From 0ee45803a0664e972bd11eb68d4711d436d9d54a Mon Sep 17 00:00:00 2001 From: Dashadower Date: Thu, 14 Jul 2022 01:34:40 +0900 Subject: [PATCH 01/45] Initial implementation of stan translator --- pysd/builders/stan/ast_walker.py | 95 +++++ pysd/builders/stan/stan_model_builder.py | 177 +++++++++ test_scripts/testing.py | 36 ++ test_scripts/vensim_models/Inventory.mdl | 340 +++++++++++++++++ .../vensim_models/Inventory_backup.mdl | 343 ++++++++++++++++++ test_scripts/vensim_models/arithmetic.mdl | 117 ++++++ test_scripts/vensim_models/repair.xmile | 101 ++++++ 7 files changed, 1209 insertions(+) create mode 100644 pysd/builders/stan/ast_walker.py create mode 100644 pysd/builders/stan/stan_model_builder.py create mode 100644 test_scripts/testing.py create mode 100644 test_scripts/vensim_models/Inventory.mdl create mode 100644 test_scripts/vensim_models/Inventory_backup.mdl create mode 100644 test_scripts/vensim_models/arithmetic.mdl create mode 100644 test_scripts/vensim_models/repair.xmile diff --git a/pysd/builders/stan/ast_walker.py b/pysd/builders/stan/ast_walker.py new file mode 100644 index 00000000..497bad5b --- /dev/null +++ b/pysd/builders/stan/ast_walker.py @@ -0,0 +1,95 @@ +from typing import Union, List, Iterable +from itertools import chain + +from pysd.translators.structures.abstract_model import\ + AbstractComponent, AbstractElement, AbstractModel, AbstractSection + +from pysd.translators.structures.abstract_expressions import * + + +def get_aux_names(entry_ast_node): + match entry_ast_node: + case int(): + return [] + case ArithmeticStructure(operators, arguments): + return list(chain.from_iterable([get_aux_names(argument) for argument in arguments])) + case ReferenceStructure(reference, subscripts): + return [entry_ast_node.reference] + case CallStructure(function, arguments): + return list(chain.from_iterable([get_aux_names(argument) for argument in arguments])) + case IntegStructure(flow, initial): + return get_aux_names(flow) + get_aux_names(initial) + case InlineLookupsStructure(argument, lookups): + return get_aux_names(lookups) + + +def ast_codegen(node) -> str: + match node: + case int(x): + return f"{x}" + + case str(x): + return x + + case ArithmeticStructure(operators, arguments): + output_string = "" + last_argument_index = len(arguments) - 1 + for index, argument in enumerate(arguments): + output_string += ast_codegen(argument) + if index < last_argument_index: + output_string += " " + output_string += operators[index] + output_string += " " + return output_string + + case ReferenceStructure(reference, subscripts): + return reference + + case CallStructure(function, arguments): + output_string = "" + function_name = ast_codegen(function) + match function_name: + case "min": + function_name = "fmin" + case "max": + function_name = "fmax" + case "xidz": + assert len(arguments) == 3, "number of arguments for xidz must be 3" + arg1 = ast_codegen(arguments[0]) + arg2 = ast_codegen(arguments[1]) + arg3 = ast_codegen(arguments[2]) + output_string += f" (fabs({arg2}) <= 1e-6) ? {arg3} : ({arg1}) / ({arg2})" + return output_string + case "zidz": + assert len(arguments) == 2, "number of arguments for zidz must be 2" + arg1 = ast_codegen(arguments[0]) + arg2 = ast_codegen(arguments[1]) + output_string += f" (fabs({arg2}) <= 1e-6) ? 0 : ({arg1}) / ({arg2})" + return output_string + case "ln": + function_name = "log" + + output_string += function_name + output_string += "(" + output_string += ",".join([ast_codegen(argument) for argument in arguments]) + output_string += ")" + + return output_string + + case IntegStructure(flow, initial): + return ast_codegen(flow) + + +class AbstractComponentWrapper: + def __init__(self, component: AbstractComponent): + self.component = component + self.ast = component.ast + + def get_required_variable_name(self): + match self.ast: + case int(): + return [] + case ArithmeticStructure(ops, args): + pass + + diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_model_builder.py new file mode 100644 index 00000000..4b6d0c78 --- /dev/null +++ b/pysd/builders/stan/stan_model_builder.py @@ -0,0 +1,177 @@ +import os +from pathlib import Path +from typing import Union, List, Dict, Set, Iterable + +from .ast_walker import * + +from pysd.translators.structures.abstract_model import\ + AbstractComponent, AbstractElement, AbstractModel, AbstractSection + + +class IndentedString: + def __init__(self, indent_level=0): + self.indent_level = indent_level + self.string = " " * 4 * self.indent_level + + def __iadd__(self, other: str): + prefix = " " * 4 * self.indent_level + if other != "\n": + self.string += prefix + self.string += other + return self + + def __str__(self): + return self.string + + +def name_to_identifier(name: str): + return name.lower().replace(" ", "_") + + +class StanModelBuilder: + def __init__(self, abstract_model: AbstractModel): + self.abstract_model = abstract_model + + def create_stan_program(self, input_variable_names, output_variable_names, function_name="vensim_func"): + self.code = IndentedString() + + + self.code += StanFunctionBuilder(self.abstract_model).build_function_block(input_variable_names, + output_variable_names, function_name) + + self.code += "data{\n}\n" + self.code += "transformed data{\n}\n" + self.code += "parameters{\n}\n" + self.code += StanTransformedParametersBuilder(self.abstract_model).build_block(input_variable_names, output_variable_names, function_name) + self.code += "model{\n}\n" + + self.code += "generated quantities{\n}" + + return self.code + + +class StanTransformedParametersBuilder: + def __init__(self, abstract_model: AbstractModel): + self.abstract_model = abstract_model + + def build_block(self, input_variable_names, output_variable_names, function_name): + self.code = IndentedString() + self.code += "transformed parameters {\n" + self.code.indent_level += 1 + + argument_variables = [] + for var in input_variable_names: + match var: + case str(x): + argument_variables.append(x) + case (str(type), str(var_name)): + argument_variables.append(var_name) + + self.code += f"vector[{len(output_variable_names)}] initial_state;\n" + self.code += f"initial_state = {{{', '.join(output_variable_names)}}};\n" + + self.code += f"array[] vector integrated_result = integrate_ode_rk45({function_name}, initial_state, initial_time, times, {','.join(argument_variables)});\n" + self.code.indent_level -= 1 + self.code += "}\n" + + return str(self.code) + + +class StanFunctionBuilder: + def __init__(self, abstract_model: AbstractModel, function_name: str = "vensim_ode"): + + self.abstract_model = abstract_model + self.elements = self.abstract_model.sections[0].elements + self.function_name = function_name + + + + def create_dependency_graph(self): + dependency_graph: Dict[str, Set] = {} + for element in self.elements: + for component in element.components: + if element.name not in dependency_graph: + dependency_graph[element.name.lower().replace(" ", "_")] = set() + + dependent_aux_names = get_aux_names(component.ast) + dependency_graph[element.name.lower().replace(" ", "_")].update(dependent_aux_names) + + return dependency_graph + + def print_variable_names(self): + var_names = [] + max_length = len("original name") + 1 + for element in self.elements: + var_names.append((element.name, element.name.lower().replace(" ", "_"))) + max_length = max(max_length, len(element.name) + 1) + + print(f"{'original name'.ljust(max_length)}stan variable name") + print("-" * 10) + for x in var_names: + print(f"{x[0].ljust(max_length)}{x[1]}") + + def build_function_block(self, input_variable_names, output_variable_names, function_name="vensim_func"): + self.code = IndentedString() + self.code += "functions {\n" + self.code.indent_level += 1 + dgraph = self.create_dependency_graph() + eval_order = [] + + def recursive_order_search(current, visited): + if current in visited: + return + visited.add(current) + if current in eval_order: + return + for child in dgraph[current]: + if child == current: continue + recursive_order_search(child, visited) + eval_order.append(current) + + for var_name in dgraph.keys(): + recursive_order_search(var_name, set()) + + self.elements = sorted(self.elements, key=lambda x: eval_order.index(x.name.lower().replace(" ", "_"))) + self.code += f"vector {function_name}(real time, vector state, " + argument_strings = [] + argument_variables = [] + for var in input_variable_names: + match var: + case str(x): + argument_variables.append(x) + argument_strings.append("real " + x) + case (str(type), str(var_name)): + argument_variables.append(var_name) + argument_strings.append(f"{type} {var_name}") + + self.code += ", ".join(argument_strings) + self.code += "){" + self.code += "\n" + self.code.indent_level += 1 + + for index, output_variable_name in enumerate(output_variable_names, 1): + self.code += f"real {output_variable_name} = state[{index}];\n" + + self.code += "\n" + + for element in self.elements: + stan_varname = name_to_identifier(element.name) + if stan_varname in argument_variables: + continue + elif stan_varname in output_variable_names: + stan_varname += "_dydt" + for component in element.components: + self.code += f"real {stan_varname} = {ast_codegen(component.ast)};\n" + + self.code += "\n" + output_variable_names = [name + "_dydt" for name in output_variable_names] + self.code += f"return {{{', '.join(output_variable_names)}}};\n" + self.code.indent_level -= 1 + self.code += "}\n" + + self.code.indent_level -= 1 + self.code += "}\n" + return str(self.code) + + def build_lookups(self): + pass diff --git a/test_scripts/testing.py b/test_scripts/testing.py new file mode 100644 index 00000000..2cdfaf7e --- /dev/null +++ b/test_scripts/testing.py @@ -0,0 +1,36 @@ +from pysd.translators.vensim.vensim_file import VensimFile +from pysd.translators.xmile.xmile_file import XmileFile +from pysd.builders.stan.stan_model_builder import * + + +vf = VensimFile("vensim_models/Inventory.mdl") +#vf = XmileFile("vensim_models/repair.xmile") +vf.parse() + +am = vf.get_abstract_model() + +stan_builder = StanModelBuilder(am) +# print(stan_builder.build_function_block(["failure_count", "repair_time"], ["battle_field", "repair_shop"])) # repair +print(stan_builder.create_stan_program(["demand"], ["inventory", "backlog"])) + +# for section in am.sections: +# for element in section.elements: +# print("*" * 10) +# print(f"name: {element.name}") +# print(f"length: {len(element.components)}") +# for component in element.components: +# print(f"type: {component.type}") +# print(f"subtype: {component.subtype}") +# print(f"subscript: {component.subscripts}") +# print(component.ast) + +# ( +# ArithmeticStructure( +# operators=['*', '/'], +# arguments=( +# ReferenceStructure(reference='a', subscripts=None), +# ReferenceStructure(reference='b', subscripts=None), +# 1) +# ), +# 5 +# ) \ No newline at end of file diff --git a/test_scripts/vensim_models/Inventory.mdl b/test_scripts/vensim_models/Inventory.mdl new file mode 100644 index 00000000..28da49bb --- /dev/null +++ b/test_scripts/vensim_models/Inventory.mdl @@ -0,0 +1,340 @@ +{UTF-8} +Sd of Demand= + 10 + ~ + ~ | + +Inventory Adjustment Time= + 3 + ~ Month + ~ | + +Supply Line Adjustment Time= + 3 + ~ + ~ | + +Minimum Processing Time= + 3 + ~ + ~ | + +Demand= + RANDOM NORMAL( 0, 200, Mean of Demand, Sd of Demand, 1111) + ~ + ~ | + +Desired Delivery Delay= + 3 + ~ Month + ~ | + +Maximum Delivery Rate= + Inventory/Minimum Processing Time + ~ Widget/Month + ~ | + +Shipment Rate= + Desired Shipment*Fulfilment Ratio + ~ Widget/Month + ~ Desired Shipment*Fulfilment Ratio + | + +Desired Shipment= + Back Log/Desired Delivery Delay + ~ Widget/Month + ~ | + +Fulfilment Ratio= 1 + ~ + ~ | + +Adjustment for Inventory= + (Desired Inventory-Inventory)/Inventory Adjustment Time + ~ + ~ | + +Adjustment for Supply Line= + (Desired Supply Line-Supply Line)/Supply Line Adjustment Time + ~ + ~ | + +Back Log= INTEG ( + BL In-BL Out, + 100) + ~ + ~ | + +BL In= + Demand + ~ + ~ | + +BL Out= + Shipment Rate + ~ + ~ | + +Cost= + Underage Cost+Overage Cost + ~ + ~ | + +Deficient Amount= + MAX(0, Back Log-Shipment Rate) + ~ Widget + ~ | + +Production Completion= + Supply Line/Lead Time + ~ + ~ | + +Demand Forecast= + Demand + ~ + ~ | + +Desired Inventory= + Demand Forecast*Inventory Period + ~ + ~ | + +Desired Supply Line= + Demand Forecast*Lead Time + ~ + ~ | + +Forecast Period= + 3 + ~ + ~ | + +Inventory= INTEG ( + Production Completion-Shipment Rate, + Desired Inventory) + ~ Widget + ~ | + +Inventory Period= + 5 + ~ + ~ | + +Lead Time= + 5 + ~ + ~ | + +Mean of Demand= + 100 + ~ + ~ | + +Overage Cost= + (Inventory+Supply Line)* Unit Overage Cost + ~ + ~ | + +Desired Production Start= + Adjustment for Inventory+Adjustment for Supply Line+Demand Forecast + ~ + ~ | + +Production Start= + MAX(0,Desired Production Start) + ~ + ~ | + +Supply Line= INTEG ( + Production Start-Production Completion, + Desired Supply Line) + ~ + ~ | + +Underage Cost= + Deficient Amount*Unit Underage Cost + ~ + ~ | + +Unit Overage Cost= + 1 + ~ + ~ | + +Unit Underage Cost= + 9 + ~ + ~ | + +******************************************************** + .Control +********************************************************~ + Simulation Control Parameters + | + +FINAL TIME = 100 + ~ Month + ~ The final time for the simulation. + | + +INITIAL TIME = 0 + ~ Month + ~ The initial time for the simulation. + | + +SAVEPER = + TIME STEP + ~ Month [0,?] + ~ The frequency with which output is stored. + | + +TIME STEP = 1 + ~ Month [0,?] + ~ The time step for the simulation. + | + +\\\---/// Sketch information - do not modify anything except names +V300 Do not put anything below this section - it will be ignored +*View 1 +$192-192-192,0,Times New Roman|12||0-0-0|0-0-0|0-0-255|255-255-255|255-255-255|96,96,90,0 +10,1,Inventory,647,125,40,20,3,3,0,0,0,0,0,0,0,0,0,0,0,0 +12,2,48,921,127,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,3,5,2,4,0,0,22,0,0,0,-1--1--1,,1|(844,127)| +1,4,5,1,100,0,0,22,0,0,0,-1--1--1,,1|(726,127)| +11,5,0,772,127,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,6,Shipment Rate,772,165,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 +10,7,Desired Inventory,469,409,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +10,8,Desired Production Start,179,249,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +10,9,Adjustment for Inventory,304,459,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,10,1,9,1,0,45,0,2,0,0,-1--1--1,|||0-0-0,1|(649,387)| +1,11,7,9,0,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(0,0)| +1,12,9,8,1,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(206,359)| +10,13,Supply Line,392,129,40,20,3,3,0,0,0,0,0,0,0,0,0,0,0,0 +12,14,48,235,127,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,15,17,13,4,0,0,22,0,0,0,-1--1--1,,1|(328,127)| +1,16,17,14,100,0,0,22,0,0,0,-1--1--1,,1|(268,127)| +11,17,0,298,127,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,18,Production Start,298,165,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,19,21,1,4,0,0,22,0,0,0,-1--1--1,,1|(572,127)| +1,20,21,13,100,0,0,22,0,0,0,-1--1--1,,1|(478,127)| +11,21,0,531,127,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,22,Production Completion,531,165,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 +10,23,Adjustment for Supply Line,351,342,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,24,23,8,1,0,0,0,0,64,0,-1--1--1,,1|(249,309)| +1,25,13,23,1,0,45,0,2,0,0,-1--1--1,|||0-0-0,1|(419,226)| +1,26,8,18,1,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(202,180)| +10,27,Demand,701,384,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,28,13,22,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,29,Lead Time,524,216,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,30,29,22,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,31,Demand Forecast,630,486,40,20,3,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,32,27,31,1,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(673,432)| +1,33,31,8,1,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(199,441)| +10,34,Forecast Period,778,485,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,35,34,31,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +12,36,1,318,230,23,23,4,4,0,0,1,0,0,0,0,0,0,0,0,0 +Supply Line Control +12,37,1,623,298,23,23,4,4,0,0,1,0,0,0,0,0,0,0,0,0 +Inventory Control +1,38,7,1,1,0,0,0,0,64,1,-1--1--1,,1|(570,270)| +1,39,31,7,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,40,Back Log,903,292,40,20,3,3,0,0,0,0,0,0,0,0,0,0,0,0 +12,41,48,771,294,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,42,44,40,4,0,0,22,0,0,0,-1--1--1,,1|(839,294)| +1,43,44,41,100,0,0,22,0,0,0,-1--1--1,,1|(792,294)| +11,44,0,809,294,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,45,BL In,809,332,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 +12,46,48,1032,295,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,47,49,46,4,0,0,22,0,0,0,-1--1--1,,1|(1005,295)| +1,48,49,40,100,0,0,22,0,0,0,-1--1--1,,1|(959,295)| +11,49,0,982,295,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,50,BL Out,982,333,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,51,27,45,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +1,52,6,50,1,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(934,219)| +10,53,Deficient Amount,790,216,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,54,6,53,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +1,55,40,53,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,56,Cost,1274,454,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +10,57,Overage Cost,1156,427,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +10,58,Underage Cost,1159,479,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,59,57,56,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +1,60,58,56,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +1,61,13,57,0,1,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,62,Unit Overage Cost,1019,423,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,63,62,57,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +1,64,1,57,0,1,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,65,Unit Underage Cost,1014,481,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,66,65,58,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +1,67,53,58,0,1,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,68,Inventory Period,492,333,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,69,68,7,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,70,Mean of Demand,850,358,75,30,8,3,0,19,-1,0,0,0,17-128-64,0-0-0,||B|17-128-64,0,0,0,0,0,0 +1,71,70,27,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,72,Desired Supply Line,477,269,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,73,31,72,0,0,0,0,0,192,0,-1--1--1,,1|(0,0)| +1,74,72,23,0,0,0,0,0,192,0,-1--1--1,,1|(0,0)| +1,75,29,72,0,0,0,0,0,192,0,-1--1--1,,1|(0,0)| +1,76,72,13,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +10,77,Desired Shipment,1146,147,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,78,40,77,0,0,43,0,1,128,0,0-0-0,|||0-0-0,1|(0,0)| +10,79,Desired Delivery Delay,1275,194,75,30,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,||B|17-128-64,0,0,0,0,0,0 +1,80,79,77,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +1,81,77,6,0,0,43,0,0,128,0,-1--1--1,,1|(0,0)| +10,82,Maximum Delivery Rate,787,53,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,83,1,82,1,0,0,0,0,128,0,-1--1--1,,1|(697,73)| +10,84,Minimum Processing Time,1018,47,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,85,84,82,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,86,Fulfilment Ratio,1103,82,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,87,77,86,0,0,0,0,0,128,0,-1--1--1,,1|(0,0)| +1,88,82,86,0,0,0,0,0,128,0,-1--1--1,,1|(0,0)| +1,89,86,6,0,0,0,0,0,128,0,-1--1--1,,1|(0,0)| +10,90,Inventory Adjustment Time,468,556,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,91,90,9,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,92,Supply Line Adjustment Time,314,417,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,93,92,23,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +12,94,1,980,193,23,23,4,4,0,0,1,0,0,0,0,0,0,0,0,0 +Shipment Control +10,95,Sd of Demand,845,401,75,30,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,||B|17-128-64,0,0,0,0,0,0 +1,96,95,27,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +///---\\\ +:L<%^E!@ +1:current.vdfx +4:Time +5:Supply Line +9:current +19:90,0 +21:Shipment Control +24:0 +25:100 +26:100 +15:0,0,0,0,0,0 +27:0, +34:0, +42:1 +72:0 +73:0 +35:Date +36:YYYY-MM-DD +37:2000 +38:1 +39:1 +40:2 +41:0 +95:0 +96:0 +97:0 +77:0 +78:0 +102:1 +93:0 +94:0 +92:0 +91:0 +90:0 +87:0 +75: +43: +103:8,8,8,3,8 +105:0,0,0,0,0,0,0,0,0,0 +104:Courier|12||0-0-0|0-0-0|-1--1--1|0-0-255|192-192-192|-1--1--1 \ No newline at end of file diff --git a/test_scripts/vensim_models/Inventory_backup.mdl b/test_scripts/vensim_models/Inventory_backup.mdl new file mode 100644 index 00000000..5a1ad7ed --- /dev/null +++ b/test_scripts/vensim_models/Inventory_backup.mdl @@ -0,0 +1,343 @@ +{UTF-8} +Sd of Demand= + 10 + ~ + ~ | + +Inventory Adjustment Time= + 3 + ~ Month + ~ | + +Supply Line Adjustment Time= + 3 + ~ + ~ | + +Minimum Processing Time= + 3 + ~ + ~ | + +Demand= + RANDOM NORMAL( 0, 200, Mean of Demand, Sd of Demand, 1111) + ~ + ~ | + +Desired Delivery Delay= + 3 + ~ Month + ~ | + +Maximum Delivery Rate= + Inventory/Minimum Processing Time + ~ Widget/Month + ~ | + +Shipment Rate= + Desired Shipment*Fulfilment Ratio + ~ Widget/Month + ~ Desired Shipment*Fulfilment Ratio + | + +Desired Shipment= + Back Log/Desired Delivery Delay + ~ Widget/Month + ~ | + +Fulfilment Ratio= WITH LOOKUP ( + Maximum Delivery Rate/Desired Shipment, + ([(0,0)-(2,1)],(0,0),(0.5,0.5),(0.672783,0.657895),(1.04587,0.859649),(1.40061,0.942982\ + ),(2,1) )) + ~ + ~ | + +Adjustment for Inventory= + (Desired Inventory-Inventory)/Inventory Adjustment Time + ~ + ~ | + +Adjustment for Supply Line= + (Desired Supply Line-Supply Line)/Supply Line Adjustment Time + ~ + ~ | + +Back Log= INTEG ( + BL In-BL Out, + 100) + ~ + ~ | + +BL In= + Demand + ~ + ~ | + +BL Out= + Shipment Rate + ~ + ~ | + +Cost= + Underage Cost+Overage Cost + ~ + ~ | + +Deficient Amount= + MAX(0, Back Log-Shipment Rate) + ~ Widget + ~ | + +Production Completion= + Supply Line/Lead Time + ~ + ~ | + +Demand Forecast= + SMOOTH(Demand, Forecast Period) + ~ + ~ | + +Desired Inventory= + Demand Forecast*Inventory Period + ~ + ~ | + +Desired Supply Line= + Demand Forecast*Lead Time + ~ + ~ | + +Forecast Period= + 3 + ~ + ~ | + +Inventory= INTEG ( + Production Completion-Shipment Rate, + Desired Inventory) + ~ Widget + ~ | + +Inventory Period= + 5 + ~ + ~ | + +Lead Time= + 5 + ~ + ~ | + +Mean of Demand= + 100 + ~ + ~ | + +Overage Cost= + (Inventory+Supply Line)* Unit Overage Cost + ~ + ~ | + +Desired Production Start= + Adjustment for Inventory+Adjustment for Supply Line+Demand Forecast + ~ + ~ | + +Production Start= + MAX(0,Desired Production Start) + ~ + ~ | + +Supply Line= INTEG ( + Production Start-Production Completion, + Desired Supply Line) + ~ + ~ | + +Underage Cost= + Deficient Amount*Unit Underage Cost + ~ + ~ | + +Unit Overage Cost= + 1 + ~ + ~ | + +Unit Underage Cost= + 9 + ~ + ~ | + +******************************************************** + .Control +********************************************************~ + Simulation Control Parameters + | + +FINAL TIME = 100 + ~ Month + ~ The final time for the simulation. + | + +INITIAL TIME = 0 + ~ Month + ~ The initial time for the simulation. + | + +SAVEPER = + TIME STEP + ~ Month [0,?] + ~ The frequency with which output is stored. + | + +TIME STEP = 1 + ~ Month [0,?] + ~ The time step for the simulation. + | + +\\\---/// Sketch information - do not modify anything except names +V300 Do not put anything below this section - it will be ignored +*View 1 +$192-192-192,0,Times New Roman|12||0-0-0|0-0-0|0-0-255|255-255-255|255-255-255|96,96,90,0 +10,1,Inventory,647,125,40,20,3,3,0,0,0,0,0,0,0,0,0,0,0,0 +12,2,48,921,127,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,3,5,2,4,0,0,22,0,0,0,-1--1--1,,1|(844,127)| +1,4,5,1,100,0,0,22,0,0,0,-1--1--1,,1|(726,127)| +11,5,0,772,127,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,6,Shipment Rate,772,165,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 +10,7,Desired Inventory,469,409,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +10,8,Desired Production Start,179,249,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +10,9,Adjustment for Inventory,304,459,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,10,1,9,1,0,45,0,2,0,0,-1--1--1,|||0-0-0,1|(649,387)| +1,11,7,9,0,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(0,0)| +1,12,9,8,1,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(206,359)| +10,13,Supply Line,392,129,40,20,3,3,0,0,0,0,0,0,0,0,0,0,0,0 +12,14,48,235,127,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,15,17,13,4,0,0,22,0,0,0,-1--1--1,,1|(328,127)| +1,16,17,14,100,0,0,22,0,0,0,-1--1--1,,1|(268,127)| +11,17,0,298,127,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,18,Production Start,298,165,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,19,21,1,4,0,0,22,0,0,0,-1--1--1,,1|(572,127)| +1,20,21,13,100,0,0,22,0,0,0,-1--1--1,,1|(478,127)| +11,21,0,531,127,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,22,Production Completion,531,165,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 +10,23,Adjustment for Supply Line,351,342,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,24,23,8,1,0,0,0,0,64,0,-1--1--1,,1|(249,309)| +1,25,13,23,1,0,45,0,2,0,0,-1--1--1,|||0-0-0,1|(419,226)| +1,26,8,18,1,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(202,180)| +10,27,Demand,701,384,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,28,13,22,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,29,Lead Time,524,216,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,30,29,22,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,31,Demand Forecast,630,486,40,20,3,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,32,27,31,1,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(673,432)| +1,33,31,8,1,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(199,441)| +10,34,Forecast Period,778,485,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,35,34,31,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +12,36,1,318,230,23,23,4,4,0,0,1,0,0,0,0,0,0,0,0,0 +Supply Line Control +12,37,1,623,298,23,23,4,4,0,0,1,0,0,0,0,0,0,0,0,0 +Inventory Control +1,38,7,1,1,0,0,0,0,64,1,-1--1--1,,1|(570,270)| +1,39,31,7,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,40,Back Log,903,292,40,20,3,3,0,0,0,0,0,0,0,0,0,0,0,0 +12,41,48,771,294,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,42,44,40,4,0,0,22,0,0,0,-1--1--1,,1|(839,294)| +1,43,44,41,100,0,0,22,0,0,0,-1--1--1,,1|(792,294)| +11,44,0,809,294,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,45,BL In,809,332,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 +12,46,48,1032,295,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,47,49,46,4,0,0,22,0,0,0,-1--1--1,,1|(1005,295)| +1,48,49,40,100,0,0,22,0,0,0,-1--1--1,,1|(959,295)| +11,49,0,982,295,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,50,BL Out,982,333,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,51,27,45,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +1,52,6,50,1,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(934,219)| +10,53,Deficient Amount,790,216,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,54,6,53,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +1,55,40,53,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,56,Cost,1274,454,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +10,57,Overage Cost,1156,427,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +10,58,Underage Cost,1159,479,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,59,57,56,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +1,60,58,56,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +1,61,13,57,0,1,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,62,Unit Overage Cost,1019,423,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,63,62,57,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +1,64,1,57,0,1,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,65,Unit Underage Cost,1014,481,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,66,65,58,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +1,67,53,58,0,1,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,68,Inventory Period,492,333,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,69,68,7,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,70,Mean of Demand,850,358,75,30,8,3,0,19,-1,0,0,0,17-128-64,0-0-0,||B|17-128-64,0,0,0,0,0,0 +1,71,70,27,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,72,Desired Supply Line,477,269,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,73,31,72,0,0,0,0,0,192,0,-1--1--1,,1|(0,0)| +1,74,72,23,0,0,0,0,0,192,0,-1--1--1,,1|(0,0)| +1,75,29,72,0,0,0,0,0,192,0,-1--1--1,,1|(0,0)| +1,76,72,13,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +10,77,Desired Shipment,1146,147,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,78,40,77,0,0,43,0,1,128,0,0-0-0,|||0-0-0,1|(0,0)| +10,79,Desired Delivery Delay,1275,194,75,30,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,||B|17-128-64,0,0,0,0,0,0 +1,80,79,77,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +1,81,77,6,0,0,43,0,0,128,0,-1--1--1,,1|(0,0)| +10,82,Maximum Delivery Rate,787,53,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,83,1,82,1,0,0,0,0,128,0,-1--1--1,,1|(697,73)| +10,84,Minimum Processing Time,1018,47,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,85,84,82,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,86,Fulfilment Ratio,1103,82,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 +1,87,77,86,0,0,0,0,0,128,0,-1--1--1,,1|(0,0)| +1,88,82,86,0,0,0,0,0,128,0,-1--1--1,,1|(0,0)| +1,89,86,6,0,0,0,0,0,128,0,-1--1--1,,1|(0,0)| +10,90,Inventory Adjustment Time,468,556,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,91,90,9,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,92,Supply Line Adjustment Time,314,417,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,93,92,23,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +12,94,1,980,193,23,23,4,4,0,0,1,0,0,0,0,0,0,0,0,0 +Shipment Control +10,95,Sd of Demand,845,401,75,30,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,||B|17-128-64,0,0,0,0,0,0 +1,96,95,27,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +///---\\\ +:L<%^E!@ +1:current.vdfx +4:Time +5:Supply Line +9:current +19:90,0 +21:Shipment Control +24:0 +25:100 +26:100 +15:0,0,0,0,0,0 +27:0, +34:0, +42:1 +72:0 +73:0 +35:Date +36:YYYY-MM-DD +37:2000 +38:1 +39:1 +40:2 +41:0 +95:0 +96:0 +97:0 +77:0 +78:0 +102:1 +93:0 +94:0 +92:0 +91:0 +90:0 +87:0 +75: +43: +103:8,8,8,3,8 +105:0,0,0,0,0,0,0,0,0,0 +104:Courier|12||0-0-0|0-0-0|-1--1--1|0-0-255|192-192-192|-1--1--1 \ No newline at end of file diff --git a/test_scripts/vensim_models/arithmetic.mdl b/test_scripts/vensim_models/arithmetic.mdl new file mode 100644 index 00000000..75e711e4 --- /dev/null +++ b/test_scripts/vensim_models/arithmetic.mdl @@ -0,0 +1,117 @@ +{UTF-8} +a = A FUNCTION OF( flow1) ~~| +a= + 1 + ~ + ~ | + +flow2 = A FUNCTION OF( ) + ~ + ~ | + +test = A FUNCTION OF( flow1,-flow2) + ~ + ~ | + +flow1 = A FUNCTION OF( ) + ~ + ~ | + +b= + 1 + ~ + ~ | + +c= + a * b / 1+5 + ~ + ~ | + +******************************************************** + .Control +********************************************************~ + Simulation Control Parameters + | + +FINAL TIME = 100 + ~ Month + ~ The final time for the simulation. + | + +INITIAL TIME = 0 + ~ Month + ~ The initial time for the simulation. + | + +SAVEPER = + TIME STEP + ~ Month [0,?] + ~ The frequency with which output is stored. + | + +TIME STEP = 1 + ~ Month [0,?] + ~ The time step for the simulation. + | + +\\\---/// Sketch information - do not modify anything except names +V300 Do not put anything below this section - it will be ignored +*View 1 +$-1--1--1,0,|12||-1--1--1|-1--1--1|-1--1--1|-1--1--1|-1--1--1|96,96,100,0 +10,1,a,244,206,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +10,2,b,271,309,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +10,3,c,436,236,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,4,1,3,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +1,5,2,3,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +10,6,test,479,128,40,20,3,3,0,0,-1,0,0,0,0,0,0,0,0,0 +12,7,48,332,127,30,30,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,8,10,7,100,0,0,22,0,192,0,-1--1--1,,1|(376,127)| +1,9,10,6,4,0,0,22,0,192,0,-1--1--1,,1|(421,127)| +11,10,0,397,127,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,11,flow1,397,165,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 +12,12,48,687,127,30,30,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,13,15,6,100,0,0,22,0,192,0,-1--1--1,,1|(531,127)| +1,14,15,12,4,0,0,22,0,192,0,-1--1--1,,1|(606,127)| +11,15,0,549,127,6,8,34,3,0,0,4,0,0,0,0,0,0,0,0,0 +10,16,flow2,632,127,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,17,11,1,0,0,0,0,0,192,0,-1--1--1,,1|(0,0)| +///---\\\ +:L<%^E!@ +1:test.vdfx +4:Time +5:a +9:/Users/hyunjimoon/Dropbox/test +19:100,0 +24:0 +25:100 +26:100 +15:0,0,0,0,0,0 +27:0, +34:0, +42:1 +72:0 +73:0 +35:Date +36:YYYY-MM-DD +37:2000 +38:1 +39:1 +40:2 +41:0 +95:0 +96:0 +97:0 +77:0 +78:0 +102:1 +93:0 +94:0 +92:0 +91:0 +90:0 +87:0 +75: +43: +103:8,8,8,3,8 +105:0,0,0,0,0,0,0,0,0,0 +104:Courier|12||0-0-0|0-0-0|-1--1--1|0-0-255|192-192-192|-1--1--1 \ No newline at end of file diff --git a/test_scripts/vensim_models/repair.xmile b/test_scripts/vensim_models/repair.xmile new file mode 100644 index 00000000..f9bbe689 --- /dev/null +++ b/test_scripts/vensim_models/repair.xmile @@ -0,0 +1,101 @@ + + +
+ Vensim + Ventana Systems, Inc. + + + + + + + + +
+ + 0 + 100 +
1
+ + + + + + + + Initial_Value + + + Engagement + + + Maintenance + + + + + + + 0 + + + Maintenance + + + Engagement + + + + + + Repair_Shop/Repair_Time*MIN(1,XIDZ(Inventory,BackLog,1)) + + + Month + average of 90 /year = 8 / month + RANDOM_POISSON(0, 100, 8, 0 ,1 , 1234 ) + + + + N of Predictive Maintenance + Predictive_Maintenance + Failure_Count + + + + + Battle_Field / 5 + + + Month + + RANDOM_EXPONENTIAL(0, 100 , 0 , Repair_Time_Rate , 1234) + + + + + 1 + + + + + 100 + + + + + 1 + + + Month + + 1 + + + + Seed for the random number generator - stream ID for the distribution to use. If s is set to 0 the default noise stream will be used. The default noise stream can be controlled using the NOISE SEED variable described below. For each distinct non-zero value of s a separate noise stream will be created. You can couple noise streams by giving them the same stream ID. When streams are coupled it means that the random selections will influence one another, not that they will be the same. For example if there are two functions using the stream ID 7, adding a third with the same stream ID will change the noise generated by the first two. Using a nonzero stream ID is most useful if it is unique so that adding additional random functions will not influence a particular drawing sequence. See the examples below. The stream ID should almost always be 0 or, if nonzero, a constant. Using a dynamic value for StreamID can consume excessive memory. +NOTE The noise stream ID for a random variable should be a number or a constant. If you make the ID a variable a new noise stream will be started each time the value of that variable changes. This will slow things and also degrade the distributional quality of the random variable. + 0 + + + + \ No newline at end of file From 73db7e8613608cf0fc22ed7be668b119e651b719 Mon Sep 17 00:00:00 2001 From: Hyunji Moon <30194633+hyunjimoon@users.noreply.github.com> Date: Thu, 21 Jul 2022 17:25:27 +0900 Subject: [PATCH 02/45] Update testing.py --- test_scripts/testing.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/test_scripts/testing.py b/test_scripts/testing.py index 2cdfaf7e..56ce112a 100644 --- a/test_scripts/testing.py +++ b/test_scripts/testing.py @@ -2,16 +2,15 @@ from pysd.translators.xmile.xmile_file import XmileFile from pysd.builders.stan.stan_model_builder import * - -vf = VensimFile("vensim_models/Inventory.mdl") -#vf = XmileFile("vensim_models/repair.xmile") +vf = VensimFile("test_scripts/vensim_models/Inventory.mdl") +#vf = VensimFile("test_scripts/vensim_models/repair.mdl") vf.parse() am = vf.get_abstract_model() stan_builder = StanModelBuilder(am) -# print(stan_builder.build_function_block(["failure_count", "repair_time"], ["battle_field", "repair_shop"])) # repair -print(stan_builder.create_stan_program(["demand"], ["inventory", "backlog"])) +#print(stan_builder.create_stan_program([(int, "failure_count"), "repair_time"], ["battle_field", "repair_shop"])) # repair +#stan_builder.create_stan_program(["demand"], ["inventory", "backlog"]) # for section in am.sections: # for element in section.elements: @@ -33,4 +32,4 @@ # 1) # ), # 5 -# ) \ No newline at end of file +# ) From 6246c219cf91d5f626d811d6c691cf2a735b3326 Mon Sep 17 00:00:00 2001 From: Hyunji Moon <30194633+hyunjimoon@users.noreply.github.com> Date: Thu, 21 Jul 2022 17:28:26 +0900 Subject: [PATCH 03/45] change model input and output signature will document on sub-types of input and output --- pysd/builders/stan/stan_model_builder.py | 58 +++++++++++++++++------- 1 file changed, 41 insertions(+), 17 deletions(-) diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_model_builder.py index 4b6d0c78..ce70a24f 100644 --- a/pysd/builders/stan/stan_model_builder.py +++ b/pysd/builders/stan/stan_model_builder.py @@ -32,45 +32,62 @@ class StanModelBuilder: def __init__(self, abstract_model: AbstractModel): self.abstract_model = abstract_model - def create_stan_program(self, input_variable_names, output_variable_names, function_name="vensim_func"): + def create_stan_program(self, predictor_variable_names, outcome_variable_names, function_name="vensim_func"): self.code = IndentedString() - self.code += StanFunctionBuilder(self.abstract_model).build_function_block(input_variable_names, - output_variable_names, function_name) + self.code += StanFunctionBuilder(self.abstract_model).build_function_block(predictor_variable_names, + outcome_variable_names, function_name) self.code += "data{\n}\n" + # self.code += StanDataBuilder(self.abstract_model).build_block(predictor_variable_names, outcome_variable_names) self.code += "transformed data{\n}\n" self.code += "parameters{\n}\n" - self.code += StanTransformedParametersBuilder(self.abstract_model).build_block(input_variable_names, output_variable_names, function_name) + self.code += StanTransformedParametersBuilder(self.abstract_model).build_block(predictor_variable_names, outcome_variable_names, function_name) self.code += "model{\n}\n" self.code += "generated quantities{\n}" return self.code +""" class StanDataBuilder: + def __init__(self, abstract_model: AbstractModel): + self.abstract_model = abstract_model + + def build_block(self, predictor_variable_names, outcome_variable_names): + self.code = IndentedString() + self.code += "data {\n" + self.code.indent_level += 1 + self.code += f"predictor= {{{', '.join(predictor_variable_names)}}};\n" + self.code += f"initial_outcome = {{{', '.join(outcome_variable_names)}}};\n" + self.code += f"observed_outcome = {{{', '.join(outcome_variable_names)}}};\n" + self.code += f"times = {{{', '.join(outcome_variable_names)}}};\n" + self.code.indent_level -= 1 + self.code += "}\n" """ class StanTransformedParametersBuilder: def __init__(self, abstract_model: AbstractModel): self.abstract_model = abstract_model - def build_block(self, input_variable_names, output_variable_names, function_name): + def build_block(self, predictor_variable_names, outcome_variable_names, function_name): self.code = IndentedString() self.code += "transformed parameters {\n" self.code.indent_level += 1 argument_variables = [] - for var in input_variable_names: + for var in predictor_variable_names: + print(var) match var: case str(x): + print("dfsdfsdfs") argument_variables.append(x) case (str(type), str(var_name)): argument_variables.append(var_name) - self.code += f"vector[{len(output_variable_names)}] initial_state;\n" - self.code += f"initial_state = {{{', '.join(output_variable_names)}}};\n" + self.code += f"vector[{len(outcome_variable_names)}] initial_outcome;\n" + self.code += f"initial_outcome = {{{', '.join(outcome_variable_names)}}};\n" - self.code += f"array[] vector integrated_result = integrate_ode_rk45({function_name}, initial_state, initial_time, times, {','.join(argument_variables)});\n" + self.code += f"array[] vector integrated_result = integrate_ode_rk45({function_name}, initial_outcome, initial_time, times, {','.join(argument_variables)});\n" self.code.indent_level -= 1 self.code += "}\n" @@ -110,7 +127,7 @@ def print_variable_names(self): for x in var_names: print(f"{x[0].ljust(max_length)}{x[1]}") - def build_function_block(self, input_variable_names, output_variable_names, function_name="vensim_func"): + def build_function_block(self, predictor_variable_names, outcome_variable_names, function_name="vensim_func"): self.code = IndentedString() self.code += "functions {\n" self.code.indent_level += 1 @@ -132,10 +149,10 @@ def recursive_order_search(current, visited): recursive_order_search(var_name, set()) self.elements = sorted(self.elements, key=lambda x: eval_order.index(x.name.lower().replace(" ", "_"))) - self.code += f"vector {function_name}(real time, vector state, " + self.code += f"vector {function_name}(real time, vector outcome, " argument_strings = [] argument_variables = [] - for var in input_variable_names: + for var in predictor_variable_names: match var: case str(x): argument_variables.append(x) @@ -149,8 +166,8 @@ def recursive_order_search(current, visited): self.code += "\n" self.code.indent_level += 1 - for index, output_variable_name in enumerate(output_variable_names, 1): - self.code += f"real {output_variable_name} = state[{index}];\n" + for index, outcome_variable_name in enumerate(outcome_variable_names, 1): + self.code += f"real {outcome_variable_name} = outcome[{index}];\n" self.code += "\n" @@ -158,14 +175,14 @@ def recursive_order_search(current, visited): stan_varname = name_to_identifier(element.name) if stan_varname in argument_variables: continue - elif stan_varname in output_variable_names: + elif stan_varname in outcome_variable_names: stan_varname += "_dydt" for component in element.components: self.code += f"real {stan_varname} = {ast_codegen(component.ast)};\n" self.code += "\n" - output_variable_names = [name + "_dydt" for name in output_variable_names] - self.code += f"return {{{', '.join(output_variable_names)}}};\n" + outcome_variable_names = [name + "_dydt" for name in outcome_variable_names] + self.code += f"return {{{', '.join(outcome_variable_names)}}};\n" self.code.indent_level -= 1 self.code += "}\n" @@ -175,3 +192,10 @@ def recursive_order_search(current, visited): def build_lookups(self): pass + +class StanTransformedDataBuilder: + def __init__(self, abstract_model: AbstractModel, function_name: str = "vensim_ode"): + + self.abstract_model = abstract_model + self.elements = self.abstract_model.sections[0].elements + self.function_name = function_name From b65153007f85aaf9f28585e918196747e1164171 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Sun, 24 Jul 2022 14:09:12 +0900 Subject: [PATCH 04/45] Add LOOKUP implementation. Refactor AST walkers to classes --- pysd/builders/stan/ast_walker.py | 228 ++++++++++++++--------- pysd/builders/stan/stan_model_builder.py | 173 +++++++++++------ pysd/builders/stan/utilities.py | 24 +++ test_scripts/testing.py | 40 ++-- 4 files changed, 294 insertions(+), 171 deletions(-) create mode 100644 pysd/builders/stan/utilities.py diff --git a/pysd/builders/stan/ast_walker.py b/pysd/builders/stan/ast_walker.py index 497bad5b..24c01dc7 100644 --- a/pysd/builders/stan/ast_walker.py +++ b/pysd/builders/stan/ast_walker.py @@ -1,95 +1,155 @@ -from typing import Union, List, Iterable +from typing import Union, List, Iterable, Dict, Tuple from itertools import chain - +from dataclasses import dataclass, field +from .utilities import IndentedString from pysd.translators.structures.abstract_model import\ AbstractComponent, AbstractElement, AbstractModel, AbstractSection from pysd.translators.structures.abstract_expressions import * -def get_aux_names(entry_ast_node): - match entry_ast_node: - case int(): - return [] - case ArithmeticStructure(operators, arguments): - return list(chain.from_iterable([get_aux_names(argument) for argument in arguments])) - case ReferenceStructure(reference, subscripts): - return [entry_ast_node.reference] - case CallStructure(function, arguments): - return list(chain.from_iterable([get_aux_names(argument) for argument in arguments])) - case IntegStructure(flow, initial): - return get_aux_names(flow) + get_aux_names(initial) - case InlineLookupsStructure(argument, lookups): - return get_aux_names(lookups) - - -def ast_codegen(node) -> str: - match node: - case int(x): - return f"{x}" - - case str(x): - return x - - case ArithmeticStructure(operators, arguments): - output_string = "" - last_argument_index = len(arguments) - 1 - for index, argument in enumerate(arguments): - output_string += ast_codegen(argument) - if index < last_argument_index: - output_string += " " - output_string += operators[index] - output_string += " " - return output_string - - case ReferenceStructure(reference, subscripts): - return reference - - case CallStructure(function, arguments): - output_string = "" - function_name = ast_codegen(function) - match function_name: - case "min": - function_name = "fmin" - case "max": - function_name = "fmax" - case "xidz": - assert len(arguments) == 3, "number of arguments for xidz must be 3" - arg1 = ast_codegen(arguments[0]) - arg2 = ast_codegen(arguments[1]) - arg3 = ast_codegen(arguments[2]) - output_string += f" (fabs({arg2}) <= 1e-6) ? {arg3} : ({arg1}) / ({arg2})" - return output_string - case "zidz": - assert len(arguments) == 2, "number of arguments for zidz must be 2" - arg1 = ast_codegen(arguments[0]) - arg2 = ast_codegen(arguments[1]) - output_string += f" (fabs({arg2}) <= 1e-6) ? 0 : ({arg1}) / ({arg2})" - return output_string - case "ln": - function_name = "log" - - output_string += function_name - output_string += "(" - output_string += ",".join([ast_codegen(argument) for argument in arguments]) - output_string += ")" - - return output_string - - case IntegStructure(flow, initial): - return ast_codegen(flow) - - -class AbstractComponentWrapper: - def __init__(self, component: AbstractComponent): - self.component = component - self.ast = component.ast - - def get_required_variable_name(self): - match self.ast: +class BaseNodeWaler: + def walk(self, ast_node): + raise NotImplementedError + + +class AuxNameWalker(BaseNodeWaler): + def walk(self, ast_node) -> List[str]: + match ast_node: case int(): return [] - case ArithmeticStructure(ops, args): - pass + case ArithmeticStructure(operators, arguments): + return list(chain.from_iterable([self.walk(argument) for argument in arguments])) + case ReferenceStructure(reference, subscripts): + return [ast_node.reference] + case CallStructure(function, arguments): + return list(chain.from_iterable([self.walk(argument) for argument in arguments])) + case IntegStructure(flow, initial): + return self.walk(flow) + self.walk(initial) + case InlineLookupsStructure(argument, lookups): + return self.walk(lookups) + +@dataclass +class LookupCodegenWalker(BaseNodeWaler): + generated_lookup_function_names: Dict[Tuple, str] = field(default_factory=dict) + # This dict holds the generated function names of each individual lookup function. + # Key is x + y + x_limits + y_limits, value is function name + n_lookups = 0 + code = IndentedString(indent_level=1) + + @staticmethod + def get_lookup_keyname(lookup_node: LookupsStructure): + return lookup_node.x + lookup_node.y + lookup_node.x_limits + lookup_node.y_limits + + def walk(self, ast_node) -> None: + match ast_node: + case InlineLookupsStructure(argument, lookups): + self.walk(lookups) + case LookupsStructure(x, y, x_limits, y_limits, type): + assert type == "interpolate", "Type of Lookup must be 'interpolate'" + identifier_key = LookupCodegenWalker.get_lookup_keyname(ast_node) + function_name = f"lookupFunc_{self.n_lookups}" + self.generated_lookup_function_names[identifier_key] = function_name + self.n_lookups += 1 + self.code += f"real {function_name}(real x){{\n" + self.code.indent_level += 1 + # Enter function body + self.code += f"# x {x_limits} = {x}\n" + self.code += f"# y {y_limits} = {y}\n" + self.code += "real slope;\n" + self.code += "real intercept;\n\n" + n_intervals = len(x) + for lookup_index in range(n_intervals): + if lookup_index == 0: + continue + if lookup_index == 1: + self.code += f"if(x <= {x[lookup_index]})\n" + else: + self.code += f"else if(x <= {x[lookup_index]})\n" + + self.code.indent_level += 1 + # enter conditional body + self.code += f"intercept = {y[lookup_index - 1]}\n" + self.code += f"slope = ({y[lookup_index]} - {y[lookup_index - 1]}) / ({x[lookup_index]} - {x[lookup_index - 1]});\n" + self.code += f"return intercept + slope * (x - {x[lookup_index - 1]});\n" + self.code.indent_level -= 1 + # exit conditional body + + self.code.indent_level -= 1 + # exit function body + self.code += "}\n\n" + + case _: + return None + + +@dataclass +class BlockCodegenWalker(BaseNodeWaler): + lookup_function_names: Dict[Tuple, str] = field(default_factory=dict) + + def walk(self, ast_node) -> str: + match ast_node: + case int(x): + return f"{x}" + + case str(x): + return x + + case ArithmeticStructure(operators, arguments): + # ArithmeticStructure consists of chained arithmetic expressions. + # We parse them one by one into a single expression + output_string = "" + last_argument_index = len(arguments) - 1 + for index, argument in enumerate(arguments): + output_string += self.walk(argument) + if index < last_argument_index: + output_string += " " + output_string += operators[index] + output_string += " " + return output_string + + case ReferenceStructure(reference, subscripts): + # ReferenceSTructure denotes invoking the value of another variable + # Subscripts are ignored for now + return reference + + case CallStructure(function, arguments): + output_string = "" + function_name = self.walk(function) + match function_name: + case "min": + function_name = "fmin" + case "max": + function_name = "fmax" + case "xidz": + assert len(arguments) == 3, "number of arguments for xidz must be 3" + arg1 = self.walk(arguments[0]) + arg2 = self.walk(arguments[1]) + arg3 = self.walk(arguments[2]) + output_string += f" (fabs({arg2}) <= 1e-6) ? {arg3} : ({arg1}) / ({arg2})" + return output_string + case "zidz": + assert len(arguments) == 2, "number of arguments for zidz must be 2" + arg1 = self.walk(arguments[0]) + arg2 = self.walk(arguments[1]) + output_string += f" (fabs({arg2}) <= 1e-6) ? 0 : ({arg1}) / ({arg2})" + return output_string + case "ln": + # natural log in stan is just log + function_name = "log" + + output_string += function_name + output_string += "(" + output_string += ",".join([self.walk(argument) for argument in arguments]) + output_string += ")" + + return output_string + + case IntegStructure(flow, initial): + return self.walk(flow) + + case InlineLookupsStructure(argument, lookups): + lookup_func_name = self.lookup_function_names[LookupCodegenWalker.get_lookup_keyname(lookups)] + return f"{lookup_func_name}({self.walk(argument)})" diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_model_builder.py index ce70a24f..8196f1f1 100644 --- a/pysd/builders/stan/stan_model_builder.py +++ b/pysd/builders/stan/stan_model_builder.py @@ -1,40 +1,34 @@ import os from pathlib import Path -from typing import Union, List, Dict, Set, Iterable +from typing import Union, List, Dict, Set, Iterable, Type from .ast_walker import * - +from .utilities import * from pysd.translators.structures.abstract_model import\ AbstractComponent, AbstractElement, AbstractModel, AbstractSection -class IndentedString: - def __init__(self, indent_level=0): - self.indent_level = indent_level - self.string = " " * 4 * self.indent_level - - def __iadd__(self, other: str): - prefix = " " * 4 * self.indent_level - if other != "\n": - self.string += prefix - self.string += other - return self - - def __str__(self): - return self.string - - -def name_to_identifier(name: str): - return name.lower().replace(" ", "_") - - class StanModelBuilder: def __init__(self, abstract_model: AbstractModel): self.abstract_model = abstract_model - def create_stan_program(self, predictor_variable_names, outcome_variable_names, function_name="vensim_func"): - self.code = IndentedString() + def create_stan_program(self, predictor_variable_names: List[Union[str, Tuple[str, str]]], outcome_variable_names: List[str], function_name="vensim_func"): + # Santize vensim names to stan-compliant identifiers + sanitized_predictor_variable_names = [] + for var in predictor_variable_names: + match var: + case str(x): + sanitized_predictor_variable_names.append(name_to_identifier(x)) + case (str(type), str(var_name)): + sanitized_predictor_variable_names.append((type, name_to_identifier(var_name))) + case _: + raise Exception("predictor_variable_names must be a list of strings and/or a tuple of the form(T, Name), where T is a string denoting the variable's stan type and Name a string denoting the variable name") + + predictor_variable_names = sanitized_predictor_variable_names + outcome_variable_names = [name_to_identifier(name) for name in outcome_variable_names] + + self.code = IndentedString() self.code += StanFunctionBuilder(self.abstract_model).build_function_block(predictor_variable_names, outcome_variable_names, function_name) @@ -49,6 +43,26 @@ def create_stan_program(self, predictor_variable_names, outcome_variable_names, self.code += "generated quantities{\n}" return self.code + + def print_variable_info(self): + var_names = [] + max_length = len("original name") + 1 + for element in self.abstract_model.sections[0].elements: + is_stock = False + for component in element.components: + if isinstance(component.ast, IntegStructure): + is_stock = True + break + + var_names.append((element.name, name_to_identifier(element.name), is_stock)) + max_length = max(max_length, len(element.name) + 1) + + header = 'original name'.ljust(max_length) + "stan variable name".ljust(max_length) + "is stock" + print(header) + print("-" * len(header)) + for x in var_names: + print(x[0].ljust(max_length) + x[1].ljust(max_length) + ("V" if x[2] else "")) + """ class StanDataBuilder: def __init__(self, abstract_model: AbstractModel): self.abstract_model = abstract_model @@ -65,6 +79,7 @@ def build_block(self, predictor_variable_names, outcome_variable_names): self.code.indent_level -= 1 self.code += "}\n" """ + class StanTransformedParametersBuilder: def __init__(self, abstract_model: AbstractModel): self.abstract_model = abstract_model @@ -76,10 +91,8 @@ def build_block(self, predictor_variable_names, outcome_variable_names, function argument_variables = [] for var in predictor_variable_names: - print(var) match var: case str(x): - print("dfsdfsdfs") argument_variables.append(x) case (str(type), str(var_name)): argument_variables.append(var_name) @@ -99,59 +112,83 @@ def __init__(self, abstract_model: AbstractModel, function_name: str = "vensim_o self.abstract_model = abstract_model self.elements = self.abstract_model.sections[0].elements - self.function_name = function_name - - + self.ode_function_name = function_name + self.lookup_builder_walker = LookupCodegenWalker() + self.variable_dependency_graph: Dict[str, Set] = {} # in order to evaluate 'key' variable, we need 'element' variables + self.code = IndentedString() - def create_dependency_graph(self): - dependency_graph: Dict[str, Set] = {} + def _create_dependency_graph(self): + self.variable_dependency_graph = {} + walker = AuxNameWalker() for element in self.elements: for component in element.components: - if element.name not in dependency_graph: - dependency_graph[element.name.lower().replace(" ", "_")] = set() - - dependent_aux_names = get_aux_names(component.ast) - dependency_graph[element.name.lower().replace(" ", "_")].update(dependent_aux_names) + if element.name not in self.variable_dependency_graph: + self.variable_dependency_graph[name_to_identifier(element.name)] = set() - return dependency_graph + dependent_aux_names = walker.walk(component.ast) + if dependent_aux_names: + self.variable_dependency_graph[name_to_identifier(element.name)].update(dependent_aux_names) - def print_variable_names(self): - var_names = [] - max_length = len("original name") + 1 - for element in self.elements: - var_names.append((element.name, element.name.lower().replace(" ", "_"))) - max_length = max(max_length, len(element.name) + 1) - - print(f"{'original name'.ljust(max_length)}stan variable name") - print("-" * 10) - for x in var_names: - print(f"{x[0].ljust(max_length)}{x[1]}") + return self.variable_dependency_graph - def build_function_block(self, predictor_variable_names, outcome_variable_names, function_name="vensim_func"): + def build_function_block(self, predictor_variable_names: List[Tuple[str, str]], outcome_variable_names: List[str], function_name: str ="vensim_func"): self.code = IndentedString() self.code += "functions {\n" + + # Build the lookup functions + self.build_lookups() + lookup_functions_code = str(self.lookup_builder_walker.code).rstrip() + if lookup_functions_code: + self.code += lookup_functions_code + self.code += "\n\n" + self.code.indent_level += 1 - dgraph = self.create_dependency_graph() - eval_order = [] + self.code += "# Begin ODE declaration\n" + # Enter function block + self._create_dependency_graph() + + # Identify the minimum number of variables needed for calculating outcomes + required_variables = set() + bfs_stack = [] + bfs_stack.extend(outcome_variable_names) + while len(bfs_stack) > 0: + variable = bfs_stack.pop(0) + required_variables.add(variable) + for next_var in self.variable_dependency_graph[variable]: + if next_var in required_variables: + continue + bfs_stack.append(next_var) + required_variables |= self.variable_dependency_graph[variable] + + #print(self.variable_dependency_graph) + #print("rv:", required_variables) + eval_order = [] def recursive_order_search(current, visited): - if current in visited: - return + # if current in visited: + # return visited.add(current) - if current in eval_order: - return - for child in dgraph[current]: + # if current in eval_order: + # return + for child in self.variable_dependency_graph[current]: if child == current: continue - recursive_order_search(child, visited) + if child not in visited: + recursive_order_search(child, visited) eval_order.append(current) - for var_name in dgraph.keys(): + #for var_name in self.variable_dependency_graph.keys(): + for var_name in required_variables: recursive_order_search(var_name, set()) - self.elements = sorted(self.elements, key=lambda x: eval_order.index(x.name.lower().replace(" ", "_"))) + self.elements = [element for element in self.elements if name_to_identifier(element.name) in required_variables] + self.elements = sorted(self.elements, key=lambda x: eval_order.index(name_to_identifier(x.name))) + + + ################# + # Create function declaration self.code += f"vector {function_name}(real time, vector outcome, " argument_strings = [] - argument_variables = [] + argument_variables = [] # this list holds the names of the argument variables for var in predictor_variable_names: match var: case str(x): @@ -164,34 +201,46 @@ def recursive_order_search(current, visited): self.code += ", ".join(argument_strings) self.code += "){" self.code += "\n" + ############# self.code.indent_level += 1 + # Enter function body for index, outcome_variable_name in enumerate(outcome_variable_names, 1): self.code += f"real {outcome_variable_name} = outcome[{index}];\n" self.code += "\n" + codegen_walker = BlockCodegenWalker(self.lookup_builder_walker.generated_lookup_function_names) for element in self.elements: stan_varname = name_to_identifier(element.name) if stan_varname in argument_variables: continue elif stan_varname in outcome_variable_names: stan_varname += "_dydt" + elif stan_varname not in required_variables: + continue for component in element.components: - self.code += f"real {stan_varname} = {ast_codegen(component.ast)};\n" + self.code += f"real {stan_varname} = {codegen_walker.walk(component.ast)};\n" self.code += "\n" + + # Generate code for returning outcomes of interest outcome_variable_names = [name + "_dydt" for name in outcome_variable_names] self.code += f"return {{{', '.join(outcome_variable_names)}}};\n" self.code.indent_level -= 1 + # Exit function body self.code += "}\n" self.code.indent_level -= 1 + # Exit function block self.code += "}\n" return str(self.code) def build_lookups(self): - pass + for element in self.elements: + for component in element.components: + self.lookup_builder_walker.walk(component.ast) + class StanTransformedDataBuilder: def __init__(self, abstract_model: AbstractModel, function_name: str = "vensim_ode"): diff --git a/pysd/builders/stan/utilities.py b/pysd/builders/stan/utilities.py new file mode 100644 index 00000000..a43bf5fc --- /dev/null +++ b/pysd/builders/stan/utilities.py @@ -0,0 +1,24 @@ +class IndentedString: + def __init__(self, indent_level=0): + self.indent_level = indent_level + self.string = "" + + def __iadd__(self, other: str): + prefix = " " * 4 * self.indent_level + if other != "\n": + self.string += prefix + self.string += other + return self + + def add_raw(self, string, ignore_indent=False): + if ignore_indent: + self.string += string + else: + self.__iadd__(string) + + def __str__(self): + return self.string + + +def name_to_identifier(name: str): + return name.lower().replace(" ", "_") \ No newline at end of file diff --git a/test_scripts/testing.py b/test_scripts/testing.py index 56ce112a..b34eeae8 100644 --- a/test_scripts/testing.py +++ b/test_scripts/testing.py @@ -2,34 +2,24 @@ from pysd.translators.xmile.xmile_file import XmileFile from pysd.builders.stan.stan_model_builder import * -vf = VensimFile("test_scripts/vensim_models/Inventory.mdl") +vf = VensimFile("vensim_models/Inventory_backup.mdl") +#vf = VensimFile("vensim_models/arithmetic.mdl") #vf = VensimFile("test_scripts/vensim_models/repair.mdl") vf.parse() am = vf.get_abstract_model() stan_builder = StanModelBuilder(am) -#print(stan_builder.create_stan_program([(int, "failure_count"), "repair_time"], ["battle_field", "repair_shop"])) # repair -#stan_builder.create_stan_program(["demand"], ["inventory", "backlog"]) - -# for section in am.sections: -# for element in section.elements: -# print("*" * 10) -# print(f"name: {element.name}") -# print(f"length: {len(element.components)}") -# for component in element.components: -# print(f"type: {component.type}") -# print(f"subtype: {component.subtype}") -# print(f"subscript: {component.subscripts}") -# print(component.ast) - -# ( -# ArithmeticStructure( -# operators=['*', '/'], -# arguments=( -# ReferenceStructure(reference='a', subscripts=None), -# ReferenceStructure(reference='b', subscripts=None), -# 1) -# ), -# 5 -# ) +stan_builder.print_variable_info() +#print(stan_builder.create_stan_program([("int", "failure_count"), "repair_time"], ["battle_field", "repair_shop"])) # repair +#print(stan_builder.create_stan_program(["Demand"], ["inventory", "Back Log"])) +for section in am.sections: + for element in section.elements: + print("*" * 10) + print(f"name: {element.name}") + print(f"length: {len(element.components)}") + for component in element.components: + print(f"type: {component.type}") + print(f"subtype: {component.subtype}") + print(f"subscript: {component.subscripts}") + print(component.ast) \ No newline at end of file From 64eb14039d79b72c6015c79b0132bdc5af7df1a1 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Sun, 24 Jul 2022 15:19:07 +0900 Subject: [PATCH 05/45] Automatically identify stock variables and initial values --- pysd/builders/stan/ast_walker.py | 14 +++++- pysd/builders/stan/stan_model_builder.py | 57 +++++++++++++++++------- pysd/builders/stan/utilities.py | 2 +- test_scripts/testing.py | 2 +- 4 files changed, 56 insertions(+), 19 deletions(-) diff --git a/pysd/builders/stan/ast_walker.py b/pysd/builders/stan/ast_walker.py index 24c01dc7..9bf254a7 100644 --- a/pysd/builders/stan/ast_walker.py +++ b/pysd/builders/stan/ast_walker.py @@ -85,7 +85,7 @@ def walk(self, ast_node) -> None: @dataclass class BlockCodegenWalker(BaseNodeWaler): - lookup_function_names: Dict[Tuple, str] = field(default_factory=dict) + lookup_function_names: Dict[Tuple, str] def walk(self, ast_node) -> str: match ast_node: @@ -152,4 +152,16 @@ def walk(self, ast_node) -> str: lookup_func_name = self.lookup_function_names[LookupCodegenWalker.get_lookup_keyname(lookups)] return f"{lookup_func_name}({self.walk(argument)})" +@dataclass +class InitialValueCodeGenWalker(BlockCodegenWalker): + lookup_function_names: Dict[Tuple, str] + + def walk(self, ast_node): + match ast_node: + case IntegStructure(flow, initial): + return self.walk(initial) + case SmoothStructure(input, smooth_time, initial, order): + return self.walk(initial) + case _: + return super().walk(ast_node) diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_model_builder.py index 8196f1f1..3fbd771b 100644 --- a/pysd/builders/stan/stan_model_builder.py +++ b/pysd/builders/stan/stan_model_builder.py @@ -13,31 +13,34 @@ def __init__(self, abstract_model: AbstractModel): self.abstract_model = abstract_model - def create_stan_program(self, predictor_variable_names: List[Union[str, Tuple[str, str]]], outcome_variable_names: List[str], function_name="vensim_func"): + def create_stan_program(self, predictor_variable_names: List[Union[str, Tuple[str, str]]], function_name="vensim_func"): # Santize vensim names to stan-compliant identifiers sanitized_predictor_variable_names = [] for var in predictor_variable_names: match var: case str(x): - sanitized_predictor_variable_names.append(name_to_identifier(x)) + sanitized_predictor_variable_names.append(vensim_name_to_identifier(x)) case (str(type), str(var_name)): - sanitized_predictor_variable_names.append((type, name_to_identifier(var_name))) + sanitized_predictor_variable_names.append((type, vensim_name_to_identifier(var_name))) case _: raise Exception("predictor_variable_names must be a list of strings and/or a tuple of the form(T, Name), where T is a string denoting the variable's stan type and Name a string denoting the variable name") predictor_variable_names = sanitized_predictor_variable_names - outcome_variable_names = [name_to_identifier(name) for name in outcome_variable_names] + outcome_variable_names = self.get_stock_variable_stan_names() + if not outcome_variable_names: + raise Exception("There are no stock variables defined in the model.") self.code = IndentedString() - self.code += StanFunctionBuilder(self.abstract_model).build_function_block(predictor_variable_names, - outcome_variable_names, function_name) + function_block_builder = StanFunctionBuilder(self.abstract_model) + + self.code += function_block_builder.build_function_block(predictor_variable_names, outcome_variable_names, function_name) self.code += "data{\n}\n" # self.code += StanDataBuilder(self.abstract_model).build_block(predictor_variable_names, outcome_variable_names) self.code += "transformed data{\n}\n" self.code += "parameters{\n}\n" - self.code += StanTransformedParametersBuilder(self.abstract_model).build_block(predictor_variable_names, outcome_variable_names, function_name) + self.code += StanTransformedParametersBuilder(self.abstract_model).build_block(predictor_variable_names, outcome_variable_names, function_block_builder.lookup_builder_walker.generated_lookup_function_names, function_name) self.code += "model{\n}\n" self.code += "generated quantities{\n}" @@ -54,7 +57,7 @@ def print_variable_info(self): is_stock = True break - var_names.append((element.name, name_to_identifier(element.name), is_stock)) + var_names.append((element.name, vensim_name_to_identifier(element.name), is_stock)) max_length = max(max_length, len(element.name) + 1) header = 'original name'.ljust(max_length) + "stan variable name".ljust(max_length) + "is stock" @@ -63,6 +66,22 @@ def print_variable_info(self): for x in var_names: print(x[0].ljust(max_length) + x[1].ljust(max_length) + ("V" if x[2] else "")) + def get_stock_variable_stan_names(self) -> List[str]: + """ + Iterate through the AST and find stock variables + Returns + ------- + + """ + stock_varible_names = [] + for element in self.abstract_model.sections[0].elements: + for component in element.components: + if isinstance(component.ast, IntegStructure): + stock_varible_names.append(vensim_name_to_identifier(element.name)) + break + + return stock_varible_names + """ class StanDataBuilder: def __init__(self, abstract_model: AbstractModel): self.abstract_model = abstract_model @@ -84,7 +103,7 @@ class StanTransformedParametersBuilder: def __init__(self, abstract_model: AbstractModel): self.abstract_model = abstract_model - def build_block(self, predictor_variable_names, outcome_variable_names, function_name): + def build_block(self, predictor_variable_names, outcome_variable_names, lookup_function_dict, function_name): self.code = IndentedString() self.code += "transformed parameters {\n" self.code.indent_level += 1 @@ -97,8 +116,14 @@ def build_block(self, predictor_variable_names, outcome_variable_names, function case (str(type), str(var_name)): argument_variables.append(var_name) - self.code += f"vector[{len(outcome_variable_names)}] initial_outcome;\n" - self.code += f"initial_outcome = {{{', '.join(outcome_variable_names)}}};\n" + for outcome_variable_name in outcome_variable_names: + for element in self.abstract_model.sections[0].elements: + if vensim_name_to_identifier(element.name) == outcome_variable_name: + component = element.components[0] + assert isinstance(component.ast, IntegStructure), "Output variable component must be an INTEG." + self.code += f"real {outcome_variable_name}_initial = {InitialValueCodeGenWalker(lookup_function_dict).walk(component.ast)};\n" + + self.code += f"vector[{len(outcome_variable_names)}] initial_outcome = {{{', '.join([x + '_initial' for x in outcome_variable_names])}}};\n" self.code += f"array[] vector integrated_result = integrate_ode_rk45({function_name}, initial_outcome, initial_time, times, {','.join(argument_variables)});\n" self.code.indent_level -= 1 @@ -123,11 +148,11 @@ def _create_dependency_graph(self): for element in self.elements: for component in element.components: if element.name not in self.variable_dependency_graph: - self.variable_dependency_graph[name_to_identifier(element.name)] = set() + self.variable_dependency_graph[vensim_name_to_identifier(element.name)] = set() dependent_aux_names = walker.walk(component.ast) if dependent_aux_names: - self.variable_dependency_graph[name_to_identifier(element.name)].update(dependent_aux_names) + self.variable_dependency_graph[vensim_name_to_identifier(element.name)].update(dependent_aux_names) return self.variable_dependency_graph @@ -180,8 +205,8 @@ def recursive_order_search(current, visited): for var_name in required_variables: recursive_order_search(var_name, set()) - self.elements = [element for element in self.elements if name_to_identifier(element.name) in required_variables] - self.elements = sorted(self.elements, key=lambda x: eval_order.index(name_to_identifier(x.name))) + self.elements = [element for element in self.elements if vensim_name_to_identifier(element.name) in required_variables] + self.elements = sorted(self.elements, key=lambda x: eval_order.index(vensim_name_to_identifier(x.name))) ################# @@ -212,7 +237,7 @@ def recursive_order_search(current, visited): codegen_walker = BlockCodegenWalker(self.lookup_builder_walker.generated_lookup_function_names) for element in self.elements: - stan_varname = name_to_identifier(element.name) + stan_varname = vensim_name_to_identifier(element.name) if stan_varname in argument_variables: continue elif stan_varname in outcome_variable_names: diff --git a/pysd/builders/stan/utilities.py b/pysd/builders/stan/utilities.py index a43bf5fc..1c2c9b33 100644 --- a/pysd/builders/stan/utilities.py +++ b/pysd/builders/stan/utilities.py @@ -20,5 +20,5 @@ def __str__(self): return self.string -def name_to_identifier(name: str): +def vensim_name_to_identifier(name: str): return name.lower().replace(" ", "_") \ No newline at end of file diff --git a/test_scripts/testing.py b/test_scripts/testing.py index b34eeae8..732ba433 100644 --- a/test_scripts/testing.py +++ b/test_scripts/testing.py @@ -12,7 +12,7 @@ stan_builder = StanModelBuilder(am) stan_builder.print_variable_info() #print(stan_builder.create_stan_program([("int", "failure_count"), "repair_time"], ["battle_field", "repair_shop"])) # repair -#print(stan_builder.create_stan_program(["Demand"], ["inventory", "Back Log"])) +print(stan_builder.create_stan_program(["Demand"])) for section in am.sections: for element in section.elements: print("*" * 10) From 4f1e37e57ed37bb1772bb6c8f3c8ee8a15fd3e19 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Sun, 24 Jul 2022 17:50:03 +0900 Subject: [PATCH 06/45] WIP static RNG function --- .gitignore | 3 +- pysd/builders/stan/ast_walker.py | 68 +++++++++++++++++++++++- pysd/builders/stan/iteration_counter.hpp | 15 ++++++ pysd/builders/stan/stan_model_builder.py | 24 ++++++++- test_scripts/testing.py | 22 ++++---- 5 files changed, 117 insertions(+), 15 deletions(-) create mode 100644 pysd/builders/stan/iteration_counter.hpp diff --git a/.gitignore b/.gitignore index 95022f46..bffabcdb 100644 --- a/.gitignore +++ b/.gitignore @@ -11,4 +11,5 @@ tests/cover/ tests/htmlcov/ .idea/* docs/_build/* -docs/tables/*.csv \ No newline at end of file +docs/tables/*.csv +venv/ \ No newline at end of file diff --git a/pysd/builders/stan/ast_walker.py b/pysd/builders/stan/ast_walker.py index 9bf254a7..68701219 100644 --- a/pysd/builders/stan/ast_walker.py +++ b/pysd/builders/stan/ast_walker.py @@ -153,7 +153,8 @@ def walk(self, ast_node) -> str: return f"{lookup_func_name}({self.walk(argument)})" @dataclass -class InitialValueCodeGenWalker(BlockCodegenWalker): +class InitialValueCodegenWalker(BlockCodegenWalker): + variable_ast_dict: Dict[str, AbstractSyntax] lookup_function_names: Dict[Tuple, str] def walk(self, ast_node): @@ -162,6 +163,71 @@ def walk(self, ast_node): return self.walk(initial) case SmoothStructure(input, smooth_time, initial, order): return self.walk(initial) + case ReferenceStructure(reference, subscripts): + if reference in self.variable_ast_dict: + return self.walk(self.variable_ast_dict[reference]) + else: + return super().walk(ast_node) case _: return super().walk(ast_node) + +@dataclass +class RNGCodegenWalker(InitialValueCodegenWalker): + variable_ast_dict: Dict[str, AbstractSyntax] + lookup_function_names: Dict[Tuple, str] + total_timestep: int + + def walk(self, ast_node) -> str: + match ast_node: + case CallStructure(function, arguments): + function_name = self.walk(function) + match function_name: + case "random_beta" | "random_binomial" | "random_binomial" | "random_exponential" | "random_gamma" | "random_normal" | "random_poisson": + argument_codegen = [self.walk(argument) for argument in arguments] + return self.rng_codegen(function_name, argument_codegen) + case _: + return super().walk(ast_node) + + case IntegStructure(flow, initial): + raise Exception("RNG function arguments cannot contain stock variables which change with time and thus must be constant!") + + case SmoothStructure(input, smooth_time, initial, order): + raise Exception("RNG function arguments cannot contain stock variables which change with time and thus must be constant!") + + case ReferenceStructure(reference, subscripts): + if reference in self.variable_ast_dict: + return self.walk(reference) + else: + return super().walk(ast_node) + + case ArithmeticStructure(operators, arguments): + # ArithmeticStructure consists of chained arithmetic expressions. + # We parse them one by one into a single expression + output_string = "" + last_argument_index = len(arguments) - 1 + for index, argument in enumerate(arguments): + output_string += self.walk(argument) + if index < last_argument_index: + output_string += " " + output_string += operators[index] + output_string += " " + return output_string + + case _: + return super().walk(ast_node) + + def rng_codegen(self, rng_type, arguments): + match rng_type: + case "random_normal": + lower, upper, mean, std, _ = arguments + return f"fmin(fmax(normal_rng({mean}, {std}), {lower}), {upper})" + case "random_uniform": + lower, upper, _ = arguments + return f"uniform_rng({lower}, {upper})" + case "random_poisson": + lower, upper, _lambda, offset, multiply, _ = arguments + return f"fmin(fmax(fma(poisson_rng({_lambda}), {multiply}, {offset}), {lower}), {upper})" + case _: + raise Exception(f"RNG function {rng_type} not implemented") + diff --git a/pysd/builders/stan/iteration_counter.hpp b/pysd/builders/stan/iteration_counter.hpp new file mode 100644 index 00000000..d5e18d26 --- /dev/null +++ b/pysd/builders/stan/iteration_counter.hpp @@ -0,0 +1,15 @@ +static int iteration_counter = 0; + +// https://discourse.mc-stan.org/t/generating-random-numbers-in-the-model/3608 +// https://discourse.mc-stan.org/t/is-it-possible-to-access-the-iteration-step-number-inside-a-stan-program/1871/6 +// https://mc-stan.org/docs/cmdstan-guide/using-external-cpp-code.html +// https://discourse.mc-stan.org/t/hoping-for-some-guidance-help-with-implementing-custom-log-likelihood-and-gradient-for-research-project-details-below/24598/14 +namespace vensim_ode_model_namespace { + inline int get_current_iteration(std::ostream* pstream__) { + return iteration_counter; + } + + inline void increment_iteration(std::ostream* pstream__) { + ++iteration_counter; + } +} \ No newline at end of file diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_model_builder.py index 3fbd771b..3d02c3bc 100644 --- a/pysd/builders/stan/stan_model_builder.py +++ b/pysd/builders/stan/stan_model_builder.py @@ -12,6 +12,13 @@ class StanModelBuilder: def __init__(self, abstract_model: AbstractModel): self.abstract_model = abstract_model + self.variable_ast_dict: Dict[str, AbstractSyntax] = {} + assert len(self.abstract_model.sections) == 1, "Number of sections in AbstractModel must be 1." + for element in self.abstract_model.sections[0].elements: + stan_varname = vensim_name_to_identifier(element.name) + assert len(element.components) == 1, f"Number of components in AbstractElement must be 1, but {element.name} has {len(element.components)}" + self.variable_ast_dict[stan_varname] = element.components[0].ast + def create_stan_program(self, predictor_variable_names: List[Union[str, Tuple[str, str]]], function_name="vensim_func"): # Santize vensim names to stan-compliant identifiers @@ -28,7 +35,7 @@ def create_stan_program(self, predictor_variable_names: List[Union[str, Tuple[st predictor_variable_names = sanitized_predictor_variable_names outcome_variable_names = self.get_stock_variable_stan_names() if not outcome_variable_names: - raise Exception("There are no stock variables defined in the model.") + raise Exception("There are no stock variables defined in the model, hence nothing to integrate.") self.code = IndentedString() @@ -99,6 +106,11 @@ def build_block(self, predictor_variable_names, outcome_variable_names): self.code += "}\n" """ +class StanTransformedDataBuilder: + def __init__(self, abstract_model: AbstractModel): + self.abstract_model = abstract_model + + class StanTransformedParametersBuilder: def __init__(self, abstract_model: AbstractModel): self.abstract_model = abstract_model @@ -116,12 +128,19 @@ def build_block(self, predictor_variable_names, outcome_variable_names, lookup_f case (str(type), str(var_name)): argument_variables.append(var_name) + variable_ast_dict: Dict[str, AbstractSyntax] = {} + for element in self.abstract_model.sections[0].elements: + stan_varname = vensim_name_to_identifier(element.name) + variable_ast_dict[stan_varname] = element.components[0].ast + + for outcome_variable_name in outcome_variable_names: for element in self.abstract_model.sections[0].elements: if vensim_name_to_identifier(element.name) == outcome_variable_name: component = element.components[0] assert isinstance(component.ast, IntegStructure), "Output variable component must be an INTEG." - self.code += f"real {outcome_variable_name}_initial = {InitialValueCodeGenWalker(lookup_function_dict).walk(component.ast)};\n" + self.code += f"real {outcome_variable_name}_initial = {InitialValueCodegenWalker(lookup_function_dict, variable_ast_dict).walk(component.ast)};\n" + break self.code += f"vector[{len(outcome_variable_names)}] initial_outcome = {{{', '.join([x + '_initial' for x in outcome_variable_names])}}};\n" @@ -197,6 +216,7 @@ def recursive_order_search(current, visited): # return for child in self.variable_dependency_graph[current]: if child == current: continue + if child in outcome_variable_names: continue if child not in visited: recursive_order_search(child, visited) eval_order.append(current) diff --git a/test_scripts/testing.py b/test_scripts/testing.py index 732ba433..4991294b 100644 --- a/test_scripts/testing.py +++ b/test_scripts/testing.py @@ -4,7 +4,7 @@ vf = VensimFile("vensim_models/Inventory_backup.mdl") #vf = VensimFile("vensim_models/arithmetic.mdl") -#vf = VensimFile("test_scripts/vensim_models/repair.mdl") +#vf = XmileFile("vensim_models/repair.xmile") vf.parse() am = vf.get_abstract_model() @@ -13,13 +13,13 @@ stan_builder.print_variable_info() #print(stan_builder.create_stan_program([("int", "failure_count"), "repair_time"], ["battle_field", "repair_shop"])) # repair print(stan_builder.create_stan_program(["Demand"])) -for section in am.sections: - for element in section.elements: - print("*" * 10) - print(f"name: {element.name}") - print(f"length: {len(element.components)}") - for component in element.components: - print(f"type: {component.type}") - print(f"subtype: {component.subtype}") - print(f"subscript: {component.subscripts}") - print(component.ast) \ No newline at end of file +# for section in am.sections: +# for element in section.elements: +# print("*" * 10) +# print(f"name: {element.name}") +# print(f"length: {len(element.components)}") +# for component in element.components: +# print(f"type: {component.type}") +# print(f"subtype: {component.subtype}") +# print(f"subscript: {component.subscripts}") +# print(component.ast) \ No newline at end of file From e91255391141c0329cf7809f0ce46d391604b006 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Sun, 31 Jul 2022 23:51:21 +0900 Subject: [PATCH 07/45] Update test models and initial value generation --- pysd/builders/stan/ast_walker.py | 18 + pysd/builders/stan/stan_model_builder.py | 1 - test_scripts/testing.py | 2 +- test_scripts/vensim_models/Inventory.mdl | 66 +-- test_scripts/vensim_models/Inventory.xmile | 242 ++++++++++ test_scripts/vensim_models/Inventory_GBM.stmx | 378 ++++++++++++++++ test_scripts/vensim_models/Inventory_PN.stmx | 413 ++++++++++++++++++ .../vensim_models/Inventory_backup.mdl | 343 --------------- test_scripts/vensim_models/demand-supply.mdl | 360 +++++++++++++++ .../vensim_models/demand-supply.xmile | 241 ++++++++++ .../vensim_models/demand-supply_wolookup.mdl | 343 +++++++++++++++ .../demand-supply_wolookup.xmile | 229 ++++++++++ test_scripts/vensim_models/prey-predator.mdl | 183 ++++++++ test_scripts/vensim_models/repair.xmile | 101 ----- 14 files changed, 2442 insertions(+), 478 deletions(-) create mode 100644 test_scripts/vensim_models/Inventory.xmile create mode 100644 test_scripts/vensim_models/Inventory_GBM.stmx create mode 100644 test_scripts/vensim_models/Inventory_PN.stmx delete mode 100644 test_scripts/vensim_models/Inventory_backup.mdl create mode 100644 test_scripts/vensim_models/demand-supply.mdl create mode 100644 test_scripts/vensim_models/demand-supply.xmile create mode 100644 test_scripts/vensim_models/demand-supply_wolookup.mdl create mode 100644 test_scripts/vensim_models/demand-supply_wolookup.xmile create mode 100644 test_scripts/vensim_models/prey-predator.mdl delete mode 100644 test_scripts/vensim_models/repair.xmile diff --git a/pysd/builders/stan/ast_walker.py b/pysd/builders/stan/ast_walker.py index 68701219..a6896f78 100644 --- a/pysd/builders/stan/ast_walker.py +++ b/pysd/builders/stan/ast_walker.py @@ -92,6 +92,9 @@ def walk(self, ast_node) -> str: case int(x): return f"{x}" + case float(x): + return f"{x}" + case str(x): return x @@ -152,6 +155,9 @@ def walk(self, ast_node) -> str: lookup_func_name = self.lookup_function_names[LookupCodegenWalker.get_lookup_keyname(lookups)] return f"{lookup_func_name}({self.walk(argument)})" + case _: + raise Exception("Got unknown node", ast_node) + @dataclass class InitialValueCodegenWalker(BlockCodegenWalker): variable_ast_dict: Dict[str, AbstractSyntax] @@ -168,6 +174,18 @@ def walk(self, ast_node): return self.walk(self.variable_ast_dict[reference]) else: return super().walk(ast_node) + case ArithmeticStructure(operators, arguments): + # ArithmeticStructure consists of chained arithmetic expressions. + # We parse them one by one into a single expression + output_string = "" + last_argument_index = len(arguments) - 1 + for index, argument in enumerate(arguments): + output_string += self.walk(argument) + if index < last_argument_index: + output_string += " " + output_string += operators[index] + output_string += " " + return output_string case _: return super().walk(ast_node) diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_model_builder.py index 3d02c3bc..ef82fab4 100644 --- a/pysd/builders/stan/stan_model_builder.py +++ b/pysd/builders/stan/stan_model_builder.py @@ -133,7 +133,6 @@ def build_block(self, predictor_variable_names, outcome_variable_names, lookup_f stan_varname = vensim_name_to_identifier(element.name) variable_ast_dict[stan_varname] = element.components[0].ast - for outcome_variable_name in outcome_variable_names: for element in self.abstract_model.sections[0].elements: if vensim_name_to_identifier(element.name) == outcome_variable_name: diff --git a/test_scripts/testing.py b/test_scripts/testing.py index 4991294b..ef27f796 100644 --- a/test_scripts/testing.py +++ b/test_scripts/testing.py @@ -2,7 +2,7 @@ from pysd.translators.xmile.xmile_file import XmileFile from pysd.builders.stan.stan_model_builder import * -vf = VensimFile("vensim_models/Inventory_backup.mdl") +vf = VensimFile("vensim_models/demand-supply.mdl") #vf = VensimFile("vensim_models/arithmetic.mdl") #vf = XmileFile("vensim_models/repair.xmile") vf.parse() diff --git a/test_scripts/vensim_models/Inventory.mdl b/test_scripts/vensim_models/Inventory.mdl index 28da49bb..14efa411 100644 --- a/test_scripts/vensim_models/Inventory.mdl +++ b/test_scripts/vensim_models/Inventory.mdl @@ -1,7 +1,7 @@ {UTF-8} Sd of Demand= 10 - ~ + ~ ~ | Inventory Adjustment Time= @@ -11,17 +11,17 @@ Inventory Adjustment Time= Supply Line Adjustment Time= 3 - ~ + ~ ~ | Minimum Processing Time= 3 - ~ + ~ ~ | Demand= RANDOM NORMAL( 0, 200, Mean of Demand, Sd of Demand, 1111) - ~ + ~ ~ | Desired Delivery Delay= @@ -45,39 +45,42 @@ Desired Shipment= ~ Widget/Month ~ | -Fulfilment Ratio= 1 - ~ +Fulfilment Ratio= WITH LOOKUP ( + Maximum Delivery Rate/Desired Shipment, + ([(0,0)-(2,1)],(0,0),(0.5,0.5),(0.672783,0.657895),(1.04587,0.859649),(1.40061,0.942982\ + ),(2,1) )) + ~ ~ | Adjustment for Inventory= (Desired Inventory-Inventory)/Inventory Adjustment Time - ~ + ~ ~ | Adjustment for Supply Line= (Desired Supply Line-Supply Line)/Supply Line Adjustment Time - ~ + ~ ~ | Back Log= INTEG ( BL In-BL Out, 100) - ~ + ~ ~ | BL In= Demand - ~ + ~ ~ | BL Out= Shipment Rate - ~ + ~ ~ | Cost= Underage Cost+Overage Cost - ~ + ~ ~ | Deficient Amount= @@ -87,27 +90,27 @@ Deficient Amount= Production Completion= Supply Line/Lead Time - ~ + ~ ~ | Demand Forecast= - Demand - ~ + SMOOTH(Demand, Forecast Period) + ~ ~ | Desired Inventory= Demand Forecast*Inventory Period - ~ + ~ ~ | Desired Supply Line= Demand Forecast*Lead Time - ~ + ~ ~ | Forecast Period= 3 - ~ + ~ ~ | Inventory= INTEG ( @@ -118,53 +121,53 @@ Inventory= INTEG ( Inventory Period= 5 - ~ + ~ ~ | Lead Time= 5 - ~ + ~ ~ | Mean of Demand= 100 - ~ + ~ ~ | Overage Cost= (Inventory+Supply Line)* Unit Overage Cost - ~ + ~ ~ | Desired Production Start= Adjustment for Inventory+Adjustment for Supply Line+Demand Forecast - ~ + ~ ~ | Production Start= MAX(0,Desired Production Start) - ~ + ~ ~ | Supply Line= INTEG ( Production Start-Production Completion, Desired Supply Line) - ~ + ~ ~ | Underage Cost= Deficient Amount*Unit Underage Cost - ~ + ~ ~ | Unit Overage Cost= 1 - ~ + ~ ~ | Unit Underage Cost= 9 - ~ + ~ ~ | ******************************************************** @@ -183,7 +186,7 @@ INITIAL TIME = 0 ~ The initial time for the simulation. | -SAVEPER = +SAVEPER = TIME STEP ~ Month [0,?] ~ The frequency with which output is stored. @@ -299,9 +302,8 @@ Shipment Control 1,96,95,27,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| ///---\\\ :L<%^E!@ -1:current.vdfx 4:Time -5:Supply Line +5:Sd of Demand 9:current 19:90,0 21:Shipment Control @@ -337,4 +339,4 @@ Shipment Control 43: 103:8,8,8,3,8 105:0,0,0,0,0,0,0,0,0,0 -104:Courier|12||0-0-0|0-0-0|-1--1--1|0-0-255|192-192-192|-1--1--1 \ No newline at end of file +104:Courier|12||0-0-0|0-0-0|-1--1--1|0-0-255|192-192-192|-1--1--1 diff --git a/test_scripts/vensim_models/Inventory.xmile b/test_scripts/vensim_models/Inventory.xmile new file mode 100644 index 00000000..b4bac220 --- /dev/null +++ b/test_scripts/vensim_models/Inventory.xmile @@ -0,0 +1,242 @@ + + +
+ Vensim + Ventana Systems, Inc. + + + + + + + + +
+ + 0 + 100 +
1
+ + + + + + + + 100 + + + BL In + + + BL Out + + + + Widget + + + Desired_Inventory + + + Production Completion + + + Shipment Rate + + + + + + + Desired_Supply_Line + + + Production Start + + + Production Completion + + + + + + + (Desired_Inventory-Inventory)/Inventory_Adjustment_Time + + + + + + (Desired_Supply_Line-Supply_Line)/Supply_Line_Adjustment_Time + + + + + + Demand + + + + + + Shipment_Rate + + + + + + Underage_Cost+Overage_Cost + + + Widget + + + MAX(0, Back_Log-Shipment_Rate) + + + + + + RANDOM_NORMAL( 0, 200, Mean_of_Demand, Sd_of_Demand,0) + + + + + + Demand + + + + + + Demand_Forecast*Inventory_Period + + + + + + Adjustment_for_Inventory+Adjustment_for_Supply_Line+Demand_Forecast + + + Widget/Month + + + Back_Log/Desired_Delivery_Delay + + + + + + Demand_Forecast*Lead_Time + + + Widget/Month + + + Inventory/Minimum_Processing_Time + + + + + + (Inventory+Supply_Line)* Unit_Overage_Cost + + + + + + Supply_Line/Lead_Time + + + + + + MAX(0,Desired_Production_Start) + + + Widget/Month + Desired Shipment*Fulfilment Ratio + + Desired_Shipment*Fulfilment_Ratio + + + + + + Deficient_Amount*Unit_Underage_Cost + + + Month + + + 3 + + + + + + 3 + + + + + 1 + + + Month + + + 3 + + + + + + 5 + + + + + + 5 + + + + + + 100 + + + + + + 3 + + + + + + 10 + + + + + + 3 + + + + + + 1 + + + + + + 9 + + + + diff --git a/test_scripts/vensim_models/Inventory_GBM.stmx b/test_scripts/vensim_models/Inventory_GBM.stmx new file mode 100644 index 00000000..eff13c9e --- /dev/null +++ b/test_scripts/vensim_models/Inventory_GBM.stmx @@ -0,0 +1,378 @@ + + +
+ + Inventory_GBM + 830452bd-02c3-47f8-b4e7-e140d931b14c + isee systems, inc. + Stella Architect +
+ + 0 + 100 +
1024
+ + + + + + + + + + + + + + + + + + + + + + + Desired_Supply_Line + Production_Start + Production_Completion + + + MAX(0, Desired_Production_Start) + + + Supply_Line / Lead_Time + + + Desired_Inventory + Production_Completion + Shipment_Rate + + + Desired_Shipment * Fulfilment_Ratio + + + Adjustment_for_Inventory + Adjustment_for_Supply_Line + Demand_Forecast + + + (Desired_Supply_Line - Supply_Line) / Supply_Line_Adjustment_Time + + + 3 + + + (Desired_Inventory - Inventory) / Inventory_Adjustment_Time + + + Demand + + + Demand_Forecast * Lead_Time + + + 5 + + + Demand_Forecast * Inventory_Period + + + 5 + + + 3 + + + Backlog / Desired_Delivery_Delay + + + 1 + + + 100 + BL_In + BL_Out + + + 3 + + + Shipment_Rate + + + Demand + + + 100 + + + Mean_of_Demand + Change_in_demand + + + Demand * Volatility_of_Demand * NORMAL(0, SQRT(DT)) / DT + + + 0.15 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Desired_Production_Start + Production_Start + + + + + + Adjustment_for_Supply_Line + Desired_Production_Start + + + + + + + + + Adjustment_for_Inventory + Desired_Production_Start + + + + Demand_Forecast + Desired_Production_Start + + + + Supply_Line + Adjustment_for_Supply_Line + + + Desired_Supply_Line + Adjustment_for_Supply_Line + + + Supply_Line_Adjustment_Time + Adjustment_for_Supply_Line + + + Supply_Line + Production_Completion + + + + Lead_Time + Production_Completion + + + + Desired_Inventory + Adjustment_for_Inventory + + + Inventory + Adjustment_for_Inventory + + + Demand_Forecast + Desired_Inventory + + + + Inventory_Period + Desired_Inventory + + + + Inventory_Adjustment_Time + Adjustment_for_Inventory + + + Demand_Forecast + Desired_Supply_Line + + + Lead_Time + Desired_Supply_Line + + + + + + Desired_Shipment + Shipment_Rate + + + + Fulfilment_Ratio + Shipment_Rate + + + + Backlog + Desired_Shipment + + + + Desired_Delivery_Delay + Desired_Shipment + + + + + + + + + + + + + + + + + + + + + + + Demand + Change_in_demand + + + + Volatility_of_Demand + Change_in_demand + + + Demand + Demand_Forecast + + + Demand + BL_In + + + Shipment_Rate + BL_Out + + + + + + + + + + + + + + diff --git a/test_scripts/vensim_models/Inventory_PN.stmx b/test_scripts/vensim_models/Inventory_PN.stmx new file mode 100644 index 00000000..521ea2b1 --- /dev/null +++ b/test_scripts/vensim_models/Inventory_PN.stmx @@ -0,0 +1,413 @@ + + +
+ + Inventory_PN + 7e42f0bf-eb95-4011-bee2-e5f141d49d4b + isee systems, inc. + Stella Architect +
+ + 0 + 100 +
1024
