{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example notebook: Use _exoscene_ to model the coronagraph image of an exoplanetary system, based on the example PSF model from the Roman Space Telescope Coronagraph Instrument (CGI)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Other Rights Reserved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.ndimage\n",
    "import skimage.transform\n",
    "import os\n",
    "import pkg_resources\n",
    "import pandas\n",
    "import csv\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation\n",
    "plt.style.use('default')\n",
    "matplotlib.rcParams['image.cmap'] = 'inferno'\n",
    "matplotlib.rcParams['font.size'] = 16\n",
    "matplotlib.rcParams['image.origin'] = 'lower'\n",
    "#matplotlib.rcParams['font.sans-serif'] = 'Helvetica'\n",
    "\n",
    "import astropy.units as u\n",
    "import astropy.constants as c\n",
    "import astropy.io.fits as fits\n",
    "import astropy.table\n",
    "\n",
    "import exoscene.image\n",
    "import exoscene.star\n",
    "import exoscene.planet\n",
    "from exoscene.planet import Planet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results will be stored in this directory (approximately 1.2 MB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = os.path.expanduser(\"~/exoscene_demo_results\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Use the exoscene Planet class to define a 3-planet system "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set star parameters to 47 UMa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, with the astroquery package you can directly query SIMBAD:  \n",
    "\n",
    "from astroquery.simbad import Simbad  \n",
    "customSimbad = Simbad()  \n",
    "customSimbad.add_votable_fields('sptype', 'flux(V)', 'pmra', 'pmdec', 'plx')  \n",
    "scistar_simbad_table = customSimbad.query_object(\"47 Uma\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.802089084311351 pc\n"
     ]
    }
   ],
   "source": [
    "sptype = 'G0V'\n",
    "Vmag = 5.0\n",
    "plx = 72.4528 * u.mas\n",
    "dist = (1 * u.AU / np.tan(plx)).to(u.pc)\n",
    "print(dist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set planet system parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: ErfaWarning: ERFA function \"dtf2d\" yielded 1 of \"dubious year (Note 6)\" [astropy._erfa.core]\n",
      "WARNING: ErfaWarning: ERFA function \"d2dtf\" yielded 1 of \"dubious year (Note 5)\" [astropy._erfa.core]\n"
     ]
    }
   ],
   "source": [
    "system_name = \"demo\"\n",
    "\n",
    "T0 = astropy.time.Time('2026-11-01T00:00:00',\n",
    "                       format='isot', scale='utc').decimalyear * u.year\n",
    "wavelens = np.array([575]) * u.nanometer\n",
    "\n",
    "# Saturn parameters copied from the NASA Space Science Data Coordinated Archive \n",
    "# planetary factsheet, https://nssdc.gsfc.nasa.gov/planetary/factsheet\n",
    "rad_saturn = 9.140 * c.R_earth\n",
    "mass_saturn = 95.16 * c.M_earth\n",
    "\n",
    "mass_star = 1.0 * c.M_sun\n",
    "long_node = 320 * u.deg\n",
    "long_node_d = 290 * u.deg\n",
    "\n",
    "sma_b = 2.3 * u.AU\n",
    "sma_c = 4.24 * u.AU # ~ sma_b * 2.5**(2./3) for 5:2 resonance\n",
    "sma_d = 7.7 * u.AU # ~ sma_c * 3.0**(2./3) for 3:1 resonance\n",
    "    \n",
    "mass_b = 1.0 * mass_saturn.to(u.M_jup)\n",
    "mass_c = 1.0 * u.M_jup\n",
    "mass_d = 1.0 * mass_saturn.to(u.M_jup)\n",
    "\n",
    "rad_b = 1.0 * rad_saturn.to(u.R_jup)\n",
    "rad_c = 1.0 * u.R_jup\n",
    "rad_d = 1.0 * rad_saturn.to(u.R_jup)\n",
    "\n",
    "albedo_b = 0.3\n",
    "albedo_c = 0.4\n",
    "albedo_d = 0.5\n",
    "    \n",
    "P_c = np.sqrt(4 * np.pi**2 / (c.G * (mass_star + mass_c)) \n",
    "              * sma_c**3).to(u.year)\n",
    "\n",
    "t_peri = T0 - 2 * P_c\n",
    "\n",
    "argperi_b = 291.4 * u.deg\n",
    "argperi_c = 90 * u.deg\n",
    "argperi_d = 178.9 * u.deg\n",
    "\n",
    "planetb = Planet('b', dist=dist, a=sma_b, ecc=0.07, inc=55*u.deg, longnode=long_node,\n",
    "                 argperi=argperi_b, tperi=t_peri, mplan=mass_b,\n",
    "                 radius = rad_b, albedo_wavelens=wavelens, albedo_vals=[albedo_b])\n",
    "    \n",
    "planetc = Planet('c', dist=dist, a=sma_c, ecc=0.02, inc=57*u.deg, longnode=long_node,\n",
    "                 argperi=argperi_c, tperi=t_peri, mplan=mass_c,\n",
    "                 radius = rad_c, albedo_wavelens=wavelens, albedo_vals=[albedo_c])\n",
    "\n",
    "planetd = Planet('d', dist=dist, a=sma_d, ecc=0.10, inc=48*u.deg, longnode=long_node_d,\n",
    "                 argperi=argperi_d, tperi=t_peri, mplan=mass_d,\n",
    "                 radius = rad_d, albedo_wavelens=wavelens, albedo_vals=[albedo_d])\n",
    "\n",
    "planetlist = [planetb, planetc, planetd]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Quantity 291.4 deg>, <Quantity 90. deg>, <Quantity 178.9 deg>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planetb.argperi, planetc.argperi, planetd.argperi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Quantity 3.48769021 yr>, <Quantity 8.72669502 yr>, <Quantity 21.36398359 yr>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planetb.P.to(u.year), planetc.P.to(u.year), planetd.P.to(u.year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create output data directory if it does not exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(data_dir):\n",
    "    os.mkdir(data_dir)\n",
    "#os.listdir(data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Use the exoscene planet_cube() function to plot the time-dependent positions and phases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Lambert sphere phase factor is defined by:  \n",
    "# $\\Phi(\\beta) = \\frac{\\sin\\beta + (\\pi - \\beta)\\cos\\beta}{\\pi}$ \n",
    "### where $\\beta$ is the star-planet-observer angle. (e.g., R. A. Brown, et al., 2009, https://doi.org/10.1088/0004-637X/702/2/1237)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "imwidth = 100\n",
    "pixscale = 0.2 * u.AU\n",
    "numpts = 101\n",
    "plot_time_span = 4.0 * u.year\n",
    "time_series = T0 + np.linspace(0, plot_time_span, numpts) # years\n",
    "#time_series = np.linspace(0, plot_time_span, numpts) # years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "phase_track_img = np.zeros((imwidth, imwidth))\n",
    "\n",
    "for tt, time in enumerate(time_series):\n",
    "    res = exoscene.planet.planet_cube(imwidth, pixscale, planetlist, epoch = time)\n",
    "    for pp in range(len(planetlist)):\n",
    "        if phase_track_img[ res['coordlist'][pp] ] == 0:\n",
    "            phase_track_img[ res['coordlist'][pp] ] = res['phasefunclist'][pp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Position and Lambert phase\\n From 2026.83 yr to 2030.83 yr')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.tight_layout()\n",
    "plt.imshow(phase_track_img, origin='lower', interpolation='none')\n",
    "plt.colorbar()\n",
    "plt.title('Position and Lambert phase\\n From {:.2f} to {:.2f}'.format(\n",
    "          time_series[0], time_series[-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Use the exoscene compute_ephem() function to make tables of planet ephemeris, phase, and flux ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "plnt = planetlist[1]\n",
    "epoch_deltaT_series = T0 + np.array([0, 0.15, 1, 2]) * u.year\n",
    "\n",
    "tseries, delx, dely, beta, phasefunc, orad = plnt.compute_ephem(tarray = epoch_deltaT_series)\n",
    "fluxratio = phasefunc * plnt.albedo_vals[0] * (plnt.radius.to(u.AU) / orad)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "beta angle: [32.99995445 33.55279824 52.01671657 85.62766263] deg\n",
      "Phi: [0.85827864 0.85396324 0.68847183 0.35735419]\n",
      "Flux ratio: [4.54117774e-09 4.51722636e-09 3.60479933e-09 1.82396464e-09]\n"
     ]
    }
   ],
   "source": [
    "print(\"beta angle: {:} deg\".format(np.rad2deg(beta)))\n",
    "print(\"Phi: {:}\".format(phasefunc))\n",
    "print(\"Flux ratio: {:}\".format(fluxratio))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Planet b (written to ephem_demo_b_2yrs_4epochs.csv):\n",
      "     t (years)  delta x (mas)  delta y (mas)     phase    r (AU)  \\\n",
      "0  2026.832877     101.810122      -5.798827  0.037307  2.139067   \n",
      "1  2026.982877     117.182401      34.865681  0.072199  2.147128   \n",
      "2  2027.832877      27.390529     137.718275  0.653965  2.351257   \n",
      "3  2028.832877    -135.904177     -51.967373  0.656681  2.444445   \n",
      "\n",
      "   fluxratio_575  \n",
      "0   3.714353e-10  \n",
      "1   7.134427e-10  \n",
      "2   5.388895e-09  \n",
      "3   5.006562e-09  \n",
      "\n",
      "Planet c (written to ephem_demo_c_2yrs_4epochs.csv):\n",
      "     t (years)  delta x (mas)  delta y (mas)     phase    r (AU)  \\\n",
      "0  2026.832877    -125.605841     105.395815  0.858279  4.155200   \n",
      "1  2026.982877    -146.540388      78.868121  0.853963  4.155714   \n",
      "2  2027.832877    -224.808914     -79.747553  0.688472  4.177001   \n",
      "3  2028.832877    -207.836319    -224.081129  0.357354  4.230613   \n",
      "\n",
      "   fluxratio_575  \n",
      "0   4.541178e-09  \n",
      "1   4.517226e-09  \n",
      "2   3.604799e-09  \n",
      "3   1.823965e-09  \n",
      "\n",
      "Planet d (written to ephem_demo_d_2yrs_4epochs.csv):\n",
      "     t (years)  delta x (mas)  delta y (mas)     phase    r (AU)  \\\n",
      "0  2026.832877    -226.799181     293.515606  0.770247  7.453440   \n",
      "1  2026.982877    -247.575618     280.112442  0.764113  7.420390   \n",
      "2  2027.832877    -353.625757     193.385052  0.706420  7.243729   \n",
      "3  2028.832877    -443.903280      73.501628  0.594884  7.073320   \n",
      "\n",
      "   fluxratio_575  \n",
      "0   1.052714e-09  \n",
      "1   1.053654e-09  \n",
      "2   1.022192e-09  \n",
      "3   9.027753e-10  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for plnt in planetlist:\n",
    "    table_fname = os.path.join(data_dir, 'ephem_{:s}_{:}_{:}yrs_{:}epochs.csv'.format(\n",
    "                               system_name, plnt.name,\n",
    "                               int(round((np.max(epoch_deltaT_series)\n",
    "                                          - np.min(epoch_deltaT_series)).value)),\n",
    "                               epoch_deltaT_series.shape[0]))\n",
    "    \n",
    "    exoscene.planet.write_ephem_table(plnt, tarray = epoch_deltaT_series, table_fname = table_fname)    \n",
    "\n",
    "    ephem_table = pandas.read_csv(table_fname)\n",
    "\n",
    "    print(\"Planet {:s} (written to {:s}):\".format(plnt.name, os.path.basename(table_fname)))\n",
    "    print(ephem_table)\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### High-cadence ephemeris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote ephem_demo_b_10yrs.csv\n",
      "Wrote ephem_demo_c_10yrs.csv\n",
      "Wrote ephem_demo_d_10yrs.csv\n"
     ]
    }
   ],
   "source": [
    "tstep = 5 * u.day\n",
    "tspan = 10 * u.year\n",
    "\n",
    "for plnt in planetlist:\n",
    "    table_fname = os.path.join(data_dir, 'ephem_{:s}_{:}_{:}yrs.csv'.format(\n",
    "            system_name, plnt.name, int(round(tspan.value))))\n",
    "    exoscene.planet.write_ephem_table(plnt, tbeg = T0, tend = T0 + tspan,\n",
    "                                      tstep = tstep, table_fname = table_fname)\n",
    "    print('Wrote {:s}'.format(os.path.basename(table_fname)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Simulate the coronagraph planet PSF scene using an example coronagraph PSF model, and functions in the exoscene.image modules."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load CGI PSF model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.DS_Store',\n",
       " '00README.TXT',\n",
       " 'os9_psfs_r_offset.fits',\n",
       " 'os9_psfs_angle_offset.fits',\n",
       " 'os9_psfs.fits']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hlc_psf_path =  pkg_resources.resource_filename('exoscene', 'data/cgi_hlc_psf')\n",
    "os.listdir(hlc_psf_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of PSF model: (10, 19, 200, 200)\n"
     ]
    }
   ],
   "source": [
    "psf_cube_fname = os.path.join(hlc_psf_path, 'os9_psfs.fits')\n",
    "psf_r_fname = os.path.join(hlc_psf_path, 'os9_psfs_r_offset.fits')\n",
    "psf_angle_fname = os.path.join(hlc_psf_path, 'os9_psfs_angle_offset.fits')\n",
    "\n",
    "psf_cube = fits.getdata(psf_cube_fname)\n",
    "psf_hdr = fits.getheader(psf_cube_fname)\n",
    "print(\"Shape of PSF model: {:}\".format(psf_cube.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CCD pixel to model pixel scale factor = 0.238\n"
     ]
    }
   ],
   "source": [
    "#cx_os6_hires = psf_cube.shape[-1] // 2 # True center of the high-res OS6 array\n",
    "\n",
    "data_pixscale_as = (21.08 * u.milliarcsecond).to(u.arcsec) # Pixel scale of CGI science camera\n",
    "hires_pixscale_as = psf_hdr['PIX_AS'] * u.arcsec\n",
    "hires_pixscale_LoD = psf_hdr['PIXSCALE']\n",
    "\n",
    "data_scale_fac = hires_pixscale_as.value / data_pixscale_as.value\n",
    "data_pixscale_LoD = hires_pixscale_LoD / data_scale_fac\n",
