0+0534',\n",
       "  '4FGL J0924.0+0534',\n",
       "  '4FGL J0932.7+1041',\n",
       "  '4FGL J0932.7+1041',\n",
       "  '4FGL J0935.3+0901',\n",
       "  '4FGL J0935.3+0901',\n",
       "  '4FGL J0935.3+0901',\n",
       "  'PSR J0922+0638',\n",
       "  'PSR J0922+0638',\n",
       "  'PSR J0922+0638',\n",
       "  'galdiff',\n",
       "  'galdiff',\n",
       "  'isodiff'],\n",
       " 'par_names': ['Prefactor',\n",
       "  'Index',\n",
       "  'Prefactor',\n",
       "  'Index',\n",
       "  'Prefactor',\n",
       "  'Index',\n",
       "  'Prefactor',\n",
       "  'Index',\n",
       "  'norm',\n",
       "  'alpha',\n",
       "  'beta',\n",
       "  'Prefactor',\n",
       "  'Index1',\n",
       "  'Cutoff',\n",
       "  'Prefactor',\n",
       "  'Index',\n",
       "  'Normalization'],\n",
       " 'config': {'optimizer': 'NEWMINUIT',\n",
       "  'tol': 0.05,\n",
       "  'max_iter': 40,\n",
       "  'init_lambda': 0.0001,\n",
       "  'retries': 3,\n",
       "  'min_fit_quality': 2,\n",
       "  'verbosity': 0,\n",
       "  'covar': True,\n",
       "  'reoptimize': False},\n",
       " 'niter': 1}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gta.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1307d14",
   "metadata": {},
   "source": [
    "Ok now all the basic analysis that we usually do is done. \n",
    "\n",
    "Quite easy isn't it. \n",
    "\n",
    "Let's have a look at the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ec5cdd3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 14:36:51 INFO    GTAnalysis.print_roi(): \n",
      "name                SpatialModel   SpectrumType     offset        ts       npred\n",
      "--------------------------------------------------------------------------------\n",
      "PSR J0922+0638      PointSource    PLSuperExpCuto    0.000     40.73      1041.1\n",
      "4FGL J0924.0+0534   PointSource    PowerLaw          1.158     76.73       346.2\n",
      "4FGL J0922.6+0434   PointSource    PowerLaw          2.065    412.56      2736.3\n",
      "4FGL J0935.3+0901   PointSource    LogParabola       4.029    309.22      2252.7\n",
      "4FGL J0914.4+0249   PointSource    PowerLaw          4.273     53.55       983.7\n",
      "4FGL J0932.7+1041   PointSource    PowerLaw          4.808     59.17       387.4\n",
      "4FGL J0909.6+0159   PointSource    PowerLaw          5.602    872.50      2940.1\n",
      "4FGL J0935.5+1152   PointSource    PowerLaw          6.186     19.16        53.4\n",
      "4FGL J0909.1+0121   PointSource    LogParabola       6.205   3326.87      8486.5\n",
      "4FGL J0930.9+0033   PointSource    PowerLaw          6.462    321.84      2612.9\n",
      "4FGL J0855.0+0720   PointSource    PowerLaw          6.782     20.92       582.5\n",
      "4FGL J0950.2+0615   PointSource    PowerLaw          6.967    116.89       476.1\n",
      "4FGL J0946.6+1016   PointSource    LogParabola       7.047   3280.96      7051.5\n",
      "4FGL J0952.8+0712   PointSource    PowerLaw          7.627     -0.00         0.8\n",
      "4FGL J0857.7+0137   PointSource    PowerLaw          7.906     22.78       340.9\n",
      "4FGL J0947.9+1121   PointSource    PowerLaw          7.915     23.22       157.9\n",
      "4FGL J0859.8+0053   PointSource    PowerLaw          8.013     33.67       105.7\n",
      "4FGL J0946.2+0104   PointSource    PowerLaw          8.181     91.22       492.3\n",
      "4FGL J0849.5+0456   PointSource    PowerLaw          8.309      0.24        87.0\n",
      "4FGL J0905.6+1358   PointSource    LogParabola       8.397     22.66       283.4\n",
      "4FGL J0955.2+0835   PointSource    PowerLaw          8.417      0.33       116.1\n",
      "4FGL J0848.7+0508   PointSource    PowerLaw          8.467      0.42       122.8\n",
      "4FGL J0914.1-0202   PointSource    PowerLaw          8.904      1.25       240.9\n",
      "4FGL J0948.9+0022   PointSource    LogParabola       9.153   5729.18      9068.0\n",
      "4FGL J0845.4+0442   PointSource    PowerLaw          9.362     -0.00         0.0\n",
      "4FGL J0848.9+0205   PointSource    PowerLaw          9.462     -0.00         0.2\n",
      "4FGL J0858.1+1405   PointSource    PowerLaw          9.518     16.79       572.5\n",
      "4FGL J0912.5+1556   PointSource    PowerLaw          9.592      0.31       119.5\n",
      "4FGL J0909.7-0230   PointSource    LogParabola       9.667      4.00       452.3\n",
      "4FGL J0847.2+1134   PointSource    PowerLaw          9.941     -0.00         0.0\n",
      "4FGL J0917.3-0342   PointSource    PowerLaw         10.415      0.65       202.5\n",
      "4FGL J0842.5+0251   PointSource    PowerLaw         10.585     -0.00         0.0\n",
      "4FGL J0928.4-0415   PointSource    PowerLaw         11.005     -0.00         0.0\n",
      "4FGL J1003.4+0205   PointSource    PowerLaw         11.235     -0.00         0.0\n",
      "4FGL J0920.3-0443   PointSource    PowerLaw         11.380     -0.00         0.0\n",
      "4FGL J1008.0+0620   PointSource    PowerLaw         11.385      4.17       513.6\n",
      "4FGL J0835.7+0936   PointSource    PowerLaw         11.872     -0.00         0.0\n",
      "4FGL J0839.8+0105   PointSource    LogParabola      11.937     -0.00         0.0\n",
      "4FGL J0922.4-0528   PointSource    PowerLaw         12.115     -0.00         0.0\n",
      "4FGL J0840.8+1317   PointSource    PowerLaw         12.153     -0.00         0.1\n",
      "4FGL J0948.6-0338   PointSource    PowerLaw         12.221     -0.00         0.0\n",
      "4FGL J1012.3+0629   PointSource    LogParabola      12.441     -0.00         0.1\n",
      "4FGL J0936.5+1847   PointSource    PowerLaw         12.646      0.91       230.1\n",
      "4FGL J0831.8+0429   PointSource    LogParabola      12.707      1.25       287.9\n",
      "4FGL J0949.2+1749   PointSource    PowerLaw         12.987     68.32      1812.4\n",
      "4FGL J1008.0+0028   PointSource    PowerLaw         12.989     -0.00         0.0\n",
      "4FGL J0918.9-0625   PointSource    PowerLaw         13.084     -0.00         0.0\n",
      "4FGL J0829.4+0857   PointSource    PowerLaw         13.285     -0.00         0.0\n",
      "4FGL J0958.0-0319   PointSource    PowerLaw         13.386     -0.00         0.0\n",
      "4FGL J1016.0+0512   PointSource    PowerLaw         13.455      0.54       193.2\n",
      "4FGL J0837.3+1458   PointSource    PowerLaw         13.799     -0.00         0.0\n",
      "4FGL J1017.8+0715   PointSource    PowerLaw         13.826     -0.00         0.0\n",
      "4FGL J1009.5-0034   PointSource    PowerLaw         13.832     -0.00         0.0\n",
      "4FGL J1010.0+1416   PointSource    PowerLaw         14.009     -0.00         0.0\n",
      "4FGL J1018.4+0528   PointSource    PowerLaw         14.029     -0.00         0.0\n",
      "4FGL J1019.7+0511   PointSource    PowerLaw         14.379     -0.00         0.0\n",
      "4FGL J0825.8+0309   PointSource    PowerLaw         14.471     -0.00         0.0\n",
      "4FGL J1018.9+1043   PointSource    PowerLaw         14.600     -0.00         0.0\n",
      "4FGL J0848.0-0524   PointSource    PowerLaw         14.772     -0.00         0.0\n",
      "4FGL J1018.2+0145   PointSource    PowerLaw         14.781     -0.00         0.0\n",
      "4FGL J0952.1-0607   PointSource    PLSuperExpCuto   14.795      0.75       238.7\n",
      "4FGL J1010.8-0158   PointSource    PowerLaw         14.877     -0.00         0.0\n",
      "4FGL J0902.4+2051   PointSource    PowerLaw         14.995      4.15       469.4\n",
      "4FGL J0854.8+2006   PointSource    LogParabola      15.026     20.08      1035.9\n",
      "4FGL J0958.6-0522   PointSource    PowerLaw         15.072     -0.00         0.0\n",
      "4FGL J0855.4-0714   PointSource    LogParabola      15.405     -0.00         0.0\n",
      "4FGL J0839.4+1803   PointSource    PowerLaw         15.461     -0.00         0.0\n",
      "4FGL J0942.3-0800   PointSource    LogParabola      15.482     -0.00         0.0\n",
      "4FGL J0821.1+1007   PointSource    PowerLaw         15.491     -0.00         0.0\n",
      "4FGL J0856.8+2056   PointSource    PowerLaw         15.574     -0.00         0.0\n",
      "4FGL J0827.6-0059   PointSource    PowerLaw         15.622     -0.00         2.1\n",
      "4FGL J1026.9+0608   PointSource    PowerLaw         16.080       nan         0.7\n",
      "4FGL J0847.9-0702   PointSource    PowerLaw         16.131     -0.00         0.2\n",
      "4FGL J0906.3-0905   PointSource    PowerLaw         16.224     -0.00         0.0\n",
      "4FGL J0910.6+2247   PointSource    LogParabola      16.395     -0.00         0.0\n",
      "4FGL J0826.0-0157   PointSource    PowerLaw         16.439      0.00        10.4\n",
      "4FGL J1023.7+0038   PointSource    LogParabola      16.475     -0.00         0.0\n",
      "4FGL J1011.3-0427   PointSource    PowerLaw         16.526     -0.00         0.0\n",
      "4FGL J0831.5+1747   PointSource    PowerLaw         16.646     -0.00         0.0\n",
      "4FGL J0947.6+2215   PointSource    PowerLaw         16.777     -0.00         0.0\n",
      "4FGL J1027.9+0252   PointSource    PowerLaw         16.793     -0.00         0.0\n",
      "4FGL J0833.4-0458   PointSource    LogParabola      16.820       nan         0.1\n",
      "4FGL J0908.9+2311   PointSource    PowerLaw         16.859     -0.00         0.0\n",
      "4FGL J0921.7+2336   PointSource    PowerLaw         16.963     -0.00         0.0\n",
      "4FGL J1022.7-0112   PointSource    PowerLaw         17.014     -0.00         0.0\n",
      "4FGL J0930.9-1015   PointSource    PowerLaw         17.035     -0.00         0.0\n",
      "4FGL J0953.0-0840   PointSource    LogParabola      17.128     -0.00         0.0\n",
      "4FGL J0829.0+1755   PointSource    PowerLaw         17.179     -0.00         0.0\n",
      "4FGL J0959.4+2120   PointSource    PowerLaw         17.245     -0.00         0.0\n",
      "4FGL J0812.5+0711   PointSource    PowerLaw         17.295     -0.00         0.0\n",
      "4FGL J0906.1-1011   PointSource    PowerLaw         17.304     -0.00         0.1\n",
      "4FGL J1021.1+1626   PointSource    PowerLaw         17.424       nan         0.9\n",
      "4FGL J0816.4-0007   PointSource    PowerLaw         17.739     -0.00         0.2\n",
      "4FGL J0812.0+0237   PointSource    PowerLaw         17.942     -0.01         1.5\n",
      "4FGL J1002.8+2140   PointSource    PowerLaw         17.946     -0.00         0.1\n",
      "4FGL J0812.3+1143   PointSource    PowerLaw         17.963     -0.00         0.0\n",
      "4FGL J1014.8-0537   PointSource    PowerLaw         17.967     -0.00         0.0\n",
      "4FGL J0809.8+0507   PointSource    LogParabola      18.072     -0.00         0.0\n",
      "4FGL J0927.2+2454   PointSource    PowerLaw         18.302     -0.00         0.0\n",
      "4FGL J0811.4+0146   PointSource    LogParabola      18.302     -0.00         0.0\n",
      "4FGL J0843.0-0853   PointSource    PowerLaw         18.336     -0.00         1.9\n",
      "4FGL J1002.5+2215   PointSource    PowerLaw         18.404     -0.00         0.1\n",
      "4FGL J1018.1+1905   PointSource    PowerLaw         18.446     -0.00         0.0\n",
      "4FGL J0956.5-0958   PointSource    PowerLaw         18.677       nan         0.3\n",
      "4FGL J0836.2+2141   PointSource    PowerLaw         18.717     -0.00         0.0\n",
      "4FGL J0856.6-1105   PointSource    LogParabola      18.844     -0.00         0.0\n",
      "4FGL J1028.4-0234   PointSource    PowerLaw         18.933     -0.00         0.0\n",
      "4FGL J1036.5+1231   PointSource    PowerLaw         19.241       nan         0.6\n",
      "4FGL J1039.6+0535   PointSource    PowerLaw         19.269       nan         0.5\n",
      "4FGL J1040.5+0617   PointSource    PowerLaw         19.469     -0.00         0.0\n",
      "4FGL J0827.0-0708   PointSource    PowerLaw         19.478     -0.00         0.9\n",
      "4FGL J0804.5+0414   PointSource    PowerLaw         19.484     -0.00         0.0\n",
      "4FGL J1018.3-0703   PointSource    PowerLaw         19.592     -0.00         0.0\n",
      "4FGL J0828.6-0747   PointSource    PowerLaw         19.660     -0.00         2.6\n",
      "4FGL J1027.6+1828   PointSource    PowerLaw         19.852     -0.00         0.0\n",
      "isodiff             ConstantValue  FileFunction      -----   3461.01    146249.9\n",
      "galdiff             MapCubeFunctio PowerLaw          -----   6576.94    177462.3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gta.print_roi()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b77fe36",
   "metadata": {},
   "source": [
    "Voila. Source names, TS values, and distance from the ROI. \n",
    "\n",
    "Of course there are much more information. It also can be written into files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2798c3ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 14:42:28 INFO    GTBinnedAnalysis.write_xml(): Writing /data/users/ysong/Fermipy_Tutorial/PSR J0922+0638_v1_00.xml...\n",
      "2021-06-09 14:42:28 INFO    GTAnalysis.write_fits(): Writing /data/users/ysong/Fermipy_Tutorial/PSR J0922+0638_v1.fits...\n",
      "WARNING: Format %s cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %f cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "2021-06-09 14:42:41 INFO    GTAnalysis.write_roi(): Writing /data/users/ysong/Fermipy_Tutorial/PSR J0922+0638_v1.npy...\n"
     ]
    }
   ],
   "source": [
    "gta.write_roi(name+'_v1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72a384f5",
   "metadata": {},
   "source": [
    "Now this output can be read with numpy, and it has all the details you'll need in it. Let's have a look. \n",
    "\n",
    "Note that this is similar to a data frame, and you can idnex it as such with keywords."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "44c9697e",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_v1=np.load(name+'_v1.npy',allow_pickle=True).flat[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5b85d8f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['roi', 'config', 'version', 'stversion', 'sources', 'lck_params'])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_v1.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a248333b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'PSR J0922+0638',\n",
       " 'Source_Name': 'PSR J0922+0638',\n",
       " 'SpatialModel': 'PointSource',\n",
       " 'SpatialWidth': None,\n",
       " 'SpatialType': 'SkyDirFunction',\n",
       " 'SourceType': 'PointSource',\n",
       " 'SpectrumType': 'PLSuperExpCutoff',\n",
       " 'Spatial_Filename': None,\n",
       " 'Spectrum_Filename': None,\n",
       " 'correlation': {'PSR J0922+0638': 1.0,\n",
       "  'galdiff': -0.1209032332738038,\n",
       "  'isodiff': 0.1053919700914546,\n",
       "  '4FGL J0914.4+0249': 0.00019658513878484935,\n",
       "  '4FGL J0922.6+0434': 0.05999764822775202,\n",
       "  '4FGL J0924.0+0534': -0.05278737856833796,\n",
       "  '4FGL J0932.7+1041': -0.0018249295535273126,\n",
       "  '4FGL J0935.3+0901': -0.01974064613132802},\n",
       " 'model_counts': array([1.67418577e+02, 1.70025409e+02, 1.58208296e+02, 1.36959871e+02,\n",
       "        1.12683598e+02, 8.92700494e+01, 6.79044363e+01, 4.95216072e+01,\n",
       "        3.47985788e+01, 2.34379734e+01, 1.47212034e+01, 8.48582311e+00,\n",
       "        4.41652146e+00, 2.03818559e+00, 8.13158230e-01, 2.71803691e-01,\n",
       "        7.42759062e-02, 1.55495674e-02, 2.25853982e-03, 2.07100535e-04,\n",
       "        1.04620572e-05, 2.47656544e-07, 2.19711183e-09, 5.22959454e-12,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00]),\n",
       " 'model_counts_wt': array([1.67418577e+02, 1.70025409e+02, 1.58208296e+02, 1.36959871e+02,\n",
       "        1.12683598e+02, 8.92700494e+01, 6.79044363e+01, 4.95216072e+01,\n",
       "        3.47985788e+01, 2.34379734e+01, 1.47212034e+01, 8.48582311e+00,\n",
       "        4.41652146e+00, 2.03818559e+00, 8.13158230e-01, 2.71803691e-01,\n",
       "        7.42759062e-02, 1.55495674e-02, 2.25853982e-03, 2.07100535e-04,\n",
       "        1.04620572e-05, 2.47656544e-07, 2.19711183e-09, 5.22959454e-12,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00]),\n",
       " 'sed': None,\n",
       " 'ra': 140.558425,\n",
       " 'dec': 6.639806,\n",
       " 'glon': 225.4198710000222,\n",
       " 'glat': 36.39243198570519,\n",
       " 'ra_err': nan,\n",
       " 'dec_err': nan,\n",
       " 'glon_err': nan,\n",
       " 'glat_err': nan,\n",
       " 'pos_err': nan,\n",
       " 'pos_r68': nan,\n",
       " 'pos_r95': nan,\n",
       " 'pos_r99': nan,\n",
       " 'pos_err_semimajor': nan,\n",
       " 'pos_err_semiminor': nan,\n",
       " 'pos_angle': nan,\n",
       " 'pos_gal_cov': array([[nan, nan],\n",
       "        [nan, nan]]),\n",
       " 'pos_gal_corr': array([[nan, nan],\n",
       "        [nan, nan]]),\n",
       " 'pos_cel_cov': array([[nan, nan],\n",
       "        [nan, nan]]),\n",
       " 'pos_cel_corr': array([[nan, nan],\n",
       "        [nan, nan]]),\n",
       " 'offset_ra': -6.994405055138486e-15,\n",
       " 'offset_dec': 7.105427357601002e-15,\n",
       " 'offset_glon': 7.691805174347977e-06,\n",
       " 'offset_glat': -2.6108364405974527e-06,\n",
       " 'offset_roi_edge': -6.999999999999993,\n",
       " 'offset': 0.0,\n",
       " 'param_names': array([b'Prefactor', b'Index1', b'Scale', b'Cutoff', b'Index2', b'', b'',\n",
       "        b'', b'', b''], dtype='|S32'),\n",
       " 'param_values': array([ 5.00860477e-13, -2.12663027e+00,  1.00000000e+03,  1.49297962e+03,\n",
       "         1.00000000e+00,             nan,             nan,             nan,\n",
       "                    nan,             nan]),\n",
       " 'param_errors': array([3.74620289e-13, 4.82490308e-01,            nan, 1.35994889e+03,\n",
       "                   nan,            nan,            nan,            nan,\n",
       "                   nan,            nan]),\n",
       " 'ts': 40.72795937297633,\n",
       " 'loglike': -185305.80259502633,\n",
       " 'loglike_scan': array([-185327.68293857, -185308.52888486, -185307.9396461 ,\n",
       "        -185307.42437581, -185306.98199186, -185306.61145338,\n",
       "        -185306.3117582 , -185306.0819405 , -185305.92106864,\n",
       "        -185305.82824318, -185305.80259503, -185305.84384813,\n",
       "        -185305.95162418, -185306.12511769, -185306.36354829,\n",
       "        -185306.6661594 , -185307.03221704, -185307.46100876,\n",
       "        -185307.95184255, -185308.5040459 ]),\n",
       " 'dloglike_scan': array([-2.18807543e+01, -2.72670061e+00, -2.13746185e+00, -1.62219156e+00,\n",
       "        -1.17980761e+00, -8.09269127e-01, -5.09573949e-01, -2.79756253e-01,\n",
       "        -1.18884395e-01, -2.60589292e-02, -4.10777226e-04, -4.16638825e-02,\n",
       "        -1.49439930e-01, -3.22933445e-01, -5.61364044e-01, -8.63975148e-01,\n",
       "        -1.23003279e+00, -1.65882451e+00, -2.14965830e+00, -2.70186165e+00]),\n",
       " 'eflux_scan': array([0.00000000e+00, 7.84921682e-07, 8.36302975e-07, 8.87684268e-07,\n",
       "        9.39065561e-07, 9.90446853e-07, 1.04182815e-06, 1.09320944e-06,\n",
       "        1.14459073e-06, 1.19597202e-06, 1.24735332e-06, 1.29912733e-06,\n",
       "        1.35090134e-06, 1.40267535e-06, 1.45444937e-06, 1.50622338e-06,\n",
       "        1.55799739e-06, 1.60977141e-06, 1.66154542e-06, 1.71331943e-06]),\n",
       " 'flux_scan': array([0.00000000e+00, 3.05054341e-09, 3.25023322e-09, 3.44992303e-09,\n",
       "        3.64961284e-09, 3.84930266e-09, 4.04899247e-09, 4.24868228e-09,\n",
       "        4.44837210e-09, 4.64806191e-09, 4.84775172e-09, 5.04896781e-09,\n",
       "        5.25018390e-09, 5.45139999e-09, 5.65261609e-09, 5.85383218e-09,\n",
       "        6.05504827e-09, 6.25626436e-09, 6.45748045e-09, 6.65869654e-09]),\n",
       " 'norm_scan': array([0.        , 0.00031628, 0.00033698, 0.00035769, 0.00037839,\n",
       "        0.0003991 , 0.0004198 , 0.0004405 , 0.00046121, 0.00048191,\n",
       "        0.00050262, 0.00052348, 0.00054434, 0.0005652 , 0.00058606,\n",
       "        0.00060693, 0.00062779, 0.00064865, 0.00066951, 0.00069037]),\n",
       " 'npred': 1041.067392687812,\n",
       " 'npred_wt': 1041.067392687812,\n",
       " 'pivot_energy': 423.7142937554327,\n",
       " 'flux': 4.830821458311782e-09,\n",
       " 'flux100': 4.830338368031619e-09,\n",
       " 'flux1000': 1.1756954175762661e-10,\n",
       " 'flux10000': 5.3916118955380975e-15,\n",
       " 'flux_err': 1.5495189806628002e-09,\n",
       " 'flux100_err': 1.5495153014365941e-09,\n",
       " 'flux1000_err': 3.247953320190991e-11,\n",
       " 'flux10000_err': 2.7372463093689977e-14,\n",
       " 'flux_ul95': 6.114866649181857e-09,\n",
       " 'flux100_ul95': 6.114255152220481e-09,\n",
       " 'flux1000_ul95': 1.4881983862523975e-10,\n",
       " 'flux10000_ul95': 6.824716676008079e-15,\n",
       " 'eflux': 1.2429970667657743e-06,\n",
       " 'eflux100': 1.2429892287552977e-06,\n",
       " 'eflux1000': 1.8523811355594515e-07,\n",
       " 'eflux10000': 6.02135650550925e-11,\n",
       " 'eflux_err': 2.706901567511559e-07,\n",
       " 'eflux100_err': 2.7069003191276514e-07,\n",
       " 'eflux1000_err': 6.506966772598339e-08,\n",
       " 'eflux10000_err': 3.1060927937756993e-10,\n",
       " 'eflux_ul95': 1.5733889927807712e-06,\n",
       " 'eflux100_ul95': 1.5733790714062652e-06,\n",
       " 'eflux1000_ul95': 2.344748967676516e-07,\n",
       " 'eflux10000_ul95': 7.621849078073791e-11,\n",
       " 'dnde': 2.341745261632662e-12,\n",
       " 'dnde100': 6.26986120787913e-11,\n",
       " 'dnde1000': 2.563454798756198e-13,\n",
       " 'dnde10000': 4.615040349517428e-18,\n",
       " 'dnde_err': 4.894768501553299e-13,\n",
       " 'dnde100_err': 3.382102511435571e-11,\n",
       " 'dnde1000_err': 6.043181558983965e-14,\n",
       " 'dnde10000_err': 2.0480591072631547e-17,\n",
       " 'dnde_index': 2.7964317817462194,\n",
       " 'dnde100_index': 2.1936104252801463,\n",
       " 'dnde1000_index': 2.7964317817462194,\n",
       " 'dnde10000_index': 8.824645346427824,\n",
       " 'spectral_pars': {'Cutoff': {'error': 1.3599488896631893,\n",
       "   'free': False,\n",
       "   'max': 1000.0,\n",
       "   'min': 0.001,\n",
       "   'name': 'Cutoff',\n",
       "   'scale': 1000.0,\n",
       "   'value': 1.4929796204442654},\n",
       "  'Index1': {'error': 0.4824903078868885,\n",
       "   'free': False,\n",
       "   'max': 5.0,\n",
       "   'min': -1.5,\n",
       "   'name': 'Index1',\n",
       "   'scale': -1.0,\n",
       "   'value': 2.1266302745729515},\n",
       "  'Index2': {'error': nan,\n",
       "   'free': False,\n",
       "   'max': 2.0,\n",
       "   'min': 0.0,\n",
       "   'name': 'Index2',\n",
       "   'scale': 1.0,\n",
       "   'value': 1.0},\n",
       "  'Prefactor': {'error': 0.00037462028880526084,\n",
       "   'free': True,\n",
       "   'max': 1000.0,\n",
       "   'min': 1e-05,\n",
       "   'name': 'Prefactor',\n",
       "   'scale': 1e-09,\n",
       "   'value': 0.0005008604766859191},\n",
       "  'Scale': {'error': nan,\n",
       "   'free': False,\n",
       "   'max': 1000.0,\n",
       "   'min': 0.001,\n",
       "   'name': 'Scale',\n",
       "   'scale': 1000.0,\n",
       "   'value': 1.0}},\n",
       " 'spatial_pars': {'DEC': {'error': nan,\n",
       "   'free': False,\n",
       "   'max': 90.0,\n",
       "   'min': -90.0,\n",
       "   'name': 'DEC',\n",
       "   'scale': 1.0,\n",
       "   'value': 6.639806},\n",
       "  'RA': {'error': nan,\n",
       "   'free': False,\n",
       "   'max': 360.0,\n",
       "   'min': -360.0,\n",
       "   'name': 'RA',\n",
       "   'scale': 1.0,\n",
       "   'value': 140.558425}},\n",
       " 'catalog': {},\n",
       " 'assoc': {},\n",
       " 'class': '',\n",
       " 'psf_scale_fn': None,\n",
       " 'radec': array([140.558425,   6.639806]),\n",
       " 'RAJ2000': 140.558425,\n",
       " 'DEJ2000': 6.639806,\n",
       " 'covar': array([[ 1.40340361e-07, -1.67476567e-04, -4.89047026e-04],\n",
       "        [-1.67476567e-04,  2.32796897e-01,  5.85811742e-01],\n",
       "        [-4.89047026e-04,  5.85811742e-01,  1.84946098e+00]]),\n",
       " 'model_flux': {'energies': array([1.00000000e+02, 1.18984170e+02, 1.41572326e+02, 1.68448656e+02,\n",
       "         2.00427235e+02, 2.38476681e+02, 2.83749498e+02, 3.37616984e+02,\n",
       "         4.01710764e+02, 4.77972217e+02, 5.68711273e+02, 6.76676385e+02,\n",
       "         8.05137777e+02, 9.57986497e+02, 1.13985228e+03, 1.35624377e+03,\n",
       "         1.61371538e+03, 1.92006585e+03, 2.28457440e+03, 2.71828188e+03,\n",
       "         3.23432512e+03, 3.84833488e+03, 4.57890930e+03, 5.44817720e+03,\n",
       "         6.48246840e+03, 7.71311119e+03, 9.17738129e+03, 1.09196309e+04,\n",
       "         1.29926322e+04, 1.54591755e+04, 1.83939715e+04, 2.18859143e+04,\n",
       "         2.60407733e+04, 3.09843979e+04, 3.68665285e+04, 4.38653328e+04,\n",
       "         5.21928019e+04, 6.21011719e+04, 7.38905637e+04, 8.79180735e+04,\n",
       "         1.04608590e+05, 1.24467662e+05, 1.48096814e+05, 1.76211764e+05,\n",
       "         2.09664104e+05, 2.49467092e+05, 2.96826348e+05, 3.53176365e+05,\n",
       "         4.20223965e+05, 4.99999995e+05]),\n",
       "  'log_energies': array([2.        , 2.07548918, 2.15097837, 2.22646755, 2.30195673,\n",
       "         2.37744592, 2.4529351 , 2.52842429, 2.60391347, 2.67940265,\n",
       "         2.75489184, 2.83038102, 2.9058702 , 2.98135939, 3.05684857,\n",
       "         3.13233776, 3.20782694, 3.28331612, 3.35880531, 3.43429449,\n",
       "         3.50978367, 3.58527286, 3.66076204, 3.73625122, 3.81174041,\n",
       "         3.88722959, 3.96271878, 4.03820796, 4.11369714, 4.18918633,\n",
       "         4.26467551, 4.34016469, 4.41565388, 4.49114306, 4.56663224,\n",
       "         4.64212143, 4.71761061, 4.7930998 , 4.86858898, 4.94407816,\n",
       "         5.01956735, 5.09505653, 5.17054571, 5.2460349 , 5.32152408,\n",
       "         5.39701327, 5.47250245, 5.54799163, 5.62348082, 5.69897   ]),\n",
       "  'dnde': array([6.26986121e-011, 4.27757879e-011, 2.91131864e-011, 1.97575947e-011,\n",
       "         1.33626996e-011, 9.00095056e-012, 6.03366694e-012, 4.02137134e-012,\n",
       "         2.66190336e-012, 1.74771625e-012, 1.13641814e-012, 7.30456710e-013,\n",
       "         4.63114761e-013, 2.88860866e-013, 1.76704654e-013, 1.05624349e-013,\n",
       "         6.14228922e-014, 3.45683027e-014, 1.87114817e-014, 9.66960983e-015,\n",
       "         4.72888750e-015, 2.16576547e-015, 9.17394741e-016, 3.54124821e-016,\n",
       "         1.22391902e-016, 3.70878849e-017, 9.61061863e-018, 2.06732546e-018,\n",
       "         3.56331045e-019, 4.71868064e-020, 4.56643150e-021, 3.04273622e-022,\n",
       "         1.30051451e-023, 3.27733806e-025, 4.40477997e-027, 2.80218480e-029,\n",
       "         7.32103604e-032, 6.63404371e-035, 1.70534255e-038, 9.79030654e-043,\n",
       "         9.44356856e-048, 1.09086570e-053, 1.00861113e-060, 4.62165414e-069,\n",
       "         5.93296723e-079, 1.08242850e-090, 1.25162927e-104, 3.50929397e-121,\n",
       "         7.60557729e-141, 3.26932148e-164]),\n",
       "  'dnde_lo': array([4.07286649e-011, 2.90150010e-011, 2.06021196e-011, 1.45588271e-011,\n",
       "         1.02189311e-011, 7.10633665e-012, 4.88182089e-012, 3.30432874e-012,\n",
       "         2.20098215e-012, 1.44362664e-012, 9.33925851e-013, 5.96666910e-013,\n",
       "         3.76402254e-013, 2.33946040e-013, 1.42515598e-013, 8.42851093e-014,\n",
       "         4.77212031e-014, 2.54978562e-014, 1.27366102e-014, 5.91985853e-015,\n",
       "         2.55006502e-015, 1.01221571e-015, 3.67105441e-016, 1.20241636e-016,\n",
       "         3.50346989e-017, 8.90949453e-018, 1.93160437e-018, 3.46954413e-019,\n",
       "         4.98790124e-020, 5.50609827e-021, 4.44117535e-022, 2.46686250e-023,\n",
       "         8.79223986e-025, 1.84843696e-026, 2.07363025e-028, 1.10170576e-030,\n",
       "         2.40515759e-033, 1.82216070e-036, 3.91816171e-040, 1.88252676e-044,\n",
       "         1.52037913e-049, 1.47108895e-055, 1.13974691e-062, 4.37772831e-071,\n",
       "         4.71221289e-081, 7.21065563e-093, 6.99490654e-107, 1.64570815e-123,\n",
       "         2.99348768e-143, 1.07172145e-166]),\n",
       "  'dnde_hi': array([9.65196372e-011, 6.30628286e-011, 4.11403117e-011, 2.68127746e-011,\n",
       "         1.74736220e-011, 1.14006858e-011, 7.45728645e-012, 4.89401289e-012,\n",
       "         3.21934892e-012, 2.11586015e-012, 1.38281447e-012, 8.94246012e-013,\n",
       "         5.69803395e-013, 3.56666007e-013, 2.19095560e-013, 1.32366242e-013,\n",
       "         7.90586038e-014, 4.68654126e-014, 2.74892251e-014, 1.57945251e-014,\n",
       "         8.76933601e-015, 4.63393326e-015, 2.29256507e-015, 1.04293648e-015,\n",
       "         4.27569754e-016, 1.54387120e-016, 4.78172404e-017, 1.23181445e-017,\n",
       "         2.54559599e-018, 4.04387024e-019, 4.69522029e-020, 3.75304408e-021,\n",
       "         1.92367135e-022, 5.81082556e-024, 9.35657964e-026, 7.12734742e-028,\n",
       "         2.22844311e-030, 2.41529389e-033, 7.42234094e-037, 5.09156652e-041,\n",
       "         5.86570713e-046, 8.08916402e-052, 8.92563426e-059, 4.87917145e-067,\n",
       "         7.46997239e-077, 1.62488893e-088, 2.23959509e-102, 7.48318839e-119,\n",
       "         1.93235490e-138, 9.97317254e-162]),\n",
       "  'dnde_err': array([3.38210251e-011, 2.02870406e-011, 1.20271253e-011, 7.05517986e-012,\n",
       "         4.11092245e-012, 2.39973520e-012, 1.42361951e-012, 8.72641551e-013,\n",
       "         5.57445559e-013, 3.68143902e-013, 2.46396335e-013, 1.63789302e-013,\n",
       "         1.06688634e-013, 6.78051409e-014, 4.23909064e-014, 2.67418928e-014,\n",
       "         1.76357116e-014, 1.22971099e-014, 8.77774347e-015, 6.12491531e-015,\n",
       "         4.04044851e-015, 2.46816779e-015, 1.37517033e-015, 6.88811658e-016,\n",
       "         3.05177852e-016, 1.17299235e-016, 3.82066218e-017, 1.02508190e-017,\n",
       "         2.18926494e-018, 3.57200218e-019, 4.23857714e-020, 3.44877045e-021,\n",
       "         1.79361990e-022, 5.48309176e-024, 8.91610165e-026, 6.84712894e-028,\n",
       "         2.15523275e-030, 2.34895345e-033, 7.25180668e-037, 4.99366345e-041,\n",
       "         5.77127145e-046, 7.98007745e-052, 8.82477315e-059, 4.83295491e-067,\n",
       "         7.41064272e-077, 1.61406464e-088, 2.22707880e-102, 7.44809545e-119,\n",
       "         1.92474932e-138, 9.94047932e-162]),\n",
       "  'dnde_ferr': array([  0.44491393,   0.39798013,   0.35273006,   0.31010727,\n",
       "           0.27145304,   0.23854976,   0.21342457,   0.19765448,\n",
       "           0.19128545,   0.19231769,   0.19750152,   0.20369387,\n",
       "           0.20880477,   0.21242055,   0.21668915,   0.2276044 ,\n",
       "           0.25509545,   0.30906285,   0.3942129 ,   0.51060316,\n",
       "           0.65758289,   0.83612875,   1.04941719,   1.30278194,\n",
       "           1.60359897,   1.96125536,   2.38723634,   2.8953327 ,\n",
       "           3.50196398,   4.22661499,   5.09238838,   6.12668201,\n",
       "           7.36200595,   8.8369602 ,  10.59740032,  12.69782484,\n",
       "          15.2030255 ,  18.19004991,  21.75053564,  25.99348637,\n",
       "          31.04857404,  37.07006705,  44.24150338,  52.78125026,\n",
       "          62.94911871,  75.05423342,  89.46439621, 106.61722683,\n",
       "         127.03341846, 152.52500299]),\n",
       "  'pivot_energy': 423.7142937554327}}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_v1['sources'][name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "16d46eda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.830821458311782e-09, 40.72795937297633)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_v1['sources'][name]['flux'],output_v1['sources'][name]['ts']#,output_v1['sources'][name]['ra'],...."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fef9a1a0",
   "metadata": {},
   "source": [
    "Now we want to see if there's any source that has large TS values yet not modeled. \n",
    "\n",
    "Fermipy does this very easily. \n",
    "\n",
    "First we need to assume the type of sources that we want to find. This is just an estimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3e1239ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'} #We try to find Power Law point sources with an index of 2."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4814db75",
   "metadata": {},
   "source": [
    "Now we can specify this and find new sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e28707dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 14:54:41 INFO    GTAnalysis.find_sources(): Starting.\n",
      "2021-06-09 14:54:41 INFO    GTAnalysis.tsmap(): Generating TS map\n",
      "2021-06-09 14:54:44 INFO    GTAnalysis._make_tsmap_fast(): Fitting test source.\n",
      "2021-06-09 14:55:36 INFO    GTAnalysis.tsmap(): Finished TS map\n",
      "2021-06-09 14:55:45 WARNING GTAnalysis.tsmap(): Saving TS maps in .npy files is disabled b/c of incompatibilities in python3, remove the maps from the /data/users/ysong/Fermipy_Tutorial/sourcefind_00_pointsource_powerlaw_2.00_tsmap.npy\n",
      "2021-06-09 14:55:45 INFO    GTAnalysis.tsmap(): Execution time: 63.94 s\n",
      "2021-06-09 14:55:45 INFO    GTAnalysis._build_src_dicts_from_peaks(): Found source\n",
      "name: PS J0916.7+0956\n",
      "ts: 29.224341\n",
      "2021-06-09 14:55:45 INFO    GTAnalysis.add_source(): Adding source PS J0916.7+0956\n",
