uncleaned.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.interpolate as interp\n",
    "from scipy.signal import savgol_filter\n",
    "from matplotlib.collections import LineCollection\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import pprint\n",
    "import os\n",
    "import base\n",
    "import anz\n",
    "import viz\n",
    "import video_tools\n",
    "import glob\n",
    "import imageio\n",
    "plt.rcParams['figure.facecolor'] = 'white'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "   camera experiment                                                bps  \\\n0       0   location                                     [r2_in, r3_in]   \n1       0   location                                     [r2_in, r3_in]   \n2       0        paw  [r1_in, r1_out, r2_in, r2_out, r3_in, r3_out, ...   \n3       0        paw  [r1_in, r1_out, r2_in, r2_out, r3_in, r3_out, ...   \n4       0        paw  [r1_in, r1_out, r2_in, r2_out, r3_in, r3_out, ...   \n\n   region_ix        region                                                 xp  \\\n0          0     [0, 1362]  [197.48735809326172, 197.23240661621094, 197.1...   \n1          1  [1418, 1600]  [197.11984252929688, 197.1650619506836, 197.24...   \n2          0     [0, 1361]  [197.97021675109863, 197.7529067993164, 197.80...   \n3          1  [1418, 1494]  [197.63018035888672, 197.70695686340332, 197.8...   \n4          2  [1498, 1600]  [197.77474403381348, 197.75, 197.5369205474853...   \n\n                                                  yp  \\\n0  [87.59857940673828, 87.35967636108398, 87.5290...   \n1  [87.16648483276367, 87.12472915649414, 87.1937...   \n2  [89.24113941192627, 88.973388671875, 89.138649...   \n3  [89.07569026947021, 89.07324028015137, 89.1090...   \n4  [89.21258640289307, 89.18599891662598, 88.9457...   \n\n                                                xitp  \\\n0  [197.48781948965717, 197.3178564271041, 197.16...   \n1  [197.19539816616467, 197.1947175424559, 197.19...   \n2  [197.96847453600333, 197.8256410164234, 197.75...   \n3  [197.72059227662595, 197.716767662421, 197.713...   \n4  [197.6407970170944, 197.64067424990742, 197.64...   \n\n                                                yitp  \\\n0  [87.59499478911395, 87.33362120389744, 87.4566...   \n1  [87.18370546752557, 87.18544143428886, 87.1871...   \n2  [89.23752740645863, 89.01945063495215, 89.0508...   \n3  [89.10102199864306, 89.1038487907622, 89.10668...   \n4  [89.11754645941886, 89.12070269382787, 89.1237...   \n\n                                                  xv  \\\n0  [-0.16996306255308014, -0.16996306255308014, -...   \n1  [-0.0006806237087744194, -0.000680623708774419...   \n2  [-0.14283351957993773, -0.14283351957993773, -...   \n3  [-0.0038246142049445098, -0.003824614204944509...   \n4  [-0.00012276718697989963, -0.00012276718697989...   \n\n                                                  yv  \\\n0  [-0.26137358521651777, -0.26137358521651777, 0...   \n1  [0.0017359667632916853, 0.0017359667632916853,...   \n2  [-0.21807677150647464, -0.21807677150647464, 0...   \n3  [0.002826792119137167, 0.002826792119137167, 0...   \n4  [0.00315623440900481, 0.00315623440900481, 0.0...   \n\n                                                  ap  \\\n0  [93.1807185147454, 91.83477039414291, 90.83842...   \n1  [91.03588518392232, 90.83868948452019, 90.8874...   \n2  [93.55997588174178, 93.06381348931397, 93.4398...   \n3  [95.32639558506906, 95.05511338541721, 95.2968...   \n4  [95.21373891596822, 94.930196778234, 95.241919...   \n\n                                                aitp  \\\n0  [92.52709660926712, 92.52709660926712, 92.5270...   \n1  [90.98728505802269, 90.98728505802269, 90.9872...   \n2  [93.42818554762286, 93.42818554762286, 93.4281...   \n3  [95.18647971850638, 95.18647971850638, 95.1864...   \n4  [95.16338825840012, 95.16338825840012, 95.1633...   \n\n                                                  av  \\\n0  [0.06441559593229158, 0.06441559593229158, 0.0...   \n1  [-0.0024783754922452776, -0.002478375492245277...   \n2  [0.02209652958658239, 0.02209652958658239, 0.0...   \n3  [-0.001437505087022828, -0.001437505087022828,...   \n4  [0.011178021576495212, 0.011178021576495212, 0...   \n\n                                                  aa  \n0  [0.0, 0.0, -1.3877787807814457e-17, 0.09581126...  \n1  [0.0, 0.0, 4.336808689942018e-19, 0.0111101770...  \n2  [0.0, 0.0, 0.0, 0.005846104095593319, -0.00216...  \n3  [0.0, 0.0, 2.168404344971009e-19, 0.0033638515...  \n4  [0.0, 0.0, -1.734723475976807e-18, 4.442428547...  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>camera</th>\n      <th>experiment</th>\n      <th>bps</th>\n      <th>region_ix</th>\n      <th>region</th>\n      <th>xp</th>\n      <th>yp</th>\n      <th>xitp</th>\n      <th>yitp</th>\n      <th>xv</th>\n      <th>yv</th>\n      <th>ap</th>\n      <th>aitp</th>\n      <th>av</th>\n      <th>aa</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>location</td>\n      <td>[r2_in, r3_in]</td>\n      <td>0</td>\n      <td>[0, 1362]</td>\n      <td>[197.48735809326172, 197.23240661621094, 197.1...</td>\n      <td>[87.59857940673828, 87.35967636108398, 87.5290...</td>\n      <td>[197.48781948965717, 197.3178564271041, 197.16...</td>\n      <td>[87.59499478911395, 87.33362120389744, 87.4566...</td>\n      <td>[-0.16996306255308014, -0.16996306255308014, -...</td>\n      <td>[-0.26137358521651777, -0.26137358521651777, 0...</td>\n      <td>[93.1807185147454, 91.83477039414291, 90.83842...</td>\n      <td>[92.52709660926712, 92.52709660926712, 92.5270...</td>\n      <td>[0.06441559593229158, 0.06441559593229158, 0.0...</td>\n      <td>[0.0, 0.0, -1.3877787807814457e-17, 0.09581126...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>location</td>\n      <td>[r2_in, r3_in]</td>\n      <td>1</td>\n      <td>[1418, 1600]</td>\n      <td>[197.11984252929688, 197.1650619506836, 197.24...</td>\n      <td>[87.16648483276367, 87.12472915649414, 87.1937...</td>\n      <td>[197.19539816616467, 197.1947175424559, 197.19...</td>\n      <td>[87.18370546752557, 87.18544143428886, 87.1871...</td>\n      <td>[-0.0006806237087744194, -0.000680623708774419...</td>\n      <td>[0.0017359667632916853, 0.0017359667632916853,...</td>\n      <td>[91.03588518392232, 90.83868948452019, 90.8874...</td>\n      <td>[90.98728505802269, 90.98728505802269, 90.9872...</td>\n      <td>[-0.0024783754922452776, -0.002478375492245277...</td>\n      <td>[0.0, 0.0, 4.336808689942018e-19, 0.0111101770...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>paw</td>\n      <td>[r1_in, r1_out, r2_in, r2_out, r3_in, r3_out, ...</td>\n      <td>0</td>\n      <td>[0, 1361]</td>\n      <td>[197.97021675109863, 197.7529067993164, 197.80...</td>\n      <td>[89.24113941192627, 88.973388671875, 89.138649...</td>\n      <td>[197.96847453600333, 197.8256410164234, 197.75...</td>\n      <td>[89.23752740645863, 89.01945063495215, 89.0508...</td>\n      <td>[-0.14283351957993773, -0.14283351957993773, -...</td>\n      <td>[-0.21807677150647464, -0.21807677150647464, 0...</td>\n      <td>[93.55997588174178, 93.06381348931397, 93.4398...</td>\n      <td>[93.42818554762286, 93.42818554762286, 93.4281...</td>\n      <td>[0.02209652958658239, 0.02209652958658239, 0.0...</td>\n      <td>[0.0, 0.0, 0.0, 0.005846104095593319, -0.00216...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>paw</td>\n      <td>[r1_in, r1_out, r2_in, r2_out, r3_in, r3_out, ...</td>\n      <td>1</td>\n      <td>[1418, 1494]</td>\n      <td>[197.63018035888672, 197.70695686340332, 197.8...</td>\n      <td>[89.07569026947021, 89.07324028015137, 89.1090...</td>\n      <td>[197.72059227662595, 197.716767662421, 197.713...</td>\n      <td>[89.10102199864306, 89.1038487907622, 89.10668...</td>\n      <td>[-0.0038246142049445098, -0.003824614204944509...</td>\n      <td>[0.002826792119137167, 0.002826792119137167, 0...</td>\n      <td>[95.32639558506906, 95.05511338541721, 95.2968...</td>\n      <td>[95.18647971850638, 95.18647971850638, 95.1864...</td>\n      <td>[-0.001437505087022828, -0.001437505087022828,...</td>\n      <td>[0.0, 0.0, 2.168404344971009e-19, 0.0033638515...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>paw</td>\n      <td>[r1_in, r1_out, r2_in, r2_out, r3_in, r3_out, ...</td>\n      <td>2</td>\n      <td>[1498, 1600]</td>\n      <td>[197.77474403381348, 197.75, 197.5369205474853...</td>\n      <td>[89.21258640289307, 89.18599891662598, 88.9457...</td>\n      <td>[197.6407970170944, 197.64067424990742, 197.64...</td>\n      <td>[89.11754645941886, 89.12070269382787, 89.1237...</td>\n      <td>[-0.00012276718697989963, -0.00012276718697989...</td>\n      <td>[0.00315623440900481, 0.00315623440900481, 0.0...</td>\n      <td>[95.21373891596822, 94.930196778234, 95.241919...</td>\n      <td>[95.16338825840012, 95.16338825840012, 95.1633...</td>\n      <td>[0.011178021576495212, 0.011178021576495212, 0...</td>\n      <td>[0.0, 0.0, -1.734723475976807e-18, 4.442428547...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# FILTER_WINDOW_MS = 125\n",
    "# FILTER_WINDOW_FRAMES = FILTER_WINDOW_MS * FRAME_RATE // 1000\n",
    "#\n",
    "# def compute_angles(a, b, c, degrees=True):\n",
    "#     '''Computes angles between 3 point arrays of shape [points, coordinates]'''\n",
    "#     ba = b - a\n",
    "#     bc = b - c\n",
    "#     dp = np.diagonal(np.dot(ba, bc.T))\n",
    "#     cosine_angle = dp / (np.linalg.norm(ba, axis=1) * np.linalg.norm(bc, axis=1))\n",
    "#     angle = np.arccos(cosine_angle)\n",
    "#     if degrees:\n",
    "#         angle = np.degrees(angle)\n",
    "#     return angle\n",
    "#\n",
    "# def interpolate_polyline(polyline, num_points):\n",
    "#     # tck, u = interp.splprep(polyline.T, s=INTERP_SMOOTH_MOTION_COEFFICIENT)\n",
    "#     tck, u = interp.splprep(polyline.T, s=10)\n",
    "#     u = np.linspace(0.0, 1.0, num_points)\n",
    "#     return interp.splev(u, tck)\n",
    "#\n",
    "# ### LOOP OVER THIS\n",
    "# from collections import defaultdict\n",
    "# dd = defaultdict(list)\n",
    "# marker_regions = marker_regions_per_video[0]\n",
    "# for cam in [0, 1]:\n",
    "#     for k, regions in marker_regions[cam].items():\n",
    "#         for region_ix, region in enumerate(regions):\n",
    "#             bps = BPS_CARE[k]\n",
    "#             bps_ix = [bp_dict[bp] for bp in bps]\n",
    "#             x = marker_xys[cam][bps_ix, region[0]:region[1], 0]\n",
    "#             y = marker_xys[cam][bps_ix, region[0]:region[1], 1]\n",
    "#\n",
    "#             x_av = np.mean(x, axis=0)\n",
    "#             y_av = np.mean(y, axis=0)\n",
    "#             x_itp, y_itp = interpolate_polyline(np.array([x_av, y_av]).T, len(x_av))\n",
    "#             x_velocity = np.diff(x_itp)\n",
    "#             x_velocity = np.hstack((x_velocity[0], x_velocity))\n",
    "#             y_velocity = np.diff(y_itp)\n",
    "#             y_velocity = np.hstack((y_velocity[0], y_velocity))\n",
    "#\n",
    "#             angles = []\n",
    "#             for paw_bps in BPS_ANGLES[k]:\n",
    "#                 angle_ixs = [bp_dict[bp] for bp in paw_bps]\n",
    "#                 angle_coors = marker_xys[cam][angle_ixs, region[0]:region[1],\n",
    "#                               :2]\n",
    "#                 angles.append(compute_angles(angle_coors[0],\n",
    "#                                              angle_coors[1],\n",
    "#                                              angle_coors[2]))\n",
    "#             if len(angles):\n",
    "#                 angles = np.mean(angles, axis=0)\n",
    "#                 angles_smoothed=savgol_filter(angles,\n",
    "#                                               window_length=FILTER_WINDOW_FRAMES,\n",
    "#                                               polyorder=0)\n",
    "#                 angles_v = np.diff(angles_smoothed)\n",
    "#                 angles_v = np.hstack((angles_v[0], angles_v))\n",
    "#                 angles_v=savgol_filter(angles_v,\n",
    "#                                               window_length=FILTER_WINDOW_FRAMES,\n",
    "#                                               polyorder=0)\n",
    "#\n",
    "#                 angles_a = np.diff(angles_v)\n",
    "#                 angles_a = np.hstack((angles_a[0], angles_a))\n",
    "#                 # angles_v=savgol_filter(angles_v,\n",
    "#                 #                               window_length=FILTER_WINDOW_FRAMES,\n",
    "#                 #                               polyorder=0)\n",
    "#             else:\n",
    "#                 angles = []\n",
    "#                 angles_smoothed=[]\n",
    "#                 angles_v = []\n",
    "#                 angles_a = []\n",
    "#\n",
    "#\n",
    "#             dd['camera'].append(cam)\n",
    "#             dd['experiment'].append(k)\n",
    "#             dd['bps'].append(bps)\n",
    "#             dd['region_ix'].append(region_ix)\n",
    "#             dd['region'].append(region)\n",
    "#             dd['xp'].append(x_av)\n",
    "#             dd['yp'].append(y_av)\n",
    "#             dd['xitp'].append(x_itp)\n",
    "#             dd['yitp'].append(y_itp)\n",
    "#             dd['xv'].append(x_velocity)\n",
    "#             dd['yv'].append(y_velocity)\n",
    "#             dd['ap'].append(angles)\n",
    "#             dd['aitp'].append(angles_smoothed)\n",
    "#             dd['av'].append(angles_v)\n",
    "#             dd['aa'].append(angles_a)\n",
    "#\n",
    "# df = pd.DataFrame(dd)\n",
    "# df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "ename": "UndefinedVariableError",
     "evalue": "name 'camera' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "\u001B[1;32m~\\anaconda3\\envs\\anipose_env\\lib\\site-packages\\pandas\\core\\computation\\scope.py\u001B[0m in \u001B[0;36mresolve\u001B[1;34m(self, key, is_local)\u001B[0m\n\u001B[0;32m    200\u001B[0m             \u001B[1;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mhas_resolvers\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 201\u001B[1;33m                 \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mresolvers\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    202\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\anaconda3\\envs\\anipose_env\\lib\\collections\\__init__.py\u001B[0m in \u001B[0;36m__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m    915\u001B[0m                 \u001B[1;32mpass\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 916\u001B[1;33m         \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__missing__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m)\u001B[0m            \u001B[1;31m# support subclasses that define __missing__\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    917\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\anaconda3\\envs\\anipose_env\\lib\\collections\\__init__.py\u001B[0m in \u001B[0;36m__missing__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m    907\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0m__missing__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mkey\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 908\u001B[1;33m         \u001B[1;32mraise\u001B[0m \u001B[0mKeyError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    909\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mKeyError\u001B[0m: 'camera'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "\u001B[1;32m~\\anaconda3\\envs\\anipose_env\\lib\\site-packages\\pandas\\core\\computation\\scope.py\u001B[0m in \u001B[0;36mresolve\u001B[1;34m(self, key, is_local)\u001B[0m\n\u001B[0;32m    211\u001B[0m                 \u001B[1;31m# e.g., df[df > 0]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 212\u001B[1;33m                 \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mtemps\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    213\u001B[0m             \u001B[1;32mexcept\u001B[0m \u001B[0mKeyError\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0merr\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mKeyError\u001B[0m: 'camera'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001B[1;31mUndefinedVariableError\u001B[0m                    Traceback (most recent call last)",
      "\u001B[1;32m<ipython-input-13-e89f1e5a1305>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m     20\u001B[0m     \u001B[0msns\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdespine\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     21\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 22\u001B[1;33m \u001B[0mvec\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mdf\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mquery\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"camera=='CAM1' & experiment=='location' & region_ix==0\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     23\u001B[0m \u001B[0mregion\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mvec\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'region'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0miloc\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     24\u001B[0m \u001B[0mx_av\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mvec\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'xp'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0miloc\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\anaconda3\\envs\\anipose_env\\lib\\site-packages\\pandas\\core\\frame.py\u001B[0m in \u001B[0;36mquery\u001B[1;34m(self, expr, inplace, **kwargs)\u001B[0m\n\u001B[0;32m   3467\u001B[0m         \u001B[0mkwargs\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"level\"\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mkwargs\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mpop\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"level\"\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m0\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m+\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   3468\u001B[0m         \u001B[0mkwargs\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"target\"\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 3469\u001B[1;33m         \u001B[0mres\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0meval\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mexpr\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   3470\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   3471\u001B[0m         \u001B[1;32mtry\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\anaconda3\\envs\\anipose_env\\lib\\site-packages\\pandas\\core\\frame.py\u001B[0m in \u001B[0;36meval\u001B[1;34m(self, expr, inplace, **kwargs)\u001B[0m\n\u001B[0;32m   3597\u001B[0m         \u001B[0mkwargs\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"resolvers\"\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mkwargs\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"resolvers\"\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m+\u001B[0m \u001B[0mtuple\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mresolvers\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   3598\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 3599\u001B[1;33m         \u001B[1;32mreturn\u001B[0m \u001B[0m_eval\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mexpr\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0minplace\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0minplace\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   3600\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   3601\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mselect_dtypes\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0minclude\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mNone\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mexclude\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mNone\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m->\u001B[0m \u001B[0mDataFrame\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\anaconda3\\envs\\anipose_env\\lib\\site-packages\\pandas\\core\\computation\\eval.py\u001B[0m in \u001B[0;36meval\u001B[1;34m(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target, inplace)\u001B[0m\n\u001B[0;32m    340\u001B[0m         )\n\u001B[0;32m    341\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 342\u001B[1;33m         \u001B[0mparsed_expr\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mExpr\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mexpr\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mengine\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mengine\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mparser\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mparser\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0menv\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0menv\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    343\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    344\u001B[0m         \u001B[1;31m# construct the engine and evaluate the parsed expression\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\anaconda3\\envs\\anipose_env\\lib\\site-packages\\pandas\\core\\computation\\expr.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, expr, engine, parser, env, level)\u001B[0m\n\u001B[0;32m    796\u001B[0m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mparser\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mparser\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    797\u001B[0m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_visitor\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mPARSERS\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mparser\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0menv\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mengine\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mparser\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 798\u001B[1;33m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mterms\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mparse\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    799\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    800\u001B[0m     \u001B[1;33m@\u001B[0m\u001B[0mproperty\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\anaconda3\\envs\\anipose_env\\lib\\site-packages\\pandas\\core\\computation\\expr.py\u001B[0m in \u001B[0;36mparse\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    815\u001B[0m         \u001B[0mParse\u001B[0m \u001B[0man\u001B[0m \u001B[0mexpression\u001B[0m\u001B[1;33m.\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    816\u001B[0m         \"\"\"\n\u001B[1;32m--> 817\u001B[1;33m         \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_visitor\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mvisit\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mexpr\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    818\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    819\u001B[0m     \u001B[1;33m@\u001B[0m\u001B[0mproperty\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
