{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4a76a86a",
   "metadata": {},
   "source": [
    "===================== Import Packages ===================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "286ea960",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os, pdb, glob\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from astropy.table import Table, join\n",
    "import linmix\n",
    "from astroquery.vizier import Vizier\n",
    "import warnings\n",
    "from astropy.logger import AstropyWarning\n",
    "warnings.filterwarnings('ignore', category=AstropyWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6868c611",
   "metadata": {},
   "source": [
    "===================== Define Functions ==================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "08433f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(catalog, join_key='Name', join_type='inner'):\n",
    "\n",
    "    \"\"\"\n",
    "    PURPOSE:    Get data from literature with Vizier\n",
    "\n",
    "    INPUT:      catalog = ctalog name on Vizier (str)\n",
    "                join_key = column header to join tables, if multiple (str; optional)\n",
    "                join_type = way to join tables, if multiple (str; optional)\n",
    "\n",
    "    OUTPUT:     t = data table (AstroPy Table)\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    ### GET FULL CATALOG (ALL COLUMNS, ALL ROWS)\n",
    "    viz = Vizier(catalog=catalog, columns=['**'])\n",
    "    viz.ROW_LIMIT = -1\n",
    "    tv = viz.get_catalogs(catalog)\n",
    "\n",
    "    ### IF MULTIPLE TABLES, JOIN THEN\n",
    "    for i, val in enumerate(tv.keys()):\n",
    "        if i == 0:\n",
    "            t = tv[val]\n",
    "        else:\n",
    "            tt = tv[val]\n",
    "            if join_key in tt.columns:\n",
    "                t = join(t, tt, join_type=join_type, keys=join_key)\n",
    "\n",
    "    return t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "83cfe569",
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_mc(x_data, x_err, y_data, y_err, ind_dd):\n",
    "\n",
    "    ### MAKE DETECTION ARRAY (1=DET, 0=NON-DET)\n",
    "    ddet = np.repeat(1, len(y_data))\n",
    "    ddet[~ind_dd] = 0\n",
    "\n",
    "    ### calculate linmix\n",
    "    lmcens  = linmix.LinMix(x_data, y_data, x_err, y_err, delta=ddet, K=3)\n",
    "    lmcens.run_mcmc(silent=True)\n",
    "    A, Ae = np.mean(lmcens.chain['alpha']), np.std(lmcens.chain['alpha'])\n",
    "    B, Be = np.mean(lmcens.chain['beta']), np.std(lmcens.chain['beta'])\n",
    "    D, De = np.mean(np.sqrt(lmcens.chain['sigsqr'])), np.std(np.sqrt(lmcens.chain['sigsqr']))\n",
    "\n",
    "    print('\\n =====================================')\n",
    "    print(\"\\n LinMix_Err terms:\")\n",
    "    print(\"    A  = {0:.2f}\".format(A) + \"+/- {0:.2f}\".format(Ae))\n",
    "    print(\"    B  = {0:.2f}\".format(B) + \"+/- {0:.2f}\".format(Be))\n",
    "    print(\"    D  = {0:.2f}\".format(D) + \"+/- {0:.2f}\\n\".format(De))\n",
    "\n",
    "    ### print to file\n",
    "    # pname = '../mcdust/B' + str(Band) + '_D' + str(DistLup3) + '_b' + str(BetaDust)\n",
    "    # rn = len(glob.glob(pname + '/*rn.out'))\n",
    "    # fname = pname + \"/\" + \"{0:02d}\".format(rn) + '_rn.out'\n",
    "    # np.savetxt(fname, np.c_[lmcens.chain['alpha'], lmcens.chain['beta'], lmcens.chain['sigsqr']], fmt='%1.2e')\n",
    "\n",
    "    pars = np.array([A, Ae, B, Be, D, De])\n",
    "\n",
    "    return pars, x_data, x_err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dae84fab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def assign_mstar(ms, e_ms):\n",
    "\n",
    "    # INDEX STARS WITHOUT MASS MEASUREMENTS\n",
    "    f_ms = np.copy(ms.mask)\n",
    "    ind_nomass = np.where(ms.mask == True)\n",
    "\n",
    "    ### DISTRIBUTION OF LUPUS STELLAR MASSES\n",
    "    ### FROM Mortier+2011 (2011MNRAS.418.1194M) FIGURE 9\n",
    "    hist_values = np.array([ 5., 5., 12., 12., 12., 9., 9., 9., 1., 1., 1., 1., 1., 1.])\n",
    "    log_mstar_bins = np.array([-1.05, -0.95, -0.85, -0.75, -0.65, -0.55, -0.45, \n",
    "                               -0.35, -0.25, -0.15, -0.05,  0.05,  0.15,  0.25])\n",
    "    \n",
    "    ### RANDOMLY ASSIGN VALUE FROM RANGE SEEN IN LUPUS (MORTIER+2011)\n",
    "    ### USE MEDIAN FRACTIONAL ERROR OF MSTAR FOR LUPUS SOURCES WITH KNOWN MSTAR \n",
    "    mstar_probs = hist_values / np.sum(hist_values)\n",
    "    randm = 10**(np.random.choice(log_mstar_bins, len(ind_nomass[0]), p=list(mstar_probs)))\n",
    "    for i, val in enumerate(ind_nomass[0]):\n",
    "        ms[val] = \"{0:.2f}\".format(randm[i])\n",
    "        e_ms[val] = \"{0:.2f}\".format(randm[i] * .20) \n",
    "\n",
    "    return ms, e_ms, ~f_ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac129a52",
   "metadata": {},
   "source": [
    "========================== Code =========================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "42eabfdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### LOAD IN LUPUS DATA\n",
    "T = get_data(\"J/ApJ/828/46\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35f9e31c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### GET STELLAR MASSES FOR THOSE WITH UNKNOWN VALUES\n",
    "mstar, e_mstar, ind_mstar = assign_mstar(T['Mass'], T['e_Mass'])\n",
    "T['Mass'], T['e_Mass'] = mstar, e_mstar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9824f13",
   "metadata": {},
   "outputs": [],
   "source": [
    "### INDEX (NON-)DETECTIONS\n",
    "ind_dd = T['F890'] / T['e_F890'] >= 3.0\n",
    "ind_nd = T['F890'] / T['e_F890'] < 3.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f263f58e",
   "metadata": {},
   "outputs": [],
   "source": [
    "### DEFINE PLOT VARIABLES\n",
    "x_data_lin = np.copy(T['Mass'])\n",
    "y_data_lin = np.copy(T['MDust'])\n",
    "y_data_lin[ind_nd] = 3.0 * np.copy(T['e_MDust'][ind_nd])\n",
    "y_err_lin = np.sqrt((T['e_MDust'])**2 + (0.1 * T['e_MDust'])**2)\n",
    "x_err_lin = np.copy(T['e_Mass'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "626d29fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "### CONVERT TO LOG SCALE\n",
    "x_data = np.log10(x_data_lin)\n",
    "y_data = np.log10(y_data_lin)\n",
    "y_err  = np.array(0.434 * (y_err_lin / y_data_lin))\n",
    "x_err  = np.array(0.434 * (x_err_lin / x_data_lin))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1aa1899",
   "metadata": {},
   "source": [
    "## GET MCMC FIT WITH LINMIX\n",
    "pars, x_data, x_err = do_mc(x_data, x_err, y_data, y_err, ind_dd)\n",
    "A, Ae, B, Be, D, De = pars[0], pars[1], pars[2], pars[3], pars[4], pars[5]\n",
    "yfit = A + B * xfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "471e2d7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x144f61970>,\n",
       "  <matplotlib.axis.YTick at 0x144f47f70>,\n",
       "  <matplotlib.axis.YTick at 0x144f269a0>,\n",
       "  <matplotlib.axis.YTick at 0x145824ac0>],\n",
       " [Text(0, -1.0, '−1'),\n",
       "  Text(0, 0.0, '0'),\n",
       "  Text(0, 1.0, '1'),\n",
       "  Text(0, 2.0, '2')])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### SET UP PLOT\n",
    "mpl.rc('xtick', labelsize=15)\n",
    "mpl.rc('ytick', labelsize=15)\n",
    "mpl.rc('xtick.major', size=5, pad=7, width=1)\n",
    "mpl.rc('ytick.major', size=5, pad=7, width=1)\n",
    "mpl.rc('axes', linewidth=1)\n",
    "mpl.rc('lines', markersize=5)\n",
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "xmin, xmax, ymin, ymax = -1.3, 0.6, -1.6, 2.8\n",
    "ax.set_xlim([xmin, xmax])\n",
    "ax.set_ylim([ymin, ymax])\n",
    "ax.tick_params(which='minor', axis='x', length=2.5, color='k', width=1)\n",
    "ax.tick_params(which='minor', axis='y', length=2.5, color='k', width=1)\n",
    "ax.minorticks_on()\n",
    "ax.set_xlabel(r'$\\mathregular{log(M_{\\star})}$' + ' ' + r'$\\mathregular{[M_{\\odot}]}$', fontsize=17)\n",
    "ax.set_ylabel(r'$\\mathregular{log(M_{dust})}$' + ' ' + r'$\\mathregular{[M_{\\oplus}]}$', fontsize=17)\n",
    "ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "plt.xticks(np.arange(-1, 0.5+1, 0.5))\n",
    "plt.yticks(np.arange(-1, 2+1, 1.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "606121da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x145960940>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### PLOT DETECTIONS WITH ERROR BARS\n",
    "### ONLY PLOT THOSE WITH KNOWN STELLAR MASSES\n",
    "ax.errorbar(x_data[ind_dd & ind_mstar], y_data[ind_dd & ind_mstar], yerr=y_err[ind_dd & ind_mstar], xerr=x_err[ind_dd & ind_mstar], ms=0, ecolor='lightgray', zorder=3, elinewidth=0.5, ls='none', capsize=2)\n",
    "ax.scatter(x_data[ind_dd & ind_mstar], y_data[ind_dd & ind_mstar], marker='o', facecolor='lightblue', s=40, edgecolor='black', lw=1, zorder=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "72b4dcef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x145810ac0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### PLOT NON-DETECTIONS\n",
    "### ONLY PLOT THOSE WITH KNOWN STELLAR MASSES\n",
    "ax.scatter(x_data[ind_nd & ind_mstar], y_data[ind_nd & ind_mstar], marker='v', facecolor='lightgray', s=40, edgecolor='gray', lw=1, zorder=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9b02e4a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1445e82b0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### PLOT LINE FITS TO POPULATIONS\n",
    "xfit = np.log10(np.arange(10, 1e4) / 1e3)\n",
    "ax.plot(xfit, 1.20 + 1.80 * xfit,color='lightblue', linewidth=2, zorder=2, label='Lupus')\n",
