{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from notepad2 import WaterStorage, Heatpump\n", "from pyrecoy.forecasts import Mipf\n", "from datetime import timedelta\n", "from pyrecoy.framework import TimeFramework\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import cufflinks\n", "cufflinks.go_offline()\n", "from numpy.polynomial import Polynomial\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import plotly.express as px\n", "from pyrecoy.colors import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup & Load data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Tsource (VDG)Tsink (VDG)MW (VDG)Tsource (NDG)Tsink (NDG)MW (NDG)DAMPOSNEGForeNegForePos
2018-01-01 00:00:00+01:0064.267667141.5310000.019.642333147.7793330.027.2-57.5458.6236.246667-12.529333
2018-01-01 00:15:00+01:0058.802000139.4230000.019.613000148.0260000.027.237.0237.0229.64666721.791333
2018-01-01 00:30:00+01:0065.176667139.8636670.036.170000147.5276670.027.233.1033.1032.34666731.408000
2018-01-01 00:45:00+01:0065.254000139.7100000.035.220000147.7630000.027.233.1033.1037.12600033.512000
2018-01-01 01:00:00+01:0064.926333139.5593330.018.850000148.3060000.027.333.0933.0934.80533333.970000
\n", "
" ], "text/plain": [ " Tsource (VDG) Tsink (VDG) MW (VDG) \\\n", "2018-01-01 00:00:00+01:00 64.267667 141.531000 0.0 \n", "2018-01-01 00:15:00+01:00 58.802000 139.423000 0.0 \n", "2018-01-01 00:30:00+01:00 65.176667 139.863667 0.0 \n", "2018-01-01 00:45:00+01:00 65.254000 139.710000 0.0 \n", "2018-01-01 01:00:00+01:00 64.926333 139.559333 0.0 \n", "\n", " Tsource (NDG) Tsink (NDG) MW (NDG) DAM POS \\\n", "2018-01-01 00:00:00+01:00 19.642333 147.779333 0.0 27.2 -57.54 \n", "2018-01-01 00:15:00+01:00 19.613000 148.026000 0.0 27.2 37.02 \n", "2018-01-01 00:30:00+01:00 36.170000 147.527667 0.0 27.2 33.10 \n", "2018-01-01 00:45:00+01:00 35.220000 147.763000 0.0 27.2 33.10 \n", "2018-01-01 01:00:00+01:00 18.850000 148.306000 0.0 27.3 33.09 \n", "\n", " NEG ForeNeg ForePos \n", "2018-01-01 00:00:00+01:00 58.62 36.246667 -12.529333 \n", "2018-01-01 00:15:00+01:00 37.02 29.646667 21.791333 \n", "2018-01-01 00:30:00+01:00 33.10 32.346667 31.408000 \n", "2018-01-01 00:45:00+01:00 33.10 37.126000 33.512000 \n", "2018-01-01 01:00:00+01:00 33.09 34.805333 33.970000 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "start = '2018-01-01'\n", "end = '2018-12-31'\n", "end2 = '2019-01-01'\n", "\n", "mipf = Mipf(\n", " start = start,\n", " end = end, \n", " tidy=True, \n", " include_nextQ=False,\n", " folder_path=r\"C:\\Users\\Shahla Huseynova\\Recoy\\Recoy - Documents\\03 - Libraries\\12 - Data Management\\Forecast Data\"\n", ").data\n", "price_data = mipf[['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos']]\n", "price_data = price_data.resample('15T').mean()\n", "df = pd.read_csv('smurfit_demand_preprocessed.csv', delimiter=';', decimal=',')\n", "index = pd.date_range(start=start, end=end2, freq='1T', inclusive='left', tz='Europe/Amsterdam')\n", "df.index = index\n", "df = df.resample('15T').mean()\n", "\n", "for col in price_data.columns:\n", " df[col] = price_data[col]\n", "\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create asset" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "waterstorage = WaterStorage(\n", " name='MyStorage',\n", " max_power=10,\n", " min_power=-10,\n", " roundtrip_eff=0.90,\n", " capacity_per_volume = 50 * 1e-3,\n", " volume = 1000,\n", " lifetime = 25,\n", " temperature = 368, #K\n", " min_storagelevel = 5,\n", ")\n", "waterstorage.set_freq('15T')\n", "waterstorage.storagelevel" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def cop_curve(Tsink, Tsource):\n", " c0 = Tsink / (Tsink - Tsource) \n", " return Polynomial([c0])\n", "\n", "heatpump = Heatpump(\n", " name='Heatpump',\n", " max_th_power=40,\n", " min_th_power=0,\n", " cop_curve=cop_curve\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "e, h = heatpump.set_heat_output(10, 140, 90)\n", "\n", "cop = -h/e\n", "\n", "cop_check = cop_curve(140, 90)(0)\n", "\n", "assert cop == cop_check" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inputs" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "Tref = 0\n", "Cp = 4190 #J/kgK\n", "MWtoJs = 1000_000\n", "Tstorage = 95\n", "storage_output_power = 10\n", "# This might be 25 MW according to literature" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def power_to_mass_flow(power_MW, Tsink, Tref, Cp):\n", " return power_MW * MWtoJs /(Cp*(Tsink - Tref))\n", "\n", "def energy_to_storage(hp_heat_output_MW, process_demand_MW):\n", " return hp_heat_output_MW - process_demand_MW #MW\n", "\n", "def Tsource_calculation(Tstorage, discharge_power, Tsource, process_mass_flow): \n", " discharge_mass_flow = power_to_mass_flow(discharge_power, Tstorage, Tref, Cp)\n", " \n", " combined_mass_flow = (discharge_mass_flow + process_mass_flow)\n", " if combined_mass_flow == 0:\n", " return 0\n", " else: \n", " return (Tstorage * discharge_mass_flow + Tsource * process_mass_flow) / combined_mass_flow" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Tsource (VDG)Tsink (VDG)MW (VDG)Tsource (NDG)Tsink (NDG)MW (NDG)DAMPOSNEGForeNegForePos
2018-01-01 00:00:00+01:0064.267667141.5310000.019.642333147.7793330.027.2-57.5458.6236.246667-12.529333
2018-01-01 00:15:00+01:0058.802000139.4230000.019.613000148.0260000.027.237.0237.0229.64666721.791333
2018-01-01 00:30:00+01:0065.176667139.8636670.036.170000147.5276670.027.233.1033.1032.34666731.408000
2018-01-01 00:45:00+01:0065.254000139.7100000.035.220000147.7630000.027.233.1033.1037.12600033.512000
2018-01-01 01:00:00+01:0064.926333139.5593330.018.850000148.3060000.027.333.0933.0934.80533333.970000
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" ], "text/plain": [ " Tsource (VDG) Tsink (VDG) MW (VDG) \\\n", "2018-01-01 00:00:00+01:00 64.267667 141.531000 0.0 \n", "2018-01-01 00:15:00+01:00 58.802000 139.423000 0.0 \n", "2018-01-01 00:30:00+01:00 65.176667 139.863667 0.0 \n", "2018-01-01 00:45:00+01:00 65.254000 139.710000 0.0 \n", "2018-01-01 01:00:00+01:00 64.926333 139.559333 0.0 \n", "\n", " Tsource (NDG) Tsink (NDG) MW (NDG) DAM POS \\\n", "2018-01-01 00:00:00+01:00 19.642333 147.779333 0.0 27.2 -57.54 \n", "2018-01-01 00:15:00+01:00 19.613000 148.026000 0.0 27.2 37.02 \n", "2018-01-01 00:30:00+01:00 36.170000 147.527667 0.0 27.2 33.10 \n", "2018-01-01 00:45:00+01:00 35.220000 147.763000 0.0 27.2 33.10 \n", "2018-01-01 01:00:00+01:00 18.850000 148.306000 0.0 27.3 33.09 \n", "\n", " NEG ForeNeg ForePos \n", "2018-01-01 00:00:00+01:00 58.62 36.246667 -12.529333 \n", "2018-01-01 00:15:00+01:00 37.02 29.646667 21.791333 \n", "2018-01-01 00:30:00+01:00 33.10 32.346667 31.408000 \n", "2018-01-01 00:45:00+01:00 33.10 37.126000 33.512000 \n", "2018-01-01 01:00:00+01:00 33.09 34.805333 33.970000 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# First base case simulation\n", "# Then opt case simulation\n", "# Inputs are Demand, Tsource, Tsink\n", "# Constraint: Storage level\n", "# Functionality: \n", "\n", "# - Set new storage level\n", "# - Save COP, new Tsource\n", "\n", "dam_prices = df['DAM'].to_list()\n", "pos_prices = df['POS'].to_list()\n", "neg_prices = df['NEG'].to_list()\n", "tsinks = df['Tsink (VDG)'].to_list()\n", "tsources = df['Tsource (VDG)'].to_list()\n", "demands = df['MW (VDG)'].to_list()\n", "qhs = len(df)\n", "\n", "bl_hp_th_loads = [None] * qhs\n", "bl_hp_e_loads = [None] * qhs\n", "opt_hp_th_loads = [None] * qhs\n", "opt_hp_e_loads = [None] * qhs\n", "storage_levels = [None] * qhs\n", "to_storage_list = [None] * qhs\n", "from_storage_list = [None] * qhs\n", "new_tsources = df['Tsource (VDG)'].to_list()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Tsource (VDG)Tsink (VDG)MW (VDG)Tsource (NDG)Tsink (NDG)MW (NDG)DAMPOSNEGForeNeg...bl_e_load_MWbl_th_load_MWbl_copopt_e_load_MWopt_th_load_MWstorage_level_MWhto_storage_MWfrom_storage_MWopt_Tsourceopt_cop
2018-01-01 00:00:00+01:0064.267667141.5310000.019.642333147.7793330.027.2-57.5458.6236.246667...-0.00.0NaN-0.00.05.00.00.064.267667NaN
2018-01-01 00:15:00+01:0058.802000139.4230000.019.613000148.0260000.027.237.0237.0229.646667...-0.00.0NaN-0.00.05.00.00.058.802000NaN
2018-01-01 00:30:00+01:0065.176667139.8636670.036.170000147.5276670.027.233.1033.1032.346667...-0.00.0NaN-0.00.05.00.00.065.176667NaN
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5 rows × 21 columns

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" ], "text/plain": [ " Tsource (VDG) Tsink (VDG) MW (VDG) \\\n", "2018-01-01 00:00:00+01:00 64.267667 141.531000 0.0 \n", "2018-01-01 00:15:00+01:00 58.802000 139.423000 0.0 \n", "2018-01-01 00:30:00+01:00 65.176667 139.863667 0.0 \n", "2018-01-01 00:45:00+01:00 65.254000 139.710000 0.0 \n", "2018-01-01 01:00:00+01:00 64.926333 139.559333 0.0 \n", "\n", " Tsource (NDG) Tsink (NDG) MW (NDG) DAM POS \\\n", "2018-01-01 00:00:00+01:00 19.642333 147.779333 0.0 27.2 -57.54 \n", "2018-01-01 00:15:00+01:00 19.613000 148.026000 0.0 27.2 37.02 \n", "2018-01-01 00:30:00+01:00 36.170000 147.527667 0.0 27.2 33.10 \n", "2018-01-01 00:45:00+01:00 35.220000 147.763000 0.0 27.2 33.10 \n", "2018-01-01 01:00:00+01:00 18.850000 148.306000 0.0 27.3 33.09 \n", "\n", " NEG ForeNeg ... bl_e_load_MW bl_th_load_MW \\\n", "2018-01-01 00:00:00+01:00 58.62 36.246667 ... -0.0 0.0 \n", "2018-01-01 00:15:00+01:00 37.02 29.646667 ... -0.0 0.0 \n", "2018-01-01 00:30:00+01:00 33.10 32.346667 ... -0.0 0.0 \n", "2018-01-01 00:45:00+01:00 33.10 37.126000 ... -0.0 0.0 \n", "2018-01-01 01:00:00+01:00 33.09 34.805333 ... -0.0 0.0 \n", "\n", " bl_cop opt_e_load_MW opt_th_load_MW \\\n", "2018-01-01 00:00:00+01:00 NaN -0.0 0.0 \n", "2018-01-01 00:15:00+01:00 NaN -0.0 0.0 \n", "2018-01-01 00:30:00+01:00 NaN -0.0 0.0 \n", "2018-01-01 00:45:00+01:00 NaN -0.0 0.0 \n", "2018-01-01 01:00:00+01:00 NaN -0.0 0.0 \n", "\n", " storage_level_MWh to_storage_MW from_storage_MW \\\n", "2018-01-01 00:00:00+01:00 5.0 0.0 0.0 \n", "2018-01-01 00:15:00+01:00 5.0 0.0 0.0 \n", "2018-01-01 00:30:00+01:00 5.0 0.0 0.0 \n", "2018-01-01 00:45:00+01:00 5.0 0.0 0.0 \n", "2018-01-01 01:00:00+01:00 5.0 0.0 0.0 \n", "\n", " opt_Tsource opt_cop \n", "2018-01-01 00:00:00+01:00 64.267667 NaN \n", "2018-01-01 00:15:00+01:00 58.802000 NaN \n", "2018-01-01 00:30:00+01:00 65.176667 NaN \n", "2018-01-01 00:45:00+01:00 65.254000 NaN \n", "2018-01-01 01:00:00+01:00 64.926333 NaN \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "waterstorage.set_storagelevel(waterstorage.min_storagelevel)\n", "\n", "for q in range(qhs):\n", " demand = demands[q]\n", " tsource = tsources[q]\n", " tsink = tsinks[q]\n", " \n", " from_storage_MW = 0\n", " to_storage_MW = 0\n", " \n", " # baseline\n", " bl_hp_e_loads[q], bl_hp_th_loads[q] = heatpump.set_heat_output(\n", " heat_output = demand,\n", " Tsink = tsink,\n", " Tsource = tsource\n", " )\n", " \n", " if neg_prices[q] < dam_prices[q] - 10:\n", " # overproduce/charge\n", " desired_hp_load = min(demand + waterstorage.charging_power_limit, heatpump.max_th_power)\n", " e_load, th_load = heatpump.set_heat_output(desired_hp_load, tsink, tsource)\n", " to_storage_MW = th_load - demand\n", " to_storage_MW = waterstorage.charge(to_storage_MW)\n", " elif pos_prices[q] > dam_prices[q] + 10:\n", " # take from storage/discharge\n", " from_storage_MW = min(storage_output_power, waterstorage.discharging_power_limit)\n", " from_process_massflow = power_to_mass_flow(demand, tsink, Tref, Cp)\n", " tsource = Tsource_calculation(Tstorage, from_storage_MW, tsource, from_process_massflow)\n", " e_load, th_load = heatpump.set_heat_output(demand, tsink, tsource)\n", " from_storage_MW = waterstorage.discharge(from_storage_MW)\n", " else:\n", " e_load = bl_hp_e_loads[q]\n", " th_load = bl_hp_th_loads[q]\n", " \n", " opt_hp_e_loads[q] = e_load\n", " opt_hp_th_loads[q] = th_load\n", " storage_levels[q] = waterstorage.storagelevel\n", " new_tsources[q] = tsource\n", " to_storage_list[q] = to_storage_MW\n", " from_storage_list[q] = from_storage_MW\n", " \n", "df['bl_e_load_MW'] = bl_hp_e_loads\n", "df['bl_th_load_MW'] = bl_hp_th_loads\n", "df['bl_cop'] = df['bl_th_load_MW'] / -df['bl_e_load_MW']\n", "df['opt_e_load_MW'] = opt_hp_e_loads\n", "df['opt_th_load_MW'] = opt_hp_th_loads\n", "df['storage_level_MWh'] = storage_levels\n", "df['to_storage_MW'] = to_storage_list\n", "df['from_storage_MW'] = from_storage_list\n", "\n", "df['opt_Tsource'] = new_tsources\n", "df['opt_cop'] = df['opt_th_load_MW'] / -df['opt_e_load_MW']\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "line": { "color": "rgba(255, 153, 51, 1.0)", "dash": "solid", "shape": "linear", "width": 1.3 }, "mode": "lines", "name": "DAM", "text": "", "type": "scatter", "x": [ "2018-01-01 00:00:00+01:00", "2018-01-01 00:15:00+01:00", "2018-01-01 00:30:00+01:00", "2018-01-01 00:45:00+01:00", "2018-01-01 01:00:00+01:00", "2018-01-01 