5-auxiliary.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Auxiliary tests notebook\n",
    "\n",
    "This notebook contains code for auxiliary tests.\n",
    "\n",
    "Copyright (C) 2020  Andrew Sonta, Rishee K. Jain\n",
    "\n",
    "This program is free software: you can redistribute it and/or modify\n",
    "it under the terms of the GNU Affero General Public License as published\n",
    "by the Free Software Foundation, either version 3 of the License, or\n",
    "(at your option) any later version.\n",
    "\n",
    "This program is distributed in the hope that it will be useful,\n",
    "but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
    "MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
    "GNU Affero General Public License for more details.\n",
    "\n",
    "You should have received a copy of the GNU Affero General Public License\n",
    "along with this program.  If not, see <https://www.gnu.org/licenses/>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import copy\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "%load_ext rpy2.ipython\n",
    "import networkx as nx\n",
    "import src.__init__ as init\n",
    "timestep = init.TIME_STEP\n",
    "timesteps = int(1440/timestep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "classified = pd.read_csv('data/classified-berkeley.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from importlib import reload\n",
    "import src.interaction_model as im\n",
    "im = reload(im)\n",
    "# Data segment 1: 08-09 to 10-09 (61 days) -- all occupants\n",
    "overlaps1, similarities1 = im.interaction_matrix(classified['2018-08-09':'2018-10-09'])\n",
    "# Data segment 2: 10-09 to 04-24 (197 days) -- occupant 10 gone\n",
    "overlaps2, similarities2 = im.interaction_matrix(classified['2018-10-09':'2019-04-24'])\n",
    "# Data segment 3: 04-24 to 06-17 (54 days) -- occupant 5 gone, occupant 10 gone\n",
    "overlaps3, similarities3 = im.interaction_matrix(classified['2019-04-24':'2019-06-17'])\n",
    "# Data segment 4: 06-17 to 08-09 (53 days) -- occupants 5 gone, occupant 10 in space 13, occupant 13 gone \n",
    "overlaps4, similarities4 = im.interaction_matrix(classified['2019-06-17':'2019-08-09'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adjust similarity calculations for different data segments\n",
    "overlaps1_2 = (overlaps1 + overlaps2)/(61+197)\n",
    "overlaps1_2[9,:,:] = overlaps1[9,:,:]/61\n",
    "overlaps1_2[:,9,:] = overlaps1[:,9,:]/61\n",
    "overlaps1_3 = (overlaps1_2*(61+197) + overlaps3)/(61+197+54)\n",
    "overlaps1_3[4,:,:] = overlaps1_2[4,:,:]\n",
    "overlaps1_3[:,4,:] = overlaps1_2[:,4,:]\n",
    "overlaps1_3[9,:,:] = overlaps1_2[9,:,:]\n",
    "overlaps1_3[:,9,:] = overlaps1_2[:,9,:]\n",
    "overlaps = (overlaps1_3*(61+197+54) + overlaps4)/365\n",
    "overlaps[4,:,:] = overlaps1_2[4,:,:]\n",
    "overlaps[:,4,:] = overlaps1_2[:,4,:]\n",
    "overlaps[:,9,:] = (overlaps[:,9,:]*(61+197+54) + overlaps4[:,12,:])/365\n",
    "overlaps[9,:,:] = (overlaps1_3[9,:,:]*(61+197+54) + overlaps4[12,:,:])/365\n",
    "overlaps[:,12,:] = overlaps1_3[:,12,:]\n",
    "overlaps[12,:,:] = overlaps1_3[12,:,:]\n",
    "overlaps = overlaps[0:18,0:18,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   ],
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    "\n",
    "font_path = 'MyriadPro-Regular.otf'\n",
    "prop = fm.FontProperties(fname=font_path)\n",
    "matplotlib.rcParams['font.family'] = prop.get_name()\n",
    "\n",
    "font = {'family' : 'Arial',\n",
    "        'size'   : 9}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "x = pd.date_range('00:00:00', '23:59:59',freq='15T')\n",
    "fig, ax = plt.subplots(figsize=(4.8,2.5)) \n",
    "\n",
    "timeoverlaps = np.sum(overlaps, axis=1)\n",
    "std = np.std(timeoverlaps, axis=0)\n",
    "toplot = list(np.mean(timeoverlaps, axis=0))\n",
    "#timeoverlaps = np.sum(timeoverlaps, axis=0)\n",
    "ax.plot(x, toplot, color='k', label='All ties')\n",
    "plt.fill_between(x, toplot - std, toplot + std, color='gray', alpha=0.2)\n",
    "\n",
    "ax.grid(True, which='both', axis='x', linestyle='--')\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%H')) \n",
    "ax.xaxis.set_major_locator(mdates.HourLocator(interval=1))\n",
    "ax.xaxis.set_minor_locator(mdates.HourLocator(interval=1))\n",
    "ax.set_xlim(x[0], x[95]+1*x.freq)\n",
    "ax.set_xlabel('Hour of day')\n",
    "ax.set_ylabel('Frequency of interaction opportunities')\n",
    "ax.legend(loc='upper left')\n",
    "#plt.gcf().autofmt_xdate()\n",
    "#plt.show()\n",
    "plt.savefig('overlaps.pdf', dpi=300, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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   "source": [
    "import matplotlib.dates as mdates\n",
    "font = {'family' : 'Arial',\n",
    "        'size'   : 9}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "x = pd.date_range('00:00:00', '23:59:59',freq='15T')\n",
    "fig, ax = plt.subplots(figsize=(4.8,2.5)) \n",
    "\n",
    "social = np.genfromtxt('ground/survey-updated/berkeley-social-adj.csv', delimiter=',', dtype=float, skip_header=1, filling_values=0)\n",
    "social = social[:,1:]\n",
    "\n",
    "strongest_social = np.zeros((len(social),timesteps))\n",
    "for occ in range(0,len(social)):\n",
    "    strong = np.argwhere(social[occ] == np.amax(social[occ]))\n",
    "    strong = strong.flatten().tolist()\n",
    "    if len(strong)==len(social): strong = []\n",
    "    strong_overlaps = np.zeros((1,timesteps))\n",
    "    for tie in strong:\n",
    "        strong_overlaps += overlaps[occ][tie][:]\n",
    "    if len(strong) != 0: strongest_social[occ] = strong_overlaps/len(strong)\n",
    "\n",
    "toplot = list(np.mean(strongest_social, axis=0))\n",
    "std = np.std(strongest_social, axis=0)\n",
    "ax.plot(x, toplot, label='Strongest social ties')\n",
    "ax.fill_between(x, toplot-std, toplot+std, alpha=0.2)\n",
    "\n",
    "org = np.genfromtxt('data/graphs/org-adj.csv', delimiter=',', dtype=float, skip_header=1, filling_values=0)\n",
    "\n",
    "strongest_org = np.zeros((len(org),timesteps))\n",
    "for occ in range(0,len(org)):\n",
    "    strong = np.argwhere(org[occ] == np.amax(org[occ]))\n",
    "    strong = strong.flatten().tolist()\n",
    "    if len(strong)==len(org): strong = []\n",
    "    strong_overlaps = np.zeros((1,timesteps))\n",
    "    for tie in strong:\n",
    "        strong_overlaps += overlaps[occ][tie][:]\n",
    "    if len(strong) != 0: strongest_org[occ] = strong_overlaps/len(strong)\n",
    "\n",
    "toplot = list(np.mean(strongest_org, axis=0))\n",
    "std = np.std(strongest_org, axis=0)\n",
    "ax.plot(x, toplot, label='Strongest org. ties')\n",
    "ax.fill_between(x, toplot-std, toplot+std, alpha=0.2)\n",
    "\n",
    "ax.grid(True, which='both', axis='x', linestyle='--')\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%H')) \n",
    "ax.xaxis.set_major_locator(mdates.HourLocator(interval=1))\n",
    "ax.xaxis.set_minor_locator(mdates.HourLocator(interval=1))\n",
    "ax.set_xlim(x[0], x[95]+1*x.freq)\n",
    "ax.set_xlabel('Hour of day')\n",
    "ax.set_ylabel('Frequency of interaction opportunities')\n",
    "ax.set_yticks([0, 0.05, 0.1, 0.15])\n",
    "ax.legend(loc='upper left')\n",
    "#plt.gcf().autofmt_xdate()\n",
    "#plt.show()\n",
    "plt.savefig('overlaps_soc-vs-org.pdf', dpi=300, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "classified_day= pd.read_csv('data/classified-berkeley.csv', index_col=0)\n",
    "classified_day.index = pd.to_datetime(classified_day.index)\n",
    "classified_day['Day'] = classified_day.index.day_name()\n",
    "timeoverlaps_mon = list(np.mean(np.sum(im.interaction_matrix(classified_day.loc[classified_day['Day']=='Monday'])[0], axis=1), axis=0))\n",
    "timeoverlaps_tue = list(np.mean(np.sum(im.interaction_matrix(classified_day.loc[classified_day['Day']=='Tuesday'])[0], axis=1), axis=0))\n",
    "timeoverlaps_wed = list(np.mean(np.sum(im.interaction_matrix(classified_day.loc[classified_day['Day']=='Wednesday'])[0], axis=1), axis=0))\n",
    "timeoverlaps_thu = list(np.mean(np.sum(im.interaction_matrix(classified_day.loc[classified_day['Day']=='Thursday'])[0], axis=1), axis=0))\n",
    "timeoverlaps_fri = list(np.mean(np.sum(im.interaction_matrix(classified_day.loc[classified_day['Day']=='Friday'])[0], axis=1), axis=0))\n",
    "timeoverlaps_sat = list(np.mean(np.sum(im.interaction_matrix(classified_day.loc[classified_day['Day']=='Saturday'])[0], axis=1), axis=0))\n",
    "timeoverlaps_sun = list(np.mean(np.sum(im.interaction_matrix(classified_day.loc[classified_day['Day']=='Sunday'])[0], axis=1), axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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   "source": [
    "fig, ax =plt.subplots(7,1, figsize=(3, 5.7))\n",
    "ax[0].plot(x, timeoverlaps_mon, color='k')\n",
    "ax[1].plot(x, timeoverlaps_tue, color='k')\n",
    "ax[2].plot(x, timeoverlaps_wed, color='k')\n",
    "ax[3].plot(x, timeoverlaps_thu, color='k')\n",
    "ax[4].plot(x, timeoverlaps_fri, color='k')\n",
    "ax[5].plot(x, timeoverlaps_sat, color='k')\n",
    "ax[6].plot(x, timeoverlaps_sun, color='k')\n",
    "for i in range(7):\n",
    "    ax[i].set_ylim(0,max(timeoverlaps_mon))\n",
    "    ax[i].set_yticks([])\n",
    "    ax[i].spines['top'].set_visible(False)\n",
    "    ax[i].spines['right'].set_visible(False)\n",
    "    ax[i].spines['bottom'].set_visible(False)\n",
    "    ax[i].spines['left'].set_visible(False)\n",
    "    ax[i].xaxis.set_major_formatter(mdates.DateFormatter('%H')) \n",
    "    ax[i].xaxis.set_major_locator(mdates.HourLocator(interval=2))\n",
    "    ax[i].xaxis.set_minor_locator(mdates.HourLocator(interval=2))\n",
    "    ax[i].grid(True, which='both', axis='x', linestyle='--')\n",
    "for i in range(6):\n",
    "    ax[i].set_xticks([])\n",
    "ax[6].set_xlabel('Hour of day')\n",
    "ax[6].set_ylim(-20,max(timeoverlaps_mon))\n",
    "plt.savefig('overlaps_days.pdf', dpi=300, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single instance of social/organizational tie difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/andrewsonta/Documents/Stanford/Research/occupant-network/venv/lib/python3.7/site-packages/ipykernel_launcher.py:11: FutureWarning: Addition/subtraction of integers and integer-arrays to Timestamp is deprecated, will be removed in a future version.  Instead of adding/subtracting `n`, use `n * self.freq`\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6)) \n",
    "ax.plot(x, overlaps[6][11][:] , label='Social and organizational tie')\n",
    "ax.plot(x, overlaps[1][8][:], label='Organizational only')\n",
    "timeoverlaps = np.sum(overlaps, axis=1)\n",
    "toplot = list(np.mean(timeoverlaps, axis=0))\n",
    "#ax.plot(x, toplot, color='k')\n",
    "ax.grid(True, which='both', axis='x', linestyle='--')\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%H')) \n",
    "ax.xaxis.set_major_locator(mdates.HourLocator(interval=1))\n",
    "ax.xaxis.set_minor_locator(mdates.HourLocator(interval=1))\n",
    "ax.set_xlim(x[0], x[95]+1)\n",
    "ax.set_xlabel('Hour of day')\n",
    "ax.set_ylabel('Frequency of interaction opportunities')\n",
    "ax.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple lunchtime test using Interaction Model without the Jaccard weighting [Note: not in paper]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "totaloverlaps = np.sum(overlaps[:,:], axis=2)\n",
    "totaloverlaps_lunch = np.sum(overlaps[:,:,np.r_[0:48,56:96]], axis=2)\n",
    "totaloverlaps_work = np.sum(overlaps[:,:,np.r_[28:48,56:76]], axis=2)\n",
    "interaction_lunch_adj = totaloverlaps_lunch[0:18,0:18]\n",
    "interaction_work_adj = totaloverlaps_work[0:18,0:18]\n",
    "\n",
    "%Rpush totaloverlaps\n",
    "%Rpush interaction_lunch_adj\n",
    "%Rpush interaction_work_adj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.csv(interaction_lunch_adj,'data/graphs/interaction-lunch-adj.csv', row.names=FALSE)\n",
    "write.csv(interaction_work_adj,'data/graphs/interaction-work-adj.csv', row.names=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "interaction_adj = array(as.matrix(read.csv('data/graphs/interaction-adj.csv')), c(18,18))\n",
    "interaction_lunch_adj = array(as.matrix(read.csv('data/graphs/interaction-lunch-adj.csv')), c(18,18))\n",
    "interaction_work_adj = array(as.matrix(read.csv('data/graphs/interaction-work-adj.csv')), c(18,18))\n",
    "info_adj = array(as.matrix(read.csv('data/graphs/info-adj.csv')), c(18,18))\n",
    "prob_adj = array(as.matrix(read.csv('data/graphs/prob-adj.csv')), c(18,18))\n",
    "support_adj = array(as.matrix(read.csv('data/graphs/support-adj.csv')), c(18,18))\n",
    "socialnorm_adj = array(as.matrix(read.csv('data/graphs/socialnorm-adj.csv')), c(18,18))\n",
    "org_adj = array(as.matrix(read.csv('data/graphs/org-adj.csv')), c(18,18))\n",