    "print(\"CCD pixel to model pixel scale factor = {:.3f}\".format(data_scale_fac))\n",
    "\n",
    "r_offsets_LoD = fits.getdata(psf_r_fname)[0, :]\n",
    "r_offsets_as = r_offsets_LoD * hires_pixscale_as / hires_pixscale_LoD\n",
    "angles = fits.getdata(psf_angle_fname)[:, 0]\n",
    "\n",
    "Np_psf_hires = psf_cube.shape[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show radial offsets and position angles present in the PSF model cube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PSF model radial offsets (lam/D):\n",
      "[1.  2.  2.2 2.4 2.6 2.8 3.  3.2 3.4 3.6 3.8 4.  5.  6.  7.  8.  8.3 8.6\n",
      " 8.9]\n"
     ]
    }
   ],
   "source": [
    "print(\"PSF model radial offsets (lam/D):\\n{:}\".format(r_offsets_LoD))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PSF model position angles (deg):\n",
      "[ 0. 10. 20. 30. 40. 50. 60. 70. 80. 90.]\n"
     ]
    }
   ],
   "source": [
    "print(\"PSF model position angles (deg):\\n{:}\".format(angles))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply half-pixel shift to place center at (99.5, 99.5)  \n",
    "This is necessary to ensure that after binning the PSF model down to the detector resolution, the array center coincides with the image center."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of PSF model cube: (10, 19, 200, 200)\n",
      "New array center along both axes: 99.5\n"
     ]
    }
   ],
   "source": [
    "offset_psfs = scipy.ndimage.interpolation.shift(psf_cube, (0, 0, -0.5, -0.5),\n",
    "                                                order = 1, prefilter=False,\n",
    "                                                mode = 'constant', cval=0)\n",
    "\n",
    "Np = offset_psfs.shape[-1]\n",
    "cx = Np // 2 - 0.5 # New array center in zero-based indices\n",
    "print(\"Dimensions of PSF model cube: {:}\".format(offset_psfs.shape))\n",
    "print(\"New array center along both axes: {:.1f}\".format(cx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set detector downsample parameters and test an example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "npad = 8 # pad before and after array edge before binning\n",
    "max_detect_width = 1.1 * u.arcsec\n",
    "test_hires_psf = exoscene.image.get_hires_psf_at_xy_os9(\n",
    "        offset_psfs,\n",
    "        r_offsets_as.value, angles,\n",
    "        hires_pixscale_as.value,\n",
    "        delx_as = (-0.1 * u.arcsec).value,\n",
    "        dely_as = (0.2 * u.arcsec).value,\n",
    "        cx = cx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get scene array dimensions and data mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(216, 216)\n",
      "(51, 51)\n",
      "0.00053487055 0.00794841662054061 14.860449297869547\n",
      "0.18250671 0.18250626010537888\n"
     ]
    }
   ],
   "source": [
    "padded_test_hires_psf = np.pad(test_hires_psf,\n",
    "                               ((npad, npad), (npad,npad)),\n",
    "                               mode='constant')\n",
    "\n",
    "cx_padded = padded_test_hires_psf.shape[0] // 2 - 0.5\n",
    "xcoord_psf = ((np.arange(padded_test_hires_psf.shape[0]) - cx_padded)\n",
    "              * hires_pixscale_as)\n",
    "ycoord_psf = xcoord_psf.copy()\n",
    "\n",
    "binned_test_psf, det_xcoord, det_ycoord = exoscene.image.resample_image_array(\n",
    "        padded_test_hires_psf, hires_pixscale_as,\n",
    "        img_xcoord = xcoord_psf, img_ycoord = xcoord_psf,\n",
    "        det_pixscale = data_pixscale_as,\n",
    "        det_width = max_detect_width,\n",
    "        binfac = 10, conserve = 'sum')\n",
    "\n",
    "print(padded_test_hires_psf.shape)\n",
    "print(binned_test_psf.shape)\n",
    "imwidth = binned_test_psf.shape[-1]\n",
    "print(np.max(test_hires_psf), np.max(binned_test_psf),\n",
    "      np.max(binned_test_psf) / np.max(test_hires_psf))\n",
    "\n",
    "# Check conservation of flux\n",
    "print(np.sum(test_hires_psf), np.sum(binned_test_psf))\n",
    "np.testing.assert_allclose(\n",
    "        np.sum(test_hires_psf),\n",
    "        np.sum(binned_test_psf), rtol=1e-4)\n",
    "# Coordinate center\n",
    "np.testing.assert_almost_equal(0, det_xcoord[det_xcoord.shape[0]//2].value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7ff0cc85b8d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2a5qGDRs2AADOPvvsnOPV19dj4MCBAIA1a9b4HmNvd4+nKArGjBlTcD/380L75eL9998HYOZCIgiis8lVkEMObQxKu1vXFArJdc0EQRBHD/TfHnEU0FEOo/b0CerHEeQ8aq+AFN63s0SjQo8P60OLJKLnwbheklYI1dXVGD9+PADgiSeeyNq/bNkytLS0oLa2Fuedd17ua2AMU6ZMCRxv7dq12L59O1RVxUUXXeTZd8kllwAAnnrqKRiG4dm3d+9erFq1CgBw6aWX+vb73e9+h9bWVs++1tZWLFu2DADwzW9+M+f8bbZu3YpXX30VAJz7QxAEQRBE96Mr1k9EOCQcEd2crnAY5TNOEP6CTiHiUW6xiWyXBHHUULTN2moFMmfOHDDG8Pjjj+OZZ55xtm/evBkzZ84EAMyePdtTtWzJkiUYOnQorrjiiqzxZs2ahUgkgtdeew2LFi1ych3t2bMH119/PQDghhtucJxJNrfccgv69u2Lbdu2YebMmdA0DQDQ2NiIq666CrquY8KECRg7dqyn36WXXopRo0ahsbER06dPR1tbGwDgyJEjmD59OhobG3Hqqafi4osv9vS7+eabsWLFCuc8Nm+99RYmTJgAXddx8sknZwlVBEEQBEF0I7po/UQEQ8IR0U0Jchi1J79OqQSjfMcMdx/l+grGf9zuB7mOCKKraWhowPz588E5x1VXXYURI0Zg9OjRGDNmDD799FNMnDgR3//+9z19mpqasGfPHhw4cCBrvGHDhuGxxx6DJEmYPXs26urqMGbMGIwcORIffPABxo4di0WLFmX1q66uxrPPPotYLIaf/OQnGDx4MM444wzU19djzZo1GDp0KJ588smsfrIsY9myZejduzeef/55DBo0CGeccQYGDx6M559/Hn369MFvf/tbJ4m2zfr16zF58mRUVVXhtNNOwz//8z9jyJAhOO+887B3716ccMIJWLFiBRRFKfIOEwRBEARB9BxIOCK6IfmKC/kIKR2dZyiXgFQKukowKuac9KuFIAAAXC9Nawdz5szBypUr8ZWvfAWNjY3YuXMnTjvtNCxZsgTLly+HLMsFjTdt2jS8/fbbmDRpEuLxOLZu3Yrhw4dj3rx5eOedd1BRUeHb7/zzz8df//pXXHHFFWCM4b333sOAAQMwc+ZMbNq0KculZHPqqadi8+bNuOGGG1BZWYn33nsPlZWVuPHGG7F582acfPLJWX3uvPNOTJs2DSNHjsSBAwewceNGtLW1oaGhAQ899BDeffddjBgxoqDrJgiCIAiik+nC9RPhDxNBtXWPYVpaWlBTUwPzn1tyQHQfgl4LPxGiqwWjIHK9n3IJKoWKNe25zkLe88UIQEHX0uN+5RDdDgGAo7m5uaBS7oVg/535ZNOFqK7Kv+qX71itGo4b88cOnS9B5AOtn4jiKG6NxIp4zwkUE7JSzLolbM60HiKONmj91JMhrzbRTchXNOqugpH7/GGLhFI4h4q9Rnf/XIswjvaLRxKOjtA6giAIgiAIgiAIIggSjogu5lhwGWVSiDBT6Hilxh47l9hVytAzhu7zWhFEByMMgBf58yMouSNBEARBED0IWj91O0g4IroptliRj2OlO4sQ+QgzYf06i1zztF8Hyl1EEIXAOAcrsqoH4+TcIwiilHRkmGEHrl9YyBpE5Po9GbZ+ydU37H51REi/TXde3xJEx0Lrp+4H/RdIdBFBVdOA9Nuy1KIRa2crBaLA1lXkOnehv4CDfsVQbgyCIAiCIAiCILofr7zyCsaPH48+ffqgoqICY8aMwSOPPALeTjFq3bp1mDx5Mvr164eysjKcfPLJmD9/PhKJRGi/bdu24eqrr8Zxxx2HWCyGESNG4Pbbb0dTU5Pv8eeddx4YYznb9OnTC76GgoWjjz76CI899hhuvPFGjB49GoqigDGGBQsWBPbJZ/KMMfz3f/+3p99TTz2Vs8+rr75a8EUTXU0+eXVykY/AUogAlKuEfEeISd2VXPeW1HuCyBtulKYRBEEQBEH0FLpw/XT//fdj4sSJeP3119G7d2+ccMIJ2Lx5M2677TZMmTKlYPFo6dKlOOecc7BixQpEo1GcdNJJ2LlzJ+655x6ce+65aGtr8+335ptvYuzYsXj66adhGAZOOeUUHDhwAA899BDGjh2LTz/9NKvPaaedhoaGBt/2xS9+0TnO/ThfCg5Ve/jhh/Hwww8X1KehoSFw36FDh7B161YAwD//8z/7HtO/f3+MHDnSd1/v3r0LmgvR1RQruuTjxmmvpThon98vB/scx7KNOCzRN4WuEUQ+MG6A8eJ+7xVr1SYIgiAIgjia6Kr107p163DXXXdBkiT85je/wZVXXgkA2Lx5My688EKsWLECixcvxu23357XeLt378aMGTNgGAYeeOAB3H777WCMYc+ePbjwwguxYcMGzJ49Gz/96U89/VpbW3H55ZcjHo/jtttuw4MPPghVVdHY2IjJkydjzZo1mDFjBl566SVPv0ceeSRwLqtWrcJXv/pVRKNRTJ06tcA70w7hqG/fvpg0aRLOOussnHnmmXj88cfx/PPPh/Z55513AvfNnTsXW7duxVlnnYUTTzzR95gJEybgqaeeKnSqRLeio5NE+42fLWoUWspVQPiM4xaSMsc71oSkjsx9REmyCYIgCIIgCILoHixYsABCCNx4442OaAQAo0ePxuLFi3H11Vfj/vvvx7//+79DVdWc4y1atAjJZBIXXHABZs2a5Ww//vjj8eSTT6KhoQGPPvoo7r77bgwYMMDZ/4tf/AIHDx7ESSedhMWLF0OWZQBAbW0tnn76aYwYMQIvv/wyNm3ahDFjxuR1bf/zP/8DAJg0aVK7zDcF/7c3d+5crFy5EnfffTe+9rWvobKysuCT2gghsHTpUgDAv/7rv7Z7HKK707WiEXN9mRuk3C2orzN2d8rd0xm5mjpK4DnWw/6IHg+FqhEEQRAEQRRGF6yfWlpasGrVKgDAjBkzsvZPnToV1dXVaGxsxJtvvplzPCEEXnzxxcDxxo0bh1GjRkHTNCxfvtyz74UXXgAAXHfddY5oZFNfX4/x48cDAJ577rk8rgxoa2tzxmyv7tKlcSZvv/02du/eDVVVccUVV3TlVIgOoVSCRZBokTm+5DSP4OMIQorZXMcFNqZkiUnZIpK7j9+8SpUPqVSCULFiUq68R3YjCMLGtFoX3wiCIAiCIHoKXbF+evfdd5FKpRCLxXxdPKqq4swzzwQArF+/Pud4e/fuxSeffAIgOHWPvd09nq7r2LhxY8H9wvj973+Pw4cPo7a2Fl//+tfz6pNJwaFqpeQ3v/kNAOBrX/sa+vbtG3jc5s2bcdVVV+HAgQOorq7G6aefjmuuuQYjRozorKkSBdPRDpfM8b0uIfOB5LPPq9gGn92AVxDi5nhWuVcGZoWxuc8fJJq0Jx9SIfcvX/23FLmacoWu2eeh3EcEQRAE0XEUU/SjIz/kCZtXcZVbhShm3h3lnM41p/bk1sxnbAr1J4h8aWlp8TyPRqOIRqNZx+3YsQOA6ehRFH+ZZPjw4Xj99dedY8Owj4lGoxg0aFDgeO5jATMvkqZpnv359AvDDlO7/PLL8wqx86PLhKNkMolly5YByG2X+tvf/oa//e1vzvPly5dj/vz5uO+++zBnzpy8zpVMJp3nmW8eopR0dliaZG31F4scocgTfhb+h1qAp4+xFinOjFynZ64FTHYupDCRpr34zzvfvE3Ct789z1ILSIUs7npConGix8INoMjkjhSqRhAEQRBEj6KE66e6ujrP5nvvvRfz5s3LOvzQoUMAwotv2fvsY8Owj+nVqxcY878Wv/Hcj4PmUsg8Pv30U/zpT38CUFx6oC6zBaxcuRJNTU2oqanBN77xDd9jevXqhX/7t3/DmjVr8OmnnyKRSODdd9/Fv/7rv8IwDMydOzcrA7kfCxcuRE1NjdMy3zxEqeg+ohGDbIpGTpiZq7HgZo4npYUjliFAuUPTMnIh+c2tNPjnVMrOvRSO//FBYXYEQZQCxgUY50U2ElUJgiAIgug5lHL9tG/fPjQ3Nzvtzjvv9D1nIpEAAEQikcB52U6leDye8xraO57dL6xvIfN45plnYBgGRo4cGVjFPh+6zHFkh6lNnToVsVjM95iLL74YF198sWfbF77wBfz6179GbW0tlixZgrlz5+Laa69FVVVV4LnuvPNOzJw503ne0tJC4lFJ6WjBKPMcLsEoQywyd7gEIL9wNeYv6gjBXftM15EQZoia40IKciAJHhC+Bme8/MmzGlzWdeRnS2eAcx1AphOpUAdSPqFr+UJV1giCIAiCIAiCKB3V1dWorq7OeZytSaRSqcBj7CimsrKyDhvPrY3YOZeKmYcdplZsMbIucRw1NjbilVdeAQBMmzatXWPcd999iEajaG5uxhtvvBF6bDQadd4w+b5xiO5EgGiUsc3c4XUWOcmy3c4iy43kbgA8riPnPC4RKr+ZBrmA8ukf7CzybpDgDckLq/KWObbLLcVy3EuCIIqHqqoRBEEQBEEURhesn/IJ/8onnC1zvKamJgjh/+G433jux0FzyXce27Ztw6ZNmwAA11xzTc45h9EljqPf/va30DQNQ4cOxb/8y7+0a4zq6mqccsop2LRpE3bu3FniGRL5UYhAEuS4yddp5BeW5nUZecUiOCKR/dgZMcOpI0Q6p5HpOpIhYMD++WYu842wTy/cCbR5WsgJTdyYvyjjKxZljJOV6DvASeVBcMDqJ2BYfbjHhZR2IHF0vvOIXEfEMYYwis9DK0g4IgiCIAiiB9EF66eRI0cCMKuh6brumyB7165dnmPzGS+ZTGL//v0YPHhwXuMNHToUqqpC0zTs2rULxx13XLvnYbuN/uVf/gXDhg3LOecwusReYIepXXPNNYGJovLBzgiu63pJ5kUUQilEo/acA45o5H7uFo3cziIAgXmMnO7u7a6cRn5hbt7ZyYH7C8k9lLNvlrsoQzTyuIekwJZ9bMb8sxxI7uvqzLxHlGOJIAiCIAiCIIjO4/TTT4eqqkgkEo5Lx42madiwYQMA4Oyzz845Xn19PQYOHAgAWLNmje8x9nb3eIqiYMyYMQX3y0QIgaVLlwIoPkwN6ALh6MMPP8S6desAFGeXMgwDH3zwAQBgyJAhJZkbkQ9+yZPzCZXKRCC/RNi2s4i5chpZ4WZMBWMKJKaAOc3cJzEVkqSYjSlgkCBZx0rWcc7joKTZHvHIdjBlJ8YOvoLCBRBPn6yQNGQn/XbN374uZm/PuC/e67ebao2pmudwkonbIXfu1zaf68n1uuZ/JwjiWIAJXpJGEARBEATRU+iK9VN1dTXGjx8PAHjiiSey9i9btgwtLS2ora3Feeedl/saGMOUKVMCx1u7di22b98OVVVx0UUXefZdcsklAICnnnoKhuF1Tu3duxerVq0CAFx66aWB53/rrbewd+9eRKNRTJ06Ned8c9HpwpFtlzrrrLNw4okntnucJ554Ak1NTZBlOa8XjigFpRCM2nMee7MrPM0nNM3sKbuEnwxBKEduo/A8Rh3/oxIsNGU4hDJcU2nHVFi+I+8+j/PK40CyjgkUxsh5RBAFQTmOCIJoNyyk5YKHtMw1g7dJrDywKXKfHK1XYGMsFtpyzSuc8L4MSnBjkfAW0re414EgiEC6aP00Z84cMMbw+OOP45lnnnG2b9682Sm2NXv2bE+1syVLlmDo0KG44oorssabNWsWIpEIXnvtNSxatMjJdbRnzx5cf/31AIAbbrjBcSbZ3HLLLejbty+2bduGmTNnQtM0AGau6Kuuugq6rmPChAkYO3Zs4LXYUV6TJk3KKydTLjpdOMrXLtXS0oIrr7wSf/nLXzzbDcPAY489hn//938HAMyYMcM3XpAoJZmLlEIEI78/jMU5jfxC0ySmpl1GHleR6aiRmQqJSVnNLSCZZ3cnyA4hDwVbFOO8CZpDRsU4vwTgzr0IaJluJNuBBM/9KIXzqBSQeEQQBEEQBEEQRMfT0NCA+fPng3OOq666CiNGjMDo0aMxZswYfPrpp5g4cSK+//3ve/o0NTVhz549OHDgQNZ4w4YNw2OPPQZJkjB79mzU1dVhzJgxGDlyJD744AOMHTsWixYtyupXXV2NZ599FrFYDD/5yU8wePBgnHHGGaivr8eaNWswdOhQPPnkk4HXkUgk8NxzzwEoTZga0A7haM2aNejbt6/Tnn32WQDAwoULPdv37duX1XfdunXYuXMnVFX1VeTccM7x7LPP4uyzz0bv3r0xZswYnHXWWejbty9uuukmJBIJTJgwAQ8//HChl0B0Ke0QFNyuIr98Rpkuo4wcPlI+QlBOCvtkqCjRyIdMcStrf1beouzcTlnOK1cYnOO4ckLV0uKROW6mgFMq8Sif1ybfT1cJohvCeWkaQRAEQRBET6EL109z5szBypUr8ZWvfAWNjY3YuXMnTjvtNCxZsgTLly+HLMu5B3Exbdo0vP3225g0aRLi8Ti2bt2K4cOHY968eXjnnXdQUVHh2+/888/HX//6V1xxxRVgjOG9997DgAEDMHPmTGzatCnLpeRmxYoVaG5uRm1tLb7+9a8XNN8gmAiqDRfA6tWr8eUvfznncR999BGGDh3q2fbtb38bP//5z/GNb3wDK1asCO2vaRqWLFmCtWvXYsuWLfjHP/6BeDyO2tpajB07FtOmTcPUqVPblVy7paUFNTU1MP9ppX9Ig/ELTSuEYtxGyMtp5CcamTmOspNgZ8/EnB8X3KqiZj83k60Lz3ZuPbeuydoH1/P0uGHXaFvEg+6Au2qcTXb1OACe+2D2TYtBntxMdsU41+thX6vz3e86nWsEYN8T59rssfL59REegpdN2C95qrhGlAIBgKO5uRnV1dUdcgb778xnL49AdUVhC4yssY4Y6Dvxww6dL0HkA62fOpuOusfh6zmJxYL3ScH7TIL/hhs8HtpTCK3dY+e6ptC8k7k+YAxxmTvrpHaRa11N6yGiu0Hrp55MPsG5Hs477zwUqDU5/OxnP8PPfvazvI5VVRWzZs1q13mIjsDH5WL9Ec7fXVOgaBR2dIholB4xf9Eoc3umaOTtzAMXCv73IvMPv/t5wBwFzyMBd3ZuJyAjf5ELz3PX7bXFI8YkCAEwBkAAggFMWIsiJuUVnlcaJAQvlqzJEQRBEARBEARBEJ1CwcIR0RPIzGfk3pMt6DCwPMSj9oeo+bqNQkQjKUeia7do5GyzXDZCcF+nkdPPIxrxEKdRviKLfZztChK+91jA8IhiuciVq4lBghDcFIsYhxDmczAEiEcAGJzqBMIRd+y5hr2+AoV/amrP3e8+5nNOgugeMM7BitRcGYWqEQRBEATRg6D1U/eDhCMihNyiUTCF/KCyjGd2iFo6Z09miFoQheYzsgWiTNHIxC0K+YhGnnEyQ7gKJR3C5ohHjuvIFd7mcSKFh73Z+AlOAoblMOJWbiNnh694BGGY52JwiWVhzqBS0RnnIIgOhPPi38K08CEIgiAIoidB66duBwlHhAt/p1FhgpEf+YSomedkfnNgkicMK1eImnlG7nEdufP7ZOY0CnMaCZeryC+nUW7BKJcrxn29aUHGTzwyNR0549q4dbyRzn0UICa5RTVuTytDMHI/dotHsO6vgDkfJniGwypXCFl7XEfOzO1ZZ2wn5xFBEARxNFNM/o5c/xAFf7CkyOG5PsrUvsGjMjW0b8o4HLhP5Ah5t3NMBvYPyxHJwu+lLPknnwUARQ7eB4TPWzNaQ/tyHnw/inkNc/eltRFBEKWFhCPCh1JUISvinD5V1LKOycAO5eKCOwKJyPijyl0CkDvRNee6Z3tggmiPiJQrLK2QvE+ZgootBmWKR9Z1Oi4gpF1D7u8Zopkf9j3yCEi+4pE9pp3jKO0AMkMUO9MRFHQuyntEdGPoEzOCIAiCIIjCoPVTt4OEI8LCP3dR+yjih9RTDcz96VFG6FpISBr3+WTIFn/cglGoywjwrZwWLhi1V7zIdO7YY2eKR0BmyJpXNJI94pEtLIUhMcm5X4xJWeKRcEQiKSvfUXbIWmcIOCQeEUcZtPAhCIIgCIIoDFo/dTtIOCIQlgw7H8ITY7enklo69MztnHGLILZA4n4cWOnMndwayMplJOxS81kuo0LC0kolWriVG3/xKH0m+765Q+kAISTPvQHzz3PkjMIkcJHuz2C5liy3l3OvhR0CKIeErBVP7mp9JB4RBEEQBEEQBEF0FiQc9XhKndcoX2XXJyF21iHe8vLOZh8XjbekfGbi6o4SjDpKpHDHj4U4jzIEMYB7r5+lhTcBA1yEJw83q9fB62LK3OYTsmaSj+sod54j9/vA/ThbRKKk2cRRgjBcMaHtHYPe6wRBEARB9CBo/dTtIOGIyKL4ZNg2+fywB+c2ChzVJWxkTtUtGjlOo4zcRnALSe12GAHtT/bszDDP4+z5WEJaiPPIvidCGJCgenUclv79m0/1Oa8I5xJqmJSusgZu5ToqJFG2+7ryc7hlnyNjTgWfmyA6ByonSxAEQRAEURi0fup+kHDUoykuRA3IJ0l0rvP67U2HVeXK0RNW6cLrLvIXjABAQLM6FFIprRTimnsMP7Ejs/SZy33kFs18nEf2cyE4JEnxTNtOIh44K3dYmuuxGUJoJ9+WzXvL0s6n/FxAwfcul2DpH8JG4hFBEARBEARBEERHQsJRj6XUolH23nzPy8BCHUbOiB6xJOzMIWIRUITDyO8agLD7lztfj32+zDFFxuMA8QiAYJo5BwFLyNHBmAQuNPM7t8QgS09xBCF4RbqgHFGA133kFY+4y4HUOWS7jyhsjejGUHJHgiAIgiCIwqD1U7eDhCPCQ2dXUss8n8dtFCLI5EqE3TGCkZuMvEsF5O7xzBfCNZb7/JkKWaZ45B+6xgQgLIGIMUDY3VzmJXfCa7/7GObiSocIup1GQaFk+ZB/uJpnHjnFI3IdEd0EWvgQRA8muDAFADAWvl8