+ + + + + + + + + + + + + + + + + + + + + + + Desired_Supply_Line + Production_Start + Production_Completion + + + MAX(0, Desired_Production_Start) + + + Supply_Line / Lead_Time + + + Desired_Inventory + Production_Completion + Shipment_Rate + + + Desired_Shipment * Fulfilment_Ratio + + + Adjustment_for_Inventory + Adjustment_for_Supply_Line + Demand_Forecast + + + (Desired_Supply_Line - Supply_Line) / Supply_Line_Adjustment_Time + + + 3 + + + (Desired_Inventory - Inventory) / Inventory_Adjustment_Time + + + Demand + + + Demand_Forecast * Lead_Time + + + 5 + + + Demand_Forecast * Inventory_Period + + + 5 + + + 3 + + + Backlog / Desired_Delivery_Delay + + + 1 + + + 100 + BL_In + BL_Out + + + 3 + + + Shipment_Rate + + + Demand + + + Mean_of_Demand + Change_in_pink_noise + + + (white_noise - Demand) / correlation_time + + + Mean_of_Demand + NORMAL(0, 1) * (second_term) ^0.5 + + + 2 + + + 100 + + + 10 + + + (SD_of_Demand ^ 2) * (2 - DT / correlation_time) / (DT/correlation_time) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Desired_Production_Start + Production_Start + + + + + + Adjustment_for_Supply_Line + Desired_Production_Start + + + + + + + + + Adjustment_for_Inventory + Desired_Production_Start + + + + Demand_Forecast + Desired_Production_Start + + + + Supply_Line + Adjustment_for_Supply_Line + + + Desired_Supply_Line + Adjustment_for_Supply_Line + + + Supply_Line_Adjustment_Time + Adjustment_for_Supply_Line + + + Supply_Line + Production_Completion + + + + Lead_Time + Production_Completion + + + + Desired_Inventory + Adjustment_for_Inventory + + + Inventory + Adjustment_for_Inventory + + + Demand_Forecast + Desired_Inventory + + + + Inventory_Period + Desired_Inventory + + + + Inventory_Adjustment_Time + Adjustment_for_Inventory + + + Demand_Forecast + Desired_Supply_Line + + + Lead_Time + Desired_Supply_Line + + + + + + Desired_Shipment + Shipment_Rate + + + + Fulfilment_Ratio + Shipment_Rate + + + + Backlog + Desired_Shipment + + + + Desired_Delivery_Delay + Desired_Shipment + + + + + + + + + + + + + + + Shipment_Rate + BL_Out + + + + + + + + + + + + + + + + + + + + Demand + Change_in_pink_noise + + + white_noise + Change_in_pink_noise + + + + + + correlation_time + Change_in_pink_noise + + + + + + second_term + white_noise + + + SD_of_Demand + second_term + + + Mean_of_Demand + white_noise + + + correlation_time + second_term + + + Demand + BL_In + + + Demand + Demand_Forecast + + + + + + diff --git a/test_scripts/vensim_models/Inventory_backup.mdl b/test_scripts/vensim_models/Inventory_backup.mdl deleted file mode 100644 index 5a1ad7ed..00000000 --- a/test_scripts/vensim_models/Inventory_backup.mdl +++ /dev/null @@ -1,343 +0,0 @@ -{UTF-8} -Sd of Demand= - 10 - ~ - ~ | - -Inventory Adjustment Time= - 3 - ~ Month - ~ | - -Supply Line Adjustment Time= - 3 - ~ - ~ | - -Minimum Processing Time= - 3 - ~ - ~ | - -Demand= - RANDOM NORMAL( 0, 200, Mean of Demand, Sd of Demand, 1111) - ~ - ~ | - -Desired Delivery Delay= - 3 - ~ Month - ~ | - -Maximum Delivery Rate= - Inventory/Minimum Processing Time - ~ Widget/Month - ~ | - -Shipment Rate= - Desired Shipment*Fulfilment Ratio - ~ Widget/Month - ~ Desired Shipment*Fulfilment Ratio - | - -Desired Shipment= - Back Log/Desired Delivery Delay - ~ Widget/Month - ~ | - -Fulfilment Ratio= WITH LOOKUP ( - Maximum Delivery Rate/Desired Shipment, - ([(0,0)-(2,1)],(0,0),(0.5,0.5),(0.672783,0.657895),(1.04587,0.859649),(1.40061,0.942982\ - ),(2,1) )) - ~ - ~ | - -Adjustment for Inventory= - (Desired Inventory-Inventory)/Inventory Adjustment Time - ~ - ~ | - -Adjustment for Supply Line= - (Desired Supply Line-Supply Line)/Supply Line Adjustment Time - ~ - ~ | - -Back Log= INTEG ( - BL In-BL Out, - 100) - ~ - ~ | - -BL In= - Demand - ~ - ~ | - -BL Out= - Shipment Rate - ~ - ~ | - -Cost= - Underage Cost+Overage Cost - ~ - ~ | - -Deficient Amount= - MAX(0, Back Log-Shipment Rate) - ~ Widget - ~ | - -Production Completion= - Supply Line/Lead Time - ~ - ~ | - -Demand Forecast= - SMOOTH(Demand, Forecast Period) - ~ - ~ | - -Desired Inventory= - Demand Forecast*Inventory Period - ~ - ~ | - -Desired Supply Line= - Demand Forecast*Lead Time - ~ - ~ | - -Forecast Period= - 3 - ~ - ~ | - -Inventory= INTEG ( - Production Completion-Shipment Rate, - Desired Inventory) - ~ Widget - ~ | - -Inventory Period= - 5 - ~ - ~ | - -Lead Time= - 5 - ~ - ~ | - -Mean of Demand= - 100 - ~ - ~ | - -Overage Cost= - (Inventory+Supply Line)* Unit Overage Cost - ~ - ~ | - -Desired Production Start= - Adjustment for Inventory+Adjustment for Supply Line+Demand Forecast - ~ - ~ | - -Production Start= - MAX(0,Desired Production Start) - ~ - ~ | - -Supply Line= INTEG ( - Production Start-Production Completion, - Desired Supply Line) - ~ - ~ | - -Underage Cost= - Deficient Amount*Unit Underage Cost - ~ - ~ | - -Unit Overage Cost= - 1 - ~ - ~ | - -Unit Underage Cost= - 9 - ~ - ~ | - -******************************************************** - .Control -********************************************************~ - Simulation Control Parameters - | - -FINAL TIME = 100 - ~ Month - ~ The final time for the simulation. - | - -INITIAL TIME = 0 - ~ Month - ~ The initial time for the simulation. - | - -SAVEPER = - TIME STEP - ~ Month [0,?] - ~ The frequency with which output is stored. - | - -TIME STEP = 1 - ~ Month [0,?] - ~ The time step for the simulation. - | - -\\\---/// Sketch information - do not modify anything except names -V300 Do not put anything below this section - it will be ignored -*View 1 -$192-192-192,0,Times New Roman|12||0-0-0|0-0-0|0-0-255|255-255-255|255-255-255|96,96,90,0 -10,1,Inventory,647,125,40,20,3,3,0,0,0,0,0,0,0,0,0,0,0,0 -12,2,48,921,127,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,3,5,2,4,0,0,22,0,0,0,-1--1--1,,1|(844,127)| -1,4,5,1,100,0,0,22,0,0,0,-1--1--1,,1|(726,127)| -11,5,0,772,127,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 -10,6,Shipment Rate,772,165,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 -10,7,Desired Inventory,469,409,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 -10,8,Desired Production Start,179,249,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 -10,9,Adjustment for Inventory,304,459,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 -1,10,1,9,1,0,45,0,2,0,0,-1--1--1,|||0-0-0,1|(649,387)| -1,11,7,9,0,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(0,0)| -1,12,9,8,1,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(206,359)| -10,13,Supply Line,392,129,40,20,3,3,0,0,0,0,0,0,0,0,0,0,0,0 -12,14,48,235,127,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,15,17,13,4,0,0,22,0,0,0,-1--1--1,,1|(328,127)| -1,16,17,14,100,0,0,22,0,0,0,-1--1--1,,1|(268,127)| -11,17,0,298,127,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 -10,18,Production Start,298,165,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,19,21,1,4,0,0,22,0,0,0,-1--1--1,,1|(572,127)| -1,20,21,13,100,0,0,22,0,0,0,-1--1--1,,1|(478,127)| -11,21,0,531,127,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 -10,22,Production Completion,531,165,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 -10,23,Adjustment for Supply Line,351,342,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 -1,24,23,8,1,0,0,0,0,64,0,-1--1--1,,1|(249,309)| -1,25,13,23,1,0,45,0,2,0,0,-1--1--1,|||0-0-0,1|(419,226)| -1,26,8,18,1,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(202,180)| -10,27,Demand,701,384,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 -1,28,13,22,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -10,29,Lead Time,524,216,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,30,29,22,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -10,31,Demand Forecast,630,486,40,20,3,3,0,0,0,0,0,0,0,0,0,0,0,0 -1,32,27,31,1,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(673,432)| -1,33,31,8,1,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(199,441)| -10,34,Forecast Period,778,485,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,35,34,31,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -12,36,1,318,230,23,23,4,4,0,0,1,0,0,0,0,0,0,0,0,0 -Supply Line Control -12,37,1,623,298,23,23,4,4,0,0,1,0,0,0,0,0,0,0,0,0 -Inventory Control -1,38,7,1,1,0,0,0,0,64,1,-1--1--1,,1|(570,270)| -1,39,31,7,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -10,40,Back Log,903,292,40,20,3,3,0,0,0,0,0,0,0,0,0,0,0,0 -12,41,48,771,294,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,42,44,40,4,0,0,22,0,0,0,-1--1--1,,1|(839,294)| -1,43,44,41,100,0,0,22,0,0,0,-1--1--1,,1|(792,294)| -11,44,0,809,294,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 -10,45,BL In,809,332,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 -12,46,48,1032,295,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,47,49,46,4,0,0,22,0,0,0,-1--1--1,,1|(1005,295)| -1,48,49,40,100,0,0,22,0,0,0,-1--1--1,,1|(959,295)| -11,49,0,982,295,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 -10,50,BL Out,982,333,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,51,27,45,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -1,52,6,50,1,0,43,0,2,0,0,-1--1--1,|||0-0-0,1|(934,219)| -10,53,Deficient Amount,790,216,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 -1,54,6,53,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -1,55,40,53,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -10,56,Cost,1274,454,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 -10,57,Overage Cost,1156,427,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 -10,58,Underage Cost,1159,479,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 -1,59,57,56,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -1,60,58,56,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -1,61,13,57,0,1,0,0,0,64,0,-1--1--1,,1|(0,0)| -10,62,Unit Overage Cost,1019,423,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,63,62,57,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -1,64,1,57,0,1,0,0,0,64,0,-1--1--1,,1|(0,0)| -10,65,Unit Underage Cost,1014,481,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,66,65,58,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -1,67,53,58,0,1,0,0,0,64,0,-1--1--1,,1|(0,0)| -10,68,Inventory Period,492,333,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,69,68,7,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -10,70,Mean of Demand,850,358,75,30,8,3,0,19,-1,0,0,0,17-128-64,0-0-0,||B|17-128-64,0,0,0,0,0,0 -1,71,70,27,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -10,72,Desired Supply Line,477,269,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,73,31,72,0,0,0,0,0,192,0,-1--1--1,,1|(0,0)| -1,74,72,23,0,0,0,0,0,192,0,-1--1--1,,1|(0,0)| -1,75,29,72,0,0,0,0,0,192,0,-1--1--1,,1|(0,0)| -1,76,72,13,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| -10,77,Desired Shipment,1146,147,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 -1,78,40,77,0,0,43,0,1,128,0,0-0-0,|||0-0-0,1|(0,0)| -10,79,Desired Delivery Delay,1275,194,75,30,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,||B|17-128-64,0,0,0,0,0,0 -1,80,79,77,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -1,81,77,6,0,0,43,0,0,128,0,-1--1--1,,1|(0,0)| -10,82,Maximum Delivery Rate,787,53,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 -1,83,1,82,1,0,0,0,0,128,0,-1--1--1,,1|(697,73)| -10,84,Minimum Processing Time,1018,47,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,85,84,82,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -10,86,Fulfilment Ratio,1103,82,75,30,8,3,0,0,0,0,0,0,0,0,0,0,0,0 -1,87,77,86,0,0,0,0,0,128,0,-1--1--1,,1|(0,0)| -1,88,82,86,0,0,0,0,0,128,0,-1--1--1,,1|(0,0)| -1,89,86,6,0,0,0,0,0,128,0,-1--1--1,,1|(0,0)| -10,90,Inventory Adjustment Time,468,556,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,91,90,9,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -10,92,Supply Line Adjustment Time,314,417,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,93,92,23,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -12,94,1,980,193,23,23,4,4,0,0,1,0,0,0,0,0,0,0,0,0 -Shipment Control -10,95,Sd of Demand,845,401,75,30,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,||B|17-128-64,0,0,0,0,0,0 -1,96,95,27,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -///---\\\ -:L<%^E!@ -1:current.vdfx -4:Time -5:Supply Line -9:current -19:90,0 -21:Shipment Control -24:0 -25:100 -26:100 -15:0,0,0,0,0,0 -27:0, -34:0, -42:1 -72:0 -73:0 -35:Date -36:YYYY-MM-DD -37:2000 -38:1 -39:1 -40:2 -41:0 -95:0 -96:0 -97:0 -77:0 -78:0 -102:1 -93:0 -94:0 -92:0 -91:0 -90:0 -87:0 -75: -43: -103:8,8,8,3,8 -105:0,0,0,0,0,0,0,0,0,0 -104:Courier|12||0-0-0|0-0-0|-1--1--1|0-0-255|192-192-192|-1--1--1 \ No newline at end of file diff --git a/test_scripts/vensim_models/demand-supply.mdl b/test_scripts/vensim_models/demand-supply.mdl new file mode 100644 index 00000000..e764d617 --- /dev/null +++ b/test_scripts/vensim_models/demand-supply.mdl @@ -0,0 +1,360 @@ +{UTF-8} +Order Fulfilment Ratio= + Order Fullfilemt Ratio Table(Max Shipment Rate/Backlog Adj Rate Desired) + ~ Dmnl + ~ SKU multiscale issues + | + +Order Fullfilemt Ratio Table( + [(0,0)-(2,1)],(0,0),(0.2,0.2),(0.4,0.4),(0.6,0.58),(0.8,0.73),(1,0.85),(1.2,0.93),(1.4\ + ,0.97),(1.6,0.99),(1.8,1),(2,1),(2,1)) + ~ + ~ | + +Supply Start Rate Desired= + MAX(0, Supply Rate Desired + Supply Line Adj Rate) + ~ Widget/Month + ~ | + +Safety Stock Coverage= + 3 * Critical Ratio + ~ Month + ~ proportional to CR? -> 10 * CR?? + | + +Order Fulfillment Rate= + Shipment Rate + ~ Widget/Month + ~ | + +Backlog Adj Rate Desired= + (Backlog - Backlog Desired)/Backlog Adj Time + ~ Widget/Month + ~ For backlog, unlike `supply line adj rate` and `inventory adj rate`, + x-coor of `adj_rate` locates between [`desired_state`,`current_state`] and \ + [`adj_time`] + y-coor of `adj_rate` locates between [`adj_time`] and \ + [`desired_state`,`current_state`] (preferrably adj_time is the highest as \ + being nonnegtive) + | + +Supply Line Desired = + Forecasted Demand Rate*Supply Lead Time + ~ Widget + ~ | + +Critical Ratio= + 0.8 + ~ + ~ Ratios are aggregated result and hence we don't need time delay for this \ + information diffusion. + | + +Backlog Desired= + 0 + ~ Widget + ~ | + +Max Shipment Rate= + Inventory/Shipment Lead Time + ~ Widget/Month + ~ a.k.a. Backlog Adj Rate Desired. John has Inventory/Minimum Order \ + Processing Time but the denominator (MOPT) can be seen as Shipment LT. \ + Wish to argue this is symmetric with Demand Adj Rate + | + +Shipment Rate= + Backlog Adj Rate Desired*Order Fulfilment Ratio + ~ Widget/Month + ~ WRONG: Desired Shipment*Fulfilment Ratio (has to be the rate), \ + Inventory/Shipment Lead Time + | + +Demand Rate Adj Rate= + (Demand Rate-Forecasted Demand Rate)/Demand Adj Time + ~ (Widget/Month)/Month + ~ Actual demand rate is the desired, forecasted demand rate is the current \ + state. So their difference divided by the adj time becomed adj rate. + | + +Inventory Safety Time Desired= + Shipment Lead Time + Safety Stock Coverage + ~ Month + ~ | + +Supply Rate Desired= + Forecasted Demand Rate + Inventory Adj Rate + ~ Widget/Month + ~ | + +Demand Rate= + RANDOM NORMAL( 0, 200, 100, 10, 1111) + ~ Widget/Month + ~ = Desired Shipment. Exogenous. The generated value above does not have any \ + affect as it will be replaced by time series data randomly generated on \ + python platform. + | + +Forecasted Demand Rate= INTEG ( + Demand Rate Adj Rate, + Demand Rate) + ~ Widget + ~ | + +Inventory Adj Time= + 3 + ~ Month + ~ | + +Supply Line Adj Time= + 3 + ~ + ~ | + +Shipment Lead Time= + 3 + ~ Month + ~ Min Shipment Time?? + | + +Backlog Adj Time= + 3 + ~ Month + ~ Desired Shipment Delay by HR + | + +Inventory Adj Rate= + (Inventory Desired-Inventory)/Inventory Adj Time + ~ Widget/Month + ~ x-coor of `adj_rate` locates before [`desired_state`,`current_state`, `adj_time`] \ + (which shares x-coor) + y-coor of `adj_rate` locates between [`desired_state`,`current_state`] and \ + [`adj_time`] + | + +Supply Line Adj Rate= + (Supply Line Desired-Supply Line)/Supply Line Adj Time + ~ Widget/Month + ~ x-coor of `adj_rate` locates before [`desired_state`,`current_state`, `adj_time`] \ + (which shares x-coor) + y-coor of `adj_rate` locates between [`desired_state`,`current_state`] and \ + [`adj_time`] + | + +Backlog= INTEG ( + Order Rate-Order Fulfillment Rate, + Backlog Desired + Order Rate * Backlog Adj Time) + ~ Widget + ~ | + +Order Rate= + Demand Rate + ~ Widget/Month + ~ | + +Supply Rate= + Supply Line/Supply Lead Time + ~ + ~ can be modeled as DELAY3(Supply Start Rate, Lead Time) but as its stan \ + transition is not implemented yet, we are using first order delay. + | + +Inventory Desired = + Forecasted Demand Rate*Inventory Safety Time Desired + ~ Widget/Month + ~ | + +Demand Adj Time= + 3 + ~ + ~ | + +Inventory= INTEG ( + Supply Rate-Shipment Rate, + Inventory Desired) + ~ Widget + ~ | + +Supply Lead Time= + 3 + ~ + ~ | + +Supply Start Rate= + Supply Start Rate Desired + ~ Widget/Month + ~ | + +Supply Line= INTEG ( + Supply Start Rate-Supply Rate, + Supply Line Desired) + ~ + ~ | + +******************************************************** + .Control +********************************************************~ + Simulation Control Parameters + | + +FINAL TIME = 100 + ~ Month + ~ The final time for the simulation. + | + +INITIAL TIME = 0 + ~ Month + ~ The initial time for the simulation. + | + +SAVEPER = + TIME STEP + ~ Month [0,?] + ~ The frequency with which output is stored. + | + +TIME STEP = 1 + ~ Month [0,?] + ~ The time step for the simulation. + | + +\\\---/// Sketch information - do not modify anything except names +V300 Do not put anything below this section - it will be ignored +*View 1 +$192-192-192,0,Times New Roman|12||0-0-0|0-0-0|0-0-255|255-255-255|255-255-255|96,96,90,0 +10,1,Inventory,670,168,40,20,3,3,0,3,0,0,0,0,17-128-64,17-128-2,|||17-128-64,0,0,0,0,0,0 +12,2,48,1447,169,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,3,5,2,4,0,0,22,0,0,0,-1--1--1,,1|(1296,169)| +1,4,5,1,100,0,0,22,1,0,0,0-0-0,|||0-0-0,1|(926,169)| +11,5,0,1149,169,6,8,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,6,Shipment Rate,1149,198,15,21,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,7,Inventory Desired,669,248,35,21,8,3,0,18,0,0,0,0,0-0-0,0-0-0,|||17-128-64,0,0,0,0,0,0 +10,8,Supply Start Rate Desired,60,289,32,29,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,9,Inventory Adj Rate,544,290,24,17,8,3,0,18,0,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,10,1,9,1,0,45,2,3,0,0,0-0-0,|||0-0-0,1|(626,245)| +1,11,7,9,1,0,43,2,3,0,0,0-0-0,|||0-0-0,1|(601,300)| +10,12,Supply Line,339,168,40,20,3,3,0,3,0,0,0,0,17-128-64,17-128-2,|||17-128-64,0,0,0,0,0,0 +12,13,48,204,167,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,14,16,12,4,0,0,22,1,0,0,0-0-0,|||0-0-0,1|(276,167)| +1,15,16,13,100,0,0,22,0,0,0,-1--1--1,,1|(227,167)| +11,16,0,247,167,6,8,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,17,Supply Start Rate,247,196,32,21,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,18,20,1,4,0,0,22,1,0,0,0-0-0,|||0-0-0,1|(566,166)| +1,19,20,12,100,0,0,22,1,0,0,0-0-0,|||0-0-0,1|(435,166)| +11,20,0,497,166,6,8,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,21,Supply Rate,497,195,28,21,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,22,Supply Line Adj Rate,217,291,34,18,8,3,0,18,0,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,23,22,8,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,24,12,22,1,0,45,2,3,0,0,0-0-0,|||0-0-0,1|(277,256)| +1,25,8,17,0,0,43,0,3,0,0,0-0-0,|||0-0-0,1|(0,0)| +10,26,Demand Rate,894,643,33,27,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,27,12,21,1,0,43,2,1,64,0,0-0-0,|||0-0-0,1|(409,208)| +10,28,Supply Lead Time,474,295,31,25,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +1,29,28,21,1,0,0,0,1,64,0,0-0-0,|||0-0-0,1|(500,243)| +10,30,Forecasted Demand Rate,669,471,40,20,3,3,0,3,0,0,0,0,0-0-255,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,31,Demand Adj Time,958,536,32,25,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +12,32,1,225,453,8,8,4,4,0,4,1,0,0,0,0-0-0,255-255-255,|||0-0-0,0,0,0,0,0,0 +Supply Line Control +1,33,7,1,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +1,34,30,7,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +10,35,Backlog,1165,478,31,20,3,3,0,3,0,0,0,0,17-128-64,17-128-2,|||17-128-64,0,0,0,0,0,0 +12,36,48,989,475,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,37,39,35,4,0,0,22,0,0,0,-1--1--1,,1|(1097,475)| +1,38,39,36,100,0,0,22,0,0,0,-1--1--1,,1|(1023,475)| +11,39,0,1054,475,6,8,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,40,Order Rate,1054,505,27,22,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,41,48,1338,479,10,6,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,42,44,41,4,0,0,22,0,0,0,-1--1--1,,1|(1295,479)| +1,43,44,35,100,0,0,22,3,0,0,0-0-0,|||0-0-0,1|(1223,479)| +11,44,0,1256,479,6,8,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,45,Order Fulfillment Rate,1256,508,36,21,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,46,26,40,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,47,6,45,0,0,43,0,3,0,0,0-0-0,|||0-0-0,1|(0,0)| +10,48,Inventory Safety Time Desired,776,261,31,24,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +1,49,48,7,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(724,237)| +10,50,Supply Line Desired,339,241,34,22,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||17-128-64,0,0,0,0,0,0 +1,51,50,22,1,0,0,2,1,192,0,0-0-0,|||0-0-0,1|(292,291)| +1,52,28,50,1,0,43,0,1,192,0,0-0-0,|||0-0-0,1|(404,227)| +1,53,50,12,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +10,54,Backlog Adj Rate Desired,1053,390,27,26,8,3,0,18,0,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,55,35,54,1,0,43,2,1,128,0,0-0-0,|||0-0-0,1|(1120,428)| +10,56,Backlog Adj Time,903,360,28,19,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +1,57,56,54,0,0,45,2,1,64,0,251-2-7,|||0-0-0,1|(0,0)| +1,58,54,6,1,0,43,0,1,128,0,0-0-0,|||0-0-0,1|(1098,272)| +10,59,Max Shipment Rate,948,221,27,26,8,131,0,2,0,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,60,1,59,1,0,43,2,1,128,0,0-0-0,|||0-0-0,1|(811,209)| +10,61,Shipment Lead Time,901,301,31,28,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +1,62,61,59,1,0,45,0,1,64,0,0-0-0,|||0-0-0,1|(943,266)| +10,63,Order Fulfilment Ratio,1055,215,27,28,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||14-124-247,0,0,0,0,0,0 +1,64,63,6,0,0,43,2,1,128,0,0-0-0,|||0-0-0,1|(0,0)| +10,65,Inventory Adj Time,639,359,34,24,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +1,66,65,9,0,0,45,2,1,64,0,251-2-7,|||0-0-0,1|(0,0)| +10,67,Supply Line Adj Time,338,354,40,23,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +1,68,67,22,1,0,45,2,1,64,0,251-2-7,|||0-0-0,1|(284,331)| +12,69,48,946,473,12,7,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,70,72,69,100,0,0,22,0,192,0,-1--1--1,,1|(890,473)| +1,71,72,30,4,0,0,22,0,192,0,-1--1--1,,1|(772,473)| +11,72,0,841,473,6,8,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||15-128-255,0,0,0,0,0,0 +10,73,Demand Rate Adj Rate,841,504,44,23,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||15-128-255,0,0,0,0,0,0 +1,74,30,73,1,0,43,2,1,192,0,0-0-0,|||0-0-0,1|(752,502)| +1,75,31,73,0,0,45,2,1,192,0,251-2-7,|||0-0-0,1|(0,0)| +10,76,Supply Rate Desired,403,402,31,23,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,77,76,8,0,0,43,0,1,192,0,0-0-0,|||0-0-0,1|(0,0)| +1,78,30,76,0,0,43,0,1,192,0,0-0-0,|||0-0-0,1|(0,0)| +10,79,Critical Ratio,773,417,38,15,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,|||87-129-255,0,0,0,0,0,0 +1,80,9,76,0,0,43,0,1,192,0,0-0-0,|||0-0-0,1|(0,0)| +1,81,26,30,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +10,82,Safety Stock Coverage,775,354,55,29,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +1,83,82,48,0,0,43,0,1,192,0,0-0-0,|||0-0-0,1|(0,0)| +1,84,61,48,0,0,43,0,1,192,0,0-0-0,|||0-0-0,1|(0,0)| +12,85,0,1658,191,111,54,8,7,0,0,-1,0,0,0,0,0,0,0,0,0 +1. Adj = y-mean(Desired, State) = y-mean(Desired, Delay) +1,86,26,73,1,0,45,2,1,192,0,0-0-0,|||0-0-0,1|(896,568)| +12,87,1,543,516,8,8,4,4,0,4,1,0,0,0,0-0-0,255-255-255,|||0-0-0,0,0,0,0,0,0 +Inventory Control +12,88,1,1064,579,7,7,5,4,0,4,1,0,0,0,0-0-0,255-255-255,|||0-0-0,0,0,0,0,0,0 +Backlog Control +10,89,Backlog Desired,1164,406,34,24,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,|||17-128-64,0,0,0,0,0,0 +1,90,30,50,0,0,0,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,91,89,54,1,0,43,2,1,192,0,0-0-0,|||0-0-0,1|(1116,384)| +1,92,59,63,1,0,43,2,1,192,0,0-0-0,|||0-0-0,1|(1009,234)| +1,93,54,63,0,0,0,0,1,192,0,0-0-0,|||0-0-0,1|(0,0)| +1,94,79,82,0,0,0,0,1,192,0,0-0-0,|||0-0-0,1|(0,0)| +1,95,89,35,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +10,96,Order Fullfilemt Ratio Table,1055,125,58,23,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||87-129-255,0,0,0,0,0,0 +1,97,96,63,0,0,0,0,1,192,0,0-0-0,|||0-0-0,1|(0,0)| +1,98,56,35,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +///---\\\ +:L<%^E!@ +1:angie.vdfx +4:Time +5:Demand Adj Time +9:angie +19:90,0 +21:Overage Retrodictive / Underage Predictive Control +24:0 +25:100 +26:100 +15:0,0,0,0,0,0 +27:0, +34:0, +42:0 +72:0 +73:0 +95:0 +96:0 +97:0 +77:0 +78:0 +102:1 +93:0 +94:0 +92:0 +91:0 +90:0 +87:0 +75: +43: +103:8,8,8,3,8 +105:0,0,0,0,0,0,0,0,0,0 +104:Courier|12||0-0-0|0-0-0|-1--1--1|0-0-255|192-192-192|-1--1--1 diff --git a/test_scripts/vensim_models/demand-supply.xmile b/test_scripts/vensim_models/demand-supply.xmile new file mode 100644 index 00000000..a47efa56 --- /dev/null +++ b/test_scripts/vensim_models/demand-supply.xmile @@ -0,0 +1,241 @@ + + +
+ Vensim + Ventana Systems, Inc. + + + + + + + + +
+ + 0 + 100 +
1
+ + + + + Widget + + + Backlog_Desired + Order_Rate * Backlog_Adj_Time + + + Order Rate + + + Order Fulfillment Rate + + + + Widget + + + Demand_Rate + + + Demand Rate Adj Rate + + + + Widget + + + Inventory_Desired + + + Supply Rate + + + Shipment Rate + + + + + + + Supply_Line_Desired + + + Supply Start Rate + + + Supply Rate + + + + Widget/Month + For backlog, unlike `supply line adj rate` and `inventory adj rate`, +x-coor of `adj_rate` locates between [`desired_state`,`current_state`] and [`adj_time`] +y-coor of `adj_rate` locates between [`adj_time`] and [`desired_state`,`current_state`] (preferrably adj_time is the highest as being nonnegtive) + + (Backlog - Backlog_Desired)/Backlog_Adj_Time + + + Widget/Month + = Desired Shipment. Exogenous. The generated value above does not have any affect as it will be replaced by time series data randomly generated on python platform. + + RANDOM_NORMAL( 0, 200, 100, 10, 1111) + + + Widget/(Month*Month) + Actual demand rate is the desired, forecasted demand rate is the current state. So their difference divided by the adj time becomed adj rate. + + (Demand_Rate-Forecasted_Demand_Rate)/Demand_Adj_Time + + + Widget/Month + x-coor of `adj_rate` locates before [`desired_state`,`current_state`, `adj_time`] (which shares x-coor) +y-coor of `adj_rate` locates between [`desired_state`,`current_state`] and [`adj_time`] + + (Inventory_Desired-Inventory)/Inventory_Adj_Time + + + Widget/Month + + + Forecasted_Demand_Rate*Inventory_Safety_Time_Desired + + + Month + + + Shipment_Lead_Time + Safety_Stock_Coverage + + + Widget/Month + a.k.a. Backlog Adj Rate Desired. John has Inventory/Minimum Order Processing Time but the denominator (MOPT) can be seen as Shipment LT. Wish to argue this is symmetric with Demand Adj Rate + + Inventory/Shipment_Lead_Time + + + Widget/Month + + + Shipment_Rate + + + Dmnl + SKU multiscale issues + + Order_Fullfilemt_Ratio_Table(Max_Shipment_Rate/Backlog_Adj_Rate_Desired) + + + Widget/Month + + + Demand_Rate + + + Month + proportional to CR? -> 10 * CR?? + + 3 * Critical_Ratio + + + Widget/Month + WRONG: Desired Shipment*Fulfilment Ratio (has to be the rate), Inventory/Shipment Lead Time + + Backlog_Adj_Rate_Desired*Order_Fulfilment_Ratio + + + Widget/Month + x-coor of `adj_rate` locates before [`desired_state`,`current_state`, `adj_time`] (which shares x-coor) +y-coor of `adj_rate` locates between [`desired_state`,`current_state`] and [`adj_time`] + + (Supply_Line_Desired-Supply_Line)/Supply_Line_Adj_Time + + + Widget + + + Forecasted_Demand_Rate*Supply_Lead_Time + + + + can be modeled as DELAY3(Supply Start Rate, Lead Time) but as its stan transition is not implemented yet, we are using first order delay. + + Supply_Line/Supply_Lead_Time + + + Widget/Month + + + Forecasted_Demand_Rate + Inventory_Adj_Rate + + + Widget/Month + + + Supply_Start_Rate_Desired + + + Widget/Month + + + MAX(0, Supply_Rate_Desired + Supply_Line_Adj_Rate) + + + + + +0.000000,0.200000,0.400000,0.600000,0.800000,1.000000,1.200000,1.400000,1.600000,1.800000,2.000000,2.000000 +0.000000,0.200000,0.400000,0.580000,0.730000,0.850000,0.930000,0.970000,0.990000,1.000000,1.000000,1.000000 + + + + Month + Desired Shipment Delay by HR + + 3 + + + Widget + + + 0 + + + + Ratios are aggregated result and hence we don't need time delay for this information diffusion. + + 0.8 + + + + + + 3 + + + Month + + + 3 + + + Month + Min Shipment Time?? + + 3 + + + + + + 3 + + + + + + 3 + + + + diff --git a/test_scripts/vensim_models/demand-supply_wolookup.mdl b/test_scripts/vensim_models/demand-supply_wolookup.mdl new file mode 100644 index 00000000..c6246a54 --- /dev/null +++ b/test_scripts/vensim_models/demand-supply_wolookup.mdl @@ -0,0 +1,343 @@ +{UTF-8} +Supply Start Rate Desired= + MAX(0, Supply Rate Desired + Supply Line Adj Rate) + ~ Widget/Month + ~ | + +Order Fulfilment Ratio= + MIN(1, Max Shipment Rate/(Backlog Adj Rate Desired+0.001)) + ~ Dmnl + ~ SKU multiscale issues + | + +Safety Stock Coverage= + 3 * Critical Ratio + ~ Month + ~ proportional to CR? -> 10 * CR?? + | + +Order Fulfillment Rate= + Shipment Rate + ~ Widget/Month + ~ | + +Backlog Adj Rate Desired= + (Backlog - Backlog Desired)/Backlog Adj Time + ~ Widget/Month + ~ As desired backlog is 0 and loop is couterclockwise, we have + arrow from \ + state to adjustment. Desired Shipment Rate + | + +Supply Line Desired = + Forecasted Demand Rate*Supply Lead Time + ~ Widget + ~ | + +Critical Ratio= + 0.8 + ~ + ~ Ratios are aggregated result and hence we don't need time delay for this \ + information diffusion. + | + +Backlog Desired= + 0 + ~ Widget + ~ | + +Max Shipment Rate= + Inventory/Shipment Lead Time + ~ Widget/Month + ~ a.k.a. Backlog Adj Rate Desired. John has Inventory/Minimum Order \ + Processing Time but the denominator (MOPT) can be seen as Shipment LT. \ + Wish to argue this is symmetric with Demand Adj Rate + | + +Shipment Rate= + Backlog Adj Rate Desired*Order Fulfilment Ratio + ~ Widget/Month + ~ WRONG: Desired Shipment*Fulfilment Ratio (has to be the rate), \ + Inventory/Shipment Lead Time + | + +Demand Rate Adj Rate= + (Demand Rate-Forecasted Demand Rate)/Demand Adj Time + ~ (Widget/Month)/Month + ~ Actual demand rate is the desired, forecasted demand rate is the current \ + state. So their difference divided by the adj time becomed adj rate. + | + +Inventory Safety Time Desired= + Shipment Lead Time + Safety Stock Coverage + ~ Month + ~ | + +Supply Rate Desired= + Forecasted Demand Rate + Inventory Adj Rate + ~ Widget/Month + ~ | + +Demand Rate= + RANDOM NORMAL( 0, 200, 100, 10, 1111) + ~ Widget/Month + ~ = Desired Shipment. Exogenous. The generated value above does not have any \ + affect as it will be replaced by time series data randomly generated on \ + python platform. + | + +Forecasted Demand Rate= INTEG ( + Demand Rate Adj Rate, + Demand Rate) + ~ Widget + ~ | + +Inventory Adj Time= + 3 + ~ Month + ~ | + +Supply Line Adj Time= + 3 + ~ + ~ | + +Shipment Lead Time= + 3 + ~ Month + ~ Min Shipment Time?? + | + +Backlog Adj Time= + 3 + ~ Month + ~ Desired Shipment Delay by HR + | + +Inventory Adj Rate= + (Inventory Desired-Inventory)/Inventory Adj Time + ~ Widget/Month + ~ AR (Adj Rate) = Desired/Delay. Desired Inventory / Delay Inventory Adj; \ + Not directly connected to Adjustment SL Start Rate; Only through Desired \ + Supply Rate + | + +Supply Line Adj Rate= + (Supply Line Desired-Supply Line)/Supply Line Adj Time + ~ Widget/Month + ~ AR (Adj Rate) = Desired/Delay; Desired SL / SL Delay + | + +Backlog= INTEG ( + Order Rate-Order Fulfillment Rate, + Backlog Desired) + ~ Widget + ~ | + +Order Rate= + Demand Rate + ~ Widget/Month + ~ | + +Supply Rate= + Supply Line/Supply Lead Time + ~ + ~ can be modeled as DELAY3(Supply Start Rate, Lead Time) but as its stan \ + transition is not implemented yet, we are using first order delay. + | + +Inventory Desired = + Forecasted Demand Rate*Inventory Safety Time Desired + ~ Widget/Month + ~ | + +Demand Adj Time= + 3 + ~ + ~ | + +Inventory= INTEG ( + Supply Rate-Shipment Rate, + Inventory Desired) + ~ Widget + ~ | + +Supply Lead Time= + 3 + ~ + ~ | + +Supply Start Rate= + Supply Start Rate Desired + ~ Widget/Month + ~ | + +Supply Line= INTEG ( + Supply Start Rate-Supply Rate, + Supply Line Desired) + ~ + ~ | + +******************************************************** + .Control +********************************************************~ + Simulation Control Parameters + | + +FINAL TIME = 100 + ~ Month + ~ The final time for the simulation. + | + +INITIAL TIME = 0 + ~ Month + ~ The initial time for the simulation. + | + +SAVEPER = + TIME STEP + ~ Month [0,?] + ~ The frequency with which output is stored. + | + +TIME STEP = 1 + ~ Month [0,?] + ~ The time step for the simulation. + | + +\\\---/// Sketch information - do not modify anything except names +V300 Do not put anything below this section - it will be ignored +*View 1 +$192-192-192,0,Times New Roman|12||0-0-0|0-0-0|0-0-255|255-255-255|255-255-255|96,96,90,0 +10,1,Inventory,697,226,40,20,3,3,0,3,0,0,0,0,17-128-64,17-128-2,|||17-128-64,0,0,0,0,0,0 +12,2,48,1474,227,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,3,5,2,4,0,0,22,0,0,0,-1--1--1,,1|(1323,227)| +1,4,5,1,100,0,0,22,1,0,0,0-0-0,|||0-0-0,1|(953,227)| +11,5,0,1176,227,6,8,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,6,Shipment Rate,1176,256,15,21,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,7,Inventory Desired,696,306,35,21,8,3,0,18,0,0,0,0,0-0-0,0-0-0,|||17-128-64,0,0,0,0,0,0 +10,8,Supply Start Rate Desired,87,347,32,29,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,9,Inventory Adj Rate,571,348,24,17,8,3,0,18,0,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,10,1,9,1,0,45,2,3,0,0,0-0-0,|||0-0-0,1|(653,303)| +1,11,7,9,1,0,43,2,3,0,0,0-0-0,|||0-0-0,1|(628,358)| +10,12,Supply Line,366,226,40,20,3,3,0,3,0,0,0,0,17-128-64,17-128-2,|||17-128-64,0,0,0,0,0,0 +12,13,48,231,225,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,14,16,12,4,0,0,22,1,0,0,0-0-0,|||0-0-0,1|(303,225)| +1,15,16,13,100,0,0,22,0,0,0,-1--1--1,,1|(254,225)| +11,16,0,274,225,6,8,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,17,Supply Start Rate,274,254,32,21,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,18,20,1,4,0,0,22,1,0,0,0-0-0,|||0-0-0,1|(593,224)| +1,19,20,12,100,0,0,22,1,0,0,0-0-0,|||0-0-0,1|(462,224)| +11,20,0,524,224,6,8,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,21,Supply Rate,524,253,28,21,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,22,Supply Line Adj Rate,244,349,34,18,8,3,0,18,0,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,23,22,8,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,24,12,22,1,0,45,2,3,0,0,0-0-0,|||0-0-0,1|(304,314)| +1,25,8,17,0,0,43,0,3,0,0,0-0-0,|||0-0-0,1|(0,0)| +10,26,Demand Rate,921,701,33,27,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,27,12,21,1,0,43,2,1,64,0,0-0-0,|||0-0-0,1|(436,266)| +10,28,Supply Lead Time,501,353,31,25,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +1,29,28,21,1,0,0,0,1,64,0,0-0-0,|||0-0-0,1|(527,301)| +10,30,Forecasted Demand Rate,696,529,40,20,3,3,0,3,0,0,0,0,0-0-255,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,31,Demand Adj Time,985,594,32,25,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +12,32,1,252,511,8,8,4,4,0,4,1,0,0,0,0-0-0,255-255-255,|||0-0-0,0,0,0,0,0,0 +Supply Line Control +1,33,7,1,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +1,34,30,7,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +10,35,Backlog,1192,536,31,20,3,3,0,3,0,0,0,0,17-128-64,17-128-2,|||17-128-64,0,0,0,0,0,0 +12,36,48,1016,533,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,37,39,35,4,0,0,22,0,0,0,-1--1--1,,1|(1124,533)| +1,38,39,36,100,0,0,22,0,0,0,-1--1--1,,1|(1050,533)| +11,39,0,1081,533,6,8,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,40,Order Rate,1081,563,27,22,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,41,48,1365,537,10,6,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,42,44,41,4,0,0,22,0,0,0,-1--1--1,,1|(1322,537)| +1,43,44,35,100,0,0,22,3,0,0,0-0-0,|||0-0-0,1|(1250,537)| +11,44,0,1283,537,6,8,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,45,Order Fulfillment Rate,1283,566,36,21,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,46,26,40,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,47,6,45,0,0,43,0,3,0,0,0-0-0,|||0-0-0,1|(0,0)| +10,48,Inventory Safety Time Desired,803,319,31,24,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +1,49,48,7,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(751,295)| +10,50,Supply Line Desired,366,299,34,22,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||17-128-64,0,0,0,0,0,0 +1,51,50,22,1,0,0,2,1,192,0,0-0-0,|||0-0-0,1|(319,349)| +1,52,28,50,1,0,43,0,1,192,0,0-0-0,|||0-0-0,1|(431,285)| +1,53,50,12,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +10,54,Backlog Adj Rate Desired,1080,390,27,26,8,3,0,18,0,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,55,35,54,1,0,43,2,1,128,0,0-0-0,|||0-0-0,1|(1121,457)| +10,56,Backlog Adj Time,987,428,28,19,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +1,57,56,54,0,0,45,2,1,64,0,251-2-7,|||0-0-0,1|(0,0)| +1,58,54,6,1,0,43,0,1,128,0,0-0-0,|||0-0-0,1|(1122,315)| +10,59,Max Shipment Rate,975,279,27,26,8,131,0,2,0,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,60,1,59,1,0,43,2,1,128,0,0-0-0,|||0-0-0,1|(838,267)| +10,61,Shipment Lead Time,928,372,31,28,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +1,62,61,59,1,0,45,0,1,64,0,0-0-0,|||0-0-0,1|(971,326)| +10,63,Order Fulfilment Ratio,1082,273,27,28,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||14-124-247,0,0,0,0,0,0 +1,64,63,6,0,0,43,2,1,128,0,0-0-0,|||0-0-0,1|(0,0)| +10,65,Inventory Adj Time,666,417,34,24,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +1,66,65,9,0,0,45,2,1,64,0,251-2-7,|||0-0-0,1|(0,0)| +10,67,Supply Line Adj Time,365,412,40,23,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +1,68,67,22,1,0,45,2,1,64,0,251-2-7,|||0-0-0,1|(311,389)| +12,69,48,973,531,12,7,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,70,72,69,100,0,0,22,0,192,0,-1--1--1,,1|(917,531)| +1,71,72,30,4,0,0,22,0,192,0,-1--1--1,,1|(799,531)| +11,72,0,868,531,6,8,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||15-128-255,0,0,0,0,0,0 +10,73,Demand Rate Adj Rate,868,562,44,23,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||15-128-255,0,0,0,0,0,0 +1,74,30,73,1,0,43,2,1,192,0,0-0-0,|||0-0-0,1|(779,560)| +1,75,31,73,0,0,45,2,1,192,0,251-2-7,|||0-0-0,1|(0,0)| +10,76,Supply Rate Desired,430,460,31,23,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +1,77,76,8,0,0,43,0,1,192,0,0-0-0,|||0-0-0,1|(0,0)| +1,78,30,76,0,0,43,0,1,192,0,0-0-0,|||0-0-0,1|(0,0)| +10,79,Critical Ratio,800,475,38,15,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,|||87-129-255,0,0,0,0,0,0 +1,80,9,76,0,0,43,0,1,192,0,0-0-0,|||0-0-0,1|(0,0)| +1,81,26,30,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +10,82,Safety Stock Coverage,802,412,55,29,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,|||251-2-7,0,0,0,0,0,0 +1,83,82,48,0,0,43,0,1,192,0,0-0-0,|||0-0-0,1|(0,0)| +1,84,61,48,0,0,43,0,1,192,0,0-0-0,|||0-0-0,1|(0,0)| +12,85,0,1617,96,111,54,8,7,0,0,-1,0,0,0,0,0,0,0,0,0 +1. Adj = y-mean(Desired, State) = y-mean(Desired, Delay) +1,86,26,73,1,0,45,2,1,192,0,0-0-0,|||0-0-0,1|(923,626)| +12,87,1,570,574,8,8,4,4,0,4,1,0,0,0,0-0-0,255-255-255,|||0-0-0,0,0,0,0,0,0 +Inventory Control +12,88,1,1091,637,7,7,5,4,0,4,1,0,0,0,0-0-0,255-255-255,|||0-0-0,0,0,0,0,0,0 +Backlog Control +10,89,Backlog Desired,1194,390,34,24,8,3,0,18,-1,0,0,0,0-0-0,0-0-0,|||17-128-64,0,0,0,0,0,0 +1,90,30,50,0,0,0,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,91,89,54,1,0,43,2,1,192,0,0-0-0,|||0-0-0,1|(1139,393)| +1,92,59,63,1,0,43,2,1,192,0,0-0-0,|||0-0-0,1|(1036,292)| +1,93,54,63,0,0,0,0,1,192,0,0-0-0,|||0-0-0,1|(0,0)| +1,94,79,82,0,0,0,0,1,192,0,0-0-0,|||0-0-0,1|(0,0)| +1,95,89,35,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +///---\\\ +:L<%^E!@ +1:angie.vdfx +4:Time +5:Backlog Adj Time +9:angie +19:90,0 +21:Overage Retrodictive / Underage Predictive Control +24:0 +25:100 +26:100 +15:0,0,0,0,0,0 +27:0, +34:0, +42:0 +72:0 +73:0 +95:0 +96:0 +97:0 +77:0 +78:0 +102:1 +93:0 +94:0 +92:0 +91:0 +90:0 +87:0 +75: +43: +103:8,8,8,3,8 +105:0,0,0,0,0,0,0,0,0,0 +104:Courier|12||0-0-0|0-0-0|-1--1--1|0-0-255|192-192-192|-1--1--1 diff --git a/test_scripts/vensim_models/demand-supply_wolookup.xmile b/test_scripts/vensim_models/demand-supply_wolookup.xmile new file mode 100644 index 00000000..1a2ff205 --- /dev/null +++ b/test_scripts/vensim_models/demand-supply_wolookup.xmile @@ -0,0 +1,229 @@ + + +
+ Vensim + Ventana Systems, Inc. + + + + + + + + +
+ + 0 + 100 +
1
+ + + + + Widget + + + Backlog_Desired + + + Order Rate + + + Order Fulfillment Rate + + + + Widget + + + Demand_Rate + + + Demand Rate Adj Rate + + + + Widget + + + Inventory_Desired + + + Supply Rate + + + Shipment Rate + + + + + + + Supply_Line_Desired + + + Supply Start Rate + + + Supply Rate + + + + Widget/Month + As desired backlog is 0 and loop is couterclockwise, we have + arrow from state to adjustment. Desired Shipment Rate + + (Backlog - Backlog_Desired)/Backlog_Adj_Time + + + Widget/Month + = Desired Shipment. Exogenous. The generated value above does not have any affect as it will be replaced by time series data randomly generated on python platform. + + RANDOM_NORMAL( 0, 200, 100, 10, 1111) + + + Widget/(Month*Month) + Actual demand rate is the desired, forecasted demand rate is the current state. So their difference divided by the adj time becomed adj rate. + + (Demand_Rate-Forecasted_Demand_Rate)/Demand_Adj_Time + + + Widget/Month + AR (Adj Rate) = Desired/Delay. Desired Inventory / Delay Inventory Adj; Not directly connected to Adjustment SL Start Rate; Only through Desired Supply Rate + + (Inventory_Desired-Inventory)/Inventory_Adj_Time + + + Widget/Month + + + Forecasted_Demand_Rate*Inventory_Safety_Time_Desired + + + Month + + + Shipment_Lead_Time + Safety_Stock_Coverage + + + Widget/Month + a.k.a. Backlog Adj Rate Desired. John has Inventory/Minimum Order Processing Time but the denominator (MOPT) can be seen as Shipment LT. Wish to argue this is symmetric with Demand Adj Rate + + Inventory/Shipment_Lead_Time + + + Widget/Month + + + Shipment_Rate + + + Dmnl + SKU multiscale issues + + MIN(1, Max_Shipment_Rate/(Backlog_Adj_Rate_Desired+0.001)) + + + Widget/Month + + + Demand_Rate + + + Month + proportional to CR? -> 10 * CR?? + + 3 * Critical_Ratio + + + Widget/Month + WRONG: Desired Shipment*Fulfilment Ratio (has to be the rate), Inventory/Shipment Lead Time + + Backlog_Adj_Rate_Desired*Order_Fulfilment_Ratio + + + Widget/Month + AR (Adj Rate) = Desired/Delay; Desired SL / SL Delay + + (Supply_Line_Desired-Supply_Line)/Supply_Line_Adj_Time + + + Widget + + + Forecasted_Demand_Rate*Supply_Lead_Time + + + + can be modeled as DELAY3(Supply Start Rate, Lead Time) but as its stan transition is not implemented yet, we are using first order delay. + + Supply_Line/Supply_Lead_Time + + + Widget/Month + + + Forecasted_Demand_Rate + Inventory_Adj_Rate + + + Widget/Month + + + Supply_Start_Rate_Desired + + + Widget/Month + + + MAX(0, Supply_Rate_Desired + Supply_Line_Adj_Rate) + + + Month + Desired Shipment Delay by HR + + 3 + + + Widget + + + 0 + + + + Ratios are aggregated result and hence we don't need time delay for this information diffusion. + + 0.8 + + + + + + 3 + + + Month + + + 3 + + + Month + Min Shipment Time?? + + 3 + + + + + + 3 + + + + + + 3 + + + + diff --git a/test_scripts/vensim_models/prey-predator.mdl b/test_scripts/vensim_models/prey-predator.mdl new file mode 100644 index 00000000..0f968aa4 --- /dev/null +++ b/test_scripts/vensim_models/prey-predator.mdl @@ -0,0 +1,183 @@ +{UTF-8} +gamma= + 0.01 + ~ [0,0.05,0.001] + ~ predator birth fraction + | + +predator birth rate= + (gamma*Prey)*Predator + ~ + ~ | + +delta= + 1 + ~ [0,2,0.01] + ~ predator death proportionality constant + | + +predator death rate= + delta*Predator + ~ + ~ | + +Predator= INTEG ( + predator birth rate-predator death rate, + 15) + ~ + ~ | + +alpha= + 1 + ~ [0,5,0.1] + ~ prey birth fraction + | + +prey birth rate= + alpha*Prey + ~ + ~ | + +beta= + 0.035 + ~ [0,0.05,0.001] + ~ prey death proportionality constant + | + +prey death rate= + (beta*Predator)*Prey + ~ + ~ | + +Prey= INTEG ( + prey birth rate-prey death rate, + 100) + ~ + ~ | + +******************************************************** + .Control +********************************************************~ + Simulation Control Parameters + | + +FINAL TIME = 12 + ~ seasons + ~ The final time for the simulation. + | + +INITIAL TIME = 0 + ~ seasons + ~ The initial time for the simulation. + | + +SAVEPER = + TIME STEP + ~ seasons [0,?] + ~ The frequency with which output is stored. + | + +TIME STEP = 0.03125 + ~ seasons [0.001,0.04,0.001] + ~ The time step for the simulation. + | + +\\\---/// Sketch information - do not modify anything except names +V300 Do not put anything below this section - it will be ignored +*View 1 +$192-192-192,0,Times New Roman|12||0-0-0|0-0-0|0-0-255|-1--1--1|-1--1--1|96,96,131,0 +10,1,Prey,390,94,53,27,3,3,0,3,0,0,0,0,17-128-64,0-0-0,|||17-128-64,0,0,0,0,0,0 +10,2,Predator,532,200,53,27,3,3,0,3,0,0,0,0,17-128-64,0-0-0,|||17-128-64,0,0,0,0,0,0 +12,3,48,203,96,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,4,6,1,4,0,0,22,0,0,0,-1--1--1,,1|(306,96)| +1,5,6,3,100,0,0,22,0,0,0,-1--1--1,,1|(236,96)| +11,6,0,267,96,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,7,prey birth rate,267,119,26,12,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,8,48,558,96,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,9,11,8,4,0,0,22,0,0,0,-1--1--1,,1|(524,96)| +1,10,11,1,100,0,0,22,0,0,0,-1--1--1,,1|(464,96)| +11,11,0,493,96,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,12,prey death rate,493,121,27,14,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,13,48,356,203,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,14,16,2,4,0,0,22,0,0,0,-1--1--1,,1|(448,203)| +1,15,16,13,100,0,0,22,0,0,0,-1--1--1,,1|(383,203)| +11,16,0,409,203,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,17,predator birth rate,409,224,30,10,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,18,48,709,201,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,19,21,18,4,0,0,22,0,0,0,-1--1--1,,1|(668,201)| +1,20,21,2,100,0,0,22,0,0,0,-1--1--1,,1|(603,201)| +11,21,0,629,201,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,22,predator death rate,629,227,34,15,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,23,alpha,194,158,17,13,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +10,24,gamma,322,252,18,19,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +10,25,beta,589,147,24,21,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +10,26,delta,722,255,10,31,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +1,27,23,7,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,28,1,7,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(330,147)| +1,29,1,17,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(382,164)| +1,30,1,12,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(429,142)| +1,31,25,12,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,32,2,12,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(523,165)| +1,33,26,22,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,34,2,22,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(562,258)| +1,35,2,17,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(479,241)| +1,36,24,17,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +12,37,0,1476,209,200,200,3,44,0,0,1,0,0,0,0,0,0,0,0,0 +Population +12,38,0,1168,646,200,200,3,44,0,0,1,0,0,0,0,0,0,0,0,0 +Phases +10,39,TIME STEP,335,699,50,11,8,2,0,3,-1,0,0,0,128-128-128,0-0-0,|||128-128-128,0,0,0,0,0,0 +///---\\\ +:GRAPH Population +:TITLE Population +:X-AXIS Time +:SCALE +:VAR Predator Population +:Y-MIN 0 +:Y-MAX 200 +:LINE-WIDTH 2 +:VAR Prey Population +:Y-MIN 0 +:Y-MAX 200 +:LINE-WIDTH 2 + +:GRAPH Phases +:TITLE Phases +:X-AXIS Prey Population +:SCALE +:VAR Predator Population +:Y-MIN 0 +:Y-MAX 200 +:LINE-WIDTH 2 +:L<%^E!@ +4:Time +5:beta +9:Current +19:131,0 +24:0 +25:12.012 +26:12.012 +23:0 +15:0,0,0,0,0,0 +27:2, +34:0, +42:0 +72:0 +73:0 +95:0 +96:0 +97:0 +77:0 +78:0 +102:1 +93:0 +94:0 +92:0 +91:0 +90:0 +87:0 +75: +43: +103:8,8,8,3,8 +105:0,0,0,0,0,0,0,0,0,0 +104:Courier|12||0-0-0|0-0-0|-1--1--1|0-0-255|192-192-192|-1--1--1 diff --git a/test_scripts/vensim_models/repair.xmile b/test_scripts/vensim_models/repair.xmile deleted file mode 100644 index f9bbe689..00000000 --- a/test_scripts/vensim_models/repair.xmile +++ /dev/null @@ -1,101 +0,0 @@ - - -
- Vensim - Ventana Systems, Inc. - - - - - - - - -
- - 0 - 100 -
1
- - - - - - - - Initial_Value - - - Engagement - - - Maintenance - - - - - - - 0 - - - Maintenance - - - Engagement - - - - - - Repair_Shop/Repair_Time*MIN(1,XIDZ(Inventory,BackLog,1)) - - - Month - average of 90 /year = 8 / month - RANDOM_POISSON(0, 100, 8, 0 ,1 , 1234 ) - - - - N of Predictive Maintenance - Predictive_Maintenance + Failure_Count - - - - - Battle_Field / 5 - - - Month - - RANDOM_EXPONENTIAL(0, 100 , 0 , Repair_Time_Rate , 1234) - - - - - 1 - - - - - 100 - - - - - 1 - - - Month - - 1 - - - - Seed for the random number generator - stream ID for the distribution to use. If s is set to 0 the default noise stream will be used. The default noise stream can be controlled using the NOISE SEED variable described below. For each distinct non-zero value of s a separate noise stream will be created. You can couple noise streams by giving them the same stream ID. When streams are coupled it means that the random selections will influence one another, not that they will be the same. For example if there are two functions using the stream ID 7, adding a third with the same stream ID will change the noise generated by the first two. Using a nonzero stream ID is most useful if it is unique so that adding additional random functions will not influence a particular drawing sequence. See the examples below. The stream ID should almost always be 0 or, if nonzero, a constant. Using a dynamic value for StreamID can consume excessive memory. -NOTE The noise stream ID for a random variable should be a number or a constant. If you make the ID a variable a new noise stream will be started each time the value of that variable changes. This will slow things and also degrade the distributional quality of the random variable. - 0 - - - - \ No newline at end of file From e7fa259df61f47db608b9f62acffa6183406d5bd Mon Sep 17 00:00:00 2001 From: Dashadower Date: Sun, 31 Jul 2022 23:55:59 +0900 Subject: [PATCH 08/45] Allow non-default outcome variable passage --- pysd/builders/stan/stan_model_builder.py | 21 ++++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_model_builder.py index ef82fab4..fa06edb8 100644 --- a/pysd/builders/stan/stan_model_builder.py +++ b/pysd/builders/stan/stan_model_builder.py @@ -1,6 +1,6 @@ import os from pathlib import Path -from typing import Union, List, Dict, Set, Iterable, Type +from typing import Union, List, Dict, Set, Sequence from .ast_walker import * from .utilities import * @@ -20,7 +20,22 @@ def __init__(self, abstract_model: AbstractModel): self.variable_ast_dict[stan_varname] = element.components[0].ast - def create_stan_program(self, predictor_variable_names: List[Union[str, Tuple[str, str]]], function_name="vensim_func"): + def create_stan_program(self, predictor_variable_names: List[Union[str, Tuple[str, str]]], outcome_variable_names: Sequence[str] = (), function_name="vensim_func"): + """ + + Parameters + ---------- + predictor_variable_names: List of name of variables within the SD model that are handled by stan. The code for + these variables will not be generated, but instead taken from the argument of the ODE system function. + outcome_variable_names: Sequence of name of the variables which are the return values of the ODE function. + Normally this will be the flow variable for each stock. If it is not specified, it will automatically identify the + stock variable names and use them instead. + function_name: Name of the stan function to be generated. default is "vensim_func" + + Returns + ------- + + """ # Santize vensim names to stan-compliant identifiers sanitized_predictor_variable_names = [] for var in predictor_variable_names: @@ -33,7 +48,7 @@ def create_stan_program(self, predictor_variable_names: List[Union[str, Tuple[st raise Exception("predictor_variable_names must be a list of strings and/or a tuple of the form(T, Name), where T is a string denoting the variable's stan type and Name a string denoting the variable name") predictor_variable_names = sanitized_predictor_variable_names - outcome_variable_names = self.get_stock_variable_stan_names() + outcome_variable_names = self.get_stock_variable_stan_names() if not outcome_variable_names else [vensim_name_to_identifier(name) for name in outcome_variable_names] if not outcome_variable_names: raise Exception("There are no stock variables defined in the model, hence nothing to integrate.") From 8c9f767b6cf8de8993ec19d1875ec3b8477854e7 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Sun, 31 Jul 2022 23:59:31 +0900 Subject: [PATCH 09/45] Add missing semicolon --- pysd/builders/stan/ast_walker.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pysd/builders/stan/ast_walker.py b/pysd/builders/stan/ast_walker.py index a6896f78..4b3d80e8 100644 --- a/pysd/builders/stan/ast_walker.py +++ b/pysd/builders/stan/ast_walker.py @@ -69,7 +69,7 @@ def walk(self, ast_node) -> None: self.code.indent_level += 1 # enter conditional body - self.code += f"intercept = {y[lookup_index - 1]}\n" + self.code += f"intercept = {y[lookup_index - 1]};\n" self.code += f"slope = ({y[lookup_index]} - {y[lookup_index - 1]}) / ({x[lookup_index]} - {x[lookup_index - 1]});\n" self.code += f"return intercept + slope * (x - {x[lookup_index - 1]});\n" self.code.indent_level -= 1 From ce5bc6570f7d3a5706dc82744643f68b68e6f124 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Mon, 1 Aug 2022 00:03:37 +0900 Subject: [PATCH 10/45] Add float to auxillary name walker --- pysd/builders/stan/ast_walker.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pysd/builders/stan/ast_walker.py b/pysd/builders/stan/ast_walker.py index 4b3d80e8..8042fdb1 100644 --- a/pysd/builders/stan/ast_walker.py +++ b/pysd/builders/stan/ast_walker.py @@ -18,6 +18,8 @@ def walk(self, ast_node) -> List[str]: match ast_node: case int(): return [] + case float(): + return [] case ArithmeticStructure(operators, arguments): return list(chain.from_iterable([self.walk(argument) for argument in arguments])) case ReferenceStructure(reference, subscripts): From f37c720fd539227041f7fde1bdd3b5002731a734 Mon Sep 17 00:00:00 2001 From: Hyunji Moon <30194633+hyunjimoon@users.noreply.github.com> Date: Mon, 1 Aug 2022 00:27:25 +0900 Subject: [PATCH 11/45] Update explanation on outcome signature --- pysd/builders/stan/stan_model_builder.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_model_builder.py index fa06edb8..7482b8fd 100644 --- a/pysd/builders/stan/stan_model_builder.py +++ b/pysd/builders/stan/stan_model_builder.py @@ -27,9 +27,9 @@ def create_stan_program(self, predictor_variable_names: List[Union[str, Tuple[st ---------- predictor_variable_names: List of name of variables within the SD model that are handled by stan. The code for these variables will not be generated, but instead taken from the argument of the ODE system function. - outcome_variable_names: Sequence of name of the variables which are the return values of the ODE function. - Normally this will be the flow variable for each stock. If it is not specified, it will automatically identify the - stock variable names and use them instead. + outcome_variable_names: Sequence of name of the variables which are the measured as system state. + Normally this will be the observed outcomes among the stock variables. If it is not specified, it will automatically + identify stock variable names and use them. function_name: Name of the stan function to be generated. default is "vensim_func" Returns From 2903470404030bbe86950eba92ebf9f190732a2b Mon Sep 17 00:00:00 2001 From: Hyunji Moon <30194633+hyunjimoon@users.noreply.github.com> Date: Mon, 1 Aug 2022 18:59:25 +0900 Subject: [PATCH 12/45] Readme for PR preparation --- test_scripts/readme.md | 46 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 test_scripts/readme.md diff --git a/test_scripts/readme.md b/test_scripts/readme.md new file mode 100644 index 00000000..641033c6 --- /dev/null +++ b/test_scripts/readme.md @@ -0,0 +1,46 @@ + +## 1. What is this PR doing and how? + +The main contribution of this PR is inference; its benefits are estimating parameter values and calibration. Currently pysd is doing a good job in data generation conditional on parameter values but lacks modules that retrieves parameter from the generated data. This pr aims to fill the gap; Stan is a computational Bayesian statistics package, with a large eco-system, providing state of the art inference algorithms including MCMC (HMC-NUTS) and variational inference. Given the following input from users, our output is posterior samples of estimated parameters. + +1. dynamic model in .mdl or .xmile or .stmx format (SQ1. can we support .stmx?) +2. user's classification of + a. assumed parameter: + a-1. time series of parameters (e.g. temperature or demand) + a-2. fixed value of parameter (e.g. size of one franchise branch) + b. estimated parameters (e.g. delay time of shipment) + b-1. prior distribution + b-2. prior parameter + c. observed state (stock variables with observed data) + +(SQ2. don't we need input signature of driving data? e.g. demand in static approach) + +Under the hood, files under `pysd/builders/stan` folder, we parse `xmile`, `mdl`, `stmx` files into one Stan code file `.stan` and pass it to Stan's state of the art MCMC infefinference engine, which serves inverse process of pysd's data generation. Stan has a large ecosystem that supports both calibration and model expansion (e.g. hierarchical regression, generalized linear). + +These are tested in `test_scripts` folder, and main parsing logics are in [here](https://github.com/Dashadower/pysd/blob/master/pysd/builders/stan/ast_walker.py) and [here](https://github.com/Dashadower/pysd/blob/master/pysd/builders/stan/stan_model_builder.py). (SQ3. could you add some detailes on each file in the last sentence?) + +## 2. Future plans +Our work is casting dynamic model into statistical model structure (JQ1. may I classify it as stochastic process; especially isn't representing demand with three parameter approach seems like gaussian process whose `alpha`, `rho` are `scale` and `1/corr` param) as part of [Bayesian workflow](https://arxiv.org/abs/2011.01808) project which provides principled guidelines for model building. + +Two works are planned for the near future and it would be tremendously helpful some support could be given in the second. + +- Developing case studies on workflow which includes the following: +``` +1. prior predictive +2. calibration +- posterior credible interval and SBC +- sensitivity check on prior distribution and prior parameter +- compare posterior with prior +3. posterior predictive +``` +- Translating vensim and/or stella function using python [here](https://github.com/Dashadower/pysd/blob/master/pysd/builders/stan/ast_walker.py) +Currently `integration`, `random generation`, `lookup` are completed (but need testing) and `smooth` is in progress. (SQ4. am I missing something? JQ2. are we missing any main structure?) + +## 3. Our question +May we ask what test or other functions would be need for pysd for our PR to be most helpful? Thanks for the awesome package! + +--- +Q. I am making the notebook but is this necessary for pr? Could the pr and making notebook be proceeded parallelly? +SQ5. Could you give us a code review as we can receive Jair's feedback? I am curious on +- the interface btw `ast_walker` and `stan_model_builder` +- the role of `RNGCodegenWalker` From 02971a38e23886ee9c21f35e8dbfd66ee435e73e Mon Sep 17 00:00:00 2001 From: Hyunji Moon <30194633+hyunjimoon@users.noreply.github.com> Date: Mon, 1 Aug 2022 19:27:13 +0900 Subject: [PATCH 13/45] Update readme.md --- test_scripts/readme.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/test_scripts/readme.md b/test_scripts/readme.md index 641033c6..b3b803b7 100644 --- a/test_scripts/readme.md +++ b/test_scripts/readme.md @@ -4,16 +4,20 @@ The main contribution of this PR is inference; its benefits are estimating parameter values and calibration. Currently pysd is doing a good job in data generation conditional on parameter values but lacks modules that retrieves parameter from the generated data. This pr aims to fill the gap; Stan is a computational Bayesian statistics package, with a large eco-system, providing state of the art inference algorithms including MCMC (HMC-NUTS) and variational inference. Given the following input from users, our output is posterior samples of estimated parameters. 1. dynamic model in .mdl or .xmile or .stmx format (SQ1. can we support .stmx?) + 2. user's classification of + a. assumed parameter: a-1. time series of parameters (e.g. temperature or demand) a-2. fixed value of parameter (e.g. size of one franchise branch) - b. estimated parameters (e.g. delay time of shipment) + + b. estimated parameter (e.g. delay time of shipment) b-1. prior distribution b-2. prior parameter + c. observed state (stock variables with observed data) -(SQ2. don't we need input signature of driving data? e.g. demand in static approach) +(SQ2. might we need addition input signature for `estimated parameter`? currently we `predictor_variable_names`: List[Union[str, Tuple[str, str]]] is a, `outcome_variable_name`: Sequence[str] = () is c. also could you explain the difference of two current signature types?) Under the hood, files under `pysd/builders/stan` folder, we parse `xmile`, `mdl`, `stmx` files into one Stan code file `.stan` and pass it to Stan's state of the art MCMC infefinference engine, which serves inverse process of pysd's data generation. Stan has a large ecosystem that supports both calibration and model expansion (e.g. hierarchical regression, generalized linear). From 46df7c6535ad58335b4d6737fe10c8a6ce5294a8 Mon Sep 17 00:00:00 2001 From: Hyunji Moon <30194633+hyunjimoon@users.noreply.github.com> Date: Mon, 1 Aug 2022 22:42:46 +0900 Subject: [PATCH 14/45] Update readme.md --- test_scripts/readme.md | 19 ++----------------- 1 file changed, 2 insertions(+), 17 deletions(-) diff --git a/test_scripts/readme.md b/test_scripts/readme.md index b3b803b7..fe39a5de 100644 --- a/test_scripts/readme.md +++ b/test_scripts/readme.md @@ -3,7 +3,7 @@ The main contribution of this PR is inference; its benefits are estimating parameter values and calibration. Currently pysd is doing a good job in data generation conditional on parameter values but lacks modules that retrieves parameter from the generated data. This pr aims to fill the gap; Stan is a computational Bayesian statistics package, with a large eco-system, providing state of the art inference algorithms including MCMC (HMC-NUTS) and variational inference. Given the following input from users, our output is posterior samples of estimated parameters. -1. dynamic model in .mdl or .xmile or .stmx format (SQ1. can we support .stmx?) +1. dynamic model in .mdl or .xmile or .stmx format 2. user's classification of @@ -28,23 +28,8 @@ Our work is casting dynamic model into statistical model structure (JQ1. may I c Two works are planned for the near future and it would be tremendously helpful some support could be given in the second. -- Developing case studies on workflow which includes the following: -``` -1. prior predictive -2. calibration -- posterior credible interval and SBC -- sensitivity check on prior distribution and prior parameter -- compare posterior with prior -3. posterior predictive -``` - Translating vensim and/or stella function using python [here](https://github.com/Dashadower/pysd/blob/master/pysd/builders/stan/ast_walker.py) -Currently `integration`, `random generation`, `lookup` are completed (but need testing) and `smooth` is in progress. (SQ4. am I missing something? JQ2. are we missing any main structure?) +Currently `integration`, `random generation`, `lookup` are completed (but need testing) and `smooth` is in progress. Some parts we are missing: `if then else`, `step` (using `if then else`), `pulse`(point mass + divide by dt), `delay` (hard..), `material memory` (not possible in stan as it does not allow). ## 3. Our question May we ask what test or other functions would be need for pysd for our PR to be most helpful? Thanks for the awesome package! - ---- -Q. I am making the notebook but is this necessary for pr? Could the pr and making notebook be proceeded parallelly? -SQ5. Could you give us a code review as we can receive Jair's feedback? I am curious on -- the interface btw `ast_walker` and `stan_model_builder` -- the role of `RNGCodegenWalker` From 12039a92bd769967b33105e68ce7fe4564faada1 Mon Sep 17 00:00:00 2001 From: hyunjimoon Date: Sun, 7 Aug 2022 03:54:28 -0400 Subject: [PATCH 15/45] Extend to Hierarchical models --- .../vensim_models/prey-predator-hier1.mdl | 189 +++++++++++++++++ .../vensim_models/prey-predator-hier2.mdl | 190 +++++++++++++++++ .../vensim_models/prey-predator-hier3.mdl | 197 ++++++++++++++++++ test_scripts/vensim_models/prey-predator.mdl | 109 +++++----- 4 files changed, 631 insertions(+), 54 deletions(-) create mode 100644 test_scripts/vensim_models/prey-predator-hier1.mdl create mode 100644 test_scripts/vensim_models/prey-predator-hier2.mdl create mode 100644 test_scripts/vensim_models/prey-predator-hier3.mdl diff --git a/test_scripts/vensim_models/prey-predator-hier1.mdl b/test_scripts/vensim_models/prey-predator-hier1.mdl new file mode 100644 index 00000000..77cd6f32 --- /dev/null +++ b/test_scripts/vensim_models/prey-predator-hier1.mdl @@ -0,0 +1,189 @@ +{UTF-8} +region: + region1, region2 + ~ + ~ | + +******************************************************** + .Control +********************************************************~ + Simulation Control Parameters + | + +predator birth rate[region]= + (delta[region]*Prey[region])*Predator[region] + ~ pred/time + ~ | + +predator death rate[region]= + gamma[region]*Predator[region] + ~ pred/time + ~ | + +prey death rate[region]= + (beta[region]*Predator[region])*Prey[region] + ~ prey/time + ~ | + +Prey[region]= INTEG ( + prey birth rate[region]-prey death rate[region], + 100) + ~ prey + ~ | + +prey birth rate[region]= + alpha[region]*Prey[region] + ~ prey/time + ~ | + +Predator[region]= INTEG ( + predator birth rate[region]-predator death rate[region], + 15) + ~ pred + ~ | + +alpha[region]= + 1,1 + ~ fraction/time [0,5,0.1] + ~ prey birth fraction + | + +beta[region]= + 0.05, 0.05 + ~ fraction/time [0,0.05,0.001] + ~ prey death proportionality constant + | + +gamma[region]= + 1,1 + ~ fraction/time [0,2,0.01] + ~ predator death proportionality constant + | + +delta[region]= + 0.05, 0.05 + ~ fraction/time [0,0.05,0.001] + ~ predator birth fraction + | + +FINAL TIME = 12 + ~ seasons + ~ The final time for the simulation. + | + +INITIAL TIME = 0 + ~ seasons + ~ The initial time for the simulation. + | + +SAVEPER = + TIME STEP + ~ seasons [0,?] + ~ The frequency with which output is stored. + | + +TIME STEP = 0.03125 + ~ seasons [0.001,0.04,0.001] + ~ The time step for the simulation. + | + +\\\---/// Sketch information - do not modify anything except names +V300 Do not put anything below this section - it will be ignored +*View 1 +$192-192-192,0,Times New Roman|12||0-0-0|0-0-0|0-0-255|-1--1--1|-1--1--1|96,96,108,0 +10,1,Prey,382,107,53,27,3,3,0,3,0,0,0,0,17-128-64,0-0-0,|||17-128-64,0,0,0,0,0,0 +10,2,Predator,524,213,53,27,3,3,0,3,0,0,0,0,17-128-64,0-0-0,|||17-128-64,0,0,0,0,0,0 +12,3,48,195,109,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,4,6,1,4,0,0,22,0,0,0,-1--1--1,,1|(298,109)| +1,5,6,3,100,0,0,22,0,0,0,-1--1--1,,1|(228,109)| +11,6,0,259,109,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,7,prey birth rate,259,132,26,12,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,8,48,550,109,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,9,11,8,4,0,0,22,0,0,0,-1--1--1,,1|(516,109)| +1,10,11,1,100,0,0,22,0,0,0,-1--1--1,,1|(456,109)| +11,11,0,485,109,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,12,prey death rate,485,134,27,14,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,13,48,348,216,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,14,16,2,4,0,0,22,0,0,0,-1--1--1,,1|(440,216)| +1,15,16,13,100,0,0,22,0,0,0,-1--1--1,,1|(375,216)| +11,16,0,401,216,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,17,predator birth rate,401,237,30,10,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,18,48,701,214,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,19,21,18,4,0,0,22,0,0,0,-1--1--1,,1|(660,214)| +1,20,21,2,100,0,0,22,0,0,0,-1--1--1,,1|(595,214)| +11,21,0,621,214,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,22,predator death rate,621,240,34,15,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,23,alpha,186,171,17,13,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +10,24,delta,314,265,18,19,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +10,25,beta,581,160,24,21,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +10,26,gamma,714,268,10,31,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +1,27,23,7,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,28,1,7,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(322,160)| +1,29,1,17,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(374,177)| +1,30,1,12,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(421,155)| +1,31,25,12,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,32,2,12,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(515,178)| +1,33,26,22,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,34,2,22,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(554,271)| +1,35,2,17,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(471,254)| +1,36,24,17,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +12,37,0,1468,222,200,200,3,44,0,0,1,0,0,0,0,0,0,0,0,0 +Population +12,38,0,1160,659,200,200,3,44,0,0,1,0,0,0,0,0,0,0,0,0 +Phases +10,39,TIME STEP,327,712,50,11,8,2,0,3,-1,0,0,0,128-128-128,0-0-0,|||128-128-128,0,0,0,0,0,0 +///---\\\ +:GRAPH Population +:TITLE Population +:X-AXIS Time +:SCALE +:VAR Predator Population +:Y-MIN 0 +:Y-MAX 200 +:LINE-WIDTH 2 +:VAR Prey Population +:Y-MIN 0 +:Y-MAX 200 +:LINE-WIDTH 2 + +:GRAPH Phases +:TITLE Phases +:X-AXIS Prey Population +:SCALE +:VAR Predator Population +:Y-MIN 0 +:Y-MAX 200 +:LINE-WIDTH 2 +:L<%^E!@ +4:Time +5:alpha[region] +6:region1 +9:Current +19:108,0 +24:0 +25:12 +26:12 +23:0 +15:0,0,0,0,0,0 +27:2, +34:0, +42:0 +72:0 +73:0 +95:0 +96:0 +97:0 +77:0 +78:0 +102:1 +93:0 +94:0 +92:0 +91:0 +90:0 +87:0 +75: +43: +103:8,8,8,3,8 +105:0,0,0,0,0,0,0,0,0,0 +104:Courier|12||0-0-0|0-0-0|-1--1--1|0-0-255|192-192-192|-1--1--1 diff --git a/test_scripts/vensim_models/prey-predator-hier2.mdl b/test_scripts/vensim_models/prey-predator-hier2.mdl new file mode 100644 index 00000000..874cd945 --- /dev/null +++ b/test_scripts/vensim_models/prey-predator-hier2.mdl @@ -0,0 +1,190 @@ +{UTF-8} +kind: + kind1, kind2 + ~ + ~ | + +******************************************************** + .Control +********************************************************~ + Simulation Control Parameters + | + +predator birth rate[kind]= + (delta[kind]*(Prey[kind1] + Prey[kind2]))*Predator[kind] + ~ pred/time + ~ | + +predator death rate[kind]= + gamma[kind]*Predator[kind] + ~ pred/time + ~ | + +Predator[kind]= INTEG ( + predator birth rate[kind]-predator death rate[kind], + 15) + ~ pred + ~ | + +prey death rate[kind]= + (beta[kind]*Predator[kind])*Prey[kind] + ~ prey/time + ~ | + +Prey[kind]= INTEG ( + prey birth rate[kind]-prey death rate[kind], + 100) + ~ prey + ~ | + +prey birth rate[kind]= + alpha[kind]*Prey[kind] + ~ prey/time + ~ | + +alpha[kind]= + 1,1 + ~ fraction/time [0,5,0.1] + ~ prey birth fraction + | + +beta[kind]= + 0.05, 0.05 + ~ fraction/time [0,0.05,0.001] + ~ prey death proportionality constant + | + +gamma[kind]= + 1, 1 + ~ fraction/time [0,2,0.01] + ~ predator death proportionality constant + | + +delta[kind]= + 0.05, 0.05 + ~ fraction/time [0,0.05,0.001] + ~ predator birth fraction + | + +FINAL TIME = 12 + ~ seasons + ~ The final time for the simulation. + | + +INITIAL TIME = 0 + ~ seasons + ~ The initial time for the simulation. + | + +SAVEPER = + TIME STEP + ~ seasons [0,?] + ~ The frequency with which output is stored. + | + +TIME STEP = 0.03125 + ~ seasons [0.001,0.04,0.001] + ~ The time step for the simulation. + | + +\\\---/// Sketch information - do not modify anything except names +V300 Do not put anything below this section - it will be ignored +*View 1 +$192-192-192,0,Times New Roman|12||0-0-0|0-0-0|0-0-255|-1--1--1|-1--1--1|96,96,108,0 +10,1,Prey,344,161,53,27,3,3,0,3,0,0,0,0,17-128-64,0-0-0,|||17-128-64,0,0,0,0,0,0 +10,2,Predator,486,267,53,27,3,3,0,3,0,0,0,0,17-128-64,0-0-0,|||17-128-64,0,0,0,0,0,0 +12,3,48,157,163,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,4,6,1,4,0,0,22,0,0,0,-1--1--1,,1|(260,163)| +1,5,6,3,100,0,0,22,0,0,0,-1--1--1,,1|(190,163)| +11,6,0,221,163,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,7,prey birth rate,221,186,26,12,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,8,48,512,163,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,9,11,8,4,0,0,22,0,0,0,-1--1--1,,1|(478,163)| +1,10,11,1,100,0,0,22,0,0,0,-1--1--1,,1|(418,163)| +11,11,0,447,163,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,12,prey death rate,447,188,27,14,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,13,48,310,270,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,14,16,2,4,0,0,22,0,0,0,-1--1--1,,1|(402,270)| +1,15,16,13,100,0,0,22,0,0,0,-1--1--1,,1|(337,270)| +11,16,0,363,270,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,17,predator birth rate,363,291,30,10,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,18,48,663,268,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,19,21,18,4,0,0,22,0,0,0,-1--1--1,,1|(622,268)| +1,20,21,2,100,0,0,22,0,0,0,-1--1--1,,1|(557,268)| +11,21,0,583,268,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,22,predator death rate,583,294,34,15,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,23,alpha,148,225,17,13,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +10,24,delta,276,319,18,19,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +10,25,beta,543,214,24,21,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +10,26,gamma,676,322,10,31,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +1,27,23,7,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,28,1,7,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(284,214)| +1,29,1,17,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(336,231)| +1,30,1,12,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(383,209)| +1,31,25,12,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,32,2,12,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(477,232)| +1,33,26,22,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,34,2,22,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(516,325)| +1,35,2,17,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(433,308)| +1,36,24,17,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +12,37,0,1430,276,200,200,3,44,0,0,1,0,0,0,0,0,0,0,0,0 +Population +12,38,0,1122,713,200,200,3,44,0,0,1,0,0,0,0,0,0,0,0,0 +Phases +10,39,TIME STEP,289,766,50,11,8,2,0,3,-1,0,0,0,128-128-128,0-0-0,|||128-128-128,0,0,0,0,0,0 +///---\\\ +:GRAPH Population +:TITLE Population +:X-AXIS Time +:SCALE +:VAR Predator Population +:Y-MIN 0 +:Y-MAX 200 +:LINE-WIDTH 2 +:VAR Prey Population +:Y-MIN 0 +:Y-MAX 200 +:LINE-WIDTH 2 + +:GRAPH Phases +:TITLE Phases +:X-AXIS Prey Population +:SCALE +:VAR Predator Population +:Y-MIN 0 +:Y-MAX 200 +:LINE-WIDTH 2 +:L<%^E!@ +1:Current.vdfx +4:Time +5:alpha[kind] +6:kind1 +9:Current +19:108,0 +24:0 +25:12 +26:12 +23:0 +15:0,0,0,0,0,0 +27:2, +34:0, +42:0 +72:0 +73:0 +95:0 +96:0 +97:0 +77:0 +78:0 +102:1 +93:0 +94:0 +92:0 +91:0 +90:0 +87:0 +75: +43: +103:8,8,8,3,8 +105:0,0,0,0,0,0,0,0,0,0 +104:Courier|12||0-0-0|0-0-0|-1--1--1|0-0-255|192-192-192|-1--1--1 diff --git a/test_scripts/vensim_models/prey-predator-hier3.mdl b/test_scripts/vensim_models/prey-predator-hier3.mdl new file mode 100644 index 00000000..6cec89c2 --- /dev/null +++ b/test_scripts/vensim_models/prey-predator-hier3.mdl @@ -0,0 +1,197 @@ +{UTF-8} +pred kind: + pred1, pred2 + ~ + ~ | + +prey kind: + prey1, prey2 + ~ + ~ | + +******************************************************** + .Control +********************************************************~ + Simulation Control Parameters + | + +predator birth rate[pred kind]= + (delta[pred kind]*(Prey[prey1] + Prey[prey2]))*Predator[pred kind] + ~ pred/time + ~ | + +predator death rate[pred kind]= + gamma[pred kind]*Predator[pred kind] + ~ pred/time + ~ | + +Prey[prey kind]= INTEG ( + prey birth rate[prey kind]-prey death rate[prey kind], + 100) + ~ prey + ~ | + +Predator[pred kind]= INTEG ( + predator birth rate[pred kind]-predator death rate[pred kind], + 15) + ~ pred + ~ | + +prey death rate[prey kind]= + beta[prey kind]*(Predator[pred1] + Predator[pred2])*Prey[prey kind] + ~ prey/time + ~ | + +prey birth rate[prey kind]= + alpha[prey kind]*Prey[prey kind] + ~ prey/time + ~ | + +alpha[prey kind]= + 1,1 + ~ fraction/time [0,5,0.1] + ~ prey birth fraction + | + +beta[prey kind]= + 0.05, 0.05 + ~ fraction/time [0,0.05,0.001] + ~ prey death proportionality constant + | + +gamma[pred kind]= + 1, 1 + ~ fraction/time [0,2,0.01] + ~ predator death proportionality constant + | + +delta[pred kind]= + 0.05, 0.05 + ~ fraction/time [0,0.05,0.001] + ~ predator birth fraction + | + +FINAL TIME = 12 + ~ seasons + ~ The final time for the simulation. + | + +INITIAL TIME = 0 + ~ seasons + ~ The initial time for the simulation. + | + +SAVEPER = + TIME STEP + ~ seasons [0,?] + ~ The frequency with which output is stored. + | + +TIME STEP = 0.03125 + ~ seasons [0.001,0.04,0.001] + ~ The time step for the simulation. + | + +\\\---/// Sketch information - do not modify anything except names +V300 Do not put anything below this section - it will be ignored +*View 1 +$192-192-192,0,Times New Roman|12||0-0-0|0-0-0|0-0-255|-1--1--1|-1--1--1|96,96,108,0 +10,1,Prey,344,180,53,27,3,3,0,3,0,0,0,0,17-128-64,0-0-0,|||17-128-64,0,0,0,0,0,0 +10,2,Predator,486,286,53,27,3,3,0,3,0,0,0,0,17-128-64,0-0-0,|||17-128-64,0,0,0,0,0,0 +12,3,48,157,182,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,4,6,1,4,0,0,22,0,0,0,-1--1--1,,1|(260,182)| +1,5,6,3,100,0,0,22,0,0,0,-1--1--1,,1|(190,182)| +11,6,0,221,182,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,7,prey birth rate,221,205,26,12,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,8,48,512,182,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,9,11,8,4,0,0,22,0,0,0,-1--1--1,,1|(478,182)| +1,10,11,1,100,0,0,22,0,0,0,-1--1--1,,1|(418,182)| +11,11,0,447,182,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,12,prey death rate,447,207,27,14,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,13,48,310,289,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,14,16,2,4,0,0,22,0,0,0,-1--1--1,,1|(402,289)| +1,15,16,13,100,0,0,22,0,0,0,-1--1--1,,1|(337,289)| +11,16,0,363,289,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,17,predator birth rate,363,310,30,10,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,18,48,663,287,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,19,21,18,4,0,0,22,0,0,0,-1--1--1,,1|(622,287)| +1,20,21,2,100,0,0,22,0,0,0,-1--1--1,,1|(557,287)| +11,21,0,583,287,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,22,predator death rate,583,313,34,15,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,23,alpha,148,244,17,13,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +10,24,delta,276,338,18,19,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +10,25,beta,543,233,24,21,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +10,26,gamma,676,341,10,31,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +1,27,23,7,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,28,1,7,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(284,233)| +1,29,1,17,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(336,250)| +1,30,1,12,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(383,228)| +1,31,25,12,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,32,2,12,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(477,251)| +1,33,26,22,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +1,34,2,22,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(516,344)| +1,35,2,17,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(433,327)| +1,36,24,17,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| +12,37,0,1430,295,200,200,3,44,0,0,1,0,0,0,0,0,0,0,0,0 +Population +12,38,0,1122,732,200,200,3,44,0,0,1,0,0,0,0,0,0,0,0,0 +Phases +10,39,TIME STEP,289,785,50,11,8,2,0,3,-1,0,0,0,128-128-128,0-0-0,|||128-128-128,0,0,0,0,0,0 +///---\\\ +:GRAPH Population +:TITLE Population +:X-AXIS Time +:SCALE +:VAR Predator Population +:Y-MIN 0 +:Y-MAX 200 +:LINE-WIDTH 2 +:VAR Prey Population +:Y-MIN 0 +:Y-MAX 200 +:LINE-WIDTH 2 + +:GRAPH Phases +:TITLE Phases +:X-AXIS Prey Population +:SCALE +:VAR Predator Population +:Y-MIN 0 +:Y-MAX 200 +:LINE-WIDTH 2 +:L<%^E!@ +1:Current.vdfx +4:Time +5:Prey[prey kind] +6:pred1 +6:prey1 +6:prey2 +9:Current +19:108,0 +24:0 +25:12 +26:12 +23:0 +15:0,0,0,0,0,0 +27:2, +34:0, +42:0 +72:0 +73:0 +95:0 +96:0 +97:0 +77:0 +78:0 +102:1 +93:0 +94:0 +92:0 +91:0 +90:0 +87:0 +75: +43: +103:8,8,8,3,8 +105:0,0,0,0,0,0,0,0,0,0 +104:Courier|12||0-0-0|0-0-0|-1--1--1|0-0-255|192-192-192|-1--1--1 diff --git a/test_scripts/vensim_models/prey-predator.mdl b/test_scripts/vensim_models/prey-predator.mdl index 0f968aa4..22b3c769 100644 --- a/test_scripts/vensim_models/prey-predator.mdl +++ b/test_scripts/vensim_models/prey-predator.mdl @@ -1,35 +1,35 @@ {UTF-8} -gamma= - 0.01 - ~ [0,0.05,0.001] +delta= + 0.05 + ~ fraction/Time [0,?] ~ predator birth fraction | predator birth rate= - (gamma*Prey)*Predator - ~ + (delta*Prey)*Predator + ~ pred/Time ~ | -delta= +gamma= 1 - ~ [0,2,0.01] + ~ fraction/Time [0,?] ~ predator death proportionality constant | predator death rate= - delta*Predator - ~ + gamma*Predator + ~ pred/Time ~ | Predator= INTEG ( predator birth rate-predator death rate, - 15) + 4) ~ ~ | alpha= 1 - ~ [0,5,0.1] + ~ fraction/Time [0,?] ~ prey birth fraction | @@ -39,8 +39,8 @@ prey birth rate= ~ | beta= - 0.035 - ~ [0,0.05,0.001] + 0.05 + ~ fraction/Time [0,?] ~ prey death proportionality constant | @@ -51,7 +51,7 @@ prey death rate= Prey= INTEG ( prey birth rate-prey death rate, - 100) + 30) ~ ~ | @@ -85,48 +85,48 @@ TIME STEP = 0.03125 \\\---/// Sketch information - do not modify anything except names V300 Do not put anything below this section - it will be ignored *View 1 -$192-192-192,0,Times New Roman|12||0-0-0|0-0-0|0-0-255|-1--1--1|-1--1--1|96,96,131,0 -10,1,Prey,390,94,53,27,3,3,0,3,0,0,0,0,17-128-64,0-0-0,|||17-128-64,0,0,0,0,0,0 -10,2,Predator,532,200,53,27,3,3,0,3,0,0,0,0,17-128-64,0-0-0,|||17-128-64,0,0,0,0,0,0 -12,3,48,203,96,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,4,6,1,4,0,0,22,0,0,0,-1--1--1,,1|(306,96)| -1,5,6,3,100,0,0,22,0,0,0,-1--1--1,,1|(236,96)| -11,6,0,267,96,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 -10,7,prey birth rate,267,119,26,12,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 -12,8,48,558,96,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,9,11,8,4,0,0,22,0,0,0,-1--1--1,,1|(524,96)| -1,10,11,1,100,0,0,22,0,0,0,-1--1--1,,1|(464,96)| -11,11,0,493,96,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 -10,12,prey death rate,493,121,27,14,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 -12,13,48,356,203,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,14,16,2,4,0,0,22,0,0,0,-1--1--1,,1|(448,203)| -1,15,16,13,100,0,0,22,0,0,0,-1--1--1,,1|(383,203)| -11,16,0,409,203,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 -10,17,predator birth rate,409,224,30,10,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 -12,18,48,709,201,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,19,21,18,4,0,0,22,0,0,0,-1--1--1,,1|(668,201)| -1,20,21,2,100,0,0,22,0,0,0,-1--1--1,,1|(603,201)| -11,21,0,629,201,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 -10,22,predator death rate,629,227,34,15,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 -10,23,alpha,194,158,17,13,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 -10,24,gamma,322,252,18,19,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 -10,25,beta,589,147,24,21,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 -10,26,delta,722,255,10,31,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +$192-192-192,0,Times New Roman|12||0-0-0|0-0-0|0-0-255|-1--1--1|-1--1--1|96,96,89,0 +10,1,Prey,379,137,53,27,3,3,0,3,0,0,0,0,17-128-64,0-0-0,|||17-128-64,0,0,0,0,0,0 +10,2,Predator,521,243,53,27,3,3,0,3,0,0,0,0,17-128-64,0-0-0,|||17-128-64,0,0,0,0,0,0 +12,3,48,192,139,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,4,6,1,4,0,0,22,0,0,0,-1--1--1,,1|(295,139)| +1,5,6,3,100,0,0,22,0,0,0,-1--1--1,,1|(225,139)| +11,6,0,256,139,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,7,prey birth rate,256,162,26,12,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,8,48,547,139,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,9,11,8,4,0,0,22,0,0,0,-1--1--1,,1|(513,139)| +1,10,11,1,100,0,0,22,0,0,0,-1--1--1,,1|(453,139)| +11,11,0,482,139,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,12,prey death rate,482,164,27,14,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,13,48,345,246,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,14,16,2,4,0,0,22,0,0,0,-1--1--1,,1|(437,246)| +1,15,16,13,100,0,0,22,0,0,0,-1--1--1,,1|(372,246)| +11,16,0,398,246,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,17,predator birth rate,398,267,30,10,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +12,18,48,698,244,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 +1,19,21,18,4,0,0,22,0,0,0,-1--1--1,,1|(657,244)| +1,20,21,2,100,0,0,22,0,0,0,-1--1--1,,1|(592,244)| +11,21,0,618,244,8,11,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,22,predator death rate,618,270,34,15,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-0-255,0,0,0,0,0,0 +10,23,alpha,183,201,17,13,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +10,24,delta,311,295,18,19,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +10,25,beta,578,190,24,21,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 +10,26,gamma,711,298,10,31,8,3,0,2,0,0,0,0,0-0-0,0-0-0,|||253-128-8,0,0,0,0,0,0 1,27,23,7,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| -1,28,1,7,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(330,147)| -1,29,1,17,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(382,164)| -1,30,1,12,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(429,142)| +1,28,1,7,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(319,190)| +1,29,1,17,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(371,207)| +1,30,1,12,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(418,185)| 1,31,25,12,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| -1,32,2,12,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(523,165)| +1,32,2,12,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(512,208)| 1,33,26,22,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| -1,34,2,22,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(562,258)| -1,35,2,17,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(479,241)| +1,34,2,22,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(551,301)| +1,35,2,17,1,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(468,284)| 1,36,24,17,0,0,43,0,1,64,0,0-0-0,|||0-0-0,1|(0,0)| -12,37,0,1476,209,200,200,3,44,0,0,1,0,0,0,0,0,0,0,0,0 +12,37,0,984,93,200,200,3,44,0,0,1,0,0,0,0,0,0,0,0,0 Population -12,38,0,1168,646,200,200,3,44,0,0,1,0,0,0,0,0,0,0,0,0 +12,38,0,974,492,200,200,3,44,0,0,1,0,0,0,0,0,0,0,0,0 Phases -10,39,TIME STEP,335,699,50,11,8,2,0,3,-1,0,0,0,128-128-128,0-0-0,|||128-128-128,0,0,0,0,0,0 +10,39,TIME STEP,324,742,50,11,8,2,0,3,-1,0,0,0,128-128-128,0-0-0,|||128-128-128,0,0,0,0,0,0 ///---\\\ :GRAPH Population :TITLE Population @@ -150,13 +150,14 @@ Phases :Y-MAX 200 :LINE-WIDTH 2 :L<%^E!@ +1:Current.vdfx 4:Time -5:beta +5:alpha 9:Current -19:131,0 +19:89,0 24:0 -25:12.012 -26:12.012 +25:12 +26:12 23:0 15:0,0,0,0,0,0 27:2, From 19033121f9a128a969ad99f3705aa2394c74f090 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Thu, 11 Aug 2022 09:25:08 +0900 Subject: [PATCH 16/45] Substitute match with isinstance. --- pysd/builders/stan/ast_walker.py | 366 +++++++++++------------ pysd/builders/stan/stan_model_builder.py | 71 +++-- 2 files changed, 214 insertions(+), 223 deletions(-) diff --git a/pysd/builders/stan/ast_walker.py b/pysd/builders/stan/ast_walker.py index 8042fdb1..6882d367 100644 --- a/pysd/builders/stan/ast_walker.py +++ b/pysd/builders/stan/ast_walker.py @@ -1,4 +1,4 @@ -from typing import Union, List, Iterable, Dict, Tuple +from typing import Union, List, Iterable, Dict, Tuple, Any from itertools import chain from dataclasses import dataclass, field from .utilities import IndentedString @@ -15,21 +15,22 @@ def walk(self, ast_node): class AuxNameWalker(BaseNodeWaler): def walk(self, ast_node) -> List[str]: - match ast_node: - case int(): - return [] - case float(): - return [] - case ArithmeticStructure(operators, arguments): - return list(chain.from_iterable([self.walk(argument) for argument in arguments])) - case ReferenceStructure(reference, subscripts): - return [ast_node.reference] - case CallStructure(function, arguments): - return list(chain.from_iterable([self.walk(argument) for argument in arguments])) - case IntegStructure(flow, initial): - return self.walk(flow) + self.walk(initial) - case InlineLookupsStructure(argument, lookups): - return self.walk(lookups) + if isinstance(ast_node, int): + return [] + elif isinstance(ast_node, float): + return [] + elif isinstance(ast_node, ArithmeticStructure): + return list(chain.from_iterable([self.walk(argument) for argument in ast_node.arguments])) + elif isinstance(ast_node, ReferenceStructure): + return [ast_node.reference] + elif isinstance(ast_node, CallStructure): + return list(chain.from_iterable([self.walk(argument) for argument in ast_node.arguments])) + elif isinstance(ast_node, IntegStructure): + return self.walk(ast_node.flow) + self.walk(ast_node.initial) + elif isinstance(ast_node, InlineLookupsStructure): + return self.walk(ast_node.lookups) + else: + raise Exception(f"AST node of type {ast_node.__class__.__name__} is not supported.") @dataclass class LookupCodegenWalker(BaseNodeWaler): @@ -44,45 +45,43 @@ def get_lookup_keyname(lookup_node: LookupsStructure): return lookup_node.x + lookup_node.y + lookup_node.x_limits + lookup_node.y_limits def walk(self, ast_node) -> None: - match ast_node: - case InlineLookupsStructure(argument, lookups): - self.walk(lookups) - case LookupsStructure(x, y, x_limits, y_limits, type): - assert type == "interpolate", "Type of Lookup must be 'interpolate'" - identifier_key = LookupCodegenWalker.get_lookup_keyname(ast_node) - function_name = f"lookupFunc_{self.n_lookups}" - self.generated_lookup_function_names[identifier_key] = function_name - self.n_lookups += 1 - self.code += f"real {function_name}(real x){{\n" - self.code.indent_level += 1 - # Enter function body - self.code += f"# x {x_limits} = {x}\n" - self.code += f"# y {y_limits} = {y}\n" - self.code += "real slope;\n" - self.code += "real intercept;\n\n" - n_intervals = len(x) - for lookup_index in range(n_intervals): - if lookup_index == 0: - continue - if lookup_index == 1: - self.code += f"if(x <= {x[lookup_index]})\n" - else: - self.code += f"else if(x <= {x[lookup_index]})\n" - - self.code.indent_level += 1 - # enter conditional body - self.code += f"intercept = {y[lookup_index - 1]};\n" - self.code += f"slope = ({y[lookup_index]} - {y[lookup_index - 1]}) / ({x[lookup_index]} - {x[lookup_index - 1]});\n" - self.code += f"return intercept + slope * (x - {x[lookup_index - 1]});\n" - self.code.indent_level -= 1 - # exit conditional body + if isinstance(ast_node, InlineLookupsStructure): + self.walk(ast_node.lookups) + elif isinstance(ast_node, LookupsStructure): + assert ast_node.type == "interpolate", "Type of Lookup must be 'interpolate'" + identifier_key = LookupCodegenWalker.get_lookup_keyname(ast_node) + function_name = f"lookupFunc_{self.n_lookups}" + self.generated_lookup_function_names[identifier_key] = function_name + self.n_lookups += 1 + self.code += f"real {function_name}(real x){{\n" + self.code.indent_level += 1 + # Enter function body + self.code += f"# x {ast_node.x_limits} = {ast_node.x}\n" + self.code += f"# y {ast_node.y_limits} = {ast_node.y}\n" + self.code += "real slope;\n" + self.code += "real intercept;\n\n" + n_intervals = len(ast_node.x) + for lookup_index in range(n_intervals): + if lookup_index == 0: + continue + if lookup_index == 1: + self.code += f"if(x <= {ast_node.x[lookup_index]})\n" + else: + self.code += f"else if(x <= {ast_node.x[lookup_index]})\n" + self.code.indent_level += 1 + # enter conditional body + self.code += f"intercept = {ast_node.y[lookup_index - 1]};\n" + self.code += f"slope = ({ast_node.y[lookup_index]} - {ast_node.y[lookup_index - 1]}) / ({ast_node.x[lookup_index]} - {ast_node.x[lookup_index - 1]});\n" + self.code += f"return intercept + slope * (x - {ast_node.x[lookup_index - 1]});\n" self.code.indent_level -= 1 - # exit function body - self.code += "}\n\n" + # exit conditional body - case _: - return None + self.code.indent_level -= 1 + # exit function body + self.code += "}\n\n" + else: + return None @dataclass @@ -90,75 +89,71 @@ class BlockCodegenWalker(BaseNodeWaler): lookup_function_names: Dict[Tuple, str] def walk(self, ast_node) -> str: - match ast_node: - case int(x): - return f"{x}" - - case float(x): - return f"{x}" - - case str(x): - return x - - case ArithmeticStructure(operators, arguments): - # ArithmeticStructure consists of chained arithmetic expressions. - # We parse them one by one into a single expression - output_string = "" - last_argument_index = len(arguments) - 1 - for index, argument in enumerate(arguments): - output_string += self.walk(argument) - if index < last_argument_index: - output_string += " " - output_string += operators[index] - output_string += " " + + if isinstance(ast_node, int): + return f"{ast_node}" + elif isinstance(ast_node, float): + return f"{ast_node}" + elif isinstance(ast_node, str): + return ast_node + elif isinstance(ast_node, ArithmeticStructure): + # ArithmeticStructure consists of chained arithmetic expressions. + # We parse them one by one into a single expression + output_string = "" + last_argument_index = len(ast_node.arguments) - 1 + for index, argument in enumerate(ast_node.arguments): + output_string += self.walk(argument) + if index < last_argument_index: + output_string += " " + output_string += ast_node.operators[index] + output_string += " " + return output_string + + elif isinstance(ast_node, ReferenceStructure): + # ReferenceSTructure denotes invoking the value of another variable + # Subscripts are ignored for now + return ast_node.reference + + elif isinstance(ast_node, CallStructure): + output_string = "" + function_name = self.walk(ast_node.function) + if function_name == "min": + function_name = "fmin" + elif function_name == "max": + function_name = "fmax" + elif function_name == "xidz": + assert len(ast_node.arguments) == 3, "number of arguments for xidz must be 3" + arg1 = self.walk(ast_node.arguments[0]) + arg2 = self.walk(ast_node.arguments[1]) + arg3 = self.walk(ast_node.arguments[2]) + output_string += f" (fabs({arg2}) <= 1e-6) ? {arg3} : ({arg1}) / ({arg2})" return output_string + elif function_name == "zidz": + assert len(ast_node.arguments) == 2, "number of arguments for zidz must be 2" + arg1 = self.walk(ast_node.arguments[0]) + arg2 = self.walk(ast_node.arguments[1]) + output_string += f" (fabs({arg2}) <= 1e-6) ? 0 : ({arg1}) / ({arg2})" + return output_string + elif function_name == "ln": + # natural log in stan is just log + function_name = "log" - case ReferenceStructure(reference, subscripts): - # ReferenceSTructure denotes invoking the value of another variable - # Subscripts are ignored for now - return reference - - case CallStructure(function, arguments): - output_string = "" - function_name = self.walk(function) - match function_name: - case "min": - function_name = "fmin" - case "max": - function_name = "fmax" - case "xidz": - assert len(arguments) == 3, "number of arguments for xidz must be 3" - arg1 = self.walk(arguments[0]) - arg2 = self.walk(arguments[1]) - arg3 = self.walk(arguments[2]) - output_string += f" (fabs({arg2}) <= 1e-6) ? {arg3} : ({arg1}) / ({arg2})" - return output_string - case "zidz": - assert len(arguments) == 2, "number of arguments for zidz must be 2" - arg1 = self.walk(arguments[0]) - arg2 = self.walk(arguments[1]) - output_string += f" (fabs({arg2}) <= 1e-6) ? 0 : ({arg1}) / ({arg2})" - return output_string - case "ln": - # natural log in stan is just log - function_name = "log" - - output_string += function_name - output_string += "(" - output_string += ",".join([self.walk(argument) for argument in arguments]) - output_string += ")" + output_string += function_name + output_string += "(" + output_string += ",".join([self.walk(argument) for argument in ast_node.arguments]) + output_string += ")" - return output_string + return output_string - case IntegStructure(flow, initial): - return self.walk(flow) + elif isinstance(ast_node, IntegStructure): + return self.walk(ast_node.flow) - case InlineLookupsStructure(argument, lookups): - lookup_func_name = self.lookup_function_names[LookupCodegenWalker.get_lookup_keyname(lookups)] - return f"{lookup_func_name}({self.walk(argument)})" + elif isinstance(ast_node, InlineLookupsStructure): + lookup_func_name = self.lookup_function_names[LookupCodegenWalker.get_lookup_keyname(ast_node.lookups)] + return f"{lookup_func_name}({self.walk(ast_node.argument)})" - case _: - raise Exception("Got unknown node", ast_node) + else: + raise Exception("Got unknown node", ast_node) @dataclass class InitialValueCodegenWalker(BlockCodegenWalker): @@ -166,31 +161,33 @@ class InitialValueCodegenWalker(BlockCodegenWalker): lookup_function_names: Dict[Tuple, str] def walk(self, ast_node): - match ast_node: - case IntegStructure(flow, initial): - return self.walk(initial) - case SmoothStructure(input, smooth_time, initial, order): - return self.walk(initial) - case ReferenceStructure(reference, subscripts): - if reference in self.variable_ast_dict: - return self.walk(self.variable_ast_dict[reference]) - else: - return super().walk(ast_node) - case ArithmeticStructure(operators, arguments): - # ArithmeticStructure consists of chained arithmetic expressions. - # We parse them one by one into a single expression - output_string = "" - last_argument_index = len(arguments) - 1 - for index, argument in enumerate(arguments): - output_string += self.walk(argument) - if index < last_argument_index: - output_string += " " - output_string += operators[index] - output_string += " " - return output_string - case _: + if isinstance(ast_node, IntegStructure): + return self.walk(ast_node.initial) + + elif isinstance(ast_node, SmoothStructure): + return self.walk(ast_node.initial) + + elif isinstance(ast_node, ReferenceStructure): + if ast_node.reference in self.variable_ast_dict: + return self.walk(self.variable_ast_dict[ast_node.reference]) + else: return super().walk(ast_node) + elif isinstance(ast_node, ArithmeticStructure): + # ArithmeticStructure consists of chained arithmetic expressions. + # We parse them one by one into a single expression + output_string = "" + last_argument_index = len(ast_node.arguments) - 1 + for index, argument in enumerate(ast_node.arguments): + output_string += self.walk(argument) + if index < last_argument_index: + output_string += " " + output_string += ast_node.operators[index] + output_string += " " + return output_string + else: + return super().walk(ast_node) + @dataclass class RNGCodegenWalker(InitialValueCodegenWalker): @@ -199,55 +196,52 @@ class RNGCodegenWalker(InitialValueCodegenWalker): total_timestep: int def walk(self, ast_node) -> str: - match ast_node: - case CallStructure(function, arguments): - function_name = self.walk(function) - match function_name: - case "random_beta" | "random_binomial" | "random_binomial" | "random_exponential" | "random_gamma" | "random_normal" | "random_poisson": - argument_codegen = [self.walk(argument) for argument in arguments] - return self.rng_codegen(function_name, argument_codegen) - case _: - return super().walk(ast_node) - - case IntegStructure(flow, initial): - raise Exception("RNG function arguments cannot contain stock variables which change with time and thus must be constant!") - - case SmoothStructure(input, smooth_time, initial, order): - raise Exception("RNG function arguments cannot contain stock variables which change with time and thus must be constant!") - - case ReferenceStructure(reference, subscripts): - if reference in self.variable_ast_dict: - return self.walk(reference) - else: - return super().walk(ast_node) - - case ArithmeticStructure(operators, arguments): - # ArithmeticStructure consists of chained arithmetic expressions. - # We parse them one by one into a single expression - output_string = "" - last_argument_index = len(arguments) - 1 - for index, argument in enumerate(arguments): - output_string += self.walk(argument) - if index < last_argument_index: - output_string += " " - output_string += operators[index] - output_string += " " - return output_string + if isinstance(ast_node, CallStructure): + function_name = self.walk(ast_node.function) + if function_name in ("random_beta" , "random_binomial" , "random_binomial" , "random_exponential" , "random_gamma" , "random_normal" , "random_poisson"): + argument_codegen = [self.walk(argument) for argument in ast_node.arguments] + return self.rng_codegen(function_name, argument_codegen) + else: + return super().walk(ast_node) + + elif isinstance(ast_node, IntegStructure): + raise Exception("RNG function arguments cannot contain stock variables which change with time and thus must be constant!") + + elif isinstance(ast_node, SmoothStructure): + raise Exception("RNG function arguments cannot contain stock variables which change with time and thus must be constant!") - case _: + elif isinstance(ast_node, ReferenceStructure): + if ast_node.reference in self.variable_ast_dict: + return self.walk(ast_node.reference) + else: return super().walk(ast_node) - def rng_codegen(self, rng_type, arguments): - match rng_type: - case "random_normal": - lower, upper, mean, std, _ = arguments - return f"fmin(fmax(normal_rng({mean}, {std}), {lower}), {upper})" - case "random_uniform": - lower, upper, _ = arguments - return f"uniform_rng({lower}, {upper})" - case "random_poisson": - lower, upper, _lambda, offset, multiply, _ = arguments - return f"fmin(fmax(fma(poisson_rng({_lambda}), {multiply}, {offset}), {lower}), {upper})" - case _: - raise Exception(f"RNG function {rng_type} not implemented") + elif isinstance(ast_node, ArithmeticStructure): + # ArithmeticStructure consists of chained arithmetic expressions. + # We parse them one by one into a single expression + output_string = "" + last_argument_index = len(ast_node.arguments) - 1 + for index, argument in enumerate(ast_node.arguments): + output_string += self.walk(argument) + if index < last_argument_index: + output_string += " " + output_string += ast_node.operators[index] + output_string += " " + return output_string + + else: + return super().walk(ast_node) + + def rng_codegen(self, rng_type: str, arguments: List[Any]): + if rng_type == "random_normal": + lower, upper, mean, std, _ = arguments + return f"fmin(fmax(normal_rng({mean}, {std}), {lower}), {upper})" + elif rng_type == "random_uniform": + lower, upper, _ = arguments + return f"uniform_rng({lower}, {upper})" + elif rng_type == "random_poisson": + lower, upper, _lambda, offset, multiply, _ = arguments + return f"fmin(fmax(fma(poisson_rng({_lambda}), {multiply}, {offset}), {lower}), {upper})" + else: + raise Exception(f"RNG function {rng_type} not implemented") diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_model_builder.py index 7482b8fd..0ff2aaba 100644 --- a/pysd/builders/stan/stan_model_builder.py +++ b/pysd/builders/stan/stan_model_builder.py @@ -39,13 +39,13 @@ def create_stan_program(self, predictor_variable_names: List[Union[str, Tuple[st # Santize vensim names to stan-compliant identifiers sanitized_predictor_variable_names = [] for var in predictor_variable_names: - match var: - case str(x): - sanitized_predictor_variable_names.append(vensim_name_to_identifier(x)) - case (str(type), str(var_name)): - sanitized_predictor_variable_names.append((type, vensim_name_to_identifier(var_name))) - case _: - raise Exception("predictor_variable_names must be a list of strings and/or a tuple of the form(T, Name), where T is a string denoting the variable's stan type and Name a string denoting the variable name") + if isinstance(var, str): + sanitized_predictor_variable_names.append(vensim_name_to_identifier(var)) + elif isinstance(var, tuple): + var_name = var[1] + sanitized_predictor_variable_names.append((type, vensim_name_to_identifier(var_name))) + else: + raise Exception("predictor_variable_names must be a list consisting of: strings and/or a tuple of the form(T, Name), where T is a string denoting the variable's stan type and Name a string denoting the variable name") predictor_variable_names = sanitized_predictor_variable_names outcome_variable_names = self.get_stock_variable_stan_names() if not outcome_variable_names else [vensim_name_to_identifier(name) for name in outcome_variable_names] @@ -104,21 +104,21 @@ def get_stock_variable_stan_names(self) -> List[str]: return stock_varible_names -""" class StanDataBuilder: - def __init__(self, abstract_model: AbstractModel): - self.abstract_model = abstract_model - - def build_block(self, predictor_variable_names, outcome_variable_names): - self.code = IndentedString() - self.code += "data {\n" - self.code.indent_level += 1 - - self.code += f"predictor= {{{', '.join(predictor_variable_names)}}};\n" - self.code += f"initial_outcome = {{{', '.join(outcome_variable_names)}}};\n" - self.code += f"observed_outcome = {{{', '.join(outcome_variable_names)}}};\n" - self.code += f"times = {{{', '.join(outcome_variable_names)}}};\n" - self.code.indent_level -= 1 - self.code += "}\n" """ +# class StanDataBuilder: +# def __init__(self, abstract_model: AbstractModel): +# self.abstract_model = abstract_model +# +# def build_block(self, predictor_variable_names, outcome_variable_names): +# self.code = IndentedString() +# self.code += "data {\n" +# self.code.indent_level += 1 +# +# self.code += f"predictor= {{{', '.join(predictor_variable_names)}}};\n" +# self.code += f"initial_outcome = {{{', '.join(outcome_variable_names)}}};\n" +# self.code += f"observed_outcome = {{{', '.join(outcome_variable_names)}}};\n" +# self.code += f"times = {{{', '.join(outcome_variable_names)}}};\n" +# self.code.indent_level -= 1 +# self.code += "}\n" class StanTransformedDataBuilder: @@ -137,11 +137,11 @@ def build_block(self, predictor_variable_names, outcome_variable_names, lookup_f argument_variables = [] for var in predictor_variable_names: - match var: - case str(x): - argument_variables.append(x) - case (str(type), str(var_name)): - argument_variables.append(var_name) + if isinstance(var, str): + argument_variables.append(var) + elif isinstance(var, tuple): + var_name = var[1] + argument_variables.append(var_name) variable_ast_dict: Dict[str, AbstractSyntax] = {} for element in self.abstract_model.sections[0].elements: @@ -218,9 +218,6 @@ def build_function_block(self, predictor_variable_names: List[Tuple[str, str]], bfs_stack.append(next_var) required_variables |= self.variable_dependency_graph[variable] - #print(self.variable_dependency_graph) - #print("rv:", required_variables) - eval_order = [] def recursive_order_search(current, visited): # if current in visited: @@ -249,13 +246,13 @@ def recursive_order_search(current, visited): argument_strings = [] argument_variables = [] # this list holds the names of the argument variables for var in predictor_variable_names: - match var: - case str(x): - argument_variables.append(x) - argument_strings.append("real " + x) - case (str(type), str(var_name)): - argument_variables.append(var_name) - argument_strings.append(f"{type} {var_name}") + if isinstance(var, str): + argument_variables.append(var) + argument_strings.append("real " + var) + elif isinstance(var, type): + var_type, var_name = var + argument_variables.append(var_name) + argument_strings.append(f"{var_type} {var_name}") self.code += ", ".join(argument_strings) self.code += "){" From af542c47d076d5de9fa15ba320301d43fef084d2 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Thu, 11 Aug 2022 09:25:49 +0900 Subject: [PATCH 17/45] Autoformat with black --- pysd/builders/stan/ast_walker.py | 92 +++++++++--- pysd/builders/stan/stan_model_builder.py | 170 ++++++++++++++++++----- pysd/builders/stan/utilities.py | 2 +- 3 files changed, 207 insertions(+), 57 deletions(-) diff --git a/pysd/builders/stan/ast_walker.py b/pysd/builders/stan/ast_walker.py index 6882d367..2bd24ef1 100644 --- a/pysd/builders/stan/ast_walker.py +++ b/pysd/builders/stan/ast_walker.py @@ -2,8 +2,12 @@ from itertools import chain from dataclasses import dataclass, field from .utilities import IndentedString -from pysd.translators.structures.abstract_model import\ - AbstractComponent, AbstractElement, AbstractModel, AbstractSection +from pysd.translators.structures.abstract_model import ( + AbstractComponent, + AbstractElement, + AbstractModel, + AbstractSection, +) from pysd.translators.structures.abstract_expressions import * @@ -20,21 +24,34 @@ def walk(self, ast_node) -> List[str]: elif isinstance(ast_node, float): return [] elif isinstance(ast_node, ArithmeticStructure): - return list(chain.from_iterable([self.walk(argument) for argument in ast_node.arguments])) + return list( + chain.from_iterable( + [self.walk(argument) for argument in ast_node.arguments] + ) + ) elif isinstance(ast_node, ReferenceStructure): return [ast_node.reference] elif isinstance(ast_node, CallStructure): - return list(chain.from_iterable([self.walk(argument) for argument in ast_node.arguments])) + return list( + chain.from_iterable( + [self.walk(argument) for argument in ast_node.arguments] + ) + ) elif isinstance(ast_node, IntegStructure): return self.walk(ast_node.flow) + self.walk(ast_node.initial) elif isinstance(ast_node, InlineLookupsStructure): return self.walk(ast_node.lookups) else: - raise Exception(f"AST node of type {ast_node.