    "ground_adj = array(as.matrix(read.csv('data/graphs/ground-adj.csv')), c(18,18))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ESIAEYkAAChhWcJ4kudjMKkomcMsmfffdd/LNN9/I5MmT5eSTT5aHH3446LT58+fL9OnT5eOPP9YV8l588cWg425+0AXthhbzkgAJkAAJJJRAihlSBMu3gosgrOy8PbJi0Ry55557VPZ+/frpGu+hLmTGjBnSqVMnM9Y4VS3gjh07BmXDdMZYMe/333+XX3/9VVq2bBl03M0PKmA3tJiXBEiABEggoQSwsEKysWb3meFItikvuYLUPqiJuo1xTtWqVcOeumrVKrVskaFGjRqyefPmoLx169ZV5X3jjTfK2rVrpU+fPkHH3fygAnZDi3lJgARIgAQSSiAtxbiejR+6Qrr9MKS8jIpSIa2OnHTSSQeUHWvE79q1S/Pt2bNHoHADE1zOp5xyigwYMEB+++03XeJ22rRpgVmsv7MP2BoVM5IACZAACSSaQLZZjjDFKOF9ObnWW47R2OlpduquTZs2MmvWLL1M9Ae3bt066JKrVKkiUNJIcEdXqlQp6LibH6XSAs7JyZH169e7uc6E5E1Ls2+hJURAVkoCJEACpYwALGCditKsCWybcsw5Wdl2U1FeeOGFMmXKFMHnunXr5KOPPtJqWrRoIegfvuKKK9TqRaAWIqAHDx5sK0axfKVSAf/xxx/qIqhTp06xC/LTjkWLFsmqWS9JrVq1/CRWSFnq/GtgyP1+25ntw9lsQjFCP1VpSLszs0uDmGbtVzvrJZEXk5nlolMykYJKVkJrj7bybGPNppnnAZ+2SV3WGXbqDtNcjhw5Uvbu3SsVK1YsrGLhwoWF3998881ixwsPuvhiJ5GLAuORFYCaNm0aNootHjLY1JGZmWmi9ewfEpsymYcESIAEyjOBNCxHaBZWqGysWtuEOTj2upk6yxQcqHxD1XOg46HOKbqvVCrgohfB3yRAAiRAAuWDgPbnpieLG9MmyWjgipYWcDwpUgHHkzbrIgESIAESiIpAqi4tmC/VXHRLZJlz9mW7mDw6KgntT6YCtmfFnCRAAiRAAgkmAAs41ShffNom5HQzd7RtudHmowKOliDPJwESIAESiBsBWMB5RvlmWA4rgmDZJoYvx4eRkVTAcXtsWBEJkAAJkEC0BDAECeOAs8x4YNuErMlGcfstUQH77Y5QHhIgARIggbAEoEjhfc5wMQ442yze4McBKVTAYW8zD5AACZAACfiNQJ7RpFhe0M2w61yzcpI5y2+XIlTAvrslFIgESIAESCAcgWQM6jXqNDXVXqHC+5yUZO+yDle31/upgL0myvJIgARIgARiRkDVKJSwKmK7apKMBqYL2o4Vc5EACZAACZBAWAKwaOGKtk35xmLG9JV+S7SA/XZHKA8JkAAJkEBYAnBBQ/VWSLdfjCHbTF+M6Gm/JSpgv90RykMCJEACJBCWACxfRELvM/NB26bcvDxJNXNI+y1RAfvtjlAeEiABEiCBsARSzJAiGLMV3AxDMso3x8W44bCVe3yACthjoCyOBEiABEggdgT+mojDhQVsrOY0FzNnxU764JLLjQLGsoBYEYOJBEiABEig9BJINbNgQQmnuwiq2mdc1lkuXNbxouM/p7jHVz558mQ59NBDpVKlStKpUyfZtm1byBqwwPLJJ58s1atXl2uvvVYXW0bGHTt2yH333SeNGjWSJk2ayJgxY0Ken+idXHc40XeA9ZMACcSDgC5HiMUYTGW2W76xvdwEbcXjOlBHmVbAe/bskcsvv1waN24sI0aMkIkTJ8rQoUOLsV2yZIlcccUV0qZNGxkwYIC8/PLLqnSRcdSoUfLYY4/J4MGDpXXr1nL11VfL999/X6yMRO746aefVO5EysC6SYAESCAeBGABZ5mFFVKMVWu7Qa69Wf5bjrBMK+ApU6bIpk2bpE+fPtKzZ0857bTTZNy4ccWekddff90M0s6XIUOGyL333istWrSQV155RfO99tprcuKJJ0qvXr1k+PDhus85VqygKHds3769ULl/++23snnzZrPqR558/fXX+jl16lT57LPP9Duqmj59usBy37BhQ2HNH330kezbt6/wN7+QAAmQQFkiUOB+ThH0ANtumLSjYrr/elzLtAJes2aNPne1a9fWz1q1asn69esLFZjzUB5yyCH6ddWqVfq5c+dOVX579+5V9/Xq1asL9+OLk093evgP3ORwdyP17dtXPvjgA5k9e7Yq2kceeUTmzJmjcl1zzTWa59JLLxUobcf9DOt9y5YtkpGRocf5DwmQAAmUNQJYCSnHGCaYWMN2g6WclW0ftBUvZv5rEnh45U5/b1pampaKz9zcXNm1a5dUq1atsKYLLrjARMilyVVXXSWHH364BCrczp07CxTeZZddJvPmzdNz0C8ciwQZ0EhYsGCB/POf/1TL988//9S6b731VkF/NtIbb7yhfdRNmzaVG2+8UeCCHjt2rKAhAUXNRAIkQAJllYB5hZsxvZhYwz6oFgsx+HEmrDJtAUOZIWVlZeknXLNQclWrVtXfzj81a9aUt956S4+h37hdu3ZmoHeywHJG3/Add9wh8+fPl44dO0p6errUq1fPOdXzz/PPP1/uueceDRhDQ2HhwoXSsmVLtWod1/K6dev0NyxmJyHA7MILL5Tnn3/e2cVPEiABEihzBIzu1SjoDDOsyHZLMS5oBG/5LZVpBdygQQPl7biMYdk2bNiw2HAkuJwRqPXll1/KhAkTBL8R9Zxi7jT6V7t06aLW70033aTK/OCDD47ZfTz33HPl888/l1NPPVUVb7NmzbSu3r17a3Q2ZOnevbs2EAKFgDK+/fbbtSEBq5mJBEiABMoigTy1gJMk20ysYbvB+VywipK/iJRpF3T79u3V2kUENIKZ0J/ar18/vQPjx4+XV199VaOc0Y96/PHHa6AVFB6inBH1jASLctiwYTLGDD+ClYwEBRirVKNGDW0AoHwEhTnpnHPOkbPPPluys7PVCsd+KGokRG9jQ5o2bZp+8h8SIAESKIsEkk0fMIzZjHR7+3EvzsnxnwVcphVw5cqVVYEigrlHjx5y7LHHSv/+/fWZxNCjSZMmqbLD8CIEPY0ePVoyMzPlkksuKcx3yy23qKJD/3DFihXl2WeflVatWiXkucZEInCBM5EACZBAeSWQZ7Sv6SGULBcuZVjA9j3G8SNbphUwMHbr1k26du2q0cGBfbd33XWXYHPS008/rZYugrQC+4jr1q0r3333nUYfwzqFW5qJBEiABEggMQSMHaKrIWFBBtvknGObP175yrwCBsjU1FSrwKnAoKaiN8AZylR0P3+TAAmQAAnEkQD0rtGobhQw8kIJ+y2VCwXsN+iUhwRIgARIIDIC6MmF8YtlCW0TcqbCb+2zRAXssxtCcUiABEiABMITSE5KVhd0hovlCDPNcoS5LhR2+Nq9PeK/JoG318fSSIAESIAEyhABWL5wKWM+aNsNM2eluugzRkwQ5oNADBGGpRZNWFcAo2EwQdPy5cuLHrb+TQVsjYoZSYAESIAEEk0AitToUzMJR4r1lmYsYNuJOBB0+8033+jMg1gh7+GHHw66ZMw8+NxzzwnWCbjhhhvkv//9b9BxNz+ogN3QYl4SIAESIIGEEjCGrxmNkmyGIWEokt2WY9YjTEu1G8EyY8YMnYkQwbuwgL/66qug6/3www91qCqm/8XImEGDBgUdd/ODCtgNLeYlARIgARJIKAEsrID+3HSzGIPtlrNrk8z58kNp27atbiW5jTFzojONMRQsVqULTFjk54UXXlDXNCZswvdIE4OwIiXH80iABEiABOJOAMoXCyuYGSmtU3r1uvKPDhfLF+NGHfCc6tWr64I9yIi1ATAXRGDCBE///ve/dSEcTNp00UUXyfXXXx+Yxfo7LWBrVMxIAiRAAiSQaALwJOeYeaBhCdtuGAOcmW2nsjGt76xZs/Qy0R+MmRIDE2ZUxII4SGvXrg2auCkwn813WsA2lJiHBEiABEjAFwTQB5yR5m5iDURNV7QctoRV5aZMmaKry0HRfvTRR3rdLVq0EPQPYx32X375RZepnTt3rrz00ksRc6ECjhgdTyQBEiABEog3AURBI6I5zSywYJsQfpWVY2cBY7rhkSNH6prrmP/fSVga1kmPPvqoHq9QoUKx1fWcPDafVMA2lJiHBEiABEjAFwSgfDGrlYu1GHTqylQTOe0mBSrfUOcd6Hioc4ruowIuSoS/SYAESIAEfEsgzVjAmIwjwwRi2Sb0FbuZutK23GjzUQFHS5DnkwAJkAAJxI1AjlG+sIAxttc25ZvFCDGFpd9SqVTAWJR+wYIFgvFafk4bN26UfPOw7Nq1y89iCvoxmEiABEigNBBIMSHN+eY/NxZwqukvxjl+S6VSASeb1k+VKlXMfKD+a9EE3mDIh/WF/S7n6tWrZfFH/XVWl0D5/fj9lVnL/ChWMZmyUkyoZilI67buLQVSYtiJ/3lmZtkF+SQa+KYd+xItQlT140lAVLObxRX8p3oLEJRKBYwotcaNG1ut8RvVnY7y5OnTp2tDAbL6OW3fvt3P4lE2EiABEigkAMczFCosYduEnC6y2xYbdb5SqYCjvmoWQAIkQAIkUCoJQJmiPzfFRRBWQQS0vcKOFxgq4HiRZj0kQAIkQAIeEEBAlbGCTXyNbUL/r5txw7blRpuPCjhagjyfBEiABEggbgSc0J8KLixgLEfoxzACKuC4PTasiARIgARIIFoCMHxTTFRzrouoZlrA0VLn+SRAAiRAAuWeACxguJ8zLNf3BbAUcxJm0PJbogXstztCeUiABEiABMISgB4tUKhhsxQ7AKsZbmi/JSpgv90RykMCJEACJBCWQMG0ku4UKsYN0wIOi5QHSIAESIAESODABPKMCZyOACwXo4qSzSBgNzNnHVgKb3LQAvaGI0shARIgARKIAwFYwJgZzc1EHBAry4dh0FTAcXhgWAUJkAAJkIA3BNAHjP5cNzNbQWmnsw/YmxvAUkiABEiABMongUj6gKGs2QdcPp8XXjUJkAAJkIBHBLCur65u5MIExoI4UNx+S3RB++2OUB4SIAESIIGwBBBQhWFF6WYyDtukSxjiJJ8lKmCf3RCKQwIkQAIkEJ4A9Kjjhg6fq/iRJBcWc/GzY7On3ChgzJwS7Q3A2r45OTmSkZERm7thSvVCzpgJx4JJgARIIMEEnLmg01NdWMApInkupq6M1yX6b2oQj6988uTJcuihh0qlSpWkU6dOsm3btpA1vPHGG3LyySdLrVq15KqrrpLly5cXy3fOOedIhQoViu33Yke0cmZlZcltt90mTZs2lebNm8v999/vhVhRleGsVuJ8RlUYTyYBEiABQwBR0GY1QqNQ7TcF50MLuEwr4D179sjll18ujRs3lhEjRsjEiRNl6NChxR7itWvXyrXXXitNmjSR0aNHy6RJk2Tw4MFB+V588UX5/PPPg/Z59cMLOV999VV56qmnZMCAAdK9e3d54IEH5JNPPvFKxIjKeeyxxwQNC+czokJ4EgmQAAkEEHCUFmKqbDfYyvb2ckBlMf7qXEuMq0lM8VOmTJFNmzZJnz59pGfPnnLaaafJuHHjigkDq3jfvn1yxhlnyCWXXCL16tWTDRs2FOZbsWKF9O/fXy3Lwp0efvFCzvXr15sVQlKka9euaulDvMBr8FDcsEWtW7dOXnrpJRk7dqzyDJuRB0iABEggUgKqeJMkLTnFeks178ZouyAjFbek88q0Al6zZo1ee+3atfUT7mUoqrw8OC/+SkceeaT07dtXFXWzZs1k9erVQS7c6667Ts466yxp27btXyd5+M0LOW+44QZp1aqVHH300epKP/XUU6VLly4eSllyUXAzw3XfokUL+f333+XRRx8t+QQeJQESIIEICWgktFHE+babqcePw5DKtAJ2+nvT0tL0NuMTgVS7du0Kuu3Z2dkCNzQUM6zIvXv3yp9//ql5YNHNmDFDnnzyyaBzvPzhhZw7duzQ/m3nGmD9bty40UsxSywLrcshQ4aoyxkKGA0dJhIgARLwmgCUL1KGmdnKdkszQ5b8GItSphUwLF4kBCghwc0MJVy1alX97fzz6aefyjvvvCMIxFq8eLG6quG23r59u9x+++1yzDHHyIcffii//vqrnvLss8966mKNVk48WFB+ULiQf8GCBbJ06VLte3WuMdafaMDceeed6urv3bu3NnRiXSfLJwESKH8EMJpXhyGZqGZENtts+SafHy3gMj0MqUGDBvp0rlq1Sj/hWm7YsGGxvoCFCxfqcURLIx1++OEydepUtYphWX777be66UHzzy233CJXXnmlZ8ORopUTFj2Ubt26daV69eoqZs2aNcW5bkfuWH7WqVNHI8QR7LZz507ZvHmzwJ3PRAIkQAJeEkDgFZSwm/V9sX5wNk7yWSrTCrh9+/Zq7UIpwDU7e/Zs6devn96C8ePHCyKHR40aJW3atNF9Dz30kPTo0UM++OADOfvss3X40o8//lh4y4YNGybvvfeeYF9RK7owUwRfopUTsuAaEMGNSGi41GENx7MPGJ4FeBIQ0Y0hX4EJ/edMJEACJOAFAWciDrf6NJVTUXqB376MypUry/PPPy+9evVSxXrsscdqNDNKWLJkiQ43grXWoUMHHZ6EoUYff/yxtGvXTu677z51V+McJ8HKQwrc5xyL5jNaOVH3ww8/LFu3bpVBgwZpPzaGVXXu3DkasSI6t6jyjagQnkQCJEACYQjAlQwl7Eahot/Yj4sxJJn+Q7cNiTBY4rd70aJFUqNGDR0uZFMrZq/asmWLVX4ERKFsL9L06dOlUaNG1q5YL+TMzMxU0d1MGIK+bcjp1XV7wS5cGa/MWhbukK/279ib7St5wgnz07It4Q75an+darGbfc6rC83MyvWqqJiWsyszJ6ble1X4az1OCFnUiDHj5JPv5spxF1wR8nionSt+/UGyls2Tt59/KtThYvuefvpp+eijj6R+/frywgsvhPR4ItD04osvlu+++67Y+bY7ynQQlgMhNTXVSvkifyKVkBdyQvG6Ub4OI36SAAmQQGkgAGs210yHhU/bDXHTObl2tiYU6jfffKMjOjA7IryLoRKGroaaMTFU3nD7yoUCDnfx3E8CJEACJFC6CCCiGRNrYIlB2y3JzF2ZbM5DfAy2khKGnWLaYhhE3bp1k6+++qpYdsQOIXbHCXotlsFyBxWwJShmIwESIAESSDyBZDOpJHpO0Rdsu+3avF6+nvSBnH/++bphdsNwCaNHnKGh8IhiREdgwjwHGCWDCZqiTVTA0RLk+SRAAiRAAnEjgHkM3Y7prV6vvrTt2FndypifHvP+h0uwap3JmjCqA8M7AxOCerHgzXPPPaeBr6+//nrgYVffqYBd4WJmEiABEiCBRBIwE2BpFDRmt7LddOywXRewDumcNWuWXiL6g1u3bh10uVh1DlPuYq4FuMCrVasWdNzNjzI9DtgNCOYlARIgARLwPwFM5a9K2IWomCrX/