      "2021-06-09 14:55:48 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0916.7+0956       : ['Prefactor']\n",
      "2021-06-09 14:55:48 INFO    GTAnalysis._find_sources_iterate(): Performing spectral fit for PS J0916.7+0956.\n",
      "2021-06-09 14:55:48 INFO    GTAnalysis.free_source(): Freeing parameters for PS J0916.7+0956       : ['Prefactor', 'Index']\n",
      "2021-06-09 14:55:48 INFO    GTAnalysis.fit(): Starting fit.\n",
      "/data/shared_data/anaconda3/envs/fermipy/lib/python3.7/site-packages/scipy/interpolate/fitpack2.py:279: UserWarning: \n",
      "The maximal number of iterations maxit (set to 20 by the program)\n",
      "allowed for finding a smoothing spline with fp=s has been reached: s\n",
      "too small.\n",
      "There is an approximation returned but the corresponding weighted sum\n",
      "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n",
      "  warnings.warn(message)\n",
      "2021-06-09 14:56:10 INFO    GTAnalysis.fit(): Fit returned successfully. Quality:   3 Status: 156\n",
      "2021-06-09 14:56:10 INFO    GTAnalysis.fit(): LogLike:  -185288.620 DeltaLogLike:        1.866 \n",
      "2021-06-09 14:56:10 INFO    GTAnalysis._find_sources_iterate(): {'Index': {'error': 0.18710199006205327, 'value': -2.3633451817936635},\n",
      " 'Prefactor': {'error': 3.8651818235657875e-14, 'value': 1.751830890902589e-13},\n",
      " 'Scale': {'error': nan, 'value': 1000.0}}\n",
      "2021-06-09 14:56:10 INFO    GTAnalysis.free_source(): Fixing parameters for PS J0916.7+0956       : ['Prefactor', 'Index']\n",
      "2021-06-09 14:56:10 INFO    GTAnalysis.find_sources(): Found 1 sources in iteration 0.\n",
      "2021-06-09 14:56:10 INFO    GTAnalysis.tsmap(): Generating TS map\n",
      "2021-06-09 14:56:13 INFO    GTAnalysis._make_tsmap_fast(): Fitting test source.\n",
      "2021-06-09 14:57:04 INFO    GTAnalysis.tsmap(): Finished TS map\n",
      "2021-06-09 14:57:14 WARNING GTAnalysis.tsmap(): Saving TS maps in .npy files is disabled b/c of incompatibilities in python3, remove the maps from the /data/users/ysong/Fermipy_Tutorial/sourcefind_01_pointsource_powerlaw_2.00_tsmap.npy\n",
      "2021-06-09 14:57:14 INFO    GTAnalysis.tsmap(): Execution time: 64.10 s\n",
      "2021-06-09 14:57:14 INFO    GTAnalysis.find_sources(): Found 0 sources in iteration 1.\n",
      "2021-06-09 14:57:14 INFO    GTAnalysis.find_sources(): Done.\n",
      "2021-06-09 14:57:14 INFO    GTAnalysis.find_sources(): Execution time: 152.41 s\n"
     ]
    }
   ],
   "source": [
    "srcs = gta.find_sources(model=model, #specify the models we used above. \n",
    "                        sqrt_ts_threshold=5.0, #This is defining the TS threshold of defining the source as detected. \n",
    "                        min_separation=0.3) #This is the minimum angular separation of sources. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08c17dfc",
   "metadata": {},
   "source": [
    "This process automatically updates the model by adding these newly found extra sources. \n",
    "\n",
    "The sources named as PS JXXXXX+/-XXXX.\n",
    "\n",
    "Example: PS J0916.7+0956, 3.5 degrees away from ROI center, and TS = 34.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1f6e131f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 15:00:01 INFO    GTAnalysis.print_roi(): \n",
      "name                SpatialModel   SpectrumType     offset        ts       npred\n",
      "--------------------------------------------------------------------------------\n",
      "PSR J0922+0638      PointSource    PLSuperExpCuto    0.000     39.12       969.4\n",
      "4FGL J0924.0+0534   PointSource    PowerLaw          1.158     77.25       359.0\n",
      "4FGL J0922.6+0434   PointSource    PowerLaw          2.065    418.30      2782.1\n",
      "PS J0916.7+0956     PointSource    PowerLaw          3.562     34.59       630.4\n",
      "4FGL J0935.3+0901   PointSource    LogParabola       4.029    313.05      2271.8\n",
      "4FGL J0914.4+0249   PointSource    PowerLaw          4.273     55.87      1038.5\n",
      "4FGL J0932.7+1041   PointSource    PowerLaw          4.808     59.25       382.1\n",
      "4FGL J0909.6+0159   PointSource    PowerLaw          5.602    872.50      2940.1\n",
      "4FGL J0935.5+1152   PointSource    PowerLaw          6.186     19.16        53.4\n",
      "4FGL J0909.1+0121   PointSource    LogParabola       6.205   3326.87      8486.5\n",
      "4FGL J0930.9+0033   PointSource    PowerLaw          6.462    321.84      2612.9\n",
      "4FGL J0855.0+0720   PointSource    PowerLaw          6.782     20.92       582.5\n",
      "4FGL J0950.2+0615   PointSource    PowerLaw          6.967    116.89       476.1\n",
      "4FGL J0946.6+1016   PointSource    LogParabola       7.047   3280.96      7051.5\n",
      "4FGL J0952.8+0712   PointSource    PowerLaw          7.627     -0.00         0.8\n",
      "4FGL J0857.7+0137   PointSource    PowerLaw          7.906     22.78       340.9\n",
      "4FGL J0947.9+1121   PointSource    PowerLaw          7.915     23.22       157.9\n",
      "4FGL J0859.8+0053   PointSource    PowerLaw          8.013     33.67       105.7\n",
      "4FGL J0946.2+0104   PointSource    PowerLaw          8.181     91.22       492.3\n",
      "4FGL J0849.5+0456   PointSource    PowerLaw          8.309      0.24        87.0\n",
      "4FGL J0905.6+1358   PointSource    LogParabola       8.397     22.66       283.4\n",
      "4FGL J0955.2+0835   PointSource    PowerLaw          8.417      0.33       116.1\n",
      "4FGL J0848.7+0508   PointSource    PowerLaw          8.467      0.42       122.8\n",
      "4FGL J0914.1-0202   PointSource    PowerLaw          8.904      1.25       240.9\n",
      "4FGL J0948.9+0022   PointSource    LogParabola       9.153   5729.18      9068.0\n",
      "4FGL J0845.4+0442   PointSource    PowerLaw          9.362     -0.00         0.0\n",
      "4FGL J0848.9+0205   PointSource    PowerLaw          9.462     -0.00         0.2\n",
      "4FGL J0858.1+1405   PointSource    PowerLaw          9.518     16.79       572.5\n",
      "4FGL J0912.5+1556   PointSource    PowerLaw          9.592      0.31       119.5\n",
      "4FGL J0909.7-0230   PointSource    LogParabola       9.667      4.00       452.3\n",
      "4FGL J0847.2+1134   PointSource    PowerLaw          9.941     -0.00         0.0\n",
      "4FGL J0917.3-0342   PointSource    PowerLaw         10.415      0.65       202.5\n",
      "4FGL J0842.5+0251   PointSource    PowerLaw         10.585     -0.00         0.0\n",
      "4FGL J0928.4-0415   PointSource    PowerLaw         11.005     -0.00         0.0\n",
      "4FGL J1003.4+0205   PointSource    PowerLaw         11.235     -0.00         0.0\n",
      "4FGL J0920.3-0443   PointSource    PowerLaw         11.380     -0.00         0.0\n",
      "4FGL J1008.0+0620   PointSource    PowerLaw         11.385      4.17       513.6\n",
      "4FGL J0835.7+0936   PointSource    PowerLaw         11.872     -0.00         0.0\n",
      "4FGL J0839.8+0105   PointSource    LogParabola      11.937     -0.00         0.0\n",
      "4FGL J0922.4-0528   PointSource    PowerLaw         12.115     -0.00         0.0\n",
      "4FGL J0840.8+1317   PointSource    PowerLaw         12.153     -0.00         0.1\n",
      "4FGL J0948.6-0338   PointSource    PowerLaw         12.221     -0.00         0.0\n",
      "4FGL J1012.3+0629   PointSource    LogParabola      12.441     -0.00         0.1\n",
      "4FGL J0936.5+1847   PointSource    PowerLaw         12.646      0.91       230.1\n",
      "4FGL J0831.8+0429   PointSource    LogParabola      12.707      1.25       287.9\n",
      "4FGL J0949.2+1749   PointSource    PowerLaw         12.987     68.32      1812.4\n",
      "4FGL J1008.0+0028   PointSource    PowerLaw         12.989     -0.00         0.0\n",
      "4FGL J0918.9-0625   PointSource    PowerLaw         13.084     -0.00         0.0\n",
      "4FGL J0829.4+0857   PointSource    PowerLaw         13.285     -0.00         0.0\n",
      "4FGL J0958.0-0319   PointSource    PowerLaw         13.386     -0.00         0.0\n",
      "4FGL J1016.0+0512   PointSource    PowerLaw         13.455      0.54       193.2\n",
      "4FGL J0837.3+1458   PointSource    PowerLaw         13.799     -0.00         0.0\n",
      "4FGL J1017.8+0715   PointSource    PowerLaw         13.826     -0.00         0.0\n",
      "4FGL J1009.5-0034   PointSource    PowerLaw         13.832     -0.00         0.0\n",
      "4FGL J1010.0+1416   PointSource    PowerLaw         14.009     -0.00         0.0\n",
      "4FGL J1018.4+0528   PointSource    PowerLaw         14.029     -0.00         0.0\n",
      "4FGL J1019.7+0511   PointSource    PowerLaw         14.379     -0.00         0.0\n",
      "4FGL J0825.8+0309   PointSource    PowerLaw         14.471     -0.00         0.0\n",
      "4FGL J1018.9+1043   PointSource    PowerLaw         14.600     -0.00         0.0\n",
      "4FGL J0848.0-0524   PointSource    PowerLaw         14.772     -0.00         0.0\n",
      "4FGL J1018.2+0145   PointSource    PowerLaw         14.781     -0.00         0.0\n",
      "4FGL J0952.1-0607   PointSource    PLSuperExpCuto   14.795      0.75       238.7\n",
      "4FGL J1010.8-0158   PointSource    PowerLaw         14.877     -0.00         0.0\n",
      "4FGL J0902.4+2051   PointSource    PowerLaw         14.995      4.15       469.4\n",
      "4FGL J0854.8+2006   PointSource    LogParabola      15.026     20.08      1035.9\n",
      "4FGL J0958.6-0522   PointSource    PowerLaw         15.072     -0.00         0.0\n",
      "4FGL J0855.4-0714   PointSource    LogParabola      15.405     -0.00         0.0\n",
      "4FGL J0839.4+1803   PointSource    PowerLaw         15.461     -0.00         0.0\n",
      "4FGL J0942.3-0800   PointSource    LogParabola      15.482     -0.00         0.0\n",
      "4FGL J0821.1+1007   PointSource    PowerLaw         15.491     -0.00         0.0\n",
      "4FGL J0856.8+2056   PointSource    PowerLaw         15.574     -0.00         0.0\n",
      "4FGL J0827.6-0059   PointSource    PowerLaw         15.622     -0.00         2.1\n",
      "4FGL J1026.9+0608   PointSource    PowerLaw         16.080       nan         0.7\n",
      "4FGL J0847.9-0702   PointSource    PowerLaw         16.131     -0.00         0.2\n",
      "4FGL J0906.3-0905   PointSource    PowerLaw         16.224     -0.00         0.0\n",
      "4FGL J0910.6+2247   PointSource    LogParabola      16.395     -0.00         0.0\n",
      "4FGL J0826.0-0157   PointSource    PowerLaw         16.439      0.00        10.4\n",
      "4FGL J1023.7+0038   PointSource    LogParabola      16.475     -0.00         0.0\n",
      "4FGL J1011.3-0427   PointSource    PowerLaw         16.526     -0.00         0.0\n",
      "4FGL J0831.5+1747   PointSource    PowerLaw         16.646     -0.00         0.0\n",
      "4FGL J0947.6+2215   PointSource    PowerLaw         16.777     -0.00         0.0\n",
      "4FGL J1027.9+0252   PointSource    PowerLaw         16.793     -0.00         0.0\n",
      "4FGL J0833.4-0458   PointSource    LogParabola      16.820       nan         0.1\n",
      "4FGL J0908.9+2311   PointSource    PowerLaw         16.859     -0.00         0.0\n",
      "4FGL J0921.7+2336   PointSource    PowerLaw         16.963     -0.00         0.0\n",
      "4FGL J1022.7-0112   PointSource    PowerLaw         17.014     -0.00         0.0\n",
      "4FGL J0930.9-1015   PointSource    PowerLaw         17.035     -0.00         0.0\n",
      "4FGL J0953.0-0840   PointSource    LogParabola      17.128     -0.00         0.0\n",
      "4FGL J0829.0+1755   PointSource    PowerLaw         17.179     -0.00         0.0\n",
      "4FGL J0959.4+2120   PointSource    PowerLaw         17.245     -0.00         0.0\n",
      "4FGL J0812.5+0711   PointSource    PowerLaw         17.295     -0.00         0.0\n",
      "4FGL J0906.1-1011   PointSource    PowerLaw         17.304     -0.00         0.1\n",
      "4FGL J1021.1+1626   PointSource    PowerLaw         17.424       nan         0.9\n",
      "4FGL J0816.4-0007   PointSource    PowerLaw         17.739     -0.00         0.2\n",
      "4FGL J0812.0+0237   PointSource    PowerLaw         17.942     -0.01         1.5\n",
      "4FGL J1002.8+2140   PointSource    PowerLaw         17.946     -0.00         0.1\n",
      "4FGL J0812.3+1143   PointSource    PowerLaw         17.963     -0.00         0.0\n",
      "4FGL J1014.8-0537   PointSource    PowerLaw         17.967     -0.00         0.0\n",
      "4FGL J0809.8+0507   PointSource    LogParabola      18.072     -0.00         0.0\n",
      "4FGL J0927.2+2454   PointSource    PowerLaw         18.302     -0.00         0.0\n",
      "4FGL J0811.4+0146   PointSource    LogParabola      18.302     -0.00         0.0\n",
      "4FGL J0843.0-0853   PointSource    PowerLaw         18.336     -0.00         1.9\n",
      "4FGL J1002.5+2215   PointSource    PowerLaw         18.404     -0.00         0.1\n",
      "4FGL J1018.1+1905   PointSource    PowerLaw         18.446     -0.00         0.0\n",
      "4FGL J0956.5-0958   PointSource    PowerLaw         18.677       nan         0.3\n",
      "4FGL J0836.2+2141   PointSource    PowerLaw         18.717     -0.00         0.0\n",
      "4FGL J0856.6-1105   PointSource    LogParabola      18.844     -0.00         0.0\n",
      "4FGL J1028.4-0234   PointSource    PowerLaw         18.933     -0.00         0.0\n",
      "4FGL J1036.5+1231   PointSource    PowerLaw         19.241       nan         0.6\n",
      "4FGL J1039.6+0535   PointSource    PowerLaw         19.269       nan         0.5\n",
      "4FGL J1040.5+0617   PointSource    PowerLaw         19.469     -0.00         0.0\n",
      "4FGL J0827.0-0708   PointSource    PowerLaw         19.478     -0.00         0.9\n",
      "4FGL J0804.5+0414   PointSource    PowerLaw         19.484     -0.00         0.0\n",
      "4FGL J1018.3-0703   PointSource    PowerLaw         19.592     -0.00         0.0\n",
      "4FGL J0828.6-0747   PointSource    PowerLaw         19.660     -0.00         2.6\n",
      "4FGL J1027.6+1828   PointSource    PowerLaw         19.852     -0.00         0.0\n",
      "isodiff             ConstantValue  FileFunction      -----   3488.26    146331.0\n",
      "galdiff             MapCubeFunctio PowerLaw          -----   6549.04    176609.8\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gta.print_roi()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93477c07",
   "metadata": {},
   "source": [
    "If you want to be more cautious, fit the ROI again!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "57519137",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 15:01:49 INFO    GTAnalysis.fit(): Starting fit.\n",
      "/data/shared_data/anaconda3/envs/fermipy/lib/python3.7/site-packages/scipy/interpolate/fitpack2.py:279: UserWarning: \n",
      "The maximal number of iterations maxit (set to 20 by the program)\n",
      "allowed for finding a smoothing spline with fp=s has been reached: s\n",
      "too small.\n",
      "There is an approximation returned but the corresponding weighted sum\n",
      "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n",
      "  warnings.warn(message)\n",
      "2021-06-09 15:02:05 INFO    GTAnalysis.fit(): Fit returned successfully. Quality:   3 Status: 156\n",
      "2021-06-09 15:02:05 INFO    GTAnalysis.fit(): LogLike:  -185288.619 DeltaLogLike:        0.001 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'fit_quality': 3,\n",
       " 'fit_status': 156,\n",
       " 'fit_success': True,\n",
       " 'dloglike': 0.0012502240133471787,\n",
       " 'edm': 0.0013698212712260748,\n",
       " 'loglike': -185288.61917883522,\n",
       " 'covariance': array([[ 4.80270192e-02, -5.89977459e-03, -1.32913792e-03,\n",
       "         -7.04259190e-04,  6.40836865e-04,  2.20729445e-04,\n",
       "          6.33537577e-04,  4.84329541e-04,  2.44372278e-04,\n",
       "          9.02902245e-04, -5.57998369e-04, -1.70317770e-07,\n",
       "          9.14092897e-04,  1.28738594e-03,  1.57287893e-04,\n",
       "          2.72874155e-05, -4.10026581e-04],\n",
       "        [-5.89977459e-03,  1.56272995e-02, -1.25818792e-03,\n",
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       "        1.04249566e+00]),\n",
       " 'errors': array([2.19150677e-01, 1.25009198e-01, 9.31742381e-02, 5.20783513e-02,\n",
       "        3.31112525e-01, 1.30778386e-01, 2.72577315e-01, 1.50490396e-01,\n",
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       "        2.92436624e-02]),\n",
       " 'indices': array([168, 169, 186, 187, 189, 190, 204, 205, 207, 208, 209, 366, 367,\n",
       "        369, 371, 372, 374]),\n",
       " 'is_norm': array([ True, False,  True, False,  True, False,  True, False,  True,\n",
       "        False, False,  True, False, False,  True, False,  True]),\n",
       " 'src_names': ['4FGL J0914.4+0249',\n",
       "  '4FGL J0914.4+0249',\n",
       "  '4FGL J0922.6+0434',\n",
       "  '4FGL J0922.6+0434',\n",
       "  '4FGL J0924.0+0534',\n",
       "  '4FGL J0924.0+0534',\n",
       "  '4FGL J0932.7+1041',\n",
       "  '4FGL J0932.7+1041',\n",
       "  '4FGL J0935.3+0901',\n",
       "  '4FGL J0935.3+0901',\n",
       "  '4FGL J0935.3+0901',\n",
       "  'PSR J0922+0638',\n",
       "  'PSR J0922+0638',\n",
       "  'PSR J0922+0638',\n",
       "  'galdiff',\n",
       "  'galdiff',\n",
       "  'isodiff'],\n",
       " 'par_names': ['Prefactor',\n",
       "  'Index',\n",
       "  'Prefactor',\n",
       "  'Index',\n",
       "  'Prefactor',\n",
       "  'Index',\n",
       "  'Prefactor',\n",
       "  'Index',\n",
       "  'norm',\n",
       "  'alpha',\n",
       "  'beta',\n",
       "  'Prefactor',\n",
       "  'Index1',\n",
       "  'Cutoff',\n",
       "  'Prefactor',\n",
       "  'Index',\n",
       "  'Normalization'],\n",
       " 'config': {'optimizer': 'NEWMINUIT',\n",
       "  'tol': 0.05,\n",
       "  'max_iter': 40,\n",
       "  'init_lambda': 0.0001,\n",
       "  'retries': 3,\n",
       "  'min_fit_quality': 2,\n",
       "  'verbosity': 0,\n",
       "  'covar': True,\n",
       "  'reoptimize': False},\n",
       " 'niter': 1}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gta.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8f0fae36",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 15:02:43 INFO    GTAnalysis.print_roi(): \n",
      "name                SpatialModel   SpectrumType     offset        ts       npred\n",
      "--------------------------------------------------------------------------------\n",
      "PSR J0922+0638      PointSource    PLSuperExpCuto    0.000     39.23       967.6\n",
      "4FGL J0924.0+0534   PointSource    PowerLaw          1.158     77.35       358.1\n",
      "4FGL J0922.6+0434   PointSource    PowerLaw          2.065    418.42      2772.2\n",
      "PS J0916.7+0956     PointSource    PowerLaw          3.562     34.59       630.4\n",
      "4FGL J0935.3+0901   PointSource    LogParabola       4.029    312.89      2271.4\n",
      "4FGL J0914.4+0249   PointSource    PowerLaw          4.273     55.96      1037.0\n",
      "4FGL J0932.7+1041   PointSource    PowerLaw          4.808     59.25       384.1\n",
      "4FGL J0909.6+0159   PointSource    PowerLaw          5.602    872.50      2940.1\n",
      "4FGL J0935.5+1152   PointSource    PowerLaw          6.186     19.16        53.4\n",
      "4FGL J0909.1+0121   PointSource    LogParabola       6.205   3326.87      8486.5\n",
      "4FGL J0930.9+0033   PointSource    PowerLaw          6.462    321.84      2612.9\n",
      "4FGL J0855.0+0720   PointSource    PowerLaw          6.782     20.92       582.5\n",
      "4FGL J0950.2+0615   PointSource    PowerLaw          6.967    116.89       476.1\n",
      "4FGL J0946.6+1016   PointSource    LogParabola       7.047   3280.96      7051.5\n",
      "4FGL J0952.8+0712   PointSource    PowerLaw          7.627     -0.00         0.8\n",
      "4FGL J0857.7+0137   PointSource    PowerLaw          7.906     22.78       340.9\n",
      "4FGL J0947.9+1121   PointSource    PowerLaw          7.915     23.22       157.9\n",
      "4FGL J0859.8+0053   PointSource    PowerLaw          8.013     33.67       105.7\n",
      "4FGL J0946.2+0104   PointSource    PowerLaw          8.181     91.22       492.3\n",
      "4FGL J0849.5+0456   PointSource    PowerLaw          8.309      0.24        87.0\n",
      "4FGL J0905.6+1358   PointSource    LogParabola       8.397     22.66       283.4\n",
      "4FGL J0955.2+0835   PointSource    PowerLaw          8.417      0.33       116.1\n",
      "4FGL J0848.7+0508   PointSource    PowerLaw          8.467      0.42       122.8\n",
      "4FGL J0914.1-0202   PointSource    PowerLaw          8.904      1.25       240.9\n",
      "4FGL J0948.9+0022   PointSource    LogParabola       9.153   5729.18      9068.0\n",
      "4FGL J0845.4+0442   PointSource    PowerLaw          9.362     -0.00         0.0\n",
      "4FGL J0848.9+0205   PointSource    PowerLaw          9.462     -0.00         0.2\n",
      "4FGL J0858.1+1405   PointSource    PowerLaw          9.518     16.79       572.5\n",
      "4FGL J0912.5+1556   PointSource    PowerLaw          9.592      0.31       119.5\n",
      "4FGL J0909.7-0230   PointSource    LogParabola       9.667      4.00       452.3\n",
      "4FGL J0847.2+1134   PointSource    PowerLaw          9.941     -0.00         0.0\n",
      "4FGL J0917.3-0342   PointSource    PowerLaw         10.415      0.65       202.5\n",
      "4FGL J0842.5+0251   PointSource    PowerLaw         10.585     -0.00         0.0\n",
      "4FGL J0928.4-0415   PointSource    PowerLaw         11.005     -0.00         0.0\n",
      "4FGL J1003.4+0205   PointSource    PowerLaw         11.235     -0.00         0.0\n",
      "4FGL J0920.3-0443   PointSource    PowerLaw         11.380     -0.00         0.0\n",
      "4FGL J1008.0+0620   PointSource    PowerLaw         11.385      4.17       513.6\n",
      "4FGL J0835.7+0936   PointSource    PowerLaw         11.872     -0.00         0.0\n",
      "4FGL J0839.8+0105   PointSource    LogParabola      11.937     -0.00         0.0\n",
      "4FGL J0922.4-0528   PointSource    PowerLaw         12.115     -0.00         0.0\n",
      "4FGL J0840.8+1317   PointSource    PowerLaw         12.153     -0.00         0.1\n",
      "4FGL J0948.6-0338   PointSource    PowerLaw         12.221     -0.00         0.0\n",
      "4FGL J1012.3+0629   PointSource    LogParabola      12.441     -0.00         0.1\n",
      "4FGL J0936.5+1847   PointSource    PowerLaw         12.646      0.91       230.1\n",
      "4FGL J0831.8+0429   PointSource    LogParabola      12.707      1.25       287.9\n",
      "4FGL J0949.2+1749   PointSource    PowerLaw         12.987     68.32      1812.4\n",
      "4FGL J1008.0+0028   PointSource    PowerLaw         12.989     -0.00         0.0\n",
      "4FGL J0918.9-0625   PointSource    PowerLaw         13.084     -0.00         0.0\n",
      "4FGL J0829.4+0857   PointSource    PowerLaw         13.285     -0.00         0.0\n",
      "4FGL J0958.0-0319   PointSource    PowerLaw         13.386     -0.00         0.0\n",
      "4FGL J1016.0+0512   PointSource    PowerLaw         13.455      0.54       193.2\n",
      "4FGL J0837.3+1458   PointSource    PowerLaw         13.799     -0.00         0.0\n",
      "4FGL J1017.8+0715   PointSource    PowerLaw         13.826     -0.00         0.0\n",
      "4FGL J1009.5-0034   PointSource    PowerLaw         13.832     -0.00         0.0\n",
      "4FGL J1010.0+1416   PointSource    PowerLaw         14.009     -0.00         0.0\n",
      "4FGL J1018.4+0528   PointSource    PowerLaw         14.029     -0.00         0.0\n",
      "4FGL J1019.7+0511   PointSource    PowerLaw         14.379     -0.00         0.0\n",
      "4FGL J0825.8+0309   PointSource    PowerLaw         14.471     -0.00         0.0\n",
      "4FGL J1018.9+1043   PointSource    PowerLaw         14.600     -0.00         0.0\n",
      "4FGL J0848.0-0524   PointSource    PowerLaw         14.772     -0.00         0.0\n",
      "4FGL J1018.2+0145   PointSource    PowerLaw         14.781     -0.00         0.0\n",
      "4FGL J0952.1-0607   PointSource    PLSuperExpCuto   14.795      0.75       238.7\n",
      "4FGL J1010.8-0158   PointSource    PowerLaw         14.877     -0.00         0.0\n",
      "4FGL J0902.4+2051   PointSource    PowerLaw         14.995      4.15       469.4\n",
      "4FGL J0854.8+2006   PointSource    LogParabola      15.026     20.08      1035.9\n",
      "4FGL J0958.6-0522   PointSource    PowerLaw         15.072     -0.00         0.0\n",
      "4FGL J0855.4-0714   PointSource    LogParabola      15.405     -0.00         0.0\n",
      "4FGL J0839.4+1803   PointSource    PowerLaw         15.461     -0.00         0.0\n",
      "4FGL J0942.3-0800   PointSource    LogParabola      15.482     -0.00         0.0\n",
      "4FGL J0821.1+1007   PointSource    PowerLaw         15.491     -0.00         0.0\n",
      "4FGL J0856.8+2056   PointSource    PowerLaw         15.574     -0.00         0.0\n",
      "4FGL J0827.6-0059   PointSource    PowerLaw         15.622     -0.00         2.1\n",
      "4FGL J1026.9+0608   PointSource    PowerLaw         16.080       nan         0.7\n",
      "4FGL J0847.9-0702   PointSource    PowerLaw         16.131     -0.00         0.2\n",
      "4FGL J0906.3-0905   PointSource    PowerLaw         16.224     -0.00         0.0\n",
      "4FGL J0910.6+2247   PointSource    LogParabola      16.395     -0.00         0.0\n",
      "4FGL J0826.0-0157   PointSource    PowerLaw         16.439      0.00        10.4\n",
      "4FGL J1023.7+0038   PointSource    LogParabola      16.475     -0.00         0.0\n",
      "4FGL J1011.3-0427   PointSource    PowerLaw         16.526     -0.00         0.0\n",
      "4FGL J0831.5+1747   PointSource    PowerLaw         16.646     -0.00         0.0\n",
      "4FGL J0947.6+2215   PointSource    PowerLaw         16.777     -0.00         0.0\n",
      "4FGL J1027.9+0252   PointSource    PowerLaw         16.793     -0.00         0.0\n",
      "4FGL J0833.4-0458   PointSource    LogParabola      16.820       nan         0.1\n",
      "4FGL J0908.9+2311   PointSource    PowerLaw         16.859     -0.00         0.0\n",
      "4FGL J0921.7+2336   PointSource    PowerLaw         16.963     -0.00         0.0\n",
      "4FGL J1022.7-0112   PointSource    PowerLaw         17.014     -0.00         0.0\n",
      "4FGL J0930.9-1015   PointSource    PowerLaw         17.035     -0.00         0.0\n",
      "4FGL J0953.0-0840   PointSource    LogParabola      17.128     -0.00         0.0\n",
      "4FGL J0829.0+1755   PointSource    PowerLaw         17.179     -0.00         0.0\n",
      "4FGL J0959.4+2120   PointSource    PowerLaw         17.245     -0.00         0.0\n",
      "4FGL J0812.5+0711   PointSource    PowerLaw         17.295     -0.00         0.0\n",
      "4FGL J0906.1-1011   PointSource    PowerLaw         17.304     -0.00         0.1\n",
      "4FGL J1021.1+1626   PointSource    PowerLaw         17.424       nan         0.9\n",
      "4FGL J0816.4-0007   PointSource    PowerLaw         17.739     -0.00         0.2\n",
      "4FGL J0812.0+0237   PointSource    PowerLaw         17.942     -0.01         1.5\n",
      "4FGL J1002.8+2140   PointSource    PowerLaw         17.946     -0.00         0.1\n",
      "4FGL J0812.3+1143   PointSource    PowerLaw         17.963     -0.00         0.0\n",
      "4FGL J1014.8-0537   PointSource    PowerLaw         17.967     -0.00         0.0\n",
      "4FGL J0809.8+0507   PointSource    LogParabola      18.072     -0.00         0.0\n",
      "4FGL J0927.2+2454   PointSource    PowerLaw         18.302     -0.00         0.0\n",
      "4FGL J0811.4+0146   PointSource    LogParabola      18.302     -0.00         0.0\n",
      "4FGL J0843.0-0853   PointSource    PowerLaw         18.336     -0.00         1.9\n",
      "4FGL J1002.5+2215   PointSource    PowerLaw         18.404     -0.00         0.1\n",
      "4FGL J1018.1+1905   PointSource    PowerLaw         18.446     -0.00         0.0\n",
      "4FGL J0956.5-0958   PointSource    PowerLaw         18.677       nan         0.3\n",
      "4FGL J0836.2+2141   PointSource    PowerLaw         18.717     -0.00         0.0\n",
      "4FGL J0856.6-1105   PointSource    LogParabola      18.844     -0.00         0.0\n",
      "4FGL J1028.4-0234   PointSource    PowerLaw         18.933     -0.00         0.0\n",
      "4FGL J1036.5+1231   PointSource    PowerLaw         19.241       nan         0.6\n",
      "4FGL J1039.6+0535   PointSource    PowerLaw         19.269       nan         0.5\n",
      "4FGL J1040.5+0617   PointSource    PowerLaw         19.469     -0.00         0.0\n",
      "4FGL J0827.0-0708   PointSource    PowerLaw         19.478     -0.00         0.9\n",
      "4FGL J0804.5+0414   PointSource    PowerLaw         19.484     -0.00         0.0\n",
      "4FGL J1018.3-0703   PointSource    PowerLaw         19.592     -0.00         0.0\n",
      "4FGL J0828.6-0747   PointSource    PowerLaw         19.660     -0.00         2.6\n",
      "4FGL J1027.6+1828   PointSource    PowerLaw         19.852     -0.00         0.0\n",
      "isodiff             ConstantValue  FileFunction      -----   3486.55    146250.2\n",
      "galdiff             MapCubeFunctio PowerLaw          -----   6550.36    176710.8\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gta.print_roi() #see if this improves the results?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "525d8760",
   "metadata": {},
   "source": [
    "Doesn't look like it. But it's still good we tried to fit again to confirm. \n",
    "\n",
    "Next we would want to do source localization, aka tryiong to fit the spatial parameters of the source. \n",
    "\n",
    "Usually the default settings by the function wouuld be sufficient, but in case if not, there are a few things that can be twicked. \n",
    "\n",
    "dtheta_max is the half-width of the search region. \n",
    "\n",
    "fix_shape is to indicate if you don't want to fit the spectral parameters again. \n",
    "\n",
    "free_background is to set everything free in the ROI to fit. It might be the best way to do but freeing this is extremely slow.\n",
    "\n",
    "free_radius sets the sources within certain degrees of ROI center free to be fitted. An alternative to the above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6e058a75",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 16:33:34 INFO    GTAnalysis.localize(): Running localization for PSR J0922+0638\n",
      "2021-06-09 16:34:04 INFO    GTAnalysis._localize(): Localization succeeded.\n",
      "2021-06-09 16:34:04 INFO    GTAnalysis._localize(): Updating source PSR J0922+0638 to localized position.\n",
      "2021-06-09 16:34:04 INFO    GTAnalysis.delete_source(): Deleting source PSR J0922+0638\n",
      "2021-06-09 16:34:04 INFO    GTAnalysis.add_source(): Adding source PSR J0922+0638\n",
      "2021-06-09 16:34:09 INFO    GTAnalysis._localize(): Localization completed with new position:\n",
      "(  ra, dec) = (  140.6559 +/-   0.0492,    6.7878 +/-   0.0499)\n",
      "(glon,glat) = (  225.3170 +/-   0.0443,   36.5488 +/-   0.0543)\n",
      "offset =   0.1769 r68 =   0.0740 r95 =   0.1194 r99 =   0.1481\n",
      "2021-06-09 16:34:09 INFO    GTAnalysis._localize(): LogLike:  -185283.889 DeltaLogLike:        4.730\n",
      "2021-06-09 16:34:09 INFO    GTAnalysis.localize(): Finished localization.\n",
      "2021-06-09 16:34:16 WARNING GTAnalysis.localize(): Saving TS maps in .npy files is disabled b/c of incompatibilities in python3, remove the maps from the /data/users/ysong/Fermipy_Tutorial/psr_j0922+0638_loc.npy\n",
      "2021-06-09 16:34:16 INFO    GTAnalysis.localize(): Execution time: 42.41 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MutableNamedTuple([('name', 'PSR J0922+0638'),\n",
       "                   ('file', 'psr_j0922+0638_loc.fits'),\n",
       "                   ('config',\n",
       "                    {'nstep': 5,\n",
       "                     'dtheta_max': 0.5,\n",
       "                     'free_background': False,\n",
       "                     'fix_shape': False,\n",
       "                     'free_radius': None,\n",
       "                     'update': True,\n",
       "                     'tsmap_fitter': 'tsmap',\n",
       "                     'make_plots': True,\n",
       "                     'write_fits': True,\n",
       "                     'write_npy': True,\n",
       "                     'optimizer': {'optimizer': 'NEWMINUIT',\n",
       "                      'tol': 0.05,\n",
       "                      'max_iter': 40,\n",
       "                      'init_lambda': 0.0001,\n",
       "                      'retries': 3,\n",
       "                      'min_fit_quality': 2,\n",
       "                      'verbosity': 0},\n",
       "                     'use_cache': True,\n",
       "                     'prefix': ''}),\n",
       "                   ('ra', 140.655885394029),\n",
       "                   ('dec', 6.787834776304419),\n",
       "                   ('glon', 225.3170403316819),\n",
       "                   ('glat', 36.5487736685713),\n",
       "                   ('xpix', 68.5322266512814),\n",
       "                   ('ypix', 70.98038265552267),\n",
       "                   ('deltax', -0.09677733487185236),\n",
       "                   ('deltay', 0.14803826555226038),\n",
       "                   ('skydir',\n",
       "                    <SkyCoord (ICRS): (ra, dec) in deg\n",
       "                        (140.65588539, 6.78783478)>),\n",
       "                   ('ra_preloc', 140.558425),\n",
       "                   ('dec_preloc', 6.639806),\n",
       "                   ('glon_preloc', 225.4198805553923),\n",
       "                   ('glat_preloc', 36.392434596541904),\n",
       "                   ('ra_err', 0.04919139710867354),\n",