U8aGMxGKB+xS5IrRv2PorZRwO7ZvSmwP3GTy8bzG/LEWOP/ncymvph8TC/x1S5fJ2901oWuA+IRKhfcMIK3oC5PrAEkDIh4UE0S2g9VO3g4SjHknxYVbFl6X3W5T4J7023TG2EBQ0XmY+I3compE+JkssSvfNd74eEcjjlCpEALHdOxaCA5B95hX2WqUFpLR4xAEhWWNJ1uLPdghJYEJ2uY2kwMWh8LknfonH3YKNKebkkyTbHycBeMkg8YggCIIgCIIgCKJYSDjq8RTu9mh/+fUAUcCdFNtzHu4tBw8gSOTJKRgBluOo1IKRe3/4pz+e+Xqe8XQFOY+wlX8Iljt0LW/3kXVs+qFdgS38nF7XUTpJts+R8BduSpMjikLWiKMCLor/xKvYqiIEQRAEQRBHE7R+6naQcNTjCP6HPR+3R7ZolK/bKCC3UZ4EhU+5w9DM58W6i3yEogyRyBGImPtYa1+eybyZ9aOXNX9mhtIxZ5vbhRQ0d5/QNXAIFuw+sufMhOzM208wCrrvxSfJ9qf0riOC6GK4KIHVmhY+BEEQBEH0IGj91O1oX1ZkokfSftEohFxCi5+YAe40CO7kLkrnL/ITjdKJsoNJC1pOwm7HDSWBQTZFI2u7HeolMQWMSZZoFN4YU6xmHu+EizljyunjnW3MNb+w+2UJRtYXbAEJhnWPuCthOM+qOheW26j0lOYXOYlMBEH0RObOnQvGGBhjWLBgQeBx69atw+TJk9GvXz+UlZXh5JNPxvz585FItD+3CkEQBEEQPQ9yHPVovCJEmNuj/eFpQNFuI7fg4xI3/ELRzO35JrvOmIufs4hJPm4iyeMs8iT0zrc6nP2YGWYonj1f5kro7XIhMY8YFh6253EgCbOvx31kVV/LciAxyUnEaV+TO3l2TmGJmbmWvO+VfPIMtS9Btj+UJJvoZnAO8CIFTvrEjHCxbds2LFq0KOdxS5cuxbXXXgvDMDB48GDU1dVhy5YtuOeee7By5UqsXr0a5eXBSX8JgiAIosug9VO3gxxHPYr2/fD5i0btCVEr5KTcvwH+7iLhctr4zi8TP9HI5SxiqukMskQixhSkHUNp95HEVDAmQZIUSJLLSRTSPMe5xki7kgJcSC4HkjnvfBxIrvsJlzMrwIHkHO7cY+t5WKU139xOhf5qodxExDEK56VpBAFACIGbb74ZqqriK1/5SuBxu3fvxowZM2AYBh544AHs27cPmzZtwo4dO3DiiSdiw4YNmD17difOnCAIgiAKgNZP3Q4SjggPwucrm/aGqIU7fMLnZVhNM88vdB/BiLta0Plt0YeZZXCZAsaiYCwKyW5SBBKLQJIikKUySCwKWYpCkcvM71IZZCkCRY5BliJmY9G8m8SUdD+nRZ3mmQOLgDEFkjVHBhVgSgECki0WCdNNJHRrmwYhNAihgwvdClXTXY17Wr6vLeDnJgsSD/3fN4W62yhcjSCInsITTzyBt99+G/fccw/q6uoCj1u0aBGSySQuuOACzJo1C4yZvyePP/54PPnkkwCARx99FJ9++mmnzJsgCIIgiKMbClUjCqQQ0SgkRC2PkC5zdMP1LCgkzW9ebvzPzaA6z5kTfpZ2Ajll633C1dwuGynPa3Fm6lfVLLPQmOe5AlMsM6ukMWHddftynPCwsKTUZjiYU32Nmcd6qq/Z54VZhc26Olf/QqEqZwRByR2JUnHw4EHccccdOPnkk/G9730PN954o+9xQgi8+OKLAIAZM2Zk7R83bhxGjRqF7du3Y/ny5bjppps6dN7HPkUUHcn5wUzutU3wyYP360Y8tGtKbw7cJ3h437DzSlJ4aKTEIuFjh9wPI8e8OG8L3BdPaaF9DaVP4D6Jhf8rJUtlgft0I/y8oa8/U0N7shzvLRH63qS/OUQ3gNZP3Q4SjnoMpXBlFCca+ZOZZ8nI2J+d36hd4Wgs7cph1h9byQk/SwtFzCUi2eKQ5IhFdn+/0Kz0/vS1+M9PYnByCQGKeRwDuCunkZPnCOnE1fZ2MO7Jg+TNgRRWhS0z9xGDt/oaADv5t/D2sQWzLPdRZjU1hoCFcFCuIbdCls51VFx1NRKsiG6E4C5ltr1j0MKHAL73ve/h888/xwsvvABVDf6nce/evfjkk08AAA0NDb7HNDQ0YPv27Vi/fj0JRwRBEET3g9ZP3Q4Sjog8Kf4f8ULdRh7aIRpl5i9yJ7tO5y2Ck2PILRi5xSK3UJQpDoXhd2w6EbWVfFoYznGSNV0uzP0CtlDkdAZjMrjQTCFH6DkcSEG43Edg5r1l5j0zz8Od39P23AqtuMbA2plQvZSJsgmCII4NXn/9dSxduhTXXHMNvvSlL4Ueu2PHDgBANBrFoEGDfI8ZPny451iCIAiCIIgwSDgicuAnGOQSBPINUcsjsXNRgpH59mZMtQQgr1AkWYmuZceBpFpzlSBZwo6f+GNWH8tlAQ7ab47L7eplrvth90k7khQIxk0nEoMjJElQIAQHF7aoowc4kFh+7iPYlmbLfeQ4kuQsoZ+5qrB5t8tpt1imC8k5Ph/XkXursHoV65ajympEFyJKYLWmT8x6NIlEArfccgtqamrw4IMP5jz+0KFDAIBevXo5uY0y6d27t+fYIJLJJJLJpPO8paUl32kTBEEQRPuh9VO3g4QjIoT2/LSGiEYe2iMa5cIa052HyBKIAKTdRJJiiUMqJCY5gpHsEo7s49MjW4IPsgUfoHBHji1McZEOzXPcPeBpl5MAZGaKNdzWV0RG6BgzZ+bnQMqd/yjYfSRggNm3nqXnli/tdx0VRmecgyDaDcXoE0WyYMEC7Ny5Ez/96U8xYMCAnMcnEgkAQCQSnC8mGo0CAOLx8LwwCxcuxH333VfAbAmCIAiiBND6qdtBwlGPoD2Oje7uNHK7VLy5jMzKY5KTw8h2GMlSBAwyZKaAMRmy40SSIEFOh4z5lpe3r9AroPCsnEyFiSuSb74k1zamOuKSxOycR4YlJKkQMFxV0VTLleSqkubKfySc+56H+4jZ12JNI0NASl9s5liWQFWU68jdrz3OI8pvRBDEscG2bduwaNEijBkzBt/61rfy6hOLxQAAqVQq8BjbRVRWFpy4FwDuvPNOzJw503ne0tISWs2NIAiCIIhjExKOCB868h/vPESj0O22wOAVETwJsJ18RYrHYWQLRabTSIbEFEiQIcPrNHKLOW5HkACHBNlXLHIfk9kvDD/hKDM8TnIuVTbHZaabisFwxB4BDg4dDJIjaVm9IYSW4T4KIqPymjkZOO4jyGkxKM88VWnXUcckrCa3EdHtoU/MiCL49re/DV3X8fOf/xySlN/vXTsMrampCUII33A1O0TNPjaIaDTquJMIgiAIotOg9VO3g4SjY54wl0ZmIuKgn858fujycRsV6zTKnIe7ypldGU0BY4opDkkKJKZAYVEwJlvfJchQTcHIFpKc+XpFHAEjHRJmhYq53UTcTl7t7Decx84YGQJSZkU2+/dZZk4ldyicW0iyw+7MKmtWTiShQQgDhtCt8DLJyYEkLKeRAIeAlpH7KCh0zc995BKPAN/Kaek8R36uI++Rwa4je789F3cOKPrlTxx9CB7wY1DgGETP5N133wVjDBdddFHWvuZms2T6f/7nf+KnP/0p6urqsGHDBowcORKA6Srav38/Bg8enNV3165dAOAcS3QMuf9utf+Hm+Uoxy5EcKl3PaQ0fa6xYxH/hOs2FWq/wH32B3VB5HJsh31wp/Nk4D4ASBrB+bmSWmNo35T2j8B9TAp37YUVVQmq0msTlgYhc32Zfd5SVFMmiK6D1k/dDxKOCBQv5xYrGuWD/+LLPI+/aCSzqJPDSHKFpqks5ghGDDIkkT03zri5DwD3uT/24sXtMBLgzh/yoMWPvQhwFhKuSmoSk838Rm6hKChsjnmdT9yq/iaEAd11fmHPXugA5IzcR3m6gPxC10LC+bKmWpTrqBRV1khwIgji6MUwDHz66aeB+w8fPozDhw87IWr19fUYOHAgDhw4gDVr1uCyyy7L6rNmzRoAwNlnn90xkyYIgiAI4piC6l73eHL9I1/IP93hopEZXuVt+c3NPYd0XiMzPE1xRCNJikBiKmQpAplFoUhRqFI5IlI5oqwSUVaJGKtERJQh6rQYVEQ8TYECRSiQhOSZoynCGDCgWY4eHYbQoPMEDJEEFxoMoUMXSWt7MqsZQrOaDkPo4JZbSIDDEJpLgOKuKzZdUjJUKIg6TUXMbKwMEakcqlQGVSq3WhkUqcy8F1LUvDdSBBKLOjmgbNEt+N6boW0CIm/JPn2/MhOVe/NRhTvhMt9z9HEBcRTDRWka0SOxw8382rXXXgsAmD9/PoQQ2L17NwCAMYYpU6YAAJ544omsMdeuXYvt27dDVVVfJxNBEARBdDm0fup2kHB0TFOsTbWQELXcolH+Zw0Ko8oUjbxOIwYZshSBIpmikcKsBvO7DBWqiEJFBDIUqCICRShOkzJ+HDjjZrJpKwzNLRrZAhAXmiP62M+50PNomiU0aVkCUlB+JHcOJttBJbO0oCTDfK6wKBQpBpkpUKQoJKZAlqLWfbJzQIWJRwGvu+DO6yJC7OJZ4lG7aK94RCIT0c3gJWoEUQCzZs1CJBLBa6+9hkWLFkFYJYn37NmD66+/HgBwww03YODAgV05TYIgCILwh9ZP3Q4KVSOKILdoVIhgBN/kzdl5jdzV09zhabZoJLMoZKYiwspNIQVRKEKBDFMcUoTiyW1kh6JJgpspppkO+zcNt4QcAxoM2KKQ4biLhJXniAvdnK3gzjbfO8a8QpokKQB0CFiJvMEhMRWG0CBDBWewAuZMwcgvJ5NgpogjQzVFLGjgMMAkyQl9M4QOSADnZj4oAR0QgGBaQNianYQ8nfMo7Rqyk2gbga+vJ98RM1/b7JC1oFxHQXirrREEQRD+DBs2DI899himT5+O2bNn4+GHH0b//v2xZcsWaJqGsWPHYtGiRV09TYIgCIIgjhLoP7Bjlo52GwWMHyQaWU4XpxVM5vks54ydw0iy3TVqltsoIkyHUUREoFqPFSFDEsxsrh8D02VkCki228gtGpkOoSR0OzSNp2DwlPU4CS6SEEIPbJynzO+244gnwXnaheR2Mdkha375kuzcTJKQIAvVbHYoG4tCZbH0PZBiaUHNCudLu7T8nEf2vfa+BwoJWcuecClC1nJBHysQ3RD6xIzoIqZNm4a3334bkyZNQjwex9atWzF8+HDMmzcP77zzDioqKrp6igRBEAThTxevn1555RWMHz8effr0QUVFBcaMGYNHHnkEnLdv0HXr1mHy5Mno168fysrKcPLJJ2P+/PlIJBKh/bZt24arr74axx13HGKxGEaMGIHbb78dTU1NOc+5YcMGTJs2DfX19YhGo+jXrx+++MUvYs6cOdB1veBrYML2L+fJRx99hFWrVuEvf/kL/vKXv+D999+HYRiYP38+5s6d69tn3rx5uO+++0LH3bZtG0aNGhW4b8GCBXjjjTdw6NAhDB48GFOmTMHcuXPRq1evQqYPAGhpaUFNTQ3Mf16P1aoDxVxXIaKRSxDwE42CRCKnGpkd8sQzHEeZ+Y3sJNhpt5EkRcCsMCw7p5HCoo7TKCLKoFghaY5Y5HHOmBgwwJmAAR0606EhBZ3p0GHmJNKQABe6lctId8LRTKeRBrgFnqzryrxz3vtiVkkzBTAG2akEJzPVdFEx1Xxu5TiyQ9MkIcH+skPYbIFJhw7OLMFLaM512GKXzpOO2OWIWtBc9999772vtddV5iMOul7brNfX2hf8GgcR9F7OpyJgPuMTPQ/zfd7c3Izq6uoOOYP9d6ZxsYrqsuL+zrTEBWpnah06X4LIh56xfiqUsPuQ64OyYqqqRdvdN6zimjl2SFU1tX9o32OxqpoQwWMXU1WNi3iO84a9TuHvrVxV1cJSDtC6iQimZ6yf7r//ftx5550AgOHDh6OyshJbtmwB5xwXXXQRXnzxRUhS/kaIpUuX4tprr4VhGBg8eLDHAXzmmWdi9erVKC8vz+r35ptvYuLEiYjH4+jXrx/q6uqwfft2tLW1Yfjw4Vi7di0GDBjge84f/vCHuOeee8A5x4ABA1BfX49Dhw5h3759SCaTaG1tRWVlZd7XALTDcfTwww/jpptuwuOPP46///3vMIzwcpBu6urq0NDQ4Nv8bhZg3rCxY8fi6aefhmEYOOWUU3DgwAE89NBDGDt2bGilkZ5LZy3mMl0k5rZMcYRZ4gZziRxZgpLHyZK5cHCNbwsWluAi2cIKU8w8RpbYYiaPNh1GKiJQhAzzS4JiNSnjPnHXly262E4jWzQyRNJyGaXdRdxxFGkQMJsphOlZzd4vhNk4T4ELlwvJch/pPOk4kIQw52O7kNJ3WnJC1yRIkGGG46mIWO6qKFRXjieFRa37pbqcR5JzT/0r4IUvHnwXHi5XmV+y7MJcR2FzIDsGcRQgStTaydH+idnHH3+Mm266CXV1dYhGo6ivr8fNN9+Mjz/+OO85Hzx4EH369AFjDIpCEfoEQRAE0e3povXTunXrcNddd0GSJDz99NP48MMPsXnzZmzatAkDBgzAihUrsHjx4rzH2717N2bMmAHDMPDAAw9g37592LRpE3bs2IETTzwRGzZswOzZs7P6tba24vLLL0c8Hsdtt92Gjz/+GBs3bsTevXvR0NCAXbt2YcaMGb7nfPTRRzF37lwMGjQIr732Gg4cOIC//OUv2LFjBw4dOoQVK1YgGi38w4eCHUcLFizA+vXrcdZZZ+HMM8/E448/jueffz4vx9G9996LefPm5X2u1tZWjBgxAgcPHsRtt92GBx98EKqqorGxEZMnT8aaNWswceJEvPTSS4VcwjH+iVnnJsQOdKC4RCP/s9gOI0t4sHMEZTle7PN5q6jZbiNFKnPCsCJSuRmaxso9YWkRoUJ2ySw2OgwrIM2AzgxoLAUDOpIsDgM6UqINukhC5wnLraNbwo7bqWMAHqdRfj9OfvfNTl7tztlkCmHpnE0MEiKsHJKQoCLi5Gyy77UtLNm5mgzo0FjS/C7M6zCvKWmJYG7xS7PEraDXwPWaZ7rLCnSWee9Vsc6jXNAnZ0QmnfeJ2WeLIiX5xKzvrFSP+8Rs69atOOecc/D555+jpqYGI0aMwIcffojm5mbU1tbinXfeCXQqu7nmmmuwdOlSAIAsy+2yZxMmx/b6qb2E5XIs7oONMFdRmCsIAAQPcbOwcAG1X/n/F7jveH5iaN8ECxaR92NnaN8j+sHQ/WFUqv6futv0waDAfW2sNbTvgfh7gft0oym0r8RiofvD4CJMkM/13irG7UbrJiKIY3/9NHHiRLzyyiu46aab8Mtf/tKz7+mnn8bVV1+N2tpafPLJJ1DV8N/DAHDrrbfiZz/7GS644AL88Y9/9Oxbu3YtGhoaoKoq9u3b51kLLVq0CLNnz8ZJJ52E9957D7Kc/luzd+9ejBgxArquY+PGjRgzZoyz7x//+AdOOOEEaJqGTZs24aSTTsrruvOhYMfR3LlzsXLlStx999342te+VrDFqRB+8Ytf4ODBgzjppJOwePFi58Wpra3F008/DUVR8PLLL2PTpk0dNgfCDUO+opHjMGLe5h0un7cfy3gmZ7iN0iFdMlSoLJ3TyHTepEUjGTKY9eWGM2HlMtKRYiloIpkWWSzRyO0y4iJluo2gWU4iV9l6X7IDbu3jhTAcJxK3xCkhdBhWOJnbeZR2HRngzPsH33Ye2a4jJ6cTIoiKMkREGVQWQ4SVW3mP7JxHVrU1lwDkn+8oTb7imNndx3nUrhxX5pkJgsiPo/0TM8MwMHXqVHz++ee49NJLsX//fmzcuBEff/wxLrnkEjQ2NuLyyy/P6ZxatWoVli5dSmXnCYIgCIIIpaWlBatWrQIA37XJ1KlTUV1djcbGRrz55ps5xxNC4MUXXwwcb9y4cRg1ahQ0TcPy5cs9+1544QUAwHXXXecRjQCgvr4e48ePBwA899xznn1PPvkkWltbcc0115RUNAK6eXLs9t6wnktnuI0Az9vGDnNyPbddRllCUcA2L/75dbwuFzhhVhKT0mXpmWrJQwoUoVjhaRIUJ0iNZYWnCScVNocO3fIfaY5oZAjdyQfEecpKbp0pGFmuo4KysmWISMIcQzj5k9K5lOy8Sk7oHPxFI0XITjMFM1d+JyhQEPXcK9kSjOxk2dkhayEEVY0LCktk2fmQCg9ZA0g8Io46uii544IFCyCEwA033IArr7zS2T569GhHMLr//vuhaeG5TmwWLVqEZDKJCy64ALNmzQJj5s/r8ccfjyeffBKAaY3ODB9v7wdAL7zwArZu3Yra2lr86le/cpxMFRUVeOqpp1BbW4u///3vWQstN4lEAt/61rcwcOBA/Md//Ede10kQBEEQRDegC9ZP7777LlKpFGKxmMfFY6OqKs4880wAwPr163OOt3fvXnzyyScAgIaGBt9j7O3u8WwnUaH9AGDlypUAgEmTJmHHjh24/fbbceGFF2LSpEmYO3cuPvroo5zzDqJThaM333wTU6dOxVe+8hV885vfxAMPPIADBw74HlvMDeuZdK5lPFPISbtV3O4iv5aPeGSOmMaVe8cK5TJFIyWd48iqKBYRESjCdt3YqbCzBSMAMCzBSGMpqyWREnFoIg5dJKHxOHQetyqfeV1GXoeRK9wMMhhT/Zu1P/N+OAKS7T6ycydZLie7ipthi0cu95FbRLLdVDIkyJCgCjNMLypiUIWZ98gO5XNXW5Nc99OeXy7XUb54BCTAdf3pOXtfYwp9II4xBAN4kU0U9nNxLHxiZve77LLLUFVV5dlXVVWFqVOnAgCWLVsWOO8FCxZg586deOihh6zwKoIgCIIgjgpKuH5qaWnxtGTSP+H9jh07AJjrk6CciMOHD/ccG4Z9TDQaxaBB/uGyfuPt3r3b+WDP3p9PP8453n33XWf76NGj8dBDD+G1117Dyy+/jB/+8IcYNWoUfvOb3+Scux+dKhz97//+L5577jm8+eabeP7553HHHXdg+PDheOqpp7KObe8N8yOZTGa9YXoemWJFoQRXtrKdJN58Ri5xwM955DOP0BCojJxJdhUyZgVoycysNGYLFWYFNduLwzxX7nEZMcNxG9mCjGFVHuPCm88ouPKbS0izq6RB9TRT9FIs4UsOqHZhjS24lUTbFJTshNlOsmwrSbZbNJKsX4xuv49iCUim80pxnFjmY9UlvJktP7dRftJ9Zngiyxq7s0LWyKFE9DyOhU/M/vznP7ern822bduwaNEifPnLX8ZVV13lewxBEARBEMc+dXV1qKmpcdrChQt9jzt06BAAoHfv3oFj2fvsY8Owj+nVq5fj1M5nPPfjoLn49WtubkY8bua3u+uuu3D88cfjrbfeQiKRwK5du3D11VcjlUph+vTpzvqsEDpFODruuONw1113YcOGDWhsbERbWxvWrFmDCRMmIB6P4/rrr3dsVTbtvWF+LFy40PNmqaurK/KKuhPuvEN+FCMWuc+RHivLJeKuouVUPcsQDjwupELwulVMZ4wKBgmyVbbeLFVvhWNZIWrMEo3cAo0dGGYKRaZoZCbDTphuI7Qhxdug8TYncbTbaSSE4YhGtreHMRUSi0GSKiBbTZGroMhVUJWadJN7QZGrIEtVkKUKMKkMEos5TqT06+QSj5yqa+ZcTNdREhoSZg4mK/m1AAdnwhGMZNeXYgXpRYTqVJhThVlhTUY6ZM3t3mJMzQgly4+gEEXnvWA7xtzOKwpZI45xBGclaUDP+cQslUph7969efVzn8NGCIGbb74ZQgj813/9V/jFEQRBEATR7Sjl+mnfvn1obm52ml04JBO7QmwkEgmcl12NzBZowmjveO5KtUF9/fodOXLEecw5x/Lly3HuueciGo1i2LBh+J//+R+MHTsWuq7jRz/6Uc75Z9IpwtHNN9+MH/7whzjjjDPQp08flJWVYdy4cXj55ZcxZcoUCCHwve99D+4Cb+29YX7ceeednjfLvn37SnBVPZyMpMd2iBrgdpnILpGgiFN5wqbcQpTsCBJp95H9lXbg2HBLbEhnJtIt8cV08nBXXiHO9bTTyMpn5HcP0tXQzKbIZWalNzkGWYpYVd/M54pUBlmKQmJ2eJhiupGYkiXSmOKRmXNJCA4huDU/87uTJNv1lYkdpmeLR2b+o7TjyHYdOfcx0xHk+e6Dzz1Jj+HvcMt2HtnbM8WiUolHFPpGdDHF2qzthp71iZmd9DpXP855lov4iSeewNtvv43vf//7JU8MSRAEQRBEJ1DC9VN1dbWnBZWij8XMKoipVCpwWvaHdmVlZTkvob3j2f3C+ubqN2HCBPzTP/2Tpw9jDP/+7/8OAPjTn/6Us8BIJuH1ODsYxhjuv/9+vPjii/jwww/x97//HaNHjwaQfcPcz23yfeGi0WjgG+ToJpfTqBAKKCOfp2jk7SdbZ+GwnTX29nSp9lwn9w9Tk5gMSbiD0lxzteBIh6hp0KEzAxpLIcVSSIk2GNCg8TboPAndiFvJqZNwHED2mMzKAWTnVpLKIDEFqlQGialmziDIkKF65mKe2XDlKkpCtyqnmWJVChDp/Emw5suEDg6AgcPgEiABOlcACdCQBBjAwSEJ7ghjdpYi2x/G3e8TAWsmCgSLmqFvjIMz+7sCCYAhNPN+C7/XhiO8/HDme89+3dJzEAxgwnrXMbhEqPR7Iz+8SdQJ4lhm3759nnKyQX/XjvZPzArpl9n34MGDuOOOO1BfX4+77747cL4EkT/F/I0JX1uxHMtwWapo95l1tAXui6l9Q/uO4MGCazkL/j0AALuwJXDfofj/hfZV5KrQ/ea6LGBsfWdo3+qy/oH7TuQjw+dVFvw/xN4j74T2FQguQCBL4dcbthziIvj1zdmZIAhf8vlQLZ8P5zLHa2pqghDC98M3v/Hcjw8dOoTjjjsur341NTWQJAmcc4waNcp3TvYHaq2trWhsbES/fv1yXodNl1dV+6d/+if06dMHALBzZ/qXfuYN86OQF+7Yo6v/WXaFqWXgCEoZ4Wr2tvaQOabkyp2TmS/JnQybOxmB7KxAApyZEpJdRU2AW9XLTLeR3cvOaeQe187pJLEIJBaFLEWgSmVQpXJEpUrEWBXKWDXKUI1yV4uySsRYJaKsElGpEqpUDlUqgyxFQkPETBHJyq0kzHxHAtzJdeT+yswRZY8jue6JKbWZriM7jbbj2nJVPvMKf+18zbJe/+D3SntC4wjiqECw0jT0zE/McvXL7Pu9730Pn3/+OR5++GGnEhtBEARBEEcZJVw/5cvIkaaIvHfvXui67nvMrl27PMfmM14ymcT+/fvzHm/o0KFOFVp7fz79VFXFsGHDAAR/uOjebhh5mjcsulw4AuDcGPcL1N4bRpQip5GbdP4Zv9xGmW4jOw+RX/OMyrIFn9wz8Sbillha3JAgOa4jIB2OZn+Z2YxMt1GSJZBkCaRYHCkRR5IfNt1GRsLJa2RWNnPlNGIyGItClsqgyFWIKr1QrvZFjTIYNcpg9GX16C/qcJxRh0HGYAzhgzCED0QdPw5D+EAMMQZjiFGHgbwOA8Tx6MOGoLdShyp1IMrVvogoNVCkCkisDIxFvSKKlSybCzPfkc7NpiFhVn9DCjrTHfnIe8/scDU791E6ZE0R6RxREjPzHTGrah3s17RgMceb5yhT8Eu/T9wha5kusUIrrOVyy5EgRXQdpYzRz5eO/sQs3/Ha+wGQ/YlZPv0kSXJcWG+99RaWLl2KiRMn4uKLL855XQRBEARBdE+6Yv10+umnQ1VVJBIJbNq0KWu/pmnYsGEDAODss8/OOV59fT0GDhwIAFizZo3vMfZ293iKojjFTQrpBwBf/OIXAeTWT6LRKPr2DXefZtLlwtFnn32Gf/zjHwCAIUOGONuLuWHHPp31j3Bh53G7VHwrqTEpcF/4wD55cizMumH+49l5f2xBxYABzlw+HWGYZe2FYTl4DDOvUUbonCNmMMnjMopI5YiySpSLKlTxKlSKclSzGKpYFFWyiipZRbVifZdUVDAV1ShDlShHuahETFQgyioRkcrNZNVSJC3aZDpwhMt1ZM2RCzM/E2fcSZAdWpkOdgibOxNUOtwP8L42xeemSruX3AKS5zwhbiR7FIIgCuNo/8QsEomgvr4+r37uc9glaN955x0MHDjQ0+wqcoZhONt++9vf5rp0giAIgiB6CNXV1Rg/fjwAM19iJsuWLUNLSwtqa2tx3nnn5RyPMYYpU6YEjrd27Vps374dqqrioosu8uy75JJLAABPPfVUljNo7969WLVqFQDg0ksv9ey77LLLAAAvvfQSPv/886xz/upXvwIAnHPOOYEFVILocuFo8eLFEEKgpqbGWdjZtPeGHdsE/SNdjNMol2PDNa4n8bVPbqOMEuxOC8h9lIswx0uQY0m4wtN0y2lkgENjGjSkLMdR3FVFLQ7dSFhOI28ybLfTSJWrEFNqUKH2Qy95MGoxGMcZAzGI98NgpQr1kQocXx7B8RUqhlfKGFYpY1ilhGGVMoZWKhhaoaKuLIYhkTIMYb0wiPfDAGMQajEY1cpxqFD7Iar0cpxHdtLstG9KgxA6DCs3ks5Nx5GOJFIsZSX7NkyBzPWaSiztOrIrzTEnZE1NB7Ex+7Gc0w2WHRaX8R7wCVdMv/a2mymd2Nw82C85N0Ec5XCpNK0AjoVPzOzn7fngqLm5GZ9++qmnffbZZ85+e1s++Z0IgiAIgugCumD9BABz5swBYwyPP/44nnnmGWf75s2bMXPmTADA7NmzPTkYlyxZgqFDh+KKK67IGm/WrFmIRCJ47bXXsGjRIse5vWfPHlx//fUAgBtuuMFZZ9nccsst6Nu3L7Zt24aZM2c6FWQbGxtx1VVXQdd1TJgwAWPHjvX0mzRpEs444wy0trZixowZaG1tdfb94he/wPLlywEAd9xxR8H3psP/O3v//ffx7W9/G++//75neyKRwI9+9CP853/+JwBz8plJMNt7w4iOJUj88bpVSvzW8gl388NdXcwWj+yk2BwcOjPdOplV1NxuI1umcUQrK3zLdBqVI4JylIlyVPAKlLMIKmUFlYqESpWhUgF6RQSqVY4alaNa5egV4eilCtREBKpVoDrCUKXIqFZUlCOKCl6BmKhABOm8R2bFNdkn55FhXpmwXEbCABeGp8KaeZy/+8id6wiAGd5nVVVzjnG7gYrIPeTrOoPs6zzzF48I4highFVB8uVY+MTM7ve73/3Os+gBzISOy5YtAwB885vfdLZ/97vfhRDCt3300UcAAFmWnW3XXXddzmsnCIIgCKIL6IL1EwA0NDRg/vz54JzjqquuwogRIzB69GiMGTMGn376KSZOnIjvf//