    "ax.plot(xfit, 1.20 + 1.70 * xfit, color='darkviolet', linewidth=2, alpha=0.3, zorder=1, label='Taurus')\n",
    "ax.plot(xfit, 0.80 + 2.40 * xfit, color='green', linewidth=2, alpha=0.3, zorder=1, label='Upper Sco')\n",
    "# ax.plot(xfit, 1.45 + 1.80 * xfit,color='lightblue',linewidth=2, alpha=0.3, zorder=1, label='Lupus - Only Known Stellar Masses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b4cf8253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GknAhhBBCiDgJdY8erOgzelCjUkCpQE0bAHmpedxoWkj93734O3uTdL1VQ9ZiI4Y0qX5PRpKECyGEEELEQZs3wFFnG53+YPSYRRfEG3gbFZEFmdNSpzG9/QbqT1xV/Z5jwDZHqt+TmSThQgghhBAxpCgK77a4ONfUGR09qFaB3eSm2X2OnrR6um4WWe/OpL3TH/1cvVWDfYkRvVWq35OdJOFCCCGEEDHiCgQ55myn2dObWKcZtKTpG3B2XkRFJEnP6ygkrWEafiXSfqJSq7DN1ZNWKNXvqUKScCGEEEKIMVIUhSsdHk41dBDss5383AwzvkAFtZ01APi7QuQ1zCEzOC1aJTekRXq/pfo9tUgSLoQQQggxBv5QmJP17dR09o4eNGs1LM1N5VLbW9R31RMOK7iqg8zquJFcfR7Qp/pdpEelkur3VCNJuBBCCCHEKDW4fByra8Mb7J1qMt1qYkGWmZN1x2h2N+PvCtFVGWS+ejF2fTYABlt39TtVqt9TlSThQgghhBAjFAorvN3USXmrK3pMp1axNDcNu1lDaXUpre42uqoD+OthUcpy0rUZker3vO7eb6l+T2mShAshxAQVCAQIBoPXHNdqteh0uiREJMTU0OYNcMzZRkef0YPZZj3Lcm2oVAFev/w6Lc3ttF/0owloWZqyHKvWhiG9u/ptkeq3kCRcCCEmrJaWFhobG685brfbycnJSUJEQkxuiqJwoTUyerBn7aVaBTfZrRTazLgDbl6/9AaNlR146oPo1QaWpKwgVWclfb4B6yydVL9FlCThceZ0OnE6nZSVlSU7FCHEJJORkYHVagUiv2scDgcQqYQLIWLLHQhxzNlG01WjB1c4bFgNOjp8Hfz17ddpLO8k7FMwacwsSVlBelYqWYtN6FLUSYxejEfymzrOdu3axZYtW5IdhhBiEtLpdFRXV7N//37Ky8spKiqipKSEWbNmJTs0ISaVKx0eTtW3E+g3ejCF+ZmpaNQqmjqbea30dTrrPABYNKkssa4k90arVL/FoOSyLM4eeeQRjh8/zvPPP5/sUIQQk0ggEGDDhg0UFhbyze3b+evhI3xz+3YKCwvZsGEDgUAg2SEKMeH5Q2GO1LZy1NkWTcBNWg3vm5bBTXYrGrWKy5edvPrbv0QTcKvWxnum38LsD2SQNlsWX4rBSSU8zhwOR/QWsRBi6goEArS0tJCRkRGTRZMbN25k7759rHtsK7fd8ykMJjNet5vXDh5g745tAOzevXvM54HYxy7ERNDg8nG8rg1Pn9GD01KNLMpJQ69REwoolJ2o5M0Lx1G6d73MNGRx6/I1pM8ySfIthiRJuBBCJEAwGKSxsRGr1TrmRPbixYvs2bOHdY9t5c77Px09bjSbueuB9QDsefpJNm3aFJPWlFjGLsR4FwornGvq5MJVoweX5KRRYDUB4G4I8taRct5qOwNKpEJeYM/nA+9bjSFFUisxPNKOIoQQE8yBAwcwWyzcds+nBnz89nuLMVss7N+/P8GRCTGxtfsC/LmqqV8CbjfruW2mnQKriVBAofGUh6N/e5u3Wk+DoqDSqLhhwWw+9KH3SAIuRkR+WoQQIoGcTicazdhmBJeXl2N3FGAwmQd83GAyk+XIp7y8nKqqqjGdCyAUCo35NYQYzxRFobzVxdtXjR5ckJVKUXoKKpUKd32Q5jNe3m17h0pvOQD6NA2LlsxjYcFN0n4iRkyScCGESCCHw4HJZBrTaxQVFXHw0CF8HveAibjX7aaxtpqiohJmzJgxpnMBeDweKioqxvw6QoxH7kCI43VtNLp7Rw9a9VpW5NlIM+gIBRRaznrovOLnXW8Z1b4qVBoVqdO1LJ5/E3Mz5yYxejGRSTuKEEJMMMXFxbi7unjt4AsDPv7awQN4XC5KSkoSHJkQE0t1h4fXLjX2S8DnpKfwgRlZpBl0uOoC1Py5i84rfs55zlDtq0Jv05B5s4EVC5ZIAi7GRCrhQgiRAFqtFrvdHpONdGbPns369evZu30riqJw+73F/aajPLdjG+vXr4/ZvPBYxi7EeOAPhTld386VTm/0mEmrZlmujewUAyF/mMazXrpqAoSVMG+5T9KsNGKdrcNs17MoZxHT0qYl8R2IyUB+owohRALodLqYbiW/c+dOIDIF5aUf7CDLkU9jbTUel4v169dHH4+FWMcuRDI1un0cc/YfPViQamRx9+hBlzNA81teQj6FoBLkjOsEbksbmbMM6PRaluUtI9eSm8R3ICYLScKFEGIC0ul07N69m02bNkV3zJwzZy3FxcWyY6YQAwiFFcqaO3m3pf/owcU5aUyzmgj5wzSc8uCqjWx0FVACnPYeQ5nhxpZpQKPWsCJvBfYUe7LegphkJAkXQogJKhAIkJuby5e+9CWcTmd0Y7BAICDzvIXoo8MX4KizjXZfMHosy6RnucOGWafBVdtd/fZHRqP4wl7OGU6gm+1Do9Oi0+hYlb+KdFN6st6CmIQkCRdCiAmqpaWFxsbG6Mc9E0zsdru0jwhBZPRgRZubs40d0dGDKmCBPZU56SmE/QoNxzy4nIHo5/jUHspzTqFLCwBqDFoDqwtWYzVYk/IexOQ1pZNwt9vN73//e1555RX+/ve/U1VVhUajoaioiI9//ON88YtfxGKxJDtMIYQYUEZGBlbrtYmBLKAUAjzdowcbrho9uNxhw2bU0VUToOVsb/UbQLF7uWQ7haKKfI5ZZ2Z1wWpS9CkJj19MflP6N/WBAwf4zGc+A8D8+fO5++676ejo4I033uDJJ5/khRde4K9//SvZ2dlJjlQIIa6l0+mk7USIAdR0ejhR104g3JtgF6WnsCArFQIK9UfduOt6W1M0ehWauV7eCp0gEIpUxS16C2umrcGoNSY8fjE1TOkkXKfTsWHDBr7whS8wf/786HGn08ldd93FyZMn+cIXvsCBAweSGKUQQgghhiMQCnO6oYPLHZ7oMaNWzfLu0YNd1QGaz3oJB3qT85Q8HUphJycajxMMRxJzm9HGqoJV6DX6hL8HMXVM6ST8wQcf5MEHH7zmuMPhYOfOnbznPe/h0KFD+P1+9Hr5hyiEENdTWVnJ/v37uXDhAjU1NeTn5zNnzhxKSkpkYouIuya3n2N1bbgDoeix/FQjS3LSUAeg/ogbd33/6nfmQiOdqc0crz1OWImMLMw0Z7IyfyVa9ZROkUQCyE/YIBYtWgSAz+ejubk5OnVACCFEf4FAgI0bN7Jnzx6MJjMZOQ6a6mrxedxodTqeeOIJPvOZz7Bz505pnxExF1YUzjX1Hz2oVatYnG1lmtVEV3WAlrd9/arflnwdGTcZcHprOVV7CkWJPJZjyWGZYxkatSbh70NMPZKED+LixYtApGUlIyMjydEIIcTwBQIBWlpaor+7ev47Xgnwxo0b2btvH+se28pt93yq3+6dP9u+jXmLl7N33z4Adu/eHZcYIPK+GxsbUalUZGVlScI/BQw0ejDTpGe5Iw1DSE39EQ+ehj7Vb0Ok+p2Sq6OytZKzDWejj+Vb81mcuxi1Sp3Q9yCmLvlJG8T3vvc9AO644w4MBkOSoxFCiOELBoM0NjYSDAb7/Xc8XLx4kT179vDgVzZz5/2fxmAyA2A0m7nrgfU8+JUnePf0cT76mc+zZ88eKisr4xIHRN53S0sLzc3NcXu/YnxQFIWKVhd/qmqKJuAqYEFWKu+flkHYGabmz65+CbilQEf+Byyk5Oq40HyhXwI+0zaTJblLJAEXCSWV8AH8z//8Dz/96U/R6XRs27ZtRJ/rdDpxOp3XHC8rK4tVeEIIMW4cOHAAs8XCbfd8asDHb7+3mBe/vwMAs8XC/v37efzxxxMZophkPMEQJ+raqXf5osdS9VpWOGxYFA31b3rwNPYm31qjmsyFRsw5kZTnXOM5Kloqoo/PyZzDDVk3JO4NCNFNkvCrnD9/nrVr16IoCjt27Ij2hg/Xrl272LJlS5yiE0KI4etbEKisrESlUsX8HGVlZWQ58qMV8KsZTGayHPl0tbeRlZtHWVlZ3IoSPX29YvKq6fRwsr4df6j3e11oM3OT3YrrSoCacy7CwT6939N0ZCwwotGpUBSFM/VnuNx+Ofr4jfYbKcwoTOh7EKKHJOF91NTUcMcdd9Da2soXv/hF/u///b8jfo1HHnmEu++++5rjZWVlrF27NhZhCiHEsPQsKK+oqGDWrFmYTKaYn2P+/Pn8+pVX8HncAybiXrebxtpqUtJsNDprmD9/bb+RsLHk8Xiiu4aKySUQDnOmvoOqvqMHNWqWOWxkqHQ0vunB03RV9XuREXN2JM0JK2FOOE/g7Oy9MF2Uu4jpadMT9yaEuIok4d1aWlr40Ic+RFVVFQ8//DDf/va3R/U6DodDJqkIIaaM4uJiNm/ezGsHX+DO+z99zeOvHTyA1+1CBXhcLkpKShIfpJjQmj1+jjnbcPUZPZhnMbI4x4q/OkTNORdKn8p46nQd6TdGqt8AoXCIo7VHaXQ1AqBWqVniWEJeal5i34gQV5EkHOjq6uIf//EfOXfuHB/72Mf4yU9+EpfbtkIIkQharRa73R7dvr7vf8fa7NmzWb9+PXu3b0VRFG6/t/ia6ShzFy3j5Z/8kPXr18d1XrhWqyUjIwOVShW39ysSJ6wolDV18U5LV/SYVqViUY4Vh8ZA8xEf3uY+1W+TmqxFRkz23u99IBTgSM0RWjwtAGjUGpbnLSc7RXbCFsk35X9L+Xw+/uVf/oUjR47w4Q9/mBdeeAGNRuaDCiEmLp1OR05OTvTjvv8dDzt37gRgz9NP8sJ3v0V6di7N9bX4PB60Oh3vnDoWnRMeTzqdjrw8qW5OBp3+IEedbbR5A9FjmSYdy3LSCNcq1Ja5+1e/Z+jJuNGAWttbQPMFfZRWl9Lh6wBAp9GxMn8lGSYZOyzGhymdhIdCIT71qU/xpz/9ife9730cOnRIdsYUQogR0ul07N69m02bNl2zY+bcuXMpLi6WHTPFsCiKQmWbm7caO+jJsVXA/KxUZhtMNB/z4m3pbUvRmtRkLTZiyuqfzngCHg5XH8blj2zgo9foWTNtDVaDNVFvRYghTekk/Ic//CEvv/wyAFlZWfzrv/7rgM/79re/TVZWViJDE0KICWfWrFkyflCMmjcY4vhVowcteg3Lc21onFB7vn/12zpTT/r8/tVvgC5/F4evHMYb9AJg0plYXbAai96SmDcixDBN6SS8tbU1+t89yfhAvv71r0sSLoQQQsRJbaeXE/Xt+EPh6LHZNjPzTCm0nfD1r36bu3u/s65NYdq97ZRWl+IP+QFI0aewpmANJl3sJwMJMVZTOgn/+te/zte//vVkhyGEmCIqKyvZv38/9fX15OTkUFJSIm0aYkoLhsOcaejgUnvv6EGDRs3SnDTMDSrqj3lQwn2q37O6q9+aa4cntHhaeLP6TYLhyGJNq8HK6oLVGLSy67UYn6Z0Ei6EEIkQCATYuHEje/bswWyxYM8roLG2ms2bN7N+/Xp27tyJTqdLdphCJFSLx8/Rq0YPOiwGbrKk0nnaT0tr73FdSqT325gxcNrS4GrgaM1Rwkqkkp5hymBl/kp0Gvl3JcYvScKFEFNSIBCgpaWFjIyMuCfAGzduZO++fax7bCu33fOpfiP89u7YBsDu3bvjGgMk9j0LMZiwonC+uYt3mrvoqXFrVCoWZqdia9TQfMrbv/o9W0/6DQNXvwFqO2s54TwR3TE1OyWb5XnL0ahl0pkY3yQJF0JMScFgkMbGRqxWa1wT0osXL7Jnzx7WPba132Y2RrOZux5YD0RG+23atCnurSmJes9CDKare/Rga5/RgxlGHYusqXjeCtLW5o8eH6r6DVDVVsWZ+jPRj/NS81jiWIJapY7PGxAihuSnVAgh4ujAgQOYLRZuu+dTAz5++73FmC0W9u/fn+DIhEicntGDr11qiibgKmB+hoWFPgvtb/jxtUXaT1QqSCvUk3drynUT8PKW8n4J+PS06Sx1LJUEXEwYUgkXQkxpTqczrht0lZeXY3cUYDCZB3zcYDKT5cinvLycqqqquMUBkb0RhEg0bzDEibp26vqOHtRpWGS1EjoXor29T/XboiZrsQlj+vX/TZY1llHeUh79uDCjkBvtN8Y+eCHiSJJwIcSU5nA4MJniN76sqKiIg4cO4fO4B0zEvW43jbXVFBWVMGPGjLjFAeDxeKioqIjrOYToy9nl5URdO74+owdnpZmY3m6ks9SP0rMhjwqshXrS5xlQqQfu/YZIRf2threoauu9YL0h6wbmZM6J23sQIl7kno0QQsRRcXEx7q4uXjv4woCPv3bwAB6Xi5KSkgRHJsajyspKvvGNb/B//s//4Rvf+AaVlZXJDmlUguEwJ+vaOVzTGk3ADRo1y21pZL+jo+Pd3gRcn6rG8d4UMuYbr5uAh5UwJ+tO9kvAb865WRJwMWFJJVwIMSVptVrsdjtabXx/Dc6ePZv169ezd/tWFEXh9nuL+01HeW7HNtavX5+QeeGJes9i5CbTGMsWj59jzja6+owezE0xUOgy4Tka6N2OXgVpRQZsc/XXTb4BQuEQx53Hqe+q7/5cFUtyl5BvzY/b+xAi3uQ3sRBiStLpdOTk5CTkXDt37gQiU1Be+sEO7I58Gmqr8bhc0QQrERL5nqeisYyAjPUYy2SMowwrCu80d3H+qtGDN6ZaML0L7o7eiSh6q4asxUYMaUOvxwiGgxypOUKzuxkAtUrN8rzl5FjkZ1lMbJKECyFEnOl0Onbv3s2mTZuiO2bm5uZSXFwsO2ZOIqMdARmPMZaJHkfZ5Q9yzNlGS5/Rg+kGHXO9ZgLHgwT6Vr/nGLDNGbr6DeAP+SmtLqXd2w6AVq1lZf5KMs2ZcXkfQiSSJOFCCJEgs2bN4vHHH092GGKcGc4Yy5d+sIP9+/ePu58fRVG41O7hTEMHoe4mbxVQlGLGVq7G3xmMPldv1WBfYkRvHd40Im/Qy+Erh+nyd0U+X6NnVcEqbEZbrN+GEEkhSXicOZ1OnE4nZWVlyQ5FCCFEAox07GU8xlgmYhylLxjiRH07zq7e0YMpOg1zfWZUp8IEe5JytQrbHD1pRcOrfgO4/C4OVx/GE/AAYNQaWV2wmlRDauzfiBBJIkl4nO3atYstW7YkOwwhhBAJMtKxl/EYYznYOMrKyspoS1ROTg4lJSWjaomq6/Jy/KrRg9OMRrIrtYQ7w9GecENapPd7uNVvgA5fB6XVpfiCkeQ+RZ/C6oLVmHUDX6QIMVHJiMI4e+SRRzh+/DjPP/98skMRQggxDiVijGUgEGDDhg0UFhbyze3b+e8/vMY3t2+nsLCQDRs2EAgEhn4RIBhWOFXfzht9Rg/q1SpuCqWQdUZDuLO3+p1+gwHHe80jSsBbPa28ceWNaAJuNVh5z7T3SAIuJiWphMeZw+HA4XAkOwwhhBBxNtoRkMMdY1lQUEB9ff2wJp5cHUsspq+0ev0cdbbR5Y+0umiUMHmqABm1aag7ep9nsHVXv1NHthNto6uRo7VHCYUjr59uSmdV/ip0mokxmlGIkZIkXAghhIiBsYyAHM4Yy5FMPOkby1inryiKwjstXZQ19R09CLODelRKK4rXAmgivd/z9KQV6lGphtf73cPZ6eSE8wRhJVJdt6fYWZ63HK1a0hQxeUk7ihBCCJFkPWMsKyoq+OpXvsI/feh2HvvqV6moqGD37t1jGjM4nOkrZouF/fv3X/OYyx/kb1eaOdcnAU/TarmxLgVTdW+ibUjXkHerGVuRYcQJ+JX2Kxx3Ho8m4LmWXFbmr5QEXEx68hMuhBBCjBPDGWOZqOkr7kCIDl8QAwp53cdMigZ9GwQNKhRTJGlOK9STVWgecfINcLH1Im83vB39eFraNBblLBrVawkx0UgSLoQQQkwg8Z6+4guGOVnfTq3XG80STGo1M+qMmNp6k2NtVpB2arHk60aVNL/T9A7vNr8b/Xh2+mxutN8oCbiYMqQdRQghhJjERjJ9pd7l44+XGqnt8kYfzwnpmfNObwKu0qjIWGAkc5FxVPEoisLZhrP9EvB5WfNYkL1AEnAxpUglXAghhJgA4jl95dPr19NuzuBkdUv083QqFdMbDVhbeltfjJlashYZ0aWoCQRUI45HURRO1Z2iuqM6emxB9gJmp88e0XsSYjKQJFwIIYSYAOI1feWBh9fxsS9/nYtt7ujz00Na8i/p0IUiN8xVGhUZ8w2kzuxtPRlpPGElzPHa49R11UVeU6ViUc4ipqVNG9V7EmKikyRcCCGEmOR6pq9s2rSp346Za+78CB0pmXi6R5+ogYIWAxlNGlREkm1TlpbMRUZ05tF3sAbDQY7WHKXJ3RQ5j0rNsrxl5Fpyx/rWhJiwJAkXQgghpoie6SuuQJBjznaaPf7oY5awhmmX9Rj9kWRbrVWRfqOB1OmjW3jZIxAK8GbNm7R6WgHQqDWsyFuBPcU+tjcjxAQnSbgQQggxRSiKwpUOD6caOgiGlehxR7ue7Hot6p7qtz3S+601jW1+gzfopbS6lE5fJwA6jY5V+atIN6WP6XWFmAwkCRdCCJEwlZWV/dohSkpKBtylUcSePxQZPVjT2Tv5xBBWMb3GgMUTWXyp1qrIWGAgdbp+zOdzB9wcvnIYdyDSa27QGlhdsBqrwTrm1xZiMpAkXAghRNwFAgE2btzInj17MFss2PMKaKytZvPmzdFt2ceyK+T1SOIP9S4fx+va8AbD0WOZXVry6/RowrGtfgN0+joprS7FG4wk/GadmdUFq0nRp4z5tYWYLCQJF0IMiyQyU08gEKClpYWMjIwxJ8gbN25k7759rHtsK7fd86l+I/L27tgGwO7du2MabzIT//EiFFY429RBRWvv5BOtoqLAqSe9K5ICqHXd1e9pY69+A7R52yitLiUQCgBg0VtYM20NRu3o5ooLMVlJEi6EuC5JZKauYDBIY2MjVqt1TN/jixcvsmfPHtY9tpU77/909LjRbOauB9YDkdF5mzZtuu6F3VAXBVfHG6vEP5YXI4nU5g1wzNlGhz8YPWb1aJjm1KMPRqrd5hwtmQuNaI2x2buvyd3E0ZqjBMORc9qMNlYVrEKviU2CL8RkIkm4EFPMSBOKWFYwJ2oyMxS5S3B9Bw4cwGyxcNs9nxrw8dvvLealH+xg//79PP7444O+zkguCmKV+I/0vOOBoihcaHXxdmMnPUsvVQrkNeqxt2lRoUKtU5F5kxFLQezeT31XPcdqjxFWIi0vmeZMVuavRKuWVEOIgci/DCGmmGQlMiM990QwVe4SOJ1ONBrN0E8cRHl5OXZHAQaTecDHDSYzWY58ysvLqaqqGvR1QqHQdePpeRxil/hPNO5AiGPONpr6jB40+dTMcBowdY8eNOdqybw5dtVvgOqOak7VnUJRIml/jiWHZY5laNSj/7kRYrKTJFwIMaiJlsgkutKeiD7noSTiPTscDkwm06g/v6ioiIOHDuHzuAdMxL1uN4211RQVlTBjxoxBX8fj8VBRUTHk+ZxOZ8wSf+if3I9nVzo8nKpvJ9AzelCB7FYdjmYdaqW7+n2zEUt+bH9OLrVd4q36t6If51vzWZy7GLUqdkm+EJORJOFx5nQ6cTqdlJWVJTsUIfoZTnUzlokMxD+ZSWSlPdZ3CUZrItxdKC4uZvPmzbx28IV+X6serx08gMfloqSkZFivN9hFQU+S7nA4Ypb4933d8cofCnOqvp3qPqMH9UEV050GUrtHD5pztWQtNKIxxDYxvtB8gfNN56Mfz7TN5Kbsm8a0uY8QU4Uk4XG2a9cutmzZkuwwhLjGcKqbsUxkYPwnMyMx0e4SjIZWq8Vut6PVju1PxezZs1m/fj17t29FURRuv7e4312D53ZsY/369WO+WOkbb6wT//GqoXv0oKfP6MH0Dg3TGgxowio0+kj1OyUv9hdovz/2e557/jnamtqwZdl46P6HuHnezTE/jxCTlSThcfbII49w9913U1ZWxtq1a5MdjhAjMlETmbH2MA9HrO8SjFY87y7odDpycnJi8lo7d+4EIncHXvrBDuyOfBpqq/G4XNH++aEMdVHQN95YJv6xuhiJpVBY4e2mTspbXdFjmhBMqzdERw+m5OnIvMkQ8+q33++n+NPFHNp/CFNKCll5+TTV1nLg+wcm1VoIIeJt/PxGmaQcDgcOhyPZYQgRNZKEItYVzEQlM2PtYR6OWN8lGK2JcndBp9Oxe/duNm3aFJ0kk5ubS3Fx8bB/fkZ6URCLxH805423dl+Ao7X9Rw9a3Gpm1BnQB9WR6vdCIymOgRPhsawjCCthPrnuk7zyi1divhZisk5PEmIwkoQLMcUkK5EZzbnHs4l6lyDZZs2albD2nFgk/uOJoiiUt7p4u6mTnrWXV48etOTryLjJgEY/ePV7tOsIQuEQvzr8K3514FdxWQsxEdY3CBFLkoQLIa5rIiUyiWwbSFSf81DGY6vEeJPIxD9e3IEQx+vaaHT3jh40+lTMdBox+dVoDN3V79z4JK+BUIAjNUd4+ZcvY0pJmdRrIYRIFPmtLYQYlomQyCS60h7LuwSjNZnuLoiBDTV60FKgI2PB9avfAxnu2omwEqbV04o2rMXX6MOeF5+1EBNlFKQQsSJJuBBCjNJEuksgJh5/KMzp+nau9Bk9qAuomFEXGT2oNarJXGjEnDO6P+XDWTvhCXg4XH0Yl9oFajBnm2ly1sRlLcREWd8gRKxIEi6EEGM0Ee4SiIml0e3jmPPa0YMFDQa0YRWWaToyFhjR6OI3j7vL38XhK4fxBiMXASadiS8/8mV+/r2fy1oIIWJAknAhhBBinAiFFc41dXLhqtGDBQ0GMjq1ker3IiPm7NH/+R7OOoJ2bzul1aX4Q5Ee9BR9CmsK1mDSmeK2FkLWN4ipRn7ShRBCiHGg3RfgmLONdt/AowdTp+tIv3Hs1e+h1hG0eFp4s/pNguFIHFaDldUFqzFoDUD81kLI+gYx1UgSLoQQQiSRoihUtLo529TRb/Sgo0lPdqsWnUlD1nIjJnv8/2Q3uBo4VnuMUDiySDLDlMHK/JXoNL1TV2QthBCxIUm4EGLUKisro3+Ec3JyKCkpkT/CQoyAp3v0YMNVowdnOA2Y/RpSZ+jJuNGAWhu/3u8etZ21nHCeQFEiVwLZKdksz1uORj3wBBVZCyHE2EgSLoQYsUAgwMaNG9mzZw9miwV7XgGNtdVs3rxZtq0WYpiqOz2crOszehCwt2rJa9KjN2rIWp2Y6jfA5fbLnK47Hf04LzWPJY4lqFWx3fJeCNFLknAhBDCyLaM3btzI3n37xrxttWxTLZItGXdzAqEwpxs6uNzhiR7TBVRMrzdgdWuwztSTPj8x1W+A8pZyyhrLoh9PT5vOwpyFqFSJOb8QU5Vc4gohgN4to4PB4HWfd/HiRfbs2cODX9nMnfd/OjoruGfb6gf+7Qn27NlDZWVlzM4pRKwFAgE2bNhAYWEh39y+nf/+w2t8c/t2CgsL2bBhA4FAIC7nbXL7ee1SU78E3Nap4YYqExnoyF1jJvNmY8IS8LLGsn4JeGFGIYtyF0kCLkQCSBIuhBiRAwcOYLZYrrtttdliYf/+/QmOTEwFgUCA+vr6MSfJfe/m/ORvJ/mPX7/G7r+e5OFNW9i7bx8bN26MaaxhReFsYwd/u9KMOxhZ9KgOwQyngZlOAxkzDOT/QwqmrMTcoFYUhTP1ZyhvKY8euyHrBm6035iQ8wshpB1FCHGVobayLi8vx+6IzbbVsk21GKmeuydWq3XULUw9d3PWPba134YzPXdzIDJ+b9OmTWNqTemJVWM0c7rVO+DowRSDlqz3GDFmJu7PcVgJc6ruFDUdNdFjN+fczEzbzITFIISQJFwIcZWhtrIuKiri4KFDMdm2WrapFskwnLs5L/1gB/v37x/T9I+eKSNHnW14VJE/t5HRgzqyW3WkzTaQfoMBtSZxrR+hcIjjzuPUd9VH4lGpWJK7hHxrfsJiEEJESBIuhBiR4uJiNm/eLNtWi6Qa6o7N9cTybs5gQopCu8eHDujZeN7oUzGjzkCaVkfWLUaMGYn9ExwMBzlSc4RmdzMAapWa5XnLybHIBjlCJIMk4UIIYPhbRs+ePTtm21bLNtVitIa6Y3M9sbybM5CaTg9n6tpRqzTMoB2IjB7Mb9Zjm2XANi+x1W8Af8hPaXUp7d5IPFq1lpX5K8k0ZyY0jniQ/QrERDWiv3yXL1+OSxAajYb8/Ml5K8zpdOJ0OikrKxv6yUKMQKz/8Ixky+hYbVst21SLZIjX3ZyrRw8auo8X1OvIDJvIeo8JY/roqvdj4Q16OXzlMF3+LgD0Gj2rClZhM9oSHkssyX4FYqIbURI+c+bMuIwtys3NpaamZugnTkC7du1iy5YtyQ5DTCLj4Q+PbFstkiUWd09ieTenR7Pbz1FnW3TyCUBKlxaNz0qO3ULWDSmo1Ikf++fyuzhcfRhPIHJhYNQaWV2wmlRDasJjGYnh7CEwlv0KZI8CMR6olJ6VI8OgVsdnomFubi61tbVxee1k61sJX7t2LcePH2fp0qXJDkskSSx+8W/YsIG9+/bx4Fc2X/OH57kd23j4oYeGtVFOPGITYqK45mJ2gLs5w/l3EFYUypq6eKelK3pMHYKCRj25ih77YjMGW+Kr3wAdvg5Kq0vxBX0AmHVm1kxbg1k3cC/8eNKzaLuwsHDAtqOLFy9SVFR0zYSbHq8+t4e9Tz9JRUXFgBdTQ72+mLpOnDjBsmXLEpKvjTirzs3NJRwOx+x/k53D4WDp0qXMnz8/2aGIcWCsm9PEcqOcWMcmxETSczenoqKCr37lK/zTh27nsa9+lYqKCnbv3j2sBLzTH+QvVU39EvAUj5r5l00U5ljIf78laQl4q6eVN668EU3AUw2p3DL9lgmRgA+H7FcgJgNZDSXEBJKo0WpCTBWzZs0a8b8VRVG42ObmrYaO6OQTFHA065gWNGJfY8KQlpzkG6DR1cjR2qOEwpHWmHRTOqvyV6HTTLw7XINNwRnrhBvZo0CMByNKwt///veTlZUV0wDi8ZpCjHejHa8Wz9Fq8kdJiKF5gyGOO9uod/ujxwx+FbPqDDimm7HN0Sel97uHs9PJCecJwkrk8iDLnMWK/BVo1ROz5jbYFJyxTriRPQrEeDCif5V/+ctfYh5APF5TiPFutOPV4jlaTf4oCXF9tZ1eTjjb8PdZSpXVpmWm10TuKhN6a/Kq3wBX2q9wuv50dJOgXEsuy/KWoVbFZz1XMsl+BWIymHz/MoWYxIqLi3F3dfHawRcGfFz+8AgRe8FwmOPONkprW6MJuDaoorDWyMJ0K9Pel5L0BPxi60VO1Z2KJuDT0qaxPG/5hE3Ah5qC0zPh5mfbt/Lqc3vwedxApBDx6nN7hpxwI3sUiPFAfvqESKCx/uKPx2i1WMUmxGTU7PFztKYNd592rbQuDUVuE45l5qQn3wDvNL3Du83vRj+elT6LBfYFcRkpnCjD2UNgLPsVyB4FYjxI2F9bRVE4ceIEdXV1pKens2zZMgwGw9CfKMQkEotf/LHaKCcesQkxWYQVhbLGTt5pcUF3LqsOQ0GjgcJcM7YlhqT2fkPk7+rbjW9T2do7DWlu5lzmZc1LYlSJI/sViIkuIUn4T37yE5544glaW1ux2+20tLSg1Wr50pe+xBNPPBG3+eNCTEbyh0eI+Or0BzlypZX2YDCagKd41MxxmylYYkafmvzqt6IonK4/zZX2K9FjC7IXMDt9dhKjSo7RTLgRYjyIexL+pS99iX379vEf//EffPzjH+f73/8+d9xxB1euXOHRRx/l5MmTvPzyyxP6tpkQySB/eISILUVRqGx1c6ahg3DPnyQFHC165tlTSF9kGBd/q8JKmOO1x6nrqgNApVKxKGcR09KmJTkyIcRIxLUE/dvf/paf/vSn/O1vf+Ohhx5Cr9ezefNm3n77bT7ykY9w+PBhTp48yTPPPBPPMIQQQojr8gZDvF7ZwqnG3gTc4FdxU1sKyxbayJhjHBcJeDAc5M3qN6MJuFqlZpljmSTgQkxAcU3Cn3nmGb785S+zYMGC6DGlz2in3NxcvvWtb/Hd7343nmEIIYQQg6pp9/CH8kYaAr2zv7Patawx2ZizKhW9JfntJwCBUIDS6lKa3E0AaNQaVuavxJHqSHJkQojRiGs7ytGjR3n00UfZsGED0LsZyN69e/n73/8OwL333ktNTQ319fWyKEwIMalVVlZG+/hzcnIoKSmRPv4kCobDnLzczhWfN9r7rQ1CkcvEnAVWdCnjZ72SN+iltLqUTl8nADqNjlX5q0g3pSc5MiHEaMU1CQ8EAqhUKi5cuABAOBzZwauuri76HL/fH32uEEJMRoFAgI0bN7Jnzx7MFgv2vAIaa6vZvHlzdKKNTjfxthSfyJq7fBypbsOjim48T5pLw8I0K1k3jo/e7x7ugJvS6lJcfhcABq2B1QWrsRqsSY5MCDEWcU3Cb7rpJiorK/nzn/8MgNfrxWw2s2nTJh544AEA/vCHP2Cz2cjLy4tnKEIIMahAIEBLSwsZGRlxSYY3btzI3n37WPfYVm6751P9Zrvv3bENgN27d8f8vDD0e5tq1fmwonD2Sgflbne/0YMz3SZunDd+Wk96dPo6Ka0uxRv0AmDSmVhTsIYUfUqSIxNCjFVc77V95jOf4d///d9pbGwEuKay4PV6+epXv8pDDz0kYwqFEEkTDAZpbGwkGAzG/LUvXrzInj17ePArm7nz/k9jMJkBMJrN3PXAeh74tyfYs2cPlZWVQ7zS6Az23gKBABs2bKCwsJBvbt/Of//hNb65fTuFhYVs2LBhUt6d7PAEeO18I+We3gTc7FWz2mBj0dK0cZeAt3nbeOPKG9EE3KK38N7p75UEXIhJIq6Z77p161iyZAnve9/7OHbsGFqtlgcffJCioiIuXLjAbbfdRjgcZuvWrfEMQwghkubAgQOYLRZuu+dTAz5++73FmC0W9u/fH5fz9yTfVyfhfavzP/nbSf7j16+x+68neXjTFvbu28fGjRuvea1AIEB9ff2ES9AVReHdK5386VITnerunS8VmOY28IGiLHJnm8ZV+wlAs7uZw1cO4w9FWjbTjGncMv0WjFpjkiMTQsRKXNtRVCoV//Vf/8WXv/xl3vOe91BUVMSMGTPYuHEjZ8+e5SMf+Qh79uwhJUWu6oUQyed0OtFoYlsNLS8vx+4oiFbAr2Ywmcly5FNeXk5VVVVMzw29621CfbZd76nOr3tsK3fe/+no8Z7qPER2ZN20aVO/1pSeqrrVah2ybWe8tLm4vSGOXmyhWROMlp0MARULU1IpmGced8k3QH1XPcdqjxFWIv3qmeZMVuavRKtO2CbXQogEiPu/aJ1Ox/e+9z3+7d/+jT/+8Y84nU7S09O59dZbmT9/frxPL4QQw+ZwODCZTDF9zaKiIg4eOoTP4x4wEfe63TTWVlNUVMKMGTNiem6AtrY2qqur+x0bTnX+pR/sYP/+/SPeEGo8LUKtqnZxuqOToKZ3NG6OT8eyQhvGlPGZ0NZ01HCy7mR0nG+OJYdljmVo1OOrVUYIMXYJ+y1UUFDAQw89lKjTjRtOpxOn00lZWVmyQxFCJEFxcTGbN2/mtYMv9Ks693jt4AE8LhclJSVxjaOpqYn29nbC4TDvvPPOqKrzfavpgxkPi1AtFhtnKrtw6vzQnbtqQ7DAlErhPEtczh0Ll9ou8Vb9W9GP8635LM5djFola6aEmIzGZylgEtm1axdbtmxJdhhCiOvQarXY7Xa02tj/Spw9ezbr169n7/atKIrC7fcW90tMn9uxjfXr18etVaOnvSYnJ4fU1FQ8Hg82m41GZ/WIq/Mej4eKiopBzzXaNpdY6WmXOdMYoFPXWzm2BbSsnGnDYhm/YyAvNF/gfNP56MczbTO5KfumcdkuI4SIDbm8jrNHHnmE48eP8/zzzyc7FCHEIHQ6HTk5OXFrk9i5cycPP/QQe59+ks+8fwlfuvuDfOb9i9n79JM8/NBD7Ny5My7nBaIXFn0vMO68807cXV28dvCFAT9ntNX5ZC5CDfhClJV3AODXRlo5VGG4QZfCBxZkjesE/FzjuX4J+JzMOdycc7Mk4EJMciMu+9TW1sbkxFNlLrjD4cDhkC2FhZjKdDodu3fvZtOmTdHFirm5uRQXF8d9seJAVf5p06Zx33338bPrVOc/+clPolar+7WjhMNh9Ho9dXV1A46VTdYiVJ83RLs/iKIKo+8+ZglqWDndhi1Vf93PTSZFUThTf4bL7Zejx26030hhRmESoxJCJMqIk/CCgoIxX52rVKq4zOMVQojxbNasWSNe6DhWPVX+q+3atYvU1FT2PP0kL/1gB3ZHPg211XhcrlEvoEz0ItSgL0TZ+U4q9B7CejAoQWYE28nXGlg4z456HFeSw0qYk86T1Hb2FrYW5S5ietr0JEYlhEikUbWjKIoypv/1bF8vhBAiOXqq8xUVFXz1K1/hnz50O4999atUVFSwe/fuUbXmFBcXx6XNZSCt1T7+9nYzF4wewt1/yfThSNI9Lz91XCfgoXCIIzVHogm4WqVmWd4yScCFmGJGtQpJpVIxffp0SkpKuPfee7HZbDEOSwghRDxc3Z4Sy+p8Ihahhnxhys918Y7eTTCld/RgvtbAwpkpdLTp4rLANlYCoQBHao7Q4mkBQKPWsDxvOdkp2UmOTAiRaCP+TfX444+zf/9+Kisrefrpp/nOd77DXXfdxdq1a7nzzjsTNv9VCCEmsmRtZjNYe0qs9CwyvV6by2i1V/t5y9lBg7V3x05tWMWSHCvTMiLtL6Y4vrex8gV9lFaX0uGLLCDVqrWsKlhFhikjyZEJIZJBpfTsCDBCr7/+Os899xwHDx6ktbUVlUpFeno6n/jEJygpKeGWW26JdawT2okTJ1i2bBnHjx9n6dKlyQ5HCJEkg21m4+7qSvhmNvHU9yJjrItQQ74wl866OK9z4TP0/snK0uhYMTMdk3b8b2TjCXg4XH0Yl98FgF6jZ3XBatKMaUmOTAjRVyLztVEn4T0CgQCvvPIKP//5z/ntb3+L3+9HpVIxc+ZM1q5dS0lJCXPnzo1VvBOWJOFCjF89m7xkZGTEPQHesGEDe/ft48GvbL5mM5vndmzj4YceittmNj0S+X7HqrPaz9tXOqlN90N3m7dagQWZqRRlpUyIMX5d/i5Kq0vxBDwAmHQmVhesxqIfvxsHCTFVJTJfG/OccJ1Ox8c+9jFefvllnE4n//mf/8nq1auprKxk27ZtzJ8/n1WrVvH73/8+FvEKIUTM9WzyEu+pTT2b2Tz4lc3cef+noxNEejazeeDfnmDPnj1UVlbGNY5Evd+xCHrDVB3t4o3GVmozehNwq1rDB2dlMcdumRAJeLu3ndcvvx5NwFP0Kdwy7RZJwIUQsd2sJz09nc9+9rO8/vrrlJeXs2nTJrRaLceOHeOPf/xjLE8lhBATTjI3s5lIOi77OHOsjZMpnbjM3dO0FJiTZuaDRXashvFdve/R4mnhjStv4A/5AbAarNwy7RZMOlOSIxNCjAdxWUL+97//neeff56DBw8SCEQW0PRsnSyEiK9kLfibDJxOZ1x/VyVrM5urhUKhuL32WAS9YZxn3FxQu2mz98ZoUqlZMS2dLPP43Xjnag2uBo7VHiMUjryPDFMGK/NXotNMjAsIIUT8xSwJf+edd/j5z3/OgQMHqKqqQlEUjEYj9957L/fffz933HFHrE4lxLgyXpLewRb8bd68eVIt+Isnh8OByRS/KmWiN7MZjMfjoaKiIm6vPxqdl/1cuujikt1HQNu7VGmaxcji3DR0mpjeuI2r2s5aTjpPElYiVXx7ip0VeSvQqKUYJYToNaYkvL6+nhdeeIHnn3+ekydPoigKKpWKW2+9lbVr13LPPfdgtVpjFWvMHT9+nD/84Q8cOXKEI0eOUFNTA0Q2IxJiKPFIeseyYG7jxo3s3bePdY9tvWbB394d2wBGvOBvIi3gmwiKi4vZvHkzrx18gTvv//Q1j8dyM5uJIugJU3/aw0XcNDp6e9R1KhVLHWnkp06s1o3L7Zc5XXc6+nFeah5LHEtQqybORYQQIjFGnIS73W4OHTrE888/z5/+9CdCoRCKonDjjTdy//33U1JSQkFBQTxijblt27bx61//OtlhiDiLVyIZj6S3Z8Gc1Wq9JtbrvY+eBX/rHtvaL7nrWfAHkbnNmzZtGlGV/nrxTCZXb2ATL4nYzGY4EvV+h9JR5aem3E2l3Yu3z+hBu0nP8jzbhBg92FdFSwXnGs9FP56eNp2FOQsnxAJSIUTijfg3cHZ2Nh6PB0VRorNfS0pKWLJkSTzii6s1a9awcOFCVqxYwYoVK5g5cyY+ny/ZYYkYi0ciGa+k93qu9z6Gs+DvpR/sYP/+/THbHXEyifcGNn3FczOb4Urk+x1IwB2m6bSHqpAHZ34ApWf0IHBTtpVCm3nCJa7nm85zoflC9OPCjEJutN+YxIiEEOPdqCrhKpWKGTNm8MEPfpCOjg5+9KMfjeg1VCoVu3btGumpY+7//b//l+wQxAQV76R3oAWCPYvpBnosXgv+xusCvolMp9Oxe/duNm3aFLPNbCYKRVHorArgfNdDld1LV8/kEyBNr2VFnm3CTD7poSgKZxvOcqntUvTYDVk3MCdzTvKCEkJMCKO6F6koClVVVezbt6/fsaGoVKpo3/h4SMLF1BLLyRfJTHoHWjwYrwV/43EB32Qxa9asKXVXIuAO03jKg9Pv40qBj3Cff4pzM1KYn5mKRj2xqt9hJcypulPUdNREj92cczMzbTOTF5QQYsIYcRL+4IMPxiMOIeIulpMv4p30DhTr9RJiWfAnxitFUei8FKDhHS+XM720ZfYZPahVs9xhw242JDHC0QmFQxx3Hqe+qx6IFJkW5y6mwDox1kQJIZJvxEn43r174xGHEBNKvJLe0S6Y67vgr6O1BYCu9jZS0myogJd/8sNRLfgbLwv4xMQUcIVpOu2l0e2jqsBHQNdn9KDVxOJs64QaPdgjGA5ypOYIze5mANQqNcvylpFryU1yZEKIiUT+ssaY0+nE6XRec7ysrCwJ0QiITyIZrykX11swN9T7+O53v8v//u//8sv/fKa7HSaPJmctPo+bG264ge9+97sjfZtJX8AnJiZFUeioDND8jpcam4/GaZHRg/XVl3n9vw+hc3dQOC2frAm4kZQ/5OfN6jdp87YBoFVrWZG/gixzVnIDE0JMOJKEx9iuXbvYsmVLssMQfcQrkYzHlIvrjSEc6n184QtfoLyigk9/bds1IxOf27GNL3zhCzInXMSdvytE82kvrV0BLuVHRg8GAwH2bPsaf/zl/uhM/V9OwI2kvEEvh68cpsvfBYBOo2N1wWpsRltyAxNCTEiShMfYI488wt13333N8bKyMtauXZuEiES8xGPKxWjHKcqccDEWsbjYUhSFjot+Ws77qE8L4JzuR+nuNNmz7Wv8+eWXYjpTfyixvoB0+V2UVpfiDrgBMGqNrC5YTaohdcyvLYSYmkaUhM+ePZvs7GxKS0tjFkA8XjOZHA4HDocj2WGIBBoPUy5kTrgYi7FebPm7QjSd8tLZGaAqz9dv9KCrrpo//nJ/QmfqQ2wvIDt8HZRWl+ILRvaRMOvMrJm2BrNu4OlIQggxHCNKwi9duoTX641pAPF4TSEmupGOU5Q54SIZFEWhvcJP2zt+WlICXJnhI9Tnx3ZOegov/+L/N6EvEFs9rbxZ8yaBUACAVEMqqwtWY9QakxyZEGKiG3E7SigU4sqVK8OaCy6EGJ2RjlOc6nPCKysroy1BOTk5lEzABX/jwUgu/sJBBX97mFAwjKcgTECrkKMAQdCoVKQZdRg8fi5WVMTlAnEosbiAbHQ1crT2KKFw5LXSTemsyl+FTiOtWUKIsRtxEt7U1MTMmTNjFsBE25pYiPFoqs4JDwQCbNy4kT179kQX/DVOwAV/48VwLv6UcHf1+10/PkOQqtz+owcLUo0syklD3z16MF4XiEMZ6wVkXVcdx2uPE1YirTVZ5ixW5K9Aq5alVEKI2Bjxb5PJVAF/9dVX2bZtW/Rjv98PwOrVq6PHnnjiCe66666ExyampljMCY/lyMREzwkf6WK6jRs3snffvjEv+JMpMMPj74j0fns6gjgzAzSkB6C7jqJTq1ick8Y0a/8kfiJeIF5pv8Lp+tPRv3e5llyW5S1DrZp4M82FEOPXiP6yhsPhoZ80gTQ2NvLmm29ec7zvscbGxkSGJKa4kYxTvLoF48tf/jIQ25GJiZ4TPpLFdLGcCDPVp8AMdbGlhBXay/20XfDj1oaomubDY+z9e5Bl0rPcYcOsu7aVJV4XiGN9T4O52HqRtxvejn5cYC1gce5iuWsrhIi5KX1f7aGHHuKhhx5KdhhCjMhQLRjnz5/nF7/4RUxGJo5nMhEmdq53seXvCNF40ouvI0ijLUhtVu/oQbUKFmSlUpSect0kNR4z9cfyngbzTtM7vNv8bvTjWemzWGBfIAm4ECIupnQSLkSixaLtIVYtGLGILV4LIoezQDCWE2FkCsy1lLBC2wU/7Rd8+DRhLuf76EzprX5b9VqWO2zYjEP/rMRjpn4sKYrCucZzXGy9GD02N3Mu87LmJTEqIcRkJ0m4EAk01raHeG3KM9LY4r0gcjgLBGO54G+iTIFJFF97pPfb3xGi1RLkSk7/0YNF6SksyEpFox5ZhfjqmfqVlZV84xvfSOpUG0VROF1/mivtV6LHFmQvYHb67ITGIYSYemSViRATyHBaMMwWC/v37x/xaweDwX7/fz19q/E/+dtJ/uPXr7H7ryd5eNMW9u7bx8aNG4d1zkAgQH19PYFAYMTxFhcX4+7q4rWDLwz4+Hhc8DfeKWGF1vM+nP/rwtMV5FKuj0t5vQm4UavmvQUZLMy2jjgB7ysQCLBhwwYKCwv55vbt/PcfXuOb27dTWFjIhg0bRvXzMBphJcyx2mPRBFylUrE4d7Ek4EKIhJBKuBBJMNLNeHrEa1MeIJr4DNWaEc8FkSNZTBfLBX+JngIzHvnaQjSd8uDvDNNlClGV68PfPXpQo4SZpg1yQ34uZqNhzOeKV0vV1a7XYhUMBzlWe4xGV2TxvVqlZqljKY5U2fFYCJEYU/cvjhBJNNLNeHrEc+ZyW1sb1dXVQz4vngsiR7qYLlYL/hI9BWY8UcIKre/46KjwE1IU6rIC1PcZPahVq7jZZsZVX4NKyR7z+eLZUnW1wVqsAqEAb9a8SaunFQCNWsOKvBXYU+xjOp8QQoyEJOFCTCCJmLnc1NREe3v7oI+PpwWR433B33jnbY1UvwNdYTz6MFW5A40eTEMVDFBRH5tzJnuqjTfopbS6lE5fJwA6jY5V+atIN6XH/FxCCHE9koQLkUBjbXuI58zlnvaYnJwcUlNTB33eaKrxg01RidWCyKsX/InrC4cU2t7x0V7hR0GhyRakps/oQRWR0YNzMiKjBz3BSKvSaNuo+opnS9XVrr7IcwfclFaX4vK7IufSGlhdsBqrwTqm8wghxGhIEi5EAsWi7SFeM5d7LgyGukAYSTV+qCkq3/72t0cVqxg9b0uQplNeAq4wAU2Yqlw/nSm9yWqqXsuKQUYPjraNqq9EbmPf9yKv09dJaXUp3qAXAJPOxJqCNaToU8Z0DiGEGC1JwoWYYOLVgjHcKv20adMoKSnhZ8Ooxm/YsOG6C/DC4TBPPfXUoOeU7eRjJxyKTD7puOgHoM0S5PJVowcLbWZuso9t8slQkrGNfaevk5NNJ/GHIu/dorewZtoajFpjzM4hhBAjFZck3OVy4fV6ycjIkJ3GhIiTWLdgDLdKHwwG+eIXv4her2fvdarxw1mA9+zTT/K1r31t0PNO9e3kY8XbHKTpdKT6HVIrVNv9tKT1jqI0atQsc9jISRl48kksp8ckcht7rVaLxWbhZMNJ/EokAU8zprG6YDV6jX7Mry+EEGMx5t+o58+f5w9/+AOvv/46R44coa6uDp/PB0RmrtpsNubNm8ctt9zCe9/7Xj70oQ9hNEr1QYiJTKfT8cMf/pDHH3980Gp8shfgCQgHu6vflZEEtMsYosrRO3oQIM9iZEluGgbN4NtGxHp6TKK2sW/xtfC2+23CSmSxaaY5k5X5K9Gq5SawECL5RvWbqKuri+eff569e/dy7NgxILLr2NUURaGlpYXDhw9TWlrKf/zHf2C1WrnvvvtYt24dK1euHFv0E4DT6cTpdFJWVpbsUISIqZ5Fevfff3+/4z2L6WKxAE+2kx89T1Ok+h10hwmjUJcZoD6j/+jBRdlWpltNCb9jmYipNjUdNZysOxn925RjyWGZYxka9dgWlgohRKyMKAn3+Xz88Ic/5Fvf+hbNzc0oikJmZiarVq1i+fLlLFq0iKysLNLT0zEajbS2ttLa2sqlS5c4evQoR48e5a233mL37t385Cc/4Y477uCpp55i8eLFcXp7ybdr1y62bNmS7DCEiLmhFunFYgGebCc/cuGgQmuZj45Lkeq3VxemyuHD3Wf0YKZJx/JcGyn65FaE4zXV5lLbJd6qfyv6cb41n8W5i1GrZJNoIcT4MaLfwEVFRdTW1pKWlsanP/1p7rvvPj7wgQ+gVg/9i23Dhg0A1NTU8Itf/IKXXnqJ3/72t/zud7/jxz/+MevXrx/dOxjnHnnkEe6++27KyspYu3ZtssMRImGSsQBvqvM0dle/PeHI6MG0ILXZfsLdhW4VMD8rlXndowcnowvNFzjfdD768QzbDG7OvnnSvl8hxMQ1oiQ8GAzyzW9+k8997nNYLJZRnTA/P59HH32URx99lMOHD/P0009TV1c3qteaCBwOBw6HbIMsJo/hLtKLxQI82U5+eMJBhZa3vXRejszzDmjCXM7109Fv9KCG5Y500gcYPThZnGs8R0VL752TOZlzuCHrhiRGJIQQgxvRX7ZLly5hMAy8en401qxZw29+8xv8fn/MXlMIEV8jWaQ31gV4U3k7+eHqW/0GaEsJcsXhJ6juXaczu3v0oDaOoweTSVEUztSf4XL75eix+fb5FGUUJTEqIYS4vhEl4bFMwPvS62VUlBCTkWwrHz+hgELrud7qd0ilUJPjp9naO3rQoFGzLDeNXMvknUgVVsKcdJ6ktrM2emxhzkJm2Ma20Y8QQsRb3O/xXr58GbvdPugCLo/HQ2NjI9OnT493KEKIJJFt5WPLXR+k+YyXoDdS/XYZQ1Tl+/FpehdfOiwGlubYMGgn72LEUDjEsdpjNLgagMhY3KWOpeSl5iU5MiGEGFrck/BZs2bx85//nOLi4gEf/81vfkNxcbGMIhNCRFVWVkYr5zk5OZSUlEjlnEj1u+VtL11XItVvBYV6ewBneiD6HK1KxcIcKzOSMHowkQKhAEdqjtDiaQFAo9awPG852SnZSY5MCCGGJ+5J+EDzw/sKBALDmq4ihJgYxpJABwIBNm7cyJ49ezBbLNjzCmisrWbz5s3RHvKpunOmqy5Ay1u+aPXbqwtzZZqfLm1vASPDqGO5w4YlyaMH480X9FFaXUqHrwMArVrLqoJVZJgykhyZEEIMX1x+U3d0dNDW1hb9uLm5mcuXL1/zvLa2Nl588UWZHiLEJDBUAv3d736Xzs5OMjIyBk2kN27cyN59+1j32FZuu+dT/aap7N2xDYDdu3dfN4aWlpbrnmOiCfnDtJz10VXTW/1uyQhRneWjp/lEBdyQaWFepgX1JK5+A3gCHg5XH8bldwGg1+hZXbCaNGNakiMTQoiRiUsS/swzz7B161Yg0qP3hS98gS984QsDPldRFJ5++ul4hCGEGKORJLVDJdChUIhHH30Uq9U64GtdvHiRPXv2sO6xrf3mihvNZu56ILKPwJ6nn2TTpk2DVtaDwSCNjY2DnmOicdUFaD7jJeSL3FEMaBRqpvtp1fUuvrToNCx32MgwTf4F7i6/i8PVh/EEPAAYtUbWTFuDRT+6kblCCJFMcUnC77jjDmw2G4qi8MUvfpG1a9eybNmyfs9RqVSkpKSwbNmySb1jphAT2XCT2uEk0HuffpJ7772XwsLCAV/jwIEDmC0WbrvnUwM+fvu9xbz0gx3s379/0i/yDPnDNL/lw1Xb2+vdYQ1xOddHgN4Wv1lpZm7OTkU7BVr62r3tlFaX4g9FRtqm6FNYU7AGk27wXVuFEGI8i0sSvmrVKlatWgVAe3s7H//4x7npppvicSohxDgw3AT61VdfZe7cuWg0mmueU15eTpYjf8At7gEMJjNZjnzKy8upqqoa8DmTYYG3y9ld/fZHku2QSqF+epB6Q+9+CgaNmqW5aTgm8ejBvlo8LbxZ/SbBcOQOgNVgZXXBagza+IzNFUKIRIj76p0nn3wy3qcQQsSZ0+kcMHHuUV5ejt1RcP0EOjeP5ubmQV8jKyuLxtoafB73gK/jdbtprK2mqKiEGTMGngHt8XioqKgY8LHxLuTrrn47e6vfHkuYy/k+3Er/0YNLctIwagf/fkwmDa4GjtUeIxSOXGBlmDJYmb8SnWbitxsJIaa2uN/DPHXqFAcOHOh37A9/+AO33norq1at4nvf+168QxBCjJHD4WDGjBmD/q+oqIhGZzU+j3vAz/e63TQ6a8jMzMRutw/4Gv/6r/+K29XFawdfGPA1Xjt4ALfLRUlJyaBxBoPBfv8/UXTVBKj5iyuagCsoNE0P8k6eJ5qAa1QqluSksTovfcok4LWdtRytORpNwO0pdlYXrJYEXAgxKcQ9Cf/KV77Ciy++GP348uXLfPSjH+XSpUtotVq++MUvXnfagRBi/CsuLsbddf0E2uNycdddd13/hRSFfd/awqvP7Ykm9F63m1ef28PPtm+FIUaeTjQhX5j6o24aT3ii7ScBs0LlDX6uGH3R7u90o47bZmYxy2ae1LO/+7rcfpnjtccJd1+EOFIdrMxfiUY9NS5AhBCTX9yT8NOnT/O+970v+vHzzz+PWq3m5MmTvP7663ziE5/gxz/+cbzDEEKMglarxW63o9Vev3Nt9uzZrF+/np9t3zpgAv3cjm08/PDDFBQUDPpaPX3lt/7LPex9+knWv28Jj/7zB1n/vsXsffpJbv2XezBbLOzfv/+68fb9//GsqyZA9Z9duOsiVXsFhc7pYcqmeWjv7n1WAfMzLdw6PXPSz/7uq6KlgtN1p6MfT0+bzjLHMtSqyb8AVQgxdcT9t3p7ezt2uz368f/8z//w//1//x8ZGZFNFW677TZeffXVeIchxJQRy90mdTodOTk5w3ruzp07gcgYwZd+sAO7I5+G2mo8Lhfr16/n29/+NpcvXx60v7y8vJzsvGlsfOo73PO5L/C/rxyirakRmz2b9/3TR8kpmE7FW6cm/MLMoDdM8xkv7vrelpmwEWpnB2kM+ukpf6foNKyYIqMH+zrfdJ4LzReiHxdmFHKj/cYkRiSEEPER9yTc4XBw8eJFAFpaWjhy5Ag/+MEPoo93dnbKjplCxECyd5vU6XTs3r2bTZs2RS8CcnNzKS4uZtasWXg8kdnODocDk+nasXJFRUUcPHQIn8dNTsF07vncF/o9PhkWZnZe8dPyto9woLetxj8dylM8+IK9iy9npplYmG2dEqMHeyiKwtmGs1xquxQ9dkPWDczJnJO8oIQQIo7inoT/y7/8Cz/4wQ9IS0vjL3/5Czqdjo985CPRx0+fPs3s2bPjHYYQE8JYdnwc626TsYpn1qxZo5rjXVxczObNm3nt4Av9Zo336Okrv97CzPEq6A3TdNqLp6HPglEjNBWFuOL3QncB36BRsyQ3jbwpMnqwR1gJc6ruFDUdNdFjN+fczEzbzOQFJYQQcRb3JPwb3/gGjY2NfOMb38Bms7Fv377o7e2Ojg7+67/+i40bN8Y7DCEmhNHu+BiL3SZjGc9Ahuov7+kr37t9K4qicPu9xf0uJJ7bsY3169dfN/7h9rAnUufl7up3sLf6rUxXcSHVg8vf2z6Tm2JgaW5sRg/GsiUp3kLhEMedx6nvqgciG7ktzl1MgbUgyZEJIUR8xf0vleU6C6ksFgvV1dWYzQPPFp4MnE4nTqeTsrKyZIciJrGJsNvkcPrLh+or73l8LOdIlKCnu/rd2Fv91hhVtM0JU+FzoXSPA9eo4OZsK7PSxj75JNktSSMVDAc5UnOEZndkfrxapWZZ3jJyLblJjkwIIeIvqeUitVpNWlpaMkOIu127drFly5ZkhyEmmKE2x7nasDbLGWK3yYEkeqHjUH3lE0VP9TuoBAnaOtF2pqLL11GR5qXV17sZj82oY4XDRmqMJp/EoyVppIbbwuQP+Xmz+k3avG0AaNVaVuSvIMucFdf4hBBivIh7Eq5Wq4dV3ZkIUw1G45FHHuHuu++mrKyMtWvXJjucCWsi3V6PhcEWLw6m76LG0e42OZBkLXQcqq98vP48BNyRySc91W9FHyKQ3oFSYObtgI+gr7cl5YZMCzdkWlDHaO53vFqSRmo4LUzeoJfS6lI6fZ0A6DQ6Vhesxma0xS0uIYQYb+KehG/evPmaJDwUCnHp0iV+9atfMW/ePP7pn/4p3mEkjcPhwOFwJDuMCWui3V5Plsm8qLGv8frzoCgKnVUBWsv6934bctV4gQqfh6Aq8us2RadhucNGZoxHD06EliQAl99FaXUp7kBklrxRa2R1wWpSDalJi0kIIZIh7kn417/+9UEfczqdrF69mrlz58Y7DJEAY5nsMZhE3V6PR+yjMdqFhbFY1BjLeIZrpF/3WP08DHXekVTaA+4wTae8eJt7e7+1RjXKjWrOuzpw+MEedBFWqTDpNFjVWroaPHQNGeXIxKslaaSud1ezw9dBaXUpvqAPALPOzJppazDrJu+6ICGEGExSe8IdDgef/exn2bZtG5/61MDVGzFxxHKSBiT29nqsYx+tsSwsHOuixljHMxwj+brH8udhsPOOpNKuKAqdlwK0lPlQQr3Vb/MMHbV2P5UdnRiUyPE2vYUFjizyU+M3ejBeLUkjNVgLU6unlTdr3iQQivTEpxpSWV2wGqN2ao1jFEKIHknfCSIlJYXKyspkhyHGoeHcXh9qG/OppGdRY0VFBV/9ylf4pw/dzmNf/SoVFRXs3r17wrft/PznP8eUEt+fh76V9p/87ST/8evX2P3Xkzy8aQt79+2LjlMNuMLUHfbQfNYbTcC1JjWm5TrOpHZS2eHu97or82xDJuCBQID6+noCgcB1nzeY4uJi3F1dvHbwhQEfT2ZLUqOrkcPVh6MJeLopnVum3SIJuBBiSktqJfzs2bN8//vfl3aUSWakkz0G0/f2en315d5tzLPsvO+fP0ZOwfSY3V6fTAuDR7tZTjIN52emoqICe15+TNotBvp+D7fSvrHkS6S58vpVv1Nn6mjKDVLW0taz6zxqFczLtJIa0pNiMFw3Hhj73Zh4tSSN1NUtTHVddRyvPU5YiewImmXOYkX+CrTq8TPLXQghkiHuvwVnzZo14HSUtrY22tvbMZvN/OpXv4p3GCKBRjrZYzBFRUX88r/+i51f+yJ/PvQSphQLWXkFNNVW8+L3d/CBj95HQ82VmNxeH+/bnU92w/mZmTVrFv/18q9i0m4x0Pd7OHdeXvz+Dp77yfN8/r5/A0BrVmO+ScdZXyfNLX1GDxq0rHCkk2rQAokbwxqPlqSR6tvCdKX9CqfrT6N0t+XkWnJZlrcMtSrpN2GFECLp4p6E33rrrdck4SqVivT0dAoLC/nkJz9JRkZGvMMQ49BQi+OKi4t54okn+OuvDw64EO9n27cSCgYHvb0+XhZbTkTjcQTgfffdx7Zt24acAPMP//APw66E963AD2dho92RR52vCm92I9oUFV4D1DYHMSgKed3Ps+i0WFRaWurctIzg/cXibsx4mrNe2VrJ2Yaz0Y8LrAUszl085g2JhBBisoh7Er5v3754n0KMEyOdpDGs2+8qFQ/9vycHbQ949uknx/b6o4x9skr0CMCRfN1nzZrFxz/+cX42RLvFe9/73iFfa6ALtOEtbKwh770zyZ89jTK/i5ouL3R30Zh1Gpbn2sgyj270YCzvxiS7Jend5nd5p+md3njSZ7HAvkAScCGE6GNqZxwipmI9SePAgQOkJGju8Xja7jxWRnMnIJYjIYdz/pF+3R977DGsVit7x9huMdB5hzVr3e3i3keL+Xt7K95QOPrYdKuJRdlWdJqp3WahKArnGs9xsfVi9NjczLnMy5qXxKiEEGJ8kiRcJN1gi/LGOvd4Mi22HI2RLvSL9UjIWI991Gq15OXl8eMf/5jHH3885u0Ww1nY+LGSB6jWp0F3Aq5Xq1iSm0Z+6tjXQEz0uzGKonC6/jRX2q9Ejy3IXsDs9NlJjEoIIcavmP+2H+429Veb6gnTVDbYoryxzj2WxZYjM953XOxbvY5Hu4USVnjqC9+h60qAvU8/yYvf34E9N59GZzUet4t/vG8tn/h/W6PPzzbrWeawYdKOfRIQTOy7MWElzPHa49R11QGRdT+LchYxLW1akiMTQojxK+ZJ+EDb1L/88su8/fbbfPjDH2bevMhtyfPnz/P73/+em266iY985COxDkNMAlNlK/Z4G+7IyFjvuDiRLqz9HSGaTnnxtYd56l+/x2c/9iivHD5Il6YZY3YW8z54J9kF04HI6MGb7FYKbWbpcQaC4SDHao/R6GoEQK1Ss9SxFEeqI8mRCSHE+BbzJPzqbep3795NQ0MDZ8+ejSbgPcrKyvjgBz9IXl4eYuoZ6vb7WOceT/Tb+7Ey3JGRsd5xcSLciVDCCu3lftou+FHCkTF6KhXc9N65LHtgM8fr22ny+KPPTzNoWeGwYTXItB2AQCjAmzVv0uppBUCj1rAibwX2FHuSIxNCiPEv7tnJjh07+PznP39NAg4wf/58Pv/5z7N9+3Y+85nPxDsUMc4M5/b7WOYeT+Tb+8kw1e48+DtCNJ704u/ordjrU9VkLjLSoPbz98tNBMO9G/LMzUjhxqxU1FL9BsAX9FFaXUqHrwMAnUbHyvyVZJhk5KwQQgxH3JPw6upq9PrBR3bpdDqqq6vjHYaYoMbT3OOJZqR3AmK94+J4vROhhBXaLvhpv+Cjew8ZVCpIKzJgLtRyurGD6k5v9PkmrYYVjjSyzEPvejlVuANuSqtLcfldABi0BlYXrMZqsCY5MiGEmDhUSs9WZnGyYsUKmpubeeONN8jNze33mNPp5D3veQ92u50jR47EM4ykO3HiBMuWLeP48eMsXbo02eEIMaBr5oQPcOdhIm985GuP9H73q35bNWQtNtKuDXKsrg1vUEYPXk+nr5PS6lK8wciFiklnYk3BGlL0KUmOTAghxi6R+VrcS1TPPPMMH/7wh5kzZw4f//jHKSoqAuDChQscOnSIUCjEz3/+83iHIYQYhsl650EJK7S966e9/Krq9xwDqYU6zrV0UV7nij5fp1axJCeNAuvYRw9OJm3eNt6sfhN/KNInb9FbWDNtDUatMcmRCSHExBP3JPy9730vb775Jk888QS//OUv8Xg8AJhMJj784Q+zZcsWbr755niHIYQYgWTvuBhLvrYQTac8+Dt7K9x6qwb7EiMefZi/XGmmwx+MPmY361mWa8Osi83owcmi2d3MkZojBMORr1WaMY3VBavRa0a3Q6gQQkx1CWnWvOmmm3j55ZcJh8M0NkbGWNntdtTqyX+L1+l04nQ6KSsrS3YoQkwpSlih9R0fHRX+3uq3WoVtjh5roY6Kdjdv13XSs/ZSrYIFWakUpafI6MGr1HfVc6z2GGElciGTac5kRd4KdJqJ25okhBDJltAVU2q1espNq9i1axdbtmxJdhhCjFllZWW0RSUnJ4eSkpJx26LibY1UvwNdvdVvQ1qk9ztogtdrW2l09x89uNxhI01GD16jpqOGk3Un6Vk+lGPJYZljGRq13CkQQoixGHESXltbG5MTT5XZ4I888gh33303ZWVlrF27NtnhCDFi1yzWzCugsbaazZs3j7vFmuGQQts7PjouXlX9nqsnrVBPdZeXU5faCfQZPTgnPTJ6UKOW6vfVLrVd4q36t6If51vzWZy7GLVq8t/FFEKIeBtxEl5QUDDmW7UqlYpgMDj0EycBh8OBwyE7x4nxIxAI0NLSQkZGxrCS540bN7J33z7WPbaV2+75VL+xhXt3bAMim3LF49wj4W0J0nTKS8DVp/pti1S/Mas4Wtd21ehBNcsdNuwyenBAF5ovcL7pfPTjGbYZ3Jx9s7TqCCFEjIyqHWWsUw3jPBVRCHEdwWCQxsZGrFbrkInwxYsX2bNnD+se29pvAx+j2cxdD6wHIhspbdq0aVitKSM593CFQwqt5yPV7x4qtQrbvEj1u8nj59ilNjx9Rg9OSzWyKCcNvYweHNC5xnNUtPTudjoncw43ZN2QxIiEEGLyGVUSrlKpmD59OiUlJdx7773YbLYYhyWEGA8OHDiA2WLhtns+NeDjt99bzEs/2MH+/fuTMk3F2xyk6fRV1e/0SPVbY1ZztrGTC639Rw8uzkljmoweHJCiKJypP8Pl9svRY/Pt8ynKKEpiVEIIMTmNOAl//PHH2b9/P5WVlTz99NN85zvf4a677mLt2rXceeed46Y3VAhxfU6nE43m+ovrysvLsTsKMJjMAz5uMJnJcuRTXl5OVVXVkOcMhUJDPmcgVy8K/dR9xdh8eXRU9q9+p9+gxzpbT4c/yLHLLbT7etveskx6ljtk9OBgwkqYk86T1Hb2rvtZmLOQGbYZSYxKCCEmrxEn4Vu3bmXr1q28/vrrPPfccxw8eJD/+q//4tChQ6Snp/OJT3yCkpISbrnllnjEK6agQCAw4BoCrVYrF31j4HA4MJmuXxEuKiri4KFD+DzuARNxr9tNY201RUUlzJgxdLLm8XioqKgY8nk9BlwUWhNZFHrfhx5kyyPfRqfVYczQkLXYhNasoqLVzdmmDhk9OAKhcIhjtcdocDUAkbudS3KXkG/NT3JkQggxeY16ROEtt9zCLbfcwg9/+ENeeeUVfv7zn/Pb3/6WH//4x+zatYuZM2eydu1aSkpKmDt3bixjFlNMS0tLdL58X3a7fcqNvEy04uJiNm/ezGsHX+jXE97jtYMH8LhclJSURI9db5Rhz8XUcBdmX29R6HPbt6FSwY927sI6S4cnGKa0uv/oQas+MnrQZtTFdVHoRBYIBThSc4QWTwsAapWaFfkryE7JTnJkQggxuY15VZJOp+NjH/sYL7/8Mk6nk//8z/9k9erVVFZWsm3bNubPn8+qVav4/e9/H4t4xRSUkZGBRqPhl7/8Jdu3b+eXv/wlGo2GjIyMZIc2IWm1Wux2O1rt0Nfgs2fPZv369fxs+1ZefW4PPo8biFTAX31uD8/t2Mb69euZNWsWgUCADRs2UFhYyDe3b+e///Aa39y+ncLCQjZs2EAgEBhRnD2LQh/8ymbuvP/T0Up8z6LQB77yBC/+7me0qGqo6fTy2qXGfgl4UXoKH5iRhc0YSbh7FoVOlclMw+EL+jhcfTiagGvVWtZMWyMJuBBCJEBMN+tJT0/ns5/9LJ/97Ge5ePEiP/3pT/n2t7/NsWPH+OMf/8iHPvShWJ5OTAHXtCM4Cjj0q1+xbdu2cTejeqLQ6XTDvoMQCATYvHkz4XCYZ59+kpd+sAO7I5+G2mo8Llf0ewDDG2X4ve99D2DQC4C+1erhLgr91o9/yofWbYweN2rVLM+1kZ0iowevxxPwcLj6MC5/ZOGqXqNndcFq0oxpSY5MCCGmhrjsmPn3v/+d559/noMHD0arX0MtABPjU7Jv4cdyRnUsJfvrkijBYJC2tjZ+8IMf8LWvfS3aZpKbm0txcXG0zWS4owwfffRRYPBFoaFQCLfbjcvl4t13LpDlyL/uotDM3Dyaqy+RF+yInE+rIU2jxdPkparp2tcWES6/i8PVh/EEPAAYtUbWTFuDRW9JcmRCCDF1xCwJf+edd/j5z3/OgQMHqKqqQlEUjEYj9957L/fffz933HFHrE4lEigec52HK9YzqmMpmV+XZJk1a9agYwiHW7V+8cUXuffeewddFNqzcNPUmYFdmU5j7ctDLAqtQWf/Fxr0aSzOtjLNahp08eVIF4VOVh2+Dg5fOYw/FGndSdGnsKZgDSadjG0UQohEGlMSXl9fzwsvvMDzzz/PyZMnURQFlUrFrbfeytq1a7nnnnuwWq2xilVMMeN9RvVUMtQ4w+GOMqysrLzu6wU8kX5tV12Qu2+9h2cOPHXdRaFet4t/uec+bp+ZhVnX++tsoMWhubm5I3rP8XC9RauJ0OJp4UjNEQKhyB1Kq8HK6oLVGLTSuiOEEIk24iTc7XZz6NAhnn/+ef70pz8RCoVQFIUbb7yR+++/n5KSEgoKCuIRq0ii4cyUjrVYz6iOpanW2jDUOMPhjjIsLPwkdrv9mjaeUECh5W0v7fUuyI+0SMwsmM2Dn1zHz7ZvRVEUbr+3uP90lB3buPf+h/jELUui1e8BRxrWRkYarlu3jq9//evDWpAaa4FAgM997nM8++yzA8b1ox/9KO53VBpcDRyrPUYoHPnZzTBlsDJ/JTrN1LiTI4QQ482I/xplZ2fj8XhQFCXaF1pSUsKSJUviEZ8YJ4YzUzrWYj2jOpaktaG/4Y4yfOCBB65ZFOqqC9Dylo+gNwz6yDFjuobcm1PYffuPUKeo2Pv0k7z4/R1kOfJprK3G63bxwMPr+MmPf9Sv/eR6awh+tmMbarX6mjUEgUCApqYmFEXBbrfHJRneuHEjP3vuuRHFFSuBQICquirK3eWEiCTg9hQ7K/JWoFHLWh0hhEiWUVXCVSoVM2bM4IMf/CAdHR386Ec/GtFrqFQqdu3aNdJTiylmNDOqxcACgQB/+ctf+Pd//3eamprIysriscce4x/+4R+um3QOd5xhzyjDvdepWveMMuwR8odpOeujq6Z3dKEGDWn6DHLmWdDq1NR0+vjoV7ax/JPr+d9XDtHZ1MCsjFQ2PPJZblpwY78YRruGIBgM0tzcDEQmPMU6CU/22obqtmq87V5UWhWowJHqYKljKWrVmCfUCiGEGINR3ZdVFIWqqir27dvX79hQVCpVtG9ckvCJYSQzpWNtNIldoiTz6zJSbrebRYsWU15+obuFJ48Lbx7hQx/6EEVFczh9+hRm88AtPyMZZ9gzqnDPEKMMIVL9bj7jJeTr/b1hytaStciC1phOIBTmmLONyx2R1pScgums/fwXWZhhpK32CoWzr/2ej9c1BMmMq6KlgvLmcvKJ7Hw5PW06C3MWys6hQggxDow4g3jwwQfjEYcYp0aShMXDzp07+82ozsrNo9FZg8flYt26df0Su0RK9NfF4/HgdDpH1Ra0bNkyLlZe5NNf23ZNK8S+b21h2bJllJWVjTkenU7H7t272bRp06CjDAeqfqt1KjJvMmIpiFSgm9x+jtW14Q709t3nW4wszk0j7PfRxsBrFEa7hqBvf3881j4ka21Dl7+LLn8XGUpkU6v81Hxuyr0pZq8vhBBibEachO/duzcecQgxIJ1Ox1NPPcU999zDq6++SnNzM1lZH+LOO+9kyZIlU2Y8oM/nw+124/P5RpSE/+Uvf+H8+fN8+mvbBm2FePbfN/O3v/2N97///TGJZ7BRhi5nd/Xb31v9NudoyVxoRGtUE1YUypo6eafFFX1cq1axKNvK9O7Rg57u4wNdjIx2DUHf/v54rH1I9NoGRVE423CWS95LoAW9oic/mE9hRuGYX1sIIUTsSFOgGPcyMjK49dZb2b59Oz/96U/51re+xa233irb1g/DN77xDQwm83VbIQwmM1u3bo1bDCFfmIZjHhqOeaIJuFqnwr7URM5KM1qjmk5fkL9UNfVLwDNNOm6bkcWMNPOw2ieKi4txd3Xx2sEXBnw8WWsIEhlXWAlzqu4Ul9ouRY8VpkvyLYQQ49H4b2id4JxOJ06nc8S3+0UvnU43ZSreQ6mpqcHpdA77+VeuXCHLkTfErpMOrly5MqKf0XA4PKznuWoDNL91VfU7V0vWQiMagxpFUbjY5uZsYweh7qeogBuzUpmbkXJN8n29XvzRriHQarVkZmZSVVXF9u3baWpqiukM70StbQgrYY7VHqO+qx6IrMFZnLuYHFMOLbRMiPULQggxlYzot3JzczOZmZkxD6Jn++/JaNeuXWzZsiXZYYhJIj8/H5vNNuznT5s2jb+/cfi6rRDNzlrm3/Ie5s+fP+zXbWtro7q6etDHQ74wTWe8uOuC0WMavYqMm4xY8iMXVN5giON17dS7fNHnpOo1LHekk24c+KJrqF78kSwO7euJJ56I6wzvnTt34vF4oqMW7Y48Gmpr8LojFfCxrm0IhoMcqTlCszsy5UWtUrMsbxm5lsgGRclc1yGEEGJgI2pHmTlzJl/60peora2NyckPHTrEihUr+OEPfxiT1xuPHnnkEY4fP87zzz+f7FDEFPT444/j87iv2wrh83rYvHlzzM7ZVROg+s+ufgl4ikNH/j+kRBPwmk4vf7zU2C8Bn20z8748G/72FgKBwDWvOxw9i0MrKir46le+wj996HYe++pXqaioYPfu3QMm031neP/kbyf5j1+/xu6/nuThTVv42XPPsXHjxlHFcnVczz77LG+//TZf/uKj/ONtH+ArX/4Sb7/9Ns8++2w0rkAgQH19/Yjevz/k5/CVw9EEXKvWsqpgVTQBF0IIMT6plOHMFuy2aNEi3nrrLfR6PXfccQf33Xcfd999NykpKcM+4alTp3jppZd46aWXqKqqQq/X87Of/YxPfOITo3oDE8WJEydYtmwZx48fZ+nSpckOR0wwY5mOMn/+fN69cIGH/t+T17RC7PvWFubOmROT6ShBb5jmt66tfmfebCQlrzvJDIc509BBVbsn+hyDRs2y3DRyLcboIsnCwsKEbA518eJFioqKrpnh3ePV5/aw9+knqaioSMgozJG+f2/QS2l1KZ2+TgB0Gh2rC1ZjM9riHKkQQkxOiczXRtSOcvr0aQ4cOMDmzZv5zW9+wyuvvIJOp+Pmm29m2bJlLFy4kKysLNLT0zEYDLS1tdHa2sqlS5c4duwYx48fp76+HkVR0Gq1rFu3jieffFK2uRdDqqysjI69i2W/7kRhMpmYPXt2v2PD/ZpEfpEs49l/38z+Z75JZq6DZmctPq+HefNu4Pjx42OOp/OKn5a3fYQDvdf0lnwdGTcZ0OgjN9yaPX6OOdtw9Rk9mGcxsCTHhkGbnDXi43W2+HC4/C5Kq0txB9wAGLVGVhesJtWQmuTIhBBCDMeIV+oUFxdz33338Zvf/IZnn32W3/3udxw/fpzjx49fd4JBT8F91qxZPPTQQzz00ENMmzZt9JGLKSEQCPC5z30urv26E81IvyZms5nz58v429/+xtatW2loaODG997CE088MaKxhAMJesM0nfbiaehT/TaoyFxoJCU3EkNYUTjf3MX55q7oc7QqFQtzrMzoHj14tXjM6x5IsmZ4D6bvzPLr6fR1crj6ML5gpJ3HrDOzZtoazLqB34cQQojxZ1TL5TUaDR/96Ef56Ec/SmNjI3/+85954403OHr0KHV1dTQ1NeHz+cjIyCArK4t58+Zxyy238N73vpfly5fH+j2IcSIQCEQX2cYqMe7br3v1RjM/27ENtVrN7t27x3ye68Ueyyp8LL5Go/2avP/97+ePf/xjzGLrvOyn5dz1q9+d/iDHnG20ent7nDOMOlY4bKToB//1E4953QNJ9AzvofSdWT6YVk8rb9a8SSAU+ZqmGlJZXbAao9YY9/iEEELEzphnVtntdj7xiU9M+p5uMbRgMEhjYyNWqzUmSfjFixfZs2fPNf26fTea2fP0k2zatGnMrSkDxR4IBNi4cSN79uy5puLcM2ljpO9zrF+jeH5Nhhtb0NNd/W4cvPqtKAqV7W7eaugk1H0XTAXMz7IwN8OCepxsm15cXMzmzZt57eALA/aEJ2u2+GCa3E0cqTlCKBypmNuMNlYVrEKv0Sc5MiGEECMlm/WIcWs4/bpmi4X9+/cP+hqjmTbRY+PGjezdt2/AqRl79+0b1dSMYDDY7/9HKhZfk7HovOyn5i+ufgm4pUBH/gcs0QTcGwxxuKaVU/Ud0QTcotNw6/RMbshMvW4Cfr054PHQM8P7Z9u38upze/B5Iv3VXrebV5/bE7MZ3sN1vfdf11XHm9VvRhPwLHMWa6atkQRcCCEmKNm9QcRcrPp5Y9GvGwqFcLvduFyu68Z0dS9uvCrOPeepr6+npaVl2J/XI549zNfrRw64wzSf6V/91hrVZC40Ys7p/TVS2+XlZF07vlDvZj6z0szcnJ2KVj30Nf9Qc8DjYbSzxeNhsPd/pf0Kp+tPR9fW5FpyWZa3DLVK6ihCCDFRSRIuYi5W/byx6Nft6bEdKqare3HjPTUjKytrRJvu9IhnD/NA/ciKotBZFaC1zEc42Nv7nTpdR/qNRjS6SFU72D168NJVoweX5qbhsIzvXuWe2eKbNm2K9v7n5uZSXFw8LibwVLZWcrbhbPTjAmsBi3MXX3chvBBCiPFvzEn4Bz/4wRE932AwYLPZuOGGG7j99tu55ZZbxhqCmKRi2a87VHW+pwrc87x4VZxHuwlNj0T2MAfcYZpPe/E0XVX9XmTEnN37q6PF4+foVaMHHRYDS3LSMGrjP+EkVmbNmjXuxhC+2/wu7zS9E/14VvosFtgXSAIuhBCTwJiT8L/85S/R/1apVAy298/Vj6lUKrZu3cqKFSt47rnnmDt37lhDEUkW637enn7dvdu3oijKNRvNjKRfd6hK+NWTQeJVce7s7KSqqmrU7Tqx/Jpcref7p9Fo6Kj001LmQwn1qX7P0JNxowG1NpIAhhWFd7pHD/Y8S6NSsTDbysy0gUcPiuFRFIVzjee42Hoxemxu5lzmZc1LYlRCCCFiaczZ0t69e2lubuYb3/gGHR0d3Hbbbbz3ve8lNzeyZXJdXR2vv/46r732Gjabja997WtotVqOHz/OoUOHOHLkCLfffjunTp0iIyNjzG9IJE88+nl37txJOBzm2e5+3SxHPo3d/brr1q2LWb/u1bHHq+Lcc4EylguVeH1NdDodGRY7Tce8eJv7VL9NarIWGTHZe2Pu8gc5etXowfTu0YOW64weFENTFIXT9ae50n4lemxB9gJmp8++zmcJIYSYaEa0bf1AmpubWblyJaFQiF/96lcsXrx4wOedPn2aj3zkIxgMBkpLS7HZbFy+fJkPfvCDVFZW8thjj7Ft27axhDIqHo+Hp59+mhdffJHLly+TkZHBHXfcwbZt28jPz4/ZeWTb+rHpO6t7JP26Y5nLvWHDBvbu28cD//bEgBXnhx96aMQzymM5S320X5OBKIpCR2WA1vP9q9/WmXrS5/dWvxVF4VK7hzMNHf1GD96QaWFe5vgZPThRhZUwJ5wncHY6gcgdw4U5C5meNj3JkQkhxNSQyHxtzEn4//k//4f//M//5E9/+hO33nrrdZ/717/+lQ984AP83//7f3nmmWcA+O1vf8tdd93FwoULOXXq1FhCGTGv18sHPvABSktLcTgcvO997+PSpUscOXIEu91OaWnpNVuFj5Yk4RPP1TtTXl1xniy7dQZcYZpOefC29PZ0a83d1e+s3qq2NxjiZH07zi5f9FiKTsMKh40Mk4zJG6tgOMix2mM0uhoBUKvULHUsxZHqSHJkQggxdUyoJHzmzJm0tLTQ0dExrOenpqaSlZVFZWUlEJmXnJKSgtFopL29fSyhjNjjjz/OU089xZo1a/j973+PxWIB4Dvf+Q5f+tKXuPXWW/v1vI+FJOETVywrzuOJoih0XPTTet6PEu5T/Z6lJ/2G3uo3gLPLy4mrRg/OTDOzcJijB8X1BUIB3qx5k1ZPKwAatYYVeSuwp9iTHJkQQkwtEyoJN5lMaDQaurq6hnyuoihYLBbC4TAeT+8os8zMTLxeLy6XayyhjIjf7yc7O5v29nZOnDjBkiVL+j2+aNEizpw5w7Fjx1i2bNmYzydJuBhP/F0hmk558bX2Vr91KZHqtzGzt/odDId5q6GTynZ39JhBo2ZJbhp543z04EThC/oorS6lwxcpZOg0OlbmryTDJGtkhBAi0RKZr425hOVwOPB4PPz3f//3kM999dVX8Xg8OBy9t1e9Xi+tra1kZ2ePNZQRef3