01:15:00+01:00", "2018-01-01 01:30:00+01:00", "2018-01-01 01:45:00+01:00", "2018-01-01 02:00:00+01:00", "2018-01-01 02:15:00+01:00", "2018-01-01 02:30:00+01:00", "2018-01-01 02:45:00+01:00", "2018-01-01 03:00:00+01:00", "2018-01-01 03:15:00+01:00", "2018-01-01 03:30:00+01:00", 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baUC/vil/z4QzxJ5M6OFZEMgiATux5/CZ59KZg46jswYen8Wc4FWFSgCePYVq+fiUG2JPfGxR6DmB2FPoNSD35YfYk3sbIAeFR4DFnoUffGiJMTW1tXTbnfcqELpww/+e/sAj6u8bNm5M+Y3Fnptvv5sqq6rox5Mm0vChg9V9fP8XK1ZS2/IyiD2FV7VQ4kInALGn0GtA7ssPsSf3Nij0HEDsKfQaEJ/yQ+yJjy0KNScQewrV8ih3LgmYYg/nRQSfYfvvZ3nwsKBz173/oB9M/B7d+/d/Kc+fvnv2VlkXsefoIw+jhR9+TOecOYnWfbWenp75IvXu1YsWL/0MYk8ujYx3g0AuCEDsyQV1vFMnALEH9SHXBCD25NoCeL8QgNiDupBrAhB7cm0BvD8bBIq2rczGa2zf0dhuz2Z/txN7+Cbz77yFa/47C5TIwx47vXr1SBGC2LOHf3tpzus04qDhtHrNOurVs7v6r+45lGnhsY0rU4J4HgSyRABiT5ZA4zWOBCD2oHLkmgDEnlxbAO9PV+w5e9Y9tE+7PnTJoWMBEQRCIQCxJxSMSCTmBEpfuoBarJmf9VzWHnM71fcc4VvsYXHnhRfnWB45LPCwiMNbtPi3B/45g6689FfUvl2F5dkjcX7Y86db187Kw+fluW9C7Mm6tfFCEIgBAYg9MTBCgWcBYk+BV4AYFB9iTwyMkOUszPliMU1b+AhNHXYajd5rUJbf7vy6oJ4945++hgaW96fbjp4UmzJkkhGxy3Mn/D4lGS5n3GyVSTnj/GxSxR6uQ3wd1XUkxNE4V8As5S0fPXt4SxaLPRdfcK4l7nBA5sk/bIrPI9u4WOzp3q2rivlz7HdGq9+cPIfSxQ3PnnTJ4TkQyDIBiD1ZBo7XNSMAsQeVItcEIPbk2gLZf/9jH79Hty27iy7sdx6dMvjA7GfA4Y1BxZ4xT11OQ9oOSYzYI3Z5bcLdKYT44Ii42So2lSbkjCRV7OE6xNc3Wh+RmPYSsumRXA4J+InZY3diF2dZTu3SxR6J4yNFgtiTQ+Pi1SCQSwIQe3JJH+9mAhB7UA9yTQBiT64tkP33J0Xs4QVskhavEHuy3xbMN0Lsyb0NkIPCI+B1GhcTYU8dPVgz/42PVRdvH/63xOyB2FN4dQglBgFbAhB7UDFyTQBiT64tgPdD7Cm8OgCxJ542h9iTe7tA7Mm9DZCDwiNg57UjHjsi6tz61+l00c/Ptk7f4r/LiV28XWuvPr0h9hRe1UGJQcCdAMQe1JBcE4DYk2sL4P0QewqvDkDsiafNIfbk3i4Qe3JvA+QABOJOADF74m4h5A8EviYAsQdVIdcEIPbk2gJ4P8SewqsDEHviaXOIPbm3C8Se3NsAOQCBuBOA2BN3CyF/IACxB3UgJgQg9sTEEAWcDYg9hWd8iD3xtDnEntzbBWJP7m2AHIBA3AlA7Im7hZA/EIDYgzoQEwIQe2JiiALOBsSewjM+xJ542hxiT+7tArEn9zZADkAg7gQg9sTdQsgfCEDsQR2ICQGIPTExRAFnA2JP4RkfYk88bQ6xJ/d2gdiTexsgByAQdwIQe+JuoQLJ38J1a2jDjkoavdegAilx8GI++P7b1LpVCR3edyD1KK9QCfBRrmcOOo7OGnh88ATxBAgEJJCJ2LO2qpJeX7GUThl8YMC34nYQ2E0AYk/h1YZ8FXs436P6DFAG477vtmV3qaPXLzvkBPVvvvh3Gc9zZdk5XyymLm0qaFj3niR5NvMk/beeX4g92bGY29gZVOxxsm9UJbGrN07v0tcBPLfla2jJKDpu7xHUplULGtK1J+YQURkK6SaaAMSeRJs3fwp34f97mJZUfUbPnfD7/Ml0lnMqg9+F/c6zFswQe7JshAJ/XSZij9PCoMCRovgBCUDsCQgsAbfnq9jD4zOP16+t/ojerZmrLMFiz+G9hijhhy99PM+VqcY/fQ0NLO9Ptx09SX1AssuTnQ0g9mTHYm5jZ1Cxx8m+UZUkSNvV1wEy3+1VO5JWl86jivo91EdNbjevTbg7quwiXRBIJAGIPYk0a/4VCmKPt80g9ngzwh3REoDYEy1fpO5NAGKPN6Ok3RFkwZjNstfW7qKd9Q1UUVZq+9p8E3suPfRYmvjybyH2ZLMS+XjXDfMfo+fWzbYVOSD2+ACIW0CgwAlA7CnwChCX4kPs8bYExB5vRrgjWgIQe6Lli9S9CUDs8WaUtDsg9kRrUfHsOfOAg+n8t26G2BMt7sCpQ+yBZ0/gSoMHQEAjALEH1SEWBCD2eJsBYo83I9wRLQGIPdHyRereBCD2eDNK2h0Qe6K1KMSeaPlmmnqhiD2nv3gzbaqpUuEcsI0r01qD50FgNwGIPagNsSBwxtP300ZaiZg9LtaA2BOLqlrQmYDYU9Dmj0XhIfbEwgxZzQTEnmhxRyH2tK7rTGO6HUmXHDo22swXQOqFIvZMnHUdba+th9hTAHU634v4/OxX6IGHZ1jF6NWrO1156a+ofbumw3NqamvptjvvpfcWfmjdc9nU82n40MHWv7duq6Srb7yFVq9eZ/3twGH70YVTfkKtS+23BqfLDWJPuuTwXKgEvv/EfVRZsgpiD8SeUOsVEguXAMSecHkiteAEIPYEZ5bvT0DsidaCUYg9nGMORs1Bn3FlRqBQxJ7jn/0D1Tc0QuzJrLrg6SwQYLFn4QcfWsIM/3v2y68qwae0tJUSerp07kxnTz5N5Wb5ylV07R9vp5O+O57GjTnKEoOG7b+f+rdcD894kkYdejD13bN3qKUoOLFn+gOP0IeffpqiwJnqmqm+6QpeVKpbqFbNw8Qg9ngbTTx7Tun4Y7rwW4eqB7J9Ghdvtzt76Gh1RCuuwiMAsafwbB63EkPsiZtFos8PxJ5oGUPsiZZvpqkXitjD9ZAvbOPKtMbg+agJmGIP6wg33363Ene2bK2kB/45I0Vn4PwsWPSx9fctW7fSrX+dThf9/OzQhR27sheU2MNCz+yX55LubiWuVqKusfqmG0A3DrtncRp8iVoXdYUqlPQh9nhbWsSeCW3PoEuPHJUTsSfbx3Z6U8Ed2SQAsSebtPEuOwIQewqvXuSz2HN8p5Poy+oNeXH0epgBmrmWwrMnnLaadLGH2/egTj3o0nn3QOwJp8okLpWVm3bkrEx7dmrT7N2m2KNrBy/Ned1WJ9AFoQ7t26stXN27dIlk25aZ4YIRe9gwq1evpREHDU9R3NhALOBcfMG5aq+dKf7wb7169bDcrEzxJ2e1L2EvhtjjbVCIPd6McEe0BCD2RMsXqXsTgNjjzShpd+Sz2MOCB1/v1sxV/+V/H95rCN227C717wv7nUenDD4wpyYTzx7Jl12e7Gwgf3ttwt0p+Ze5CsSecMyadLGH6wvXlSVVn0HsCafKJC6Vqf/+kN5dvjXr5Zo2cT/6xl7tPcUe1go2bNyohJuHH3kyRTeQh2UX0eQfTlSxe+xi9kyeNDFlW1dYBS4IsUdX4D5dsixF7LETb8R7Z9JpJ6p9d/qeOtPzp7GxMSxbFHQ633/yPtpe8iXNPP53Bc3BrfBHPHee+nlC+Rn0myMPU/+f/3bmwOPU/7Jx8fsu7Hcunbzv7snp2qpKemPlZ3TyvgdkIwt4Rw4JbN5WTe3bllFxcfBMPP7Je3Tbsrtp7vimRQ4uEEiHwKat1dSxXRkVFaXzNJ7JRwLSd5hjT67LUltbR7vqG6htWSvbrPB4aS/2DFZ9IV9xKNOEZ/5AA8v70+G9mvJllyc7Gzj16TJX4bLfOvqHuTZT3r//hncep+fXzbYdO3fU1lFjfQOVOdRBs/B2c7goAflpu9JOROzhdYDUoZ6136Q1pW9T2a5edM4+x2MOEaWxMki7KOIBOY6ePXqAZj3Ei9MOIN2zxy4mD+sRN0y7g8xQMhmYxXo08WIPw3vhxTmWm5Qp7pi/MxlT7Dn2O6OtCNqm2MNfGXFlTuDcl/5FVS2/pIeOnJp5YglN4Xtv/EqV7KCSo+l3Iyeo/89/O63PWDptz+yceMHv+0nPM+m4fvtblJ9d9gHdu+Z++s9htySUPIr11wUv09KqL+nmQyd9vcgOvtJGPUE9CoMAf2BpmlcGr4NhvB9pZJ+A9B3m2JP9nJhvbCT+3FfkUBd5vNy/pOnDzAd1b6j/8r9Hdh2kxky+4lCm01+dRnu33pu6VrSiOZtfts2TnQ2c+nSZq+xZPJDuOPRnuTdTnufg9o+eUXaxn2O510Gz6HZzuCjx+Gm70k4+r/lcZYXXAVKHuu84mNa1+R8VV3ens/qPx1wzSmNlkHbnDuUZPJ1/j5rbuPQSOO0A8toZZO4sCpNK4sUe83g0gSdxe75YsapZIKUgnj1hGqOQ08I2Lm/r27lGZztAs13MHidXbu8S4Y58IcCBufmr20NHXkwdKtizJ/hCG/UkX6wd73xiG1e87RNF7rCNy53qTW/Oon4duqW9HUy2cfVoX0rPrZttu7UsnW1cPUv60Iyxl0dRJQoqTWzjmqfEnvOHnqi2P5rbBguqMqCwsSDgJvaIaON2Ghc7jjw980U658xJ1jHrpjNJmAVNvNhjwjKVNcTsCbM6pZ8WxB5vdk5iz/juY+iyEad4JxDCHRB7QoCYh0lA7MlDoyU0yxB7EmpYl2JB7HG3uYg16R5z7ib2zFz8MfXu1IaWrN+sFtp6PB+vmD25EHsWrltDG3ZU0ui9BiWmoUDsmUdFu9rSBfucDrEnMbU6vwviJvZwyUTweW/hh1ZB9e1Zdr9XtG1LV/zmgkhO5yp4sQenccWjwUHs8bZDPok9POGavmgOpTv59KYR7A4cGR+Ml3k383uv6i36z5E3wrMnM5R4OkMCEHsyBJiHj+da7OHxjE8KmjrstBQRobZ2F+2sb6CKslJbqhJ4ln+MMkBzGGJP+xYdaAdto420knoXDaWT9x6lPIWOfPT31L9rOY3v/p0UsYdt8vjnr9OqxkXNPC1krpILsYfHKmadJO8PiD3zVPtioTFXnj3cxsz2H3ZXOueLxdSlTQUN694z7KSRXoETKHixh+1vRsQ2gyPpW8H0IEwFXndCLT7EHm+c+ST2xG3LDo6M965fbnfIBPqpUbdA7MkMZeCnuS0t2/IVXXJoduJyBc5glh+A2JNl4DF4Xa7FHqf356PYc/5bN9MFQybSwHZ7WpblhSxflS2+tP4mJ2k5iT0yJvADTqdxQewJp/FA7Mm92JONOaS0w+dO+H04FQepgMDXBApO7IHl40kAYo+3XeIi9pzR6wz66YGjrAzbCTsQe7ztmU93QOzJnbVkCx0mgE02gNhjXxfPnnWP+mH62HNyV1kjenOSxZ7jO52UsZAbxLOH5xF3HHIxHdB59zYniD0RVdyQki0ksWdXXQPNPukqkvlur9qRtLq0cMUeCEAhNaICTwZiT4FXgLgUH2KPtyXiIvbIFz/JMcQeb9vl+x0Qe3JnwbNm30lrd2wkiD0Qe9xqYZIXBV5iD/8+qs8A6lFeEUpD3V5XTUu2rqIDvxZEovTsMcfTdAoQV7GnM+1J/5lwRTpFSvsZbONyR5cNDxU9B15tl++V7Y58CERVbR29euo1sRR7Tu/9fTp3+Oi066bXg3Z9eJL7dS8e+D08AhB7wmOJlDIg8L0nb6WdLbZhQePCEGJP+hUs2xOc9HMazych9uTOLhNnXUfba+vRN35tAnj22NfFJC8KvBaMYffv729cTLzdSbYnQexpHrPHzzauivo9st5vQexxHqu82lEUo5yfd+aL2BP1YSjMQdqMxJnkWGF84WNPFLWzcNKE2FM4to51SZM8UQ0LPMSe9EmGvRhIPyf5+WSYYs/9B99CA7qV5yeIHOQaYk8qdIg9EHtMAmH37xB7iMKI2QOxJ5wBQ9/GZQYK3lG7ixrqG6jcIUi4ngM/wks4Od6dip93Quxp4qWLPdKn3bfkGYg9YVfKAkwPYk8BGj2ORYbY422VXIs9fFLAlR/ebE0CJcfYxuVtu3y/I0yx5y/7/4mG92kXGZKknbwWhdiTz8cDZ/ZvAAAgAElEQVQTQ+yB2AOxJ5VALrZx8TsloLNTgOZciD2Sr6SexiXzQD6Zik9Lg9gT2VQiJWHmnk3PHog92bFrobwFYk+hWDrm5YTYQ8ptfGC73nTBkO/bWivXYo+IOmaMgZvenEXPbHqCONDkp9tWqAChuQzQzPl5Zf28FLfXsL/8xrw5hZ69fBJ7kmbrKMSeKII+c7vjK+pTw9IVe1jgmr5oDp09dHQij7ZN8hjq5R0QdpuHZ4+3Z09cxR6ZJxWy2LN2ay1tr61r5kHr1Y5CnzgQ0RVzn6RXK19Qx6azOGV3wbMHnj1R1D2kuZsAxJ6IaoN0XrcdPcn3G/iZgeX9aEnVMmvfpu+H8+xG80uU28QhTkV7ZPFcenH1W3TfUZekna3nV/6XllSubCbqsP33rehP9xxhn7ZMYvg40zV1K1TwyPc2LrbyUbx+OL3645+nnS+vB53EHhECWATiAHu8t9hO7GHPIL5G77X7FBCvd9r97iUk2e3ZD3sxkE6+8/mZQhB74rhYFu7mF3IRLri9DSzvT9/tfzBNW/iI7339UYg92eKXrtiTi4VONtt8tvhns0zyLi/bhd2/O4k9nB9dRAjj6PVsBGjW64YdK6/TuLjc5w89kW5bdpe1aIfYk72WINu4nj/mThr30hT1YjvPHrEJjxd8mXFepB3xOqNt6xK6/ZCLIy+E6YkUZ7HHbW4Jz57IqwpeECEBiD0RwYXY4w42X8WeMIL/8cD94dYl9NDRl6VA0vfrOg2I/Pd8FXvCWoxA7Imo03JJNl/EHhZBpsy/yvUrolMx9foZ9uk+6VrMSeyRNsCTehZ7Du81RC3E/H7NTprYw0JylzYVrh47XoKBHxvxe4KIan7SDOuesPrXsPITZjpetoPYc43qB5w+LkLsCbM2Zj8tEXt4C/QvPvg1xJ4Jd0diBIg9kWBFojEgALEnIiPEVexhT5CB7XtT25KyiEruL1k3scfN3dNf6tHdFYbY47TQ8iv28AKP98rnm2dPWIsRiD3R1W+nlPNF7PFaFLqR81oQZZ86kZfYU7KjBw3rOKjgxR4/MUsyqRtie6++Jxd1RN4ZVv+ayzI4vVu4O8XMgNgDsYfrjsQV5P/vV/iOY3038wSxZ54lcAX5qBHUthKSwK7uwLMnKM1k3//87FfogYdn0JhvH0FnTz5NFXbrtkq6+fa71b/77tmb5B6TxGVTz6fhQwerPy9fuYqu/ePtVLl9u3WbnmZYFCH2hEXSSCeuYg/n645DLqYDOme2lSZTbG5iT5wHaog9dzUL0Ox3G1dYixGvBRe2cWXaOps/X4hiz0/7TqYzhn4rfJgBUvQSe4qru9MBnfaB2PO0+2KXkUPsCVDxYnYrxB53g3iJnV5CdlK2cUk9ifMcMp2mFVTs4XGhvLTEcRtX71Z9qVN5K7rzW1PTyU6gZ/JpG5fb/D6bYs+M8VNp3KxfKg9lnMYVqLpl7WYWchZ+8CFVVe+wxB07sYfvuXDKT6h1aWmzvHEaTzz1HF3xmwuUOCTX9AceoREHDbcEoTAKBbEnDIo2aUDsCTY50fd/x3mghtgDsSeiLiPWyRai2BP1yRt+DO4l9hTtKqcDKw7KO7Hn7Fn30D7t+qQVzNkuZo/XYhdij5/aFt97IPYEm0+Zd4uYc9tRZ9JZr13bbJtrmGKPxDviPGT7NK6kij2Xv/4Izd36srIbe7bw5Razx0vs4d/7dy2n+476TeSNHmKPf8Ti3X/9YaepA1sg9vhnl+07WahZvXot9erVQ/2XvXmCiD1879U33kKTfzgxVFHHiQPEnohqCMSeYJMTiD1Ehb6Ni11o+3Xo5nhig9QoePZE1Gm5JAuxJ/vM1YT+/z1M79bMbbZo0hc1HOA132L2+BFnnIhD7LEnE5bnZG5quvtbIfYEm085iT36IlI/GQliTxxr/e48yTgQlthDDS1pYMWeWRV7zuh+Fv10xEhb0HE5jSsunj0Qe5pXkxXb1+WskfZp273Zu0XsOeXECdbWrQ7t2zfbxuXk2bNg0cf0wD9n0JWX/orat6uIvGwQeyJCnE9ij99FdpioeHLRqXU5PfSdptMAIPYEF3taNJZSfVGtZZa4n8bltRjxuwBMV+w5ouJYuu6IE8OsxgWTFsSe3JgaYk9z7hB7IPaYBBCzx1/MnmyLPWW7etGs712Ztc4zU88eP4Hes1YY7UWhiz1E6uTfbHr2TGh7Bl165ChfYs/MEy9V25j46lU7klaXZidmD8SeXNRuf++8aN6f6Z0Nn/i7OcS7bh35Szqoy76OYg979NgJP14xezjBF16cY23xMmP36HF9wigOxJ4wKNqkEWex56aDLqJDezQFhxKhxe0khzARra2qpNdXLFX7UFsUF9Ezx/0OYs/XgIN69ph2gdjTRMQpZk8YR9yG2RbyKa1CFHviUF+Cij2PHjWNepR7fyXK9WlcfoVduzYCsce+5/AS0/OpvzHzCs8ed+t52V5+z7bYw9uFXj31mqxVvUzFnkz6pSgLGVTsKarpRG1btnGM2cN5jbPYc9vo09U2Jr4g9jyjODx3wu+jrGKxTzuunj369q2TvzeBHv/PzJQAzUE9e6La3gWxJ6IqHmexxzztKpsDnH5ksN6BwbPHv2dPSV1bqivZHbldqnChiz1yLHJpUWvaUPxZymkc6bTHiLqGvEwWYk/2zMbHx1/59kP05PG/toRL8wu50zYuvycZ/mDm7bS1fkuoE0ivBadOMJP2CLEHYo9JAJ49TYKK04IQYo+//jubc2F/OUr9gOV3Gxc/ZRcvSR83IPY0t0AcPHta13WhP42ajJg9QRpIDu4Vbx45icsuYLP8zS5As5OoA7EnB8bM5JXpTGb5Ge6Al1Qtiyywnd2kKJsDHMSeJs+TJVWfNZuY+fXscaqXcRJ7bnz1dZq5/cGUk9+8FoN+66HTNi69bvHR9Prxmem0x0zaf9KehdiTPYtKPb56v4vpqc/+p2L2mF/IMxV7vNpiOqUNkmYm7RFiD8QeiD2pBLzaHsQefz2a3zmIv9TCuyuoZw/EnvTYx0Hs4ZzzickI0JyeDbP1lCn2iEhTua3KOl3LTezhfNqdxgWxJ1sWDOk96Uxmky72rKneSLe8/wS9tXm+ErP0L1Hw7PHv2QOx5z11IoUu5jATiD0hdV42yUDsiY6tmbJej3l7bT6IPRz37dn1z1Jb6urLWyid8VE4xUHsue/wK2hguz2zVyl8vMlrwe8jidjeEqdtXCzCjt5rkGJVW7uLdtY3UEVZ82N1+Xep5/z/uR3zpQdUl3/fdvSkjNh72R5ijz+8cRd7OO4Nf0Tjy+00Ljuxh9vQ45+/TqsaF6nn88Wzp2NdP9pcsswqs93cz591ve+C2OPNCHc0ETDFHvmbfpQ63/PAwzOaIdPj8ZixevjmsOP1cJrYxhVRzU1nMpt0scc8kpPRi9sxxJ7dYs+M8VNpydZVdGDnpgmlXHKEpVOVLd4ygGZ+fwq1LSmLpFbLhNuMZSIDZJeG/lTbWKNsCs+eSEyQs0Qh9mQPfVCxp3fRUDqw4yB6ZtMTzY5Udsq11+IwaGmdYgs5pZPO+ChpxUHs4S+vBxj9c1BmYd8ftk3Dzl8m6cVJ7NG3SqYj9gwqGUHj+oywjtAOIzaYl+1lfqV7DGTjNC7E7Mmk1u9+VvrXTMQeSUNSdRJ7JK7mqD4DfMV/8yqhzFuDBGjWY/bwh2H21OZLtrGZH/r0PGRy4AzEHi9r4vd8JQCxJwLLLf2qis7836/UF5w9yrrQK+vn+f7a2bf1XrS85otEbuOC2NNU2by2cUkQRXNA8xJ7OO0oFyFeYo/urZUNsefOEVfRsO49iWOcTF80xzp+WiYH2MYVXucGsSc8ll4pBRV7OD2p835j9ngtDr3yqP/OaUmcLLs4EXZphS32jHniKhrSfiC5eUgIV7+M7PItaUTZzwZhb9qB/53EQJ5JEnu4jZw18Pi8E3tO2mMcPbb575agrH+g08dafZ4XVOzhsXzDjkrlOcU2Dyo4JD1AczbEnjD6Sb1fyrbYk4l3FsSedEcfPBd3AhB7IrDQghXb6Bcf/FqJPXyx+66bEi1Z4E6xZ0kfWlO3AmLPhLsjsEzmSboNBn5T9xJ7ZCIYttjDXzz8Co9uCx0nz57WuzpTy+LWWfPskUWbPjlhF1+IPaQEsEvn3UNTh51mbTnwWz/t7it0sYfr2KBOPZS4GPWVjthTVt+FqltsSMuzh237XtVbdNWw89OqKxJrjL++5krsOfLR39MBnfaB2FOAYg97Ipz6ylTfdd9P+xXBQsZgXUTI1LMnl2LP1fufT1d+cEczViL+igcFM5JxntsWX+O6fUdtIZLyRyH26HOjdIJuZyr2HPH0+XRg+SGu/Yif+hP2Pdn07Mmm2CPCDK+RuL5xLMuq2jrKxLMHYk/YtQ/pJYEAxJ4IrBiG2MPZ8iMQBc1+LgM0w7OnyVrZFHv4XWcPHa0WqZkKVV6ePVy2kvoKevmEaWlv4+rRpjPdN2aKa7U2JyMQe5rjCnvCVohij2qr/c4j3u6QiSdK0D46HbFH3uHXa0X37NGDf+pbO/zmG2KPX1LR3hemt1a0OQ2euptnT9h9Hefu+ZX/pesXPmDNwcIUe/i0nZEdR9CrlS8oEF7buPxsS/GyvQgzsg3G7kRWzkuhiz3Z7OeDtAI7sef03t+nc4ePph21u6ihvoHKy0pJF+BM4d3vNi6n9sSnnXZpU+H4wcNJnHPz7Akq9hzf6SS1XdltbTTmqctpSNshaQl2ws8ufS7H+O5j6LIRpwQxXcq9Xt5qwgoBmtNGjAcdCEDsiaBqxFXs2V5bR+NemqIWMOwe+/qKpWohk4kSHgQfxJ7siz36AJyJ2MMD/fw1n6uB1smzR+oCD5TpbuNqW9qCZoy9HGJPkIZlc2/YCyCIPed6LsoyNJn1uJ3YQ/Wt6LUT7mh2j/nOJIg9XjEj7GL2wLOH1DjOVyFt43ISYTJti/cteYbuX/xsJGKPmTcvscfP/Exs376kI50zZGwzD70wxB72nGMPjCR79uST2CPCQyZiz0WDf0SLN61V6wC5nOYOXmzsxB4WKnnOyJddzB5d7OHYcxt2rqGdu0ro/KEnWtsc9Zg93Fa8dkq45XPt1lqategrmnyYfXD9qMUeL281iD2Z9tx43okAxJ4I6kauxB6ZJDt9nZV88WDNl0S19zOZCANTXMUeJ08buzJnIphIevK+6cdMoRdWvUnH9j6UepZ1Vt4Duot3GNu4whJ7dDfvfBF7uJ4v2/IVXXLo2Kx6ZoTRVjJNIxtiD79D+PrJr+TpL/v/ico6bKUdO0pC3xaVSbn1xbJMuqSue010/ZTf7z22Yo/h6akvcvV0kyD2eNkQYo99TYLY0+SFF8YlYs+Mb1+vxuZ0PHt4K+2U+VelbOe3y1uYYg975tj1ARB7/NWKbPbz/nLUdJedZ08YYk9Ffe9m4omX2HN4ryG2p6E67RoQbzEvsUd4cJynqMQeKZuTZ5CX2HN056PpqkMmBjFdyr25Ent0D/+0M48H85oAxJ4IzJcrscdLiIDYY2/sXIk9v/vmqXTJO7fSTQddRIf2GNxM7BFXTnZd5Uu+kLhVWTNwaL6JPbUlG2hIp74q0LTT5Xcbl+wB5y/dcZ3ERdD9qCS9FsxB32vn2ROk3eh5YrHnsvdvJT5WPNMjh81yZFLuuIk9vCWyDbWztlboE9SgYg8vPG945zH61/jzFbI4b+PysiHEnmjFHq4bFXW96d8nnRW0m4jsfqdtXE4ijNeWE6+Mitgj42k6Yo++7ZnfJ0evm+8udLFHDljgscArZs/lr/9bnVLqJOrpdkonDEJc5wlhiD3nvTSdPqx926p+fBqXX7GH29OVH96shMtcij1DS0bRorrXXbdxudnw7gVz6KFV/3Z83kvs8Wqrdv2K3hflSuyR9/IHfl0MPvzp8+nCgWeFJpJ79av4PXcEIPZEwD7fxJ5jnv017dfmgNAXXiZa85SG8tKSWBy9HmTR6iWo+alO8j4JxKzHBdE9ey7d9+d04yd/Vd4+fOn76Z3ek+9ij5TRbaIGsce7lnktmL1TSL0DYk/2t3GZNspE7DG/aELsSc8DRDimcxqXftJQ0Pbn5/6wPHviKPZI/2PGzHASYTJdtMv7IPZMIgnQHNU2Lr1v8hJ7vOyaVLHn9BdvpuW7FqutUBwkWwn2X8eP8buN6/hn/0Bbilb5Fnv4I+OX1RvU/SLwyAnDdnFzTCGDRY4bFjxANSVNadh59khAbF0IdfPs6VU7klaXzgsk9vBWsk+3raDpY8+hs175Iy2pWkZX7/NbalNWR//46FX6v2N2i9pRiD36zgm/Ys+UgZPoziUPK2GGhWe+Mtme6yj2zDzXEn+211XTkq2rlJiKK3kEIPZEYNNsiz0S9OuGt5523c/q5NnjNYCGhShqsSddV8W4ij0STJHFnl2Ntdag6WYPiD27T+OCZ0/qV5xM2rFMNp8adQt