8.-METRICAS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8- MÉTRICAS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " La rápida expansión de NILM y el desarrollo de diferentes algoritmos, han hecho que sea esencial proporcionar una evaluación de rendimiento mediante el uso de métricas de desempeño. Las métricas de evaluación, comparan los resultados de la desagregaciónn (predicciones) de los modelos entrenados con los datos del set de validación (mediciones reales de cada proceso). NILMTK cuenta con el cálculo de métricas de evaluación mediante el uso del MeterGroup para la validación de los resultados mediante el set de validación \n",
    " Vamos ahora analizar en nuestro dataset diferentes metricas como son FEAC,F1, EAE ,MNEAP y RMSE  que  resumimos a continuacion en el siguinte cuadro:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![metricas](metricas.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En primer lugar cargamos las ibrerías y realizamos otros ajustes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "import warnings\n",
    "warnings. filterwarnings(\"ignore\")\n",
    "plt.style.use('bmh')\n",
    "\n",
    "from nilmtk import TimeFrame, MeterGroup, HDFDataStore, DataSet\n",
    "from nilmtk.metrics import fraction_energy_assigned_correctly, mean_normalized_error_power\n",
    "from nilmtk.metrics import rms_error_power, f1_score, error_in_assigned_energy\n",
    "\n",
    "data_dir='./data/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Métricas individuales del modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Librerías\n",
    "from nilmtk.metrics import f1_score\n",
    "from nilmtk.metrics import error_in_assigned_energy\n",
    "from nilmtk.metrics import mean_normalized_error_power\n",
    "from nilmtk.metrics import rms_error_power\n",
    "from nilmtk.metrics import fraction_energy_assigned_correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medidor de cabecera para desagregación:\n",
      "ElecMeter(instance=1, building=1, dataset='DSUAL', site_meter, appliances=[])\n",
      "\n",
      "Dispositivos para validación de entrenamiento:\n",
      "MeterGroup(meters=\n",
      "  ElecMeter(instance=2, building=1, dataset='DSUAL', appliances=[Appliance(type='fryer', instance=1)])\n",
      "  ElecMeter(instance=3, building=1, dataset='DSUAL', appliances=[Appliance(type='LED lamp', instance=1)])\n",
      "  ElecMeter(instance=4, building=1, dataset='DSUAL', appliances=[Appliance(type='incandescent lamp', instance=1)])\n",
      "  ElecMeter(instance=5, building=1, dataset='DSUAL', appliances=[Appliance(type='laptop computer', instance=1)])\n",
      "  ElecMeter(instance=6, building=1, dataset='DSUAL', appliances=[Appliance(type='fan', instance=1)])\n",
      ")\n",
      "\n",
      "\n",
      "Done!\n"
     ]
    },
    {
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       "    }\n",
       "\n",
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       "        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>Fryer</th>\n",
       "      <th>LED Lamp</th>\n",
       "      <th>Incandescent lamp</th>\n",
       "      <th>Laptop Computer</th>\n",
       "      <th>Fan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.420</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.453</td>\n",
       "      <td>0.741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EAE</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNEAP</th>\n",
       "      <td>1.138</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.484</td>\n",
       "      <td>1.150</td>\n",
       "      <td>0.502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>17.417</td>\n",
       "      <td>7.339</td>\n",
       "      <td>22.688</td>\n",
       "      <td>13.816</td>\n",
       "      <td>12.651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fryer  LED Lamp  Incandescent lamp  Laptop Computer     Fan\n",
       "F1      0.420     0.789              0.756            0.453   0.741\n",
       "EAE     0.002     0.001              0.011            0.002   0.012\n",
       "MNEAP   1.138     0.349              0.484            1.150   0.502\n",
       "RMSE   17.417     7.339             22.688           13.816  12.651"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "labels = pd.DataFrame({'Instances': list(range(1,7)),\n",
    "                       'Meters': ['Main', 'Fryer', 'LED Lamp',  'Incandescent lamp','Laptop Computer', 'Fan']})\n",
    "\n",
    "\n",
    "\n",
    " \n",
    "\n",
    "#Set para validación\n",
    "valid = DataSet('ualm2.h5')\n",
    "valid.set_window(start=\"2023-02-24 17:40:01\", end=\"2023-02-24 18:51:27\")\n",
    "\n",
    "valid_elec = valid.buildings[1].elec\n",
    "\n",
    "print('Medidor de cabecera para desagregación:')\n",
    "valid_Main = valid_elec.mains()\n",
    "print(valid_Main)\n",
    "\n",
    "print('\\nDispositivos para validación de entrenamiento:')\n",
    "valid_Devices = valid_elec.select(instance=[2,3,4,5,6])\n",
    "print(valid_Devices)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#Datos de Dispositivos: predicción\n",
    "# Se carga los resultados del mejor modelo\n",
    "#dataset=DataSet(data_dir+'NILM/Results/CO_30s_first_pred.h5')\n",
    "#predictions=dataset.buildings[1].elec.submeters()   \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "index = list(labels['Meters'][1:6])\n",
    "met = []\n",
    "e = []\n",
    "\n",
    "\n",
    "\n",
    "dataset=DataSet(data_dir+'NILM/Results/CO_60s_median_pred.h5')\n",
    "\n",
    "\n",
    "predictions=dataset.buildings[1].elec.submeters()      \n",
    "\n",
    "#Metricas\n",
    "f1 = {}; eae = {}; feac = {}; mneap = {}; rmse = {};\n",
    "\n",
    "f1['F1'] = f1_score(predictions, valid_Devices)\n",
    "\n",
    "EAE = error_in_assigned_energy(predictions, valid_Devices)\n",
    "\n",
    "for x in range(len(EAE)): e.append(EAE.iloc[x][0])\n",
    "eae['EAE'] = e\n",
    "#da error esta medida AttributeError: 'tuple' object has no attribute 'instance'\n",
    "#feac['FEAC'] = np.ones(6)*(fraction_energy_assigned_correctly(predictions, valid_Devices))\n",
    "#feac['FEAC'] = np.ones(6)*(fraction_energy_assigned_correctly(predictions, valid_Devices))\n",
    "mneap['MNEAP'] = mean_normalized_error_power(predictions, valid_Devices)\n",
    "rmse['RMSE'] = rms_error_power(predictions, valid_Devices)\n",
    "met.append(pd.concat([pd.DataFrame(feac, index=valid_Devices.instance()),\n",
    "                      pd.DataFrame(f1),\n",
    "                      pd.DataFrame(eae, index=valid_Devices.instance()),\n",
    "                      pd.DataFrame(mneap),\n",
    "                      pd.DataFrame(rmse)],\n",
    "                     axis=1).set_index(pd.Index(index)))\n",
    "\n",
    "dataset.store.close()       \n",
    "print('\\n\\nDone!')\n",
    "metrics=met[0].transpose().round(3)\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Carga de datos de predicciones y GT en memoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "gt=valid_elec.dataframe_of_meters(ac_type='active', sample_period=30, resample_kwargs={'how':'first'})\n",
    "\n",
    "\n",
    "labels = pd.DataFrame({'Instances': list(range(1,7)),'Meters': ['Main', 'Fryer', 'LED Lamp',  'Incandescent lamp','Laptop Computer','Fan']})\n",
    "                       \n",
    "Meters={}\n",
    "for x in range(len(labels)): Meters[valid_elec[x+1]]=labels['Meters'][x]\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "#resultados de predicción modelo CO 30s\n",
    "\n",
    "CO_60s_median=DataSet(data_dir+'NILM/Results/CO_60s_median_pred.h5')\n",
    "elec_CO_60s_median=CO_60s_median.buildings[1].elec\n",
    "\n",
    "#resultados de predicción modelo FHMM 30s\n",
    "\n",
    "FHMM_60s_median=DataSet(data_dir+'NILM/Results/FHMM_60s_median_pred.h5')\n",
    "elec_FHMM_60s_median=FHMM_60s_median.buildings[1].elec    \n",
    "    \n",
    "    \n",
    "\n",
    "gt.columns=list(Meters.values())\n",
    "pred_CO=elec_CO_60s_median.dataframe_of_meters(ac_type='active')\n",
    "\n",
    "pred_CO.columns=[list(Meters.values())[0]]+list(Meters.values())[1:6]\n",
    "\n",
    "pred_FHMM=elec_FHMM_60s_median.dataframe_of_meters(ac_type='active')\n",
    "\n",
    "pred_FHMM.columns=[list(Meters.values())[0]]+list(Meters.values())[1:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análisis particular del cálculo de cada métrica\n",
    "\n",
    "Metrica FEAC ( da problema con AttributeError: 'tuple' object has no attribute 'instance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Carga de datos de predicciones y GT en memoria\n",
    "#gt=valid_elec.dataframe_of_meters(ac_type='active', sample_period=60, resample_kwargs={'how':'first'})\n",
    "#gt.columns=list(Meters.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predictions_Devices=elec_CO_60s_first.submeters()\n",
    "\n",
    "#frac_1 = valid_Devices.fraction_per_meter()\n",
    "#frac_2 = predictions_Devices.fraction_per_meter()\n",
    "#fig, (ax1, ax2) = plt.subplots(1, 2 ,gridspec_kw={'hspace': 0.1, 'wspace': 0.1})\n",
    "\n",
    "#frac_1.plot(autopct=lambda p:'{:.0f}%'.format(p), kind='pie',labels=list(Meters.values())[1:6], figsize=(8,8), ax=ax1,   colors=['tab:blue','tab:orange','tab:green','tab:olive','tab:purple','tab:cyan'])\n",
    "#frac_2.plot(autopct=lambda p:'{:.0f}%'.format(p), kind='pie',  labels=list(Meters.values())[1:6], figsize=(8,8), ax=ax2,  colors=['tab:blue','tab:orange','tab:green','tab:olive','tab:purple','tab:cyan']) \n",
    "\n",
    "#ax1.set(ylabel=\"\")\n",
    "#ax1.set(xlabel=\"GT\")\n",
    "#ax2.set(ylabel=\"\")\n",
    "#ax2.set(xlabel=\"Predictions\")\n",
    "#plt.savefig('./data/images/5.4_FEAC_CO_60s.svg', bbox_inches='tight', transparent=True)\n",
    "#pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Métrica F1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta métrica mide la precisión o asertividad del modelo entrenado mediante el cálculo de la media armónica utilizando las métricas Precision y Recall. Estas métricas son calculadas internamente por NILMTK mediante la matriz de confusión, vease la ecuación de la figura siguiente."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![metricas](metricas.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precision se entiende como la fracción de cortes de tiempo en los que se predijo correctamente que un proceso estaba activo, pero realmente se encontraba apagado. Por lo contrario, Recall hace referencia a la fracción de cortes de tiempo en los que se predijo correctamente que el dispositivo estaba encendido y realmente se encontraba encendido ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fryer</th>\n",
       "      <th>LED Lamp</th>\n",
       "      <th>Incandescent lamp</th>\n",
       "      <th>Laptop Computer</th>\n",
       "      <th>Fan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>42.0</td>\n",
       "      <td>78.9</td>\n",
       "      <td>75.6</td>\n",
       "      <td>45.3</td>\n",
       "      <td>74.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Fryer  LED Lamp  Incandescent lamp  Laptop Computer   Fan\n",
       "F1   42.0      78.9               75.6             45.3  74.1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(metrics.loc['F1']).transpose()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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onD0NhoGDYEAAGAACsBI7VihAOtA+YKXYudWL0xeZoNcPzXnic4Kxt2s2ao8I5py35mR/YE44KIX3yH9gPrIZ+gNvSUYvBsEbiuCe9WMAgHH7X8EIhAlP/RIMnvlr0PyyiPcDANu2DeZXLwobm4a1GcK9RGqSBBAx5wiBt+4ceMcvgv7gezDs3wi2ex+Y/kMR30tBwFtqwJtr/BkCrM2RJk/Cgi8eG7BANEB/5FPo2ncMdaNl2gkd3Vk9eMsY8JYxEZf5mrMwkJ8PprcRhgNvg+3dH3G+UNYAvmR8KJCFt4wBiF8BsuOii9Bt3Y7yGTNAAPgmXwjvcfPAObaDKfkEvXv2YM+bX2DCLQITupbPiMDYdu/0wuummDSzEBDtlyYAFujrT4P3uOXwGQ+DKX0r8DwHuPpATNG1bMOKQfP5pBcC5f3pJkyl/ugTQggs9fUJ9UEJI9BDxadB++abb7BmzRpcffXVqTFfxgESS4MWQG9vL/LzJRyNw8CNmALHeX+Fs/+w37E0fHHqTeBL68EXhQcFBCJ3ehsBSsGV1qH4yaGIWkJ5yShYrUC4qVJGL3ETQZfHCGPt6fDVng73rB/D1bUHpi3/6zc7hYErGgXvhGXwjj0TXPlEUFPxUCPvA9N/GMxAm9+3pSQyLQxlBUID5wUfsPaNPO20hL9RDpRSNL/5JipmzcK5UQSFUWeeiVFnnhlxTdf8MYpevxwvPukAqwO+f7nfz4y1HUPR65eDLxwJT8MSeCYsRa+3XHLeWRsbQRgG37/YC8PgUFoTg4Hg4tXyfmsFBQwuXl2A77714s1/ONHfy6OiUjC/ZCKvNAHRYSkhLBKCDnYU9KfeCecpPwfb8TV0HV/7tV89+8H2NYEaLfA0LIV71lWhFDnuGVfAuOMpGPb+E9CZ4DzxBngnSPi+BIU4iTmnWQgPcqm1GoDkXkcYP6NWd47/b87jX4+2Y6D5FYGggDBtrM/ljzh0dIIvGgm+eFxE+h3G3h7BoGmZdiJHdxntY5BufGk9XCfeoKgPa1MTahYtwhmPPx5xPbi/fHnnndj3/POghPGfr4H0UJo+IwK/aV83h8qRLMDokV9ZKblfHjt2DABgHjtWsn379u2y3QwrBk0ONJDAliSYiywS4ujFeDB16lT1E9fGqUFTUkyeLx4bnwaCENCCEfCFaTQpa4g0n/BebS4+QFKylIKQdlzFZDiW/QWuzt0w7P0nqD4P3vGLwFVOl3eaZ3R+BrdURmgQ9s174ezsg6W+HiUTJ8bzNYrg6urC5ssvx0m//a1i7VzQ3FRSxqD5oFhgYuytMO34K0w7/orJugJ/BGxA88CV+f9/1GnzUXToNRgGI7VD3rFngo8jJ1+hsRf4x5toq1iBssJtEU7cQi2VliA21cWYcwENN1c1Pea7qdEC10k3w3XSzfENRti3lpkMId1k1ioQ517HGsCXTZD3G9OZwI2YEuX5yP6F49MU4qSdkjNCiFMffhj6wkLZdkt9PXwOBwbb21HC6CJNmxo9IwjvA8dRWPspJk5hkp9zMtAUg0YIqQHwHIAqADyAxyml/0sI+RWA/wLQFbj1Tkrp28Ln5Uo9BTVokGHQKKWwNzfDYLHAKOETIxik8Ono92cU8WnQZE2cqQajj1x8nFeb/kCUSkiW0gtQjnbciClwRtvElQxHtOF7YD92DPqiInTv3InyGTNS0k8Q/Y2NAABLXZ3yhwOax5IyBt/uonChCEbYJbS5AOtzAO07IrUNACysAWRKJCPlnnIpBhf8Jq50BSa3G8tP2I3i444Dv/HSyCg7TsPmpjhNdWqsV9Gc0zBjK2Ieo2jQMkE7LfugibWPqZ9zI0+N4mQFYMzixSiZNMl/9rKG7DgjeC/6+3hQCpSWM2mbc1or9eQDcCuldBKAkwBcTwgJJsz6I6V0RuCfiDkD5LMdE5aFsaQkqn+Zu78fPmf0Ek6Bt0X+mQR3nHbEqUHr6OhQYTBSm75GJUvJvEoZpJ1w8fNeLN+0CYWjRuG9ValPt2ELMmiJuAEENtfSMv/W0m2YioGL/wFP/RLQODZaSinamgfhdg/NXW/NfAyecXfcuaRYoxEVs2f7pXYJ7aNmEadQkJE5p2UtkNDHK8phmaNdJIR7sJwmKFG69R84gMNvvhn1bC0YORJV8+ZBZzKJtMZaPSMI50Ffj1/xU1LGpm3OaYpBo5S2UUq3B/5/AMBeAKPifV7Ot0tnMsE8dix0eVHSYTDMkKYtGoahBq0wivo5pciWjUvBhq8K7UR084EwDMpnzsRgWxu8dntKu7M2NYE1GlEwenTsmwUIbqglAQatr8MJrno2HEv/hP6rt8N+7iPw1C0GNUhHKtsHKF58chB7vva/hyupg2Ppn2RNzHJo+egj7H36afGGr+HIMLGpTlqgVGPOiYUp7Woehdq9aOYmVWgnYjK0Szux9jG1c675rbewefVq8DL+4UEcfusttHz4oXida/iMGFevw4+uL0BFZXQTZzJzTlMmznAQQsYCmAlgC4D5AH5KCFkN4Cv4tWx9Ke0wXgYtizVoNNPZrCWkIy1ST4lPixoQ9t95sAvbf/YzlEz2K5etTU0onx7bDyleWJuaUDRuHJhEglnCfNDOvzgPZVPCmDx9vj9QYsIygFJYj+5BGd8VyMLud3LvPfotAL8GjrPUwn7+0wnVgj28cSMOvf46Tnjg5MgGjUrkABSZ6tKObBGmAG3RDZBgMrQrFMTra5sorI2NyK+qgiFG6qgdv/0tCkaPxqRF2XFGgPOCZQlKy/17JJemM0KTDBohpBDAegA3UUpthJDHAPwafpXQrwGsA/Aj4XPd3d2YP38+dDodOI7DAw88gLlz52Kwqwuujg6YGxpAGSbUDvj91rxeLwjDgPf54Ha7odfrQxGhLMvC5/OBZVl/rjKOQ7iMQSkPt9sdaud5Hnq93v9OQkLP63Q68Dwf0c4wDBiGCbVzHAdKaUQ7IQQcx8XdHv5NJspHsJM+HweeeMCyLBwOBziOA8dxaG1tRWFhIdrb26HX62GxWNDd3Q2LxQKPxwOn04mqqiq0t7fDYDCgqKgIPT09KCkpgdPphMvlCrWbTCbk5eWhr68PZWVlGBgYgMfjQVVVFbyUILzUuNNuQ4+9FV6vN/R8QUEBWJaFzWZDRUUFent7QSlFRUUFOjo6QtKI3W5HZWUlurq6QAhBaWkpurq6YDabwXEcHA5H6J1Kv8nT14KwGEvw8I9H6ptaWlrAcRwMBgOsVivKy8thtVpT+k0jBt0I396OfdeB/a9+htOffx4A0Pb11+Crq1P2O9Vcfz0Ylwv9/f2Kv6nWPRiYgwQNk/ToNjDwulySv1O7g0F//jhUHT8v9Dt19b8B4E64L34UTeNmYrCfosrkVvxNppoauPv60NsziPBqqQ5bP9qbm9PyOyU796b4IpMZH2vvBOvJF/1OXV1dofWal5eXlrlXVsBGrgGfC263O+17RCLfxHd3IlxH4fbx6OnpkfydWlpawDBM0ntEtG8a66MIt9XY+nrQ39mZ1j0i0bk33hNpeuzo7kXBKLfomzweT+ifkt+pd98+GEaNwuDgYNRvKhw3Dt3ffAPfwgKEi4V93Z3o7+PS8jslM/e8x5rR+KUbRWYGEybrwYNFc3Oz5O/U39+PvLw82d8pGkgyEQbpACFED+BNAO9SSv8g0T4WwJuUUpEH9meffUYnTx6q8Wiz2WA2m+Hs7IS9pQVlU6eCkQkksDY2gjEYUDRmjGR7qP/BHjBhuYdoXpm/5mQYjhw5gtNPPx1TpkyBw+HAddddh4suukj4qqj48ssv8fzzz+PRRx+VbP/222/xq1/9Ci6XCx6PB8uXL8f111+Pzs5OrFmzBt3d3eBcdlzxvSX4wQp/Nm2uZHwoWWiQNgDgcrlgksh3lWqYnz/Ln3YjAOtlmzSZYZsMtKH4qXmhv/nCKliv+lLyXjVox7Zug/nVoQz4739ahN1b+rFq/348O3o0Zt1+O2auWZPWMcQL444nkf+JP7ltRxuHLsvpGPXzv0neK0W7LXfdhb1PPYXLjx1LKur6yKZNeO+SS/C9XyxALfNV6Lp9yWPwNpyb8HvTCctf54JxdIb+7r9qS0TB6iBUWa/eQZT8aWgvpbo89F+fxmSuScDw7d9R8P7Q/HdPugiDZz8gea8atDNuexz5//7NUJ8zfwznaXeltc9EUbj+EuiPDe1tAxe+CF/NyaL7EqXbCxMmoHbJEpzy4INR7/vqnnvw9UMP4We/mwaDVftnBNu+Cy/MXYBx9TosPj8PvhHTMHCpdMWiWLTbvn37toULF86RatOUBo346yI9CWBvOHNGCKmmlLYF/rwAgGSdoVh50N5ZsULkQzZuxQpMvuoqFIwciXe//33Rsw2XXooJP/gBXD092HzFFX4H7ad+F/52yT5nzJiB1157DTabDaeeeipWrFgRijLleR5MEoePzWbDNddcg+effx7jxo0DpRQffPABAOCaa67Bddddh0WLFsHduhvfu/JnGFczEvNmT4OcD1pXVxdqamoSHk+8yBZ/IJHjbBSTiSq0E0RW9bcPwlJXB11eHhY+8wxKjz8+ZV05OzvRtH49xp53HgoT8EELN5l8s92DPXs347I7aETJsyCkaGdtaoJ5/PikU+IEI1D7OhyorQ5r0HJEXZzmpkzMOU2bhkXRr9par/HW9c0E4k3tkgjd3P39cHV3xxUNbqmvB/X5YO3nUBG2VWj1jPDY+uGw05CvbbrmnKYYNPh9zX4I4BtCSLD69J0ALiWEzICfGzoM4BolL6U879/wU+KDpcwHzWw2Y8SIEZg/fz4WLVqEvXv34v7778cHH3yA119/HT6fD5dddhlWr16N9vZ2/PjHP4bJZIr6g27atAmLFy/GuHH+hKaEECxcuBCtra0YHBzEokWLAAB5RiNuvnoVXnz9XT+DJvP9UodnWiB0etbqpi9V208GqtBO0H9f5yBKT/VvemPPky+0mwi6d+3ClrVrUT5zZkIMWjhzW1LGwONww9XdjTyJUHMp2s287TZ4bMnXAiwaMwZEp0N/5yAQxqBpOSdVvIKBKnNOytGd0hTtoalFXEmlg/eqMH6ptDiaRZxCQSJ00xcV4aKtW2P6nwFhAlWXFxVhxYC0ytzaDjUDGIpWpzLpSYDk5pymGDRK6b8hreaRTKshRLQ8aIRhsHTjRtlnPQMDOO2RR1AkqEkZhKmsDEs3bgRx9QPWI+Fvjzqm1tZW9PT0oLq6GjNnzsQ999yDffv2YfPmzXjrrbfA8zyWLFmCZcuW4cEHH8SVV16JlStXYt26dTh48KDkO1taWjBqlDi4tbW1FaMjDlWKMSMr0dYRLMItPVFKAxUW0g7BJNbqYSkaV5QNXw3ahUfzUUrBsiSUoNZ26BA6/vMf1F98cUoOH2tTE4AEc6ABERt+0IHW2tQkyaBJ0a5i1qzE+hWA0etx6e7dKN35e2Bv81CDRjd8AHELBqqsV0JAGX0k08h7pasbZBoKHN1VoZ0wElKrgigg1ijLMBqJ0I1h2bj3kbJp03Dxjh2o+uIWoOPYUINGzwjrIT8PMKRBk18Xycw5TaXZSBZyedB0+fmhMk9y4NzuONMVxKdB27lzJ5YvX45rr70Wf/zjH8EwDObO9ddx3Lt3L/bt24fly5djxYoVGBgYQEtLC5qamjArcEDNnj1bdgSjRo0KlY8IR3V1deR1SnG0rRPVleWBocubONWAOIGjRg9LoUQeQ32ddoQxiIQQXH57PWbedhsA4OimTfjk2mvh6u6We1oRbE1NMJjNMJWXJ/YCgQYNGMqrJoSQdq6eHhx6/XW4ensT61uAvBEjQHSRG6dWhQIAcad3UWu9Zk3KAyFjG0WboQrtsoVukNA+yjAaidDtyKZN2PPkk3HdyxqNKKqtBdEL1qtGmVtHRxcIAYpLAyxUmubcsGLQ5PKgmUpLUSChcQoHYZhQSajoN8aXB23GjBl44403sGHDBpx++ukAEPI7mzBhAqZNm4Y33ngDGzduxMcff4ypU6eirq4OO3b4s6oH/yuFs88+G++++y4OHToUuvbhhx9i1KhRMJlMeP99f8Ful9uFPz7xIi5ZfnZw8JLvCwYLpB2i0H2Nqv5FUqU8g6YG7cQmk5pr1c8AACAASURBVKFNKyihWmWYIKWwNjbCXF+fsDYufGxmCwGjY0NaOSGEtOvctg0f/OhHKfuW1k8+wfuPfRxZakWrPmg8F6pBCPhr/sqVuFFrvWZN0tA4mQwgQ+tVo3QDEHeKkkTo1vjyy/j2scfivr9p/Xpsf+9I5EWNnhEzVp2LG35eBJ3Ov09GE+KTmXPDikGTA+X5mPWw4k1US0UatHhyp0Vi8uTJOP3007Fs2TIsX74cq1atgs/nww033IAnn3wSK1euRHNzs+zzZrMZf/7zn3HbbbfhvPPOwznnnIM9e/YAAP7yl7/g+eefx7Jly3D+6hvw/fMWYf4JgRxZMocuFw9jmgIIN06tblzxOs4CKtEuTDr7ersH6x8/As7j37jMgWz/qWJqbAcPJm7eBCI0aAxDcNFjN2P6zdL1H4W0S6qCgQRsBw9i7+Y9GLCFrX2tajMUFPxWa71mj0ClMdpJVP7QKkQaZRlGIxG6WZuaYFawl7R88AG2v3co4ppmk/zyXuj1YedpmuacpnzQkoUcIaxNTSCERN34CcOAUjoUUCB7Y2wN2pgxY/CaoGr9RoH/21VXXYWrrroq4trIkSPx1ltvyfcdhilTpuAf//iH6HpVVRWeffZZAADbtUdgNpFm0BwOB8oTNWkpQbYkv1Tg06IK7cIY27ZjHLpauVDZssKaGjAGg6yWSilWfvklvA5Hws8LHaLLJtTCIyNBCmlnbWyEsaQkpjtCvAiu974eHmaLf01rVSgQ+5/Ja4FUW69Z4pIgnHPRHLZVoZ0oWl2jcw6QKGsnvdcppRulFLamJlTPnx/3M+a6Ohzoc8Hj0cNgCJxVGqQdpRTvr30EU8q9qJ/op1c0IT6ZOTesGDS9XppIlOfByLQFwej10OXlgVKRjkwAdSsJPProo/jXv/4Vce25555DSayi7kDctTirqqoSHZ4yZItvhoJST2rQLnzx9/XyKCkfMn0xLAvzuHGwpYhB0+XlyZZEiwuC37TrQCsOvHEvZt1xh6gygZB2SiXuWAi+q7eHR+146fFpBUqqV6i1XqVM69rKmhmAcL1GYW5VWa/Zss8BcacoUUq3wdZW+AYHFWnDQ5GcPTwqqwN7hQZdEpydnWjcvBVjFoflNkvTeh1WJk65IAHK80CMvEqmsjKUTJwYu7yNkMlJM4N2/fXXY+PGjRH/4mLO/IMT/C3NoMXKZpwqZI9PS/y1/VShXVj/fT08Skoj5/KCp5/G/D+IcjorRseXX2LLL34Bd18SVdQEv2lXYxt2rVsHh0RQi5B21sZGFDc0JN63APlVVdDnGdDXPeSGoNmUByKhQF52Vmu9Zo2pToHGW+31CkCTTEYQ8bpzKKXbwBG/L5kSd4lwjbfc+LSAoLUiFMEJRBXik5lzw0qDJnRsJoTA4/GAclzSiS/D3ir4W5MypR8yGjSPxxNBKznNY8oh2ri0t/gAKKrtpwrtAv273dSfHLEUETmpgik3kkXb559j96OPYtbttyf8DqHPiKV2JAA/8yVMYSOk3dI4zftxj4UQlI6tgtfbNnRRq2Y6oVCQ6TmH7MnnJc4fJ3+sqUE7ymRT5HB8zK1SulXNm4fVR4/GtFyFwzxuHAhD4LCH+XVr8IwI+sqWlg/xFNGE+GTm3LBi0IRRnIWFhbDb7TiydSss48eDFhfLPOkP8e/euRMVs2fDGOU+4h6AvnkowpIaCuHVq2QiVAJKYWjeHnHJY6oFCAEhJFQzDAAsFuXFqBMaUpb4Zog2/CiLTxXaEQLK6OB2eTBmHIsRVWxETqqB5mY0vvoqjvvhD5FfKS4NFC9sTU3Ir66GvrAw9s1yEBzixWP9CZetTU0YvXBhRJuQduaxYxPvVwYXPnIDCj65e+iCBjd8ABJaIHkznVrrNWt9RqMwt6rQLlsqCVAaN+0SoZu+oEDR/br8fPzkhatR8N1LoWuE0x7trE1NYPU6FJnjCxJIZs4NKwZNWOqJEIKioiLwra3QjRoVNdzV8c03+OKqq3Duhg2oiFKPk+nvg+WLX4b+5iy1sM1YKXt/xsB5UBI2Tsro0D/3cslbu7u7UaBwMSUEkW+GNiVyJRu+arRj9DBbfLh4tb+vPt4XOsQdra3Y/pvfoGLmzKQYNGtjY3IRnBAz3aaKCuiLiiR95MJp171zJ9o//xzHrV6dHIMohDByOFvmXBShIGPrVauMhkgLJM/cqkK7bPFBoxxImAUoWmoXpXTb8cAD0BcWYspPfqJoSKywZqVGzcMjJowCwwy5gkQT4pOZc8PKB00uD9rstWsx5uyzJduCCHL7vhgRbEL1uVa1QJqTKoHsKfWkoLafatrHeHKhJRkoYG1qSj7FhYB2RGeApb4e9qNHRbeG0+7o++9jy113pdAVwY+OfS34+3MO9PUEIrw1ul6VmOkypvHW6GEp9qPKLO3E6YS0Sbd0nhGNr7yCzi1bFA+pccshbPj74FBaLA2u17n/8z+48PcCZUeaztdhxaBJ5TrjOQ6unh7wMgEEQejy8wEgdoqBLHEAFdenk9+0PB51viFrTJwK8qCpRTswBryzwYl//C0wP8M0QUEtVTK50DwDAyAMk3wUpYQm6NzXXsNZL7wg7jOMdtbGRhSMHh1ah6kCYfU4cohDT1fAryVbhAItzDmhQKXR9apEoFKFdlliGhYxjimiG+/1YuDw4VCORiWw9wziwF4fBgdpYIzapJ04cjg9c25YMWi8RKJZR0sLXmhoQOOrr0Z9Nl4NWtZsWgryKjmdzjQPJoBs8c1QsOGrRjtWj+6OsDx/YWMkhMDS0JAUg2YoKsKq/fsx5dprkxmlpLO7wWyWrEwQTjtbCsyrUjDX+iuI9AYiw7JGKNDAes1egSqztMuaSgKcUIhPzT430NwMynEJrefi0f6avaFITo3NuYHmZmxYsAAtuyKtFaminRDDikGTipYI1tfUx5DM9UVFqJg1C4YY6shsSRWhhMnIVB60rPEHynAeNADgCetPsVHmN+MLDyVLXR3sR45IPaoISZsYJZzd+777Dh9fdx1sBw9GNAVpRylNjXlVAsbiYuQXkKENX6Maby3OObBCgSpL9roo1gJVaCfsX4OO7gDSdkYEBcWEGLQx/n6G1qu25lz//v3o3rkTLBEog9I054YVgyaVBy2oEdPFcNIzmM1Y/v77GHf++dE7yRLHWSUmTtXyoGWJD5o4ilNeIleLdg4HgccTlntHMMaTf/97rEzA5yOIPU8+iY+uuSZmSbRYkJp3nNuNxpdfRu+330a0BWnn7uuDx2ZLaZLaof71KCljQhu+FqPCAIgZx0zn8gKyJw+aAkZDnTxown1Om0KBElcOJXTzDAzAWFKSkMBVOLICDIOw9aqtORf08y2uKopsSNMZMayiOBkJ6d83OAgAqYsME6n9PRE5qTQDoXYqitrfYJBvSymyxDdDrM2QXyZq0a6v22/eLA0waMKNy5BkEei2Tz9F3549CRdJD0HCB8083m9mFAYxBGlnKi3F5S0toOmok8joMaqGhbVfmxJ5EGLGVv6wVGvOiZzdNbpelVRhUIN22WIaFjGOKZpz9d/7Huq/972EhsTojRhZww4dpxpbr7amJhgsFuQVRAYkpmvODSsNmhSD5o1TgwYAby1bhu333x/9pkBOqghoUIsWbxFcACgqKpJtSyWyxTdDSR40tWhnKDBi8nQ9yiqkNWjO7m58dsstaPv884Teb0tVmSXRpm+AoagI+VVVIh+5cNrpTCbFeZPiAqvHaWeZcN5FARcHrR6WGlyvWSNQKQiwUIV2WZOQO35Hd9XOCEaPS64owKkL/ek2tMbcBlMRERq/EJ8M7YYVgybMgwb4y0fMXrsWBSNHxnx+4MiR+Px4skH1r8DE2dPTk+7R+JEtG74CfyC1aDdibAmWrMhDkTmwZAVjZI1GfPfMMwmFtlOeh/XgwZQ46ctpM8x1daJcaEHaHXj5ZWy9+26kA2KhQJvmJi3OuWyJWBcxGpmmnchP2Zf2koCJQIkgqoRu/7roIux9+unEBqVxNxhLQwNGnnmmyFUiWmBKMnNuWDFoOp2YCSmeMAEzbr0VeXFUk9fn58dOs4HoOak0AwU+LfHX9kwS2SJZKvBpUYt2LldkGhkhoyGnpYoHjtZWcE5napz0ZbQZpZMni9wAgrRrfvttNKe4zNNQ/wbYrDyefMSO73Z7NSsUKKn/qtacyxZTneZoR4jYRK3FvU7kBpM83bx2O1o++ACe/v7ExsTq0XzQh2ces/vdEjQ2507+3e8wZ+1aiTNCXgGSzJwbVgyaVJoNd18fBo4ejcv5WVdYGDvNBiD259Lg4lPiAJoL24+EFmn3ym+34q1/hvUl4exuqa9PKFmtx2ZD2bRpKD7uuGSGCEBegzbv/vux7O23I9qCtLM2NqYlgjPYfzCKs6eb06xZXYkGTc3ULhHQoCsHAG3SThTJqb15J/R7TAXdrIFI7UTdJSijA8MC3Z08ert5Ta3XCB5CpTk37Bm0PU88gb9Pnw4q0SaEvqAA3kBQQVQIuWUNLj7RZhplArlcrjQPJoBsMZko0KCpQTve54O1axBmS9hyldi4pMyI8aB08mSs+OgjVM2bl8ww/VCQf8/lcoHnOAwcOpSWHGj+/vXQ6QgsxYFUG1pcq5AIEsjwnAOySKDSIu2ywd9WQXBFvHSzHjgAAIkLXIw+FAjV18NrSvlx+I038OLEibA2NSkyDycz54YVgyaZB83hAGsygZEpAxWOEXPnonzatJj3ZUUuNAVOx2rlVRLa6TW74StIfKkG7exHjoDn6FCKDUjnkCueMAG6goK4zPRpg0xOKmd3N95esQKH33wz1FRVVQVHSws4tzttGrSgtjuUakOzuffiNzdlKm+hZgUqLeaQE+4ZWpx3Ctxg4qVbUEA0jxuX0JAoa0B+AYHBCPT1cJo6I6xNTXB2dvrrHas054YVgyaVB83rcMQdHTZn7VqcdN99sW/MgqLfUhnd5aBaXqVsKSKswL9ADdoFzZalZdE1aFOuvRbf37lTcTTkR9dcg09++tOkxugfEwdChzTVFAQgfsHIYDaj/bPP0LNrV6i9vb0drq4umCoqEioLEw+CwTGlZay/mkDWzDkNrFcRk6FV2mkwh1wW7HVKBNF46WYoLsaoBQugy8tLbFCMHoQQv0DVq631amtqQn51tT9ll0rrdfjnQXM4Ul7fLys0QQpqhZlMpjQPJoAsSE8CQJF0pAbtgo7/kRq01M25zq++QvmMGcm/SGrTCgQGsAYDimprI3zkTCYTKmbPxqp9+5JOkCuLwLyvGcvC49HDp1YdS4VQUupJrfVKBYKJJi0FUJZDTj3aiSM5NRfHKXKDkWcH4qXb8VdfjeOvvjrxMQXm3Lh6HbweaEprG+4rKzJxpmnODSsNmiyDFqdGYccDD+DVE06IfWMWOM+KDvAoEygvUWlHIbIl5YES/wI1aDdizhzMvWgm8vLDoiAl5hylFO+tWoXdf/5z3O/mPB7Ym5tT4wMmUeYpHOa6uggGLZx2SSfIlUPgt2uYpMfi8/NgYNKQDDcVUHBYqrVesyKdEKDInUM92mVBmSwN0i2o/DjlTBPOPMckDmTIIKzh9YJVOl81xaARQmoIIR8SQvYSQr4lhNwYuF5KCHmPEHIg8F/JuFWpPGgTfvhDzLj11rj659xuDBw6FFuaFy0+DTIanDD0XH7D7+vrS/do/MgWk4lo8clrM9Sg3YgTTsCJ3z8xkomRMKsTQtC/b5+iXGgDhw+D8nxKfMBiSZWWQBBDcH319fXh8zVr0pYDzT+God+OUgqfy522vpKB0KcwmkSu1nrNinRClIo1e1qgncjKor0zQkl6knjo5uzqwgvHHYeDr72W+KDCxkApBfVqg26814v6Sy7BqIULASgT4pOZc5pi0AD4ANxKKZ0E4CQA1xNCJgO4A8BmSmkDgM2Bv0WQyoNWs2gR6laujKtzfUEBKM+DixF1kRUbl0gil2cyysrK0jwYP7IisglQ5IOmBu26v/4absE+JUc7oZYqFpIpbCxCDIm8YtYsVJ54IrwDAwD8tDv23nuwHz2afN9yCIyBUoo/PWDHZ5sH0tdXMhDNucyv16wQqKQSckfRxqpGuyzwQVMiiMZDN2tTE1xdXTAkU3UgwFz3dHN49HcDaNrZmfi7UghGr8dJ996LsUuX+i8oqMKQzJzTFINGKW2jlG4P/P8AgL0ARgE4H8CzgdueBbBC6nmpNBt9e/fCfuxYXP0HnatjRsFlwcalhMMfGFDp0MqGTQvKtBnppp3X4cCGM87Arre/iWyQoZ2lrg62gwfjSisD+J33xyxenBIn/ViBKXUXXYTF69eH6ob29/Rg4MiR9KXYAADCgoKAEIKCQoK+Hg7gNWjmVGBuyth61aJApUB7BuRoFw6x7568IBoP3YIpNpLZS4LnVFERA5cL6OvIYER6GDw2G3zhihsFdUyTmXOaYtDCQQgZC2AmgC0AKimlbYCfiQMwQuoZKQbtvcsuw1dxmlCCwQS+WLnQsmDxKSn47VHLcTobQs8BiQALecky3bSzHToEALCMipTC5LS2loYG+AYHMdjWFtf7q085BYtefBFGiyW5gQIxfdCEsDY1AZTC0tCQfN9yICS0XkOpNjS4XpU4Hau1XoUHtibNdKJIxOhxb+rRLgusBaKchcnNOWtTExi9HoU1NYmPKUA3g9EvUPV3qpRYOAa+fughPD92LPiAG5WSwJRk5pwmozgJIYUA1gO4iVJqi9eB2Gq1Yv78+dDpdOA4DhdeeCEq7Xa4KEV3dzdYloXNZkNFRQV6e3tBKUVFRQU6OjpQWFgIlJej8uyz4eU4HD16FIQQlJaWoqurC2azGRzHweFwYCJYhP8c3e2tQEk/PB4PnE4nqqqq0N7eDoPBgKKiIvT09KCkpAROpxMulyvUbjKZkJeXh76+PpSVlWFgYAAejyfUnpeXB4PBAKvVivLyclitVni93lB7QUGB7Dc5e7oQHrvqo4zsN3EcB7fbjfb2duj1elgsFnR3d8NisaT0m3ra2hHBBnBetLa2xv1Nod8JgN1uR2VlJbq6umR/p+A7lX5TvscZsTC6+2xgTTbJb+I4Dp2dnQn/TrG+qenLLwEApgoLYB8a04C1Fx6HQ/RNhfX1KJ45E62HD6PaYon5O/V0dqKiqiolc89gPRbx+/ooQXNzc8Q3bbn0UljmzMHs//f/4GlpAQDk1dSgubk5bXPPwuhAOA9Kyxg07fOhuXE/ymvqUvo7JTv3eFsfjOG044Hm5mbJbwpfr8nsEbG+ydNrRWHYmHifJ62/UyJzr7KQQXH4lgI2VPtQ6ps4jkNfX1/Se0Ssb9L7+Igzor+nC77C/rT8TonOver+3ogzwuHy4mhzs+Q3FRUVoa2tLerv1Ll7N/JGj4bX50P7sWMJfZPB2R3aQ0rKGPR2utAcNqZMzb2+/fthqq5Gb38/WJZFoccVod1y+3i0NTdLfhMADA4Oyv5O0UDSFt6eIAghegBvAniXUvqHwLV9AM6glLYRQqoBfEQpFdWl+eijj+j06dMjrj1bU4PjVq/GSffem7IxFrx9PQwHhmoH2s99BN4Jy1L2/lTAtOUh5H35h9DfzhN+CtfJ/y15b3NzM2pra9M+JjLYjeIn5oT+5vNKYb16e9r7VQrzs2eC7T8U+tu6ejP4EmkzXLppt3PdOmy7917812trYdn5cOi684Tr4Tr5tqTf/9LkyRh/4YU48Z57kn4X27kb5peG1oGvYjIGfhBZ3um1005DwciROPvll/HVs8/i2DPPYMmGDSGzZzpg+csMMK5+7N7pwb82uHDxZ5tRNGlm2vpLBPn/uhHGfRtCfzvOeRCeiZKeHKqtV/3BzSjceFXob8/YBXCc/1Ta+1UCMtCG4qeGKmDwhVWwXvWl7P1q0a5ww5XQH/4w9Lf9vCfhHb8w7f0qQarPiG8efhicxxN3UJ4UiKMLxX/1Z1LYtNGJA/s4/OBod8LvSxX+ecopKKypwdkvvQQAsDw2FYxnyHTZf80uUJO0FSIW7bZv375t4cKFc6TaNKVBI35V2ZMA9gaZswDeAHA5gN8G/rtB4nFRmg3K8/ANDipO3BkLWaG+1mLYfjYEVwASfi3yprp0084anhwxDKmgncdmw2B7O0wVFUm/C0Bc/kCWujr0fOP3pxtz7rmYc/nlqek7GgLjqB7N4qTTDGD1mtr2AMjXMJVC5tLiaG+9ik3D0X/bTNFOi2b1VJ8RU3/2s2RHFHFGjG/QwVBoBM9xcVUCShcoz8N28CBGnXFG6JqS8mLDJs0GgPkAfghgASFkZ+DfEvgZs0WEkAMAFgX+FkFoCvU5nQClosNNDr179uC5MWPQ/M470W/MAmd3kb9IlAlkMET3FUoVRJmqNejTAig7LNNNu8n/9V84+fe/l8hJJU+7D370I2yOg/EJRnumykk/nmSr5vp6DDQ3g/N41Jt3gXGUlbM45UwTCkcUx3giA1DgdKwW3bKhYooSYQpQkXbC30+DtEvlGcF5PJFO9AkiXPlRP1GP088uzChzBgCO1lZwLldkKiIFwSnJzDlNMWiU0n9TSgmldBqldEbg39uU0h5K6UJKaUPgv71Sz3NcZHQWo9fjjCeeQM2iRXH1zxoM8Nrt8Nrt0W/MgsUnnEDRMpNbrdZ0j8aPbJAqAdl6klJIN+0qZs5E7ZIlojkXS4PW++23Md9tSzGDFk/yRktdHSjHYaC5Ge8sXIidf/iD6J6UI2wcLifFQHNz+vtUCMLF73Ss1nrNCkuBguhXQEXaCfZbLSVcDSGFZ0Tbp5/i2VGj0Ll1a3JjEvx+nkEP3P39yb0zSejy8jD3179G1ckn+y9QKv4903RGaIpBSxbCPGiswYC6lStRMmlSfM8HTKG+GGk2smPxxT+BysvL0zyYAAIpD0J/Ul6TKQ9EzE+UTT+dtPMMDODwm2/C2d0tYTKRn3OWurqQlioarI2NACEoSrCwsQhx1EQsnz4dE1atgtdux+ChQ2AkchemHGHjWP/CID65I32JcROGgtp+qq1XkSCqvX1OaRSnerQTjEODVpZUnhHBiOyiZP37wuYcx1E8+tte7H700eTemSRMZWWYev31KJ4wwX9BKuI6SiBjMnNuWDFoQg2ax2ZD27//DXecmXzjz4Om/TIeSnJ5qaZBC0t5EIIGaSdegJnRPvbt2YPNq1eja/t2RWZ1c319SEsVDSPmzMH0m2+GLkX1CeMxDZdMmoRTH34YCKTESWuKDYlxlJQxsB7SngZNiSYocxpv7VsKtKJBU+KSkCmk8oywNTVBX1SUvD8rISEmm2UJzMUMrI0Hkntnkujfvx8DR44MXVBRazusGDRhRGrf3r14e/ly/wEXB0J50GJq0ITmJu0tPiU5qbxeFZkkkVSuQQZNwQJMJ+2CPmLF9fUS5ib5ORc0WdpiVBQYfdZZmHPXXUmOMgxx+mVQnkfXjh0AUmhejQYmkkFztHfFFsJURqwkv+FQa72KLAUaXKviIIHo/j452oUhhWdEsJB4SmrqCtarksoo6cCWtWuxefXq0N9KA1OSmXPDikHT6yM3tWDC2XijOBmdDsddfjlKp0yJcWMWMBkK1NdVVVVpHswQNB8ZxnMgVGB2JfJOqumkna2pCUSnQ+GYMeLNM8qcszQ0YPzKlTCWSJasBeAXZmwHD4YSL6YEQhOYDJPxzgUX4Is1a0AYBkVjx6aufzmEjaO0zL/lBRMAawYKDkvV1ms2FEtXkJAbUJF2WWgpiCaIxqJbkEFLBcLPiNJSBramg7HrY6cR1qYmmMMFSQUlsoDk5tywYtCEnGpQStbFGcUJAKf88Y9+p+xoEG6eGlx8Sko9xUqWl1JonbmVcpyNIhWmk3bWxkaYx43z+2kp8GkxWiw484knUHniibL3ODs78eqcOdj7VOryWsVbfLlw9GgAwMgLLgCrQlSd0MQJALZADVKtQEnZHbXWq7iSgMbWKqDYxKke7YRWFg3676Vwzh1/7bUYH2fN65gIY3hKyhj4nM64K6OkGpzbDbuwHJ2CWs1AcnNOewmBkgArCMcNMmhK8qBRSkE5LqrzslgLpL3FJ4osjWIyKUhxnrhoEAdYeKCpVMkizWP0DT+dtLM2NYU2hkQ0j56BAdnCxaEUGymSegHErQUK9jl97drU9R0NAgbttLt+gvJZs9TpO14oMKurtl6zQAukJCUOoCLtRH7KWnSDSd0ZMeUnP0nFiPwIY3hqxrGY9z93QqdWrk4BBg4fBuX5iH1SHJiSvjk3rBg0IXwJMGivn3YazOPGYeFzz8nfJNJmeMBzHCBQwwaZPDkzUtR2QkL5XxJpp76hScTzNOriEzK2aYWIdr6Y35duhP92xO300wsAwxBQVi/52wL+349l2ajtwvdLtsvMj7Oeew58UCus0Nz0+W234fCbb+KSb4aKrBOWBSEElOdh3b8fQGoZtHjrSZrHjwcAOI8eBVQwcYb7Jen1BJOXnQH3yJFiuse55oJCnNJ2wjAgjIzRQpQHTV6zqNp6lTGrJ/v9ybZHQGGx9EzTLhooz6eEPpTnQSXqUgvbqccd2usAgA/QTup5QikopaH9I7zd2dkJzu1GUW2t/PxWgPB9o6ycxfGXXQSuqEi0JlM1n6LRSzJXpEKtbTJzblgxaMIoztGLFuGsv/0NhuL4E1OyJlMcUZyRi+/Ap3vx7gWR0SvFEydi5eefAwDeWrpUlB+mYs4cLN+0CQDw+hlnoG/Pnoj2kWecgXP/+U8AwKtz5sAeHkUCoHbZMpwVYCJfnDgR7t7I1HCT5o3C0rP9ocqP/G4ASxsOoWy89OfYbDaURPFXSikkpPI3zjoLPV9/HXG5+pRTsOSNN1QZ0hdr1uC7p5+OuKbTATetNQOMDp9cfz2a/v73iHZTRQVW7dsHm82Gr372Mxx5O7KkUdHYsbg4EJzyr5Ur0fbJJxHtpVOm4ILAtY2LF6NbszdnLAAAIABJREFUEMhSedJJWBb2TqVOxwWjR8PZ0YGnR4wIXfv+11+jcPRo7Fy3Dtvvuw+syYSCUaOivkcR4tQCBftsfOkljD/11NT1LweJOfdCfT08NlvE5QmrVvkjTAE8U10t2tSPv+YanHTffeBcLjwrQbfpt96KOWvXwtXdjRePE1Wiw5xf/hLTb7xRcohKNEFqrVe5PGjU58PTlZWi+6fdeCNO+OUv4envx98kgj9mr12LGbfeCvuxY/i7oCQfAJx03304/ppr0P/dd/jn/Pmi9lMffhgTVq2KvBin32MQqtFOocZ7229+A11+PqbfdBOsjY1YL+GeMP8Pf8DEK65A986deGOhuGzUGU88gbqVK9H22Wd45/zzRe2LXnoJY845B0fefRfvC+kI4Lwx+zFi/EIc/Oc/8dHVV4vaz//wQ5RPn459zz2Hz265RdT+w+ZmWY29IghoN9jehhfrxBrvuXffjak//Wla6VUxezbOfPJJFIetZ9Hem8Y5N6wYNGGQQFFNDYpqapS9o7AwFFwgB+Hi4z0esEYjpt9yS8hfKS8s98lxl1+O0YJkuQXV1aH/n/zjH8PZHVlvLDyfzJTrrhMdJsVhmo/pN90kyuI8qv8tAAOwD1B4PUDfoRaUyXxPRapK/cQBsW+GB5N+9CMMdnZGXC8M/G6vnXYaxp1/flL13WTHEpAIxyxejPzA70HcNpi2P4GQIMgYMG75cpGmSR+I+K2oqAC3ciXKZ8yIaDdahuqyTfjBD1B9yikR7XlhNJ90xRVwLF4c0V4oZAAU+u4FDzI+zC8zWO+y+pRTMOvOO1E6eXJqtZRxmkwqZs/GGY8/jvLTT09d39EgkeR3+i23iPLElYUFB8264w6RY3JFwCzK6HSYdeedom6qTjoJgD8aXK69d88evHvRRTj1oYcw+qyzhhoVBPWotl4lkoZ2bd8Oc12d5PdVzp3rf8xkkv7+ANNlMJsl20fM8ZcjNFVUSLaXSTB18fo9BqEa7RSu1+5duzBw+DCm33QTTGVlkt9fPtNfPza/qkqyPZjvs6i2VrI9uIcVNzRg1p13wvjty2BsLaH2opqR/vdMnix63uv1Ij/AlJfPnClqN48blxrmDOJIXINJLz3fAkxZOumVX1mJ8RdcENmgwL8bSG7OpZRBI4RUAjgbwHQAxQD6AewC8B6lNO3emT6BCrRn9244OzsxesGCuN+hKyiAq6cn+k2CH2Ti/LEYc/c/ZW+f8IMfRH3dxCuuiNp+vIQ0E46pP/2p6FrR3z8H2g7BZPIzjO4Bp+zzvb29yA8wHGlHmCaIUorOXd+gdulSmMqk2UdnZyfsR4+mZShb7rwThzZswKV79oSqTTD9h2F5dsi8TVk9apcskQ0c6e3tFS9gAeovvjhq+4TLLos9WKEjagyflrzyclltTdW8eaiaN0+yLRnEG5hCCEHdRRfh2LFjkC4vnFqIxsF5MO2GG6I+E00gYPR6zPxv6aLSgN+lQq7d2tSEwfZ2OIV7jIKyO6qtVwGT0dPuwhtnnYWzX3kl6vfr8vKithstlqjteeXlku1HNm3C/uefx7z77x+6qFCboRbtlGrQPP39KBjpZ5BMpaVR6VNQXR21vWjMmKjtlvp6zPzv/0bRK59B1z6kGLCNHg0OQOnkySidPDnimWPHjiE/EI1YPn06yiWY5ZRBsNcZ8g1Rvyfd9BJBoVk9mTmXkihOQsgkQsg/AOyBv5amHkB74L8/BPAtIeQfhJDJUV6Tcnz39NP4+NprFT2jLyiIaeLMphIoBqNfqee2yzNoqoYwh9HO7QJe//71aHzlFdnbDRZL2kp9uK1WEGEwiMIkhGrRTihVajIwRWGAhWrzThR1nTnaGQPuFh7BnFaSz0s1uoUlDQUAl8vfr1GBy0gq0bNrF/Y88USEVlhJ0WpARdop1KC5+/thsKghroRBgS9Vps4IAJqL9FcaJJAM7VKlQXsGwAMAVlFK3cJGQogBwPkAngSQetE9AGGpJ6/DoShAAADGnHMOSiZOjH6T4Af518OfwPvqj7AghSkLkkZgEhFCYDQRuAfkmU5VTZxhm0Bww4/mI2gsLoYnTdm/Pf39osNGnDA0+hLJlLlJa5sWIJGZXCPmJlG6iAwKVMFDWCR0KBAM1FyvYPQhhtYVkPGU+PSmEsG16u7vH3IRUBCJCGRwvcbSoFmt6jO+Cvz3MnVGABpUgCgU4pOhXUo0aJTSEymlr0oxZ4F2T6A9bcwZIM6D5kuAQRt/4YWYfvPN0W8S/CDWDrvIRyzTCJ/UY+tYmGvHyN7b0dGhxpD8CDss3XFI5Ibi4vRp0CQYNKVJCNWindh3T2ObFqDY3KTavNOQRM7odNAXFkYKHZSPSI5MQaImR1ZzvUoJVJnSoBnCGLQQFEbUZW69RndJYAyG5MskKYQS/z11zwjlEbCqQqGlIBnapcwHjRCyBsDHALZRSjNiQxDlQbPbQwXQ4wXv9cIzMABjSYls2QrhRHY53CjL0KYli7CNa9nKfFgvXw1xILEfhQoS+SaL8GhElzO2Bq16/nw4WlvTMhZ3f784zYRw8cXY8FWjXVbWRYzO3GaMdhkuzVb3ve9FViuRksijJEdWc71GuCQ4M8ugSZmHxclWtbJelSUzv0QQxa4KFAhU6s45bde6FjO20dmoZGiXyiCBswD8AgAhhHwJP7P2CYAv5TRr6YZvcFAxg7b7scew9Ve/wuqjR+W1b4KJ7B70ZkztLwdxKLBGAnYZZRJ5LGfuZFC3cmUoejMIcQFh7dENgPakSihPGqoWxMmRM0u7+evWRV5Q6HSsKsJ+w4bJOhgu/DUYfWbGZ7BYwJpMkVH2Cs1NqkG0XrXnM6qk2oya0Hyta4VWlmSQslJPlNKz4Y/cXADgHQCzAawH0E8I+ZQQck+q+pKDMA/ayevWYe7ddyt6R5Chi5ZqgwoiEd2DvoxJlbIIW3wf/MuFDRddLnur3W5XY0R+hC2+6lEsFvzy6lBKDbUx49ZbxRG2CrVAatFO834ZgESy1egbvmrzToPMbbjjsFLGVs31Gq6RKq9gUb9sUZS704sRJ5yAK1pbMTIsPYvo8I4hUKm3XuP3e7QdPoz3Vq1C17Zt6R5WJBT4oKl7RmgnqEcKSgNTkqFdSmtxUko5Sul/KKXrKKUrANQD+CWABgA/T2VfUhDmQSubMiUit1Fc7wgwaFEjOcM2AY4DGmaWoWzqVEX9pB1hmz7no7AdPiJ7a6VE0sl0IXwymy0Mjls0O2r+nH3PP4/namtT7odGeR4em00UYSOsmRfLZKIa7USVBLS1aQHKGQ21aKc15vbja6/F66edNnRBoQZNzfUaLlC1tXDoyoQpLgBJlxOp2rlRkLH1GkULNNjWhiPvvAN3moKhZCEUqKKsV1XPCK1nSVAoFCRDu5QyaISQMkLIBYSQPxJCtsGfduNEAL8DcFIq+5KCMA9a0/r16N61S9E7ggyaLxqDFjaRdTqCJVfUYZxEJuJMIpzLN5oIPFYxMxJEV1eXWsOKUAf3dHFo27Uv6u2EZeEdGEg5g+bq7sbzY8di75NPRjYodDpWjXZSan81Q9/jgULnWdVopzENGqPTwRVW+UOsBdII3RCZ7uPj91zYcu8fVetbNBaex8fXXouDr702dFGjc06JWT0YMKK2FUZJugh1z4gYqY8yDZGfcnShIBnapYxBI4R8C2ArgOUAdgP4AaV0JKV0JaX0D5TSrdHfkHr8+8YbcXD9ekXP6AIJ5bxR1JLinFQam0BABKNhyiPgvV5Zs61cMEQ6EO7TtX2LB++ufTzq/XJ5o5JFkOETR3Eqk45Uox1hQIWRfVrToin0B1KNdgodttMNUWSyQpOJmus1PGmoy0lhMKtUbFwChGFweOPGCFOgUp9R1WinQCiQ3YvSDQXCqLpzTltBPULEW3M4dH8StEulBq0VQAGAWgA1AEYTQlRKT+9HeB40nuMSChKwBMpg5AeyOksibAIdPezDo7fvRMeWLYrHmzZQGrFxBasJyDE5paWlqgwLQATtXC4KY6Ep6u2SofUpQPB9wuSQYsfZ6NJRpmgHIOOMhhDiHHLRNy61aCfKg5ZhidxYXAzO6QTnDsROKWRsVZ1zEYmlKYxFmWPQAInE1SLaaWS9Klirob1ITQaNUnGy6yjrVc05J9Y+alwQjSEUJEO7VAYJLAIwEsDtAAYA/AzAQULIFkLIA4SQtNsAw/OgBbVFeoUlFoJlH6LV8Aw/eFxOCo+LD2neNAEaGSxRUqHD+AsvxFCByUioqr4WpNkwFuZFvT1dGjRZs4LC4svqmps0ngtN4WGZKfNwplOUCIUOpYytqnOOjdzrjEWZ3edEiasVpsVRzcSpYK2yRiMKx4wJ1cpVBSItkC5qahd1TZzaFkTVdINJaQ4BSikHv5lzK4B1hJBiAFcDuAXAzQBSWJlZjPA8aEEfMp3CHCQ8x2GwrQ0Gs1l+wSjMhq86BJvB6HF5KPzpX2VvN6u4MVBBmg1jqTHq/fmVlWi49NLoGs0EICu1KnCcBdSlndZMdUIoLYGiFu1ELgkZZmzLpk7F5KuvBhPU+CtkbFWdcwHacRyF1wsYCzPLoAnNw2JzU/QjTTXaKdCgTbrySky68so0D0gAhUml1Z1zQo23xkycKu5zqS6WXgbgtLB/0+A3fW6GPy+aaghGYSqtJODq7sYr06bh5HXr5BeNQIMG+AsAawVCiTzWhi9MT5JWhG1cbieFuSA6g2YqK8Npjz6a8mGUTJyIGbfeinxBhI3SxZcp2vk71xaDplSyVI12GqNb5dy5qJw7d+iCyNQUfVvOxJwjBLh4dT6Yc05Wr28JFI4eDXdf39AFhQEWatFO69pupTnQ1JxzIrcSjdFOzX0ulZUEvgUwEcAh+BPUPgTgE0rpoVT1EQvhhCgcPRoXfPopChRqXkJRnNGCBNhIvwxCAL2amZZjQWCms9qA58aMwbz770fDpZeKbnc4HCgvL1dlaOEb1+IVeaDT58R+hlJQjhvSOKQAZVOnSqdGUbj41KWdOLeSpuI4hYelVminQd89n8sFwjBgDQbFNUwzMecYhmDMOB3sI0uRSeqd8Ze/RPwtzkkVXRjN2JyLwmRs+cUvwLndOPl3v0vzoKKMJwZjq+acE69XbfmgKRXik6FdKoMEfg1gNKW0nlL6I0rps4kwZ4SQpwghnYSQ3WHXfkUIaSGE7Az8WyL1bHgeNNZoROnxx8NYUqKo/1AUZ9Q8aEP9jKhiMe0EE4iMf1dGIDiADHkGeO12WUf7qqoqNUblR9jiGz1Gh4qxsR0oX5k6FV+sWZPSYTi7u+Hs7hZdV5oHTVXaiWrUaUz1rzC6SS3aKa2LmG5YGxvx7MiROLRhg/+Cwjxo6q5X/5xz2Hl8t9sLZ0+Pen3HA4UO26rRToE7QtdXX6F/X/R0QymHQkFUzTmn+TxoKtIuJVwFIcRAKX2ZUtoW477o9iw/ngGwWOL6HymlMwL/3pZ6MDxIwHb4MPb89a+Sh3DUMTIMdPn5USsJhP8gEybrsehcg6ZyUgk5fEO+ESBE1tG+vb1djWH5EVh8Ph/Fd7u9sLXF3vB1+fkpj+Lc+stfYsMZZ4gbFPqgqUo7US40jW1cCgMsVKOdxjKTByOHg+tRnOA3uhZIzTkXPCw723m8ud4JW3OLan1L4dCGDXjnwgvBB3NeanS9Smm75eDu71fdRUapFkjdM0KYB01bgqhS7WMytEuV2udrQshthBBJeyIhpDpQTH1HrBdRSj8B0BvrPpl+Qv/fs2sXvlizBs7OTsXv0RUURNegheWk4jjqTwBLVfQLiQXBZkB0BhjMZtlM1cIKDOlE0HzjsFO8ud6Jlq+bYz4jCq1PAdxWq3Rgh8LFlwnahaAx1b9SyVI12mkwzQYQljpGw3Mu+BuGfG1j+IymG4MdHWj96KNQJKe4WHp05la9ORe/idMjtxelE1pdq4BYoNKYIKrUfy8Z2qWKQfv/7H13mBvVuf571LXSrrTNuzYG2xjbYEwxJvRgJ6aEGjrEAZIAuZeSQhoBQiDJJf1eAoQW7g2BFEJCB1ON6c0GGxuMcbfX3l3vrrZIu6qjcn5/SJqVNCNNkebM8f70Po+ftTQjzZlX55z5zne+7/2OA9ABYC0hZBMh5FlCyMO5vxsBrAGQTyDQi28RQj7ObYHK7lsWZnHmDSw98heH33QTZpx1VuWTcj/KI3+J4omHY3x1IhldJWepOGYBfCxXb5aSCd+tnNgrSa2vAYRgUFYYUuvgM4M7ESbLRZRCGktV+WHJijupYWtyJQG7HTaPZ3w8Sh6WlbfpTBmvuWx1Z4O5BbWVjVtOuJMrL1ZmlyVRZi4yEpJFisKigGWfk5Zm42whypC7mkRdU0oHAfyQEHIjsqWdDkK2cPoIgN8AWEkprWZWvBfZGDea+/s/AC4rPSkQCODYY4+FzWbDrEAARwOgdju6urrg8XhgtVoxOjqK9vZ2DA8Pg1KK9vZ29Pf3w5sL8g+Hw5h2/vkIBALo7u5GS0sLAoEAmpqakE6nEYlE0NnZiQxssCI7cTX5CUZHAojDhVgshs7OTvT19cHhcKCxsRFDQ0Nobm5GLBZDPB4Xj7tcLrjdboyMjKC1tRVjY2MQBEE87na74XA4EAqF0NbWhlAohGQyKR4vd0+TUZzgkEhTTD/vPKS9Xtl76u3txZw5c9DX1we73Q6fz4fBwUH4fD4IglDTe5oUicODbHIFAMCaQldXV8V7SjkciI+MoKurq+h36ujoQCAQACGk7O9U7p7CgQD8++6Lrq6uonvaNxZBoXTucCiMcH9/2XvaunUrOjs7df1Ocn2v0j3Zk2kUpqLEI2Po7eoy5HfS0/caU4miFd9waAyjg7Gy99TX14eGhoaKv1Mt7mlqySyXjEUQiUQM+52U+l4sFoPD58NwTw92796NjuhY0e8ajgkQEomy9xQIBMTxqneOUHtPM5JpOJHNuAaAqDCGcG+voXNEpXsKCdlFwOCuXQjabDgwlSj6bXsHBmGzD5W9p61bt2LvvfdW/TtVc09+YgUp2FkJDg8iFI4W3dOktjZ4Z82CZdIkjIyMMOl7nZ2dCPXsRKH4QwoW9Pf3l70nQRDg9Xpr8nxSuqepKYpC7YXI6Ig4Hox+Pqm5p854BIV+5KHgKOzxeNl7CgaDmDlzZtnfqRJIufqMZoIQMh3AUkqppNJ5pWNvvfUWnZcrjr72jjvw4c9/jq91d2v2oo3t3ImMIMC3335lz/HdvwCW2BDu/v0YZh9gw+ee/hS0gVGWiwKsfR+h6V9ni69THYdg7KKny54fDAbhZ7SCs29aCu8L38Km9Uk882gMX7n1ZDRc/c+Kn9n6+OMY3b4d83/4w5q145F58zBl4UKJhEfDqz+B85N/iK+ji/4LiUMuKfs9LLnzPvFV2He9I74eO+tvSE37PJNrq4H/ngNBkuOhASNXrQMc5bObWXFnGdoE399PEl+nW/bD6CWvGH7dSlh3zz3w7r03pp9xBhzrHoFn+fXiscSBFyJ6wm/LfpZln3O/eStcH/0fXn85jjUfCPiPF36DxIL/YHJtOfSvWIGlp5yCkx99FFMXL0bTXxfDOrJVPB665BVkWsrP2yy58999AEgqJr4euXo9YOdD0NzauwpNj54rvk51zsfYhU+WPZ/pM2Lj0/C++F3xtTD7DERO+SOTa6uB5/lr4Nj8nPg6fMpdSM4+vez5StytXr161eLFi2XlDGqqg2YUCCGTCxIQzka21qcEhcZmKhwGCIHVXVmpXg5vX3stkuEwznz55fInWe2glGbLn7gIV1uccu5rSinS8ThsMnwIAsOtMmvxlomrQblO2cxzz1U8RyvmX3cdvPvsIz0gCdiu7L5myt2eprCt4Ppnxh1nxdIBYN7VV4v/1xqwzbLP5fv/4Uc7MOdAu+nxe662NvhnzwbJh7NI+lzlRxpT7iw2FM5uJJ0ENXeHWITWUA5T5zkOxmsRNNZrroY7jrQhsiCE/BPAewDmEEK6CSGXA/gdIeQTQsjHAL6AbFUCCTKZjPj/g779bZy/apWuQqV2r1esRFAO1GJDMglkMtli5Fztk8sYGe/+4Af49/z5sqfHYjHZ941APoh3v/1tWHJZAzxe5Ri0TDqNxMgIMjUUS5xz6aXYSzaLU1vwLFPu5OJaeEFJ/VcA/HDHYfarMDaGSG9v9oXGGDSWfS7/sPQ2WjB5L6vpiwLfzJk49/33xbGr1bhlyp0k2F36oA589BGeOO64ogLwTKAxjootb3wnQ2nNuq6GO+4MNErpVyilkymldkrpVErpnymll1BKD6KUHkwpPbOcnEdhtoSjqQlN06fraoOizAYgalIdeZwDU/a28hWwLbOqtDc1IREMQm5L2wwdtIYGC6bsbYOVKBtdWx55BH+fORORntqk+KcFAUPr1kEYHZUc41XLCwDf2U0lWcyUWAFSeXphpoPGYYms96+/Hs+elNt2lYzXyhO+GX1u4/oktm9JccFdETjW81IjkBwbGMDI+vWy87Kh4Fh7T6qDxtGzFZCp/MG5DloehBArIeQhlXpnNUehDtr2p5/Ghoce0vU9diWZDQCw2uFwEHx+sQtT97Fx9bCUuq8dcPr9yAgC0jLWvBlaXju3p7BhXVLV6iivG1UrqY3wzp146vjjsfOll6QHGWrcaIUWbSXmkMkcVgIz7jjcGnb4/aLsjXRFzl+fe//NBNZ8IHDhfXzhnHPwST52lOvxqpyNmJ/T2GdxaqtewfYZwfFCFJDR3qvs8eZBBw2AWCz9JAAZpXONgKVAzX/Lv/6Fz/78Z13fo8ZAo1Y7UimKWDQDSikXE5cImdRzSXp6ARyOyiv2WiI/aX28WsDbryVUrY7ybS8ntKsVFSdFjYYGS+54js0o/R2VPI8AO+4khi0HvDn9fqQiEWSSSc31JJn2uYKYUaebcLFTENywQVTe1xpLZQZ3ImS2OPPyQex10EqMRYVtOrbzHH/jtRDSbXXjuDNii/MPAH5OCGEeDllooCWjUc2F0vOYcfbZOO722xUuZsf2LSnc/fswBvoyXExcImTc/qJ6uYyeWGNjI4tWZVGgg+ZyQZWR4ahgXOpB3tBzNDVJjmmd8Jlyp2LCNw0Sw1Z5UmLGHWc6aECJnpfGbTq24zX7OyZiFC4X4eJh6fD5xhdrGrebzJjr8pDjLn8fzCsJSEI5KnuBWPLGm26hBBLj1rg+Z4SB9m0APwIwRgjZRQjZmf9nwLWKkEqNE5cKh2HTaaC1z5+P/c4/v+I51GIXtbycLr6SBOQs/Ja5c3HI978vGmqFGGJYX48WrMizyRXKg6/mHrSckSrrQdMYm8GWu2Kjh6c+p2eLkxl3crU4TZYXKlx0SNXweepzdmQyFIIAuFyEi4BtZ357WEdiihlznQiZuc6z116YesIJsLBU6gc0bw2z5I3HkIQiaBRHroY7I2Q2LjbgO1XBZhu/nWQkAs9ee+n6nlgggOCmTWhfsAA2l0v+JKtdVMN3uQmSHKwsRUhWlTb458zB4TfdJHt6s8aC8lUhN2klYoDPr06exNXaisOuvx6thxxSkybkPXFy2wpas8KYcsfxxKU1uQJgyJ3FCloiGopMSpURaRTa58/Hkb/8JZzNzUCPNuOW9XgVJXHc4GJb3eH3ZzNgSw1bFYkppo5XGe5mf/WrmP3VrzJqUAE0em1Z8iZZiHLQ5wohF+NdCdVwV3MDjVL6Rq2/Uy0KZTZS0ahuD1rPa6/hjSuvxHkffADfzJnyJ1nGJy6nE1wZaJIObbWDZjJIjIzA4nDAUeJyjcViaJLZ7jMEuS2TeDy7ZaJmm87mdmP+ddfVrAlTjj8en7/zTrhaWqQHNXqC2HJXGpvB0xanNk8GwJg7qx1IFRpoSVMNNN9++4lC2JKAbRWSB6x4oxYb3C6Cb37XC4eTcJFR13rQQdnwBI1bTQBj7jiWxdEjFWHWPMfDoqAIGrX3quGu5gZaLoPzZgBfAdBKKfURQk4CMJtSeletr1eIQgPtnHfeAc3oy1XIVx6oJLVBrfZcXEa2SDsPE5eI0oBtqx2JYBD/mDULR/7qV5h35ZVFx+PxOLOm5Seti7/pgdWqfpsu2t8PUIqGGqR7+2fNgn/WLPmDGleWbLnjN7tJa20/gDF3FjsIxq9ntmhoJpnE6LZtcLW1oUHS5yo/LFnyBosDFgvJersBCBz0uQU33ggAIImSeFrO+pxELkWGu2UXXwy714tF993HqFE5aDQymPLGeQya1l2WargzKklgHoCvIls7EwA+BXCVAdcqQqEOmt3rlQ0CV/U9Oc9bxUxOqwMz59hxzKKcoggHE5cImcBZMUlAJo6LqTZQrjP7/BZ4Gy2qB99zp5+OFWW2aLVieP16DH78sewx6VYdT5pUHE9cpdtNKrwZpmrImcxdfHgYjx99NLY99ZRsSEIlsO5zw0NprHg7gUg4w9c8p1GeBGA915X8jjJ9bmz7dmXNTQMg8b5zpYNWGmvLUZ8DmGrvGWGgnQ1gCaX0PeTkNiilPQD0BYRpQF4HLZNKYcVPf4rd77yj8Al55LdGU+Fw2XOoxYbpM2047MisgcZTwLachW+xWmFvbJTNhGSrcWNDPEax4q0EhgJp1fEFTp+vZlmcq3/zG7x5VZn1gsYAUN50lUxDqQdZhTfDDP09ESYbGoWJLzxrUlGrHQO7M3hreQKxGOXiYbn18cfx7wULEB8eLD7AWZ+TbHHKhCQkgkHmGZwAZBZUlRei/z+PVQkYau8ZYaAJKNk6JYS0AzA8DSQvs5GKRrHu7rsxuGaNru9R5UGz2BEcySAayW2j7gHxQE6/X1Zmw1UuEcIAUIsDo6EM3no1gaFARjVvDr+/ZlmcQjAom80KyMfvVQJL7niW2dAqtgqw7nd8ifxanU5Y3W55mQ2FCZ9pnyuItc1mcZr/sMwkkxjbvh1CSXacklQEwHq8KntthVCIvQYaoHkh+v8U3i7AAAAgAElEQVTzWC2FVimmargzwkB7FMBDhJAZQLbQOYC7ADxiwLWKkDfQkjnPl14dNO8++2Dx3/6GSUccUf4kqx1PPBzFK8/n9pd56kRltIEcfr+sF8qto6C8bhRlhamf8J0FyuvVIhEMllfu1pihw5Q7joVqpRO+sg4aW+6U6yKyhjO36NAasM2SN2rJxtoCuZrDHPS5ce9jyZpfhfYea+4KUerxTgsCUtEo8yoC2bZoMzLYPiP4G6tF0Ki9Vw13RhhoNwLYAeATAH4AmwH0Avi5AdcqQl4HLe/5snu9ur7H0diI6aedBs/kyWXPoVZHTmw1GzzLw8SVR7nBN+/qqzF7yRLJ+SMjI0zaBQAgVsRz1aacLgJCM0BGuR5nLT1oiWCw/KpVY/AsS+54zgrTGpcBMO53HBZMd/h82UWHxi0T1rzF4xQ2G2CzqZPFMRqihtxIyXzAeZ8r5S4jCJh53nlomTePXZvEtmgLSWDKm0ROiKNQDh3ae9VwZ4TMhgDgWgDX5rY2BymjSrB5HbR80KVemQ2ayaB7+XI0TpsG/+zZ8ucQGxJxCmfOQOPKg1Zmwp914YWyp7e2thrdonEQgriQXRe43AXcWawVPzbz3HPRfuihNWmCUCHuQ2uGDlPuOPagaY2jAthyx6M6+eE33ZRNZOr7Y/EBhdp+TPtcTu9xfJ4z35vhFBOeih98arY4zR2vxdzZvV4suv9+du0phMZST2x5s4ISS3bxDowv4hWeEUygQ3uvGu5q7kEjhNxGCDmTENJMKQ2wMs6AcZmNvIGmd4sThODliy7C1sceK3tKKgWk0wVGBk8PyzIxLfHhYYxs2CA5f2xsjEWzRCQSOQPNpZ67zqOPxuyLa6OB/MW//AWzL7lE/qBGTxBL7vYoD5qKgG2m/Y5D43baqadi8nHHac4cZtrnLA4sPtWFr12VnUt5SExxtbdj75NOgstXMr9z1ud4Hq/ShWhl45b1M0JNBqwp0KG9Vw13RmxxRgD8AEAPIWQtIeROQsi5OW+aocgbaJ1HH41vBALoPPZYXd9DCFEsmB6PZrflxC1OXjoQUDb9/OPbb8fTX/yi5HRBYLsqPvQYH676gReOnEKJGu6E0VEEPvoIqVis6utPXbwYLXPnSg9Qqnm7iSl3KnSVzILWckUAW+54fFiO7dyJ/vff1yyOzLTPWe2w2QgaGnKPCg76nLutDSc98gimHnFw0ftKsXsA6/FaeVHQ/coreGjvvXUns1WFUk8oT30O4DYOTaJ3avA8V3MDjVL6U0rpQgDNAL4DIA7gAQCG5+kW6qBZrFZYrPpdojaPB6kKBprD68FJp7swdXruGhxMXCLKeIEcfj/S8ThSJcJ5TLWBAFgdDni8FhCi3oPW+8YbeGbxYoS2bKnq2sLoKHYsXZoVvi0FTYNg3OFLiUXRrc5Wk4rTVSWgqxYnW00q/ozbT++7Dy9dcIHmRQFrLa+V7yTw2SfZNvIQuydCYyYiwFjPS2FRkAgGkYpEdIfiVAO5es2VwPoZwa2kEGPtPSO2OL2EkJMB3ALg1wCWAHgJWWPNUOR10Ha/+y7e+cEPqtLNsjU0VPSg2RsbcfACB1rbsg9wHlbkeUi3TLITV7mi40w1bgB8+lEUq1ckxNdqqjAUFpeuBqHNm7H80kvlV6061PBN1UHjZFUJQHPQMWCythIH49Xh8yEZDoMmedZBc2DNhwK2b8k9IDmIQQOApxYtwtu/vqf4TRUPS6Z9TsGDlp/LzMji1LpVx/oZwa0WmsYMToA/HbQRZGU1dgO4jFI6lVJ6AaX0bgOuVYS8zMbwunXY8Je/6C71BGTj1yopPMfGEujrTSOVynlcuHpYykse5LW/So0cpinUADauCePTjwvamFZeHZUzLrUiL9UhlyQgMRQ5S9vnTQ2/EFoLCAOsJQ/4q2Oa79OJSEkpGAPT9jXDki9px1esbSoWQ2xwuOg9JS8QwNd4NdVA0+h9ZP2MkHrQzB+vgPbYPaA67mqexYmsnMbnkZXbOJ0Q8gaANwCspJQaOrrzW2b5rUndSQIAjvvDH8SanHLoWrkZr/5vBFd82wt/C+ErFbjMdpOzTLknh0N5YqslEvHM+IQPqDI0xFJVVWqh5e9dVmYjrb1cEUvupB40/vtcJTDtdxwat6JXOJyAt2AOV+p3LHlLw4JEAnDm2sfLVpPT70ditKTSi4qHJdvxWrooKO5zQjAIm8cDi115rNQapQYPT30OAJdJPQBkYveUeamGOyNkNm4FAEKIFcBhyJZ+eh6AFYA+YTKVSKezgfvJaBTEaoWlCmLaFyyoeDweyW7ROd17gA5abqJoPvBAHHfHHWicPr3oeCgUgp/hKi4eTaOpfdxAU+PNcNZoi1P0oMndr0YNNIAxdxxu0+WhVZ4EYMud1Lg1n7t8H4xHEkDhIluBO5a8JcPZpBwXZ3JCDr8f8Z0DxW+qWBSYO16L57n2BQsAQmAKShd3Ct5H1s8IKXd8LAwkvBnc52puoBFCWgAszP37AoA5AFYh60UzFKIOWjgMu8czHoSuAwMffIDowACmn3aa7PFE3kDLZSLyEpsBQCZJIDv4PJMnY46MvERbWxuLVolIxNKaPWh2rxfH33sv2ufPr+ralTxoeowMptyVGowcbNOJ0JgVBjDmjsOYlvb583HCP/6B5g0/Kj6gsCpnyZsQyRlo4kJUyGY7m2VY5OD0+xFaVxwjrMbjzXa8liamFD/c9z37bOx79tns2lMIjXJCrJ8RauqYmoFyzo9KqIY7I2LQugF8F8AogO8DaKaUHkspvdGAaxUh70GjlMLZ0lLVd2148EGsuLF8kxPhOJxOwGLh0INWxtDIpNMIrF6NcHd30fFQjUooqQGlFIl4elz4Euq4IxYLZl14YVnhYLXY78ILceqzz8ImVx+tjGFbCUy5K2kPT4kpeoJnWXInVSc3nzv3pEmYdsopcDuLY2WVJn2WvDVOn4Hv3+zHAQcV8MeBN2PK5z+PGccdVPwmZ31OKYszFYuBoUxocVtKhaU56nMAuByvAHQlQ1XDnREGWjOldBGl9GZK6XJKafXCVSqR7+xH/+Y3uGD16qq+y6agg5YYi4nbmwC4WJGLkGTo5AZfJoNnTjgBmx9+uOhwPvuVBQghuOauE3DsF5zjb6qc8AfXrMHg2rVVXd8zZQoml9PH0xFHxZI7buMyoK9YOkvueDRuM8kkdr78Mob7S5IEFLhj2ucAELtDXIgC4OJhOfvii3Hc1V8uek+Nx5spdwpe22dOPBHLv/Y1du0phMbFKOs+VzrX8eIAkcRgGtznjNBBSxBCvkAIeYAQ8lLur1Qd1QDYaxhsaWtoqJjFOe+iU7H4lAIvDAerShFlrHyL3Q671yuJ42KtcUPsDlithcatOvf1Oz/4AVbdemtV1+5evhy7li2Tb5cO9zVbHTRO4zIA6RanionLVO44mPAz6TSWXXQRtnxasoblSAetf+VKvPx0GJHwuJePB+MWAGhaKPZAcaa9J0kSKBkjQjAIR2Mjs/YUt0WbsDRzHTRe57oJoIN2BYB/IStM+wSychsPE0K+qfLzDxBCBggh6wreayGELCOEbM79bZb7bN5SXfWrX2HNbbdVdR92jwfpeByZlHzHmHTwAZg5e/zH4SUNGKg8+Bw+nyQTkqXGzdiuXXjloXUI9I8XSFc74Tt8vqqTBD6+806sLdc3ONfykniBOInLAPR50NhqyFXOqDMDNpcLVpcL8XiBOLLFrhjfxZK3kfXr8cmHURQpFnHA3dbHHsN9J/0YoWCBgcbZeFUSR06EQvLZ5CygcbfAfB00TuY6jbF7AH86aNcBOJFSeiOl9E+U0p8AOCn3vho8COBLJe9dD2A5pXQWgOW51xJYc5UDupcvz5ZQqQJ5iY5yXrSeVRswFBg3MniYtERUGHxOv19i5HgYKlmHd+3Cp2/sQDRSMLGq5M7p91cts5EIBstOihLDVkUMGkvuuK1PB5TV3qsEttxVzqgzC05fE+KxQiND2WvLkrf8XOHiLJzD1tAAmqFF3KlZFDDtcxW8QJlkEqlIxBwNNEDzYpQpb5DTQTO/zwFyuyzG9jkjDLRWAOtL3tsIQFXUPqX0TQDDJW9/GcBDuf8/BOCsSt+RzGVxVoOZ552Hs958s6wW2vIb/hsfvjfeyXlYkYuoYOU7ZAw0axUlsbQin0VZmCSgxUCr1oMmBIPlJ0UdpWNYcsejVIQIHStLltxJa/vxwZ3D5yv2oHHGmxAKwWolsBUMBR4elqKGXFybB43peK2QiVhR7ocBpMLSlbljOlYBfuNtdVSbqYY7I4Rq3wZwGyHkx5TSKCHEg2zJp3er+M4OSuluAKCU7iaETJI7aXBwEMceeywu3LEDHwQCWHPbbbjmmmvQ19cHj8cDq9WK0dFRtLe3Y3h4GJRStLe3o7+/H15vVqItHA6jo6MDg4kEiN8PtyAg0NODpqYmpNNpRCIRdHZ2IjE6VmRkpIUYBgYGEIvF0NnZib6+PjgcDjQ2NmJoaAjNzc2IxWKIx+PicZfLBbfbjZGREbS2tmJsbAyCIIjH3W43HA4HQqEQ2traEAqFkEwmxePl7unAktVRd28/WhztCAQC2Pvyy9Hk86Grq0u8p97eXjQ0NKCvrw92ux0+nw+Dg4Pw+XwQBKGm9zS4cyeA4hX58MBuOGYkFH+nOCFIBIMYHBxEJBJBR0cHAoEACCFoaWlBIBCQ/E6l9xQfGUHabkdXV5fknoS+bhRGhGSIFV1dXRXvadeuXUgmk7p+p3J9r9w9RUeHUbS3nxbQ1dVlyO+kte+RcAgFaR8YiyYQHhqqeE99fX0YHR2V/Z1qfU/R0TD2KmhfKhFFb2+vIb9Tub4nd0/E7UJidNzIyMBa1Dfl7ikQCIjjVe8cofaeRvv74WywFkkWDezuhtvWatgcoeaeErmqMYUetGQG6OrqqnhPu3btyg4djb+TnntyBIaL5pOUEENXVxfa2towODiI6VdcAd/BB6Orq4t538skE0XembFoHJFIf9l7EgQB8Xi8Js8nNfeEuIDCJVUqkeXO6OeT0j11hIaLxFyjiSR27dpV8Z6CwSCcTmfZ36kSSK3TfAkhkwE8AuAYZD1hLcgaZ1+hlPaq/I7pAJZSSuflXgcppf6C4yOUUkkc2jvvvEPnzp2Lv+27L/a74AIc/Zvf6L6PcHc3dr70EqaffjoaOjqKjqXicTw0ZQqO+6ITR30++1hKtc/F2JLndV+vlvD9aT4s8RHxdfCbq0AbWsueH41G0VChakItse6ee7DippvwresaRSMtsvjXEOZ9RfGzwc2bEenpwZTjjwexaHf+ZtJp/KW9HfN/9CMcdsMNkuP2rS/Du/Q/xNfCjBMQOfP/Kn4nS+6QSaP5jzPFlxQEwe9sM12TCgA8S/8Djq0vi6/Dp92H5H6lkQrFYMmd86MH0PDmL8TX8UO+jtiinzG5diUMvbcc/qVfR9uk7Co7452M0OXvVfwMS97evvZajLz6GC69fHwtH/rqi8i07c/k+uUQ6enBIwcdhBNPd+GQBdlHefS4G5FY8B8VP8eSO2vPB2h67HzxdWryAoxd8DiTayvBf+9BIMKY+Hrkyo8BZ1PZ85nOcwAaXv4+nJ89Ib6OnPjfEOaex+z65eBY/xg8y34ovk7sfw6iJ1eOd1fibvXq1asWL158uNwxI7I4d1NKFwKYAeAMADMopQvVGmdl0J8z/PIG4IDcSalcQL+zuRmuKoX1Rrdtw3s/+hFCW7dKjuW36VwatbxYoZL7enT7dux88cWi48PDpTvKxiGTSsHussFZkACrljv/rFnYa9EiXcYZkJX4OHfFCux/2WXlGlf8WsV2E0vuYLGCknF3OQEFaLrCBxhCokzOF3dKmlRmof2A/UTjDFC3xcmSt+Nuvx1Lbji06D0e5jpnSwsOOuVgtLYVzAWccVcpczgVjSI2OIhM2qTxqzEGjSlvAL86aDpCOarhrmYGGiGkgRDyK0LIM4SQnwEIUEpXUkq7lT6rAs8AyAvGfA3A05VOvmDVKsz/4Q8rnaKIfOxZSkYLjdfAWREV9sm3PfEEli1ZgrQwPkBZiiUe/J3v4D//+a3iKg8qM3RiAwPY/tRTiOvs8MRigX/WLIlHdLwd2mPQmAtNSqoJ8NHvtMa0AIy5U8ioMwtD6z7FujUF/X9P6HMcJFjY3G4suuwYTJ023jY1sjhMuatQmq3rhRfw8OzZGN2yhV17itqibTHKus/xGm9bWv9YTZJANdzV0oN2F7Iesw0AzgPw33q+hBDyTwDvAZhDCOkmhFwO4DcATiSEbAZwYu61BDZb7ULqxCxOGQPNO3UqTv3H/dh7ekHwHy8WPlA5SUCmYHp7ezuTZsm1B4Bq7obXr8erl12Gkc8+03XZSG8vPrn7bkklhTy0FhAGzOCuVHDV/IclAKmRzRt3nNYx3fHScrz4dByZTHYSpyqyX1ny9tZ3v4u1bxVvWPDysEwLcQhCYZIAX9yV/paFi5hEhZJzhiOTBinwvFMQgFQOZDf9GcGNzEbpPKdsc1TDXS0NtFMAnEQpvS73/9P1fAml9CuU0smUUjuldCql9M+U0iFK6WJK6azcX1kXSjKZRGxgAC+edx56Xn9d/50AsOWC/eSqCdi9Xkw5/nh4vOP0caNJlUmD0HHRotLBJ1d0vL+/n1nzPvrd7/D+w8X5Imon/GoLpgc3bsTKn/4U4VygsAQ6pCJYcgfIGI2cPCyl6ed8cSfVQeNjvDobs556MRtRhWHLkrcdzzyD4b4SIV1O+tzDP3wCLz1T0DbOuKu0xSlms5tioMks4BXiWFnPc5Ksa06Fao1+RtTSQPMUZFruAuCr4XergtVqRWJkBD2vvor40FBV35X3oMkZaKGtW7Ft6UtIJgtWb7x0IIXBl/egFRo5+cwTFuhevhx9G0rCEVVyl19tCjoNNHHV6pPvmlIdNOUJnyV3AGRiMzjpdzrKZDHlTjLh82Fk5A20eL7aE0e80UwGwugonB5n8QFO+pyzwV6sg6Zii5Npn6sgjpwIBmF1u2F1Oks/ZTx0SEWwnuekVRj4GK+snxG1lNmwEUK+AICUeQ1K6as1vJ4s8gaVvcoO5fT7cf6HH8Il457sfuUVvH/DDdj/R17Y7bli6Zx0IKXBl1+xVSv4qheJYBCNnQ0Axg1otd6Maj1ogpL2kCRw1ggVmuog1VZKwpxyyyXQMekzBacxLU5vNlsm70FTE9PCCsLoKECp2MY8eNlWd3lsiA8WlnpS9mawhETouoC3inqMBkOPkcEc3OqgsX1G1PLbBwA8UPB6qOQ1BbBvDa8nQTqdHjfQqhSqJRYLmvaVb25CJouTlz1ypWBt/5w5OOWpp9B60EHie+FwGK2t5WU4aglhdBTOWdOK31Rb6qmpCSCkag9a2YlRYwFhgC13ALhVxNcTv8eSO163hp1eN4ACPS8VBhor3sTxUmKgccOd245QTJtQLdPxWsHbPePLX0bbYYexaUcpSuc53ngDJJ5kXkISpNwZ+4yomYFGKZ1eq+/SC7vdLpZmKlcBQAs+vf9+NM2Ygb1PPLHofSEYhN3rhcVSksVJqfmaVKXbDyUWvqOpCVOOP77ovY5yWY0GQAgG4Ww6sPhNlRM+sVhwxosvwrv33rqunQgGYXE4YHW75b+/tJ6kiomLJXcAuCz6DUCm33HGHadJAm1z9sHXrvLA589Fm6gwbFnxlhEENE6fDre/eDeCl90CV4N1fGsYAFURsM2yz8l5u/OYesIJzNpRComxw9tYhZz3kY9tdT3ex2q4M6LUk2lIpVKw2O3wzZpVNs5ICz6+4w7sePZZyfv5Irc8alJJB19xR6eUYtsTT2Bw7VrxvUAgwKJpSAsCGiZPRkN7scawlgl/0uc+h4bOTl3XP+y663DBqlXFEh+FUDBu5cCKuzz4TT/XLrPBkrtKGXVmwuGwoH2SFQ5Htk/yxJt/9mxcsHo1pi+YXnyAkz4346AWfO6Ygt9VhaHBdLxW0PIKbdmCGOO5QwTnYxUAv3WHdUgxVcPdhDLQAGDqF7+I81asgG/mTOWTFWD3eGSTBMT4AUkn4sDKVxh8hBC89d3vYuujjxa9xwJWhwMXrF6Ngy8o9khq2R7etWwZup7XV7HB1tAAz157lT0uMW5VTFysuBPBqSdITwwaU+449TzSVAKrVwjo2ZmbO3jjDeC2z82Y68eRx40H2Uu8LjIws8+RtJDdZQHwwjnnYOUtt7BrSyF0CHKz7nPShSgfW5yl7VBj3FbD3YQy0GqpgwYANo9HVgftqF//Ggvvu0+mADMHnUiFF8jp8xUF2re0qKpjXztU4b5ed889+PiOO3RddsODD2LzP/9Z/gRJ/J7yhM+aO149QVJ9IM644zTomNA03lgWx5aN2TGgZludFW87nnsOL5x9NqLR4p0BXry2qXgCoWAGqVQ+fk95/mfa54ilaJcFgDjXCcEgnDXY5dEDSbwoR31OhOTZykefkz5fjZ3nJpSBlkwmsfFvf8PSU09FJlW9N8vW0CDrQWucNg0tc+dWjDEwC2qCtR1+f1EWJyv39dAnn+CFs89GYGuxzIYW3hwlxqUWbHjoIWx78snyJ+iIozLd9c9BnwP2gC1OSaknDhZTyMa0OF1Ekw4aK95Gt25F7xtvwOooldngo8/tWBfA/94RxlAgp/vIEXciJN7HFDKpFJLhsDkitYAubzcPvHEBSSJZfYtTNaxWK0a3b0dg1SpYauBNs3s8YtJBITb+9a/oX7GCz3phKoT0nH5/kZHT1FS+SG4tEenpQe8bbyCTLnH5anhYOkuMSy1QSm2XuK9VrMhZcSeC0606PZlhTLnj1IOGtACXmyAeV5/FyYq3RDAIYrPBXpJwxct2Uz7XZ1yiRNmbwXq8SrbqMoKy3I/R0KFZyJ43TnXQJM8IY7njT+ipSqQiEdiqlNjIY9H998saeu/feCP2/8Y3MGtfDjWpJPEF0vY7/H6Eu7rE12lGBXvHy5sUu/Y1edByxiWlVPPefiIYrLxqlUxcyhM+K+7y4NITRKkku0nNdhNL7ngtlk4ySbhcRJTZULOtzoo3cUFjK/GgcWLcOp1ZzsYlSvjqcwBkt+oSwayBZpYHTU/dXOa8SRZUHMxzgK5dlmq4m1AetLwOWumKTy+cfr9E8DYtCEhFo7kkAf48aGos/CP/67+w+KGHxNcRmW1cI1BWh0zDhO/0+5ERBKRjMeWTCyCqoleK+6hQw7QcWHEnotRLwIPrX1LmSbl0DMCYO8mEzwFvAJBOwunWtsXJcrw6/X5ut5vceQONQ+5EyIQkuFpb8fk770THEUewbUseOrzdrHkrXajwED4EQJf3sRruJpQHzW6319SDtmvZMgysXIkFP/mJ+J5QWOSWx+0mFWKrTTNmFL3u1ClboRVCuQLBGgzbOZdcghlnnQWry6V8cuG1x8YASiuuWiUaNyomLlbc5cFj3KOeSQtgzF0FVXczQTJJfOlMF6zWnMyGCi8QK94aJk8GsVpltuk46HMAnM5s7JkW76Pp4zWThNM/GbMvvphpO4raoEPvkTVvvJa00+N9rIa7CeVBSyaT8E6bhvYaKTT3vfcePr7zzqL3Cr1APE5cpNQzIDPhD61bh3X33INMMtvevr4+Fk2Dw+9H+2GHgdhLS8eo583V2oqm6dNBLNq6rtPnw9f7+jD3iivKn6SjjAcr7kRIVuTmGxrSQunqDDSW3HFp2AJAOgmP1wKXO+dxVMEdK96O+uUv8YX//V8u+xwA2G0pLDrJiX1m5NrH5XiV1oCN9vdj4MMPkU4k2LZFbIN2mQ3mvJWG5nCyoNLjfayGuwlloBFCcMTPfoaF995bk++zezzIJJNIC+OdQ4yjamqS8aBx0IlUWPh9776LFTfdhEQuWNVuZ1OLbd6VV+LMV16pKoU60tuLtbffjrGCGDq1sDocFYsTSzMRlVfkrLgTwWPRbx36cQBj7jgMRwAAZJLo3pnCW8vjoJSqins0vc9xYtxaMikcfrQTnVOyUhZqFgbsuZPusnQ9/zyePekkxIeH2bZFbIP28cqaN4mcECd9To/3sRruJpSBZrValU/SgHy5qEIttLZDD8X5q1ej85hj+NSkUmHhiwXTc8amj7EeTzWex2h/Pz78xS8wvH69pmuOfPYZ3r3uOozt3Fn+JB2rI9O542HiUpE5LAem3EmywlIAzbC7fhmQdBJ9PWmseFtAIq5uy4QVb89+6Uv45K67uE2wQCaJ4EgGI0O5IGyOuMtDznMrKNUENhhSj7fyeGXNG6/iyHq8j9VwN6EMtFQqhRfPPRcf/PznNfm+fMH1Qi00q8OBpunTs8c41KRSY+Hn47Dy3sDBwUHjGwbg9SuvxHvXXy91X2tJEsh1dq0F04ObNuGz//s/JMfGyp+kI0mAFXciOJy49MRlAIy5I0Q6FjgYr8gk4XRltzfjcapqm44FbzSTQeDDD7Nedh55oxRIJ7H08SiWv5AryKliQcV8vEqqzQhIBIOwOp2wlakJbDh0lCtizxuvST3aw2Cq4W5CGWhWqxUjGzYgPjRUk+/LJxsUetD6V6zA2ttvz8YPcPiwVBOwXWrksFodDa1di2hvr9TDomFrWDQuNWqhiVvTFe5Vj/ua+cqSw0WB3iSB+qoc2ay+XPxZIk5VeR9Z8JYMh0EzGTh9Pi5jbUHTIKBZiZI4BSUWwKK8g8Le4y3dqlOU+zEaOhaipnseOYlBky5GjR2vE8pAo5QiFYmInq9qMePLX8Y3BgbgnzNHfK/3zTfx4S9+AWKx8Bl4LNmmkyn1VGLkCAKbzi/ki8xLBp/61ZFDpwdNlTikjomLFXd5SNLPOXhYSkvHqEsOZ84dh9vDeR00IJuNqMb7yIK3IkkcHrPVc21wuQkSMajyngHs+5ycRImZZZ4AfVU/mPPGo9cWkHryVPS7aribUDIbmUwGyUgEthLtMr2QE6lNBMxYA5AAACAASURBVIOwe72w2O2yGTpmQ42F3zhjBi5YswbuSZMAADGNmmJ6IeoqVRGwbbFaYW9s1FzuKa+KXkmCRfLAVjH4WHEngseJSzJpqYtBY84d5x60eJyqWhSw4C1RSU6IB95ybXDmPGhqvbY8jNeDv/MdzTsANYVkEa88XlnzxnPcYxFUlHqqhrsJZaDZbTbQdLpmHrRITw/W/OEPmHPppWg7+GAAWc9N3ovDpQdNhZFhdTjQuM8+4msWGjepeBzpeFzeg6aRt/M/+AB2jeUzMoIAd1tb5eoDjDVu9IDLiUuH5xEwQ1uJv2xEkhHQ2m7Bd25ohN0ORDjRpLLYbJiycCE8e+0Faik2Jngo9ZSfM/JVGDJE3aPM/PEqoH3BAqZtKIU0lIMf7T0RnMagSYXgjdXem1BbnElBwNQTT4Rv5szafF84jA0PPIDQ5s3ie4lQaHybjEN9IMkDu8zg++Tuu7Hz5ZcBsNG4yQgCpixcCN9++8mk7WvjzT1pEmwahWqPvPVWXPTpp5VP0uFBY6+rxJ+0i2TSUmmgseaOS+M2nYLFQuBwkOzigZM+1zJ3Lk558km0z5/Pp7RLrg1zDrTjtHPcXGrvAZD1oHUvX47gpk1s21EIHQsq9jpopV5b8+c5AMw15CaUgWax2XDyv/6F6WecUZPvE5MECgqmC4UBnpKJiwMrX6WW17p77kHXs88CABwOdVtS1cDR1IRTnnwSM848UyazSduEv/mRR7Duvvs0t0GpdqeebEQW3BWBx7I7OuRJAA6448CDlufujWVxbFyfVDXhM+etVKKEA29GfqxO6rRi/3l2EJs6TlhzJ7coeO3yy/HZAw8wbUcRdMjiMOeNw3hRQE6iRNn7WA13E8tA06gurwQ5mY2TH30UJ/ztbwCq0/MyDCpXRw6fT4yDaGxsNLpVxSBWUIwbS4RmgIz6grI7X3gBGwtqiarByltuwTolAWMdHjTW3PE4cekR+AXM4K5UC838VXne+7huTRI7t6VULQpY8Lb+z3/Gvw87DKloVNomHua5XJ+Lxyl2bk8hnlA39zOf60rGa0ZIKNcENhjShaiykcGcNx77HCAj8qs811XD3YQy0IRwGP888EDsfuedmnyfnMyGze0e3+LkcEUurScpP/icfr+YCTlUI1mSSuh57TX8e/78rMAsIVUNQIffL2ZlqsWOpUsRWL268kk6XP8suCsCj65/HYYtYAJ3HMag5fucy50LdlfBHQveort3I7xrF6xuN5fb6vnfbmB3Gv/+axSB3ep+S7PHqzA2CkCmHjFL6PB4sx+rMpn+lLJtgxxKdywM5m6PMtAIITsIIZ8QQtYQQj6UHEd2YoHCVpZaWB0O2L1eZFLjP8rKm29G9/Ll2RdcTlylQnryHcjp94uZWs3NzUa3CtGBAYx1dcGad/dWUT6msO1qIeQzSCtAjw4aC+4KwWUJlFIjUcWKHGDPHZcLqtx2YT7YnZc+l8glQxFCOJV2GTdsgfGC6UpgPl5LuBOCWQPNrCoCgD6ZDeZjlRCpc8HsfpdJZ3d7cqAgqrT3quFujzLQcvgCpfRQSunhpQcy6ew2Wa2yOAHg0p07cdiPf5z9/mQSn9x1l+iJ4XHikoqGyrtgHQVGDosU6rzHK79yrGZ72On3Ix2PIxWPqzqfUipqsFWEDg+a6VIRPBgZEq8tnzIbXCYJ5OUi3HzJbBQtaHiUdimQ2QAAtXXH2ctsFBsZidFsJRNTPWg6YtCY8wbw1+90ZqvXZTZyMMJAK0SiVOxUYuHzEDyrbovzmN/9LqvlBiCu0tCpBmL9uXzsRRWGRn5yE0IhVdmcybGxrCp6JWkOSqWCuSq8GSy4KwKHfU5vJQH23HEY15LzeLvdBJExdQYaC96K1O5LPaIc9DlRZkOjB83sPtc8xYdTn3kGzQccwLYdhdCh5cWcN2QXVCQ1btyQTBKmbnLqDOWohrs9zUCjAF4mhFAAf6KU3l94MBoOAwAuvPRShC0WnHPOObjmmmvQ19cHj8cDq9WK0dFRtLe3Y3h4GJRStLe3o7+/H96cuG04HEZHRwcCgQAIIdh1//1I2u046FvfQmj7dgCAxeNBV1cXJkdiKKymFh0LYldXFzo7O9HX1weHw4HGxkYMDQ2hubkZsVgM8XhcPO5yueB2uzEyMoLW1laMjY1BEATxuNvthsPhQCgUQltbG0KhEJLJpHhc7p72GgvCWdCmcCyB3V1dRffU0tKCwNAQmpqakB4bQzqdRiKRQF9fH+x2O3w+HwYHB+Hz+SAIAmKxWNX3FB0agrWhAYMjI3A4HPBkCArXbclYGD1DcVW/U9spp2DxwoWw+HzYtWvX+D0FAtl7SqcRiUTENqWDQbg7OxGxWBAMBuXvqcGFQkc0JVYkBEHxd0qn0xgYGND8O6npe3L3tE9Jfe94ZLT8PTHqe9PjkaI2jUXjCObiLirdk8PhQFfBeDGq7+WP75eiKJxShwP9GEGXIb+T2ns6QIjBBuDUs90ghGDbyChGo10V76lwvOqZI9TcU/OhhyIyNoaRkRFgLIzCkPZMMo5IJGLY76TmnjyhETQCcDiyES2RsIB4PK74O6XTaYyMjGj+nfTe034lm1TR+BiajpuP4VAIyS5z+t6cZLG7MTAcAhkdrXhPjY2N2L17d9XPJy33NI/Yitjr7toG0thp6BxR6Z4CXduKnxEWG7q7uxXvCQCi0WjZ36kSCOUh8E4lCCFTKKW9hJBJAJYB+Dal9M388eXPPUdH7r8fx991F+w1qibw9OLFcLW14eR//QsDH36IZ086CSf961/Y+8QT4Vz9f2h461bx3PihlyG28OaaXFcvPC98G45Nz4qvwyffgeT+X5acN/DBB9j6+ONYcOON2D0ygmnTphnarg0PPYSBlStx/N13AwCaHlwEa2iHeDx06WvINM8wtA0VIUTQfO+B4ktqcyN4zWeKH+vq6jKcu0LYdr6Dxie/Kr5OTj0a4XP/yez6cnB+9Gc0vPlf4uv4od9AbOEtip9jzZ1n6ZVwbH1RfB0+9V4kZ53C7PpyaHpwIayhLvF16GuvI+OfXvEzrHkjsWH47z9MfJ1x+RH6zzXMri8H247X0fj01wEA2zYn4Z13JOxXPqH4OdbcuVb+Ee73/kd83dNxIXbbj8D000+H1ems8Enj4Fn6n3BsfUl8HT7tPiT3+1LFz7DmDQB8fz4KlvC4ARO87F3QxilM21AIEu6H/89Hiq8zDe0IffMDxc8pcbd69epVixcvloRsAXtYDBqltDf3dwDAkwCOKDxub2zE4gcfrJlxBmS3S/NZnMlwGCBkvOA2lwKOpUJ68k7S0NatWH///YgPDsKlUfRVD/b/2tdE4yzbLv3bTZGeHrz9ve9hcO3aGrVOnwYaACbcFWFPqF6hUmbDbO64yIDNjdeubSk8+1gUQlQ5mIo1b1xKuxSM131n2eGfoi4Q22zudqzYiNe/+U3QtHpZoZpDx1Yd87EK/vqdVATe+GfEHmOgEUI8hJDG/P8BnARgXeE5tdZBA7JSG3kdtL0WLcI3BgYw6fCssbunlnoCCgqmB4Nwu92y5xgJKXfqH5apeBwbH3oIwY0bVZ2/+5138NKFFyLc3V3+JB2BswCYc8ejJpVUvFHdxMWcO84m/Gwbsv1+NJTBxk9TiI9GFD5hPG+UUvx12jSsue227Bsc9rnC0j89u1Lo3jis6mPM5zpJkkAEFocjK19iEvTooJnxjOCu3+nIfgWq426PMdAAdAB4mxCyFsBKAM9RSl8sPCHc04N/17jOWaEHDcgW6yZ5Q5C3NGBIxTfLdSJHgYE2MjJieLuePuEEvPmtb42/UUXAdqFxqQaj27ahe9myyifpCJwFwIS7InAZ6K4vSYA5d7xN+AVtELMRx6KVzgZgPG/JcBjJsTFYbLkxIJdNZ3ZoTME89+7rCbzz73UVTh4H+z5XvNBLhGNw+v2KVU0MhQ5PEHPeICcpZLLHW2cWZzXc7TEGGqV0G6X0kNy/Aymlvyw9h2QyoKnaZhi529vFwtw7nn0Wb3/ve6CZbKQ2l5pUpWVYynnQctu0QjCI1tZWo1uV1acrRBXeDEdB29Ugf56jgnq3Hg00AEy4KwTPXiARKsqfAHXuCtvg0mCgGc2bIMlWt4KS8UcFAQWoiVt0KP7tXG6CRFTdb8m8z5U8xBNj8YrzEAvoqfzBmjcA3GUP6ynzBFTH3R5joKlBJp0W1f9rhaN+/Wt8+ZVXAAD9K1Zg66OPjnvQOF6Ri6jkQSMEqWgUY2NjhjdLKCwyDxnPnoaHpcVmg93rVe1BS4RCIFZr5dhEnasjFtwVgUMdtNKJU22pJ9O542i85uUihNGw4keM5i0/roq0urjTpBrvcy4XQSKizrvCvM+V8JaIxEwVqQWgKwaNOW8Af31OZxhMNdztaTIbFUEzGdgbGgz7/ry6tggOH5Zq44EaOjtxWSAAYrGgq6tL9pxaIS0ISEWjxRN+lQ/Lhs5O0ZOphEROdLPStoLEm6LSgyYIjN3uHBsZIrjljrNST5SKbXC5CRo8RNXOodG8STxoQJa79HgCA8kIoDAxjqrgt3PmDDRKqeLWIes+V7oQXXz5ERg76gambZBAR+UP5mMVcsLSZm9xlibgGT/PTSgDjVAKWw0zOIFsDceNDz2EE/7+d6kXSKKGz0FWmEpDgxAilsTq7Ow0tEmiSG0Rd9VVYThv5UrV5zqbm9Eyb17lk3RmcRrNXSm4i8uQaQO/3HFWSYCms9uFAJp8Flz1Iz+CZ5+j+DGjeXO1tGDOpZfCu88+4nvUakeR6WO2cVsw17rcBJl0BqlIRDGDn3WfK51/m1qcsM6ezbYNJdBT+YM5bwB3tXMl85zKLc5quJtQW5xWrxf7nHxyTb8z0tuL7uXLkYxEitW1ARkXLAcK2xoMjZW33ILP/vIXRbG8qttktWLOpZcWG0kMvY+H/+QnOOXJJyufpNODZjR3EuwJlQR45Y43j7fO5AqjeWs+4AAcd/vtaJo+ffxN3vpdwfUPOMiO83+/BFYVcgZm97m1L2/EwAfK2lmGQke/Y84bwGGf0ycnVA13E8pAc7W3Y95VV9X0O/Nlo5KRCCw2Gxo6OsYP8rjdJEkSKG/l73rpJfS+/rrhKdSu1lYcd/vt6DzqqIJ2Vfew/PT++/HuddfVoHVZqM1+LYXZMhume4EA3ROX+TIb5nofS/vcC0+Ese6eexQ/ZzRvaUGQhA/wlmBReP3GJgsm7b/PeNZpBZjZ5yileOMfa7FLKaPcYOhJiNrTpJiMAEmXeh7rMhuaYETqct5AS4XDOOXJJ/HFBx4Qj/HWgQDIxBeUf1g6/H4kQiE4HOoeqLqblE5LJ/ySuAet28PDn3yCrueeU3Xusosvxke//33lk1Rmv5bCaO4kkMRRmd/npFlhvHLH2YKqpM/17hIQWL1a8WNG87bm97/Hg5MnF49ZiSi3yf2uoN9HIxmsf2VdZZ3DHMwcr4kEAArzkwR0JEQx5w3gTwhepxRTNdxNKAMt+OmnWPHTn9b0O/NZoamoTPo7h5pUWlZHTr8fQjCIUC4o2Chsf/JJPNDejtDWreNvlsYXaHRfO/x+1VmcAytWIKrkZtYZ6G40dxJwrkkFQLXMBhfcmYmSPudyW1X1aaN5S4RCsHu949nqAH/bwwXzxdgoxWt3LFVVWYR1nyucf/MF3c2W2dAz1zEfq+DPaytZDDN4RkwoAw2UwlZjV6yzuRm+WbOQSaexbMkS7Cjw2pTKCZjegQCpoVPhYZk3ctra2gxtUiIYBCiFI6cnB1RfhcHp9yMdiyGdqFwah1IqZnFWgt5Ad6O5k4BLTSrtQccAe+542x4u7fPOBpsqbT+jeRNKY23B38Oy8LfLi/zywJ0EBV6WRDxnoJnsQdPj8WbOG8BdDJokuUJlKEc13E08A63GOmiTDj8c561YAd/Mmdj54ouIFLrROetAAKRWfoVO1NDRAZvLZfyKXCaLU+rN0LZl4lBZTSAZDoOm08qrVp1xVGasLLnrdzo15OoetOI+72qwI6GCExbjVbKg4a1qSolQLaCusoiZfS7vQeNui5NTD5rUa2v2tnopb8bvFEwsAw3jMWO1hqx4I28dCNqyOI/4+c9x7vvvI5k0drIVgkHYPB5Y7AVtqVYHbdIkNE6bhnQ8XvnacppOMpCmnqsbfEZzJwve4tB0ZiMy5463mJYS3vyT3PBMmaL4MaN5k+g9Qma3wGzvY6EHzQmAEFUeNNZ9rpC3qdOsuPyWeWg/7DCmbShuENU1Xs2Y53jrc9JQDuPnuQmlgwbU3kCLDw/jlUsuQccRRwCorZaXIdCwxZmH0Ro3iWBQLC2VR7Xbw9PPOAPTzzhD8TyayaDzmGOKNJ1koVMqwgx9IGop1qQi6STMjEKTZMByyl212+q1Rumi4OgzpmPekqcVP2c0b/udfz5spYLfvHkfC65PCIHT61blfTRTB81qJWjyWTBqYqH0Qu09AKDEChBlP405Omh89TnpFqfx89yE8qA52trQPHduTb/TYrOh/733MPTxxwBQbGjwFjhLM5JOVMnQ6HvvPbx80UXYoSK4thpM/eIXccDll5e0i82WSeM+++C0pUux16JFlU+UbA3zoUklC+6yEdnrA+mCJKnHbM8jn31u7je/idlf/Wrxm7zNdSV9/uw/XY/DfvxjxY+ZqYO2c0cKK5ftRqbG9aI1gVPtPTnwFjNqht7jhDLQGiZPRtshh9T0O/MryWQkgsZp0+AsKHzKnTK5XAZnBemRxMgIdr38MiyRiKHN2vecc3DI974nbVsBtHozon19eOGcc9Cdq5NaLfQGgHoM2lKvBJ4DtgH1HjTW3PHnQSu+/vb1o3jmhBMUM46N5I1Simh/P9JC5aQZs6umlHLnn7E3XCqKUrPvc+PzyI4tKbz3UgDEamXahkJIfjdOxyoA/jxoOuWEquFuQhloVEZvq1pYbDZYnU50HHUULvjoI/hnzRo/yN2qUtv2Zj6eLh1WLtBcDeJDQ9JsyyrjgYjVit7XX8fo9u0Vz9v2xBN47MgjdchsqNv9t5ox2fIm7yJZlXPKHWcTfun1kymCwOrViA8NVfyYkbylolH884ADsO7ee4sPSPqcyYkpJdztePsTbPrHPxQ/xr7PFWdxOt0WQ/Q6VaM041qlkWHKPMfbToFOKaZquJtQBlpo/XoMf/ppzb/X5vEgJedl4uxBqSVBABiPpxvt7zesTQDw1KJFeOf73y9+s0rj1qkyizPS24vQ5s3SmJoSSESGVQ6+0dFRVefVFNwtDEonLnXeR+bcSSZ8vrJfXd5sqSKlPm0kb2Ld3NKsZ868tqV9ftOL7+LjO+9U/BjrPlekgxanYsapadBpZJgxz/G2U6A3W70a7iaUgQZA8UGsB5MOPxw7li7FsiVLig+UalLRDJAxUZNKYmRUflDmJ2FXjb2OpRBCIWVdJY3GrcVuh83jUXyYJUIhEItFsYiyZHtY5RZne3u7qvNqCd5iM/S6/llzJ+1zZpeOKebN0ZgNHlfq00byliiT9Sz5Tc0uu1PS5x1NjWLGdiUwH68FvMVjFC6XuQbanjJWAXDnQdNTIguojrsJZ6AZIbNx0iOPYO8TT8TgmjXSgzx50Uo9AgpbTc7mZjROn46YgSnUmWQSyXBYWVdJx4Tv9PkUU+uFnGRAkSq6bEO1Z78CwPDwsKrzaooqNeRqDp2rcubcSeRJ+FqRu7zZxaVSnzaSN1k5IYA/iZKS6zt9TUgEg6AKVTWY97mCsZCIUzhdMLfyx54yVgH+QhJ0hsFUw92Ek9motVBtHoKMNhCA7MSVLoivSguAzWVIG5Sg1cK3NTTggtWrsWvXLsPaVG5FLp3wtW83tc2fD/ekSZWvL6OKLget28PieSZMtrzpA+ldlbPmjrekHkklgSYP2hcsKKq4IQcjeRPkRKXB33aThDufDxlBQDoWq7iLwny8WqygxApC0/jKZR6kU0CEpgFi0qNXZxanOfMcX+NVb7Z6NdxNPAPNgC3Od37wA+xYuhQdRx4pOUYttmJNqkzKPE0qnXvkRrqvhTIr8lpk1J3wt78pntNy4IFwNjcrf5nOGDRTXP8S76PZAdvqq1cUwsztJgDmr8hLeHN4PThz2TLFjxnJm2/WLBx+883w7r138QHuqlcUX9/hzxq1iWCw4jPAtPGaTsNqJbBagUgmpdr7Umvozbg2hze+xqveRXx9izMHx6RJsBiQbRLdvTv7/XKeGI4mfYmRo2LwvfXtb+PtG280qEXZwsCH33yzVP6EkSbVIddei2N+9zvlE3WujvoNTrCQhSQ2Y8/c4mTOHWdxVNKQBPN588+ahUOuvVYqWcEbdyXXn3Xml7Bk0yY0KIiCmjNeHaCUYvkLcXRtS5nLnU7tPTN4486DpnOLsxruJpSB1tDRYcj35rdNmw84QHKMq8BjHRZ+cNMmRDdtMqpFcLe345Brr4V/9uyi92vhQVv7hz/ghbPPrqp94vV1lnryKiUfGABJBQszV5aZdDY5JgdKLIBF3SKJNXfcbXHKeDNe/spX8N7111f8nJG8Rfv7MbZzp+R97rbVS65va/LD3damGGtqyni12iEkgI9WChjoS5s6XqXznDoDzQzeuPOgSUKI1C3iq+FuQhloRik02z0euDs68Lmbb5Ye5CnwWIcnw+HzIWlgCnUiGMRYVxcy6ZLs1tLOrYO32OAgBj78sOI5j37uc/jgF79Q/rJSw1rlytIU8JTdpHNVaQo4m/Dl4oFiAwMY3brVnPYAWHvbbXhq4ULpAe64K57ro8Oj+PCXvzREZqlqWOyIx7OBLy43MXe86lTDNwU8zXOAbo93NZhQBlpYQbRUL8rqoAFcb3GqWR05/X7FtP5qsPXxx/Hv+fMRHxwsPlCaYapj8Dn9fqQiEWTKZKFSShHeuVNV1pTe1VHYYJFfWfAUsK1zaxhgz510y4Sz2D2LHQ6fT3E8GslbIhiUJvSA/+2mVFzA2v/5H7EkXzmYMV6p1Y54LGeguYjJHm99C1FTeJPMc2Zvq+tbjFbD3YQy0IyIPwMAz+TJSIbD2Pbkk5JjpVthpk5cOjJ0HH6/oZUEVGeF6TTQgPK6UaloFJlkUlUWp97spg6DttUrgdbAuK0V9AYdAyZwJ/F2m63lJVV1d/r9inpeRvJWNuuZMw9a6cPa3pyNmVMybs0Yr7DYkch50Jwucz1oejOuTeGNM2mX0vAlFtztUQYaIeRLhJCNhJAthBBJoAY1qITGtNNOAwCk43HpQZ510FQ8LP2zZ8Mzc6ZhadSJYBBWtxtWp7P4QA22hh0KBlpZVXQ56NRBCwQCqs6rKWqwPVwz6DRsARO442msyl3fkjXQEgoGmpG8CWU8aLxvNzn8LQCUNeRMGa9WO5ICBSG5LU5TPWjanxGAWfNcifPD9Gz1Uu7U7RZUwx3HASPFIIRYAdwN4EQA3QA+IIQ8QyldL56jJEaqE2XFGyENnnV9+CdkvCasNgBYQ8XBvRJPiwzmXnEFmr70JcPqwwmhkPyWScnEYBnrgfv1WzR9d9tgL6YeNBXuD+6Cu0d6jUhXdlu1sfdluF+vvP1tHfysuH0qDQ0z6uqVts2x8SlYhzYybwcAEKHY+6rFg8acu1zlj3xSA6EZuF+7GTCpNqJt96qi19RqR9uhhyp6gYzkLREMomHyZMn7pb+rvft9QON4rSVK+x2xO+FoalI0bk0ZrxY7Zs6x4/s/zc7HyQ/+iIxbubC7EbCObCt+Q+V45WGeswR3aH5G1BLWkS0lb6gzn6rhbo8x0AAcAWALpXQbABBCHgHwZQCigWbUFufmhx8GACTHxqQHSzq4Y+uLhrRBF1Ra+I5wGNufeaboPVdbGyYfcwwAoPuVV5CMRouON3R0iLpwO196SVIM3Tt1KtoPO6yCwG/J4IsH4Vr7kKr25jEDwIxzgJ7Vj2PnG8UeQLuDoLnVggMPsWPS2FtwrX1X03er5a6lpUXb99YCpQ/LnpWw96xk3w45aIhBM427AmFp18d/Zd+GcrDYMefSSzHn0kux49lnJV7tphkz0HrQQfD7fJLxCgC+/fZDy9y5SCcS2PnSS5LjzfvvD//s2UhGIuhevlxyvHXePCy44QZVckLW4c2wDm/WeIMGwuqAw++HMDqKtCBg54vSebh5zhy07L03UtEodr3yiuR4y4EHwjdzJoTRUfS8/rrkeNshh6Bx2jQkRkbQ+9ZbkuPthx0G79SpiAUC6HvvPfF999ogrKEk9p1lg81G4Ni0tLp7rSHULkR5mOcssUHNzwgjoXYxWg13e5KBtheAQsn7bgBFyrExQnDsscfCZrMhnU7jnHPOwTXXXIO+vj54PB5YrVaMjo6ivb0dw8PDoJSivb0d/f39YipsOBxGR0cHAoEACCFoaWmBZcYMAIBlr73Q1dWFzs5O9PX1wW63Y19rA3jNg4lRO4aGhmTvKRAIoKmpCcPr1uG9q69Goqen6LPNRxyBLz7yCARBwJvf/z5i3d1Fx9sXLcKiBx9ELBbD61dfjeTISNHxztNOw8J770X7mWeicWQEiUQCfX19cLvdcDgciAyEoGLjURVWvi1g66Zi93NziwWXf9uLU85y6/rO3uEwfB3ZNrtcLrjdboyMjKC1tRVjY2MQBAGdnZ3YsmULOjs74XA4EAqF0NbWhlAohGQyKfaTavpe/ndKp9OIRCLo7OxEIpZBZbUn85C2NaCrq0vVPfX19aGhoaFoPPl8PgwODsLn80EQBMRiMfG4w+FAY2MjhoaG0NzcjFgshng8Lh6v9Dvl+16j3QtbOqFwF+YglrEh0N+PeDyO1664QpL8MuvrX8esH/0I/T09WPX1r0s+f8A112D6VVchMjCAN2WOH3z99Zi8ZAkiu3bhbZnjh996K1rOOAOUEESj0aK+ZwvFMaNG91lrZCwO7Ortw2mvvYbBUAgDPT14Veb+DvrhD+E6/XS02myyFtKFhwAADxlJREFUx+ffcgtazzoLJBCQPX74b38L/0kngXZ1yR4/6o9/hPe445D67DO8XnLc4yXYfbAdC080p8pMOQjEia6uLsXxJAgCvF6vZDwZOe+1EIrKNTXMRSxjx0B3t+I9BYNBzJw5U3Yu7+vrq3gNYkYJBz0ghJwP4GRK6RW515cAOIJS+u38OW+99RadN2+eIdcvl91k3/IiPM9dBWJe/QBZUGJF+Ky/IrXPsYrn7vroI3hKYsRsDQ1omj4dABDcvFnysLA3NqIxpzY+smEDaEnBdUdTE7xTp1a8rvfJS2DfKV2JakUomIGQKObfagVa2vR5VFOt+2NsyVJVcWgjIyNoVlOpoIawDG5A07/PAUlGlU9mjOjCW5A49BuqzjWDO/fbv4Zr1Z+YXlMNMu5WjC55DtSbNb2H16+XnONsboZn8mQMDw0BMuKXrtZWNHR0IJNKISijbehub4e7vR1pQUBoyxbJ8YbOTrjKrPZJIoSmv58CS7hX660ZjsSBFyJ6wm/F15l0GsGN0i1/d3s74jYbfF4vgpul3r+Gjg64WluRiscxum2b5Lhn8mQ4m5uRikYxumOH9Phee8Hp8yEZDhdpydm3L4f7nd/B4yVo8PAT9k2tDoyd8zDSUw5XPNeMsQpK4X3sAth7P2B7XRVIdRyCsQufUhUeocTd6tWrVy1evFj2R9iTDLSjAfyMUnpy7vUNAEAp/XX+HCMNtEqwDqyDbfdqgGaUT2YAarEiNeUIZNrmqDp/cHAQbW1tBrdKBqk47NtfgyVigrp3GWRczUjO+ALgVLd2M4s7S7ALtl3vmJ96ngchSLcdgNReR6j+iCnc0QxsO9+CdcQYSR49oPYGJKcdLxpnSjCrz5HoIOw7XpfEf5mJTNNUJKcvUp3UYxZ31r41sPV/zNEzwobU1KOQadlP1fmmPSOSUdi3vwpLdFD5XEbINLQiOf0LgEOdAK0Sd5UMtD1pi/MDALMIITMA9AC4CMCSwhPSpWKojJCeNA/pSewNw1ohEomYM/hsLiRnncL+ujWEWdxl/NMg+Kcxv24tYQp3xILUtIVITZMRY91DYFafow1tEOaex/y6tYRZ3KU7D0W681Dm160VTHtG2BuQnH06++vWENVwt8cYaJTSFCHkWwBeAmAF8ACltEg22m7nNRqMb3Qq1K+rozzq3OlHnTt9qPOmH3Xu9KHOm35Uwx0/G+IqQCl9nlI6m1I6k1L6y9LjyTKK8nVUhlKgYh3lUedOP+rc6UOdN/2oc6cPdd70oxru9igDTQlBA0sWTWQ89dRTZjdhj0WdO/2oc6cPdd70o86dPtR5049quKsbaHXgiSeeMLsJeyzq3OlHnTt9qPOmH3Xu9KHOm35Uw92EMtD2lIxU3pBKmVxCYw9GnTv9qHOnD3Xe9KPOnT7UedOParjbY2Q21OD5558fczqd5tS82YMxPDzc1tLSwk8e8x6EOnf6UedOH+q86UedO32o86YfKribtnjx4na5AxPKQKujjjrqqKOOOuqYCJhQW5x11FFHHXXUUUcdEwF1A62OOuqoo4466qiDM9QNtDrqqKOOOuqoow7OsEcZaISQRrPbsKeizp0+1HnTjzp3+lHnTh/qvOlHnTt9MJK3PcJAI4R4CCF3AXicELIkV4+zDhWoc6cPdd70o86dftS504c6b/pR504fWPC2RxhoAH4BoAnArQDmA/iNuc3Zo1DnTh/qvOlHnTv9qHOnD3Xe9KPOnT4Yzhu3BhohxFvw1wvg15TSNwH8EoCFEHKTme3jGYSQjtzfOnc6kOOtEXXeNIEQYiWEuFHvc7pQ73faQAghub8e1HnThPrzVR9YP1u5MtBIFk5CyGMAfkkIsVNKwwCmALgIACilQQC/BXAeIUR/mfgJCELIvoSQRwD8ihBiyXHXgTp3FUEI2Z8Qch8h5LuEkKYcb20AlgB13iqBEDKTEPINAKCUpimlMQCdqPc5RRBCZhFC7iCEXEkIaa6PV3UghOxHCPkTgB8SQqZQSiOo9zlF1J+v+mHWs5UrA41mkQDQnvt3fu7QLQAuIoS05V5/DOB1AKcxbySnIIT8CsDLAN6glF5OKc3kDv0cde7KIhc38HcAWwEcAuA+QshsADcCuIAQkld4rvNWAkLI1QBWAfgeIeTcgkP18aoAQsj1AJ4E0ANgEYA/5w79DHXuyoIQ8jMAjwPYBGA2gH/kDtX7nALqz1d9MPPZypWBBmRX5ADCAF4BcBwhZCqldDWANwHcBgCUUgFAGkDAtIbyBweAEUrpvQBACOnMrZA+AvAa6tyVw/4ABimlvwfwnwA2ALgUQAjAs6jzVglbAVwB4KcAlhBCXABAKV0D4FUA/5N7XeeuALktuTCACymlvwPwdQD7E0IOLRivde7k8Snw/9o72xi5qjKO/57d1r6pLRBpS5GCKFA1CCSSqrRGFBsUIzExaAI0RAmi4FsXNdhoxKBfkBjiFxWlaBBDYzUBjUaFQAK0hQKbikQ/VcTaDw0iSNnaso8fzhn3dtjXZzpz7sz9/5JJ7t57JnvuL+fe88x55UJ3/w6wCdifW70fJ1WOel6nQfVriGJ1a/EAzcza87CX9BD+BXgBeL+ZLQOuBd5pZleZ2QZgPTBOgzGzITMbBnD3EeB4M/uGmd0N3ALckVuAvgC8K3elyB0T41eAPwFjZnaGux8CfgMsAtaRAg+VuTZaz6y7/47UmvEE8CxwdSXZJmCd3E3KAeAX7v6kmS1w9zHgcWA4X/8icjcp7r7V3fea2TmkOmIZ8E1LSx18Hr3nZkL16yypQ91aJEAzs+VmdjVApbmwxenAand/ENhDan69I7/EPgksJjUtftfd7+ldrutBuzt3f7lyeTOpwGwBLiVVBJ/L44IuJwUejXRnZkurf/vEJrTzSd0l5+XzjwLPAG/N3q5AZa7d3Xjl2EnddNuA95nZm/L5A8BlwBLk7ghyV9M/8/HBXBGcTQpy5Y7JvbXxKmDE3S/Ix1/Rey4xgzvVr1MwyXuufN3q7j39AF8FngZ2AG+onP8YKWBcCXwL+B6pAD0A3NDrfNbxM427jwPD+fh1lfNnA7uBxaXzXtjbZmCUNIjz4nxuCPhoPr6C1KW0Nv+9FthdOt91+EzhzlrPayXdCuDbwOb892mAlc5/n7g7D7incn1F6bz3g7dK+tZ77tWl8176M5M71a9z9la0bu1ZC5qZrTWz3bmAbCT1g/8rX1tFaqqGNHhxPWna9DuALwMnmdlZvcpr3ZiFu9cCrfE/1f7vNcDO3ua2XpjZjcCbSS0Se4H35haLFcDxOdl9wD7ga5amT58M7DSzxb3PcX2Yxt1K0vO6sNVV7O77SL8uN5rZi8BFpBdcI5mlu1aX5jJgR55o8SSwodIF3yhm422Sr50GPAL4JNcawwzujsnJVL+2MYO3pZSsW3sYoS4nt1Dkv+8HrsnHQ21pT6wcHwesKh1hF47u5+JuIXAOacDsH4G3l85/IWdGaq7/LXBqPncdsGmK9EPATcCvSOPSzi19D33kbjiX0R3AdmBd6XvoF3f5+q2kcStbm+ouUOaWkgKM+5r8ngu6U/0a89bzutXyPz7qmNmxwEeA2939UF47ZDxHpgaMAIeBm70ypsXMFrv7gTwQ2fzIfuBG0IG7he4+ZmbvAV7v7j8pcgOFaPeWz/2YtKbZAtIv7UeBeaQFBnfmNEvc/cVc5o7zI38pNYIO3C1y95csLVB7kbtvLXIDBTkK5e5K4JC7bymR/1J04G2Bp7F7HyQ9r416z0FH7hpdv3bgrUzd2qXI9MPAfuAp0kA6aBuPQhqc+P183OrjPQH4QRZQPMIu8enQ3Q9p7q+hV3hr+QHOAn7e8kiaJdca87Myl7mTSt9DH7s7ufQ99Km7W4GVpe+hD7019j13FNw1tn7txzLXrTFozwBfAq4kzexa7e6el4Voja24K187xiei+GeB69z9713KVz/Qqbt/FMhzHXiFN/j/TJzXAIs8P23Aw8AeM5sHPEfy9nSBPNeFTt3t6X2Wa0Mn7kY8z+ZsIJ2Wuaa+56Bzd02tX/uuzHUlQHP3XcDtpEU//wx8Jp8frwh4HngIWF353pi7/7sbeeoXOnT3XI+zWxum8pZ5GDjVzD5taeeArwPPu/thd39JZU7uonToTs+rvM0ZPa8x+rHMdRSgmdkiy6uHt+NpX779pO1MzjCzd7clOUiaRdfIAiN3MSLe3P0w8FnS4rPbSE3X1/cqz3VB7uLIXQx5iyN3MQbJW3iSgKVd3R8Dtrn7tdOkWwZ8AjjF3a8xs9OBv3kacDff80C9JiF3MTrwtsbdnzKz+aQy/98eZbk2yF0cuYshb3HkLsageeukBW2Y1Dd7vpmtmSpRbhrcApxrZv8BPkVer6ZpAUYFuYsR9XZVnvl1qC4PXgHkLo7cxZC3OHIXY6C8zboFzcyG82C61l58q4ANwCnAme7+oUm+M0TahmMr8Ebgenf/5VHKe98gdzHkLY7cxZG7GPIWR+5iDLq3eTMlyLMYbgCGzewP7v57T2tynQBc4O6XmNmomV0M7HP37a3velqja8zM7nT3n3XtLmqK3MWQtzhyF0fuYshbHLmL0RRv03Zx5gF0u0jbRPwVuNHM1ufLLwAP5uNR0sC6y80mtihpHdddQjeQuxjyFkfu4shdDHmLI3cxmuRtpha0ceAmd/8pgJmdCXyAtMHqUmCTpf3jXiatvjvqlT7T6nEDkbsY8hZH7uLIXQx5iyN3MRrjbaZJAruAu2xiU9/tQGvB1FHgXtKWCeeTFn/baA3fYLqC3MWQtzhyF0fuYshbHLmL0Rhvc1pmw8y2AI+5+y1t562fotISyF0MeYsjd3HkLoa8xZG7GIPsbVbLbJjZsKWZD8uBX+dzbzGzJd3M3CAgdzHkLY7cxZG7GPIWR+5iNMHbbNdBGwfmkzYafZuZ3Q2M5HN91adbALmLIW9x5C6O3MWQtzhyF2Pgvc1lHbS1pP0fHwJuc/cfdTNjg4TcxZC3OHIXR+5iyFscuYsx6N7mEqCdCFwG3OzuB7uaqwFD7mLIWxy5iyN3MeQtjtzFGHRv4b04hRBCCCFEd+hkL04hhBBCCNEFFKAJIYQQQtQMBWhCCCGEEDVDAZoQQgghRM1QgCaEEEIIUTMUoAkhhBBC1AwFaEIIIYQQNeN/nx6aKOaClvAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (10, 4)\n",
    "\n",
    "fig, ax1 = plt.subplots();\n",
    "ax1=gt['Fryer'].plot(lw=4, c='tab:orange')\n",
    "pred_CO['Fryer'].plot(ax=ax1, c='darkred', lw=1.5, ls='--')\n",
    "ax1.legend(labels=['fryer','Pred_CO'], fontsize=9, loc='upper left', ncol=1)\n",
    "ax1.set_ylabel('Power (W)')\n",
    "\n",
    "plt.savefig('./data/images/5.5_F1_CO_30s.svg', bbox_inches='tight', transparent=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (10, 4)\n",
    "\n",
    "fig, ax1 = plt.subplots();\n",
    "ax1=gt['LED Lamp'].plot(lw=4, c='tab:orange')\n",
    "pred_CO['LED Lamp'].plot(ax=ax1, c='darkred', lw=1.5, ls='--')\n",
    "ax1.legend(labels=['LED Lamp','Pred_CO'], fontsize=9, loc='upper left', ncol=1)\n",
    "ax1.set_ylabel('Power (W)')\n",
    "#ax1.annotate('TP', xy=(250, 188), xycoords='figure points', xytext=(285, 210), fontweight='bold',\n",
    "#             arrowprops=dict(facecolor='red', width=3, headwidth=10))\n",
    "#ax1.annotate('TN', xy=(320, 38), xycoords='figure points', xytext=(400, 70), fontweight='bold',\n",
    "#             arrowprops=dict(facecolor='red', width=3, headwidth=10))\n",
    "#ax1.annotate('FN', xy=(300, 38), xycoords='figure points', xytext=(530, 70), fontweight='bold',\n",
    "#             arrowprops=dict(facecolor='red', width=3, headwidth=10))\n",
    "#ax1.annotate('FP', xy=(187, 188), xycoords='figure points', xytext=(220, 210), fontweight='bold',\n",
    "#             arrowprops=dict(facecolor='red', width=3, headwidth=10))\n",
    "plt.savefig('./data/images/5.5_F1_CO_60s.svg', bbox_inches='tight', transparent=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (10, 4)\n",
    "\n",
    "fig, ax1 = plt.subplots();\n",
    "ax1=gt['Incandescent lamp'].plot(lw=4, c='tab:orange')\n",
    "pred_CO['Incandescent lamp'].plot(ax=ax1, c='darkred', lw=1.5, ls='--')\n",
    "ax1.legend(labels=['Incandescent Lamp','Pred_CO'], fontsize=9, loc='upper left', ncol=1)\n",
    "ax1.set_ylabel('Power (W)')\n",
    "#ax1.annotate('TP', xy=(250, 188), xycoords='figure points', xytext=(285, 210), fontweight='bold',\n",
    "#             arrowprops=dict(facecolor='red', width=3, headwidth=10))\n",
    "#ax1.annotate('TN', xy=(320, 38), xycoords='figure points', xytext=(400, 70), fontweight='bold',\n",
    "#             arrowprops=dict(facecolor='red', width=3, headwidth=10))\n",
    "#ax1.annotate('FN', xy=(300, 38), xycoords='figure points', xytext=(530, 70), fontweight='bold',\n",
    "#             arrowprops=dict(facecolor='red', width=3, headwidth=10))\n",
    "#ax1.annotate('FP', xy=(187, 188), xycoords='figure points', xytext=(220, 210), fontweight='bold',\n",
    "#             arrowprops=dict(facecolor='red', width=3, headwidth=10))\n",
    "plt.savefig('./data/images/5.5_F1_CO_60s.svg', bbox_inches='tight', transparent=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tW7cWZc+fP48ZM2bAYDDggw8+AABMnjwZt956K0aOHIk5c+Zg4cKFmDBhAgghLaZHS0pKsGrVKgiFQnz00UctjpuUlIT33nsPjzzyCIxGI0wmEyZPnozhw4fjzjvvxPjx4wEA8+fPR2qqS7yFR9zVd/78+Rg9ejR69OiB3NxcAEBWVhbEYjHmzJmDO++8Ey+88AIeffRRaO03g9tvvx233HILG7O6kJCcjIQ+fTxud9xw/H072zJ9OjL69EF/L1NxGo2Glf2CBY/Px8mPP4Y0JyckDppZp/M4MgkANosFuspKiNPSWK+pKZwyBd3nzsW5b9yuVIjjhbNffAGN1YpUD8EVjjZv1mrjDpobItFfzQ0NsBqNOPb22+h0882ck9pw3B+FXvpxKO1W/NRTKHYTuR4rBGK7mHLQ/A0SoDZb0OrQvn17nHOz5mn37t1uy8+cORMD7VoyDp5q1VhXrVrldt97773X7TozBz169MCXX37p8v28efMwb94852er1YrbbrvN+XngwIHOOqWnp2PLli1e67to0SIsWrTI5TxftNK+WbNmjUuZQKJVlQcOoObYMXSbM8ft8Dw/IQGEx/N7fUP9yZOQ5ed7LaNQKPw6dqDwhELwRKKQTA1YTSZQi8Wr46U8cADfjhuHMevWod3IkayObzObIc7IgLm+HjarFTyOLk3gIodefhmpPXsCHhw0YYAvJbFOJPpr87WajZcvc9JB44lE4Hl5fkbqPhcLBGK7mFqk0HxKjQ0OBy2Yjlo0wcVMAkwo27IFex55BPAwRUoIgUAqhaWx0e12X5i1Wq9vlQBQW1vr17GDgUAqDcmD2Bl84WUETZLZJEXoT6DAV4MG4eTHH6PdTTfBZjT6V8k2ikWng8WLQ+twquMOmnsi0V+bRyhy8XeRFxT4fMkKpd0u//QTts6cCWOMZisIxHYxNYLmNxFy0N555x2/920dFBAOAqlvKLBotRBIpV7XsE3ets3rWipPUEqbju9jmiiSOoJCqTQk6+tsZjNSunaFNNOtHjQAQGJ/K/THQdNXV6PLrFlot2gRJyUHuIxZqwXfS/Jl57pLP19KYp1I9Fd9M30xLv4u3efORfe5c72WCaXddJWVuLR1K0xqtVeJoWglENvF1AiavymLHI6ZtzU3sUy0pnrytU4KAFK6dXM6E2ywGo2gNpvPEbRIDv0npKeHJM2TRKHAjL170XH6dI9lBImJ4EskrB00i8EAc0MDJAoF0tPSYOOoNA4Xcbw0yNPTPZbJvPZaTN+zB+lxqQ23RKK/Zg0YgBvsgWFcHEFjQijt5hz1DWEwVySJT3Ha8VcHTSiXIzE3F9IIiRhGGq7qx/nCMYLmjT/Xr8f5jRtZH5taLGg3ahSSfMhzVFVVsT52sJi2cyeGvPVWRM5NCIFEoXCbFcEbjvLmxkZ81r49/vz661BULyZxvDQYvIz0i+RypHbvDoGXUba2TCT6qyQzEx2nT4c0Jwc8Lxp2keLnu+/Gtttv91omlHYLNJiL6wRiu+gcOvGAv+GsYj+mwGIJrurH+cKi03ldJwUAx+15CwsnT2Z1bKFMhjFr1/osJ4vBSDnV4cPY+8QTuO6VV7wuaL7m8cdbqNgzwTHiltKtG0BpTCuIBxu+SIRbjhxBo5doc5NGg9Nr1iD3hhuQVlQUxtpFB5Hor9UlJbDodJh1/HjYz80ErRddPQehtJuwWeRxLBKI7WLKQfMXm9UKXXk5TA0N8ZtaFHH9smWw+FDmFiYmwqTRhKlG4eXY229DX10ddG0lvVKJqn37YPWxgL+LH3k0xWlp6Hnvvcjs3x9EKPQ7G0FbhPB4kOfnuyStbo65sRH7nnwSg197LX4v4wjH3n4b9adOYcbevZGuilssOh3EkVyqkZKCpE6dQKJ0oCCUxNQUp7/RiOozZ6BXqUD9jAKNdqI1ilOiUEDuQwbDX5Vq1dGj+Ozqq1HuQR7FQWMEF/0qDxzAxRAkGHYsZPY1faxXKqFimZRbXlCAAc8/j+ROnSBKTY07aCzQV1fj8GuvocrLSIxD+yxWRyMCJRL9Va9SQZKZiV2LFuHom2+G/fy+YBKtHkq7pV19NW4uKUHO4MEhO0ckCcR2MeWgBaqDRm22oESrXLx4EYWFhZg0aRJuvPFGt1pkvti7d69bbTEHx48fx80334xJkyZhzJgxzgjL6upqzJ07FxMnTsTYsWPx2Wef+TwXV5PM++LEypW4+P33XssI/XTQzBoNdPYcpd7IYjnFF0xCLbPhS+j0+PLl2DhqFKvoZ5NG40z0npiVFXfQWNB4+TIOvPgiEryMCMdlNrwTif5qUCohzshAdUkJVEeOhP38vmCyVCSS97loJxDbxdQUpy8dtO/sOS2bUzh1KrKuvdaZOkeYmNhCV6vLrFnoetttMNTU4Cd7KPIEBhIXffr0wYYNG6DRaDBkyBBMnTrVGS3pSOPkLxqNBnfffTdWr16NwsJCUEqxfft2AMDdd9+Ne+65B6NGjYJer8ctt9yCwsJCF3HZ5lgsFmduzmji6LJlyB40CO3HjvVYRuCnFIWZgbo2ACiVSuT7GMULFUKZLCQjJb6S0DuQKBSgViuM9fWM13EefPllnF69GnMuXkTmpEnIiN/4GeP4rb2tQeMJBOAnJMQdNA9Eor/qq6shyczkbM7JdiNHIqN3b69lQmk3s1aLbbNmodsdd6DTjBkhOUckCcR2MeWg+Qu12TyKnQZKUlISMjMzMXjwYIwaNQonTpzAyy+/jO3bt+Prr7+GxWLB7NmzMWfOHFRWVmL+/PkQi8Vef9CtW7di7NixzhybhBCMGDEC5eXl0Ol0GGVP/SORSPDAAw/gs88+8+qgRStMojiLn34a1zz+OPtjO6b5fLxZhkLmgimhGkFLSEtDRt++PiVMxBkZAJoeQEwdNMdoAgAUzJyJdhxMHs1VmDrOoWoXsUC4+6vVaIRJo4FEoWj6XTg49Xz966/7LBNKu/GEQlT88gtyhw0L2TkiSSC2iykHzZeel7uRL0opao4cgSQtDSNWrUJiTg54bo4jTk9nNHLWmvLyctTU1CAnJwd9+/bFiy++iFOnTuGnn37Cd999B5vNhvHjx2PixIl44403MG/ePMyYMQOvvvoq/vzzT7fHvHLlitsUT+Xl5S4PvPz8fFT4mKqL1ihOs07nc4Qrwc8caEzU9AEgLYIRwOKMDEgyM4OeLqnTjBmM3mSbZxNwJED3hV6pdOrSpcjl0JaXQ5qTE1FHN1pwPNzTfMgBTd25E6KkpHBUKeoId38lAgGm7NgBcXo6qvbuhaGuLqznDxahtBtfJAIRCDjpvAaDQGwXU2vQ/NXzkmZnQ5qTA3l+vlvnzB8OHz6MyZMnY+HChXj99dfB4/EwYMAAAMCJEydw6tQpTJ48GVOnTkVDQwOuXLmCc+fO4ZprrgEA9OvXz+Ox8/LycPnyZZfvc3JyXL6/fPkycnJyvNbV3xRZkcRmtcKq1/scTaj+/Xcc+Ne/fEZ7tiaxXTsUTpniU9laGcE1VD0XLcLMo0cjlsvS4WixkcrQK5XOiLGDb76Jz3v0gNm+Ji2OdxxTnGq93ms5Wbt2cQfNA+Hurzw+Hxm9e0PWrh2SOnWCLDc3rOf3hUmjwSf5+TixcqXXcqG2W6iyonCBQGwXUw6aPyNBhBBIs7MhkstBKQ1aSos+ffpg48aN+OabbzB06FAAcK4769q1K3r16oWNGzdi06ZN2LlzJ3r27IlOnTrh0KFDAOD81x2jR4/GDz/8gPPnzzu/27FjB/Ly8iAWi/Hjjz8CAAwGA5YtW4Zbb73Va12jcQSN6XSP6sgRHH71VZhZSm3kDRuGG1eu9PmgS4rBB+HB//f/sGXaNJ/lZPn5GLp8OTLtLx5MaD6ClmwfBdZXV/tX0TZGl5kzcfuZM8js0sVrudNr1uAMg+Cgtki4+6v63Dmc/PhjGOvrcd3SpRixalVYz+8Lc2MjLFqtzxHsUNtNkJgYsyNogdgupqY4/YHabLBZLLAaDFCfO4fkLl0gCrGYYVFREYYOHYqJEyeCz+dDLBZjzZo1uP/++7FgwQJ8+umnXtegJSUl4b333sMjjzwCo9EIk8mEyZMnY/jw4Vi+fDkee+wxvPHGG871bYMGDfJan0jmk/QXoUyG2X/+6XPE05mbUKtFKLTVIylRUrFnD468+iquX7YMsiCu5dJcuICGsjKf5YSJieh8882sjt334YeRbHcwhPahf71SieTOndlXtI3BEwohTk9Ho4/sDWc++wygFF1mzQpTzaKHcPfXqr178etDDyFv+HAkpKSE9dxMYLqUI9R2y+jbF4kcG10MFoHYLqYcNH8MYTUaUXfypHM9DYKQML19+/bYsGFDi+9aJze/8847ceedd7b4Ljc3F9999x2jc/To0cOtfEd2djY++eQTVvW1hTlJfDAghDC64fmbRmT/M8/gzw0bMPPYMa/ltFotMuyL3sONsa4OV3bsgKG2NqgOmlmrZZyXVnngAAifj4w+fRiVL1qwwPl/m915jkttMOPi1q1Q/v47Mm6/3WubE0il8VFJD4S7vzoSpYszMnDy449x5vPPMcmHNFA4MTOciQi13Ub9738hO3akCcR2MTXF6Y+el0PDyTESQzkm2vrOO+9g0qRJLf7qgrzQNBp10LRXrqDkuedQf+qU13LNR9DYYFSrYWOwNi87gvlbhSHSvGISHevglwcfxKFXXmFU1tzYiPpTp5zrAfPsgQVxB40Z5Tt24Pjy5T7bnL/af22BcPdXfXU1BImJECYmQl9djer9+2Hj0DPGMa3oK9gqkve5aCcQ28WUg+ZPkICLg8ax0aRFixZh06ZNLf5S/YxM9EQ0JktvvHwZR5ctQ+OlS17LCezT1WwfWBadjtEoUmVlJavjBhPn6GCQVb4tOh1jB02iUDB2sJQHDuCr666D8sABAEC92Yzif/4Tmf37+13XtoTZ3iZ9tTmuyjlwgXD3V4NK5Vxz6RQR5tBvI05PR9e//Q0yHzpdobbbb48/ju2tZpRihUBsF1NTnK0XOhJCYDKZvIqwOh00+ygS1xy0cMBFiQOTyeS1Xs61Ez7WC2b264e/lZUxnrJrfnwmTkokRx+dSYaDPFqS3rs3RD6iVx2IFQqoz51jVNbhyDkeWAkSCXovXuxfJdsgjpFNX21OKJMFvU3ECuHur/rqaqfuX/PlFlyJsk3p2hVDli3zWS7UdtNVVKD+9OmQniNSBGK7mHLQWkcjymQyNDY2wuBFYqHh0iVUHTgAmpaGxoYGWDUamBMSQl1VTmGxWHxqyIUbQghkXpwvx5SlLyeKJxRC5EcHMTOc5ktm6MiEAmFSElK6dgVfLA7qcQcxnLIEmpwtg49F6w6cDpp9vWdycjK0V67AYjAguVMn9hVtYzhS8vhqc8X/+Af6P/NMmGoVXYS7vw5fseKvrCSOFyoOjaDZrFYQHs/nS3qo7cbVLAvBIBDbceqpTAjJB7AKQDYAG4D3KaXLCCHPAlgAwDGX8iSldHPr/VvreRFCIJfLvZ7TlpAAkdmM5JQUZBYUBH4RUUhZWRkKouzaLa1uep4wNTTg4NKl6DBxIrKvu47x8fNHjgQYRLeqVCokshydCxby/HzM2Ls3Iud2IFEoYNHpYG5s9Jm7U69UNjnM9huWSqXC8fvvh81kwsQtW8JR3ajGotdDKJX6bHNsR4vbEuHur+L0dIjT0wEA0pwcZA4YABJAmr9gc/Ljj/Hbo4/itpMnnSPb7gi13fxNyRcNBGI7TjloACwAllBKDxJC5AAOEEK22be9Tin9j7ed/dHzSunSBSn2sH+zVgtQ6vNBE2tEchTIX5jqoIFSHP/vfyHLy2PloPW8915G5aLRdr74csAAdJk1C70ffNBn2cKpU6Ho1w88BrlcHdM9jrf15ORkiDMyUMPBBNJcZOz69bCZzWjwMdJQtW8fzn/zDfo99VTcWWtFOPurzWrF4VdeQbtRo5BZXIzcIUOQy6EITsC+Ho5SCCTeRYhCbbdYDmwJxHbcceUBUEorKKUH7f9vAHACgGtOI8/7sz6nrrISdSdPglKKL/r1w76nn2Z9jGjH5CX5MlfpdscdmFtRAamPZNsCP6M4mURwApG1HaUU344fj5MffxzUY7zNCHwAACAASURBVKrPnWN8s5S3b4+cwYPBZ+CgdZs9G9e++KLzs8lkagoyiEtCMIIQAr5I5LPN1Z04gePvvQeTWh2mmkUP4eyvxro6HPr3v6HyIjoeaSwMl4qE2m4pXbsia+DAmFwDHojtuDaC5oQQ0gFAXwD7AAwGcC8hZA6A39E0yuaiNVFTU4PBgwdDIBDAarVi+vTpWLRoESorK5GYmAg+nw+NRgOFQoHa2lpQSnHlww9x/O23MeXkSZCEBKiVShgMBiiVShBCkJaWBqVSiaSkJFitVmi1WmRnZ6OyshJCoRDJyclQqVRITk6GyWSCXq93bheJRJDL5aipqUFqair0ej0MBoNzu1gshkQiQV1dHdLT09HQ0ACTyeTcLpFIIBKJoFarkZGRAbVaDbPZ7Nzu6ZoUCgWqqqqca7gaGxuRlZXl8ZoqKyuRnJwcndd05YrP34kIhdCr1SgrK2N8TT8MGoTCKVPQfvFin9cEICy/k7u2pzx8GEk9eiCtujoov1NGUhJAKYwA6uvrfV5TbUUFTEeOgN+hAyTt2nm/pnbtkN2rF8rKypCUlISamhoYRSKYGxvx56lTEMvl0dX2wnyPuPz++0goKIBo4EBnf3V3TWr7mtuys2eRKRJx+prC/Ts56h+Oa6otLW2qB4Dq6moYr1zBrwsWoPi55yDt25cTbU+nVoMnFqOhsdHrNZlMJlit1pD9Tu1nzIDkhhtwpbw85tpefX09ZDKZx2vy6gdxUUWeECIDsBPAvyil6wkhWQBUACiAFwDkUEr/r/V+v/76Ky0qKmJ1rr1PPIHTa9ZgTlkZNgwZAllBQUyL5rnDaDQiIcoCI85//TWqSkow8F//8ll2dceO6Hzzzbju5ZcZH/+Tdu3Qfd48XPvCC17LRdp2n3btig6TJmHwq68G5Xh6pRJrunXDdS+/3EJU1hPa8nJ83qMHBr/2GrrPneu1bOVvv0GWn+8U1TUajbiwdi1+eeAB3HLkCOQ+Qv3bOo7fuvill7y2ubLNm/Hj7NmYsmMHMnr3DmMNuU84+2v5zp3YMm0axm/ahJzBg6H58098UVyMG/77X3TxkX4vXPy6ZAkubNqE231EUEb6PhfN+LLdwYMHD4wYMaLY3TZOTXECACFECOArAJ9SStcDAKW0ilJqpZTaAHwAwG3yP3/0vJqrprdV/aBIann5S8WePTj7+eeMygoTE2E1Ghkfm9psTTpoDKI4I207gVQa1LUbTFO/OHBICPjSQqOU4vvp01H6wQfO7yorK5EzZAiGvveez6T0cf66VzHRQQOCL2AcC4SzvzqyCHBZBy33hhtQNH++z3KhtlvZli34vFcvaC5cCOl5IkHM6KCRptXDHwI4QSl9rdn3OZTSCvvHaQD+cLc/z4/oGEfoOtD0UDIHWfQzGvCmE8dV2Kjd33rkCKvIKYteD4CZkxJp2wU7+oknFKJwyhQkFRYyKs8XiSBKSfHpoJkbGmA1GltEiolEIiTl5DA+V1uG2myw6vUQSKU+25xAKgUIiTtobghnf22t++fUQeOQg1Y4ZQqjcqG2G7VYoL18GeaGhpCeJxIEYjtOOWhoWmv2NwDHCCGH7d89CWAWIaQPmqY4LwC4293Ofjto9gd919tug9WLZlqs4kuKhIuwyRfJNqzduXCWwfEjbTtF377OMP5gkJibixtXrmS1j0ShgMGHg+Z4WImbOWhyuRw2sxnVBw4gMTcX8vbt2Ve4jdA8atlXm8ssLsb/qVScFKCONOHsr1fffTe63HorRPacwc6AJQ45ziaNBjyRCAIfWoqhthsXnddgEYjtOOWgUUp/AeDuruKieeaO1jpoTLj67393jkB0uukm1vvHAjU1NV5FYblI85FPXxx75x1YDQb0WbKEUXleQgJ63n8/o/U7kbbdDe+8E7FzO2CS7qn1aALQZDuxQoHvxo9H8T/+wUjWo61iMRgglMkgtC+A9tbmuKSzxTXC2V8JIUholpaPx+cjf/RoTr2I/HDLLeCLxRj/9ddey4XabkIOOq/BIhDbccpBCxR/1PBzb7jB+X+TRgOjWt3mFisHO7dnuGCaLqV8504YVCrGDlpCcjIGPPsso7LRajtPXNy6FTvvugsTvvsOaVdfzWifwa+/7jObgaFVFgGgyXYCqRRCmSwuteEDSUYG5ly8CADQaDRey5obG7H3qafQcepU5A0fHo7qRQ3h7K/Hly8H4fNbrPEazXDdbLiw6HQQp6X5LBdquzVPgxVrBGK7mHrVsvmhoaI8eBCaP/8EABx65RWsHzQo2NXiPHr7mqtoYsy6dRi3YQOjssLERFbrtKwmE4xqNWxWq8+ykbbd7y+8gC3TpwfteOaGhqZpDxbpsVK6dPH5UpM5YABG/u9/SOrQwfmdw3ZihcK5oDqOb5i0udOrV6Pm2LEw1Ca6CGd/PfvFF7j0ww9hO58/MJ2JCLXdxOnpKJgwARJ70FEsEYjt2ryDtv3//g+H/v1vAH9FxMWiWJ43vOUqjQXYRudW/vor/ldYiOr9+32WjbTt9CoV6k+eDNrxHEEyTAMwAKD2+HEce+stWL0IMkqzslAwfnyLLB0O20kyMnxOkbZ16k+fxo7581FbWuqzzXExWpArhLO/6qurW6y5BIAt06djF8MsJeGAac7hUNstMTcXI1evRtbAgSE9TyQIxHYx5aD5kzW+eTSgUCYDKHVG8bUVsrOzI10F1vz68MM4/emnjMoKZTJWaxvMLKQmIm07YZCjOJ05TlmsmaguKcH+f/7Ta9L06pISlO/a1eI7h+0kmZnxKU4faMvL8ef69TBpND7bHOHxYjq3YSCEq79SSmFQqVzyW5o1Gug4JGtkYRhsFen7XDQTiO1iykHzSwdNp2uhgwbE5jy4NyKt5eUPf65fj5qjRxmVFSUn+8w11xynkxINOmgyGSxarV9pztzBOMdpMxyjBN5GwY699RZ+e+yxFt85bNf7oYdw/Wuvudstjh1zs5Q8TNpcsPXxYoVw9VdzQwOsBoPLlJ2AYzknez/0EPJHjfJZLtR2ozYbPu3SBUfeeCOk54kEMaODFihsZTaaawsBbddBE/tY4M1FLFot4yjOfk8+iX5PPsnq2AAzJyXSthNKpU3t2Gj0GSrPhNSiInS9/XZGuTUdOEYJvI2C6VWqFgECwF+2U/Tt60dN2xbNXxqYtDmJQgHC54e6WlFHuPqrsb4efLEY4lZtXiCVQldR4WGv8NP7gQcYlQu13QiPB7NWC2N9fUjPEwkCsV2bdtCcNz37gz6zuBjXvfwyRG1M1VzCYnSJC9jMZtjMZlajPGxgM80Xadsld+6M/NGjQf2QmHFHwbhxKBg3jtU+DsfL2xSnXqlEeq9eLfez20575Qqq9u1D/ujRrKZW2xLNXxqYtLnpv/4a6ipFJeHqr/L27XHHlStAq5FtIYdG0GwWC7Tl5ZBkZPi8l4bDbrGayScQ28XUFCdbHTSeSISRn36K9uPHAwBSunZF0YIFSLALC7YV6upc8s5zGjOLKUgAKN+9Gz/OmQNDTQ2j8pn9+6PvY48xcgAjbbsOkyZh9OefB82x8SdARsIg3ZO+utplPY7DdtUlJdgxfz4ayspYn7utQPh8iBUKCGWyiLe5aCactiOEuGjSZV13HfJuvDFsdfCGtqIC6/r0wZ/r1/ssGw67xeq0fCC2iykHja0OGl8kQsG4cUju1AlAU4qf2tJSmHzoDMUa6UFUog8HVoMB0uzsFiKQ3tBVVqLs229hZNhRMvv3xzWPPQYeg/YUbbbzxY758/EVS6kZoUyGW44cwdV3u03wAYvBAHNDg8sUp8N2jmmgeKCAZ7r97W+4/dQpiJKSGLW5I6+/jpLnngtDzaKLcPXXi1u3YufChS6pA4vuvBPXLV0aljr4gs1SjnDYLdgBT1whENvFlIPGVmbDqFbj4tatTg2m+lOnsOH661Hxyy+hqB5naYiy/GfSrCzMKi1Fl1mzGJV3plhh2Pn1SiXjSKtI26589258dvXVUB05EpTjWXQ68BMSWO8nz8/3uB9PKMSU7dvR+ZZbWnzvsJ1zDVtcC40RTNpc9e+/4/JPP4WhNtFFuPqr6tAhnF271q++FC6cDhqD0fdw2K3D5MnIjkEd0kBs16YdtPpTp7Bt5kzU2B9usaxm7A2TF/2qWEDI8nctee45bGQQ2QRE3naEEOgqKoI26ts8Ny0bzqxdixMffeR2G4/PR0afPpC1a9fie4ftmAQZtHVKP/gAP//97wCYtblYnS4KlHD1V71SiYS0NBfB5z/efRcf5+XBajSGpR7eYLNUJBx26/fkk7j6rrtCfp5wE4jtYspBY6uDZmmldyVkOdISK0Sbxk3t8ePYOnMmaktLGZVnK9xpYSjeCETedsGOPDbrdIzX9jXnwsaNOPnxx263qc+dw8lPPoFRrW7xvcN2ouRk8IRCr0EGbR3V4cOotC/8Z9Lm2GbPaCuEq78alEqXNZcAAB4PVr2eE84zG0mdcOrHxRpxHTQ7bHXQzK3m4B1DvVzoPOEk0lpebNGWl+PS1q2MHS5RUhJk+fkAwyjf5tp4voi07YKtGm9pbGQsX9IcsZdsAFW//YZfH3wQplYOmsN2hBBM3LzZ4xq2OC1HNhnroMUdNBfC1V/1Hhw0x32FC85zSrduuPallyBjkLw9HHbbuXAhvorBTAJxHTQ7fstsODIJtNERtEhLRbCl9cinL1K7d8etLNZosZnmi7TthEF+qeg8cyakOTms95NkZsKgUoHabC6Ra461Za1FO5vbTtGvnx+1bTuYtVrnb82kzUmysiBWKEApBSEk1NWLGsLVX/liMWR5eS7fc0lrM6lDB/SwT5v7Ihx24wkELkEVsUAgtospB43tjah1FAtPKMSQt95CRp8+Qa8blxGxECXlAq1HPoONRauFmGHS3kjbTpSUhMIpU5Do5mHgD0yFK1sjycgAtVphrKuDuFXUkr66GkKZzOX3am678t27oS0vR5dbb/Xr/LFO85cGJm2u9+LF6L14cairFXWEq7+O8yBdwaURNH11NQy1tUjp2tXlpao14bAb17IsBItAbBdTU5xWq5VV+fwxYzB2/foWD+Out9+OtKuvDnbVOI261dQT12ktMOwLq9GI72fMwLkvv2RUvue996L7vHmMykbadqKkJNy4ciXyhg8PyvGMajVsfojeOqZz3K0jM6hULkmjgZa2O/v55zjwwgusz9tWkGRlIalDBwCRb3PRTKRtJ+/QAd3nzuWE1ubpTz/F+kGDYGWwiD0cdovVdZOB2C6mHDS2OmiJOTnIGzasRVob1dGjqD91KthV4zQZDEeLuIJAKkVSx46MR9B4QiGu7NgB9ZkzjMp3nD6dsZp+tNnOGzarFf8rLMQRP/JiFkyciHlVVUjp1s1lm16pdJneBFraTqxQQK9SxeQi4WBw44cfYshbbwFg1ubKd+7ElmnTOJWYmwuEo7/qKiuxeepUlO/a5bIttXt3DH7tNSQVFoa8Hr4w63QgfD4jKZBw2E0glYJaLIwcxmgiENvFlIPGdgRNeegQLmza1OK7nxcswMGXXw5mtThPpN8q2dL1tttw8++/Mx5BIzweBFKpM6zcF7XHjzOWfOCC7T7v1Qv7/vGPgI/DRriyNXyRyEVSwMHwFSswbMUKl++b206SkQGbydTmRKL9gUmbM9TWonznTsbizG2FcPRXbUUFKnbt8jgaRG022Fg+q0KBpbERAqmU0dKgcNgts7gYPe65x69sJlwmPoJmh+3b95nPPsMvrdbctEX9ILbRr9EIm/UN34wYgWPvvsuoLBdsRy2WoDg2bIMvWuxrMGDPo4/i0rZtLtvE6emQ5+e7fN/cdpJ4NgGvfD9jBko/+AAAszbXVjUdfRGO/uqY5ncXxdlw6RI+ysjA2c8/D3k9fGHW6RiniAuH3XKHDsW1L74IQZgS2oeLQGwXUw4aax00N3pXbdFBi7SWF1uOLFuGn+bMYbUP0zQiNrMZNpOJ8egcF2wXrDYbSPAFXyTCyZUrUbVvX4vvbVYrDv6//wflgQMu+zS3nbc1bHGAil9+gba8HABDHTRHRHobu5f5Ihz91fGS0Tq1GfDX78KFZwybaPVw2I3abDA3Nvq1BpbLxIwOGiEknxCygxByghBynBCy2P59GiFkGyHkjP1ft0kY/dFBa/0gjtV8YN6ItJYXW+pOnEDNsWOs9knr0QPSrCyf5cwsxBsBbtguWA4a2+CL5hAeDxKFwmUEzFhbi0P//jeUhw657NPcdlnXXotbDh9GZnEx63PHOjazGTazmZ0OmmMErY3dy3wRjv7q0AN0t+6Sbdq5UNJ93jz0f+YZRmXDYbeLP/yAVe3bo/aPP0J+rnASSzpoFgBLKKUHCSFyAAcIIdsAzAXwE6V0KSHkcQCPA3is9c58Pp/dyXQ6l+kcQWIiLJcu+Vn96CTRjwdyJGGj9O9g5OrVjI8NMHdSuGA7QZBeKsQZGbjm8ceR2r27f/srFC4jYN4eVs1tJ5BKIWcgmNkWMbdynJm0uYSUFKR0787pXJCRIBz9VSiTIb13b7f3KL5YDBDCCcc5h0Xey7DYjUPOazAJxHacctAopRUAKuz/byCEnACQB2AKgGH2Yp8A+BluHDS2uBvi7XnvvZzoPOGErWMbacxarV/rpJjAdh0WF2zXYcKEoBwnMScHfR991O/9JRkZLiNojs9iN9M9rW139M03kVZUhHYjR/pdh1jEYhfvdNyrmLS5pMJCzNizJ6T1ikbC0V+L5s9H0fz5brcRQprkJDgwxak6fBiCxESkdOnis2w47Bar6yYDsR2nHLTmEEI6AOgLYB+ALLvzBkppBSHE9W4P9lGc1y9bBtpqvrstTrFoNBqkprqdNeYkFjdT074oef55NJSV4cYPP/RaTqxQ4IZ330XWgAGMjssF2/W8776gHMfc2AiTRgNJZiZ4LCVrAECanQ1jfX2L77wtmG5tu2NvvYUOkybFHTQ3KIqLIc3NBcCNNhetcMF2V//975wQQ9+1aBGSO3XCiFWrfJYNh92CnbaOKwRiO046aIQQGYCvADxAKdUwzRCgVqsxePBgCAQCWK1WTJ8+HYsWLUJlZSUSExPB5/Oh0WigUChQW1sLKhJBkZeHsrIyyOzRLNWlpRDW1yOhTx8QQpCWlgalUomkpCRYrVZotVpkZ2ejsrISQqEQycnJUKlUSE5Ohslkgl6vd24XiUSQy+WoqalBamoq9Ho9DAaDc7tYLIZEIkFdXR3S09PR0NAAk8nk3C6RSCASiaBWq5GRkQG1Wg2z2ezc7vaaKIVCoUBVVZXzmhobG5GVlQWlUun2msxmM4xGY9RcU0K7doBUCoPB4PGaWv9O9efPQ3nwIOrr631eU9Lw4ag1GCCy28TbNZnNZlRXV4fld/LW9gw6HYzNzunP71S2fj2O/+MfGPTNN0jv1o31NXV47DEMyMxs0Z/KT58GANSaTGi8fLnFNQkEApSVlTmPKUhNRWNlJcrKyjjb9gL9nfy6JrkcvT/4AGnp6aioqGjRXz1dU2ZGBr6bMAGFt96KjjNncu+aIvQ7mc1m1NXVhfSaSh99FJLCQhQuXOj2mrovXgy1Wt2i7Uei7Rk0GiSJRCgrK/N5TYmJiaioqAjt72SXo9DW1aGsrCxm2p7NZoNOp/P4O3n1hYIpDEkIyQIwGkBvACkA6gEcAbCNUspopRwhRAjgWwA/UEpfs393CsAw++hZDoCfKaUuipg7d+6kvXr1Ylzf02vWILljR2Q1S9B66JVXcHDpUsyrrvZrFCEauXz5Mtq1axfpaoSU3YsX4/K2bZhVWuq1nKGmBvVnziC9Z09Go3RcsN2eRx/F+a+/xu12Z8hfTqxciT1LlmBWaSmkQYraojYbTGo1RCkpLnpLrW23ecoUWI1GTPr++6CcO1Zh0uYopViZmYleixej+Omnw1Qz7hOO/vppt24oGD8e17/+utvtFp0ONosFoqSkkNbDF5927YoOkyZh8Kuv+iwbDruZGhpw7M03UTBhAidGGIOFL9sdPHjwwIgRI9xO3QUlipMQchUh5EsApQD+BkAIoNL+798AHCeEfEkIKfJxHALgQwAnHM6ZnY0A7rD//w4A3wSj3vuefhp/btjQ4jsuJbMNF21BvV0olTJKxFvx66/4bvx4NFy4wOi4XLAdPyEhOFGcrdY6saW6pATbZs+G9soV53eEx0NCaqpbMczWtpO4CTKIA1Ts2YP111+PWvvLBZM2Rwhpiu6NsemiQAl1f7VZrTDW1Lid0newZdo0/HTHHR63hwszi2CrcNznRHI5+j31VEw5Z0BgtguWzMbHANYCyKWUjqaULqaUPm3/dzSAHPt27wuAgMFocuhuJIQctv+NB7AUwChCyBkAo+yfXWCb6smtDhqHktmGC4WXmwkX+WbkSPzBUEjWgUAmg0Wn89lZ2Krpc8F2QrvMRqAK3OYAhGoBwKRW4+LmzWi8fNn53fHly1HqYd1fa9uJMzKcUZ9x/sKgVKKu2cgv0zbHlcXoXCLU/dVYWwtqs3l10AQcyDlJbTZY9XrGa3nDdZ/TV1e7rGONdgKxXVAcNErptZTSLyilRg/bTfbt1/k4zi+UUkIp7UUp7WP/20wpraGUjqCUdrH/W+tufzY6aFaTqUlbqLUOWoxGknijqqoq0lVgDKUUqkOHWKewSe7YEVnXXecSFNKa1pIGvuCC7YIV/WTRasEXi8HzM+rIEampbzYKdnbdOlz64Qe35Vvbrvjpp3E7w3ypbQlLK20+pm1OkJgYH0FrRaj7q1NWxstDWcgiq0mooJRi5KefouO0aYzKh+s+90VxMQ79+99hOVe4CMR2QVtkRQh5FMBOAAcopRGRAmYTzuoU5XSTSQBoWyNoMobpPriA1WAAKGU9ytNl1ix0mTXLZzm2I2hcsF1zB41p6hZ35I8dC5mblExMcWidGZqNgumrqz3qqrW2XSB1j2VavzQwbXOZxcVIjPG1pWwJdX8lhCB36FDIO3TwWIYLU888Ph8F48YxLh+u+1wsZvIJxHbBXAU/EsA/0LSUbC+anLVdAPZ6GlmLJM4HcasHfdbAgRj3zTdIKiyMRLXi+KD1aEK0HT8UZPTujZ733w+eSBTQcXIGDWIlXtkax6iBQ/uMUgqDSgUxwyF+9blzKH3/fRTddReSO3Xyux6xhr9J7If+97+hqE4cL6RedRXGtVrX3BouTD2bGhpQuWcPMvr0YZRhJVzEooMWCEFL9WRfa5YC4EYAWwD0Q5NURj0hZDch5MVgncsTbHTQJFlZuPnAARROndry+4wM5A4ZApFcHuzqcZZGBovnuYKZpdK/g8s//YQv+veH+uxZr+U6zpiBEatWgfCYdQ0u2C6zuBgDnn0WCSkpAR2noawMDRcv+r0/TyhEalGR01E0NzTAajS6zSIAuNrOVF+P0g8+8PkbtTUS8/KQO3QoBBIJAG60uWiFC7YrGD8eve6/P6J1aLx4EdtmzXLJneuxfJjsJuTA+rxgE4jtgqojQSm1Athv/3uVEJIC4C4ADwEYBCCk8d5skqXzBAK3o2QmjQaXtm1DZnEx5AUFwaweZ8ni0BuULwiPh9xhw1hPxdksFmjOnYNJo/FaLqVLF0bK2g64YDub1QpzYyMEEgn4AYyi/bpkCYz19Zjy449+H2P6L784/2+sqwM/IcFt0mjA1XbiViNwcZroNGMGOs2Y4fzMtM3te/pp1J8+jTHr1oWqalFHqPvroVdewbmvvsKMvXvdRi4DQLuRIyMuxux80WU4Khuu+xwXpn+DTSC2C2qydEJIOiFkGiHkdULIATTJblwL4BUAA73vHTgWHwvAm9Nw8SKOvvlmC0kAoEn5/OcFC1D522/Brh5nUUZR5JysXTuMW78euTfcwGo/pmsLlYcOsfrtuWC76v378b/CQlT++mtAx7HodH4lSveEvKAAd5SXo9NNN7nd3tp2zilSDtiUyzBtc4aaGtQHqI0Xa4S6vzZcvAhzY6NH5wxoytihuXABNpaZb4KJpyU+ngjXfa7orrvQfe7csJwrXARiu6A5aISQ4wBKAEwG8AeA2yiluZTSGZTS1yilJcE6VzCoP3UKJc8+C215eYvvYzUfmDeYZmqIZphG5x55/XXsefhhxsflgu2c0jCBRnG6yU3LliNvvNFC44kQ4nG6uLXtBBIJhDJZiyCDOMBvjz2G7yZNcn5m2uZicTQiUELdXw0qlccRYwdnPv8cX1xzDYy1bsUIwgJbSZ1w3ec6zZiBwilTwnKucBGI7YI5glYOIBFAAYB8AO0IIWFdac1GB81TUuxYzQfmjbS0tEhXgTFXduzA2t69naKdTHGOoPlYD+BOG88bXLCdMEjiymyEKz2hq6hA+a5dAICLW7di5z33eLS5O9tJs7Nh0esDqkOsoa2oaPEwZ9rmuLAYnWuEur/qlUqPay4dcEEM3cJyijNc9zm9Uon6GJPaCcR2wQwSGAUgF8BjABoA3AfgT0LIPkLIfwghIXeL2eigeYrWcz7I29CNjQvTdEwx1Nai8dIl1lpdCampaDdqlFd9IoC9VAUXbBesl4pgTHGKFQqY1GpYTSaoDh7E2bVrwReL3ZZ1Z7sZe/fi+jfeCKgOsUbrlwambU4glcKq10d0Ko1rhLq/6pVKnyNoQg6IoecNG4ax69cjMS+PUflw3ecOvPQSNjcbLYbVBOHZ78Ev/z0s5w8FgdguFEECJfa/1kECDwLwTwGTIWx00DxFA/L4fPAlkjY1xZkU4ZxwbPA08ukLaVYWxqxd67OcWatFAos3Hi7YzuFQBtpmr33hBUhzcwM6RnMtNL1SCXFamsectu5sxzR6ti1h1mpbtHembS6lWze0HzsWNrPZb/HhWCPU/TV/5EgorrnGaxkuzNJIMjOR58ORbE647nOtZTZkX8+B8PJeAIB2+Asw9fpbWOoRTAKxXVAdNEJIOoAbmv31QtPU509oca0NCQAAIABJREFU0kXjDN70riZu3uzzLSiWYCNPEmnCoYPG5thcsJ1AKsU1jz+OzP79AzpOx+nTA66Lo9/olUoYlEqvGmjubHfuyy9x+aef4hpezbDodBCnpzs/M21zHadNY6wU31YIdX8d/NprPstwYZ1zzbFjUJ85g8Jp0xitkQrXfU4olcKs1YJSCn79BadzBgAJx7+ISgctENsFO0igGsC/ASQDeBNAV0ppAaX0dkrp+8E6lyfYGKLorrsw68QJtw/jjN69kZiTE8yqcRptFK2381cHDQA+79ULh1991WuZYcuXo/eDDzI+JhdsxxMI0PfRRwNy0GxWKyr37oUuwJQusvx8KIqLAdine7w4aO5spz5zBmfXrYONRUR2rJMzZAiyB/4VBM+FNhethNJ2lFJGibGTO3XCwKVLkRRBMeZzX32Fnffcw3gBe7janEAmAyiFVa8HT9dSbocY2KX34wqB2C6Y8wkvAGhHKe1MKf0/SuknlNLzQTy+T9jooAnEYkizstw20AubNuHi1q3BrBqnyc7OjnQVGJPcsSMKJk70SzXfpFbD0CxPpDsU/fohraiI8TG5YjtdZWVA8hRmjQbfjR+PP32ooPsivWdPTN66FRl9+oAvFkPevr3Hsu5sJ1YoAEphqKkJqB6xxMB//Qs977vP+Zlpm7v8449Yc9VVrANqYplQ9teqffvwSW4uKnzI3UizsnD1XXdBFsE0XGzXm4brPidstgac6Fs6ZMQSncFDgdguKA4aIUREKf2cUlrho1xCMM7nCTZBAuc3bsTRN990u+3osmUofT/kA36cobKyMtJVYEzh1KkYuWqVX6HLvqLaKKU4s3Yt6k6cYHxMrthu46hRKHn2Wb/3ZytcyYRxGzZgyFtvedzuznZxLTTfMG5zhEBfVQVzQ0NoKxRFhLK/GpRKWI1GiJKTvZazWSyoLS2NaBtnG60ervtczpAhGPLmmxBIJCAGdYttxGIISx2CTSC2C9YI2lFCyCOEELcrjAkhOfZk6oeCdD63sHlol23ejJMrV7rdJkhMbFMyG2xGHqMZX3nerEYjdi1ciIvff8/4mFyxXaA57NgKV3pj05gxOPTKKz7LubOdYw1bXAutCUopVnfogCPNIluZtjkurHXiGqHsrw6Hy5fMhlmrxYbrr8e5L78MWV18wXatbbjuc6ndu6Pr7NkQJia6TmlGqYMWiO2C5aBdDyALwBFCyGlCyCZCyBr7v6cAHAbgCCAIGWyiOC06nceHUVvTD0r28cbHJXbdey82jh7t176CxESvDyt/AhC4YjuhTBZQ2L6jvQcjk4C+uhoVe/Zgy7RpKN+922M5d7aTZGZCmpMDK4vR8FjGajC4pCdj2uaCLRkkOvo/yNeMh/SnJwCrKSjHDDeh7K/66mqAEIh9OGjB0i0MBDNLOaFw3edMDQ2oLimBSaMBz1DfYhuxWQBr9N0XArFdUKI4KaUqAA8TQp5EU2qnnmhKnF4HYCmA/ZTSkFuWTaonb0O8gY5GRBsqlQqJQUzxE0qMtbWwmfx7OLQfM8brTcmfAASu2C7gEbQgRseKMzJQc/QoTPX1uPruuz2Wc2e75E6dMOv48YDrECu4+12Ytjmm2TOYwKs5g8QdTamUBcpS2KQKGK57KODjhptQ9le9SuVVVsYBTygETyiMqA7adUuXwmpgPiIVrvtczbFj2DxxIsZ9/TVSTPWuBSx6gM+NWQumBGK7YOugmQDstv+FHbY6aJ4exL5GWmINrowCMSEQtft+Tz7pdbtzmi8KR9AEUmlA04Ip3brhxo8/RiqLAAlPSDIzoTxwoOn/XqI4uWI7LuMc2WzWJpnaLSE1FR2nT0digNp2ACC4srfFZ9HJ9TAMfBDgQKozNoSyzWVfdx3EDDUUI/2MSSosZFU+XH3VGSSg1YJYXR00YjGAJkRee5INER9B4wpMQpwdWHQ6j0PR/Z58En0feSRY1eI8Jj9HpCKBRauFKCUlNMf2QwSXK7a7at68gN7IJRkZKJw8OSh1ae6UedNB82S7XffeC3n79uj76KNBqU80Y7GnyWr+0sC0zYnT0jB8xYqg1IPX0DJnMV9zGXxVKayKq4Ny/HARyv7aacYMAADR10H60xPg15yCsddsGPve6VJWGOE8qWe/+AKJOTnIuf56RuXDdZ9rngaLwIODFpaaBI9AbBdTDprNZmNcdsqOHbB5WOcizcoKVpWiAn0U5T40a7V+jwj8+tBDKN+9GzeXlLjdntq9O6bs2AF5hw6Mj8kV27UfOzag/RsuXYL69GnkXH89+AmBBVsrrrkGp1atAuB9BM2T7epKS5vW88SBUC5H19mzW2hmRaLNtXbQAEB4bmvUOWihtJ2xvh6ipCRISt6B6FxToJF01wswFwyFLa1zi7LXvvRSRJ8zvz//PHKHDmXsoIWrzTXPskB4bqY4zdy437IhENvFVF4VNtESPD4fAk85Ag8exOHXXvPowMUaXNHyYkLBhAnIHT7cv50JganeTae3I5BKkdG7NxJYDElzxXb66mrU/PGH3/tf+v57/HDzzTAFQZKh25w5uO7ll5HRpw8EEonHcp5sJ87IiMts2JG1a4chb76JjN69nd+xaXOfdu2KkuefD7ge7h20HwI+brgJZX9d27s39j31FIQXdrT4XlB52KVs4eTJyLr22pDVxRfelvi4I2w6aM3ylPKM7kbQos9Bi7gOmgNCCJ8Q8kmo9c48wUYHbe8TT6Bsyxa326r278eBF1+E2T694Ate/QUIynZFZYQJwB0tLyYUP/00rpo3z699fUXn1p8+jRMrV7pEzXmDK7b747//xcaRI/3eP5hRnABQtGABpmzf7rWMJ9tJFIq4zIYdarO5LN1g0+ao1RoUHTR3DppAdRI89cWAjx1OQtVfLQYDzA0NkKTIwK8712Ibr9E1O0fdyZNQHTkSkrowwZuKgTvCdZ8TyuUYvmIF8kePdhGqBaJTC40LOmgAnMnSRwNgPtfYCkLIR4SQakLIH82+e5YQcoUQctj+N97dvjyGiZYppSj94AOoDh50u725F+8LwYUdSPrfaMi/ngP5Z5NAdNGngC7yQ5U/UlAW09itEUilsOr1Ho9RtW8f9ixZAqNa7Xa7O7hiO4FUCpvJ5Peor6WxESAEfA+jymxQHjiA/3Xu7FViA/BsO7FCAb1KxWpNaazy54YNWKlQoP70aed3bNpcUBaj26zgNbp/yETbKFqo+qsjQ0mi0PWZQRpd9dv3P/MMfn0oMlGwNrMZNpOJVTBUuO5zPIEAHadPR3JBHojV6FogCkfQArFdKKY4XwfwHCHE31jYjwG4W1DzOqW0j/1vs7sdmTpoVoMB1Gbz+AaRYGzqULbz+30cyATp9qdB7JpAgpqTSNy8MOo0guRyeaSrwAib1YqPFAoc+s9//Npf4EN/yOImYs4XXLGd86XCz4ex2Z76xZ8MDS51kclgrK3FLw884LWcJ9uldO6M9J49YeXI+r5IYtHpmu5VzdokmzbnSD4dCERbDULd5zkWnouulHih6q+ONZOJxHXk151zK4hgkIA/o+XhvM9V7NmDuiPu1wkTc/SNoAViu1AECdwHIBvAQ4QQJfBX0AWl1HNivr/K7CKEdPDnxEx10LxpPgnPfg/5oab0NKJvF0OYKYC5m/voNtHxteA3XGm5/5X9kP78T+hufIkzIeiXt2+Hsc51uLhgwgQIxGJc+O03SNxM6xVOmQKeQADVkSNQnz3bYhshBB2nTwfQNGKiuXChxXaeUBi0qEAHFp0OoBQCPxexZ/Tpg6vmz/d+fLCL4qypqYGMheBjqGi+uJbNGjoHbFO/eMMRudlw3nsqXk+26zp7NrrOno3y3btdggUSkpPRzj6V665di9PTkTdsWAC15xbu7lVs2lwwRtDcTW86j1/+O4hWCZroORgkXKjPnnU7bVg4eTJ4QiFUR4/i3G+/IaNV9H7H6dNBCIHy4EFoWrVZnkCAwilTADQtfWm8dKnFdr5YjA4TJkCvVDblnjVfBFrdnnhuRtAiKYYulMlw88GDrO4T4bzP/bxgAfIH9UPHHq7biJVbDppj7W+7G28E0JT/tvUMjMZmQ9+bbwYAXPzhhxZLp4RyOeBF2DgUDtrsEBwTAO4lhMwB8DuAJZRSF4+jvr4egwcPhkAggNVqxfTp07Fo0SJUVlYiMTERfD4fGo0GUmPT0GmDwQCDwYCqqirIZDIIGsuR/8PDEAmbfEqziSJx2yM4rU+AsGAArFYrtFotsrOzUXWlDEV73efyTPjjMyiJApqrZkEul6OmpgapqanQ6/UwGAzIzs5GZWUlxGIxJBIJ6urqkJ6ejoaGBphMJud2iUQCkUgEtVqNjIwMqNVqmM1m5/bm16RQKFBbWwtKKRQKhfOaLHo9zu3cifMffOAiTDh+3z4IU1NR/v33uOQm7VXyoEGw8fk4vGIFLn36acuNPB4yRo6EXq/HwXfeQfnXX7fYzJfJoLjxRpQfOgRddTWumjgx4Gu6aJ/mMVKKsrIyZGVlQalUghCCtLQ0KJVKJCUltfidKisrIRQKkZycDHNBAYqefBJ1Oh30NTXO7SKRCHK5HKrycoDHg6q+HkajkdHvZDQaUV1dHfDvBACNjY2sr0mlUiE5ORk6a9MIh66+Hiqz2XlNTNte1s03QzZkCIxGY8C/U6X9BtVh5kyvvxOPx0NZWZnHaypZuhSq335r2Savugr9u3RBamoq9j3/POqPHm2xPb24GJbCwpD1p0B/J5PJBL1e79L2PP1OdVVN65fKVSpkikRoaGiA0Whk/DtljhwJgViMsrIyv69JUn4KnpSnCCgMRzagMm8M42sK1X3v9NKluLB+vUsdeUVFEMrlKPvsM5xcvtxlO79fP4hEIpxeuRLnWt3nBFIpeH36QCQS4Y/33sPFVvc5cWYmSI8e4P9/9r4zPI7qbPs+M9t31bWWZNmyJPfeMAaMG8IYbMC0AA41tC8Q2hcINQmQ8kIICaG9b4AQEkwxxTSbEmODbYyNbWywjbssS5astmorbZ+dOd+PLdL2mdnd2ZW/974uX6CdM+2emTPPPOV+Ro5E6by5yOd3RmyfWk9EzBFu+FTzA9dR6XuvmxBoXC7k2GyirpNWq0VLS4sizxOr18PWEb2K29rRAsFul/08pfre2/Tgg2hbtw5XHDqEtrY27HjkEVjDejnnn3IKxi5dCovFgq333w/78f68TcOIERj7yitRzxUASDbmefg9aGsopZP8f5cA6IDPG/d7AGWU0hvC19u0aROdPHlywu33HD6MD+fPx7wXXgh6gcBzyHnvcqhav4fXS+F2U+j1BAxDIBhL0Hvlx6Cm/rJo7Q//gmHjozH3QQkD1+y7wJsngC+ohpBbkTEFZE9vLxxREhVzq6vBqFQ4fuAAcqOI/OaNGgXCMHC2t8Mdpfoxf8wYAICjtTUisZ4wDPJGjcKGW25B+3ff4fIY+X5S0FtXh3dPOQXz/vu/MfrKK2Vtg/d4QFgWTJTz/fbBB3H4zTdxbUOD6O21tbWhJAtkWfoaGtC6dSsqzjtPlgct1eDsdrA6XVSeA0jEna2pKcL7w2q1yBkxAgDQd/x4xEcHq9ejr64Oh1aswJnPPJOyoodM4bs//AF7nnkGP2tvD4aflb7ntDtfhGHz48G/KasJpnUAAFe5ELZl0fsaiwVxdkGz/10wfS3wls8CVzEP0EoLDVFKfZ7+sHda7siRYFgWTosFzbW1KCoqClmeN3o0CCFwtLXBE55/SgjyR48GANhbWiIKLgjLIs8vgSIc3YqiNcujHlv3Lw4Bqn7X2vZHH8X+F1/E9S2R3rV0w97Sgtp33kH1RRcFn6VEUPKe+3DBAhhzGFx+dl3EMse830TVlcsUPr/0Ulhra3GF33Pb19AA3h2aO9djt6Ny+nQAQO+xYyF5woxGg9qurp01NTWnRNt+yj1o/grO3wJYDqCIUppHCDkHwBhK6fNytkkpDZbBEEJeBrAm2jixOmj5Y8bg+paWkCRk3bd/harV18tdpSJQqfrDk4y9DaY1t6DvsrcBlQ7wuqDb8ULINj0jF0Pd+A2Ix+e+JFSA/tun+8+BsBDyKnzGWn4VBFNpSkOggqEYQn41+IIqQNPvinZaLHB3dwcnqWigBgPy4zyo+iFDgk2so8FQWgpDLMkEszllkgnJVho2ffkl/nPZZTj/889RcuqpEcun33cfJtx8s6RtuiS0S0knckaMED3ZRsOJDRvAqNUomzMnJccj5hol4s40bFjc5TkV0TMmWr7+GnXvv4+ZDz8MtUTF9GzDkFmzMOm220JyA6Xcc5RSCB5PUtp24SFOz5gLoT3Q3+hb1fgN2PYfwReO8s2P4eCcYHuOgemuA3F1g5qG+ubBvOFgrI3Qfv8KtAfe66/Q2/0vUEYNb/lscJULwBePB19QDZpgziQDjKlo0JvNUDkcMec6Q0lJXG0yY1kZUFYWe/vW2C3KGHs7hLzhwb9HX3EFSk8/HZTSlOR9SkFffT2+e+wxFE+dKnrOUHKe84XlO6Iuy7YqTqfFgsKJ/VqA0fi0Dvjgj9rBoasr5vbTEeJ8GkA5gKsABHQs9vl/l2WgEULKKKWBT42LAUQVfNLAA1WTPyQiCGB6m8D21IHpPgqm9wSoNgdCwcigkUQ1vpcI03Mc+u/+J7gdt4ti+3YWo6o9KBvmo0jVthumj2+Ce+q1YDuPgHH0Gx1UpYdj4R/Atu+F6eMbQaJoHRPKg+05BrYnfl5OKiAYS8BV1cAx/7eoffttbP/tb3FNfT00udEDFenUuNGbzfDa7ZJ1d6JBm5+PibfeirxRoxIPjoJERQLaggJoCwokbTNbdNA4ux2de/cif/Ro6MI8BGKw87/+C2qjMWUGmhiki7uAOK7TYpHc0ibbULF4MSoWLw75TQpvm++8Eye++gpXJqGRx4Tl2XKV86Fq/g6stR4AQHgPct86HxQEQs7QEE8R4VxgbNFz2CijBgRv9PlS4KBu3Ax14+b+8So9+IIqCAXV4POrIRRUw1s6HUL+CLi6urD2iisw7d57I/gaiHQ+r6qW2FECxtYSYqAVjB+PgvHj03Ys8eCV0XNYyXlObTDA0xkjPy/LDDRXRweK/d6xWEiGu3QYaBcDGEUptRNCBACglJ4ghJSLWZkQ8haABQCKCSFNAB4BsIAQMg2+EGc9gKgdmFU9x5CzIUF7mBPb0drMY9c2D+Ys1CIvP7Lyk9OZse2rOujKJqEM/fHi8AkjAPfUa0GNZnirzoJz3m9g2JS8MGQyYOxt0P74Jihh4LSwYDQaXzJiDLS2tmJEEt6XeAi8LF0dHUkbaKZhw3DaH/8oe33VgD5v0VD3/vsQvF6Muvxy0dtMJ3dS0Fdfj0+WLMFZr74aTGqWAq/Dobiyebq4G2igDXZwNhuIShUiqi2FN1UKktGZvtAwnJBTDm7kOWB3vRTyOwGNKJqKByJIk4QhXidUlv2AZX/wNwoC95Sr0TXkclh27kxYEJG255VSqFq+i7k4XGrD3tyMjt27UT5/fsqKc8QiMP9J1UFTap6b+fDD0Oz6J9D3ccQykkWdBKggwGmxxO2WAiTHXTpkNjwIM/wIIWYAogTCKKXLKaVllFI1pXQYpfQVSuk1lNLJlNIplNILB3jTZKGnS8D+PRy8XOSXGyUMuPP/CgCwVy7xue3jHa/aCNfMfnvRPf0G9F75MRxzfw335KvADTsdgjEzOUravW/Adfww9GZzXDe6LgXaV7EQCI2m4mXJu93g7HbZ+ljqBB60g6+9hoNRiiXiIZ3cSUEi72AieB0OxV8U6eIu+FFwEhhoG265BavDPEJSeEuFnEN4iFPIKYd78k9B2dTqkQumoXBPWg4+X7zXk4BCt2cFsOZhAIibigGk755j+prA2GO3JwsXq23ZvBnrrroK9gzkoMVTMYgFJee54mnTUFoRowNJFnnQKKU49/33E+ZDJ8NdOjxo7wL4NyHk/wK+8CSAvwFYmYZ9yQLn8b3g1ZpIo8U1+y7Q6rkAIfC6edgu/CeMq2+BqvNg1G25pv0MVF8Y8htfMgV8yZTQgR67Pw/jKNjuYyBu8WKoCSHw/nDuMTDW40HNIgIKz5Ht0JvjW+/6OO14koV55kyc98EHwYKCZHDso4+w8ec/x2U7dgQTc6VgoBRFNHjt9phh4FhIJ3dSkLQOWgplNsQiXdzpzGboioog8NG1uwYTOLs9QpdPCm8qg8EnTMpxYCS0wus/AAcYV3/BPGVUoIZiUIZF3xUfQLP/XbBdtWC668D0NUcNV1IQCLnDIBRUQzAU+eaq7jow/jwjr3kiXDNuATd6SbCQiuk+CvWxr8Ba9vnGdteB8cTuiOA54que1OfGfxmm655TNUdWbw5EuNRGIm9+OhHYpxS9RyXnua79+9Hy9V5MKIxclk2tnhiWxdC5cxOOS4a7dBhoDwF4EsBeAAYARwC8DOCxNOwrBILKAK68v78ZNRT78s0KqsHnVYBx9oDpqYOj4SMAW4CKmeBMfpVfRg2uaiHc024AIcSnU2O3Q8irQN/y1VA1fQv1sfVQH1sHtrcJAMDnV8E9U2RSucYIfsgk8EMmIZ0NoVQNG5Hz4XXBv51dVhgq4++xu7sbuRINE7HQFRZi6Pz5KdlW0DUv05DQ5udj8p13hiR1DoTX4YAhThJwNKSTOylIZHwmgtcvVKsk0sUdq9HgqiNHUr7dTMBrt0OTnx/ymxTeAi9hzuGQVd0b4T0zlQKMr9iIN0+Ac/4jAw7W5QuHDhS1JSyEnLKoxQPEbQUoQHWRxyUUjIS7YMBHGKUgjo5gsQHbXQfN4TXB/DaH3VcgNmTLfeAqV4DmDI16Pmm751pCDTQ+pzwk3BsuVht41pLu8iADY6+5BlUXXghdHP2tcCg5z9W+/Tb2/892THg4Unctm4oEbE1NaN++HeU1NXGfrWS4S7mBRin1ALgbwN3+0GYHVUjLw5tfBduct2Mu900bNXBs6AOwBZ7lK8HHqG4KEXhk1fCOmAvviLlwzn8ETOdhMPZWeIeeCqizw4MSgHfEfHhGnQdNra8+Y85CLViDBcTZDaqPngAfXnaeSlBKUf/RRzCNGAFzgmTKRJCT3DoQKoMBpz76aMzlcgoZ0smdFCRroC395BPJBRLJIlu4y2ZwDgeMQ0ONDSm8mWfOxJS77wYR2WUlHJH5Z9ENHwCASgehQHx4kmolGIyE+PJ8jWag3FeB7Zp+A3I+vA5s5yFodQRl5SwMzlrQt86HffHT8I6I/DBM1z2nag7NP+PGnA92Z7/mWiwPWia6CbBabcJQcDiUfFbVRiN4rwBBoGCYsChXFhlobdu2YcPNN+OSLVviGmjJcJcOmY2/AtgA4GtKqaJJIGJlNgjLQmc2g4nTI+vyXbuil6YTAqF4LITisXIPM+1wzP011PUbQLxOjByjBuCC56tfwzPmgqjjSUcrdLRrQCl8D6ipDHxBtV/DbRjA9N8qVG2Et2x6iJxHLBBCsPnuuzHqyiuTN9BkKP2Hw221ghASNZQpJw+rr68vKzoJEIZBzYoVcWUG4qF4ypTEg1KMdHK36/HH4e7pwel/+lNatq8UvA4HVGEcSeGtZPZslMyenXhgDER60OIYaAqDmkrRd9k7MK6+CZOwA5Om+eZz4uyC6cPr4Tr1drhm3x30+AF+7vQaMNbjYPpaIJhKIRSOipDvII4OsB0HQbW5vpy4eJps7j6wnYdCfvKMWgJdiIEW6kELzGGZCHEe//xzdO3bh2n33CN6HSXnuWD4lwPCX8HZVCQQ6L+aqEggGe7SEeK0A7gHwEpCyBEAG/3/NqXbYBNroE296y5MveuuuGNUWZJbJAc0txzOU++AZtOfcOI4D3MJA8ORT6A58knU8VFvnb4TcauSKKvx6RRVnw2veSLY3sZg6IG4Q0VrDQYKz4FN0Ox/F3x+NfjicYBGupHFORxgtdq44qeJ8M706Rh52WU448knI5ZdtmOHZE0ijyd7+q5WLl0qaz3ObkftO++g7MwzZRt4cpBO7roPHUJ3mKL3YMTEn/88Qu9NCm+C1wtPby/UJhNYGU2bwyU24nrQMgCqy4Pt4hUwfnYHNHVfBH8noNBvfw6ao2shGP3eIipgZFc9NI7WkN6ifE65bx6rOBNs52Go69aBbf0hJJ9OMJh9hpoq8qOdeGwgtP/dwxeMBB/2AU/sFkDwBj90c6uqcN6HH6JgwoSU8CAFjWvXon7NGkkGmpLzXNBA81BotWGGcxbloDktFhCWTRh5SIa7dIQ4fwMEBWtPA7AUwD/hswPkv1lFQC0nCTYG9r30EqjXi0m33ZaybSoJ94ybYN/wOt5dcRBLL9Fj/OTUFuwS3gP18a+hPv51wrFGjQPuxiMwfvErAICgzYPjnKfAVS+StM+h8+ZBk2TTXrXRGDOsICdHJ1t00ACgdetWsDqdZE+lq6MDW+65B3Off15RAy2t+ntDhqB506a0bV8pTPr5zyN+k8Jby+bN+PySS7BkzRqUnXGG5P1HVnBml4EGAFDp8OFbNuSpx+GcmYdDjCW281CEdyscbN8JsLv/Dez+d8wxjMMSon0ZD96hp/jCvfpCME6fCCmhPIijwye2C988NHTePFHbSzU4Gfmmiuqg6X3vcS5a6nQWhTidFgt0xcUJ0weS4S7lMhuEEBMhZDF8+mWPA/gpgP8AuDPV+woHF/WKRmL33/6GrfffH3fM8c8/R11Y37VBBVaDrpHXAgAMxsw2bTcYCRz2/q9Rxm2FfuPvIlqyJMKws86S9NUXDSqDIWpirtfpxPZHH0Xbtm2SttcapYVWprDl3nux++mnEw8Mg5yqrlQgndzpi4vh6ekBn0UeTqmglKKvsTEiDCaFt2AyusxQWnjulJAjSs5ScXT+uA9OYzVsl7wBwZDZxu1ef46cYAp9MQ/kUuB5HF21Cl37YncfSBfkpHIoOc8NO306rrrJiJzcyPfQQCZiAAAgAElEQVRWNhUJuERooAHJcZeOEGc3fGKyzwK4gVIaXZ8iDWBEJsK2bdsGe3N0desA1EYjnG1tccdkO+yMb4JQTayBZ2hszSK3xwu1eVRQpVvQDyiF764D4xig70Mp2M4jQRVxMTAYCZrqQ40xtrcRxGEBNYpPVg24lHWFUeqvRSJQnRsOT28v9j77LEzDh0vK2ckWmQ1AvuaVV4ZwZSqQTu4CSdCujo6IJPvBAq/djnemTsWpjz2GyXfcEfxdksxGktWCkR40aVXOSoBSCldHh69rybDT0fvTT2Fc+8u43n3BVArBVArWcgCEd0cd4y0eB8JzYKwNIIJX1LF4h0yGZ8yFvn0YS0NEdZm+VvADbLYNN9+M6ffdF7OqPF3gbDbJBpqS85zRSJBbHj3Ylk0hztP+9KeI3qzRkG0yG48BmAuf3Mb5hJBADtp2Smk6FSZE5w+JkRQIqeIcpAiIw5KL/gx7nKqdnp4e5IeV8guFo+CtXBB9BUp9OkV166Bu2Aji6oaQO7y/BYtpCECY4NjJkw9hoqUOQt9XIWKObPs+eKvEG2ibfvELuLq6sGzdOtHrhCPWdfXK7POpkZHXky6oTSZZ92wqii/kIJ3cmYYPR+GkSRHN1AcTYl0XKbwlpbdFadbnoAEA19cH3uWC3i8bQY1m2C5e4W/x1xQyttergWH4lP4cWM7p6xBTtx5sxwEIhmJwVWeBq6oJhiMheMFYG31c0NjaelSb59O/9M99NI4HjWFZsHp9Rt4xXocDaolJ60rOc66W46jf5cGIalVkp58sKhLIGT488SAkx106ctD+AACEEBbADPhaP30KX/5ZWstAeJHClNG0hcKhNhiSbpGSaTjb20EYBtoEZb5WqzXCQIsLQiAUjoK7cBTcp0TmyIRD6zf0uPUPQvvjW8HfVZb98FYtFL1br8ORdBhu3HXXgUYpJpGjrg3I4C6NUBkMcHWKatgRgkyFONPJ3bCaGgyrqUnLtpVCLN0/Kbwlo7dFnJ0gfH+ImGpyAG3mNf/CEfgQ1YV9hAoFIyEUhApaWxoaMGJggZJaD656Ufx8WEYFoaBKkoQIEOltZOyhEZlMvWPO//RTyaF/Jec5R2M91q52Ydnl+ggDLZtCnD/+/e8oPf10FE+dGndcMtylQ2ajEMB8/7+FAMYC2AmfFy2tUKnEnU40baFwSP3CyEaM/ulPYZ41K2HVY7EEwUI5sJ84gWMff4wxY8oxsOkVa5GWf8HZ7Un3ixx52WUxtw1I96ClmztRoBTE2Qm1SpAV4hw6fz4u3bYNJpFfhKlCVnCXbrh7wdhafer7emmh+VgfDVJ40+TmYsZDD8E8c6akfQPRNNCyL7wJ+CRmRixdivxR8dvyAcrecxE5aGF8ZipKQxgmpLerGCjJm4bxhZw9nsgcZcK7ASr0R2gyBM5mw7aHHsKsRx9NaKAlw106QpxNALYD2ATglwC2UEoV8UuK9aAZSkpgTPAyOvV3v8Opv8ts0/NkkVtVhdyqxF99VqsVxjSGt2yNjdj28MMoeumJMANtf8x1osHrcCQdhnP39MDd04PcysqIbQPSw3zp5i4WVE1bodn/Htjuo748QXcv5g7j4R5mhPGzO+CpOhveygVRVdrDoTYaE1dvUgpEaeMTAomTZjq5491ufHLBBRhz1VUYd11/Zw14XdDueR2MwwI+dxiEgpH+3o8UbPcxX96ltQFgWF+4vqAKfH4ViNcZbNPGWI/HzFvy7dwDxtro25azI/izoMuHkF8FPr8yRFmfqvTwjFkKvizUiOJsNgCRnk0pvLFaLabfe6+oseGICG9mkQbaQORWVeHsFStEjVXyeRVMoQYtCddCMxjg9V9jJbH9kUdgnj4dVRddJHodJXnTEJ+XLGbNn9cFqJX19ocj4LUVUySQDHfpMNAKKKVxZq/0QWzDgvM++CDNR5IdaPziC+iKimCeMSPuOLHVr3Kh89/ENo8OFCSoL8Ra6wGPTZTgLeALTScbhtv1+OOoffddXFNXF/J7+YIFuL61FUSixlq6uYsGtmUnclYtj/i9eAgLwAUcXg3N4dWghIGQW+EzMgKiw/lV4AtG+oozvE6oj3+Nzs/fQtu2LZgxkwEpqvBp1RVUAwwbLBRhe+oTJuh6i8fBft7zPuFPEUgnd4xGg659+2Ctre3/kVIYP7kNmvov07bfuMfk6gHT+j1Urd9HLNPufR19l70LvrT/a9w0fDhm//GPyB83LmSsVN7sJ06A0WhEvUxCjncwSGxIhJLPa7wqTgA465VXFM/7BICDr74KgeMkGWhK8qYhvo9lLooHDfCFOekgMtCS4S4dOWhuQshCANcAKAdwAsDrlNK0z4qp1EFr2bwZh157Dac/+SS0WZJjJBVb778f5pkzsfDll+OOS7fGTeAmdnVaIRRUg+0+GlzGWg6AL58lajvT7r0XOSPiN35PhHiVjnKEPDOhg6Y98H7U3zssPNqaeUyYogYhBIQKYK31YK31UNd/FTKWqo2AwIHwHuzd5MY3X7kxc1oO2M7DYDsPyzouVcdBGD+9HX1XfSrKm5ZO7ggh0JvNwYkUANRH1mTMOEsEwntg2PR79P3k3aCqvXHoUEy69daIsVJ5+/Css1C5dCnm/PWvktYbLBIb3z/1FA688gqu/PHHhOkcSj6vEQaavc3nifZf34Lx4xU7lgAopbIiEYrqoME3P3Ox0uQ4J5Dh4vlg3qMIAy3bdNBuAvA2gFYA7wNoAfAmIURkV3H5EGOpUkHAmiVLUPvOO3HH2RobcfS99+Du6UnV4SkOpwI6LWKgzskBq9PB2dEB3hyqnK3qEB/mHHf99ShfKL6oIBpURiMEjoMQdq+0fPMNttx3HzwiyqYHIhM6aGxHpEI+VelRW6vCZx+64BWhCEA4ezABnPNQMAzAssnr5ak6D0J95FNRY9PNnb64GM52f9WwxwbDpj+kdX/RQBk1+NzhoGxsmZsAVC3fQX30P8G/XV1d6D54MOJelcqb3GT0wSCxAQCO1lYIHCeqw4iiz6vG5Cus8IPwHhC/cC0AnNiwAUffe0+54wHAu1yggiA511ZJ3liPFdffasT02dE/mLNBasMlwYOWbTpo9wFYRCndHfiBEPI2gFUA4rtykgQr4gH1Ohxo+/ZbjDjvvLjjgs1sB2klJ2e3w2u3i2qKm+7cAkIIdMXFcFos8JqnQHN4dXAZ2y6uUIAKAroPHoRx6NCkPJqBECnncIR0DujcvRsH/vEPnPLrX0vanuL5Z1QA2xEqLdi7fA148wR4VS8Bax9C34Trkde5BWzXEVGb5DhALdJ5SBFFPDIsN02/7W/gRp0X0gMxGtLNnX7IENiamvzH9GxIFR1l1PCMXgK2px5MzzEABEJ+JfiCkRDyKwHB68vv6zkG1noclNUOCBFXxW/0TQgEY6lPUzC33NfehwogfS2+kHHfCV+iMwDNwQ+hbt7ef8zf/AlcVQ3AqlH/8cf45pe/xJX79sFY1m8cSeVNFad7RjwMBokNQLxgKKD88yqYSsF29X/0MbYW8AZfVf3h119Hx+7dMQuX0gG51epK8kZcVn+6RozlWVDJOerKK1G+cCH0IorWkuEuHQZaEYBwt8ghAPLVRVMITqQoZ9BAy0Az21Qg2MhVRAWJGMM2WVzw+efQ5OWB79gZum+RHjRPby8+OPNMzP7jH6OGfcRi4HUdaKDFkjRIBCW4GwjGehyE6/9oELR54M0TAUKgNvpy+WyTbwIZ/hjgsYPtOebPITsW7JXK9hwD8fiSk/ncYXDlAKq8dvTcvAWMtcGfd3YMhArg86uCOWxUXxTRVBoAmO6jyF2xKNhih+2qhfrwGnDjlsU9l3RzZ545E5q8PDCdh6H94Z8hy1wzboZrzn1p3X8ICAOaWw5vbmiY0Dt0FnLfWNzPXc8xaH98E+6p1wXvSZ2tFvAWBAsLovImeKGu/Qzafe8AXhc8Y5fBM+EyQKWDymCQpYMW6UHLzhCn2EgBoPzz6jPQ+j+UGFsr+CGTAMgXlk4GXqcTrE4n2YOmJG+Mqwc//uCB3kAwcowaVG0ImfOyod2TSqcTnW6TDHfpMNA2A/grIeR+SqmDEGKEr+XTljTsKwRiqjjFVusFZDZkCTxmAWJpA0VDb28vChI0fE0WAVkTnoSGONnOwwDPAWz8/EGvTAMqHKWnn44z/vKXCBkVr8MBRqMBI1KqJQAluBsI1hIa3uTN44NGU1A1PnDPaozgh0wCP2QSQoJklII4LAAVQI0lcH19I1S5HKihGLyhOKKaMBGEgpHwjLsoJDdOv+1v4MYsDTaHjoZ0czftnnsASmF4f3mIEjyfUw7Xqbenbb9SIBSNhmfiFSH6gLptz4AvGgNm97sAgMI1V4NojeAqzgRXdTacTCUKdf6qW4GD5vAaaL9/BewAj5e6eQf03/4V7inXQK1Tx44EeGw+L2KgQrX3OKjKAKGgytfg2w8KAsGUnMRNuuDq6EDh5Mmixir9vIbnoZEBeX2xupqkE6Zhw3B9c7PogroAlOSNuLqxY4sHhcUMRo5R+4zc7v6iLpIFYrVHVq6EwHEYe801Cccmw106DLSfA1gJwEoI6YLPc7YFQGTZWYohpkggqBif4EWvzsnxJQBKvJGzBQXjx+PC9euRW12dcKxZYnWXHDStW4f2nTsx4/77IRhLguEmwnvAdtX6DI044GQq/Ycjf+xY5I8dG7l9u12W8acEdwMRnn/GF/fzFmGgxQIhIS22znzmGfDO5CY916l3QXPwIxC/0jrbcwyagx/6vDgxIIo7rwuqxi1ge45BMA7xhxirAZUexNER9AwSjw1C7jBfGDKvAsRjh7r+S6hr/wN107chm