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      "\u001B[1;32m~\\anaconda3\\envs\\anipose_env\\lib\\site-packages\\pandas\\core\\computation\\expr.py\u001B[0m in \u001B[0;36mvisit\u001B[1;34m(self, node, **kwargs)\u001B[0m\n\u001B[0;32m    399\u001B[0m         \u001B[0mmethod\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;34m\"visit_\"\u001B[0m \u001B[1;33m+\u001B[0m \u001B[0mtype\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mnode\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__name__\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    400\u001B[0m         \u001B[0mvisitor\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mgetattr\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmethod\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 401\u001B[1;33m         \u001B[1;32mreturn\u001B[0m \u001B[0mvisitor\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mnode\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    402\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    403\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mvisit_Module\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnode\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\anaconda3\\envs\\anipose_env\\lib\\site-packages\\pandas\\core\\computation\\expr.py\u001B[0m in \u001B[0;36mvisit_Name\u001B[1;34m(self, node, **kwargs)\u001B[0m\n\u001B[0;32m    533\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    534\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mvisit_Name\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnode\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 535\u001B[1;33m         \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mterm_type\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mnode\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mid\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0menv\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    536\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    537\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mvisit_NameConstant\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnode\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\anaconda3\\envs\\anipose_env\\lib\\site-packages\\pandas\\core\\computation\\ops.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, name, env, side, encoding)\u001B[0m\n\u001B[0;32m     84\u001B[0m         \u001B[0mtname\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mstr\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mname\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     85\u001B[0m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mis_local\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtname\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mstartswith\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mLOCAL_TAG\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mor\u001B[0m \u001B[0mtname\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mDEFAULT_GLOBALS\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 86\u001B[1;33m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_value\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_resolve_name\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     87\u001B[0m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mencoding\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mencoding\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     88\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\anaconda3\\envs\\anipose_env\\lib\\site-packages\\pandas\\core\\computation\\ops.py\u001B[0m in \u001B[0;36m_resolve_name\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    101\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    102\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0m_resolve_name\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 103\u001B[1;33m         \u001B[0mres\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0menv\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mresolve\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mlocal_name\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mis_local\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mis_local\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    104\u001B[0m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mupdate\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mres\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    105\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\anaconda3\\envs\\anipose_env\\lib\\site-packages\\pandas\\core\\computation\\scope.py\u001B[0m in \u001B[0;36mresolve\u001B[1;34m(self, key, is_local)\u001B[0m\n\u001B[0;32m    215\u001B[0m                 \u001B[1;32mfrom\u001B[0m \u001B[0mpandas\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcore\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcomputation\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mops\u001B[0m \u001B[1;32mimport\u001B[0m \u001B[0mUndefinedVariableError\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    216\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 217\u001B[1;33m                 \u001B[1;32mraise\u001B[0m \u001B[0mUndefinedVariableError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mis_local\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mfrom\u001B[0m \u001B[0merr\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    218\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    219\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mswapkey\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mold_key\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mstr\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnew_key\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mstr\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnew_value\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mNone\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mUndefinedVariableError\u001B[0m: name 'camera' is not defined"
     ]
    }
   ],
   "source": [
    "def make_multicolored_line(x, y, plot_args):\n",
    "    points = np.array([x, y]).T.reshape(-1, 1, 2)\n",
    "    c = np.arange(len(points))\n",
    "    segments = np.concatenate([points[:-1], points[1:]], axis=1)\n",
    "    norm = plt.Normalize(c.min(), c.max())\n",
    "    lc = LineCollection(segments, norm=norm, **plot_args)\n",
    "    lc.set_array(c)\n",
    "    lc.set_linewidth(2)\n",
    "    return lc\n",
    "\n",
    "def make_plot(x, y, plot_args, title, ax=None):\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "    ax.set_xlim(x.min(), x.max())\n",
    "    ax.set_ylim(y.min(), y.max())\n",
    "    lc = make_multicolored_line(x, y, plot_args)\n",
    "    line = ax.add_collection(lc)\n",
    "    plt.colorbar(line, ax=ax)\n",
    "    ax.set_title(title)\n",
    "    sns.despine()\n",
    "\n",
    "vec = df.query(\"camera=='CAM1' & experiment=='location' & region_ix==0\")\n",
    "region = vec['region'].iloc[0]\n",
    "x_av = vec['xp'].iloc[0]\n",
    "y_av = vec['yp'].iloc[0]\n",
    "x_itp = vec['xitp'].iloc[0]\n",
    "y_itp = vec['yitp'].iloc[0]\n",
    "x_v = vec['xv'].iloc[0]\n",
    "y_v = vec['yv'].iloc[0]\n",
    "\n",
    "plot_args = {'cmap': 'viridis'}\n",
    "f, axs = plt.subplots(nrows=1,\n",
    "                      ncols=2,\n",
    "                      figsize=(15, 5),\n",
    "                      sharex=True,\n",
    "                      sharey=True\n",
    "                      )\n",
    "\n",
    "make_plot(x_av, y_av, plot_args, 'Position', ax=axs[0])\n",
    "make_plot(x_itp, y_itp, plot_args, 'Position (smoothed)', ax=axs[1])\n",
    "bound = (x_av.max() - x_av.min())//2\n",
    "axs[0].set_xlim(x_av.min(), x_av.max())\n",
    "axs[0].set_ylim(y_av.mean() - bound, y_av.mean() + bound)\n",
    "axs[0].invert_yaxis()\n",
    "axs[0].set_xlabel('X (pixels)')\n",
    "axs[0].set_ylabel('Y (pixels)')\n",
    "\n",
    "f, axs = plt.subplots(nrows=1,\n",
    "                      ncols=2,\n",
    "                      figsize=(15, 5),\n",
    "                      sharex=True,\n",
    "                      sharey=True\n",
    "                      )\n",
    "\n",
    "make_plot(np.arange(region[0], region[1]),\n",
    "          x_v,\n",
    "          plot_args,\n",
    "          r\"$\\.X$\",\n",
    "          axs[0])\n",
    "\n",
    "make_plot(np.arange(region[0], region[1]),\n",
    "          y_v,\n",
    "          plot_args,\n",
    "          r\"$\\.Y$\",\n",
    "          axs[1])\n",
    "axs[0].set_ylim(-5, 5)\n",
    "axs[0].set_xlabel('Time (frames)')\n",
    "axs[0].set_ylabel('Velocity (pixels / frames)')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "(0.0, 500.0)"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1440x360 with 6 Axes>",
      "image/png": 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8nAXArlixQjj+j3/8gwXAfvnllwHXMRqNbL9+/dipU6cKx1566SUWAPv4448HnEuO9+/fP+B4//79WQDsa6+9FjLOMWPGsMOGDWNNJlPA8Y8//jhkjPG+XgaDgQXA3nzzzSGPF46jR4+yANjbbrstrvMplI6CzrOdP8+uWbMm5DUMx4oVK1gA7AknnMA6nc6Qv5dKpeyGDRuE406nky0tLRXeQ5Zl2ba2Nlar1bKDBg1ijUajcNxoNLIDBw5kdTod297envC5LMvNw+J5XAyZo4Of4y233MICYPfs2SMcu+OOO1iVSsVu3bo14Nyamho2JyeHveaaa4RjCxYsYAGwb775ZsC5d955Jwsg4njEfP/99ywA9oUXXgj53auvvsoCYBcuXBhw/LnnnguJRXa7ne3Vqxd76qmnsm63O+B8Ev9++OEHlmVZ1u12s71792ZLSkrYtrY24Tyz2cwOGDAgJDaR937o0KGs1WoNuDaJZa+++mrAcbfbzY4bN46tqKgQvgebN29mAbALFiwIea4XXnghm5OTI8TKu+++mwXANjY2RnjlArn++utZAGxLS0tc51O6L9kSV8aPH8+WlZWxHo8n4Pgpp5zCFhYWCvNsXV0dq1Qq2Tlz5oRc44477mAlEglbVVUlHJs6dWrA3ODxeNjy8nI2NzeXra2tFY47nU72pJNOYiUSCbt//37hNRk5ciSrVCrZbdu2hTye1+uN+DiRjpEx9OvXj50wYULAcavVyur1evacc84J+Ztg3nzzTRYAu2bNmpDfJTIXJ/J6tra2siqVih09ejRrt9uF8+rr61m9Xh8wr7KsP/5PnTo14jwczzokkXmVZbn3rbS0lL311lsDzo/0fkydOpUFwD700EMhv0vke1FdXc0CYDUaDVtTUyMcb2pqYpVKJcswDPvss88GXOeiiy5i5XI5azabhWPJ3McsWbKEBcBu2rQp5HeU9EFL/7o4ixYtQnFxMUpKSnDcccfhzTffxAUXXCAY+TEMI+yueL1eGAwGtLS0CDvUf/zxh3Ct008/HQC32wJwpoB2ux1/+ctfkJubi++++w4At5Po9XqFa0RDq9UK/3Y4HGhtbUVbWxvOPPNMmEymsOVed955Z0BJyYQJE5CTk4MDBw4Ix8iu6j333BPwt+eeey6GDx8ec1xGoxGfffYZLrjgAqhUKrS0tAg/FRUVGDx4cMAudiSam5vDqpg2b96Mo0eP4rrrrkNRUZFwPDc3F/Pnzw85/5133kFlZSXGjRsXMBaXy4UzzjgDv/zyi7Dr8umnn0IqleLOO+8MuMaNN94YUZlQUFAQ0q1px44d2L59Oy6//HI4nc6AxyXlMeQ1SOT1UqvVUCqV+OOPP0JKW8JBdt7jLQOiUDoaOs923jxL5rQvvvgibFlGMDfffHPAbuqpp54KAJg0aVKA8lWhUGDixIkBz/ebb76B1WrFHXfcIZQZAFxJzu233w6LxYJvv/024XPjoXfv3rj00ksDjpH3nqgsWJbFu+++iylTpqBPnz4Br6dWq8WkSZMCXs/Vq1ejV69euPrqqwOu+8ADD8Q9rubmZgAIG+dWr14NqVSKe++9N+D4zTffHPCaANzr1djYiOuuu074fpAfUjpPxr5582bU1dXh2muvRX5+vnANnU4XNn6KH1ej0QQce+edd5CTk4NZs2YFPKbBYMDMmTNRU1MjfAbeffddMAyDa665JuDclpYWXHDBBTCbzVi/fj0A/+fyo48+Clu2GAyNc5Rgunpcueaaa1BfX49vvvlGOFZdXY1ff/0Vc+bMEebZDz/8EE6nE9dff33I92bmzJnw+XzC44dj8+bNgkK0d+/ewnGFQoH7778fPp9PKKfeunUrdu3aheuuuy7AwoIgkSS3bJVKpZg7dy42btyIHTt2CMc//PBDmEwmXH/99TGvEWuujHcuTuT1/Oabb+BwOHDzzTdDpVIJ1ygtLcUVV1wRcax33XVXSGOHRNYhicyrAPd5bGhoCCkXjcV9990XciyR7wVh1qxZgtIY4NTMw4YNg0QiCWnkdOqpp8Ltdgvrl2TvY+ic3zHQ0r8uzrx58zB79mwwDAOtVouhQ4eGTJLvv/8+nn32WWzZsiXEH6m9vV34d1lZGYYNGyYEuu+//x6lpaUYPXo0pk6diu+//x4LFiwQ5MRTp06NOT6LxSLUBh89ejTk9+LHJwwcODDkWEFBQUDtcXV1NSQSCQYPHhxy7rBhw7Bnz56o49q3bx98Ph/eeOONiDXw4cYRDMMwIV1FAAheIeFK2UaMGBFybM+ePbDb7VGlui0tLejXrx+qq6vRu3fvECmpXC7HgAEDwr6mgwYNEmTf4scEuJulRYsWhX1M4mGQyOulUCjw/PPP484778SAAQMwYsQITJ8+HbNmzRJupsSQ16+7dPygdD/oPNt58+zUqVNx9dVXY+XKlXj33XcxYcIEzJgxA3/+85/DzqXB1yOJjnClivn5+SHPFwBGjhwZcu6oUaMA+Of2RM6Nh3CvA7nRJWNsbm5Ga2srvv7664ixQrxQO3ToECZMmBAy95eVlSEvLy+ucZF5OVKcKysrC0lKKZVKDBw4MOBzRz4rc+fOjfhYJN6Q15aU7okJd4wgLrcTP67ZbI5a5tTY2IihQ4diz549YFk2agk6GeNtt92GNWvW4JZbbsEDDzyAU045BWeffTbmzJkT9r2hcY4STFePK3PmzME999yDVatW4eyzzwYArFq1CizLBpT9ke/2jBkzIl4rmh9WInMpSX6ccMIJMcefKNdffz0ef/xxvPHGG3j++ecBAG+88QZKSkpCytjCEWuujHcuTuT1TPdcGe86JJF5FeASdfn5+SE+atEoLi6OGKfi/V4QwsXX/Px8lJWVhRjuk3sGEneTvY+hc37HQBNVXZwhQ4ZEncw+/vhj/PnPf8bEiRPxwgsvoF+/flCpVPB6vTj77LPh8/kCzp8+fTqWL18Oo9GI77//HtOmTROOL1iwAE6nE99//z1OOOGEgJ3OSFx++eX47LPPMG/ePEyZMgUFBQWQyWRYu3YtnnvuuZDHBxAykRPEk3+qEwD5+yuvvDIg4IohGftoFBcXY9u2bRGvH2584YIYy7IYPXo0/vGPf0R9rEh/H4vgXWbxde69917hJiQY8h4n+nrNnz8fF154IT7//HOsW7cOH374IZYtW4Y///nP+O9//xvwd21tbQAQNThSKJ0JnWc7d5596623cP/992Pt2rX45Zdf8Oyzz+KJJ57A888/j9tuuy3g3EjPK9LxcOONh2Tm4WhEGx95LPLfGTNmxK2KivTexTt+Mi+TeTr4GvFen/z/008/jeOPPz7s3xA1RbKvbaQ4V1xcjH//+98R/44shsnz+eKLLyK+H2QxXVhYiI0bN+Lnn3/GN998g59++gl33303Fi1ahLVr1wreiwQa5yjBdPW4UlhYiPPOOw+rV6+G2WxGTk4O3nnnHQwfPjzAq4h8X1etWoWysrKw14q2IZHMvJuJxX+/fv1w9tln45133sHf//53HDlyBD/99BPuu+++uLp6R5srgfjn4kRez3TPlYmsQ+KdVwHOn+q8884LUXElOkYg8e8FkNx9QXDcTfQ+hs75HQNNVGU5b7/9NlQqFX744YeAL32kDmvTp0/Hyy+/jLVr12LDhg1Cudjpp58Ou92O//u//8POnTvDyjGDMRgM+Oyzz3DVVVeFdKpIpCQiHAMGDIDP58OBAwdCSlD27dsX8+8HDx4MhmEEY/JkGTVqFNatW4eWlpaAEj9iCBhOcRDu2JAhQ9Dc3Izp06fHlC4PGDAA3377bYhBn9vtRnV1ddw75UOGDAHATdSxXoNkXq+ysjLccMMNuOGGG+D1egXjxXvvvTegBIeUtYiDGoWSTdB5NjzpmmcBbn4YNWoU/vKXv8BgMODEE0/Egw8+iFtvvTVtixYyb+/atStE/bl7924A/gVCIucC6VlYkR1mk8kU1+s5cOBA7N+/P8BIGeC6FxmNxrgec+TIkWAYJqCMgzBo0CB8/fXXMJlMAaoqp9OJ6urqgMUwiTdarTbm2In6LdxnLJ7PnZghQ4Zg//79mDRpUkxD2yFDhuDLL79EeXl5XKWtUqkUp512mqAS2L59O8aNG4fHH388oCMlwMW50tLSsCbzFEo4OjOuEK655hqsXr0aH3zwAYYNG4aqqio8+eSTAeeQ73ZRUVFS87x4Lg0meC4lKqFwXejiIdY8PG/ePHz++edYvXq18BjxlP0B/nvYcHNlInNxIq+neK4MLudMZq6Mdx2SyLy6c+dOVFVV4amnnkpoPJFI9HuRKsnex1RVVUEmk0VVtlFSh3pUZTlSqRQMwwRkmFmWxeOPPx72/GnTpgltWN1utzDxjRo1CiUlJVi8eDFYlo2rvp1MxsEZ//r6+oBuCskwc+ZMAAjo3AFwXYVilaMA3E7Rueeei48//lhoyyqGZVmh3jwa5AY1+Brjxo1D3759sWLFCrS0tAjHTSZT2PayV199NRoaGiLuZIhl0zNnzoTX68ULL7wQcM5rr70W9+ID4KTTo0aNwiuvvBK2RMXj8Qg7Aom8XjabDTabLeD3UqlU8BMI3m0i14tHik6hdEXoPBuedMyzbW1tITukeXl5GDBgAGw2GxwOR8xxxMsZZ5wBrVaLf/7znwHtqc1mM/75z39Cp9PhjDPOSPhcgPNXirTTHi8SiQRXXHEFNmzYgA8//DDsOWI/jAsvvBCNjY1YtWpVwDmJLBiKi4sxYsSIsO/fhRdeCK/Xi2effTbg+MsvvxziJ3bWWWehpKQETz75ZNjXwW63C6/j+PHjUVZWhpUrVwaUcVgsloTa3QNcbPX5fGE7XwKBsfWqq64CAPz1r3+F1+sNOVf82orjOqGyshJqtTrk+Xm9XmzatInGOEpCdGZcIZx33nkoKirCqlWrsGrVKkgkkoAuqQBw6aWXQqlUYtGiRWE72BmNRjidzoiPMXbsWJSXl2PFihVoaGgQjrvdbjz99NNgGAYXXnghAOC4447DyJEj8eabb4ZNbMVSGOl0OrS3t0c877zzzkOfPn3w6quv4q233sLJJ58cdzfqE044AXq9PuJcGe9cnMjrecYZZ0CpVOLll18OiIUNDQ1499134xo3IZF1SCLz6ieffAK1Wo2zzjorofFEItHvRaokex/z+++/Y9y4cbTjX4ahiqos55JLLsFHH32E6dOn4+qrr4bb7cbq1atDEgmEwsJCjBkzBtu2bUNFRUWAr8e0adPw3nvvQS6XCwa10cjJycGZZ56Jd955B2q1GhMmTMDhw4fx6quvYsCAAQHeIIly7rnn4qyzzsJrr72GlpYWoW368uXLMWbMGGzfvj3mNV5++WWccsopmDJlCq6++mqccMIJ8Pl8OHToENasWYOrr74aixcvjnqNs88+Gzk5OVi7di3OP/984bhUKsVzzz2HSy+9FBMnTsSNN94ImUyGN998E4WFhThy5EjAde6880588803uP/++/H9999j+vTp0Ov1OHLkCL777jth9wAAbrjhBrz66qt4+OGHUVVVhYkTJ2L79u14//33MXjw4LiMXQFuZ+ntt9/G9OnTMWbMGKHVuc1mQ1VVFT7++GP87W9/E1ryxvt67d+/H1OnTsVFF12EUaNGIT8/H3v27MHLL7+MAQMGhHx2Pv/8c4wePTrumwEKpatB59nIpDrPrlq1Cs899xwuuugiDB48GHK5HOvWrcNXX32FSy+9NK7SwXjJy8vD3//+d9x666048cQThblv5cqVqKqqwquvviqYaCdyLsCZub/xxht45JFHMHz4cEgkEsycOTPACD8ennjiCfz666+49NJLcemll2LSpElQKBQ4fPgw1q5di3HjxmHlypUAgL/85S/497//jRtvvBGbN2/GyJEj8eOPP2L9+vUBCuBYzJ49G0uWLEF9fX1AKcp1112H5cuX47HHHkN1dTUmT56MLVu24IMPPsCgQYMCYpFWq8WqVaswa9YsDBs2DHPnzsXgwYNhMBiwd+9efPzxx/jkk09w2mmnQSaT4ZlnnsEVV1yBiRMn4vrrr4dMJsPKlStRWFiI6urquBVqpHX6smXL8L///Q/nn38+ioqKcOzYMaxfvx5VVVXCRs2ECRPw6KOPYtGiRTj++OMxe/Zs9O7dG/X19di8eTPWrl0Ll8sFgGtecuzYMZx55pno378/7HY73nvvPZjN5hDD5B9//BFWqxWzZ8+O+zWnUDozrhDkcjnmzJmDZcuWYfPmzZgxYwb69OkTcE7fvn3x8ssv44YbbsDw4cNx1VVXoX///mhubsaOHTuwevVq7N69GxUVFWEfQyqVYtmyZbjoooswYcIEzJs3Dzk5OXjvvffw+++/469//augMmIYBitWrMDpp58uzA2jRo2CwWDAunXrcPbZZ+P222+P+HwmTZqEzz77DLfddhtOOukkSKVSTJ8+HSUlJcJYrrvuOiHpsXTp0rhfK6lUiosvvhhr1qyB0+kM8D5KZC5O5PUsLCzEokWL8Ne//hUnn3wyrrzySthsNixfvhxDhw7Fpk2b4p4rE1mHJDKvfvLJJ8LGTjpI9HuRDhK9jzl48CD27duHZ555JmNjovCkrX8gJa2Q9rZPP/10zHOXL1/ODh8+nFUqlWxpaSl74403sq2trSyAgFbWhHvuuYcFwM6dOzfkOgDYk08+Oe5xNjc3s9dffz1bVlbGKpVKdtSoUezy5cuFdtLh2qZWV1eHXCdca2+LxcLeeeedbElJCatSqdiJEyey3333HfunP/2JVavVAeeGa5tOxnffffexQ4YMYZVKJZubm8uOGjWKveOOO9hdu3bF9RxvvvlmtqCgIKAlOuGjjz5ijzvuOFahULB9+/ZlH374Yfbrr78Oaa/Nslxb1xdeeIEdP348q9FoWI1Gww4ePJi9/PLL2a+++irg3KamJvaaa65h8/PzWY1Gw06bNo3dsmULO27cOHb48OExXzsxNTU17E033cT279+flcvlbEFBATt27Fj2wQcfZI8cOZLw69XS0sLedddd7HHHHcfm5uayKpWKHTRoEHvnnXeydXV1Aderrq5mGYZhly1bFutlplA6HDrPdv48u2XLFvbqq69mBw0axGo0GjYnJ4cdM2YM+8wzz7AOh0M4L9xzJUR6DyKN9+OPP2YnT54szMOTJ09mP/nkk7Dji/fcxsZG9uKLL2bz8/NZhmEC3oNIczT5/AXHCqvVyj722GPsqFGjWJVKxep0OrayspK94YYb2N9//z3g3MOHD7N/+tOf2JycHFan07Hnn38+W1VVFTMuiKmtrWVlMhn7zDPPhPyutbWVnTt3LltQUMBqNBp26tSp7MaNGyO2Hd+xYwd7xRVXsL1792blcjlbUlLCTp48mX3sscfY1tbWgHPfe+89dvTo0axCoWD79evHLl68WGiLLm5BHu29J6xatYo95ZRT2JycHFapVLL9+/dnL7roIva///1vyLmfffYZe+aZZ7L5+flC7D777LPZf/3rX8I5H330ETtz5ky2T58+rEKhYIuKitgpU6awH374Ycj1rr32Wra0tDSkHTylZ5ItcYWwadMmFgALgH3nnXcinvfLL7+ws2bNYouLi1m5XM6WlZWxp512GvvMM8+wdrtdOC/S3PDjjz+yM2bMEL6jxx9/PPvaa6+Ffay9e/eyV1xxBdurVy/hsS688EJ28+bNUR/HYrGwc+fOZUtKSliJRBJ23qipqWElEgmbk5PDWiyW2C+QiD/++IMFEHYeSHQujvf1ZFmWXbZsGTtkyBBWoVCwgwcPZv/5z3+yL774IguA/eOPP4TzosV/lk1sHcKysefVmpqasDGMEOmzEOk4Id7vRXV1NQuAXbRoUdyPESmeJHIfs3jxYlapVLItLS0RnwMlPTAsm2bHUAolw4wePRputztj9crB1NTUoLKyEsuWLcMNN9zQIY8ZDq/Xi6KiIpx44on48ssvO20ciXD33Xfjgw8+wP79+yMaJ1IolK5HR8+zlM5l/vz5+Prrr7Fv3764jIUzxbPPPov77rsP69evx6RJkzptHPHS0NCAgQMH4sknn8Qdd9zR2cOhUCgxqK+vR79+/XD99dfj1VdfTfjvzz77bFitVvz8888ZGF383H777Vi2bBnq6uoimrJnmhdeeAH33nsvGhsbe4w/n8PhwMCBA3HZZZdFNaanpAfqUUXpsoSr3f7888+xc+fOAG+QTFNRUYG77roLjz/+uFAWkGnCPfdXXnkFBoOhQ597KjQ0NODVV1/FE088QZNUFEoXpavMs5TO5bHHHkNraytWrFjRIY/ncrlCfKIsFgteeuklFBYWYuzYsR0yjlR58skn0bdvX9x8882dPRQKhRIHL7/8MrxeL+bNm5fU3z/77LNYv349vv766zSPLDzhfBrr6+uxatUqjBo1qtOSVADXVOnFF1/sMUkqgFuLORwOPPLII509lB4BVVRRuiwLFizAli1bMG3aNOTm5mLr1q148803odfrsXXrVvTt27ezh5gxrrzySjgcDpx00klQKpVYv349/v3vf2PQoEH43//+h5ycnM4eIoVC6Qb05HmW0nns3bsX55xzDi677DIMGDAA9fX1eOutt1BdXY2XX34Z8+fP7+whUiiUbsR///tfHDlyBIsWLcLUqVOzpjLhyy+/xP3334+LL74Yffv2RU1NDV577TW0trbi//7v/3Deeed19hAplIxBE1WULsvatWvx5JNPYvfu3TAajSgoKMD06dOxZMkSDB48uLOHl1FWrVqFl156Cfv374fFYkGvXr1w7rnnYsmSJejVq1dnD49CoXQTevI8S+k8Wltbcdttt+HXX39FU1MTZDIZRo8ejbvvvhuXXnppZw+P0oP44IMP8Pbbb2Pz5s0wGo0YNmwY7rvvPsyZM6ezh0ZJIwzDQKVS4dRTT8WKFStCTOO7KlVVVbjvvvuwYcMGtLa2QqVSYfz48ViwYAFmzJjR2cOjUDIKTVRRKBQKhUKhUCiUHsfkyZMxYMAAzJo1C0VFRVi7di2effZZvPjii1E7zFEoFAols9BEFYVCoVAoFAqFQulxtLS0oKioKODY5ZdfjvXr16O6urqTRkWhUCgUWWcPoLM4++yzk6pPXnrlMqz7+A9UThyEF35cnP6BUSiUrOasaVq0tnljnyiiqOy0rPFL6EkkGyduHPcgjuytwykXjscj/6adwCgUSiA0TnQdgpNUAHDCCSdgzZo1Mf822RiRLr59fz2ev3MVPG4v3tryN/Tq13NMrSmU7kwyMQLofnGixyaqWlpakvo7hVYBANi78RCcdheUakU6h0WhULKc1jYvNnxVntDfTDw/ufmIklmSjRNupxtgWTAMk+YRUSiU7gCNE12b3377DSNGjIh5XrIxIl001LTA4+YWs8YWM01UUSjdhGRiBND94kSPTVQli8/tE/7d3mREaf/iThwNhULparAAfPDFPI/SfbGbnQDDYPsvezt7KBQKpQtC40TX5bvvvsOaNWvw5ptvdvZQYuJyuoV/G1vNnTgSCoWSTmiM4JB09ANWVVXhpptuwnHHHQepVIrTTjst5ByWZbF06VL069cParUaU6ZMwdatWwPOWblyJRiGCfl55ZVXMjp+l8Ml/Lu90ZjRx6JQKNkICy/rS+iH0n1gWRYmfsHgdno6eTQUCqVrQuNEV6SmpgaXX345LrzwQlx77bVhz1m+fDnGjx+P8ePHo7m5uWMHGITb4U9UGVpooorSfWhut2Dd1iq0GC2dPZROIvEY0R3jRIcrqnbt2oW1a9di0qRJcLlcYc958sknsWTJEjz99NOorKzEP/7xD8yYMQM7d+5EaWlpwLnff/891Gq18P8DBw7M6PidoqDQ1mDI6GNlitqaFjgdLpT2LYBGp+rs4VAo3QpuF4T2qOipmNut8Hl9gISBw+YES0sAKRRKEDROdD3a2tpwzjnnoLy8HO+8807E8+bNm4d58+YBAMaPH99RwwuLeE1ibOmpC3pKd+Td7/6HVd9sRo5aiXXP39LZw+lwaIzg6PBE1cyZM3HhhRcCAC655JKQ+m6Hw4Enn3wSCxYswG233QaAax1bUVGBZcuW4fHHHw84f8KECdDpdB0zeAAuuz+51paliqp7Zi+Dqd2KK24/A1feeWZnD4dC6XZQuW7PxdhiRlGffLTUG+Dz+uB2eaBQyjt7WBQKpYtB40TXwWaz4fzzz4fL5cLnn38OrVbb2UOKC3Hpn6HF1IkjoVDSi9HqAADIZB1e/NVloDGiE0r/JJLoD/nbb7/BZDLh0ksvFY5ptVrMnDkTX3zxRaaHFxOn3QWwwMDR5QCbnZlOlh+3tAd/+SmUTMGChZdN7IfSfTC3WdBS2w6JlJtfnbbwymEKhdJzoXGi6+DxeDB79mwcOHAAX3zxBUpKSjp7SHHjsnOJqtL+xWAYek9P6T4cbmwHADDomYr0ZGJEd4wTXW5W27t3L6RSKYYMGRJwfPjw4di7N9SYdtCgQZDJZBg2bBheffXVjI/Pze9eHNpxBK31how/Xibw+bgPMllIUSiU9OIDm9APpftgt3C7gFJ+fnXYnJ05HAqF0kWhcaJrcMstt2Dt2rV45JFH0NbWht9//134cTq79vxNFFWNR1rRXNvWyaOhUNKHx8t1s2S7YfIlXhKNEd0xTnS5TEV7ezt0Oh2kUmnA8fz8fNhsNsHXqqysDEuWLMHbb7+NTz/9FCeeeCLmz5+P5557LqPjc9r9MlufNzsleayPGzdNVFEo6YcF4AWb0E88xGpEUV9fj/vvvx/HHXccdDod+vXrh2uuuQZ1dXUh19q9ezdOP/10aDQa9O7dGwsXLoSXvykQnkccTS0ooQiJKjkXwxzWrr3QoVAoHQ+NE12Hr7/+GgBw5513YvLkyQE/9fX1nTy66IgbPDmoepfSjXB7uLWqr4cmqpKJEd0xTnS4R1U8hDOeJRlV8ruzzjoLZ511lvD7c845B06nE48//jjuvPPOsCWGy5cvx/LlywEg6U4dYo+qbE1UEUWVNEYZJoVCSY5M7GrEakSxefNmfPLJJ7jhhhtw4oknorGxEYsXL8ZJJ52EnTt3Cl5+7e3tmDFjBkaMGIE1a9bg4MGDuPfee+Hz+QI8ABNpakHxQxJVMj5R5aSKKgqFEgYaJ7oGNTU1nT2EpHE5/J1lnXaaqKJ0H9zenp2oAjJnpp5NcaLLJary8/NhNpvh9XoDVFUGgwEajQZyeWRT2ksuuQTvv/8+ampqwnb/S0enDqdo98KbrYkqLyn965l1vxRKJmGBjNSJx2pEccopp2Dv3r2QyfzT+tixYzFs2DB89NFHuOaaawAAr7zyCux2Oz7++GPo9XqcccYZMJlMWLx4Mf7yl79Ar9cn3NSC4sdm5hJVcgUXq2jpH4VCCYbGCUo6EJup01hD6U64eVUOEVf0NDIVI4DsihNdTlJTWVkJr9eLqqqqgON79+5FZWVlXNfIZCtwlyP7S/98pPRPQhNVFEom8CX4Ew+xGlHk5eUFBBUAGDp0KDQaDZqamoRjX3zxBc466yzo9Xrh2GWXXQa73Y5169YB6PpNLboydosdAKBQ8YkqWvpHoVDCQOMEJVVo6R+lu+J284mqHq2oonGiyyWqTjrpJOj1enzwwQfCMZvNhk8//RTnnHNO1L/96KOPUFRUhP79+2dsfOLSv+xVVFGPKgolU7AZqilPhu3bt8Nms2HEiBHCsXBJ//Lycmg0GqFhRaJNLSh+7BYuMUUSVdlYjvHoTStw50Uv4khVY2cPhULpltA4QUkHAaV/GUpUsSwrbHBTKB2FX1HVMz97ycSI7hgnOrz0z2azYe3atQCA2tpamEwmfPjhhwCAc889FxqNBg8++CCWLFmC/Px8oZbR5/Ph9ttvF67zpz/9CRMnTsSYMWPg9Xrx3nvv4b333sOLL74YM1OYLF6vDx63F+AVW9moqOICTqDfF4VCSSMs4O0CG0A+nw933nknhgwZgjPPPFM43t7ejry8vJDz8/Pz0d7eLpwTq6mFQqHI6PizFbuZU1QpNUoA2amo2vDjXvi8Phw+0Ijywb06ezgUSveDxglKGghUVKU/1tx89t9Re6gJNz/6J5wzZ3Lar0+hRMLt4RJV3h5a+tdVYgTQuXGiwxNVTU1NmD17dsAx8v/V1dWoqKjAgw8+CJ/Ph7/97W9obW3F+PHj8c0336BXL/8N87Bhw/Dmm2/i6NGjYFkWI0aMwKpVq3DVVVdlbOxETSWTS+Dx+LIyUeV2+XdfenLLTwolU7CIX35LaG5uDvDNE/vpJcuCBQuwfv16rFu3LsTbL1LDCvHxeJpaUEKx8Wbq+gIt+g4phcfjjfEXXQufzx/b2pvNnTwaCqV7QuMEJR2IPaoyod5tqTPA7fKiem9oty8KJZM43dx61dtjFVWJxwig+8WJDk9UVVRUxEyQMAyDhx56CA899FDEc5YuXYqlS5eme3hRcTrcGH7iYFiMNhzdX5+ViSpngMcWTVRRKOmHgReJ3aAXFxdj06ZNaRvBv/71Lzz99NP4z3/+gxNPPDHgd/n5+TAYDCF/YzQahZ2RVJpa9HQcfKKKYSQ4dqBB+P9swWbx78pvXLcHp180DtocVSeOiELpjtA4QUkd8T29w+YKWSCmCsuXEh3cVZu2a1Io8UAUVSzLGar3PF/lxGME0P3iBDUpSgCv24s9f1ShtZ6Ts2VjoirADL6HZqkplEzCAvCxif2kk48++gi33347/v73v+PPf/5zyO8rKytD6sKPHj0Kq9Uq1Jqno6lFTyWnQIfRpwyDWseV/rmcnhh/0bWw8l0LGQmDTev2oe5wS4y/oFAoiULjBCVVWJaFPl+L0vIiyORS+Ly+gKqJdOD1+ACGgcVkT+t1KZRoeLy+gDnPy/a89WoyMaI7xgmaqEoALy9DJB5YWZmoEi2asnH8HUF9vQHv/Oc3fPn1DuHYZ19sw18XfYjVn23pxJFRsgUvvxMS70+6+PHHH3HFFVfgtttuw3333Rf2nHPOOQdfffUVzGZ/Wdd7770HtVqNqVOnAkitqUVPp2bXMez4ZR+UGq7m3i0qzcgGrPyCRKHgBNfpXvhQKBQOGicoqeBxe9Fc246W+naoeE/EdJf/kdJCi8GW1utSKNFwB1kmeHtoBVCiMaI7xokOL/3LZrxB3fKysetfoKKqZ37xY7F+w0G8seJnaNQKnH3maADAtz/sxtbtR2C2ODDr/BM6eYSUnkisRhSHDx/GrFmzUFlZiT//+c/4/fffhb8tLi7GoEGDAADz58/Hiy++iIsvvhgPPPAADh06hMWLF+Oee+4RWsyqVKq4mlpQQrFbOUWSWsuVy2VdoopXVEllUgBuuLNMEUah9GRonOg5ECN1hVIOfYEWUrkEDqsTOXnatFzf4/ZyFiEMA5PRlvayQgolEq7gRBWtAEor2RQnaKIqATxu7ovD3cBnZ+mc0yn2qMq+8XcExPxYnMaTSrng7KGvGSUGLJDWXQ1CrEYUf/zxB4xGI7Zt24aTTz454LxrrrkGK1euBMDVi3/33Xe47bbbMHPmTOTl5eHuu+/G4sWLA/4mnqYWlFDsfKJHnUMSVdmV6CGKKpmci3Mk7lFCaWk1Y+euWpw2hZY5URKDxglKqjjt3P28Qi2Hj2VhbLHA7UrffC32v/K4vLBbndDoqF8hJfO4PNx9EwN+ruyBa69MxQggu+IETVQlACn9k8pI6V/2KZKcNhcKS/RobTL13JafMSALM7HpPyn39Lh73mRJSRwfm/7gEqsRxbXXXotrr702rmuNGDEC33//fdRz4mlqQQlFUFTp1AACuzJlA0RRJVdwiSpa+heeH9btwdK/f4aCfB0GVhSjvLyws4dEyTJonKCkAoktCpUCMhnZWEjffE0qMBgJA9bHwtRupYkqSofgIhtkDAOwbI9VVGUiRgDZFSdooioB/IoqvvQvy9qOA4DJYENrkwkAVVRFwsUHenFpJOk2kW2t5ikdTyZ3QShdH6Ko0uizVFFlcSAnTwOVmvfYoomqsFitTnjcPjQ1m9DQaKSJKkpC0DhBSRWSSFIoZZDxnoKetCqquNJCiYRBToEWFqMd6Je2y1MoESGlf3yeqkcKK2iM4KCJqgQQElV8e8VsTPQY263Cv7Nx/B2Biw/0YqkpTVRR4oUFAy/tU9Ej8Xq8cDnckEgYaHJ4RZUjvea2mcZitMFssCG/KAcA4Kalf2ERvy7t1GiYkiA0TlBShRinK9UKYV2SzntUcn2ZTApDiwV2qzNt1+4JeL0+SCQM9fVKAhJfJQwDH3qmoorGCA6aqEoAL/niyLK365+pTZSo6oEZ6nhw8LtUXq8PPh/LBRo+q+2iizZKHGRKrkvp2tgtnJpKpVNBoZQDyD5FEin9U6qyc/wdhUv0uhgMXFw1me1Y+c6vUMilmH/DtM4aGiVLoHGCkgqCokolB/h7VE8a52viUSXhPVp7ok9QKtx577vYtbsOkycNwtLHLuns4WQVbq8XYwf3wZ4jjfB4fT32s0djBE1UJQTZqSC14FmZqKKKqpjYbH4FhNPphlqtANkQoeoCSiyoXLfnYjP7O/4p+ESPuNNqNmA18YkqWvoXFZcrVFHV3GzGx2s2AwBuuv40upNOiQiNE5RUETyqlHIQwUk6PaqIWTvxaKVrhsSwk9hPNQEJ43R5sOVALeSkqUsPFFbQGMFBE1UJ4AkyU8/GDG9AoqoHSinjwSqSNzudHj5RRRRVdNFGiQUDL0vluj0Rh9Xf8U+u4BVJWeZRZeG7/qnURFGV3uT8oX31UCjl6NO/MKsTOW5RLCCJKnHZjcPphlql6PBxUbIFGicoqeGy+xVVXg93P5/OLq1kkyWb1zydidMRuGakxA/xqCK2Kz1zvUpjBEATVQnh4wOBhCiqsjDDa2z3e2mks2uhyWDFHz/thy5HhcnThqftup2BUimDRMLA52PhCOrYRRVVlFiwAHy0rrxHIiiqdCJFVZZ1/bOR8kUNl2RJ5w79/9ZXYfkzX8Jhd+Lym6bhzAvHpu3aHQ1RVPUq0UPNJ/XsIvWcyeSgiSpKRGicoKQKMTtXqhVw8UmRdJqpO3iPKqmUNJDqicmC5HHw749UQr/niSIkqvjNrHg8qm5d9jG2Vdfj8mkn4JbzT8ro+DoCGiM4aKIqAchOhUyevaV/bqcb2hwVrGZHWs3ptm2oxrOPfAy5QopPNy5O23U7g4ZGo5CEJH5VZOPf5fKAZdmsVgJQMg+V6/ZM7BYHSsqL0HtQCeRKLry6syxRRRRVaq0SQHpL/3ZtPYKaqkbk5KrRWGdI23U7A6KubWwy4cjRNgCAze4vGzeZ7ehVou+UsVGyAxonKKkg9qgiG8/uNG4seD0+DBrZG1YzV2Xg9dKN2kSwC6WT9HueKCS+ComqOIQVR1uMsDpcqG8zZXRsHQmNEaCpukQQElUybgGSjYmqlkaTYJabTkWVh38t2OwTmYXgEe0afffDbgD+SVKnVcFCO59QosCynFw3kR9K98BmsqPpSAtcdregqHJnqUeVRkhUpW9xUnOgEQBgNtrx67e70nbdzkDsUUVUc3aRv6GJT/hRKOGgcYKSKkKiSikXNtC9aVT9221OHNxVJ2zc+qiiKm58PlZQ2HaDZVGHE1z654lDWMHyC1C2OyxEkVyM6I5xovs9owziJR5VJCCksQ1sR2Exikv/0hd0SBKvO9QRu8n7ygAffLwRgF92arY40Npq6ayhUbIEH5iEfijdA6uJm1+1uRrBo8qVZWbkZeWFGHZcP6j4RFW6Sv9YloWh1YIRx5cD4JJV2YzYr5B0gw1WVFEo0aBxgpIKQqJKrYBMwW2gp9OjiqiBBY+qLLQ76SxsdpdQiZHO96SnQGxWJAz57MVeW5KEajfJUwFIPEZ0xzhBS/8SQOj6l6WlfyzLCmUdQHqTSmQx5vOy8Pl8QpeQbEQcVEiHR7GJZEurBRX9izp8XJTsgOvUkb2ff0ryWPnkiyZHndHSP4/bK8ShdLNt/QF4XF6cfPYYAOlTVB2racGuLYehz9MAAMxZrjgSJyCd/HtMElW6HFWA4opCCYbGCUqqCB5VKjkc/L1qOku1XXwjEKHTeRZuzncWLqdbSJxk22ZVV8BJFFVSYqYeO/skKP+6SUKVxggOmqhKACKplWZposphc8Hr8UEmk8Lj8aa19M9h9e8kuxwewYg3GxGX/mn4Fu3iYy2t5g4fEyWboJ06eip+RZUa8jSX/h2tasCTN72Bmt21kMgkeHvL35BXlJOWaxPcLg88Li8kUgnU/NyXroXPsZoWDB3ZB/0HleDHL3fA5fTA6XBDyb9O2Ya4sQZJStn40j+LxQmDSL1MoYRC4wQlNSQSBqMmD0FhWZ5Qsp3Wrn+Coip0w5YSnYDScKqoShgSX6W8LM0Tx2ePlAf6uo2kisYIgJb+xc3hPbV45f53AIgVVen7Mqxcuho3n7YEF5TfgZn9bsfR/fVpuzbh49fXAQDkSm78OzYewo6Nh9JybSvfKQoA7KLyh2xEvAAhKjpxgKalf5RokE4difxQugdEUaXVayBXyFA5YSD6DC5Ny7Wrd9Xi0M5j8PlYyGRSVG0/kpbrirHz/nsardKvCEtToqrhWDv276qFRqeCLlcNAAEK32wjQFHF/1sc+2iHWEo0aJygpErTsTbsXH8ADMMI65L0Jqp4uxMZ7fqXKE6Xf4OKKqoSx+XhP3uS+Ev/PLzZfzxJrWwgmRjRHeNE93tGGcJqsgmtxwXTwjSWzv3vxz2o2VPH7Wi7vbBlwLD7g9d+CPj/usOt+O2rnWm5dkCiypbdiSqPJ3QnhEyAffvkC4sSCiUSXpZJ6IfSPbDxvkTaXDUUSjn2bjyEA1uq03Ltmj21wr8dNhf2balJy3XFkESVWquEnI9z6Uq4GA1WAIA+X4McPZeoymafqoAdc1L67mOh4L1i3LRMhhIDGicoqWBs4TZN9QU6yAWPqvTdn5Ky9XAWGJToOJ0iD0O6ZkgYEj+lfOlfPP5obv7zmc61eWeTaIzojnGClv7FifitJzLYdJb+WYN2ltNZZ07wqyH9z0YiS0+u0mrxJ9Yc2a6o8oRTVHEv3rE6AxqbaekfJTIsGFpX3kOx8uVeGr1G5FGVnrncarKjfEgvKDRKVG07AnO7NS3XFWPj53G1TiksfNI1fpOBe21y8zTQkURVN1FUkX8bjDbu3wxDFVWUqNA4QUkVUxuXqMot1AnrEk8avfEEj6osbiDVWYSLD5T4cZLSP6KoimO9TdZu3WWTiMYIDvoKxInH7f+SkHaZ6UxUmYRFB5cQyYQBL+vzAT4Wf54/TTgmTVOiSsJA2IF3ZLuiSvRek8WGeJK0WB0hf0OhUCg2Eyn9UwtdmNwuT1raJe/+4yCO7GvAhOkjAQR2cE0XYkWVUi3HkNF9kVugTcu1jW1EUaVFv4HFKCnLhcmQ/mRbRyFORLndXvh8bEAnQJqoolAomcTY5ldUyRR8oiqNi3TiUSXYnXQTk+qOQFx5QWNB4giKqjhL/1iWhYv/G5eXvt7dCaqoihOXQ5R84efqdCWq3C4PepUXwmKwcddm/DsZ6cLr8cLt9IJhGJSWFwrHpdL0JKqOHW7hVGAMk/Wlf2SCZBguMHu9voCdJGsGyjIp3QtfwgaI9AawOyCVSzB84iDkFeshkUgglUnh9XjhcXsFhVKyNB5tBQD0HlACADC3Zy5RpdEqwUgkOLCzFvr89CSqxIoq1seiqd4IYwaeQ0dBdsnlcincbi9cLk9AOSBdnFBiQeMEJRWMLZy6P7coB3I5X/qXRvWOO0RR1X1KqjKNK6D0j8aCRCFJJ5mUJKqiz32uMJYt3YHEYwTQ3eIETVTFiVNUzkZKQNOVqLKZHTi4/WjAMUeakyHE80qjUwYkp9KVqGpv8RuMHznUhHEnDU7LdTsakpgCuLp8t9vLJapExvkWC01UUSKTXEvZ7hNYezL7Nh2CsdmMgtI8AIBcKeM3CdwpJaqsZjvM7VYoVHL04jcaLBlQI9l4r0G1Vin4knjStDix212QSBjk5muRxye/SPIqG+lfXoiCfA2qqpuFRJVbpKhypdErhtL9oHGCkgosywqlf/p8rdCNPJ0eVURRRWKXlypV4iag9I/GgoQhr5k/URX9PsQher0d3aTUMrkYAXS3OEETVXHiEvsu8WUc6dpdsJpDfToctvQmQ+wi7xGJKDklSUOiimVZtPG+Tb3LC4USj2yEyKblcql/p9ztDZgkrWl+byjdCxbd09CQEh2n3QVjsxkyuRT5pbkAuESVw+rkdqZzkr92fXUzRp44CLmFOcjJ0wAAzBks/dPkqPydntKwO2m3OVG9vwEanRKl/fKhz+eegzEDPlsdxe69dWhrsyI/XwMruFIP8c55uhJ8lO4JjROUVLBbHCguy4c2TwOlWgE5X/rnzqhHFZ3T4sVJPapSwhVc+hdDGGIXdVl0dpPEII0RHNSjKk4CFFVpLv2zhTGUtadbUcXvlGt0KkgkYkWVNOVr220uOB1uyBVSNDcY8N83fspak1ySqJLJpH5FgdsbUPdPFVWUWPT0drI9keZjXGlecd9CYY6VK+QAALcrNc/B6l3HsOuPg5DKJMjh1UiWDJTNOR1uaPVqaHNU/nbnafA8qdpdB6/Hh7J+hVCpFMjln0M2l/6RNvAK3jSfK/0T+5J0j5tlSuagcYKSLBajDfU1zTA0GgEAMr70Lx0bCwTBo4pXVKXTl7e7E9z1Lx0+lT0JUr6Xn6PG2MF9hO5/kbA73dCrlQAAR4r3W12JRGNEd4wT3e8ZZYiARBVfK+tLUwtM0vFPIvoipjtRJeyU61QBjxPryx8P7XydfFGJHgOHlgEADu2tT/m6nYGb3zGSyyRQkPbsHm9A6Z/V5qRBhxIRlgW8rCShH0r2Y2g2Y8jYAagY1Vc4JrQMT3GX++AOrjR80OhyaHPVGH3SUPQdUpr2echitMNqskOlUQpdpNLR6elYTQsKinNQyb82/kRV9iqqyI20ik9GcqV/3dMng5J+aJygpIKFL5vW5XFzaSZK/4hHlb/0jyaq4oVsWqhUcui0KupZmCDEK9judON/VbUxy/ncXi+MdicUcilKC/UdMcSMk0yM6I5xovs9owwhTlSRMrB0TdokUSVec6Tdo8ri7+YklaS39M9icmDk8eWoHNMPMy+bCLVWiZee/Dzl63YGZJdcJhcpqjxeePja/JLiHJSW5sKW5YbxlEzCwJfgDyX7MTYbcWDzwYCJXE7UNil2cXW7PBh2QgXKh5VBpVaiavsR7Fh/QIgd6YKUoWtzVGn1qGptNqOt2QxdrhoAkJuvwaDhvaHTq1O+dmfhDqeool3/KHFD4wQlecxCoooroyZdtzNR+qfgywqpoip+SKLK7fbCbHHQJF+CkPI9aZxm6qTU0unxYn9tc2YH12EkHiO6Y5ygiao4cTm4hYZUJkF+Mec/kq5JmywOxMbm6VZUuV0ejJo4EH0HFAUoqtKRqDIbbdi19QiMBhtOPWMU7DYX6o+1pXzdzsDjJYoqqT/w84bqAOBwuFFXZ6CLEEpEWNCd8lSpqqrCTTfdhOOOOw5SqRSnnXZayDksy2Lp0qXo168f1Go1pkyZgq1bt4act3v3bpx++unQaDTo3bs3Fi5cmBFTWLKZoVQphGNC6V+KXVyrth/Bvi01yCvmjK7yirkdQwPvDZgurGZSIq6ETM59Lj1pmOtMvJ+WPpdbVOnzNDi4tx77dtamfO3OQNx0o7SXHmVleXAGKapojKBEg8YJSiqQZhokUaXSqTBq8hDkFurS9hh+M3UujlGPqvghpX9kXUcVtokhlUiQr1NDzr9+vliJKpHy291NkoLJxIjuGCe63zPKEGQRcuVDF+HaxZcASL9HFUkayeRSqDTKtFybYDHZsXPDIVhMDjAMAzBcskoiST37StRF6iBDx2wsjxMUVTIpZLyZMJeo4p6LNM4OFJSejReShH4ogezatQtr167F0KFDMXTo0LDnPPnkk1iyZAkeeOABfPrpp9DpdJgxYwYaGhqEc9rb2zFjxgwwDIM1a9Zg4cKFePbZZ7Fo0aK0j9nNb2bIVXLhGFFUpepRZea9nIg/VW4h999HLl8GmyV9qiobn6jS5qiF0r90JKrMRm6MObyiKodXUpkyYAjfERD/KblcCqvVhfp6A1zOYI8qujChRIfGCUqyWIIUVWBZ7Fx/AA1HWtL2GP7SP74MvJskADoCl6AI4tZY6YijPQmL3Yl2i11Yc/lirCcD1Mye7Fx/hiPRGNEd40T3e0YZgnT9U6gUQkIpfYoqbnFAvpAetxetDYa0XJtAFGFKlRzp/v4S9Zdaq4REIhG6RaXDhLejIXXRMrkksPSPdKAgiSpv95gEKemHBQMfm9gPJZCZM2fi6NGj+OCDDzBy5MiQ3zscDjz55JNYsGABbrvtNsyYMQMffPABGIbBsmXLhPNeeeUV2O12fPzxxzjjjDMwf/58LFq0CP/4xz9gMpnSOmang1dUqf2KKpngUZWaosrCJ3R0vCJJwSfDGo60ovFIa0rXFiMoqkSlf+nwqCJlKqRjoUanhETCwGZxZuUNvOBlKJf6N2fcXpqoosQNjROUVCCJqpxcbtNCrkyPeleMRMpAl6cR4lg6YkFPwWF34/gx/aDgTe5pPEgMtzew619MRZXbCwYQCt883SCpmkyM6I5xgiaq4sRJEj3q9CeqLAYbRk0aHLATn+rCJhgyfoVKDq/PP2GyMb780Vj7wQZcOmUp/vv6OgCARsupwORkYk5jrXxHQRJSkUr/iAKNKqoo0ejpOyCpIu5MGo7ffvsNJpMJl156qXBMq9Vi5syZ+OKLL4RjX3zxBc466yzo9X5zzcsuuwx2ux3r1q1L65hdojmWQExoU1k8+Hw+WIMSVRD5ENRVp8+PwSooqtLb9S+49E8ikQj+VBb+MbMJt8uvqCILEZfbE1DeQRcmlFjQOEFJFrORK/3T8ipVoXFHGs3ULUY7LAab4MPnoxu0cbNl62Fs23YECnIP0MlJvo3bD+NvL3+FT7/d3qnjiBeyGSQTSv+ir7lcHu5zT9Zorm6SVKWKKpqoigu7xYG6g1w5iUKtEEoi0pWo2r3hIHb+dkCY0ADuJtdpdwmLn1QhCyWFUgafxx9sUlFXNdYZYDLY0HCsHQCg1nBKAn/5X/a153aLSv/EiSqSzZelOUlJ6X6wAHysJKEfSmLs3bsXUqkUQ4YMCTg+fPhw7N27N+C8ysrKgHPKy8uh0WgCzksHYtUtIR273DazAz4fC7XOnzyaOXcq+ldyHVZr9tYlfe1gAszUhS5Sqc91RFGlz/Obp5PyP3OaDeE7AhIn5HJ/nAj1qMq++EfpOGicoKSCz+1DYWkudERRlYZNkWCI2lXwqMqAt2N3xGCwQZ+jQt8++dDw66LO3rj46qfd+Oz7nXjrkz86dRzxIlS3SOIr/SNm6hLe1qazE4PpIJkY0R3jRPd7RhmgZtdRbP1+J8oryzD1khOFjK3Px6ZcB+vxeGBq5QxxVVq/L9X/1u3FRYPvwUevfJvS9QlOUemfuM6cZZNfhJASP/IaqPnxk4SbKwsTVR5xSYfMvwABuEy9hHpUUWLCwJvgDyUx2tvbodPpIJVKA47n5+fDZrPB5XIJ5+Xl5YX8fX5+Ptrb29M6prCKqjR4VFmCyuYA4JTzTsDsW85ASd8CfPWf9fjhk02wW1NXJgV6VPFzXRpu+HILdBgysg90OaJEFa8EMGehT5XQ8U8ug4JPVDn49594G1LzXEp0aJygJI/VbEdrgxFSvumFP9ZkIFHFX5t6VMXHzl3HsGPnMRQV5fgVVZ0cD9r5OEviVVeHlO5J4zZT5z3B+MRWJhVVHed/lXiM6I5xgiaq4oD4gxT3KYBKowTDMP5kVQoT978e/C+uG/sQ6mtaUNwnX5DXAoDH7Qbr9aG9KT1dnUj3DoVKHjDmWF/+aEiDOgb6FVXZW/rnDjBT5yZ0u8ON3mV5KO9b6K+XphJoSgToTnnHwDChAZncQIh/F+m8cMcBYPny5Rg/fjzGjx+P5ub4y+qIR5UijEdVKosHwTQ3VxNwvLSiGE3H2tBc246/37ICDYdT96pSqORQaRRc1z9Zekr/fD4f9u04hgO7aqHTq4TjgqLKmIWKKrLbK5MICxE7r6iTiZS4FEokaJygpAKJKTK+9DgTiip/0wjiUUUTVfHQ0GCAXq/GyBF9RJUZnbtx38Z3iZRJYyeqTp/7T5x0xbP4dcvBTA8rIh6v34YFiMdMnTufrM3dGVL/rd93GPOWfwyz3ZmR64uhiiqODn9G2dh23MovFLSiHe2ivgUo7J2f0k38no0H0VJvBADkFuYEqgNYrjPfrg1VSV9fjFhRVTGsDP0GlXAPk0K+hXSzIJAb9mwu/QNYjBndF3165wWU/tXVG9DQZKRd/yhx0dN3QDJNfn4+zGZzyHxvMBig0Wggl8uF8wwGQ8jfG43GsEorAJg3bx42bdqETZs2obi4OO4xucMoqhRpWDxYLQ7klehRUl4YcLxieO+A/ze1W5N+DIBLKNUfboXD5oJapxRK3FNdnNhtLrAsC7XGXzYPAL365KGwRC/4YmUT/tI/mRAnHA6+vF7Ge8V0g9IDSmahcYKSLMFqp3QrqliWhcdFHoN2/UuEPXvrYTLZ0bdvgRAfPJ2c5MvRcZtEEfbnAnC63GDZzk1MCh5VogqmaBBFFbFnyVTp3/zXP8HvB47ghbW/ZOT6wVBFVSckqrKx7biFNy0kteAAYGwxo7WuHb4Uvshavb8MIr9ED4nM/3YQZYDVlJ6beFKWIlfK0aeiCCdOG849TgqKKoYJ/PgQqaY8i7tcuNxebN9xDM0tZgwaWIJRI/rAzPu2SCV+JR0N2JRIsCzT43dAMk1lZSW8Xi+qqgIT+cGeVJWVlSFeVEePHoXVag3xrkoVofRPKS794z2qUij9c1gdMDSZ4A2aT7U5arz5+6M4YQr3PExtlqQfAwBcIh9DiUQCmdzfhTYVqbtg0K5TBRxnGAatTSaYslFR5RaZqRNFFVHUKSQYUVmGQQNLOm18lK4PjRNdi3g20bsSbr5KQi4oqkisSU+iiiQppDKJoNqi3qzxYbFwMS8vTyMoglydrKg6dKQFABBrj93nY+Hl14WdWTdCFFFEAeaNsVYliipS9eLOQJLN7fUKyq4mU2r3W/GQTIzojnGiw59RNrYdt/I30qS7BiA2yU1+AaLU+D2p9AXagFI6f6IquZv4X9ZuwyuPfoLfv90JIFBRBUBIq6eyACFJG+5yjLBzoMhArXxHQUr6JAwDqVSCnbtrcZhv/y6VSvz10rT0jxIFLytJ6CceOlqNGu+1OoOTTjoJer0eH3zwgXDMZrPh008/xTnnnCMcO+ecc/DVV1/BbPaXUL/33ntQq9WYOnVqWsfk5Eu/lGqxmXrqiiq7lZOYq0UehoSyimL06scprUxtqSmqnHZ/Z1uA68yXjhJ3cSdBMULXvyw0U3cJHlUiM3WHG2NG9UXlsDLs3luPHbuOdeYQKVkAjRNdh3g20bsSRFEl4ysY/LEmPQ2YSPdAmUzqrySgpX9xYeYTVTqdKqAyozMhCl9PjKojq90pZKgcnbiGc3sCS/9irVWd7iBFVQaqq2yi75apA0r/gMRjRHeMEx2eqMrGtuNWA2kD61dUCaqhFHyYAm7+Gb9RN+AvybOa7Empdz56/UesWfkzPn2Lkyf6d/u5cZMFSCqJKpb1D/SWBefh7IvHA8ju0j+SLZdIJBjMl0ceOtQsHBMUVbT0j9LBdLQaNZ5rZQqbzYYPP/wQH374IWpra9Hc3Cz8v81mg0qlwoMPPoilS5fipZdewnfffYfZs2fD5/Ph9ttvF64zf/58KJVKXHzxxfj222+xfPlyLF68GPfcc09A7EgHwg63qPRPl6tBUZ98sCnMFw4Ld0OkCpOoAgB9PheXUi398yfa/OOXylIvW4icqOL+PxsTVSTuy+RS4V7A4fRg+85jqKszAOBes44zXaVQOHpSnEgnsTbRuxoe/v6abJrL0+CHKEbwa5VLhcYaVFEVHxY+Zuu0SlH33E5OVPHvnSfGe2iy+Kt4bLxKuDMgiqh47VaCE1WZMFO3Of2vR0d4VGWSbIoTXU4j1hXbjhMzdZ3Ioyod3ZxIAkpfoEX/YWVCMADAJYBYrqugLYkbeWIUTOp6iZk6UVQxcdb9RkPswSH21yI37tnY9c/HT4YSCYPepXkYUFEEBZ94EyuqvFRRRYkAC8AHJqGfeOhINWq818oUTU1NmD17NmbPno3ff/8du3fvFv6/qakJAPDggw/ioYcewt/+9jecf/75MJlM+Oabb9CrVy/hOvn5+fjuu+/g9Xoxc+ZMLFq0CHfffTceffTRtI85nKLK6/WhpbZdUEUlQzRFFQDkFPCJqhRL//yqW5EZvKj8L1mslgiJKr4DoCULPapI7ONK/wK7/qnVCiFOdLYvCaXrQuNE1yLWJnpXw2+mziuq0mymTvypZAqZ8NoEb5rTRHx4SMzLyfErqjq7C6ygqIqRwAlIVNk7J1HFsqyQmJKTKpY4zdTl/Fo0Ex5VYkVVh5mpJxgjumOc6HIzc1dsOy6YqYu6LpFuTp4UkjHE32rB6zdi9u1nhQZKPtlkNiS+Uy4koliSqOL9R0iiij8vFY8qcdtycZLNr6jKPo8q8npIJAxUKjmqa1pQw9d2SyQMpFRRRYkJkxGpbkeqUeO9VqaoqKgAyyfqg38qKioAcMn4hx56CMeOHYPdbsfPP/+ME044IeRaI0aMwPfffw+73Y76+nosWbIkJL6kg2geVanECYc1uqIqvzgHA0f2CVDkJvU4Ni62qjT+RJXfUD35udwSwaNKUFRlYaLKX/on8yuq+E0rpVIulCvEKrOg9GRonKAkD0lUkSqJdCuqhNI/kaKKxIH7/vwSzh10P5684520PFZ3gmVZWPiYrdOp/LGgE5trsCwrKJRieTeZrJ2vqCKqL5lU4u+0HqeZOnm9M5GosooUVR1T+pd4jOiOcaLLJaqArtd2XCKTYPDx/ZFblCMcS8fuBdmlJosmRhI4boYB8otycOxgU8LXJi8BUQgFe1QxaSj9E+8Wk2AJpN/UsSMhhn2MhAlRT1GPKko8cC1lmYR+0kE61ajxXovix0XMtDPmUaUK+3ttjhqHdtXi2MHGpB8DECnCRKWLsjSU/jkdLuj06siKqmws/XOHKqqcfNc/lVIuUqLRDQ1KeGicoKSCX1HFxRjiVeVxe4X7/pSuL5rjgu97bWYHWJaFid/ETxfmLGysEYzL5YHb7RUabXQFjypx0ibWOJwuN0oKubWuzZ4ev7NEEftTCdVBMdaqeVo1xg7uAwVJVGXCo0pUQWV1umKWUaZKMjGiO8aJLpeo6optx4/tr0fV1sOBHlVkpzyFTg7kORLpbnCGU66QwdBqQe2hxBNVEmKWzgcWV1DrdCYNZupEBnz9XWdi2rnHicbd+RNzspDXQ8IwkMkCa6Ml4uQVVVRRouCFJKGfdJBONWq816L4KSkvxqiTh0EpUiQJSfsUDG4d/A5nJEUVKUm3pLhoEDYz1GlWVBkdsBht0IheFwDIyWIz9YCuf0Gl7kqVLKO7upTuA40T2Umym95itv+8J6UxEJUuqe7gOrWmzw9JMGuXy/yKKn7NQuY/myU9atiGY224dMoTuGzqUtQfbUvLNTsLwZ+KVxB3iUSV6LFjlSAaTHY0tXLNZzpLUeX2+sv+4q1iqW014n9VtRn1qCKKKvIYFkfmVVWJxojuGCe6XKKqK7YdN7ZwX1p9gU44RpIxrhR2ykkHDQkfBIIVVXK5DDK5FD98vDHha/s9qrjHKCrLxYDKMqEsRZIGjyrSLp0sZoRxp1mC3JH4RKV//l0kv6lfpFp9CoXAIvEdkObmZuHGd/z48Vi+fHlSj51ONWq816JwHNh8CDt/3ReQUJKnoQNqTI8qPlFlTtFM3cErqlQiM/V0eFQ5HaEm7QCg0SlQUKTr1BbYySKRMBgzqi+Ki3NEHiQiRVUXKPegdG1onMhekt30Jrx4+wo8e9Nr+PnjP5IeA/GQIvfb4n+n497bK+oqKJNL0atvgbCJ4bRxmxrm9vQoqo4dboWp3QaZUoYd/6tJyzU7C7vdif79CzFwAPe56AqJKnFyKlZMsovWtJ3lUSVWVPkbf8X6G5/wN+L/TyfEoypPo8aIviWwOjObqEomRnTHOCGL+ttOQNx2/OGHHwbgbzs+b9484bxzzjkHTz/9NMxmM3JyOJliptqOm9v4RFWhKFGlTH2nXEj08AkRSVCiSqGRw2pxwJrMjnNQIqp6bz1a6o3+0j/ywUhhlUAmPJksMN+pIBNzFnpU+aKV/kkYSKWBCUAKJRy+BPcAiouLsWnTppQeU6xGFe9cJKNGjfdaFD92Xvmk0amFY+kt/YuUqOKUvqTpR7I47WHM1NOgqHKEMWkHAJVaibYWS1YajlssTmzfeQyDB/eCQkEUVdxrJPaoykZVMaXjoHGi5+Hz+bD2zR/A+lh8/fYvOPXiE5O6DklGBSSqlDLYrU4u3uRE+ss4ry9SjUqlUjQea0Mu37jDYuJiTWl5YWoPwlN3pBUAF4Maj6XXY7ijcTo9OHy4VfBWCt60+O33A/jny9+htDQXzz01p0PGJFb2+lgWHq9PUAUFY+c3lkoKcqANUkF3FGS8MqlUqA6KpagipX6k9C+TXf+cbg92H2uCswPie6IxAuh+caLDFVXZ1nbc7fLAZnZAIpUEmqnLUzdTJ6ocItcNzioS9ZMlibptwaOKT7KQRUhw6V+sut9okAVGsKJKl6tGr955IeWb2QDJ8ErDlPkFKqqyUQdA6QhYFvCyTEI/6SCdatR4r0XxY+dNwdU6kaIqLaV/0c3UdXxcMhtsKZVyh1M+SdPhUWUP9EckEOWWo5N2bVOBlPkFepAQRZW/VCYbk3CUjoHGiZ6Juc0qNO1x2JJXZAgeVQpRx+00KqrEpX+C/5XHB5fTA7vVBUgYbPu9Ki2d/8wGm1DCaM/CeCDGJfL2Avwb9+R4faMR9Y1G7Nvf0OFjIjNItA0UG7+x1NRmxqFjrZkeWljEZurxVv+Q5JZCmrlGJgyAsQN6Q8mv/50ZVkwnEyO6Y5zo8ERVtrUdN7X61VTiRFI6SjrILjVZDNy37Fq89b8nhN+TRJXVlPgCJLj0j3hUkcWBUGaYikcVyXrLAz9GUqkEjXWGtBstdgQkecgwoaV/EonITJ0qqihR6AzzQ7EalUDUqOecc45w7JxzzsFXX30Fs9ksHAtWo8Z7LQqH1+OF0+4CwzBQakJL/1wpxIn8Ej2GTxgYUVGlUMmhVCvgcXtTWviQhJK46x9Ry6ZW+sdfNyhRRRpwuJyerJtP/YkqKVRKOYYNLRUWJgGKKlr6R4kCjRNdh1ib6OmivdHgf0xz8v58xB+XbIYA4kqPNCSqXH4PXdLZ1OP2wGywYuT4AQC45IE3Dcn42iOtwqa/PYUY1hUQb2IAnBfj6FF9oebjH/E0zLQRtxiyiULWhe4o3soO0aaatZPei4DSvzhFFeT1JHHYmYJ/dCRaLXb8r7pOSJ5l4jGC6Swz9a4UJzq89I+0HY8GaTv+0EMPRT2PtB3PJOY2C4BAfyrAb2CYiqKKSEFJ8kOrV0OrV0Mql8Lr9kKhlEGhksPlcMNpd0GlCb9QiYbP6wPLssJigSiq0uFRJSiqggzS9LxnismQfSa5gpm6hPG/RiwAhiiqeBkq9aiiRICrK0//HoDNZsPatWsBALW1tTCZTPjwww8BAOeeey40Gg0efPBBLFmyBPn5+aisrMQ//vGPsGrUF198ERdffDEeeOABHDp0KESNSpStsa5F4SCqJ7VOGbihIXSHTV5RdWR/PfZvOSzEnHDo8jRw2l2wGOwRuwPGwmEjXf9EZuppMOf1m7QHJqokEgmUKjmcDjecDjfUScS3zoKU+SnkMigUUuzb34C8fC7uqVTytHh7Ubo3NE50Lcgmuhjy/9XV1aioqEjL47Q1GoV/21JoJEGSUWJFlVKj4DYtXMnHG+H6bv/1paINC5vFiV2bqsFIGbAs4HK6haqQZBGrau3W7FZUuYMUVW6XBzt2HkPlsLKA4x3pXygoqhgAbPSyOLGBurmzElVekqjyV7HEVFTxf6NVyiGVMBlJIjk9fHI4g4btYjIVI4DsihNdzqOqq2G3OlE+vA/6DC4NOO5fgCT/ZfCRRE/QJC+VcYkqqVwKrV4Nl8MNq8meUKKKLJa8XlYwfJcrZCGdBdlUzNQFRVjgNf2JquxTVPnN1CVgGC5Z5Q1jsO5N4XWjdH+8SM+uhph4bqQffPBB+Hw+/O1vf0NrayvGjx8fUY162223YebMmcjLy8Pdd9+NxYsXB1w7nmtROMjOuDpHHXDcX/qXwoZGGNPcYEZOGIi6mmYY28wo7pOf1ON4PV4U9tJDp/cnusoHlUCukKWnpFAV6kOgUmdrosq/a06UYiQppVTSrn+U+KBxousQzyZ6OmhrMAj/tqfQNY/MN+K4IJVI4LS7UmryFHJ9uUx4DI/LK3T6YyQSsF6uFFCji3iZuAhIVNmyO1EVrKjye936qzUAbq3hcnmE8zIJSZ6R9Uy00j+7w5/ktFg7J1ElqKOkUpFgIIaiil9PK+VyeH0sHBlo5kWSX8QHqyMUVZmIEUB2xQmaqIqBx+nGkT21IYoqQWKbltK/wESPTCaBC5wJn06vRnuTCRajHYWleQk/hs/nE8r+xAsFfyeF1BVVsiCPKj3vmWJK0dy3MxDM1PlgIpVK4PX5lW9SqqiixIAF0ia/FdPRatR4r0URG54HqplI6V8qylvBNFcZOVybDTZUbT8KY4sl6cexmh1obTQJXWgBoLG2Hbs316QU55wRzNT9x2xw2FNXAHQkpMOfQiGDkr8XIDfXAV3/aJygRIDGiZ5Je6MRCpUULoc3LkXV/y3/Dh88vxaVEwbhobduEY4TlW6AmboqjaV/bn/pn0ykArJZuFgnlTDwedPzWOL5P5Xy9a5AsKKKWLuQNYO4zN1scaCwIMUsXxyQeEXWfa4oiSpS+scwgN3pjmq8nin8pX8Sf+lfnIoqDf8dsKWgYo8ESUzJpZkzbBeTqRgBZFecoImqGJD6a0nQFzUdpoWRSufIY8nkUsHAPdHOf0LW3usTdisUokQVk5ZEVfiuf/p8v7lvtiGYqUv9iSqIujMG745QKKFkTq5L6ZrYBUVV+ERVKnFCKMGQRw7XuXxH2vZmc8RzYuG0h5b+ydJQ+ueIYKYOZK+herBHFcDFhJLiHKjVfo8qWvpHiQyNEz2R9kYDXHY3GIkUTrsLXo83pCGRmKpth9Fc2w6JtEY45vX64PNx7d/FaxO/718aSv9c/rgjjgNEUSWRcffG6TBud4rmf1t3U1RJAtcMbrcoUWXumEQVSfxIGAkAb9SyQ2KmrlYqYHO4YLU5kRukFM80Hq8PORolNEqF324lVtc/Dyn94+5fSIe+dEISVUoZMVPPtKKKxgiAJqpiQiaXkESVMnXvEb8ZeVDpnygZUlFZBrlCmviNvND1T6SoEnmEMHFmqaPhJQmcCIoqYxYmqrxhFFUEcelfKq8bpfvjy5Bcl9I1ISUcal1woir1rn9kxzqaoiq/mPMCMLQmn6giMSbQTD11Pw2/oip0/CR55cw2RRXxqFLIhOfl8fjQ1GyGWqUQPKpo6R8lGjRO9Cyeu/k1fLnyRwDcxrHT7oLN7EBOvjbi31hN3H20uPLCI1LZij0RFek0UxdKC6VCHHC7PUKiSiaTwgl3So1CCOL1jSPLE1VEUeUv/QuswiCxVCJhYLUlX/qZCERBpVLKUJCnjRqXiKJKo5LD5nDB3AmJKpfHC7PNCRYQqlhiiSqIelnLb7TZM1D65yBKannHKKoAGiOATuj6l20QmWZwMobsbqdS0iGotYIUSZfddTZ6lRdi6qzxMLfbsGN9FawJltGRj7ZXnKhShiaqUinJj1j6l6dGvwFFKCzO6ZCa/3TCivyogMBEFTVTp8RDZ7Udp3QeERNVafAyFEr/ohjW5hXnAAAMzaakH8cZRvlEFkepdHaKVvonKKoc2bU4EXbN5TIoeR8ysnmh0yn9iqo0dMSidE9onOh5OG1Orp4HgIrv4hqr/M/cbgWAgIQU8f0L3uQmVRPpUFQBwIhxA1BUmisYtntcXqHMnTx2Oh4r0KMqO0r/HA43LrtsGebd+AYMok15V1C89gsPuDeeJIl8PhY2W8ds0JDkmcfrQ22jIWpJOlEiaTVKqBQyNDQbI56bKTyi0j/Bbzlm6R/3nHRq7ntlz6CiSsWv/10Z9qhKJkZ0xzhBFVUx8AZ15iOko6TDf+3AYHPGZZNxzpWnQKlWYN//agAkU7fNfVhbG4345M2fAASW/gkeVekwU5cHvjZqjRKtzWYcrW6B2WgXzNWzAZKYDJuooooqSpxQuW7PwmKwof+Iviiv7B1w3O9lmPwNqX/3PLR0jpBXRBJVKZT+EdNzdRhFVUpd/6KYqfPJq2zzqHKKSv8kEgYKhUw4lqNTiRQIVFFFiQyNEz0LhShZP2RsBexmJ6wmK4CiiH9DElViXv3re2AYYM595wUcJxsj6UgemQ027N5cjSFj+vk35t1e2MzcWkROFutp9qiydZKBd6Ls31eP5iYzmpvMaG01I49f54QqqviOibyHkjiWdtSGN0mo+McS+XGL8nTQqBQYP7Ic73y2Edv312H8qP4dMk6C2+M3Uyelk7HM1EkCUCcoqtJ/T+HgS/2UclL61wGKKhojqKIqGkf21gqd+YJVT+nwqIpU+qfNUQuLBSVfhpGsHNbnZXFoTx13LXF7cCYdHlXhFVUAUFKWBwBorDckff3OgOSf/KV//uw0VVRR4oFrKZvYDyW7aaltw+Hdx4R5leAvEU+DoipKd6D8XrkYNKYcLJP8Z4ksFlTqMIqqFOY7Z5jrEkhMyr7Sv0AfEpUoCafTqgTfxo5sQU7JLmic6HkQCxGtXg2byYFd6/fHbIBRUJILIHAOdtrdYFmgV7/ABJewMeJIg8rJRhqEKIV7X5ZlYeX9GNNhfyI8Fq+oGjWuP5wON1oak1cGdxR799UL64H2Nn8yMVhRJQuKoeIkUUc12zCa7Bjcv1gwGo8Wz/fXNGHvoUZU9ClAn5JcHGlo75AxiiHG6HKZNGEzdZ2KVypm0ExdzauoM+1RlUyM6I5xgiqqImBsMeHG4/8i/H+IoirFkg6WZUVG7ZE/WCq+ZXeiiSqG8X+pGX7o6e/6F95MHQBKSnNRc6ARzfVGDBneO+T3XRUfP4GTLH6gR5VEpKiiiSoKhcLRWs/dzBWW5QcclytS8wxhWVb4W1J+EY6cXA0O7jwW4qWYCE5bqKJK6FiUZMKFZVmUDyqBRCoR/FPEFJbkYMDQXoJhfLYQnKhSit4bXY5KKP2jHlUUCoXg4hMyN//jGmz4chsArgtgNA7v5TaaxUocB686IuWDBH/pX+rzqdDJlt8sl8mlcDk9sPClin7j9tQey+32wOvxQSKVQKNRwuvxYe/2ozjljJEpXTfTWMwO+LwswADtItVbJEWVj5T+id7HjtrIOFzXjqrDzejXu4B73AiJKpZlYecV0H1L81HbZEROUCfjjoDETZlIHOBjo6+5iHBCryGlf5lIVPFeX0LpH43vHQFNVEWgtqoBGr1aqB8PXgDoi3Iw5ISKAMVNIpCEiETCQCKJvLhQCYqqxOSw4vQTUQeJPULIxntKZuqk9C+KoqopyxRVJHFHuiJKRe+NVOp/r0i9OYUSDmqA2LNorYuUqOITFknK0MlmhlQmiRon9AWcGa+pLfrufDSIT5QqbOlfcol5p8ON3VuPQK6QhS39Y31A9f5GmE0dYyqbLoiZupJfrCkUcoDhdoCVCpnIhJ5uaFAiQ+NEz0LcsCK/hG+AEcNXkOXv5j2iGELWA+LGF4AoeZQGRZU9KBkmlUsBpwdetxeFJTlpS4o5bH7F7cBhZdi3qxZHqpsAdO1ElV3kq9XeHo9HVaCZOpCa92MiHKltBQDo+M9LJEWVy+2F18dCLpNCy98HODNgSh4L4jcli1NRxbIsPL5AjypbBkr/OlpRBdAYAfTgRJXX48W37/yEnAIdTjx3bMjvG2taYDXaRCVgwZ35pDiwpQb9h/dJ8vH5BUgUg1wgBUWV6N8kES32qBKMGTNU+te3fyGKe+nR2pK8Z0pn4I1ipi5WVNHSP0okWKBbym8pkfErqvICjsuUMgw+viKkvDteSFmFLErZHwDo+a5RpvbkO60KZuri0j85MVNPbufQauYSUFqdMuzvNcRQ2JptiSq/mTrAeVUBfvUBWaRQjypKJGic6HmIlVB5JFHVFF1RRXxkxapcQe0UpHaRq4gnYhoVVfwcLZfLYIcTJoMNrU1mlFYU8eNKLSEgJO/UCuTkqmFss8JsiG4w3xWw2cSJKlHpXwRFFVkziFW2HVX6l5+nxYghZdCooyeqiJpKrZJDyY+/MxJVgpm6VBKXRxV5HWVSib/rXwZL/zR8oirTXf9ojODosYmq1vp2PHX1P6HUKPCZ5d2Q33uCShGCFVXaXK5dpzVGx45IRDJSD4YEiYQVVaIPt8vphkTKYNAIfwkeI8gpk09U9e1fhBy9OqBtLiEnT4PmRhOa6ju+Y0QqCF3/mNBElVQq8beapaV/lChQA8SehUwuxahThqG4X2HAcbVWhcN7auF2eeCwOYWNh3iJx58K4DxPJFIJ7BYH3C5PzPPF7NhwEHU1LULbcbHy1q8MSjJRxV9Tqw/f3poksKzm7DDQJRBzWrIYIRscan6h6PcloYkqSmRonOhZOPn7eKVaiTzeeypmp1b+Fl2cfIqsqCKl5unwqOKTFvwahGy2kMSSMk2KKpPBiuLSXAyqLPNXkNi7fhdY4unYq1eusLENhHbpJYkWEkPFKqqOKv37Y2s1rDYXTpkwmH/cCIkq/nOjESeqOqEsXzBTl0mF6p9oXf/cQmJLCpVChuMH9hbK79MJMVPXKDsmUQXQGAH04ESVlW8n6vOymFV4HXR5WrxzcJnw++AJJDgZo9VzHR6sxuR2sD1C2Vz0DyExU3cmaqYuSsKaDTb4vCxKy/2LKKKoSqXrX9WeOjTVG8IuiopKuC5UrU1d3xRRDEnchVdUMULQoaV/lIh0U0PDns66D9Yjp0CHgWP6I69YLxy3mmzY9dt+KFRyFPUpCPgbhmGQW6xHS20bDM0mlPYvTugxPXyJWazEE8Mw0OdrYWgxw9xuRUGv3Lgf48UF7+PYoWZI+Bs78eJH2A1OskSBKKp0OeF9LjQ67ni2dHoikNI/oqQ658zR+GTNZlx7+ckAgLxcDUaP7CMsViiUEGic6HE4SZJHo0BeiR6jTh4GdU74JD6B+KG6nR6wLAuGYSJ6VAld/9JY+qfWBCaqXHyCJl2lf23NZjQ3GNG3okhQpNqTbB7VkZDSv8ZGI1pElSOCoopX2wqbFkQZ1wmKKgf/HpHkTaSNdhv/uVErFZ2qqBLM1MWKqmiJKpGiSq2QY3t1PXwsC4/XB1kKvp3BCIoqpSLg/zMGjREAenDXP+IRxbIsrAYbGg83Y3bveYIk0hsk2Y+oqEoyURV/6V9yXf9Y0UQklUsxZHRfDBSZmvvN1BO6bACkBS6pixdTwC/ksi5RFaX0j1NUUTN1SnRYcHXlifxQujYOmxNLr3gRD5z5OL55+6eA3x3bXw8A6DO4NKyPlOBFksRcSLyt4lFICT5VYdqZR4PEQmL2Ki4RJ/Ep9dK/CIkqofQvuxJVPq8PGo1CaNt+2ewTsfL1GzB92nAA3M39jl21MGWZ9xal46BxoudBEkxKjRJyhRw7f92Ho/vqov4N2UxmWVYwVI9tpp5Ojyq/mToQWKoHAK4UvYBa+LhY1CtXSIplQ6LKJlJ9iRNODQ0GAJFL/8Rqpo6wEPF4vPB6fZBIGCjk0b0Tu0rpn6CQkkkFUUW06p/g84niyeZM7+doWFkxxpSXIlejwqjyUmhUith/lALJxIjuGCd6rKKKJBrEn31jswnNR1tQWlES0GEDCDUM1/CKqtYGAx655B8YcnwFrn744vgfnySqYmR7VerkSv/Ekqr2ZjNa6o3Q52n8v02Doiq485EYoqhqaTILu0DZAHk9/N5k/nFLJYyQwKIeVZRo0F2Q7sH+zYcAADU7j0JflANDozFkc6K1rh3lw/ug//C+Ya+Rz5d4tMfwIgkH8TiKJ1FVUVkGp92VcKKK7IjLFTJMOK0yICalagoulP5FVFTxiSpzdiV0LFYnvF4fVKJNGrXoppV2/aPEA40TPQunULKnFBpteGIkAsRKEpfDDZlc6k8iBZWSp6sTHwDY+bFqeB8sQVHFq24KSvQYPXFg1CYf8dDSSBJVesEf0ZkFpX9iM3WyXqypaYHJZMfgwb1w0mSuzC7Eo0q0tuwIM3WSaFIqZMJYPBFK0gVFlUoBJb8J4/Z44fX5AhpLZRqPyExdSmxqoogDxAosANCoFLA4XLA53dBr0tO10OP1YfOhWjAMoJBJseNoA4r12rRcOxo0RvRgRVWk5GzdwUYAcXhU6Ymiyo4NX27DN+/+ktDje6J0zBOTvKLK/wTD7ZTHk6WOhZuUP4RRVKk1SvQpL0RuviYr6s0JRBJL3u/Iiipa+kcJDzFATOSH0nV5bt4raDzcDAPfRrzpSEvA7xsON+PInlrBHDcYcrw9GUUVv+CIZaYOcPNx49E2mBNMVBE1k9vlgTOoZISUpierqLKYoieqBI+qLFJUeb0+eL0+MAwilvaRco+O8iChZB80TvQ8nKKuf0SN6Y6RVBIv0F0OF9wuD3xeH2RyacgGhlzwqEo9UeUIUVQFJsEkEgY7NhxCa2NqPrTtfNlcUS891OrsUVSJx0g2cixWB6oPtUACBmpecRat619HlP6Rsj+lQiaUwUWyLiGKKo1KDoZhhPJFUureUYgTTxLBTzny+eR1JBtEWr40z+pI3+eIlPmp5HIhiddRZuo9PU70WEUVgQ1K1NQfagROHx1H6R+nThIMDhNUDJEb/1jdoLR6DQaM6AudSA0VD+IEFFE0iR+LmKknW/vHsmxMo19DmxVWiyOrWnSTzwN5eYK7/lFFFSUeumOw6KnUVjVgwHEVwv83Hw1MVFn4xBCJCcH0HtQLI08ampTBbbxm6gDXwAJIrPTP6/UJaialWobps8YF/F5QVCXZvc4WS1GlzT6PKif/PiqV8ohKYUFR5aZxghIZGid6FmTDWalRCpsPsRRVbJCiSioLr6YC/JvR6TBT7zOgGEVlecLjkKQ8KSvMyePUJNYw5c2P3/42mmrbMeeWGZg8Y0TUxzG0WKFWK5Cbr/V7VHXxzW2j0QaTqJEWST6ROKkSl88TFZMntPSvIzYynHxppkop92+gRFBU2QVFFTd+pUIGl9sDp8stHOsIPCIzddLYKj5FFd99NwOlf3Y3/9ooZFDK+LJIT+bLImmM6MGJKrlSBrg4ZYxEwsDnYyGVS2Ezc5NPaOlfYKJKrpBBqVZwOyQMk3BVKEl0xCr9yyvOQfXeuoR3LdigL7VCJQu4qRay1Ekqg8SdLSLerPPS5nS0yu0oiPqMyJnFcleplAnZHaFQgmHRPXc1eirHDtTjjQX/BgAU9yuEKshvyWLgEkO6/PAycJlcil2/7cewsQMSfmyyiJGHUa0Go+cfPxFFFTlXq1dhzm1nYsp5xwX8XlBUJTnfWS0OyBXS2KV/lmxKVPE71FHeEylVVFFiQONEz8Ln80GrVyO/RA+FWi5sPrhjGDKL516X0w1GwmDg6H5CNz4xgpl6iokqn8+HLT/tg8/HQsdXjwilf/z8l1eoAwBYgjqfsyyLDT/shdvlwYFdR2MmqmwWB+xWFzQ6VdZ0/Xvj9R9hNju4tSPLCokVoVudSBQgCy79EyuqOmAT38HfQ6iUspjNoFiwOL6yL0p5j2GlQgazteM7/4m7+MWzVvV4fDhuQBmKcrnPpFaVfkWV8DrK5cJGVOYVVTRGAD249I9IYxn4vwBejxcWvhtgLEUVAPQf0RfyJM3UPG4PRk0ajIGjw/uaEPKLc6BUyWFqt8IaFBCiEfylJm1rg0nWo0rwNYlys04CW7K78Z0BUaIxYbv+SaDRKDB6RB/oIpgDUygANcntTljarUIZREttG3b9ui/k94B/hzkYctxsSKwkD/An+WMpb7nH0fCPE3+DD2O7FRIpgz4DSvCnG08LeRxSmp5s6Z/L6YHb5YUyQvzR6lQo7ZOfqCC5UxF7fkRCnqK3F6VnQONEz8Fpc6GtwYD2RiOkUqlw7+yJUVZFml1IZRI4bS64HG4c2nEUhubQUnK1ToWK4X3Cqq0SwWpywOdjodYpheQXiQ0kJuXyzTus5sB1SWuDUahMsFtjJwqs/CaFVqsUDNoTtTrpaDT863suv7FDFErCBr7I0iVa6V9HbHgLGyuK2Ioqk8WJrXuPwc53duwsQ3VBISUTdf2LZqbu9WJbdT3qWjlBh1ZQVKWuLCQ4eEWVSiGDsoMSVQA1Uwd6cKKKEPzZJzvYwR5V4byk7BaHILFlkVjCx+3wYMf6/ajlPbEiwTAMSvsXAQCOxjhXTHACKthHyt/1L8lEVRQjdYKwY9TB9c2pIHT9Y0ITVVKpBF6PDzt216K1zdIp46NkASz1HulOiI1dGQkDi8EaoBK18ObqkRRVOUkonQg+rw9Dx1YIMSAa5HESKf0ztVnh87IRE2H+0r/kbqiJ55UyQtmANkeJpnoDGusMWbOh4RIUVZFLIYRFHVVUUSJB40SPQvB84jc5Ey3983p8cDndouYXofOPTC5FzZ5atNS1pzRWstmhF8W0PgNKMGriAGGeJoqq4NK/o4eahXNaGmJXghDTdrVW6S/96+KJKrIBQdY/5Pl6wli6CIkqX6iZekeW/ikVfkVVJG8sou4jirBOS1R5/Gbqgp9yFFGFm5ivi8zUgcx4VCnlMsGjKuNKsyRiRHeMEz0+UYWgBBNJPAVPIOE6WwT4RiWY7xEy71Fudgnjpg1Hr/JC1Nc0x3394ASUImihwKSaqOKDZSSlFuCf7NxZsgABRIkq/vWRycSKKr/PF90pp0SCmuR2H6RSiTC3X7ZgFvSFXDdTo2g3myiqdBEUVeS4JYlEldPuwr7Nh2DkDWejkUzpH0lq5UYsW0ythI2UcERKVEkkEuTkcqUl5gQUw52JQ/CoikdRlT2xj9Kx0DjRs7Dzfn1E7eQ3U4+u+vCJkgoOi1M4X64KnX+UvCIp1a55RP2rF8W09mYTdm6o5h5HJYeOn7eDS//amv2xqrneEPOxSNm3RqeEQinDmAkVGDKiN1yu9Klh0g1Z05DNimilf8GKquKiHBQV6QKOZRJipq5Syv0K6QiP6+SfFzFRFxIynaWokvq7/nmjeFQJXQKlmTNTt/OvgVoug0JQVGX2daFm6hw0URWUpyElgfGU/kVamMQDafEarmNeMH++9Uw0HmnFvx76IO7rh5b+BT5OPFnqaLjj8E7xJ6qyx6NKMFMPU/onlUqEjD1dgFCi0dMDS3dBl68FIwE0OUr86c5zUdArD0Bgoorc1EdqeJFTkLyiiih7pXGU/uUW6TBiwkCowniXRMLYxi9IIiSqUi39I4oqUtIRjhzehN6UQMliZyKU/kUre5dRRRUlNjRO9BwcRDnE+/LF61Elvkd32ESJqjCKKrIhkGqiytQeGtPE9/pKtRzaHL7zeVDpH+nip9Iq0VRniPlYJFGl1irBMAyOVrdg99YjMLZ13XhA3jOFInBDgiirAkv/+EQLn8Q6VtuOlhauIqMjuv6JS9VlQcbuwZB4RRIxnaWoIq+nXCYRVf9EOV/UJRDIjJm6Q9T1TyHrmK5/AE1UAT3YTF2AYSCRMoKJtqCocnu5JBbp/iYLTVSJuzwlqkwiZofBSqdw5ORrIJNLYTHa4bS7hF2TaISW/gUpqpjYX/5ouBMq/cueRBXpLBE2USVh/KUwVFFFiQA1QOw+MAwDuUKG/F55yCvORW4xp6hqb/aXNKi1SpT0K4S+ICfsNQRFVVIeVfwNWByJKm2OBrs3HhJKEeOBLEj0BRESVWQ3OMn5zmmPXvoHAHqiqEpg3J2JoCYOs1AkyIkXCO36R4kAjRM9C6H0j99IiMejKrjTmcPmFEoHw60d/Iqq1NRI5nZS+udf44jXEAqVHDq9Gjl5amhz1PD5fELVSVsTt4njdnngsLlgtTigjeDp6vX6BNUtKfsrKMpBe4sF7a0WFJfmpvQ80oXX4wtYA5K4HKKockcp/eOTUuL3tEPM1J2k65/M34EwQoLM5Qos/etdkot2kw2uDq6KEZRpUn/XPy8bj6KKe345KiWUcllaS/McpHuiyKPKSc3UOwSaqGJZ9B3WF/klemz/aQ/cLpKoCvyAh1VUiRNVCSqTEmk7zjAM8ov1aK5rR1uTCWVx+JUEG89FTFSlaKYeTRGWlWbq/OvBRPCoktFuTpQ4YGlw6RYU9y3Ep5veETYiBh1XAa/HCyuv/jG3W7B3w0EoVHLklejDXkOfr+PPTTxRRZRMsjjiRCqlf5EUVf5S5yRL/4hHlTpyUoeYwJsM2VH6F0/XP+pRRYkHGid6Bi/c8jp+Xr0RAKDSxu9RJZT9MQBYwG5xQptLFFVhSv806Sn9c9hdGDiiN3r1LRCOkXv93AItBg3vDZlcCpvFCbPBDq/HB4mCT1TxpX/5RTloaTCirqYFQ0aFbxpFTNM1WqWQ6Mrnva/a4yh3zzT799XjjeU/orHRiDdX3SRsYAulfyqSqOLN1D1hFFVB5XbisrtklcqJEE5RFan0jyiElHz8stldOFzXBnsaTcnjwW+mLhU+F1G7/gUlqmQyCZxuD+wuD1iWjdiZPhHsYkUVXxLp6oBqIRojaOkfwDCQSiW44JazAPgTSMGlf+HM1LUiWWyiJXT+0r/YiioAKOjFLYLIbkUsYpmpCx5ViZpr8QiGjvI4Sv+y0EydJKhIfTTAJSupoooSDz29S0d3gZEwkEgkkEr98/+On/eioaYJPp8Pd5yyEABQNqhX2BgBcIuH4RMHobR/MexWR9hzIkHiUTylf0LCp90at8JXImVQPriX0MEpGLKLnKyiiuyWq6IqqvhxZ4miSjCnpR5VlBShcaJnsG/zIZhEJXGAqOIgShIgeOOUK/2L7A+rSFPpX1NtOw7trgtQbZHxGtussPAG6sFqIYCLQyPHVaDfwGJUHl+O9ubICScb79ul1vqrRPJ5/6a2ls5vWHRgfwM2b6pGW6sV27YeFo6TBAUpjSPrAaH0L4qiyuv1x+aOKP0DC4yp7IOSopyYiiq3MH7uean4z1iHJ6oEM3UJpMSmJso9TbBHlYof/7bqOrzx9Ya0jMkpJKpkUEj9iqpkfZ7jhXb968GJKnGCVa6UiYIG6foXh0dVOhRVcZT+AUABv1vf1hi7iwYQxqMq6HGEut8w4/7tm13415I12Lv1SMTrC13/4lFUZdHNOpkMyY1BfoEWxUU69O2dD61GIZR00J1ySiRY2s2p21LYm9thbqlth8VgQ+2BBjASBidfMD7i3zAMg6ajrTiyry5hVZUn6MYxGgqVHEq1Al6PD3a+zCQWDUfacKSqMaKHlCzFhIu/61/kcvXi0lz0qSiC05EdJeLidt+RoBsalFjQONFzEN9nq3lFlUQqAcMw8PnYiAoXoqgSElVWp2AbEs4fNl1m6m1N3DqjoMRfeid+PFIS6Pcw9I+/Zl8Ddm2ugVItx96tR6L6VNksTuTkqgWfQoBTYgFAe2vnK6pa+OSizeZEU6NfJBBqpu4NOB5Q+kfMwPkElbeDS//ajFZs31sLt9sXW1FFvLf48av499zZ4Ykqv5k6EVUkoqgiCcRth+qx8ttNaDOnvgkmLv2TSSWQShj4WBaeKCbvqZJMjOiOcSKu0j+DwYCff/4ZGzZsQENDAxwOBwoKCjB06FCcfPLJOP744zM8zPTDivyn5AqZ0H2P7G6EKKrCJKqKehdAk6OCzewAG6V+NhxuR2T5bjh6DyhBvyG94l6AsEFfnlAzde6/wV9+lmWx9K534fX4UNq3AJXHl4e9vjtKsCRkpUcVH0xIXbTV6kJziwVguKBClBUd0a2DQqF0LYr65AMAWuvbYOA3DXoP7IVrH7006t/pC3PQWm+AqdWCkr6FcT8eiUMyRWxFFcCV8DXbXTC3W6GJ4AsiJlYJurAQSbJ822+mHmVDhgGOHW7NHkVVHKV/8hS7JVIolO6DWDVFFFUMw0CmkMHtdMPj8kAaZrOAFXWh9nm5RFU0RZVcIYNEwsDj9sLr8UZU+caCqKAKROXs4scjpeJ+xa1/niNznpp/nhZzZBWx1eqE2WhH73J/TCwpzUVpn3zBZL0zaRGpwdpFm0xkA4nENX/XP78JOMGvYuJ+J147kGOZxMonLXUahWBdEjlRRbr+8YkqfjPG0cFruKJcLY4b1BtqpVxQVEXv+sffJ5FElWhjz+JwwWx3oiAnfLObeHGIFFUAoJDJYHe54fZ4IZcm9z2jxEfULMlPP/2Ef/7zn/jss8/gcrlQXl6OoqIiKJVKbNmyBStWrIDVasWAAQNw/fXX49Zbb4VeH96no6shkTBCxz+ZQi4kXNxOD64fcx+ajrQGnB/eTF0NGz8J+7wJlv4lYKZOOHqgEcbW+OSwwXLE4McRanaDzjO2W7ndEQmDjT/tw8XXnRr2+q5EzNQz5FG14J5/w2i0454Hz8PgIaVpuabQ9Y/v1CEOODKZVJD00gUIJRq0rrx7UsQrqlpr22HgO//l94pt+JpbyO0Sm9oSK2cgiSRZnAuOnDwNmuvaYWq3ole/2Amx2Imq6De2sSClf9EUVcRE1xbnJkxnI5EwGDG8NwojlEsC/hIEqrylRIPGiZ4B6fgHIKAZklzpT1SFa5IkKKok/tK/aIoqhmGgUCngsDnhdLih0SW3gCYWI/nF/gYh4s1uUmYuk4Vu3BJ1FTFQt0ZJVFlMnC+hTq8Wjqk0CjTUtqM1TpuTTNIsTlS1+RNVZE2jUpHSv6Cuf6J4GlL6J0q4eIPWjcfq2mG3u6DRKNCnLD8tz8HKf/a0GiWkksCkWTB+RRUp/eP+29Glf9sP1aHZYEWuViUolqKV2AUrqlRBCnSrI/XufwwDjOrXC4V8wkspk8LucsPp8UITf6PlhKExIkrp34wZMzBr1izk5eXhk08+QXt7O6qrq7Fx40b88ssv2LlzJ4xGI3bt2oVbb70Vn376KQYMGIDPPvusI8efNPm98qDgA4NcIRVavbocLhzdWwenLfCmOVzpX7HIaDDRG/lEzNQBf0tbW5RJX0xI6V+wmbokfN1v/ZE24d+OaPJhFqgc3RdFpZETk8SnI1MeVVv/dxgH9jUE7Hqkwu9/VIWU/oklvHK5RJgI3bSkgxIRKtXtrhT2ycfIk4cht1gPA18ekVcce3NGzxvEmhI0iCVNPeIxUweAwaP7YfTkwXHHCSEORVAHpVrCJnT9i6KoUvN3eVHjTRfCbLJjz+7aqHFNRhRVWdRIhNLR0DjRU7AabQDD3XeLjZ1jVR34BEUVN5/YYyiqAJGhui35+bS9iVdU9QpWVHFjD1ZUiec5oq7S5JBEVeQmGeESVfou1FxDrZKhhFeViRVV/tI/7rUONlMXbyxJpRIwDF/G5WMDklPBZup/f/4L3HDHW3jk8dVpew5W/nOg1ShFiqrwSZ9gM3jiUdWRpX9WhxMtRisYhlNW+RVVkRNVgvk6v0GkDEpUWeypb4K1mu3YebRR+E4qSOe/jBqqJx4jumOciHj3e/bZZ2P16tXQ6XQR/5hhGFRWVqKyshJ33303NmzYgLa2tojndyUKe+dD41PD5TDziio+URWhras0jLRPXMLhSzBRlaiZOinjIOaDsRDXxMtkEhSXBe76R+r6ZzHYMGR0HxzYVQdHlBa3FrMDe3ccw4ChkZVMcr5cxZ2hLzJJDkYz6o2XI0db8cI/v4GBLz8hdeUy0fsuk0mFxFVHdOugZC90F6R7Uti7ALt+3QeJVIITTh8NABG7/Ykhiipjgr4bnjCeF9GwmGzYsb4Kpmvj88KKtWGiUMowdEw/5EVRD0WCZVm/R1WUMjmiqLKnsLDqSPxq4sjvibBJQzc0KFGgcaL74/V4YTXawTAMPmp8Ddocf1JGJg/0xg3G5wtSVMXwqAL8mwKuFFQkeUVa6PM1QtIo+PH8iSpunhOvf8h9eXyKKu53OXp/mXo6m2tYLU6uCoKJXv0RicPVLWhqNAIMA6PBPx6iPFKr/KV/LMv6zciDKnCkUgk8Hh+8Xl9Q6V9gfCBdBFvb02ckb+FFFzqNAhY+xkZWVHV+6d+Mu18BywI5GqXQ9W94eQl06siypRCPquBEVRoUVXbeo0rNr9kVMr7zX4bXgjRGRElU3XfffQlfbOLEiSkNpqNh+F0KmVwqTMLOCB/ocIoqrcgAMNHEBVkgxFv6R3Yn7HEmqsSKKo/bE7JjI5ipByWp21rMOLCjFpAwUXe44yldFMzUM7Cr7PF4uSQbwyQVgILZvuMoGhuNXDUowwg3BsRrBOASfn5FFU1UUcLDAt1yV4PCbSzkleTC0GSEqdWM4ScORtnAXjH/jiiqSLlgvCSaqCKLoPgVVaGlCmJkcin2bz8a17WCcdhdKOqlhy5XE9UrhSiq7NZsS1RFaSRCu/5RYkDjRM/A1GbBqJOHQa6UBySpAL/3oCfCZi5JAEmkDAaM7gdtrlpIaskjKar4MutkFapejxfVe+rBMEzAY3Cb6iyUShnyCvlElTTUi4+U/un45JPVFLv0TxtGUWU2pJaoYlkW9968EocONOLUacPxyN9mh5zT1GjE2v/bgrLeeTjrvONDfu/k53qVSg676PUUFFUqzhOMGOJHitcRE1WijQyfj8WGTdUAgEEVxTCa7cgN+rwkg40ft0atFBJOkRTS/kRV55T+WR0uuDw+gPEnm6RSBnuONKFQH9ljKqT0TxGcqEpdUeVw84kqPnk3pLQQBTp1RhVVNEZwJN31b+/evVi9ejXq6urSOZ4OhSRrpHKpSFEVfnIP51HFMAxe+OERANE7EoTDlaCZesKKqqAMVLCxbiRFlbicwRlFUUUMZePyqMpANl5s0JiO7qDBpZtEai2W8MpkUkE6S7s5ReYfH6zD5Nv+iZuf+6izh9I5sNxnMpEfSvZQ1Icr+T66rw57/qiKy7C2tKIYcqUMLXXtCT2W/8Y3zjjB39haTfGVTcRSVJHjrqAb1cbadvzy1Q78+PnWiNe2mh1objDCEENFJiiqsqT0Ly5/RqKooqV/lEjQONEjMLWYsfPXfWg+1hryO6GJU6zSP6kU1btqUV/TErNc29/5L7nkglvU0VtcpqgQNvPdgqLKX2EgTr5wcx4p57NGWbOEK/0j/lepKqpuu+Z1HNrXCCDy+mzzhkN4d+UveP7vX4T9PVnnOBxu2ESKX2GDRy4TfGvdbq+wga0ISlQNGFCMwYNK4HZ7AkUEogSfk1fsMAywedsRGFJM1BH8iiqlv+tfBGPy0K5/0Uv/VvzfH5h+0zLc/tSHaRnrsSYDACBfq8JfrzgdgL+qJZq9TixFVTo8quz894IkqhqNFmw/0gCHJ4NqsyRiRHeME3Hd/d50001gGAavvPIKAOC9997DlVdeCa/XC51Ohy+//BInnXRSRgeaEfg5WCaXCjfkpFSBoM3VwGq0hVVUAcCQsQMBJN4VSeial6CiKlmPqkiJqmCPKvHOTqQdmd1bD2Pr+ioA0RVV8gwqqgISVQkmCcMRkqhiiJm6yKNKJqU75XHQbLDA6fagyZA++XK24UNiuyB0zyR7KO5bgKot1TDw7ap1ebHL4or6FMDt9KD+UFNCjyV4VMWpqNLl8ouDKL4gYgSz9gjXVyjDl6a8/ve1+OXLHZDKpdixsQa3L54V8rdkjs7RR98VVmuzrPSP7DpHU1SR2Ee7w1KiQONE98fI+xKG8zIk6w5PpEQVUVTxm+pup0foGB7JNqT/8N5obTTCkKAfIsEVQbElTozlFgQqqsQVJWTMQqIqLjN1//pEq1NCImFgszjhcXvjjn3BGETm55ESVXXHuI0jr5cr3RMn5oDADRpxMpHYmcjl3JrA6fTA6/UJGxPBzU9qa9thtjjAsv61Rr9+BcjP96uE7HxiUSKVwOv1wRznWi8WfjN1hUgBF6eiiv98Rir9O3SsFVa7C0cbDWkZa4vRil75OgztV4ypxw8CAMj4z74nyjrPn6iK5FGVhkQV/1kgRu3kMTLrUZV4jAC6X5yIS1H15ZdfYsqUKcL/P/LII5gzZw7q6upw1lln4ZFHHsnYADMJmZSkMn/pX3DAsPJZ/UiJKs4ojwHLsgkZqrsSNFNPVFEV7JlFEl0EUtoWnH4NVFS5wnZa+HDFL9i64RAAQBnFY4sEmEyYqYsn8WhtS+PFGzRxk9dHGtT1jyqqYkPiCdPdZss4YcHVlSfyEw///e9/MXbsWOh0OvTp0wdXX311iKKVZVksXboU/fr1g1qtxpQpU7B169aQa+3evRunn346NBoNevfujYULF8LbAa2SuwMjT67E0PGD0M6bqevyIkvSCb0HlgAA6g41JvRYHn7ujNdMXZsTu9xCTExFFVEaByWqDu9vAMDddNfw/w5GKOuIUb6gVpNEVXZ0/RNid5QFlFwWWhJDoYihcaJnQJpu5BaFJqpkMRJVrKCo4uYTl9MdU1EFBmhvNqE9ya55gq1H0PXFibHcAq6UnaiJwymq8op0OH7yYCiUsogd24hHlU4UIyQSCSacOgy9+xeiJcnn4POxaBd12G1pNuHzTzbjwN76gPNMJjsYCbd+C7dR4hTFPVdAooooqvxrArfbG3BcDOki7vX5S/+OHm1DY5M/mWjnVT9EFWSMc7MpFkqFHHl6NbQaJZQKGUYP643C/PCba4KZepCiyhFBUdVm5JKBxNM3VZoMFjS2W5Cv838eSKfCaOs84rkVqetfJkr/OiJRlUyM6I5xIq5EVVNTE/r16wcAOHDgAKqqqvCXv/wFpaWlmDdvHrZs2ZK2AXUGXKKKN8VzucMGgGjlHYL8NYEPbKxdkWD8iar4vnChpX+BRnQkiRC800CCIKm7Dle6IE6CKVRxlP5l4IscWPqXfkUVmXjlAaV/EqqoioN0vB/ZTfq7dPzf//0f5syZg5NOOglr1qzBU089hZ9++gnnn3++YLgKAE8++SSWLFmCBx54AJ9++il0Oh1mzJiBhgZ/MqG9vR0zZswAwzBYs2YNFi5ciGeffRaLFi3KyKuRSVavXo0xY8ZAqVRiwIAB+Mc//hFyTkVFBRiGCfgpLY3cBCIWBaV52L/poLAAiUdRVdy3EL36F6G4X2FEH8RwJOpRpdETj6r0lP6RxYp4Z5nzDmEwYmw5AKDhWPgGKuF2y8ORvWbqsT2qaOkfJTI0TvQEiLIpXNMNlVYJqUIWufRP8KjikyEuT8xGTEVl+QCAlrrkGlu5HNG7CjKMf+NbGmbjliSt8otyULO/AQd310WMEW0tZihUMhQUBzbtstmcqDvShtrDoeWS8dDSZMLQEb1RPrAIAHC0phkvPPU5/vn02oDzNvxWBZbvgGcOiple3lOKCBoiJ6r8nXE9Ybr+AYFeXuK1hvjfRFFFkkTpUFS1tltxtLYNPi+LHK0SDBjs2FeH2kajcM4Lb/2A2x97H2vX7fTHtqBEVSSPKtKJL113/ES5peZ91gCRai/J0r+R5b2EZFcqBJf+kWSYIwu7/mVbnIhrm7agoACNjdxO8LfffovS0lKMGjUKAHglUfpvxlavXo2FCxdi37596N27N26//Xbcc889AedUVFTg8OHDAcd69eoV8CJGJ9Sjyuf1cTLWIDlIJEUV+Xu3ywOP2wtF9HtygUTN1NVaJZ+Vj29KiLf0j+zYbP55P/61ZDVaGrgdDJlcCpfTA6fNFXJDLk7SRLtZLyzRY/T4ioxIa47UNEOjVcJmc8EXodVqIgRPguT1ES8Q5WJFFS3piAibtrCVvaQ7V/fvf/8bY8eOxbJly4Rjer0eF154Ifbt24fhw4fD4XDgySefxIIFC3DbbbcBACZPnoyKigosW7YMjz/+OADglVdegd1ux8cffwy9Xo8zzjgDJpMJixcvxl/+8hfo9bG72HUFfv31V1x88cWYO3cunnnmGfzxxx944IEHIJFIcNdddwWce/nll+P2228X/l+hUCBZhp84GP0qe6OlzgAgvkSVVCqBw+LE3sMH4bA4BcPbWCRa+kfUS4mW/kXanRdK/0Sq2N1bDuPooSaMHNcfMrkUbc1mOOwuqNSBz4lsJuhiKaq03CaKI+sSVZHfE6lUgmFDSjmDXY83ZOFCoQA0TvQE/IqqnJDfseDK5oIV/QSSqCILbbfDDU2OCoPHlEOdE36xUdQ7DwCE+JQokWICSZBJZVL//bGMJBFCzdRlMikGj+yD//26H8cOtaCsXyGCaW00weXwoCCoLLJfRTF2bKrB0epmjDtpcMLPoaGuHbu3H8PgYdyGlCtMVYeh3YqWZrNQJ/X8U2vxt+cuF35PPKMUSimcTq9wjYDufnIZhgwpRY7eAJvNiYJ8LUaP7gu1JnBdJ6iCgt5n8brDQRKQChlgdcKUoqKKZVn8tvkgAKB/nwIwDON/vzz+5/L5up2wWF3QapQY2K8IvUtyhUSVjj8Wbp3n87HYdbA+5HmkglBeJ3q8+BRVQYkq0d/vOtKIsoL45iqrwwUJw8Bgs6MsP/BvhK5/HaioAjLjOZVtcSKuNOM555yDhQsX4qWXXsKTTz6JSy+9VPjdzp07UVFRkfJAxJAFyMSJE/Hpp59i7ty5eOCBB/D888+HnHv55Zdj/fr1ws/atWtDLxgRv6xWrpSh/4i+UItuqssGlQj/JtLNcISTv8ZCIpVAX6iNu6RDrVPC52XR1hifFDbYt4ksBoTH57/8JKnw+3e7UFfTKpR4kIVROJ8q8aIlWqJNpZJj++bD+ObTrfj8w41xjTteDuxvgM3KqcuCfbaSIcSjShpGUSWXUpPcOEhDJWbWk26prtvtRm5ubsCxvLw8/rG4z/9vv/0Gk8kUMD9rtVrMnDkTX3zhNwv94osvcNZZZwUEkMsuuwx2ux3r1q1L5Wl3KI899hhOOeUUvP766zjzzDPxyCOP4Pbbb8djjz0Glytw3iorK8OkSZOEn7Fjxyb9uL0HlaLpSCvsRDEUR+kf4O/w5HbFb3LrETykMl36Fz6RIleQ0j//mFsaTRg5vgKjJwzEkJF9oM1RoTVMXIpbUaWWQ5ujgsvlyQo1JlmwKGK8J4dqmlF1qEnYdaZQgqFxonthajXD4/YEqBIaDzejqE8BivqGJmoENU6krn/8eyaR+RVVjUdaULXtcMTkd1HvAuSV6CMaYMfCHaGjd1lFEcZMHoxJM0YKxyTE6NotVglx86NUJkVp33wADI5Vh3ozer0+tDZzarPCXkGJqgFFGH5ceUjJebwQ76k+5QUoLcsVNu7Fqt262naUVxRBreae56YNhwLWcKTsT0liIB8rXS4vRo3qi7Fj+0MiYeBye1BT3YKjR1px+HArduw4FvLekPUEKa2TklJAsaKKV1qTJIs5TpuXSNgdbjz18tdgAJw6kUv2CUpf/nmaLA4U53EJVKPZjm37arF9fx2Ucu4552iUOHisBUfqQ5vANLdbBE8rZ5rWQ0RRpVL4P3ukusXrYyPeH7iF0j/eo0oWGJtNtvhey9e+/QMnLliG2c+8E/JYJFGl4cem7hBFVWZK/7ItTsSVqHr22WcxadIkvPLKK5gyZQoee+wx4XeffPIJzj777LQMhtDRCxCpVAKJRAK7xQFLu9+Ar/5gU4DkNhLky5+Iobrd4oCp1SqYdsdCreVu9u1WZ1w388GJquDdbrKLQM7TB9Usk1KQcIkqcfCI5lFVwu/stLVYsPajzTHHnAgHDzQKsuOH738PV16yLMZfRCeSmbpUpKSTy6TC//uiTJo9nXQkDrMZrvNGegPL3Llz8fPPP2PVqlUwmUzYv38/Hn74YUybNg0jRowAwHVilUqlGDJkSMDfDh8+HHv37hX+f+/evaisrAw4p7y8HBqNJuC8rs7WrVsxY8aMgGNnnnkm2tvbsX79+ow9LsMw6DPYXzqojTtRRfxI4o8TRL0aKZEUjDZXjVGTBqMgTJlJMCzLCmOJ3PXP3xCDLL4O7q7Frk01KCrlbnSsZgfawxj3kmSZLoaZukwug8vpEX66OvGU/gEI2b2mUMTQONG98Hq8uKT0RpyjugL7NnJKFq/Xi2P762FoMmLCmWNC/kYqC6+0IQSX/rkcbtj5DVqVJrwqt1effBiaTKjadiSp50Hm4GCPqoGVvfHUv2/BQ/+6NnT8IkWVh09aSWUSaPVq+Ly+sJYlbc1m+Lw+5BZoQ+ZSrU6FPduO4EiCzUcI9bVcYqVf/yKIl0Jtog60LU1mHKlpCfi79na/r5X4dWAYbo3g9fpgt7uwc+cxVFVxYxte2RsAsGdPHey8KkodpJgWFHEuYo7vX0cQSOmfWq2AXCYJ6DKYDKTbX2GBDlfMmgjArzgink42uwvVfDfKxlYTnC6SnOPeDz1fiWMKkzQ7XO8v54zkYZUoDleooophGCFZ5YmwC04UVXL++UkkDL5bOg8v3TILAGCK0//ycFM7wAIapRw/7T4U8Dt70Ng6xKMqiRjRHeNEXImq3NxcvPnmm9ixYwfefvvtgMzZzz//jKeeeiotgyF01AKErKdJR42h4waGnEMmYleUSUNKOvwk0KbS33Y2vkSVTC6FTC6Fz+uLmjQTrh+ULFCqAxNK5DmT04IDpdAFMUyLW5eoM2JwMBPTq8yfsQ1JlKWIxewQxsx1yEhNJusNKh8kZupiU0SpTBIgn6WG6uGhCTykvab8vPPOw8qVKzFv3jzk5uZi2LBh8Hq9+Pjjj4Vz2tvbodPpIJUGJjXy8/Nhs9mEJH97e7uwexJ8Xnt76M5ZV8XhcISU8CmVnHJ0z549AcfffPNNKBQK5Obm4pJLLgkpGU+UgrI8AMCIk4ZCE6O0jUDUSfHM3wSSSIrmkShGo1Vh5+9V2L819vMj/ldSmUS4cQ6GYRi/1yA/FhO/maPP1yKvkPMWaW8N7fDpdLgxbFQ/9OqdH3Ms2eRTJbTwjpGokgftXlMowdA40X04uq9O2Pi18/ejx/bVY88fVbxHYXHI35C1Q6RkNlknSIUNczccJFGlDa9ULeFL7JqOtSZ1L+Y3U49tSxLuXphs+kqlEmiilHWvfP4rKFVyjBpXEfI7Ja/mSlYVBgAjxvRFWZ98zL3ldFx1wxRIpRI4nR6hxK6NN1uffMpQ9O7DxajWFn8cI4+tUsmFud7t9sJq5ZI2Wt73t1+/AvTrVwCn0y1s7KuC1lvC+8fHjnC+SyTJpdUo4Pb40NxqweffbMfab3eguTV0IygWFn6cOo2/miY4JpHEFMMAjfxmk0IuE9aHSoUMCrkUTrdHSCIR2s12DKvgPtMOlzst9/2CR1XQZ08mvF7hHyO49A8ACvVaVJQUAACMcSqqVAo5euXp4HB58O2OKjSKOpd3jkdV4jGiO8aJ1B3GMkBHLUD+svIW3Pf6fFxwy1kAgGETBiGnINDUjywQnFHaWwqtqBNQVAXvlMQD2UFxxnEzTwLmyp8fxnv/WxKyw05qzEkgDDY8zy/SoaiXPsC0nOAQmQFHC2a5+VohQRWvIiBeQmuiU/PBCq5/JjsgYgkvmeSJvNRNd8rDQgJWT85XcTsh8f80Nzdj/Pjxws/y5csDrvfDDz9g/vz5uPPOO/HDDz/gv//9L9ra2nDRRRcF7GYGt1fmxsKG/C7SeeGOd1UGDx6MjRsDS4o3bNgAAGhr8+/2XXjhhXjppZfw3Xff4emnn8b69etx6qmnwmg0IlmuWXQp5j7xZ0w467i4XzN5EqV/QhvseLvDEjP1OMoGYhmpE4IN1Y182+/cfA3yed+VcIqq2ppW7Nt+VEhCRcOfqOr6nf/i8agC/PcOtPEGJRI0TnQfmo60QMdXJhj4bnV2PlkQvK4gkJLuiKV/Xr86CQDcTo+QqAq28yBo9WroctVw2t0whtlAiAVREkXsKigi2PaEZdmAMZMxEpsOMRt+2g+nw42bF5wX8jshURVmozweqvY1YPf2Y8jRqzH9zFG46obTcMeD56GwOAevL/sWAFfpAQB9ywvRt5xL7gUmqvwbEiRR5XJ5YOXVYVr+uWm1Khw92oaGBmNERRVJ/Lj4WED+P1zpXw7f8a65xYSn//k1nnrhS3z/c+LKFDM/zhzR5yQ4QUYSQwwYQXmmUgaqmfTa8KqqpjYz9tU0QyphwLL+pFcwkRRHDa0mXPXYO7jxqfeEY+EUVUBsnyp/oiowJufwSbp4S/9+3VuDRoMFFSUFWLNhN7Yf5jy43B4vSnK1KM3TQcF/5jvSo6qnx4mIM9GECRMSehCyQEgHiSxAJk2ahL59+2LPnj149NFHceqpp2LHjh0h9ZcAsHz5cuENa25uxtjTA6W4uUV6mNssECc9Trt0Mlrr2zH4hIqI4xV2RTKcqFKqlbAY7XDYXMiJ0F5UuD7/hdbolMgJY/YrmKnzH7rgJBvrY9HSaILVFKpUEu96K6N0/WMYBgv+dgkW3fXvuBda8RKsZkr16xBqps79l7QZB/y7RzK5BHDG50l28HATfv3jIHqX5mHGlOEpjjJ+9h5qhA8shpQXB/hsdQTBRv49kXhbxBKKi4uxadOmiL+/9957ccEFFwSoV48//nhUVlZizZo1uPjii5Gfnw+z2Qyv1xuwC2IwGKDRaCDnfQfy8/NhMBhCHsNoNIbdGemqzJ8/HzfffDNee+01XHLJJdiwYQOeffZZAAh4/i+88ILw71NPPRUnnXQSjj/+eKxYsSLEdB0IjRPhGDZhEIZNGJTQeJMp/fMKXf/iTFTxHlW2ODoGJZKospq5RRIAGNttADhFVX4RtwAztIQuiFr5xVphHGWIWaWoilEuSZDF0a2I0rOhcaL7UHeoUbAOMbTwiSp+Hlbrwquf/Iqq+Er/PG6vkPxSRUhUAZyqymI8hsajrcgLY+IeDeJRFc89e3DpH3keEilXfaDW8vN6UKLKbnXCbLRDrpChoDh0fMQfK9lScKuFNPLwv+4VA4pRX2vAzm1H8NP3u/H+u1x1Tn6+Fr3KctF/QBFsoo0ScekfqaxwuTywWgMTVcIGlNsrqLXUERVV3oD/Dyz942JfXi6XqGpsNqGslx51DUZ8/9NeTDulEiUJvJdmfpw5os+ePGjzhBjGS6USQdWnDHrf9ToVWgxWmCwOlBT4H58kruQyKbwuD+xOt9AlkOB0ezD38f9g0sgKXHf+xAB119EmA/YcbgIYrkxRp1EKJYRijyoyPiByLA2nqAIAnUoJCcPA6nDB7fVCLo28FmJZFhb+PRhUWoCtNXVoNXPfZ6PNgdo2E/K1amHtTBJVjgQqqZIh0RgBdL84ETFLMnLkyIR+0sn8+fOxZs0avPbaa2hvb8dXX30VcQEyZ84cnHrqqZg3bx6++uor1NXVYcWKFWGvO2/ePGzatAmbNm1CcXGoDDe/V2Byi5EwuPvlG/H46r+gckLkzhNkTImYqQsBKIG2mURR5bDH3nUmiiomUkmHJHqiinQJtIRJVPUp95tCxgpmYo+TdJLum//gXW9JOEWVPHFF1dff78br7/6Cl978IV1DjYvrHnkHcx95F8caDB36uIB/16Ondv9jkf6a8r179+L4448PODZs2DCo1WocPMj5YVRWVsLr9aKqqirkb8U15JWVlSG140ePHoXVag2pNe/KzJ07V0hWFRQU4OKLL8bChQsBcN1fIzFq1CgMGzYM//vf/8L+PlacSBaZUEIX/42NO8Guf0qVHBKpBE6HO+acG2+iinTEJTfupPQvt0CLwhI9istyw5ZotPCJqqJecSSq1NmUqEqw9I823qCEgcaJrsXu3btx+umnQ6PRoHfv3li4cGFCHc0domSMoKiyxEhUkQV4hDmCFUr/pELyxm6JrqgCgL6DSlDQKxdmkeduvETyqAqHNMhMndyXk01dMsbgRFUD7yHVq09e2DWQKsXSP6uZTybp/K9ReUURAODokVbU1bbD6/VBrVaguJceCrkMh6tbYBC9XmSeVyrlgYqqoEQV+Z3T6RaSTSpV+EQLWWcICqGgdUx53wIUFxFzcwfqGoxQKmXYe6ABO/fUJvQaCKV/os8JeV9I6Z+DNM8SJXiCk0SCosoauPll5pN6pKmIPcx79fbajThwpBmrvtiII42BZWC1LX5F+7FmAzcewUw9uqLqvIdex+Q7/oltB+sA+D23ghNVEgkDvUYJBoAxhsrc7vLA4/NBKZeiJJfbgGsxc5tyrRbuv4U5fj9SFW/Y7kjgfi5RkokR3TFORJyJVq5cmZYHSIa5c+di27ZtuPnmmzFv3jxoNBo89dRTuP3221NagMQivyQwURXv4mDgmH6QyaWoP9yE3oNK4tr9Jl84aRKlf//P3pfHO1LV2Z+q7Ot7efv+et836EagZRMasJEWRMZRcUZ0tEU2AUdBUWmBwQVlZhQQwW34OQjihijIKAgoeyMNve/db9+z76nU74+qe6tSqSSVpJL3Xr+cz6c/8PIqlZtKXt17z/ec89XSxjutIs+Tg2ZUiROhkqgh3aOC/kyiKhZNYOf2Y5TiLDR+cg2L2ZxpQVpJVJUpqVJ6n8n1McoVVeKkXExGFaPIAqsWyMsVwYPqBvLdm9PWP53P19vbm3Vf27t3L6LRKO26unHjRrjdbjz++OP4yle+AgCIRCJ48sknsXXrVvq8zZs34+6770YwGITLJSyKHnvsMdhsNpx99tk6j7xyMBgMuPfee3HHHXdgYGAA8+fPpxPmaaedVvD51bav0KynIqrERH1l1GidZhgGdpcVIV8EkWAM7obcytukRmUQ2bAkE0I3q6BPWLS56uzwNDkxPuxH/+FM5VkqxWFqXNisaQl2pxuaWUBUJTUSVURtkKopqmrIgdo8MTPg9XqxadMmrFixAk888QQOHz6Mz3/+80in07QNeyFkElXCJjxSQFFVKDaEOCMYVsgKTMSSiEVImHpuoiqdBqZG/aqOiEIoKqPKlEm4UCJGvPfZcxBV4yN+rDypB72L1PdzZM6Jx0okqkiOlExR5XBacfK7FoBLcThyUAhC/+Rn34NTNy7Grnf6AWQquIjayCKz/iUTKYSUiiqqtuKQTvMwmQzZXf/EfYBSUSUnqoKhOPoGpmA0sLBYjIKVjmGwcF4z9uwfLro4T8LU5SomZZg6seuZjIJLBMi0/gFAb2cjeD47MJ0oqixmIxBWJ6qm/BFhH8AAwxMBrJgvNaEZHJMTVX4s623NnVGlCFMf8YbA8zz29Y9h7cIO+riSqAKA1noXfOEYQrE4mupyr4dCMVGBZrWgySUcNzgpjHHHUYEQa3RKRFXVrH8VOOdsmyeK8mPxPI+BgQH09/dj7dq1cDjy289KxXRtQDxt9Znj0GiZCkyEcGzvIG77p//Gv//wU9j0kXcXfE6aI2HqlSGqCAFFbpBKkEtEpKdKO4pEVEUyHvcqLB6FAvRM1IOvb1U5K/ejzL9m+STQ0OCgE4k8TJ1Myp0dHpjNRk3h+axCuVZtTEeWBFlc1ULV9cNVV12FG2+8ER0dHdi8eTNGR0dx++23Y968ebjooosAAFarFbfccgvuuOMOeDweLFu2DPfccw/S6TSuu+66jHN973vfw2WXXYabb74ZR44cwbZt23DTTTdlNMqYLfB4PPB4hDDU+++/Hxs3bsxbydm1axf279+Pz3zmM9UaIgCJEMqVR6KG9nnNMFtNRVmn7U6RqApFCxBVGq1/pJocT2Fi1A93nR3zlrXBaDKgSVQhT4xm5n29+dIB2BwWvOeitQUJHUBm/VPJMplpINa/QhlVSptFDTVUGrV5ojQ88MADiEaj+M1vfgO3243zzz8fgUAA27Ztwxe/+EVN7zcmkiNNXQ1070Aes7lyEVXinJBjLSkPUyeZUYlYEgzDwGzLTSTZ8oSYF0KymIwqhVIonSL5VIaMcUTCmeMY7pvEnn8cx7zF6kQVaf5UKlFFsnUdisD5aCSOvbsGccrGReiZ14TObiFsWx6WTsCneaxc1YX2znpMirmMiQQn2QpVFFVAtpoKkF+nTIGCPHOJdvg1GXD2xiX426uH0NPVgEaPoO4ptrMeIdScsmtgpHMSUVSJNk+TEYSpUlr/otEE3j4wiIBibg6K3+2ulno01juywtYBYNIvKdSGRDss/V0gjOZ6ByxmIw4PTmDThiWyjKocijQujSTH0b0FIbZyWf8AiVDyhfMrqoKiU8lls6LJ7QAP4Jm3D+DPOw/CLo6nQa6oqlKYeiUw2+YJzavf+++/H3feeSdGRkbAMAzeeOMNnHzyybjssstw1llnqWZ9lItqb0DqFYoqrURVQ5v0PLfMw5sPkvdcO5FAKigxDYGzkvUvF1El/kHnsP453erWPxKa29HVgI/fcAE6exuRD4To0Tt4PKu6ULaiSjrf1FRYyqNSCVOfmgphaNinqVLOMnOPqEqJ3705S1PxpfnK8+H666+H2WzGD37wAzzwwAOor6/HGWecgW984xsZBYNbbrkF6XQa3/jGNzA5OYkNGzbgz3/+c4YS1ePx4Nlnn8W1116LLVu2oL6+HjfeeCO2bdum65grjVdffRV///vfsW7dOgQCAfziF7/AM888g7///e/0mD/+8Y/4+c9/josvvhgdHR3Yt28f7rzzTvT09ODKK6+s6niljCrtC5ujewbQt3+4qIKG1pyqZDwJu8MMV13+roUmSlQl0X84BN9UiM4XTeLcNzGSSVQd3DWErnnNqG/UVsyiRFWepiUzBYsWtyDN8TAZC2RU1brD1pAPtXlixuDpp5/GhRdemLGx+vCHP4ybb74ZL7zwArZs2VLwHERRNTEwhaFDIwCAiNj9L1dnWJZkPBXIqGJYJkPhZHWY88aG0KJ2gc25GopRVJE9EiGoqKLKkN/6NzboAwC0dqp3hLVYSiequFQasWgSLMtkNfJoFG11e3cPIhiIoatLIKpMMmsfQSyWwu5dA2hqdqlb/5xKRZWoBlLpcC4pqlIZP6dlTo5EkhBVgpoqGk3gWN8Eli9tF65FkXld45NBzOtqRK/sGisVVdTeKCOnLAqSiBRkEgpCJiDuQyOxBPYfH6M2QjkmRaFDe5MbQUWg+YH+cYz7wgADvLlfULTlsv7J8x7lhN2+fkEZl0pxMLGsagaV2y6sh/zh/OpCoqhy2sxY1dOKM5bNw8v7jyHJAfEkBzCAQbafpoqqSmZUVWCOAGbfPKGJqLr77rvx1a9+FTfffDPe85734Nxzz6W/O+ecc/CLX/xCV6JqujYgJrMRn//RVXj2f/+OHc/v0WzLq2tySQFrGjocAWVmVGmx/hVQVNEbJU+6/imJKmFiDfgyFVWkU0bPwhacfeHqguOQOl3pR1Sl0zyVk1LoqKgCpK4dBpalr0O+D5JcW4P1T3xuNYkqeZVmOprzkO/enFZU6fzWGYbBZz/7WXz2s58teNytt96KW2+9Ne9xK1aswHPPPafnEKsOk8mExx57DNu2bQPLsjjzzDPx0ksvYfVq6b7U3d2NsbEx3HDDDfD5fGhsbMR73/te3HXXXVVXBZRi/ZPC1LU3RCBq2EKd/1KpNCKheEGFl0lm/es/LCwMe0TLhttjh9FkQNAfRSyaoF1e33r1MPa/049PfO58TWOuhKKK53n87MHnwXFpXPnpc4q6hrmQTHJ4++1+sCxTUFFlrCmqaiiE2jwxI7Bv376MfQ0A9PT0wG63Y9++fUURVYBEUJEw9VzB58YCjZjkYeomOVGVx/Yn/30piqqiMqqMmYoqQriRex+1/imK633iPNLSUa96XksZYepE8WSzm7P2P43NLvAAgoEYDAYWLaIi2GzK3qfQ7oUGlt7rk4kUWIbFmjXdlPSiiipCsmhQVJEihnytTkgss8mAD33gFMzvbUZzkwsDw0K2U6LI+JQj/ZM4NjBJFVmAXOWb2fVPTlQprX8kg0r5+mFRgWQX1W9qiqqpgKCoGp4IYNeREfo4z/MYEK1/S7qbYbOI83/Brn88IjKiitgN40kOSS4Ns0rxqF5UlCkVVTzPg0vz4MHDZDAgQBVVFjS7nfjwu9fiynPW4/tPv4zjE15YTEYsbJWEGU6rGevnd6LBmb/QVzYqsI2abfOEJqLqvvvuw+23344vfvGLWeGCS5cuxYEDB3Qd1HRuQC78+DnY9/oh7Hh+D612FIJZ3opU4+ac2KOKsv7ZSJVEg6KqQEYVIV9oRpViomxscaOxxY3jh0ZxaM8QFq3oACApqjwau08U8uCXAjIpMgyjWw6SkqiykJu17PKRa0nYfS0qMUJEVrMRnryCPx0N+Mi1nMtEVSWqIDVkYv369VndYZVYs2YNnn322SqNKD9omHoRUnFyr9Pa9Q8QrH+sgSmoqOI0nrtrQQsYhkEqxSEWSWDl+nk0W4RlWaw5dQGioTgmhv3oWiCEzw/1TQIAOnryK24J6hoc6OhpQFID+a8VkUgCjzz8EgDg0n86BU3N5ROTUlcnc0G1ak1RVUMh1OaJmQGv16vaocrj8cDr9WY/QQUxWWEgHBAKvKRDXy5FFSWzc2ZUSQVnuRUvX8c/+e+1uC+ywaOlywNHAaUtII2fEFScIqPK6jBj9SnzM/Y5h/YMIhSKYfUp89GzoEX1vOUoqr54zcMAMtVKBE2yDoOtbXV0nGqKKqI6Eogq8nsOwyM+vPNOPzadv0p4riKHV9nxD5CIFqXiTL7vSFJFlQGrlndi1fJOAMBPHxHmsGKsfzzP42jfBABgQU8Tfdwoy03keZ6Say6HFU0eByb84SySyEyzmDK/o15/BBaTAQ6r+F1TkIo8z6NBzITqH/fj+PAU/Z0/FEMoGofNbMTR4Skc6B9HLJFEOJqAzWyEzZwp+JBfLzlRRcixBOlYqFKMqnMQRVXmemjYG8Dm238CAHjzu5+TrH/i+zl75QIAwKlLerLOCQhumTePDlJlVaVQmyM0ElUjIyNYv3696u9YlkUsVry8NB+mewPCiOyEVrVTx6JWGEwGcElO8+Zc2XZWCyykSqLBHqFVUUWGS6rqDCM81ruoFSe/exH+/Nt/4Af/8SQO7BqAw2XFuZecjI6eRixY2qZ6XiUqEaZObu4CUaUPGaIkqkhVhFXZjBRTKWemwfontySm09XfJHHie01PB0s2QzCHOboacsBUgvWvJEWV2450GoiE8m9SSC5hIYv71FgAO18/gss+eSaOHxzF7jePYfM/n0p/n06lsXdHH4YHptC1oBmRUAzeiRBMZiOa2rSRQxaLCUN9U5gSCyF6QN7BaXIipBNRpd7VSQ3FFDRqmJuozRMzB2rEM8/zqo8/+OCDePDBBwEA4+NCIwm5GpQoqch/c2VUGcRNLpfjHiF3XhBFVfuCZixe05v3vdjs6pY7LQgHYhgb8AIalo6SdVFUVCn2NQ6nFbu2HxPUKykOBqMBz/x6O/bt6MOWj5yGBcvaVc9rtkph6rk+AyX8vjD++z+exNiQD+B5fPr6TVnHdHQ1YN6CZgQCUaw9WbqG1PkhKyJxMkWVu84Gp9OCeDxJrX9OZ2ZGVTLJYfHiVnR3ZxdnSMSLMqNKcIcI74/ME8ogdotCsaUF/mAUbS1uOGxmeOqkXCWGYWBgGXBpPsNG57SZMeENA2y29Y+QP3LrH5dOIyTuQ0lYu3J8gXAM7xwcgs1ihNHAYswbQjSehM1iwsC4DyYDi7WLO3FwcAKT/jD8oShCsQQ4Lg2XwplEib50OsPJEhVfkxBVZpW1TB1VVGVa/yaD0s/jgRBC0Tjcdgua8wSuy0FshokKz++1OYL2bsuPRYsW4YUXXlD93YsvvogVK1boOqjpRhrFBZ2/91/Owup3LxWeq3FzXkqYel2TE54WNxIaqgyFMqqIVIgcR61/4oSQTvNo7RT823veOo5UkoNvKox/vHwQQ32TaGjWpqiiYeo6/jGnZUQVAV+mPpJLi586sXCKiwIywchtoKSCUqhSPjTiQ9+goCyoLlElky9PA1kkKaqKf+7NP/0jLr/rYTy746DOo6oeeED3drI1zH5Q0r6Yrn8pbWSSHKSaXsj6RxblpkKh4DJbRMhPOv5J1fYOMadw6LhQwR3sm4TVZsLady3QXOypqxcW00qreTmQE1VTk6E8R2pHNCopqgqB2npqiqoaVFCbJ2YOPB4PfD5f1uN+v19VabV161Zs374d27dvR3NzM+LROGKyjXAkQLKprFh2ykI46+1Z5wAk5VEuooo2RTKwaOlqQEt3A4aPjCMgu7epgUSQxCMJeEf9+ODiz+PDq27O+xwC2mRDg/WPKqo4oqjKtP6xLAsHzbsV5qNXn9uL+kYntnzs9JznZVlWlo2obb685mM/xEvP70c4FIfBwGLzpSdnHdPaVofjR8bhnQjBLiNDzCqKKvKejEYWPA+EQnHE4ymEQ+pd/5KpNA4eHIVPZQ4zKIoWRgNLxQKcYg9mVhSlqLqsCKJq0hvGoWPj8AdjWSQf+WySqTQ9Z4vMIZNLUZWQKapIR0GX3UI79CmtfyOTYp5xcx3WL+tGR1MdhsVA9cFxP5JcGjaLiRJJk/4wOC4Ns9FAX5OOmVr/0hndBamiitoms7+zdXZhreJXZGSN+6U1wYg3CH8kjkAkDrNKzpUaaCGKqxxRVcoccSLOE5oUVTfccAOuvvpqmM1mXH755QCAsbEx/PjHP8Y999yDhx56qKKDrDrEDXYuNZIaGIWVrhAktl77a7AMA+94sGClnOf5gtY/qqhCpvXv6q++H90LWrBweTsGjk5kPe/YgVEAyNmtQwl6E9fV+keqTAzoPULHjCoDy9Dr09XZgOf/lDnBSzf6/O/p4V++gr+8sA9gqksYZSqqpoGoElVc6RKYqtcP9MMbiuLA4DjOW7dY76FVBzyAE3CyqKE8kIp4MYqqVAmKKrtTG1ElWf8KZC3J7NtBcQPmzCCqBGtB/1FBWdB3eByxaJJmKmpBnUeoYvoLbMCKgXdKOtebrx/B6WcsKfucJOxdzd6hRE1RVUNe1OaJGYNly5bRzuIE/f39CIfDeRs4Edz9ifux5+UDdCMQCUaRTqcxeGgE+14/CJNZ/X5RKBqDKOIZlkEkGMVYv2CfytfNFchsvBTwhYXMrBCDZCJVsMur1m6wgCx7SRx/SmH9A4S825A/ilAgCrvTgonRAFiWQYfYcS8XLFYTkokUEvEkzazKBZ7n4ZsKC9VRhoHbbVXNGO6eJ9ngemT/Twrq8owqTqZ+ImRRLJZEOEw6CmYqqsjxatmFkvVPOqfBwCKd5sBxaRgNbIb1L+M60K6C2tcNUz5h7mv0ZH9PjAYWcQgFbWLXa2tyw2o2wmhk8Z5TM9fdZhVFVUC8Bi6HlRJbSusfIaXaGt2IxJMYmvBj0h/Ggs5GDE4I+VRdzXXwhoQ1xag49ztt2bZWsk9OcWlE4pKjiGRaEVuimqLKbbegq6kuq9/WmIKoCkRj4vHq6kclyGtVkqiqzRECNBFVn/rUp+D1enH77bfjtttuAwBcdNFFsNvt2LZtGz760Y9WdJDThdxqJJVji7R40YyqosLUtfnO5SRVLqKKvDdCrJFNy5LV3ViyugsAUN/kgMFoyCBx7E4ztn7xfWjryj/JEFQiTJ1TVVSVeU5Z1buQeoH6vPOQbxOToQySqBTSplTI38t0KqpKec/pgkrA2YGaXLcGJUrJqCLWP0NRRJXGrn8JdauBErRbYYqjXWBJsw0AWPOu+Thp4yK89Oc9+PQXNuOvf9gBAOhdpJ4/oga3qDjweSujqHr+ub1Ysbob556/sqxzRkXrn82qQVFl1Ka8rWHuojZPzAxs3rwZd999N4LBIFwuQV3y2GOPwWaz4eyzzy74/JBX2vRabGbEownEwnHExbV6rkwpYv0rlFFlMLBgZWoRtywgWw3ybnt+mZo0GorB1JD/uUURVUQRRhRVpAAvm1PIXBEKROk5PU2uguvsBUtaEQrGEI0k4KpTV6QRBPwRJBMc7A4zHn3mCzmLLzabGUuWtSMQiGYQVbSzXYaiSrr2NnGs8XgSIWr9s9Lfs6KdDgxDSS85KKEny70ysCyS4Og1y0lUWYonqibFua+hPpuokgeqO+xmrFvehaYGJ557+Hr89bWDWL2kI+N4KaNKev2geA3cTiusORRVu48MAwDaG900yHzSL4zrrf0DAIDOlnocH/MBAMZ9wvfUqaJWpmHqyowq8f+TKSmIXu39Dkz44e3MtP6Ni0HvbfVORBNJ2sXQrUKUqYFY/5IVnt9rc4RG6x8AfOELX8DQ0BCefvpp/PznP8dTTz2FwcFBfOELX6jk+KYFpWyWCeGkVVFVSkaV1aGt619h25+kAEsrZKfym+Sp5yzHVV++mB7PAOjobsR7P7hBc0fESoSpUx+8/P3pqagqoHIzGfPfoPYfGsFln/gBnvnrbml406Womoa7HJEyl0RUEZI1q/4xy8AX+a+GEx6UtC/B+leITJLD5tSWT0LVWhqtf6kkh5CfKKqkjcOCZR1obqtHc3sdPnPJ9zA26MNFHzoFZ2noCktQ5xHOp6eiKhZNYt78Jqw/dQH8vghef/VQ+eeMFWH9U3TEqqGGLNTmiRmBq666ChaLBZdddhn+8pe/4MEHH8S2bdtw0003aWrO5BsX1CMsy9A8qkggSokqS477Bc0qKpBRxbAMLDIVp7sxP9kk7xDun5ARVRoyq8j8pMX6Z6Bh6mJGVTIzgwmQEVX+KCbHhOvU0FI4OsQ3FcaR/SOIaBjzxJhgM2tqrYPFasq7P7n3p5/Cw7++DqvWSiHZJlnOFIE8o0oe7k6tf06J0DDLSD01RRXZq8jD1Mk+oyBRRRVV2sPUiaKqQcVyKu/UODoRxI69AwAvFP7PPW1JdkaVLEieIBAi6iOLpKiSkXw8z+O13X1ocNvwvnevQKO4XpgMRDDmDcLjsmNBRyM2n7oMdaKCaUIksRwqfytEncyleaqikr+mpKjK/s6axOcqs6SmAhG4bRaM+EIYmPQXragyVUNRBRQ/R5yA84QmtoGElrtcLlxwwQX46Ec/ive+972oqxNae953332VG+E04KwPvgubPnYmLrn6Qs3PURI/hVASUaVRUVUoSB0AGEYk1kRigATrKisRxOJBOAdbEXYOIDN4XK9gb6nrn/RY2RlVGda//J+J5MtXv0EdPjoOm9WUwYRXkzCa7oyqVJor+bW5aQh/1x81T3kN2SD2j0qHqWtVVHEa86/kDTFCKtY/g4HFZ2+9GEFfFFNjAQz3TeDCyzYUpagiRJWeGVUD/VM4dnQCzS3CRrOUDlJKSBlVha1/ylbgNdSQido8MVPg8Xjw7LPPguM4bNmyBbfddhtuvPFGfP3rXy/8ZF5QTC06eR5+OfwgnKKKxT8ZpEVlsnZXoqD1T7ZPkHcXdxdQRVkdFoGsYgCfrEFFoTkBKE5RZTIbUdfgoO4GsiY2mlSIqqBEVDW1FCb/HCLhF9YwZu9kCE6XVdN51UDtbSoZVayBhcVK7G3yMHWJ0JDnPOZTVHEK6x+gJhbIfH4pGVXReAotjS40qiiqjFTdlZZyKvOsL9Ssf6SDnttppcSWvCvh13/0DMa9Ibzv3SuxbH4rGsWA8glfCHuPjeLpV/aiyW2Hw2aBy2EBywA+0QKoav1jpTEzPLCiV4ieiSaEsH1ir8+lqAKyCaX+ST9VUYViiRIUVdWw9hc/R5yI84Qm69/555+Pq6++Gt/+9rdht0sMbV9fHz7xiU/gpZdewjXXXFOxQVYbG85fiw3nry3qOQwhNzR3/Ss+TF3q5FFAUVUgn0p4XQaLV3XRoMNcWShyiweAgl5xJRiGgclkQDLJIZVKw2zW/n7V4J0KYXxUql5RlK2okk7AFlBUkUp5rtytA0dGhQ2N7DTTl1FV/U1SKkW6/hX/2kSNpaHJy8zGCVjVqKE8FGv9S6fTUtGhmHlCXEBHw4XC1NUXxkqQOSEWiYNLpWGxmjIqyICwEbvnkc/gjRf3Y/UpC9BeIH9ECYfTijUb5iESjiMWTcCqQbFUCBOiyqFRVB/oQ1SRjKrC4zPUFFU1FEJtnpgxWLFiBZ577rmin3fgH0fgYfaje2kH6prcOOm81ZgYnMLgwWFaVLbkKPBSoiqXoooWndmMtbdLJXtIDrvTilgkgalRfwZRFQ5G8zxLAFVUaSCqWJaBfypM761SBpPM+lcnKaoSSQ6NLS70LCqccesUiaqQBqJqfNSPUDCGJo1NnpSgiip5RpXMpkfsbZFwAskkB5PJkKmiMmlVVEluEEpeEUVVqoCiqgiianDYi7GJYEbHP/peZRm7iRwB7nKohamnUhxWLWpHe7MbVku2omrPkRFM+sM495TFMLAsmuudWNjZiHSap9lVPa0eAEIge5ovQFRRRVUavnAMe46PgmGE/UIyxSFOrH8qRbdcDphRr/R3EYrFESw6o0r8zlRDUTXHoWn1+/jjj+Pxxx/H2rVr8fLLLwMAHnroIaxevRqTk5N47bXXKjrI2QBis9OqnJEyqrTvyKmcN1q+ospkMuDgrgH0HRLC0cnmSUlUKSW0hTY1atDT/vet23+Pz1/7/wBkWhvLzqgqQVGVq1LuF+0xVpl0mrShrQbk4+KmRVGVLvm1iaJqVvuy+Vo3pxqyYSzS+icvHmhpz02gWVGlUa1lFO/5QX8UC5a1Y82pC1WP8zS5cMFlG4omqQChoDEy4MWhvcPw6tShb2JctIM0C1V2ZdhrKSBElVVDwYYqqriaoqoGFdTmiRMKl1y7GQDgaXEjFo5j59/2yTKq1De/JKMqV9e/tCzmwiyS443t9ahryk/I2MUCc9gvhLqTn6MFGjEBkqLKbCl8j1MqhZDmseLkXrSLnWABoL2nEcvX9yKRSGF82IfJsSBVS+VDMYqqSfFe72nKrzTLBTMlqrIVVQaDRBIGg1E0NzuxdGm76vOB/IoqufWPZRVEFSkcKcgWi8UIHsDImF/z+/GJyuc6ly3rdxmKqpT6a8pBSCx5RtWUP4Jdh4aRTvPU+hcXLXnHh6fAcWks6GjE4u5mAIDHbcPhwUkcGpjAsNgNsK1RmJcJMRUU/1bUMqrkXf8IMUQyooKROHheOEZt7yZlSUl/YzzPY8QnI6qiCZp9VbSiqpLzewlzxIk4T2giqj74wQ9i586dWLNmDc4++2ycdNJJuOaaa3DdddfhjTfewNq1xamPTkQU2/UvLbsJagUJZIzrkFFlVIQ45lJULV/Xg7t+/EmceeEqLF7ZibWnqW9S8sGkMgmUiklZdSgjiL5MZkNOVBXaFJoKVMp9Ygt3ujESh1Yt0mi6u/4t7BA2qsXaHXmep2OvFqlXMcxxT3kN2SALWK3Wv1KC1AHtGVW5ihNKkApvJBjDkX3DmBKVSnqjQayGT03oS1S1tOpn/YtEEzAaDUWFqevZ8baGEwy1eWLWY8n6Bfhz+nFccs17AQAL181D78ouRENRulbPpaiiYeQ57hG8TFFLyJLJYR8NS88FomIK+sIYPjqOiEhcFOoEC8isfxoyqliFKigeT2LPP45jakyaI1iWwd63+jAxGsDYsA8A0NxeV/DcThexDBYeM5kzGhvLU1TJ7W3kPRmNUkZVMBjD+FiQdv6Tni/NoUq1MSBXBIkB7ayUUZUvJxgAFs5vxvnnrEBLsxsvv3FY0/shRFW9iqJKXmjXoqiymLKvDQlOt5pNsIrWv6hI9Pz4iVdR57TinPWLqBqLWP8m/WGMe4MwGw1oF4kqhziXBsJxNNXZ0dmc/d2Q8rykPQIh3Ei2VK73QPZrcuWTLxzD4o4mdIpjCMXiGA+EYTayqLNnk3uq5xUJMGX2FYFue69aRpU26x8AtLS04AMf+ACeeuopvP3221izZg0+/elPw2Qqzgp2ooKGqWvcYHMlZVQJf9AFQ3JTaSxb10MrMGqQckfyE1VWmxknnb4IJ52+SPM4s16L3BhLWLDv3zOIvmMTWLSkDfMWtsDhkiZoVkYolZsBRT4Ph92M7gKKAHrtciiqfAH1nBUuxdGbayWRkVE1DYTPvj6hTT1XZKVBfsNPzfqsqhOvqlFDeTDbzPC01mnu9ErvyUUEqQOCjQ4AEgWImVz3fCXI78mGwVMgyLdUNIpEFamOl4NEIoXeeU1IJjl63pgORJXXG0YqxaFJQ+Vesnaoz3vxRIraOmqYq6jNEyca6pvr0Ld3EHaXDTGR0LDkyqgqsDYmzgtGpqgCALuKUkYOq90CMAyS8RRe/P2b9PGoBqIqQa1/hecdmrMkrvXU5hQXJc0iGB8WVEGtHZ6C55YUVYXtilNiAbuhVEWVLIeRgBMjLAwGFlYxo4rkU3kU2U9XXXUebtv2GyQSnCphIg9Tb2p0wuW0Zlv/kuqd68wmI5oaHPjzC3vwzp4BbDylsFjAL+5B6t0qiipZoT2hKaMq2/pHivBWixF2mwlLeprhslsQjSUxPBHA/uNj+M4Nl9LjpYyqMMb9ITAMsG6x0F2QhKeHojFM+COUuJJDyqjiKFHV3uiGgWUxKQoD1Gx/wuOiUEK2v/CHo9jdN4o2sXtmIBJHNJGE0cCqKrrUUChMPRiL06D48lCbIzStmCcmJvDBD34Qn/jEJ3D99dfjzTffhNFoxJo1a/CjH/2o0mOcFSDqpYp2/bNp6/qXjCew763j6D8wkvMYpR0vRf3R+i+cV53UgxVru6ltohj8x1d/g7vv+D1+/pMXMTkRwu63B+jvMrKkys2oSqfBAPj+f34M//Wdj+Y91qiQ8fI8j2SSo21rfX71ibXSbUwJpjujiqjbilWQybt5VDKgMFjAOqsL5ngFpIZsMCwD33iwoHWbQOrKV9w92eaygmGE7JB80G79ExVVom2krkJEFdlkTOlAVE1NhvDOjj5MjAdgFzeJeiiqRsV8xIaG/BkxgKxbYo77/ieu/SkuuPwe3HnPH+H16dftsIZZhNo8ccKBZARGQlGkkhxYlsmZ90TUsrkUVZmB3pIooBBRxTBil0CGyTAb6B2mzhozyRbapVZOVNVLjTLqGxxYvq4HTW1aFFXaM6qIoqqhgCUyF4h6LDOjKtv6FxH3XvWeTKWSy2WlXfHUrptk/UtjciKEeDyZRVQlciiqAGDp4jYAwPH+KU3vhyqq3CqKqowwdWL9y/1Zk8wtNUWVzWKC2WTEgb5x9I340D/qxdsHh9DVUo8G2Wt7XDawDAN/OIaFnU1YuaAdrVRRJczPYXF+bvFkry8MrKRII4X4RJLDgYFxjIhZU+Yce1e1rn8BcQ3mEl/bL6qyGpw2zTELkvWPUxWo+CKFv7eaUFNUaSOqVqxYgZ07d+KFF17At771LZx00kl47bXX8PnPfx7XXHMNLrrookqPc8aDfLk1d/2ThSRqBZH7Fur6RzuN5JEHy4mqVDKF3kWtWHf6woKtykvB+GgAe97ux9R48ZYOslkbGfJjoG8SgODZVkKrkm3nO/24+H3fxUc/cn/m66S0WzHlYYQA8Knr/gcXXHYPvvfAXxCPJ1HntqFdpftItUJ1p9v6l6ZEVXEkmTyM8Y0D/XjwT6/qOi5fOIq1N/8XTv/a/ZgM6dddTBVzfGKpIRtSK29tfxd00V+sosplA8/zBSvRSY1EFamsE9tIfYWIqrZOD5av6dLFKjcl5lw1NLpoNVwXokrMCWlrrS94LNkQqJHuo2N+mEwGxOIc/u/5Pfjvh4oPca7hBEBtnjjhYBMJFmK3s9gtOTe/hgIdpEnh26Do+md3F1ZqWCymjEiM+mYXJjXkHJWUUaVQVMkL3m6RqPJPhfH68/twYGc/tXnnQzEZVWmex9KVnWhuK63rn8loQM+8ZrR11NPHMogq8VqQYnu9QlHlkO211BVVmcVto9FAVVZZJJ/KfN/V7sH8nkbYrJl7n5/84iX88OcvIhCQ5vpYPIl4IgWzyQCbSpZic6ML3e0ecFyakjeWPPs+qqiSkXhRUVFlMZuoAioSS2DcJ8y7LQ2Zn6+BZVEvkqtvHRjEivlSmD5RMJFCdWt99ncjM1dLuF5uMfdtaEooHuVSVNGMKtm+KBQTPkcSnB6OCXvqeoc22x95TwZWIIJJUd4fieKan/0OX3rsT/BHCisBNaFGVGkjqj70oQ9hx44d2LhxI33MYDDga1/7Gl599VUMDg5WbICzBTQPSnPXP1IpKSJM3WFBQ4sbVoclLzFDugLm65wkhPkx4Hkek2MBHNg5gOMHR4veFGlBi1g9GR/VHgYICORTQJSwpnkeX/3CY/T/s4/Vds5AMIJoNIFJRWAvV0RmGNnYkQ3n+GQA6TSPeCKFZ57bjaPHJ1R96pVtYypBTohNh/VP3rKvGKIsKmtvu6d/DD/44yu6ZlWl0mk6oezuH9XtvFngAfBMcf9qOOFBw1M13gdKzaiyiwv8aCieV1HJ0ep3oa5/wu+J5by+RHtFIdjsZux9ZwCDYkGiVMRiSbz+6iEAQGOTE1ZxIS1vn10qRkeERXFra+ENkclkhM1qUr0H7jkwgqOy9+nWEC5cwwmG2jxxQoKonaJioUBrwTjX71dvXIKGtnqYZSQFaZiRD8p5wzcexOjxwvfWYhRVJGMrncodIUIUVT5xzd3Y7Na0zna6bfA0OXMWGP727G58/xt/wP89+RYO7hnC4f3DaGwujagym43oOz6Bw4fH6GPyPQFpnhEX5xBPQ26iSk0RJWUsSblX1DapyKhSI7oaGxw42jeJN9/uo4/xPI+fPvYyfv6r1zAkIyB9/ihWLmnHKWvnqRKkwVAM/cNehaIqX0aVQejwJyu+x2lGlRF2SlQlMSEqg5vqsxXHl79nLQwGBku6m3He+iX0cfJ8Ps3DYTWjWaWjpZFmfKVpIb5eJKpGpoiiKgdRpdJZMyQqquocVqyd145mt7Cm8RRBVAGynCqRaD48OoXn9x7F7/+xF6MBHbI2S5kjTsB5QhNRde+998Juz5YQAsBJJ52E7du36zqo2Qi2WEVVCdY/m0OozAwfm8Dw8YmcxxFric2R32tLJpPRAS8AoLG1sBy3FJAqx/hIcURVOBRHIi7cAI4cGkUsloTTZYFHvJHJb8Las8FyPU4mpcJ/5HJFVTSWgD8gVHxe2X4Yv/nDW1jQ24RL37cu63nVI6qmV1Elt79qUVW9caAfL+w8jKmgpHLiAaQBbL3317qNi5BUDIBj417dzltDDVogLeo1KqpKzKgyGFjYnEIxI5/Vg9gcSGZFLpB5Ip3msWxdN5oqNE+4xODXYA7rtFbs3NGH//3J3wEANpsZZoukqCqH+I7Hk+joqMfSpW1wq2R/KGE0sojGknRzI8fBw6NgACxbLFSWmVoORQ01nBCwOqVCAZA7nwoobP2LRRLY+fIB+CeCYBjhPs0aWKrEyge1rt/Dx8cLPi8ZL4KoMhBFmJizlFAhquok6x8ANGkIUgcAi9UE70SIPk+JJx59DX/41Rv41f97CQDQ1uEpqjmVHGRuTiUlGxexmBkMLOrq7djwrgVgWAYLF7VkZRRmKKrUrH+sZP0j55Sr0Xiep9Y/tfneIX6HwqKiK55IweuXrot8ne/1h7H7wDDGp9SJEoOs22BSRQGXdbyBxa5DwzjcL+05SUaVzWKC3SYpqihRVZdNNq1Z3IEvXHEu3r1mPlYuaJPem9UMHkA8xSEcS6DBlc01GGRd/1JpYcz1LhvmtzXQz8mcw76oFnpO4j/cNiuGvAEcHRP2Ax6nOs+RC8qOgofHJGvmwFRx+90ackOXZOdaoDqoioTXaHcqpesfwzBYsq4HALD/reM5j4uF83caISCV8nGxE4cW33gpaBY3NqPi62jBzh19+MzHHoDdbobByNIbcb3HAbtKhUqrcogoDFIpDh/72A/o45So0rAAkE9qY+NBrF7RCYZhEI+lcLRvAm6XDUsWtGY9L5WsfkZVsfY7PSD/LLSEol9932/wuR/+Hrv7RtEulwwzwBuH+nUbF7kWPIBwvPi8tGLA88X9q+HEB7V5aPybVMv70AqHWNXPZ5tIka5/BTYkNEzdH8W+Hf0FO06VCpdI/gQD5RFVx4+O0/xRs8UIg4GFyWwAzwsh66UiFk/h4KFRDA56NeVYEIt6PJ79msf6JtDb3YCVSzsBANFYZe9HNcxM1OaJEw9miwlGkwFpLo3eFV3oWtyW81gjtYPnCFMnBW3R/QBI689CWLCqG0ZFTEY0FCtI1tMwdQ1d/yjZklKEqcvW0e56O046fRHqRRVSs8Z9BlnnR3Nk8pLHw0GBdOgo0AQpHwwGFqxBsHGlOTG6QrZHa2lxY+/eIUxOhHD40BjNPSSwyix2ai4ZpUXSaGAzCKMUl8aSRa1YtqRNdU9oMRthYBkkEikkkxy+9q0ncMnHpfiSiCz/lxBYnjr1YopBZqMj5E2+rn9SJq+0bqFd/ywm2El+l5yoUlFUnbqyFx98z1pc/cEzMuZPh8z547JbVLOmnDYzelrqwcjG4bRacGR0Cn3jXizulDr4KSHPkiIIiVY/l82C3mYh2H9hWyOWdzXnvA6q51YEqkcSCdjELojekD7Wv2LniBNxnsh5x2tpacFbb70FAGhubkZLS0vef3MdpHqh5UvC87yUUVVkBWDByi40ddRjtD+3hDcWJda//BsKsgEhnTgaNdgZSsGSVZ3o7G3EO28e07wJGR/1Y3w0gEgkkdEpz+m0ShlV8mvNa1NVyfNhRmUKrwULWrB6VTcMKlUoJeSKqsnJEHbuHqQtUDva6rBudff0Wv/kXf+mQ1El+/9iOv8lUxyGJxVByjoOX07gpYoYV0mY457yGrJRrKKqVOsfINgmACknRfX84jgKZlSJv0+IyiC3p7iqo1bQ7lBlKqp2vHkMDIBbv/4B3HTLxQCEBTVQXk4Vef8Ws7bCHOnoF1chx/YfGsXx/il0ttcD0Kcj4VzCZCCMJ17dhS/97KnpHkp5qM0TJyRIoPrxPQOI5enSbTQbsexdC9GQowue3HlBg9U15tre/r/X4JJPnSP8wDBo6WrA8PFJTBQoGEsZVRrC1BWWNqn4Ic0pRpMBY0NeDPVNorXTgwXL2zWN30a6nOfI5PWLSqtQKAazxYilqzo1nTcXlNmzHCcRgwYDi43vXkyVUy6FVZthGKpMVmuoRbv+EZWW0ZBx7dJcGgcOjeLIUXXFG8MwlByLRBMZHfjIYwQSUaU+T8ttiFqsf3I1EwGJ6bCajbCYRBItySGV4jCv3YMGFUVVLtgtZqyYJxT2653q5FoixaFv3IdYUur6ZyO5YXEOBwcncjZJMlMySRo/OdZps6C3uR4AcHhkEi5bcRZ8iQQTzn14dIpmbQ14dVJU1TKqkPNOdM0116C1tZX+v9Yk/DkL8fJoIQbIMQzDFH1d3R4HJoZ8mMhjoyNh64WsfyQk9+lfvoaWznp09DQVNRatWLikDSODXnDpNLb+0334yrf+CSvX9eZ9jryqYLWZqdTU7rDQNq5KpNN8QeuenDiR81r79w0hEIxpUlTRlsJcmnb6s1hMSCQ5DA35sGxRW5ZPvbnRCS49DWHq00CvyyfqYogyNSKPF8+hJmMvFvKJNldLWd1wAvrEaygPhgLVcyVKtf4BgF0kqkJ5iCpyHy10fiMNUxWOr1PJkNADRFEV8BfX6ODIwRH84Xf/wOR4ALd980PoOzYBngfmL5QKaAsXtyIaTSAWS8JdonCYKKMs1sIbOCC3oup43yQmpkKwWkzoaKsHAERrRFVRePPwIG773z/DbjHhxV1HcNaqBdM9pNJQmydOSNhcNgS9grrE1Zg7ONxgYLHv9cM5152k+MoaWNQ1OmF3WdEoktta0NLZCAAwmli09TRibGAKx/YOoTkHMQYUmVFFcpYUYerK4seC5R0YPD6J0UEv3BpJDKqoCmcrqhKJFDwNDkyNB8BAUIF1dJWuqCJjjsdTwnuwmrJya911NoTF9b5LxfotEIhp1SKodJ1E8svAZHSykxwduUlIh92MYCiGcCSepQwuhqgyGiRldyJPLhY9XtHZEZCKL1aLSSDRrGYEI3EMTfhxbNhLSSQtYFkGo2LOlMOqvmclAgIho0oYs038fkYTwnu3W9Sfa5TZ83ieB8MwCIrXy2WzYGVPK36/fS8SKQ7LOotUVCmsfxFZ53LdGjbV5ojcRNVtt91G/3/btm3VGMusBqlyaLH+lRKkTuAUq86hPIt5LWHqgDSZjA36hOPtlbFwWqwm3P2jT+CmT/4YUxNBfOVz/4vfvvBl1WOnJkPYv3sQB/cN08dsNjN8YvXE6bIiJHaf4hXUsSZFlcz+xQP4j7t+j1u//H46uWjLqJKsf8GwMBab1YSgOK7lS9spgUUwPhmiFZpKY6Z0/QOKsx7KbYIumxmRRApcOo0kx8HCatsc5kM1iSrmBKxq1FAelDaJQiCEVimKKqljUh5FVVKboooG/or5IyQcV2+Uqqh69aWD+MNv3kRdnQ0vvbAPI8M+sAYmY+MyNhbA0IA3o3NRsSCLc62KKrN4HNlUvLnjOF7bfgQ7dvfDZGSxedNK2IlqoGb9KwoR8XpF4kns7R+btURVbZ44MWGTKW7q8hBVrEhQpQtY/xiWwbsvPhnvvvjkosZx/odPw5KT5qF7USv+51tPYtdrhzA6kNuNkUqlMG+poHjSFoORmVFF7OrKQu0pZy3F9hf3IxpJoKO3UdPYbdT6l62U2fXWcRzYMwSzxYhoJIG2rgYsWdGh6by5kK2oyiSqGmWfo8ulRlSJZIrK/C4pqoj1z5BhB6TqrTwOG5oFFU3QxiAMIxTc5fOHVkVVKsUhmRJJyTxrAJqvJdtXkNe3isUYm9WEYCROM7TsOQinXGhw2TEZjFKFkhJkf51O81S9RF6DdAvPRY6xLAMjyyIlBrGbjAbYLSasX9gJj9OK9Qs78d51S7C4oxmL2ooTayitf0RN1dNYD3+kcLdKLajNEXmIqlwYGBjA8PAw2tvb0dXVVYkxzUqQrn9avlOlBKkTuETvbzBHwCAgs/4VyBJREmV1nsp0cwKA7l7pBpDOQ9g8+evt+N+fvJjh+a5vsCOZ4jAxEYLDYZGqCYrTaCFllFa0Z5/dg1u//H6Zd1y7oiqZSiMkBmaefsp8TPoi2HT2CtTX2VXtHmqS4EL492/8Bv/Y1Y+Lz12Fm/7tPE3PyQxTr35GlZxA1JJRRQ5PZnTlSMBiMYJLCD56S4HOZFpQNevfCSq/raE8FGpFrkRKo+JJDa3dDVi4qguJPEodqqgqZP0zS5sR1sBSEkxv2OxmGI0GxONJRCLxrByQXOg7JoS8MiyLu+/8Pdo76rF8VVfG4ptYsZNlZFQR26AWSwwgKarIon7Hrn488dRbsNstWLqoDcsWt9P24TVFVXGQZwwOTPimbyDloDZPnLBo7W3G5JAXIW8Y7sbc62plB2kliulGrQabw4plJ88DADS31yPN8RiUdbZTIh5J4sieQdic2u69OTOqFHPKsrXdiEYSYFgG7T0aiSqRxI+oKKr6j01g1boe2OxCELfBaEBXb3mOEGUHxnRKSVRJSjC3O3sO/Pq2DyASTWDtmp6s3ymvk7zrn5BRJRal8tg6neJ+LhxJ0KI4qQnLFVUAsG5lF1qb1KNcyGskk2nwvKBWyve6cgUWASGHrGIxhpBGElFVnOjBKc71uQxGmYoqkagSFVRxcR1jz6PiMhkNSCWEorfJaED/hA9vHh7Eh89cB7vFjDuveG9R46XnJdY/ce8SSwrzeN+kD00qofBFozZHACiCqPrBD36Au+66C0NDQ1Q+197eji9/+cu4+uqrKznGWQGmiDD1cogqqqjKR1SJap5CiioDmzmZuBsqUykHAKfsxp5vs0AW7qQyc9mH3gVXvR0/e/B5gGEyFVUlEFWpHJWrYhYELpcV69Z0w243ISQqqho8LlyzdRPNJVGT0pZiwxubCCKeSGHCq73VqTyjiquyoorn+YzPxRuMorkuPwFKDleSWmajEbFESrdsr1TVFFUnZovYGsoDkfVrVVSVE6bO88DhXQPwT+a+b+TaVCghr6y76myaM1KKBcMwOPOCFfjrn3bine3HcNpZSzU/b+WabqS5NIZH/Bga9OGkUzIVNoSoUuvApxWS3UEjUWWWrH88z+O3T/4DsXgKX7pxM05ZvwAOuwV9A0KXoHAkjkAwCrdKpb6GbEREYs/Asnjz0OA0j6ZU1OaJExUmsxEh0frnzkEYALJg6xxd/0hxU497rrtBWIflU9lGxLW13aGtGMEqbGFJOqdk3iO7F7TgF3+/FW6PXXPcicVqAssySCYEO558ntr55jHs2tGHm+/8IM7dvKasbq4ENNJDfA/yrn+ApKJiGCHqQ4lTTsmt6qTWv7Ss65+MfKGF8jzWP1K4+f2fdsCr2P9Fo9K8duDoGHbsHsDHLz8971gIwZNPTSUcTxRY0rqls6UOHredznHEskeyq4pVVP3LBeuxZmEH1i1WzxmT2yTJ50LIMBIIn89uaDKyiCaEY+0WiWjLZ3nUAmX+VVS2t9WnYVNtjgA0dv27/fbbce2112Lz5s344x//iO3bt+OPf/wjNm/ejOuvvx633357pcc548EUEaZOb1YlTD4uTxGKqgJd/xhWqaiqTPYIIEy08sp8rlBbsqEgN0V3vR0rVgnKPZPJgJbWOuqdV05OWhRLSZXOezzPZ7SNLQSrxYQd7/Tj2PFJavFzOiz0pi1/H3KUYsOTnqH9ZpVIcnCJ1Rc9JvBiIH+5hR2NODYylftg8hzxXT7y17foYyzDwGLKlGKXCzlpl9RIFpSMOR5+WEM2WFpV1aioSpRu/bM7ifUvt/ycy7GpUIJsEMxmI1a/q7IWqwVLWrHqpF7s/Mcxzc95/aUD2P12Hz7x2XOxYlUn3n/Zepy/eXXGMeR+XJb1j2RUaczfIIqqRCIFry+CYCgGh92Ms89YRtuNE+Xw0IgfX7rztyWPbTaC53n8169fxH//+m9FNd0ApE0ADx5DU4GcQbozHrV54oREx0Kp0x8hiNRA7r2Fuv4p1+qlwC4WiyOB3HNCRHQIlK2oUiFc6hudYFlWM1HFMExO+9+QSPATe7ceGcomU37rX3OLC41NLvTOKy7HCFCz/rEZuVVarH+kO96fX9yLZDIFu8WETWcsA5CpqBoXi1PNOZR8lKgiWWTGAvO/QlGVTvPYeWAIh46P0zEtndeC7tZ6RChRVZyi6uyTFuH6y8/EWWvV1xdyoo9cQ4eoqCKfly2PJV+ZJUVIOmuZTg2X1QKryUjPF01K+9q4Xs2zamHq2hRV9913H7785S/jjjvuyHj8ve99L1pbW3Hffffha1/7WkUGOFtAu/5pCVPnpIDEYqElo8piM2HFKfPhLkA8KQOq3Q2VI6oAwNPoxJjYYTDgj6DZmp1qm+LSwh+aODSb3Ywly9vx4X95N7a/fgTv3bIOm7esw/Wf34xnnnobP/jen+lztdjc1NqTS91UGE2h3Q3idZ2aCqOpSfCtO52ZFSi1KkUpiqpSiKZxbwhBkUCrdkaV3PZ3eHgSz751CBdsKKCM4DP+g44GN85evQAv7D4CQB/10+7+EWx98Nf0Z02WxHJwAk4WNZQH2vVP46ac06h4UoND3JSE84Spm8xGNLa6tXf9S6Ro6/JK4ZSNS/Dj7/0FwwNT+NTnLii4AYlGEggGYjCZDVh7ci86uxvQ0OjMWvBLRFUZ1j9xEa5WhFADIbTi8RQGh7wwGln09jRlvCebbEE/OOKjavW5gEg8iYf/700AwKfedyocBu1V+LCYydLkdsBkYNE/7sWKnrYCz5qBqM0TJyTaFwiNHDytdehekjs7iSqSchFVJXYHV4NDVATlU1RFSeZqgdgQAmlOE1VICW3FD62w2c0IB2OIhBNwiZlLPM9jeMALAGjvyh0KXyyU1j9CHhHSbf78FvzyV9eVdG5K6Imfp8FoQEuLG8uXtQMMr8nR0dbihtlsRCKZQpoX1sUnr+3FX/6+D76AsB/keR7jYufs5hwEKRFIJJIkczH//M+yDM3C4tJpRKIJ8LxAnMnFFv2jPvp9KFZRVQisOCem0mlKNtktJjCMdE3zWf+UyidCLJUbKZLmecSSqayMqlWdLTg0XrhIrwm1OUKboioajeKss85S/d3ZZ5+NWEyf0LDZDLK41EKWcGVUSVx1dtQ3O+Gss+fMOpkc8WPPG0cLq7sY6b9mi5FOBJXC//vDTZgndmIK5AjMTcRTAC/IfgGh84fDacW/XX0u7vvJv8FqNcFiNcHhtGSRQVpIGbWNCpEra80BIESV1xdGVKxkOBUTu1q711IyqiiJUwRhJbcJFhNmrgcIGUe+2lpeX/nelne14ObL30OrIAkdKhPHJ/wIyzpyVL7rX5H/ajjhYSCVySKsfxabGeYiOugQkE1JJI+iKhiIYHI0UPC+197bCLN4P27trC96LMVg3qIWrHvXfLR3NdDNSD6MjwqFj5bWOjAMg+YWt+r7MVv0IKrEKqzGajG1/iVSGBz2gUul0dOZ2ZnKZjPj/LOXwWw2gk/zGRaOEx1BmUoiHC3OJkEsJo1uOwamAuif0KkVeLVRmydOSCw+aQGWvWsRWAOLxevn5zyuUEYV2U/o0fXYIXaqC+dpVhEVFVV2p0brH7Fk0a5/2nIPtWLF2h6sOrkXUVlzonAojvkLW7HmpHlw69jYgxBSuRRV5cBht2Dt6i76GgYDA68vgr37R5BIcJoyqpIpLmP+MhkNWDy/Ba1NLhzpE3Iag+E44okUbFYTVe0qQd9ngsOapR1YtqAwwU9VVVya3rddsvN3NNeBh6QYs2psOKIVRqqo4ulrCKHoEiGWq+sfIM8VFhVV4n/LVVQRAiwhfn7EUuiNxBDL0Z2+aNQUVdqIqksvvRS/+c1vVH/361//GhdffLGug5qNoKSTFutfGRlVBqMBaY7H8PEJ+CfU80e0Zo8QlnrFuh48sfM/dLkhF0JbpwdtnR4EclgX4/EkwEjBtTaZfVE5WSt/1kLmqG1UyGNa37/bbQPLMggEY/D5I2BZBg4FUaVWFS9FUVXKcyZ9Yfr/VXb+SWSc+P4LWTp4noeSvyOfq7IKUg7G/cGMn/WyE6qCh+ArL+ZfDSc8pDwPrWHqHOLRREnzhF1D1z8aFltgnnDXO+i90VKkpL9YMAwDs9mEXW8dx8G9QwWPH+gTOli1tGarc+WgYeplKMLIPKFdUSURVZNTIfA8UF+XmUFlNLD46ue3wGEzw+uPIKpLrkVhTPpCuOjqH+DCz9xX2cYSeRCQdUUKxYqz7hHrX71DuJ7eUHGdImcEavPECYulpyzE9166E/979D6Y86hLDDQXSf2+VM5eQQktiqqISAgVr6gSFnEkQsNUQKWjFRNjAez6x3GEZGMOBaPYvaMPI4NeXdWnJqqoEt6DnkRVMsXh7Z0DiEaE+5bRaMjoZKdUb6nBqiBizCYj5vc0IRSOw+uP4P7/eQEHDo1g9fIOnHpSbnKUhqmnOLyzfwjHhworf2hOFZdGKCLFnRB0NEvzr13MFtMTrEqYutHAwiabi/NlRy7rasbK3laqeCKKKnOZRJWymE7O73HqlDVZyhxxAs4Tmj6lzZs344tf/CKOHTuGSy+9FC0tLRgbG8Nvf/tb7N69G9/+9rfx1FNP0eMvuuiiig14poKliqrCzIAUkFjaF6q1qwGBqTBGB6bQoLJATyaKs4yYNIbD6gGrzYSRQS+mJoKqv5dbS+x2cxYBJIdSkaYlOFwtyJ0SVRozw1iWQYPHgYnJEALBGNJpPu84CUpRVBHyrRhF1aTMFlptRRUZJcswSPN8wc9EjYgjnwNRpemhqBr1Z5K6lbb+1VrK1qAE7fqnUVFF7lUmjcSIHLR6niePhIS1a1mIf+qW9yHkj+LU81YUPZZisWhZO17/+wEc2jeEsy9YlffYA/uGYTSyOOmU3AtzQB/rH+neZ9E4X9LXjCcxOSUUDxpydNYlNsFYlbr/hSIJTPkiAAP0j3gxv1NbJy49kaGoihVH0EVihKgSCFlfeBYSVajNEyc6CoWgF1JU8Tpa/+wa7OBRMUy96IwqYv0rw66uBqdYcAnKxkxyF/XuPqsMU9eTqJJ3+ANIRpXkwtHyWkolr8lkgMVsxKrlnXjtH0fxi9+9AYYBdu4dwsmruvOMRXhdUqzNR44RCIqqlKCoIrm8Nuk70t7kxsLORhwemtTd9gdI+2suzVM3hMlggM1iBiDsd/IpqkZ9IezqG6UZWnpZ/8wypRbP8zSjqruhDgdHJso6N0FtjtBIVF1xxRUAgMHBQTzzzDM5fw8IFVGtFeMTCsV0/UuXdwNs6WrAwXf6MTowheUqsmJSnSm0wSHf/1I2QqWiqUXogDIxGlD9fVxGVEUiCbjqcjPTymqKFiJILUyXWv803LAJ5s1rgsliRJhUF1Rktg6HGWFZa91qZFSl0zzqXVYEwzb4gtGqZ1SR90g+mUKEkJriihC4JoVctxwoCbOKKqqAE1J+W0N5oNVnrUSVuKgq5f5MrBuRPNVzqVV24U3FRR8+regxlIrFy9vR1ulB/7H8C739ewbxyt/2AwyDBYtb8x5rNks5W6WCFFEsWhVV5kxFFQA05siBJFlVsQpngBHIrXZ7Do9MC1EVipZu/RsPiMSfS8yLnI2KKqA2T8xxSHbw/N2o9QhTlxRVuYsX0aIVVZlEW1Jn659TLLiEZAWXcFAYo0OjPVErjDnC1PUgCeXWNUDY+0m2SV5T0cimQlQBQGdbPd1/ToqdJpvyBPiT10ikJAtdIRhkYfDhaPaep7HOgcODk2BYoE5jx8hiYJQRfXJF1er5bfCFoghG43nD1ElXQlIQIRY93ax/KQ5JLg0uzcPIsvjOR99X1nkzUJsjtBFVR48erfQ4Zj3YIrr+lXsDbBU7XYz1q0s2NVc1xMEWak+qJwhRNT6mTlQlFO3Dm1vyWzrk0EIEJVQk1vEirX8AEArFMTTsg13seqGmqPrRPf+Kj3zmR/TnUmx4ZGLT+twjAxPYdWAYNpsp4/lVA3k5klFVwFaiprgi1RNlp45ysH9oPCOkv+Jh6jXUoEChTYkS9D5ego2CKKryZVSRcbBFEPTVQEd3oyZbx2svHcTRQ2P40L9sxMkFuhFKGVXldP0TFVUa7Y8mkwEMI9hhJKJKfQNBquXVUlSRzYbRwGLPkWG876yVVXldOUpVVA1PBuCyWbBp3WIs7hAINj2tf68d6kf/pA8b5ndiXktD4SfUUEOJKBimTpQ2JXQIV8JsNcFZb4fRZEQsmoDVlq1AoRlVGskGpVKIzFkmncLUJaJKbv0jiiptZJpWmJRh6kUojgvBoCSqjCwlf7h0WgpZz/M55yKqPvbBUxFPpPDHZ3dhVHSqNOZppEVeI5USSUUN