1qCij4uIix0DjTu/Rt3Hlq2cPl41PDPEz8ZLT74KeiObktZNLscetj+TxQ04DC+M0Li+hMl89e8ISY5zaV1jpZ1Ps8TtmyyIMYo+99fVt1vm2jUva+/0H30J3fPCfFA8o+dAl/a3MVfnQiemL5jT7QJlUsWfirOvUKb2ZiD168GauRV6ePRIfh++VwMj6CcOPHjWN1tasofPfulmJL6aQYb6PTxo24zGKPXWxp6imE43pOZJe3Py8qux6zJ50xB597BKxh/uRNsVt6PE1T6cIR3ERe+RUZj9ij58+1I/YM2fFInVSn8wzJN0Xli2komKiM4Z+i3qUVyib+HlnJvNNPBs+AYg94TOlbIs90pBfW/2Rq9jz2Pw1dNu6q5SS+/iy12gVfWh10um4JwZFd8FbN9N7Gxerx1i9D9uzx0s1d8qv00Jm4qzr6egeh6gTD+Ry8uzhe/lq16YlNTQ00l4Ve9DstXPVdqQDNKX89o/+TS8sf5doVzk5efa0b9tIq3aspuO6HkfPrn82ZbDzYu4k9nBsFT52nT1n0nFvDhKzh+vX0q+q1BeoS4b8hI7f+2A1EZCBWyZu5ikj+uSA8+g0oATx7FlY9R4NKz9QtQu7WEM8aeSLJ45yapkXY/k9nfrGp7z0LOuSUif8vi/IfcKIgx5edtApKbFxOA9rd2yyYkX5SVcmmxB7/Is90lf4FUDEDvko9kje/ZbV7QOD2b7XVG+kBxfMp34duil382yJPXbHMGfi2ROWGJOu2OPnVCdOO5tij98+FGKP+5bXMMSeXQ01VNNyo1W9ZHGeLc+eX/S6mP6zZhatalxk5cFJ7Plo+0fq45c5lylksWfuxnfojwseofqi2mZzLWnXund4ELFHhB9d7OlN+9Gw7r3ouXWzrXXEyA4jaNph56j3pyv2mP1bELFH324mW8SdxB4O1mwGe86W2NOpTbntyWYyT/YSe86edQ/t066PFY/SK1afndjDnjzjZv3S8uwxvX/lGfYsMmOB+e23/cwtcU92CEDsiYBzXMWeB95YSfduudZyDZSFf7Y8e/JN7LHj4iT2SCetL3qYL2/FmtB/uFXLTv9/N9Dymi9SAjGb27hkQJYBVh/svKrr9SN+Rod3P8C6zeyw+YeoxR5+h3yB0gPcmmKPGRjcFHucBpQgYo/+xcgUeyR9zpe5l9mLs5f3gdPz3AZY/GOhL8rLaXsDv1PaYZCtKBB7mrZx8eXXsyfJYg9PqqctfER5OOl9n5PYYwodQcQeiZ8i7TdbYo9dHvNZ7DH7W6f+Z+wzV1BpQwU9/d3fEB/6IF9zo+iv/C4axv7naqpuubrZ0cfYxtVkFSexRxaF8qHH7uh1FgH5o1Jji50pJtbFnobSzfSN8kMiOY1LbCheG3omnMQeGdflo5D0yXp94GeDelVmay4ctC15efa8/MViumXJ36m2aJuVNMd84w9dfFR4j/apx7LzTabYw+z4unvxY0pI0z177MQevldECfHs4TTvO+o3Vp3UxSW/nj0mmyBijx4nK5tij75VzMy/3TYuHtPsYhb6FXs4zfYtOlC31p1Vm7T7qCf5kDTNtj/zswUqTISsP4Td88fcSW1LSyxPLZ4b88XeXV9s+4r+OvaMZl5cQesz7s8+AYg9ETCH2GMPtRDFHlNxF0FDj80je5m5U7Y7ZjKI2HPmoONShASIPXOtyiiLdfHgYTY/3fMcatOyBcQej34QYk98xB6eWL636VPlmWle3N8c1rcP8Ve7ge12Hy8rEzl2vefFe9CYPab3o/4VMGqx57oFf1enFoYl9vy072Tlku51JUXs+dt7r9N/Vs5RX2f9ePDKOMRCnl+ByIul0+9+xR72LmkoW1ewYk/Pkr40ffxkR8xOYo/Yj8UQ+TpvftgQUdpMPEXsKVtnLf7DPnrdr9gj/YwuQriJPVweL48Hs8zS5p22iaVbzzN9zkvsefLT9+n/Vt5r+xo+EGF4n3bNPG10sefOEVdZJ8eJkGYn9vAL9L+L2CN/cxN7Shvb0Xc6H6O8UXQhQk/PrgDpiD3sdfSvCRdYHyKK69rSq9+92YrZw9u43Dx7WNjQ88n5chMC3bY5RyX21FMtVbdoiofkVtedxB6nj6aXDvglfbhpuToQRgQi3e52W/Yyrd94PnoCEHsiYJxNsUdvsF7buC557mV6s/EROrLiWJq3eb7lBuv3awYHmFy8aa1ypecvu9X1NVSzs14R9HPk6Tlzb6JPKj9T93tt47p6v4ubuTnq7+c0uOyDOvWwtqjYTRz5Hq/jofWAyQO7dqRt9Vtp6daVKjDawFbD6LKRJ1gLJ9Oz56xXbqILh06kX8y7IaUmyQAVhtjTorHUcs31qq5BxR7+cvv6iqU0qs8A1y+4QbZxcR7Fs4e/OBzSsx89tvpplXX9y78+CLKdOCBdXYtKdZ/bgJKJZw9PZHRPKs7ngG7lltjD72YWfMkXbbvAqnoe2CWXr9F7eQe2y5VnD7eTDTsqVR5FdB3d6od09ZgjvaqU+l0mm/889Drao0NnxOw54fee3KLy7DHd4/WMcN3eVrKKOD7a7dqJdmabiYvYs0dZFyt4ppTDzKvU17DEHjPui5MhkyL26Iv5oGLPmKcupyFth0RyeMOSbSvprNeaPI295g/ZEnu47nH/L/EP4xKg2et0NIg9TXHUzC2wfuqW3v6lzTudOOXZ6Ud0Q1RiT9XmLrS61dvWot6PwGMn9kix3cQec/7H/xbeTifVyTPiIeQWs0f/WCrzTP1DBM8p9Zg9bmKPnYdqJmJPn7I96K5jzrY8Yu5Z+jjtWza4Wb9qevac0vHHNGvDbNpO62nuCXdYtctuDuBU14OKPTwnXlO3XHkMndr1h/To+n+q94rdObbWmf/7la9+O6LmgGTTIACxJw1oXo9kS+zROzJu6G5ij1scCD9ij7nFxuxsuHP0OkpZf8YUe/SySAdvpmcKLfKM3rGbHZ6f4Mv6ZLgz7UkbaWWKibsV70mPj7tC/c0uD7r6LQ+23NGDdrVZ26xDTMezx6u+6b/7EXv4fvkiI8/aDRQ6u3TFHjPvptjDg7jUHXPLlS7M6Ol4iT1yr9MXIxE5uP4wh4l9xltij3z1lLYk7Ur/MsoLEz0Pfk9LkPpq2iiIff3ea25v4PKIq7teB/0EGuZ3ymTzuv1/TqN6D8tY7LlkwU22kx2/5XO6L93tdZyeLoDoX5HZHVz6SL4v19u4kiT22PEUG/JHiYn7HEJT3/sj7ajfEZpnD8SeSa7NTPfs8TM3SLfNyoENfhbk2RJ75IOR9P9JFXv0NhBnzx4O/N6CSlO2i2bDs6cQxJ6+rfeilSvbU0PXBaGJPTJ3F69A8wRZU0TJVOzRx3vZbqTnQV9XsLfOK1vmUGXDFuUhmE2xh/MkW55lvWAnvJtiDwsvc7c2eWXq4ReiFHv2qT6JyjttsOJcmp5e7Cn2iw9+Tew9dXK/wz2F+nTHBzwXLgGIPeHyVKlB7LGHmg9ij92WKX2A8iv2UENLouJdvsWeXxz2DRUszW4bV5Aqmg2xp0tDf3ry+EusbNlNFvVTI/T8+xV72KNqyc6Ftl8PMhV7eDC799SxagHvJPY8v2I+La6br94vQokepA5iT2ancfFkwY+XQZC6z/faiT0cLPzOOZ/TlNF7Ky8up4v7Jz71bvZ3r7Pcv/WYU7mO2fPoqNttYy/o5QnDs8fOky2qbVw76xpoee3nKaeb6UKl+aWZhbdMY/aEIfbcdNBFdGiPwYGqpxm3KNDDPm52Sj8Tzx6IPU0HLDjFBqqt3UU76xuooqy0mYX0+CH8o5P3glc/yHYN27MnX8Qeu4W7m9jzg5m3p2xv9SMk6oaLu2fPoW2+Q2/ueFFlWT963W0bF2/LmbDPvrYBk9et6Ri62NO6tifNPumqZu/TbSnMwxJ7zMZX1NCSGot3pfxZn1t7iT2t67qoeYBZN9hT/c9H/riZF7zXNq4kiT3m1i6JjeRjiMItOSQAsScC+Pko9gwtGUX/N/YM14WQqPSsMNspy34mLZJGpp49+gQ2TM+e0MSer0n63cZ17NBu6gjLfBB7zK8zUYg9+mkEpou/k9hzXIfT6Nktj1h12MmzZ+8dx9A/TjnVVezRTyCA2BO+Z082xR6/3j7Sp8kWQq5IptjDXj0ycfMaOsLexuUUeyFsscdu4hpE7DFP7lALk6evSeEmk3z+ulpZsirvxJ6gi0g7Bl71J+jvEHt2bwdLV6SKwrNHggybXg5iXz/zprDFniPaf5uuG3WayoKTZ494NItXlYyn2YzZE1TsMeelQdtp3MUevU/wK/ac3vv76kRZkw3PsaIQe3hu/+qp1+RU7LHrO4OIPfy8eZCJvh3KnJOyyLi1fkvKOCZ5MD2G892zB2JP0JE5HvcXhNgz/YFHaPbLuwO1Tp40kcaNOcqywNZtlXT1jbfQ6tXr1N8um3o+DR+6+6vd87NfoQcenqF+O3DYfgXrkg4AACAASURBVHThlJ9Q69LmX3Ikwbc+X0e//uj31ukt5vF+TqbX95zadTb6c/y1mvdNypXpNi5O54o+f6Bnqx6kC4ZMpJWbdlinrchENQqxp2dpT3WEIAcD0y/umHmf6sDSYbSmdo3qRHWvGt09M6jYwydUbGn4im487EcqFo8pHLm5nvr27Pm6MHEVezo09qYtRatS6o85gDlt48qF2KOf/uMk9pjijpPYw/u+/33SWRmJPRzLgk+saFHXlupLtqfEInJr3/xbPm/jalPfhe444lzap0Mf1W78bmnicovdxA14r/pD6METzgx1JJT2qbc7iD3NPZ7cYvZA7GmqknaCgd+6ZFep4+zZY26j5vzrk3pe+HCg5wdXP5jWaY52PN5c+zFd8s6tvmI/5PM2Lq8OLhtijxxVbrfQdBJ7ZJyPs9jDAec5vpK5RVmfF3vFg9LtI21+RPe96W/LHwitrnvVAa/fZRu1fp9fsUfi6EDs2UNth1LrGY9tXHbrLzexx61vz0TsObLseHq38t1YbePSxwX2wL9vzBSv6ovfY0Ag8WJPTW0tPfzIk3TKiROofbsKWr5yFV37x9vpF+edqQQd/v22O++lYfvvpwQg/v3Wv06ni35+NvXdszctWPQxPfDPGXTlpb9Sz7NwxNfZk5u+ithddu67fo67DiL2iPeQPqhlErOH0/lJhyvU0ey8T53FJN4DK/n2itnDz/uZtJiePfwcx+YxXZxFhZf/yqkLIpz5FXvOe2k6raj+MkVxl7LIfvwkiT2T+o2n8wZ/16qW8qVSPFXkB94L7xXJ30nsYRfXmw/5hRUYOxuePdKmuB7EQeyxizHFgqQuSpl9Q1xi9hzz7K/VEa12QQid+jR9snnbyF/RN7rsk7bYI/2MiG5hjoMQezIP0Owk9rxX9ZYVJFJvj2ZfDM8e+xqdb2LPcV2PU0dyy8LH/NCRabsNIpxB7OmtPlA4XV4BmmV+la9iz4zxU9U2d5ln6uWxi0enz4vTEXv4qPLn1s1OtNjD43/Vpi5qG9cZ3c+iB9fdZ3vcusncLUAz3xulZ0/nnYNpY6uPLbs4xSK1aydOnj1yQiU/Y65z9HRknLPbBhyV2MOsJV5kVDF75HRKYekVs0cXe+TEvkzHAjwfPYHEiz0mQjtxhwWciy84V4k55u/8W69ePSxPIFP8iYvYo0dQtxOWvAI0J1Xs4UXq9tr6nIk94kIr9cQpQHNY27jMgchJ7DHrbZAAzaawB7Fn9ylj+iI4rmKPTFog9qRa6KhnL1Cn3plbKfUAzYW8jUsWWObHADuxxxT+sY2r+Va2sKd3mWzjsvPssYuN4tdL2U/Z/Ig97IXLnr8zV72RlaPXo9jG5cXCz0eydLdxmTFR4iz2dKzrR5tLljXDxf3x9Yedpra5Z0PsGVQyggZ07lhQYo/EWMz0NK6oxR794y+/KwyxR5/7hin2yEm3coAHn44pp1jxh3Q/AZqzIfbImiEdsYd3CDxz3O+8ujj8HgMCBSf2yJatyT+cqDx77MQb8d6ZdNqJKV4/bC/T8wdiz24CPUv60oyxv3Ws1nYBmvnmTD17SuoraFj5gco7yBQtwhR7Hlk8l+5f+ixVN25VXxa211Wrr012p3EJBP0oSv3LAQ9aZw08XnlP8UBbKGKP2IoX0LrwZdYBidnDJyhccuhY+sFzd9CqxkWKu7lIkO0FHDh6Q3FTTBVzUqhXSqdtXHpMAq+YPU6ePUkVe/S2m1TPHl0EEw9ExOxpajm6oOtX7NFPtLETe6RNmqKjW4BmSXPu+LuoqKjIatbs+XFAp31cjwiXdMMI0Bw0Foj0/fxfvyfgBZ0fJlHs4TLx2PD+pk8h9qTh2cN9GosXfNiAasf9zlNzDn2hGZdtXHbxEjnPptgjx29LeXTPHtleLW0naDsV73ruZwrJsyfOYk/Zrl5U3XK1Mqkp9vxt/jzlkeTncvLsCSr2jGx7FE078gcpr7Tre2W8kZhdNxxwkTrFStqg3RYomYPI/DfuYk+QD4Z+bIR7oiNQcGKPuQ2LxZ4XXpyTEofHFHuO/c5oK4aPKfZUVtU0s87TSz+gu1bdS8NbjlK/Ldj1Oj131G2eVhz/yoXUtq4XbS9p6tjcnln05Xa6ZMnlVppjW/+Q1tavcHyX5MnMxN8P+hP9+J1fqz//qOxS+kf1jfTHA86nzzfsUGWQPEx86Y8p+dL/rafplmf9GXb1LGvVQj3av00/lW/9Eg7yX073sjcetcrHrOyu83r/hE4YsL/101mvTVOePTOO+Y31N8kHl3P/DgOsdPkGnb88wH/j5+X9YptZyz+k2z7/G/E7mZXd1a/N3vSXkRc1ezenefpe49VzbLvR+3am37x/R8r7uf6YXLwq0ZjOo+mX++/exsWcOH8PffGcZT8/3D7asI7+/MFTtLV+syo7M+NL6ibn7YbDTlV/07lI2lymWTX/dMwu21PswCzMOtCjxZ60tn6lakP8HrE3Pyd1WWwt7zdt58SvR8036b5jJ6k0+T3f6zlW2UGe13nx/39z3Sf02Y5lquzyTrP+SR2RvNm1A3lm0l7H0qS9xnmZMqPf9faul4FtKfmQPPt5kd52bxx+Pg3r2NRuPqxZQE8ffa2fJOjfi9+mB1Y/bPUzYgdfD/u8SeqC3g+Y9cUpKZ2L1PNejUPo3m+fq5hxfeJ6wJfenzilp9dLP/dLOk599U0Dr6Ohe7Sl41++RHkg2V3j2p9AnTrupIVblqp+3ExTuEh71vu1b7UZQ1eMPE7Zlcup51lv4+b4oLcFaUPM6tDu+1pjiP4+zpP5jP4uvfx6G9bTnHnUbbRb6iE67rnraf/2A60+yY6NpMt95Ffbdqr86WOF+YzYXPo5/t1vXbJ7v8nAZ5X2fZtT+rrt9H5bT9huPNXZm+Ov70y53OiHpdjgg61LlNhjjm9mPyf2tLOdnzzzc5N7TaL31y9XY6/MEZze09DQQA2NRCUtipsl79SOzRudbCL3sV3b1vVWY5bTpY/Pet9nzillruI1fvN7ZHzgtsXspT7ofYjdXPWDLUvVXEYur7mvcLKbe0kabAdJk8eNta3fVj85jW3ynDkf9KoDwov7htkb59Dk7mfSUX0HUPeytl6PRvo7z2N5PqRf0hbqGxromaUf0N9W24se3Vr2oLP6TqS/fPJYszlg8frhahuXzNfM/lbmn05/l3mani+e2z87/rfW/E7/zZxzSDt1m+dyelz/pE5ynZM6ddtb81znmea7pb7KGMDv1euIuc6x6yPt2qtd36vXa07n94OnqDWbtEFmd9/hU1NsKv2wPv+VuafejuzWX051XdKU98p9+njIawb594DKE6m803rV/9nZXV/v6PasKG8daRtA4pkRKCixh0WcDRs3pgg7mXr21O6sa2aBJxcvoDuX/40OKD1c/fZ+7Wv00pg7PS11zOwpVFG3B1WWNAURc3tm4apK+tXHl1ppjiubRGvqVzi+S/JkZuKWwTda6ZxZ8Vu6v/J6unnERbRsfbUqg+ThxOevT8mX/m89Tbc868+o07halahHpbx6OsJB/svpTp37iFU+ZmV3Ten7Uzpx0HDrp9Pn/JGqauvpyXG7PY4kH1zO4R0HWunyQzp/SYT/xs/L+8U2wpTfyazsrv5le9Pdh+3u0OXdnOaP+k9Qz7HtjhnSlS6ef2vK+7n+cN0Jco3tegz9+oATrUeYE+fvH5/NtOUsN5rcpKxSds63bivO27QjmuJW6VwkPS7T89UPO2ad7amz6F/eL6WsPVr0UeKlvEfszc/p3NnWel71ujSwxQhaUt/0RdO8OB1Ok99zcu9jlR3E9jov/v9vrP2YPqtapvgJJ7P+CSfJm107kGd+1H88/ajfhCBmDXyv3t71MnA9lnxInv0krrfdmw+6kIZ3GpTSHv2k8af3n6RZ618i6Wd61nyTHjx+sp9H1T3rqrbTf7/8LKV9mw9LXdDrs1lfnF4oXDgmFQfflqtLfT/a0GIZsVdaPTWJLOWNXWlAWT/68ZAjaWjXnnTfB/+ljzatoGlH7o7lptfLP4w4kzbsqFLPbq6pooEdu6nn7C63vnpY7wrLfnbPcnvhexZsWqL6cblMBtKe9X5N2hrnm+u73mfqbVwfHw5odTi9v3N3HyVtiNM6rMdgawzR38d50tuPWQ/18ut9YEqax/yFSPPs4fT0PsmNK/eRXA/l/s83b6FPtqyicXsPTXnMLk2/dcnu/SYD3xXf541O6eu2c2JkN57q7M3x12eWXG/zw1JssHDzYrXgM8c3s5+Tsd9PfbDLHD/H71i3rVaNRzJHcHrPrl31VNfQQG1KWzZLzqkdmzd61Vu2a9u6PVz7Sn181vs+c04pcxWv8ZvzKO3yO09do9hLfZD0zTmhlGvB5iVqLiOX19xXONnNvSQNtoOkyePGmq/FHhnbPqh5m2Z955ZmfaM5r/Gqt8LrWz0Hqz6Cr6BpeL0jnd95HsvzIf2StrBzVz397p376Z3K9xyTZrZ8mXNtEXtkvmb2t3bzT/2e0sZ2Kv6ffvHc/sFv/4bOe+PmZu8z+3ppp27zXF3s0dcD/M6b3/iv6zxTz5dev/Q2rtvXqU7/bM791lzSrr3qfe+i9WvUWM/jvMwrOR9X73++WmtJG+S550Ojd3+E1sdFff4rc0+9Hdmtv5zqqfTt8l65T9qd1CP596DtJ1FZ5/Wq/7OrDz/s8UP659qmD7nM9PAuB9Pe7bvRaUMPSqdq45ksESgYscdO6GHG7KmTjzF75ixdTld+2rQA5yvdmD1lu/ag6pZN4pLfAM3sWriw6j2qa1HZrJq6BaJ22sZldyRpkADNeiai3Maluzvr24lO6X4KPbbuMdsmm8k2rr4tB9HyXYsDdQVhxewxj41m2/H2NT55ii8vN3BxC3bKPPPzs41L30bDadlt49LzqtclJ7dwSYddZtlddmKf8cq1Xe7nOoRtXKmW09suux9fNOwkmr5ojto66Sf4PKd2w/zHlGt8ugGa3bbISW4zCdBsF7dETWjqd5/iYdZn6W/kWZ2FXi95K4rE++E66hanwykOgRy97pRPaZfD+7Sj9zcuJo4RIJe59dHuNC7Jk1OAZruYPSYPYcVpZXMbl5+jtvVtXFwPpbw3vvo6zdze/JSpbJ/GJVuWeItrOleU27i8TvtMJ79+YvaYMWfM8U1vK/rY76c+2OWZn9NP6pFDHJzeU1u7i3bWN1BFWfPTWf3GE4k6Zo/efwXdxnXf0b+kiS9cT42tN1nBe4McvS5jrVv90Le7OB1Pz3aQmD1227jsDu7gd2a6jYvTGFo6kn62/zjrQAq9LNzmpg47jUbvNcizCUj8Kd6WHvTyOo3rZ/+9mT6r/twxWdlKZPIVscdrG5eesNMpp3IPizO3j5xKl71/q3X6lfxmbvsx27ddAVLEnq/HYhlnpe/2wzOTbVz6/MeuvZrjKY/1EqZB2Mu/pQ3aBTeOchuXHqaAg5ab25r9xuzR25+cmnxg+SGuW6j92Af3REugIMQetxO08vU0LnMioYs9z4/9M7UtKUupOX4mHn7FHrcq6VfsoYaWVNHYTSUVptgjMV4kjzxI8le6V0+9xsp2uqdxOYk9boNfULGndW1Pqildo/LamfakjZTquuvVHYQt9vCXm5eO+1PKKQWcB4g956aYQiYxcY/ZM/2YKTTx5SYvtyD7rc1jW9NZ/CVR7OldNJQuO+gUmjL/KsWU+z8+ka1LmwpLDGPOEHua+l+JV2N3mqI0qHRi9vhZ3HuJPea4mY9izw7aRi+fME2hNIMN89960370rwkXNBtGvAI0p9PevcYqiD1NhOIs9sgCVfKpx0XUxwRZwHL/x0KzCDPSJzrVBY57OH3hK/TYmqdcRfWwxB6ew509dLStcCNthvtridkj+XaykbQxPyd+ZSLmRi32HNJmDL21YzYNbDWcluxcYNVL85RcvR442TRssaeopiM1tt5szVl4vZBLsccu1k4+iT1n9DqDfnrgqNDEHq+PV17jAH7PDoHEiz0SkHn16qY9n3IdOGw/azuXec9lU8+3YvTw/c/PfoUeeHiGelR/zslEMonhAYIvv1+/JTiciB9uwomb2CNfo/T8xU7s+XqxWV28wTb+RLqePeagnG9iT1FNJ/UVjS83jwKnuhe22COTNXOxn2SxhwXDV9bPUyKkePkwB/73ERXH0nVHnKgWUvoVZ7GHB/cPNy1XniU8Kb1/cdNxyhB7UltROp49nIIeoF081ljcWbu1llaXzqOyhq60b9ngrHn2NJavVQVL17Pn209PpTbULiWQsBmg+fnPFyovLfPKZ88ec9zMR7FHXwjZiT1ObT6I2HP1fhf78mTwmsJC7GkiVMhijy4Muc13whJ7THGG6+CyLV+pQyD44t+L6ltRSVEp7Sre7bkeVOyZ+dkCuvGTv6qxgQNIs3jE3sI8LqTjuecl9vzo5ZuabfMy5ycyh9H/Lp49+5V+kz6sbYqDJJfTR8xse/aY5ci12GPnkROG2MOnd536SlPIh3QCNItdzHWj9O3yu9RlN8+ehg5LacnOhZY3n/RTvJY1PXsg9niNdPH4PfFiTy4wx1HsufXdmfT4mqddceiePS8u/pye2fSEpaCbi32nhHx79niIGYUq9tgNbEHqcCGLPU4LTzt+XE+dtnHpJyCYYo+5rUzSlkWULIrt2oEMumcOOk65+EZ5md4R/C6IPecpsUu/5HhU/nsYYg+ffLOkdiF1LtqTNjWsowZt22O2tnFlKvbIRwf91Ci9/+eF10OL3qF5219pVoVzIfZsr62jcS9N8Vw0e3n2JEnsWbJtJZ312rXWYlO+0Ich9gTdGuPUz0HsaSIDsafpSHW/Yk+3nQfQV63eV8+YHmdmH27WVVPsMbesOo0BvYsH0r/GpQbT5fc7efbodVtOP+P+P65ij4gLelt1EnX0093s2nbYnj1JFXt4jnDXYb+mHu2btoDqc7ZMxB59HLvpzVlqHaeLNX7Enm3t5tOauhUQe6KcpGc5bYg9EQCPo9gjWyjciquLPfe//78Uj6R8FXs431W1dbbbuE7ueQJd9I0JKccK2004zIU8M9Rjx7h96ejdqi/9a8zu4NB6nBq7o9eTIvaMbvVDmrPT+TSuKGL2RCn2iAca25rdwGXbjryzZUMFzTl+mlWX7hxxlXIV5y98e7btpv5/3MSeFo2l9Mpxt/vqAaPaxnX5YePUdic313rJYBQxe6RcIv7ZwfDysNM9e0yRWtITAZH/LZ6b+r26sJJpzB43sYe3nZ289yj1lZkvfq8IlLqQyUGqZ3/3OguHKfZc/d//0Ibiz5rhyoXYs2DFNnWkrdeiuZDEHvGY0BfDsqC2O/o9iGcPxJ7donHSY/ZEvY0rHc8eMwYRe874jdljijM/mHk7ba3fYru91JyLObUbu/YgfQ33t6saFxELJCtqvqAeRQPowRPOpMvnPklrd2yg6WPP8TX+Ru3ZE0Ts8RoPIxV7dvWgypZrM97GNbLDCNpVU6bqjZ+j1/XxLx3PHh7zhZvMF5jTWb3OoXu3XKvGLolxxxUiLLHHjDXE5fUSe9hzp7rVl7SlaJWn2NOirq2K4+k19vqq5LgpUgIQeyLAC7HHHqq5YHQbNOS3ooaW1Fi8S3XuuteE0xcYs9NxE3vkXn2LQthijzkwFIrYs0/1SfRpWdMXBbsrTLHnrNl3KpfTIGKPuIXrAZrNhbm+jUsW6OagrL9Tr6N64F7TGygunj0iWvrpAqMSe04dfKAKju1nAZkPYo/0VyZTN7HHtEOUYg+/S8+Lk9hj5skUe377xiOusdb0diJtXcQl/q/fmD171R9CX7R4S+HU05w7/i4q+vo0Log9TbXNtBHHTYHYEyzYddIDNP+072T62/IHPGPuiTCY72KPBMe9ceQ5dOm8e1K2YkubceqX9D5c0jGDMXN9Ob339+nc4aNTunyzD5d5JS+k/33SWaqt6u/1GoOTIvaYH5j8BGhOsUMGYk/Zrl5U3XK1Sq59iw7UsLPc2qovHr96H6p7ZydZ7Dmi/bfpulGnWZ5FXEcrS1YpNvqHbPn/+jYufc6czvZEr3qP38MjALEnPJZWShB77KGmI/ZIShB7/FXUuGzjClPsueyQE6y9zHancdlNhGSy6nS6R5eG/sozodDEnsP69qEXVr1pVSa/J2lFJfYcN2AY/W3lPb7EHnFJdstz0NO4wvbscWqlEHvSC9DcvmZf2tr6k0jEHv7q/ucjf0x/n79AncYVh21cmcQ/iFLsYVZb6zZbiyMWaPmrbllRe2rVophO7zfB2iL5g6fvorYNXeme751M3GY5/pnpFSGLXRHUnQLcmotB/iI/7bDd3hD6ohqncTV5DZoc9