3TO+03Gy/QHwnna/4Xm0EdBLwHj7ELOquXgm11gWYBhCMA5oDm6FpqjayG2MRDj7IJ+2zOYNcwLXlsA0wfXgC8YCcJ7fKHb7roI8dRYoEYzwGZP14yBGLF0KfJEaKAByj+v4VIbjK2f74AOGqVUdBecVEHq/hTjjVIQlxVqNcBxvnevYCoLNdD4zHvQDr/+OgCIMtCS4S4dVZwtAOYTQoYBGAqgmVLalGC1lMArIjuaMAyKp09P6BIvmjQJVx06lKpDUxxqoxHm6dNFje3q6oIhzSryrVu3Ys9zz2H6fffBa54IzYAXA9uxP6GBlqp+kZzdDuvRo8itrIQmt18VnQpCSMhTLJTgbiDCQ8IDDbTS007Dsg0bRAl2DoQmJwfIyUk8MA6E/BHwTLgM2n1vB3/TbXsGQgxxVsJ7wBzfAwPt9rXksbX5PHgF1T5dMo0JqoZNUDdujppzEhH2GLjMnydXX8th7WonLrvagCKzL8zAjZgPbuTipM411aDGIXDNuAX6bX8L+Z3jKNSa5F/co8eqAPQBx7+O258yHrI1/wwAZj3yiOixSj+v8aQ21EYjKM9D8HjAahMXkaQCXfv3Y8+zz2L6vfeKNmoBBXnj7CACB7WGgPMAVKUD1YXlHGeBB81psaBwwoTEA5Ecdykz0AghBgC/BjAJwC4AjytlmElBwfjxWLZ+faYPI+1o+/Zb9Bw+jDHXXJPwa0mqu1sOdMXFoF4vPD09vkrOAXIHbPs+YPylcdfnRHo+E6F7/36sXrwY57z9NoYv6m/vMuepp2RtTwnuBiIyxNk/SWhyc1E8ZYrkbe5/+WVoCwsx8tL41yARXKfeDs2B90EEX0CV7W1Ezsc3xBwfoX5nbYCqZWe0oRGIZZwB/UULLAv09VLYbRRFZoCyGjjmPxo1jy7TcM24Gdq9b4Bx9IcVJ0xRo3y4ClSlF1W5RlkNPOMvBdXmQrP3TTAeX3K6rU+Aw04xpFR+Loy3dJrsddMJgecBQQAjUmJJ6ec10oPWX9E37vrrUX3ppaKPPRWwNTbi6DvvYOL/+T+S1lOKN8blU01QawhsvQKoLh9UFdr1IBuKBJwWiyiJDSA57lIps/E8gAsAHARwGQBZbzxCyD8JIe2EkB8H/FZICPmCEHLE/9+oAV2xrZ7EwOt04ourr0b96tWJB2ch6j78ENt+8xtRrmwl3NeBalKnxeJLah8AMYUCedXVOPOZZ6KGJ6UgGAZMUXWukiET4rKG5BlRwoIfIArr6e3FgVdfRc9haTpmB155JSX3uZA7HJ6Jlye9nVTBYPTd+w47hWAqhX3xM5L7KSoGjRGOeb8J+cl82lwMe/h19Ny2D73L18A5+254S6ZC0Ob5uhL4//H51XCeeiesN2yBo+ZxOM98ENYbt8Ix7zfgC0dhxxYP3no1etibEhZ8fhU8VTVwTb8J9oV/gGPOA3BPuBzc0Fngc4fBU70IrlnZkbMXjs49e/BqSQkav/hC1HilQ5zxGqZrCwqQU1EBwijXtsgrs++uUryRgIGm9lWXC9p8UFWo6FmmDTTe44Gnp0d0YUq2hDjPAzCDUtpCCHkOvlZPd8jYzr/gM/ZeG/DbAwDWU0qfIIQ84P/7/vAVxeigHf/8c+x64gkseuMNGMtjVyUxKhWOf/opiqdNQ+UFF0g8hczDabGIktgAgLa2NoxIUgA2EQLVpE6LBfzEsEIBy/4QAcdoMJSWior3J0Is+ZRdTzwBVq/H1LvukrQ9JbgLIDz/TCgcFdIyyNPbiy333OMzZMeMEb1dzuFIOrcvAOes26E59DGIR5qeXDwIpqHghp8O4uz25U31NoJQHlSlB19QBaGgGlSbB8Z63Fep6jditSPGAdiJzjE3wnrDoxnv35cI3NgL0WcoBtv+I7zDz4ClgwFxMyggTLDYw3Xa3WhoaEh8z2lMcE+/Ee7pN4Jv+B24bc+g77z/9nXv8Df/5guqIeRWJCzQyWYEqtW1IpOwlXxegWghztbgXGetrUX96tUYc/XVkrs8yIVYFYNwKMVbwEA7e4lvXqP6fEAdpkqbYR00V2cnAHEaaEBy3KXSQDP6889AKW0khEhP6PGtu4kQUhn28zIAC/z//28AGxDFQBNTzupobUXnnj1Agq8WRq0Go45T/ZTlcFksoiQ2AMCkQGN4XbCbQCeEvNNBNTnBlzjj7gXT1wQhN3Z/VEdrK2xNTSiaMkWW4n8AAUMkvHqqce1a6MxmyQaaEtwFEG6geYtD8/bkavd5HY6kq2MDoDll6Lv4dWj3vAZmgChnxDgQONUFUJeO9xkKOUPB2NuCraUYZxf4orHgqs/25dkNNN55D4i7z9d8PJpRzzl9FaoqPcjDJXC4NVlvnAXgHX4GvMPPAABsveF8gBAsDfNuSr3nVKZcgFK4hi+EakyGJdhTjIAQsdiXpZLPKwBfiI7VBnu3Es4BePoAbS6sR4/iu9//HkMXLFDMQPPKLLZSireAgaY3+J5XjzZKiDPDOWjGsjJc39IiOnSZDHepNNBU/hZPJMbfSKLdU8kA46+FEBLVNdTZ2Yk5c+ZApVKB53lccskl+MUvfoHW1lYYjUawLIt2v3Ble08PVF4vzGYz2tragiTabDaUlJTAYrGA0engslrR0NCA3Nxc8DwPu92O0tJStLa2Qq1WIy8vDx0dHcjLy4PH44HT6Qwu12g0yMnJQWdnJwoKCuB0OuFyuYLLdTod9Ho9uru7UVRUhL6+Png8nuByvV4PjUYDq9WK4uJiWK1WcBwXXB44p97eXpjNZnR1dYFSCrPZjN4TJ5A7ejQ6OztDzokQgsLCQlgsluA5dXR0wGQypfWchlRWYuG338KYn48eay/UOVUwde4JXjuheQ8auoWY59S1Zg2+e/hhnPftt9AWFSU8p1jXyejP97CcOAG32x08J7fNBsZsRm9vr6TrZLFYwPO87OsU696Ldk6Fx3ZgoBnlzBuJhoaG4HWy+bteWE6cQEtLi+jrxNntcFOKhoaGlNx7be58mGY+lPCcenp6wDDMgOs0GnmjTwm99xxOlHo80e+99vb416mzDaVLlsA4YgQaGhqSOycJ1ylVc4S9uxt6/z058N6zWq3B51XMOXF+I7avsxN23xyasXNK9bzXe8LnLe10u6F2uUSdE8Mwip6TR1cMrb0/NUG1+SkcH3sTOv1dS5rq6kCGD49+7xUXo/fw1yiy7gVlNGgdMg/FFWNlX6funh5oCwrQ3NEBncsl+pxYlkVLS0vK3k+xrlNRyzGYADTWe9FQ58WkkTo4bK6Qzk6csy9k3svkO7e1qSnhOTmdTuj1+pjXKR5IqpL/CCH1AOJtjFJKE2s++LZVCWANpXSS/+8eSmn+gOXdlNIIn/aGDRvo1KlTw38OwfdPPoldTzyBn7W3J9S8WjlpEoYuWIB5zz8v5rCzCq+PGoWqiy4SlfwuKmSSYug3PgrdD/8K/u2acQucc+6LqZu1+5ln8N1jj+HaxkZoBCs0deuhPrYOTHcdqLEEnqqzwFWfDaHQpw/lk2CoA3F0BFXdqb4AlFLUvv02zKPLUWxmwDh8X+CvXXQvhp0yAQv+8Et4h50eEjqMByW5y3nrAqja9wb/7rvoNXhHzAv+TSnFq2Yzptx9t+iOCALP41WzGTMeeADT71NQuBWZue8GE96bPRuFEyfirH+GiuxK5e3wG2/g6zvuwOU//ICciopUH2ZG8e3DD+PwihW49vhxUeMzcc+ZPrwW6oZNIb9xQ2fhWNnN+PjCK7Fo5UpUnHNO/0K/tIymbh3Ux9aHdsBgtfBMuAyu6TdCKBDxOnX3+vTt+k6AavN8gtOmUsmFMkrxptv+PPRbn8K3m9zY/JUbt676JUj+UBg2/S44xjX1ejgXPJr2Y4mFlm++Qf3q1Zjx4IOiKv8Tcbdr166dNTU1p0RbljIPGqW0MlXbioI2QkiZ33tWBqA92iAxOmicwwFWqxUlSJo3evSgbZR+yebNopNPS0S0q0gFdj3+OIzl5Rh77bXgi0Pz0HS7XoL2h1ch5I8A1UZyzu6oAwhQuOoiqLrDWhj1NkLV8h2w5UkIplIQjz1qDpSgywc1mDGrtwmkLdRNzvf2wmDZjpyPfgZv2Qz0XfIWoEpc+q4UdxC8YDtDZV/4sBAnIURyWx+GZXF9a2tG9P6U4i4TOlOpgDeGNp9U3krnzMH8F18Unac1mDB07lxJc7Riz+sAuKbdANXxb4IagYBPSDj/oC9lgfy4GqrRajD2dqiPrYP6+OaYVbuEd0O79w1o9r4JvmQKwMR451EvmN4mMI6OyEVqA/j8KnAjF8M18xZRH6NK8UZc3QD6W8+5qQHaiCrOzIY4LTt3Yv9LL4n+CE6Gu3QI1aYDHwO4DsAT/v9+FG2QGB20nIoKDJ0/X9ROz/vgA/FHmGUwlJYmHuSHxWLB8OGx879ShfpPPkFOZaXPQDNHasgQgQPbVRt1XW+XC2oVIo2zMAwsY49Y5uoBXD1oOcFDrQGKzf05ixodCeY9qFp2QbtnBdwzbkp4Tkpxx3QfCzY3BwDBUOwTDw3DRV99JflFnExOXzJQgrstv/oVmr78EpfvFCffkU3g7PaouUJSecutrERuZWUKjyx7UHHuuag491zR45V6XgfCW7kAfZeuhOnzO0LmJ63QCwAge1Yhh5VWRU1AoWrbnXhgGHZ+60anxYlzLnBAZdkHde1nsC/574TVzUrxRgbIbAAAR/XQRhQJZLaK02mxQGUwRHSjiYVkuMu6zFlCyFsAtgIYSwhpIoTcCJ9htogQcgTAIv/fsjD+hhtwzsqVqTnYLIWtqQm7/vQn9NbXixqvlHdBbzYHk3r54vHwFo0TvS7nSY1oJwCsec+B7Zs9Ib/dclcOzljQ7zHTbX82OFnEg1LcxROoHYjc6mpJBpqjrQ1b7rsPHXv2JB6cYijBHavTwdHaqrj+VSow/+9/x5irr474XSpvnr4+tHzzDVxdsYs2BivsLS3wSuiCkSlPKl8+C73LPwE3ICUhJ5fgtntNmDAlfuSHqvTwVJ0NPgW9UFtO8Gis7/fkqToOIHflBVAfXhN3PaV4Iy4rAECt9u3PI2iiyGxk1oPmtFigE1mAByTHXdZ50CilsVpC1SRaN5U6aADw3R/+AEdr66DLQes5dAjf/+lPGDp/vqgv58JCZfrY64cMQd+OHb4/GBa2Za9C9/0rYNv2gO2pi+qOD2DqKRqMHOO7vhQEfNkMeKrPhnfYaWDbf4Smbj1UTVuCXqaAG18wloDpa/Y1CPdXUqk1BB4v69OUyhsOSnzb1RxbF2wizrh7odv+PJzz4ruxleJOFUegdiCOvP02GJVKtOiss70dB/7xDwydP1+WyG0yUII7vdkM3ukEZ7P5OiYMIlQsjt7xQCpv1tpafHrBBVj05puSvE2DAR8uWIARS5bgzKefFjVeqec1GqihCLZl/4Jux39Dt/1ZMPAEtfrCIZjK4KmqAVdd058Ty3NQ134K3a6XoWr/Mep6Eftk1BDyKnztz5xd8Hh3QK0J/TglHhtMn90OV/MOXxu0KHnASvHG+EOcAac+RzWgYakmma7idFkskqpuk+Eu6wy0ZCBGB23jrbfCa7ej5rXXEo611tai5+DBVByaonD6tYHE3kQWi0WZBNDi4mATd8AnyTDQACJuK5ieBsDrjlhX7//XSxgIBVU+iQU/+NLp8Ey5BvDYwVrrIegKIxNhqQCmrxnE2QXy8V1w5hei78pVAHx8fX377Zi28ByMRX/Db+2e1+Ceei2EvNiJ1UpxF68H50Ac+te/wGi1og00TqZwZSqgBHeBZ8DV0TGoDDTe7Ubzpk0onDAhQq9RKm+Ba6t0Y+50Q+B5uDs7Jb0slXpeY4IwcJ16O9yTlkPVugvfPf0iyoarUV3FAwwD77DTwVXV+MS8wz0vrBrc2GXgxlwIpucYiKMz7q6ooRhC3vAQg8v51jIQkx2e0WOhOfJJyHjd7n+DeOxwLHoyQpZGKd4CUYvKUSrc9VAO7NNnAe6wSEaGQ5wCx0lOIcoGHbSMQ4wOWt/x46KT59VG46Cc1FwStYFyB/SkTCcMJSVQ6XTwulxQ6SITU6k2z5f4GgXtO3aAUatRPC1Oy1+UdkkAACAASURBVBmNMaJLQRCEgZA7DMgdBnVufsh1dXd3o3HtWlRf9DwE1bdgbM2+VXgP9FuehP282B5UpbhjLaEhznANtABUBgM8Vqvo7QZ7nGbAQFOCu0AowtnejtyqLO0iEAWujg6sveIKzHn6aYy77rqQZVJ5S3X3jGyBu6sLVBAkGWhKPa+JQA1F4KoXYfdHN8J59dUY8pP/Er8yIb4KTjFVnGHw2u3Q5OfDft7z8JafBv3Xvw/JbdUeeA9Ua4Jz3iMhBqJs3tx9UJ/YBvAeeMtmRHRWCEfAQGNZApYFYCgE5UM/2DPdSWDJxx9LSplI5p47qQw0MfA6HDCIrKpQGY2DclJzdnSA1WqhFukx4Hk+8aAUYMqdd2LKnXfKWnfbr38NlcGQksINlcEQVIMGBog35ubDOfUeGNfeE1ymObwGnqpFoDllEdsBANbOAcZpgEak8KPghar5O6iPrYeqZRcoq4GQ71N15wuqAU2UxFPOHtKjkbKamCX2KqMR9paWqMuiIXDuyTahlwMl7ru80aMx/sYbB10FIxdHUFQqb7HEmQc7At54KQaaUnOdWCj9jtGXlPjef4TAPfUaeEunwbTm/wQ/SgFA98O/QNUmuM64FxB4MH3N0Db+AG1Dh69TR/cxEM4Ob+lUcFVnw1t+KsD6Y5K8B0xPPdSNW6CuWwfViW3B3rwA4B0yGVxVjW+doGePgth8ItUBA81uF7D9aw/K5xxDSVXos5vpHDRAWl5ZMvfcSWWgiSFCimq6ymAYlAaaq6MDuuJi0TeR3W5HsYSkx0zA63CIbk6bCNPuuQe8u/+rLGikGAzwjDsX2u9fgWqAx8r0n9jdBQImsGAqBZ9fDWooBo3BO/G6oGr6Fow7zMPVtFXS8fNFY2K251FLnPB5jwdEpcpIiFOJ+y63shJn/PnPad1HOjDwngyHVN7kdpjIdrj8BpqUeSHb5jqVwaCo4bzojTdC/uZLJqPv0jeR8+5PQj4C9Tueh+bIp2D6mkB4D6Kpfalav/cZc5oceIdMAtPX7G/DJsTcv6p9b4iWYyx4OWDnNg8Mh2pRMnpu6MIM5qC5e3qw+c47Mf7mmzF07tzEKyC5e+6kMtBE6aDZbKINtNzKShROnAiB58GICJ9mC+Y+9xw8feJ7IZZKiKcnA1tTE7Y9/DAm3XorSk47TdK6nMORMiPCPGNG6LZtvsIAlcEAEAbOMx9GzgdXSdomY2uNK/GRSsTKPwP8HxUSJvyRl16KkZdempEqR6XuO4HjwHs8Kes3qgSCuYFRjlkqb4xGg5oVK1AwTnzV9GBATlUVTv3d75A/erTodZS658RC6gdVOiDkV6Lv4teRs+oKnxSRH2xPnaj1iacPaokfmYkQeJV7HY7IVk9RcpSVgqO1FfVr1qDqootEr5PMPZd1MhvJQEyRQPmCBTDPnClqe+Ouvx4Xrls3qIwzACAMI0rhOIBE7SZSifrVq9F96FDigWHwOhwpC8P1HD6Mhk8/Df7NqNXIHTkyyJm3Yg48VWenZF/pwMBS/XDMeuQRXPbdd5K3mQn5AaXuuzfHjcOOxx5TZF+pQrzcQKm8EUJQuXQp8kaOTMmxZQtyKiow+fbboR8StfNfVCg514mB0lGazy6+GAf//e+I34XisbAt+zeoOvUfMXx+NbwlU0Ehfo5hc3xzMedwZJXMRjCsrtA9d1J50MS8ZOa98IICR5JZbP/tbzHk1FNRef75osaL8TymAgMr6qQilqq6HNS++y72PP00fubvZ1e+YAF+EpD/8MO++GkIW//iT86P7l0ilILva4XG3hKiEp4Igi4fXOVCcFVnASodmO5jvibh1uOAEOcjg1GDq64BNzr2ddVITEg99tFHaPziC8x97jnFjTSl7jtdcXFQf2+wwDxzJs59/33kjx0bsUwOb81ffw21yQTz9OmpOLysQF9jI6jXK6n4Q6l7TizO+/BDxYSiBZ5H88aNKJk9O+pyvnQqbBe+AtPqm0M6sQj6QrgM5WBLxvmki/z5r+pjX0J97EswztD5XDCVgS8cBa5iLrjqGggFvg8DYrdAXf8V1A0bQeyhzyPV5EAoqAZf4Ns+VzwReLQKXpststOB1+nrfJKBj8rAPCJFBy2Ze+6kMtDEVHFKQdP69djx6KM4+/XXkTNIegZSSvHj3/+OySqVaAMtT4K3LRmwWi00ubkhUhticfYbb0gqbY4HtcEAKgjgXS6o9Prog7Q5ovq92e12GHVqMNbjPq01T/zwIp87HHzp1Jg9R5NF2/btaPj0U8y4//7Y5zYAlp07UffBBxnR+lPqvtObzbLuuUxCV1iI8gULoi6Tw9s3v/wliqdOxcJ//CPJI8se/PDkk2j68kss37dP9DpK3XNiEa2aPV2Il9cYHDPsNFivXQ9V2x4I+kJfD2Ndvm+eC4tgcKPOBagAtv1HMPY2CDlDwedXAero26dGMzwTL4dn4uUJj5UAUJtM4DkOYNWgjApE8HUKIlTwfciyyndACTgXpHjQkrnnTioDLVGrJ7fVinemTcOsRx+NKF2Puj2XC1379sFttWKwKCh5enpAvV7oJVj4HR0dEQ9fuqAfMkSWN0NsQqYYDJQdUOn1qH3nHRx+/XUsfvddsNrE/TcHoqOjA8YRIyAUjoJQOCplxygXnXv3Yu+zz2LSrbeKMtBSmdsnFUrdd3qzGV379ycemEXoOXwY3fv3o2LJkggPixzelE5GVwJOiYKhgLJznRjUvvsuug8cwKzf/jbt+wpWqydoUUSNQ8BVh6Z4xOSNMOBLpiAdtbHX1Nf3e/VVemCAV494naAZMNAIw8BYXi6p/2sy99xJlYOWyIPmtdkkaUSpB2H1U8D4UcrCl4r8ceNE9zALwOt04uiqVehraEjJMQSFO/3XtffoUbRs3gxGhis6277I1RI1r1IZOpYKpbjTDWgxNlhw/PPP8eUNN0CIklcrh7dsSEZPNeQYaNn2vLZt3YrDYZWV6YIYD1osZIK3gSkX4d0EwGVGC23CzTfjyr17RWupAv/rQQsiUSVaPG2haAiWp/ur/AYDpHYRAACPx5N4UIpwtogODuFwWizYcPPNmPvccykJNYdfV84vvSLloQtASe7EQKqkgtduz4gGGqAcdxWLF8NYVgZKacZ6MUqF124HCInqBZXDm8pggKcncW/ZwQSnxSK5MjUbn1cpVdfJonj6dNE6oAORCd6+//OfoTIYMPkXv4hSKOCKkRmcfUiGu5PKQBOE2PorwIDKKJEvpICnhxtEX55cXx8YjUaSNpBTQrPhTMAbR3JADsrmzcPSTz6BqaIiuH25Rkq2cSdVlJTV6WRN2KmAUtwNq6nBsJqErXyzCvE+GuTwpjYYYD9xIhWHlhWglMIlsWk1kJ3Pq9fhABUEWR+IUpBbVYVl69fLWjcTvDWtWxc00KAO+1DJUCXn13fcAWN5OWY88IDodZLh7qQy0BJVSwQ9aCJdvNr8fJTNnStJsiLTqDj3XFwvQUkeUFYb6OiqVdj34os4/5NPRIcUU612ry8uDsnRSyYPK9t0laS29Vnw0kvpPJy4UFIHzdHWBm1BwaDRQosXepbD2/QHHoCQZd6jpEAp5r3wAnIlSodk7fPqdGb1vZkJ3gZKkFA2XAstMwZa89dfo/T00yWt8786aH4k0kHTFRZi9PLlEc2HY8FYXo4lH32EofPnp+LwFAMhRFIoR0ltIE9vLyzffRcMxYoBl+J+ke6eHhxZuRJ9x48DAEzDhqFo6lRZ28o2XaUhs2bh+tbWmBWA2QSluLN8/z3enjIFrVu2KLK/VIBzOGK+sOXwVjhhQvw+toMMhGFQfcklKJb43Gbb86o2mXyGiAIeqhMbNuCD+fNhra2VvG4meFObTMG5n6rDDTTlc9AopXDK8Nomw91JZaAxCVzEBePGYd4LLyBvVOar7dKFfS+9hG8fekjSOhqFdHgAeVpoXom5g4ngbGvDpttuQ7tf++yUX/8aNf/6l6xtKcmdGDAsK0lXafPdd+PHv/89jUcUG0pxF7jnBpPUxsyHHkJNjHxNObx17duHI2+/nexhZQ2cHR1o2bw52AVELLLteR1/ww24rqlJUtW9XDjb29G1d68s/bBM8BYi4huWg5aJIgHOZgPvdEouTEmGu/+vDDSp7WwopXj3lFOwNwMaUXLRvGkTmjdtkrROjsim6qlAYCKSUlVXcvrpuGDtWuRJaOkSD1LDgPGgJHdi4Onrw5b77kPL5s2ixjeuXYvuDElQKMVdMgLJmUJORQWKJk2KvkwGb/Vr1mDTrbdCyLJm4XLRtnUrPr3wQvTW10taL9ueVyWRTBVnJnjTFhSA9evERbZ7Uj7EKUcDDUiOu5PKQEukg3bglVfwakmJ6PAaIQT2lhY42tpScXiKwNneLtnC7+zsTNPRRCJwc0vxZmjz8jDklFNS5kELr3Rcf9112Pab38jalpLciQKlOPCPf6Bj925Rwzm7PWO5L0pxpzIawer1g0pq49jHH6Ppyy+jLpPD22CUDIqHYMsdiZ6nbHteuw8cwIZbbkHP4cNp31e8/q6JkAneTn/iCVzqT0uIVsWpNASOQ/GMGTANHy5pvWS4O6kMNJUqfs2D1+GAwHGSEsIHm36QS4Y2UEFBQZqOJhI6sxnF06dL+orr3LsXh1asiKoJJQfhWmFd+/bBITNPQEnuxCBofIqo4qSUprTHqVQoxR0hZNB1E/jhqadw4J//jLpMDm+p9BpnAwLXUmo+ULY9r+6eHhx97z3Ym5vTvq9kPGgZ5y0Lqjjzx4zBsnXrUDZnjqT1kuHupDLQRMlsEAJWhMJ6AEo3s00Wzo4OSRIbgLIl1JqcHCxbvx5VF14oep3GL77A5rvukhyijgVGowFh2eAXpTdOQnYiZFvZPqNSgdVqRUnDCB4PKM9nTKhWSe5mPvQQRi9frtj+kgVnt8f8kJTD28looGkLC8Ek+CiPWC/LnlepuoXJwDh0KIbOny+ZMyAzvB3/z3/wxdVXw+t0RgjVEi67rmM8/K/Mhh+JDDTOX7oupcJRaSHBZMC73dCbzTANGyZpPZcrM6rMYuG120FUqpQ1FSaEYNmXXwb1vzibTbaRko3cif2o4N1u5I4cKdnjmiooyd2oyxP3/8smeP06aNEgh7dg94xBMpclgktGKgeQfc+rkgbamKuuwpirrpK1biZ4szU24vinn4Kz2bIixLnvxRdxZOVKXLhuHRgJfb+T4W5QGWiEkHoAfQB4AF5K6SkDlyfSQZPjKRlWUwNdYaG0A80QWK0Wl+/aJXk9pTVuNt52G0Ap5v/P/4gaH8+bIBdFkycDSD7Ml226SgCgyc8HTfCxAgCa3Fz8xF/JmgkoyZ2zvR22piaYZ8xQbJ/JINU6aGXz5uGiTZuQJ1E3LFsx/f774ZbQti+AbHteg8LSWd6tJlM6aIDfeA2v4syAgWY9ehS248clGWdActwNKgPNj4WU0qhZ/ol00ErnzJH81TX797+XNH4worW1FSNS0EJJLNzd3XBIENNNR55U3fvvQ2UyYdjChRg6f75s6RWluRODy3fuzPQhiIKS3O176SXseeYZ/KytLe2K7cmCUurz9sfRQZPKmzYvb1AJbidC4cSJstbLtudVbTJBV1wMIvGlLwff/PKXsDU1YfE770heNxO8DfT6UnW4B035EKezvV1yziOQHHfZPVNJRCKZjZGXXooZDz6o0NEoj5bNm/HZxRdLbiqu0+kSD0ohpCZsc2lo6L37mWdw8NVXwajVOHfVKoy+4gpZ21Gau1Si59AhfHrhhbBkyKBTkju92QzK83B1dSm2z2Twkx07MPGWW6Iuk8Obu7sb+//xD1kipdmIo6tWyap8zLbnVZObi6sOH8bYa65J+75sjY1wyawozARvwbxJux0I7ySQgRw0V0eHZIkNIDnuBpuBRgGsJYTsJIREzF6JDDTObpdcCfjNPffgw4ULJa2TKViPHkXzxo2Sv8b0EoomUoGAgSYmDAcAp/3Xf2FxikU2U1WdqzR3YrDn2Wex64knEo4Lin1mKHFcSe6CWmiDoJKTEILc6uqY3n45vLm6urD1vvvQPki8q/Hgdbmw4eabUb96teR1s/F5VQrxulMkQiZ40+TlBfslh3cSAK98iNNpscgSFE6Gu8EW4pxDKW0mhAwB8AUh5CClNKjKarFYMGfOHKhUKvA8j0suuQS/+MUv0NraCqPRiK+vvBJQq7F41Sp0dXWBUgqz2Yy2tjaY/I3RbTYbSkpKYLFYQAgB53LB1tyM7u5u8DwPu92O0tJStLa2Qq1WIy8vDx0dHcjLy4PH44HT6Qwu12g0yMnJQWdnJwoKCuB0OuFyuYLLdTod9Ho9uru7UVRUhL6+Png8nuByvV4PjUYDq9WK4uJiWK1WcBwXXG40GsGyLHp7e33ncfQoAICYTGhoaIh5ToWFhbBYLMjNzQXP82hubsbYsWMVOydqMoF6vehuboaLYeKeU/A6DR0q6ZwSXScvwwA2G/Zv3Ijvb7sNpz7xBDRTpkg+p/r6epSWlkq6TmLvPannFLhOdevWgevuxpjbb497nXr9xgpHCFpaWpK69+ScU2trK7q7uxV5nqz+jwHLsWPoNRiy4jrFOqe+9nb0rl0L3YwZKJowIeKcLBZL8HkVe50s3d0AgI4TJ6BrbFT8nFI57xF/7pmDYeBwOCRdp/r6egwfPjyrzmndTTfBPHMmRl93XVrvPZfVCnVREdxut+Rz8ng8sNvtys4Ro0ejZt062O12cLamEEPAae1ET0+PotfJMHYsjBMmoLu7W9I59fT0YOTIkTGvUzyQVEkXKA1CyKMAbJTSpwK/bd68mU6Mk5vwwdy5MI0YgUWvvy56P98++CAOv/kmrpUYNswEtj7wAGpXrsQ1EtW1bTZb8MZSAic2bMCBV17BGU89FaykjIcjb70FTX4+Rpx3XsqOYf2116KnthbznnsOHy9ahEVvvYWKxYslb0dp7sTgyxtuQNe+fbhs27a44+o++ABf3XgjLvnmGxSMH6/Q0fVDSe66Dx7E+2ecgQUvv4yRl16qyD7lovvAAbw/Zw4WvvIKqi++OGK5HN48vb1YUVmJU3/3O0y+/fZUHWpGYPn+e3xcU4NFb76JinPPlbRuNj6vKydNwtAFCzAvzR1r3ps9G4UTJ+KsGPp68ZBp3tR162BafVPwb65yIWzLXs3Y8UhBIu527dq1s6am5pRoywZNiJMQYiSE5AT+H8A5AH4cOCahzIbDIbkaUOUPhQ0GQ9bZ3i5ZAw0A+vr60nA0sVG+YAHOXrFClHEG+EJ2tStXpvQYVCYTvA5HMLwn1/WvNHdiIFYaJihcmSGhWiW5Mw0fjgUvv4yS2bMV26dcJLoucng7mXTQ5IrUAln6vCokhl525pkYMmuWrHUzwZurqwufXXwxGj77LEJmIxNVnHKRDHeDxkADUAJgMyFkN4DtAD6hlH4+cEBCodo42kKxoDIYQHkegscj8XCVh37IEJinT5e8nifLzy0dVZynPvYYzv/ss6TUtYHs5E5tNIrKK1ObTCicPBmaDPUnVJI7tdGIkZdeKlkjMBMItuSJcU/K4Y1hWbA6XcbyDVOJQB6hnITtbHxeVQaDIvp0c/7yF0y69VZZ62aCN8IwaN64EX319RE5aEpXcXbs2YM3xo5F88aNktdNhrtBk4NGKa0DMDXemIQ6aDKqAYsmT8aYq68GHQRNhk8XkRgeDUpr3LitVqyaPRvTfvUrTLjxxoTj01HFGUjAbvX3epO7/WzTVQIAXVERNLm5CcdVLVuGqmXLFDii6FCau/YdO0BUKlkfMUoikQdNLm+XbNkCbaZb9qQAI5YuRf7YsTCWlUleNxuf18HQTjCTOmic3Q6ENUsHp6wHzdnWBpfFIqkLUQDJcDeYPGgJkUgHbfIdd2BYTY2kbQ5ftAhzn302Y+1wlECiRMVUQ5OTA1dHB5wi95tMK6ZYaNu+Hd8/+ST0Q4ZgxNKlssWIleZODKbfdx+u+OGHTB9GQijN3Tf33IPvn3xS0X3KQSIPmlzecisrTwotNG1+PoaccgqYBB/k0ZCNz2tOZaUsb6AUCByHFVVV+PHvf5e1fiZ4YzUaMGq1L8Uow50EnEl4bZPhbtB40MQgkczG9F/9StZ2A/lnUlpEKQ2B4/DBvHmYfMcdGPPTn0paV+kSasIw0BUXi9JCE3gevMuVcgO5fds27HriCVx7/DjOXrFC9nYGc9n+nmefRdP69Vjy0UcZ2X8m5F0Gg8xG5fnnY/n+/dAVFUVdLpe3w2+8AZXBELXwYDDh+OefQ+A4VF5wgeR1s/F5TXdxAODLv/ZYrYDMXOpM8RZoWxdpoCkb4nR1+LTx5bQXS4a7k8qDFs+AErxe2Jub4ZXYF6vxiy/wqtmMju+/T/bw0gpnRwd6Dh0CL6PvlyZFPS6lQKxYLWEY/PTQIdm5E7EQCB8lm/uRCe4S4cSGDfjPFVck5Le3rk6W2GeqoDR3OokCyZkCq9XCUFoa00Mkl7cDr76KI2+9lcyhZQX2vfgi9so0arLxeVUCXn8rKbkfupnirWjqVF8xSHiIU2EDzdneDpXBICuSkwx3J5WBxsfJE7M3N2PlpEmoW7VK0jZZnQ5UELI+RyDogpVh4Vtl9LRLFmJfloQQ6M1mUTlVUhCYqHY89hjeHDdOdpVuJrhLBKfFgqYvvoC7pyfuODlFM6mE0twFBZKzvCK7eeNG7Hr88ZhCznJ5UyuUjJ5uOC0W2SHBbHxe97/8Mj6/7LK07iPZavVM8bbkww8x/d57o7R6UjbEWThpEkYvXy5r3WS4O6kMNJUqdsQ2IDsg9YUU7AeW5QaaKwkDrVhGuXqyqDj3XFH5gM6ODnz3xz+ia//+lO4/cB/YT5yA1+mUHb7OBHeJoBYpqcDZ7SnP7ZMCpbnTm83gXa6sb0zdvGkTfvjrX4EY96Rc3gLhosEOp8Uia54DsvN5tZ04gdZvvknrPpKtVs84b6w25E/CewBBucK90VdeiTP+/GdZ6ybD3UlloMXzoAW+HKXKNQRuaG+WT+pBbaBB4kGbeMstmHH//QnHOVpasPsvf0Gvv0tCqhAwvJ3t7UkZKdn4RR5S/RQH/7950KouvhhL1qwBq9UmHpxBBAznWB8Ncnk7GQw0gefh7uyUpYEGZOfzqjYYwLvdELzetO1Dk5OD0cuXI0dm0+5M8bblV7/C13feCRACGhHmVM6Llsy1+V8Pmh/xQhdemS5elV8BONsnNm1BAcrmzZPl+k9U/ZouCByXsB+nV6ZhnQhl8+bh6mPHUDB+fFLbzhR38RD8qEhgoBWMGwfzjBlKHFJUKM1dzvDhKDvjDLBZnoeUSPdPLm8qgyHrvYeJ4O7qAhUE2SHOrHxeFRARzq2uxrwXXkBhnE478ZAp3vrq69G1bx8AZLSS8+0pU7D1gQdkrZsMdydVFWc8HTS5Ll5tfj4m3HIL8kaPTurY0o2KxYtltSoCMqNxc/S997Dhllvwk127kFtZGXNcMHcixZ4eVqMBq9EkXSGajbpK2ry8/9feucdHVd4J//ubyT0hF0hICDchEMSiohQKFXTFFm1rrX1Xt+iq1F26llXXt9X23dIiWKvU1e3nta37ru22aq2X6pbWVtMVLYpVuWMRVBQQwyUEEnIPk8tknvePcyaGZDKTPCcz50zyfD+ffEjmOWfmmS/n8jvP5feQW1aGL0qXP8D8tWsTVKPIJNpdR3Mzh198kaLzzyd36tSEfvZgiNWyqett/r336lbJM2SMGcPS3bu7H5wHixfP13CjQWdLy5CPtQ2jlHKUhcAtbynZ2QQPH7b/yATqu8skGCARo0lVKESgpoZUzYTeJg+aTbRINW/6dObddRc5EycO6j3TRo1iwQ9/mBRLxOjiRo6bNDsfU6y0B/Fajqi9vp6ta9aQkp3NGV/4gvb7eDGvUv6MGVy9bRvjL77Y7apEJdHuOpubefWf/omjGtnAE0msvH+63tJyc+MWACQK8fnIHj9eO5+bF8/XrNJSqyU7jpNX9j31FI8UF9N86JDW/m5567kqiltdnO319aiuLu1xj07cDasAze/391uWV1bG2bfeSqbG2IVgWxvBQGKn9Q6WPy9bxss33KC1b7YLA8XDB3usmZy6XdOxCLa1sfvHP2bcBRdw3re/rf0+brgbKp5bvJid993n2ucn2l143JLXc6Fd8vjjXF5R0W+5rrdjb77JllWrCHmwm2+g1Lz1Fm898AAdTU1a+3vxfJ20ZAlXvPwy2ePHx+0zgqdOEersJEUzJ5db3nquU9p3uafEBGiBEycAtGIHcOZuWAVo0QjU1tJ44EDMMU+ReLK8nO133x2HWg0dTR99pL1eaLTANl4MNEAru+oqbjh8mFFRukF16Nmt4AQ33MWiq72diiuuYN9vfhN1u4YPPqDTxcWjE+3On5ZGWn6+53Oh+fz+qF2cut5O7trFnoceSupUG8c3bWLnvfdqXcfBm+drInA6i9Mtb/nl5R+Pk+29YHpnYhpNnKwiAM7cDasALdoszvcfe4z/njtX6+kxJTvb8xe1ttpa7SbYJs2nUSeEWzNi3SzF57NmtMVYJWKwhC9UW++805olpIkb7mLhS03l2Ouv0/zhh/1uE87t52aaDTfcZRYVdT8Re5W37r+ffU8/3W+5rreBTh7xMoHaWnypqd1DJAaLF8/Xunff5XeLFnHMXhc4HoQfRHVb0Nzydtby5Vz6zDNA3y7ORK0mkFVczCdWrCB3yhSt/Z24G1YBWqxJApKSgk9jBpfXF7MND2LUSbEBUKS5nxP86emcc9ttjJ0zJ+p2h9avZ/N3vzvkyUV9KSnd6RZ0jokwbriLhfh81oy9KDficJf9UI/tGwxuuMssKiJgL9viVT544gmqXnut33Jdb92txskcoJ04QUZhofaAdy+eryhF3TvvdC8nFA/CM4N1H3S94M2t5Z7yZ8xg/j33aHdBz2OxHAAAGKlJREFUO3E3rAK0YJRcJZ2traRmZWmd2F7PH9Te0IAKBrVb0Orq6oa4RgNj7urVMQeyV7/5JnsfeSQu66CGWxSczBB1y10sUmI8VOgmbh5K3HC38MEH+ZuHH0745w6GYGtr1P8XXW+JSOcQb5ysIgDePF+7Wzbj+P9SPH8+Zy1frr2/W94OPvccz8yZY7V6p7o0SaCx0dH/jRN3wypAi0as3ELRiNUa4TYqFGLa0qWMOftsvf1dWvomGAjE7G6KdbNywjX26gRO3t+rywbFXNbH52Py5ZeTV1aWuEr1wg13eWVl5EyYkPDPHQydMbqedb0ly6oo0Wg7eVL7QRS8eb4mIkA74/LLmbt6tfb+bnnram+n+eBBOpqb+7agdSYmQNu6ahXPfvKT2vs7cTes8qBFXerJwXibGddd1++yK14gs7CQi/7jP7T3d6v5euOKFTTs3cvfbt7c7zbxHCcVHo/o5P290PQficLZs6MGIpmFhXzmV79KYI364oa7k3v2cHTDBj7x9a97MmFtqKuLrkAg6kODrreShQu58cSJmPnxvMwXX3zRUSDjxfN1qCYsRaOztRV/Whq+KMOAouGWt57Bq1tj0JwMHwLTxdlNtDxoM5YtY87KlVrvW37ddZT//d/rVivuhLq6HEXpx48fH8LaDJzMASyY3hnHFrTdDz0EQKGDbPpuuYvF4kce4ZOrVrldjai44a5m+3a2rVnj2YkCXYEA/oyMqN3uut58fn9SB2dgfYc0zYSh4M3zNSUri3GLFpFdWhq3z3j5+ut53kG+R7e8da8r3NradxZngro42xys/QrO3A2rAC3adNbSRYuYcuWVWu/b2dLCKQ8mOAzz3i9+wWPjx9Om2dedo5mV2ymZRUW019dHnVkbCgbj1oJ27PXXKV6wgHEXXKD9Hm65c8qx11/nienTqdmxw7U6uOEuPH4pngOynZCak8NXq6qYdcst/W6j6629oYE37riD6k2bdKvnKp0tLbzxzW9yfMsW7ffw4vkqPh+ff+45yq66Km6f4bQnwi1vPdcVdrMFzUmA5sTdsArQonFyzx6aDh7U2nfr6tX87sILh7hGQ0egpoZQR4f21HO3CN8so82q++yvf80XX3opLp/vT0+nrbZWO6eSl9l6551RExd3NDXRdvIkkuQtKoNloOld3CYek2JCnZ3s/eUvqduzZ8jfOxGcqq5m76OP0lxZ6XZVko5ga6urM7Z1yRw7lomXXkp6fn6EAC3+LWhKKQIOUlg5ZVgFaNHyoL36ta+xbc0arff1+izOtpoaMgoL8WkmxGtxaQHlAedCi9P4v4b336dx3z6OvvKK9nu45S4Wp6qruxcZjoTTxJVDgRvuuh8KPBqgNR86xMYVKzi5e3e/2+h66x7r5OFrWTTCD3JObpZePV8rrrhCezHugdB56pSj2epuecudMoUlTz1F0Zw5kJr4Lk4VCjFn5Uomaq5zDc7cDasALVYeNN0niFQ7QPNqS0vADtB0KS4uHsLaDJwx55zDvLvuinrB3bJqFXsfeyyu9XDyZOmWu1jEeqgIz/B0M1GtG+4yPd6CdurYMfb/5jdRx8jpevNnZoJI0iaqDTtxMmDbq+droLaW1qqquL1/8NQpRw9jXvDWdxZn/Ls4fX4/Z998s6NhME7cJVWAJiKXicj7IrJfRPo8bsTMg6abZiM8UNGj63E67SOvcelmNWrSJM6+9Vayx43rd5sP162jZvv2uHx+eMC0kyDFLXexSMnKinoj7s6D5mKA5oa71JwcvvL228xasSLhnz0QBrL2rK43EfF8yqBotA1BC5pXz9fU7Oy4Bs6zVqxg0mWXae/vlrfO1laenDmTd372M3BhDFpHUxONBw7QpbmMIjhzlzQDUETEDzwEfBY4AmwTkT8opd4dyP5OniBSeswkcbPFoT+mXHEFqQ5mNsWrCzEWSimaDx7En5nZb5DmpOUzFuevXMnGm25y9GTplrtYhFe/UEpFrGPe9OmUXX21o24Pp7jlzst50DoHEDg78ZZeUAAezAU2EIKtrfjS0sgYM0b7Pbx6vsZKLO2UcxwsZwfuefNnZBA4fpz2+npUyqTTy47vIvNV/dxuA6Fq8wH+9G8VfOWBpRRN1XswKG1pIfNAlIkCuV/qtyhpAjRgHrBfKfUhgIg8DXwJ6A7QQi0tHPzDH07bqfDcc8meMIGutjbtG3HJ/Pl86gc/INTV1ef9w58xavJk2hsaIi7RUnT++eRMmECgpibiDKriefPIKimh9dgxTmzb1vfzP/1pMgsLaTlyhJqdO/uUl193Hen5+VrfDWD06NHa+zpl3aJFTFi8mLKrrwasFo4JixcD1kzDeKbZGIps+m66i0ZeeTmlF12ECgapfPHFPt3zo844w/WM+m652/fUUxxev54pX/5y92vZpaWMtZNRVv7pT31mFo+aNInC2bMBIl4DcqdOZcysWYSCQSorKvqU50+fTsHMmQTb2ji8fn2f8oKZM8mfPr276zXaMenE29K336YzwnUSYMzZZ5M7ZQrtjY1UbdzYp7z7OlZbS3WEdSPHzp1L9rhxnDp+POJMy5IFC8gsKur3Ola6aBHpBQU0V1ZSu2tXn/IZy5Yx65ZbHAULXj1fU7OyaLLTMfTnN3yfOFVdzfGtW/uU93ufCIVIzc2lZMEC7bU43fLm8/vxZ2RwcvduDhQqzg4pfD7hRHUXDe8egE0HTtu+/CxrmNPxY1001p9+zfP5YNqZVvmxI0Gam05/UPGnQFm5VX70UJDWFkXVh1avXEHV82Q063U4ZsTaYNHwCNDGA4d7/H0E+FTPDVoqK9lx882n7fSp++8n5+KLmfuTn5A3bRqVlZUUFRVRV1eHUoqioiKOHz/ePRW2paWF4uJiampqEBFGjx5Nc04O46+5hhMffMCGr361T8VmrlrFWTfeyIkdO/hLhPJZa9cy85prOLxpE5sjlJ/74IOc+eUvs3/DBnbcemuf8jk//znTL7uM9yoqeDvCQNJ5Tz5J2YUXDuo71dTUkJubS1dXF1VVVcyYMYPq6mpSU1PJy8ujtraWvLw8Ojo6CAQClJSUUF1dTVpaGqNGjeLkyZMUFBQQCARoa2vrLs/IyCAzM5P6+nrGjBlDc3MzHR0d3eWZmZmkpaXR2NhIYWEhWRMmUPnCC1S+8AIAOWVlLFm/nqamJv56992oYJCOjAza2toG9Z1aW1u7P7O/70RaGohQHwigWlq0vtP+/fspKSk57Ts1NjbS2dnZvX92djZ+v5+mpqZBH3uD/U7dn7lwIQs/9zkOV1XxyvLlhHo10U++5hrmrV074P+neHyn6upqsrKyBvydhurY+6CiguoXXuDgc891+yhesoT5P/0pfr+fV2+6iWCvgb2lV17JvPvvB2DDP/wD9Ap4J157LXPvvpv2lpaI14gpy