__class__.__name__} is not supported.") + raise Exception( + f"AST node of type {ast_node.__class__.__name__} is not supported." + ) + @dataclass class LookupCodegenWalker(BaseNodeWaler): - generated_lookup_function_names: Dict[Tuple, str] = field(default_factory=dict) + generated_lookup_function_names: Dict[Tuple, str] = field( + default_factory=dict + ) # This dict holds the generated function names of each individual lookup function. # Key is x + y + x_limits + y_limits, value is function name n_lookups = 0 @@ -42,16 +59,25 @@ class LookupCodegenWalker(BaseNodeWaler): @staticmethod def get_lookup_keyname(lookup_node: LookupsStructure): - return lookup_node.x + lookup_node.y + lookup_node.x_limits + lookup_node.y_limits + return ( + lookup_node.x + + lookup_node.y + + lookup_node.x_limits + + lookup_node.y_limits + ) def walk(self, ast_node) -> None: if isinstance(ast_node, InlineLookupsStructure): self.walk(ast_node.lookups) elif isinstance(ast_node, LookupsStructure): - assert ast_node.type == "interpolate", "Type of Lookup must be 'interpolate'" + assert ( + ast_node.type == "interpolate" + ), "Type of Lookup must be 'interpolate'" identifier_key = LookupCodegenWalker.get_lookup_keyname(ast_node) function_name = f"lookupFunc_{self.n_lookups}" - self.generated_lookup_function_names[identifier_key] = function_name + self.generated_lookup_function_names[ + identifier_key + ] = function_name self.n_lookups += 1 self.code += f"real {function_name}(real x){{\n" self.code.indent_level += 1 @@ -122,17 +148,25 @@ def walk(self, ast_node) -> str: elif function_name == "max": function_name = "fmax" elif function_name == "xidz": - assert len(ast_node.arguments) == 3, "number of arguments for xidz must be 3" + assert ( + len(ast_node.arguments) == 3 + ), "number of arguments for xidz must be 3" arg1 = self.walk(ast_node.arguments[0]) arg2 = self.walk(ast_node.arguments[1]) arg3 = self.walk(ast_node.arguments[2]) - output_string += f" (fabs({arg2}) <= 1e-6) ? {arg3} : ({arg1}) / ({arg2})" + output_string += ( + f" (fabs({arg2}) <= 1e-6) ? {arg3} : ({arg1}) / ({arg2})" + ) return output_string elif function_name == "zidz": - assert len(ast_node.arguments) == 2, "number of arguments for zidz must be 2" + assert ( + len(ast_node.arguments) == 2 + ), "number of arguments for zidz must be 2" arg1 = self.walk(ast_node.arguments[0]) arg2 = self.walk(ast_node.arguments[1]) - output_string += f" (fabs({arg2}) <= 1e-6) ? 0 : ({arg1}) / ({arg2})" + output_string += ( + f" (fabs({arg2}) <= 1e-6) ? 0 : ({arg1}) / ({arg2})" + ) return output_string elif function_name == "ln": # natural log in stan is just log @@ -140,7 +174,9 @@ def walk(self, ast_node) -> str: output_string += function_name output_string += "(" - output_string += ",".join([self.walk(argument) for argument in ast_node.arguments]) + output_string += ",".join( + [self.walk(argument) for argument in ast_node.arguments] + ) output_string += ")" return output_string @@ -149,12 +185,15 @@ def walk(self, ast_node) -> str: return self.walk(ast_node.flow) elif isinstance(ast_node, InlineLookupsStructure): - lookup_func_name = self.lookup_function_names[LookupCodegenWalker.get_lookup_keyname(ast_node.lookups)] + lookup_func_name = self.lookup_function_names[ + LookupCodegenWalker.get_lookup_keyname(ast_node.lookups) + ] return f"{lookup_func_name}({self.walk(ast_node.argument)})" else: raise Exception("Got unknown node", ast_node) + @dataclass class InitialValueCodegenWalker(BlockCodegenWalker): variable_ast_dict: Dict[str, AbstractSyntax] @@ -198,17 +237,31 @@ class RNGCodegenWalker(InitialValueCodegenWalker): def walk(self, ast_node) -> str: if isinstance(ast_node, CallStructure): function_name = self.walk(ast_node.function) - if function_name in ("random_beta" , "random_binomial" , "random_binomial" , "random_exponential" , "random_gamma" , "random_normal" , "random_poisson"): - argument_codegen = [self.walk(argument) for argument in ast_node.arguments] + if function_name in ( + "random_beta", + "random_binomial", + "random_binomial", + "random_exponential", + "random_gamma", + "random_normal", + "random_poisson", + ): + argument_codegen = [ + self.walk(argument) for argument in ast_node.arguments + ] return self.rng_codegen(function_name, argument_codegen) else: return super().walk(ast_node) elif isinstance(ast_node, IntegStructure): - raise Exception("RNG function arguments cannot contain stock variables which change with time and thus must be constant!") + raise Exception( + "RNG function arguments cannot contain stock variables which change with time and thus must be constant!" + ) elif isinstance(ast_node, SmoothStructure): - raise Exception("RNG function arguments cannot contain stock variables which change with time and thus must be constant!") + raise Exception( + "RNG function arguments cannot contain stock variables which change with time and thus must be constant!" + ) elif isinstance(ast_node, ReferenceStructure): if ast_node.reference in self.variable_ast_dict: @@ -244,4 +297,3 @@ def rng_codegen(self, rng_type: str, arguments: List[Any]): return f"fmin(fmax(fma(poisson_rng({_lambda}), {multiply}, {offset}), {lower}), {upper})" else: raise Exception(f"RNG function {rng_type} not implemented") - diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_model_builder.py index 0ff2aaba..60f42962 100644 --- a/pysd/builders/stan/stan_model_builder.py +++ b/pysd/builders/stan/stan_model_builder.py @@ -4,8 +4,12 @@ from .ast_walker import * from .utilities import * -from pysd.translators.structures.abstract_model import\ - AbstractComponent, AbstractElement, AbstractModel, AbstractSection +from pysd.translators.structures.abstract_model import ( + AbstractComponent, + AbstractElement, + AbstractModel, + AbstractSection, +) class StanModelBuilder: @@ -13,14 +17,22 @@ def __init__(self, abstract_model: AbstractModel): self.abstract_model = abstract_model self.variable_ast_dict: Dict[str, AbstractSyntax] = {} - assert len(self.abstract_model.sections) == 1, "Number of sections in AbstractModel must be 1." + assert ( + len(self.abstract_model.sections) == 1 + ), "Number of sections in AbstractModel must be 1." for element in self.abstract_model.sections[0].elements: stan_varname = vensim_name_to_identifier(element.name) - assert len(element.components) == 1, f"Number of components in AbstractElement must be 1, but {element.name} has {len(element.components)}" + assert ( + len(element.components) == 1 + ), f"Number of components in AbstractElement must be 1, but {element.name} has {len(element.components)}" self.variable_ast_dict[stan_varname] = element.components[0].ast - - def create_stan_program(self, predictor_variable_names: List[Union[str, Tuple[str, str]]], outcome_variable_names: Sequence[str] = (), function_name="vensim_func"): + def create_stan_program( + self, + predictor_variable_names: List[Union[str, Tuple[str, str]]], + outcome_variable_names: Sequence[str] = (), + function_name="vensim_func", + ): """ Parameters @@ -40,29 +52,53 @@ def create_stan_program(self, predictor_variable_names: List[Union[str, Tuple[st sanitized_predictor_variable_names = [] for var in predictor_variable_names: if isinstance(var, str): - sanitized_predictor_variable_names.append(vensim_name_to_identifier(var)) + sanitized_predictor_variable_names.append( + vensim_name_to_identifier(var) + ) elif isinstance(var, tuple): var_name = var[1] - sanitized_predictor_variable_names.append((type, vensim_name_to_identifier(var_name))) + sanitized_predictor_variable_names.append( + (type, vensim_name_to_identifier(var_name)) + ) else: - raise Exception("predictor_variable_names must be a list consisting of: strings and/or a tuple of the form(T, Name), where T is a string denoting the variable's stan type and Name a string denoting the variable name") + raise Exception( + "predictor_variable_names must be a list consisting of: strings and/or a tuple of the form(T, Name), where T is a string denoting the variable's stan type and Name a string denoting the variable name" + ) predictor_variable_names = sanitized_predictor_variable_names - outcome_variable_names = self.get_stock_variable_stan_names() if not outcome_variable_names else [vensim_name_to_identifier(name) for name in outcome_variable_names] + outcome_variable_names = ( + self.get_stock_variable_stan_names() + if not outcome_variable_names + else [ + vensim_name_to_identifier(name) + for name in outcome_variable_names + ] + ) if not outcome_variable_names: - raise Exception("There are no stock variables defined in the model, hence nothing to integrate.") + raise Exception( + "There are no stock variables defined in the model, hence nothing to integrate." + ) self.code = IndentedString() function_block_builder = StanFunctionBuilder(self.abstract_model) - self.code += function_block_builder.build_function_block(predictor_variable_names, outcome_variable_names, function_name) + self.code += function_block_builder.build_function_block( + predictor_variable_names, outcome_variable_names, function_name + ) self.code += "data{\n}\n" # self.code += StanDataBuilder(self.abstract_model).build_block(predictor_variable_names, outcome_variable_names) self.code += "transformed data{\n}\n" self.code += "parameters{\n}\n" - self.code += StanTransformedParametersBuilder(self.abstract_model).build_block(predictor_variable_names, outcome_variable_names, function_block_builder.lookup_builder_walker.generated_lookup_function_names, function_name) + self.code += StanTransformedParametersBuilder( + self.abstract_model + ).build_block( + predictor_variable_names, + outcome_variable_names, + function_block_builder.lookup_builder_walker.generated_lookup_function_names, + function_name, + ) self.code += "model{\n}\n" self.code += "generated quantities{\n}" @@ -79,14 +115,28 @@ def print_variable_info(self): is_stock = True break - var_names.append((element.name, vensim_name_to_identifier(element.name), is_stock)) + var_names.append( + ( + element.name, + vensim_name_to_identifier(element.name), + is_stock, + ) + ) max_length = max(max_length, len(element.name) + 1) - header = 'original name'.ljust(max_length) + "stan variable name".ljust(max_length) + "is stock" + header = ( + "original name".ljust(max_length) + + "stan variable name".ljust(max_length) + + "is stock" + ) print(header) print("-" * len(header)) for x in var_names: - print(x[0].ljust(max_length) + x[1].ljust(max_length) + ("V" if x[2] else "")) + print( + x[0].ljust(max_length) + + x[1].ljust(max_length) + + ("V" if x[2] else "") + ) def get_stock_variable_stan_names(self) -> List[str]: """ @@ -99,11 +149,14 @@ def get_stock_variable_stan_names(self) -> List[str]: for element in self.abstract_model.sections[0].elements: for component in element.components: if isinstance(component.ast, IntegStructure): - stock_varible_names.append(vensim_name_to_identifier(element.name)) + stock_varible_names.append( + vensim_name_to_identifier(element.name) + ) break return stock_varible_names + # class StanDataBuilder: # def __init__(self, abstract_model: AbstractModel): # self.abstract_model = abstract_model @@ -130,7 +183,13 @@ class StanTransformedParametersBuilder: def __init__(self, abstract_model: AbstractModel): self.abstract_model = abstract_model - def build_block(self, predictor_variable_names, outcome_variable_names, lookup_function_dict, function_name): + def build_block( + self, + predictor_variable_names, + outcome_variable_names, + lookup_function_dict, + function_name, + ): self.code = IndentedString() self.code += "transformed parameters {\n" self.code.indent_level += 1 @@ -150,9 +209,14 @@ def build_block(self, predictor_variable_names, outcome_variable_names, lookup_f for outcome_variable_name in outcome_variable_names: for element in self.abstract_model.sections[0].elements: - if vensim_name_to_identifier(element.name) == outcome_variable_name: + if ( + vensim_name_to_identifier(element.name) + == outcome_variable_name + ): component = element.components[0] - assert isinstance(component.ast, IntegStructure), "Output variable component must be an INTEG." + assert isinstance( + component.ast, IntegStructure + ), "Output variable component must be an INTEG." self.code += f"real {outcome_variable_name}_initial = {InitialValueCodegenWalker(lookup_function_dict, variable_ast_dict).walk(component.ast)};\n" break @@ -166,13 +230,19 @@ def build_block(self, predictor_variable_names, outcome_variable_names, lookup_f class StanFunctionBuilder: - def __init__(self, abstract_model: AbstractModel, function_name: str = "vensim_ode"): + def __init__( + self, abstract_model: AbstractModel, function_name: str = "vensim_ode" + ): self.abstract_model = abstract_model self.elements = self.abstract_model.sections[0].elements self.ode_function_name = function_name self.lookup_builder_walker = LookupCodegenWalker() - self.variable_dependency_graph: Dict[str, Set] = {} # in order to evaluate 'key' variable, we need 'element' variables + self.variable_dependency_graph: Dict[ + str, Set + ] = ( + {} + ) # in order to evaluate 'key' variable, we need 'element' variables self.code = IndentedString() def _create_dependency_graph(self): @@ -181,15 +251,24 @@ def _create_dependency_graph(self): for element in self.elements: for component in element.components: if element.name not in self.variable_dependency_graph: - self.variable_dependency_graph[vensim_name_to_identifier(element.name)] = set() + self.variable_dependency_graph[ + vensim_name_to_identifier(element.name) + ] = set() dependent_aux_names = walker.walk(component.ast) if dependent_aux_names: - self.variable_dependency_graph[vensim_name_to_identifier(element.name)].update(dependent_aux_names) + self.variable_dependency_graph[ + vensim_name_to_identifier(element.name) + ].update(dependent_aux_names) return self.variable_dependency_graph - def build_function_block(self, predictor_variable_names: List[Tuple[str, str]], outcome_variable_names: List[str], function_name: str ="vensim_func"): + def build_function_block( + self, + predictor_variable_names: List[Tuple[str, str]], + outcome_variable_names: List[str], + function_name: str = "vensim_func", + ): self.code = IndentedString() self.code += "functions {\n" @@ -219,6 +298,7 @@ def build_function_block(self, predictor_variable_names: List[Tuple[str, str]], required_variables |= self.variable_dependency_graph[variable] eval_order = [] + def recursive_order_search(current, visited): # if current in visited: # return @@ -226,25 +306,35 @@ def recursive_order_search(current, visited): # if current in eval_order: # return for child in self.variable_dependency_graph[current]: - if child == current: continue - if child in outcome_variable_names: continue + if child == current: + continue + if child in outcome_variable_names: + continue if child not in visited: recursive_order_search(child, visited) eval_order.append(current) - #for var_name in self.variable_dependency_graph.keys(): + # for var_name in self.variable_dependency_graph.keys(): for var_name in required_variables: recursive_order_search(var_name, set()) - self.elements = [element for element in self.elements if vensim_name_to_identifier(element.name) in required_variables] - self.elements = sorted(self.elements, key=lambda x: eval_order.index(vensim_name_to_identifier(x.name))) - + self.elements = [ + element + for element in self.elements + if vensim_name_to_identifier(element.name) in required_variables + ] + self.elements = sorted( + self.elements, + key=lambda x: eval_order.index(vensim_name_to_identifier(x.name)), + ) ################# # Create function declaration self.code += f"vector {function_name}(real time, vector outcome, " argument_strings = [] - argument_variables = [] # this list holds the names of the argument variables + argument_variables = ( + [] + ) # this list holds the names of the argument variables for var in predictor_variable_names: if isinstance(var, str): argument_variables.append(var) @@ -261,12 +351,16 @@ def recursive_order_search(current, visited): self.code.indent_level += 1 # Enter function body - for index, outcome_variable_name in enumerate(outcome_variable_names, 1): + for index, outcome_variable_name in enumerate( + outcome_variable_names, 1 + ): self.code += f"real {outcome_variable_name} = outcome[{index}];\n" self.code += "\n" - codegen_walker = BlockCodegenWalker(self.lookup_builder_walker.generated_lookup_function_names) + codegen_walker = BlockCodegenWalker( + self.lookup_builder_walker.generated_lookup_function_names + ) for element in self.elements: stan_varname = vensim_name_to_identifier(element.name) if stan_varname in argument_variables: @@ -281,7 +375,9 @@ def recursive_order_search(current, visited): self.code += "\n" # Generate code for returning outcomes of interest - outcome_variable_names = [name + "_dydt" for name in outcome_variable_names] + outcome_variable_names = [ + name + "_dydt" for name in outcome_variable_names + ] self.code += f"return {{{', '.join(outcome_variable_names)}}};\n" self.code.indent_level -= 1 # Exit function body @@ -299,7 +395,9 @@ def build_lookups(self): class StanTransformedDataBuilder: - def __init__(self, abstract_model: AbstractModel, function_name: str = "vensim_ode"): + def __init__( + self, abstract_model: AbstractModel, function_name: str = "vensim_ode" + ): self.abstract_model = abstract_model self.elements = self.abstract_model.sections[0].elements diff --git a/pysd/builders/stan/utilities.py b/pysd/builders/stan/utilities.py index 1c2c9b33..10c83f44 100644 --- a/pysd/builders/stan/utilities.py +++ b/pysd/builders/stan/utilities.py @@ -21,4 +21,4 @@ def __str__(self): def vensim_name_to_identifier(name: str): - return name.lower().replace(" ", "_") \ No newline at end of file + return name.lower().replace(" ", "_") From c744e176564f15352eaf303bd41c9bd33720653a Mon Sep 17 00:00:00 2001 From: Dashadower Date: Thu, 11 Aug 2022 09:37:51 +0900 Subject: [PATCH 18/45] Minor update to logic --- pysd/builders/stan/ast_walker.py | 4 ---- pysd/builders/stan/stan_model_builder.py | 1 - 2 files changed, 5 deletions(-) diff --git a/pysd/builders/stan/ast_walker.py b/pysd/builders/stan/ast_walker.py index 2bd24ef1..64f22d1f 100644 --- a/pysd/builders/stan/ast_walker.py +++ b/pysd/builders/stan/ast_walker.py @@ -41,10 +41,6 @@ def walk(self, ast_node) -> List[str]: return self.walk(ast_node.flow) + self.walk(ast_node.initial) elif isinstance(ast_node, InlineLookupsStructure): return self.walk(ast_node.lookups) - else: - raise Exception( - f"AST node of type {ast_node.__class__.__name__} is not supported." - ) @dataclass diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_model_builder.py index 60f42962..de6312c9 100644 --- a/pysd/builders/stan/stan_model_builder.py +++ b/pysd/builders/stan/stan_model_builder.py @@ -314,7 +314,6 @@ def recursive_order_search(current, visited): recursive_order_search(child, visited) eval_order.append(current) - # for var_name in self.variable_dependency_graph.keys(): for var_name in required_variables: recursive_order_search(var_name, set()) From b6305e537c06113337ce4d131f3e10f23087d8c2 Mon Sep 17 00:00:00 2001 From: amoon Date: Thu, 18 Aug 2022 02:57:28 -0400 Subject: [PATCH 19/45] Add demand_supply model workflow in notebook file --- requirements.txt | 3 + .../Prey-Predator-Demand-Supply.ipynb | 2244 +++++++++++++++++ test_scripts/data/hudson-bay-lynx-hare.csv | 22 + test_scripts/data/n_logn.png | Bin 0 -> 26868 bytes test_scripts/stan_file/demand_supply.ipynb | 1685 +++++++++++++ test_scripts/stan_file/ds_data2draws.stan | 103 + test_scripts/stan_file/ds_draws2data.stan | 21 + test_scripts/stan_file/ds_relational.stan | 655 +++++ test_scripts/stan_file/pp_data2draws.stan | 65 + .../stan_file/pp_data2draws_maprect.stan | 75 + test_scripts/stan_file/pp_draws2data.stan | 45 + test_scripts/stan_file/pp_relational.stan | 17 + test_scripts/vensim_models/arithmetic.mdl | 117 - .../demand_supply_pink_sterman.mdl | 601 +++++ .../demand_supply_white_sterman.mdl | 365 +++ .../vensim_models/ds_white_sterman.mdl | 365 +++ 16 files changed, 6266 insertions(+), 117 deletions(-) create mode 100755 test_scripts/Prey-Predator-Demand-Supply.ipynb create mode 100644 test_scripts/data/hudson-bay-lynx-hare.csv create mode 100755 test_scripts/data/n_logn.png create mode 100644 test_scripts/stan_file/demand_supply.ipynb create mode 100644 test_scripts/stan_file/ds_data2draws.stan create mode 100644 test_scripts/stan_file/ds_draws2data.stan create mode 100644 test_scripts/stan_file/ds_relational.stan create mode 100644 test_scripts/stan_file/pp_data2draws.stan create mode 100644 test_scripts/stan_file/pp_data2draws_maprect.stan create mode 100644 test_scripts/stan_file/pp_draws2data.stan create mode 100644 test_scripts/stan_file/pp_relational.stan delete mode 100644 test_scripts/vensim_models/arithmetic.mdl create mode 100644 test_scripts/vensim_models/demand_supply_pink_sterman.mdl create mode 100644 test_scripts/vensim_models/demand_supply_white_sterman.mdl create mode 100644 test_scripts/vensim_models/ds_white_sterman.mdl diff --git a/requirements.txt b/requirements.txt index e0947fec..7b2f5217 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,3 +10,6 @@ openpyxl scipy progressbar2 portion +cmdstanpy +IPython +arviz \ No newline at end of file diff --git a/test_scripts/Prey-Predator-Demand-Supply.ipynb b/test_scripts/Prey-Predator-Demand-Supply.ipynb new file mode 100755 index 00000000..f83a7daf --- /dev/null +++ b/test_scripts/Prey-Predator-Demand-Supply.ipynb @@ -0,0 +1,2244 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 123, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "hBkiivD5LW7e" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import pysd\n", + "import cmdstanpy # 2.30 is fastest (as of 08.12.2022) `cmdstanpy.install_cmdstan()` \n", + "from cmdstanpy import CmdStanModel, cmdstan_path\n", + "import arviz as az #!pip install git+https://github.com/arviz-devs/arviz\n", + "az.style.use(\"arviz-darkgrid\")\n", + "import os\n", + "from IPython.display import Image\n", + "#import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# set your working directiory\n", + "os.chdir(\"/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "lxLXpPsoj6a2" + }, + "source": [ + "# Structuring Uncertainties in Dynamic Models: \n", + "## Predator-Prey and Supply-Demand Dynamics with Bayesian Approach\n", + "\n", + "Angie Moon, 07.2022" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RlXBUmm_j6a3" + }, + "source": [ + "\n", + "Three source of uncertainties in dynamic model \n", + "\n", + "Predator-Prey: parameter and measurement uncertainty \n", + "\n", + "Data: Lynx and Hare Pelts in Canada\n", + "\n", + "Mechanistic Model: The Lotka-Volterra Equations\n", + "\n", + "Statistical Model: Prior Knowledge and Unexplained Variation\n", + "\n", + "Computational Model: Stan Program\n", + "\n", + "Demand-Supply: parameter, measurement, process uncertainty \n", + "\n", + "Mechanistic Model: Little's law?\n", + "\n", + "Statistical Model: Prior Knowledge, Unexplained Variation, Amplified Variation \n", + "\n", + "Computational Model: Stan Program\n", + "\n", + "Conclusion\n", + "\n", + "Exercises and Extensions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Three sources of uncertainty in dynamic model\n", + "\n", + "Parameter, process, and measurement are three sources of uncertainty in dynamic model. Statistical inference that returns uncertainty interval is the main step for verifying/validating one's dynamic model and we first suggest Bayesian framework which empowers us to model parameter uncertainty. Second, we introduce specific mechansims to propagate each source of uncertainties so that synthetic data and inferred parameters conditional on the synthetic data are properly generated. We illustrate with two examples: first with prey-predator model which includes two uncertainties: parameter and measurement. Second, in the demand-supply example, we introduce two formulations with and without process uncertainty and thereby analyze its effect with the focus of its interaction with the other two.\n", + "\n", + "The absence of process uncertainty in predator-prey model is understandable as we infer `arc_parameter`, not `node_parameter`. In the demand-supply example, however, we can infer `node_parameter`s for `demand rate`. The main difference of `arc_parameter` and `node_parameter` is the uniqueness of uncertainty source. `arc_parameter` is unique given the two nodes, but there can be multiple `node_parameter`s given a node, which amplifies uncertainty. This additional uncertainty is parallel to $dB_t$ term of Brownian motion which is regarded as $\\sqrt{dt}$. The example of the `arc_parameter` is `alpha`, `beta`, `gamma`, `delta` from the predator-prey model and `mu`, `sigma`, `rho` of the node `demand rate` from demand-supply model. `mu` and `sigma` are location and scale paramter and `rho` is autocorrelation parameter (which may be `dt-dependent`? ). \n", + "\n", + "Jair's vignette on comparing the state variable of three data generating processes, a) noraml random variable with fixed `mu` and `sigma` b) adding `rho` i.e. multiplying `pink noise` to a), c) geometric Brownian motion with `mu` and `sigma`, shows state value measured by `inventory` state remains the same across different `dt`s only for b) and c). If the purpose is to match the state values, scale parameter for a) should be adjusted proportional to $\\sqrt{dt}$. However, it remains to be seen whether a) possess internal consistency for each resolution i.e. for each `dt`, would a) pass SBC when the same value is used for `dt` in integration (as part of generator) and `precision` in ODE solver (as part of inference)? If parameter's shape of uncertainty is preserved for each resolution, the author is willing to be not bothered by state value's high sensitivity to `dt`. This is based on the belief that what we call \"model\" is in fact combination of statistical and computational model and it is natural that our inference is affected by the parameters of computational model. This is why both software and hardware specification are required for replication in computing. Unfortunately, sensitivity of hardware parameters (e.g. `precision` in ODE solver, `adapt delta` in HMC) are less tolerated than that of software (e.g. statistical model's parameter)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predator-Prey: parameter and measurement uncertainty\n", + "\n", + "Predator-Prey population model by Lotka (1925) and Volterra (1926) are deterministic system dynamics model with two state variables (population of predator and prey), four flow variables (each of their birth and death), and four parameters (one for each flow variable). A statistical model learning parameter uncertainty and measurement uncertainty simultaneously is suggested which treats deterministic solutions of Lotka-Volterra equations as expected population sizes. To be specific, `p` number of constant parameter is replaced with its own prior distribution with prior paramters to model paramter uncertainty. Also, likelihood distribution and random error's scale parameter is added to represent measurement uncertainty. Stan, as both the language and and an optimizer, first encodes statistical model to be cast into optimization problem, then returns samples representing distribution that maximize the posterior via HMC. Data of Lynx and Hare population collected annually between 1900 and 1920 is used. \n", + "\n", + "For model checking, we run prior predictive check (PPC1) and simulation-based calibration (SBC) for verification then posterior predictive check (PPC) for validation. With parameter's posterior distribution that passed two checks, we use Bayesian forecasting system to propagate uncertainty to predictive distribution. The importance of the sequence (pre and post data) are discussed focusing on the different roles of sythetic (for PPC1, SBC) and real data (for PPC2). Posterior predictive check shows the model fits observed data well meaning full Bayesian approach can be used to estimate past and future's population.\n", + "\n", + "Lastly, assuming another scenario where PPC2 failed and we illustrate model updating. Based on the new findings in the new SBC paper, calibration should be conditional on not only parameter distribution but also on observed data, we introduce rejection sampling. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "bhoL22XVj6a4" + }, + "source": [ + "## Data: Lynx and Hare Pelts in Canada\n", + "\n", + "The species of interest in this case study are\n", + "\n", + "- hares: prey, an hervivorous cousin of rabbits, and\n", + "- lynxes: predator, a feline predator whose diet consists largely of hares.\n", + "\n", + "Spikes in the lynx population lag those in the hare population. When populations are plotted against one another over time, the population dynamics orbit in an apparently stable pattern. Population oscillations can be modeled with a pair of differential equations similar to that used to describe springs. The first plot is the number of lynx and hare pelts (in thousands) collected for twenty years. The second plot is the phase plot of number of pelts collected for lynx versus hares similar to that of the dynamics of a spring in phase space (i.e., position vs. momentum)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 225 + }, + "colab_type": "code", + "id": "dX9-7-Qbj6a5", + "outputId": "e6827254-f9ef-4906-df7e-46577e7ffc67" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0.5, 0, 'year'), Text(0, 0.5, 'pelt (thousands)')]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lynx_hare_df = pd.read_csv('data/hudson-bay-lynx-hare.csv')\n", + "\n", + "# data viz\n", + "pd.melt(lynx_hare_df, id_vars = 'Year').iloc[[0,20,21,41]]\n", + "pd.melt(lynx_hare_df, id_vars = 'Year').iloc[[0,1,20,21,40,41]].rename(columns = {'variable':'species', 'value':'pelts in thousands'})\n", + "ax = lynx_hare_df.loc[:, ['Lynx', 'Hare']].plot()\n", + "ax.set(xlabel='year', ylabel='pelt (thousands)') " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 298 + }, + "colab_type": "code", + "id": "zxIexRJoj6bA", + "outputId": "68b09259-ba06-48b9-f3b0-cb2e38192899", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'hare pelts (thousands)')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(lynx_hare_df.loc[:, 'Lynx'], lynx_hare_df.loc[:, 'Hare'])\n", + "plt.plot(lynx_hare_df.loc[:, 'Lynx'], lynx_hare_df.loc[:, 'Hare'])\n", + "plt.xlabel('lynx pelts (thousands)')\n", + "plt.ylabel('hare pelts (thousands)')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "REdoJXx2j6bK" + }, + "source": [ + "## Mechanistic Model: The Lotka-Volterra Equations\n", + "\n", + "The Lotka-Volterra equations (Lotka 1925; Volterra 1926, 1927) are based on the assumptions that\n", + "\n", + "- the predator population intrinsically shrinks,\n", + "- the prey population intrinsically grows,\n", + "- a larger prey population leads to a larger predator population, and\n", + "- a larger predator population leads to a smaller prey population.\n", + "\n", + "More specifically, the rate of growth of the prey population is proportional to the size of the prey population, leading to exponential growth. The prey population simultaneously shrinks at a rate proportional to the size of the product of the prey and predator populations. For the predator species, the direction of growth is reversed. The predator population shrinks at a rate proportional to its size and grows at a rate proportional to the product of its size and the prey population’s size.\n", + "\n", + "Together, these dynamics lead to a cycle of rising and falling populations. With a low lynx population, the hare population grows. As the hare population grows, it allows the lynx population to grow. Eventually, the lynx population is large enough to start cutting down on the hare population. That in turn puts downward pressure on the lynx population. The cycle then resumes from where it started.\n", + "\n", + "The Lotka-Volterra equations (Volterra 1926, 1927; Lotka 1925) are a pair of first-order, ordinary differential equations (ODEs) describing the population dynamics of a pair of species, one predator and one prey[5](#fn5).\n", + "\n", + "* u(t)≥0 is the population size of the prey species at time t, and\n", + "* v(t)≥0 is the population size of the predator species.\n", + "\n", + "Volterra modeled the temporal dynamics of the two species (i.e., population sizes over times) in terms of four parameters, $\\alpha, \\beta, \\gamma, \\delta \\geq 0$, as\n", + "\n", + "$$\n", + "\\begin{eqnarray}\n", + "\\frac{\\mathrm{d}}{\\mathrm{d}t} u\n", + "& = & (\\alpha - \\beta v) u\n", + "& = & \\alpha u - \\beta u v\n", + "\\\\[6pt]\n", + "\\frac{\\mathrm{d}}{\\mathrm{d}t} v\n", + "& = & (-\\gamma + \\delta \\, u) \\, v\n", + "& = & -\\gamma v + \\delta uv\n", + "\\end{eqnarray}\n", + "$$\n", + "\n", + "$u(t)$ and $v(t)$ are rendered as $u$ and $v$. The factor $\\alpha$, $\\beta$ are the rate of birth and shrinkage relative to the product of the population sizes where as $\\gamma$, $\\delta$ are the shrinkage and growth rate as a factor of the product of the population sizes. Both u and v have positivitity constraints. as long as the initial populations are non-negative, i.e., $u(0) \\geq 0$ and $v(0) \\geq 0$, because the rate of change in each population is a factor of the population size itself.\n", + "\n", + " is the growth rate of the prey population, whereas \n", + "\n", + "### Four behaviors in the limit\n", + "\n", + "One way to understand systems of equations is to consider their limiting behavior. In this case, there are four behaviors.\n", + "\n", + "1. If both population sizes are initially positive, the populations will oscillate in a fixed pattern indefinitely, remaining positive.\n", + "2. If both population sizes are initially zero, the population sizes will remain zero.\n", + "3. If the predator population size is zero and the prey population size positive, the predator population size remains zero and the prey population grows without bound.\n", + "4. If the predator population size is positive and the prey population size zero, the prey population size remains zero while the predator population shrinks toward zero size." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Rkg3Z5Zbj6bK" + }, + "source": [ + "## Statistical Model: regreasion framing and uncertainty embedding\n", + "\n", + "### Solving the inverse problem\n", + "\n", + "For a given legal value of the model parameters and initial state, the Lotka-Volterra model predicts population dynamics into the future (and into the past). But given noisy data about population dynamics, how do we solve the inverse problem, that of inferring the values of model parameters consistent with the data? The general approach in Bayesian statistics is somewhat counterintuitive, as it involves formulating the forward model then using general principles to solve the inverse problem.\n", + "\n", + "Specifically, a Bayesian model requires a mathematical model of what we know about the parameters (i.e., a prior) and a model of what we know about the data generating process given the parameters (i.e., a sampling distribution (The choice of prior is no more or less “subjective” than the choice of sampling distribution—both are mathematical approximations to some process of interest and as such must be validated for utility)).\n", + "Mathematically, a prior density $p(\\theta)$ over the sequence of parameters $\\theta$ encapsulates our knowledge of the parameters before seeing the data. A sampling distribution (The sampling distribution p(θ|y) is called a likelihood function when considered as a function L(θ) of the parameters θ for fixed data y), which may have a continuous, discrete or mixed probability function, $p(y | \\theta)$ characterizes the distribution of observable data $y$ given parameters $\\theta$.\n", + "\n", + "Bayes's rule gives us a general solution to the inverse problem, expressing the posterior $p(\\theta | y)$ in terms of the prior $p(\\theta)$ and likelihood $p(y | \\theta)$[10](#fn10).\n", + "\n", + "Stan provides a form of Markov chain Monte Carlo (MCMC) sampling that draws a sample $\\theta^{(1)}, \\ldots, \\theta^{(M)}$ from the posterior to use for computational inference. Posterior quantities of interest may be expressed as derived random variables using functions $f(\\theta)$ of parameters. Such functions may be used for posterior expectation calculations such as parameter estimates that minimize expected square error when $f(\\theta) = \\theta$, or event probabilities such as the probability of the hare population falling below some fraction of the lynx population, when $f(\\theta)$ is some indicator function. \n", + "\n", + "### Uncertainty embedding for forward-backward symmetry required for calibration\n", + "\n", + "The Lotka-Volterra model is deterministic in that given the value of the system parameter and initial outcome state, equation solutions (simulated outcome value) are fully determined. However, for empirical research which use posterior inference from the real data as it final forecast, forward model should be re-designed. This is because symmetry of forward and backward model (i.e. data generation and its inference) is the theoretical justification of calibration. To pass this internal consistency test (or with enough resource, SBC which is rank-statistics based), we need the two process to be the mirror image of other. This is why we purposefully embed uncertainty components, waiting to be captured in the inference step. The purpose is to test resilience and identifiability of our models evidenced by the perfect retrival of prior distribution for every uncertainty we embedded. \n", + "\n", + "### Linear regression analogy\n", + "\n", + "Like in a simple linear regression, we will proceed by treating the underlying determinstic model as providing an expected population value around which there will be variation due to both measurement error and simplifications in the scientific model. Consider the typical formulation of a linear regression, where $y_n$ is an observable scalar outcome, $x_n$ is a row vector of unmodeled predictors (aka covariates, features), $\\beta$ is a coefficient vector parameter, and $\\sigma > 0$ is the error scale,\n", + "\n", + "$$\n", + "\\begin{eqnarray}\n", + "y_n & = & x_n \\beta + \\epsilon_n\n", + "\\\\[6pt]\n", + "\\epsilon_n & \\sim & \\mathsf{Normal}(0, \\sigma)\n", + "\\end{eqnarray}\n", + "$$\n", + "\n", + "### Adding measurement uncertainty (epistemic)\n", + "Before embedding parameteric uncertainty, linear predictor $x_n \\beta$ with predictor $x_n$ (row $n$ of the data matrix $x$) and coefficient (column) vector $\\beta$ are deterministic. The only source of uncertainty is from the measurement. This is expressed by assigning a normal distribution to error term $\\epsilon_n$. Equal expression is with latent error variable $\\epsilon_n$ as follows[17](#fn17), \n", + "\n", + "$$\n", + "y_n \\sim \\mathsf{Normal}(x_n \\beta, \\sigma).\n", + "$$\n", + "\n", + "### Adding parameter uncertainty (epistemic)\n", + "Next, we add parameter uncertainty by coding estimated parameter as a distribution rather than a fixed value. This distribution is called prior distribution and from our example, Normal distirbution is chosen to endow the uncertainty to the four estimated parameters $\\alpha, \\beta, \\gamma, \\delta$. Considering their role difference, $\\alpha, \\gamma$ as multipliers of $u, -v$ and $\\beta, \\delta$ as multipliers of $uv$, prior parameter are chosen as N(1, 0.5) and N(0.05, 0.05) for each. For this selection, refer to the original case study [Carpenter18](https://mc-stan.org/users/documentation/case-studies/lotka-volterra-predator-prey.html).\n", + "\n", + "### Adding aleatoric uncertainty\n", + "Lastly, initial population is added to the list of estimated parameter. Instant reason for this modeling decision is measurement noise; as population cannot be directly measured pelts (our data) are used as its noisy proxy). However, more fundamental reason is aletoric uncertainty, namely unmodeled uncertainty. There are factors that impact predator and prey population size other than the current population size. There are variable environmental effects, such as weather, which will vary from season to season and year to year and affect population sizes. Infectious diseases occasionally spread through a population, reducing its size (Hewitt 1921). There are also more long-term environmental factors such as carrying capacity (Carrying capacity is roughly the maximum population that an environment can sustain. It is often modeled in the system dynamics as an asymptote on population size.). However, our forward model is restricted to two differential equations involving two stock variables (`simulated outcome`) and four `estimated coefficient parameter`s (four flow variables can be expressed with the other two). Hence, after restricting the architecture, we are reaching out to the best version of ourselve by declaring the initial population as estimated parameter. In Stat/Machine learning terms, Stan optimization algorithm returns `estimated parameter` value that maximize log posterior among the feasible (restricted basis function) space defined by the modeler in the form of stock-parameter relationship.\n", + "\n", + "Continuing on `simulated outcome` and `observed outcome` coflow, `observed outcome` can replace `simulated outcome` also known as state-resetting but we maintain the error term to compensate for measurement error and unexplained variation in the data (Challenge: check whether this is equivalent to the original text \"Solutions to the Lotka-Volterra equations replace the linear predictor xnβ, but we maintain the error term to compensate for measurement error and unexplained variation in the data.\"). In the case of population dynamics, the data $y_n$ consists of measurements of the prey $y_{n, 1}$ and predator $y_{n, 2}$ populations at times $t_n$[18](#fn18).\n", + "\n", + "The true population sizes at time $t = 0$ are unknown---we only have measurements $y^{\\rm init}_1$ and $y^{\\rm init}_2$ of them. The true initial population sizes at time $t = 0$ will be represented by a parameter $z^{\\mathrm init}$, so that\n", + "\n", + "$$\n", + "\\begin{array}{rcl}\n", + "z^{\\mathrm init}_1 & = & u(t = 0)\n", + "\\\\[4pt]\n", + "z^{\\mathrm init}_2 & = & v(t = 0).\n", + "\\end{array}\n", + "$$\n", + "\n", + "Next, let $z_1, \\ldots, z_N$ be the solutions to the Lotka-Volterra differential equations at times $t_1, \\ldots, t_N$ given initial conditions $z(t = 0) = z^{\\mathrm init}$ and parameters $\\theta = (\\alpha, \\beta, \\gamma, \\delta)$. Each $z_n$ is a pair of prey and predator population sizes at the specified times,\n", + "\n", + "$$\n", + "\\begin{array}{rcl}\n", + "z_{n, 1} & = & u(t_n)\n", + "\\\\[4pt]\n", + "z_{n, 2} & = & v(t_n).\n", + "\\end{array}\n", + "$$\n", + "\n", + "The $z_n$ are random variables, but they are deterministic functions of the random variables for the initial state $z^{\\mathrm init}$ and system parameters $\\alpha, \\beta, \\gamma, \\delta$.\n", + "\n", + "The observed data is in the form of measurements $y^{\\rm init}$ of the initial population of prey and predators, and subsequent measurements $y_n$ of the populations at times $t_n$, where $y^{\\mathrm init}$ and the $y_n$ consist of a pair of measured population sizes, for the prey and predator species.\n", + "\n", + "In summary, the measurements, $y^{\\rm init}$ and $y_n$, are drawn indepently from a normal distribution centered at the underlying population sizes, $z^{\\rm init}$ and $z_n$, with noise scales $\\sigma$.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vTYEL9m4GIFi" + }, + "source": [ + "10 Bayes's rule for parameters $\\theta$ and observed data $y$ is $$ \\begin{array}{rcl} p(\\theta\\,|\\, y) & = & \\displaystyle \\frac{p(\\theta, y)}{p(y)} \\\\[4pt] & = & \\displaystyle \\frac{p(y | \\theta) \\, p(\\theta)}{p(y)} \\\\[4pt] & = & \\displaystyle \\frac{p(y | \\theta) \\, p(\\theta)}{\\int_{\\Theta} p(y | \\theta) \\, p(\\theta) \\, \\mathrm{d}\\theta} \\\\[4pt] & \\propto & p(y | \\theta) \\, p(\\theta). \\end{array} $$\n", + "\n", + "11 The matrix of $\\theta^{(m)}$ values (parameter by draw) is what is returned by Stan.\n", + "\n", + "12 The convergence result (as well as error bounds) follows from the MCMC central limit theorem when $\\theta^{(m)}$ are drawn according to $p(\\theta | y)$ with an appropriate MCMC algorithm, \n", + "$$ \\begin{array}{rcl} \\displaystyle \\mathbb{E}[ \\, f(\\theta) \\mid y \\, ] & = & \\int_{\\Theta} \\, f(\\theta) \\, p(\\theta | y) \\, \\mathrm{d}\\theta \\\\[4pt] & = & \\lim_{M \\rightarrow \\infty} \\, \\frac{1}{M} \\sum_{m=1}^M \\, f\\!\\left(\\theta^{(m)}\\right) \\\\[4pt] & \\approx & \\frac{1}{M} \\sum_{m=1}^M \\, f\\!\\left(\\theta^{(m)}\\right) \\ \\ \\mbox{ for some finite } M \\end{array} $$\n", + "\n", + "\n", + "16 Gauss initially noted that the maximum likelihood estimate derived from the normal error model is identical to the least square error estimate derived by minimizing the sum of squared errors, $ϵ^⊤ϵ$. With Markov, Gauss further proved that it was the lowest variance unbiased estimator.\n", + "\n", + "17 The latent error variable may be defined in terms of x, y, and $β$as\n", + "$ϵ_n=y_n−x_nβ$.\n", + " \n", + "18 This model makes the assumption that the underlying population sizes $z_{n,k}$ and measurements of it $y_{n,k}$ are continuous. This is a very tight approximation to counts when the numbers are in the thousands." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "KbkjXoUBGIFk" + }, + "source": [ + "### Multiplicative error and the lognormal distribution\n", + "\n", + "It is common to log transform positive-only parameters so that they are no longer constrained to be positive. On the log scale, we can then take the error to be unconstrained and additive, just as in linear regression.\n", + "\n", + "$$\n", + "\\begin{eqnarray}\n", + "\\log y_{n, k} & = & \\log z_{n, k} + \\epsilon_{n, k}\n", + "\\\\[6pt]\n", + "\\epsilon_{n, k} & \\sim & \\mathsf{Normal}(0, \\sigma_k)\n", + "\\end{eqnarray}\n", + "$$\n", + "\n", + "where the $z_n$ are the solutions to the Lotka-Volterra equations at times $t_1, \\ldots, t_N$ given initial populations $z^{\\mathrm init}$. The prey and predator populations have error scales (on the log scale) of $\\sigma_1$ and $\\sigma_2$.\n", + "\n", + "With additive errors on the log transformed scales, the result of transforming back to the natural scale (by exponentiation) leads to multiplicative errors.\n", + "\n", + "$$\n", + "y_{n, k} \\sim \\mathsf{LogNormal}(z_{n, k}, \\sigma_n).\n", + "$$\n", + "whenever\n", + "$$\n", + "\\log y_{n, k} \\sim \\mathsf{Normal}(\\log z_{n, k}, \\sigma_n).\n", + "$$\n", + "The $\\mathsf{LogNormal}$ density accounts for the non-linear change of variables through a Jacobian adjustment (The [Stan manual chapter on changes of variables](https://mc-stan.org/docs/2_23/stan-users-guide/changes-of-variables.html) works through the Jacobian adjustment for this particular change of variables.)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Translating the Model for Generation: pysd Program\n", + "\n", + "Code is auto-translated using pysd. Initial conditions such as the lenghth of simulation and `assumed parameter` are explicitly set. Prey-predator model doesn't have any `assumed parameter`. \n", + "\n", + "Having set the four `estimated parameter`'s prior as N(1, 0.5) and N(0.05, 0.05), we set `alpha`, `gamma` as 1 and `beta`, `delta` as 0.05. Comparing to the observed states, simpled version of prior predictive check is not too bad. Full version of prior predictive check is much easier once the model is coded with Stan, hence will be revisited after. For calibration purpose, we use generated data for the next inference step. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "mod = pysd.read_vensim('vensim_models/prey-predator.mdl')\n", + "# list(mod.doc[mod.doc['Type']==\"Stateful\"].iloc[:,1]) # ['predator', 'prey']\n", + "assumeall_res = mod.run(initial_condition=(0, {'predator':lynx_hare_df.loc[:, 'Lynx'][0], 'prey': lynx_hare_df.loc[:, 'Hare'][0]}),\n", + " params={'alpha': 1, 'gamma': 1, 'beta': 0.05, 'delta': 0.05},\n", + " return_timestamps = range(lynx_hare_df.shape[0]))\n", + "state_dt = assumeall_res.loc[:, ('Predator', 'Prey')]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Default time_step is supplied from the vensim model which is .03125 here but can be changed if different precision is needed. We aim our model to be on continuous time (as opposed to discrete time). Whether the time step is small enough can be heuristically checked by comparing the value of state variables for the given time_step and its halved version." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.03125" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dt = (assumeall_res.loc[0, 'TIME STEP'])\n", + "dt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From comparison below, we judge time_step as .03125 is small enough to be considered as continuous time." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "assumeall_res_halfdt = mod.run(initial_condition=(0, {'predator':lynx_hare_df.loc[:, 'Lynx'][0], 'prey': lynx_hare_df.loc[:, 'Hare'][0]}),\n", + " params={'alpha': 1, 'gamma': 1, 'beta': 0.05, 'delta': 0.05},\n", + " time_step=dt/2,\n", + " return_timestamps = range(lynx_hare_df.shape[0]))\n", + "\n", + "state_halfdt = pd.DataFrame(assumeall_res_halfdt.loc[:, ('Predator', 'Prey')]) #, columns = ['h_Predator', 'h_Prey'])\n", + "state_halfdt.rename(columns = {\"Predator\": \"h_Predator\", \"Prey\": \"h_Prey\"}, inplace = True)\n", + "\n", + "comp_dt = pd.concat([state_dt, state_halfdt], axis = 1)\n", + "#plt.plot(comp_dt)\n", + "plt.plot(state_dt.loc[:, 'Predator'], state_dt.loc[:, 'Prey'], color='r')\n", + "plt.plot(state_halfdt.loc[:, 'h_Predator'], state_halfdt.loc[:, 'h_Prey'], color = 'g')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "assumeall_res.loc[:, ['Prey', 'Predator']].plot()\n", + "lynx_hare_df.loc[:, ('Hare', 'Lynx')].plot()\n", + "prey_pred_df = assumeall_res.loc[:, ['Prey', 'Predator']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Translating the Model for Inference: Stan Program\n", + "\n", + "Using `stan_builder` in pysd (in process), Stan code is generated. User input is `time`, `estimated parameter`, `observed state`. Code is auto-translated using pysd. Initial conditions such as the lenghth of simulation and `assumed parameter` are explicitly set. Prey-predator model doesn't have any `assumed parameter`. \n", + "\n", + "Having set the four `estimated parameter`'s prior as N(1, 0.5) and N(0.05, 0.05), we set `alpha`, `gamma` as 1 and `beta`, `delta` as 0.05. Comparing to the observed states, simpled version of prior predictive check is not too bad. Full version of prior predictive check is much easier once the model is coded with Stan, hence will be revisited after." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To compose one stanfile which consists of six blocks (data, transformed data, parameter, transformed parameter, model, generated quantities), users should input three priors: relational, variational, demand. The table below expresses each prior's mathematical identity and location within the program.\n", + "\n", + "| - | `demand_prior()` | `relational_prior()` | `variation_prior()` | \n", + "| ---------- | -------------------- | ------------------ | ------------------------ | \n", + "| type | objective function | set of equalities | probability distribution | \n", + "| Stan block | generated quantities | function | model | " + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [], + "source": [ + "from pysd.translators.vensim.vensim_file import VensimFile\n", + "from pysd.translators.xmile.xmile_file import XmileFile\n", + "from pysd.builders.stan.stan_model_builder import *\n", + "\n", + "vf = VensimFile(\"vensim_models/prey-predator.mdl\")\n", + "\n", + "vf.parse()\n", + "\n", + "am = vf.get_abstract_model()\n", + "stan_function_builder = StanFunctionBuilder(am) \n", + "prey_pred_relational = stan_function_builder.build_function_block([\"alpha\", \"beta\", \"gamma\", \"delta\"], [\"prey\", \"predator\"], function_name = \"vensim_func\")\n", + "\n", + "stan_file_path = os.path.join(os.getcwd(), \"stan_file\", \"prey_pred_relational.stan\")\n", + "with open(stan_file_path, \"w\") as f:\n", + " print(prey_pred_relational, file=f)\n", + "\n", + "# TODO should remove `functions{}` manually (issue 6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Draws2Data" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "10:00:44 - cmdstanpy - INFO - CmdStan start processing\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "dc0153ebc5c84671a763fe9d8c37a505", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 1 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "10:00:45 - cmdstanpy - INFO - CmdStan done processing.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "sf_path_draws2data = os.path.join(os.getcwd(), \"stan_file\", \"pp_draws2data.stan\")\n", + "sm_draws2data = CmdStanModel(stan_file = sf_path_draws2data)\n", + "\n", + "N = lynx_hare_df.shape[0] - 1\n", + "times = np.arange(1, N + 1)\n", + "\n", + "data_draws2data = {\n", + " \"N\": N,\n", + " \"times\": times\n", + "}\n", + "fit_prior_pred = sm_draws2data.sample(data=data_draws2data, iter_sampling=30, chains=1, fixed_param=True, iter_warmup=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first plot first ten sampled (out of 4,000) trajactories and briefly check the range are not too extreme. As can be seen from the figure, blue and orange real prey and predator are comparable to its prior predictive corrspondance (green and plum).\n", + "\n", + "We have real data as an external reference so as long as the predicted ranges are not too off, we give a pass to prior predictive check. Summary statistics such as N^th moments can be used for comparison. Few comments:\n", + "\n", + "a. we use real data below as a representation of our knowledge, so prior predictive check is not double dipping (using data twice)\n", + "\n", + "b. Bayesian prior corresponds to frequentist's regularization so having a tighter prior than posterior is not unnatrual; simply our determination to find a model concentrated around certain model configuration\n", + "\n", + "c. if tight prior is well-placed, it prevents diveregence from frustrating geometry and boosts sampling efficiency" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "#compare with real \n", + "ax.plot(state_dt.loc[:, ['Prey']], label = \"Real_Pey\")\n", + "ax.plot(state_dt.loc[:, ['Predator']], label = \"Real_Predator\")\n", + "ax.plot(pd.DataFrame(sm_prior.y_tilde[:,:,0]).T.loc[:, :5])\n", + "ax.plot(pd.DataFrame(sm_prior.y_tilde[:,:,1]).T.loc[:, :5])\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Data2Draws" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "15:52:37 - cmdstanpy - INFO - compiling stan file /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan to exe file /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws\n", + "15:52:49 - cmdstanpy - INFO - compiled model executable: /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws\n", + "15:52:49 - cmdstanpy - WARNING - Stan compiler has produced 3 warnings:\n", + "15:52:49 - cmdstanpy - WARNING - \n", + "--- Translating Stan model to C++ code ---\n", + "bin/stanc --include-paths=/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file --o=/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.hpp /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan\n", + "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 10, column 4: Declaration\n", + " of arrays by placing brackets after a variable name is deprecated and\n", + " will be removed in Stan 2.32.0. Instead use the array keyword before the\n", + " type. This can be changed automatically using the auto-format flag to\n", + " stanc\n", + "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5: Declaration\n", + " of arrays by placing brackets after a variable name is deprecated and\n", + " will be removed in Stan 2.32.0. Instead use the array keyword before the\n", + " type. This can be changed automatically using the auto-format flag to\n", + " stanc\n", + "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 56, column 4: Declaration\n", + " of arrays by placing brackets after a variable name is deprecated and\n", + " will be removed in Stan 2.32.0. Instead use the array keyword before the\n", + " type. This can be changed automatically using the auto-format flag to\n", + " stanc\n", + "\n", + "--- Compiling, linking C++ code ---\n", + "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -c -include-pch stan/src/stan/model/model_header.hpp.gch -x c++ -o /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.o /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.hpp\n", + "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -Wl,-L,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.o src/cmdstan/main.o -Wl,-L,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws\n", + "rm -f /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.o\n", + "\n", + "15:52:49 - cmdstanpy - INFO - Chain [1] start processing\n", + "15:52:49 - cmdstanpy - INFO - Chain [2] start processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chain [1] method = sample (Default)\n", + "Chain [1] sample\n", + "Chain [1] num_samples = 400\n", + "Chain [1] num_warmup = 1000 (Default)\n", + "Chain [1] save_warmup = 0 (Default)\n", + "Chain [1] thin = 1 (Default)\n", + "Chain [1] adapt\n", + "Chain [1] engaged = 1 (Default)\n", + "Chain [1] gamma = 0.050000000000000003 (Default)\n", + "Chain [1] delta = 0.80000000000000004 (Default)\n", + "Chain [1] kappa = 0.75 (Default)\n", + "Chain [1] t0 = 10 (Default)\n", + "Chain [1] init_buffer = 75 (Default)\n", + "Chain [1] term_buffer = 50 (Default)\n", + "Chain [1] window = 25 (Default)\n", + "Chain [1] algorithm = hmc (Default)\n", + "Chain [1] hmc\n", + "Chain [1] engine = nuts (Default)\n", + "Chain [1] nuts\n", + "Chain [1] max_depth = 10 (Default)\n", + "Chain [1] metric = diag_e (Default)\n", + "Chain [1] metric_file = (Default)\n", + "Chain [1] stepsize = 1 (Default)\n", + "Chain [1] stepsize_jitter = 0 (Default)\n", + "Chain [1] num_chains = 1 (Default)\n", + "Chain [1] id = 1 (Default)\n", + "Chain [1] data\n", + "Chain [1] file = /var/folders/4j/8mx5dnzd1p34_5y5r19b4g5m0000gn/T/tmprr2akp_3/z6cbxpi_.json\n", + "Chain [1] init = 2 (Default)\n", + "Chain [1] random\n", + "Chain [1] seed = 1234\n", + "Chain [1] output\n", + "Chain [1] file = /var/folders/4j/8mx5dnzd1p34_5y5r19b4g5m0000gn/T/tmprr2akp_3/pp_data2draws_r4ltbib/pp_data2draws-20220816155249_1.csv\n", + "Chain [1] diagnostic_file = (Default)\n", + "Chain [1] refresh = 100 (Default)\n", + "Chain [1] sig_figs = -1 (Default)\n", + "Chain [1] profile_file = profile.csv (Default)\n", + "Chain [1] num_threads = 1 (Default)\n", + "Chain [1] \n", + "Chain [1] \n", + "Chain [1] Gradient evaluation took 0.000256 seconds\n", + "Chain [1] 1000 transitions using 10 leapfrog steps per transition would take 2.56 seconds.\n", + "Chain [1] Adjust your expectations accordingly!\n", + "Chain [1] \n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: ode_rk45: initial state[1] is inf, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: ode_rk45: initial state[1] is inf, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: ode_rk45: initial state[1] is inf, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: ode_rk45: ode parameters and data is inf, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[1][1] is nan, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [2] method = sample (Default)\n", + "Chain [2] sample\n", + "Chain [2] num_samples = 400\n", + "Chain [2] num_warmup = 1000 (Default)\n", + "Chain [2] save_warmup = 0 (Default)\n", + "Chain [2] thin = 1 (Default)\n", + "Chain [2] adapt\n", + "Chain [2] engaged = 1 (Default)\n", + "Chain [2] gamma = 0.050000000000000003 (Default)\n", + "Chain [2] delta = 0.80000000000000004 (Default)\n", + "Chain [2] kappa = 0.75 (Default)\n", + "Chain [2] t0 = 10 (Default)\n", + "Chain [2] init_buffer = 75 (Default)\n", + "Chain [2] term_buffer = 50 (Default)\n", + "Chain [2] window = 25 (Default)\n", + "Chain [2] algorithm = hmc (Default)\n", + "Chain [2] hmc\n", + "Chain [2] engine = nuts (Default)\n", + "Chain [2] nuts\n", + "Chain [2] max_depth = 10 (Default)\n", + "Chain [2] metric = diag_e (Default)\n", + "Chain [2] metric_file = (Default)\n", + "Chain [2] stepsize = 1 (Default)\n", + "Chain [2] stepsize_jitter = 0 (Default)\n", + "Chain [2] num_chains = 1 (Default)\n", + "Chain [2] id = 2\n", + "Chain [2] data\n", + "Chain [2] file = /var/folders/4j/8mx5dnzd1p34_5y5r19b4g5m0000gn/T/tmprr2akp_3/z6cbxpi_.json\n", + "Chain [2] init = 2 (Default)\n", + "Chain [2] random\n", + "Chain [2] seed = 1234\n", + "Chain [2] output\n", + "Chain [2] file = /var/folders/4j/8mx5dnzd1p34_5y5r19b4g5m0000gn/T/tmprr2akp_3/pp_data2draws_r4ltbib/pp_data2draws-20220816155249_2.csv\n", + "Chain [2] diagnostic_file = (Default)\n", + "Chain [2] refresh = 100 (Default)\n", + "Chain [2] sig_figs = -1 (Default)\n", + "Chain [2] profile_file = profile.csv (Default)\n", + "Chain [2] num_threads = 1 (Default)\n", + "Chain [2] \n", + "Chain [2] Rejecting initial value:\n", + "Chain [2] Error evaluating the log probability at the initial value.\n", + "Chain [2] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[1][1] is -1.11329e-08, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [2] \n", + "Chain [2] Gradient evaluation took 0.00135 seconds\n", + "Chain [2] 1000 transitions using 10 leapfrog steps per transition would take 13.5 seconds.\n", + "Chain [2] Adjust your expectations accordingly!\n", + "Chain [2] \n", + "Chain [2] \n", + "Chain [2] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [2] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[18][1] is -9.37576e-11, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [2] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [2] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [2] \n", + "Chain [2] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [2] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[7][1] is -8.03465e-12, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [2] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [2] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [2] \n", + "Chain [2] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [2] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[3][1] is -1.57324e-09, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [2] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [2] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [2] \n", + "Chain [2] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [2] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[5][1] is -1.69268e-09, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [2] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [2] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [2] \n", + "Chain [2] Iteration: 1 / 1400 [ 0%] (Warmup)\n", + "Chain [2] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [2] Exception: ode_rk45: initial state[1] is inf, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [2] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [2] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [2] \n", + "Chain [2] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [2] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[1][1] is nan, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [2] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [2] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [2] \n", + "Chain [2] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [2] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[3][1] is -2.49891e-13, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [2] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [2] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [2] \n", + "Chain [2] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [2] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[3][1] is -2.88284e-09, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [2] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [2] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [2] \n", + "Chain [2] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [2] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[2][1] is -3.82941e-11, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [2] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [2] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [2] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[6][1] is -6.65845e-19, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Iteration: 1 / 1400 [ 0%] (Warmup)\n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: ode_rk45: initial state[1] is inf, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: ode_rk45: initial state[1] is inf, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[1][1] is nan, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[5][1] is -3.09655e-10, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[9][1] is -2.14703e-10, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Iteration: 100 / 1400 [ 7%] (Warmup)\n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[11][1] is -4.71201e-11, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[9][1] is -1.07383e-11, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Iteration: 200 / 1400 [ 14%] (Warmup)\n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[2][1] is -2.79091e-13, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[14][1] is -1.71864e-15, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Iteration: 300 / 1400 [ 21%] (Warmup)\n", + "Chain [1] Iteration: 400 / 1400 [ 28%] (Warmup)\n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[10][1] is -9.23562e-12, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[2][1] is -5.8469e-10, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[8][1] is -1.11463e-11, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[9][1] is -3.67641e-10, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Iteration: 500 / 1400 [ 35%] (Warmup)\n", + "Chain [1] Iteration: 600 / 1400 [ 42%] (Warmup)\n", + "Chain [1] Iteration: 700 / 1400 [ 50%] (Warmup)\n", + "Chain [1] Iteration: 800 / 1400 [ 57%] (Warmup)\n", + "Chain [1] Iteration: 900 / 1400 [ 64%] (Warmup)\n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[18][1] is -8.03506e-11, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n", + "Chain [1] Exception: pp_data2draws_model_namespace::log_prob: integrated_result[1][1] is -8.10007e-16, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Chain [1] If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n", + "Chain [1] but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n", + "Chain [1] \n", + "Chain [1] Iteration: 1000 / 1400 [ 71%] (Warmup)\n", + "Chain [1] Iteration: 1001 / 1400 [ 71%] (Sampling)\n", + "Chain [1] Iteration: 1100 / 1400 [ 78%] (Sampling)\n", + "Chain [1] Iteration: 1200 / 1400 [ 85%] (Sampling)\n", + "Chain [1] Iteration: 1300 / 1400 [ 92%] (Sampling)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "15:52:59 - cmdstanpy - INFO - Chain [1] done processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chain [1] Iteration: 1400 / 1400 [100%] (Sampling)\n", + "Chain [1] \n", + "Chain [1] Elapsed Time: 2.635 seconds (Warm-up)\n", + "Chain [1] 0.856 seconds (Sampling)\n", + "Chain [1] 3.491 seconds (Total)\n", + "Chain [1] \n", + "Chain [1] \n", + "Chain [2] Iteration: 100 / 1400 [ 7%] (Warmup)\n", + "Chain [2] Iteration: 200 / 1400 [ 14%] (Warmup)\n", + "Chain [2] Iteration: 300 / 1400 [ 21%] (Warmup)\n", + "Chain [2] Iteration: 400 / 1400 [ 28%] (Warmup)\n", + "Chain [2] Iteration: 500 / 1400 [ 35%] (Warmup)\n", + "Chain [2] Iteration: 600 / 1400 [ 42%] (Warmup)\n", + "Chain [2] Iteration: 700 / 1400 [ 50%] (Warmup)\n", + "Chain [2] Iteration: 800 / 1400 [ 57%] (Warmup)\n", + "Chain [2] Iteration: 900 / 1400 [ 64%] (Warmup)\n", + "Chain [2] Iteration: 1000 / 1400 [ 71%] (Warmup)\n", + "Chain [2] Iteration: 1001 / 1400 [ 71%] (Sampling)\n", + "Chain [2] Iteration: 1100 / 1400 [ 78%] (Sampling)\n", + "Chain [2] Iteration: 1200 / 1400 [ 85%] (Sampling)\n", + "Chain [2] Iteration: 1300 / 1400 [ 92%] (Sampling)\n", + "Chain [2] Iteration: 1400 / 1400 [100%] (Sampling)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16:09:38 - cmdstanpy - INFO - Chain [2] done processing\n", + "16:09:38 - cmdstanpy - WARNING - Non-fatal error during sampling:\n", + "Exception: ode_rk45: initial state[1] is inf, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: ode_rk45: initial state[1] is inf, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: ode_rk45: initial state[1] is inf, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: ode_rk45: ode parameters and data is inf, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[1][1] is nan, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[6][1] is -6.65845e-19, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: ode_rk45: initial state[1] is inf, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: ode_rk45: initial state[1] is inf, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[1][1] is nan, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[5][1] is -3.09655e-10, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[9][1] is -2.14703e-10, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[11][1] is -4.71201e-11, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[9][1] is -1.07383e-11, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[2][1] is -2.79091e-13, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[14][1] is -1.71864e-15, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[10][1] is -9.23562e-12, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[2][1] is -5.8469e-10, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[8][1] is -1.11463e-11, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[9][1] is -3.67641e-10, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[18][1] is -8.03506e-11, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[1][1] is -8.10007e-16, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Exception: pp_data2draws_model_namespace::log_prob: integrated_result[1][1] is -1.11329e-08, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[18][1] is -9.37576e-11, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[7][1] is -8.03465e-12, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[3][1] is -1.57324e-09, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[5][1] is -1.69268e-09, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: ode_rk45: initial state[1] is inf, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[1][1] is nan, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[3][1] is -2.49891e-13, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[3][1] is -2.88284e-09, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "\tException: pp_data2draws_model_namespace::log_prob: integrated_result[2][1] is -3.82941e-11, but must be greater than or equal to 0.000000 (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/pp_data2draws.stan', line 24, column 5 to line 26, column 47)\n", + "Consider re-running with show_console=True if the above output is unclear!\n", + "16:09:38 - cmdstanpy - WARNING - Some chains may have failed to converge.\n", + "\tChain 2 had 400 iterations at max treedepth (100.0%)\n", + "\tUse function \"diagnose()\" to see further information.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chain [2] \n", + "Chain [2] Elapsed Time: 717.22 seconds (Warm-up)\n", + "Chain [2] 291.561 seconds (Sampling)\n", + "Chain [2] 1008.78 seconds (Total)\n", + "Chain [2] \n", + "Chain [2] \n" + ] + } + ], + "source": [ + "sf_path_data2draws = os.path.join(os.getcwd(), \"stan_file\", \"pp_data2draws.stan\")\n", + "sm_data2draws = CmdStanModel(stan_file = sf_path_data2draws)\n", + "N = lynx_hare_df.shape[0] - 1\n", + "times = np.arange(1, N + 1)\n", + "y_init = prey_pred_df.loc[0, ['Prey', 'Predator']]\n", + "y = prey_pred_df.loc[1:, ['Prey', 'Predator']]\n", + "\n", + "data_data2draws = {\n", + " \"N\": N,\n", + " \"times\": times,\n", + " \"y_init\": list(y_init),\n", + " \"y\": y.values.tolist(),\n", + "}\n", + "fit_posterior_draws = sm_data2draws.sample(data = data_data2draws, iter_sampling = 400, chains = 2, show_console = True, seed = 1234)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Exception: ode_rk45: initial state[1] is inf\" may be because ode integrator is stiff; which may be resolved by using ode_bdf from [this](https://discourse.mc-stan.org/t/exception-integrate-ode-rk45-parameter-vector-1-is-inf-but-must-be-finite/13953/2?u=hyunji.moon) post." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Inference Data" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 4.0\n", + "1 6.1\n", + "2 9.8\n", + "3 35.2\n", + "4 59.4\n", + "5 41.7\n", + "6 19.0\n", + "7 13.0\n", + "8 8.3\n", + "9 9.1\n", + "10 7.4\n", + "11 8.0\n", + "12 12.3\n", + "13 19.5\n", + "14 45.7\n", + "15 51.1\n", + "16 29.7\n", + "17 15.8\n", + "18 9.7\n", + "19 10.1\n", + "20 8.6\n", + "Name: Lynx, dtype: float64" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lynx_hare_df.loc[:, \"Lynx\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "ename": "InvalidIndexError", + "evalue": "(slice(None, None, None), 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3621\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/_libs/index.pyx:136\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/_libs/index.pyx:142\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: '(slice(None, None, None), 1)' is an invalid key", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mInvalidIndexError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [150]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mlynx_hare_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mLynx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHare\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/core/frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 3504\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3505\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3507\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3628\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3623\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3624\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3625\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3626\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3627\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m-> 3628\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_indexing_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3629\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[1;32m 3631\u001b[0m \u001b[38;5;66;03m# GH#42269\u001b[39;00m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:5637\u001b[0m, in \u001b[0;36mIndex._check_indexing_error\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 5633\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_check_indexing_error\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[1;32m 5634\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(key):\n\u001b[1;32m 5635\u001b[0m \u001b[38;5;66;03m# if key is not a scalar, directly raise an error (the code below\u001b[39;00m\n\u001b[1;32m 5636\u001b[0m \u001b[38;5;66;03m# would convert to numpy arrays and raise later any way) - GH29926\u001b[39;00m\n\u001b[0;32m-> 5637\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n", + "\u001b[0;31mInvalidIndexError\u001b[0m: (slice(None, None, None), 1)" + ] + } + ], + "source": [ + "lynx_hare_df.loc[:, (\"Lynx\",\"Hare\")]" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [], + "source": [ + "# # stan_data = az.from_pystan(\n", + "# # posterior=fit,\n", + "# # posterior_predictive=\"y_hat\",\n", + "# # observed_data=[\"y\"],\n", + "# # log_likelihood={\"y\": \"log_lik\"},\n", + "# # coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", + "# # dims={\n", + "# # \"theta\": [\"school\"],\n", + "# # \"y\": [\"school\"],\n", + "# # \"log_lik\": [\"school\"],\n", + "# # \"y_hat\": [\"school\"],\n", + "# # \"theta_tilde\": [\"school\"],\n", + "# # },\n", + "# # )\n", + "# # data = az.load_arviz_data(\"non_centered_eight\")\n", + "# # az.plot_ppc(data, data_pairs={\"obs\": \"obs\"}, alpha=0.03, figsize=(12, 6), textsize=14)\n", + "# # plt.show()\n", + "idata = az.from_cmdstanpy(\n", + " posterior=fit_posterior_draws, \n", + " posterior_predictive=[\"y_hat\"], \n", + " log_likelihood= [\"log_lik\"],\n", + " observed_data = {\"y_hat\": lynx_hare_df.loc[:, (\"Hare\", \"Lynx\")]}\n", + "# dtypes={\"y_rep\": int} if Poisson family\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Computed from 800 posterior samples and 20 observations log-likelihood matrix.\n", + "\n", + " Estimate SE\n", + "elpd_loo -644.91 36.16\n", + "p_loo 469.16 -\n", + "------\n", + "\n", + "Pareto k diagnostic values:\n", + " Count Pct.\n", + "(-Inf, 0.5] (good) 20 100.0%\n", + " (0.5, 0.7] (ok) 0 0.0%\n", + " (0.7, 1] (bad) 0 0.0%\n", + " (1, Inf) (very bad) 0 0.0%" + ] + }, + "execution_count": 161, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "az.loo(idata)" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_ppc(idata, alpha=0.03, figsize=(12, 6),l)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [], + "source": [ + "fit_posterior_draws = posterior_draws" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "positional argument follows keyword argument (3705576184.py, line 14)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Input \u001b[0;32mIn [125]\u001b[0;36m\u001b[0m\n\u001b[0;31m log_likelihood: \"log_lik\",\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n" + ] + } + ], + "source": [ + "dims = {\"y\": [\"N\"], \"x\": [\"N\"], \"log_likelihood\": [\"N\"], \"observed_data\": [\"N\"], \"y_hat\": [\"N\"]}\n", + "idata_kwargs = {\n", + " \"posterior_predictive\": [\"y_hat\"],\n", + " \"log_likelihood\": \"log_lik\",\n", + " \"observed_data\": [\"y\"],\n", + " \"dims\": dims,\n", + "}\n", + "#idata = az.from_pystan(posterior=fit, posterior_model=sm, **idata_kwargs)\n", + "idata = az.from_cmdstanpy(\n", + "# prior=prior_pred,\n", + " posterior=fit_posterior_draws,\n", + " prior_predictive= [\"y_tilde\"],\n", + " posterior_predictive= [\"y_hat\"],\n", + " log_likelihood: \"log_lik\",\n", + " observed_data= \"y\",\n", + " # dtypes={\"y_rep\": int} (if poisson dist)\n", + ")\n", + " #predictions=[\"slack_comments_pred\", \"github_commits_pred\"],\n", + " #predictions_constant_data=[\"time_since_joined_pred\"],\n", + " #coords={\"developer\": names, \"candidate developer\" : candidate_devs},\n" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'list' object has no attribute 'items'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [116]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m idata_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_hat\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_likelihood\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_lik\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobserved_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdims\u001b[39m\u001b[38;5;124m\"\u001b[39m: dims,\n\u001b[1;32m 7\u001b[0m }\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m#idata = az.from_pystan(posterior=fit, posterior_model=sm, **idata_kwargs)\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m idata_stan \u001b[38;5;241m=\u001b[39m \u001b[43maz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_cmdstanpy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;43;03m# prior=prior_pred,\u001b[39;49;00m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior_draws\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;43;03m# prior_predictive= \"y_tilde\",\u001b[39;49;00m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#posterior_predictive= \"y_hat\",\u001b[39;49;00m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;66;43;03m# observed_data= \"\u001b[39;49;00m\n\u001b[1;32m 15\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43midata_kwargs\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:846\u001b[0m, in \u001b[0;36mfrom_cmdstanpy\u001b[0;34m(posterior, posterior_predictive, predictions, prior, prior_predictive, observed_data, constant_data, predictions_constant_data, log_likelihood, index_origin, coords, dims, save_warmup, dtypes)\u001b[0m\n\u001b[1;32m 765\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_cmdstanpy\u001b[39m(\n\u001b[1;32m 766\u001b[0m posterior\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 767\u001b[0m \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 780\u001b[0m dtypes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 781\u001b[0m ):\n\u001b[1;32m 782\u001b[0m \u001b[38;5;124;03m\"\"\"Convert CmdStanPy data into an InferenceData object.\u001b[39;00m\n\u001b[1;32m 783\u001b[0m \n\u001b[1;32m 784\u001b[0m \u001b[38;5;124;03m For a usage example read the\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;124;03m InferenceData object\u001b[39;00m\n\u001b[1;32m 830\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 831\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mCmdStanPyConverter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 832\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 833\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior_predictive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 834\u001b[0m \u001b[43m \u001b[49m\u001b[43mpredictions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 835\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 836\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior_predictive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 837\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 838\u001b[0m \u001b[43m \u001b[49m\u001b[43mconstant_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconstant_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 839\u001b[0m \u001b[43m \u001b[49m\u001b[43mpredictions_constant_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpredictions_constant_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 840\u001b[0m \u001b[43m \u001b[49m\u001b[43mlog_likelihood\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_likelihood\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 841\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex_origin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_origin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 842\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 843\u001b[0m \u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 844\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_warmup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_warmup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 845\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtypes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtypes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m--> 846\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_inference_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:463\u001b[0m, in \u001b[0;36mCmdStanPyConverter.to_inference_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 446\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_inference_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 447\u001b[0m \u001b[38;5;124;03m\"\"\"Convert all available data to an InferenceData object.\u001b[39;00m\n\u001b[1;32m 448\u001b[0m \n\u001b[1;32m 449\u001b[0m \u001b[38;5;124;03m Note that if groups can not be created (i.e., there is no `output`, so\u001b[39;00m\n\u001b[1;32m 450\u001b[0m \u001b[38;5;124;03m the `posterior` and `sample_stats` can not be extracted), then the InferenceData\u001b[39;00m\n\u001b[1;32m 451\u001b[0m \u001b[38;5;124;03m will not have those groups.\u001b[39;00m\n\u001b[1;32m 452\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m InferenceData(\n\u001b[1;32m 454\u001b[0m save_warmup\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_warmup,\n\u001b[1;32m 455\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 456\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior_to_xarray(),\n\u001b[1;32m 457\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_stats\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample_stats_to_xarray(),\n\u001b[1;32m 458\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior_predictive_to_xarray(),\n\u001b[1;32m 459\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictions_to_xarray(),\n\u001b[1;32m 460\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprior_to_xarray(),\n\u001b[1;32m 461\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_stats_prior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample_stats_prior_to_xarray(),\n\u001b[1;32m 462\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprior_predictive_to_xarray(),\n\u001b[0;32m--> 463\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobserved_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobserved_data_to_xarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 464\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconstant_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconstant_data_to_xarray(),\n\u001b[1;32m 465\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions_constant_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictions_constant_data_to_xarray(),\n\u001b[1;32m 466\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_likelihood\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog_likelihood_to_xarray(),\n\u001b[1;32m 467\u001b[0m },\n\u001b[1;32m 468\u001b[0m )\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:65\u001b[0m, in \u001b[0;36mrequires.__call__..wrapped\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m((\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mcls\u001b[39m, prop_i) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m prop_i \u001b[38;5;129;01min\u001b[39;00m prop)):\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:412\u001b[0m, in \u001b[0;36mCmdStanPyConverter.observed_data_to_xarray\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 409\u001b[0m \u001b[38;5;129m@requires\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobserved_data\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 410\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mobserved_data_to_xarray\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 411\u001b[0m \u001b[38;5;124;03m\"\"\"Convert observed data to xarray.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 412\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdict_to_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 413\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobserved_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 414\u001b[0m \u001b[43m \u001b[49m\u001b[43mlibrary\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcmdstanpy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 416\u001b[0m \u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 417\u001b[0m \u001b[43m \u001b[49m\u001b[43mdefault_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 418\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex_origin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex_origin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 419\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:306\u001b[0m, in \u001b[0;36mdict_to_dataset\u001b[0;34m(data, attrs, library, coords, dims, default_dims, index_origin, skip_event_dims)\u001b[0m\n\u001b[1;32m 303\u001b[0m dims \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 305\u001b[0m data_vars \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m--> 306\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, values \u001b[38;5;129;01min\u001b[39;00m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m():\n\u001b[1;32m 307\u001b[0m data_vars[key] \u001b[38;5;241m=\u001b[39m numpy_to_data_array(\n\u001b[1;32m 308\u001b[0m values,\n\u001b[1;32m 309\u001b[0m var_name\u001b[38;5;241m=\u001b[39mkey,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 314\u001b[0m skip_event_dims\u001b[38;5;241m=\u001b[39mskip_event_dims,\n\u001b[1;32m 315\u001b[0m )\n\u001b[1;32m 316\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m xr\u001b[38;5;241m.\u001b[39mDataset(data_vars\u001b[38;5;241m=\u001b[39mdata_vars, attrs\u001b[38;5;241m=\u001b[39mmake_attrs(attrs\u001b[38;5;241m=\u001b[39mattrs, library\u001b[38;5;241m=\u001b[39mlibrary))\n", + "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'items'" + ] + } + ], + "source": [ + "dims = {\"y\": [\"N\"], \"x\": [\"N\"], \"log_likelihood\": [\"N\"], \"observed_data\": [\"N\"], \"y_hat\": [\"N\"]}\n", + "idata_kwargs = {\n", + " \"posterior_predictive\": [\"y_hat\"],\n", + " \"log_likelihood\": \"log_lik\",\n", + " \"observed_data\": [\"y\"],\n", + " \"dims\": dims,\n", + "}\n", + "#idata = az.from_pystan(posterior=fit, posterior_model=sm, **idata_kwargs)\n", + "idata_stan = az.from_cmdstanpy(\n", + "# prior=prior_pred,\n", + " posterior=posterior_draws,\n", + "# prior_predictive= \"y_tilde\",\n", + " #posterior_predictive= \"y_hat\",\n", + " observed_data= \"\n", + " **idata_kwargs\n", + ")\n", + " #predictions=[\"slack_comments_pred\", \"github_commits_pred\"],\n", + " #predictions_constant_data=[\"time_since_joined_pred\"],\n", + " #coords={\"developer\": names, \"candidate developer\" : candidate_devs},\n" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Computed from 800 posterior samples and 20 observations log-likelihood matrix.\n", + "\n", + " Estimate SE\n", + "elpd_loo -644.91 36.16\n", + "p_loo 469.16 -\n", + "------\n", + "\n", + "Pareto k diagnostic values:\n", + " Count Pct.\n", + "(-Inf, 0.5] (good) 20 100.0%\n", + " (0.5, 0.7] (ok) 0 0.0%\n", + " (0.7, 1] (bad) 0 0.0%\n", + " (1, Inf) (very bad) 0 0.0%" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "az.loo(idata_stan, pointwise = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'var names: \"[\\'y\\'] are not present\" in dataset'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:71\u001b[0m, in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 71\u001b[0m var_names \u001b[38;5;241m=\u001b[39m \u001b[43m_subset_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvar_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilter_items\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilter_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwarn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:149\u001b[0m, in \u001b[0;36m_subset_list\u001b[0;34m(subset, whole_list, filter_items, warn)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(existing_items):\n\u001b[0;32m--> 149\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39marray(subset)[\u001b[38;5;241m~\u001b[39mexisting_items]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m are not present\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m subset\n", + "\u001b[0;31mKeyError\u001b[0m: \"['y'] are not present\"", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [112]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43marviz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_ppc\u001b[49m\u001b[43m(\u001b[49m\u001b[43midata_stan\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/plots/ppcplot.py:261\u001b[0m, in \u001b[0;36mplot_ppc\u001b[0;34m(data, kind, alpha, mean, observed, color, colors, grid, figsize, textsize, data_pairs, var_names, filter_vars, coords, flatten, flatten_pp, num_pp_samples, random_seed, jitter, animated, animation_kwargs, legend, labeller, ax, backend, backend_kwargs, group, show)\u001b[0m\n\u001b[1;32m 259\u001b[0m var_names \u001b[38;5;241m=\u001b[39m _var_names(var_names, observed_data, filter_vars)\n\u001b[1;32m 260\u001b[0m pp_var_names \u001b[38;5;241m=\u001b[39m [data_pairs\u001b[38;5;241m.\u001b[39mget(var, var) \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m var_names]\n\u001b[0;32m--> 261\u001b[0m pp_var_names \u001b[38;5;241m=\u001b[39m \u001b[43m_var_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpp_var_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredictive_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilter_vars\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 263\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flatten_pp \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m flatten \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 264\u001b[0m flatten_pp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(predictive_dataset\u001b[38;5;241m.\u001b[39mdims\u001b[38;5;241m.\u001b[39mkeys())\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:74\u001b[0m, in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 73\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin((\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvar names:\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00merr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124min dataset\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m---> 74\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m var_names\n", + "\u001b[0;31mKeyError\u001b[0m: 'var names: \"[\\'y\\'] are not present\" in dataset'" + ] + } + ], + "source": [ + "arviz.plot_ppc(idata_stan)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "a = {\"Prey\": prior_pred.y_tilde[:,:,0], \"Predator\": prior_pred.y_tilde[:,:,1]}," + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unhashable type: 'numpy.ndarray'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [93]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m dims \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_likelihood\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_hat\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m]}\n\u001b[1;32m 3\u001b[0m idata_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_hat\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobserved_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdims\u001b[39m\u001b[38;5;124m\"\u001b[39m: dims,\n\u001b[1;32m 9\u001b[0m }\n\u001b[0;32m---> 10\u001b[0m idata_stan \u001b[38;5;241m=\u001b[39m \u001b[43maz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_cmdstanpy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior_pred\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior_draws\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43my_tilde\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#posterior_predictive= {'Prey': posterior_draws.y_hat[:,:,0], 'Predator': posterior_draws.y_hat[:,:,1]},\u001b[39;49;00m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;43;03m# observed_data= \"\u001b[39;49;00m\n\u001b[1;32m 16\u001b[0m \u001b[43m \u001b[49m\u001b[43mlog_likelihood\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\n\u001b[1;32m 17\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlog_lik\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mposterior_draws\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_lik\u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#predictions=[\"slack_comments_pred\", \"github_commits_pred\"],\u001b[39;49;00m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#predictions_constant_data=[\"time_since_joined_pred\"],\u001b[39;49;00m\n\u001b[1;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#coords={\"developer\": names, \"candidate developer\" : candidate_devs},\u001b[39;49;00m\n\u001b[1;32m 22\u001b[0m \u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:846\u001b[0m, in \u001b[0;36mfrom_cmdstanpy\u001b[0;34m(posterior, posterior_predictive, predictions, prior, prior_predictive, observed_data, constant_data, predictions_constant_data, log_likelihood, index_origin, coords, dims, save_warmup, dtypes)\u001b[0m\n\u001b[1;32m 765\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_cmdstanpy\u001b[39m(\n\u001b[1;32m 766\u001b[0m posterior\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 767\u001b[0m \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 780\u001b[0m dtypes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 781\u001b[0m ):\n\u001b[1;32m 782\u001b[0m \u001b[38;5;124;03m\"\"\"Convert CmdStanPy data into an InferenceData object.\u001b[39;00m\n\u001b[1;32m 783\u001b[0m \n\u001b[1;32m 784\u001b[0m \u001b[38;5;124;03m For a usage example read the\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;124;03m InferenceData object\u001b[39;00m\n\u001b[1;32m 830\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 831\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mCmdStanPyConverter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 832\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 833\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior_predictive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 834\u001b[0m \u001b[43m \u001b[49m\u001b[43mpredictions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 835\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 836\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior_predictive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 837\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 838\u001b[0m \u001b[43m \u001b[49m\u001b[43mconstant_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconstant_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 839\u001b[0m \u001b[43m \u001b[49m\u001b[43mpredictions_constant_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpredictions_constant_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 840\u001b[0m \u001b[43m \u001b[49m\u001b[43mlog_likelihood\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_likelihood\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 841\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex_origin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_origin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 842\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 843\u001b[0m \u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 844\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_warmup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_warmup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 845\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtypes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtypes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m--> 846\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_inference_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:466\u001b[0m, in \u001b[0;36mCmdStanPyConverter.to_inference_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 446\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_inference_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 447\u001b[0m \u001b[38;5;124;03m\"\"\"Convert all available data to an InferenceData object.\u001b[39;00m\n\u001b[1;32m 448\u001b[0m \n\u001b[1;32m 449\u001b[0m \u001b[38;5;124;03m Note that if groups can not be created (i.e., there is no `output`, so\u001b[39;00m\n\u001b[1;32m 450\u001b[0m \u001b[38;5;124;03m the `posterior` and `sample_stats` can not be extracted), then the InferenceData\u001b[39;00m\n\u001b[1;32m 451\u001b[0m \u001b[38;5;124;03m will not have those groups.\u001b[39;00m\n\u001b[1;32m 452\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m InferenceData(\n\u001b[1;32m 454\u001b[0m save_warmup\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_warmup,\n\u001b[1;32m 455\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 456\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior_to_xarray(),\n\u001b[1;32m 457\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_stats\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample_stats_to_xarray(),\n\u001b[1;32m 458\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior_predictive_to_xarray(),\n\u001b[1;32m 459\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictions_to_xarray(),\n\u001b[1;32m 460\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprior_to_xarray(),\n\u001b[1;32m 461\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_stats_prior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample_stats_prior_to_xarray(),\n\u001b[1;32m 462\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprior_predictive_to_xarray(),\n\u001b[1;32m 463\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobserved_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobserved_data_to_xarray(),\n\u001b[1;32m 464\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconstant_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconstant_data_to_xarray(),\n\u001b[1;32m 465\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions_constant_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictions_constant_data_to_xarray(),\n\u001b[0;32m--> 466\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_likelihood\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_likelihood_to_xarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 467\u001b[0m },\n\u001b[1;32m 468\u001b[0m )\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:65\u001b[0m, in \u001b[0;36mrequires.__call__..wrapped\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m((\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mcls\u001b[39m, prop_i) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m prop_i \u001b[38;5;129;01min\u001b[39;00m prop)):\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:65\u001b[0m, in \u001b[0;36mrequires.__call__..wrapped\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m((\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mcls\u001b[39m, prop_i) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m prop_i \u001b[38;5;129;01min\u001b[39;00m prop)):\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:312\u001b[0m, in \u001b[0;36mCmdStanPyConverter.log_likelihood_to_xarray\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;129m@requires\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 309\u001b[0m \u001b[38;5;129m@requires\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_likelihood\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlog_likelihood_to_xarray\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 311\u001b[0m \u001b[38;5;124;03m\"\"\"Convert elementwise log likelihood samples to xarray.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 312\u001b[0m log_likelihood \u001b[38;5;241m=\u001b[39m \u001b[43m_as_set\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_likelihood\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 314\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstan_vars_cols\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 315\u001b[0m data, data_warmup \u001b[38;5;241m=\u001b[39m _unpack_fit(\n\u001b[1;32m 316\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior,\n\u001b[1;32m 317\u001b[0m log_likelihood,\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_warmup,\n\u001b[1;32m 319\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtypes,\n\u001b[1;32m 320\u001b[0m )\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:592\u001b[0m, in \u001b[0;36m_as_set\u001b[0;34m(spec)\u001b[0m\n\u001b[1;32m 590\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [spec]\n\u001b[1;32m 591\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 592\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mspec\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[1;32m 594\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mset\u001b[39m(spec)\n", + "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'" + ] + } + ], + "source": [ + "dims = {\"y\": [\"time\"], \"x\": [\"time\"], \"log_likelihood\": [\"time\"], \"y_hat\": [\"time\"]}\n", + " \n", + "idata_kwargs = {\n", + " \"posterior_predictive\": [\"y_hat\"],\n", + " \"observed_data\": \"y\",\n", + "# \"constant_data\": \"x\",\n", + " \"log_likelihood\": [\"log_lik\"],\n", + " \"dims\": dims,\n", + "}\n", + "idata_stan = az.from_cmdstanpy(\n", + " prior=prior_pred,\n", + " posterior=posterior_draws,\n", + " prior_predictive= \"y_tilde\",\n", + " #posterior_predictive= {'Prey': posterior_draws.y_hat[:,:,0], 'Predator': posterior_draws.y_hat[:,:,1]},\n", + "# observed_data= \"\n", + " log_likelihood={\n", + " 'log_lik': posterior_draws.log_lik\n", + " },\n", + " #predictions=[\"slack_comments_pred\", \"github_commits_pred\"],\n", + " #predictions_constant_data=[\"time_since_joined_pred\"],\n", + " #coords={\"developer\": names, \"candidate developer\" : candidate_devs},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unhashable type: 'numpy.ndarray'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [81]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m idata_stan \u001b[38;5;241m=\u001b[39m \u001b[43maz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_cmdstanpy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior_pred\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior_draws\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPrey\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprior_pred\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43my_tilde\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPredator\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprior_pred\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43my_tilde\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#posterior_predictive= {'Prey': posterior_draws.y_hat[:,:,0], 'Predator': posterior_draws.y_hat[:,:,1]},\u001b[39;49;00m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mPrey\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_data2draws\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43my\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mPredator\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_data2draws\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43my\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mlog_likelihood\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlog_lik\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mposterior_draws\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_lik\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#predictions=[\"slack_comments_pred\", \"github_commits_pred\"],\u001b[39;49;00m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#predictions_constant_data=[\"time_since_joined_pred\"],\u001b[39;49;00m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#coords={\"developer\": names, \"candidate developer\" : candidate_devs},\u001b[39;49;00m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# dims={\u001b[39;49;00m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"slack_comments\": [\"developer\"],\u001b[39;49;00m\n\u001b[1;32m 15\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"github_commits\" : [\"developer\"],\u001b[39;49;00m\n\u001b[1;32m 16\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"slack_comments_hat\": [\"developer\"],\u001b[39;49;00m\n\u001b[1;32m 17\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"github_commits_hat\": [\"developer\"],\u001b[39;49;00m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"time_since_joined\": [\"developer\"],\u001b[39;49;00m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"slack_comments_pred\" : [\"candidate developer\"],\u001b[39;49;00m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"github_commits_pred\" : [\"candidate developer\"],\u001b[39;49;00m\n\u001b[1;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"time_since_joined_pred\" : [\"candidate developer\"],\u001b[39;49;00m\n\u001b[1;32m 22\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# }\u001b[39;49;00m\n\u001b[1;32m 23\u001b[0m \u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:846\u001b[0m, in \u001b[0;36mfrom_cmdstanpy\u001b[0;34m(posterior, posterior_predictive, predictions, prior, prior_predictive, observed_data, constant_data, predictions_constant_data, log_likelihood, index_origin, coords, dims, save_warmup, dtypes)\u001b[0m\n\u001b[1;32m 765\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_cmdstanpy\u001b[39m(\n\u001b[1;32m 766\u001b[0m posterior\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 767\u001b[0m \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 780\u001b[0m dtypes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 781\u001b[0m ):\n\u001b[1;32m 782\u001b[0m \u001b[38;5;124;03m\"\"\"Convert CmdStanPy data into an InferenceData object.\u001b[39;00m\n\u001b[1;32m 783\u001b[0m \n\u001b[1;32m 784\u001b[0m \u001b[38;5;124;03m For a usage example read the\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;124;03m InferenceData object\u001b[39;00m\n\u001b[1;32m 830\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 831\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mCmdStanPyConverter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 832\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 833\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior_predictive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 834\u001b[0m \u001b[43m \u001b[49m\u001b[43mpredictions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 835\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 836\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior_predictive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 837\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 838\u001b[0m \u001b[43m \u001b[49m\u001b[43mconstant_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconstant_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 839\u001b[0m \u001b[43m \u001b[49m\u001b[43mpredictions_constant_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpredictions_constant_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 840\u001b[0m \u001b[43m \u001b[49m\u001b[43mlog_likelihood\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_likelihood\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 841\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex_origin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_origin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 842\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 843\u001b[0m \u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 844\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_warmup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_warmup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 845\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtypes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtypes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m--> 846\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_inference_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:462\u001b[0m, in \u001b[0;36mCmdStanPyConverter.to_inference_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 446\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_inference_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 447\u001b[0m \u001b[38;5;124;03m\"\"\"Convert all available data to an InferenceData object.\u001b[39;00m\n\u001b[1;32m 448\u001b[0m \n\u001b[1;32m 449\u001b[0m \u001b[38;5;124;03m Note that if groups can not be created (i.e., there is no `output`, so\u001b[39;00m\n\u001b[1;32m 450\u001b[0m \u001b[38;5;124;03m the `posterior` and `sample_stats` can not be extracted), then the InferenceData\u001b[39;00m\n\u001b[1;32m 451\u001b[0m \u001b[38;5;124;03m will not have those groups.\u001b[39;00m\n\u001b[1;32m 452\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m InferenceData(\n\u001b[1;32m 454\u001b[0m save_warmup\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_warmup,\n\u001b[1;32m 455\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 456\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior_to_xarray(),\n\u001b[1;32m 457\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_stats\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample_stats_to_xarray(),\n\u001b[1;32m 458\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior_predictive_to_xarray(),\n\u001b[1;32m 459\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictions_to_xarray(),\n\u001b[1;32m 460\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprior_to_xarray(),\n\u001b[1;32m 461\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_stats_prior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample_stats_prior_to_xarray(),\n\u001b[0;32m--> 462\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprior_predictive_to_xarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 463\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobserved_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobserved_data_to_xarray(),\n\u001b[1;32m 464\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconstant_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconstant_data_to_xarray(),\n\u001b[1;32m 465\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions_constant_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictions_constant_data_to_xarray(),\n\u001b[1;32m 466\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_likelihood\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog_likelihood_to_xarray(),\n\u001b[1;32m 467\u001b[0m },\n\u001b[1;32m 468\u001b[0m )\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:65\u001b[0m, in \u001b[0;36mrequires.__call__..wrapped\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m((\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mcls\u001b[39m, prop_i) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m prop_i \u001b[38;5;129;01min\u001b[39;00m prop)):\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:65\u001b[0m, in \u001b[0;36mrequires.__call__..wrapped\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m((\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mcls\u001b[39m, prop_i) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m prop_i \u001b[38;5;129;01min\u001b[39;00m prop)):\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:227\u001b[0m, in \u001b[0;36mCmdStanPyConverter.prior_predictive_to_xarray\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;129m@requires\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 224\u001b[0m \u001b[38;5;129m@requires\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 225\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprior_predictive_to_xarray\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 226\u001b[0m \u001b[38;5;124;03m\"\"\"Convert prior_predictive samples to xarray.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 227\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredictive_to_xarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprior_predictive\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprior\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:231\u001b[0m, in \u001b[0;36mCmdStanPyConverter.predictive_to_xarray\u001b[0;34m(self, names, fit)\u001b[0m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredictive_to_xarray\u001b[39m(\u001b[38;5;28mself\u001b[39m, names, fit):\n\u001b[1;32m 230\u001b[0m \u001b[38;5;124;03m\"\"\"Convert predictive samples to xarray.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 231\u001b[0m predictive \u001b[38;5;241m=\u001b[39m \u001b[43m_as_set\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnames\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 233\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(fit, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(fit, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstan_vars_cols\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 234\u001b[0m data, data_warmup \u001b[38;5;241m=\u001b[39m _unpack_fit(\n\u001b[1;32m 235\u001b[0m fit,\n\u001b[1;32m 236\u001b[0m predictive,\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_warmup,\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtypes,\n\u001b[1;32m 239\u001b[0m )\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:592\u001b[0m, in \u001b[0;36m_as_set\u001b[0;34m(spec)\u001b[0m\n\u001b[1;32m 590\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [spec]\n\u001b[1;32m 591\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 592\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mspec\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[1;32m 594\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mset\u001b[39m(spec)\n", + "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'" + ] + } + ], + "source": [ + "idata_stan = az.from_cmdstanpy(\n", + " prior=prior_pred,\n", + " posterior=posterior_draws,\n", + " prior_predictive= {\"Prey\": prior_pred.y_tilde[:,:,0], \"Predator\": prior_pred.y_tilde[:,:,1]},\n", + " #posterior_predictive= {'Prey': posterior_draws.y_hat[:,:,0], 'Predator': posterior_draws.y_hat[:,:,1]},\n", + " observed_data={'Prey': np.array(data_data2draws['y'])[:, 0], 'Predator' : np.array(data_data2draws['y'])[:, 1]},\n", + " log_likelihood={\n", + " 'log_lik': posterior_draws.log_lik\n", + " },\n", + " #predictions=[\"slack_comments_pred\", \"github_commits_pred\"],\n", + " #predictions_constant_data=[\"time_since_joined_pred\"],\n", + " #coords={\"developer\": names, \"candidate developer\" : candidate_devs},\n", + " # dims={\n", + " # \"slack_comments\": [\"developer\"],\n", + " # \"github_commits\" : [\"developer\"],\n", + " # \"slack_comments_hat\": [\"developer\"],\n", + " # \"github_commits_hat\": [\"developer\"],\n", + " # \"time_since_joined\": [\"developer\"],\n", + " # \"slack_comments_pred\" : [\"candidate developer\"],\n", + " # \"github_commits_pred\" : [\"candidate developer\"],\n", + " # \"time_since_joined_pred\" : [\"candidate developer\"],\n", + " # }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'var names: \"[\\'Prey\\' \\'Predator\\'] are not present\" in dataset'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:71\u001b[0m, in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 71\u001b[0m var_names \u001b[38;5;241m=\u001b[39m \u001b[43m_subset_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvar_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilter_items\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilter_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwarn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:149\u001b[0m, in \u001b[0;36m_subset_list\u001b[0;34m(subset, whole_list, filter_items, warn)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(existing_items):\n\u001b[0;32m--> 149\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39marray(subset)[\u001b[38;5;241m~\u001b[39mexisting_items]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m are not present\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m subset\n", + "\u001b[0;31mKeyError\u001b[0m: \"['Prey' 'Predator'] are not present\"", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [66]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01marviz\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43marviz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_ppc\u001b[49m\u001b[43m(\u001b[49m\u001b[43midata_stan\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/plots/ppcplot.py:261\u001b[0m, in \u001b[0;36mplot_ppc\u001b[0;34m(data, kind, alpha, mean, observed, color, colors, grid, figsize, textsize, data_pairs, var_names, filter_vars, coords, flatten, flatten_pp, num_pp_samples, random_seed, jitter, animated, animation_kwargs, legend, labeller, ax, backend, backend_kwargs, group, show)\u001b[0m\n\u001b[1;32m 259\u001b[0m var_names \u001b[38;5;241m=\u001b[39m _var_names(var_names, observed_data, filter_vars)\n\u001b[1;32m 260\u001b[0m pp_var_names \u001b[38;5;241m=\u001b[39m [data_pairs\u001b[38;5;241m.\u001b[39mget(var, var) \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m var_names]\n\u001b[0;32m--> 261\u001b[0m pp_var_names \u001b[38;5;241m=\u001b[39m \u001b[43m_var_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpp_var_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredictive_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilter_vars\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 263\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flatten_pp \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m flatten \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 264\u001b[0m flatten_pp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(predictive_dataset\u001b[38;5;241m.\u001b[39mdims\u001b[38;5;241m.\u001b[39mkeys())\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:74\u001b[0m, in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 73\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin((\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvar names:\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00merr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124min dataset\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m---> 74\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m var_names\n", + "\u001b[0;31mKeyError\u001b[0m: 'var names: \"[\\'Prey\\' \\'Predator\\'] are not present\" in dataset'" + ] + } + ], + "source": [ + "import arviz\n", + "arviz.plot_ppc(idata_stan)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Posterior Predictive checks based on estimation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fit_draws2data = stan_model_draws2data.generate_quantities(data=data_draws2data, mcmc_sample=fit)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HfhfnzdooHKL" + }, + "source": [ + "The output displayed in `xarray` format shows full information of how posterior is approximated conditional on user's input specified in U3 and U4. draws posterior approximation such as `method_variables()` which are parameters for the optimization algorithm. Although users unknowingly (sometimes intentionally) use default settings of default value of `method_variables()` (a.k.a hyperparameters in machine learning, and sometimes looked down upon as \"nuts and bolts\"), they affect the sample more than we know. Just as sensitivity checks w.r.t. different parameter values are recommended for SD models, variability of `method_varibles()` can also compared with outcome. However, estimation being computationally heavier than data generation, not much literature as far as the author know address this problem seriously with the exception of recent paper on deciding good enough posterior approximator after comparing the output from different precisions. [An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models](https://arxiv.org/abs/2205.09059).\n", + "\n", + "With `draws_xr()`, clicking the disk icon would show specific value of each row. Specific value of Stan's sample values can be further inspected with `summary()` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fit.draws_xr()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fit.method_variables()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "godbsO8CoN_V" + }, + "outputs": [], + "source": [ + "fit.summary().round(decimals=3).iloc[1:10,:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Code\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(stan_model_draws2data.code())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(stan_model_data2draws.code())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We recommend users to use positive instances of `divergences` and `iterations at max_treedepth` as potential signs of sampler's failure to explore the entire posterior. `divergences` counts when hamiltonian at each iteration is not preserved; `iterations at max_treedepth` means the number of post-warmup iterations which hit the maximum allowed treedepth before the trajectory hits “U-turn” condition of HMC-NUTS algorithm. Both can result in biased sample.\n", + "\n", + "Small data makes the geometry of posterior distribution highly curved, thus the sampler may encounter difficulty in exploring the posterior space hence fail to fit the model. Increasing synthetic data by varying parameter values can be a potential solution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f'divergences:\\n{fit.divergences}\\niterations at max_treedepth:\\n{fit.max_treedepths}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "for \n", + "alpha = np.random.normal(1, .5, 10)\n", + "beta = np.random.normal(.05, .05, 10)\n", + "gamma = np.random.normal(1, .5, 10)\n", + "delta = np.random.normal(.05, .05, 10)\n", + "assumeall_res = mod.run(initial_condition=(0, {'predator':lynx_hare_df.loc[:, 'Lynx'][0], 'prey': lynx_hare_df.loc[:, 'Hare'][0]}),\n", + " params={'alpha': 1, 'gamma': 1, 'beta': 0.05, 'delta': 0.05},\n", + " return_timestamps = range(lynx_hare_df.shape[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fit.method_variables()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "O6db3n9kTDve" + }, + "source": [ + "There were no divergent transitions[27](#fn27) reported. The $\\hat{R}$ values are all near 1, which is consistent with convergence. The effective sample size estimates for each parameter are sufficient for inference.[28](#fn28) Thus we have reason to trust that Stan has produced an adequate approximation of the posterior.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "NyLuBnr7_e8Y" + }, + "source": [ + "27 Divergences occur when Stan’s Hamiltonian solver diverges from the true Hamiltonian, which must be conserved, because of numerical problems in the stepwise gradient-based approximation of the curvature of the log density.\n", + "\n", + "28 With effective sample sizes of roughly one thousand, standard errors are roughly one thirtieth the size of posterior standard deviations, being in an inverse square root relation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Angie 0731 ends here" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RcyvwyW1oPZ0" + }, + "source": [ + "\n", + "## Comparing the fitted model to data\n", + "\n", + "Using a non-statistically motivated error term and optimization, Howard (2009, Figure 2.10) provides the following point estimates for the model parameters based on the data.\n", + "$$\n", + "\\alpha^* = 0.55, \\ \\\n", + "\\beta^* = 0.028, \\ \\\n", + "\\gamma^* = 0.84, \\ \\\n", + "\\delta^* = 0.026\n", + "$$\n", + "\n", + "Our model produced the following point estimates based on the posterior mean,[29](#fn29)\n", + "\n", + "$$\n", + "\\hat{\\alpha} = 0.55, \\ \\\n", + "\\hat{\\beta} = 0.028, \\ \\\n", + "\\hat{\\gamma} = 0.80, \\ \\\n", + "\\hat{\\delta} = 0.024,\n", + "$$\n", + "\n", + "which are very close to Howard's estimates. [30](#fn30) The posterior intervals, which are quite wide here, may be interpreted probabilistically,\n", + "\n", + "$$\n", + "\\begin{array}{ccc}\n", + "\\mbox{Pr}[0.47 \\leq \\alpha \\leq 0.63] & = & 0.8\n", + "\\\\[4pt]\n", + "\\mbox{Pr}[0.23 \\leq \\beta \\leq 0.33] & = & 0.8\n", + "\\\\[4pt]\n", + "\\mbox{Pr}[0.69 \\leq \\delta \\leq 0.91] & = & 0.8\n", + "\\\\[4pt]\n", + "\\mbox{Pr}[0.020 \\leq \\gamma \\leq 0.029] & = & 0.8\n", + "\\end{array}\n", + "$$\n", + "The effect of these estimates is plotted later with simulated population orbits.\n", + "\n", + "Error scales for both populations have the same posterior mean estimate,\n", + "\n", + "$$\n", + "\\hat{\\sigma}_1 \\ = \\ \\hat{\\sigma}_2 \\ = \\ 0.25.\n", + "$$\n", + "\n", + "and both have the same posterior 80% interval, (0.20, 0.31).[31](#fn31) \n", + "\n", + "## Inference for population sizes\n", + "\n", + "One inference we would like to make from our data is the size of the lynx and hare populations over time. Technically, we can only estimate the expected sizes of future pelt collections and must assume the population is somehow directly related to the numbers of pelts collected. Howard (2009) plugs in optimization-derived point estimates to derive population predictions.\n", + "\n", + "Rather than plugging in point estimates to get point predictions, we will follow the fully Bayesian approach of adjusting for uncertainty. This uncertainty takes two forms in inference. First, there is estimation uncertainty, which is fully characterized by the joint posterior density $p(\\alpha, \\beta, \\gamma, \\delta, z^{\\mathrm init}, \\sigma \\mid y)$.[32](#fn32)\n", + "\n", + "The second form of uncertainty stems from measurement error and unexplained variation, which are both rolled into a single sampling distribution, $\\log y_n \\sim \\mathsf{Normal}(\\log z_n, \\sigma)$. As in the Stan implementation, $z_n = (u_n, v_n)$ is the solution to the differential equation conditioned on the parameters $\\theta = (\\alpha, \\beta, \\gamma, \\delta, \\sigma)$ and initial state $z^{\\mathrm init}$.\n", + "\n", + "## Posterior predictive checks\n", + "\n", + "\n", + "We use posterior predictive checks to evaluate how well our model fits the data from which it was estimated.[33](#fn33)\n", + "\n", + "\n", + "The basic idea is to take the posterior for the fitted model and use it to predict what the data should've looked like. That is, we will be replicating new $y$ values that parallel the actual observations $y$. Becuase they are replicated values, we write them as as $y^{\\mathrm{rep}}$. The distribution of these replicated values is given by the posterior predictive distribution,\n", + "\n", + "\n", + "$$\n", + "p(y^{\\mathrm{rep}} | y)\n", + "\\ = \\\n", + "\\int p(y^{\\mathrm{rep}} | \\theta) \\ p(\\theta | y) \\ \\mathrm{d}\\theta,\n", + "$$\n", + "\n", + "where $\\theta = (\\alpha, \\beta, \\gamma, \\delta, z^{\\mathrm init}, \\sigma)$ is the vector of parameters for the model. Our two forms of uncertainty are represented in the two terms in the integral. The first is the sampling distribution for the replications, $p(y^{\\mathrm rep} | \\theta)$, which is the distribution of observations $y^{\\mathrm rep}$ given parameters $\\theta$. This term encapsulates the unexplained variance and measurement error. The second term is the posterior $p(\\theta | y)$, which encapsulates our uncertainty in our parameter estimates $\\theta$ given the observations $y$. Here, the integral takes a weighted average of the sampling distribution, with weights given by the posterior. In statistical terms, we are calculating an expectation of a function of the parameters, $f(\\theta) = p(y^{\\mathrm rep} | \\theta)$, over the posterior $p(\\theta | y)$, which can be written concisely as a conditional expectation,\n", + "\n", + "$$\n", + "p(y^{\\mathrm{rep}} | y)\n", + "\\ = \\\n", + "\\mathbb{E}\\!\\left[ \\, p(y^{\\mathrm{rep}} | \\theta) \\ \\, \\big| \\ \\, y \\, \\right].\n", + "$$\n", + "\n", + "\n", + "## Stan code for posterior predictive checks\n", + "\n", + "Stan defines predictive quantities in the generated quantities block, which is executed once per iteration.[34](#fn34) The code declares variables at the top of the block, then defines them in a loop over the species, then over the times.\n", + "\n", + "```\n", + "generated quantities {\n", + " real y_init_rep[2];\n", + " real y_rep[N, 2];\n", + " for (k in 1:2) {\n", + " y_init_rep[k] = lognormal_rng(log(z_init[k]), sigma[k]);\n", + " for (n in 1:N)\n", + " y_rep[n, k] = lognormal_rng(log(z[n, k]), sigma[k]);\n", + " }\n", + "}\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "drawset_pd = fit.get_drawset()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_init_rep_draws = drawset_pd.filter(like='y_init_rep', axis=1)\n", + "y_rep_draws = drawset_pd.filter(like='y_rep', axis=1).to_numpy().reshape((4000,20,2), order='A')\n", + "y_rep_draws.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predicted_pelts = pd.DataFrame(index = lynx_hare_df['Year'], columns = {'Hare','Lynx'})\n", + "predicted_pelts.iloc[0] = y_init_rep_draws.mean().values\n", + "for i in range(2):\n", + " predicted_pelts.iloc[1:,i] = np.mean(y_rep_draws[:,:,i], axis=0)\n", + " predicted_pelts.iloc[1:,i] = np.mean(y_rep_draws[:,:,i], axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "min_pelts = pd.DataFrame(index = lynx_hare_df['Year'], columns = {'Hare', 'Lynx'})\n", + "min_pelts.iloc[0] = np.quantile(y_init_rep_draws, 0.25)\n", + "for i in range(2):\n", + " min_pelts.iloc[1:,i] = np.quantile(y_rep_draws[:,:,i], 0.25, axis=0)\n", + " min_pelts.iloc[1:,i] = np.quantile(y_rep_draws[:,:,i], 0.25, axis=0)\n", + "\n", + "max_pelts = pd.DataFrame(index = lynx_hare_df['Year'], columns = {'Hare', 'Lynx'})\n", + "max_pelts.iloc[0] = np.quantile(y_init_rep_draws, 0.75)\n", + "for i in range(2):\n", + " max_pelts.iloc[1:,i] = np.quantile(y_rep_draws[:,:,i], 0.75, axis=0)\n", + " max_pelts.iloc[1:,i] = np.quantile(y_rep_draws[:,:,i], 0.75, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(21, 5))\n", + "plt.plot(range(1900,1921),lynx_hare_df['Hare'],color='red',marker='o')\n", + "plt.plot(range(1900,1921),predicted_pelts['Hare'],color='blue',marker='o')\n", + "plt.fill_between(range(1900,1921), list(min_pelts['Hare']),list(max_pelts['Hare']),color='grey',alpha=0.4)\n", + "plt.title(\"Hare\")\n", + "plt.xticks(range(1900,1921,5))\n", + "\n", + "\n", + "plt.figure(figsize=(21, 5))\n", + "plt.plot(range(1900,1921),lynx_hare_df['Lynx'],color='red',marker='o')\n", + "plt.plot(range(1900,1921),predicted_pelts['Lynx'],color='blue',marker='o')\n", + "plt.fill_between(range(1900,1921), list(min_pelts['Lynx']),list(max_pelts['Lynx']),color='grey',alpha=0.4)\n", + "plt.title('Lynx')\n", + "plt.xticks(range(1900,1921,5))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Posterior predictive checks, including posterior means and 50% intervals along with the measured data. If the model is well calibrated, as this one appears to be, 50% of the points are expected to fall in their 50% intervals.\n", + "\n", + "\n", + "The uncertainty due to parameter estimation is rolled into the values of `z_init`, `z`, and `sigma`. The uncertainty due to unexplained variation and measurement error is captured through the use of the lognormal pseudorandom number generator, `lognormal_rng`. The additional noise in the measurements `y` over that of the underlying population predictions `z` is visualized in the plots." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "z_draws = drawset_pd.filter(like='z.', axis=1).to_numpy().reshape((4000,20,2), order='A')\n", + "z_hare=z_draws[1:101,:,0].T\n", + "z_lynx=z_draws[1:101,:,1].T\n", + "plt.plot(z_lynx,z_hare,color='blue',alpha=0.8,linewidth=0.1)\n", + "plt.xlabel(\"lynx pelts (thousands)\")\n", + "plt.ylabel(\"hare pelts (thousands)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot of expected population orbit for one hundred draws from the posterior. Each draw represents a different orbit determined by the differential equation system parameters. Together they provide a sketch of posterior uncertainty for the expected population dynamics. If the ODE solutions were extracted per month rather than per year, the resulting plots would appear fairly smooth." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "y_hare=y_rep_draws[1:101,:,0].T\n", + "y_lynx=y_rep_draws[1:101,:,1].T\n", + "plt.plot(y_lynx,y_hare,color='blue',linewidth=0.1)\n", + "plt.xlabel(\"lynx pelts (thousands)\")\n", + "plt.ylabel(\"hare pelts (thousands)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot of expected pelt collection orbits for one hundred draws of system parameters from the posterior. Even if plotted at more fine-grained time intervals, error would remove any apparent smoothness. Extreme draws as seen here are typical when large values have high error on the multiplicative scale." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RlcVAAh1CGxv" + }, + "source": [ + "29 The posterior mean minimizes expected squared error, whereas posterior medians minimize expected absolute error. Here, the mean and median are the same to within MCMC standard error.\n", + "\n", + "30 Discrepancies are to be expected in that we are finding a posterior mean wheras Howard is finding a posterior mode. We do not suspect the priors have a strong influence here, but this could be checked by varying them and comparing results, i.e., performing a sensitivity analysis.\n", + "\n", + "\n", + "31 This suggests they may be completely pooled and modeled using a single parameter.\n", + "\n", + "32 In well-behaved models in which the data is broadly consistent with the prior and likelihood, estimation uncertainty is reduced by larger samples; as more data is available, it will overwhelm the fixed priors and the posterior will concentrate around the true values of the parameters.\n", + "\n", + "\n", + "33 This is “testing on the training data” in machine learning parlance, and while we would not trust it for final evaluation, it is an easy way to spot inconsistencies in the implementation of misspecification in the model.\n", + "\n", + "34 The log density and its gradient are typically evaluated many times per iteration to follow the Hamiltonian trajectory of the parameters given some initial momenta." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "bqAvgOUjj6bP" + }, + "source": [ + "# Conclusion: What are the Population Dynamics?\n", + "\n", + "Even with the strong assumption that the number of pelts collected is proportional to the population, we only know how the relative sizes of the populations change, not their actual sizes.\n", + "\n", + "\n", + "## Predicted population cycles\n", + "\n", + "\n", + "In the same way as Volterra (1926) plotted the cycles of predator and prey populations, we can select draws of $z^{\\mathrm rep}$ from the posterior and plot them. The variation here is due to posterior uncertainty in the value of the system parameters $\\alpha, \\beta, \\gamma, \\delta$ and the initial population $z^{\\mathrm init}$.\n", + "\n", + "## Predicted measurements\n", + "\n", + "In addition to the estimation uncertainty discussed in the previous section, there is also the general error due to measurement error, model misspecification, etc. In order to simulate the number of pelts that are reported collected (which may itself have error relative to the actual number of pelts), we must additionally consider the general error term. That is already rolled into the variables $y^{\\mathrm rep}$, so we plot those here.\n", + "\n", + "\n", + "# Exercises and Extensions \n", + "\n", + "The Lotka-Volterra model is easily extended for realistic applications in several obvious ways. I ran out of steam before turning this case study into a book on Bayesian modeling. I leave the next steps for this model to the dedicated reader. [35](#fn35) Even if you don't plan to do the exercises, they provide a concise description of where this model can be taken.\n", + "\n", + "1. *Simulation-based calibration*. Write a Stan model to simulate data from this model. First simulate parameters from the prior (or pick ones consistent with the priors). Then simulate data from the parameters. Finally, fit the model in Stan and compare the coverage as in the last plot in the case study.[36](#fn36)\n", + "\n", + "1. *Forecasting and backcasting*. Extend predictions another 50 years into the future and plot as in the last plot. This can be done by extending the solution points in the transformed parameters, but is more efficiently done in the generated quantities block. Next, extend the predictions 50 years into the past and plot.[37](#fn37) Is there anything suspicious about the long-term uncertainty measures?\n", + "\n", + "1. *Missing data*. Suppose that several of the measurements are missing. Write a Stan program that uses only the observed measurements. This will require coding the data in long form [38](#fn38) as\n", + "```\n", + "int J; // num observations\n", + "int spec[J]; // species for obs j\n", + "real tt[J]; // time for obs j\n", + "real yy[J]; // yy[j] == y[tt[j], spec[j]]\n", + "```\n", + "and coding the model as\n", + "```\n", + "for (j in 1:J)\n", + " yy[j] ~ normal(zz[tt[j], spec[j]], sigma[spec[j]]);\n", + "```\n", + "Only this part of the model changes; the ODE is set up and fit as before with the complete set of time points. What happens to the computation and posterior inferences as increasing amounts of data are missing? How can the missing data points be imputed using the generated quantities block?[39](#fn39)\n", + "1. *Error model.* Replace the lognormal error with a simple normal error model. What does this do to the `z` estimates and to the basic parameter estimates? Which error model fits better?\n", + "1. *Sensitivity analysis and prior choice*. Perform a sensitivity analysis on the prior choices made for this model. When the prior means or scales are varied, how much does the posterior vary? Does the model become easier or harder to fit (in terms of effective sample size per unit time or divergent transitions) with different prior choices? What does this imply about the number of digits with which we report results and thus the effective sample sizes necessary for most inferences?\n", + "1. *Model misspecification.* Swap the coding of the lynx and hare in the input so that the predator is modeled as prey and vice-versa. How well does it fit the data? How does this provide evidence for the folk theorem?[40](#fn40)\n", + "\n", + "1. *Multiple species.* Extend the model to deal with more than one species. Treat each species as as having a mixture of predators and a mixture of prey affecting its growth. Simulate data and fit to your model. Do the equations become stiff as more species and interactions are added? How much data is required to identify the model as the number of species and their mixing increases?[41](#fn41)\n", + "1. *Covariates.* Suppose we have measured covariates such as temperature, water, and plant abundance. Set up a model where these have further effects on populations modeled as unconstrained multiplies of the existing species size. Simulate data using your covariates. What does the existence of covariates do to the uncertainty estimates compared to using a model without covariates?\n", + "1. *Joint mark-recapture modeling*. Suppose that we also have mark-recapture data for each species over the same time period. Find or simulate such data. Build a mark-recapture model such as Cormack-Jolly-Seber and jointly model the population in terms of the CJS model and the Lotka-Volterra model.[42](#fn42) How can you scale the population dynamics model in order to work at the same scale as the mark-recapture model? Does this reduce uncertainty in the population dynamics? Does the joint model improve on fitting the mark-recapture model or Lotka-Volterra models separately?\n", + "1. *Measurement error*. Suppose the number of lynx pelts collected is affected by the size of the population. In particular, suppose a larger proportion of lynx are captured when they are hungry. How cold this be included as a component of the model? What parameter would be introduced? Simulate data and see if the true populations can be recovered with the model.\n", + "1. *Carrying capacity*. How can a carrying capacity (upper bound on population size) be incorporated into the Lotka-Volterra model? Formulate the model in Stan and fit it to simulated data. If the data is simulated near the carrying capacity, how does it fit using the simpler Lotka-Volterra model?\n", + "1. *Cross-validation and predictive calibration*. Cross validation as such doesn't make much sense for time series. Nevertheless, it is possible to fit data to initial sequences of a time series and predict the remaining sequence. How could this be achieved here?[43](#fn43) Do you need to write more than one Stan model?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RtBQ8nGYE5kM" + }, + "source": [ + "35 If you complete a few of these exercises and write them up, please [let me know](mailto:carp@alias-i.com) I'd be happy to extend this case study and add a co-author or publish a follow-on case study.\n", + "\n", + "36 This basic validation technique was extended and converted to a statistical test of MCMC algorithm fit by Cook, Gelman and Rubin (2006). Their test of Bayesian calibration relies on data simulated from the model being calibrated by construction in full Bayesian posterior inference. As extra credit, apply this test to validate the model.\n", + "\n", + "37 *Hint*: the initial time will have to be changed in the call to ode_integrate.\n", + "\n", + "38 Long form is known as “melted” in R’s Tidyverse.\n", + "\n", + "39 There is a chapter on missing data with more details in the Stan manual.\n", + "\n", + "40 *Folk Theorem* (Gelman) When you have computational problems, often there’s a problem with your model.\n", + "\n", + "41 *Hint*: see Michael Betancourt’s case study on mixture models on the Stan web site for general advice on mixture modeling.\n", + "\n", + "42 The Stan manual chapter on latent discrete parameters describes how to code the Cormack-Jolly-Seber model for mark-recapture data.\n", + "\n", + "43 *Hint*: Considier using the [`loo`](http://mc-stan.org/loo/) package for leave-one-out cross-validation (Vehtari, Gelman, and Gabray 2017)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_aj_OKChEyF5" + }, + "source": [ + "## References\n", + "\n", + "* Cook, S. R., Gelman, A., & Rubin, D. B. (2006) Validation of software for Bayesian models using posterior quantiles. *Journal of Computational and Graphical Statistics*, 15(3), 675--692.\n", + "* Hewitt, C. G. (1921) [*The Conservation of the Wild Life of Canada*](https://books.google.com/books?id=8hVDAAAAIAAJ). Charles Scribner's Sons.\n", + "* Howard, P. (2009). [Modeling basics](http://www.math.tamu.edu/~phoward/m442/modbasics.pdf). Lecture Notes for Math 442, Texas A&M University.\n", + "* Lotka, A. J. (1925). *Principles of physical biology*. Baltimore: Waverly.\n", + "* Rogers, K. (2011) [The rise and fall of the Canada lynx and snowshoe hare]( http://blogs.britannica.com/2011/06/rise-fall-canada-lynx-snowshoe-hare/). *Encyclopedia Brittanica Blog*.\n", + "* Stan Development Team (2017) *Stan Modeling Language Users Guide and Reference Manual*, Version 2.17, [http://mc-stan.org](http://mc-stan.org).\n", + "* Vehtari, A., Gelman, A. & Gabry, J. (2017) Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. *Journal of Statistics and Computing* 27(5):1413--1432.\n", + "* Volterra, V. (1926). Fluctuations in the abundance of a species considered mathematically. *Nature*, 118(2972), 558-560.\n", + "* Volterra, V. (1927). *Variazioni e fluttuazioni del numero d'individui in specie animali conviventi*. C. Ferrari.\n", + "\n", + "## Source code\n", + "\n", + "All of the source code, data, text, and images for this case study are available on GitHub at\n", + "\n", + "* [stan-dev/example-models/knitr/lotka-volterra](https://github.com/stan-dev/example-models/tree/master/knitr/lotka-volterra)\n", + "\n", + "\n", + "## Acknowledgements \n", + "\n", + "Thanks to Simon Woodward, Arya Pourzanjani, and Leo Lahti for helpful comments on earlier drafts and Peter Howard for providing the provenance of the data and the original case study with the data. Thanks to Joss Wright for patching an illegally formed data object.\n", + "\n", + "
\n", + "\n", + "\n", + "## Complete Stan program \n", + "\n", + "\n", + "Here is the complete Stan program for solving the inverse problem for the Lotka-Volterra model, including posterior predictive checks.\n", + "\n", + "```\n", + "functions {\n", + " real[] dz_dt(real t, // time\n", + " real[] z, // system state {prey, predator}\n", + " real[] theta, // parameters\n", + " real[] x_r, // unused data\n", + " int[] x_i) {\n", + " real u = z[1];\n", + " real v = z[2];\n", + " real alpha = theta[1];\n", + " real beta = theta[2];\n", + " real gamma = theta[3];\n", + " real delta = theta[4];\n", + " real du_dt = (alpha - beta * v) * u;\n", + " real dv_dt = (-gamma + delta * u) * v;\n", + " return { du_dt, dv_dt };\n", + " }\n", + "}\n", + "data {\n", + " int N; // number of measurement times\n", + " real ts[N]; // measurement times > 0\n", + " real y_init[2]; // initial measured populations\n", + " real y[N, 2]; // measured populations\n", + "}\n", + "parameters {\n", + " real theta[4]; // { alpha, beta, gamma, delta }\n", + " real z_init[2]; // initial population\n", + " real sigma[2]; // measurement errors\n", + "}\n", + "transformed parameters {\n", + " real z[N, 2]\n", + " = integrate_ode_rk45(dz_dt, z_init, 0, ts, theta,\n", + " rep_array(0.0, 0), rep_array(0, 0),\n", + " 1e-5, 1e-3, 5e2);\n", + "}\n", + "model {\n", + " theta[{1, 3}] ~ normal(1, 0.5);\n", + " theta[{2, 4}] ~ normal(0.05, 0.05);\n", + " sigma ~ lognormal(-1, 1);\n", + " z_init ~ lognormal(log(10), 1);\n", + " for (k in 1:2) {\n", + " y_init[k] ~ lognormal(log(z_init[k]), sigma[k]);\n", + " y[ , k] ~ lognormal(log(z[, k]), sigma[k]);\n", + " }\n", + "}\n", + "generated quantities {\n", + " real y_init_rep[2];\n", + " real y_rep[N, 2];\n", + " for (k in 1:2) {\n", + " y_init_rep[k] = lognormal_rng(log(z_init[k]), sigma[k]);\n", + " for (n in 1:N)\n", + " y_rep[n, k] = lognormal_rng(log(z[n, k]), sigma[k]);\n", + " }\n", + "}\n", + "```\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "## Licenses \n", + "\n", + "Code © 2017--2018, Trustees of Columbia University in New York, licensed under BSD-3.\n", + "\n", + "Text © 2017--2018, Bob Carpenter, licensed under CC-BY-NC 4.0." + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "20Stan_lotka_volterra_(1).ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "local-venv", + "language": "python", + "name": "local-venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + }, + "vscode": { + "interpreter": { + "hash": "58113edcaccd8903e77075e096a62a8175d10cdab2dffe2be6bcc9cb981b6aab" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/test_scripts/data/hudson-bay-lynx-hare.csv b/test_scripts/data/hudson-bay-lynx-hare.csv new file mode 100644 index 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np\n", + "\n", + "import pysd\n", + "from pysd.translators.vensim.vensim_file import VensimFile\n", + "from pysd.translators.xmile.xmile_file import XmileFile\n", + "from pysd.builders.stan.stan_model_builder import *\n", + "\n", + "import cmdstanpy # 2.30 is fastest (as of 08.12.2022) `cmdstanpy.install_cmdstan()` \n", + "from cmdstanpy import CmdStanModel, cmdstan_path\n", + "import arviz as az #!pip install git+https://github.com/arviz-devs/arviz\n", + "az.style.use(\"arviz-darkgrid\")\n", + "\n", + "# set your working directiory\n", + "os.chdir(\"/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts\")" + ] + }, + { + "attachments": { + "db58966d-7db3-43ac-9114-da7b079d88c4.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "07c14386-a246-4f4d-a4e5-ad85fa338c19", + "metadata": { + "tags": [] + }, + "source": [ + "## U1. Draft\n", + "From mental model to SD model.\n", + "![image.png](attachment:db58966d-7db3-43ac-9114-da7b079d88c4.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "9dcead94-0f1f-4396-8b41-65c41d68df57", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "original name stan variable name is stock\n", + "----------------------------------------------------------------------------------\n", + "Adjustment for WIP adjustment_for_wip \n", + "Change in Exp Orders change_in_exp_orders \n", + "Customer Order Rate customer_order_rate \n", + "Desired Inventory desired_inventory \n", + "Desired Inventory Coverage desired_inventory_coverage \n", + "Desired Production desired_production \n", + "Desired Production Start Rate desired_production_start_rate \n", + "Desired Shipment Rate desired_shipment_rate \n", + "Desired WIP desired_wip \n", + "Expected Order Rate expected_order_rate V\n", + "Inventory inventory V\n", + "Inventory Adjustment Time inventory_adjustment_time \n", + "Inventory Coverage inventory_coverage \n", + "Manufacturing Cycle Time manufacturing_cycle_time \n", + "Maximum Shipment Rate maximum_shipment_rate \n", + "Minimum Order Processing Time minimum_order_processing_time \n", + "Order Fulfillment Ratio order_fulfillment_ratio \n", + "Production Adjustment from Inventory production_adjustment_from_inventory \n", + "Production Rate production_rate \n", + "Production Start Rate production_start_rate \n", + "Safety Stock Coverage safety_stock_coverage \n", + "Shipment Rate shipment_rate \n", + "Table for Order Fulfillment table_for_order_fulfillment \n", + "Time to Average Order Rate time_to_average_order_rate \n", + "WIP Adjustment Time wip_adjustment_time \n", + "Work in Process Inventory work_in_process_inventory V\n", + "FINAL TIME final_time \n", + "INITIAL TIME initial_time \n", + "SAVEPER saveper \n", + "TIME STEP time_step \n" + ] + } + ], + "source": [ + "vf = VensimFile(\"vensim_models/ds_white_sterman.mdl\")\n", + "vf.parse()\n", + "am = vf.get_abstract_model()\n", + "stan_builder = StanModelBuilder(am)\n", + "stan_builder.print_variable_info()" + ] + }, + { + "cell_type": "markdown", + "id": "477f3043-0f99-4bcd-9c78-da7e53081fd0", + "metadata": { + "tags": [] + }, + "source": [ + "## U2. Classify\n", + "\n", + "| variable name | `est_param` | `ass_param` | `obs_stock` |\n", + "| ------------------------------------ | ----------- | ----------- | ----------- |\n", + "| adjustment_for_wip | | | |\n", + "| change_in_exp_orders | | | |\n", + "| customer_order_rate | | V | |\n", + "| desired_inventory | | | |\n", + "| desired_inventory_coverage | | | |\n", + "| desired_production | | | |\n", + "| desired_production_start_rate | | | |\n", + "| desired_shipment_rate | | | |\n", + "| desired_wip | | | |\n", + "| expected_order_rate | | | V |\n", + "| inventory | | | V |\n", + "| inventory_adjustment_time | V | | |\n", + "| inventory_coverage | | V | |\n", + "| manufacturing_cycle_time | | V | |\n", + "| maximum_shipment_rate | | | |\n", + "| minimum_order_processing_time | V | | |\n", + "| order_fulfillment_ratio | | | |\n", + "| production_adjustment_from_inventory | | | |\n", + "| production_rate | | | |\n", + "| production_start_rate | | | |\n", + "| safety_stock_coverage | | | |\n", + "| shipment_rate | | | |\n", + "| table_for_order_fulfillment | | V (lookup) | |\n", + "| time_to_average_order_rate | | V | |\n", + "| wip_adjustment_time | | V | |\n", + "| work_in_process_inventory | | | V |\n", + "| initial_time | | V | |\n", + "| final_time | | V | |\n", + "| time_step | | V | |\n", + "\n", + "The rest is `aux_var` which are derived from the defined." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "a3d2b4a9-532f-4f3f-858e-64b4877c1997", + "metadata": {}, + "outputs": [], + "source": [ + "ass_param_lst = [\"customer_order_rate\", \"inventory_coverage\", \"manufacturing_cycle_time\", \"time_to_average_order_rate\", \"wip_adjustment_time\"]\n", + "obs_stock_lst = [\"work_in_process_inventory\", \"inventory\"]" + ] + }, + { + "cell_type": "markdown", + "id": "33ac5c16-f572-46ba-8d79-062f601a5e24", + "metadata": {}, + "source": [ + "## P1. Relational_prior\n", + "From SD model (`.mdl`) to Stan ODE function block (`.stan`). No new information is added." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "d1a14086-45cd-4f99-9e69-51aa57dea790", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "functions {\n", + " real lookupFunc_0(real x){\n", + " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", + " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", + " real slope;\n", + " real intercept;\n", + "\n", + " if(x <= 0.2)\n", + " intercept = 0.0;\n", + " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", + " return intercept + slope * (x - 0.0);\n", + " else if(x <= 0.4)\n", + " intercept = 0.2;\n", + " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", + " return intercept + slope * (x - 0.2);\n", + " else if(x <= 0.6)\n", + " intercept = 0.4;\n", + " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", + " return intercept + slope * (x - 0.4);\n", + " else if(x <= 0.8)\n", + " intercept = 0.58;\n", + " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", + " return intercept + slope * (x - 0.6);\n", + " else if(x <= 1.0)\n", + " intercept = 0.73;\n", + " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", + " return intercept + slope * (x - 0.8);\n", + " else if(x <= 1.2)\n", + " intercept = 0.85;\n", + " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", + " return intercept + slope * (x - 1.0);\n", + " else if(x <= 1.4)\n", + " intercept = 0.93;\n", + " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", + " return intercept + slope * (x - 1.2);\n", + " else if(x <= 1.6)\n", + " intercept = 0.97;\n", + " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", + " return intercept + slope * (x - 1.4);\n", + " else if(x <= 1.8)\n", + " intercept = 0.99;\n", + " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", + " return intercept + slope * (x - 1.6);\n", + " else if(x <= 2.0)\n", + " intercept = 1.0;\n", + " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", + " return intercept + slope * (x - 1.8);\n", + " }\n", + "\n", + " real lookupFunc_0(real x){\n", + " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", + " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", + " real slope;\n", + " real intercept;\n", + "\n", + " if(x <= 0.2)\n", + " intercept = 0.0;\n", + " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", + " return intercept + slope * (x - 0.0);\n", + " else if(x <= 0.4)\n", + " intercept = 0.2;\n", + " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", + " return intercept + slope * (x - 0.2);\n", + " else if(x <= 0.6)\n", + " intercept = 0.4;\n", + " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", + " return intercept + slope * (x - 0.4);\n", + " else if(x <= 0.8)\n", + " intercept = 0.58;\n", + " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", + " return intercept + slope * (x - 0.6);\n", + " else if(x <= 1.0)\n", + " intercept = 0.73;\n", + " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", + " return intercept + slope * (x - 0.8);\n", + " else if(x <= 1.2)\n", + " intercept = 0.85;\n", + " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", + " return intercept + slope * (x - 1.0);\n", + " else if(x <= 1.4)\n", + " intercept = 0.93;\n", + " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", + " return intercept + slope * (x - 1.2);\n", + " else if(x <= 1.6)\n", + " intercept = 0.97;\n", + " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", + " return intercept + slope * (x - 1.4);\n", + " else if(x <= 1.8)\n", + " intercept = 0.99;\n", + " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", + " return intercept + slope * (x - 1.6);\n", + " else if(x <= 2.0)\n", + " intercept = 1.0;\n", + " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", + " return intercept + slope * (x - 1.8);\n", + " }\n", + "\n", + " real lookupFunc_0(real x){\n", + " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", + " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", + " real slope;\n", + " real intercept;\n", + "\n", + " if(x <= 0.2)\n", + " intercept = 0.0;\n", + " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", + " return intercept + slope * (x - 0.0);\n", + " else if(x <= 0.4)\n", + " intercept = 0.2;\n", + " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", + " return intercept + slope * (x - 0.2);\n", + " else if(x <= 0.6)\n", + " intercept = 0.4;\n", + " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", + " return intercept + slope * (x - 0.4);\n", + " else if(x <= 0.8)\n", + " intercept = 0.58;\n", + " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", + " return intercept + slope * (x - 0.6);\n", + " else if(x <= 1.0)\n", + " intercept = 0.73;\n", + " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", + " return intercept + slope * (x - 0.8);\n", + " else if(x <= 1.2)\n", + " intercept = 0.85;\n", + " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", + " return intercept + slope * (x - 1.0);\n", + " else if(x <= 1.4)\n", + " intercept = 0.93;\n", + " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", + " return intercept + slope * (x - 1.2);\n", + " else if(x <= 1.6)\n", + " intercept = 0.97;\n", + " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", + " return intercept + slope * (x - 1.4);\n", + " else if(x <= 1.8)\n", + " intercept = 0.99;\n", + " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", + " return intercept + slope * (x - 1.6);\n", + " else if(x <= 2.0)\n", + " intercept = 1.0;\n", + " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", + " return intercept + slope * (x - 1.8);\n", + " }\n", + "\n", + " real lookupFunc_0(real x){\n", + " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", + " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", + " real slope;\n", + " real intercept;\n", + "\n", + " if(x <= 0.2)\n", + " intercept = 0.0;\n", + " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", + " return intercept + slope * (x - 0.0);\n", + " else if(x <= 0.4)\n", + " intercept = 0.2;\n", + " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", + " return intercept + slope * (x - 0.2);\n", + " else if(x <= 0.6)\n", + " intercept = 0.4;\n", + " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", + " return intercept + slope * (x - 0.4);\n", + " else if(x <= 0.8)\n", + " intercept = 0.58;\n", + " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", + " return intercept + slope * (x - 0.6);\n", + " else if(x <= 1.0)\n", + " intercept = 0.73;\n", + " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", + " return intercept + slope * (x - 0.8);\n", + " else if(x <= 1.2)\n", + " intercept = 0.85;\n", + " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", + " return intercept + slope * (x - 1.0);\n", + " else if(x <= 1.4)\n", + " intercept = 0.93;\n", + " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", + " return intercept + slope * (x - 1.2);\n", + " else if(x <= 1.6)\n", + " intercept = 0.97;\n", + " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", + " return intercept + slope * (x - 1.4);\n", + " else if(x <= 1.8)\n", + " intercept = 0.99;\n", + " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", + " return intercept + slope * (x - 1.6);\n", + " else if(x <= 2.0)\n", + " intercept = 1.0;\n", + " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", + " return intercept + slope * (x - 1.8);\n", + " }\n", + "\n", + " real lookupFunc_0(real x){\n", + " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", + " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", + " real slope;\n", + " real intercept;\n", + "\n", + " if(x <= 0.2)\n", + " intercept = 0.0;\n", + " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", + " return intercept + slope * (x - 0.0);\n", + " else if(x <= 0.4)\n", + " intercept = 0.2;\n", + " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", + " return intercept + slope * (x - 0.2);\n", + " else if(x <= 0.6)\n", + " intercept = 0.4;\n", + " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", + " return intercept + slope * (x - 0.4);\n", + " else if(x <= 0.8)\n", + " intercept = 0.58;\n", + " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", + " return intercept + slope * (x - 0.6);\n", + " else if(x <= 1.0)\n", + " intercept = 0.73;\n", + " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", + " return intercept + slope * (x - 0.8);\n", + " else if(x <= 1.2)\n", + " intercept = 0.85;\n", + " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", + " return intercept + slope * (x - 1.0);\n", + " else if(x <= 1.4)\n", + " intercept = 0.93;\n", + " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", + " return intercept + slope * (x - 1.2);\n", + " else if(x <= 1.6)\n", + " intercept = 0.97;\n", + " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", + " return intercept + slope * (x - 1.4);\n", + " else if(x <= 1.8)\n", + " intercept = 0.99;\n", + " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", + " return intercept + slope * (x - 1.6);\n", + " else if(x <= 2.0)\n", + " intercept = 1.0;\n", + " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", + " return intercept + slope * (x - 1.8);\n", + " }\n", + "\n", + " real lookupFunc_0(real x){\n", + " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", + " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", + " real slope;\n", + " real intercept;\n", + "\n", + " if(x <= 0.2)\n", + " intercept = 0.0;\n", + " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", + " return intercept + slope * (x - 0.0);\n", + " else if(x <= 0.4)\n", + " intercept = 0.2;\n", + " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", + " return intercept + slope * (x - 0.2);\n", + " else if(x <= 0.6)\n", + " intercept = 0.4;\n", + " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", + " return intercept + slope * (x - 0.4);\n", + " else if(x <= 0.8)\n", + " intercept = 0.58;\n", + " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", + " return intercept + slope * (x - 0.6);\n", + " else if(x <= 1.0)\n", + " intercept = 0.73;\n", + " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", + " return intercept + slope * (x - 0.8);\n", + " else if(x <= 1.2)\n", + " intercept = 0.85;\n", + " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", + " return intercept + slope * (x - 1.0);\n", + " else if(x <= 1.4)\n", + " intercept = 0.93;\n", + " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", + " return intercept + slope * (x - 1.2);\n", + " else if(x <= 1.6)\n", + " intercept = 0.97;\n", + " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", + " return intercept + slope * (x - 1.4);\n", + " else if(x <= 1.8)\n", + " intercept = 0.99;\n", + " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", + " return intercept + slope * (x - 1.6);\n", + " else if(x <= 2.0)\n", + " intercept = 1.0;\n", + " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", + " return intercept + slope * (x - 1.8);\n", + " }\n", + "\n", + " real lookupFunc_0(real x){\n", + " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", + " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", + " real slope;\n", + " real intercept;\n", + "\n", + " if(x <= 0.2)\n", + " intercept = 0.0;\n", + " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", + " return intercept + slope * (x - 0.0);\n", + " else if(x <= 0.4)\n", + " intercept = 0.2;\n", + " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", + " return intercept + slope * (x - 0.2);\n", + " else if(x <= 0.6)\n", + " intercept = 0.4;\n", + " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", + " return intercept + slope * (x - 0.4);\n", + " else if(x <= 0.8)\n", + " intercept = 0.58;\n", + " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", + " return intercept + slope * (x - 0.6);\n", + " else if(x <= 1.0)\n", + " intercept = 0.73;\n", + " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", + " return intercept + slope * (x - 0.8);\n", + " else if(x <= 1.2)\n", + " intercept = 0.85;\n", + " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", + " return intercept + slope * (x - 1.0);\n", + " else if(x <= 1.4)\n", + " intercept = 0.93;\n", + " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", + " return intercept + slope * (x - 1.2);\n", + " else if(x <= 1.6)\n", + " intercept = 0.97;\n", + " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", + " return intercept + slope * (x - 1.4);\n", + " else if(x <= 1.8)\n", + " intercept = 0.99;\n", + " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", + " return intercept + slope * (x - 1.6);\n", + " else if(x <= 2.0)\n", + " intercept = 1.0;\n", + " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", + " return intercept + slope * (x - 1.8);\n", + " }\n", + "\n", + " real lookupFunc_0(real x){\n", + " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", + " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", + " real slope;\n", + " real intercept;\n", + "\n", + " if(x <= 0.2)\n", + " intercept = 0.0;\n", + " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", + " return intercept + slope * (x - 0.0);\n", + " else if(x <= 0.4)\n", + " intercept = 0.2;\n", + " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", + " return intercept + slope * (x - 0.2);\n", + " else if(x <= 0.6)\n", + " intercept = 0.4;\n", + " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", + " return intercept + slope * (x - 0.4);\n", + " else if(x <= 0.8)\n", + " intercept = 0.58;\n", + " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", + " return intercept + slope * (x - 0.6);\n", + " else if(x <= 1.0)\n", + " intercept = 0.73;\n", + " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", + " return intercept + slope * (x - 0.8);\n", + " else if(x <= 1.2)\n", + " intercept = 0.85;\n", + " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", + " return intercept + slope * (x - 1.0);\n", + " else if(x <= 1.4)\n", + " intercept = 0.93;\n", + " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", + " return intercept + slope * (x - 1.2);\n", + " else if(x <= 1.6)\n", + " intercept = 0.97;\n", + " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", + " return intercept + slope * (x - 1.4);\n", + " else if(x <= 1.8)\n", + " intercept = 0.99;\n", + " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", + " return intercept + slope * (x - 1.6);\n", + " else if(x <= 2.0)\n", + " intercept = 1.0;\n", + " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", + " return intercept + slope * (x - 1.8);\n", + " }\n", + "\n", + " real lookupFunc_0(real x){\n", + " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", + " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", + " real slope;\n", + " real intercept;\n", + "\n", + " if(x <= 0.2)\n", + " intercept = 0.0;\n", + " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", + " return intercept + slope * (x - 0.0);\n", + " else if(x <= 0.4)\n", + " intercept = 0.2;\n", + " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", + " return intercept + slope * (x - 0.2);\n", + " else if(x <= 0.6)\n", + " intercept = 0.4;\n", + " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", + " return intercept + slope * (x - 0.4);\n", + " else if(x <= 0.8)\n", + " intercept = 0.58;\n", + " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", + " return intercept + slope * (x - 0.6);\n", + " else if(x <= 1.0)\n", + " intercept = 0.73;\n", + " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", + " return intercept + slope * (x - 0.8);\n", + " else if(x <= 1.2)\n", + " intercept = 0.85;\n", + " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", + " return intercept + slope * (x - 1.0);\n", + " else if(x <= 1.4)\n", + " intercept = 0.93;\n", + " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", + " return intercept + slope * (x - 1.2);\n", + " else if(x <= 1.6)\n", + " intercept = 0.97;\n", + " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", + " return intercept + slope * (x - 1.4);\n", + " else if(x <= 1.8)\n", + " intercept = 0.99;\n", + " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", + " return intercept + slope * (x - 1.6);\n", + " else if(x <= 2.0)\n", + " intercept = 1.0;\n", + " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", + " return intercept + slope * (x - 1.8);\n", + " }\n", + "\n", + " real lookupFunc_0(real x){\n", + " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", + " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", + " real slope;\n", + " real intercept;\n", + "\n", + " if(x <= 0.2)\n", + " intercept = 0.0;\n", + " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", + " return intercept + slope * (x - 0.0);\n", + " else if(x <= 0.4)\n", + " intercept = 0.2;\n", + " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", + " return intercept + slope * (x - 0.2);\n", + " else if(x <= 0.6)\n", + " intercept = 0.4;\n", + " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", + " return intercept + slope * (x - 0.4);\n", + " else if(x <= 0.8)\n", + " intercept = 0.58;\n", + " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", + " return intercept + slope * (x - 0.6);\n", + " else if(x <= 1.0)\n", + " intercept = 0.73;\n", + " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", + " return intercept + slope * (x - 0.8);\n", + " else if(x <= 1.2)\n", + " intercept = 0.85;\n", + " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", + " return intercept + slope * (x - 1.0);\n", + " else if(x <= 1.4)\n", + " intercept = 0.93;\n", + " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", + " return intercept + slope * (x - 1.2);\n", + " else if(x <= 1.6)\n", + " intercept = 0.97;\n", + " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", + " return intercept + slope * (x - 1.4);\n", + " else if(x <= 1.8)\n", + " intercept = 0.99;\n", + " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", + " return intercept + slope * (x - 1.6);\n", + " else if(x <= 2.0)\n", + " intercept = 1.0;\n", + " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", + " return intercept + slope * (x - 1.8);\n", + " }\n", + "\n", + " real lookupFunc_0(real x){\n", + " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", + " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", + " real slope;\n", + " real intercept;\n", + "\n", + " if(x <= 0.2)\n", + " intercept = 0.0;\n", + " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", + " return intercept + slope * (x - 0.0);\n", + " else if(x <= 0.4)\n", + " intercept = 0.2;\n", + " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", + " return intercept + slope * (x - 0.2);\n", + " else if(x <= 0.6)\n", + " intercept = 0.4;\n", + " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", + " return intercept + slope * (x - 0.4);\n", + " else if(x <= 0.8)\n", + " intercept = 0.58;\n", + " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", + " return intercept + slope * (x - 0.6);\n", + " else if(x <= 1.0)\n", + " intercept = 0.73;\n", + " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", + " return intercept + slope * (x - 0.8);\n", + " else if(x <= 1.2)\n", + " intercept = 0.85;\n", + " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", + " return intercept + slope * (x - 1.0);\n", + " else if(x <= 1.4)\n", + " intercept = 0.93;\n", + " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", + " return intercept + slope * (x - 1.2);\n", + " else if(x <= 1.6)\n", + " intercept = 0.97;\n", + " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", + " return intercept + slope * (x - 1.4);\n", + " else if(x <= 1.8)\n", + " intercept = 0.99;\n", + " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", + " return intercept + slope * (x - 1.6);\n", + " else if(x <= 2.0)\n", + " intercept = 1.0;\n", + " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", + " return intercept + slope * (x - 1.8);\n", + " }\n", + "\n", + " real lookupFunc_0(real x){\n", + " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", + " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", + " real slope;\n", + " real intercept;\n", + "\n", + " if(x <= 0.2)\n", + " intercept = 0.0;\n", + " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", + " return intercept + slope * (x - 0.0);\n", + " else if(x <= 0.4)\n", + " intercept = 0.2;\n", + " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", + " return intercept + slope * (x - 0.2);\n", + " else if(x <= 0.6)\n", + " intercept = 0.4;\n", + " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", + " return intercept + slope * (x - 0.4);\n", + " else if(x <= 0.8)\n", + " intercept = 0.58;\n", + " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", + " return intercept + slope * (x - 0.6);\n", + " else if(x <= 1.0)\n", + " intercept = 0.73;\n", + " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", + " return intercept + slope * (x - 0.8);\n", + " else if(x <= 1.2)\n", + " intercept = 0.85;\n", + " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", + " return intercept + slope * (x - 1.0);\n", + " else if(x <= 1.4)\n", + " intercept = 0.93;\n", + " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", + " return intercept + slope * (x - 1.2);\n", + " else if(x <= 1.6)\n", + " intercept = 0.97;\n", + " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", + " return intercept + slope * (x - 1.4);\n", + " else if(x <= 1.8)\n", + " intercept = 0.99;\n", + " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", + " return intercept + slope * (x - 1.6);\n", + " else if(x <= 2.0)\n", + " intercept = 1.0;\n", + " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", + " return intercept + slope * (x - 1.8);\n", + " }\n", + "\n", + " real lookupFunc_0(real x){\n", + " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", + " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", + " real slope;\n", + " real intercept;\n", + "\n", + " if(x <= 0.2)\n", + " intercept = 0.0;\n", + " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", + " return intercept + slope * (x - 0.0);\n", + " else if(x <= 0.4)\n", + " intercept = 0.2;\n", + " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", + " return intercept + slope * (x - 0.2);\n", + " else if(x <= 0.6)\n", + " intercept = 0.4;\n", + " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", + " return intercept + slope * (x - 0.4);\n", + " else if(x <= 0.8)\n", + " intercept = 0.58;\n", + " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", + " return intercept + slope * (x - 0.6);\n", + " else if(x <= 1.0)\n", + " intercept = 0.73;\n", + " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", + " return intercept + slope * (x - 0.8);\n", + " else if(x <= 1.2)\n", + " intercept = 0.85;\n", + " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", + " return intercept + slope * (x - 1.0);\n", + " else if(x <= 1.4)\n", + " intercept = 0.93;\n", + " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", + " return intercept + slope * (x - 1.2);\n", + " else if(x <= 1.6)\n", + " intercept = 0.97;\n", + " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", + " return intercept + slope * (x - 1.4);\n", + " else if(x <= 1.8)\n", + " intercept = 0.99;\n", + " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", + " return intercept + slope * (x - 1.6);\n", + " else if(x <= 2.0)\n", + " intercept = 1.0;\n", + " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", + " return intercept + slope * (x - 1.8);\n", + " }\n", + "\n", + " # Begin ODE declaration\n", + " vector vensim_func(real time, vector outcome, real customer_order_rate, real inventory_coverage, real manufacturing_cycle_time, real time_to_average_order_rate, real wip_adjustment_time ){\n", + " real work_in_process_inventory = outcome[1];\n", + " real inventory = outcome[2];\n", + "\n", + " real change_in_exp_orders = customer_order_rate - expected_order_rate / time_to_average_order_rate;\n", + " real expected_order_rate = change_in_exp_orders;\n", + " real safety_stock_coverage = 2;\n", + " real minimum_order_processing_time = 2;\n", + " real desired_inventory_coverage = minimum_order_processing_time + safety_stock_coverage;\n", + " real desired_inventory = desired_inventory_coverage * expected_order_rate;\n", + " real inventory_adjustment_time = 8;\n", + " real production_adjustment_from_inventory = desired_inventory - inventory / inventory_adjustment_time;\n", + " real desired_production = fmax(0,expected_order_rate + production_adjustment_from_inventory);\n", + " real desired_wip = manufacturing_cycle_time * desired_production;\n", + " real adjustment_for_wip = desired_wip - work_in_process_inventory / wip_adjustment_time;\n", + " real desired_production_start_rate = desired_production + adjustment_for_wip;\n", + " real maximum_shipment_rate = inventory / minimum_order_processing_time;\n", + " real desired_shipment_rate = customer_order_rate;\n", + " real order_fulfillment_ratio = table_for_order_fulfillment(maximum_shipment_rate / desired_shipment_rate);\n", + " real production_rate = work_in_process_inventory / manufacturing_cycle_time;\n", + " real production_start_rate = fmax(0,desired_production_start_rate);\n", + " real work_in_process_inventory_dydt = production_start_rate - production_rate;\n", + " real shipment_rate = desired_shipment_rate * order_fulfillment_ratio;\n", + " real inventory_dydt = production_rate - shipment_rate;\n", + "\n", + " return {work_in_process_inventory_dydt, inventory_dydt};\n", + " }\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "am = vf.get_abstract_model()\n", + "stan_function_builder = StanFunctionBuilder(am) \n", + "ds_relational = stan_function_builder.build_function_block(ass_param_lst, obs_stock_lst)\n", + "print(ds_relational)\n", + "stan_file_path = os.path.join(os.getcwd(), \"stan_file\", \"ds_relational.stan\")\n", + "with open(stan_file_path, \"w\") as f:\n", + " print(ds_relational, file=f)" + ] + }, + { + "cell_type": "markdown", + "id": "75a0b7de-7c90-4823-9b38-7370f3cea02f", + "metadata": {}, + "source": [ + "## U3. Specify\n", + "\n", + "#### Estimated parameter $\\theta$ \n", + "\n", + "- declared in generated quantities block for `_draws2data.stan` and model block for `_data2draws.stan`.\n", + "\n", + "| `ess_param` | (min, mode, max) | distribuiton type| \n", + "| ------------------------------- | ---------------- | ------------ |\n", + "| `inventory_adjustment_time` | (6,8,12) | N(8, $1^2$) |\n", + "| `minimum_order_processing_time` | (1,2,4) | N(2, $.5^2$) |\n", + "\n", + "\n", + "Q1. Can `msr_err` (min, mode, max) be helpful info?\n", + "Q2. Shouldn't `msr_err` distribution determine `family`? Then, `Poisson`, `Neg_Binom`, `\n", + "\n", + "#### Assumed parameter $X$ \n", + "\n", + "- declared in generated quantities block for `_draws2data.stan` and data block for `_data2draws.stan`.\n", + "- specified with its actual value or series or lookup function (aggregation)\n", + "\n", + "| `ass_param` | value/series |\n", + "| ---------------------------- | ----------------- |\n", + "| `customer_order_rate` | N(10000, $100^2$) |\n", + "| `time_to_average_order_rate` | 8 |\n", + "| `wip_adjustment_time` | 8 |\n", + "| `manufacturing_cycle_time` | 8 |\n", + "| `safety_stock_coverage` | 2 |\n", + "|`initial_time`, `final_time`, `time_step` | 0, 10, .125|\n", + "|`table_for_order_fulfillment`| lookup function|\n", + "\n", + "\n", + "\n", + "#### Latent stock $Z$\n", + "\n", + "- declared in generated quantities block for `_draws2data.stan` and data block for `_data2draws.stan`.\n", + "\n", + "#### Measurement error\n", + "\n", + "- `msr_err` is specified with `family` and its parameter\n", + "| `msr_err` |??|lognormal, inverse_gamma|\n", + "\n", + "\n", + "#### Observed stock $Y$\n", + "\n", + "- declared in generated quantities block for `_draws2data.stan` and data block for `_data2draws.stan`.\n", + "- $Y \\sim$ `family`(Z, `msr_err` )" + ] + }, + { + "cell_type": "markdown", + "id": "6671aae2-5375-4056-b4f0-83cda80ba708", + "metadata": {}, + "source": [ + "## P2. Variational_prior\n", + "\n", + "\n", + "- based on `est_param` specification (a = lower_bound, b= most likely, c = upper_bound) in U3, its prior is automatically set to $\\theta \\sim N(\\frac{a+4b+c}{6}, \\frac{c-a}{6})$ using [PERT dist](https://en.wikipedia.org/wiki/PERT_distribution)\n", + "\n", + "| `ess_param` | Prior distribution | Prior parameter| \n", + "| ------------------------------- | ---------------- | ------------ |\n", + "| `inventory_adjustment_time` | Normal | loc = 8, scale = $1^2$ |\n", + "| `minimum_order_processing_time` | Normal |loc = 2, scale = $.5^2$ |\n", + "| `msr_err` |lognormal, inverse_gamma|\n", + "\n", + "Q3. feedback on PERT?\n", + "\n", + "Q4. how do we usually determine `msr_err`'s prior parameter?" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "edd2203d-618e-464b-b3f2-15ed26b1e7cf", + "metadata": {}, + "outputs": [], + "source": [ + "initial_time = 0\n", + "final_time = 10\n", + "time_step = .125\n", + "\n", + "N = int((final_time - initial_time)/time_step)\n", + "data_draws2data = {\n", + " \"N\": N,\n", + " \"times\": np.arange(1, N + 1),\n", + " \"customer_order_rate\": np.random.normal(loc = 10000, scale = 100, size = N),\n", + " \"time_to_average_order_rate\" : 8, \n", + " \"wip_adjustment_time\" :2,\n", + " 'manufacturing_cycle_time' : 8,\n", + " 'safety_stock_coverage' : 2\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "217fa4b0-cd0f-4fb5-a3ac-647da6531ed3", + "metadata": { + "tags": [] + }, + "source": [ + "## P3. Draws2Data " + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "b393e0fd-470f-43f5-a535-866f689aed24", + "metadata": {}, + "outputs": [], + "source": [ + "# first argument is `ass_param` and the second is `observed stock`. Design for `est_param` is under-development including how to express multi-levle prior\n", + "ds_draws2data = stan_builder.create_stan_program(ass_param_lst, obs_stock_lst)\n", + "#print(ds_draws2data)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "a5d2525b-e441-4e79-a0f3-8d5c2c953c63", + "metadata": {}, + "outputs": [], + "source": [ + "sf_path_draws2data = os.path.join(os.getcwd(), \"stan_file\", \"ds_draws2data.stan\")\n", + "# with open(sf_path_draws2data, \"w\") as f:\n", + "# print(ds_draws2data, file=f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16713c97-3371-4ecb-aa04-310ad0480034", + "metadata": {}, + "outputs": [], + "source": [ + "sm_draws2data = CmdStanModel(stan_file = sf_path_draws2data)\n", + "fit_prior_pred = sm_draws2data.sample(data=data_draws2data, iter_sampling=30, chains=1, fixed_param=True, iter_warmup=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3939e7a3-259b-47a3-9360-0b17a54726b3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:                  (draw: 30, chain: 1, y_init_tilde_dim_0: 2,\n",
+       "                              y_tilde_dim_0: 50, y_tilde_dim_1: 2,\n",
+       "                              sigma_tilde_dim_0: 2, z_init_tilde_dim_0: 2,\n",
+       "                              integrated_result_tilde_dim_0: 50,\n",
+       "                              integrated_result_tilde_dim_1: 2)\n",
+       "Coordinates:\n",
+       "  * chain                    (chain) int64 1\n",
+       "  * draw                     (draw) int64 0 1 2 3 4 5 6 ... 23 24 25 26 27 28 29\n",
+       "Dimensions without coordinates: y_init_tilde_dim_0, y_tilde_dim_0,\n",
+       "                                y_tilde_dim_1, sigma_tilde_dim_0,\n",
+       "                                z_init_tilde_dim_0,\n",
+       "                                integrated_result_tilde_dim_0,\n",
+       "                                integrated_result_tilde_dim_1\n",
+       "Data variables:\n",
+       "    y_init_tilde             (chain, draw, y_init_tilde_dim_0) float64 30.28 ...\n",
+       "    y_tilde                  (chain, draw, y_tilde_dim_0, y_tilde_dim_1) float64 ...\n",
+       "    sigma_tilde              (chain, draw, sigma_tilde_dim_0) float64 0.01 .....\n",
+       "    z_init_tilde             (chain, draw, z_init_tilde_dim_0) float64 30.0 ....\n",
+       "    alpha_tilde              (chain, draw) float64 0.55 0.55 0.55 ... 0.55 0.55\n",
+       "    beta_tilde               (chain, draw) float64 0.028 0.028 ... 0.028 0.028\n",
+       "    gamma_tilde              (chain, draw) float64 0.8 0.8 0.8 ... 0.8 0.8 0.8\n",
+       "    delta_tilde              (chain, draw) float64 0.024 0.024 ... 0.024 0.024\n",
+       "    integrated_result_tilde  (chain, draw, integrated_result_tilde_dim_0, integrated_result_tilde_dim_1) float64 ...\n",
+       "Attributes:\n",
+       "    stan_version:        2.30.0\n",
+       "    model:               pp_draws2data_model\n",
+       "    num_draws_sampling:  30
" + ], + "text/plain": [ + "\n", + "Dimensions: (draw: 30, chain: 1, y_init_tilde_dim_0: 2,\n", + " y_tilde_dim_0: 50, y_tilde_dim_1: 2,\n", + " sigma_tilde_dim_0: 2, z_init_tilde_dim_0: 2,\n", + " integrated_result_tilde_dim_0: 50,\n", + " integrated_result_tilde_dim_1: 2)\n", + "Coordinates:\n", + " * chain (chain) int64 1\n", + " * draw (draw) int64 0 1 2 3 4 5 6 ... 23 24 25 26 27 28 29\n", + "Dimensions without coordinates: y_init_tilde_dim_0, y_tilde_dim_0,\n", + " y_tilde_dim_1, sigma_tilde_dim_0,\n", + " z_init_tilde_dim_0,\n", + " integrated_result_tilde_dim_0,\n", + " integrated_result_tilde_dim_1\n", + "Data variables:\n", + " y_init_tilde (chain, draw, y_init_tilde_dim_0) float64 30.28 ...\n", + " y_tilde (chain, draw, y_tilde_dim_0, y_tilde_dim_1) float64 ...\n", + " sigma_tilde (chain, draw, sigma_tilde_dim_0) float64 0.01 .....\n", + " z_init_tilde (chain, draw, z_init_tilde_dim_0) float64 30.0 ....\n", + " alpha_tilde (chain, draw) float64 0.55 0.55 0.55 ... 0.55 0.55\n", + " beta_tilde (chain, draw) float64 0.028 0.028 ... 0.028 0.028\n", + " gamma_tilde (chain, draw) float64 0.8 0.8 0.8 ... 0.8 0.8 0.8\n", + " delta_tilde (chain, draw) float64 0.024 0.024 ... 0.024 0.024\n", + " integrated_result_tilde (chain, draw, integrated_result_tilde_dim_0, integrated_result_tilde_dim_1) float64 ...\n", + "Attributes:\n", + " stan_version: 2.30.0\n", + " model: pp_draws2data_model\n", + " num_draws_sampling: 30" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit_prior_pred.draws_xr()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "515002a4-35be-4752-a705-d09f70a9c43f", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "#compare with real \n", + "ax.plot(fit_prior_pred.loc[:, ['y_tilde']], label = \"\")\n", + "ax.plot(state_dt.loc[:, ['Predator']], label = \"\")\n", + "for i in range(len(obs_stock_lst)):\n", + " ax.plot(pd.DataFrame(fit_prior_pred.y_tilde[:,:,i]).T.loc[:, :5])\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "960d1e72-a5e4-4dcb-995f-e9d689c26149", + "metadata": {}, + "source": [ + "## P4. Data2Draws" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "f91d6908-3ff1-4787-b80a-f43b85e0a6ff", + "metadata": {}, + "outputs": [], + "source": [ + "sf_path_data2draws = os.path.join(os.getcwd(), \"stan_file\", \"ds_data2draws.stan\")\n", + "with open(sf_path_data2draws, \"w\") as f:\n", + " print(ds_draws2data, file=f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b22c5ede-6e25-41da-9a97-cc2d641d2c7a", + "metadata": {}, + "outputs": [], + "source": [ + "idata = az.from_cmdstanpy(\n", + " posterior=fit_posterior_draws, \n", + " posterior_predictive=[\"y_hat\"], \n", + " log_likelihood= [\"log_lik\"],\n", + " observed_data = {\"y_hat\": lynx_hare_df.loc[:, (\"Hare\", \"Lynx\")]}\n", + "# dtypes={\"y_rep\": int} if Poisson family\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b44779b8-93ce-4539-a892-695e121c684a", + "metadata": {}, + "outputs": [], + "source": [ + "az.loo(idata)\n", + "az.plot_ppc(idata, alpha=0.03, figsize=(12, 6))" + ] + }, + { + "cell_type": "markdown", + "id": "472ae07f-9550-47c0-be46-791ff7f3c234", + "metadata": {}, + "source": [ + "## P5. SBC" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "local-venv", + "language": "python", + "name": "local-venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/test_scripts/stan_file/ds_data2draws.stan b/test_scripts/stan_file/ds_data2draws.stan new file mode 100644 index 00000000..44395179 --- /dev/null +++ b/test_scripts/stan_file/ds_data2draws.stan @@ -0,0 +1,103 @@ +functions { + real table_for_order_fulfillment(real x){ + # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) + # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) + real slope; + real intercept; + + if(x <= 0.2) + intercept = 0.0; + slope = (0.2 - 0.0) / (0.2 - 0.0); + return intercept + slope * (x - 0.0); + else if(x <= 0.4) + intercept = 0.2; + slope = (0.4 - 0.2) / (0.4 - 0.2); + return intercept + slope * (x - 0.2); + else if(x <= 0.6) + intercept = 0.4; + slope = (0.58 - 0.4) / (0.6 - 0.4); + return intercept + slope * (x - 0.4); + else if(x <= 0.8) + intercept = 0.58; + slope = (0.73 - 0.58) / (0.8 - 0.6); + return intercept + slope * (x - 0.6); + else if(x <= 1.0) + intercept = 0.73; + slope = (0.85 - 0.73) / (1.0 - 0.8); + return intercept + slope * (x - 0.8); + else if(x <= 1.2) + intercept = 0.85; + slope = (0.93 - 0.85) / (1.2 - 1.0); + return intercept + slope * (x - 1.0); + else if(x <= 1.4) + intercept = 0.93; + slope = (0.97 - 0.93) / (1.4 - 1.2); + return intercept + slope * (x - 1.2); + else if(x <= 1.6) + intercept = 0.97; + slope = (0.99 - 0.97) / (1.6 - 1.4); + return intercept + slope * (x - 1.4); + else if(x <= 1.8) + intercept = 0.99; + slope = (1.0 - 0.99) / (1.8 - 1.6); + return intercept + slope * (x - 1.6); + else if(x <= 2.0) + intercept = 1.0; + slope = (1.0 - 1.0) / (2.0 - 1.8); + return intercept + slope * (x - 1.8); + } + + # Begin ODE declaration + vector vensim_func(real time, vector outcome, real customer_order_rate ){ + real inventory = outcome[1]; + real work_in_process_inventory = outcome[2]; + + vector [2] dydt; + real time_to_average_order_rate = 8; + real change_in_exp_orders = customer_order_rate - expected_order_rate / time_to_average_order_rate; + real expected_order_rate = change_in_exp_orders; + real safety_stock_coverage = 2; + real minimum_order_processing_time = 2; + real desired_inventory_coverage = minimum_order_processing_time + safety_stock_coverage; + real desired_inventory = desired_inventory_coverage * expected_order_rate; + real inventory_adjustment_time = 8; + real production_adjustment_from_inventory = desired_inventory - inventory / inventory_adjustment_time; + real desired_production = fmax(0,expected_order_rate + production_adjustment_from_inventory); + real manufacturing_cycle_time = 8; + real desired_wip = manufacturing_cycle_time * desired_production; + real wip_adjustment_time = 2; + real adjustment_for_wip = desired_wip - work_in_process_inventory / wip_adjustment_time; + real desired_production_start_rate = desired_production + adjustment_for_wip; + real maximum_shipment_rate = inventory / minimum_order_processing_time; + real desired_shipment_rate = customer_order_rate; + real order_fulfillment_ratio = table_for_order_fulfillment(maximum_shipment_rate / desired_shipment_rate); + real production_rate = work_in_process_inventory / manufacturing_cycle_time; + real production_start_rate = fmax(0,desired_production_start_rate); + real work_in_process_inventory_dydt = production_start_rate - production_rate; + real shipment_rate = desired_shipment_rate * order_fulfillment_ratio; + real inventory_dydt = production_rate - shipment_rate; + dydt[1] = work_in_process_inventory_dydt; + dydt[2] = inventory_dydt; + + return dydt; + } +} +data{ + int N; // number of measurement times + array[N] real times; // measurement times + real customer_order_rate[N]; +} +transformed data{ +} +parameters{ +} +transformed parameters { + real inventory_initial = 2 + 2 * 10000; + real work_in_process_inventory_initial = 8 * fmax(0,10000 + 2 + 2 * 10000 - 2 + 2 * 10000 / 8); + vector[2] initial_outcome = {inventory_initial, work_in_process_inventory_initial}; + vector[2] integrated_result = ode_rk45(vensim_func, initial_outcome, initial_time, times, customer_order_rate); +} +model{ +} +generated quantities{ +} diff --git a/test_scripts/stan_file/ds_draws2data.stan b/test_scripts/stan_file/ds_draws2data.stan new file mode 100644 index 00000000..665d94de --- /dev/null +++ b/test_scripts/stan_file/ds_draws2data.stan @@ -0,0 +1,21 @@ +functions { +#include ds_relational.stan +} + +data{ + int N; // number of measurement times + array[N] real times; // measurement times + real customer_order_rate[N]; + +} + +transformed parameters { + real inventory_initial = 2 + 2 * 10000; + real work_in_process_inventory_initial = 8 * fmax(0,10000 + 2 + 2 * 10000 - 2 + 2 * 10000 / 8); + vector[2] initial_outcome = {inventory_initial, work_in_process_inventory_initial}; + vector[2] integrated_result_tilde[N] = ode_rk45(vensim_func, initial_outcome, initial_time, times, customer_order_rate); +} +model{ +} +generated quantities{ +} diff --git a/test_scripts/stan_file/ds_relational.stan b/test_scripts/stan_file/ds_relational.stan new file mode 100644 index 00000000..ab2c4ff3 --- /dev/null +++ b/test_scripts/stan_file/ds_relational.stan @@ -0,0 +1,655 @@ +functions { + real lookupFunc_0(real x){ + # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) + # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) + real slope; + real intercept; + + if(x <= 0.2) + intercept = 0.0; + slope = (0.2 - 0.0) / (0.2 - 0.0); + return intercept + slope * (x - 0.0); + else if(x <= 0.4) + intercept = 0.2; + slope = (0.4 - 0.2) / (0.4 - 0.2); + return intercept + slope * (x - 0.2); + else if(x <= 0.6) + intercept = 0.4; + slope = (0.58 - 0.4) / (0.6 - 0.4); + return intercept + slope * (x - 0.4); + else if(x <= 0.8) + intercept = 0.58; + slope = (0.73 - 0.58) / (0.8 - 0.6); + return intercept + slope * (x - 0.6); + else if(x <= 1.0) + intercept = 0.73; + slope = (0.85 - 0.73) / (1.0 - 0.8); + return intercept + slope * (x - 0.8); + else if(x <= 1.2) + intercept = 0.85; + slope = (0.93 - 0.85) / (1.2 - 1.0); + return intercept + slope * (x - 1.0); + else if(x <= 1.4) + intercept = 0.93; + slope = (0.97 - 0.93) / (1.4 - 1.2); + return intercept + slope * (x - 1.2); + else if(x <= 1.6) + intercept = 0.97; + slope = (0.99 - 0.97) / (1.6 - 1.4); + return intercept + slope * (x - 1.4); + else if(x <= 1.8) + intercept = 0.99; + slope = (1.0 - 0.99) / (1.8 - 1.6); + return intercept + slope * (x - 1.6); + else if(x <= 2.0) + intercept = 1.0; + slope = (1.0 - 1.0) / (2.0 - 1.8); + return intercept + slope * (x - 1.8); + } + + real lookupFunc_0(real x){ + # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) + # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) + real slope; + real intercept; + + if(x <= 0.2) + intercept = 0.0; + slope = (0.2 - 0.0) / (0.2 - 0.0); + return intercept + slope * (x - 0.0); + else if(x <= 0.4) + intercept = 0.2; + slope = (0.4 - 0.2) / (0.4 - 0.2); + return intercept + slope * (x - 0.2); + else if(x <= 0.6) + intercept = 0.4; + slope = (0.58 - 0.4) / (0.6 - 0.4); + return intercept + slope * (x - 0.4); + else if(x <= 0.8) + intercept = 0.58; + slope = (0.73 - 0.58) / (0.8 - 0.6); + return intercept + slope * (x - 0.6); + else if(x <= 1.0) + intercept = 0.73; + slope = (0.85 - 0.73) / (1.0 - 0.8); + return intercept + slope * (x - 0.8); + else if(x <= 1.2) + intercept = 0.85; + slope = (0.93 - 0.85) / (1.2 - 1.0); + return intercept + slope * (x - 1.0); + else if(x <= 1.4) + intercept = 0.93; + slope = (0.97 - 0.93) / (1.4 - 1.2); + return intercept + slope * (x - 1.2); + else if(x <= 1.6) + intercept = 0.97; + slope = (0.99 - 0.97) / (1.6 - 1.4); + return intercept + slope * (x - 1.4); + else if(x <= 1.8) + intercept = 0.99; + slope = (1.0 - 0.99) / (1.8 - 1.6); + return intercept + slope * (x - 1.6); + else if(x <= 2.0) + intercept = 1.0; + slope = (1.0 - 1.0) / (2.0 - 1.8); + return intercept + slope * (x - 1.8); + } + + real lookupFunc_0(real x){ + # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) + # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) + real slope; + real intercept; + + if(x <= 0.2) + intercept = 0.0; + slope = (0.2 - 0.0) / (0.2 - 0.0); + return intercept + slope * (x - 0.0); + else if(x <= 0.4) + intercept = 0.2; + slope = (0.4 - 0.2) / (0.4 - 0.2); + return intercept + slope * (x - 0.2); + else if(x <= 0.6) + intercept = 0.4; + slope = (0.58 - 0.4) / (0.6 - 0.4); + return intercept + slope * (x - 0.4); + else if(x <= 0.8) + intercept = 0.58; + slope = (0.73 - 0.58) / (0.8 - 0.6); + return intercept + slope * (x - 0.6); + else if(x <= 1.0) + intercept = 0.73; + slope = (0.85 - 0.73) / (1.0 - 0.8); + return intercept + slope * (x - 0.8); + else if(x <= 1.2) + intercept = 0.85; + slope = (0.93 - 0.85) / (1.2 - 1.0); + return intercept + slope * (x - 1.0); + else if(x <= 1.4) + intercept = 0.93; + slope = (0.97 - 0.93) / (1.4 - 1.2); + return intercept + slope * (x - 1.2); + else if(x <= 1.6) + intercept = 0.97; + slope = (0.99 - 0.97) / (1.6 - 1.4); + return intercept + slope * (x - 1.4); + else if(x <= 1.8) + intercept = 0.99; + slope = (1.0 - 0.99) / (1.8 - 1.6); + return intercept + slope * (x - 1.6); + else if(x <= 2.0) + intercept = 1.0; + slope = (1.0 - 1.0) / (2.0 - 1.8); + return intercept + slope * (x - 1.8); + } + + real lookupFunc_0(real x){ + # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) + # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) + real slope; + real intercept; + + if(x <= 0.2) + intercept = 0.0; + slope = (0.2 - 0.0) / (0.2 - 0.0); + return intercept + slope * (x - 0.0); + else if(x <= 0.4) + intercept = 0.2; + slope = (0.4 - 0.2) / (0.4 - 0.2); + return intercept + slope * (x - 0.2); + else if(x <= 0.6) + intercept = 0.4; + slope = (0.58 - 0.4) / (0.6 - 0.4); + return intercept + slope * (x - 0.4); + else if(x <= 0.8) + intercept = 0.58; + slope = (0.73 - 0.58) / (0.8 - 0.6); + return intercept + slope * (x - 0.6); + else if(x <= 1.0) + intercept = 0.73; + slope = (0.85 - 0.73) / (1.0 - 0.8); + return intercept + slope * (x - 0.8); + else if(x <= 1.2) + intercept = 0.85; + slope = (0.93 - 0.85) / (1.2 - 1.0); + return intercept + slope * (x - 1.0); + else if(x <= 1.4) + intercept = 0.93; + slope = (0.97 - 0.93) / (1.4 - 1.2); + return intercept + slope * (x - 1.2); + else if(x <= 1.6) + intercept = 0.97; + slope = (0.99 - 0.97) / (1.6 - 1.4); + return intercept + slope * (x - 1.4); + else if(x <= 1.8) + intercept = 0.99; + slope = (1.0 - 0.99) / (1.8 - 1.6); + return intercept + slope * (x - 1.6); + else if(x <= 2.0) + intercept = 1.0; + slope = (1.0 - 1.0) / (2.0 - 1.8); + return intercept + slope * (x - 1.8); + } + + real lookupFunc_0(real x){ + # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) + # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) + real slope; + real intercept; + + if(x <= 0.2) + intercept = 0.0; + slope = (0.2 - 0.0) / (0.2 - 0.0); + return intercept + slope * (x - 0.0); + else if(x <= 0.4) + intercept = 0.2; + slope = (0.4 - 0.2) / (0.4 - 0.2); + return intercept + slope * (x - 0.2); + else if(x <= 0.6) + intercept = 0.4; + slope = (0.58 - 0.4) / (0.6 - 0.4); + return intercept + slope * (x - 0.4); + else if(x <= 0.8) + intercept = 0.58; + slope = (0.73 - 0.58) / (0.8 - 0.6); + return intercept + slope * (x - 0.6); + else if(x <= 1.0) + intercept = 0.73; + slope = (0.85 - 0.73) / (1.0 - 0.8); + return intercept + slope * (x - 0.8); + else if(x <= 1.2) + intercept = 0.85; + slope = (0.93 - 0.85) / (1.2 - 1.0); + return intercept + slope * (x - 1.0); + else if(x <= 1.4) + intercept = 0.93; + slope = (0.97 - 0.93) / (1.4 - 1.2); + return intercept + slope * (x - 1.2); + else if(x <= 1.6) + intercept = 0.97; + slope = (0.99 - 0.97) / (1.6 - 1.4); + return intercept + slope * (x - 1.4); + else if(x <= 1.8) + intercept = 0.99; + slope = (1.0 - 0.99) / (1.8 - 1.6); + return intercept + slope * (x - 1.6); + else if(x <= 2.0) + intercept = 1.0; + slope = (1.0 - 1.0) / (2.0 - 1.8); + return intercept + slope * (x - 1.8); + } + + real lookupFunc_0(real x){ + # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) + # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) + real slope; + real intercept; + + if(x <= 0.2) + intercept = 0.0; + slope = (0.2 - 0.0) / (0.2 - 0.0); + return intercept + slope * (x - 0.0); + else if(x <= 0.4) + intercept = 0.2; + slope = (0.4 - 0.2) / (0.4 - 0.2); + return intercept + slope * (x - 0.2); + else if(x <= 0.6) + intercept = 0.4; + slope = (0.58 - 0.4) / (0.6 - 0.4); + return intercept + slope * (x - 0.4); + else if(x <= 0.8) + intercept = 0.58; + slope = (0.73 - 0.58) / (0.8 - 0.6); + return intercept + slope * (x - 0.6); + else if(x <= 1.0) + intercept = 0.73; + slope = (0.85 - 0.73) / (1.0 - 0.8); + return intercept + slope * (x - 0.8); + else if(x <= 1.2) + intercept = 0.85; + slope = (0.93 - 0.85) / (1.2 - 1.0); + return intercept + slope * (x - 1.0); + else if(x <= 1.4) + intercept = 0.93; + slope = (0.97 - 0.93) / (1.4 - 1.2); + return intercept + slope * (x - 1.2); + else if(x <= 1.6) + intercept = 0.97; + slope = (0.99 - 0.97) / (1.6 - 1.4); + return intercept + slope * (x - 1.4); + else if(x <= 1.8) + intercept = 0.99; + slope = (1.0 - 0.99) / (1.8 - 1.6); + return intercept + slope * (x - 1.6); + else if(x <= 2.0) + intercept = 1.0; + slope = (1.0 - 1.0) / (2.0 - 1.8); + return intercept + slope * (x - 1.8); + } + + real lookupFunc_0(real x){ + # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) + # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) + real slope; + real intercept; + + if(x <= 0.2) + intercept = 0.0; + slope = (0.2 - 0.0) / (0.2 - 0.0); + return intercept + slope * (x - 0.0); + else if(x <= 0.4) + intercept = 0.2; + slope = (0.4 - 0.2) / (0.4 - 0.2); + return intercept + slope * (x - 0.2); + else if(x <= 0.6) + intercept = 0.4; + slope = (0.58 - 0.4) / (0.6 - 0.4); + return intercept + slope * (x - 0.4); + else if(x <= 0.8) + intercept = 0.58; + slope = (0.73 - 0.58) / (0.8 - 0.6); + return intercept + slope * (x - 0.6); + else if(x <= 1.0) + intercept = 0.73; + slope = (0.85 - 0.73) / (1.0 - 0.8); + return intercept + slope * (x - 0.8); + else if(x <= 1.2) + intercept = 0.85; + slope = (0.93 - 0.85) / (1.2 - 1.0); + return intercept + slope * (x - 1.0); + else if(x <= 1.4) + intercept = 0.93; + slope = (0.97 - 0.93) / (1.4 - 1.2); + return intercept + slope * (x - 1.2); + else if(x <= 1.6) + intercept = 0.97; + slope = (0.99 - 0.97) / (1.6 - 1.4); + return intercept + slope * (x - 1.4); + else if(x <= 1.8) + intercept = 0.99; + slope = (1.0 - 0.99) / (1.8 - 1.6); + return intercept + slope * (x - 1.6); + else if(x <= 2.0) + intercept = 1.0; + slope = (1.0 - 1.0) / (2.0 - 1.8); + return intercept + slope * (x - 1.8); + } + + real lookupFunc_0(real x){ + # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) + # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) + real slope; + real intercept; + + if(x <= 0.2) + intercept = 0.0; + slope = (0.2 - 0.0) / (0.2 - 0.0); + return intercept + slope * (x - 0.0); + else if(x <= 0.4) + intercept = 0.2; + slope = (0.4 - 0.2) / (0.4 - 0.2); + return intercept + slope * (x - 0.2); + else if(x <= 0.6) + intercept = 0.4; + slope = (0.58 - 0.4) / (0.6 - 0.4); + return intercept + slope * (x - 0.4); + else if(x <= 0.8) + intercept = 0.58; + slope = (0.73 - 0.58) / (0.8 - 0.6); + return intercept + slope * (x - 0.6); + else if(x <= 1.0) + intercept = 0.73; + slope = (0.85 - 0.73) / (1.0 - 0.8); + return intercept + slope * (x - 0.8); + else if(x <= 1.2) + intercept = 0.85; + slope = (0.93 - 0.85) / (1.2 - 1.0); + return intercept + slope * (x - 1.0); + else if(x <= 1.4) + intercept = 0.93; + slope = (0.97 - 0.93) / (1.4 - 1.2); + return intercept + slope * (x - 1.2); + else if(x <= 1.6) + intercept = 0.97; + slope = (0.99 - 0.97) / (1.6 - 1.4); + return intercept + slope * (x - 1.4); + else if(x <= 1.8) + intercept = 0.99; + slope = (1.0 - 0.99) / (1.8 - 1.6); + return intercept + slope * (x - 1.6); + else if(x <= 2.0) + intercept = 1.0; + slope = (1.0 - 1.0) / (2.0 - 1.8); + return intercept + slope * (x - 1.8); + } + + real lookupFunc_0(real x){ + # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) + # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) + real slope; + real intercept; + + if(x <= 0.2) + intercept = 0.0; + slope = (0.2 - 0.0) / (0.2 - 0.0); + return intercept + slope * (x - 0.0); + else if(x <= 0.4) + intercept = 0.2; + slope = (0.4 - 0.2) / (0.4 - 0.2); + return intercept + slope * (x - 0.2); + else if(x <= 0.6) + intercept = 0.4; + slope = (0.58 - 0.4) / (0.6 - 0.4); + return intercept + slope * (x - 0.4); + else if(x <= 0.8) + intercept = 0.58; + slope = (0.73 - 0.58) / (0.8 - 0.6); + return intercept + slope * (x - 0.6); + else if(x <= 1.0) + intercept = 0.73; + slope = (0.85 - 0.73) / (1.0 - 0.8); + return intercept + slope * (x - 0.8); + else if(x <= 1.2) + intercept = 0.85; + slope = (0.93 - 0.85) / (1.2 - 1.0); + return intercept + slope * (x - 1.0); + else if(x <= 1.4) + intercept = 0.93; + slope = (0.97 - 0.93) / (1.4 - 1.2); + return intercept + slope * (x - 1.2); + else if(x <= 1.6) + intercept = 0.97; + slope = (0.99 - 0.97) / (1.6 - 1.4); + return intercept + slope * (x - 1.4); + else if(x <= 1.8) + intercept = 0.99; + slope = (1.0 - 0.99) / (1.8 - 1.6); + return intercept + slope * (x - 1.6); + else if(x <= 2.0) + intercept = 1.0; + slope = (1.0 - 1.0) / (2.0 - 1.8); + return intercept + slope * (x - 1.8); + } + + real lookupFunc_0(real x){ + # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) + # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) + real slope; + real intercept; + + if(x <= 0.2) + intercept = 0.0; + slope = (0.2 - 0.0) / (0.2 - 0.0); + return intercept + slope * (x - 0.0); + else if(x <= 0.4) + intercept = 0.2; + slope = (0.4 - 0.2) / (0.4 - 0.2); + return intercept + slope * (x - 0.2); + else if(x <= 0.6) + intercept = 0.4; + slope = (0.58 - 0.4) / (0.6 - 0.4); + return intercept + slope * (x - 0.4); + else if(x <= 0.8) + intercept = 0.58; + slope = (0.73 - 0.58) / (0.8 - 0.6); + return intercept + slope * (x - 0.6); + else if(x <= 1.0) + intercept = 0.73; + slope = (0.85 - 0.73) / (1.0 - 0.8); + return intercept + slope * (x - 0.8); + else if(x <= 1.2) + intercept = 0.85; + slope = (0.93 - 0.85) / (1.2 - 1.0); + return intercept + slope * (x - 1.0); + else if(x <= 1.4) + intercept = 0.93; + slope = (0.97 - 0.93) / (1.4 - 1.2); + return intercept + slope * (x - 1.2); + else if(x <= 1.6) + intercept = 0.97; + slope = (0.99 - 0.97) / (1.6 - 1.4); + return intercept + slope * (x - 1.4); + else if(x <= 1.8) + intercept = 0.99; + slope = (1.0 - 0.99) / (1.8 - 1.6); + return intercept + slope * (x - 1.6); + else if(x <= 2.0) + intercept = 1.0; + slope = (1.0 - 1.0) / (2.0 - 1.8); + return intercept + slope * (x - 1.8); + } + + real lookupFunc_0(real x){ + # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) + # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) + real slope; + real intercept; + + if(x <= 0.2) + intercept = 0.0; + slope = (0.2 - 0.0) / (0.2 - 0.0); + return intercept + slope * (x - 0.0); + else if(x <= 0.4) + intercept = 0.2; + slope = (0.4 - 0.2) / (0.4 - 0.2); + return intercept + slope * (x - 0.2); + else if(x <= 0.6) + intercept = 0.4; + slope = (0.58 - 0.4) / (0.6 - 0.4); + return intercept + slope * (x - 0.4); + else if(x <= 0.8) + intercept = 0.58; + slope = (0.73 - 0.58) / (0.8 - 0.6); + return intercept + slope * (x - 0.6); + else if(x <= 1.0) + intercept = 0.73; + slope = (0.85 - 0.73) / (1.0 - 0.8); + return intercept + slope * (x - 0.8); + else if(x <= 1.2) + intercept = 0.85; + slope = (0.93 - 0.85) / (1.2 - 1.0); + return intercept + slope * (x - 1.0); + else if(x <= 1.4) + intercept = 0.93; + slope = (0.97 - 0.93) / (1.4 - 1.2); + return intercept + slope * (x - 1.2); + else if(x <= 1.6) + intercept = 0.97; + slope = (0.99 - 0.97) / (1.6 - 1.4); + return intercept + slope * (x - 1.4); + else if(x <= 1.8) + intercept = 0.99; + slope = (1.0 - 0.99) / (1.8 - 1.6); + return intercept + slope * (x - 1.6); + else if(x <= 2.0) + intercept = 1.0; + slope = (1.0 - 1.0) / (2.0 - 1.8); + return intercept + slope * (x - 1.8); + } + + real lookupFunc_0(real x){ + # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) + # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) + real slope; + real intercept; + + if(x <= 0.2) + intercept = 0.0; + slope = (0.2 - 0.0) / (0.2 - 0.0); + return intercept + slope * (x - 0.0); + else if(x <= 0.4) + intercept = 0.2; + slope = (0.4 - 0.2) / (0.4 - 0.2); + return intercept + slope * (x - 0.2); + else if(x <= 0.6) + intercept = 0.4; + slope = (0.58 - 0.4) / (0.6 - 0.4); + return intercept + slope * (x - 0.4); + else if(x <= 0.8) + intercept = 0.58; + slope = (0.73 - 0.58) / (0.8 - 0.6); + return intercept + slope * (x - 0.6); + else if(x <= 1.0) + intercept = 0.73; + slope = (0.85 - 0.73) / (1.0 - 0.8); + return intercept + slope * (x - 0.8); + else if(x <= 1.2) + intercept = 0.85; + slope = (0.93 - 0.85) / (1.2 - 1.0); + return intercept + slope * (x - 1.0); + else if(x <= 1.4) + intercept = 0.93; + slope = (0.97 - 0.93) / (1.4 - 1.2); + return intercept + slope * (x - 1.2); + else if(x <= 1.6) + intercept = 0.97; + slope = (0.99 - 0.97) / (1.6 - 1.4); + return intercept + slope * (x - 1.4); + else if(x <= 1.8) + intercept = 0.99; + slope = (1.0 - 0.99) / (1.8 - 1.6); + return intercept + slope * (x - 1.6); + else if(x <= 2.0) + intercept = 1.0; + slope = (1.0 - 1.0) / (2.0 - 1.8); + return intercept + slope * (x - 1.8); + } + + real lookupFunc_0(real x){ + # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) + # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) + real slope; + real intercept; + + if(x <= 0.2) + intercept = 0.0; + slope = (0.2 - 0.0) / (0.2 - 0.0); + return intercept + slope * (x - 0.0); + else if(x <= 0.4) + intercept = 0.2; + slope = (0.4 - 0.2) / (0.4 - 0.2); + return intercept + slope * (x - 0.2); + else if(x <= 0.6) + intercept = 0.4; + slope = (0.58 - 0.4) / (0.6 - 0.4); + return intercept + slope * (x - 0.4); + else if(x <= 0.8) + intercept = 0.58; + slope = (0.73 - 0.58) / (0.8 - 0.6); + return intercept + slope * (x - 0.6); + else if(x <= 1.0) + intercept = 0.73; + slope = (0.85 - 0.73) / (1.0 - 0.8); + return intercept + slope * (x - 0.8); + else if(x <= 1.2) + intercept = 0.85; + slope = (0.93 - 0.85) / (1.2 - 1.0); + return intercept + slope * (x - 1.0); + else if(x <= 1.4) + intercept = 0.93; + slope = (0.97 - 0.93) / (1.4 - 1.2); + return intercept + slope * (x - 1.2); + else if(x <= 1.6) + intercept = 0.97; + slope = (0.99 - 0.97) / (1.6 - 1.4); + return intercept + slope * (x - 1.4); + else if(x <= 1.8) + intercept = 0.99; + slope = (1.0 - 0.99) / (1.8 - 1.6); + return intercept + slope * (x - 1.6); + else if(x <= 2.0) + intercept = 1.0; + slope = (1.0 - 1.0) / (2.0 - 1.8); + return intercept + slope * (x - 1.8); + } + + # Begin ODE declaration + vector vensim_func(real time, vector outcome, real customer_order_rate, real inventory_coverage, real manufacturing_cycle_time, real time_to_average_order_rate, real wip_adjustment_time ){ + real work_in_process_inventory = outcome[1]; + real inventory = outcome[2]; + + real change_in_exp_orders = customer_order_rate - expected_order_rate / time_to_average_order_rate; + real expected_order_rate = change_in_exp_orders; + real safety_stock_coverage = 2; + real minimum_order_processing_time = 2; + real desired_inventory_coverage = minimum_order_processing_time + safety_stock_coverage; + real desired_inventory = desired_inventory_coverage * expected_order_rate; + real inventory_adjustment_time = 8; + real production_adjustment_from_inventory = desired_inventory - inventory / inventory_adjustment_time; + real desired_production = fmax(0,expected_order_rate + production_adjustment_from_inventory); + real desired_wip = manufacturing_cycle_time * desired_production; + real adjustment_for_wip = desired_wip - work_in_process_inventory / wip_adjustment_time; + real desired_production_start_rate = desired_production + adjustment_for_wip; + real maximum_shipment_rate = inventory / minimum_order_processing_time; + real desired_shipment_rate = customer_order_rate; + real order_fulfillment_ratio = table_for_order_fulfillment(maximum_shipment_rate / desired_shipment_rate); + real production_rate = work_in_process_inventory / manufacturing_cycle_time; + real production_start_rate = fmax(0,desired_production_start_rate); + real work_in_process_inventory_dydt = production_start_rate - production_rate; + real shipment_rate = desired_shipment_rate * order_fulfillment_ratio; + real inventory_dydt = production_rate - shipment_rate; + + return {work_in_process_inventory_dydt, inventory_dydt}; + } +} + diff --git a/test_scripts/stan_file/pp_data2draws.stan b/test_scripts/stan_file/pp_data2draws.stan new file mode 100644 index 00000000..9854941b --- /dev/null +++ b/test_scripts/stan_file/pp_data2draws.stan @@ -0,0 +1,65 @@ +functions { +#include pp_relational.stan +} + +data{ + int N; // number of measurement times + array[N] real times; // measurement times + + vector[2] y_init; //init measured stock + vector[2] y[N]; //measured stock +} + +parameters{ + real alpha; // est parameter + real beta; // est parameter + real gamma; // est paramete + real delta; // est parameter + + vector[2] z_init; // init state value + vector[2] sigma; // msr error scale +} + +transformed parameters { + vector[2] integrated_result[N] + = ode_rk45(vensim_func, z_init, 0, times, + alpha, beta, gamma, delta); +} + +model{ + // U4 parameter uc + alpha ~ normal(.8, 0.1); // 1,1 + gamma ~ normal(.8, 0.1); // 1,1 + beta ~ normal(0.05, 0.01); // 0.05, 0.1 + gamma ~ normal(0.05, 0.01); // 0.05, 0.1 + + // real alpha_tilde = 0.55; + // real beta_tilde = 0.028; + // real gamma_tilde = 0.80; + // real delta_tilde = 0.024; + + // U4 parameter uc + sigma ~ lognormal(log(0.01), 1); //-1,1 + + // U4 parameter uc + z_init ~ lognormal(log(100), 1); // E[log(z_init)] is `loc` of lognormal + + y_init ~ lognormal(log(z_init), sigma); + + for (n in 1:N) { + y[n] ~ lognormal(log(integrated_result[n]), sigma); + } +} + +generated quantities { + vector[N] log_lik; + vector[2] y_hat[N]; + + for(n in 1:N){ + //posterior predictive + y_hat[n] = to_vector(lognormal_rng(log(integrated_result[n]), sigma)); + + //elementwise log likliehood + log_lik[n] = lognormal_lpdf(y[n]|log(integrated_result[n]), sigma); + } +} \ No newline at end of file diff --git a/test_scripts/stan_file/pp_data2draws_maprect.stan b/test_scripts/stan_file/pp_data2draws_maprect.stan new file mode 100644 index 00000000..356b4413 --- /dev/null +++ b/test_scripts/stan_file/pp_data2draws_maprect.stan @@ -0,0 +1,75 @@ +functions { +#include prey_pred_relational.stan +real partial_sum_lpdf(int[] slice_n_redcards, + int start, int end, + int[] n_games, + vector rating, + vector beta) { + return lognormal_lpdf(y | + n_games[start:end], + beta[1] + beta[2] * rating[start:end]); + } +} + +data{ + int N; // number of measurement times + array[N] real times; // measurement times + + vector[2] y_init; //init measured stock + vector[2] y[N]; //measured stock +} + +parameters{ + real alpha; // est parameter + real beta; // est parameter + real gamma; // est paramete + real delta; // est parameter + + vector[2] z_init; // init state value + vector[2] sigma; // msr error scale +} + +transformed parameters { + vector[2] integrated_result[N] + = ode_rk45(vensim_func, z_init, 0, times, + alpha, beta, gamma, delta); +} + +model{ + // U4 parameter uc + alpha ~ normal(.8, 0.1); // 1,1 + gamma ~ normal(.8, 0.1); // 1,1 + beta ~ normal(0.05, 0.01); // 0.05, 0.1 + gamma ~ normal(0.05, 0.01); // 0.05, 0.1 + + // real alpha_tilde = 0.55; + // real beta_tilde = 0.028; + // real gamma_tilde = 0.80; + // real delta_tilde = 0.024; + + // U4 parameter uc + sigma ~ lognormal(log(0.01), 1); //-1,1 + + // U4 parameter uc + z_init ~ lognormal(log(100), 1); // E[log(z_init)] is `loc` of lognormal + + y_init ~ lognormal(log(z_init), sigma); + + for (n in 1:N) { + y[n] ~ lognormal(log(integrated_result[n]), sigma); + target += partial_sum_lpdf(log(integrated_result[n]), 1, N, + } +} + +generated quantities { + vector[N] log_lik; + vector[2] y_hat[N]; + + for(n in 1:N){ + //posterior predictive + y_hat[n] = to_vector(lognormal_rng(log(integrated_result[n]), sigma)); + + //elementwise log likliehood + log_lik[n] = lognormal_lpdf(y[n]|log(integrated_result[n]), sigma); + } +} \ No newline at end of file diff --git a/test_scripts/stan_file/pp_draws2data.stan b/test_scripts/stan_file/pp_draws2data.stan new file mode 100644 index 00000000..436e2bbd --- /dev/null +++ b/test_scripts/stan_file/pp_draws2data.stan @@ -0,0 +1,45 @@ +functions { +#include pp_relational.stan +} + +data{ + int N; // number of measurement times + array[N] real times; // measurement times + +} + +generated quantities { + vector[2] y_init_tilde; // simulated initial stock + vector[2] y_tilde[N]; // simulated stock + + vector[2] sigma_tilde; + vector[2] z_init_tilde; + + // U4 parameter uc + real alpha_tilde = 0.55; // abs(normal_rng(1, 0.5)); + real beta_tilde = 0.028; //abs(normal_rng(0.05, 0.05)); + real gamma_tilde = 0.80; //abs(normal_rng(1, 0.5)); + real delta_tilde = 0.024; //abs(normal_rng(0.05, 0.05)); + + // U4 measurement uc + z_init_tilde[1] = 30; //lognormal_rng(log(30), 1); + z_init_tilde[2] = 30; // lognormal_rng(log(30), 1); + + // U4 different msr_err + sigma_tilde[1] = 0.01; //lognormal_rng(-1, 1); + sigma_tilde[2] = 0.01; //lognormal_rng(-1, 1); + + // calculate prior predictive + vector[2] integrated_result_tilde[N] + = ode_rk45(vensim_func, z_init_tilde, 0, times, + alpha_tilde, beta_tilde, gamma_tilde, delta_tilde); + + y_init_tilde = to_vector(lognormal_rng(log(z_init_tilde), + sigma_tilde)); + + for (n in 1:N) { + //posterior predictive + y_tilde[n] = to_vector(lognormal_rng(log(integrated_result_tilde[n]), + sigma_tilde)); + } +} diff --git a/test_scripts/stan_file/pp_relational.stan b/test_scripts/stan_file/pp_relational.stan new file mode 100644 index 00000000..3e512117 --- /dev/null +++ b/test_scripts/stan_file/pp_relational.stan @@ -0,0 +1,17 @@ +functions { + # Begin ODE declaration + vector vensim_func(real time, vector outcome, real alpha, real beta, real gamma, real delta ){ + real prey = outcome[1]; + real predator = outcome[2]; + + real prey_birth_rate = alpha * prey; + real predator_death_rate = gamma * predator; + real predator_birth_rate = delta * prey * predator; + real prey_death_rate = beta * predator * prey; + real prey_dydt = prey_birth_rate - prey_death_rate; + real predator_dydt = predator_birth_rate - predator_death_rate; + + return {prey_dydt, predator_dydt}; + } +} + diff --git a/test_scripts/vensim_models/arithmetic.mdl b/test_scripts/vensim_models/arithmetic.mdl deleted file mode 100644 index 75e711e4..00000000 --- a/test_scripts/vensim_models/arithmetic.mdl +++ /dev/null @@ -1,117 +0,0 @@ -{UTF-8} -a = A FUNCTION OF( flow1) ~~| -a= - 1 - ~ - ~ | - -flow2 = A FUNCTION OF( ) - ~ - ~ | - -test = A FUNCTION OF( flow1,-flow2) - ~ - ~ | - -flow1 = A FUNCTION OF( ) - ~ - ~ | - -b= - 1 - ~ - ~ | - -c= - a * b / 1+5 - ~ - ~ | - -******************************************************** - .Control -********************************************************~ - Simulation Control Parameters - | - -FINAL TIME = 100 - ~ Month - ~ The final time for the simulation. - | - -INITIAL TIME = 0 - ~ Month - ~ The initial time for the simulation. - | - -SAVEPER = - TIME STEP - ~ Month [0,?] - ~ The frequency with which output is stored. - | - -TIME STEP = 1 - ~ Month [0,?] - ~ The time step for the simulation. - | - -\\\---/// Sketch information - do not modify anything except names -V300 Do not put anything below this section - it will be ignored -*View 1 -$-1--1--1,0,|12||-1--1--1|-1--1--1|-1--1--1|-1--1--1|-1--1--1|96,96,100,0 -10,1,a,244,206,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 -10,2,b,271,309,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 -10,3,c,436,236,75,30,8,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,4,1,3,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -1,5,2,3,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| -10,6,test,479,128,40,20,3,3,0,0,-1,0,0,0,0,0,0,0,0,0 -12,7,48,332,127,30,30,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,8,10,7,100,0,0,22,0,192,0,-1--1--1,,1|(376,127)| -1,9,10,6,4,0,0,22,0,192,0,-1--1--1,,1|(421,127)| -11,10,0,397,127,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 -10,11,flow1,397,165,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 -12,12,48,687,127,30,30,0,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,13,15,6,100,0,0,22,0,192,0,-1--1--1,,1|(531,127)| -1,14,15,12,4,0,0,22,0,192,0,-1--1--1,,1|(606,127)| -11,15,0,549,127,6,8,34,3,0,0,4,0,0,0,0,0,0,0,0,0 -10,16,flow2,632,127,75,30,40,3,0,0,-1,0,0,0,0,0,0,0,0,0 -1,17,11,1,0,0,0,0,0,192,0,-1--1--1,,1|(0,0)| -///---\\\ -:L<%^E!@ -1:test.vdfx -4:Time -5:a -9:/Users/hyunjimoon/Dropbox/test -19:100,0 -24:0 -25:100 -26:100 -15:0,0,0,0,0,0 -27:0, -34:0, -42:1 -72:0 -73:0 -35:Date -36:YYYY-MM-DD -37:2000 -38:1 -39:1 -40:2 -41:0 -95:0 -96:0 -97:0 -77:0 -78:0 -102:1 -93:0 -94:0 -92:0 -91:0 -90:0 -87:0 -75: -43: -103:8,8,8,3,8 -105:0,0,0,0,0,0,0,0,0,0 -104:Courier|12||0-0-0|0-0-0|-1--1--1|0-0-255|192-192-192|-1--1--1 \ No newline at end of file diff --git a/test_scripts/vensim_models/demand_supply_pink_sterman.mdl b/test_scripts/vensim_models/demand_supply_pink_sterman.mdl new file mode 100644 index 00000000..5109905e --- /dev/null +++ b/test_scripts/vensim_models/demand_supply_pink_sterman.mdl @@ -0,0 +1,601 @@ +{UTF-8} +Adjustment for WIP = (Desired WIP - Work in Process Inventory)/WIP Adjustment Time + ~ Widgets/Week + ~ The adjustment to the production start rate from the adequacy of WIP \ + inventory. + | + +Adjustment from Inventory = (Desired Inventory - Inventory)/ +Inventory Adjustment Time + ~ Widgets/Week + ~ The desired production rate is adjusted above or below the forecast based on the \ + inventory position + of the plant. When desired inventory > inventory, desired production is \ + increased (and + vice-versa). Inventory gaps are corrected over the inv. adj. \ + time. + | + +Backlog= INTEG ( + +Order Rate-Order Fulfillment Rate, + Order Rate * Target Delivery Delay) + ~ Widgets + ~ The firm's backlog of unfilled orders + | + +Change in Exp Orders= + (Customer Order Rate-Expected Order Rate)/ + Time to Average Order Rate + ~ (Widgets/Week)/Week + ~ The demand forecast adjusts to the actual order rate over a time period determined \ + by the Time to + Average Order Rate. The demand forecast is formed by first-order \ + exponential smoothing, + a widely used forecasting technique. + | + +Change in Pink Noise = (White Noise - Pink Noise)/Noise Correlation Time + ~ 1/Week + ~ Change in the pink noise value; Pink noise is a first order exponential smoothing \ + delay of the white + noise input. + | + +Customer Order Rate = Initial Customer Order Rate*Input + ~ Widgets/Week + ~ Customer order rate is exogenous. A variety of test inputs allow users to try \ + different patterns, + including a step, pulse, sine wave, and random noise. + | + +Customer Order Rate 0 = Initial Customer Order Rate 0*Input 0 + ~ Widgets/Week + ~ Customer order rate is exogenous. A variety of test inputs allow users to try \ + different patterns, + including a step, pulse, sine wave, and random noise. + | + +Delivery Delay= + Backlog/Order Fulfillment Rate + ~ Weeks + ~ The average delivery delay is given by the ratio of the backlog to the \ + current shipment rate. + | + +Desired Inventory = Desired Inventory Coverage*Expected Order Rate + ~ Widgets + ~ The desired inventory level sought by the plant. Experience suggests that to \ + maintain customer + service by providing full and reliable deliveries, the plant must maintain a \ + certain + coverage of throughput (demand), estimated by the demand forecast. + | + +Desired Inventory Coverage= + Minimum Order Processing Time + Safety Stock Coverage + ~ Weeks + ~ Desired inventory coverage is the number of weeks of the demand forecast the plant \ + seeks to maintain + in inventory. This inventory coverage is required to maintain delivery \ + reliability by + buffering the plant against unforeseen variations in demand or \ + production. It consists of the normal order processing time plus an \ + additional term representing the coverage desired to maintain safety \ + stocks. + | + +Desired Production = MAX(0,Expected Order Rate+Adjustment from Inventory) + ~ Widgets/Week + ~ Desired Production is the demand forecast (Expected Order Rate) adjusted to bring \ + the inventory + position in line with the target inventory level. + | + +Desired Production Start Rate = Desired Production + Adjustment for WIP + ~ Widgets/Week + ~ The desired rate of production starts, equal to the desired production rate adjusted \ + by the adequacy + of the WIP inventory. + | + +Desired Shipment Rate= + Backlog/Target Delivery Delay + ~ Widgets/Week + ~ The desired shipment rate is determined by the backlog and the target \ + delivery delay. + | + +Desired WIP = Manufacturing Cycle Time*Desired Production + ~ Widgets + ~ The desired quantity of work in process inventory. Proportional to the \ + manufacturing cycle time and + the desired rate of production. + | + +Expected Order Rate = INTEG(Change in Exp Orders,Customer Order Rate) + ~ Widgets/Week + ~ The demand forecast is formed by adaptive expectations, using exponential smoothing, \ + a common + forecasting technique. The initial forecast is equal to the \ + initial customer order rate. + | + +Initial Customer Order Rate = 10000 + ~ Widgets/Week + ~ Initial value of customer orders, set to 10,000 widgets per week. + | + +Initial Customer Order Rate 0 = 10000 + ~ Widgets/Week + ~ Initial value of customer orders, set to 10,000 widgets per week. + | + +Input = A FUNCTION OF( ) ~~| +Input= + 1+STEP(Step Height,Step Time)+ + (Pulse Quantity/TIME STEP)*PULSE(Pulse Time,TIME STEP)+ + RAMP(Ramp Slope,Ramp Start Time,Ramp End Time)+ + Sine Amplitude*SIN(2*3.14159*Time/Sine Period)+ + STEP(1,Noise Start Time)*Pink Noise + ~ Dimensionless + ~ Input is a dimensionless variable which provides a variety of test input patterns, \ + including a step, + pulse, sine wave, and random noise. + | + +Input 0= + 1+STEP(Step Height,Step Time)+ + (Pulse Quantity/TIME STEP)*PULSE(Pulse Time,TIME STEP)+ + RAMP(Ramp Slope,Ramp Start Time,Ramp End Time)+ + Sine Amplitude*SIN(2*3.14159*Time/Sine Period)+ + STEP(1,Noise Start Time)*Pink Noise + ~ Dimensionless + ~ Input is a dimensionless variable which provides a variety of test input patterns, \ + including a step, + pulse, sine wave, and random noise. + | + +Inventory = INTEG(Production Rate-Shipment Rate,Desired Inventory) + ~ Widgets + ~ The level of finished goods inventory in the plant. Increased by production and \ + decreased by + shipments. Initially set to the desired inventory level. + | + +Inventory Adjustment Time = 8 + ~ Weeks + ~ The inventory adjustment time is the time period over which the plant seeks to bring \ + inventory in + balance with the desired level. Initially set to 8 weeks. + | + +Inventory Coverage= + Inventory/Shipment Rate + ~ Weeks + ~ Inventory coverage is given by the ratio of inventory to shipments. + | + +Manufacturing Cycle Time= + 8 + ~ Weeks + ~ The average delay between the start and completion of production + | + +Maximum Shipment Rate= + Inventory/Minimum Order Processing Time + ~ Widgets/Week + ~ The maximum rate of shipments the firm can achieve given their current \ + inventory level and the minimum order processing time. + | + +Minimum Order Processing Time= + 2 + ~ Weeks + ~ The minimum time required to process and ship an order. + | + +Noise Correlation Time = 4 + ~ Week + ~ The correlation time constant for Pink Noise. + | + +Noise Standard Deviation = 0 + ~ Dimensionless + ~ The standard deviation of the pink noise process. + | + +Noise Start Time = 5 + ~ Week + ~ Start time for the random input. + | + +Order Fulfillment Rate= + Shipment Rate + ~ Widgets/Week + ~ The order fulfillment rate is equal to the physical shipment rate. + | + +Order Fulfillment Ratio= + Table for Order Fulfillment(Maximum Shipment Rate/Desired Shipment Rate) + ~ Dimensionless + ~ The Fraction of customer orders filled is determined by the ratio of the \ + normal shipment rate to the desired rate. The normal rate is the rate \ + current inventory permits under normal circumstances. Low inventory \ + availability reduces shipments below customer orders. Unfilled customer \ + orders are lost. + | + +Order Rate= + Customer Order Rate + ~ Widgets/Week + ~ The incoming order rate, equal to customer orders. + | + +Pink Noise = INTEG(Change in Pink Noise,0) + ~ Dimensionless + ~ Pink Noise is first-order autocorrelated noise. Pink noise provides a realistic \ + noise input to + models in which the next random shock depends in part on the previous \ + shocks. The user + can specify the correlation time. The mean is 0 and the standard deviation \ + is specified + by the user. + | + +Production Rate = DELAY3(Production Start Rate,Manufacturing Cycle Time) + ~ Widgets/Week + ~ Production is a third order delay of the production start rate, with the delay time \ + determined by + the manufacturing cycle time. + | + +Production Start Rate = MAX(0,Desired Production Start Rate) + ~ Widgets/Week + ~ The production start rate is the desired production start rate, \ + constrained to be nonnegative. + | + +Pulse Quantity=0 + ~ Dimensionless*Week + ~ The quantity to be injected to customer orders, as a fraction of the base value of \ + Input. + For example, to pulse in a quantity equal to 50% of the current value of \ + input, set to + .50. + | + +Pulse Time=5 + ~ Week + ~ Time at which the pulse in Input occurs. + | + +Ramp End Time=1e+09 + ~ Week + ~ End time for the ramp input. + | + +Ramp Slope=0 + ~ 1/Week + ~ Slope of the ramp input, as a fraction of the base value (per week). + | + +Ramp Start Time=5 + ~ Week + ~ Start time for the ramp input. + | + +Safety Stock Coverage= + 2 + ~ Weeks + ~ Safety stock coverage is the number of weeks of the expected order rate \ + the firm would like to maintain in inventory over and above the normal \ + order processing time. The safety stock provides a buffer against the \ + possibility that unforeseen variations in demand will cause shipments to \ + fall below orders. + | + +Shipment Rate= + Desired Shipment Rate*Order Fulfillment Ratio + ~ Widgets/Week + ~ The shipment rate is the desired shipment rate multiplied by the fraction \ + of orders filled (the order fulfillment ratio. Shipments fall below \ + desired shipments when the feasible shipment rate falls below the desired \ + rate, indicating that some products are unavailable. + | + +Sine Amplitude=0 + ~ Dimensionless + ~ Amplitude of sine wave in customer orders (fraction of mean). + | + +Sine Period=50 + ~ Weeks + ~ Period of sine wave in customer demand. Set initially to 50 weeks (1 \ + year). + | + +Step Height=0 + ~ Dimensionless + ~ Height of step input to customer orders, as fraction of initial value. + | + +Step Time=5 + ~ Week + ~ Time for the step input. + | + +Table for Order Fulfillment( + [(0,0)-(2,1)],(0,0),(0.2,0.2),(0.4,0.4),(0.6,0.58),(0.8,0.73),(1,0.85),(1.2,0.93),(1.4\ + ,0.97),(1.6,0.99),(1.8,1),(2,1),(2,1)) + ~ Dimensionless + ~ The ability to ship is constrained by inventory availability. As the inventory \ + level drops, the + fraction of customer orders that can be filled decreases. When inventory is \ + zero, shipments + cease. Unfilled customer orders are lost.\!\!\! + | + +Target Delivery Delay= + 2 + ~ Weeks + ~ The firm's target for delivery time. + | + +Time to Average Order Rate = 8 + ~ Weeks + ~ The demand forecast adjusts to actual customer orders over this time \ + period. + | + +White Noise = Noise Standard Deviation*((24*Noise Correlation Time/TIME STEP)^0.5*(RANDOM 0 1\ + () - 0.5 +)) + ~ Dimensionless + ~ White noise input to the pink noise process. + | + +WIP Adjustment Time = 2 + ~ Weeks + ~ The time required to adjust the WIP inventory to the desired level. + | + +Work in Process Inventory = INTEG(Production Start Rate - Production Rate,Desired WIP\ + ) + ~ Widgets + ~ WIP inventory accumulates the difference between production starts and \ + completions. + | + +******************************************************** + .Control +********************************************************~ + Simulation Control Parameters + | + +FINAL TIME = 50 + ~ Week + ~ The final time for the simulation. + | + +INITIAL TIME = 0 + ~ Week + ~ The initial time for the simulation. + | + +SAVEPER = + TIME STEP + ~ Week [0,?] + ~ The frequency with which output is stored. + | + +TIME STEP = 0.125 + ~ Week [0,?] + ~ The time step for the simulation. + | + +\\\---/// Sketch information - do not modify anything except names +V300 Do not put anything below this section - it will be ignored +*View 1 +$-1--1--1,0,|12||-1--1--1|-1--1--1|-1--1--1|-1--1--1|-1--1--1|96,96,121,0 +10,1,Inventory,931,341,40,20,3,3,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +12,2,48,399,341,10,8,0,3,0,42,-1,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +11,3,0,781,340,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,4,Production Rate,781,355,49,7,40,3,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +12,5,48,1359,342,10,8,0,3,0,42,-1,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +11,6,0,1135,342,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,7,Shipment Rate,1135,357,44,7,40,3,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,8,Desired Production,638,713,58,7,8,3,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,9,Adjustment from Inventory,902,540,45,12,8,3,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,10,Desired Inventory,992,602,53,7,8,3,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,11,Expected Order Rate,991,694,40,20,3,3,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +12,12,48,1133,696,10,8,0,3,0,42,-1,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +11,13,0,1078,696,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,14,Change in Exp Orders,1078,716,35,12,40,3,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,15,Inventory Adjustment Time,842,479,52,12,8,3,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,16,Desired Inventory Coverage,1107,619,29,18,8,3,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,17,Time to Average Order Rate,1126,818,44,18,8,3,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,18,Order Fulfillment Ratio,1127,452,47,16,8,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +10,19,Table for Order Fulfillment,1196,535,43,16,8,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +10,20,Work in Process Inventory,637,341,40,20,3,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +11,21,0,519,341,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,22,Production Start Rate,519,358,60,9,40,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +10,23,Manufacturing Cycle Time,743,446,41,16,8,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +10,24,Adjustment for WIP,644,500,54,9,8,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +10,25,Desired WIP,748,554,36,9,8,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +10,26,Desired Production Start Rate,476,507,54,16,8,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +10,27,WIP Adjustment Time,630,581,46,16,8,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +10,28,Customer Order Rate,853,42,60,9,8,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +12,29,0,1033,398,20,20,5,4,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +B +12,30,0,1035,435,39,13,8,4,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +Stockout +12,31,0,862,396,20,20,4,4,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +B +12,32,0,864,433,39,13,8,4,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +Inventory Control +12,33,0,561,456,20,20,4,4,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +B +12,34,0,563,493,39,13,8,4,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +WIP Control +1,35,3,1,4,0,0,22,0,0,0,-1--1--1,,1|(839,340)| +1,36,3,20,100,0,0,22,0,0,0,-1--1--1,,1|(726,340)| +1,37,6,5,4,0,0,22,0,0,0,-1--1--1,,1|(1245,342)| +1,38,6,1,100,0,0,22,0,0,0,-1--1--1,,1|(1050,342)| +1,39,13,11,4,0,0,22,0,0,0,-1--1--1,,1|(1051,696)| +1,40,13,12,100,0,0,22,0,0,0,-1--1--1,,1|(1103,696)| +1,41,15,9,2,0,45,0,0,192,0,-1--1--1,,1|(839,519)| +1,42,1,9,2,0,45,0,0,192,0,-1--1--1,,1|(922,475)| +1,43,10,9,2,0,43,0,0,192,0,-1--1--1,,1|(968,558)| +1,44,16,10,2,0,43,0,0,0,0,-1--1--1,,1|(1026,614)| +1,45,11,10,2,0,43,0,0,192,0,-1--1--1,,1|(996,613)| +1,46,9,8,2,0,43,0,0,192,0,-1--1--1,,1|(738,699)| +1,47,11,8,2,0,43,0,0,192,0,-1--1--1,,1|(814,743)| +1,48,11,14,2,0,45,0,0,192,0,-1--1--1,,1|(1010,742)| +1,49,17,14,1,0,45,0,0,192,0,-1--1--1,,1|(1137,748)| +1,50,18,7,2,0,43,0,0,192,0,-1--1--1,,1|(1135,396)| +1,51,19,18,1,0,0,0,0,0,0,-1--1--1,,1|(1137,484)| +1,52,21,20,4,0,0,22,0,0,0,-1--1--1,,1|(561,341)| +1,53,21,2,100,0,0,22,0,0,0,-1--1--1,,1|(461,341)| +1,54,21,3,2,0,43,0,0,192,0,-1--1--1,,1|(724,305)| +1,55,23,4,2,0,45,0,0,192,0,-1--1--1,,1|(755,382)| +1,56,20,24,2,0,45,0,0,192,0,-1--1--1,,1|(665,441)| +1,57,25,24,2,0,43,0,0,192,0,-1--1--1,,1|(680,500)| +1,58,23,25,2,0,43,0,0,192,0,-1--1--1,,1|(767,507)| +1,59,8,26,2,0,43,0,0,192,0,-1--1--1,,1|(491,568)| +1,60,26,22,2,0,43,0,0,192,0,-1--1--1,,1|(489,390)| +1,61,8,25,2,0,43,0,0,192,0,-1--1--1,,1|(742,585)| +1,62,24,26,2,0,43,0,0,192,0,-1--1--1,,1|(552,545)| +1,63,27,24,1,0,45,0,0,192,0,-1--1--1,,1|(654,535)| +10,64,Desired Shipment Rate,1053,238,42,16,8,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +10,65,Maximum Shipment Rate,979,478,43,16,8,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +10,66,Minimum Order Processing Time,1066,537,47,16,8,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +1,67,1,65,1,0,43,0,0,192,0,-1--1--1,,1|(951,436)| +1,68,65,18,1,0,43,0,0,192,0,-1--1--1,,1|(1068,477)| +1,69,66,65,1,0,45,0,0,192,0,-1--1--1,,1|(997,526)| +1,70,66,16,1,0,43,0,0,192,0,-1--1--1,,1|(1110,571)| +10,71,Inventory Coverage,984,290,55,9,8,3,1,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +1,72,1,71,1,1,43,0,0,192,0,-1--1--1,,1|(940,298)| +1,73,6,71,1,1,45,0,0,192,0,-1--1--1,,1|(1075,317)| +10,74,Safety Stock Coverage,1219,603,36,16,8,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +1,75,74,16,1,0,43,0,0,192,0,-1--1--1,,1|(1170,631)| +10,76,Initial Customer Order Rate,707,45,48,16,8,2,1,43,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +1,77,76,28,0,1,0,0,0,0,0,-1--1--1,,1|(0,0)| +10,78,Input,707,109,23,9,8,2,1,43,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +1,79,78,28,0,1,0,0,0,0,0,-1--1--1,,1|(0,0)| +10,80,Backlog,1135,128,40,20,3,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +12,81,48,971,128,10,8,0,3,0,42,-1,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +1,82,84,80,4,0,0,22,0,0,0,-1--1--1,,1|(1064,128)| +1,83,84,81,100,0,0,22,0,0,0,-1--1--1,,1|(1001,128)| +11,84,0,1027,128,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,85,Order Rate,1027,145,32,9,40,3,0,42,-1,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +12,86,48,1292,127,10,8,0,3,0,42,-1,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +1,87,89,86,4,0,0,22,0,0,0,-1--1--1,,1|(1260,127)| +1,88,89,80,100,0,0,22,0,0,0,-1--1--1,,1|(1200,127)| +11,89,0,1232,127,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,90,Order Fulfillment Rate,1232,151,47,16,40,3,0,42,-1,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +1,91,64,6,1,0,43,0,0,64,0,-1--1--1,,1|(1070,281)| +1,92,80,64,1,0,43,0,0,192,0,-1--1--1,,1|(1079,161)| +1,93,6,90,1,0,43,0,0,192,0,-1--1--1,,1|(1248,241)| +10,94,Target Delivery Delay,951,231,60,9,8,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +1,95,94,64,1,0,45,0,0,192,0,-1--1--1,,1|(1009,210)| +10,96,Customer Order Rate,1368,607,49,16,8,2,0,43,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +1,97,96,14,1,0,43,0,0,192,0,-1--1--1,,1|(1257,686)| +1,98,28,85,1,0,43,0,0,192,0,-1--1--1,,1|(892,111)| +12,99,0,1154,215,20,20,5,4,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +B +12,100,0,1156,252,39,13,8,4,0,42,0,0,0,0,-1--1--1,-1--1--1,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +Order Fulfillment +10,101,Delivery Delay,1172,34,41,9,8,3,0,42,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +1,102,80,101,1,0,43,0,0,192,0,-1--1--1,,1|(1120,57)| +1,103,89,101,1,0,45,0,0,192,0,-1--1--1,,1|(1241,82)| +10,104,Desired Shipment Rate,1260,445,46,16,8,2,0,43,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +1,105,104,18,1,0,45,0,0,192,0,-1--1--1,,1|(1213,455)| +1,106,10,1,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +1,107,28,11,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +1,108,25,20,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +1,109,94,80,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +10,110,Customer Order Rate 0,1683,394,39,12,8,3,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,111,Input 0,1590,379,75,30,0,2,0,43,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|128-128-128,0,0,0,0,0,0 +10,112,Input 0,1647,589,75,30,0,3,0,40,-1,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +10,113,Pink Noise,1977,533,40,20,3,3,0,40,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +12,114,48,2109,537,10,8,0,3,0,40,-1,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +10,115,White Noise,2004,636,35,9,8,3,0,40,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +10,116,Initial Customer Order Rate 0,1590,438,48,12,8,3,0,42,0,0,0,0,-1--1--1,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,117,Noise Standard Deviation,1897,664,44,16,8,3,0,40,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +10,118,TIME STEP,1911,596,75,30,0,2,0,43,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|128-128-128,0,0,0,0,0,0 +10,119,Pink Noise,1569,539,75,30,0,2,0,43,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|128-128-128,0,0,0,0,0,0 +10,120,Noise Start Time,1661,520,75,30,0,3,0,41,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,121,Pulse Quantity,1736,550,75,30,0,3,0,41,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,122,Pulse Time,1743,586,75,30,0,3,0,41,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,123,Ramp End Time,1584,678,75,30,0,3,0,41,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,124,Ramp Slope,1548,620,75,30,0,3,0,41,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,125,Ramp Start Time,1546,650,75,30,0,3,0,41,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,126,Sine Amplitude,1753,614,75,30,0,3,0,41,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,127,Sine Period,1740,652,75,30,0,3,0,41,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,128,Step Height,1653,693,75,30,0,3,0,41,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,129,Step Time,1706,673,75,30,0,3,0,41,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|0-0-0,0,0,0,0,0,0 +10,130,Time,1544,571,75,30,0,2,0,43,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|128-128-128,0,0,0,0,0,0 +10,131,TIME STEP,1544,596,75,30,0,2,0,43,-1,0,0,0,128-128-128,0-0-0,Helvetica|10|B|128-128-128,0,0,0,0,0,0 +11,132,0,2067,537,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,133,Change in Pink Noise,2067,554,60,9,40,3,0,40,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +10,134,Noise Correlation Time,2134,617,49,16,8,3,0,40,0,0,0,0,0-0-0,0-0-0,Helvetica|10||0-0-0,0,0,0,0,0,0 +1,135,116,110,1,0,0,0,0,0,0,-1--1--1,,1|(1649,433)| +1,136,120,112,0,0,0,0,0,0,0,-1--1--1,,1|(0,0)| +1,137,121,112,0,0,0,0,0,0,0,-1--1--1,,1|(0,0)| +1,138,122,112,0,0,0,0,0,0,0,-1--1--1,,1|(0,0)| +1,139,123,112,0,0,0,0,0,0,0,-1--1--1,,1|(0,0)| +1,140,124,112,0,0,0,0,0,0,0,-1--1--1,,1|(0,0)| +1,141,125,112,0,0,0,0,0,0,0,-1--1--1,,1|(0,0)| +1,142,126,112,1,0,0,0,0,0,0,-1--1--1,,1|(1683,600)| +1,143,127,112,0,0,0,0,0,0,0,-1--1--1,,1|(0,0)| +1,144,128,112,0,0,0,0,0,0,0,-1--1--1,,1|(0,0)| +1,145,129,112,0,0,0,0,0,0,0,-1--1--1,,1|(0,0)| +1,146,130,112,0,0,0,0,0,0,0,-1--1--1,,1|(0,0)| +1,147,131,112,0,0,0,0,0,0,0,-1--1--1,,1|(0,0)| +1,148,111,110,1,0,0,0,0,0,0,-1--1--1,,1|(1631,366)| +1,149,132,113,4,0,0,22,0,0,0,-1--1--1,,1|(2039,537)| +1,150,132,114,100,0,0,22,0,0,0,-1--1--1,,1|(2086,537)| +1,151,113,133,2,0,0,0,0,0,0,-1--1--1,,1|(2031,580)| +1,152,115,133,2,0,0,0,0,0,0,-1--1--1,,1|(2067,596)| +1,153,134,133,2,0,0,0,0,0,0,-1--1--1,,1|(2123,564)| +1,154,134,115,1,0,0,0,0,0,0,-1--1--1,,1|(2064,654)| +1,155,118,115,1,0,0,0,0,0,0,-1--1--1,,1|(1947,624)| +1,156,117,115,1,0,0,0,0,0,0,-1--1--1,,1|(1964,653)| +1,157,119,112,0,0,0,0,0,0,0,-1--1--1,,1|(0,0)| +///---\\\ +:L<%^E!@ +5:Pink Noise +19:121,0 +24:0 +25:0 +26:0 +15:0,0,0,0,0,0 +27:0, +34:0, +42:1 +72:0 +73:0 +35:Date +36:YYYY-MM-DD +37:2000 +38:1 +39:1 +40:3 +41:0 +95:0 +96:0 +97:0 +77:0 +78:0 +102:1 +93:0 +94:0 +92:0 +91:0 +90:0 +87:0 +75: +43: +103:8,8,8,3,8 +105:0,0,0,0,0,0,0,0,0,0 +104:Courier|12||0-0-0|0-0-0|-1--1--1|0-0-0|192-192-192|-1--1--1 diff --git a/test_scripts/vensim_models/demand_supply_white_sterman.mdl b/test_scripts/vensim_models/demand_supply_white_sterman.mdl new file mode 100644 index 00000000..1fcb6d95 --- /dev/null +++ b/test_scripts/vensim_models/demand_supply_white_sterman.mdl @@ -0,0 +1,365 @@ +{UTF-8} +Adjustment for WIP = (Desired WIP - Work in Process Inventory)/WIP Adjustment Time + ~ Widgets/Week + ~ The adjustment to the production start rate from the adequacy of WIP \ + inventory. + | + +Change in Exp Orders = (Customer Order Rate-Expected Order Rate)/ +Time to Average Order Rate + ~ (Widgets/Week)/Week + ~ The demand forecast adjusts to the actual order rate over a time period determined \ + by the Time to + Average Order Rate. The demand forecast is formed by first-order \ + exponential smoothing, + a widely used forecasting technique. + | + +Customer Order Rate= + 10000 + ~ Widgets/Week + ~ Customer order rate is exogenous. A variety of test inputs allow users to try \ + different patterns, + including a step, pulse, sine wave, and random noise. + Initial Customer Order Rate*Input + | + +Desired Inventory = Desired Inventory Coverage*Expected Order Rate + ~ Widgets + ~ The desired inventory level sought by the plant. Experience suggests that to \ + maintain customer + service by providing full and reliable deliveries, the plant must maintain a \ + certain + coverage of throughput (demand), estimated by the demand forecast. + | + +Desired Inventory Coverage= + Minimum Order Processing Time + Safety Stock Coverage + ~ Weeks + ~ Desired inventory coverage is the number of weeks of the demand forecast the plant \ + seeks to maintain + in inventory. This inventory coverage is required to maintain delivery \ + reliability by + buffering the plant against unforeseen variations in demand or \ + production. It consists of the normal order processing time plus an \ + additional term representing the coverage desired to maintain safety \ + stocks. + | + +Desired Production = MAX(0,Expected Order Rate+Production Adjustment from Inventory) + ~ Widgets/Week + ~ Desired Production is the demand forecast (Expected Order Rate) adjusted to bring \ + the inventory + position in line with the target inventory level. + | + +Desired Production Start Rate = Desired Production + Adjustment for WIP + ~ Widgets/Week + ~ The desired rate of production starts, equal to the desired production rate adjusted \ + by the adequacy + of the WIP inventory. + | + +Desired Shipment Rate= + Customer Order Rate + ~ Widgets/Week + ~ The desired shipment rate equals the customer order rate. In this model \ + there is no backlog of unfilled orders: unfilled orders are lost as \ + customers seek alternate sources of supply. + | + +Desired WIP = Manufacturing Cycle Time*Desired Production + ~ Widgets + ~ The desired quantity of work in process inventory. Proportional to the \ + manufacturing cycle time and + the desired rate of production. + | + +Expected Order Rate = INTEG(Change in Exp Orders,Customer Order Rate) + ~ Widgets/Week + ~ The demand forecast is formed by adaptive expectations, using exponential smoothing, \ + a common + forecasting technique. The initial forecast is equal to the \ + initial customer order rate. + | + +Inventory = INTEG(Production Rate-Shipment Rate,Desired Inventory) + ~ Widgets + ~ The level of finished goods inventory in the plant. Increased by production and \ + decreased by + shipments. Initially set to the desired inventory level. + | + +Inventory Adjustment Time = 8 + ~ Weeks + ~ The inventory adjustment time is the time period over which the plant seeks to bring \ + inventory in + balance with the desired level. Initially set to 8 weeks. + | + +Inventory Coverage= + Inventory/Shipment Rate + ~ Weeks + ~ Inventory coverage is given by the ratio of inventory to shipments. + | + +Manufacturing Cycle Time= + 8 + ~ Weeks + ~ The average delay between the start and completion of production + | + +Maximum Shipment Rate= + Inventory/Minimum Order Processing Time + ~ Widgets/Week + ~ The maximum rate of shipments the firm can achieve given their current \ + inventory level and the minimum order processing time. + | + +Minimum Order Processing Time= + 2 + ~ Weeks + ~ The minimum time required to process and ship an order. + | + +Order Fulfillment Ratio= + Table for Order Fulfillment(Maximum Shipment Rate/Desired Shipment Rate) + ~ Dimensionless + ~ The Fraction of customer orders filled is determined by the ratio of the \ + normal shipment rate to the desired rate. The normal rate is the rate \ + current inventory permits under normal circumstances. Low inventory \ + availability reduces shipments below customer orders. Unfilled customer \ + orders are lost. + | + +Production Adjustment from Inventory = (Desired Inventory - Inventory)/ +Inventory Adjustment Time + ~ Widgets/Week + ~ The desired production rate is adjusted above or below the forecast based on the \ + inventory position + of the plant. When desired inventory > inventory, desired production is \ + increased (and + vice-versa). Inventory gaps are corrected over the inv. adj. \ + time. + | + +Production Rate= + Work in Process Inventory/Manufacturing Cycle Time + ~ Widgets/Week + ~ Used to be delay3 but changed to first order delay + DELAY3(Production Start Rate,Manufacturing Cycle Time) + Production is a third order delay of the production start rate, with the delay time \ + determined by + the manufacturing cycle time. + | + +Production Start Rate = MAX(0,Desired Production Start Rate) + ~ Widgets/Week + ~ The production start rate is the desired production start rate, \ + constrained to be nonnegative. + | + +Safety Stock Coverage= + 2 + ~ Weeks + ~ Safety stock coverage is the number of weeks of the expected order rate \ + the firm would like to maintain in inventory over and above the normal \ + order processing time. The safety stock provides a buffer against the \ + possibility that unforeseen variations in demand will cause shipments to \ + fall below orders. + | + +Shipment Rate= + Desired Shipment Rate*Order Fulfillment Ratio + ~ Widgets/Week + ~ The shipment rate is the desired shipment rate multiplied by the fraction \ + of orders filled (the order fulfillment ratio. Shipments fall below \ + desired shipments when the feasible shipment rate falls below the desired \ + rate, indicating that some products are unavailable. + | + +Table for Order Fulfillment( + [(0,0)-(2,1)],(0,0),(0.2,0.2),(0.4,0.4),(0.6,0.58),(0.8,0.73),(1,0.85),(1.2,0.93),(1.4\ + ,0.97),(1.6,0.99),(1.8,1),(2,1)) + ~ Dimensionless + ~ The ability to ship is constrained by inventory availability. As the inventory \ + level drops, the + fraction of customer orders that can be filled decreases. When inventory is \ + zero, shipments + cease. Unfilled customer orders are lost.\!\!\! + | + +Time to Average Order Rate = 8 + ~ Weeks + ~ The demand forecast adjusts to actual customer orders over this time \ + period. + | + +WIP Adjustment Time = 2 + ~ Weeks + ~ The time required to adjust the WIP inventory to the desired level. + | + +Work in Process Inventory = INTEG(Production Start Rate - Production Rate,Desired WIP\ + ) + ~ Widgets + ~ WIP inventory accumulates the difference between production starts and \ + completions. + | + +******************************************************** + .Control +********************************************************~ + Simulation Control Parameters + | + +FINAL TIME = 50 + ~ Week + ~ The final time for the simulation. + | + +INITIAL TIME = 0 + ~ Week + ~ The initial time for the simulation. + | + +SAVEPER = + TIME STEP + ~ Week [0,?] + ~ The frequency with which output is stored. + | + +TIME STEP = 0.125 + ~ Week [0,?] + ~ The time step for the simulation. + | + +\\\---/// Sketch information - do not modify anything except names +V300 Do not put anything below this section - it will be ignored +*View 1 +$-1--1--1,0,|10||-1--1--1|-1--1--1|-1--1--1|-1--1--1|-1--1--1|96,96,90,0 +10,1,Inventory,332,355,40,20,3,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +12,2,48,-200,355,10,8,0,3,0,32,-1,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +11,3,0,182,354,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,4,Production Rate,182,380,41,6,40,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +12,5,48,762,356,10,8,0,3,0,50,-1,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +11,6,0,536,356,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,7,Shipment Rate,536,382,37,6,40,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,8,Desired Production,39,727,48,6,8,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,9,Production Adjustment from Inventory,303,554,43,15,8,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,10,Desired Inventory,393,616,44,6,8,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,11,Expected Order Rate,392,708,40,20,3,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +12,12,48,596,707,10,8,0,3,0,50,-1,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +11,13,0,511,707,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,14,Change in Exp Orders,511,721,55,6,40,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,15,Inventory Adjustment Time,243,493,43,10,8,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,16,Desired Inventory Coverage,508,633,25,15,8,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,17,Time to Average Order Rate,507,812,37,15,8,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,18,Order Fulfillment Ratio,528,466,52,8,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,19,Table for Order Fulfillment,597,549,36,13,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,20,Work in Process Inventory,38,355,40,20,3,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +11,21,0,-80,355,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,22,Production Start Rate,-80,383,50,8,40,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,23,Manufacturing Cycle Time,144,460,34,13,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,24,Adjustment for WIP,45,514,45,8,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,25,Desired WIP,149,568,30,8,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,26,Desired Production Start Rate,-123,521,45,13,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,27,WIP Adjustment Time,31,595,51,8,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,28,Customer Order Rate,770,555,50,8,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +12,29,0,431,408,20,20,5,4,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +B +12,30,0,433,445,39,13,8,4,0,40,0,0,0,0,0-0-0,0-0-0,Helvetica|8|B|0-0-0,0,0,0,0,0,0 +Order Fulfillment +12,31,0,263,410,20,20,4,4,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +B +12,32,0,265,447,39,13,8,4,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +Inventory Control +12,33,0,-38,470,20,20,4,4,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +B +12,34,0,-36,507,39,13,8,4,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +WIP Control +1,35,3,1,4,0,0,22,0,0,0,-1--1--1,,1|(240,354)| +1,36,3,20,100,0,0,22,0,0,0,-1--1--1,,1|(127,354)| +1,37,6,5,4,0,0,22,0,0,0,-1--1--1,,1|(647,356)| +1,38,6,1,100,0,0,22,0,0,0,-1--1--1,,1|(451,356)| +1,39,13,11,4,0,0,22,0,0,0,-1--1--1,,1|(468,707)| +1,40,13,12,100,0,0,22,0,0,0,-1--1--1,,1|(551,707)| +1,41,15,9,2,0,45,0,0,192,0,-1--1--1,,1|(240,533)| +1,42,1,9,2,0,45,0,0,192,0,-1--1--1,,1|(323,489)| +1,43,10,9,2,0,43,0,0,192,0,-1--1--1,,1|(369,572)| +1,44,16,10,2,0,43,0,0,0,0,-1--1--1,,1|(427,628)| +1,45,11,10,2,0,43,0,0,192,0,-1--1--1,,1|(397,627)| +1,46,9,8,2,0,43,0,0,192,0,-1--1--1,,1|(139,713)| +1,47,11,8,2,0,43,0,0,192,0,-1--1--1,,1|(215,757)| +1,48,11,14,2,0,45,0,0,192,0,-1--1--1,,1|(439,751)| +1,49,17,14,1,0,45,0,0,192,0,-1--1--1,,1|(558,750)| +1,50,18,7,2,0,43,0,0,192,0,-1--1--1,,1|(543,407)| +1,51,19,18,1,0,0,0,0,0,0,-1--1--1,,1|(538,498)| +1,52,28,14,2,0,43,0,0,128,0,-1--1--1,,1|(559,724)| +1,53,21,20,4,0,0,22,0,0,0,-1--1--1,,1|(-38,355)| +1,54,21,2,100,0,0,22,0,0,0,-1--1--1,,1|(-138,355)| +1,55,23,4,2,0,45,0,0,192,0,-1--1--1,,1|(158,391)| +1,56,20,24,2,0,45,0,0,192,0,-1--1--1,,1|(66,455)| +1,57,25,24,2,0,43,0,0,192,0,-1--1--1,,1|(81,514)| +1,58,23,25,2,0,43,0,0,192,0,-1--1--1,,1|(168,521)| +1,59,8,26,2,0,43,0,0,192,0,-1--1--1,,1|(-108,582)| +1,60,26,22,2,0,43,0,0,192,0,-1--1--1,,1|(-106,403)| +1,61,8,25,2,0,43,0,0,192,0,-1--1--1,,1|(143,599)| +1,62,24,26,2,0,43,0,0,192,0,-1--1--1,,1|(-47,559)| +1,63,27,24,1,0,45,0,0,192,0,-1--1--1,,1|(55,549)| +10,64,Desired Shipment Rate,635,431,54,8,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +1,65,28,64,1,0,43,0,0,192,0,-1--1--1,,1|(657,438)| +1,66,64,7,1,0,43,0,0,192,0,-1--1--1,,1|(590,387)| +10,67,Maximum Shipment Rate,380,492,58,8,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,68,Minimum Order Processing Time,467,551,40,13,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +1,69,1,67,1,0,43,0,0,192,0,-1--1--1,,1|(352,450)| +1,70,67,18,1,0,43,0,0,192,0,-1--1--1,,1|(469,491)| +1,71,64,18,1,0,45,0,0,192,0,-1--1--1,,1|(582,472)| +1,72,68,67,1,0,45,0,0,192,0,-1--1--1,,1|(398,540)| +1,73,68,16,1,0,43,0,0,192,0,-1--1--1,,1|(511,585)| +10,74,Inventory Coverage,435,290,46,8,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +1,75,1,74,1,0,43,0,0,192,0,-1--1--1,,1|(362,305)| +1,76,6,74,1,0,45,0,0,192,0,-1--1--1,,1|(491,297)| +10,77,Safety Stock Coverage,620,617,53,8,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +1,78,77,16,1,0,43,0,0,192,0,-1--1--1,,1|(571,645)| +1,79,10,1,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +1,80,28,11,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +1,81,25,20,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +1,82,20,4,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +///---\\\ +:L<%^E!@ +5:Table for Order Fulfillment +19:90,0 +24:0 +25:0 +26:0 +15:0,0,0,0,0,0 +27:0, +34:0, +42:1 +72:0 +73:0 +35:Date +36:YYYY-MM-DD +37:2000 +38:1 +39:1 +40:3 +41:0 +95:0 +96:0 +97:0 +77:0 +78:0 +102:1 +93:0 +94:0 +92:0 +91:0 +90:0 +87:0 +75: +43: +103:8,8,8,3,8 +105:0,0,0,0,0,0,0,0,0,0 +104:Courier|12||0-0-0|0-0-0|-1--1--1|0-0-0|192-192-192|-1--1--1 diff --git a/test_scripts/vensim_models/ds_white_sterman.mdl b/test_scripts/vensim_models/ds_white_sterman.mdl new file mode 100644 index 00000000..57d3419d --- /dev/null +++ b/test_scripts/vensim_models/ds_white_sterman.mdl @@ -0,0 +1,365 @@ +{UTF-8} +Adjustment for WIP = (Desired WIP - Work in Process Inventory)/WIP Adjustment Time + ~ Widgets/Week + ~ The adjustment to the production start rate from the adequacy of WIP \ + inventory. + | + +Change in Exp Orders = (Customer Order Rate-Expected Order Rate)/ +Time to Average Order Rate + ~ (Widgets/Week)/Week + ~ The demand forecast adjusts to the actual order rate over a time period determined \ + by the Time to + Average Order Rate. The demand forecast is formed by first-order \ + exponential smoothing, + a widely used forecasting technique. + | + +Customer Order Rate= + 10000 + ~ Widgets/Week + ~ Customer order rate is exogenous. A variety of test inputs allow users to try \ + different patterns, + including a step, pulse, sine wave, and random noise. + Initial Customer Order Rate*Input + | + +Desired Inventory = Desired Inventory Coverage*Expected Order Rate + ~ Widgets + ~ The desired inventory level sought by the plant. Experience suggests that to \ + maintain customer + service by providing full and reliable deliveries, the plant must maintain a \ + certain + coverage of throughput (demand), estimated by the demand forecast. + | + +Desired Inventory Coverage= + Minimum Order Processing Time + Safety Stock Coverage + ~ Weeks + ~ Desired inventory coverage is the number of weeks of the demand forecast the plant \ + seeks to maintain + in inventory. This inventory coverage is required to maintain delivery \ + reliability by + buffering the plant against unforeseen variations in demand or \ + production. It consists of the normal order processing time plus an \ + additional term representing the coverage desired to maintain safety \ + stocks. + | + +Desired Production = MAX(0,Expected Order Rate+Production Adjustment from Inventory) + ~ Widgets/Week + ~ Desired Production is the demand forecast (Expected Order Rate) adjusted to bring \ + the inventory + position in line with the target inventory level. + | + +Desired Production Start Rate = Desired Production + Adjustment for WIP + ~ Widgets/Week + ~ The desired rate of production starts, equal to the desired production rate adjusted \ + by the adequacy + of the WIP inventory. + | + +Desired Shipment Rate= + Customer Order Rate + ~ Widgets/Week + ~ The desired shipment rate equals the customer order rate. In this model \ + there is no backlog of unfilled orders: unfilled orders are lost as \ + customers seek alternate sources of supply. + | + +Desired WIP = Manufacturing Cycle Time*Desired Production + ~ Widgets + ~ The desired quantity of work in process inventory. Proportional to the \ + manufacturing cycle time and + the desired rate of production. + | + +Expected Order Rate = INTEG(Change in Exp Orders,Customer Order Rate) + ~ Widgets/Week + ~ The demand forecast is formed by adaptive expectations, using exponential smoothing, \ + a common + forecasting technique. The initial forecast is equal to the \ + initial customer order rate. + | + +Inventory = INTEG(Production Rate-Shipment Rate,Desired Inventory) + ~ Widgets + ~ The level of finished goods inventory in the plant. Increased by production and \ + decreased by + shipments. Initially set to the desired inventory level. + | + +Inventory Adjustment Time = 8 + ~ Weeks + ~ The inventory adjustment time is the time period over which the plant seeks to bring \ + inventory in + balance with the desired level. Initially set to 8 weeks. + | + +Inventory Coverage= + Inventory/Shipment Rate + ~ Weeks + ~ Inventory coverage is given by the ratio of inventory to shipments. + | + +Manufacturing Cycle Time= + 8 + ~ Weeks + ~ The average delay between the start and completion of production + | + +Maximum Shipment Rate= + Inventory/Minimum Order Processing Time + ~ Widgets/Week + ~ The maximum rate of shipments the firm can achieve given their current \ + inventory level and the minimum order processing time. + | + +Minimum Order Processing Time= + 2 + ~ Weeks + ~ The minimum time required to process and ship an order. + | + +Order Fulfillment Ratio= + Table for Order Fulfillment(Maximum Shipment Rate/Desired Shipment Rate) + ~ Dimensionless + ~ The Fraction of customer orders filled is determined by the ratio of the \ + normal shipment rate to the desired rate. The normal rate is the rate \ + current inventory permits under normal circumstances. Low inventory \ + availability reduces shipments below customer orders. Unfilled customer \ + orders are lost. + | + +Production Adjustment from Inventory = (Desired Inventory - Inventory)/ +Inventory Adjustment Time + ~ Widgets/Week + ~ The desired production rate is adjusted above or below the forecast based on the \ + inventory position + of the plant. When desired inventory > inventory, desired production is \ + increased (and + vice-versa). Inventory gaps are corrected over the inv. adj. \ + time. + | + +Production Rate= + Work in Process Inventory/Manufacturing Cycle Time + ~ Widgets/Week + ~ Used to be delay3 but changed to first order delay + DELAY3(Production Start Rate,Manufacturing Cycle Time) + Production is a third order delay of the production start rate, with the delay time \ + determined by + the manufacturing cycle time. + | + +Production Start Rate = MAX(0,Desired Production Start Rate) + ~ Widgets/Week + ~ The production start rate is the desired production start rate, \ + constrained to be nonnegative. + | + +Safety Stock Coverage= + 2 + ~ Weeks + ~ Safety stock coverage is the number of weeks of the expected order rate \ + the firm would like to maintain in inventory over and above the normal \ + order processing time. The safety stock provides a buffer against the \ + possibility that unforeseen variations in demand will cause shipments to \ + fall below orders. + | + +Shipment Rate= + Desired Shipment Rate*Order Fulfillment Ratio + ~ Widgets/Week + ~ The shipment rate is the desired shipment rate multiplied by the fraction \ + of orders filled (the order fulfillment ratio. Shipments fall below \ + desired shipments when the feasible shipment rate falls below the desired \ + rate, indicating that some products are unavailable. + | + +Table for Order Fulfillment( + [(0,0)-(2,1)],(0,0),(0.2,0.2),(0.4,0.4),(0.6,0.58),(0.8,0.73),(1,0.85),(1.2,0.93),(1.4\ + ,0.97),(1.6,0.99),(1.8,1),(2,1)) + ~ Dimensionless + ~ The ability to ship is constrained by inventory availability. As the inventory \ + level drops, the + fraction of customer orders that can be filled decreases. When inventory is \ + zero, shipments + cease. Unfilled customer orders are lost.\!\!\! + | + +Time to Average Order Rate = 8 + ~ Weeks + ~ The demand forecast adjusts to actual customer orders over this time \ + period. + | + +WIP Adjustment Time = 2 + ~ Weeks + ~ The time required to adjust the WIP inventory to the desired level. + | + +Work in Process Inventory = INTEG(Production Start Rate - Production Rate,Desired WIP\ + ) + ~ Widgets + ~ WIP inventory accumulates the difference between production starts and \ + completions. + | + +******************************************************** + .Control +********************************************************~ + Simulation Control Parameters + | + +FINAL TIME = 50 + ~ Week + ~ The final time for the simulation. + | + +INITIAL TIME = 0 + ~ Week + ~ The initial time for the simulation. + | + +SAVEPER = + TIME STEP + ~ Week [0,?] + ~ The frequency with which output is stored. + | + +TIME STEP = 0.125 + ~ Week [0,?] + ~ The time step for the simulation. + | + +\\\---/// Sketch information - do not modify anything except names +V300 Do not put anything below this section - it will be ignored +*View 1 +$-1--1--1,0,|10||-1--1--1|-1--1--1|-1--1--1|-1--1--1|-1--1--1|96,96,100,0 +10,1,Inventory,586,375,40,20,3,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +12,2,48,54,375,10,8,0,3,0,32,-1,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +11,3,0,436,374,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,4,Production Rate,436,388,41,6,40,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +12,5,48,1016,376,10,8,0,3,0,50,-1,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +11,6,0,790,376,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,7,Shipment Rate,790,390,37,6,40,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,8,Desired Production,293,747,48,6,8,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,9,Production Adjustment from Inventory,557,574,43,15,8,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,10,Desired Inventory,647,636,44,6,8,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,11,Expected Order Rate,646,728,40,20,3,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +12,12,48,850,727,10,8,0,3,0,50,-1,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +11,13,0,765,727,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,14,Change in Exp Orders,765,741,55,6,40,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,15,Inventory Adjustment Time,497,513,43,10,8,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,16,Desired Inventory Coverage,762,653,25,15,8,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,17,Time to Average Order Rate,761,832,37,15,8,3,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +10,18,Order Fulfillment Ratio,782,486,52,6,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,19,Table for Order Fulfillment,851,569,60,6,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,20,Work in Process Inventory,292,375,40,20,3,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +11,21,0,174,375,6,8,34,3,0,0,1,0,0,0,0,0,0,0,0,0 +10,22,Production Start Rate,174,389,50,6,40,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,23,Manufacturing Cycle Time,398,480,34,10,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,24,Adjustment for WIP,299,534,45,6,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,25,Desired WIP,403,588,30,6,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,26,Desired Production Start Rate,131,541,39,15,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,27,WIP Adjustment Time,285,615,50,6,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,28,Customer Order Rate,1024,575,50,6,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +12,29,0,685,428,20,20,5,4,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +B +12,30,0,687,465,39,13,8,4,0,40,0,0,0,0,0-0-0,0-0-0,Helvetica|8|B|0-0-0,0,0,0,0,0,0 +Order Fulfillment +12,31,0,517,430,20,20,4,4,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +B +12,32,0,519,467,39,13,8,4,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +Inventory Control +12,33,0,216,490,20,20,4,4,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +B +12,34,0,218,527,39,13,8,4,0,50,0,0,0,0,-1--1--1,0-0-0,Helvetica||B|0-0-0,0,0,0,0,0,0 +WIP Control +1,35,3,1,4,0,0,22,0,0,0,-1--1--1,,1|(494,374)| +1,36,3,20,100,0,0,22,0,0,0,-1--1--1,,1|(381,374)| +1,37,6,5,4,0,0,22,0,0,0,-1--1--1,,1|(901,376)| +1,38,6,1,100,0,0,22,0,0,0,-1--1--1,,1|(705,376)| +1,39,13,11,4,0,0,22,0,0,0,-1--1--1,,1|(722,727)| +1,40,13,12,100,0,0,22,0,0,0,-1--1--1,,1|(805,727)| +1,41,15,9,2,0,45,0,0,192,0,-1--1--1,,1|(494,553)| +1,42,1,9,2,0,45,0,0,192,0,-1--1--1,,1|(577,509)| +1,43,10,9,2,0,43,0,0,192,0,-1--1--1,,1|(623,592)| +1,44,16,10,2,0,43,0,0,0,0,-1--1--1,,1|(681,648)| +1,45,11,10,2,0,43,0,0,192,0,-1--1--1,,1|(651,647)| +1,46,9,8,2,0,43,0,0,192,0,-1--1--1,,1|(393,733)| +1,47,11,8,2,0,43,0,0,192,0,-1--1--1,,1|(469,777)| +1,48,11,14,2,0,45,0,0,192,0,-1--1--1,,1|(693,771)| +1,49,17,14,1,0,45,0,0,192,0,-1--1--1,,1|(812,770)| +1,50,18,7,2,0,43,0,0,192,0,-1--1--1,,1|(791,428)| +1,51,19,18,1,0,0,0,0,0,0,-1--1--1,,1|(792,518)| +1,52,28,14,2,0,43,0,0,128,0,-1--1--1,,1|(813,744)| +1,53,21,20,4,0,0,22,0,0,0,-1--1--1,,1|(216,375)| +1,54,21,2,100,0,0,22,0,0,0,-1--1--1,,1|(116,375)| +1,55,23,4,2,0,45,0,0,192,0,-1--1--1,,1|(410,416)| +1,56,20,24,2,0,45,0,0,192,0,-1--1--1,,1|(320,475)| +1,57,25,24,2,0,43,0,0,192,0,-1--1--1,,1|(335,534)| +1,58,23,25,2,0,43,0,0,192,0,-1--1--1,,1|(422,541)| +1,59,8,26,2,0,43,0,0,192,0,-1--1--1,,1|(146,602)| +1,60,26,22,2,0,43,0,0,192,0,-1--1--1,,1|(145,421)| +1,61,8,25,2,0,43,0,0,192,0,-1--1--1,,1|(397,619)| +1,62,24,26,2,0,43,0,0,192,0,-1--1--1,,1|(207,579)| +1,63,27,24,1,0,45,0,0,192,0,-1--1--1,,1|(309,569)| +10,64,Desired Shipment Rate,889,451,54,6,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +1,65,28,64,1,0,43,0,0,192,0,-1--1--1,,1|(911,458)| +1,66,64,7,1,0,43,0,0,192,0,-1--1--1,,1|(836,396)| +10,67,Maximum Shipment Rate,634,512,58,6,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +10,68,Minimum Order Processing Time,721,571,40,10,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +1,69,1,67,1,0,43,0,0,192,0,-1--1--1,,1|(606,470)| +1,70,67,18,1,0,43,0,0,192,0,-1--1--1,,1|(723,511)| +1,71,64,18,1,0,45,0,0,192,0,-1--1--1,,1|(836,492)| +1,72,68,67,1,0,45,0,0,192,0,-1--1--1,,1|(652,560)| +1,73,68,16,1,0,43,0,0,192,0,-1--1--1,,1|(765,605)| +10,74,Inventory Coverage,689,310,46,6,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +1,75,1,74,1,0,43,0,0,192,0,-1--1--1,,1|(616,325)| +1,76,6,74,1,0,45,0,0,192,0,-1--1--1,,1|(745,317)| +10,77,Safety Stock Coverage,874,637,53,6,8,3,0,32,0,0,0,0,0-0-0,0-0-0,Helvetica|||0-0-0,0,0,0,0,0,0 +1,78,77,16,1,0,43,0,0,192,0,-1--1--1,,1|(825,665)| +1,79,10,1,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +1,80,28,11,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +1,81,25,20,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)| +1,82,20,4,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)| +///---\\\ +:L<%^E!@ +5:FINAL TIME +19:100,0 +24:0 +25:0 +26:0 +15:0,0,0,0,0,0 +27:0, +34:0, +42:1 +72:0 +73:0 +35:Date +36:YYYY-MM-DD +37:2000 +38:1 +39:1 +40:3 +41:0 +95:0 +96:0 +97:0 +77:0 +78:0 +102:1 +93:0 +94:0 +92:0 +91:0 +90:0 +87:0 +75: +43: +103:8,8,8,3,8 +105:0,0,0,0,0,0,0,0,0,0 +104:Helvetica|10||0-0-0|0-0-0|-1--1--1|0-0-0|192-192-192|-1--1--1 From 76eea02901a4b38281357daaf88447040c1031a9 Mon Sep 17 00:00:00 2001 From: amoon Date: Thu, 18 Aug 2022 03:01:33 -0400 Subject: [PATCH 20/45] Relocate notebook file --- test_scripts/{stan_file => }/demand_supply.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename test_scripts/{stan_file => }/demand_supply.ipynb (100%) diff --git a/test_scripts/stan_file/demand_supply.ipynb b/test_scripts/demand_supply.ipynb similarity index 100% rename from test_scripts/stan_file/demand_supply.ipynb rename to test_scripts/demand_supply.ipynb From ae761d3b07f1ef0bbcf743a046605b4947b181b6 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Thu, 18 Aug 2022 20:53:39 +0900 Subject: [PATCH 21/45] Update return signature of ode function and change deprecated stan functions --- pysd/builders/stan/ast_walker.py | 12 ++++++------ pysd/builders/stan/stan_model_builder.py | 13 ++++++++++++- test_scripts/testing.py | 12 +++++++++--- 3 files changed, 27 insertions(+), 10 deletions(-) diff --git a/pysd/builders/stan/ast_walker.py b/pysd/builders/stan/ast_walker.py index 64f22d1f..8e42652f 100644 --- a/pysd/builders/stan/ast_walker.py +++ b/pysd/builders/stan/ast_walker.py @@ -140,9 +140,9 @@ def walk(self, ast_node) -> str: output_string = "" function_name = self.walk(ast_node.function) if function_name == "min": - function_name = "fmin" + function_name = "min" elif function_name == "max": - function_name = "fmax" + function_name = "max" elif function_name == "xidz": assert ( len(ast_node.arguments) == 3 @@ -151,7 +151,7 @@ def walk(self, ast_node) -> str: arg2 = self.walk(ast_node.arguments[1]) arg3 = self.walk(ast_node.arguments[2]) output_string += ( - f" (fabs({arg2}) <= 1e-6) ? {arg3} : ({arg1}) / ({arg2})" + f" (abs({arg2}) <= 1e-6) ? {arg3} : ({arg1}) / ({arg2})" ) return output_string elif function_name == "zidz": @@ -161,7 +161,7 @@ def walk(self, ast_node) -> str: arg1 = self.walk(ast_node.arguments[0]) arg2 = self.walk(ast_node.arguments[1]) output_string += ( - f" (fabs({arg2}) <= 1e-6) ? 0 : ({arg1}) / ({arg2})" + f" (abs({arg2}) <= 1e-6) ? 0 : ({arg1}) / ({arg2})" ) return output_string elif function_name == "ln": @@ -284,12 +284,12 @@ def walk(self, ast_node) -> str: def rng_codegen(self, rng_type: str, arguments: List[Any]): if rng_type == "random_normal": lower, upper, mean, std, _ = arguments - return f"fmin(fmax(normal_rng({mean}, {std}), {lower}), {upper})" + return f"min(max(normal_rng({mean}, {std}), {lower}), {upper})" elif rng_type == "random_uniform": lower, upper, _ = arguments return f"uniform_rng({lower}, {upper})" elif rng_type == "random_poisson": lower, upper, _lambda, offset, multiply, _ = arguments - return f"fmin(fmax(fma(poisson_rng({_lambda}), {multiply}, {offset}), {lower}), {upper})" + return f"min(max(fma(poisson_rng({_lambda}), {multiply}, {offset}), {lower}), {upper})" else: raise Exception(f"RNG function {rng_type} not implemented") diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_model_builder.py index de6312c9..b2ed1186 100644 --- a/pysd/builders/stan/stan_model_builder.py +++ b/pysd/builders/stan/stan_model_builder.py @@ -350,6 +350,10 @@ def recursive_order_search(current, visited): self.code.indent_level += 1 # Enter function body + self.code += f"vector[{len(outcome_variable_names)}] dydt; # Return vector of the ODE function\n" + self.code += "\n" + + self.code += "# State variables\n" for index, outcome_variable_name in enumerate( outcome_variable_names, 1 ): @@ -377,7 +381,14 @@ def recursive_order_search(current, visited): outcome_variable_names = [ name + "_dydt" for name in outcome_variable_names ] - self.code += f"return {{{', '.join(outcome_variable_names)}}};\n" + for index, outcome_variable_name in enumerate( + outcome_variable_names, 1 + ): + self.code += f"dydt[{index}] = {outcome_variable_name};\n" + + self.code += "\n" + self.code += "return dydt;\n" + self.code.indent_level -= 1 # Exit function body self.code += "}\n" diff --git a/test_scripts/testing.py b/test_scripts/testing.py index ef27f796..b95c57ab 100644 --- a/test_scripts/testing.py +++ b/test_scripts/testing.py @@ -2,7 +2,7 @@ from pysd.translators.xmile.xmile_file import XmileFile from pysd.builders.stan.stan_model_builder import * -vf = VensimFile("vensim_models/demand-supply.mdl") +vf = VensimFile("vensim_models/ds_white_sterman.mdl") #vf = VensimFile("vensim_models/arithmetic.mdl") #vf = XmileFile("vensim_models/repair.xmile") vf.parse() @@ -11,8 +11,14 @@ stan_builder = StanModelBuilder(am) stan_builder.print_variable_info() -#print(stan_builder.create_stan_program([("int", "failure_count"), "repair_time"], ["battle_field", "repair_shop"])) # repair -print(stan_builder.create_stan_program(["Demand"])) + +ass_param_lst = ["customer_order_rate", "inventory_coverage", "manufacturing_cycle_time", "time_to_average_order_rate", "wip_adjustment_time"] +obs_stock_lst = ["work_in_process_inventory", "inventory"] + +#print(stan_builder.create_stan_program(ass_param_lst, obs_stock_lst)) + +f_builder = StanFunctionBuilder(am) +print(f_builder.build_function_block(ass_param_lst, obs_stock_lst)) # for section in am.sections: # for element in section.elements: # print("*" * 10) From dc961b65beb6235c43db66e73dc3f3cf27ce42f0 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Thu, 18 Aug 2022 20:58:03 +0900 Subject: [PATCH 22/45] Change output signature of ode call --- pysd/builders/stan/stan_model_builder.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_model_builder.py index b2ed1186..925c79cf 100644 --- a/pysd/builders/stan/stan_model_builder.py +++ b/pysd/builders/stan/stan_model_builder.py @@ -222,7 +222,7 @@ def build_block( self.code += f"vector[{len(outcome_variable_names)}] initial_outcome = {{{', '.join([x + '_initial' for x in outcome_variable_names])}}};\n" - self.code += f"array[] vector integrated_result = integrate_ode_rk45({function_name}, initial_outcome, initial_time, times, {','.join(argument_variables)});\n" + self.code += f"vector[{len(outcome_variable_names)}] integrated_result[T] = integrate_ode_rk45({function_name}, initial_outcome, initial_time, times, {','.join(argument_variables)});\n" self.code.indent_level -= 1 self.code += "}\n" From 8cd38f2a2109a312c7964e83b1487506e3abad55 Mon Sep 17 00:00:00 2001 From: amoon Date: Thu, 18 Aug 2022 07:58:25 -0400 Subject: [PATCH 23/45] Update notebook to include full flow --- test_scripts/stan_file/demand_supply.ipynb | 598 +++++++++++++++++++++ 1 file changed, 598 insertions(+) create mode 100644 test_scripts/stan_file/demand_supply.ipynb diff --git a/test_scripts/stan_file/demand_supply.ipynb b/test_scripts/stan_file/demand_supply.ipynb new file mode 100644 index 00000000..97422c06 --- /dev/null +++ b/test_scripts/stan_file/demand_supply.ipynb @@ -0,0 +1,598 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c1026b9e-9c63-4bbf-afea-4d3b8c4a4898", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from IPython.display import Image\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "import pysd\n", + "from pysd.translators.vensim.vensim_file import VensimFile\n", + "from pysd.translators.xmile.xmile_file import XmileFile\n", + "from pysd.builders.stan.stan_model_builder import *\n", + "\n", + "import cmdstanpy # 2.30 is fastest (as of 08.12.2022) `cmdstanpy.install_cmdstan()` \n", + "from cmdstanpy import CmdStanModel, cmdstan_path\n", + "import arviz as az #!pip install git+https://github.com/arviz-devs/arviz\n", + "az.style.use(\"arviz-darkgrid\")\n", + "\n", + "# set your working directiory\n", + "os.chdir(\"/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts\")" + ] + }, + { + "attachments": { + "49465262-0d0e-4530-924a-86d154fd9c04.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "c6c216c5-4f22-49a4-8cdc-e04fa3a0b098", + "metadata": {}, + "source": [ + "User-Program Workflow \n", + "- model parameter \"prior calibration\" (specification for desired data)\n", + "- prior-predictive, SBC, posterior-predictive\n", + "- purpose: analyze, statistics\n", + "\n", + "\n", + "Six software Workflow \n", + "- policy parameter \"prior calibration\" (specification for desired behavior)\n", + "- behavior-parameter classification/mapping\n", + "- purpose: prescribe, science\n", + "\n", + "Details in https://github.com/hyunjimoon/DataInDM#supply-of-silkroad-project\n", + "![image.png](attachment:49465262-0d0e-4530-924a-86d154fd9c04.png)\n", + "\n", + "i) User-Program workflow (August)\n", + "\n", + "ii) structural dominance analysis, pattern recognition (September)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6866c023-2d95-40e4-a7bf-2b65254f9d85", + "metadata": { + "tags": [] + }, + "source": [ + "# 1. User-Program Workflow (Analyze)\n", + "\n", + "| Step | Goal | Program's work (P-rows have `.function(input)`) | User's work |\n", + "| ---- | ------------------ | ------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------- |\n", + "| U1 | Draft | `Vensim` assists U1.a() | a. Translate mental model to SD model |\n", + "| U2 | Classify | `PySD` assists U2.a() | a. Classify parameters `est_param`, `ass_param`, b. Select `obs_state` among stocks |\n", + "| P1 | relate | `PySD`, `.build_function_block`(U1.a) | |\n", + "| U3 | Specify_project | | a. Supply value or series of `assmed_param`, b. Choose `family`(:= dist. of `msr_err_scale`) |\n", + "| U4 | Specify_regularize | | a. Choose `prior_family`(`est_param`'s prior dist. type) , b. Set `prior_param` (`est_param`'s prior param) |\n", + "| P2 | predict | `draws2data.stan`, `fit_prior_data.sample()`, `fit_prior_data = (U2.ab, U3.ab, U4.ab)`: Prior predictive check (opt-out prior) | |\n", + "| P3 | infer to verify | `data2draws.stan`,`.create_stan_program`(U2.ab, U3.ab): Infer parameter from (synthetic) data: SBC | |\n", + "| U5 | Specify_tolerance | | a. Set precision with `iter_sampling` (:= # of samples), b. Select posterior approximator |\n", + "| P4 | infer to validate | `Stan`, `fit_post_draws.sample()`, ` fit_post_draws = (P1, U3.ab, U4.ab, U5.ab)`: Posterior predictive check (opt-in prior) | |\n", + "\n", + "\n", + "##### Q. family and prior dist change\n", + "How often does the measurement model (family) change? Can all (or some) changes be covered with prior change (e.g. adding hierarchy; family poisson to neg_binom is the same with gamma prior for rate)?\n", + "\n", + "What scenarios does user decide to change prior distribution (not prior parameter)?" + ] + }, + { + "attachments": { + "db58966d-7db3-43ac-9114-da7b079d88c4.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "07c14386-a246-4f4d-a4e5-ad85fa338c19", + "metadata": { + "tags": [] + }, + "source": [ + "## U1. Draft\n", + "From mental model to SD model.\n", + "![image.png](attachment:db58966d-7db3-43ac-9114-da7b079d88c4.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9dcead94-0f1f-4396-8b41-65c41d68df57", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "original name stan variable name is stock\n", + "----------------------------------------------------------------------------------\n", + "Adjustment for WIP adjustment_for_wip \n", + "Change in Exp Orders change_in_exp_orders \n", + "Customer Order Rate customer_order_rate \n", + "Desired Inventory desired_inventory \n", + "Desired Inventory Coverage desired_inventory_coverage \n", + "Desired Production desired_production \n", + "Desired Production Start Rate desired_production_start_rate \n", + "Desired Shipment Rate desired_shipment_rate \n", + "Desired WIP desired_wip \n", + "Expected Order Rate expected_order_rate V\n", + "Inventory inventory V\n", + "Inventory Adjustment Time inventory_adjustment_time \n", + "Inventory Coverage inventory_coverage \n", + "Manufacturing Cycle Time manufacturing_cycle_time \n", + "Maximum Shipment Rate maximum_shipment_rate \n", + "Minimum Order Processing Time minimum_order_processing_time \n", + "Order Fulfillment Ratio order_fulfillment_ratio \n", + "Production Adjustment from Inventory production_adjustment_from_inventory \n", + "Production Rate production_rate \n", + "Production Start Rate production_start_rate \n", + "Safety Stock Coverage safety_stock_coverage \n", + "Shipment Rate shipment_rate \n", + "Table for Order Fulfillment table_for_order_fulfillment \n", + "Time to Average Order Rate time_to_average_order_rate \n", + "WIP Adjustment Time wip_adjustment_time \n", + "Work in Process Inventory work_in_process_inventory V\n", + "FINAL TIME final_time \n", + "INITIAL TIME initial_time \n", + "SAVEPER saveper \n", + "TIME STEP time_step \n" + ] + } + ], + "source": [ + "vf = VensimFile(\"vensim_models/ds_white_sterman.mdl\")\n", + "vf.parse()\n", + "am = vf.get_abstract_model()\n", + "stan_builder = StanModelBuilder(am)\n", + "stan_builder.print_variable_info()" + ] + }, + { + "cell_type": "markdown", + "id": "477f3043-0f99-4bcd-9c78-da7e53081fd0", + "metadata": { + "tags": [] + }, + "source": [ + "## U2. Classify\n", + "\n", + "| variable name | `est_param` | `ass_param` | `obs_stock` |\n", + "| ------------------------------------ | ----------- | ----------- | ----------- |\n", + "| adjustment_for_wip | | | |\n", + "| change_in_exp_orders | | | |\n", + "| customer_order_rate | | V | |\n", + "| desired_inventory | | | |\n", + "| desired_inventory_coverage | | | |\n", + "| desired_production | | | |\n", + "| desired_production_start_rate | | | |\n", + "| desired_shipment_rate | | | |\n", + "| desired_wip | | | |\n", + "| expected_order_rate | | | V |\n", + "| inventory | | | V |\n", + "| inventory_adjustment_time | V | | |\n", + "| inventory_coverage | | V | |\n", + "| manufacturing_cycle_time | | V | |\n", + "| maximum_shipment_rate | | | |\n", + "| minimum_order_processing_time | V | | |\n", + "| order_fulfillment_ratio | | | |\n", + "| production_adjustment_from_inventory | | | |\n", + "| production_rate | | | |\n", + "| production_start_rate | | | |\n", + "| safety_stock_coverage | | | |\n", + "| shipment_rate | | | |\n", + "| table_for_order_fulfillment | | V (lookup) | |\n", + "| time_to_average_order_rate | | V | |\n", + "| wip_adjustment_time | | V | |\n", + "| work_in_process_inventory | | | V |\n", + "| initial_time | | V | |\n", + "| final_time | | V | |\n", + "| time_step | | V | |\n", + "\n", + "The rest is `aux_var` which are derived from the defined." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a3d2b4a9-532f-4f3f-858e-64b4877c1997", + "metadata": {}, + "outputs": [], + "source": [ + "ass_param_lst = [\"customer_order_rate\", \"inventory_coverage\", \"manufacturing_cycle_time\", \"time_to_average_order_rate\", \"wip_adjustment_time\"]\n", + "obs_stock_lst = [\"work_in_process_inventory\", \"inventory\"]" + ] + }, + { + "cell_type": "markdown", + "id": "33ac5c16-f572-46ba-8d79-062f601a5e24", + "metadata": {}, + "source": [ + "## P1. relate\n", + "From SD model (`.mdl`) to Stan ODE function block (`.stan`). No new information is added." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d1a14086-45cd-4f99-9e69-51aa57dea790", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "functions {\n", + " real lookupFunc_0(real x){\n", + " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", + " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", + " real slope;\n", + " real intercept;\n", + "\n", + " if(x <= 0.2)\n", + " intercept = 0.0;\n", + " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", + " return intercept + slope * (x - 0.0);\n", + " else if(x <= 0.4)\n", + " intercept = 0.2;\n", + " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", + " return intercept + slope * (x - 0.2);\n", + " else if(x <= 0.6)\n", + " intercept = 0.4;\n", + " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", + " return intercept + slope * (x - 0.4);\n", + " else if(x <= 0.8)\n", + " intercept = 0.58;\n", + " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", + " return intercept + slope * (x - 0.6);\n", + " else if(x <= 1.0)\n", + " intercept = 0.73;\n", + " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", + " return intercept + slope * (x - 0.8);\n", + " else if(x <= 1.2)\n", + " intercept = 0.85;\n", + " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", + " return intercept + slope * (x - 1.0);\n", + " else if(x <= 1.4)\n", + " intercept = 0.93;\n", + " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", + " return intercept + slope * (x - 1.2);\n", + " else if(x <= 1.6)\n", + " intercept = 0.97;\n", + " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", + " return intercept + slope * (x - 1.4);\n", + " else if(x <= 1.8)\n", + " intercept = 0.99;\n", + " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", + " return intercept + slope * (x - 1.6);\n", + " else if(x <= 2.0)\n", + " intercept = 1.0;\n", + " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", + " return intercept + slope * (x - 1.8);\n", + " }\n", + "\n", + " # Begin ODE declaration\n", + " vector vensim_func(real time, vector outcome, real customer_order_rate, real inventory_coverage, real manufacturing_cycle_time, real time_to_average_order_rate, real wip_adjustment_time ){\n", + " real work_in_process_inventory = outcome[1];\n", + " real inventory = outcome[2];\n", + "\n", + " real inventory_adjustment_time = 8;\n", + " real safety_stock_coverage = 2;\n", + " real minimum_order_processing_time = 2;\n", + " real desired_inventory_coverage = minimum_order_processing_time + safety_stock_coverage;\n", + " real change_in_exp_orders = customer_order_rate - expected_order_rate / time_to_average_order_rate;\n", + " real expected_order_rate = change_in_exp_orders;\n", + " real desired_inventory = desired_inventory_coverage * expected_order_rate;\n", + " real production_adjustment_from_inventory = desired_inventory - inventory / inventory_adjustment_time;\n", + " real desired_production = fmax(0,expected_order_rate + production_adjustment_from_inventory);\n", + " real desired_wip = manufacturing_cycle_time * desired_production;\n", + " real maximum_shipment_rate = inventory / minimum_order_processing_time;\n", + " real desired_shipment_rate = customer_order_rate;\n", + " real order_fulfillment_ratio = table_for_order_fulfillment(maximum_shipment_rate / desired_shipment_rate);\n", + " real shipment_rate = desired_shipment_rate * order_fulfillment_ratio;\n", + " real adjustment_for_wip = desired_wip - work_in_process_inventory / wip_adjustment_time;\n", + " real desired_production_start_rate = desired_production + adjustment_for_wip;\n", + " real production_start_rate = fmax(0,desired_production_start_rate);\n", + " real production_rate = work_in_process_inventory / manufacturing_cycle_time;\n", + " real inventory_dydt = production_rate - shipment_rate;\n", + " real work_in_process_inventory_dydt = production_start_rate - production_rate;\n", + "\n", + " return {work_in_process_inventory_dydt, inventory_dydt};\n", + " }\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "am = vf.get_abstract_model()\n", + "stan_function_builder = StanFunctionBuilder(am) \n", + "ds_relational = stan_function_builder.build_function_block(ass_param_lst, obs_stock_lst)\n", + "print(ds_relational)\n", + "stan_file_path = os.path.join(os.getcwd(), \"stan_file\", \"ds_relational.stan\")\n", + "with open(stan_file_path, \"w\") as f:\n", + " print(ds_relational, file=f)" + ] + }, + { + "cell_type": "markdown", + "id": "75a0b7de-7c90-4823-9b38-7370f3cea02f", + "metadata": {}, + "source": [ + "## U3. Specify_project\n", + "\n", + "#### Assumed parameter $X$ \n", + "\n", + "- declared in generated quantities block for `_draws2data.stan` and data block for `_data2draws.stan`.