748Us+SKKpdsz+MRPyeeB+pcOtneZKCOJZ7PIKom/GEEo3Es7miC2577O0vUXyFxntFbUZVIcdT2Vy75VUM2NF3R3t7eSo9j1oNhi1BUlUlUNbbXo62nkeY4KaG1qkGGqlf1QwuaRKuimqLq1b/txxuvHKaEAssyqPfkDktUkjDEJ58PSTVFVQlEFanQEzuIw5496Stl36WQRuQpWpVVB4+PwWYxoaneib4Rb0kqrnIghalLk3A+qBFGZCI162j9m1JYQ5IaVS2loibXrUEJVrGoLwSSp2QqIZjU5rSgY34z3DlaVANSFbwa2YTFoKO7ASzLYHTIi0QilTMTamTYBwDo7GooqAoj54jHSyeCTGYDli1tR51b2yaOYRgsWtgKjkvD5bRizaqunBsIK7H+lTG+YhAWs1JSXBrv7C+cBVYJZGRUFaGoen7nYbx9ZBibTlqMljqh666e1r/P/78/wBeJ4Yp3r8OXLn2PbudVQ22emNuopvWPYRiYTAZ4xwOIBKKqRFWkVOtfKpOoKsadkA8u8V4rJ6rC4hidFbL+SYoqSf1ULrKtf4YM8oo+ni+jSkFUmcXvTnOjC6uWdeKPz+7ClJhP25jDYi5/jWSJiqqQiovEbDLCZjEimkjB49K/KZe8a6I8TJ1kFB4cmoA7R3g8ADipokoYOyGqyldUiQUwjkM4nsDa7jY0OPV9/7U5Ik+Y+rFjx4o+WTqdRn9/fznjmbVgisioIoRKIf96LpgtRoz0TcI3qZ7zRFrEFqpqEK9yKRuhUtHWUY8Va3tUpczbXz0C76SUJWS2GPNW1ZVt3rWQOS6Xlb5vgv/+/p8BSDdjLSAbn3Sah8ViVN0olRL2rgSxiWp96jsHhhCNJ9FY78h4frVArX8aw9TzWv/EyUmPDn2TwXDGz5Xv+je3ww9ryIYUPKux6x8pOJSiqHLZMHR0HEd2D+Q8hi6Oq1io0AKz2YgNGxdj5boeDA/kDnodGxY6vbW21xc+p6ioUsso1IqJiRD27R+GZGzW8JypEA4fG8c7uwfwzq4BuN3q1WYSBFs1okpUVNmtJkz4QtR6Uk3EEil6JcNFhKmP+oQ1wrKuFngcVpy8oBONOm6OSLemYb/6+kpX1OaJOQ2DIhdJibSO1j8AcIrrwqAvrPr7SCgGs8VIFbmFYDCqK6qMOiuqghnWP+H/tXYm1AqloqqtvR5Ll7XrQroRckiy/jFSQDinLaPKpuj+azJK17it2Y0Vi9sB8Fi2sBVtze6c5yGvkSxCMUa7/qXT8IWiaG1woq3RlXEM2RPV6fy5AMhQn9FsLQOLelloeYM79xwgt/6luDRS6TQYRpuaLB/kYeqBaAzv9I1gcErdLVQyamHquYmq1atX44orrsBzzz1XcIM9MDCA7373u1i0aBEeffRR3Qc5G0AVVRqOpS1nDaV9oSxiJSSeo5W1VsvI/X+4CU8f/Db+/e5/LmkcpaCrtxH7dg/g1b/tR0xRRTWapK9je6cHJ79rYd5zZSuqCm8Ax8YDtFJCsFfsLlXMYkBe5c/VBlZJVJWkqBKfk+a1bW6PDQpdsDziTVtLwLyuUBJVJYSpG3VWVCVTHALReMb+Uo9OgjlRoXayqVQK3/zmN7F48WJYLBZ0dXXhxhtvzHxpnsddd92F7u5u2Gw2nHXWWdixY0fWufbs2YPzzjsPdrsdHR0d+NrXvjY3swWrCCnPQ9t1lhRVxS/6ScZINBzPOX8XE6ZedfA8dv7jOPqOqudUxWJJTIwH0dTi1kZUmcu3/hFbnrKynQ8ucdEejsTBMNLPSlTf+pcAA2F+8QaimPSrb1wriUAkRm99xVj/CFHVWu+Ew2bBP44MYtfxEX3GFBXairMMk5GhVRHU5ok5D0lRlYOoSpfnvlDCVS+sC4Ne9a7b0VAciVgyo9t2PtCcJZJRRYvkemdUVU9RRfZPRw6PYv++Yap2LQcG2uFPyoWUd/1LaVA32xQKOPk1bm12Y8/BYQyN+LH/0CiaFSSS2lhSxSiqDNJzJrwhjE6FYLdm7nuIjc5dgYwqg/xayVRpdvGzMbAMVU2pgfwuFEsgkZJsf+V2jZSHqfvCwveyPo8FsWiUMkecgPNEzhXwvn37cOedd2LLli2w2+1417vehVWrVqGpqQkWiwU+nw9Hjx7Fm2++iT179mDNmjX49re/jcsvv1y3wc0msFW0/lkpUaW+uEsVGWioZ4vXQrDZLViwqBXDg14cOzyKZbLuFHKF2edvfT/WnJzfckqDJhmBH9Fic8tHZhWTA2DJIKrUb5DK61qKuokSVRqfenRAJKrqhAVJKZ0GywF5OcLRFcqCUmuNTv6WlFLsUjEZEhZlDKR7+MHRCdz8yFP41kdnT4fST3ziE3j22Wdx2223YdmyZejv78eePXsyjvnmN7+JO+64A3fffTeWLVuGe+65B5s2bcKuXbvQ1tYGAPB6vdi0aRNWrFiBJ554AocPH8bnP/95pNNp3HnnndPx1uYEaPVZa5i6xqYYajCZjTCaDEglOSTjKZhVyBVq/ZthiioA6J7fjNdfOoj+o+Oqv3/xL7vR3OKGu96Gzu7COUKldP37/e//gQd+8Cy6uhrw4EP/JiOqtHc1cooWGp4H3C5rzo0IqZZHq0hUAYDDZkEskcK4N4zWxtxV+EqAWE/yVcLVMOoVlE6t9U66UQnrpEQb9YXgsVvhj8bx+uEBDEz50dWgPSdzJqA2T8wekD1AmkuD53mVNWN5HcKVcInW44A3t6IKAGwaVTFUJSyu85ra3Fi4oqMkFbAa3B47Vp7cC7vMihgOkTB1fTOqSKGcrDfjMZHQsJavDqPXSdyjCF3/pM9ei6JK2cTDLJu321vcYFmGkk+dbbnvWZR0It3zNHX9I9a7NCaIvbA+08b+u+/8GxiWqUgurhQ8n6YFZ6OBpWRdIdLJaSUZVXGqmNUjS0peTPeK9nOPQ/+MrkpgNs0TOT+pzs5O/OAHP8C3v/1tPPbYY3juuefwu9/9DiMjI4jFYmhoaMDSpUuxZcsW/PSnP8X69et1GdCsBe36p50sKZWoshQgqpI6Bxrqjd6FLTi0fwRHD41lEFXyyr8W7kxJTGm59vkUPsUo3DIUVTk6pCithKXkRXHU+lf4uYFQDAaWxfIFrXCK35FqK6qKtv6pEFlkUWbWiagKRmPobqzDkC+AlOx6eCvZ0lzny/6nP/0Jjz76KN5++22sWLFC9ZhYLIZvfvOb+NKXvoRrr70WAHD66adj3rx5uPfee+mk8cADDyAajeI3v/kN3G43zj//fAQCAWzbtg1f/OIX4XZXd8M6V1BsmHo5iipAUFUFfRFEI/EcRNXMVVR19jSgzuNAMEfXwhef3YO3tx/DzV//gKbzlUJUHTo4ing8hTGx8UcpiiqnrLrszhMySxVVVbL+kZwRl8OCSX8YE95QgWfoD3JfnwpGaDVaCzwuG9Yt6ECLjKiKxIvrGpgLY4EQfOEYGl12LGhtwOGRycoSVbV5Yk6DYRgYjAZwKQ5cisuyzJW7V1CCKqpyWP+i4eJsdawio2rw6DgO7xnSbU6xWEzY/VZfRrRGLJpAfYMdrjp9s4BMCkUVbZ6hQzRKVpi6gZWsf+k0XQfnu25KlZr8Z6PRgPaWOgyO+GC1GPMGmpPok2RSUiZpHX8qlcakTyj8NimIKvIZVWI5QTOy0hzCEUENbDYawIqsVaH34JApqkjzrHKD1AE5UZWCX8xcrNNTUQVUpOvfbJsnCn6lXC4XPvWpT+GRRx7B/v374ff7EY/HMTw8jOeffx7/8R//USOpIO/6p4Es0cCe5wPpOpRTUZXQ2PVvmtDUKnxxpyYyF8ekgiG/ieeDpEzQZjMD8hMnxSjLSDXD7bJi8cIW9fNlZVRpPj0FyTPTosYaHvdjKhBBIsnJJsYqd7fLsv4VT1QZGGHstOtfmTa9cDyJ/kl/1v3ebKwckcvwxf0rhJ/85Cc499xzc04qAPDyyy8jEAjgQx/6EH3M4XBgy5YtePrpp+ljTz/9NC688MKMCeTDH/4wotEoXnjhhdLecA0FoczzKAQpo6p0ogoAYmF1CxO5f87EeaKjuxF+XwSH9g5n/Y7Mse2d9Vi1rjvr92owlUBUkc+JvF5UtKcpuy/lg1NWxMiVTwUIGzIAiMVLz9AqFot6mtFYJ2w2poOoklu6i8nIenVfH3YcGUKDyw6ryQSGEfKuCs01WkAIM4fVjNcPD+DIWO6MND1QmydqIPOCWk4VDVPXoesfALg9JKNK3foXCYrZdRptdcrcxXL3NkoQu1s0Iu11Av4ofFMR1TD4ckDmwVRKULcRoorkG5Z1bpHQyOj6RwPWeU3XTbknUhJX1//be9Dd4cFHLn1X3r0Midbg0uI+sUhF1WQORVUlQdwuiWQaaZ6H2WiAyWjAOWsX4opzT8LWi07N+3yX3YLupjqYDQbEkxwWtzeht9lT9rgoUcVx8EWEolq9zoqqYueIE3GemHml1FkKQkpUI0y9cEZV6SG81UBjk+CfnhwP4vH/9xKe/NXr+Ptze2mQ+o23XoyVa3sKnicts/4BGhVVeTaJbDFElbjxCQRjMLDq11lpJSxFEksW31qeOjgmhAt3tNRltHOtJrK7/uV/fWVeGJCtqIqnytu8RcQFh/Lz1SJ5Lhk6e8pfe+01LFmyBNdeey3cbjfsdjsuu+wyDA1JHbv27dsHg8GAxYsXZzx3+fLl2LdvX8Zxy5Ytyzimp6cHdrs947ga9EU1M6oAyb4RCakTVTM5o6qhSehaNDWZTaBMTYbw+kuHEArG0JLH4iCH221Fz7ymoqzQO3YcByDlWhFFlaWEjCoAqMuz+bNVOaOqb3gKh/rG0dzgxJLeZiRyhDlXEnKlrNZiRCKZQjiWgJFl4bJZwLIMbKLiIaqDGo1UxUnOyESwwtldtXliziNf5z+yXtXL+kfC1PsOZBcAAMn6Z8/hElBC2c2O7G306vpH9jryPNuIWHjJ5WQoFYT4icWSSCY48LzQ6VWXrn80Gka0/hkN9DGtGVVdHQ249xsfgUUkzpRB4BvW9uI/v/5P+OSHN+Yfi1FSR5GxFB6/ZItc0NWIVYva4dEYuK8HyPc/Ke4F7KJCall3C/798nNwxbkn532+zWRC/4Qfg1MBxJJJHByegDdUvqNCHqZOHBq6ZlQBFcmomm3zxMxboYr43e9+hzVr1sBisWD+/Pm45557so7RGvRVDTBFWP+4FIcV71qI3uUdJb2WVuufXp039EZjs0BUDQ968fvH38C933oad9z8S7zz5jEA2jNAiFVP6rioJR8sj/WvxDD1jnb1zZKyqlFK1z+6ANDw3oZEoqq9uS4j0YNUSQAArEdJREFUqLGakKx/ws+F1CNqGVZkArcSFUSZm6hoIimOSUFUaZA8lwydJ5aRkRH87Gc/w44dO/Doo4/ipz/9Kd5880184AMfoN8rr9cLp9NJLWYEHo8HkUgEiUSCHldfX5/1Gh6PB16vt7T3W0NB0DwSjRlVUlOM0u7jVjE7LxZRJ6rIOPTaVOgJT6NAVHlViKr+Y0LAendvk2YVrNVqRt+xCYyNauvIk07zGB8Xjk0kUuC4NFX8FmX9E3NUWppcWLm8M+dx9XU2rFjaXlnyXIaxKeG6uhxWHDg+jgnf9Fn/lP+fD14SWOu00s+e2v8S+hFVpMX4eGB2EVW1eWL2gSUFjGR2QU5v69+ZF5+EtWcuxfDxSdWOsJSo0qyoyiy+kABl3RRV9mxFFSm82HXOqFqyrB1t7fU4fnScWrD1CFIHZNY/8W/QwDKSbVKWUZWvC53ZZMCalV05BQ5mkxGtTYVtVjRMnSNh6hoUVeIx8UQKb+zuw76jozBXcX9JVGAJ8fvlsBSnpqMZkIkk4uLfmTLzqxTI40nMRgPW9rSh0amvJbUSRNVsmydmJJPx0ksv4bLLLsMnP/lJfOc738Frr72Gm2++GSzL4oYbbqDHaQn6qhYoUaWBjIhF49jz+mGYS7wJWmyk+porTF3fzht6gxBVI4Ne2mIckG7iVru26/LPV2zEP1+xEZ/5+EM4cngsQz30/f96Bul0Gtdcf0FGxUBv6x8AdLSpS0j16PqXIta/PAQbIHzv/KEo5nU2oKetHkGRxKw2UUVukuRaFgpTV82oEq1/VrM0uZQDsoHJUlRViKjSKr+VY3x8HBs2bKA/b926FVu3bqU/8zwPnufxxBNPoLGxEQDQ3t6Os88+G8899xzOO+884bVVvsO8wo6Z77hqNlaYayjW+pcUF8ulKqrsVFGlnv9D7QYaKqrVhsttg9HIIhyKIx5LZqiYRgZ9AIDueU2az+euI23OtVVRJydDgtpT7MAQicRLzKgSNlNjE0E48+S+sCyLPQeGsaC3WfO5y8H4lBBI3ixagaJVtBwSJDIUVRqJqqBgWZLbK+wWM4AIIrEEoE1glxN+0b7R4haI0vEKKqpq80QNgEyVpFLAIJ+JXkRV16JWuBuc2PnKIex+4ygWrOzK+D0lqrSGqSsUVeQ9sDqtrdSIKtL1T+8w9Z7eJowM+9DS6qYKLj1sf4CkWiKfZ6aiSltGFYHdakI0mih5bS+38Qk/a1dUEWt6NUkqQNpLkaK1rci9s1x1W6kw9YEpP97uG8HHz9JvPVXKHAGcePPEjCSqbr/9dpxxxhn40Y9+BAC44IIL4PV6cfvtt+Pqq6+G2WzWHPRVLRCWW4uiimSGWDS2gFXCWsj6lyBd/2bkx4uGJid6FzQjGIzBYGDBcWnwkKpHxXRVAiRlFbn2HJfGE799EwDw6c+cm0FU6UXckEwRm82Elmb1VrBKoqo0RZVwYx4c8+HVHUdx2rr5qsd94tafY/+xMQBCgC/pgrT/2ChC4VhGqG8lUayiSu33ZALXy9IRFYN2KVHFZL5ORcAXd+7m5mZs37495+89Hg8WLFhAJxUAOOOMM2A2m2lrWI/Hg2AwCI7jMqogPp8PdrsdJpOJnsvn82W9ht/vV62M1KAPCnV4UoI2xSjRwm21k4yq/AWNmaioYhgG9Y1OTIwG4J0Koa1DKgYMDU7BajOhrbNe8/lIPlRAI1E1NRlEfb0doVAMqVQa4XCcFoaKyag6/9wVePgXr4Dneaxc1p7zOIc4p4dzqN/0RDyRgjcQhcHAokG0AkVzFL0qiQyiSoPK8Nu/+iseeX4HAMDjkqrWdrG6HtHR+tda58S8Jg9cVn03w1mozRNzHmSdrtbOncZb6LhWOXPLSfjbk2/hpad2YMuVZ0qvlU7TucKq0VZH1t6EoEpz+qp0JVVwtvVPqz1RK+YtaIbRyOKdHX0Iia+ht6JKsv6x0nogrS2jioCMqdRGSUpysZiMqniCBMxXt7hFrYdEUVXkHlFNUaU3UUUiSqwmfb4zFEXOEcCJN0/MvBUqgB07dmDTpk0ZjxGy6pVXXgGgPeirWiB7Di1ECJ0M7KXdaAta/0iY+gzNqGpocuH4kXFMjQcziIqoSLxZbcX9oUvWP+Hay4NZ44rw3LyKqmKsf6KiKhpNokm0qSiRpagqkqgiBB4ATPkieOwPb+Y8Vi4H9rjtlIR5bedxHOxTb/FeCZC3qDWjSu2akInUJipJyt2ARMXvAPk81nS3ia9d1mnzQ2ep7vLly9VfhufpZ79s2TJwHIdDhw5lHKP0kC9btizLO97f349wOJzlNa9BPzAMk0FWFYKkqCpt4UOsEdEc5Ie0UJ2Z88S6DfOwcl0PJseDGY/7vRHEoknUFdH1ySUqqgJ+rYqqMHy+CFUlh8MJREtQVDU1uPDQ9/4VT//6Bixe2JrzOLu4FojkmNP1xNCYD6sXd+CMkxbQrA898p2KRbHWP/kmTp4DInX+04OoEv5WWuqcODbhxa7+kbLPmRe1eWLOI1+YOqez9Q8AFooqqsEjYxmPR8NxNLbVoam9XrN1j2XZjOgNvcPUTSYDDEYWqRSHpEgwUKJKZ0UVwzC0SB70C8rNYvII88FIrX/Czxld/7g0JfoMGjqPk4yqYhpQyCFZ/4TBmLRkVInjj4lraS3P0RNkP0OKd/ZiiSpZ0Zta//QkqjiOOj+sOlgKM1AB699smyeKupvwPI/+/n68/PLLCIcrJ4mOxWIwmzO/iBaLcFPau3cvAO1BX9UCJTm0WP/ExVCliCoSpj5TrX8mk5FaMdSuV7GTg0GRx5SUTfhxRThtXqKqmNeUL5pzbJiUVrNignyB7IkoX9i73IrYUGfPuKx6LnIKITtMvUBGFZfOuu7kuTbdrH+Ziiq6sOIr1xFR7y4dF198Md555x1MTEzQx1588UUkk0msXbsWALBx40a43W48/vjj9JhIJIInn3wSmzdvpo9t3rwZzzzzDIJBiQB47LHHYLPZcPbZZ+vw7mvIBWr/06AgoRbuEu0HzZ0erNgwXzUTi+d53fNP9EYywWH3jj4M9Wd2XvOLHavqPNq7DlmtJpjMBiQSKdrNKR+IwshgMIAHEApFkUxyYJjMfEIt6OpsAMMweRV0Dnv1FFV7j4xi58Eh8JAqzbFZQFSZxKYlK3paccMHJCUIIarC8fJJPpORxZL2JjQ6hfVJTCU3SE/U5okaDHnC1MmaUc+GF63djTCaDJgY9mXkF0ZCcUwM+zQVUeSQz2mczrmHcvIoFk0inU4jKhb6bSXun/LBJsaOBIOCslIvokr6/Hg0eBwwGaWQdk6mqNJiw5OIqtLWr0ZFBEG+XCz6HKqomi7rX2aulqNIpZu8mBHTkagyGcQc3RSHqHheW4mFxVyoRNe/2TZPaL6b3H///ejs7ERvby/OPPNM7N+/HwBw2WWX4b/+6790GQzBokWL8MYbb2Q89vrrrwMApqaERavWoC85HnzwQWzYsAEbNmzA+Li+KhOmiPBqMjnYSqwIGE0GsAYWXCqdVYXhuDTSaT6jej8TUecRyB2XonNEW0e9RGJpBCEJibUug6hSLMBJKLmlTO85WVibzcaciwhl6GGxiqqUYsGQT/ElJ6o8bnsGyVVMS/ZyIfmXpUk4H9QzqkhIrljtL5OoiioyqshVVOs4qBt0roBs3boVjY2N2LJlC5588kk88sgj+Jd/+Rds2rQJZ5xxBgDAarXilltuwV133YX77rsPzz77LP7pn/4J6XQa1113HT3XVVddBYvFgssuuwx/+ctf8OCDD2Lbtm246aabMlrM1qA/iun8R7r+GUssOKSSHPZsPwrfRDDrd/INxUzNm+nsEWTpOYmqeu3zBMMwcLm0q6oiotXE5baAYRnc+R9PAhAs35W4XoSoqoaiamwyCJvFhBUL2yRLxDQQVcVmVBFrxaa1i9DZWEcf723xYM289ozzlYrj414cGJ5AvUNYn1SaqKrNEzUYNISp63nPMRhYbNy8FotWd6P/4Ch9PCKSM44iu7nJrWRSl0L99h42mf0vEo6D53lYrKaKdKuliqqAcC10s/4Zpf3hlDcMi8WYmVFVhBLtqn89Gzd+ZhM+/bEzShsLq7T+aVdUEaLKUmURBFVUiWMuWVGVSFJVmB7KJ5fVjGUdzbCajHSfYdObxKuAomq2zROa/tLvvvtu3HTTTfj0pz+N5557LiNr55xzzsFjjz2my2AIrrrqKjzxxBN46KGH4PV68cwzz+C73/0uAGQQU1qDvgi2bt2K7du3Y/v27Whu1je0lKGCKi1EFbH+lZZRBQDL18/DsvXzszo6ETWV0WSYsRsQQPocG5ozbXMfufIMeBrUrXS5QAgcEjguJ+9iMXXrX2OTSq5UEZcrQUMFc9+wlZe/2HwsZZU53+dplC0M6ly2jAV2tEotzwFZ+Kc4nEKKKkJkyd8ZeZ82kUwsX1ElWv8UiqpCQe8lowIVELfbjeeeew4ejwcf/vCHcc011+C8887DL3/5y4zjbrnlFtx66634xje+gYsvvhiBQAB//vOf0doq2Y48Hg+effZZcByHLVu24LbbbsONN96Ir3/963pfiYpjtnWHLU5RJSpjS1ws5wtTJ0S2stAzk9DR3QAA6D+WWVSSiCrtiioAWLm6CytWdSIUVA+XlyMqEkbr188HzwPhcBwOuwWd7eqNM8qF3VY969/eIyOIxpPoaKmjG7FqzhEEySIzqsgmyWHLLPBFYkm8c2wY/nD57cZDopKuQVRUJVJc5RqS1OaJGiARBWpzAvnu6V105tM8Du3sR99BydoaCQp/P1o7/hHUNThQ3+hEKpnSXVEFSPukaCSBu7/6GxiMLDa9b61u5894LTF2hAS26xWmTjOqxPWxxWKiZB7HpWlRWov176TVPfjARSdh2eLcmYdaxkKK9kVlVFG3TnUVVcpOhcUSVUYDC5PBgDTP03u8Hooqh9WMfUPjOD7ulULa9VRUlTBHnIjzhKZP6r777sPtt9+OL37xi1mBf0uXLsWBAwd0GxAAfPKTn8Tbb7+Nz372s9i6dSvsdju+9a1v4brrrqMXSGvQV7VAw9S1EFXh8qx/ADBwZBz+yRCSCrVMaobnUxFceMlJeOOlg1h3ynz85L7n6OOlBCTSyoQGRRVR0ZRL4c2b14T585rQlWfjoiSWJqdCONY/gdZmN2wabrTJFIfeDg+OD3mFB/J8tUwmA8AD5562GEYDm5HNpUd2h1aQNT21/mkNU2cYagMll033MHXCnpE8uQpa/7RUNYrFokWL8NRTT+U9hmEY3Hrrrbj11lvzHrdixQo899xzeY+Z6ZiN3WGlxakGRVWiPEWVRFRl28kqsaHQG4uXt2PeohYc3DuMeDxJG1j4vELsgLu+uDbQE+NB7N09SNVS+UC6PpEcLNLxL9lQvmpHDZL1r/JEVUR8L3VO6yxTVAnHKDcYDiux/pX/Hoh90GWzwGI0IC6G5Opt56CozRNzHvkyqqg9W+fGL73L2vG3P7yFY/uH6WPhQHEd/wgioThCgSj4tER+6Kl2WrC0DSzLYHIigJFBH7hUGpsvW6/b+eUgjapC4pypt/WPbA8tFqOMMCouTL38sWTmxxaTUSVZ/6ZHUcWneZy8uAvNdcUVqQCh8J2McAjHxM9WB6KqTsxK9EViWfsW3VChOslsmic0fVIjIyNYv179xsCyLGKxwhXKYmAwGHDvvffijjvuwMDAAObPn09zp0477TQAmUFfS5cupc9VBn1VC4zsD6kQys2oAqTNi5Koop2iZmjHP4LLPno6Lvvo6eC4NH79v6/CJ1bJSwlIlIc5AplEVSymbv1TVScVcUM447TFOOO0xYUPlOEXv3sDP3nsZXzinzfikx95d8Hj02kexwenKHPD5xkg2QBvOk347ssJump2dFIqGgtmVIm/X93Til19o0jzPBhkZlRFylRUpXkeZqMBZK1HrX+VTFOv4KlrEDAbu8MSm4emMPVEeYoqYi2PhrPn5zRHOv7N3IJG97xmTI0HEQrF8LH3/ScSCQ6b3rcGLrcNq9f10k5+WmETNyFRDaol0tjDYbfAZjNJP+vcaYrALo4tGIrh/p89j6uvPKcirwNIpJTNYqYdDKclo0o2T2sJBpZCcDO/s6QDFNmAlIp0mqdElcNihsVkRDzFIZacXURVDbMLBkXXv3Q6TddzZL2qt6Jq5SkLsPbdi3Fs7yB9LCwqqoq1/rFy618FCiB2uwV9R8ax+x/HMTrkAwC0FtHxtRjYREVVLJbAqtXd6OzQR0GrzIEym42UfEwXmVFVLqj1jyqqCr9mV2s91izpQEK8B5urvG5gZV3//nFwAO9a1l30OewWMwKROFVU6dH1r84mEFX+SIySdxWx/s1xaLqbLFq0CC+88ILq71588UWsWLFC10EReDwerF69Gk6nE/fffz82btxISSitQV/VQlFd/0SiylKG9c8k+muJgooglUxh2Um9WLiys+RzVxMGA4sfPX4N/bkURZW8zSug6PqXpagiRFX2eSpxP5C/TorTLrUFsr9L+UhQTlF5W724Aw2iGiAyHdY/Wde/fCpDMlnWO21S1VD8j12nan8gGkcixeGkee1Yv6ATK7paAGRngOkJvaW6NWRjNnaHbelpRMeCFqQ05MZ1L2rF6tMXl9wUwybeSyMqVrdUcuYrqgwGFh+/+lwYjSxi8RRi0QReen4fhge8WLikteix002IBqKKWPBsNnNGl79KEVXyzcKOXf0VeQ0CUriwWU3TqqiSq6iSGu7FiRwhuA4LIarKzzLkeaFAYmBZSk5VMqeqNk/UQO5jk8M+XN5zNd7f/Glqw+MrZP1betI87H3zGPoPjWFixAdAsog7irT+0fwlTv+ufwBwxvkrsP70Rdj+8iFEIwk4nNasbFu9QDKq/L4odu3s19R4Qwvk18NiMYJhGNVsLy3Wv3JhMhqwbkUXbOJ9U0uYeirF4Z0DQ5gUuyFWO0zdaMh0zZhK+H6R+3lYR+ufyWiAw2JGmud1DWmXoxLWv9kGTVf0hhtuoBXqyy+/HAAwNjaGH//4x7jnnnvw0EMP6TqoV199FX//+9+xbt06BAIB/OIXv8AzzzyDv//97/QYEvR1xx13wOPxUEuHMuirWmCKsv6JC8UyFr2kE5RSUcUl09j31nG0dFYmS6MSIHJboESiiiiOVKx/JEuKQCJ/ZBMCn/ljpZBMcgCjfRJXhq/nC2Mn6iBSedh85kr0j/rwk9++WlVFFe36x7IwsIzQ0STN04lGCWLFNLAsJfXIkXp1/SPX5pINq3DW8vn4276j+PnfdxQdbl/DzEKh7rBnn3123u6wemcraoFvLICx/klNBY39/ziGiWEfzCXaD0jWSDSsYv0TSQIt1dTpxFmbVuLkUxfg2ad2YuD4JP7+vKCsXn/qgqLPRbNOohq6/lGiypShTrbqlFmihi0XrsGTz7xTdP5GsYiI84HDZpaIqlgSPM9XNddSbv1LqNielMi1ESCKqkiZXf9C4vOd4vnI68QrHahew5wGUbXGQjEEp8JgWODIzn6s2rhEClPX2fpnc1jw7ovW4a+/3Y43nt2DzVdspNa/ookqg2RdrEQn2Q0bF+N/f/g8Du0dhqvOiqUruyp2nyLFjBAh7Yq0QeaCvDhNLOykMMul5RlVVbD+GVns2DMAt/g5mzQUfBpEq50vIBJVVY6WYZlMMUIpyrOFbQ2wm420KKMXoVRnt1IlrsVooIq1GvSDpk/qU5/6FLVU3HbbbQCAiy66CHa7Hdu2bcNHP/pRXQdlMpnw2GOPYdu2bWBZFmeeeSZeeuklrF69OuO4W265Bel0Gt/4xjcwOTmJDRs2ZAV9VQtsla1/RFGVlVGVmvmWDiVMZgOVHpXSZUMZpp7P+qemqDJbDEgkOU0kY9FjY5is82oNZ1Uel68dLQ1GlE1001Et52U5UwaWBZfmwKXTOas2RFFlYBlq+ZPC1E0Z5ywVJIuK+NxpMGOlwtSBmly3CtCrO6yS7KokaBVVQ3h0udY/mlGloqjiZoGiCgDcdXa46+z4l63n4P7vPEXv7V29TUWfi1j/tCiqKFFlt9BCCFB6XpgWnH/WCjz5zDslKT2Hx/z42a9fRZ3Lhqs/dlbeY4nC1mYxwWg0YOXCNqS4NJIprqqVcvk8rSWjipBZuYiqUJkFGWIdJAoti1F4nViZhZK8qM0Tcx4NbfXoXtKOuPj95dPAwbeOYdXGJRXNLlp16kK8/dIBHNs3BAAIiyREsWHqhJQif8+sQf9OsmvWz8Oet/uR9McK5p6WA1I0D4vFHadORFWGokrcuxlYiXyxWkxYtqStYopdOYhtrxi7YZNHIKr8JGS+yvtLmufFE6Kq+L8HfySO3f1jqBcbZdh06PoHAPV2K4a8AQA6B6kT1OYIbUQVAHzhC1/AVVddhVdeeQUTExNoaGjA6aefjrq6Ot0HtX79+qwNiBq0Bn1VBYx2osrV4MDKUxfBWVe6fLUQUaXVXjYTwDAMVqzuQiKeLCmjqqe3EQF/BAnxWiRlFdBc1r/OzgYMDHqRTvPweBzo6W7EupN6y3gX2qG1wxIhn1oanRibDGV1AZRDaf0DQAPbq0tUCf9libQ5xQljy3H/Jmong4HNsmOSjYIvHCur2k875zCEqNIW9F4yTlD57UzDVVddhc9+9rN46KGHcPnll+P111/XpTvsgw8+iAcffBAAMD4+nvX7csDSRaKGjbl4nzCVqOKhRJVKRhU3CzKqlPjQv54Jny+KeQtaUFdkkDpQXEZVTFRd2WwmfPWrl+BLX/4lAoFYRSveJC+qFAXsoePj+MNzu2A0sAWJKjL/kPnhyMAkovEkkqk0KhXFpAa5okpLRlWsQEZV2YqqWKaiimxkKmb9q80TMwaPPfYYHnvsMbzyyisYGRnBT3/6U1x55ZVVee2wP4L+A8OIyZpekPszr1DK64kN71mO79/yGP7vl68CDIN/vLgPYAC7q7h9CVVUievvStwju+dLXdrrG4oP0tYKMkdERDGB06UPcaS0/skf47g0/IEo9h0YwblnVT5f2Sze13gAJ63ooo088qFRVFQFIyJRVe2ufzSHuHSiitzXSeatXoqqpZ3NMBhZ7Owb0SX3KgO1OQJAEUQVALhcLlxwwQWVGsushtJ+lg/H9gzi8M5+WGz6K6o4saphqHJXhnLxXz/+ZMnPjYTjOLh/BH7RP03yVwAgFlO3/n3lK+/Hl7/8ON5+px8N9U5865v/XPLrFz1ejS3IKYkjEiv57BGUjJERVXarZOuoFkjFg0F2aKMaaKAjy8oUVcLvDAYWFpMB8SSHeJKDtcQKiHQd2Yz/1qx/sxuV6g67detWbN26FQCwYcMGXcdMFExaFFUJkWA2lxmmrppRlaqe1UAvNLW48OU7P1jy84mtQwtRxRoYNDU54XRasXRpO5xOKwKBWEWtcbSwUML9mpDuXDqdl9RPpjgkUxwMLEPDX01GA6LxpCaySC9w6bTQOIMRNkz6ZFSVR1RFkyksbG1Eu8ed8TrxVM36d6LjV7/6FY4dO4aLL76YNueoFohKMy77/pJ1fKWsfwDQ0tmAk89aioPv9OONZ3fDNxXCP332PLz/yjOLOg+Z00ihmK1AzlLPwma0dzdgeMBbUaLKXWfDwsWtSIrZv3opqhiGoY2tCVElD1OXBAaV37eRLn/JFIe39gzgIxcXXuM01DlQ77LSNXq1iSpW0dldS6dCJZwiCckAOGVxFxqcxRe71OCPxLCzbwQ9jfVY1NaoyzlryETOb1uhtoVKXHTRRWUPZjaDYTMZ33yIipUTWxk3QUpUxXMpqmYXUVUO6kVZqs8rEFVaFFUGAwvyURmMldt8qJ1Za8U8zWUSLIk8lV0lGQMINkpPnR1V5WPE12JYBkbxbyKV52/CwDJYt6ADTXUOSVElO9xqMiGe5BBLJksmqmrWvxMTs7E7LO36V4AUIB2UGIYp2W5mc1iw9KReVYs5Ry3is4eoKhfE1hGN5CeCeJ7Hgf0jCASiaGhwAgBWrewEz/NYvLhysQL2IhRfShDVLM8DsXiKqrOyjpOpqQiZRcL6E1UkqhLJFBZ2NAIMcHhoMq9amIB2/TOqW//KJarCsQQOj06iu1FwCZDqeFRD44OSUZsnZgQee+wxsCyLUCg0fURVRPr+pghRVaGufwQf+/xFeOnpt3FoZz++//QXYLGZi1bZ0o5sVFGl/96jZ34zhge8AFCxIHVAuPcfPjiKxlYXAP0yqgDhOnFcmiqaJKJKyqgqRSlULEiBguxVc80VcjTU2eELxqht3FxlIQQpqPFlWP8cVmEddHzch8lgBP9+6dm6jM0mFjv7Jn20aKIranNEbqLq4osvVs3XUQPDMJqsDCcyGGr9K7z5jYSEjh7FesHlyBemDswuS0e5kIiqMADJK29XkbTKiSpeFvxdMTAMlHcaLWG+QPYiJZlHUSXPeiIwGlh4A1GEouW17S4GNExdrCABQCgSh8epvriIxJPYcWQIva0eUFpPxu5ZzUb4I8JGrN5R2gIllaWokkIsK4ba5FI1eDweeDxC84h83WG/8pWvAJC6wxLVVDWhVVGVjEu2v1JVPHanFft39IFhmIyW5/LXn0vzhNaMKq83jEAgCofDgqYmgai65ZYtVRhf6QrYQCgm+/9obqJKtMfZZb8nHZS0kEV6IcWlcXhokla5kxxX0N4dF8dnMWcrqtbNby95fqDnVyi2rNUIU6/NEzMCFV0DFoBR/D7HZfclUkhQU8rrieXr52P5+vllnYPMaYRcq0Txw2ozw+6wIBKO581qLRekwE/2VXpmRhkMIlFlUlr/ePqeKpmBSGDKIqoKkyt2qxkMI7o6GKb6RBWTqagqx/rnCwv77+6mel3GJi+gl1pMz4vaHJGbqDp69Gg1xzHrQRRVWtQrUXFRWRZRRRRVCvIiNQcr5fUeQcIpKaqEaxCJJOD3RehxPM9nTvx8prWuWghHYkin+YKLD07hx86nqEqrZBmQ55USzlsqJOsfQ7M9/u07v8T/fVudFEjL7I1S1z/pulh1aBFOCT8mU1GlNdS+WDCo+cqrgdnYHZZUm1MFupwRpWyptj9AIKGsdjNikQRikQTNrALmpqLKZtemWBoa8sHhsGDevKaqdsGzWqSMqmIz+QJBOVEVQ2uTW/W4SDQBg4HN2JyQrobVtP4R9ZbZaISBTRbsDgsAcXHzqAzytZiN2HF0GM3u8ixBNANLPD8hrCqVUVWbJ2oAJHJETlSRv8VKdNHTGwZFIbVSdvKrvvBehAMxvOusJRU5PyA2doKwP6irt8Pu0E8hQ5bmkqJKisagTpgKqNGUIPdPsla3a1BUsSwDp82CoJjdVfWMKqWiqgRimRBVXJpHg9NOlbjlwiYLdtQ7o6o2RwjIeVV7e6sTLH2igHb9K8BUpZIpxKNJsCwDi630P5TCYepzp1JOgnWJokq+4I7J1EtykophGEn9U2Wi6vUdx/Hw46/gyn/emPe4dFEZVdmZM+Q7UE2iCjSkGuB5Ydz5rm+al9ROEkEl/Q2RSaCczkvKrK+a9e/EwGzsDksVVQUUyIl4eUHqBHanFbFIApFgTEFUpbF8/Tx0ykJqT3TYHWasWNNV0DoyNupHOBSHx1O5LBQ1GAwsLGYj4okUYvGkpko3QaaiKjuTjCAaT4Hj0pmKKpIxU4A81RNJUUFgMrIwGQzg0ikkkikYDbnfc5PbAZvFlBWmbtWJUEqkMhVVLXVOLG5vBMfX5okaKgeSJ5uUxVSQjCoapj6DiSqq+BeLK5Uqflx4yckVOa8cJPsoFIojmeTgdOhn/SPrW6JoIuvRNMdXtQkWKUyAB8Bos/4BgNMhEFVdLXWoLzJwv1xIGVXCz6VkVBFiqs5mwbsWd+k2NjlRZat1/asINK2C+/r6cv6OZVm43W643eoVvLkChnYlyL+oIflUdpetrGptroyqNLF0mGbuxKY36uvsWLWuG55GYWMhV5nFxMk/neYREy0VhMxZuKAFIyN+LFhY/Q2rSYN0Vkk+5dtIEDJKaf0DqlspJxOJoFgr7LtP0W6FLLX8ye/LREpbTk5IWmHxrLj1r9apoyqYjd1hidWusPVP+L6bylBUAYJqd2osgIiCvIhFE9j75jFYNC5STwQYDCx27xooWCWPi9ferpLtVWnYbGbEEylEY8URVUGNRBWZDy1mOVFV/YIGUVSZjAbUOa2IeUOIpzjkirfleR5HRyaFMGJFAwSqfCozSyqmsP7FkykcHJksO/sqJ2rzRMXg9/sxPDxc8LhScwr17AxLrX8yyy9RvKp1c55pkBRVlev6Vy0Q6x257labfvMjIfDOPkPIyqTWP1lGVSkETLFgWQZGA0sbWGidZ1x2C4YBDIz5q5KlJQdRUJWTUeUSm5f5I3EkdLSPylVUyvzEslGbIwBoJKrmzZtXkFTp6enB9ddfjxtvvFGXgc020K4cBb5UZMNAOjKVCiJRrSmqAJvDjJ3v9KOzqwGAgqgSJ//f/uYN3H/fswAjTRA33vhe3Hjjeys6Nkbx/5dvWY/Hn3xTUw6JZP0TP+siu/6R502X9S+DtCpwvJxgy7D+6bAJ4aps/QNQq4LUoAopo0qbosqsg6IKQDZRJQb3WsvoPDvbQDYhpKNTLhCiylLmtS8Fa5Z3wheIIBxOoKFeu6KLS6dhNhuEDn55rI0kdN1mySaq8lnL9UZKRlSR+SkaT+bMMkymOPC8sGFRblLMRgMYRsi54tLpjIYixYBkUVkVGVWVsv4BqM0TFcLjjz+OT3/60wWP05LBqwY9O8PS+5JsTZhSdP2bzgytQqBzmjjmmaz+KgSTyQgekpLNrGPm0JO/vgE73unDgnmCipmVK6oqmO+lBqNRIqq0WP8AwGmX1GUuHZVmWsCwDDoa3fCGokhx6RLD1CVCrsmtT8c/ALDLrX+1jKqKQNOn/cgjj6Crqwvvfe97cf/99+Pxxx/H/fffjwsvvBCdnZ247777cN555+GLX/wi/vM//7PSY56RYFhtiqpIkASplyedzGn9S849osoiLrpJhz95/kucqqik6zGd1SmXGM4YjhQOOJcUVQyWLmjB4nnNOb9faZWuf8TSUc2QXJ5a/xicslSQ1+bzbdP3yLIyekrF+pcs3fpHrg2pyhirZf0r5l8NcwJS1z9tiipzmYonkoMYCWYSVaQVukXHivFMh7wtdz4QwkbPTYpWjIwH8PaeQQwMe7M61uZDIBRDIsEhHEnQzkxqIESVVU5UkY1yBUOKlZAyqgyUNCNB72qgQecqnwnDMLSSHS/DvkgIKbN4Lku1wtRr84Tu+NSnPgWe5wv+mwkga/WEmvWPqMFnMPmTlVE1i3MPTTInitVq0j2jcN2aHrhF6zm5btXu+geIXfsYYU7Uuld0yYLlnVVWG5sNBgxNBmgESClZXp0NdXCIc02jSz9bv3x/YzNVyPo3x+cJTSuxv/zlL3j/+9+P73//+xmPf+Yzn8F1112Hl19+GQ8//DCcTiceeOCBOamqkrr+5f+WkA1DOUHqQG6iiobkVrkrw3SC2FcIKaWmqJJ7v6spTbbZTAhF4jRkn1QiQuHCRBV5DsuyOHx8AikujWQqDYs5e/xUNTTNiiqpkyJw1ZaNeGn38bxy5pRKt0I1658uiio2MzOLq6CiqibXrUENkvVPY0ZVmWRJLkVVXMzuKycncbaBEDKFguwTVFFVfRLPKW4Gvnjnr/Gl696Li85bXeAZAhIqdnc1kPnQltH1TxuBpyeSYkabyWCg83E0nvseTwgoS46ih8VkRCyZQjyZgr3Ez42QZ2TjQcivSiqqavNEDcT6J1dUEesZUVQxM9j6RzOqxDXaTFZ/FYKctLFW2BZP16FcGpy46q2G9Q8AjAYjgDisRaiG5eSUW8duiFpAwtvJnqiULK+upjpExLnRrSPRZquwoqo2R2hUVD3++OO45JJLVH/3/ve/H0888QQAYPPmzTh+/Lh+o5tFkMLU8x8XmAqha1Er5q8oL8wtd5i6yMzPKUWVuKAkiqpUGkuWtgmPicoB+Y2tmoqqPz5yPT79sTPpz06ndqKKk5E4tOqdY5PFqSiqyHuubkaVZP3T0nWQWhZzVK6sJh3C1PnMa2OQdVupGOZ4BaQGdUhh6vm/e4kYsf6Vt1h25FJUReeeoopYbArdD6fT+ueUbQB8/kieIzMht+3ls5VLiirpvUmKqmqGqUvhwYRYiua5x8cV+VFK6KF+UmZU0XOmaoqqEx179uzBr371K/zud78DAGzfvh2/+tWv8MILL1T8tan1T/bdTYn/P5u6/p0InWTlhaFK5zdKXf94mllZTesfkKmsLYSF3U10X1ltRZXRwGbsEUwl/D1YTEY0uOwwGVjYdCSUrBXs+gegpqiCRkWV1WrFSy+9hE2bNmX97qWXXoLVKiyGeZ6Hw1HdTjkzBZKiKv8GZHLYh4FDo1h12uKyXo/cUFOKvA3uBJDfFgszJe04pNM8+o5P4MD+EfAABod8ePCB59Ar625V7bBHuXzYLRJVwXDuwFsCKXeKhcVkRDSWzLmZUO36p4Eo0huEqGUYRhMhRAk2A4v//swlCERiWDO/nf6ehqmXsQFRkmHGKoSpn4iTRQ3lQ6uiinSAMull/cuRUWXRqUXzbIBJc0aVPvlgpcAhywwbnQhqfp5aAxE1xPJkVOXLQNQbSZn1j8zfkTzWv5giP0oJSXlbekGj6hlVtXlixuCXv/wlvv71r9Of77vvPtx33304++yz8fzzz1f0tQ2m7MZIZB2flq2PZioIiZZKZq9BZxtMVVRUSdY/nhZ4q6WoMrAMwAPzuxo1P8ftsNK9hLvKGVWAYFckzopSrH8A8OydW3W3c2YoqvS2/tXmCAAaiaqtW7fijjvuwOTkJLZs2YLm5maMj4/jiSeewAMPPEC7Kb388stYu3ZtRQc8UyF1/cv/rZoY8QEAmjo8Zb0eaVvumwwileQQDkax67XDOLZP6HRinEPWP4ZhYLWaEIslEY8nMTjopb/juDTe3H4UPb1N0vFVllHLKwFul3ZFVVpmWSObrFyBt2rdYaajm5Nk/dOmqKKqMYbBepWWsWQDEooUJvYKvga1/kmVrEqhJtetQQ1S9Vlj178yK38NrXXoWtiS0VEKkDKqrPa5Q1QZtWZUJXLnIVUapEkKyzAF8y7lkFv/onmIKqqoki2uzRqvi54geVgmo0HKIcxr/cv/mehBKpHXMItzLVFU5eu2Wy5q88TMwLZt27Bt27ZpeW1qSZa5I8j8MBusf1ld/2axm8Moy6iqtKLKILP+kQiMUixtJYEFwAAhDVm5BD3tDfT/HdPQEddslBFVJdpL9SapAMAmz6iqWf8qAk1X9Y477kBDQwPuvvtu3HvvvWAYBjzPo62tDXfffTfNpPrnf/5nfPKTn6zogGcqGGr9K0BUDQkkSrlEVUi0BfzpkVfw/k+chQM7+vBfX3wURnGhO5sni1JgsQhEVSyWwPCQN+N3RiObUS1mUN1JX77IcLtsWLeqG3YN2TCSna+w9U+961/1w9Ql65/0+vmUS3LVmBpOX9qLNw8N4K0jQ2WPiVWEqVfc+ldDDQqwxXb9K3OxbDCwGDg8Bv9EKOPxuZlRJaqQZffQ0VE/WlrcGQvY+DRmVNEqO8/jrV39mp+XkG1y43kIn5hKmLpWAk9PyMPUNVn/Eil6vBr0sP4Ri1+WoqrS1r8a5jQMNH+HR0NbHaZG/NSePBu6/hFFFXciKKpkpEMxtrhSwBqkBlyk0GwoUSlULMg21VNE97t5HQ04e8MieFy2qoW+yyHkVAnE2kyKtulurEOD04apUJTmPeqK2hyhjagCgBtvvBGf+9zn0N/fj5GREbS1taG7uzvjBrpy5cqKDHI2gGZUFVJUEaKqvb6s13v/lWfhlWd2ou/gKHzjQbrxoR3O5pCiCpBsGt6pCDwNDnR2eXDgwCgAgUXnOBlRVeXilFxR5amzY8eufk2ToJzEIQv0RI7NBKfobAdMl6JK+C/DMHTBoklRZVD/UJZ3t2AyFEUqzSOeTOXMKMkHuggQ/0YNtTD1GqYJ1PpXKKMqrlNGldhhKBSIZjweEzOqrHMoo4p0dCJkfyQSxzfvfAJDgz589F824pIPCG3mSZj6dFj/5HPFyFgAPM9rqgLLCRotiip5mLp5OjOqDFLXv0iscNe/XNY/i46KKhKiLnUSrIWp11A5kLV6NBzH1IgfgMz6x2cXIGcaDIos1NlMVMn3TdW0/pE5Sd51sJIge4u6IrrPu51WfOuG91dqSAVhkX0200GU5UKTy4FkKg2WYSrSSbw2RxRBVAHChrm3txe9vb2VGs/shcauf/7xAFo6PWjqLE9R1dBahwUrOtF3cBTe8QANVSevP5MY52qATCpTUyEMDnjR0uoGa2CQTgttiJPJ6pE1WZCtMRx2M0xGA2LxJGLxZF7CSs36lztMXa3rX/XD1ImikGFkiqq8RJWoGmPUJx633YpAJIbBSX/pRJUiTJ2tKapqmCYYNCqqUokUrHYzbGV21yFEVSSYSVTRMPU5lFFlVBAyL/x1L3a+MwAA2LdnCJd8QDhuOq1/7a11qHNZEYrEEU+k4AtE4akrXPVOqHS6VYO6oqr6ylvyWiYjS61/0TydXWOFrH9m/cLUadc/8ftSya5/tXmiBtL1LyXPqFIqqmbQxlwJKaOKWP9m7lgLwSQjQ6oRpt7ZUQ9PvR39wz4ApWcvFQuaNeWsftZUqTAZZQ1AZtDfA8Mw+NMtn0SdvULXsjZHaCeqhoaG8Ic//AEDAwOIxTLzYhiGwbe+9S3dBzebQBVVeb5VPM9j+NgEYuE4msu0/gGAp9kNAJgaC1J/OJ/mAZaZ0RNbJUA6NPm8YQCAy2XDV7d9ANde/T/Yv38E/oD0nS1kz9QbpCDOMMLkVOe2YWIqBH8gCmtz7skw0/onBm4WtP5JnzutdFVTUSX+l2UYqu5KcWnsPDKEG37we9Q7bfj1bR+nx6dVCDYlCqnJCiF3mHqFvge1AMQacoAoqtIFMqoS8RRikUTZ2SSk619ORdUcyqhSWv8GB71o76jH8JAP/QNT9Li4aEEzTwNR9aH3b8CH3r8B13zpEQRCMYxPBLQRVYkiFVWWac6o4oj1z0jHkm/cCa1d//KQXYUgZVQpuv7VwtRrqCDUu/4Jfx+k8DyT1/NUOS+OeSaPtRBMVVVUMRgc8oFlWUpMViuj6ntfvRwj4wEs6GkqfPAMwUxVVAGoLElVmyO0EVW//e1v8ZGPfAQcx6GlpQVmc+bitkZUAQwryThzIeSPIBaOw+a0wF6E5DIXGloFoqrv4Ahcnsxui3PN+keqHz6fkN3ldlvpYzzPIyhTFKQKbBJ1B7HDidIqSlQFo2gVyUY1FGP9U+v6Z1LZgEz6wrj+27+G0cjif27/WIlvKDfImBlGFqae5jERjMAbiiIYzQxvlMi43BNPuRsGjlda/yqrqGKAKqeg1TBboGzlnQskWNdYZrtjqqgKZBaXpIyqOWT9U9wPDx4YwdCQDx2d9airl8ggKaOq+kQVgcVsxLH+SXj90YLHptN8RjEinofwsVlMWDa/FU5ZNpl0Xao3LxIFmMnIwmUzo7e1Pm+xIqaRqCpH/eRx2LCssxlWY2ZGVbxCGVW1eaIGQGYHlxUhCekzf1UX2ue3zOgvirJByIlg/WNZBo1Nroq+Fl2Hcml6762WE2Z+dxPmd88ekgrI7Ig404iqSqE2RwjQtBL78pe/jAsuuAA/+9nP0NDQUPgJcxBENZPP+iflU3l06T6wYsMCLDupF1wyRb3t5Ftdte4RMwQk+DYgLuxdLluGvUFOTlWzDTcgEVREHUF84f/50LPYdOZyfPCik1SfV4z1L8VJ6isCA8uCYYTcKC6dhoFlEU0kcWhgAgA0558UA8n6x9CJOMVxdNxKMZuyI58a9FNUidY/0qGT5ytyDQDUqiA1qIJuSgp1/ROJKlOZZImzTj2jytPkxKp3LYDdWX7BZLaANTBgGCDN8eC4NIIieTcy4sfQkA+JRApms3FaM6oIXKIlIxgq3O2UKDFMRgOSKS6vMmlgxId9R0YzLHSUqKqkxU2BpEisGQ0GGAwGHB/1YTIQznl8oYyqBqcdzW5HWeqnY+Ne9E/66bWpRkZVbZ6ogXR2lTd5SIuKw4NvHoV/MjSjozxYRcTErFZUGQ1gAPApnirvKwWDLBojxVVXUTUbYZFZ/2by34PuqM0R0PRX0d/fj+uvv75GUuWBlq5/QW8Y3Uva0bGwRZfXXLFhPvxTYTz32zcxMeLL+J1hrimqSJg6sf7JFFWAlNuxbl0Pbrppc1XHRuyghA+pF1UOu/YN4ennduV8Hifr5EcCbxM5Fs25LHRKFQELht74YmXYJHKBvFdWpqji0jziKUJUZf59qHUrVKJcokpuoQQIiVb5QPUaalCCZlRx+b/LSZEsMZVpP3OIpHhYQVQd3jOEXa8fgdU+dxRVDMPQinkqxSEYjIIBUCeSeX5RjTudXf8InGI2WTBcmKiKy4J4Vy1pR1d77liBqDgP2qdZUZWSdf3TYj0k79Gcc13DYzwQRiQPSVcISjLMqoNKq4YaCoGs1eUqW0JacVz+rsgzAYRw4bk0Fq3sRFNb3TSPqHSYzNL9paHRWdHXkhNVXJUVVbMR5hls/auhstD0aW/cuBH79++v9FhmNchEkk9RFZgMof/AMIxG/Sq1jbJJQb6pmWs3vHedtgjNzS70HZ8EALjcNlXrxqmnLsI571le7eEBkJRV7W31dKMwPhnMeTwvI3HUbHxyEMJHKbs2KmTZcouIlk1QsZCsf7LsAo6juS9ExUSgDDpXg7lc6x9VbUmvsa63A6u625ColK2DL+5fDXMDmhVVSZ2IKrcNHfOb0dxZn/F3R4grYg2cK5Bn/QXEa1BfL9jmvSJRxTAMWlpcGZ3xqg1JURUvcKRUvDAbjdh1YBh7DgznPJZ01rPJQvTdTis8dXZEE6WTPMUiQcPUDXRuy1eIINlTuax/UiB76e8hK0zdbML6+Z1Y0FK5Am1tnqjBaMqeEwhRRZRVMzmgnIwtkeRwaPcgguJ9dDZCvm+qNFHFyoqlySpnVM1GyBVuxhlM3OqNYueIE3Ge0LQKvueee3DFFVfA6XTi/PPPR319fdYxdnvhwM8TGUQtky+jinTwKDcgVw4Shhv0R5BMpmB1WBCLJOiGaK6gra0OVpsZ6XQa8+Y3w1NvV62IT8dEQL4R9DvCpRGJJgCGgc1qzmk/k2dUUeufygaX53mpjbHiPMLEm6QEVUqm5AhF4mhp0NeHT/bCxPpHrIchWevxeJKjXZpIR8C8RFUZiir5tZHbC4+OezEViiAcT8BuqUCg9Ak4WdRQPliNGVV6KapMZiMmh32Ix5KIRxOw2gWlDiGqnHONqBLvJbFYEuFQHAwjbEiOHh2njTjGxwMIh+NwuqavIxIhqkIaigmJhKg2Ip3v8ihlqaJKRlSZTUZ4A1GEIoVJMb2QVFVU5SZv0+k0muuccNnUu2DqQVTFFTlYTosJbx4dhNHA1iziNVQMJIeQ53mwBhZpLk3nB24WdP2jyiCRXJvNGVXysXsUub+Veq1UiqPRJNXq+jcbQXKgG1y2ytyLZypqc4Q2omrNmjUAgE984hM5vyCFrAwnOsgfUVYIjwy5VC/lgGw8SAUmFhEIgbkWpj5vfjP6+yZhtZoQiyVhMhtVSalpUZrJyBsAsMlsF4MjPqRS6YxuIwTy/CazSmcY6TiJpFL+fRJFFdkYZCiqKrAxkVv/AIGASnHpjNeKJZKUqKp01780zcxCxrWps1kwFYogEI2j2V2BylltcqlBBdXOqAIE1VQ8lkTQH4XVbkE6nUZEzD6yz6L21HrAaBLuh36vUPV3umx0Q+LzRcBxaYTDAoFlt6uTItWAZP3TrqiymPPbwwFJUWWXqcVc4msFNLyWXkjIFASF1MIAEEkkMe4P5UyWJfNJtEQ7e1pmTydZKGajESaDAUmOQyLF5VRzlYXaPDHnIV+rW+1mRIIxSlSlZwFRpcyomsnqr2Lgrq+s+EJu/QOEdX6+dfBcB9k/zzkyrzZHaCOqfvKTn8wtBrMEkOuTT1FFg6b1VFSJpIdy43OiTBZaQarJhIixWk2q39npIKp4GVkCIMtSkkimVIkqeX6TiZEsK9nH5SZ7aOc9TsX6VwGiSt71j7y+QFRJygC5hY9a//LcX8gGId8GrNB4DEzm34NbbCfrj+hvf8QJKr+toXzQjKoqdf0DAGe9HVNjAYT9ETS31yMaTiCd5mFzWOac8pZY//z+MNau64HDaUVbWx0WL2lFLJZARLwn2u2Wad00uBxFKKooUWWCgWXAiV0AlTkeqRSHRJIDyzAZYepOkZALVZGospiNWNjRCLvFLCtE5L6/J6gCq4D1r8SMKtLZz2w0ZHzuDosJvgiHcDyhP1FVmydqQCZRZbFbEAnGZNY/cQ04g4kqqeufuNaa5XPKr/94IyKROJrydOTWA1lE1Sy/bpUGcYO4prGAVHXU5ggAGomqK6+8Mu/vk8nqZRvMVGjp+sdVwPpnE6uhXIoTmFfa9W9u3fQIUZWWEVUAqPWMYFo94OKXRG67AASrhkPl5iu3/pEMNDVVEQncVFPqKavV8u6H4UooqhSknFD9SCEUlVv/ZEQVmaTzLMTIRqaUNuGECFNuOt02YSMYiFaAqAJqVZAaVEEWo2mueooqYu8LiR1R52o+FQBaEAgG43h7Rx8WLW7F8hUdOHhgFMMn+xESM6Gc06w0czmtsFqMNNg9H+LxFBb3NmNedyMGR3yIxpOIJ1Iw2jLnmYgsSF1exKGkWCVI+xzwh6I4PDQJnufpZ5I3oyopEUlqsFnKs/7l6irotJrhi8QQiifQ4KyAwqI2T8x5GMW1q9VuxrINC/DKH9+ihefZoKiSii/C39Bstv4BgLvODndd5aNs5ERVT1eDarG6BglkfnBYKxDVMZNRmyO0hamrged5PPfcc/j0pz+NtrY2Pcc0K0HD1PNY//gKWP8s9mxFld1pQV2FgwBnGihRJV5jkk/1+z9+PuO46bBE0owq8b9KRVWuTBFOppSSrH+5FVUGDYoquZ1QTh7pBfJeifWP54XXOzw8Se0Z8m6DXI5sLTnKsv6R96s4f51IDFZEUYVa+GEN6tCqqNIrowoAnGJXu1BAQVRNYwbTdIEUcMJhopwyo55Y/7xhhERLpNM5vVXbrnYPkkkOh46NFTw2FInj4LEx+ANRqpRKqMwp0TgJUs+cf0iFuhIK21wg85HRwEoZVSpzG4E800oN5WZUKYPUCUh+YSSu/1wJ1OaJGgBXoxP1LXWIheMITAjNdbgkB17WeGYmW8LI3ofmLNUIF00g+8B0mkd//xQmJ0LTPKKZjZMWdaK31YMNS7qmeyhVRS1MvQSi6rXXXsMNN9yAzs5OnH/++fjd736HD3/4w5UY26wCUUnlU1TRMHUdbZQkTJ2TVegjoTjc9ZUNApxpMBjZDALQIi7G7XaLJO9BfkKk0lDLqALUNxWAzLbGMjCwDHgAIxMBSjYRcDLllRLdbR4s7m3Oyqji0jym/OHS30wOyLv+yX8+MjxFCSo5USW9x9y3Isn6VzxRlUqrK7bqqKKqQpszvsh/RWJwcBBOpxMMwyAUkhY4PM/jrrvuQnd3N2w2G8466yzs2LEj6/l79uzBeeedB7vdjo6ODnzta1+b8zmD1YDBoC2jKiVunI06EFVEOUUIqnBwLiuqhPsAIaqsNjPqxSwSny8yYxRVrc1uWCwmjE+GMOXLf58Oi7mUTrsZ5jw5VZFodpA6IBFV1QxTl4gqg6ZCRDypjaiKlZhRpQxSJ3CKRFW4QkRVbZ6ooanNA9+YHwDQ0FEPAEilUpKais3OHp1JIMWX2WBTnEkghWWyBq4pqvLjmkvejd9+/Upce+kZ0z2U6qLYOeIEnCc03VF27dqFW2+9FQsXLsTGjRvxwx/+EKOjo7jnnnswPDyM++67T7cBzVbQjCoNYep63shtdpn1T4am9jrdXmO2wCzb1FlllpnMatT0081aFVVS9hQrfK8Y4Df/9zbdmBBweRRVU4EIDh4fp68h7/o3UQGiioD8PdD/yn6XYf2jRFLuhZipDOtfWhY0Lwex/s1WRdUXvvAFOJ3ZqslvfvObuOOOO3DzzTfjySefhNPpxKZNmzAyMkKP8Xq92LRpExiGwRNPPIGvfe1r+O53v4vbbrutnLdcgwZoVlQldFRUuQUiJuQXAsR9EyE0trrRvai17HPPNrS01sHltiEkknU2GVHl90UQCgr3A8c0K6pYlsGpJ8/HulVdONY/mfdYQjA5HVaYTbk7/0ViCZiMhqz5x2Y1g2UYRGLJjAzDSiLFSfd9k6FwmDpVVOXIidJLUaUkqhwiUSXvWqsnavNEDVaHhTojHG4HVp6+GIvWzZsVHf8AeUaV1CChhsJgmMzw9LkW11KDNlRDUTXT54mcd5QjR47grrvuwurVq7F27Vp85zvfwfLly/Hwww/j4MGD4HkeJ510Eow5wi3nGmjulAbrn54yXqKoItUMgoaWygYBzkTIiSqLbDEuJym4PIq3SiE7TD07o0oNnOz78tkrzoJbrPKnFYqqdB5FFc3uEENm5RlVk75IUe9DC9IKK5/aNz2TqCLH51FUifeYZIrDX94+iE/d+zjufeolTePhcii2OhrcWNnZSjcouqLCFZC//e1v+NOf/oR///d/z3g8Fovhm9/8Jr70pS/h2muvxaZNm/D444+DYRjce++99LgHHngA0WgUv/nNb3D++efjqquuwm233YZ77rkHgUCg1HddgwbQrn+FMqqI9U+PjCrR+keUVN6JICZHA6rE9okOjksjGIgiEJARVaL1z+sLwzsVxqpVXViyZPrjDFwOC3bsGsDRvom8x5GsQYfdLFn/VNSnsXgSyRQHuyWTqGJZBqes6sHapZ1Vy6kiBQqTwaApoypRyPpXZkZVIetfuMSQ9ryozRM1iCDduy02E3a/chC7Xz6AdGp2hGyztVDwkiFfl5pqBF8NSpQyR5yA80TOv4xFixbhq1/9KlwuF374wx9iZGQEf/jDH3DFFVfA5XLp8uInEpRWJzUQgkHXrn+EqJKd22Izz8nJgtgegEyiSs6WKAm9qoASVcJArBZThh0xkVBfoCttcaxCKkyQT1FlFRfwMUJUyTbIk379PfHKrn/k2stHHJNtJvLlaxHQMPUkh+d3HcEbhwbw5Ot7tY2HV++I6LSYsXtwFMcnvJrOUzQqNLFwHIfrrrsOX/va19DU1JTxu5dffhmBQAAf+tCH6GMOhwNbtmzB008/TR97+umnceGFF8LtlsjsD3/4w4hGo3jhhReKfKM1FANWq6JK3DibdOg05nDZwLIMwgGBhJgcEWwmjW31ZZ97tsHlEki7oKicstlMqK+3g2GAaCSBPXsHsWvXAOz26Q9s7Wz3AAAGh315j6OKKrslr6IqJpKfVgVRBQDHhqbw9v5BGrheaaRkLdnNxsKKqkShMHWRoCu56x8lqjKvjVMsKoVjs8siXpsnZhc+evMluPTq83H2B98FAEgludmnqBLV+rM9TL2akF+rWrZXDaqoIFE1W+aJnHeU3t5e8DyPXbt24fnnn8fLL7+MVAnWm7kChoSp5yWqRPWIjjdyu8uGFacsoMTAz/72Vfxu77d0O/9sglxRJbc3XPS+dZSoUOY7VQPKV1S2DY/nqAKn05kkCyGslKqweCKFJfNasGxBtpXHrlRUyYiqiQLZJ6WAR6Z6TP4bAGh02RCU5UJpyagy04p7CkNTwibbVICIfXHPEVz/kyfw8Av/EM6vGFCLW5C5jgX0vwYMKifVfeCBBxCLxXDNNddk/W7fvn0wGAxYvHhxxuPLly/Hvn37Mo5btmxZxjE9PT2w2+0Zx9WgP6iiqkDemp6KKqvDjHSaR0wkNCZHRaKqde6pbp1igHxYDE232c2w2czYcsnJ6Oz04M3tRwEAS5a2T9sYCbra6wEAAyP5yfQQVVRZ8oapkwKBRYWoIrlVkQpZ3JRQy6jKS1RR61/ujKoNi7owr9VT0nhyWf/a6lxY19uOdAUiA2rzRA0El159AT57979gxWlLAAh5tmS/xeaJRZgJoMUX0n26pgzSDHnkRaE1bQ1zD6XMESfiPJFzFXz06FG88soreOSRR/CrX/0KjzzyCDweDy677DJs3rx5Rof7TQcMRhbdS9vhbsrdbU8ejqgXGIbBnu1HaZbJXK5mmGULcLmi6nM3XAjWwGBqIjQ9lg7xxkH+Zho8dly2eR1e33EMA8O+3BlVChsdmdSU1r9INIEDx8ZUO05as4gqYcFf57Siq6W+jDelDl4xZuWQJoNRDE5IctCUBkWV02pBT3M9GIahFo9Ct59ndx3GX3cfQZNLyJ9R2iJb6kSiyh/CX/cchj8Sw5nL5qNRrxbkFRDuTU5O4qtf/Sp+/vOfw2TK3mx6vV44nU4a2E3g8XgQiUSQSCRgNpvh9XpRX1+f9XyPxwOvt0IKsxoAABabGQtWd8NeIMhcyqjK/pyLhc0h2EqiYoD4JAnubakr+9yzDS5CVIXFDnhiY4ue7kb8/nf/AGtgcOmlJ2PpDCCq2lvrsWJpR8G1lhSmbpGFqatZ/0TVkErumV2cL0ngeqUhzyY0aQhTl6x/6ktWp82C7YcHciquCiHFcVjU2ojWuswmNAmOw47jwzhj6bySzlsQtXmiBhkYhoHRZEAqySEpqhvVIh1mEuwOC9p6Guliby66OUpFhqKqdt1qUEOFTECzaZ7Iewc8/fTT8f3vfx+Dg4N45plncMkll+DXv/41Lr/8cgDAQw89hO3bt+sykNkOhmHQv38YU3lk+jy1Rek38ZDqBc/XqhmkoxMAWBQbvOuuuwC3ff0yLFjQUu1hQXmncTqsuHHrJixfLGyGCln/CLFJFixDYwH8+Fcv46v/9QfwPJ9h/VDCRq1/JExd2CD4QzH8Y9+A7uG5hENjFESVnLDa2zcqOz7zParByLLoG/dhKhjB/gGhXXuhjJ+pUGb+lpIIa3IJdp+JYBif/98/4tbH/w+vHDie95zFgOH5ov6Nj49jw4YN9N+DDz6Ydc5bb70Vp556Ki666KLcr6uyqeUV1tN8x9UKEJUFn+ZxZGc//OPBvMelEqTrX/mLV5J/EhPJGZPJiMWru9DQMvcs/ERRFYlkElXdPY0AhPtXT29zhjp3utDU4MSeA8N4Z+9g3uPUrX/ZhBN5TM36Z7dNv6IqP1ElErc5NnNmowFGA4tEiqPHFoNQLIFDo5OIK8ZAMqsqkmWI2jxRQzYMomqQqGpnuvWPNbAY6Z9CXCTWamHq2iEnqmpd/2pQQ7FzxIk4T2hajbEsi/PPPx/nn38+HnjgATz11FN49NFH8dvf/haPPPIIlixZgr17tWXGnKggJEK+DCRq5dJRykuqF1TJMsMntUqC5LkYDOyMIuwoeaN4nGyGcnf9IyROZkbVjr39+PHjr6C10YWRiSCC4kbF5chNVA1P+BEMx6TAS5YFl04jlkjCadOvw5Vk/RPGesH6xXjilT1gGIGsWtzZBIfs9ThCsOapGtJqfzyJiLh4SxYgqsbF/C0pvyvz/CaDAVdsXIdn3jkIbzSq6ZyaUUKgYXNzc17Sf/fu3fjJT36CF198ET6fDwAQiQhknN/vh8FggMfjQTAYBMdxGVUQn88Hu91OqyYej4eeQw6/369aGalBPyiDZ3NBz65/VFEl3icO7xzA5Kgfbo8j39NOSBBFVSyaSVS1yJqPdHbWV31caqhz2WA0sgiF44jHk6qWPUAgmRbPa4bdZioQpp5PUVVdooqjRBVLyacUl0Y6zasWLQiJZcmxmWMYBk6rGb5wDKFYAg3O4v5uSAi7TVHgIo084pVsulEEavPEiQ+jyYg4EkiIKviZvqa3iPeOpFhwnUlr75mODKKqpqiqQYkS5gjgxJsnir6jmM1mXHrppXj00UcxOjqKhx9+GIsWLdJlMHI8+uijOPnkk+F0OtHZ2Yl//dd/xdDQUMYx8+bNA8MwGf/a2qanWw+5OSttWQQ/uOUXeOirjwPQV8orKarEn2f4pFZJENWMSQcVgp5YNK8ZC+c14+Q1PRmP58sTAbJD0gnZQjYco5NB9A1NSV2fVAgnq0jyPPant/DYM2/JKtnCueJxfRfgSuvfZ7ZszPj9waEJ/G3nEfqznDjLBadVeF+hWBzRhLCRKlQ1JzlecXHDxqow+xaTEfUOG7VYJtP5c4OKgd6e8oMHDyKZTOL000+Hx+OBx+OhvvKuri5cd911WLZsGTiOw6FDhzKeq/SQL1u2LMs73t/fj3A4nOU1r0Ff0HlCc9e/8q1/Vpn1j+d5BMRsOtccJKqIoiqRSGH1mm40NgoW4GYZUdUyQyyRLMugoV74jCa9ubP0hkZ9OHhsHGaTMX+Yep6MKgclqqofps4wkv0vyanfgwnxls/aR+aJcAlkWy6iqvKKqto8UUMmiAUsGRO+czN9TW+xCX8zKfFvpGb9047MMPWZ/TnXMD2oREbVbJsnyirXOhwOXHHFFbjiiit0GQzB73//e3zkIx/BNddcg7vvvhvDw8P4yle+gosvvhjbt2/PIHo++tGP4rrrrqM/m83T062HVspTHILeMB6+67ew2Mz41O1CYn4skpAdq6OiylCz/lGIl7W5eWZZWk4/ZSFOP2Vh1uMkGDZXtTbb+if8l1g9AMAfjGJc3MSoKarkrcgHx3xwiscYjSziydK7JOWC1PVP+JmQbJRIZRnEkikkkimYTUYphyuP9Y+8h1A0AZIjX2jjMOQVcrCiyWTGOORY2t6MHz+/HRB/l0zpaIPU2Vd+xhln4K9//WvGY3/605/wrW99C0899RQWLFiA3t5euN1uPP744/jKV74CQKiSPPnkk9i6dSt93ubNm3H33XcjGAzSDq6PPfYYbDYbzj77bH0HXkMG5PNEPhjNBrR1N8JqK38+s4kd7GLhOOLRBJLxFMwWky7nnm1oaXHD5bbC74tgajKE5s9vBiAoq27+0sVIcTza2mcGUQUAjR4HxiaCmPCG0ZGjSyOxjpvNRjQ3ObFqiXq+Vl5FlbjZnA7rHwCYDCySKQ6JFJcVaA4UzqgCJLItFC2+Q19ORRUh/irVSKg2T9SgALF7JxKzRVElElUpkjs3s8c7kyBf99YUVTWoogIZVbNtnpj+IAYVPPLIIzj55JNx77330sfcbjcuueQS7N+/H8uXL6ePt7e347TTTpuOYWaATCZpLo3JYS+efPA5MAxDiSp50DVTAUUV6IZ/7k4SpIrsdFqneSTaUO+2Y8mClpxB4rmsf3Kiat+RUfzi6TcBqCuq5BPh2FQIC7ubAUiKKr2JKvItJ9Y/opTieR4MhNBbP7FnmIw5rXlykBbhe/qlbKt4MpXTA83zPMLxhPj/ABj1vwu6IRL/diq2GdEBTU1NOOecczIeO3bsGADgzDPPhNMpKENuueUW3HHHHfB4PFi2bBnuuecepNPpDDL/qquuwve+9z1cdtlluPnmm3HkyBFs27YNN910U0aL2Rr0B23lnYcU5bg0Ro9NgGGYgqHrWiBZ/xII+gR5t6tep6YBswxt7fUIBsSOfzYzurob6O8uuHDNdA0rJxbPb4HRYEAwGM15TEIk7c0mA2KxJHYeHMY5py/JOi5vRhVRVEWrRFSJ932jOD+ZjQZE4kkkkxyg8pUv1PUPkOaJUFmKqswlMSWqKqSo0hu1eWL2wyh+50iY+kwnfixiwYMUX+Z0sbxI1MLUa5gOzLZ5YkYSVclkEnV1mVVN4nVU62w2EyARVdLmmZdRoYR0APTt+ke8o1SxMocnCZIP1tg0sxRVucDzPA4cGcP6Nb2qvyckDquw/smJKrnFQ812mpRllby5px+rxAB3QlT94PG/4ds3XEIr2+VCCtoTfqaKP/H3hKgKRuJocNll1r/cfxMOi6gISaaoao7nBZuIvMI+7A3gx395A1PhCLX+ERhUCC1i6yBH5gvzLRbFtIiVj6Fc3HLLLUin0/jGN76ByclJbNiwAX/+85/R2tpKj/F4PHj22Wdx7bXXYsuWLaivr8eNN96Ibdu26TSKGnKB2CLyWf/CfoFMcrhtumxSiPUvFo4jQNSXnrlJVNntFtjtZkQiCZxz7vIZvwnkeWDnvkGMTy3PeQwlqsxGmZ08+15GihIzIaNKqahaNb8dE4EwYkn1wkmyQJg6AJq1WBpRJZx/Oqx/xaA2T5z4mG1h6lZq/RPXcjXCRTPkBdpamHoNaih2jgBOvHliRhJVn/zkJ3HppZfi4YcfxqWXXoqRkRF85StfwXve8x6sWLEi49if/OQn+N73vgebzYbzzz8f3/3ud9Hbq77xryQM8pBccu/hpeR7XkYiVCKjip57hk9qlcR9D34SqRSHlI6EQyVB8qNiKrkgQ6M+BIJC5d+gsP7Jq97BsHAMwwA2a7aVx24zw2IygGUZLF/YjnGvEDJOCM6X3j6GWCIFp00vokr4L0sVVZkEkUvcTARFe4Ym65/ifTEQbsSxRAqTgQg+fu9jMLAszlg2D4+98g49yMAylLBSU2xZTMaMG7qum5Eq8OlXXnklrrzyyozHGIbBrbfeiltvvTXvc1esWIHnnnuugqOrQQ1SmHrue1RIVD05dVI92exSmHrAK5x7LgapE5xz3grs3T2IpcvULXIzCfWios4XyKOoItY/k0RUqWZUEeufiqLK5bCgucGZUVCrJEgRhhRMhib9ODI8pZqRxfM8zRrMl1FFChqhWOnWP3su61+lFFW1eaIGBYym2WX9IxZyWnSc4eOdSchUVNWuWw0qqJI2ZybPEzOSqHrf+96Hn/3sZ/i3f/s3fPzjHwcAbNy4Eb///e8zjrvkkktw2mmnoaurC3v37sXXv/51nHnmmdi5c2eWIgsAHnzwQdqmcXx8XNcxG2TWv3RK+malkhxMZmPGApDRU1ElW7ixBnZOtw1mWQZms3FGtBbXAtKRT81+95lbH8GUuGElxCYhW8IyoorkcWxY2YOPX3pq1nkuPXcNLj13De768f/hied34fQ188RzSd+TaEy/zn9pRetS5aLFKS5qSKfCfEQSfY6CqCJ/Sdf/6Ancevm5GPGGAAYIKirpFqMRSY5Dg8uOld2tUEKZhRJVaeleEjQGGtYw9yCFqef+ggS9+oadmyxGsAYWyXgK4UAUK05ZgPnLO3Q592zETV9433QPQTPqRKLKn4+oEkkUi8kg6ySbfS+L0zD17PnRZjFjfCqECV/u0HY9kVJsah15gtATKQ5r5rfDyLJ554nWOgfmtzQglqM5ST7kDlMX1lcVUVTV5okaVECtf7NEUUWsf2mx+GKsKYM0wyDLKzarZPPVMMdRmyMAlND1rxr461//iquuugqf+9zn8Ne//hWPPvoopqam8IEPfCCjEv3f//3f+MhHPoIzzzwTW7duxTPPPIOhoSH89Kc/VT3v1q1bsX37dmzfvh3Nzc26jlleKU/J7FYJsULIyzYmek48ckWVniHtNVQeRFEVVyGqUim5Ai9TUeW0m9HSKNgbw2JIv6nAJNfSIBxPCCI5n6lnThW1/okePaNiY+G2C/lhpOqt7GyoBuXmgWD/4HiGzWPEG8z4vdlkRJJLY9QfohsjOayKaxaJ62h74Yv8V8OcgJYw9aBIFjjr9FFUMQyDBSs60dLpwejAFPa8cQTxaHW6u9VQHuqooiqS8xiqNjJrVFSp3E+dououGClejVQKOEX3WYdNypfiFBZ2jkvjnaPDGRmFarCYTf+/vTuPb6rO98f/Otm3ZulOC21ZWyrKYlVAFAQcLLIoKgNeLzLqdMoIOg7jwsCgI1yW69W5MowgOopeN0T9CYwyLojLCH4VGTek7DuF7k3a7Mn5/XFyTpMmXdKeNjnJ+/l49CGepsnn06R557zP+/P+4ERVHS42NEU9nraW/qkVfEVVD1VpU5wgrfCJHo9EKqr4ZupUURU9qqgiHYo2RiRgnIjLFO7ixYsxY8YMrF27Vjg2YsQIFBUVYdu2bZg1a1bEnxs2bBgKCwuxf//+3hqqILiZui+oKbPH5QGghZ8NTzyIIfiNTi5SnyHSOzTtVFR5AieyGWkGaAJXwPlkzpnKBtTWN6N4cLZQbaXu4CoWn6hqDpyIyJjQiiqxsMJSPgTGHJRIZRgYtGro1EqhF4rQMJ5pO0gzDIPUFB3qbHYU9c3A8Qu1cHv98Pr8IYmq706eD/m5FI0KDc1cJUJ+hjnsfsMSVSJVVDGgqyAkMv49ur1m6mIv/QOAxlobqs83oKGGS+YmazN1qbEYueeprYoqlmXhdrf0b2ovUZVm1mN4US50mshL/wCgqbl3ElVCRVUgPvBVs80OFyY9+CzcXi9Wzr8BE0cODtrxr/0Yl27kKhBrrNFXhbVdURXoUdUDG21QnCCR8D2qvG6+oiq+L0CrAu8nfJUw9ajqvODzN9r1j7RGMYITl4mqiooKzJ07N+RYYWEhtFotjh071uHPx2L5W3Azda87vKIquHluTy39S+ZG6lKkbadHFd8Evbq2qaXvU+BDPV9t9fPRCzAGdjjsqBFjViBR1SQsGwxKVIlYUcWvcOX/BmUyRngopUIGg0YFu8sDa2DJokIug0ohb7eiCgB2r/wNaqzNMOu1uPaPz8Dt9cPt9aHRzp3ABfej4lkMWtQ1OaBQyDAoOz3sPoMTVUqZLKw/SbfE6aYPJLaEpX8RNj7guZxuZOWlwZwp3s5a2sD7BCWqpMXUQY8q/oKGQiGDXC5raaYeoQLo6OlqHDlVHXGJCV9R1WR3Csdcbi88Xp/wPTF5W1dUBRJVlXVWWANj4JNHnkAVvbKDC3EZJi5RVd3YlYqqyImqnu9RRXGChFIKS/+413287+Qtl8ugVCngCSTW6Dyk84KfW1oySSKiGBGfS//y8/PDqqIOHjwIh8OBgoKCNn/up59+wqFDh3D55Zf38AjDBVdUhSz9CyQBgntUiVkaG1pRFZdPJ2kDX1HlbJUo8nh8IUvV+JJgvhKPPzmRy2VobOI+1He0vj3VpENOhkk4QQ5d+ifekrfWS/+Alh335HJZywf/wIeaJqcbbq+vU1eT0o16KOQyvP6H26EL/O6qGrmr53ySSh34G2AAjB/aHy8tnI2n50/HtUP7h91fcI8qj9+PH85ciGqu7WHY6L5IcujM0r/GGhsunq4VmtSKQRdIVPG7/om1rJD0LLNRi0EFGUJCp7XgRurB/41UUcUf00ToUZUSYenfP744gEnlf8PkBX/rxgwiE5Z8t0pUnbpYL1xA4MfCx7uOYkRGoKKqulsVVaG/m97Y9Y/iBAnWUlHFvSal8LlerW1J8EphvPEiuEcVVVSRSKKNEYkYJ+LyHaW8vBxbtmzB4sWL8fHHH+PVV1/FTTfdhIKCAkydOhUA8N5772Hu3Ll49dVXsXv3bmzYsAFTpkxBXl5eWOf63sCX57ZOVPENEUMqqkSs+ArtURWXTydpA19R1XrpnbXJGfL/fADjq474XQ2z01OE23S0LMKo1+B8dSOa7C6YDBrMu/EKXDNyAPf4IlZUKeUyDM3LhFGvEY7xfaoUspYr/vwVan4uyiheu/kZqcIyj1pbaO+WNxb/BwDuIoReo0ZhTgYuH9A34v20XvpX2WCLeLuo0Zpy0oaWZuptV1Q5mrgTdE3Q31B38YkqW2BZIVVUSYPFrMfRk9U4fKIq4o58fCN1VeDktr2lf/z7fMRd/4SKqpZE1af7jgBo6W0lprCKqkBSttZqF5JCDYEdbT2BCuIOE1UmAwCgujH6RBUDBjnmFGHnQF6PVlRRnCARtPSoCiz9k0ACo3hUAXQpXIyhyqDOC+1RRb830kpXYkQCxom4XPp33333QaVSYcOGDdi4cSPMZjPGjRuH1atXQ6/nTlD79euHqqoq/O53v0NDQwPS0tJwww03YNWqVTAaxVsy0VlC7xGfD96gDzXC0j9vLyz9o0SVpPDN1FtXVNU2NEGtUggnG/yHeb5MmL/CPH/WaKx75TNYm50d7nSo13EfwN0eH7x+FkX9s7H/0FkA4vaoanK6cfB0FcZcUiAck8llgM8HhVwuJIf45rRu4Wp5dG9FFr0Wp6sbUGsLPSnJNqdAxjDwsyz6ppravQ+lXA6G4d7XGQZoFrGyjGk7D0GSWEtFVXuJKu4EXasXb8mV1hDYVS2whIwSVdKgVilg0KvR1OyCtckBszH0eRN2/Au8/3emmbo2QqJKq1FBxjCwOz3w+vw4dqZGuA+P1wdPJ6teO6t1M3VDYNe/xuaWizTWwL8726MqVa9DltkAm8MFn9/f7g6BrZ2vb4TD7YVRG5oc1iqVGNInHc0uN/x+VtT+ogDFCRKudY8qKXyuP33kIuw27u+VLph3XkiPKiX93kg4ihFxmqhiGAYLFizAggUL2rzNZZddhl27dvXiqNoX0qMqpKKqh5f+BVVU0dpwaRGW/jlDTyrOVjaEnGjwV1r4D8ksy1VX5eVYhA/zHZ1E6AJXilkAhfkZGNgvTThhEbOiih93cHN3fumfInjpn8fLNQLu5ElIawP6pOFcvRU2hwsahRx5mRYM7JMGvVqFlxfNxsXGJlw+ILfd+2AYBiq5HC6fD0q5XBiLKBLwqgbpPv6CRrsVVYGG1mImqviKKmdgl1BKVElHqlmPpmYX6hvsYYkqV6ulf+0nqrj3eXWEpX8yGQODTgVrswvNdhdOX6jH94fPQ6mUw+P1wdrkRJpZL9qchIqqQDIpRaeGxaAN2XmVT1oJPao6iBEKhQxOtxd2lweNdidSDZ17jbu9XjjcXihkMmFJOU+pkKOy3gab0wWb0wWTTrwqRwAUJ0gYoUeVRJqpA4BC1fK3SZVBnccn0/Pz0pBiEPm9hSQGihHxufRPiuRBPap8nvBm6j5fyzFxK6po1z+pEpb+tUoUHToRug03f9U59OqLHFnpLZWDHe36J5MxIVfS5TJZjySqWhJPLSdD/ActuSw0URV8shLtlWqFXIZqazPqmuxwen2YN34U1t4xFQzDYHhBDn4xfAgM2o5P9PkGvWqFAmYRT0KSfU15b3njjTcwatQoGAwG5ObmYt68eTh/PnT3x4KCAjAME/KVnZ0dk/Hy79e+dhJVzkCiSmMQM1HF3VfNhUakWHToU5Am2n2TnpUaSCrWNoQvaWu99I//b+tEldfnh8frA8OE9uYLNqKoL7LSUlBVZ8PFWm4ZtDaQ1GpsitzMvStYloXXH7rrn1GnQX2TI2THvsZWFVWdqehKTeF+V5V1Vvz7+LlOjccaWO5o1KkjtmXgk1P8DrJiojhBWhMqqgLnEVKoUApe7kcXzDuP/0x/6nQt5SNIRNSjihJVoglupu4JXvrnCt/1T8zAE5ycksKVF9JCrVKAYQC32yv0agK410pmmkH4/9Y9qgDuCnpwH6iOdv0DWpJZKYGf65GKKk94RZWMCfSoksuE406Pt9NNciPJNHK/nyYndwVeq+5a42n+xI4B0GB3tn/jzmLBlb1F80Witn37dsydOxdjx47Ftm3bsHbtWnz++eeYNm1a2K56t99+O/bu3St8vf/++zEZc2eaqdsDS/90Il5h5e/L7/MjI8cCrdiVIaTHpKUaYDHrYLWFJ0qEZuqtlv65W/VUcvH9qVTKNntkerw+XKy1obLGiqp6LlGl14YvyesufuMLGcMIFyiyLNz7eUOTEyyAgTlpMKdwOx5G08fQYuB+Zv7TW/BfWz/Bqar6Dn+mMfC+33rZH88ciJeixQcexQkSgUJIVAUqquJ81z8g9POnFJYqxovg35VWI97mKSRBdCVGJGCciMulf1LEJ4l8bVVUBfUkEbPPQXByigKEtDAMg8z0FFTV2OBwepBi4IL9DwfPoaqmCfzGeS27/rU8vyqlQkh0cUsBO37u+ZJsvtLIZNSieEBWmztKdQW/LXrwLoT80OQyRria7/Z4g66UR//4mYHGuXb+BKyDXQ/bkpdmRl2zQ9QNDoDEvKoRb1577TWMGjUK69evF44ZjUbMnDkThw4dwtChQ4Xjffr0wejRo2MxzBB8T8H2lv4JFVViNlMPanT7i9uuFO1+Sc8zGTSob7CjrsEe9r2Wiqr2l/45hR3/wvtT8fpmmQEAZy42oLquSXjsyhpr2AYf3dG6kToAZJpbLsyUXlGInfsOCVVX0SwPTw0kqtxePzw+H1a//Qk2Lril3Z+pOFcNgKvqisSs4+6zwd4zFVWEBFME/oa9bilVVHFjzsy1UEVVFIJ3/dNq235vJsmLYgRVVIkmuKLKG6Giyhey6594v3aZTAb+HFsKAY2EkjEMWBZoDLpa3hy08xIQ3qMKAFQqORiGEaqRIu0IFfZYgZ/nK6lYP4sDJy6i1hp+AtRVLRVVQYkqcB3L+2enhuz619X+VACQYeL6pfA7RGk6aCbfFv4KvI8VuWNhku/S0Rs8Hg9MptCG+WazGQC3vCgedaqZerP4zdRvmDsGw64ahCsmFmPEuELR7pf0PL53iS1Cssjl9iLdokd6Kvd+yL+/tq4obNnxr+33yb5ZZm5JdV0TzlU3wpyiRf8+aRg+OCdiz6uu8vHL/oI+r6QadcKFmUv7c8tymxxcHOxsjyoAIX2p/H4WSrkc1Y1NEW/r97PY+OFXeG7X/wMAqNu4f2Hpn9gVVQDFCRKG/7znFXpUxf/ner4KrOpcPZQqSrh0Vp9sM3SBjY6ooopEFG2MSMA4Ef/vgBLBJYy4T1ruoO2cPYF/hy79E7d6g79KL4WARkIZU7RgAez74RTsDm4Zm625VaKK71EVnKgKPOf8hxp/FCfm/MmM0LTTI14T8UhL/xiGARigxmoXKp+c7uClf9EnmXIsRhTmZghX59vqu9KR+ddcjkW/GIOrh+R36ecjYUBrynvDXXfdhS+++AIvv/wyrFYrDh8+jGXLluG6665DcXFxyG1feOEFqFQqmEwm3HrrrTh16lRMxsxfbe7tZuoGkw5LN87H8ufuRv6Q2PTnIl1jDCyBs9rCEyUOlwc19c3weALvg4EqW1szF0uef/NLPPI/23D8TA2AyDv+8frnpsHvZ3Ho5EVUVjeiweaAUiXH90fOo07EixmRKqrkMhnMBi2Uchn6BHZrtQUSVXyVbmcSVTOvugTXFvfHH266FmaDFp//fAJnahoi3nbl27vwf5/vx+maBshlDMx6bcTb8b0LT9d0vIwwGhQnSCTC0r/A5yMprJQI7lGlVEV/4TFZORxu2AMbnPA9awnhdSVGJGKcoKV/IpLJZfB5fcKVEINZJ+z6F7wcUOw153yljNhbJ5Oex18t/59nP0ZmagrGXjEwrKJKKVRUBS39CySb+A/7Xl/Hyabn/jQHjU0OZKamcPcRuF8xd7uLtPSPL/nTqpXCcZfHK1wp70pFVU6qEYfPVwvLsTXKrgX5kgF9UTKgL575aC8+wJEu3UeYBF0nHm9uvPFGbN68GXfffTfuvPNOAMDYsWOxffv2kNvNnDkTo0ePRt++fXHw4EH8+c9/xjXXXIMff/wxrCKrp/F/w752/l4dgcoZjYiJKgAwp6eIen+kd6QEXge2CH2inM7QSil+aZ/b40VNfRNeePsrAED/vlzzfHU7iaqCnFT4WRZHz9TAandBq1ZylU4Amh3uNn8uWr4IiSoA+OSJcgBcNaRcxsDl8cHj9QmJLWUnNosZlp+NZb+cDKNOjR9OXcAPqERloN8W70RVHexON744eBJWuwt/X3ArLh+Y2+by+SnDh2DvkdM4U9MAv58V73MWxQkSAd9MnT9nkElgKV3wTn+qdt5jSKjgnf5o6R8JQzECAFVUiYqvlOKX+zU12FFznrsK11O7/nGPKwv5L5EOtUohLHnYtecQvF4fnC4vgl8iEZf+8bs8Kfhm4B2/prLTjSgsyIIlcPLB30frxrvdEbGiKlCLqlUpQnb9687SP7VSgSxTy4l3V3tU8XRdbMZOYmf37t0oLy/H/fffj927d+ONN95AXV0dbr755pD326effhpz587FNddcg7KyMnzwwQc4f/48XnzxxYj3u2nTJpSUlKCkpATV1dWijlnY9a+dpX+mtBSkZZtFT1QRaWqvosoZ+KzBX41nGEZYQlJxvGX32G9+Os3drp2TyAyLAUa9Gm63Fwq5DLmZJhh03Guw2eFq8+ei5fX5oFHIYdREfn0zDCP0UbQ5XFHHiSyzAVqVEn0sXHy40CpR9cCLOzDnf19HtbUJN115CUoG9m23x+OwvlkAA7z3w2Ecq6rt1BiIdFitVjz66KO48sorYTKZkJ2djZtvvhmHDx+OyXgMZj36DukjrODpTP/RWFOoqKKqK1JSghNV9BmUkEji/x1QQvhEkTdo6d9XH3yPQ98eD62oEjmhJCSqqKJKcoKfM1uzE02Baip10Dp/vpl6613/AGD7M7/B3tcX465bxkT92MqeqKjyhveo4j9waVRKIaHkcHtx9By3HEXRxSuGeekmyALVWl3tUcXTiXwVMNlLdXvD4sWLMWPGDKxduxYTJkzAL3/5S7z77rv49NNPsW3btjZ/btiwYSgsLMT+/fsjfr+srAz79u3Dvn37kJGRIeqYO2qmzrIsTlWcR+2FBlGX/hHpEiqqIvSocggVVS3vX7pA0urshXoMyudev7X1NmSmGjA4r+3XM8MwuLQwF85AFVNetgX6wMlTs1O8iiqvzw+Xx9du3DEEkm1NDnfQEvHo4kSWmUtU1Ta1LFs8W9sAl9cHOcNg9pjLcOeEUR1+blIpFBiamwkA+PfJ81GNoSMUJ2Lv9OnTeO655zBlyhS89dZbePbZZ1FZWYmrrroKZ86c6fXxeN0+nDt6Ea5AFaMULkAH7/pHFVWdx1+EANrf6IIkL1r6R0v/RMWfhHj4ChWGwemKSrz59D+F8nVA/IQSf39i71xGeh4b1PmuqdmFpkB/GrVaAYebOwnhE0rykF3/ApVU3XjO+fs4c6Eec5e+jJGFuXho3iQ4XR6cvFAPsCyKCrKiuk+XO8LSvwCNsqWi6lxNI3b8v5+523ahogrgTkT43lzdrajSi11RlYDBIt5UVFRg7ty5IccKCwuh1Wpx7NixDn8+Fu+XHTVT93p88Pv8kCvkwk5KJLnxV90jJaqcrvBEFVdR1YzDJ6pw9BRXEdhgc8Lp8ULdTjN1AOiTbhT+bU7RQh9IGIm59M/TxtK/YHxFVZPTFbQ7bHR/D5kmA2QMg/N1VgBckurvu76B3enGHeNHYfH0azr9HnDFgL74f0fP4N+nzmP26MuiGke7KE7EXP/+/XHs2DFotS1Jg2uuuQZ5eXl44YUX8Oijj/bqeFpX3UohUSWTt3wepV3/Ok+vb/ncqaEeVSQSihGUqBIT33/E4wxdSmVMNYScmIjdo4r/sJWdnybq/ZKed9+vJqLksnz85flP0NTsQlUtt0xBp1GiIbAToCJCxVykRFC0+Pu4WGeD3emBK5AYK1/7Jg6cuIjLi/ribw/eGlXpucsbvvSPX2KtVimE5BgAfHXwNMBwV6y7Ii2lZYen7lZUiZ2oSsSrGvEmPz8/rCrq4MGDcDgcKCgoaPPnfvrpJxw6dAi/+c1veniE4Tpqps5fRVfTMgASkKJTY/jQ3IhLQxytlv4F//vcxUYA3M6yTrcXYIC8PqntPtagfulQKxVwebzITE2BXssv/ROzoqrj5uhCosrharl9lCfsaSla+FkW9YGKqm+OnsXXh89gRP8czJ9weVSJ6oGZaai2NePwhZqoxtARihOxp9frw46lpqYiPz8fVVVVvT4eoUdV4HUvjUQV97ckV8jognkU+GXaDMNIomk+6X0UI2jpn6jkgTdrb6Ci6sop3JU3mYwJWfondo8q/v6czeJ9mCS9o0+WCVdfMQgAUFNnwyd7K5CdYcQ1Vw4WbqOI0Ezd4+1+Xym+kskeWD7CkzHc49Q12nHgWGVU98n3uwpOpPFVT2qlAkadBn/5zQwUZFmE73dmN6dITLqWK6CdabTbHoNGhcvyRNoNjQXgZ6P7IlErLy/Hli1bsHjxYnz88cd49dVXcdNNN6GgoABTp04FALz33nuYO3cuXn31VezevRsbNmzAlClTkJeXh/nz5/f6mPm/YZZl4feHJ6uERJWOElWEYzRq8f3Bc/jm+1NgWzVWdUZY+sf3obpQw1USXXFpPuQKBpcNyUHRgPYrZEcU5gp9BjMs+palf6ImqjquqBrSNx2X9e+Dpi70qOKlGrgLGbU2LlH15p4fcKauEXPHDUe6MTw50Z7B2dxFwLN1jcLvp9soTsSt6upqHD16NGz32N4gLA/nm6mLvEt4T+DjGiVbosN/9qXcHomoKzEiAeMEVVSJiL/ywS/90xu5E2mXw42sfqlCY3XRe0kFXpf84xFp4XuQWJtdePefPwAAjEFNFvkP9ME9qu657epuP25wXwEAaGxy4rV/fiv0IzlRWYdPvj2Kywbndvo+XYEPV+qgKik2KFGlkMswYfhA/HTqAnbuq8D5WmvUV8p5KYGTKLmM6fZVPK1SiR/OXOjWfYRIvFgRd+677z6oVCps2LABGzduhNlsxrhx47B69WrhKnm/fv1QVVWF3/3ud2hoaEBaWhpuuOEGrFq1CkajsYNH6BlyhRw+rw8+rx8yVehrX0hU0TIAEqBSKqDVKOFwemB3uKHXtfQuEyqq1OEVVVqVEkMHZGPqhGJcfmk/NNgcGNgvvd3Hyu+TCq1aCZ/fD6NBE5SoErOZeseJKrfHhx9OVKKm0R7UoyrKRFWg4rYuUJl8oYGrVi7IbL+qLBKDRo2rh+Tjy8OncPB8FUbk50R9HxFRnIhLixcvhsFgwJw5c3r9sfkLk/zumJKoqAoMkZb9RWfI4Gw889R/ULwnbaMYQYkqMbVupq4OlHU6HW6crDgfdjvxcK9kfVByg0iHTqsCw4S+H/XNNgv/lkdY+jcgr/0Tjs5Qt1o+aHO48L9vfAZzUMLzwPHokjd8RZU6aCleUV4mjp2vRf/slhOEbEsKztdyV/y72qPKENg1SoxSc1r6Jz0Mw2DBggVYsGBBm7e57LLLsGvXrl4cVcfkChl8Xl/E5X+09I9EYkzRwOH0wNrkDElUCRVVmtY9qoDTlfVQKRW4/uqhnX4chmHw8cbfCifL56oaIGMAn4hXaVsSVW2/76cFdqatsTbDG6g8jDZRlaJVQymXo9nlhtXuQq3NDrmMQUaU1VS8fmlmAKdw4MxF0RJVFCd6RmNjIyorO64GLyoqCju2YcMGvPLKK3j77beRlha5ncamTZuwadMmAOjxnWGlsOsf/xlMCkm1eKJWK1A8tPMXgknyoRhBiSpR8W/SCpUCmf3ShO3FXXY3PEE7AYq99I9fQaKjRJUkMQwDtUrB9REBVwas06qFf4ffHiG9nrqqrQ/+/FKLvCyLcDW7s4RlGkHj+9uiWWG3yzC3nCx0NVGl7WZfqmBi7/oHlqILiayloXr435bbwSUeaOkfCWY0aHGx2garzYE+mSbheHsVVUBLtW40FEHvxyaDBiyAi4GLCmLwejuuqEoLJJPqbHYY9Wr0yzAhRRvdXBiGQWqKFhcbm1Bx7iIAIMuU0uUT/8v6ZeHdfQp8e/Ic/mPcyC7dRxiKEz1i69at+PWvf93h7Vovpd2+fTsWLVqEtWvX4uabb27z58rKylBWVgYAKCkp6d5gW+GX/rFgUXTFAJgyUkS9/57AX0SlpX+EiIxiBPWoEhMfYJob7ag6UwtF4ET6ZMU5uIP6AIl91YFluQ9+OtrOXLIeuXcK0izcVWS1SikE/OBqITZwVVuhkItSRRScTAq+O1cgYXb6Yj0OnrwIfyevpjtcHjhdXqQZtFB3kHzKMBmEfyu72Bi+ZFBfPH33dDy/4NYu/XywnqioSubtZEnbWhqqhz/xTqqoIhHwS8GtttCd/4Rd/4KSU7qgfxu6+ZlAr1VDIZfD7vQISbHu4pujt5uoSmmpqGpsduJMdSNkXYh5A7LTAAb49thZZKboMf6SAV0bNICC9FQ4PV6crWvs8n20RnGiZ9xzzz1gWbbDr2B79uzBnDlzUF5ejgcffDBGI29ppu5xeVHxzXHYreG7fcadwJ+m6G1NCEly0caIRIwTlKgSEf8mzZfsqjTcCXj1mbrQ24ncOU8eKKGnHlXSNXncUEwcy5Whc72jwt9t+PParvZ0ai244blCLgNY7lG9Pj9kDAO9RgU/y6LZGdqfxOXxwu5ww+4MbbB7sd4GBoBWrepwZ8sME3fFXMYwyDIb2r1tW1K0Glw3bBBGDex+6bRWKWJFFduFL5I0+L8NflenYC09qihRRVoYDVyiqrFVokqtUmBEcV9YgmJ/Qd80FORyy6wNuu4lqhiGQWpgGV5dY3O37ovXmR5VfEVVbWMzHIFqdG0Xql5TtGqwLLD/+HlUWZuRntK1ZX8A0DeNq2Q7I1aiiuJE3Dhw4ACmTZuGG264AevWrYvpWPiLlH4v36NKCskfboyMBJYpEiIZXYkRCRgnaOmfiPhKKX43J1UbJxtiL/3b8v1/AQgvYybSMjA/A6YULfJzU4U3m5CKqkDlnKKLS+VaC15yd/Xw/vj+aCXqrNwOSTqNCka9Gs1ONxqbnEjRtSwrffmf3+DZbXthSdHio7+09Aeqqm8CAGSndlyqnpqig06thMPlwYgBIjWm7QaZjIFCpA9ZDACG/hZJG4RdnSL0qHJTRRWJICMtBX1zLCEXB/x+Ft/9fBYutxeZaS0bA6ToNTh5ri7w7+5XWaeadLhYZ0Ndox25meZu31/nElU6pBl1aLS7kO4OLG9URZ+oyknlfi8Hz1ZhQFYqxhbmdWHEnFS9FlqlQrSeQRQn4kNVVRVuuOEGGAwG3Hffffj666+F7xmNxl7f+U+hlF4zdaFHFVVUESIaihEcSlSJqCVRxb2w+Iqq3iLGcjASO9MmXYppky4FAHz93Ymw7/MNbcXqAxDco+qOqVdg2OFz+OubXwDglo+YDFqcr7GiscmBvkEnKM7AFW5vqxPtykAfk0xLxxVScpkMXz61EBfqbUJ1Vax1tVdWROE5CEIAhDfLDSYs/aMeVSSITqfCmcp6VNfahGO3lD8Ll9sLnVYVssRvUH6G8O8Ufff7VgoVVYGLGN3lCVQSKttrpp6iQ63VjialCzmBJFxXKqpyA4kqq8MFMEBR38wujJjDMAz6WIw4XlXX8Y07i+JEzP388884e/YsAOC6664L+d748ePx6aef9up4Wi5kcH8nUkhU8egchBCRUYygRJWYhEQV31C6jb43lB8lHTHoNTAaNEgxtJxo8JV67V2JjkZwjyqNUglDULNavVYFU+Akp6EpdLmJ3cWdTLs9oUuXztY0oiDbgiH9MtBZ2Zb4aRQqZiNQugpC2tJeM3Xa9Y9EYjFxyaLaem75HcuyqKlvAtjw5X39+lgwOD8DR05Vi1Jl3S/bggE1VjTYHN2+L6BzFVU6jQraQMVtU2DpeVc2zxiWl430FD1qbM2Yfnlxt6uhzDpxN6yhOBF7EyZMiKvVCHyPKr6HYUdtFOJC4PfHSGCohEgJxQhKVIlKaJIbqHxp82SDXnikA8WD+2Dn5oUhx3yBDy6iLT0IuvqlUsqhD7xeTXoNLhuYA2dgyUVlTWhPDlszl7hyebzwen1wuD2otzqweec30KgUmPcLcXfB6S1ysa4GJug6cSKO1kv/nM0ueD0+GMy6ll3/NCLvQkkkLc3CVZ3WN3JVTXaHG34/oFHJ8dL/zAu5rUIuQ78+3I6tw4Z0f1m10aDBsfO1OHWxvtv3BXQuUQVwVVVnXY1oCiRvu7L0b3BOOqwOJxgG6J9liX6wrVj0IvYBpThBIuDjg5SW/vEvY6qoIkREFCMAUKJKVEKTXKGZelsfrOjNnESP/4AvZuVPmkkHt8cHvUYJg1YFBcPA2uSEw+lB6dVDcexcDY6fD13qUBO0BKSh2Ymy/9kqnMR0ZXlG4mEpGU3aFLzpxoKxy3HiwFlM/OUYPLSJ2059yMgCmDON7d0FSTKp5kBz8UBFVW1DMxgA6akpMKaEJ09W/n66aI+d34drzH66UuxEVftLrdOMOpytaRQqeDVdiC1KuRyf/LkMaqVClLdkURNVFCdIBEIzdQklqibNHAWNVoW8QV1fWksIaY1iBECJKlHJWzVTV6oUGDQ8D0e/P93qlvTCI9HjX1dyEXeB2bmuXPi32+vjcqgskJtpwrAB2XC4vdj6yXe444bLkZPO7Xrk9/mhVMjh8fpQb7XDH/RGKuVE1e1jR4h2X4m4RSwRR/9h/WDJMsHv94MNVN86Astr66sacfjfJzF22shYDpHEGT5RVdfAJarqG7iLBamBJYE9KS+bq0Q6dUGc3kzeQO+djiqqclKNqGls5vpLoWsVVQBgFHG5nriJKooTJFzL0r/A5z1F/CeqLinpj0tK+sd6GIQkHIoRQPy/A0pI613/ZHIZrHVNsRwSSSB9Mk0w6NXI69P9JQyRZKcbwQSq/Yx6DcwpOhTmZSAv04zvj5wTbnfkXA08Xh8uKchGk8MlLHUFpJ2ounfyGPHujGWj++rA1q1bMWPGDOTm5sJgMODyyy/H66+/3uohWaxatQr9+vWDVqvFtddei++++y7svn7++WdMmjQJOp0OOTk5WL58OXy+8H5JpGecPXIBB/Yegc/rg0wpB2Qy/PzNcQCA3cYlrHQRqmRI8koz63DF8HykWfTwen2oqW9CZloKsjNNPf7Y/TLNuGRANhRyGXz+7nd27ezSvxSdBudqrXB6uM074iG2mHUi/11SnCCttF4aLmYFPSFEYqKNEQkYJ+gdUERCosrHNxZkYIn4QZKW/pHo/WbOOHz44kL898M398j9y2UyzL5+BG68uhiXF/UDAFxRnI/TVQ341/fcLoQT7/sbbHbuCveBkxfQ7HTDG9QUWhcHJxOJ6KmnnoLBYMBf/vIXbN++Hddddx1uv/12/PWvfxVus2bNGqxYsQIPP/wwduzYAYPBgMmTJ+PChQvCberr6zF58mQwDINt27Zh+fLlePLJJ/Hoo4/GYlpJSehl6GPhC5yEOwJ935oDDat1KeI2bSbSptGocP5iIw4crsSpc3U4eaYWVbU2ZKX3/GYUWo0SlbVWHDtbi5rA0sPu6Gyiit8N1hOIL12tqBJTttmAkv65sR5GmyhOSJ+Cr6jyS2fpHyFEOqQWJ2jpn4j4ExA26ErIHQ9Px7PLtuLskZYnl9acknj1u7kTQv5/xGDuQ/kPR8/j1IU6NAWWYSjkMnh9fjjdXm7JYIC2jZ0ukwoLMCJvKbtjxw6kp6cL/z9x4kScP38eTz31FBYtWgSn04k1a9ZgyZIlWLiQa8I/ZswYFBQUYP369Vi5ciUAYOPGjXA4HHjnnXdgNBpx/fXXw2q14rHHHsNDDz0Eo5F6I/W04F3/PC4vwLJwOzzwerxCRZWeKqpIK4MLMnDuQgN2/asCx07XcMf6905PmJx0E+oa7ThX3YistO4lxzqbqMo0G4R/KxVy0Xa77Q6DRo19J891fMPOoDhBIpC3uuAtiV3/CCHi64EYAUgvTtA7oIiEE5DABzGGYXDF9Zeh36CsWA6LkC4bkJOG/jlpqKyz4Zalm8Gv8uN3HnS6vXC6vcLt42F5RlwQuVQ3OKjwRo4ciaqqKgDAnj17YLVaMXv2bOH7er0e06dPx86dO4VjO3fuxJQpU0ICyJw5c+BwOPDZZ591Z8akk+SBJtI+rx9ul0c43tTogJ0qqkgb/uPmqzCoIAPWJidYlkXx4D4YXJDRK4+dk8G9X5xvtQNsV/AVUh01U+cTVRqVAtcOi4/+N2l6kXuCUZwgrQhL/6iiihDSA0v/pBYn6B1QROk5qcgfmis0yGUCuzu1DjQsVVQRiZDJGChkQUtVAy/dFJ0aAOBwuuF0t5xs09K/ADbKry7Ys2cPiouLAQAVFRWQy+UYPHhwyG2GDh2KiooK4f8rKipQVFQUcpu8vDzodLqQ25GeIwtshuD3++Gyu4XjTQ3N1KOKtKlwYBYcTg/e/eB77Pn2OI6frkZuds/0K2ytuH82Lh3UB9X13e+5yVdUKTtoEp1pNiBFq4LT7YUtUMkba6kGsRNVUX51AcUJaeGbqfPnEZSoIiSJRRsjEjBO0DugiGz1TTh18FzQ7mzcr5eRUU8qIl0jhuRCrWq5+p1h0uMXVxYCAGwOV0gCnyqqOAzLRvUVrV27dmHbtm249957AXBrxQ0Gg1Ctw7NYLLDb7XC73cLtzGZz2P1ZLBbU19dHP1ESNf6Kuc/rg9vZkuRtbrRTRRVpk0zGYFhhjvD/A/Iyeq3RsiVFix+PVuLg8Ys4faHlfcLt8WLPjyeiuq+WpX/tV1T1z04FE6jczbb0fC+uzhB/1z+KEySU0MNQqKii8wdCklW0MSIR4wT1qBKRQsX9On3ewNK/wIesy64uxPnjVTjx8zmwfpZaVBFJeeiOSWhyuvH+3oMAAJVSAU2gsa212Qm5jIEv8KKWUz8FTpR/5NXV1SgpKRH+v6ysDGVlZRFve/LkSdx+++2YOXMm5s+fLxxnmPAPtHz1ZvD32rpdpONEfPyJiM/rg8vhBgK/d27pH1VUkbaNHz0Ye/99HB6PD9dfM7TXHrd/bhoA4IvvjsFi1OKB/5iAr346hb+8/inSTXqkGfUozO9cv6zO9qhiGAYr5k2BUiFDXkbvVI51RKWQw6hRi3eHFCdIKwph17/AZypF+wldQkgC60LCINHiBCWqRKRUcr9OfltZ/krIjF9PxIxfT8SZw5XwenzIGdA7DVAJEUtuuolLSPlZKOQyaAJJ2WanGz4/C5mMgZ9l0ex0d3BPSYAFEGUDxIyMDOzbt6/D29XV1aG0tBR5eXl45ZVXhOMWiwU2mw0+ny/kKkhDQwN0Oh2USqVwu4aGhrD7bWxsjHhlhIiPX8rBL/tjZAxSLHq8+fRONNbYAFBFFYls1LA8XD4sDy6PFyWX5fXa4xb0ScWQvAwcPVuD/++zHzHpqkLsO3ga52usOF9jxdc/n44iUcX3qOr4osa1lw7o1rh7woiCPuLcEcUJEgGfmOJPCmnpHyFJqgsxAki8OEGJKhHxFVVCyW6r6pJ+Q0T6gENIL/vNTWNx8ORF/OvHE5AxjFBR5Qg0g9ZrlMi0pGBY/+xYDjMuMOha+W1H7HY7pk2bBrfbjffeew96vV74XlFREXw+H44ePYrCwkLheOs15EVFRWFrx8+cOYPm5uawteakZ/CB3+Xg/naUKgWsdc2oPlcHsCyuv/1qqDS0hJaEM+jVWPGHGb3+uBq1Eq+s+E889dqn+Nd3x3DiXC2szS5kp6bgQp0Nh05Xdfq+OltRFa9sDnEuxlCcIJGE9aiiKnVCklJPxQhAWnGC3gFFpFDxJbv80j8qkSaJQ69TAeCu9PEVVXyiqm+GGW8+Ng+3TRges/HFFZF36fB6vbjttttw5MgR7Ny5E5mZodULY8eOhdFoxNatW4VjdrsdO3bsQGlpqXCstLQUH3zwAWw2m3Bsy5Yt0Gq1GD9+vAgTJx3hr5A7Aw2iNVru78rRxP3/6NLhtLyGxKVhA7JxtqoRX/10Eh9+fQgX6mwo7p+FukZ7p+9D6omqVDH7VFGcIK203vVPTj2qCElePbDrn9TiBFVUiUhY+kdXQkgCeuj2ifjtrHEwaFT4MtBA1xHY8U+lpLeSENFeBengs+hvf/tbvP/++3j66adRV1eHr776SvjeyJEjodFo8Mgjj2DFihWwWCwoKirCU089Bb/fj0WLFgm3LS8vx7p16zBr1iw8/PDDOH78OB577DH8/ve/D9lilvQcvkcV30hdm6JBk82JhmorAMBgFnlnMUJEUpDD9ao6ePIilHIZBvRJw8GTFyGTyeDx+qDsRD8dnVqJUYNzYdJLc3mrWexEVTQoTiQ8Pj7Qrn+EkC41tU6wOBG3Z5dvvPEG/vu//xuHDx+GyWTCpEmTsGbNGuTktOx6w7IsVq9ejQ0bNqCmpgZXXHEF1q1bhxEjRsRkzMLSP75HFVVUkQRiMmhhMnAf0vmlf85AokqtpIafgq6sK+/g1/fhhx8CAO6///6w7504cQIFBQV45JFH4Pf7sXr1atTW1qKkpAQfffQRsrKyhNtaLBbs2rULCxcuxPTp02E2m/HAAw/gsccei3LApKta96jSGjTQpWjQ1MBVpaSY9W3+LCGxlJdlBgDUNdrRL8uMpb+ajD89uxNnqhpwsrIOg/tldHgfpy42YP+Rc/jPX1zew6PtGaJVVFGcIBEoqEcVIQToco+qRIsTcZmo2r59O+bOnYt7770XTzzxBCorK7Fs2TJMmzYN+/btEyqV1qxZgxUrVuCJJ54QMn6TJ0/GTz/9hOzs3u+VIyz9oyshJMFp1Nxbh8vNNcZVKeLyrSRmxF5XfvLkyY4fk2GwdOlSLF26tN3bFRcX45NPPhFpZCRa/BVzj9ONIaMKkDMgC3KlAk0NpwFQoorEL41aidmTRuDtT3+Az89iaEEWRl+aD/VhRacTVY3NDgCA2SDNnS0tevEqHilOkNaEZup0HkFI0uuJHlVSixNxeXb52muvYdSoUVi/fr1wzGg0YubMmTh06BCGDh0Kp9OJNWvWYMmSJVi4cCEAYMyYMSgoKMD69euxcuXKXh83v/SP9VOPKpLYUlO0GD4oB25vIFFFFVWheqgBIpE+/kKL0+HG4f0nodFrkJWXhmM/cIkqWvpH4tnMCZfizmlXQCaTQSaTwaBV4+jZGhw7V4PrUdjhzzc0cYkqk0QTVSqFiIkDihOkFaGZeuC1Ie/EclpCSIKiGBGfzdQ9Hg9MJlPIMX6rQ/7Ne8+ePbBarZg9e7ZwG71ej+nTp2Pnzp29NtZgLbv+8T2qKFFFElNOuhnfH6/EoTPVAKiiKpT4zQ9J4uBPPPgeVWqtChNvGw2Au7ih0atjNjZCOjK4XwYyLClIM3GVfwU5qQCAk5V1nfr5xmYnAEi2R9XU4WLtZkRxgoSTyRkMGzsE+kBlLZ1HEJKsuhAjEjBOxGWi6q677sIXX3yBl19+GVarFYcPH8ayZctw3XXXobi4GAC3TaJcLsfgwYNDfnbo0KFh2yX2Fj5RRSW7JNEZ9RoMyk1HmpH7MEU9qoKwSPrAQtrGx4XgRNXICcX40//9FluPP007/hFJGZCThuGDcjr1uvX6/LDZXZAxDFJ00kzImnQiJdgoTpAI5HI5Duw9gqb6ZgB0HkFI0upKjEjAOBGX74A33ngjNm/ejLKyMphMJhQWFsLn8+Gdd94RblNfXw+DwQC5PPQE2WKxwG63w+129/awoVSGbitLS/9IIuuXaUZVQxMA2vUvjD/KL5I0hF3/XHyiSgm9UYurp42CwUTL/oi05Gen4vuj5/Hp/qPw+lrezHx+P1iWFargAcAaqKZK0akhp12RKU6QiORBy0spUUVIEos2RiRgnIjLd8Ddu3ejvLwc999/P3bv3o033ngDdXV1uPnmm+Hz+YTbRbqCx38oivS9TZs2oaSkBCUlJaiurhZ93K0rquiDGElkwUs3VNRHIQTDslF9keTBn3h4hIoqaVaWEAIAWrUS2Wkp8Pr8OFfdCABodroxtnwdxpQ/jRPnW5YE/ni8Ev0yzSi9Sqzlc9JGcYJEEtyXSk6JKkKSVrQxIhHjRFyWQSxevBgzZszA2rVrhWMjRoxAUVERtm3bhlmzZsFiscBms8Hn84VUVTU0NECn00GpVIbdb1lZGcrKygAAJSUloo9bSFTxyTKqqCIJzBi0BIKaqRPSOfyJR3BFFSFSNig3HWCBH46cQ362BT8eq4QvUFl+9FwNBuSmAQAOn6mGXCbdZX+E9AaqqCKEEE5cvgNWVFRgxIgRIccKCwuh1Wpx7NgxAEBRURF8Ph+OHj0a9rNFRbG5WqdQhjZTZ6iiiiQwQ9DJBi39ayXJ15STtrVUVHkBcD2qCJGyqWOLUdXQhEOnqwAAR85WI9NiAAAcO18j3G7bngM4eaEe140YGJNxxh2KEyQCWdDFd0pUEZLEqEdVfCaq8vPzsX///pBjBw8ehMPhQEFBAQBg7NixMBqN2Lp1q3Abu92OHTt2oLS0tDeHK1CqWm0rSwGGJDBjUKKKmqkHYQH42ei+SNIQdv1ztzRTJ0TKRg/Lh06txJuffIfGJge+O3IOVfVNUCnlOHDiAgCgocmBmsZm6DUqDOqbEeMRxwGKE6QNwRVVdB5BSJLqSoxIwDgRl2UQ5eXleOCBB5CTk4PS0lJcvHgRjz/+OAoKCjB16lQAgEajwSOPPIIVK1bAYrGgqKgITz31FPx+PxYtWhSTcbf0qOL+n5b+kURmDOpRpaQeVUES86oGEQd/hdzr5iqqVJSoIhKXotOgMD8TJ87XYc8PJ3DsHFdF5fb4cPDkRbAsixOVtWAADCvIpv6dAChOkLYE96iiiipCkhXFCCBOE1X33XcfVCoVNmzYgI0bN8JsNmPcuHFYvXo19Hq9cLtHHnkEfr8fq1evRm1tLUpKSvDRRx8hKysrJuPml/5RjyqSDFKCelSpaelfKAoupA381XKPi0tUaShRRRLArPGXYdmm9/HxviM4V92IYQXZUChkUCsVqG5oQr3NAbfXB42aerIJKE6QCKhHFSEEAMUIxGmiimEYLFiwAAsWLOjwdkuXLsXSpUt7aWTtU6oDv05a+keSgNmggUohh9vro4qq1ii4kDbwm394PYEeVTpKVBHpu/qy/sjLtuCz77k+omkmPaobm/Dd0fO4WGdDnc0OAEg1amM5zPhCcYJEQBVVhBAAFCMQp4kqqVLSrn8kiQzNz8Kowr7IshgwrH92rIcTP/h15YREIJNzccHj9gEAVBpKVBHp02vVGNIvE3U2O/pnp2Lx3Al44rXdAIBaqx31NgcAwGLQxXKY8YPiBGlD8EVuSlQRkqQoRgCgRJWoWpb+cf8voz4MJMH978KZVE0Vhm1pVEdIK/zVcqqoIolmVfmNAACv1weFQo40E5eUCklUUUVVAMUJEllwRRWtzCAkWVGMAOJ01z+pUqoVKB49CAzDXTGXUUUVSXCUpGpDkm8nS9rGXyGXyWXof2k/aA2aDn6CEGlRBOJCqpHrKVrb2Iz6wNI/qqgKQnGCRCAL7lFF5xGEJK9oY0QCxgmqqBKRQinHz18dBROopKKlf4QkISrXJe3gG+U2NdjRePQitDp1jEdESM8QKqoam+Hx+TF8YA76pKbEeFRxguIEaQMt/SOEUIzgUKJKRAph5zPuhUUBhpAklYBXNYg4+CXhXje39E9HS6FIgsq0GDAgNx1unx/fHzmPOpsdWZSoakFxgkQQfO4gp6p1QpIXxQhKVIlJoaIeVYQQUHAhbeJPPDx8oiqFlv6RxKTXqHHsfC0MWjXqbHaoVQpkWShRJaA4QSKgXf8IIQAoRoB6VIlKqQq98sHv7kQISSa0pry3vPHGGxg1ahQMBgNyc3Mxb948nD9/PuQ2LMti1apV6NevH7RaLa699lp89913sRkwWk48+Gbq1KOKJCpLClctWN3QhPwsC4b0zaCeOwKKEyQyuYKW/hFCuhAjEjBO0DugiPiKKh5DFVWEJB8WgN8f3ReJ2vbt2zF37lyMHTsW27Ztw9q1a/H5559j2rRp8Af9TtesWYMVK1bg4Ycfxo4dO2AwGDB58mRcuHAhJuPmT0L8PhYyuQxqLe36RxKTSc8lqhqaHTh1sR79+6TGeERxhOIEaUNoRRUldglJSl2JEQkYJ2jpn4haelRx6MohIUkqAa9qxJvXXnsNo0aNwvr164VjRqMRM2fOxKFDhzB06FA4nU6sWbMGS5YswcKFCwEAY8aMQUFBAdavX4+VK1f2+riDr5DrUrTCLrGEJBpToFrQ4fQAAC4d0CeWw4k/FCdIBMEVVXK64E1I8qIYQRVVYlIGVVQxDEMnIIQkqyQv1e0NHo8HJpMp5JjZbAbALfcDgD179sBqtWL27NnCbfR6PaZPn46dO3f22liDBe/opDPSsj+SuNRKBTQqBVhwjdWH5mXGekjxheIEiUAupx5VhBDQ0j9QokpUcmVLcGGomoqQJMVyW8pG80Widtddd+GLL77Ayy+/DKvVisOHD2PZsmW47rrrUFxcDACoqKiAXC7H4MGDQ3526NChqKioiMWwQyuqqD8VSXBGPfcar6pvQgEt/QtCcYJExldU9SvMgUxBp2mEJKcuxIgEjBP0DigiuVwmJKho2R8hSYoFWNYf1ReJ3o033ojNmzejrKwMJpMJhYWF8Pl8eOedd4Tb1NfXw2AwhFyhBgCLxQK73Q63293bww7pP6ILNJsmJFGNuSQfAJew0qqVMR5NHKE4QdrAX8w4c+g89TAkJFl1IUYkYpygRJXI+OV/VK5LCCE9Z/fu3SgvL8f999+P3bt344033kBdXR1uvvlm+Hw+4XaRlmDzSwMjfW/Tpk0oKSlBSUkJqqurRR93cGywZBpFv39C4km6SQ+jTo3i/KxYD4WQiMrLy1FUVASDwQCLxYJrr70WH3/8cewGFLjQLVfKIaMeVYSQJEbN1EWmUMnhdnmpPxUhySwBy2/jzeLFizFjxgysXbtWODZixAgUFRVh27ZtmDVrFiwWC2w2G3w+X0hVVUNDA3Q6HZTK8AqPsrIylJWVAQBKSkpEH7dKo0BKqgG2+uawnWIJSTQLZo7Fb2+6OtbDiE8UJ+KCw+HAwoULUVhYCLfbjb///e8oLS3FF198gdGjR/f6eBhw5w8KpbyDWxJCEhrFCEpUiU2hUABw0dI/QpJZAjY0jDcVFRWYO3duyLHCwkJotVocO3YMAFBUVASfz4ejR4+isLAw5GeLiop6dbw8lUYFW10TwDAwmHQxGQMhvYUu2rWD4kRceOmll0L+/4YbbkD//v3x2muvxSRRxe1LH76TOCEkyVCMoKV/YlOouCsg1EydkCTFsoDfH90XiVp+fj72798fcuzgwYNwOBwoKCgAAIwdOxZGoxFbt24VbmO327Fjxw6Ulpb25nAFprQU4d96I/WoIiQpUZyIW3K5HGazOSY9DDmBiioVVVQRkrS6EiMSME5Qul5k/BUQuopISBKjqyA9rry8HA888ABycnJQWlqKixcv4vHHH0dBQQGmTp0KANBoNHjkkUewYsUKWCwWFBUV4amnnoLf78eiRYtiMm5jql74t54qqghJXhQn4gbLsvD5fGhsbMTLL7+MI0eO4Pnnn4/JWBi+okpBiSpCkhrFCEpUiU2upIoqQpIdm4BXNeLNfffdB5VKhQ0bNmDjxo0wm80YN24cVq9eDb2+JRn0yCOPwO/3Y/Xq1aitrUVJSQk++ugjZGXFprlzSqpB+LeOKqoISVoUJ+LHli1bhKXker0eW7ZswZVXXhmTsfCnptSjipDkRjGCElWiExJVVFFFSJJi6SpIL2AYBgsWLMCCBQs6vN3SpUuxdOnSXhpZ+zQ6NWQKGfw+Fip1eDN3QkgyoDjRUxobG1FZWdnh7YL7FE6ZMgXffPMNampq8Oqrr2LOnDl4//33MWHChLCf27RpEzZt2gQAPbIzLP+6kFNFFSFJjGIEQD2qRKekiipCkhsLbqeOaL464eeff8akSZOg0+mQk5OD5cuXw+fz9excSI/gNt0ghCQtihM9ZuvWrRg6dGiHX8EsFgtKSkpwww034P/+7/8wZswYLF++POL9l5WVYd++fdi3bx8yMjJEHz9/bkqJKkKSWFdiRALGCfq0LDI+sFBFFSFJjBW3XLe+vh6TJ09GcXExtm3bhmPHjmHx4sXw+/1YuXKlqI9Fet6gEfmoOV+PzH5psR4KISRWKE70iHvuuQf33HNPt+5j5MiReOONN0QaUXT45T5yBdUSEJLURI4RgPTiBCWqREZXQAhJbiwAtpNXNTpr48aNcDgceOedd2A0GnH99dfDarXisccew0MPPQSj0Sjq45Ge9dSHf4z1EAghMURxIn6xLIu9e/eif//+MXl8vi2NTE6JKkKSVU/ECEB6cYLeBUXG79JBS/8ISVIsy10FiearAzt37sSUKVNCAsicOXPgcDjw2Wef9eRsCCGEiI3iRFz44osvcNNNN+Hll1/Gp59+irfffhszZ87EV199hSVLlsRkTP5ApooSVYQksa7EiASME/QuKDI/yzU/k8noV0tIsmL9bFRfHamoqAhp/AoAeXl50Ol0qKio6KlpEEII6SEUJ2KvX79+UCgU+OMf/4gpU6bg/vvvh9/vx7/+9S9MnTo1JmPin2u5nFZoEJLMoo0RiRgnaOmfyLLz03H0u1O45qbLYz0UQkis9EDvEbPZHHbcYrGgvr5e1McihBDSCyhOxFxBQQHeeuutWA8jRHqOBWqtChl9LbEeCiEklnqoR5WU4kTSJqoqKipQUlLSM3eeD/x91378veQvPXP/EVRXV/fI7iOxlGhzSrT5AIk3J4fDgQMHDnTrPsZOuRI1NSeiftzg96OysjKUlZWF3CbSBg0sy9LGDT2oR+NEDCTa3yuQeHNKtPkAiTcnihOE12Mxoh9w7uC3eLdks/j33YFE+3sFEm9OiTYfIPHm1N040ZUYwT9uIsWJpE1UFRUVYd++fbEehmhKSkoSaj5A4s0p0eYDJN6cxPjA+c9//lOEkYSyWCxoaGgIO97Y2BjxyggRB8WJ+Jdoc0q0+QCJNyeKE4SXaDECSLy/VyDx5pRo8wESb07djRM9ESMA6cUJaqRECCFxrqioKGzt+JkzZ9Dc3By21pwQQkjyoThBCCGkPVKLE5SoIoSQOFdaWooPPvgANptNOLZlyxZotVqMHz8+hiMjhBASDyhOEEIIaY/U4kTSJqpar9eUukSbD5B4c0q0+QCJN6d4nU95eTnUajVmzZqFjz/+GJs2bcJjjz2G3//+9yFbzBJxxevroasSbT5A4s0p0eYDJN6c4nU+FCd6X7y+FrqD5hT/Em0+QOLNKV7nI7U4wbAs2/FehoQQQmLq559/xsKFC7F3716YzWbcc889eOyxx2gLa0IIIQAoThBCCGmflOIEJaoIIYQQQgghhBBCSFxIqqV/P//8MyZNmgSdToecnBwsX74cPp8v1sOK6OjRo/jNb36D4cOHQy6XY8KECWG3YVkWq1atQr9+/aDVanHttdfiu+++C7tdPMx769atmDFjBnJzc2EwGHD55Zfj9ddfD7mNlObz1ltvYezYsUhLS4NGo0FhYSFWrlwJt9st3EZK82nt3LlzMBgMYBgGTU1NwnEpzWnz5s1gGCbsa+PGjcJtpDQf0juk9FwnUpxItBgBUJyQwpwoTpCukMpznUgxAqA4IZU5BaM4ESoe5iMpbJKoq6tj+/Tpw06aNIn98MMP2Q0bNrA6nY5dunRprIcW0bvvvsv27duXvfXWW9mioiJ2/PjxYbdZtWoVq9Fo2L/+9a/sRx99xJaWlrJpaWlsZWWlcJt4mffo0aPZuXPnslu2bGF37drFLl68mAXArlu3TpLz2bhxI/vHP/6Rfeedd9hPPvmEXbNmDavRaNh7771XkvNpbe7cuWxWVhYLgLXZbMJxKc3pxRdfZAGwn3zyCbt3717h6+LFi5KcD+l5UnuuEylOJFqMYFmKE1KYE8UJEi0pPdeJFCNYluKEVOYUjOJE/M1HSpImUbVq1SrWbDazjY2NwrG1a9eyWq025Fi88Pl8wr9vueWWsODicDhYo9HI/vnPfxaONTU1senp6SEv+HiZd3V1ddixuXPnsgUFBSzLSm8+kfzxj39kTSYT6/f7JT2fzz//nLVYLOwTTzwRElikNic+sAQHxmBSmw/peVJ7rhMpTiRDjGBZihO8eJkTxQkSLSk914kUI1iW4gRPKnOiOBGf85GSpFn6t3PnTkyZMiWko/2cOXPgcDjw2WefxXBkkclk7T81e/bsgdVqxezZs4Vjer0e06dPx86dO4Vj8TLv9PT0sGMjR45EVVUVAOnNJ5K0tDShVFeq8/H5fFi0aBGWL18e9pxJdU5tSbT5kO6T2nOdSHEiGWIEQHGCF29zakuizYd0n5Se60SKEQDFCZ4U5kRxQrrziSdJk6iqqKhAUVFRyLG8vDzodDpUVFTEaFRdV1FRAblcjsGDB4ccHzp0aMh84nnee/bsQXFxMQDpzsfn88Fut+Nf//oX1q1bhwULFoBhGMnOZ+PGjXA6nbj33nvDvifVOQ0cOBAKhQKFhYV49tlnheNSnQ/pOYn2XEv9NZ4IMQKgOAHE/5woTpDOSqTnOhFe3xQnOPE2J4oT8T8fKVDEegC9pb6+HmazOey4xWJBfX197w+om+rr62EwGMK2krRYLLDb7XC73VCpVHE77127dmHbtm144YUXAEh3Pnq9Hi6XCwAwb948PPHEEwCkOZ/a2lr86U9/wiuvvAKlUhn2fanNqU+fPlixYgWuvPJK+Hw+vP766ygvL4fdbscDDzwgufmQnpdoz7WUX+OJEiMAihNA/M6J4gSJViI911J/fVOciM85UZxoGWc8zkdKkiZRBQAMw4QdY1k24nEpaGs+rb8Xb/M+efIkbr/9dsycORPz588XjktxPnv27IHdbsfXX3+Nxx9/HAsXLsQzzzwDQHrzWbp0Ka666ipMnTq1zdtIaU5TpkzBlClThP8vLS2Fy+XCypUrcf/997c7ztbfi4f5kN6RaM+1FF/jiRQjAIoTQPzOieIE6YpEeq6l+vqmOBG/c6I40TLO1t+Lh/lISdIkqiwWCxoaGsKONzY2RsxuxjuLxQKbzQafzxeSwW1oaIBOpxMy2PE277q6OpSWliIvLw+vvPKKcFyq8xk1ahQAYNy4cUhPT8edd96JxYsXS24+Bw4cwAsvvIDPP/9cGI/dbhfGIpfLJTenSG699Va8+eabOHnyZELMh4gr0Z5rKb7GEy1GABQngPidUyQUJ0h7Eum5lurrm+JE/M6J4oQ05xOvkqZHVVFRUdj6zzNnzqC5uTlsvagUFBUVwefz4ejRoyHHW69/jad52+12TJs2DW63G++99x70en3IOKU2n9b4IHPixAnJzefIkSPweDwYM2YMLBYLLBaLsK68b9++WLRokeTm1B6GYRJqPkQcifZcS+01nugxAqA4wYuXObWH4gSJJJGeaym+vilOcOJ1ThQnWkhxPvEmaRJVpaWl+OCDD2Cz2YRjW7ZsgVarxfjx42M4sq4ZO3YsjEYjtm7dKhyz2+3YsWMHSktLhWPxMm+v14vbbrsNR44cwc6dO5GZmRnyfanNJ5Ivv/wSANC/f3/JzWfcuHHYvXt3yNfDDz8MAHj//ffx4IMPSm5Okbz99ttIT09Hfn5+QsyHiCvRnmspvcaTIUYAFCfibU6RUJwg7Umk51pqr2+KE5x4nhPFCY7U5hO32CRRV1fHZmdns5MnT2Y/+ugj9tlnn2X1ej27dOnSWA8toubmZnbr1q3s1q1b2dGjR7PFxcXC/zc3N7Msy7KrVq1itVotu379evbjjz9mp06dyqalpbEXLlwQ7ide5v3rX/+aBcA+/fTT7N69e0O+nE6n5OYzZcoU9oknnmDff/999oMPPmCXL1/O6vV69pe//KVwGynNJ5IXX3yRBcDabDbhmJTmNGvWLHbNmjXs+++/z+7YsYO94447WADsunXrJDkf0vOk9lwnUpxItBjBshQnpDAnihMkWlJ6rhMpRrAsxQmpzKk1ihPxNR8pSZpEFcuy7IEDB9jrrruO1Wg0bHZ2Nrts2TLW6/XGelgRnThxggUQ8evEiRMsy7Ks3+9nV65cyebm5rIajYYdN24cu3///rD7iod55+fnJ9R8li1bxl5yySWsXq9nTSYTO3LkSHbdunWs2+0WbiOl+UQSKbBIaU5LlixhhwwZwmq1Wlaj0bCjRo1iX3755ZDbSGk+pHdI6blOpDiRaDGCZSlOSGFOFCdIV0jluU6kGMGyFCekMqfWKE60iIf5SAnDsoGW9IQQQgghhBBCCCGExFDS9KgihBBCCCGEEEIIIfGNElWEEEIIIYQQQgghJC5QoooQQgghhBBCCCGExAVKVBFCCCGEEEIIIYSQuECJKkIIIYQQQgghhBASFyhRRQghhBBCCCGEEELiAiWqCCGEEEIIIYQQQkhcoEQViQrDMB1+ffrppygoKMAf/vCHWA8X3377LSwWC6xWq3Dsyy+/xKhRo6DRaMAwTAxHJ457770Xd999d6yHQQghhBBCCCGEdBvDsiwb60EQ6fjqq6+EfzscDkycOBHLli3DjTfeKBwvLi7GsWPHkJaWhry8vFgMU1BaWooRI0Zg9erVwrFhw4YhMzMTjz76KNRqNUaPHh3DEXbfyZMnUVRUhJ9++gmDBg2K9XAIIYQQQgghhJAuo0QV6bKmpiakpKTgxRdfxPz582M9nDBHjhzBkCFDcPjwYQwePFg4rlAo8NRTT+G+++5r82cdDge0Wm1vDFMUkydPxvDhw/Hkk0/GeiiEEEIIIYQQQkiX0dI/0iNaL/2bP38+SkpK8N5776G4uBg6nQ433ngj6urqcPToUVx33XXQ6/UoKSnBDz/8EHJffr8fa9aswaBBg6BWqzFkyBC89NJLHY7hpZdewmWXXSYkqT799FMwDAOfz4f7778fDMMICbaCggIsXrwYK1asQN++fWE0GgEAe/fuxYwZM5CTkwO9Xo8RI0bg1VdfDXmczZs3g2EY7N+/HxMmTIBOp8OIESOwf/9+NDc341e/+hVMJhMGDBiA119/PWyc27ZtQ0lJCTQaDbKzs/HQQw/B4/EI3z979ixmz56NzMxMaLVaDBw4EH/6059C7uOWW27Bq6++Cr/f3+HvhRBCCCGEEEIIiVeUqCK95vTp01i+fDlWrlyJTZs2Yc+ePSgrK8OcOXMwZ84cvPXWW/B6vZgzZw6CC/0WLVqElStXoqysDO+99x5uvvlm3HXXXfjHP/7R7uPt2rULY8eOFf5/1KhR2Lt3LwBg8eLF2Lt3b0jC57XXXsNnn32GZ555Blu2bAEAnDp1CldffTWef/557NixA7fccgt+9atfRUw43XnnnZg7dy7efvttsCyLW2+9FXfffTdycnLw1ltv4aqrrsK8efNw9uxZ4WfefPNNzJo1C1deeSW2b9+ORx99FJs2bcKSJUuE28ybNw9nzpzBpk2bsHPnTixduhQulyvksceOHYuLFy/ixx9/7MxTQQghhBBCCCGExCVFrAdAkkddXR327t2LgQMHAgB++OEHPPHEE3jppZcwb948AADLsrjxxhtRUVGBoUOH4ujRo9iwYQNefPFF3HnnnQC4ZW6VlZX485//jGnTpkV8LJZl8e9//xt33HGHcMxoNAr9qAoKCiL2pvrHP/4BjUYj/P+cOXNC7vPaa6/F2bNn8dxzz2Hu3LkhP/uHP/xBGCM/jwkTJuC//uu/AABXXnkl3nrrLezYsQMLFiwAy7J48MEHMW/ePDzzzDPC/ajVatx7771YsmQJ0tLS8PXXX+P111/H9OnTAQATJkwIG/cll1wCuVyOr7/+GsOHD4/4OyGEEEIIIYQQQuIdVVSRXlNQUCAkqQAIjb8nTpwYduzcuXMAuKoomUyGm2++GV6vV/iaNGkSvvvuO/h8voiPVV9fD5fLhfT09E6Pb9KkSSFJKv5+7rvvPuTn50OpVEKpVGLTpk04fPhwxJ9vb24mkwkZGRnC3A4fPozTp09j9uzZIXObOHEinE4nfvrpJwDAiBEjsGTJEmzevBmnT5+OOHaFQgGz2YwLFy50er6EEEIIIYQQQki8oUQV6TVmsznk/1UqVdhx/pjT6QQA1NTUwOfzwWQyCYkipVKJ+fPnw+v1orKyMuJj8T+vVqs7Pb6srKywY/Pnz8eWLVvw4IMP4sMPP8Q333yDu+66S7j/tuYXaW788eC5AcDUqVND5ta/f38AwJkzZwAAW7ZsQUlJCR544AHk5+djxIgR2LVrV9jjq9XqiOMihBBCCCGEEEKkgpb+kbiWmpoKhUKBL7/8EjJZeF41MzMz4s+lpaUBABoaGjr9WAzDhPy/0+nEe++9h/Xr16O8vFw4LlbD8tTUVADApk2bMHLkyLDv8wmr3NxcbN68GX6/H19//TUee+wxzJgxA6dPnxbmCXBz5e+TEEIIIYQQQgiRIkpUkbg2ceJE+Hw+NDY24vrrr+/0z6nVauTl5eHEiRNdfmyXywWfzxdSlWWz2bB9+/awpFZXFBYWIjc3FydPnsSvf/3rDm8vk8kwevRoPProoxg7dixOnTolJKqqq6tht9sxZMiQbo+LEEIIIYQQQgiJFUpUkbhWWFiI8vJyzJkzBw899BBKSkrgdDpx4MABHD58GM8//3ybP3v11Vfj22+/7fJjm0wmXHHFFXj88cdhNBohk8mwZs0amEwmWK3WLt8vTyaT4cknn8R//ud/wmq1orS0FCqVCsePH8e7776Lt956Cx6PB1OmTMG8efMwZMgQuFwuPPnkk8jOzsbQoUOF+9q3bx8YhgnZ5ZAQQgghhBBCCJEaSlSRuPe3v/0NQ4YMwXPPPYfly5fDaDSiuLgYd999d7s/N2vWLPzqV7+Cw+GAVqvt0mO/9tprKCsrw7x585CWloaFCxfCbrdj/fr1Xbq/1n75y1/CaDRi1apVeOGFFyCXyzFgwABMmzYNKpUKcrkcl156KZ5++mmcOXMGOp0Oo0ePxocffhgyp3/+858YP358yFJAQgghhBBCCCFEahiWZdlYD4KQnuB2u9G3b1/87W9/w2233Rbr4fQYn8+H/Px8rFmzBnfccUesh0MIIYQQQgghhHQZ7fpHEpZKpcKDDz6Ip59+OtZD6VFbt26FVqvFnDlzYj0UQgghhBBCCCGkW2jpH0lo/FK9xsZGmEymWA+nR7Asi7///e9QKOjPmRBCCCGEEEKItNHSP0IIIYQQQgghhBASF2jpHyGEEEIIIYQQQgiJC5SoIoQQQgghhBBCCCFxgRJVhBBCCCGEEEIIISQuUKKKEEIIIYQQQgghhMQFSlQRQgghhBBCCCGEkLjw/wOIsbjg+Y9KHwAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vec = df.query(\"camera=='CAM0' & experiment=='location' & region_ix==0\")\n",
    "vec.head()\n",
    "region = vec['region'].iloc[0]\n",
    "a_p = vec['ap'].iloc[0]\n",
    "a_itp = vec['aitp'].iloc[0]\n",
    "a_v = vec['av'].iloc[0]\n",
    "a_a = vec['aa'].iloc[0]\n",
    "\n",
    "f, axs = plt.subplots(nrows=1,\n",
    "                      ncols=3,\n",
    "                      figsize=(20, 5),\n",
    "                      )\n",
    "\n",
    "make_plot(np.arange(region[0], region[1]),\n",
    "          a_p,\n",
    "          plot_args,\n",
    "          r\"Paw angle (degrees)\",\n",
    "          axs[0])\n",
    "\n",
    "make_plot(np.arange(region[0], region[1]),\n",
    "          a_itp,\n",
    "          plot_args,\n",
    "          r\"Paw angle smoothed (degrees)\",\n",
    "          axs[1])\n",
    "\n",
    "make_plot(np.arange(region[0], region[1]),\n",
    "          a_v,\n",
    "          plot_args,\n",
    "          r\"Paw velocity (degree/frame)\",\n",
    "          axs[2])\n",
    "\n",
    "axs[0].set_xlabel('Time (frames)')\n",
    "axs[0].set_ylabel('Angle (pixels)')\n",
    "\n",
    "xlim = [0, 500]\n",
    "axs[0].set_xlim(xlim)\n",
    "axs[1].set_xlim(xlim)\n",
    "axs[2].set_xlim(xlim)\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "anipose_env",
   "language": "python",
   "display_name": "anipose_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}