5dz/sqVNFdVRSyfdtttnPeNbyClpdaawaNGUVlZGfE71dTUdJ+vOv9PrZWVvB6hDnPXrqXg85+nee9eNkUo//RPfkLWwoU079rFlgjlFz3yCKmzZ9OweTPbI3Qhf+bZZ2HaNOo2bGDn7bf3Kb+sooLguHGceP553o6Qw++LGzcSyM11dOzt37+fiRMnDvp8GurrXu//J19BAZPnzaOyspLQvn0Rj5H5Dz9Mzrx5dO7axcYI5Rc8/jiZs2YRePNN3uh1HwSYfd99FC5ZovWdOjo6yMnJSfg1oquri7SiIg5VVHCoooKZ3xlFWhq8s6uTHZv7djvesdoKsHZt7+Dtnac/ZKWlwb98xyrfsaWDvXtOHxKVnSOsuN0q3/J6Bx/uC3bvl5nlTguieHHpi0iIyNXApUqp5fbf1wPzlFLdEc2rL72kJvZ68sweN85q1h8Cgm1tNH34YZ/Xw58RPHWKpo8+6ls+fjzpeXl0trTQfOhQn/KcCRNIy82lo6mJliNH+pSPmjSJ1Jwc2hsbaT16tE957pQp2k9GAPX19RQMkaPB0vs7+dPSyJs2DYCmgwfpam8nv7xce5HfaKhQiM6WFtJyc7Xfw013A6Xu3b6jANLz8+OaGHMguOUu0nmclpvb3fVZ/957fRI/p+fldS+WHNFnQQHZ48ahQiHq9+7tU54xZgxZxcWEOjtp2LevT3nm2LFkFhbS1d5OW11d1HGZTr11tbfTeOBAn9ezSkrIGD2aYCAQMSVRdmkp6fn5dLa2Rkx14fg6dsYZpGRl0V5fT+uxY33Lp04lJSNme0RUvHq+dnV0EAwE4naf8KWkkDd9unbro6v3iPDxEAoycee3SKt/n5bmEIFTfWOXomIrk0FzU4i2wOnlIlA41ipvagzR3nZ6uc8HY4qs8saGEB3tVnlWtpCdE7/h+n9etG7HJZdcEnEtqWQK0BYAa5RSl9p/fwdAKbU2vM1f/vIXNWvWLJdqmLzU1tZS6GAW6EjGuNPHuNPDeNPHuNPDM97am0g9+Aq+tnq3azJgWlpbyMnufwzaptA5/QZoydTFuQ2YLiJTgKPAUuDanhtEy4Nm6J/W1lZvnHxJiHGnj3Gnh/Gmj3Gnh2e8pefSeWb/Y7a8SFVlJZMnT+5/gwjjMcMkTYCmlAqKyC3Ai4Af+KVS6rRMnNHyoBn6p6SkxO0qJC3GnT7GnR7Gmz7GnR7Gmz5O3CVVHjSlVIVSqlwpVaaUuqd3ebTF0g39U+3hdUa9jnGnj3Gnh/Gmj3Gnh/GmjxN3SRWgxaKhocHtKiQlv//9792uQtJi3Olj3OlhvOlj3OlhvOnjxJ0J0AysW7fO7SokLcadPsadHsabPsadHsabPk7cDasALVlmpHqNaEtkGaJj3Olj3OlhvOlj3OlhvOnjxF3SpNkYCBUVFc3p6envu12PZKOurq5w9OjRtW7XIxkx7vQx7vQw3vQx7vQw3vQZgLvJl1xyScR1pIZVgGYwGAwGg8EwHBhWXZwGg8FgMBgMwwEToBkMBoPBYDB4DBOgGQwGg8FgMHiMpArQRGSU23VIVow7PYw3fYw7fYw7PYw3fYw7PeLpLSkCNBHJFpGfAr8VkWvt9TgNA8C408N408e408e408N408e40yMR3pIiQAO+D+QCPwDOA37obnWSCuNOD+NNH+NOH+NOD+NNH+NOj7h782yAJiI5Pf7NAdYqpV4D7gF8IvI9N+vnZUSk2P7XuNPA9jYK421QiIhfRDIxx5wW5rgbHCIi9r/ZGG+Dwtxf9Uj0vdVTAZpYpIvIfwP3iEiqUqoFKAWWAiilGoD7gKtERH+Z+GGIiEwVkaeBe0XEZ7srxriLioicKSL/KSK3iUiu7a0QuBaMt2iISJmI3AiglOpSSgWAEswxFxMRmS4iD4rI10WkwJyvA0NEponIw8AdIlKqlGrFHHMxMfdXfdy6t3oqQFMW7UCR/XO1XbQaWCoihfbfbwOvAl9IeCU9iojcC6wHNiql/lEpFbKL7sK46xd73MCvgQPAucB/ikg5sBL4OxEJZ3g23nohIv8M7AC+ISJ/26PInK8xEJF/BX4HHAX+BviFXbQG465fRGQN8FvgA6AceMIuMsdcDMz9VQ83762eCtDAeiIHWoCXgYUiMkEptRN4DfgRgFKqA+gCalyrqPdIA+qVUv8PQERK7Cekt4BXMO7640ygVil1P3ATsBe4AWgE/ojxFo0DwHJgFXCtiGQAKKX+CmwA/t3+27jrgd0l1wJ8RSn1b8BXgTNFZHaP89W4i8w7wOeUUv8O3A7U2q3eb2HdHM35GgVzf9XCtXur6wGaiPSuQxXWSfg+0AwsEZF84Fbg0yJyk4hcClwIhBjBiIhPRPwASqk7gLEicpeI/BH4MfCE3QL0DeACuyvFuOPj8SvAHqBNRM5USnUCFUAmsAgr8DDHXC/C56xS6kWs1oy/AnXAih6b3Q4sMu4icgr4rVLqHRFJV0q1AW8Bfrv8mxh3EVFKPauUqhKR87HuEfnA3WKlOvjfmOtcLMz9dYB44d7qSoAmIsUisgKgR3NhmBnAZKXUG8BHWM2vT9gXseVAFlbT4v9VSj2fuFp7g97ulFJdPYq/h3XAPApch3UjuM0eF3QDVuAxIt2JSF7Pv9XHi9CmYnWXLLRf3w4cAWbZ3m7EHHO93YV6/K6wuunWAZ8Rken266eA64FsjLvTsLuajtm/t9s3gvOwglzjjsjeepEG3KGU+qz9+7+a65xFDHfm/toPEa5z7t9blVIJ/QG+CxwCtgBTe7y+FCtgHAfcC/wU6wB6Dfh+ouvpxZ8o7q4B/PbvRT1ePw/YDWS5XXeXvX0P2IU1iPNK+zUfcLX9+41YXUrz7b/nA7vdrrcXfvpxJ+Hztcd2JcBa4Hv23+WAuF3/JHG3EHi+R3mJ23VPBm89tg9f53LcrrvbP7HcmfvroL25em9NWAuaiMwXkd32AbIMqx+83i4bj9VUDdbgxQuxpk0vAP4PMElEZieqrl5jAO5ygfD4n5793zOBrYmtrbcQkXuAs7BaJKqAS+wWixJgrL3ZK0A1cKdY06fPALaKSFbia+wdorgbh3W+ZoS7ipVS1VhPl8tEpBW4HOsCNyIZoLtwl2Y+sMWeaPEOcGmPLvgRxUC8RditHNgGqAhlI4YY7grszcz9tRcxvOXh5r01gRFqMXYLhf33RuAW+3dfr20n9Ph9DDDe7Qjb5eh+MO4ygPOxBsz+GZjrdv1dciZYzfX/A5TZr30LuL2f7X3AA8DvscalzXP7OySRO799jG4BNgOL3P4OyeLOLv8vrHErz45UdxrHXB5WgPHKSL7Oaboz91c9bwm/t4r9wUOOiIwG/hfwmFKq084dErIjUwHuAILAj1SPMS0ikqWUOmUPRBZ1ej/wiMCBuwylVJuIXAxMVEr9ypUv4BK9vdmv/RIrp1k61pP2diAFK8HgVnubbKVUq33MjVGnPymNCBy4y1RKBcRKUHu5UupZV76AiwzBcfc1oFMp9agb9XcLB97SlTV27wtY5+uIus6BI3cj+v7qwJs799Y4RaZfAmqB97AG0kGv8ShYgxMftn8P9/GWAj+zBbgeYbvx49Ddzxm5T0N9vIX9ALOBp8MesWbJhcf8jLOPuUluf4ckdneG298hSd39FzDO7e+QhN5G7HVuCNyN2PtrMh5z8RqDdgT4NvA1rJldk5VSyk4LER5b8YxdVqA+juLrgG8ppQ7HqV7JgFN3R12osxfo4w26Z+KMAjKVfbYBm4CPRCQFaMDydsiFOnsFp+4+SnyVPYMTd3coezbnCMTpMTdSr3Pg3N1Ivb8m3TEXlwBNKbUDeAwr6ee7wM3266EeApqAN4HJPfZrU0o1xqNOyYJDdw0Jrq5n6M+bzSagTET+WayVA1YDTUqpoFIqYI45404Xh+7M+Wq8DRpzvuqRjMecowBNRDLFzh7eG2Wty1eLtZzJmSJyUa9N2rFm0Y3IA8a400PHm1IqCPwLVvLZdVhN1ysTVWevYNzpY9zpYbzpY9zpMZy8aU8SEGtV953AOqXUrVG2ywf+EZiilLpFRGYAlcoacJeq7IF6IwnjTg8H3mYqpd4TkVSsY74jQVX2DMadPsadHsabPsadHsPNm5MWND9W3+xiEZnZ30Z20+CjwDwRaQG+jp2vZqQFGD0w7vTQ9XaTPfOr0ysnngsYd/oYd3oYb/oYd3oMK28DbkETEb89mC68Ft944FJgCnCOUuqLEfbxYS3D8SwwDViplPrdENU9aTDu9DDe9DHu9DHu9DDe9DHu9Bju3lJibWDPYvg+4BeRl5VSLykrJ1cp8Fml1FdEZJeIXAlUK6U2h/dVVo6uNhF5Sin1ZNy+hUcx7vQw3vQx7vQx7vQw3vQx7vQYKd6idnHaA+h2YC0T8QFwj4hcaBc3A2/Yv+/CGlh3g8jHS5SEf/e6hHhg3OlhvOlj3Olj3OlhvOlj3OkxkrzFakELAQ8opR4HEJFzgM9jLbCaB9wu1vpxXVjZd3epHn2mPX8fgRh3ehhv+hh3+hh3ehhv+hh3eowYb7EmCewAnpGPF/XdDIQTpu4CNmAtmbAYK/nbMhnhC0z3wLjTw3jTx7jTx7jTw3jTx7jTY8R4G1SaDRF5FNiplPpxr9clmaJSNzDu9DDe9DHu9DHu9DDe9DHu9BjO3gaUZkNE/GLNfCgGXrBf+4SIZMezcsMB404P400f404f404P400f406PkeBtoHnQQkAq1kKj54rIH4E77NeSqk/XBYw7PYw3fYw7fYw7PYw3fYw7PYa9t8HkQZuPtf7jm8AjSqlfxLNiwwnjTg/jTR/jTh/jTg/jTR/jTo/h7m0wAdoE4HrgR0qp9rjWaphh3OlhvOlj3Olj3OlhvOlj3Okx3L1pr8VpMBgMBoPBYIgPTtbiNBgMBoPBYDDEAROgGQwGg8FgMHgME6AZDAaDwWAweAwToBkMBoPBYDB4DBOgGQwGg8FgMHgME6AZDAaDwWAweAwToBkMBoPBYDB4jP8PwoNfIfKUf8MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (10, 4)\n",
    "\n",
    "fig, ax1 = plt.subplots();\n",
    "ax1=gt['Laptop Computer'].plot(lw=4, c='tab:orange')\n",
    "pred_CO['Laptop Computer'].plot(ax=ax1, c='darkred', lw=1.5, ls='--')\n",
    "ax1.legend(labels=['Laptop Computer','Pred_CO'], fontsize=9, loc='upper left', ncol=1)\n",
    "\n",
    "ax1.set_ylabel('Power (W)')\n",
    "plt.savefig('./data/images/5.5_F1_CO_60s.svg', bbox_inches='tight', transparent=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (10, 4)\n",
    "fig, ax1 = plt.subplots();\n",
    "ax1=gt['Fan'].plot(lw=4, c='tab:orange')\n",
    "pred_CO['Fan'].plot(ax=ax1, c='darkred', lw=1.5, ls='--')\n",
    "ax1.legend(labels=['Incandescent lamp','Pred_CO'], fontsize=9, loc='upper left', ncol=1)\n",
    "\n",
    "\n",
    "ax1.set_ylabel('Power (W)')\n",
    "\n",
    "plt.savefig('./data/images/5.5_F1_CO_60s.svg', bbox_inches='tight', transparent=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Métrica EAE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La ecuación de la figura representa la diferencia entre la energía total asignada yˆt(n) energía total consumida yt(n) por el proceso n de la industria en kWh "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![metricas](metricas.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Fryer</th>\n",
       "      <th>LED Lamp</th>\n",
       "      <th>Incandescent lamp</th>\n",
       "      <th>Laptop Computer</th>\n",
       "      <th>Fan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EAE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fryer  LED Lamp  Incandescent lamp  Laptop Computer   Fan\n",
       "EAE    0.0       0.0               0.01              0.0  0.01"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(pd.DataFrame(metrics.loc['EAE']).transpose(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>kWh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Energy_pred</th>\n",
       "      <td>0.015917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy_GT</th>\n",
       "      <td>0.012283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EAE</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  kWh\n",
       "Energy_pred  0.015917\n",
       "Energy_GT    0.012283\n",
       "EAE          0.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_Devices=elec_CO_30s_first.submeters()\n",
    "e1=predictions_Devices[5].total_energy()[0]\n",
    "e2=valid_Devices[5].total_energy()[0]\n",
    "eae=round(abs(e1-e2),2)\n",
    "\n",
    "pd.DataFrame([e1,e2,eae], columns=['kWh'], index=['Energy_pred','Energy_GT','EAE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Métrica MNEAP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este indicador que representa la ecuacion de la imagen  consta de la diferencia entre la potencia asignada yˆt(n) y la potencia real yt(n) de cada proceso de la industria, por cada intervalo de tiempo t. Después de sumar todas las diferencias de potencia, el resultado es normalizado por el consumo total de la energía del proceso "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![metricas](metricas.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Fryer</th>\n",
       "      <th>LED Lamp</th>\n",
       "      <th>Incandescent lamp</th>\n",
       "      <th>Laptop Computer</th>\n",
       "      <th>Fan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MNEAP</th>\n",
       "      <td>1.14</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fryer  LED Lamp  Incandescent lamp  Laptop Computer  Fan\n",
       "MNEAP   1.14      0.35               0.48             1.15  0.5"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(pd.DataFrame(metrics.loc['MNEAP']).transpose(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (10, 3)\n",
    "fig, ax1 = plt.subplots();\n",
    "\n",
    "#umbrales de potencia\n",
    "a1=[20,1250]\n",
    "\n",
    "gt['Fan'][a1[0]:a1[1]].plot(ax=ax1, lw=2, c='tab:purple', marker=\"D\")\n",
    "pred_CO['Fan'][a1[0]:a1[1]].plot(ax=ax1, c='darkred', lw=1, ls='--', marker=\"D\")\n",
    "\n",
    "ax1.minorticks_off()\n",
    "ax1.set_ylabel('Power (W)')\n",
    "ax1.legend(labels=['GT Fan','Pred_CO'], fontsize=9, ncol=1)\n",
    "\n",
    "err= abs(round((gt['Fan'][a1[0]:a1[1]]-pred_CO['Fan'][a1[0]:a1[1]])[1:6],1))\n",
    "\n",
    "aux=40\n",
    "i=0\n",
    "for e in err:\n",
    "    ax1.annotate((str(err[i])+'W'), xy=(aux, 140+(abs(err[i]))*0.2), xycoords='axes points', size=10)\n",
    "    aux=aux+63\n",
    "    i=i+1\n",
    "\n",
    "\n",
    "\n",
    "#plt.savefig('./data/images/5.6_MNEAP_CO_30s.svg', bbox_inches='tight', transparent=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (10, 3)\n",
    "fig, ax1 = plt.subplots();\n",
    "\n",
    "#umbrales de potencia\n",
    "a1=[20,1250]\n",
    "\n",
    "gt['Laptop Computer'][a1[0]:a1[1]].plot(ax=ax1, lw=2, c='tab:purple', marker=\"D\")\n",
    "pred_CO['Laptop Computer'][a1[0]:a1[1]].plot(ax=ax1, c='darkred', lw=1, ls='--', marker=\"D\")\n",
    "\n",
    "ax1.minorticks_off()\n",
    "ax1.set_ylabel('Power (W)')\n",
    "ax1.legend(labels=['GT Laptop Computer','Pred_CO'], fontsize=9, ncol=1)\n",
    "\n",
    "err= abs(round((gt['Laptop Computer'][a1[0]:a1[1]]-pred_CO['Laptop Computer'][a1[0]:a1[1]])[1:6],1))\n",
    "\n",
    "aux=40\n",
    "i=0\n",
    "for e in err:\n",
    "    ax1.annotate((str(err[i])+'W'), xy=(aux, 140+(abs(err[i]))*0.2), xycoords='axes points', size=10)\n",
    "    aux=aux+63\n",
    "    i=i+1\n",
    "\n",
    "\n",
    "\n",
    "#plt.savefig('./data/images/5.6_MNEAP_CO_30s.svg', bbox_inches='tight', transparent=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (10, 3)\n",
    "fig, ax1 = plt.subplots();\n",
    "\n",
    "#umbrales de potencia\n",
    "a1=[20,1250]\n",
    "\n",
    "gt['Fryer'][a1[0]:a1[1]].plot(ax=ax1, lw=2, c='tab:purple', marker=\"D\")\n",
    "pred_CO['Fryer'][a1[0]:a1[1]].plot(ax=ax1, c='darkred', lw=1, ls='--', marker=\"D\")\n",
    "\n",
    "ax1.minorticks_off()\n",
    "ax1.set_ylabel('Power (W)')\n",
    "ax1.legend(labels=['GT fryer','Pred_CO'], fontsize=9, ncol=1)\n",
    "\n",
    "err= abs(round((gt['Fryer'][a1[0]:a1[1]]-pred_CO['Fryer'][a1[0]:a1[1]])[1:6],1))\n",
    "\n",
    "aux=40\n",
    "i=0\n",
    "for e in err:\n",
    "    ax1.annotate((str(err[i])+'W'), xy=(aux, 130+(abs(err[i]))*0.2), xycoords='axes points', size=10)\n",
    "    aux=aux+63\n",
    "    i=i+1\n",
    "\n",
    "\n",
    "ax1.set_xticklabels(labels=['10:00','10:30','11:00','11:30','12:00','12:30','13:00','13:30','14:00',''])   \n",
    "#plt.savefig('./data/images/5.6_MNEAP_CO_30s.svg', bbox_inches='tight', transparent=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (10, 3)\n",
    "fig, ax1 = plt.subplots();\n",
    "\n",
    "#umbrales de potencia\n",
    "a1=[20,1250]\n",
    "\n",
    "gt['Incandescent lamp'][a1[0]:a1[1]].plot(ax=ax1, lw=2, c='tab:purple', marker=\"D\")\n",
    "pred_CO['Incandescent lamp'][a1[0]:a1[1]].plot(ax=ax1, c='darkred', lw=1, ls='--', marker=\"D\")\n",
    "\n",
    "ax1.minorticks_off()\n",
    "ax1.set_ylabel('Power (W)')\n",
    "ax1.legend(labels=['GT Incandescent lamp','Pred_CO'], fontsize=9, ncol=1)\n",
    "\n",
    "err= abs(round((gt['Incandescent lamp'][a1[0]:a1[1]]-pred_CO['Incandescent lamp'][a1[0]:a1[1]])[1:6],1))\n",
    "\n",
    "aux=40\n",
    "i=0\n",
    "for e in err:\n",
    "    ax1.annotate((str(err[i])+'W'), xy=(aux, 140+(abs(err[i]))*0.2), xycoords='axes points', size=10)\n",
    "    aux=aux+63\n",
    "    i=i+1\n",
    "\n",
    "\n",
    "  \n",
    "#plt.savefig('./data/images/5.6_MNEAP_CO_30s.svg', bbox_inches='tight', transparent=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (10, 3)\n",
    "fig, ax1 = plt.subplots();\n",
    "\n",
    "#umbrales de potencia\n",
    "a1=[20,1250]\n",
    "\n",
    "gt['LED Lamp'][a1[0]:a1[1]].plot(ax=ax1, lw=2, c='tab:purple', marker=\"D\")\n",
    "pred_CO['LED Lamp'][a1[0]:a1[1]].plot(ax=ax1, c='darkred', lw=1, ls='--', marker=\"D\")\n",
    "\n",
    "ax1.minorticks_off()\n",
    "ax1.set_ylabel('Power (W)')\n",
    "ax1.legend(labels=['GT LED Lamp','Pred_CO'], fontsize=9, ncol=1)\n",
    "\n",
    "err= abs(round((gt['LED Lamp'][a1[0]:a1[1]]-pred_CO['LED Lamp'][a1[0]:a1[1]])[1:6],1))\n",
    "\n",
    "aux=40\n",
    "i=0\n",
    "for e in err:\n",
    "    ax1.annotate((str(err[i])+'W'), xy=(aux, 140+(abs(err[i]))*0.2), xycoords='axes points', size=10)\n",
    "    aux=aux+63\n",
    "    i=i+1\n",
    "\n",
    "\n",
    " \n",
    "#plt.savefig('./data/images/5.6_MNEAP_CO_30s.svg', bbox_inches='tight', transparent=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Métrica RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta métrica de la ecuación de la figura es muy utilizada en la comparaciónn de diversos conjuntos de datos, consiste en el error cuadrático medio de la diferencia entre la potencia asignada yˆt(n) y la potencia real yt(n) del proceso de los applicativos , en cada intervalo de tiempo t ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![metricas](metricas.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Fryer</th>\n",
       "      <th>LED Lamp</th>\n",
       "      <th>Incandescent lamp</th>\n",
       "      <th>Laptop Computer</th>\n",
       "      <th>Fan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>17.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>22.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fryer  LED Lamp  Incandescent lamp  Laptop Computer   Fan\n",
       "RMSE   17.4       7.3               22.7             13.8  12.7"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(pd.DataFrame(metrics.loc['RMSE']).transpose(),1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resultados de todos los modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculando Metricas: CO_10s_first\n",
      "\n",
      "Calculando Metricas: FHMM_10s_first\n",
      "\n",
      "Calculando Metricas: CO_10s_mean\n",
      "\n",
      "Calculando Metricas: FHMM_10s_mean\n",
      "\n",
      "Calculando Metricas: CO_10s_median\n",
      "\n",
      "Calculando Metricas: FHMM_10s_median\n",
      "\n",
      "Calculando Metricas: CO_30s_first\n",
      "\n",
      "Calculando Metricas: FHMM_30s_first\n",
      "\n",
      "Calculando Metricas: CO_30s_mean\n",
      "\n",
      "Calculando Metricas: FHMM_30s_mean\n",
      "\n",
      "Calculando Metricas: CO_30s_median\n",
      "\n",
      "Calculando Metricas: FHMM_30s_median\n",
      "\n",
      "Calculando Metricas: CO_60s_first\n",
      "\n",
      "Calculando Metricas: FHMM_60s_first\n",
      "\n",
      "Calculando Metricas: CO_60s_mean\n",
      "\n",
      "Calculando Metricas: FHMM_60s_mean\n",
      "\n",
      "Calculando Metricas: CO_60s_median\n",
      "\n",
      "Calculando Metricas: FHMM_60s_median\n",
      "\n",
      "Calculando Metricas: CO_5min_first\n",
      "\n",
      "Calculando Metricas: FHMM_5min_first\n",
      "\n",
      "Calculando Metricas: CO_5min_mean\n",
      "\n",
      "Calculando Metricas: FHMM_5min_mean\n",
      "\n",
      "Calculando Metricas: CO_5min_median\n",
      "\n",
      "Calculando Metricas: FHMM_5min_median\n",
      "\n",
      "Calculando Metricas: CO_10min_first\n",
      "\n",
      "Calculando Metricas: FHMM_10min_first\n",
      "\n",
      "Calculando Metricas: CO_10min_mean\n",
      "\n",
      "Calculando Metricas: FHMM_10min_mean\n",
      "\n",
      "Calculando Metricas: CO_10min_median\n",
      "\n",
      "Calculando Metricas: FHMM_10min_median\n",
      "\n",
      "\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "from nilmtk.metrics import fraction_energy_assigned_correctly, f1_score, error_in_assigned_energy\n",
    "from nilmtk.metrics import mean_normalized_error_power, rms_error_power \n",
    "periods = {'10s':10 , '30s':30, '60s':60, '5min':60*5, '10min':60*10} #, '15min':60*15, '30min':60*30}\n",
    "\n",
    "dir_predictions = data_dir +'NILM/Results/'\n",
    "m = {}\n",
    "classifiers = ['CO', 'FHMM']\n",
    "methods = ['first', 'mean', 'median']\n",
    "index = list(labels['Meters'][1:7])\n",
    "\n",
    "for p_name, sp in periods.items():\n",
    "    met = []\n",
    "    for method in methods:\n",
    "        for clf_name in classifiers:\n",
    "            print('\\nCalculando Metricas: '+ clf_name + '_' + p_name + '_' + method)  \n",
    "           \n",
    "            #Datos de Dispositivos: predicción\n",
    "            input_dir = dir_predictions + clf_name + '_' + p_name + '_' + method +'_pred.h5'\n",
    "            dataset = DataSet(input_dir)\n",
    "            predictions=dataset.buildings[1].elec.submeters()       \n",
    "            \n",
    "            #Metricas\n",
    "            f1 = {}; eae = {}; feac = {}; mneap = {}; rmse = {}; e=[];  \n",
    "            F1 = f1_score(predictions, valid_Devices)\n",
    "            f1['F1'] = F1*100\n",
    "            EAE = error_in_assigned_energy(predictions, valid_Devices)\n",
    "            \n",
    "            for x in range(len(EAE)): e.append(EAE.iloc[x][0])\n",
    "            eae['EAE'] = e\n",
    "            #da error para FEAC\n",
    "            #feac['FEAC'] = 100*np.ones(6)*(fraction_energy_assigned_correctly(predictions, valid_Devices))\n",
    "            mneap['MNEAP'] = mean_normalized_error_power(predictions, valid_Devices)\n",
    "            rmse['RMSE'] = rms_error_power(predictions, valid_Devices)\n",
    "            met.append(pd.concat([pd.DataFrame(feac, index=valid_Devices.instance()),\n",
    "                                  pd.DataFrame(f1),\n",
    "                                  pd.DataFrame(eae, index=valid_Devices.instance()),\n",
    "                                  pd.DataFrame(mneap),\n",
    "                                  pd.DataFrame(rmse)],\n",
    "                                 axis=1).set_index(pd.Index(index)))\n",
    "            dataset.store.close()        \n",
    "    m[p_name] = met\n",
    "\n",
    "##Guardamos resultados\n",
    "f = open(data_dir +'NILM/metrics.txt',\"w\")\n",
    "f.write( str(m) )\n",
    "f.close()\n",
    "print('\\n\\nDone!')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Otras Metricas: métricas Promedio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'F1':         CO-first    CO-mean  CO-median  FHMM-first  FHMM-mean  FHMM-median\n",
       " 10s    75.243233  76.344218  75.866203   72.789120  72.656615    63.613193\n",
       " 30s    72.745803  71.686967  71.673752   68.926765  68.547529    68.997192\n",
       " 60s    63.431515  64.996651  63.177128   60.988986  65.369718    63.684522\n",
       " 5min    0.000000   0.000000   0.000000   13.888889  13.888889    13.888889\n",
       " 10min   0.000000   0.000000   0.000000    5.000000   5.000000     5.000000,\n",
       " 'EAE':        CO-first  CO-mean  CO-median  FHMM-first  FHMM-mean  FHMM-median\n",
       " 10s         0.0      0.0        0.0         0.0        0.0          0.0\n",
       " 30s         0.0      0.0        0.0         0.0        0.0          0.0\n",
       " 60s         0.0      0.0        0.0         0.0        0.0          0.0\n",
       " 5min        0.0      0.0        0.0         0.0        0.0          0.0\n",
       " 10min       0.0      0.0        0.0         0.0        0.0          0.0,\n",