\n", + "- specified with its actual value or series or lookup function (aggregation)\n", + "\n", + "| `ass_param` | value/series |\n", + "| ---------------------------- | ----------------- |\n", + "| `customer_order_rate` | N(10000, $100^2$) |\n", + "| `time_to_average_order_rate` | 8 |\n", + "| `wip_adjustment_time` | 8 |\n", + "| `manufacturing_cycle_time` | 8 |\n", + "| `safety_stock_coverage` | 2 |\n", + "|`initial_time`, `final_time`, `time_step` | 0, 10, .125|\n", + "|`table_for_order_fulfillment`| lookup function|\n", + "\n", + "## U4. Specify_regularize\n", + "\n", + "#### Estimated parameter $\\theta$ \n", + "\n", + "- declared in generated quantities block for `_draws2data.stan` and model block for `_data2draws.stan`.\n", + "\n", + "| `ess_param` | (min, mode, max) | distribuiton type| \n", + "| ------------------------------- | ---------------- | ------------ |\n", + "| `inventory_adjustment_time` | (6,8,12) | N(8, $1^2$) |\n", + "| `minimum_order_processing_time` | (1,2,4) | N(2, $.5^2$) |\n", + "\n", + "\n", + "##### Q. Can `msr_err` (min, mode, max) be helpful info?\n", + "##### Q. Shouldn't `msr_err` distribution determine `family`? Then, `Poisson`, `Neg_Binom`, `\n", + "\n", + "\n", + "#### Latent stock $Z$\n", + "\n", + "- declared in generated quantities block for `_draws2data.stan` and data block for `_data2draws.stan`.\n", + "\n", + "#### Measurement error\n", + "\n", + "- `msr_err` is specified with `family` and its parameter\n", + "| `msr_err` |??|lognormal, inverse_gamma|\n", + "\n", + "\n", + "#### Observed stock $Y$\n", + "\n", + "- declared in generated quantities block for `_draws2data.stan` and data block for `_data2draws.stan`.\n", + "- $Y \\sim$ `family`(Z, `msr_err` )" + ] + }, + { + "cell_type": "markdown", + "id": "6671aae2-5375-4056-b4f0-83cda80ba708", + "metadata": {}, + "source": [ + "## P2. predict\n", + "\n", + "\n", + "- based on `est_param` specification (a = lower_bound, b= most likely, c = upper_bound) in U3, its prior is automatically set to $\\theta \\sim N(\\frac{a+4b+c}{6}, \\frac{c-a}{6})$ using [PERT dist](https://en.wikipedia.org/wiki/PERT_distribution)\n", + "\n", + "| `ess_param` | Prior distribution | Prior parameter| \n", + "| ------------------------------- | ---------------- | ------------ |\n", + "| `inventory_adjustment_time` | Normal | loc = 8, scale = $1^2$ |\n", + "| `minimum_order_processing_time` | Normal |loc = 2, scale = $.5^2$ |\n", + "| `msr_err` |lognormal, inverse_gamma|\n", + "\n", + "##### Q. feedback on PERT?\n", + "\n", + "##### Q. how do we usually determine `msr_err`'s prior parameter?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "edd2203d-618e-464b-b3f2-15ed26b1e7cf", + "metadata": {}, + "outputs": [], + "source": [ + "initial_time = 0\n", + "final_time = 10\n", + "time_step = .125\n", + "\n", + "N = int((final_time - initial_time)/time_step)\n", + "data_draws2data = {\n", + " \"N\": N,\n", + " \"times\": np.arange(1, N + 1),\n", + " \"customer_order_rate\": np.random.normal(loc = 10000, scale = 100, size = N),\n", + " \"time_to_average_order_rate\" : 8, \n", + " \"wip_adjustment_time\" :2,\n", + " 'manufacturing_cycle_time' : 8,\n", + " 'safety_stock_coverage' : 2\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "217fa4b0-cd0f-4fb5-a3ac-647da6531ed3", + "metadata": { + "tags": [] + }, + "source": [ + "## P3. infer to verify" + ] + }, + { + "cell_type": "markdown", + "id": "bb81280a-e5a7-4b6c-aebb-48a18dc44f61", + "metadata": {}, + "source": [ + "The first argument is `ass_param` and the second is `observed stock`. Design for `est_param` is under-development including \n", + "##### Q. how to express multi-levle prior? Auto scale?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b393e0fd-470f-43f5-a535-866f689aed24", + "metadata": {}, + "outputs": [], + "source": [ + "ds_draws2data = stan_builder.create_stan_program(ass_param_lst, obs_stock_lst)\n", + "#print(ds_draws2data)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a5d2525b-e441-4e79-a0f3-8d5c2c953c63", + "metadata": {}, + "outputs": [], + "source": [ + "sf_path_draws2data = os.path.join(os.getcwd(), \"stan_file\", \"ds_draws2data.stan\")\n", + "# with open(sf_path_draws2data, \"w\") as f:\n", + "# print(ds_draws2data, file=f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16713c97-3371-4ecb-aa04-310ad0480034", + "metadata": {}, + "outputs": [], + "source": [ + "sm_draws2data = CmdStanModel(stan_file = sf_path_draws2data)\n", + "fit_prior_data = sm_draws2data.sample(data=data_draws2data, iter_sampling=30, chains=1, fixed_param=True, iter_warmup=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3939e7a3-259b-47a3-9360-0b17a54726b3", + "metadata": {}, + "outputs": [], + "source": [ + "fit_prior_data.draws_xr()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "515002a4-35be-4752-a705-d09f70a9c43f", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "#compare with real \n", + "ax.plot(fit_prior_data.loc[:, ['y_tilde']], label = \"\")\n", + "ax.plot(state_dt.loc[:, ['Predator']], label = \"\")\n", + "for i in range(len(obs_stock_lst)):\n", + " ax.plot(pd.DataFrame(fit_prior_data.y_tilde[:,:,i]).T.loc[:, :5])\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "12626a31-08bc-4a5d-ad74-39916446e4ff", + "metadata": {}, + "source": [ + "## U5. Specify_tolerance\n", + "\n", + "#### Q. how to set 10^-2, 3,7~? ode_rk45 precison" + ] + }, + { + "cell_type": "markdown", + "id": "960d1e72-a5e4-4dcb-995f-e9d689c26149", + "metadata": { + "tags": [] + }, + "source": [ + "## P4. infer to validate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f91d6908-3ff1-4787-b80a-f43b85e0a6ff", + "metadata": {}, + "outputs": [], + "source": [ + "sf_path_data2draws = os.path.join(os.getcwd(), \"stan_file\", \"ds_data2draws.stan\")\n", + "with open(sf_path_data2draws, \"w\") as f:\n", + " print(ds_draws2data, file=f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b22c5ede-6e25-41da-9a97-cc2d641d2c7a", + "metadata": {}, + "outputs": [], + "source": [ + "idata = az.from_cmdstanpy(\n", + " posterior=fit_posterior_draws, \n", + " posterior_predictive=[\"y_hat\"], \n", + " log_likelihood= [\"log_lik\"],\n", + " observed_data = {\"y_hat\": lynx_hare_df.loc[:, (\"Hare\", \"Lynx\")]}\n", + "# dtypes={\"y_rep\": int} if Poisson family\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b44779b8-93ce-4539-a892-695e121c684a", + "metadata": {}, + "outputs": [], + "source": [ + "az.loo(idata)\n", + "az.plot_ppc(idata, alpha=0.03, figsize=(12, 6))" + ] + }, + { + "cell_type": "markdown", + "id": "f5191cd0-da48-4846-bc30-6d11304479a7", + "metadata": {}, + "source": [ + "###### Q.brms state\n", + "```\n", + "Confused about brms family quas\n", + "quasi(link = \"identity\", variance = \"constant\")\n", + "quasibinomial(link = \"logit\")\n", + "quasipoisson(link = \"log\")\n", + "```\n", + "\n", + "###### Q.hierarchical auto-scaling, formula (+others)\n", + "https://github.com/hyunjimoon/DataInDM/issues/9" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "local-venv", + "language": "python", + "name": "local-venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 76f5485d355d088da8b9fe3854a001243282bdd3 Mon Sep 17 00:00:00 2001 From: amoon Date: Thu, 18 Aug 2022 10:37:54 -0400 Subject: [PATCH 24/45] Add tables to workflow --- test_scripts/stan_file/demand_supply.ipynb | 2 +- test_scripts/stan_file/ds_relational.stan | 592 +-------------------- 2 files changed, 9 insertions(+), 585 deletions(-) diff --git a/test_scripts/stan_file/demand_supply.ipynb b/test_scripts/stan_file/demand_supply.ipynb index 97422c06..731b943c 100644 --- a/test_scripts/stan_file/demand_supply.ipynb +++ b/test_scripts/stan_file/demand_supply.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "id": "c1026b9e-9c63-4bbf-afea-4d3b8c4a4898", "metadata": {}, "outputs": [], diff --git a/test_scripts/stan_file/ds_relational.stan b/test_scripts/stan_file/ds_relational.stan index ab2c4ff3..e11c9bfe 100644 --- a/test_scripts/stan_file/ds_relational.stan +++ b/test_scripts/stan_file/ds_relational.stan @@ -47,607 +47,31 @@ functions { return intercept + slope * (x - 1.8); } - real lookupFunc_0(real x){ - # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) - # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) - real slope; - real intercept; - - if(x <= 0.2) - intercept = 0.0; - slope = (0.2 - 0.0) / (0.2 - 0.0); - return intercept + slope * (x - 0.0); - else if(x <= 0.4) - intercept = 0.2; - slope = (0.4 - 0.2) / (0.4 - 0.2); - return intercept + slope * (x - 0.2); - else if(x <= 0.6) - intercept = 0.4; - slope = (0.58 - 0.4) / (0.6 - 0.4); - return intercept + slope * (x - 0.4); - else if(x <= 0.8) - intercept = 0.58; - slope = (0.73 - 0.58) / (0.8 - 0.6); - return intercept + slope * (x - 0.6); - else if(x <= 1.0) - intercept = 0.73; - slope = (0.85 - 0.73) / (1.0 - 0.8); - return intercept + slope * (x - 0.8); - else if(x <= 1.2) - intercept = 0.85; - slope = (0.93 - 0.85) / (1.2 - 1.0); - return intercept + slope * (x - 1.0); - else if(x <= 1.4) - intercept = 0.93; - slope = (0.97 - 0.93) / (1.4 - 1.2); - return intercept + slope * (x - 1.2); - else if(x <= 1.6) - intercept = 0.97; - slope = (0.99 - 0.97) / (1.6 - 1.4); - return intercept + slope * (x - 1.4); - else if(x <= 1.8) - intercept = 0.99; - slope = (1.0 - 0.99) / (1.8 - 1.6); - return intercept + slope * (x - 1.6); - else if(x <= 2.0) - intercept = 1.0; - slope = (1.0 - 1.0) / (2.0 - 1.8); - return intercept + slope * (x - 1.8); - } - - real lookupFunc_0(real x){ - # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) - # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) - real slope; - real intercept; - - if(x <= 0.2) - intercept = 0.0; - slope = (0.2 - 0.0) / (0.2 - 0.0); - return intercept + slope * (x - 0.0); - else if(x <= 0.4) - intercept = 0.2; - slope = (0.4 - 0.2) / (0.4 - 0.2); - return intercept + slope * (x - 0.2); - else if(x <= 0.6) - intercept = 0.4; - slope = (0.58 - 0.4) / (0.6 - 0.4); - return intercept + slope * (x - 0.4); - else if(x <= 0.8) - intercept = 0.58; - slope = (0.73 - 0.58) / (0.8 - 0.6); - return intercept + slope * (x - 0.6); - else if(x <= 1.0) - intercept = 0.73; - slope = (0.85 - 0.73) / (1.0 - 0.8); - return intercept + slope * (x - 0.8); - else if(x <= 1.2) - intercept = 0.85; - slope = (0.93 - 0.85) / (1.2 - 1.0); - return intercept + slope * (x - 1.0); - else if(x <= 1.4) - intercept = 0.93; - slope = (0.97 - 0.93) / (1.4 - 1.2); - return intercept + slope * (x - 1.2); - else if(x <= 1.6) - intercept = 0.97; - slope = (0.99 - 0.97) / (1.6 - 1.4); - return intercept + slope * (x - 1.4); - else if(x <= 1.8) - intercept = 0.99; - slope = (1.0 - 0.99) / (1.8 - 1.6); - return intercept + slope * (x - 1.6); - else if(x <= 2.0) - intercept = 1.0; - slope = (1.0 - 1.0) / (2.0 - 1.8); - return intercept + slope * (x - 1.8); - } - - real lookupFunc_0(real x){ - # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) - # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) - real slope; - real intercept; - - if(x <= 0.2) - intercept = 0.0; - slope = (0.2 - 0.0) / (0.2 - 0.0); - return intercept + slope * (x - 0.0); - else if(x <= 0.4) - intercept = 0.2; - slope = (0.4 - 0.2) / (0.4 - 0.2); - return intercept + slope * (x - 0.2); - else if(x <= 0.6) - intercept = 0.4; - slope = (0.58 - 0.4) / (0.6 - 0.4); - return intercept + slope * (x - 0.4); - else if(x <= 0.8) - intercept = 0.58; - slope = (0.73 - 0.58) / (0.8 - 0.6); - return intercept + slope * (x - 0.6); - else if(x <= 1.0) - intercept = 0.73; - slope = (0.85 - 0.73) / (1.0 - 0.8); - return intercept + slope * (x - 0.8); - else if(x <= 1.2) - intercept = 0.85; - slope = (0.93 - 0.85) / (1.2 - 1.0); - return intercept + slope * (x - 1.0); - else if(x <= 1.4) - intercept = 0.93; - slope = (0.97 - 0.93) / (1.4 - 1.2); - return intercept + slope * (x - 1.2); - else if(x <= 1.6) - intercept = 0.97; - slope = (0.99 - 0.97) / (1.6 - 1.4); - return intercept + slope * (x - 1.4); - else if(x <= 1.8) - intercept = 0.99; - slope = (1.0 - 0.99) / (1.8 - 1.6); - return intercept + slope * (x - 1.6); - else if(x <= 2.0) - intercept = 1.0; - slope = (1.0 - 1.0) / (2.0 - 1.8); - return intercept + slope * (x - 1.8); - } - - real lookupFunc_0(real x){ - # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) - # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) - real slope; - real intercept; - - if(x <= 0.2) - intercept = 0.0; - slope = (0.2 - 0.0) / (0.2 - 0.0); - return intercept + slope * (x - 0.0); - else if(x <= 0.4) - intercept = 0.2; - slope = (0.4 - 0.2) / (0.4 - 0.2); - return intercept + slope * (x - 0.2); - else if(x <= 0.6) - intercept = 0.4; - slope = (0.58 - 0.4) / (0.6 - 0.4); - return intercept + slope * (x - 0.4); - else if(x <= 0.8) - intercept = 0.58; - slope = (0.73 - 0.58) / (0.8 - 0.6); - return intercept + slope * (x - 0.6); - else if(x <= 1.0) - intercept = 0.73; - slope = (0.85 - 0.73) / (1.0 - 0.8); - return intercept + slope * (x - 0.8); - else if(x <= 1.2) - intercept = 0.85; - slope = (0.93 - 0.85) / (1.2 - 1.0); - return intercept + slope * (x - 1.0); - else if(x <= 1.4) - intercept = 0.93; - slope = (0.97 - 0.93) / (1.4 - 1.2); - return intercept + slope * (x - 1.2); - else if(x <= 1.6) - intercept = 0.97; - slope = (0.99 - 0.97) / (1.6 - 1.4); - return intercept + slope * (x - 1.4); - else if(x <= 1.8) - intercept = 0.99; - slope = (1.0 - 0.99) / (1.8 - 1.6); - return intercept + slope * (x - 1.6); - else if(x <= 2.0) - intercept = 1.0; - slope = (1.0 - 1.0) / (2.0 - 1.8); - return intercept + slope * (x - 1.8); - } - - real lookupFunc_0(real x){ - # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) - # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) - real slope; - real intercept; - - if(x <= 0.2) - intercept = 0.0; - slope = (0.2 - 0.0) / (0.2 - 0.0); - return intercept + slope * (x - 0.0); - else if(x <= 0.4) - intercept = 0.2; - slope = (0.4 - 0.2) / (0.4 - 0.2); - return intercept + slope * (x - 0.2); - else if(x <= 0.6) - intercept = 0.4; - slope = (0.58 - 0.4) / (0.6 - 0.4); - return intercept + slope * (x - 0.4); - else if(x <= 0.8) - intercept = 0.58; - slope = (0.73 - 0.58) / (0.8 - 0.6); - return intercept + slope * (x - 0.6); - else if(x <= 1.0) - intercept = 0.73; - slope = (0.85 - 0.73) / (1.0 - 0.8); - return intercept + slope * (x - 0.8); - else if(x <= 1.2) - intercept = 0.85; - slope = (0.93 - 0.85) / (1.2 - 1.0); - return intercept + slope * (x - 1.0); - else if(x <= 1.4) - intercept = 0.93; - slope = (0.97 - 0.93) / (1.4 - 1.2); - return intercept + slope * (x - 1.2); - else if(x <= 1.6) - intercept = 0.97; - slope = (0.99 - 0.97) / (1.6 - 1.4); - return intercept + slope * (x - 1.4); - else if(x <= 1.8) - intercept = 0.99; - slope = (1.0 - 0.99) / (1.8 - 1.6); - return intercept + slope * (x - 1.6); - else if(x <= 2.0) - intercept = 1.0; - slope = (1.0 - 1.0) / (2.0 - 1.8); - return intercept + slope * (x - 1.8); - } - - real lookupFunc_0(real x){ - # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) - # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) - real slope; - real intercept; - - if(x <= 0.2) - intercept = 0.0; - slope = (0.2 - 0.0) / (0.2 - 0.0); - return intercept + slope * (x - 0.0); - else if(x <= 0.4) - intercept = 0.2; - slope = (0.4 - 0.2) / (0.4 - 0.2); - return intercept + slope * (x - 0.2); - else if(x <= 0.6) - intercept = 0.4; - slope = (0.58 - 0.4) / (0.6 - 0.4); - return intercept + slope * (x - 0.4); - else if(x <= 0.8) - intercept = 0.58; - slope = (0.73 - 0.58) / (0.8 - 0.6); - return intercept + slope * (x - 0.6); - else if(x <= 1.0) - intercept = 0.73; - slope = (0.85 - 0.73) / (1.0 - 0.8); - return intercept + slope * (x - 0.8); - else if(x <= 1.2) - intercept = 0.85; - slope = (0.93 - 0.85) / (1.2 - 1.0); - return intercept + slope * (x - 1.0); - else if(x <= 1.4) - intercept = 0.93; - slope = (0.97 - 0.93) / (1.4 - 1.2); - return intercept + slope * (x - 1.2); - else if(x <= 1.6) - intercept = 0.97; - slope = (0.99 - 0.97) / (1.6 - 1.4); - return intercept + slope * (x - 1.4); - else if(x <= 1.8) - intercept = 0.99; - slope = (1.0 - 0.99) / (1.8 - 1.6); - return intercept + slope * (x - 1.6); - else if(x <= 2.0) - intercept = 1.0; - slope = (1.0 - 1.0) / (2.0 - 1.8); - return intercept + slope * (x - 1.8); - } - - real lookupFunc_0(real x){ - # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) - # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) - real slope; - real intercept; - - if(x <= 0.2) - intercept = 0.0; - slope = (0.2 - 0.0) / (0.2 - 0.0); - return intercept + slope * (x - 0.0); - else if(x <= 0.4) - intercept = 0.2; - slope = (0.4 - 0.2) / (0.4 - 0.2); - return intercept + slope * (x - 0.2); - else if(x <= 0.6) - intercept = 0.4; - slope = (0.58 - 0.4) / (0.6 - 0.4); - return intercept + slope * (x - 0.4); - else if(x <= 0.8) - intercept = 0.58; - slope = (0.73 - 0.58) / (0.8 - 0.6); - return intercept + slope * (x - 0.6); - else if(x <= 1.0) - intercept = 0.73; - slope = (0.85 - 0.73) / (1.0 - 0.8); - return intercept + slope * (x - 0.8); - else if(x <= 1.2) - intercept = 0.85; - slope = (0.93 - 0.85) / (1.2 - 1.0); - return intercept + slope * (x - 1.0); - else if(x <= 1.4) - intercept = 0.93; - slope = (0.97 - 0.93) / (1.4 - 1.2); - return intercept + slope * (x - 1.2); - else if(x <= 1.6) - intercept = 0.97; - slope = (0.99 - 0.97) / (1.6 - 1.4); - return intercept + slope * (x - 1.4); - else if(x <= 1.8) - intercept = 0.99; - slope = (1.0 - 0.99) / (1.8 - 1.6); - return intercept + slope * (x - 1.6); - else if(x <= 2.0) - intercept = 1.0; - slope = (1.0 - 1.0) / (2.0 - 1.8); - return intercept + slope * (x - 1.8); - } - - real lookupFunc_0(real x){ - # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) - # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) - real slope; - real intercept; - - if(x <= 0.2) - intercept = 0.0; - slope = (0.2 - 0.0) / (0.2 - 0.0); - return intercept + slope * (x - 0.0); - else if(x <= 0.4) - intercept = 0.2; - slope = (0.4 - 0.2) / (0.4 - 0.2); - return intercept + slope * (x - 0.2); - else if(x <= 0.6) - intercept = 0.4; - slope = (0.58 - 0.4) / (0.6 - 0.4); - return intercept + slope * (x - 0.4); - else if(x <= 0.8) - intercept = 0.58; - slope = (0.73 - 0.58) / (0.8 - 0.6); - return intercept + slope * (x - 0.6); - else if(x <= 1.0) - intercept = 0.73; - slope = (0.85 - 0.73) / (1.0 - 0.8); - return intercept + slope * (x - 0.8); - else if(x <= 1.2) - intercept = 0.85; - slope = (0.93 - 0.85) / (1.2 - 1.0); - return intercept + slope * (x - 1.0); - else if(x <= 1.4) - intercept = 0.93; - slope = (0.97 - 0.93) / (1.4 - 1.2); - return intercept + slope * (x - 1.2); - else if(x <= 1.6) - intercept = 0.97; - slope = (0.99 - 0.97) / (1.6 - 1.4); - return intercept + slope * (x - 1.4); - else if(x <= 1.8) - intercept = 0.99; - slope = (1.0 - 0.99) / (1.8 - 1.6); - return intercept + slope * (x - 1.6); - else if(x <= 2.0) - intercept = 1.0; - slope = (1.0 - 1.0) / (2.0 - 1.8); - return intercept + slope * (x - 1.8); - } - - real lookupFunc_0(real x){ - # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) - # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) - real slope; - real intercept; - - if(x <= 0.2) - intercept = 0.0; - slope = (0.2 - 0.0) / (0.2 - 0.0); - return intercept + slope * (x - 0.0); - else if(x <= 0.4) - intercept = 0.2; - slope = (0.4 - 0.2) / (0.4 - 0.2); - return intercept + slope * (x - 0.2); - else if(x <= 0.6) - intercept = 0.4; - slope = (0.58 - 0.4) / (0.6 - 0.4); - return intercept + slope * (x - 0.4); - else if(x <= 0.8) - intercept = 0.58; - slope = (0.73 - 0.58) / (0.8 - 0.6); - return intercept + slope * (x - 0.6); - else if(x <= 1.0) - intercept = 0.73; - slope = (0.85 - 0.73) / (1.0 - 0.8); - return intercept + slope * (x - 0.8); - else if(x <= 1.2) - intercept = 0.85; - slope = (0.93 - 0.85) / (1.2 - 1.0); - return intercept + slope * (x - 1.0); - else if(x <= 1.4) - intercept = 0.93; - slope = (0.97 - 0.93) / (1.4 - 1.2); - return intercept + slope * (x - 1.2); - else if(x <= 1.6) - intercept = 0.97; - slope = (0.99 - 0.97) / (1.6 - 1.4); - return intercept + slope * (x - 1.4); - else if(x <= 1.8) - intercept = 0.99; - slope = (1.0 - 0.99) / (1.8 - 1.6); - return intercept + slope * (x - 1.6); - else if(x <= 2.0) - intercept = 1.0; - slope = (1.0 - 1.0) / (2.0 - 1.8); - return intercept + slope * (x - 1.8); - } - - real lookupFunc_0(real x){ - # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) - # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) - real slope; - real intercept; - - if(x <= 0.2) - intercept = 0.0; - slope = (0.2 - 0.0) / (0.2 - 0.0); - return intercept + slope * (x - 0.0); - else if(x <= 0.4) - intercept = 0.2; - slope = (0.4 - 0.2) / (0.4 - 0.2); - return intercept + slope * (x - 0.2); - else if(x <= 0.6) - intercept = 0.4; - slope = (0.58 - 0.4) / (0.6 - 0.4); - return intercept + slope * (x - 0.4); - else if(x <= 0.8) - intercept = 0.58; - slope = (0.73 - 0.58) / (0.8 - 0.6); - return intercept + slope * (x - 0.6); - else if(x <= 1.0) - intercept = 0.73; - slope = (0.85 - 0.73) / (1.0 - 0.8); - return intercept + slope * (x - 0.8); - else if(x <= 1.2) - intercept = 0.85; - slope = (0.93 - 0.85) / (1.2 - 1.0); - return intercept + slope * (x - 1.0); - else if(x <= 1.4) - intercept = 0.93; - slope = (0.97 - 0.93) / (1.4 - 1.2); - return intercept + slope * (x - 1.2); - else if(x <= 1.6) - intercept = 0.97; - slope = (0.99 - 0.97) / (1.6 - 1.4); - return intercept + slope * (x - 1.4); - else if(x <= 1.8) - intercept = 0.99; - slope = (1.0 - 0.99) / (1.8 - 1.6); - return intercept + slope * (x - 1.6); - else if(x <= 2.0) - intercept = 1.0; - slope = (1.0 - 1.0) / (2.0 - 1.8); - return intercept + slope * (x - 1.8); - } - - real lookupFunc_0(real x){ - # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) - # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) - real slope; - real intercept; - - if(x <= 0.2) - intercept = 0.0; - slope = (0.2 - 0.0) / (0.2 - 0.0); - return intercept + slope * (x - 0.0); - else if(x <= 0.4) - intercept = 0.2; - slope = (0.4 - 0.2) / (0.4 - 0.2); - return intercept + slope * (x - 0.2); - else if(x <= 0.6) - intercept = 0.4; - slope = (0.58 - 0.4) / (0.6 - 0.4); - return intercept + slope * (x - 0.4); - else if(x <= 0.8) - intercept = 0.58; - slope = (0.73 - 0.58) / (0.8 - 0.6); - return intercept + slope * (x - 0.6); - else if(x <= 1.0) - intercept = 0.73; - slope = (0.85 - 0.73) / (1.0 - 0.8); - return intercept + slope * (x - 0.8); - else if(x <= 1.2) - intercept = 0.85; - slope = (0.93 - 0.85) / (1.2 - 1.0); - return intercept + slope * (x - 1.0); - else if(x <= 1.4) - intercept = 0.93; - slope = (0.97 - 0.93) / (1.4 - 1.2); - return intercept + slope * (x - 1.2); - else if(x <= 1.6) - intercept = 0.97; - slope = (0.99 - 0.97) / (1.6 - 1.4); - return intercept + slope * (x - 1.4); - else if(x <= 1.8) - intercept = 0.99; - slope = (1.0 - 0.99) / (1.8 - 1.6); - return intercept + slope * (x - 1.6); - else if(x <= 2.0) - intercept = 1.0; - slope = (1.0 - 1.0) / (2.0 - 1.8); - return intercept + slope * (x - 1.8); - } - - real lookupFunc_0(real x){ - # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) - # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) - real slope; - real intercept; - - if(x <= 0.2) - intercept = 0.0; - slope = (0.2 - 0.0) / (0.2 - 0.0); - return intercept + slope * (x - 0.0); - else if(x <= 0.4) - intercept = 0.2; - slope = (0.4 - 0.2) / (0.4 - 0.2); - return intercept + slope * (x - 0.2); - else if(x <= 0.6) - intercept = 0.4; - slope = (0.58 - 0.4) / (0.6 - 0.4); - return intercept + slope * (x - 0.4); - else if(x <= 0.8) - intercept = 0.58; - slope = (0.73 - 0.58) / (0.8 - 0.6); - return intercept + slope * (x - 0.6); - else if(x <= 1.0) - intercept = 0.73; - slope = (0.85 - 0.73) / (1.0 - 0.8); - return intercept + slope * (x - 0.8); - else if(x <= 1.2) - intercept = 0.85; - slope = (0.93 - 0.85) / (1.2 - 1.0); - return intercept + slope * (x - 1.0); - else if(x <= 1.4) - intercept = 0.93; - slope = (0.97 - 0.93) / (1.4 - 1.2); - return intercept + slope * (x - 1.2); - else if(x <= 1.6) - intercept = 0.97; - slope = (0.99 - 0.97) / (1.6 - 1.4); - return intercept + slope * (x - 1.4); - else if(x <= 1.8) - intercept = 0.99; - slope = (1.0 - 0.99) / (1.8 - 1.6); - return intercept + slope * (x - 1.6); - else if(x <= 2.0) - intercept = 1.0; - slope = (1.0 - 1.0) / (2.0 - 1.8); - return intercept + slope * (x - 1.8); - } - # Begin ODE declaration vector vensim_func(real time, vector outcome, real customer_order_rate, real inventory_coverage, real manufacturing_cycle_time, real time_to_average_order_rate, real wip_adjustment_time ){ real work_in_process_inventory = outcome[1]; real inventory = outcome[2]; - real change_in_exp_orders = customer_order_rate - expected_order_rate / time_to_average_order_rate; - real expected_order_rate = change_in_exp_orders; + real inventory_adjustment_time = 8; real safety_stock_coverage = 2; real minimum_order_processing_time = 2; real desired_inventory_coverage = minimum_order_processing_time + safety_stock_coverage; + real change_in_exp_orders = customer_order_rate - expected_order_rate / time_to_average_order_rate; + real expected_order_rate = change_in_exp_orders; real desired_inventory = desired_inventory_coverage * expected_order_rate; - real inventory_adjustment_time = 8; real production_adjustment_from_inventory = desired_inventory - inventory / inventory_adjustment_time; real desired_production = fmax(0,expected_order_rate + production_adjustment_from_inventory); real desired_wip = manufacturing_cycle_time * desired_production; - real adjustment_for_wip = desired_wip - work_in_process_inventory / wip_adjustment_time; - real desired_production_start_rate = desired_production + adjustment_for_wip; real maximum_shipment_rate = inventory / minimum_order_processing_time; real desired_shipment_rate = customer_order_rate; real order_fulfillment_ratio = table_for_order_fulfillment(maximum_shipment_rate / desired_shipment_rate); - real production_rate = work_in_process_inventory / manufacturing_cycle_time; - real production_start_rate = fmax(0,desired_production_start_rate); - real work_in_process_inventory_dydt = production_start_rate - production_rate; real shipment_rate = desired_shipment_rate * order_fulfillment_ratio; + real adjustment_for_wip = desired_wip - work_in_process_inventory / wip_adjustment_time; + real desired_production_start_rate = desired_production + adjustment_for_wip; + real production_start_rate = fmax(0,desired_production_start_rate); + real production_rate = work_in_process_inventory / manufacturing_cycle_time; real inventory_dydt = production_rate - shipment_rate; + real work_in_process_inventory_dydt = production_start_rate - production_rate; return {work_in_process_inventory_dydt, inventory_dydt}; } From c921d21d56bac9ad21765d7e91d4376595625175 Mon Sep 17 00:00:00 2001 From: amoon Date: Fri, 19 Aug 2022 16:14:27 -0400 Subject: [PATCH 25/45] Update notebook for relation input (issue #16) --- test_scripts/demand_supply.ipynb | 1655 ++++---------------- test_scripts/stan_file/demand_supply.ipynb | 598 ------- test_scripts/stan_file/ds_data2draws.stan | 126 +- test_scripts/stan_file/ds_draws2data.stan | 43 +- test_scripts/stan_file/ds_relational.stan | 34 +- 5 files changed, 396 insertions(+), 2060 deletions(-) delete mode 100644 test_scripts/stan_file/demand_supply.ipynb diff --git a/test_scripts/demand_supply.ipynb b/test_scripts/demand_supply.ipynb index 2fed7ea8..e6d68605 100644 --- a/test_scripts/demand_supply.ipynb +++ b/test_scripts/demand_supply.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "id": "c1026b9e-9c63-4bbf-afea-4d3b8c4a4898", "metadata": {}, "outputs": [], @@ -29,725 +29,207 @@ }, { "attachments": { - "db58966d-7db3-43ac-9114-da7b079d88c4.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "07c14386-a246-4f4d-a4e5-ad85fa338c19", - "metadata": { - "tags": [] - }, - "source": [ - "## U1. Draft\n", - "From mental model to SD model.\n", - "![image.png](attachment:db58966d-7db3-43ac-9114-da7b079d88c4.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "9dcead94-0f1f-4396-8b41-65c41d68df57", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "original name stan variable name is stock\n", - "----------------------------------------------------------------------------------\n", - "Adjustment for WIP adjustment_for_wip \n", - "Change in Exp Orders change_in_exp_orders \n", - "Customer Order Rate customer_order_rate \n", - "Desired Inventory desired_inventory \n", - "Desired Inventory Coverage desired_inventory_coverage \n", - "Desired Production desired_production \n", - "Desired Production Start Rate desired_production_start_rate \n", - "Desired Shipment Rate desired_shipment_rate \n", - "Desired WIP desired_wip \n", - "Expected Order Rate expected_order_rate V\n", - "Inventory inventory V\n", - "Inventory Adjustment Time inventory_adjustment_time \n", - "Inventory Coverage inventory_coverage \n", - "Manufacturing Cycle Time manufacturing_cycle_time \n", - "Maximum Shipment Rate maximum_shipment_rate \n", - "Minimum Order Processing Time minimum_order_processing_time \n", - "Order Fulfillment Ratio order_fulfillment_ratio \n", - "Production Adjustment from Inventory production_adjustment_from_inventory \n", - "Production Rate production_rate \n", - "Production Start Rate production_start_rate \n", - "Safety Stock Coverage safety_stock_coverage \n", - "Shipment Rate shipment_rate \n", - "Table for Order Fulfillment table_for_order_fulfillment \n", - "Time to Average Order Rate time_to_average_order_rate \n", - "WIP Adjustment Time wip_adjustment_time \n", - "Work in Process Inventory work_in_process_inventory V\n", - "FINAL TIME final_time \n", - "INITIAL TIME initial_time \n", - "SAVEPER saveper \n", - "TIME STEP time_step \n" - ] - } - ], - "source": [ - "vf = VensimFile(\"vensim_models/ds_white_sterman.mdl\")\n", - "vf.parse()\n", - "am = vf.get_abstract_model()\n", - "stan_builder = StanModelBuilder(am)\n", - "stan_builder.print_variable_info()" - ] - }, - { - "cell_type": "markdown", - "id": "477f3043-0f99-4bcd-9c78-da7e53081fd0", - "metadata": { - "tags": [] - }, - "source": [ - "## U2. Classify\n", - "\n", - "| variable name | `est_param` | `ass_param` | `obs_stock` |\n", - "| ------------------------------------ | ----------- | ----------- | ----------- |\n", - "| adjustment_for_wip | | | |\n", - "| change_in_exp_orders | | | |\n", - "| customer_order_rate | | V | |\n", - "| desired_inventory | | | |\n", - "| desired_inventory_coverage | | | |\n", - "| desired_production | | | |\n", - "| desired_production_start_rate | | | |\n", - "| desired_shipment_rate | | | |\n", - "| desired_wip | | | |\n", - "| expected_order_rate | | | V |\n", - "| inventory | | | V |\n", - "| inventory_adjustment_time | V | | |\n", - "| inventory_coverage | | V | |\n", - "| manufacturing_cycle_time | | V | |\n", - "| maximum_shipment_rate | | | |\n", - "| minimum_order_processing_time | V | | |\n", - "| order_fulfillment_ratio | | | |\n", - "| production_adjustment_from_inventory | | | |\n", - "| production_rate | | | |\n", - "| production_start_rate | | | |\n", - "| safety_stock_coverage | | | |\n", - "| shipment_rate | | | |\n", - "| table_for_order_fulfillment | | V (lookup) | |\n", - "| time_to_average_order_rate | | V | |\n", - "| wip_adjustment_time | | V | |\n", - "| work_in_process_inventory | | | V |\n", - "| initial_time | | V | |\n", - "| final_time | | V | |\n", - "| time_step | | V | |\n", - "\n", - "The rest is `aux_var` which are derived from the defined." - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "a3d2b4a9-532f-4f3f-858e-64b4877c1997", - "metadata": {}, - "outputs": [], - "source": [ - "ass_param_lst = [\"customer_order_rate\", \"inventory_coverage\", \"manufacturing_cycle_time\", \"time_to_average_order_rate\", \"wip_adjustment_time\"]\n", - "obs_stock_lst = [\"work_in_process_inventory\", \"inventory\"]" - ] - }, - { - "cell_type": "markdown", - "id": "33ac5c16-f572-46ba-8d79-062f601a5e24", - "metadata": {}, - "source": [ - "## P1. Relational_prior\n", - "From SD model (`.mdl`) to Stan ODE function block (`.stan`). No new information is added." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "d1a14086-45cd-4f99-9e69-51aa57dea790", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "functions {\n", - " real lookupFunc_0(real x){\n", - " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", - " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", - " real slope;\n", - " real intercept;\n", - "\n", - " if(x <= 0.2)\n", - " intercept = 0.0;\n", - " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", - " return intercept + slope * (x - 0.0);\n", - " else if(x <= 0.4)\n", - " intercept = 0.2;\n", - " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", - " return intercept + slope * (x - 0.2);\n", - " else if(x <= 0.6)\n", - " intercept = 0.4;\n", - " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", - " return intercept + slope * (x - 0.4);\n", - " else if(x <= 0.8)\n", - " intercept = 0.58;\n", - " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", - " return intercept + slope * (x - 0.6);\n", - " else if(x <= 1.0)\n", - " intercept = 0.73;\n", - " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", - " return intercept + slope * (x - 0.8);\n", - " else if(x <= 1.2)\n", - " intercept = 0.85;\n", - " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", - " return intercept + slope * (x - 1.0);\n", - " else if(x <= 1.4)\n", - " intercept = 0.93;\n", - " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", - " return intercept + slope * (x - 1.2);\n", - " else if(x <= 1.6)\n", - " intercept = 0.97;\n", - " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", - " return intercept + slope * (x - 1.4);\n", - " else if(x <= 1.8)\n", - " intercept = 0.99;\n", - " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", - " return intercept + slope * (x - 1.6);\n", - " else if(x <= 2.0)\n", - " intercept = 1.0;\n", - " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", - " return intercept + slope * (x - 1.8);\n", - " }\n", - "\n", - " real lookupFunc_0(real x){\n", - " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", - " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", - " real slope;\n", - " real intercept;\n", - "\n", - " if(x <= 0.2)\n", - " intercept = 0.0;\n", - " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", - " return intercept + slope * (x - 0.0);\n", - " else if(x <= 0.4)\n", - " intercept = 0.2;\n", - " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", - " return intercept + slope * (x - 0.2);\n", - " else if(x <= 0.6)\n", - " intercept = 0.4;\n", - " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", - " return intercept + slope * (x - 0.4);\n", - " else if(x <= 0.8)\n", - " intercept = 0.58;\n", - " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", - " return intercept + slope * (x - 0.6);\n", - " else if(x <= 1.0)\n", - " intercept = 0.73;\n", - " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", - " return intercept + slope * (x - 0.8);\n", - " else if(x <= 1.2)\n", - " intercept = 0.85;\n", - " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", - " return intercept + slope * (x - 1.0);\n", - " else if(x <= 1.4)\n", - " intercept = 0.93;\n", - " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", - " return intercept + slope * (x - 1.2);\n", - " else if(x <= 1.6)\n", - " intercept = 0.97;\n", - " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", - " return intercept + slope * (x - 1.4);\n", - " else if(x <= 1.8)\n", - " intercept = 0.99;\n", - " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", - " return intercept + slope * (x - 1.6);\n", - " else if(x <= 2.0)\n", - " intercept = 1.0;\n", - " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", - " return intercept + slope * (x - 1.8);\n", - " }\n", - "\n", - " real lookupFunc_0(real x){\n", - " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", - " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", - " real slope;\n", - " real intercept;\n", - "\n", - " if(x <= 0.2)\n", - " intercept = 0.0;\n", - " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", - " return intercept + slope * (x - 0.0);\n", - " else if(x <= 0.4)\n", - " intercept = 0.2;\n", - " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", - " return intercept + slope * (x - 0.2);\n", - " else if(x <= 0.6)\n", - " intercept = 0.4;\n", - " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", - " return intercept + slope * (x - 0.4);\n", - " else if(x <= 0.8)\n", - " intercept = 0.58;\n", - " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", - " return intercept + slope * (x - 0.6);\n", - " else if(x <= 1.0)\n", - " intercept = 0.73;\n", - " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", - " return intercept + slope * (x - 0.8);\n", - " else if(x <= 1.2)\n", - " intercept = 0.85;\n", - " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", - " return intercept + slope * (x - 1.0);\n", - " else if(x <= 1.4)\n", - " intercept = 0.93;\n", - " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", - " return intercept + slope * (x - 1.2);\n", - " else if(x <= 1.6)\n", - " intercept = 0.97;\n", - " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", - " return intercept + slope * (x - 1.4);\n", - " else if(x <= 1.8)\n", - " intercept = 0.99;\n", - " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", - " return intercept + slope * (x - 1.6);\n", - " else if(x <= 2.0)\n", - " intercept = 1.0;\n", - " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", - " return intercept + slope * (x - 1.8);\n", - " }\n", - "\n", - " real lookupFunc_0(real x){\n", - " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", - " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", - " real slope;\n", - " real intercept;\n", - "\n", - " if(x <= 0.2)\n", - " intercept = 0.0;\n", - " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", - " return intercept + slope * (x - 0.0);\n", - " else if(x <= 0.4)\n", - " intercept = 0.2;\n", - " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", - " return intercept + slope * (x - 0.2);\n", - " else if(x <= 0.6)\n", - " intercept = 0.4;\n", - " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", - " return intercept + slope * (x - 0.4);\n", - " else if(x <= 0.8)\n", - " intercept = 0.58;\n", - " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", - " return intercept + slope * (x - 0.6);\n", - " else if(x <= 1.0)\n", - " intercept = 0.73;\n", - " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", - " return intercept + slope * (x - 0.8);\n", - " else if(x <= 1.2)\n", - " intercept = 0.85;\n", - " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", - " return intercept + slope * (x - 1.0);\n", - " else if(x <= 1.4)\n", - " intercept = 0.93;\n", - " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", - " return intercept + slope * (x - 1.2);\n", - " else if(x <= 1.6)\n", - " intercept = 0.97;\n", - " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", - " return intercept + slope * (x - 1.4);\n", - " else if(x <= 1.8)\n", - " intercept = 0.99;\n", - " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", - " return intercept + slope * (x - 1.6);\n", - " else if(x <= 2.0)\n", - " intercept = 1.0;\n", - " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", - " return intercept + slope * (x - 1.8);\n", - " }\n", - "\n", - " real lookupFunc_0(real x){\n", - " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", - " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", - " real slope;\n", - " real intercept;\n", - "\n", - " if(x <= 0.2)\n", - " intercept = 0.0;\n", - " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", - " return intercept + slope * (x - 0.0);\n", - " else if(x <= 0.4)\n", - " intercept = 0.2;\n", - " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", - " return intercept + slope * (x - 0.2);\n", - " else if(x <= 0.6)\n", - " intercept = 0.4;\n", - " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", - " return intercept + slope * (x - 0.4);\n", - " else if(x <= 0.8)\n", - " intercept = 0.58;\n", - " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", - " return intercept + slope * (x - 0.6);\n", - " else if(x <= 1.0)\n", - " intercept = 0.73;\n", - " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", - " return intercept + slope * (x - 0.8);\n", - " else if(x <= 1.2)\n", - " intercept = 0.85;\n", - " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", - " return intercept + slope * (x - 1.0);\n", - " else if(x <= 1.4)\n", - " intercept = 0.93;\n", - " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", - " return intercept + slope * (x - 1.2);\n", - " else if(x <= 1.6)\n", - " intercept = 0.97;\n", - " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", - " return intercept + slope * (x - 1.4);\n", - " else if(x <= 1.8)\n", - " intercept = 0.99;\n", - " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", - " return intercept + slope * (x - 1.6);\n", - " else if(x <= 2.0)\n", - " intercept = 1.0;\n", - " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", - " return intercept + slope * (x - 1.8);\n", - " }\n", - "\n", - " real lookupFunc_0(real x){\n", - " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", - " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", - " real slope;\n", - " real intercept;\n", - "\n", - " if(x <= 0.2)\n", - " intercept = 0.0;\n", - " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", - " return intercept + slope * (x - 0.0);\n", - " else if(x <= 0.4)\n", - " intercept = 0.2;\n", - " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", - " return intercept + slope * (x - 0.2);\n", - " else if(x <= 0.6)\n", - " intercept = 0.4;\n", - " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", - " return intercept + slope * (x - 0.4);\n", - " else if(x <= 0.8)\n", - " intercept = 0.58;\n", - " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", - " return intercept + slope * (x - 0.6);\n", - " else if(x <= 1.0)\n", - " intercept = 0.73;\n", - " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", - " return intercept + slope * (x - 0.8);\n", - " else if(x <= 1.2)\n", - " intercept = 0.85;\n", - " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", - " return intercept + slope * (x - 1.0);\n", - " else if(x <= 1.4)\n", - " intercept = 0.93;\n", - " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", - " return intercept + slope * (x - 1.2);\n", - " else if(x <= 1.6)\n", - " intercept = 0.97;\n", - " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", - " return intercept + slope * (x - 1.4);\n", - " else if(x <= 1.8)\n", - " intercept = 0.99;\n", - " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", - " return intercept + slope * (x - 1.6);\n", - " else if(x <= 2.0)\n", - " intercept = 1.0;\n", - " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", - " return intercept + slope * (x - 1.8);\n", - " }\n", - "\n", - " real lookupFunc_0(real x){\n", - " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", - " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", - " real slope;\n", - " real intercept;\n", - "\n", - " if(x <= 0.2)\n", - " intercept = 0.0;\n", - " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", - " return intercept + slope * (x - 0.0);\n", - " else if(x <= 0.4)\n", - " intercept = 0.2;\n", - " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", - " return intercept + slope * (x - 0.2);\n", - " else if(x <= 0.6)\n", - " intercept = 0.4;\n", - " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", - " return intercept + slope * (x - 0.4);\n", - " else if(x <= 0.8)\n", - " intercept = 0.58;\n", - " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", - " return intercept + slope * (x - 0.6);\n", - " else if(x <= 1.0)\n", - " intercept = 0.73;\n", - " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", - " return intercept + slope * (x - 0.8);\n", - " else if(x <= 1.2)\n", - " intercept = 0.85;\n", - " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", - " return intercept + slope * (x - 1.0);\n", - " else if(x <= 1.4)\n", - " intercept = 0.93;\n", - " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", - " return intercept + slope * (x - 1.2);\n", - " else if(x <= 1.6)\n", - " intercept = 0.97;\n", - " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", - " return intercept + slope * (x - 1.4);\n", - " else if(x <= 1.8)\n", - " intercept = 0.99;\n", - " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", - " return intercept + slope * (x - 1.6);\n", - " else if(x <= 2.0)\n", - " intercept = 1.0;\n", - " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", - " return intercept + slope * (x - 1.8);\n", - " }\n", - "\n", - " real lookupFunc_0(real x){\n", - " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", - " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", - " real slope;\n", - " real intercept;\n", - "\n", - " if(x <= 0.2)\n", - " intercept = 0.0;\n", - " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", - " return intercept + slope * (x - 0.0);\n", - " else if(x <= 0.4)\n", - " intercept = 0.2;\n", - " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", - " return intercept + slope * (x - 0.2);\n", - " else if(x <= 0.6)\n", - " intercept = 0.4;\n", - " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", - " return intercept + slope * (x - 0.4);\n", - " else if(x <= 0.8)\n", - " intercept = 0.58;\n", - " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", - " return intercept + slope * (x - 0.6);\n", - " else if(x <= 1.0)\n", - " intercept = 0.73;\n", - " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", - " return intercept + slope * (x - 0.8);\n", - " else if(x <= 1.2)\n", - " intercept = 0.85;\n", - " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", - " return intercept + slope * (x - 1.0);\n", - " else if(x <= 1.4)\n", - " intercept = 0.93;\n", - " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", - " return intercept + slope * (x - 1.2);\n", - " else if(x <= 1.6)\n", - " intercept = 0.97;\n", - " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", - " return intercept + slope * (x - 1.4);\n", - " else if(x <= 1.8)\n", - " intercept = 0.99;\n", - " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", - " return intercept + slope * (x - 1.6);\n", - " else if(x <= 2.0)\n", - " intercept = 1.0;\n", - " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", - " return intercept + slope * (x - 1.8);\n", - " }\n", - "\n", - " real lookupFunc_0(real x){\n", - " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", - " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", - " real slope;\n", - " real intercept;\n", - "\n", - " if(x <= 0.2)\n", - " intercept = 0.0;\n", - " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", - " return intercept + slope * (x - 0.0);\n", - " else if(x <= 0.4)\n", - " intercept = 0.2;\n", - " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", - " return intercept + slope * (x - 0.2);\n", - " else if(x <= 0.6)\n", - " intercept = 0.4;\n", - " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", - " return intercept + slope * (x - 0.4);\n", - " else if(x <= 0.8)\n", - " intercept = 0.58;\n", - " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", - " return intercept + slope * (x - 0.6);\n", - " else if(x <= 1.0)\n", - " intercept = 0.73;\n", - " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", - " return intercept + slope * (x - 0.8);\n", - " else if(x <= 1.2)\n", - " intercept = 0.85;\n", - " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", - " return intercept + slope * (x - 1.0);\n", - " else if(x <= 1.4)\n", - " intercept = 0.93;\n", - " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", - " return intercept + slope * (x - 1.2);\n", - " else if(x <= 1.6)\n", - " intercept = 0.97;\n", - " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", - " return intercept + slope * (x - 1.4);\n", - " else if(x <= 1.8)\n", - " intercept = 0.99;\n", - " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", - " return intercept + slope * (x - 1.6);\n", - " else if(x <= 2.0)\n", - " intercept = 1.0;\n", - " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", - " return intercept + slope * (x - 1.8);\n", - " }\n", - "\n", - " real lookupFunc_0(real x){\n", - " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", - " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", - " real slope;\n", - " real intercept;\n", - "\n", - " if(x <= 0.2)\n", - " intercept = 0.0;\n", - " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", - " return intercept + slope * (x - 0.0);\n", - " else if(x <= 0.4)\n", - " intercept = 0.2;\n", - " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", - " return intercept + slope * (x - 0.2);\n", - " else if(x <= 0.6)\n", - " intercept = 0.4;\n", - " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", - " return intercept + slope * (x - 0.4);\n", - " else if(x <= 0.8)\n", - " intercept = 0.58;\n", - " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", - " return intercept + slope * (x - 0.6);\n", - " else if(x <= 1.0)\n", - " intercept = 0.73;\n", - " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", - " return intercept + slope * (x - 0.8);\n", - " else if(x <= 1.2)\n", - " intercept = 0.85;\n", - " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", - " return intercept + slope * (x - 1.0);\n", - " else if(x <= 1.4)\n", - " intercept = 0.93;\n", - " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", - " return intercept + slope * (x - 1.2);\n", - " else if(x <= 1.6)\n", - " intercept = 0.97;\n", - " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", - " return intercept + slope * (x - 1.4);\n", - " else if(x <= 1.8)\n", - " intercept = 0.99;\n", - " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", - " return intercept + slope * (x - 1.6);\n", - " else if(x <= 2.0)\n", - " intercept = 1.0;\n", - " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", - " return intercept + slope * (x - 1.8);\n", - " }\n", - "\n", - " real lookupFunc_0(real x){\n", - " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", - " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", - " real slope;\n", - " real intercept;\n", - "\n", - " if(x <= 0.2)\n", - " intercept = 0.0;\n", - " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", - " return intercept + slope * (x - 0.0);\n", - " else if(x <= 0.4)\n", - " intercept = 0.2;\n", - " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", - " return intercept + slope * (x - 0.2);\n", - " else if(x <= 0.6)\n", - " intercept = 0.4;\n", - " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", - " return intercept + slope * (x - 0.4);\n", - " else if(x <= 0.8)\n", - " intercept = 0.58;\n", - " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", - " return intercept + slope * (x - 0.6);\n", - " else if(x <= 1.0)\n", - " intercept = 0.73;\n", - " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", - " return intercept + slope * (x - 0.8);\n", - " else if(x <= 1.2)\n", - " intercept = 0.85;\n", - " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", - " return intercept + slope * (x - 1.0);\n", - " else if(x <= 1.4)\n", - " intercept = 0.93;\n", - " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", - " return intercept + slope * (x - 1.2);\n", - " else if(x <= 1.6)\n", - " intercept = 0.97;\n", - " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", - " return intercept + slope * (x - 1.4);\n", - " else if(x <= 1.8)\n", - " intercept = 0.99;\n", - " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", - " return intercept + slope * (x - 1.6);\n", - " else if(x <= 2.0)\n", - " intercept = 1.0;\n", - " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", - " return intercept + slope * (x - 1.8);\n", - " }\n", - "\n", - " real lookupFunc_0(real x){\n", - " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", - " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", - " real slope;\n", - " real intercept;\n", - "\n", - " if(x <= 0.2)\n", - " intercept = 0.0;\n", - " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", - " return intercept + slope * (x - 0.0);\n", - " else if(x <= 0.4)\n", - " intercept = 0.2;\n", - " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", - " return intercept + slope * (x - 0.2);\n", - " else if(x <= 0.6)\n", - " intercept = 0.4;\n", - " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", - " return intercept + slope * (x - 0.4);\n", - " else if(x <= 0.8)\n", - " intercept = 0.58;\n", - " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", - " return intercept + slope * (x - 0.6);\n", - " else if(x <= 1.0)\n", - " intercept = 0.73;\n", - " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", - " return intercept + slope * (x - 0.8);\n", - " else if(x <= 1.2)\n", - " intercept = 0.85;\n", - " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", - " return intercept + slope * (x - 1.0);\n", - " else if(x <= 1.4)\n", - " intercept = 0.93;\n", - " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", - " return intercept + slope * (x - 1.2);\n", - " else if(x <= 1.6)\n", - " intercept = 0.97;\n", - " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", - " return intercept + slope * (x - 1.4);\n", - " else if(x <= 1.8)\n", - " intercept = 0.99;\n", - " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", - " return intercept + slope * (x - 1.6);\n", - " else if(x <= 2.0)\n", - " intercept = 1.0;\n", - " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", - " return intercept + slope * (x - 1.8);\n", - " }\n", - "\n", + "49465262-0d0e-4530-924a-86d154fd9c04.png": { + "image/png": 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EjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMwGlAAMEGEQYBBiFo27Ztug1doht58eLFpUCBAocJaFzgR0iELZ6phxiFQOAFHY+N6zz27dvnynOesr48773Qw2vqEw8H0o+XEnimHX/w2tfjte83eC6rMoMGDZKffvpJqlevLtdee63Url1bs5qEtsqjLuOnf8bE+6SkJMeDZ8bAOR879SIjI52ExzkOLyrxmv55+MPHlvmaP+/j9eV93eB5XhMjdbw4x2vfpm+LZ8r6urz380w9f93PO++DB+fpJyEhwY3f16Uc9bnO4c/7NvPnz6/ZW850awYWwf4p79cM5WHs2/H90yd1Ml+nro+V1xzU5+C8Hw/tU5e+f+8Ixk05+uTwMdEOR1bt+Djo08dNWT8WH5dvk2v059cG533MXPOxp6SkuDVGOTj6dihDHR7Bdvz5YAyU4T0cebAu4RHkznX6py3P2bdFOfr3bfpn2uXw73lNfQ7iDJ53J+0fI3CKEUhN3S+ffj5HM3bNko2bE9mUNWTCcW/qWAsXyidFCufXrVfDpHixSH1E6L2UQ+LWJcq27UmyW6W7hG27BPnOVdDq3FZITK1bVpa+t7TQ7TdLuTqnGDobzgkSGPjmJPni67mh7YB1zRSIzCNPPd5FOrQ79kxx3w1ZJO9/OFXWrt8uB/Tr+wxdmw/f214uuai2W7MnGOIJVX/ltd90fHMkWbM3cjeVLF5A+j/SSVq3qnjSvlv4DjNxLvtpMnEuezZ2xQgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJG4DQggACDSIM0Qza24cOHy9SpU925Sy65RDp27CiFCxfOEGqCkgx1vATk5TGuc85LPJRZsWKFrFy50ok6NWrUkDJlyjhhzYtA1KG8r8P7YD9MA21y+POUpb4Xd4Lng+24Sgf/ef3112Xo0KFOnLv++uulfv36Ti4iRiQi2vB1d+3aJXPmzJFRo0bJ9OnTndSUJ08e1x99kq2OsbRo0cIJeEWKFMmIxffp2+M9r/0YeObhJS/GwcOLTFzz76lLTDyCMlWwjB87ZTl8ec4HYwhdPcSQ95mv8x7pCoFy2LBhMnr0aCdSEg/XYIXkxWvm3M87sVWrVs2tl7Zt2zqpMNifj4lzlOU9/dAO4+ZgTLzn8GV4TVn655wft+/fX+eZgzJcC/bn26Quh38fLOdfMz7aICbfV1B687HQjj9PXcpyzfdPvJyjDIfvk3O89u95Zq3NmzdP4uLinHjYpEkTJ2RSlvZow4/H1wu+55yPy/fPe877a/4913md1eHP+2fKcjBPxMJc+zZ9fd7bYQROZQLp6Qdk9pw4eerfoyVmzVYdakiaI2tcvvBcElWmiJzfraa0aV1Js4AVDl0PAKF+dPQWGTFquYz/baXExm2VtFQVT7l39FYk+1eXDjWl761NNctWcXc6UP1Pf0l8qRpPHs2AZ/fzn477uDtYtHiT/N9L42TuwnX6ORz6W+Dqy86RW29uKsWLRxy1vcTEFHnhlXHy08jFmtGN76UDUq92aXngnvZyToMyf/l6yxzwN9/Nk3cGT5f1G3dIqmbVa9wgSh68t40TSU/WeuQ7zcS5zOQPvTdx7hALe2UEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI3CaEQjKPTt37pRx48bJV199JTNmzHCi2A033CDXXHONVKhQwck7yET8kOnFGp4Raji8IJQZIZIV8tlvv/2mP8znkU6dOjlhLSIiIkMyoo5vMyj++LaDffIauYmDPnnPg8O34c/x3p9jrG+88YaTwRDeguIcYhBjC45hx44d8uuvv8oXX3whc+fO1R+oi8tZZ53lhD8y8m3fvt3F0aBBA+nevbsgO/mtX7OKh/jIWsfhRTH3Rv/x4wzy5ZwXl/wYguPiui/D+cxlfNs8+/Z9Od+Of5+5LqwQ58aMGePmDbGLsnv37pVNmzZJTEyMiy0qKkpKlSqVMZ6KFStKq1atpHHjxhniHPXonznz80V/jM2PgRiDYw/GzGvaCB7ERxvU8bFz3Y+TtnkEr/nXtOXXPWX8nPs+fJw+Pt77+aIe5WmLvvz54Bg57wUz3yex0V7w8GuYNrZs2SJffvmlk+dgytosXbp0xtqmDH34fmnHx+vPc47rPkbfXzCGYD1fnrVPWfogJl77tn354Dz5OeQcffmxUtYOI3AqEoiN3SGvvTlRRo1Z6u4x7g/ko+JFI6RFs0py7VX1pFbNksc09ElTYuWrr+fJnPnrZHtisqvDPUp2uhuubio3XNdQs9XlO6a2Tkah3UmpTgrMqfd9vXqlJSJ/KAPryWjb2jg5BBAbn39xjAwdvkiSNCsbR1TZM13WuUYNz9LP69/vZ+y4lTLo3SmyLHqz7Ne20venyy03Npcbrm2kWRLzHbX+77d+4lc3b94lX3+3SKbPjHX31yU96mg2vcqauTbviTd+sAXuMRPnssdp4lz2bOyKETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBIzAKU7ASzPIMJs3b5bBgwfLhAkTZOPGjYJI17p1ayfOIYWFh4c7ocYLOYgzXrLhNVKYvwY2n4kM6QoZ7+eff3bSGVnsWrZs6bLYUSYoIlEPKYF2vATEa+LkvG8TgYvzbJdK31z3ghDtIQH5NrjOwTNbtZJFrUqVKk5OQnpD5uOaL09Z+tq9e7cT54h97dq1LmYf97p161wWOsaUN29e6dGjh1x55ZVSqVKlDBGL9hCRvCDFe+IkNuKmDy9wEbPv34/Fj4cxcy1Ynvcc1Kcc9RkD7zlo37fHdR60y+GZuzf6j++H9jl4z2uER9YE27X6uFkTU6ZMcSIkZdq3by/nnnuuIEFSL1++fFKsWDGXLc3HHIyNc4yfOKnPw4+BuGgjKBdynfocjM+LXX4dcI6DctTluo/f1wue8zFRhgfvKcczbQXZ+2u0zWvKEyvleU27XPPCGdf8OfgH16aP07dJG74v1t+GDRtkwIABbk2Rta9fv36ChEjblGW8fvyMl3a4Rhz+oO/g4a/7uSPWzCxox9ejLdj7/qjP4etznhgoRz1eM2Zio207jMCpSGDfvjT5bshCeePtibJ1+x5d+3xm7ZcSuh3rtVc2kisuq6Ofd8cn+CAKffbfefLVd7P0e0Y/l/X2YcvWiuWLyv13tdEtKivpfXW4aPtnsE1JSZUfflwqX38/V5o0jJKbejV2ItWf0Ze1eWIExo6LloEqb0bHJOhnMN95Inf3bSVXXdngd2XHPXvSZNA7U3StzdW1tlcTHLJ2C8gjD3aQ9m2r6Of9P+eze+PGnfq3RF6V7k++vMn3lolz2a9BE+eyZ2NXjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI2AEThMCCDNspYq8gxRWoEABiY+Pd/JP165d5aKLLnKZxfjx0ctCiDNIcVu3bnUZs8hQxnWEm8jISFceoYoMZd9++61MnDjRCTfNmjWTmjVrOsGKLVtLlCjh2kHSQjYqWbKky9zGa9pLTk7Wbe6incjFNR70gTyHyIXk5+Uu5B5ENrLDkQmNDHBeDuL5nXfekSFDhrgMemTTO+ecc1x5Py4vJyED7dmzx2Vc+/rrr51AdsEFFwgsyASGVLZo0SJ55ZVXZPny5cKYevXq5dojNmQk6q9fv97V5TVtwqNo0aJuDPnz53eri7h4ME6YI1ExLqQk2kJEK1u2rJPRqECMPOC+Zs0aFz+8k5KSXBY86p155plu/Mwj4hNlaZc+ChYs6K7RLqIbzJhTHn789Ovf057nQxY+sgd+/vnnLr5LL73UZdujP8ohG5KJj/54DQfahxlzQjzMKwfxsoUvMcEFgczXhRVjZn0gbNIWLOFDLMROe4ybMRAva5Exsg5hS3u8JmZioG+206Uu9fy4aY/5ZB1Rn3ZgRr+UJ8sgzLwgRlnuFd8PsTNe+iEm4qYuWQnJJEd7rFXaJC76Zw3TJnEz/vnz58tbb73l1lS5cuXkvPPOc8zon/fUoy3GTx+cY53DnDkjFtYCc0b/tA97xkQd+oE7zH3clCMWONA2fOFPnF6ApC/6YTw80z68OHjNww4jcCoTWLlqqzz3whiZOWetpOn9xtaqYWG55MpL60mv6xvrvRb5h4YfH79bHn9qlEzTLFupqSq96r20T5+vVxmv941NtN0CnMo4/H0XOsG9l3HpiBehW/SQVJvVfZqcnCrDRyyV9z6cJjsSk3U8DeTmXo0yZfn6/X7o2Pd18GMhIxbiy6rfjALZvPDthS4fvf9gM79XF37BGEOfX8HaodeO2hFlT2wsvt/QnB3fmHyEiYl7dKvgX2XshBW6TkLieb2zy8gTj3aUalWL+WJHPM+bv0GFu0kya66uX5Uz9+oau+TCetL3luYSFVUoY45CMWZmRHPHFq+v7wMIzj3X/PrNjsHh9bPv05cLPYd6y65NHwvP9J+VOMe9FhJfjxx7cAzBtkLticxfsEHv4Z9lpZMZD2gWwCLy6ovdpUaN4pmLH/b+j47hsEZO8hsT504yUGvOCBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbgf4sA4k1cXJzbkvO///2vk67IrIb4w1al1atXl549ezrZDRnJ/5iIZDN16lSZNGmSk8coj6yDBEbmNTKRIcgtXrxYvv/+eycHIeUgjiEuIVN17NhRGjVqlJG9DZnn8ssvdwIaQhJCH2LaBx984LK+URaJj4xxyEvjx4+XyZMnOzmIsl7UKl++vNsylIx59IM0hGQ0cOBA+fHHH6VWrVpuTHXr1nVSETMW+iE9JI8xBmQtJDEyziFGkSkPeY4YOVavXi0vvPCC67927drSp08ft10rdWNjY50oCBvYIk7RPrIUPBHtGAvSIOISohzyFNn+2BYWtvzQyzXkqRYtWkjbtm1dFjIEJuQnxvHJJ584nohUnEOSQuxCdKIPxED6nj59upPUaJf5btiwoXTu3NltmYs8Rcwc9Ml13vt55hwPziGBjR49Wj7++GM3N5dddpmbD+YK8ZHYg33RN/UqV67s1gPbuCLDMR9w6d+/v2NVQbcChg1tIHGxTpC2mD/mkvMzZ850ZRHUChcu7DIAsu0vGdron3XGtrqsB+YccRKZDC7ET/vMN2vu7LPPdmuQNUN/CxYscGuZtRZcx8RKzE2bNs0Q2BBLX331VdcfMTI/XpwjgyHSG32RjZAtj4nXr00EOFiQwZF7jBiRB9mmlfLIa9xjtIuMyDrv1q2bGx/cZ82aJR06dJCrrrrKlfHCINsgI3gi951//vluO2SEPLIrjh071q0B7jvmAzkQGRAO8GDNkEUQEZRxECvtwpjrbdq0cWsWxrQPS+4lDsr5deJO2D9G4BQisG9fuvykctkrr4+XLVt3H/ycPCBdOtSUvrc2k8qVip7QaKdOi5XX35oiC5esl/Q0FaL13ip/VlF56L62mnWuguuP+y0mZpusWr1V9mo8YblyOuGpQvki+lkRysYZDIIMY8uWb5ZNmtWOLT7z5M4ldWojLrMlZ8i227YtWd77YIb8MHyBbNuRLPnCw6RRg3LStnUVJ85pl+4+j4zMI9WqFNXvqciMur4v4tq2PUVi12yTuLXbJVYfW7cm6We7fmcVjtDPy0Jydq2SUu4s5PVQv75u5me2veWxZUuSbNi0U3ZsZwvbHFKseISULR2pn6f5tI3Qd3Tmuv59wtZkWbZssyTu3OtOFSkcrp/zpXVsuWR1zFZZsjRelq/Yot/re6VokfzSrGl5qamCU7iO/SAWFwOcZ85aK9ErEyQ5Jc1lFqxcqYg0aVxOt9CNyCjr+83qmYyE6zck6pxtlzVrt+l/jLBdv8tzSdkyBYR5q1JFxW/djtfPR1ZtZHVuxMhlmj1uksQoa/jnUdZ392stl19SR8dxZJY2mL6h6+u/X89WLnt0fYkU07Hfd1db6dqlms4VWUtFdiftc+trTew2WRu3Q+K37FJ5Oo+U1HmvUrmI1KtbWuXtUJbcrOLasydVFizU/4BB54CDuM5pUFa/w8L175E0mTV7ncyaE6d/5+yR0qUKuvVYX7cFDgsLrd9Nm3Y53jt3aUY8DaiUSqNVlVEw8xxx0s9KnZ+1Ov41a7ZrnLv1HoBrIf07oLicXbO4xk2cR0ZJu5nFuasua6AZI+vp926iWx/rN+zQOU+VctpexQqF9XtPRfgyBXXujsz+SDzHK85RBwEyRmOP1Xs6Ru+dXbtS3P1SvvyZbm2w1rK6r48c0ck9Y+LcyeVprRkBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACPwPEli6dKkTxBBw6tevLxdeeKGT0BCEELV6qjiHQMRrDqQyZKMPP/zQyVJINchIyDmIRxwITc2bN3ciFxnnkNw4aB9RCmmMOjwPHz7cPciUxZaniGJki0MCQ4h64403ZNWqVa6966+/XjN61HD9/vLLL7JkyRKXTQzRiMxulENuog8kO9pCpELgIqPeDz/8IIhujIlnn0mMH7H5cZUHZWkLcfCbb75x4hbSHFJSVFSUE5DmzJkjr732mouvcePGcsstt7ixIWpR76OPPnLZ9hClqlat6up4MYtzSGfIcLCbPXu2E58Q7TgQDxGyaGvZsmVufIhO1113nZOtEKyQHMlShlyHCEWbXj5buHCh+1GeWGkHGY5+GBNzzUHfF198sZMIEaA4GLf/MZ86sODgHNeYdwQu5p22kBxpg3VBRkHmEQHLC2JkeSNW5hBB74orrnDCHq9jYmLkrrvuknnz5rkYWQdwIhbmkCyDrCfGQHvEQz+IX0iLlCfjHWIZ80s777//vrAmELsQHNnulL6oA0eEPMQ2YkYKY60i2rE+2Y4XYQ1ZjoxrvEfuQ2RDmoQ/fXLu2WefdVIcXKiDIEo9+qtXr56b688++8xlfGNuYA87YkBeZH5hx7oh1mnTpsmnn37qODFfSHXcC7TJPYI4iHyKqNqlSxe5+eabncjHnCD+IXgiDcKHWFmnjA2ZjnrMgY8PLvQPa9YO0h7rhblC1GPs9IeYSswIlvAiDlgwD/5g/H69+HP2bAROFQJbVTAb9PYUGTZikWZr3OvEo0q6nWrfW5tLx/ZVnSR2ImNFBHpT22cbTdrXm8k19+DdHeTKy2vr/Rymnw/75bMv5srQYQtlx84UvT/DpHOHanLd1Q1UTMp3RPexsds1i9x0mTk7Tj/vNPtooXC5587W0qJZlH6G51CRd49Kc9NlyLAFsl0lntA9LBKeN7dERuRxn/M0SnayihWKuKx61A3KQwhZk6fEyndDF+hnsco/SXsE6WmPSkds/RmucUfk18yvKpqdf15N6dypuhQremSsfL/AeJhuFztnXpyKeMmye9c+SdnLNtgak0ptCFsliufXbUWrSqcOVTNkq8wDnzFznY57qsSo/KUgpaZKTzf1bOKErlG/LFORbYfjR7Y24iteNMK1eenFdVTuK6iflyky5IfFMka3RF0bt1W2KZs0lRnzqZBWqGC41FYJ8IZrG0ndOqXdGDP3z3u+LnftTpEfhi2WseNXykYVwnbtVjZ67gwdEHwLROZzMuFVV9SX5k2jsh1PVu0jXT3wr59kyowYJ/kxn40bRMnj/+qof++ceUSVzZuT5ImnR8pULc987k3dL9061ZI7+iJ9kqXugAprW+Xjz2bJosWbdA5TXPzJe/Y5QTNC2ReIDNe/EUrIRRfUdrKhfuUcccSt2yGvvj5R5i/a6P5mCFeZ7dknz5PyUUVc22PGLpd1GxNdRsUIFdvYKvbyS+tKp46hdTFmbLRuXTxH4lQ2TNWtkVs0qyg33tBIKlUs4tYB6yQmZrsrM3f+OpUAfZypkkulNuIsWCCfNKhfRi7pUVvFvFKHrVcCpg0vzu3StcpWyBWiVApVOXPjxl2SsG23ruMUN+eRbBerjzKlIuXi7nWkXTsVSlUiDR7M9fGIc9wzlP9Wt31evGSTjiFZRcIUnZN0yZsnTNvPqwJduLRqUVkuv6zuMUuawZhO5LWJcydCz+oaASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJGwAj8TxNAgkEmQtxBxGJb1Xbt2jkZiYG99NJLThQiexxCG4ISWd2Qar777ju3lSmiDVIRWbSQfshKxpaQiDhIOsg5yGcjRoxwUlT37t1dWa6TeY7+kXfIjoXcg1yF7MY1xDlkozfffNOJVoh4V199tZN+yJSFYIVchmCFaEe2LOQ05DiEIjKrIaiRYQ4hCxGQfhDvbrzxRifOkf0ss/zDe8QwJCwyzpG5DJkJeRB5Ck5kVkOQI7sb8hayIZIXWcEYD3WRsxDuEKtoD85jxoxxmb0Q19jeFSmM7HFIZ4hKjJ2+GBPCF9eQFMkqh2hGVjMygxHXoEGDnPhHecaJJBar2e6QpcjQxtgQFcncRqY75oYMZGS2Q5xCQqSuFyKZ86Asx3uOkFyRw8WHpIU4B2v6ZOxkzCMesuYh77FeEK2YQ1jBA2GPLHvIbgiLzM8999zjmCCcwYmxIzLSDnPIOkNMI3se64v1h1AHK+afcZF9Db4wQpwjcxtMiYGMdHAjBuYK6Y/5ow5SJfPKekDqpG3KszaYU67BClmNeWSsZPBDKiNTHpIj6x2Rjb6Ye8aLJEe2N+JBimNNE4+ff9gTO5njWDMVVPBExnv77bfd2oGFF+OoR3uIbdyf9El/N910k7u3mBc4EieMWeM9evRw7XIPUod7j3Xl1y/rgHXCfUMs3Mcwpt06dchcFO6EP/oaN26cy3yHjEe73LN8Zvj1EFod9q8RODUJxK1LlKf/PVqmz1rjhJpUlY/at66q4lFLzVZWwkk9JzryESOXylvvTpHVa7bKAXVS2Urz5uubqfTVWO/L/E6ce33QZPni69myXaU3RC4kpn59W7jMaZn7X7p0s7z06gSZ6raATZciKtc99XgXJ/qRWeyd96bJ9yrNJSZqdjDstIMHn/vIPQhBHGkq7FVTuequfq1UMKviRCPOI5h98918+XHEUt2iMl6/B9KdkMdnAonlqE4btAWv8zQ73x23tdDvn2Luc4M2OLg+bfoa+fKb+TJbtxFN2JYk6Xy28D8XVygWsvCRZa9MqUJy+cV15fxutTRrGRnwQu34f8dPiJGXXxsr0au3uP4Rkapq/HHrd8hmzaDG+EJieKgGsSFHXX1ZA2nTqqL8MmaljBy9RDYnJGkByhJJaDzEmivXGVKnVml5+P72Ks+V8t1mPDNmstT996u5MmZ8tMQn7Mr4rKQtjhBfMsmeIVUrFlN5rL70uLCmfj8cLmVlNJrFiw8+mikffz7TZUAENmLkYw93lA7tkAoPt9rg+9Cj+l2p2RKJII8KWnf00b/lLq/rMrP9OnalfPHlHJmt0iKynBuzgvVs/ZrIpX/DNahbVh55sIN+Px4+j4TIdsZPPjNKZs+Pc2Mky+FL/7lQFuta/HbIPDe3oe8M1kZoXs9UGbHXdU2k5/UNZeSo5TLo3cmyRjPzMS9tVB679642LiMg5adNXysffDzDbTeblJx1nGmaXTE8by5p2jBKtxxuqhnvztI5O7RIaCcozgGEcTInPHjtYtTx6Fs9F/qeK1YkQq64pL5cenFt/Y8WCjBcdzDfxyrOIciOGLlcRdP5smTZJsc6xCPEmrY8l8I6n82bVnTbNddQ+fOvOkyc+6tIWz9GwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJ/OQF+jPMHP9Rlfo/Yg8QzcuRIJ7axPSNiG6ISZcmahZTD+b59+zqxhu0eydL1zjvvOIGrffv2TqojsxbiTugHQH6IDGUpQ7RD6kF2QtBCjEPiQQhCqkPmIjvbkCFDnOR0ww03OIGKsohzbGX5+uuvZ8hrCExkxqIu19kKk5gYCw9EImQosrgh7iEhkdmLAwEPEY1seIhziEJenPNsiJsD8QjZymcCY3z0i8y0ceNGFw/9MhYkLGQwpDGyl33++eeSkJAgjAWxDAkO0QwJkHGSnY32br/9dtc/0hnbnPpMdAhajJ+xwZ82ySCG6IakCFPOIVuRmYxMdHBlLGRKo4+hQ4e6H3/pH1kNQYuYkKXYahUJCoEMWYzXHDDgB2MezKVfM5z3GedYK4hzsCZrGtIYAiOciBdZkTZhQX3aQppEGGQdkb2sTZs2Ti5DnCNzHwzvv/9+Jzh6WRL5D0kPyYuxkSmOrIJkm4MX84tkds0117j5RS4bPHiwm19Et54HsySyzry0xnVkROJDwPTzATPWCOeRyDiQzZDmWLfIZIhjXbt2dXE/8sgjTpxEYkRmZB0xXzDjgA19MhbWCK85hwgIB7ZwhRP8EBu5B9999103JoTGfv36Sfny5V17iISsDbblZd0wn6xp5p05QYBDcCPjHO+5f4mVfrl/4YhUyngRP5kD2kT8ZB7JEAh/stSxTpkz1j7iH3PNmoE9a4+tZVkLlOFhhxE4lQmwbecD//pRlq3Y7CybfSq1Xdq9vtx1e4vDJJoTYbB4yWZ57Y3fZNrMtfq5nq7CWrpcfGE96XNzM5VxC7nPT7LSffnNHJchjsxwPc4/W/r2aZ6lOLdsWby8MpD2Yp2EhDjX/5HO0k7lt9cHTdLsdvNcZreQJHUwcv5M0NvZfQfq/c0b4qhaqbjceXtrademkpOyuP7O+9NdLGyT6Q9VofTlwUZ4pS/1o8hlvGt7bjVto4XUqoloGPrMoJ0vvpwn33w/X1bFJrhyZGSTHIfEPd7SjqtyQL9H1CqMjAiXDm2raea3BlKt6uEC14SJsTLgtXESHRMS56hMREhRjDW/ZjrLqc9JKjHxncQYyeZXuFA+l1Vt6/Ykl/WPOu7QWET7ZRyUJea8mkntqsvOkVtuaqKfqeGhcgf/Xbd+pwzWTH8/jVqimdv2qESoFXUMdIXQRl8HdAyOwcHxVIwqptumtpZW51Y8Qno7rPHAm1jNqPdI/5Eyb+E6dzZMhb5uXWrpmjxXpfCIjJI7dctapLh3PpjiMvilpe2XxudEabmW0vCcsxyTR7WdH0cs1nWiGf60JmOHuT/U79Nzh/5uvPmGpi6LX6FCoczDvhz3ydP//iVDnEN07Kgi3/yFm1RcJAPgwXnUtkL9HHDce/dsKtdf11C3Q14ib78/VWLjtrt7oLWKc/fc0cptlZqamq73x0T58lvNyqjS3MElpC0SKLGJzuuhOGn/6ssbqJDXRMroNr/BNZdZnKOJ0CrRWm69EJ/OEaKjzhHB0j+C5b13tJZLLqqtfwuEaQ+u+DGJc8Q3bPhieXfwNN1iV1lgxxJ3qHPthiD4PnXdOWmPPrp0qKHb8B4+p67jP+kfE+f+JLDWrBEwAkbACBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMwN9PgB+IEXa8BMV79+O4hoZkw2sEGbJSkaEMAQgxB0GMssg1XEOkQZxDkqI9MnchsyGRIf8gjiE1+R8pfT+8Rxgj85UX56699lonxiE0cR3ZCikI0QvRzWdBY1tIL84hvJFJDcEHiYqtJokD4SgmJsZtRUnWM8aD8MS2nWQ4Q07zccOALV8R58i6lTnjHDETj3+QNQxxDsmMLHJk1kOqghtyHteRnBDBkNnILIaQhMzGFpkISmQGQ1ZirMSGoIUMhkhGP7feeqtrDxGM8ZFxjcx+iH2IgQhXiGLvvfeey8KGpEj2L2IhU9gHH3zghD2YcY3+yYhG+4h1HAhTbLdJPMh3jAVJi3jI8oZkhVjFwTni8iz8OZ4ZN5KjF+coi8SIcIb8hqTFHJDdjwxwxE8Z6iGosc5YL6wV+iXTILIcMhnCGhJh+fLlnfDFnDIGBEhkLdaM58j44EWWONYc4hzrgi17P1IBk/jatGnj2mOeYYJoiIBJPbYjRqxjHREXHMngR6Y5v1WpX79kgkPQY+1wXzDXHE888YQTzujntttuc/Xg5g/mjSx3zDeSJduz0ib9kE2PrIhk3yN25tyLc8wNMdxxxx1SoUIF1y8cEedYhwirSGzBjHO0iVwJf8RJstjxgC/nYEg2PNYIfZIVj3mBE/cVIiFj58E17ivuWWJii13mnOx4xMp9B0/qcwTH7Mduz0bgVCGwaPFGFeeGu2xwOfVzLMcZB6TnNU3l1pubqGx8uDz0R8e8XrenfFkzxI2dEK33XZoKawekc/vqLqtdlcpF3efGyRDn2ut2k7+MiRYkp717U2XSlFiJXhXvRKXcmiGsWpUS0rBBWZf9DMErXeMorRm2mjQqp99thfRzXHT71/Xy4itjZPGyzaHPAP0YOEO3Zq1eubjKWOXclqf79qYrr+0yV7OYrYxJkFbNK7mxeHGOj45Jk2PkVZUFl0XHq0ekIhHSkH5+litbWKpWLiaFVfZj21e2XV2xMj60dasCJiNd7tw55dorGkqvGzQjX7H8GdiPFOdEcoXlkHPqRkm386q5rUnJ9DZ/4QaXMY/MdBx8liHXEVfpkgWknWYUbNokSjO55dVMagny+ZdzVehK0DKhriqUKyJPPtZZGjUsGzrh2hD5/L9zZfBHU112Ox2MEw8b1DnLSW3VqhSllCxaslHFxQWybgPfByrUqfTW5tzKmsWujWY8DX0HZzSazQtiffGVCTLkx/lue1yKlS5ZUJ7t38XF7astUSHz3y+MlbkL4uhaUlXcu+m6pnLzjY0yhMt7Hxwmo8eucGssUrc7rV6tpNQ5u4zL6LdufaL8otfY4hZG+1TqbK1zeacKenVqlzzssz+zOMdXIduPpqqsV6pEQWnepJzeL+H6fbJJ5ixY57b2vfC82nK7yp9sBzzsx8XyzuCp2YhzafKcjmOYCn5kxUOArF61uG6dW0bOKltAtmiGwB9VvNu4Wf/+UzZkrCMz4D0qujVrWk7/DtGFqwdjOEKcUzB5codJPR1zw4ZldDvhSN06eI+MGr1UVq1J0HuDv4c0C6Tel+dqFrh7VHKsc3Ypd471ciwZ51bpGnrtzUkyYdIq/U5O0x4PaBbICN1uubpmliuvXPJoJskUzUi3VCZNXa1/V+5luhy3+zTrXge9b/PkyenG8Gf+Y+Lcn0nX2jYCRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBIyAEfhbCYR+FD5cCOMcDw6eEYnIFkbGLsQ1tthkC0p+yEaeIesW4hxZyxCekKyQvwYMGOAyxCHksM0jcpg/kG8QhZCmeCbjHGIcApLPfOXLI6DRPwIT22X21ExhCHo+4xwC3MCBA912lmzNifCEEIdQxnaSZOFCcKJPpB76I0sW7SKgITYhlSFtIQoRB8Jbr169st2qFS7UR0hC+iOjGtIRkhgiE6wQoBCiiBWZDW7IfmwHS8Y3JCPEODKLITTBArEsmEEOIYxxsi0pghXbtyJnkUmNfhgTYtRbb73lsoqR4Qx+CHz0QcYwJDAvltEHY6cPhCneI+IRI1IUUiFxky0QOQqhjgxlSHUclKdff8CSGODB3BE/c4+QxXnGTXY8pEtiWb9+vcs0R3usH+rxoC/aZn0hoCEaIkU+9NBDLnMf6weREcEL4csLhsiWiHOMga1aYcX4kDnJnIc4Bw/EOSS4j1ScI6MbolefPn3cHCDNkX3Nb9eKOMn2rQh8rCEytdEfMQez5BEvdRkncSOs0S5S3H/+8x+3JtqoOIfEVr58+YzxMi62eUUuRXRD9IMjD9pDFGUeqEtGQuYGEROpk618kf0QClkDxABHRFCYc6/CDlb0yVzBA8GTLIesf8bFnBIn9xVbBrPeyS5Yr149x5B2ERNffvllJy6SUY+xI2Qyz/TJnDEW7nfGzb1f4aDMx/qgDA87jMCpSmDuvA3y8GPDVejZpvdiDilUIJ/07tVErrqifkbmqRMde3x8km6tOl4lpWX6OaWfNyr/IJuxRSqyGZ8bJ0ucS00lox3fbfvkzbcmy/CRi2V30l6XyY3tX6+/tqF+poYyqZETC+koLCynG3tycqo889wYGT1mqZPaGDfbl3bvVluuvKyeSkwF9bMlJNVSdkvCbvl1bLTb2vWiC8/Wz6Birp01a3fIq69PkPEqEjFejvy6/Wx3zaJ3QbeznbQVRoY25ZC0e59M10x8X+vWsMuiN0u6ilhsIcu2rXfe1lK6nlddv3NDWT4zi3O5tY3zOtWUntc21kyuhd1Y6As58dvvF8lLuq1rsmafY17JBle5QjHpqdnP2rapnCGWpaZqttSRulX825MkTmU3DjLU3XNHG421RkbfmzbtkieeHqlZ/tjSV8VjFa66qPzY6/rGcnatEhniU4rKgNNnrJXnXxqn2ce26uenSIlikbqV7bkq91V3/FwnR/lnxsw4+c//jZEVKj5ykKHsuqtUJtRtTwvqFqgpKWkydNgieeu9KQe3jD3gxndH33OlXZuK+vdIiNl9D/2o4twyqVa5hNyodevWLeOy7+XVLU/3qKQ2bvwqGaBztWkL/1GCSM1qJaTvLS2kTWsy5IXaoP/M4hznGBsCW5+bm7utbXPpOtqxI1mzyy1TiWylXH9N44xYfhimGdk++D1xbpz8+PNilckKOKaNG52lceZxvPbpmv5St8f94us5smmzbsmr/zuzYD7N5BeaIz9W/g7JLM7Bnux0nTpU1cy2+d2YmL9ly7fIR5/MkInTYiRF14gbj66TO902t/WUcV7H41jEubfemSqffzXLZXlk2+GS2mcv3Yr5/K7V9O/sQ+Lnho2J8va702Tkr0tl1+697t5q0aSiPPJQexVSj02qdIH+wX9MnPuD4KyaETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBIzAP58APxbyw7uXXIKiC0IQws5PP/3ktnMk4xkCDdIczxzIMwhdCD9ko0PYIaMYUhCCGFnWkLaQebx0RJ+0jYDDQV0yfSGHcQ7xDQHOZzlDxkJeIkMaklRPFecQoXzGOTKJ0RdyXLt27Vx9+kIWQhAjPmImFp7p32ecQ8RDXqMe25GSJY+tK8mIlp04R30eXpxD+kO6QvhC8KIPWCE5IeERJ1nbuM72mQhQPnse8hbCnWeBMIXIRAxk8mKsyGCMnfEhzsGnvEpRzBXs2IqTDHFkWKMtRDFiQJwjCxlbxCIvUhchClGLbGIIYgh7ZAT0vGENm6A4R+y0x+HXCa9ZNxycgweSFtIXc4/EhYyGOIdMSFY2sgYiayEQImoxZtrw689n7ENmrKACFkIg4hzzh5TGdrMIkZRHPEPsQpxjXpG+mjZt6sQuRDEy+rE9KWuIrHdsLYwERwzEx3yTaZCtb2mPdcwcstZ//vlnJ6UhKCJlwp65QCojNuJm/VKPg3uBLUzJyEZ78H3uuedcfzBnHbHO4AN/5pG5Yr3Dm/gR7xA3yfjHeo6Ojs6YSzLpwdWLc2Scu/fee90aQISDP3OGGEe73Gts1UqbxMr6YE2xFphvBFceyK7w4BrjYl2xVlhv1EOARJyDC/cD42OszLVfB/SPnEhMPFgnjJGDchz+vXtj/xiBU4jA4iXxct9Dw1RySnDbfEZG5hW2q7zmqvr6WZT7pIx0bVyiDBg4Qcb9Fso4hzDW9twquqUmW1UWc59DJ0ucQxLj2KMyEH0O+XGRSjopTpa64pJ6clNPtiDNOpPe1GlrdAvYCbJUt60l6xltdetcy2XfI2tYVgf9IHHlzx+WIWshB3325UwVifbol0vo++U2lauuvLyuCmuH5Hvf3r596TJv/gZ5690pMmPOGrfTJZnnOrat5iQuGBFLUJyj4SoVikr/xzpJ/XplfFMZz2QSvO/h4bqNKJnfDqg4mEdu6dVMY6ingnmejHK82LkzRe5/+EeZOjNWv4/3S0FdAzfqGrgqUBa5794Hf5BtKobxvVGjSinNsnfuwS1YQ8x9o8hz7w6eLl98NUd27EyWfOF55MKuteT221poxrNDIpUvn9UzwuG/Hv9ZsxSuEMQxjioVi8nzz3TVz/ESsla3PEUSmzgFOTFd9qamyWU96qqg18JlttOvFHfc99Bw2adt3XBdY+VUMkMuDF0VIevc8y+O1XZWa9a/VKlUvojc1ru5dO5ULWM+KZuVOFdIM8z1f7SLZkyrrO0eYpCkUuWunXt0neXT76LQ34hHE+de1rFs3LRbrr6yvpyjWRERK4MH2fUGDpooU2fEana4dP0eC5MH7mrrtlb1YiXfV16c27krRfKp5HmhSp+33txU/26LDDan320is2bHySDdInnWfP2PMpywma7bp9Z02RMrVwqt96OJc9u3J8uTz/7iREGy1hHDDVc30ax/jbOc6xXRW9xaQ4gkhgrlCssTus0ymfP4Tv4zDxPn/ky61rYRMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI2AEjMDfSoAf6vghlx/dEFx4z4PXZPYicxoSElnlkGTIZEZmMsQaylEGqYfMc7xG2CF7G1u4IqEh4SAukeXKi3D0R/u0wWtkKuQkpCXOBcU54qJ95DRkMoQ4snAhPtG2z5CG4MOWkohrZB+jHjLelClTXMxs9UnmNcQe+uMaghSyHPIa7SEukbkOqQ35D2kNGRDZyB+0y7g5aAcJi7aQtdj+EjkOuQtpbNGiRfL+++87hvSNvEZGOAQnMqIh1Pm+eY2MxRgQwnjAonfv3m6cjJ+tRJHDkMQQnWBIP2xlypas9Mc4Eb78Vq3IUsGMc7TJXNE+GefoF97IWWRU8+IcGeyQyRDWmDvEMH94Bswdh3/PM/URFhHnyEaGOEcmONYR8iMHbfKAtxfQqMv64gFvBC3kuPvuu8+JaOeff74TMIPiHPGzZsgOh1TnM86xXsgCiBDGmkMMbN68ucsABw/Gznu2fkWGo2/WLhnp4MzaRUBkHcGUtclYyKhG3KxBxk4dDpjSBuMh7piYGJdxjmfuBcQ5hEnWDWX91sfEh2yGmIhMiIBG5kJkQBgiq/lsgHAdNGiQiw05z2/VyvyxDmiT2Bkb4hxSIOIcB/IrbX6k2faInfXDGmH9woM4yGJHX8QBfx5k5uMeZr0wdrLUUZ85858RzBdjIg4evCYeePj7hNd2GIFTkUD0ygR54OFhsnzVFlWxWOcqvmimrD69m+m9krVgdrwc5s3fKAPfnCiz5q51YhZbTV7ao54KSk31s+9MvR9PXsY5L86RcW7AwN9k6PBD4tzlFyPONc7IOJd5HF9/u1C3Ip0m6zZu13tfNOtbQRWjOkuLZlHu8yBz+azeI3w989yvMlyzhyHEkYELAe7O21sK29JmdyC3jRq9XJ5+7hcnm7GNLNvD3tUPOY3to884QpyrXbOUPNO/s7Zb7Ihm1+hWsv96YoQsXLJBP+fTXRa5uzXDXw/NjOczlPlKZJ178ZVxKhku0Ox8oa1CL+leV27R7XqLFA6Jbj+PWiGPPz1CknSbTUS2yy9qILfc1FjKlC7om8l4ZiyTp8TKcy+N1S1bt2nsOaVZowry+CMdNGvfsWcWG/6TZuId9Ju2oVupautnaqa523qfKz2619TvkbXy3MtjZcNGtlnVrHZFI+S2W1rq+Grq3xthGbHExm7XvyVy6fd/hH6uH/45TpxkDXz73amamXCJJKpAiDhHBrkunavp90H2GedyaaZCMhj2vbW5lChxuJSW0Xngxe+Jc8S/fv0OCc8X5rbwzRwn30PrVfAbOGiKy9rIFr95NKPeA3e3lcsuqZMRJ+UOE+c0y+FVlzWQm3s11r8jQv+hSCAkd9+9+/4MlTxnyfbEkBBZr3ZZefDetioZltaiOY66VWvcukR59PERMnfRel3vaVK2dGG5s28Lad+2iq6znMHu3GuEvqee/VV+mxzttidm6+C7b28t53U5XFQ8ouJJOGHi3EmAaE0YASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJGwAj8MwnwYyEP5BYeCEG8JzsWshByGWKY33IUwYysbxyIQ4hbsZpdDckGAQe5BlEJ2ea9994TsoUhD5F1jsxaQekGwcb3Q3Y0JCgOL3IhPXGdbF+IPwh8xITgg6DGdbKSsR0rgg9yEYIPYhDSF/LW3LlznThGHTJm0SdCFtuGkpGsWLFibqtWZCNEPNohExhiGsITYhVyEEw4vATEM+IRmds8HwSzrl27OmGJfhg7cSM0UZ9xIYAh8yF2sW0pGfqIGWkKNmQ4Y0taRMXy5cs7EREZCz5kbWOrUqQntj9FjoOSR51YAABAAElEQVQN8h5SIfEgkHXQbTOZR9pBjKpfv77rm8xvjJEMZGQTY5zIToiNZPBDiiKL3pw5c5ykBUPGw7jgFFwnngPneJD5jj6pD1fYcw5xDkkNuQ9hkPki+xssELvgRH0v0CFeeXGLdYUgRgY35DLEQ+rQD1IaTBDFEMm8lMh4GB+SHlyIGyaMD7Zk5qMOghhiJNugIk+ydtjmlAxsiGbMCW1Sh/lFaCMrH5naEPXohzh4ME7i5sF6peyzzz7rtndFZGSOuWdYu4yVeaRNtmnlfmKdse0qbXmpjnWFtEkMxEh8bCPMumCL3zvvvNONmzjokwx1rAG4wxducKE/riH/sR64B2kTnsTDVq2sBURROCHrsUY42F6XzIOsB+LkeoUKFdz9wHXmzM87dYiFg3EQE2vErxN3wf4xAqcYgbW6rehT/x4t02evlf0qsCG1detcQ+WXlnpPZy96HQ+G74Ys0gxkUzO2AkW86nNjM5cFrFjRfHof/jPEuYFvTpEvv5mt4ppmitOjq26DCody5Y5d9oqJ2S4vDhgvk6et0r8v9G8R/d8zj3eRC8+vdYSwlpkh2c8ef3KkzJ4Xp3JammZ+Y8vUVnJRd7ZIz5WlOPf0E52lapUjxblNm3bKo/1Hqay4xglKRc7M5zL8Ic4FhTBiIM7BH4YEqq3bkyRvnjDp3KGaINqRqWzv3lT55ruF8opuabonZZ8TLPPnz62Z5HJn+fmoH9na5z6V7PbpZynfjft1q9Ti8soLF0rlSse+prZtS5a77h8mc+at1e8BkZy5cqiAV16FvWa6vexSGTZisX5f79U1S3a+6tJPs83VqF5cY8pM9tD7AwdY47pFrm7fu2jRRt1qd6WMn7hKErYl6XdBulStVFwFvBaaRa7SYfOVOeNcHpXCHnmgo85rzSN4Hurt0KvfE+eIl/EF4w79TcHfI6myfMUW+XbIQpk6PVYz/u1WngckIn8eJ871uPDQuqJOZnHuyktD4pzfnvhQRKFX48avlDffmSzLV8br+PdL6ZKF5PGHO7hMgnz3HS3j3LJl8fLAv4bLas1YqWjdHEXky6NMQplkickftMd3607NAIlkx9pgW+CbNLshAmDmTIi+3sl6NnHuZJG0doyAETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBP4RBPyPcfwQx2semQUXfqBDWEKsQYRC7EKCIjMZohHCDHIMB1IPog9ZyhCA2Cq0vEpfZM0iexttIS4h9JCxjvJsE4nUhNxDpjOkOWQiZDJEO8qTpYsMcYhjbHuKeLV48WKXWQw5jCxoZAlDeCJLGOUQvRB8yP5FebZqRbAjEx0yHBnq2NZyvG5picyFCIRkRJ+IP2T1IpMZmcAQmsgQ58U5zwlWPGgL6YjscYiFSIP0z9g5ENnIEjd48GDXZ8OGDR0b2CGtwYx+2M4TGQoRacaMGU6oQkhEukICjIiIcCwRxdg2ExGuRQvdtk358R7GsSqZMT62nUW4IjZ4Is4h2SFLIbAxFqRGhEjq8Z75QgpDnEPEQzZkLokTQRFxjvkPrhvG53nw7K/RL8Ia0iT1EeSQ0BgP4uPEiRPdekDiI07m2MuMMEQwY7tQBC/Gds8997gsfF5kY8tT+lq7dq2TEtluFpkQMRMuCFysL0Qx5oa1yvhYt8wFGQCJj7lmbmHPfNEX6xwxjTWB/Mma4R5gbdIWYhxzSNzEhzgKS7ZXZS2StY1YqNO/f3+3VSvrtKcKesTNOFk3ZN9DYqNd+mJsPHNPsI6JAzHQy2rMNX3AFDmVtU3WRfpjbbC+qUvsyHXMKWuH8bGmkPG4D+gXMY546JP1iWDI2FiHrDXGxn3EsW7dOrfumDfuYcbOvDFHjANBEW7MB1Iq80Zsfj3QNwfcGLsdRuBUI7BdtxNlW83vh82Tnbv2OpmlWqVi0kflIbJG5ckT+o78o+NO0gxmAwZO1Gxm8yVZtzXlvkPQe+SBTnLFZaFsWf8Uca7/0ypj/7TIZYoTlZluvK6p9LqhoWZdy3fMwyfTGltqLlm+yYlIxXRr1qce6yxtWldyY/+9htgylWx1v45b4bYNZUvOu3U71Jt7NXHb5mbeqpWMc9mJc1sSkqT/06NV4AttQfp74hz8P/50lnz06QzZogIZ4lyn9tWctIc4xxr5+LOZ8vHnM52ExxySFY3HsRy0X6FcEXn2yS5Sr+6R28pm1wZi1SefzZIPPpkhCVt3u6xzxYsWkLatKunWoKtlc3yiq5pXBb7eKl9dd+05KpQdyq4bbJcYkOWWr0iQMeOiVUJcLfFbdso+lcX4btCvALf2o8oW1qx2zTQDWnVd+9lnnOPagOe7u3kN9pPd66OJc74eUhzCYUzMNl0H0TJx0iqXcS9FMxmGvpe0pAabK1dOuV8zzl1xad2MOLl+vOLcgoWb5FUVImfNjdP1mu7u96cf6yrdutZw6/Vo4tzMWXFyz4NDZatu2cq64OC78li+Lpnfgrpt8MXd60jP6xsf133mOjrOf0ycO05gVtwIGAEjYASMgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBIyAETACRuCfTcCLLfxQx8O/J2r/4x3ZxhDL2LqUjFVIM2QmQ44hIxhSjJdhEIgQotguFRkHgYyMVohQCEJkrKIMEhwPhCqypbGFKMIW0hDCGGUR5JBvkHPYlhNRh3K0izREBjcyZdEOshI/2rI1JzEiTXnZDEEPOQ5BjGvIRQh6lKE+WeeQ9BCd+vXr5+JApiLDFjIWIhGyGTIW5z0jzwxWCFoTJkxwmb7YYhXJDCGpfPnyjiNjpi8ENrKB8Z42GdPy5cud3EUMbH1LfIwFGYk+EJzYFpSMd8RMFjNEOyRB2mQsCEkIU2RxQ+RCtEL2YuwIXfBEfKQN5DEylyE7EqtnydiQxBAVEecQtBCtkP2Q3Tp37uyy5NGmF78YOwdM/OHXDUxom/qsIaQ7v8Xn/PnznaTFM2sAqY0H46AewhdxILkx93FxcfLII4+4zH2wuOaaa1y2N+oyf8wt8hlzyPgQBGEFDyRDsiDClXWLfIY495FuV8qcMW6y1yEEMi7WENvtsrYZMw9kRjLo+biXLFni5gZO9MP4YcTBOmnTpo2TF5HJyDjn5TfiZjyMkwP+ZElE7iO7HWuZNukbDqwJxsC2vMSO8EhfiKiMi7VA7Mh4jJ1yCKu0SyZF5o/xsSa4lxgDEhzMKEc8bCvM2kGcY75Y76wD7jXqcFCPMSB5cg8zn/D0Yh3zwAP5kbXH3CHy+XXBs79f/PpwDds/RuAUIUC2sVGjl7lsYhs0SxkyFNnOLr6gjmbw0mxrZx17trXMSPh4/Xn0cnn3/amyMmaLCnOafUy/I2pVLykP3NNGmjSOcv39E8Q5JJ5H+4/UeJdkiHO39GouPa87Rz8zjtzmMvNY/fuxKju98fZkWaFb3zKucmUK6xalnXS713Lus8SXy+o5Rbfg/M//jZOfRi6SZH2NOHfz9c3clqlFi+Q7roxziHNPPhMS59ja80TEua2a+e3Dj2e6LT337VOBS4MvV+ZMKa1b2ebMeXShmL8LSuuWrjdc20i3lS2S1dCzPRcbu00efPQnWaRbzipAV47PYv8ZTZa0Jufo30Caba7hOWWzjCc5OU3maha/L7+ZL9Nnxep2tKm67kTCdUtXxNCUven691mq+5swqmyRYxbn3nxFs9E2i8o29uCFYxHniGGhZsH76tv5MnHKav2PB/a6DG55cofi3JuS5taFDl7P53QZ505UnFu0OF4GqDg3c/YaJ86Fh+eSZ544T7p0OjZxbppumXvXA0PcNrd8drCFbdRZhfU/itCtcQ/OV5BD8DVzGB4eJq1aVpTzdGvcyMiTszV0sI/gaxPngjTstREwAkbACBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMwP88AX5wC0ot/n3wHHIWohFbP5JFDHmH7Fw+c5avg0DHa7a0JDMVcg5lEeeQaMhyhVSHzIMUhDyEIITUg0CG0MRrtjUlYxZSE+IQklmJEiWc7IXwRXY12kYeIyMaohPCGXUphxhELMhTbLuK6MU5RLzxKtAhM9E37SAdMT76QVpi+02EIeIiCxsxIPORPY6MeJzn8Hz8AmA8jIvyZBlDHCJWRCIOfvBGVGNrTtqFEQIYUhYHsiBsiNMLWIhkbJtJOz77F2IX15kP+kLkQrBDWqJ9JEOy+cEdQYt4Ea/IXIZoiBTlM5BRHlmLzGZsGQsTrnlBD0ERMQtZClGqUaNGbo4QzCjrRSjPgmd/cA3ZirYR2phDxsED0Q9RCwGLOUHKRNJjzqiHiMY4kOYYC/2xJsh8R7yMjflgrhk3vFkvCGjUgylZz7w4x5zTF2uQbIIwhR8iIVukst0qGfrgSFvESp8IcJRn3mmL+FhrSHOMCwmTeWc+YImgxhoiZh7MPe2x5SvyIsIp69Fn1oMVjMmYhwTJGqA8cwwjxgdT5g9xD7GUbWXpB5GQOWXMMEFgI06y2jFPxMqckl1u9erVblqIzfeNGIigx33MWvXZAeGEgOfHjdTq55f5ZOyw5pl+WRd+zmBGDNzHrCHipG5mydKvEXs2AqcagRXRCfLs87/I3AV8Bx1wmbeKa6a0Xjc0lh4XnO3EsaM4MEcg4WM1fstu+ffzY+S3yStVAktz9xyC2jWXN5AbezZR8aqAnmPL5MO3as2nQlO3LrXkjttbSrGiIQk22AHbQ74y8DeZNjPWbS2LFNb/kc7Svl0VJ+JRNlmzdg3QMkOHL5JdujVkgchwufzienJTz8b6OZm1CEes3/0wX79b01TSEulxfm3pe2tzKVumYLD7331NBq8BAye47VbJ4BWu2dBCIlK1jNiyayBha7I89ewoJ03Biy1I+93SUmNu5KSi4804d7LEuV2aifDLr+fKoPenKBuEvjTpe3MLt2VqgQKh7a2zG9OJnkfUe/3NyfLN0Pn6vaVb6GZaiPyNcv1VDaVP7xb6/XPkvO5VKe77oYtk8EdTZcOmRMmhcldkhErnpQvpGqsp5zQoKz/+tMRlGtyRuMdlxjvWjHMnU5zbsydNRo5aKoPenSLrNu5wawWxr4zKiR01+1/L5hXl6+/my8+/LHGSX1jYyRHnJkyMkTfemiTLojfp3xChzICPPtTBCYF8Rx4t4xzX731wmGzcnChpeh/XPbu0ZipsLc2als88VSe6FE64volzJ4zQGjACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBIyAEfinEfBiTDAuznnpBTkG8QfBBwkGYQ7pDFmHH1t9OX4cRNhBwkFOI8scWakoT1nK0Q7SDwIS0hCZxbzAhthD+zyQkZDIYnXbUcpSH8kNOQ5Bh7YQiiiD8ES/ZJ1DDGKLUWJAPKMOwhSx0Q5ZsyjPmMimhTSF9IRIRJuUJx7OIRwxZiQmZCLkPGLL6qA9xozgRV+MmfEgD3EQH7IZcSHpETdiFHIbcXIdLj4++mcsxEc8CHC0S4w80x/jhw+xMw+M0/PmmYN4qUNcCGp+LDxzzbdD37SLJEjcvKZNxDAkRQQ1BERiYhwcvgyxEy/v6YuDZ+oTIww5kNoYsy+LiMWcMB/+GYmR/ilHedjAkLWCeEkdzjMfni1rhX7gyjnmkLnyccKauWGtwRM2bNFLhjXEM+Qx5E7PAkmOsdIH/RMvh597xsk6Zn3AnphpG/5whSFSGswQApEPYUTcrAsfN4x4+HJkiUNGY6yscRgwBvggsPGePuDKwdwwNsYNA8ZMFjnuEeKhXdizprgXuT/on3VPzNThHA9eI+1RjjEzz369+3mlPeJhTLRLvLTDOeJjzLRF7NyvHLDza5B27DACpzIBMl398ONieWfwVNkUv9N9JpLJq1L5opolrKF0UCGtiGY8O9Z7ARFu0+bdMmToQnn/42kZ0hznK0QVlQfvaSutW1XU+yx0b3F+8Icz5ZMvZrgtH/PkziVtzq0qD9/fWu/pAkegX74iXl5+9eSLcx9/Olu3JJ0umxN0W1D9SqhVvYTG0F7q1yurnwdHhJHliS1bkuS5F3+VMeNXqoiU7jLs3XHruXLt1Q2cgJhlJT1Jf0uXbZbHnhyh2eoS9HNwvxQulE/u7tdaul9QUz9/c/1tGeeQ14YOWyzPvzxW9qTs09jSpVO7Gi7LW7WqZHLNblSh8/779VjXT7A1RMt58zcIEmB0TPxha5B1U7J4AXns4Y7SRrdvDQs7MpBRmvHwtTd/k9i4bdpsDmHr3GuuOMdtE1ywYLh+d+yRt9+bqlsJL5AdiSl/izjHeKdOi5X+Osa167fpfXGGFNXtgXto1scrL6+vgmmkbNE1+errE2XE6KUnTZyD36dfzHXb8MYn7HRrrmnD8nLf3a2l9tn8xxNHF+eiVybIg48M1zUbr38vsGbzS1+VPS/uUcttL8zYsjtY87ry9ZFD5zW7UifvvIlzJ4+ltWQEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI/APIeAFHsLhB1kenPOCDueRZhCLOBBleHgBirJBeYp6CDVetEFE8m0i6HCerF7Uox2uIfogFHnJhvYog0REe5SjDAIZzxzEQz+UIxbOU472fZ1gm5SjDm37eGmPPqnDwXs/LmKlvI+NODl49q957/nRPg8OH68vRxnfD21Szo+F/ujfc+Ea9fx4uc7h42JslKVNYqQ813jQBw9eE4PvkzL06+PyZeiH8zxol7kiLg7a9zHBi7pc9wd1fRmu+bH661yjXdrgoAxt+3L0R1x+TVCeg3H7ufTtco0sdTzThpfPKO9j5Jlxc92zoDxrhDj8Na4jzn388ccZ4hzb5iKcMU7i8334/umHtvy6oS/fLtd8f9QlNuLnoDzj45lz8IO9vwYD6vpy8ODgnG/Dj8sz4TrnqOP50Q7XiZtn6nP4NUw5z59xcJ73xEodzvm58n37sXONg2fGR1uU5ZkYOEcdz43x+fXlx+rboIwdRuBUJrB27Q63ZeOv46MlXaUtdVn0flWxu3CkXNCtplzQtaZUqlhEPwsQkA8XXUK3GkIt2ShTZdHizbrd5FwZ91u0boMZyjTHxTDdcrJ3z6Zy1RX1pUggOxhZ7r78ep68+8EUiVdBCHGoQZ2y8tTjnaVChcKHYaePOXPXqQw1UeYt0MyuGmN2Gedee2OSfD9sgcs4F5E/r3TvVkv63NL8sCx2Pnbu8V/HRMubb09SQQupO/T5ccPVjTXzXkNXJ6vPAV/fM9GPNHn+xTHy7Q8LJEVZKBWVsQrLvXe20WyklSR3FnIXnzPbVOD64su5MvigaAiTerXLyB19W0nzpmc5Jn9XxjkmYOq0NfLQoz9Kwjb9PtP34fqd0Fuz911xWR2VjrOWKhlXSkq6itq79LN3v35XFdD1E/qOOWxSj/Jmd9I+eeY/v8ovY5e69libHAcO7FdGZeWl5y+UMqWPFCyRD+99cLiMm7hCP/P1uybXGdLz2ibS+8bGKnOH/uMAtqF99/1pf6s4h5j4xNMjdXzLVUxMk7x5wqRr55py712t9D7J58a6efMuGTho0kkT55ibxbpNK1Lh9FmhbVrhdUmPOtLn5uYq65/p+j1axjn4ITVOnLJS51j/blBZtFXzStKvTwupW6e0fl8fKTNyz9D/9u3J+h8P7HFSLtkCs7q/XBAn6R8T504SSGvGCBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbgn0WAH9948IMbD/+eKP05HzHX/OHLBt/717694HteUyd4+L58P779zOWCdX7vdeZ+KZtdm1mVRULi8NKPe6P/UNaXD8bmz/ly/tmfD9bjWrAu77lOn/SX+Zq/7tviejA+f55y/sjcHu95BMfDe45gf5zz77O6Tr/B68HXtBVsn/fB9ngfPILtH2u5YH3/2rfDc7D/zG1mfs8Wtx999JHb7pZtSXv37u22Qw3OgW87OE5/LtiXj4Vnf92f83WD1zhHOR7+un/29Y727PvJqt7vXaNd5hHhLSjGZdXO0WLIfN2Px/fPdd+uP+ffZ65r743AqURg9px18uSzo2X1Gs1kyteJfuVxD+xTGaZR/XJy6UV1pVbN4pqlMVwi8ufWDI1hek+GZLnduh0qGbumTI2VIcMWypp1oQxffG3SRl7NmNalYw25uVdjFfCKHoYNSe2nEcvktUG/yXrdppLyJYoVkCf+1UHatGb71VBxPe0y2X3w8Uz5edRi2aFbd1I3K3EOge/t96bIl9/Ok8SdZNfMKU0bRslD97XRDJeh/mlv69YkSdaybAkbH79bXhwwXqZMj3FjptczC+aV665qJF06V3NbtgY/Q+l7m4pDO3fu1ayd+VQgC8n2Q4ctkfc+mCqxa7c6yYxyLZqUl6uvPMdtDcpWof7PCca6ceNOHc8KjXWObNy00w2WMXc/v670Vl5RUYXcZ9LfKc5t0Bgf6/+z24KWLT3ZlrNShSJy1WX1pV2bypqZlG13Q38jMSa2u2Wr3rnzNsjI0csc/wfvbaNjCQlZoRk9tn/pb9yElfL8S2Pddqtn6HarMC0QmVcu7VFX7urXUmXqUEbZYIvr1ifK40+Okplz17jylHn2ia5yns4lbXBsUxFw0DtTdKvWxW6dVChXREJbtdZQOfvQ3zarVm+Vp//9i8yeH5fR1snaqnX37r1y821fy/xFG9y6KFQgXG69qYVcekkdvc9CoiFrBHFu5K/LMmWcq5cRJ9xfGjBBvmVb210pkk+3Cb6ke125SddQ0SL5D1tzq1ZtlQ8/nSm/jouWpOS93KR6P+eWO/q0lEsuqq2ZW/Ny6qhbtcLwjbemyudfznT3o/4lLvv1w+O8TjXlas2WV7NGCcmrnxPQpj2+x9n6d3XMNhn96wqVX9fJlZedI127VDso5dLin3OYOPfncLVWjYARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJ/GYG5c+fKBx984DLOeXGuQoUKLlsbQfDDOQcCg5cY3An7xwgYASNwFALqtMjn/50rX3w9R+LWb5X9KsU540Wf2HaULF2VyheR8lGFpVzZM3Ub1QjNApkuSFVxKinFxibIqlitpw357JF8JiHwtG5ZWW7q2UjFO7aAPPygDNuUPvwY25RuEZymPJqZrEWTCnLFpfWcbEWbmzbtUilvsYydEC27k1LcZxx1sxLn9mqmu6++WeCy2G3dnuRkp6KF88tlF9WTli3KO5Fro7Y3aUqME5Euu7iONG0SJd8NWah1pul4trsgaZ8MeK1bVNLtQCtLieIRTtIiS1jC1mSZO3+9ble+Q7emrCOdOlRx1+j79UGT5TvNOpeoch8H2fvKlS2smcRqSC2ViQqqkMe5LQnJmplvlYw/mJ2PDF30Wat6KZe169yWh7a0/TvFudTUA/KebuVLJsH4rbsde+KPjAyX9q2rSpNGZ2Vk8tul0taGjbtl9tw4mTRttWYiS3P8nni0k5xVNrQdu4NyHP/Ex+/SjHc/hyQ4zcaXruuhepXicv/dbd18ZvV9h5z1lIqgXnYj2981VzRUgbGBlCwRofzZBna9So7TZJbGuiclVcqVKSy33NRMGut4yGhXuHCECnq55c8U57Zt2yP97vle5i1c5243hNQuHWqo8NZEyp1VyK2HCRNj5JPPZmqZ9W7d5FCz8v4720rnTlUlOSlViut4IiNyZ4hzzEFYWC6pe3Zpadu6sm4FX8jJriDfkpAkX383T0W9g23JGSq7iXTTtdn31uZSUYXIkPB6bOLcrNlxMvDNyTJX4/fZKmFZp2YZt96rVC6s90WYCqrcM7t1G/atuuZXqqC7VUqXKCj339VWxdSqWcqPx7FEjlrUxLmjIrICRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGIF/LgFkioULF8rQoUOFzHPNmjWTHj166JZq5Zw4x3UeCATBrEj/3BFZZEbACPzTCJCp7aefl6ogNU+iV29x2y+6tGkqs/H5whaiSEtk/NITofD1MydnTn2ozMPDp5fiMrJau7ZV5dqrGkiVyodnmguOPSl5nzz59Gj5Zdwyt62nfpq5z7EoFfQq6hax7vNPM3Jt0QxxtKsum5YIZcHMSpwjzhkz18pT/x4tsXFbXVtuu07NPHdWaZWRtPNNm3e6LFm1a5TWrVRbS+tWFVUETJWXX/1Nho1YrNtsp7g+6HCfbmNJ/UIF8kqkZtvblZzqpDg4lNP2brulhVzUvXZG1qzVKgcNfHOSbmG5WrOv7XPtIP/t1e0sEbjOLJhfZcQ02a5iHZ/ZeTQuwDHuksULyC03NpPuF9ZS6fDQ1qZ/pzjHXMXEbpO33p2i2d9UXNTxh+SqUEbC/bApGNpWNFGlLeYL4ZDtUZEBmzYsL4//q6N+X/0xcQ4Z8fshi2TQe5Pcdr7humVwx3Y1VJw7V0qUOHKbVuLdvTtVZcyfZMJk3X5Y1wOcSxWPdCJZrZqlXLbAX8etkIVLNoZkNK0TGREurVpWcmODPXNat06pP1WcQz598JHhMmHSCt16WFem/v/MQvmkQ7sqUltF02SV0Ib8sEiWR8e79cE4WEvnaQZHyrHN7zWazbBB/dLyymuhjHNkdeNIc1ujp8sZui059wmNb9uRrM+6tXsu1pxmDta5qhhVTFm20nugkps3veDus6Nt1Uo5jq++ni+DP5ku6zeQMVJP6OcF9wvZKsPz5naZ88hIyZrPoWZsmOtbdD4KyN13tNEsgCbOOZD2jxEwAkbACBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACGRPIDExUdauXavbC27VbE8lpUyZMm7rUkQ5tjDlCG5nyg/sdhgBI2AEjpfAxElr5McRC2WWbt+6JWG3ynKafg4hBiMmu48VjBn9zEGaCteMcWVVKLtEM7Gd362mFNGtTH/voM6ixZtcprbps9aocKOCjVZIVzENUY+mkfIi8udx204mJe393a1a6SsxcY+88Mp4GTFqiaSoeMU2kgyCNjnYrhO5p1a1Urrd57lybssKThpavnyLbj05W8ZPXCVkqyM2ORAaN6958Nnqt6UsqdvK9umtIvOFh8Q52p86fa1msFsgU2fE6ja2yYfacbKSQ5URExIgglkV3Ua2e7ezpVu3Gm6ctOOPv1ucI47Zs9e5LT5nzl4ju5L2heyqg98zjpOW8Zz1hXtdRLO2de5QTXqrDFi8+O+vAz/WrJ6jV27V7WJHyDzNlFa6ZEG56/bW0qVTFZUVD8mFmeu9+/50+fS/s3Qed7s5ZBtRZK6QfHZAmefUbYRzOhGU7YkRF7m+V7PktW1ZRYXKc6V+vbJHinO5c8mbAy6R5s2iMneZ5fsfNFPiu2zfG7fdSXqtW1SWe+5oJdWrF3d9f/PdAnnrvckqBe7U2EJZB4mB9QnH/Jq1kSyOCIqh1SvuHknVrH+1q5eRxx5uL40alnPi3DdD5ssu3f6V1R76R5+1EoIcxxluvhDmxImZFaJ0y93L62nGxKpSQLeJ9QfFj1Wc26kC6LffL5Svvpsv6zRbIxz9PcPNS19ud9yDnw+EkEvHU7tWab13mkvTxvwHAGrD/omHZZz7E+Fa00bACBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBiBv4IAWWZSU1PdI3fu3E6WQ5RDnEMEQFygjJfoTJz7K2bF+jACpyaBbduTZcyYlSqArZHYtVtlo2Zo26nZxELZuxgzag4iGRKabq+q2zGS1YqMadWrldBMc1WkedNyxwWHbR8HfzRDZs9b5+Qf336uXDmkRNFIlYOidKvTkjJmXLTMmb9WUjSDW8liEdL/kc7Stk1lJ8P5DokrRreO/fCTmTJtWqxsUimJLUeRdrhGmwV0q9GWzSrKdVc3cNvIItNxkMVrqGb5GjM+WtbEbZPtmqVrv2YtyxCRtAzthOfJLXV0O0zqt2xRUXLnJovXoSM+frd8P3SxSnjRsk6zce1Qmc9vZ0kp2sir3Apr5rBqVUtIz+saauawsipJheI41JLIbyozDnhtrCxfFU9NzUZWSp7p30X+n73zAI+i6sLwAZJASCCk0iGhh967dEURREFsgCIWRAFRUcGGCmIvv6Ii9goqgtIVka6A9N576CQhAdLDf76zTNiEJBQRSPyuT9zN7Myde9+ZXTaz736nUsVg99XsPspxPj/sV5n31zYrQYrj8ki/lir3VT+jJCbkrK++WSqffbVQDuh2SFrrcE1VFdOaa5pboTP6XrvugEyaslYWL9klu1QEOx6nAt2pBq6Yk5cm6AXoPiuEhVgJ3FYty2uJX/9M5+Vse7bbY8cSZfQnWi52/AqpXLGoDNXSr+XLBWS72aFDx+18gjx5JFJL9uoAMUYc55AgXwnX8xSJaMtW7pYDh1Su0/MZ4l9QoI906lBVbulaS0sFBwgSBJ8fPkPPy10muoHRu2/eLM2anNv5PXHSWvno04WyXZ9HkN0g5Q1UcS5cxTnwio6Ok/c//FMTFzfK4chjKtdhWkjty2NJfg3rllVRLklWrdmXJnNiLkEqJbZuUUnuvrOehIUGyLTpG2TilHV23mO+cZqgeBLWGnYCmU0b7vp455dgfd7UVSmw6401pGaN4rqv9OIaOK3W/T313BTZvP2wzTusTKC88/qNUjU8xPpy/198fLJMmrxOZszcJBu3HNBxnkh3rmP36gSq/FpApVo/fb4VlzZaRrZB/dJaBtrLvat/5T7FuX8FKzslARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIggUtDwEk6StZP1HHfPWXOkeawHPcpzF2aY8K9kMB/gQCknrXrD8paTYTbHREtx7QMZKKmYSUmpWrJRS27qJJUARWJIBtVKB+sYluIVNCyrI6Edr6MkDw3Z942S/lCshzKOgYEFpTaNUuoqBRmr32QczZtOaQiXIrtt2OHcN13YKavfUjD+mP2Nlm+co9EHjlhiWIQuwr7FZCKOt5GmnZVLizgDKEM4161ap8sWxGh8lyUxGipyRNaUhbCUoH8HuJd0EuKFyssjRqUVlEwRPy0v6xee5dqct+q1S5+0SrlnYhzzcu3UAEbf7kwf5MCS5XMvOwoxoJSqb/+tlH27j9q+ymjZWw7aZpfSIgvHk7XUPp2ypT1sn7TQTtWhTSp7+p2FVWQKnlGshfKzS7RJLk587ao2JdgIl/d2iWsdK2vb/50/Tq/xGv50CXL9srfKs8dOBgrcbq/JD0fIA56F/QUP51XubBAk+YutDyrsy/nFuPEuYFjj/OsbesK5yRcHdZjPlXLD69dv1+OaxKbp6eHFCqcX6W1otK2lZYm1bTWiZPWyGrtG/MopOV466m8eFXzcnp8Cylrl0g5afJa2aYiJsZRUMWzO251SXXO+LK7xbhnz90mBw/FulIOw4vb+HHszGnTjSFrzvh9o6zQcy42Jt5ENoyzgqYQ3tCpmo49UX77fZMy2Kflf5PsfK1Vs6SVdC1VqrCdExgbzpM1aw/o8+ewyoInJD4u2daHsId0Pd9C+aWYlretqgJqg3ol9bw9nTLnPgeIcwdUlv3p59Wyb3+sve8IUdnulpvrKJczzzln2927o1Xy3C6btx62eWCsJtaqoOjt7aH7LiS1a5WyfXtpct+lahTnLhVp7ocESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAE/gUCSJJDcyQ5ZxeOLIdbrOPpmXXZOmcb3pIACZDAhRLAa02Clj6FtIbyopBfMqZVXWjf7ttBAopT6cZDk7Dyq6h2MRrkLpSB9VJ56kJKQ8ZpupoO64K3xxwgJJ3Qfi7mvC4Gm3/SB7gi/RTnwoUKk/9k/+e6LSSurMaI8w1CaHalX891P/90PTy/UEY2s+RBJATiuXc+40QaXHIyxEbX8/Wfju98tgdzvG+5WM/h89m3+7oU59xp8D4JkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJ5FACkFaQOueUZ8U0cB8lXNFQpjWr1CNbgf8jARIgARIgARIggf8QAYpz/6GDzamSAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAnkPgIQ5jJrWO4uyrnfz2x9LiMBEiABEiABEiCB/xIBinP/paPNuZIACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACeQ6AhnFOUeQw3J3ec5ZnusAcEIkQAIkQAIkQAIkcAEEKM5dADRuQgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJXGgFHoHMX5FJTU22YWOa+/EobO8dDAiRAAiRAAiRAApeaAMW5S02c+yMBEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABErisBCjOXVb83DkJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkMClJkBx7lIT5/5IgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgAQuKwGKc5cVP3dOAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRwqQlQnLvUxLk/EiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiCBy0qA4txlxc+dkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJXGoCFOcuNXHujwRIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARI4LISoDh3WfFz5yRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAApeaAMW5S02c+yMBEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABErisBCjOXVb83DkJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkMClJkBx7lIT5/5IgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgAQuKwGKc5cVP3dOAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRwqQlQnLvUxLk/EiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiCBy0qA4txlxc+dkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJXGoCFOcuNXHujwRIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARI4LISoDh3WfFz5yRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAApeaQK4S506ePGn8cJsnT540lu730xbyDgmQwDkTwHPKeX45G+F5xeeWQyPr28zYYe28efNmvREfIQESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAES+FcJ5BpxLiUlRZKTk+0nNTXVhJ58+fKJh4eH/VDw+VfPoyuy84yiV8ZBXspzIquxXMoxZJz/+fweFxcnSUlJ6eQ5Ly8vwQ+eZ1diy4q5+1j/bf54LcLrUnx8fDp2kOZ8fX0pHrofDN4nARIgARIgARIgARIgARIggVxIICUlVVJSzvwymvtU8bdp3rynf9wf4/0ri0BK6klJSda/9fW4ntT7OHb5PPKIR768en2EX5C7so4WR0MCJEACJEACJEACJEACJEACJEACJEACZyeQo8W5kydT5dix43LixAk5cuSIHDp0SGJjYuT48ePiqUJP4cKFxd/fXwIDA+2+r6+PeHp6pZNVINwlJiaa3OKOK3/+/CYFuS/Lafcxt4SEBL1Am5Ju6AUKFFAOnumW5aZfUlKSJSYm1s6J7ObliJWQmHAfx9zb29uO+8VMA8P5GR0dbeepMx7wL1SokAQEBDiLrujb+fPnS0REhMlzzkDLlSsnFStWlODgYGfRFXMLYW3Pnj323M5qUHnt4rZLrMXxhwSI5wbOgYsl1OG1aNeuXbJy5cp0rzGQ5jp06JDjX2OyYsvlJEACJEACJEACJEACJEACJEACLgJr1x2QbduPSEJi+msz7nw8PfJKgH9BKVa0kAQF+ejfivn0b1NPk+nc1+P9y0cgWWW5uLhE2b0nWrZti5St+nM48rj4+3lL2VB/CS3jLxXKB+mX5PAFQwp0l+9Icc8kQAIkQAIkQAIkQAIkQAIkQAIkQAIkcH4Ecqg4d9IEnuioKFm4aJGsW7dedqucEqm/J2iyE0S4fJo0BxHKx8fH5KTq1atL3bp1JCw0zIQlNWOMVFSUXuzastWkIHd0VcKrSOXKVdwX5aj7SNs6cuSwbNy4UQ4fOpxu7HXr1pXSZcqkW5abfomOjpIFCxbI5EmTs50W5DgPFdggSxVWia1kqVJStWq4lCtXXs8Zf71AfXGS1FatWim//z5TNm/alDaewKAgqVevrtx0U5e0ZVfynZeGD5dly5frReK4tGG2adPa5K+qVaulLbtS7kAYfeXll+XAgQPpkt7cx+f6VriHvUbgdaJ48WImAlapUsVeMzw8PP+xQLd//36ZO3eOjB37vaXOYf945QkpWlTeffd/+lpU2H1IvE8CJEACJEACJEACJEACJEACJJDLCLzx9myZMn29XqtKznJmrr9P84lnvjwqzHlJnVolpccddaWsyliQ6C7Wl7uyHAAfyJJAqqbKxccnyarV+2XMD8tl/Yb9em0kSeL1eCYlp1jSXAEvD7225CW19LjdcWttqVa1mOTXZacuPWbZNx8gARIgARIgARIgARIgARIgARIgARIgARK4/ARUnNMrQDmsIUENQsq8efNk2tRpsnbtWjl8+PDpdClcmVJxDC2PylEQoypXqiRNmzWTdm3bSp06dSR/gfz2+ObNm2XK5CmyePFi+935301dbpJu3bo5v+a4W4hz4DJ+/HjZsH5DuvHf3+d+adWqVbpluemXvXv3yldffqVi0rtnnZYljalgiQSwkiVKSI2aNaRFixbSXM8VvyJFzrr9uawwZcoUGfXhKFm6dGna6sWLFzfpbNjwYWnLruQ7ve/uLQv+/FPiND3PaTd0vkF69uwpjRo1chZdMbdIeru63dWyc+fOLMU5DBavD0iYK1iwoBRVma1SpYpSt05dadO2jZTQ8wHy7T9p2P+ECRPkww8+PC0d6utTCT3+06ZPs0TMf9J/zt/WJTDn/HlwBiRAAiRAAiRAAiRAAiRAAiSQOYGhL/4qEyatlsSklGxFKtdlLFfpTy9PD72OFSI331RD2rauZGl0lLAy5/tvL90TcVS+/3Gl/DF7s+zZG63H8bQA6fxF61xY9fTIJ6VLFpGetzeQ6ztU1i/LFfi3h8f+SYAESIAESIAESIAESIAESIAESIAESIAE/iGBHCnOHTt2zBLFXn/tddm8ZbMkJSa5MOhVREsR07KLKNWYoj8n9cdpQZry1bVrV+nVq5eWUShr6yzSxDpITTNmzHBWs9sHH3pQhgwZYt/qRZ8Q0ZwfZ0WnnKezHLdYhm8CO8ucdbHM+cEy53HcOs15HLfuLeO6+N1ZB7fOONy3SU5O1qSrufLWW2/LMjdhC+u8/MrL0qNHj7S5YRl4uY8Fy5y54D4a1kFz1nMfL5Y5j2Md5zFnfWebjH1iufuP+7bufWD5ubbsxDnrU4+RNbd9O31DomrZqqU8/PDD+k3hWs7i07eYZ4bt0CeazQ13Mhy/zMS5YsWKyXXXXSfDXxqebv7OnFFGNGM/6DojK4era7eucaCPjNs7x8ZZ39mPM/bM+nb6xLweeeRRwXMlLu60OIfxQy6FiOreMhujsx8bm8PffSO9734OOutjHniGOOPHJpAdz9ayE+cgy9kh0o7Rt/trBJIqUdp5oB7/a9pfY/Kcs6/M5uU85ow34/xQLnbatGny2WefpSXOYZvixYrLmLFjxM/P74xjisfRj9OnwwXHAT+5q6V/rctdc+NsSIAESIAESIAESIAESIAESEDk+WG/yc+TVZxDqdZTfwJ5aGnWvHldv+glBv2b13VNBfet4Y7+XViymJ/cc1cj6Xh9uH7h7599sYvH4sIIfPzpQvnim78lKhrXQ/SYnTqGdr0C14fcu9Vf9EhKaOkgeWRAS2nVopwlBrqvwvskQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJXFoEcKc5t0pKXP/7wo4waNUqQPuc0Ty27WURTwvz9/eWEJmNFR0fLcb11ZCFc1KpRs6bcfttt0qNnD4GABxno888+l9mzZzvd2O0999wjDw982CQdyC0o/4ryjxDSnIYkOw/9NmmSftsUSVwJug72DbklKSnJfpx1IfsgvQpiFhr6i9eysljPaRhfQS0Z6Z5yhbFjHewbP0m6HYRA7MMT5Wh1DCgzmQ9CjW6PhjHGxMTI/PnzLelq5cqVzi7s9tnnnjXhyUt5Fda5gSFEI/exYEWkcGEs2BfGgT5TtG/noiDEHszHy8vLxuZextN5zEPHiL7jtcRnXmWAZDf06zQwwLHC3JxjiePo5eWp+y5g/TsCkbPN2W6zEueMmY61kI4BLUnngv2jvG9a0zlVqVxZuvfoLjgH3BsYYIw4b5xzAcswR4wZ5wN44Xf3lpU4d+2118rzLzyf1h8EqXxaHtYrv5cULlzYzr2Mc8cxwpidc8eRqrA/nAPYt3NOOOPAOsdiYyVZx47xoqFfZ7w4N7Ec502sruesg/XQJ86RsWPHWtlf7Ndp9erVs7S5smXLOotsW3uu6Hq4xQ/6w7EHI/DB+Ypj4d6wTszRozZGZznWKaQldNEHzi3MHXPCc9yZm7NuxtssxTmdN/rMr+cBuGDOeI2wc+8UG/QdGhoqQ58fKq1bt047/zEOnMd4nifrcx7bp550PRfByc4BfT5gfs5xi9Ly0atWrdJSvb/bPJxxBgYESv/+/cRbnwvo10pMu70W4DmF50BqSqqND+cb+sXYz0UcdPZz5d+e+sThyh8oR0gCJEACJEACJEACJEACJEACF0QgozhXoICH1KpWQgICfKy/5KRUiYw6LvsPxurff4lyQsuAJmkZUPz1npJyUurWKiV972sqTRqX0b87z/5FsgsaJDfKksC778+XL7/9W49LopVl9crvKb4FPcWvcAFJ0BTBBC3jGnsc1wyT9DqBdqMHLm++vNK0YVkZ9EgrqVghKO0aQZY74QMkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAKXjUCOFOf+XPCnfPHFFzJ58uR04EK01GLH66+XevXrmWiDUq5L/l5igouzYkjRECvh2PfBvgKhacmSJYL+tmzZ4qxit02aNJEWLVuYwHSbinYo6bpu3Vo5cOBg2nrVq1cXXx9fXXZANmzYIBF7I+Tmm2822WvHjh0SERGRti6EuqpVq0r9+vVtGfa3YsWKdOsUUFGsefPmUq16NZONIOYcPHhQdu/aLbt277J19+3dJ3HxcYJ1AwICpHTp0paMVrxEcSmsUo2HykmHDh2yErbLVyzXcrbzZa/bOLBzCFu1ateSkiVLWgLfURWW5syZIxizewOD8PBwk3Ug7kwYP0EOHzmcTnCDPFW+XDnZtGmzLFm6JE26giCFbZHeBcbbtm4z4an5Vc2lQYMGJkEdOXxE1q1fJ5s2brK5QUjLo9+4hlQUEhIiYWFhUq1aVQnQPiASZZSt3Mfqfj8rcQ7CXvny5eWWW26xc2L3nt3yt54fK/U4uDeIYDfddJM88eQTaYshN0HExLFYsdJ13MAN4pWfXxEJDg6y8VaoUMG4QhB05KnMxDnMr5mWg4XAuWzpMkEyGfYBOapY8WLStGlTKadcIUqhHxPLVFzEMYI4un37dgG/eD0uySpcgRvOCUieGH/t2rUtVRECHqQzJCru27fPhD9MCjwrKAscP/CFvLhLy4rOmjVby44k2rzBO8A/QFC2+KdxP9l+4xNOi3P169WXxo0b236wAc7XI0eOyGY9F7Zu3SoHDh7Q8/eQyZ5gHxAYYGyQUIf5gxFkM2yHuaOsMEoum2in+4aU2eKqFrInYo+sWbNGIo9ECkrc4jkWGBRoY8zqf1mJc5DiunTpoudVNYk9FmtMZuucD+pzBlKqNeUNse6FF1+wdXEu49hjTnie41gdjT6aJk8ipa5gQW9BomW5sHJSt25dO4YQE6NVnFup4hz4Y45omiVnz90BDw8wiXTbtm0m16Gsq9Nw7iOVELLq2jVrjQFK4kLkw3Mq9zSKc7nnWHImJEACJEACJEACJEACJEACmRHIKM41rAuhqoVUqhhsq+M7XPiBMLdw0S75eeIqWbZyj/6drl+01D+ZklTO6tW9odx3TyMJCXZ9ETCz/XDZv0PgvQ8WaOLcYivRWqqEn6bIVZZOHapImdL+di1m9+4oGfPDcvljzmYVIOPsmgYSBIMDfeTOOxrKrd1q6rVFlmz9d44OeyUBEiABEiABEiABEiABEiABEiABEiCBf04gR4pz8+bOk88//1ymT5+ejkBhv8IqDNWRJk0am8QCuSUyKtLkFEhFkIUK+hS0NClIXc89+5ysXr1aIFpB2nJvgSrBBOtPkApRo0ePNvFl+rTpsnXb1rTVGjduYuLUDpWY9u/fLzGxMfLkk09q6ceTMn/BfJNsnJVLlSwl7a9tLz179rRFEGkmTJigMt46ZxXxKegj9/e5X2644QaVcuJ1XPtk4sSJJs5AzkP/sTGxlpQGAaigikUQpSDAtWrVSpo0bWIiHUScYcOGq6y21cblngSHnUHMKqKyV7iKfCNHvmfS2jtvv2PiW9pg9M5dd91l5UQh8CCJrH+//rJbpaGUFFfqHph2u6WbyX4zZ860FECnSAWkp/Aq4Ta+v/76SyIjI00su+322+Tqq68WjHHiLxNl/Yb1sn+fslNBKFEFMKTuFdBtIZAVVbkKglOr1q1MOsRcHRnNfZwZ72clzkEiq1O3jqsEr159hnw2adIkEyidPiCLValSRe7ofof07t3bFkN4gjS1YMECmQ8Rcd9eGy9kQhxrLxWrfPS8gshYWdPqIBxCisN4kQ6WmTgHaaxkqZISWjZUdu3aJdEq4SH1DCmAhQoX0gvolTQV8Gapr5Ih5LkTmto3/ddfNUVwgY5li/GMOxFn0hnEM3wTHalnSJvz10Q2CHwoNYrzAo9//PHHJoji+YDm5ekllSpVEgikEDohef7222/y/djvNVEtwdbBMahWtZo8N/Q5eeXlV2TpsqUm4dmD+j8cR5yrEEgTdZt9ehx/1TEuWbJU5bLd9pw6flzT3FQu9MRzT4+rXxE/mzPOVUh3ZcqUsWOK82vgwIEqBkIeO2nJez6+PnJV86tki84XciWSFnE+PPXUEDvPnXFkdpuVOIdj9eyzz8g111xjc4FM+NGoj2T5xNn0kgAAQABJREFU8uUCEdK94bmM8xtyJNjgPI6I2GvrISHOuOunG2CP56OPyoGQ2nAOYDuMFeLc7/rc+Pbbby0lEP1DnCuqku/oj0ebmDtb0y5/+eUXG4Oz/6rhVVUODLJjvRf71OdH165d7DkJWTb3NIpzuedYciYkQAIkQAIkQAIkQAIkQAKZEcgozjVpECpPDmqdJs65bwOBbuWqvfLW/2bL0hV77O/NRBXnrmsXLv36NtNtmF7mzutS3Efi3Ngfl+mXD4Pl/t6NpUH90pqmn77SwLFjifLJ54vk+59wbSHOhuXl5SFXt6kijw5ooV8CLHQphsp9kAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJXACBHCnOISHsuzFjZMyYsa7SobiyqA1SFeQ4JG4hgQ3pVCWKl5ASJUtIWGiYlC5T2uQmJ0Gqf//+JqVFaZoUUrvcm5WxVAkJyVjjx/9kpSpRHhZpX05DehkEHSS8OWVGndKb06ZOk7Vr1zqrmiAEmeaxxx6zZWN0/F988aWsXrUqbR2ISkOGDLEyobt27pJJkyfJLz//YmIVJC1IXUjo0omazOOUl0QZzAaaZHfLrbdYIhVSvwYNelx2ajpZrAqBEJfcG8pDglPNGjVkzNgxJpBBEkLynnsbMGCACWSQm1BysuP1HWWnSl6pKnihgdE9994jHTp0kPE/jZdPP/00bXP0jwQujNdJ0oKsiIQ1pKFBWPt5ws+WMIZ5QDyC1JRPy1nEx7tS1CCdBQcHy1UtrhKk/kHQQnIZEskgLWXWsM0+FSG/+upreffdd9OtgjK4EMpu1cQ5NMiOixcvtnK9zooQ2lq0aGFCGdLY0JA2CHFq8qTJlh6Gr4I740W5XBwblO/EcswZiYddu3aV5irPoczp1KlTZdSHo2Tp0qXObkyo8/DUC6166mL++MG8rEEeVBZ33HGHzbtS5Up2DowYMcIS8o7q+eqksmG8mDMS4/DjNJxLHTt2tHKzZUPL6nNljJUkxvHDOB22r7z6irRs2dIEzg/e/8Dm6ZTshTDZ4foO8vTTT0ufPn1kgUp77hLmDZ07S69edwkS5Hbv3m0JkFOnTDUpEmPJc+p8hRrlzBHjw7lRW7e5+eau0rZtW2OG86vLTV1MUMQ62BYMIIkhdRGJb5grEhtHvDxCQkNDsVqWLWtxzkuee+45O2chROIcGPneSEtFxD6chteSQYMGScdOHWWHPo/+97//yfr1G9JS43DcwRhJfzFHVfp00uR03CgF/OCDD0rnGzvbcfrpp5/kfWWLMq/WtO8S+to04/cZVtoZAu2XX34pixctdnavr18lrKztHuUKcQ8N0u0DfR8wATVtxRx/h+Jcjj+EnAAJkAAJkAAJkAAJkAAJkEC2BM5HnENHxzVp7ocfl8u7o+br9ZEk/Xs6Vcu1lpEBDzaTRlr+M18+/h2VLfCL/ODHny6STVsOya1d62jCfAm9Npc5/+07ImXwM1Nk9bp9dq0HX7OrV6u0vPhce60okJuS4y8yYHZHAiRAAiRAAiRAAiRAAiRAAiRAAiRAApeZQI4U5w5q+tqvv80wmeWIlg5NSkrW5C9N3XLEIzeokOSKqaQCsQwlQmvUrGHSDSS0999/30q5QoaDbObeQsNCrawnUsSGDRsmX3/9tSWquYtzzvqQbCwJTW+ff36opcJBlrpQce76jtfL77//Li+9NEL7ijHpCOOFNOYkdJlMhJQyNynuOhXY7ryzp60zatQolfJWa1rXVhV70idpVVGBraTKhBUrVpRnnnnmXxHnHDa4hQQFPlV1vygTC2EMIhlStHDcPL08TWgsU7qMynNeJmEd0hKfJmnpdpCUICJ1VdEqNDRUkLR2Iu5Epsfb37+IynhH5OtMxDkwhJxXRGU2iFxIOYvX5DD3Br43a9Jbv379TAzEet98/Y189913lk7orAtBroTKTUixO6znDuQqpJDhHPTVhDicb0M0GQ2pbkgXzCjOoR+U+ERKGeRCiF6WYOd2DtesWVN63d3LUv+WaoobpMyoyKi0eUNAQ7oZmG3dstWOo/tzoJYKij16dLfStCgNPPjJwZZq5ohYOCYvDnvRBDuUDX7xxRdVttxpUiIeq6FiJVLg2rZrawJeVuIcpFScrxD7kBwIZtgeYiVkVUh6R7TMKuQ493Ko7VSaQ6phy1Yt7TF3cc7hjFv0hR9wra9S4vDhwyVUz4PsWlbiHM69W2+91cqpntA0PBy3SVryGc9/57nkjP3Z5561VLy5c+fKeyrXGVscHx2Ln6ZbVihfwca1bNkyOwd0hbQhIdGuV69egteRCxHnnI6cuWOfPXv0kD4P9Dnr3J1tc8ZtnpwxTI6SBEiABEiABEiABEiABEiABC6QwPmKcykpJ2XylHUy7NXfVKJL1L/RT0pYmUAZ8FALademgn4ZLZ+NBH+j4s9Q5xYL9U9H+zs1K7nLmYKznfO7a1v87e1agn3ix7VcTBbD36cZm7Nv59ZZ3/lbNpNN0rrIOAZn7FjB6c99DOgzs3lhXXy3ErdoTj+uMdiif/S/9RsO6hfb8llpVg+PvFn2FRubKO++P09+nrRaj1uCVotIlaqVisqLQ6+TWjWLZ7kdHyABEiABEiABEiABEiABEiABEiABEiABEri8BHKkOIe0MaSYjRs3TtO8pskhTaRCwhVSvyBipWu4sKY/kHcgnl111VVy+x23W8oWRKVZs2bJF59/oSUw56fb7P7775fHBj1mKW/Y7oMPPshUnEPam5eKX/m1bCkEqMGa3IYEuskq41yoOIcEL5SiHT9+vElMGBjKl4aHV7H0L4hkP/w4TsuGzjP5yxk40tQgfSGpDDxQWhZlZtesWeOsYrcvvPiCJZmBCeSm7du3W4nZi5k45+wQchiSw1BCFOJcqVKl5IAer9nK3bmoGRAYYOVe77nnHpPkxo+fYGlckLicBgEN8tzV11wtmzdttuQ/CFoZW+s2rW3Rd99+d0biHB5wUvtwORXJeejDGQceDw0Lleuvv97SvSDRIYXsqaeekimTp6igeTqVEAJgp06dpEzZMlqWdI+MUMkR5U5xbuLiLIRNlDi9tn17LXG67AxxDusU0ZKqdevVtfKey5evkF16TuOcdFrRYkWtXCySxiC2oQwqxoN1MG4ksN1yKj1vrKYvoiSsI8WhD9f50E0GDOhv6w96bJAlykFgcxpK0nbW5DiIdUOHPp+WvAgpDyzfeOMNkxohuGUlzmEuH6oIOVPlOeeYYPuwsDAZPGSwJbNNmjhJj+nvWpZXv3l9qqGsabdu3WwdlEnNVJzTvtEXziF/lVjr1a0rgwcPllKlSzndZHqblTiHlVE2FmVtcQ7g9SJJ5VP31w0PfR5X0DTJZ7SkKxj+/fffMmfOXOUen3Z8MDecJ0ibe/311+055swd+0Ay3r333SuQHy9UnHMS+woUyK+vLV76nL1Vxdg7z1qmFvvPOe3MD15yztg5UhIgARIgARIgARIgARIgARI4O4HzFefi45Plpwmr5K335uiXBl3iXCUtEzrgwRbSqmV5TcDPq3+bpkhMbJxWGUiQY7EJ+mW8JF2eR7wLeIpfkQISFOij13s8MxXNMOL4+ET9Ahmuo6WaIOddwEOvTfiaIJaYmCz79sfoF8SO69/4qZqqrv0F+Yh/kYK2b/0zXa+jiF4jSdX9x+s44uXE8QS9TTRpDWPw8fGSkGBfu0Vlgcwa5nnw0HFNcNdrOydT9YuJ+fX6g4/A14uJidcv2J2wn1TdmXcBVDUoqPPyNXEQY0BLVjnt6NF4OXDomN2e1PEWLOhlDIKDfPV+1gxcPVy8/yMp8PMvF8m3Y5dJdEycsatcoagMffoaqV8v+2sYF28U7IkESIAESIAESIAESIAESIAESIAESIAESOB8CeRIcQ6TRFLYgf0HNFFtiyxbukyQ+rRt2zZL7oLgBBkGt06KFLaBNAX5pm2bNjJs+DATj2bPmm1y2Zw5c7BKWnuo30MmTEEKQkM6XcZSrVhetGhRqVKlilStVlVKligpza9qLvPmzrPSrtmJcxC7UJ5x9erV6MaaU6oVUtCbb77leuzUN2ZRdhayFlLMMCakya1fv16OREY6m4ufpp/VqlVLGjZqaAIXymaO1HGvWrkybR3cQXlOyFjoB6JXVuJcfxWuunfvbgl2kbqf67VU626Uaj0lJ2ZXqhX7gfCEcp+QwypXqmwi1UodCwQwHCuneasUCEmsfLnyNqaIvREmoUFKchrWQQpc+2vbm8T2y8SJaZKXsw5uXxrxku3nh+9/OEOcg8RXWFPLypUvp9smW2odhK3oqOi0UpuYE0r9Xq2JYU8++YSsUKHtlVdflQULFriuDJ/aWWhoqEmAEOQgsmFexzQ1zhGwICXefvvtJjrt3LXzDHEO2yERbrieh+D0ww8/yLRp02T7tu1p0ymkY4VMOPCRh7Vc6SHZoKVCkXi4bfs2LTF6wM51SJtHo49KhJanjdTUNHcJEPPo0rWLPP7449bnV199Jd/qeedeHhhlZVGaNkoZfPHFF2lzhDTYpUsXefSxR60sLYStrMQ5MHz55ZdNaHT2H6iJfC21X0iamN/MmTOtXCyeG05DudtON3SypDqUOs1MnIO0iucXkiKRkBgWGiYNGjaw567TT2a32Ylzma2P54IjeSJNsKcmN0KMQ+Lk3oi9sn7DeivZC2H38OHDkpiQeCpJ74glJOLY2ycHpzqHMHffffdZ2d4LFef8NBmxogqjNXXuJUuWNBmvatWqJiJmNoecucz1+pozx85RkwAJkAAJkAAJkAAJkAAJkMDZCZyPOIeEtV27ouWjT/+SKdPX6bUtfOHvpDSsV1b6922mAlZp/QJlvCxeskeWr4iQrdsPScSeo7L/UIzk1+sDRUN8JTQ0UJo1DtUvnpWS0qWKmOyWcZQbNh6UXyatkR07oyRZZbOwsoFyW7daEqiC3F8Ld8rU6etl5eo9eq0kVcqU8pd6dUpJ61blJbxKMZPRIOqtWrNPvyi4W7ZsOyS790TrdRxUO8gjwSrtlSxZWMvKhkrjRmWlfFigSnweGYeg14Ui5ctvlshelfRSVIBr1iRUWqsYuP/gcVmyZJfuf69s2HxQk+uTTZirU7uUtGlVweZVuFB+K2OLNLh5C7bLor93yFbtL0HXLarCXqWKIdKieXlp1jRUihcrlKVAeMag/sECJM698948+WXKKksKPKkyYK3qpWT48x2kYgWWav0HaLkpCZAACZAACZAACZAACZAACZAACZAACfyrBHKkOIc0NZTZRMITpKXdu3dbAt3BA5o8pyU8Ibbs27tPtmsZxgP796dL4YI8V6duHUvSKq+pUhB5kMp2hjj30EMmqWF9tMzEOYg2V7drZ3JSuXLl9Nu3/oJSoT9qGhwkJXdxDuIL0uCe1EQ6CDajP/7YRKKNGzamHWBHnCusZSCHDxuustTBtMfQP9LFBjw8wJZBZEOy3bHYY2nrQEQKDgm2EqKQ27IS515+5WUT5zA3rLdDOT3xxJPyJ+Qwt9a3b1/prqU+IWCB50033SR7VdBy5KiziXNIl4OUBXkL4l8h30Ly6WefmqAF2ctpGDfEOMwfLTkl2UQ29/Q0XHztpaUvW7VsKRNVmvv+++/TJcA5fX046kOpVq2ajNNj8O677zqL7Rb9Q8KCjIY5xMXHybq162TW7FnphDWk49VS8enrr7+SmX/8IaM/Gm2Jb+6dFVJJsaCOGcIVksvi9JxM1vNRD66tBrZXNb9K+vTpI4cOHzpDnCvi728C53sj37P1MZ9vvvlGUJLVaZDGet/T25L2IPBNmTrFSqke1vLEsXrcXclzySYB4j5EUefYoA/IbzhmTw7Wc04bkgdHaslRJNM56WiQxMAE58Gff/5p62FOSGa8q9ddViYWC7MT53CevvDCi5Ym5+y/hJ7vHVU8Q2obju+iRYtMFP15ws+2D+d/7VUGHfHScC3f63mmOKfjwHG4ofMN0qRJEwkODhZfH18t2epr0prTR2a32YlzSG9EaiMa5oofyH2Q5PA8q6updhA0cf7itWThXwutFO2+/fuspOxxLfGKkrM4T1EmGiV67difklzRb/Xq1eW++++z8tAXIs4hybKG9oHjj7TFAP8Ae20pqOew85qE/eT8RnEu5x9DzuBiE8DrqPNair6d16mM+3HWweO5vWGu/4V55vbjyPmRAAmQAAmQwH+VQEZxrnH9UHnisZb65cGgNCT4czJF/y4/cPCYVlZYL999v1SORJ+w90BIVet8fXV56IFmJsFN/3WDfPv9ctm9N1KvlGiVhbyuH/QB8S5VhS3fgvnluqvDpecd9aVy5SD9Gzp96tucedu1rOgcWbthv5UUDVfRbNDAVpYA98HoBbJnX7R46DZ4D4bUuXz58kjNqiWl910NTVybOXurfDd2qazftF/3d1IrPeS1dTAh1xhOatJ7HhX4yst9dzeSunVK6t/drhKzzqQX/71bBj87SXbsjtL3vyJNlEutmiXkr0U7VJg7oDxE+3C9H3b6DCsTJL3vrC/Nm5aTJUt3y5gflsuqdXtNMPTQv6Px1hjrokxq0eBCckuXWtKtay39oqufPebs+9+4RYLfoMGTZPEy1xdOPTzySavmFWTw461t///GPtknCZAACZAACZAACZAACZAACZAACZAACZDAPyeQI8W5VatWycKFC7V0w+lEMlzMQ+nLcC0HGhcXZ+Uz52kp00ULF2n5iSPpSCG96qWXXrIyikgSGz36Y5kze3a6de7u3VseffQRK+8KQSyzUq0+vj4mNfXt+4B+e9Y7bfvM0uSQdNepU0dLscMHwG+99ZZM0zKzKPPptOzEOZQE7dz5Rhk48GETZ1AWFOlXEOichnFC/AEDiFDof+TIkZaG5qyD22eeecbK1RYsqAKRloCEePj4oMfPKFd7q5aGvO222yzBDel2/fv1N5HI6ets4lz9BvUFJT67du3qbCLvvPOOfKQi2lEtOeo09BMcEqKiV2mbG4Q5iF242Ok0XAju0KGDSV6//vqbpv/9kKk4N/L9kbYO0gEzinM4Pxo1aiSvvPKKpovls/Nk7ty58uUXX8qatWtPJ4bpuVS+fDkVIH80meyTjz85Q5wrVbq0yoDFrCQrjqdJazpeZ8QYb43qNaTbLd2M7ygtZbp06WkpDkmF1113nYx4eYRNcdKkSSaWuZfLhUwIcQr8Phr1kUzXpL5YPedxbFHGE6l1ZXQckPBQrniPnhMQupyWUZyDTPbB+x+Y1Ok8J5CMh3MTt5BM0dAvSriiXDGkSbTsxLnII5EyXJ9PO1XAdESOED2e7VQqHfr8UCth/Neff5kYiFQ9p2EOSFF8SVP3IKFmTJzDcxpllbFvJLidT8tKnIN0VltLIZdSsU8/X9CL8PnES+fro5Ii0hErVa4ktTUJEGVkse7vWl72qy+/krn6WoJjjKv5kNqQXIhkyMIqUKLMLRi4i57/VJyD3Ne+/TUm+GJs//oV/vOBe1HXzf3Cz0XFxc5yNQG8tqMcN5JoId7jdRqvSzVq1LDXJbw243XRWW/79u1Wbh1pnJB/c2PD6yrkbEjM+HcFPNhIgARIgARIgARIIKcRyCjOVSwfJLfcVEu/rFXEdQ1BLyTExSdZadRlK/bK4r+3S1R0vP3NiusiAUW8pe99zaT77XXlk88Wy+dfL5Koo3Eqq6k2Z2IZBDfX1YhUTafDPchuKJnauWNN6Xt/EylW1DcdtnkLdsjID+fJuo34smmqVCwXrIlvYfLHnM2yOyLK/h6GlAcJD1Ib3oP6+uSX226uY+lzL706U8W9KP2bGvvWoeo1EOevO4h02AYDwWhaNqugomArKafJc+5tydIIeXqofkFwT6TuT7/Q5ukhiUmua0EoR+vt7aX9pOp1jhQdI3qyDqVUiSKWvAfxbs++SLueAbEPf8PbtaRTDJI1rS/Qv6CWuG0pnTtVtRKu7vu/mPcx3w0bD5g4t2X7YZsPStx27VxT7u3dSAID9O96NhIgARIgARIgARIgARIgARIgARIgARIggSuSQI4U5yCmIQEOiXKuq3GuMqyQfJCShbKgEKOQJoeUrm1bT5cFhSBUr149ef2N100Ig6j00UcfyaxZs9IdoGu0VCfEsVIlS0l41XBbJ2OpVshPKGfaWyU794a0M5RhdRelkLrVUKWtBx7oY6uOGjVKVmm51WOanOc0R5wrrQLZ22+/IytXrNDp6dU3bZC+UKLy0UcftQ/KkQ62QMe+S0unouECJcYDuey222+zi5ooifreu++dIX3dpiVEr7vuWkskQ5oVPpR+ZOAjlrrn7A99oownOISFhcnixYtl7JixcuzY6YS7s4lzbdq20fk+IM2bN0d31r75+hv55JNPZNPmzWnHDuJXnTq1pWPHjpY6d+TwEUv2itMkL6f5qqTYoEEDQfnKP2b+od/AnqrfIE52Hk67HTr0OUsK+37s92eIcz6+vlKlcmXpdffdxutozFEr8ztbpUlHJENHkLgqq0D19ddfm9j41ptvCSRMXCh2WouWLSxND0l6EKr27N4j8Zr6Bn64aIzzrHat2ipp1dYSI0vOSJzDh/8Q51BaFm3y5MmaxPdlutQ/iHO97u4lTZs2lQH9BwhKojpj8FRJAsf7Bi11WlrlufnzF9g5fAIlQ0+1jOIcFv/y8y/y+eef2/F01st4W7xECXnooQdNesQ80LIT5zD/d//3rpZzWSiplronJpyirOhzejx8CvrIDBXQIAeu1dQ7p3mrEHbTTTfKiy++aGVnMxPnUCoWJWNxDp5Py0qcg1yCcscQ9iCgeGrSHc4pCCnOXJ39QNh477337Ngh4dJpSAJECVU8h5DYh/K369at1eS5BGeVf5w456/PCSTtoQRu7m7ORyu5e5acHQmcjQCEOfw7ANkdMjuSdfHaitcmlO1u2LCh/fsP+RulrWfrv1vDhg2z1/833njDyjmfbR858XEk6+L91HfffWf/Dg0cODAnToNjJgESIAESIAES+I8TyCjOqd8l/n4F9b2ep5GBHBdzLE5luTj73csTyWmabq/XF1Di9Jo24XL3nQ30ekawvDB8hoyfuFIFMxXjvLXqQICvhGiymp9fAS0PmmSlW49EnrBtkZAeWjpQnh1yjSbih1qfzqFwF+dQJjUo0FevN5yU2GMJmsZeUEoV9zP5a//BWNl/INYS12to4tyTj7XWdZLkscE/S4yWjIWc569iX9EQP01J97ZrFjt2HrFtkPoG1y2/zmGojqFjh3BNzDudOpdenMtrY0ZqXNky/lIuNMhS2nBJbOeuSFmhZWNjYuLRnUmBKC/roSALFc4vlSsUNZkOPFAyduOWAyYegl98QpJ0bF/dytxWqBBk12scBhfzNj4+xRL4Pv9qkRyOOmEsSyrDAQ9eJddeU8WO48XcH/siARIgARIgARIgARIgARIgARIgARIgARK4eARypDg3f/58/SD1K4EYZilPuJKmDUlQKJ+JsqhokKGQyObIRliGD6Dbtm0r7773rpWQXLxosaXJ/fbbb3g4rUFig5iEpJdXX3vVJKqM4hxKOfbv31963tkzbTvcmfn7TEv1mjFjRrrlEKEgieHKIRKqEpAOdmrsWNER5yCsQdqCgIcPzk3G0guCRVTwady4sck6K1autKQrlIlEw0VVPNajZw9Nputsc4Y4+Mabb2r5zyW2jvM/pGRB+GrWvJkMHz7c9vHQgw8ZT+zPaZD9IPBAFNq3b1/aWJzHzybOtW/fXh5UAQvCm9PmzJ5jcwNvJ6ELZToraNncG7WsaLlyYSaBQRo8eOh0qdrwKuE2N5QQPXDggOzff0BOuolsTv9Vq1U1CQuCXsbEOVwhxTeQnTKdOGsgeuEbyWDsNIhUTZs1NcEPshrkhEkTJ6WTBpHs10yFwHpa1jMmNkZ+GveTJeLg29goUYKSmo88MtCkQZQpzZg4d67iHI5nHU1IQ9qfU14V4yysY6yv58n9992vF4Z3y4TxE0xcc2eSmTi3YcMGExA++/SzdM8LZ+64vUaTzu5WubCllsV1WnbiXBG/IlZ2GKVm4/V8tPNVWfuqqIhzEiVQkZ6EZDY8jobjUEklxttV8sS+oqKizkyc02+cPzXkKRPIIAeeT8tKnMM5+9rrr5mM5/SH5w5+MjZIiK+rkPKxJlK6v4aUULGwdZvW0q1bN1m+fLk+17+WHZr85H4O/dPEOZSlvfHGG+WFF1/IOKxc9vuZ3HPZBDkdEjgrAUhzSDh9/vnnTZBG+WwI03gvg1Q5vJfYr4mgkNCfeuopk+iw7PHHH5fQ0FD5WEu/4/U+Nzb8e4/E388++0xQPh7zZyMBEiABEiABEiCBnEYgozgH+8uVynb6OgQS1/TKjk1NlTlc5NG/qfNLgzpl5K6e9TXlraR+2SuvDBvxu/z0y0pNQS8gjRuWlQZ1S+sX/4pKieKFJTIqTiZPXSvjfl6p4phL3ioa5Cu9ejaSnt3r6RfH1Ng71dKJc0hoO3VNpEJYiNxwfVVp2KC0lYVdu+6ATJ+xUa87RErXG2urwFdflq/YJ/0f+8nGWL92KV23jFQNL2bCG+S7ufO2ypffLpFduo1eIlG5zkNu61ZX7r27oV5jOp28llGc81Sprnp4cbnl5lrSplUFvX6X30a7e3e0vPm/OTLvz22WzIeF+BO+SGFvuUrT7G7RUqy1ahZXPnlk2/ZITeVbJDPnbJLY2ARLzCte1E8GP9ZW2rWtaOucQnDRboBu85bDKjX+KivX7tXrTJpUr8ezft0y8vQT7aRixfRJexdtx+yIBEiABEiABEiABEiABEiABEiABEiABEjgohDIkeLcES3ZNWfOXPnwww8tcQ3JLI5UhPKPVrJMr1wlQ4o6lUoGqc5bpZm6KjpBeLn55q52pW2LJp+NHPm+TJgwwcQwhyrEHvRTVj+UHjfuRxk7dqyWB/1RNm3a5KxiSS/9+vU7Q5zbtHGTrf/FF1+kiULYCGNDqhUu8Hl6eNr+3EU1R5y7udvNsuTvv1XwecPKXx5TgQfzQwkMyEgeWr7ixPET+s1ZlZD0Ah0S0iDDdb/jDisNWqFCBbvouW7dOhnx0ghLS3MkNYwDc4NABKFtzNgxtu7rr70u48ePt4Q1d0kI8hz6t4uoYKo8nccvRJxDuTWIhUjcQ5lalNVF3+gLkl7+Avn14uYxnd9x2xf2Dy4QtyAElitfzsUtUYVCTD5Dg5yID9q/VpnpDHEuw7rpftWDgm82Y3skpeEYdO/e3bhD8vtBS8Mu0FS3EzpezN9SgPRYYGw4NhA0cSwxXqQDVqteTYYMGSI4Ftj+QsU5SGVXtbhKHnv0MRUJD6WJojgmOBeKa3JdgibdQfBDKpr7+QTpAqllTz/9tB1zzBeJgUh+gwx4NPqo67i6gcD5iZS3rl27qIhRNu2R7MQ5SGIrV6yU1157zeQ4jAPnCZ5zvsoHTHCckcinO9Tz11PL7SEt7wZNnLtJkHqYpTinkgTWuyjinB7jAprehLRJ9/LBaZPMcAfHFSVyca4e0ePrSIle+b1UYi1iAit4RkVHaTpUXNprDbqpXKWy3HfffSYO4rXlfS2RG68MrOk4Sqi4OuP3GXbO43EkKkHidZqJc5rG98ILLziLcumtvhiykcB/mADS41B6fujQoQKxGa+1eF8BcR+vx3h9/1vfD0AYW6OJnXj/gtdvJNoOGjTI0jiRmovXe7xmQQLGdvh3PmPDv7VYBz9YBz8ZpWHn33dnOdbFMvSX2froE4/jFttk3K/TD8birOuME/82uD+ecbz4HX3j3w/8QOLHj7Mct9ge/TrvcdBnxjHYBvwfCZAACZAACZAACVxGAhnFOZRYzefh/gUu3Nf3NjpGCFdIoisa7CuNGoRqWlklqV7NJYVhCi+/9of8vXSXtGpRUbp1raHXBAqnm9m+/THy9HPTZfGynfoeKVXlsgIqotWRB+9vmi71LKM4h06wz3t7NZGuN9VIt+5yLR+7di2+yBEmoWX95a9Fu+T1t2ZKzRolVYirpUl4RdONAe/Phg6bIVN/XaN/KyfrtbV80qF9NRnYr7mEhJwuGesuzukmUqViUXnogaYmzbkn06Hz6dM3yFsj58keLSOLVkCT7jq0D5c+9zTR6wV+tsz5319/7ZR3P5wva9bvNQYQ6p554hrpcmNNHcuZ75Od7S70NjY2UT79fJF8/9NyiY6Js+MYqAl+vVVY7KZSn6+vK1nwQvvndiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAv8ugRwpzuFD171798qsP2ZZubL1G9ZLVGSUfXDq+tZuql6ocl2EzKMXHfFBql9hP0uPQ9ocRCSkxaHFxMSY5Pb9999LxJ4I+zYqLtihQVTDh9E//PiDiXUTJvwsm1Fi9FQrrX3c3+d+ufXWW51FdgtxaO7cuVYSc+2atSYz4cIhroTig2dIV9WrVbMEmQidh9MgYT2m0tIdKsAhLe+332Zo+c0/tAzkek2oO2IfdtsH1LoBLrTiw2H0BckGSWwdru9gc7QPlnV3SLH5cNSHMm3aNJXJDuqHy64Pt3E5FpIYksy+G/Od7R6lSJGahnHH6PhNEtKrtkhPw7hKKAeks0FKO66iIhpkt7vuutPKuU6cONFSAO2BU/+7+up2cu+999p+nOUY/86dOy1BB+lzEBFxDFLwwbv+oGFeNjftP0TnhtSyG1UiCg8Pt7E4fWV1i2QeiI4ffPBhVqvYckfZwb5Q2jcoMEiq6XFBEl+zZs0EpX/RUMoWpXGnTZ0mK1etMkktWSW5dMcCEoL2ExISIrVr17bUthYtWljC4Qw9jp9++qks03QypxVTKQJlcJ959hlbhPRElKL766+FziqWnmjC4I2dBWVc/5g5UyWtaElS0QL7NklB9+ujIoFfEdeF4r1796Vt768CX5s2bWwfOCccwQCSxjvvvCOrV69Jkw2wEcbvp9u88MLzVoYWEqHT+j7QV8f2l4mDzrKOHa+3cxWljyG+QYicqWNcs3aNpu/p+eomWZo0qv3jfC2m0lh7TbXD2Cpr6hwEVYh/3bv3kG3btjnd2zk+6PFBVtIWz8PzaZBpb+x8o4m1eE1Aw/HG/l966SXppCVuz6Xh+fD99z9YCV2IG5Az8FzGfPAcgrwI4TNWBTrIk07D8b1Tnxs4B5AMNfqj0frN+FPpkLoSEh9//uVnkyynTJlix/7vv08nQ+I53UlLFw8eMtjpMpfeOs/CXDo9TosEzkIA/6aiJDR+kDL5vKbOhYWFpdsKr4/4Nw0JsbVq1dL3CY+ZUPfII4/YexksxzrLli2z11NI8TVr1rQy1OgI75nwvgSy+gpNc0WKXWhoqK2D11akguLfQYjAEO7xWlaxYkX7txl9YoxIWcVrPcaG91RokP7wXmylJuBGRETYv5kQz/HvE7ZBCp7zXguvn9gv1kWJ+cDAQOsP/UL8z0qgwz7wngHla/FvMtbHexuME6I49oF/N1avXm37BR/8G4wy2mwkQAIkQAIkQAIkcKUQyCjOhQT5SJ2apcQ/wEffB+GLlfgSn4eVPUXJ1aJFtUpAsUJSvlygvvdKL3pBYtPLXFKlSlH9+/Z02VPMFaJc1NF4Gf3xn/Lz5DVWSrWIn7d0u6mW9OvbzGQzh0lGcQ5/5951RwO5757GEqilWrNrBw8ekx27oqVKpSB9L1cg3apInEtMTJHvf1wpX3yzWA5oqVeIc+3bhcsj/Zvrl+hOi37u4lyyprR17lBd+qk4FxZ2ZkLbTt3foCETZc061zWPQE2uQ4rcdddW0ets6RkdVQavvz1Hpvy6VhLikyRexzP4kTZyx+319Mt9F1diS9GSsb9MWifvj5ov+w4cNRb583tI21aVVRRsoe+HT883HSj+QgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkcMUQyJHiHOjZB8ExsZbUgnKJ+CA2+mi0pZIgoSVf3nzi6eWpFx69TSoqU7qMlTerXqO6Jbm4H4FVKkNBkEHaS0J8giQlozyqmBiD1JchTw0RlNtcsOBP2aMf3joNj13X4TqTrJxlzi0+RIaMhh98CO2MyVdLxQYFBUqjRo1k69atsmXzFmcTSzvrqkl4KJGJ+aHcJGQlm9/OXfYhNoQgJK1BWoPQ5l/EX0LDQrXkRDv9ULmMFHCTndAxtof0tW3rNkv8gvij11jtg2+kYqHUGxo+dEfi1axZs+xDauwHkh/kKcwTJVDxofrOHTttHNgGwhPKekLAw7aQv9xb/Qb1VZBqbx/Quy/Hh91gslAlseUrlsu+vfvsQ32njCckL0dIKhta1niE6gfm3rr8XBo+VJ8/b7788ssv2a5uUmU+Dz1PvDTFxttkJsylmn7wH6Af6ru3w4cOmxCGee7ctdN4xcfFW6ohkgwxXvzgg33IBY0aN9QyKF4mAyCNDalz7mmFAYEBtt4tt9xiu8ExnjNnjkC0dBqO8dXXXG2lhZGM+JsKWBAfjqiU5n58ID4U1QQ3cEXaodNwfCACdu/R3QQ+iBFo6AOC26KFi9Il1CHFDklwPXv2lPJaOte9ITEP5VadY4THmjRpIi1atrBUPYgSGBOkvGXLlut5skOOxhyVOE1iwwV4zMW3kK8JGuXKlZdrr71GeZdQRp4mO2Db119/XfZGnBZJIVJASq1Tt45t5z6es93H8234sOEmb5i0emoDMOl1d6905YOz6wsJiXh9gOR5SBP/cN6CM1IffXz0eKu0AUEDZYXdx17Au4C0bdPWxg7BY8rkKfYaYPvSJyC2eeaZZ+w5jOSo2dr/hvXr04YCGQ/CaBdN/svdDa9GbCTw3ySAf+eRIgdZDq8zkHpvu+22M2Dg9RWvRZDe8G84UjrxfuWJJ56w9zx4vcDrDP7txGsfRLQ+ffpYIh2WoUw2EljHjBlj22MZXnPxGtuhQwdLs0Ny6MaNG+W5557T9zoLTD7D2PDeAPtHvygf++KLL1rJWGz/xx9/yMsvvyyLFy82KRlCHeRx/EBmGzBggAwePNheN3/66Sd56623ZIf+24D3LngdxfuLHj16aFnzR+xLCmdMXBdAhMfYP/nkE3nwwQfl2Weftf0hdQ/jxb+5jkSHMeLfml69elnSKriwkQAJkAAJkAAJkMCVQCCjOFe3VmkTxMqXCzJxDilzKKOKlLUCWtY0qy8VZDYXiGoJCcn6t3qyXquIkkWLd2lp1fWyddsRSUxOEX8V51yJc030PZvrCxDoJ504l6zJ8CrojXj++lMi2vn9nYa/uSHLYQyHDx+TFSv3yZTp62XlGlQZcCXOXX9tdXn4oWZZJs5BnOuoqXT9+jZVYdDFxX2+RyLjpN/A8bJi9R5bHFCkoDw+sLV0uK6KXps6PS88mKJ9vfbmLPlxwkr9AluiXuNLkYfuv0pL1tbXa2invyDo3v+F3t+0+bCMeHWmLFuxW5L1fTNaeRX/Hnu4tTRvGnqG+Hih++F2JEACJEACJEACJEACJEACJEACJEACJEAC/x6BHCvOuSOBYLZv3z6XVKTJbEgw81JpycdXxTL94BRlHpFK4qSkuG/r3McHrpBiIF0hGQUX/pAoVVA/4EXCSaxKevZYfJyziX1Ai9QUfEidWcMHwxDo8EExxgRJCBIaxoKUsEhN6cJyp0FUKxpSNC09zFl+XIU1lOlEsgukqZRUvfip80JfSGSD5JXdhVV8wI1EPoiFjvhkKXx+fumkNnwgj+QwfCCPD+khGSGJpkSJEpYaA3HIKcOJsWGfEIAKqQyIeWAb9+an/YMPUr6yavhQHsk0OH7RUdEmBYYEh6jAVUyCgvHt5czZZtUflkNGcPrNbj0n/Q8f4mM/OD7ZcURfEAiQmoMxgxXOmwD/ABPXcDzQT8bzDMIhzi2cp07DvrAuBAM0K/mp/eHWaRgf+OEHDfuGRLBj+w6TuFAyFMenfLnydq5j24zHwBEfIRM4c8NxxnFEkh76dBrEusJ6LANV7PTySn/McHywDdg6rYge3yJ6Hron0+ExlNl1ztfII5EmavgH+Nt5hOOK8yVjwzjwPAFP94ZkNgiJGZm6r5PZ/az6wxxxPmc2hsz6sWX6WpCkzPbs3mMyyPETx01QCQ4KtlQmiKx4bXB/LmM7lOzFMUZiUkbWeG6FhoaanHos1lXu1f3Y43HwDdTnV+5u5/eBTO5mwdn91wjgtQFyO8p64/V15MiRJoqfjQP+HUeSJZLn8LoJeRrplg0bNjTBGWXX8b4F5V+xDGmmI0aMsPcMKP8NMRoS3rhx40yUR1npV155xeRupNdhe/x7AcEfpbLxbxeWQf6+6667TIBD+ViI90h6g+QL4R/jQilwpMrhtXvgwIH2g9Tbhx9+2P79QH8YE/4dRZlqiNz33HOPlaXGv1MZG/6tff/99+Wzzz4zGRAla5csWWLlaqdPn27vTSDoQ1jHftEn9g0xGaXA2UiABEiABEiABEjgSiCQUZxr2jBMhjzR2gSxCx0fhLkTJ5I05SxGlizdIwu1fOq27YflSOQxiTmWoBUD8JfqSf0bv4AmztU2IQ3lTZ3mLs4hqa5YcCEZ9nwHk72cdc52i+tmkOWiouP0vViELFi4SzZuPqh//+r1s6Mn9O9o1/UGJOpde3VVTZxrlm3iXHbiXFxcktz34DhZumKXDSs7cQ4rvKGJc2N+XKap+S5x7oHezaXXnfW10sC5fSHzbHPH48eU89vvzZOJU9boe+ZEXXJSU5+9pVf3BnLHbXX0usOZ72/PpV+uQwIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkcGkJ5ApxDvFwrhKtellQ7+PHkYRwix8nbSs7vM62GdfBtlk95vSfcRvndwg8zrbOus6ts9xZF7fOY+7LtIN083NfL9P1023s+iWzfeGRjFywnjNmPI7+sQ5uM+vD2X92j6GfrJqznXOL9Zw+ndusts1uuXt/2a2HfaA5t9mt6zzm8ME+0JxxOrfOemm3uh7WdNZ3lqdb/1zWOdWHs39nzNkdH+wr4zHGsqz4pBsTVjzVMls/q3Uv9HzFvDK2LPeRccVMfs+sP6yGMsd60DLZIvtFDnewcMZlfelmWR7fLB7DntKOyzkee2yT+9r5H4fcx4Az+q8SgKSP9M8nn3zSJFuIcxDAztbcxTkI+k8//bSVRcd2SGGDXAaJDP127txZvv32W9tP165dTXzDehC/v/zySxPWkDKKVDck0yHJDaXXW7VqZSmgSLeDfI9y9uivU6dOJuRh3Einq1u3rkl3KKUOKRvLIcxBJMYtSs+jb2z/wAMPWLocxHGsO3nyZJPvKlSoYOOAgJexZSfOIREPQt79999vKXaQ/iEAIukX++7fv3/G7vg7CZAACZAACZAACVwWAhnFuSYNQuXJQa2lUsXgCxpPYlKKVl2Ikum/bpB5f26XiL3REh0Tr3/nihTQVDl83y1RU+hST6ZKId8C0rVzLXm4X9alWiHOVSoXLM8MuVoa1C99TmPCNgcOHZPZc7bIH7O2yLYdkSrQHdeUu1Qdg5aQPZlHU4aT9YufJ60U7dVtKmsKWwv9ksPpL0dmLNWanTiHRLt7H/hB/l5+ZYhzSXoMJkxcIx9/ukgi9kcbs/xakrb1VRWl/0PNJSw04Jw4ciUSIAESIAESIAESIAESIAESIAESIAESIIHLTyB3iHOXnyNHQAIkQAIkcN4EKM6dNzJukGsIQIBDeXSUM0UKJsS52rVrZzo/CLuOvIukOghqjz76qCWSfvXVV1a+Fevs3LlT3nzzTfnxxx+lX79+Vs4VSaQQ2SCsIf30iCbzorz9lClTtEz0bCuX+s4771gKHMrGzp8/37bF9k7516lTp5o4V7NmTSsBi9Lsn3/+uZVPfeihh0xcw8CRgIey2yiVDnkNpWBfeOEFK0kLkQ3l0JFcirGu1/LUH3/8sUl8YNC7d+8z5p6dOIekT+wLZcPRsO57770no0ePlr59+9p+z+iQC0iABEiABEiABEjgMhC4mOIchDUIZ198vUhWrIqQmNh4yaeJbkEBBaVZ43JSv24pWbxkt8yYtdEe8yvsfU7iXOUKIfLM4Gukfr2SZyWEL66uX3dQvhm7VBb8tU0OR56w76b5+nhJ7RqlpF2bSrJ7T7QlsR1Uuc7TM5/kJnEO72WXa8LeW+/MlVVr90pySqp9QS+8cjF5bGAraVCvlKbL82/ds55IXIEESIAESIAESIAESIAESIAESIAESIAErhACFOeukAPBYZAACZDAf48AP0z47x1zztghgNQ1lEx99tlnrWRpVqVaIcyhXDbKs6JkO8qyorQrxDSUfv/kk0+kbNmyJqPhcUhwkOkgjyGNDil0Y8eOlT/++MOEOWyLxEuUUUfqXY8ePWwblE+FOLd48WJLrevevbutA1Hvt99+s2UVK2qChgpwSKVDCVaM/ZZbbkkrp42y1Cit+umnn9r4kFwHKQ4pcCjfjhLk+KARDf0izQ6lVdEnRMCMLTNxDmVikYyHsb/99ttSq1Yt2wxS4IcffmjL+vTpIy+99FLG7vg7CZAACZAACZAACVwWAhdTnFu7dr+MHLVAFv69QxI0VQ7lV9u0rCRdOteQKlWCJZ++z/vi6yUy5oelEnU0Tv4NcQ6lWd94a7b89sdGidVypfny5pF6tctIty41pW6dUhIc7COTp66XUR8vkD17j+Y6ce6AlqJ95fVZMmfeZonXJDz8VVs0pLAMePAqua59FX0PrYl7bCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAjmGAMW5HHOoOFASIAESyG0EKM7ltiPK+Zw7AQhkW7duleHDh8ucOXOs9Omdd95pcpl7LwkJCfL777/LY489ZvLZoEGDrLQrxLnQ0FD56KOPTKBDfxERESbBff3115YGh1Kp6B/pciEhIdKuXTsTzYKCgmTZsmW2bkZxDmIahDts6+XlZYKbuziH8qiTJk2yPocOHSrdunUzEQ/7R5IdEu/Gjx9v4lybNm1kyJAhsmnTJlsPZVkhDKJBokP/hQsXlmrVqukHvVXcp233zybOQRJECh4akvQgzmEZysJi3mwkQAIkQAIkQAIkcCUQuFjiXKqWPX3rf3Plx/ErJPZ4gpZDFUtye+C+JlK5UrC+J8sjx44nWvnQseOWSfS/JM79tXCnPPnMJDl05LiWYk2VOtVLC8bQtElomjT204Q1Js5F7Mtd4tyJE0kyavRf8tMvK01MxPmFcrj33NlIut1cU/yLeF8JpxzHQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkcB4EKM6dByyuSgIkQAIkcDEJUJy7mDTZV84jgDKqEN8gm7Vt29ZS0iCXIREODTIa5DEIYW+88YaJbyh9imQ5JLSFhYVlKc6hhOq1114r9957ryQlJVnpUvwOUQ1lTr/44guB+Ha+4hwEPoh8KNWKfUBS8/X1tbEuX77cUuhWrlyZrlTr2rVrLc2ua9euWsbL9bzHHJCChzKyrVu3lvDw8DMOIMW5M5BwAQmQAAmQAAmQQA4kcLHEuWhNentxxAyZOXuzvr9LURIn5YlH28mtN9cQb28vIxMTkyCjP10kP4xfLkdjTifODejXTLw1nc5p8xbskJEfzpN1G/frFxtS5VxLtSYnp8iEX9bJK2/+LifiEiVBE9fuvK2+iXPFihVyupcfxq3WcSyQvftjck3iHBL+xqsQ+NV3f8vuvdECkRGy4rXtwqX3XQ2kZMkieAOfxsD9DsrVFijgYeu7L+d9EiABEiABEiABEiABEiABEiABEiABEiCBy0+A4tzlPwYcAQmQAAn8RwlQnPuPHnhO+xSBlJQUWb16tUlls2fPlo4dO8qTTz4pkOcgmEGs+/bbb02ag5w2YMAAue+++2TWrFny+OOPn7M4h3Kvr732msl52CfKsaKE6vr169PEuW3btplcl13iXPny5eWZZ56x7Z566ilB6VaURK1du7aNFTLeq6++mpaIh9S6//3vfybZoaQrtgkODpbjx4/LuHHjZMSIEVK/fn2T7ZzkOPeTg+KcOw3eJwESIAESIAESyKkELpY4F6FlT0e8+ofM+3OrJKekmqR1V4/G0uP2OlKsqK+JXHsiojURbaHMmLVRjp9IFL9C3nJjpxpyT++G4uXhoYKdhwlcFyrOxcUlqRS3St75YI7ExyfpOFLkunZV5b7ejTX1LkhThfPKsWOJ8qWWi/1hwgqJjDxh4hzKyfZ/sLkE+Bew5GFfXy9ZuixCnh46RXbuidR+TkrH9tWkX9+mUr5ckL4XTn+0E1XQu/eBH+Tv5bvsgYAiBeXxga2lw3VVNMXYI/3K+tsbb8+RMT8uM7kvSWW/B3o3l1531pegwIJnrHsuCyAXgtnoT/6UNev3W9IetoM417J5BQktE2BjzsybS9VjVblyiDRvGipBQT7nsjuuQwIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkcAkJUJy7hLC5KxIgARIgAXcCGT4NcX+I90ngP0Lg2LFjVvoUJU0higUGBlrpVT8/P4HMhmXe3t4muGGdggULCkqnQrALDc28VOs333wjffv2FSS8obTrvHnzpFKlStK4cWOJioqSJUuWyKFDh/TD1VQrk/rII4/Y7SuvvGJSHQS3jKVaUb4VCXfDhg0TyHcQ6CD7lSpVSiDUYZzbt2+3BDmUhUUpWfQ7c+ZMGwMehyRXvXp12bFjh43Jx8dH+vXrZz8e+kFuxoZt3n//ffnss88s2Q7zh9iHMcTHx8vbb7+dVqo1MjIyrVRrnz59WKo1I0z+TgIkQAIkQAIkcNkIXCxx7oRKa8M0cW7abxskMTFZRP+cKlPKXzp3rCaNGpSxMq1Tp6+XP2Zv0VKu8TZf7/we0qRxmFStUkwSE1KkdavyUrtWCVnw5w5594PzT5xDytqUaRtk2Cu/SuwxLRerLaCIj7RvV1natK4gBb09Zc7c7TJp2lo5cCjGAtgg09WoWlyuvaaqJidHS3iVEOlwbbisXrNPhjx75YtzmPP6DQfl3ffnysK/d7nS/tz+lIU856QqG5AM/0M64NWtK6sU2EwqVQw+QwrMsDp/JQESIAESIAESIAESIAESIAESIAESIAESuMQEKM5dYuDcHQmQAAmQgEPA7dMGZxFvSeA/SADyHMqWjhw5UtasWWOlVfHhW758+Sx9DuVQu3XrpmkaXpKQkCBz5syxdLiyZctaGl2JEiWM2r59++TTTz+VH3/8Ue6880558MEHZe7cuZYCh3KpKAELWa1JkybSoUMHmT59uixYsMDud+/e3dLtli1bJv3795cuXbpoOoinfiibaPtDOlzp0qUtNQ5Jc5s3b5bRo0fL1KlTLUEuICBAGjRooKW+ki0RDyVdkZCHdDnIc++8845s2LDBZD3Mq3LlygLBDSl7GFNmDYl7SLFD6h7mg/5QBhbCHDigbK1T4hXlZ7/66isbE8rPIlGPjQRIgARIgARIgASuBAIXS5zDXD7+bJF89/1SOXT4uKS6xZt5aSnQFKTQ4T2kilxISHN/PEUT3RrVKyMPPdBMGjUsc8HiHMawctVeGfLcVNm1O1L3gSWu5umRV0W5k7bMQ+/bGNxWwGNBAT5yb68mcsdtdWTN2n0y+JkrW5wD4j17jsp7H87XErkbJS7OJSw6cz6XW4hz17SpIv37Ntf3wBTnzoUZ1yEBEiABEiABEiABEiABEiABEiABEiCBS0mA4tylpM19kQAJkAAJuBGgOOcGg3dJwIS5vXv3WiIbUt1QsrVkyZIm0F0oHnxAefToUdmyZYultEF6QyIc2okTJ6zEKoS8YsWKZZuU4ewfKXWxsbGWXAfhDSVkIdfh/tatW+Xll1+WhQsXChLqevbs6Wxm+8LjERERAskOCXhFihRJe5x3SIAESIAESIAESCC3Ehg24nf5ZfJqSVSBSr02adwgVAY90koqVgg67ykfOBir8vTLcqgAAEAASURBVNximTlrk0RGn5CTKsQheQ5fuvDx8ZISxfzEr3B+2bL9sL5fi7PEN+zT1ye/tLyqgvS8o45UDS8mfy3caTLY+o37TXCrUqmoDHm8ndSt4/pCRnYDi49Pls+/XCK/TFkt+w8elVR1yTAGtAL5PSU42FdKFi8sO3ZFaurcMUnVMWIMnp55pUJYiNzVs560v7qyrFq9X0u1qoC3J8pKz6JUa98+TaRcWKCt7+rR9X+Uau3z0DhZklaq1Ucee7ilXNu+cqalWt9+d558r6Vaj8clipVqvae53NmjnpaK9Xbv9qz3j59I0vKsf8mEiav1PXVc2jzPuqHbCjjuEOeQOFeh/JllaN1W5V0SIAESIAESIAESIAESIAESIAESIAESIIHLQIDi3GWAzl2SAAmQAAmAwKlPVwiDBEggxxBAOt6ECRPktddek1q1alk5Vgh+KJ2KBLvXX3/dyre++OKL0rBhwxwzLw6UBEiABEiABEiABP4tAt+OWS7z/9omyUmpJoTVqF5Cut5YQ0qUKHxBuzysaXM/TVgtK1ZGSIyWS0Wp0ILeHlKxYlG54fpwlcMKyrdjlsmK1RESp5Ib0uga1C1jkln5cgGWQrxx0yH5WWWwHTuOSJKKbeVCA+X2W+tIWGjAOY0pJiZBpk3fIH8u2iZHIiGU5REvj3w2pw7tq0id2iVl3PjVMv/PbXI0Nt71WHE/ua59uDRuVFryawnZzVuOyBdfLVa5LlZSkk9K0yZh0knHX6xooTPGgPS6d96bLxs27bfH/AoXkJtvqiUN6pcWpNtlbON/XqMlazdLfEKS9p0qXW6sJW3buErJZlw3u9+374iSiZPW6H4PqGDoEgCzWz+zxzD2xo3Kyg1aUjezuWW2DZeRAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAlcOgIU5y4da+6JBEiABEggHQGKc+lw8BcSyAEEUIp1/vz5Vgp106ZNUrduXalTp44l0M2bN8/S5/r162flXpFkx0YCJEACJEACJEACJPDvEICQdejwMZXQPDNNUotVYS0yKl78/PJLEb/zS1o79xGflEOaKocvRQUF+Z6RFJeQkCL7D8SKT0FPfdzn3LvlmiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRwiQhQnLtEoLkbEiABEiCBjAQozmUkwt9JICcQiIyMtHS5cePGWXnWqKgo/cA2vyXNderUSfr27Sve3v/Wh7M5gRDHSAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkkBMIUJzLCUeJYyQBEiCBXEmA4lyuPKyc1H+GQFxcnGzZskW2bdumSSZ+UrlyZSlevPh/Zv6cKAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQM4mQHEuZx8/jp4ESIAEcjABinM5+OBx6NkQOHnypDg/2axmD+XJk0dLWrl+zrYuHycBEiABEiABEiABEiABEiABEiCBCyVwUjfklZgLpZcTt+MRz4lHjWMmgUtDgK8Pl4Yz9/LfIMDn03/jOF/sWfK8udhE2d/5EOD5lxktinOZUeEyEiABEiCBS0CAl2svAWTu4hITSE1NFZQu3bVrlyQlJZkUl90QChYsKCVKlBB/f//sVsvVj0EyBLe8efOelVeuBsHJkQAJkAAJkAAJkEBOIeB2jTX11Jjx110e0QfwGNSczP7cO7UdtnG6yIvV3RqWO33mc3Xm9ijuujrGek4ftgS/oOEXfX+Z6f6dFZwdYE/OOPXLLGm7w/ZoeXR0aZ2778DZCCu5lrs/mn5keOTU+mkr4U7aL67H3VbTBWe00w8722EV19JU7d8ZUdpwTz+su0/R3/BIRtpY6ewNe9GvBp36z9VTHu0rrTfXCq6OsBtnMLZEH3R44ve0x/Ioffy4GvrKe2o+doM+3Zuu4BqBayHuOyPBkny22LUM/7d7+j8cVvsFj6fdd63njAW/ube0IbqPDivhJ92kMXpnazyALe1ZoLdoJ0/t0rXM9ej/2XvzaNuOqm67dnea29/cdDc9SSAJBDH0vIAEFJsxULH/AwcOO2xwKPaKooJBA/a9Q4aCgjLs0OGnYoOK0omAQBISQnrSJ7fvzjm7/Z7nV2udc4LJK8n7fkr89rp3nb33WlWzZs2aNatWzd+aVe/UfBsltVQ6fuGyHxP+mbOl7DdPSzXnxnV+cIRa8lci/m1bze+bJFz6+c2feqP+ar9LKIxUCm3SMuNGCkmCZM3P9QRy5ilnTWu2NE2TxFPyqa9VJvV2rZlJkmi6iSpfLbbeafjJhfWL661UU9VivFtT1xKq1Or3ynhD2D7eXG4KacnU6y2hprj1m+b51Gtc2iwBbyfJf0jbXqgFK5Ham9rrZKy3HqQMb2y+aQk8T3PaJ9NsXKkFj/kiR1pSJW67PPBoKXl1ndf1HxtppbJRMs/u0K0tzI2pve9TaK9XXg5aLawX21vtp6VUPqr0qjXjrnqwOZEJK5f5Vv+0EifhTB7M0Bybvnplc13l6oG3G/43KrledrU1UqicVXtmQihgYCoHtY7SlFKl9sASuMyh3vtv457fNn7xo2W0s1nqTapW99s0lSQEKuNq0uZD7jaSNvxbmhcfUGiba4NOmy8ly4pHw4by2yBinTaIrX/bTKApz5HIrw/QH35vHE1C2z72plJL024kSmmm9Ky8WLdav4ZC7jz0nw1O5KbN034+dL4H3qnlP4QoNyW1h1Y+TbuhITVJS8Vf1puPlpF1YdaUD/W3pfAfk1tPD+u4cddvM8YXz9iNB/DUanz93JCsdCyppenvVt9a2ny2/G+6XSnJQatI2IwH2HmubzJelURtz456sLmCKcoLtS/VVI10ueyYnySW/5CHHG0QrekfKpd21KNptzZbvdgUtsGPl6s9Ngd5WnFJvhXXZhoPGGtN1J4b1ZamRzjc3PWgV0m1rZsUSdsWsXEll/njHc/N5aih2sTNFkTinA2BNkdL5aE/W5uzmX61t9L3qJqwcb/9lqLa+WJ+5M9DFxV60mz1yqStkNvvtUx/1eNTf3u15aBJko823UPx0JbbpvNTrWIcdoZVf24i3aar9Npfm0usd9q6mMK6VKlt5OKbt7yQZ532x6br3KqHtOoIbD9R7jP6U9d5T462LIl5/u+OVpErTw+eUnqfDq0Hz12vtv2tlZB8WyYnl8Llpiq3wmgvbaasVjdWZPPlT/luTk+Ph+a92ovW2tTUG3830/D7Jhm1pNvEDxCzMjWBcmvqmM+2Vm2m+llnerWvVzItv8rIf01t2zKTSDvvBe9t8NUm2ShBHtYpJH2916asJba/NvJtfKspmt+1yE0/qv7M6BvekhM/axvJ3fqMMnk2Sqsl1rS1Fg/WCg+leeZ7AF+h/iB/HjRhq/MtBbn+lCPsobMkiZ7Sv8JfmyXJ23b2x0YbtJyFBHcekCX5HuyPNfWovLR5N7MvnfZ6kvLnwWmbSt6k6TyeczMhrqwfmwTc0t6gqY1p59S1vz5YG63TesAXqXm29ZLqZhm1idt03Gu/tgy0n23Sh6zEeoJP68scOPdpiWmeaC6BuQTmEphL4P++BP7DyPZ/v4g5xbkE/oslcPz48fL2t7+9XHnlleXQoUPrpRtVToDY5qPX65XHPvax5eUvf3n5oi/6os23/n/zfTwel3vuuafce++95fTTTw+IUFnNj7kE5hKYS2AugbkE5hKYS2Augf9eCTh3/dR5Wa65/NosoE5Zv9TF4aL8gM/ujBv896OjY6ZLWj5M3m0WOvWBjjhdqnWZ1nyb/KW5PuSaVBc4exLjs676urDeJY0Ls7ma5dWet9ty24ICOpAOB+XV/HzBWTsbebFm6pBeWnWh24TcmVArK9cfBDtXui4Kj5OuG6651zIQ2tUFbV5/brjh+WU6ubTi/mgdXdCsjkrBLjjZGpBICxAwdc1bs0aGXOoojzxXVAamzJ0nyFqnA6VUOTeyMH+nh6R7qTB3dea5MC5fJPKDl1ek7aHoUq4/GgZqKbbXNOd0NqFZe0ihz0mpJJi1fq4mn02v+Fh0JgXlr/Pc0EUvoFJ4zYjPusi+CP2BnMgbmWfKQ8amFNCjdVCUKQBA+ZBqdQVaY7lQj6Z8M4N/octX9Yqs4WMK3epQt0ZNOdRdeTdXuJ6WqjI0TaepGO0zm1gpLlmkBXbV4DUy+2khcIBcOk35laqU1S81oroR1PmqvRDjv32k6jTX5dcsHGMEO5ytknZSBjAu9IlvaOFCZLbed0hbS6l5lXnKhK7cKy97qeWPqezUdoPdJZL1bRqTWx8Pv5tcJtQb+pDl5zBhhGVd4abbCx9m9axaRM9VHspCMJV6aTuSNY94PVptCkfk957FKR37lr96vEzVQT9nU/TUYrUfbdfga7RFZZsN0h7SbJPKtgeXoOVZ+7V6YqX6lBGJpB6kshzatSMv5jCZR0vA7y3R5rrVX8cyeS0dxoy0Ifqlqk7IP+ZM+/A7atLQVLXX2978HBP4mdI/eshTvqv++yW/Kj/kM29lSKYq2KWmUfJTamePom3teCTpqEzdE3wZUjV7xyJlU1//yTKHHzZPe7TViWlorluuaayXTaILtosBHUC3Z7tJZLzE1QUIUvaECzZMbWIuzbD3Y3RPpmhMuJSHRuqpJanJX2tBau5hy+hvs+gQqU1sovqHT8pJHfyjztEPTTRb4lR/TMsl8yjGJrmp6y3/jrhszzMRpzZY/jnSpb1qPm0/ja50JRS+J0giek5ZlDvm1ogUI3STK2WBjAsNXXtQLSEMc7dCh7xer0hVeTTt0jKponWx21G4dAbKp/SphlDZyKhMhi2qP4IXdb5PHdtaWYrtb9Iqd7nprPdjMps/9+unfUxLY4+Rjoe4694IKvTHNKE8xTaa1no4iiuZeqS+tdDKo985HHrWuDmGciwZ8rKNvG115M3RXWWfDUk84US+sz71tXnCa7UYbW3k1DHJxnJUim6TLkmh2W37uxc4Lau2gLMM7aL1xJ6Y13skaJLyywv+aSrg13ph/Uq1Mu1VP5MhKTf+qNtyip5y9OyLtqNHCpR+U0bTNtFBSSEC9U+667Rbdpqi5EENaUuP6uSnUl2JXSod+oay9Dp/Yju5N2OMcTzvlmVuMN/Jba2mpzxL1fvRUH5pfxmXopcwVxnkU13ht7ppIc0J6XSradJrM+SUm1NsxshG5bvjexfZOD5wt/aNOo6HqnqgUfXgq3M2+0VsUy56z1bFwsPegEw9zuSQoFQtxzGHi16ybtoaU9Xep0ZxJn3Nwl9uq1urzQVtHO3WEGlISoJL1su+UKWmZklx4Bhjs0u3z32qHL4cnDmUj31plv5kIolpPTZ4rVJJLquvscHskVZZLHTKqNuMn7ZBbBjlSMa0HnxRjjmoYNWWZgyBoRnK4Pgx1iaieAukSXPJrH1bQ0jfVxc85KRy46+2lPaK9KVmavOHY65pbz1rOm3LGNrrNoYUpu5LjnlAPfiR7MhiowbNPT9MZ6srewVqZumjOwjAMbnDuFpL1FK1R81Xf3nVUwlZWJsqjPDbg+v+3HwkmWXaGJZvK9nPTUg/mm1liCARt7oDylagESqkNISUp/7YTm2Ras/G6CDNSst5ypjJRS+DLdck5T/zZlw1rfxzmsX2kmjSj5DFWn1RvYetgydtXa9XR6gqN+VhHltAog92WNdWxq2930gbVuBlhv747FfnVWSRH4/1pO2F9nPzTRNZF/tbW3/1SVuwRD2QK6xiJkLXvjONzZCWmuU/impI2yeZeXCnjyiqHqyXus6PxBqCfKs6oCw+9Wjb1jJaLa5plGilIZ2W70ZGNjCXYyvCHEnWmTSf+qOBULbKXzsssKxSXU/KVevDLANyPutsrrHjl/NANbBKITKClWp/V7nD/IBcncxMLMe0ESN/PSovmWNnHDCtcvC6bVCP2osqb5WC12uZm6vXZGvqahotou2KOGbLnIwpyWBtGE+Y63U72FdopU/wzV6f/hAdn6YG3necrzMxqcld7TVKUrlEi2UY+tbRU45rKuaWbQHcX/fRcU2zY5+qfJlJIq3xltmGepufK5ClLgh6thJZT7WnE/Sty2mW9oROO96s04l8reXGsdHqG9fWE0grtbEt/aEeOqZX3qXDI23u+N1zvXi+e7T0QyoJ1FeeE6J/6N50kYzc5V5uqwKceW5jotuhUtEpklS5cj/fqi3M/A39aeeED+wppt04pF9LkVJ9PqsabAuqC8qbI4z4RV49qfMYm2A2ePN5dr2ibedPJq5Hxn4+smMOnHtkcpvnmktgLoG5BOYS+D+WQIbq/2MqcwJzCXwmSeDEiRPlne98Z/nVX/3VRJ4THLe6ulpuueWWcvTo0YDDzjrrLCbkPIYwq73gggvK133d15XnP//5n0nV+C/jZd++feVNb3pTefOb31xe+tKXlle84hU8xDcT5P8yLuYFzSUwl8BcAnMJzCUwl8BcAnMJbJaAi8ktcK4Fz61fY4XSf65mjllEdTnYe4ssWA5YyJyN/cPtONtYfHfRleRZz+S631c54ybgc5G8WchuFkf1Na2SWJpLeEkCpglBErBiK+hGwJWLxWZx5qiTAHwDa6pcyVRSh3xDkHm3gIIpp5FLOgJLdMJ6G4djMroCa36dbN4SAeOCK4vfLhQLIpoEgKArgWs4J0M+RcBr6EvAQ+nInWkk1pxepU4pyMwwPLF+0sKJPpMvksq/9Ewi6Mjssit4pUf+flaLXTyWltd1nJIXvoJ38rK3x/V+Z8Aiew/Hmc8fOp5wyko0vMg3Wb1n0bZD5Mj3yoB1q+R0/At+8V+XhfE+7RDXimUJqvCQCLTiu2XtW5BfZwZAAbCUx8y6piDgHMh6BHHdWsp6GVkMKEP/c4BTihOy+iTiB1zA3dsTcqF7CPc7svDT0yYybz8ODJyD8mBdyC/12dR81JHnjPoboiLrcCR6Qcex9bQWyZN2smD0w4YIUIu0ytR0XOuqO4J2Oo27pquzANnCrGVRaX5XeekSbjQCbms7wlSlTdm6wSzbPwFamZvGXI2jaQjMImqBvizQ35bwmXfob23fqe1jHWz/TvQD2eBNGNq+lhOXk24b4WMD/nXLEtVbaNstngfLh4CMKIQ+ji6UoYLVoBEBwSeApk6/Xya0HyRyxK0tCAZAVZzYAOcmOoejbyShP+lsEcQ5I50tbY0kqYt9hmPXfN2xbQiohDNKb9sMkI+6lBzIegoQaIqcA6rBTcO9BtPY8FJlpVStjDzaEgPaKlBNbQR6qPO6011AV+zPUE9C28SCqKMNZdKmkupguqhkPTkCShX0QKYZfVgoQgVRIT7oLNEWnQCOpNf0i/QJy1F/K29+MwJMoK/2eTu7jNT/+VTnKoDGwnUyKV8TWCv0DEY71GWgsFRJ2653nC9YaOsZLbKutQ9wg1K1i5aVqjb6w/emfrne1FsbpCi0AZa4iG72oN2znadbyEOb6ZS3apL0DFBEh+cap7W13bFB3FO8aj3kc2pfdHNPA8rDLTqquqYnUNklkTRDp6m7+gqoJQ5ZEV0THH+CPWxj62B68na0GxQ6RVZyUeWnDO15KhmX0FVtqN/T5tJOufCFLE0tx0gwetrBpoCI4Oxiy8SxjNGwYWgu83cRBXKssAflX4SqcGrpXq8s2pLaKh26lO1F+zCGUL5bG6TUk3ICnFzdd4yqBGod+wCksEmMmpzUg9TVCZ4RmqTmh110q2O/tN6khAond5BPbTdt1pC20GZZW8hb1TVS+gOgTqc/JI32z55Fm+uMV37er9SoCz/grzWl3hErthZZTckxwubbY7SS6pV9oFpKGgrgHG01liZ1AQmlWtv/1EJzyL//rMGYtujoqHZ8gc0k8wtnz74kLxzyQhLSWi4OcW0J7TTGrmqTQlNmpEEWc9VWai4ioFl0SK69a+kkbn7V9Pxt+OBG7iZdwFEC1RyxALHRjrX/2k+hwvgnpYAabd8WgIoIAsoKzaaNTOiRApWGpz2o5u+TNjKwjTonKFO+AZ5gl9Me5Hd+NO2sQHwlcux0tjZtKFFlLPQJOCgK2eksksb5BfLNAH2CTxvXunLGMNqjpU/JkTmFyKeGkEvjZrysYFtujJcBzlE5D7J20F8B/maZcEGLraSoMfpHuVXd0kU7TPis7ZRGctzzX1qIsqeUDc5SkulPIcifmfWgr2oHnGM6GjJz4rRE257+6pjgT4/20/FdUF8uCCqg3ay6BXpSVgf7MEWnIg7SKTn1WX1apF90nfByzOg7+PtjZ7uOC+aPijsKCnD1n5w4BtqHlYXirNomD30LabHC9glYWusOua4Gq/9cbHS0VX3b3LkBrHJPXVPOCDRt5RgIX7Sn9isQa9q4m/kjtLRx0FTW7VihjKRVa9UMkNLiSvSYxtKaZz6UlpB/x1/PegjMHWIFxo4fHD2IWuOBzDbPEBm9RdLRt+tIYYm1VPPUtlOIgmA9qRs/tQVqgMBrASwqvbSoJnnoLSSKpL0QenIFH9ZB+XrNSgIOz9HMmclUjzZb5n4t6Em61pp6B/i4ExlKL+zDE7aZMYIJGpcW+Y5MpbdOy6/KSdvkWUccZnlJO0Im/UzwyGMdkWSecawwdmzqfNbnF6WY5xjKto9ShylAV3tMxzlq5E06+nQIxY7buWQE3Y4c+PDwUg7o0Ffq6QX7rScJkFeqkLTWkXLCm3Wv9TdHTaTUWyG2n83N6A/XtC8AeeybEV7+ap+3lBGDgGak347vmlDe/rB/aBiQSJ5f8pNyfF4Y8fwhIKuHvB37TGlt1ps+hsWrnh7w3JxWSXWs92r/DvqbclJd7tgGjfXhl3Ks8wpmf2RDRpZpgdKxSSIs6XrRNlEnGBemgsqcG+3gHmM86ey/9Xmk5nd8H6HntuWAcuvorIzRdfq8Jas5Wv8eE7bYGOfPPcYA8tn2Qvq7ecmHtPDQtkd9XvZpTArwIe98q/Wr7WYVbF0rUmtdZdZUydT1Dpfz7OpnczM2h7FImjPacmJb5p5jDO3N5KDbcf7G/VoEmkhpzqHpVz6L8BQLPUeiQcYiiOWI/YKuGqM14VERudWaTbRbXPOvpzriGoC8yr12IeNjo6u2PkWYgRNdBPXcUqh9A7nYh8ic/msy7WnneCimb/N80nXeYprQ4wv322fFaunsa7UgZdkeLVf1Hvn4H5lIIsmtpXM6Ujq2WI5SpyC7hTJV5pEhd0JZGk0R0rfu0rWfZDzvHOU3dqyzBSIAfpHPlPEp2Ri/e331hL49oe4UIP3Nz1xVPs59OX0OpL/NmpdY2vUjOGgOqG5irn5Vcx2Lnb/ah3xO2cJv9Fomwgjs8SLJjP7s+sCUuWEF6MKScvGws9CeMpj1DX7mBc4ILike9h+Ac9UEPOyc8wxzCcwlMJfAXAJzCcwlMJfAXAIPkIDTqrW1tWLkuXaKdeutt5YrrriifOADHyhf//VfX77ru76LCVyduQoSc7vWwYB3bnk90jwC6vo4I9o0FuBWpp5e8/6Et5w9ze/pde+3NDZfb/NL23Sepmu3RzWtND/18L5lPFQ66XnPw/z+lm7Lv9e91vJquW29/O49o80JMnzDG95QXvayl5VXvepVZWHBiXalLa3NvLXX2zL93MxDy/NmHrzWXm952ExTGvPj0SEB218dGw5ZMGh079HB+ZzLz1QJLC4uxv5qG+bHXAJzCcwlMJfAhgQcc9tjs430ugvpjsMCOSbYzzUWnE2jG4b11azeugbtNQEps4zZG/Rc2R5yb5QFcME/pCWfRUrbhVQdBq6WusjrEn0WTk0AXSP/DMnrXRfJXTaOf8gsOUiXRXI+q6c9UQnWSOTSeZ9F7wEMmmrCwji1Yb2Vk1VnHQ4WHecvzid/hB8W7QV11BQ6p5iTy04jDxlUHmaQL//5qxLgb10d5rdXOS0k/Fg29OAzYBmuC9gxuXXy0Mk75KzLyQAOkKdu5UZgpBCEVl0CfQSnn1FRuYquU3kKAGqC41feeyy0V+CMDo6aLo5O6iePkWjjB22junADCckMdWrkJoM6+PME0QI4SBGgCbzKfxU9zysuyLuYTeopi+ny5FGd135XBgIc5I+88RZRlrf43UnoOH6kHkoBeekc5p4OE0+T6mRJdELypwyvKwedLrYegjVKmq1nW1cvA62Fc03Z6ByubcdfBRilouYQj4tKpW6yBTgHTUEAcTgZHUueoKPeh3cURMCDmiPdSps6kiYOPOXKKYtd6t62dyPq+GimcepRBvzQRLTlQjmOvijNJfIuTnDs4lAVYDDDQRxHgiXBs8G6LLnKV2cLz4rIYBRnC+4zLhlUTuEJTjNl5FWbGnrctFBuBACZhFXuOk/GFKJs+J/a6biPTCWkPGWCdEpAkI2gGJ00tedhLUAQKNIRujkMcI72Q5cWsCRdIlJUvynE5E0fHlTUxArGJKMVTL35anH8SrvlU75lHJ0LD7Yg5eN0SU3VY+SozEa2l8Vw6sOUp9QL8uojRVYbJliDNu3aziqv9IzkqENMqtPtXEG+3FLj+9R1aYIzaKQcad8+DnCfmf1ngXxGFgqd/wH0tnLjtzxYKVPCErzznXzhPxn4Bj+6OcNk0tiHoJp2JYOOJnRIwFgUCv7afhs6sX8WgixMTqrwZqEcsf3Kg+/yk0++G5WoG+Cc6xPmpT1pD8vtaA9kSRrIXh2aan/sCzj5hExpfWw91cuk6oX0lU21c+omHAreQGYBbMCH9VWrdZ0mHx1N3Ronqp42ubFvEOvYxz3I0wE4bF4sDSVUK9/a5ETwDLP2X7hCEMlpAZSS8Q7hV5deHXMG1NF6JqoO+iMGZkRlBAiabQBfi4I2HJ0aOpGejajd59OWjBVOQ2s3uGXBnEbWDBdc8Hqj+eEn4KUoKT+pY9oI3ex0WX8KkEPns2ALaKYmUKIvKye1xfGmJxAE7ixwqn7KJLaxS3spp4BZk8Kxibu2qw0jV6DjpwFiy6waIz0cuPLkkcTww79E+dQY6mV1PKIvDzE6Q2zFAJ1crI2e2wGH2ULIX/scWZhP/WK9KjhZ2YS7tBH9UPlJvgInapubrxumScdnB2fvOhAb9mSzGz2iYgDuZowrY9qugrS0PfYRsoYBHbrKp5ERdRVoV3tuU19+KRfYyJFxVKa4bFfw9GePMgKSgJZRfwJ+NGcK417Kgw/JZfBUJ8hom0gfIkliQXZED79zMWBkwFNTAer0sR7jbB2d/a1DHP1E1jMAE9FZ+0fy27jW0STIU+HkhvqnFnDSVoJsbGPHztQ1ymDlyB9byGX6qW3VEAjP/IBV6kQ2m5qerATTdyr4WZ7ghUnLrHOMrAI3SJGx2Bi03ENPtBMOLywsUgT8pps6n7A8skVGtiGFSI9rAjdVg42DMmDRyHc1ZXofhKmHGfhwHK4KYi4yp20y6nibQ9oS2bhdL5sZXTI7/8KqZOFFIHO6JSBccVSr6N4CvC3Yn7RRABOmTJxrJCnkzr0ewAP1kOR550A18LRvdZ17W28HGmgOGRtXyb0AYGGRuVLaMGA3+gYExgxi1ld52H4C1JxNagu0+2P4k/iE676M0OdzIQB4+w8EtNkQEpij/ZQnNTLstILgWsAk3LX+vngSWUA3bcjt5GaASARak0NhjM44j5Cjdv7Wi61IBthi7PKtGPnI4WejY8o31/wiBQHUygba6T/Ypei7eeRJSJu2A/0hY4C2yPkBBzJqqcaOQbMeIrRUupaPJlkUTNughOUBO8scY2L5063YOMcUKJoufcZGJ63AEmckkStk4V/NamXafnIn5Sp3u5pzKLtZplq2NdSMrOhkaTqyDHTNeaCJODJfQH4zwXPUvwIJqYvjclpSnivf8lXrbj09myR8SL9e8KJHm99ysFNctv62Vz28QH3SUSsvuW62EG4+8jt3kj7fIlNnUPLDEVIC54y6R+9D3HnOpKFn9LkpczDneB2AwY543le1ZVFQ3Qj7p9a2Y5SSVke4VKvRJubnxmECb6PP1KO2LfJWcWyzejv5HWs2qmHBtY0FwWXiatqk54/5PaA5ydgNG45FPYCNEwHOApR5CUH589/5Zk85kkTMpZPUETZAbcOSV1C49LrqqRa7vhij7VBGeWnDlxd8YSVGiNpDK8A5ChijVOpFeEBn0hNj7wG1QUNuK8tkihSqbZM55aJNbw/zCrqr8D2E6/w8/YVE/Pc5U2hu7cvVpgS0Zb/V3qQ8XzmANoVCnrrDrwOoZUPSdQMPHwd8hmqPGe2veNRSSg1wrp2H+nKfmpRxp20bZcA/Kcuc/XDCOKaZEeedw0+FFf1odDESsV6e/OeyKbqMvRUYZzrnXpzY1UScJEGHxugk+rP6ISDc1ZIUkE9ptEct3iumbcpNeTDH/woQFvgsD0iCPgbn3qhzDT5ti1yrl/klveZakzomyctRCAGN6hC2YQqgncmW74Wo5xkCGHO7A/ixvIa6w77ZK12vymsFojuXcAzPM408WMa6LPme+thSfE2d7EPyh2bn4YW8qZ9tk2RVD5FzAKDwJ/2Aoi2WMuoQ4fjJGKOekNExKzaoCq4Seph/58C5hymwefK5BOYSmEtgLoG5BOYSmEvg4Ujg5ptvLq985SvLe9/73vLt3/7t5Yd+6IfWs/swe+zYsXLDDTeUa6+9thiB7TGPeUz5rM/6rLJ3796ytLQU4Nndd99dPv7xj5ddu3aVPXv2lI997GPlk5/8ZLZ6feITnxjw3Y033lg+8pGPJLLdxRdfHBpufyrQyPx33HFH8gqUu/rqq8tNN92UrVGf8pSnlHPPPTdlyZg8CfwT8HfNNdeU+++/vxglz3Kkt3Urb6MyGZXv66+/vhhlz/zyft111yXt53zO55SVlZVswSoNeTvllFPKpZdeWs4888zUw0h8H/rQh8qv//qvl7/7u7/LdrXKx3Lc5tY8Z5xxRrnkkktSnnx5/ROf+ER4lJagPHkYjUblnHPOCRDP30bye9aznpVof3feeee6vHbs2BG5KOPt27cz2c5Mfb095l8+syWgTv3N3/xN+aM/+qPom7o8P+YSeLgS0H4J+NBufNM3fVN50YteVLQN82MugbkE5hKYS+A/SiCOl2axur2rw21shAM+BWQYo0Jnpwv5A8MSsUCqg9B17QWcO13movH6uLCKI6BDOJCRgBXmYQGxuQLr4rgruS6aulBq5mavrSnOQX0LSWOpDP9r2nLK0+21SH6XXIf4qFxqru49ll350dWZxBWDuxyDvsvNxErDoYXjAprG1XE5HzclS9k6IliOBgUREABRbqzOmProbG3cj/BZHQrOQnROuCCfLWqZq1pWyiNfFpb51AEg/3w0f/g0KX+81rXu3jehhx+epuHTaEY1/kKN38HTAXLjTESQKodRgE/wj+D7dQU5vMuDWwhOcJzpBBrgsNMRIz8BqVku9+OMho8ADMKHDFi+i9C2m+2iI0Ahc0/eMo32C/eTBl4AX8QNj3wrkJCtuSjbdnWL0bHbxVk+uZSdbkWBdUYqm01wJkifm+NEZqGF8UxY14AKbAf+CVzqjklr0RwTKlKBbLBBG+i0tT0SpYpE6qu+WCXgRor+Ezhp/XvZgg2ngav3hG4x0oP1sTzFMEFHRzILU2ley0t/qDALATRGNOpEUSCo2DzNY7SqbJ+o1lX9yTZJzVY44cl0zEnUYXwTtU7WgayJdBhiXlezKXvQKyfIo5N7qxIdHSNQgM6EJepLtABexjJvfEqkI1lkUca0QdqZ3pHnnwaMYGJ+D+lrOp2iF7QL7NowyIFPDunEEcMnhaUrWkb45KYOXcFCceaLIjKfdcPPstZfBVx7QgnS3kiAyBICd2ZEFFOv1og0tIpDRGeLbbOI83eAcGbSAXimkrgdnbbGiHmURiqywmMX5xyS45cH6dEjG0q7lTYl0lrrVNNXXF1ltob9n5OsSo/UBNKSt1BJ9a10gt3YLwAoTnukpJF6RGXooH+qfCK6dQ/XsicnQYeoKNR7BQcyFq4sjdkGcYTsjfTSXUS9AY7SRywvYEj4DeAQ/XPrIYSe/4pf8VSgGCIgg9vqpu/BTw6BGDhSjVrp/QAOaJTcpa4mU8fUPJSUNpKqF3R0K22cnOmblGMEP5xZ9iO3zbLcgDB1fvHPI1bWNobGFPlOp/Rtdck+n3Ls16SWAU6dtrF7+SOgDDkIgEIO2hL7jY5HE3fkg/YVWCeQouohROgb9jQxItFpQFuChIx2aeQlgVnaxzXbnPqoHX3y92MjKh0j7nSMusWAIIfZIiz1hQ9rJZBCG9NX/shUZ7I2RRkgk74AOMpx6+S1OJ/VFeyswtVuyCaDyRCFnA4E/ihu6kcDCgLL4UcUkC/ohhGqkhE+Y3NNhOBihihHyBKskETwjBaatoQH/qevWYZy1nkcrkV8do5wjboa6UMQp47r5rAukx4R6aBshJKudo682km3Ja9jD+kZl6d4jpWp9wbIpsv9GuGO+4wHs8UxcnDklL4j54B+Q89NlexZ9EH6teO24Hj579HRgqmAj2F/VG0NW3XGdlIPcSaoEfVVr2rZferUEexBRY1uqTM9/VFbqK7ahgpJOXGqKR2BqtTRiHI1WpR9dxtmoY4XsAPxmp4MyWc01kn/WH527dvIT/ufeqR/qDdpQMrFniRqmzAfapaya1vaHiYTuBR5Wi9Ot1hUd/u0v1stCi4QPiZV/6u5GhNtk4DUKTIXOCmfdYaDEYVm7B1p7I8Bc1isvynYKCxGbHOGo1HrdrZxD7AJ8kykKzKvjemD6HWvy1qeYEJVhvKVOx8POLo0lrTGyFEwlbOjPnWXrdTW9jJvw4MU8pV6ZMtRALsBjFsfKGttBLb00BPB/YLjqXXsb2yUyjE7FtkswneHSHQzwHqRPGVV2goUQA0gkR6Md41IYwQcbyJX5ylTGKrb4VEeTv4WpGUFbQOB6EZtsvQe47BgfbtojZ6HntvB0K3IHj0VzDmh7hP4pur89lZj1yAYcIoV4XoRPAEzsiNA2nmC2m8fdY4nPs24Tyuh2C/boKmNDLCbamjHx7Sj+Z1NC1N0DusZQDj9STs5mwhwwG5gR6eM26s0hMEgF9H7pdEKdeI2vwJyoMzhYAVgr/qFTcr8D5oxvMrKOS59SdtiOeTVZglvduvcGRW0HlU7neE7HvhLgIz5PRoBQMP6TtE/um3mX04VnTv3kUe6pLw58faTdp1ha7Sr2icBNwUQb+Z32EUqzfyhbVOvmwmZxXbVduNCDvU4wMUQxl4SuVa72m6X6H0BQhOfWxhzu4xDvR5AK+1IQ6MS8i9XpGffF0wiH/KjXjih8VNFMM2mQzpa6qkRUDNHRt+tpEYAWoLKOg0qpwIL0XHuV1KtQKgf+pLiN9FGnI2U7c+0ADZ5gqxc06r9l7awGOYZtkLlhXSQjV1Vj/khOKiHjc9Qt4l+Gj4aXsfhGlXR+nJkbMGuUJvIIRchbJ+1fpZnP4rs1RuS2e+pt/wZBTeVVBresyN6yGR7fIosq8wpwkqlMsz9SKMo5aofmXqL3pSXgpBbd7nqjrJv5qDifMbYRbmrYw28QEG552iNdtq0XoosHOfT7rV+3ol8IwPkCh/qVI3WZetYMWkrF3/bnlbK68pBY+vpeF/lGJmRzOp1sTVupzsxipZ21tzqCtcFyidMJc97As+dJqoz2h77lBQ6/SNkYEzGxo6ZgwY4Zz/zuUQWPP1Khg7lWKbxKZ0LZayzD8KnbeOcyrGK5NAhn4ktwwvaWD6m9C0je9rWXk8SbG+X9uii/wHvKnzo12cqWgC6shSi0Kc08jov9KRvwGQimtm/nS9D2Ge0FGgJ5MmzGOVp43Kvlpy/AuSsrtcTtRJ9V//GMGndKSB1N7Ka868AtOCxm8iL1sf0tT6kNgOn2uanfyk0+sIPCebkEpmMeDzlGcc27naZ40N3kmdlbBDl+3zdZdzKM7HzM+5bNY9cswwu1JKkaVkC3gW0ccMXU8yXXOqmreD8BzrKLde91hBJo8G7dcduCpyNripb9HR9XDS5hYZvbJ1FWR9tuM84/ua2L0r0iDLcdR6mMtHSPo85v/BQWyrwkR+5hL2XrtUIT+q+tkU7UnmfASymMH7Xj7bu2ixJ+Fsa9jV/+CKf+lIjJ2LlmIchWHiVHmldL8Lu5NkvV9TP2t8rtVx82H+wQU0tH3bWeYa5BOYSmEtgLoG5BOYSmEtgLoH/TAIC5374h3+4vO997yvf9m3fFuCcE2wn54LSBAG97nWvC9DM6EeCg0477bTygz/4g+WrvuqrQv5tb3tb+bEf+7Gybdu2RGQTBCfwTBoCxJ797GeXN77xjQGWmcFF7K/+6q8uP/VTP5XJo/cEqAnGc8tYI73Jg5G7Hv/4x5fv/u7vLi9+8YsTEU6e3vrWt5Zf/uVfznazgveMonfqqaeWb/zGbyzf8A3fUE466aSA6uRJwN+TnvSk8p73vCf5raNgFHk2mpzANaPqSUOgirwKHhS49rM/+7Plz/7szwK+E5D3whe+sHzf931fAHU/8zM/U77yK78ysjFSnXmV4RVXXBHef+EXfiH5fvzHf7zcfvvt5XGPe1x4UEZG9ZNPQVY//dM/HRCe9ZCGEe2UjWnOO++80PrP2nB+/zNDAgcPHiy/9Eu/VP7wD/8wQEn1eX7MJfBIJKCd1WZ9zdd8TbaIbrfQfiS05nnmEphLYC6B/0kSaJcInSe2xwOusYg5wQE1HJ0oqzjpTjCXXMvKZo+3vHFGub0ei7ou7rv10xL3jIIRTxpLni7SC54Y4gQUwNNhAX6AoyMAAxdVdY6xmKpTIQvSpBkCtDObzhLTDfEYDPGg+2+RlXiwCiziMq/F0SAYL1vEsdJKIJkywIHSY3F1vCjgyCVXoVO6EHWL+1vHru5pN8cCAkAZPRwUiUjHmqt1E8QQpxBMVNCLi7EuHcuToBVOPusb7i4oUx785E13xOjasA6ISNSKQM3//tah6oK8l7PAzDW/JS3frFeAOIIQyISrN1uadpnT9nGcBXziPRwqOpmzRRsODSP0uERtNC0jmRhtIGAZnASCTuRHB4PlziZEZMDRreyzoK6zI7XTK4KjREeQDhccPl2jhOn4YdG840K1a9VIyAXxgFisOxU2cpHyyXZoOJR1ojMLJ2IATgXadhGSC3g5FqDfN1qCbS9wDTCEW4yu0ZZr8CQQpI++GQXF5xt9Yh10bNEtGZUS/A9Z9J9RmcjRRXnqYVSWvt5BTl09uvF1g464XihjAaBZtjqaHUeiOJ2ic9AUjIHMFMyEOqpPtiNaH6c2X6g3C/bUDrcO+gVYlLrEuQ3psGR6Ki+4LfXCaWIrBKSC02LKG/16suwjI1RpBgBpCeSR4JM4VEE0ZAvftBOJVCB4iuOJep7AayQIaCs1GoxxxQ+RnVsc9YlUgTNBR7enXAs8HCC/juAXnApUKfLNzTQe+qq8AeQNYVe90Bln25lWZ0To+FfRRU5+1v5nX1fu2WKROvWtlIK2YeDVaIcrg7WyBoBQmgPquQjYQTASguXKuKwOsCGLQkkE4ApIov9Rlm3ofXUQlaRPsbUb9dNhE6cht1tnqLqtjJGoAq0yMxNAgRF9IUVx19pYH1MqzyE8TgDE2fcE9A3QTY8JsprQDrgVYysCFMZB24ExeiB2jsgY9LNu7yg+vkMSI+LLLmS2Iw7eFXS+i4N+K8C53ghnD7yMesv0U9oJMJ+qAVsUJOxYpyE/6OiJWggLOgAFL000YvQ/dWxBpzfOuGyVq2jQndUF+KTBjF40UJm4bhPg94qcrGfaD50RDNM6xWILKNfN8uQleo8zz+0Gx0Y/UQm43oGQaZWKkYoE0+l40yk+HAkE4P4AQCyOyER+jJeexLKNHXXLrD76pWN0hq7K9qwXiDJ1ob46X8OXoEThGNBS6shzYDuSQzBdwBv0qylAwRnbzuGOjK1DtbETbBiLvguK6WMvLDPRZcZY+QgAR65bUsJ/j7FEEEwXW9NNVDban3bqAQ7oUhflI8jELQuNdNRDJxYnW6BLDewnbHkmAGNBOwr/XfqoMhkNzQOdZYGVgoNopxXkhhxsgPgaoe13wT1u/WjfFPgZZ7FycVxTH9H3bIfrWAaffUEJ6gKd0zPCVTb8VzuFkXaxn11gOYJPum55BlK8oy0jY+wG+YYA50RfyvM05Qo0VNrQoQzBbdm2FPCtY45g5kWd2tgnyxFMIVhyivFeQz8EzjlydtHJAfS057XhtUdEcYOwgFz1RyBsn/sCkdbQafuw2/tmTCGb40WPunugNvDMNRytghoTMZHKqqeJqGofFaSAvsYw+JP8btfaZdtRAXc0GLyixyhxbypwjrLgN/pPWjmdYWe6tKlRGcd9tiqLMx0HNfKrkXCsh/bEscUcVEgZ6KyWljKBKaP1aAMpkPtekiFk4zhMrgll8BU5kYtTO6U9miAbr7vFMsykf0nZMXNiHSIP2hd+EinK/HJC+8nLhD6oHXZsVO49tmTX/k3c5hLwhDJyDOpCp8v8YG1IP4DXruD1ESfFOg9CHJETzYR8yGoeZG9/0rZIo4MeDiYACmFYTIojmtuhCjZGw5LfeZKbFeuk72JTlYuzK7kTyGV/62L7Y9uxZQIa1iCinswECQC6ULpb1F3G+Cn6O6ZvOz758sIEQEYv8xTb2bma/RIdZ345YXzruYUwhnJlDbCB4CDstP2LCkG/9p0p7WmUKkqkK1AnbIQMBuxHfWPflC99X7CDEYnHlDOibyD+5FNfnLf1aRftAmIgn/rGbJL+kZFRWwLvPfXftLzsa1EnaAvgE0ijz9iAPBGmdRsv1D4e4Bw670g4QL/7yEl55bChBHJNhd6pU/ReItCOegv57RarC6tHsX+OyYDqkJ9bia8sHqcMbA1Ue7ShsqsgNHjW3mmX0BVfuvDFF5ot+UqXcpBBxqGkyQwZOs5lhmogclPHq+JQDe5RD8A/gtb6zOGGlDtC5guOE6hnokShQx0ncgjOaHgTEeHoWejRltpcT6vNRx2L0KwucrGMAEgEpaQ9lAkHN2ZGfzVXo8gd5gqZM9ofSFbBKOojpwYGbWv7qPdzpADSO/7RlyrgibRNn04kMO2OzLeZwkdyU7TjFWMQc0RfMpnQvm5dWHxxAL3rL2Nf0K8J8/fMpWhTs+e5SHuCTmqLZaeWwF3bHTlZDV8a11amObRL8KvuTwGUWqX+wDk0fQZ+HYe0/ZmLk0d7MVG+EHZMbp/rIsjUW/1HvzhqBFl49XCsst/E5npNYQrEUd7hPuXZdwKk59PofuPMOavsLUsQo6C9aew3fUzj5+FHyvcH6RuaXhL0oz5iUNADxlno1KjEyCH7cdJO8GFrTdEpAV19AT8Zi6izLxYMsHukcI6uZOEkJURPFKQMqGgxfpZFewnooi1rjson4iOlY6r6bTbkj2ycZCjvOm54PdzwxcO89EfklQ4Q4Jw0HI8cs2hXBrwucy7tzRSA92SEXpLNx4wuvAuc6wkkRl4+54yok6LTNmR6yPdO/zAqyjjGfBf4LKwBZqetIwfaQ3FYvR4q1gGgPAJY6ty4w1zU5yT1aMzz5ATCfUBrjsXaMyuqDlmggE35suoTxnP1t6duUg/5UV49tysVOMdV+4xj4VR55dS6c408zq185jRSm3OWhDeDyITQ6gLOOn3myeivEeccaSFC+7tuIOF0B+5RPxsC3i3f8cTxosdY6jNPj7M+i1M+jAcoR1l9FKEPCGyNNYwZ41qA5ORVR5CCRTV6oo1RL/jkqPbDsUMJkMgOR/0GfdJQH+e3Y8tNv6vzg47b9cq7Y2KiAsKHz3/oW9SNMSdjT/qW7VBJuyVp5qxGqUtZ9mvGdL47N6nPXbQfYxzNzKkeOo9FXs5prAFzVO3wiJcJZsx/8zJB6kMfdl5PKkn7xbp11WXrBv0hdms2RkfREa91+tivvusEtf86l6Ig+pw2BZ741+ceF3NN2n0NDcfM0HXOI7UX6QfwDwhwNt3JTdM4YtpHZAR9QV6KNj+55HfbWLCj+mJET9MtLS5zMp7BhzLv0PbcSAafozNPIl0IceeRHnPg3COV3DzfXAJzCcwlMJfAXAJzCcwl8GlI4KGAc4cPHy5//Md/XK688sqyc+fO8sVf/MXlvPPOC2jsL/7iL7J94Ktf/eryJV/yJeXP//zPyw/8wA+UI0eOlCc/+cnl+c9/fsBvAsME0QlMM1Lb05/+9IDD/uqv/io0f/7nf7589md/dnnTm94UAJlgEfMbYUmQ3jve8Y7ywQ9+MPkEqhkNTiDfj/7oj5bdu3cn3UUXXZRIdm9/+9sDfhNw9opXvCIR8ASt/fVf/3V4NTLcM57xjOSRH2ncdddd5Wu/9msDrDPCnfX46Ec/Wl7ykpeUn/zJnyzvete7sk3ru9/97nL55ZeXb/7mb049TCfvnw5w7id+4idSD4FxggAFElo/wXavfe1rA+z7/M///NRRwOBf/uVfFtvkpS99aXnNa14TMOKn0YzzJJ8BEjhw4EDAmEYxNDqhOjM/5hJ4JBLYv39/toe+8MILy8tf/vJy9tln1wf2R0JsnmcugbkE5hL4HyQBHRyeLmK24LnWwdL+PnrkWLnqI/+e6MX3HdhXDuOodDF8iYVTo6BNWCg1OgAryvh8WLh0AZ/FVZ2MXvZwidqFYwESLjH7PQvILsGazgs4klwId0GcdVOWgnEwyF/j0I0zBGe1jkUXzicsnnqyfE9KwWE4jSzXBVXWdAVPubDtQi1csWaLC5BF5jjxXajl1oA6GIVEnt1iVoer69AzoubFj0KdRtCLQxpeWuCcfOmS0RFZt4bRKQ6HypF/On/ES1h2TheC+e4COH+55oJ1lUddtq8yEBikk12Hgby63C24r4/jQ+CcUb6mOJ8r4ALKOhLgRQeF20UJbHH9WgCCUah9S7tPZAZ5chtE6wbsBA50mFlf08oZwA+dHf7GiSh4zroOkIuyK7xVb/Q4AQs6G3T2W4tJFsIpV6AE3+OMaBzQgtAEUuA1KVvw5Czh+AloJtsoupguo7Qf3p0h7ag7rIsuDQQVQL2CASmFvFtwwgPXYBHdKFA0nDwClHBxvqfOUXffjnfxXN2Qf52GYht0FrkVqJGWerzR3+sZ6YZ64ESpwDlKoxhrGicb+XUeznQqontTQGq6vnU+dGkcJAV/APXkg/YeIZOhdVdppcBHn3rgcqAM6eJACgAMqSEblVsfoFGsBOUEtJM6C+bE+Qs4wFiDU/gb0k5jnLA99lnt0aY9HPV95NUBjDSGnyEsADmibJnWsaSTW1BY7TtuaVi3bqNKlKlji2wBp9Jk0fHoMvfi37Tqaos6EYesOqFDGukYxQt5qwE6DnWGL+EQ0vkefQA8MSHdCo6NIQ4ONLEsrKFDOGXSLy0ffTBS5BqKqLw6yGmGU3mADKvLpmqWvBmZx96iUupMTCQff0bK0OJuWsyqk8a6jWh/dc4+KVfWzYg2HfTH3jSmTWc6p5S3QArBRqSb6ITUeRWKpMNhnS3j4LNP/Tu0u1sI94mm1+0dr30bgE6HfjFCsKvoidGjtqFwSwEXU0ecgUNADgJ27NeLgCUEp1hDIzHJbwAF8OUWQ3AAqIB+pYPROtNQgrWMZiZYaQTQauQ2ZchtifxLABZU3FW3J8ORp/3RyviPkvgb6XFN/VY+8I/+IFrkXp1PAmWMCFaBc8hYnyY0E+FJW0biLp9UnQhWIiHgZ4BzkL6GisURrI7Lx4BrAjQsfxEv8BiA5xS71SOP9THaqODICXVwq1Xtm8A5IzzJuRYg/R59FuSgPo4E5qBXPVAEAa1AR0Cu7Yzw4JPIGFDoCUQ0zKidn+s0v4zbPXEaw6iReQTkBehiNA3oxVDCA7eH2JLYfvreAJAumouhBPRF3zOPkTuF5mizutAa0dZjAH1GL1N37Qd9gZCUH1AXBQv8FIzRg5NlAABAAElEQVRrm5c4l7EJ3B/QrrLpVq8Cwqk8dNFRKrxAVLaFMWVzb4Kzdwz9AJkpT8lmzIM7W7iLbmQLWfRG2QlKF0A1g0bsH87TblBLjg3kJwplH3kNqLdAlRnAS6VuWwqCMorZAF0QmGX/rlG3aFyc6jP4iUOd9F34F6ioDlXAGc7NjBnYGtpacEodJx3vaBbo6RSf0gfdFkz9gHpO68ndhl/7If0VJbSV1aVEVIXP9JXwilVF3tqwCQ5infAL4Y+2wjaNkPUCshxQP53V6sqUtjMiVIfxcBHA8IR6Tmg7qh2+UF0OoUnoGp/1UCOREfln0lLfLJ8rXfSL1s13hFJ5Q/9sw9QFAJF9VJBDgA5cHPNlLOiV6ymPa32+CBQQsGu9jcZln+2g6wK16P2c9ghkh01VT3UyG2FR7hbUXz6nRLh0XiH4V3mTPG0lP3Ew25GolhHNjGtGYWUV2Rlp0G6jA96oQZlnNe3Vwy72AV0JqBGwNUKG9qlFyukhPwFzbonop+3ltrSCaIxKpF1wPiGIwabPCxBcDKiBsTjzO3KqA0p4mTRG/RW2OSKKqvOfSSGyKlsRDwSkCcwCWCfw1vGRBuE+tihoavIhv+gUOinoMGMcBScaHbyZ2rEmwBN0VltEkdQb3ePkQj7tk7bshH6Udtc+B9AACIAyjepUwd4Kzb7urFZ7Dh0uCYgTjOucqceYpoaMiGI8Yr7m+LWI0qqHwrYnzCsds+2TIB+plxYPruDB9p1SnnMDeVeucEj7Mp9lvjpChoVtwBOZl7k41ji2SYCStmYI4EFb1kEWfW2iNog06ph1dH4nwHAAUGIR4A7axb3j6AOR6mhHRE0abImypO+p70AeoyeOL9kOD3okjSwF+3aA9Tv/GgKGHAI2DoCQujr3i11CpnahNe7PjNJJZgFmbpU7EPhDWUOiRU9QMufpPXUSPauAPxmi3eG0Rj+ClcamKpe0RRSZMqDmi+b24x5zrbzsYQ2ZoziuVaAkjNsJbftIBl6Q18YB3zmg3nYoGyaHAvRL/V3BOVgKXtRGTMy5aXv6RcG+aBt9TrJ1smU7OmCJ6yXJs1vUOzfhojMA7YqAKhqXcQu7gM4LCBQALuhXPkcAgHw1xJccBswvxvSdsZ0GOXbTT2k39RP5CjpP1DHZtj3awwIDahE8w3dsRaJc+V3ZhtPmGpdqWgEsbQ2YnzAHVT5DX+hhTipgzy047clT5nWZj8OJ40jHesIbylPLU1j2YcqiZchDPfnr8yGZ0VN+C9A3PfOORfrCQHvFCXVoqo+CsJDbeCu2AbC0wFufPRgz7TFVP2sfV+ctg4aodY0dcbzkQNcCnDOFNkwe0B9lECCRQGxo8+RBex7jhCZ0KrCwrU/VxNRPGaV+0oAeum3vHtDntY3jMZFCaZeFReuMzTM6KrZBOyofDGfwbqRwbD5zpVHAetYH+ybIDE7yfAJ/eeaE54DV6UvOkb2m/GID5EOArwBMQLcCoXhI1IRWXRWERLsZ/W6GTNMPkLkALOdL6lhsuvN0bRE0BOkJWLT9gGeTTl1v9QI5RD7aUKKgznbDpXMJojR3D6En6Ao5FYR2gRlP6DtnsnnSbrCMqcIW8KzAXAtB04Mom/GtbRftWIY18mWeAL2MpVBwnDFHfRmPWZsgN+zOhBfGnK/7zOeYK9i7x3xHQo7xPm/VSG/oKTmqXlDH9BPaRqHRJj2fJ50zoZe2vmlRWdLRJwDVCS4T7JVt2ps+LgguQDLsbOjCX6I8qo+xPbXtjbBm0QLNCiC5etjvbEfqxHXXWASe+wwvzQqU1TYy7+OFAceMGYCz9FkJyANycYzkohegxTfaTF32JRLeiaF62GEj8DLmFZ5xnLMmmihz4r51Vm/QjTFjls/WA8Y2m22Kbjnv7WMPfFZwHsmADY/WScCttJ1H7aJYbJN1hHZlB3nZl8KZfz2pK7xZLzLxnycEAMhn7j2zXHzJxex8dV7ZtfMksttupPE/2eTNsrhcafPxSI45cO6RSG2eZy6BuQTmEphLYC6BuQTmEvg0JfBgwDkf4N3WVNCX264aFU3gnIeAjt/5nd8pAtm+8Au/MJHfBK0JnDMq0utf//py+eWXB5RmVLjf+I3fCDjOT4FjRl8zzd/+7d8GGPZ5n/d568A5wSFGefOaD0yC2Iwa57aoguAEyX3/939/toE18puR4zwELL3lLW9J3he84AWhL5/mcZvV5z73ucUIcOZ369Z/+qd/SgS8JzzhCeV7vud7sr2rEef+9E//NNHtvuALvqD85m/+ZiLe/dqv/Vqi4X3Hd3xHedWrXpWyfvd3f7f84i/+4qcNnPuXf/mXbPWqHM8///yACn/rt36r/MEf/MF6dDm3ufVQlkbzM6qf/D/vec/L9fmfz3wJtMC5q666KsA5dXF+zCXwSCQgiNbomEaqnAPnHokE53nmEphL4H+qBB4MOOe81UXgCsYp5Z477y6//8a3lKuvurocGx7HMcaiM4umyyxsC3IaA44w4lqHyDVjABNrLmCyoKmjc4FPF7x1jrgIqhOO1X+WRl30Z9EVZ5DOBkE6OjzjZHJRmUMHgfl0Jukgb53pWfA1D1d1bgeixMKub8RXQBvrviyk6ux00dVVYtaaucjiso5H0rkdmZQ7vHghsOX4kaNlvLpSTtqxrWwB9EOoMtlkzRmXoQAPFt8D+mOBvm61KIfQgC7VhJaL2ZVbOfefjhvXceMMYmU3DgVSupXVVGet3oHwyQI0de/h2BJYQbZK08wkMIJJgFasQ1svtypd40x0GO9Dz8gYOsZ6yF/HqyAxI1PodJviFFtZWSvD1WHZwosn27YiaxfeWdhWOon8pDMEB54RiLLgzWK6jss+271Z5swtgTpbKU2Zs1DuojgyFKimgyfRS3D+yIvO24kgH+onAJBLZRnwyjJyts5rOE9G2YcSwlBzpXvi2/Is0AsOW8Cpr0Tdrm+oA4186pqfAvWmOo7hbzLFicUCvE67CY6PE8eJdj1EG3C4bduyFDlEyVjwn0LbCEU1PZGYaBmBgjq0Ev1L+dkG6Eb0TnCVzmScCBOANFMdHzquYNe3/LO4H3AdqgKfbkXsQr4RwgT4CHRcQPekFR2hHoIm1Pm6TZnaVx3jRnlRWQRR9ZFhT7ACzropjqcOUaWmk4M4KUbl6Inj5ciJtbJz90k4Eti4lWyqt4AkgU41KgGaiLxtE/UugAr4rY50Wk/dh4eA6bguf1ZHOyDYIyBaalev6VJno2DaRudrT8AYeqOuAzvC5wkYgMqJXwiQhLawjmtdIo/3iMZGn7bNBzyDuqWiEWS6OJYm6OQQPtQhXGMUbnvjBloblcPHjpXFbTj0iUxeIzuph9aJOnJSEHwiN8rxe9SHdlP2iY5EEnVLAF8F2wn0wUFHW3d19umwoV0DMCTSRxfwnO0xIUqefTxbRFK3EQ1lX4Dh1FZBq5tG+urrlMJxFEcm2+cZQWkVJVcqW3CcLwAYEwI2WtpaVmkHPKPpv32Ac8fXjpUTOL7dym7b4nZAV/KlvG0XI3eibzE8Vg+7gD4YrS8ReUg48h56s0Bdlty2ENmsoD9DgDkz+NfMSUtZRNm4YiQkt/20re1DgtfgCp2BEnUfRl6CI9R/run8t7+pr9QZYnGgjrBD2rg4VLG79hej2hhFRVEZ1ebE8aNl5TgAwq0nlyUi7unMNJJcdW5aJ2SMw3KEPkOZX9htnWmU6ziRKHdx4kMf/sbIW5CMUUUEGtXW0NlKH0FvjOpRHc7UE0PsOMJABB1suI43+DXaCqxmrBHs1qH9+kSqUlYg5pChLUf/40K2DQQ5ErAlvy1fx6AAa4Ezx9km+SA6umX71rK0hfJwDAoS24KOGYFVgMQIOQhLDmiL9tVeGs3JPiLQzYip9kfBzyOBc+ovjsW+NNDJJZEr1huQ5oizRqyTF+oifXgRgNYF+GIeo770OoejL2WGjZ5op6kPNIxAlX5th9bJTfQZZSwwyggjMU7RDNMhZ0GN2MnDJygbHdq2bXtZEuSjLUP+SBR+4c/8OP2NKKez36heVI0y7YtUl9ojHdLW+ilc7bsOVK/Z/zKOqs/qNTwJhjQqm7a9H1AEVkbAlBF54CUgFNKp2gHAU0dtCgTpO8xB6A5jjJEzggH2ZgJYZlJ2kG8HbQgfRIK0LqvY0BMnABVS1tYtyJ25jCnJDaX6r/Jr26nnwpqsFTaVdJEr5Tm/sC7Z5l2ZNHo0BvAooGoAaGlhTDspDcAcawvVPkZ+0Kq4L/oDsojdAXSlPewArDC6TrUN2BH7GGOcQHIBEto12U2krsgbHQWsJBgw0UQB7h4+vFKWB7vKtqXdgGuwJNwjcBEyR4/RmVV0VkDXGCCKW45muz2IOjYIjBLQ1APAKeBY0NwQ8LgSWABgMQBgMSHPaIFPdAZuqj2h7wkmcTtA50RGpRQ8ZF16lJfohpEnNo9/1lZARH9IW9COE8aN0cK2RJmalkPo7nFsCuC98Q6GCPLQDxfQTSuvfT904hDRdWjh3TsBLEDNaLDqCXTt3QFeky7AVXSlRgpW3dRF5znW1XKth7YOGdeaIA84zCnMjfYUOIL8tH3AZMnoGG1Zjou0IfmHzpmQtcC5gaBh6j1hTjyBNyYRnNqYCvoV/Cbgv8s8syegEzn1kaX8CPJZQVePHV8tTGd4OXo3kXboO8zHBBINmYd0sK2JRASIYYEy6SWx7fZ7we3aFwHX2gdHBSM+i60KQJq+qe4tEQFvaShYRPA0oDn7vnWRX/qiLxZ0lTsy7BrBk3w14hxfkYUmxfmS4LweoA2sLLYJMCUgfwYQhUud7RfS4pMMY6MwYXvHREM8gR3tI8/ty7v53CJ+EBtH3bBx9oMeeqZl1oxXO8UnMrPs2B3mZraJQCDbom7VyvzX+WAD4HJ+altPATVr37o+u1CfmpG80A9BbGvlEyPCV+exuW5hmw/rlMp7kTSWi94EoKo9UB8EnjACaPcPHTycaE47d51U+gsAW5KfOnCoa9mmE5vo2FyfcQQhC5bm03EVuWjRwlOqbhnKF1uNnLxRgU3YKaPXGXURfmrULHQ320ky5kG99lL550A/QifAFp8fvE85ObxHOc6Psdk5SKedyDyPe8pd23icceLYsdWyc8fusmWbWzGq90Z7hUfSCEKrICzth7pm+Z7YT+YBDMCcltdcd5yRN9vNlw74tN/3sOczPo1CFhCiEbX6zEfJH+DceBflLKM/qhz9DJp88OmzCnnTJpQU2TgG+YyJTlquvNLvrFRAqrYjfVkg2fFj9AnASEuLuwC6IcUAiqBHyQFUR67y70Gpzo/y1d7geGn7OI405WRuZ36kh20Q6DiOsSQT5Vt352/tSyLOlbS1zuMFUnfoz3lOhn9Fqb3I/AAWHBd8CSzPl9CpXJHIgZmji733CBgqICxeVmHsO3xkP8ESmNNsxbIBUBLKmu1W6bNd2sB6TWhTwWeJ6s2zUI2irX33ubzy5/O5/TKgLvppp3MSec9k+mv+fdjofcgXWWMKHbYHzNEWAEq7pbDxe9s2yjOYfRZOBOLLveNDD9rJzBXtW14OwKZYK226cqNQyJsH+QjoBbA5wpYePnof/qmlsrxlOwJwPixwC/tjaGf0fAZI3MiWgsdqP1B5YDK6oe0TdAcnPkPkmVEZIZ/0Qa6jM77Epk12/QSlteW5rv3jZQb4CMCQl7acHWaccV5hO/IvB7KxfwWsGxCfwDl0IhHavd7UiVSC/rwu4FE+RyDfjh9FErOdZStztp6RmgNQsx+qffaJ9tAeWw/aCD0PCJnvjn/2d69NkU2Ad4mUuC16mjmUVaUtpvTHPnNXIxX6AoO2u8/ag6N6bWDkigycB1lwXsqb0Ect0+1oma8gTHi0/p7KLsRJ63dOQZPY1zFj5IiXA3bt3l6e+5znlcufd3m58MLHhdYU0HDH8S9lUJSV9KyqzpeHfzDPRWLzYy6BuQTmEphLYC6BuQTmEphL4P8TCTwYcM5tVv/+7/8+W5bu2LEj25MKasvEGi4+/OEPlx/5kR9JdDi3phTg5vamz3zmM7NV5RlnnBGA2e/93u9lK1Mjqgk283CrVUFpgu/cIvYrvuIrApxrAXdvfvObs11pm/YNb3hD0r7sZS8rj33sYwNscytWAXWnn346k1UWPVjgNzLdb//2b5dTTjkloDYjupnmAx/4QPmWb/mW8KtDVUDeoUOHyn333Rd+rKuAJ3//wz/8Q/mTP/mTgNzcOtatYt3OVZCbIBaj1BlV7+EA5wS/CUL8zu/8zkTCk9/rr78+29TK87d+67cmCp1RNuTvhhtuSH0FGH7v935vyg2j8z+f8RKYA+c+45voUcPgHDj3qGmqOaNzCcwl8N8ggXaZsAXKtfPTuihcyk033FR+5rVXlttuu72cftbesufMk8uWZSJKsUDZLvu6TBxADo4NI4ixpIxDECcii9kdFpLXHVE6pFhcdkmVNdE4wHUGx1GgQ1PnQJw4zcon6eNMZAHbReUayQtHCpldLDdmyRpvwa8JnGPeJ4VOHC249aGr29CFXvP2cFTMSLfKWvIa3jid9CMc6mO8k7fedHM5emhfecIljyunnLSLNV9qAH230BuxODx1kZjVVPFerp+zFMwpb813PqklPOi4UBZw4sI681TBIy4KV6eCi9IsNkNEf4l1rVtp4UQWOBVHXNaLQ1vgX4Bz0BOkpPN1iPxW8Hy6fadt4BakrDLDq7VnYZo0OmrjBMIReRTn1p133lsO7T9c9p6+lze2Twn4J44HuJ7gkBdY5lZ6LmQLIHAx3uhvxhAx+g0lUSedBTqHrIuOHOFTtgCuPcp0i1UdRWOcXRN0YA1awEpcNcehLZBABwttRnnW3TKMWEBNkAlvu+OQGExPEKFsP45OHAX9naTFKU6KZZwAw+M4IVnc7y3ztjmRtEc4tnhqoZ44n1hAv/XWO8uBQwfL9p07ymPOPbdsX6JOtNeY8gWd2GI6J9UaCocvFvnhFaWo3/mUfx39ReAcgBQdE4mMoBPZf2TT8eo/QQNuJTXBuT/mbXsjMagzRt7rC6CSjhxCXsc1WdOwHZ0dowPQdbslbm7ZgVMLJ8oIENDqMbYvQod6e2jYM6pTfnKIMtfKTbfdVm6/+97ymAsfW04+5QxYB/iljlOxrv0KntC8OM82Iu/Q19AT9VT+1TegApzIF/6tq9Al81fnilKi/UnjlksTgDiTzjZOnS+CzsyHLOUbB5RgT511glXiENK5T3scx1k2BSy5nQgPy/BplKIpW86NiNJgT54IREJH+kSA0gG8Sh+84857yk0331bOPPf8ctb55xNhxHap7dUCr8iKRNVBPrUN/kr7QQ9+jLw2AkQW4Bx8JWob+tsVDEZb9QQwARLItrBGVOLEHLB1LaA1QCQdQUk6Du2H6kYcO+gLUcd6OIIGOIY7RK6Y0lYdnP9GmpmxHesqADpp9lcPlwHb53UWcQhuO72cwCmuA3wRsaIx5ZP33VHuO3B/WQac+5gzzyo7tuCUgn/txIj6BLiAbbCf6YISfFyBc9oW9Ug6cIhtFZ+gLTXC0AiwTsCN0HDLKtvEvEZ0cysvt/RSdpEn7RenMmkE8ArGE6zntmqCw6ZsN9vXaTg5UdZWAa8g4+UtOP7QUSMcDgGFCMwVoFAAA4wFh2lDqOdtt95e7r/3YNl72gXljFPPLlsAKqXP6fjUoQjdMWUNQY/p5I6DFnlXQBDqhRy0N8rE1q1bwgoqcUtWbAj1tb8KxDCaaHSAOmh3xUYZ3UZQWQ+gj9FVqGqAYF4Ww2Gkmh760Qd40Fk7XibYFNWsu7gbMPhWxInj//gB/P+09wK/B9uwH4JHAIkhibvuvbtcc93HymPOP7ec85iz6Z82Ae0OaitRzhCJWxEakwuJchPrqJ7SbxAabQmYHASLfI/ol4k4B9+OlY4ty9gxQcY6oYeDFcY2+NGBSDsGSEyBbkXcQW97RsajjaPTODozDBjZB+dvl77WmQAGpw21rYMlnalYrck9ADmI5gUoqo9ju9sTZKceoRMYN8GIB1nXuZm+uIr+X/S4i8suQKzq/AyQTk+bbb9AxtNpk5dt5wL2jGbSh6ib/GtPaBrsnLaCk5JsD22O99aBc7StY8YQPTXqmABSgSFqQJ/+0we4UHOj8KRFTNQJQaNH3fGhMl2rAKrONsYFwKoDtuPuru4HLM54MzitDJZd32LMgX9BcvfevY+XLu+n3Xvl7DPPKLt3bqffS9VZSbikfRy/1XzBI+g3HNd/jHHwEMAA+mjqOOzpr0a40aZOCtFaGNeMjrbUOMcds1cpI7w7JnImShP5HK7dlnAm+ItxskP/y5bmgHxmgOhOrAKKMwdblS1iawRETVZwlq+uATwAbMk4N2Pb0iFydU5zz7795Yabbi+7t59aztl7ftmxjfYHuGf7ufX6AB1Rp8Rnai+VqcAoxwBHcOvq/ErdEnA8pA2Ma6Y1WQJwsCSYm+22Rwtu7UkbCN40KqAAIOo7oW3sa4579ne3LXUMt48KjBIIxG3aVvtG3Y7eiy4w9i3tAWNwWlmxrQTOGd1TQP0E8CMgLyPXDdBDQVFD+P7oJ64tx1eOlcdf+njqSNQp9h43EqfjgTzEJggCsG2Rf8ZJ6mV0XEHdtnWNNCpwDmaooXNgLZYtPlCPmXd0jFZEu0zkCzsKTJbfgKACPqAe5HFLRXt8F74c9wfIesJ8hz1LuQr0dwVbQ3S4rdj7yWA7czij+whMw6pQ9ICOIiDTue4YXg+wU8mdd98X4M455z2G9djd2C3HZOZXGk64tJ2m2GEjWYl/hCF0lrHDTuf4QV0HjM8CUAKccwJLGYy2mY+5XfyScw0oraJra9BP9GR1H+3tCJpg/E/EOXREkHAdc2EYUgHN8bXPeNAB/DhkPJwuo9vbiNDLmC9Ap0abowzaxEafoi8dGmLfvnvLncxnFtDDM/aeTR88hT4oEJd88N4RNKdewp39TpvvEXBFON6op3PfCpzTTgJoRB+PHbPNAMbCi+AYozNZd8fw9WeRULTdOZVf5sDNOOk4Q4umovytCeCh4SOGLXbMe8wBHXzQD8GuApPt/xNeOrn6Y9fAy/FyIS9u7jn5FMjYbukYpFFXkZc6m6vOobD3WMJJADGkYEwysnCiJ5NNbnvIaY2B+OjRY9wbAVrbwfiMjaR/yF9eQqLu2RpTgBk6rPxpbUpJRRs52i+so1Q9Kl8B7phO3mKrSIbsAiJSJuQR3LK2Ni633X43QQAOl3PPuaDs3XsGZTIW05drVDb6vGO+fQ5b60s8kV/oct0xMeUrL77yz3kXmss8caHsO0jbMz7t2tpnHnGEdj1CdpSIiLcB+3SZZyGnrmPhWECU0X/p0/RBNdj5htpfwaiC1Zo60c61jsrdgq0bvKJ3jhOCIPvMT04AzL3p5pvLyoluOe3Uc8vpp52MDUaXiOBW8ysfZUa1oBTJpu2pD/SQBHxqU4xgzDwLW65c6JW03xIvGJgLm7XMnAC1Y2iibblPdp+H/ZzRLytwznEGfmPfKJfvsJu6CZwTxG33Np/zc58/lajt7otB9hvHOO2YY7GR9JzvyflVV3+kHDl6sJxzzunlVAINLCLfRAEjnS9i+VQ9YUyduS06L38ImndbaLJyz4O2bOob+xDAFs/kaycD5jqHcZjnt+0Hys5d9wHUY64AEGrK6dbbgx7zKwbARIFmHiFR5372C0Z/5sTwCO0eNt+XVlKWqahPXqBJ3eFQ/YZfGTLqu3M19piHz0G5/7595aZbrkdHzypnnnU6OuqYy5xjJHieUrBVtnv6vzZA/aAM7YQ2zxcDAvDURguCDKhtK/6yPi+KAIJjHroFAP4iEQTbtHU+gVzUdedkngDGuj3GNcbP2O9c94U128MCSc9YEd0CxOccy3Gr44s+1D3gM3TcFxX7vHjSZx6r7qpX+gPvvP0AY8Ee5qYXxu6hces0lZtSSXtRXgCk/HLbeKNmCjxXl53DO4cR/Kr+dssZZW1leznBi3cdrvnS3QKPqsqjRpllZGd89yU9IzirdZZUBehcscoOLSfdyVwnBc9gBQChiXzGSvRGsmSNhPELgeZ77AWg7OPH1vDnfbzcw7PTs575jPLl+Drd9aqHbvjymMXZ3302J2N+14ry+xEcc+DcIxDaPMtcAnMJzCUwl8BcAnMJzCXw6UrgwYBzx48fz7alRmMTlHbOOedk69TWUemWqgK73Gb1ta99bSLAvfKVryyXX355Iru5jargtN///d9P9LcXv/jFAdTJkxNlo8+98Y1vDNhO4JxAtF/5lV8pX/qlX5pIdgs+jXEcPHgwUdlM/8IXvrCcd9555ed+7ud4K/VwueCCC3g4ctLMfJVJq5Hk9u3bl6h2r3vd6xJFzghx1k/w2pd92ZcxN2UKTlrpCv5zK9j3ve99iQBnna3XMd4m/PIv//JipLmHC5x7z3veU6644orwZYQ7eRK8J6BKHozQ5wO5gDnT/fM//3O2YNy2jSgNPqhzWKb1cKtawXpG2Jsfjw4JzIFzj452ejRwOQfOPRpaac7jXAJzCfx3SCCONhcrOVqgXDuH8rffr7322vL6n74ygInnv+AF5clPe0rZTWRfF1mNdOX2oPgi2cpPpwKLsMwndUmwJl39S66F6lBxYZMjEXD4FIzjSquOZ3YxwwFiAi64uOonNHQOuL2lEZV0liwBvFlykZayBcr4lvRxVnKPEmXMbZ5cbu/BB/4NtvfERQH/k7UjrC+zSA5YRSDEGs7gNeq2BmhnhXnikEhl//ru95Y777ixfP4XvaCcxwJ7H6cmSBoW9XESBTGno5864Wz0J6vIzcJyBaoI+HDB3jfk3W52DYeD2+p5fcCZqFmN08n6C4DJdpLUoUYHEyCjzKx/bQ++5HsclMhjwD0BK0ZsOwEIQgfkEo6QBSLl4R4mihMyEMzCQrb+qIWBTuxuuXv/AaIFXldu/+Rd5ZKLLy5Pf+qlgJJwwpLLNhCQocO7sOVRHHks3kvAFhCYJz+61HVUVKBKbZsezoeFyWFkchRYxwruIBynOr3Kduq2A7o7cAIDrtB5xD2dlzoodMYauaUYxURwGnRwBaAY5F65q8wOX1dWjxwvyzsvKks7nwAPOKPHd5eD995UVnDYLJ9yQVnceQZ6QX1dIUfOJ1hc/8AHPlhuu+v2svfsveUZT3tqOWkrWwUhryHONaPfddVL5a1DV92iamTOGcdJ88s2E0wAM3E+GuVmAZ3qwKdbUSk3wZEzZD3BiTliK9xE5EEX1UmdOH2cQdIRmLSGQ0GgZkBuAMp6qwfK6NA1BCW5BUfcttLbc3Hpbz0NEM99ZbLv+kTx6O24oAx2X1p6SzrpiTxDlLJ//bf3l2s/dl15xjOfXS646IllkegJ+ju68FSBKvCoEwTnjpHUbMsaxYx2S5PpMKGvAvgIIJA6LtHObgg7ABjWxdErQIZND9Et64/+lD05A76iPmPoj9E/oyfGSYEseziabInemC3acJoKHDmw/46ydmJYdm85pezafjIAM6S2drDct+9uwGTAY7bvIFLQyThhALsAcjp6dLVc9dFry7vf88Fy2dOeVZ72zOcACMGRTnmCXt0qrgKwaKQ4PqmQjiaicaQNBTZS7zHyHuJU0pkYQJ9gVJzLCwAygcwiK7aMQudyAAgE2kdfXC7H6TtrOL11PApukrS2B63hD/WlVv3QxIEFCGLtyH3o6B1l2w6i4+3Yiw4AzkHXTtxzTXR4y+5TS++UxwNIODk2aoBdWKGffvjaq8uNn7y57FheLs986pPL6TjTgZyg+oArKMooUNoxo3sJ9AiYlu6obXCLwzXv89nHEauNc1ulGQCraR/ZUz+j9PQAbWR7XIAdY8APKzP6odtmAbDgBvJWF3FiA/bChYku67jEqUafnCKv2ZgtEsvBMj56Vzl24D4Au52y++RTy9L2k4gKSKQnAMbHj+3HObdUtuw5DbztSfSFZeo3Lf/2/o+WGz9xW7nkcZeVx1/8WWypZKQNdEvHJ1xoV4yytooRjXM3jkXABvA6sN/ErqNLgpK0sfRZQcY9IqX1AZr2iDIpHR3FI9op20dj5/DoopfoJ6CJGVG+KuiDfo7N0lnsVpdjjNkU+Qx0rAPYGR+5raztv4VoltjRU59QulvPLNOVfWX1vqto44NlYdvesrDjIoAuJyNjHKW01HXXXF3e/td/wUt6Ty9Pf9YzywLtKEpoQlS0mXbFCCZEA5kRDSSAaTSnAnhq39P+LIBYyrjGmDbGDhtpz20Xl2i3JfrQwgj9JDLicEEwC2axCyhgxngL2GZI20Vu1L2CI+kC6KXgAIqi3vZHPtfup/1uLUfZWh1FLNv2XAA+l+grk+vLffffXFZX+kSTO69s3QEAF0e5wN2+toyoj7cyTvzbVR/Dvdsrn/vCzy1nnAKIF1CMetEXoKl9nMITkcACEqcPCpyLs9h74QcZIDHr2ycyYsCQjBFuld0BRKtsIFjtVpzTRG7s7uHchbPcqHqOaIxx9Os+AEHN14zxwu2zBaihyNgqgAgnbiwrB6+nPuOy9ZRLy5Zd56Mn+8qQa4cOHGQScE7ZdurjAVszHqELjr0fu/YT5ePX3whQYbE89UmXlfP27kVGjgfaNMd19IQS0bqc1flt//CO9az2SFtbDYV1kUEd/rQ72/n12Gq0T/0QBdcdJwRXL+fTeUFAefRB5y8T+heNiM3F8SyKjrFmgDz7s/1Ex7sdIOO90eFdO3fRn05jLsOc58DxcmjfnURCGpSdJ+8t3W2nMh4SrY15x9U33lze/e73l7P3nlOe9MQnA4bYDTADoADgJiZSSA7AAm0u/mqM3gheN3KjtbaGOu5tH7f71g6OjSDa30Jbjsvy9Ah25zB9+EhZHQDM1C6yTeIiEZ+AKcEnEdWwtGPGPKMC2k+72DbnZerKMPMibCyy6wIQLMfvLcfvuQ4bc7Qsn0TbnfT4skKUrZF6AnBO0GIBoNkBMCB03eila/ThI+jQX77z78r+Q/eXz3vhC8rZp2GfhsTbwo5oS9wmOf0OAKZRY523UCvoaGvoO/TV9EFsk9voClAxkixcA4Jla2b0ve94BmhBYKrbsKb9etuht5M6wiNgPs0UVos/Ss/xl9qjH323eEY/jfyzMjxSjt17Vxkd3ldOO/30ssDcZQ0ahfFH25Ftj5GXY4w6CDflrrvvLldfeyPrvIfLZU95cjn/wnOwNfAHHwLSAyymHoiDOQBzjiga+dFDtya2cgJTfaFB1XQ7ee1jj3ZeQLaLjId95r59X1BADxxXhsyHBVQKZpgA2phNqCtjB4ILaCWRu5CXkaFgI317wDjUOXY37wHcmDFhEV1cOO1cmGItFpuY9nPyoY7Dp+ggQfc3XH9d+egHPwRIp18uvfTScvY555VFQK+CCcG4Z07Ywy6gMukrZLYj8bsCuGypgFr5zHq0dpF2P3powhzpk5x3AEg6vTz1qReXc85H55d4BkDXtRwRUqTCd2wC0kkdBdoE4OI9XlTI9aSDCcvPWEVbeygA21vbZS+wIdDTnlvWyhP3RgCz3/63f1X2H7yvPPu5zw6gpcucyj6m1lio/ywi/YS2d66TTsd326VDfxEcEpvPfDPRyyjn4x/fz3zw35P+SU96Qnns404vy8vIi7mc80y0Cr1gDpR+DZgXvZ/Sh2rZyhF+rYKsqDT+zqQHudrnBBI1PPKlpocnKokofAmCPkiU7g9/+OpyG+PFZZc9vVxy0aWJSKYeu1Vvu92wnSRRYbFpAsCrTiDHyFAmPJGlMnYmCbj31lvWyl//7Sfge3d5+pP3ls++tFN27QKwy7PKiPmfz2Udo+7Rpom6arhR2kcwsC9NUDr6ySf1su8bdVKdSRvSXyvwDelbZ8q27gJhjdrXQwcHS6Oy/8Ad5T3v/Zdy+NCJcjF1u+SSx/PCGFtJM5+0nxqRUpuvnCzREcQ+Ejlabv5JV/kKQkPu9IGVY8vllhtn5V/ffwttewj9PxXw7xll5zb6sTpPe9QXqlQAn9Hso17zpTL1L43GRy0h9+QgcyyvMab4PKfV9XkLuWo38vxFuwkca7cbFYD553/2/5R9+/eVpz3taezMcRE+H4DV8GD7aVsqoAkdmmATEbNHSs54Bz/qCv0PDjnREOcCPPfd+Iluef/7uvhhADc/sVue9JQR4FHGFWQxY/DpjBnb0WWBc9kalHlsxlfpcU6x+Dx5pY0y/09fswRuI4I2sl5evIMXgXMVUCd/ta0FtH7i+pvKe9/7vvLZlz2pfNaTLqKfkJe6DddMRbuRVF2KvqNbRhO1LhkP1Q/bNP3SQgWH04+Y/9xx67Hyrnd/CNDhfnTjwnLpE88vJ53E+OOzE+mzjbP6ERAcdImy1u3ywlSiuQnAI+IvZQlCzBbSkZ42nHLCB+A59KgC53gmE1RMWiPHO64scAo+XcPO3HjTDeUqnvGXF3aWZzzrOeXUU3keSb9WXrYWpJqTG3yrP4wY7RbfiSQoz85tsBsVkNcrh+4/uXzg/fvLTTfeVM47b0t5ypPPKafuZS5gxLtovfXQLtEXGUMTcS91Vifk/Qifgi6JUjhmnoVtcW7cQRZ5kRAq1lcbzh8bJtcz17Id0JEDB1bKP/7D28u/vOsd5eKLL8AH+eLynOf8L8rTpmn3nSdaFiLG7kC81u8R/p0D5x6h4ObZ5hKYS2AugbkE5hKYS2AugU9HAg8FnHvb294W0JYR3ASSnXwyC+lM9DycLBrRzchyPrS8853vzDamL3jBCxLtbfv27QHOveUtbwkQTtCaW5t6PBRwTqCa6a688koWx+pTjgC3t771rQHauX2qAD4j0y3zBPGSl7yEB9K6val05UnH514WE92a1QhyAudu4+3AV7/61eVFL3pR0ghucotUwXXWwW1hn/jEJ5YzzzwzW8K6Ba0gvYcbcU7QndvPCiTcs2dPAH5eEzgniPA1r3lN+dzP/dzI0Ch4V1xxRbn66qvD18U4BbOA0dRDx69gOoGJl112mdWbH48CCcyBc4+CRnqUsDgHzj1KGmrO5lwCcwn8l0vgPwPOOZ+66uqryut/5vU41vu8lPFl5XnPeS4LxCfF1eF2WC5Y6hdjjZoFTJa7s8jP0rMOAxdtSTNhLoardH0B1xmwbhQ/naUukjl+lSzvcqE5XENeI5zGahb+x7orScsCrYvtOB3xrrIF2qAcgzddI74fb5yzZRaHWccFfwAA4d472J3xIMAIInudfBZ7VG3Hf0I+6A7hcQzA5x//5u/LJ268pnzxi19YHnvRY3CesKjtVojyoyOGBXTXk1NRP+Enp3N5PVv+pI5GRMj2rngYXFpGNESbEgRj7a0xznsctHUp3mV745t4IkfpkpbbIR1hKUIOi8t6t59Uew0gklvX9I8eLlO2Iz8GmKfHM8Zg9y7wMSzEA4DpUkdlci/AuQ/9+0dxVt4SJ+XzPueZOCx1/ej2lnVLx4nhXl7kq4euA47mZ9bCcSAIjpjhXPWGgLnZiTsBm+A4PX5XmQ4PQgunVmcPYAwi/SyeTrSfUwAtsIUTyMixICecdz0Ww7OVpAIlspvOvrx5jqNZ0Nz43veUI/ceKEunPKtsOe3ylFlWry2HbvkgW5ctlp1nX1aWTn8CghVMIu8FJ94JwAL/Vm67+45yzoXnlmc9/WllF1ERdWkZVWYYByKSZgG/j2POPPFPuFgP19Xl43faAUErdgESRhDorB0o02P3A2w7gNPCrbfcEo82Q48GbDO2uO1kMGbUGXBEtDkO1qbBAA8EOAddXV6Lq5R1+O6yes87y/To1WWwFVDDmc8rnd0XlNnBm8rwznfxQtKhsuUM6njms0tvy57U8RhRh975jneUaz56dfm8z/+CcsmllwEsQ9uhbx3jMKU9gQfBP04a5ERDpY52E9uvccvChbUUzEDEIrfyGhHN49ihMjm0v4xXAEVMcXSwdWmfqGkLy2fgRKTPLJyCVwInF/3MrVDdSkwpGT2ji4Mj2+CxnU4hMkVnfFfZ/8kPliMHj/BseW7ZdRr9aTvOFYBYt99yXTkGYGLPGaeXPXsvAO+EM4Vtad0S698/dE35h398D46f55Xnfs5zcHyz/ZtKT7N0cR72cGbrJGvrlo5CJKW0IJEJorvYmhH9Wgea9sRoPmV0tExP3FEmx9DVVQBFgKbsq322Bexvoe12ADpZOg1QB+2JNM0T2yXhOHLUU3nAmW20tWOHy/F9t5Yj93ykbD9tR9mGLnYWHldma2vlvuv+DiDBDWXXmeeWhXP/F7bmPKQEYBe+TwBU+MBH/r1ce8O1bE+3VJ6PM/3M04m0wz2rmYhijZ3EisALNkyHkHY04Br6M8/mRk/UNRRwpg5+gBDj6cEyWd1Xxsfvp0/irAJIJMCma8TG5bNLdwtAvq3oxBYqhcNJR5bOy4gXmfGftqMY64tjuDe5uwz3X1sO3P7JAHZ3n3Ve6Z50VpmtTsqxu28th++/qezYs71sO/uC0tuJfvR3ACTpl39+5wf/X/beAzjS87zzfDp3A93Ig5yBGUzOMxQ5JCeQQ1IUKSuvvVdlSz7b6xyrHM5Xtvfssldey3WydvfOV1snyVpZoiVaEsMwSBQzhzOcnGeAGQCDnFN3A53v93+b4LG09q4lr+5EF5rsAdD9hTc87/O+3/f8vv9jVy7esJ3b9tv2bVutrAqITe6CehQBBQKw2GwxtKhxqC9RTOJfpVn0OWpCXc4W8vmUlQLRONRrmT6k/3LYaA4IKMs4K/hQYooyBlGZ9ESwAa/SG6MgVuDN8ZxvIUjqUnTjmwUoOtU6IM6VyYuWHD2LIiLQVftB81X2WHrhtiWGXkAFa9RKqrrwQQfME+uAqSilRD47f+asfeOrX7X7D2Gjhw6bH98mtbBiWlCKTDsqPaofyMvNAM5+CPYDhyoQLJfnQ2VK22lcCp4mcSafAwUkp9wYgbKhLePAhXho1MTCkSYUCpsZhwCKKI45tUcNC9kGdVOgUMFyvQReFKRWtDyIH71gCyNDTJGobzbeaYFqIJjCeRvuY2wuFay6bqtVNWyyQAlplUmZ5lKpAkbeHhy3l0+dtTkArocfvg8AizEixUaO7ZW/VnsS8C4oherb9VBdNNdRAN74B2Y4zS9KDeoDanBzW34KcIu5IjlrK/izTIoAKlBwkPYPhSucnXoi8jUAO0A88jUCzJz6jo7rFgjAzPKvgCAe2ig/fcIWx46jbJOxmubDFlq3j2NyjvHjNjs2gvpVu1W07bVQFYFkoB0pkZ08d54g8zVSmZbY3fveZ+3cs/Jhk8Wyu2FQnBs4ZRGweOerYv+yHhAgRe/xll3TGfpf4yfD/LAwhFjjGDBlAhvFj2LDoWA1vqYBP1hLfzAfAmegw8M+QErqSxpP1upUozikAseWG7bkwmWbHOsj6JxxwfBYfTvDCMWp8WmbGrxkkVLAuWbA66pN1K+OeHvQTly+QaaLF21jdw/QyT6rqkWNSsF8ABYPYMhqam8Hf3MeNa2DlCiRKu58rHNIzPd8VgAwzLOecaAi/iWPH02mJ2xJcC3/learrNRbi33WFVXjQuXUUQHyYtOoKgw8ZyB5SG+tf/yyUR5oSM/129zASR4AWLBoA+BN/Z2szQRQ0r4oacrTKXieZ852Po9PpAK8SPt/7ZknWNuM2QcevN+621ooB3AKqUVdOj3XoNRMkLdAVhXCwQhFiPOdbqOMuC3KprmTNtLch38pLKB6m+SdRn0SWFBqgJD89F81c3Y9zgpgGMgTeUnsTeu4Yl01XygVtAfoXnOh4Pvk7LjN3r5hK1Mj1tTZYZGGDdh4LSYDfIpaFjKJqwagjteRgFdH7K0zF2xiYsbuYp7o2dTuYAmtIQUEweC4seEeRJDzZgwprbZ6UyqjDp5iG6W5ZEu+pye1jqV+HhR9cwCLucVp1m70AcfLAGAWAPEjqN9GwlLAJa2eB2AYEEzrBoyCv/lJO2n1qlJKCVX9Wpi7YstDr1p8bsxirRutpG037cO+LhW01iHaWvuyPWCDh/f1S1ftxKuvcb4gYOBOa+loJ/08a26NecBAB8xjJ7ofXUwhyCHYXZCnoA75BaXr1sDR4dUEmianxnP2/PMX7ZWXr6BC2GMf/LHNtn1PCEBY0IpWusLU3zZM11rYJfVhV8qpskrNjDrpoQ78cvFVnKO0tnNQGR9qXMjP8ag5bwGemjeljCi10+IZ0um8ff3vHwNKGrWjD97HdcUmxoUeBmBzjlBslWKPYWSUSl+ozm+f1v3g+oSfecEgrLsFimh8fvfFBXvsse9Q5xQ++h7u1TdZRZX2FbBN3+DniirCtJczdoA0gTo6K+XmiK4cmntdSVhfUXjOz3kcOMcmvNTnKo6DcPSBdmds6rxxssa8dvxNu36tDyWoe7nHvpdrD/ULXYW9uWtLNRO26ZGCM+A0hs9bYDugueYtNb+OSRkdQEMrsEwi+07WPvu541xa1dij72+3B+8PWX2j5hTWn4Kq3m4nSuvmVDe/8LtSVeeZNwWjSwFYII/sw711ItVPP995qYDqffoPKAnH4/pGzx/N8NDAiy8/Z/MLs7Z1y3aunXYAzgG84thkI85OXHu9fTx3HvyW2lHt+663zlns8wDHC9q5t8we//pZHkSYwjY67fB9qErWCNorgunqY1mR/JaO44osh+rKr36W7ancnNSdnrUAY8O1I22sVKpFVTHVjE1kVMDGAj2dXehvxk5muWBf/eo3bHQcP3PgIPDXZovF6Bv+kxqZ3J7bVWsw2lNjSNdEPJ7CUTWO9ICOtqffsTn1oeB0jYHjr6fs77+etaHROTt0uMSOPhyyhkafhRn/Sivr0fWm3jqkk93UMamrmzi0VuOYHE/Qn07l0rxqPaV2oaauT/muqDit9lIb62C83YsrI9ZIl5kPv/P8q7Z3/x6UyjYhpiBfyWlQfBNI7dKVYp/FkSb7VH1kJ2+/ZFxa86h1KZNOzKrGLp1N2Oe/+Lwb3wcPHrAHjm6yxiZ8rw6hPikO9GJxNG6xS49HAO8SX8qnAOA5CE/nc53Ifuon1Un7q4/46e4/MIboL5cGXSamr/hcfjIL4N3b1+uyQvl5AOjwIR52aGxlV8aXyvC9L+1MO8t+886HF48npdNi2na1Iq3B3DY5HLS//9ptO/nWJeJ75fbIo5tRz6yycJh9uQdDT7r21/YeHI/AZvWb6xPnv+c5OPcJBM7lSN2L/bqHdDQvyy9RFpWxeEWjcqm8xfGjlOh5HjSbnVm2J5962o4de9xa2hrswz/2qN19z13EHdWG2AJtILsvjkn1m5vQ+anjff+vNXDu+2+ztT3WWmCtBdZaYK0F1lpgrQXWWuCf3AL/EDi3mqr1d37nd6y8vNxBZLt3c1OBl+C5/v5++8Y3vuHAuYcfftiefPJJB4gdOfKDg3MC1g4ePOiU6EqQb9drZGTEpUrVuaR+J1DvV37lVxzE91d/9Vcuxam2U5D09u3b9uKLL7IwDjtlt9HRUQfODQwMOHDu0Ucf1aZ28+ZNB81985vftJ/8yZ80KeUJdJOynmDBX/qlX3LgnFTuUgQUVlO1/uIv/qJL9ypFutUUtFLIUzkE4EkF77HHHnPHFggnwO/d4JzgPUF6el27ds3+7M/+zM6cOePO/+M//uNcXOlizrjhNGGvvvqqU6kTALhp0yb3+do/P/otsAbO/ej30XulhGvg3Hulp9bKudYCay3w32oB3WDUulE3wVfXOf/Y9u5mpLtB+l9v8b3f6W+93M11fq6eQ39rTXjxwiX34IYCwB/60Ift7gMHeHK8ihvk3CTXfW/uT+oIuk2p25ZagenG7+pNbN3W1K3R1W107137KPRMbIkgj24hKwDCFzqI3mzgjsFHurerbbUPt111i557ydx4Jd2LAm0uLSgnVQDaz81gBUD9qlOGG7M84DGLskVgHHWWugYL7Nxt3jqC6ASYCSG4k2VIQ/LcU89a741z9mMfud96etZz41+PpbN+VsDqnRfHJDjpbuyqjKo7N8RdDF03kBVtoU10319qLpSOqhDqoaxKg1fcQfWgZdyNeH2iwCvf8b/qp31cbIKfajAXWNLv+t59yTYcf5l3kGC6f2Lclk6dsWF+VhLcWcea2Q8gyObsgL0AG0wS+Dlz7qxdu3yNwMg2rg/uQTlAGAQQi85B2YPQLUrr5e5Za18dgXMUD6RteHEsmZQLoHKj2ptF2Qc4I45qUxwFLgVmfKQtVUBYgZsQoFVZeTXt3gJ8UumgD3NAIpEFzqVAgoIwxUCTglYoZ82dtczt52yW9Xuk4W6LtTwEeMPZE2ds/NobQGshq2nfbyXNO8wLlKfAjcq4SBDvZdb7Q6MT1rm+x+7Yu8fKUBhU8KxA2qUMaYAUgFK/Kv2fgixSJ1O9XDpaBYLUAQQyFHiX+SgQAIVk2SkU8Kb6SMsza8tc36QAN/N85wPE8JGqsrKqwWJVTYBYdRjgOo6J7biGo73od4FOUiOUJfkA5wqLIxYffMbS86dgOGmfzgcd+JCbuWbL/d+xaVSiytr3WbT1XqCgBvYqsUVSAn33+efsKhDr0QcfsK07twO2KeDiNBP4F79Af0p1zAUOlAYIQ1GQQumGGAlsgQqiOpVqCbpUvX3ZBUstjdv85IitzI1jywA8fK40tApYxUhXWU4ZgxUEYsragZOAGggMSsVPBhJQyicFz2SULu5FcDTVb/O3Xgacm7cy9itr6wZOIwgCjHTzOkpWREbrW1qtpmkT7ddIeWKWQPXxzIVzAB8v2fvuPGgHDx1CTQlwjoGvSzrF3F3gTB0jh6DW5HOlN1XQxKl7oYjkAlJsLJVDZ9zAfIIEllARS8zeBIRANYP/NBwVZA6RJitc1QgXuBHTpI4oKxUBC/ZXQ8l+2FjglVrQkyZoGJ8D1rluM0PHrbKp0irad8LBbAW4SNrIuW9afuGK1QCUlWy4zzylG9hNkBrNgqLYyXMn7VLfRcC5iN138F7SmQJhOHvD5qioSzXGmdSfhIipJeeUj6HOThHO2RMb8MKy2A6IAfgoGZ+1xZnrlpzrJU3lAspl2DljRVbnEwCJwl+oGpAVRSAoIloMCFJBQ9pB/oZ4s/O3VNfZkSc3aMsT522i/6ZLf1jdsd78te2wIFmbH+y1mZHrgFcVVt6BmldlGyZX4YKKL738pl1B+WL3rn22bet2i1bhC3RwVx/1j+yGoBv/5QRA4Q/k0bWF3IHis4pqKjjv1fjjiwKgZToxSP9dYcwAJCWps0AaBX6pSQHVokh1lUXr1gGxNtGHsqlKDqQ+JGCvgypA6cAHDiiYhFTJy5OnbGH4NRSiUla2/iEL1uzk+IO2dPMpW8anldV0WmnbYfNVdCA0gqokjv3C2Sv2jS//vR2996jdfd/dwFDFgLFTG6Ww9BRnJX2iYDjqWHxJhRPogbdUS72SZ+WltneNrpSqy5POj6Zmh3A5gjtXSHVNUQElI4Ey0tVVW2k1AGRVG3wHfo8ApIPmNGeqnoKZFeB19SUN1kofjNoZmx+4RXt0MQYPW5D7M57CORu8eoI0eFmra9hmde07LBClvfAxTOiM/xUb4n7RiydP2TST86Pvf7+11gNBM1EUA+4MSI0JB1fonLhtTq/hjwUzDvneBfVBfKir1L8gQl0fFFATlTrc3BxwIimA8SL4YlT26OsS1hbhSB0Koy2YEuVBgTMLjCmYT/ap8SrlIMEXgi1dCkggtfzkKzY7+LIlFnPMCw9ZScMBthmxzPBLNjV8y7yxNqvq3GvhmiZYO3wQwNpb9OGFczesFIWrA/t2k62gwQVhlTLMrYHcuTgfR9LLQWwyRTcBYztamLjFCbbH3C4fr7Lpft8KKkWJ0WvmjY9TbdKLU0u5IqWkLI3EAG17AE07mdMAp1ByE2ivLpRHnQXucQAAQABJREFUe8dc3DmZr1JDlpi7ZOMo4WZRAqxvbAaS66bBI5YaHrXx/pMG62RV7SiT1uwG3m1mrIXs5OXL9swzz9uOzTvszn13oOzJPIHCi1uLMP+R9F2NqTO6H/pNKIKbQ1Rp+Vj9L4iV8aXyKy18YWXeVsZ6bXm63xLpeRQRGcvMBUGC2yH6KRJG9a16A4DmRhZ05bg1asXB3bgGwnREjyoqAF8QDcdbZl6d7j9BCti4VTTuNn/TQRoL9VWt72g/H4tDrehyKKO5FNQqDHsvMQc/duyYTc1M2iMP3Ac4RzYPncvZGjan8cB+6hcHkjLYlErdzcKqm6sav2gQyg0BIQhAzgDmJqbxNVO3gcpQJ2W+F6CKBB0+ALWmKL6mrMNKy3dYuKSL4wNaafzRnu6YQLEyWKc86cbiEse7bVP9lyw5Lai/i3l9O+3TxPlQnMMHFwEXyqH/XfuY3RoYtBOnT9s4mUV0P7eHVJ9+/KjWteIb9FbHuDUK6zLNyVnGntbZUswSOKe3205gAX3h1gLLPAQwe9vmRm86gJUJ0SnuKSW0mMMo6YCrWbOVVDQz13cDDDZSPlkH8xBtJbMprpqoJms4MCfLz5y2pYHnbGF2xM2FZZ3qQ+qneU8HVcUYI8Wu0+9mN1DOffP1N3h4O2g79+yx5o4OHkLQtpojOBd9p7WxNhaMo3PKBwjILW6jbWk7ba1/9KILx0fz9tRTAkfPkMK00z7xr7bY3jtLUYZdcnOcj3WusBdXCH7Tf9pRqo9S69RL6w2nHKb+cy/Gi8YM22p+1Om0jdYdAoiU3lrm5AXkE4ijazP5rCwA4GN/95hNTI0Ctz0IOMcaQ2sTttVbZ5Z16/z6qVrqM1e2t8uia7Ei4MoWNKDqqtI//fS8/efPP8k8EbePffRBewi4rLpaeyvNusP/KQPHwy4wYt7F+YdfeBU/U58W517Nv1To7bO7Td7+Z7WcOq/cgvpA/lBQdDy+iOLWcbty6RoKUPfYHpQR9bC+plc3t2ln1zDs52MdxhjHoLgWIgWwqwXeBdvUuPFqgff2K03K5VOnMvbHf3qMc7baxz+8yR55f8jqGoE2gbNcO7CtSqyG1NCVOq/OqZ5QqlbNvcXlg9a/apdiu2vdob+Kb7W6Cllsd9cWTGRvFxlwbt5eeuUFQLdJwLlNto3rq4ggc/rBnZv9BDLKFvRS++SoTFH9UYCpjiTLLfasvlM55mb99tYbefvyl05bMjVuj3ywxx5+pNPqa3moiLbV1gID9ZKqXdFG3Z/v+kffqxarL9WkeI2Dfjsfyq9wpLc30TEFeWq8FourL3jEbMVjj331mzY2OWV33nXANm/fbNEYoDV+U6psujfgxympFKv9L58vyEwtqXG6Op6KLakuL37z4stL9rdfWrLbwzP2wEMAkB8pIzU6qcUB5wSe6ZpBywVBlnogK++uBVUP/Yc9CGRl3Subc2sK5g3XU/LjzpmodXnTrO5P/lB93eZ8vvq6ernPnn3uRdQn99j+O1DqZqJwPsPN99TMtXkRCFabFGfCYp2Lx1B55HCdlqJrV6nAnz6xTEzvSRR8x4HmDgHpbmItwVgKqD5v9w5l0lLBFZR/PMxpUhPWt6v9VDynzvT/nse1q6uMavP2sbSFjuc+YRSr3vytlOB9N2/Zm8ff4PgFO3LfEWttwX9TCPluNco741HHUBu5g7Cz/tAP3vIKznHxgVIL5/Dv0yMF7HTAXnn9tO3cUWYf+wRK0lvXOdU+p6jn+kl1YRwUD+WOp3IJoCymahWUSFmkPk/7atvii434RKnXNS6UetXtjI3KjzlwDtB/biYJOPcE76+7tdqHiBfK3wTeBudUF6eg6cabji0LL1o5v3zfrzVw7vtusrUd1lpgrQXWWmCtBdZaYK0F1lrgn94C/xA4p4vey9zEEux17tw5+73f+z37xCc+wYWCz6U1VZpVqckJmpNCnCA0pSI9cuQHA+cEokmprZ50ADrnAw88wMV71qVRlVKbgqAC0ZQC9rd+67fs+vXr9vM///P2a7/2a65MUpf78pe/7KA7pUPVMQSeSHHue8G5GzduOGjt2WeftZ/7uZ97JxXqpUvFIOvzzz/vVOCkgKcySHlOEN1P//RPu+MJklN9f/VXf9X2799vf/mXf8kTtbWuTJ/97GftiSeesMOHD7tz/EPgnG7iqGx//dd/bV/4whfs/dzg/Y3f+A0HBep8x7ip9qd/+qfW3d3toDop+q293hstsAbOvTf66b1QyjVw7r3QS2tlXGuBtRb477WA1pNa2ygwozWk1kD67N0Q3SoEp8/10ncu4KU7o2+/VsE5fb76Wt1vddvV73ScS5cu27//9F8QuPPbhx79EDfY7yJIAwjFvW3urXL84lG0j25X6k+dTWGlIiRW/EA3U/X2EWxQFCvNndakAvJ8Db/CvXHVjRvnCjxyw1xPpXP31R3PQXoc38WT+NinAAXqT/pe6XuU3lD3gB0QxA12wRIZKWCNjln/49+ykt4+q+ghTctDD5qvqwMxuRCQioJv4EOJpD3PE839fRfswx87Yt3ruzknqjCkLpUKgeri6uhuBHNMqaEQldHnegI8kyaQomDV20GxYiPopjFbAPMUePv4zsOj6EozJwWpAhCN7lZ72E91kBIHsWdXD+2mlIXubvbqDzahqVzjZmg74h0W4YGUwK1BG//WU3bhRp91HzlqbXffC4RFcMcVgkOQOnBqccpOnT1l169esa3bt9q9BGI9rL8V1C+GOBQW9JKajcOT7szddF69809/6Ne8+oKj6nftRQJSlNsmben26zY72sfT5FmUCqLATlHLQv8szaNssrRklWG/lXQBSNUBEJW0cGjalZvnxRv2ukGum+aqJJUnjVB+9i3AuadtgWuRUONdCAZ9gBg47RU/ZZO9xwHnwlbVusdKmrbzeS1F4ni0x+LinL308ks2PDJFWrMtdtfe/RYj1Weea54CajuFAMEK7MrLzXml9JQyntK3KnAXIKAUKEgFQI2M0kqWgLZ+F9GZW7SlweMo4F3l+omgZBQ1HZ7qV8rAFEp0ceoYAYior28hHeJmAuLUU0o0egESYhRs65AEhRhcdQsJgIiBJy09e5KUpdUW6X7UfKSoy05fhjn7Nqn5pi3WtssirQdI1aoASJktLKVRnHuBVJEXSJ94r23bsQnFOdpRgXsCTG6UEHjykiZVQSeBi5AU9JeCTcBw2JpTjyHoVMC2hPL4UcaRSuDCzBCqRrdQY1sE6PKj/gSyhZ9JoUJn2E4VUcnS+nYLNO41T3m7S/uWov8D2K6ASxdwztImUn0j3Wth5aYlbr1kSRTsIpUoAbUDogLOZQH0Bnpv2DIQSGNDi1U0AJGEWilnOSBN0k4hw/H8t1/Av9xrhw/djy2RHpNuCzAgxK0VFQAxUhf1oo6MqbSLWgJvADgq/aZAhKKNaayxbSZuqdkBmxo9Yyvx29gE8EoI1SdItni8j3Syo8BPAGBS+KsHxiyp50TYlNRQNOAU2MXX5iUjoXKoQKhXJsau2vTga1bZXGFlrTtRWtoCOLdsY+e/ARh5xapaOi3cfQQVH1RmchyPMZGiTd+68KZd7jtrZQQnDzJWG2qbKLdUaAjYYW6CLJUaWuUT2CgVhaLCFudnA77Br8rnKdBIcInyCZCIz8wCs5215cXLFg2uWE2okfSJZZZKzFs8AUiEHZTVd6EItAdwhz4E2lCKYQG9Sg/pwEMVgLcUkyxPOs9JYNX+PqCWgNV09MCEtgF15W1uoM/mhqgj4FxZB6mUAYEgY/ELPnv1lTfsMmpCu0iLJQX6SEUl58GXY/pFbFPg3Gownz4yVCMVINULG81RFv4l0K65QHOD5rwlgKubNj92HvFA/AKpnsvCqGvii+LAmIsJlKEAocubGq2yZTsKaj0cTL6B+qCLJfqymBZLeLYcGOcAusrMn7D50ZdI3Zq0aOejwJP7LIfiXPLmkxYfB6yqbcN2DwIGtgKHkX6bva+c67cn/stzdv89D9qd998JkAi0Ql+hrYYSDkeHVFE9hSmszob0IKMNG2J/p6rH3ObK4YKafJpF/W7pNgpp15mLpgHlSIGtlLzss5JKU85Zi2EH5fWdFgAazpcDYADnSlVHB1XgWH7G9Rvj3zyM2wyw1fgZiw8MADCp3+9jXqjBFi/Y8I3TwLkplMh6rA7bDUVbaXoILFoeOTG7PXTLvn3iLZsH/Pjgwx+2jro6fD0AkbM/2s+NP7bVnMV5UwxO2WUQf+fPonZIv8kv5ZQ+T2pz+D8slrYFqAVgnZodRMGPVMOlwFakyfSTaixDWlW63KLltVba1GPedVssG6xnLyALrQXcfKtgKsAVbV1AJswjJbDJl2xxAHAu4bGqtocZw/dyvtuWHfmOzY70YZstwJ07UWJtAhCSelaYBy177cr5fisBzH0factbWusAtAQaUFatCYrDwNVJAXuVvaguxDyCfbr60AzyD0oTphTg1M7ml2ZtYajPCqTCLVM6zBip5pi7VlCESZNu1QMsWFO/Cej7boYL8wQKly61OudzDw2IOOE4GiyMaKYPwcxXbWK4n7VMzmrrm62saT2blFiaB1qnh14F3spYeRtKbVX7gZXoR8C5Ny+dtWefftZ2bd5ld99xANVH5jofbYW9KAU9KCZ1YOypu9Xn2KdgG61LvKRcc2sWNYJoSEFjUowCPM8vT9n0zUv4hUGmFqXGjdFu2D/1Ts2iYpYKAnduBvq+x42ZDP5bLiXAcZUC3KXZdBKgmvcFd86TKpl0pANnmI1XAOf2m7/hPubqWmyHVUZhmVTetDN+N8fawEf5te6jk2yRwfi1Y9+2qelJ4M6jgHMtRf/ivhckAyTJJ6phXinIGWte6u4gD1VZ22ncUD6+4B/6GT+wEh+2ubEbNjvZS5lIcB2qtLDSjjKm4mnSkWamUBRq4l7lvVbp1PEATmlzKe8UAH7EAAheELsiMLsAmL48w9geuoaPHrSWrm6yB+8AtG7inMwLlEv9oPW32kp9LxfVf/sW4NwpoKspu/fee23T+o0WwjYFbJCvmeKyEz4BOpo3CCq/C4/XIQLUTdspHbcH1SB3ggDlU3fygEOKcTE1eMPtH6uMkpK6YHEUPWdRuswxh1WS4r62aYOFAIktspW2x5sBZ2Dm7MODKu4sHBaHJ1Dfu/AW68BjtoDiXBnzS6z9AeyjlTZnvEnVlDEiwE8wDh/Slh7AuWt2Ch8TBNTbtnu/NXS08SAAZQTIl7f0Yp9oU3Em1QgrpU5y51qvOagIe3UVcj5e5eJP/NUE4Nyzz4zbt1+4BADUYB//eLftuytspTEUmbEKgXM+5naP5nfOVDw+bopm1LpINuFlPemUCkm7rJNKjbnYMXzNcFBf+YFj1P4uHSm7ZVnjS+VNqtz0KNVknsafffWrjwE/jtvDH3gQpSbGPMfTSkzjju7g2GyrdQXH0u9KwesucvB5MtEMJ5SP9QogVf3V5fw8dixpn//CMc6fIMPMYR5Sb7aqanlhfDBtJ+CIJueajc+cPy6uG4rwFtckGKrqobOy8qcMxTU5u2h4FYcEvxdtk/pRF07rbEBNLiWvBda+x1Gc6yXt9V1cE+7csY12Ka4vtfSiOrxpPx2PcZ+jrALNAqSsdnMj9c5iVzp2gDHkVOo4dpp+PHMqZX/4vx1jx1b78Y9ssfc/GCLltCqkeZGWYju1n9pIvjPIB2qfNPXKAfnKVN1Yke9h7tYCUsXJsRbQNab6WGPWAbocQ2XUGkx2QbHUTaQY5WGcV162uYVx1OZ6eBBgB3AwSrbs6XqR/WQPsgUtQ3X8LICwu3Zm3tA6RsfRKHG9zYE1d+Aq7fQbWWI/V1kLT9tDj3TY+x9uBpwDEnfb8AiM1izsHGTQUSL+41y8dQ73VhnVOW9/pw00BwloU196qYc2pHhue5dWnOsAN7a0L99rjHEJ6RTnJqan7c4D77OeLRuttAQ/SYU05rKslWTXAcB2lUFtrn/kfjRcdPRi6dwfrsy6PtZnL760aH/7X5Jck83b0Qeq7dGPlllzg8A5QWkqP0fU8fFNHhTBpZiplxA2KWka62KXnpbNpOIrmFX2wwRBexePocrJ1pxv43c1CcsS93LjiTa8cvmmPQ84tx9wbt/+rfgc/Bn7KE2pOkh8tzrdAXQcXmMkB4CtscgWzta0DtB4d35DnYpfO/1m2v6Pv2YOwkcfvf99xLu6rLUdO9PxqL/K4WxJf6nY6gxHeEoZUB+oj1wp+V4/5dvUdtinxrz6kLWOtnV1pHJeHYhXcXzxN3NXOpUFnBtiLL6GXecQljgEYEZKd1o5JzgRXyAA09moKsRLPsHZsP6lXGoKfeUeAuBnlg/kvyaH8va3Xx6xV988R+amMvv4v9pmmzaj8IyLcfMPP9whOYfA7mKD8YMPZa+OamXl7CBMziWgvQgoawypXWhr5nbdF/FzjSXfLlVEn1P8ZP3EQwILc4BzT37Lnnjya9bR3uQyat2j+xxas3ES17ac2N1v0TmLR3a//SD/rIFzP0irre2z1gJrLbDWAmstsNYCay2w1gL/xBZYBefeeOMNk6ra7/7u77KQ8zgFtccff9wpqOlQenpQim8C6Y4fP84Ct8UBXrrw1HZ/9Ed/ZEeO/ODgnGAxgWbt7e0OSBOgdvLkSZdyVSlc//iP/9iC3GT++te/7s6lBbpuzCh9q6A3lV8AmyC0n/qpn7KrV6/+V+Cc6jU2NuZAOKWObWtrs0OHDiGxnnbqb1KtSyQS1tXV5aA8pWx96qmnHAQnJTulh/2Zn/kZE3wncG54eNi2b99uzc3N7jO1pVTq1A5SlPvHwDkFkU+cOOGgw9M8nSk4TjfrVbaXXnqJRXfWQX2CBHXetdd7owX+JYNzglf11hgS/PBu6OFHsXd0c03l1c/3Qnm/tw3XwLnvbZG1v9daYK0F3ostIB+s9+pLvwtsW4Xo9Pnq95pfVl+rv/9D361u8+7v9PvqPkVwjoch/vzT3ED32ic+8EG7k0BXrCRCqjXdEFUYg5vPBEy8rLG83FEtBLlJK6qBeSOPmlshiWrXCgENKcRxjCCpVT2kZcqhiJyLKJhJHQis+riBaqTgK0iNJsm8g1qFVEK8gEo+3cznnPkIN3q5+ewjUOVZQqEMQCvPcaUYoZvnPgLVfsAfD+oVChSn+vvt1lf+zipQSC7r2WwB1qk51pmQK8BKAGakvssup+31737Xhm6et6Pv38/T2qjTxAm8xrmRCxSnm8p+brZ7STfpLaW2kjYh+ELUB9aLciZJeYqSmIIMLnUm9fOH2JYy+wgG6hayAu6S5skv8zvKVBneBdbLXgWMgjzhTxDfU4EqUqlulHNCvkPChnRhKaew5AKQCgBxl98TBbTh7aOd7MxFW3rqmPXdHraGu0ihu/cO0n5Wc95SC5K+sFASsCnSjJ6/cIoUfNdt8+ZurgtQyOIcGYJWCrbohrOqpJi26gtJREMq3SF9B2DiITiuNKRSbMpyg3tZkS5u2EeDqP8sD9BORIK4ie1FwUedoLoW4qgojdy0AmmqPJVALh13WKFuF/tRV07hQclFUFqBFIVZAtsKhvjp18LSZcsNvUyQec6CLfdapO0R2hRgYf4Nm7j5JkE1n61r3mIl9Rs5FfAmymoKSiVIT3n61DmbmJy3ttZu27EN8Iyou9SqCl5sKkDADNsQOKf4jACKPCnllDbSl50AwhwrlitHatQMacYojyKPUnEroHZVSM84eMuA5JSGTilm86Tjmxzpt9mpeUDSWlvXsQ9ADIDKSEnjIqyAXIqkY6sCLPRyEFRq2FaGv2vZWVK1RlFz63oEW9yKcstVywygcEFqN6U8Czdvx9aBXQpltpwooNDypvVdv2GHjhyw7i0dtAvHJYDjU1pK/lNaOFBU7BWIJ12KAl4ZYIMgKFKxAkTQYzQ8fUtEJO9ABgUQMwSNk7aMLckmIkHsVkAMLQTZZ8n+s5ab6LdAWb2FN91n3mrqhx2CpRLQYwyiEqg2zsu2CXR5ObaHlJrJ22/ZyiLKZzXtVgI45y0vpbsB51DMS6+krKmhifSR3cSO6mijUqfGdv7GRa7bXiNV634CsQdRaSLwTuyaxsP+6L8gbalUcYw7Ku/sNCNbJcjoz8/zFZAUdc9zPKVAUpDfpZXMCCQaYhe+95GuMocaGZBPId1rqclLtjBPusjSdos17UKRrROfoJRCnNdFlLAfBXeJFCpIpcAjFbPkeB/AKABcSw1vAINAD7a7YKOXvmXBJKlaW1ot2IzNB1vwESojIBHtdhlVyxv910j5FSFt1D6rra6h+NQFm1Qfeml/2V0em5GVegkqOsOTGZFGU+US9FoAbksDSeFyACpIH0Za1lyasZYfJEWygLpa2g54EnXDzMIlVFNGEF2rs7IG1BqbUMdDdS4LOOdlMCjlsgjFvOTuFDz2LuEThkijeM0mbw8BvkRQ8wKcAyQTHJJkXMeHLqK6FCKlcBf+qJX4H6lMCQKePXXGrt/oR+F9s0sLHSnDv7oBJ5iDeUFziwuC0rECqwKN7FuFHdGv2KkUxQQJaExn3ZjBrtUPpBfMo84i3NcjsFFqoBy3gOrlCupQE5NjUFdhq+/ZayXlOwl+Nrk5zOvBNwGDFfAbSi2q9LRqRA+fZeZRSpp4k3sDOSvp/CA2useypNlM9z/NvILNr2uxYOMegN9q1N9QZGLP/ssT9sLjJ+zwgQdt690AS6VLTHMcS71FIDSAPwkQDBR0VLRBfBBdmCMYaFmlSRPQQFmou2tvfKrUeAqAOZnFMdo+Tf9hA8yTNA5Vz9nywHlScwLykNY5svEoTCB2VYi+DVtxXAAjBh3nJ3UpALDHAyRCms/81BXG4TA2CDDedtT8lTWM6fM2cv2kzc/FgbhQKWrexFxVT9vLN6gSK64tXzl32RaYC95/9EFrqWIeWcZG1HYCSLUhZcZpYtNhS5OW0cs4DK7MUoxRPme+IA2yUpFmgWkL+AUHaNKHhZVRYJA4NgPAQqppeRLDd2dGbpP6eMghBrGWDeZrvRO30Mm5gCyg8z156peZoK7MGQomM749gLIFVDtXbp+0pYQPQA74sfZu6j9g+bEXSFN7HZXMdfhSwO0yVMwAp/IA2lcvDNjAtXEU4CK2c/cWq64FlsbWXNp5+QweENB1uWwwJ1VX1jaO0uT3guYqF2QvzulS8FR5GKiWxkfkUWGKpJWODTuNMFeH1I/YyNQ1m7t9nfYtB3Q7hG3tZuDWwbcArWhMkE5aAGWB9s9pTAJPegqkQp29ZePAjwIza5s7GW/djAVStQ4P2tzwyyiWCpwDpIzuBIZVGlifXei9ZK+9/Ibt6Npq+3bss9JKUE7SpSltrHyin7nPSz8KzlVqZvlLv5Q/NS+sTFJeoDaBVtipUuVpLmA39qP+C9Mo4TFONTeiFuYRbZ/DdlFTmxucZC4vs+oNd1uoaRvFrGZsMB4EnOepn7NPzU/LBMZRwOOzLGnLJ25e4cGJnJUBFPrqjuKCUEbUWpDjau4quLlLI0w+kTeqtllSUD9+7BVsddIefuBe6xIoID8GpCgVYI8f34d/o/AcQ+sXBqFAxZwgMEF8nF/wgNYr+D31sy+Av2WcZzNzliLtdVhrWl8Fx8TX4EfyqbOk5buGiHGA5eNOq+8+TIZq5i9AILk4CEn8I2smfJT8s4cx6WXtkQf2nRnqtTlUXZtQHYsClyGtR9szjph7pfqmOrKaxPawF9aWwygkX0CVbRJ4Zx/phDcAlmG9lIV3CuiAcnsor5FSvcD2GdIO6+1hXgssj+ODqDPQaSHLug2oQesckRw+EWJJUucBqqtPqSTnZA73ov6XGAM2RWl0Zgo1yjYguLtYk95JGzKX0mY0AGVWimyBiEV/jjFg3pdseYTsHgD/pS17LdbxIOvZZqqBr0hx7ygrmJ9zANmpoQSp3rw+aOdOX0MJrtnaN99lIdaILKWxH2yMYwrWifBwS6wERVjemu5XONZyMmcry/zkd82bwbDPSkt9QHKo37LNxEjennyaFJsv3uCzFnv04UbbtBPAP8xxaa4gByqPsU+U7eXKeDH8ilMMa/gEKayTrNGz9IfcXJDrmRBgVBZIkJW+hShLlPP5+CyB+mo8rl7jb4AuQTIZyqU1QpQUyn6UUJ/kAfDpuRkeAj8E+NrKvWxWlIBhgnZ82J/K4MpfwjFkDpQhxbXPCoC6gJhiWkO6mQcaSqKqL33Ids8eS9kXv/QiZczaBx/ZZ3e8rwY/DljEulrqlrLdGHUsITU7zSjLcaOneFmRx18CW7KeYPUIsIIGZZCrMKqcxgdEK3w8BAPIwonSlGd2VvcsgbjYRt4slc5i6inaJGVXr5wDtB61O1ANbGtbz3c0AuPNAYXMF35/0GJlaBIzZfux1yxzito4RTutKpqxB+vOILYADk6ZVdYzZ7P2h3/0LP3caB9+eKMdPQwYr2UOnVigrUNcB8boBy6bHB7LstABdxqGSfoqSb9kuC5TeSNcCwa4JtWycYXxWco1ViymzzgeO8zOMLczptQGdDKQMudgDMcTy3bxIvMgYHDPpi7rZB2bB0TN6dqF9ZnsSQ9ElVeGLEr/webx3EjBFuP0H74gp23cXKHv8BkV9EfUY/MzeTv9OuDcl3nAhHX30Qfb7NDBSvqraP9S2Qxx3Sa7L+HCLIhd66Xyy3ZWaOP4QtE+BJ+FuMYMYxeqe4Y5SP1UVqbrbFZTLA0W4/iHFa54wvQf9pIBxs8BYke5nta5nnz6KQcJ7t5/wFo72h3YmqaPVUHBY0Hso4T6lWF/ck/q4zjXI8u65mV9gnNwY8hLv5Qyr0fLSRbN9f/Lr8btK19K2sDQoh29r8Luf7iKscc4wsYENUa4ppaNKu2nnwe8dBzO6Hz08gp2spgnVbmA3eI1fYgy6Fo8kybpPfNqCXVmeOIzzObnUXxlSRJgXEr4PcX9Bq3bI/iHkaFJAMhXbc/ubbZt82Z3DZmiPQThUjWUy6gjx45RP8wV28QPUL8U7wx1pXaUj7EK7B9GcTTMvI45oTiXsc/9h1eAtwEDj+wGnmu2ihp8BQNJ7RSmfhrbstUA++cZQ2lsUzampW2S4y/RN4Llwqz5AnS0bhPkeOCvhPFSFiveV9Gom5pI05f0IfcWAvRvIoHNsl2YdpDKo+bBi8TvuDSw3bv3oLZZgQ1muI1Ae7MW1YMYUeoXoexazjDz4UtpX8ZIegX4XQ1Ln3g5QISnGGOV+Bnsbmwwb1/52zHAuRu2Y3sNqVq7rLmFaxmBnfRWKAwsX0q/89a53SUTNqr6qS7ypQl8tvyKX+MWu5bN6vkjKc5XVmAH+Kgs8sq6xl1aBPzlGigULLZXMpGzmekEqamP8VDOE9a1HnCOVK333HM3x2ONwjkEo6o+77rlhS394K81cO4Hb7u1PddaYK0F1lpgrQXWWmCtBdZa4L/bAoODg07t7dSpU/apT33KfvmXf5mFHDeduCoXCKQ0qZ/5zGdY4M+/E+jsQQHjF37hF0ypSgWKSSVNinB33323/cmf/AmL0dJ3wDspt33gAx8wKcfpNTMzY1/4whd4WuirDj6T4toXv/hF+9znPudgPJ1b6VQV+BQoJ/U2Kcvt3EnQjO+0v1Kq6riCS7T41s3CLVu22M/+7M86tTqdX6p0n/70p10K19/+7d926Ve1v6C0ixcvuhSrzz33nKun4LStW7c69Td9p88PHTpkSlWrVEqqv9LAHjhwwNVD0KDKILU5pVZVGQQV7tixw7VZKBRyqncC59QuSuOqMij1qsqgl9rthRdesL/4i79wkJ+CyKpzU1OTffKTn+SJw487ENBtvPbPe6IF/qWCcwJLBaIKDFUaZcGiUodcteXvp3PkVzQG9VNw7A8LwNPYE1CrPpFv0Pj8YZ3r+6n/P3XbNXDun9pSa9uttcBaC/yotoD8vNY1ermgLn/rM71W5w8BzquvVZhO2+itbVa308/v3ffdf69ur2NJWeASKlf/7i/+xCIEAf6new/bnvp280zPke5p1N34D3CXOAZQFG0nXdbGTvM1EvhkTsqNE1DtHSb95ICtLJDmS8E64JNYTbmVtHXAmpBirBHAAZACSsNypBtM30DpqLePIPoCN1O5mRwqA9apsVBnq1VtI6BeQxQIlY3CIio1VwYtdf6aJWcXbYWbyFI2CkRJa9fZaSUdLQTCUaBAaW3kuWesioBgWQWB3OYNtlCJgk5XhzXcsYtUfC3c7fXalTOnbHb4KsBVk5XQrMvXSUXWn7D0IoFeAn7hmlIrXd9mwQ1d5qsFrCEokp8jzSVgwuLNfuCNGQJ7BFS4ax8sR8FqQ7fF1ncTyK/mM9qbuT/HNtm+ftce85OkcuMGczgStZLKWlRImi2wqd18zUAkhEByKKhlBlEmu44CUj/pCQkK5AWvoQZS0d1isU0EutMJW/nOK5YiKDAmxZ76BvM0NpvFai1S22m1pEsMdXfYEu11a/CyzY1ft7bmMmurj1KvCdRVgI0AkiJEAQICdrh5rqCdl4CjtwZQxrOIss05gt5z8BIoyRGIXUwHLU4wNliyztbVVBN0oLEUjFXQlpSQ7k42AfECKcFyt69YFmWqvCC+DfdYtvEurn2CCKQQzUElKpeg3eK8AS500z7CTfUQ4EdhZoCvVyzQfsQiHR/ClgjOLr5uU32vcZN/2cpr6gmw0ZdpP/uipAHkJqWdsbEZFwRoxKbWrQN+SwIekFYzzU3+PEH+CJEp3ZRP81DPMipgsRZSw5VWWGr6jKWnSZtKXbxeYIZUOfZEdKG03EJlNdigoBIgAa5PPARDBA1AbVKHEUsM37Qh1nPR0hKrXb8D1aouBihwFhGEAikzc0tjKLcNWwq1HkEwYdTNAgSGs/M34QgAgWKkCe18kAD1JsC5m5YbeJWUm7cttA47AIDME+jHsB1IOjAwThAvZTv270atrcqpiS3NznA8GAwCKIJGpeySByrKZKstVEHaQ6WY4/zLpOsNBErZpgSIFYWsFLAY8KqfgLWnDBv1qo5SnSJyJKBA8k8oCmYG3rDczdMUoczCWx+2gOBHRRypQyE3DdvQiy1h00kgLhdcJZBCFDM1N07wEoup7maMbaBJgOCmR2ziymmy85KWr5JUorR9HkWBLDALcU4bnJm2kbFx276jB+WGVssTuFyeTQClpl2ANBTlvIylHDCul9SZftSpBD8uL8ZJjUj9UIjxEL2UykKKoLongDIC7RsAIPUAGDh1HmC8QkbgFX/nb6O+cx6ltnECslGr6dxh0cYetgOsI9BTABxUetnUzASswaKzdY0VHwGfFOebQ0UqCtBQ1r7Hcv5uokQLNnnxWxaav2SxCsDcilbahL4geCUQwIMvm5hK2dwSak3lMWtqI0UkvmUFVb8sMF4Q2wyWVAJrRAiSAVMQEo7UrgPOABSZBRCbmCKYTYCK62wBy0uAtTlAyBipA0OxZkAWASPYpfoOEKoATGcrw/iSUzbGWEyhoFlFOsQyoBY/MBgFwodMU8d+jo+NJjkefaggZjBMu1P/uell6r3OKtsBrEjzWiBonBhBKWXwFAG2HClS6QMA1oQC/kTKhscmCdRmSXe93erkjwoL1G8E+0eZCEAnHK6EM6EP8REZ7DpQuQ0grBPocNxSE2cI3CXMz1ySR41pCX4nj9piaVmjhSsBWOh7iBr6krEFLOIRZIFtZAFShgcAMv1z1rh5p8VQjssXOtwY9OZHUDq7BSSHD6XPCqiUBsICStGTSlLnxZsorgUZg48yDnZbYW7Ysv3PwTDdAqSrQsmrwwGGS0BaaaCPxZG03b40yz2K/Va7oZLzjAJyTJPCGWCUew8l3GtRkNKNWSAiH/bnB7jOZcdtkVSGfuaaiD/G8AlaYoH6AxdF6qpQ6KIvCGpLqS0fYCwCLXkBXgRqZUdeob1JU83XkQ2kI206zPzNWGXOsxXBaHrQED+aYf5kXhKAXAqo4ifFXHoae4htsVDH/aiv0YYr522q94Ql5qeYD/GBVYx/FP+yBKqVFhX6wlbwOf0TwLQW40FEQJ8C0BBqWRgzvkEQEf8DZ2aBgtL4hUL5Zpfa0Tc7aPnx89gtwBC+NgmclcBHZwvYaKyGwDTjUJFpxqiHFNcOwtP6hPGcR+Fr4dYJ6jLFGOwCJj6EO8KXcjJBPoUEsPH8DcY+9qxmwl+VCMBPjVp6EpAzGbSyDR+yYPO9mEi/5UZesMXhKzA/JVZSi90yZpbTwBQExWeHZ+AwU6SbZw7rXs94Zk4ALk8DvBUYOwG2DRG5VnukUbILo1bnL6evUVhKYWtePyAYwf4M9pcE4skCCMVIVxyuxOaAOp2qrEAtulT8tcfmzBbO2WL/m6h7rqBSud9KUVfzhNrxM/JFwFWoVOYXLgKzLNoy83AAoKFEcCI+ZXmescJxS1s3AFMDG6PumWE9swhkHQguoozYgM20o8KGii5h/rGZYRu9NWLbe7Zxvd5N/5IyNoP/kCIn9hcCKgtpvgdgywv2Au71lx+gnenP+ZMUmDoyRnmUwZLYRh54LBQlTSx9KOiTDmYs0ocErp36bg7fD5i70HcFfz1u1Z3dzNt7mXPxiYC4Xs6djw8x56Ggl8DXZYALUawT2JHPLKP6SFp21g6RlkOsOw5zbEDGlQnmR/qb1MWaq/RwBY3FORn3zP3+mm47fpm1F+vKvTs2WDXwa2ZuiH0Y5/RLIAxYCsjvAvlAIv5ojDTH9bZC6uAEc7LmrpBgekFl2H6GNU2klHUTvsaD/6HzmQqBNpVuVHMwfszSV/B9p1EvnWeYrLOGjQeYKuhzUilbBjhsSb5mCFVM/AFznIDYEkALQYALrP0W5+NWv3E7a719HLuZfVIoLfbin/FD+H/Zk+BNKY4mGOyzcxnglFJ8KXAnEEVmaZB2njEfQJUfu/aiWprHH6ZQoCtUdFkIBUnBg+mhFy3IQwkeH34kXwqs5LNFbMqPPyurqOdBDflSjBOwS+yeh3nTC+hH56Gies4m8aU1rLmr2qlfNXYB+EoBMZdpBFxvobx3kz6Jk2KZNRXH8QqcpO5z8ZSrW6zjCOBcJerFPBSBD0ot4FNZ/2QB2JU6NAKwOjtbsOFBwI/YNlv2b7frk5U2MLFis4ucgzrIjzZShl3bK4ElgEUowuBt7kVdnOYBkCVbXGLNiX+qZB3R3hmybTuj1tpMWr/ZvD3xrTHuMw9joi12x64qi5V7bJL7V/GlJNcp3HfbWmlbtgWtqUUgB23HkdLLBRsbztnl8xm7dj0BIDkL3B6wBtZZ5QDiM1xjFFjzrV9fZnv3V/AdgOrFOTt3fsGWlkJWU8VDHKw7JsZZy3pnUFyt535ZNce6aDMo77Y09wC/lFlvb4FjpRwEGArmmCPztnFjqW3ZxPqKNdz4aM5uXF2xvt4lB/T7UBMM4GsaW6K2ZUc15w9bBd167JmMff6LJ1jneO3AHT0o+JbaGPfQpwDIY8ztGzoaKEPY2jqBCoFgRJCxpLDpqZxdu5KiHedsckJgZ5SHPqJWX4PuMuDKDA837NwXtR27UHXl4Zypkaw9fWyOudiLOi6K0uGCjYzSlij1traHgHt4CAhArqWujbYvIV4wi91ybcaYY6liNazDt26touwAiLj84cGsXb64bAP9S/SHQErUWYEC21orSKVZZp09wNT4ntOn0sRTXrHEcr3ds6fd1rf5bWJmAfU35rVI2JrbgrZvL33eDpDG9mLeBAnNzOXtzIUVu3p5gQdallhz+OnDCqupBeynTL23bpJytZYYQi3XER6gubx99zuTwFV+a+K6SFDSGNeeS8lxbCtP+cPMp2FAJNaZyRDljpOaE6XM5Cz95bGmxjLr2djg7KKUcoyP5ezsuWXiJShUAqhmuX4O44dqayvtwL0xW78J/7NYsJOvZe0rXxkwLi9t3+5a7CPs2m0OO/XjG1s7q+mDSlvfBeQHbEf1HDA3T/16e1N28s0FgCKtzTzWUBe1qsoSfI7PphdWrLXNZ3cfjHB95re+GzmEEzI2PDSH2hvXP6zNJ8aGaXtgwJ4W276z3kYnrxGX8rAm7yJOE0LtchHYk2sX1npe/H0ZyrAbe8oAeGNWVeWnbsR+Luesry9JTGaJNSNrNoC1muoy20Jfb98b5PoL9WHAuS//TcJu3c7go0usc0PAxYFmaD9W59S5jHvglTzMFeHcjEOuOzL4sqV5s5t9WZTi4narj4cJ8HFaTzc0oV6Mj5wD+m9oiNmeXTHq6qcPC/bMc4sov/msshxwryRHW07gb8dR8Yu6/WZmB/EPDQBb66z3Rp4+ZK7D/+KkqFPQurqrbNfOEsrhYxwptpWy/lsZxiAgJ2Cz179sFdVZ/Eylbd1RZnXVPjtzMmv/4T+eBJxL2+7tG629vdy1x9TcJHMb/dJQY9u3lVv3pgBjDIiTa0jCVUCPBRu8mbULF1L4NMZKbhn7iFod7yz1Hx4ftA0bS3iAqN5q17HGAYZ8+slh4nkr9Ok65uEgQvrjgMpj7BO03fu6mLtyNjo8iY0CUpLqfHRYxxYMLTgQABlorrub9dyOCmuo9zsI79KlBG0xT1vgj5bxM1zXxXjAqas7bPvu5HqI7aZuk1L4y8P20mv91tXeAHzYzNqdMo7OcIwF4heV1LHKtm0PMsYEEOvSCXAVXzI8lLVLl3WORZsn/lfNfZ+mxkrWxwGEPIALefDu8JFSW7+hlLUx2bnO5uyVV/usvKLc6usqOUaWY0wxFWe5tj+JXb6M2l0dinP327337GFcyncLmZN6Lu3kMFkGigbLP+MFOCeMcO211gJrLbDWAmstsNYC/yNb4J85O/2PLMrasdZa4P/nFlBQUxCXYBYBX4LV3v0SNDNKoLGvjzQnc3MsqhsciFJWxs1VbrjqleFCRcfQ34LQVgOcq58LLFtVTlNwU8fUdzqfoDKlflXq0o985CP2m7/5m+58Y2NjDqTr6OjgiZPoO8HT1fNJZlpwnEA6QTEbNmxwqVxXy7RaLwVldW5BOqsvfafzqk46Tnt7uzuXoCApzqm+Oo4+V7BX8Nw0kuAqr+qv+qi9BOVIpU51l3rdunXrXJBYddQ59VPfrf69WrbVcqhsalOBglKvK+fRNEGJgpJ0jrXXe6sF/qWCc0NDQ06JUmmIBXb+/u//vn30ox91EN3320NqozNnzrjxv3v3bqsjnc4P46Ux/Ad/8Af21ltvuZ+PPPKIG78/jHP9MI65Bs79MFp17ZhrLbDWAv9ftoDWWqvKn6vg8urfKofWitpmdX2mbVbXSVpj6Tutn/TST22/uo1+rn63uubUT70Ezl2+fM7+3Z//oUUJej2yYbNt5AZ9dhhgAFUxxVHdCosAbKy+0arft9sid20nMEhw98xNS7550RYG+l2AJE9AMAfwEAAEiXaRPm7/3QBVBPs4QH560OYBtBdOn7Ps4DAxRZ49R1VEwNYSUJKvq9U2PHjISturibUkLXVzwOZfOGnLZ1Ao40ZtFiArTUB+BTWS2u4eW7eTVJ5lJTZ0+rhNvfqCVU8NE+wkPSEwSwqVkEjPems6fJeVbtzAo+FhWyCYkJq6adHMpE2fv2pT58bMP+eSmlFmFIUIlguyq9yzy2LbODbtk+lFDeb0KZu71WsRbujT+MBlQE4E58o2b7Pa/QTIWXd7ePo+B+A0jxrTyqmTpHkcdUF8HzeqFQANlpST3qrVymm30KZmAnBpBw9OnzxlU5cAOwi0hahXAWghztPVlRs7rO4AKRIJJia+/ZLlSZektEnpWJmlga1SBIDDDRus7Z6jVrptGwJbIdbeKCkBHsQCSw4GStIeHpRAwgTvQvSJlKdE582jiOEHrAl03mWRwrTFrz5jQQLNkoxIASKBPJEmlvar6LB1te2Af0CEsgBgHyJg2BXRuQI3u5dHsJGLlhu9AlQUscD6eyxTfwBVEODGBEDWRJ8lZy4B2fQRvEXtDCgmxJswrwUIOK0ssV3nA6Qx/TDBckUb3rTZ3heBTUhliLKUAu1pnlJfSQhg4IY9QJgfG4iUCrSpAMojuDU9SvBnmf4DrCPFkq4FpMC2vISijLfK1m3/AMBRgy31A1kMfsfKUMQIhFAmQnUuAXjkwU5iDV0AET3UD5gKQ80TIPL6UFzJLziYIz81aEO91zF3r9Ws3wgoSUCclICCIXJABIuAg0mBSXnUmGipIDYYwnYKlM+fBsisbjN/x1Gyu24EThmw3K3XbZ628aDEYGUiC1AykbqKVFYAkogaWi224kOdIE4KxyWiTGGAOSkrCLhUyrAsgJTPAwDatJGgKxDmBNd3gBZB+ieMrUmRR0p1obJKCzR0Ait00HesH/PAVOpLTi01KSLwgDAnAOdOELQLWXgj4BzgFQfFVoA5FnotPvoGYNQNoAzgDNRcAkhAlAMOFeLLrE0JxDRsKaZeLAEIAuhYuHoGpcQZ1pBAT8BkOUAhrphtHqhlFrgqQD3aWmvkPiwxTXAGVUkxE2GpagA/5vBdSY5dCoRT2szYCldTtwngmQkrBZAM6TyMqwRKV55IA1AJ0FAdICgqlAUCcOhxUD3gH1TyoDotPXbRpodvWxwAs757G+pxPaS2VNAfcGyOVK6jVy0xiUoW8E6YvkdIxsEbKYJu8yiTlK/fbOWdey0lcC49b3MXv2m+oVNWSuDMD3iZA77JQPoQi8FnYLOo3QXDjRYh0G6AG0vAVAngPI8njsIO7Ycqks8TIdCEP2G8V/V0k36SMTd406Zu9gHTAPIAHuDpbUXAKOpMZTWbATbxozGACCk1UE8vyihSsoJepatO2uCNU4wT4E4U1MrqUOCKCO7kITNsMzGMahe+Lyf5FVJyeQGv5Bdy+KE48FpJrMMqOrYBLdcBTCzb0jAKaAPH6bUE35XhGwkYyj5Rc1kGZAvis5tbBd4GACoASQBE8wT/zMqxD4BQxqBSHqZJt1nRSjrmGpQVp3ttvvd5/NAckE4VCkm1wCPcGwk1wgEzrtbJjyI5I26HgFvOh6oa6koe1F1ywAJjAygceoZRnNuIzb2PTgLGow/zievYKH4XuMpQjRTzWgps6fGhdpoCrAXqCROoCwicK9+F0hzwy83nbGX8Bj6PezDlDQB5QFfYi/pcwlk+gsu1re2MHxIWr6DQhFpYmjGltNsl3B4J4UsRTgU8Iu1o4yb6WmBar40PXsXFFgj00wYF4NxlYCGApGgTaSYBNOhYxkMFkBT2R98GmNekRFaYetHSpB5djGOD7Q9YtPko5wJYW0FhbgbVRJTzFpJXsQeU9fB1XuC0cmwunIgD5TAjrSMd6/oiOFdIXMDfnbQ4fsuPbyhlTCmgu4LfT6MGJLVRf5DyhVCRCwMoo86TnxHUNUDDS0kWIBNH5iH1ZzJHiln8YriBdKCogPrGAKWvvQwMhW+sJL008yCJrAGvyq1qXZtVoT7px78K0i0wFvKc1yclJ0Cg/MwNW7z1AuqqAy4tdLj7MP63kz6jzQFvVibwo7PMJSmAKozAi3JkCZCfX8A94OlKvsyimz9igbaDzGu3LIei58IAaUUZKUHKUgCyW9F5gNs90AqlrC+iNbXmBaJPs3ZJxCcpD7AR0EKAbX2QOjnWOelEyCqaN1PHRtYogKQ3r7ImWgDCZpyxblpm7kkD3VYzpiJ1u/BHjZSvFLCPsYW/SKOk5jXueS2ftuQg8PfErJXXbaW9UIYNdAGi4o9RN1sZfg7VtuOWkqIXc63SScaou1/3vZgDMkFAaJTlAi3rNQCYVwds4cYzgF/Mh0Ad6SDzWBYlOhTM4vhXT2LFWtqAlv1l2CwwXgroD/BMa44AwBulxv8BgwFf+mKbsL+PMSctABseY9z3AVkFLI79L5HS2R8G1K/YChgB0Ei7F/CfHoLf/MNaTIqcQEUAa/HbpIqeuGhVzRWAqPuhPFkH4mMKSR5wGL1gC+M3Ad0XgDcAylDvCQEgK5V6EtgjGltnIUGPNUcYYJxj4ZplJ05ZFjstALzgQBkb+CTWoLlwORDhFps36oaKYxXKTNmZIVsBjvBlGeP4v6AgftUSGFgqXCUAyMHWDkuwfpqYoBzLQ8yZKObgaz0ZKdNVoJS0gfSzWwCVG10ZpEorlWClsIVgZED3WXr4NKlcWTsxQVVv2A3j100zAPolxwB4z1Be5gtSeScZf1IpijGfScorHs8AtnqtfsseizQLnGvF7hdR7nwTGPMafoE1EKeR5lIKv5tlkAVQPi2JdFhpBfdDgfRTC/ik5UWX7S+sCRL4MUXdFldQ6EFJtKLzXg4wa4uXv2SBlXEHRXnxJVnG2wJjLVLbCHDPwyexVtZklJn+y9KuUo5Fk5Aq8jDMMCDI4EmrYO1c3XYXgOJdtD/rnrlZ6oYvnb8I5N7LWFmxEh7kKAG+9nJ9kcM3JrG/WNddPLRCOVic5hNA58wr8Zl+AM1pfDu+DPuW+mQuVwEYUWtLy+vtzRt19tr1JhsHzsrZIv6BdQBr0qpYyA7e1Y6CUilgNqnqX7oO0AKQn2R8AgVKdTOICm1TS4b09c12x/465h+Pfeubw/bssyPAXgBJDYDJqOot8ADFwjwPguB/u1pL7IGjVXbPYcEhjFPafWI0a8dfnraXX16y/gGAKwDaaGmM9q9wa/1hHjqJRRfIGNNlH/1YF+0TsKefGbRvPdFngyOoQfKAQIz73iupKauomAfsaLW772G+RF3z9siUDfYX7I3XkwAvMXw3fUc/K7ludUUS1cuYHTzAA6lAnCdOLthbJwHgppjb8CFBIPEsarqNTWHK22kH7q4B2vHa84Bz/9cXzjpQrKWxAfW1KCAL0NrshFsP11bW2537wnb/+6uA0VCYZLhOAM2dP7Nk3/n2MA/wMmdh9xFA9mhJGetpoKtZbClx3j78sTp7+NFGYLqY3bqSsT//zEUbHE1bI4BueRnQDWPYFxA8XgF4Vck8H7YRYKpTJydtCHUxKR778I05+roSym/Xrhb6pwrlqWU7/sYM282jgMtcQv2kaid1t7q6kL3/oVa7894o1xZmZ8+m7U/++E0bm66xnuYm0gej5Eb8YD5B2nbsrbw8hRhAox08VG/d7cy7+NoFVMfOnF+2v/vGsN26yYNElCOCD1M/BrGBOBDylRtn7cce2mmf+p83kI7YB5iTsf/zP92wt05nrYl7qBXlwMI8TODxjltnR9j2vw/fyPi52Re3y1emAOPwz7rWYyEQ5LqkpibIgx6t2B7zQzpgr78xbSdPMa7xvz4B/iJ3WasJTvyxD62zAwfL3HNGp1Cc+9LfDtgQx2sDJorFQN2XEwBsY0B7s0Ba5aTc3GT3HymzjvYikDQD5Hfu/JJ997uDtM8881DYykqY692iFPAuDjjKdeD79pZQv3r8f9COA+h9/fEVO3PuijWTWrsKu10GgKWWtntXO4Bdg1U3ANNNmt24FrITp+btNmBkDhU0PyAbA9X56x1byu3BBxuBrkqAm5bsxdcyNjoBMEd6a7TCaI8UoGXY9u5rtYc+iM2t99sbry7b33w+YddvAXxVSV0MQAupscQS6794wgFuu3cCQn2kg3hNGJeBkhpQ4Y2rWVItJ+3UKR4aY+0YZU0f4mGHEP04D1gbZ57asbUN5TGBf2EbHMjaZz/Xb1dvRp1CZU05cKWPBwpyk9bWUoGycw+pmoGvxhMoIi85eFVKuEEpIKLwWgp0thH48f7D65iHAnb67KK9+jp9PaGFDjAl61Wp+UYii3YXwOV9DzRbN7Yhxbn/+J/OMr6TVlfThJ1x3cl10SIQ/vIyYDHr9D07a+2BD9Ta5m2sqZnXEws+u3opba+8OEdbL+GjWC2Usg4rJZ00sP7SkheFvjN2+N5a+9c/uYm+Dzmo9zP//qS9eXIMQLLbjakM6svmnSbNfMTuP7oFgJSHV5hjrlyaRwVynvTJCVtYlPlN2+EAAEAASURBVIIcvpK1hAfot7urxA4dbrOujjK7cmUWBbcRGxqWT2ae0zUfdipVyO5uv33o4122YXPMZkh9/dUvD9mxb/fh/6qtkYdhsqx5FhiLSzz85mN9s31Lqz34UDVAMQ9NlfPQEhBy/620ffeFETv51jjzLdcjrEkreFiohDVhHvXt0Ym4VZYt2M/9m2ZssMamRwv27WNZ+7//5tv4JB7ua2nD7/ponwUrB/pLLF9EffV12wz0/OGPHMS/8vCPC0WugnNSW+UDusy9VZ0f8LUGzv2ADbe221oLrLXAWgustcB/qwU0Q6291lpgrQV+FFpAQNoqOCeVtX/7b/8tNxV0gb72WmuB91YL/EsE5wQuPP30025cnj17lhslMfuJn/gJ+/Vf/3UHef5jPSSgYRV0WIUZtO0qyLbEjRKlZ1a65X/O693gxLuPMzIy4gA/pUSW2uWjjz76Drz77u1+VH9fA+d+VHtmrVxrLbDWAt9PCwiUkwKoCyCgXCzfrwcU5Lu11tNDGHrooLISOIO/NV9o3olDP+jBCD1ckCRt6gpKRfpeD0q0tra+83CCyvIPgXPXLp61//3P/shCpFTbEqu0+gWQgFDEGrvarLwVxVRSgkxeQYmCu+/l7c1WexQgrqbGVl45ZYtnLhHsNKtc3wV0A/RGACeRSpKxjEDlxi0Wbl1PejeU3c6etKvPHLO5qUmUDaqtnqBvmGC7UmuNJuO2SMq/DXftA6IA1Bi/bSOvn7ChN96yCgKb9R3rLdqAQpVC1AsAFQSRKlF8C9ets3jfZRt77ltWzVPckcZ2822/E1Co2bx1qKd0tpsX9TgPQXLCsjAmPHn/8lN28bsvAzv5rRsgpqmlnW/iPJRx0SZIuVRTV2/r7z4I8IAaNKrKV8+fdco3Xd2dFiG4rsDnHJBNvqLSKoACow0tSAWs2NLFK3b12WdRihlxfVTfTfkqgCSITcQV0I8SqNzSSfC4xjJTEzbx2qs2eOYcgduCbVi/icB1Aze3SykDKnGVpbZuzwaAgJhlANqzzz1vs6RqiW3capFNW1EqQ8WP7X2dG83HU+/E8WkZ0sAtATjN3rTxW30E6bIEwsstTNAlTzA2TUrVPCp0cdqzpHsPge8DQA1jlrj0NFALqi+oZXm4ae+v6iIg3gzg1AikhBoWEEpRhUiBdOARgp7kyUMRsM9WRi8BG/QTqK0wf9udlq7eD7YEdDZ53RL9F1HsGkWZJG9l6wBkADgSBEQKfBZcEGjDQ0IbUVfq/hA30IGsFk7b9JXvkuZoknQ5KEBUVQNVCgIDmCQglwQiiwJHlfC5ALf4sAKIBOirq0lVV0t6QKBDoLsVKb0RVA6UNVjVjo8AidQBbHwHGOhl0sURcCftpKdkA28ACIKKoaoGCxJIB00juA6aRqBPKn0+AtSF+QHL0TZTpLMMEjQt6+ohBV8T7cET8AR8EkMAmLd7XTAiVo1CGkG8JH2dI6iSnRmzCOMgVt9ugQ2kNqvc6Noq2/uKTQ7dIMUudaHvSoHbDMAoQyAuR+pTcvwSyCE4QWA+Q2AhRPrFcLSc4Ja+QtUGtYNl0v+EAvVW2bHVggAe2YmrNt53C4Algtpjs5WgMOcHgPEAKHkrG+BY6vAhAqmIWNJ2SBzQhwmC9P2MNWAAVNlyfB/qOmKBWmAI6COBAnNDwBBjl4Ep4lZaQyAelZ8UIIYnSeBoZoR2iFh1yz7AoM1AOATSgRvmrp61NP0bLgFLiBJ0JVWbYBwFtucWJjhvkkAgRSAQl0Z9ISwIlIBNHqogi4LNEv2cRJmuGnWLiu6NnKLWpm4N2RJgYBTgIEpfB8ubyeRZa74odStvYthgJ8AiWYAKLJTwEMQpKhm20keKuQsoMU4wzFBja9/CeAXW8aGOlwCSGThly9iq0lXFSPkYQiWrqK4zSVnxpXxevXEb5dhLukr82MqcTZ39uuUH37IYqZciBNN8ZbVORSiNT0gukKKS8VVGfbw8xJZKALkAiSqdYymKIXp+bgWoJ01fL8wnCQajGrJ1M1BELaqT/TZ6/Rplz7kgawioM0D9kL9E0awdaA44MILCEt1HqB2VJxTQUJrypAcQ8zxuw33nCeTWWG3bPU4Zy6O0ULTn8lSvTfaeol5LVoYiUai0ijEFqEoK1ER8GtUOgtRl660SnxDAl6WBeRYBZOK3XrUQ0FIZioVeVJdywKIr2HUSpcwCqcCi9HcOdTcp44VRH4uW44eAxVKkhkouDXDsW/gKlDg2HLbIul0O7pztewbYbQabrTUDJA1EUQ5V3fA3HmAuOpSfgA7AEnkACz9YFrmdLDVyyyaHrwOSJKyGMRgBxPP4mhln+K+xN20GgDekYDtQbVBqUkCFaSC0JDBxnuCq/E8ItTJv1R4AEdSR+p62BYDXHO3vX9cJRFTPuQEVgZjyKA95UL6JokZUCK4Qfo6jaAjAQZk9wGDIgwHfcd45yhZsAcQEuqom5ffyFRsdQB2VsRlFha68qpN5lz4LMX8yl3iBmz2AcxnAOULQcqVWIplRjpcZf4X3a8BPtGXb/QCxR/ieOSsOTDN4FhWzawBa8/i0kAWoI9pSFkRdzwMcvbKQpN+Avbc8gC3ShvGrNnfjBDY/gO8jpSApy0vd3BLDHoEDmWvTQCpl9FcYhbEUfbq8MAo8QDox2S1AnWCn/DKpQoEdM6SDLus8QppU/MLQecte/DYw86JlAdIKte0AwYxBVBQj0VpSlpFOE2CHDoXBo00IdHqkmsc586QyXRx5nfmeeayBtJStdyPeBlyUmHYA9uwwdUwv4A+D+AR8ohdVQtKdG+BgFqWxHApXsR0fBSzjOhjgN4c6ycyNC7YIWBRhri+tKqaT9YqSmZsy3xIqevxKYkvgOkAPFKSiNUDspCnL8EDqEuNwbhrwEBCnZeN20r3iUyambfwa8BAARrQcFT3UFsMVjG/AUT/gmldzB7AuE6RWUvgsHnZgPvR6gJjmzwCovuVUlkoB7EobdjNNoIS3HEYp7ZZN9D4FmNVr0eqYhfAbAqaJFJt3EiiQVGpW2WilnVssgOIclbXM6C2buv4EarAo9FE3D2PUIvWoj7FWQr2xABxewVgJ0pdKCVyIsg3j28dDAjlUDFcSqGwyf/nxgyU1mwAyP8E+S6gRHqP+/Ywh5oN1PGQA6Ohnri8t6SB9HL4aYFPnd84Kn5znIQYPKXTzi7fwwZfxX8NW1VZvoZYdpIPewHhOW2rsnM1jp0nm9zC+uKoGVU3guRT+JU56zxTnrakAEmu7G8jzCMcGFJujX6cuAm0BmIv6YcRLeSbDGnQev+kpX2dVLZ1AjvTX5G1SSt9kiHoZV4xlFJ7yQGGLSFYuTgwyH80CyVShgtcJ6kvqvtt9rJ0n8Gthq4zVA8DUA0pVY++tjPUWxuE6eD+AM0Amr+BV1krk6ICVuewAwKWZeWwZJbPObTycASjPWMxO3wCMfNXSSzygQaYOXwXrywDQKcDiEuvGOArJHsrUvA1wrmk/frqN7/CXY8yt84OoAALKonAnFbokqnyLACJ+xkg54G5OUPn8JMfLAqBWsXZn/CSZH/lsCdXTJCp6UQDkim7azoCnLvxnxsQY27N+wS7C5a0MF9ZgjGfN9QwIzIvxw2TBpQMpn5O8F105ZpnrFxhTFesqrJK521exFxtl7cGaZOEWfj+FTdawRqxAyZGHOfKAiSnWMgXmbq0vynoOs3Y8Sm+h6Bnn4Zq52/TZLPVDfTZCGlzg8iVUAtPLIdYpUTtzsWDPvokS2cqd1kJ79mwAfmFJmVxRO8wBeVQD64QAhabsa48/A4wipak91rO+GX+cR80NkNY7anv2NgLtNKO45LUnv3WbTCZXbWSi3FobO2wrAEhzE8BX3EhnOGz9QwN21/56+4l/3WWbtjD+4KCOH5+xbzz+lvX2raA81omyEmA06sKDA4t2vW+G9OqXrLHOR1rU3faxjzUDkXCep8btK393zi73JWwd4Nz2jfW2oTtinV1SsELtrY11Q9Rjx0+MkRnmNOlH51Ft2k/526yyCtALmd3l1Ixx2WAbu9bZ0ADKWc/0o+g2Ypt7OlH5aqK+KKChcukLxm3b7gbSJVYBFHrtuacz9tefB4hHZaoK37Cxu4F66oEDv125OgXkdZtUlgv2yU/tsEP3Mx5J03nmTILjX7MTJ64C+TSy/XaU8mLYVs6u3li2azdRAitctU99stse+UiT1VeXWu+llP3pp8/ZhWsDqMtFraerhTTs9Vw3lgLt+K2+Mch4itvXHjtlN64vWitjctf2DsY40C0PVskf1daG2CdmZ8/3A+1dB+4roX67bNOGKuZC3PFSAthoFMW5etu1twL1Tj+AZNb+1//lDTtzq8TW80DW9u6CrQf4KTAP3+ifsht956y+IUJ/7LEjB1H4Zs14/dqyPf7EsL3w6m1ArSrbubkVoKkMRa1Zu3zjNkDkgI2ievzJTxy1f/Nzm6yyAXBuOGOf/UyvPfda0ip4QGvLetTVNgdRMgsDKwZ5+D5qA30F++Y3eu3mwLi1NNWjSt6CEhj2C2i6lJzGtmLcw611aoVf/sp5rjXCtguYqKcHgBhXOc/18nJ6yXbtCaMOhsoyF8QX3sr9P+y9B5Bc53mm+3b39PRMT855picCg5xzzoGIJEgqreS1tNLalst2+ZbDdZAtyVrZsr3SqmSJsphJMYEBRCaRiEzkATA555xzp/v8B+LWbpW1VbJ8a2W5pzQiCfT0nPOn8/f5nvO8+vE/V+peZQPHiJFvZhZjOsV6WKGiqlUP+J7JdefAvjRtWAvQRCnn4QOfDnMc5/lcnZVZQP/NVGq8C4tgh8qrulXRSIyxP1Lb1+foK/81WdnZYbp2xadXX5vQhWtXlRLj19wZ2fRhivKBm3Ppv5wCu9Jy7bp3d1qvvT6uG/c6OTeHZs/DdpbMusYebmSgV8kJQcs6FwA4e+1wrR7W2BnLWA3nxVl9aAQKo+z9cnJitXpjsvKBGS9/NK3nn5vUjfttAIwDmlUagZkvnfdKUl19FzayB9hTJ/Qbn18NBJmq2ESMix0+kpcmdOwUa+ZQo9YsSeM80gGnArQJ51ldxx7BDlA7V08cZAzPc6ihLqDvfr9Tl++x6th7NDPPp0UAa9nZbmx8buYh1wPg5ZdfvqZbt9qAXks0Z/ZM+o0HBpj8o/RjdFzQMqe1tzpIMGpWLcbWvJx8RBLpGOkA5LmODWPVzC+O0OKlBsiN1M1rPn3v+9d07yHrJZ8nZxTla9H8TO7DuBif3XpY0cTv7dbBQ7O0fRdwK/u8lgYTs9wPoFvJ7w1obuksjGtxjJEB1p4R1pqAOtlXPb4rV//5i5i3c8NZ4wP622/f1NmPqnigJUH5uWmaPQsTZ2GM0rOdyvPw2SUVIHLQprdeb9KlK1WsiTGaU1rEzxurN6ZJDHzu6HHsfnyOYa/++ms3VVUzSBtkafH8QsyKLiu2taevk88Aw9qwtUBFrMXcFtDrP23TkeNlCIdt8uTmAOBlMmajAWF9AKdV1oNaa1Yl6/FDccrMwybX6df5c/06/NZHgLgTKi4qxahZylowoeoaM3dHAGJ9KuTY/+D38rVhU4K4hFhr2j/983FeN6YSEgZmluRy/OmKiiD1oOqcyh4cUcmMRMC5rVq3ZiHgnGEQDDhnrmMA8CFwjvYIfYVaINQCoRYItcCvaAuYi1boK9QCoRb4VWiBT8C5Z555xoon/drXvmYVR38Vji10DKEW+EVa4NcRnDOAwze/+U09//zzlhXIQA9FFM+/9a1vafPmzdxU/d+vp8YSZCAH8zrz78a8aEyKxtZo/vvKlSv6+te/bkERZq5v2LCBm9F2igvT1vub133yngaeMLCEASzM+3xiIjL/bd7LwBgGrjC2IhPPbKAK8+/m50Pg3C8yckOvDbVAqAVCLfD/TwsY664xgBqIuaysjPiKDp5Qp7DOzV9j0zLW0YULF1KAWGKZg801wFx3qqurKQxdtSK3zc93A1kZuG7Hjh369Kc/bdl//1dw2vz7/7x2UCRvuH9X//SXf64JzEw5FOZLKFouWb5aaWsorOVSZCQDyF/ZobaTH2It6FYmca2Jc2Zr6vY9TdQ3KKoQM9n6FcSjAgnw5D9VSstaFaSgbg+Pke9BlSaPfaDaW9ewunGjfD3gVkkpQAEAhTE2cf2apgjspnBjHyGC58ol3Tp/FgvxpBavWaPUuQutGC7LgEJbGDgGlQ4MEHBIS50a3nhBcRRLo+fOl3PLLtlzCy3I5BFoQgEfYiVIEXj0ow81+M5rGmptU9zcZUpbt0PhxH5SKVbXg/uqu3xZgfYOChqlWJ4SKQY3qqYLo/OCBcpcskhh2VkAZg4MU0RLhvH8Ntda1Ejy1XAj/tQ5Vd69r/zMPBWtX6eIEoAQQC8eBwfqAXLE9GSj6BU053fjY9WfOys3RYVUjHFJ61ZaRW8TpemniG5zYBMkZg+6Rd4H92i7I8SYtGKh26KklespPmdQwMd6A4xno2Dmw2zF7WyAjgr1Nj5kzExSgMWel5Yth5tiLdYYP4BQoAWDRd+AInIA8EoA57jpP3rvuMIoMtsTKSRSnHUkzQIIAYIxsZ4UjDlL/kmfcvyPbmJTnfQ1Av/d1wi2LnRdQAK5cmYtB2aag0wE21zLNeCbCo4+WnFZcxSRamA1U8DEoNaGRbDpKvsSP2a9LUAZmKB450A/ZrCySxY4FQtYGU8fmiI3/wdc0aWJdgpWA20UCdjTBJzYz2KA0vIsEMoeRbEYO0mAyMjh5rPAQneR4xHBNRdDkTtPQ3XnNNR0ATjApbi8FZi1VtN+6YwJCtQUv2kI5gOAB0di2Y44ctsU7YkpZqKRIvwooFBKNoV5TF5Ai1RLiLilnesxeVHkT8jCZMO3DePANJa/yX4MZ/W3LUAwmj5wlW4jjhhwrgejTdV5dVFdcQEkJRSUKjzdY7V1kLnvBUQcawVExVbnZw6507EclKzgWPOwK3CKYw0YY8rU34mFCADHQEQuTAvGgtNYjWGK4k5S/sJHbQLwYgCxIABsEMAtaPqS4oMdONBuoDnAqcAQcEzDTSt+LjIe4K6AsRWbC20yCPhXo/Y6Cl8YnJJT0+TOKbTgpgBFj+mhe4yzS5rEapeSswK70gLmG+apvmZ1PMDYQxEnNi1GaXmAIPGzGc8murJDE70P1NFQhp1mEDAJkCUxT+7M5URM0naYECYxOfQ2PNRQYzmFUUBTs0aEp6i7oUljXY2CTSTmcRbQqDE/cZxYh8x6E8T+hluCb+xPxvwGLGDHTOQnUnWEMT82inUitpB2wTbE2kYnY/FqI+7wFudKYTrVA8zDexpLz3Qb8XrV6mnBmMj+PIWiUHzhEiJji6n296v79hsKAorEsFePzOW8U2ZyDEBOk2Ma7yJOk0jKyCkAUWbkmBfDHGBdVDYQLJCKDUNRAKCll/fuaWpRPKbKVMzpTox5o03dwG/E+gGc5BViIcIcxILD2GAuYp0LmFg+9v/k+DFWidgiOlA+7C/DNdjTrqq3u/GROS5nI2PU/K5JoJUmgBNMXxRec7h2JKQD/2EZC2KiCo5Vaojx3dc2jCENW9iMJczhGRQaAYMbb2uo4YKi7eNKJoIuPHUJ8xcomgjGiY4GDbZV0X6tQDZAYmklzKll9CH9AbwaBASb7Liq0bZLtDMRXKUbAcsWYV2qVlfdST7fTDK3WWNSlmNg8/A5JAmrRTznxPjB1GMzWiYHpENwHNgPwJao4IE6AKlejjMxTXGF9CGxtQas8o7WqKP+rLwAwakAJJEJyzg/+hfo1I/FbLALSx3nGU+BOGrGQTkSgUSGMN/Xvcc1gJhPzikqdzmcCQCyKx5TDHh1R5VGqq9h9OpmrSeG0oDqhbOYW0X0BWsR9kA/FsPBxnrW3XTF5s3FOAfsN3xHrXXYUVnzUjIXMNYWMD6BrIAJjYDIFglwTCFwGsoZZx+gMzGGmNug0TVSexkQpoa5DNyTvxkL1PJHpi/WxL66OxawGJ1AFCrmOgdwW5AmCjJGJ+s/1lAvwE3aLCXM3gponcr51am97JoGutowl7iVbOAja4wCujEufX3YKYksD/jGKfpzLcOkF4atM5brhSuZMR4GRAzAF+jFhlp9DuMcsEzxesC5UgC3MuLTP6Tdpxj7ixSevYL1AirExHhj7TO2TP6Db74YqgZWt/lGaTNiw5vLNNhfBcDmJUoYy2fKEvofwKefNbH+KmtaF8BnBmvJDN7TrLEYp4CTfS1nNd6GmSzcrbh5exF5beT4uuWvv6JeoHk4RcXnFWCNw7QFDM1ix7FXywuwNEAE8zDF4Gj6MCUT618KYJ+B6FnTxnt61NyIjRGjZ/6cQtZNYpeB2JruAdsSQ5aRV8x7ciyAvXQ+10TWUgyR/BJAMjqU9ZhBwnljvpnCuNZ6S8NcDx0R9FP2KjmTAdwBM/1EaPdz7n3tt5WYRDRgfj7rVx5FYIB2+m6qrlZdrZ0KB9BLKJ4Ns15E31PwB5zrrHlPEY5+4pc9HDtjOzqf8+PzPPDnIH3o76tlrQEwBdSMyud6nsw6GgHQxNowOchDAhjwgmNEO2JzjCvYi4BuXB3l5wC6upjX7NkKGGesi3YgskfuXdZp5jCLkDXGbAawBsjXGLbD7rvq7QBSw6CbUjKPsVJEWnEKY6EXUyxAWS/7L0DtuAzizAF9jFVsCvCzD9huuL1aKcRhJuavlD1jM3+XZYHLaDAfzXWzAeC8WGCA1bs11NasISC8xJwUTFt2IFcgfeD52Ayg2ey5tCvQG1DXwLAxBF6Rv7PMAh2iCjysX3a1N7Swdo8ojX1SUgbwm2um1Yc2bKlmD2ZdF+1xgGVmHrKfBCoOelkXuq9pBKuid8IJSF3K2sC+E8rL2OJGsXCOYO6MAEaNwwoYhgXUjCU/4GdfewORl80AM5MqYg8aiW3Q56CvGJuOyS5gOYx9AeYGRtAghkk2D5x6pcaIqbUDNvrYg/uIeI9KK7Tmht1pDHdAvL2VrKX3gOwAErMxiZZsoF8G1XHvWex03cBvM4HfNgLLcW3GxstE5pjoPzNOWWPM3to0K2pVjgFInj7swwo4xXEmk/wRZdaoCNa14Q7m03X1N1WT/Josd+FcYEsezOB6EwR49zN2p7HUBdizRczYpLDiA7Qj6xBz2zo39uRmE4cfmHNmjZkYYL0eVUNtpz44W69blVjBMj+FYWsB4AlRi7HGncuPmntirPeDPQBlxzp1/PRlLV48U7t2zQQ2CeMzD68xax17s5hY9gs8bABTz2sb9M7bmPFGc7V2RaF27HQrO9/YB4N6cHdcr791Cwg0oMf2zNbmTcDBAPrPP3+PaMnbRB8WatfOxVqw0G3BZs2NAV28MKYjJ85w72xaj+1arqeezAE8sxMP2cd73VRNw5BWL1upnTuB1+YSbQn45wLANZ8BIth7fHiqW8+9yN511KltmxZr9VriFoEPuUCa52KAkjkPju3y+UkrYtYVMajdu2cALwE1R9mACNm7cC3lWSOgHLB/AMwT70/rn545r1r2n2tXrNG+vSWaNRvbKB+tqioCAHLNul9xiXOZo9175zwy5B1r0ekPb9MPPqJKF2rlylzFE71p1rMPTwErnaRfRsr0mc/SBvsygVmjVPdwCuPcVZWV3wfuytWnnt6M9SkagxdR0gwjI1+7dWNQr7x0jTmVoA3rZvEQLyAPMajm/Ey/88wCn9V8Ovp+ua5eayJqNlebNwAvzcNQSESnnz72QzFHAxlGAQWabcztW3791df4nNvo0tbFBfr8p/jcWeqgr4IcCzarc2VqbevWcj7r7sfolcLaeeFiI4APY9SWoU0bZ2D7igAqpP1o2w/Pturd986qmvX0s08f1Jd+cz77HQPOTet7f1+lM1e9AFdxPMycTlQtVljGYKTbyeeNMF39yKf3j7RgC57U+rVZWrMuBhjQWBOJs2Y/5KavjUn22LEuHT78QCWFc7RrRzL9x4MttNEkZmy6mRhQY++18XAL53DDp2f++QEQZzNRrYU6uL8AaJTPQKwJDx8S4/oGJllfirYABR7cz0NEbM9P0UdvvlNrQVD/6XOL+ZzPHp/jHCQq9CMMaW+8W6+2/jDtWF+kL38lSQV5Du4T+/TKa+O6fvuq5hcnaPeOUi2eF6UkGHoXhrcIHqyJ5JjOne/XCy+OEBPq1Zo1Cdq4nQfk0vksYDqQycgwZl/O/HmI0e/tOxqeSMAyVqhVq4jKjWN/zWt4Tok5YgM0M2PDpkvnvPrJT6Z06W6V5s+M0L7Hc6x55XbbuXfh1/Gj3Je4UqfN6xcxfjHkMUcrK0b17PPtxLuGaen8SH32KfZ0vP/wKPG2t0b1/rF7GNkGtH7lPD35RBaRy0Q4Nwb1D/+9Tx8Rszwjc1qHHk8lVpaH5+h7bmUTC06EbptX3/r2hxgf+7Vm5VJt2ZyPFZl9GecFl2xuHXDLwKazHwzpg1OdlqF1x45iLVjA50jex+bwc2990jLwxsVhPAfSvsmY+d73L+pO2R3NxyR6YN9iolBNLL2NYwroPG164dIZrViZoQOHZisd6Pneba/ef58H/9qbgfew3G0uUOEMQHgi5C9+NA7wO6SKhhod2JGpL/ymxwLnTLt/4xtlOnX2Pu+RzvhdzvlFKSHFPFhk1hozvgLiMqiXnjVwYZsKS1K1dWumioqBLjk3tjQ8LEV7sGbWVfn0ox9expYcrzWrS1gDiZqmr4N89pucBlDnIYnkNH6O13Y02vXGq22AjDwMFGvXY4DDq1dl0eckSA2xzhzv1f2yIPNhQk9+KlWlc6NUjaXyncPtwM4PmOPJQIMzuB+FYZk5UsHcPfxWla7f6VdhVrz+8PfzsUbGqacjCDjHnHj2HY53WDu2zdXux+YTQR3BOj6hU2fex0L4ugoKUmnnfYBzxIibhYXzNo9CBbmWhcA5miP0FWqBUAuEWiDUAr+qLcCOI/QVaoFQC/xKtIApoL733nt66623UGpv11e+8hU2luyYQ1+hFvh31gK/juBcRUWF/vAP/1CVlZX6zGc+o48/xrDDn/3lX/6lnn766f8trtUAEg+IrTt79qz1TwM/mGjjLVu2cFNjjRWNbOb5u+++a4FyO3fu1P79+y2w7u7duzyJVqgVKyhYYiAyX8Y2dPr0aQuO27ZtmxWHbOAI0863bt2yvk3MsYlYNhHHs2bN0rx587gBkmiBGiZSNmSc+3c2iUKHG2qBUAv8WrWAMc3dx3J2EnOZuXaYr3isZQaYNn/XQ7E3EkBl9erVevLJJ3n6O8da4y9cuEAh5YTq67HCAUibhyzMWm/sob/9278tj8fDjUdza58byBSr/ldwLkjRrqnsnp7/8z9Xb1WVcrCWLMVUMHfPXiWuWawgAIwNmCvQPKTBt4+rtewW9p1IZS3AIlRTQ7GtXi6eHk7ctlbhMzDsAHOZYqMpMPspbjr8YRo9c0Xjb71PAbVJCeuWKn7PGoVR5LRRILRsHtzQNTe4w7j7629sUdvR94mBOauUghwte/oJRRdhtCImxXosn2hZYzkxUIb5DnS2qPqlZxRfV664JQB5+x6XDaDIMvZww9yP2cXPDVgTx9V9+HVNvPuaYmiD2F37FLllO4Y1DEw0zTggS8vx0xq7ztP+/Fl0RjpQQocedHYoc9Fi5a4g2pTz9AP3BYg6dIUTpkjBMDDilfdOpe6+fYwnyfs1Z9kqZW/bKieAoA0wAX0YZiMKXqYGDXDgAwJqBZprvfKRCrjRnrx9vVwbAVMAFg2sFsQ+ZoryJvoxODYif3WFRk6+R0xNszybdgH7bQXmSKXteDlFAxOLSCkdkwRWre4ydWPOG/XFAS0tAF4qoGgMAIfjKNBbRkTheYq8bXKmY8qbASTn79Xg3aMU3weBIOYBPm6HDaHIbKIug4+guSDtbbU1Y9BGcS/oBSgbrtRIJ5AX5iXETIrNn0k7UuFweGgPxmDtaXn7mjC5AbMAOjhiiVU0FSYHEa2dFwBF3sMOMqK4onUU9B/jxjhgY285JpUrFogWl50LjGlsLx6Kc8Aq4w1I7i5pvAVgZRBDkrHVJXmUVLQUuMwUsIFliIk08bHjdW9iqLrAeRP1OWM/yYSFRBde1giGoqj4WMUUbZUjfTNjCSCUmDmfaWdbPz8/zFEE+Kaq558A2MFqhMlqoKUV2CMJIxEQWAaxbfx7kALLZON1DTdcp7hLnFIR0IKBiwDn/EBpPgxDI1XnZQc+c1O8d83kwQmMJX7gocnaC0AinYpJw6KYvwQmKp9jSQCWYGy0XdEosMoUlhVnJGaWPCIsZ3Cs4TMtcE5eYtGaMWw1N8qJFSgpP5+5CNTaXa76mjYAReKpZqyHR+E4icOzoFTGuvHpPCo/EJ1FEdo+jU1vpAEbDiYkvo3tIiUTKCGLc8B4EBwnfrPttloAct3ArUnYhZwG5MJoRnUNkOIqIqgjrElTiJKWKS57CfPTgHONar1/S/3Y3JLzsRYULOU8ZtPOGLb8xDMP3FRX+WXAuw4K02FWzKsrdyddPN8q5kyNd1vmolGiYxNcfsZGMSa7FHUacA5YJI1IzOg8gKvoFYwlzEGASAGgUfNtoqcodYLtTAHNYfwDnBrpIMKzl/UpHCNa9kI5EwFLiH8MTmAJ6uLcgR8joogRy1sI2LaUyZTE+QFGMb57gQaHiQhNzi8BnFvKGCtmPgLO3X2Tfi3D7IfJqHAddp45tAtQBKCX18T40b8o3oBrORoAj9gsIh7zgV4wDhpIQL4m1kwglPpqJdAXifkFCJg8jO1htTU2ElPoV+6MEqIHZzEmcxj/jG3WUjx/8Jo+OFz8WXzbiMYLAF35+muIvL3PFCVaL4nYsSSsTpiHbBEDgC73gEjqgOxsxJLNYq5wrGHAXETDBSfvabz9lnqwrLhtRKUVY5wDFJoAMuhpvMWcuYg5hAiyvCXAtIC9gGUGivB31wLVXWVe3MN4g13Ps4Z1cSfrF3OVKNAgQKa/67ymG0+Qwkn0aOFauVKX0w+YMGtOYOXwYepcynVlAzAgAJGwvgVN9CVxjNY1iugwC2YhhpWI4OmuO4CG1czKKMVnAaVkMiaATEzRfnKgjDF6Coh0msL6SoVF72SuABUDFwYnmgF7zwNwXME2wnHOOMB5LOMUGjRd9zYmxWrgZMxC+VsAA4nmBuCzom2B7UbKjzBWAU1YO12ZRYBjQOExs7lGGaNbs4Jt1zUCJDcVxKrKuhCZyHWIONVuA56EpyoxdzV20kWMC4xjPgBvYEiba4Jj5lrBCmNGahixhk7iGb2dteprKGfcTj8CcrJp6+hiluwewN3r6qjDWBWMUHIGAHKOMTAyB7leBgfvcR7nOQ/WxsRiJc7e+DNwDqNR2Q1glH4gEaxdBfM479kcSxrrGmNm4K61bg1jCvUB8Zg1Kz57JuN0IWBhKcdIlDgWmkDHZWKJ38e4ZweyXgOcVgzvA7xceQ5QjCtEIde4LNY1F+soNIede1FBis20BGdnRiurDmB4YKKbtb2OiFADuWGuTE0ApgbgjAaKxbDqY4766ojoHiHOM2eJXDkrqYRncazAvkBH/rZ3NdkEPMc5x87aIVfeBtmn+uQzoA+QxBRAaULRbCx9s1krch9tCxgX060X1dXZqAmsnUkAh4mAzQ4LxKYSja1xqq9HrbT7OJaZ7OJkJeQS2dvTy16oGUgC00vBIiA+4DcX4By/I0hR2oex1BRxbdNE6/nZf6iHfUUzMGK1RpoqSNnsA2SaI1fWqkcAMoCrt6dabTW3sRd1YWtJAkCcCZCZxfuwNxonbryhSh1V1RAJAGnFANhESQeJOhtra1BX/VHMjqMY3ohFTl3N2swcY5/kB3QaaLoBJH5Nkb4BriUzFVm8j/6by5LPwwRcg/yjDzRQdQxmqg5bKGbEgm2M6UnW32vE3w4ryUP8szGYxRQxnngIj31g0MfFnAI1HcqaDfRIvHTQy3pIROkoprJhQGI3fZdQtJzrKvY75u44sHRPxQdE1bZTnC8B8KJ/6D8zB30YbUc676mr9irQs1nreBAjYz1rEP2L2TIIZG6iPc21z6I3iEsOYByd6Gwi8rZJielRRPMFAemaYDKjMPIxxzMYH+HpIFou9WMVHKn/UMHWS8pmDrrZN0z7wrHAYqPlQcIk7KLRgNk2F7AdfWgL5/cAA3MAXCnMfgsw1jbIufP64ToNtt/AGsp11IHNNRmQOIW9Be05Bfg5CDjnG2DNxsQTmTMPAyBrKftb/pCHAQB4gb0nRnpUNHsewN0yTTsKaUdcOQDcJvY8SLx6YJJxNInRMsC1sKdMox0PLLjQPCgRwfU3Ko+1Mp15QfRxcJr47747PITAfonrkBMYN4b9ko/1tfPBS8zBUQDkZYrJ2ctex2ONe7OGMqmYgezV2LYxIek//gVQLzhUpqkugGj2Xc74JCV4SnhYohhGknUPoDVQexaYr4u90VyFF2/HIpzNes8+GyNgsP0iEc3Huf7x8CXgnKPk6UeAcnCM86cPAeqMXZLsY755PfvJAR7eqS6v1dkLtbpZgZ019RCgx0qtXQnUA1QWxrXDEcVaCFnW1hDQ4Tc7gdcuY1zzAG/MBN5iX5XAwwNAOA7222a9N59dejsDGOeqdezIA6bCLB3Ym6+tu4nWzuJ6y+FWlI3rn354B6hyEGCkRPv3eoC6HPpv376oKvbSK1ctBYybi20pnHhP3o+YwcuAU889BySMNWz7tqV6+kkekgBWOXG6X++8e0sdRFbu27VKu/clq6CEmUvMOQdEm3NszJUzp4f1wksX1NM/qo0blmrjpmxlAu1EcH4uoB47x93P7zlzYkIffljPWtmtrcAm8xYlYxZjL2vaguZzcJ0w72snKvE0sYbP/OQ8cZItOrhvg558ysP6Qd+y7DTTXm+/1amTHx4D2inSvn3LlBgfqTdfa9H1Ww94YDdGe/fPwYCWwP6Nzz/Qh+dPe/XTV8cB8a7x2TFXu/ZiVUtyq7ZiQn/3nY8AvOqA4mbqy19eiYWbNZIHfljOiWwM6ONrI3r1pRsa6I/QqhXF2rotTtl5GJOJWOTZLb5s/J1X775dpfMXAMC5lm7fOkeLl3NtwW7GFpkHJWgH3s+0hZfjuXHdx4PBNzBsJ+rgtlx9/vORPDAA/MjfVdV4+QzciE2tAdhsng4+low5b0pHT9zT+cutKmC937+/SHPmuDgGroW0yflzPRjHzhGFW6YnD+7XF7+4QEmZ2CeNce7vqnXttlOrFyXpC1+O1qwFRH1as8SF7c+tS2cAgd5pAoAb1tp1QHkYsrJzTb+YCFW7BWcN9Pn13jvtev31+8rKyOOhtFwtXU48ZRJ7atYIF9cHUrLNlMdkSqTsVZ9+8twDonY7GfelyA6yeU+uHYzjes7vh8900xdurVrs0uc/4wKcCwK29evoBz0Y4ez6/f+ngDkQjtmY9gIMvHqNcf1MjcobvNq02qP/8qVE5WXbsY/59NobxMxW3tCWtfl6Yn+eSmc6sXhZ3cL6RePQ7ucv9NE+A2psG2UOJGnjToxtOU7F8QCLmzEaxoGPEhV75+6kXn7jhvpGI3jgu5jxjP0cE6OTNcrFmDCgqDlPutFqtxdf8OpWRas2rI7SU59PxeYH8MprRkb8Ovx6l954o0qzuef85OMpKppBVPGNLj37QhV7Go8e35OmT38KSI15OArceR/j3utv1ujm7Q4tmlsEIIfxbpGB1Pz6zj/063qFQ8tn2/Sl/xILoOey5pYZfeahosZav7797Y/U1NykFcvnA9/OkAczZBjGUjvHE057mIeuThzt4bsdgLRH24FOV2BSS0xl/Y00Fm8+/5lxz4drM68/vuLVf//eh6qtqyHmdTX39eeqsNhpgWydHYBzH0zruZff1szSJB16eqmy0+P00blpnT7DZ0aiZHfsytfGzUQvZ3D/geM07/fqC9O6dLMCmDBO/+k38lTg4YE6r0h7uYtJsZy2KtTv/te5rA1uPlbwU/SdeQDBxzrehAXw5Wf7da+8RXlFbu3cjcWwNNKy6Jp1MpKxYmJU7wLv/eB/YDCeiNbaVfnasiNGmdlmrmKopa3DaA8uQgDbAR5ksumVF9v0wblbWBjd+tznFmjl6hRgS7tlwjv+LrDvWTOGWrX/iWTaPUE3ro6zlvBgEQbjPQdy9dj+NKWlEysOXNnR6uccW3X8VD8PP0Xq976arrWca3dbwDLOPfviOxj5fXrq6fnas3cG8KOLiOlhIrFfA0x9Xfn52dq/75DWrd4IkMtAs3bLZj8COGcaw3yZxvwlvkJRrb9E44V+NNQCoRYItUCoBX5eC/ySV6ef97ahPw+1QKgFfuEWMOYoA8iYoqixiRjQ5hNryC/8ZqEfCLXA/8UW+HUD5wyI8Pzzz+s73/mOZZn73d/9XX3wwQd65ZVXrLhW89/Z2dyI5MvY4QwwZ15rYDUT6WoMcaZNstCq/8mf/IkVuffyyy+rvJw4IW5om581MJ6BJl588UXLYGcgPRPDZ74MhPf7v//7FgT3ve99z/p7A1qY32/+22j2k5KSLAjPRJump6frL/7iL7jhts/6uxA4ZzVj6P9CLRBqgVAL/F9pAXMNMWu2AeYM7GzWbBPL6vF4uOHJDf6ODuI/LujatWvWdeKrX/0qT4Ovoig0RUTPbevb/Iyxixpbnfn3TZs26bd+67es9/h54JyJZWu5V6ZX/+wv1PKgTAXpGRRQ1mvmnp1yz6b4F8/NS27aBnnqfeLMZdVcvUaBYVLFy5bLTnGqufyhBih4JhcXKDYTMxWgX1yehwIp9rlEAA6HSz0nzmn4MIAWla3YXevl3rYYO4opSJviJYZVbtoHANvsE+MK1tSq5ci7gOdX+R0LNe/JJyjm5VNYpvDppLBrPpZS2LfOB6gr0N6i2lf/WXE19xWL7cO17wnZAe2C3Ck3RTnzlLXRNDiA8lpeeVH+k9y4TYhX5N6DCluLYQsI0EbBL4DKYuDsZfWeA7Tg9XEzizSOueVBRSXFkwhl5Bi7WZYieZQ/KoP4xFzOD+VEcJTi+O1yXT8CZMAN6Xlb1il1JQVe+s7crQ9yE97HTXgHT3rbxqc1WVOtujMfYFe7pdJCjxK2ARwuw1pFRFXQRlSRVbjmNH0AXRQ5fRUPNHr8PZU3Nitv8w5lrNtkxdDaKOgEeUo+aJ+ijScsO5MPE1ZfPcYUZw6F8RWKSPRQqInm/AEhgAn8jR9g0mqiCAvMU8LvpQg7VHZMkZMDcnkw6hXuwsJUwD11U2Xghr6pdKEsCVDcNtFrQQqlvuFm4hEBhHpa6D8nhbE8CslACwArJgrUNwwAR8Scc6od0w+GtnQK/m4PNVysUk7icgFZvPXvaghzYWTBGsCVvXyOoZ8oJA/c5yl5nsyP9wBxFS6gTSiwA9UEp+r5+8uabADK6iXaDiNONABNAsCaK2Ue50dbUwi3bGGNhzGknOHY4igA76fAW6RJAIPJlksYqYh5KwT2SF1LlFkS/W6KnxRlwyim203IG//kHP0Y7ozxJwBUNk6fGSgjMmcRw5XxbKfPsWpN12Ncq78hF+ax8GIAqhRAGRtGGRQdxmo12nAOaKGcaFmiRYs3AE7lwcJg7mm+qJH+QSx1C0neW8WP8DkujD4CQ/B3Y+qqOwPs044pIhVYZT7SMUAap4f35iWiuNcOONdYx8vDiB4l8hAQyY+9pgkAKioNe2HReuxFBZwP7YG5yI9+zWeALSrp9Ogj29wYtizgn572Ro0CTURlllBkJFY5OoffgaVmpArw5LLaaoEuUrA5AfiFJzEuDFxEISM4dlvD1e9rdGBc0UBR0dhwHFGYHYG4WstvaZx4PwO9xps2CyvhOJjnfqw2g1h7aq9pqr8J0wrFqmxgTRNL6Z4FlGSKQ4MaA86ZoG2jgTbceflAUqlACe1E9XYrPTUOuAAILQagN5hsgTpeB+MTKpWyupwUg8ICBgoEmuoA6kXREA5Ql5DBGmrscNHpjGuiDQETB4mGHACqS0gmzq9wESAPcJGJs8WwFBxv1kA90CTwSiLHEF/A7wwDaAGc67rzppyDD7F/YVks5NjjDWwE+Ik5yMQXj1eewoL5wLA5lgHQ7QE6zF6qYMwMxiTtj9loHGCvv+GhIjGOWWtlqgHnhtQBpBlOVGVaUbFc8bSbAecMREGlcsr0YWCKPpykqMc6CZw51dMIuAJ85RuxYiUjiWSUC9gjkgKtqxWz2EUKYy2KBRJNy8YEZUAQLGmyj9KNDzXdCTgHkOvyAzMVcZzMqUn0HH2