       "                   ('dec_err', 0.049944915545761935),\n",
       "                   ('glon_err', 0.0443372211002496),\n",
       "                   ('glat_err', 0.054300082536623176),\n",
       "                   ('pos_offset', 0.17686496404124538),\n",
       "                   ('pos_err', 0.04879995129547136),\n",
       "                   ('pos_r68', 0.07400879046986827),\n",
       "                   ('pos_r95', 0.11943532612329509),\n",
       "                   ('pos_r99', 0.14809800342920182),\n",
       "                   ('pos_err_semimajor', 0.05533886896958728),\n",
       "                   ('pos_err_semiminor', 0.04303368122230955),\n",
       "                   ('pos_angle', 46.769021637927935),\n",
       "                   ('pos_ecc', 0.6287106376268099),\n",
       "                   ('pos_ecc2', 0.8084861579859159),\n",
       "                   ('pos_gal_cov',\n",
       "                    array([[ 0.00196579, -0.0003534 ],\n",
       "                           [-0.0003534 ,  0.0029485 ]])),\n",
       "                   ('pos_gal_corr',\n",
       "                    array([[ 1.        , -0.14679152],\n",
       "                           [-0.14679152,  1.        ]])),\n",
       "                   ('pos_cel_cov',\n",
       "                    array([[0.00241979, 0.00060409],\n",
       "                           [0.00060409, 0.00249449]])),\n",
       "                   ('pos_cel_corr',\n",
       "                    array([[1.     , 0.24588],\n",
       "                           [0.24588, 1.     ]])),\n",
       "                   ('tsmap',\n",
       "                    WcsNDMap\n",
       "                    \n",
       "                    \tgeom  : WcsGeom \n",
       "                     \taxes  : ['lon', 'lat']\n",
       "                    \tshape : (10, 10)\n",
       "                    \tndim  : 2\n",
       "                    \tunit  : \n",
       "                    \tdtype : float64),\n",
       "                   ('tsmap_peak',\n",
       "                    WcsNDMap\n",
       "                    \n",
       "                    \tgeom  : WcsGeom \n",
       "                     \taxes  : ['lon', 'lat']\n",
       "                    \tshape : (5, 5)\n",
       "                    \tndim  : 2\n",
       "                    \tunit  : \n",
       "                    \tdtype : float32),\n",
       "                   ('loglike_init', -185288.61917883522),\n",
       "                   ('loglike_base', -185288.61900955572),\n",
       "                   ('loglike_loc', -185283.88883282273),\n",
       "                   ('dloglike_loc', 4.730176732991822),\n",
       "                   ('fit_success', True),\n",
       "                   ('fit_inbounds', True),\n",
       "                   ('fit_init',\n",
       "                    {'fit_success': True,\n",
       "                     'fit_inbounds': True,\n",
       "                     'xpix': 68.54908487686686,\n",
       "                     'ypix': 70.89793671628767,\n",
       "                     'zoffset': 48.15959180638927,\n",
       "                     'peak_offset': 0.5584900320381028,\n",
       "                     'pos_err_semimajor': 0.0650089641981166,\n",
       "                     'pos_err_semiminor': 0.05160769199150316,\n",
       "                     'theta': -2.382879666862875,\n",
       "                     'pos_angle': -136.52894799878234,\n",
       "                     'pos_err': 0.057922038992278733,\n",
       "                     'pos_r68': 0.08784312142878936,\n",
       "                     'pos_r95': 0.14176115822088967,\n",
       "                     'pos_r99': 0.1757816985792957,\n",
       "                     'ra': 140.65418604229282,\n",
       "                     'dec': 6.779590505886115,\n",
       "                     'glon': 225.32500821148224,\n",
       "                     'glat': 36.54331108423976,\n",
       "                     'pos_ecc': 0.6081070805698211,\n",
       "                     'pos_ecc2': 0.7660178144741181,\n",
       "                     'skydir': <SkyCoord (ICRS): (ra, dec) in deg\n",
       "                         (140.65418604, 6.77959051)>,\n",
       "                     'pos_gal_cov': array([[ 0.00276212, -0.00038026],\n",
       "                            [-0.00038026,  0.0041274 ]]),\n",
       "                     'pos_cel_cov': array([[0.00348644, 0.00078029],\n",
       "                            [0.00078029, 0.00340308]]),\n",
       "                     'pos_gal_corr': array([[ 1.        , -0.11262048],\n",
       "                            [-0.11262048,  1.        ]]),\n",
       "                     'pos_cel_corr': array([[1.        , 0.22653258],\n",
       "                            [0.22653258, 1.        ]]),\n",
       "                     'glon_err': 0.05255585440754089,\n",
       "                     'glat_err': 0.06424485556287934,\n",
       "                     'ra_err': 0.059046115506704015,\n",
       "                     'dec_err': 0.05833588554544134,\n",
       "                     'pos_offset': 0.16907009084755498,\n",
       "                     'loglike': 24.079795903194636}),\n",
       "                   ('fit_scan',\n",
       "                    {'fit_success': True,\n",
       "                     'fit_inbounds': True,\n",
       "                     'xpix': 68.5322266512814,\n",
       "                     'ypix': 70.98038265552267,\n",
       "                     'zoffset': 51.32748678903225,\n",
       "                     'peak_offset': 0.11872355731227104,\n",
       "                     'pos_err_semimajor': 0.05533886896958728,\n",
       "                     'pos_err_semiminor': 0.04303368122230955,\n",
       "                     'theta': 0.8162734155183138,\n",
       "                     'pos_angle': 46.769021637927935,\n",
       "                     'pos_err': 0.04879995129547136,\n",
       "                     'pos_r68': 0.07400879046986827,\n",
       "                     'pos_r95': 0.11943532612329509,\n",
       "                     'pos_r99': 0.14809800342920182,\n",
       "                     'ra': 140.655885394029,\n",
       "                     'dec': 6.787834776304419,\n",
       "                     'glon': 225.3170403316819,\n",
       "                     'glat': 36.5487736685713,\n",
       "                     'pos_ecc': 0.6287106376268099,\n",
       "                     'pos_ecc2': 0.8084861579859159,\n",
       "                     'skydir': <SkyCoord (ICRS): (ra, dec) in deg\n",
       "                         (140.65588539, 6.78783478)>,\n",
       "                     'pos_gal_cov': array([[ 0.00196579, -0.0003534 ],\n",
       "                            [-0.0003534 ,  0.0029485 ]]),\n",
       "                     'pos_cel_cov': array([[0.00241979, 0.00060409],\n",
       "                            [0.00060409, 0.00249449]]),\n",
       "                     'pos_gal_corr': array([[ 1.        , -0.14679152],\n",
       "                            [-0.14679152,  1.        ]]),\n",
       "                     'pos_cel_corr': array([[1.     , 0.24588],\n",
       "                            [0.24588, 1.     ]]),\n",
       "                     'glon_err': 0.0443372211002496,\n",
       "                     'glat_err': 0.054300082536623176,\n",
       "                     'ra_err': 0.04919139710867354,\n",
       "                     'dec_err': 0.049944915545761935,\n",
       "                     'pos_offset': 0.17686496404124563,\n",
       "                     'loglike': -185283.99475409105})])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gta.localize(name, make_plots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db00df4e",
   "metadata": {},
   "source": [
    "And we can check the results of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "04251eaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('psr_j0922+0638_localize_peak.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "debc3cdc",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-39-01bb55fa89d3>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-39-01bb55fa89d3>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    Now all the things are read for us to plot the TS map without the central source and make SED of the source.\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "Now all the things are read for us to plot the TS map without the central source and make SED of the source. \n",
    "\n",
    "To do TS map, we need to again use the assumed model for sources above when we were finding sources. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bd42a9f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 16:38:00 INFO    GTAnalysis.tsmap(): Generating TS map\n",
      "2021-06-09 16:38:03 INFO    GTAnalysis._make_tsmap_fast(): Fitting test source.\n",
      "2021-06-09 16:38:55 INFO    GTAnalysis.tsmap(): Finished TS map\n",
      "2021-06-09 16:39:05 WARNING GTAnalysis.tsmap(): Saving TS maps in .npy files is disabled b/c of incompatibilities in python3, remove the maps from the /data/users/ysong/Fermipy_Tutorial/ft1_pointsource_powerlaw_2.00_tsmap.npy\n",
      "2021-06-09 16:39:05 INFO    GTAnalysis.tsmap(): Execution time: 64.49 s\n"
     ]
    }
   ],
   "source": [
    "maps=gta.tsmap('ft1',exclude=[name],make_plots=True) #You can change the filename as you like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c388a77b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rU8O8okyTzk/DgXqCDErw5XUkNO6+iJAxqp5gA6BjqBQOJPAm0zhqM1yqVNp0Hbj3dVaCahxryuYgEgWraqOZ1MI57gIi+h37EAORRgPI+Xz169P1s+q/YV650kVoOgeaWrNl3muwAh/LJEtfWuZguRu7825QQmWJUNUaPNT1/kZ/3e+DlSeSlHZiTSyd9QxIiPU+txYoC+WiAeCrK4Qw0xLRruWkI8M1bitn5ChY9dl2KCPdBwIE/hSM9MYpGOkDYyClBh0AQLNKpXWDAjIexJKMKmKESTMcMVYgUfIqADRZZqccEWYBN0qc5EbALpJbiVRe6DpKD+eUYBd9BQAcNzW7Ss4naqbU6xy93rOzCK/AyjJNkuQmrPOl2PbWhpXpX1id3Gbzo5EAM6kx3FGpZFl6+kB2igtICjTnlBy6yxiDZkty9sw5Le+EFcd3s1A7jSc+Z0FLCUQrygGHIJ1rz/tzGRTnBACZG6Rx6/0szK7xMPbImZG+EuQT3hA4BSM9EM5HaN7xHJO3K9m0vAqwUw+xBDfSAIQbgakDkukUeVE7g0YwiItSBd0h2pmHvY4daRdqoVqmARFkPMyqG0SyJbJ8+IgTUCx7UPKFqOu25LEqTboO3E5M4qP8ed3C0DHEUgmlQBrk2nsiPq70GZGgi1HjgMlM/SDm834EOSLQxQWaf/M2uJHB8F9H4LpRlVLdHLbzigHnwkKxbYINXlVHx5iI7yJjtg9EEGbzTA6WDU3FJAUIEiDBaDZFgvp7LNnuE9P+7QsiMdJVjXJGjkXoCB6SvHqqSvldVdX//BIyiRPWqkEbUywNR4nz1dXRnZl3wS5qmj6gInKEfNhMxO0qyB6Y7P5UptkPp2CkB0JpUD2zuH6nZjzK1w3Ka0YwQCgBYcBOCcUUYA+YecjeYewFZXx4nARYArgJ4GkNNA5UR+laYZXwyhz5J5H8FuLiW9dZ8gjgOATFXCaJPUpiGhjdkkePh31hUdh2LHHLC0hy2sCtFBLrfUZERNVETQ0Jvl+9PH5mGpSonpeoLxh2OoQtC5V9Onf4mrRvmv8NDmnq3RUfyYQuCGD0YbrR84U694wetMegNODmY+/KUyBfN0o0T2ZyPc+Rr4MEVRQxaxk3yZWv74eo7BoHY6IPyKHXjaL8uihAaLTlwoZztpw04CExWE8E1v1wCkZ6gCsHOwuwU4YYAAS4IRAswY2hPxOBrfTBpKABB3kCBcKwIdSNw/CFh5mLShMnM8CHrKOnpokLMWsQQhSdI9XzYGG/uy0Q2UY6XSBMqueGRBMlJT72W41z3w1roylbHS3UsbJhmAYimo1BWeiEPZ2CEDM/QctFK+3g47GafVAOQi/1Q1LRNA2KawcKBmYaScGbbNAfqLfBMnRNPUJZwNroTntkf4w+71/Y9ntZllov38MHIATRQDpypPSUnVLlAWZw4rSJntTN/uNM6r2q0VKN88eR9R8A1+hzbQuDpj7cXkDnuNRnZ8vLvdfv6SFL70/YGadgpAf49WsMXnmC8QXDl4TmnFA/JfgBUD8LCIUy54spANFAxMxc5naMnzr4xuP8P75Q58RXX0eYTLOCAMBNaWDyDkj6ehQakDBrtmQdumqWVd1wl3X6IVqAT6bABHtNvlSW4JefK5l0XkEur9W2fja/aUxFrM6MZQl69gThbASezDQjMU8W30rKW1WmSZ1mxYfO6/umvwVyPUH55ddQDkrQi2tVoKwrSSVbe+DeCIN3CiLweAwMBkBTw19u7mJ79Lc3Rn1Aoj+HzLWb9Y3vJ9mou8O+DwkBzAQeDnJ7AAL0PjmkQaUIwmwGzKubz9WOwW1WTM2rlhSdOzB3SqF7KrJ26bgcB7LwGh+DEWN3DEZWlQ875xPXQNzNn6885ogu0cdEwGH6tAf4ke4Ep2CkDxoHrj1MLRAChAjBAqEAwkAgViBWVTFKy1fOiKZZBQUIrvaaDWkcwmy+sb+MrJpYkp9JCLkF90aQWn2vRDyepON7cMhiExUDUlgl2ZrtjcMoH2NUbRDdMOGRP/NanxFiEInW5fcdt/dKRHbRAj4Rg9eNOy0CjwHHGCYTyBpISIqqI5yzB5KcFakMsDyAI3rsBB/AMRuZyx+IGaZD30dWKKS6ZPGtx0dTtUjgXUDK5N0DT8i7GIwcS977Bgjw/YFqmkOOfcw4BSM9oH1LVO3CRsmq3Ch/xE6iNLcCSHvnQRgIyZ2wAPisQOO8KnCiGgDAAulzUcWyJkiJP6fkM7K2pKA7qIVgI/3KFuCB7gBlNttfytgdl3Ogqwm4Y+XdlbYuvrg1n+IkV55VCHWnF0zEqmBkobnfjZ1ybNAWMydhMl0brIhzkKvr6DNSdxrErbgWia8A3MqkmUsiR0i90zHakIvErFNoSyR3iXTfpL+Xu9Ky0ewa0Urpbl+EIPlcEAGNhuqcCiC8/uI4ZaobxMueGchEYu8+U4c2nQO2H9udQNjk90vZkK3GZ/u+7y7oXtMHKvc9YTtOwUgfBG2ixo2AjYAdgRuV7coVAQyYGVppbpb36t9mXMI77TQK59qggilnKbZ5USQzLlDMwGwqT8RSj1DHFjy9p2FgNNQFMBLstFfL/pOD1DXkxaVa1yfDtnWy40zsqyHTWW5Ol70fOuNYF4ysXXyIQeMR6PwMVNU6rmp9MOIvL3f8gCt2t8cCUeRHFB1uxP3vkMJ8DqzzAblrdJVZpOoZslYD8lTqPOT+DaJqmmS6dmERnp7r281mwNUBY0/PH7Xk7BtE3O0DjGTNe+CIpOuaO3ATxPtcptnL+OxY9ze15GV5AMoar4nwg45/M+IUjPSBBM2MNAJDAlMDoRCEQOBC50nqrpnRtlogIA9YIjS1b5UrxgDRWZHKEmBCwHztwpmRdwESh7XjrqZbm5XYnTafkhZMo7JpGfOCG+U2Ql/3vJRS0DuMb6HuvQSzisC6DT5o6SXs8P77Yl/lRXTURFr4uhmHqJo6fGhHUDfcN4LE+ym0LsMdC3AlTA9z1+WQORT7BSUhhOx3Q6zZILqtTNg+ipqH4r/RmU9CHM/RyjSH4IEQSE6ckf1wCkb6QAQ0naP8hoUUDHZDuDHDDQnzwAgFYGoNQCAq56XGg4O2Sh80Hq5xqpIx6ixJEkDDIeRiDADgV18gvPLqatIpYpBQ2EVb63UTlPcQ4pVlB2kcwtV1u2gZAzLRVAkAXVwALz0FDIOncyCl653TCX9e3eS7JKIfMYhdVg5sS+9L40AUiborxsqGEVyPHWTwCNcTULT1bsm/R0LHnnurH8Kqw8sS5q1vAcoCMpkivP5CF954fR98l+C7gLqPQa4nKnevmxwIc3QjpbMxwlufQQoDvpyBX78CnEO4nuxVUglelVvJ7yVcXYNj9i3MDswO5dJr6z3S+zt+CPdE8sLpoKnd7mWa2/gM4YBresKDwSkY6QMRoG7A1zOgsCgsg0IBCoxmTIAQyEGJq6LeCOSD9tGoHQqJ7HNe7O4powHC+RBCgL2eRcOvsDrQ75Z0Ni1akurra8o4wUMqD+n0eEmNvcBqP+2ejiCWYZjBFG2tqzpLabHKarwny12C5M7D6z6L9qbpN8n08rLYA338EG4cay1kPISMB+AQYudZF0t0e6SZH4ncuC9SI8okawfajAjKAhgO4C+G8AODIgBmXgMNg2YzNc9L2DGjoGWaxOfQHjU+mbId2XvkUWNp/K5xt9+fZhse0DUNIPgDOFvhGHyvR4hTMNIX3oOcliG4CTCVRzAEO9dSTDEVFJOgv5s04GttzkXOoyDEMk282aIlNhHlSVCi/G9t6cV77emxSlmwjG4X0bWv0aCFSLSclCTDzoErB/EMEoEYbfoFa0HkW0vqYyDZeqfyxJIqYGvX3m7vnF1q8JS6wZrWBbSvY2fXD6HvROi9mtwRAU3rjZJLZWt69CwQnbu/X/X+yWdklb3/jdc+kPQ/kOXENBrGTFuTA1WSDlGR9D7lufrzcKXOuZlH1OFoKLZ/T9lnZGE8ceNQ2pxF7H2/vMHhG/8wyjQPBEEOqww/FrHesXEKRvogmRfNK5Ax4MIiyXbFEEJBKCYe9qoB1w7mlSvIJDayAmBcA1dHw6Ig2sysbtTG/MVlbqm+SRWwtTSz8OIdCZfdNCeg5Lq6Bl/NIMl4zRjtPkoEiFXDJeL9CZ2dgCPxTABkclz+XZDYKG/N+xDlrrtwbge+jWaC+PwMNBpBqhrhtdd6KzGkqXtnRPKxPkC6FvYpCIulMlWNrPCFKWwngNotCEpZL+29ssIbJV6PtYHcvr4Ve4KMAb/lJYTnF6DagV55XU3/iCIPiVpStPPgqznYGtDVFDJVVZj4Ngjv49KZfEbawbTtCng8Bg1KiHOqrLlHo7GHhlvpT/OI4Q/MjBxy7GPGKRjpg1gWkWSG5APgW7kvAHAdwLUDVSrhRadUUBjK7PPM/vdeg5DUd2MX+eRtLAqZIBeb4sWxEZDt6ffGOv+DTv+Z5Z/rmJRQaLZlRvYdk+G2M+w+OMCRE84pedj7mBWSVv2EDWRFSq/Z5X3SIaTKq1U4ok/HUUDaGiCUBixaNhEmrPwAMeMo0XtDs4XSOVW/ST1zRlaBSdVhfods45sM3vn7L9Oc8OhxCkZ6IFS1mj8VHmJtm14XUdv3QDBzB76u8q5XPQFUWstMaOa1kvKiX0DaYckqr4BdUuzHRJLbBoFQBZhJKzlNBmspWGpqLStsa+zWTbsny/awSDajsmgn+pgCT2RWJn3NWs6IaDM1rHLmXPf6IJDpTBewxu123BEh3kNms6xUSt4lxBsex3hNlOi6PTOWiMjps+1Nik1B6l1BQjYXpDqWsEhVXm3vGcQsJUCzuQbMdQ00ShQhE/sdAW0QLWs4WB1omWapEzRUTiuprBYJ3Ce0cLWD2ddnpItdyqd9MnULrrGHD29XnDIj++EUjPSAVJXW9L0HlS3HQTMjAeIJPHeg6Vzr1/MKoW50h0YMawjNvEG4vG6zIBseqNauHfuXQ3p/SC3thCqopDJJKJfcXtvafJQDr+UjsPp9nI31uiUCbIhZodisDta2RmlJ7tp4LYUBGzMjvVPmwWtH4mm/w44GEfXv6CK2sUdh1l/L4G+Wb3Z4r43XJ2XCdjjPrSMtNEEJ30hZD6DNBnXJut4pSTl2982vTaUpLAVlW+6T7DOy8EMNSNBoMJJN8U7IcM2GzEhPPhKVpW5O6ma951KXBwSszyDmhp07v/1REIQQZP+A4pBjHzNOwUhP5B2m9+DGgasG4hlMWs6gRgOMBUOz2JnWDgr4x7SzSs3qOn4ky+UaYtr+sDunJavQaWaXGv9RJIOSX3T6TDLOuMM9epnmoUE6Hhn7LnadTFofn5FDFtcF19gUXB/IM9Emi42WYKKpXyY4My82jAyy8DmJ6AYfCTt6zSSfkZVjSmXVsN4P580K747IGfEe8Lxo5piaanLMnMRGnCH1+jnhDYFTMNIXQcmD0jjgm6+CLvXhsOOhLtQu7sCC6L+jeRiVJYrREM6F1SWZFdgozT0GkgplVYYmlWzgVVHDcVfZzZCk/w8biLLBI1xeg2bzjmqmfS9hAgOQYHO/ni53pncw8ohlrl1zuRvluuWfLYOotfd3DjtnereRnLPV9or7lQ342VP1yalqhMtLHb+JLp0hINTN7i6j8Z6Tq2tQ3UCS/X7i1aRrE7s16xiSuiaWE+PPiCh6zHRKW1uwljOS/HNusRXAY8ZGzkhPlVqYV6C6Xigrki1AQ22YiOdPgUEJvp5Cvvp1LeWsOZesepbuAKcyzX44BSP7IJUyZnNQ3UQTMt/WKOPuquvbAcMohiVc7XfngOwizd0XKY1JrKYoqxakrlFTiMcAHaVClCNveStVnjQ3F1hi7ZsTW4Grxfvi69LisJMDa7dHxYHW9veCNenmltC7ZUFNhnh39LmJCVQUkNEABGRVTi6TEAHoaTiXpLMpcE1IzeAAILhMPM4drIEbpcSF43YI6Bd8RpbLC7sq096EUM7IkTIj4aZHERnOG7owHiCMSxjntxO57+n592B47D9vv1nvslMwcghSrwgAMA3IeKDQTImWJEy7m6sbGBI0VY/JOdWrtz1U3b4XqU66TaoZLelzI71tRNTl05SFWthDCbxbJbUrMi9A7M71bZYAACAASURBVEQaSzerdq+phr9TZiRdr1XvB+hOvizUK6JvX5AjIJc01slsVx1jTL7OYV5tHHMqgUnjkLvcHtpSHli7kEvQRnqJQNolA9NyMNHjfSWIkpk777sQUGwJ0PN9tC4QWeNLE0R9Rng4zBkmWWXsd8IClDNyC0vJ0j0oTQO+moEqB7qevvFLt28ynIKRQxB3SyIBmAUIMfiJAcqx/s55UOy7EmZzGAS4qr/B1lZ0mm9R3JGSVxJqPr7jmdAeEzMbKTtCWL3wLGQ0OPfSofMz/dG+k3a6ftEJdtVnTbXonSeeDdeLy0LJtCJqGb4tgDomiECDgaqKnEO4DjsRb6ksQWdnygPxfv2YRR1D83XM3zu3C++u2aLkygtsJlkHD//iEnS1uLCLBIjfo4tqLkcFiNtk761BLBlkpVrKjqRyTu51JEvPAHH2pSHvEWrkDKSImg/S+ZlmfGYzdWB9bBm2O0a4DWlv5x4EEMt1AVK9Gv/d7BzQ3zXkQALrst3+mwWnYOQYiHJR4s6CmcyZcr09wFoD3/RUfmxCKnUY00oZ08TczTJ0H+wllUHqeEqSMipdaeMd7jzWTCpHJ7AmmeiuXh0Lxx5oANYl/95W+a2z8O49T6esGYCtSeMVafV2LAd8Z0tBdP5Zh8yYf5/M+JhAe6o4iUlpXpZbu/mmwUHGfvvikfGebs30LGZu83MTwiJ/6IFenxNnZD+cgpFjI2hGBIhyRGtzF1tbGjT1EYKRjosokt9J6lhaN9lSPj2sPB7nLEZ6Xdfzg6zV86DDe/ReyYfdBSURDL1Xb5XkYTG/vVR2rzLNFogPoJjBERHl+OzC34lpfRqNQOORZjYur3v3vslku6QU2GHRyceE0BIoNyE28eOiAA+H4LLQhobL77PhvZULEqcG2S2Ds3SCGCyY/TroEoNMq0JDbFGwLN0lIg0aUuDElI0JIbUqwahTuovIFv75e4iZvuFATdbOx0BZaG+p4hrwa0jex0TKXJYleDQEoM35HkOZyB3bDj7dw+dnakyYDSHDYg+nE95QOAUjx4bEFvBRYkiGIZHYZwuL+eTA7p9p0rIWNBoBgzIreMQ57a67tEjScABcnOl4XJQn1o2WloDWYrszqUvd6GuWkVLxld+viVjPXV/OjIQjTD4SdHHvEh6FNi+2KavEpIHIk3NQoy0BegUjHdOy9ty8dVKV9L3ugpQ1KKx2hk4clVXGUZuIvqTZAcmGY3sgcmP2oeNRbAZJ0QRLP0NszojEveE4TtOWaoj03m5qzQwxlA8VFj+DqpawcO3JWkhZ6vM6HkBKC64b/TmwWTF2DHSlq2fxWfXhUQQjR3VgTfNbYXXeslY9m5omVpHXcOEeELww/FrL412OP+JgHhFOwcghiDtmAG1KEcglkmzxHhcTUxi4ZrF8krHrgp52nbG8gtR2vmkWupsuoHGgRJwNIZJr2+wNijYz0iv9uc9OsecxbHi7kmbH69j1iMmZpF08NpLfinOgeZ07ye6FuAAeZJyVAiTi6EvSbZoXg2GJ/W1WXQ8RbMwEL7cl6DToa/knEesWhh0VLG1wXWg2xhjQeBSDEIaUBWAYVDUqD+96+KSSZLL2j3JiFCUoSCawagfs7eMRH8DxnKEw4CRR9l4D9NvcjEvkwcRSRA6sHgF8l8DK8Zp1TR27/Y9WZSLTMQkpaxg9YtQkUdpA/iE1dlyBAEI4QE0T7tIu9gHhFIzsg0SEGw6yt0G366w02klU6jpaleuCURQWrpNqX1hQdtx16a43NkxrGl0Y6+aGP0cXYToFdfX4cQLP6e7hEDIe6k5sMtWMyJJl+31ha8femNIlwy15cXnS62QCbqR4V2UNuj+X6GBKpH4pkyly59a+2MSv6AGyBfjJOcAGMp2qm2xnzKGqVNkyq1SBs2YsejJqg42U9fIeMpvrTt1a9XcAbkhnRQQym93M3HQbL27xRknfHZ2NQU8uIGWB5u0XqJ4XCIbghgRhwvB1j+HXZqAmgK9noFnV8rIAyKCEDAsgQAOKstBnYzqLna6dkmLXEWK9R3AebAhhaOHHFjwfgAcDDcBmstXBdWesyg4mMndqnEmR97VrKfGQsRx4bu882MaWF2dj0HCo/kLXE0jjlDg+HABBtPS0lFHk0bAtI8d7d2F+i93N267eW1yf7xknzsh+OAUjfdEl0Bmjk57ErrPUyYwki+rOomUKA7fMGUnS2p09vdqyge4U4vtsKBkspPq7qfy0uFj9HOI8iLmNyx9AXXZrMAIoryCqI4RYd8LdmKzTeG/vCUxE/VJ62mbcCmLTNrIWqMzNck/eUe4gX15Q23SOFw+RTiuAdL93Fq90vVdix+ucv7voVxIGBapnBWYvGQQL+GFq8mdgJyVM5cG1a0uI6ZkzDCmMrlGFBYnolG4Ysi2wjuqjkO5/wwiWABPLrC6aqN3FjjxyI4iWy3nbu1Hr8XuUTA/8XF1pL1mr3Y2bps0qGdNy26qbC21Xvi7ea1aLqc0+BsnZPiBl5+6BWHzCreIUjPQBx1bi4/GCsVmyMQeA1H01/z+QH6KitG2ZJkk9C6u7oV3KI2x0Bzke53KDBC3T9JpQJLLSoy8FJX5IOt5osMKDAUQkp8NzU7e7qtkSwVizlS+SGuUtlF0Wdp1rjl91zR7obmsB3gOzOYQpNhO7GWTRjpsrSkZpXp2Fb8i4z8bKTUolEyLtLzSZHqwIImMyL0Beeor5Oy7gzg1e/3cWs7cLhACVx2h2ZHBlIFOCuWZdsERyFpJ8ADt9hsh57WuTdtRda/E1kMbBx6Z79rUpuCrBL6ba7LJu2jYGx8C2UmJd547OvTcEfeaBqMTrk5ldBde4zBlRTha17QGAtpwMZDt3eK/E6pRlTNm95FXTHY503VR3K7ndJw7njDyCOegWcApGeoAKC744h7z0VB/6qgaqBkQO0qBduBHLNkuLhClszoyQMaDRUFOasxkwrzZPCNExlcZjhJcuQC6AZpVmZPqkj+N4JKaudREqQHXRBk9J/jvivLuEMaCkIrmLYCSVwqzZnBlJZZRNWPVwp9IN4eEQ4nblvjgHf3W19XU7vWVhQYNB7mDcvQfJGNB4DHlyhlBahHEBYYJ9bQaaV4cvzsR6/w8HqN96hst3l2guCFffVeM9//ZraLzB5XyAxhtM8RTDVwxKQyhfNTkQkesJxDnQVB06wQThKHV3TsmPfjs3SJoafjLVjOfXX4EpCm10eT3ZUxG0Z7YhbPCS2fh+nezf1td2SKIx63UjEO0Bn6S9yesmtcNIgUOco8jo903WQOpa/Ymc06Ck01D0BvfukTUmVM7I/qWWQ459zDgFIz2QUspioxqDWUscUXWQU6sprbgEu0xgBQ6a0IUJFDoEWvE7L2j6+wDAtEoEQ4CYaPlOEIq7mfS5gai6uTsC2S5lmoPwgHdYG7HD9T/4K+qqTTonoyQl32WRSEHfMocHaO+lVAJhQAwABgrW58SwwAeBSnQ7pYg0htTPh1QVpYRsfTZujG9LKUN8UCm5cxDimFl5uH4WC7jN1hFb4H0MRtL17Vz3TPBPCsNVXaJFoLwdqJFdVD+1Wc6ufxO3ZRos/vxWuTUn3DpOwUgP0HCoyhNAyxYjJc1R5bR+3mi5Q+pad2NLZNKitNn0TLyHTGf6eu/bHcq6yU/UWE2m07bNeZ7UTSs/tRYo1LAppUHXfyCthdNohPDsPKa4I1HMB013i0CifJLqBjybIyyTRA81A1uFOEERHdHwbMX5H9Tkdcyx7Fin0Xu1VRct/C7ebwSArFFFFhHocqKk2W4qfpVrKxslNJaFNkGsqlzGzLLdqArj2qO4FkAA+40C/zB8GyCE0DDEEUYvGMXUw04DqPKZFJ6eMwmiO21ikC9AhY/29Fq+y1mADc37vA+6qCb56CFqlru+r/rczyk72rijNP9zTYAdllqCWbjvOCufEm+N6hqSSr7d+bGbsaU1ZZgO1y1tmABkFRZ8JM0ei2i8J8KBvWlOapoTtoLKUhfmtJMrDMQQmAg0L7RHS6wvd7tOJixIe2WpE2g0idroZyABMos+JRzr7aZtWidCrTafKPJANqR8o0RYBgX8WBUT3GgwQl6AJgUj0XiKowzYGBCJqhO6nhXHzjKIwDDdXmbkIQUi9wSJaoXVvwytoR0b5RYRQa6vNR2/HDinFHu8h4lJ0/LDgablk6w6GZZ1+plwE2DnAhBQvmDMxgM9pVdXVTsF7ExgqmiLH7kgeQydZo5gankGsVTApQGVhZJDnVvd9SCp4USUk/LY0Pd+PlJ5MviAYli2XZO7WTQgd6OWIKqy2jRO2TD/EVoFYJJyQ+dllIWWzWfz46me9sSJM7IfTsFIHyTLdRENPAAIEcQquY841qmnqw+3Hc6IlkVMZJIXmnUhgkxUqrnS3CdNsLU25ROm/J7JX0Qa104ACRxJiqmVevC6cAQBKICqBmaiahxK8scOKZe80XJU8iYxBgJ/e0FIB7depnlMWFXyWPWayF3q3ahuVYYrcYu4letK41bX8ZMfS/6nRN6G3mu6gLQNGsHRF8R5UNWgmHhQYBRXDDeKQXbQYKS4EtiZh5mrZ4507vnFzxDdVMsC1DiVvCMGLQ3lDOMqZP72sIQYqxkVs6eL7JsIrnEwZbHovcOsGTFiJeQmv5d945/EmSsLoFBuEDG3PjN+z8aMJzwYnIKRPoj9F8gFlRsWBiAglBby/AwUBEYEuLoGQaWR3UnMLqhpWG2fBwPgyTmatz2BMKH42iX469+EpIl0KSBJ2RRiAiazG8x18R5ULTYu4+EAdHGuwcjVlTbQA3TX6IHw+gvQbLYoEe3ubsoyS/MkqoDQJal53NpkvZPpWReH9vV4wH1Bsp+KD6ubhHW70a48wZpyWpJ7L5RZdAFO5EKgrf+vDZSXVV0S1OOmqhYC7ryAxEwi6hr8GmEQgHJgQP4MgxdmYZzjbziUX5uokufyKpeJlsdBxoCfXEDORqDpXMsCUQWWPWbWcUZiZoTPz+CKQjcK8wogfrBN2R4CfONQDmLbga6T7GikGYvpHPBXMRA5IBtjDOj8HDIaaEY3ZYWncxUApDnpnhHAJ9OzPXAKRvqgs1CRRCdIIg1IjAEJwFZT0KtuJyWwRjVNsry2FmFQojm3qlR4UahWP0hcWJZO0k1JL41J/15hrBXfByFAotEagHa3UtftrmaZa5AM0tJ7ECvn1VMkCTIOmmC2gA3fHZu+m3l4aEjlh1X27ukl2dJ/z+8jcjm0tBF/1kmb73RZFiTV0QI/lWaMact9cTebZOZoGtC8AnmL4nqAULT3IYnAXsffV42WQtf1J2HSTUNh8+ZhV2JjysBxOjbtvNN9f/K1WImmdjgv0n0ZnWQBlWwXVgPoI1xHova7FWbAxiafHLkpD0Ty64XgD+i8e8ixjxmnYKQHJPX6AHSiZV2QxRD8QBd4czXIxDxZkuva0i6anrFpsy2xIYEUsQFeVQNVtb3+uYuqoq4hk0k2Ylv4XRDN4iwv+B0fFZRF289Ff5mdZQ+1Nc+ktFhWaAl1uoDcaZkmkQBvE/sqkbrqkXVlhg75jwg33ycFz2uyIjwY6P0d0+rHUCfkoDuVOFMg0l2c8jiisoZjX5kAmDqAPMC1BzVJqrvBf8N7yGQKCgKZV6tLOWsQ0nWdzUFgdZdNvhd3tcitIgLvcowt2ixpz15Ga7/nXcqCUAdWY9sSNqBNHeV6Asyt8otyVmrP65g2f43T9hbMkLh8SSTuSxC9f++ZM3LCfjgFI31QWJ1EfVAZbAxEQslwZwbCQHE5gB0NNTXduAVPjgVpb2ryZQzAAHvRTWhhEJ6MQTMLurzq1yhrTRpeqqptapdf26ntew/I0gOcnS0jc5158efEq9P1m8bUHVfHjp4HA6CI3Y2pigoJAOJh+gYjh6bSbzMVf6C3Sdf4KZ9vKROxdde54vPlLr2jIajQRY3qJp7qgICkw4vSz53UX5pip+XzEumOl5CiXnAVYJoArhxQ1eotssGATLxX75HZPC7Mu5dXcmA9myJUPrZzuOPyTGxSmMe0y8JKys8gY9oWFLu+V2oWuSLwSc/nAlF4BXzjYTtj1pJdg3B1lYOZVW0qesOrXX47b7YBLAqrt82arPRdwh+opvH3/gnuB6dgpAdkabEnr1JYYtLMhminTQALi32CKSx80y4oElQZQI0HV5GQ2ng9p1u6JTdxGToBwsaFauE8W3wJ4i46mxYlmWO21j5gl7OMRGYEdPFizqUC4p6ckceAQ67brs39sPsaKkG0QVs383KUJjrcLmhR4ZJJ00C0+I7vkzx7LMOXDDcgmAYQQxDXkbITbV1wRNQKvi+hMTn9ZtXPCkXc3rgN+XsXMXOw82cmyn2uFszF9siGLWRGuuOJZWB9v87vCYdl3YKoVUkQgBGDcNH7KXdwXuJA+bvr4xCEEQ5Q04Q3KTfpFIz0gSHN4ke7Yr6eg2Y6gZq5WmWb62p1Gp0ItjBouj4jLy4h19qXoXg9krJqbQ4VnGuzImzAsVnZgkdC6hpMnNPguVPwOolwNyjZJSARjzCZqawzIXXS7OFpsOrn4j2IortrWQAh9igRAaJfwINU06SsTtr17TqJ75K52IQQSdHpXBuHuGPdOabVQ92ALi+zeiQFPMvN8QAsZsRWpfLjfUnWgs7O4nfbaUHgfbYHz1ylsoB7NoIfWky/xWL2VoKdARRUPm/mhTZcA5T3tK6BXFyEKMqG+zQ0TPcZhaANBo8VbBOBRyMtJTiHMJut//6CR0hD3vX9g8+fc6dANXq8mJeeK9F3rqTg1EsrE5G72HC/ucbDdGTay+VXQDcVqTynLTR8Kw/fcv72vNwqslLA6KHjr+qoEtTPxuNxbr4nV1fwVw64o+rNKTOyH07BSB+kBystxD7og2UYtvEQQ/pgrDqOOJqetQ9fmEfPkMlkY32WmNTILL63pIcuGZ1R5/do1G+EZLNte+YPbF9EtUHcHl1qt0HiIs6m3RknsmwMxO6UwLoL0vcUyw/ifeTc3FEn0VvaVUPUhjyfPZeUOvbc6VfoBEXLjpgibRPJotCmacNSiafzShfOeaVBN7elOhiGH1m4sUF9QaifCoIlDF4nsCMEG30ljMptl99zYfHM5YXQizCZYynsUH7sg1R6iR2At7Z+2KeE15MnQsZAzkYIF2OwMcB83loCdM+1w7O3kBnJwUecl5gXOUMxkKSmXrCN3xmpLIPOPOwc0NR5rLlZ38VY/900wNTcWTBywn44BSM9QNMKFEzbMZQ1StesYwBFKa8akN2cxM0qO/gu1gQGEkQfKHR4A0kanJqcxRbbEncMWxfwLumt20fijvu0SBBtGw60gRWQiay9OSPHAi0utvn/06RaFHmsYYtk9L5wsO9CtO7O38ny90CsGbuiiA7E8TqY9hh4r06+qaFjsm+XAHhAoJkIahzMTL/z8tIglAQzU6MzrtSETGIDRwwGbVdeQO/7bofsptH7qWl6LXRJ2ktLz+3BiJwJcm69Cui2kbKoSJkt7W2lho112wywa+WeCPvLjstL8J2uvd2O5lSWUdUUy0GdQAJloUGQ1+A0b8y24QZHKmV6GeAAYhtLgZR9kSTOyXeFgMMUMQ8sD3xnOAUjffDKaxDYPCnTcKgpaI5kvMTTsJFMZUzLMwl+0fRsGZsWjuA73iD6Oi4L0POnkEGpOvvLq9zpc+suKQYiPBrmYEREQN5revouA5Lg4a8noOl0YReegpHePiPHQIdcS5GECY67W7v4yOhoj9A07tjYtUyzDul+7sqJXcfsLKXhz89Ag4Fafc/mN9Lu4jyIteSYLeS7Qa/3auE+mcK+PoKZWlwIMHzdgBuBnXpwE8C1B8oCEqXzNBxkB2EAkMsr+Nd0oc/eKD0DxKSm4aMHI7GB3LYx7au02gY24NGwJbjGwCO89jpwqdm9G2WpaB6HwQBo6tyPKJsmduCdhymiBUAnEKGLc5XhlgXCqFz8fF5A/kzN8F6/1O+rz7wTM9P5fIYB8ELZmapYugJ0nl5jRnlsHO4zcj89hu4bp2CkB6SqICRtL5ngAZ8kbZ0byMSOX0u+FaYwuTdN/zdfmqRi+lPt2Xn1a1ahu0jlLplBO7Xn9Pd+Q9wboeONkois0u5S7yczwjnNnMphMHxjhyXHXrgeEnLdghd3x91skVF5OoWg10K4bYoGIPuIeCXI3mj/nuTULko2Adhpo4ufAFx5Tcf7aLgXy6IE9eXJKq9838Qx78HNyZwRPv5isLHnCtDJxB0/IKF0nbrBWxCgrgHilQFGLrWl4IJUbr3Kwsa5AJN6/6R+V/HZkeQLUloI6SVQoinUJyQEsLU678gOn72bXVmF7vOY5o0gm7lxJzwInIKRHpDGQ9AhiplGb/SyAEaD1oOENUVI8zko+XE4pwTWVZmRVMftPEjbSJFS15AXl8BEdfypCdlWCV3asXqPMJu3dV1A0923xc/Yc5K9UwJrCh6BduKO/ifwAgpNm3WK10nqun/NG7j1TIpe7iO8R0yjZ/VU8pHwURQxneXyi8yr9j1jQzwELdu0HXC3ZCtEwNc1eB7lu15iYNJJyTNrxjEE3f36oJymA6PoNPaVmZGuu+2KHTwVZWy5EFoSeVcOm4KutVkR7ljPH5CZTE0zgSxL1hYStd7Lri0TicfaAEmC9s4iLPJRlBi8uGxIUcKOhzDf8jYl3TvX9o3xAcEQ/NBq4qIOYAeANWMB4dacbstn4tEQ4a3P4J4MYeYOfDlT75luf6W6QTaj7M7Hh2YKe+Dw3jRvzsDpFIz0gDQ1hIrFHxZed8vMkMICliGWVa57XYKs8iFYApi5JbB2scQ8zzvRxq2dmMQ5+Fdfi//YY9EJHcJid6K9jbRIXuT78yrumjOS1UndHViWPoaWN9FHRZNPruWftUqnB4iVJb9cgnFqMDY37eLb+T2IcuuCnfhIsRTA1Tz2w+lkC5L9N2mGChy5W8l7pD7QD4QIQVryZffnADpk8RXN3mLZiAal7sY7jsYL129DeYY6JM+NxPNtH8PEBpoAMAvZk0USL2sZq+7DxBFKfi6Ji4ZIDl1SWHlbwowG8G97DvNiArma6GcRUd4GM/zIQAiwIXKPomEkAJjsCr36cycfHBqPUL88xvzlAsWVxbCJRP55MlWLzUcTkTU+yzwaAvbugpEAQsD+73fIsY8Zp2DkWCBCLvX5zk4uPsS21Eu9ksCadirpgd+1dfmxdtcSojLhiOdc8R77gA2r/8NS+eZWMwsSoFu3xfcQaYO1fTNID0oZdCx0vU82fS+bskJBdJfrgwYiSV6asnbWZF6WpNf7GBxGQuaxQDHoyfLtjhuxrJNdhtDa298nf4ipDWr2KU3Eppp5Y5RIvd2gLNoMpDKIDwGma2LHlDlo4Lgxq/W1XDlw7bXkZjrB/rb5IQZE5AQc/9yYZ0NY2FBJYPU5YVKjtBMeNE7ByL5I5L6UDozZEbgAjv04pGnyw2yHQwCAc6u9PyTWb4GUvuy3487lhX0mw5RCvi30Gc+NxmcMAcE8fRIdGOtFxdAxJ39py1xdn43kWbFQBtvnPQ/1GekB7TFz62+y+O+lbAGAvOMntMG2NG7xexbRhnrda5sMrLok4tjxN7ePTzJU7/cvSS2YkQV4H2DKUr0qBgPQeKS/ms+1XLUKXYJqPA+A/V1H9ylpplKvtaDhUBfueQXp6fVlzs9ATy70M00m7WeK3wedjSHnY+XxzOYQ5+FrD2sIVDWtb0wQSK3lPfIeg1mlpo6V+ihlczIAcn290eFVjRcDaDZH8eoU5IbguQNVtbpcN82i3Xw+j6rBMBhAxne31J3KNPvhFIwcgAW+RZp8o0wxWSinSdIOdTfh3ZpJ5sCFKsv29j3FQ1ODRBhjEAD1DYhpYwqxr2VwOHoDsyUCpAC5DLfw+zcS9l38tr5mkTSZj/A3mzmKczdLQlH1JR0vHSWwmlaSvm8g0h1/h2QuIWizy+gLgpFuIpSTsL41w8LY74gXtIC4GSFjVM0notep1zlIF+6zkQYVnY7NOXgoC8ioBJzOcyQC7z0Ms5Zkguh3JK3BnfjQWhOkXj/R/AyA8t028ogCkmU9T+e6aLnYq8j5jl390sYkZZqZleh/Rzjc9OwUjJywC7o7vrIESu2yK/HBRN3onzg5pfqqHcRgBMglh1x7lbC6Np/eyxYLjpKrX9uxgn8DgY3ybCTK9NIuWaWd2hQrXE9ux5QNiFyIOMlJuD355S1g50V6j0AkKyeGg2wpLnW96EUSXVhVkRSzGbGksnMTOAnaGbqIGRJOfwzUqIRUCRbHtYsao+uvs5y9CEHAA82MkDW5TT2ATEy9LeQybd9GeQCSykm81yabQJul6IPg1W05ercslL9iwEFV/HnTQJoGblppZmQ6zw7SWUUVCcw34DsGe1s9kTqSXeeButFAJDZC3OTdIrFhIvHj4Gi9mfHmDMEOAbEGBzY2Fjs/0yZNdQOazkHXU007Tmc6eRndZRVnusPyZMDDAXg0BJ+NwE/OQaNRy4lIxMlMalMWOb/0HPzSc33tijEt/P0GAhuCOI8wnWopK9Wsnz+FvPOtwNvfAj6L12/dbr1zTXtDRInLdR3lmbzfee4atzjGFIjrffkMeNvLoJefgy4uwOdn4LORBill2fY/GQxA0aKbRqNICN2yc18u+0QDLVgbs1Utp+NGM76VA6dM8OTxWMe4pJyRFIwMB8CgbJtjQoODnQKErvJnV0jICiSKZaJVVvzrPlc6h9QNZDrVEsse3WulbrT8Mp3HLIYG45I4GVUFmlcaeMznkNkcfjIBEyG88irk6gphOkWYtcfnMXYyRsnbJMxmWzcSuZFj7ISuc+wE4eoactVpJrqSi+QRrq7gv/FK72uxL4LQwX/64LOf/Sx+4id+Au9973txdnaGd77znfjRH/1R/O3f/u2N137uc5/Dhz70IZyfn+PZs2f4OLuIPQAAIABJREFUyEc+gn/6p3861kc/CG+81esukLT31kIKq1r6yBxPkfryjstaA+98u7PPDcRiTT3t9ijVxVNwwtnjAptcBNOO8Q0WkBARfEzFLuz0iCA2lk/u4jPnlC89noDkthDv/WRoJcMCUhZqLFUWOVgg09rJE3Xu4aRYWpZcLgeMC544SUnTcXft4/GSeFVJKWXaMkEXEn0vYDrB7W1+16vOnYPnPe7rmGXt9hfqhZwR8W02cO1rNUBpqgbWcltq67rsSlhPLt6V79V93tIxiTfmE2l1/XnE+149ig5FiGWaff/0NT37vd/7PfzLv/wLfvZnfxZ/+qd/it/+7d/G17/+dXzgAx/AZz/72fy6v//7v8f3f//3o65r/OEf/iF+//d/H//wD/+A7/u+78M3vvGNY1+G3jiVaXqC4kRMwwHCy0/gno/AMwf7zato7ORbZne0VxYmmODVY6Sw4Ehm1a6lApghTPoZYro2yiZDHXt64FLPtYJEx2UBOtM+DDKZIszfIClJIhjDUVIbP9NsFhckhpnXQNPExmMbJrZDyypxIuSB7vZFRFPUS2n7Xmn1x4ZY3iDDoIsL0MUZZFCgefkM7szC1AFmMgL5AL7WnTNCyGl7cQ6UHL99lMODWz+K+EwB8R6uqjY9zwRK9zhR67zZNC0vYZvNemqgl4Kh7s85Eh2hZRqCLnSgSLiU6FBsjHIX9sg4rEXH9wckWsdNHjZuR/Zp556T1G0Y2KukJM5pmSfETVUQUBGbHhYW9OQC/vkZqPLgyNcJLsBYXm8N4JyWfrI7awGqG4Rd3KIB/f6Trfz5mQa+00goFlmnCO58qD0yVQfg8K69/Y793d/9XbztbW9b+NmHP/xhvOc978Fv/MZv4IMf/CAA4GMf+xgGgwE+/elP48mTJwCA97///fjO7/xOfOpTn8Jv/uZv7j3mY+AUjPRF0ryXJZonQ8zeUqK8MjCvT9WAKdWhl1K6DNEdvrVKEAP09c7rDnNQ6q4xuUBWdXzYoqHZpt4NRQEaj3XSrJvcZO5RL4xxZ8jWwjtdfLJxFgCqqsxBSOZOtzcW3cVTWaq6QgSYUWu0lHwYHpB/yNFMz/IJWReRwoIuzuBfvkAoDaq3lKjPGKYB7JkFeUFZWlimVqKb+tD4uDsN0gYhicg4GmblCrzvECdjaaUoINECPqk2yFHe9W6TFudU/w1jO1JOSoS2vadW0ZP8S0LsOeXzxT3etU3jTqTpQ9oxHEKEF4lKJw0qusEFFRYoSoSLEZpnQ5i5B18PQE0DFwRszVofGWmcktCNAY1H2j4AAGa807XMrRmKAjIeIoxLsAjoUs307sU1+gFhORABgPPzc3zXd30XvvSlLwEAnHP49Kc/jR//8R/PgQgAvPvd78YP/MAP4I//+I9PwcijQ5Boax3ALsDUAq6VWZ5TlCvSo8YyXO1ByRwNkXRXBGBQwj3RPjFce5UH58lzB2JqJJPl/+9LZk2EROy5u+9O8Efua3PDDj55TQSJ3XLvbtejkuJONuSQRWlBUvqAg8aUFSo14KWygAwKiGlNq0gAYcCNCBQIprbgZgCqDXjeIVynRa67Uw0Bwqyy3iR7j6+jVJJJf6L/hzCBmvZ4IKnJDDbKvZNcOH+22H+lc54gAu76ZKTvWOKxD/m7OgSJLN9pCgn4m6UiL2AX2mZ0bDS2jB23116fIACpF4vOk7t7soj32ojReW0ZMGdQ4xCWZPgPBdrHe//S3iHHJrx48QKf+9znclbkH//xHzGbzfC+973vxmvf97734c///M8xn88x7GTo7xqnYKQnso269zCvTjAEwNc15PIKMplqDXPFTt1aA9c4oCxy4KG9NYD6SYHJ2wzEAINLQXnlUVyXKF+/As3mW5vfhfgaQBdMSp18d0wn82iku1If9lKmJMId+jTa2xYAxR0eM246sMbf7dTL4khInZMX3i055aasyK4gygRFadwtKoEOP0VSctHFOeTtL8MPLMSwBiOGQB7gRuAHhOplvYfrc8bwwsDMAkY+dtd1DjKft3Lcjh8FAA2mYzYkVJ1SZKGlIZQFpDCxdIJIggydcVp9bSwN5RJHpwwiQdpdNqx2jn1y3i6iQSACMLPait+4FnETcWw5eV8cWzrcVRgBi1waCR0DtCitteqnJIZB4yG8sbCFAQ8GCKsa3gUPkQDxrO0xUrfeHdU+UtcaeJg5uKpAhaoXc+PB+BkeSqB4rDLN5eXlws8HgwEGg8FO5/jJn/xJTCYT/NIv/RIA4JVXlMD70ksv3XjtSy+9BBHBa6+9hm/91m/de9yH4hSM9IVoXw44As8q8HUBns6VGb6hK6ctjFrBGwOxDLEEX+qkXj1hVC8RQvo2xAABKMvoq7BtTJ2uvqmuL7RbClSPsW3nVcP9jJKSQiG1CN/gxbB4XEfquaHoy0yQtQS4u518xAcQ4sWJnXwXX7BjNirxF6zd3xhr21scg3RJlCW5VBRwZyX80IJC7BdDAAUBe4JjghsDwQLkCOwYBRNCaXXX3AmWAbS8iGx01lqOZ6JyJpvqtZauX4nI4jGpq3KSyS9nB1MAC2SHY2KGxK7biVcgQfQ2XhpvlhW/EYnLyUjuxv0sWsLq/tx7LZOlINIYBLB+x8Zoh+ZVj0G6/gFteXPteJbmrViihXPwjYvcna7r7xuzRvOud71r4d8f//jH8YlPfGLrcb/yK7+CP/iDP8Dv/M7v4P3vf//C7zbNC0eZMw7AKRjpCxEAqqGX6RQMtN4fG9KOplA1DWZz2BczyKCAe8sQfshwY0JzIQgFQEIIlhAKYPT8Auw8ZDKBf3G5efFN5YvO5H7j9V2L6463idQ1MJ31yqZ037dNo/Y4NvkQbAEbhg+iSqO7tNpOjpadrqXtoLSXEIwBGtJSAwQ7G7ClPi5ht2uw19ij2VjezXfLF1kl0llkOjb7FNUkFDM4yTDKTGotI/poPMcMnluIZdipBTcGwRLKSUBx7WFmHnw1hczmmhlJZoA3smDajoA6QWnqBSMSbeJnc3BUk9Fkpk35sqmWcrRo+dxrvg9Kbq7DAcKzM4SCNcDyggCAiyJLWXNQAmhAtIt1+UNH19o99m6h508X/UBCyN9bLsN2gzEmwBRIWwlbmOMp27YYoK18xB5IVgTQ63FYmUbxpS99aYHfsUtW5JOf/CR+7dd+Db/+67+On/qpn8o/f/nllwG0GZIuXn31VRARnj17tveYj4FTMLIPYqTuX3sBenG5UxMwW1g0tUN4cQlyHnxxBrx1iPqMUT0lVG91QBHghxbNOcGdM4avnGFIBPOaBV3v4BuQdx+rm1+RMaDBoE1zxvJIqKrDSK/e567BO0/UabezBRwby3FZRNn0lpLGkVLYKcO0sKBHOSGMWkyTtRBOJk87sPoTJNqH31ZqmRhEDBjNeEFi8JAIwF3peOIIJOI1oSXqdmW5IuBXr/T1PuTdrYkS2LIsMBwPAUOg2unuuXHqOzGbL5Aiszx64ZrERSZljZJjZjTHkqtrYDLVoda1BjZA+9ylYDgonyE357txadSvhIoC4WKE+duG8GUMRtToUyXK6XotS0IfwqJ3yBi6hnVlodf6+VPU73oZYZBMGAFuglqvz6q27w7QZkmYEYbq/eGMgbEmtm5Y/74Hjz0dL35hs/BAOOMZxyrTPHnyZCEY2YZPfvKT+MQnPoFPfOIT+MVf/MWF333Hd3wHRqMRPv/5z9847vOf/zze85733CtfBDj5jByGkCbA7U9DKtNIslmOk6kwIBbA0MOOHcKZhzsTuBHgR/rASyKyHoLu8TcWAmn/7AEJ0gYiR56s2bBmhJdTyHeFfTwtdsWtKoCW/Do6149iMzUqbCtVT/8fM0FIPWW4ld/COeVSNJ0/czW/wnQGnszAVzPQ9UyNs+axX0tqjbAtq5AtzbklrQJthqJpNAuZett0njuJwdROCqJ0PSIZVgwiBwbwQcCm5ZAsPBsPIRA5FpJNf/y+w8BolnZk4M4MmnMLf1YijIeQ0SAazkVH2qUgz3v9Ny/Lpm8LKRDJRnenZexXf/VX8YlPfAK//Mu/jI9//OM3fm+txY/8yI/gj/7oj3B1dZV//sUvfhF/+Zd/iY985CN3OdyVOGVG7gimUAJrkrlxXYOCEsybC8F//u/+E75t/Dq+Mn2KV+djfOWbzzD52hBmXsLMhoc/5MFDYDSV3Wlx3gtpV5UIaCmzkXa1t5C+ZiaExi3KNzfhSAvGjV4Xwq29dQiQay1T3PC4uI++JMuIZlMSvyMyJttyU7Rw5+TZkcy9RGJjR9FsSO6Wa/Xec+19I75j9Z3e0hjQdKalFR+yh052rgUWyKSgyMUwBkD0/4i9Z9T1VKWiMpm25cRNHAEJbbZk1WtSqaHr1OoFZuZBjsFN0MyID0Dd437ri67ybJ8O1J3jcz+qHTKzGamsCsR7QtV9zYVBM2bU54TmXInJ5aWFnQvK64DBNytw40Hz6BnSOHBUP4VIFmZCPyL3rmCTy3YU71eK2UkAkMkEYTo9/vvuibtulPdbv/Vb+NjHPoYPf/jD+KEf+iH89V//9cLvP/CBDwDQzMn3fu/34od/+IfxC7/wC5jP5/jYxz6Gt7zlLfi5n/u5vcd7LJyCkTuCLaJfRvCQKvZXiA+uGwt+8C3/L/6L0RfwpScv42vNU/yfw+/Ef3zpO1C/ZlCOC5hdiKzbEDzExV3fJgnvhvJB4hMI0BLRDvE22AI2jBBCfy7LoYgBFgEdtUXrJ7LA5E/o7tLuM3ecxuiaXOYgSi6opE0Hz8exK24kKIagsskUSDjfymlZ/W8WeBqHZMK6qXZuszMYDBb62SCql3bqlLztHkxeMYlPYxgUgvplONFgpPEQL2Df3Or9lstUkkpJ/a7jcomNSCB9vosUkKTsjzVoRozmjDB/mVC9JQCBUF8RuCEMXyWQL2HmAYVoN2CKqigEgZPYf8sw6lvYkFA0SsslUyZQUQDRJA9NDTycWAQCQjiAMyI9j/2TP/kTAMBnPvMZfOYzn7l5vnhfvPe978Vf/dVf4ed//ufxYz/2Y7DW4oMf/CA+9alP4a1vfeve4z0WTsHIscGLLecT7LBUAmsiYgIgJzoRVoQv189xYWb4/+bfgi/OnuMLrz9HcQWUVx5m2hxuYLWBM7L8Ol1QV09uItJOYncANnxT2ruMfT07NnE2On4mKXijXG8PmXSprw0t2TWRg+9QdrwS2T1bdLGCtIuXj52ljYGQSnRRGOD/Z+/dYm3Zrmqx1scYVTXnXI+99j5n+4W5foFwCLFlWQRubqwAAt/ABWQT85FI+KJ8IFnix0Iy/MTYH4kEscQXhhiLKBJSJExARJasGwyYKJcgkRAlDg4i8bGNsX3wOfux9lrzUVVjjJ6PPsaomu+ar7XXPmc2aWuvNdesmlU1q8boo/fWW8sNmAg0rkRBlTlofzjJmoTrsFRpc5vDjNmZPAvmh4GT4lueKMB+riWpKbdYICRomMEEwCgwOKjDHhgzomLTxzkzGcWSRJLVn/GscQ7sNm835qgyW1uYCcMbghLvQcAw6jvxmivkVxpmrKCqDGpkpLMskPmjfLk5HYBGFVBV08JprXPeGKGjK55/c+zhHg4/v5rxhS98ofN73/3ud+Pzn//84Q5mBxyDkV2wYBJUJwOo05M5DYrs7ETq0b0ikeJ06ZBfaxSPDf7nF9+G/2fwOvx/Lz2P8YM+8pcN3vD3FfovPARdDUWjZFuEAImMgbp7Ae4XUMFhc5YQ2nTbLJC9jmn3G3QIVrOiZ7NoaySs6+jZNMUdCcFVCDZ6RSCEemCCVotiI2kOHXyKZuTJd8ok7IKg8SAHGAzfyhK4JinBnJ0AhYHvZSjv92B7Cv2XK2TfkBZOHoVuGO+b1vU2dilLUSjNBKl3fyGaH+rxNfjySgiktV28750+t8Wh8UI8ZqPgNYnoWZatDlR3wTri9ozmB4BGuTS1z4dd2cCbCRkt3qTsw5y6m9STawy+dYL8JEN1Vog22YnHxRue4LVnV/jyi/fxOBsguwZYFdClA9VOVIidgy1EOE6/9jkoLxotcE74RLGDytoQvG/wDERuSCBUR5sMeAbqUkozYUy6TbjpMs0rBcdgZFfMTMyklbh9EgFeSzrTe5gih3Xc1KwRVqiWoSfAo6sBrNMYP+wjf8mg94BQPCyBx1fwk8lOkz9FSW2tk/Q81XY5D0XR8s6QA5ZkFh5KKNOsfpOcX5ca/7TCJEDKr7+07XPWEqjNEWrjqlvrpuumlTl5Gqz/NOanH+REubaArkMHCUspxCjUJxp1n5Bda2Rx0qutCNkBi4O2tpncFpN3VFjlzMAXMhwpoFEzXrzRxp8zhWi6B8jqnglMkJZ6j5vJjCxDu4Mp6qoY6f5BnoX3yLUmAGwBaG5MMtl3/y686H5wWUFdV9BE0HUe7m/Ga8+u8O9dfBNXVYF/Ou8BpGD7BJ8pkWPXos9iQxpODXJwT2wtKFgBEJoywTbXgqJkv9bSEUYkCwTnUqn0UFo922Ib593Z7V+NOAYjuyBqjrQeNq5qYDhu6prB0VdrBVsH0l/Q+NBPJigA9F/SGH7tFA/6A/T/SaP3EqP32EM9Gcvqoqp2WqWx59S9g9EYykv9f9FgH2XWASBZvG8i8R63aWcBqDVQrjgPMkaySkmh0UPl+epgpFU66DIoxdbW7n2409tS1YiexbbZ5JeTZ9LyG3/2IZNUljcuWb1SwIjFPoC9B40nIEXQSkGXBbxWUJaRtEri+9v7NqbRIgl6K1zV8MNRZ/VdQAJDDhMWTSqoSbi2ZbXQiLA5ft48+Ikka0XgspTPRFBb1QSlFIwC4DyUXZKNuQmwtCazQsOl0Wq6NBOPLTolV7V4NTkH6Dx9920vp1Xnw2UJdXkNKisMvl2gOjOoRhm+8Zo7OM8nGFcZfMGwllGfEOqzDKoW/yGqPVxsCb7bhx2WUNaDxjVUWc2Tj0mBdDDe6/eFQDuepCzH7LVgryR4bksPaJ3k5G/SDqIrovvuLtu/GnEMRnbFzIPgx2NQVcnDdvdCVjOaYQoDb10zeU1KqIeXMKMJzoyC7fXhc4XBSw69BzXMdQ08eCQD/K4lkSTH7CQQuV4hxxxWS4nBDghZtavEe2ZENTbaiQOLO3AWbV4UoHt3wZkB1RZUW+h+AXs9XvmxncmGiX+w3UqKayuurlpKNijy0O4a3lAUjdlbbAUdjoLOxi1ZvZFq2mxjNqeuQQDMsA8QxHsEkIzFgpbWmDYnY4B+D5xnUMOxBF5VhzR8S+dEtqmgtIaKnk3jcSMJv4rTswniwsE5cFklrRQV/WesazqjbposPXOcQi4NE/agj+k2Z274TD0RpCOtQeOxLHRC2QsAMJ4AsStoxT