       " 'MNEAP':        CO-first   CO-mean  CO-median  FHMM-first  FHMM-mean  FHMM-median\n",
       " 10s    0.685631  0.673075   0.679720    0.781605   0.782902     0.785067\n",
       " 30s    0.696204  0.705955   0.711977    0.817371   0.806076     0.816093\n",
       " 60s    0.737223  0.731813   0.724600    0.741792   0.678579     0.761275\n",
       " 5min   0.998978  0.998978   0.998978    0.738979   0.738979     0.738979\n",
       " 10min  1.000000  1.000000   1.000000    0.574848   0.574848     0.574848,\n",
       " 'RMSE':         CO-first    CO-mean  CO-median  FHMM-first  FHMM-mean  FHMM-median\n",
       " 10s    13.284980  13.035849  13.069940   14.929643  14.939067    16.256626\n",
       " 30s    14.489492  14.740025  14.864228   16.055981  15.622608    16.049776\n",
       " 60s    14.890328  14.781449  14.782334   16.144353  15.074322    16.137228\n",
       " 5min   19.068689  19.068689  19.068689   14.276961  14.276961    14.276961\n",
       " 10min  18.044777  18.044777  18.044777   11.335130  11.335130    11.335130}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_mean = {}\n",
    "\n",
    "#cols=['FEAC','F1','EAE','MNEAP','RMSE']\n",
    "\n",
    "cols=['F1','EAE','MNEAP','RMSE']\n",
    "labels=['CO-first', 'CO-mean', 'CO-median', 'FHMM-first', 'FHMM-mean' ,'FHMM-median']\n",
    "per=['10s','30s','60s','5min','10min'] #,'15min','30min']\n",
    "\n",
    "for c in cols:\n",
    "    m_mean[c] = pd.DataFrame([#CO FIRST\n",
    "                             [m['10s'][0][c].mean(),m['30s'][0][c].mean(),m['60s'][0][c].mean(),\n",
    "                              m['5min'][0][c].mean(),m['10min'][0][c].mean()],\n",
    "                            #  m['15min'][0][c].mean(),m['30min'][0][c].mean()],\n",
    "                              #CO MEAN\n",
    "                             [m['10s'][2][c].mean(),m['30s'][2][c].mean(),m['60s'][2][c].mean(),\n",
    "                              m['5min'][2][c].mean(),m['10min'][2][c].mean()],\n",
    "                              #m['15min'][2][c].mean(),m['30min'][2][c].mean()],\n",
    "                              #CO MEDIAN\n",
    "                             [m['10s'][4][c].mean(),m['30s'][4][c].mean(),m['60s'][4][c].mean(),\n",
    "                              m['5min'][4][c].mean(),m['10min'][4][c].mean()],\n",
    "                              #m['15min'][4][c].mean(),m['30min'][4][c].mean()],\n",
    "                             #FHMM FIRST\n",
    "                             [m['10s'][1][c].mean(),m['30s'][1][c].mean(),m['60s'][1][c].mean(),\n",
    "                             m['5min'][1][c].mean(),m['10min'][1][c].mean()],\n",
    "                             # m['15min'][1][c].mean(),m['30min'][1][c].mean()],\n",
    "                             #FHMM MEAN            \n",
    "                             [m['10s'][3][c].mean(),m['30s'][3][c].mean(),m['60s'][3][c].mean(),\n",
    "                              m['5min'][3][c].mean(),m['10min'][3][c].mean()],\n",
    "                              #m['15min'][3][c].mean(),m['30min'][3][c].mean()],\n",
    "                             #FHMM MEDIAN\n",
    "                             [m['10s'][5][c].mean(),m['30s'][5][c].mean(),m['60s'][5][c].mean(),\n",
    "                              m['5min'][5][c].mean(),m['10min'][5][c].mean()]],columns=per, index=labels).transpose()\n",
    "                             # m['15min'][5][c].mean(),m['30min'][5][c].mean()]],\n",
    "                              \n",
    "m_mean\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Maximos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>10s</th>\n",
       "      <th>30s</th>\n",
       "      <th>60s</th>\n",
       "      <th>5min</th>\n",
       "      <th>10min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>76.34</td>\n",
       "      <td>72.75</td>\n",
       "      <td>65.37</td>\n",
       "      <td>13.89</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EAE</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNEAP</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>13.04</td>\n",
       "      <td>14.49</td>\n",
       "      <td>14.78</td>\n",
       "      <td>14.28</td>\n",
       "      <td>11.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         10s    30s    60s   5min  10min\n",
       "F1     76.34  72.75  65.37  13.89   5.00\n",
       "EAE     0.00   0.00   0.00   0.00   0.00\n",
       "MNEAP   0.67   0.70   0.68   0.74   0.57\n",
       "RMSE   13.04  14.49  14.78  14.28  11.34"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=['F1','EAE','MNEAP','RMSE']\n",
    "\n",
    "maxs=pd.concat([m_mean['F1'].transpose().max(),\n",
    "                m_mean['EAE'].transpose().min(),\n",
    "                m_mean['MNEAP'].transpose().min(),\n",
    "                m_mean['RMSE'].transpose().min()], axis=1)\n",
    "maxs.columns=cols\n",
    "maxs = maxs.transpose()\n",
    "round(maxs,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correspondencia de Indice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>10s</th>\n",
       "      <th>30s</th>\n",
       "      <th>60s</th>\n",
       "      <th>5min</th>\n",
       "      <th>10min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>CO-mean</td>\n",
       "      <td>CO-first</td>\n",
       "      <td>FHMM-mean</td>\n",
       "      <td>FHMM-first</td>\n",
       "      <td>FHMM-first</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EAE</th>\n",
       "      <td>CO-first</td>\n",
       "      <td>CO-first</td>\n",
       "      <td>CO-first</td>\n",
       "      <td>CO-first</td>\n",
       "      <td>CO-first</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNEAP</th>\n",
       "      <td>CO-mean</td>\n",
       "      <td>CO-first</td>\n",
       "      <td>FHMM-mean</td>\n",
       "      <td>FHMM-first</td>\n",
       "      <td>FHMM-first</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>CO-mean</td>\n",
       "      <td>CO-first</td>\n",
       "      <td>CO-mean</td>\n",
       "      <td>FHMM-first</td>\n",
       "      <td>FHMM-first</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            10s       30s        60s        5min       10min\n",
       "F1      CO-mean  CO-first  FHMM-mean  FHMM-first  FHMM-first\n",
       "EAE    CO-first  CO-first   CO-first    CO-first    CO-first\n",
       "MNEAP   CO-mean  CO-first  FHMM-mean  FHMM-first  FHMM-first\n",
       "RMSE    CO-mean  CO-first    CO-mean  FHMM-first  FHMM-first"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=['F1','EAE','MNEAP','RMSE']\n",
    "\n",
    "maxs_i=pd.concat([m_mean['F1'].transpose().idxmax(),\n",
    "                 m_mean['EAE'].transpose().idxmin(),\n",
    "                 m_mean['MNEAP'].transpose().idxmin(),\n",
    "                 m_mean['RMSE'].transpose().idxmin()], axis=1)\n",
    "maxs_i.columns=cols\n",
    "maxs_i = maxs_i.transpose()\n",
    "maxs_i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x828 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (10, 11.5)\n",
    "fig, (ax2,ax3,ax4,ax5) = plt.subplots(4, 1, sharex=True, gridspec_kw={'hspace': 0.35})\n",
    "cols=['F1','EAE','MNEAP','RMSE']\n",
    "ylabels=['[%]','[%]','[kWh]','[-]','[W]']\n",
    "per=['10s','30s','60s','5min','10min'] # ,'15min','30min']\n",
    "\n",
    "labels={'CO-first':-0.35, 'CO-mean':-0.22, 'CO-median':-0.11, 'FHMM-first':0.01, 'FHMM-mean':0.13 ,'FHMM-median':0.245}\n",
    "\n",
    "c=0\n",
    "for ax in fig.get_axes():      \n",
    "    m_mean[cols[c]].plot.bar(ax=ax, width=0.7, edgecolor='black', linewidth=0.5, legend=False, cmap=\"jet\")\n",
    "    ax.set(ylabel=ylabels[c])\n",
    "    ax.grid(b=None)\n",
    "    x = 0\n",
    "    for p in per:\n",
    "        ax.annotate(\"▼\", xy=(labels[maxs_i[p][cols[c]]]+x, maxs[p][cols[c]]+maxs[p][cols[c]]*0.03))\n",
    "        x+=1\n",
    "    c+=1\n",
    "    \n",
    "ax5.set_xticklabels(labels=per, rotation=0, fontdict={'fontsize':12})\n",
    "fig.align_ylabels([(ax2,ax3,ax4,ax5)])\n",
    "ax2.set(ylim=(0,120))\n",
    "ax2.set_yticks([0,25,50,75,100])\n",
    "\n",
    "ax5.set_yticks([0,50,100,150,200])\n",
    "\n",
    "ax2.set_title('F1-Score', fontsize=11, loc='center')\n",
    "ax3.set_title('Error in Assigned Energy - EAE', fontsize=11, loc='center')\n",
    "ax4.set_title('Mean Normalised Error in Assigned Power - MNEAP', fontsize=11, loc='center')\n",
    "ax5.set_title('RMS Error in Assigned Power - RMSE', fontsize=11, loc='center')\n",
    "lgd = ax5.legend(labels.keys(), loc='lower center', ncol=6, bbox_to_anchor=(0.5, -0.6),fontsize=9.5)\n",
    "\n",
    "\n",
    "#ax2.annotate(\"90.5%\", xy=(0.68, 98)); #30s FHMM-mean\n",
    "#ax3.annotate(\"0.19\", xy=(6.22, 1.4)); #30min FHMM-median\n",
    "ax3.annotate(\"kWh\", xy=(6.22, 0.9)); #30min FHMM-median\n",
    "#ax4.annotate(\"0.24\", xy=(6.22, 0.7)); #30min FHMM-median\n",
    "#ax5.annotate(\"41.5\", xy=(6.22, 120)); #30min FHMM-median\n",
    "ax5.annotate(\"   W\", xy=(6.22, 90)); #30min FHMM-median\n",
    "\n",
    "\n",
    "plt.savefig('./data/images/5.7_All_metrics.svg', bbox_inches='tight', transparent=True)\n",
    "\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (10, 8)\n",
    "\n",
    "fig, (ax2,ax3,ax4,ax5) = plt.subplots(4, 1, sharex=False, gridspec_kw={'hspace': 0.35})\n",
    "\n",
    "#cols=['FEAC','F1','EAE','MNEAP','RMSE']\n",
    "cols=['F1','EAE','MNEAP','RMSE']\n",
    "\n",
    "ylabels=['[%]','[%]','[kWh]','[-]','[W]']\n",
    "labels=['FHMM-first', 'FHMM-mean' ,'FHMM-median']\n",
    "c=0\n",
    "\n",
    "for ax in fig.get_axes():\n",
    "    \n",
    "    df=pd.DataFrame([m['30s'][1][cols[c]],m['30s'][3][cols[c]],m['30s'][5][cols[c]]]).transpose()\n",
    "    #if ax==ax1:\n",
    "    #    df['FEAC'].mean().plot.barh(ax=ax1, width=0.7, edgecolor='black', linewidth=0.5, legend=False,   color=['greenyellow','darkorange','maroon'])\n",
    "    #else:\n",
    "    #     df.plot.bar(ax=ax, width=0.7, edgecolor='black', linewidth=0.5, legend=False,   color=['greenyellow','darkorange','maroon'])\n",
    " \n",
    "    df.plot.bar(ax=ax, width=0.7, edgecolor='black', linewidth=0.5, legend=False,   color=['greenyellow','darkorange','maroon'])\n",
    "    ax.set(ylabel=ylabels[c])\n",
    "    ax.grid(b=None)\n",
    "    c+=1   \n",
    "    \n",
    "\n",
    "ax2.set_xticklabels('')\n",
    "ax3.set_xticklabels('')\n",
    "ax4.set_xticklabels('')\n",
    "ax5.set_xticklabels(ax5.get_xticklabels(), rotation=0, fontdict={'fontsize':12})\n",
    "fig.align_ylabels([(ax1,ax2,ax3,ax4,ax5)])\n",
    "ax3.set_yticks([0,1,2,3,4])\n",
    "ax4.set(ylim=(0,1.8))\n",
    "ax4.set_yticks([0,0.5,1,1.5,2])\n",
    "ax5.set(ylim=(0,270))\n",
    "ax5.set_yticks([0,100,200,300])\n",
    "ax2.set_title('F1-Score', fontsize=11, loc='center')\n",
    "ax3.set_title('Error in Assigned Energy - EAE', fontsize=11, loc='center')\n",
    "ax4.set_title('Mean Normalised Error in Assigned Power - MNEAP', fontsize=11, loc='center')\n",
    "ax5.set_title('RMS Error in Assigned Power - RMSE', fontsize=11, loc='center')\n",
    "lgd = ax5.legend(labels, loc='lower center', ncol=3, bbox_to_anchor=(0.5, -0.8))\n",
    "\n",
    "\n",
    "plt.savefig('./data/images/5.8_All_metrics_2.svg', bbox_inches='tight', transparent=True)\n",
    "\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FHMM-first\n",
      "                          F1  EAE     MNEAP       RMSE\n",
      "Fryer              63.736264  0.0  0.836495  16.357420\n",
      "LED Lamp           76.991150  0.0  0.623026  10.629197\n",
      "Incandescent lamp  79.104478  0.0  0.388289  21.491051\n",
      "Laptop Computer    62.801932  0.0  1.705811  18.111343\n",
      "Fan                62.000000  0.0  0.533235  13.690894\n",
      "\n",
      "FHMM-mean\n",
      "                          F1  EAE     MNEAP       RMSE\n",
      "Fryer              67.052023  0.0  0.715534  15.055000\n",
      "LED Lamp           69.523810  0.0  0.690773  12.084146\n",
      "Incandescent lamp  86.713287  0.0  0.268644  17.491502\n",
      "Laptop Computer    69.724771  0.0  1.698797  18.054960\n",
      "Fan                49.723757  0.0  0.656631  15.427432\n",
      "\n",
      "FHMM-median\n",
      "                          F1  EAE     MNEAP       RMSE\n",
      "Fryer              64.088398  0.0  0.830106  16.326393\n",
      "LED Lamp           76.991150  0.0  0.623026  10.629197\n",
      "Incandescent lamp  79.104478  0.0  0.388289  21.491051\n",
      "Laptop Computer    62.801932  0.0  1.705811  18.111343\n",
      "Fan                62.000000  0.0  0.533235  13.690894\n"
     ]
    }
   ],
   "source": [
    "print('FHMM-first')\n",
    "print(m['30s'][1])\n",
    "print('\\nFHMM-mean')\n",
    "print(m['30s'][3])\n",
    "print('\\nFHMM-median')\n",
    "print(m['30s'][5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resultados del mejor modelo\n",
    "En vista de los resultados del obtenidos, se propone la elección del modelo FHMM entrenado con datos promedio de dispositivos cada 30 segundos. Este modelo solo se considera adecuado para la desagregación de datos promedio de potencia activa medidos en cabecera cada 30 segundos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (10, 8)\n",
    "\n",
    "fig, (ax2,ax3,ax4,ax5) = plt.subplots(4, 1, sharex=False, gridspec_kw={'hspace': 0.35})\n",
    "#cols=['FEAC','F1','EAE','MNEAP','RMSE']\n",
    "\n",
    "cols=['F1','EAE','MNEAP','RMSE']\n",
    "\n",
    "ylabels=['[%]','[%]','[kWh]','[-]','[W]']\n",
    "colors=['tab:red','tab:blue','tab:orange','tab:green','tab:olive','tab:purple','tab:cyan']\n",
    "c=0\n",
    "\n",
    "for ax in fig.get_axes():\n",
    "    df=pd.DataFrame(m['30s'][4][cols[c]])\n",
    "    df[cols[c]].plot.bar(ax=ax, width=0.7, edgecolor='black', linewidth=0.5, legend=False, color=colors)\n",
    "    ax.set(ylabel=ylabels[c])\n",
    "    ax.grid(b=None)\n",
    "    c+=1\n",
    "\n",
    "\n",
    "ax2.set_xticklabels('')\n",
    "ax3.set_xticklabels('')\n",
    "ax4.set_xticklabels('')\n",
    "ax5.set_xticklabels(ax5.get_xticklabels(), rotation=0, fontdict={'fontsize':12})\n",
    "\n",
    "fig.align_ylabels([(ax2,ax3,ax4,ax5)])\n",
    "\n",
    "ax2.set(ylim=(0,125))\n",
    "ax3.set(ylim=(0,1))\n",
    "ax4.set(ylim=(0,0.7))\n",
    "ax5.set(ylim=(0,110))\n",
    "\n",
    "ax2.set_yticks([0,25,50,75,100])\n",
    "ax3.set_yticks([0,1,2,3])\n",
    "ax4.set_yticks([0,1,2,3,4])\n",
    "ax5.set_yticks([0,50,100,150,200])\n",
    "\n",
    "\n",
    "ax2.set_title('F1-Score', fontsize=11, loc='center')\n",
    "ax3.set_title('Error in Assigned Energy - EAE', fontsize=11, loc='center')\n",
    "ax4.set_title('Mean Normalised Error in Assigned Power - MNEAP', fontsize=11, loc='center')\n",
    "ax5.set_title('RMS Error in Assigned Power - RMSE', fontsize=11, loc='center')\n",
    "\n",
    "df=pd.DataFrame(m['10min'][5])\n",
    "\n",
    "for x in range(5):ax2.annotate(str(round(df['F1'][x],1))+'%', xy=(x-0.18 , df['F1'][x]+4),fontsize=9.5)\n",
    "for x in range(5):ax3.annotate(str(round(df['EAE'][x],1))+' kWh', xy=(x-0.25 , df['EAE'][x]+0.1),fontsize=9.5)\n",
    "for x in range(5):ax4.annotate(str(round(df['MNEAP'][x],1)), xy=(x-0.08 , df['MNEAP'][x]+0.03),fontsize=9.5)\n",
    "for x in range(5):ax5.annotate(str(round(df['RMSE'][x]))+' W', xy=(x-0.18 , df['RMSE'][x]+5),fontsize=9.5)\n",
    "\n",
    "plt.savefig('./data/images/5.9_Metrics_FHMM_30min_median.svg', bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating total_energy for ElecMeterID(instance=6, building=1, dataset='DSUAL') ...   "
     ]
    },
    {
     "data": {
      "text/plain": [
       "active    0.099792\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_Devices.total_energy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 ElecMeter(instance=6, building=1, dataset='DSUAL', appliances=[Appliance(type='fan', instance=1)])nstance=1)])])"
     ]
    },
    {
     "data": {
      "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>(2, 1, DSUAL)</th>\n",
       "      <th>(3, 1, DSUAL)</th>\n",
       "      <th>(4, 1, DSUAL)</th>\n",
       "      <th>(5, 1, DSUAL)</th>\n",
       "      <th>(6, 1, DSUAL)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>active</th>\n",
       "      <td>0.015834</td>\n",
       "      <td>0.017322</td>\n",
       "      <td>0.03087</td>\n",
       "      <td>0.012283</td>\n",
       "      <td>0.023483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparent</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reactive</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          (2, 1, DSUAL)  (3, 1, DSUAL)  (4, 1, DSUAL)  (5, 1, DSUAL)  \\\n",
       "active         0.015834       0.017322        0.03087       0.012283   \n",
       "apparent            NaN            NaN            NaN            NaN   \n",
       "reactive            NaN            NaN            NaN            NaN   \n",
       "\n",
       "          (6, 1, DSUAL)  \n",
       "active         0.023483  \n",
       "apparent            NaN  \n",
       "reactive            NaN  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_Devices.energy_per_meter()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replicando el entrenamiento del modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model for submeter 'ElecMeter(instance=2, building=1, dataset='DSUAL', appliances=[Appliance(type='fryer', instance=1)])'\n",
      "Training model for submeter 'ElecMeter(instance=3, building=1, dataset='DSUAL', appliances=[Appliance(type='LED lamp', instance=1)])'\n",
      "Training model for submeter 'ElecMeter(instance=4, building=1, dataset='DSUAL', appliances=[Appliance(type='incandescent lamp', instance=1)])'\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='DSUAL', appliances=[Appliance(type='laptop computer', instance=1)])'\n",
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='DSUAL', appliances=[Appliance(type='fan', instance=1)])'\n",
      "Done training!\n",
      "\n",
      "\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "from nilmtk import DataSet\n",
    "from nilmtk.legacy.disaggregate import CombinatorialOptimisation\n",
    "\n",
    "train = DataSet('ualm2.h5')\n",
    "\n",
    "\n",
    "\n",
    "train.set_window(start=\"2023-02-24 14:17:10\", end=\"2023-02-24 17:40:00\")\n",
    "\n",
    "\n",
    "train_elec=train.buildings[1].elec \n",
    "\n",
    "#dispositivos a entrenar: [Lights_1, Lights_2, HVAC_1, HVAC_2, HVAC_4, Rack]\n",
    "train_Devices=train_elec.select(instance=[2,3,4,5,6])\n",
    "\n",
    "#opcional: asignación manual de estados de dispositivos\n",
    "num_states={train_Devices[2]:2, train_Devices[3]:3, train_Devices[4]:4,\n",
    "            train_Devices[5]:5, train_Devices[6]:6}\n",
    "\n",
    "#entrenamiento y exportación del modelo\n",
    "CO_60s_median=CombinatorialOptimisation()\n",
    "CO_60s_median.MIN_CHUNK_LENGTH=1\n",
    "CO_60s_median.train(train_Devices,\n",
    "                        num_states_dict={}, #en otro caso: { }\n",
    "                        ac_type='active',\n",
    "                        sample_period=1800,\n",
    "                        resample_kwargs={'how':'median'})\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print('\\n\\nDone!')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'states': array([0]),\n",
       "  'training_metadata': ElecMeter(instance=2, building=1, dataset='DSUAL', appliances=[Appliance(type='fryer', instance=1)])},\n",
       " {'states': array([0]),\n",
       "  'training_metadata': ElecMeter(instance=3, building=1, dataset='DSUAL', appliances=[Appliance(type='LED lamp', instance=1)])},\n",
       " {'states': array([0]),\n",
       "  'training_metadata': ElecMeter(instance=4, building=1, dataset='DSUAL', appliances=[Appliance(type='incandescent lamp', instance=1)])},\n",
       " {'states': array([0]),\n",
       "  'training_metadata': ElecMeter(instance=5, building=1, dataset='DSUAL', appliances=[Appliance(type='laptop computer', instance=1)])},\n",
       " {'states': array([0]),\n",
       "  'training_metadata': ElecMeter(instance=6, building=1, dataset='DSUAL', appliances=[Appliance(type='fan', instance=1)])}]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CO_60s_median.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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