112lvb6ewsPCaBBzgnnvuAeCVV15JaFxCxJOiKLSV+6j9q7tfAm6drSfv1pR+CXiLx8+fLjX1S8BzUwzcNjNLEvAYcQfcvH7l9WgCbtAaeM+090gCLoQQU8CYk/CPf/zjKIrCww8/zO9+97tBn/f73/+ehx9+GJVKFU1wIbJgE2DOnDljDWVEes472FVOz/EzZ84kLCYx9QQCATwezzX/G+s88YH4O0M4/+6mtcwXbT/Rpahx3GImc4ERtaZ39OD55k7+ermZru7Z3xoVLM6xsiY/fULN/h7PuvxdvH75dVz+yB1Ak87ELdNuwWqwJjkyIYQQiTDmWWKbN2/m1Vdf5fz589x5553Mnz+fW265JVrtdjqdvPHGG5w7dw5FUbjhhht44oknop//05/+FIA77rhjrKGMyOXLlwEoKCgY8PGe4yPd+luIkWhpaaGxsfGa43a7PWbjHhVFob3cT9u7/Xu/0wr12OYZosk3REYPHnO20dJn9KCte/RgqowejJk2bxtvVr+JP+QHwKK3sGbaGoxaucMghBBTxZj/qqampvK///u/fO5zn+PgwYOcO3eOsrKyfs/paTv/+Mc/zo9+9CNSU1Ojjz322GN86UtfYtq0aWMNZUR6etjN5oF3RExJSQEim6uMhNPpxOl0XnP86q+JEAAZGRlYrZHKp9PpjF68xmrDI39Hd+93e5/eb4uarMUmjOm9FW1FUajq8HCmvoNgn2UiN2RauEFGD8ZUs7uZIzVHCIYjs9jTjGmsLliNXiMTZoQQYiqJyV/6zMxMfvGLX3DhwgV+/etfc/r0aZqamgDIyspi0aJF3H333QPuijlz5sxYhDBu7Nq1iy1btiQ7DDFB6HQ6qqur2b9/P+Xl5RQVFVFSUjLm6StKuLv6faG3+q1SgbVQT/o8Ayp1b1LtC4Y5Ud92zejB5Q4bmTJ6MKbqu+o5VnuMsBKZMpNpzmRF3gp0mok/6lIIIcTIxPT+8pw5c/jyl78cy5eMm55xhG63e8DHeya19K3aD8cjjzzC3Xfffc3xsrIy1q5dO8IoxWQWCATYuHEje/bswWyxYHcUcPDQITZv3sz69evZuXPnqOaQ+ztCNJ704u/orX7rUyPVb4Otfz93XZeX41eNHpyRZmJhthWdjB6MqZqOGk7WnYzeGcxOyWZ53nI0aumxF0KIqWjKNnlOnx7Zga66unrAx3uOz5gxY0Sv63A4+k1/EZNLLHe83LhxI3v37WPdY1u57Z5P9duRc++OyO6xI9mRUwkrtF3w037BR09HiaINoZnuwV6Uhd7Qm+wFwwpnGzu42NZ7EarXqFiaYyMvVfqSY62qrYoz9b2LvPOt+SzOXYxaJRc6QggxVcUlCVcUhba2NgBsNhuqcdhPumjRIiAyD3IgPccXLlyYsJjE+BcMBmlsbMRqtY4pCb948SJ79uxh3WNbufP+T0ePG81m7npgPQB7nn6STZs2Das1xdce6f2+uvptWaDmSn0N2eH06PFWb4BjzlY6/b3PzUkxsDQ3DZNMPom58pZyyhp714TMsM3g5uybh/V7MRAIUF5ezosvvkhDQwPZ2dl88pOfpKioaFLs1iqEEFNZzMowHR0dfOtb32LFihUYjUaysrLIysrCaDSyYsUKtm/fPuxdNRPhlltuIS0tjYqKCk6dOnXN4wcPHgTgn//5nxMcmZgKDhw4gNli4bZ7PjXg47ffW4zZYmH//v3XfR0lrNB63ofzf13RBFylAttcA3nvT0Fv6b/48p3mLv5S1RRNwNUqWJRt5T356ZKAx0FZY1m/BLwoo4iFOQuHnYCvW7eOBQsW8O3vPMNv//QXvv2dZ1iwYAHr1q2LyxhLIYQQiROTSvixY8f46Ec/Sm1tLVdvwBkIBDh+/DgnTpzghz/8IYcOHWL58uWxOO2Y6PV6Pv/5z/PUU0+xceNGfv/730cnonznO9/hzJkz3HrrrTHZLVMkTqIqh06nE41m9ElreXk5dkcBBtPA03kMJjNZjnzKy8sHHZMZDij420OEg0D37uZqnQq9VU2HTkXHFQiFIsm2JxDiSGMzzZ4+owcNWlY40kk1TNmutJjq26qk1Wp5q+Etqtp6v3fz7fMpyiga9utt3LiRF196acB2ped2bMNkMo2oXWk4cUt1XQghEmfMf33r6ur48Ic/TGtrKykpKXzmM5/hAx/4QLSXuqqqij/96U/s2bOH6upq/vEf/5GzZ8/GbAbyWDz++OP88Y9/5I033mDOnDm8733vo6qqijfffBO73c6zzz6b7BDFCPRUDvfv348pxYI9L5/G2hq2bdtGSUkJzz77bMySDIfDgclkGvXnFxUVcfDQIXwe94CJuNftprG2mqKikmvWJShhhdZ3fHRU+NF3X/P2VL/TivT9Jp+43W4uXrzIEWcbbnovGuZlpDA/K1VGD8ZQT6uSJdXCmcYz1HbWRh9bmLOQGbbhry+JdbvScOIea4uVEEKIkRlzO8q3vvUtWltbWbp0KRcvXuQ73/kO//zP/8zChQtZuHAh//zP/8wzzzxDRUUFixcvpqWlhW9961uxiH3MjEYjf/7zn3niiScwm8386le/oqqqioceeogTJ04we/bsZIc4JQQCAerr6we9vT7cXSX7Vg73/O9JvvObP/GTv53k4U1bePGll9i4ceOY42xpaRnTa/QoLi7G3dXFawdfGPDx1w4ewONyUVJS0u+4tzVEzV9dtJf7o4svDWka8t6fgm3uVaMHQ2HONkbm3Ie6n2zWaXj/tEwW2K2SgMfJuYZz0QRcpVKx1LF0RAk4xK5dSQghxPg15kr4//zP/6BSqXj22Wex2+2DPi87O5tnn32WpUuX8uqrr/Kd73xnrKeOCZPJxNatW9m6dWuyQ5myhqrEtbS0cPLkSV599VWam5vJzMzkrrvuYsmSJdE7KomoHAaDQVpbW7HZbGPeTGf27NmsX7+evdu3oigKt99bfE27wfr166OxhkMKbe/46LjYm3yr1Cpsc/WkFfavfgPUu3wcd7YRCAaxqU0EUTPdamJRthWdRiZyxEMwFNl8hy7IVmWjQoXNaCPYGqSqdWQ778aiXWm4elqWhBBCJNaYk/ArV65gtVqHNUVk8eLFWK1Wrly5MtbTiikiEAjwta99jWeffRazxUKWI58mZw07d+5k3bp1/OhHP0Kn0w2rcvjSD3awf/9+Hn/88THFlJmZGZPb9jt37gQiFwcv/WAHWY58Gmur8bhc0TnhAN6WIE2nvARcvbO8DTYNWYuN6FP796WHukcPVvSMHlSp6dSlsDw3jfzU0bfPiOvzh/ycaThDGmm0aFsIa8KszF9JpjlzVK83lnalkfJ4PFRUVIzpNYQQQozcmJNwo9GIx+MhFAoNuVAtGAzi8/kwGmUO8WQ01gVeAy123LRpE788eHDAxWk/27ENt9vN008/PebKYTgcJhAIoNPpUA+ySU2sK4Y6nY6dO3fy6KOP8uKLL1JZWcns2fdx3333UVRUhEatpfltLx0X/dHPUalV2OZ1V7+vaidp8wY46myj0x+MHss261nmsCV18slkH7PnCXgorS7F7/eTRho6tY5l05aRZkwb9WsWFxezefNmXjv4Qr87Oz0Ga1cSQggxcYw5CV+wYAFvvPEGL7zwwpA7Qr700kv4fD6ZODJJjXWB19WLHS9evMiLL7543RaTvU8/yVNPPTXmymFPNXDatGmDLriMR8WwpaWFUCjEvffeGz0WCoVoqGlCuZjav/qd3l39tvRPqBVF4d0WF+eaOumZTaRWwc12K7Nt5qTO6U/kYtlkcPldHK4+jCfgQYOGTm0nSwuWjikBh5G3K42FVqvFbrePucVKCCHEyIz5t+7atWt5/fXX+dznPodWq+WTn/zkNc8Jh8M8//zzfP7zn0elUvHAAw+M9bRiChhJi8lErRxmZGRgtVqjH4dDCp2X/LjOKai7t5JXqVWk36DHOvva6rc7EOSYs50mT2+1PM2gZYXDhtUQn+R2JHc8xjpmbzyPz+vwdVBaXYov6APAaDCyuGAxJl1s2n527txJOBzm2Z52pdw8Gp01eFwu1q1bF21XGiudTjcuplUJIcRUM+Yk/DOf+Qy/+MUv+POf/0xJSQmPP/44//AP/0B+fj4+n4/Lly/z+uuvU11djaIofPCDH2T9+vWxiF2MUyOdoR0Oh9Hr9dTV1fVrBRlJi4lGo+GTn/wkP7tO5fCTn/wkarV6wHaU4bSaxKNiqNPposmlpylIy2kvQbc6OrbImKEhc9HA1e8rHR5ONXQQDPfO5p+bkcKNcR49ONw7HrFYLDtex+e1eFo4UnOEQCgyocdqsLK6YDUGrSFm59DpdDz11FPcc8890UXJWVkf4s4772TJkiXj6ushhBBi5MacTajVal555RW+8IUv8Oyzz3Lx4kUqKyujj/ds3qNWq1m/fj3PPPPMoD23YnIY6wztHiNtMfnZz36GRqNh79NP8uL3d2B35NFQW4PX7Rqy9WE4rSbxqhiGgwqtZT46LvXp/daoSL/BgHWW7prqtz8U5mR9OzWd3ugxk1bDCkcaWebYJYFjlcjFsonU6GrkaO1RQuHIhVu6KZ1V+avQaWKfFGdkZHDrrbdy66239jsurSNCCDHxxeQ3udlsZvfu3Tz22GO8/PLLnDhxgqamJgDsdjtLlizhYx/72JhX8YupZaQtJjqdjmeffZbHHnssuggwJycnutBxPFYOPY1Bmk57CXp6e7+NGRqyFpvQpVx7sdrg8nGsrg1vsPf5yRo9ONQdj1iM2Rtv4/OcnU5OOE8QViJff3uKnRV5K9Co47Pwte+dEiGEEJNLTMspM///7d13eFzlmT7+e/poNBr1MiqWi9ybJFvFdgg2IYGlmBKICTiQBWcdQgtkk12SrMFkN+WbsLsJIazBiR1aGjghQMKyAZwf2JYtS3KX5aZiSaPep5f394c8ozbqM3M0o/tzXb4un3NmznmOzpH06Jn3PO/cuXj88ccDuUsKI4EerjGVh9NUKhWWLl2KnTt3Shr7eDwugY4zdvTWDq1+JyzVIGbuyOq32yNwuq0XFzrNvnUquQx5qbHINEjTenC8TzwC0WZvJrXPq+uuw4nmE75P94wxRuQb8yGX8ZM9IiKaPH6mSQETjOEazz//PKxW66hDTMLx4TS/1e9EJZJytVDpRiZ0XTYnjpq60DO89WBaHKJU0rUeHE+4Pizrz8WOizjTesa3PCd2DlalrpK08wwREYW3SSXhL7/8csAOzA4pNBHhOMRkNG6nQOcZG3rrnL51MoUMCcs0iMkeWf0WQuB8Z3/rQe+zl3IZsCLJgAXx0rUenOinBoFoszcT2uedbTuL8+3nfcvz4+djecpyyeIhIqLIIBPez1YnQC6XB+wX/0wb6xls5eXlWLNmDcrKypCfny91OBRilhYX2o/b4LINVL+jkpRIXO2/+m1xulHW1IVWy9DWg2uNcYgNUuvBYHA6nXjwwQcHZjwd1mbPO+PpTCSEwKmWU6jpqvGtW5K0BAsTF0oXFBERBVUo87VJlZc+/elP8+PXSTKZTDCZTKisrJQ6FJKA2ynQcdqGvssD1W+5Uob4ZRoYstV+33O5x4pjzd1wDmo9uDC+v/WgQh5e33/h2mbPIzw43nQc9T31vnUrUlZgXvz0J8chIiICJpmE79+/P0hhRK5du3ZN+iFBigyWZhfaTwyrficrkbRaC2XUyOq3w+3BseZu1A9pPSjHWmMckifYenAmThEfbm32PMKDssYyNPU1AQBkMhly03KRaciUODIiIookM/O3YATZvn07Nm/ejMrKSmzdulXqcCgE3A4POk7Z0dcwtPqdsFyDmDn+q9+tFjuOmrpgHdR6MCtGi9WpsVBPsPXgTJ0iPpza7Lk8LpQ2lKLN0t9iVS6TY036GqTp0ySOjIiIIg2T8CAzGo0wGo1Sh0EhYm5yov2EDW77wFCSqJQr1W/tyGTa7RE409aL81daDyqEBwnChjlpKciOj5nUsac7Rbw/M3na+EBzuB04XH8YXbYuAIBCrkBhRiGSdEnSBkZERBGJSThRAPitfquuVL+z/Fe/u+39rQe77QOtB5O1Chj6rEjRTu5bMxBTxPszU6eNDzSby4aS+hL02nsBACqFCsWZxYjTxkkbGBERRSzOMkE0TWaTEw0fmYck4LpUJTI2RvtNwIUQON/Rh49q23wJuFwGrEyOQW6KYUoxTGSKeJ1ej9dee21K+49kZocZB+oO+BJwrVKLDVkbmIATEVFQsRJONEVuuwftp+wwNw6tfieu0EKf6b9q7K/1oEGtRIExDrFaFaxWK4Dxp4QfLhBTxPsT6a1Ee+29OFR/CHaXHQCgU+mwLmsddCr/X0ciIqJAYRJONAXmRifaT9rgdgyM/dalKZG0SguFxv8HTPU9VlQMaz2YEx+N5X5aD443JfxwgZgi3p+ZNG18oHVaO3G44TCc7v4/omI0MSjOLIZWqZU4MiIimg04HIVoEtx2D5pLLWgps/oScIVahuT8KKQW6Pwm4E63B6WmLhwxdfkScK1Sjk9lJmBViiEgvb/vvvtuWPr68MEbv/G7PZymiA+FNksbDtUf8iXgcdo4rM9azwSciIhChpVwognqa+ivfnucA5XsaKMKiSs1o1a/2yx2lJq6YXUNDOvIiNEib5TWg1Odpj0QU8T7MxOmjQ+0pr4mlDWWwSP620Em6ZJQkFEApTxyzpGIiGY+/tYhGofL5kH7SRssTQNdTBRqGRJXahGd7n/st9sjUNnei3MdZt86pVyG3BQDsgxRo848q1KpkJqaOqU4n3/+eVitVuz5wVP47c9+jGRjOloaG2Cz9FfAn3/++UnvczrxzET1PfU41nQMQvT/IZWmT8Oa9DWQy/ihIBERhRaTcKIx9NU70X5qWPU7/Ur1W+0/ceuxO1E6rPVgUpQaa42x0KmC9y2nUqnwq1/9Ct/+9rd9M2ampqZiy5Ytks6YOVNUd1bjVMsp33KmIRO5abmj/kFEREQUTEzCifxw2TxoO26DtWVQ9Vtzpfpt9J/MCiFwscuCU6098D57KQOwPCkGCxOiQ5LsqVQqLF26FDt37gz6scLJufZzqGqr8i3Pi5+H5cnLmYATEZFkmIQTDdN72YGO0/Yh1W99hgoJK0avfltdbpSZutAyqPVgzJXWg3Ha0SvQTqcTLpdrxHqlUjnrK9eBcrrlNC51XvItL0pchMVJiyWMiIiIiEk4kY/LeqX63Tqs+r1Ki+i00RPihl4rKpq64RjUenBBvA4rksbvfNLR0YHW1tYR65OTkyNqLLYUhBA43nwcl7sv+9YtT1mO+fHzJYyKiIioH5NwIgC9dVeq365B1e9MFRJWaKFQ+U+knW4Pjrf0oK7H6lunVcqxJi0OqdGaCR03ISEBBkP/LJkmkwlGoxEAIqobiRQ8woNyUzlMvSYAgEwmw6rUVZgTO0fiyIiIiPrxNz3Nav6q30qtHImrtNCljv7t0WZx4GhTFyzOQa0H9VrkpsVC46f14GhUKpVv2IlCoZjUBD3kn9vjRmljKVrN/Z8wyGVy5BvzYYwxShwZERHRACbhNCsJIdBb60Rn5dDqd8wcFeKXjV799giByrZeVA1rPbg6xYA5Y7QepNBwup043HAYndZOAIBCrkBBegGSo5MljoyIiGgoJuE06zgtHrQft8HaNqz6vVoLXcro3xI9dieOmrrQNaj1YGKUCmvT4hCt5reS1OwuO0rqS9Bj7wEAqBQqFGYUIiEqQeLIiIiIRmLmQLOGEAK9NU50VNoh3BOvfgshcKnLgpPDWg8uS4rBogC0HqyursZrr72GCxcuICcnB/fcc8+kZ7ac7axOKw7VH4LZ0f8JhUapQXFmMQwag8SRERER+ccknGYFp7l/7LetfVD1O0qOpNVaRCWP/m1gdblR3tSNZrPdty5GrcBaYzzix2g9OKGYnE489NBD2L17N3R6PZKNmXhj3z7s2LED27Ztw/PPP882hRPQ5+jDocuHYHPZAABRqiisy1yHaHW0xJERERGNjkl4kJlMJphMJlRWVkodyqwkhEBPtROdZ4dVv7PVSFimgVw5ehW7odeGiuYuOAa9b36cDiuSDVD6aT3odDrR0dGBhISECSXPDz30EPbs3Yv7v/0MPnPHF6GJ0sFmseCDN17Hnh9/DwDw4osvjrmPyR4z0nTbulFSXwKHu78/u16tR3FmMaJUfMCViIhmNibhQbZr1y7OXigRp9mDtmNW2DoGOpgodVeq30mj3/pOjwcnWnpQ2z3QelCjkGONMRZp0dpR3+dyudDa2gqDwTBuQnzp0iXs3r0b93/7GdzwpQd867U6HW68dxsAYPcPnsKTTz455tCUyRwz0rRb2nGk4Qhcnv5PN2K1sSjOLIZaoZY4MiIiovFNvJcaTcn27dtRVlaGV199VepQZg0hBLov2tGw3zwkATfMUyPj6ugxE/B2qwMf1rQNScDT9RpcOzd5zAR8sl5//XXo9Hp85o4v+t1+7Z13Q6fX47XXXgvYMSNJc18zSupLfAl4QlQC1mWuYwJORERhg5XwIDMajb4JWCj4HH1utB2zwd45kHyrovur39rE0W93jxA4296Hs+19vnVKmQyrUg3InmTrQZPJBIVCMeZrLly4gGRjJjRROr/bNVE6JBkzcOHCBdTW1o66H7fbPeq2SNXQ04CKpgoI0T9MKCU6BWvT10IhH/trTkRENJMwCaeI0F/9dqCrygExaPp4w3w14pdoIFeMnkT3Olw4aupCp83pW5egVaHAOLXWg0ajcdxJd3JycvDGvn2wWy1+E3GbxYLWxnrk5NyD7OzsUfdjtVpx8eLFSccYrmq7anGi+YRvOcOQgdy0XMhl/FCPiIjCC39zUdhz9Lph+sSCzkq7LwFXRcth3KBD4nLtqAl4f+tBMz6safUl4P2tB/X49JzEoPb+vvvuu2Hp68MHb/zG7/YP3ngdVrMZ99xzT9BiCDcXOi4MScCz47KRl5bHBJyIiMISK+EUtoQQ6L7gQNe5odXv2AVqxC0eu/ptu9J6sGlQ60G9SoG1xjgkRE1tXLFSqURycjKUyvG/rebPn49t27Zhz/97BkIIXHvn3UO6o7z84+9h27Zt4/YLn8wxw1llayUudFzwLeck5GBp8lIJIyIiIpqeyP7NTRHL0XNl7Hf3oLHfejmScqOgjR97bHBjnw0VTd2wuz2+dfPidFiZHAOlfGJVVZPJhPLy8nFfl5+fP+ozAc8//zyA/i4ov3vux0gyZqC1sR5Ws9nXJ3w8KpUKqampE4o5HAkhcLLlJGq7BsbFL01eipyEHAmjIiIimj4m4RRWhOdK9fv8QPVbJgMMC9SIWzR29dt1pfVgzbDWg/lpsTDqJ9f5pK6uDkePHoVer/fbGtDpdKKvrw9JSUmjJuEqlQovvvginnzySd+MmQsXbsXdd9/NGTMBeIQHFaYKNPY2+tatSl2F7LjRx8gTERGFCybhFDYcPW60Vtjg6Bmofqtj+qvfmrixq98dVgdKTV0wOwfea9RrkJ8aC41y8l01cnNzsX//fqSkpCAvL2/E9oqKCrjdbuTm5o67r3nz5uG73/0uamtrx3wIczZxe9w42ngULeYWAIBMJkNeWh4yDBkSR0ZERBQYTMJpxhMega