3vx2Pwt18CGYOTQQHgDON7Gi+/7gRfze9hXObgnocjoDrXKC8MdM3wGUFXHi5XsM7DPlegHBcgz8iuDPLLazH49E1QGW0QaNAH7l2Ii/nDSwnYZzlsqUwaTRNVILJqEMmzeGueqyN2xjEY2TdisEFKVp5BMEgrQl1Ntx5yXYOIoMc18usefAaYkYceB9GoNaqumx+bD5LmgfR1iBWFZ0AvWZV3kaif+ZuUaW7JyodFFYuZpvnuMXPTXqW1dSqeFtZe59CiG8iAuvbwpQLVvgmmFiGcL4dSwUIS9LLPn/E9kf9CWSu0ih7s3gyflQzwnHQJSTBiwbXY2u9VFbsrt6Xd8tsVbf0Tz8330d6n1uu7bSJ/xDPUuEJ25WB7Cmqo8WTYg61DkKgkY+MNQExiaQEFnys454GehusRlAV8Hjq1aFpOnogabhWREKbXaQjFY19Xrr0lOJZptsMxGNkUszoBix7wEJDw5RPwSFYs2jvYSSX19/B3fz0E6Qm01jgngs801MSCagcqZblNJgvS4TuuANoaD7Ms95mBMKpXTknLd0mLx/OeEbfi0AJIKhjEzWRO0vvKEvzgkaT940rKOfhdVqotxcmdr2MMND3D+3Gwn2+ukxoOpc0QSJMD6gWkz5nja8S+dNrnVLfFtlyMBcc/VVaMhGTnoJxDrjWyIpNAOKbY2861Ab6qQVfXcrzjiYhmtTgeSb0WTakgGdSl737aACmaTwJoVsKbkJOnT77hG8TyxpQ5nw/ZB9Nch7pORpd7aKKXwzBG5PQj+Xf23mvp9qgiCIvVNqkZs7Vi06AVqNcD2qGG96DraMwp28SAAnUt17pXgMwJYG0wwFyxuIllrAeP0KstskenqE7PMX7pDDxg+DseIIA1YAcEZwGXaSgHVCcExx7l6zVGpYIuAa8JxaAHiuXg2Fp9eiLlGQAYTWRhNBqvL2MGnRwANyOstgM8VOow2nb7VyOOwcgWSJ0Yq+Y17+AnTtKUADQxXNkakDhIggMSrFgLbYLKodGg2oo+RBzU95WO9IvdbqeInYpAcE3adBOS4qLjjOlWaKjCNMZks8GItXDRqTKUich7KW/tgKRbgT1cx3guC/YTS3RTLZtdShaBfBhN6VKqnjiIye0xFT1zPG0DskhKZAqr2vZqtP3dB62cuTNLnidSxkoZDi91fsql60Lq/TPffV0tlPqP14SB1do4Uxu1WmGjlH/cHiGj085cEUkQUNXwzu8vMxL1MTxLt8tcMBJMCGM5psjBk1KCsqg461xyJEabWOs9eDJJjt3xWWXvwE6DtJeJf9AXTskkBDjL7qdwX7tHl6An11BXZ7i4U6B3KY7iVz0Fn7Oo1OYEMlKmIQ9U54Tae/i7hPIxIRsCyipwHrhFmWk8nooCPOjJ+DYciaJvWXYLutt/VySLpqfdQn/E3nAMRm4IyijYJSs7DiszOA+iQtKWSoHOzkAnJ+DRGO5xvZ+HTunGt2NGThuQ7hI4TBPD2gHJrkgT2rqBR0oiep3OyDJV2Pau4qR4A+3I07oKHb6vaBKnPJI7g9ZQoe7Pk1KC2m2OZUOCp5BaxfCMXAy4N7hmkcvAnLRC0vfst0uzM3PKmETyI0JnGLfVb9tBXa9IQcicMd+y1uTwN9Fl2fFeb7ffRuL4gntPn5+CTk+TCqpcd5+CvNSVFTgtBDSdQCrIsifdl5lzZJIWW2OSJH9U5l2JuK21MFclCk1wGaG6I07j2RWgJwyQlGu8IbgCsOyBAYENxOJCAb5nQP0CFAMqSPBLZSUluViG9h4rtY18uJ84mCZ6KSvdRvIqADgmuB1KLbts+yzjGIxsgW06I0xmpJtm0f6qCv5JaJVTBDIanGdwr70DX2hk374Wo7x1JnHrQASVZ6A8l9TueNyqO4dV47Kxah8PfSDR0soPaj6PrQUpwK3okiGtoc7OhCg8nsBdXc0FT6kkcOiBa5u251A6YQeZZJlFd+POuWTIHj2RToINjz0JsHXtOuFgmDirw9I1G9E+n7iqjwFg5NpYK2Warq2YkcsQzkXlmQRqZ6dCHq5q+OuhlCWCh066dv1CunIur0HWNsezKNBovea9T+7AWyGWlgAxkJw19ItQGnjtfYzeLE6pZmihKgdNBHpylZyIY8ClnBPJ/V4hZY4Q9C38ftpl4PE4LHa6eWjF585fD6G++i308hz5P12geHwGnzfXyRUKo/sKdgBUdxiOPMzzFvU/MsyQ4HKgupMjxym0UqCg3sujcSpdp9IxsywomBafT0srx9V24WLqNuHIGdkOx2BkF2wwSJvcwC0JRmQAqAEW7xgwA4rgega2r2GKbH910shL2CtLb0NssEJWpFYHf6SAoMOQuAbtvz0NobFNESdJz6KfFnQ3ODNQJorQbTDwxnLJpt9xOyjdIf2dVqye03fHnkF6w2yNbCjK8UaljAiMAeeZtOdqBXaqIUUaA+7l8IMcKv59HdWkdUzsGWofz1rU6lkyYZIicC9DfaZBHtJODUAbeT6ZubmOHGwcnG/UnRcc++zrbGtRZsXy41iGNqdNaY1ikMHnGpwp+EyBY7CqAc4Yjh36uQfHDmQCWAsPThstvBdHjZDd7DVWFHR5VggQyoHdlN7iETeMYzCyKSIJcENoo9eqqLJzonYJgGqLHECWG6jH1yuzA53B3HJRfQpPdFg1M7pnl5ReE4ywtGhCq2ndlFmyZhuznQuHWGF11FZJ4BZheDyBurwCGQMej7uXl8JnqpOBtEmfn0NfXEj2IIpDdeJcxKwKtrs2bdO6dP5+Zcmi0z6j/kddSxbRBmJkngnBs98DZwbutIA7yWCYhYdlbdPREe/BeE+FwDyW97zvUJIMWjqJIBt9gEKWMbbmrsoAsWeoysKMxKBPjyz0uAaNSyG7zpLAPQMQ8vTK42uRdynP5R4KROU5Kf913VZRv2c8hn40hM4MONPgTEOPM7i8gBkRVKVQVwx86wwn33iCwbc9dMnIhhaqlO+MsiwoN7N0GMY23ZamUMN9uZ0Zj65gVvA7qKjyUYH1iM7YYsWojUZd1svT5jFlWUo6k66HwONLQCm4qureSbAGQhTcy662+/wNz0Pp0Da4bH+xbh7IftN/XB6ItDtXNjIZ64iVsvrL4IUYy7aWoDF0kXQ9ttSZcXoC9DK4uwPQxXlTplijNyE7aV0foNu1mb2nF5Wrtilhxe2A1MUE50DjSRNMaAWQAQZ9uDsn4EyhPs/h+qJ+LKtyPV22cw6BGBWIws13xLwmMxI5SlHVOEz2mKApo/puq3cqa5iRA1kP82QCGk2kjLHoefculWaEO7K8fZ6i9sigL8J8sTxmraiYxnNdc2/F6+Svh1CeAa1EKr/IoYocJ8xwA4PepQZKxp1vEC6+bJE/qkCOoce1ZEGdF4E2raWFOnBIqN8XS4Jaurfk2tr9kfWfEhwIbgejvF22fZZxDEYOgQUrF5MZTEbl2k2TsZlCw6q/LTobN4EZBVda502DUHO+pWS2rZRXI9fCYeXqevX2ALlAGu1aFlugAdLp/SvIh1tjYWtyUOR0TgKsVrmRtSipcuBdrUQk2raetXge3vn1uhdTu4p5a4K+AAAgAElEQVTlgw2zPSyZAD2x8j1VtagkW9tSK52REWgf/5KPo8xIm3BUbw3GgFPfzCJvqQU+WwlRB4a98FZqC1IKugwBkiJ469GrCGbk5HXvARt0ltYoKEduUeIYHfGqxDEY2RYLnDUBBA+Yli17gOnl8I+H0t4YWwwX6EiIj8zMYPgqeUApy6FO+tLKOZ7AT8pQplk1mPkmBb3MVG3q/aEGD2yjCN8ZafW90/bYaIKPpEf/+BKYlMCDJ/APHzdy9O0VZyuQmCKrAomnsfTzW+q8aeJvtZdOYdNyFdDwP9pInVJezqeqJQNQFICR580XBmwk26VqbsTblgWEsXPEheuQkdC1VhFYI7+LRKZ87tp1BTP8w8cwlZR3eDKBr+opjR7V74P6PflOQwsvvIglxmsiF6zR0tF3L8DPXYiHy6SU7pNAGk7HGPgZlPgdkk2J5apF5TQJ+CGtuKEdXANQmYEpDHjicPKwRv5PQ+mWiQsEQIKsIO+fJOrD8UGLsV7SVtlHOTpck2k9qP3stgs870ZCfTWtPds4BiPbYEEgktoKgzsu0Fo1ATCFgXPCGmcgiIBFG/q2j0tLsfAZURzcFyiKOxkj6fiqWq/A2q5tbzKQhbT3wbBrlmCb7cM2fjIR3YyrK/irq+XvnylXgeaD48Wb0TSR0suKWUbRme3Ds8F+AxJuq0yUTi3eA9xyvWXfSJ9raT31WoEYYVXeCkQWdJyk7EgwY4vPJnXJrKzrPusAPxyJR9QiPg+RcGH6fSmz1K2MSUu8LSGSe/s92Is+mIDsESdV29TlFHSEJIgM3JeohxIn77RYamVQvAcrlbSBKHC/SAl52Jc1smEJdTVsPpNagWrk+8R27/AaOUqWAAcplQJge7PjqN+RM7LLts8ybt1ZX11d4SMf+Qje+9734v79+yAifOxjH1v43r/5m7/Bj/7oj+L09BQXFxf4mZ/5Gbzwwgtz7/vbv/1b/MAP/AAGgwF+/Md/HN/85je3Pj4yBqrfhxoM5N/pKfT5KdTFHah7F1D37oKeuwu6dwF1fiZkwkEfOs9gPaZWfFNksvhabOdzLrUrzq0S943ofTE7OSst/26o84adB5eVmAM60R7QRsmYu+w44gooiCBt9oHc/Hulgmjl90g6WLSHmr4EJ2vuN1p8X061E4fPjrwK0h3vozQJ94SEm2eh3DC/LQe9CdSVGO2NLfTYQk8cdOmhJrIiF9v5JRNSVLptd/20z6F1XHt/FkLb80K9jNCOy5OJZEJCh138m/yugmlhhuh+DOugJrXwNYIXVgoCZhGDgpBVjKrIHLug4tgUgs9okBiF6JKzcOB16ZBdWYg2YTyeh5fSzCL/o31gKht0g/Cgnf+9GnHrgpEHDx7gU5/6FMqyxPve976l7/u7v/s7/NAP/RCqqsLv//7v43d/93fx93//93jPe96Dl156aeq9P/uzP4v3vOc9+NznPofnn38ev/ALv7D18VFRQN29kH/P34N6/h7wmufBb3wt/He+BvWb7mPyludRvvl5uNfdBT93AVycw/QLOKVBRVChTPoLMw+id6nsEFUrOw/kG58MNUqnJhN9hPg5SoS3VCSe3QDY1rKav7ySa6AIpDQ46qNE/YZZqCZNfUQLQb9B5VlIw6u5oDOZ3vV7UDEAWPN9SwCTNd0QgExMwcU33q/U0sVIK+919zEpCUTOz0Dnp5IpKwopb85uy1Ky8cMx+GoI/WgI83AIfTkRQuhwIpP5eLyYRJyC0XbXSpxU1VRQlq5j+xlZeg4dW+djlmcJYdOPJ/BX19IN5X3Tqh25ZHF8yDMxyNMaXFXQD6+hH16DRhPASqdK0yrsYx1BJmtrRedoPJFMTVXJ8bSOibSW76BXyPiV5ZK91CKQB6XgPEPnphFlm1vYxKC49YwGPZo59/B9jHXMzXm8khcbryDcujLNm970Jjx69AhEhJdffhmf/vSnF77vox/9KIqiwGc/+1mcn58DAN797nfju7/7u/GJT3wCv/ZrvwYAePnll/Hiiy/iE5/4BADgXe96F77jO75j6+MTnQN5qDhwQzjP4PqZ9NUXGq5Qoh1QGiGnEUFnGs5ztwkzDBzpkTxUZiIeS0zdzpZDNiDy7QWpvdWlFbUyobVXtUzNjoPLWqRbJjrzei/6FbPznhKjMgYAZQHuQACNH7Dqe4gcKgolARXKNW71trFdlo0WFdggj78Q8X4hTqWMpsSpQLWFr61Mxh0L8T56KEVhrXiusXy6y7236b0bWqRJkRBHZ8YBInk9/pye4cT5CBmRRbWkthrruk4nCmZ2oQ0XipqsiBLSsLUeKtdgraaPM2ZwOJDyA5mWWZRuN9adeQZwVGDdDrcuGOki1GStxWc/+1l88IMfTIEIIIHMD//wD+OP/uiPUjBy7949EBF+7/d+Dz/90z+NT37yk/ie7/merY9PzKsmIKNB/R44U4BC8LoAVOmgrAdZaW0T910Howh2UgF1tdqwKn1QIGYG4tdBJuDomtrmWra6A7iqkAzmbgqxldPLBKMAeNsy31tU+9+GM7IGlOWgzMjq8QD17JvCNCnVJa5E+nttARIzM64qJLXOVfuMBm5x+zk9jPB5XsGXJSiQFjkE4wv1OdrbJ9E0Ly3OdRUMBxd8ByHo4aoChuMUXEGp1Ca/VAa9RfyMEvJMoSSR50BtwWnC9t2eW2D+70pDn58CRSGTeSCqclWvV1VmD0A3RoPGSHYiBAMEyHdXVi3J93jtKjmHSEqN1gNdE53xWaxq0buJGVpSgFGicpsZ+EEOqwm46MNfnEANjSwcnAPFBQSQ2oop3mftFuPZz53VAorX4hl4Do+cke1w64KRLvjyl7+M8XiMd7zjHXN/e8c73oE/+ZM/wWQyQa/Xg1IKn/70p/HBD34QP/dzP4c3vOEN+OM//uOtP5trK+Z3xoB6RZOWDGJEunSppY3GlazWrINWBFdWm8kyRyO9QyEFHouNs/albbIVglaD0gRX27lJr429HyeRTE55JhNHbbuTL28RmJG0S+b+EEoQSQQvBiEd7s1FjsuL3+jAVeO1krIlwc02OUgv2BdFP5KyDM7Ca9q7qxrgofwMNHyEVQEEqVT+oWCTIFnMrCGhx2PctjsqBjd3zsGnAzGtG4lDsR+O5oKx6ZNqBY7tQKTfE/JtyMxS7FaxwQAwfp+TUrpw2udPWPocLQPbGn4IQJG0DecZoHNwP4fvZ6hPM1SK4e9kQQIegPVQZQ22TsjFOqhLB1HH1DmzIriYdbEG9Ob2BEc8M3gmg5EHDx4AkKzHLO7duwdmxqNHj/D6178eAPD+978fL774Ir7+9a/jrW99K/LoHroNYuutliCEU9TPIA699bWTYMS6RCIzmYZzfDOlj67p4DjQ7ePh3mVfK9o/iQj+KZDQEut/WZr7lmNlhjHwgebq94c4zzQJkux/isiopnkk8b3eJc2N9L4VwQi1CJGym7if5l4iY5rPDqRRFYiyEjBkgDGypjArSlWb3N8tAi8H9VIC5NwojCNdNVq8bzpQbCiVMAPkZX+h7RfOpTEJi7KJYVLfGOwBr5Acj2eO1zuGjm3O7aRGmz+yabk5fGbz67NBNvfY0ZvmVUpgfSaDkYhVA+7s305OTvD2t7+9035ffPFFfOUrX0FRFCiKAnXo6//v//G/wcnJKbxjOO/hwv/eMbx1cM7DWw9XW3jPcLWDdw7OM97yvW/Enfvn+A//1TvhJlX4m4ez8r93Hs41P8vvsu/2exa9r/3eKKvgagtv7dx72LP87gEfara+quX98b3xPc6Dfev38P/Ua86DtRDXvHOwY1GP9D58Vmsb4Qo2xxGHFTJZ8BjxcytFUsG19yZVGWNWqqqfmdTwSsw8C/r0BHT3jpQyRmMx4qvpsBmg9io/cA3IQ4J6ZI0MuA+eKBMRCIyTuehrLDi11GEStFFSd4pvSkKUQZ30QScnjU6H86CTAfjOKdgowIo4lyeCykxoL58RGtsQlOdCCu714M4GsHcKMcMzShYszoGGYywyiFOBY6PC4kcRQykPIgfjSxAIBAVlNFQO0N0+wAxtRCVVKQK9ZgACi0CZVlA67DP8rBRBqeb12ffE35VWoPC7LgroIgMVOdT5KahnQIXB6+6d4p//u2/GXc5gHEMTQTFDE8I+NLQCVF1DOQdFFD4f0Fqnz9Gm+Uyd6dZxaGjTHJM2Ov0ft5Gf9dR+4mvD0TXuv/aP8cILLyDLMpRlibIs8Za3vAWve93rNv5uV4F37IjhYzDy7OC5554D0GRI2nj48CGICBcXF1vv/+Mf/zh++7d/e+71X/6P/ysMnn8e+s45VKahezkoNzAEaO+hAWj2IGvlwQkPuu7l+M9+6Sfwpf/z6/jy//5l0HAEpSk9OFMPj24/TPJzVmTQujWIaJUGkfaAoeLDaLQ86HEgUGpmW/lsMhpaq1B+pjAQyT7ToLXidWodw7ZoBy0xyGGWAAYMFIMCb//334Zf+K8/OBXQ+NB+yCzbgpt9xPdI6dyn9yH+3vp7+p9b+wjvjW2HcXsW5l36mRG6Elrvndpu5v3A/O/xc9u/N6qerfe0/p99XV5q/czAm773jXjvv/4hfO9/8D3z2aaiEB8XQkjx2zBJ22bni7CoNbj1EoVBlKZfbF4jgEgJ30o1WYr0Ni/8AqLQpk0EFdp6iX0THBCFZJrcg/HzZBHeHIMkXWQipSwDZVomcWaZpDMDZAZQgAqvf+ebn8eb3vYa/Mv/9J/L+wLRkhQt/F/F41QL/hbOUwUnbtIERXKViCgIyFITdKj9PUuyeIgLisWLCWfd9PsXbONaixn2DM8cm3HgtUYsXp2d9PD615yjfksFtmGbyjX79AxvPXxZoZrI4kcWTK3FWFjQTS3QbPO3qffNvN5s69PCsP2+8WQEQBoY2vjQhz6ET37yk1tf8yP2h2cyGHnb296Gfr+PL37xi3N/++IXv4jv+q7vQq/X23r/v/Vbv4Xf/M3fhLUWVVXhpZdewlvf+lZ841tXMFUPtiKwDulHBVDtoYeVrHYmFTCeiGpoNLzKDH76P/+P8H/82d/if/n9fwv3+HL9QRAlWefYfre0AyHU4BOpjURJ0Y/Hy1d0FIy0wnt34V1EsiexBzmbBuk4EKfVlmpWYKDmdZ3n4k4LBjnXDOxK4Zf/u1/EFz7zl/ib/+n/ChOQSv/PTgBx/6TCgB+CpXhNlNFQmZEJyzmE+VE+tzWxyGVUYfIM+ydMTZTxtajvQK33As2KvZmEW39r7Sv+Lj83743HlPYBTE/smP+5vaCqSourh9d46esvT/2BSFbs1OsBBMkSBOXaZffAoo6Hudd4/m/yf6sEQ6JwSlqBlUrBGrxMWGhl0NLBxk4f5+CtnQr2wLKKnAruvGsFnwyO6gW9AgjlWS+bgo2GL0Jpxjqw9fjZf/0v8OX/+x/xv/3JF+FDyyuHTGQ7KI0TejuAbQfVzAB6fdCgD6+VZEYKDVQOGFVA7YDLa9QPHsn+Qwuua2cRuZVZDJN6O7BoZyhvqitF9XrCp+r14J+/C+5n8JnCf/Ff/if40pe+if/h9/5XqIl44FBlkwAaOck88dU1eDhKGkpEBF+W4HK9VcYusCGt9sILL+D+/fvI8xzGGHnO9wzPO5Zpjt00zw6MMfipn/op/OEf/iF+/dd/HWdnZwCAf/iHf8Cf//mf48Mf/vDOn6GUQp7nyPNcBkoAuH8P1RsuMHx9Lh2QtVh/65KRK4KqHJRRYl0e2fJVBS5LaDCqlx/BPbnu9PmkNej0BFQU4NEYbkUKnfp9qDuhq8hKKyMpErnlZWl3FidPDj/vAq4rKa902dds21/79QXbluMKL77wbfy/fzMvZrdw30nxs0nVR34EhZQ5gGRIBudEivpplmJi0JHM9VqlhjY2OMbv+xf/Dv7yf/xr/Jv/9s/n/9iWc0/kzM323xWzXA0AjVpo/JkoaV5EsmIn08NNjqMoGvLlc3fB/Vz26QIHwop2xw//y+/DV//uW/jLz38JPB7DP7le3sm1BvruXeC5C5l4TehEsU7k0p0HXw/hr64wJ9UfjzlqeSzoOnoqIAKyTHRGihzcz+AGBvCAqz0MQzoIKytlsCoY5YVxEJ5Tx5YyQuqF0VBPruC6dqztyHN77rnncHp6utW2XXHsptkOtzIY+dznPofhcIirIGX9pS99CX/wB38AAPiJn/gJDAYDfPzjH8f3f//34yd/8ifxK7/yK5hMJvjoRz+K559/Hr/0S790kOPiTMNnCi6nYK4lKz/yAAc9DNYanJlpZ03P0FrBTcrNuA+R3LdG90EEp+KAH3QZ0qS86oT2OLh13Vc8ro6TjVKEld40s/uOjqpwoY1RN5OfCWl5QH6OEtUL97UkaHqa6EpMXodFHTaHwiINGx+Il8C8urCi6aaVDQmjS7fxkvlJEuGaQFYycckN2Dqw96lEFJ894gX6LJsgBjtA42IbyaaLgs54LvF+Jg/wU+okmcq8SYYxem95I//IsSiwpjGIG4PGkOmRNmmeNv4MGiUbE1sPYcx4xFPHrQxGPvShD+FrX/ta+v0zn/kMPvOZzwAAvvKVr+DNb34z3v72t+MLX/gCfvmXfxkf+MAHYIzBj/zIj+ATn/gE7t+/f5DjolEJM7TIrzW8ptBBAxAzXKHAmsCZgh9kQlQLqWmqauhMw1YzafAVEx47Bx4OU2vjqqiCqwq4HoaVl2RGknHZPhBX0os8NDYAZbnY2xsDHo3gR6O1+yK1xpsmvZGg+j2oiztSDphMpEyWZ6CzU3AUqjNa1CyDjDh7L8qVrhFaSylkLWZoXY5zJ8SyRhTwaotRzU4GmpaupNuQSs/TX0lHGfGoqxE1P5JfiPPgEKy0NVFWHTtluXw3ka3Nvgk4vYev6vnrE+9da6HKSrKXtRXr+tZzwtbLvosMxAwVzeuiZskG4LIEPX4i35vRjU6H0QC03JuZmTYZjJ1OWkOdnQIDIaZiPJFy6nhy+PsxQumkCZOC+vMz8NkAfpCjupejPtFQNaNWAAYi/qh9oG/GMnXLCC9m/XxZQl3KteGqkmdw3X2ttNhrGJMyzrex1fdYptkOtzIY+epXv9rpfe9+97vx+c9//rAH0wKVFWhUI7vO4A2BNaQkzgAbgtME15PVgaoNirIP5T2YJtBGw9mZB21K1Kc1AYX//WjU6bh8VUNB3isCaUv8LrZEMtJiDsZe2wU5pBXo9CQM9F40B9bsS+nQTbNyx2EV2e+BL87AmqCuM9l/kcPdO4XPDch5CR5rJ5NDWHkyiRBUktYmJXoOxgCTCWiLiWgrMM9fj1ZAMp31Ofzh7AwS7xTkGagCWFnAYbcJJAY4eQbyUU7dixy6MQB7kPNzmR/xnAkr80kppZmygh+OGmn1UC4ipSSD5jzQ74GsbbkGd4cfj0FBODCKqlGvAPdyKRtGCXcgmQxGCX0YA75zBnd3ALIeaigibKR1p+dmZ8S25OhDE4/ppA931oM7yTC50KhPCLok1Iqh+hq+p6EqDXjI9xOEzmY5b1w6uMgTiZYUyoN5efZPro1wVjAxkunCjvfTAbCrv8yxtfeI9agtVFnDjC18cgkNE0U02lLUOP5EQqlWMJmBrRfUhduy0zs8UE2nRXfp684Ict5zHhLbIOivdCXcaa26nU/stqhqKVvVFmwdyPgU58n7WvuKJYFgqQ6i4LcRSjpxNXtbMKO78NSgdHLt3UqdNqbZgR2CEoWpek4seyzaX+zOieWX6KmyoDwQ5eBjFo0y8aLhLe8DcQX2wTGXwHUtGRkicFQWDp8pZcWWCSEzyHqQ9cI1sXvMdm4AynPQ6QmgNdyggO8buEJLJlgB3gAWXlRZ4zUG5DsJgdzK75k90CUbwGF/dXAPjoZ+twzHzMh2OAYjG8A9voR2hHwo0sh+0AMXOtVPoUg4JYXUUdkocC8HWQedza/wk8spdozs2bdsv5ugZF+InSDbDsgRbC346lo6hMbjTvwZUtQQiJfuWM7ZXw9BVrp5fBwA2YPqU6igJUHOiV9QUMyNypZwLnUJwGhwkQOR+/O0DfjaZZwNVEAPNUzr81PQxR3AefiHj+CHw+XHENpjE0iBsnA/BRL1xve+1sKRiqWXUH6Je0kTdovQ3BjK5eBBD1zk0mZbVUAdBn8lx0RGi/aIzoXwah2oroFNyyMh08VM4EkJUhUwVqCgQppk2hFW/Rk1nU5KiQPv1UTO72oohPiy3Ouz3QV0forqnz0PVyi4voYrCC4j2D7gM8kQV5pBfQXXUzBXojHCozH8aLTWXgC8Xo0VkO/VXw/le7gtpN4j9oZjMLIBuCzhaQgV0rrKe7ALkvA94RowAWRIZgIVZJCNhjEGdslDuZZo2uXYDr1CUCSdBzsca/J5UWqtvHcEKVpfpon7j109UewKCOl8IdERcwhEol6FAhQ3SqRKN9mQqJipOhrH3QQ2GHi7eDxtjTChw7pujs5+OhuV+AfOgUltV3KgoF7atrtfcH1SwB8IzBJoaHCmA49IA6r5fM9iQQAiaT/OALIqycNvhRiUxGSQraf/RgQok65LNOIEs3Sk1DYFIlyvIF0fCJwZ1OcGtq/gjQQirCGlagWwAiwYKpO/p+3WSBJMf0i396z18rkFOGZGtsMxGNkGnhtFZc+iNeIYDA/y4thLnkGVlTa+qoYyCs5OT6oxhTuVeqVmsFb9ntRpywr++nr5A8tSb5bt40TcmiRaHQ2dVhOtFWVjRCZterumidlabGK+p7twRuY+JKhxkgJXNdTlNfRYOC+xk4HLUloOI8MfkEyNZwCijEkAeDzZT3lqWxCJbf2O5OF9gicTqCdDaYuuVkwO0WzRTwcKdHICOh2IvsmjxzLJtsmL4f5bSmSNmZC2Cd7SriiVLBxSq6zzUJVM6pRnydoBwWAvZUbaZZJ93QNJMyZm2wJPJAYi7e4SG84z8C5SEK30WpLvXhDLs5lBfaJhe7FbSf7pkqFqCUac9SiUhhl76OsSNO5ALt2xTfe24hiMbIdjMLIp4iqvTRANAkvkAITuGrIeNKlB1yNwVcEs6qbx80REMpms5ntF0kNQj6/Bk3L1qiAcC2lKZLxYG08eGOzhh1i9n2CglpxLIbwAXiWg1hW8ufle526a1mc0Pzv48Ti5D0+/r5UdAYI8eciaWA8ejqQDYAvi4j5BJtwLADAeb3Qsh8qW+etr8Hi8vC01HUCU1a8SWZi0Ai7OUL/mHGpSQ1sLHwJCroIaa5yYFwXP3LgKc7sLZRmi3b0xQHC7jS28AOQ1yOofRsNrDWQaPtdQlQPVwSdnU+fs2aCjfZ1amTtAN0Tf0HmU2orrGjyZSBYxuncDabFxU8EpFxnKOwTbJ5gxQ5eAcgxdA2DJkKD2yHIDc1UDLz2SLOWqQBVo2qw7dIdtjX21wx9xcByDkU0RiXBtolYQT4JiKOvBVjgjMpFJTXthN80CkFaNJkZcocUywiYPlmo0NyQ9bUQMTZdgu3o/SWE0BDFb1fX3BNUm+G6DRQHQ1EThZHD3gaAbHU83STEfcoWnGn7FRjhkmWbToDJm7pgQ2d1MaCbdyEmK2ZC1+/ONkWEXtA35wjNEHuCocwGIs6xRkl+M2YmYSeM1QdcsWvfXVNARX5uVfA+t5On4ALnGzBKA+JbCqiIQBfKwX9B9tU/EDDBz6I4ByInQI/nmf+8AVzmYQknGKXBhVj63h7w/b/IzFuCYGdkOx2BkQ5AxYrgVNSsASfuWUgfmUSkSw94L6WwyAepaXHsXdNNMQWlQvw86P00y2FS7Jj3bAeyckC61BopCUs5nJ3BnPZBjqJdz0JMrIYPN2YtT0z6a53KeRCBrDy7XvAxKK1k573PCT/sILq8OADH8WKzdsQmxst1ZUtu9r/C4tnKUbR2OXRFLPwhkz5sQQOPm+OnRJfLgaB05RABAGTcBRhIEm/8O2McgoYvbLYPhpJV85niiWCGIRNCwMPAkAbBsK8dBwUSyE+K1VdTYNARvnMRFCq29qGrhhITjTOcSlUtjVinuWisZH05P5IXHl/BBGHLvSKRSD/3gCe7+fQ+uUFCORSzOM1TlxGBQAfm3K/TQg37xEVwgmK7Nmh1Q+feg+133sditPffVmsc5BiMbgjID7hfTrYHeN+JJcZB0vkU48+szI9HzpFeAT/rNy7ZlENYFYdUqA2EG5Bn8eR/lcz0oyygqKxNuVUmQUVtMqRlGgmGeieSzItDw6d0mSWek66p5E8yUdNi7jQlyFCYdqEim3PPE7kP5YouBdVk2RTo3wnfK/uaaM8K96R9fgq6HU/4krEKpIpRhVgZeLYfe9Z+5oB26FXwDkC6aUJphktIgUyi31rYJDrqA1JQ2R+rO6hXgzIB7BvZMnis9ttAj2TdNapCVkhCXVfje61TiSpYGvQL+jsiZ06QEVnHJdoWXTiD/6DFyQDJGcdwLYx7b4LT87YfQpwbu2y93f4aO5ZMjWjgGIxuCYxZEt0onNvAKYg13ZlWgQwZlkc7I3P69b6SjN8kGzLxX2iZrEAA1rmGGRkpHVS2f4Vur0DbiStLKIEyAGJTdFKLqYyAeKq1AgwH03TvgqhaRqpsg73UEe07ZlIPM6jFbBeyNI8CeQWFyfRo6DTGzQUAgkOppAbNFiKTWZifrP4ga0iplGTiXbBDZkP1ql1oDvGf5qHhZkhqvtIBP8WQWfRcsJZUkM+9DMGQ0uNDwmQ7lIVFrdv0MygYtHK3kmtT13OqYw/Vi61rZlNY4sc19EcX0Ai8pmdbNcj2YwWUVyraB1Op98naCZ9jKQis6zDOwDZ6incOxTLMdjsHIhuDxWHQvWtblABav5MLAqo0Mop1SvVUNGk3kYQoPP0dJ6qUdA/MdF1xVsgLVCjQaI78cyOuTSRAEs/MpeubUGuvdEBhPZICq6sWfu2eQMZKG1kpKTEFV07/mObi3VdCXY6hv/pOQefdRXpitKW8zaHkHH1oMaRoAACAASURBVMfuAwzEicAa0/YdA0MxAVaLJyrv4MsdRcd2gXdg9oDKQUUhpQxrgSp2fNm5YyOtQUWR7m22a/gIIZtAJwNASanSn4u0upo0brIJMYj3DAUSl1lAhM+IgH4PCiHbVDddWHPfRzw+UlKuDe7TvmdgzwqwotASS/ADA5wIF8MMDch66FEFFewKeKKnrhdbYdzEkhMHvZJtEctJ6vwceM09cKahX76Ee+nlqYUK1xb85Mn8Dlry7m5SQhE9HbJ3K1BNHJ0WN8dXNeBuZgwDjsHItjgGIxsi+lQAEMJdS4MiBSehBh7T5CakxJfpjMjGYdXnnKg0KgXAyP7XTXLpOHQYIEJAUlfgGiKDHgeJuCKOTqRzJ9jSRGgJSd0I4krWGFnJ9nIoTagLSW1T7aF0UHnctRrSbh/dFYfkXMQyEHtpr+6KdeS9p51ZChlEio62zKGTyy0m7EbTOGC9iFYkjirVkLeNlGHAwsMiH0usrYCH5ZlV1HpdqSCEZsQA0zmwDxopq86NpdMndWgpBZ9L+UcyMgBrgs8I5Bhklei41bppMW4HkjFYslbk4ON12PYejiaAwerBnuTwuUZ2nQeJ+tY97Ry4XH3NXW2h9VOaRNtE4RiURCVbABRsCI643TgGI5siEq9iJwY3DwErNKQ1hBWMZ5gTsa33aNj88/v1YC/6G2QtONZlAalZrxhwVJ4lUhtfD+EnM74cgSOSsEIk6qkidf5o8KAHf9KTyaq0MFcV1HAiA/CyQGrpfsMKM55zLH2QAniBg/ANon2/+EWuzs415GFFoCyXe2XdCpRv6XfcgphBjoC6Fo5VKNMsPLeoWYIlWchFCOUZGA1YJ/oXjiXzGCTZAUh2wEs7PioH5TzUSGTbqawa/tekbJ7/DlLkFHVDtIYPUunSRRT+7hjaN50qgAQqyfU7WhPMfo/xHs6MmPo5v5kaKTWBGMOBJxPoh9fQWgvpPvDcNlEedtalcvSNoPUMq/NTUL+fOEnw3OjQPIVn+5gZ2Q7HYGQbcNNSxx6IRWZiAvV7oEFDQIX30Gfyu9cZyGSLCV6h/ZGtBaqZQaBeMdAQyYN4cS6rsKoGJjPpW+/A5e1fGhCRGNjlGfxJD9VzPSijwJMa+uE1aDiGX1eymt9p0+IcdSmCImeUgX+aYmKU56AzISQq7+cDyTioKg3VK0CFaUpsq9qziXDrafnewV1dza/uF3XQWLtx9wVpBeSZTO6x/OmcaKTUtsk+KAKVPdAkB08qqNpCDcdSzqzFeZZHYwkWw3Gv//DQshxb9HUw1kwnJMGIsiw/R28rrcBFyIjmwUjPoTXeBL5P5LLkmRync+k964+rpX3iAP/kGjQcSRaotkkhljRkkdUOupZwMZyVrOWNgZQ8O8YA9y5g755Ih8+4FmXg4Rh8+WSjTsR94RiMbIdjMLIPMKfVDlppWQCA1jCBOOdDHZNXZNujD4zslqf+f+awbTsukajaslwPttJeydbtd7UftBz2csyHBrf0Jp7CAHswtAL7Tu/dBLFd1gddEmsli+B8IpszEURCNBg4BqIogj7Q1D66ZuPaLstBP4R8EEVsv81xCkJAACdDP7EpINXwHvgAWS6OpoDswbZFaG7xZ0jNt0QvNBh0/kYzIxTJx4pErC7Xjeq1p6bjB7hxkjYzgXcIKHbZ9lnGMRjZF0K7YdSqIK3FfjzLYE568N7D370jgUZZLh5YotX4yYk8SCF9DefCoLmovMPwoxEUe+lv35HUti+QMUI4BISAG4ixCxEH+RjAMUONKmREUr9/fAV+dCnEwC3SrmKmhiaQYScB4YyWRSqZeC+kt025ICELs4miJFcVcCnkwJVEYWb4qga57rLwS4PY2dVx2P82oCxvSgWLMn6zJbJOO23xebYkFTNLZ4h69FiMGQNpWywNfCpDUGbCs1aF1m4L8i6UZHzitmyEyGHIM5EB0Bp6WEGVNmVMAMBnSjIhhmD7Gj4jmLEXjpcmqNOBGM5ZC391ncpUvqpTxxkHm4fOgVIUoIu/zt6mU+UgKR3P7XvBd+Ks65YZ2fW7bSNkhzg3cAMDVXloIhkzQ0YxlZyOuPU4BiP7QlxNBLM2MploA2QZdJ7BO4Y/G0BPojT5zMMYVxtRtjoYuzFsWF0tNxTjsoSLnJBbsqInYxoZ83bb64KAJJ1Wa8VFZQ3lGYoI7nok6fxFWYxV5xv/vui6LZDiT2lvzzLpbxyMRN0M7rySTWWYLvCu8zGtM8pLk1kM0rZpD40qvVkGQr1Q2Xf6czpezzCZy0S4ZXkxdLW42oZ9zUzYgZSa3hsk331tJTMSA6swcabAbt01itmNdjsxEWhcQVknwUPU6hgU8IMMDILPCbYQXyttlHTuFLkcZ1mL02+rpJhOZZtM3kbv7TaRC2ekSzASiMi8Y+dN7JZRolLtMwISKVmMMQ8hQtgFHrST6Nku2z7LOAYjhwJ70fQgBe0dnPNCwCw7CgIRAUWelBuVDxmSsKqbW31HUpsxqbsminFxVd24pDu70J4YCH8xVTqX9k0b+KaTiFmIrN5DKYJf5lS67ny2SOunTo1X+GpqYQp+452IXDkptzQLs5fP2RahfDoXiJAC6aAyPOjL8+KD4BgzVMtVWgLLDbIj8Tp4CYZoXMqzXEtWhoLxHGsFKMmOuFyhHijUfRF/U3UGVXkYz9BeMp7Uk7Zm4bHEQKmd4dpg0j1AKdLZjmUa9hKI7Fo6YQk2oo5Sdm1BlQeNK1BZP1U/qSNnZDscg5FDIKRW3fUQNBoBT85gKwv+x28tj9Zj+pSlbMBGgwcDsFFQEwvVK4TVP5lIkGPtwu4LKgop9eQ5+HQAGA31+Aru5YdNO+8NBCVsa7jrSDhsPnPVHO+rGup6CG6p2xIR7PhmpOjZ1tNloG3cUTdJmd8UZiefdvv2jmBbJ8Ljwuu0KAM1K2AGHO6+nH3WUheKFuLwcxcSAFyPwFfXDSWilnPyIRDZpDwoztQkxNBJ695lcQmmfl8WCncGsH0NO1AY3VeozwAzIricoCtGUSjkRkGVGbTzkmmZTOAuhdA+ZSjY9fop3bRIr+vAWZS5XfL+zpwR5r0ECmzF+4ZKgvr2A+SjiYyPo5GU5Krq9j2HR6zEMRg5JMJArNnD1RZ+NOq+LRE4N6KNAEDVOcg60TdY4SBKmWh0oMjB/RysNXSeSUozymgfArNp/k2IiWmb+bZOUgR2N7TKaR9znDBXlMduNShwRg5tFrYt10RNH9dUSYY9krHcARAnccoyuL7IrPNE2miZgzljzIZsO6Exp+B29lxRFJL9I0paI64H2AGDvPzMipAVotLKXov4WpZJkNS6J4kIvGnb1Ozx7AGdOSP7RBhfk/gbs3B91oyRh8aRwLodjsHIvhGN01r259qoTo69IDHCchen8D2D6m4OVyhkQ4OcCFQ7qNoCPJ7uqGgrTl7cgb9zAs50GGgBVfZlNRZrzsBaV9qkhholotcZ5e3jwZ/ScBEoRWCl5/U1omw80KR899xp0yb6dUEUkGqIsbcgiGkHJIfuFNqArDqVpl8w4R9K+4W0lvb7PAefDWDPRRU1HxegqyE8Wp1xs8cJTPOaTNaYJEZy9bLzDh48VBSgfg+cZ7CnGepThXpAsCcMe+qF0EoEXQG6UsiuFKgOnT7jsQjfRQXWqKexxFBwIUI5dPkFWkBuDtcNwNJzXMsZOeC9l8QAI/cnlrGfEo5lmu1wDEb2DMoMVFHIymosk6c2urPrJw96qO/24PoKo+cNbB8oLgnkCuiJg7oaz0f9MRDpFXDPnaG8PxClRyM3tRoXMIN+450DgMoSrrZLJ0zKc6g754BWUI8u4W7KtXcmo6K0Emntfm9qwJ8ye4siVJuQJLseywagLHTjROG625IljucRAriD6KqE8geAMCF0+B6WBS2HXNGGgJ8HPbg7fZQXGVgB2ZMMKmizqGB62HQa+SZLhiarQ3kGGgya4GBSSiCz4IsnIiH6Fjn4pA/fM6hPDKoTQn1KqM88cG5hewqup6EqQnZF6D9Q0BOS0uwo6KMkXsoWpTZulZyWdfSlwEPOJZWDgEbheQYryzQH8FdqQ5SmNzO4POL24RiM3ACU0Z1M8qbAAHlAOfmforFd1EyY0T1gZlnQxfeGlj8mEtOvpFHREOw64ZArjHWrJSIhExIBmQiUpU6kdHy3ZcZH+k6Y16tz3gTmmo+iASEdKGsT77vbjKhPETIQgFwOsBBVvfPTZZXZgCQiTNrSfeXBumpl6lTrbdR0WQU1VjahpVejycJQOBDFgJHAmrWoOjNh4TO/E9aSv1tji2cRQFuBnUTP2tmYp9D9sm8cyzTb4RiM7BlcW5kqW6Q3k3UPRqi20BML5YLJU0bIn1hkL41AVQ2+Hjap2kRIDJ0oVQ19OUSuACgFV4jgkrqeNKqTcWW0RguDaytS3YrmXTz3gLYM+pR+SGsVr2LrZa8HOpUOG9XqKEqKmLckBRF1QOS7ebrHNNfaq8RojjIj7sfj8X5XqNyYLD7tc1+IMOGR1tLNkhsQM7IrKfupq4lYKVQVyBcNKTciZuyIABIOFg364DunADMUM3y4hykE/omb0jJ+5H4BXwQuGAHKhgWHA3xoTWXNQA74DPC58EYAiJZLBzXUtViYEYkLg0YArXmfX9tltjIzsiYbQ3kOFTJMfjh+5rMcvGOZ5hiMHLEfeAeuph/mTco08B6qEs5B5hjQBP2kgroaAlUNX1YLB0p20pqI8URWKJmR0YwINKngJ+VGHhbc8kQ5SJtcS9MjBUftGrUiqEwGN68NOM9E+0PrUBteHUw9FWygA/I0QJkBslxEoQ6BbUjLNw0i6VQzUorREydKv2UlVgPWyby86hmJWiFZBt8XdWV9LQ7TkTRMQAp8oLQE3pkJ8vQ6BRjkAXIsGhlRJ0MxWAPeAF4TfDCgW2huuU1r78Lr0uKJzJ57mz+15Lqs5YysuJ5Rk4i9B03KlQrVR7xycQxGbgCdCazsgbKCGpaAUlBGWlxpFEy6rE0th1MPdyDNivqnB1Xiz6Ccl4EzBCIbrVjZN2TXRSWHtm139H6JgUUHVU8iCmJuYQXpGcm0jllUK4Nomi8rOSfnRBtCa3jPt3riv43gJOjVGu1bGQPKpbMEdQ0fOUKH4m88reyJc6BxCQUIHyrTMs8GxVXvfOcOJK4qqKsJYhdHCtpjSTSWTpUTwzwAUAR9JYE2eYaqGWaiUZ0rVDaTkoySIMWMQ7ASvoKVQnDbiNbFTaNactQkit/97HVYsf+dummck+xrzHo+4whVv522fzXiGIzcALTRcLVbP2Awwz+5AoXJIplnVVUqzUwRwMJgoYJ2AQAhus2QTf2k3MzVMxwLr1B1lckrl2Apz0U51jvweNK4mq7SNQn27gAah1IXZLjhJXV7OpDjH5fgR5dA1qi6knPPfDp3Dtum3ZehvQ/vpLV8Roo7doRQUYDOT8VU7noEfvQ4pOYP0CL5NFouQ9bGlx548LCxlydp43VVFbQrPLRaM6kGt1tcPgGuh4hdHLMZSyYFUlUQLWQgq4ERQI8JRAq6yJHnGfygh2x4hvKOhi0IdiD3QfHYQ9UsmZNojIcVpNVNO1YiZ6YooO7dFUO/x0+SmjOZrDGYXJMd3cWbxpdlIHzv0Ep9i+BBoKMC68Y4BiM3AJPpbpkRyOoVNG1Vvkh/A0CTWtVaJnYlvjeJcR+NouwSBdO1B7NmGwrpah0MAMOxkPdgBfGDWabT0Z54lZgD8sxKTIUJwwdfkTarP7Lzj1iMhXLwi8ooQbocWoFDGQHGJDfb21512RjM8JMySJL7tAqNWUDvPdbOBewBr8Ds5vlbU+8TjxwK+6d29hAQyfnAg8ouewAA3VMgrwACdMXBYC8EjkTLV80L2nE7QykJRIxuMjhxt0TgZZ5SLexEYN2TENptwZHAuh2OwcghMDMwKd2xTIMwKMYFf+yXXzTABEt5aA117wLu/h2AGfrBFXB5NWUSNafZsENKd/o46yaQABouglLCX1kxMHJVy6oSSIFTKiXFrMx4DABwk4n8TpSCrlfM4KW0eBgBm/nU7IJWqyVFd1PnQWUtwa+1YRWO26GXknRSVvAaNkEU/5vxqpH/Q2vv0m1jp1S3cmRaVIQy41RHm3OAkoxl9rAPXRbwmUIvkFv12EJNLGhcSVtvzB5MHfN6E7tVxwYSgjo/fiIB6aghNsfMZpfOsJt27T3ilYdjMHID0EbD2dYAuiqd2pEESSoIKBU53L1zjN44ADlgUDvQeAJiD16UUUlcj10HdJYySchopFje+07p4iltgAXv57IERhKsuDAQi8uqbHMoUaybBrVKTzzcb4p6uWuvakzigCYNX1WS1XJeum6U2s4wcJ+InJaYwQG665gswwqibafMyKbXI6qx2uZ3AImoSbWFUhr6qhDd2cA3YWslYLFWukwW3fO7lryYxWizLOcXKRvomHQ2ynsVwDOBjqJnG+MYjBwSQWRK51nnzMimYGYxlKsYYICsb0TAbkLrIugzJFXHPZaD4gq1rbCaCG63QMdjL/B8kHNZx8GcDVQoGjECMtm2VW23PYB9cUOCPHzkd3T67LTtZsfAnqXcMHMBp3xgNuVfxe2jA/DMAoFDwIFaYUqGPj5XnqU9GNl0pnOFPs/Kvy/DDt+XdzuUaV5hCLHkTtu/GnEMRg4FpaFOBqAiR3Y6gJtl6e9aJvGyoiHnoB5eoR/2SY+ewF8PA5t/wURygPZLrqpGh6A5wN12SpTKF76dNg6lm1dKZoRtDT8K3J6bOqfYft7mAjgXygbhtX0EE/vYx2zQFLksvGDCjVmU2FnmXCpVdto/ERgEZTRUUTSvKwV1fgYM+kBZwb38YL09QhtKQ52eAEUBlCXck+vp7Ip38NdDYMa7KumUEIHOzkSNuK7Fi8V7+KqeN8o0JmW9Fv39UHgq3jRHvKJwDEYOBFIkctG9HnSRSTASsZdOidYKaTyGulSSpRiOZBDa1+d0OpYD8ApIQUV58bRKf2UR3QAc9JxWfv0z35notHFSDKVdJpZYCtx3Z0QKlKKK7ALZdSV8mDghTwXJi5yCZ46ZmUFBQwQA4IMcer8Hf9YXsTOtN2+/zHLxpAFAajh36As7w7JcKkZ5Jm3XWSAWhyCL1ALLAWqO/SYtCZw9ckYijgTW7XAMRg4E9iwkTSJoQFp7gf0GCKHFkKq4WgoT27PcHtfKHoX+nOlA7ojDgT3YK5DyaUDcqdS3b8fjqcxi9IuJH9WWFA+B+pSZ5ILgavb42mTNGOh76bihSSmByKRKSqsboSzlbi7L7tc0LjhqAqyVQKTIgdMBiBn04DHco0fzpxXLWR26YPaFzgTWtj5RlM9f5ldzaGPHA+EYjGyHYzByKPjgsjkpobwNpnR7eKhaDzM7BzjI4BhstLepZ+/tmPbhmhvIiu3JZGchpGd0UNsJHYW7phC6M9ih1Umz3bUXga49Xu8gzMbMyf+GFEkGZKa0lLpXoiw/M0Az7eCzXSLh3vDOQxGmLRCch798AhqO4GPpZxN4B3c9bLrHOpZOEoE1iP0xANw5xfiNZwCAgWfROmk/d0rKWORxo+JZznnoDtk0MlmTdYvfR1XPZ4ba5nprrCuOeGXgGIwcEOwcQAylSLppDvk5OEDmpStiALFMHbLzfmjKpEzFldOrKYh42ojtnreUILxQa6MdeLU8VtqvUZikGwQBMZ7mtbD38xot3GRalnKx1mHbyTS2EjsHODH3i27cvGjyPxAheh18IOgrpaQjaR3WCctFUBAs2ich+sA4dtNshyPj6JAIA5c2SjIjsyAClO62ilVaunNMJoqrmWkm7pa9+Y2DCOpkAHVxB/r8dLpltMO2s6Redi6lbafcU3fBrvT2rlA6SWurkxPo83OoXu/wn7sL4jEbM3ePqTxbLC6nNFSvFyTEF/ydZ2wBumL2fph5LemwhPuDQ0syV7X8a/09BlNkMvnX70OdnkCdnTb/BoPwd5OeQ6luhExfUBlOho7Op8/eCfGc2v9WgT18WYJHY6iXH2HwlUsMXngMPLqcy0ayc/BlKe+/QZJ37BZc197bfGdV870taVmOY8EUD2ibjN8NIw43u/zbFFdXV/jIRz6C9773vbh//z6ICB/72Mfm3vfzP//zQoie+ff2t7999xPfEcfMyKHBLDoji0oNMfXssDqjEFd2sVPAmDTgkmqlm/ddo+8CUqBeD3Q6kNT4eCJtimu3a9X4p+r2zZO4iVjcU0WU1Y4OrZkQl8nopIrbeYTZ0wqwaxtsLHUwMyiYoMR7LPqrzLZtUyaOywQpES6MOTY9h/b90NbAaQmdzXkltcm/U9u3dqtDUNHriZ5LK8BlHWTInQsy6yoQTJWUOqKzNHtg4uHdnvhYKZPYLhOtuM+DCCAjlI8ePZaXF5V+vQOXYV/tQOfAwXgc39Z21GygXZJI1qp1Pz4DXXTy2OzCGdl8mwcPHuBTn/oU3vnOd+J973sfPv3pTy99b7/fx5/92Z/Nvfa0cQxGbgDaaPhlZZouT2YUqVJKWPXGgJxOqwdqdRbMpp1vBNYCQbJ9nyliSfk+G6nZKXifJkEkfYmOg+hTSEVPqYJGBN4BgqHhlBKrD9oyXdLxHdH2OpIsxxLr1mX3QwjU2++ToCkLwUgB7hfTm3hO/khRVZVZdEZIi/kkQns5UvfKfgL+6UCkw3VstbenaW7ZdjeVPWh9TnQlP0RHjdx7T6f89KzgTW96Ex49egQiwssvv7wyGFFK4Qd/8Adv8Oi64RiMHBpEITOyYADz8zXr2W2BYIR3etIMqnkGqi1wRaDaNisG73Hj8t1BI4HGY3BQmtwYS7IBRFLDfyZAKpUmKJgAci8HBU8dBvZD8N0zOOqLtLIKlJlQpoBkeZQKzqpBqt/WwHiFVcEmCBkNdXoCurgjwciTK/gn1002hBkM3WRugkt0+nxmqF4BGoTVXZi4qMjBpwNAa7jTAm6QS2E6HLK5KqGC8iwgBo/Mkk2hQR/U64EHUmYjoCkPVbvL0Tc/b7ifWAJbtm1w8AYQXHAPNBYEg0zSGlzbxIk7iNZI1MUBbt3zswhPo5tmoRfVM4YjZ+SQCDeIyMEvGRS6PFyhXkpZBs4zcJFLQJKFVV9rFb43nsUG4LqCn0xECGqDwWLdsdKzkhlpTeRNySyaCMr38zS+l07fRSzF+BmOR+zKiGWOWW6PtWGb/XRPwRhwX7IXlIWOi3bZZTboad/rIXtDWZb+Ic+AIgf3CnA/h+tnsCcGtm/k/xMD15csI4xppOYRghFjgMyAe/LMUXjPXgb9XcgB7e0XIPErbsBIMomyaXXQzAiAm+N97QG8h3+HxHg8xute9zporfHGN74Rv/iLv4iHDx8e+FPX45gZOSTCw6OM2q6bJnQ2AEj1X3IeqGrJiNR1mBR8MuG6rV0QizB3rERiWw5ZsavWIHerMTNRsnPgSSk+L9aC+n2Qc/DjyWJxqwNg5Zw52zY5i3A/JT0IIvGpCaqme50UgrYJrAWNxR8l8UCSdogXommeSYAXs2VEzfHUNXhShm3CdkTgQsPnBq6n4YqQaawZ5AGaVXfVGgySTFAhpFUaV4k3EzNc6ToAt2eCDNYT6s456M65BIyPHsNdPtnrx1CWT5fDnAM7D48DZkZepXjyZPq7K4oCRVEseXc3vPOd78Q73/lOfN/3fR8A4C/+4i/wG7/xG/jTP/1T/PVf/zVOT0932v8uOAYju6CjvsbKzMg6xP2G1DSckxJN6iKoGlvyW1gGWIq2AmgMtEwG6hUgIviyhNq3VsWhMHPNuargnzyRFWq/Bzo/lbIB840FI6vQ5mdQdJIFpsiB7LyURWJLbOC+kMZ+A5KobTIpgatruReqWvhR3oeMhZQnKXYmBe8cMferAZYOEgpuw6rfC+7OCr6XwfU07IlGPVAgD2TOS1AfEb1fwj9lDLhXgKoadCXWCuxccjKGMem533twtg2IkgQ+7l1g8s/ugjyjsE50SPb5Ob0C6mQAdh48GqVuGMfHYCRiX2Wa7/zO75x6/Vd/9VcXdshsgg9/+MNTv//Yj/0Y3vWud+EDH/gAfud3fmfu7zeJYzByA9BawW4qlDQLZsCFYMN7sHXJRGtK3OkZR/QeASTl/ExqjISVNCOQJIEZjYtbgFhGQsgQeA+ekXBnZlDbwlYR4KMU+579jZglyI7XSeuGSzR7DZnmC8yRzI0QSOkt0vpKNZL4aZ9+2jgwvZdE2mcfDti7gpQ8N+EfEw5GYk2fM3M/x+f06NyL3WstYduvf/3rOD8/Ty/vmhVZhve///04OTnBX/3VXx1k/11xDEZ2AXcblHWmUY63XxFzWcI/vpxOUcd2v7bD55ZIqdcl5lsHR6tTwI8nQk6s7bPbTRMRzAzxKLSkTjYwV9sD5m6JVPJryi/Ic8kIMEuWId5frVJI5I5QUUjQ0FIh3Zvib9CfEG2PDCrLJBCYTCQbSNS0jMd7fpbcHDMYVSWBujEwj3tQPQPyBcgbgAE9cVCWQcGigbQCl7KtryzIOeDBI9HDmPnOptpLtYbKQ7mrtk3p5qaen1huC340RAS6GqL3NQnE+Opq++8mZiq1kixIXTVcoXh9w/mSIjA0bG2PmZE94vz8fCoYOSSYObmkPy0cg5EbwK56GUnM6UCIEw3Y36i51hy8A7cGclL0bHBGFiBqwPhJCfA4vLjHwOr/Z+/NYm3Lqrrx3xhzrrV2c849dauKoqSx+9DE5o+mQogGUCpGReIbBA2xASImStQHozEYUUyUmAgR5UUN0fiCNMEXI4RIjBiCnxJDghFKY8OHUFXcqnvvaXazmjnH/2HMOdfa++x9dnfOuU2dES637jl7r37NOeYYv2aVfsSilXEEi3JbFUEEQqeJVGZYGDPVkWjU1jS6fecgbnfVXQBJZZS8V3fcfq9tR8bPdo4xaaCccpwLSTox2BjwSQGuMlhmSFjNc6VtFPryIgAAIABJREFUGnLSYkzCdn1dA87B3z6cbcHM6OLoPikkTrP795f2/rQA0gCsNUbdf2/d1tbSjtVYCuKKiFTrlOzVp9Ro47t6LgDWe0htdWHs2KbBJSuwfuQjH8F4PL7jdN+rZOQSYifMyCVEWu3eZQOAYkbuwWSEIqOBAechzY5lfOoI3oX2RQQRJlXQdapa0QiPQsJBpMknkJKQmYk+hg+Ttpe2PRJbPIucY7cNLxCGYj/mFVWJAGmN8GYorkvOdfbf4XxEW2fUeKDxQOMgnYnVO7/QVycqgKZJXrweb6wQdRVnL3oyjc9DUSR1WEj7HCQvp01uzKLk1jkI88x1JmsVu+MdUHGbuDqn/jSLkpH5RG7VtbnLxqFNY1fiz7bf/djHPobRaITj42MAwL/927/hIx/5CADgta99LW7cuIE3vvGN+PEf/3G85CUvARHh7//+7/EHf/AH+I7v+A78zM/8zPYHfQ5xlYxcQpi7nBUidaPtkI7K5d0Q9wy1dz6IFXBprbq0Ord19SBJtWcZ+Nq+0lUzC+nnEEPgSQ2aVkDdADdvaZsrCeDNXbvQVpTOfaboBov5j0rCBwgz4IJCq0NSJ4V4daZe+2TOWPFJSNoA+CMBjcapJZJwHF011nWfi67kekhEuHKgqgGVFWQ6VcG+qkr+M0w0m9QRqXT8oA+pa/ijE0jtIE1HynzGCye4CV8EjqvTPuHrD0D2B6Cyhty8BV+WAdgb7sm6+55jsUXKti/LjuGgMvt4fw/y4AHQOPBoAqlqUFnCnYzUZLCLJQlWAVGxF95DyvJCq7zP5fi5n/s5fOlLX0r//vCHP4wPf/jDAID//u//xsHBAZ7//OfjPe95D55++mk45/AN3/AN+MVf/EW8/e1vx3A4vFOHDuAqGbmUMNagqe+eSf5UeJ2gANxVq5J7hk0zF8QUyubcOpNuu1qOHimGgTyD9Aulqu5pMmIMg5mV+ZFZUEmh+nHWxN9inaRS4OrMZA8obkdkhv6aEhi2LZj1PGKOkSZ1BZnLcURIgbPA6kl+ASU+7SqAdanxoMYlr5QoaOa9B+YvHTGQaUI486tFmLFo3YBzx/i2xxLZTXkG38tbLG/05NnmOYugcaE2r5o/v4BN8UUGsuGZ8x5SEyAerjldGWl1T5SptFHyeo/GnRA9A4D/+Z//WfmZj370o1tt+zLiKhm56AgKrHdzZQTA2QNYaDtctpU3G75zraM5ka+ZnxOD+z21dY8gx65aqHNKuQ76Fzo5mdmV/ZqUUHEOaMJEUWv7gixDDMFbAgYZJDPgKoOpDsBWe/wgzCYXZ+7E65xD7fEk19FUVZAWJxBoritbJZ1rFmnbAIJJWgfQvXbpfu4diiv6AHhOz2b0CiKCWANYPVZTOpDzbTVpWio1tW5S0iuC00A+8ZDJ9BSeZtlxypyi7blGaMcIADkewXgP1A18Va+niNt9l1PFY4Fo35J9y3gMcysAnstKwa1hbPPOzwJYo4ZMxK74u6vyemEhtBvu4znq2nuVjFxkhInLZHc3ZmRVkA0uwd7DV7i0hOSOAlhnzMxmS/CcZ+AHDiD9AjytIOZEJ8OqTqyDWOKmsIolIl1ZG9bJbzJdb+ksPky0AplOdWWeWU1GcobrEUAq5FWIgIscNJ6CiIPhm1mBI+lMPp3jkY4vDQEtJkV8CyhdJ1EMYlzUK7TNFOTeXUxGNkk2F4iUJV0aoDWIA1JFCZmFZAZwAh7XIOdAo4kmkGWpAGPfPW9/upskAj+ZAJM1gchrsuy2ishocQ7+1i3g6KhtzaxxLSP2hYBwPzc4ThG4W4ego5PWSNAYoNZ9u8bNJSOhLejdXaGvc1lxpzAj93pcJSOXEPeM++w6cYlUGyI6GzNy4UBBDq7IoY2Q2CicpN5hjRoXAqd74eIBhERkW9pcrAhED6PYyjCUqiPCgDDgcwPq50E9FYCJCdWW5x81PkTaCtUu19v79a/Duvc2CaMtiXDNKLZuVlwL70WTuEXbuZsiVmD8mveFY7uQZyUCYkSQ8qqILd2gbSJosUmnKiO7xiU5Dl/F3RFXycgmsamQUJhIjNlSDv4uCV11+e370VsGzbNpOmX5OMHPtESWbmjuvq27gjQMQBkx6hrX2V6gyEqega7tB+Ajw0WmRSx9R+feaDZX1zOUyLVCJFFfpWkgzGgGjKbHcDnB5QA3gHAPdpIjOwoeLcMheG8IP5ps1Bqa2XVgiWxtihfNICcTeOcQXXnPOo6oe7Pq3kaaqaCeLf+H906qCnwy0W2FPySif4eJlLKp4lM6rSJiUqp7xJNcVHRZJttUG1Oieja9m2wGHvbVfbmj2pzuZyfBWN9BWCtlyqY6/2SErAUPBvqOTUv46fRctnspIeHPLt9/DsZVMnLRIXJvYEbOilgavuTgoIiZItrEh0REqbMmUC2XDOZzQl+aKKxYdcdkI6y4KawkU3WAucVSZBa+l4GcgKZlSKBaYzya8YFRr5qtkrpAJY30Wpczmh6h6QGuILADIAxXEIQU3IoiA4oCNC01IdimX7/KWXqtY5eNtHLIsOp3rLq3coa8ftznZAJqMpAUCigGdFJmBjXqN6N2CgDgIULgYJQnQCu0dh4xv9In9fuB+OXXeFV1YNV9iZTwXpDI9wKZTE4/g5u2llILpj1O1Rk5p8qIMao1w9yK390jcacArPd6XCUjG4S5/gC4UlXNTcCcxt5HbZpLDDanqb0S1ThjKX1THZJ1Vn/E4KIAiiJ4AFXJK6WbkEg6BrQDuzFgS6B+X9sk0RVWBBiNIBNJni9bq+fG/cVBK+AJfQY4IbieluKll4OGA/VeaRr9WqzcbED7nPk7XB/9q4OHiQkaEEChOygOi2g1qqvdsU143Y6QysSrVDoBYcKkxmnSQ6QgYecgkA5gd41rtIq1dNbPRNtvKxlju1Yjo2Ej0GHcnMPiKJw7FwVoOIATwA4HmsjtCnb3olUvw9sl0Vdxz8VVMrJB+G98FPbYgb72rK66qmqt1d5SzEgA96XBqFveBy5fln0+7nDP9pTomXdaUuegCUr56o1ErED32q7wEyFjQNf2IcM+qKyAw+P2PodkSKyBZAbkvcqKO624UJ6Dihx45CH4vrrMessg52G/ZtWgLZwTYbuJm0TADuBGABB8IOo0A4IrAGGGt4TqWo760QOY/T5oWoOfZch4ogDaTfY5R/0lw63y56APZBlgDSSokdLNQ7hnntn6uZEgN75rW1CcUzn+pgENehDLEMvwhYUYhmUGV7Uynkpl1sBYcHIDXnPCprYSFv8d9z/zDi/QfVk5fqzbflyW+IiDNARMpipgVlXbJcAL9p30Th5+CM0LHoQ3Bvah6+DBANI0p8DBm4Q0NXwQ77onGTjP0VbLLnGVjGwQbpABlSL01xokwmdUgXX5wJYSks6K867S17hTSckiAGtUEUVYQW94TGtrQFgDKWwASDLI0enxJeQ0XWApkbIMXJHBDXKAAW8Y7HzCKnQBl8JrtgHmqKLkBSTUSm8QIEb/9hkAJviM4XpWhb4AdaM1mkBJs37rJU20ST9FE5GovSG9PCQjOpywDdnRtoySdSnDK7fjk3leqnIQQQzDZ6wsG2tUfdgYwITKF9F6VRFg9r5cFJ131f5XJE3iVWG5C0g+lwhgbunlaPZzOAiolyfa905xh1rD5xFXbZrt4ioZ2SB40gB17N2f1jxYNjAsxYzEiTWzMIOOBsPd8BJ2waJ5ru0G5+DH4/WPb8ckZqHoWQQnNk1ncF3ddgGQlCAprnyDBP48SFKcg5yMwM4DdQ1fVjOy174swYfHMJNSn4O4nagu2TTK3hABHMDOKaakbhTEKZ3nZ9GkywbcK5ImRMSvUJYBActgpgIhgTdaGdHsDGG/+m9XEKoHLFyfYSYZiqrRhKSswHHC6LaeOtcyXve4fwpVn4SlsbZ1WBYBlTVoUuokMpns1AagLA+YEb3WuzF4BAIFwtK4TJgjcgwqa9XKqCqtitQKhGWjcvtrebuk68VQedrTv9s4IvNlHXG3NRyDyRhtG1qr2IsISN6x8hppwihyVNcsGghw0AddP9CKYl3DT+9gVeOKjXNPxVUyskHwpAFVC0SPOnLKEDr1+6VtmlBGJaM9VzArrmDkA6X0gk5knYhgUWPAw4Gal1U1aN1kacaPYrsBSTEjC/w+4qppg6SNQlLFw0FY+WoSQVUdGDCdY/QO/vAYNJ60Xh/dAbxp4L52Yw4U27YyyOkEQcp7VLVPF3xQmqAZskhGPB6rMaDhEBRow8hmX1Mhgpk6UPByAXGi98ZFlTDQ9AjTAwZ5RjYRmOkAxhjQtFRBOecB73TV3D0mF9pOHdaSTjpFatEovTn8znnt7x+fpPbl1hMAqSYJ9Xqa1G1rEhnxK86pgNtkqkBka1WxtMhAk0rBnHXHA8f5tnq1TnUmPZPnNOl2fYfOAu/O73/F9mg4aJ+julYszS7tmo6Oix/kqPYZDQn8fgb34B54VAJHx0BZrnec5x1d4Lhzl9s2uWLTbBVXycgGQXUDuHZCVA+KORltoK2QhAl0JYA1GH8trLbcyaze6zmIiLqpbnIskSmA8G5tITdPpJURsmFw3nE1F71WwKwUT2OUE7soIhU2evbMV2Dm2QfiIDAgDklOHf54UV+XkPzM72Ph9YjOusZAigwSlEvjPZDMAEHkkbxiRzQRofRzAApc9QA70eNI2+dQ2XAQ3yqsUjDDE2YQh2NNgm1WqxWsst6L9EIkVI92bjF6H67N9pUF6rTC0vVM1Sqv4mfOJ8o0vNKno1HeHY11NT/W3p5vzz1W2QBATJuAdsewdd9Vr1Viqh1MKRAnsEI6RnpRVpTNVnszzRvprbPvuzoo/Nnl+8+9uEpGNonDY4jvuFhypyISgxhkgK6k7ypqrzQN/PFJ0qJofT8WvMCXJc3uHXwFEAffjskU4pyqNq4R3CtAw6GeS1nqKm+TsjsbcJFDQOD9fa0QBEOurc+bSbcTbOMpYBMWCYOJFxDmmAcrB2elwfqyBN+4BXuU60Ab3XunZdsKOWOiJSJQwGI0j1zD9JFCqyGlBzehNZMThPUcsrG2ZrgRwAM+J7AH7BToP+tgpg6m9jCjShNqIsiwnxLgWMlBoy0kChUCsB4HDEMyC98rEhNFwvWjsgYal0TfZmTet4luu8tvgR1hA3NtT5lQQMJ+kNEEDER6DbxvWzSJbh1ce4EEor30kC2M7lZur4GMRqGiZUB7w5BEVJC60cQtgI+VPVa3irvL3jWv4GCpavCTz+CB2gHHFYpnRzA3butn+n2Yfh8yGsMdHy89H8pzcL+nh1vVbQt1m4pYhxTQVh/v5cTmuRNXycgGIaMRhPL0cGs5l2Z77kBY1Zj0b14BYBXntJwZTLCIKLmlnwpiJGXQXfUfVoVXHQFpmo3LrWQtaNDTiQ8AEet21pSgJmPA1mhVZtBXWW8ANB5v376isKqPvWQvy9U7paO8usk1DhgUOT6GjDtAT+D0RL1su8yq7GoMmr0M44e0EmInBFNJoKjqR20pMFOtfJjSgRqBKwzIA6byyEYN7HGlraJpnXxlpNBjIieJSksccTgmVVKkyJKkuh/kSrUMx05ewLEFFUGf5/A8btqC6wYxaTtp0G+rAfOVBqetKZ34ZsXgJAizxUrJHYlzfqfFOU2EmUGDPijv68+DiR2sTXggmZaajDmvLbIz3rWYLPhnb4LGE8i4hBmNIUfHQJ6B9obqDdQ4IDBjFgXZoIMSo9bFwjYifYluDgdp7tT9w1WbZou4SkY2CecA6kykoVR9SvcivkBWB0BjDZyX1W0X8SBSnAaJQCIjoes62h1U74WMP04GwZ6duIM0P0vkiQmcWXgJ3yXaviISEgupKtC0DLsOx1VXiyed5E2zHStEYrWBg4MucRjwgxR3WPnPKKN2qZqBAkyNBHVVqPS70UQkJqvsCEb0c1R5cNWAnNfkZOpgjyvt3zeKj0HjNNGJ11Q6LZzus5uYEgWkZ+Fzg/paHhg7QdDNCzLLMFPFYUTV1HOhjsaIK91NJqYIMGeGFHl7LvG4Q5JCAGg0mr0HIuAoWJdZEGUKTu4a8d2DEZ9HahrFjBAlv6SISxImUDTc837tZEy8gOoazjkwJGmaSMDprKSRB2XiGGQMVhlkkrWqkhvoyjOJ/nm0CneJq2Rkq7hKRjYIX1YQy20fPVljB6aDMbOrOlYmgsksBHNumUA7uEYqY8BZUK9oV2/hHZ1NQtYfKDaOLitoF42HmUmuLbmStQkcOFOCTxoWJn2OjCqwKrjQA9FddVMsTcB3+Mm0Lf12sSDzkwzR7Apr00pMp9SeGAdEulokagdfp5LlCCJ66R4TKXizIphpg2ySwWVAvUdoeqR0YQtAAFMTilsCrhzs4UQZLUTax39mDPPkTS3Re2mZPPGYQrtKYkUueu4w6+RkDdxBD/V+hqbPmF5nuFxX1GCtqvRuGWQjj6KwsLeOwFFl9jwYYZFVZAxQVaoZsurShwQTJUP2BmgeHEJy1XrxmarDmUqTNWsZdDLSL4Z2je/cB44VFgD+6Bh+PN7tfO4UBkyCUm0wFCTn9f3aG2rlK3gsCRFoWqU2ydrhHXzp4aoG5Bs1FSQGjccKrl6RSPqy1Ooak4oNrkENpn4f/PCDiMaL/vB4Nqm8k3Hl2rtVXCUjm4Ro6Zai51RE3Qd6W1wFt7RIbQkYw4oZ6RqvAUi0vLhyTiZsJm1fgBnBqQun2OxYEZiJOCiEqkjaRZwAu5b1kYkS2wDMYDaqMxKSiVWrpZXhHaRagf+ItMqQcEoEKW+67/j5CJqNk38EgMbrscSUTQKWQ1shihMRhlYmTEhGAHhDUEovUsKmeBjoKnc6hZ8EKe2YCBvTCq8l7xyCWPVuScdEikvxluAzFVRzOaVj4IbQFPq3z/XeiTHBy+ecIq7gaYHOy7LwkmztxRB80BXxmU4SJKE9FRcVHQCliOjPgBZjBJxtxnevRKwyuk61NbCixMxWXLfxT/LOg5nb93Xdr8fknRiS552kfPkYFIUHYTuJ9TKc3VXcE3GVjGwaEnQljAHv70GGfcigwPSRAXxGyG9WyJ4+1FV8pVx7tgQvCJ4bvVmwWFTkDEkJ7w1V+bNugFpL7jORhNFWUH/ZgPs9kDHwZbnWqjKe38L3f8PJWKYlhIKC4hI6cKw+8GCgVQNmTeoAPfcssHGqOlQTNpQyX3hgy7/Lg4GuhGN1yhhgMoV75ubmKqnWAsTg/T34R65DLINcAIzWgcmBNuEE0Fa7nFNFUOdgDifoFQZNz0DYghzgM0IzQALtux4DBPCgUNpqTHCjo3CcSBfMp91VMJlGjyWr1WjPWthjvR/kLJqeBTcCV6gnDqBsHvLaupG9AdgYyGgEf3KGlsUi9kQ87pmD84rriCDqdUJ8WGk7kHhkgFYs+zl8b3a442mlGKZOlU6BywFnUdXAZKIfrtcDbp99bJewYmczSw2eu/5qLljpmDMtNfk0rO+eCGQ6bd+zNYMC5kSgVeCtImimyLQMei9ng7ylqsDHCsqVu8y3JkKVdvn+czGukpFNI6DTiRgy6KF5aA/VAzmOv96i6RP2v8zYH5UKFpyWkGkJ5uDaG+SzZX+IJBTVNCp53gSWw94Afq8PrhpgNFZdj/mns6MmuRShnlnw/p7qKpwQ3DLth/ltnNObIHUFd9YEHgbNVC4e9OAzC+lnEALM8RScZXAuAELXTaZ2COr3gOsHQGbhBjl8bmBv56Cj4+BcvOa1CSqsyDLI/hDVwwN4SzCVB1ceXDYwk1Lv/TyFMyS7fjIFEYGzDBkRTJHBZ0OQMJoi4ke0ousKTUbMIMixR0p2XDXG1X3cV2QaBDZNqiJIuMahjUOZBecZrAjIF3B9RtMoZoWKWJERrTIwIHsKNCbvVdeDZLGWRYf2HUvS2r6cpz6fYYS3LCIbpyz1GI5Pkm6LHfQUY5ArQ4jGU/iYKEfxspjIeTeDQ9i67XTJwltkDLgoEiZK5pPByK5xToXvUks53IfJdLNnHSEZGQ7hQTB5tvoLyyLcb1kj7/NVrTomwOb+VBcdV5iRreIqGdk2WLUXfGHgcy1je4tZFkyYWIxhuKAYOiNhbhjgDNS0gCtyweukboIw0ZInM7U5llcKRERXPvHz6LRKYnkemMVvXGZa3mUfMQGW4QqjFaLSgjMDgcxUDy76eKhxWrGoDZgo0V03jghcFWWcQBhc+aQ/gsYFJkdHwyQwpeKxCAPSOBWbYwYHMCtlUHM8BsRq6wRgiNVrKHHejPiazGryV+T6zIVtQjykrAAJk64LlQHSZ1SiHkfVgEsDM8lAntTjhBEYO4rpFkNwwxyUG5i6AU9Lfa4qTslODIo6JT4wwrxcjMhfrGIGGitKThobYliF2mSWNSO+BbAmDZL435cV6yqwAguTnTRmrLqg0p7fDE6BNLml7nU4A7yr+DAH1zRgQ0s/d96RTDNjBGFDAJ2EO4ommu7wdxV3YVwlI1sGGQO3V6B8MEM1ZDQ99QQRpiT9LVUNX9UwltFMSvhpCZNnrXbDoKdma2UDGk10YBhPQMeqpeHH46QDkEqvnXI1Ba+TU4DBSLMMnH1xPqxETSuH3StAg4Eex2iitGXndlPP3CRCO4hcixHxPYvyoRxiCIUhoN9TAa48V2rxBR+XH4/BgfpKeQbuIvU32XfEmRgDmpTIvnai1zy2BYKuTFT9hNcePgVAdGL6eC2bEwAua2QHPXhLcDnD5QSfAyURXEEwU4GpLOABM21N/ZBnoDyD9AtMvm4PzdDAjhyyowpUe5hbx5Dbh5oUBRl3cUjJkRweAdMS5jjD4HgKWAPfz+B6NlRf1AnX9RlH39SDMGHwdIHeoADVDjwpNbGeF80jNUGkqtbrEKTYz/UeR2A4HPyJVyZVdBdmhtT1qVaGSAdIvA7GaI1j2CQoy8HDPkCs7/8ZFcEEBgdmGCSxonNmAhUp6HUzq6pr2vYucm3VyXQKd3SyNCGRqoI/Eriy7sLALjZiotmRQ0CWga/taxt8MoUfjRM+hqkBlrOLz/nYrgCs28RVMrJtEMEXFk0vOKXGqgghDbzRtZMNwwUBIal14CVkqt3Qy5QWWSqCXMYTlah2vi2XdlkWMaJGQGyzxGRkhh4aVga+lfaOAxjluZbVo5x3GPTkDNDYuUbsEYeyuBDBW0bTY/gMMBMD5AYCagfcC2YjSFXB1U3LpCFqdSg2jQgMbBrQsbIwZDrVSoRzpx1N4302wbgt3oO6TlUhrhy49iDPCmLNBJAAbGWCKxgmY3AJACGZMcqMccMc04cyVPuEbMAQE9pG0wo4sSAEvEi8Lw4AifbwnQdoqr4mxDB5pgyxzMIfDOB6FvW+QfmAsm1MZWFHPXDlYJhDFUZO4Z8o0EcJgOxSaj/ruYj6IWsyfGYArJ3vX1aQYdVJIdIW7VntSQoy/RFYzwFnER+rVQZ6oTUMMUnzU5MQraCpn01ktpycsSFtr/i6WYjHPvegTsUGDsQ2WRbIoAcpcjARqK4BF3SZLlHVlCKgfIfvPxfjKhnZNpxSKft9g6zP4NrAW6B3swGNp0DZKjh2FVilaRI4kU8yUKO+HggKpdEfozuQdMvFqews1CYesRxPrMqnMeEoggKorVVjAkjMCQC6z6h7ASD5YjhqWwfnNRgvmzC8AAFXYxnoFwY+Y2SHFWzj4IOMPuX59qqM60aciD0rpXee4ryO+m2kBfvAbOkoQUowD1vIVIiskchGQLzvHoQaMAweV8gyhusx7ESNaEyJ8EdUobUOLJy43czC9zP4vmKamoFiPUxt4EuGHeQw/b4myNOyXU13MBTknGp0lKFl5h3QWJDLgb3+6ctICNWHJRNAE9hV8bkzWv2K4Fv1ijlDhTTIvVMWtSZUrEsCRXoXPRBtU925lak4r+MDVuNUyCj2ppssz1RD1nl3YyITP88UhAFZQbtdwcMV4ZwHd0UFt2gxrRUR15M2046F1Di1eOguIKII5VXc1XGVjGwZvqphvnoD/cMRJM8wHPYglmFunsDfvK2r35AAqFFeGNzj6tsYcNMkcGECnFXVadCed5DSzUzoIjSjTUE2AxkGH1xTqe9QbQCU4omyajErgS5LU9UekKbRyglaT48o7X0uSUlkTxDaiaKjZ+KPT0DjCegoQ+9wpBNyVauqY1Wr/sBgoMdz0ToCZ9ASyWrLg0ROVzbCOWm1ihMORzp9+ZXXMq7u0rF4SOMhjWKJzM1D2LJCjw4wvW5g+oCdAHYq4FpgRw5m2igmRcL3c4tmv0C9b1EeEOprgZ5rWZOYSQ9F6UDTEjSdKsbIS1sci+X/jrZNpGFzvwc62AOUsxKSEP0jhiBOn8E0uXeqeKoIqtRZ4gzIoEwm7yGjMfwknv/chEykCXdRgHoF5Po1SGbAt0/gn7kJcmpjsG1C0tUZuRMhdQV3FHEcZ1Q2oqxAnoEaUtbRDu9q8p8iVisC77VK2zRrC775lIzwarwKgCQjsM07LdLiWjiYejLpuOHDAi/sA2wUB3VZcQVg3Sq2TkZu3LiBW7du4Vu/9VtP/e7f//3f8eCDD+Lhhx/e6eDu6ojgPwBUZUrVNAyMJ6eoaTPeNHGyAyBV3QqDRbDYWS/lPOuFMCuWFXq+qfqRypke1JhQKnctyDauHrrHEFo/aXXvIobg/Fs3ST8lTtpdsG1wk/XOn5bzvpPRrSTNxQwoOEaXubJpdEGJQSiNSqtsnFIgrPLwptJkhFxnH6T/J1ZbMt4SxKo+CWUR9CrwBUMKA/JZogELgtPtfIQJSYS0hdM0CSBNXoGs5OJ/q+gdBcYOJb0Y1VDRFmLQ8Yg4jvgnPr8OsxW1iKvJM6369Qr4vrY7aZxp6zE6/W7b9Zlv0+wai7a16lnYQWl45+/FyV1UJE/qZu02pXcexlxGn2Yu0jMT9HNceGbumJz/FWZkm9g6GXnb296Gg4MD/Omf/ump37373e+IUupuAAAgAElEQVTG0dERPvCBD+x0cHd1SOynOwhNAziO4Kdla7wVQtk08xQ73670uz+uN2hDxFK6VzG1KDNOVQ2xqq0AZu3JAzohVIFO3MWZADoRZRnkgX1IkSmDYjQBGqfqk2d4S6w+zsD66QT3+8k/RMI1I9/BZ4QJy9eNAiuBVmJ925jXt9hwW+IcMC3Dd0+3WXhvqOaAQAtOLSvIyK1O5gKgMFJtT00AoitVVDWMYewNMzQ9Eypg+pFmYOD6BmZiAcPwgwJumKHpG7giUIFZ4AtCZQByFPRD9mDHHn1mvefTEjIahypaB/+0KKkSAQlgxw57Tyl+pf9UifyrWh2UaamVkE77RwK4O7VZoiptcIGNJnmJMUbQKszeEJTncI8coLlWqCBbpvczMwxT5KCqBt24CXd4tN09FtH8gc12VYZOdYx6hTodx+cumEaeBQbd4EC1ahjuzc4VwwAGJTaq9eMcMIrqqeslOakysu51S2DpLY47SgNEvae9gY5vZaXPW123wPOqgqcLbO9exbnE1snIpz/9abz3ve9d+Lsf/uEfxi/90i9tfVD3SignPrRURqOFn+GwUjjl2hv4/tsagsVtaHgA6ucgTQOqG+3tM6vdPBB6/b61so/JiBfVhsgyoMjRPDhEvZ+pr8lhpl4nVX2m0dVmx6pBwUgr0gulqiEyiwkh7yCNgz8vUSPiWUGoTas93p3Wbehuvt+HPLCvE/SkbF1wJ8GPZ8WgeybIUiTJkbMIMmuQ5Rn8sEAzzOAtoRka+IxgM733vpeh6Vu4Hin7xgBgwGVKG9b8kOB6BnbEMOUQ2XEGPgntGqfJbfIaOePYzaRBf9KozPrTh3BffSokFmdrVlBX3j8IkHX7/akNORgADx7A9XKMXzzE5Ho0AxRwEIKzPQMzdcjGU5V5F9oYYyQCUCz7b4NRiqDSoClEw0GQz9fqD40moNHkzOdo7WOtF7R0t9pQtKPQCqQUGdBEavr61RbvYzKyZnKxQ/JEAcBP1gKDPvz+QNlbIdFF/Du0F72/eJ2iFFdtmq1i62TkmWeewUMPPbTwd9evX8eNGze2Pqj7KWLZ0p2nRPZ8UKc0GiYQGA7YAVGQbAQNxupCBMpG8zoi1daY1rCWwKVT4bULMghTV9BZwO48FZGY4M9T0Eh8W7q9AFl9SfodbUXhQsLrtZPQMiJRQHNUQ41FKIFASCXjvUFq0/hM4HseJIQ66JO4nFAeWQgDOQAzrZSeDoDEa9UiMq1ipct70LSCGYVhRARwCkheqz0lXp+tuk6VpMiuikljNLyjQQ9urwc3sKj2GPU+gWsBQJBGYCqESUB2uu7iveqM7OLaG/x9uu0eipXRDSoNlxrxnjYNqNSk0C/SHjoDxJ0ArJdxuF5SJZWqGlwGE8hGx6sIEj93IP5aB4erZGSL2DoZef7zn4/Pf/7zePzxx0/97vOf//zSROW5FvHldM0sAPVcoutpE6moQVGSykr/RPov0FZjnL60UlWpKgEAlE3ATQOTZbNmblES+xxDJhMd7KKx2oKJi5nPdwwROZulseu2j49BZakS1dEvIxggrhKo2yhC+4dEQP0cCBgNU3pQo5RdQPR/VnVIXA9ohgK350EHFR596AgZe4yqDGWd4eSoh6ZfID8yGHwtw17Gik25NYKMCESsLI84j4ooQPvpG6CbnWHEC1xZrqXiKc7BH50kPFBkE1GeAVkQrwq/c488gONvGqIeEE5eSCgf8rATQu9rBDsFsgnBjmvwdDeTPhEEUPdmKqQzQdR6LEHBxzKeqCHiMgxGdCfekQ20U4jAH51oyzmCtOffyX5fW2su6CB1rrW/xGQEPgCVUQNyC3Rygq4ZpHSA15dNz74fk5GvfOUr+K//+i+86lWvmvn5P/zDP+BXfuVX8K//+q944QtfiF//9V/HT/3UT221j62Tkde85jX4nd/5HbzmNa+ZAbH+x3/8B971rnfhR3/0R7fd9H0Vxmqb5EIqI7EiQp1VWKIHhxUYBWO2jjiSvqy+8zkdUKSpVXEzftarDshGOJY1IyUgwPLBgrCb/sTCHV/cm+7LUjE5mVW/HWsvBEQn0hrpwUnCbVAjYK9A1qirIAbwRuXjvfWQzKPfr/AN125haCqMXI7KGXw5v46bNzOIYZiS0HvWwjCBT6zawC8CEHsHv6Q9ueaJtG0GIsBmetQBpJp+TgQ3sCj3Cc2QUF8T+GsNamORHRHYBYn6JqgXR8DzFnO6915z9y2fk2R2GY89nCeqWg0Ll1SLWjPMO5SIhFjZ+gkMHjQd/FWIS01GdIf613gMTMLCLIGf3Z1JRO7TeOc734nPfvaz+Jd/+Zf0sy996Uv4kR/5EUynU7z0pS/Fl7/8Zbz5zW/Go48+ih/6oR/aeB9bJyO/9Vu/hb/+67/GS1/6Ujz++ON40YtehP/93//F3/3d3+Hhhx/GO9/5zm03fe9EFxC5ZDUTkxHfbPhisIHZG6qUd6xehOQgDraU55qEFIWyC4DWfE98657aBX7WAbxa16fL0LF9c+rnWyYEq2StV1wPY/hyZbjPI6IyZKnAzVZZdANpb+CMBC18JlA5eTyFNTphi+nolIiAnVcxuQzwOeBzAeUe+/0SLxnewIFVDIqB4AvF1+HvDoco8xzkDLJRhmyiw4PNLGhagutGTRcvoHWXWDLRBdsafc6DjYIZNyiOVQ6/ukbwuYUdE7IRYCYCO/bgcQWahsl0/jnuUMnPbh1hloq8KeA5YUZY23ajsbZ8ynLxuxV3GwS8lr5rl+xxsyykqoIYoDtV4dmaTTNv7rdNEhHaTOLQMsGinID4y6023Idsmn/8x3/EG9/4xpmf/eEf/iEmkwk++MEP4vWvfz1OTk7w+OOP473vfe/lJiMveMEL8NnPfha/8Ru/gY9//OP45Cc/iec973n4iZ/4Cfz2b/82XvCCF2y76XsmoraHiKhs9IIXaCmAdUVwnoEeug4/7OtEMJoEim5osxgD6vUAa9Swb19tVHmsAmdUN5DjkwRSlUZSSyQNigsmlFR23VWFcn6A2aZFQQR/3snIRQ7qYZszVZ9N9hV1FxaZxgGplJ8mt6pShd/JtKXHAsGsTrRKQFAAawFI4ZH1ajw6PMb37v0HHjVHeJArPMCMzw/+C7eqPr508CCeyQ7ALoOZAC7voRhaZMcFsmkFHhlIWSYw7blFdErmoA6cWcXFTLWlyIdj9L+WIesbNH2VO7YTID8U2KlHflSDjkaQskrsrFPXNlginNWCmZGDJwaHCs1aCq5R6C3KqI8n8Cej9ZLRVcld99m4U20cAFKWaroJnDqfbSsj3CtaZt1oHPSZtkxIxAXqeWvGKIkivvGhbRX3owLrV77yFXz7t3/7zM8+/vGP4yUveQle//rXAwD29vbwtre9Db/2a7+21T52Ej17wQtegPe///27bOLeD+Yz7c1nMCMbblcMA4aSdggx6/sUZd2LHGIN/CBHs6eVEQuoWVXk3jsPwLWAygjMW8dEa0e9hbTCNGovu+lAykzn06bproovK7bGG/BS07hYgiaiBOxE3ai4XadkrtofCiYlLwpqFYR7oJ8zEBgIhky4xj1coxIH2RTDvMKzPQfXywABmh7B9himMlqls1aToLXPZ82KxPzXoi6J14mcGqfy8oZgKhXZ5BowtcDUmnhJUAydN+ZLERK5s8LPK7Buo2/uJVHTd8Ke3K2x5Hyc87D5llNKNPLktvW8i7aRsm06Va27EDN8L8VkMsHBwUH69/HxMb74xS/iLW95y8znvvmbvxk3b97cah/nosD6xBNP4JlnnsF3f/d3Yxh0Fp4LEVc8EgfO+WAD29eKhY/y7WsOTOKc+obE7wSDO+wN1BOkMKgPenAFo3zAYPqQvnj5YY5s7JEfO/S8B52Mk1T2jJPlWjoAu1HvAKjx1nCgGiZlBX98vDbAkM4BwEo24DcyC5mGFf3dOjkExdVFQdaCD/aVPlxVkMOjBIakmByEgVymRvEkxyP0ni7hDSE7YfWQmTKeyB7Bx/f+P3xdfojv6v8/fEv2LL7mrqMwDa4VU/T3Skyu5zB9AjcEbwxcTrBHe2AbfEwm07Ofo8C64P1gXFaWnSrB6e9I3cCfjDThCiDgqEkCEVBVw4xrQIBslGmSNBHkxw5m6mFGFTCZnq5Kda/tOi0A6SQjQQsIwHrCXwFfJaMRhCjpgKwVqyqRUZPjLo61KiNdoG6MSCEO592KOHbMQde8jmQtkjrwQK0KZDwBjR1Qb3Y+W8d9CGB98YtfjCeeeALf//3fDwD4zGc+AxHBy172spnPzSctm8ROychf/MVf4O1vfzuefPJJAMA///M/47HHHsMb3vAG/OAP/iDe+ta37rL5uz/8YtlwAGkwNkWm9FRC6yOxzovlRRU3ibQVkwUhq0BvbHoG0wfVqG/6EGH6iG4zv0XIjw2aHqH4Wk8phXXdAate8JNObYkbrKVS6vchg5569ozHa2ur8HlQe40BDQcq5ATo/u/WOAsnYgxobwi/N9CW3fGJTnZNwBHNfT7KedtnT9A3BDvNUF3LwA1j0uvj89dfgK/2DpCRQ49q3PYD5NzgWjbFfr/E+FoPTWFQlQbaeGcM9nKQE/C0Cs8y0PKIT+MzyFrQ3hDSL0AjqwmMX8IqCpYH0vk+0FaDVH22hgFgpwWysSYjduxgJg1oUsIFXMbCl1KWtL5OfazTponss00iuG1vHKtadHdrAt2JdZKRxPwzmK0oRyycfkj/Tu7J69279J4YAxQFZD8sjJ0HlSeXl4zch/EDP/ADePe7343XvOY1eN7znof3vOc9MMbgta997cznPve5z+HFL37xVvvYGvr84Q9/GG9605vw2GOP4X3ve9/MgPjYY4/hQx/60Labvm+CDIOjFHyUCqc1LnmgBpK1uqLPbEhIDFxh0PQMXI/hCgUnkgBmQrBjgh0HQF8pQNQXcac1PC4s4nMgqgEgQQ2RykrL6Bscx6kV1CZBlAzVTrEb1v1+x4DrQmN+X+HYwR1athegVso2qvVAsRL0SLjx4FrAlYArwJww/vfZB/Afzz4P/3T4jfi7k2/D/z3+P/jf8QN4ZjrEuMoArz128pQk3iX6Ha0hzZ8m9HAM8F6daNeR9ZdOGzGKWwVMjJDqi0QZfKq96uicE+sqGeVd1r1vd7xe+zS0aMna2efjPGPLcxcvKwGs2qoNf4JoGfUKUFBkVhAzzyYim0RskYXWHs2Zjl7FdvH2t78dN2/exDd90zfh2rVr+MQnPoG3vvWtpxKPD37wg3jlK1+51T62roy8613vwpvf/Ga8//3vh3MOb3vb29Lvvu3bvg1/9Ed/tO2m7+2Iq8KQfNheDu8EXBQAdB0p5RlZfnQkzXPIsA8ZFIkl4S2jPshQXtOyeXWNgmU7MHhSmQbFoUc28siOavDxCDIea4tmF5pblLjuxJn4D2nlw4VY6cJECvRt1l+eqGrs5qDXqLRK1qrbbxCh2mgzEZzs/MX2/bvmenHQ7CrFdvxB/K3boGMLH83Llh1TYNNI0CPhkwqWCMWhVdXSKaO5uYdpBvzz9Qfwf/e/GWQEnHkQCVxlgJpBFYNLwE7CxC+iGKaQkJDQYjlvaqnkUcI+VcOMWW+lKwIygTEW8VG5BQzBTtQI0JQe9qRSka6oOrwjPVYQ8AaX4RI9s+P1Vv+U5+B+DyBWl+JoZnleoNaYwIfFxCbPvXMOvOo9C+2mOMaRYdDBNbgH90CNB1PA9UQG4ULw1FyilBZA0m6/qlV8D9hN5XqLIOwIYD23Izm/eNGLXoTPfe5z+JM/+RPcvHkT3/u933uKXfPUU0/hFa94BX7yJ39yq31snYx84QtfwO/93u8t/N2DDz6IZ599dttN3/OhWgOaQLA1Cl41HVn2db5rTKqGCAFiGGIZLmeV9s4BnwFiAZoC+YmAayA78chOGphJcOqtFwuKbXhCCyo6qycT/btTft94t3S6BbH6S61UtDGqsdJhR6y3jfYeEBBlWC4m4rVlAnynPB1E7OB9unZSVWtPPhSwRtEegGur1YSSwI3ATgAxAFeMZpTBW6C55gArgCP1rfEKFKUGYJc2rAnUGRP/TPUjVnC8rAR7LzwPw8lMT+K+GxV4M6VTxlDTkZDf0VBRAmYkqYzeZUFEKgjHpJNsBDyvYTew1vbj+AOz8b3yzq+ufEWlV5h0b6XI4PpqNsrRfVe41YrpJiRrVmySrxJw+YZ59yG191Of+hQee+yxMyU7Hn300Z2KEFsnI4PBAIeHhwt/95WvfAXXr1/f+qDu9RAvoMBgYWZ4kXaQXLFyiN+VpgFPSjCgst9G/UbyjEHewFtty4ghBaweOXDtYU9q0KRti4hzO7+McYAiCitGVnCen0wWrIoXvEhbDpLp2m0SEgY6a4F+TymWeRbwK9HefP1BduNkKMa61GjxOvh0D8mz6pMwb7f/brsH0BZJ1SA/bMBNqHARIKzA1maAUGmz8DmSWCw3QHFL0Dv0sBMPqry2/kRaPZPu9Qz701aHQCDKvAmy9UkjZ4sQJkjGyYVYOIzZIqpwGv7sWpLXY8dGbrUzQa3+z1p04O0Osk1cw/mm5GlZBXQDqn6sYm4aa1N7RbQi0u8DRY7qkT0cv7gAN8C+F1gilSaYTLTFXFVa3aXwDseFRhTGa5qkIo2o4lvkShVHuKdSAedkcfVcjMcffxyf+cxn8PKXv/zC9rF1MvKKV7wC73vf+/C6173u1O/+/M//HK9+9at3Oa57N6L4jlcbbuYgeMZm5Vf1+wE1X1WQk7Ga3hlOVN/ce9hx0D4ICY4Z16CTifZHqzoIpLnkKrxzzzS0nchaRahbq2JOC2Tiu+2cdmW5XYuIdqH25hmo34NYHbTEBDr0OtEt+24bqwCJnX2lCSs8I8SBVbMNHTmCP8OgDC+K2ZhUyJ81yE5Mq1QKwBcWPjfwhcHk4QwuDxM9azWkd9MhP65V3XXagGKVLVRHwMEEMJxzOq3INOvOxTQ7gW4UwfRRrCbgEttFwV8J/nxotBKM+7Y1oCNjtI1iTGL3nGeIiJbxRc9bvCRQKNslCVCnzSrLAMTnEBuJnhmT2tCjrytw+H8YXANc9zEEQKUDn+SgulHgM5CSDMQ29l5fz21SgibTmWSXegWkV+giCgBQA0fnfsqL4z5k02y9KNsgtk5G3vGOd+CVr3wlXv7yl+ONb3wjiAgf/ehH8Zu/+Zv41Kc+hX/6p386z+O8t0IkNf4M86wU/KoyY9T3iKucOPhz6NlXjfZVO0GT0Df3PjAsmg4AcFGLZdPz8e3KVsJxLAqanZQoCrAuoauuCtpS9Ky1qw+rZq+mcGuXneNEa4yacW18BFtGkO9PE0xc9S4qfXdp4vPXff7z8b45pTdS7ZJsOodrRAJkYwPypFUIVtAq1z6BRMk5kJO25RIrIzQrFJbAmHHf3WOMn4n/7OJkwjZOtRXDZ7T6HZIRJoXf79iWmQ8RUaO8uzmcD62M9holDyqR0xiJ7uc4aM0Qzl08zXvZSPSMvOgjHw0eZT2shb6bcYGm/03WBqdp106cIndGX+Q+TEYuI7ZORl72spfhYx/7GH7+538ev/zLvwwA+N3f/V18y7d8C/7mb/4G3/md33luB3lPRpjAmQl+XvBsXb0R5yEmMGLixDQtT4FJE9Cuu01jQEWhE/povJm+yPypBMVWiqtQY1R/YWE5mFup+jxT86oIot0wmNe8TjMHqys/f3yiRoFR/I1JvUHWGIAp0IGp14NMp6Btyu2xwrXJ8ad+OpI4mWJfIpYk/Mz7VtgriqAxtzgTILEuAACN04F6ysnBmZow4XsPKtXhOWeCzTlglAjkBHbUaEWkdno9I1UcSOXylBzHZKKqtbLnGfOzAXE4riwLhxm+m2dqwOY9/MkIfjTWZ8lLa2tAmoT4jOAKBolVkS23QeURAGV5C06eq4AkNs2WIc7BjyatP8o5h9RNog4n3RTSKiCsBcaThe+mBMNGyizMoFAw+WRy6rlOBnPAxu/eJgqsUtWQo2PwZIrhlwsI9cFO0H9yCnNzlCoi0jSzTtjOQ9CAXBYSbCjjMDpYTytNupsGdKxjotQ1UF1ej+Z+VGAFsNN7sU7spDPy+OOP4wtf+AL+8z//E08//TQefvjhGdO853qICJhEKyPRtC7GuglJACvGnqhMOi9Vd9UZJ6qIi+Agr00EahrQZLK9aFJA+otXrZIzKbcRW2KtytV7r26v2+x3WWVk1bUTlUvfJgHS7bOWgfuFLuBPRksNzs46hq0ifc+3wNYwcbfHR0p1DYlhMgezNrFVFOcza45I3CAZ7IUEmbwP7Q4HG1shhKRWymWTQKI0rbRC5nw7MGW23U903/Uq/qVKspye/VQ9ybLWSykmUEUOGfRSW4mmZWgBCaibRBM0GckJXBMkM6C6k7CtCqLEtEJVQZrZZ0liZXLbENm6xbNWBD2W+aAs0+ekWsJWC+8wmUK9rKKg26IqypYDxUbJSFPrgsFaZE8V2A8tFnPzBDSaqK9PWaYqmQRGHpwLz2bw3rImVEhMqPyFBKSq9fuxWuIu8J48R+Lxxx9fzZaCJi3L8KRnxVbJyI0bN/DHf/zH+NSnPoWvfvWrAFQa/vHHH8fP/uzP4qGHHtpms/d2dErl0YGTjIHJM5WY7vUQlQa5S31dBjoT9ZEhIDnxdrUU5nt4cXUsQSacxANlwA4kv5ndJJbXOueYGIXjT7z/LYIXGeVdhvaD+KTRAe81sersV72IllSGziMiCDJWt4p8JgET59tWXCzPJ28X1kQjlLLTJN3Vzohl7u7EG/8WdfxNDB4bklxAB/5GE16J0vR5noztFChMoJMcbEyq2CUgaPw7TiRRSydoiFBZtxiaAETuXHRlWzSqqcO1Cp615njrU8bj8cS248zv5AytjM41mvnZnRYkE6/nL6tF2sQ5UF21Wh/nGBt70/hwvNMS5miq93iqjLFU7fUtQDdF+B1VSukWo+MONU4xc8kWoLOA2EbWf9u4T9s0r371q/G85z3vwra/cTLyyU9+Eq973etwdHQEYwwefvhhiAieeOIJ/O3f/i1+//d/H3/1V3+F7/u+77uI472zwQYg0/a2Oy9zm4AEjQVmUJ7BDPoK/7g2hDCDigxcFPqideSrZya3mKxMJjrod5OWJZGOpIMb0NUlp8GXItjwHEB+FOl/pOdJeY6ufLc4DwpVnKVeIemYFyP9FTMy991UXboYEB4Q7munmkN7w7Dr0KduGvjbh9tXXlYE2Qy8v6dMoP0h3F4fYGiLRdSHhaZVwoIAgGQW7mAAX1iVTSfS1XJs1RhWMK81EPYpMUgeMEBIEkhzAA9tbRkL5ADXDiyiFZIICiSCXNuD9DK4QY7qeg4xhOLmEPaZnoKvJ9O0yvWTqU6G4XmnPFdwcS8HTUrI8UgnkbJMjJx0TRoHLhugItjbU/XdKSvg5m34ql4/OQyJ5LJPLk1+u5iW+C6ehXO5xJCmgT88UrzRiuOQqoJfYzzZJrzza62c9UBazSF38xbo6Fi3EY1A0d4LDsaD+jXRez+ZJoYNwpgrzkHGk9S+6n7/vLFFZ58b7stk5B3veMeFsmk2Shdv3LiBH/uxH8PBwQE+9KEP4fDwEE8++SSeeuopHB4e4i//8i8xHA7x+te//r7VGYlJR9KzCH8iLiGWzcmqqZjJDJzzWv4ugtlYZtNATNaexoAAKSFJFEPv2snnrD9eP5vUT8Mq44IuRlLIRCcJA5AArwmrss625kIxI93PXN6AIq4zYVurE3uufyjLFt+z84ouriLP4PtWWS+FheSqyyCZ1eTCtH8kM/C5/h3VSk9VRKKGSlQ0ZZq5rjRfLeAAGLWxGsLpu6kikikbpx4y6iGj6RtIT5MMfcZN0tkBZisSYjqCdHXQUVnoLSMKom08aFKBT8bquzSZqvdSvQamJ55/9305tZvFOKjue3/qZ3dBqNJxtRoTFasn/vyTp00BrPEeRBdoPxppIhoxWmEsA9COr7qjMLbVoR2rqsQISanU1cz3ReRyKyNXsVVsVBmJaquf/vSn8aIXvWjmd4PBAG94wxvwPd/zPfiu7/ouvP/978ev/uqvnuvB3vHoSlQTqZFT7EH3ekmoLA283oOLrGWhOIEUGXwv10G/2VP672gCunm7LWnPrQy2O1ZJoLWZ4991AIrMBq+CV6rC6UBNwAvESSpM2gAgVaE9XOcWa5OE7UZVxqQVkOeai7DpDJ4LvFBiUGdy2PFciRZMNFVbCt9IgyKYg1G/r89JXalp3BkldYmsJRFNyCKOQxAm5vCcNKFkzQzjPUxmVY20y6qwBmJDgpCZ9CwCUN0Q71XTJDOnBm0hbdV4ImCQgbwFTW3AJmnCo0wbAUm3QhcmjkYp5umaxepZaN/RbVYTxarSz0kQHQsaEclT6WQEdopvkSpMNnXTJutnJbxE4EIlx+HUO2bZtU9V/V4v/AfPtmK8b8/DoUUb3ulWzUXEorbUoo8VBXhvCOn1YLINkvT5xcWilld4Fmg40N+VZXJmlqiw6hRUnRY/8+Ec5BIl4e9XAOtFx0bJyCc+8Qm85S1vOZWIdOPrv/7r8eY3vxkf//jH78NkRJ+SSKOL/XIqcsj+QKsf4Q850X52kcM5HazFe7j9As1QVQajTkh+u4BtHDCdgmLJGdC+ukQQ4BbVjR3AaGduE0jbFQmYlIA/oExdM6nXgwzVNZOqWs+rLEFxIlmwTbIWNBzqShqA6ffg6gaU2Ra0d9bAGGX44+Htcu5R6C1ORgCkcZDpVAe3dVbiAKLGA2UWfHANcm2oughVfXZ/P7ZPQlIS1CWU5QLoAFzVOoFPJpoEnIw6tuktfkBCNc73M/g4WYgmD+SCtwsRvA2Uz/BzICQilkCGIDYDBODMwNiAOXECNAqKjfTMWE0honb12nXMjWDoGjq5xIjP0GCgyYhz2tqpG3UmPjrRj8UJZ52EMySVNICEvHQAACAASURBVByAru0HJtEZ7KiQgJp+Dx4BWwO072LdAKhPvQeXGmsmCbvvp6OVc0bFhfs94MEHIP2+tkS22T6wPKEsCsj+QMHNRJCyAvkS3rWVk7OuhDQNRC7RJe8+VGC9jNgoGfnCF76AX/iFX1j5uVe96lX4wAc+sPVB3dWRHCW5BQiaTul6LpgJ3klrFDYPmCNoUmKNrhARWkE+rGwjnU0upyKwMhbhO7xATU+C70jEJCQ55jUwIzPHH0GTshpvsix2XQlFgN+2y5RwnchmwfyrowMBrL4mgRqOkMiCRPEdQKLnivehchBAfqghrrOS9+EexD8xWD8tgHoHhVaNzzQZoWbunDl+NpyaMLwzmsyUdWqfmNKDHLUy7XWTWoZLn8tTwO3Ybuz86VYhtr2vneTurGvvnSYpUYp/w072vRtLEpy1sCWB9u8bd0oDaa1dx4RkGcA+PQchWTWcFnJXcTmxs3v6GrFRMnL79m088sgjKz/3yCOP4Pbt21sf1F0bod9OgUJJRaEcd2vSKpEkCkN5oKxgReAbBzpR7QETV9lM6jdjtPQuwz7QL9p9OQ+uak1EpiVkPD7NToi4leB8CSCtJC8kKSFqDeTi6rJbGnUeMGGl4rwCaAFNpHynbRSpoFE7JTBHACgALe6uaeDLNfEAQOgPn5G0rRuR9ugcEI+te++JQFwtH6PZgHtFMHgrQIPApGoc6PZxkOlf8OU4kIdEVADQeJqmw6QNUtWQ0UhbFE2TgIDiOoBKEfhpBRlPlL5b5KBekCm3DFfwjHiYt4SmxxADtRc4bPS5NLo9bwh+oJNALQBJBq4EvSc9+GQKU9UY3Fb9C5q0z+vM87ju5a+qFnPUzFUhYqWjCxzVE16yMa3E+JORVldE4JfRX4G0SpeqVLE+rtv7EoXeuvequ5/LinPeF1mrLUQifd9itcq7xa2TufCTKejGs3C3jzs8rE0OgEFZuJ/VgnGrrkDjacDkMWB7Wtnj0XqPFRFA5vIE0ARnl2rW+f5zMDZKRsqyRDavebBoo9aiqu5DXnekrkbApjVaAg8vLHmvDzyFSkgQm/JNYA8QAZkFZ0YngsxAnFZLfM/qd4P3BjlRj5mg64HO5I8oYBQZPHmubRERFQtybvvWzqpLEIGqXUxCXLmG/waQJtN43bpo9tYVFKm1QtYqK2BatiVh18CH1fXacR4DdWxxuECT9l7bcaQJZDqnJUFMmqjmmcpSx3bV0QgSBOhmetvz6qQIyRwaSF2DxuGw6jrgRBoVcEusiHayToNzuD9xYiEgKKiqv4vPGGDA5QyfEbwF6qH+DTDsmLW1aCjojoTPGgS5eIYtBT2j2g801sRHGr1nu2htJOGthZR3dfNNVbhYLVkxaUpdrXdMcRt1DV/7WZbcoqrKNpL9d1sYE+jj6rAt862zFRGvrRuNNlavFS+grCNXP18dEdWswbTU9m2/B8ms0no3CK02b/SVreMKM7JdbEztfeKJJ2BXeHx88Ytf3PqA7uqYcY/s6DeIaEk6/juuoERgLKvOSLxmmVX2gWX4wqTKCJApNoBIS+VOwJaVUsmsk0kTqGvzq7LApqAARNw5zhjYdZLEUobOItAtsT9l7tr9XNJ9mAMiUtJNMWv1rk+dQ9qBnP75iraW0pdDeyOW94tclS4bB3ZOx7ZotT6/L+eAhoCgFBl/lgDKC63RYwuQEhuLssBKQYJzhAnZBFyrW7yp1Krx6RzJaauHvICdwIP0vxvdOtdANMjTsri2bFgEDpqcEJE6+IqHqZRmjIB/STT1XdlbXaDysphvH5xTtSBtJmgCiQ+uuB01XU2mAyB50Neq3/Ex/PQedWLzWm3bVTVWqb0ddtaqexKFIF3H1XvBw6zO0xXEq4s2VQYynW4E8L8bHZivYjY2Tkbe9KY3rfxMRMTfdxFfrogViaqnzqtmSBDIQhYuqxewVWovQgXF93I0w0zL4kN13/WW4DNAiMBO1KvBCezUgpzATAvYYa6iT4cF6DhLK2R4SRTL6OS5k4bAWRN/KJtLg9mBZgVQ9sxDEQepOv4vne2qLooC5Cizy52CFwRFyqvvlPoDmLRLSY706VNJiQ+TvDGagBmBDHpwB31Q7WCY1O68DlWKqGsQx+Ggl0BZluTMpa71s3PnuejYqciBLIMMevB7RWK/xIQktouisduybaouQxBJqxuQ1Yoa1wxyBBKGGAE5ghBDGDCVetWQl+BN40DewucMEoArgak8TOnAxxNtI1aVVrXOqz24DBt16nPn246MvXEmpOdaPGaSWM4zwGTghx9E/XUPgLzA/g/DP3VvJiPS1PBjTQx2SSSV2mtANmuT9LPuTff6ntFuk6qCC4u9RKkXv/7C5CKA/GfuD1dtmi1io2Tkz/7szy7qOO7N6ADipHFtn5/a1QEbrYxE113J2n69y4PPRkZwBQJ4kMCNOqaS6CqVPMMXVpORKGVNFHAH7rSgz7aJSJysmZauuC+kN75km0SkIpHBdwW0mT8MkRq+acXBt/ovXR8XorattUxILYJJmeEtK4bDGE0CvQR56rCCjpgD50Ky09mm86cnzw54tzWbm2sFGm2pRGYPUaczH7Uu1rnniZ2DNGAqKJbAEHAjoUXY+U4AJ1Id6Lus5nmmdODSterAsqStctFx3vtLkJBlmBCftFYks3A9q8nIuo7Qd2NE3NcuAGFEBVZK78OpcuiKYzjzd5G9d87mfhcSO7ZprpKRNeKnf/qnL+o47pmQplEzKmNUATIabkXAY9MAtk5eIQYCLwJ/bQCfG4xfNMDoEYbPCfUe4ApAWCAWgAD5ESE7AbgK2DkXf6+NezFGe6eRSRGOJ1ZJ1lKi7K6kuwqTmQU/cKA4h/EU/tatc7dA3yQ4eFAkeeh1Vm1xck/Owa2lOx1cQ/PwviZbTiAi4FEJevJr8KPJYjpwMGoTcqDxFBmg174Dqk1JT2KciiZzTgGAFLVZYlWnA8KkogANBvrFUisMYA64IG21iOEknw7DQKOeLYl1smQCiRUU6vVANrR1at2uCeBVMQyx+t/6rOkXfcYgIzDjWo3ySof+WPv0VKl5GTUOcnQCX5Yt2+Eio3vtDAfM0WqA5Wa7SNnI8s9UlR7Dzdsown2QoCB6T0YApgPYjhIfvi/EYGtawPllJ6Z3S1xVRraKezidvzORWCTLMAllmVoE3O+BADjn0QR9kZNHDU6+XuALD3/QwPaazmYI06cKCDHslJCNBKZGcCplMDxguV2ZRRl56ES3VntmRsyrI2ltDLgogAeuwe33wLct6Pj4jspcE6scvFSVAtu657CkjN9twwBhMt7bA/IM1Quv4+gbe6EVoZWA3s0CvaNRAsSdWnmJT+ZcMp4khlDaf1Qz7WpfAJBAj9VkcTz7HUCF8ohUav6Ba1qxODpJLc4IYNUJGK1SafzTEd86s+WT58q0igDMRg3HOAiWScaQRim9XOo1cz0D1zPB2l2AsgLXDeRkpLohMTH0Gyjsnlek6644KnFnVLSA9fELafMy87VF+48Junv2JnAzsAbvZRArddrO4jc+FRUrzOCJwwLiHqheXMVdF1fJyLZxFnI/+mp4aXVGukG6AiXrYTOXPDi8J1R5Dp+Hir42rlWltQ6l8uCgGsvjM8CsVWXyLi2SWBVkZ05JlHFRNaDGwd/hlQ2h1R+YFUdaAxgXTdZYGUxSZGgGBs1AaaoR2+HtamxTYikBkK5VfaAsA0jKs2QtklPoMkZIFGczqi3jM6v6M90ZMNKU6wZchYSwDqqjkVXlZSkwjwhtUhZE5LQlI+pN470ChKNAWWzZCLRd08icb03cv9KSdwapLjjgdnXu1p7QIph0I3DzGZEKI+sAwS8bi3BRIUqjpl20JEQ1gTaSg79f46oyslVcJSPnHYEJIE4HVUbQGXFee+21gCtADAAj6OU1mD0y4+GF8Ox1i5Jz2GNG71kAI4BLj+zmRJOEoxHk+CR5z6w1AHcqBpQFjRTDQJbrZDWZqh5EVYG+9iwos/DR7+MOJiQRwErxeJ1X7YdlIa3mCREDmQGyHO6BPbhhhvEjGUYvBIQE/RuE/EjbEQBa1sn8JqNhoR5QW3GJrCn9B5BloEE/KId6pfAGb6B5Qz0yRj8blCXdfgFyHvYkS/oaqCptIzkPMwl6LeMJfGhZ+apekXyqeizlOaSvx0RlpZURkWCch+ApY8GVR367BFUNzIRhg8IqlS74gmgSlPbb3iTFxvjdJmbu98EH1xSXc/sQfjRa/uEAjiSjei4gTv4mpz+72fMr0SX2XsPfx4rnNrgdkbb6uEWFR5xK/jfT8nKJC129oruoJXRF7d0urpKRbWJV6TeU1iECwwTvfKJUkgPYkcoVkCCzDplxyINY2KhfYeIIjcu0n0+6UqVxqfba40nL3lgXz0EtYDMKtiEKpTGFVoKH1AIX9WHugpebiJSh1NU2WRVznyFr4PsWzdCi3gPqfb037pjgxyEpRAcrsGB7i66zBNYRgp8OGW41RRp1pkXVwbx0t9/RIZHcwhdGvX06JoNx0vcAKCjxSlUngbOVFTBAn0FjIJYDHTuwamLLBoA3Cqj+/9l711hZtqtc7Btz1qO719pr77PPA5MEzMU+CbqxjS1jxZZsxUa8hIwuIBRZRAHlT2SudYUQucYSso3/WAKM+IXsIBKByB8wD4kYWQok+AauQMhCBiuIXODYPATY55z9WGv1q6rmHPkx5pw1q7u6u7r6sdY+pz9pa6/Vq7teXTXnmGN84/vAcE64VfhoIEbHAmOLwa/PwGxDVmw75CyVa0cEmkyBNcFIyDxqAGkWRPj2gZoy8oRFI7sa9u2S5fFdMVW1tc5IbxBFwnfH2eUJh8UpGOmDdROBEh8SAO6BUbCVgb6cgeYphi8nsKlGNSKM1QAvjlOoYYWLO1Mk2iLRFsPzOSaVkrLCgGSycAqg0AqUpTWhs2vQYFlKQ0DwXZFUP5olhVXndyw/jHiXimCtEE2DjH7H/VOayHVSCqq0SGYG+eMEg6/KoJ0/YOSPLdIrn2FaXfJohdOgUJkWgmiWwjxzgeLpoezv0QBqXkJdjusgInTVOI+bqoICkDoNEC8ahri802YCtokfQfJFM8t7yQhXBGUpDqcsTjekNdJcSiN6bqBmRS0mFV9vy2JK1nZ9vHjdjpwJLkqo8RQAJPvTBU4aHIkz7lN691Vy4IwsTKouu7ivctDecQs4K6ZrmaYL6ZhqITT5PdIFWnqv4w0BNWcs1v+5BQurEzbjFIzsGZQm0r0BSA2VAFOUwFdegkoSnE/mGHz1DGaUIH+YobhIMXs6xePXKiR5hft3x3ju7Br/kNzD7O596Bkhnejgf+OdcFFV6w2/Yjj+gW/b9WqidWvyCm5DOClan4qPSbF7HKiVUpKx8N43XYMFIvGDOTsTxdtJASoNzoiQzOT6ZY8r6EkFfTUHXKZpqwHdleMoS4F7d2CHGcZff47r/1yDKsbgYYZkZjH6pxz08FE9OLpMi3n4WLppHifAS+JJJO62ZeN7aMj/bxhUw0Acw4hHDBVlyKqRVuCJyPrrsoJ6nAuHZDwNfjexMVwI1qoW1cs98SbsdBq6MFr30walgDQVTkyS1PyROEjvS2BdWOFTlgkZ2hjYArcvILkFE67tEoxsEjBc8HSCUo3AkOOA3pGopaXeKTkPcrm/y8q5bPvnbl9n2QEnzkgvnIKRPaNpPW+htXJy8CWorEBZhiTRUFWG7CoBk0I1JMxnGpVilEaJ5oOy0tKrXNuoN+JLklqJk1S3VUbjANUSOS+0ia75jPxHYN5vK+U6iAim607xOhadPxyZF1oLKgE9q5Bey7noSQU9LUHzopYf73pQ8T60FoJslqAaEKoRoEqCmQBkxQlXt5EhnagaW5YyDLA609Xlesctr1yLDkowZ4XHErJpLvDUWmS2FTk+jivDePO9bUpDu4J5Own5Bm/FE7J76FssH4bbZEu5IfamOWEJbBlqLwrQtShh0NHxXC1vnAi0qsWG8Xcfx3HCUXEKRvYMrqpgi87GgMAwpWvBJQImExAAVZQYjhIk0wTKJGCdwAw0Hj3I8OD8AjROcPESIbtk6IJhRxko1bCZcADUtIJOEtjxpJUk2QqtQednYsddGekQ8ZyMtSdlnSiYQ1sAdIA0MSnlFt5G5peu+yAlfIxhLiWJmRiv6aKEunbS6vNCzt8FI/VnVwdbajAADYfuFzdZ3b+H4mvOUQ21eLto0YdJJxbZowp6PHcdKKsCjdjXZ7eJTkjJqXRFkMuUzOe1wWJYKcLpp7gdl0XUpss1iTZ+re8xeZK0J1zvA16mPZLqp0Ra032wFfa1LYF1RWsvl3H57OZLIrcRosDaYSxZEysGor03mXRlOHgz0qIUHtl8HkrL4X+DqNPMusCcumdU94QTgbUfTsHINoiUVQG0DnSeWS6/SJnG+q4KUlAQN1MUOdI0gR5nUNUAQAqTAeWZQjXKkMyAwcsW2dhClQwzSIBBgvIiQTlSSK8tRuPzwKVYK3YWVs1aJM0vBqC5gRrPZELeRH5zJYmlbW7J49gWSgmBtU9KnNIUdpAKIXM2CcFg+PZYOCJSBnHn5rsJVgQkNMhFFySY5RGqp84wfSZFNVCoRiTidQCSiUV6WUBdz2CqNV1J+7p25AKRJHGTpRBipfQzXk6LOxluripguoZ0uMvxEQl3x5kg7lWzZrGsqLVMXlaykP23u1De8bDmyVD/vEF0KtNs5Ioox/fKxBLBm5GmiQSdSoHKunTZbJ+XsiKMCYam5HlXx/7qXqUBxS445bK2gBoNpQVxOITKc7fq08sBSrR6UlrBxnbxqlYHhRHdED01yK4tsitG/ogxeMDIHjHyS4vk2iCZVtCzCmpeQc2lPVgXdtlBdBGLwRPg3ISt6IlUZmVL60YcI11NQmDtAy5L0KwUrkTkdhv+rSBjroXX3TAW7AZJ1ipodOg5IxkDyZiRTAzUpJS69RFAypE5s1RatokCz2gjfJmu7d+uCFmWPWcTyAm/laVor5Rl8N/p9vn2AMzfb09cN80tgDXO5ViJ7QKlmSgMO/uKjfCEZF+e0Z64r8QANOa5xePsYgedD0Qyt/8sq9uAX6G4urrCBz/4QXzHd3wHnn32WRARfuqnfqr1vX/2Z3+Gb/u2b8P5+Tnu3buH7//+78cLL7xw3ANuwSkzsgXoNc+BlEvzGyt6H87KfUl/AQCYobSbUCOl00AgnYtmRDotkD5IxI1XE1iJlTdNizpgcB4oanKG9DyDmpag6VxWgW0rTi8ipSik29kYqMkMikgyItNZnb5vPeGI/HcDtXKlCA157s6dQwb20WPQRAiZvjwh+iq6ZurHplsdujC4KIDxRFZsd0awowycKaiKkTCQXVv5eWKQ/93L4IePYJ1i6cFBCnR2Bh7m0kKuFZBLl49X6d23dPpGMMtzsVETpefmjQUeX4KdGjFCun799Q7t7S0daRsVWG8DbqCzrQustdBaQ1+cy7M2GABE4OkU9vK6k2Iw0hSUZ0LU1xqcaEdQVuAKYkEwmwmXqcWCwAczNBqB7pyJzcHVGAolsEa6Zq+4AQLryy+/jF/8xV/EN3/zN+N7v/d78Uu/9Eut7/urv/orvPvd78ab3/xm/Pqv/zpmsxk+8pGP4F3vehe+8IUv4Nlnn93hwHfDKRjZAnw2AKvMrY5Z2iC9OZ6qWmv/i5kR8i26RLUAldd/cHV6ito62RuxASLZrpR8xtm2BxO3RUQSz2z9+1iItIkGKlOvKjdNlseexKL9NlxptzgO9j4v8fvjVa9ffQGtg1rrNv33lCTgRAl/x3m6KGakVwbJpISaFOBHlzCPL90Hj3DtvNJqlrqyk1tZ+hq8Wl+rPxgOUdrwirzGbE+u9SVLImcxsCj/7992i6OR0OZ6u8pGzABpEu2XRAN5JpnDqhJCKtP6Q/ZjllsosMuMsHYqwRZhXESbDUFUbqZEgweSIaRZUcstHAE3wRl57Wtfi4cPH4KI8NJLL60MRj7ykY8gz3N85jOfwcXFBQDgrW99K55//nl84hOfwE//9E/3P/AdcQpGtgC7yZAqG2zhoTXIsgxsLa6XeiEY4aoS8pX7rLjG2jpYiFPa/mefojQWNJ1JMFIZN+GWNeehcbBWXvfbhiOCzudgdt0VfrLeFIz0mUyVbth99+n5V16B1WV4guppFJSwMY5cuEBCBeD1NvzfKNpGWBGXu5VRqGLoqWw/mZRQ1wVoNu9NoNx6/06CngY52ImoUVEBcEFuXI7y12OfWYoNHKq9Q4nqKmWptB3P59tlnrgmO7Zdhy5GeQeFOz9AMnGtpN9bSqC1lZFumvkcXAlfg7QWbZugZRTfj9QMILQoJktZhST7zAyaWtCMmlmRWB064sSF7CdzKNlxWdbmfcfADWRGugTPVVXhM5/5DH7oh34oBCKABDLvec978Nu//dunYOSJgQJQQRxLfQDgrcPnLvK3qrFiUVpLMOLZ3rM5qCiWW0nXDTCRn4w1RsoPxgTTstbPtrVKsoG5HtfKhcHrZcW+d+qiiPRWCsnAbKUaC3nAmCGaIWki5a00DUZxXrPDXl2Di4iECtT+O2zrdmTfigsJCsMA2XX13qIuSZVFWhqQYajLqZjdVY7E3Of6bZmFolxItZRlsKMcdpBAWSsDmqmCyR+ggwdS0OIAdg4gGiJTh3Z4dgEunZ2Bzoag2VzIwZHDcResO04fjBxNSXQBKkuhLu4IX+p6DL6+9gdWv+mWlWc8rLFQimCurmoiqjN9XCIvx2Vk/1KSgHJnX2CtSCGULNnnSrqx7GSylPGMgxBKE4DESZ2m8gzKou3m3MdvC/72b/8W0+kUb3rTm5b+9qY3vQm/93u/h9lshsFgcANHdwpGtgIZromfTocBlpurzwX9A1IEE2cuerVyWoDqBzfogvRpM7RGJucYhxrcfGaEthwIIs2MBr/D//MZEq6VRLlhAEiBGwJ26qGIsk1Arbi65fWT0lczQyMKp9YFN/26f7aGF4fSWnRjfEp7MVPhskHMUbnPqigXHGUHbikXAUAzU+HLnOTO7dD7uwk8gToZobVX+vEBI6WwdV1U4d6Mwc6k0bhnqjJAWbhMb/u2aPH+AKSkCldePWI2aV9lmsvLy8breZ4jz/Pe23355ZcBAPfv31/62/3798HMePjwIb72a7+29z52wSkY2QLqKw8AqyQj4UsozHWpYCHtS1pDp0kzC9JjoFd5Drp7IZOOmwS5LMGPHocHbiusOQZhvntCYP9JleJOHhUJUrWtYOOJNXN13uFA0tWjITAcLJWSeJBJ54jLUilHmKPRQMowg0xKFtMC+l9eFBM1toHM2ScQUXkOOj8HDzKYUYrqLIGeG6A0wXVXnHB3YO53uD9kBZlLwJU5RV7XmaX8NdcKPByICi0p4SG5+5BdDV8In8LLWVSv9Lo14Tp5k0WgoRnCxrSWJ/cO317OJMRr61fM+zdztNaujUX29Yy0wWf6AJdR3Hc57YCBJlvbUK6V7NuK68M1qT7AGPDllRhTOh2c4IDtCMptJTl/L0jJPJH70RTgEo4nV2xXytsVeyrTfN3XfV3j5Y9+9KMrO2S2wbqSzk1ypU7ByBaoXnwZQEfDNgAgBZUmqHZs76RBDjobCbPcT9xzDXp8ud9VoW+J86WMXQZaVZdEOq1endAR5bmUY86GoDwFDwagNHXmaL7cQkCaBCE45a4vnY9g756BU43qLIUZaKRXJZLHVw3xr8b5bkN8dEJqnGcwgwQmVyDD0EC9Ha0dz+eAD7XWoOFAJkUPxyMSTgwAcgFbntWtzC5YDkdGUsIiYkmpj4ayzdksTAQEU0ttO8l1O5/XQbBfBR8DfgLzafryMJ1Kcout+P78M+IVbg8QjPQqd60KNmL+FIBGJmzPMEYUpwM27Wfh2rFFKPGEF7psy4vgwd2zni/nPZ1u2H28L/7hH/6hwe3YJSsCAE8//TSAOkMS48GDByAi3Lt3b6d97IInLxfo8LnPfQ7k0rSL//7kT/4kvK8oCrz//e/HxcUFXve61+F3fud3+u+0x+pPawU2XNfWe0Se3meB5kX4h3mxnN7cFa6bZyUPZZtNGSMtnb7rp83/hsRFWA2H0Odnwn04P5Og4nwESjVM6lr7IOlWLkpwUYBmhXjOTObSIl35a1SCpiX0rBLzt8Ix79uM8PpcP2aQtVCF6MOouQEVFah0irbrdEz2BZfC9oJt3k9GauyVCL0xQGV97uHaO5I0+1UnIKUcT4IdDSQ7FU24ct51FvDWINbs6QufwfP/lCw29GKwF8MTzW8pkdSDkkSyeVl2lP0FnZFdsVh+3vScRrIJsR1H8LU6NngP/wBcXFw0/u0ajLzuda/DcDjEF7/4xaW/ffGLX8TrX//6G+OLAK+AzMjHP/5xvOc972m89oY3vCH8/PM///P4/Oc/j9/8zd/El7/8ZfzwD/8w/uIv/mIpBdYJvEX+zbXwKq3E5y3LpJFhFUN+3W7nc9iXXm7UkcMKcc/obFK2aTtFEQSk2vQFQhdIloGeugseDcTf5SwDpwrleQIz1JjfS8GjAXA9BqoKdjqT9uera1H2ZBaXV2NA0yloPAYlCZKLO1CjgXjPeMn3vpOHH+CYheFflEgfTJAkCjSvQLO5dDfNZsJPiVtODwA2BjydhuwTJYm0GLvuBXizu6sx+HpcdxX46x91F1DqDOaGA5hnLmCGKdI0qTuCjAW4FGJ2GZdmbqjdGxDzNK2kpV5reSL7dAg5h+1G0JWmYAb0009BvTiGefCwuV3m7duJj4GWjIg6PwMNh+CyhH18tWTCuPdD2Ic3zarjW3O/SXnXEdtTEVgjQ/V9AUiQeaTY8bbKwSdJgu/5nu/Bb/3Wb+FnfuZncOfOHQDA3//93+MP/uAP8GM/9mOH2XHX47vRve8Bzz//PN7+9rev/Psf/uEf4iMf+Qi+/du/HQDw2c9+Fn/6Q76I/QAAIABJREFUp3/aLxjpAaUVrLURmXP7h7Xh3AocdhDc17bbunlixCZzeQY7ymCzBNUogU0VyjMNTghV4kTg4DMjRb3thdQ0GwMy4klBSSKtvPM9pvJdayIZC8wKySgEfxuftWkRv9s3fOcKyXWRgdqRVz1ZL3QRlEs6NGFVzwwgCVwXmycwowTJdSLX0ETltTZPkRvUnxGtEHZB2Ab9inWb0nXpE0AIcDnPpFOOmt1xAG5XELIK5Dxd8kz4TEfoDvKLD6XcmLdvrOKbNUz1ahJ70BVSdHw5+BvAZz/7WYzHY1xdXQEA/vIv/xK/8Ru/AQD47u/+boxGI3zsYx/D2972Nrz3ve/Fhz70oSB69swzz+DHf/zHb/Lwn/xgZBNe97rX4Vd+5Vfwjne8A1/+8pfxR3/0R/jYxz7Wa1uU5yBOEJszrRyYXIqQSAJydXFHiJWTCez1uLZk7zqwHXIAdB0rG89pn4g7XqwFzQ10ZaFKA1aEdJIgLRjZoxLqagzr1GIbaDnOYJQ2m0kAU9UZgF3N2rgoJEMDhAGvQYRddMXdN5z9en1ArmXSD/y6arY9l1WomzcQdyQYI5nh6RTJS9fQ1ynU5UQyULFCKVF9nl7TAcC6Tom+oBAEtLWEKuFQjUaAtVB5Lh1i05mQlLfEUqDKrgQzngLT2a0vxSxxQvy9x06gbzav7RAO/Fx7PaWwANsXFu97oCZUO7I7DQYhAEdV1Q0FcOWqjF7RCqwA8CM/8iP4u7/7u/D7pz/9aXz6058GAHzpS1/CN3zDN+Cbvumb8LnPfQ4/8RM/gR/4gR9AkiT41m/9VnziE5+4UfVV4BUQjHzgAx/A+973PoxGI7zjHe/Ahz/8Ybzzne8Mf//Jn/xJfNd3fReee+45KKXwsY99DG984xt77UsNc2FrGwOy1knArw652RgoAhgE+9QFONXQjxMoxxS3kyNoM3QAJaLj0eWc9rNDJz7mNAHIlwyMBap68kumJfQ/P4T9yotCmtw0mDoyJVvAXo+lfALUpQygX/eRg53OZIICwuBISVI7+R64rZe0dk6kXGukmKJ17PIlQdvm5hxdR3YcF1NWQgTWGrYsl693o0uMAp9CgrE9nrPjEVGWuXLUrLF9UiSlhzsjeNIu2JGbp9PtJlxrpONCTkQmM3dO/OixkClvcxYknqQ9T8IgXC+ezUM7bKfs4I6ZLnYByF54IxFI1febL1XHrr58PoId5C5LOQ9CeGxZPptloFy94oORL3/5y53e99a3vhW///u/328nB8QTG4zcvXsXP/qjP4p3v/vdePrpp/E3f/M3+Nmf/Vm8+93vxu/+7u/iO7/zOwEAzz33HD7/+c/jhRdewP3791t7rDsjyEiLzgQp2rhwCnLwCuI74y2x3faOjsB/OOCKb9WgFq/itHYiVgvse7+St6I1YL3C7JaDJDuSp5fEJ69/kCTbCZ01N1r/6D9O4iME1aKXcEB0bhf3WY2N76sdTzutop3KpWScqP817YMFEvT+Nx9ZMuwbXjcH0XfYd19sAaxoI2cW4S+1RSCyI+oyzRHaQxXVZRkPZpeRjtrYb4LAekIvPLHByFve8ha85S1vCb+/613vwvd93/fhjW98Iz74wQ+GYASQGubrX//6ztv+6le/ii996UvI8xxpmqJ0kuG/+B9/Cmdn5zCVgams/F9WsMbCGgtT+f+NvGYtXvtffx3+q+s5nv9vnodhgEsDMy9hqwpmMocpy/D55me5/tn/s7b5Xvc/W17/OzOskVWfJSWZmsrAzAtYE+3DMqzr0PCfZ8uwzo7eGiufqWTVYW29L+s7DNIUTARTSKrUWmkftZbBENl8ZgCDHDTIRQ8jTWTFs+iXYy3sdN6LDOqt6yl1mYtEMj9eedVeXQctja2wMGmwMZIxQUvKf+uDjgLFthJUVYLNQjq+C7q8zxMzuwSqkdIlZaloqxRFdzO0DcdhvcIxL2ea2BjweCwBIJEE9oraLRFWIQqKvXaKpPXN0lv2BpfFoOEQ6v49ud+LEuzaqO31eClLqtyCR7lJV2v5nZSCUiKoqLRq/h79rNMUKhFekegBUr1NrZrbSzSUls+K8a5sV2vlfqb67+417fftfh+eSyfG9/6774YxJnw2/HPv1Yn7PUmgFEEnWvbvztXvQydatpEoKK2br6UaOkvDaypRUERiVE0U9uG3PZ6O8a+e/yW88MILSNMU8/kcZVnita99LV7zmtfs9au+rQTW244nNhhpw7179/De974Xn/rUpzCdTjH06fMt8dGPfhSf+tSnll7/xL/9JYyGd6DSRG72qgRB2neVQnhAtX+wE4X3/k/fjpe+eoU///N/hMpTaGYoMDQzaDaDYtt4YP0DnGQKSuf1a0otvY8WBoYwMLX8Xf65hz5xgwrcoEMEpSnsg6IBwQ9IYaDTtDT49UUIZphdmVtWc/71wVmO59/yDXj/z/73supibvwfAiJG9Jp1CyQGOzl5r0oaPmtskJcO7w3tmrz0Grv0fWiLdfvj6L2NzzED/nVg6TPyGsKEHf5OBMD97M/LgaPPygv16zH87099zV38d//+3+Db/4f/dum6d83grNLaoETXXQteFdcYabEOnAXAC5r47dT/hx0svRaL5cmPFL1XfvflKiKSYyEKGR2K3tf4jKpb/0EAKfd5pRDtEkSELE/xkd/4n1HOy+Zno234Z8Tvxz8P8fuUirbv9qcSLWUupSRAgAQdEu9TeN52gXHt3vEzEi9S/HMSFiD+b5brxUn0er3IaX6msYgxtVDcv37Hf4lyXjYWRMsLKbnPq8qiKKp6AVZUboFXL5LCIi9arDFpcJrK9ksDW1WoihLVdA5blI33GmMxm08BoLGABYD3v//9+OQnP7nT9V7CDZVpnnS8ooIRoB5od1GS++QnP4lf+IVfQFVVmM1mePnll/GN3/iN+P/+9G+RpqPa32Q6dRyLFStBInzLd7wZ/+mL/4jf+a0vwOailknMYmb2zy+Ks+uhSaN+0vDkvySRiXg2RztJsFlm8W24tYBQcxWqnCS5ThOoQQaVJlBgOU+SLIXSCXSeislZooFhDjofyCCdalCikExL0INHoLIEKoN//7++H//h1/8jPv9//nlY0VE88McDvSIoP/koBZVnUM6KXN05A1IxNCRroZjB4wkwm4ZthAllcRJLNJT30DAWxPWE56+VT0svfdZNfP5vbb/H2wGo5qJg4T5emrT9hMXNe939WBUGL/3TA/zLl76y07OwCGa4GrxokdhRDptpqMKAHo8DB4UrEzgEIfiJgq/o1xDMLb4WAjQiCe6sDeO8COmJtgTD82OKpcCwNUj024dsm5lhS1FTtZbxoV/9d/jM//J7+OrfvwgwGgGvD1JtFLz6n/3rIWgGwKSko9RpovD5GYrnLmASBTUpQddz8KyAffEBqsursD8fCHAcePtA3V8LFyjEWcpepUKloVxrLJfV+i64NRicDfB/XP0qfvZ//AVcP9pM0PA8EEoSIE0lAJ/PO/Ho1GgkTQFaB/M8GAM7m7eWCyvINl944QU8/fTTGAwGSJJk91bkFpwyI/3wigpGHj58iM985jN485vfvLN4i1IKWZYhWxAMYhZSVO2wuyY1T5IdsdbV7RVgzjOUZwmSqUF+NYGaTqUtdJsBYHFy2TQAMbtWUAKPjeO68OogaHG1vdha7M4t1L6VTJ1sLDCe1R0dgPA18hxIGFQRWGVgZpiMUdlKSka2grWEfFIi/+cr8HgCns5QzEt85e9fwt9+4cvrL0fcfeHOM4g9DQfA0/dg8xSqqMQ8yxjYB49gXQtcK5ykuxoOoO7dlYH66grm0eO1x3JQxOWFuJul5R78jh9+D/6v//3/wZ9/7v/d7/5JQT99H3jmHsydAR590zkmzxEGLzOe+k9T6KsZ1IMr2Jdebk6MPTqZKEmgzs+kFON4RNIpJatfoCZL9u7qaeHTVP/bv8UX/u8v4u/+8h+3314ENRhIiVD7Z0DDPAs8fopQaULuTBbTaYXkX65gvvriYRclbSCSUub5mSyyrse9g5FtCaz+WaU8B42G9T3S4T6hLAXfOQMSDXJE57VaQu66Pv300w1V0xNuD57YYOQHf/AH8fVf//X4lm/5FjzzzDP467/+a/zcz/0cvvKVr+CXf/mXD7PTHu1q0nNfDzBWK5ERtxBlUW93vSOTvROY4btNen3Www/gPpMA1IJs1javE5EMxokoqXKWgLMENtcwmQIngEkJrAkmV7UZnstYxNeuFZ5n4bUE2rIARICOXrfc6bsk336cJiLFP9nBc2Zf8GqT/tqzPXr3KWkFm2rYTKM4JxR3GbogVEMNKlMoxwEia8N90YtISJGom7//jAEwD5MOVx22u+7Zag3Gd8usBighq5NW4Vm3mUY1IFRDgi4JqlRQlUbqn591x3qgMYJcB1N49noi1hnpDJ/JVirKeHUAicAZK1UL/d0WsuqpTNMLT2ww8qY3vQm/9mu/hk996lO4vr7G/fv38c53vhO/+qu/ire97W0H2SdXBozCSWzboGK5+gNiqW3nBejxNfQkEQNezqFmTi49yHVvcQf64AWQh/KAfhOr9h/aO5UjMaap84MoAplQfFIIlKbi65KlsHkCm2nYxJUdDJCWYnmfXlei8umyMOTlvjdMJmyMCF9F1zHoiwCg6wnUPBEBtOlUpNTb/IKcImdz8wyazUFEYTV+YxAyCUCplEpciWGXduUGHMmyNcvggj7SWro0ZiW01hi9ZAEoDB5ZJJMKal5FBGQWETagF7mXjZFtWZZJ3fNDsiwQXDtlW7Z8Njh+vnYAl5X4/GglJb5EQ18OMHyQw+QKydRCTw30VMpa646zkf3btQsnbNRllZiFSEtqJ1XnWGeksZs0E7I6pLS9+J1xUUo21GdwO4CrCmo6lwVO0aKlc5M4BSO98MQGIx/60IfwoQ996Kj7lHLFvPu9wlLPr2aFyLlrDV0ZUHkOKivwxJVo+nRhMIdSwo3Ad1RoDRqNwMPcnROBirJuu3POspwmEowME5hUgRMh7ZFlpNcV1NxAXRcyGFaV44b4tly9Pg1vl7M93hGUjBUDJq1hi0I6X9oGcSKoLK0HTW9Xbi3sWMS0eNah+6Yl7b9XMEuA5zgbZMzKrqCtBmifrvcTVMukEFQuKyl3KWsx+ucMySRFOq6gL+dBfj/ooOzUWeMUd31rduK8OdISZNKmKNsewcx7aU/lqgxlUcrFVVYlCUb/lMLkwmECA3parg8o3XMUMgA+4F7oAOoFUlK2HAvBc5eOMA6Zkea1o0EOdVdKI9YuB5BcFjDbStWXJTCdydhQFrX/0glPLJ7YYORJgeiMSLusF6KieQGqzPoa55MAUhIQaSUtutbpsCz2/6+CX+hXLEZzfjUNuMFX1e6/1r2564AVSl822MyLYd8akToWbRMAjTJOmKC7TlCHXqX5DNC2GbW+iEphoXxRVSCtoGcV0oSgpxWoKCUgtXsIRBzkO1nYjstgMYBWufZt0BI87u2SukwWW9GiYQBUOiNHa4NXChV1NnAt4tLiHksSIg5msavukV3FGfHf3+L32DiI7bNXXn9oq7buI4AA7BLKHkGl5VbiFIwcGOLaa4NGhHp8CZrOpJ11Nl9eifhUeRcRqUOITMVBxLq0sct4UJLUOiFe3j3ehjFibW9ZAozSQhGBWdL7qrRQkxJqPAMVpUwwWoOGQ8m8nJ9BnZ/Jtaqq7bRBXAkHLvW8lrfgOoVsnAIHanKd1iDmzfs/QnDAVSmWAsDKUuHGWNBn1Xhhpe3vPb8d10kFpdz/LsCbToGigC4r6DQRjZjpVK7hfL6XQMR/J1AkgaoLMClLHQejAM3n28fzUcmJMulw46JsGOBRmjlztR2fMReQ2KIEKWlZpXnRFPszNmjVrNoGl1UzI7uvDjx2QUjXQMR35iHKoiwcBy1wRng+h334CACk22UP4LKCvbyUxc8iwb5Rwoa7VnvZbceDw6lM0wOnYOTAIEVSS3XkUTszUkde836xwmaZsI/Oro8e4LXvc5yRRMimrAlkqbZ198fuNSAAJ6PPIMNSoqksyLB0ucwLJ+1tHS8gBbSCzYQf4VVBeVsS3xZ1aCn3NAc1AqTtMNGrLeWPjS5B0TpQ3ULMNsosLJrh+TJbmqKW7ycR66oqwM6lRVopmQz2UZpZhD+mOJBMNABXsuizmnckYNJK7i3A3Sci+sYMEQvTeul+8O/d7hxq4jhX1drnfyWOpW67CRRlKonr59VdE1OZJZ0Urqr9215YA56b5Xm7reOsul2ZkxPacUtG1ycESgNYmAw3TI5iGtV98GLLoJu0mOQNA7xXkhzk4PMRkGjYLAGnyul8yMoZnnMBx7/wgmyzypkEOuE0Y0UsyznfBtfZspJFeOEGMa0BbZtp+UN3ILlAhmdz8cDYgdx329DIEq1Ko/hSkDFgrev0saJQMvA+OXBaGL3Ljl7rAmiudL3nCrOT3VdAaWS/2zgk+2yIomamZ1FgjC28HUHratsdq3/v0RcL+0RUygT7QKvD9TR18Bq242CdHwyAXteJ0kyetW3lDjxi8vENlcBPOiP9cApGtoAa5iATTUqkNpZUgjdNV7SQMY8K37HRNnh4kqPWoIs7KF5zByZ17rUEpNca6oEQHLkoQ0rWZ3tUVYkCpV9hAzKwPb6uHVf9QJckIMuBNEqpEBZJEZip07Xfy+UoCtiHD2XFvKvc+22BW6kDqPkgjb/HHjySraIkkWxV9N4lEbwdJmY1yEXEigg8ntT3Q7y/ygBcgueO6LwYMKwCUTCDhNaB/Ct/omb3jBM2I27Rb4mDGaD/hHmbsNB9thEbxie20kEYgkvtul26XCeloc7PZKEzm8M8btfQ2YhdJAz2gVOZphdOwcgWIK1BFmBXhiDlJ8XVWg9KbRmM3GY4o0BoDU4TmFzDpvWKkZNYy6P2FWF2InFJBSqreoXN0vrJpeu+iCYzquR9DZO8loxNCE4O2L1yG5yVe6HLJfG6JVjBqWHrSjkcWrUDAr9mD9eeSMTN/D7i79pnYvzEVJZ1ENR13778qZ12ScRrILhLxR3Oxx+XUqFt+VbjGPpFESQzEl0jpZr3zBqQcvdAlgrPrIMR6QmvHJyCkW2QOjn3qgJb5QbvhY4Yv9J0A+W2ZZqDINLP4KJYJpylWU3kq6plpn4kdy7mdho8SFGNFEzmJioGqNLIYnVaD99VUJTg6+taWIlIgpHZsgS0SNUz7GQaWmtRllErp5skAVcfXqGPcVPYpcVXaajhQLgYRdH6ne18bFEQAqBZYmn57oIwlnc9LiuZdPYkusZFCb68kizbIrHbc0ZcR4Z0R3UIROLSTJDjrydKrqpQ6onvv9bN+nvOIJQy42OkNJMOH2P2/331ARHU+TloMADKotWI7xBgK2MeIhfoBsl6XSaOVB0s7ujPc+O4JcPQk4RTMLIFKE2lo4IUQhCypFfhiFMsk2MgsN4gKE1kcnNGWIuDEmUp1J1zSU97WeUYbqVDWgGupmvzFOVIwaQEYgIY0HNJd7fqWzCLnsDjsp4IPR9gUWPAMeQJDDOetMu2+1KDT8Frv61bksXwZaQeAZKX56YkAY/HMD30JNYqiDq3WiLndhtHEyu+OwCyak0T4W6kiXzWi9Kt+mxHcFnArEnlszXbWybE95nXSEF0bZxGCy9MmMzcfv1Wpf9dkE55BirKXt/XvkFaQ13cAV+cgSYzkA/wDhwkWePKNO55bzuuulXfwY2VPnvFniy+Y6vxTeHEGemHUzCyDdh3CqwOLkjXnAgCoNMEnKRQg0Eg+x19wnRuuCsl0NVCStzUry0NymzBTKDSIJkxyEA6ZBjQcysdMbFeyCL8gA7tylurJz/aIsW8dwXGhQxXH/SSQAcCaVT08HcghW4Au/uZO5RbgtIrkXiQeEn9+NhuCbEzSJv73/09bBlQNmj+wAsOLl7jPYme3STY30PVmmfxALDWrvam8QGis2fw+iDh/nPHTKXzp3lSazQnzkgvnIKRLcDzGbhcs9olghoOQHcvpMvkzhA0GoBf8wzoX30dqDLAoyvYhw+PU1LwYl3GANM1CovW1pO5z+z4FeUiR8BYoKqgXr7EufcesdKiS9M57ONLIa5uGkisEa4HsPI6CB9kwzXyLZn7hDPagyPNhvPZ5vvagVTLxoCvx2BH/tvbfRJ1GTQ6gzpsn8sK9tHjRhuvP1bphV3ohrmpDJXSUHfuCAnSyr0aOoL8MXnNDp8RWfhumbG6w2gVrHSC3RpZchblYHLfhRf+642O2S+2vOxNE5fLXKu+GOJNG1olXJUwDx+BrpJaXXdbbKPTdMKtwikY2QJcVmCj1j+QWQoe5uAsRfXUEJRpFOcZqnsjqNJAzwqX2o46Gg5+4LaboZhfoYTU9sKgoiADOTNwPYZygztXwnrnshIxrK6DwIaBTToddt9OL6Sp8GhKAqlCvqpjkQGtgS0AYIvW1S5oiEBteR7WaeSs3DQFg8ObJHaSl8ofDqScNKeGtH8IFnw2pOVe7RVQ+KxpDzPNg4BdJ5qb1I/VCdZo7Y0QyP6p8I6IrXDpFvhJPJ/vpKFTl39vLhA5lWn64RSMbAHKc9DctmfRnDIhDYeo7p/BDBNc/Rc5eKAwflqjuJchmRqox8K58OWOo0xum1YzVQVyCpBhpRspLcbZkTBQswWXLiNRVWHA712aaIFSriRwEyidGmxZtmtyHPp72yYIcccURJ7c8S5Nqps0ZHYAWw6eQiuPfZHHwXZ9htDfg4sGcW3BrtJ1iTRJwFpcg6U86SfkZllp1b0qnJEtTp4UaDgADYdAWUIttDzvXQiuK/z1PSKx2xq7ZJQnhyKS81yU0rJ/oDJMkLa/SZzKNL1wCka2AA0HIkg2nS4RLj0xyz51jsl/NsT8jsLDfw3gDHj89Rbjr02QXyokj4eimeEFpQ6ZHenKtygKId0B9QARd1t4ky7AtV4qF3wUstpckNIO79tlJe62cZQxdFECn1kkzf0KbfGcjlHL7nLiC5M7ZamIeZVrylaHmhRd6YeB9u27ji7fGu7ND3k6W/0MkBJbgCSRgLksnbYJlgISShNXWkvqfy4QQelLFR1LXowlSfP246tX+3TnHPbuGWgmky3N5xIMWgtidqW+467WpRTSsVy2Kcjegru1xLeJut9EN2hPdgFtuGmdphN64xSMbINNHQpuorYJwWaAGVpoBVSJhU0gRmu6ZvTfmgCYW4IirjkdRAog94T7oKRhfnWYgYWoNt86Om5L7b8LPLfHuduyUevv1b3tt23SWfd2qrUntAaY12pJBO+YJHHvk4ldMnDN4/BBDpSuy2ku4K9Jqt0n1G0unz8v9u7CSrRSCCQBtXn1aGZwrDOy/Mcn67nqiVOZph9OwcgW4MsrWZUtZEXi1DdNC+SPKpDRGP6zBlUKycuEwUOL7NJATQqwr13f9tHJnWdwFybVHGjceazuiNlNmVNS/r0+uh26Hl98TrcBzIgtPkkrab9dxdkI9+mGScG1SwNozSZQktSeLj7AKEtnkNeFm+R0PeyKbipXclHnZ8BrnoUdplBXM9CjS0dIlfKDz0YG/ZvMeehMZ9KiXpZi3ue7ZjqC2ZW8VhnltfifYDKFZhbDQL+vRb2dtu347+TQZMvYHDBNACvmfWG/ewoSrFdgXcSrIAgJOJVpeuEUjGwBc3kForR+YWH5xMxQ8wLpZQGqUgy/qqCtQvaAMXhQIRmXoOm8Xqk9KfCpz0g8DewG2g1OuDthlWbJvnBkdcqDgS2IkqCn0SoY5c3xSDnhrjVkVB1/z3ZZlyZJQHnuMgCu82qunIDYmklVRRkbRyhtC8gpFadgOhth/uwZyvMEeaaRFqWY9BUlqKrEYsAfR5oFywCezaVkVFXbC5CR6LFSkiwb5YX3RLwXr147mYrHkrtmAXZN4Bc0iY7AH4u7WQY5YBlkbPv57QBr1rT2nnDCGpyCkV3RcLnVYvA2KZAAyC8TJExQ1wZ6Iq+jKJ+MrEgbfLcAUbNr4EDnohQJ417VHINTu94C3H3Hxkr3SFXrNyy/tRu5jy2DvBFeh6CZiMDBK2jFe3zJhVSzzdYbH0Zk1ViUTM0NdEJQ8wpwip7+c0Ge3mcnKun04qqqtW56TPDC315NxI0F++rPMIgXSpkwAKvaXXhh8eKdg71dwsE4Jf7a+iDhkNo1ba29rzacMiO9cApG9gTpa69gHz4CzWZI0hR3H18gqYCzv7lE+qV/AZcl7HTWMNoTY4wnp5YqA70vvxw2oBL5boK+OAfyHJjPYS6v9xeQ9G3hvE1gbxZnYCdNmfJWSX7bgYDrW4tX3JfstGZCWSjWpVnMNjkhNDUcgO7dFW7FeAJcjwF4LRnRdaGzkXy+KCWrMZ0i/acHSLIUNJmBr65rUmiWygTrlTqrCnZWy4+v1Z6JtSgWz5FZyqhV2V7aCeUOFTKDrAAyRjgtWgN5AlIKXBkQCvlMyo3PAJBzSMUQ0gKuBLznscB3+UXmlD5bdIh231Wtva8mnDgj/XAKRvYFN8Db2RxUFJJCnhdQ1sK+9BDm4aNlgSVyg7FVR2fa94Ljhxytdc6PaXkuIlavFNfcfcNze7p0TnSd7DYFfD5D5rH4c7QPUiTy8XkGaAWaF83FHymn6yI8FDYWKAp5Xq7HUq4rHCcFUiYKBmy+VOKF3Cxv7JppGFy26McEKs6qbXiysLVguOAiIniTJ+j6jIiytUswUL/Xv49qE8K9jgWek+KCxJBdXDCl3Cd4FWfkhBM24BSM7IolczFZfRLEMIuIYJz40NKKlNesVCPPFTZ2uXX2psC2Hit3lEvfBKWcyeB0BrYMnhf7zcYcSzPkBtDaDbKn82RXDkLoEuG6hTRufSYKNvJIoqFmkUfh24KnMnFyUdSS7WUlk3TDlC4B0kw6VlxJgIG6c6YNEYFTDQfS+lsKpyRkK7yWCa/wpvGI9XS8lggAHt/5AAAgAElEQVRHujx+f/69vgMoIrYCAKEUHR0WuwZeVz5ZbD/vAk9Y1aph+RD0Vtr2peo2/s6t0BGsN8o7NGKSdVy+vQ0KrKcyTS+cgpFd0PagujZZtvKQKAWY8aR91drWUutBCupsKMS8+Rx24jsPbrik05hE9riCa9k+kaz27WQiAck+xZuOqRnySoM14LkBE4GYQUUZyKgh0wfreCKZdLmkqbS2NzIQHL7ThtYN4CZLL7rneBdWSh3Ic9BwUG/Lcl2eWjUBeT2QJAFd3AEPMvFAGU9AXrCPWcotvKYz2nNSGpkgBdIt77XR+ZVV3ZUWMlndA4yG+FtH7ZBQnvEBiStnrVvYqIjgaifd9xVOw24I5PaEEFQCsPM5eG6awW9VyWs3AGKWFvQdPv9qxCkYOQSiFTcp1cu1t1F3XUwrv1rgRM/YGCmkngKH2wWfUaAWTxZfHvAurd70b1UKfzEwJ6qzFJGMOzOjsQVPUt1kYKlEi4S0lqxIloIhJR/2ZRNvb7A2GomO1x1nY7+K6hJS4/0tx7ftpONNLLd5f9SG3DimtS3H/eXU1xrl7ROeQNy2LyXkZlb6Zvh4p8xIL5yCkT2D0gxqKA69PJ2KpDlznT7suLpny7DTmaw6gaANwIdWbb0J+MF/Uc/CtfYG2/F9Oh6vykrdYOkmuM3ehLNzX/BygOw7N+R7i7RpjJEGE6/FsalTh61kFKLfSWtpo/XXx5N1Zxs0TlyWhkZDVM9eoLzIkIwrJESgohRjwqKoMyRdVvex3ogXXUsS8DCX/RkLzOf1d7nDPdXqLrwJ3vlZKfHrcfcW1qjB2qKUlfm6kpdHi7P1WtGzPYKrEnYKKeFFGTVvCEhZBn1+Jq9dj4Hpns00T9g7TsHInkFZChoNpeOkLKG08B5Ca12XYIJqQh4zg9JM2iLT9CDaALcCLQP1ohz9Pn1vWrGP0s0uwYzWEpBQ96B1HcLHD6in0kpoVrU9QvinSCZnZrl/N02s/ngXnhWuKmA6rTvSYvfgNSCtgSwFBjnKezlmTyXIEwU9zuT6jCe1JglvUGBt+6NTX6UkAWcpONWykIizE7tgXUl31UecyzalEM5OmgCzyNah7Z5wJbhOCO3cNemWLUMfgzPCDC6L5ddc0KaGA9D5WejOwvTwh+Rx6qbph1Mwsm8YIwZyVkhpSrsyDVvAbn5Iw+rYdwUA9cqjjQT7JKAnqYyI5NoZ4zxPDnzuN62w6q3u9x047Gl75Eioi0TLxSCRNBAk2t13RxXVHSauq8yXYXrB1ivxrc+jYqiKQZV1/0xDmyXojLR+uE1d1DpVWGl5hs8ulIcxkNwKXhtoIZO0NzLzwr1grQX6xF77zEiyfBfkMiZH78I7lWl64RSM7Bl2Phd1SKD2pAhy1JtXu5TnoNFItB4ur8GlU5Jc51h6m0Ekq5QsEzLqeNL5HGTxxrXz6DGwrVpn/P4dMyuibXHYDqW+oCSBGo2kRXdW27w3JllvGUDkyoosRng+mPRvq6qdMj+BQ+T32WU74bth6FmF9JqQjCvQZAbMi4ZSa4MzEpsRhtNc3rf1qrClC0SUhp3NRI7+gCJja2Gdv9R8Dut5FMUeO9JanuNeomde2blDW/ZG+O9wOoV1hpFPTMnzVY5TMLJveLa9Q2hP7ThgUpqA8kyyKzE56yaCkD2tVnyKnICtDMOCN80tm5g3guq09VbokYpfeQj77mggJWUOrUGp47RYhgTYC2q8SgUSJFdVXdNfaoPfEdsGbe55ospCFxaqNOIlUy0HRkRYDkQcaZJUi6dOzJ2ZzUXcrSj22wHWB04bCIWIr3lOxaHQR/TMk4uhyQXjuz8DIeAFjn79T2WafjgFIweGdxvtjF1S1/tEwwBwt5U6F4UIzQYp744chkN70/QBLUxMmgKnhUZD0GAgaqCPHsPOZsc5ntiocUGTo9f2Wq45GwOezoS744meiyt+l1EIwYe1y6XFfQYiXeGD/CwLfA6bOrXYqgLKonHNAuE8SWuPHv83R8Jd1RkjE79bSGwKRHbU9Fh1rg2LCt9pZAw4dKB45+39L3DY2q11RiTT5TOCHb9XpaWNF6g9j+IWajmYrY5jbziVaXrhFIwcGFormG1be52mwk2b6XnSrSjH9hy4mEU5s4jY7CQ6FJsG39CJdFvQslKmNBHNiyQBnr6H8t4QelKC5oUzjjtsySUQRRFlQ6gngTBMZC3HbI3ovSxi8X2OWLhELrxppBl4NADnCWymYBMCE8TZ14sSAj4lApUmtakcUHvdEMGWa7IL1nQmmFOaQOW53OPT7TU9mhtzWZ/ofgjPbZytjUsiB7g3bR+dkTYy6gZQKkaKEtAp1wpugclUskDGOEPIWzR+nLAWp2DkwAjEzS5gccIl5puL6hexj4AoJoZuMVGS0xk5OPqUo7xug7OaJ6VglQIrAmu6Hf4cW/JfvLDWSlfffX4Zu5YAt+XmOCInGQaVFipVoLIWO4uf0UYAHGmjMN+SrOWuOOAiRzgjR7r3vVHiLcOpTNMPp2DkwAickY7g6RTWSUevNfs6NNiRyUjtJ50bt2p2KNOowQBQBDUaSbvkoUhoPt2rNbgo16/QXFDFVkEN0yBLToNc9DSMgR6XULMidIxsnLyolhFv5Re0aDk0DskyiDzxsqV0sgk+25MmUHfO5VpPpjBXV4ebeL1PSl/NHCdzDqbO9wVPZ9LkoTWy64kEkZMpzHRWe0YBjmPhumPmdcYklozf10KBy0pC9E0y9o0PrfhOfJko1m9pe249t+VAGTvmIxnlGQOei90Gw38/FuyykexUeYMRKeh45Y9TmaYXTsHIAeHTlRybim0YALiqbg/7e4+EyqXtbgBlmVy/PA/yzocAKRLJcq27pYuZAUIdiGSpdJgoAoyFmhWgWQHblbjoVUo9GXTFMQK6PVhhC7C7z/qQJb1/SZKAhkPRojBWDOoOZmmvEBSFeXsNFFLkPJuwPrCNJnKOxcc8VonLMUBOdv2gJEhv7rdq+wt8IOlW2nC9Nj2zh3qmHayx23fT9ABbBpUlWKl2nkhUThUdlIMf0gk74hSMHBB1MPIKSe8eEVxVUESwzon1YPuJ/UO2IX46G3kAkv5nipRCt/y+N5zfujJf8H9ZUOddV7cPInpu0gjqoaXzmCn3bEjYhh06a7yYV+vqniLBtbgFl1TzOnNLR4z/E0e8h0M8t1GQUU+YC908kSMxEYngoWvNtfMNarM3iINlRha/D6clEgi67rX6QJwJ6Q3h1Vpq2QWnYOSA8KzyrQmsx8Itdq210ylAgL0eH7ZcZQ3s1HW9dJyASRGglZBWnbgVAFCiZYKvjEzqazdST0hrg6CYb9PyPXmJcxi7WZ3XdVGoi3Pg/j05VmNEGbWsYB9fhk6ZJf2UfXV6AHWpoC+saSdfukBEjUbShuyFx5iBsgxutZsySOyyXweBLws68z8f+BBQO+suklGTBHTnDJynUJdj8Fdfuj0u3gv3xkE4I206JMybr4G/yY5g3Nfc746Lz5v+Tm8Ip2DkgFi16jmhA9wgvbZzYV/ow4mhup2w0YnhVEe3Rt8aviuxMKr1ZD4/wSkS/5Q8AxIFKipwZUBW7Au8mNlBsY/vcxW3xpW9KE0d94Ol5GK1ZFM64qDOs0oF88Dg0Oo8ZBr79t4+iQYPMnCWyKTssybcvfx7LFjXcbRPeB0SXkx03JJzXsSJwNoPp2BkB6jBAEjToKfAbmLydWi/QrDb6Iy0wJvvAc4uO1KL3An7epgXtQ3i7S+kzYONeof9ewXW2wa2DJ5MhMcSn69bxaKqGvLiazYk/2/qMFq3ii8KR4K0ofVUumIAGgxAee4M06KUvzFQkxlYEWhWSHtrUa7mKu1NG+SA36XnvmjX5eT3pQiwCjTIpQXbWFFF9arGbSqihzzMNAGdnUmAUZaBhIk0kcyVNULCBEDuWWFm0ONr+cxEsnikNdTwTDJAVeUyiDfANfOaLE6PhZm31hnZuItjy7mfcCM4BSN9oTRoOJQUuRUiGlkOAx2A4F65a2aEshR090J2e3UFU1bYVYhsn4i1DZo+HE4ePM+DHDwaglmbDQNvXVbJmRjayQRw5R1f35eJ0KXfNw2g8Yp4B34G++BUDqTmkJAC8oE4SHuNl/AdWfB4AliGnUykTHULJei3gSe1+oxD/QdR9qQ8B/KszlpNp3IdWjtODtSeSkKWpjtn4ESDpvM6YM9zINFOmr5ckNAv5fsCQlcPaQW6cw6+OANN56BZC0H3SKDUWQXAn86erx3z7udGRySxnrppeuEUjOwCrWQAMd6Zs3kX+RXCNq29K+Enits2OcdYMkwT3QrKXPtrlcgK3ilfbpqD6bYpsC4OstbUapZw5D3HAzi6TozvHmhMxrb+TshN1l4fxfMmjHny/I5WwXMunF4PjNmqNONxkHsuvnesBSzV2SzlXjMUWlRjzlGjrTi8Fn3+pp+R6Nj4UIFcG25ZicqDrPzb5fOvRpyCkZ4gJasZHg1AswIYW+dFUd9JKnBGdru7uCjBl1cA4Jj0B1zF9nDYDdoG0WBJiahXUpYCzz2N6s4Aal5BPboG5gXseAK+ulq73Rst03ip7uAu215aCq3B3qHWd3l0xa7n51Lk5Fbd0prrAo3JTO4Xy04pdijZgdkc9uoaMAa2uEEtG0CcgEm5e2g/QRHP5oAqazl6QEpUQPO1VZ9n7J/06IJFLgrwg0eAItiiFI0TRSCa1Fm12BgzHNTCPWUZ9nosjsPOTPOmwFUJO3aHZexRdEZ8KzoRSel6HdfpwO3MJ+wHp2BkFyQanGqgcpPWAodgXwRWrkrYiZQ89uZh0QaisLommO5zasvDTlpJeSnLUN0ZoLg/QDKtkBYVSClQWW7s0ghGeTcAzz9gJnctVit+UiKkQs+HOWYrt9cIgVJAnssxWwYBsGURfGIoTYA8Aw9zUFk5jsgB76VOB+/utdACv+OE4QMIExn5+e+sKIXPY3ljgNto7d0nSLqXzPV4aUGx9bfARoKuskRwtr4pRPo8fCSdEWgNyqUcSVV1uyobpzJNL5yCkZ4QEuNMHryilK4P25SV9gPaVmWaeEUetSDyGlGsvSAefBu8j4X3+BXehswJGxv8aPT1HKlWULPYsn3zREg35U2jdAikCKhT0LN5q8YDV1V9/XYQ8+oFFwST1mL4ZrWsrKO/A3D6IRIIrlxFb1B73f+xc23gFneGxH/f4jjDecVlA3cfkzG1V5Anm6+JtnvHIp4X4hxyG9d6Mcu2DVzGst5PHezDOyN34GAdlp1LsPY4mZHbjFM3TT+cgpG+sAb24UPg8rLZ/x4hcEa2qFsrz/ovK9jJpLb83jXNuK6+Gg10YbBc1JlwQYgvSZAxsAVWli+4KsX8azaHApA+zMCVgR2Pm4qJa6CUOn4w4rpR6PxMvgelwImos9KjK1mFRYEYG+Ps2R0Xw3VvHDRwjMBVFSZ0KiunTGoBuOyMD2arCjyZSgfQdLZ8/f0kqrV8P0cyuguBnJd4j/+2IuClJHVZKw5S7uKmu/De6N6xsznIuwkvBCrLB9W/PZWSFOriAqRVsxRpt8g0LkJpqOGgtg3w2iQjKbvRvADP5hs1ZoKj876fqVgz51BZpUV44TvFmzV9TngicApGdgBX1VqZ8l6tvURCitR7HDDileTi66i7QYLvxrrByvuKOKXFleULnzomBk9nMmGWFexsO87LjVQRFEkGwYuYubZFSrQbdJv+Oj5rRXClnWMLP/pgVVHtO7L4FusIndWKbBMpmUS8+dihV9GNg2OAUHf7uIBY7q/F43RE3DQVbRTLAHfgOC0Ira1bve902r6rJ0mCm+xerqPXJmELgntefRebc9deCV9+Bdxzvv9AOSxmeP213SvYusD/luEketYLp2BkX/BdFdFES4HdH70vMkbzaAyk7H7eZw04VvH0KpxZKoRGRaJSWRSOoR+9t3mQEnj4lXg4R+eb0mbO5fYrxN6I5LrNw3bMB3PRC8Tvv/IkSCWtsr7jAWiQQHtzRvbRFeDaQylNwGUFAi2VnrmqgrCoGg4kYHTfDSknOa6VvL54bFscH6WZU8y0cm06cEHUcAB1cUcyUZOptMj7jphIy4KSBOrZp2EvRqB5BTWeil7HZAp7fb3+GJ0svCf8wthmOcsHl7us7o2RDFSadCpFdoL3sInF9ZQCJlMpPblncglxxqJveWhhO+El37kF3zVXByMqTUWIr0XJV52fC9ejKIU7s+neWBHMsXGcGaC97Lg4zhrbOqwdAqcyTT+cgpF9wN/4rmVynehZIBzKG+T/sgopVjZGCIareBt94YMH7UiDgxx0904Y1NgYUYM0pn3h5FbfXFgpCajmAEUaMmguciW8RsCWbP9efJs9wKs9AgBZYaJJyy7LtRoO3OsO87mUbry/zS4D3g6raPLfaZYBuoAsnnW9Ta61TyhJgEEePsuzOYJOSpKIvD0peJ2YGt0CSUqT7hL1gDw/gxx8/y6YCMrxLSjwSYyIfOU5kOeonruL+TMD6JlF9nIKKlyQdX29eh9R6cXr4pAyrqzGLqNULwj6xiJsnL3AnAJ5eGf4oDFq3ybv2wSsdP1tiA32JSsvltDcmBb8cny7uCtJs1Ky0Mkyua+a6SiosxH4jtNGmc/B8zX3RgiCWu47a2Bn6z6r6nEWACkLHEFc+IT+OAUj+8QigVUrCUQWU/o+MGgp35BPkx8j1x8/4M6nRDpp0JiEZKKNMx6SJSG1MFnFJYxV++mIrYzK9ky8ZBYbeTJyLgR3jopkgLMWXBnnkcL9gsZoYvSDpi3Kfil0f60sBwn0lYx874cSMlm+FVnXdfiY3MmL33HH79a/7ks+m95rWQi22t2HXmujoa0hXUJkWbQcmMGJAozeroODnLx4uNedZ42N37JDqYHtYShD0ffECk0jwEVE98Suz4WovQ5qnxxfvsxSsFbibVS5YBwESvw9vTz7s7GgymV+Vz03EUdNPtPvuG9MFoCx+vnr+vlXIU7ByD7ALaZNRNCDAdgyVJ7XXRg+5UpqueMBCLVhAtpr5rseqmUQOeLfbN7srtAaKmRtSFbJgKTAp9Pm+cG7YppGliSsoLTrCtoiVd9AmL82PJlKC+k3SXZ3NPU8F8vg6zEwnwNpBho5Iqtrj6WyAh5dSilhlXvsimNtZJQUQY1GoPMz+f3RYyk1uGPpAkqSEMzwdAr4YyJIRiLejmVAO60Ln+L2GZ24fXwxvc4uKEvScNx+e20rbskIzJrHF2uatNwLdjKBetllw2aiG9Eod7kuGMznUI8nyF2mg7UChjIxktYb9TZEqVXL++UF+cNsBjg+005zWNxmu7ihXcpxfjFjDFZGOgvllLXv7QK3PXVxDnzNM7B5ivKpAYq7CVgDVU6wCSG/tBi8OIcqDDjPoM5GoLsXy5kPa2AvL0GTicsgL2vcUJrVSsb+elXV9hYYVrK4NbH2eDospzJNP5yCkX2h5UFRqZaX01Qmtvh9q8J9T2A9ZHnCT55VBVZiINYw53KBSEhzFmWdeQjbqLkljSyJ90DxBm7M3VL1CwjaE10SI1laO+j2zS54hHKUSHKLUFYuKplKgTM3IbMLsrZYdTbq7K7uT3kGPh+KMNn1ePvj9UTGiP9R32Ntp8eNUmJ83huFs3wJK7jLWrBZJkSGyVj5Dh0JvEm7UlFLOYrLCvbyWki4ZbUUsAQCLhHUrJBOqzQBn2ewiqB9ENyl1BXL9/vPVRVYFYB1HVw7ZUbaCMJR9m4XUuuqzzku2JK20R6yA5SmqC4GMMME0+dSTO8rcAJUQ4A1YAYKukih5hpGEyhPwLkLDhcPf51AWUxO9hlioJbG3/a6eWL3jpYLW+NEYO2FUzBySFgrZZqyu8olV5WsKPbNGQk7cBmNsgJ0CaKqJgoCdQkp2v+mNtyw2verTiIhMKYZyOs6eIO8rhN3CEY2vJ8tuCil7FCWext0wjn7DJJWIJsB1oLKyrkJb7cvESNzpTCvlppnUi5QLA6t2pGBuwZUXmwPzRLg8vtcut6q/vdV4HDweql+tgALsVkml8xxqpTI5Rflcuuw/4w/7Ya/jArkVdFTKUEzAhVlKKVhOqvPy09abQGFd/VNfOfOQlkKrhxE0TFQxIMwRibUbVtkPe/Cd/7s01tKaclGRBwJsIUdT3dv0Y6vCxGqAaG8AGwKlGcMmzHACvljjSSRcyMjz0hQno4zNms7nljKOCjBUWZEgkPfDLD9QoO0htLkk3Un3FKcgpFDgRlkKlhjtyodcFHAeGLaISJkv/JnCx6bWsDMIaystAYVopIaVszr2gJ9NiTPZVJNUyFJMjsio2lfka9AmEc2BiNcl5C6vL8r58EasJUgiqpKdB0GOZBm8nqfTgmnNUFaB40IHg3AgwRsGDpNt1a/5biTYsPx7CQZHmdOlA5tpqsOlC0LqTl1vkTMoMFAAvTrcUswEpU3XIatEeRmmXRhAODZLBjHBaJzUTSPZbEkFhF4oZy7sguMYE3TZZmktTv2+1H37oLvnEEVJexLD4C2gGRNGYbSJJQtxZdpBTGzB1SWQt2/B3bXB8ygykBZhtklGIm6i3ymtjwnzJ61sANGcn+Gs2GB6/QC2aVGdiUdXMp1uoRr6oJJAOAS68USywJcNTkjdSDXQ0zQ6+fk6mjByKlM0w+nYOSAUH71uG1q8RhwZZbF2ML/TpAsPxFhLVFuEX4C0FrIbdat+IlkVd011bqqTNM24G97zRqETKzP2FgjA6gioNQgJ+m9cwbGZQpWrt63SUkfO60bMh+r/u5W/nZhot6i9NEIRBzhNNT/TZ1ttP7c4+zdYnu2onbqhLUyMVquW7UVAQxQFAyRI24i0ULUDGWqKDhfd27++EMpyan02j1qfihn2ukCB9a8XntkG1h2EyyDSUozrBlJajBIK1ylDNaA1fJ9kJUScPyMhExnl/35BRN86Wmxq2sL+DLmni5FJ5wIrL1wCkYOiMWsw63Dmkks6IL4c2ibsBdT4W7CkVq8kNCYuM6SeOXVDgPwyjJNGJR6rCpjJdnhULgm1q34V5UP/Klax0dxGis7e4EYA1gNmhVQlXH7L8IE7smiOxnIdb0+23Y/rSNpLryHJxOgLBqBCRct1zgy/GsEaTFxc+aWttbKBOOzbaE8U2dB5DOQZaYXgvPHNJuFyZKN69pRSlqHAQmA8kx0ePwxlyXoaiKfc/tvZLDWXTNSoLMRaDCQTIwjcHZxru4CNhZ8dS2ZTI91JohKtz/Pq7Y/m0G/fAWaDjB6MUN5rmAGClMeYTbMkT7USMaMdMJAYUDWZUaiRUzIqm1lIrnwPW5T5k0SydIqxw06GeXdepyCkQOC6JYHI+vgVyebxg4/OPjzDIqQLiCxDGSpcC1ICR+mw3hUz0XN1bVfrTZ0IbYFKdBgADoTLQxEglKtwYgfFCsLLvfwfdpIu6QqQFMLttbpMtTCXCACCmxN/t0KbUJvXQK9LpOCFfn/LsGOtxoIxEVH9gzBgnGlMSDwR0JbtXXdPjoS31tFFLdGuqDic1YUNEwAiJ7HwJWFfKmhKAOpOXBmNl8BOV6tJRA5GwpXpSzks1vYRKwDVyX4eixKx0oFPk9r0BfUWHVn3gvP5uCHj6EmUwxfPEM5ylCNCKw0zEghe0zIxhbpxAr5uKqEpBoJOfYqES52dG0BcfUd1EHrEV2NT2WafjgFIweE8jojr3AEfoCvLXuNCKc9AK8rsAUpt9YZad1h+wprG7VQa4Sc6j1NjN2seruvcggLbwC+jOcnBTe5kgaCyuahpbVjQSuvHrv0noWSUaRu2WlC63rdjAETgaAR6iqOYNowtrOO/7GoQ9Kxg4QtS+ofqAOxmNRK4otESQJ2GiQwNmTQvFZLZ3gzw6KUoGZRy2UdOpbrhPgJuYYqUl1deUjbHb+Q0C30tEI6TkEGyIaEqiSkV0Ays9BT4zrzttz+AeC7+EL29hhOwh6WsVM35JO6gN0Rp2DkgHiiMyN94GrwXMnP5EiePJvVhLauadq2Mo1fYbVMjjHZEEDUwbPALXHkXXt5DRpPanLeqonYf26PYGNgr8d1R0rcteSErYKi7xYr8BhdRbvUIAfluRjpTadLqXBKkrC6DMrCw6EQcJlhL6/3YqonZTBnQx+TTlv4SvLaQlu156l0gTVChAQk8LNOeXY4qPefpaK2C0jw8djpysSy6l3LHMbAPnoMurp2gZQTWdsU/K5w8F7egei9sFmYcNsyaj2yFGw5yNGn//QQF9MKdpBg9NUMZqCQXlXIHs5Ac8lekXyofWPbLBh2ABeFqDd7LR/fkn/CrcUpGDkgSNHR5cxXYhdtg23gWy1NvWrmopTJbgvPltYyjUcLjyTIZPsVuzOvaz1vZmHtd++43i9cCp3dz8t/j1bth1zReVJlkgTCcuu1dWqlIYPju6aMEQ2RvtexMTHFZOpNfJQVk/i2RHG3WCANOQ+XGWGgdmsmCuTW3t1IzNIO7H/vKH5G27hAu0D7IPDlMhYxQG0sVJ5CFSPYLIGelqDxLLS8k9ow1rSZdq58b89xyz3jIAJGoyBXfxQcmcD6uc99Du95z3ta//bHf/zHePvb377DwRwPp2DkgFBqjRbDOsQeGkBdF1/1dk/WAtzEv6wGu1VavesxxqqrQbrZglAFXgSs1JFbV4GLWhKR74bK3Pl02X+kCAqKZMyB4wRgfbBI/m19j627PPrAlRpq/kVLpqh07dsrSmg1UTXKQjhjujBJbQOqBfHkEKLMVMcW5VY4AmxfH5bQ9eYnP+Vkzkspzdh9+czIzjp1FrET3dvaeHFb+PEhqO82v1NKUsksKCXlqtkcVJbQlqHSRBSJnWGfjQUU1+5T1R0uLcRUSjOo8zNAK/C8kKwd+o1f3tfnWCDsyBnp+bmPf/zjS0HJG97whv4HcmScgpEDgpSC7TlYBxKj1kBRrDWUouEQ6t5dAABfXcGO0Zh8yItsWcBkO5UAACAASURBVO8ou4cVVOx+GsOVR7isloOiGC3S6JQkoklBBDWQW5N02lwdxYHaguU8gDA5PhHlsQ3dTATdL5hV0srog1TSjqw4ny+t7u187lRrV5BWnd5K46XZbFlRuAt8OS3P5XsGRH/GtqjHbglKE6jRCCL2hX6lIx8k+I4wa8FXV3Ivr1IN7Ysu59mX+LklSGvpLiOxCvBdOD7Ip9SRQUkB8znsxGm8XF1LBik+ZO8AvWmf0cKDqyrSMpLPqrMh8JpnwKmGengtBTjv1LvN+OWyUrZ85XP3nn/++ScmC9KGUzByQKhN6co2LJiSkRsceU26UlQu3VfpsxRdUrs7YuMKqO14fTDhTec8VK3nQHEmh4Cg5xC97gcyAOBV+ZPbmhXZAFFqXVGi6gwC+2vq5OLbd9YzOF21vRV8HiDKYK3SV9kBOxnbAaKlYRlMrtNJvgAJbndt436SoJpiY/J/fW3ZE74RBfxeednxYXb5LvxCDEkCTjU41UCit+peWgTbXesm2+5wx0zWEzpu7YpTMHJI0Jackah1VYSanKYFM1qttP1K8+wM5v45AEAXJWjWXAGzMYEcuC+59JCijyXgtRbSn1Lg6RTmckEC3qshag0aDmodB78iThLwMBcbeX9npqk4hjLLSn80lOCsLOsOmKKo51NeKNPcVqzjDbCVlbhPm2+7aSfyRLmTnD/WZBrk013AmaRQ52fiv1NVTqND7meeFzJ5ude20ZBogy1KkPt8mwFbKxzplQ1EC2XuPFG8C2316ghCRMNlDvaZkGRhWmCWtmHmhZKrkXHJIApQrCzCNu3TWSOw+xkkRozq3l3QaADOMwlEXHArmbMtCPDNnXW/J/aAm2rt/cAHPoD3ve99GI1GeMc73oEPf/jDeOc739n/QI6MUzByQKhtRM/8CtIHIkDd9hmLOsWrWN+WmWcoL3IQM9Qj5wPCUb7gQOleNgbEVOuKpFJm4VSktkmNm4frykUSVIzAd0byh7ICVUaMz0YSjITOiDSREhMg275zJgPXbC4pW6PqYOZJKM0sYhXBdofvi7T6/9l7s1jptqtc7BtzrrWqand/c46PwWAibMcJuhzLlkAoBHSNEgIPvJgIWVi6QUiJ4odEcB9AlpAw5CVIiMtLkC0hRVhINFIsJEKjG5InkK7AVqQAyQuWgXBx7NP8zW6qajVzjjyMMeaaq3bVrmbX3v9/fGpI5/x716611lzdnKP5xvcBIFBZSclmwE56D0Bmy4SUBejkGHw0FlzBVBSFqa6lPLQjvmOpxXBjKXOlmUPStICfA064Sihmrbzf7GZgT0CwZ6V2nti5G4HaUuJDc0Jsk7hRmUY2XaD/LwvgwQnC6RHgIAzOjJSh2rmMd9tMxdbHw+0SMbrt+fn54OPRaISREfNl9uDBA/zMz/wMPv7xj+OVV17BV77yFfzqr/4qPv7xj+OP//iP8SM/8iO3GMz92cEZuUOTbpq4FYCUSgWjshIIMYMhi75FEEMQYgSaFsVlIx553SJaW+udnNR1kiwD2iE4oOtBd6ktFJA0uNLCJ54Nq023nfATAEAt36EqO4Yh4V2PL7F92udSjsBg8dgbWPeubcPuik2NU4ZACajuo9Sgke3g3ngPlIU4p4pdgmX68hbZ2x4TwKo24GQ3XVsFWot4pPxMXVAsjWZy3omOrll+b9bpQ5k6MtB3EW2RkeDIcOs6VxYBst6DqhI0GSM+PEbzeAzqGH7egdqYZc6G+kEDxt13wnu+pb3//e8f/P7Zz34Wv/RLv3Ttex/72MfwsY99LP3+gz/4g/jEJz6B119/HT//8z9/cEYOJsRJsiiXAsjyHnw1FQDgqm3OTsEPTqVt8uIqodR5XoueRb6mqPBafPIUXvcZpzNEY/Lc9wualVkMoMoBEuG2Hajt4CIn/AqdnoqDofoioiki1OA8mwFzAQWyOk9UFsBoJA7LI8masPOgQp0wa7U008lJnB6Xas3gCMwEZLmuE2kjU7DtSlr8Xcz24bJ25D1lCbjtQGDwdCqidGlxuTuHhKoK7vREiMLqBtw0oKpEPBojnI7gpwSqG8k4YD/nSkUJ9+B0cEyJosM152udYxrrGqSLdGxbxKsp4sXFfu/5C7JU/ig8eDbrn4kl5yTvdHbttuVUYb4ZM7JkP24yBj04A58d49l/dIKrb3UYP2E8+CrDNaG/p9m2Oeg21nW69y+DEXMqGe66PQD80z/9E87OztLny7Iiq+zhw4f4sR/7MXz+85/HbDbDJJM2eFnt4IzcoZG19joCykr+tUzBiu8LbqICtQE0lw4DCjeDt+JsBjLCqDvOBpjgl6SxMQBAyiIj2RyqSmG0dC4xiVJnUZl026Q6s0VeIQiI0BEojgfHu8aimLNtKr01nLFoemG63M8JX08777vU4UjbkvekoRFDX9/fVxlkjRGRaBAVRa/S7DxQOHDhBpLwAPYzJqfHrEp55hRjRTFeb+WkNUDdrDQmFPSbK0y/7Ebe9fghFRhc+azdkq8kRt64TJOsLMCjEnFSojklNA8ZviGwv2E/pG3rjkBdJ+/6fXEprbOIjfn3Vm4P4OzsbOCMbGuWIb01uPue7OCM3JURwVWVrJWjkS4QN78srBkD91T7+a+miflwHQfDplTYtzVxHJq1aX+aTBBfOZOFqPSIhYOfd/BvX0hGZK4tghn7KQGqbeNkkQHAVQUEjXq0BJT4Mbquz4Y4EkzJ8aRPu9e1gOS2lR0fnDAP0//7vr5Gtb1t2cJAolUlmJoQBOSXL6D3GNGz8k/Aq/Oj9OqxcOCCAE997X6POAzWZ4JKZUwNUcT55nUfUW9bYrFumm8mC0Gyh5w9y3dhzBsBWAebNC3crIbzHsdvHANwmDyJqN6egS5nygy8iFUxkDdJYHOwgT19+hR/9Ed/hI9+9KMYj8cvejgb2cEZuSsjB380ETDmZKyKryvIv8w4Ij4/By6vpJRhBETrFpRbRjMbmwHdNohA+PQI8289QSwJ7ZFDqAjVZcQxM9xlKanIvIRgk6N3Pd8IgDiugKaTzoYYwVNVgG0bKUd4L1FfUYCPJ2hfOQaYUc1q0PNzjZBveW02TVXvEvnvClZVkjg3GYOOjwVkeH6xkGK/vyiRu1Yo7r2TezcaAVWJWHrE0kmnhhHh7TquRQ/BngMugOMJ4oMjUNDFkEic1Xlc3fr9omzPGKF1lq55EPzFXZKoRcO1bTO+pgFfXQFdh+O/H2H0ZITioob7+tvg+Rxxdr2szZ3Opcbm+jJkRNT2VabZ1D71qU/hO77jO/A93/M9ePXVV/F3f/d3+LVf+zV84xvfwG/91m/tPI77toMzckcmKqQFGIKzsOiavAerhDdlJRv7u2AcdHG6S7XW29ial8Vk4GNBCBUhFgB7+88J9kPPPWExlPqcikLQ/MVCmj3vLGIDtVmXkXKxOKepXerLQ/vCd2w6wSZuBtt2DxPl4uKVt4CTS+d/57aps0UOZNffNlFqczLOnJ2OnwEWzSJrWWgJuNGeKd1m0wyZ3O5votRIZOX/UCzNXba9r8OMLDMbXwhw8wZF4eBmLbhtJetxo2bUy+WIAMB908F/5CMfwe///u/j85//PC4vL/H48WP8wA/8AH77t38b3/u933uLgdyvHZyROzIqCuDhGYJ3aL/9FRTnc6Dt4K5moKsrmbAtom9bAVuGKFFC2200YRjVt6SiXxLHRUm2uPQII0IsCRQB3zBcJ/gOLhyoMPpu7aJxwpfCrzwUfgFt5+W2A9pOWi9VPTUZkS5yTlLQngRvwgwuMsrxfWAxbprwnDpWZTHkXIFEfbGud54wqaySyBc37bXMGncdMJ9LWW+hlLfXOdp5uPFI7m3TXGMkJe9B45FktU6OwScTIa3yThYbInBVSkllXsg128JRo6JQjIAbAFRjXSuuyMFb+adp5bkoCm1zBqhpZdzKWXFjuXQb53MXu+fFk0MQJlnv9T26u+MzY3vMCKAq3x3o/Ap+VgN1gzibr5aSyA/4LrfPfOYz+MxnPvOih3FrOzgjd2VlCT4ZIXqgeVxJsN4EOEDAfd6Dj8YCKmtamVC7LtX+15oBSbVV9mXg+CKNfOEFtBgLQvSA6wCKDOogi5JFzRkwlUgwH93pSHAGI300WwUSLtB7D9hJDdxKBJAcI4nA3TmnRp+loKIQB0txLLYQ4hZU4uSdUHFbqj0X0QOU9K3tOWnu6HzJkbScG016fk72LFYCYOVxhXg8AjsBIRJDMiSF0tsXhWbEtnASySW+GUCDR+3iYgDuUjvPjMXXO8mueXVqSTMkKlVwL2VN4N5LMkstBsT5Hs/3hnOKG5KeDcykHNoOuLiUgMUCs320gN+33ZbX5F3qYB2ckbuyGOGg9dkImZgLWTBZkf/UKIto0yqjaOjBfYsP5AK/RyIQ8w5o5PMBF8ILqKPmIFqad6jOA+AACuKMuCbCzTtQK+eZKJ613ZPaFu6yAZUezvL7ddNzZWBJ+pyk7GOKqxS0XmvYha6AM62cbTJIdr1zvEh+D/L95J0bkQEnUR5HujXfCzPLcwIMxOzIxfSZHDfiGhfGHoN7joJtIXYD8LJxySQgbVGkewEA1EUQE6gzJ0o1iCaTjFhMQczLnKkE1C2FfReQTFADgPpsUO6MkXdg7qUUhCXYgb2XzIz31/R2BscjgqtKaRm+TUdN/qzeZ6fHXTlA1tqfA6YXgfXblLhMnyovVzOL1mUGch4Ihi4wOi8f54vFkbwoBtZ3uh2ckbsyZvg2Ah3DNxGx8uDCwXun3TFRJlUFqtqkvHRCdh5OU/XW3kpFATo9EfzFvAbN5jCVXAACaLxFeWClLU4AC/uXyYTh3n6G4+lc23SXRAp1059P24JDRDy/ALWSJaL3CL19ePpcwGpaix60iGrLMJcFeFyCiVBc6cJdFcDjB1Iaq0rZx3SGeHm5ERCVihJUFqkkwJGlDFMIgVec1+KQZEJ/YAVoWjYndcnsfg+4aRCsWyCbYFOwGIKWoRYnaQI5v79FMAbEq2k/Dj0OTSagUQUajcBnx2DvE96HGPBzGXtyRpwDnxxJySfEdO9NgG3AC2PlLu/hzk4RX3sEMMM9vRTwc9dJW2fIWsVNEVhZgXkkQosEWSS5C8InYiRpA6eSkkPlTk7gTk8Rnj1bff1WYWgSLX7mpHJc7yTs415lRHB7a+s22QnvRZDz7ATUduBnzwVcmhGpGcnjWnNeZB6sXT+btyxAEexRTKzNzNJtyF03KIX243QSnDknwccuYokHe2F2cEbuymKEiyz0yEG4M3RGTOn2hAFY0zlDhovIF+GykIxAWUg7Y9fpoq+IednwbtLROZBwKZ25tnkqp0EqqThN5VPGvxKsw0IZZ6MseHwm6fVY95o6rPwmg2uRlWiIGdz1ERWXEgmjLLRteHMZcfIuTXimREoLv6/MHjPruezh2q/rlFr1d3vW9mmL55NKU6UyrXpxRuwyMwOWqcrbeb0Xp1LxQuSjZCqs8yl/pkhZe4tCHExmccANDGvHUY0ZA6v2Y6T+GXEO5FnLeAtdVrbgGg7Ja8v4sndoi+s6AGjfVG7Y173K5CSW6lntvFtKvC5xVA3KxPnemRlug3OxOY2KondAlo3TANplITwy1/hjst8ty+I0e/eieEcOZZqd7OCM3JFx14Gmc1AbUH3jos9oPL8UnYcYew6ERarjxX2FIFTVQAJ9ggg8HoHHshCkz436u22lPBF8HwUCkmHxfpg52aRLwibrTMgvB2kmwqimGWI48n16r9kE3cdoJA6U8ahwTM4ERSNEy0oCCLJALERR7vwSNL3hUd4QkJhAktl2g9IIFrAqgJYX0NP15/f0RVnKntztGMh70NFEuF3UGYFzQBdBIcuIMMs1iX1GxRYzHkkJjwA4LaVx1/XcEXp9eTqFf0uft4tL1SXK+HdMpymnow8BNBMuCimbKbGe90AFEBdDJ0lxTBxY6ODb5vp9tMyDtSqv604hB/LQ73h9lpZpvOxnAXJVCTo+GjoLbYt4frlZpiQvRQ4ycSpsdzUVX7PrBGDadYPyIMfNummSeGcWsEib/kjfcaXjj1HmtqVjzRwvIIGnqSyFafY8D/LWDmlvRlH+u83270Y7OCN3ZBwCcHkJrlvwv/96ekFjVpJJTsK6+iZn/B6OUho6Ho0Qj0pQW8CNlIVSOTlIaeRh6WtT2D0+klbjptGMSruQTl4WZfeOiHU0UFEI8yXQO1asoLNs3ANzHq4tZfujCWh8JOOq62uYDsOI5NE4IwOk2gQYaoSmgdHN2+KUQKVVCXd6IhPcGjMsAwBwXfddGwawY06kawOSOQ4vJ8Yu3m3dnLwDH08QHh5JuUyfcd8GUN31Gi+Wute/c1n0WarCy/PiSIDdVr4EssVeFlOazWFKv4tYBVJcCEyCHlDgc5v2lcyc9/zzHHTJUTiBmiVKr/pMkffDceRmY/KUQMzMpPpM9ordoVzD0ZFc18kIsSpAV3MRlgxh7XFJy1xs2T22snEER4d4eQUoWeEy50Y6kTYYK7O8Y0CPHVEQOyZjmbdc3QcvwHCesoAoc3yoLIDTY8TxCK7woHkNdC+AFO2QGdnJDs7ILrZh+o8gzgfaVtLSwCAbsjVS3CJM+9fMuhbCwvf050E0v2z7LSzRbfMSYp9114Rj2l7afwuQZ02tEgg9sHAlNs0WNJuXImtZiofODBNADApOslT23XXn1suPXv9+yny8jJ7HErNSzTLQ7R6MmeG6IBwf0dqsqZ+MOXvWHPoMVYxAsJKjLkIZWdZi6h8YZsiW4nDIZAcWovpFBlZHQMwi6lTS0BR/KnFS5thkYMrcadn0OVAhR3GYA1DLwr7WIVkArQ/Oa/GrVlKqSvBkBBQe8ahCHBXwzOKkWMZpWdnJsp5a5pByKMl7JCfeHz+suAcAwKLJtZWxXA8CBO/hHSgK/sMcVfkeD7ZB5DQPEKmzZ5nh8BLyjxzsRjs4I9ua8ymqWokLSJOqKn4G664wByGbxLZ8YSQaiRLxN11PDuYcOEZQ24GuZkIYNK/7NCoLICxOp6JjYxH/YsfI0oP2NXmbrKnrUguyqQtvZDqBuMkY3aunoMDw2g7YY2iUQ2JhsidHQFkKdkMnKGsBvEbmpGOOTQu6uEyZk5vGyW2HGGe632x/5Aaf7UV87y7Nnj8vQEyqSinLKehzX8ZtB37yFH46A0aVtKo7VW7uNKK2ax76xYty8LJlTCyLs5ihSItvX8Jb1nFD3ikeSfECWsKx8zUiPgA94Z45HNa2PNI24KIAHU/gzk7VcQpISsNAD05WIOzKMiBJpobKAvToIfhoDDerwW89SaXNlfeDCG40kkyBlSOIwLO5yChk14DKSgQDyxLxlYdo3nuMWDo0pw5hRBg9q3A8reHOS/DlFeLV1fBQVSVCes6BrTylqsuG97HzX1eW2jgzssoUD8cGOs5B2W0HupqCW0iQlWV1GQCFCHrrCagoEJum7/a5b6eEcbuy0LvUhzo4I1taKlUAOkktyZLoC0LOyXvLcX+LAHOqqVMXQG0Q1lHnpI22U56Strs+2XEAN3F52nJtVsMcKc08REgZaOvxa+28LNAdl0LffV6B5qVENgoMJaLrQzK0vPGTkANR1/MRLBszB3AdNnu/Y1je8mmTK0dw906ZKbSkN6pA47G0js/mu92zVRbFwcHVDO5YFFS58H331CA7EvssnUWuQK+2nKXc84U/2brn0ynglFnukTkjRiDoM9Zf26X3mgnzUtYYlYlAj6sCGEkbK3edlJAoW5R5g04pc6yKAvF0gu5sjKJwoItR76jdYFTJveuzNDrXzOaD7IZQ8I/BkxHC2Qj1owKhJDSnhG4i5bOj4zFc2wkOY9GZKQrB/pQFaFYrmFgF75TCn9pWSAXXqR9H3iEzos4mc+IoSviszNGzzMlgO84zZuGl6KC5bzr4bxY7OCNbGkdOE/qyyShnzHSjSjx2lX6+ER+yCYg0N2tr9SRZ5KhKqcZYugAsWziJ9fu/C1OsBUEwLcVV2wMbSTsYNFVObomTp6yaiexN0/nL0vp7M2MeJc3c3MVkt6rlU7t3zPkV4PMGGRlSGnbtdOFxJdmkO7hOHBnk1MGdzWWsi6BQy0po98zKMWg0zLLjrdqi2Xh7gB7YuqoMmgNpvet/NmcoQrgeUpknKi5qgdvlGrh1AeBtzkOQjjoyIHThBW8Vwo2gUu46aYE3rR1yPRMxIFla70GTCfjBCeJRhe6kRCgJsZCyra8ZRc3C7WP4sXUOlHa4oAnJeVzqIC4d9C1EBiMDdQOaqkNr2TJ9Dri9zkB8sG8eOzgj21qU7AKApS+1m4xBZ6eSZj05BjsHd3qCCEhr2rLUbNYzn9D2K/Zv35eDabtiCKC6FSdkOrvO15DbkgXvXtOYUbJJfH6JwhYq5YdAjBKlESUQXRoj0LeRFgqAdE5ovq/ujuTIVaWk64sCfHmJcL66BXsny+99vlAoqM+dHIsQXozA+YXwOqw5Phm5V1mCjyeIp0dwZQM8K3tw6L4sCniXQwDV9SB7Zd020iJLPSNuiMCCxjrlf1+8FuuM+TofS/a3619Xp4AZubYPZ3gTYga6rNSTOr6Wj8n4UJLzqABXGJlh28E1uv1krE4bgyyrt/iuMiPOZlJSzT82B41UJHE8Aj04w+zbTtGcigRDO1EgcQOUU0Z1HkCXU/DV1Wp256zDiapKyspNm3hguGtXXs9r13YXb0TLLvH8Qq6JjQkA11JySaDZl90OANad7OCM7GI3OQmOhAeBCM46BQrRLGGb/FbZFgqzg/a5FH1JN8m2E/mLMO46iWRtEiRKCxIvi64MoOh9ckQ4R9Pf1XlY+t/KCfdhholRzAHKQu6v95vRqFsknWj3KVHu35lxrz7MwYNKTp+noyYnOgev9niIZLt0AS2k7K/9WTtZZP+6b3/9e8QsqZmFyHyTLA1lC7qdo5UaKEbBzUTIc+tdeqbIrRDxy67p9YMpKLYspeQ5cejGogUlYGHABYZvGa6N4li1PZPx9Qu0cH4GuOcbsrlLLG7Y2rvSQgCbPIaRmrXtWj2hl8qyeHLn7d+FdnBG9mnMEk1czZTQq08109FEXqbprE9BWzTECgyjDV78LJJG00r7YNBaK/d18RdG+HOTZWJrBqoc1MON3MgRXFHI8DNeB1H0FbpxalpQruy5p/GlLIWmz7lpwOcXUhKb1ynq3ptIod17/Vl2rjosqrOSygmTsQB9M9DuMsdTFkX5l2Y1vGIF4iaaR/sw7nFJVppA4cFKeEdtCVR6z6xM0oVeEuEuUvG8BCvlSDAjTEDTCllXFFZYmteSSbCW83WlTXOWmZNuT8KsAKDnl/B12zteCU+zXTmKigI0Ggko9sGZtFafjdGNBaxKEfBzue7FnOHnEX4mWY5VrcjctOBn5zqHWGmK+2zENp13zNtr0+SmcwOVJTAZS2fNMydYl5dRofdge7ODM7JnkxdYAHPUtX0kMhmn9lJuGu20yV6uNZGdmaWDAYDarl+onAMc99Hmy0Z8QSQT6PGx1MwVzEhASjvDIr2iEKFBzSQl0GpVyoLmCJjOBTyZE1/ddojmJAEw0DF3HcL5+fVzMcrxjqRst8e0LDmScVjK3zJH4xFQlj13hhJHpeg5L99BSw/TqTjIIQjm5D7MnuVA6TnkwoOPRtIl0Qqb5uCcZzVwNdUF8w6e3WXvV/SaJdFoXGUauGmB2Qzh/HJzR9OcjCUyDFwHxKdPhdZ+NBIZB+d2aq+nqkrEXvHhCbrTEbqTEt2EEEZAMQOKeYQLQDGN8PMAN+uEy2dFdoHbBuFpM8QuLZKfbWgxa9He/uSczBHqiISHJ4An+LoFuef3pm14WzsAWHezgzOyZ0uZkBB6npEQZOJLLZceDKW/3uYNswXbFkLrUiBCUlfSFP9Wiqh3bTqxudEIeHAiDkXTijOVk04ZNXzejQFJFVP++T4V4HJbYJy88av5hLPL5OH8tWMOLHIqI7BGmhSzcpaBUQ3zAHVUyVpcIa2qRaFR5f2bMNIq62mIQzxBft9z4irNjiWG033pqywfoGBdIMzA7MR5YmZha93GoTeQ5SowqjGYdh3QdSCjLL+NEUkJDgAFwHWAaxm+YbiWUcw6uDrA1RsCP/Oxa4fLtqXJpSXWZeNedp2MOyQIWZ5rOgHo77MD7D6MccvgZG8jeUfZwRnZtynAlclJCaETgTHqOnmxY5A0awiIkZe3ki4zLc+4k2NhWLTPuqxt1TIytv/65UhrirqwB736GOevv4bmxGH8NGD8ltDlu8tauCeYZeEMAQhHOh8G4RUIQdK3dr7bEo8tzpDLIsQQpBQD3BwRW/lm1wyU83DHR0LqNK+vR9KWIg+hL9cowym5jI9D+TWg5Tk3GslC/ugB4Byab3kAPDxTThjcrKdzF8YxCZs5ZtBkBDhRIbasHhc+Y2MVbI47PpJuk7pBePvpnbVrCgFZDSYHh7E4iFpaJekt3XxfXXujvlQCbs9rCVSI+rLJNscJKvRn4E4VohxdBMQZobwIGD2p5b06nwKzuTyru4hmbpitHW6zgGdbsETOtozugFk4VOoadOVBFxcgcoiz2YvhCznYvdrBGbkL05eYOIJjTAtcivoKjfxqWh0lLBop2E1bNcGqnxGiTGy6nwQCveNT3Ng0kqeiQDwZY/oeh+YBgZ2Hbyr4OoDqhbbMruuZUIGsYyP2omt6rhudp02OgyhvhT7IppPvLhO1DcfwL1WZ6PCH+5Y20jSle2uP5dTKPDDt4oDW2uN4BHZAdySSAc55kN+Qa2WfZgDMECUb1gVQAbl/XegF6Sy17wiAF6zAuAI5ZeG8q8SOjY8IHErFj+zorW3yPOh3Nua9WWUhSCkTkPc+Mvyc4YhRXrRwz6eiqnt+0Zcy7ym7wMxDltpF07IrMy1tM7bMEQPA9E6Hend26KbZyQ7OyB0aEfp0r1HCMwtRl5Ud0hfXjc7rzAAAIABJREFUgFaVapvbVurrBpZVQifjomBmWZQ6w4684FIN9wy07nKOozcjiplkRsrzBtQGyYq0Cy3P5K6nvNsWPJ2JEzLXiM/Q/jdYXr6gyVgisMurBDS8d0v07DfccxOIa1vQXDMDGbCQjTMCdn7GkaEsvAy4mZDiyeRfSJbFolI79i7gW2MhNkbOG/bBGSbDXc0EK9G26gQ4caj1s5SOb1pxxNpuZdljr2ZOSS14L16GezBwM8fdonRnnVC82zXXUmdyRKOoD7MDuHCIhYCWuVQGUyuJ7Sj7sKutE8pbydHyzWQRt6skfxNfmpvs4IzcoRHJ5DNAsRsxEnSi3iTVr1LtAMCzOTgXDDOCMCUCo9NTIUFqGpCf3VwGWjZp3MHkz20D7lrQW09w+v8U4HEJmrfihGhniIkHCgujV/9sOJZY16C3n8g+c0KrdY6cYmzcwweIj85AzHBfB8LL2i6YR+zTWe+8ZHwXXNfgyKLSaqBbQLgrpnMgMsonM7nGZZm6hEy7wxw5brH14uiqEnRyLOO8miI2uLE8AQDx8hI8mw2cofy5HZCjAeKcWMnuNrYsK7bkuWGlD1/83PbhxiMhLwwB4fJqcyff8FLWRda225dPtaMsAbnNPCGWDuwJYSSOiWs8/EQlExYdz3xMy85zD8a8prV3FcvxPu0FdxIeAKy72cEZuUMjIkGXA/3LwUohD2wNDpPNM6G9xASLRBxGHIf6HhsNdMFBsp3u86VQnIW7mqmicJDFlTktsimKM1Du4uFtkd7G7Bob50bhNJW8hGRin7bIA5Nb0lyJN2NftO2XFDPB5kiEMHTGAF3kFXQYo9TuO5EMSOPxGWjWe+lo2QVH4oxzJQp/ySaA6SX3jiN6rSNv2ZYoTKGAnOttIntrg0/PwBA8O8jq5I7JsgXVutY47oy9oZxYbVtL3Cw9fT4rAzMXkAyJfeYI5CltQyZ4x9wr5AJ3gsO4tTbNbS11Ab0ceLmDbW4HZ+QObSUILn22QWQPLDgembOwsE8OQSJpFZzbCEFPDv7kGHR8NNB94LpGfH5xMyhvS+OuA19eyqKobZQABkq46bN9KOPmC4qCU93zKyBGxE2ZSC097tbrciQzgGpRJPBgLgXAISDO5thEvM/G3XcZxeG+ug48Yx2qnq+W7+hyJjomZpaJ816iZqP53tLB47YDplNxII6P4R6cCVj78kpaY7fhXlGJeA6QcyIHdp2wcIYdMBxEApguCmEnPTkWDFUh+jNyAnIt6XKK8MZb6zt2zJGazeVnK+Ns8iyog5j4SrbgFBnsIwQwOVAJ0KgCxiM0j8eYvmbnJA11RU3gwoGDg6sq0GQil8Uuz9FEWsQjg5+fJ0bffWFKmHdQ7V1mmSO58fOkPEZUFPJeGLstx+uBzV3aATOykx2ckTs0cu7m52rTh46XEGOt+F6cboH60iiGjibgR2cStXkB1brzKWg2BxoNePeAPWFVjk0TzIIqLoB+0jGl0F0tdyJYGDB5rkJxHLdidDSVZgI2mrSpLKQjZFQB07kAchH6S8hCjLX26NZxccMxDfAHZPNtI5o/8fJSnNOyyCJGSNeIA+BdT729hVnniKtKcUROJoJr6brkCG3lQxiwM2dx33JMubnRCBiNQMcThPc8QKw8YukQqx6rQwxUb5WgZ8/13Rpy/lxrvsowOgC2i76ZpSPoFuUDGWOAo7EoJE9GaM485q+YBg1AgREqB/byH8pCHBfTynEOfHqMcDYBdRHO9Ky2ZW1ec657Ifq10rQxwW7gjJAj0GQsPCVtKwFNiPcPmzs4IzvZwRm5QxNCxz2hke7wAeUgqXFy0ibKxkS59wNxElYbfh6vYWeWYUa2sVRjdzapUZqUETNxtE0dksTrQWn/xoWRiL3yn7tOIruYlZ82O9D137eOpLXDyso8luL3C6WpTQHU1/av0b4Ck9F2UnYz+nrvXxw3BPWLMI8qhHGBWAnAk5WTw3XosxtWZsoWLMF+LnojEUb8koCo5pDkx74pi7ivd1j5W1zLyrYq/CIU5RhSunHgUdmr/morOFclYumFbbbwvSpxjmvj7LnYctxba9PcwDliQUQftNycLRFemwB2/m5YfA92p3ZwRu7QnHP3mx7c1khJnq6uQDFIPbkqQYXv+TYcbSqXc33fOYOjTTgxLI+aF8IXWoYZ2fC4PR/LRMZhmYEQZNEMQTICbYeNotvIYIdeCM170OlJJijW9F0umpXgyyvwdCbp+Q1FxnI6ejOJWLflemAADLStlINqzRIZU2+edbJJO8cSbNLxwSps9vwcdDUFW2txVcr+t+1UWvW8bGnkPejkGPH0GOHBGLP3VgiVZA8oMlwAaBrhgnSjOJMlyJlsl+7YLfxquC8BD1MlIGHuOsTZ7G6CB12EqW1BjjB+swYgbdzsCUyAbxmxEt0mPDyCG1eyrSWFxiXC2IO7CHdyJEDXupHOnK4bPK/J4d6iNXipI7fq3uYdRot/Y5ax2Of2Xp+eKgdNfZ0hN2rm1WelyV3nr9vYITOykx2ckX3atZfwdtH9fZksynN1TsagUCRw6c6y8zkodldnZpdtlI8FVSnpbJc5I23XY+usrr1F+7N1fRgegUcVqBWFXNaUNzvpCorWdr3F/bf27YHdZiJl7p0/IoB9it4HwFDKroUjIG6Y1WBGnNcg14BGI7hR1WdGdrBbPS9mTqJ/npToJh7txCFUgG8B10LeyfwSOx1v5mysjO6XAc5VrJFU0JDse/uuDSyMj0KEv2pRVR5cEMJIsj/UQUCsJUDRX3ueLEsEOMSqgGtLGXPTqFxFBIce8EvebwUi5hhXXLqFe2t0BSYUmDoLh85FvwNK7zWNR5Jh9R688I6lsqW1n78IO7T27mQHZ2QPRkUBd3QkQLkMsEjk+m6awQY7tNYt2yaPOMy2WQBt0lURMfJOwa+SGqWiSBPf1u14HKXDYnHM2+xiF6AfVPjMrvuoQvOeY4SRA7HU1V0XUX5tDP/0OdC00qq54vzIewFCmmqvo0TlTp1kWriTlDHnIm87RPdGGb7w4c0b5c/AjW3cbihtH7u+PJYfK1ssNsND6H3uOsHkkFstU38fFoXh1U0bFIXD6MIjlCQZkcgpQwIAXDrQ0RjkHSIwxDEBg7KAbLBwL4z3w3sBgE/GQN3ANU3f7gzsCYuRlf9mgkNyV2OUhUMsHRALuEJ5jCwJWRBisZBp89aFA8GHFT51LzEvobPfsly7vMRlqscr3gnv4Qp1Vjpajo9j3d7A4F2ngnpFTw0wOGYcvosHe+nt4IzswaiqQK88Alcl3OUU8ekzKQV4d/3lW0WedJNWSQ7GtJdaJcRl2/7l57bbLipL4NgADi7R1pNGIESWpt2w1JDt9zbR4c6YEZv4IBFiPKpw8f4RmjOZgNkDrgEejTzGhQdN56C6Btcrxuq9dCDk6rmAODuxE6BcK50fu9B7D2xVCesGIyW8W8cZIuJ7VaJZ7wG8vUOSxr5Ne6SBT+sgvC23seQY3SKrwBF8NRXV2i5gDFmABTcBKUd4+S9WHvH0WJ5zZpCV26CXwPvUjSJEbctvDnkPPjtBeDCBv2pA8zkcpn1nEfVjk393e64BgJsGoRU8knMevu0ESNxOEDVLEr0DOyCOHEI1DFbMIYsAYulBheu7jBYdDytPbvFQrswqLXsuDVhbKCEfR2COLBMzvF7cdakMQ1UlBIb27C6hlr8v1tlFO/CM7GYHZ2QfRiTodaPtNgp0BRHu5RCruEOMS8AmAOMUAHaa9BLA9JpDpOC8dQJvL5kxAbEAYgVED+FkICCWBPZeKMdvKEXZfYQC/rjwGlkLFX9quY1xt7bNfZhz0i10m31sez+XZepuMwH7LK2+TavpYnbQ+Fa6DtR2cPNOuGU0I0BEiPp9Ykh2IDqlnlcBy3xfwGalSqeg0cIJh01ZglwYgJyFi+WWUbo5f0ACDxORaNEAiOzgADCTdHIv3BOKhp2RjCcU9Lo3ptZdummczp2sZVAN1gwwnDv4KXsYwvKy2ctgB8zITnZwRvZhIUhro3UXJNr33MtX44VWszSp3xA1JW4IknR4zF7CyJKBKRVI5wXDsEzKfJVZdA0gsbqi7aTQbt8pC4lgJmOJxKczhIuL/b84VgJZ1xZ9k3EEYhBa9DbCtwzXkKxTLUTVtBbadHRhM4eRCHw0RjweATHCXcxF8K3rEG3yUaCdnkA/lqX3dHeHcXCqBr5cAjod+AnGbZL4Ozj70sIY12S13HgsZYmotPq3EbJTEKN79Ah47TEAgN54gvDWW2uvDZUV3IPTxCuR1IkVTEzM8MwSKLg+WODSg9WBo1YXuqoEHR8nEbpEiqcyDgMW5TQAvefOiePTyL7o5EhbavUedx3i83NhoN3X+8IKEuYIFAVc2wGFhzOWVu9QHFWIlQcYoBDF+eoE20SBRVailexeNALCQUfY9S63tcNieXe3sZxZlkajvvstY502PpQ0f1oWjkgxbwd7p9vBGdmDcYjgWia/FNGR9vUvTj75RL+hXgU5q12TODzIaqGk7au58qkj6epo2ptLJalNVdKkzCz75xZgSrV/c1aoqkCnJ+BK6aan0/2mQgegNteXDHaZwLVzhkIQafVOIysCfAP4NvZka5tEhY4QRyXaswquiyLup8qzAvobRtR2HjdytOyDKXLTdDQL18VS0bktj0/jEej0REQam2Z3Z0QxGeQd6PQY8285AQCMp3Pg7fXXhqqyfx7rBpjXqd2YjUreyo6WtdQMDCfxQX1vFBuETt8zQSXLrQtGNLf4Lhs42MG1nTg2gAhZVqVk0rxkLWg2A7bhAFpnBh5WrBfXTb+o63n62US6hVSvSDJGIWlaJVI5bYmFtdEOvdgth7VdZoQoe98BEVSMBaiQ7iw7V6prCcJ0nlzqHL4sJimp223/LrSDM7KFUVmBfJVKIrFpeyei6wTZnajNlTYatHJBtQVrXY2co/bbyy8rBqf116oSh6RoZKFoFiaXlHkYzhhSbuh7+gWAq9uR0IfD6MvjLRfQJQC3wbk6yTDwTenOLD1/rSU1ATojqIsoZhHRC4AVLO2PftqB6nZzZyqyODZNlChzgPfRUg+54SR0DfBoLbYebjIWMGldb9wKagDU+1RhvZUtwzqtOs8Q4Gd6Tm13czRu+y0L8LgCjzQrGCLIZ2Br5eMAIpg1u6jHp047l8wZUXkCASNz3wzBa947+1vTguZLPD3nAMe7lxTWZdDUYSIXwEryRd7JeWv2Bwr4lL+HngMmZoBVdWAQbjjPDWylNo0FPiZaqdlc8k7mKz/ErUh3WkzjXGr5+593ib3oEvKhTLOTHZyRLcw/fghfHUmraIygp88RLi56ZlEznbic9zIhVNWQ8dMWUiu9AP0ESv2Llb4fg4hrpf1LxoCjSws3vAOVJfjxA4TTEfxFDde0AgGzxYso0SXLbmz/UdLRCfWudek0Efs0FqobUGSh/97Bg7dj52WMVBO240asALz1i5txfoBIsAbaxbHIb0GzGkdfm2E08nBNgKs7UBdBzy7AF5erafPtPjgHDhFEHWhao4gKUJs3soCxlMkYJQhtmhCvaccAQsRWFsJ/8t5XEScl/Fvn4H//tfXOhROOBRpV4LpBeH6+OeX6XZgRaa34W1p0jJ57CS0+gLQPvrxC+c/yEZ/fXP5L1/H4GM0rxwhHBYqLEoWT9m1hoFWG28SjsgQb5GjwHJoKsbWL2jsjf189Hg4B8fwCNK+l3HM0ARceTAW4XO78b2TrlIItc0M8eIbTsabTfn7JcSE587FmXWk0kuvBvJ4e/yZjXHdG7HkwJ3w0kkxIVcIwdjrIgXpzksDIgdEW2Nk+HQnjblVKBquuM+r9F/V+3BY7dnBGDrbORiV4pCnYLih/hXrkbeZMAH1EyNoPv2R31ha4EieySAuZGzNS+GbiWd4jjguESSF0z0XR8wRkDhCqUhZR6zpRBVcsLqA5aIzkBUusiLukEnM8RZqcl7MlCvh32WXpswvQFD8KuQ/EUVLQ+fi7AHc5B9VelIKnQgnP05mc9yr9k4wnRRwMklS3lY80OyTdAw7kkfgZVurYmBNVluiOK4SjEu6i2ihqJquhjyq5dzuKtd2LKdcLeW0nrsp0b8gtoed2Tu7b5ZXgUG4iTDMnSHk94sgjjBx87cClgotdtriljCL376CSviW14NyicV6g38c6Y05lIeIIjEcQCV1IJ09Hy7MFayzxzsQV2VMrY15jItUfuu7mZc15EOu1VIbWnfh9Mosx4hrHhgVe3osjUlWAd+CySCR8YO4zIXYCrY5+abDQO1KwjjKSzLSxtr6078fBltrBGdnCeN6AWl0UQkCc19cXsjR5WaQVe4AViRdPlbAiJl6KfGE3QOFGA1KEvkUOXQf/tNQadZMiDCqL1JOPVx+Dj0b6nVqcKiDVkW86VjIFAw7UQM2yFHrKwNS1RDmWeUGQSfAGW9mJZI4MUY+TsVR829eRjQkVvhH1Wu8lm6OLhnETLM3uGDOnpZEB2VfdCLdIjJLO56gYm9h31NwE+LPjzhyKty7gRyXo/DKN+aYyDIcgPB5Rav42biqr1IUyEAZblSpfR1mefxdY/r1CyiMIETQZw4UhCNg/egg+OwEKj3g8Qqw8/GUN//Y5uG2B2RyxroWf5/REwNddJ39btXwqiRWRliLLAggR5fMafl7AX9ZwFzOg7SQ7ZmPKWuH7i6mlgKR0jGEGYQdLHCVzAP5KQKXzBu5SGF7jbIkwI2WkYkuieA5BoSs3jOk2HUyWGcqUku093dXYMiNOsiBUFsBrr6L91jPEQlqNuSBQxyinHahj+Is53NNLma/qumd/tn2GJeUX5RHhyAOSPSICl8KGS1XVO56BgGV4qbuwQ5lmJzs4I1sYX14i8jxNWNze8OJqySEaR4dG2jSZgM4E/IfzC0Wxr3Jo1g1IyxqNkpY5koX3eakLn4hF0ViFwyZjNN/6EM3DEn4WUL09h6ulIwQXF8ujeSA5PRSjTjTqcGhGIqV1iRKFtJuMQcfHQIyIzzOQpQHQdolaLBqyiNYWGF142K4lq2DgYmdLfuDF88wW78QvUZX9YhZD71SZxaiO2WaMo6x4otB2oKtZoto25VqqKlBVilO1uECxCN/lv4MINB7BHR/JM3l51WOWrl27PopciznJ22UX0xhEoLJEGFegGOEmkz7Lpot+fO9jzL7tBLEi1KceoQLGz0Y4HhVw0xbu+SXcxQUwGoEfP0CclHCXNfDkOVY54lQWCWeDQh1dZvi3zuGJBLw6m6nOUk/cdyPvS36elh2IrKWGLTEeyhHDuqBewzMty/5ZFxuv4IhZAMTvqwNrYJq9tfLmrTtTNONEZQH38AEwqnD54Vfw1usFYomUNSmugOOve5RTxlGIoG80QCPyBRtJCXDPj4Q5wK5RB6QE+aEwJHUdXHePzkhUcNqttn/32cEZ2dKk4yQsxQQsmnMuPVgpCrE2QyAtrHzbfvmE8SBQ28prYOlpXbTJaVujJ8SC4DwBnqTFcZPUrApXcVbeuUsjwpLSlGYg/LAdOi04ix0PKYraoHZsKW+gn8SIhPSp4x5PsLCoUOyzEAK4XeNl5XicLdsm1y1CpCW/XbA8q3e6BHyt6s7snFCga1s5M4OcQ3dUoTsS2vFYArHUcoUj0UgxZ5ZIRBkXnr/0OdDfi6oSB9Hp+2OlGG1TZnNALCPCKzpgcrMspN2HeMv3MN8vb+l033dNIQNTJ62iPT03RPosViV4XKE5dWgeMmLFcK3oBLnG7jH33Tw5g/EmZti5EEDwEBIVp1gb17cGA0C8XfnpYHdvB2dkC6PxGNRkYLG1qW6AwSnSJu9A47G2r0VV1HQq0nUL0JgZiyYKGaK+lBbchFYngmsj/FwUPwHIwp4Q7lgeSdrvIYgkvbZOXitzGPiNHGKcpXbCdfTglmHBOrIr7h0C6rreics6gG5llvHKacFDkLRxCD2jZr4JAPLx+nbr7mW2YNnvbOJ9monZZLzciMgZVIOHRiPQssjSnBTTnXFLsjnZMZfqxFi0WZUIk1IxEQ4UjmVRUOKv6fvGuHqv8Fv4mlHMgWIa4c/nUhpUXA+HCJrVImVfNylzpAPosyFFAbznMepvOQUTwc+EzMw1Hej5pZR3TLAQ0NZK0zxZfxmREWyJ0x1xq8h2Q2MLaoDNnpd9mWbJDEyKrgNmM3G8b4n5ZAPTn56ge+8DtGcVnv2HDh/4nv8XpQ/4v//+fSi/UcEFYPw0oHrWwD/NRCW3bddVh5K7CDjJLFJVgScjxDNhz3XProDn95UWAYwP5Vbbvwvt4IxsYVQKEC8R8Kz7Pilxl6YPURRS6y68eOqlilSlKHkP6O8YtDOngqt0gTJOBUAEtpoI1xqivsdfyOFXj8EItiwNvtJxYRnDxvwTCm5LGBqLeJbNv7cp82xpxrvCTds7GksGxZs6IOuOt0O7Lred6HJ5DzoVPNJKHaGsY2ugDmyOoOF/TGwQgBB7hT6SdgQuBDTKnkCltEyzJ8RSsm7zRw7NA4CCcro0DN+I4wHVVZHBa0tsjEDdqMOn+Bnr8hiPgVGF9tERrr61AhMwfupQXjngiuCfIzmKuSOzsVqrPWuRQfkluSWQcyNbdEjv08hJ6XY8AjckpaU98HawZmFpPELzoEL9sMD8fR3+q2//dwCA//HJj6L+RgUKQHnZoXw6A11OEeb17t0v9t5FlsCmLMBHI7QPRgCAat6BLu6RrfWAGdnJDs7IDibp8BUdE/n38lIDaUmm8NrupzNfV0mUMq97kSgrGSxyNACbP6j2fYu8vAMXPoHIwICfbznhWokq0TWjX9hsQdtFmyUovXWWVt8nlf7WxorBIZfhUG4AfeYlnk0t//5NmIZs/7kDMYgg9fqRAVs3qPvnWaSkmrrKFrIo1LQopp0oIntJt8eC0I2lNBNGhFiJBpA4JAzXRBUV1LGZ8KA+L9z1QoO8iuuCFXJAEoGbIjMFBcAmcG4EwoaZsrxd/N0UkXIUTSVHKo65ZYkktwxcbOU3REYxDeCCMHqjwO9//XsBAFdfP8bxU0L1jOGnregkdXtyyJSVlgDQ1KO0tup5reDog73MdnBGtjAOksGgqsxwIzcxnEqrW2rhLQrwqEI4Fml7fjQGO0L5bA7XtKC6RqxrobLW3n8qC0lfdl3CLWwaQTCztBoSgcsCXJUIRwXaEwcQUFyi166wltV1+1wESJKTEtR4LLTXFxdbR/jX9vmijVmo040XY8OSycaWAX1TNuAaLsMPuSkyzg7rzrBsjbCEClhaWF/D4Fg9CVxWXsye22trcCLS6rkcEmYmMvj8EqU+U/FkAh55hPEI9QOHbkyoHwDtSUQxJfiGUT3rUFzU4OlU2ntHlfBa5CUwLbMMHO/8PBhwAWCHJHYXK494PJGFkBmYzcUJWXY9886VjGcjOXgZYZa0a298N995psDYcH4J8rM+iNC/bWtOGXktI0LeAV2H8hvnKJ6WeK04wz88/wCYgPd+PWL8tEV52aF44zn4agaeTvfiCHIIiM8vQP4KOC/hnmjX4myOWO+R/XadHQCsO9nBGdnGOMoz5gwp3934DuVcGUlszQFcSFQXRh7sAV8V8IUHd1quSDwN2kOvGQNG2K6mm4ErAQAOiCYhnnMxbGtZtgeA8kn4HpS5iy2MYymA9T5tBwXdrWwAXg5DoCj1tPiD7ytnBxr9nUnp5hXrstgObFkmy65taVa6SEBeW6+6DpjNQV0JV5WIhYwzlkCsAC4hPHlOHAjfqgZMCEJqZbotRL0j0t6seEwJOE0iu0BynVA4gIv1HU1Zd8s1wTor6bybMiNAVtK9pWmHFbyTMo3NC7Ma1LQYvzHCaSX3e/SsQ3HZwl8JfT/aZn+BiGLWuIMy4mqHUAjgcEtF6S3HcSjTbG8HZ2QL49kMzEVCnq8DW5EuMNZ2KnoutVx0IhTaleDOZ7Lvpk0ARg4y6RP6lLy11V7j9lg2VqWARgji0ACgtsN4VKKYl3B1gL8UoSxums01WgYHUfDYvF7ZbXKwFaZdQTQewVUncv1mM5EYgJUYQtJuQVmCHp4hHk/gpnPh9lDA5rpM1Eb3xFqLrbsiK5VcI4ZrW2CmJcWjMaiVLp7oCbGQ8kz13KG4AkZPW5RvTUGXU8S5tkYrCNoE7Vbij4xbJUT45zOMx4Uo70YGBZnw47gAlR5+VgnxlV3bxYzlAkh18PlCSUeqYneUGhm0Td/M90LawswGAn9ZFym7T+SkvAmS0ouWRoq3ChzrVw14DAUssylf78vSvjJyvXebk/kOtYMzsoXF6QyRyo2/P2ARDRGMFriIcLNamR7VAahrxOl0YUJWsjRr0VVqeST9kxsmMpL6t3RaOOmuUZIpHyLcuBK2QyMAq+vVTKTrjBlxPhcAnP6+D1vFwPrNZjQeAw9EII6eEoivekeXI+Ak6qdRhfDwBO2jMcrzAu7iSjE8N6bmNr4fqbuirCRaVY6aZcRwUdWKqavgjo+AwoMCgwtNUtTCI1FdMEZvzIA3nogGj2o5SWZlfWaOI4uj23ZwTx2qyEKkVhXg0oG9QxgXAAHuKiN/W4GZWez8ApQ0UP64eEU2um5bm3YJAVjL90LKDURdp3xFL6ceUXIwiYDQyaVrG7AKF9JsjuLJs77l1vteZdnmwNu+7IscLC8SHMy43fm8C+a9ZXZwRu7SrJyjzgE0pZ7q1kbM1K1IUWuHA2WLCnk3qOGvZdPkiAS2BUBt2wtphdA7RLwGxLjOEuh2A2DmO9mWRcw7nmfC9ACi97PsSznQlFmwEcw9z4bReOdj2CWqJweUSroG9E5OJuR4TV9lYSFhkv/AUKVkiKBger7zjMSaa5Zxt5AuxKRAS9JyJwAQO/CC47AzVb49w8xaJtvcmbvpHHa2Vel+l2V4XoJ3LAl5OifziX1uGa+OgMaBqBMK+KAkgvmcd9trbWb7eZHz0KGQxWSWAAAgAElEQVRMs5MdnJG7MiK4shC2yKrqNVNilpbMhasWcBjyMYMQEm01kTK4GoDW2iQXo6aFh5mNcj6ypFO1zGTHTdHvrpObgS0t6iOSfW7CpLjKtumm0ePv1MmzzZCM+VMJ5OAc0LaipbLNcbnnY+HZvNcuCkE7k9RJjRn