7nj5NTU3Ys2cPDhw4kDXesGHD8Nhjj0GSJMyePRt1dXUYM2YMRo4ciQ8++ABjx47FokWLsvpVV1fj2WefRSwWw09+8hMMHjwYZ5xxBurr67FmzRoMHToUTz75ZFY/xhh+97vfYdCgQfj973+PQYMG4ayzzkJdXR2+9a1vAQDmz5/vrBMLoeD/0tasWYO+ffs67dlnnwUALFy40LPdLnmvaRp+/vOf49RTT0X//v1xxhln4IwzzkBtbS3mzJkDzjlmzJiBH/zgB1nnau8NO3bp4rAd9z/1LMRh4spzU4rE2L5TYdnilR2yZbtu7NxGKaaZbiOWgoYUUiIOHUkzr5FIpvMawaxqBqSrxzEWhSJVICJXo1ypRZXUH7ViIAbwPhggV2JILIK6ChlDKzmGVhoYUZXEiMoERla14Z+qj+Cfqg9jZNURnFDVhhOq4hheqWFohY76SmBwuYRB0SgGyJXox/uit+iPSrkvypTeiChVkKQYGNT0fRccELoleFlV2SzHkSaS0GzXkUtC8hOPJDBIgkERiidcLdM11L7XRcoSh7JapniUEa7WMUmyGbr854cgOpGj/ROzSy+9FKNGjUJjYyOmT5+OtjazgtCRI0cwffp0NDY24tRTT6VcRgRBEARBlJQ5c+Zg5cqV+MpXvoLGxkbs3LkTp512GpYsWYLly5dDlguLoJk2bRrefvttTJo0CfF4HFu3bsXw4cMxb948vPPOO6io8K+gef755+Ovf/0rrrjiCjDG8N5772HAgAGYOXMmNm3alLXmshk2bBj+/ve/43vf+x769++PzZs348iRI/ja176GP/7xj5g7d27B9wQAmAjKdBnA6tWr8eUvfznncR999BGGDh2KpqYm/PSnP3U+kTx48CBSqRT69++Pf/7nf8YNN9yACy+8MHSs999/HwsWLMAbb7yBpqYmDB48GFOmTMHcuXPbVVGtpaUFNTU1sOtNHR3kmmch/+hnlugMegtkJMZ2ws1UK9+PAndSbElSrP3Z7iDHIcNd7h7B0zmFnDL03rk5rhsWhcQUJ8dQRK6EwqKIMDM/UFSUQUXETPgMBZKVRZ8zYQWrcSRZAhpLIiEOQxdJJIwWU3gxjpiuI5G0ElFbbiOmWMmwKxCVq1Eu9UYv9Ec1r0KtVIYqRUbfGEOFIlAb1VEuc1RHUohIHOWKBlniVqAZQ8qQoXEZrZqKuK6gSVPQqkloTDIc1gQ+0zQ0iziapENoRSPajEYk9VYYPA4u4t7qbiwKxhSoShVkFkWZ0gsqYqhAL6iIoIJXQIaEKFSnopr5KgurjpyBJEwxLc7aoLEk4qIFukgiaRwOuCeZWfcl5x6ZQpDiJNYOchUJ6xqENZ75nFuV7AAhNDgV5LLeC6VKgF3QrzvimMV8fzU3N3vK25cS++/Mp3NrUB0r7u9MS0JgwILmguf7wx/+0FkcDB8+HJWVldiyZQs455g4cWLW4mfevHm477778KUvfQmrV6/OGu/Xv/41pk+fDs45Bg8ejP79+2PLli3QNA1jx47FW2+95bv4ef311zFp0iQkEgn069cP9fX12LZtG9ra2jB06FCsW7fOd/GzZcsWnHvuuTh06BBqampwwgknYOfOnWhubkafPn3w9ttv4+STT87rXuzevRvDhg2DLMuBeZ+I3Byd66diKWb9FV6xhiE8v4Qs1+Q4dzC60RS4LxbJLu3s5nT5/MB95SwSuA8A/oa/Bu47FP+/0L6KHF6enotk4D4hMte2Xo4vGxe4b5gYErgPAD6U9gbu23vkndC+AlrgPlnKcb08EbxPtIX2LQ5aKxFB9Iz1E+FPYRmRAJx33nmBVVX86NWrV7tVLZtTTjnF84kpkUkxolHx5/IIBT6ikedYJgECEChu4S6E4VnLucvSAzq4NQcBjhRLQYAjyeIwhAZNmN8NkQTnuiWQmEmyzWswBRHJym0UlatRIdWiCn3Qh/dCLzmKflEZvSJA/5iOGlVH31gCFWoKvcraoMo6ymIJUzhiAkIwaLoCTVPRmihDXFPxWaIchzUVMTmCppQEWVKhpiQwzgAJEDIHt4UWziGQdAkqBiAAgycBCdCt7xpLAgLQWczcDw4zfXi28yjtNEq/bsg4gkFyZ0pCoe+drMp57t/9wnxt0uexxzbPY+Z2KvycecwKtCAiOhUutcsq7R2jfe/ZOXPmYPTo0fjxj3+MjRs34sCBAzjttNMwffp0fOc732nXJ2YnnHACFi5ciLVr1zqfmF155ZW44447EIvFfPvZn5jZHwC99957eX0AdOqpp2Lz5s34j//4D/zhD3/Ae++9h379+uGyyy7DPffc4ymoQRDFEfYzWoxbOvxnnzH/csk2ESVYWNCNYFEhFxHJ/9NtGzkkH+Ue5p8g36Yp7p/QHgDOjIXnJJ1eH/5vye4j/r9jAODnjatD+x7QtwXuGyHXhfYdbAwK3Lc/h9ilGdnJaW0UuSy0rxGynuZ6rtc/V5ntniL8EkctXbh+IvwpWDgiiHzz0DDIPk4Vm+JFATNzkeGIDmZlMQ4Ie3TXdifjkZ0XSLMqlGmWmyfb6cSYAokpUFgUKouijJejnKkolyVUKECFwlGj6qhUNdREEyiPJFFdcRiqoiNWloAsG2nhSFOhayoYE4goEehchgSgTZdhCOCILqFclnCYq4iKGBQWhSJFYYgkGJcyRBQz9bUQ6VxHQpjXnv5izpH5LnmZr4jUvtcpn1A3xiTPJ4Th7xeCINrDpEmTMGnSpLyOnTdvHubNmxd6zLhx47By5cqC59HeD4Dq6urw2GOPFdwvk6FDhxb0oRdBEARBEASRhoSjo56uSSbMfKq4uQUHdyl2WwzwOksKxBJIgj4g4cx06DDrGHeiaB06DKZBEwkYMJ1GhtBg8KQVKmeLR2m3EWMKFLkMEbkSZVINqnlv9BLl6BVRUBtl6B8z0CeiY1DFEVRHEujX6xDKy+Ko6tMMJZZEpKoNTDbAJAHBGYxEFEZKRVVTJeJt5YgdSqEqXg7GBGLJKAwRgcQkGCIKoVdDZzp0KQkuNBhS0nIdcUAY5lwZIIQOLiTHcWQIDRKToCEFIAJdGJafJ/zH3J3sXGJStjjPJPO8OfGGp2W6mOznAtwSjYz08Y4DyT6f14FUWqcQuY6IzkNwQBT5iZmgT8wIgiAIguhB0Pqp+0HCUY+nuB+oYhJem+JB+/raziHB7O8GOCRIrrpqthBhMC3tOBKmwGTn1xFWTh2/udluIwVRREUUESajXGYolwUqFY4KRUdlJInKWByVlUcQq2hDrFcr5FgSSq8jYIrh6B5yPAKeNPMCMJkjlYxACIaqZAwpQ0aFoqBNlxGTGCKQERERKCwKiamm+8kKG8tMHJ3OF2S4asdZzioISGCWP8kMCvNLll1K/Cru2e8R213kdhpluo46DxKPiE5CtK+qR9YYBEEQBEEQPQVaP3U7SDgiujVmaunMbWmhwQ5V4+BgzM4LZEpIOnRwxs3k2MKAITTownLx8JSZlNnOI2SLCEwCYyokFnWSb1fwSlQiiipFQXUEqIlw1EaT6BOLo29VC6oqW1Ez4DNEao4gOvAQWKUAelcCsgIoCsA5pCNHgLZWyJVxRFvKwSSBSHMKKUMBYwJxQ4bGGeKGhARXkdDLcYRVIiW1ISVFrJA6V9iY4E6eKC50GJChIWHlNiqDziQYlqPHzmNkpaJ2yUrp+yiF5DPIhyABMbOqXq7QNHNbZwlJJB4RBEEQBEEQBEHkgoSjox4zwfAxjUiHqJlCg2I9TgsMZv4i03Vkw5m1TRhWiJrmOHPS/YwsF46d4FtiqpnfSESgMhlRGYjJAjGZo0zRUaGmEIsmECtPIFLVBrX6CFhvBlRWQPSuhVBUQFEBbkCKRICyBGStCUAbos1t4JqM8mgSiVQEZbKBmMwRkyVEJAYVClREoCJmuo6synVmXTThqpRm5TlihpXnyEj7jpiAJITjwUoLSN7rNe9b5+YW6jqnEUF0LkIwiCI/8Sq2P0EQBEEQxNEErZ+6HyQcEUcddriZxOCIJUxY1dyYBgbZctQYVvpszUmMbQjdqqSmpcu/W5hijJROii1FoYoookJFTJZQJjNUKAaqFAPVkYQZolZ1GGW9WhDp1wSptwFx3GCIympofYdAyFFAiQLcgHzkM0htLZAlCXJZE6LxJgBA1eEK6LqM6kQZ4oaMFs0Kh5MUlIlytElmkmydq1Z1DTPfkF1djQn7fugwhAbGJBhMgyQkGNABBjDBIIEB1l0BAM7SgW2dSahg5M5tlHdeJaC4yiDkOiI6GC6ZragxSjMVgiAIgiCIowJaP3U7SDg6ajAz3PiTr+uo9D89QnAwlh3mJGD45rsp6lyZTiErx5EtCtneGvtOGFYolykYaU4yZtO15L4X2fePMQkSZChQoECGKjGoEqAygYhsICIbiEZSUKIpKLEUWJkBVEQhKqthVPYCr+gHoVYCcgwwEoAkQygRSIebwbQU5Io4lLYEItEUVFVDTNERkThUJqBIDDJjULgMGarjgLLzHHnnLTsuLL/qamaGo/SdM5+JQMGId5ELqGsdSCQeEQRBEO2l/X+7GAtfhstMDdxnsFS7z2sIPXR/o3wocN8n+vbQvjG1b+C+eaccCe07btWI0P3lr/40cN++b00P7fts828C9x2UW0P7ViAWuE+ScvwrFfIZmJTj9WdS8Npe91l7uxF5f/jme+Yc+2nNRBA9ERKOiHYhLFHCLbiY26SM40oTAmUKUZJTft4+H2DmOWKQwIQEibmK1jsJo42020hoThl7+CTGZpCdhNQyVMhQoEKCyhgiEhCRBKISR1TVEFE1RMqSUCrjYFUSUFUFo7oWRtUAoGoYZKUSkhyD4Dp0tQo8UmG6jjiHVHUYSjwONZZENJJCVNERs8LVorKEqMSgGmaSbJmprsWFBPcqxBTQODjXIaS0kGYnBJeEKSCZacPhEY3cApMtvPnf/MIXxZnvg64hcw70sQPR+QjOSlAVhKzWBEEQBEH0HGj91P0g4eioohSuo1JMg5uhRLkOyxCNSpn0WFh5j7iwBCVLMOECkJx8SJZoJMzKa+GY7h0byXL4SEICAwNj5rgSA2TGwZgwm2KASRxQZEBRIJQIhBIFk2OQ5BhkKQbOdDA5BiHH0nmPFIApBmRVh6LokBm3GiAzM2xOguRyG8lO2fr0TbBzP6WFNDsJOGOSJ9cRRLrmHGe2fOT/epTidfITEUOPL7nbyO/cruTiWZDriOgYKEafIAiCIAiiMGj91P3oDrYAomSE/fPdcW4L2/ViPuYdF3JkuYRsQYoLr1PGqbAmzITY9vN0sxNjW30Ez6s8vcSYKRrBEosYIDEBWTbM57IpHAlFhZCjEHIMkmSKRrIUhSyZQhLkmCUsKYAqATKHZDVZ4pAla3wAiks8clc88wv/M11YhnM/HIcVdOhMd8Qiz3eX68ju4y+ulfa1zPu90WFha2G/8uiPC0EQBEEQBEEQRCbkODrqCHMdAel/9CWfbcWcVVhnTTtzzPxG6XOZz72PM4UCW+RAToEpfZ3mub2OIEcIEgbAVHAY1izS5dwdQcnObdROMYILAS4A3VK+DcFgcAm6roBrZkPiMFgiDqa1QUo2w9CaIIQOrlSa3/XDgHYYTE+B6TqgccBQwQ3JbIKBF6SKZ9wPkb4fjngGDslOCg4Obr824I5gJDIEo7QIGBK65oP7tc/3+OyN7Xl9/O5ZrnmEOY8IosRQckeCIAiCIIjCoPVTt4OEo6OSXOIR0CE/Ke4QtYxwNTsHkedwlxDQ/vCnjDxKjljlOsIKVwOTkVlW3n4u2iEeOQKLEBDCvOu6gCMccW6JPppi7tBSkFJxCLUNXGt1rlgIHdDjYEYCUioB6JqZXkmXIbjkWDGFsGyZyO/VExBgTria/9y5SLuL7LvIM75sl1j4a9S+1y9TTMx5fIlyYhFEd4Fi9AmCIAiCIAqD1k/dDxKOjlryEY/yGaM9vdIV02x3isddxOxvaYdL+thgGFjojMzzKk5yawHuqaqWdh2ZcFcIXSHXJmDmCTKgwYAODRxJLpA0GBKGhIQhI65FEE/EkDxSDjmqIdLUBAmtkKs+AUuYVUNEpBxGpAKM65DbDkGKN0Fq+gzsUCN4swz9cBm0RBSJZBRJXUGKy9AEgyEAQwgYMGBA94hhfsKKnahcCPN+2MnCBQxwSNCZDgnp/Ejp8DRvaJsQhd8vL5abTFhV0qw8R36uM+de5zxfofPJ95OJINcR5ToiCIIgCIIgCIJwQ8LRUU0pxKN8xvdPvO1OgGw6S2RHCLCFA+fYDspZwwVPJ8MWHJzBkxPInmch2MKUlSUIOgwYQkAXgM4ZUoZsCj2GglQygkgiAn4kBqa2gR1uhQRAjhyEiMTAImVg3IDU1gIpcQTscCvQlgCPx2AkVaSSERiGAo3L0LgEjTMzik3AkXXS4X35zz/zbcGR3pZOmu2fNDxdda70+L0W+Z2rI8QcClkjOh5K7kgQBEEQBFEYtH7qfpBwdNTT0eJR5ulMAcI8q+S4XWA5XJgn/5HXbVKoAOJ7+gx3jTmu6ayRmDmndHhaDlGASWCCO5JEOpcSBxc6dJ5ESo4jzpJo41Ec0SS0qhJimoymVBRyG0dFSxWEYFBiKahtMUTxOVhFM5TWZkCNQESiYNwAEgkgmYA4mABvjSDxj96IN1fi8OEKtLSV43AqglZdQdxgSBhAknMkmQad6TC4lreYw11Jyk2xSIMkJKuiWnq7znQY0NJuLZFOJJ6+2fknEM/Eef3d4mKGkJgZyui8NzosMXa+kOuIKCEUo08QRwFhv/OL+QEM/1sihB66X+fJkL7tn5fO46H7m/FZ4L6U0RLat1ztF7ivIhp8PQBQUX1K6H7j+OGB+/pEQ7uGfoh4SDoYfl7eO3Af5+GvYTGEfvCZ8/XP9b9BMX+XKK0A0QnQ+qnbQcLRMUFHikdp15GAWZo+vcud5ygtHjmhSgF/1Nq12PHJqQQoVmiZ7W6Ss5I0Z/7RtfeZolfAqVyhcIbQoLEUNG6GqyWscLW4riCuqTiSKIMiGyhrqQAAKE0JSFoCEloBhYFFVIBzIKUDSQHeEoXWUoHUkTIk28oQT8QQ1yJIGApShoSkwaBxAU2Yfie3uGNXhMt5qzzJwc3rNRNlw8l3ZGOmzbYTahfubgqdh0s8yry37hkQBEEQBEEQBEEQ3RcSjno0xTgrrKpenqTKrhxHOYbNLR75CWHcOR9jcMQdDg6GtOvIFq6yRnTELMlMsC0ka66Ga3wGAR1caNB4G5JMRZtUhhYWg2pIiKVUSExCRI7AEAyyxJFIRcAFQ3lLJaraYlBiSUSq2iCpOpiqA4KBpyrAUxEkmyugxaNoPtQLR9rK8dnhKhxKlqExGUGTJqNFA47oHG0ihaSUQEq0QedJ8xMtywGUD9yqlsaYleNIpAUkzriZv0lo6cTYwnAEIyd/VDsEJCdMziUe+iXHFp68Te7rskUvd4rwoDdT5nukyE8lCKIDoOSOBEEQBEEQhUHrp+4HCUfHDJ0Tspau5JUOTUuHI6UdLu6wtXRfIys8qd3zsAWrgEsOEqYYJB83VDrXjSlYGBBCBxe66TgSSWhIISF0JAwFbTrDEV2CyhRUahEwBpTHy8G5BEnmiCSiEIYMJhuQVR2Cm5XXDF1G8nA5kokojrSVozVRhjYtgiO6gjZDQpvOkOICSc6hwUCKpSxxx8gQcaw8UmBmuF2IYGLfY844IOzE2OmwNA5viFp7re/ukLSsfYHhih2XS4kgugsUo08QBEEQBFEYtH7qfpBwdEzR0eKRK0m2WzwSgHBcJoCwDSKBUylULLDcTRamSJF+7A5X4wJOriNzCpawwgAmZPO7LXYxFUJoZs4kd6JkoUNAhm7EwSBBZipaJBXgANMZNBEBIKNNV2CIMlSmIkjoCsoVDb2OVCKqaCiLJaDIBiTJHNMwZBhcQluiDAlNRVOiHEc0FZ8lYmjWZHyWlNGUAppSBppFAq1SK5LiMDQeh8FT4DwFAS0k35BXtHEqrAlAMFOoMZjhhKpxYThV5wyhZYSp5e9sMl8Dw6me5ohyIa6zLLHI121EEARBEARBEARBdAdIODrm6AjxKGhM/2prTr6jgDCl9s3AdDoJZjjuJjvPUb5ITIJhhW8xqzy9KSK5ExtauZJcriOdJ5BibUiyMiREGaJcRlyXIDOGVt0UtKJSBCkugTGBiBxBylAgSxyKZIo2XEjQDBltqSiSuoKWVARHNBWtuowjuoQ2HWjTBeLcgAYdGlLQkAAXGrgVUpYvXBiQXaF6tlgkuZJUe/MmwXEh+d33wgh6T9jjuULU/ELXyIFEHGuIEiR3JC2VIAiCIIieBK2fuh0kHB2TFCIe5ZvnKChJtlmVzO06AkznEeByHzm4Ezd7hQSTPEqku8rNZ+Y58sttJDEZXMBxD3GmQ4IKwXRLPNGs6mq2ICXAhA4OwOAS7BoghqxBl3UkeTW0VDUqdQUJQ0G5oqBZkxGTBWoSMUQkjjJFh8IEFNtxJBgMLiFuyEhyCa2ajLghoSnFcEQ3nUathoYm1oYW1oQjOISk0YKUccR0GwnNElXseyNZycLN0Du/6+YwILnuteGqnmbnODJsYcp2HGU6j1yvmT/BQpFfjiThuobg9wBBHDtQjD5BEARBEERh0Pqp+0HCEdEu3LmOzOdWuJKV6ybtNrKFBW+em6LPbwsQGb8P7OpqmZjikQYATuhael7uJNm2eCSBgYMLHUxo0EUSEleRktoQZwqiIgpwQNUlM0k2Y0hZv5xUSUKKS5CZgMxM5YzDFI8ShoQUZ2jVJCQMUzQ6onO0cQNxaIizNqRYHDpPmvmNnOTj3Nf9k5lHyg8uvImo7UpqmfdNeISpwnHnuhI+Ccozcx1lD0DV1giCIIiegxDh5ek1o7VDzmvweOh+HcHzisjVoX1bk/sC9y14919C+9469pXQ/QcTZwTu+2Pr3tC+wloD+pEQh0P7GpIeuE/nR0L7hjmpdSMR2pWHzLnYsH4W8gFz7rHD/hkniwdBHKuQcHTM0pH5jjJzHcF8bv1xFFaOIVscYK5peASDnAKS3zVYDicosJMrC5bOc8QggwsjK88RAEhMhQCHZB0vMRWCSRDczunDnbOaczUAJGEY5nkMKQUua0hKh5GU4ygTFWjTaxDTI2jRVEQkCRWKBFUCYrIMiQEyS3u6DAEkDUDnQJshkOIchw0dCaGjhR3GEekw4mhB3GhGih+G5riNktluo4zvDDIYkyC5xBphBam55RsOAwY0V24jzSVQGR6hKvv1CXlP2TmvQsMTMxJw+whF3mpq7lejIwh6/9GihygdQhSfnDHbuUkQBEEQBHHsQuun7gcJR8c0+YpHhYar2c9Edsia5YBxV9kqzmGUPqd5Pnur5WyCASEkV+iaN1wts4IagymucNsRY4WwCY/45ZJarLxKZr4jCZr1KV2SqQADZEmBLgyAA1EhwxAyVMl0HzEwKMy+OwAXgM4FNCGQMDiSgqNNpBBnKcdplOJtMEQSXOgQVrOv3Y05umxWVfMJU0uHgfEMGSa93Uwmng5Pc19z8eTKdZSPu6grRCOCKDElsFqDrNYEQRAEQfQkaP3U7SDhiLAoPNcRIGWErKXzHQFWziNn9HRiZvNBPsKBe06WEGGfy+2MYaY4xcE9SaGdUaxtMlRLMFHBYEBIEVOgQcTqDyvfke4INQICEBo4OIRhCjk6j0MXSSRYM+JyM1QWQwurhgIFMV4OxZAR1VRIkCC7xBMzm5IODoEkS0FjKcSlNqREG5LiMHSeRMo4DIOnYPC4JRwls1w4DMy8B1aSb9ttlBm2ZoejSa7ntqPIyW1kJcW2k4Hbj8376z5v/iKON0zRu90ciVvju8LlhH8oXufTHeZAEARBEARBEATRfSDhiCgNWfmO5IxtpXR4ePMYZYarBeU5cjuMwAAmzHAywTg4NDAhAVY4G8sUMgSHYBq4MGuT6UYCwkp8zSXddB8xFRwcEpOgiZjpboIESTBwS03TkAJnHEkkYDANKRGHhgQ03gZdJGHwFLhIOkm7s/EKY35uo+y7ZaTvk50gWxget5F/+FiBCO4JU3TPzSMa+fTrXMhtRHQeQkimK7KoMUjQJAiCIAii50Drp+4HCUfHPIVWWLP75DOmLQZYVdZc+Y484hHgCEhmh0L/cXefzw6FMxNZM8ctY4pBXHBIzFuSXnKLSCwdSgfJymMkwRSNgCznkXmssJxHBiDiMJgEjhQMrkBjRyAxFQm5GQwyFCkKmamQmAIJsisRN5zz2tXMBDh0njTzDFmCkRmipgGO68l9r6x7yBTTZYTgqmpcGI7Ly7lea7uZ10iHgGGdz0q+nVVNLV/8wxQzQxTdbrPg8xQj6oSHx+Uen/64EB0AZ8VbpclqTRAEQRBET4LWT90OEo6IdhKQ78hPPAIKFIuyw5w853E5mTz7BAdjclaeIxun6psV4sWF4es8kqBY4hGAQPeR4egMHBy6YeVTggEdMmSmgDHZI94A6QoZhtDBhQZufTdzKFmiUUAFNfMa0vfck8spQyRijvPKfW47LM2VCDsrKXb6PrvveqF4cly555EZouZ7vvadM5x8ErETBEEQBEEQBEEQmZBwRPiQb74jG1e+Ix/xyBwxO3TMT0Ao6JyeUvXpcDVAcQQSd2U103nkEnMkS2ySJHChgUkyuFBg8JQ1d8nM+8MMMOd8dgInzXIlmfmGDMTBIENn5o8Uc74HlaTnabFGWGFpHoEqU+iQnNxGjKmhbiPz3nJw4X0uLOHIrqLGLdeRndvIqdzmmmPBgorLYZYpGHlxu48yr7kYEac9jiUSjYiOQwhWgqog9IkZQRAEQRA9B1o/dT9IOCICyEc8yhUGlw4d8riP0B7RKPtcfuFqtoDkhE0J7oSn2bhD1wQzhQaJyQBPmNskDiYkGNwUfhwnEIPpdkKGQ8YSymxxhkF2RBOWEZubmRzcCdnKUzRyh2L5JcQ2r9kwnVduB5ElGjl5jWzhyiXoeF+TwgWYzNc4vSPzHCXIp1QySDQiOhZRgqogRVcVIQiiCHL9nQj7+Qz/2c21FjL4keCRWTS0b3vHBQBdJAP39VGPD+3bGHJNqxLPhPZ9+/3eofvD8mUmtX+E9pWlqsB9YdcLAIf1g8FzsqrttgcjR18eOq/i1lDhBUnIpU10PV29fnrllVewePFibNq0CclkEieeeCKmT5+OW2+9FZJUeO6ldevW4f7778fatWtx+PBhDBs2DFdeeSVmzZqFWCwW2G/btm1YsGAB3njjDRw6dAiDBw/GlClTMHfuXPTq1Svr+NWrV+PLX/5y6Fx+/vOf45Zbbin4Ggq+6o8++giPPfYYbrzxRowePRqKooAxhgULFgT2effdd3HPPffgS1/6Evr27QtVVdG/f39MmDABL774YmC/p556Coyx0Pbqq68WeglE3uTzw+YVO9KOHO7Zbu4znJZ9qnzfipbA4krm7E7wnBZHDDMUzXM1ZoiazFSzwWwKi0JhUahSOVSpDKpUBkUqgyKXQWJRSCwCiUXNRRpTrGpmzLleAWEKM8KAEBq4SIDzNggeB+dHPE3wuLldJMBFAkJoZnMElcycRi6nEVQwK38Ss/Mc2ZXVspxNhtPsnEbpSmocnOtW2Bz3hMgVXN0sSPwRPN2AwNc9WCzrDGjhQxAEQRAEQRBEmvvvvx8TJ07E66+/jt69e+OEE07A5s2bcdttt2HKlCngvLD/W5YuXYpzzjkHK1asQDQaxUknnYSdO3finnvuwbnnnou2tjbffm+++SbGjh2Lp59+GoZh4JRTTsGBAwfw0EMPYezYsfj0008Dz1ldXY2GhgbfdtxxxxU0f5uCHUcPP/wwHn744byP//DDDzFmzBjn+bBhwzB06FDs2rULr776Kl599VVce+21ePLJJwPVu/79+2PkyJG++3r3Dv90giiWQsLW/ELWJLidR6XFTJZthqRZAoWVf0j45dfJQGJW6JrlVAKzQss4IDEOcIBJEpiQwIVkCVOWqwmaE75mnif7HuUnwPjNzw6vs4Q722lkiUS57mV2biNvHiPOdUeoEi5xJ3vO7cltlB2a6OtkCnRYdZaYQ6IR0TlQVRCCIAiCIIjC6Kr107p163DXXXdBkiT85je/wZVXXgkA2Lx5My688EKsWLECixcvxu23357XeLt378aMGTNgGAYeeOAB3H777WCMYc+ePbjwwguxYcMGzJ49Gz/96U89/VpbW3H55ZcjHo/jtttuw4MPPghVVdHY2IjJkydjzZo1mDFjBl566SXf855++ulYvXp1wdcfRsGvRt++fTFp0iT8x3/8B/7whz/g0ksvDT1eCIHjjjsO//mf/4n9+/dj165d+Otf/4rPPvsMjzzyCBhj+O///m/87Gc/CxxjwoQJeOedd3zb2WefXeglEFnkFiPCcf9QhjmPclfRYlkuJ/fztNBgu44EDMd1xK3cPebjdOl57hIuJCthtQwl3ZjlOkIUKmKIuJxHEbnSdB9JZZClKCQp4uNAshNhZ86d59HcpF8HJzSNKY7TiLlyGklMDaimxj3NsNxMdk4jWzQSVjU1IdIikp9LLPi1yCSzgpqfuyxXiFpn/HMsOuk8BGFiW62LbQRBEARBED2Frlo/LViwAEII3HDDDY5oBACjR4/G4sWLAZiOJE3T8hpv0aJFSCaTuOCCCzBr1iwwZs7p+OOPx5NPPgkAePTRR7PcQ7/4xS9w8OBBnHTSSVi8eDFUVQUA1NbW4umnn4aiKHj55ZexadOmgq+xvRSsFsydOxcrV67E3Xffja997WuorKwMPX7IkCHYuXMnZs+e7bFFSZKE73znO7j55psBAI899lihUyFKgpTx2O8twZA7bC3kn3GPUBBi7WOZc3GfPwhbqLKTZNuVw8wwrfTsss9rlrSXnZA11QpZcwtIZgibK3zNCmGTpTJILGJ9j1riTtQUeCwRyR4/fV+Dm3ms9cVkx2XEIDtOI+aT48iNW6xxmi0Y2UKRp5qaSyhyHED5JKgO+rUR9Nq6RLIQh1bHQ4IRQRAEQRAEQRDZtLS0YNWqVQCAGTNmZO2fOnUqqqur0djYiDfffDPneEIIJy2P33jjxo3DqFGjoGkali9f7tn3wgsvAACuu+46yLI3h2x9fT3Gjx8PAHjuuefyuLLS0BHxQx5isRjKy8sD919wwQUAgP/7v//r6KkQWQS9/MU6kHKJA+FxoWnnjt8cwl1H2fmOrApisJNDG5AsQUYSEiQhQREKFKFAFipUEUWEmYJRhJWbTSqHKpUjKlemRSQ5ls6BJEWcZgpHaSeSWQXNJQr5fdlCkeUwMgUjy2XEFCunkauKmpXbyHNX3OFmjutK9yTBTotG6SpqtiOodEKO20mV4arKEo2CXGj5UsicSTQiuga7KkixjSAIgiAIoqfQFeund999F6lUCrFYzJNqx0ZVVZx55pkAgPXr1+ccb+/evfjkk08AAA0NDb7H2Nvd4+m6jo0bNxbcL/Pc1113Hc4//3x84xvfwJ133om//e1vOeccRpdXVUskzEpWZWVlgcds3rwZV111FQ4cOIDq6mqcfvrpuOaaazBixIjOmuYxSHuEoVz5jtyVz4LyHcGz32zFJkbm1qysCmuCg0OHIcxE2IbQIEMFZwZkSODg1tVLzpf9DDDDvSRI4IxDZgoMqOAwYECDJCvgQockNAhhwGBmqJft6GGWUGUKQHA5e/K4Rtf98Sa8tsLXAkQjGyHc7qu0gGQLRnZOI69o5Bei5vcah/zizXpt3WO5jgmlo4QdEoyIroXKyRIEQRAEQRRGKddPLS0tnu3RaBTRaHZlyh07dgAwHT2K4i+TDB8+HK+//rpzbBj2MdFoFIMGDQocz30sYOZFskPh7P359HPz0Ucf4aOPPnKev/TSS7j//vtx66234uGHH85yMeVDlwtHv/vd7wAEq2kA8Le//c2jkC1fvhzz58/Hfffdhzlz5uQ8RzKZRDKZLmmZ+eYhgrDFAPc//YUky87AV2DIF/d5bYHKTFbNBLd2Wy4jaJCgWgISh8Ss8vNwh2e5R/YKRzIA7ghQHDJUGMwUnwyhwWAaJKZACG4KSODQeRKCGZCE4iSgBgAOzZRcWFrMCb1Kn3A9WzBy73cfl3lNjoCUp2iUf4haOwhMvF2qhNhusTJzO0EQBEF0NMU7tIMwK56277yMBf9TED4ucDgVXKmnT3RIaN9R0rjAfZ+XN4b2/VzfHbo/oTcH7lPlPqF9ZSkSuO+IfjC0b0o7FLjPt1KwC3exkEy4iIf2FSJs7GLXOWHvPVpDEccWdXV1nuf33nsv5s2bl3XcoUPmz3pY8S17n31sGPYxvXr1cnIb5TOe+3HQXILmUVZWhunTp+Oaa67BqFGj0LdvX+zatQu//OUv8fDDD+O//uu/EIvF8OCDD+acfyZdKhy99tpr+P3vfw8AmDVrVtb+Xr164d/+7d9wxRVX4IQTTkBNTQ22bduGxYsX43/+538wd+5c1NTU4Dvf+U7oeRYuXIj77ruvIy6ByCLEdZSFj+vIFkYc94zbkeQvHgGyKYAwA47ryAlN0yCEAgOAZLmAZKh2LwcJZtia/Zhb5+TMFI8UoYCDQ2cqBAwY0E0XEjOFI5mpZiJuazFmO5GEiDjhYoDbFRS+0DCv1rvY8IhFgjvPM8PU7PHT59S93wNFo3wIWKhmioKhAlmxDrOsk5d4PIIoHUIUn9yaHEcEQRAEQfQkSrl+2rdvH6qrq53tfm4jIB0JFYkEC81233g8XPgtZjy7X1jfoHmcffbZWcXDRo0ahR//+McYOnQovvvd72LJkiW49dZbMWzYsJzX4KbDcxwFsXfvXlx99dUAgG9/+9s499xzs465+OKL8ZOf/ATjxo1D//79EY1G8YUvfAG//vWv8d3vfheAmay7tbU19Fx33nknmpubnbZv376SX8+xTebbpIgf4kBBIeytmKv8vCtnjpPjyK4gxmEIHUIYTq4jLixRiXnnYoetuauuKUJBRESgighURKzHUUREGVQWhcpiZjJtVubkQlKkGBTJSrQtRSGzKGQpAokp5ndJMSu0MTWrMcjpx0zytKzrFl5BynEYeVxGusttZfiIRt47WRSCp1vga5S9hyCOZexyssU2giAIgiCInkIp10/V1dWeFiQcxWIxAEAqlQqclx3FFJZmp9jx7H5hfQuZh813vvMdDBkyBIZhYMWKFXn3s+mS1ejnn3+OCRMm4LPPPsN5553nlLYrhPvuuw/RaBTNzc144403Qo+NRqNZbxgiG3fC5mwKEY+yQ5LyS77sOoftPMpKlO2u8OaXKFuDXWqeCw2c6+BCgyF0GEIDFzoMaE6Zep3pVngbd1xGkuvLFpBUmOJRVMTMhhhiosJsqESMVSLqblIlInKlK6l2OSJyJRTJrNJmi0m2oOQ0SckSjMJyGtnXaeZZ0hzRTNiV1BwxTTNfC6FnOI1yhajlU1EvGGF9pSlViBpBEARBEARBEERpyCcMLZ9wtszxmpqaIIT//z1+47kfB82lkHnYyLKMs846CwCwc+fOvPvZdLpwdPjwYXz961/H1q1bMXbsWKxYsSJQ9Qujuroap5xyCoD2XXjPJvtlzxSL/MWjziBMPPIj+4fQDlODK2TNFlYctxEMM49RSMiYJBgUIUMRsvPY7URShCUowXQhqSIKlVlOI0ShIgaFRSExBQqLQnY5i2SmQHY5jByRCOnmJqiKmlMVTdgikDeXkV8S7OzwtI4RcLLFwlKHqBFE90dwVpJGEARBEATRU+iK9dPIkSMBmJFRuq77HrNr1y7PsfmMl0wmsX///rzHGzp0KFRV9ewvZh5u7HGDri+MThWOkskkJk+ejPXr1+Pkk0/Gq6++iqqqqnaPV8yFE7nJdh/Z+YjSRwQT4jrKWWErWzzK2p51btNFYyYSNB023HbecB0GT1muoyR0kYQBzfquI8VSZs4i5nUepc/EIFtfipARESpUoToupIgVxpYpIEVYGSKsHBGUm2ISM91Gppikmq4jpviKSOZ5vTmN7O92SJrbYWQLRvY1wwlb0zwuo+xE2GGiUeY9dld9C/9FnFs0IrcR0TPoinKyxLHH73//e9x8880YO3YsjjvuOEQiEfTq1Qvjxo3Dww8/HGqDX7duHSZPnox+/fqhrKwMJ598MubPn+/JoUAQBEEQ3YmuWD+dfvrpUFUViUQCmzZtytqvaRo2bNgAAFl5hPyor6/HwIEDAQBr1qzxPcbe7h5PURSMGTOm4H758P777wMAhgwJL3TgR6cJR7qu47LLLsMbb7yB4cOH409/+hP69u3b7vEMw8AHH3wAoH0X3rMI/ye969xFYbhEKib5hKwBfiFr5lPLfeMO5bJD1wS3kljrrnxHRjpYjYWLWraYZibTZk5OJNuBJEOBAgWyMIUgBgkSkyFDhQwVEmQwJlnuIxnMbpAguUSyoJxG5tW6XVJ2TidvxTTHZeTcoYx7VDD5/6og0YggCKK0PPjgg3j00Ufx/vvvo6ysDKNHj0ZlZSXWrVuH7373uxg3bhyampqy+i1duhTnnHOO4+4+6aSTsHPnTtxzzz0499xz0dbW1vkXQxAEQRDdkOrqaowfPx4A8MQTT2TtX7ZsGVpaWlBbW4vzzjsv53iMMUyZMiVwvLVr12L79u1QVRUXXXSRZ98ll1wCAHjqqadgGN4Imb1792LVqlUAgEsvvTT3hVm89tpr2LJlCwA411kInSIcCSFw3XXXYcWKFRg0aBBWrVqFQYMGFTXmE088gaamJsiynNcLR7SfcGGpsDCy8PPITktTuHiUzndkuPL9JC3XUdJ0HfEEdJFECm3QRBIaS0KD6TzSWAoaUtCZAZ0ZPnl64IhHdiibO4TNFo/MxyoURE3xiKmOgOQOS/O9c7YQBK/LyE58bV+XfZz5WHMJRrZwZgTkM8r12hQmJgrXlxcSjYieDTmOiFJwww034M0330Rrayt27dqFDRs24P/9v/+HdevWYciQIdi4cSPmzJnj6bN7927MmDEDhmHggQcewL59+7Bp0ybs2LEDJ554IjZs2IDZs2d30RURBEEQRDBdtX6aM2cOGGN4/PHH8cwzzzjbN2/ejJkzZwIAZs+e7al2tmTJEgwdOhRXXHFF1nizZs1CJBLBa6+9hkWLFjm5jvbs2YPrr78egPk33nYm2dxyyy3o27cvtm3bhpkzZ0LTzMrdjY2NuOqqq6DrOiZMmICxY8d6+l1xxRV44403wLm76rbAiy++6MzvggsuKNipBABMBGVqypPrrrsO//3f/4358+dj7ty5vsfcdttteOSRR9C3b1/87//+L0466aSc47a0tODmm2/G9773PSeJE2A6jZ588kncdtttSCQSuOmmm/DLX/6yoDm3tLSgpqYGpgDRUxbkmSFn7j353YP2JTn2ij3OuRxHjb3dW3reHNXrrDE3uquBZc4h41xMBiCBMdWUeaSIGSImRUwRx8o7FGHlYJAQYeWQhAQVEUsUMkWgtLMo+z4JO7U2E1bOJNO5ZCfd1q0QOAOaVeFNA4dhPrdEH9MBpVmPeVoccrmJAIALzblut7DkyWNkPbfn5n+fchEcouY9imWcJxMSjYjuiimmNjc3d1ixBPvvzN8vGosqVSlqrFZNx/+3YmOHzpc4elm2bBkuu+wyDBo0CB9//LGz/dZbb8XPfvYzXHDBBfjjH//o6bN27Vo0NDRAVVXs27cPAwYMyOtcPXP9lIvs9YtN4WHdmQTnYWQsuLyzuT84f6gQyfDThqQUqIyFl28eKn0hcF+NCE9P0Ybw8MkDUnBV5GbdP3+ITVL/PHCfYYRXZw7Lh5nrM/iw90Axrz9BdA09Y/30wx/+0NE1hg8fjsrKSmzZsgWcc0ycOBHLly+HLKd/98+bNw/33XcfvvSlL2H16tVZ4/3617/G9OnTwTnH4MGD0b9/f2zZsgWapmHs2LF46623UFFRkdXv9ddfx6RJk5BIJNCvXz/U19dj27ZtaGtrw9ChQ7Fu3boswalXr15obm5GRUUFTjjhBESjUXz00Uc4ePAgAODMM8/EK6+80q7Ir4JfjTVr1mDy5MnO88OHDwMAFi5ciCVLljjb3333XdTV1WHdunV45JFHAJjl4m688cbAsd955x3nMecczz77LJ599ln06tULw4YNg6Io2LFjh2PHnjBhAh5++OFCL4EAR/FmMwmFhT2Z5xQQeQtVbjFJ2GMwCcwRj9xzcI9pO3UABm71lcE5IKxQNM50QLLEGMkKDROAxGRwcChCAWcciuCQLHeQHZ5WSuxk3eZjf9HI3tb5ghEQ9j4JX/CQaEQQBNEZjBo1CgA8YWf2p4sAMGPGjKw+48aNw6hRo7B9+3YsX74cN910U+dMliAIgiC6OXPmzMHo0aPx4x//GBs3bsSBAwdw2mmnYfr06fjOd77jEY3yYdq0aTjhhBOwcOFCrF27Flu3bsXw4cNx5ZVX4o477kAsFvPtd/755+Ovf/0rFixYgDfeeAPvvfceBg8ejClTpmDu3Lm+FdXuv/9+rF69Gps3b8bevXvR2tqKXr164fzzz8cVV1yBa6+91skTXSgFC0eapqGxsTFre1tbm2fRYsfiJZPpTzX27duHffuCPy1wU1FRgQceeABr167Fli1b8OGHHyIej6O2thYTJ07EtGnTMHXqVDBGn3h1PQwlFwaY5POJlyUUBYpHgNd5BEuossUjCRA6uJAgQYUhNMjM+g4VBtMAAQhmQGeAJCToTIcilPRZLKeULSBxZkta3PO9UISrX07RyJPryM+JBZ97kovCRKNwqHoaQdiUItSMQtWIMNatWwcATiJNwMx/8MknnwAAGhoafPs1NDRg+/btWL9+PQlHBEEQRLeiq9dPkyZNwqRJk/I6dt68eZg3b17oMePGjcPKlSsLnscpp5ziCZnLxS233IJbbrml4PPkQ8HC0XnnnYdCotsKPd5GVVXMmjWr4H5E4RTiAmrP6L6ihOAZFdMs3NvcyaLtgmzOBuTtPIJgQIjziDMOzjQIiUOHWXHNFpJkqDCgOEmwTdeRBDBAsoQVRzRi6YpsBnRwcFOMApyQNAOalZRb94SoGULzOI3shN6hglGow8hzt0IopWDkN4d850EQxyZCSBCiOIdnsf2JYw/DMPDJJ59gxYoV+MEPfoCKigosXLjQ2b9jxw4AQDQaDcwpOXz4cM+xfiSTSc8HgC0tLaWYPkEQBEGEQuun7gfdTSJv2p8k26YIJ4pTol72bPNPmO3FLayYCaa5lTBbA+fmd0PoMIRmNljNeqwzHTp0GFbTLbFJZ97Hdo4jWzSyhSRuiUbuZNf2czuvkTlPb04jJ+TO40YKqpaWfdU5bihINCIIgji6WLJkCRhjUBQFdXV1uPXWW3H++efjz3/+sycf5KFDhwCYuQ6CnNm2xd0+1o+FCxeipqbGaXV1dSW8GoIgCIIgjhZIOCI6AZHxLLeYYFccczcwyRKLzKTXgOSIR6aAFPR2tkO+DMCuPiZ0cJ5yqq3pPA6Nt0HjcWg8jiQ/jKRItwQOI8GOIMniSLAjiLM2JJFIN2a3uFmhjSWhIwldWA1JaDAruRlCM0UrS6jiQoPBU46IZQpaKcuVZM9Xg4AGCD2gWhpHulparmTlfsmv7ZZ5NHPd3zBINCIIP7hgJWkEAQCDBw9GQ0MDzjrrLCeh9ZtvvolnnnnGU643kTCTDLurvmQSjZrJk+PxeOAxd955J5qbm52Wb7oBgiAIgigGWj91P4pLVU4cxXgTZHdsuFppYJBM9w2TwITtvpGsFEu2cBGetDs75xHAoZnilJDAwMGFJVSlT2yeW3BwGJCYDAYOwydcLZ3o2rAqrBnpUDQnJM2AIfTsRNiufEbmYO7ryCc0Lfzu+RNeMa39kGhEEAAAziB4kT9PxfYnjhmmTp2KqVOnOs/Xr1+Pm2++GT/60Y/w+eef4+c//zkAOIk2U6lU4Fh2CFpZWVngMdFo1BGYCIIgCKLToPVTt4OEox5DQK6hrkRwa0pe8cIt2rDMPEh2riNLzDGTQsPJeWQeEiQeZVZb4xAwxSImTGGIMQlc6JCYAonpMFgSElPN/EZMdtxPkhUyxzLm7ghHLlHICUtzCUamkyhbMOKWo8gcK59cRrncRZmUWigipxFBEERXcfbZZ+OVV17B8OHD8eijj+IHP/gBjj/+eCcMrampCUII33A1O0TNryoLQRAEQRCEGxKOCIfu5jrKEo2sbULwtPsIZt4jWzwqyHlkC1eCm/mzhQ5AsZxCunWcBC44ZGZut0Ujd94he07e8b2CkZ3LyE6GnSkaCaE7CbBNwQhov2iUv7uoO73eBHEs0tVVQYhjn0GDBuELX/gC1q9fj82bN+P444/HyJEjAZiuov3792Pw4MFZ/Xbt2gUAzrFEewlz/xZWsrkgsirPepGkkCU+Cw5hBACDtwbuO5z4KLTv/6lHAvdVqgNC++biiHYwcF9S+yy0rxDJkL3hmTtY2OvoV+jFc14jZG/YPoLo2dD6qftBwlGPxhuuBoSLR9m5icLFmXzPmR9uF5IzIYCZApIZugYr55EtwNh9MufociYJO9m0BEA1BRymm84jpoBBBmMSDGblWrJyLElOsu4Ax5HLNeQJR8twGLkrpnkSXzsV4/zmn69gVDqhyD8vFbmNCCIXtPAhOgNd1z3f6+vrMXDgQBw4cABr1qzBZZddltVnzZo1AEzXEkEQBEF0J2j91P2g5Ng9ivz++c8neXVQ33zP7T1HrnEy36aWaOMWb5yk2RaeimtBuM4rOAQ0p+qaGUrmTlatO8mqudCgczPJtZP82mqGK+G12aw+XLeqqWlph5GfaCR4O0SjoITX7iNCElyzdOLx/CHRiCAIojuwe/dubN68GQAwevRoAABjDFOmTAEAPPHEE1l91q5di+3bt0NVVVx00UWdN1mCIAiCII5KSDgifBE+X6XDT6wyXI/d+zMEEEfcMKuAMWa7gIIrrgVVDLPnYopFwlV1LQnhVDZLgfMUDB6HwePQjTh0bjUjEdycY+IweNKq3hY3xxMpS5RKmudyV0tz7rUrSXZgtbRMwchbHS2rIppbIAoSiwoWkGxINCIIP+xPzIpt7eWVV17B+PHj0adPH1RUVGDMmDF45JFHwHmhblGTdevWYfLkyejXrx/Kyspw8sknY/78+U4VryC2bduGq6++GscddxxisRhGjBiB22+/HU1NTaH9Pv74Y9x0002oq6tDNBpFfX09br75Znz88ce+xx84cADz58/HpEmTMGLECFRXVyMajWLYsGGYNm0a/va3v7XruruSjRs34t5773VCy9y8+uqrmDBhAnRdx9e//nWMGDHC2Tdr1ixEIhG89tprWLRoEYQwf0/v2bMH119/PQDghhtuwMCBAzvnQgiCIAgiT7p6/URkw4S9kuhBtLS0oKamBuY/2D3xDZV/0uRw8g2h8jt3WtwwhQpL9HG5iBjzCj6ZOY/SeYW489x28Jiz8UsuHfbPUuacEDIvb59s0nMyz+0OQcs3h5G5J5vc7iLvhna8tlk5m3I5xHrcrxHiqMb8XdDc3Izq6uoOOYP9d2b9V89BpVpcVPhhTcfZf3q74Pnef//9uPPOOwEAw4cPR2VlJbZs2QLOOS666CK8+OKLkKT8fz8sXboU1157LQzDwODBg9G/f39s2bIFmqbhzDPPxOrVq1FeXp7V780338TEiRMRj8fRr18/1NXVYfv27Whra8Pw4cOxdu1ap7S8m61bt+Kcc87B559/jpqaGowYMQIffvghmpubUVtbi3feeQejRo3y9Fm1ahW++tWvAgD69u2LwYMHQ9M07N69G21tbZBlGY899himT5+e93V3NatXr8aXv/xlAMDAgQMxZMgQpFIp7N271xHezjzzTLzyyivo27evp++vf/1rTJ8+HZzzrNds7NixeOutt1BRUZH3XGj95EfwfQjNjYN8HN7BOXBYjmwTslwVsjf85z4sx1Gu3EoRtX/gvqJzHOldleMo5L1OOY6IHkXPWD8R/pDjqEeSb76azkXA8FmQWAmwAxJlp8Ulr/PI4z5yntvuoyCsvEOW+8hxIEFLu5BE0nQi2U0ENGt/2lmkZbiLdCtptp/DyLwb+TuMMtxFWY6iXC2ccNHIb54EQXQ169atw1133QVJkvD000/jww8/xObNm7Fp0yYMGDAAK1aswOLFi/Meb/fu3ZgxYwYMw8ADDzyAffv2YdOmTdixYwdOPPFEbNiwAbNnz87q19raissvvxzxeBy33XYbPv74Y2zcuBF79+5FQ0MDdu3ahRkzZmT1MwwDU6dOxeeff45LL70U+/fvx8aNG/Hxxx/jkksuQWNjIy6//PIs59Txxx+Pxx9/HPv27cPBgwfxt7/9De+//z4OHDiA7373uzAMA9/61rfw//7f/yv8pnYRo0ePxsMPP4yLLroIFRUV2L59O7Zv346ysjJMmDABv/rVr7B27dos0QgApk2bhrfffhuTJk1CPB7H1q1bMXz4cMybNw/vvPNOQaIRQRAEQRA9F3Ic9dhPzMKuO189sUSOIyDLdWQnnXa7jvzEI+fMLveRx+WTmT8IYe6eTDLm6MwzvS83aaeRM9ecQkwmwQ4j/7llH5ebjHlk3auA40g0Io5KOu8Tsz+PPxeVSpGfmOk6/nnV/xY034kTJ+KVV17BTTfdhF/+8peefU8//TSuvvpq1NbW4pNPPoGqqjnHu/XWW/Gzn/0MF1xwAf74xz969q1duxYNDQ1QVRX79u3zuIcWLVqE2bNn46STTsJ7770HWU47MPbu3YsRI0ZA13Vs3LgRY8aMcfYtW7YMl112GWpra/HRRx+hqirtnmhtbcWwYcPQ2NiIF154wcnlkwshBE477TS8//77ePTRR3HjjTfm1Y9IQ+snP8hx5IYcR5nnJccRcSxx7K+fiGCoqlqPRSB4sZNP5bNiHUoh5xAcgrkrlmUf616MCRiOqCSEt+qaYHBVXLP6OqJIUNU1eLabyzrrWGsBwPK8/tzCS/oM2bRHMPKrpOZduIpcC5XARSGJRgRRKF1RFaSlpQWrVq0CAF83z9SpU/Gtb30LjY2NePPNN3HBBRfkOL/Aiy++GDjeuHHjMGrUKGzfvh3Lly/HTTfd5Ox74YUXAADXXXedRzQCzMpf48ePx6uvvornnnvOIxzZ/S677DKPaAQAVVVVmDp1Kn7xi19g2bJleQtHjDGceOKJeP/999HW1pZXH4LITfDfwpx/b4sw/ucamws9cJ8aKioBEgsWk3UjRFQCkNQ+bdc+IHfl1/BrziH+sGjIvhzieYhYFi5IAd3B0U8QRyNUVa37QaFqPZqwf/w5ssOn3PtKcU6e5b4JWxSIgD/cbmEkKHTNXBSkk2ab/bzup3C898MveXhwQvGgexkU5hUckuaZtyeRtTtsTfa07NHDPwH1h0QjgjhaePfdd5FKpRCLxTxijI2qqjjzzDMBAOvXr8853t69e/HJJ58AABoaGnyPsbe7x7OdRIX2A4A///nP7eoXRjKZxKZNmwDA974QBEEQBEEQ/pBw1OPJRwDgCBeSijungMgI5TKs7zxQLMrELZK4Q9qYO+zNOaa94pFN5v3I1cyryW7ZV5HLZeTJYeTsz7y+3AQeFxhSRxBEeyhlVZCWlhZPSyb9P+nesWMHANPRowTYvIcPH+45Ngz7mGg0ikGDBuU93u7du6Fpmmd/Pv3sxM/59HOfI4jW1lasX78eU6ZMwe7duzF16lScc845oX0IgiAIgug6qKpa94NC1QikRYwu/uES3JqCZD22w8+4FX4mWY8lMzwtwE1jh67Z2bsYgxO2Bpiha4AMwczcR9mha0BHCGT+5K5w580DlX1M1n3wi7fPqpLmdnZlX2vheZgIgvCDCwZe5MLF7l9XV+fZfu+992LevHlZxx86dAgA0Lt378Ax7X32sWHYx/Tq1QuM+V+L33jux0Fz8evX3NzsJL3O1Y9zjpaWFtTW1mYd06tXLzQ3NzvP+/fvjx//+Mf4t3/7N98xCYIgCILoHpRy/USUBhKOCBdheY86Cr9cRxyAnDPXUX7ikdnHFo8AS0ByhBTJJ/eRsM4TJh6VQjwJF40KEoxyJGcEk3ImtPTfT6IRQXQX9u3b50nuGI365+xIJBIAgEgkEjiW3Tcej+c8b3vHs/uF9S22X2ZfN1/84hfR2tqKxsZG7N69G//4xz/w7LPP4rzzzsMXvvCFwGshCIIgCIIgvJBwRGTQWe4jt0jFISCZQonlNLIdMcx2Cgk7BI1bCbAtN1Kh4pF1akeQEjwthQQmz7ZxiyilvD9FOIxyCUY5yVX1zU2pRaPMe0iiFHHsUcrkjtXV1XlVBYnFYgDMkK8g7DC3srKyDhvP7mf3dT8vpF/Y+TL7uvnDH/7gPD5y5Ageeugh3HffffiXf/kXbN68GSNGjAi8HoIgCIIgug5Kjt39oBxHRACd8U+8T64jwBIxXCKNLeQ44oY391GuyiLMkw/Im/cITPLmPrK2Aa6cQg7eJNXFk530Okw0ysphVIholOf9Cg5RK+X7wS+XEwK2EcTRTVfE6OcThpZPOFvmeE1NTRDC/3eB33jux0Fz8etXU1MDSZLy6idJUl5iWkVFBe655x58+9vfxpEjR7Bw4cKcfQiCIAiC6Boox1H3g4QjIoSwRM5+xxaDO0wsvc2TKNsjGJmPheMOMrIEkexqa0CmeGRXXUsLSFb1Naa4trMMEUkKaEEEH+8Zm6Xn4u7nKxiVRDTyuo3C8xqVgiDBKPMYgiCKYeTIkQDMami67l+Se9euXZ5j8xkvmUxi//79eY83dOhQqKrq2Z9Pv0gkgvr6+rz6uc+RDxMnTgQAp7oaQRAEQRAEkRsSjog8CROQihGNssUK3yprjrjhFY8AtxPJX0Cy8ROPAFcOJee57DnOLdRki0hu8heUssbIchhlzgWFC0ahZIeoBdNR+ZwI4tjHTu5YbCuE008/HaqqIpFI+AokmqZhw4YNAICzzz4753j19fUYOHAgAGDNmjW+x9jb3eMpiuKUvS+kn/t5of1yYQtpQYIaQZQWv4qq7mbkaO0fm/O2wKYbR0JbGLJcHt6kysDGmBraPB+g+TTGooFNYrEcLRrYcr+KWkgzQlv465QLFtII4timK9ZPRDgkHBEF0p4/fPmM6bc5XdLeEY9E2nkk3PtFdvian4AUJh5lu4/s8DWrZS5givjKWhA5cypBWJr7/vk6ssLyGmWECZJoRBBFIUQp7NaFnbO6uhrjx48HADzxxBNZ+5ctW+ZUIjvvvPNyjscYw5QpUwLHW7t2LbZv3w5VVXHRRRd59l1yySUAgKeeegqG4f2dvHfvXqxatQoAcOmll/r2+93vfofW1lbPvtbWVixbtgwA8M1vfjPn/N38/ve/BwBKjk0QBEEQ3ZiuWD8R4ZBwRHQzMsKm/MKsLPEI8OY9sp+LvFw0QKZ4BIS5j+zjXT8yWcJPHvgen5nrKCPZd3tFI/thSHiaub+zkmETBNFZzJkzB4wxPP7443jmmWec7Zs3b8bMmTMBALNnz/ZULVuyZAmGDh2KK664Imu8WbNmIRKJ4LXXXsOiRYucXEd79uzB9ddfDwC44YYbHGeSzS233IK+ffti27ZtmDlzJjRNAwA0Njbiqquugq7rmDBhAsaOHevpd+mll2LUqFFobGzE9OnT0dbWBsBMcj19+nQ0Njbi1FNPxcUXX+zpd/fdd+Oll17yJM8GgIMHD2LWrFl44oknwBjDrbfemve9JAiCIAiC6OmQcER0E9zuJT/xKO08st1H7rxHbveR24Hk17zkEI9c7iNvCFtGKFoOe3W2YOQfyuZXIS5vbIdRoMsoO6dRxzqNAHIbET2drkru2NDQgPnz54NzjquuugojRozA6NGjMWbMGHz66aeYOHEivv/973v6NDU1Yc+ePThw4EDWeMOGDcNjjz0GSZIwe/Zs1NXVYcyYMRg5ciQ++OADjB07FosWLcrqV11djWeffRaxWAw/+clPMHjwYJxxxhmor6/HmjVrMHToUDz55JNZ/WRZxrJly9C7d288//zzGDRoEM444wwMHjwYzz//PPr06YPf/va3ThJtm7fffhvf+MY3UF1djVNOOQVnn302hg4dikGDBuHBBx9EJBLBL3/5y4JD3AiCIAiC6DwoOXb3g4QjopsS5DxKiyLuvEeAN9dRe5I7+4pHgEfwyQohA5BfguzcxxUtGgWSXaEux2Dtn4cH+mVNEKIE8fntXfjMmTMHK1euxFe+8hU0NjZi586dOO2007BkyRIsX74cslzY75xp06bh7bffxqRJkxCPx7F161YMH/7/t3fncVFd9//4XzMwMAiCgIhKQASNe6igMZFY81W0MfDTuDVo+nHDxCRtTGMV6xIlgQQjjTGxnyQPjYnNp5IFlyg1ycei2EY0fhAMccHUjaUqVlEYVJaBOb8/6EwZZt+YgXk9fZzHA+6958y5l5F5877nnhOFtLQ0HD16FL6+vnrrTZw4ESdPnkRycjIkEglOnz6N0NBQLFu2DMXFxTqjlNSGDx+OkpISLF68GH5+fjh9+jT8/Pzw7LPPoqSkBEOHDtWpk5aWhpdffhkjRoxAdXU1iouLcfv2bQwdOhQvv/wyfvzxRzz77LMWnTcRERF1LGfGT6SfRBhaW7cLUygUCAgIQOsf8HxDuZ62