D5JTvj0irmXFLGnRWMrFTPNFK1kYWqKLl5beU1hR8TBy0acYmH+bMs6umvBHHpL2+YqYk9pbS86b/AJlmcl52tUnwHUo7zCdYJ38vPX0lcNqYd1yFzvf6tW0FUL7qFt9JVjGiJWOZ3GlS3PHs6gPn5nU+zRAz3roJw8e5z+7sez56PtH9G5g05RH7LkMj17+O+ym8L8iNiqqJR2NtZaH2b0OEX8nN1OCxFj9HGM4+/Jqcm61y3Xf32deuygvekfS2bTqh2rVUweiD3+1lv5dBfEngisBbEnvmKP7sUjUfWz6dlzzLO/ptqibcqlM8nbuMIUe3T31rDEHhn4vMQePVgz1+ognj08ieOAe3Hy7BnQrZze27g4NmLPgZ0HqYW2H88et3hI3Ocy7xveerqZa7ZbbBA/Yo+Tt47+Vcvu1BK9FwxL7OnWZZd1mppdL8vvcdvGJXVY4gfxIpyPBF5cN79ZPXX6ssmTySCePRzQd9rCR1R2g57GZTKUviAMzx5Om0+x5CtbYk/Nznqac8rVtgOk1MV0XeKjFHvMxZG+iJE6pS+gK+p6Kw8GuzYrW8pbNVTQzuLKZm1fPzHJXAyap1vqYs8+JQdTeUkblVVujyX1FdSjpK8SqfjieCl86VtG7Rb7+tHrdn2+3le9vfYj+rJqA53Yv+kwDv3ycygG389bJn/UfwK9sP4l2qddf2pT14UaG4i+rN5A71W9RY0tdqoFGPN0upzEM7uDBExBUZ//6OnrcxTh4HQalzynTiNcsYiu/OAOKylTnOc4adxPsx0aSiqtk7v8xuxhXjUlG1T6XjF7+B67ckgdERHErl8yWTC3ozsfTVcdMtH6ifmObHsUTTvyB9bf7Oyul80cC+R0JEfjOizuRew5f/ZD9P7O19weVwz4KmSxxwmQk6jD93Pd5fGR4/BxO+TLSezZTuvpuK7HpRzuYSf2yNY+TstJ7JG8Sl1hu0loAP7Nr9jjtD3XFHuG92lnHdwBsce1KeXtjxB7IjBdJmJPcUMraihu6lTCOI1LFkH3fzzL9vQUvfh2MXvuP/gW4gVirmL26JMImZTw4uHKD5uC+pmX1zYufWLTobE3PXPc79KK2TPj29fTG8tXKOXeLWaPDAzmcaRxE3tMjyDzmHnT/l5fBpMi9uiDLde1IGIP38/1g0+6OPfAw+mCt5rqbC63cSVJ7OFJWNuSMnW6iXnykD6BsxN75LQUmYS7xezxGiLc2r88K54R+oRbXwDofb3bNi7eS3/JO7c6ZsmP2MOL4HqqpbbUVYkvpsggE0QnsUeCltt5zdnF7NGDnIcp9izYtITWVG+gXo1DVcwejosxsttg2lJTZZ2uo4MyY/bwb6bYs/SrKutLexSncW1v2Epzv2c/dgURe8RjRi0+SnvSmto1xIsNWZAMKz+QFla9F9o2Lp2jHqNK/q4fR8zl8CP2yLOc3vMr5quPIJ1b9EgRa9kG7J1RX9Q0J3ITe7zaqfzOi+tOrcuVJ5JfsYe/1OsCg3gi3fPxU/TepsX018N+3ez1fsUefrBkRw+qa7PWiuvBW072rRigWMjVrXhPenzcFc3ew2XYXvQVtW3sluLZM+eLxc3mSXr75IQW7JhHw9uMTHmPvIBPpdqjxUBaRR+qP0k/J32r3ZxQ/xgj6ZjzWL0/5vFIjml3E3v0I8HNuugWs4fvDVPsMeeXdn3E3Qvm0EOr/p1iJzexx4+4a8da5myTnrqfVpQ0bXV1uiD2OLPxEnuc6rmeoh6eQcZFs58srmtrHdggz+piD//N7aORXk8g9vjt7XGf1Z9XVdc2Ake4BHSxp7axRh1p6LVdwq7x6kqumUNzIsGT1jV1y1VnobvxySKIv+TaHZWrp2sn9vxl/z8Rq75+xR4WQXqWdbYFGtY2LrvJprwwiNjDA6Aes0ImBuYixu4+zgNfUYo9+h5jvzX0qC6j6A8jm75W8yX254CsTlua+D5zEu0p9pQcTLeN/Yl6h92XwT1rjqCVrXdPVM38mzF7erTpQkt2LrBu0wPUOXn2iK2dtnHxolZcWM33+/XsCSL2sAh5z0ezVEBGvX6Yk9gkiD06F6++Tb7Uz1z5huqDftR+Kv1j6zQqqxqk4q5UlX1KLIZcdtApKpC10yX1TCausrgVvkdUHEvLtq+y+PfvVk6ffVVF76//gl6tfCHFg0D6z2yJPdK38H/NAM38N79iD/fjpleFzsuP2CP3S78WVOzhsWbu1jm2/UlZfReqbrEhRRSNSuy5f+mzxCL62b3OUWKP1Asun10wVS+xR8YVu3hbnOYfXn+ePtq21KpfTluP+N6F69aowON6HAPhbG4XE3vodvhu/4OVNxSX4/LX/02LN6+jXmWd6f3qt6i8sat6xK4eSVrSf4YVs0evY3bjr3w44fu4HG3qu9LjJ/7M1bNH0uSxZ+222pT6xO8Y1WsInfr6BSndAd+7b+kB9OaGhTRpwDHqN7f24NiZfN3mnLbxmItzfRGmLw5/8d8/UUMjZSz2FO0qp8aWVZbYU1TTiQ7sMDRl8cceLV1a9qB+bXvT0X32o9F7DbLGeOlf9G1cdmKTeMVwP8GXBGx1WmTacQhT7GlovYnudoTI9gAAIABJREFU+GiGyoub2OM4Jnx9tLlTgGZ+ruWOHvTz/b5n1RO97Kbg7rWNy4/YY3cYip3YI+MXp8leTte+fz/NGLv7MA+9zG5iz6mPT6e1rVOPTbeb7+h9hvwuR6+XF7WnqsatKY/JVne7tNzmkm4Bms2xLpvbuNzqkF3MHslrELGHBVIR3N3WKZIXWXPx9spN9V9RZcMWxy4rLmJP552DaWOrj1Py6UewdOuL8Vv0BODZEyJjmTjI9hxuAGvqvlBH2HktiNzc7eyy6Cb2XL3/+TS6z1BrIsCdTrbEHqeJrEwC9UHCbXA3fzOD3zpN8PJR7OE8n3nAwdYXLmsgru5ODWXrAtVQU7RJV+z52awHaVHd6+rdujAjmTmo+Bt067hz1T/txB6viZsp9piThzDEHjdwXmKP/iVXT8fNs0cvM7e5TTVV9ODqB5tNYsMQe85+7gEl7vJld5yr3j/I5Jb7gHQ9e2T7obCQLRh6/eDglZt2NHlGrK1Zo7wuxu35LUtwlD6o867BtLHlx8STQr1+223lkqPR9S1afhqE3UTLztsHYk9wzx43sUdso7eTbIs9vIjaq9U+1KdsDzqg617Kg2NN9Ua65f0n6K3N86kz7UkbqSn+he7ZIycI6p5ashWITzC7bcl9RF+78otQZlcX//7Ry/TwkpeUJ8ijR01Tt3BMDlk0cEDM7bX1zU6g08UeEbjT9TiTMp7R/Sz6cOtSSzRwyrfXaVx6Oe3alp6uLqC4beOSNHu36UVbtxc1E3uqNnVRJ9XoF49vFfW9LZFC/xDgp1/Q73ELHN56V2eqablR3W567jFTnl9xX/rT126iuvoGumjwZNqwo9ISYPi5IJ49ki83kUnPu85bbCfPSj/n9n4W16mx0fLacWKnf3ASDjKeLNj+rjqJR79+O2wy3fLuTMsLSs0P+u0+vUznwn/fVrLK2j7iNWewy6MuZjp5fPM4c/7QE0MTe1iI5YvFNjvPHr9ij92hD15CsM5A5kitGtrRzuJtrtXfy7PH7mEne3jZya/Yw2O7bHP32gZttY8MAjQ7AQrTs8ernzTzIGIPH8O+sTE1JpN5b1zEHnPeJn0kAjQHHYGyez/EnhB5m2KPObG4/e1X6dH1/7QVftwmW3aLIDexR78/iGeP3dabPw25hg7Zu7tvz54gYo+b50quxB45StBuYqWLGmF69jiJPfK1L0gV5cnwkqpl1gQrXbHn+0/cR6tL56lXRyH2sH1Nzwy7iYwpruhu4l6ePW7cvMQep8lHELFH3MvD8uxhb4HFm9YqMWXKiw/RjhbrVRH9ij28rWP/bj3Titnj9MVTBnqJ5SAna/Eknr0ubj/k4mZij9jFj9iju/wLTz/twW5BekrHH9OF3zpUPS4Tcog9yRN7dOFY+ghzG63UIV3skbpgLu6dFkJ23kMXz/07vbd1Ee0qrrQeMz0hzO0wcqOIPSzUcMBjbkvpij1SFvPklDiKPVx+s49kZl5iT2/aj07ud3gknj26zc36oPcZp75wHW2vraNBbQfQJ9Uf06zjd4tTUYo97KHDAWBZyLQTex5f9pqnkOOnH9X7aOHgttjnDxn3L362WdJ2noum2KNv1/OTN75HF3ucPCnCFnv0eGdhiD3Mk0+H4u1y6Yg9flg5ij0uHxSjFnv0HRCFLvbYCSimXfVYQdnaxqWPH9L+nfLq5dDgp57inugIQOwJka2X2OMWYDQKsYcnIeNemqIGRD+ePXZij5vLrh26IGKPWweXrtjDX2L7le5D08c2ndjBNqmqraNXT206DcYMxmxu43Kb2Gdb7EmnanZt2YPW71obe7FHL5vdpCLXnj1xFHv0k6Z4W55cfsUeLhMHRE4nQLOb2CP5kMk2B7Wev+0dalVSRA8dfVlgsUcmMmYw7kzFHl7YX3rkqNDFHq8vncLHacLNvydlG5eU1c2zh0XLKfOvsuqvKUA4LZL1NDlAs9M2rmyIPbzYnv3d65p10cc/+wfaUrQq5e9OYg+ncdnwyZY3iB73QbyDC0Xske1/el/iJfZwvdFPWQo6Xrp59qQj9shWInk2HbGntLYn1ZauUUn4ER31+qF79nht2/bLSv/gFJbY84Pn7rC2QuqePX7zpN+XC7Hnk8ql1KK4SG2xDEPs0csTldjjxDbIHFwfx8LYxgWxZ3eA5lyIPbe+O5MeX/O06mecAjTrYo/MySH2pNNT5f4ZiD0h2sBN7OEF0LPrn1X7Oe0UULvFlDS0dD17FqzYpmIZ5KPYo+99lcWQvth126evLx7GPnMF1dQUF4zYo0+W5aufbKMIErMnas8eL7Gnc3F32tiwrllAZN4W8fqKpVasJHYddYrZ49a0dc+egW2G0NzKF6zb3SbZufTsyRexR18UzBg/lcbN+qVtH+Tk2SOLXt17Tp/U++my7b7y6m7QYXr2+BV7eHHPlwR61cuhx2dzC9Ccbswep60F6QRoTmcb192LH6OWRaXNYqQxg3TEnr8cdCVN/+JfCqEZsycbYo8p0Ikt7b64un0wsdtCoIsYmYo9Hev60eaSZY7CmvwQ9jYu9tocWDpMBVDXRRDmw3HUdpD7thOOvzWoxQjXbVy5FnueH/tnOvOlP9PG7Ttp/46DUsrJXNMRe+w8adz6u6jFHjvRy62/Mz8myPP6nFfqmunZ46dfN+9JR+xhjzAqKlKCk14Wt7ame/29v+lTKi8tcRR7pr76L5qnHbsufZzb/Muct5nltOtX7I5LD8ow38Se1rs6UU3LTWoNxe3roU9fVVvCM7nisI2L6ltZW4SdysL2Pm+fU6lLmwq6dN49zWLmybzr0n1/ThP6D1dx4/SPKpKuzINkTsDpHttvmO1pXHr7gNiTSS3L/bMQe0K0gZvYoy8gIfY0QfejZuuThXTEHp5Y8HsKxbPHnDTEdRuX3uzcJo+muGJunwtD7OlZsldKIMx8E3tKi1rTk8dfktKT2cXsyZZnjy72XH/YaSoWlZ3gXGhij9tQo0844y72HFl2PL1b+a5rwHeuA21blikRVYQ3EXXMGF/piD0X9juX5m59OS/EHplc2y3Y9GOXo/DsMftWu21cTl57fMLWkp0Lm1VbOyFVT3fME1dRTekatZBmr0Nd7PHjIcgvZGb8P7eYPbkWe37Q57s0c8WbtL26iA7otI9VTn3radDg0UHFHj1OTRSePUHFHqc+bp+KAXTy3odZMdz4vjDEHjnlUAJT2/Kub0WtGyqsGExOcw83sUcPbO0l9tjFMPS7Rc38sCsx6+w8tQpR7LFbD2S6hIuF2OOjEGxvjvXGfSp/9DHFQ3N7sJuHLM+bz53zf/TRjvfVUfJBxB6n8BLYxuXDiDm8BWJPiPDdxB7zC4L52ig8e2Z++gnduPTPalBdsG0RvbKhKeCu44BcfRJ9WvZEys+52sZl5tFvgGZ+Tp94Quw5V9nfy63bDOzs6dlTNIxuHT9FmSmdAM1JFHv007/0L47mgivdAM2mZ099Q6NyJ+fLjB/iJPa0aCyl+qJaC7/fAdrPIk0WgXZiDy9qN9avTTkRMA5ijxx5nK4HhfQ3fr7aJkXsYfvaTTbNhSH/W4/lBLHnGtsJugS2tBN72HuEPeOCXtLnZCL2OC0mvcSeIx/9vQq8HpXYs6O6zPLK+N6eo1UQ/HQuqcfSf577+jT6aOuSZkk5xeyRG7kf08Wenzw6q9k8ym/+7LZNefUbInDEWezhMsi4F6Znj87GKWaPGz+/nj2SBteFdMQev/bnMizb8hX169BNxWMyT43U02lf1I22Nn7lN2nb+/LNs0cKoa8HMgJgBBA3BXmnubM5b3Ly6PQSeyVAs58yeIk9fKLX8l2LrRAOXmKPtMOgYo9TXv3OJf2UFfeETwBiT4hMwxZ7uu08gL5q9X6z0ww4y34CNOtfmJ5b96IK3Ot2xSlmj5lPP+668kycxJ6K4g50/TfPVlljDwc9j/qpbXancWVSNUWki8yzp34g3XrCVJVFiD3NLZUNsYdjUbE7OV9+xR4zp34HaD9iD29VfWbTE1asCW6H4tnDk+Qe7UtTxJ6imo7U2HqzlSXxcsjmNi6Z7ELs+VLZjYUHsbVeN/TJbFCx58iKY+nVyhcsEd7Js4fHq0Gdeqgg5HaT5NQ4QM09e/iI5V1t1qZUcSmTnwDN+oLOT8BQc2uK08LAzbNHvFN4YTfmqcvVFj/+G58y9dy62Spgqz5u+B0TnMQefl4Xm/20a7ux2LSPPubaiT33H3wLXfDWza7eYPp73Dx7ZB6ji0l+uZj3cRrSfzqxyKbYo+dvr/pD6IsWb7kWbXz3MVafqos9vG3Sbrtoupz4OeGgn1QWJL0kiD2Dir5FS6s+c93GZTcf8stJ+hCJw+cm9vhN0+2+qMSes3qdQ//eeL9te5d+M52YPVKWbIk9/D47z/M4ij0izsvcH2JPGC0kOWlA7AnRliL27GqsbTbQ6h0Gf63jvez6ZTfR0AdvngzqVxCxx28RoxZ7zDIG2caVr2KPTK6/0WmfvBV7Hh11O018daqKNyXXQRB7XJuVXl/NU+fC8uyJm9gjiwE7zx47sccEaC6K9S17Ok8//ZndV167mD0Qe4KfxhVU7BF7eXn2SLBTp+O0+fmjuo5UgqLdNi63I2GzJfbcs/Rxqi5uOiVPLjexRxbRusjG5exU3pKW13xBV+9/Pl35wR1+qnzKPXETe/6y/59U/EC/F8QeIvMkNTt2TmKPl0eBXzuY9diPCOqUNo97Z/ccQaNebQrOHsY2Lv1d2fDs6bV1AK1tWZl1scft5Np0bCnP5ErsOXvWPVRdt0OdQPaNkoPp3br/BSrGXX2+R3/7alPK9vtACWg3u23j4tviJPasqVuhRFc7z9oOjb3V4QDpiD0DOnek19YtUFS436ksWaWEOr3sXtsG/X44TNdOeC4zAhB7MuOX8rSIPXYu/XqjsYu4H2exJ8jEwdxzrAPKROxhr4EvqzekbAtwMp0sKuR9uYzZw3nkSY6X2NOzpC/N3J6eO7odhzA9e+zSL2SxZ4+yLmrB6XZ1aehPG4o/U7eYE6psiz0ckLJb6862EyNzgOaTuga27+1LjDbLn09ij9438JaTfPDs+dOQa2hVzWrXo6bZBo3lTd4tfOy9XGEGaM6V2MNl2R2XoLlnT67Fnp/2nUx/W/5As24hqNhT2tiOOrXsQDyxT2cBqy9Q7BYqufDsSUfsGUTfokdqbkzhyduNC8WzJ25iz0GN+9E7RR+mPWPmenfujq/okJVvqzTYk7Ok7Wbbo9rTeUk6bUUXUfyMAXts6kNrWtdS69YNNOv4a21P44rCsyfIh9Eg7HIl9uieoiN29KT5bZpOoPN7Td3VhR4rHuDp+eYnvaSIPaZzQBDPnh7tW1untHbeOZh2ttgGscdP5cmjeyD2hGCsOV8stiKkc3J2Yo/eqZpiDz9/5Ye7t/hIluLi2RNE7NGPNzbRZiL28ISZLz0GhJvprujzB7p2xe+abqlvRa+d0PR1NNtHr/M77cQejp3Ss6wzrdqxmljo6VJbRB+0+CKE2tiURJvGttRYV6qCEnIQw7V1y9UpKE6XW8weu2cKWeyReuhmLH2RFaXY07JFMbUqKXbdxuW23cEUe7iN+hWjzfKbYg/HMNqv/QBasP09tUg3t3E58ZPYR5l49tgtvHXPnnwUe3QeTuxMsYfHloeWvEQDO3ZL2ULHz3O9OHXfb9BDn75CO4srI9vGpY9nvN2Qv+ourptvFUEX5/WtWnZldBN77O7fr/SbdNcxZ1MQzx4/22f4XeY2Lt3LQs+Ll9jDeTz3wMPVNie5dJf8IOOvOXfIZ7Gn+KsDaH6721PMqos9HDR3SNshGX3dj/M2rtiJPfUD6Z0WzWMa+Z20mGIPtwv9yGe/6Tjdl47Yo4/NQcQe/QOBPq6Yc8ygZZJtXJ1al9ND37nYCteQC7GnqKElNRbvalYEuz5Fv4nz6rWNKwyx558N/Wl16bygiJvd7yX26LEY5WEOjD5t4SPWvCtbMXvcPHv09eKAbuWO23/N7do81uhiD6dTXbxBxXaEZ0/G1Ss2CUDsCcEUukePU0foJvY4KbDZFnvsUAQdQA8tOo1uGv9tW6rZFHvMwGcyMY+L2BNCtQs1CYg9cy2eZowGc+HG//ZyZ3ebEJUXtacRbY6ga799XIoNpR9wcoc1AzTzNi6+5BhYPTG9T8lU7OGYIbxY9rpMscfk5lfskecyEXvsFt5uYk+6sVE4r16TXy9uaoHQ7zwVkJMvp/HAr9hT2WIV7axvoIeOvswSt+14mPk2J4FhxeyR8ouoY06MoxR7OIjp9Qf93DEOEI9XtaWrU9qzn0U2lyksscfOPT7fxB7mwQug0XsNIonZw14T+1YMUGzT8ezxEnv4nW59tZ92B7HHD6Wme/gDT5LFnjtHXGV7VLVOqGV1V6pruYMaW263vEF1gTrodmOTvog9bUtb0Iyxl+dU7HGqGV7jXTbEnol17en/Ne5BG1t+5L8CO9zpJfbYPSbrIg5xwPOadMUeHn/ebHzEVxlkTOCPAyuqv2zmVBCm2CMOCxB7fJkmL26C2BOCmZIs9rBXyKrGRb4pcdyfe08da3t/rsUeXrD+9u3pVNmwReVPFhlebrd29+kLL68Jp51nj2+gWboxrmLPoJIR1LNNFxXgVRbF/F/+2i2L04mzrlNbHoJcMojxABr06HV+j5fY45WXjnX96Onv/ibltjiKPV5tQy9A3MUeEb10+4kInySxZ03dF+qIVvaiEfsVqtjDtuYtlZMGjLHd/jZgxwRq17EyUrHn/7N3HmBSVWcff7f3wi4sS1l67yAKRlTEgqhYUGwJsVfsRj9bbLFHo1ExicYWYyzYQRRRRAUR6U06SF8WWGB7mfI9/zO8lzNnbp25swXnPk8eyc69p5f3/M5bkP/HY+/UPRCgfLlxBbRfiarDgj00ZcMx7+X1raE0e+T9lGGPHKUqHNhTv7sLLc96I2iNbJPShnbWHjL5sNp7rdZhaC9k+gvEXHHDQTOAwfPfLws7GpdcXjvQsSF99kQKey7vNJKuObCRhu0L7NVua/YYadaZjQH5EhZmZU4iu7EmUDRgT2Xcbjq95RkiKhdkncbQ7LGaO2a/M0TVs3JAu0Wq2RNJ2dRvI4E9WNegnaYLezpdRn//9XXXiopgLzi7cFQutW1l2FOWuM3QPFK91MGFSJe8XM2MS94zYrDHte5r9IRisMeFLsBEr6DdwoHt4abZ47R5ogV7ZO0EO9pGepo9Ty/5iD7dPl2rUkPBnmNbD6TzO58UVlQVp+0f7vtOYc/Qmvb07LkBM7loRuNSVYllyAYQefcR59EjS15rdrCHx57cXy8vmEdv7XotSGNA/p3NX/j2z03NnqfmTtcES9mMy0m0nqYOe/TmBgvR9wy4hB5bFupvxc58srrptJOGW5o9AAtJyfWNDnv0tMnMNHueGHaVuFV324yLIQRHPlT7Agdq3MzK8NbOIRvpqJo9wzJH0ryKWSHdbVRvs3HBsGd42sn0U/UMO0Mo6J3GgD0wTZ51xvOaZk8ksAemE4jWV59hDvEjhT08PtyCPZjHX64oaTjY02okTdsdPObsyEeOB5QLmj1X5rSj6zZ9SUfk9W+SsMfpWIom7MFBXl4LDyfYI+ZcfSGVJwV8y4Xjsyec8Wv0TbRgz60tz6Bn90x1s6giLbdhD9IcnN8jBntc76mmlWAM9rjQHxwylQUHK589Tx1xCx1d2FvL2Q0zrsRfR9OAXtWaTxuYBBil60KVDZNoqrAHC/qCfctpzr45Wtntwh580DG1E+VTB+1Q4ESzBwvp+M4n0j0L/hHNpo8obcewp7oNPXvewaga37wdouni5ADs5F3VlAXAp5xK6IByM27VGI2t2SPDnrdW/Cgcu/ItvpEZlxyS/LV1U8gt2MPpysKuevtj1Z74HY6gRXSN1ONCxoMTnz2c13FZp9LgVp3EzaZT9Xi7t7wsREP77vW14QlmTsavHYEzEjMulCU+sZ58nqQgzZ58KqK9tFUXCPAf3TTjcgp7UAb1gKPXVk599vCe3BCwx+iwGAnscXoA5TbjMZlS24ZqU4Kdn2Ksn9Z2JM3est7U2bfZWDXyI4S1yw3NHuRt54AbbvvIdeP+MQLbR8QNoIX+ZUFygyzjoZzdMrrSWv+PYp1yC/a0rDyS9mSYRyka5mtN8+J3BXVVtGBPD297Wpuwzc5WoPvOlWnZdN32OUGwZ3DmLnp1z5qw05Q/PC37CJpWttBRWvIYczqWZrQ7gk7evlDAimPb9aNnS2c43qfUwvJFjroWxtVnCtMxtx8AVX/qPreTpay6VkQJybr+S5FZiieVahNrRL6NDXtyanrRv0+9UqyHkKv0zm96/YQ1EEEThnduravZc1v2sfS3sh9cb1urBDGGllQsou9KAo7Q1UdPtpNhT5w3WYu8G9PssWrt5vN7DPa40FeykKDnzEsVXGSSjN+sYA/eUZ3Aqd/IsOcP7S+gawaOahTY07n6JPrPeeN1WzUSM65INXtQINU3AtIEFLNzyEv3taJe6b2DYM+G8m00dfdUS78BmYnpoj0qPFUujLboJOEY9pTl0gtHHUeerqdHrNmTXp9DVUkHbFXMjt8SOwk1JdjDmlEMe94/4THhvFt9og175Ahi4cAeblO3YA+iv4zoeqw4kELF/tudi22blDY32COv72awJ3nBc/TXvFTDIS5rdEBb4aJpL9huMzUEbiQ+e8KFPVbhXX9rsKdH3O8ERHD68DjQAyaAPdme9mGBHpTj5nYX09+3/0+3SA0Oe5KG06L6n5w2T9D70DKYdvZDhmZcQ8tyaUF2wPQbjwp30caFCZ2Es1gBe5bvpDUZn0ZUJiEz7u9Gvtz1punIh2Z+0ax/IilUW38e7YgrDTuJcb50uqxsMY3N7SnSgHZwnn8PfZUWOPRH+nTw+GhLYryjZGTY4RT2vFy+ka7O6kJDD2TSyPRUejppD6XW5ZAnwUuehPDAjAx7sP8d27YXPbfzQ0d1agovZ1fnkD850xY4aWzYI9a0LteK9dDuxc3xWacK1wK3pQ6ic068Thf2RGr2GG4/oi7Tdn2lRS1U07GCPfL7MdgTbi80ve9isMeFPrFj6iALXdBGeOe0G7WcjYT77Jo8KksNbK5OYM9RWUfTM8dd2iiwByrw7427XLdVI4U9TNztbMqqGRcKFAns4T5gdX9Z0wAO01bW6lN0F4ZXgyTBsGdn1V76cttccTtpFukAAs4/WtZQzSmTIoY9TioI7ZHc+Exa4YssCgNvYrm+ttQlPeBElB+z8YXf8ETqsye7Jpc+P/dJkZYKe+7qdT2d3nVgSLPIsOdfqz4VTnjxROKgGe2AB3NL3tg5ipGdtY0LmlqfRzVJpa5p9kBYGtH9xLAOpU5hj5MxqL5rV0A0yyNasMdJ/w2tyKcFmXu1Ysqw57jPbtRu+1DWdeWrqTypxLBK8rjil8zMuHgMRgX21BfQVfk9dW9Z3TTjMtoHItHssWtSZjQmowF7/phzMv3ngL5pWUPDniPih9BC36JIpi/hYPr5eU8Zw54DmbQg59Dhndc5zhRtnOlpR2XZi8ShcfKy2bQj076fw4gK34Aft61Lox3J1WHn2K60Ez2QNFMAEjxZ1TmU6fPRzgzjKKFhZxbGh6yZavdThj1ta5Pp4vhsAXsifWStLKExVHREVEyBIi2nm99n1eRR+cFzjpvpOkkL0OZvNUtswx5Oe+JeP134x5ebFOy5o7SGPmzfmzZWbdJtAsjufx9zLR3z49OWTZTiy6La+MD8tNqbjTTSLTOJvdAgLRCDPS40sx2BWhW6vhj9LLHGRziwR3UIBs2egZ13CnVjFi4fX/BBSLhdF6prmkS0YI+sWmikPSUXzC7sqYkro/qDi5lV28gQQIY9bhz2rPKO9u8MezhEsdUhwxL2eAqpPDFgk+3640si0gkJ6iQf+JcQoSXrWlH37EMaW0ijQWDPwQMG8lNhj6r5h/DZeBDqE1AGv7+w4mPypQTUr7OoVdih1/E9r02yyQfG9GvH/x+Nn32Tk2Y1fDccMy7AHn9Gm7DAWnODPS29XahjUi6N6HSUaEM9M5k/1bekuPJt9jR7LLQV9DqqqDyXtmYd0mBQfdLwN9AAWlf1K5Un7zbsbyewJ3ALnEHlCTssBUpZs2f67q9F/jd0upZuWH6HYVkSPRl0tSeOXkoNvW1vWz2E2mRQEDCwWvs4o9eOvY+6ZxeJ/wsZwEgYtjITMptgdsuipsEXRXqwB23YN6eb8A/m9vNSh2PohtXrCaGpkTdVFpKv1VLH0bhQrrj6DPInVZoWsaiuI21N3hxRNZzCHjUzUU9vMvmyttLIjJPop33zqSbZnqZqRAVv4I8BNXak1IWda7vSDvRA0iwN9oSdUJQ+xDrhSTQfb3LWOFRDyxLt8vvKMtN12W6RL9ibR+/lBy54W1W3pPJEn7hAiT3RbYELyurovexkks8ZdnI8a1c+3df+AJ1YlkllaYf2TnzbWJo9KNP8/HrakVimW4UuFZm0OyGBytOcrVFtEtrRTu92w2aJwR47I6bx3onBHhfaPhzYIztBNYY9LajsoD2tfPO7/NfldNOyN4JURQF70touEgJ4lqcNTTvrQbp81pOGqnwuVFs3idTSwTRjwrW6v0Wi2eO0vHpCsp5mjx37XM77cIY9+XFF9M9j7qBiz2bhSNrqkAHYc1V2KV0Tl0ytPB1od2KwI82supZUnhz5TZfTfnf6vlPYk1/fm5IS4qk4fqXTrILe5wMG/siw5+SM8TSjcjLB1vuC1D1Ud+Qt4huO9od/B8Ge9ICvBtn/D2cSFHrd04a6p3U0NHfQOxBC6Mmt60X70g75qoikwkOShlNczV5HoXshLK31V1B5YrDPETvlsAtg7fgFsZNfpO+wkIk1Znf5VtqaFHorh8MFHltmXHWtaNo5jxhqK+iVN602i6pTDt3O41OzAAAgAElEQVSyG8EeaFMA9rDWqV5aWfUFwIiaE04xTj2FNO2sh0JuQQXsSUwT7yb74qkuPqCxpvfIsOe1dVOJ/F662e+jR0y0jJDO2Ip6mpKZFJJk26pB1CYzPgj2YA+ryVts2aW3tDqDNtTH0Q8H5oloWlGBPZX9aUfGcsuyGL7gTSZKCD6gx+8eSGPj99Kn+eH7XzHKD9oO1yYcHQJ77GjjhlNJN/YZjL8vR1xEv1v4T90i9CrPodVZxgcjAXuIRJ0P50fPZMxJfQF7bohfRHfntnTyWZN995gDqTQnp4Zy6lJpVIWXPs6rj7isOKh/2jqgXSlr9keccCwB0xYw2h+smg39dX/SLDohbQSVKfCkUWFPbnlEYFav3m3jCmmH3/gCNwZ7rEZL4/4egz0Rtv/UDcvoydWTLFNRb6ke6n8jjerQT3xnCHuqc7QFpL2vFx11YAPNSMukgRmZNNsb7GwTsMfTKRBpysoG3bKwEb7A4QjVZBoS9uhVIT++Ne31HRLInB70DGGPFFkgwqZrtM/RFs8P+xMtrlhIr2561xL2dKjIpXH+TfRcVguhHaPe8mdX54bcdDRa5Uwy1oM9dkFBJPXBAWPaqbfSPUu+ox9LF5AnoVzTKIIGxwXp+4WJHB7AnmoqozTK1rWBt4Q99QXUI6W9obmD0TxwOj/M2gOCD56FCetsN5uAPd79phokthMzeNHNOkZaFnwvTKQOrKTylEPmVJyuI9hTm0fTxj3uCPao5X+o7+00qlMP8Wd57bYDe3BQIfJRWWrwbSc0Ws+f9nTQOI7zAsLEaWZiZu0YAnu8ddSzujrIzEbve0PYU9GP2mQnB80NwBBoo1g9qTWZ5En0a9oARrAHIO+HUffQiO8CDu2dPImVBeTJMDaXc5IWvytgj2+bdqgMJw2jbzBGn0ntGAJ7OtUPpV+TFriZlWtpYS2ePvh4Onr1Z7pp4nf1ICe/GCfGOpHfgSmKHa0l1yrYRBIC7BlaXxmVcdfYVbQaI3bLF4M9dlvK3fcigT3DEzfSPbktyZ8QDPugmbU7reEvPIdW5NGOpAr3YU9tCu1IqTVs+BjscXdMup1aDPZE2KJ2tHp0hc+8cXTn0aPFT3Zgj5xGnieDShV108StJ5Cn6FvxWoo3nb496o+GN1URVtnyc2iF3HvMGBrQuk3Qu40Ne0IKrnPraVY5Gfa0T2tLCZ5M2ly/Vhd2WDZSE3sBAuvdrQfRk/vnCPMmK80eFJ/VuvVuoNwSfqLdTPCd0z0hjxYmbYx2VkHp823y6J8PRX/gwyJgz0Xl31L9gCsovmQRnVieTeUppYbjLNWTT2+ddLeWfmFGVtCaAqCVS2m0NdkgjLELZnFWjSdgj8/rqJ3hQ2ZtojcEGFjl1Zx/hwbU+vI1mkanXJfT9idRZ6qgSbkphlXkeZfoS6TvRtxiyy7fKDE2J2TNM34Pfbmufo9uGfkdlKMqsZ48ScFO6bP8rYg8CUEaP076S4Y9r/8yWWj29KgnWpBtrpI+tspPU9LjQrJKrmhPvtT95Ek8ZOIVbuQbM58G8/01dGScsWNtJ20Q6buAPT1r/bSqvTtae3J5Ju6vpZdS2wjwIZtxQYNqR/qSSIsele8TvYk0w7eLTkgKdYqPDKOxlzU1yByVhlUSzdvfgdr5/bS8RfBFZUPkHe08Ej3p5EmMPACHrD0VjXEX7XZorukfU5ZKc7KdOwpv6U2jo6vLdLVGG6v/UrxplFUfR3tSIx+Pcn9amXHGYE/THv0x2BNh/4QLe3A7OqltItUPvMIVR8rqbWRWbUsqT2l4qszNeXXHS2hCv99prQvHv+d/e09Qazc3gUeNGsDCfVZtnjiMN/enR3wWrfUFzDjswJ4W1Vm0L62cZC0evrFsrI3OaR+k1KdQcryHyhO8Tj+N6H0904GCuHZU4t9O0Jq6oK6YLqSAFgyrCJu16RneLJqaUK5Fh3hq7nSaUvqR+B5Ayx8X36hjFICgorqW1mQaACed1oQPmQOJftNb9Yg6oQl+3KOuFRV76wzrjLGxJTNYW0auhjxGhtZ2pAUp4fsz6Z3ch95oQXTS9n1BpnQw4VyWmkB1KcaAJVrzP2dvLzqQv5pubncRvb7+C+hTUPfkAkuNsTP8eTRVJ5JQuGBHb+hk+VOpPE7/wPDq3mK6Ir+wSYw4yAo9EvJpdd7MqJYnCPaU96YdWauiml8kiWfVpVN5sruHI1mTGBdD3oNA8bdi9hVJf1h9G+dLIn+Efvus8uDfG0sLK1prqN16N4n3IryIshtaPpJ9wMi8MbE+PeSyo0m0aZQKEYM9UWpYl5KNwZ4IGzJs2FPXkV6umEpVE36MSNWei29X9TzC6tr+/PikPvTIKTeLuk1ofTn1pLV0367ZQd83N9iDG0BPgkerAzssO1w25aL6NNqaFIi20bb2SNqRMt9Wf8MMg1VYuU8Plzax1QBhvIT2melZSkOzOmlfy4IJblGmVC4Uvx2TOpJq0kPNeuRs+dYFMODd4y+kG1b+ojk2Rl54zEwRwqiCo08Ae4rL/bTdIpSwnOhvcQzh0FntSyaPYv5kt7GTPWlUlxh+xBw1n3iYWMXFkS/+kN+XrNp8XTMz+Vu3y6EdvCoLyZ9RTEel9KXlZVuoPt5Laf4MS1O/oQltaIHXue8nu+1u9Z7qD8nq/Wj/Hu/JJJ+kzRSN/HCAorpM0V/JtTmmcFDO343Dtd1DXiT1Rh6F1J12pupH4sTh597vP6YfDnxDg9OP1tZjmLnfuehZqvZWE9XkkT93QyTF+E1+C1cF8PEVsl5VtY7Id1JifRp5DspAGIdwvN0mrkdIZNJwx5cTSCXLVahnuHnqDRA35lhDDDzbZwQDKGT7+4aojMt5NGYfymORyxGDPS53sMvJxWBPhA0aLuyB07/b/Itp59GT6IGVz0RYCqK4g0JwxAm5lECHmjb0txOvofPmPEjDfcdRx5piei89EFWIn+a+EOd70mhvYnVU1Lxd6gZHychqmm2rBtKOdGu/FUbCVnPvW0cNF+bLCD1rGBHBm0zptRnUxuehDWm1IU5W1SzlWySEBu6T1a/JwZ5f9qVQdcumH5I4UiFKdlie7MukuvgKSvBkkNdBpBfRv74kguFRQ91gOx3Gbh4+9PJOqmtB9cmBiHN2n/T6tlQff4A8PtKN5BS3v2tEh+t8KqK9tJXi/Unki6sXfRRpZECruhVUHEMlmXPEa0neLPKUFwTVQfUxpnegdHLIDNqjy4uESZZVVCz5m4Ttx5C3XaC8Zo/RPEMfViXtMP02tT6fapIOAfCxuWNpyv4pdF6LS+mTxTvJV7BEHPq5bSKd03qFwR7XOr6jgD046KiyIB9+oGXZJbdAi7CHv4+d+heqqPFQej1CtgegPj8oK0AZyp/sy6K6+HLtv/wO6l9LNY76xao/mtPvRn715IiSXJ+E2jzyGmheJ/gSyBt/SKtXvmCAo3bvzgHUqtvqENgjyzc8ttK9LakqwVybPqu+NZUnhefI202ZitexcPocskYipQXNv3DSsfONVZ3PybqKpiwpptzCYtqTEXo5afW9nTJE6x0nZcuNKxBBAILW5gjBZiT1ksvO/47BnkhaNPrfxmBPhG0cLuzhbJPr86lOEloiLE6T+vzs2iL6JGUrQSNmWHk2zckNNnWKRHWyKVU0WrfYDV3HdE+S8LOBJ6E+g7wWYW/V8uHwF+dLjuhmLZp1drK5hl0O5fDXIHnqFFYOIwsHsTB3UR0Ihl1Hgw/tAg0cPOPiSByWITh64ZzX4VjTDkYH1fndBA8j44+jWb7vHTUPboTTvanCj43s26tz9Um0Ke1r7dBplqhaBxx685Ja0DbSifwWBmRweyxmHxhCZTmLRJXcbH+x/ngy6LSC0TRr12xqm5VLa2qCLwqM2hFapKu21tHi3ZtCgAPK2LLsGNpdMFX4W8PDpjVJtbl0Tecr6MUd5hcv3Lftqa/ol07e4fRrwk+BNbO2BXlTguGUChkYNrX0daU98QGNDs3E52CfIo8VVYvFIR/PpCGP0h3zJpHX56evxz1Io576ka4f1Zk+3DVZlEGNdKUehFPrcqlDejda6wl1kDxp6IN0+9wXtINbz6pxtGHvPs3/X+GOi6gkd2YA+OiYzfABF3XwxdeKd+RgEUb9lOTJoCR/TgjUUcPUGx2geyQOpZ21OzVH33zQWLqljG59dwV5O38ZBHvCGfvoq7jkCsP9jGHP/sSN9NVZj4ZEmVMPP8e/8RL9vt8RdPXQYfTmnK20ZEuZgFKLaoLXGtn8TTMV97YLcmqOdqqs9RiWDb9X+Q9o45v7AX/Hw1FI+6YcRZuqNlpCCkeLYTRfPjhH5DEum8jpQSAjMIS52S2jK62rO+S7ClEEy5NKqKiuA931u8uF30nZZxnGY01cGVFNixAn5BiTmGPI7/3R99C5XzwaOr51NJISqwvJk2Yc5YibUw9kyU1tBptQpgrffm2fNfMvZtV9cFtAiJ6YYByGWy8NW4Ev0L94Dq41Zt9gre+T053u/3g1teu1RluH5bwL6gZRSXLj+Qrj9R99V+/1Bc1XGWrL65M8nrkuWOMXly8MkpPiS3uTL69xTGPl8h6fPpa+q5oigHfsabotEIM9EfZNpLAnKHtlobNTtBZVBbQv3d1oHXby5XeicWPmJP+m8i5uj3x+P/lTnd1Eu1n+9MoeVJVh71Ck5utGP2IDiC8ZpEWFc7NubqSlmjrG+5LI57Ld/38Kz6Grtk6j2qRA1IJWe7vQ7nx7zp9ZbdzOGHKjv8JtU6ODvd3wynDo3To/l9ZVbhROpys98ZoQBPgD6MgmZ6pGAv5/y+oM2p0R8FvD6vw4mMVn7go53IRTxzPSL6S2+Vn08tZX6PiME+m7ym8sk8GN8JUte9HfKucFHb6Hlt1EC7KftwV7umd0EW3CDwTdy7uP1TQC5EIIYYuj/8TXE5wp/7DjkOme3vwWcD21lDL9BY4Fdb0G6Jc4glZ4Aqa5Tg/TIdoo9RlBgizG0t9P/L1Iu8JTRedOe9TyQIrDzv8NuITy4wvp7RULaa7/3aBio4w39jtHtOfg/B60vqRSOzC39efRe2cERy+TNSxkoRuHcwi2CKzQI6+Q0uOyadI3v9LEEzvRt2t30H8OPB2ASQl1lOJpGXTgY19ogCwTFwSiczH4uTjvWvq+9AcanNeTFu7cTsXxKykjPoemnXk/XTl5OtXFldF/zhsvYM+zF/aj19ZNEaCA5x2vCXrwp3tGVw0qJPiTyRtXR9gvpp9/exCkuDL3Pnrj51U0YMBuUbYb+59NDy94m5IS4nRhEfejiCJXuUGMK8Aeb+HPunshlxHfTex5Pj3zy5tUn3zoEkiFPZw+xvcL8+aKKGnQannm6BvprnmvGMKey05NFvOXv3c6PlF31MOXUWwYmQ1pdvP9Thz+3z7/fEvYw/02sEM2FR+opYpaD72zfgZ9tQ++pw498j5qBHs6JfSjcu8BoWWmPqjzyFbDaNbueSHzXIU9cr9ZLnKN9IK836RVd6TqtM1B66m8ljiBPWjnY1seRcvKVtK+xMC6y9+PzT6V7jz2HPE3hj0ox4sjbhbjjkEbZAo8GJes3cVjGFE0VSDC6cuaabYgiFQ2tRt4bDNs0usm9POS0jXaPts9eUAQ5HLStVnetiJ6Yriwx0zLUPVnJbeNKr9xAAEuu1572/E9KdfdiQZkPC42JfNmTue4rFPp+/Ivg/ZFlKO4rCZoLZHrhrUGDyIq640HaCx+tP2LIFgUDdcdehpEemMDeWNPWZP+Ed3f7QF6eP1DMdjjZBI1wrsx2BNhox875QbX1LjVhc6saLwBnp45gT6veCvCWoT/eTQWnPBL03hfWt20NUTJulWfTuvTPg8rKzf60Qj2tE9sQds84UEwbL7xvkD0ofS4HMcChtwYUMuuyVus/aljUg8RTc3NZ/YpT9OYqU9qPkQQbnZ7nj2nxEnVrcnrJ8PbWllAlG/52vt60bb41W5WwzQto4OTLOSYJQCfL4V5OQHY421LlbVerc63ZQyj73etowWZgQOgKvgAqvSo82qhtlt6u9CehI2E8TuoKCfkljycRsGa2q0gQ0ABCJT4r9UDn0Qjup8o3pUP23ZhD/r23J7D6O2N07SsrGAPvwhzDxX2qEIr+iwpIZ6SE+PFZ04Fdc4L5cQDE5qTM8bTjMrJ4v87PUxzG6Hf8v1FNLyoiKbU/kOruwx78Ec9QV7uE4CeD0+7lzIT08Wf5QiXOTU96UDqGlFGhj2ntj+a5mzeoh3acKh+f/S9QaY4evsxH44BYPQeaJbcsPwOMQYAq7gcgD9tEjtSQWo+LaqaSz+MfZEu+vz5IM0c+HIBCMCDAybgCecze10pVdZ4aHT/Apq+vISO6ZFH985+Lwj2cB9YwR4Z0KCMV0x/RYAc9O2fut5CT36xnmbeeSi4AtdT72LLCPawGZXaRlxGowMx//24D+8SsKi1tw/tSvhFjO/JqxYLcxr5W2ivDMwcooFBADyAt2MG1WlzEcAU2j71fd+0msZBY9kO7BkRdxGV1XjohT/0dwR7uCDryrbS5T88ElQuFfac3+E0+mLLgiDYxrBRb23isWcEG5AZa/00ddgjy1VY04ZljKSfqmeEwJ6K+ioxXhzBnvIiurzoErrkmCIxTwFP+fnTgAtpVKce2lwEVIV54qyr/yj6WYY98ZWF4pCOPjnjw7+RL6lCzFsz2GMFqPQGql7dABwAi0sSV4Vo+MlpXNbjDHpzyXxtn7V7MWNUjnD2EC6/Gdwygz2qxqAd2NMj7ne01v8j2b0cswveUH/13bzyHlSatTZIZuD1zgr2CI3KkkpD2IO6vrLms6CLg+yyI6iyKp68hda+Ne1cBOPC7Lzuw2jy7v9ZrpMJxUdSt+wiAXuwb2HPi2n2WDZbo74Qgz02mv+LGbPozbffF28OHtCXbp54JaWmBA6gx0++3zWzFSewhxeRy1reQm+u+tbw9slG9SJ6xQ1IEFEBovyxbO+dXNadOif2Fwuc+jQF2IMNQ69sdprIjX40gj3DcofSvP2hJgQol5n5B28eEJrwyLfTduqkvqPe8jgReGRhAUInHj2zBpT5nClPaSYaQ/YU0aKW9kLNYgxV13lDVLrhp6M+oZzkmztZ0Bjf6mL6ZPuXQTfk4bSP/I0ZuDE62NsFIyj7Ea0706w9s4XAdEeH4bSwZDN9Wr8gAC22L6dFtQGfHzLwaO/tTmV1JdTDm6DBIBkaNATsUW/Guc1QjruHn0njZ/0pSOi+MPUuerfmCVPNHoCA0wpOoVN6dKYbfzpkRmQGe3i9Qf56sEcVRNsnd6QD1Yecy4cLe+T6yxcNTmAP0sBhCr7qMredRr2yu9GzF/UNAi1OYQ8DAO4PGfbwvJdhDw4/eIcPbSrsgYp9anl32p+6Omh/t4I9yJ99tJzXe7AGe7g+D8/6mpbuW0cfnnNd0G+sLSSXf+P+Errz6MCNr97DGgfaHDjow8Ep7OF00IYwP6mo8VJhTkC+kZ9owJ6Zv64N8lnI/chyFa9DMszE4fbjsXeKwzQePfDG/Y+2cAp70nYPo7jcrVS36WjytVhvqtnz1Zl/pjqvj7LSU+jKmc/SmupD0F09/AAEdm2dTpkpibrtyoAWYw/rwQf73iAel7IpET5WYY88L+3AHqSBNaA5wB4uq7weqrCkems/w4Oy0QEe68I9Q8/TACvGjN6c47ZP3DCWvr3pDDHueJ/GgbuquBMVdNxG7427nC746DUqT9zmCPYIU6PElZRloXGpVw+Y4R3feiC9tOWVoH1H9U12OMCepJWXBAHbh/rergE5jBE9uKaujXjPSObE/JO1XuH7EBpMuNiA2e5VPc+iB9Y8ps1dtT9Y/pblIDbRU2GPKkdAc+fnTftoS+6nmqwgX+ghzQ9//Ya2eX7VlAtQt2Pb9jG9jOIyjqKraSa9bLiXYLwklAyi584dTt8VL6YPf1lJqenVmqUA1uGfS+eLtkC+y5a1oq7t/THYE6mg3YDfx2CPRWMvXbGK3vzf+/TAXbdRTnYWvfpmQDX8iksuFP91E/Zk7u9GNSkVtux3WbgGVcUDstoYjxuQoDHKbTdPeUHHgo0N3Ujo5YOD3VsEu2Ww+9641Fvoo5rn7L4e9J4b/WgEe1QIIGuomNmruw17+JaHK24X9qBt+PYO3xod+FkI1w5hScPpivbDaOKmvwe1tdH44HRVXwz8d8CuxVVzBWSSxyXa9+31MzTAFNYAUD4yAzdsQnRztyuCBA3+RiubgV8Zcdjvc4kQnPhwx4czVUNFFvCHJA2n9WWrqHtCLi1MDoQU5z7ErevArglR1+yB0HfAu1/TjOEDGh+wsDbI4wpmMf/e/0gI7JHfGZs3TjvUy2uLXHd1zDDs8SPaUe6GEM0eVRAdkN2XNu07FLLdCexJqU/RzBLlsc/q2+gHJ7CH5zX6fOuOVNpS4tNgD/srUGHPHz+YLHwfGT1msEcW+Fmzxwr24BuYpC5KfC8I9qCv1pRtoVdHX2VrmsnQAVo00Mr5csVuUV+AjqeXvStgBLQKjLSFjDLidQZlmlL6kdYH/P/5O7QNzHpm7PpOCOs8NriNZdhjVoZowB6UUU6X5wLLVbym8LqwuvIXmn7mo6JqTmFPWtGKIGfIRhAAh7Ybhx0d8P0DR8+t9AMVYMzLsOfxBR/QtF0ztO6ye9PN9efyyPNehT18UP2twp7XTryVZm9ZH6TphPaq3jCcEjr+SFlJ6do+yHJGC08XzUxLnkvqGmM1zwAboPHG4w5rKGTC3et7CR9a5w0N+PdhjbxzP/5HiL8YuY95DYb2J9aZjJREU41LrFmvr52qFRN7zxktx1JRZr4GezaX7aa9yatC/GbJsAcHd2hCqb6ibC1oFvKPWRp6dVffN9Psab/1atpWFAAW2CdmnRXsX80u7DHaq1S5Tg+gqvvz44NuEWcvjLXTsiZQbeJeunToQHHpw7IijxM248JaxlpkPAbwNzzQ1ON2ki8nef2TTfGcwB6WQ4z6B1pTCPID82Bol8LktEvncu3ymMuM8mLtkX+PafbYnTmN+14M9li0P+BO27aFNObkkeJNFf64BXvgkPLmvCH0yr6NtlTsZdiDyemq7yBuE1+SoL2+3PWGvmgYEgiV54MRMBp3SLubuywQwtkqfCYYCb3IGYthy8ojdSMDuFuy0NSsFnSz/F2BPeVFFL+3T4jPHhUcqLdyRgdPu7BH+LqBps1BJ79qpBautwp3rGAPH+TDhT0Qqm4ceBqNn31TUNPz3EU5vfF1QmsH6tjdkgcIp6N1Hh91TO0s1I/xwJdLr5RBIqILhAS0FzuIxe9W/lrCGXdqn8kCEgtkfHDm9PkblA3mKqd06kdPrH4pJHv0PwtJdmAPtEBwKAbsWle+lrpn9dAEVc4T68+AXtUaDNOrMwTERG8W1aYYhOA+CKegsXJUxwLtIC6bSrDQAzMImBXiqffVUJ+svpopiaxVgjn59ca1dNmxRfTwN99rKtfy2FMPhfLhj332yPMT9UDegILsI0sPksnz6rTWJwufPvw4gT2yM09ZAGYhD2nK44MPpPiuTWKnkEOFXF92VMuaPSqI4PLCZ42Z1qIR7GFVf7SpE80eI9hj9wDP5VZhTzhz0egbhjTsK4T7QG+9BcThsnAbc9ss27VTOGlOik81BU4NAXu4TEawRzZtcwp75MML2tQI9gyqH09XDjnKFPbwGqgHe/LjW9Ne3y7bZg1OYA/yTU7y0IyzHwsyVdQ7mJqZcbGsYqTZw36d3ByvZmnpaVvwOsdllee4rOmEv9esPkmYYwFgMMTA37E25/m6hUTTQppuwh4+JMuwB1o+MDuUH1uwB3K3LyXE/5wKe7g93lm+QIM9MBWFWwfV5EmGPWbygp2LShWKWI0Rdhw9iMbQEvqCzCKBmcEeWb5V13uUoaFgj5AvE8qoha+I/jL4OgF71PKocxpjDdqSgMFYr2VgyDIcNEHlejDswSXlzUecQt+v3UMfLt5Mnq4Bc0OnsOfV4kmG57hOpefT9p1pprCHfQLGYI/ViG+av8dgj0m/1NTW0t8n/ZsG9O+rwZ7NW7fRcy+9SrdcfwV1LGpvqNkjDqAIh54biLhh9fBiYeWbgNPRgz3xCNeZUCsO3HiMbqSsyiLnkbDpVC2yhd53sN2EzehvAfbgEHjX8SMsYY9Tp3B2+8PqvUhgj50IKlb5q1CE31cPH8OSTqd59QHfQkYCN36zC3swF67v9gctko5Rmrg15ttt3izNbrc4nXBhj3zQlNuOD+6y0MU3uCwEMAzBd39ofwFdM3BUkCAgwwKkA/Vz3O7LEMiqv+TfOcSvUZ+psAf+X67tfZauZg/Xe8netUFmSZy2HuzBgfPVFTPpin6jxH9lgZ21DViQls35ZNgzZkiG0LrQizqEvKH2/a9Fs3UFf/zOdeR5jr/J4EYek/g3lwdaEwBxLKzxNxi/0OSAH5GJJ3YWt2H+jGIBQ81gD9LmcaAHe/iAZhf24CD14MDr6OlfDvksMYM9WNNlwO8U9nA7OoU9aE887HSYHTTjb9GAPR+vWUJl1fVCc0fVoDicYA/WhHdOv8kQ9sjjza5mD49fGc7JDpqT2y3XDZ/OBz35cCRDJDPYgzVO1qqKFPYYwX7M/zGdB5jCHo7UpQd7eL7YBYNOYQ80QGR4x/so/sumWeyLqcpTHRTNjw/q8rvcb/gbH8zN9mUn+4rdd/W0LWCuV+uvEUmgXkawB/24fGFnXdhTVH0cpfizKa7VqhDo7BT29Cq5nl65bKBYm5O92UKDhjV7GPbI9b1i2pu0zjuf0ryttLnA7YoofsW+dULTTtXsQVsMyusZUl4j2DN/czHdtuIBsacw7EGasmnsY0Ovo8fnfSba0Qz2mGloynOe+8RO/3KdIZ++uvtZKkzLp33Vlbqh2xn2FCZ00vxz8V7VVGAP2m/Syk9oQIseQisz2n2cVpsAACAASURBVLCHfbnhYgT/Y99jKuwBHPXH12lOo3EePL31KUJrmL9t1W21aFecTzPjczXFAoyXpVsPaL7a8P5Oz2bNH6yseRSDPXZGfdN7JwZ7TPqEYc+pp4yigf16izftwh4jkxaj7DI9bendUXfShTOfoorEHZYjhRflJ7o9Qv3aZtIZ399CA5JG0IoD6wRkwtMQsIchgezM0O0wvHJjWIWf1Gu4oUkn0oJ664g6et+iX7g/Tkm5mG4adhSd8/XDIf5R8B4evFtYcyQVp1o7TbPsZIcvTEi7i96qfsLhV4HXowl7rm53Bb28/VWtXGjHr2oDTuDk9lULPvW4gEka5gSeLmldaFl9IPqP/ACePHb0eLpr/X2UUp9PSXEpunMI6X22YblWFswXvfQ4bS6bCnvkvtYr8z1zPxDpot5ndu0v5qb8cFvj9+rSVqLPWscX0asjbtfqel+v60V98PAc57bAGJPLjnTwoI3x789//Y62J613NA7UMav2mQp70pMT6OKOY4L6lb/hei8/sJ7uXvpiSDnQflw/Xvfkl7j9eHxgXcSDvoOa+s6aUq3fOE+06Sd/HCPeU9ub00b/X/nV24Zzk+vI85zzlMcuj0n8hnJurN4o1m354TEmv4vfz/nHYg32cP9Be2zKyIAprvzwmOc2ljV78C3yxVOzrb+AR2iHn0rWaO0izyvU67EBN9IjB7Ws1H5T81bBfaekHvTrQUfm8tjHuOQxqo4PAJSi5I7Uwl8UMsfkdnlvfjEt31FOj5zVXetjtDfq+NjR52lFu+/TddS/bRZ9XP4f3bmtjiPuA3kOooxXdj9DjNmLO55K32xdSbsr6gTs4fn3y55d9OelL1PP9N4UXzKQlqV8IH5H+lh/5DLZmWBcjtFZZ9KNgwPA1q2H5wnaE2Oe+0Cdu9yWXBYeG9w2KA+PN3Usq/NyZfUSoXHA45fT4jGJtQnjJ7X9ct1+6pLWiTZW/yrak/OS5yuX6cwvHxftznX5v+7X0LFtAjIYP2Zl5rqiXOjrP3+6jjp2OEDrsz7Rvjda/zH/RxX1Ed8YmXFhPqbklNB7o+4gP8xK4uPpuRWf0delM8VY2uXbSur8N+p3rj+3Jf7Lc5THJfc1+rhFSga9ecJNQXuZPC/VucPf8t7I5eB9pKS8VlsTuexm+7Jb45fTQR+/v+5n2p++PChp1KPSUyX+tsG/KGjMyHsEvn/9Kw9dOLQNLfXMDloDu5UHImr5CpaGrENqOxnV69GFH9Pcyu9o0L6JYp3CuMv0tBNtVlQ/jIrX9qK/nNVdyOHqfME6Le/l3K4YY51apom1COlincGeij5BH/fL6R5SXozj/20ORHnCw3PI5/PRmbNvE3OyML6DkK24rDy2Hh94Az268CORPu8Va6pWUW3S3qAyq2b1vO5BnpHnPD4yO6fI/nW4zn8ueogeXfMStcpMpso6r/heL5AA0i6I7yDaVx6HLN/qyQy8hqll4jIH7U/lRSLKHsK7Y47yWiPPIXmu8DzB2o/2RPv9b/MXYj9An2APVMvE7Z7v7UJ7EzaKtuuQk01T90wTefLaxe2orsXyOYLlP+yV7y7YqcEepDm8oKcYQ10S+tHzx1xJV8x+Rqw9eCDT/fuUQERL/ja/6y+iXdEen516t7aGYLys3FFBH18X0C7CI8vKsoyJ8kOW4fWU5QC7cNvt9SOWnr0WiMEek3aKRLMnHNjz1nG300vLZtGMikNRAeJ8yYLW4smq6knl6WvEv3nxerTzw9SnTQad8+Pt1D/xGFq1Jo/Im6zrWNAphEEeVpo9erDH7HbAqLlVh3JG73WuGEObMoNDlVoN9SvbXEb/3vm61Wu6fid4swFkemLAROqUn0YTvn8mZKPDe20rj6W1Oe9R6+ojaVeafdgTTnvJlcH32EBPTLqI/lcbACNOn2jCHrX9T0q6kL6uf1eAmXbJHWmjf5FucT/+XcAmG+2Np3NqZ1ruCTjuxYPv8dTvb09/OepcunfT/WLTxaMniCC9zzet0MYC5oucnlqIcGAP8rj/549Euqj36Z37ibkpP9zW+J0qCunf5Y+LA8I/h9+i1fXu7teK+uDhOc5tgbrJZRfpEIl6yf92MgbUMav2mR7subBodNC84m+43ivLNtB9K0LNuNCuXD/8G+ue/HD74W+o58NHjTP8nfNEm06++BTxntre/DH65tpv3jWcm5pmT/rv6cpBQ8Rn8njB/+cxyb9tLt9D1w8ImPjyw9/I7+K3819ZpsEe7j+9+nM/47/cxjLswbebajaJ7Bj2oB3m7V6rjWf5nao6L/2l70R6fF0gqpjab+o4UWHPqBYn0Mx934rX5PmFccljVB4ffGDonNpJHIp4jrFgL7fL5IW7aOXOSnrwjC5B7a32+4NTN1LfNhn0WeV/DWGPPI64D3gsjv/mr+Tz++nyrqeJMXtB0SnUI6UXrYCAW/WaNv/ktkCey1M/oKQkDz3Q7wrqnd/ayZQKqg+Xw3ECJh9Mmj9frKNoT4x57gN17nJbcpvwuiaXiddYdS6q2fPeJ49fXo8wJq1gD8bEppoA7OG85PnKZTr366cE7OG6PNLveuqb3TWoOGZl5rqinBjvD07dEAJ7jNZ/7E8ntO8jvlFhD8Zwqqcl1R4ooPQWu4PWrhdWTRXzhIGJOv+NupLrL8MenqO8L/CaiD7um91NrIny2iTPS3Xu8Lf8DpeD+62kvE5bExsD9hity3I90EbymJH3CIyRN7/20fghrWm5d4623uD9rmXnUEZyPJXnLgrZ6/X2Fr0+4n4dWDpRrFMYdxn17USbIY3VS7vRg2d0FXK4/KCMmBO810HzIo2yxRzBGAPswVqEdLHOAPZ4qZZqq9Opb4sOIbLO2V2OoPe2fiWy4HHB+bH83zquSKwJXFYeW5g/Tyz5ROTNewXPV7nMKuTD/2e5S57z+MYp7MF+8eeVk4JgjyZf1+cL8MSaPa3iOoj27RI3hKo91bQzYRVdnHInvVP9LGX4WoXIDLxnqmXiMscD8GQFIAj2UsooFusLxh6vNfIckucKzxPMObQn2u/drdNFu/Df1H2c253zl9eh968aoMl53I7qWlydsIda1gwUbcDyH/bKyYt2abAHaQ5r1UOMIR7L1/70nAZ7INP988SAb1n+Nq/LLyJNtPOHJ91Jq/buonc2zKH+CceItFE2fuT1BeU7qrAT/Vz8q5BpIcsw7GE5IAZ73Nzd3U8rBnss2tSJz552/r5U7N0sbr6cwh6oOn562r2iNKOmX6+VSlanfa7PE/Tn5S8I1TtevPC3AUXZdP/c92lUwTB6/NNi8a3ejZRTvyzhwB5/1hYCoMJC6uSRy2YGfnpUjqO1GaHRsJAXO/dU872h09X04q/Gnuj5fT3oIttYc/+cNe3REL9KeK97xThalPMCtak9inam/Gxafdk2urDsBCrODhymwnlQ7tbZKTQmfQK9XvKSoV0up41xuj1uZVBWdmEPQl3XUq2uXykjMy61/cfl/pE+2v8foZbdLb0LLa79QbfaM0cHQAHaG4/6LqukV5a2pL8dfxHd8stdps6Tkd5Ha5ZoY2FwyrG6efM4sjLj4vC1cuGRx+2z3hHpot7jeg4Kms94t8P+s0TUBfyOOqHcUP1/+8S7tKSWbS0Tf8fDc5zbAvNfLjvSwYMxLv/byViSxyzKcm77U4PmjAp7YEpwSdfTg97hfuZ6Ly1dS7fOD3UYjnb9S/8btf7iecXl5fbD/0c9nxl5UVBV5N85T4zfr645S7w34ctJIeMbf0ff/P6zNwznJteRy49v5PEir9Fmbcvf8Pjld0f/bZ4Ge7j/jNLkMc9tLK+P+HZ9VUCzh6PQoMxzdq7SxrP8Dsy9/jb0VrF34FH7Ta2LCntObXUSfbk74ByZ5xzG/jMD7tPGqDw+eN50y+hMWZ72Wpn473K7/HfudlqytYyePj+gtcFtp/b7n95fRYOKsmnyvjd01x61HTkd7ku0J9rh+j5niTF7SbfTRTvwPFPnH8ryj28308cH3qS05ARtb3Yyp+T6yGPKaRpG7z89+0eaVvlfMa4hM8jjV97vuC25Tbgf5DLxeFPnopo3733y+OX1CGMS/8b4gTNkPVNBjIn1lZvEOOK85DnJZTrp44eEDMHzW51LvBbiv3pllscR+vmOyauoc6fyINnBaP0/LeMPdErHfuKblt1WBa0XKDfm0m2z3iHKLKYPTr6L6r0+Sk9NoqcWfSTmCZtx6ZVZry9Z3pNlDZ6jfVKOpBdHXqbtKejjgS16iDVRXpu6Jwyl4rqdos3VfHm9lE24khPiaC9tE+trRa2X1lHAPxzPAzltt/z3qKbC3BZG67K8BqCN5DGj7gH/+ryWJhyNteb7oPUGMhnWDfnvnK/e3qLXP9yvQw7cKNYpzAFAbMh4SGPFoi701/G9hRwuPygj5oS81+F39BHGWJdW6WItQrpLkt4XDprxQJ4Z2B5lPiQXoe6QJd5cHzB/l9er2novjZl5oygLzLiwJnBZeWw9e+QtdP9Pk0XevFegbB6qJYAFflTzPVlGG5Z6Ms2rCYS953oYrU2yZg+nCTnmtgXPClkVY45N84SfS28X2ucvIX9dAJh1Te8qxiTqhAdtcXn2vWL9N5rzenI5z3F5/5RhD8YerzVyvVRzRx4raE+035sbPheyG+ahOjZRPm53zh//vXPIOJqxcg/94eh2mkzL7ai3FvMYY/kPe+Vbc7dpsAdpHtOmtxhDXL7ff/ME7fRsCYyR2qPo7TMvFf/mb3k9wzry9TkPaN3Hv0+/bZj2N/TRG0vmC1n9+cF/pn4FbbTfIMvweory3briz/T92FAtbqPxEft7w7dADPZYtLmTaFyyx3Ij2GMWiYft5VWP7yw0wXbz7iXPBQSqrScIDZ7nxp6khY4Ui8xTP1JGciKV5S4IMeOKFuxBvnE1eeTN/0UQczzhwh6AnnikVRisGcMbhll4cSMbfPjqeGjFC+SLqxO2quzIV+16K9jD/QObbUQzwONP3Sf+i/L1rDyXFmQ/L+y4Vcd8ZnlxeOZwpz/DntMz/0iv7XjFsu312sku7DFy6Iiy2/XZg/5A2GUByDK6Ctt0BnzQoOKIGlY+e2TY8/yoPwjbaSM/OSifHMEEgme/9MG6ESlkoVuAVYNoXLxRy/2GPNh5pFwWua539bqenlz+Jj11xK2U5s8R5WafIZwWwvRyhL3Xj/wbdSsICEHs00v12YPf4EgYbVtaXWkajlNvnMnpoSzndzgtKA092MPhszk99p8Tjs8euUyy8009vwr8OxwlXtNzvCgnnKq+cPZJIhk1TDGnjb6BHwV2fG00J7n8+F322YM+tBMxSfbZI+eh57PHKE07PnuQNsMe1Q8Dz1O8A8jx/LA/ib0DD/sBMlpvVNgD584cZUgWgmW/UnqwB98VH6gN8r/E0Tw4b9lBs9zear/D91FhTqq2/6llV9tRju4GX0poT7SDHI0L7cDzTJ1/nL6ZXxg767VaDjvf2H3nie9mC58KVg6auS2NHDTzuoL/Wo1vdf3htVLeF8xgz4AW3WnZvnVB/ldkR7bsn0J10Kx3axwtnz0TWl9Owwp6C5897OOC+4THGfvr+nj03Vro9bdW/Egvb35Tgz12b7pZ3mO5AXnwHFUjpmGewZ8LfPJwfyIQwAU9h9Nd814RsqGarxxtjeuRmZIgDoVIHw/7SON5oK57Thy6G41fFSTI67Lemi2vAWgjeY7L72Pte2lKta7PHshkgzpkBzlu5nzt+uzhKGv3dfgLje5fINYSQGzIeOwvSM9nD/zQ7akup/z4Qs2vC++Z8AuFPR17l+yzB2VD2nDmK/sTRN2xjnE0Lnm9qq6tp3FfPkYnFAwnX12GWBNUnz0vDL+dSstJyF28V8BXE48DeXzLfY18h2QNoe+qphDKjLTDhT2Y2zfNe1oX9qAd26W3pK82rqH65D2azyJ5fMJnz3t7Axr6euuUqYPm3QO185AMezBXeK0xgz08VjAO2Uci++lTxybKx3Oa5Sp1rPHahXfZj5LsoJnHAcaYHZ89nP750x/VYI+cp+qzB+vId+Mf1qYq/45oc/JjJsuww3uU76W5i+hfFwRrONvdx2LvNUwLxGCPjXb+YsYsevPt98Wbgwf0pZsnXkmpKSni/8vRuGTYg0PrkNTjaa4/EKrdaDFVhQh5oRAT3ttOuyGTYU/S6osE7Hn50oHaQRDv40AxsCiHFpcvJG+7QyYv+C1asAch+/BAmygpu4S8Pr+t8PFyuzBsMAIGkcAeLOiXz3qS1lVuNA0R7AT24OCAh6GWHuwxM8+Sf2Nh3cZQ1H2lqcAeCKuIIqWOedVBM9dXBiDc/0iDBR0V9gzO7U/flx+yWe+ROFREr8JNmBXs0XNSKjv61ZujKF9FXAm12P87KitL1aKMqbc+8rd2YI8sjBsdNnFI5ihe8vtWsEcNyWt3TIUDeyBwqc5VIbw6gT0FdYPow3OuCyqmXdgzeeTTIgyvnCcSMoM9Rr+J9bGqtZjPhwPsmTT0QXHQQBSzSGHPuW3OpA93fib6yBD2SMI0r9Vuwh4eIEYBDGKwJxBtjMevHEGuKcGewfk9aPHetYaw54uTJlFmSqImV/HeEQ3Yo4ao5zGGPLE3OIU9fDAK10GzU9jDewevVzw3Ghv24EItNSmBqqnMluwr75nyB9GAPXIAA6ewR5ZH7MAergv3E69R2DONYE9CfByl17XThT2PHXOhFvBAhT0+r48y0lOIAbAe7BmU30PsjRwEwS7sOS5nlIA8dmEPnHzXrz5d00Dh/cAK9gBg3vrOShGGnh1UO4E9MtTkMrNck1d6PB3IXhiwupDMuMKFPQxm+Lym7j8oy9KKRTQwc4huwAE7sKdjSmda4ZndLGDPG3O2CofgsafptkAM9kTYN0awR76d0TtIqtle0flCurTPCeLPRpo9EIQALXAjAzVJPCqJZdizdMduGtrbT4vrZmhe761gj6r1YteMC7AHYS9x++vP2KmpaDpp2qYAe9Lr24ZEEJG1PGTNHjuwJ7vsCCrLXqjbDHJekcAe9Flcfaa4Lbmpy3V075LnHGv2YEP07+5NyyoXhWhUqYXnG5ipu6aHaEip0QF4PLHAzuZRZrCnX+IISk6MFxukLFzlJOTSuV2ODdI2we/ixt4G7NE78FjBHgiGB7z76YL8y4Q6Kxzhqgde9cbTDPY8MewqWltarEVtQlpmmgW8DhzOsAcHnPfGXR4W7JHBlgxoQoDOwZDqeoeKeH+y0Pg73GCPPHcihT0yrHUb9sjRytAHVqHKI4E9dR6fFkEOkW0OJ82e46bcQJn+Au2WGLCH4Q+vfTN/XSvgX0pcKu2J3xAENe1qMEWq2WMFe3jcslxlph0TqWaPegnhBPaw1sYxbTprmj3hwp6zP35ORHZCiGVoHdrV7Gko2IOoWDDbrJLMfcxkO8iZVw4dJLSc5Ke1tw/tSvgl5FMj2HNczgn06IiAzxG3NHvkS42mCHugaRNX2UYX9siXK+HCHm2Mf/M2RQv2YPzWrD4parAHcpscqVGuE+RGyG2ALABU3N+Dy6+h9elTAy4wGgD2oExYn1jG1NPsSfSnkCcu4BJBll94XeNvzTR7AO4mLnhQ1BNt0liaPTHY4+S02zjvxmBPhO1uB/ZovmR8SfTD2Bc1Uww5a6hZgrybwR5sitd+/SqtrP3ZGvZsPSAADNQe+UDasvJI2pNh7DhYhUEa7GnzswjFC+/1fCgaU3CKWExZwyhc2MNtw4KXHc2edTRHF2jIG7nctmaaPbKfHz01Yz3Y86evPqMFv+4nf4v1upo9CKv5a8JPQeGVUR7Z9l3OS1XDl8sOs7UNO+vJ2262ri8edmgH2HNv7xuFmqyVCZ3aTvj/8SWDaEnpGg1oGE0L3lTkcc/vqrBHvWnmfM1gj3yTw4I+Dikt07IEKMEhBho9YzoMFdAkmrAHZYFApMIehkyotxPYo6d6zAJ79+QB9NrJE4OavTFhz209rqQ7fgk4iMYjj1f0K4f/HfPFnVThOyDeCceMqyFgDzsM1jtUdM/oIjT+eF07XDR73IQ9D/W/kR446O/HbdijrjPRhD3Ii81jDgfY8/KCefTWrtcEFOcDAh8c9GCPeijSO2BE24xLD/ZcN/0tcYONp7nAHm7L2tr6iGEPa2Pw/og5NrHn+fTE+me1QxwD7JOzz6Y+LQu1C4OTPnqQ7hlyMY3q1EOTK93W7EG5Lht0pKZVYiUyQ4a7cdjRIabEstwha9gYwR5oBt49NBCRzwz2QIvxljc32TLjakqw57ReXenVFTPptIKT6a/rXhKXXHZhjwwPYMZlV7NHXgPswp6z244W6wxr9rBGtZFpH84yt726J2zYszp+Jp3QoQdNKf0oyMyQLRusYA/ahkPQc39D02lNxoei+jB99mVvIV/eKrHehGPGZaXZg3ysYA+0w8oTtzmGPXG9P6K6+HJtbcDc4LkSCeyZvbaUXrlsYND0tmvGFYM9Vqti4/8egz0R9oEd2MMaArhhhZ0kFgF43ZdvSvRgT5I3i1IpW9yqQP1Q3hQtNXt0YI+VLxkj2IMm8nb+UoMIOOxpsOeghlG4sIcXYyewZ+OmLG0jkbvPFuzZPZBatCqlvRTwzC8fYtmXjJwm/35D29vpgsEBGIcHGlRym8hmXFwOtTxyXvl1vWlvYgCgabCntDf504uDoA5gD+or5yWXL1zYA7DDUCgc2AMhszZlZ9DsMYI9aFfcKPNNhVPYw5noHQTDhT2qjxO9PncT9hTEF9GHYwKh1OWHYY/eLaMbsAfRY/D44+tDwpwazR3cGgIcss8gdZ7IsEcWLqIBe2BqcefRo4PajA8+djV7IJyu9SwIWj85wcMJ9izeu4a+PxBw9B4x7DkoDCMt7E03/hSIiBeDPSFT2PQP0fTZIwvi4cAeHJIHtA443cT30Pj5eOydpvWJhmaPrInH43b0Jw9RbeIemnVGwKm43hOJZg/83Nx4xEk0YcbjmuYz52HHjIvfjRbsYX9YvC/Ia55Ze1j57EEf1/prNF8t8uUK0tXz2eMU9kCjGXBINiVE2ipoYZ80kcIefA95DDIo0uR0WSaTffY0BdgDH1XjOo8Ikid5LAPyVVTF04+l80N89siaPQ0JexiQ8/7OcpFT2APLhHO/eJRy0hKF9j9DG8g5XB+YcaG/0Jf8d4wd9Gk4sIfLzLAHeS5f2FkLYGMEe1Ae+BCatXuegDFcPvbZ05iwJ7XX1yFlsgt7Hj6nF9299m6h9Sn77DFaU2Kwx9l+35TfjsGeCHvHDuzhDRUHbIY9yFZeLPVgj+obRN4U4awMTrWMzLiQ/rlD29DTK/5Dpb4AQDhcYY8RXOHDCTZJ9tkDc7FThvk0h6MAcTs9gQhqDCHkIYE2Ky6roe8uPRQhDb+rsKeFpwt1rTpDOGi2A3sAcVhDSYM9knopl8EN2MMhjzlNlE+GPcenj6XK4s6ONHvkQ76c7rFt+wghD2PXW5NBVRlrtYOnEFir5grtNmyaaPuC1HyxkXffcwWta/lq0E2OekPZkLAHgBWqwG5p9ujBHLRbtGEP+qFNShsBO/Q01+S+k510NhXYI2sfaIcxjKODZn56h2nVjIvnI8OhLzYto/UllcJhowp7oLEEJ5q4KcfD6cMZ9N2DLtX+brZtwMQDN7Z6quY/bdpF22p20A87fhF1UG39OV3VQfMZuRfS1P0B/28sbOPfsoPmvLQMenjpv7S1DL+zY2KnDpr9SRWaz7fDGfagjbAWNUcHzcWV5cJnFWs4skyhXZwc9OGjrj164IBvofXGrDzWncCeel9NCEjR0+zRgz12zMrswJ4JbSfQqHaD6eo3lhI7FEV9VD83ch0bG/bgIuihIy4XsN0J7DlnylPCPM9MsweHXexpeakZtLl+rS0HzU5hD2Sm8b0H24I9vP7p+VKDr7BbhpwuugZzVPbv9tTc6ULzA0+0Yc+/ls6k/257LwigR+KzR2/vUMey2h6yvx/eA3iuRluzxy3YwxqI0F5q6rBHXR8igT1i/LbtE2S+z06+jTR70EZtEjsFuTNgJ8qRwB44Ese64gbsOXZwPc2s+5+AcDHNnghBQgN8HoM9ETayW7Dni9HPUmZiurax4R9Y4HHwWLBzk9jYnMAedpaFW4Ri769Ci8NN2DOu3Rj6YN8bmjkZSPy8rduoW9sk+nr/NN2wq3pNHYlmD/uEiQT2yBoeerBH77CJeqiwZ/KI5+nJaetNYQ+ElZLElUKbB+m+sOJj0S/Rhj3qIV+FPVDPhd+lLzcts23GZQV7kAecDCOaAAufEGAYeKq3Nrh5YVCG9pV99vC4cRv2IN0X1/5XHI7lhyErC8ZumHE1Juxhjapowx52mDyiQzcqzMiiJXvX6qr+oxx8c21mxoU5wmkFHcYigD2cDgSnf+9/JAT2qLddcgQjKxMXJ1uJ7EzSKLII0pMF7ed/eU9AeyPYg0O/epAPF/Ygb/ZR1dRhD6DxVX1PDQJxetG45PZkM67mDHtUCNPUYA+XRy5nQ8Me3r+xX7sBe9jRP9cpGpo9MAP/06BzHcMeI+0fea3hPU32JcL7Lf7rhmZPpLCH9ylZ9oK8IO8VcrSwaMMeVbuBD+pW0bh4jKgOmhsD9rx27H3UPbtIy5qjyQEq7Kuu1KJaqjKC7D9KT7NHvUhEBkZmXE0J9vizt5Cn6FvNBJbDwMtrKEP0SDV79AC627CH54qVGRcC+lw2/zZXYA8uzeHKIwZ7nEh+jfduDPZE2PZuwR75NgYbG0JRn100im47bqh2uxwO7Jnw2eu0pXaT67CHQ9iyORlgz5ItZUIFU/YTZNW8dmEPnATi1oo1Xer7vhnkgBJwQFbR5Xz5RsRIsydc2IP0ZWeh6BuooJpp9nCITfYL0lRgz9VFV5GvvHWQE2KjfmNoES7sWXlgHX097sEQFd3GgD3yZq4He0a2GkZryrbQcZmnhaJt/QAAIABJREFUBrWN6iND/laeo2wmIaspq+3aEJo9DQV71BvlSGGPmh63nVMzLlmzxwj2sH8yo9CjRho4Vuub0e/hwB5eLyKFPa+deCvdOut12kYrdYuHuSj775JhD5sk40M59Lq89vKBwU40LrUAVj574L8L4YPlRw+kcjp39bqeTu86UPNpw/BM1aQ10+zBgej90feG1dWs5QVHmmwyFVZCFh+FY8Ylz69oaPbowZ7xnUfR5E0zDcNoy+aH+N4MsNrR7HET9uBgOzj96CCNvWjAHoxnjFOnmj2HO+yR5/nhDntkzSXMg0g1e/S0vdhnD7Rs2ERMD/b8eejv6c7FT2k++dAPFZ4qWle3VHMKLy9PzQL2ZBSLywyW5axgD7dfOGZc4cCewrR8ykvOo3kVs7TLUjPNHruwB/IN6uCGZk8M9kRjJ49emjHYE2HbRgv2wH8OwkkP7JDtCPZc9M+FVJiTqoXBY22KSDR7RvcroBk1/9V8tGCheO+0O+j2T36gndtzRQtGA/bIGw8fJsxgDw7X7635iTaWb9UOMrxJ/mnOKzRv/wJSzbjchD0wDQE157LCmeKMsk+0ESZvLI2p2TMscyTNL1ml+exBWRDRSo44pU4LdqhoF/acnDGeVpZuCdLsgYC2cX+J8MHSFDR7AHv0Do+s2cMHDWyyctvYhT2ygOC2Zg+PdYSiR//hgfkcmyqxvwTURfWVpLfkyYf1cH32xGCP/c0kEthzYv6J9HPJapGZbMZlV7MH49osBL0Z7JFBUziwB6Y9M85+zLCh1pVtpct/eEQzX9F7UY5Wid/NYA9DHdUsrqFgj/0REdmbDQV7zv78EeHvjtcL3qN5XODQhD02rWiFqBCbqrdOKqRd9cUEjarX105tMrBHhgbcA0ZmXLyWyoc3N2DPW8sW0MtbXyH4J5tW8pUwH1Zhzx0zP6CfqmeEmGjJo8YK9sB3HgId3DXvlaAoQUiDTXj7phxF/zzpCk3m5PnlxEFztDR7nMIeyCyFZSeKC0j25SPvc0Z7sjoT9fyWmF3SGK1ZZhcGKrhUx2Vjwh4GQbJmD+aAvOY4gT0d0tvRlqrtjeqzx+8y7NEbSxxYRA/yW2n2QGYrzEkR7iZYrorBnsj2yN/61zHYE+EIMIM9jx1zoTBjwEKAx8xnj6rZI8MeDpkqC+lGPnugXYKHzbiwaXy2cj1tSvs6bDMu2HneveQ5TXBjKoy8lm4NROIB7Kmo8VC3ggz6+6Z/umLG5RT2cBvKGyVvkmx3HU3Yg3ZgiAHhgjdH/J3bjLWB9GAPHBtWJG4Liqblhs8e9bYGN+4w2WIHzXZgj2wqh43+8hmTaF3dsqDZg3fYZw+0haauXxYEe+SXVdjz+pF/00AZC5+R+Ow5+ZN7gvxF8Gas5+NFPTzC/xJCYjZl2CNDnYaGPVfk3EeXjgiohMsaXjHYY38ziQT2YP7C5w+ehoI9iBTJcxY3wnicwh7WCrIyhzPThOM1Vm5pN2CP0cHPrsaL/Z6PzpsNBXs4GqgKe+Ab55OtM8W+rwd72DdWc4U90OpBaHs1EpAbsEfek3AZ0iU3kI+s2cMRu4y0HTGqrGAPf3vNe7Oof9t8eq/sxRCfPeo+2ZxhD+qCKKO/RdgzsvZW+su4XmLNxmOk2cNmfWaaPW7Cniumv0I9szsIx8duO2jGPIJZPmApR+PCpdijc74QZ5+yvB9F3l/+kEUy7IGrC5jk8SWfHApd9YNlpNljFxzy6m8Ge/idxxd8YAv2IK2Jfc8SGqyQx4orykUSg3P7a1qIDIpimj3R2X+bQ6ox2BNhL8lRiXDoem3dFCHwYOFg2MPq7BCSsXBiciYnxGsRodTFWKjZSZo9chGh3rl4/3I6NfsCXQfNKuzBt+yfIlyfPXZhD4APHtm8yap5+UYQvomgno8ydkztTHP97wY5lLWj2WMGe1igagjYA4fHP2zeEhR+NL2+LU0/+wGtbfRgj3AGfdC/Erdb//JLafUWf0TRuFTYI+cthEQbmj0q7GHhUz10MexBmpNXLbYNe9hvEYPRSH32qGMQ2hAPDj9fu7FU/QHI9UA/wHFeDPbcoTWLPIZisMdqVbP+vTFgz4NL/yki8YSj2aPCHkT1eXDgtVrENvnGnLUAVTMuXusbEvaoZkFGZlzNHfZALthetUdoMGBte2XNZ1RdmUb+3A0hmk92IjsZjWD+VoU9suxjB/bI/lei6aDZyozLrmaP7MfNbc0evQsIVXNEvvCz6hu9g728n05fXiK0vxkmIb1fygOXhH2y+ooDotwu6Gsnmj3dqk+n0/sWhe2g2chnj1PNnmjCnopaD435eqKpBqLcT2roeLUP3dbswWUwR7RyA/ZweWUQH45mjww73IY9fPZAun1zuolw8ZgLOP/AxQRfgMN/l13Yo66VTRH2yGMJsGdXWS1lbz+DptxylPZTNGEPu7Dg9rWWfmJvNEYLxGBPhK0uHyrNYM/dQ8/TcsI38AOw07NF+5uZZo9eEeXJK/+uB3uwuT+y5c+uafawtoys2fN/Y7rR6P4FYcEeFp54I5EjOrEaeHOBPegXmL1BBVNPw4hvEezCHphUQHsqktDr0YA9rHpuBns44pDebaSq2RNt2MOhKe1o9tiFPTjwwo+U/Mg+e6JlxoX8cGCW64K/RWLGNTztZGEmgMfKjOtwhD1tykbRnq1FIdENm5KDZvbZE65mDy4i+MJBNeNCtLGapL2i//XMuFTYA4GaTU3wjWoegcNlY8Ie9XBsZcbV3GEP+kBeD9A/t0z5WvilUOsWgz2HonE1F9hjR0y1q9mjHtzx/1lbjw/gkcAeaCOf2q/AFPbAZA3aHbyXyetRc4A9KLeVBqLcZ7AAQKRHI9DdJGGPL4l6JA+kV0dfpVVl5Ke3C82ZcM24Ggr2XNTlJLp72hz6duK4qMOe0Z/dS70y+uhG3zSat5Fo9rToP5NKfAFTWtUfEMOeIZ4LNLiFMsRgj50V9PB+JwZ7IuxfO7BHDiGJ7KDK2CmrFX2174sGgT28Mbml2cPOLGXYA+0f+BfCw0DDTtPKC9bhAHvkOuvBnjunfSu0ltyAPTANi9vXjfwt1lPr7BRCuOyb5j0dZAaG8rDwxDfubmj24DD15JJ3qCpph1Zl2YxL9YWkjgUz2NMuvaUWfU6vPeUxI6INlbYU/q1wU8k3uapmTySwRz5I4d/ss4d94chldBP2QLBCpDDVkSoLyG7CHhlM8fyWzdsOd80eNpdsag6a4Ux5/Kw/Ba0XV3e8hN7Z+LUYdk7MuMxgj9y/hwPsUdebcGGPnT2sqbzTnGAPIk69deZloulimj2LBRiRtU2d+oSR29FKs4fHq7wHNzTsYflADz7bgT2oA+/xqC+0NaDJkpG3R7QlAnr8vtvJtGtHrnAtMK1khtB6c8tnD8vUds13rGAP5Ck8ozr1EP9lx+7sS8mpzx43NHv0nPjKZq12NXsgdxak5geBCTkdGZrhTAGzO9ZKkjW9EfUJ7iRUU0p5/eV0cRFx67srxOVNtDV7wjH1jQT2tBz4rVAUiMGeprLzNo9yxGBPhP0EcINnrWeB2Kz1zLjkTVzO7uwv7qfKWo8IewjnmvImbGTGxe840ezhjTHL016Y1Rg9yNPXaqn2M0eoUc24+DA46ZtNNHtdqVAbbGjY0/+ITQSHyGXZC7UNvCmYccltqwd7Zq7fTA/N/pweGHE6PbT41aDQ63pmXGaaPeivhJJBQuvHDuxhH0JGsOfNnzZQfa93dIcHNpbFVXPpjJZjhYNlPCyQHpd1Kv1cOl+ogMtmXFaaPfDzsLJ0sxZmXYV9kfjswWZa5T+ghVVvjrBH76ZWvgGMFuzpmNSD/nvK7Zq9P/o6BnvamUYGcrqN2DXjkv0lsGYP7zPIMwZ79B00q/0Rgz2/15qkMTR7huUOFQES2GePfFBpyrCnXa819GvCT5o/j2j77FHlQLswIRzYg3bHft0jr5D2VJfT08ve1Q7S0dbsiRbsGdwtkV5dMTNE40E1P0R72W1bPQfNbsMeo/2DL1zChT3sW9HIZw+giBw0Q97nI4U9nq6fkT91n247x2DPw4TzGNwFyD6C5HFg5LMnBnucSlux99ECMdjj0jjAZsLhVc/89Enh4JV99hjBHqjccdhDuRhmPnvswB6YEP3fad2CasYk2Q3YwxEbkAFDJzPYE1efQf6kSkOIEI4ZF4j9lZOn05r0j5zBng1j6ZTf1QjHZ0JA6nKtcHbKNu2qs16jvmOAxmZmeqZKerAHgGrSN7/SxBM70RXfPRkW7ImraSE2URn2dM1tTTf3nGCq2WMJe+ZsJYS013uMHEOyDXNeWoaI9LG2tFi7pVy8dw19f+Bb3QgiGI/w07Rg5yZaXLqG3jn9JoJTZRkYRQJ7ZB8Woq9an0wwpeQ+eaj/jTSqQz9RVbXPVTMuvKOGemVhBeMmwZNJ5E0mb0qpqKuenbeRYGl2gxsN2KPnawn10zM5Q7sA7OIWKQZ7ogN7eiQODVKT57knQz2eY5HAnu/GPyzGpZEZV1PT7IGZB0NldT2yE40rBnvMzbjgo8UsKpqRWIQxBOiPEORY+2QtDL7o0vPZg/UXe25jwh4OFcz7PsCKkRnXiLZ9aPqKEi2KE+o5stUw4TxZBjJuO2iW08bewNrSdsRU9M3SikU066xngl63C/fkA3hzgz2yRoheW/1WYY+ZaR/vBdxe7FeIZdrE6kL69ryHQs4RDDytNHvY9YCe7BMO7PnipEl0/vTHGlWzh+VW1sDic4CZtpHeeEQ6cCK907M5BnvsLG6xdyJugRjsibgJQxOY8Nnrwvny30deJsLINjTsYTVIuWRWsAfCDMxh7Gj2yIu3HdiDGwKO/KS2lmrGBefGgwrbCWCAMiFUL3xJqD57woU9SSsvoZNH7Wg02KP2CTZW9ldjV7OH21OGPYPyegofGmZmXNGEPSykytomQpiQQkdaTTUWAFg7KBLYowIaFfaooZflsjmFPRibpZV14gY4XNjDDqTlcjQU7JFDwsoq1Qx7iqv3UmZ8rhZhz02fPXqCoNXhRP5dzweT6o+G1w55PLHTeo4SBDOuuIpCeuWygUHDNFo+e1hNX7W5PxxgD0DNlNKPIvLZYwbZY7BHfyU1MuOCBuXVg0doH8nQz2pNVn/nb58YdhXdPvcFSopP1Q4rhxPs4f3MSgMvmrAn3L5R/cJYrafymuOWz56bjhsgNGzYDAl5yCZUZpo9srk594OefxzZjKuxYA983f111CF/nEZ9ZmXGZfQd6o3LpK6pvcSlAK99smuI6tp68nl9lJGeQnyZw2ZckcAevVDxcjhxI9gjm9W9t/d1XTgjp2PXjEs2nzfbN9mfnJkZV5t2+2lL7qdCXjOLxmVn7uBiUYXAVnOXZV3IyHY1e+B7dcnWMhpzRIZYe9mZuiozIupzzGePVQ/89n6PwZ4o9LksTOl5b7fKMlLNnnBhT/2BAqrJW6wVz8iMK5qw5/TMCYHw7QdhDwqDxfBwhD0ctlqFPf74OqKaPBFJBWZceLbnfaI59JZhT9uaYVTc8jMa0KLHbwb2yLf+Rj57VNgDPycT+v1Ou8k1gz0Yg/HJlUGaBWaaPbJdebiwh2FUU4M93ZMH0Ib9u4RzSb7xO5xgz5DcfrRo/woC7IFatRpRIlqwx2oPsKvZk7p3MO0umKpdKLDmxfdjXxRZ8Pz47tLrHWn2bK7ZKNLFw3OFNe8goJo5aGaoHImD5hjssRohob8DIE5c8KAYC7KDZrUt3YA9HFWU92eraFzNTbMnHNiDAywiirI2pJE2rNpzesDaee8HfB+V1OwVWrJW+4he+nqaPTDRvqDncPIlltONPx3SGJIduqtpYS399/iAqbcMZu3CHn5PjZipXgzIsAcuBSA3cpAQtUzR0uyBrHDX8YdAajRgj3ohijzktpFhDwfOiBbsketnB/bsiv/FEoTIZyQZ2mFewMSQoaGbsKdL53JhFeAG7AlnroYDe9S219MmwjiPwZ5weuTw/yYGe6LQx80V9qg+fezCng/m76RnL+4rNls8qoNmJ5o9zQn2vLlwKf27+CVRZ1MzLl8SZfkLQnx+GMGeVH8O5Sa2EP6VGPZUt/qJVtb+LPKSYQ+0efj25NERF9Afvn5CaJXJj6xqz44g2SSEBQdodUHboXXfBbSNAmFY+elLJ9A/T79Qd6aoMLMhNHtkQYcPs6+edgldNv+2ICFIBjT8Df/NDPZAXTgzJTGovk0Z9kwe+TTN3rLeVjSuv/84lz7Y90bI+ODbYFWzB4ImhIfDFfawWUlzhT3QQoPQyuNbPcjbhT1waMqR5bDmwJcA0sXDc0UWUJsS7DEDQzzQfws+e/hw3ViwZ9GmavrB/yYdHXchrfD8IJqeAfFvAfZw+zcW7DESZe1oJ6jfqgBqyd61QbAHMoVegAKkowd7YHo+OLe/puljptkTDuyxEuOjBXuuLrqKJgwYapU9RaLZo8IetD006wa0biPylWEP9xvDHvx/uClQNWH0gK9sxgXz4p21O0391MmwR5bxZc0eOFq2eoxgD38nj18rZ8j8u5WD5hjsaU0w7bZ6jPxVwSE6m8WivwHpYqHXrVqzcX+PwZ4otD8miOz0zI4wKhejoTV7sJngttYM9kzfPpemlXwlHN6qmj1LtpQFTfSGgj3XvzuXklptFE3HPnfwb/lgXhBfRB+OuU/7m5tmXCzc4b9msCeltg0lJ8bbhj2FCZ1E6HbUiWGPr2CJJijxxsr9IG+ADJBQJggFeHIScgXAkc24oHKfmZJA74++V7zD5njXj00LCZlqdnvVVGAPNBeMyoL6qbDn/RMeozbp+aLuqlmIlf8lNRpXY2v2oDzypqz6imKhXH6P1xv404KJVkPDnvdH30Njvp6o67zR6nDiphlXDPYERoJ86+4U9vROHUg39DtHaJXwXANUhqnBpn37tXWL9xmj8MM8Jq20YZ34VeM0f2uw59Qu/enVhfMF2G0ozR5cGPx7f8BsHfsLHhX2ABxCQ0SWIWQ/Yrz2or+SE+Lpk9PvM5TQZO039SUVVsiHE739QP5ebi8nZly8l8CnIS5mGlqzx6ihrNZTve/MYA/PYyewBzILTM3ZrEuFPfd+/zF9X/5l0FrkRLPHcJAc/CFasMeubO8m7FE1nMxgj9mY0PPZk+tvT/vjttGkoQ8Kp92ybxo1rWjAHj0NLbdhz5PT1pM/o5g+r3ir2Wr2yGZwQWtXTLPHain4zf4egz1R7norwVUv+4aGPXx4NYI9L186UGjtsJpyuLAHKsEby7cGaY6otxZONHtY5RMChBHs0TM5c8tnjwwKzAABQxf1kGOk2QOhCI8R7BlEY2gJfaEJzEawh+vOm6UKewrT8um1kyeKvH4rsAdjGFFH5L6wA3vEAeGbt7VxJjto1oM9sgNUPX8DPO/ZQXMkZlyRwB4I4dDaMYM9HJbXTTMujjLV2D57YrAnMBIjgT08dnkeyesMO7/nPNQDhtH+58SMy86B67cGe2Tnw00J9rDJsh3YI+9PeuOkIWAP8jXLR/bZw/KAno8wMzGTwcrkEc+LSx63n+YAe1SH0JB95HE7cuqNNDBteJCGipN6xWBP8Kgy0uwJd+y6pdkT6di3o9mDPOTLMb6chnsAPPA3J2vKymeLSMvH39sx43pu0ef04c7PbENjllFjZlxu9dLhlU4M9kS5P99a8SOd3Lk/FWZk2c6pqcEeOEPmRz24AhIYafawqi8WHzho5tsc1WmfHI1LD/Ygb75Fgorwxk1ZhDKFA3u+XF5CA4ZuMnTQPPqTh4jqMinT34pKMucYOteW2wP/toI9ad5W9OE51wWNATPYkxWfQz/s+Vk4WsMja/a82P+vdMPyO8KCPTh8IWrcXfNeCXKcp8Ke43JOoDl75wtNLjPNHggNHIVO3kRhCvXlxuX0wry5BK0bJ48ekJHT1jPjsqvZY3S4lP9udCNrBXtWV/5C0898NMg3inwA0HMqGIM9xxmGybXTD5E4aH5txyt0yaAj6fW1U8UYH5zXI8TnQ1P32eOWGZcMey5MvYsWl661bcYVgz1OVrfovotDBKIc4kbeyBcMHIquKduiGwXOqnTyt/g3nu1Ve8QesHi9x1CzZ2L339PcrVvFXFdliJcXzKO3dr1G6fVtafrZD4g0rcw1+B38V09TzC3NHqt83IQ9djWBrPpI/d0JFOFvG1qzxwr26I0HJ/VqzrDn5Oyz6f5jx4iu0bs4clOzp7nDHj1nyXpnFBn2XDf9LVrhmR0UkbQpwB4jEyqz+R/z2eN0dfztvB+DPU2wr6MFezqmdBaLmvpYafaYwZ7iA7VUUevR/PUgbdm7PSCN27Cncm9LeufaI7RqqJu+Kjioh+zHF3xgCHtYqOCIUFa3xnybbQV7EGYRfmXkB7Bnb90++mbs02IT52hc0OzBpotNamBRjvjELdjDjidV4UmFPbLDTbtOCGUgg/YAyOCICG5MM9kBqVwPmA80Bdgjh7WWNRjMNHswd2DO1BiaPYh6UlpZTwmplbY0e6rid5M3ro7ccNDcFDR7ZNhjNM9/K7BHBvHw9wAobNdnT3OBPcnebPpDr+OFmapqxqmnYebGmtWYabjl+NduHTjCnZ4Zl+ojTW5vvTkWTdiD8o3o0E1cwMmyAtdTXQsaSrOnucMedtqs57MHYbwRREI244KrA4aOVrBHz2zltwJ75PF43Id3Cd9Hskwbgz3mK5QV7FHHkWyN4WSM2V0nZZhtFo3LSXra2vXN28IlhwrA0Qaz15aKaKM4d+wqq4357AmngZvxNzHY0wQ7Lxqwh6upmqzg75HAHr3mM4M9Kw6so2/2fqN9ZseMCy/reZ6XFzg7Zlz8fkPDnj4VF1H/tvl0w7H9g5pLdeQtYE9lGR3bvhs9csIZ4l1oLzUE7GEwwz57woU9slaO27AH7aCaRcoOaO347NEbr26ZcYUDe7hOf2h/AV0zcFTI+FBViOWDh56fHtUvj+xIU/0NfTx51WLhjNeOGRcKB1MuN2GPHki0ErDw++KquYSIU25p9jQ12HPR58/TAe9+0S88rtmpunyYjlizx5dEFF8fpHUJ2ANtzQXZz4vxaOWgubnAHpgp3zP0vBAAbQZjm6B4YLtIDQ17Pliwk/6+KxANTPbZk+rPopeOu5m6ZwectY7+7F7qldFHO7A2NOyRoUpTgD1Gmqy2O9riRav1VO9zdezsrNpL5397j3jVyGcPm2/qwR7sQ3Cay2ad6nr7+ZrV9MT6Z7X0se+9OOxuGtiyk2HtnNSrOWv2yG0F3z+4DGxqsAd+KWtTdoq+YgfNz17YjwZ2yLYcxuG4urBMVHohBnsCbhrYAiMGe5yMnsPn3RjsaYJ9aQf2GB2k5dCFdg62vDjjEJlPRfRrwk/aZxyNy0yzRy8PM9gjDsS7Zmif2YE9UEtvmZalRR9Q82zqmj33dfgLFeakhmx8erBn9/pehAgGHMWgucEeuW9+K7BHdTap2sNbHSaNhB09YdYp7EFUjczEdM3XkHy4CRf23FTwAI0/sq3oai4jryNy/68r20qX//BIyBIBwZ8hRriwh8Ha4Qp71LUBfQWTSF+rpUGH6X6Jx9Jc/7thR+NiXwuqZo8T2HN1x0toQr/faY7Om6rPHsCe98ZdHjIe9W7Lm6BY4LhIDQ172CQV/f/FlgWivGs9C0KcsKtaO3rmCtHU7GlqsMdxxzr8wAkU4aT1xg5fioQLe+QLBRX28NhhmGTHT4qTejVX2CObZaJtmirs4YivzRH2qNNJ1eyR/S86nHqGr8vrm5uwS88XEwoRLuwpriwXkV5Zk54rFIvG5dZIaLh0YrCn4dradk6NBXvU6AqRwp6H+t5On26YH2TGZQV7EMoyLSlBqNobOTZWG5IjObAQZ2XG9fSSj+jT7QF/A/IhR46y5KYZl5nvET60os/7JY6ga3ufRV1bp2uhv+3Aniumv0I9szvQnUePFiqaOz1bRN30HDQbmXGpmj0P9b+R0E7Q5HBixmV7kIf5YlPU7GnKsEd1Hv3Tpl10xy/3B419I5MzhlTsoBkfYTw83+8pGtwxYF5oBnvwu54mYQz2WA9+u7AHt+VYK2UfA3J/yppvcprcb1jj45Ir6f6B14i1Gocsp5o9nHdTd9AMM+Z/jJ4Q0vh6ByjrHmr6bzQm7OF9Rg90qyAHpqyZKYlBDWoUbUZ+yYmDZuyjnTqWiehkMdhjPXatYE+79JbUJbdAi9zJmjtYi4w0e8xgD9wBjJ99kygYHOXCD5Sejzu55A0Fe6xby/qNcKNxqSlHG/YgiMri/ctNQ67LZZK18jiAQ1PT7NFzNWHmD0eFPXaCCliPgOA35PWtKcMeo3oB9lxzckv6sXS+mKex0OtOR0DDvx+DPQ3f5pY5Hi6wB4s+HzCwEeBQYAV78A4eJ7BHXbitYA9UzOGUVc8Jm5s+e3CwvnvaHPp2YsDLv/qoBzo9vxEMey4blUd3zZ9E5XG7SXXQLKfrFuyB6cY9c96NwR6pcY0cNDd12NM3ryO9tSMQZlS+QVXLzXCGx+FvBfagTWQn40bCrFXIcMuF3cELDQF7YAoXV5OnmQXw+FZhD/u3kSOI4GIAoAmO4psL7DEyBT5cYQ9HzzQyUXQwHG29Kmv2OIE9thLXeckJ7MHnege8pmDGFW797X7nBIpwmnptJWv2yEE1EjyZ1DW1F43pMFTIbeHAHt578F+741U257VqC7kN9Oph9X2kv7sFe/Q03tz02WO37dVx0trbh3Yl/CL+zHKGfGlp1n5uwg67/dTYsEcuZ0PUH2tz8YEaEYDCiRmXUXsC9shmejHYY3fkNd57MdjTeG1vmHNThz0n5p9IDw4/37D8bMbV2LAnrqZFiDM7FFoP9iytWESzznpGiwLihmbP+pJKmvTNr/TsRX1128oJ7EEa533xCO3ybW0Q2PPF6Gfp/GlPC9gzofXldPXQYU1ipqgbI6KU3ynUAAAgAElEQVQv+Ooy6K7jR4T481FNllRVVK5QpD57VGiCfOF8EppWLMSaOYB1asaVk5BL75x+U9DhRc9/D/JGvjyW7cIeRGJ7dMSFWuSPzWW7qS6hTNQlHM0eWcUbaTQ1zR5AC6OnsRw0NwTsYY0thiBGsIc1ISxhz5QbNP8/rG3UlEKv/9ZgD689Tg9w4S70RrBnWOZIevr4i7Rk7Zho2SmDm7CHZRU15DfKEW0HzXbqGsk70YY9vLfxWjkq+WJ66OTjtb1PXvPNxqSsGWi0V8vtYGSyotdWMdgT3CoIdrGnulxE7YvkYTOfHnmF9N7/t3cv0FZU9x3H9wWRp5r6CMrSxMSoNShqnjapRslSVKyt1teSNNRiqtGAMdgmrrpiddloWmN8VKMpaEgWPoh5mQcGoiA+0zxQlFo0GiP2BuITEPQi99K1R/dx7mbmzJ458z+z98z3rtWV4t2zZ8/nv++cM7+zZ86Kh9SSdXfm+qrwbp+jzLHq45/92N2Jq8fit87lmWNFHe1b9Yr247odYY+rVL3aEfZ4WE/fw56kbw6KM+qwZ1X/M9HT3qtc2aNvUbAfZqfHmRT2mKWaeVf2TJr3NbVh9BO5X+D0OHwOe8wtbWZFlsubr278KbX7FMT+XVVhj+1Q9jN7zEWrywOai4Q98Qfu6u31Mmj9EGf9Q9jTjVm+5bkh7Zk9ndzGVXbYo8+d5nxB2NOdeZK1l258amzGkBb22O8XCHuyqlbu77sd9ugPivRz4syFvGvYo8epf9JWWdoqeS7ECXvKnVNJvaU93yVrz/p9jPl2vKy23f59njnW7bEV3V8ZYY9+BtCkfd+pdt5ueDQMVvYUrUb3tiPs6Z61855cwp60zjp5QLPrM3ukwh5zTPFP0/V/y7p9Iu02LvNNRPa93+3Cnk/f/l31/p12ib5KPv4sjDTvIm+kTF8XLPqJevyVlep7x322tYrCHqu5jct1ZU/8GSqdPLOnTmGPvlBu92ai7JU99lzR/Z+406lqxkfe/LQz6fdJn75Hy9TX/UYt+ZsrWpvEL5Skwh7zzWAmpCLsuX6Lh8w6n8wLNpRe2WNWny1+/petbzrMu7JHn6viYQJhT8FiC27WzbBHH4a9v6T9VxX2JDHHz6Fm/tvn4rwre1yfOSNY9kFdF3mPkuc2rqxVq3owZjVn0hwp6pDnQrwuYU/8+YzGLek2rrTnRBa1bup2eeZYKEY67Hl+3Ua16ISLShsyYU9plGIdEfaI0RbvuAlhz9DNW6v+no2JF1HdDnvavQnMWgJf5I2U2V/8Cflpqz+uvev36nd/2hDdCuZyG5dr2GO/abAf0FynsCfrjY+2t79NImlOuD6zxzXMMe3a3cZlPxywG2FP/BksrOxZGoW+7S5oip/p07d0DXvOe/9UdeGKr+R+QLPZc3w+6X3qbx751E6fHfTV62m3cRH2SFS+3D4Je87c4ptk4sLx2znKCnuy3jOUW+Hs3jp5jxLvPelZN1nBXdL7mrLmZJ4L8bqEPUnVJuzJ/hso2iLPHCu6j25vp8OeV/v6Mz9EzzMuwp48WtW0Jeypxr3tXjsJe37+6J8Sv+bb7PDQH81UA5uGqRnvPzn6T+b2nKRvv9DfkvHU6g2DvjJcj8188p92ENN+Nkf9bv1T0W1c9r3BZqnn9iNHp36detHloGY87R6+ptvYK3uqCnvsN1JJF5TxbxI4e8G31bI37o9uGdPHqO+TnjB2l0HDT7oXO+1NbHxDU+uxO/QM+qpFn5fY2nWzx+o6j/R2+quC9dcE65+0cEjbPvHSqkEXD2X45Okj/neadnzmv5u/b7td/NkIeg7F791PGouZU7q/515+SX1q/4+qIUN6IiuXe//jAaTe5rS9joke7qv3deDY96g9tn/HFnPYdk4715hji6/csseUVDf9d/WbVb9XH9htJ7XLqB1Sz8fxZxLYf2uSLx1Jrl+9f5H6yStvf816/Hznsgw+qc+k87455zz84hOZrxFJ8yWpJq5zxcX0xVfWq+23G6V6enqiOeRy7O2+3cnlm59cxkWbLQWS6tMN77Q52K5GRS7s+vreUBv7B9Q2o968nYGfwQJJ71Nc/2alLPX+9U/S+yepfZb1gOak8cXDHtf3PFLHWbd+i5wTfDd4sPcp9drG/o6f1+T7cTK+wQKEPR7OiE7CnqzDib6Kt29TFMQU+XH5VEafIJeveVL94vh/LbKLjrfJCnvmr3xAfWXZnLbf+mD6yPqUbuGKXnXbskfVrBPffBhv0R/9ZuDgHT+iLjnsmNQuzl0wT/36jbtyPx/IJewpOu66bBcPJLJWAoV+zHkfhBk/3pfWrFfv2GZUK+xxsUgLe1y2pc3bAq7npLqbxcOeuh8rx9c9gSIXdoQ93atPyHvS31409eO7Rf9X9k887Cm776b35/JhVtONOP4wBAh7PKyTdNijDznrOThpLC5hj/6GpBVrn1WzJ32mEt2ssEd/Yj39oa+1DXuW/fEFdfV9v1Ez/vKDasIuO4ofh8ubgYt/sVgt7LuFsEegGk0Ke8wzKM6bcEruT3cIewQmn2OXhD1vQhH2OE4YmokLEPaIE9diB/qbWccM36r1QNsyD4qwp0xN+kKgngKEPR7W1eewR18Uu35bgoe0rSGFeByEPXIzqklhTyeKZYQ91xw0Ux2wQ2df+drJMYS6rQl7Ltpvupr4rn1DPYyOx03Y0zEhHZQkQNhTEiTdFBYg7ClMx4YINEaAsMfDUvsc9njI1ZghEfbIlZqwx82WsMfNSaKVCXtuPPgCtee25d8OIDFmiT4JeyRU6bOIAGFPETW2KVOAsKdMTfpCoJ4ChD0e1pWwx8OieDAkwh65IhD2uNkS9rg5SbTKuj1VYp8+9knY42NVmjkmwp5m1t2noybs8akajAUBPwUaEfbMnnOrWrhoSasCU6ecpI46/NDWv9esXacuuuwK1du7Ovpv5583Xe2/7z6t389fuFjNmTsv+veBE8arc84+XY0YLvftC4Q9fv6xVD0qwh65CujnTP34pe9HO6j7A5o7USTs6USvs20Je970I+zpbB6xdXkChD3lWdJTMQHCnmJubIVAkwRqH/a83ten5t76A3XCcZPVdttuo/6w8jl1yVevVp8787Qo0NG/v+raWWrCfuOjAEj//srrZqvPnzVNvXu3XdUjjz2u5tw8T134pS9E2+vgSP9Mm3qK2Dwh7BGjDbpjwh658pkLacKe9saEPXJzMKtnwh7Cnqw5wu+7K0DY011v9ralAGEPswIBBLIEah/22ABJ4Y4OcGbOOCMKc+zf69+NG7dzayWQHf5kARf5PWFPEbX6b0PYI1djwh43W8IeNyeJVoQ9hD0S84o+iwsQ9hS3Y8tyBAh7ynGkFwTqLNC4sMfcsjX11JOilT1J4Y1ZvTPllOMGrfrRE8Fe+SMxOQh7JFTD75OwR66GhD1utoQ9bk4SrQh7CHsk5hV9Fhcg7Clux5blCBD2lONILwjUWaBxYY99G5YOe+5ccPeg5/DYYc+RR0xsPcPHDnteXruh9Plx7L3nqiHP76++fsjJ6j07jiy1/1MX/UfU382H/VOp/dKZvMDVv/xv9YuNt6g7Dv56rp395KlH1Td7b1T/OO4f1DF77Jdr26Y0Nkb6ePP6NsVIH2f/wGY1pKdH9fS4H/UFD92ulr1xf2uDr0w4W+273fvcO6BlJGDmaNPnZ3//ZjVkaI/KMQWZQQiICGzevFlt3qzUkCHMRhFgOs0UGNATkDmY6UQDWYE/23aU7A7ovSOBoMMeHdRcevk1iQBJD1LWIc4LL744KNjpdGVPf/9ARwVI2vjQOz8bhT13/92Zpff9Vz+9JOrzx5MvKL1vOpQV+Le7l6iFG29Ri4/8Rq4dfe9/H1bXPHODmr77Gepv//yAXNs2pbEx0seb17cpRvo416zboLYdM1L15Eh7zl10s1rad2+L6cqPnKsO2H6vJrGVcqxmjjZ9fr6yboN6x5iRKlfiWEoF6ASBwQIbN25SbwwMqNEjtoYGgUoE+jZuUgMDA2okc7ASf3b6psDQoUOg8Fgg6LAnj2tS0KO31yt1fH1mzz1/f1aeQ3Rqe/QdF0ftfnbsl53a08gfgaK3cZnbP85575nqhH0O9OeAPBoJt3G5FYPbuNycJFrd/cwT6vJltzb+3M23cUnMLvosIsBtXEXU2KZMAW7jKlOTvhCop0Ajwp5236Dl87dxEfbU84+u6FER9hSVy96OsCfbSLcg7HFzopWcAGGPnC095xMg7MnnRevyBQh7yjelRwTqJlD7sMc8kLm3d/Wg2sVv87LbnH/e9NYzevRG8xcuVnPmzou2T7o9rOxJYR7QTNhTtmzY/RH2yNWPsMfNlrDHzYlWcgKEPXK29JxPgLAnnxetyxcg7CnflB4RqJtA7cOeEAtG2BNi1eTHTNgjZ0zY42ZL2OPmRCs5AcIeOVt6zidA2JPPi9blCxD2lG9KjwjUTYCwx8OKEvZ4WBQPhkTYI1cEwh43W8IeNydayQkQ9sjZ0nM+AcKefF60Ll+AsKd8U3pEoG4ChD0eVpSwx8OieDAkwh65IhD2uNkS9rg50UpOgLBHzpae8wkQ9uTzonX5AoQ95ZvSIwJ1EyDs8bCihD0eFsWDIRH2yBWBsMfNlrDHzYlWcgKEPXK29JxPgLAnnxetyxcg7CnflB4RqJsAYY+HFSXs8bAoHgyJsEeuCIQ9braEPW5OtJITIOyRs6XnfAKEPfm8aF2+AGFP+ab0iEDdBAh7PKwoYY+HRfFgSIQ9ckUg7HGzJexxc6KVnABhj5wtPecTIOzJ50Xr8gUIe8o3pUcE6iZA2ONhRQl7PCyKB0Mi7JErwqr169S5i29Sz6nl6t7JN8jtKPCeCXsCL2ANhk/YU4Mi1uQQCHtqUsiAD4OwJ+DiMXQEuiRA2NMl6Dy7IezJo9Wctp2GPReNn6km7r5Xc8ByHuk5d81Vv319CWFPGzfCnpyTiualCxD2lE5KhwUFCHsKwrFZaQKEPaVR0hECtRUg7PGwtIQ9HhbFgyF1GvawYqV9EQl7sic5YU+2ES1kBQh7ZH3p3V2AsMfdipYyAoQ9Mq70ikCdBAh7PKwmYY+HRfFgSIQ9skUg7Mn2JezJNqKFrABhj6wvvbsLEPa4W9FSRoCwR8aVXhGokwBhj4fVJOzxsCgeDImwR7YIhD3ZvoQ92Ua0kBUg7JH1pXd3AcIedytayggQ9si40isCdRIg7PGwmoQ9HhbFgyER9sgWgbAn25ewJ9uIFrIChD2yvvTuLkDY425FSxkBwh4ZV3pFoE4ChD0eVpOwx8OieDAkwh7ZIhD2ZPsS9mQb0UJWgLBH1pfe3QUIe9ytaCkjQNgj40qvCNRJgLDHw2oS9nhYFA+GRNgjWwTCnmxfwp5sI1rIChD2yPrSu7sAYY+7FS1lBAh7ZFzpFYE6CRD2eFhNwh4Pi+LBkAh7ZItA2JPtS9iTbUQLWQHCHllfencXIOxxt6KljABhj4wrvSJQJwHCHg+rSdjjYVE8GBJhj2wRCHuyfQl7so1oIStA2CPrS+/uAoQ97la0lBEg7JFxpVcE6iRA2ONhNXXYM3TVh9XiaaeXPrqj77g46vNnx3659L7pUFaAsEfWl7An25ewJ9uIFrIChD2yvvTuLkDY425FSxkBwh4ZV3pFoE4ChD0eVlOHPVs9M0ktOvv40kdH2FM6adc6JOyRpSbsyfYl7Mk2ooWsAGGPrC+9uwsQ9rhb0VJGgLBHxpVeEaiTAGGPh9Uk7PGwKB4MibBHtgiEPdm+hD3ZRrSQFSDskfWld3cBwh53K1rKCBD2yLjSKwJ1EiDs8bCahD0eFsWDIRH2yBaBsCfbl7An24gWsgKEPbK+9O4uQNjjbkVLGQHCHhlXekWgTgKEPR5WUzrs2XroEPXDyRd4eOQMqZ0AYY/s/CDsyfYl7Mk2ooWsAGGPrC+9uwsQ9rhb0VJGgLBHxpVeEaiTAGGPh9WUDnvGDB+q5k36Fw+PnCER9lQ3Bwh7su0Je7KNaCErQNgj60vv7gKEPe5WtJQRIOyRcaVXBOokQNjjYTUJezwsigdDYmWPbBEIe7J9CXuyjWghK0DYI+tL7+4ChD3uVrSUESDskXGlVwTqJEDY42E1CXs8LIoHQ7r6gfvVd1/+trp38g25RnP740vVVU9fn3u7XDupQWPCnuwiEvZkG9FCVoCwR9aX3t0FCHvcrWgpI0DYI+NKrwjUSYCwx8NqEvZ4WBQPhvSdpb9U3+y9MXdoQ9jjVjzCnmwnwp5sI1rIChD2yPrSu7sAYY+7FS1lBAh7ZFzpFYE6CRD2eFhNwh4Pi+LBkAh7ZItA2JPtWyTs+ebS+9R3er/T6vyag2aqA3bYK3tntEAgQYCwh2nhiwBhjy+VaO44CHuaW3uOHAFXAcIeV6kutpMOe3YeuYO68fCzu3hE7KoMAcKeMhTT+yDsyfYtEvaYlWWmd8KebGdapAsQ9jA7fBEg7PGlEs0dB2FPc2vPkSPgKkDY4yrVxXbSYc+eo/dQV31yShePiF2VIUDYU4YiYU8nimWEPfMnfV2N2WpUJ8Ng2wYLEPY0uPieHTphj2cFaeBwCHsaWHQOGYGcAoQ9OcG60ZywpxvK4e2DsEe2ZqzsyfYtI+zJ+4Dx7FHRokkChD1Nqrbfx0rY43d9mjA6wp4mVJljRKAzAcKezvxEtibsEWENvlPCHtkSEvZk+xL2ZBvRQlaAsEfWl97dBQh73K1oKSNA2CPjSq8I1EmAsMfDahL2eFgUD4ZE2CNbBMKebF/CnmwjWsgKEPbI+tK7uwBhj7sVLWUECHtkXOkVgToJEPZ4WE3CHg+L4sGQCHtki0DYk+1L2JNtRAtZAcIeWV96dxcg7HG3oqWMAGGPjCu9IlAnAcIeD6tJ2ONhUTwYEmGPbBEIe7J9CXuyjWghK0DYI+tL7+4ChD3uVrSUESDskXGlVwTqJNC4sGf2nFvV8hUr1IVf+oLabtttolquWbtOXXTZFaq3d3X07/PPm67233efVp3nL1ys5sydF/37wAnj1Tlnn65GDB8uNg8Ie8Rog+6YsEe2fIQ92b6EPdlGtJAVIOyR9aV3dwHCHncrWsoIEPbIuNIrAnUSaFTYo4OehYuWqHHjxrbCntf7+tRV185SE/Ybr446/FD1h5XPqSuvm60+f9Y09e7ddlWPPPa4mnPzvFZ73Yf+mTb1FLF5QNgjRht0x4Q9suUj7Mn2JezJNqKFrABhj6wvvbsLEPa4W9FSRoCwR8aVXhGok0Bjwh69Oqe3d5X60Af3HxTe6HBHBzgzZ5wRrfSxwx/9u3Hjdo6CIP1jhz8Sk0GHPXtvOF7NOnFS6d0ffcfFas/Re6irPjml9L7pUFaAsEfWl7An25ewJ9uIFrIChD2yvvTuLkDY425FSxkBwh4ZV3pFoE4CjQh7dNCz7NHl0e1XK558elDYkxTemNU7U045btCqH114e+WPxGQg7JFQDb9Pwh7ZGhL2ZPsS9mQb0UJWgLBH1pfe3QUIe9ytaCkjQNgj40qvCNRJoPZhjw5z7lxwd+s5O3a4Y/9eF9cOe448YmLrGT522PPa6xtLnw9H3DVd7fnqcerav55Yet/H//xS9b5R71X/fvDJpfdNh7IC8/7nt2rWH29SCz55Ta4d/fCJR9R1K2fl3i7XTmrQ+J/vvU09vPE+nNrUcsPrG9WI4cPUkJ4e54qb+Wc2yDt/nXdEw0YIrH9toxo1cpjqUe5zsBEwHGTXBTZt6lebNm9WI4Zt1fV9s0MEtMAbm/rVwObNajhzkAlRocDIEVtXuHd2nSUQdNijg5pLL0++8DUPUl605MHWw5XjGOa5Pc88+9yglT5JYY95no/+nR326Dee/CDQDYG+jZvU1sOGqp4cF9p6XD968hH1jZWz1J0T84VE3Tgmn/bxxftuU8vWLlfzj77Yp2F5NZbX+95QI7YeprjO9qosjRoMc7BR5fb6YPv7B9TAwGY1bNhQr8fJ4OorsKl/QKmBzWor5mB9ixzAkY0eSdjjc5mCDnuKwNore3x8Zk+R42Kb+gu8vHaD2nb0CDV06JBcB3v740vVVU9fr+6dfEOu7ZrWWN/G9fBLK9Q9JxL2pNW+yG1cTZtHHK+sALdxyfrSu7sAt3G5W9FSRoDbuGRc6RWBOgk0Puzx8du46jTBOJbyBAh7yrNM6omwJ9uXsCfbiBayAoQ9sr707i5A2ONuRUsZAcIeGVd6RaBOAo0Pe3Qx16xdpy667ArV27s6qu35501vPaNH/1s/4HnO3HnR78ztYSOGD6/TPOBYAhAg7JEtkl4B9fCq/1OXHHaM7I4C7p2wJ+Di1WTohD01KWQNDoOwpwZFDPwQCHsCLyDDR6ALAo0Le7pgyi4QEBEoGvaIDIZOGylA2NPIsnt10IQ9XpWj0YMh7Gl0+b04eMIeL8rAIBDwWoCwx+vyMDgE3hYg7GE2VC1A2FN1Bdg/YQ9zwBcBwh5fKtHccRD2NLf2HDkCrgKEPa5StEOgYgHCnooLwO4VYQ+ToGoBwp6qK8D+jQBhD3OhagHCnqorwP4R8F+AsMf/GjFCBCIBwh4mQtUChD1VV4D9E/YwB3wRIOzxpRLNHQdhT3Nrz5Ej4CpA2OMqRTsEKhYg7Km4AOyelT3MgcoFCHsqLwEDeEuAsIepULUAYU/VFWD/CPgvQNjjf40YIQKRAGEPE6FqAVb2VF0B9k/YwxzwRYCwx5dKNHcchD3NrT1HjoCrAGGPqxTtEKhYgLCn4gKwe1b2MAcqFyDsqbwEDOAtAcIepkLVAoQ9VVeA/SPgvwBhj/81YoQIRAKEPUyEqgVY2VN1Bdg/YQ9zwBcBwh5fKtHccRD2NLf2HDkCrgKEPa5StEOgYgHCnooLwO5Z2cMcqFyAsKfyEjCAtwQIe5gKVQsQ9lRdAfaPgP8ChD3+14gRIhAJEPYwEaoWYGVP1RVg/4Q9zAFfBAh7fKlEc8dB2NPc2nPkCLgKEPa4StEOgYoFCHsqLgC7Z2UPc6ByAcKeykvAAN4SIOxhKlQtQNhTdQXYPwL+CxD2+F8jRogAAggggAACCCCAAAIIIIAAAgg4CxD2OFPREAEEEEAAAQQQQAABBBBAAAEEEPBfgLDH/xoxQgQQQAABBBBAAAEEEEAAAQQQQMBZgLDHmYqGCCCAAAIIIFCmwB9WPqeuvG62+vxZ09S7d9u1zK7pCwEEEEAAAQQQaLRALcMe/ebxkq9erda9+qo6cMJ4dc7Zp6sRw4eXWujZc25VCxctafU5dcpJ6qjDD239+5HHHleXXn5N9O9x48aqC7/0BbXdttsMGoMep+5n5owzBv0uPn69gd13qQdCZ4UFXu/rU1ddO0utfuGFQfVds3aduuiyK6J+k+peZIdmX0uXLW9tfv5509X+++7T+vf8hYvVnLnzon+nzXvdprd3lZo29ZRBw4hvu82YMeqCL87gwqtIobq4TTfnnzks5mEXCxzoruKvjfY5KumQssIe+/Uw6fXU5dynxzVu3M6DXqf1eDj3BTrR2gzbfn8m9ZqWNTft82Xae7m0uRk/jrT3kfWrXthHFK95/PynrwnuXHB36dcjWXPQPselvTfk/Bj2vEt7ba36WthcD/X2ro6GmPSeIO1a2N6Wa+Hic7SWYY/hkDq56pP53Ft/oE44bnIU0piT7efOPC26+LbfvOo3k8seXd46yccnsP0Cbn439dSTor7sfxcvNVuWLWBe1F9dv0F97KAPty4idL0feOhXav1rG0oLe/Q8uP0HP1VTTjkuCi713P7P629qhTL633Nuntfan37h1j8m1ImHj4cfdsigsMfe1v532W70V45AN+efGTHzsJza1b0XMzePPGLioEC6SNijz0e9f1zdOr/qc9sLL77Yej3NOvfFw5ykD2Xi503OfeHPTPv9lj4iew6VdZRZczP+Opz0Xq7d3LSPI+m4yjoO+ilPwJz7dtxhhy3OUxJhT9Yc5PxYXm1D7amqa2HztzBhv/HR67d9bdzuWjhpWx1cmevsUGtR1bgbFfbEX1jthFG/KG94bYPasOE1pVdP5PkkyJ6U9uqJtE8uk9JMu63dd1UThf1uKWBqc9CHP6Ae+tVv1ZmnfzpqdP2sbyv93340f0ErfLE/fYkHLuZN3KhRI9X9D/1a2WFMkr39xtH+VCbtoiVpZY/9JjLrk3bmgh8CZcy/pFrnueBlHvoxF3wbhR32JJ1jzKrWV9asyXUbV9LFS3zFTtr8TfrkmnOfbzOns/G4vl9KW3ltzoeH/MVH1W3fvyMajMvrsRl1fO7p//a1q2+IPlgxtyfaH8KY7ZLmZtIHNvFgsjMptpYSMHPw0EM+phYveUCZwNu+4E5b8dDpazLnR6nKhtuvPfe6dS1sX+OmnZ+TroWTXsfTzp/hVqZ7I29M2KNJ5/98kTpq0mHRygg92RcuumfQSgh9wW5uX8nzKUrSBY/en1lVkbY6J23pmp7QZiy6n6Rbvbo3RdhTmkD8gkZ/8qx/xu0yNvoUWv9v/I3ZPfc+qHbffbfoTZ+9EsyceF1ueTBjib8hGPvOnaLbyUx6rtukBTZJYY+Zn2N33DH6tHzRkgcTb/ViJvglUNb8i19ouF4sMQ/9mgu+jUYy7Im/Nuvjdj33JV1Qc+7zbeZ0Pp74+6ekZ0DZq2Ljr4nmtVl/WKPfv+VdWR2fm6v/9PwWIWba+8qkuWmPJe02m87F6KFMgfi5T/drVvOsePLp1v9vn7fs94R2rfPUnvNjmdWsR1/xsKeb18KugY1r2JPnurwelSvvKBoT9tjP7LEvhpM+RXFdcmlva5+Y84Y9+g/kW3PnqVfXrY+eO8R9iuVN+DJ7ir+o7/6uXaNP8UaPGhmt8Hnm2ecGhT3x/SatBIvf5pc1Rnv7pFsm8oQ9JlB85tmV6smnfp9rVVvWWPm9nEBZ8y/+QqtXWbiGy8xDudqG3rQXq7MAAAtrSURBVLNU2JO28jV+u1jaua/dc1E494U+4waP335mT/w5Jfb7Nfv8Zz8s3PXTZHveJV3A5Al74rfp6tdlntkTxhyNn/v23vO9URitz0/x4CcpCIzPs/jFeV/fxi1WiKVJcH4MY450e5TtbuOSvBZO2m/S+TTpXJl222uea6VuO/u8v0aFPfatNPFbtYqGPfbzA8yFs/7fIit77ElvJvzhh31iiwdL+jyxmjA2+4ImfjFhJ9r2gxq1jwnx8qTV8fvBzfxKWo2RJ+yxV/vYn3w2oZYhHmNZ889eIZT0AG/bh3kY4ozp3pglwh770299NHnOfWm3ccXnO+e+7s2Rbu3JvIcav/fe0XsyOwjS4zBBStIthWlfahAff9LcTHoNzhP22PPVXo3eLT/2k0/APveZC15zW5dePa3DHvtDlfg803PW3AL4ypp1Tg925vyYr05Nam2HLt26Fu5kZY+uT/x2W1OvPLfVNqnGWcfamLBHn1zjD3cqI81MCno0eCfP7HFNQrMKy+/lBZJW1Ji9xk9yw4dvHX26ox/Yp99sFl3Zk3SBbfZnvzHM88we15Vo8qLsIY9AWfPPvKjq1YT6J+srsJmHearUzLZlhz1JFzJ5z31pz0WJP+8n7207zaxueEcdD1n0l2skfSubPqqkgCZrZU/a3IxfsOd9Zk+e1brhVaPeI7ZrF3+9NA+Wz1rZo4XM+UqH0R/64P5tH3TP+bHec6rTo4tfV3bzWthevJD2mICklT1Jx6z/JrL+Fjq1quv2tQ574i/S9ou4/Qle3pU97d4A2PtK+zQnaYLbJ21W9vj7p5f3Yts8U8euqcvKnrSTZFK4pL8hLm1+pj2gOf78Kj7d9nfOxUdW1vzTfdrPLrFvezX7ZR6GMTeqHmXS6639bYHLV6yInpmX9YDmtFWKec99aSt7OPdVPVvK278+P/3XTXPVsZOPaD0U2T5nJb2+/fDHd6rDPvHxLeZi1tzL+n38dbhdkJgWRMa/dY6VPeXNE8mekl6XzQoFczuh3n/8A8C0lWGzvnWL6unpUTNnnBF982/ST9YcTHpgs+7HrAxPC831f7fnHO8NJWeOXN9VXQvb5960ueoS9rhcJ8kJht9z7cKetG9ZMEn5wkVLoqrtssvY6H/Np9h5wh77KfpmGsTvC283jqTt40vT7KVrPLPHzz8014tt/SIdr6m+fXDMNqPVERPfvDXP5SRmL7s0Iva3es15a3VGfC7qtknLIeMPhI4vbc/zTXR+VqYZoypr/mmtdn3FNZmHzZhbRY7SvlXVft2Kn2NOPv5YtXTZY9FFTFbYY39ziBlb/PwVb2Of++zt7fMb574i1fZ3m6T5Ys9F+/XQvI4mnd/afXFC1tzM+ptoNzftbXlmj79zLj6ypNdS8990O30bl/4wJe3buExf7VbQxveXNQdNaJP23pDzYxjzKu8ofbkWbjfPs66F43OT27fyzoDB7WsX9nTGwdYIIIBA8wT0Rc4dP12gPnPalOiNKD8IIIBA0wSyVkk0zYPjRQABBBAIX4CwJ/wacgQIIIBARwJp31TUUadsjAACCAQkQNgTULEYKgIIIICAkwBhjxMTjRBAAAEEEEAAAQQQQAABBBBAAIEwBAh7wqgTo0QAAQQQQAABBBBAAAEEEEAAAQScBAh7nJhohAACCCCAAAIIIIAAAggggAACCIQhQNgTRp0YJQIIIIAAAggggAACCCCAAAIIIOAkQNjjxEQjBBBAAAEEEEAAAQQQQAABBBBAIAwBwp4w6sQoEUAAAQQQQAABBBBAAAEEEEAAAScBwh4nJhohgAACCCCAAAIIIIAAAggggAACYQgQ9oRRJ0aJAAIIIIAAAggggAACCCCAAAIIOAkQ9jgx0QgBBBBAAAEEEEAAAQQQQAABBBAIQ4CwJ4w6MUoEEEAAAQQQQAABBBBAAAEEEEDASYCwx4mJRggggAACCCCAAAIIIIAAAggggEAYAoQ9YdSJUSKAAAIIIIAAAggggAACCCCAAAJOAoQ9Tkw0QgABBBBAAAEEEEAAAQQQQAABBMIQIOwJo06MEgEEEEAAAQQQQAABBBBAAAEEEHASIOxxYqIRAggggAACCCCAAAIIIIAAAgggEIYAYU8YdWKUCCCAAAIIIIAAAggggAACCCCAgJMAYY8TE40QQAABBBBAAAEEEEAAAQQQQACBMAQIe8KoE6NEAAEEEEAAAQQQQAABBBBAAAEEnAQIe5yYaIQAAggggAACCCCAAAIIIIAAAgiEIUDYE0adGCUCCCCAAAIIIIAAAggggAACCCDgJEDY48REIwQQQAABBBBAAAEEEEAAAQQQQCAMAcKeMOrEKBFAAAEEEEAAAQQQQAABBBBAAAEnAcIeJyYaIYAAAggggAACCCCAAAIIIIAAAmEIEPaEUSdGiQACCCCAAAIIIIA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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[['DAM', 'POS', 'NEG']].iplot(dimensions=(1100, 400))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "('bl_cop', 'opt_cop')", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3620\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3621\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3622\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: ('bl_cop', 'opt_cop')", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32mC:\\Users\\SHAHLA~1\\AppData\\Local\\Temp/ipykernel_24068/1091589963.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'bl_cop'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'opt_cop'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3504\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3505\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3506\u001b[1;33m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3507\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3508\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3621\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3622\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3623\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3624\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3625\u001b[0m \u001b[1;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: ('bl_cop', 'opt_cop')" ] } ], "source": [ "df['bl_cop', 'opt_cop'].describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df[['POS', 'NEG', 'MW (VDG)', 'opt_th_load_MW', 'storage_level_MWh', 'opt_cop', 'to_storage_MW', 'from_storage_MW']].iplot(subplots=True, shape=(8, 1), dimensions=(1200, 1000))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df['bl_e_load_MW'].describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for col in df.columns:\n", " if col.endswith('MW'):\n", " df[col + 'h'] = df[col] / 4\n", " \n", "df['nomination_MWh'] = df['bl_e_load_MWh']\n", "df['imbalance_MWh'] = df['opt_e_load_MWh'] - df['nomination_MWh']\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ex = pd.DataFrame({\n", " 'A': [1,2,3],\n", " 'B': [4,5,6]\n", "})\n", "ex.index = ['i1', 'i2', 'i3']\n", "ex" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ex.loc['i2', 'B']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ex['B'] " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ex[ex['A'] > 2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def calc_costs(df, nomination_col, realisation_col):\n", " dayahead_costs = df[nomination_col] * df['DAM'] \n", "\n", " imbalance = df[realisation_col] - df[nomination_col]\n", " imbalance_costs = pd.Series(0, index=df.index)\n", " is_pos = imbalance > 0\n", " imbalance_costs[is_pos] = imbalance[is_pos] * df['POS'] \n", "\n", " is_neg = imbalance < 0\n", " imbalance_costs[is_neg] = imbalance[is_neg] * df['NEG']\n", "\n", " return dayahead_costs + imbalance_costs\n", "\n", "df['bl_costs'] = calc_costs(df, 'nomination_MWh', 'nomination_MWh')\n", "df['opt_costs'] = calc_costs(df, 'nomination_MWh', 'opt_e_load_MWh')\n", "df[['bl_costs', 'opt_costs']].sum() / df['bl_costs'].sum()\n", "\n", "rea" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df[['storage_level_MWh', 'opt_cop', 'to_storage_MW', 'from_storage_MW']].iplot(subplots=True, shape=(4, 1), dimensions=(1000, 800))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df[['POS', 'NEG', 'MW (VDG)', 'opt_th_load_MW']].iplot(subplots=True, shape=(4, 1), dimensions=(1000, 800))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#per month\n", "# -+10, 2.3%\n", "# -+20, 3.1% saving\n", "# -+30, 3.7% saving\n", "# -+40, 4.1% saving\n", "# -+50, 4.5% saving\n", "# -+60, 4.0% saving\n", "# -+70, 3.5% saving\n", "# -+80, 3.1% saving\n", "# -+90, 2.7% saving\n", "# -+100, 2.3% saving" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#per year\n", "# +-10, 1.9%\n", "# +-20, 3.5%\n", "# +-30, 4.5%\n", "# +-40, 5%\n", "# +-50, 5.1%\n", "# +-60, 4.9%\n", "# +-70, 4.7%\n", "# +-80, 4.5%\n", "# +-90, 4.3%\n", "# +-100, 4.1%" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "interpreter": { "hash": "5ec59cbcf9a87043036a8796d27aab156a33bd65b7023fbb41d7014b6ac2d886" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.5" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 }