3St3APb9CGaNQ/YcwIHYCcF0IbaOTyjgnHp0hTirQ1VycpBC1ZKPlovFIMlYDTo+YJAViBcQRhFdkxihmEVR3iXp/Y8sE4hACYmQ4Y5f1Hu7kGNwIJby1FcPo/83ZX3LNE7vruuhZ8VpUVdfwDGvfkxycjKwrLOcUMU2oNc5/nNfizGUlYSoruGPhzxiUdV9k1sSOTw4xRDiiPtPKvRwGA8L2bOXrTJ15AC7e9h3OCPIMcJ2aBiDXCV3cWGXj1hYjbnWweF8Dfbns4IzclWm/PStdeNLL2GTBHCjaAiAWhkFPSRUUbZeiVwZ6WfBlppMucwS3pVBdcx7BbN6hs3y4lLhCqCplEpitiPQ33eemQWUO9qQVi8u+zBYoO55TFdhdnC67J00LTGf9orUAxDSHFKrp4gDB+QAL3SAsTgy2vATGKaLcDOG4gg8MV5ZSVnQqY+C1iyyLYAU71S8esQBiISBT1wK+ZVDbieTBFo6iHU9wU0IGGOsoC5n3cM6rZksEFcLISiEi2nhua04czWUCl0QsBFurB6/Zy95hpRgVy5Lxq+T/rrIYwPUC429ZSGkvfUf5Ye7YEb/RkgMY9PozkuCglcOsdBaMJ6h3nJPCs8fN1/Ymo/45NYmGpBDODIp0f87IwXaygzNyV8YRBO7JmOJCxJYDBm3Soiy6NxBhNnFxgLBrauRholZEJLX+TXg+NEqTHxcyNJlRWUm0DEi78aoFlwju5Djp0CQBOdoxzW0p6E0zIzoRkrufmUbo8AFYZ9Nt+QtM8Mt4RPR5EIrrbOIOEdy28ozEhejRlE+B4TXb9B5wBNoOdDWH7yLclSgay3Pb75e0ddiifCIBVhKA6lmNk3+u0E2A0fOI0bOA4koyOun5WqzrrxwO9wKEC58TsTjeTSvFmTYAhbwvRAT2HuSNRXXhWJs+i9wXfpKzZ63BzKCo9PeWIUolShWIHI/SfTQtICjDaDo/QBdg2rx9HBI4cN2II2yltB0VtKkQ8Uw2MPQm1yfLWg3mGtMlsjFdYyPW5yin3M+Yk9dy3dx0Knoe0tVTDLvQgN4xui87lGl2soMzclemDyRrGn8R30Hew52eSJRTeKGIB+DqJnEvJFbKtpMIEQHhPIAurwaHoqpSwbKIeDW7kfk0dVZk5aJlvBTu5Bh47RXAO7g3nyK8/WRpCpWKEnjPK2hfO4OrO/i3zsGz+XVp9k0sV2b1HhunVrKSxnBwe6pDm8X+eg14X25xjMRqa2WQqgJ3QRd+0uNIViteXoH8vJ90AUlJR0gGzspxkPIAOSOjW1aKMMcggjsRFqSvfQPkfQ+WzLBEBM3iECmwWtpCw9Pn8izPZnj1ySNwWYDqJpFZxaurvsRgafN112xZy2me3TO20G6kDpxioBxJBrHknsNlF1ZbBiBqeT3PiuJpSOnOAYCvpojPnmsLtFe5hxHo9FTe6UJB5ypfAFOjtdJEJR1AxCwK4Bs4JNw0iJF7Ub9sf9sYeQ/34FTkKeZzxOcXN0tS6PPixqPEEROn04XysDrMzNdLl8lpCdfTnvoM7momFimt3oVcm6ZNDvVGmlH7tIMzspMdnJE7Nc2GLAJNUxRViphUWYDH2qUTI6hTRk3SMoCjwcuc3ivbj30Hvgc2Lh2OYkVoA2Es78AjwUe4Yg2HQ1kgjL1u5npOlR0sKfK+rGZZiRxAfEtLET+gInAxLWL9l7TGHoKkn/31e50m3W24Xswh1UWFsxLeYjSZwKP5OavjEC8jnMq5p7JMvAMsA+t1JwJF4S4hOw/Sa6et7IbD2XpyzwGQhoWy9L+WJFn/TQBfLe2YGGZyRArfj22ZZVmtrc4/LACKd1jAjLeGQrh53kgbaOnJrxZ45BiBTTmCFrSldjKi1KEj5Rkb42ZK2gd7eezgjNyVEUm/vaUISVhPyWv0PxqBH50hnIwQRwXaE7kVk/+PRMMj5+0IWTRxLdqIQyG6NZwS+QLKN0VWTQt3Ie16PK+XZkVsW5rOUT6vQAP+gB2wGxkQTk5v87T60lbnPUcYksXIft5HN4PdwwDJKCn5FzdtnwpP39UMUNf1mAQr6Q1AklbeucFZWjJuWeQ1s+LoeveM0s4vS3lzCIh1PQBspn2m76w+9lZmDn7bCXg2KlZEo2JnZZK6QXz6rCdUuynzMOD/sDKROTgGyGxgwpOppAq9VqkkFgWUXJsyt+vB3Aulo4Sj2EABfNgdkmGD1ik/L5s3ckbosgC1RSoRrsxapbmm7Z+5a63S5sfd4Ngo2JyKQlSYvQNPZ4jTWzCk2v3PrevkXUpg/XvMNhwYWHeygzNyF5ZeRtb6fgbSqkpJ5U4maB8doXlQops41GciOFZejlC9CSQsgU1YKbJaWACtG2bTCHQTZlHlKMH5hTgjN3SLcGTw1Qyu8JLRqevEX7JLRJrOY1sg4n1MNvtirFw0LUHE6fTm0lIqVVCvP5PRmF/bbhFbsuE4YOWdwd/WUIYzC1/N5kfb3TgDbTbaqg6ACllg48MThKMKxfkcNJ8DmOtmNzkjrlcqZo3w82dQNYqY+pKQOIUERMuaOHFaprOUlbFOtqUlkCUA1ZvGZ+c+fP83dbDC4DPyXkpJZSHBUVH0+KBVcwkzuG3WCmDe5IwkR2Q8Aj04leMDwGy22zvMnIDUlAGYB3QJ90x6xpxhlnbc/t1oL3E+fL1dXl7iZ3/2Z/G+970P4/EYH/3oR/F7v/d7g+80TYNPf/rTODs7wwc/+EH84R/+4b2Nz4i7qCiFwGg8Ah0dgSYT8LhKlOwUTdmU4Vqtc2fMhOQ93Hgk9dq7Tj/mnRI5R8TK7wtnANVtv0BayvQWJmvyN0mEsE3ZatP0tnUO2CJqKfTbmPMJoEi2SHnXp8Gt8+au2EnXGVFylNKCp1oybHV6ZlAX4dpwnVXYzq8UbRzK26Lz7jKO/fNnmcmQKQNreSjt27rJiiI5RjBnZpOsxya2yz4yB+uaaUs2NW3KIDCvaIve+HiUgLtUViufk5StCFKSvnUgYfen6zQT3bcLDxyyg73U9o7OjPz4j/84vvSlL+FXfuVX8OEPfxi/8zu/g5/8yZ9EjBGf+tSnAAC//uu/ji9/+cv44he/iH/4h3/AT/3UT+Gv//qv8f73v/9uBmWRhyOJ1JyDOzsBPTgTYbGjEeJEaK1j6eCaiGIWMH4zggKj+PozxOfnqZOGHMEdT0APziRL8fxCxOgWJ7l9LtyKT4jhCiCn4Nkbvns1FUfEgH6joi8t3YdDYRPOS0ICNTAF5QJIwM/bGnkPd3QEeAfqOhGfy9lSd1m4nIc7PlLqeQFrAlCOhk6Ix4wqfptM3J6MlK9HflEn3iseQyNgUSwWdlhfFOC6kWxN26XWZPIedHYqrczTGeLbT1JX2qLTzU2TxAdTaYQcqOlLMOKAeNDxkWjutB342fOe/+embNI2ZlmrbY2cdKqYhIRl4RoAT56CzgsBLCeQ/ZbjzDIt5B3c6SmoKuG/5TXEN98Sdub8NNRxiFEI/Mg7LQPvfn2Mj0W6ChVkXBbAZKy6XldAS7eqnGxl6/B4m2z/LrR3rDPyJ3/yJ/izP/uz5IAAwA/90A/hH//xH/FzP/dz+OQnPwnvPf78z/8cv/iLv4gf/uEfBgD86Z/+Kf7qr/7q7pyRzBwRABGRiqcTcFWgOy7RTRTs2US4wHDzgOL5TCayi8v+BTYysaIAK9ERZvN+gllJZb0H20IoLjatUM4rFmYZuHJb24oO/iWOfJLyLWl3xz5ulT4TVAgPBsU+M0AIw/bJbcZpgEYFVgPogaAGoia6mYL+rsx4I/IuEqCvz3Omp2MttFbmVFp9KgugrMBHY/CkglsEliaQeP/vMqeLg0rTl+ogeS9A9Mmo54I1/M66h/gOHXZy1JeRFiUkYrgdTgPonUI7jteuwMKBTybAkyVZ3ASYboFZXIlB2sq03MUkIoqJ80gzXzy/ZzAr3xIz8i51Rl7eWXyN/cEf/AFOTk7wEz/xE4PPf/qnfxpf+9rX8Jd/+ZcAgA9+8IP4whe+gDfffBNf+tKX8Bd/8Rf48Ic/vNMxqax6HozBHwg0GsGNx3CTCdxkLEypZQn2Hnx6jPbxBPNXx6gflWjOPLpjB/Yi+uU6TZlqt0QywwM0LehqBrqaCcFWTt2+b0fESgq7puJDlsLeV0rf+Yz91GuqfgHQ95LWWdmAyEs0Xna2EIC6lohSKdIRI9xoBHdyLG2OAOCL4fN6w33lyL1eSNf1ZQYrSxhI8L4nymzMeQfYshIeESVgqWUmUhttUQCj0ZC47YZzuRGEac+aXZMQwPMadDkTEHJyRDZ4Jje9nqlEtfn7ZJ18ewNbXzsAD4/TdeBZLUKGF9P1arwk5T83HsEdH0u2b9n8Cmw+J+Xvm/EjWXbrYC+1vWMzI3/7t3+L7/qu70JRDE/hIx/5SPr793//9+MXfuEX8KM/+qN47bXX4JzDL//yL+P111/f6Zju9BiujtciCipKeZE0cjNkPY0qMBG6V09x8f4RugnADmBH8DWjmEYUVwxqg3AzGL2zWQZspEyh9Vq0ZhPVPiYcLTHJwrmEOGrpJtRnQkyJM4Rerv0mUb9V+6Q+EnfG6gpI5AP03Cu37em/a4sB3Gx2HTc17joE5ZrJafjp9EQyG61QbrvJCK4qEWs9vt3bZel4jqIcTQ6OYx9V5l0JBhC8reM3cCTXZQ6GuIfcCUnaPoZl8RjgB/J9oCpBx0dg7xJPyM5mQGJAdFeUK4adG2Zj9vVcGkGZF4DsOgBpsi2ymzubXYsIKb+cnwNth/CNN28u71KPRaKjI9DxRDqjnjy9nrGxUucqIHA2lnRM67gC0lx0r3TwdIuDvaSB1V3bO9YZefvtt/GBD3zg2uePHz9OfweA1157DV/+8pfx1a9+FY8fP05/38nyrMHiC2FAP5toveqlgMAqrR5LjfBInBL5Zf2ENWyPvLkj4LZ06Glx8zLQ5JSsGqfVjBejFqNkXkefvYm5nvY8UZDnx3+ZnRHgbsYXjUWX5F4BgikqvIiWAaujwWXPCS8w2S6LQvepmbGuJXXx64vjUWE2AP1+spZwy0IlR8YrCZm9ozcceqvbFVne5cjXJRn2+Wwaj8mLKJFtalrWIvBmDpN2ICUOlwK3z6RqAMM5d8p9t8oeyjQ72TvWGQHWtJBlf3PO4UMf+tDG+33jjTfw93//9xiNRijLEq1Sfv/PX/ofcDI5QQwRoQuIIcrPIWpWOyJGRoyM0EW89u2PEZnxgX/xbWhLhwD9mzEOzjvENoC7iNBJzTvO5gjTOUIn6P0QAmJg3a8cL2bHTp/Zd4L8jSMjBEGWy+cxfd5vI3+zzxgErsZg7xE7iS5jCAizGmFeJ86UGBnsPLgo0sTPANh5xLIEE4GbFmFeA11Ed3WFMNPttW1ycFz9LO07RvhSqLedUaSnzEhU1H4A2MS17lCPZhPbkOY8UWkDfQS3h4mHlXNDgMRtT2DWND2OAlBcxZqdkQNGI/CplHqo6xCNCdhUpreY3KW0SX1Uq8dIWbM1rampUwjonaGUoeHUOSFyCtbmKSWShBvwHnQ0QXhwBDDDzVrJHnVhpUO00Zpo1zP0v1vnG/QdWFyUnWr9OFUFdppJdeokOeV3sc/s71QUcIWDsBfFwX7IubRd/rnz7trf5DPdzjs47+W72b7IEbzPv9v/7O0z79Ln6T9H+PYPvw9nr57hp375k+nzfH/9fx6+LOAKD1cV8GUpvkkIIHD6nvcOrtBx2u+O4Ap/bZ++8IPtfOnhnIMvHC6vLvFt/8EX8dWvfjXN6XVd4zu/8zvx2muvbXCzNzeOEXyLzMi7tbX3HeuMvPLKKyn7kduTJ08A4FYZkM9+9rP4/Oc/f+3zX/7Er2JcTdJD7wt9OcoSfjyCLwv4UQlXejjv8V/85H8CZuDP/+3fgE4mcKUHFfryQCQ1HEHUfSuZFMppA5rPJYCDBg/eo6yKbGKwyWP5y07O9ZOGTRxrPrfJyY8qmaSIpPpDpEEZDSdRL8ysabLMx0PiDOaT4K72b5vfTT9HdVhkLbKf+5bEpf+ao8NY+Hz4HeMrWPx5sC2zYCWX/Qxc3zYHQrJkxERbBuAYBt+X7yHbV7bPwQ/Z39L/9AfD6DDj+GyC/+7f/CtMz6fpO2v9HiKAIJT0IwWwppZt7ktiZF/POhSy1TtfyKnQVtzEuYOeJAzoAZ/UBxD9v0hCffY56Rj7/aAvizpZqm2slL0fKAVYSSQ4LWJOvBS2DxvX2eMTRGb85//qX6bnPf97/5k4F1j418aYFnf7+x4sBRIKjk1OvQYmuUM/cPizgGTwd32XYheyAEb+bj/bNosBkB0rfR4iHr73AYqqwCvveyQBm/7d9t21bT8Gcgj6DkYGQhsQmwbdvJHj25h0P3kAmALBzoLB4WeRJfkVg+x73olT+LGPfWxwPT/96U/jc5/73F7uzcFuZ+9YZ+T111/H7/7u76LrugFu5G/+5m8AAN/93d+9874/97nP4Td+4zfQNA2apsGbb76JD33oQ/jq//WPKKi8voHzifWx52QgfPf3fQht2+F//1++hHhyJChzT4kCmtoA6iK49IgjkUP3b50jvvWkp9KO3JMSQTEjJhu+qhVv2/SwllrceAT32qvgo3HPQdAF8PkFwvn58JTHY9DJcZ9uZxbA7tmJtNXVDWhWy3ifn4Pruo/cCP1CoT/bhG0Oz7/4T/9j/Le/9lP41//ysyhOjgSX42ShcY4ScNARUm3YHCpygqFwypSZvoc+s5IvKLSw2EgTVP9zWuwWFz4nkSpIxAp1s3RN830BgDNBL8NdGLgybdIv0oMFH8OfZXHOvmfYGuvaYcZ3fd+H8H/+H3+DN/7xLaz0RhbDf1WapuMj8NmRLPgXU/BsLo5E6NI+ch9Idp3t234mUhCt0+2FDIqcU9p57rkhstJKfgxyHmz+BTkAxkysThxR9q9u3rZ9S673st2jM4SzibxPF3OgbhHnNeLFZWI6Nuf0v/zXP4bZ5Rz/6+f+t4Ezak7l0BFG5hQIFT2XFeCcZDln82uObQz9+dq/uSOR7y93KPZu2q23kjRvB/vYf/Y6/vv/6b/Gv/lvrgdzuRkDq3HlpOyZtmLD2JjTBmuyjwMPWB1Q7+Wd8R4dyb6+8pWv4D3veQ+qqkJVVXtzEgd2KNPsZO9YZ+QTn/gEfvM3fxNf/OIX8clPfjJ9/oUvfAHve9/78H3f93232r9zDuPxGONcrnuFURY9AZA0sQNgPkGIoHmtQDQPLj0QkbpnqAvwjb54XUiIf2caJNo2CGa4yxl4KrwecTq9hiExptfkrABrH25TvaTJGHw8QTiuQG2U1HHbAbPrj0msa5CJk9l5j0cC2o0RdDVTvhQD9DE4cK/qucYun10hhoi3//ltkH/Wl2nyzgq91tYtkNQ7vYd7+ECcqhCAi/+/vfMPjqK+///zvbu5uySXkED4lRbaapCaBm2ngpEqxnGC/G6ZQgntdBi0lUT7Q8ehSMMIiK3YDjPFdphafoiKjZQAVaHoVH5J5UehVMcg8qlfsQTpgEggl0tyd7v7+v7x3t27Sy65u/y6hHs9Zm4g73vv7ntf997d175er/fr5ZeBta0BmC2t7U3ziV78dnxMxI1OVk/uJKguko4qnnbUt6Ng04jcJVHBmiLs0rj/qQq88+oJ/L93P4meD3EDRgWUrCwouTnyoehvdsoBdCm3iBKxDNbOyZGVCWR65By+1iiz/dq/IxAVA2IXTrRjkABIV1SMqr5y04gHa8TvpQzKlcqzHdtBVkVtK7Oq2dLqVKa+Z/6d8F9rxumj/5f8+QLhFweTwnll2jwsnfNM5YPHtLL52iTqbuwMosRcXEKBcLvkC4ydHE6YEFmZEETSxeVvlvKzUxwAceewc/+zl9QD1uom6SIcOnQocnNzu35+iWAS7LIKXYKVkYHF1KlTUV5ejqqqKjQ2NqKoqAg1NTV44403sGXLFqipLJREJmAq4RUhpumscCAg3G4Y8oFukvRfA1ZgnpWnwy1zGJDHBTPbDQBQdAMiFJJ6t12kKxLnLcPKLZCI/9HOMaFp0krjUqEAoJAqkwbFCtqlcJAa2TeLkEwJL4SQb6cJViKNR7g+jxIOSrP/rwgpAvsGpGkyoVWmG2aOByJkQAnpQEBaSETAXuFj7zy5N85wgK/aXiYJnYydvr2T7SJjJUTsmBj7Ad2uPkjbqqiR86HtXIk5PvkgMK3VOjLupBsFAe3f31QdRRFuF8jjkkHfLRlSuVAAYT0Y7d/HCT616vA4dW9kJ+uf6L/bzlE7fT75m6PnkK24uTKktSQY6rGcWB0qbW0DikXXVpr1KPax7SW13YxXkDpgAtqIfa06RQ1NAGrUC5202sBR/sMP+Rgyi1A+o16QYL24dKVWFtOnDFhlBAB27NiB6upqPPHEE7hy5Qq++tWvoqamBhUVFX06DrLftgwjfONU7YRl1s3XnSFNt1kuGFkaYBBUfwhKUC6dFMGQEykvs2BmgLJlojQQQQRCEAZBtAZBVkCh7Bxx47Yycwq3SyoDjU3Wc6zzG55tyhRCgHQTim5CCcrlxsIKZO30hhlRwI2aW6RSE8sCEQ8RfptxsmvaN5nI4wBy1UdE4KBzHhmaTDKX6YLudcnzCGU67YpmydNOVmUY4VLjrYH2pc/bnmpEMTm7TH0s90fUUkwnz4M1/nhycVwWHSgBdoIvw2xnGYsicqWCMAFkJGQhIUMqvACicnV0B6f4n2rKeaWqMug21P7BLSIUTkfps/YBtHEJAbEVkajvLStI0DqnNla2yH3b/RN6oMbDmgdhBTR61Y+TNM0wYivuEZYd4XI5yq9T7yZe8b8kxolkqz33BELI1U12fhsyHYWQQuG8PGTKZH7WHzGvN0dOtsJr58gBIgpI9hFkKUzd2j79GNDKiNfrxdq1a7F27drUDsTKJUGIMBO6AKgKTFWF6fXAzHKBVAXBvAwEBqlQdMDzuQq1WYfqD0D4/CDdcIp9kTsDel4mjEwNmi8I9UoTREgHNfnD6/CtdPHOmv2MDIhBOTC9WVCaW6VrKJDACgrLFQRVhTAMudqgNQTR3Cp9+sFO8gUA4TfQgAEjooBb0heVUGQG14ilvM7+nf/bFWljbK8oMnuo2wXD60ZwkAYlJPejBF0yRqfVAygChtcDI0uDCJrQGmW1XHHNB/H5lTiVS+UY4hVdUzI9MrdFMGS5hkRyD4/O+kVYpTo6voyBUcLpsYGw4hSZo6WDYzu5SRJw60SOq0NM+cAVigBFWAbJyktjj9vBfru1lWT77dYy6XdqEYkBhYLReS9EtPUloSrTSVrChCaLYsI0ZZI6PTx/IIRMjpiTYyWZuwqztb3LVVr6MmQiO02TCn9IFqKUrqVuKiNRv1/3rTQU+fLQ6WGFU8OKiGSOG3tOtI1hiVwFFjNGzi6/YWWA1jQgFIQZWfm6NwpcdgCZBOqGm6YrNbmampqwbNky/OUvf3FezB9//PE+fzHvDgNaGelX2EF3hmHl6CAZ3GgH1wkBUuwP5CciDtGKcJNf2G4RC0FWfIldDbetlm/lIHDKmFuJ1RJGkYGL0NooAE421SS0/G5c9G2LenWpUJ4lA5nLRQaX2nKXtURUkBAwXSoMtwpFCJiZGXKVU3NG2GzclTod/RV7NQrkcmlZxybONl2VfQIWNDIMCCs4O+bcshUR27IjFFkbJ/kRtTl+G6VWiM4fcqnGCsB1guJVBSAVCMV5OUh4/z1g/WkDJRozIjvLj13OwMkW24ELpjNsRdZ23dqb9nWOkRSRSJ22/g4rIz2NbUY1DHnTDxkQrSEomgIyFGT4BIShQhiEjKaQtEK0BMNmd2FCCB2ihaBdFtA0VVoorOJ5tg8/KoiSZGE7MgwoVxuhtLSCQiEZBBbvoSoERFYmKD8XpCmOBUclAq5RVPXgXhedleYcQulagi2rYqfQDShBA1qLCaHLAoSC4GRvhSpguhQYLgWGW0FwkLwMslwalEAQCAWlyybSIpTgG7jd1wwEIKw8H72WjruT4wMA6ZYiqWkyPsPtlitZWgMyJigyb0iXLFmWW83y53eacROIsKDJFN1RDyDbJWO5JOwYJuFxy4dwIAi0tFjXSSC536Oz8Tirl8LBmx3uMsljkWEAdvBvW8sLEcyWVigR6cujEAKKN1sG3QJOlV2yywDYLwrdQGgZ4VUsPeSOc6xymtZpwDMZZtil29ravay1ZMrEiqTK2l2qagU59/F1FzGe7rlpkts20Tpt/R1WRnoD26pApgxeDYYgAhqEJh+KSlAu6VX9MgW8CIak2Z9M6fLRIQNarYJ5ZiAI09/cZpKqYX+6LhUR6CaMkB5erpeIlUIoEB439FwPSFVguFWQJqAENGm0iWWJ6S0oojBZVy0sVtyOEjSgBgyZU8IwHeWGLMuJqQqYLgEjQyCULa1VaqsbWZezgFYVZJhyO8CSu5qYKd8+j0Cgz4qExjo+QCDDkqVlwqZMt3QdKUIqfda8cYrrJZM8zg4WtFwJyay0iV31V8oYgGVud8m4qSwPoFmBjaEQBPSw1a8nHjL2PtooJT1iNDA7d+dRIADDWsHTDuu6pJwsuRrP3yLdM5bbtNvKgxCOa9d5cPcEtnXWisfq6DciwwC1tMgA/WCoe24U2yrtrCpTnHtpKuhrN01nddq+//3v49ixY5g4cWKXx9NXsDKSAPbk0BFCwk8Yw0Rzix/NzQHoZgCmIdf6klBgQoUwTJARAowQhBkC2W97pgr7pmxr9UQhmBSKuvkIMq04SgEiA0TWmwWUiMDORFZPmJd5oaEAAA/ISURBVIAZgK63gkiBoaogCChGKxQzCFAQJoVA1EOm4QQJGAE0+nzQkziuQgqEmQFhEkwjAF0HhE5QdN1SSqSlB0KBrgvoIQ0GBPSQdG3pegi6GQAQAsGSORC2NNDAcd00+hoRNALQKQhhykRfZGrywWYGQBQEkQ4iU1or7JU4CSMAKFbyMNOagwkuce5gf4IAkIBCQRlva5ogQwBClWM2gwDp4fnYo79FWPtobvGjtTWQ1NzrccgEzKC8L9i/mR6KuB67+9YvoJDtRjbC95duyjQQakFTUxP0eL8RmbJqsqmE506c8crtOh+ftH5aS9kjfJE6QtbmvX/96hRAdxRFe6yNbXI7ud1uuN3udv0TrdPW7yEmLvX19fJVkz/84Q9/+DNgP/X19b32nGhpaaERI0b0yDi9Xm+7tuXLl8c87pgxY+i+++5r137hwgUCQL/+9a977Zx7EraMJEBhYSHq6+uRk5OT1JK/c+fOYdy4cfjwww8xcuTIXhzh9Ud9fT1KSkpYdl3k008/RXFxMT744AN84QtfSPVwBhQsu67TX2VHRPD5fCgsLOy1Y3g8Hpw9exbBYIJVlTuBYiwvj2UVsUm0Tlt/hpWRBFAUBV/84heT3i47Wwaf5eTk9H7Wv+sMr9cLgGXXVWwTL8sveWzZZWdns+ySpD/LbtCgQb1+jESzdvckvVmnrS/p4yw36Umv1D9IE1h2DDNwsK9XGiCxVdcD48aNw+nTp6G3CQrviTptfQnf6XsRu7gVP1AZhkkH+F7X98yePRtNTU3Yvn17VHtP1WnrK9hN04sMHToUlZWV/c5cORAYMmQIy64b5OXlobKyEnl5eakeyoBj8ODBqKysHDDm7f5Ebm4uKisrMWTIkFQPJW3o13XakkAQ29MYhmEYZsDS1NSE6urqqHTwS5cuHVDp4FkZYRiGYRgmpbCDj2EYhmGYlMLKSIIIIXD+/PlUD4NhGIZhrjtYGWEYhmEYJqWwMpIEu3fvxg033IAhQ4Zg1apVvX48IQTWrVuHr3zlK8jPz8f69etx+PBhFBcXIz8/v0/G0B9huXQPll/XYdl1HZYd0ykpTEXfbzh27BhNnjyZvF4vZWdnU1lZGf3jH/+I6gOAvve975HP56NTp05RZmYmnTlzpsN9rl+/ngBQdnZ2zO9PnjxJ3/72t2nkyJGUmZlJY8eOpZUrV5Lf74865vz588nv99OBAwfI4/HQ3Llz6cqVK3T69GnyeDz00Ucf9YwQeol///vfNG3aNBo1ahR5PB7Kz8+n0tJSeumll2L2P3ToEE2dOpXy8vLI4/FQUVERPfnkk1F9rge5JMrevXtp4cKFNHbsWMrKyqLCwkKaNWsWnThxotPtOpt/6SK/xsZGWrx4MZWXl1NBQUGH9T2SkXG6yI6IyOfz0c9//nMaOXIkud1uuvXWW6mmpqbL/dJJdkzypL0y8s9//pPcbjfdddddtHPnTtqxYweVlpaS2+2mw4cPO/0A0L/+9S/n7zvuuIO2bdsWc5/nz5+nQYMGUWFhYcyHwalTp8jj8dCtt95KW7dupb1799Ly5ctJVVWaNWtW1DFPnjzp/D1s2DDasWOH8/eECRNo586d3Tr/3mb//v20aNEieumll2jfvn30+uuvU0VFBQGgVatWRfV9+eWXSVEUqqiooNdee4327dtH69evp5UrV0b1ux7kkihz5syhe+65h9atW0cHDhygbdu2UWlpKWmaRnv37o25Tbz5ly7yO3v2LA0aNIgmTZpEP/rRjzpURpKRcbrIjoiovLyc8vLy6I9//CPt27fPkeHLL7/cpX7pJDsmedJeGbnvvvto+PDhURaJxsZGKigooIkTJzptaFPx8d5776Xnn38+5j5nzJhBM2fOpAULFsR8GFRXVxOAdm8BDz74IAGgK1euxDzml770JTp06JDz9913392hhaG/c/vtt9OoUaOcv8+fP0/Z2dlUVVUVd9vrWS5tuXjxYrs2n89Hw4cPp3vvvTfmNvHmX7rIzzRNMk2TiIg+++yzDpWRZGScLrLbvXs3AaA///nPUe3l5eVUWFhIuq4n1Y8ofWTHdI20jxl55513UFZWhqysLKctJycHkyZNwuHDh/G///0vqf1t2bIFBw8exLp16zrsk5GRAaB94aa8vDwoigKXy5XUMQciBQUF0LRwAuANGzbA7/djyZIlKRxV/2PYsGHt2rxeL4qLi1FfX9/uu0TmX7oghEioYmmyMk4Hdu7cCa/Xi7lz50a1L1y4EBcuXMCxY8eS6scw8Uh7ZSQYDMYszWy32cWGEuHSpUt45JFHsHr16k6r/C5YsAB5eXmoqqrCxx9/DJ/Ph127duG5557Dww8/7FT7vZ4wTRO6ruOzzz7DunXr8Oabb0YpHm+//TYGDx6MDz/8EF//+tehaRqGDRuGyspKpxIoI7l27RpOnjyJr33ta1Htic4/Jj4dyThdqKurw8033xz1wgAAt9xyi/N9Mv0YJh5pr4wUFxfj6NGjTlE7ANB13dHoY5Vm7oiHHnoIY8eORVVVVaf9vvzlL+PIkSOoq6vDjTfeiNzcXMycORMLFizA2rVru3Yi/ZyHHnoIGRkZGDZsGB599FE8++yzWLRokfP9p59+iubmZsydOxfz5s3DW2+9hcWLF+PFF1/EtGnTuApoBA8//DD8fj+qq6uj2hOdf0x8OpJxuvD555/HrM1jt9n3xUT7MUxcUu0nSjUbN24kAFRVVUXnz5+nc+fO0QMPPECqqhIAeuWVVxLaT21tLblcLjp16pTT1pHP/uzZs1RUVETf+ta3qLa2lg4ePEi/+c1vKDc3l+6///4eO7f+xH//+186fvw47d69myorK0lRFPrtb3/rfD9mzBgCQE8//XTUdr/73e8IAP3973/v6yH3S5YtW0YA6Pe//31UezLzLx3pLGakLR3JOJ0YM2YMTZkypV37hQsXoq7TRPsxTDzSXhkhIlq9ejV5vV4CQADojjvuoCVLlhCAqACrjrCD3R577DFqaGhwPvPnz6fs7GxqaGigpqYmp/+8efNo2LBhUW1ERJs2bSIAdODAgR4/x/5GZWUlaZpGly5dIiKi0tLSdtH2RERnzpwhAPTMM8+kYpj9ihUrVhAA+tWvfhXVnuz8S0cSVUY6knG6UVpaSuPHj2/XXldXRwDoueeeS6ofw8Qj7d00ALBkyRJcvnwZ77//Pj755BMcPnwYDQ0NyM7Oxje/+c2421++fBkXL17EmjVrkJ+f73xqamrg9/uRn5+PH/zgB07/d999F8XFxe1iQ8aPHw8gPfysEyZMgK7r+PjjjwGEfcxtIcs9oyjpPVVXrlyJFStWYMWKFfjlL38Z9V2y84+JTWcyTjfGjRuH06dPQ9f1qHY7hq6kpCSpfgwTDy1+l/TA7XY7F865c+ewdetW/PjHP0ZmZmbcbUeMGIH9+/e3a1+9ejUOHjyIPXv2oKCgwGkvLCxEXV0dmpqa4PV6nfYjR44AQFoEH+7fvx+KouCGG24AAHz3u9/Fn/70J+zZswff+MY3nH5/+9vfAAClpaUpGWd/YNWqVVixYgWWLVuG5cuXt/s+2fnHtCeejNON2bNnY/369di+fTvmzZvntL/wwgsoLCzE7bffnlQ/holH2isjdXV12L59O2677Ta43W689957WL16NcaMGZNwemKPx4OysrJ27Zs3b4aqqu2+e+SRR/Cd73wH5eXlePTRR1FQUICjR4/i6aefRnFxMaZOndoDZ9Y/ePDBB5Gbm4sJEyZg+PDhuHz5MrZt24atW7di8eLFGDp0KABg8uTJmDlzJp588kmYponS0lKcOHECK1euxIwZM3DnnXem+ExSw5o1a/DEE09gypQpmD59Oo4ePRr1fWlpadLzL53Ys2cP/H4/fD4fAOCDDz5AbW0tAGDatGnIyspKSMbpxtSpU1FeXo6qqio0NjaiqKgINTU1eOONN7BlyxaoqppUP4aJS6r9RKnmzJkzNGnSJBo8eDC5XC4qKiqiZcuW9YiPvbMAwn379tHkyZNpxIgRlJmZSTfddBM99thjdPny5W4ftz+xadMmuuuuu6igoIA0TaO8vLwOkxs1NzfTkiVLaNSoUaRpGo0ePZqWLl1Kra2tKRh5/+Duu+92YplifTqDA1hlYq2OZHf27Fki6p6Mr2d8Ph/97Gc/oxEjRpDL5aJbbrmlw3TwifRjmM4QRLxmkmEYhmGY1JHeUYEMwzAMw6QcVkYYhmEYhkkprIwwDMMwDJNSWBlhGIZhGCalsDLCMAzDMExKYWWEYRiGYZiUwsoIwzAMwzAphZURhmEYhmFSCisjDMMwDMOkFFZGGIZhGIZJKayMMAzDMAyTUlgZYZjrnM2bN0MI4Xw0TcPIkSNRUVGB//znPzG3efbZZyGEQElJSR+PlmGYdISVEYZJE55//nkcOXIEb731Fn7yk5/gtddew5133omGhoZ2fTdt2gQAOHXqFI4dO9bXQ2UYJs1gZYRh0oSSkhKUlpairKwM1dXVePzxx3Hp0iX89a9/jep34sQJvPfee5g+fToAYOPGjakYLsMwaQQrIwyTptx2220AgIsXL0a128rH6tWrMXHiRLzyyitobm7u8/ExDJM+sDLCMGnK2bNnAQA33XST09bS0oKamhqMHz8eJSUluP/+++Hz+bBt27ZUDZNhmDSAlRGGSRMMw4Cu62hqasKbb76Jp556CpMmTcKsWbOcPrW1tbh27RoeeOABAMC8efPg9XrZVcMwTK/CygjDpAmlpaXIyMhATk4OpkyZgvz8fLz66qvQNM3ps3HjRmRmZqKiogIA4PV6MXfuXBw6dKjDlTcMwzDdhZURhkkTXnzxRRw/fhz79u3DokWLcPr0acyfP9/5/qOPPsLbb7+N6dOng4hw9epVXL16FXPmzAEQXmHDMAzT02jxuzAMcz1w8803O0Gr99xzDwzDwIYNG1BbW4s5c+Zg06ZNICLU1taitra23fYvvPACnnrqKaiq2tdDZxjmOkcQEaV6EAzD9B6bN2/GwoULcfz4cUcZAYCGhgbceOONGDFiBN5//32MHj0amZmZ2LBhQ7t97Nq1C2vWrMHrr7+OGTNm9OXwGYZJA9gywjBpSn5+PpYuXYpf/OIXqKmpwYULF/DMM8+grKysXd+SkhL84Q9/wMaNG1kZYRimx+GYEYZJY376059i9OjR+OEPfwhN07Bw4cKY/QoKCjB79mzs2rWrXV4ShmGY7sJuGoZhGIZhUgpbRhiGYRiGSSmsjDAMwzAMk1JYGWEYhmEYJqWwMsIwDMMwTEphZYRhGIZhmJTCygjDMAzDMCmFlRGGYRiGYVIKKyMMwzAMw6QUVkYYhmEYhkkprIwwDMMwDJNSWBlhGIZhGCalsDLCMAzDMExK+f/kj8ehy5uaTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astropy.wcs import WCS\n",
    "from astropy.utils.data import get_pkg_data_filename\n",
    "\n",
    "filename = get_pkg_data_filename('ft1_pointsource_powerlaw_2.00_tsmap.fits')\n",
    "\n",
    "hdu = fits.open(filename)[0]\n",
    "wcs = WCS(hdu.header)\n",
    "\n",
    "plt.subplot(projection=wcs)\n",
    "image=plt.imshow(hdu.data)\n",
    "plt.colorbar(image, label='TS')\n",
    "plt.grid(color='white', ls='solid')\n",
    "plt.xlabel('RA')\n",
    "plt.ylabel('Dec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0c0afec4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-09 17:53:13 INFO    GTAnalysis.sed(): Computing SED for PSR J0922+0638\n",
      "2021-06-09 17:53:17 INFO    GTAnalysis._make_sed(): Fitting SED\n",
      "2021-06-09 17:53:17 INFO    GTAnalysis.free_source(): Fixing parameters for PSR J0922+0638        : ['Index1', 'Cutoff']\n",
      "2021-06-09 17:53:17 INFO    GTAnalysis.free_source(): Fixing parameters for 4FGL J0924.0+0534     : ['Index']\n",
      "2021-06-09 17:53:17 INFO    GTAnalysis.free_source(): Fixing parameters for 4FGL J0922.6+0434     : ['Index']\n",
      "2021-06-09 17:53:17 INFO    GTAnalysis.free_source(): Fixing parameters for 4FGL J0935.3+0901     : ['alpha', 'beta']\n",
      "2021-06-09 17:53:17 INFO    GTAnalysis.free_source(): Fixing parameters for 4FGL J0914.4+0249     : ['Index']\n",
      "2021-06-09 17:53:17 INFO    GTAnalysis.free_source(): Fixing parameters for 4FGL J0932.7+1041     : ['Index']\n",
      "2021-06-09 17:53:17 INFO    GTAnalysis.free_source(): Fixing parameters for galdiff               : ['Index']\n",
      "2021-06-09 17:53:32 INFO    GTAnalysis.sed(): Finished SED\n",
      "2021-06-09 17:53:41 INFO    GTAnalysis.sed(): Execution time: 28.36 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'name': 'PSR J0922+0638',\n",
       " 'loge_min': array([2.     , 2.22418, 2.44836, 2.67254, 2.89672, 3.1209 , 3.34508,\n",
       "        3.56926, 3.79344, 4.01762, 4.2418 , 4.46598, 4.69016, 4.91434,\n",
       "        5.13852, 5.3627 ]),\n",
       " 'loge_max': array([2.22418, 2.44836, 2.67254, 2.89672, 3.1209 , 3.34508, 3.56926,\n",
       "        3.79344, 4.01762, 4.2418 , 4.46598, 4.69016, 4.91434, 5.13852,\n",
       "        5.3627 , 5.58688]),\n",
       " 'loge_ctr': array([2.11209, 2.33627, 2.56045, 2.78463, 3.00881, 3.23299, 3.45717,\n",
       "        3.68135, 3.90553, 4.12971, 4.35389, 4.57807, 4.80225, 5.02643,\n",
       "        5.25061, 5.47479]),\n",
       " 'loge_ref': array([2.11209, 2.33627, 2.56045, 2.78463, 3.00881, 3.23299, 3.45717,\n",
       "        3.68135, 3.90553, 4.12971, 4.35389, 4.57807, 4.80225, 5.02643,\n",
       "        5.25061, 5.47479]),\n",
       " 'e_min': array([1.00000000e+02, 1.67563723e+02, 2.80776011e+02, 4.70478736e+02,\n",
       "        7.88351685e+02, 1.32099143e+03, 2.21350241e+03, 3.70902704e+03,\n",
       "        6.21498379e+03, 1.04140582e+04, 1.74501836e+04, 2.92401772e+04,\n",
       "        4.89959294e+04, 8.20994032e+04, 1.37568816e+05, 2.30515429e+05]),\n",
       " 'e_max': array([1.67563723e+02, 2.80776011e+02, 4.70478736e+02, 7.88351685e+02,\n",
       "        1.32099143e+03, 2.21350241e+03, 3.70902704e+03, 6.21498379e+03,\n",
       "        1.04140582e+04, 1.74501836e+04, 2.92401772e+04, 4.89959294e+04,\n",
       "        8.20994032e+04, 1.37568816e+05, 2.30515429e+05, 3.86260235e+05]),\n",
       " 'e_ctr': array([1.29446407e+02, 2.16905218e+02, 3.63454458e+02, 6.09017819e+02,\n",
       "        1.02049293e+03, 1.70997594e+03, 2.86529934e+03, 4.80120224e+03,\n",
       "        8.04507320e+03, 1.34806241e+04, 2.25886356e+04, 3.78503587e+04,\n",
       "        6.34234701e+04, 1.06274727e+05, 1.78077890e+05, 2.98393941e+05]),\n",
       " 'e_ref': array([1.29446407e+02, 2.16905218e+02, 3.63454458e+02, 6.09017819e+02,\n",
       "        1.02049293e+03, 1.70997594e+03, 2.86529934e+03, 4.80120224e+03,\n",
       "        8.04507320e+03, 1.34806241e+04, 2.25886356e+04, 3.78503587e+04,\n",
       "        6.34234701e+04, 1.06274727e+05, 1.78077890e+05, 2.98393941e+05]),\n",
       " 'ref_flux': array([4.03213026e-10, 2.40632650e-10, 1.43606651e-10, 8.57027098e-11,\n",
       "        5.11463391e-11, 3.05235157e-11, 1.82160644e-11, 1.08711266e-11,\n",
       "        6.48775668e-12, 3.87181461e-12, 2.31065206e-12, 1.37896916e-12,\n",
       "        8.22952092e-13, 4.91127841e-13, 2.93099147e-13, 1.74918020e-13]),\n",
       " 'ref_eflux': array([5.16193526e-08, 5.16193526e-08, 5.16193526e-08, 5.16193526e-08,\n",
       "        5.16193526e-08, 5.16193526e-08, 5.16193526e-08, 5.16193526e-08,\n",
       "        5.16193526e-08, 5.16193526e-08, 5.16193526e-08, 5.16193526e-08,\n",
       "        5.16193526e-08, 5.16193526e-08, 5.16193526e-08, 5.16193526e-08]),\n",
       " 'ref_dnde': array([5.96787887e-12, 2.12549457e-12, 7.57007182e-13, 2.69612485e-13,\n",
       "        9.60240457e-14, 3.41995192e-14, 1.21803565e-14, 4.33810441e-15,\n",
       "        1.54504097e-15, 5.50275276e-16, 1.95983721e-16, 6.98007357e-17,\n",
       "        2.48599356e-17, 8.85400983e-18, 3.15340680e-18, 1.12310407e-18]),\n",
       " 'ref_dnde_e_min': array([1.00000000e-11, 3.56155783e-12, 1.26846941e-12, 4.51772717e-13,\n",
       "        1.60901466e-13, 5.73059874e-14, 2.04098588e-14, 7.26908923e-15,\n",
       "        2.58892816e-15, 9.22061736e-16, 3.28397619e-16, 1.16960711e-16,\n",
       "        4.16562336e-17, 1.48361085e-17, 5.28396582e-18, 1.88191498e-18]),\n",
       " 'ref_dnde_e_max': array([3.56155783e-12, 1.26846941e-12, 4.51772717e-13, 1.60901466e-13,\n",
       "        5.73059874e-14, 2.04098588e-14, 7.26908923e-15, 2.58892816e-15,\n",
       "        9.22061736e-16, 3.28397619e-16, 1.16960711e-16, 4.16562336e-17,\n",
       "        1.48361085e-17, 5.28396582e-18, 1.88191498e-18, 6.70254904e-19]),\n",
       " 'ref_e2dnde': array([1.e-07, 1.e-07, 1.e-07, 1.e-07, 1.e-07, 1.e-07, 1.e-07, 1.e-07,\n",
       "        1.e-07, 1.e-07, 1.e-07, 1.e-07, 1.e-07, 1.e-07, 1.e-07, 1.e-07]),\n",
       " 'ref_npred': array([56.84016016, 56.06191361, 45.02553634, 32.76910674, 22.65463985,\n",
       "        14.91201667,  9.06590125,  5.24664063,  3.11052469,  1.8535863 ,\n",
       "         1.10556611,  0.68490224,  0.41063679,  0.24330534,  0.14402245,\n",
       "         0.08544102]),\n",
       " 'norm': array([3.68644808e+00, 6.61388584e+00, 5.40963014e+00, 1.67498942e+00,\n",
       "        3.29079887e+00, 1.34341048e+00, 1.71041480e+00, 5.60460337e-01,\n",
       "        1.53409627e-07, 2.88401044e-01, 7.28794781e-10, 3.60163934e-07,\n",
       "        1.00000000e-10, 1.00000000e-10, 1.00000000e-10, 1.00000000e-10]),\n",
       " 'flux': array([1.48642389e-09, 1.59151688e-09, 7.76858867e-10, 1.43551132e-10,\n",
       "        1.68312315e-10, 4.10056108e-11, 3.11570263e-11, 6.09283528e-12,\n",
       "        9.95284335e-19, 1.11663537e-12, 1.68399116e-21, 4.96654958e-19,\n",
       "        8.22952092e-23, 4.91127841e-23, 2.93099147e-23, 1.74918020e-23]),\n",
       " 'eflux': array([1.90292063e-07, 3.41404505e-07, 2.79241606e-07, 8.64618693e-08,\n",
       "        1.69868907e-07, 6.93459792e-08, 8.82905048e-08, 2.89305998e-08,\n",
       "        7.91890565e-15, 1.48870752e-08, 3.76199148e-17, 1.85914291e-14,\n",
       "        5.16193526e-18, 5.16193526e-18, 5.16193526e-18, 5.16193526e-18]),\n",
       " 'dnde': array([2.20002756e-11, 1.40577784e-11, 4.09512887e-12, 4.51598059e-13,\n",
       "        3.15995821e-13, 4.59439924e-14, 2.08334621e-14, 2.43133546e-15,\n",
       "        2.37024159e-22, 1.58699964e-16, 1.42831913e-25, 2.51397075e-23,\n",
       "        2.48599356e-27, 8.85400983e-28, 3.15340680e-28, 1.12310407e-28]),\n",
       " 'e2dnde': array([3.68644808e-07, 6.61388584e-07, 5.40963014e-07, 1.67498942e-07,\n",
       "        3.29079887e-07, 1.34341048e-07, 1.71041480e-07, 5.60460337e-08,\n",
       "        1.53409627e-14, 2.88401044e-08, 7.28794781e-17, 3.60163934e-14,\n",
       "        1.00000000e-17, 1.00000000e-17, 1.00000000e-17, 1.00000000e-17]),\n",
       " 'index': array([-2., -2., -2., -2., -2., -2., -2., -2., -2., -2., -2., -2., -2.,\n",
       "        -2., -2., -2.]),\n",
       " 'npred': array([2.09538299e+02, 3.70787097e+02, 2.43571498e+02, 5.48879069e+01,\n",
       "        7.45518632e+01, 2.00329595e+01, 1.55064517e+01, 2.94053397e+00,\n",
       "        4.77184434e-07, 5.34576224e-01, 8.05730808e-10, 2.46677084e-07,\n",
       "        4.10636786e-11, 2.43305345e-11, 1.44022454e-11, 8.54410227e-12]),\n",
       " 'ts': array([ 1.42294109,  9.58524343, 12.33749532,  2.15465774, 14.68444325,\n",
       "         5.11851362, 11.31633423,  1.62277823,  0.        ,  0.30920729,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ]),\n",
       " 'loglike': array([ -3648.75567637,  -9114.03549037, -27539.9637361 , -36845.76667814,\n",
       "        -35778.54475601, -27978.45596373, -18250.6324032 , -11445.06286406,\n",
       "         -6626.65697133,  -3885.51144779,  -2013.43711648,  -1055.32467639,\n",
       "          -497.26349833,   -305.91047112,   -171.78316293,   -101.94028054]),\n",
       " 'norm_scan': array([[0.00000000e+00, 4.09605342e-01, 8.19210684e-01, 1.22881603e+00,\n",
       "         1.63842137e+00, 2.04802671e+00, 2.45763205e+00, 2.86723740e+00,\n",
       "         3.27684274e+00, 3.68644808e+00, 4.39017972e+00, 5.09391135e+00,\n",
       "         5.79764299e+00, 6.50137463e+00, 7.20510627e+00, 7.90883790e+00,\n",
       "         8.61256954e+00, 9.31630118e+00, 1.00200328e+01, 1.07237645e+01],\n",
       "        [0.00000000e+00, 1.62310730e+00, 2.24695462e+00, 2.87080194e+00,\n",
       "         3.49464926e+00, 4.11849657e+00, 4.74234389e+00, 5.36619121e+00,\n",
       "         5.99003852e+00, 6.61388584e+00, 7.12974371e+00, 7.64560157e+00,\n",
       "         8.16145944e+00, 8.67731731e+00, 9.19317517e+00, 9.70903304e+00,\n",
       "         1.02248909e+01, 1.07407488e+01, 1.12566066e+01, 1.17724645e+01],\n",
       "        [0.00000000e+00, 1.76923469e+00, 2.22428412e+00, 2.67933355e+00,\n",
       "         3.13438298e+00, 3.58943242e+00, 4.04448185e+00, 4.49953128e+00,\n",
       "         4.95458071e+00, 5.40963014e+00, 5.78435110e+00, 6.15907206e+00,\n",
       "         6.53379302e+00, 6.90851398e+00, 7.28323495e+00, 7.65795591e+00,\n",
       "         8.03267687e+00, 8.40739783e+00, 8.78211879e+00, 9.15683975e+00],\n",
       "        [0.00000000e+00, 1.86109935e-01, 3.72219870e-01, 5.58329805e-01,\n",
       "         7.44439740e-01, 9.30549675e-01, 1.11665961e+00, 1.30276955e+00,\n",
       "         1.48887948e+00, 1.67498942e+00, 1.95211605e+00, 2.22924269e+00,\n",
       "         2.50636932e+00, 2.78349596e+00, 3.06062259e+00, 3.33774923e+00,\n",
       "         3.61487586e+00, 3.89200250e+00, 4.16912913e+00, 4.44625577e+00],\n",
       "        [0.00000000e+00, 1.18707966e+00, 1.45004457e+00, 1.71300947e+00,\n",
       "         1.97597437e+00, 2.23893927e+00, 2.50190417e+00, 2.76486907e+00,\n",
       "         3.02783397e+00, 3.29079887e+00, 3.52874038e+00, 3.76668189e+00,\n",
       "         4.00462340e+00, 4.24256491e+00, 4.48050642e+00, 4.71844793e+00,\n",
       "         4.95638944e+00, 5.19433095e+00, 5.43227246e+00, 5.67021397e+00],\n",
       "        [0.00000000e+00, 1.49267831e-01, 2.98535662e-01, 4.47803493e-01,\n",
       "         5.97071324e-01, 7.46339155e-01, 8.95606986e-01, 1.04487482e+00,\n",
       "         1.19414265e+00, 1.34341048e+00, 1.52826186e+00, 1.71311324e+00,\n",
       "         1.89796463e+00, 2.08281601e+00, 2.26766739e+00, 2.45251877e+00,\n",
       "         2.63737016e+00, 2.82222154e+00, 3.00707292e+00, 3.19192431e+00],\n",
       "        [0.00000000e+00, 4.04901347e-01, 5.68090529e-01, 7.31279711e-01,\n",
       "         8.94468893e-01, 1.05765807e+00, 1.22084726e+00, 1.38403644e+00,\n",
       "         1.54722562e+00, 1.71041480e+00, 1.91282976e+00, 2.11524472e+00,\n",
       "         2.31765968e+00, 2.52007464e+00, 2.72248960e+00, 2.92490456e+00,\n",
       "         3.12731952e+00, 3.32973448e+00, 3.53214944e+00, 3.73456440e+00],\n",
       "        [0.00000000e+00, 6.22733708e-02, 1.24546742e-01, 1.86820112e-01,\n",
       "         2.49093483e-01, 3.11366854e-01, 3.73640225e-01, 4.35913595e-01,\n",
       "         4.98186966e-01, 5.60460337e-01, 7.51909222e-01, 9.43358106e-01,\n",
       "         1.13480699e+00, 1.32625588e+00, 1.51770476e+00, 1.70915365e+00,\n",
       "         1.90060253e+00, 2.09205141e+00, 2.28350030e+00, 2.47494918e+00],\n",
       "        [0.00000000e+00, 1.53409627e-07, 6.00200013e-02, 1.20039849e-01,\n",
       "         1.80059697e-01, 2.40079545e-01, 3.00099393e-01, 3.60119241e-01,\n",
       "         4.20139089e-01, 4.80158937e-01, 5.40178784e-01, 6.00198632e-01,\n",
       "         6.60218480e-01, 7.20238328e-01, 7.80258176e-01, 8.40278024e-01,\n",
       "         9.00297872e-01, 9.60317720e-01, 1.02033757e+00, 1.08035742e+00],\n",
       "        [0.00000000e+00, 2.88401044e-01, 4.40304085e-01, 5.92207126e-01,\n",
       "         7.44110167e-01, 8.96013208e-01, 1.04791625e+00, 1.19981929e+00,\n",
       "         1.35172233e+00, 1.50362537e+00, 1.65552841e+00, 1.80743145e+00,\n",
       "         1.95933449e+00, 2.11123754e+00, 2.26314058e+00, 2.41504362e+00,\n",
       "         2.56694666e+00, 2.71884970e+00, 2.87075274e+00, 3.02265578e+00],\n",
       "        [0.00000000e+00, 7.28794781e-10, 2.74943736e-01, 5.49887471e-01,\n",
       "         8.24831206e-01, 1.09977494e+00, 1.37471868e+00, 1.64966241e+00,\n",
       "         1.92460615e+00, 2.19954988e+00, 2.47449362e+00, 2.74943735e+00,\n",
       "         3.02438109e+00, 3.29932482e+00, 3.57426856e+00, 3.84921229e+00,\n",
       "         4.12415603e+00, 4.39909976e+00, 4.67404350e+00, 4.94898723e+00],\n",
       "        [0.00000000e+00, 3.60163934e-07, 2.36834261e-01, 4.73668161e-01,\n",
       "         7.10502062e-01, 9.47335962e-01, 1.18416986e+00, 1.42100376e+00,\n",
       "         1.65783766e+00, 1.89467156e+00, 2.13150547e+00, 2.36833937e+00,\n",
       "         2.60517327e+00, 2.84200717e+00, 3.07884107e+00, 3.31567497e+00,\n",
       "         3.55250887e+00, 3.78934277e+00, 4.02617667e+00, 4.26301057e+00],\n",
       "        [0.00000000e+00, 1.00000000e-10, 3.80936953e-01, 7.61873906e-01,\n",
       "         1.14281086e+00, 1.52374781e+00, 1.90468476e+00, 2.28562172e+00,\n",
       "         2.66655867e+00, 3.04749562e+00, 3.42843257e+00, 3.80936953e+00,\n",
       "         4.19030648e+00, 4.57124343e+00, 4.95218039e+00, 5.33311734e+00,\n",
       "         5.71405429e+00, 6.09499124e+00, 6.47592820e+00, 6.85686515e+00],\n",
       "        [0.00000000e+00, 1.00000000e-10, 6.18790918e-01, 1.23758184e+00,\n",
       "         1.85637275e+00, 2.47516367e+00, 3.09395459e+00, 3.71274551e+00,\n",
       "         4.33153642e+00, 4.95032734e+00, 5.56911826e+00, 6.18790918e+00,\n",
       "         6.80670010e+00, 7.42549101e+00, 8.04428193e+00, 8.66307285e+00,\n",
       "         9.28186377e+00, 9.90065468e+00, 1.05194456e+01, 1.11382365e+01],\n",
       "        [0.00000000e+00, 1.00000000e-10, 1.21471498e+00, 2.42942996e+00,\n",
       "         3.64414493e+00, 4.85885991e+00, 6.07357489e+00, 7.28828987e+00,\n",
       "         8.50300484e+00, 9.71771982e+00, 1.09324348e+01, 1.21471498e+01,\n",
       "         1.33618648e+01, 1.45765797e+01, 1.57912947e+01, 1.70060097e+01,\n",
       "         1.82207247e+01, 1.94354396e+01, 2.06501546e+01, 2.18648696e+01],\n",
       "        [0.00000000e+00, 1.00000000e-10, 1.81180404e+00, 3.62360808e+00,\n",
       "         5.43541212e+00, 7.24721616e+00, 9.05902020e+00, 1.08708242e+01,\n",
       "         1.26826283e+01, 1.44944323e+01, 1.63062364e+01, 1.81180404e+01,\n",
       "         1.99298444e+01, 2.17416485e+01, 2.35534525e+01, 2.53652565e+01,\n",
       "         2.71770606e+01, 2.89888646e+01, 3.08006687e+01, 3.26124727e+01]]),\n",
       " 'dloglike_scan': array([[-7.11470545e-01, -5.58345719e-01, -4.23863774e-01,\n",
       "         -3.07970802e-01, -2.10613195e-01, -1.31737641e-01,\n",
       "         -7.12911246e-02, -2.92209207e-02, -5.47459492e-03,\n",
       "          0.00000000e+00, -3.31073950e-02, -1.19734769e-01,\n",
       "         -2.59623479e-01, -4.52517277e-01, -6.98162274e-01,\n",
       "         -9.96306908e-01, -1.34670191e+00, -1.74910028e+00,\n",
       "         -2.20325724e+00, -2.70893023e+00],\n",
       "        [-4.79262172e+00, -2.71232523e+00, -2.07283605e+00,\n",
       "         -1.52087589e+00, -1.05558142e+00, -6.76105725e-01,\n",
       "         -3.81617795e-01, -1.71302095e-01, -4.43581244e-02,\n",
       "          0.00000000e+00, -2.51501666e-02, -1.05812174e-01,\n",
       "         -2.41563197e-01, -4.31986586e-01, -6.76671734e-01,\n",
       "         -9.75213944e-01, -1.32721430e+00, -1.73227954e+00,\n",
       "         -2.19002194e+00, -2.70005920e+00],\n",
       "        [-6.16874766e+00, -2.71322652e+00, -2.06037644e+00,\n",
       "         -1.50045131e+00, -1.03172966e+00, -6.52550766e-01,\n",
       "         -3.61311400e-01, -1.56462886e-01, -3.65083224e-02,\n",
       "          0.00000000e+00, -3.15957085e-02, -1.18066669e-01,\n",
       "         -2.58673033e-01, -4.52693860e-01, -6.99426396e-01,\n",
       "         -9.98185386e-01, -1.34830243e+00, -1.74912535e+00,\n",
       "         -2.20001762e+00, -2.70035779e+00],\n",
       "        [-1.07732887e+00, -8.41091236e-01, -6.35414139e-01,\n",
       "         -4.59648716e-01, -3.13172524e-01, -1.95387907e-01,\n",
       "         -1.05720524e-01, -4.36179702e-02, -8.54852829e-03,\n",
       "          0.00000000e+00, -3.53586865e-02, -1.26870981e-01,\n",
       "         -2.73056685e-01, -4.72508838e-01, -7.23888343e-01,\n",