P5O2SZv2S9W3e5ys3WTXEmse8UL7ybbbJVr0JF6MLW9vLpuSRgb7oT9hZHz1NFdIGrndryTqcAKACrW1tWYt5W4N9efMofjJ8PU0f9Uvfe41KzGx4KBD+0tkDsZPnYnhuMJD6me8ptTH4D5TMY9K1WR4n2g0uO/fR5jYbziuMxVHSSSeBvfpxHrt9xvptxBKo3Vti2mM/R9jrETOwPjJnRn+LUrkNAL/+bBUQR0oCIjW5JFQ/TtJ1LpPoKU1YPj3dgEVJJBqEkCWJJDaP8omgVQ7oJBIdZJHEnjYJXlkLf2v7cykkS1coQ9E9mOPodK8Y0ZERETuhPGT62HiiFyUOoEgwX8SHFJN4kOiToZItI+GaPl3IkelGYEkhEr70TNbGUgetfbDugSS7Ykn4yOjXD9hBLhOP4jsh4EPETmH4biiRXXXaE1jI4MkEuMjAIzFW6bqmnqc3lisZCqOMjYySAhTMZixfjlqRBGRe2P85Ho4xxF1InpGzrSb+6h1339WX2s7D5Kpocj6WFOnY7Wf1BoWJo2IiIiIiIiIDOOII3JxbUceAUZHH7UdZdSmBUmbb9qORLKuO4YTL/Z/XM3MJI/RRJGxtlxphI8r9YXIftQTNNraBhEREZG7YPzkejjiiDqJ9okFA/P2aI1A0rcsPbRGIpn/8sbrWJY0UplZzOiPVUkjAddK1LhSX4jsi8vJEhEREVnG2fHT119/jYSEBAQFBcHX1xexsbHYsmULVCrrnt44fvw4pk2bhpCQEPj4+GDo0KFIT09HQ0OD0XqlpaV45pln0KdPH8jlckRHR2P58uWoqakx+7VLS0vh5eUFiUSCAQMGWNV/wIrE0ZUrV7Bt2zY8++yziImJgaenJyQSCTIyMkzWdeYFo66gfcLjP0kW0eafdlKl7TEt2kUotY81VrR60aJTdNkhIWSiP0LPP/2vbej6OZI5r+NqCSwiIiIiInJnGzZsQGJiIg4dOoTAwEAMGDAAJSUlWLp0KaZPn25x8mjnzp0YN24c9u/fD29vbwwZMgQXL17EunXr8POf/xz379/XWy8/Px9xcXHIzs5GS0sLhg0bhqqqKrz99tuIi4vDjRs3TL62EAJLliyBUmlqBUjTLE4cvfvuu3juuefw0Ucf4ccff0RLi3kjLZx5wcgVSPQUaxkfVaOVRNFK/OgmU/QlgUwVw8xMDrXtl5EEld5qOkkifa+vv2bHM/SaTBiR+1APtba1EBEREbkLZ8VPx48fx+rVqyGVSpGdnY1Lly6hpKQExcXFCA0Nxf79+7Fp0yaz2ysrK0NKSgpaWlqwceNGVFZWori4GBcuXMCgQYNQWFiI1NRUnXp1dXV4+umnUV9fj6VLl+Lq1asoKipCRUUF4uPjcfnyZaSkpJh8/e3bt+O7777D1KlTLboO+licOOrZsyeSkpLw+uuv45tvvsHMmTNN1nH2BSNnM/Sf1pZEkoBuAkI7cWNwFJJoPyLH3BFBFtSzYART+74a+2e8L+Zco44m9BQi9yEgsUshIiIichfOip8yMjIghMDixYsxZ84czfaYmBhNwmjDhg1mj+DJyspCY2MjJk+ejBUrVkAiae1Tv3798PHHHwMAtm7dqjMY5sMPP8TNmzcxZMgQbNq0CTJZ66qUwcHByM7OhqenJw4cOIDi4mKDr33z5k2sXLkSI0aMwEsvvWT+RTDA4sTR2rVrkZubi1dffRVPPPEE/Pz8TNZx5gUjZ7PkP6y1I5FMz+ujk3gxOsrHDo+YGeypqYSQMeaOaGKChoiIiIiIyFwKhQJ5eXkAoHdwyuzZs+Hv74/q6mrk5+ebbE8Igb179xpsb+zYsRg8eDCUSiX27duntW/Pnj0AgAULFsDDw0NrX0REBBISEgAAu3btMvj6r7zyCu7cuYMPPvgAnp62r4nm8MmxnX3BqDOyZhSSqRFI+ucFat1o5rxCZs+BZM6IofZMjWYy9ggaR/QQuSpnT+5IRF2VvikAzC3G4w0hGgwWlequ0dJipKhU940XYbwI0WhDaTJYgBYTRV+sZY+Yy1i7pgpR1+aM+OnUqVNoamqCXC5HbGyszn6ZTIbRo0cDAE6cOGGyvYqKCly/fh0AEB8fr/cY9fa27TU3N6OoqMjiem3l5eVh586dWLBggcE2LOXwxJEzLxh1BdaOQDJvDiDrR/9YW9+K0Ux6XpWBA1HnwDmOiIiIiCzjjPjpwoULAFoHqBgaoRMVFaV1rDnteXt7o2/fvma3V1ZWpnkUTr3fkn40NDTghRdeQGBgIN566y2T/TSX7WOWTHDWBWursbERjY2Nmu8VCoWZvSfXoP5Pb2mipO3xbX9xtE/UaOdPLU0e6bI0EaQPk0JERERERES2aP+3v7e3N7y9vXWOu3PnDgAgMDDQYFvqfepjjVEf06NHD81UPea01/ZrQ30x1o+MjAxcvHgRH374IUJCQkz201wOH3HkrAvWVmZmJgICAjQlPDzc/BMgF2LrSmymRiJZMgrImsm1TfWLI4mIuho+qkZERERkGXvGT+Hh4Vq5gMzMTL2v2dDQAADw8vIy2C91wqm+vt7kOVjbnrqesbqG+lFaWoqsrCw8/PDDePbZZ0320RIOH3HkjAvW3qpVq7Bs2TLN9wqFgsmjTsva0UdtGRqJ1B5HDhGRbVSw/VEzFVdVIyIiIjdiz/ipsrIS/v7+mu36RhsBgFwuBwA0NTUZbFP9FJOPj4/J17e2PXU9dd223xurJ4TAkiVL0NzcjPfffx9SqX3HCDk8cdTRF0wfQ8PRyBUYekObStpIYJ+kTPs2rP0FxQQRERERERGRK/H399dKHBliztNM5jzO1r69mpoaCCH0Pn2lr722X9+5cwd9+vQxq96nn36K7777Dr/+9a8RFxdnsn+Wcvijau0vmD7mXDBz61FnYuztJzWxH7Dt0TVDuLoFEdkPH1UjIiIisowz4qeBAwcCaF3cq7m5We8xly9f1jrWnPYaGxtx7do1s9uLjIyETCbT2m9OvVOnTgEAPvvsM/Tu3VurzJgxA0DrPNLqbceOHTN5Dm05PHHU0ReMuhpTCST+QUVErksFiV0KEZE2qQOLxEgxdSPN1NL21i57b+tNOmPnRESuxhnx08iRIyGTydDQ0IDi4mKd/UqlEoWFhQCAMWPGmGwvIiICvXv3BgAUFBToPUa9vW17np6eiI2Ntbie2u3bt3Hjxg2toh5w09LSotlm7IkwfRyeOHLWBSN3wg99IiIiIiIiso6/vz8SEhIAANu3b9fZn5OTA4VCgeDgYDz++OMm25NIJJg+fbrB9o4dO4bz589DJpNh6tSpWvvUI4R27NiBlpYWrX0VFRXIy8sDAMycOVOzffPmzRBC6C35+fkAgOjoaM02c86hLYcnjjr6glFXxZFHRNQJ2WOYNR9VIyIiInfipPhpzZo1kEgk+Oijj/DZZ59ptpeUlGgW20pNTdVavGvz5s2IjIxEcnKyTnsrVqyAl5cXDh48iKysLM3UPeXl5Vi0aBEAYPHixZqBNmrPP/88evbsidLSUixbtgxKpRIAUF1djblz56K5uRlTpkxxyFxGhjg8cQR0rQtGrox/XBGRa1EJiV0KERERkbtwVvwUHx+P9PR0qFQqzJ07F9HR0YiJiUFsbCxu3LiBxMRE/O53v9OqU1NTg/LyclRVVem0179/f2zbtg1SqRSpqakIDw9HbGwsBg4ciJ9++glxcXHIysrSqefv74/PP/8ccrkc7733HsLCwjBq1ChERESgoKAAkZGR+Pjjjy0+P1tYnDgqKChAz549NeXzzz8HAGRmZmptr6ys1NTpSheM7Mkey9231xX+wDL2HD6fySciIiIiInKENWvWIDc3FxMmTEB1dTUuXryIESNGYPPmzdi3bx88PDwsam/evHn47rvvkJSUhPr6epw7dw5RUVFIS0vD0aNH4evrq7fexIkTcfLkSSQnJ0MikeD06dMIDQ3FsmXLUFxcrDPoxtEkwtBSZwYcOXIE/+///T+Tx125cgWRkZFa244dO4bMzEwcO3YM9+7dQ2RkJObMmYOVK1dCLpcbbOvs2bPIyMjA4cOHUVNTg7CwMEyfPh1r1661akU1hUKBgIAA/GcCQHIcU9fXmkFvphJOnWmVM3u+/zrTeRM5kwCgQm1trVnLs1pD/TnzRcxsdPOQ2dTW/RYlni7JcWh/iczB+MmVWPaHi2WMxVnOjDUc9Z5j/ERkHsZP7szT0gqPP/44LMw1aYwdOxa5ubkW1xs2bJjWM4bUmQgY/6BXwbLkkTmjlNSrfriSjgiw1a/haudO5L5UsH1spSPGZhIRERG5KsZPrsfixBGR/VmaPOoMeCeWiIiIiIiIOj8mjqgDmBp1BPwnJ2wogWRpzrijRh0xQUREhmlW9rCxDSIibY68l25L/OTI31fGbjJybAFRV8L4yfUwcUQuprN88PMXERGZphKweVU0FZ8+JSIiIjfC+Mn1MHFEHcScUUf2fC176kxJIv6GJCIiIiIiIvth4ojIKCaNiMh6AhIIG3+P2FqfiIiIqDNh/OR6mDiiDuTIUUfuPMoIYNKIyDWphMQOQ6072+8jIiIiIusxfnI9XW0pK3J59k5wCAe02dm4+/kTERERERGRo3DEETlB20SHpZlgJkn+g9eCyNW1Tu5oextERERE7oLxk+th4oicTP0/2lQCqaP/53fkZN7W4G9Cos6Az+gTkWO4ahxgrF+2/i4ztvKuq14P6hiu9ncE2Yrxk+th4ohchCv+QnfF5JErXiciIiIiIiLqqpg4IjLKlZJHTBoRdTac3JGIiIjIMoyfXA8TR0QmOTN5xGQRUWcmRGuxtQ0iIiIid8H4yfUwcURkFnPnYrL36xERERERERE5DxNHRBZxdAKJCSOirkRAAhUndyQiIiIyG+Mn18PEEZFV7JlAYrKIqKsSQgJh4zP2ttYnIiIi6kwYP7keJo6IbGJtAonJIiIiIiIiInJ9TBwR2UX7RFD7RBITRUTuiKuCEBGpMRbqHGz5zHHWz5jvra6G8ZPrYeKIyCH4AUZErb8JbP1twN8mRERE5E4YP7keqbM7QERERERERERErokjjoiINCTg/QmyJw61JiIiIrIM4yfXwxFHREQaTBqRfansVMh9CSFw9OhRrFixAo888gh69OgBLy8v9O3bFzNnzkR+fr7R+sePH8e0adMQEhICHx8fDB06FOnp6WhoaOigMyAiIrIM4yfXw8QRERERkYs6fPgwxo0bhz/84Q8oLCxEaGgohg8fjrq6OuzZswcTJkzAq6++qrfuzp07MW7cOOzfvx/e3t4YMmQILl68iHXr1uHnP/857t+/38FnQ0RERJ0RE0dEREQOIoTELoXclxACAwYMwPvvv49bt27hp59+QnFxMaqrq7Fq1SoAQEZGBv7yl79o1SsrK0NKSgpaWlqwceNGVFZWori4GBcuXMCgQYNQWFiI1NRUZ5wSERGRUYyfXA8TR0RERA6ifkbf1kLu6+GHH0ZpaSleeOEFBAYGarZ7eXnhzTffxJQpUwAA27Zt06qXlZWFxsZGTJ48GStWrIBE0vo+6tevHz7++GMAwNatW3Hjxo0OOhMi6hyEDYXIPhg/uR4mjoiIiIhclL+/Pzw9Da9lMmnSJADAP/7xD802IQT27t0LAEhJSdGpM3bsWAwePBhKpRL79u2zc4+JiIioq2HiiIiIyEFsuW/Le7hkDvUk1z4+PpptFRUVuH79OgAgPj5ebz319hMnTji4h0RERJZh/OR6DN/CIiIiIptwOVlyJCEEcnJyAGgniC5cuAAA8Pb2Rt++ffXWjYqK0jqWiIjIVTB+cj1MHBERERF1Qtu2bcOpU6fg5eWF3/72t5rtd+7cAQD06NFDM7dRe+r5ktTH6tPY2IjGxkbN9wqFwg69JiIios6Gj6oRERE5iMpOhai94uJivPzyywBaV1WLjo7W7FM/vubl5WWwvre3NwCgvr7e4DGZmZkICAjQlPDwcHt0nYiIyCjGT66HiSMiIiIH4XKy5AhXrlxBUlISGhoaMHfuXCxfvlxrv1wuBwA0NTUZbEM9kqjt3EjtrVq1CrW1tZpSWVlph94TEREZx/jJ9XRY4qisrAwSicSs8re//U1TLy0tzeTx58+f76jTICIiInKaqqoqTJo0CdevX0diYiJ27Nih8zia+jG0mpoaCKF/elD1I2rqY/Xx9vaGv7+/ViEiIurqvv76ayQkJCAoKAi+vr6IjY3Fli1boFJZN47p+PHjmDZtGkJCQuDj44OhQ4ciPT1dM0LYkNLSUjzzzDPo06cP5HI5oqOjsXz5ctTU1Og9/uLFi1i7di0mTZqE/v37w9fXFz4+PnjwwQfx4osv4tKlS1b1H+jAxJFcLkd8fLzBop6kUS6X42c/+5lO/fDwcIN1u3Xr1lGnQUREZDYB24dZ27IqSGcNfNSuXr2K5557DuHh4fD29kZERASWLFmCq1ev6j3+1q1b2Lp1K2bNmoX+/fvD29sb3bt3R2xsLF5//XXU1tZadd6u4vbt25g0aRIuXbqE8ePHIycnBzKZTOe4gQMHAmgdVXTt2jW9bV2+fFnrWCIiIlfhzPhpw4YNSExMxKFDhxAYGIgBAwagpKQES5cuxfTp0y2OoXbu3Ilx48Zh//798Pb2xpAhQ3Dx4kWsW7cOP//5z3H//n299fLz8xEXF4fs7Gy0tLRg2LBhqKqqwttvv424uDjcuHFDp86RI0fwxhtv4NChQ6ivr8egQYMQERGB8vJyfPDBBxgxYgT+93//16rr0mGJo969e+Po0aMGy6OPPgoAmDp1KgICAnTqL1q0yGDdiIiIjjoNIiIiswnYYag1rBtq3ZkDHwA4d+4cHnroIWzbtg11dXUYPnw4FAoFtm7dipiYGL2jjadPn44lS5Zg9+7dqK2txfDhw9GzZ0/88MMPWL9+PWJiYjQJk87m7t27ePLJJ3HmzBmMHj0aubm5Bh8zi4iIQO/evQEABQUFeo9Rbx8zZoxjOkxERGQlZ8VPx48fx+rVqyGVSpGdnY1Lly6hpKQExcXFCA0Nxf79+7Fp0yaz2ysrK0NKSgpaWlqwceNGVFZWori4GBcuXMCgQYNQWFiI1NRUnXp1dXV4+umnUV9fj6VLl+Lq1asoKipCRUUF4uPjcfnyZaSkpOjUe+ihh7Bz507cuHEDVVVVKC4uxk8//YSrV68iOTkZ9fX1+NWvfmV0fkNDXGKOo7t37+Krr74CAPzXf/2XcztDRETUyXX2wKelpQWzZ8/G7du3MXPmTFy7dg1FRUW4evUqZsyYgerqajz99NM6yS8PDw/Mnz8f33//Paqrq1FUVIQrV66gqKgIAwcORHl5OZ5++mnLL6iTNTY2Ytq0aThx4gSGDRuGb7/9Ft27dzd4vEQiwfTp0wEA27dv19l/7NgxnD9/HjKZDFOnTnVYv4mIiDqTjIwMCCGwePFizJkzR7M9JiZGEzdt2LABSqXSrPaysrLQ2NiIyZMnY8WKFZpHy/v164ePP/4YALB161adm2gffvghbt68iSFDhmDTpk2a0cXBwcHIzs6Gp6cnDhw4gOLiYq16Dz/8MObOnYuQkBCt7T179sSf/vQnBAYG4tatWzh69KgFV6WVSySO9uzZg3v37iEkJARPPPGEs7tDRERkFyphn2Kpzh747NmzB+fOnUNwcDA++eQTzSPpvr6+2LFjB4KDg/Hjjz9i3759OvV27NiBMWPGaM37M3LkSPzP//wPAODkyZM4deqUWeftClpaWpCcnIzDhw8jOjoaf/3rXxEUFGSy3ooVK+Dl5YWDBw8iKytLM9dReXk5Fi1aBABYvHixZmQSERGRq3BG/KRQKJCXlwcAem9qzZ49G/7+/qiurkZ+fr7J9oQQ2Lt3r8H2xo4di8GDB0OpVOqNZwBgwYIF8PDw0NoXERGBhIQEAMCuXbvMOLNWXl5e6N+/PwAYHCVujEskjv785z8DAJKTk+Hp6an3mPz8fMyePRsTJkzArFmzsHHjRlRVVXVkN4mIiCwi7FQs0RUCH3W9X/7ylzoja7p3747Zs2cDAHJycrT2GUuojBkzRvMo/D/+8Q+Dx7maL7/8UjMqWyqVYvbs2Xjsscd0ivqaqPXv3x/btm2DVCpFamoqwsPDERsbi4EDB+Knn35CXFwcsrKynHBGRERExjkjfjp16hSampogl8sRGxurs18mk2H06NEAgBMnTphsr6KiAtevXwcAxMfH6z1Gvb1te83NzSgqKrK4nim3b9/GTz/9BA8PD8TExJhdT83piaPr16/j0KFDAIw/pvb3v/8du3btQn5+Pnbv3o2VK1ciKioKO3bsMPkajY2NUCgUWoWIiKgr6gqBz/fff29VPWNaWlo0I6yMLUHvahobGzVfX7hwAQUFBXpLYWGhTt158+bhu+++Q1JSEurr63Hu3DlERUUhLS0NR48eha+vb0eeChERUYdrnwdo+7na1oULFwC03tgyNJhFvaCX+lhj1Md4e3ujb9++ZrdXVlamiVfU+23px507d3D48GE8+eSTuHfvHpYtW4bIyEiT9dpzeuJo586dUKlUGDRokCaQbatPnz5YvXo1CgsLUV1djfv376OgoABTpkxBfX09Fi1ahNzcXKOvkZmZiYCAAE0JDw931OkQERFpqITELgVwn8CnqakJFRUVZtVr+xqm/OUvf8H9+/fh4eGBRx55xKw6rmDBggUQQpgsZWVleuuPHTsWubm5qK6uRkNDA86fP4/169dDLpd37IkQERGZyZ7xU3h4uFYuIDMzU+9r3rlzBwAQGBhosF/qfepjjVEf06NHD63H50211/ZrQ30x1Y+amhpIJBJIJBIEBQVh4sSJuHnzJnbs2IGNGzea7Ls+Tk8cqR9TMzTaaMmSJXjjjTcwatQoBAUFwcfHB2PHjsWBAwcwffp0CCHwyiuvaJ7d12fVqlWora3VlMrKSoecCxERUVu2LiWrLoD7BD61tbWaSa9N1VOpVGaNIr537x5WrFgBAJg/fz569eplsg4RERE5hz3jp8rKSq1cwKpVq/S+ZkNDA4DWuYAM8fb2BgCzViWztj11PWN1TfXD09MT8fHxiI+Px4ABAyCTyXDlyhXs3LkT5eXlJvuut02ratnJ6dOnUVJSAolEgl/96lcW1ZVIJNiwYQP27t2LS5cu4ccffzT4rJ63t7fm4hIREXVGlZWV8Pf313xv6HOtswc+ltRrX9eQlJQUXLhwAQ888ADn9SEiInIj/v7+WvGTIeqRuE1NTQaPUY/2NueRd2vbazsiWD31gKX98PPz01o5rbq6GuvXr8d///d/45FHHkFpaSl69Ohh8hzacuqII/UKJz//+c/Rr18/i+s/+OCDmokwL168aNe+ERER2UoIiV0K8J/AR10MJY5cNfBxRL32dfX5/e9/jy+++AJ+fn746quvzFqRjIiIiJzHnvGTucwZjW3OqO727dXU1Bh8Okpfe22/NtQXS/oBtK5m+8c//hFJSUmoqqrCH//4R7PqteW0xJFKpcJnn30GwPik2Kaol/Ztbm62S7+IiIjsxZ5Drc3V2QOfgIAASKVSs+pJpVKjdxH/8Ic/4K233oK3tzf27duHuLg4g8cSERGRa3BG/DRw4EAArYuCGMotXL58WetYc9prbGzEtWvXzG4vMjJSk+NQ77elH20lJiYCAIqLiy2qBzgxcZSfn49//vOfkMvlmDVrllVt3Lp1C//6178AAA888IA9u0dERNQpdfbAx8vLCxEREWbVa/sa7W3btg0rVqyAp6cnvvzyS0yYMEH/CRIREZHbGzlyJGQyGRoaGvQmVpRKpWYF0zFjxphsLyIiAr179wYAFBQU6D1Gvb1te56enppVcS2pZw51XGjNoBunJY7Uj6lNnToVAQEBVrWxadMmCCEQEBCgd0U2IiIiZxLCPsUSXSHwUX9vbcD0+eef4/nnn4dUKsWf/vQnTJ06Ve9xRERE5HqcET/5+/sjISEBALB9+3ad/Tk5OVAoFAgODsbjjz9usj2JRILp06cbbO/YsWM4f/48ZDKZTpwyY8YMAMCOHTvQ0tKita+iogJ5eXkAgJkzZ5o+sTa++uorAMDPfvYzi+oBTkoc1dfXY8+ePQCMP6Z29uxZvPjiizh79qzW9oaGBrz55pt46623AAArV640OmknERGRM6ggsUuxRFcIfNT1vvzyS9TV1Wntq6urQ05ODgDoHbH89ddfY968eVCpVHj//fcxd+5ck+dIRERErsMZ8RMArFmzBhKJBB999JFmWh0AKCkpwbJlywAAqampWrmHzZs3IzIyEsnJyTrtrVixAl5eXjh48CCysrI0j/yXl5dj0aJFAIDFixdrbtCpPf/88+jZsydKS0uxbNkyKJVKAK2TXM+dOxfNzc2YMmWKziP4S5cuRX5+vk7MVV5ejvnz5+PQoUPw8fFBSkqKxdfGKYmjr776CnV1dQgJCcETTzxh8DilUokPPvgAw4cPR69evTBq1CiMGjUKwcHBWLNmDVQqFVJSUvD73/++A3tPRETk2jp74DNz5kwMHjwY1dXVWLhwIe7fvw8AuHfvHhYuXIjq6moMHz4cTz31lFa9o0ePYtasWVAqldi4cSOWLFlizeUjIiIiNxQfH4/09HSoVCrMnTsX0dHRiImJQWxsLG7cuIHExET87ne/06pTU1OD8vJyVFVV6bTXv39/bNu2DVKpFKmpqQgPD0dsbCwGDhyIn376CXFxcXpXe/X398fnn38OuVyO9957D2FhYRg1ahQiIiJQUFCAyMhIfPzxxzr19u/fjwkTJqB79+546KGH8PDDD+OBBx5AVFQUPv30U3Tv3h1ffvmlVQuTOSVxpH5MLTk5GZ6engaPi4yMRHp6OqZMmQI/Pz/89NNPOH36NIKCgjBr1ix8++23+OijjyCRWJ5NJCIicjSVsE+xVGcPfDw8PJCTk4PAwEDs3r0bffv2xahRoxAWFobdu3cjKCgIX3zxhWYSbbWUlBTU19drJsN+7LHH9JZvvvnG8otKREREHcJZ8RPQevMtNzcXEyZMQHV1NS5evIgRI0Zg8+bN2LdvHzw8PCxqb968efjuu++QlJSE+vp6nDt3DlFRUUhLS8PRo0fh6+urt97EiRNx8uRJJCcnQyKR4PTp0wgNDcWyZctQXFysc7MOAN577z0sWbIEAwcOxPXr13Hq1CncvXsXsbGxWL16NUpLS5GUlGTVdZEIQ0ukdGEKheLf8ypJASuGsBERUWcmAKhQW1trdEUuW6g/Z37/wHPwltr2KHWjqgkb/rnVqv7+5S9/wTvvvIOioiIolUoMHDgQCxcuxG9+8xudwCctLQ2vvfYaxo8fjyNHjuht79ixY8jMzMSxY8dw7949REZGYs6cOVi5ciXkcrnBfpw9exYZGRk4fPgwampqEBYWhunTp2Pt2rVGV3arrKzE66+/jm+++QY3b95ESEgInnzySaxbt07vohiRkZEoLy83eV0++eQTLFiwwORxpI3xExGRO3Of+Il0MXHEwIeIyM0w8CGyBuMnIiJ3xvjJnRl+ToyIiIhsYu3kjO3bICIiInIXjJ9cDxNHREREDmLNcrD62iAiIiJyF4yfXI9TJscmIiIiIiIiIiLXxxFHREREDqL6d7G1DSIiIiJ3wfjJ9TBxRERE5CC2LAfbtg0iIiIid8H4yfUwcURERERERF2YsUly+dclEZEpTBwRERE5iIDtf5LwTxoiIiJyJ4yfXA8TR0RERA7SOtTaxuVkGfkQERGRG2H85Hq4qhoREREREREREenFEUdEREQOIkRrsbUNIiIiInfB+Mn1MHFERETkIFxOloiIiMgyjJ9cDx9VIyIiIiIiIiIivTjiiKjTaTtRHMdgErkyDrUmIiIisgzjJ9fDxBFRpyYBk0dErotDrYmIiIgsw/jJ9fBRNaJORd+ylLYtVUlERERERERkCEccEREROYgQgIpDrYmIiIjMxvjJ9TBxRNRpGBtZxEfWiFyRgO3/M/k/m4iIiNwJ4yfXw0fViDoN/vojIiIiIiKijsURR0RdApNKRK5IZYeh1rbWJyIiIupMGD+5HiaOiDoVAd1H1vhbkchVcTlZIiIiIsswfnI9TBwRdTr6kkdEREREpB//giQisgUTR0SdEgMgos5A9e9iaxtERERE7oLxk+th4oiIiMhB+Iw+ERERkWUYP7kerqpGRERERERERER6ccQRERGRgwjY/mApb5gRERGRO2H85HqYOCIiInIQDrUmIiIisgzjJ9fDR9WIiIiIiIiIiEgvjjgiIiJyECFai61tEBEREbkLxk+uhyOOiIiIiIiIiIhIrw5NHC1YsAASicRoaWho0Fv3+PHjmDZtGkJCQuDj44OhQ4ciPT3d4PFERETOprJTISIiInIXzo6fvv76ayQkJCAoKAi+vr6IjY3Fli1boFJZ16q1uYzS0lI888wz6NOnD+RyOaKjo7F8+XLU1NToPf6f//wnNm/ejP/v//v/8MADD8DLywsBAQF49NFH8c4776CxsdGq/gNOelRt4MCB6NWrl959UqluLmvnzp2YP38+WlpaEBYWhvDwcJw5cwbr1q1Dbm4ujhw5gm7dujm620RERBZRwQ6TO9qlJ0RERESdgzPjpw0bNmDVqlUAgKioKPj5+aGkpARLly5FXl4e9u7dqzdnYYi1uYz8/HwkJiaivr4eISEhGDZsGM6fP4+3334be/fuxbFjxxAaGqpV59FHH8U///lPAEBoaChiYmJw/fp1fP/99/j+++/x6aefIi8vD8HBwRZfF6c8qrZ69WocPXpUb/Hy8tI6tqysDCkpKWhpacHGjRtRWVmJ4uJiXLhwAYMGDUJhYSFSU1OdcRpERERERERE1AUcP34cq1evhlQqRXZ2Ni5duoSSkhIUFxcjNDQU+/fvx6ZNm8xuz9pcRl1dHZ5++mnU19dj6dKluHr1KoqKilBRUYH4+HhcvnwZKSkpOvXkcjmWLl2KH3/8EVVVVSgsLMQ///lP5OXloVevXvjhhx+wZMkSq66Ny89xlJWVhcbGRkyePBkrVqyARCIBAPTr1w8ff/wxAGDr1q24ceOGM7tJRESkQ9ipEBEREbkLZ8VPGRkZEEJg8eLFmDNnjmZ7TEyMJmG0YcMGKJVKs9qzNpfx4Ycf4ubNmxgyZAg2bdoEmUwGAAgODkZ2djY8PT1x4MABFBcXa9U7ceIE3n33XYwYMUJr+8SJE/Hee+8BAPbu3Yvq6mpzL4mGSyeOhBDYu3cvAOjNqI0dOxaDBw+GUqnEvn37Orp7RERERgnROtTalsJVQYiIiMidOCN+UigUyMvLA6A/9zB79mz4+/ujuroa+fn5ZpyD9bmMPXv2AGidI9rDw0NrX0REBBISEgAAu3bt0toXFBRksD+TJ08GAKhUKly8eNFk/9tzSuJo165deOqppzBhwgQkJydjy5YtqK2t1TmuoqIC169fBwDEx8frbUu9/cSJE47rMBERERERERF1SadOnUJTUxPkcjliY2N19stkMowePRqAebkHa3MZzc3NKCoqsrieKW0n4vbx8TG7nppTJsc+cOCA1vdffPEF1q9fj+zsbDzxxBOa7RcuXAAAeHt7o2/fvnrbioqK0jpWn8bGRq0ZxBUKhdV9JyIiMpcQtj9qxhFHRERE5E7sGT+1/9vf29sb3t7eOser8wkRERHw9NSfJomKisKhQ4eM5h7at2dpLqOsrEzzKJx6vzn1TPnyyy8BAIGBgRg6dKjZ9dQ6dMRRdHQ03nzzTZSUlEChUKCurg4HDx7EmDFjcOfOHTz11FM4efKk5vg7d+4AAHr06KF5HrC9wMBArWP1yczMREBAgKaEh4fb8ayIiIj0c/ZyskRERESdjT3jp/DwcK1cQGZmpt7XVOcT1PkFfczJPbRvz9JcRtuvDfXFkn4AwPXr15Geng4AeOWVVwwmxozp0BFHr776qs62SZMmYfz48Rg3bhz+7//+DytXrsShQ4cA/Gc4VfuV1tpSZwvr6+sNHrNq1SosW7ZM871CoWDyiIiIiIiIiKgLq6yshL+/v+Z7faONAPvlHmxtr+0jZYbqWtKPpqYm/PKXv0R1dTV+9rOfYeXKlSbr6OOUR9Xa8/LyQnp6On7xi1/gyJEjuHPnDgIDAyGXywG0nqwh6kfQjD2nZ2g4GhERkSOpBKCycbC1io+qERERkRuxZ/zk7++vlTgyxF65B1vbU9dT1237vaX9EEJgwYIFOHr0KPr06YO9e/caTWQZ4zKrqj366KMAWmf5vnz5MoD/DMGqqamBMDDJgzlDyoiIiJzBWcvJEhEREXVWzoifzHn8y5Lcg7W5jLZfG+qLuf146aWX8NlnnyEoKAgHDx5EZGSkyX4b4jKJI5lMpvm6ubkZADBw4EAArRm1a9eu6a2nTjKpjyUiIiIiIiIiMpc6n1BRUaHJR7RnSe7B2lxGZGSkJjei3m9NP9asWYP//u//hp+fH7755hsMHz7cZJ+NcZnE0dmzZzVfP/DAAwBaZzTv3bs3AKCgoEBvPfX2MWPGOLiHREREllEJ+xQiIiIid+GM+GnkyJGQyWRoaGhAcXGxzn6lUonCwkIA5uUerM1leHp6IjY21uJ6bWVlZeHNN9+EXC5Hbm4uHn74YZP9NcVlEkdvv/02AGDw4MEICwsDAEgkEkyfPh0AsH37dp06x44dw/nz5yGTyTB16tSO6ywREZEZhJ3+EREREbkLZ8RP/v7+SEhIAKA/95CTkwOFQoHg4GA8/vjjJtuzJZcxY8YMAMCOHTvQ0tKita+iogJ5eXkAgJkzZ+q0u3XrVqSmpkImkyEnJ8esvpqjwxJHf/3rX7Fq1SpcuXJFa3ttbS2WLl2Kzz77DACwbt06rf0rVqyAl5cXDh48iKysLM3zgeXl5Vi0aBEAYPHixZpsHhERERERERGRJdasWQOJRIKPPvpIk58AgJKSEs0q7ampqVoTTG/evBmRkZFITk7Wac/aXMbzzz+Pnj17orS0FMuWLYNSqQQAVFdXY+7cuWhubsaUKVMQFxenVS8nJwcvvPACpFIpPv30UyQlJdnhqrSSCEMzNdnZV199pcm4hYWFoW/fvlAqlTh37hyampogkUiwbt06pKWl6dT99NNPsXDhQqhUKoSFhaFXr144c+YMlEol4uLi8Le//Q2+vr5m90WhUCAgIACteTOJfU6QiIg6CQFAhdraWrNW2bCG+nPmSb/nIJNYt3qFmlI04eu7Wx3aXyJzMH4iInJn7hE/vfHGG1i7di0AICoqCn5+fjhz5gxUKhUSExOxb98+eHh4aI5PS0vDa6+9hvHjx+PIkSM67Vmbyzh06BCSkpLQ0NCAkJAQREREoLS0FPfv30dkZCSOHz+uk3Dy9vZGU1MT/P39MWLECIPnuGXLFowcOdLsawIAnhYdbYO4uDisWbMGx48fx8WLF3HmzBkIIRAWFoZx48bhxRdfNPiM3rx58zBgwABkZmbi2LFjOHfuHKKiojBnzhysXLlS7xJ1REREzqb6d7G1DSIiIiJ34cz4ac2aNYiJicE777yDoqIiVFVVYcSIEVi4cCF+85vfaCWNzGFtLmPixIk4efIkMjIycPjwYZw+fRphYWGYPn061q5dq3dFtaamJgCtCThD8yMBrU99WarDRhy5Et4xIyJyZx13x+wJO90x+5YjjsgFMH4iInJnjJ/cmctMjk1ERNTVCCHsUsh9XblyBdu2bcOzzz6LmJgYeHp6QiKRICMjw2Td48ePY9q0aQgJCYGPjw+GDh2K9PR0NDQ0dEDPiYiIrMP4yfV02KNqRERE7oaPqpGt3n33Xbz77rsW19u5cyfmz5+PlpYWhIWFITw8HGfOnMG6deuQm5uLI0eOoFu3bg7oMRERkW0YP7kejjgiIiIiclE9e/ZEUlISXn/9dXzzzTd6l95tr6ysDCkpKWhpacHGjRtRWVmJ4uJiXLhwAYMGDUJhYSFSU1M7oPdERETUFXDEERERkYMIISBg21BpDrV2b+qVXdQ+//xzk3WysrLQ2NiIyZMnY8WKFZrt/fr1w8cff4z4+Hhs3boVr776KkJDQ+3eZyIiIlswfnI9HHFERETkIK3TSNpWGPaQJYQQ2Lt3LwAgJSVFZ//YsWMxePBgKJVK7Nu3r6O7R0REZBLjJ9fDxBERERFRF1FRUYHr168DAOLj4/Ueo95+4sSJDusXERERdV58VI2IiMhBVEJAZeM9LxWHWpMFLly4AADw9vZG37599R4TFRWldawhjY2NaGxs1HyvUCjs1EsiIiLDGD+5Ho44IiIichBhp3/W+vrrr5GQkICgoCD4+voiNjYWW7ZsgUpl3Voj1i7vXlpaimeeeQZ9+vSBXC5HdHQ0li9fjpqaGqP1rl69iueeew7h4eHw9vZGREQElixZgqtXr+o9XqVS4euvv0ZaWhqefPJJhISEQCKRwNPTfe6T3blzBwDQo0cPSCQSvccEBgZqHWtIZmYmAgICNCU8PNy+nSUiItLD2fET6WLiiIiIqAvasGEDEhMTcejQIQQGBmLAgAEoKSnB0qVLMX36dIuTRzt37sS4ceOwf/9+eHt7Y8iQIbh48SLWrVuHn//857h//77eevn5+YiLi0N2djZaWlowbNgwVFVV4e2330ZcXBxu3Liht965c+fw0EMPYdu2bairq8Pw4cOhUCiwdetWxMTE4Pz58zp1FAoFEhMT8dprr+Gbb77BrVu3LDrHrkCdxPPy8jJ4jLe3NwCgvr7eaFurVq1CbW2tplRWVtqvo0RERNRpMHFERETkILZO7Kguljp+/DhWr14NqVSK7OxsXLp0CSUlJSguLkZoaCj279+PTZs2md2etcu719XV4emnn0Z9fT2WLl2Kq1evoqioCBUVFYiPj8fly5f1TuDc0tKC2bNn4/bt25g5cyauXbuGoqIiXL16FTNmzEB1dTWefvppneSXVCrFyJEjsWTJEmzfvh0HDhyw/OJ1cnK5HADQ1NRk8Bj142c+Pj5G2/L29oa/v79WISIicjRnxU9kGBNHREREDqKCsEuxVEZGBoQQWLx4MebMmaPZHhMTo0kYbdiwAUql0qz22i/vrn4ESr28OwBs3bpVZ/TQhx9+iJs3b2LIkCHYtGkTZDIZACA4OBjZ2dnw9PTEgQMHUFxcrFVvz549OHfuHIKDg/HJJ5+gW7duAABfX1/s2LEDwcHB+PHHH3VWBfP390dxcTE+/PBDLFq0CEOHDjX3knUZ6sfQampqDC5FrH5ETX0sERGRK3FW/ESGMXFERETUhSgUCuTl5QHQvxz77Nmz4e/vj+rqauTn55tsz5bl3ffs2QMAWLBgATw8PLT2RUREICEhAQCwa9cuvfV++ctfonv37lr7unfvjtmzZwMAcnJyTPbf3QwcOBBA66iia9eu6T3m8uXLWscSERERGcPEERERkYOohLBLscSpU6fQ1NQEuVyO2NhYnf0ymQyjR48GYN5y7NYu797c3IyioiKL6wHA999/b1U9ak3I9e7dGwBQUFCg9xj19jFjxnRYv4iIiMzljPiJjGPiiIiIyEHsuSqIQqHQKm2XSW9LvcR6RESEwdXEzF2Ove0xli7vXlZWpnkUTr3fnHpNTU2oqKgwq17b16BWEokE06dPBwBs375dZ/+xY8dw/vx5yGQyTJ06taO7R0REZBJXVXM9TBwRERF1AuHh4VpLo2dmZuo9zpz5a8xdjr3tMZYu7972a0N90VevtrZWM+m1qXoqlQoKhcLkObibFStWwMvLCwcPHkRWVpZmrqPy8nIsWrQIALB48WLNyCQiIiIiY/TfiiQiIiKb2WNyRnX9yspKrVWt1Euqt2fP5dhtaU9dz1hdW+u1r9sVFRQUYNq0aZrv7969CwDIzMzE5s2bNdtPnTqF8PBwAED//v2xbds2LFy4EKmpqXj33XfRq1cvnDlzBkqlEnFxccjKyurQ8yAiIjKXPeMnsg8mjoiIiBzEnoGPucuh23M5dlvaU9dT1237vSX1jL1e+7pdkVKpRHV1tc72+/fv4/79+5rvW1patPbPmzcPAwYMQGZmJo4dO4Zz584hKioKc+bMwcqVK/X+PIiIiFwBE0euh4kjIiKiLsScx9AsWY69/fLu+h5X09de26/v3LmDPn36mFUvICAAUqkUKpXK4Dmot0ulUrOSaZ3Z448/rnnUzFJjx45Fbm6unXtERERE7oZzHBERETmIMyZ3VC+xXlFRgebmZr3HWLIcu7XLu0dGRkImk2ntN6eel5cXIiIizKrX9jWIiIioa+Dk2K6HiSMiIiIHEf8eam1LsTTwGTlyJGQyGRoaGlBcXKyzX6lUorCwEIB5y7Fbu7y7p6cnYmNjLa7X9nsuJ09EROR+nBE/kXFMHBEREXUh/v7+SEhIAKB/OfacnBwoFAoEBwfj8ccfN9meLcu7z5gxAwCwY8cOnTl4KioqkJeXBwCYOXOm3npffvkl6urqtPbV1dUhJycHADBr1iyT/SciIiIi2zBxRERE5CAqicouxVJr1qyBRCLBRx99hM8++0yzvaSkBMuWLQMApKamaq1atnnzZkRGRiI5OVmnPWuXd3/++efRs2dPlJaWYtmyZVAqlQCA6upqzJ07F83NzZgyZQri4uK06s2cORODBw9GdXU1Fi5cqJkE+t69e1i4cCGqq6sxfPhwPPXUUxZfGyIiInJtzoqfyDAmjoiIiBzE1mHW1q4qEh8fj/T0dKhUKsydOxfR0dGIiYlBbGwsbty4gcTERPzud7/TqlNTU4Py8nJUVVXptKde3l0qlSI1NRXh4eGIjY3FwIED8dNPPxlc3t3f3x+ff/455HI53nvvPYSFhWHUqFGIiIhAQUEBIiMj8fHHH+vU8/DwQE5ODgIDA7F792707dsXo0aNQlhYGHbv3o2goCB88cUXkEp1w5hp06ahZ8+e6Nmzp+ZRuZaWFs22nj174qWXXrL4mhIREVHHcFb8RIYxcURERNQFrVmzBrm5uZgwYQKqq6tx8eJFjBgxAps3b8a+ffvg4eFhUXvz5s3Dd999h6SkJNTX12uWd09LS8PRo0fh6+urt97EiRNx8uRJJCcnQyKR4PTp0wgNDcWyZctQXFysM0pJbfjw4SgpKcHixYvh5+eH06dPw8/PD88++yxKSkowdOhQvfVqa2tRXV2N6upqrVXZ1Nuqq6t1Hn8jIiIiIsMkwto1XjsxhUKBgIAAtObNdJcVJiKirkwAUKG2ttZhS7mrP2ce8vklPCRepisY0SKa8GP9lw7tL5E5GD8REbkzxk/uzNPZHSAiIuqqVAAkNg6V5hP6RERE5E4YP7kePqpGRERERERERER6ccQRERGRg6gkKkhsXNVDxXtmRERE5EYYP7keJo6IiIgcRAUVJDYGLgx8iIiIyJ0wfnI9fFSNiIiIiIiIiIj06rDEkRACR48exYoVK/DII4+gR48e8PLyQt++fTFz5kzk5+frrZeWlgaJRGK0nD9/vqNOg4iIyGwqO/0jIiIicheMn1xPhyWODh8+jHHjxuEPf/gDCgsLERoaiuHDh6Ourg579uzBhAkT8OqrrxqsHx4ejvj4eL2lW7duHXUaREREZhNQ2aUQERERuQtnx09ff/01EhISEBQUBF9fX8TGxmLLli1Qqaxr8/jx45g2bRpCQkLg4+ODoUOHIj09HQ0NDUbrlZaW4plnnkGfPn0gl8sRHR2N5cuXo6amRu/x9fX12Lt3L1atWoWJEyciICAAEokEAwYMsKrfbXXYHEdCCAwYMADLli1DcnIyAgMDAQBNTU1IS0tDZmYmMjIyMGbMGCQlJenUX7RoEdLS0jqqu0RERERERETkRjZs2IBVq1YBAKKiouDn54eSkhIsXboUeXl52Lt3L6RS88ff7Ny5E/Pnz0dLSwvCwsIQHh6OM2fOYN26dcjNzcWRI0f0DoTJz89HYmIi6uvrERISgmHDhuH8+fN4++23sXfvXhw7dgyhoaFadX766SfMmDHDtgtgQIeNOHr44YdRWlqKF154QZM0AgAvLy+8+eabmDJlCgBg27ZtHdUlIiIih1JJVHYpRERERO7CWfHT8ePHsXr1akilUmRnZ+PSpUsoKSlBcXExQkNDsX//fmzatMns9srKypCSkoKWlhZs3LgRlZWVKC4uxoULFzBo0CAUFhYiNTVVp15dXR2efvpp1NfXY+nSpbh69SqKiopQUVGB+Ph4XL58GSkpKTr1ZDIZHnnkEbz00kv4n//5H3z88ccWXwNDOixx5O/vD09PwwOcJk2aBAD4xz/+0VFdIiIicihhh+fz+agaERERuRNnxU8ZGRkQQmDx4sWYM2eOZntMTIwmYbRhwwYolUqz2svKykJjYyMmT56MFStWQCKRAAD69eunSeps3boVN27c0Kr34Ycf4ubNmxgyZAg2bdoEmUwGAAgODkZ2djY8PT1x4MABFBcXa9UbNmwYjh8/jvfeew+/+tWv0L9/f4uvgSEus6qa+vk+Hx8fvfvz8/Mxe/ZsTJgwAbNmzcLGjRtRVVXVkV0kIiIiIiIioi5GoVAgLy8PAPSO5pk9ezb8/f1RXV1tcGGvtoQQ2Lt3r8H2xo4di8GDB0OpVGLfvn1a+/bs2QMAWLBgATw8PLT2RUREICEhAQCwa9cuM87MPlwicSSEQE5ODgAgPj5e7zF///vfsWvXLuTn52P37t1YuXIloqKisGPHDpPtNzY2QqFQaBUiIiJHE2ixSyEiIiJyF86In06dOoWmpibI5XLExsbq7JfJZBg9ejQA4MSJEybbq6iowPXr1wEYznGot7dtr7m5GUVFRRbXczSXSBxt27YNp06dgpeXF377299q7evTpw9Wr16NwsJCVFdX4/79+ygoKMCUKVNQX1+PRYsWITc312j7mZmZCAgI0JTw8HAHng0REVEr2wdat/4jIiIichf2jJ/aDyBpbGzU+5oXLlwA0Dqix9AUO1FRUVrHGqM+xtvbG3379jW7vbKyMs2jcOr9tvTDXpyeOCouLsbLL78MoPWZwujoaK39S5YswRtvvIFRo0YhKCgIPj4+GDt2LA4cOIDp06dDCIFXXnkFQgiDr7Fq1SrU1tZqSmVlpUPPiYiIiIiIiIicKzw8XGsQSWZmpt7j7ty5AwBaC3m1p96nPtYY9TE9evTQzG1kTnttvzbUF0v6YS+GZ6vuAFeuXEFSUhIaGhowd+5cLF++3Oy6EokEGzZswN69e3Hp0iX8+OOPiImJ0Xust7c3vL297dVtIiIis6ggABtHDLW2QUREROQe7Bk/VVZWwt/fX7PdUF5APeeyl5eXwTbVdevr602+vrXtqesZq2tJP+zFaYmjqqoqTJo0CdevX0diYiJ27NhhMBNnyIMPPoigoCDcvn0bFy9eNJg4IiIicobWZ+wt+2zT1wYRERGRu7Bn/OTv76+VODJELpcDAJqamgweo37MzdCCXvZoT11PXbft99b0w16c8qja7du3MWnSJFy6dAnjx49HTk6OZok5S6nrNTc327OLREREREREROQGzHn8y5zH2dq3V1NTY3BaHX3ttf3aUF8s6Ye9dHji6O7du3jyySdx5swZjB49Grm5uVZnym7duoV//etfAIAHHnjAnt0kIiKyGSfHJiIiIrKMM+KngQMHAmhdDc3QoJTLly9rHWtOe42Njbh27ZrZ7UVGRmoGx6j329IPe+nQxFFjYyOmTZuGEydOYNiwYfj222/RvXt3q9vbtGkThBAICAjQLI1HRETkKgRUdilERERE7sIZ8dPIkSMhk8nQ0NCA4uJinf1KpRKFhYUAgDFjxphsLyIiAr179wYAFBQU6D1Gvb1te56enoiNjbW4nqN1WOKopaUFycnJOHz4MKKjo/HXv/4VQUFBRuucPXsWL774Is6ePau1vaGhAW+++SbeeustAMDKlSuNTjpFRERERERERKSPv78/EhISAADbt2/X2Z+TkwOFQoHg4GA8/vjjJtuTSCSYPn26wfaOHTuG8+fPQyaTYerUqVr7ZsyYAQDYsWMHWlq057qsqKhAXl4eAGDmzJmmT8xOOixx9OWXX+Krr75qfVGpFLNnz8Zjjz2mU2bPnq2po1Qq8cEHH2D48OHo1asXRo0ahVGjRiE4OBhr1qyBSqVCSkoKfv/733fUaRAREZlNhRa7FCIiIiJ34az4ac2aNZBIJPjoo4/w2WefabaXlJRg2bJlAIDU1FStQSubN29GZGQkkpOTddpbsWIFvLy8cPDgQWRlZWnmOiovL8eiRYsAAIsXL9aMTFJ7/vnn0bNnT5SWlmLZsmVQKpUAgOrqasydOxfNzc2YMmUK4uLiLD5Ha0mEoZma7GzHjh1YuHChyeP69euHsrIyAK0TSf3xj3/UZONu3ryJpqYm9OrVC4888ggWL16MX/ziFxb3RaFQICAgAK15M9tmaycios6mdYnX2tpas1bZsIb6c6aX7yOQSmxbwFQlmvGve987tL9E5mD8RETkztwjfnrjjTewdu1aAEBUVBT8/Pxw5swZqFQqJCYmYt++ffDw8NAcn5aWhtdeew3jx4/HkSNHdNr79NNPsXDhQqhUKoSFhaFXr144c+YMlEol4uLi8Le//Q2+vr469Q4dOoSkpCQ0NDQgJCQEERERKC0txf379xEZGYnjx4/rJJwAIDY2FhUVFQBaB+IoFApIpVKtibRTU1ORmppq9jUBANt+GhZYsGABFixYYFGdHj16aH5oRERERERERESOsmbNGsTExOCdd95BUVERqqqqMGLECCxcuBC/+c1vtJJG5pg3bx4GDBiAzMxMHDt2DOfOnUNUVBTmzJmDlStXQi6X6603ceJEnDx5EhkZGTh8+DBOnz6NsLAwTJ8+HWvXrjW4otrt27dRXV2ttU2lUmltu3//vkXnAHTgiCNXwjtmRETurOPumPXsNtoud8xu3S/kiCNyOsZPRETujPGTO+uwEUdERETuxh6ronFVNSIiInInjJ9cT4dNjk1ERERERERERJ0LRxwRERE5SOsdM9tWReMdMyIiInInjJ9cDxNHREREDiKECiob54IRgoEPERERuQ/GT66Hj6oREREREREREZFeHHFERETkIK3DpG28Y8ah1kRERORGGD+5HiaOiIiIHEQI257Pt1cbRERERJ0F4yfXw0fViIiIiIiIiIhIL444IiIicpDWqR051JqIiIjIXIyfXA8TR0RERA7SuqIHVwUhIiIiMhfjJ9fDR9WIiIiIiIiIiEgvjjgiIiJyEAE7TO5ohzaIiIiIOgvGT66HiSMiIiIHEUIANj5j39oGERERkXtg/OR6+KgaERERERERERHpxRFHREREDmKPFT24KggRERG5E8ZProeJIyIiIgcRogWAbUOluSoIERERuRPGT66Hj6oREREREREREZFeHHFERETkIPa428U7ZkREROROGD+5HiaOiIiIHITP6BMRERFZhvGT6+GjakREREREREREpBdHHBERETkIh1oTERERWYbxk+th4oiIiMhBONSaiIiIyDKMn1wPH1UjIiIiIiIiIiK9OOKIiIjIQYRoASBsbIN3zIiIiMh9MH5yPUwcEREROYwAbB4qbVvgRERERNS5MH5yNXxUjYiIiIiIiIiI9GLiiIiIyEGEUNmlENni66+/RkJCAoKCguDr64vY2Fhs2bIFKhXfW0RE5HoYP7keJo6IiIgcREBll0JkrQ0bNiAxMRGHDh1CYGAgBgwYgJKSEixduhTTp09n8oiIiFwO4yfXw8QRERERURd0/PhxrF69GlKpFNnZ2bh06RJKSkpQXFyM0NBQ7N+/H5s2bXJ2N4mIiMjFdarEEYdaExFR56KyUyGyXEZGBoQQWLx4MebMmaPZHhMTo0kYbdiwAUql0lldJCIi0oPxk6vpNIkjDrUmIqJOR6jsU6xk7xsux48fx7Rp0xASEgIfHx8MHToU6enpaGhoMFqvtLQUzzzzDPr06QO5XI7o6GgsX74cNTU1RutdvXoVzz33HMLDw+Ht7Y2IiAgsWbIEV69eNVqvpqYGy5cvR3R0NORyOfr06YNnnnkGpaWllp5yp6VQKJCXlwcASElJ0dk/e/Zs+Pv7o7q6Gvn5+R3dPSIiIsMYPwHo+PjJKNEJHDt2TEgkEiGVSkV2drZm+w8//CBCQ0MFAJGVlWV2e7W1tQKAAKQC8GBhYWFhcasiFQBEbW2tIz6ytD5nJPARUkk3m4oEPlb1NzMzU7R+1kFERUWJhx56SEilrec+depU0dLSYlF7f/7zn4WHh4cAIMLCwsTIkSOFTCYTAMTo0aPFvXv39NY7fPiw8PFpPYeQkBARGxsrunXrpulXVVWV3npnz54VQUFBAoAICAgQsbGxIiAgQAAQwcHBorS0VG+969evi8jISAFAdOvWTcTGxoqQkBABQPj4+Ii//e1vFp13Z3XkyBEBQMjlcqFUKvUeM3HiRAFAvP7662a1yfiJhYWFxZ0L46euHD+Z0ikSR08++aQAIJ577jmdfTt37tRchKamJrPaY+DDwsLC4s6l4wIfwFtIJHKbCuBtcX/tfcPlypUrwtu7tR8bN24UKpVKCCFEWVmZGDRokAAgfv3rX+vUUygUmqTN0qVLNZ/Tt27dEvHx8QKASExM1KnX3Nwshg4dKgCImTNnaoKqu3fvihkzZggA4qGHHtIbvP3iF78QAMRjjz0mbt26JYQQoqmpSbz00ksCgAgNDRV37941+9w7q23btgkA4sEHHzR4zLPPPisAiP/6r/8yq03GTywsLCzuXBg/deX4yRSXTxzV1tYKLy8vAUCcOHFCZ39TU5Pw9/cXAMT//u//mt0mAx8WFhYWdy0dGfjIhETiZVMBZBb31943XF588UUBQEyePFlnX0FBgQAgZDKZzt2vjRs3CgBiyJAhorm5WWtfeXm58PT0FABEUVGR1r4vv/xS00eFQqG1T6FQiODgYAFA7NmzR2tfYWGhACA8PT1FeXm51r7m5mYxZMgQAUBs2rTJrPPuzNTXfsyYMQaPSU1NFQBEUlKS3v0NDQ2itrZWUyorKwXjJxYWFhZ3LYyfumr8ZA6Xn+Po1KlTaGpqglwuR2xsrM5+mUyG0aNHAwBOnDjR0d0jIiJyKfae20YIgb179xpsb+zYsRg8eDCUSiX27duntW/Pnj0AgAULFsDDw0NrX0REBBISEgAAu3bt0lvvl7/8Jbp37661r3v37pg9ezYAICcnR2vf7t27AQCTJk1CRESE1j4PDw/Mnz9fb72uSD1vgpeXl8FjvL29AQD19fV692dmZiIgIEBTwsPD7d9RIiIiF+DO8ZM5XD5xdOHCBQCtF8jT01PvMVFRUVrHttfY2AiFQqFViIiIHE8AwsYCYdEr2vuGS0VFBa5fvw4AiI+P13uMenvb9pqbm1FUVGRxPQD4/vvvHVqvqKgILS0teo/pKuRyOQCgqanJ4DGNjY0AAB8fH737V61ahdraWk2prKy0f0eJiIh0MH6ytB5gffxkDv2ZGBdy584dAEBgYKDBY9T71Me2l5mZiddee03PHsveTERE1BW0/u4XoiM+AwSEnT5r2t/08Pb21owYacvcGy6HDh0yeMNFX3ve3t7o27evwfbaHgsAZWVlmmXe1fvNqdfU1ISKigqz6qlfQyaTabVjql5TUxPKy8sNHtcVmIqN2u4zFGO1f4/95/8M4yciIvfD+Kmrxk/mcPnEkT2GWq9atQrLli3TfH/16lUMHToU/54s3W59JSKizqOurg4BAQEOadvLywu9e/dGVVWVXdrz8/PTeUxo/fr1SEtL0znWHjdc9LXXo0cPSCQSs9tr+7WhvuirV1tbq1nu1lQ9lUoFhUKB4OBgrXZM1Wv/ml3RwIEDAbTe8WxubtYbBF++fFnrWFPq6ur+/RXjJyIid8X4qevFT+Zw+cSRPYZat88q+vn54dy5cxg6dCgqKyvh7+9vxx6TORQKBcLDw3n9nYg/A+fi9XceIQTq6uoM3v2xB7lcjitXrhj97LKEEEIn6NB3twywzw0Xe7Snrmesrq31DNW1tF5XNHLkSMhkMjQ0NKC4uBgPP/yw1n6lUonCwkIAwJgxY8xqs2/fvqisrET37t0hkUj4e4wchu8tchS+t6zH+Knrxk/mcPnEkT2GWrcnlUoRFhYGAPD39+cvDSfi9Xc+/gyci9ffORx1p6wtuVyuufnRkexxw8Ue7bU9d/WcAdbUM/Z6+urev3/f4npdkb+/PxISEvDNN99g+/btOomjnJwczd3Gxx9/3Kw2pVIpHnjgAb2vxd9j5Ah8b5Gj8L1lHcZPXTN+MofLT47dfqi1PpYOtSYiIuqq7H3DRX1MTU2NwXkN9LVnzmNh+uoFBARAKpWaVU8qlWoF/qbO3Zzh313JmjVrIJFI8NFHH+Gzzz7TbC8pKdE8wp+ammr0bigREZE7cOf4yRwunzhqP9S6PWuGWhMREXVV9r7hoj6msbER165dM7u9yMhIzaSL6v3m1PPy8kJERIRZ9dq+Rtt2TNXz8vJCv3799B7TlcTHxyM9PR0qlQpz585FdHQ0YmJiEBsbixs3biAxMRG/+93vnN1NIiIip3Pn+MkcLp84Ug+1BoDt27fr7LdmqDXQ+nzf+vXrDT7jSI7F6+98/Bk4F68/OYq9b7hERESgd+/eAICCggK9x6i3t23P09NTs5ytJfXafu+oenFxcfDw8NB7TFezZs0a5ObmYsKECaiursbFixcxYsQIbN68Gfv27bPpOvD3GDkK31vkKHxvkSHuHD+ZRXQCR48eFRKJREilUpGdna3Z/sMPP4jQ0FABQLz11ltO7CEREZHrmDJligAgnnvuOZ19O3fuFABEcHCwaGxsNKu9F154QQAQkydP1tlXUFAgAAiZTCauX7+ute+tt94SAMSQIUNEc3Oz1r7y8nLh6ekpAIiTJ09q7fviiy80fVQoFFr7FAqFCA4OFgDE7t27tfadOHFCABCenp6ivLxca19zc7MYMmSIACDefvtts86biIiI3Ie7xk/m6BSJIyGEyMjIUK/9KqKiosRDDz0kpFKpACASExN1LigREZG7suaGyzvvvCP69esnnn76aZ32Ll++LLy8vAQAsXHjRqFSqYQQQpSVlYlBgwYJAOKFF17QqVdbWyt69uwpAIilS5eKpqYmIYQQt27dEvHx8QKAmDJlik695uZmMXjwYAFAzJw5U9y7d08IIcTdu3fFzJkzBQAxfPhw0dLSolN30qRJAoB47LHHxK1bt4QQQjQ1NYmXXnpJABC9evUSdXV15l5KIiIichPuHD+Z0mkSR0IIkZubKyZMmCACAgJEt27dRExMjNi8eTOTRkRERO1YesNl/fr1AoAYP3683vb+9Kc/aeqHhYWJkSNHCplMJgCIuLg4cffuXb318vLyhFwuFwBESEiIiIuLE926dRMARGRkpM5dNrXTp0+LwMBAAUAEBASIuLg4ERAQIACIoKAgcfbsWb31rl69Kvr16ycAiG7duom4uDgREhIiAAi5XC7y8/PNvoZERETkXtw1fjLF5ec4aispKQmHDh1CTU0N7t27hx9++AEvv/yy28xTQEREZC57z20zb948fPfdd0hKSkJ9fT3OnTuHqKgopKWl4ejRo/D19dVbb+LEiTh58iSSk5MhkUhw+vRphIaGYtmyZSguLtY8/9/e8OHDUVJSgsWLF8PPzw+nT5+Gn58fnn32WZSUlGDo0KF66/Xt2xenTp3CK6+8gtDQUJw+fRoSiQTJyckoKiqyaD5EIiIici/uGj+ZIhHCwNpwRERERERERETk1jrViCMiIiIiIiIiIuo4bpc4+vrrr5GQkICgoCD4+voiNjYWW7ZsgUqlcnbXuoQFCxZAIpEYLQ0NDXrrHj9+HNOmTUNISAh8fHwwdOhQpKenGzzeXV25cgXbtm3Ds88+i5iYGHh6ekIikSAjI8NkXWuvcWlpKZ555hn06dMHcrkc0dHRWL58OWpqaux0Vp2HNdc/LS3N5P+L8+fPG6zP609EroSxFFlKCIGjR49ixYoVeOSRR9CjRw94eXmhb9++mDlzJvLz843WZ4xIllq7dq0mxjIWo/G9RWQmq2ZG6qQyMzMNTnQ1depUq2YXJ23z588XAMTAgQNFfHy83qJv+cI///nPwsPDQ++kYaNHj9bMCE9CvPzyy5r3cduSnp5utJ611/jw4cPCx8dHMzFbbGysZmK2qKgoUVVV5YjTdFnWXH/1pHnh4eEG/1+0XzpcjdefiFwJYymyRl5enuZ9I5VKxYMPPihGjhwp/Pz8NNvXrl2rty5jRLLUuXPnNCtZGYvR+N4iMp/bJI6OHTtmcmm9rKwsJ/awa1Anjj755BOz61y5ckV4e3sbXabw17/+tYN63Pmkp6eLpKQk8frrr4tvvvlGs7SiscSFtddYoVBoViMytBRkYmKiY07URVlz/dWJo/Xr11v0Wrz+RORKGEuRtf7617+KAQMGiPfff1/cvn1bs72xsVGsWrVK8wd+bm6uVj3GiGQplUolxo0bJ3x9fcWECRMMxmh8bxFZxm0SR08++aQAIJ577jmdfTt37hQARHBwsOYPM7KONYmjF198UQAQkydP1tlXUFAgAAiZTMaRFQaor7mxxIW113jjxo0CgBgyZIjO0pPl5eXC09NTABBFRUX2OZlOyJzrb23iiNefiFwJYymyVm1trVAqlQb3T5kyRTNqrS3GiGSpbdu2CQDirbfeMhqj8b1FZBm3mONIoVAgLy8PAJCSkqKzf/bs2fD390d1dbXJZ6zJvoQQ2Lt3LwD9P5uxY8di8ODBUCqV2LdvX0d3r0uw5Rrv2bMHQOvcVe2XnoyIiEBCQgIAYNeuXY7outvj9SciV8FYimzh7+8PT09Pg/snTZoEAPjHP/6h2cYYkSx18+ZNrFy5EkOHDsUrr7xi8Di+t4gs5xaJo1OnTqGpqQlyuRyxsbE6+2UyGUaPHg0AOHHiREd3r0vatWsXnnrqKUyYMAHJycnYsmULamtrdY6rqKjA9evXAQDx8fF621Jv58/GOtZe4+bmZhQVFVlcjwzLz8/H7NmzMWHCBMyaNQsbN25EVVWV3mN5/YnIlTCWIkdST0Ts4+Oj2cYYkSz1yiuv4Pbt23j//fchk8kMHsf3FpHlDKf+u5ALFy4AaL1Db+huR1RUFA4dOqQ5lmxz4MABre+/+OILrF+/HtnZ2XjiiSc029XX29vbG3379tXbVlRUlNaxZBlrr3FZWRmUSqXWfnPqkWF///vftb7fvXs30tLS8P7772PBggVa+3j9iciVMJYiRxFCICcnB4D2H/GMEckShw4dws6dO/GrX/0K48ePN3os31tElnOLEUd37twBAAQGBho8Rr1PfSxZJzo6Gm+++SZKSkqgUChQV1eHgwcPYsyYMbhz5w6eeuopnDx5UnO8+nr36NEDEolEb5v82djG2mvc9mtD/3f4szFPnz59sHr1ahQWFqK6uhr3799HQUEBpkyZgvr6eixatAi5ubladXj9iciVMJYiR9m2bRtOnToFLy8v/Pa3v9VsZ4xI5mpoaMDzzz+PgIAA/OEPfzB5PN9bRJZzixFH6uGvXl5eBo/x9vYGANTX13dIn7qqV199VWfbpEmTMH78eIwbNw7/93//h5UrV+LQoUMA+LPpCNZeY3U9Y3X5szHPkiVLdLaNHTsWBw4cwMyZM7F371688sorSEpK0gQwvP5E5Er4eU2OUFxcjJdffhkAkJGRgejoaM0+vufIXBkZGbh48SL++Mc/IjQ01OTxfG8RWc4tRhzJ5XIAQFNTk8FjGhsbAWg/W0324+XlhfT0dADAkSNHNNl7/mwcz9prrK5nrC5/NraRSCTYsGEDAODSpUv48ccfNft4/YnIlfDzmuztypUrSEpKQkNDA+bOnYvly5dr7ed7jsxRWlqKrKwsxMbG4oUXXjCrDt9bRJZzi8SROUMNzRmCTbZ59NFHAQAqlQqXL18G8J/rXVNTAyGE3nr82djG2mvc9mtD/3f4s7Hdgw8+iKCgIADAxYsXNdt5/YnIlTCWInuqqqrCpEmTcP36dSQmJmLHjh06jwwxRiRzvPjii2hubsYHH3wAqdS8P2353iKynFskjgYOHAigdQb95uZmvceoExnqY8n+2q5uoP45qK93Y2Mjrl27prcefza2sfYaR0ZGan5m6v3m1CPLqa9z299PvP5E5EoYS5G93L59G5MmTcKlS5cwfvx45OTk6F0BizEimePUqVOQSCSYOnUqevfurVW++OILAMBbb72F3r17a1Z+5HuLyHJukTgaOXIkZDIZGhoaUFxcrLNfqVSisLAQADBmzJiO7p7bOHv2rObrBx54AEDr6iy9e/cGABQUFOitp97On411rL3Gnp6emiWX+bNxnFu3buFf//oXgP/8vwB4/YnItTCWInu4e/cunnzySZw5cwajR49Gbm6uwUeBGCOSuVpaWnDjxg2dop7L6O7du7hx4wZu3rwJgO8tImu4ReLI398fCQkJAIDt27fr7M/JyYFCoUBwcDAef/zxDu6d+3j77bcBAIMHD0ZYWBiA1jlepk+fDkD/z+bYsWM4f/48ZDIZpk6d2nGd7UJsucYzZswAAOzYsQMtLS1a+yoqKpCXlwcAmDlzpiO67hY2bdoEIQQCAgI0d8LUeP2JyFUwliJbNTY2Ytq0aThx4gSGDRuGb7/9Ft27dzd4PGNEMof6cTN9Zf78+QCA9PR0CCFQVlYGgO8tIqsIN3H06FEhkUiEVCoV2dnZmu0//PCDCA0NFQDEW2+95cQedn4HDx4Uv//978Xly5e1ttfU1IiXXnpJABAAtK6/EEJcvnxZeHl5CQBi48aNQqVSCSGEKCsrE4MGDRIAxAsvvNBh59HZzJ8/XwAQ6enpBo+x9hrX1taKnj17CgBi6dKloqmpSQghxK1bt0R8fLwAIKZMmeKYE+skTF3/M2fOiBdeeEGcOXNGa3t9fb144403hFQqFQDEm2++qVOX15+IXAljKbJWc3OzeOqppwQAER0dLa5du2ZWPcaIZAtjMRrfW0SWcZvEkRBCZGRkaJIXUVFR4qGHHtL80ZaYmCiam5ud3cVObe/evZrrGxYWJkaPHi1+9rOfaX4pSyQSsX79er11//SnP2l+FmFhYWLkyJFCJpMJACIuLk7cvXu3Y0/GhR09elQEBwdrire3twAgunXrprW9oqJCq5611zgvL0/I5XIBQISEhIi4uDjRrVs3AUBERkaK69evd8RpuwxLr/+pU6c0/y/U16/tNQQgUlJSNAFLe7z+RORKGEuRNbKzszXvm4EDB4r4+Hi9ZdasWTp1GSOStUzd3ON7i8h8bpU4EkKI3NxcMWHCBBEQECC6desmYmJixObNmxno2EFFRYVYs2aNmDBhgoiIiBA+Pj5CLpeL/v37i3nz5onvv//eaP2CggKRlJQkgoKChLe3txg0aJBIS0sT9fX1HXQGnUN+fr4m+DJWrly5olPX2mt85swZkZycLHr16iW8vLxE//79xbJly8Tt27cddJauy9Lrf+fOHZGeni6mTJki+vfvL/z8/ISXl5d44IEHxKxZs8S3335r8jV5/YnIlTCWIkt98sknZn129uvXT299xohkDXNG5fO9RWQeiRAG1iAkIiIiIiIiIiK35haTYxMRERERERERkeWYOCIiIiIiIiIiIr2YOCIiIiIiIiIiIr2YOCIiIiIiIiIiIr2YOCIiIiIiIiIiIr2YOCIiIiIiIiIiIr2YOCIiIiIiIiIiIr2YOCIiIiIiIiIiIr2YOCIiIiIiIiIiIr2YOCIiIiIiIiIiIr2YOCIiIiIiIiIiIr2YOCIiIiIiIiIiIr2YOCIiIiIiIiIiIr3+f1xm9/4DIRrvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1400x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,5))\n",
    "plt.subplot(121)\n",
    "plt.imshow(test_hires_psf, origin='lower')\n",
    "plt.colorbar()\n",
    "plt.subplot(122)\n",
    "plt.imshow(binned_test_psf, origin='lower')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate unocculted star count rate in peak pixel of PSF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peak angle index = 9, peak separation index = 16\n"
     ]
    }
   ],
   "source": [
    "(peak_ang_ind, peak_sep_ind, _, _) = np.unravel_index(\n",
    "        np.argmax(offset_psfs), offset_psfs.shape)\n",
    "print(\"Peak angle index = {:d}, peak separation index = {:d}\".format(\n",
    "        peak_ang_ind, peak_sep_ind))\n",
    "\n",
    "padded_peak_psf = np.pad(offset_psfs[peak_ang_ind, peak_sep_ind],\n",
    "                         ((npad, npad), (npad,npad)),\n",
    "                         mode='constant')\n",
    "\n",
    "binned_peak_psf, _, _ = exoscene.image.resample_image_array(\n",
    "        padded_peak_psf, hires_pixscale_as,\n",
    "        img_xcoord = xcoord_psf, img_ycoord = xcoord_psf,\n",
    "        det_pixscale = data_pixscale_as,\n",
    "        det_width = max_detect_width,\n",
    "        binfac = 10, conserve = 'sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute star PSF peak countrate based on collecting area and throughput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Band-integrated irradiance of host star: 5.905E+07 ph / (m2 s)\n"
     ]
    }
   ],
   "source": [
    "minlam = (psf_hdr['minlam'] * u.micron).to(u.nanometer)\n",
    "maxlam = (psf_hdr['maxlam'] * u.micron).to(u.nanometer)\n",
    "\n",
    "star_flux = exoscene.star.bpgs_spectype_to_photonrate(spectype = sptype,\n",
    "                                                      Vmag = Vmag,\n",
    "                                                      minlam = minlam.value,\n",
    "                                                      maxlam = maxlam.value)\n",
    "\n",
    "print(\"Band-integrated irradiance of host star: {:.3E}\".format(star_flux))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stellar PSF peak count rate = 8.42E+05 ph / s\n"
     ]
    }
   ],
   "source": [
    "A_eff = 3.5786 * u.m**2\n",
    "non_coron_optical_losses = 0.404 # Band 1 CBE at end-of-life\n",
    "\n",
    "unocc_star_countrate_img = star_flux * binned_peak_psf * A_eff * non_coron_optical_losses\n",
    "unocc_star_countrate_peakpix = np.max(unocc_star_countrate_img)\n",
    "print('Stellar PSF peak count rate = {:.2E}'.format(unocc_star_countrate_peakpix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7ff0d250ab38>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cw = 19\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.imshow(unocc_star_countrate_img.value)\n",
    "plt.axis('off')\n",
    "#plt.imshow(np.log10(unocc_star_countrate_img.value))\n",
    "plt.colorbar()\n",
    "#unocc_star_plot_fname = os.path.join(plot_dir, 'unocc_star_psf.png')\n",
    "#plt.savefig(unocc_star_plot_fname, dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define an approximation to the HLC field stop to mask out light from large angular separations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "fieldstop_rad = 9.0 # CGI HLC field stop radius in lam/D\n",
    "xs_p = np.arange(imwidth) - imwidth // 2\n",
    "ys_p = xs_p.copy()\n",
    "xxs_p, yys_p = np.meshgrid(xs_p, ys_p)\n",
    "rrs_p = np.sqrt(xxs_p**2 + yys_p**2)\n",
    "datamask_nan_ind = np.nonzero(rrs_p >= fieldstop_rad / data_pixscale_LoD)\n",
    "datamask_nan = np.where(~(rrs_p >= fieldstop_rad / data_pixscale_LoD), 1, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7ff0d241ac88>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(datamask_nan)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an animation illustrating the orbital motion of the planet PSFs "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load ephemerides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['t (years)',\n",
       " 'delta x (mas)',\n",
       " 'delta y (mas)',\n",
       " 'phase',\n",
       " 'r (AU)',\n",
       " 'fluxratio_575']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ephem_tables = []\n",
    "for plnt in planetlist:\n",
    "    ephem_table_fname = os.path.join(data_dir, 'ephem_{:s}_{:}_{:}yrs.csv'.format(\n",
    "                                     system_name, plnt.name, int(round(tspan.value))))\n",
    "    table = astropy.table.Table.read(ephem_table_fname)\n",
    "    ephem_tables.append(table)\n",
    "    \n",
    "ephem_tseries = np.array(ephem_tables[0][ 't (years)' ]) * u.year\n",
    "\n",
    "table.colnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ephem_demo_d_10yrs.csv'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.basename(ephem_table_fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set duration and time step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_span = 10 * u.year\n",
    "tstep = (5 * u.day).to(u.year)\n",
    "\n",
    "time_series = np.arange(T0.value,\n",
    "                        T0.value + time_span.value + tstep.value,\n",
    "                        tstep.value) * u.year\n",
    "\n",
    "Nt = len(time_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loop through time steps and create the time series cube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "planet_scene_series = np.zeros((Nt, imwidth, imwidth)) * u.photon / u.second\n",
    "\n",
    "for tt, time in enumerate(time_series):\n",
    "    t_ephem = np.argmin(np.abs(ephem_tseries - time))\n",
    "    for pp, plnt in enumerate(planetlist):\n",
    "        deltax_as = ephem_tables[pp]['delta x (mas)'][t_ephem] / 1000\n",
    "        deltay_as = ephem_tables[pp]['delta y (mas)'][t_ephem] / 1000\n",
    "        flux_ratio = ephem_tables[pp]['fluxratio_575'][t_ephem]\n",
    "        \n",
    "        planet_psf = exoscene.image.get_hires_psf_at_xy_os9(\n",
    "                 offset_psfs, r_offsets_as.value, angles,\n",
    "                 hires_pixscale_as.value, deltax_as, deltay_as, cx)\n",
    "        \n",
    "        padded_planet_psf = np.pad(planet_psf, ((npad, npad), (npad,npad)), mode='constant')\n",
    "        binned_planet_psf, _, _ = exoscene.image.resample_image_array(\n",
    "                padded_planet_psf, hires_pixscale_as, \n",
    "                img_xcoord = xcoord_psf, img_ycoord = xcoord_psf,\n",
    "                det_pixscale = data_pixscale_as,\n",
    "                det_width = max_detect_width,\n",
    "                binfac = 10, conserve = 'sum')\n",
    "\n",
    "        planet_scene_series[tt] += (binned_planet_psf * star_flux * flux_ratio\n",
    "                                    * A_eff * non_coron_optical_losses)\n",
    "\n",
    "    planet_scene_series[tt] *= datamask_nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display the planet PSF scene at one time sample, in flux ratio units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7ff0d22fd518>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_show = T0 + 2 * u.year\n",
    "tt_show = np.argmin(np.abs(time_show - time_series))\n",
    "crop = 4\n",
    "\n",
    "plt.figure(figsize = (8,6))\n",
    "plt.imshow(planet_scene_series[tt_show, crop:-crop, crop:-crop]\n",
    "           / unocc_star_countrate_peakpix)\n",
    "\n",
    "\n",
    "xtick_locs = (np.arange(-1, 1, 0.2) / data_pixscale_as.value\n",
    "              + (imwidth - 2 * crop) // 2)\n",
    "xtick_labels = ['{:+.1f}'.format(loc) for loc in np.arange(-1, 1, 0.2)]\n",
    "plt.xticks(xtick_locs, xtick_labels, size=14)\n",
    "plt.xlim([0, imwidth - 2 * crop - 1])\n",
    "plt.yticks(xtick_locs, xtick_labels, size=14)\n",
    "plt.ylim([0, imwidth - 2*crop - 1])\n",
    "plt.tick_params('both', length=8, width=1.5, which='major', top=True, right=True,\n",
    "                direction='in', color='gray')\n",
    "plt.xlabel('Offset from star (arcsec)')\n",
    "\n",
    "plt.colorbar()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}