G+VLrzwQsECOfjEAjOIhkaC0kX/8EUXXVRYzoMPPhj0u/CA5RcqYEtQzEYCJEACJJB4As58Gu7GAdvLjfUARo4cqWsCVKxYsfBEZ8bEwh3mC2YgjCZRAUdDj+eSAAmQAAnElQA8ycaeLZiP0rJm5Lc0gAtLDFS+hTs9/kIF7DFQFkcCJEACJBA7Ao4r2Y1CRV4/rgdMBRy754QlkwAJkAAJxIAA3NCpKfYFm3gtXyYqYF/eFgpFAiRAAiQQjgBmwJJ8N1q1YNascOUlaj8VcKLIs14SIAESIAHXBDB2Fv3AqS7M2oKhS5bjkFxLFPkJVMCRs+OZJEACJEACcSYANWqG36oStq0axnKKC4VtW260+aiAoyXI80mABEiABOJGoMD97G45whTjssb4Yb+lUqmAs7KyZP78+bJs2TK/8QySB0sbQtaNGzcG7ffbD8x1ighBLNno55SRYZakw+qZThikn4WlbCRAAjEhgFeArgnssnQ3LmuXRUecvVQqYAyUxmTZ+PRzwrKAVapUEb8vUo+GAlj6fRnDNWvWSJeWzUOuzem356Bln/F+EymkPB3+dVjI/X7bOeOXtX4TqZg8K79fUmyfH3e0bt/Sj2JZywT3s1mN0NV80DAwcnCSz1KpVcANGjSwXuM3Ucy3bt2qMjZr1ixRIljVu2nTJqlcubI0bNjQKn+iMm3YsCFRVbNeEiABnxCAHk3DSkguptaACxrn+C2VSgXsN4iUhwRIgARIID4E0AecZ/zQbvQpVG8uLeD43CDWQgIkQAIkUDYJaB+wGVeUBF+0ZUqCxexGY1uWG202WsDREuT5JEACJEACcSMAPapK2IVHGVlxjt8SFbDf7gjlIQESIAESCEsAehR9uliSwTbBbc25oG1pMR8JkAAJkAAJhCCg1qyZhiPV3gO9X1X7zwSmBRziBnMXCZAACZCAPwlEbAG7sJjjdeVUwPEizXpIgARIgASiJuAYvpjf2TYVeKxpAdvyYj4SIAESIAESKEYAarRAodr3ARf0/9rnL1ZpjHbQAo4RWBZLAiRAAiTgPQFnNJEbdYq8bvJ7L3XoEqmAQ3PhXhIgARIgAZ8SgEWb6mhiCxmRVa1mi7zxzEIFHE/arIsESIAESCBqAlhZ0Awssi4HCltXUbI+Iz4ZqYDjw5m1kAAJkAAJeEDA6c91YwFj3LAfJ+JwEUfmATkWQQIkQAIkQAJREXA3D7RTlZuoaeecWH/SAo41YZZPAiRAAiTgGQFYwDoW2IX5COXrw7UYpNwo4HzjfyhwXXj2HMSkIMoZE6wslARIoKwQMNo3VfuA3V0Q+o39lly0Ifwmup08kydPlkMPPVQqVaoknTp1Eiw+Hyq98MIL0qRJE8E6w/fcc49gLV+k7Oxs6devnzRr1kwOO+wwPZaZmRmqiKj2RSvnnj175KqrrpLq1avL8ccfL6+99lpU8oQ7OVo548WzqPx5eXlFdxX+xrEtW7YU/uYXEiAB/xJANDMsYARVudn8aAGXaQUMpXT55ZdL48aNZcSIETJx4kQZOnRosSfrxx9/lBtuuEHatWsnDzzwgDzyyCMybNgwzTd69Gh55plnpHfv3qrghg8fLi+99FKxMqLZ4YWcQ4YMkbFjx6rsLVu2lB49esjs2bOjEavYuV7IGQ+exQQ3O2655RbZu3dvqENyyimnyMiRI0Me404SIAF/EVDla8YVJbvZjNJO9aEJXKYV8JQpU2TTpk3Sp08f6dmzp5x22mkybty4Yk9TmzZtZMGCBfoS7ty5s6SkpOh5yAjredSoUdK/f38ZMGCApKWlybRp04qVEc0OL+QcNGiQ/Pzzz9qQgEJB2rx5czRiFTvXCznjwbOY4GZHly5d9N7NmDFDrd1XX31V1q9fr/dy9+7dcu655wos4alTp8pnn32m30OVw30kQAKJJWBWAtaIZqNTdSCSzSckzvWhCVymFfCaNWv0Saldu7Z+1qpVS1+6Rd2RyWZh5xYtWsi8efPkiCOOUDf0wIED9Zyzzz5blRp+vP322+qSdhScZvDgHy/krFy5ssDyvf/+++XGG29UhXPmmWd6IN1fRXghZzx4/iXxX9+efPJJ2bdvnzz++OPy1FNPSc2aNdVLUK9ePalSpYrUqVNHvQdz5szRhss111zz18n8RgIk4BsC+boSUpKkGAvY7eabi9gvSJlWwE5/L6xWJHzm5ubKrl279l9+8Af6fZs3b679vzNnzgw6iN833XST9gVff/31Qcei/eGlnDk5OdqA+P3332XJkiXRihZ0vpdyxpJnkNBFfqDxhft48cUXq0JGv36NGjWkadOm8tVXX8ltt90m3bp1kw0bNoR1WRcpkj9JgATiSECjoI0fGrNb2W4Qj+OA43iTUBUsXqSsrCz9hAUEJVy1alX9XfQfuCG/++47OeGEE6Rv376qrJEHyqxjx46Snp4uEyZMkIoVKxY9NarfXskJIdDHDStu/vz58sQTT0QlV9GTvZIz1jyLyl30NwLVkOD5CEwZGRmqlLFv3bp1gt9MJEACPiMA5Ws0b1KScUZbbsmaD85qu/T0009rTBAa4zt37ix2EjyN0Am9evWS5cuXFztuuyP4DWR7VinJh4hmpFWrVunn6tWrpWHDhsWGI33//fca6YzjSPXr11c3JKwgwD/vvPPUGoLyPfLIIzWPl/94ISf6NBFAhgQFg0YC+rW9TF7IGQ+ekV4zAu2uvfZadd937969mIKOtFyeRwIk4B0BREEjDBoxVbYbTimIndYvJf4DI+ybb74RjPg4+eST5eGHHw7K/+mnn2pX5ieffCL/93//FzKwN+iEEn6klnCs1B9q3769WruIgIbrEVHBGFKENH78eIHSQoAV+v+QZ+XKlXL++efLxx9/XNgXjCFJS5culaOOOkqjjBFpDDc1Aru8Sl7IiWE0aJVVq1ZNVqxYoW72008/3SsRtRwv5IwHz1AX/eGHH+rujz76qPDw559/rt8RHY90zjnnCPqoMVQK3g4mEiABfxKA4nUzt3NW5h5ZvXyZIJAUCYoVcTOhEgI1MWQ1NTVVu6Ng6Qamk046Sc/HPhg6juEWmMf2e5m2gAH4+eefV3cshuUcc8wxGs0MOOgfnTRpklq4GOOL4By4bjEcqUOHDvLuu+8qw7feeks/f/vtN3nuued0c17mesCDf7yQE/3ScJcgGvrNN9+UO+64Q8cseyBeYRFeyBkPnoUCR/AF/UtUvhGA4ykkEC8CBeas8WSaKGjLLSt7n6w33UrwdmILNyQRlwCPqdPdhviQoqNJ4GGEoQPFi+GNjucxkstPMjMvGWO+dKVFixZp4AwiWG0SApNgIdrk37Fjh8K1KfdAeaZPny6NGjXSwK0D5cVxL+TEg4WWmxN4ZlMvht5geBDGS9skL+S0qadonrlz56qc4frwi+ZP5O+WfcYnsnrrujv86zDrvInMOPO3dYms3qruld97G/RoVWkEmVq3bxnBWfE/5dNbzwhZ6RvvvCs//Pq7XHrNDSGPh9r506xvZf7338iIx4PdyaHyDh48WFq3bq3uZQTswjMGqzgw/fnnn4Ihq5gj4l//+lfgIVffy7QF7JCAQrJRvsiPlk2ikhdywiXiRvlGcq1eyBlJvTyHBEiABGAAQ3FFstnQw7wQs2bN0qzoD4YyDkwYjom+3zFjxkSlfFFmme4DDoTG7yRAAiRAAmWHAJYYtE3OAg42+S+88ELtK8YnRkM4cSOYKwKWMEaawE2Nbk0keA+dLkvd4eIfKmAXsJiVBEiABEgg8QRU99rrX+0rxphhm4SZEDE1LbrzAoecLly4UE/HMa+mrqUCtrkjzEMCJEACJOALAo4etVWoEBoK222wU6DyjdWFUwHHiizLJQESIAESiAGBglWQVKtal46JOxzVbX1SzDNSAcccMSsgARIgARLwikCBHt2vhC0L9aHuVcmpgC1vILORAAmQAAn4gIDxJesc0C5EcSKmXZwSl6xUwHHBzEpIgARIgAS8IOBYs25cysib50XlHpdBBewxUBZHAiRAAiQQOwIIpsI0lFiQwTYhv5thS7blRpuPCjhagjyfBEiABEggbgRgAUMJ26vfAtHcRkHH44KogONBmXWQAAmQAAl4RkD7gF3M44iVR6G4/ZaogP12RygPCZAACZBAWAKOHnU+w2YMOKB5fWgCUwEH3CR+JQESIAES8DeBgj7ggn5gW0kRhEUL2JYW85EACZAACZBACAJGlepeWsAh4MRjFxZMnzdvXtgFleMhg00d27Ztk8zMTJ3Q2yZ/ovJgCcbff/9dJxhPlAw29WJJSczTWqFCBZvsCcuTkZGhEZe5pW+lz4QxY8Uk4IYAlHBykn0ncLLJ72bYkhtZoslbKl3QeAk3aNBA172N5uJjfS7WksTCzlWqVIl1VVGVv3PnTqldu7ZUqlQpqnJiffL27dtVRr/zXLZsmcx65PygidxjzSbS8v85eGKkp8b1vNo1K8a1vkgqW7x1VSSnxf2clUsPinudXlYIy9fZ3JXrv07gUqmAk01IGxSb7Rq/7m6Sd7k3bNigcjZr1sy7QmNQ0tq1a6VOnTrSuHHjGJTuXZFYAqxy5cpy0EH+foFATiYSIIHYEFA1ij5dF+OAC/K6cVrHRvaipZZKBVz0IvibBEiABEigfBBAMBU2F/q3YOpK/+lfoQIuH88sr5IESIAEygQB6FG3Chjn+M8BLVTAZeKJ5EWQAAmQQDkioErYxfW6ze+i6Kiy0gKOCh9PJgESIAESSAQBN1HNbvLG81qogONJm3WRAAmQAAlERQDWrE5FiS+Wyek3tswet2xUwHFDzYpIgARIgAS8IeBuXC8UcMHgJW9q96oUKmCvSLIcEiABEiCB2BMw2rRgakl7E9icoYFbsRfOXQ1UwO54MTcJkAAJkIAPCLjq1y0wgX0gdbAIVMDBPPiLBEiABEjAxwRg96aYf1LtDWDNbz9xZfwu3o8yxe/qWRMJkAAJkEC5IMBxwOXiNvMiSYAESIAEYkkA7udkF1NhJe/vN46lTJGUTQs4Emo8hwRIgARIICEEXHiei8jnPxuYfcBFbhF/kgAJkAAJ+JiA0cCIqUpxYwEbUxOR0H5LVMB+uyOUhwRIgARIIDwBY8hClbpRp27yhq/Y+yPlRgHnm8XRXYWte8/aqkTKaYWJmUiABMorAfT/mi0FZrBlKugDtswcx2xlvg948uTJcuihh+pC7p06dZJt27aFxPvCCy9IkyZNpEGDBnLPPffI1q1bC/O9/fbbcthhh0mNGjXkqquukj179hQe8+pLtHJmZWXJbbfdJk2bNpXmzZvL/fff75VoQeVEKycKa9++vbIGb2zg7XW66667pGbNmrrO8aOPPhq2+MWLF0vbtm31+WjXrp3Mnj1b88aLZ1jBLA6gscZEAuWPwP7n3jGDbT59CqlMK2Aoyssvv1wXmh8xYoRMnDhRhg4dWuxW/Pjjj3LDDTcIXsAPPPCAPPLIIzJs2DDN98MPP8ill14qJ5xwgjz++OPy5ptvyn/+859iZUSzwws5X331VXnqqadkwIAB0r17d72OTz75JBqxip3rhZyZmZny9ddfyyGHHCJdu3bV7bjjjitWVzQ7cN24hzfeeKN06dJF7rzzTpk1a1axInNzc+W8886TjRs3yvPPPy+bN2+Wm2++WaDY4sGzmEAuduCZRSODiQTKIwEYv8lGe7nZXPmsi0B9+umnVT9069ZNdu7cWeRo5D/LtAKeMmWKbNq0Sfr06SM9e/aU0047TcaNG1eMVps2bWTBggUycuRI6dy5s6SkpOh5yAjLGK5rfOJlvnfvXs9ffF7IuX79epUbSg2WPtKGDRv006t/vJBz/vz5kpOTI5dddplccMEFct9998kll1zilYhazltvvSVpaWny4IMPykMPPaT7Qt33L774Qv744w+54447lNn333+vFjDudzx4Bl70woULBUr15ZdfluXLl+uhGTNmyJdffiloBOK5e/fdd2XevHmFp+Xl5ckHH3wg3333XeE+fiGBsk5A3cnmInV6Sct/ndyRsMHf1zfffCPw/p188sny8MMPR1JMyHPKtAJes2aNXnTt2rX1s1atWvpixYsrMCWbZlSLFi305XbEEUeoG3rgwIGaBS/DypUra+sHLugOHTqEdWMHlunmuxdywoJv1aqVHH300fqQnHrqqdpgcCPHgfJ6IefcuXO1Gsjbtm1bOfLII2XJkiUHqtrV8dWrV0v16tW1QYJ7DoXqyB5Y0IoVK/TnmDFjNH/jxo0FSg8pHjy1ov3/wDsDVzw8Az169NBn7LHHHhM0EmCRX3PNNZKamipjx46VF198Uc+CQk5PT5fhw4fLtGnTAovjdxIoswTUAQ0L2MW2edNG+co0Zu+9917d4PWyTXgnwKjB3x8s4K+++sr21APmK9MK2OnvhTWEhE+4HXft2hUSDPp90X+Kz5kzZ2qeLVu2aH70AcPFC/cp3NBeJi/k3LFjh7600biABQ/r181DZnM9XsiJxs6xxx4rTz75pIwaNUqgLOHu9TJt375d77VTJnhgX9EElzPS2rVrVYYqVarIoEGDdF88eGpFAf/06tVLzjjjDPXUwBrHs9q/f39p1KiRKtqLL75Yuxbef/99PQseBLjQ4eH57LPPAkriVxIouwSM3t2foIrttipVKsvhhx8m559/vm74W7dNq1atEjTkkWCEOe8N2/NLylemFbADDQE1SPv27dMXc9WqVUMyOffcc9Wdh/7evn376gvQsZ7hyuzXr58cddRR8t5774U8P9KdXsg5ZMgQVbgIKoI7fenSpQILysvkhZxXX321ulpvvfVWtTJjxdO552iQwOVdp06dYiice4vuCQTXoe98+vTpyjEePIsK5Pxho1HiyFuxYkW1zp0GBBqETjT/7t27tQg0II455piixfE3CZRRAmZEC0a1uNgqVaigsUBwIWPD35VtgjfNMdoQB1O3bl3bUw+Yr0wrYEQ0I6EFg4QXW8OGDQtfYLrT/ANrA8oVx5Hq16+vrRxYkXBLIsEiQsrIyNAXuv7w6B8v5ITSxYOBh6VevXoaAexct0diqmseZTnlRsLz008/FUQlOxG8UJSVKlXySkQtB/cPXgz8sTiuZ9z3oinUvYVcUNjx4FlUnldeeUUbAnArBypU8EGXAixkNBKcqPHffvtNA83QNw9LmIkEygUBNXrNP+hKtN5MfvO3HUlCjJATxIn+4NatW0dSTMhzUkPuLSM7MdwF1i4ioGEJYYgJFC3S+PHj1e0INyisDeRZuXKluic+/vhjcfqCEUWNwJi7775b4AL8+eefBRFxXiYv5MRDMnr0aHWTZ2dnqxWHoDEvkxdyTpgwQaOS4Y1AYwEWu+P29UpW9NegrxQBXo7l6AR6Bd53RL2j8fPaa68Z99Th8vrrr6uiw7548Cx6vYMHD1YXtNM6D4xihysajRV0ozgWMF4GaCygb4qJBMoPgf2K1JVCjUz5gumFF14oaOTic926dfLRRx95hrpM/+UieArDS2A5ILAFfY94kSEh8GfSpEkaUo7AJQwxeuaZZwTKF6Dx8kZCnxwsDrhz0f8LFyo64r1MXsiJyDxYfVBm6PO89tprNaLbb3LC1Qs3L3jDrdOxY0e5/fbbvRRTLrroIrUUn3jiCVVWvXv3lhNPPFHrCLzvUGZvvPGGDlXDfcW9husZKR48taL9/6DBB8vdUb6Bx5zvsIyLJirfokT4u1wQyDfWb36u/aVq/siUMN6nGCGDkQgl/X3aC/NXTuNGd9WM+OvMBH5btGiRdobD1WqTYCWg78wmP1zN1apVK1YsLDaUA2Vpm6BoEEDTrFkzq1O8kBPjbJEqmD4P2zR16lSdrMRxyR7oPC/kBE9soViHqx8RwS1btlRFFS5P4H7cSwR9HSjgAn8CaLw4fdyBZUTCE8OJ0Lft9R9roFxeff/n4IleFRXTcmrXtO+zi6kgJRT+/bulIxCu/rHHlXAV/jk0/9nQHrwPTBDin0v/kH59brYW9uvpM+SLqdNkyNBh1ufEI2OZtoAdgLASbJQv8odTCOj7xRbL5IWcbhRvpNfihZzx4BnuXha9brh0Qylf5IsHz6Ly8DcJkEBJBIwlm4cteDhpSWdE2v9bYpkeHCwXCtgDTiyCBEiABEjAJwTgTHbjvNW8PnT2UgH75IGiGCRAAiRAApYEoEzRr2ub3OS1LdODfFTAHkBkESRAAiRAAvEhkO9MvuHGolWFDbvZX4kK2F/3g9KQAAmQAAmUQCBJ/c8uo6DFhbVcQt1eH6IC9pooyyMBEiABEogZgb8sYBdKlRZwzO4HCyYBEiABEignBHQu6Ij6gOmCLiePCC+TBEiABEggFgQKI5rdDkNy02ccC8FDlEkXdAgo3EUCJEACJOBjAm5dyj5UvqBLBezjZ4yikQAJkAAJBBModEG7sYAxcYcPExWwD28KRSIBEiABEghN4C9V+te30DkD95q8PrSCqYAD7xG/kwAJkAAJ+JoA1gFWZZrrYjEGN9ZyHK+eCjiOsFkVCZAACZBAdATyC3zQOh2HbUlqK9MCtsXFfCRAAiRAAiRQnECBBYyJOHKKHwy3x+QtUNzhMiRmf6m0gLEw+dy5c6VSpUqJoWZZK5a427Nnj6xevdryjMRkw7J9CxYskOXLlydGAMtad+/eLb/++qssXrzY8ozEZNu+fbtgreFQ6/cmRqLQtcZ6da/QtXIvCURHwE3Pb1BNPpwPulQqYCyH16RJE33JBQH22Q+sJVunTh2pWrWqzyQLFgeNBCwG72at4+AS4vNr165dB1y0Pj6SlFwL5MTyhn5fyvCPP/6Q74ecV/LF+ORomzs/9Ikk4cXIqNko/EEfHdk4/3cfSeNelMKpKPPcWMCmv9gsO+q3VCoVMBZZr1GjhvUav4mCvmbNGqlevbo0a9YsUSJY1bty5UqpWbOmNG7c2Cp/ojItW7ZMGzRoLPg5LV26VBszYOrnBDmZSKC0EYioPxcn+TAQq1Qq4NL2wFBeEiABEiABbwgkOashuYmChvvZhxZwsjdIWAoJkAAJkAAJxJ5ARBYwxKIFHPubwxpIgARIgATKLoECC9hcX57LccC0gMvuQ8ErIwESIAESiD2BiCxg9gHH/sawBhIgARIggbJNIKKZsHw4BAl3iX3AZftZ5dWRAAmQQJkioBNauZ3VquAk33FgFLTvbgkFIgESIAESCEegYCZKE9XsahwwZs4KV2Li9lMBJ449ayYBEiABEnBJIN8ZhiRGqdomdUG7yG9bbpT5qICjBMjTSYAESIAEEkDAlRsa5i9nwkrAXWKVJEACJEACZYWADkOCRetmIg4dA+w/HzSDsMrKU8nrIAESIIFyQEDVqCvrdz+USM6JMU8q4BgDZvEkQAIkQALeEdBhSOgHzsu233TpQncuaMyVfskll0jbtm3l/fffL3YBP/zwg3Tr1k0uuugieeWVV4odt9nBPmAbSsxDAiRAAiTgCwL5mNFKrVk3ChXnuAvC6t27twwbNkxX3jvzzDOlXbt2ugiQA+HWW2+VN998Uxo2bCg43qFDB83rHLf5pAVsQ4l5SIAESIAEfEGgYCIOIwr6gK03KF83Cltk7dq10qZNG6ldu7aceuqpMnv27KDrh/LFCnIpKSlmnYckWbduXdBxmx/lUgHnu+gLyM7OFmzxSm5kw3rDeQmaYLy0yOnmvoXjmWv+yPft2+emqKjyumEbVUU8mQRKIYF8o0jxN4K+YNttxdoN8t93P1B3MlzKy5cvL/HKt27dGrSeN5YW3bRpU9A5zvKtjz/+uC7nevzxxwcdt/lRrhQwWihwFWCh9KOOOkpmzpxZIiPcJLR+Lr/88hLzeXFw8uTJcuihh0qlSpWkU6dOsm3bthKLfeGFF6RixYry3nvvaT40Evr166drDx922GFyzz33CBSK1ylaObOysuS2226Tpk2bSvPmzeX+++/3WkQt76677tI/ijp16sijjz56wDqK8gw84Zxzzgn6Yww85uV3W7aLFy/WFwmeFbjFirbMvZQp1mX99ttv8r///S/W1bD8skRg/5jefNMHbLs1rl9Lune5SKZOnaob3j9F05gxY/SdhPfSli1bZM+ePYVZdu/eHXL9+QceeED//mANR5LKlQK+88475fvvv5eXXnpJMjIyVLHCugmXrr32Wtm5c2e4w57tx42GkkeLasSIETJx4kQZOnRo2PJXrFghd9xxR9Dx0aNHyzPPPCPot7jqqqtk+PDhep1BmaL84YWcr776qjz11FMyYMAA6d69u+AB/uSTT6KULPh0lPfII4/IjTfeKF26dBHc91mzZgVnCvgViqdz+MUXX5TPP//c+RmzT1u2eF7PO+882bhxozz//POyefNmufnmm9Ui8Fq4uXPnysiRI2XOnDlaNF5ejsfliy++EFgJ+HvCPcUn0sKFC+XHH3+Ul19+udDKwDk497PPPtPz8XJD4/e1117TPrQPP/xQtm/frufzHxI4IAF4kl14MbU8Yyrn5+aUWHSPHj3k999/1w3GEAwc/H3B2sYzfeSRRwadj3fXhg0b5K233pL09PSgY7Y/yo0CBsS3335bzj77bLnyyivlhhtuEES5hXsx48W7YMECqVKlii3LiPNNmTJF3Rt9+vSRnj17ymmnnSbjxo0LWx4aBuj4D0x4YEaNGiX9+/dX5ZaWlibTpk0LzBL1dy/kXL9+vfaZdO3aVS19CIWH2MuEPwhc/4MPPigPPfSQFu2WJ06CYgZPtIhjnWzZQvH98ccf2gCDpwSKDxYw+qC8TGjxw1Nx3HHHyRNPPKEeGXgScnIKXmJo4P3555/y73//Wxo1aqQNnm+//VYbj/C+HHLIIYIXGjw5aAxBieNlds011+jfHZ5h/E3WrVtX4KWI9AXm5TWzrFJCIA/a1MiaY4wn2800ApOSUlxdIJ53GEZnnHGGnHvuufK3v/1NG5X/+Mc/ZM2aNWokzZgxQ/9G0FeMv2G3qUr6fOgAAEAASURBVNxEQeOPH/14+GNHgmsZCSCLJufFC6vypptuKnrY89+ODI5MtWrVEigqWA7JycFtJMiE1hgsk0svvbTwxYuGhZPQ0IBL+pRTTnF2efLphZxo+MDlePTRR+v9QHADrFQv0+rVq6V69eqq6MESysmRvWg94Xgi33XXXSdnnXWWPjNoGccyOfId6BnAs4kEdxmUGRQYuiHA0csE93a1atUE/VsYilGjRo2QxZ9//vnarYNAlAkTJujfVa9evfSl9dVXX2kDAZ9wryO98cYb2jUC1zkUNKxfnAtrg4kEbAhoH7B5N+LTNkFf57tZP9jkP/3003VDVx66LZHgusbwIyQvYoOC3+5abNn8x3FxpaYWtDmcz1B9rXjx/vOf/9TWfTxoODLAakPCJ1yNu3btCqp+5cqVapHBqsOLN1SCaw+NhmbNmsn1118fKkvE+7yQc8eOHWoVoXGBFy+sX7hTvUy41w5LlIt6nPsfWE9JPNFNgdbtk08+GXhKzL7bskVDEgkRmnD9wkMzaNAgz+XCs4duAlz/Bx98IJMmTRL8zeBlhH58R15HHjR6nMZt0X3o7nGC2BCHAWsXCh7Ja8vdcxAs0HcEknQOaKNSc7PsNyzcYK+vg67ZUb5BOz36UW4UMCwhJLw8kJwXgvPS0J3mn3fffVdb6/D3P/vss/rCQdALWuqxSqFkgwKpWrVqUJW33367KmbI7gSu4MU4f/58zQcrrWPHjvqCgzXitVXhhZxDhgxRhQumcPGjG+Cxxx4Lus5of0BO5z5D0cNtWvQ+o45wPNEtgWPHHHOM3vdff/1VRcLz4Dw30cpY9Hxbto6FjK4K9PWjH3369OmeN2Kg2OFlgRsOjZe///3vcsEFF2ijFErZkQPWAOSA27+HsWiRMCkB9kHRgiHiEuByhqcD8gZ6ddBQREMCzwMTCdgQgDVb0AcMjWq7mazmXeC3VG5c0HChoSWOljqS81m0LxVWEdJzzz2nn/jnp59+0uAozHgSi9SgQQMtdtWqVfoJ2SBXUesAx9E3h2hnJ+Fld+KJJ+oAcATn7N27V4OGigYMOPmj+fRCTihdWO9wESMhvN+57mhkCzy3fv36GiCEwCYE/CAVvc/YF44n+nxhqaNPE5uTbrnlFo0fwHPkdbJl6wx9gHxIkAV9qU7frFdy4dmDuxgMHWsVniHET8AiQJQ5+nUvvvhiDaQLbOwNHjxYXdDOPkSRo4sELjunrxf9aEitWrXSOAzHI+WV/Cyn7BJIUn8yLOCCeASrK811Pw7YqtwoM5UbCxgvFChQ9EWhtY4oTfjz8SKAOw/KCy8cdLqjj9XZ0A+GGU4Q4BSr1L59e7V2EQGNyFAE1aDfDWn8+PEqG4ZEwVJw5HLkQYALgnEQFANr8uCDD5axY8dqZCwsNi+TF3IiWGHZsmUaCQ3rCu5nr/uAwQOW73333SewuJHc8ISScTjj0zkX34t6Jbzia8sWfadQ1nhOsL3++uva/+socK/kccpxlK/zO9Adh8ZT69atgzwtRxxxhKAB5Chf5zz8/TnK19nnfFL5OiT4aUPAsYC1L9hYwHafpmQdvmRTQ/zylBsLGEjRdwp34mWXXaYWGBQV3GFo5cOVi37fK664Imi8F14OeNFgbG2sUuXKlXVICYJX4MY79thjta8X9S1ZskRlw3AoBC45yemDQ7Qp3KtoVCBhXCU2JLzUEVntVfJCzocfflitU/Rbom8WrsnOnTt7JaKWg4YWXJ2I3sWLHy5QeAmQbHhCmQUqNMd9jfsSq2TLFl0TTkPx6quvVkvTaWTESrZw5cJ9jC0woQuEiQRiSsB4fNQF7cYCdhmAFVP5Awo3s3rhakpXWrRokUZl1qtXLyLBEWGMlyoUQCwT+uYwRKPoSypcnXAjwmUa6XWFK/dA+zFGE8OYHPfmgfJ7IaczSUigRXWgejEEp2XLlmphHSgvjsNNiwZWPIaSBcqD4QjwrKDh5jbZssWfLcbhOn3HbutBfnhaIpm9J5K6oj2nzZ2xi8GIVjbn/PXzC7qvnN9+/czZU/IkP36Re8OXA0OK8v7rL8uS2dOkT5dzQx4PtXP63AUydcEKeWjUmFCHE7avXFnADuWDDjrI+eqrT1jb8Va+kQDwQk43ijcSGXEOug9KW7JlC8s+GuVb2rhQXhIIIuBYwUE7S/rhZtBSSeV4e6xcKmBvEbI0EiABEiCBuBFQp63LICzjgtZVlOImpF1FVMB2nJiLBEiABEjADwQw8gizYSW5iSFOliQGYfnh7lEGEiABEiCBUkvAKF/EQORnF8zpYHUduWbhBh0zbJU7bploAccNNSsiARIgARKImoCJf9Ao6GSYwi4SJ+JwAYtZSYAESIAESKAoAXUlGxe0m3XaseodFLfPEi1gn90QikMCJEACJFASAdP3CyXsVqGWsPRsSbXF8hgVcCzpsmwSIAESIAFvCeQba9YYwG76gPOhfGM870MkF0kFHAk1nkMCJEACJJAYAoh+1pmtXLqUc7ITI28JtVIBlwCHh0iABEiABHxGwFG+WGLQNsFqTo7tzIe2ogTmowIOpMHvJEACJEAC/iZgppfNN33A+S7GARuPteS7mTs6TgSogOMEmtWQAAmQAAl4QADDiVyOA0YfcJILhe2BlFZFUAFbYWImEiABEiABfxDYPw7YpULN5zhgf9w+SkECJEACJFBKCWAIEnzKboKqcvNcj1qKB51SaQFnmwHYc+bMkXisqBPNTcByeLt27ZLly5dHU0zMz4Wc8+fP17VyY15ZFBWA5bx582ThwoVRlBL7U7G+9A8//CBYu9fPCTx/+eUX82JyGU0a54vy+995nHGU++q0P9cEVbldXCHfh2sCl0oFjHV8sX6t3/8wsW4xlhesUaOGr/9ooND+9re/SdWqVX0t52+//SZNmjQRLF7v5/Trr7/qGtAZGRl+FlOVL+471kz2cwLPnx65yM8iqmxH9X7H9zJCwH273K9T7asLc5YizHExF7SbiOk4XmypVMB4YVSvXt33a+cuW7ZMF4O3Xeg+jvc9qKolS5ao8vW7nGjQoDFTv379IPn99gMNmtq1a0vNmv5+0cHrUaVKFUlPT/cbwiB5/N5ACBKWP2JPoMAENmOBjSvaNiFoC4rbZ6lUKmCfMaQ4JEACJEAC8SSgytRd14m73PG5GCrg+HBmLSRAAiRAAp4QUBNY8l0EYelUlD6MdaAC9uSBYCEkQAIkQAJxIQBFCgvYzcxWOMesI+y3RAXstztCeUiABEiABMIT0Ik4zOGsfeHzFD2SY6atpAVclAp/kwAJkAAJkIALApiAA2OBXVrA6oZ2UU08stICjgdl1kECJEACJOANAR3Pa9zJOZn25eVmS1KK/4bbUQHb30LmJAESIAESSDQBLMYAN7SLqSjzJckEbblYPSlO1+i/JkGcLpzVkAAJkAAJlEIC+5cjzDMzItpuuhhDijt7c+nSpXLJJZdI27Zt5f333w8LatiwYfLyyy+HPV7SASrgkujwGAmQAAmQgL8IwPKFEsan7WauIN8obDepd+/eMnDgQHnvvfdk6NChsm3btmKnf/fddzJy5EjZvn17sWM2O6iAbSgxDwmQAAmQgD8I7LeAdTEGjAW22cw5eclJkmPc0Nhs0tq1a6VNmzY6q92pp54qs2fPDjoNc6kPGTJE+vfvH7TfzQ8qYDe0mJcESIAESCChBPKTUkwfsLGAEQVtua3eukve+XyGnHvuubqtWLGixGvYunVr0FoDmFZ206ZNQefcdtttcv/990c1N707p3hQ9fxBAiRAAiRAAnEmYJQvgqrysu0XY2hYrZJc2v5UGT72f2GFHTNmjAwfPlyPT5w4UbCqmZN2794dtPbAl19+qSvy/f3vf5dvvvlG9u3bJ+edd560aNHCOcXqkxawFSZmIgESIAES8AUBEwWNhRjyjfVrvUFhq+s6/BX06NFDfv/9d92w2l7FihVl8+bNuojDjz/+KEceeWThyQcffLDAAoZlXKlSJc0byaIm5cYCxkoY0a57mpubq/0HsVxmzgs5C5+SGH5xI2dmZqauuFN0VZt48IwhAt8VjXWykfy+DrHvwFGg0kVALWATVJVlbwFrFLRRwm7So48+Kpdffrng/QXrFkt3Ym13REZjve9DDjlEi4N1vHPnzsLfbuoo8xbw5MmTde1gtFI6deoUMpINwF544QVda7ZBgwZyzz33CPoAiqZzzjknqF+g6PFofkcr51lnnaXyY71cZ8ND43WyldOpF1zRkkQkYdEUS5533XWXtk7r1Kkj+EMKl5588slCXuDWunVrzbpjxw657777pFGjRnoc7qlYJFs527dvHyQnntHAhBcDlkDECyMWyVbOt99+Ww477DBdNvKqq64qdOP98ssvcvzxx+vyh5deeqm672IhJ8ssDwTMmF5YwOgLtt0MFh077ALP6aefLp9++qluAwYM0DObNm2qyjewmOuuu06t4cB9tt/LtAKGDx8vJKxzO2LECIFfH+HkRRPcCzfccIO0a9dOHnjgAXnkkUcEY7sC04svviiff/554C7PvnshJ4ILLrvsMt3OOOMMWblypTRv3twzGVGQrZxOpQh0uOOOO5yfQZ+x5PnJJ5/oPbzxxhulS5cucuedd8qsWbOC6nd+zJgxQ70aXbt2FWwXX3yxHho1apQ89thjMnjwYFXKV199tXz//ffOaZ582sqJFvjXX3+tLWxHzuOOOy5IhmuvvVZb4UE7PfphKyesAijXE044QR5//HF588035T//+Y9x/eWp1YDnB/cdyhjXYRuN6tFlhC0G8oVLflxDNpys5WY/pqE0E2Hlmz5g202M9zLSVKFChUhPPeB5ZVoBT5kyRSPX+vTpIz179pTTTjtNxo0bVwwKQs0XLFig47k6d+4sKSkpQRFvUCQINfdaoTmCeCHnrbfeqkoHjYfKlSsLXtBPPPGEU4Unn7ZyOpVBKTRs2ND5WfgZa55vvfWWumEffPBBeeihh7TeUPcdB+bOnSsnnniiekduueUWHVaA/a+99pru79WrV2FgxiuvvIJDniVbOefPn6/KCg2sCy64QC1zuMGcBKWG57dKlSrOLk8/beWEtwPdPPhEw2fv3r0CyxldD2hATJo0Sbp166ZeBUSURtsl5NVF4r5D1qIJw07uvffeorv5O9EE8rGykbGAc/PtN6yEpGsIJ1r44PrLtAJes2aNXi1cc0i1atWS9evXa4tcd+z/By8IRK/NmzdPjjjiCIEbGgOwnQQXA1y8bdu2dXZ5+umVnBAKs7e89NJLqniK9rlGK7StnKhn9OjRAs8CPApIgS/bWPNcvXq1VK9eXRtSuOeo25Fdhdn/D9zMy5Ytk//973+CcX7o03FczQjCQDlI6N9BWrVqlX569Y+tnGgkIMFLg2cQwSBLlizRfU5jBo2tSIJAtJAD/GMrJ9zgaPzBk1SjRg3p0KFDYZdP/fr11ROFqNEvvvhCnn32Wb0/B6ja9WGM3Xz++edlwoQJei6s26lTp8pnn31W+HePCRVef/11mT59uuZBYwH95uvWrdO/nbFjx2pUq+vKeUJcCED/arKdhEPzOSfFRUTrSsq0AnZmLnGCUvCJwB8MoA6V0O8LKxefM2fO1CxQZnBToq8wVskLOR3Z0G+JBgdefl4nWznh/obHANZn3bp1g8SIB0/MSuPcc1QOj0aomWo2btwoJ510kiD68eOPP5ZmzZoVWrvwhCxatEhd+mgwIEFhe5ls5URD6thjj9VnEK5xKMRXX31VRYFs//znP+Xf//63l6IFlWUr55YtW/RvC33ATz31lFq9cEM7CS5oKGLcGyhFJ2jMOe7FJ7wurVq1EsxQhDrgEZozZ45Gs15zzTVaBRRu1apVteGFbin8bcPNjz5rNMQRCVtS3IAXcrKMyAnkG2sWBm2ucUHbbnk5ZugSFLHPkv8k8hAQrB+krP3RchirhT9+/PGFSuhHxR8u+rD69u2rivj222+XY445Rj788EP59ddf9TS03lGWVylaOdGoQMJLBO5CuCqhdLxOtnKCGWQCI1iXSHA/oh82Xjydew4LCH2NCMYqmmDlYgwflFnHjh01XgBKF/f5iiuu0P5ruH9xDNZlvXr1ihYR1W/wtJET/c/wJqCbAVbwUUcdpUFt7777riAoDhYxnknc/8WLF+uzGpVgRU62ldPxNKHh1a9fv0I5neIQCAlL9JlnnhG486EgvU7428XkCJAFDZOvvvpKA2Tg+t6wYYN6iPD3j75+KF4nUBFeEsxqBJ5QwPCUMfmTgHrToIFT0uw3M2RJp6/02SWVaQUMVzKS4zqE5YA+yUB3KI4juAYvDBxHQisd47/gzoXV8+233wr6ifCyRgrXZ6QHI/gnWjnxYkFy+grPP//8CKQ48Cm2coI3QvPBFMFvSHjhTps2LS48cf/gxYDF5bieQ/VFQ1khWAhKF8lRhlAUYApLCd0SN910kx7D2D8vk62ciMSEReYEBEFOyAhPA9Jzzz2nzySu96effipk7pWstnIi2BHJ8RRguB4aP3Dho0sHjVgk5zlCv7XXCQoYEfdgBIUKGZzGMlzMGEriWN64/4hrQMIxBOshVgRzADuNWq/lY3nRE3CimfP3ZYr1ZqxlV+sHRy+mVQmpVrlKaSYM3UBrF0oAlhCCKqAUkMaPH6+WD1x6sI6QBy80KC+4I9EXjCEpsDychMho/HFjXzgr2snr5jNaOZ0XmtNXePjhh7up3jqvrZywKKGAkcAc0chwBV555ZVBrvFY8cRwM/TjwR3vuJ6doKXA+457OGjQILV6oGThLkUXBPqCsR/yjRkzRr0KuJbu3bvjw7NUkpxo5EFxIKAJDRooB/xG3zYUB+RDhD+GSjgJ0e8Y6oNn2stkKyfkwaowd999t1qYP//8szz99NP6t4IRBOCL5wLWOtzqCIr0OuF5++CDD7QxDYZgArc0vAO4f1DI6J6BJwGBYGiAIcFiRrQr3gNoMKABzuRTAubZ0ako3axuZCzg/Fy7OaDjedVlWgEjIAQBGYhkRT8f+tGcibMRxAK3KP7Yjj76aP1DhGsMyvfCCy/Ulzfc1TjHSY4bM3Cfcyyaz2jldOqGAoarFGNXY5HcyOnU7/QbQ6mhoeA0FnA8VjwvuugifdkiMAneDlg0iHRGCrzvGNMHBYGXMJQMLNz//ve/mg8vbygN9AtiHDOUBvoWvUwlyYm4AycyFzIgYAhyIn4BLnG48qGMA93iqampOvYZfbBeJls5oewwPhnDtxD1DNc5XL9I6BNGIwKufYw6AGd8ep3QMEFDBSycbpizzz5brV4nSA3dS7CQnd+OZQ5PA7wI8C44CQ0aJp8R2D8VZW6msWotU16WUb7JGZa545ctybi1jDO9dCW4DBFlGfjyKekK4AZDgIhNfrjPqlWrVlJx1sfw0oQyRHCPTUqUnOiLQ3+o40I8kKxu5DxQWW6OI3q2ZcuW2kVgcx7uJSwtm+E5sIacBkFg2bCE8Kw5L/PAY+G+w635j3/8Q5VhuDyB+23lhGLB5tXzCYWDiGVHEQXKFOq7GznxjKDBVjRF8veFLqCTTz65aFG++31U73d8J1MogfbtKpgxLdQxP+1b8lroSWXeGfm0zH9/rPQ8oaW1uLOWr5NZe5Lk8fcnWZ8Tj4xl2gJ2AKI1bKN8kd+rl5tTt5vPsiinm+v3Oq+bexlK+UIeJ7DIa9kCy7OVE+5TbIlKXshpW0airpH1lgICCDo1Ec1u1vfVqShTKvru4sqFAvYddQpEAiRAAiQQGQEoX/TnuhxWlJ/t3ciVyAQvfhYVcHEm3EMCJEACJOBTAhqAZZRv3v7FR2zEhAUsqYnzHoWTkQo4HBnuJwESIAES8B2BJBPRnAeF6iYK2qXLOl4XTQUcL9KshwRIgARIIGoCas1ifd/9EyzZFKgKOznNJmtc81ABxxU3KyMBEiABEoiKgBlaqJNxuLKAzTm54Ve9ikqeKE6mAo4CHk8lARIgARKILwFnJqw8N+OAs43LWmgBx/dOsTYSIAESIIGyRcBYwGKWInQ3taQ5x8yG6LdEC9hvd4TykAAJkAAJhCdgFmLA7FH5mN/ZNpnZs2gB28JiPhIgARIgARIIQwBDkfJc9OnmGYu5QG2HKTBBu2kBJwg8qyUBEiABEoiEgHEnIyXbq698MyWtWRC44Dwf/Wt/BT4SmqKQAAmQAAmUTwL5+QV9uXlZLma2wqQd6ZyKsnw+MbxqEiABEiABTwgk6XKEphfYrFZnnVLMcoQ56Af2V6IF7K/7QWlIgARIgARKIICJOBCElWfWeLZNmLYyKaP46ly258cqHxVwrMiyXBIgARIgAc8JJBlr1kRgmbmdXVjAxmrOy/HfMoylUgFjMe0ffvhBKlSo4PnN9bJALJ6O9U///PNPL4v1vKydO3fKr7/+Kn/88YfnZXtZ4O7du2Xu3LnW69d6WbebsrCo++zZs42HzMULwk0FHuXF2sJff/21rpnsUZExKQY8f/7555iU7WWhgzv+TYZ8strLIllWCAJ5ZiWkfDHzQWfa9wHnZ+dIUkX//T2WSgWMdXNbtGghlSpVCnF7/LPrt99+k4MOOigua8pGc9VQvo0aNdKF56MpJ9bnzps3Tw499NCQC73Hum435aOR0Lx5c983EOfMmSMtW7aUJExs4OP0448/SrNmzXwtZ35+vmls/yy/PdfVxyQLRGvYcYTvZSxJwCTM6YzlCM2iDNbJPON5bsYNWxccXcZSqYCTjTuhSpUqUq9eveiuPsZnw6JEI6F+/foxrim64hcuXKhKze9yzp8/v1TImWJcZNWrV5eaNWtGd2NifDb+jmrXru17jwIaCJUrVxZw9WuCAgZPptgTyM/NlnysiORiOUKMGU6umB574VzWUCoVsMtrZHYSIAESIIGyQgANsZwILGDT5eK3RAXstztCeUiABEiABMIS0OFExiuS72I5QpyTlME+4LBQeYAESIAESIAEDkQA44B1Gko3UdDoA85xMXf0gYTw6Dg7LTwCyWJIgARIgARiTwDzQJuIPMk1LmXbLc+MHU5yE7RlLmPp0qVyySWXSNu2beX9998vdmEbN26U+++/Xzp27CgjR44sdtxmBxWwDSXmIQESIAES8AeBpGTBmsBJxgK23UzItOS7WLwBF9q7d28ZOHCgvPfeezJ06FDZtm1b0PXfcMMNctZZZ8mHH34o33zzjWzatCnouM0P9gHbUGIeEiABEiABfxAw1iwWVnAzDniv6S9euWerfP7553oNJ5100gGHM65du1batGmj+U899VQd2w+Fi5RrZPj99991qOHbb78to0ePjmhYLC1gxcl/SIAESIAESgOBfFjAWJAB0dCW2z5j/a7bvl1mzZql2969e0u81K1btwaN48eQwkALd/369YJt1KhRuh8KPQeR2S4TLWCXwJidBEiABEggcQSSzJhrycuXvCz7qSWrGUX9j6ZN5N577w0r+JgxY2T48OF6fOLEiYIZ2JyEWfgC553AuPQ84waHAsbEULCGYV2fc845zilWn7SArTAxEwmQAAmQgB8IYNITXYzBWLWIhrbajMI+0MxZPXr0UEUKZYoZ9ypWrCibN2821na+YDa2I488svDyMdFOgwYNdKph7FyzZo1Uq1at8LjtF1rAtqSYjwRIgARIIPEEks0YYFjBboYhmQhorKLkJj366KNy+eWXS6ZZdem8886Tv/3tb7J8+XKNjMZaBLB+b7vtNkE0NI7BDe02UQG7Jcb8JEACJEACCSOQn2ssYBOElZtpP643D4sxuByGdPrppws2KGBn4Z+mTZvqQkC4+NNOO003uKojXZeACjhhjxErJgESIAEScEsgKcX0nGIYUpr93M5JyanGVe0+SAqyOco3nJyRKl+URwUcjir3kwAJkAAJ+I5Afo6JgDaR0Hl77ed2xsINySmcijJhNxN9BrbLrsHlkJ6eXmx1E6yfioi3WK7K4oWcGKOGkPiMjIyY8fZCzpgJx4J9SwCRo1jP+0BWhW8vgIIlnAAsYPTnJrlZbxvTV+bYR03H6yLLfBT05MmTNaINboJOnToVm82kKOgXXnhBo98w+0lgeuONN/SlgVD1WCSv5IRsCIWP1QvOCznbt28vTZo0Kdzuuecez5HedddduhxgnTp1BMEU4RLu68knnyy1atWSq666SoMskDfbtJj79eun69AedthhAhnRMPM62cgJhYVgD/Q/YZ1hTH/nJKyRfMUVV+jynAgCWbBggXPI089o5YQwaLThbxDRpTt27PBUPqcwGzmRF5Mn4L7WqFFD77sz5ARrY3ft2lWXk8RzOmHCBKdofvqEgAZTGQs4x1jAtltetlHYboK24nStZVoB448KUWyNGzeWESNGCMZ2YUqxcGnFihVyxx13BB3ebgZvP/zww9KrV6+g/V7+8EJOR54XX3yxcLYXZ59Xn17ICSX29ddfyyGHHKIvOrzsjjvuOK9E1HI++eQTeeSRR+TGG2+ULl26yJ133qmD74tWgplurr32Wm0IYCabSZMmyeDBgzUbfj/zzDM6HR0UM8YHvvTSS0WLiOq3rZyvvvqqPPXUUzJgwADp3r27PPDAA4Jzka6++modOvH000/rUIgLLrggogkBSroQL+T87bff5NJLL5WPPvqopKqiOmYrJyJYIcsJJ5wgjz/+uLz55pvyn//8R+tGowtDTtDQhhcMQ1MwBpTJRwTMmN58THphPq03LMbgw+UIy7QCnjJlis5S0qdPH+nZs6dGrI0bNy7sk4SXccOGDYOOwxJ+8skn9UUddMDDH17ICXHQgOjfv79aSR6KV1iUF3LOnz9fFcRll10mUBb33XefhvUXVuLBl7feekvSjHvqwQcflIceekhLDHXfMbcruhXOOOMMlQED7Tds2KD5MQ4QwwzAE4oP5U2bNs0D6f4qwlZOzLiDbg80VmBBIkFOsJwzZ44+23h20VBYsmSJTJ8+/a9KPPgWrZwQ4fbbb5fVq1e7nqjAjfi2csLLhe4ofKKBhlmRYDkjgXWzZs2U84knniiYEQnekHgk1A2PDBqGSPBmfPfddzrXMJ7T8ePH6z1HA7Y8J12OMMUEVZnIZtsN80Anx7BLLtL7UaYVMAZHI9WuXVs/4WbEQ45+qKIJFg9avrAukJz+4uOPP17QendefEXP8+K3F3JCjuuuu04nB8fqHbFIXsg5d+5cFQ0TmUNODG6H0vAy4UWPgfJQWrjnuJeO7IH1oO6+ffsKGmh46eI8x7179tlnC2REgrsSL+FTTjkl8PSov9vKCTlatWolRx99tLrLMS8tFEejRo3USlu1apXKsnPnTv1cuXJl1LIFFhCtnCjr7rvvVs/HwQcfHFi0p99t5cRYTsxk1K5dO3VBd+jQobBr6oknnlClB/f0kCFD9H0AN3WsExoB8NYddNBB+tzhnQP3Nyx0NP7wnKIx8OmnnxY+l7GWya/lJ5m/67wILGCsnOS3VKYVsLN6BR5gJHwiQGnXrl1B9wEvLFg6sJbq1q0bdAwvPfQjxjJ5ISfcozNmzFBrPVayeiFnsgmGOPbYY1VOWJh4acLF6mVCt4Fzz1EuFDH2FU1QqrA20CBDHrwE//zzz6BsM2fOlJtuukkV9PXXXx90LNoftnKivxTsHTlh/WLwPxoZ6Kd87rnntIvk5ZdfVpG87l+NVk4I9a9//atYUGO0/Iqebyvnli1b9B0AJQvXPixKuKGR0EBHn7uTMCtSJHP8Oufbfn788cfqFj/zzDPl1ltvlXfeeUdPRf8+JoH4+eef9R4jFgD3vTwnVb6Igt6Xbb/lsA847s8MrB8k5w8Kbhy8mKtWrRokC9xjUMw4/r///U+PoT8QLr54pGjlxATjuIZjjjlG3VUIJEF69tln9Zq8uoZo5QRP9FnC04CXDCy7o446Spf78kpGlAM5nXsOpYUXaKhGFKwJvOjg9lu8eLF2UcAaRrAQEl6+WOsTfYGwRhA85GWylROWGBQuZIRbEuuUPvbYYyoK1iGFRQymcOsjBc5Zqzui/McLOaMUwep0Wzkdjxga3OjzdZ5BPCdobF144YXKGmxff/11VdBWAkSRqX79+rJu3TotAY1CJ4jSeeZgheOZhvHgNISjqK5Un4oJNeBSxjhg2820/oy7Oj5dCW7glulxwJirE8lx0cHaQh+v4152QOE4Ai3wx+ikV155RdAH1LJlS2dXzD6jlRORsbB6vv32W90cQW+55Ra58sorPRuOFK2c4AlvAyJ3EeyG+4CXSiRzqDrXGOoTLzO46xA0BmsHqWjfPvYtXLgQHxolj8/DDz9cpk6dWughgeUBqxiTrAfOA4u8XiRbOaF04ZlxLB+szIJnFo0LPLcIwEKfNdyVSF67eaOV0wtWNmXYyomgTCTHU4DhelC+YAoFB5ZI+LtCct4f+iNG/6ARBWWPYE/8jcArhIahk9BHjX5+NDKc58A5Vt4+8/PMlJLm3YEIaNuUl20UtlHcfktlWgHDPQdrFxHQeFnNnj27UMkioAEPOdyg+HQiHZEH0bOIoo1lv2/ggxCtnFCy6Mdy0rBhw9SqhFVU1Np38kTyGa2c4PnBBx9oVDK8DXiRwKobNGhQJOKEPQf1jB07VgO8HNfzJZdcovkD77uz1icsoR49eqhs6PsFMww7gqUJ6whlYcMLGRayV6kkOdF4AiMECkFOxCjAXQq3Oaxh9AGjAYNPjE3HCxqRvGgoIG7ByxStnF7KUlJZtnKirxXuevRLX3zxxereRSMGihnWMYK5MDQN3TpQzrCIY53QNYN7jHvujN9Ht5iT0DWC42hQoj+4XCfDSi1g45myTvvHDlvnj1PGMq2AEWjx/PPPa6sSL1j0PToPNQJ/4GZG4Ar6eZ3kuHcwTCaU29LJ5+VntHLCMnWsU8jlyI3r9TJFKyfkQqQuonRhrcHagIsX7nMv00UXXaTDdRBQAyXVu3dv9WagjsD7jkYLhqVh6Bb64BCUg6hsJLyEkRAMgw0JDRAvFXBJcqI/H9Y3EobBwaJHQwV91bCEOnfurNeGYzfffLMG8MCCD7Sa9GQP/olWTg9EsCrCVk5EvaOBBTc++n/RLdKtWzdliy6JgQMHKl+njzgeQVjOBTrK1/ntfMITB8WL7hEYB+U6GfezMYHNTFj24/LzTcQ0zvFbMksr7u/w8ptkJcizaNEijV607euCewktR9v8JVTt6hAUDSJVEWFrkxIlJ9yucLs5rrkDyeqFnGjpY3Pjfv7iiy+0SwCuRpsEFyMsiypVqhwwOxpeXr1oMVzrH//4h04EcsCKTQZbOZ2JQJz+Qads/AnDKnb7fKMPHI0O9HHbpGjltKkjVB6M34dnAo0Pm2QrJ54/PMtoWBZNbp8H3AN0AXkdKV9ULi9+N+w4wotiYl7Gmk9uDlnHa0Mfkm+fGyWd6tm9B1DILzt3yKJ6dWT0zG9DlpmonWXaAnagwkXn9uXknBvPz/IkJ1r64Vr7XjF3o9y9Ur6RyG4rZ1HF69QFKz8ez3e0cjryxvrTVs6SnsFEPg+x5lP6y8dqSPmSl2UfVJWHqSv9ZwBzMYbS/zDyCkiABEig/BDIz8MoBRPVrK5ou+vGOQzCsmPFXCRAAiRAAiQQkkASgrBMUG2yGYZkm5Ky9pnJO0z0tM9SuXBB+4w5xSEBEiABEoiQACxfY89KtothSLlm0g7MoOW3RAXstztCeUiABEiABMIS0OUI813O7ZwNCxiR0P5KVMD+uh+UhgRIgARIoAQC+TkFw5By9tgPQ0LAVrIPlyOkAi7hRvMQCZAACZCAvwgkpRZMRZmcXjDHv410SdlmAYfsv+b4tjknHnmogONBmXWQAAmQAAl4QgCTaiAQK9dFHzCWLXQTtOWJoBaFUAFbQGIWEiABEiABfxBIMvM65Jv+XFdBVWYQcE6m/dzR8bpSKuB4kWY9JEACJEACURMoWAs4VXL32QdVod84pUKFqOv2ugAqYK+JsjwSIAESIIGYEUhKSTWzYBkLuKIL9ZUNl3XB3OoxEyyCgl1cQQSl8xQSIAESIAES8JCALqxgxvTmZLqYitJMwoG1g/2WqID9dkcoDwmQAAmQQFgCGgUNhVrJhULNNeOAXcwdHbZyjw9QAXsMlMWRAAmQAAnEjoBOKZnkMgoaCpszYcXuprBkEiABEiCBsk9A54LG6kZuLOA8YwGbc/yWSqUFnJWVJd9//731OqaJgr5nzx7Zvn27LF68OFEiWNULOefNmycLFy60yp+oTJBzzpw5kpZmPwA/EbJCzpkzZwqWl/Rzwnq4X331la6Z7Gc5s7OzBWtBY9lFPyfwxPPp55RnFjF4/LpW0n/0L34Ws0TZVJGaZyHXxUxY+bkmaKvEUhNz0N9viDBM8AI++uijrRZaD1NEXHZDqWHx+His1RrNBc2dO1eaNGkitWrViqaYmJ+Ll9vhhx8uVatWjXld0VTwww8/6PMZbv3eaMr28tzZs2fLcccd53vFhsYM5PR7gpwtWrTwtZhYRzc7e56EW+ze18IXCpdk5nU201FWcLG4Qm6SWUEJyxj6K5VKBYyWcMWKFaV27dr+ollEGlhAeAn7Xc4U0zdSGuRMNrPflBY5K1WqJDVr1izyRPjrJ/6OsHh9erqLYJYEXALkrFGjhuA59WuCYoOcuO9+TvAmQM5SnSC+4Y2hSLYJlr9ZQslVWrp0qdxxxx2yefNmueWWW6Rz585B5y9YsEAeeughgcere/fucvHFFwcdt/lRKhWwzYUxDwmQAAmQQBkkkJtvdGmyUcD2GhUNJEl214Dr3bu3DBs2TL2DZ555prRr104bgg7RwYMHy913363eruOPP17OOeccNRCc4zafVMA2lJiHBEiABEjAFwQ0mtkoYTdzO+/KyZQ5a5bLwIED9Rr69u0rdevWLfF61q5dK23atNE8p556qqDL5qyzzio8B14ZdDvAowDPQiQxH1TAhTj5hQRIgARIwO8E8kxAVT6Ungl6s00pkiuNatSVc889V0+pUqVKiadu3bo1yJpFd9KmTZuCzoFLGq7pMWPGyHnnnRdRMGNyUIn8QQIkQAIkQAI+JqCLMZg+3eQKGdZbRmq61M6oJKeccopuiCEqmqBImzdvrtuWLVu0b9fJs3v37qBg2hyzGMTNN98sv/zyi1rGy5cvl2nTpjnZrT9pAVujYkYSIAESIIFEE8BiDPkpybIvM9NalBzJlgOtH9yjRw/B5iQoaQRgYXTIjz/+KLfeeqtzSD8POuigwsBABN+FUupBJ4T4QQUcAgp3kQAJkAAJ+JNAshldgrHAB1Ko/9/emcdIVWxhvHrY3nsmKoFAFA3igooishjRuGA08b1oRAMKbjEQcdcYUfSRyCIat6ghbriDcRcX4r4bFY1BEjVgZHFB0KD+I0IAZ6apd77zUm3PTC/nTt/uKbhfJT3dc5eqU79zb51aTlW1kT7fIrsnNbc5VO2f2267zZ199tluqxh6dDEPGDDAoaU7btw4h6mGl19+uTvrrLN0DBjTTUePHl0tyg7naYA7IOEBEiABEiCBWAnkpQXsundzzQnWds7LGHBTr16JsnTsscc6fGCAw5z+gQMHqvFFRGeeeaabOHGiwwIs4XyiBORiGuCkxHg9CZAACZBAlxHAGHC+RVa2EiNsDrIQR+vWzm1HWMm4wgO60vlq8tEAVyPE8yRAAiRAAtEQwBiw69ndtSRYiKPVbXPdSjhedXWmaIC7WgNMnwRIgARIwExAx4DF+OZ6JlgTvqVZWsB2py2zMDVeSANcI0DeTgIkQAIk0DgC2gLu0U1awC3mRPPSAm6SPQRiCzTAsWmE8pAACZAACZQn0E28oGV/X8wDNgdpAavzlvmGxlxIA9wYzkyFBEiABEggBQK6EpZszPHXVvtKWGgBy1JVKaSebhSZMcBht5J08aUfWxI54R6PnWywS1BxgFs81iWt5+4xachZLHPMv/My5xAr3/RKOI0h5jxRNhLYXgnkZBEO7G6US9QCbtF7Ystz25I7NulSkOftt992++yzj24Tdtppp7k//vijZKwPPPCA7nqx2267uenTpzusBYrQ3NzsrrrqKof5X1imbNasWXo87T9WOUO6kBcrr7zwwgvhkH4/8cQT6haPZdXqEWqV888//3QzZ850e+yxh/Kul5zXXXedbgfYt29fhwn15cKdd96pcmA/ZHwOOeSQDpd2ZpeTDpGUOWCV89lnn3X77ruv7sZy3nnn6TJ53333XRvZQx7uvffeMql1/rBFzkrvyooVKzrIunjx4s4LVOZOi5xYUD+wCt9YaAEBSwtiZxusFYw5ntiDuh7BImclnsuWLXPjx4/X52HMmDHumWeeqYeYdYtTtwfsZOxeKsSy+oVrkQaI9ZOX9aOjDNKS2e6CvMz+119/rSq3rN/ppQD2MpnaP/zww15ai37q1Kkd7pNVTbCvlZeCzYth89Ki9FdffbVeN2/ePD13zz33+BtvvFF/v/LKKx3iKHXgo48+8rKnZKlTbY5Z5Qw3yWosXjalV1mee+45PSwVC3/zzTd7Mcp6HPm1hg8++MD/9NNPVS9PQ85bbrlFZQTnk046SWWVXUaqpo0L3n33XS87lFS9FvqBPmWrMH/RRRfpb9m1pOR9UinzUunS5wLPxvXXX9/mOsiJuPCxBqmkeFlLturlVjmXLFmi6cuqO/6hhx7y0ruhuv7tt9/8tGnTCp/DDz/c9+jRw3/44YdV08YFb7zxhpfekqrXWuWs9K489dRTmocLLrigwHrVqlVV08YFr732mpceiKrXWuWUSleBmezjqnJdeeWVUq7n/X777ecPOugg/+STT/ohQ4Z4qbx72emmatpiUFTOqhfKBVY5K/EUo+sHDRrkH3/8cS+VBy3bLO+wGHX/2WefWcSs6zUnnHBCp+O/d/os/59cbz+nxyDzZ3Kuvx8/ZGSn06zXjfZSpV4SdCJeqwF++eWX9eVauHChpnL88cf73XffvUOKePFkc2UPA/P7779rISZrgup1s2fP9tKV61HYLV++XON75JFHOsRR6oDVAFvlDGnI3pReWuMqSzDAkEm21/KyPJoer4cBTkPOAw880B933HGala+//lplhZG0BKsBluXjVIcotGUtV01Dth8rmQQKMRjhTz75xKNiUxxCRSewLj5X6bfVAFvlPP/882Xzl5zfuHGjR0WrlEHYsmWLl14FL/uXVhKtzTmrAbbKWeldufbaa9VIfPXVVx4VCrxz1mA1wFY5i9O9+OKL/ciRIwvyoIIXDJm0lP0uu+xiMv5JDLBVznI8ZehJK2J4HxCk9avPuPSSFGet5G+rAf7mm2/8008/7VFJ+uGHH/zHH3/s33vvvYLuUGl/8803lZvsEuS//PJLTQ9lM/ihPAVLxIH3HAFyQ9b333/f12qA/y0GeHbTQPNnkhrgUSpHTH926C7oX375RRouzvXp00e/sai2tJw7jAVgDPWAAw5w8qC4wYMHO3RDh30jxTi4oUOH6qbLRx55pMO+kKeffrrGl9Yfq5xIT1pAujC4vJyaPFZiQUC3mbw0Dt3s9QppyInhgJ9//llFFIOi3+vWrUtVZMQvBaeOgUPnYBRkL04I3eE//vije+mll1Sve++9tyvuEp8yZYru/4kuvnoEq5xYf3annXYqbAguhVeHoZT77rtPl8yTVnzqolrlrPSuSAGtwznDhg1zhx12mDv11FO7TM6QsPROOamouptuuqngR4E1fffcc08HOcXguLvvvjt1X4paecIXQSplThoUmhUMO8HnQ3pAQtZq+l69erXDfrl4d84991wnlVN3++23Kw8xXu7WW2/VrnlsVDB58mSHoRAMkSC88847ulTj66+/7i655BLdcxdxwC9Fehn0uZUWuG5y0Fkht/ltzjfJylbbxDfD+MnLPa57fOYuPok6q5US94XxXumW07P4hkPNpk2bSlztdNwX47wY/8VGywgopBEPxizg1CQtYSet5JL3d/agVc61a9c66RrXAqP9ZtIHH3yww3hnPUMacmIPzZUrV+o6qjBwCGCcZtiwYYMLOke80BuOtQ/Q4xFHHOGkt8NJt6CT1rCTbny9DAUzCh6MEdcrWOXE1mh4ZjEGfNdddznpWXHSpVsQCxUZyH3GGWeoU17hREo/rHJWelf69eun6+qisjNhwgTljQpvmsEqZ0gTvgionKNCUxw2b97sYIjxDEl3vm62Xny+1t9WOSvxDDLMmDHDvfrqq05a8uqnEo7X8g0jes4552jlU1rrGhXKTZQ9qDxJ61f9YrARAcpDOIOWCmiooJIgvV5ahmI8/cILL3QybKIVylL3WI7l5H1GRaCbOGFZP02ybKXPixGOLOzQBhg1OAQ4MyCgFoaXSsZP9f/2f7BZM2pnqEmiBoiH7oYbblCDi1qhdKs41JpRG0wzWOVE6wYyIR8oyBCkG9FJ13ia4pSNKw058WJfc801KvPJJ5+sBgOFc5oBcgado+IED+ZSlRO0xuEI9NhjjznIgsIGlQNUvsD60EMPdYsWLXJweEFAawjs0wpWOUMPDlpqaEXI2GQb5zsUwNgsHGzrEaxyVnpXZKxSjRlavjAaCDI0lKq4VjmRKIwGHJewoH772QLYWu6tt95yc+fOdY8++qjKnaagVjkr8YQ86PWYM2eOGrk77rgjNRHRGyjdyBpfce9U2G4PLfDwHqxfv14dXIMRhkEOAb1QCGGWBnqicJ8Ml5Q12uHeSt/S7+18DtsR/mX+tMi8YawhHVuIT6IUCaErGSE8ROj6kTFg7ZIsTkbGpJw4XahhwJZTqP2iewUPE4wuWpvhYerdu3chvuI4avltlRP5kHFqLYRDeiggsA2WOI6EQ3X7TkPOXXfdVbvw4ZmMrlV877XXXqnKDP2hFwMtGbQeEaD39gGVKhnXdqeccooOPQSjjUoOWh+ffvqpfsJ9V1xxhXbJpTUdySonukQRQk8B0kelIgR076JQR2u+HsEqZ7l3BbI++OCDqgMY4FB4w9ClGaxyIk1UWiGXOAIWREBPArpX0cobO3asDkXhJPIlfheF62r9YZWzHE+kj+5xbIcn49daGS/u8alVvmOOOcbJuLsThzl9R5FGcbj00ku1CxxGV5zYtEKISis8u1FGoeJaKuA8PLfRywgv886GHBbikK5ntH6toUl0rStoWW9o1HUxDUhbZbE6YUm3nXoLy/ionz9/vno3y5QiTQbOS9Li9TIGqJ7KcLSS7lEPZyY4XshYsF4n3aTq4ADPSXk59feCBQtMolqdsKxyfvvtt142htbP/fffr7JAJjiOhQAHB3l21Os7HKv2DYeK4HhS6do05JSxddVD8N6ErMFJo1LaOGd1woLTHeKFJ3vQX/D8LNa71N697GTi4dQmPQpeppqpcxscVQJnfMv+nxoffkuhXU1Mb3XCqiQnnOngMYwQdIrnFV7ZeFalNV6Q48QTT/R4xpMGqxOWVc7AutS7AocmzEh48cUX1ektid6tTlhWOcEJToqQob3jHTzJ4ZgHL2ip2OqzKl2nVdEmccKyylmJpwxHqPxSefRiEPUDR8JqAc92eBfKXYuyQHpV9DSew1L5R36lIlWIAv+LQS78X+4H0q81zP3vDH989z5+qhtg/kxwff244aNrTTr1+3doL2jQwoskDiz6sI4YMcKLM45CxJQdvIAy70//l7E1Ly0x9Z5FgRuMArxopdamcey8884eHqnwRrUEqwFGXFY5Q7rwSIT8zz//fDik36GwrocXNBKoVU7pVdCCDV690gJqY0jaZKTEP1YDDC9hTC9BGphShgIqhPZ6x1SPUJhB/59//nm4tPAt41bKunCgyg+rAa4k5/Dhw710BRZSkrnp+mziWcYziOcyhP79+3uZsxr+NX9bDbBVzkrvCgpxTAeEPqB36fkwy2k1wFY5kfBll12mXtnS29FGDun18EcffbQ+OygvxMGpzfly/yQxwFY5y/EM09Lw/hd/8CxXCxYDjDhkvQM/adIkj+mXsYW7Z8zxY3K7+Gk9B5k/E7v182OHjIgtKz4HiUSJ21XAOB26Mq1jh+hqQlek5Xp084mh7cAjjHEk2ftRXPd1wQk491hCEjkt8VmvgaMJxkNDV2e1+9KQUwoX1WH78bdKaaPbDV3t6MKzBOgS40+W7i6MoZYaJ7ak0/4aOLGMGjVKFwJpf67U/1Y50XUL9vCITiPINBL1rMZqapZglbPSu4KhAfhgwGvXGuBRK638DmO15e63ylnufhxHHKXKgXL3oBiFP0ZY0KPcdcXHrXJW4lkcn+W3GH+dRYFhq+01zJ0+0y264z43rNneBb3O/eX8iMFu4dLFUWXb/hZEJXYyYfCyW4wvYi330iUxvMmk+/vqJHL+fVfjf6UhZ3Asqqf05XRZKs20jG+puKsds8qJsd+0xp+ryVTqvFXOSu8KfCjqHaxyVpIjjTgqxY9z1jQq8ayWxo54vhtms8hOSLnuCXwIWnOuefOW6HBkwgBHR50CkQAJkAAJdIpAXlrx2Au4pVmWpDQG8Zt2Pf71T+PVjbuMBrhxrJkSCZAACZBAjQRCC7ipp92TOtfa7JrLzFeuUZyabqcBrgkfbyYBEiABEmgkgVa0gGVhDXEoMycrk5AcDHdsgQY4No1QHhIgARIggbIEsKAG5uonmQecE6Pd2vL33PmykTf4BA1wg4EzORIgARIggc4T0AU1ZDlKrIRlDWgBYx/h2AINcGwaoTwkQAIkQAJlCWAt6G3aArZNn0NEaAHLXO2ycXbVCRrgriLPdEmABEiABBITwFrQMsFfnKrsLeC8k7WgI1zxggY4sfp5AwmQAAmQQFcR8LIDq7ZmE2wv6PM5mYgUnwWmAe6qp4jpkgAJkAAJdIqAGuEEd8raou7/O6cnuKkBl9IANwAykyABEiABEkiHgC6eLAa1VbYYtIa8OGH1TLD8qTXeWq+jAa6VIO8nARIgARJoGIEmOGGJFU4yDalbfps6bjVMSGNCNMBGULyMBEiABEig6wlgDrBs6u62JnDCanat7p894jN38U2M6nr9UgISIAESIIFICaAFnN8mazv/o5f50yQrZ7XKBg61Bqy+hZ25sKtXGiG+KkEauWIcJEACJEACOyQBtICxqEaSFnCLTEPq1cs+b7gUuFWrVjnZi9vJHvKpzSneLg0wtmRbtmyZ7itaClQsxzZt2uS+//57h71mYw5btmxxq1evduvXr49ZTIf9cFesWOHWrl0btZzYc3X58uVdunWgBRAKsqVLlybam9cSb9rXYK/dL774Qvd2TjvuNOPD1JglS5akGWXqcW2H2793YDBk2FC3Mr/RbUiwG+EW2YzBr1ntxowZo/EtWLDADRw4sEPclQ6gfJw3b56bMmVKpcsSncuJQuKbHJUoC7yYBEiABEiABBpD4KijjnKLFi1yaexpzjHgxuiMqZAACZAACWxHBObPn+/2339//axZs6YukrMFXBesjJQESIAESGBHJMAW8I6oVeaJBEiABEggUwTYAs6UuplZEiABEiCBWAhwDDgWTVAOEiABEiCBTBGgAc6UuplZEiABEiCBWAjQAMeiCcpBAiRAAiSQKQI0wJlSNzNLAiRAAiQQCwEa4Fg0QTlIgARIgAQyRYAGOFPqZmZJgARIgARiIUADHIsmKAcJkAAJkECmCNAAZ0rdzCwJkAAJkEAsBGiAY9EE5SABEiABEsgUARrgTKmbmSUBEiABEoiFAA1wLJqgHCRAAiRAApkiQAOcKXUzsyRAAiRAArEQoAGORROUgwRIgARIIFMEaIAzpW5mlgRIgARIIBYCNMCxaIJykAAJkAAJZIoADXCm1M3MkgAJkAAJxEKABjgWTVAOEiABEiCBTBGgAc6UuplZEiABEiCBWAjQAMeiCcpBAiRAAiSQKQI0wJlSNzNLAiRAAiQQCwESY3nLAAABs0lEQVQa4Fg0QTlIgARIgAQyRYAGOFPqZmZJgARIgARiIUADHIsmKAcJkAAJkECmCNAAZ0rdzCwJkAAJkEAsBGiAY9EE5SABEiABEsgUARrgTKmbmSUBEiABEoiFAA1wLJqgHCRAAiRAApkiQAOcKXUzsyRAAiRAArEQoAGORROUgwRIgARIIFMEaIAzpW5mlgRIgARIIBYCNMCxaIJykAAJkAAJZIoADXCm1M3MkgAJkAAJxEKABjgWTVAOEiABEiCBTBGgAc6UuplZEiABEiCBWAjQAMeiCcpBAiRAAiSQKQI0wJlSNzNLAiRAAiQQCwEa4Fg0QTlIgARIgAQyRYAGOFPqZmZJgARIgARiIUADHIsmKAcJkAAJkECmCNAAZ0rdzCwJkAAJkEAsBGiAY9EE5SABEiABEsgUARrgTKmbmSUBEiABEoiFAA1wLJqgHCRAAiRAApkiQAOcKXUzsyRAAiRAArEQoAGORROUgwRIgARIIFMEaIAzpW5mlgRIgARIIBYCNMCxaIJykAAJkAAJZIoADXCm1M3MkgAJkAAJxEKABjgWTVAOEiABEiCBTBH4H/zxGenQ/NLwAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "library(sna)\n",
    "library(corrplot)\n",
    "graphs2 = list(interaction_lunch_adj, interaction_work_adj, interaction_adj, info_adj, prob_adj, support_adj, socialnorm_adj, org_adj, ground_adj)\n",
    "graphs2[is.na(graphs2)] <- 0\n",
    "gcors2 <- gcor(graphs2)\n",
    "rownames(gcors2) <- c('lunch','work','all','info','prob','support','social','org','ground')\n",
    "colnames(gcors2) <- c('lunch','work','all','info','prob','support','social','org','ground')\n",
    "#png(width=1800, height=1800, res=300, file='cor2.png')\n",
    "corrplot.mixed(gcors2, lower='number', lower.col='black', upper='shade', tl.col='black', tl.cex=.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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