7zDnSft+NKVzrIZEBsjgZxi9SQ+Zm90svberCqvQ/eUeMKmQyrUgyYGzu51oODaTQarF+/Hvv378fixYuh0w10OLFYLKipqcGmTZug0WimtP/ZzOl24kjDEXRYOwAAcpkca9PXIlUfucNuiIho9mESTjOavbt/7PeI6ndeFDSxY1ew+xwulA5rPRh/pfWgPgCdTwoLC3Hw4EFUVVUNqYZXVVVBo9GgoKBg3H04nU64XP0Tzrjdblit/UNllErlrJsBEwAcbgdK6kvQbesGACjlShRmFCJRlyhxZERERIHFJJxmJOER6DrnQPeFYdXvhRrELRy7+i2EQE23FSdaeuC+8mYZgCWJeixO1EM+xer3cN5q+B//+EccOHAAPT09MBgM0Ol0uP322ydUBe/o6EBra6tv2dvzOzk5OaIfuPTH6rSipL4EfY7+CZPUCjWKM4sRq42VODIiIqLAYxJOM469y422Y1Y4ege6l6gNCiTlasetfttcblQ0d8PUN9B6MFqlQME0Wg+Oxul04te//jV+9atfQauLRkpGJloa6mGzmNHT04OioqJxq9kJCQkwGAwj1kd6y8HhzA4zDtUfgtXZ/0mAVqnFuqx10Kv1EkdGREQUHLPrNz3NaMIj0FllR89Fx5Dqd9wiDWJzxq5+A4Cpz4byYa0H58bqsCxei+6uTjiVCQEd4vHQQw/h1y+/jPu//Qw+c8cXh/T5/vWPvwe5XI4XX3xxzH2oVKoJx+R0OtHR0YGEhMCeh9R67D0oqS+B3dX/h1O0OhrFmcXQqUbOJEpERBQpmITTjGDr7K9+O/sGEmhNbH/1W20Yu/rt8nhwsqUX1d2Wgfcq5MhLi0W6Xgur1YrW1lYYDIaAJa+XLl3C7t27cf+3n8ENX3rAt16r0+HGe7cB6O///eSTTwas3aDL5Qr4eUitw9qBIw1H4HT3j9s3aAwoziyGRskHWomIKLJxvmeSlMct0HHGhqYDZl8CLpPLEL9EA+OndOMm4B1WBz6saRuSgKdFa/CZuUlIn2Tv78l4/fXXodPr8Zk7vuh3+7V33g2dXo/XXnstaDGEu1ZzK0rqS3wJeHxUPNZnrWcCTkREswIr4SQZW4cLbcdscJoHVb/jrlS/Y8ZOvj1CoKq9D2eHtR5cmRKDebE6v60HTSYTFIrJ9wT358KFC0g2ZkIT5X/IhCZKhyRjBi5cuIDa2lq/r5kst9s9/ovChKnXhHJTOTyi/9onRyejIL0ACnlgrg8REdFMxyScQs7jFug8a0fPJYdvnUwuQ9xiNWIXqMft3d3ncOGoqQsdw1oPrjXGIWaM1oNGoxFRUVHTPwEAOTk5eGPfPtitFr+JuM1iQWtjPXJy7gnYBDxWq9XXPSWc1XXX4UTzCYgrA/+NMUbkG/Mhl/GDOSIimj2YhFNI2dpdaDs+rPodf6X6rR9aBTWZTCgvLx+yrtfhQrvFAc+gdfn5+Vg3Z37AWg9OxN13340dO3bggzd+M2RMuNcHb7wOq9mMe+65J2Qx+ePva+hPfn4+jEZj0OO51HkJp1tO+5azYrOwOnX1lCdNGs1MO28iIqLhmIRTSHjcAp2VdvRUD61+xy9RwzDff/W7rq4OR48ehV6vh0qlghDCN/QEAFxOJ8xmM5Q5cyCXLRj12EqlEsnJyQFt+zd//nxs27YNe/7fMxBC4No77x7SHeXlH38P27ZtC9hDmcDUzmP413A4p9OJvr4+JCUlBT0ZPdt2Fufbz/uW58fPx7LkZQFPwIGZdd5ERET+MAkPMpPJBJPJhMrKSqlDkYyt3YXWYza4LAP1a22CAomrR1a/B8vNzcX+/fuRkpIyZEZKr4qKCphMJuTm5o55fJVKFZSJb55//nkA/V1Qfvfcj5FkzEBrYz2sZjO2bdvm2x4oUzmPiXwN3W73uF/D6RBC4HTraVR3VvvWLU5ajEWJi4J2zJlw3kRERGNhEh5ku3btws6dO6UOQxIe15Xqd82g6reiv/OJYZ5q3ApoS1s7tIY4VFdXY/HixdDpBsZeWywWVFdXIz09HR0dHZJUM1UqFV588UU8+eSTeO2113DhwgUsXLgVd999N/r6+vDSSy+Nu4+rrroKK1euDFqM3lk99+/f7/drWFNTg02bNkGj0eDkyZP4+OOPAxqzR3hwvOk46nvqfetWpKzAvPjAfULgz2TOm4iISAp8EirItm/fjrKyMrz66qtShxJS1jYXGvabhyTg2gQFMq6ORuwow08G67Q58eHxSnS1NAEAqqqqhmz3Ljc0NKCuri7A0U+c0+lEWloavvGNb2DHjh144oknkJaWhtOnT6O1tRVmsxl2u33EP7PZjNbW1pB8QlJYWAi1Wu33a6jRaFBQUAAAqKysDGjMHuFBWWOZLwGXyWTIM+YFPQH3muh5ExERSYGV8CAzGo2zasypxyXQccaO3tqh1e+EpRrEzB2/+i2EQFWHGZVtvdDNyYHieCkM0TrU1NT4KpreSmZ8fDysVqukQwo6OjrQ2trqW/Z2LyksLMS5c+eQlpY26nCI6upqXH/99UGP0V9V2F81+Prrr8fZs2cDErPL40JpQynaLG0AALlMjjXpa5CmTwvsyY1houdNREQkBVbCKWCsrf3V78EJuDZRiYyN0TDMG7/6bXa48P9dbseZtl4IAAqVGlkr8tDV1QWFQuGraFZVVUGhUKCzsxPr16+XNJlKSEjAggULRvzLysrCvHnzUF1dDYvFMuQ93qE08+fPh8FgCEmcw6vC/qrBBoMhIDE73A4cunzIl4Ar5AoUZRaFNAH3msh5ExERSYFJOE2b2ynQdtyKphILXNYrs14qZEhcqUXauiiodGPfZkII1HZb8EFtG9qtA72/Fyfo8YXPXg2NRgO9Xo+amhq0t7ejpqYGMTEx0Gq1kidTKpUKUVFRI/6pVCrccsstAEYfSrN58+aQxemtCg/+Gvr7A2a6MdtcNhy8fBBdti4AgEqhwrrMdUjSJQXoTCZnoudNREQUakzCaVosLS407jejt24geY5KulL9njt+9dvu9uBwYxfKmrrh8vQ3INSpFLg6KxHLk2MQpdVi/fr1vmr4J598MmOq4OPxV1mWogru5a0Kf/LJJ6NWg6cTs8VpwYG6A+i19wIAtEotNmRtQHxUfHBOaIImct5EREShxiScpsTtFGg9ZkXzYQtctv7qt1wpQ+IqLdLW6catfgNAs9mOD6pb0dhn862bY4jCZ7KTkKhT+9YVFhb6quEul2vGVMEnYnhlWYoquJe3Kuxyucb8A2YqMffae3Gg7gAszv7EXafSYX3WesRoYgJ5ClMy0fMmIiIKJT6YSZNmaXah/YTNl3wDQFSyEkmrtVBGjZ98uz0Cp1p7cLFrYNyxWi5DXlosMmJGTivvTaI++ugj5OTk4OLFi7jmmmvCIpkaXFmeM2eOZFVwr6KiIqhUKqxZs2bU10w25k5rJw43HIbT3f9pSIwmBsWZxdAqtUE5h6mYyHkTERGFEivhNGFup0BrhRXNR4ZWv5NWa5FWrJtQAt5lc+LD2rYhCXiKTo3PzEv2m4B7eavhNTU1YVMF9/JWlj/55BMA0lTBvdRqNYqLi/3OIjnYRGNus7ThUP0hXwIep43D+qz1MyoBByZ+3kRERKHCJJwmxNzkRMNHfeirHzT2O0WJjE3RiJmjHuOd/YQQqGrvw0e1beh1uAAAchmwOsWADZkJiFKOPnMmEN5DCryVZZfLJWkVfDImEnNTXxMO1x+G2+MGACTpkrAuax3UivHvByIiotmOw1FoTG6HBx2n7OhrGEi+5SoZEpZrEJM1sWTL7HThqKkb7daB1oWxGiUKjHEwaCZemQznIQW33XYb3n33Xdx8881ShzJhY8Vc31OPY03HIET/w7Rp+jSsSV8DuYx/1xMREU0Ek3AaldnkRPsJG9wO4VunS1UicZUWSu34yZYQApd7rDjW0uPrfAIAixKisSwpBvJxOqcM5x1SEI70ej22bNkidRiTMlrM1Z3VONVyyrecacjE6rTVTMCJiIgmgUk4jeC2e9B+yg5z49Dqd+IKLfSZE6tcO9weVDR3o6F3oPOJTqnAWmMsknThM5SEhjrXfg5VbQM9xOfGzcWKlBXjtqIkIiKioZiE0xDmRifaTw6rfqcpkbRKC4VmYpXOFrMdR5u6YHMNdE+ZY4jC6hQDVApWS8PV6ZbTuNR5ybe8MHEhliQtkTAiIiKi8MUknABcqX6ftMNsGqh+K9QyJKzQQp8xseq32yNwuq0XFzrNvnUquQx5qbHINIze+YRmNiEEjjcfx+Xuy751y1OWY378fAmjIiIiCm9Mwgl9Df3Vb49zoPodbVQhcaVmwtXvLpsTR01d6LnS+QTobz24Ji0OUaqxO5/QzOURHpSbymHqNQEAZDIZVqWuwpzYORJHRkREFN6YhM9iLpsH7SdtsDQNJM4KtQyJK7WITp9Y9VsIgfOdZpxp64X32Uu5DFiRZMCCeB3HCocxt8eN0sZStJpbAQBymRz5xnwYY4wSR0ZERBT+mITPUn31TrSfGlb9Tr9S/VZPrPptcbpx1NSFtmGtB9ca4xA7idaDNPM43U4caTiCDmsHAEAhV6AgvQDJ0ckSR0ZERBQZmITPMi6bB+0nbLA0D6p+a65Uv40TT5wv91hxrLkbzkGtBxfG97ceVMhZ/Q5ndpcdJfUl6LH3AABUChUKMwqREJUgcWRERESRg0n4LNJ72YGO0/Yh1W99hgoJKyZe/Xa4PTjW3I36Qa0Ho5RyrDXGIZmtB8Oe1WnFofpDMDv6H67VKDUoziyGQTPzZ/kkIiIKJ0zCg8xkMsFkMqGyslKyGFxWD9qO22BtHVb9XqVFdNrEq9+tFjuOmrpgHdR6MCtGi9WpsVCz9WDY63P04dDlQ7C5+v/AilJFYV3mOkSroyWOjIiIKPIwCQ+yXbt2YefOnZIdv7fuSvXbNaj6nalCwgotFKqJDRtxewTOtPXi/LDWg7mpschi68GI0G3rRkl9CRzu/vH9erUexZnFiFLx+hIREQUDk/Ag2759OzZv3ozKykps3bo1ZMf1V/1WauVIXKWFLnXil73b3t96sNs+sJ/kK60HdWw9GBHaLe040nAELk//NY7VxqIoowgaJYcXERERBQuT8CAzGo0wGkPX0k0Igd46JzrPDK1+x8xRIX7ZxKvfQghc6DTj9LDWg8uTYpATHx22rQdNJhPKy8vHfV1+fn5Ir5tUWswtKG0ohUf0DzFKiEpAYUYhVAp2tyEiIgomJuERxGnxoP24Dda2YdXv1VroUiZ+qS1ON8qautBqGWg9aFArUWCMQ6w2vJOzuro6HD16FHq9HirVyHNxOp3o6+tDUlJSxCfhDT0NqGiqgBD9f2WlRKdgbfpaKOT8hIOIiCjYmIRHACEEemuc6Ki0Q7inXv0GgPoeKyqGtR7MiY/G8ghpPZibm4v9+/cjJSUFeXl5I7ZXVFTA7XYjNzc39MGFUG1XLU40n/Atp8ekI8+YB7mMD9gSERGFApPwMOc094/9trUPqn5HyZG0Wouo5IlfXqfbg2MtPbjcY/Wt0yrlWJsWh5ToyBkb3NHRgcTERFRXV2Px4sXQ6XS+bRaLBdXV1UhPT0dHR0fEVsIvdFxAZetAt57suGysTFkZtkOMiIiIwhGT8DAlhEBPtROdZ4dVv7PVSFimgVw58YSqv/VgN6wut29dZowWuRHYerCurg4NDQ2QyWSoqqoaUg2vqqoCADQ0NKCuri4ik/DK1kpc6LjgW85JyMHS5KUSRkRERDQ7MQkPQ06zB23HrLB1DCTNSt2V6nfSxC+p2yNQ2d6Lcx0DrQeV3taDMdqIrIx6h6NERUWhpqbGVw23WCyoqalBfHw8rFZrxA1HEULgZMtJ1HbV+tYtTV6KnIQcCaMiIiKavSKrzBnhhBDovmhHw37zkATcME+NjKujJ5WA99id2F/XNiQBT4pS49q5SZhjiIrIBBwANBoN1q9fj66uLigUCl/1u6qqCgqFAp2dnVi/fj00msgZguMRHlQ0VQxJwFemrmQCTkREJCFWwsOEo8+NtmM22DuHVr+Tc7XQJk78MgohcLHLglOtPb7WgzL0tx5cmBC+rQcno7CwEAcPHvRVw+fMmYOamhrExcXBarWioKBA6hADxu1x42jjUbSYWwAAMpkMeWl5yDBkSBwZERHR7MYkfIbrr3470FXlgBjUscQwX434JRrIFRNPmq1XWg+2DGo9GHOl9WBcmLcenAxvNfyjjz6CUqnEJ5984quCX3PNNRFTBXe6nTjScAQd1g4AgFwmx9r0tUjVp0ocGRERETEJn8EcvVeq310D1W9VtBxJuVpoEyZ36Rp6rShvGtp6cEG8DiuSDBHRenCyBlfDOzs7kZCQEFFVcIfbgZL6EnTbugEASrkShRmFSNQlShwZERERAUzCZyQhBLovONB1bmj1O3aBGnGLJ1f9dro9ON7Sg7phrQfXpMUhNYJaD07W4Gp4Tk4OLl68GDFVcKvTipL6EvQ5+gAAaoUaxZnFiNXGShwZERERec3aBzPNZjNeeeUVPPLIIygqKoJGo4FMJsPTTz8taVyOHjdMH1v6Ww9eScBVejmMn4pGwjLtpBLwNosDH9S2DUnAM/RaXDs3eVYn4F6FhYXQaDSoqamBVquNiCq42WHGgcsHfAm4VqnFhjkbmIATERHNMLO2En7+/Hnce++9UofhIzxXqt/nB6rfMhlgWKBG3KLJVb89QuBM28jWg6tTDBHd+WSyvNXwDz/8EFdffXXYV8F77D0oqS+B3WUHAESro1GcWQydSjfOO4mIiCjUZm0SHhMTgwceeAAFBQUoKCjAu+++ix07dkgSi6PHjdYKGxw9A2O/1TFyJK6OgjZeMal99didOGrqQpd9YAbNxCgV1hrjEK2atZd7VEVFRVCpVFizZo3UoUxLh7UDRxqOwOl2AgAMGgOKM4uhUYb3HxZERESRatZmZQsWLMDu3bt9y++//37IYxAega7zDnSft0N42wXKgNgcDeIWqSGbxAOTQghc6rLg5LDWg8uSYrBolrQenAq1Wo3i4mKpw5iWVnMrShtL4fb0/xEXHxWPoowiqBSzp+MNERFRuJm1SbjU7N39nU+GV7+T8qKgiZ1c9dvqcqO8qRvNZrtvXYxagbXGeMTPotaDs5Gp14RyUzk8wgMASI5ORkF6ARTyyd1DREREFFpMwkNMeAQ6z9rRfWFY9XuhBnELJ1f9BoCGXhsqmrvgcA90UZkfp8OKZAOUs7D14GxS112HE80nIK7cSMYYI/KN+ZDLZu3z1kRERGGDSXiItZZbkWAcqFirDQok5WonXf12ejw40dyD2kGdTzQKOdYYY5EWrQ1YvDQzXeq8hNMtp33LWbFZWJ26msOOiIiIwgST8AAzmUwwmUwj1ldWVgIAnJb+YQMyGRC3SIPYnMlXv9utDhw1dcHsHBjKkq7XIC81Dholq6CRrqqtCufaz/mW58fPx7LkZUzAiYiIwkjYJuG33XabL7GdqJdffhmFhYVBiqjfrl27sHPnzjFfo4ntr36rDZOrfnuEQGV7H6ra+3zrlDIZVqUakM3WgxFPCIHTradR3VntW7c4aTEWJS6SMCoiIiKairBNwqurq1FVVTWp91gsliBFM2D79u3YvHnziPWVlZXYunUrDHPVMH5KN+nqd6/DhVJTF7psTt+6BK0KBcY4RKvD9jLSBAkhcKzpGOp76n3rVqSswLz4eRJGRURERFMVttnbsWPHpA7BL6PRCKPROOr2mDmTbz1YfaX1oHtQ68GlSXosStBDzup3xPMID8oay9DU1wQAkMlkyE3LRaYhU+LIiIiIaKrCNgmfDWxXWg82DWo9qFcpUJAeh3itWsLIKFRcHhdKG0rRZmkDAMhlcqxJX4M0fZrEkREREdF0MAmfoRr7bKho6obd7fGtmxenw8rkGCjlfPhyNnC4HThcfxhdti4AgEKuQGFGIZJ0SdIGRkRERNPGJHyGcXk8ONHSg5ruoa0H89NiYdSz9eBsYXPZUFJfgl57LwBApVChKKMI8VHxEkdGREREgTCrk/DbbrvN106wsbERALB792689957APrHd//xj38MWTwdVgdKh7UeNOo1yE+NhUbJGRBnC4vTgkOXD8Hi7H+QWKPUoDizGAaNQeLIiIiIKFBmdRJeUVGB2traIesaGhrQ0NAAAMjOzg5JHB4hcPZK60HvvJcKmQyrUgyYG8vWg7NJr70XJfUlsLlsAACdSofizGJEq6MljoyIiIgCaVYn4TU1NVKHgL4rrQc7B7UejL/SelDP1oOzSpetCyX1JXC6+++FGE0MijOLoVVyGBIREVGkYZYnESEEarqtONHSA7for3/LACxJ1GNxIlsPzjZtljaUNpTC5XEBAOK0cSjKLIJawS44REREkYhJuAT8tR6MVilQYIxDQhSTrtmmqa8JZY1l8Ij+TjhJuiQUZBRAKee3JxERUaTib/kQa7XY8UFN25DWg3NjdViVwtaDs1F9Tz2ONR2DuPJpSKo+FWvT10Iu471AREQUyZiEh9jJlh7MT+5PwNl6cHar7qzGqZZTvuUMQwZy03KZgBMREc0CTMIlkhatQX5aLLRsPTgrnW8/j7NtZ33Lc+PmYkXKCnbCISIimiWYhIeYXCZDbqoB82J1TLhmqdMtp3Gp85JveWHiQixJWiJhRERERBRqTMJDrCA9DvPj2PN5NhJC4ETzCdR11/nWLUtehgUJCySMioiIiKTAJDzEolX8ks9GHuFBuakcpt7+GVplMhlWpa7CnNg5EkdGREREUmBGSBRkbo8bpY2laDW3AgDkMjnyjfkwxhgljoyIiIikwiScKIicbieONBxBh7UDAKCQK1CQXoDk6GSJIyMiIiIpMQknChK7y46S+hL02HsAACqFCoUZhUiISpA4MiIiIpIak/AgM5lMMJlMqKyslDoUCiGr04pD9YdgdpgBAGqFGuuy1sGgMUgcGREREc0ETMKDbNeuXdi5c6fUYVAI9Tn6cOjyIdhcNgBAlCoK6zLXIVrNrjhERETUj1PzBdn27dtRVlaGV199VepQKAS6bd04UHfAl4Dr1XpsyNrABJyIiIiGYCU8yIxGI4xGdsGYDdot7TjScAQujwsAEKuNRVFGETRKjcSRERER0UzDJJwoAFrMLShtKIVHeAAACVEJKMwohEqhkjgyIiIimomYhBNNU2NvI8pN5RBCAABSolOwNn0tFHKFxJERERHRTMUknGgaartqcaL5hG85PSYdecY8yGV83IKIiIhGxyScaIoudFxAZetA68k5sXOwKnUVZDKZhFERERFROGASTjQFla2VuNBxwbeck5CDpclLJYyIiIiIwgmTcKJJEELgZMtJ1HbV+tYtTV6KnIQcCaMiIiKicMMknGiCPMKDY03H0NDT4Fu3MnUl5sbNlS4oIiIiCktMwokmwO1xo8xUhua+ZgCATCZDXloeMgwZEkdGRERE4YhJONE4XB4XjjQcQbulHQAgl8mxNn0tUvWpEkdGRERE4YpJONEYHG4HSupL0G3rBgAo5UoUZhQiUZcocWREREQUzpiEE43C5rLh0OVD6HP0AQDUCjWKMosQp42TNjAiIiIKe0zCifwwO8w4VH8IVqcVAKBValGcWYwYTYzEkREREVEkYBJONEyPvQcl9SWwu+wAgGh1NIozi6FT6SSOjIiIiCIFk3CiQTqsHTjScAROtxMAYNAYUJxZDI1SI3FkREREFEmYhBNd0WpuRWljKdweNwAgPioeRRlFUClUEkdGREREkYZJOBEAU68J5aZyeIQHAJAcnYy16WuhlPNbhIiIiAKPGQbNepe7L+N483EIIQAAxhgj8o35kMvkEkdGREREkYpJeJCZTCaYTCZUVlZKHQr5canzEk63nPYtZ8VmYXXqashkMgmjIiIiokjHJDzIdu3ahZ07d0odBvlR1VaFc+3nfMvz4+djWfIyJuBEREQUdPy8Pci2b9+OsrIyvPrqq1KHQlcIIXCq5dSQBHxx0mIsT1nOBJyIiIhCgpXwIDMajTAajVKHQVcIIXCs6Rjqe+p965anLMf8+PkSRkVERESzDZNwmjU8woOyxjI09TUBAGQyGVanrkZWbJbEkREREdFswyScZgWXx4XShlK0WdoAAHKZHGvS1yBNnyZxZERERDQbMQmniOd0O1FSX4IuWxcAQCFXoCC9AMnRydIGRkRERLMWk3CKaDaXDSX1Jei19wIAVAoVijKKEB8VL3FkRERENJsxCaeIZXFacOjyIVicFgCARqlBcWYxDBqDxJERERHRbMcknCJSr70XJfUlsLlsAACdSofizGJEq6MljoyIiIiISThFoC5bF0rqS+B0OwEAerUe67LWQavUShwZERERUT8m4RRR2ixtKG0ohcvjAgDEaeNQlFkEtUItcWREREREA5iEU8Ro6mtCWWMZPMIDAEjUJaIwoxBKOW9zIiIimlmYnVBEqO+px7GmYxBCAABS9alYY1wDhVwhcWREREREIzEJp7BX3VmNUy2nfMsZhgzkpuVCLpNLGBURERHR6JiEU1g7334eZ9vO+pbnxs3FipQVkMlkEkZFRERENDYm4RS2zrSewcWOi77lhYkLsSRpiYQREREREU0Mk3AKO0IInGg+gbruOt+6ZcnLsCBhgYRREREREU0ck3AKKx7hQbmpHKZek2/d6rTVmBM7R8KoiIiIiCaHSTiFDbfHjdLGUrSaWwEAcpkcecY8pMekSxwZERER0eQwCaew4HQ7caThCDqsHQAAhVyBtelrkRKdInFkRERERJPHJDzITCYTTCYTjh07BgCorKyUNqAw5HA5cLLlJMwOM4D+BHxFygrU99ajHvUSR0dERESRwpunWa3WoB+LSXiQ7dq1Czt37vQtb926VcJoiIiIiGg8FRUV2LBhQ1CPIRPeKQYpKLyV8MOHD+NrX/safvnLXyI3Nzeox6ysrMTWrVvx6quvYunSpTwWj8Vj8Vgz4jg8Fo/FY/FYM/1Yx44dwwMPPIC//e1v+MxnPhPUY7ESHmRGoxFGo9G3nJubi/z8/JAce+nSpTwWj8Vj8Vgz7jg8Fo/FY/FYM/1Y8fHxQT8G5/UOEaPRiKeeempIQs5jzVyR+jWM1GOFUiR+DSPxnEItUr+GkXqsUIrUr2GkHiuUOByFwkZ5eTnWrFmDsrKykP0lTOGB9wb5w/uCRsN7g0YTynuDlXAiIiIiohBjEk5EREREFGJMwilsROqYMJo+3hvkD+8LGg3vDRpNKO8NjgknIiIiIgoxVsKJiIiIiEKMSTgRERERUYgxCSciIiIiCjEm4UREREREIcYknIiIiIgoxJiEExERERGFGJNwIiIiIqIQYxJORERERBRiTMJpxjGbzXjllVfwyCOPoKioCBqNBjKZDE8//fS09vv222/j6quvhsFggMFgwMaNG/Huu+8GJmgKmQMHDuCGG25AQkIC9Ho9CgsL8fLLL096P3v37oVMJhv131133RWE6Gk6rFYrduzYgUWLFkGr1SI9PR33338/GhoaJr2vzs5OPPbYY8jOzoZGo0F2dja+/vWvo6urK/CBU9AF6t6YO3fumD8Xzp49G6QzoGAoKyvDD3/4Q9x+++3IzMz0XcepCvTPDeWUIyEKkvPnz+Pee+8N6D7/+7//G48//jiUSiWuvfZaaDQavP/++7jpppvw3HPP4eGHHw7o8Sg43nzzTWzZsgUejwef/vSnkZSUhA8++AD33XcfTpw4gZ/85CeT3ufq1auRm5s7Yn1RUVEAIqZAsdlsuOaaa1BSUgKj0YhbbrkFNTU12LNnD9555x2UlJRg/vz5E9pXW1sb1q1bhwsXLmD+/Pm49dZbcfr0afz0pz/FX//6Vxw6dAgJCQlBPiMKlEDeG1733Xef3/WxsbGBCJlC5Hvf+x7eeuutgOwrKD83BNEMc+HCBfHAAw+I//mf/xFlZWXimWeeEQDEU089NaX9nT17VigUCqHRaMTBgwd966uqqkRiYqJQKpXi/PnzAYqegqW9vV0YDAYBQLz55pu+9U1NTSInJ0cAEB999NGE97dnz55p3VcUWt/5zncEALFu3TrR29vrW//ss88KAOLqq6+e8L7uueceAUDcfvvtwul0+tY/8sgjAoC47777Ahg5BVsg743s7GzB1Chy/PCHPxT/9m//Jv785z8Lk8kkNBrNlK9vMH5u8E6jGe8HP/jBtJKlBx98UAAQjz322Iht//mf/ykAiIcffnh6QVLQ/ehHPxIAxC233DJi2759+wQAcdNNN014f0zCw4fdbhexsbECgCgvLx+xfdWqVQKAOHr06Lj7amxsFHK5XKjVatHU1DRkm81mE8nJyUKhUIjm5uaAxU/BE8h7Qwgm4ZFuqkl4sH5ucEw4RTzvuO877rhjxDbvurfffjukMdHkjXUdb7zxRmi1Wvztb3+DzWYLdWgUZAcOHEB3dzcWLFiAvLy8Edsn83383nvvwePx4KqrrkJqauqQbRqNBjfffDPcbjf+8pe/BCZ4CqpA3htEownWzw0m4RTRurq6UFdXBwB+f0BnZWUhKSkJtbW16OnpCXV4NAnHjx8HAOTn54/YplarsWLFCthsNpw7d25S+y0rK8M3v/lNbN++HU899RT+/ve/ByReCpyxrv3g9SdOnAjpvkh6wbqeP/7xj/HVr34Vjz32GF588UW0trZOL1AKa8G6z/hgJkU0bwIeHx+P6Ohov6/JzMxEW1sbamtrsXLlylCGRxPU09OD7u5uAP3Xy5/MzEwcPXoUtbW1WLVq1YT3/c477+Cdd97xLT/zzDO4+uqr8bvf/W5ExYOk4f0+HuvaA0BtbW1I90XSC9b1/Na3vjVk+fHHH8dzzz2H+++/fwpRUrgL1n3GSjhFtL6+PgCATqcb9TXe5Ly3tzckMdHkea8jMPq1nOx1NBqNePrpp1FRUYHu7m40NTXhz3/+M5YsWYK///3vuOmmm+B2u6cfPE3beN/Hk7n2gdwXSS/Q13Pz5s3Yt28famtrYbFYcOrUKTzxxBOw2+3Ytm1bwDptUHgJ1s8NVsIp4G677TZUVlZO6j0vv/wyCgsLgxQRzQQz7b647rrrcN111/mWDQYDbr75ZmzatAlr1qzB0aNH8fvf/x5f/OIXg3J8Ipp5fvaznw1ZXr58OZ599lksWbIE//RP/4R/+Zd/wS233CJRdBRpmIRTwFVXV6OqqmpS77FYLEGJRa/Xj7t/s9kMAIiJiQlKDNRvOveF9zp61xkMhhGvDdR11Ov1ePTRR/Hwww/jf//3f5mEzwDjfR9P5toHcl8kvVBdzwceeADf/e53UVVVhZqaGsydO3da+6PwEqz7jEk4BdyxY8ekDsFnzpw5APpnuTKbzX7HhdfX1wMAsrOzQxrbbDOd+8JgMCA2Nhbd3d2or6/HsmXLRrwmkNdx4cKFAACTyTTtfdH0eb+Pvdd4uMlc+0Dui6QXquspl8uxYMECtLS0wGQyMQmfZYJ1n3FMOEW0uLg43zdPRUXFiO2XL19GW1sbsrOz/VZXaeZYvXo1AKC8vHzENqfTiVOnTkGr1WLRokXTPlZnZycAjPowL4XWWNd+8PqJPJAbyH2R9EJ5PflzYfYK1n3GJJwi3o033ggAeOONN0Zs8667+eabQxoTTd5Y1/Gdd96BzWbDtddeC61WO+1jvfnmmwBGb0dFobVhwwbExsbi4sWLfj9Rmcz38fXXXw+5XI6PP/4YLS0tQ7bZ7Xa8/fbbUCgUuOGGGwISOwVXIO+NsZw+fRpVVVXQ6XRYsmTJtPZF4SdoPzcmPW0QUYhNdMbMxYsXi8WLF4v6+voh6wdPW3/o0CHf+nPnznHa+jAy2rT1zc3NY05bP9p98f3vf1+0trYOWedwOMTTTz8tAIioqKgR7yHpeKcmX79+vejr6/OtH21q8ueee04sXrxY/Ou//uuIfXmnn/785z8/ZPrpRx99lNPWh6FA3Rvvvvuu+OCDD0bs//jx42Lp0qUCgHj00UeDcg4UGuPNmBnqnxtMwmlGuvXWW0VRUZEoKioSWVlZAoDIyMjwrbv11ltHvAeAACCqq6tHbPNOT69UKsU//MM/iFtuuUVERUUJAOJnP/tZCM6IAuGNN94QcrlcyGQysWnTJnHHHXeIuLg4AUA88cQTft8z2n0BQGg0GrFhwwZx1113iRtuuEGkp6cLAEKr1Q5J9El6VqtVFBUVCQDCaDSKL3zhC77l5ORkcfHixSGvf+qpp0b9xdja2ioWLFggAIgFCxaILVu2iBUrVggAYuHChaK9vT1EZ0WBEKh7w7s+OztbbN68Wdx1112isLBQKJVKAUBs3LhRWCyWEJ4ZTdc777zjyxuKioqETCYTAIase+edd3yvD/XPDSbhNCNlZ2f7kid//7Kzs0e8Z6wkXAgh/vznP4urrrpK6PV6odfrxVVXXSXefvvt4J4IBdwnn3wirr/+ehEXFyd0Op1Yu3at2Lt376ivH+2+2LFjh/jsZz8r5syZI6KiooRWqxU5OTli+/bt4uzZs0E+C5oKi8Ui/u3f/k0sWLBAqNVqkZaWJr785S+Ly5cvj3jtWL9Mhej/ZOWRRx4RWVlZQq1Wi6ysLPHoo4+Kzs7O4J4EBUUg7o2DBw+K+++/X6xcudL3KWlCQoLYuHGjeOmll4TL5QrR2VCg7NmzZ8xcAoDYs2eP7/Wh/rkhE0KIyQ1gISIiIiKi6eCDmUREREREIcYknIiIiIgoxJiEExERERGFGJNwIiIiIqIQYxJORERERBRiTMKJiIiIiEKMSTgRERERUYgxCSciIiIiCjEm4UREREREIcYknIiIiIgoxJiEExGNY+PGjZDJZNi7d6/UoQwhhMDq1asRHR2N1tZWqcMBANhsNhiNRmRkZMBqtU55PzKZbMi/uXPnBi7IIPHeJ4P/1dTUSB0WEc1QTMKJiMLUb37zG5w4cQJf+cpXkJycPGTbl7/8ZV8iGB0djb6+vjH3tWPHjiHJY319/ZRi0mq1ePzxx9HY2Iif//znU9rHYElJSUhNTR1xfoB05ziahIQEpKamIjU1NaD7JaLIxCSciCgMuVwu7NixAyqVCv/8z/885mstFgv27ds36nYhBF599dWAxfbggw8iLi4OP/rRj9DT0zOtfZWWlqKpqQmlpaVjvi7U5+jPvn370NTUhKampqAeh4giA5NwIqIw9Pbbb+PixYu47rrrkJmZOerr5syZAwBjJqAHDhxAdXW177XTFRMTgzvuuAPt7e1BT3wBac6RiGi6mIQTEYWhX/3qVwCALVu2jPm6a665Bmlpafjwww9hMpn8vuaVV14BANxzzz0Bi88b1549ewK2z9FIdY5ERNPBJJyIaBrcbjd27dqFT33qU4iPj0dUVBQWLVqEr3/966MmhF49PT345je/iXnz5kGr1SI7OxsPP/ww2trasHfvXshkMmzcuHHE+1paWvDee+9BpVLhlltuGfMYCoUCX/ziF+F2u/Gb3/xmxHaHw4E//OEP0Gg0uPPOOyd17mPZtGkTEhMTcfToUZw5cyZg+/VHqnMkIpoOJuFERFNkNpvxuc99Dl/96ldx4MABWCwWqNVqnD9/Hj/96U+xfPlyHDlyxO9729raUFxcjJ/85CeoqamBXC5He3s7nn/+eRQWFqK9vX3U43700UdwuVxYuXIlYmJixo1z69atAPwP13jnnXfQ2dmJG264AfHx8RM88/EpFAoUFRUBAP7v//4vYPsdjRTnSEQ0HUzCiYim6PHHH8eHH34InU6HvXv3oq+vD93d3aioqEBeXh46Oztx2223oaura8R7v/a1r6GyshIJCQn405/+hL6+PvT19eHDDz+Ey+XCv//7v4963IMHDwIA1qxZM6E48/PzsWzZMlRUVKCysnLINu8wDW8SG0hr164F0D8eO9ikOkcioqliEk5ENAXV1dX45S9/CQB48cUXcd9990GlUgEAcnNz8d577yE6OhqNjY144YUXhrz33Llz+MMf/gCgP0G85ZZbIJf3/zjetGkT3nrrrTG7ing7haxatWrC8X7pS1/yHc+ro6MDf/nLXxAfH48bb7xxwvuaqNWrVwPAqJ8GBJoU50hENFVMwomIpuCPf/wjPB4P5s2b5/dhv5SUFHzlK18BALzxxhtDtr311lsAgKVLl+KGG24Y8d68vDxcc801ox7b2wIvKSlpwvHec889kMlkeP311yGEAAD8/ve/h8PhwJ133gmNRjPhfU2UN75QteyT4hyJiKaKSTgR0RSUl5cDgN8HJ702bdoEADhx4gTcbrdv/fHjxwEAGzZsGPW9n/rUp0bd1tbWBgCTGt+clZWFq6++GrW1tfj4448BBH+Yhjc+u92O3t7eoBxjMCnOkYhoqpiEExFNgTcRzsjIGPU12dnZAPon1hk8Ltz7XqPROOp709LSRt1mt9sBYNKV3cEPL168eBEHDx7E3Llzx0z4p0Or1fr+b7PZgnKM4UJ9jkREU8UknIhoGrwJcSglJCQAgN8HPsdyxx13QKvV4o033vCNZ/cO4QiGzs5OAIBMJvPFHGyBOEePx4Nf/vKXKCoqQnR0NOLi4vDZz34Wf/nLX4IVNhHNQkzCiYimIDk5GQBQV1c36mtqa2sBAEqlEnFxcb713rHSY/URH2sctff93iR3omJjY3HzzTejs7MTP/nJTwAEd5iGN764uDgoFIqgHWew6Z6jw+HArbfeih07duDee+/Fxx9/jPfeew9XXXUV7rrrLjzxxBPBCp2IZhkm4UREU5CXlwegv/2ey+Xy+5qPPvoIQH8Xk8FJqLdriLfVoD9jtfVbvHgxAKCmpmZSMQMDHUScTifWrFmDJUuWTOr9QghUVVVN6LXe+Lzxhsp0zvGb3/wmzp49i7KyMjz00EPIz89HcXExduzYgYMHD2Lv3r3YtWtXsEInolmESTgR0RTcfvvtkMvlqK+v9ztBTEtLC1566SUA/UMkBtu8eTMA4MyZM3jvvfdGvPf48eP44IMPRj2294HOo0ePTjru66+/Ht/61rfwjW98Y8xe5KMpLS3Fiy++OOHXAmM/gBoMUz3Hy5cv4xe/+AVeeeUVv2PyV6xYgWeffRY7duwY8qAtEdFUKKUOgIgoHM2dOxcPPPAAXnrpJTzyyCNQKpXYsmULVCoVjh07hvvvvx9msxnp6el48MEHh7x38eLF+PznP48333wTW7duxZ49e3DjjTdCLpfj73//O+677z4YDIZRx3x7HzIsKyubdNwqlQo/+tGPJv2+Dz74AK2trXjrrbdw8OBBFBQUQC6X46abboJOp/P7Hm8SHuqHIqd6jn/729+wYMEC30yfANDY2AiVSuUbfrRlyxZs374d5eXlKCgoCFjMRDT7sBJORDRF//Vf/4VNmzahr68PX/rSlxATE4PY2Fjk5eWhoqIC8fHx2Ldv35Dx4F4vvPACFi9ejPb2dmzevBl6vR56vR4bN26ESqXCd77zHQD+O6CsXbsW8+bNQ3Nzs69VYrAtWbIEv/jFL/Db3/4WdXV1ePTRRxEbGztqAl5fX4+TJ0/CYDDgc5/7XEhinC6TyeTraOO1aNEi3Hnnnb5lnU6HpKSkkPU+J6LIxSSciGiKoqOj8f777+OFF17AunXroNFoYLfbkZOTg0cffRSnT58eUlUdLDk5GYcPH8Y3vvENZGdnw+12IzExEY888ggOHz7sm33TXwIvk8nw5S9/GQDwu9/9LlinN0RGRgZ+/vOf+5a/+tWv4rrrrhv19X/4wx8ghMAXvvCFURP1mSY+Ph7Nzc1jvsbpdKKzs3NSPdqJiPyRCe+0YkRENGPcd999ePnll7Fjxw7s3LlzxPa6ujosWLAAGRkZuHTpkm/a+2D6/ve/D4fDgQ0bNuDZZ5/1O57dq7i4GIcPH8ahQ4dQXFw86WN5WwpWV1dj7ty5Uw15Us6cOYOVK1fizJkzoz5M+qc//Qlbt25Fa2sroqKiRt2XFPETUXhhJZyIaIapq6vDm2++CQC49tpr/b5mzpw5uO+++1BbW4t9+/aFJK57770XTz/9ND772c/i17/+9ahdYQ4dOoTDhw/juuuum1ICLpVly5bhtttuwz/+4z/CYrGM2N7U1ISvf/3reOKJJ8ZMwImIJoJJOBGRBKqrq/GVr3wFJSUlsFqtAPqHOvz1r3/FNddcA7PZjLVr1+Kqq64adR9PPfUUNBoNfvCDH4Qk5szMTN//U1NToVT6f7b/+9//PmQyGf7jP/5j2secN28eZDJZyKrJu3fvhsViwYYNG/D+++/DYrGgq6sLv/3tb1FYWIiVK1dix44dft+7ceNGyGSyoE1+RESRhd1RiIgkYLfbsXv3buzevRtA/3jkvr4+OJ1OAEB6ejpeeeWVMfeRlZWFvXv34uzZs2hra/NN4iMlu92OgoIC3HzzzVizZs2U95Oamjpk2dudJNji4uLw8ccfY8eOHdiyZYuvQ01GRgYee+wxPPHEE6NOPJSQkDAi7lBNUkRE4YdjwomIJGA2m/HCCy/gvffew/nz59HS0gKlUol58+bhpptuwhNPPDEjkurZzOVy+VoUpqWlscJNRAHFJJyIiIiIKMQ4JpyIiIiIKMSYhBMRERERhRiTcCIiIiKiEGMSTkREREQUYkzCiYiIiIhCjEk4EREREVGIMQknIiIiIgoxJuFERERERCHGJJyIiIiIKMSYhBMRERERhRiTcCIiIiKiEPv/AQD8AIa9UAfYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### SAVE FIGURE\n",
    "fig.savefig('../output/figure_06.png', bbox_inches='tight', dpi=100)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2d096a6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  }
 ],
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   "notebook_metadata_filter": "-all"
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
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   "pygments_lexer": "ipython3",
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