       "         -1.02591912e+00, -1.37738370e+00, -1.77711926e+00,\n",
       "         -2.22401401e+00, -2.71700382e+00],\n",
       "        [-7.34222163e+00, -2.71358112e+00, -2.03516762e+00,\n",
       "         -1.46505569e+00, -9.96823332e-01, -6.24700943e-01,\n",
       "         -3.43478574e-01, -1.48429927e-01, -3.52494784e-02,\n",
       "          0.00000000e+00, -3.22441251e-02, -1.22858332e-01,\n",
       "         -2.69542005e-01, -4.70150213e-01, -7.22678890e-01,\n",
       "         -1.02525182e+00, -1.37610918e+00, -1.77359737e+00,\n",
       "         -2.21615997e+00, -2.70232976e+00],\n",
       "        [-2.55925681e+00, -1.94883553e+00, -1.44298365e+00,\n",
       "         -1.02880744e+00, -6.95978369e-01, -4.36050144e-01,\n",
       "         -2.41997864e-01, -1.07895641e-01, -2.86843408e-02,\n",
       "          0.00000000e+00, -2.88677918e-02, -1.23187691e-01,\n",
       "         -2.77596006e-01, -4.87441501e-01, -7.48657785e-01,\n",
       "         -1.05766400e+00, -1.41128641e+00, -1.80669577e+00,\n",
       "         -2.24135661e+00, -2.71298583e+00],\n",
       "        [-5.65816711e+00, -2.69159574e+00, -1.94389863e+00,\n",
       "         -1.36117124e+00, -9.10472217e-01, -5.68543631e-01,\n",
       "         -3.18248280e-01, -1.46530183e-01, -4.31720764e-02,\n",
       "          0.00000000e+00, -2.01866478e-02, -1.12474853e-01,\n",
       "         -2.68533829e-01, -4.81526206e-01, -7.45757530e-01,\n",
       "         -1.05642498e+00, -1.40943299e+00, -1.80125527e+00,\n",
       "         -2.22882961e+00, -2.68947648e+00],\n",
       "        [-8.11389115e-01, -5.88432587e-01, -4.18197529e-01,\n",
       "         -2.87599809e-01, -1.88176974e-01, -1.14080566e-01,\n",
       "         -6.10683286e-02, -2.59476687e-02, -6.24533504e-03,\n",
       "          0.00000000e+00, -4.97587253e-02, -1.80804138e-01,\n",
       "         -3.73354519e-01, -6.14450255e-01, -8.95083288e-01,\n",
       "         -1.20870789e+00, -1.55039964e+00, -1.91634894e+00,\n",
       "         -2.30354003e+00, -2.70953908e+00],\n",
       "        [ 3.82493454e-07,  0.00000000e+00, -1.49686006e-01,\n",
       "         -2.99451041e-01, -4.49294399e-01, -5.99215383e-01,\n",
       "         -7.49213311e-01, -8.99287510e-01, -1.04943732e+00,\n",
       "         -1.19966208e+00, -1.34996117e+00, -1.50033393e+00,\n",
       "         -1.65077977e+00, -1.80129805e+00, -1.95188819e+00,\n",
       "         -2.10254959e+00, -2.25328166e+00, -2.40408384e+00,\n",
       "         -2.55495555e+00, -2.70589623e+00],\n",
       "        [-1.54603646e-01,  0.00000000e+00, -2.44328149e-02,\n",
       "         -8.75128394e-02, -1.77703720e-01, -2.88010074e-01,\n",
       "         -4.13866487e-01, -5.52126564e-01, -7.00528619e-01,\n",
       "         -8.57391413e-01, -1.02143045e+00, -1.19164170e+00,\n",
       "         -1.36722501e+00, -1.54753211e+00, -1.73203014e+00,\n",
       "         -1.92027555e+00, -2.11189504e+00, -2.30657126e+00,\n",
       "         -2.50403208e+00, -2.70404223e+00],\n",
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       "         -5.84239866e-01, -7.22214888e-01, -8.65693565e-01,\n",
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       "         -1.99083378e+00, -2.16533657e+00, -2.34263120e+00,\n",
       "         -2.52255331e+00, -2.70495366e+00],\n",
       "        [ 2.28264753e-07,  0.00000000e+00, -1.50113617e-01,\n",
       "         -3.00253178e-01, -4.50418535e-01, -6.00609545e-01,\n",
       "         -7.50826064e-01, -9.01067950e-01, -1.05133506e+00,\n",
       "         -1.20162726e+00, -1.35194441e+00, -1.50228637e+00,\n",
       "         -1.65265301e+00, -1.80304419e+00, -1.95345978e+00,\n",
       "         -2.10389964e+00, -2.25436366e+00, -2.40485169e+00,\n",
       "         -2.55536362e+00, -2.70589930e+00],\n",
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       "         -1.65272556e+00, -1.80311291e+00, -1.95352260e+00,\n",
       "         -2.10395443e+00, -2.25440820e+00, -2.40488371e+00,\n",
       "         -2.55538077e+00, -2.70589918e+00],\n",
       "        [ 2.42721399e-11,  0.00000000e+00, -1.50327443e-01,\n",
       "         -3.00654925e-01, -4.50982446e-01, -6.01310008e-01,\n",
       "         -7.51637608e-01, -9.01965248e-01, -1.05229293e+00,\n",
       "         -1.20262065e+00, -1.35294840e+00, -1.50327620e+00,\n",
       "         -1.65360404e+00, -1.80393192e+00, -1.95425983e+00,\n",
       "         -2.10458779e+00, -2.25491578e+00, -2.40524381e+00,\n",
       "         -2.55557189e+00, -2.70590000e+00],\n",
       "        [ 1.18518528e-11,  0.00000000e+00, -1.44590072e-01,\n",
       "         -2.90054826e-01, -4.36344727e-01, -5.83414331e-01,\n",
       "         -7.31221849e-01, -8.79728765e-01, -1.02889950e+00,\n",
       "         -1.17870113e+00, -1.32910310e+00, -1.48007706e+00,\n",
       "         -1.63159658e+00, -1.78363706e+00, -1.93617550e+00,\n",
       "         -2.08919039e+00, -2.24266160e+00, -2.39657025e+00,\n",
       "         -2.55089859e+00, -2.70562995e+00],\n",
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       "         -3.00410151e-01, -4.50639118e-01, -6.00883848e-01,\n",
       "         -7.51144219e-01, -9.01420112e-01, -1.05171141e+00,\n",
       "         -1.20201799e+00, -1.35233974e+00, -1.50267655e+00,\n",
       "         -1.65302830e+00, -1.80339487e+00, -1.95377617e+00,\n",
       "         -2.10417208e+00, -2.25458248e+00, -2.40500729e+00,\n",
       "         -2.55544638e+00, -2.70589965e+00]]),\n",
       " 'loglike_scan': array([[ -3649.46714692,  -3649.31402209,  -3649.17954015,\n",
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       "          -3649.75198328,  -3650.10237829,  -3650.50477665,\n",
       "          -3650.95893362,  -3651.4646066 ],\n",
       "        [ -9118.82811209,  -9116.7478156 ,  -9116.10832642,\n",
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       "          -9114.27705357,  -9114.46747696,  -9114.7121621 ,\n",
       "          -9115.01070431,  -9115.36270467,  -9115.76776991,\n",
       "          -9116.22551231,  -9116.73554957],\n",
       "        [-27546.13248376, -27542.67696262, -27542.02411254,\n",
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       "         -27540.96192149, -27541.31203853, -27541.71286145,\n",
       "         -27542.16375372, -27542.66409389],\n",
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       "         -36847.99069214, -36848.48368195],\n",
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       "         -35780.0098117 , -35779.54157934, -35779.16945695,\n",
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       "         -35778.81429802, -35779.01490622, -35779.2674349 ,\n",
       "         -35779.57000784, -35779.9208652 , -35780.31835338,\n",
       "         -35780.76091598, -35781.24708577],\n",
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       "         -18251.68882818, -18252.04183619, -18252.43365848,\n",
       "         -18252.86123281, -18253.32187969],\n",
       "        [-11445.87425317, -11445.65129664, -11445.48106159,\n",
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       "         -11445.43621858, -11445.67731431, -11445.95794735,\n",
       "         -11446.27157195, -11446.6132637 , -11446.979213  ,\n",
       "         -11447.36640409, -11447.77240314],\n",
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       "          -6628.3077511 ,  -6628.45826939,  -6628.60885953,\n",
       "          -6628.75952092,  -6628.910253  ,  -6629.06105517,\n",
       "          -6629.21192688,  -6629.36286757],\n",
       "        [ -3885.66605143,  -3885.51144779,  -3885.5358806 ,\n",
       "          -3885.59896063,  -3885.68915151,  -3885.79945786,\n",
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       "          -3887.43172334,  -3887.62334282,  -3887.81801905,\n",
       "          -3888.01547987,  -3888.21549001],\n",
       "        [ -2013.43711648,  -2013.43711648,  -2013.5370602 ,\n",
       "          -2013.64645662,  -2013.76421691,  -2013.8894367 ,\n",
       "          -2014.02135635,  -2014.15933137,  -2014.30281005,\n",
       "          -2014.45131628,  -2014.6044362 ,  -2014.76180768,\n",
       "          -2014.92311192,  -2015.08806671,  -2015.25642098,\n",
       "          -2015.42795026,  -2015.60245305,  -2015.77974768,\n",
       "          -2015.9596698 ,  -2016.14207014],\n",
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       "          -1057.42857603,  -1057.57904005,  -1057.72952808,\n",
       "          -1057.88004001,  -1058.03057569],\n",
       "        [  -497.26349833,   -497.26349833,   -497.41362573,\n",
       "           -497.5637779 ,   -497.71395459,   -497.86415558,\n",
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       "           -498.46519795,   -498.61551704,   -498.76585909,\n",
       "           -498.91622389,   -499.06661124,   -499.21702093,\n",
       "           -499.36745276,   -499.51790653,   -499.66838204,\n",
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       "           -308.01505891,   -308.1653869 ,   -308.31571494,\n",
       "           -308.46604301,   -308.61637112],\n",
       "        [  -171.78316293,   -171.78316293,   -171.927753  ,\n",
       "           -172.07321775,   -172.21950765,   -172.36657726,\n",
       "           -172.51438478,   -172.66289169,   -172.81206243,\n",
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       "           -173.87235331,   -174.02582453,   -174.17973318,\n",
       "           -174.33406152,   -174.48879288],\n",
       "        [  -101.94028054,   -101.94028054,   -102.09047761,\n",
       "           -102.24069069,   -102.39091966,   -102.54116439,\n",
       "           -102.69142476,   -102.84170065,   -102.99199195,\n",
       "           -103.14229853,   -103.29262028,   -103.44295709,\n",
       "           -103.59330883,   -103.74367541,   -103.89405671,\n",
       "           -104.04445261,   -104.19486302,   -104.34528782,\n",
       "           -104.49572691,   -104.64618019]]),\n",
       " 'fit_quality': array([3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.]),\n",
       " 'fit_status': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n",
       " 'correlation': {'PSR J0922+0638': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])},\n",
       " 'model_flux': {'energies': array([1.00000000e+02, 1.18984170e+02, 1.41572326e+02, 1.68448656e+02,\n",
       "         2.00427235e+02, 2.38476681e+02, 2.83749498e+02, 3.37616984e+02,\n",
       "         4.01710764e+02, 4.77972217e+02, 5.68711273e+02, 6.76676385e+02,\n",
       "         8.05137777e+02, 9.57986497e+02, 1.13985228e+03, 1.35624377e+03,\n",
       "         1.61371538e+03, 1.92006585e+03, 2.28457440e+03, 2.71828188e+03,\n",
       "         3.23432512e+03, 3.84833488e+03, 4.57890930e+03, 5.44817720e+03,\n",
       "         6.48246840e+03, 7.71311119e+03, 9.17738129e+03, 1.09196309e+04,\n",
       "         1.29926322e+04, 1.54591755e+04, 1.83939715e+04, 2.18859143e+04,\n",
       "         2.60407733e+04, 3.09843979e+04, 3.68665285e+04, 4.38653328e+04,\n",
       "         5.21928019e+04, 6.21011719e+04, 7.38905637e+04, 8.79180735e+04,\n",
       "         1.04608590e+05, 1.24467662e+05, 1.48096814e+05, 1.76211764e+05,\n",
       "         2.09664104e+05, 2.49467092e+05, 2.96826348e+05, 3.53176365e+05,\n",
       "         4.20223965e+05, 4.99999995e+05]),\n",
       "  'log_energies': array([2.        , 2.07548918, 2.15097837, 2.22646755, 2.30195673,\n",
       "         2.37744592, 2.4529351 , 2.52842429, 2.60391347, 2.67940265,\n",
       "         2.75489184, 2.83038102, 2.9058702 , 2.98135939, 3.05684857,\n",
       "         3.13233776, 3.20782694, 3.28331612, 3.35880531, 3.43429449,\n",
       "         3.50978367, 3.58527286, 3.66076204, 3.73625122, 3.81174041,\n",
       "         3.88722959, 3.96271878, 4.03820796, 4.11369714, 4.18918633,\n",
       "         4.26467551, 4.34016469, 4.41565388, 4.49114306, 4.56663224,\n",
       "         4.64212143, 4.71761061, 4.7930998 , 4.86858898, 4.94407816,\n",
       "         5.01956735, 5.09505653, 5.17054571, 5.2460349 , 5.32152408,\n",
       "         5.39701327, 5.47250245, 5.54799163, 5.62348082, 5.69897   ]),\n",
       "  'dnde': array([5.52941268e-011, 3.77924554e-011, 2.57933448e-011, 1.75739056e-011,\n",
       "         1.19494011e-011, 8.10538456e-012, 5.48215541e-012, 3.69523478e-012,\n",
       "         2.48063808e-012, 1.65721768e-012, 1.10075544e-012, 7.26142265e-013,\n",
       "         4.75123361e-013, 3.07873017e-013, 1.97204319e-013, 1.24591288e-013,\n",
       "         7.74375073e-014, 4.72017581e-014, 2.81127052e-014, 1.62882528e-014,\n",
       "         9.13273520e-015, 4.92467236e-015, 2.53504770e-015, 1.23480556e-015,\n",
       "         5.63195481e-016, 2.37545908e-016, 9.12881518e-017, 3.14040784e-017,\n",
       "         9.46960779e-018, 2.44111593e-018, 5.22190616e-019, 8.94692499e-020,\n",
       "         1.17712515e-020, 1.13110138e-021, 7.47829831e-023, 3.16884031e-024,\n",
       "         7.90890067e-026, 1.05149918e-027, 6.60773693e-030, 1.70240837e-032,\n",
       "         1.51820009e-035, 3.83159673e-039, 2.15348633e-043, 2.02667656e-048,\n",
       "         2.27492370e-054, 2.03412711e-061, 8.96242696e-070, 1.09879662e-079,\n",
       "         1.89908231e-091, 2.06031514e-105]),\n",
       "  'dnde_lo': array([3.68414851e-011, 2.60605839e-011, 1.84023935e-011, 1.29623339e-011,\n",
       "         9.09877709e-012, 6.35647077e-012, 4.41243485e-012, 3.03769901e-012,\n",
       "         2.07007254e-012, 1.39440229e-012, 9.28028386e-013, 6.10536158e-013,\n",
       "         3.97344582e-013, 2.55895873e-013, 1.62973771e-013, 1.02437844e-013,\n",
       "         6.32999340e-014, 3.82060394e-014, 2.23133893e-014, 1.24715491e-014,\n",
       "         6.60600897e-015, 3.29256089e-015, 1.53557836e-015, 6.66134051e-016,\n",
       "         2.66762848e-016, 9.76590905e-017, 3.22771223e-017, 9.48121273e-018,\n",
       "         2.42777361e-018, 5.29235681e-019, 9.54396422e-020, 1.37542226e-020,\n",
       "         1.51969849e-021, 1.22500655e-022, 6.78949134e-024, 2.41074650e-025,\n",
       "         5.04073591e-027, 5.61420924e-029, 2.95566571e-031, 6.38030559e-034,\n",
       "         4.76819834e-037, 1.00864199e-040, 4.75249549e-045, 3.75040789e-050,\n",
       "         3.53074365e-056, 2.64831855e-063, 9.79024057e-072, 1.00725168e-081,\n",
       "         1.46113015e-093, 1.33066483e-107]),\n",
       "  'dnde_hi': array([8.29890666e-011, 5.48057439e-011, 3.61527231e-011, 2.38261227e-011,\n",
       "         1.56931184e-011, 1.03354929e-011, 6.81121173e-012, 4.49509975e-012,\n",
       "         2.97263268e-012, 1.96956823e-012, 1.30563089e-012, 8.63638595e-013,\n",
       "         5.68127058e-013, 3.70407671e-013, 2.38624553e-013, 1.51535688e-013,\n",
       "         9.47326034e-014, 5.83155439e-014, 3.54192803e-014, 2.12729932e-014,\n",
       "         1.26259066e-014, 7.36581606e-015, 4.18504651e-015, 2.28894586e-015,\n",
       "         1.18903045e-015, 5.77806510e-016, 2.58186792e-016, 1.04017932e-016,\n",
       "         3.69365049e-017, 1.12597227e-017, 2.85712554e-018, 5.81984666e-019,\n",
       "         9.11775353e-020, 1.04439469e-020, 8.23698608e-022, 4.16532758e-023,\n",
       "         1.24090432e-024, 1.96937891e-026, 1.47723699e-028, 4.54240667e-031,\n",
       "         4.83396737e-034, 1.45553464e-037, 9.75803846e-042, 1.09519231e-046,\n",
       "         1.46577558e-052, 1.56237742e-059, 8.20460911e-068, 1.19866170e-077,\n",
       "         2.46830415e-089, 3.19005837e-103]),\n",
       "  'dnde_err': array([2.76949398e-011, 1.70132885e-011, 1.03593783e-011, 6.25221712e-012,\n",
       "         3.74371735e-012, 2.23010839e-012, 1.32905631e-012, 7.99864975e-013,\n",
       "         4.91994599e-013, 3.12350548e-013, 2.04875456e-013, 1.37496329e-013,\n",
       "         9.30036971e-014, 6.25346544e-014, 4.14202345e-014, 2.69443996e-014,\n",
       "         1.72950961e-014, 1.11137858e-014, 7.30657517e-015, 4.98474042e-015,\n",
       "         3.49317140e-015, 2.44114369e-015, 1.64999881e-015, 1.05414030e-015,\n",
       "         6.25834972e-016, 3.40260603e-016, 1.66898640e-016, 7.26138538e-017,\n",
       "         2.74668971e-017, 8.81860674e-018, 2.33493492e-018, 4.92515417e-019,\n",
       "         7.94062838e-020, 9.31284548e-021, 7.48915625e-022, 3.84844355e-023,\n",
       "         1.16181532e-024, 1.86422899e-026, 1.41115963e-028, 4.37216583e-031,\n",
       "         4.68214736e-034, 1.41721867e-037, 9.54268983e-042, 1.07492555e-046,\n",
       "         1.44302634e-052, 1.54203615e-059, 8.11498484e-068, 1.18767374e-077,\n",
       "         2.44931333e-089, 3.16945522e-103]),\n",
       "  'dnde_ferr': array([ 0.41729189,  0.38030289,  0.34408739,  0.30908863,  0.27592769,\n",
       "          0.24545548,  0.21878045,  0.19720002,  0.18192096,  0.17353361,\n",
       "          0.17151971,  0.17427882,  0.17972435,  0.18597245,  0.19180813,\n",
       "          0.19703562,  0.20295507,  0.21301648,  0.23309552,  0.27017766,\n",
       "          0.32957802,  0.41355596,  0.52256771,  0.65711229,  0.81878108,\n",
       "          1.01064132,  1.23734387,  1.50516627,  1.82207817,  2.19786509,\n",
       "          2.64432739,  3.1755628 ,  3.80833918,  4.56256459,  5.461865  ,\n",
       "          6.53428338,  7.81311865,  9.33792786, 11.15572052, 13.32238034,\n",
       "         15.90435673, 18.98067699, 22.64533985, 27.01016263, 32.20816831,\n",
       "         38.39761487, 45.76678908, 54.53970984, 64.98291376, 77.41352861]),\n",
       "  'pivot_energy': 556.2947770756764},\n",
       " 'config': {'bin_index': 2.0,\n",
       "  'use_local_index': False,\n",
       "  'free_background': False,\n",
       "  'free_radius': None,\n",
       "  'free_pars': [],\n",
       "  'ul_confidence': 0.95,\n",
       "  'cov_scale': 3.0,\n",
       "  'make_plots': True,\n",
       "  'write_fits': True,\n",
       "  'write_npy': True,\n",
       "  'optimizer': {'optimizer': 'NEWMINUIT',\n",
       "   'tol': 0.05,\n",
       "   'max_iter': 40,\n",
       "   'init_lambda': 0.0001,\n",
       "   'retries': 3,\n",
       "   'min_fit_quality': 2,\n",
       "   'verbosity': 0},\n",
       "  'prefix': '',\n",
       "  'outfile': None,\n",
       "  'loge_bins': [2.0,\n",
       "   2.22418,\n",
       "   2.44836,\n",
       "   2.67254,\n",
       "   2.89672,\n",
       "   3.1209,\n",
       "   3.34508,\n",
       "   3.56926,\n",
       "   3.79344,\n",
       "   4.01762,\n",
       "   4.2418,\n",
       "   4.46598,\n",
       "   4.69016,\n",
       "   4.91434,\n",
       "   5.13852,\n",
       "   5.3627,\n",
       "   5.58688]},\n",
       " 'norm_err': array([3.05078215, 2.16822418, 1.58131882, 1.15852914, 0.95807839,\n",
       "        0.68245513, 0.707132  , 0.56126211, 0.20034916, 0.84560619,\n",
       "        1.14848641, 0.78868027, 1.2684424 , 2.05814725, 4.15667772,\n",
       "        6.03061372]),\n",
       " 'norm_err_hi': array([3.05078215, 2.18409801, 1.59815285, 1.17679866, 0.99044389,\n",
       "        0.72597862, 0.76881743, 0.64793761, 0.20034916, 0.84560619,\n",
       "        1.14848641, 0.78868027, 1.2684424 , 2.05814725, 4.15667772,\n",
       "        6.03061372]),\n",
       " 'norm_err_lo': array([3.05078215, 2.15235035, 1.56448479, 1.14025962, 0.9257129 ,\n",
       "        0.63893165, 0.64544658, 0.47458661,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan]),\n",
       " 'norm_ul95': array([ 8.63526275, 10.25470088,  8.03621343,  3.60853449,  4.94416887,\n",
       "         2.60268847,  3.08838137,  1.78375066,  0.54129382,  1.9698374 ,\n",
       "         2.78530097,  2.13280256,  3.43032584,  5.568391  , 11.11711948,\n",
       "        16.31140474]),\n",
       " 'norm_ul': array([ 8.63526275, 10.25470088,  8.03621343,  3.60853449,  4.94416887,\n",
       "         2.60268847,  3.08838137,  1.78375066,  0.54129382,  1.9698374 ,\n",
       "         2.78530097,  2.13280256,  3.43032584,  5.568391  , 11.11711948,\n",
       "        16.31140474]),\n",
       " 'flux_err': array([1.23011510e-09, 5.21745531e-10, 2.27087900e-10, 9.92890870e-11,\n",
       "        4.90022024e-11, 2.08309299e-11, 1.28811622e-11, 6.10155146e-12,\n",
       "        1.29981659e-12, 3.27403040e-12, 2.65375250e-12, 1.08756577e-12,\n",
       "        1.04386732e-12, 1.01081342e-12, 1.21831869e-12, 1.05486301e-12]),\n",
       " 'flux_err_hi': array([1.23011510e-09, 5.25565293e-10, 2.29505378e-10, 1.00854834e-10,\n",
       "        5.06575789e-11, 2.21594197e-11, 1.40048278e-11, 7.04381175e-12,\n",
       "        1.29981659e-12, 3.27403040e-12, 2.65375250e-12, 1.08756577e-12,\n",
       "        1.04386732e-12, 1.01081342e-12, 1.21831869e-12, 1.05486301e-12]),\n",
       " 'flux_err_lo': array([1.23011510e-09, 5.17925769e-10, 2.24670421e-10, 9.77233397e-11,\n",
       "        4.73468259e-11, 1.95024401e-11, 1.17574965e-11, 5.15929116e-12,\n",
       "                   nan,            nan,            nan,            nan,\n",
       "                   nan,            nan,            nan,            nan]),\n",
       " 'flux_ul95': array([3.48185043e-09, 2.46761585e-09, 1.15405370e-09, 3.09261184e-10,\n",
       "        2.52876138e-10, 7.94432024e-11, 5.62581540e-11, 1.93913793e-11,\n",
       "        3.51178262e-12, 7.62684523e-12, 6.43586142e-12, 2.94106896e-12,\n",
       "        2.82299383e-12, 2.73479185e-12, 3.25841823e-12, 2.85315863e-12]),\n",
       " 'flux_ul': array([3.48185043e-09, 2.46761585e-09, 1.15405370e-09, 3.09261184e-10,\n",
       "        2.52876138e-10, 7.94432024e-11, 5.62581540e-11, 1.93913793e-11,\n",
       "        3.51178262e-12, 7.62684523e-12, 6.43586142e-12, 2.94106896e-12,\n",
       "        2.82299383e-12, 2.73479185e-12, 3.25841823e-12, 2.85315863e-12]),\n",
       " 'eflux_err': array([1.57479400e-07, 1.11922329e-07, 8.16266538e-08, 5.98025244e-08,\n",
       "        4.94553864e-08, 3.52278921e-08, 3.65016963e-08, 2.89719868e-08,\n",
       "        1.03418939e-08, 4.36496441e-08, 5.92841251e-08, 4.07111651e-08,\n",
       "        6.54761753e-08, 1.06240229e-07, 2.14565013e-07, 3.11296376e-07]),\n",
       " 'eflux_err_hi': array([1.57479400e-07, 1.12741726e-07, 8.24956155e-08, 6.07455852e-08,\n",
       "        5.11260722e-08, 3.74745463e-08, 3.96858579e-08, 3.34461198e-08,\n",
       "        1.03418939e-08, 4.36496441e-08, 5.92841251e-08, 4.07111651e-08,\n",
       "        6.54761753e-08, 1.06240229e-07, 2.14565013e-07, 3.11296376e-07]),\n",
       " 'eflux_err_lo': array([1.57479400e-07, 1.11102932e-07, 8.07576922e-08, 5.88594636e-08,\n",
       "        4.77847006e-08, 3.29812380e-08, 3.33175347e-08, 2.44978538e-08,\n",
       "                   nan,            nan,            nan,            nan,\n",
       "                   nan,            nan,            nan,            nan]),\n",
       " 'eflux_ul95': array([4.45746673e-07, 5.29341021e-07, 4.14824135e-07, 1.86270214e-07,\n",
       "        2.55214796e-07, 1.34349094e-07, 1.59420247e-07, 9.20760543e-08,\n",
       "        2.79412367e-08, 1.01681731e-07, 1.43775433e-07, 1.10093888e-07,\n",
       "        1.77071199e-07, 2.87436739e-07, 5.73858510e-07, 8.41984153e-07]),\n",
       " 'eflux_ul': array([4.45746673e-07, 5.29341021e-07, 4.14824135e-07, 1.86270214e-07,\n",
       "        2.55214796e-07, 1.34349094e-07, 1.59420247e-07, 9.20760543e-08,\n",
       "        2.79412367e-08, 1.01681731e-07, 1.43775433e-07, 1.10093888e-07,\n",
       "        1.77071199e-07, 2.87436739e-07, 5.73858510e-07, 8.41984153e-07]),\n",
       " 'dnde_err': array([1.82066984e-11, 4.60854873e-12, 1.19706971e-12, 3.12353922e-13,\n",
       "        9.19985634e-14, 2.33396374e-14, 8.61311992e-15, 2.43481363e-15,\n",
       "        3.09547658e-16, 4.65316179e-16, 2.25084641e-16, 5.50504633e-17,\n",
       "        3.15333963e-17, 1.82228560e-17, 1.31076958e-17, 6.77300680e-18]),\n",
       " 'dnde_err_hi': array([1.82066984e-11, 4.64228847e-12, 1.20981319e-12, 3.17279613e-13,\n",
       "        9.51064290e-14, 2.48281197e-14, 9.36447036e-15, 2.81082098e-15,\n",
       "        3.09547658e-16, 4.65316179e-16, 2.25084641e-16, 5.50504633e-17,\n",
       "        3.15333963e-17, 1.82228560e-17, 1.31076958e-17, 6.77300680e-18]),\n",
       " 'dnde_err_lo': array([1.82066984e-11, 4.57480899e-12, 1.18432622e-12, 3.07428231e-13,\n",
       "        8.88906979e-14, 2.18511551e-14, 7.86176947e-15, 2.05880628e-15,\n",
       "                   nan,            nan,            nan,            nan,\n",
       "                   nan,            nan,            nan,            nan]),\n",
       " 'dnde_ul95': array([5.15342022e-11, 2.17963111e-11, 6.08347129e-12, 9.72905952e-13,\n",
       "        4.74759098e-13, 8.90106943e-14, 3.76175861e-14, 7.73809660e-15,\n",
       "        8.36321133e-16, 1.08395282e-15, 5.45873649e-16, 1.48871188e-16,\n",
       "        8.52776796e-17, 4.93025887e-17, 3.50568002e-17, 1.83194050e-17]),\n",
       " 'dnde_ul': array([5.15342022e-11, 2.17963111e-11, 6.08347129e-12, 9.72905952e-13,\n",
       "        4.74759098e-13, 8.90106943e-14, 3.76175861e-14, 7.73809660e-15,\n",
       "        8.36321133e-16, 1.08395282e-15, 5.45873649e-16, 1.48871188e-16,\n",
       "        8.52776796e-17, 4.93025887e-17, 3.50568002e-17, 1.83194050e-17]),\n",
       " 'e2dnde_err': array([3.05078215e-07, 2.16822418e-07, 1.58131882e-07, 1.15852914e-07,\n",
       "        9.58078393e-08, 6.82455133e-08, 7.07132005e-08, 5.61262110e-08,\n",
       "        2.00349159e-08, 8.45606190e-08, 1.14848641e-07, 7.88680273e-08,\n",
       "        1.26844240e-07, 2.05814725e-07, 4.15667772e-07, 6.03061372e-07]),\n",
       " 'e2dnde_err_hi': array([3.05078215e-07, 2.18409801e-07, 1.59815285e-07, 1.17679866e-07,\n",
       "        9.90443886e-08, 7.25978619e-08, 7.68817428e-08, 6.47937606e-08,\n",
       "        2.00349159e-08, 8.45606190e-08, 1.14848641e-07, 7.88680273e-08,\n",
       "        1.26844240e-07, 2.05814725e-07, 4.15667772e-07, 6.03061372e-07]),\n",
       " 'e2dnde_err_lo': array([3.05078215e-07, 2.15235035e-07, 1.56448479e-07, 1.14025962e-07,\n",
       "        9.25712900e-08, 6.38931647e-08, 6.45446582e-08, 4.74586614e-08,\n",
       "                   nan,            nan,            nan,            nan,\n",
       "                   nan,            nan,            nan,            nan]),\n",
       " 'e2dnde_ul95': array([8.63526275e-07, 1.02547009e-06, 8.03621343e-07, 3.60853449e-07,\n",
       "        4.94416887e-07, 2.60268847e-07, 3.08838137e-07, 1.78375066e-07,\n",
       "        5.41293823e-08, 1.96983740e-07, 2.78530097e-07, 2.13280256e-07,\n",
       "        3.43032584e-07, 5.56839100e-07, 1.11171195e-06, 1.63114047e-06]),\n",
       " 'e2dnde_ul': array([8.63526275e-07, 1.02547009e-06, 8.03621343e-07, 3.60853449e-07,\n",
       "        4.94416887e-07, 2.60268847e-07, 3.08838137e-07, 1.78375066e-07,\n",
       "        5.41293823e-08, 1.96983740e-07, 2.78530097e-07, 2.13280256e-07,\n",
       "        3.43032584e-07, 5.56839100e-07, 1.11171195e-06, 1.63114047e-06]),\n",
       " 'SpectrumType': 'PLSuperExpCutoff',\n",
       " 'param_names': array([b'Prefactor', b'Index1', b'Scale', b'Cutoff', b'Index2', b'', b'',\n",
       "        b'', b'', b''], dtype='|S32'),\n",
       " 'param_values': array([ 4.11262915e-13, -2.14586117e+00,  1.00000000e+03,  2.51012398e+03,\n",
       "         1.00000000e+00,             nan,             nan,             nan,\n",
       "                    nan,             nan]),\n",
       " 'param_errors': array([ 1.89447342e-13, -3.53860981e-01,             nan,  1.96766513e+03,\n",
       "                    nan,             nan,             nan,             nan,\n",
       "                    nan,             nan]),\n",
       " 'param_covariance': array([[ 3.58902952e-26,  5.74338424e-14,             nan,\n",
       "         -3.46888282e-10,             nan],\n",
       "        [ 5.74338424e-14,  1.25217594e-01,             nan,\n",
       "         -5.97907318e+02,             nan],\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan],\n",
       "        [-3.46888282e-10, -5.97907318e+02,             nan,\n",
       "          3.87170605e+06,             nan],\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan]]),\n",
       " 'param_correlation': array([[ 1.        , -0.85673534,         nan, -0.93057188,         nan],\n",
       "        [-0.85673534,  1.        ,         nan,  0.85871689,         nan],\n",
       "        [        nan,         nan,         nan,         nan,         nan],\n",
       "        [-0.93057188,  0.85871689,         nan,  1.        ,         nan],\n",
       "        [        nan,         nan,         nan,         nan,         nan]]),\n",
       " 'file': 'psr_j0922+0638_sed.fits'}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gta.sed(name,\n",
    "        loge_bins=gta.log_energies[::2], #This is to choose the energy bins to do the SED in.\n",
    "                                         #The way this is doing right now is to use exisiting binning we specified \n",
    "                                         #in the configuration and combine two bins together. \n",
    "        make_plots=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bbcbadd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('psr_j0922+0638_sed.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c66b43b",
   "metadata": {},
   "source": [
    "Now to this point we can save the secondary results after we found all the new sources and done the localization and everything:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f8fb1964",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-06 15:53:50 INFO    GTBinnedAnalysis.write_xml(): Writing /data/users/ysong/Fermipy_Tutorial/PSR J0922+0638_newsources_localized_00.xml...\n",
      "2021-07-06 15:53:50 INFO    GTAnalysis.write_fits(): Writing /data/users/ysong/Fermipy_Tutorial/PSR J0922+0638_newsources_localized.fits...\n",
      "WARNING: Format %s cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "WARNING: Format %f cannot be mapped to the accepted TDISPn keyword values.  Format will not be moved into TDISPn keyword. [astropy.io.fits.column]\n",
      "2021-07-06 15:54:05 INFO    GTAnalysis.write_roi(): Writing /data/users/ysong/Fermipy_Tutorial/PSR J0922+0638_newsources_localized.npy...\n"
     ]
    }
   ],
   "source": [
    "gta.write_roi(name+'_newsources_localized')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c87519",
   "metadata": {},
   "source": [
    "Now for the next parts of the tutorial, let's try \n",
    "\n",
    "1) Create a new analysis by loading from the previously saved analysis results;\n",
    "\n",
    "2) Manitulate the source model (add and delete sources, set certain parameters free, etc...)\n",
    "\n",
    "3) And fit again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6a7d4d1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-06 15:58:29 INFO    GTAnalysis.__init__(): \n",
      "--------------------------------------------------------------------------------\n",
      "fermipy version v1.0.1 \n",
      "ScienceTools version 2.0.8\n",
      "2021-07-06 15:58:30 INFO    GTAnalysis.setup(): Running setup.\n",
      "2021-07-06 15:58:30 INFO    GTBinnedAnalysis.setup(): Running setup for component 00\n",
      "2021-07-06 15:58:30 INFO    GTBinnedAnalysis._select_data(): Skipping data selection.\n",
      "2021-07-06 15:58:30 INFO    GTBinnedAnalysis.setup(): Using external LT cube.\n",
      "/data/shared_data/anaconda3/envs/fermipy/lib/python3.7/site-packages/fermipy/irfs.py:51: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  log_ratio = np.log(x[xs1] / x[xs0])\n",
      "/data/shared_data/anaconda3/envs/fermipy/lib/python3.7/site-packages/fermipy/irfs.py:52: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return 0.5 * (y[ys0] * x[xs0] + y[ys1] * x[xs1]) * log_ratio\n",
      "2021-07-06 15:58:31 INFO    GTBinnedAnalysis._create_expcube(): Skipping gtexpcube.\n",
      "WARNING: FITSFixedWarning: RADECSYS= 'FK5 ' \n",
      "the RADECSYS keyword is deprecated, use RADESYSa. [astropy.wcs.wcs]\n",
      "2021-07-06 15:58:31 INFO    GTBinnedAnalysis._create_srcmaps(): Skipping gtsrcmaps.\n",
      "2021-07-06 15:58:31 INFO    GTBinnedAnalysis.setup(): Finished setup for component 00\n",
      "2021-07-06 15:58:31 INFO    GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 00.\n",
      "2021-07-06 15:58:52 INFO    GTAnalysis.setup(): Finished setup.\n",
      "2021-07-06 15:58:52 INFO    GTAnalysis.load_roi(): Loading ROI file: /data/users/ysong/Fermipy_Tutorial/PSR J0922+0638_newsources_localized.npy\n",
      "2021-07-06 15:58:53 INFO    GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 00.\n",
      "2021-07-06 15:59:13 INFO    GTAnalysis.load_roi(): Finished Loading ROI\n"
     ]
    }
   ],
   "source": [
    "gta_2 = GTAnalysis.create(name+'_newsources_localized.fits') #This is loading the analysis from what we just saved in the previous steo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa1d6a91",
   "metadata": {},
   "source": [
    "As you can see, none of the Fermi tools were called here - what has been done was merely to load all the proper files again to a new GTAnalysis class object, and set up the following analysis. If you do gta_v2.print_roi(), you'll find it should be exactly the same as if you read if from np.load(name+'_newsources_localized.py')\n",
    "\n",
    "What we're going to do next is moot, but we just really want to test out the ability of Fermipy to manipulate model files. So let's have a look on how to do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fe2cfd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "gta_2.free_sources()"
   ]
  }
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