NZVghr1EwjQEMWwYUuZSTAaC2DQCz1Vl/181cjIum8FqwFsPnVuZZDpAGzRfsZzwCRzafwnQzrkjPWiDX5dic6okQPYVByqeoYt4zYwOQGZAWkJAtwJgDHJjmDcDhsJ+AaxEZONmCfbCtmnjRMUBFrICYDR2xZt1lfQpi1Ou5qeaq40rC0pOUxnFEApk6AJbDARACxlJ5GRDzAvBNQFEz9wGKLGIM1jFv30P0Vq8IDF3RnscA6Oh7wBpT9Q4MXmV9egtrZB0xpYlAxtjjWEvtkYCvmPHCpjoZyjeBgxmn0xHMHSIuE/1Eyt7F8oZ1KhJbUc564mkX0M7AagZGMjBZGK/BpOe3AcRYUBk2PUDnCc5xbIhIVdZqN+uoIz7fDBzWvFYMlZexPt7nfDDP5C7G0sYYcgHvAQ2ZOeprvKjJ1lr54nKJRV8NGAoEDrjcVY4xBogjJRVzSD7gXFwRx8LPGWBytEpe7Gg9jZgYh4lURnOUDBwZmbkCaNr8brQ5XKcD3bc0+oA+xCrppL9iMYBOt95VoOYC44+CdL4BkDdybFju6DWq6tZaE7AZKAE4l/tV/ALAzlqMYJzjMCAgtsdo4L8IDGhexiGoJMdyQYG6U4wDYHSuB2Hpa2krY0Wkj1kPAt1HNdXygUaZUBElBtZbpQjiXgMN19TfCOBLFHcCkesRqcDyBuILsD4NAzkB+3QCXE3bgQ6JvYvNNNAh1ygZkDwAKMW9tBbAuaF61qoobGZEqPf2qR2zbsCXSIrpEmyYPwPneA9fWEBTZp3hVB18hg+jWu/w0kdDxJdiYBzpJXI0PE4JQKGO5Jm0I/CJMHcCR7VU3wMs8im90Fi+ijTqBHxn3+MGXPQRQdxb+RBDGWmthZgmaR/5ozTc0gxQydjFKJoIQO1IWmyNbQPfBin6j7bc5DWsk6xXURn0secApzYHQNKMrlGMqUSLVp9QsKuaLs2UG5Ohb3hSg5W3WCPHGNOYGwG1fO5c3hJQwICA/JMFwpoHNgdWykALazW2YNbRSUDDsEjicj3Y5oA4WagsSG2M2Lmu+0cVhx0yycDlmRs5d+51mH0Ja8H0EFBZ1QXF2omK9gBnZq3kPIDPJ1kniG+cGOkEchtiBDE/2Gfaxpgj2OL6sJYmpQKPMwzGAcdcSUTQeTDWJbL2A4FO2yKwIhEV2XjBAueSo71AZ0BS/mgAOOYW0GZ8dj7AFtBzOBAykKbNxdwDOjYxuUH2OCAprD1N/L4qDffVsyfBvEsHxydgJ02i74mwNYvaeH+rBtorYe7alUCsYGQG6yyQJpsB/p4+7q5jHcXg2U+sY+k8C+4OhLF2+1lnR+jbYSDcCcDLCWBQothtgPqBsRbWmDbmSB8R71yD2X9FMKfsiey5sTwGjeJpGMNj40caZY/rSJmrmMINwA4DGip/TQ6Mke489j9Zu+kL1l7OxhyOeQCFnuQ6D6LLGhkcH2LNbmQ+cE0i8niaBwoiMX5GAhX6Iogh5oeCvbcUrDolG/ZFB2uBvWAP8zmTNsRsGuQc2LP5Ko/KO9IvZwFx7kWHaBvmkY/Ya0Bi7xAxsIB+AWBIGpO967iGBkYA03tV3eTX5fJo1Q8tU0JqkWbNjFZGVpQVkZmdG0lcZBgPSwR19gzRf+/we9iTFebnypOXCMAFHJTnUka2jYhewBBAkoGeALa5hzp5ogqD1AIgqVwtI+oyIZWzJnq3pmJSz/y4TG0d7ZiT8vX4wZns4Zz6+jdPqZeYwu3bV2G/KlSGgZi4Ho9iVbp/06/nnv8Y2G5IGzbMAfLJ5HfZdfxkt45iwBoe8uuJfUu0eXsS0YnAQ66fgcHMFXPtunN7gofay4hsreLhVhN1ms1nrmisbFFEmxIJSqSmzWvD3Dahk6cqZSJFk9KS5cnPUEkx55kdxTfXsFQD2gFqsSc9fdyrZ1/8iM9z/Xrq0CogGeK/AarM56Su9oCOvNuj994/rdI5WdoLOJccG6nXXiE+sqpBS5clac9+D6AS0d4uhiifg+7fCuinLwM5lV3Vgf3ZGOdylZESDjg3pr/9zmXiXPuBweZjo8O0mcj1mPWI4YTBMQgAyfm9DQB5kzjIiDiVzkpUYUE8EbRRxOw6iVQ1cFJAly42W/3SUDepnOw8YhexmmW6gWrcKplp+hpIiz40Ztcb16f0199gn+DN0KE9HvrE9DOQH+O3njFz+oMWnT9Tp0LPHD2+G9AzgZjWd2/rZlmvFi9bgLEvl8hcICZAIHOcd4ipffPNmzxAfFP7dm8GnJsDOOtQS7NX/+Mfa/kcF6XNmAAPfcap/GIermBfDEKkweFwPbgd0NEjTaqpa8K05eQhg1juuSYSc5lEH7mUwvmx+9LVS4MAnuXq7BxnXCZh2YoBfMSsDDCVmeECliTiGINgPwDaDYC2F1/k4SKiXnfvKdVOojzTMOCFMeaam3x69rlBzHo2LcCy958/G23BmD/4UZsuMUYWLkjVl37HjH/ez4Bc9N/DB3498wI2+rv9Wr44Q1/5YjJtbNdlYKw33h4hHvOWdm8v0YHHcpg/dp7X4vO0dbF6tCbcf4DN7XgXYGEtIGOY8gvSuI8QT2xxgvKwfmWlMa6B/1owt/30nQe6/WCItojVvFmx/B7OMQH4Ks1FlC1x2UmAaEBal89O6sWXvKrjobptW9za+2ScsrDgGXDOmAOPHB7iHnSNslKz9eShZMaDHUNbk159vUYJrK9P7U/B3AegyHuNMyaamgBo32nWxUtdRHlm6XGArKVLTJyyV//tHzr0sDFa65a69IUvhDOenMB8dAq9yK0Ttbf49crL9zHclQM6JhM766EPY9kDsd5kP+qbaGCwsrsjWNTKuUdSQ3/k87o8wDag9dRw+jSShw1M/DLtzn7UgG7f/d45tWPv3bl9uR5/ohi4jNh3zq+3G+D27BRxyseVA3B28NBi4nET9MFJr85f6uHezbAef9KjhZjxYrAGmkOtJJ74nTe8OoFZbtXKWH32Cx4egudz05j0ja/f1Me3a7Rs6Sz95m/MxCTHhY8Hg2z0o7GhG9vnYHcE875X5z6q1ejkhAoA/vPyEjDZxvAQJdZN4LgYzrG12Q/Ydpdo4z7FEeFdUkIcdkY05xYNvElf8zp3vJk1QR6sYV14pVOXrlZq9uwkPXGoCKtelGVOnMICaCKjPwC6nZ5u1t4DyQB9Cbp4boT3ryEKPVFPABlv2AbgycMpbOE0Amj7zmujevvdCWud/OpvxWn9pmh1thDZ/b5Xz796hDbH/PiZBbQBdlPg296+IR0BnDt+7C3le3KAYp96BM5ZYhDz2BdHygeERzPd6nL+71//FQLn/vVtF/rJUAuEWiDUAqEW+LktYC5Xoa9QC4RaINQCoRYItcC/XQv8uoFzBnj40z/9UzTsb+v3fu/3+GD/BZ0/f15/8zd/wwfbPOvvTLyeAeRaW1stOO7o0aNav369Dhw4wJONEUQ4nLOsQcYk97nPfc4CJUwss7HRfelLX+Kpxa0WaPeP//iP1r//wR/8gWUcMr1iYIs//uM/tmJXDZC3YcMGC74wx2Ta2sT1LVu2zIqFfemll6zfs3v3bv3Zn/0Z8QnRCoFz/3ZjO/ROoRYItUCoBX7RFjBAtYHgTHS3+aeJZjXeum0kAABAAElEQVSgtLlmmAckzJ8ZaO7ZZ5+1zHOf/exnLfDZGOlMfPcn3zU1NXrhhReIg3logXW/8zu/o/z8fG48mlv/3Gbmvcy/m3+aL2OcawWc++mffk3ND++qIDNL67ZuVOljW4GrMMjFAIVw4z4wMKXByx+r/ALgy+i45m3arpjEeOLciInEsmozNhIbcUrh4RheAMxmYcuaCwiA9ab3zGWNHT3N3xHTtGudnJsw/MQlc2MfAwfgzqNDAwIjxtRfXqn240d1/94dFSxfpJKDj1NsLcRCQ6EOcxwMmrnfS8GKGp2JJ2+uV+MbLyqu9oGiS+cofPfjCptpCruRREGZ2+oUgKhBBoem1Prai/KdOaLklGRF7D0g++q1QAWYX3xEY/Z0avjCdXWe/Ri4xa/01csRBiUAIVRhbmlU2Aj2JEo0rkg3gEOu3EsXKJwCn6kweW8/0JVTxzSd4tbcveuVvpRrfUw6pjugLwxyDuADZDUAVxRhH5ar/IOjGqx5qDmlhUreslbOhcUUImMBD2IoemJ/MednOgfDh+/OHY28+44qAN4823cofRNPYgPu2Shy+LBM+fkOC5vkHAGTTCwlRe5AdAmgA+YpE7tpQAEaIDhKRCYwSyfRT+HEnblKNgIy9mqk7IRcxI05iSULK9xJIb2Qm/y0CRUsRgoHAg0DxIT+BPMBFq/uRiLLaA/uksckFwJrLaIvTTtwox/wxE+huLf2HYVjqonGshZmCtfRgBthgJK8XbDvuvwNgCuAUM58IMqCxxgHAJDdNzXwkEIaBEF83jyKyAacy6Cwi8FwCqsSEMkU5pr+7nZNAUzGARPEetbLmQpQwHsbsxO5ZoCU72Ep+pCfi5FzBgXeyBkUn68BfVyi+B6H6XC9BMjjB/Jz+BhMNLQ9nANzAM0BXQUmWjhHoIPONrmwukTEY4wDDPMlAAdimCH4in7B9FX7ETF7N5CwJSkMCFEpHAeQon2ctiYCdazlvKYGHhA/mCR38UbZ43OI2MO61EohvG8EmGM59qWNFPOBa4y5hti/4CBRcQ3n1MPvjozJApYBWMskXhJDjx3gRkGgn06AN8a8j0J8Sj6gBVFMfuCF5pZe+mIBbYfBMCaf94yn+M7aQcXGgGWB4BSgB33pxdbURTxrywUMQgAWJlqPdoyILeDc3LQJgOTIfdrrI3U01mGvK1Ycxq0IYLwAEBf6EuKU72q08oTGB4AKM1YDFhJpR0E0OMDcrbqpccZkcj7gHIYwe3gp8xsgbQrgqP+BJhqI6u2pkyliufIZOwasjCihDxnPRI+Otd7RRC0xxmHYqTweeIc0dTZ3E2vXp7TkBKAExlPsAuxPSRgtTdAe7QJ0Ec48dLKQBCfqNYlZZxDw0TeG3QxoxMToOoB1hF3LwAzBASJqm8oBHVqUjEEopmghShSgJHOcgAL+EaIQG4kY7KgFlMDWhClJTmAJDFFdtw8DzgHpZuUrDHuhPX4mfQdwBbyGZlATFacBOQB3mPNh8VmMYwOLAM6Z9weAs0+3aarjIca+24qY7FNsoQcAKQ+RWT/n2S43BcqUItbOBEC9YLY1NnzAMF7AGwdQmRMYwjY9oKl24Og24K4J4qXiiLvKmQVUxjppY545gTPCWLdaAWUbOpUQlqd4DGhhBqxzA5aFAThMVwDOEcdZ2wBA6wbamQPEmE8hO0CsMSakplsU0GOwOC2jbZbTP4AnAB8GvBpvvo71kLhawLnYglVAxJsBSbJoO6qNAY6t8yKvOatp1ufo/E2KjF+GBbJeEzVnWD+J+ytcLHsmgBQ/E/RR2WdtFHz2pI3jAvoJxwBFZq+G62swU44xF1KBlmcQQVsEBMNxMKahQTTYcRULxwmlJUUoNYf3i9pEewFUGuNcoBvI1JjhLioWQ1DEjF3EJS+AHWsAzDyCpa8ZCBpTWd5+1klj9+M4zEVlDLtS6+sWeDmNbSyK943GghYEDPSxljqBjYM9wBQVNymKs7pm5BBrCBg3elf9PW0AkgUwNVvpR6J/DSBEdCSSGbPMsPwBBjm4ZgGwTQ8Sd8vx+YFII1gfXWnMg9h8zs/NOmIgojqAFuDV+gfMrRTm03LinbEehnF+QTfLRT3rKBByU7V88YAeM5Zh/WR+TtSpu+IeY3tMSUDNcblAVQkF9B+WPsZocBSIpvUm4OZ1oKVWxg5WRM7PlcH1AgNfENLCxvwN9AOElV8ApKRJsa9F5RXK13xbwVrAOaIgHXkbGGp8s0Yb45zpei8FXh/zx8UYdfKDvh7ibrseAM9VAcBy9QRCdBIPGQSGnSQ+Opy+9rWclK3xmGkcrqeA7OkbONZH48JGZHSg5yTg1mmNAPO5AOfC0pbLHQA4armqnnrAnXDgc88azJ6MDaAOAe4FAY58befV2oRxDgAvHaAr3ljCws0YpX25wPrH+6wI1eG+CqUURmDNTGEdxeZV1c35A+NlYb8z9i8ivVnEMIgRzw2ADhVEX7JGso7YiEP1tt+irbrZmrDOJxHpC2Rti0ridfShAPyA2Vsqy2kfxmgBJklsoMPAhuCIipruYR0Goq0pB8xjrSrEtIr1MQD8NdTSqX6ipGMBjBMByR3GbGlZEQGlgRAn2z7WWBMwuGMA6JH1N/eJR/Av17JpDfGgQI181WewhVXClGcoongLFs4JjKZ3LGulu4DrGZGiU8BhdoA5mHbmN51IX9rYrAQCtN34HSvS2Nc3jqU3HrMdljLifG3uHPYfkVyr7UBtFeq+fxjfayeAH2uIiWINZ7xxvSFLlt9JLHUtDyOwh4gCgnFkL2Kb4OIyVAO8iqVtGPsaYL6TSeJgDbdNcR2Y9GJO5hoJ1BGD9WmaSFsnD2E4mQP2eHM9NGu/W2PAA9ONlwBILykOONbFOj/li2ddaAVsHGVNzENOZ9buYmsfYfIIg5ybw9BlgBpi3xccvQMY9zG2MPodODg+yQOEzB7IzdprGYDHifVtUW9rDeB0L5BpDrAskCYxwwJO44kLgPdqrheNwKpDSi1dwP5wsQUo+4Hjpjqw7xJR7p9gfxvAOsc+3zzoEPCz1wUsDeP+jgvbYATnFlbAGpXgwWgMTGGu16MPWQYuaLy1GapyDrHtW+ijKU0+eA3wcEjh7PGcubtY03PM1otzfPTN6OT8xwGiWUcBgKc66jXd0Wa1cRi2YQf2VXtMBuw9e2+uKfauywpUvMfUYdwApgfziWJ153G9sbP9G2V8A7TVH8P6ivGaee8qYqwBeAVG2G920n70jx3dkJ19GUfA//x8RpjWOPtdvzNflb3zdK4yjz2sHxALcBrIPi3FpRkF0Vq5NBUwJ0K9GMsuX+rQ3Ts9fL5hfvpiSVRIV4EnDJAjqHkYpowhi2GBca5cp05XKTZ6vgXOLVoRxgMcrK3sYBpqpvTDH99XY3Mz97xygW9m8xBROFY1bNLY/kwU69btOTwIw1invYYGAqq849dLLz9QP7HKa9aWANtlcG2z6djxTsC5+1yDpCcPAM5tS1QqEFWYkzWA8WU9lGOL4mF2A+QMAf81khYxqbEpbHZON/fWHJoBALNkSaxKgazGxv1ElPbq45vtGN7MZzZisGO53gPrzJkVCfAWp+KCCMWwlzppwLkX2MdynXviiTXaSrRjAq8zn5WMJc+Ac4ffO8+DuhmYmxYqNS4SMG5QFbXtRLTGau/BTMAaJ8fKeKNLKu4G9fqrXt24e5vX5xLvmkkspF1VZWP6++9e0zDjcNfOeXocM1pCMvCsg1+Eyo0dFQ/y2nX3xjhWtw6sfTzcMB7OPcs4jLQRKip0a/GScBWXhmloZFS3PsYMdrWfmFXzsxiC3ZGcn8OK1ly0KB7gDlCKeXjn1rT+8mtnWEOy9OT+Yu3bAziXCUpNn9Q0+nT6ZIPOnKlXIZ8FntzD9S9pSj89fE9llb1atXa+du/KBmLCoAV0ZWfU3b8/qbfevE9M5sfau2OtvvTFWYrj99bUTuvHP6zFGhatrRtStP9QuLILmR+cn42NxDSWXGMxvHdnXDc+7gSe68RqPc0DH/GM0WzsbQ7gp3Da0g0UJN28Nqhr11vU1QVYzIdRdyRwFlBvUWGc1m3mdSVOjQ8HeZ1PL714jwjNPoCjUuxmWYBafBakVeqwo7386qDuAlLNLrTp808nYAsL6Ec/adeNsmmMaZn63BejLEsY/CZ7cqm60g90RFwpx7hwXooFzhUWhunKZSxtAGrVtTe0azv2wcfygQ2NIY71xgwW1vQAi0PvYEAPykeIu63DTteHSTCKNiBaOypBBVnhWrTAqYXzGbcuu64xli9c7aYteFBi2sZ4jFU6e8FCALEFRHXOW+xUKhDgxbNDWPKm1Nw+pt0747Tn8TilAqQaeyIfoXX0XR7efqlRKfGpOnQwETuaHaisUW8crgG4nK+nDiZr0zquPfThFPcYWhgzh99u1kcWOJetg3tTtWQR4FyTT3/z942qa4vFbBejL3w+HKCRz6em4+kDPwvfOPDt7VuDxMe28MA50dqjZh9lImujlV/gAApzA32xtwFEvHcXOO9iPe+LYzyQzr0WYuCJKS0pCteyJVFErLJnwCR454ZP3/+nS4BzraTBLNHBA4XKZT0yBrtejHMXz08Bt54HvLNbhsdiT7I+PB3Quct9ykwf0lOfydXchdFYM7m2Ma5ry/16+3Wvjn94hwjVOH3+S1k8/O4mVt2hb3/rhu5j6Fy1uoT79bMZ25F0PGObfYTNPmEtq9NTkapkfFy42MU46SLSl7/nOhjDPfzMdLvmEo28cFGcYmOcROIO6/qVNsbaCA9kmvtIMUpivno8LtZTJ33IORPj3NXo11uvDxD1W4/tMwEIN1elsx+ZHL2TQSyZHC/w3MRkM5Bcuubz/uc+HMMk2MycJFb1QJxWbQyzwGZzkENDQc5xClOnlzVwSr/9W25t2Oy2wLkTgHMvvvI+YGiGPvW5+Zo/H3srpzk4PErM85us629a9ZIDGOfWruGzPbZ+LtIs06xCZm9gzXT+iLb8Zb5C4Nwv03qhnw21QKgFQi0QaoGf0wK/5NXp57xr6I9DLRBqgVALhFrgP24L/LqBcyYm9a/+6q+smLxvfvObFthmIlu/8Y1vWPYfY5wz8JoxRT733HP6u7/7Oysm9Vvf+pbWrl1rDQQD1H3/+99XNxEXBpQz4ISJeTVQnnnPlStX8mH1DQvGM3Gs/ydwzgB5VcTuHTlyxDLgGTNRVBSxOFgA3nzzTctCZ4C+r3/960pLSwuBc/9xp2LozEMtEGqBX4EWMDe5zbcxjJp/GmDORLSaLwO5mYLbvXv3rGuEgeOMgfTQoUNWlKv5GfN6s76bqNcf/ehH1j9N7PdXvvIVK9rbuon+s/cy//4JOGcZ5+5inPt//0JdVeXKTUsnvmS5Zu3doViMc/YYbl5iVwkMj6v7/EVVXLyBISSohbsPELlXTKF+DIMS1pBmithdPZroAuCikBaemaqY9WsVs2iBRm+Va+T4GXgAArIwzkVsw2qRmEKhjbumxmzG+RmzR2AUQw3XrY5jxyxwLn/BPBUfAITLAxhwY2QLdxpZiHX71IbBbXpkTDaAsta3XlFsY6Wi5gCzEcEaVgzMAgBAzZ97z9y05un3AGa4tsM/1dSpd7gOE+O574DC1mMKi3YBFFHExGoycv6aOi/coJAspW1Zr6jFRGSOEDlW36FgC8Yc4u8mKK73DmGXyU1VyuqVcufmy/eQKCDAuUliR2Y/tl6Zy0yBN1NeRxTnTBGDooCBD4MTFD0BDWvOnFT/wzKKacSNbidibwlxlhg8AiIGluKf+TIsSRgwov8OZqN331YVEED2ju1K24RZKT2ZwiVjApjGvNJuCvqTrfI3A3O01skfXUCEF+YbYxAi6tQq4o/fI3r0pDqJiAwHXggv2aEIYsRGyo4B8PTK5QFmKdyBkcTDfekw6yhwCNFHpgCOHWaiQSMDVRoizisw6icaKlvRRD/aja0J0w73tjkUH3YVYiir3sE416aY1GLGwQqT68dxcMyM50D3Nflq3oZB6sNeskGRRfvof8Y81pOhCgPOEcmHeSkib64V2ecHRnNMtWDOuQxQhRkDq5OJ5UzKnyE3wJcjeREN9TNwzotppv2o/E0fYq+KUnjpp4hvnI2pjp9t+UjRaUkU3zdgf1tOq8VZ/WxingxIZHMMWxGlY8SeDnbVW5WfeAxz7nSAjIQSjQFd4HGQy9iwMOV4AUimgU/CkxNlL1mvQPISzoNilDGvDAIXNpzB1kMUaEKKoko2A7UUYN8hnq3xLNaUPqIXlygmczOwQy4HADUUGAecu6oR/r6vsx2THOAcRWxX7nLaLpPXMJr9RFcCC/U0NwIlOTAlFSgqGntSF5bGtkEMSUuIHNwIBOfh9aZIb4pLzHVgAarc9A2Wmz7iDTnH8IlGpEHAgxjt/MmrmS+ptAc9jzlNGJu87ReIiazl/GdiIyOqMj6XuRdtzVPHGABg+QksS9OMgTXARYwd6lTBoYfqwGg0RKU5NZf4T8A4h5u5aKyB013IyCo1VnPDiu2MMjFmQGX2rDUU6VlHKPQFAMLGgHOMcc6NBTDS42H9SVdbI+DcwCCGFNYU4l8VNRfuIV5eYAs/kBROR2Ar+hCYYAQobYzY38D0BAWkLEUlAXoALRpznTG32UyUHNaxwSaiWHuJjk7AlmAgzcRZtLOxbQEXDRPl1FSh4c5qTFUmzhNYz8U4x3DUfRvjHBF60Zm5gHPEzCaYn6MA56O6PlSpKYDCAGauKcAMeyzQXuECheUwB4xxDiDCxryYbCVGsf4BxrlhRRfmAPWlE5vbp+4WLIzR8UQazgIUYc7YMG85wllHMDViSnIKKyJt5OvrAGipAE4YwB4CYEBUowEcTYRhkEhTG3Ynm72WczhHdPMj41yCMUpigLRFpnG8jKVJYoPbMDg2NFLexrhVyO/MyJUPAGKoGaCl8bZiKToa45xiVzJHDHQINDaEgaqdONP2+xTPbYoywEfeRtZmxqidtdxAYZ1ELjedAbyZJF5xE1DTGnk7WjRReZbCrB+zmAHn1gDb8TNeB+ODxSMcc6cdcxG/A5KINqpGOAZUQiRkZFop8wczlgsgid/BDzGehzTedU2t1SeVGGNTUsFq+mI94wjrGNCJf+oRODcEVGPAuajiPbKnAu0wNycwXhoAODKpFCDsEThnivysqIA0V1hH3yBislbTzmRAkdUAugacy6YPDFjaTULnfaxPtygmYr7M9YjasxWB2gXwagdSiy0gUjQeSE+P4FWzoltXcqBTW6AHc1OtBom69nIsUZxOTJaxvTHPWcdsAFoGotU0JjlMT22M0/DIVNaCpVgfuR7ZsUQFAOdGAaMagHebqhRMyJa7BHAugXUYyLgD49xgD+BRWh5AJOMvroADAJwLMrbHmjRVfUWDLbfkne4mThAYFYubKxNoLSmddYAyJWuF+pir5cR9TfJHXBuiPcXERt7BCHsBQ1aUHEVAo6nrrPY2yIop8gYBgvzBIYV5+2mjHoCdVk0TsewEFoxIZw4auDh2BhK+CE3QJhE2fk8roGnD+8wf4Nf8dXJwHGKtNUA9Wjip86TGG05qwIwlolojstcSENqmaWxrndgSFc41NI/rcAp7lQjO0Q6YaMC51vPqqG/ETkoRnMjVaMBwWxTXQ36awQ8/28EaUMYaUE1Eahx9kIo5rEftNcRkO1i7gIHDAU1trBsB26N9R9ABksQxBwHCJrBajrUzRrvN/ApXNK91EpVqY71CEWyt1zYDzrEWNFcBTvGadAPOpZtrSTYt5sD0yrWs+Q6AXKWmuQTE5+dx3Qby8sVouK1bQ/WsM7xVLFGtTuKEFZ7C+7JPIuraGCGHMQ5Gcd1yZ2PTzD3I7+aayXjyEztuB6D0Vn3A9ZAYTgPwlmzDDDaO8fOmwjmP6AKOlXHnZVyDn8DWs49gHbUge6DAiTFiBruAnAcA4IFRk4iatqKuDTDmYMCzlk6zn5vsZi17+DZgXCfRocSrp27jcmbGGwOH6/EkltV+IOE4rrGRhTxIkcXaze8abr0NdFdOTJ8feDNWTjfXToaRj4cyJvoGuM73A7ElEDlIVCp7DmdsAjzeUsYoELIzyYLvJniwYLIOcK75YyAqTF1ZMzUdSASabmNCDgORexTBdZYLDUAONlgzEYEmwwDebFPsrbDnTrLvGAWEnuJBgWjmV2zqXPYMBi7mWse6bmLbx7tb1cv+kkXDeijEnY0h2LKzsg4RR+vvqmIsEVE/NEJU+lzG6Hz2ZvEa4dzHO84qMtgG4J4ohyuF98VY62edI8JxqruTpFfmCw95uExse/4O9sUA6gBt7G5lH7ovfw0Afns7kOoC4N+tzE1gwbs/ZR0aB2hdJ1v2FgXNAwZcu+2cXgBKyse+EJeWwoarsGPeA27rpGHDMe7mKSx7FmtUPtturp/mMwZzPdB5nq3Fu1xbOBb2jbacfezZPLwfsAL7gUDPFZYjwLnBXrZygPlF+1mH2Mt2Xrbi0AMTxEwDi0ZEsf5gSBLX3imsuuPsk+1uYOzY3aoZnQk4Z1czUZFd7KO7OhjDPCS0dHEptqosKwJxeBDrVH1ALY0BtbYDGDVOAyv1AtkPALSlacPGNCxGDh78LCfR4aEFzj11yKPlK5h/Zg0G1mmsnSLSskyNLW1aucIDODeL9wjT3/7DOWJqMWPx2WPb9mzl5LIvZ60zv/MugMw/P4cNl+vphg0zMbxlKBpL1HtH23TsKJHY43YgnhXc14u3wDmHA5ufnTXafPHwj5drGMI2TE8B1XP8Tc0+HnwdUyf9Ozrag3UuUdu2FmrOvCggn6CazetqiUns4HzbsHZjDY2KmNCaNR7t2JKtfKxQH3yAnenls1zPDTi3injUTOaD+YV2mZjId9/u0jtHzmsWdru9gHPJPAD02qsjeljdocWLogB5MojNdCK9pj8YGzevYaF6xUvM413t35OD4Q0DGuBcOeDcP3zvOlDhOODcXB04CDiXAvwEvG0QXD618LkynL2/3eqX+roAUCLH3jXJQ8BYYYnBzfcEMOJhFANO8nFNbKf/GjFZtQGkdXUPq53oZfP5bu2aAvoaS5YnDIOal/ubpzEMAs4dmKE9u1mn040DUqqn/U6fbNKZDxuVT/T1E48lEV86rTffe6hbD7q0ZNks7TJmNYxzEUCGzFLduTOl114vA3a8oYO7DDhXCmjoUH3DtH74g2r6xa0dm9O193GMW/nAszyERecxF8Ggp4iD7g9iKOW4AYmamifV0ellrPKg00STSBrVth0zVIopa4rPvs1NATU0TKkDYKy7Z4JI8Cn2ezZtWB8BIMce1R2uu7cA5zCgTXGt2H+gWJu3ZgMQAjLyWaeWNnz55R6VPfRqdrFDX/g0Jlb2Us/8pEcXb45rwfxkjHOxwFM8XMLnV5Z8lRNj+pMXe4DaBrRsYbK+/MUEFRb9DJx7q4f3vKUD++ZoNxGyxgrnZM/Ppy1OkfWG95jm8+cgBrG2jmlrjDY2MI46sRcSQ9zV0YVxzke8bioP25kHD5inrUHV049NTZxfF/veTvZbI15McAnEq6YS2RmBgW+Ec5xQM2N959ZYIjvjiRPG7s5nU9hnHXlvAAtck5Lj0/XU44maPcuujy4365U3qnl4bZ6e3p/MvACcYx5O8iBWU2uA+9MNFjhXWpSnQwcMOBdm9cm3/r5W9W0x2rQ2Xr/5G0C02P1M/wXoP7/ZPzLGR4C2Ots47mq/Gjj2dgDTjm6itEd6lcg+f+uWTMsA53L71N7qU201Y7rZxnrEazt7Ncq+f8HcGOZBlpYDiN274wPCvczft2GqXMyDioWcn9O6LPS2B3TlvIlqvYQZMFJ79pXKkxXDvPVh1SNuN21Yhz6VqwVLYrDAsTbTFZVYA9+yjHN3tXFtrH7jSxnKzXazzXHqb751B3CuEig0X08TY+rxsMcC7jTgWIAHGdjQs8ARjc6WqZ11o67ej5kRY1ynWWv6+a7nXpBX69YVa/061opYh7VONLIemblozrGnux9AmbZIGiFiNZ95xAMMQHuvvdxPlHGNFsyLA5z1AM4BcjOvTOzrWWxzxwHehkYbAIozgWTjdPGjcb32JvsePqs8ARC5YWs4nxUAGBlvvb1AvW/59O777POd0/rqb4dr/cYodbT4deqoVy+8ekzz5qboU5+dq0VL4vkMATg3OAg49zbx2++xVubSzo9rHfeuTA3E7GYYvuwHjfWRduTff9mvEDj3y7Zg6OdDLRBqgVALhFrgX2iBf4Mr1L/wrv+e/sjEHhnTgyk0mSKWuZCb2CRTpAp9hVrgV6kFTPF0nEKbMY98UnD9RY/PjPMwbuwYC9W/9j1+0d8Zev1/vBb4dQLnzLwzYNsPfvAD5efn68knn7T+aSKV33//fV2+fNn6MwO6FRYW6q//+q/14x//mKiB3TJxq8XFxdYAMNBDOzdIzRw2MJuBIz4B54y57hNwzsB2/xI490d/9EfWz39inPNiFzHRzgagM0a6lpYW69vEutbV1Vk2InPcIXDuP978C51xqAVCLfCr1wKfAG3mnwaUM1+f7MPM9eEO9rHvfve7lln0C1/4ApEW+zEWEOvG681nErP3M+DcD3/4Qyu+e/Xq1fryl79swdOfgHLmPT95b3NHMsD7NpWV6dk//zMsSFXyxCdr9az5mrtnj6KXzAVMonJL7JW/r1vtJ06o6uZ9ipxJmrf/04qbVUr1m6hTAJ0An5UCAwNEkVap/+R7GiD2KmbpYmVu3ylbK0+4HztLvEm3EjevUdIe4iQz0rkNivECsIwj4n9e4BoK2s1N6jx9SrevXFMmxpC5+x9X+JzZFH/jKfZRXTbnam6gTgOTAM7521rVdfhNuRtqFDl7rsJ37sQ4R8whwEkACMu0n42b8kEK853HjmjgvdcVSfE0Yc9jitmyETAHIGQS0KK1TT0fXFDv9VtKTiUaaOs6uZYa0CSWhESii7ALBTG2TNQ1qvXCh+pqfai8lUuUuWq1ghTcbx87ob6xCc1ZtUY5m4ixy86h+EjbcLvXipYl9goqBcCwQW1nzqrr6hXlp2UofudWOTdi+4mKpabrtAo5fo7PR2EnDKNK4F65pt46rJr6WqVjnU0F6HNkJFKcNjfV+TJFVuJD7ZMUYduI6MOUNeWMoog9g0i8AswnqY/adgjLWNsxdQM4utKItit8HMvMoEaJWXNhvnF5KL4XbtEkZiUcFbyxcd955aQIGsAKM9J1F6NKNWcDaIT9JCZ1PoarTGAWgD/MDY+OhsL7IJaammOMl3q5kksAVjYDT3m48Q9ZRcHR206hu+KIJogSdlHMjyzCbsL5BvuxJFVe0jgF7Vggi6gCoLwY4hFtAJaY7nwAY+NNFPiIpjRxju7kdKCk1bAmC2iDRI4Xe+FkO+PnBFDFVexHcXLP+gyph7Oxv13E9HYeA0WyXBSabbGrKfybaFzOEtjPQdFfUwA1nRT5AVECUxOKA0SKziKCNA5gDMvVFJacMM7T1IWp7MvbcFZjgCt27vxHFm+gT8x70t++AWw6AIY1l4B0GohgzFRE0S5Yg0LMLff5uVNEpLYASMwgeo3oufgiJjkgAPcZAl0XNVZ/RkMUXO0RSQBUi3jv9byvaT/adqoSoOkj9TY1MQeIAiueoXA3EaeAfm1do8SvLcXEtwFwjj60RVCkpckpPDhMzC5g5RgGsr62hxYwmJiYDZBUAmxBVTDcA4wQzSv5NQx0+zgGJAx9HZUVFPxzMSEtxfREOxhjWxCbGca5/vunrZizOMC5GOA+O2akIDG97RX0P2BgRiaGlBlEoBoADGNTcNoAtcTZVQDs9HYqOpJ7KYAWDs/mR21goB6OcaIRmKX2LoVkoKyiEqiRDLU2AmAAU6QnJv5P41yAaF4vUGAAIMNlo0DvbcES+FAttURRMeeTU4mvJL7RbqIIGR9BQEKbASECmGJGiQ1tIm62q1KJceCqQJr2pIW0M2AKsI6vn9i+ujIAPMC6QiBU4DB/ONF948PqvfOSHF03sAVmKXzGXn6O8QfMFQQ6CPQ9ZH34kPWgnCnPmI5OoW1KFZ4DVBlPO3Ncxoo43IDdpqFOSPcUVwKAh1lppL2ZGOV+opWIZKNYHJbI+sFxB1jHcEwxG8etaOVgTz3juR6T9LCisHbG5hTKmQSIE8FrAeC8AeLZAMccRDhONN5VV00ZsMWo0lgT3VlYqwysRhHbxMpONNwFwmzFiJFAxCTASCrjZtKnwWZgmKabSkjEiJMH/JgAfGJitTGWBXrriGH9WKOAX87wAHHLi2Uia4OuDIpaZr0bBrA6J38tpiXiscNLtxB3uhHAhvMuB16lUBZZiJGT+RIE9LMq5azNNifgKkDUGNGZA6xhXmL1UpILiDw1v9+MUX4/pWCmIF8cP3DPNP3QXXHSgizjmCthQJgixhEyB7iIQmgbc6X7CpFjRPkVH5IjcSVQq4lqNWBcDTGlGL48O38W1RrD2GC1HriraWIJfX2NQMQxctB/MUT52oFRA3IDQrczT+9p4AE2GR/AoWcWdjmidPsriNBrAZTNB+rFlhE/i35L4EjNuoGliDVO0xi4AHVGAA/HOD5XTArrTBG8I+CmM4tLm4tjMIVC4K8pjrP/OgXrKq6v0bTFLOAzEx2aSX+y5hN7PFl/GhtWA0BTkWKKV2I5BdSb6lBn+W31cU1KATRNKiIO2bQf66ixAQbGK5iDrDP8nAMoKIBlK4o2jgZIC0vNYHywaFDsD3RjQnuIXdbrYgzw4FVeCRwU1tKGC4wFF+A14Fz6euZUCmsoxX2uyzYDkwS6iMOtBwCmD/vb6TcKx1msU8SXGsOgMd8FqH5O0B7htgHZOy7LX3caCHRM4cREurKBCKMYF6xfQcBuf+Nx9hSstYAJsXP3yJVHVHmwSyNt2Pjqailix2GUI8YSIM2ygBkIu78MePoiRrpGwIVwbIQlGL2Arkw7YGhBGQiwVKveOmxSA71KLeZ6k5clL0BPWxWQEkazlJz5RGn/f+y9CXhc13XneV7tC6qw7zsIgCS4kyIpUiJFUhIlUfsuWd7dbie2k04my0y+L1/itO0ZJ9Ptrdt2vMtuS7ZlydZOShQpiRT3nSAJkACx7zuqCrVXvfmdRzHjcUeJI09/iRRAhkkCVa/eu/fcc+9753f/f13flAEiaR9iz2hwfdr/M+QGrC+nJsmFzK+FCq6ieGfjtdZ5M6/Y7ORrlN+y9N9AB6qxWC5WNjC2UVBKu1FuA22yzwG/9hwAAOyQrD8gBShNBisauW4vMcJ4ART3U3wOEoOOcsBKSwWN59GT3ViBA84BP/rsWFpjHepcQC4KAs4BSqj3W2aWY1/cxbl2Ayw2oL56B3NuhLx2WPyovwVrUH+rvEnS3hrEpVg/0O0kBb5RU6VtpoaIE/Kfz+uWAixrPcUae4wtcoyw3tAKtlorp2bYFHH2WfHE+rGMxZq9Eut1H7C5g/GcBibrvyDTF85Lntsh/oWo/qGoN8szjlDPMbEDH5bU1FmAIlQX546KU6iD2OlmHh6X3PwCrJ3dEmG+YKJjvmeMo5yZdSj07uAapyTaeZg8p5AvMVmzSBJmPufOJgcg1QJUEn2VrOHcQK/0e4Y1tQEQ5MDulBMDTuQaxxgHkpC80gWAtIxzzl03XRgKLbBeQdaYsT0os92tQjJFZbdA1PbbKEBZEOBDUBBNYcc7ClgXAfKqWUo+QOUxGfXKUF+bxFBZLQFQKqxt4RqIf80xEdZq/UDW/cC55AEDlWZ3NSqjALqOEmLOmc8Y1644yVppD2rI4yLMIe6lNwEWo2h2/AnmPHJr4/WSrNzG3IQqLsd16AQOWKeALBKfkiBPp1AmTaIG7C1fI+4yNqyggJtFLS7F4sdgTeYAwEupmu/g85KKANKqCmcFQHgO87YSCwr5sfaKt+8C1g/Th5vExQaLFO+bYYwlp7vF46uQIKq8TuYuLo4pdgRV4ksWnOzHntnPOaaDqyWGomsEeCc0G0c1aQblKeYQcuxDDy6RHXcGLcAkC+CTCJuWgtTFi0l5ff+AHDxyBvtJILgHWqSm3i2vv3Fe9rx6WvICi+UDD7XItdcSG8ApWebc7s6YfOcHrUA4E3LtugUoXTVgDYqi05f3S2ffoKzdsFruxap1KVaPPtYYY8A1b+5Ly7d+cISYmZM7gMcefricudWUX73YBzRyDA7QLvffvVluubEQu0PyHOCpgmVXJiO9z+DeC6VWHRNMPTLH+c9MYmHZEZJfYnvZN5BhM9ISuffhYqkBlGLZwi0VWn+0xQBw1os7u+TkyXYpKwpiCbsUxbFi2X8Q+9gnFJyLYJ+4EXCnDPUs3ghAMghU+OyzY/LCi3ulZWGZ3HPXGikv8cvPnkLN7kSXNDXm8bM6WbnaKzm5KDhyTnv2oPb0U87p8mn54H2V8sC9FSilOeTceQXn2LCB+vCO21rkwfsbsNHEupn4sinErWuPLFsTaMNkwrqVEZYIMjuTBpyKyO49cTb7tvM8s1puQQWuCvVAwgtoHhhN+zqU5NqG5SdP9EkZa8hHgBLXX+9GQS0hf/P51zh+jTx8d7PceptLKgC+MsTl5e4Ulq/9sve1AWlkXNx7Rz7rybS8hMrg64d6saBtBgxskJYlLkvNi4lZ9r+ZlB//5IQcPHpcHr1nC1ati6UAcK6vJynf/mYHSoA+uWWbgnNOqapXlW7uJZmb9b16q53R9Zr+ybVFOfcwfbj/QERefuUoAFKH3HP3zXLPfQs4JuOG9JeCZYpj8zsxlkSBLio7Odcc35A8cP9SlMTLpeNSVh7/cSttNcF9ul5fNerG5FkSbRfQ0+OPD8r5C0lZtsgmjz1aSSw75Uc/CcnLe2ekstwm/+nPKqR5MWtkgPQMm8RUoe9b3x6S05eSsnVdsXz6M6j3LXjbqvXpEem6fBLVvuX0Ya3UoNzn4n1MheQ7nYu4LoDNJOnMgui41jmuMQRoNj6astQWL3Z2oThXIR/50Ersg1kr0xaIZJKrszJFHx47EgJYZaMVdt8feaQSdbsiOYdi3k9+HgNynZLtWwJ8fp7U1jOvoSA3B4z4/AuT8sRPe1FzK5JH7ytFedElx4+Nyre/104YN8p9wJYPPki8AGnNcU7tl1LAjxewk52SNUub5NGHS2UVy/LeLlO+9F/aARkB564vlo9/1GtZpmp60ucBXJr1ldI+ZAXKYwTE2rk++rCzc052vtJDW8/K6mUL5J77S1CBA5oF7osyBsMzWZlhOmg9OwOIeo51ZUi2b2uUB+iz9nZUAH94ANvwXoDINcBxTTyToP5Lm44Drx56IyVPPHkYO+IcVBwXyoI6r7z5ZpyY6UVZeVbuvb9ZNlyfx9qNMc+5HgXQffx/pGT/sTa588ag/MePlwLbedhIYQeca5Mz7Rdl4+ZqeeSxpdKAjbXDRjASoNZcwJybQeE1w7MMXW+l+FP7MIbSXvflGNfYJsdOnJFm4PRHHlouq1f5OAdilE2D2hYxvjsvxeSFl7rk1PmDwGz1cvc9KJSjZvzzJ6fk4LGLKMF55aGHG1HnCwLc0oYsB/a+lgJGBZxjrn7g3jJUPINy9GhMfvKzbh6bpOW+u+vkLnJ3IQqEdDlQX1b+x+MoyL2SkZI8Q/70D92yGaB0BChy98tp+dFPd8rSpXnyyIeWypq1+eJmGE5Pj8nOF1/m+xXUR6vlrjt2ICiwEctdHaP6xRhQe3X+prdQ2u36/W6/5sG5d9ty8++bb4H5FphvgfkW+Cda4HeZmv6Jw74HfqUFKAUfdlIoOn6cggNwRCAQkIaGBgtgUAAiNzf3PXAl86f476EFtHCqYIzaMJ44cUIKC62taf+iS9diaiQSsYqsf/zHf8xNXtG/6P3zL37vtoDGj35fLdL/r76S9xM4193dbVmePvssD48pouo8oRCDjie1WVXFOLVJVTvULVu2WLatP/7xjy0loM985jPssuRh+9tf2gdXv3Te+au/+ivr/f8cOKdAhR5f1equgnMj7DJWdTu1j9Wf65ym56fw9zgPYhXE+8IXvjAPzl1t8Pk/51tgvgXmW+BfqQU09+uccVVd7tfnY4XmdH23a9cuC8ZWe1YF4jSHa07X9+ncra+7cOGCpUqnVq2qOKev0/sWPd6vH1P/rp9l8t7LraflO5/7a5mlYFnvC8oKQIxl126UousohlWzAx1FtmRfl3S89qqMDE1LFaoa9dtuR5kDe1EerjsCbLTwUZBOJQEmsAj81c+YY4ZRm1sN7HUr9dCERHbvk4vnWnHNqpOKGzdIYXMzRTOKsDwQzaQBezgfR46Xml9Ipo4cktN794mLgsHSTTdI8FqK3hXFkqQKkOJBsANQxUVx084T5czoqIygxOpsR92rrkHct2Dn1gIwQyHaADoxtMjLtGryoDn0FmDcC8+gZjIseWtXS+VW4DjgNbw/Jd52Sbr3oaDR2ycNS1AruA6byeo8wC3UWNzYyqqaTdqJNd6w9L/6vAy2viU161ZK5Y0U8rGl69i7n+u7JNXl9dK4eZv4OYaRD8yVTXJ8Cuzat152/4dmZObgQRl5Y5/k8sTfjzqd97Zb4AnyLHW6xBxwHdIrRjG7sYELjIs9gHNYtV5olYJrrpHKbVsAcVAuQYnFoKitYEIGGzgblkBZQKAQ4NFMbAZrRuwbKxvE7sd+L5EEeKEwOAzwN4hqSBUqSo2PUdhmN37rs+JOAXGgumVfcKMkgRvQY2EUsGMcSMoB/BBDtSY0dgY4gvdiXeopocAbWApsdUXFJaMAJtUZG8UaZwJFM9Tdov1tkrYXSoCCqju/juK1KqCFUblCsbDnTUlHk+JddCvgDeAcD8PN6TMycQ770MiU5BQDjS0AOMpDaRDryUx4UMIU2jMU9J0oydgASzLYm3rKWxDhUSCDexV242fDKKrx2fGRVutngRUfBJxbLKG+N1DoehOYqhhHye3AYpsIWlTQ+FwMksWYw5p0QhVwLgMJTsGCBjj2Eril1dTWAA/Z+J6m2mm3uflsYlTVagb3A0C9QQEjxTEBEctRfMJ3JoElqaq6hXpOiHNuFPWOOvFgR2vkAXNSRM907UJZrBNYCvCvcYl4Actstgps2yjQDwKqDB7FRhB4y46NTQnQViNqX37GLxU9Yw4rUIrhM6NYAwKg5DUDTPiALEeBMUfCMFHrATO2iju3nGsDVmN82NNxxOpmJQHUFR55GftSbGBR1MkB1rEHUWNTtT5s+ATYMqFqXhRrXOkeMUdOIDp1gQv3oMKzgJirBWIDrkORLTVBX6EOmEAmqbB6C3aQ64F2gCexKh1uPSGhoVEpQV05dwHjsJxzRykS2RssVNtlpqdT7BRpvXbUyQrriDugUf60A5lkZlE17AE2INf4gxR9UY40sZQbQHEuOROS8jwUiLCvNYLLqZwAbGoF2jXHuBkE9sCetf+0zEzMoiIHoAJI48zlnIHhTNR/LOBDPaQAYAhCC/yaBSrw2WeBMGt5GfCcgi/AYUlgiqluwJ8o6gwLASjr1iHYAzgSC8nYicfFGDoiAeyn3ICmNqAj/NCABVChwpYyNXGW8TRqVXjixKQrtwz2ZiHQFjAJ4yOL0lUI8G1skEIrOaxo8QJxVwQ5dyw2e6cB+QAVa1EJA2w0AUQUSgURtgp6yNQBhqKmNQ6848oDhqgXV2kloAqvAwxRsEstih2MX4N4SQ+pJe1x7usvSmE5KpsARAqAGeSdLOBUuOcCEOcYMEgZ5wGsV1wDvJhGAeucjHUdkhw/Sm7Vi7E4Bqr1045cf2qoE4XRVviIAeKG4jqx4W/YALxFPzM2MrOojwKqmsMHaXMD6BlQtXyLzA2PYHl9QPJzHKgGrkPScwPXhwoledlQqDgzjILoBZkCfkxMDokX78gCBQhzqZy6ajiWFtHob3KN4WTgqgrfJKqBF1+11MPc2JJ6q4npHOANVG/S4xMyDfAxO3cWKMbHfPMQ134t41stY38OmAO04S2krQFByrTvaUPAgAyfHwbwc6Dsl/ICnxaVogTGOCNGDGQVzaiqnZ2SyS76gvPPr18OOIfdFlCxBc4BmhbWUfQrXEyfFNBz2GSRTg1gpuh4O0p6+4DysHB2OYgLQG+UV4wAcQrYyQGZL3ixAgvZYYmHzpKvsQMNpaU4pwyVQ8CkXODGlIM4O4m97xu8ZhSIczGKgai1oWql4NzweSx4Ub/LywO4qwIaU6VBeym/G0N1rFUm+88wpwNcOWwow3jIATUS5Dw8VWXAyqwJAKnTo20yfvGUoE3G9WwBLm3BgpfYI3cL842n+UYxSlE/ReEPj3G+WQhkUCSMAvxh3xkbaRcXwKajkBitAEjyL+Da6EMDKMnpYCpViCAqxiRKqJ37JIStq7uoXrxARzbGlsJ5qTCAUTd2fr0nyL9eyV1+jzjVFhg4LzR4UAYutwGtOChcA3tg525jrGmezIxcAIQ/hRUoeYNic7CwQtwKipUAO7npw+SApYQ23osqWzguFYsWi6+uljlgUgba++mGPCmjXwO0d8ZVSmEe63I08myAj9kQwNMg8Df22CnUXF2A216Oa8MC+YqaHXMy9/8KV+BfCSyNzWf3WQsuyS/ySDGQrgOVVlV1zRIHyf7DKBYBfOcUAUIvxd66mXZ0A8f2y/iFVxnREWxmG8VZuQ5VQsYB+Tw73gMIDhAV6rbWZgYAe6B+i7iLgbNoJxMwLY3aaVQhx7lhcVQ0ibPlPkDniExdwrrYCElBg9rmbgfiqwXkYYMB6y8WU3BSjKkhrLgnBpn20GUlv3hKWwjPOvKoqrDRf0Anpm7U4PfZMMqVqI/axzvFHaxizXSL2FXl0zYFPAZA3sfaoaOb+A1KzhIAXNTaZlHunFOb81APCm4LyDvkUDZjGHx+EoB+qgfgDoWjwlKuK9ePsumIxFgL+shfPiBJJ5AwEkXE2ahELh9H9e+S5JUFmEtaJJ7NlwlADpMcV1TXCMzHxkQXcaHzof6nsGikl/5nnTTQwXSXxM4bZVngR8O7mOtiMwD5xcAeW5xKmgAORYBwWVdlB89QzAeeL+e1zBd2D30dG6aJ22WY64wnpmXBqmXk2eVYaHtkmHk+wpgvLrBjuw607ObYDJPs5IAkuoCsx/rIDVE+B7VoADMngKlPx7iHNSnTlMZw5uJ+1j8zIg3Xinv5LayV4xI++lPx8AJX40aJA6Wp2qciyw4dg5xDFihQVWWTY6y3AdZVrVNBZXse86bbTtzScwxZB3OFnbGbjQDhjr0sUwoR2tjMgUKoB7taVZ8ksTGPHEdd9zBwEaqBi68XF7a/qVQMwPkt1jpAIsRGHmtKV7CS+ARmZNPE5MAFYqiXOQH4B6VJexmbZYA0HTaP0OTyxr6Y/PBJYhgI+M47GmXrNuZKD2sPNm57aQ8eU6Folpbdrw3Kq6+dlqrKGqwSF0t9oxtFqjbZvesYirIL5AMPrpRrNwDnYq2qa/weIJLvff+MdPdNyNprmuThB4BGHXb5zuPnZe/BU1LKWLoPwGjDap/4gGcuX87ILgCYp19gTvcm5N7bsX99qFR8gHMvvtIjL7+I6iJddN9dN8qt24qkopR1ILbKJmRRhrYz2ewyO23K2GSY/QHAoQHAdb0XA8rp7kzKU784h12oKUuWc407glJSRxewbgnSD27W+VMonT0PoHeQayrEtvXu2xfKdah+7T+K4twTrwGMzKE4d53csp1+yU2TM+0oRxlYIaI49+yrgFdlKI2txQI1V17YOS6v7rlgzS1bNzeiuFcsBUU2oDiRXSjY/XJnBqDvnHziwUp58G4Us1FAO982J//lqwewag3JjluXykMPNPLsn7UZgarfWfJpOIwi11Aa8IpNM7nYOOZx3YTawX0JefH5pPRgTX/TtlLZsAlluBLUqphXnICyej+QZhyeOjEl3/n2AOdfBiRZABzkxPI1IZ/73CuMyBqgqsVY6KpVK5b0bHbq6M7Ia68MAEf2S1PdQrn/zgKpbTBk3+F+eW4Xmzq4J7jxxmZZv87HHGe3ALDX98Zox8PSfvmUfPihW+RTn2y5As71JuQbXzsHNOQB9qqVu+93S3U9udxSrnUTi3aUytIyOaX3oKwSsRF2Qf7Br8uhY0CNLxwHSmuXm7beINu21zLXm6yfUZrlNR7GkIJLBw+n5KnnyH/2QexHl8uSxcVsVs7Kj588BziH4tzdjZbKYQntrUBudxcA0Q8HuFdPyDLsSx97tApFNA9QU1yeeWFMIrFR+fAnGmX12qDk5rAynorJQUDTJ58akYFJr9x+Q7l8+tNBqau3yaFDaXn6mVHi+BTgWovcdlsd96BoiHJuCkPbiDXCEQvNFJAca0IGvsuncNuVPlR47oePX5CTZy5J88JyVN6WSmUZaxYaw8lmGBe7O2L04bEjSfklsOY4G3QeuqNI7tpRJBfaUNX7WVj6WUPecWOBPHBXgVTX0e98uIJwzz43Cgx4mfV4iXzg/kq5DmXISxcj8r0f9En3gF/WrfTLhx5jDVaIYmMsK4ePjdOHZ1EJNGTjNS3yyP2lwJ8pLNdt8tWvdqDOGJTN6wvlQx/wSl0dynbkCO00bq0lynzQ1wd4zf1+DpvhHNBt2od9ffQhypHHj8+gllcpN96UL41Mkx7um1zkGRuvI63J2TMR+fnPUQjk3nnrZoDbexuls8OUb/4AAJ+NNNtvX4mN6RLgzivruekxFOdeT8r3H39DyssDKDUuk2UtfgC8hPzy+S7eM4yiWo3ccEMt5+q0NtLsP4it7rMZOdOJY8B2wLmPlkp9FZsgULT8v77UISfa2mX9pjLU2JZKUy2K6dznWjfHzMQKlKod7dBIUuLxrHiICx/9yCVYSpcvvXRJ9r91CuiyGdvcxdLYhAon07ULr183gapt1NmWkV883SNHT+2RTTegOnnPKingXvCpn04CnLZZ4NyD9zdJC2CbG6hYY3s34NyLgHMhNs89dF8JUG9QLnakgZ4H5Vxbh2xcW4ZaZIPU1HkFEXS5hNrfj380LHtPZmVZZVD+j/8tR7ZuccsYCn2vvJSWH/zkWYDXXHmEa7xmball1To9PSwvvbBTdr2wR+prGaN334qVLZu1UCYmgskR3ANx/QxPC0AknJnt3v3XPDj37ttu/p3zLTDfAvMtMN8C79gCv8vU9I4H/Tf/C4Ud3njjDfnbv/1bpIxHpLKSBwcUp/TnChwoBX/LLbfIn/3Zn1k2SQoizH/Nt8C/dgt0dnZaIMxzzz0n9fX13MBivaN3TL/Fl6pTjVKAHB4etmwmv/zlL/9/gJ7f4hDzL3kPt4BCwiqX7aXw56MArn9qnvtt4+dfeunvJ3DuZz/7mTVXqFKcAnLFxVqouwIh6s8OHTpkzSN/8id/Ip/4xCfk7/7u7yyg7eGHHxYFVHWs6pdCDwq4qcKpHkNV4n4TnFObVYXobkZ5RhXsampqrPe+8sor1tjXz1NwTpWGFOT70pe+ZP3+TtSD1q9fb81lCtl94xvfsIA5Vb+bV5yzmmj+/+ZbYL4F5lvgX60FdM7QDTo65+qf+nXVflXvQ/SeRBVMdZ5WIO5DH/qQVFdXWwrB+l790nWcgnNf//rX2S3djm3HZguc0zlG5xf9UkVhC5jjPRakx2ddOn9Wvg5EHeNzmjw+qeHj6wuLpGZxE4pJZTyAjsvEYLf0d/eIB8ii+bptKJY0YMF1WeZYNyos7gpiSwomkB7ol6kzFLqBZQo2XC8F12HFyEPbxInT0voWBXKsXUurS7E3qaXAw5NdAKgI55XmKW8RsJnT75NEb4907T8oY+fbpDyYK0XN9YBJBTIHRDLDQ2O17SquqwMkoKgdjcnYL5+V1LHDFIFR1liOBdkCjl2O3VJlBYAXG5wo1pkolKSxop1941XpOHMceyuvlDc1AzaVABcJoEgvNjajFgzYzKJmoAAAQABJREFUvGGtBClyR7GZm5qY4SF7IcoruRQZKRaPT0v/JQoiMdRFNq6Vkus3UwzxyERrm5zHytbAoqqsssoCA80crIvY5u/gqbU7EBRfUwNF84AkLnfI+L43UTFBvQmlueBSivS5QEFU22cB7RN5vHbVQskvKxHXVETCz++UVmA7O5u16lA0CZZXoMKUB0RQJ7ZS1htYA9lMVIpmhyQ+1IH1jhZ4UVFhA43LD0iiO/Gx7TKnUKAaR20GRaTggg8AYGA7e+5X4jOnxWdZhd0oWazb0hBlID5Y7qH4A3gz04UtIUpQgUAK60uUpfIAmowqiq+AJQqxoFZmZ+e6HYDNbkygOKSWpKexYwXmQlHEn4vKiS8HPhFLI9RyBOggnbKhXnIbak53WrUBc/aMjJ/bQyERm9/8gBRz3+31AAhitRcPY+E5MUQxKMmxKFY72eTDsW12L/ZghXwuajUgBhlUfaKTKO0AFAiqRQVrHuLUGmhngDyAuiIU53w1t+GNuImH8D6evjNuMkAGY6i89e+X+DSWahRZAwUAPainmE6UvLBsy1IItFHotGGZaPNWW7FkTp/D/vUwFqKjFNlzLVjNBWgTR4klMTdGsR84Lh2i+N9A26I4l7sQeBFrPxTlRgYvSQYwzAWoEqBw4QHsSoVS1Kj7GCiTlk1ODDu7JPCIv5CiN3aVNqpA9igWsVO9KAjEKN7mAT/qeGE3/mgXdklhiu7rJVC3DcU5Ylp970xUkrAvTU+irtN/TKJTB8XrimEtWQY0t4iqCoAKQIFBcTvrLJC4r5zPKgDYBE6cAZDqAQYAaHEBSbgL8mkTwDlAw8RMr0wNKjiCSlLNDZJbtxa1GYoboUtAOycQVRuWItrRTw4xuA+lCkOxZ0bCqIgl2ZwVALywZxVFoUhZVAncSPsBQmXnxgEm+jjfMZT6csSHAhPkA3lnjJiKSWkeBW1V+wFChBIibgAbbNOABZck0ntQIqgkOYhdBWVcwIMkCvoZoBK1NIPrs+PnZsMxwMTKLhMBxkQpKDHbQ+xQhArSDwBoNpQvk9NTMjXOuWL/WojqXRA1xowH6CwRluGTPyfHqTUgqgzFwJUK5zEEktFpCujkDyOBwoYH+z0nahZzbFgBnUJNyZdHLFNYyiamKNROydjENO4FPilf0gwAh70g0PJo1wTvrcZ2dBUgGscFnKNUzlhMcvwJAKKTkuw8Jh4+y605gFysip9aPVPAV4CjHN4G4CDAEu3TCKDqGOpjo4d4SQj70GLyNH0MtGNiuxch30/ORCS3AhuxhVi1Akxnya+zqIVNdBxG1ShOP5SLCyjOhZpZam5C5sYGETWaRV2Fz4wDe4JwuLCl9hcDdAFZxaYmOTZQ2lybTjYAOyhOlm1CsXJYxgDnioNuyVNwroBvwDIF56A5LQAnOnIE2O0CYzIueQV+gMZqYpRxCNhpZCmioUKmgJtBPmHgAwVhBUpchwB7iR5EqwrEFwDASXgBcGIyQ86KpwYktzgoeQvv4uNWAfz1As49J5PdgMDkj1wsNj1FNag+AUUjqROfGuEah4AoAEzIbVGoXj9gjZ85x+nQIucsrEwP83AIlbBywDlsQPPcQL3tMsbcZ8N6uaD6OmA1qrPkBVNBMZPrQw1zousIwNMh8QBuFZDjnQBG4qmjHRTs5DkiClBqpWsAKRoojaViAzI+eAHglGvArssPjGoHsIlR/E7O9lnAUBpV0gBxGGi4AXAMgDjZj+ojSnyDfeRPh5QQIwFgO4g4mKtpIEqUGeOT5FFseJkbYyEApKmM5BBrnmLiHyggm6EAPdMn00NdQNx5WDVvlUDtChT8AF269lnAu7cZcK54I+deyNxDYAP0qJJjavwkkDLKLQCUQQrvTlQMjQA50wVEiK2aydh3kLPVgtkAHBVA3swA1reAo7qM0T50YiGdRS4lGp1C5e48gGQveThPCpfdC1y0mfkMkGrwkIx2AllSMM8tKAb8o23IqakUrgvkDzuAOhotjGEOCjBrI685sYxzepknAMZj2LJNMwazUUPKm5dj5VolKZRPFJxL0m9lWNwGK5oBVIkn1g4GVtrIwPG5AIu9R1CDHSbusacsAxLzA80Rn2YGMMJFnvST93zkHHKgGQ8BSHWi7HgKsGkGC0hA2kA98yQgl8Yic8IsSrWZnBJiaRnKXQuJf/LlQLcMnQdIw9q3oBRIFzBUVSVV7ik7jh3y3DRis2mJpYDQaH8/Y9dHXjeIt4y6pACbZ7EKtZH/bRTTnS33AsDEZLjtLcnFUre0ScG5TTB6wG4UpQ3mLTPO3EJ8TnGuaTzhiliz+IHXoKwBEugTAEIFHx059B9qmmlUILMx7GjJvZn+89guMucU1DNXMP+gPhhHKXaOPBodmUJ9pgBQfi2Kc8skhhJlovu42AAc9XmTkQ+wy/rKQK0swRpmhufsmjtLqwE6Wd8ksememZxibJATmMP92AsL4n/xCdp2vIN5f0hyq1CvaVgioVQ+sOmkNV+W1SwAwgdywxrXsngGmjbjWAiPngXoO07bD0iQ9W4A0NoA6kGaEXCOsagFe87LnsN4QG1WlYITgKTxAdTHyMMkc8ZaNXUBAA0g+ghqmhPMbWohX7dymWWdm8mgIjrcC+B5UnwoPeYUsj7ifQZrPDM0i2Ih8Cb52QvUYAcyigLLZv0exjh9jR2uCdBjTHVamyESKCIbCzaKd+kOLMixLz36NMKQbIRcANBdcQPrP+Y7xQt0Dch4So62oqZ4gs8BnsWW0FPK+A8CrtrLgY6AmVgbuFh3Ot2s5bCjNtOc+8RRmQbSjYUzKLEB9RfQx0wpGRRYs4w/AapDGFR8i7agNrmdbJsA9kapDEVF3bSQG8Q2nnGdZWNDDHvS8NS4THNv0NkXksksNs+FNwPYoVyIwmWcMdfWPoZtaI9UoEy7bn0lIHoCJbV+YDLyTbAQcM7GOjYEaANkTgytWtkMPLZA8lHteuONS/LKK0ewqa+RBx9YLes2olYXZI3GWO/tiMp3vntQulEsXLduMQpnrJeDTnntzUn5xfPHZGjakCWLFsnqRUWWPWY/1o7HzqPmff6cVORn5J7tLcBjrPeDWXl5d6fs3HWS2o+BgtuNsn1LMeAc1qCAc1ks4tOAzmkULS+ciwBZAQyzpg2gruVDHTGbyMhQ/7icu9BPbg/I8lWLpaTWy1hn40EsJsXYE3uoHY1NpuXEyW6ZBhZfvqhGbt1eB1jildcPxOVHT+wkZ6pV6za59eZq2oVUxlqDx7Uo2Q0DTL0IeFUt99+7QZYsy5WTrWF5eWcbkNgACnQV2LjSXvkulLXscrrNlMPnUK9KdMgnH6yWB+4sBpyzy7lLEfm/v/I66lxhue2WlfIQKlllpeQx3ZRATEXYyHPp0qwcPMCaNpIvhWyo99HWCdY17W1hFL1Qb7OHZeN1xSjUslE4TLsk3EA7KDMiJxXnnrSjY0jOnw9xPqhT3YFl5zIH96lx+c+ff4X2q5aH720B+gKcK0+jkGWj7wSlKzaKvdwOOLfcAnYWLbVLW8esPP9yhxw7GyFuSlHbK0aFzwcUhpVp2zRt3QrEdR5w7mb5j59ailW5HSvOuPz3rxwnjtxyx63Ncvd9OYBzbEJhylIlL7Xx7bgYltbTqCRGMsCYml8CgE42aUet7BL3jG5nHEhvBWv5oAwD5NuZwwp5jRf4OxpNy/nOWblw6TLglBulshbmXA/Kahl54udspMhOWWDo9lsrse9Um880CmGqyjUsrefisqTZDghWIZUo9Z06m5FfvTAqJ85eQFWvUhY2V7BO4P54NkR7TXDdwPcpn9y2tVw++Yk8nh3b5K39afnFMzPSzUade+5pktv4nMoyt6VUp+y2qnlrX3a0z8ihgxMAgmzWYh3sBS5TSHFiMkP8dQL9zsqy5ZWybEkZm0+i2CynUCv0izfPJSHmk0sd04zZWRSRA1islskNG/1y4lQUBbEZ2nxS7ry5GMCxWKpraVgHqpGsUX71yyH54Y87ADHL5bGHauT6DS6ZGM/Is89Py5sHx8lXCdm0vh64k/pqJkn/9svpM52MkYBcf80SefCeQlm9BoXF/qx89WuXADT9cv3aAvnQo36gQe7HyGMapRlUKkfHUrLz1fMyPZNi7YiaNkrxau/Z3z8r7Rdwcpmzy6plKCLW5LAmmGIOirD+YXOaJ8D9VJbrow/ZfMejCdl+YxOgZDXxnZVvfHufDHG/un3HKkCzZSgqojrMGFRw7q09CdTzXuC5OkqR968j3+RxfUl5cVcXqnnkbsZ9y+IWYDA2VPH8pf2SyIGzDrmMculHbs4FnCtDUZHNJnEDcK5VTgKiXYtV66OPLZHGWhRUud+1FkUk4hQbAEZGMvT3sPT2T1v3KoXFus53yNBgUs6ebZMIlrRNTXWyaFE118R9H892fDl+NsDwHALF3r6+NO3bjVJdh+y4u0G23tjA3BaQp54YkMNHz8mq5cCPwMktLbni5b5SNynueiWF7SoW79xTP/yA9mE+SpJZee2NGXll9xHY6gwxXA84VylJAq6nPyWHDs9IK3l1dWWO/O//KV+2bvWxkSMruwDnvvujJ6RhgVPuemCRrFyp/WRgcz0sr+58Vd58fb8sbFwg999/OzUMVKnZkMRNONeIqid9yXC1wDlWGdwTvfuveXDu3bfd/DvnW2C+BeZbYL4F3rEFfpep6R0P+m/6FwotKHCg6j3Lli1D7vk/yJo17JbiwbEWpRQwefrpp+WnP/2ppcyl8EJLS8s/FKN+/eKuqg9pcWr+a74F/le3gCpKaXH1b/7mbyw1RAU7Fy9m9yKx+099aVwfPXqUHT1f5WY3x4J1rsb8P/W++d+9P1pAi/RPPPGEKCypyjUKWG3bto2bmpWW6qBCdJrLNI7+/8pl7xdwTq28dbx95zvfkUcffVT+8A//kBtrHtK+/aUgm7br97//fbn11lvlL/7iL2T37t2W3V5dXZ1l26qqQfqlwMMXgBcUjNCx6/f7rd/rvxWWuwrDff7zn7f6SK1ZVflUgYjvfe971vjVvvrKV75izUkKxb300kuWst1HPvIRC5DTvtbXKlihKkR6rHlw7u3Omv9jvgXmW2C+Bf4VW+AqAKd/6lyrf85SFTgINKX3JWfPnsXmYqll0bplyxar6HgVbtfX6hpQrbgVjP51cE7nGp3b9evX53F9j/68DXW6L33x/6TIOS7LeNBaH0PlBJW0EhQQClBCSwMOjaB64QTEqr92g5RvuAEQICl9r+2WubbzqDahWOJmVzQPlJMxLBeZZ/IbsLW5dpNln2pgy5gFiBk8fEDGzpwV7xSFRMra7IWmOBeQMAXaDBYddaw7fFVY1sWx8mq7KEP735JER4d4KDb4ARkSFCx43C4eoLs6IPU8ipSG0yWRN9/gex+2SyjqoAKSBUrIaaqXqnVrAP/eLjhT9M0AZaXaTkrf4bdkum9AvHA3XorqCvbNpFAwKi4FtlorletWiWOEXeOHX5fhzstcHwo7QEwZ7JgSvE7bI39RrRSvWy2ehUuoJhYCDEzL8KH9MnXsIAXZSR48A1zxUDsDIOTn4Xxudb0UXb9RPIubUW6alujZ4zJw7IgMdveJkwJGAUXhfNQkomx/z9ZjM3nTBiloaRKvKlocPC7tu1+HMRiQEtYFPsAoT1mt5K/neC1cXz4AAuAR1SwsClG06t0DPHaJh9pAhhSCbTx4dpgzKGYNyaQWRGo3i7/ufoqzYzLR+RLWtSHJqeY+t3oroAoADB2p4BxSc4BeuyXUdZRC+jBqGqjQKYjoKkK5gOISRfSsCwAsUCUerMvsau9l2Zuh/EbxNo4Vmh3bLicqCylAiCT9p4ovRpTCP0pm/oabATJupogF2BdpRT1qn0zPAqJpAZnCtTtJgSuGAh+KAGqD6isoBcSgsgHkEhnsAagY5OOS7EwHgnPlWvBMFoWHFMX8FCBfwao7AecqUJw7KAmAgDxsytw1FH2DAB8mBXviFMIGq7WXec1+ikXjwJzs1AfcsPF5adRQDBvKR4xFj8+PKhnKYSWrgAVRIIyPAMJRaEZZJYHqkx1IzcGDfpP2yBppLGfHebofl0BhHXZx2wANmlAf6rMUhqaA6pIeFD2AAD30jZtCsklxVivVTlSjPKpglQQWwMYtQ7XapYAgXj9uitVmEhgLuElBBn8DFogoeGXGe7HIiQDOrQMsuwE1AgAxfNsMrMfUFi6F1VQY1abobCvWdzRVIB9VpVJgKwABoCxDoTWguWzhKqCHxZbiiw0IMTN1WRIAEZAwjDqOB3SmEAplMVRMQvQn54rSXg62hm6UqlLhLpm4eEZsQAQ5wAZwI7yHwgf9FUK1IgKg6s1BKQ+wJJM0yG3jHCtMHkgwRgAy9GwiM4i+TaHEh+pfQzOxUCHD3QAuqIIUAHC4qxZjN0fMA50RSBR+6GtsJcOdhyylOB/FWjdFWxu/V3uqFDBHGrVDI1AiPuAuF+pMBCvFfmA0+mF26AKA7LCl4JODYoxdxyxFqjnGepT8mIfdX5DPRAoLYCciI60UswEvAlyX06fjgLwA6JXGIi8BYOMFAgmWoaLkoWA9BjgC9OQgn3mptjmwNjVRUosBWIwBhhieHKloWQR0VoJ62KCM9QCwAuEU1KFEhuUnUkHkZ6AWbElj9EHsMuOQbz8x6ywFOkMBkAsBzgL2o4jvcNUB3K2jfZo4X8Yp0Fc23AoQ8Tp9A/hAldGGsqCTmHYzxtNAR3qdHiw68xuaiAtsN+mX+DBANOCrA9jRxj1FBkBG1YfmsCg2AYhyiK8ACmzZSApQFagRGNqrQBIKXGnyndMcRXmyi/zA35tuEHfFJonwupGOA1KIJVleFZaBeahPEcNQGxTzp7GfRPUIFbEwVrAZinFe1NLcFGkV4lWbOEcWBQ5sMV35dZaKYYZ2sinMxJgKo8IXne4BKpgVLznGnqFomvBb4GMa1TMX7eRv3IINIrkXoCjZg/UnimggEBT3AUTsWNEBt0UZL/p5DsCcXOBZVZOcBdoxmQsZ1cwDFGt5VyqWkgiFyBiQW15tM5aSzG+zqH0ODzEX1DDeUV9iHNkcQI18Bj7TwJ0AzVhvRokdPyqH+YDmdvrXTAcpcFJQ10o+4LEDJUhbIeqGxAYJmPbtQeHspDinzpGX5yTlc0uc8eTk3OwqhUTbeFEw81RupDkBijJY/nadBFoBNGZABci7PvIKIScxpJ7izKUO2iOvvJaUWSJpAOQY6nlOYE4bfWxiswYxTRvOoGaH8hwgmBtwzluzAtinHQW4I5Yipwv42iikD+3kGtpEVWnN6bMSByaewaISL1rUclBfAvBW2/Q0uTGuaxriyQtc5S1CWc5NLidPZGcA4chRc+OXxa6x4KE9WYdkKPgbQJomuSZrKwKgvx3xsPXMcyjRocwZ6WvHPhDrTfKSgVpVxkNOYoxmEygCAcr6yfc6NmKomEYVTgM+9gKZO1CaSmPRHZsj7805pXjBcvFWkv9m2DzahRoYVpnFDcB0JfXUbAF06QlIW/IhsQaQFAFm9GL36QWYdLDWyDBvw9iyjiB/+FCAzGP8F9Wh9or9L2/OqtomCnHp0cuWFbIdm2mXeqHRv2nApAjzvwmEGahowT6bcY+yU2oEpbq+13jNKAADx0XpzNRcFqJ75tzAVmxWKCu1lFDDAMfZ9AxrO9qAc7GhuuPmhJwopRnEiK2kUeyAo3Mk4JGO4xIEyizC0s1Ruor5qoRT1HkQW9lpIL4eYCggMQNYKx+4yuUH/iR+0uQM9MD4O/GEMqAThTYzUEf/6eIJ5b6+VsZ4L5k8anFnHuAitHyIb2AY4Bs/EJoL6NlWuZp8Sf8OolqpyodsQEgBpac4bzufQ8ZgGZOVSax7i6pRDmUTAgOZNgdajIxybVHsa72smVCwTTFuUVHMABd6yjg+eXMqWyzDQxPiQhGtrILNFQWo5dk4T9ZwwjrHDPcQO8fIGceYG1mnYdnuADQ2gLvhD+l71N/c5A/AzhzyhQLYugbIzvWgrnaccXUJ8JQ1DKqLOaw7fKgDmqglRXWdQo4vaW4RN2MYiTfGFnNv72HGBaA86oMsIuk3QHVyZJL3pJlnXA7yWoD+zowDcwwTZ6zZgFwdNj/z/QzxMsZaghxfu178i26nLbD4PLkTNWAU51DYlLJrAQoBNVGKZaAwJXazbDspoxdPcn3TUgjg7MllDLK5IQbkleAzTdRJA0Ws2YCGbQBvvBHQlU2bI52WPbkJ3G1zJBmfKNayScHJAswG8J5MMm+gbOlsRLWXuSwxeVpCI1hWs1nEQwy7GN/w2qwniFPmkUnAt/0nJuTUYJkMGdto4yXAINxPoMaWYgznBOZkw/palMYCcqlrXA4cvkCOK2SMAnOydk1EWe0bk1Jf45StW+pl9UrUccnPr+29LHvfOM06p8pSgVq2FsgemItTlcHOiDz++H7sUSeAblqwElwCsOTGujUtL7zah+rcCHCXIcWArbk+nV98Mh1zyMBwv5TlpeWOLXXy0L25rHUBPfZeklf3Yuccd6PitEluuK5ASgoZ0KyXTCe5l7VCElvl/funsXlsteAdAxjcTazZWXskmS89qNg1NRcDJS2Q8RmnHDnJuB6elFxvHus6N5AQ4CVAc22VV7ZtqqE9CnCMscme/WH5+TN7mf/DcuedN8iNN1QxZ3CDxXHHJlCpe3kUq9ZXpKUJa8MdawHzcmQSW80Dbw3J3lcvYC+aAoAkB6Ke7HTnSiQZlM5RRnrsknzsvmpsMkt49meTtq6IfP2bbwDVRuXGrasABOv5OfMRH6VQ0hTWnMePTskLzw0ASLHBgc0sdsZIMjXHJt8wUKJbFi4Mcl/qkUHuT86cARAf587OBaRMjsuQh6KoFhajYrbp+mrAmyAbWGzAhkmeWR4AzsE+dUe93HqLU8rLyBWoqfb3i+zfN4Cq4Hmpxdr6vjvLZPlKh8yQvw4cDckzz3UDEcUBFFEaJD85WYvFyFeTqGP39p1G9W2zfOITLVJUYQe+TMh3vnlcOgHntm9bKDvu8Et5DXHKHMdol8lJkeOHIvL6bjZODcVZYxSJy53PnKxKfYwx54ysWVUgi1CRG+JY+w/1kEdxFkGJ2kW+iiXJ34DcgfwENr+VsmUT60ugsaNH0vLMs52kR6w/t9fITTeVEIeojALv9nWJPPUzQL9zKWkCfnv4/gJgIrdMhICxDs3K7r1dQGJsSCCWAsxXXjdrVe7t+if0viIumzcWyn/4aBFAloP+TstzL8Q49/Ny2+1lKPGhiliG6h9rFVW6JaFY8NypI5OAbP1yCdDR7qAf/aips7YMsQnHBLhd2JyLwh3rB54HHDvYL309KdqV2OHz57jnCHPP5kXhbM2qGpTlCjlvB0p7EXny6bCldn3z5jy5/eYCNsRRJ3CmsUi3yc6XxuWnP+8G3iwDgquwFOfSwK0nTidl16tY1Z7lXi8LiMk9rdPPXGFjfT3Gho6oU9atULvUfOqvThli7P63/35Zegbdsglw7tGH/Vi14urCzKUCvTomenuBsn7AfXLnGOddxHqUnMiqfhZI2WbEpL6ukM8vA4J1yOmzPdKDcryHWHbT11FiJ8R9R8BPPsKKdNsNlbK4qUBaz2Tl+z86KqMTPbLlpuVy245GchWbN5gnpgDnju5LYnP7AiITOXLHXetQeGQeYd47cqxPXtk1yPMa3UBfLoWoInp9KPcBqXWNemQAhdQHtgXl4x/GjreGMYKw3H/9yklp7egDnFsi993XILUVrOexhCYhkk9Vsc5JnwBWPt0tx88AXTPvBNgEw80VwCfr/+QEioE+WbmKDQTMV4cPsolpBMV41mdOJ9A7a4c486gN4Ld+QUZuuaNcFi31Aak75bmn++Tk8TZZubQMuHShNDcjnuAjh7PhbvfehLywawb76T4skxsYv2xA4NarozsuO1++KKdPDKDM7QV8ZsOH14WCKAqOEb8M0z6VhWn5408XyJbNORY499puVDSffAL4NizrrkNtrwFAGvgxxCaeo0cOybnTZ2T50sWoUt4h121ay3oGctsC53StxNghITHFWN/84F1/zYNz77rp5t843wLzLTDfAvMt8M4tYK263vnX74PfKDSkxaZ/KCCh8vOXf/mXFgindnpqV/mbsIjCEgo/qLKPqnQpVKLwwa8DSvr3tWvXsnj/BIvG/H8AT94HTTZ/Cf+GW0BtIVWV5Jvf/KYsX75c/vzP/5wbz1oW79wF/yNfCt1oMVahOY3lP/3TP+XG68Z3fP0/coj5H73HW0Dzn4JdX/ziFy3FQVWkURVNtYNTeG7r1q1WLlN1s2CQHcjc5OprNKZ+Mzf+tk3xfgHnDh8+bMFup0+ftv587LHHrLb79XbQtlWVOTcP9//6r//aUoLUttZxpyp0d999t/VyVYpUqG316tWWnavOTToXdVHUUJD7jjvu4KHUGevv2v5/9Ed/ZI3xnp4eC87bs2ePBXN/7Wtfs4DZLwDh/epXv7KAvg9/+MNSUFDAztFLFli3d+9eq08V+qurq7PO68iRIxaop+p0Oqe9V75Ujen3f//3udlvls9+9jOAi9XvOi7fK9c8f57zLTDfAu+/Frh6H3JVcU5VrltbWy1oTpVCVYn0vvvusxTnylFUuwrX6Z86H18F57797W9b71NlOs2N9fX1FiCnLXZ1Laifpd/61YY63ef/8xdkfGBQ1pQVy9qAT6p46p5DMd/Nw8sshZIYRfUS1NwKr1mLCBBwVSQqc8ew3USpIRVGhYcH7kktdPPaYHW55AKUuRY0I0iDeo6Lz0pg80dxKsZu+mx7p6QmKQpSbMsqoIQqjmfhIsln7rPnA7VQNMuGUR/pxFbvxHEKen0UECmeM+9lcykGLWiSYAvHb6SwTdEkwxwYP30KCKyT4smMxGiPnAbguo3rUFdr5DUoFGWBmlCjMWcHJd55UaLs7k71jQKEYd3FI3Yn86MX1SXfatSeylHhGO6WaCvFecC5bAQVBYq5WmhwoibhK0eRY91ScdVXAyugvELx1QCoyw5xHmePymQbEAVqNiYPkXXnex5KN76aOvHQfg7eIxRPsqibxTraZfI8xcqeESAx1NwomjqYe51LmsRx/Upx16CYRxel+lHHOXNeJs+eQwksQuGTYiu2iqUbUSFpqZNMHtdGgURtt2xaAI8co2iLLSiqO2q1aeecbTaezmMrFmFHvrt0PfDhDqACwIxhdo2bobehsDUUBSlOchyqukBaFORHDmELeEHwP+UeN2LFXMqkiI4tYwrwKgMc4kL5I6dsDXZ8qO/orQYKFCZqM9npCwAxKAmhipPWh+BaIAHM0H9nEk5s/LA3Ld5AUFLxTABlAIik4wBFvI7SFoopFLqJM6gI2rkCu0SgBFSVDOIgPYm9HSCCyS57mDKAFUATYCWFEpMAEEngvkDLNkS7KlAJRA1novWKoljJtRTFUS1DIcigzU0gJHP2AA5jZyxFLWXpbArAUaBVixitBGMOLG7UyFwU0o2S5QBjKPkBShlYKmWBPVKo6JkUSVXBzlKVUVAUlRktZNsBv+wF16K6VmMpwGSmAChQhTEoQqUopJsKwU1TpGa7visXxTD61ebj/SkK6bP9wAiTxICNIiG/d1GwZRzMoQqTNFGcq1vEe7CcDU1g55QkhrGuBexzAfkZqlZAvKuKW3aK4gZWehksOx0uCoaMuQTgo0nh1+Q6KMWLCWDoLF/PMZZgJUZfUc0xKMZkpzpQnOkQZC+4HkYK12aBsPw9a6j612L4rgbOmSEGaBlT+zuKbA4FJAF1TNaxNCjumS7JAGL4UKByMh4R0EJlDzATJYN0DPABcM5J/2Xp/xiAqwPCz13bhLBclYRGOU6IseQuB0hhzKH+ZQDXKHgC/YYtZ4ckh1C5mhkAOKAbFFii2KRQcJLzTAMzmozTnJJaYCzGnxNgh/iSJMAO8Rkb6xU7kJjbxhjSOKUga7oAJlEgcxSVMtbqCAMKotjUzvUeErwH6QuKyxSk4sA3SY1ZgDgnwJ4rl5yX28jv9OWAExzbRMXMAP6xoxBlAMGkAMPGJ0PWGCol5/iJ69REGDtk4gWIIwcwxYHiIRfKNyOb4mwCcM4cQUGKWDYSqJ4AEqsSInSKjhTYEs7HVQOoxDgsXCgGcJtJLjaSquB3gnMhN6EQR5qy+tetOYHY1pxmoJ7nRqHGUDtq4jATGkbpqI0LgNKh+J0id84B/6UAt3wB7OgKq4DLiGf6MDPez9hirNNWqjzp4HmXzRxHgY0cC6nnAbDyVW4GgkC1YhhomvHvK1gE2AeMiGqgAowmKovmNGN0/DQxNExsMi5Rc7GjmJbm76kE4wpwzuuqBOzEfrpigSRQYXQwTzgApdKoISVm2glZrA8BeRwGal9qIcoxLP9VAAVHmVqAVjBOAV5Hj2KNPEgRkfscYD+TwmkqhSUh+dBGId6NSqQT5aAsgExycoIYAYwFCFeLPbvmGFo8gkJkmKJkoLJJcgsJ/tggapBcB4qdtuI1ALB6HwJUowkFRacsY08hk2ykB5AtzDjk3BIAM3HyC+1r07jzFXOeK1Dtom1QPXOgyGZiHZylXcxR1K7olxQ2aFm/m7agfbQonqCPPQuAJVfQjwp5AWuiApWhEKz38XZAVzqPeS6hiCxKbxTVy8pRX6PoSvwioYWqaAfH7yMXMhdqymP42IwZVB75PH1NHQB85Uorrwpx4WK+s5FnbIGFHAPrU0Bhk/YTYO2MAtOjPbRziNeBJjKuOUtsoMGotD9Q6fIAvnoVtHaX6WRhxUA2AgA7evEKXMyZZrFt16K9CwXH7FyE/gGkLd9MrlnJeY6JiV10ZpwxQZHYAA5kiwE2ncSO5lwTcN4H3AVobTgTxP6o1YdzrFPcGn/AxqrCE2MujIedQO3LxF1FbAD7zYyQT2j7QPkCgCPWAQBSOr0YJKwsKloxtSAHZlT1OMPFz1zY33H9GUBK7W8b/e/IA2guWkAMAa9q+5M3BBvxDHGaYl2QodDtUFAYuzUIMN7LXOOi7wvqgM9RG+Pf2dAkc/gJ4pixjq1chnGSCpPTo7SJo9iKZVthCfVkxiv2wYnxi+ShCT4PUApo3wUcaI4PMreFxcAm2tF0J9C/G7izizjGgBeVSVseY5gxa2heB4TJTjOOsPjMoEpoAGo4+O+q0ktG1V9oaxOgy1OEAmUJtqbkALIJOYY1B/acKeaJWHwQWCYEuJEldRDXOl5YS6i9qy2vSWxFK4gXwDTmtiz9bU71AOShXMk52DkXtx+QjzafBtRSeNaP4hxRSyyOM4f1MkejJgh84dQ1F0C5quYmwsPiLgTyQxUx7KpDwYbYo7/yUMf15DQyl2u+Z3yJrifIx4ynxDhrKc7TBkwGNW61mwl4mNBhScwbQFk5ZYuwn2WdrcqIGWx6k5cBWTtRwwMq55o8XHsO7WFDKVLHeQYY08WGBnse49cG8KfzL+sOBefS2PxmUEg1GbdOVGxtDlTJgK4V4HfmQ7MLcwWqwEk2yhj0kxN4z+6kT4DponNJyZStBpTfzlKJ9XLHUUvpx17E2hZLaAPAhYu4kutiwPnMERFAVBPbwStzGusM4iSlwF6afIMtdE5hDQqTrGdyF3AOjB9dhwCYZlDpTIdZ0zCebcS2i3yvIczkwZocdbzStcCP1/OZHDPB3E2eT6IIJ3PalgC35Bebjsc0UBHqVpf6bXIacO7c9CKZmssnv7ORhBgtL87IooU2C7hye21yuSuO0lYYVx8H4AXwIvGYD4xfU+UCpnBjf4nNcaFTohFTTp4clzOt3ahm5cqGjbVSjX2rwhyKNU+NJGTPHmyzJ2alrr5C1q6pltIiJ4pNWBX2JeXwybicvxAG0EZ5lzYuLGD9BAlz+twobRWRm64rl/vvypVggSGnAFQOHe8FrHHKteuWyMplOZKfRzZQdUBiVseaSQy0d6TkrYOzKKUlAbGoKbGpSDc6FOY5ZFFzjixd7gMW88jlniz3ZVHp751hOcXahbW9A8XB0go/xw6iHudF7Q10lKF2/GxKXn+znThKyfq1DbJ8SS7qgYxp3jOBNeyRk2E5dfKc1FYWyro1dVILeAXdDTSaktPHUFA7NQXAhqYvCn/l5XnMqW45fcmUSTYLPLIDWG17kZRW2oB44vLcS1hrx9OyekUDymr5khckL3Fyao+YZj4dHEjJ4QMxOXsOhbIZ9G+JdRvnVYz6X/MiN+fmBgozpLcnxjPKENaagEgh3qv3S8RdYTHA1XofSnNcXxk2ojShWuQ+87TmCoC6a/PkmmscUlDIloyEIYOjWcC6cb7HpJQ127prcmRBI+fEzZBCg3ten5OTp2ckNJWyrq9G7TP59SUUe0+efUPuvWutfPSji1GIs3O92Mk+e1lG+l0oglXK2nVuVGbJ5zbWvXwnom7p6XDKCdqs6zJrlAiWmKjIZbn31dhY2OiiTTwSzLcTo4gMHEe1k3CPsR7NcK4Od1wKUMpbstILVOQj1lChm+Nc2jOy/yDjnzXomms8AE1uaoHkdWDiCQDG/ftS0tMNXFRiyPUbXQBZHI+13OBYRs62xuXAgQw1GS6Zn9Viveqjb09eiKAsPSob1xXIx7H5rKtxooqWkcNYxU5MT2BZHJQVq1B6B+h2ciPGW+lGBWpF+visY4fj0n4xJiOTcSAqfs41er0eqQIGW0GMNix0WPcWp0/E5CI2spOoPKcg7kn7nLtTGhqDxIhfljWzBkSRrPVcWl57CwvYiYysXemUDdegxIc1sI21cIK13zHa6k2UyVR1/Pr1fsYx9zAcS+O37WJCzhyL0sfc07AOLQCmRDhWulGI6+6dZiyUyj23F8iKZU5U5LI8A2GT15RXVgBobrvBRaxoW/JcQSdn4mxqAtB197i0XZqWaazt0UCh7Z2opAelscEjS5Z4pHGBi3gw5TRKeap4OT3D3MuGOreXGCW3NDe7Ob5LFtQDCCPtqG275/VBNoxEZeUabGPX5LJJAkCXdU08bEpne1r2vHaauohLVq2pB65EXT3IGJuOybkzKTlxJAvAy3mwOaCygj5hHmntNuQCzylu2xyQDz5SInXl3J/TU0//skM6UclsWloLYFqOYiSfw3MCJhaukjme9f00+fTooaicOTcrwxNx1CC5fn6XwyaDijIHfe+RpkVAdoDWJ4+HpQM1yWluQ+PkXPg7NoS5pKnRI6vWuqRuITEFHByZdsrBfRGuFbW6+hzZAJhYQSw6dXMBfXi6NS1HT6O4ivrvRsDKloXcl9H3IeaL9raYnDockr7LcQBS0k+Blz704ZxjQ/0xSv5OyO+jjHgdVtrTk1k5cTwpO3e/Rt4ckbIq1PD0PpfPiAHJX2xrZTx0yKoVgHMP3SHXb14H+EkiNJmfUbbXWNU4tr6sBdnbf38Xf8yDc++i0ebfMt8C8y0w3wLzLfDPtYDOTu/fL13QKiygBSn9UiBO4YOhoSH53Oc+Z6k6vNPVa8FJlYDeeustC2YYHh5mkcFClC/9nRa8FKZQ2ERBBFULUgW7q0Wrdzru/M/nW+B3bQGFktQ+UsHPbdu2yR/8wR9Ytqu/CTlpcVZVSb71rW9ZEI8WWB944AEW9yxW57/+3bTAr4Nzg4OD/9N1a9yokpmCSQpVai6rq6uzoOJcrMOuqtFpbvvNGPufDvb2D94P4JzOH6rso9DpokWLLMhN1fp+M8cfP37cskxV2Fpht8985jPy1FNPWeNO20HbUgtafX19FuSq9q133XWXBUGoQp2+Vl/z2c9+lodL14hCEap6qm1fiALQJIVVPReFZvV1qnCn56GvUYhO7V+17/S89DN0fOvcpeNfIYwPfOADFjipEKCCdArovRfBuaamZqttKyuvgHOGwb3229/vFIfzP59vgfkWmG+BfystcBVm03sJnRt0faY23Koip9C6zr9btmyx7I3USl3n26vv0b9fBecU1lbgTtVMdV2n6qL6Ov26+ufVuVr/3c6Gob/94hcBWGJyE3PMhsYFUkYR3sGDS1XlUSUOgwepLuxx7KUUElFwMhUUQ+EtA7icBYrI8vDXBLazAWs4KGLaddMRD60NXU/y8FdJGZOH9SbXlRkD5gmhhsXRTaAak89y6uYjfQ/XpWeqFn5mCPUYVAwygEHZBNUIfmHzYgtaiNpQCepIeVhpaVEvhCIVyk6ZiQlAlhSFbHbbYw2Wg/qrA9DOevLPE3b09TgPVR3R1zMHjlPwjSvYw/9oXwe2krZirNkoZpko3WQnUQpCkcdEISPD7mvoLwu2sLOpwF4BvJNPUR2wJEvBnEofNS7OdwoYb5Qi7yzAl4JsQDsOgCEbKmYGx7blonRGqVjhMROVHwVmMgAzJoUrLVbbUD6xV1FBqKWAjJqDnYfuZgRokIJ7apTia4iKgIJNKHe5q5nrivMkSRFTbXKpV1C8BOLJdNLWvZw3bUxhyoDiMSk4Z9jpHpmJAgKtFE/NNmAnrNawHjMUklKVsgDWtxBQ1LC16sDPKe6hWGYCzYk5xs8ozGO3qooKCiyo+oyqlxiAZPZAA8pUxAZnYkflx8jy2RTyTWwBTawYtTip7WrDFtac6wKG4DX5y/m+hjjm7zHUGRTW4dh2hTooLlvFXLpHr9dAtY+FKLFHoZ44UzBDFWMUJDMUCkQdxdDC9yDFde7HbahBqVKSA0hDi+tCAVoL8uIHInUCp6EORiWZfosAjwDgYekoqRnai75hbaRQmaoZXokdL3FGoR5lwQx2rQnOx0FMuygUU7XnXIBG6HuTnxlU8BSiMueG4ebGiQ8K/3kAid4qHjJw3TMn+bxpCtZAD9jJqi0fbrF8JpiCG4iBa0Tuhx+gtJRmrDAmqXoTd1yf0P9TPRQhgJicqIRVLwUuIFZQpVOVJQOlBfFi66rnTTyompOp8BqFH5PCB6Qox+Z6dawqHaPQCp1taoejvGBDKSkLkMSL6AdikcKNWr0ioUg88T7GsFpIGighZLX9KeYY7jpiG6BRFZFQqomN9hKDqA5RXLLaD8UAMAc+ktcChdq8QY5P7KCcY2qb0RYm1kk27AMJRgCoCdQcJmD7UGmsB0ApqMGOETghFuBsyQ+MfwPg0ECpAMKAy6QASjtngT3wJeXfwAlcun6ewnMGwEcWcCpN/KhFpEOtFAFrNL5VgSgLIKCKVnwIb7IyD/GoJUdtt3HGBBAiVn6qIAR9hHjYMasfbYw/w1dH8wWAKIB7UPhzqrId4CkdTgsSA8SWxobGqMYJ8oucOzBWeFImRqhoqdVt/WJgwnrawkP+1USLRaCPcwQKYRBw/vSPKo3RTnbAREhXTg0g1irc0+cAKSYgK6QREAyx4KuGxQBMpD+ygJwuFGts2BNC3nGtnBNto+emOQjpNAt4M7AgtRWU0bbkSsCKDKqV2dmL1KuIAdQBVd0wDrymgI0bcNIGcKBQn6qYWeNwjmsEdFC1DSMHkHAaGHG4DeU4rIYbbkTRbpMFh5lR8giwmw2oVRRs07yp0KzmigT3nnH6kFgwOS9rLFE1JvMC79AOqHTZbHqOJVxqAXALkJ5eHxZfQoyaCSDWNLmMCqGhxTaObQKFKQyiY9JWspRzK6cvyGdTx4lf1E5QeBNfA6/ntSjTUQ4lNIlRbD0tEgOlJY2NDAqsBlakEELEBsecG5WZQUAXVymW5csArHQMEi/MJYanUNJBNiui0mcj/xvA0JwEbU2bYr0KscPn6FjSPKPjjDgFJIKYokaoVqnMa27UYBifDmJAIxESjmmrh+thLEKxKLRic8xxTHI6gBHEq2W9bKCgpJBefLSbuQQ4C7DExs+0z03Ab5M1gloV23AWUDA0zYiyU1g3ZoiNEG1oQa70s6pjRVGbBNRMYHdsA5zzlK2yzteYA4rl3GwuxgTKNbouSBPXZnYW9c8ZMRTUnZvk3Pg7ij6qNqf5O6OgskJ2ChpxfXYAKVNzKk1gApIaKBMpuGZ9o6alIW1zEX+0mzkzSH4DvCrcQN5cSoyQ97HGTQOk2lCWsQGbqd96ypjVaCHbsP4wybuoTtJ4jJcp4pw1CuNUlbQMwEWFQ8NDKOrFsZmsWy2u6gZOBFVbIHmd012qFodlZ5pcYM1pSjUAVGRQcdN8w8KA42h/ELt8tuYbBdpNu+bfKsu6XOcqVc0zuTYDoA8JPwA6+os+M4B0DS+xixKsgvkm48meW2XNwzQIbcgaZaqdNovRt8QH82EaJTojrfMfOdBFrsHO0lBoEQAsixqbiSqeNqgFfhMrmd5WLh8QqwRXlIa7OTfmLyBEEhOqgyhJArWlAXxU+9cOfGCNI8BSPMutc7ZyJHnSyorIh+p8T8cRUwCJnhLJ2Ol/nZu0v8gvSLLxFs4bUNBaA3CNBCRxzznQDjbWCJLbwhTPulDXc8SJzuGaxxWAUODZUHVLhbzIAWqPDuPNF2OP/JnlubpCrprj8UEnXtWKekCi2LgHgAjddddggb6YPgVw5ZgOcr5lRSr0i1V41/cqSdBNnHXxgeRkfWyPaiONyuczV7CuyaoSpoKMqJQaatvKuRumzsPMeeSaDJBwGhLLzvi9umFegf00seEoxgY9dyGfx3yvDYeymqnqs8yJGXJdllxkJ1ZsOh8CsllzqM5nQpunhpgC+RxysM75SH/ChbcBbqAcVL5Kcuq30LeoB00MWs9zDPKg4S3k/IhzXaQDIZjMDSbvy8ZH+ZnOE/S3ruWYCxXy0rnCRO/ZsrfHElrnfSYLxhnrcKXLUEC0IFjmDQN1IsNNW9OnJhB4ZjZuAbq2UsahrjF0HHJt1pjFsla/DEAc7TSFgaHlgc58MhQplt45v0S0CVlL+8nrBQBoxUBD+fms5YjBMCDK5KRpQTLRKCt18ngOKk35AEulxTZgLhR3uUSGEuufNFDYFKpmcZ5tA1Tn6PAj1lDvi8Vd0jcSoc1QVgbiKytCoZkcpSETIS5Gp7I8EzMlCjTj5pmYSeD19Jry3M7z2MiGZcdNNXL3XZWSD+w1OR1FOQllNdqmrCQIuMcGCp1amHf0mZuh7yc/zAKwjKCwNAUQFJ7NAHqzNuSa1Kq6HKAFIWsgaFSq6I4JrnFmKm2B1yywyBNcY7FTyrjGgoDCMhyfrr/YlQE8GqSvDOwzi6W5HoVDbA0JRBTAwCyx2Jwan5Ncv5PNuC4gGEAi8kECJcNJwLOxIVQaAYhsxHWO3y4XOtLy6oEMbdcjH7ijTG6/KV/ygXNCrIE7e8hL5ISy4hwp4VxgO8kIxCobNjT3adeOj5oornHuYd1AxPjienL4zIIiE4DOZlkO40Irk+NZrg8l2zner2kRACkn1y4VNQbQEfeQgMt67zk8jN3jq4xp8urq5V5Z1GTHUpy5hOkyTIxMTaP2Sh952aBQXGwHWOTegf6fYx3S05/l/eC2EfqAMaSAZR99uHd/j1zsPMpm5GuAb5okj/NSu8m+7gSgmyFl5RwLlT0X7WiwxjYNwFSUu+ZCXtZj9A+xoeedRJlL7zECAWKP15fzrSql0zPaz6aEZlFD1CYDUFKozE/fFQMhlpWwUQAluzgKhoN9AKOngf65j120yCGNwGY5OXwu0HKMdd7gILetsygaA0OWMw7cHnI+1xdjDI8DGfVyPbpXRe+DgwzvKaCpp1/olaHxOOqDlfLYI7lsErbTRvTLCDERS/NvFC9LWKNxrrpuIFo4Sf7GPVUUResJYlTH2CTQf5z1gY0G9bGBJx/Ak0fJQF+8nJw1QT+Pc51hNoWkiAO99hzaoggosLSYfvQSV8RuT4/IkTNsAuHcli60y4rFdp5L83nA4Dp+x8fB+PuZMjiTijKbFAG7abvG6JNprn0M+86xfmZs5h6WoIwjkbeODkvH5SEArhrZcUsJcQE0xjl1onCWQMWylHFZBXzp1vsLXf+zdmB0Aati6Yo96ARjK4zyYgKwi9siCeQwtgAoi4oM3bcl3AoRo7xunNexAUntal3clwbIM8W8prCAeKVPDPqhpxdVOQC0BEHZtDDnSh+Sh+x8tsnvQ4z3vt4pIHUXucyHojnzFfGtr5+hD0f0fPSWmTEWZKz0D2blFcbghUvdsmNrjjz2YIFUV3IvSR653BtibCU5j1xAVy8W4syXJvnXWiCxfmKcpPjMacb9KPDfLDBhNMZ6nqv304d5jI9SxnNuAbmOMTRBDhinv8PEapIxo/Orh+sswZ65rIqaTpDGYe0QmXGj9JYm3mJ8rl2WLfWSAzTeFdZn3JOzRmivNM9uqlCiLOD6Te69NE4jIWKJcTyD/W6U9aUth6cQnNCe17AbPjknNZV++dTHCoGX2WTHxgK1a+3uR2GWe1uPj/sFcmcWUj2CvfPhg/twvtoHmFcv9z1wu1x/HarUrAO4qeE89U8+l2Nrc/y/wc3f38XXPDj3Lhpt/i3zLTDfAvMtMN8C/1wLWMuuf+5F79nfqy3rgw8+KPv27bMKSwoWlFA4+fjHP24BRL/LhalakNrk/f3f/710d3dbAJ3a+dXV1f0uh51/73wL/FYtoCpMWjx98cUX5ZFHHpGPfexjVuH16pu1WKoQzXe/+11LPfGDH/ygZUusgNT817+vFtBcpbGiKmj/GDj3m62h4JUCweuwSlNAS62qKysrLTUctfpVNTT9vlqY/83367/fD+Ccwm4Kp+oYu+2226y5RBVKf/MrHA5bVri7du2SjRs3Wgpw+rpnnnnG+lZQW79UTUjHoR5LoTj9evLJJ+UXv/gFN/hhuffee615SZUhdS45efKkBb+VlZXJjh07LDU5tYb9vd/7PVmxYoXVxmop/vzzz1sW46p4p/C2qs+p2p2et57HJz/5SXYsHrbgDM0TClvog4D3ypfmuk9/+tM8cFgsd977exZU4uPBGM9MrQdVfh5IWZZhupzhm2n+H77f3yuc90oPzp/nfAvMt4C2wFWoTe1ZVV1UYWvN86oWunnzZgucU6U5zc96v6Lf+p6r3zonKWSn8/l57Fd1vvnUpz5l3d/o8XVOvvpafa/+WwHqC0B2X/7SlyxVq7tvv5Md/ii16fZvqj1aWOITKFpQTADi0AIx1U0KfjzF5OGpPp22YAwe+uvrNLkq1GJ983fNufqQWX02tAjEXyiqaRFfX2shZAAevJ6CovVUlIepPFtXUQeKeFpQp7CvcAWUhqlVFS1g85RdgbwEhSLrUykyOwCDrCImQIUJKENll2PqA1f+p9fNZ1E/4HcUz1nz2HlgTqVdT4fz4JvP19qqXkxWC93Yh1oWoiiVZAGEtIgEG6OnwbWhDqAPz2kKO9eqp8fTf+uX1hVZ/6bt9Jk+n3ulwMbrFQTh4ng37Yatll4T4JSZpmipIJHCKfp6lGjSFNQUKLBTYLZFtUDJBwMxGIBqePjwdxQGOeeUQgm0mZv29NDOCulkE12c5CRdwbGQodBzTo93ocp1HssX7FbLgD2qsXT0V9EXWgDl5RRHTDcPy/nMKz2lxXQahoJMVhVlsoApJmAKIIIqi+hDbQVoDIcWuoGHbMAsqkAGbAnDRkGLo2ibaLGYc1NGBCNasc21oZx1gBr9FMXglcCK67hm+hkrw2jvBdrQK+5SLFFza3kvx6bIqzGl0KOpNrBqSQbolwVosUBDfa8Li0UbbYQl1EzHZXFPYtmYVy/2+us5RANtpYVjqrZQllq0NwVgg7i+Elb0QXaMPub9qDtZsCAQpCpOZDxATLS5Wt2wouDzVS0MQFWLSfznAs6x+l4bUFuNSmEW+MugeJ0BOslO9DBcsA0tvxZgspICMz+b3AevNIuS2VIA0NWcm8KK9D+H0Jg31H4yrRALgIYAPWlFic/JKmgJeBedaJdpoEMHql25FSspmAKRAgZYVTfO6Yo8jAY0X4xRBfL0z2ySNgNOMlNAe0oL6NikR9S609BvVNb0O8XYydBfGnt8sNVOdk6Osg3fAB/AA7fFgrsAAEAASURBVHiiYjmJYmMU9UJPtbjzqq2CeQaluYlBrhnIKJiPAlAxQB3qckTm28fiAimomdq+PH/JprCNUlU0F+ON+ErP9qLUOC6TFJD8qAXlV6niXDmngT1YFvvcJLHK+NHcYl5pePITv1boJgUokALcABZVG2mD/rNhd2nzEEMo4GS5xizAj522NNJcO21iaDzZgTI0x1mwAj+3FNBmAV8HOR+U5fJQSURhTeEkhcPCvQcA0Xpwe60EkljLsQFGacs0RTyNUge5goHAaynMap+p8h65xGYBcAoA96Ogh+UjEIIPyDGvZinHr+c8Cng3Y5WjQFZY56RFU4XRTIq8GY0rhfyAEyHviDXOW4EOBZEE0A7bSxNLtjhwS5KCKDoOtDl2bfy/E4jBxgBUFUWNUwUUoatgf/ph8Igz4CZ3eQ1KWZVAIXaJj3fz83Pio6Dq4twy3gbOIc+KeUOPoXMOVbUMIIsCYxyZ8+CYAjSV7pOZYSyMObY/F7vDmptQ4VvL+7mnQT0OylDPiGMQl+QnkzyapS9BJ4gBzXM6joE3sEfVWLGgN2BLG9ZoeEsS5jk0L+2t+RgIz0WO0XyqgF3G6gOKuppXyZuZyR542ctMYxQ0a1FKIqdko0Oooh0CkAK6CtQBSm+A8wB20cGnbaoQqUVjc6pRCvsAoprjtQ85MY47gOLYeZkeHuS6GiRQhcU16lSqHGWi6qOwVoJ405ysbnBOK870+gBYFApUIFtzNGpUqqCjalYK15IQeDFAi7aHzkEKnGp+yRDHOsfSznwI7Uzu19jP9AOznEPVqRfVT6zJihdZNs8K9c32oBgFxJdTUiVuFB5txIWpqh18cXTer9kUFRH+syvdEFJAjd8xZqy8DaSZnjpt2cpmA0XirN+E2ucK3gdkA6DjpNBt57yspA6wknIASwEhqLacSjtdgWonyRUAaoA6GQBRAxjJ5QJKV7UxLYRCZ2QZN/QarwE4pBcdXLMyaDoPY2jMT8aopqPQOXKWsYTyFWqhtv+HvTcNjuy67jxPvlyRmdj3HSgsVcXayWKxWNx3UiQlUZbt/uTx9JcZj8fyTDgc4Rg7rGnb47E8ju62PZroDrtt2bLakrulkSyTNEmR4k7Vwtr3QlVh34EEkMhEIpeX8/tfsByaiR7ZokSFW8qUQKCAzPfucu65993zu/+D4lxAgNzCRUvPzsAMdSLUN8wUVOPmQ5/1SZg5NSDpMK0XNgVD0+5qNqBIN96zKKvNnLf01AwAUZPVDh7mQAA2jp8vk27Vc6AUbURgGfE3V7Ygyq1SBS2jUuprDJIalRyl9AhQGGkrPVKtlwAfAwLnBM6iZKPps8C18hovXEW2FeMrTN8H8PFInwGDjZLSGZU73p9oJJW0DgTw1o3UguWnrliUNUOsbRu2O0y6W/4mH88rhC16AGo+wJaUdAXzCPzCULBx0nmnxiw/eZrPoxzXdQAY/2O0eRvlZswxN0OcIi4JCMxsRssD7OAbsVXBfT4EjI8SqepfZr71NMbwux7KQQFAqzLwliAsn0YVG6d7uyg7c4bSftPolAPAFNhaqpvryxOMwZLFGoeoxw7uSTtxr5DKoTWCXnJN+AOtBXHHfMnWZRW0sxQbBeeithYQdIc/9AWIoo62OqmDFRuAK/0WZSwGqndyIfoZ3+AMifWIoDlBKW7Nydgq+0Bg+VHKnGJsUVbGV0C2h9KgR/rkAHO2/DGSiJSVeUrjhBp7tJ1AQa2BnS8VdApQVwI03Fi4TLrom1bXwfq8aQ8d1Md7AB0F/dHG8gd5lYXfaAwKTAIJoc2xfwG++HT5CLf+Yz3ooxBZAJxMz56nHL4lujjsoDVbtJn+po3xA1J3di5dkFpB6zw1HG0KqFsuArSxXiuhHCkwXuUOAthpPRNg3VYG0C7h9wuMxRCNExSUKcPj/yXu5+FjPIEpggVRkiyg8Li5umbR5n0WbrvHrRXkDzFAxheDVpQF6yMfokrXVYd6HDYpc99N/FhG/au3ULwIfaM1vm4WxL5lBs4mKbtUyvRSqlb9TwhQWGtzPuthMwwnfmYtCAzrDgIIFmU9L78o8LuMaucmAJ36XL8L01eqXx6lOkFwq4BRUdKKV+nZgfXyKGpJb7+3YS+9etR6Oor2qU8O2SOPbwPywr6pvx5dtIYSABbk5h59Xtazg+yAl54t5CM0A8mUb63jMDc1JXuh9DhrjABjw8e3SAWrQB19zZ+ydcaF/J3QcO7i/L3govfPrNsrr57m+cxHsWmX3b6nwWoArzQ1aS0Ns8W9tFpAFZCDDJrDM4BFKyt5W1nk+QZbSwjcxVTW0kV76dvL9uaxoiUiWftvnmu1B4+gygnItsm9pPDqAXRJiVK+L4itUltKz5dMAF/Pb92c6/qKsanl49YaUese/sf93NqA+6nMKhlv4It24Eel73TzFvYmv3FzNG9f+ZsrLB/iqJF12qEDEasDWtItVT8us2XK3FAtrYMY0qaYJV3pDF8e5U4AnMpZplEbe+uNrL3+znkURZftuZ86YA890mE1gHjyvJ7KIdvjm3yMyh7UuopxUnR/09zNaFTZZWf6O5fWZ7dqLv+nuYoxTH0EMIUYa27JoLKyRPdZ30WA6qWQms3GUZyL2OtvoqLKOvDOgw0oztVYjeA/bErPkpsYjdoSzorVkHyyIDXP5lM5m55bdYcwqpT5g/uszpMZ6FwGZcArrFWaSG3bZU89HrMmIDYuAYhEGSh7FFsKsqYNMlgC+pIFMv/qQVWuvcR/3HfuBU5FPWlZ7Njhkdin1pmCmcpA7krtqTlTz26aU7RfEHTjibpr3GO0Fy8AY769adPzBTt8e8KOHERxDEU2D1VrQW1aD2k9qbKxhGIssN/A5VYYg7MLeQBngLIqxhltvgS0eOx0lnTNKLBnV+1jj3NQ/6E265b6Gfu5OcBjlTmML4lqDU+/eR7rT54p3VhENbTE/dSmuofcoK6rpbSEbrWw0NpGE4FgTZpgy774lUxVdkf19I1r88X3EWDTv3tpxFYBu/bv7+HgOofVgdMElTF0uYZSTgNQ4/e0xtRnitw0tZIB/NzkQHwYoDSOTQCoAtS++e6afetdn5TcafvUk7X2iadjQJkyuAJlB/SjL3zW757zyczLFNKpIjO2VBfZow5vqG98Pdfpjvxfc4n2dTwKJT+l32kZK7es5Z0+e+sl4DXo1p3YP9dfnova22+uoQA4ycH9uB2+hzbv5hk8pvU0ZWJcbDLG1J5sD2jbgXGAn6EP5+eZG3FEUezIYz23zhp1ZHTVvvafzhNLimMPQ6TVrbfhIdWD9sGgSvLxmo+os3yMj32sLm3aCy+8Zi88/3/bwLYO++QnnrTD9x6kTbWewR65r/uPqsv4Vf3c161KfZ/fK+Dc99lglbdXWqDSApUWqLTAP6UFNDv9+L4UZPqZn/kZp9IgEEFBJIFzAgx+WC8pQHzta1+z3/zN37Snn37afu3Xfs0tcH5Y169cp9IC/6UW0EPOxMSEU5xSIFVgyZNPPulsXCflpGiilK5Sr3rwwQftM5/5jEvNqcV35fWT1QIC5wRYSTltZmbm+6684CuBWgLpdu3a5YL0XV1dnBStdwH+W2ldv/vCPw7g3HfX58P+rHEoRTi9pB73X5p71D96n4BFteWtl5TmNL/cSgV+6/f/3+8Cy6RGJ+CiuppT9B+8BGcINBAse+vU9K2//df0fZbgyS9/5n+xdy4css3YJzmdWWutbAh2c4qznxOY27o4IckpSOKmip9yAtGsib+3NQbI7MbWkvYZ+NJGmb70XF55VVqg0gKVFvhRtoDWXlqbyd9rzfbCCy/Y66+/7vz+E088YY8//jgnyrvcBraeVTRXaE649TnNEfLzFy9etD/7sz+zK1euOHBOB4G6USb7//P1RXa1zwNS/8Hnfp9NUY80RZ+0e+67j9P6BPNxiMIifMoWYeNZ6Xlc6gw2PKXg4DaZ8Z38kv/xPm3Qal9TPlU/4kxdYI9lpZaW2rdWZELld3v2fEofUbCUfWzilwQT2RBXORQScMoGXMtjU39ru5a7sLlcZgNWm8fsCZsHgLM+es3Cq/MWZS0S7OpGgS3ODQkUsgHtAD+uoVPSiskV2PQPoJBCQlQCanrW0xyggqmQBJ2BenKkJZQyj9KpSb9GG//a5HVhT7crT2kI+Cmi46YLyu0uoQZQ/dUA7geu+cGEogCc4vgC+EDxiKXqxLW2oAmOuOAG31RNlQPYO0+UJ0DdPIEt4zdR0iPtUucgse9OYAG2ygmQbPABwl3cKqyPWAS4zl9fsNWFU1Rl1uKkOgyjEFPKoQCxAJiEuku8tp60sbch+DRIBwKjCLrivlyCsrGZTrBcRdCXx677JmnJMqjZbCjFIQomHoHgENCfAvpVKJ6Ea5qdAk3Za6XMqPzQ5goo6Zqu74mUuiajfCT8tcAiwcCRl2x1cd5i2w5Z9bYHiMFwX9K6zl456dQ96rv2WrITsCyCKhHgnMBDbkzZZH+k8AWSzKBsFsylSQeITaDitQ74lk2vkH1zxRr5THXbTgt23o6AVwfXZ/Md6tGnXwhvUTg6jvq51RRBwzzqShlSuOVRwAnJsBlLCjoECEYmEvUWQ3nGAwBTSrQCQdkCEIoCnRFHYmL71M3VlYsKuiRPmm2MvEfq3suktQE8GXwAQKcbFa9ztjn9LZRMlkkFeKclOh4CuOhykR2NjSDKBE7Zirrklq+iHjJDmiMF3wleAPEUUMnKZTixT4rL2r5DFm8lNavgRRm2CqBBpSisKxC/46JlQTdIhuRQw8mukiIZcM4DDiPc6z4TS9YCGaL2mGzDBuhDUCvBnRoTCnIo8C0A0wUQUUAIKpC+cdMWr79j6eV1q6/ps5r2QafMVsL2JkeuooSQQ2mly+r6bnPtD82wZVA0qlTmrEjqUVSk0suLBJZQOKniPqUF0gaN21qG9os2WRM2mmjsAXgDskBBykNRDSKMTsMzUE1V2QW4ZGeo5OTXRlAGA3YjZVxRyl+0QRQ/EEU5KliN0iN96AN9BMqyd11ABkB/SVUHfxJw4BzFxB5KqDCuTF6yVdJO1nc0WV3XMM0qcC5tUxdfAhi7AagxZPGu+4FchwGD4kAR9B3jhZgasS3qCey6mZqmD4FNGVsRAoQlgv1ZgLK11BLjspb0VNuxgZ1co4XrUy75K9pddfPoT9eveDkFNPOAOrn0JPDCBLwCql/qP+og5cQY14qg0ujVdbEGbiXWShAR2EMeEs1KLB6QSMCxxqUiaoJashNWmCVV9OwoEATo3cAwynB9jAWUcyav2tr0WQ7WYGeMw1L1bgJj2Ajjx0ELqLwJCEzPjgFvkYqLMgfDwH2AGuvr4wQN5/l32Vq7SX0t2Ck6RB2on+xSzkAvfcOuBLQUBOcAsfgbpPJOAaYBySAhQj0JRBOQrEINM1TbhzDbIGO9yUEDYhc0fgX7hARs4CMKCuhT55BALNpofeqULY8eBRZA2WXHoyih7kRQCYWqm68D80wBLA5bFLAvBlAXwa4cgKn24XoCKDOk2c0vjmGjFFWgNGqbRZQ71Q+CAxJtu0jbTdsA9ZVlV8Dagm02aGr5e3HmUfooCBAjOLdESsnSKsqhqLttMCZ97D4QZyYCKIuEmyyKLw1JkREZmy0wiRmHitK82Kj+Q4WZK91EsXkdta1jqHiQHhWYprFnP+kXmbNXpm2W9cMqyqQN3UNWj6JhOAFUiWKZ5qWAm2Tk8MHmCEZvohCXmQMCROGrChUpj/L6pKXNLo8iWLmMgGWfxfruQultmHLJ1xA4lh0pWq/v+Hc/CASG8pr8yiaHvDLL05AdKOuhQkfPwmrFmIsaUbdpBQigjFLWJKAswGYTO8eaqKPAOZRkqKRTdALQDpZR65r5jmXGSVMLjJrsvd8i7QepA2Nh8n2bHxvDf3ZaI4px0cZOKwqM08yu+VRibsjLlNaBQDP0IzYRjCs9MXM1aWFz8+P4RBReWgdRRbwD/4Cyoh4SORQgAMSpMwp2obKazoMADOIq/axSgWP3gKc+SqpFpezGdoLI2UhZNoqdBsNdNE21S6VcAiRTGwi0kssR4B6RfdI2lgdcBYSeox6bBKib2vustqfXzVMrM9O2OoLScBQ1md5dMKP7WNe0Mq6Ficq2GBtAA4X5UZfaWgHtkAwVECwPlL6K7ymTHrQepax47+3m1x/Bt9QyN9O+KgjEWxFQBnyH0rG6EaAJgFdEiW8zRRp57KHAHK/1UhifnQQOjNWiLMtY0Zwl8AoPSyNvzTdl2QO26Q5XuHkaH8OBhk1Sm87OXGAuLwKV7QHwvIO+4Dr4AgEjMmduT/swFmgb5yL4lf7gQADmQn/jOil0b9AnrJakgEi5c5vz2DipvtMbVl1Vb809pAwnRbXR9hSQC3IN1ZN7SBWMZtOP3Eepn2+SbvU8cCn2xR6KiitYQemrq7hWKMl6OdrLsqyOvsOGsAPQMmwAAJlWV6r4rRc9Qf9nl8ZtYeJ97P6q9Qz0sXYBFA5vYz0M6EcLC0byoTKkXugOcahwjEOBcx5gzcYy66v1SZf+V+sqCEn8DOkUV5k/GJ+1TfWkSt7Dmm0H5lnHZ6kMb1PBSwK5sJ1giTUvn4NkwHWRgnwF1c8cPseBJawBWC9HaZcEipbhemD7GABkJInPBNRw4JzqxEWxZcRrXb+E8YUec15p6YytA4ynUWqu7rnL4n0PYsiA7Fujm3vSj8wvOmQjUsUBsdyvxNpDflAHPUofrCEcNMfg9OmbIp8JopgqsEnr9CD97wA410+kl9Ychi+UImhRfpDPeIJ+CoCzzMGldVQnmSuCwBsh3hNIUCbqFm5oxw0A7Ac4RKO24fobpOB8660RuzSybtU1baRRTTA35+za5RkOKY3bPGmA7zrYRBaGIdt7cNBlXRBIrLWQUpbqWUhojA7y0OS86AP6U/BbgbqpSyi+TEzLCv3V2aCDShlDZeZMBzo6gxcIwwzOtXz8p34lH6/re/ye/9rzL8/ZF/7ym9Q7a5967il7/JFt1sg+lse8qmcItScNTp9jQ9iSDhItpbOoh8/a2VML1LmefS5UIYEsp+bW7N2TYxy4SNiBPR32M0812f6d+EmU2vIAklrzO3Vh2lumGaTNgxyoobZuvuaOFBCjoC18bKKIjQnQYVeNv2hO4W+0kdZc7qmMNZRrCXypbEpryTKq0Kwo+XeUNSipUU+u2P/+e3/Hs1ajPffMIXvu6XoU4biOTJhvWgPpxZ2pi7s1aUR9UuquAVjNU17PGutJc8w9JlDLkjqf+vCBBwbsmWd32PadCadul+d5OkRZw67sVAcfovVsiHZUut0ifeABYkkF0qf8/JN9VkovX4QFBvTwR4HkH1R/cC/3nKrDCGG9n/IVBfVRxwj9HMFXLC+F7Z23y/alLx/nvctkENlujz/WjyoZILzmFNqEWcV9NkQfapxp7EhM88SZUXvnvVMcBG5FCa6DOafGJm6k7crVSZvg2Wb/HTsArnpJjysoS35Fay/aXcaHb9PhHdlRUPbBfRh87n6ChvU/+futtb9QT8qBvXm0YUh9pPZwfhxIHHtXH9Db9Cv9jZHqCdhdVYOa9nrrjTn7869M2djMqj31xABAXwcpSTlsIRgU3yRfWca36JpuWcWHtfwbncrbiZPjllpctw4OyBSA4qWidvTUDb5PEkNoBp7aZQdvr7NGlO40pxQ4WCGA2QMwDwLJMVtRRg5XlIHnnLEADkt5kxJqWaL2deOKn1nc8x+ByYw1/EQZ0lEwqABV91Iz8aNaQHaspbUKevLUhv2f/+55QMYpDi3ebx//5G7r7WffQO299ZGta/IP/Vv/3UDp8uKVSfZwpm2FNKhNPMNUoe47PrFCeuVRm07FSFnbb5/8WKMd3MucWEPZmIu1x1LC3xR1YAJn4vEMGqJeOjilkaahLifhowaseuihQj2ie5bdHMA1qLgDu+lr9yioQnEtt1fCs6VgU9mKe2GHSsM8M+7Zl790HXj4lO3d12Kf+qn9tn1HncUTmluIPXAhtLQpAWUBjMOb0kQh+nzD3n17xNaW8igQ8ozMc+Q8yviXr47a2TM3rKVp2D7+zH578L4Edox6LPNfiEMissuinj8YSSH6K4CfWZrb4ID/39uLL37d+ns67RPPPmVH7uUwQ1SbElvF9fEpmsbdXoB+oDwf9lUB5z5sy1U+V2mBSgtUWqDSAt+jBT78xPQ9LvrP5k8C5372Z3/WBZ+U1u6jeGkzQIpCStF3/Phxl4JPv9NX5VVpgY+yBQTPLSws2GuvvUbwJ2EPPfQQi1c9KG0FZ48dO+ZSRN57770O2pHySOX1k9cCsgelhHv//fdd4P0HaQEpzg0NDXE6a7/72rlzp/X1baV1VfpPBfoVwFeq0D/+4z92ymeCOh9++OEf5LaVz/4Et8Ds7KT9T//zv7cXT9xNwOpuHvJ16vC759cPfmZDuB66oBNobrAjYPuGPetgo45YOKkkzFqR6e/tBLJLsLnAe3X6UCcV2XNzGzA/wU1cqXqlBSot8CNogQ1SMk1OTto3vvENe/nll03qonv37nVqc0q3ektpTuBZR0cHaYqAXfg5g/LP6OioU4y9fv26++wYwViB7M8++yzpZlqdap3AO4Hu+oyD11gP6qTz+fMX7HO//6/ZHPfspz/+Cbvv7rusrhaFLSIIeU4SuxRTxQ2ANnwj6eMUQtLutNv+BxTSGkJqJy6Iop1ONn3ZosaBoobGPbRtrc1stn8BCXgvwR63y6vddD1msi4osS5QCFdBeQde8XtBFW7DVKpNbJwqWKNNW52C9thUVjqV8lLKxlnjRibHrGZwwKruO2wl6ut/oHrhNsK5lvbDNRP4bNpKPSsKcBJi01aqaFK0U2pAlaWs8vJGaYz4bE4rCIEmHMEbBRx4H+9X0E91E6SkZzmlpZFSjmAXKszv+TvB3QCqLiy63SZ+mWsppKGXgiUegWkFOhzEo9SCitYIMiPw4Svogaqdh9ob+V0s/+bbNjE5bU2H77e6PXsJDrKNHQPi4G0CncB1XAgrxDOtvzZnS1NvEDy/BtxG0AjIq4S6QRG1nChB15p2IJEOAvsA5pz3p6gJSkWQjGuxM85/KDvFVnOUiylbnz/Pem3cNknFJrUEKSoEUd5SassYm/rxOg6btQxDpO8iWNxFk9CmupZeagdKV1I0ig34IJ+1hXNWuvYq1521CPBX1cADLiWfoLLpy8fJhhe0xt49Vt9JPQXOlZic1QuKnql8gFsFVJUWR8+RDXCSv5C+LYzKAz1WRA4iGWi0JpSgomzce7U9xI1QSaIP0HwhhuC2/1Uy96KnKGLGMivvk/bqHGI7CwjxCIjDtoANPb5i9FtVbRNKQACLzUNWjPeiaJJUiwEUUCa3oU9jEYjjas6si6uTlr72hvmoziWADWLbH+SzvYgUnbb0xLdQAE5ZYxdjrPNxVFIoI+EImZRTGAPq9IHI0nMnUV+7TDAdQAJ7KZE+SsEeD3AvXNdjiZ47UAjrcf3mVPK05gHa8elvNbsAFwVrldYzvzZF6qCbtgJEYqhihAXA6W/0i1T0kjX1Vt2yk6YCsIh0Yveyehc9whqAuRgxW10plRzUjTavWmrkW5RvFWWWflS3hkknyChJT9n0yDVSDhWtuZ1Ab+9eYJVuPi9wjmsArfAmgoHAY7MXAbRuol4FxIr6R5EUsgJ5yoByyXrU9Nr3oOgFrCalLKm4aVxJKQ6woAhMVWSBFtY4AhbNrxKEm3kfQOYGbUhgifdKnSaCEcfo+7BUs9opY9MAbdTGmBUcyDVwNAILECTEtKizi37T/qRXXZ66bLNjJwH42q2lH2ghxOdQirh59nnUa0ZIPTZs1d33E6AfpIykUWTMqo00tl36WMCa9cUrtjSP+heQVJTFpIKaJYCwIIHK2ro+q27aBYvEmIkBevByYChBR11IilUBKQnp96gaZYAmV+Yv2/rSZcbfMnauQBYFRwFL/ilaVQtcs8PKHQcRbezmGvgTiiIbV28KvJK75WJ8afziVwDnFqYBr8IFax7sR7mrjzRjpPMaow/HL7ImrreGHuDTmv2MI0GxugB2gf2UUGdcuHkZ+O6GJQAZIihbbTIW0wI3SG9Y09Bhde3ALMkh2qeVElBKd28uoZcu5QKBpM1DQbKcHkXp7jxZIm9Sb9TGKH4gByzM+AqJlEx0U78DKB4Nkt4OIEKRQforwHgVECbVn4KzL9LkUeMgKk6Z+fdsfvRlApeb1jT0uIVJ81lYnrHM9deBVaYt1rrTogOPAaz14CLURowHfJdQN6cuOHXaNsbPYEv4GOaIPH5e+yQCsmubeywO9OjV99HAKBtSRqWILfG3HOVRVTVyQMPw9VOoYgGULgCuLqHohSJinmDuBuBcQanrsL1abKi+oY9mBo4BBiOXJg201YdbjaXx+EEfUs8AqqKZuaMEPq/wANNubdsO8hyD4hI+cRa12tTqptV27bCmgT0AVa20NcqE+GYH4HElzU8FoIwsaXWXJ0+h6DUJWENwlTmMmKobb4kksFsb7d2yw3yATCnRSLlQfkq25b7k65XCFnCuBCSaQk1xFcXCCNBWFBU6qVcJFQijjJoAvIuhIOk1dPPVg6JnI9YEdEo/EtPlyY2mxEidShfpew0Qszh73JZvAs+RVq154CGUtw5RtjkrjH8H4GwcJrfHGql7Ff1hgE9bEDq2Jp9BquvM0kkUTU6zRkK5THZBB4dIzRkiWB8Fhq6RjTYPAEUD3iioTP8pvbk+LyVEiwoCYN6irQqk/c0tA9wtXMU2BQbil+WXKLHUqLS/EW8dtnAzcxdqrj5zOLM5bS0YBh8PtCGV2ZDUVqSWU5oBfLts0zdJi0tgu7ULCLl3m7Oa9akZW7561qqB32v7SbUKJJ0HsBQKK4wLK+MnbHxixFYmrjulOBKv8tkCfhT7wpYTGr/M9RGAslx4EL9ehc/kEB4+l16m2ArS85N8A/N5OTtpucWLzIOjlmGNlmdNV+RaEXxZjPRqUVK7R7HPKnyMR8pkiF/qji8VdKy5UC+Afpc6HZ8lcK6QumaTUyfw61kg5P1W03Y/7dLJ/LPlo50NsSwqcR+lnZVikh6+SyVBN6zzjIMD2StAkmeZylL4dH6N/WeZKzQdxElV2kD/VTX2AoKhyBesd2tUQTxlETiqp176HLCrIOt06gzpwN9lXURKcNaSQQA3N/4DrMlYI4Tj+OaGO1GOG3JqUrJfDMStF90az/kxLsh6KABNuQ5cOz8OOMeaq3+o35Idd2KLzAuCvTWx83Gt7bTGEsAiZEX/c+plzAdZFPkytLu/PkrPAitArsmu1MtVwJDVrT3YKHA6NsVpDu7LAOVL8EERXyxlu6CDy2kzYPt86hRANJC+VHmZ/1wrYF8CU8MoFsdqWgFit6F42wtM3uzGjAOrKWCJcubd/MMRA9a6UuQro7CZG30Tn8KYRrkzOvAw6ooAhlRM6lZhritlzIDmIy0cGQuCC4XOYPL4TtqAdY2eBOQT8Y78m3UzY10paz1gEc11Drz74G/yAWVSQAvQ8YECy8yZwpYKzM2pJXwp7RXIjfE8UcQCATKA6vP0d4lxXNPez/oPoJm1GnpI3CNva6Q2/OpXr9q331q0XL6GMci6HLW6InBhwF+wlsZNO3xnk917f4t1D21jOc7aswy0L3VpPYfg02VJHuUWvOcoWs1fQMEbUn2mSlq2aw2g9taXW7K63+mZAVvBFzoVR6k88yVPp1TO4n7UJlH1K3OBYOnn/z5jX/ir5/HRS2SzeAKFsX5rrOGq8qfcSMCeysM/3ZeeleZX03b82KS99LdjpInkMA0KmD4HQVZR8FJ6+/6dA3b/Pe12166IdZDGNExKSililtRnrrdY8+ITPPUF9qfr00z/YLMad4LJilqjYnEe/sStMz+os3s/7wnKb/IOAUBYgmpJHdfpb9oOWEbzyMn3s/bZzwLOFTrtZz+xx557NmmtHfQfF9FjlGtAvmmoUTXsRil6S/b20SX7xgvXbGmRNRXzWZB2yGZQtqRPevvqAB932L4DHJ0hNaauoc/Lr2jNopSgOrbklNZ0bcqoumuM6C/yhQqD4AL4Fx+nPwR1Bl2B6Br+nQeO00DmuIdbl+hZkcyyWoq4+VOPASsLpDA9WrQ//cI5DjwsACMN2BNP9vDsjQ/BQFy/c33ZjLAkwXas5lBaM3v9rUv2jW++YaurOijdBgRbh1+hfMwZvUMN9uBjvXbHga3DwVLhE9yEVVFmNRyQm6qNP9D13GJGY5o6Sj1Rxunznq3aqVX5YszI07ie+qAcsvWi+xyX0Bhl3LElynf+p4oqtTV+7Y3XV+1Pv7oERLVkTz/VYc99otU623muxvD11EsJ3Jhw5aAN1f6bqKpfu7lh33zhnF0AlPN4vitR3hzF2mQd2N1TY/fcP2xH7m6yDlKsxrlxiee+TQ5M0WK0OynsWUvK7ly/ymer6vSHnk50Zwe68m69cGG8D3AL5U3Nxk6BHX/vlGUZ21L/E8wsQ9FeiFNblJ1gB++fLNoff/7rpM6dsCc/9iBA5i7r7NKhBl6yZbUyTeHJX2L3WoduUJH33x+z114dQ5FPa/1G52tS6SWmyLx1DAzZw4+QsWd/FdDgpiVY+sseZQtOzZCihFiDhBkIGttuHuNvbu1MW8iHYJLU1U1nKokq6NqaX/N71QVfo7oytt2alD4rYqhOOVmlV8fwjjzpVWdGzf76SxP2JmqNt+9vRkzmNrIIJZnrNc61x8AzrcBS3q8dDEqG/wzYlesZ+9uvn7GRi8uw2qy7OPiTBtLNcZigrs63uw4P2aMP9wDC0VtM2jJPORPts5R4hpc/COG5A+U6Uudm7Rtf/7r9/fN/a/29SpX9cQ593kNiAdZN7nPMdbSdDhrSAFxIz0X6rj9+/68KOPf9t1nlE5UWqLRApQUqLfCPtsCHm5T+0cv+M3nDjwqcE7z02c9+1qXcu/3221kM6xGg8qq0wEffAgLl9FIKzhs3brh0rQKXZJNKtzk8PMyDIg9VerqovH4iW0APZLIPBdyV6vOH9Ypzuru3t9d2795tAuhka4ODg075TPeUKo7SyVXAuR9Wi/9kXkeKc7/0y79u164f5LT/p2yNzc41diCK2onArZXZBBGnkWdjn+d60mTod7faivfw/N0MLDfYBky3LWjbekh8RgqEhkazvk7PulCuQ3iIk3psjBHk0s9uE+DWJSrfKy1QaYFKC/wQWkAqoCdOnLAvf/nLbLwfdQcbtE5TKnQdftBLazWpzT366KMOqNN6TpCcUoGfPn3are0E3wmmU4rXgYEBdzBCEN3hw4fdXKy5+dZL1zt38Yr93h/8IduQnv2Lp5+y+w/dbrVJAcjoFuHsFJTZSjfIBioqFTqd7/YzgSSgcpxqh+Z0pe/Thjr4G4FDAnxxgBIUJRTyZ7uUQANlLwC2oBKi1KPaRA0glRJACrRMmcq8N6ibKRqhl05ik+4rgOKJFI50U6VhLYeB9wjwKI1ocWzGJv72m5YcHbXEzu0WeeQ+K/URtAZGKUcI7DjATUEEPq77CfwBrgkxRxDvd9GLgIAsNpnLvNen3D7BScLbrhhhIMBYDnU4pdMiaOI26yknVM8WpET7uz1cAl4eG8plyuTS6nmUG2BDm8UlFBwsQqpIrltmpz1IO4UIEkDU0HZshCsCrCKiTuNrY5xrekrHm6F+F1HM+ebLNjo+bs0PPGINd5Guq4XgVpx2rKJtUR8rAEVoKzmktImoBOUX3ybN5HmAjXXXTn6ZtgizgQ/EFARG8uqq6RfqyacUsAQf39r8V0/RFD51USQo4KcAWc5iS6Q1JDgQAaJUcD4I1FBYJQC5skaQhI1xABSv9Qj9OEwhVFfaUkEYtRcb6UTvXJ2kyOUvXbHiyNvABwsW6j0Md/QQfUQ7rJ2ymZFjliUg0oDaWF3bdu7ZRBsRfJa9SYmFYtHp2AQBeaCyIgF+KkuwjdPxdIngkKoo6e6i27A9gCSCwgHaXZo/BaU65PMuFRpW8IGFUU/U2DInbX3lEipC61ZFYDkJnBakH/2lFVTi2ORnARFqb7HwtrusVLfPsqQdpCctjDqhIKkAMJpAFBmoUhOWs3OWvvGWleYvofAHODf0oHkEqTdTp2116jVbWUYVr/12wKIHqFoX7eMKzzUooOywTJCF/ttcvWKh3BIsAkFn9Qmp+gKo/QRqeoECBxgzSiFLO/tAIQRmy7S9ghzqw4DGEWBcAMWLIgpZUgLb8BdhS4DJUN8Lc00f0HJ5CQU76hFv3IGy0ccAnbZTMz4rdUBdiDHroFICOWXS6gQ1aPIokt18FVWbNVThBgGIgGuqCVqlx2322jVU41g/oUJXj+JYkBSn5pM6DStTzCMQYkyUgLZWrmIDExYiFaZSPxUJDJax5zBgTbAKSKBKMAuLMIKz5YLKwXiRfRLsKhBoKhJIF9Ya2iTFFWBnBgUhXbcqgvfBR2jRV0Yhp4S6G7gnKYGHLIqSYSBJu3lcl7GeR8mthBpYBLsMaRziCwUMKm3eKkpsM2MXUPLqcOp3ntdGvdM2fvFlGJprTh0qCfwYjPZRN0AuATcKrlNJj4CTlecJyt+wtYXrfIzUvFxZwJxHkD2CQkMEECwQ7aUcqOABArq6YUcBADQ6j38DoG7lAONnAlKknMys3AQcuGFVAdqdfSQXD14nFR9qm5soFHkAMpH+h1Feuo3rUCbBI26xi19QMI2fffyZVBsDeeCjpRu2CPhWwE81b+vFBrpxSSjOjVPuMepYV2s1XfuA+/ZQTgBW/GQAxQuVxweM2US1rUz9wuvT/I4xLLgMX+6hLBap7kOBq5tFM8FfxosAN5Mao+rGjy5NNeUp4T82tf+QHiOT6SVg4Tl8H7gmSoRB3l9eR51tCRUzgnOB+tusFhgz1khfoOKBwfMlxyldFKBEvlRdeSkBH+uL79rC+EsEJ1FfG3gCl72Pe6DaOPK6baBsEsMmYtvux3a7XJm4ED5CwDQQFYpe5cXzLk2oUi9DEwCBMBfiS71YM6DOILBwNym1ASywRBKHUydMnXvnFbhmPhQoElb6xfI0ENEokNQN6gNIhw0E4lHLV/mWxq5zcwsWW89ZMt5msZ676ccD2ATAL/ZdBnKCDuVnzT/Ypp5ruJFXQH1w4YxN0U/IFlrbAAqW1aTQTE3YwoVzTnGuBlXE+j4U50jvxuDCPvGjCnzjTx2ZKJ+Ru4li1nFUFq/h17GhD+CCgAdsSprrQA2QW6KT+YP0jjxDBdxYxEdpwvqHlwLHgAmbKdQUKRdqkklS98ZI2SbwrExbFlBI95dRkUMhM9IFMErqyXKih3FfwzOZAve0HOPG89doROZ6gvVQgKTFPWdL48Bvq3lrH3gEKOkgQwUQceI7lpoctzJpcmv69lmsqZN6CYSnjlI3BGa0AOpwmTMoBAJZk0ZaZQ+gNhkkWBsGSggDmHqoiwUEgSmdt8d9ad4t4Ilr0edloFL0Wmk3FAIZgwLnApkJwHsU253CDeOMMeOnF/CzKSDGDot034W/2c74Tji4S3bhgCvAIh0AkLKrAOYAqVo1BmdGSXvNg2kLarnVXYPcv8qyU7OWunYW2yVJeN8grr8fwLWBuQ71Lb5CDH5B7MWVWcsvTJKPEWVL4HYWFqx9CMSjthmub4O/bGd66UBZivahzyK0bxhw1qkBFQW9qR8ZkByMKG/QnuvXmF9RcmMuKpK6W8qR4RzjnNSvaVKi+ii41gzfv2X/pGenwlyDsSi7xD5ZtPA76kYfSjGyuEr9po/Tjusozh2wZOvDDNtexjb3BPrFoGkP4A/ARKVgDSqlo9aPpJ+WglDAaNMC6opzF8ynrlK7xZDx3QThUWeMVNPe2Kj60Gf+LzJfyJqCAtbk/1Q3bFr7AFwQwBDEnhTH6dRxnuVzqCySfrYssBsAewPgfQ21V7/ZapoA21mf+CjyF7WPKt/N52VDHn1II1FevjPuN1DoW5g6D+ByA7hlmyVQRbTIEO+hfVG0Q3LJ1ZMfKALl4EuHUqTipLWRj5pvHju39CVsE+gRGy1Tj1C0gXlCcz4HZJKd2Ch2qqJge1D9vEeravli1g/UIag0f7lJ1kUngfFGaAMwIdY/OhDDJI9CWxpgOG2b9Hlt9w6LolJJZ2L3Ak4FcElliLkY386qFXiD+lJ2HyW9wth7toJaZrj/sIUGn3BKsO6oC+UVLB6i/z3UvZyipfw5a2QfkLWo9TFjWSqRAT0vMAYE6kj9SECdFIqhh2kHweO8V58V3OPmwrTzU2X5LlZckHIoT6JMuzIKvD4GzLnEWg3ACNVkH3pJ42+N/gughlfdddgirYfoNuYtAN78RgAFKc+OnSihTFYG4EGBkvFeWzVvzTUTVls9z5542QZ3JFib9VH+Xsqi9lZf0crYKb/AmnSUgP7U+h278EnvnOX+OWyDZQRdjc/g+UXAlWCjMP+JUFelR5Rit65TzDGGMQulzmTWcOYeBYJKcNBGituCk/7uhbz9hy++Rt8u2nPP3WuPPNht9aRqpXWwcWyb56ZohP0omRO/pKtsNb9pN67l7eS7AZu4DnpIEXmnU3ts7Axa786IbR/0rAdorg6/gklzf3TGdCiDPgxDjHmsX4pZ/Dt10aNTqIreoou0n5bnS8rmeSDXEmV0SmWUX2puEfbGFNpSM/nME3k22wQi6bp6vgqEgOCjKIqGUBrFX7x/Im//6l+9jN/rsk8/O2zPkrayoZE9Ourvcz3dO0z9ZA5UjXU5XiVTtsvXSvbGO5s2N8PaQWlAeYbyACzb2L/bcVut7bwtTjpN1BUpt4NuqF8BKLhEfwVZN5L42h28Un00LGK0gwfoJaivAOVZ0DOc/kbDUQygJ67FD6qbTx8KfirQXgHUHcvQ+nnGmJbmHuvOJJ0R5ZrL874dB5z787+6YRs8q3z82XZ79PF2lLeY97m23JBH3YK0WZRnXLBLGo7+oj6Xr6Xs3e+M2sQ4KX1z9UC01e7wThN1GtgdsoHbgtZGutAaoEeEHOkXntHVfzSUFBtlkyVA5hLPtyFsJEKZpPZJyd3egQDtIuBbUWC5nvFkp5SH4mOrPJfyg7ArltVcl3ZxbQFAiOWFGa8xxrNGnAzrjTc27T98LWs3STX+xCO19GHSOng2dWmh6bMwvjyKUqkOIAcwNoFzcoezc74dPZ62yxdY263gX/m9T7rhOuxycDjJGIxbdxd1RBFR7al9iHWMWdsLUdYuIdYxcsGyL48Ta1HeJOhdnqUAdIpQu9ydG1/q4xDPHLFwirJoDAuqxwYZMLgr9oT1vEVDChilfnFU/NTfAWjZY8cK9vn/65u2yJrm8aeOkLVpl7U0A667PQ7ZPVeL8byhpmQ+xmoYHwEbRwHx1AmU53Dp2Qx2QrkDqOPVtQWtZ2fchnaGrKeZ51xU3WI0vlS9pTa4ITPgmvIX5RxXo69kgxpbnjpJxeQbvwa0Z9ziZ9Q/sh2lUofbR9GX92JTHnOxfFGevs5jD24cYh8e+wtKKxuWCiifnb3pA84t2lvvjtj+fY326U/1klqVfR7WQzw103hcj/dGuP/WwYog9w64PjxxLGvXLxZYerD6ZSzIpJJ1Aesb9Gw7dtrXG7QGAFaW8u6lR4/cJs+VQeZ6fK9PG5eLCVteztpLL3zTXnnpBdvW32/PkfXgyJEjLFMFzskBYN9aD2pPQY3AiKFgfNE4H+JVAec+RKNVPlJpgUoLVFqg0gL/WAt8uEnpH7vqP5e/30rVKpDtq1/9KhP0R1NfqSv9zu/8jr377rsuPeZ3B63+ubRFpRw/vi2gdI4Kxn7+8593oFJtba1TK/nFX/zFrcWpNoUqr5/YFlBquL/4i7+wz33ucw6g+6gaQmlCpZAjpRwBdUohqhPaUuMUBFB5VVrgw7SA7OgXfuG/t+5epOE//T9YFXCA+E84Bxd/2WSzbXKubBfZILg5y9cqChfaGOThX/v2Auo2teGpTQv2INyLpUAjD/tdNQHramBDjE2rtr6A3bHXs7v50oaONvaqtFHG949o6fBhmqPymUoLVFrgv9IWEDh36tQppxKsVKtau0llTqCcnk+0+an5Wv9+5pln7LHHHnNz6MjIiD3//POkHDrDprFO1rPZyEuf0ec19yqdujYjBbHfgvB0Pb33/IXLDpxjG91+7omH7e5dKHZtrlt+foHUZGxysjMcrq2zYHsXgBRfpETSq7yRMn9xCiBglmB5yqWXUhAnSnrLMHN9sLvXAo2kTJKMHMHzALBOaWrOSqOkKFsi3ZIiTaRXC5IWNtTWCYDVTtBLQTw2k5XukDSAmxPXrZgixV1ulfqjtVKV4H0olpDKRpvL2XMXbfLFF612bhrhnQ6zXTus3AHM0E5KqsE+gmeAOIBQim4ocOehlOSjnFGcnEctCTAqS6CXwE64HiCwneu29JC2DaUJSOkS7V8ilbo/PmqlhRmCGAB8bDwHWUNHe7bx3g4C7wTmRFNTFX+dwBj1K86QBhF4wCnxKD84il4R3qs6lkgdGSLYEQAI9EnZWZonndkM8BfBHUgsFLrQMmgFWmhtBrbgpP1RlNBe+rZNzUxZzW2kBtwO+NOEckxT3KoHB2i7HoLk9I2CXtroLgDSZI+iPHfByEdIO7L5DFxnMeAVgsRlpb4ixaqHmpCiYyUAuIDUXghqaU9aaV0LOYLEIeCCpAIMBFIBsIKkWQkLhnRBY5TtSDmZJp3lZoZ2b+iyWNuDXHe/679AhDYF7imvk94VBbwy6cgEDGrTP5DHpmYu2drcikX67rMEQItgoXL6BOk/v0PQukQa026U2toAEgg0bVJ+IKBgnAAwIKahZsVVeP8M/XgdYGGSgEmWv1P/hGAJAI88AWvSxgZrd7DHTnAfhTof2CEgNS/qUQDOK7JprxRNYcaHT+Ct6JPKBzMNA/mE1E6ABD7p7nxSwq7MjpLOFMWkHXdaqWk/4BwQHR0eRT0tsLlEPWdoc75rUCBhK8hiYwZlPSAWKSyFe58AVhxECeYs6S2/RXB+nhR6KNPUA0kQxPFRS5FKhAcIEMCeAiiwlXOCbSZI7ziHbQFuCMgC2EFKhbZsYuy0WKiagDoBJ6gC1jopYBy1McGbHEGPPMEPVABC1I+4CsGnDUCILPAkaxc98/GxMkokGfowMzOKfXRY7fDHLda8h/cTiOL9aGfQBwt8AYEBZgmyCBE4CwCkZOcuWRYIKNo0aMmuoS1wDoBt/vIlgiMFa2DcJZo6CI4A8xG8kZJjiLSUXhK1CKkKyXcAfPmkxzVSQ8r2vGrqX0P9AO1Mai9RxnO5GTZR9kSa4BDfGatKp7xJH4ZQFwqHeD8WoJRYHqpXQQJ/nsBWKZ9x/Y2JEdIWprGPZkAIVOwasIkw7cazhw/cVvZpZ3xLYQ04CtgqDJinJ+KN5SVbnJ4GHOtBuWs3fdJDO6zZ/JUXsacrVteMCmEzB8+AR0rUj9IwRgA94k1k2sQOPa7nTwKE4juWGQuMK/kUD0A4kKzB3lRGII8EYxEY2c8yzqhnME5wl5hQCUXKIsHCAiBmFHAiGGokIEqnldIEAgltSgmLIK5P6lB/5gaw24QVsY+agcMoQqG2AygriNMTJFfEPhmnJcC/EnCUABfZr48i0Qr1ZIiR7nKbVTV1M/6BucZHLIfiXLI6ggpdD6pErUApBAAJ/Adj+OAYNh5BEU3wZWoMXzCGHSsgRtujZBmIqA8BXwq8T2pucaAWoJ5ieoJgMYqfwDh+URCEwDnej2qUNJOUntUpe9KHASDdrT5cso2pKUvN4isBkRoHdgBq9jMWuL8GnKBcfGl5g/HHWCwyP0oVVW1dXEGVbfq0C6zGdzzFXLQPFot2uv6ybc6PABi2WYT0kX4M1RNFUblgIJYktWmDsw8+TN1G8WEL3IfgP8FJpWuGGmKM1AOaMG8BSrmgLekRi9hREPDWC3A95q8iDyEKyocS+DAC5SVgdnGnAYCSAHNGgEC8X1xHNeysFWaucv+oVfXfz1C8l/fIP/Mrqd0VsNFNfAH2VxC4w55lhLEgIHZ+fAqbQ81m291kQG3AH07Y8uUzjM01VBubLdHastUWgAwai0HGTAif5KBUgUmoW/qrp4EvL7p2dLFIHm48oCml9gxgR4HqbaRBbOLazCdAQgEAb4gDAA78qOZp5rOo/DNwXVZBe9YgTGOAP7QXtlpmTspPXrEsduWhgBTv3G6h/nuB3gZpDKBaAToA+JYdZZxMOFsqE6j18BOqd2rumi2ny9a+7TGr6biL32HLE0dtbfK6m8sSnb20MX4U0LJAxD8EdBhEncsDnjBS5JbXbzBOKDsAvILbHhCoB0xYZkz5xWrnVz0VGDWyIqlmA/iPAPOML6iRFMmEoFGMrAP6qMXPYCcopniAIEHgpkCgDttjPly6Zln6MJePuFSdVSimBlhH+IyJAHNoAJVWH3hPY05zixS1vMgGdrKEOhjpUQk8N7T3WHUnc0IxCTiHYiUqZoK7Em0t+E3uAx0uBc4AfePx4BkgaE+nMReyflhijG+gLAUsEahOMKb4AjLQZyyEjSZ30w+0J8ARaAflx//lq2gvJgdA4xD+0G0FUmc+hI/kXqzNPJELzC8lxsvq+JhlN/NWP7wH5b99lKOH9wp6ok1Q7yoBvRYE2fEzpaPe4BaozqUWr1FXlME691u07T7anXUTapJSpCvnWQcyBoqkBPWADwTLBIArPaBKCzF3Az2ZUTfAWh9ltjKpcgXC0eHYPeM0Jiib8aXPkH5UUE4JfxDUepPgPwV0a+Yi/RKMAEsGGbuAg/k86Ykpuny2UiNTUMb5DVvBly4tA1BUD1jb0D0uhTmF37J5n3IyzxdRDyoD0waB5XVOwGdNubyAj0otWNcA6bfbDzGUsW32FspAxT7tIuhaByqkwKwDGiFsI0SfMnHSdkCxq5fwpedweaxPWQsEgas9wHjIQPwK4zGMz0v0u3m5vHaadqGvGYMqey7HcRDqHYuT0hUQyC+tMAaZ65n/PWyWDzG3cV3WpOtj11BhnLW6FtZsfXdboPkQdaMSDvamL7KMLQBC2aj2Qxz0kkP5cfoKwFrGYr33UbVnqR/zFnMRuXixaT6X4/qMH8FhLEEYX/iNZB99QP1Ik4iDw0QW6b917oUvZS3pcRiojGJbMStrIY1wkj7F/nzS0Zc0z1VpzgSEybKypU0iOjDDs48DBpkfI6yXPafAh60y7v0UQP8khxPWSC3cdofV9T+81X+iwIBEcpkgSmzrrKXnbT2zyrNT3pqqi9aYyLJfRB9xvfpWYHGp8bEe8kusNamXbS4yVlbdGqoMqBOizAEgzQBKnvK9Gdo4xXJpdtrn+kUUmpm7WQNV4f8aa2PW3oK6FNAM2dptjf2nmZkCXzkgPxTMWNdF8FOtAEd9XWFrbdS6JmB/9yLg3F8epe8K9tjDe2w36RPzpLHPsE4I4asa26usFzWnxlrWc8AtOieRo45L8+x3XSvayAXfpmc1gXAwlP2rll7GMQdElXZzsCNsvW3YIOaTYi/sKqlA4c84LMVzBfa5DJSWAeRu5X09A1HWAfQB9ZsBCJue3bDl1YyDqsL4kPrqOPu6YevqQh2M66dTvk2NFznAVeSwBusDyhSO+dbY6ll/f9yaSFcq93L8aM5++397FcCp2555eNCOHETJinljhWeoUDxC2taIdffRZtxbgFgVy4ESsO0i1z9zumg3rpecshusIAfDAqT3JHUrB16XWOu1dVRZ37YY66at/b2Rqxkje7XrCz0TL6fyts6ak2UYKV1R4a4lvTrrnvnZEv1HGVYoN9BRlHm2hlSh3Z0xa23nkAb7fSvrZaC2PKqqqFyv5QHjAN9YJ9U0xWxogL4GiFpZ9O29d4v2pa+MAvwX7aF721GJIy2EBm67AABAAElEQVQy/nc9LZ/hW3t3wjq7SeVJ+l2EtegXwHzql2Keu36jZKfeL6HEBaiIf+5oCFh/HxAcQznLeqexMWiDAyiR0t7z9MkMNreKwmxdLeMA37i8nLcsqs9N2F13f8haOlif0oba95yaKdn05AYQLL4IvxFEta+G9m4BxusSrFbn2Rqqw9NzJWejqyvA2ajEsSy2+pqo9bRVWQ+qcoj+2ptv5e1PvpZhXzVtDx5O2qE7UeIGbkrzGYGjLS0R29YXpRy0HdNQmQcsKbylacPrN4p2/VLJJq8rAxI6cPRzD30o8EqYVAP7rt3YVCMZQjKU+9LlLPaEMi9rqepo2RYXNhjnpOmuDdu2gWpraEH/Er8zRx9OjvP3pYJTfwuxxqrnGm3tXL8nxsEa7IyUv/PzJRufzHINysQ8JZC3ti4I7BUl3Sy2TF8fey9PqtZv2cLiij34wCE7dIiDEqy/0jz3aJw01NPnQ2GAP9qXucaNNtZEWfrwJmPw/Lki9iTYVDbqWRtjMEL9cjybdLeGbLCbscV9dCDh4qWsza+UqD9p7fEHqbk8qb+zVg0s2z+UBAbloJCWKMCMmp8m8CFzi0Db68xx0HbxhGftHYybziD9BOyKE56cKNnERI55iRWMDusBF8aTEWtuC7mUs4JGZ0Z9+49/tUx64DHbvbPBnnykg3sqKxAK0jxn1QEKtvRG8GHYBu/XVCJwL4tNTI2W7MYF2vEah21YviHsbZ3UsZH3lpg8Isz5QwMRlKsBA9mrEex6czRNqmyeKxIR/F8WJfRVxOszZB96C5v/NtBeH6DwM3bvEQ4lsZbh4UWDA7uhcxm7rN60ssCr6evDvSrg3Idrt8qnKi1QaYFKC1Ra4Hu2wIefmL7nZf+Z/FEBpp//+Z83BaiUGqm7u/sjKZnAud/+7d928JLSLynoVXlVWuBH2QJpNgP/5m/+xn7913/dBVE/85nPOKUvqZVUXj/ZLaAThFJ/k4+SUs2P4iV4WNCcVOh+4zd+w6Ww/lHct3KPH78W2ALnfsG2bx+2X/qlX0SdSfP4/3vtssFmA9nRSDVSZuOKfVBguoI2qNhgOTvi28Vx3yaWdApZm+xsQvOQn1MsgA2+Wy8EVayDoHMnGzrEnWzbkGcP3OXZ3mE2e9jc0IY1ceGt4MOtD1W+V1qg0gKVFvgntMAtKG4JZRYpwC4vLzsITorA2uyXerC+tPmu75o7+/v7gShCbHKu2NWrV0kBCQTFe2+t6wTF6fOC5wStS7murg4wiM/opfeW2FA9f/6ife4P/q00Hexn77nT9jWg9oPqUHZ03IKrgBfseodqUM7auceie++0MMFB4j1AYiiBXCRQdfkCQQyCq6Sgi/KHBuCX+NAOixy6y8LD2whIEUAkcJ3jPdlTZw2JO1imRctQPp8deL+tw+K37bbOg3ehAtRGJJLNWVS5Ni5fttWjqJPNjxMI3ALnPMCzpp27LDm0h/KHbfbd4zb35uvWtDKPOguKI0BtGQLH5dsGrfGRu6129y6nuOPUm4AASygNbVy9Yulzl610cxIugR1flCwiLfUW3bHXqnYfsmjPIIFZgi6kys1cOGfFM6fNm50mKI5yDnXx6gm2HLzTkgfusEgX841SB3JcPDs2YRunTtoGn9mcnwROAejhb8VGgJYdu6z+9oMW6gYkIYDqz81YlmfPjSsjlh0ZJeAM6c3pfGQnrPY2lIIOUD8gi8yrR23jbdLSEZQN1lejeFZtuZq4xbd1Wf/9D1tyOwH6OgJ4bPwrfVR5cw41r3dJDXjRPJSuvBwKAIJTCLSXCZptUs5IU5+FgRfKBFPXb560MKoIoWqeS3k2zWaoRy6G0kkX4MWQSwdKhJegR4SABlAPgQMp9pUJsGan3+O0+SmXwq265QGucQQOgqBiaB5bGAOoRL1mZpS2IWUfNucRoJEKQQDYJzVPqrm+B4D/niX4SfB/hZR8N96xLIBBtIG+QC2wuIatZ1D5ILgdRbkt2thBnJnJl2BzMQX4OI963TrgHKfXAwSRFFAMSWlsDQgp2GJVgw8TXG610uQZsuKdILjB37CfdVJFbRTCwBANVo0qT6QZsECRINJjlgsE0knfpkC6AI0ywdi566cAQQjSAAxY2z7LAOURHiHdH2pty2MWmL5mIezPZ4O/QLrEIMGQ4uo4oikp0rz2Wajz4xYgTWSJdLTrMy/a+tq4JRiHYeAfH2W7UhYwEXAimATEae0lEEd7Z1EOTKFEAzgXQP1NYzpMqjylZcvnqgA66gkOD6EowdhavmkbC6NASlyDQbyBKeXow3hVB4pwfdhNJ/VDtQXlFEYt12PwkqpQEII/d8aKKDpt5BMWHX7KYijhuSgZUGA5x7hbvgCANIJKD/9GCScWi7uAax6IhtiqBVqHrbYP9RoCxOXFMVu8eJZAyBqsaBK2hvHIQiqPX1Aq0xBB1ngz0GGCemcLqAYCtQBPeIAOHpAHEVoCwABPpKwOhQGYGrDRYJ+lUbQoF8YBHVK0gxQ/CFqypguR5ra6eTfQK/VDZYmWoA+xL30HnLDCAgAKab3wp0qJ3Ni/y6pISWth+hv4p7SJGtzaZcugKJFemXNqP7XACAkC9HmCvmsEruIdA5bsu4P2G6JRV2zh/NfxieeAHoFmki22XkSDkfqF8aMekc0q0qWGG/sBA7AfFOcKS6O0IfWTHQOfBUhp68cipmfyAPZS24t9VLdaYRK1HFJeRXg2CsVQdSgBNaJwlUdtpwEFsiRpdD2lHVYACTo5gD2aB4Szgd0tXrfFK5dQFAzTF7utumc3dQQ45LOh4jRSNGcBf88TOEVBEmhOfr8aeEvKNmnqmQdIa+gbBpzrwiaAkccAYkcBzuIAUUDX1I5UWSibYPMRrXPr2y1a14c9eKjOTeJvJmhfgokEYUNA0x5KSxJBLGVJ29W7z6llFVZvWnrqO9jrGoAXqoH4wBQB1DIwS3XzdqsGLA1GWrBPot34Mp8ga4Bofhn4V3D28tgVAnmrlLOXNMX0BWAGRsFiHugR5bsS6UFLaZSygJ1DjOVQHGhvA0gDhbmAbPa2Z1DqBAoFjild/zsrAH56gBpeYxdKH0HGC6ClAOGqGmy0A1AWKEdw8/Is750BPMEXEeHW/CPF0/w6B2+KTaQIHSagjapHmjS6qTGANoKzQSAH/Ow6EEC0qhqguxdoup/xR5mUBs/5F6rKPC5AtTiLQuj4G4wn0hb3PmRVvY/QFg2ufr6gldR5xjjKaQAl6wTqPepTmwSwAMZZWQBWSQyQzvM+Cwk2To8Czr1PQHXOajj9U0VguQgQml1HJRS4JhxpxW/cBkC6DXsCiCT1YWHuhAUWLzF0gIcIVspPK61XiQHu4yNDHbdjTj2Wn8GfT7zv/HUJ/5Wh/Ovrmg/MGprol+ZdVowDdmN3SqHn0hoKyAHk8ecvAAieYF6aQTGwD/jmUcYCYxHwqgwM4ktNcZ5rkwa5QL8WUSYKSjUouEIgf95WN8Ko6j2FSucR/DIQzhjg3PhlPk5KwkagG3xbAVtYJ99dmLZL1g+Qehq1LtT6/BQwO2plm4CNHvULA9kJgJQSy2aa9KqAMlXNgJD48JWJUYLoPlM6SXZRTVvSuoP+rq0d4ou0twmAVODdLH9TCuGY1O1wuGX6Pzt9ikAwKlb40HjnPtJXA49qXga60XxQRHGwRB4/H1sL4X8wB+q5aasAlQJkG7CRRMd2/FbSclOzgOnfQf1m1aoAFugVACLAfOwnGK1FZY10k21cH3XG4jLKdfPT+NHNrXkeH+LAOiA3JlT8TS8g/v38nLGN6eOM/1nGNoAZYFB6A5ALOCve0G5VpNCVSiuOjTGIP1XZpfKSEzA5YbnJy5Ym5XeyFyi65yBjkP4TkVGahssaQeFxBDCI+RBfUAdAGkOJS5BzljEsGCaG4meok/6T4ucqfbh4gzl+DggnzZqD1G34tSpUR0PxRuwNtVIUHZ3CZWkeP632Y04FEgxo/OBrA7QD+l2M+VWLse6LNPYA34RJ4zmFL19i/sHH40cyrJmlWBlP7rQ64NkoqUod3cX6EwPk/3xHEU3QZgaof3ZiiWrVWtfOe1FA7eMdjFsOApQKrIlRcstK4Y95KwrYWVUjaK9sa+sblgKY6hzeTx/ejf/lc4CuxblT2NVV/BhjgJN6m4yLPOvxcFMjfq8He+rAjeUB50ZIiYrK7ArrfkC2YDWHLYBRlW52A1goktgNEH0nfZxi3fo1fO88oDkAPb5mhakuxTCraeu0JiDSsMDeMnYp2wTOFqxqBuy3hnrx1FnLzl7hgGG1hbvuNa/94S0/5AMmAz1mUPYrogrsAzJqTVmF8nSYMSRV041V+qfnfgv3f4L6MRehhltcHnXptYu5WdpnGWADHwr0GkowR5CyOFiLjxF0ht1sLI0jngkgKeU5oMwA4yOPv99YAx6LtiDg2cnnUaECQM2goCiIRPa1ukYfs2arru+yZPMAy1EIDewLR0S/baVb1FwQWOfg0NS3bWlyjHXrLlRGH+XATy/vwcY59FIszMLojwFsTHNgkjkZCK4GKovVlq0uYoM+IAsHGup6d7COarKNDfplYcw81lc+h0ByRj1Yq0YYuNFwO+A1gHrTPkuVm+zKWM7efittVy8XAbPSrHmwScZmd3uNHdjTYYfvqHGQzLXLm3bs5JJdvb5qKUE7ALoCjIeByh64t9727aEMQDAv/n3B/uyvSE/MYYW9t3VbHTkdp8aXbQmF4DBjaWB7o91zT43t4f0Cx2g4xjJKczcLduHkup05BbQzy3qQvmhiWm0ERJtfLwGxTtnj97awd1VvtQBFN6dz9pWvnufZsWxtzHtJoP8ZpvO1tZTt3R+z+x5ptOZmnrMmC3b8eNbOXlyw2QX6GoArCvDfhs3t21Nnh+8GGE4A4V3M2NF3MzYOXJZOA/4zxqpiOcobtYce6rI9+2ocAHTixCZ7zq8z53XYkb3brAvgbGyCay9m3Pw9MBSxex9I2G17EtZMOcP4BildTUwV7I3X1+wcUNLyPMAlzyWdPDK2tQFg4WvOXbjMAbE+e/ppDoI0AdItFe0/f/ka/VK2/u4u7g08Ob9saRRWu/s8+9jTBwC8atj7ztqpkym7dCEDKEUqehSIY/QNzLLdeWeD3XGQQxocLrp8ddPefW8RH4H6KHNqjrEbZl3b0tViTz/ZaHfsj5vOebzzTtH++j/dxCeUbdf2FmtrqOK+604RPsgBluGdjXb3PW22d2fUwZK4BWAks+n5vJ05u4qaWwk4jDGCv5c6WTvg1zIHqaax0+3DUdKi1lo7MOLFcyV7nfa4NjJu3R16to/Z9DQwPn29Y3ubHbkvbrvvwJfj5sYAnb5zdAWga546su7Ic2gNyL+uJuHs7r772ScgXe7Vm6T8PbVuly4tAwQLnGO4M7+2Nyftrtu77OH7SKvb7tmb7xXsT/5z2q6Mp9kHjXH/KOuRDCAf8x3rlG39Cbv/3gY7eKgGv8sMBkhVxHfPL6Byf27dTh9fI9Wn5pg464cYICHrCSD0hVSJlJ0xe+C+GHXwgOF8++KXbpLyuxpFuypgwwLw4hwA2az1dDfYQ4CXUqlbBko8cSxDP/KZeQ6d8bweCuWw35Lt2pkEfmu2xrqoTYwV7MTxlJ2hHdaA8ZQWnKd6awduvffeWjt8JGmdrWFitkrV+obNzG6aFPs725uACVe59iI+hUPV7Y324P11tvcAYxCwkjNZDpJbmC/Y2VNpO3Y0S5mB6mj7NsC+WsbREnvLUxzyO3yg1p58qM3agO503u0LfzViZy6mraW9jTLGGW+rtrY0T5kC9uCjg7ZzN/6D/edZgLkzZzL0zxrjJcOannUW7RePl233rrgdOlJrfQCZOZ433n07Z6fPLdsizx+bzD1hgO3a2irbuStqDz5W58DUufGg/ccvLtobb0+QYSWJr2limRBGNS+LcngKZe24HbgLW707bv1AeQKp8xiTfNeV8+t29ljOrl0Kkw4amLetCBCqp7CwTU3nef7cQMGu1vYzhnnUo2+K9sKLZ1FerMUnNVhqednm5m7wvhI+/irg8WkOdnbZp577GNkUDvEMSDOrcegbXVN9hLX8wxc/fqhXBZz7UM1W+VClBSotUGmBSgt87xbQFPXj+1JA6ZVXXrFf/uVfdmoMX/ziFz8SqE1Brd/6rd/ioeO4vfDCCx/JPX58e6lSs+/dAqzIv+vlIyFd5NSSTvxp41MjWMFTpfL6kz/5E/vSl77EwrnWKXz9yq/8ilP/0iZ65fWT2wIC577whS/Y7/7u735kinOyMQXvYzGCCgTwWlpaXEBfwfxf/dVftSeeeOIntwMqNf+BWuAWOCeQRCqaAuD/qT4tx0k/so1x+pJgIUCdDhePAtG9eapkx0dKtsym1+YmIB1pQDbYPEK0wW1AybFyONHaCSI2ccq1fcCzB+/h62CQU59sNrOnW00gn73MyqvSApUWqLTAP9oCAuc0FwuoSKWUcmjNKc7pg/JnyWSSk9Ns3gOc35pPNacKotPnFkh9KdhOKnW6llTl9BlB6gLl9Dm9X9/1mVsvrQ8vX7ho/8fv/T5pITfsnp5W6yfglyDtUSM7wXVSgCMlyFqaDXQkCpJ7DljtQ4+h6BK33NG3bPHEO7aSmgemqCXQhYoG4JwESKKC4YDLIqRP1Ynh0s1rNvbOW5a6cMmqUVerAUAIELzLsDu6CHDhoxa366FHrLoDVbZ0xpbPnrOZtwDAJm9aAiCqluCxcWJ6nee2aoI1NcMKrDZY7vJ1m/3WK1Y7N0kQs868zi6i+DVWGuyx2OG9pC7sI8iKcgfQgD9HSsNTJ2yKcqzNLlgDqhfVVbQnSMgSgc0cQaO6of3WfA/gAgGnFQC4qVPHrYoUVa0oBEVQyyqwCw5uZIEhUuAeAGjpH+D6VbY5u2TXXv57Wz150pIAH211ABi09yob7KvcJ4xyVQdgYHxoiAAmMAAK5AsoQa+xCV+H9EFNNUFSgulL+YzFezut5a59KGokrPD2Sdt8/R2bIViV7GglcNlmRewg2NlptXsPcN1BAqhAFlLxKi4QmL9pi+Nvk9Jx2uJASHEUsYJsrG8pwqCKQjAwgYJWAoWiQHnZUpdeASwYB+JCbaK6hT6ugzEiRVh1J8H0fuASQAgUejiv/gGzAyBEgL7M9dMz71D+owRq41bf9TBBysMOSCr71/nbaUACYCdAhgTQHtEzNtwpIyCIBwiRJ1gRHXrcqvqepv1RLVo+botX30BxgdSkbO4n6csQCkmBTeybIEwAIKq6lmAyoAtv4NrzLrga5qQ/XAPBZhS0UCmJEOT3BIRVtVv1nmcAUDosN/KWbV57g4An/dbYabmqTupZy3q0AVigh8uxZuDfUj9Rbj0pYii1jhVQOyR95Pz1C7SlbzWDu8xr22V57l8CysoQYE4B9MTXAHoYo0FsZB0oqgB0l18asyqUdKpbSZ/Z+9MEifcSfD4DPPINAmLXCCKhLlXD/Ql0hXhmywPP5ThcHwT6qa7h+kAO68CjSu0WJbClNEIhgIQ8UblV4CYf5bmWAUCwBGmXZkZs9vplYC2UchqBhrD3MkpIUeDHSP0QaV1RfkShp0jwS7oIIZ4TdZCf3LEoyRy1wugJnhMJpG//GNngADFlS5sAQ0uXbH3qDH02i+8A3iBgLMAtAHCZXZkBnKHsgFF1faTPrAacWxi3xQsnUTHj/SgA1uAPIowNPZfmACLzfEmlJEHgu4xKWwbFgSAKDTECOmGur1TPGfzOOlBercAC0nJasN8Wx0YBNi6hvrTE2gr1S3xSGZgzjBJWlOB1ENWvskvJixoeAV0F7yEyKPckkMNVW5mWGhjqL30oNrYD/Ya5P39fnzlq67OnLENg30PpLYLSWQLZiigqIblV0gzymere2yzafwg4aRc2v2zz73/JgoBGMfxRGYBsA4jTw4eGUEvjLCauoJngPjaFckcpO874EJwLbIaSYYQxVoKGXce/rlDPGGvE1h0APkqRemPJZq5OOhitSkAXwfMAbRjietHEMDa+g/4liitimT4MxAAFQixYN4DG5i7b/MgF4LYoqTl3WUKKXthHgfIXF0hTOfk275tgDJLilnHo085BFPBKqAiuAsrGUR9qHJIaXy+AB2lhb14AqD3m0jrG8OsBxmIBO0wLsgKuigJuJYG0FFBNY6dF2jwMdBtFfkkiTwECyuUVygZAFdl5BAXNnaSTZByNvAxDDdBSh6+rwsYBAYMoRMfqttEW2ClKVBqDYgN9AuUe9iJopzA3SspVFNF8gXNATqRxDsQa8WmADGvXbPXaSbob0AKVupCAIPzehhTWSNMbZB6N1TZb5MBzqKXuB3AaseLlr9r66FkHk4YAJYpAmppD88xLRSavADBFQyP+hwFZnAdexP+E8dEB/JxTRALCyKLKUii3Ml/tBrbgnmtnbXEC1SiC+wnK5oUAsVC/jNFOkRbGH21bIH1okD7yykQEmVPpRgd4luZfRUHtFYLGGxbd9oglep+gb7Fx1JwyQC7ZubP4tFFgDdYIkLFFFLAiKNEFUb/KoEIUadxl1QMPAWoJnANevXTUVkkBGksWUNPBz7IGYAhaDrAit4EyIu1e3d5H2VqBc7kGPiyysQLsErNSHMAWwDiMn86gYlYAQoujgpfs2g7gB0R+/U1gDgKx+K4cqqUe4zBGmWrqgBLqd1ohNkioM8keFPtQVC9Ce3qotvnLZ23pGp9FJa+2fYBrPsW8xVwoaAigJgvcnFsaATqU0lSctmLuYxxvoraXyVAOxlnHzk9QbgFggFrjR4EpgfEwwgjQbgLgPCR4jvXPJkHjmAcMJpU2yYozMJUisYR/LLMv5gFTFYEsBI0KtGrdscdqmFdLi9M2cwFwCAWU2lr6mpNQWUALD5CrBsgvBrwUrGrC//NcCFBGSwKHEUmm7cpr+PbxY/i+rNV1HmSO3esUNMvMW6W1SUtdx0aBrMOszeLAmSHWF0WPFMTA1ymgqzAKY6192wHudmHXwN0TU/TjK8whC6x9gJ+kYgksJBB5A/8URG0u1obdotCVWUHtDcWkKt4jFbKioDdsNMg8GGYtEgJKjXaTXpN1TOrGa7T5JIwyAChKlpse8B2wawJoJlrfy+AFSiKVLQVkHMnVbFoQVbzyMumwJ2hvIKIawLkq6hiouo2/AzWtvo+SKl+oq5XKrC+woSpAZQS98IGbDsKJo16b2HaHhXuPMKfVMTbHmGKAYQGyGBTMS4K6gfxQEiuUAD5RY00yd4aizOsAX8vA6UXaKQYsFdbYYO7RXmc2u0Afkm4TiCGJ3ysUSP02ifofn0kkSS1IPUuA93nqFa3egTruPtoacJrP0xWMP30x5r0FbIV1y9RpmxtfBApqtPahw/R3J20iGn0SVu44alJA8vjPJKqWIdYEzFiwiSuAncDhQO29uw4BuB3mMz3410XUhI9R12ukpmWOYJzoE2mU69A4Q9UOeKmOtQh+uLQ0SdrxZRbOgPH0YRZgLFAgPTAbE0XgxurWw8xF9wHzknr83BcAMSfZZGhBmLXfcozRPNeOocKcQIE4xNqnyHzOTgT1Ap7CP3s+9UtzUGP6JID2ZWyK+ajrPvOaH3DtXgZYXZshhe3iuEVZv8SxrxBfBcpQ3gBWxA/DtFhN34P4qJ/CPhhbQIx5YNDs2hgjAZCwirGMuuIaapdKmVwN+BhrYT3PfJdbmkJZLm1x4COpISnd6AZfK8BAhY0Izzbd1jDc766TvnnRFsgkEEcBrUrq1qzNIii5Rus4dEJqZsh05nDktwAMffyM4Eilgw6tHbfixLdIsz5l4brbLNH3kAPn1M8FfMzK0ils9ArzyzrwLKMX+45wECScBpCdZy0C7NfUd7slAc/LKKqu6wDG/E3zgP5ikOAlFCILpJAtZIClgI6i9Tss0vOoXV7otL99dczefJ33eh1AToClKGjJb0UBY4cHWuzA7jZ8n2fvvjNnF6+MUnSezzjw4rGWSXOoqKUpb/cdATK6vc4a8NkvvlSwf//np4DUUFCjTztaUMxlTVAABppYmOfgQM52bq+2n/50p+3dx0Fk2nXketm+/e1Fe/tN4MBU2Zobm/BjEYCsgq1uxjgcyjp7c8T+5c8M2zOPtTtVr8s3s/YH//Ztuzoyz3NIK+pQPSizcS+U1nbuDNq+g8wJjLNXXpqz7xzjQAyTcwNq1wnA4gK+N8ozzkB/vd11OI5C2gptMGGXzjPnxjq5P4ddONCQyc2jhrhsDz/ca4fuZn1E+sbTZ0r22f/1DRudqbeB9l7rbPKtOr4Fx82mWJuRNnrf/mZ76mMDdnB/FT6zjApazr79xqJ98/kx1ogAgXVtqGuhMol9p1A8X1iatmkUIz/98cP2cz+33Vo6PVS38vb5P7xg7x3P08ZdDmBKVq/TLmvWD+x16M4B1qGeffv1GwBrqFqSvrqhoZNDWEngOXx8cJFDuTE7cHs7oFbQXnxlhnjKKn0R4f4Ayix41vC1Icr+6EMtduRuxiQHCo4BCX3hLy/ZtZuz9F+ndfE8nGCNWQAQXlieZg2/SJvttY8/JduQIlzAqcG9+saSvfTKedaHEdTW1BeoyLKmkErexDzqXMy9Dx2ps3/537bQ7mGXEvYrX5m3t79zDMBQKU7bKHuNK//wcNL23cEB9V1Bpz72yis5oL8xDoDlrKmhkTatAybneR11yYH+oN0NMJasLXP/y3b8JAcy/CSpSbuZVxKsjVB8Js33np1t9vRjzS6V6mvv5uzffWXJTl+dRbkRFTPgzDr2HYKoMc7xfJ1DxbG/p9o+/elh23NHEggXZbLVAPBexl59ddRuXB0nVXIj4GcvMHrSVjloMMXYzTFnH9oHQPXJerv99hCqaSX71//msp27ApzJHN7aUETtjUMnHB7o7Wqw2w82AOYF7VtvTtOPwM/rjdaGKn2SAxplKS4H5oh1sT97fzdzXwzANWUn3r+KjwbCQ2U5wiGSdQ5ZeN6SHcTeH3iw2bb11dhxUrX+mz98C0XGRcqIPTW3YkPMPcy58yjFr6MY2cba7+OfaLF7HqDtGPNLS6gNHk3Za69etavXllBva7W2RuYKJPtSKF7eQBFvGn/y6Sd67ef+xaBrMwaV/f4fnLaX3x5hnDAGUetu5NmgCRfX2xOmD5usdxsHCfDob72xbK+9PmqTMyi3ceCmnoNfLNhYj2JnbSUgxVYAxwR+JmXvvw/Ayx53A/shSpOs1KxF1Fbb2jfs4SebSW0M6DhfZX8NOPfiyxc5+FIABmzG/3AwjPVTanXFZpmXWrs67IlHW+yh+9VeKBJy4OUMgOwL37xiN69qTm4FhON5I7iCIiMqfukoY8W3zhbf/sf/rhWQtdYWUb98/dWi/ekXvs0hraL1dHVZM7BkNYqf1ck8sacTNnrzPduxo8Oe+9RT9BXgHEsrks0yP7GeQcP2FjindeUPErWsgHM0YOVVaYFKC1RaoNICP+wW+EGmph92WT6a6yld6x/90R+5NJYvvfQSk/aOH+qNFMBSMOuzn/0sJ0jecHCK4BEFqyqvSgt8Py3A/sB3vfQPfWlni9WlvthQX+dU7uzi/8Pee4DJdZ1nml9VV3d1zjln5EQABBFIAiBIgiABgmCQRFuyLI2cRtq1vfNYz6535KixvWN7JUvjIJkSJQZRjCABkgCInHNoAJ1zzjlVV9r3vxBnaK+t8dL2PrbYJTUBdFffuif959zzv+f7kDePQmY9jQ1IEnETE5M6fvy4Y+VVWlrqJFKrq6t5iN2KJPITLE6RuJ7vj9ThJ+9lCXhLGBw4cECvvPKKk3z/l6oFU8j4EJQzWNPApnXr1umee+7hITTNUUBsampyYCfri/Ov+Rr4ODXwzwHn/qHPMzhudBxluklO1pnNa1dYp64EdP6WWb3y/WkOm5O4tk3kD0E6U5TPAupIAaRLK3RxylJ6Yhs2DJwEjuWUYCKbILYJMP+ar4H5GpivgX+oBkxJztTmWltbHfU4U3+dQonE1mZmtVpSUqJVgFoGBtu8anO3PV+YcrZBc2bTWlNT46jPGRhn71u8eLHzTGPzrb3/Q/U6m5s//P0w12+qua1v/P7XgBv6tTAxQRX8vITkT+5SIIzcHAIetiENbeoHeguSUMhav4kkXokmzp8muUtSH8WXlHWrFF2c57AAPoKjWWvG5bApnU5Cug+7vdPndfPEMZaqYeUtKMdarlQR3Nc0AMEYyW4X78+qIMELRDNRV69anpfGGltVnMUJbN4fm42KBoHWgQtIaMdkY8EaS3KjpVUde/cpAbW3mCLUNu5ZI3chVnTpAGM5KLGRICT7SnIFaKTqtib2v692FORcGanKWQn0gC2qKcANtTSro6mVpGikyu/GQhSloLGaarW1NCojP0dZi1BE4r1BEt5TrJlCKL/FUzcxqalAImNqvXBR1w4fUQLwYeWCRcoEkIvEOnQWFaNpFE3cqIglo4bqQW1rur5RnYcOoezUhmJFhnLvXq84Np5DbsC5yREU5FCAKi8CSGBtfuG6fO8eUnt3l9JRuEtehR1qBqowWOdGZppVVApJRJtcUOWaBJ7prkKJoYdETgwwQw6wDskCktjhsXrNDV9V7zjJy6K1iqvYTtK/Vz03XyOpCOQVD2iWgYJeYhmKRgCIgC0R/GkJUhdJ2TCb83eeQVC1I3kbHG0CSAKOm2xCCQSwgGSxO34FmfJh1H2uargDi1k22BNi85SAmo2b+5kjMTzbfwsFqWqSl9jLLHiYPrMTwIJ08sg1Ddw+ydw7CEhDXeUWAyqSdCUBbGpBARLW0QBbUSTgIVtQCANqwaYzNqsYsAzrS4MhACr8fdcdJRs3cFbS6j3UQyH96YQmak+hAMKzUR71l34XVoO0uwGNTv0BC5Bkdp6nLJnOuHJ5eiBNLqL6U62xzhFsalBqKllJornS6U/+kRqNAEoMoUaVDLiVklZGkp0kKAncGdSFfB3XlAAUkZRTKQ+JZlcKydjRa5prfUN9ZjFIwigR9bNYQEAPVrtzwKIT2OHNAlmQ0yVjwLMdCShvBqpy8QBN1FGYZOIMloDDqIHMAS/lLa4EMEQ9r6NFDdWNlCdO2QWlSswo5u+pQC1mZZdN3aOwgy0tvZY/AVlI25P+QPmoR5NNl+XvqgeczFZM5Q6aewFtiP3aRLUmOlCGRIUpGhgpIR34B3Uwg9tC2DqOdV4EViRZT19KKl6DihjqQn0ttOFVbJL6gC5jlML499KvTDogSPLUN9JMYh6ojr7kJukdRrUmNpUxn2h1wLMIwNxwJwl8lIXSMxKUXLrU+cye5kZ+j2R/LKp/KFJGpZUiDphF8hn7Y8AGF6o7BjeYTRVEErAAsIyVYbwOl0EgsCEgLsCJRCA/L4lhMu+4MnZqrOM0kC5KPyiGRGXn099RksHiLgykOtbZrQjs3xJLFwEKbEQJEvARUGn48vOK6L2gaJJRylsGY4K6nKW0sKDzoRDhB4iLQ3HQg/qfDyBvhrge4QVCTVkAHEi8wlJ0dKAVJY8OoF2S24sA5/jsmdZ+dVa3k9jD6gg77Fgso02JLgKLTIIHt8w4BKSlMumfgKQeFqIeAIWJFmCIK4AlLfSpLGxl1yLGBoAAiBgAihpFOW6SmJCEGkws7eEiJgSJX+GRXoAsYIKRCSXxeSmAc56UEpR1YERamUewTY4kyZaQS0zNAdoD5vBhqzs72oXaGup8ACOBAH0S1bLo9IUOIOYCMgoAskwP1GHtCCzi8ih+8RYU3O7STC+KqHUH+b2xO2VDncydSD/1olAHwBoFzOhC/SRs/Z5+6nIBrpqVLvDjWEet+rroN/T5LCDNmFSAHaxSCaSM98Mawuo0AqgzMROQISkdcIhkNjDCZDuqcoMdJPuSFb3yKWLtWkd1KVDzpkYabyqMfW9U0Xq4jAKSvYBMqCH5xuijALFJ0STNAXdDgDiRwLWR6SUAOCgiAgwHhlFCRT015M4BVFyphDTA4OHr6m1EMQq71VjGbGxqBf2J+S8OFSUsiMOo1c0BjGDWRb3S9wE2+ET6KcB26weot54l/AAFlT2oqNz76FOoY042a6CtSpMoXMUxbyYxV0QwloPY/BpM5EPFbXZyTN7MxUC9W2i/HMZtl/pvndcQqk1xiSSYc0mkp5fRV6gXwKOZsQFiJ6pvcQCTiQbSAa6MexA6zSLW5KH6mAr4i03j8E2sOa8Dd6FIU/IgKmPLAeeuabbugKZQWIvIJq5lAaYb0IIibSTqX664bAfoxZQTBOrOhlUMHco9h3Jmz1X1N5+nvBNKL1wib+6D9GliAzEt1Hceq+FG2hTYBrA5kmRzEEjM7x/RzOAVAL4a3ksSf8mTQEmbgT/64EXPq7v5NtDSLLbQJHtJapuCZ5iHsmms4wPD/SiCouxKktZrcZ64EWHW0YC8YRTOfCP9Guzp5VkuqLzlqMGWZRMr2tV+nfkJFb1M6iIuuxA7WSAr6twDgO1GxZHAemcIAv0YzAyFyvVQkRu4isXxBRRdw0osWI/d7hJ+D5UUgKMprGb7mqpwSsR2NQ1ADVAUHzKgMlQYBzkgAGQQQ7/NK8cCFWgHLEvjgHMjNw8pBngyOTUTuLsC1dJcPg/7WMbgJEqrbg9qi0GU0MLxKJguounKnRgxx5pwBsDNh8qkm9+PzSinfzwOwEcivPZ9QIku7OXygKrX0N7ljuqvK4GDAYAUzMqMPS9jEPCX8rmIAe65ds31VmuktcFJeqeXL2feZR6NKOYhGGisHeiz7yr1EgHIjM06QEsE9u+hceIFc8F4XwdzJDB8xRpFGDiHXTCdmnmd+RpQzgXMS7CgbCjijXWRzB8FgE5QRlYJz88ojA7dRjiym/ZlLZFGnMKiNYBq2TTXGOmrlg9wKzcXGJTY4JtNBrKt5TrNqJ5GYzXHvBVXgFIYbQhIHsl4dBErbA4IG6ELWODEUzfQ3+wlDaHIN4zCUGJsuTJL73bmBGhF1h+12LheRElslDm3GCv5pU55w8E+YMlm9RP7DRAvXQE0mXcP1VeE7SpAHPMLcrDA5sRuAxIBXv2hVg7rAXCh6paAdaCprzKZEt8yGBL5tAlWvZTPz3VnGcd+4NjE/HVKqtiCmh999+oLKN+xPgHG9eQCdQPPubCq9dD/I1lLuCOYm1jH0kEBRZmbAF5c/jY4Y+btLlPG7EIdCFVNW7Mlr3KAcH8fZWcd4EOJNj65CAVQVP8AfXyoA8/03wSwrXVUizLK78dOejftxboXACdI/PFTJ26A8EgsKJGQZc2A9fBIn6KJp17i3gzjcJY5y8tcFkcsdTOXhULA95Md2Pd1yc86MZP5LnUxa0/qZ6yhFsWlfuJXJpb2xQx9Yijt5gbUNZVBOgCfz6EK+qgDXHLIIRxi/dt7SXON54lzgHH0z6ii+5ljMp1+NQr02dt9mzYfoe9zoCg1/Y4y9ySqeYz7vrY+DougAFWx2lFGdHkyULBErdOUAmcHeC4B4EAxKwg4Nw0sGOhpdfpofNFunW8p0UtvXcUickirOcxy190lrIl5VkGRDxSMpTqQHPd980pIJ05SnyhmrlmTpYqFtDfqcWOoXsdET6uM4VRUkAxcGKd33w/oL//mgmqbO1HzWqr71pdqQRmKyswDNY0junANkHWyDUWnlVi5Mk7iInTi1KwOfMABpdZWFOyyUUpDuTrNg+LUjC7e8On0NRQPJ2r0pc8s1NO7CrFw9aiucVK//0endJsDSAUcXtp4zwosG3n2y8K+E6WzJIZKQ6NfL75wSe1dAS1bXqR7NqY7oNIUCt5mxZoMZJye4sGetFGnTtZyuCNG69as1qLFqbQhh52wp57hQFBpWbJKyrDzREnU7Fa/9jvndLvVqxVl+Vh9RqiynDUc7VrTMKiLV64S36SHHtigRx4CjKLZr6LU9867NapuQMGuNB/gDEgoKx7ABzW8yx26cPma2jpb9dlnduiXf2kp8SOCupjTN7/RrGPnZoHzUB3bFKdFy1z8zK3MLIBrVJGvsb+3b/9N1DKHtWRpvlauKkAVCyVEoF8/c0UWKmcplPHUmUm9vrcaEAlQak0m7YEyK6q848Cl6PaqrDAOVbskYoQL1bOgnnv+huoa6lVSnKX7NyyifKiV8rxdUzOkY6eaAKTKtHNHhh7axvzJsuccCm6vvtWDqt9Nbby3gLrOB55KYL3op+yjOnGBuI8rxo7NefrlX8lQRalX57ET/eEP+nTi3Ak+O5n2W4zdZSprHi9ljFRmNoAhynsXL/j041dQRGwcckDAtfcArPGeAL6bPoD9BGDGdECmAZTA33jrEophLq1YXg6UhiIwP5tmfRtAgTcLKGtlZZJSORhy+LRPf/lSvy7eblQJ/WXD2iwtRV0vmYNPra1Duni+getNa+ejd+mhR9JRL3Opsdmn118fAMwbALKbwhq0SFk8249N0yduDgCd1QNBurTh7oX6zFMZKP1FAioGAcuqdf4G6nGJQHWrkrR2VRqqcHHAhdEoymEtiirnXz13UTX13ZRvsTbcU8rhB+Bvxl8Yq+g4wMa8rFTduhYC2utnzu3QXbTzsuU893IKehov4mniWDYWxuXlScrg+eXiBcC5vzinqppmlO0WaO3qMi1dBpTIfm5bC6qOVyfU3tHAwf9y7dpdwH4v8GqdX2+/26HrVdXME17AzIUqL47nsEGA783q0FkUBTub9aXttOEvltNmKFbTpr/39TN67/hVJaek6r5712jZQsrH+MzA5jYbJb8YYvFAj0uvvtyly9fqgVcjtGZtKftCrAnpO1PYtEcBcWZmJRPfI/TD5y+hKhipJahVrl6TxhrCwDnex5j1RE+pcjFqilgxD/fF6aXnO/T2excB0qnb1ZVataKEuotjXM04/bR7MFYrlqTq2U+loiIYqeaWoN47MExe8YZSUCRdt6oURcl0jQN11qKkeeHcCHHLr7LcBH31N/LZD08EhAbIJKb91XPvOG1y9+rF5IJQzS+xvYVJnTt/kP7yPhbEeXpi907AuY2O+AfHU5irmccccM7QQfvXnS/++FiveXDuY1Xb/C/N18B8DczXwHwN/PQasCnqZ/tliaQjR47oi1/8oj60rzSlhn+plyW9WlpaHFW7M2fOKB/Kfv41XwM/rQbsdNyHL/u7ffkDKBJw6sj+tFOWIeTp2V1h86CIDV8SNh42HCLYPPGQFOCkYmzEB5z0/Vs2OkgAjHEqm35oFsGm9mXJ1mkWz6ZMYgoklpS1P+dfn8wasP5gqpiWfLc+8XFeloS3rw9hOYuhdurSEv0bN27k1Nhdjk2cfd+S/qam861vfctJ9v/ar/2aA3F+nM+d/535GviXBuf+fo1a/J1hY2OaPB5MhJpbQ5wMDOjCzbDa+02xDpgOwM6Gjjm+WO6WPVMlRQHRMd1v3hSh3Q96VMiJR0SIOOXJdi8/vwMh/P1Pm//3fA3M18AnsQZsDr516xaKAcdkBxtsjrT51NZu9mUWqytWrND999/vQOimPmffNzVh+x17vujp6XG+Z/Vn86yBc5s3byaBAtwCvG5zvX3ZYQmbr50XCa2mm1X6b1/7LdRIGlUK2LUur1gr7t+qlHs3yVOcSy4MNY26Vo28vU9dbe2KB8rLWbUGS9IbGm9rVvyiSqU89rCiFhQ5SWVLILIiIJlm60oszy7f1sg7BwH0qpW1conyt29WVKUpYaFaQeLFVGecu+GeQsOD6jt1SpePHFNiRKTWbAPwYv3gziJpBrQW9mNHFg5QQLPpwagLO9mWH72mxCbuazlWsru2y1POWhibHedmWA8HULWyOxo+dERjP36DhCTJ6E3rlfnoVgCADBLxqA01Navpg2MauVWt8tJKBzrzdXaplcRv3hpU9jZyApqT127W0XiKAYxQh4B8Lp4hZynXuTf3qrOhQcsrKlW5FfhhwUISfZag5V5JIpNlc+4nMIhq1amz6jxxUulkhlLW36ukHQ879q8uU7JBUcKUXtzAgZ45EoI3azS39x3UJFqUtWWb0mmXCJKeLq8lm0mOWvIXOE8kY8PDWJ22XEa5jJP5uUAkWCC6vPkmCkNi/yKOakfUBZwZWXC3oisfBxVrV3/VyyhEdSsJGCUh71HAKRLCXlMqsWcMymkTGpCb8yBCfbvCBlxhbdmNKkI/llgoaqTllWCvaopYQHpznZrtOKWJviZy06ji5a9HyaqMbkC9+VGa6zoDPHYc4BOTrgUPKqb08TttP3ZTw7dQAgQ8SsggUQY05eGawYgsSHYsL9tOYPtayzWmeT/PQSg6ubPWylu4FqUlgAarBwPLWg4CkqGgRiI5ec0Tikws1WQDynj1p0mexAENbqb+NgM1AOoABoSBNZDcoYLI0lknpO8ir0Kd1pLYP6K+1noAnQQSkAA7+SvkSsjn/ZyD7z6rkZZTPJMFlFK4ASBvA7BhAuUCEh3ENrP+iKJRf4rHfs9TRhlRRguPYFXYAjjX3g4wV+4oyHgA8VwxqFT4sH1qAxxsOQlQAQzjwZoH6M5btpW6q+Qj6T8z3FPfBWxvWjRNAjV7EQlakjOTXR2qrwEcBfIoRNUnNpdrmspiEBjQga5MMZC2JHHicjF+3CP0CRKv2ND2N9ySh6RLOtBRNKqB7hjK52vDru2CevmZGzm/1NwKLFytXwCSoAYRGjhOcnu/pnkWjSb5nli8/ifgXJP6AOeGx1BoKMhUGm1ocJQLpUqDL/z9wEW1KKAZvAlkGJuJAlwh7ZFEP8UCzZSZxlurNN5Zo0TUIhJKUVOMSVV3UyPAYLMyseyKNxu17JXUCbEG0MqxowuSfDFQgFjiSOlZIp3ktK8fNTn6aXAuAmAFdT7K4I67k/CfIok+2X9JCZGoC+XxHJ27nD6TxpgbRYWvUaP11eLhmXvAirN0Ewu3lYBzwxq9/JzcqCzGZAPqVFJfGSuoY0AmVHZCADKzrdeA6Lq4PxKSqFtFpqIYl4UyZPpqxms8MFQP4kU1GgBKjAUOS1kElJWbr+mOPvXUdgKcYN1VulzeHK4bSxwzSMBih0EsgFcO7IFyowtlLvzvgNQAODuBH2nDhMwK1Dg3AIcA1JIw9FOOgeZbQJm9yiwA8gUscSVQb1j3hUaaNNN2E1u7HiCCNKWUUzeppcR6YKrOemyTzyvagxpECQpNgCAuQNowdRPqw5IRaHJipA5OJqS4rLsUV7IDAKPMuCf6biv98RRg02kHYkqsBJzLXI+6VJOG6gCBImex5LsLxcwH6JqlgAgALMQXtxsQwsYgexystOmnY/y9DYvGGnWjQDQ2GVIaAFAG4z0q1pTKUK0b5j5aX0aRCbAMVbso+m8EZQ9Fmj0kAA7wX6CzylFiiV0KvJq6DuiznaG9V2PNNchGFyu27DEU4VAvZQ/ErEr9AyiZ1b8nN8CI252IZWsFls8b4S4Xk9Qnns+0AuaeUX8dFsfKRAEPa8Zk2qXvigabAQYBYuNL1gGNA9lGU59e5hfazS9UwUiug8fQlNGot2ApC9ASmOigXo7BQDYAp2ajyAXskUz/BogKjVxXT8s1Ets+krLY6GUTY+MNhEb1ZARbwpZjqFJiS5lWARh1P0JM+fTDbmL6OQ2jvJOS4cGuEMXLTAAp9obCWCiHscQaagV6nWpGfXaCfoJSIIqG8Rmoa2UBZxEXgwA9oYEzWL4eIb5hC4sCXnzhCsC5y/LV7yf5iqpYxSbG7haASsAUS2UylxlgDWlEzEaNiO+FCDheLCBd400apz+NAJzEo4qTWryCuLCO37M2vIKq7EGUvAbpJ4sVnbeOfgHAh0pqeAYbT3421Uk7AsamLN4jb/4D9Hss1jrPqLPplsM95ReTJLeYB1hlc50pMPraANn6ugAfQ1ikYRtZsoQxvph+jNIaYyXQ26L+FtQKgd5zlpTS/zMRQwXCv16HUhvAQf5ixRTQhokVjGXgVdrMeRFi/vtf3CR1AWkCU+2aBV6d6anGHhRYpHA9hwlsDQKqPNqqwZbbGh9oBw5ClbdoGeueJXdixjgxqP20OpqbiJ8kygHnYgoXUVaS1t3dmrl1RHGAUgmZBu3TBwHuXB4OUwBtD7ecR1Wqlv40SWwr4f4fReUSIBzwLQz8OweQN9ZyAHCrFRtarHwrn3Qgp176tgvYKw11Nm/Wdq65AsYKkBgLbLopWC7JaVsb2sMsPdxUhkLc5wT1OdALjAgInb1wE9bcC3gPfYWY3Vezl/dUKxEAN76YhDcwMcQRoFYHSoJ1zGnVSo41UJ65ugCoDMUwJgHGMGOclVkYmJHgRl9EmYy5frgV1SAAjkyA+/j4GdqFOYMH7LjMddjTPsIYQDEUBTAf8P5Q2zkA4FvKxRIzFrhzZjqZ7zUCKHdifYsVOzHfFU99R2YyJ/IAbvE0zEECW9fYhM/aKYxyVjgIMDuGzedAFfEESDNlLYcBaH+WsGGUw6ZRopsC+o9GoTg2m3Ik3cc1WdOEsaxHEbKrDcvvqQlVrkKxrGAD48DirBVvgD+Zj+ZSmYdQBg5iTe5ifdB3BtiwURH0xQgWfF4g28gMQD3a353EuAjTrwY4mNB8FsaQsQHwn7RgC79PjLr2OvAycCFliyrZxr3Q91F+tPV2GCXfcMDWa5SOP0wC1BUC8p24jTocayxUudBeVAaAfXQmc0x0MWcdAPTajgPodANTsi7Jvx8AuYCys2YA+vN1n9Bg61HaYIbDLfdyQOVR6pE2tPqzRQUANQ8e/J3x4AammgKI7LnNehMIFTXGKetTaQA1BYC2WOUqivEN6BYYveUApTPA22nEvmQOArioq/G6RvV3oy7K2igJyNaVYGOJdSDrCBcQvkMZWdVaF2X8uSJQGCYuTwOJz7bUKJY1i7cQKDBnLeM9jhOQzK0Nl1CJ6nCUq3NLOGwBOGqNG8a21d91U92s3Q3izmC+i2YtwATFz5O4PmMYsCOCsR5mzRVm/RQY4lBI6zmNAQXF5Tyu03WL9NKbBs369cSjG7BWZBykEv+4VfhM5ji46p6Q3n3br1Onx1jbDHNoPUfL7wLgSwFo4W0RzO/RUajQ0qfcoTjcifz6y++eVx3g3M4t9+jTTxXw3IdVItdrYg/qtb1dOnnqICpni7RnFwdqkrx6/c1xHT1dz/V92vVYOc+KWSiOugHzwoAufuA+lLJ6rulze/L16SfygaqiVFM3o6//X2eB1Zq1ZkWFPv+5tVq6OI56YmZgOp7CdeHcmTF97wfnsTNP0UMPL9TDj8UrI9fs1ikb62XrZhPDAb391i3guVrU6jKwEl2jVWtYw/L5Vj7fHGaL7Il5OUhq08VVYLXf+T3AwI5YPXxPiT7/2TjsMO/UxY3bc1g6XgMw68H6dL2e3JkByBRCFatF+w7WOZa9O3csBD4EDkMBawLw8oNDY3r9jTO6WlWln3/6Ef3KLy9j/otQe6tf3/6LAZ2+7NaS4iiU6BK0eiO2mThDRLI+MxvQDw5M6e13WAejWLr9kQqsP9OABnlu5MZDgLUxHOqYZJ/vVYCvl16/pcU84z3zRIbWoOYWFc8BCerJTxljqAgvZTO1v6tXgvrb719VO4dk7llXomc/vRi70ETW4dKtGwF957lOHC0StWWjV596hoNg/OJrr83q9X1Dzvr8K/+pQqvXYttLnY1jbXr61Jy+90ITe44BPWrg3K+mqLQYWPEM7frSiM5fPa+1K/O1e9digLBInldQjCR22ZqR80I68sE4gN2EunvntO3BBO3YlajMXKK9s6bkfewLTFHGa9fGONR+FZviNO16tFz3bYlVInXMeRmL1KiLsQbnetHc13Hu6b+9OKgLQGLrV2bqU09yzyu453gXwFxIb77ehuVws9betUR7nsxA4c+UBif0t891ABrH64FNsfrCFzjgxLp+nD56AZvVN96oUU3tNDBWhT71dIZW3mW2oQH9+V80j88dfwAAQABJREFU68yVSS2mDT/7c8XauC4WNT6OIVjnI9Y1NQb0h398Qt0c/ntgyyo9trPUAcNctLGFDNtznaV87+8zNThTER7T448XY5ebpLRMfsh1ZjnEZ3NgFGWLYg68BDj359+6oJs1DUBzd6HuuAD74kiAOCyDmwHwDgUAXA9R3zna8zRWrllefmdOr7+N4iMxcDNA6/aHgcOKOLRHPzp2IqDvv4LVMAcQf2Frsn7lPxQCzqEWR//9+h+f06FTl5RblKVf+dKDgKepQGncB1OM3b+PAwEtddL3v9uu2romLVuZokd3lQEJJjF3cNP83zqXn8+puoIgzDeY+4NAmQ8u0yOPMF6xoXU4cZ6RrA/awQkP6/vezii9+Hyr3j14kT4TraefXIPiHop3gHlTtMkbe1G4O+nn4M2kfvFz2VjnxtFOc3rltU7smHv1AIDnnl05Kir3iGKotiGkl7je0XOTKmW989Vfz9B996NA2BXSQWLaX33/XaWkRuvxXWu4rzQVFqHEB6D39t7XdeC9N+jTBcTG3dhW3+vsU/0PcI65yOYR/kdrOV/88bFe8+Dcx6q2+V+ar4H5Gpivgfka+Ok1YDPxz/7LbJG+9rWvOapLv/3bv83C+nMssmxq/ue/TM3p/PnzDpS3YMECfeUrX+Hhg035+dcnugZsX8q+LEfD84rzsk0ADqffUTSaYaHsu/PvKf7e0ulRfZtX7b1eDYx6NeEzAI4VtbMzYk8O9FdOd3vZ6FheOqPPPNDNiaxOHmJP6a233tLKlSv18z//804i1mBRUyl58803VFV1U08//bSjAmbwnCVi51+fnBqwOGcxyiykX3zxxX+y4tyHoJz9aQl6gy8TUf3Iwz5s9erVuvfee50+Z5asZg1nAMBHX6bC+e1vf5v+V6V5cO6jNTP/9/+vNfCvDc79/fuxeI2IAeMGmG5KOnwmyIZHQC1NYQ2yYdaDQ9jULJsTbLpZHtf2CaPZvCmvcGnPjghtWuPBDsNFUoDvs+FhQ8PmgvnXfA3M18Antwb6+zm5ffGi3n//fceuNSsrCxUNFFJYk9nhG4PpZjgZ/sgjj2jnzp1atmyZcyji6NGjztxt4J2putrhHFOvMwjP1nkG29kab9EirMaYq+314fxtfw8BUTRfv6bv/e7/rm6U50pS0rVt5Tot27kHVgRVkQwUzcgghIZQT9r3juovnANa8qgM5dhQd5u6mrCIBIDLWoMiBipykSixudP4HaxHDV4Lz6H+c/ychvYDNJGkztq6SSkPbgR6yiaRaYc/DK6zRTAJTBT2gq2t6jl4UDcunFdBSYkWPrFHkZwgdycC/1iwJAC7sfFzoZwVsgR/W6tqX/yxUpvalLTiLnkfR8EM4MbJeAYtGUsZATzwi1LHG3s1uvdtpQGGpezaqRjuJYIT/WGUpYK9g+o6dFC9J48rF4WglMJKcrvTamxvIfmaocylixVL3UYAMLpSDdrCziSGNfMEagrnz+rsawB5LOhXbrxfGWz6RuSi7hRja3Qmi/AcX5SPupg1QO/9wxq5el0L8ouU9NDDqOStI+sCOBJJ+fD3DgHKzPF+D0oNrnqgobf26lZtnQoe2KacBx7ks/OpOxLABrfZkh0IQ6Em2ugkIMk1ypuhBAATT0opu+4p/IzPHrkJoHCEk/a95ARRByzHJjVkqjZvoWozAJSxGjBwF7xCJfdAP3EegWkXyx6hJsLRHb5nCWCs84Zvk9hsQJFDQCnFSgJaiAD+4j+opwAjNB4iod6NlRV2WUXbgJVK+F2rCw7x9JxSoP5dFGHG4cG2yIsSjtuSNyRRR26fAsqYUAr9Pq7MIJ+FJKSy5ZltVaj3uOZaLqB+0k+SJ6iYdPpaCeBS9jqACRKcIW7Yjx1Y5wdYD57RLDY8KSt24tJbpqmmC5qoP4cdLkmGEoCdjI1kR/idn1QfF6B9AD+oJ1fAoLkB2h7oqh94BJAzGRW2xOx7UKYqIxFK8h31jOl2FBGbT5OAiAUu2or6Eta3WOUFg4CPqMlM1xwkW0oSF5jFU/4YaldLgEKvKti4F3WwHmzlAPGKt6GIaElhoKQg6i19JzXVeMBRZfNiCZgIlBNVsoXPLLZG5nNbFET1ZgRr1kkS/hmoEsZjkeTra1N7Uz/J2jKsxtYAafF+IJ9wkAQ8AIrbzTjkCs4yg7YMBztR6bmu/q5qFAcGUddIRakOa2AgCvzfgIPqUPI6TbK5Dss81PSKAb8yl3Ef+Q4MFR45ranqVzmA5Zc3ez0wCKpdgJ7BfkC82msanx4F/spVUvkqoAXqzJ5VQ1iWDt3AGvgisFcfSoQx9FHUcnKBN2Loc/QtzRqAchPlphvYsqKIVVwEfJSoXuKfD5UlU4GIK14GxARY5C6gQAZc2LUBIQy4oqtCPPHvQaxW21AeAi4a6yF5jKVXPmVIol7IQoUnscNuo30BMtKSZuHTSM6nrcQaEpUdQJEwdsfTQDnTPe3cI0n20g0s5ADnUFwbufJ9RQxedmJBZOWjDnhi2fEwahzh4ToFmk4A5RCrDWblmSihuBLwjD6ayD2j8RjG9jaAReFA801s4rD8qsyTJy9LU109KJZhT4caU2rZGqDX5dwr9Y0CmwG1ZNP5/TtfLjcDD2AjABQzgoLQiClGciguoxAwIXUNDBMxFSvhubbr2Nw2kTzFerQcCDMHgCQql8sAAaBWM8vPh6hbL8qYKRXAYxlF3B/wbjeqH+2XFU8mOLZoNd+/l3tJJhYYUNUMmApk0Xfxjpoy0Ki7kLgRQzuzB4EUD33nGPDse8BZM4ot3YzS3f2oeTVpAGgnCYWW+CLifybwBbCILX7DqHdZQswpnm2EAOe6GIOBsVsou91QP0BHBGp6mQXrUJxiLogANJ2jzvtPAzi+Ql+ZVLTdByAQFUffn0HdsutOHOi44FjcxSza44Bz4dEOrFrfwXayHtEkoLgKxmYG1zTQmjgdGj2PrfPbwFqAFIATsTm0RTnlN8Uvxr5L3fSPixq4eZbPiAUGWaCYBFLKA9c00tHGNQuUUI5FYMYqR+WKXgw4wfMA8AWNQk8xaC4G+0TqcgpAE/vT0fZ6FO5Q97E4mrOazwGO4/5DQ8DwwCBh5sl0lMii0xfRDtnUEwAm1qCBNmJdD3EfUCkRkC0S264wfaL/JkDh0ACqUqmMQeAfg6FdOfwefXS6ERjnNCqEXNc3yPM5tnu5lDGHWJpJGwIXhU12cATQ7fYhbDCjULsCfsTeO4SqWLCBhCzQUeTCB+m3W+ibKMGaSqDFX4sytnfKPGawYAibT/dMG2qD1E3rLX6OlVs+rgc5QOXefO4JKKXnNOU4wMEkH/FwE/eB2p43iysxtucGWWMc5H6PcniJ+lm4W1GFD/CZKMV1kiTHQjsMTJONPXFCFlCcp5hbQKUVCDvEzwcBqELQKym5WDoD/rnTLM4QE7EADoy0a6C1HgC0FxgoC1A7zbFS7brZBOSbqLQi5kjmSVcs8COAn2XUnfhpD4DM8zanQtAy9XYANnAdrOI92L+m5QHaoiTqAqCTa4KYfhsL5mqWH6jlFBY6cFcINTMbK27myrmO8yjxNdLmccTuhfIWmNVtNMpgWNbVnFSCG0vPgmWAc1tYI7Cm8EzQxjVAcSdRdb2iOPp6kvWNUuDsGOBs7JFtrRGaQomtaa98QNzu1GLFlz2Nctk4MPQhWmGQz1qKutkjXHMRKrqo3wHauplXrRUt1LgAt9iNJOYBhnVVAfKhfIZSaDJWsonE+wjU0cJAvn7sdbur3pIXMDqtaBFT1YPcRykXQJl2GqVBoKTh2vMAIH7FlhNLChlLXuomMODEwjlsPH025FnnkqqXa6qPeRe1yH6gYpSdEuLmsNiswbIzXt48oNKkh+kfVrdjgIDNjJ3TWIxexUIPO8eiBYBzcVjVd7BkGVUq+1FxOXfBZ9L/3el8RRILmSuCFM3Z6wRABtYLzfZhN1qtPkBqX2AYhXgA3bT7gGKYm6Ow5hxpRPXyGnQQIHw66phZq4ldd3MtiAb6gL+/GsD1NipygyplXZ6QdzddndjG2toZx7ODtD9xYM4OrwDKuZqB1W4AzrUCNo4xXFDjA2Lz0MZu7GndqPiEI1hvjV2Xv5U1XTd9K7MSO2bWGKwv5q7tZ2z4FFEMaFfM/BlXRAUS8zlQbTaRYeAJe7lwHHExr4ZmW+XvvQisB7AHPRGNNXdiNoqYCYXcfwKxGVXLpkOcSUBNr+guDlZw+AQINcI2MGyN3Q+I2/geKvvjKEVvpBsCL3pZJ4g52wDf6Vm+gA9RD2T24E/AfObCMCCpj/WxL4a2K6hk7mYNZfOgi7W/y5TcGjTaeAlFwm5Un+KAyImxLCrHG1o0MjiHtfpdAIMA3SjumlofZrfOms1sLZ25nk0Wl2uIvzMnATVOdLYx5HyApkVUP3EUi2IXnTk40aTO2wCWY1PAq6irllQSE229SvsxH4f6b2iwHjVtxnkS/TvaAFsvPw+xtqB8YZSDw3OUg/YMErMi54CVh1iv9qJsl/mQLvWu0I/3VbMHNKw1y+/SuvULlJbD+jMLF4JMYDEsVidRxDp2wKcjhzlYgq3imtVZWrA4UzkFfCZgUgrwUFzstLys/8UY3v+uX997HkXv4Sk9s3u19uzEwjHHfib19AHOvdmrve+8DWBTqqd3r0MBLFEv/GhIl643afnSJO3eXYJ6WjR2t8yvtNTxU349/5JfjfVV+rkngFN2ZKMw5kYhakr/5b+eRE2uVw9uXu4oteXkMhfyW5wP0sxEWFcuTejll4FDeyK1oDJbq9clqLAkHuU1DweM7L5dmpkM6OBBlN2O3uLvEYBGi1GcAyzPYI5E7SoThTfOSrD8cqIoKm9+/effPamhsSzt2Q5Y9my0CoBoZgDsmlr9XKcVl6YGVZTcBTiXDiDn1+tv1aKs1sUe8yI99WShysoB19hDM9W7CxcCuKZc0eFjp3n/Vv3yLy9XKmpdbVzrL7/ZpRu3E3Xv6lg9+wuUYYXZPVNEXoPU5dmTPu19Gxh5eESLl+Si0JXFXjaq35QvPRVQDIWxOe7r/YOT1DEK64DN967LATRMpn09WCTjKoG7RBoqwwZyjQyGdOE8tpTf53ARqpiPbi/Xbuo8L4/4w89bG4P6m78ZBlqM1GqW1Z//XKziURR77nsj+uD0rCrK4vTl/y1RpQZKco9B1mS3rgX0rb/u0M36aW3dkIXinIFzETpzMqAfvTqBYuBVbb2vxPmcxQuBe7nnn0xYNmq4n0nasE9Vt3rZC4jXhvuwFM4FbIz34ADjArzEBph6rL41ox//6Ka6OrEIXZYF3JmgnEL6Mm1osGEC10XcEDV0wLnTPn3nR6O6Xt2k7Zuz9ewzBeSdmNdsOUBM3/vmsF54sVbFhSV6BvW4RYCRFy8N62+ea5XXW6xP7U7QY4+jtseUZe4dTU1BvfLjBp05PaxlC4r19J4MbEqxam0L6pvfbtYVgMr1KM19+VczUCajDfkcuqhTzM7OkL7xrYuo1tWpuKSUA/sLVVLK4am0SNqRNmQccO5Lxw5P6PCBAfWhfL18WaaWLmefJT/GgeeSbQwyVq1v2JbBlUsozn3rnJraWrVl81165tOVKl8QyZrJxbNTUKeOzVGnqKSVJ2vXEyuVn5Wkk8dn9d6RPuC6cT35TCnQZDyqeOxDMAgv0ed/+EpAh8/e0s57k/RLXyh2wDkfgNp/+a9XdPzCFWx18/WVL2/CEjfJqWN4ckYiaurkBvs6w/rRS226cOm2ElNRs0PZsrwSy2WsYNmWoJx8Ds1efXNG33/uNLbFIS2sKMbpJ1e5eazz2G5JYe85FSXJKNSuQ6xhetoj9NIPO3T0RBWKkKn6zKeXMr4SeK4FluQz33vfr/cOsv/Duv7ZZ9JRsIvX8ROTgHNA+4DHTz1egSJkDIcTOBxCvfQCeb70/UHtf39SGclR+vX/mKzNBs51h7B2BRj9wbuo1mXq059ZDqAXzyOSG4hyQvvefk0H3t3LzwDxdu/RvZvu+wk4Z9HLCXtOHLPo4Xb+53z7Y/1nHpz7WNU2/0vzNTBfA/M1MF8DP70G7ixwf/p7/v3/1BQYutik+NKXvuQocX33u99VEZY/ptD1918Gmdj7DQT5n8F19j4DRP7oj/5Ihw4dchSWTC1i/vXJrQF7mIBbA7pAxABLh9EJ9mJYNBtL6fOF2SAO61JVSNdrQ2rmQcBADPYkLFfoLBr59f/+urPVfGdRaSPVTqasXxmhP/hypFYt8zkqJH/2Z39O8jXPAUMrKtlE+MlvBzk+ZEnaP/jDP3ROHH31q191bDQNcpp/fbJqwFTmnnvuOX3961934uA/VnpLtlvMs9hnynGpqZyIQvXG7H83bNigTZs2cfppAQ+oPJ39T17/muCcxd1/6svKNP/6910D/3+Dc/9Qbd3pcmFdJHa//UFQt7ED6OPEZdsop0+xdTX2IEDwtp7pZX979UKXPve4B0uMCOWzGWqbcyb6aXum813yH6rh+e/N18DPdg1MYi/UjhKVfdk6zOxVbS61OcqeTwyoM0iukOTrpz71KQeeM2tWs1g3tbnly5frsccec8A5mwNPnz7tfNlBHQPttm7d6oDtNn9/dN4LAtm1VV3XS7/3f6oD2K44I0tbNz2gpbuwRVu8RIHEODaRSVBhyTRy+IBqThzDWmRWy7bez+nzGTVVA+C0kXwGMsjF6siTkS53ZamiyorhbIA0CGqDJ85q9PAxw0aUtpnE20aS+ekkplD0gYxxkuwuN89W1EHg5m1173tbtbfZyF2zUsVP7gaQqlQoNgklFmytKFsEu9CRJJTxHlWoo0U1L76ipMZWpS7HanTHE4ooXUiGJgoQivndQIwQybxZ7FTf+LHGDx/G3gd1i0e57t33kHRkvUKiMUTSZPDkUfWfOKRooJfMFasB4xLVVY0dXm+PUrC8MktSu28X5YpaA6THnwb7zZ06oevv7udaWNI/uF2Ja1HvoR5MFY+L8/mzDuwVpq4naxvUwCn1iRu3tRA1q+QHH1Ikhw1cgEdk+fg/98w8EHDfqfNQHdZab7ylmyhilDz0kHK2PeBYzpHV5X3s8PP+MICC/I0ksU+TTL8BlFCkVCATDxZXLmxqZWoZY6iwdJxTfydJ2JxKRZehkgaYMFZ1QNEkTKPzKW8RCdG4MvKv0XAQrPe4FYMmDbgwBSizw5qZqFcPymFTqG9ZgjkrZbOiEyzBzP1H+MgzXoMX2w8w0aeYzOUImjxEPXJNYBEXFqEhYLRA3TtYf6F8VwYoUQl0Y4mWESxma1CjIyGcUliMqgkQQMwCag87q7lWhQePA8Jc0Fh3N2UNAJKgMFUKRGhKXiSywjbB+kmCogY11nlWU2EsfBY/wr2hONd0RZONF0jypQDO3QdIsAq4I8Gp6xDtGgScw3APa8ZxFMf6EIVBfaYXcGqilXuPVQpJ2MiUu7AWTaUM1AsA0STgwgSKVolkLKIBgSIySNADqwW5Y9dkqybrDgPToFCTmqvIikeBcxY6CVo/CeixPmzI8leiLrQFoKqI3wPMoA3Do6dh4w5hCdfF7aEW4iSx19CXGUemOkbCdq4H+KwT9TaaPLWoEBtVEsO9WAF29JOwX4ziD3Z4iXm0OYltF/BciGylMw7oiqia8MRJ3VeRRD+HNVWfAtimJaFOmJS1kCEDsMOzqSarUIw5gSJTEyo/ZQB8KOwkL3buI2QZnAksG6vfRB1oRlGAc7H5a2gC1DGHGzWCJeE4gJkpziWUoRSZUMnn0weBGk2VaLz5CuAcAEKMx7GKi8jczsMrCXMPD7qhDmxHbwB/XsW6DIWhwnyUFxPV10KyeRTYIw2bpCJg3mSyjSTf6XT0VQaLtQplDFtfDfegxoPFJQpn/ahPeGOxtM0rA7hCmT2K7BLxI0wsG2zlcwZqSWqFlFhCMh/LuoArlT6ACtJkB33tNvAK6ibFhdi13o3FI59pVq3XX5Jn7Bo2jkVY8ALOxWPhakppfiATgJZg6xFUsm5ozOdHvSdTSWXL7ii2xS3hNhkjcz2OlfAAtp4u7ERTKrBTLsCqqhvbuuYBJUbnosi1FvhxEe2VQXLLEpE2ECmmbSAAvRmQE5oCdhgELAOQMlgoOb0AhUJU0eKpS6Bit69F/uYzQCSMFyDMNOwVTYEyHJFOXREPUGSa7bgFwIfaFXWUWoGVbBZWn8DZE93Vjg1zcgLKlUAg7pQtzr2EAUZDg23y9RwF+DqJDRu699b38x+jD5fdaefwqDNWg+17OViCtXLpNkVnbdb0ICpCAGnJQGZJ+ZtoQ6CdyCyuS8Hsi6JRkZQNSDeMxR1jaKLrMnZ0zcQi7MpRlItDdctsb9EQoRp8QFfnNVq9DxUoilhq4NfD9NFUJx6aKqav5wxA4iklRKOUAnTlSqUdhzsUrHsX2BS1JyxVveUP4vpazr0wTiy7OX5FgeZ3NdVaIw/9ISqXOAqEbHaPePkRq/qJcVUaqEEFjb2b5JwC4FJufQxwBsVXdyI2jqUPAQ0vxTI2kXhAm/NfoVRkmhkeGwsz3P8MYNQI8yJgoIvvpWJ3G5WGopzBLGa16GtEXRLQGRDa7AZTKtbJm7iAumLeREnSPY2iU/thALJ6hVOKlVCxEbYxhfuo1hBz8hT2fMlZAFOoDEYkUT5XGnVMDJ8BQiWOzqBk5x7HcpfxFYUKmKdwM3GRMpqKlgFTwGcjt4+yTwWwloeVOEBYuBc1ruaj9C8Owy0AiM69n/owgIr+T9OZeqzVocVzlxt7S98wSpG3HLDJQJ/EZFQfM1cRJtOJpbwnEpC6+wQqnKZsR/WWoRiYeR/lz6CNeYOf+b37KLA590FdJ1QCQhfYZ7YwjI7Rt+sZUhnE55XyAge6Ioppe+5l+jqx9gBJYeY8nBky8xc6aqGmrsWA5V65N5S0hlBDmxpuU0YRB//yseEdBNqvbaeVAD6AemNyljEH5SkAlORi74Mr3+mjllEGfAz7arCkvIKSHnXKYdYEVERTUdVyxXH/Tp+mH6Pw1FNXRxcHPihA1ROltwBjnDfAd6LsiSLiSFsT//YCYWLbXIAlMAnpyU7U2mrPoJ6FxXLxXczZ98MfozTrmqR8KFt2Ym/bfA6wbgrlP9q48GEeLukfQDf4XXJ667YCrftgD2sAK4sBWJ9GUWhKAw1HuebIT8C5LXSlMurIGiOgCGKj2+RuAfzFGAwDw/mBAse7W2m+GayQsYRE+cxtCmcWkwD2A8T8nqr9igHUTUKty5P3EA1JPVOLYd8IMDUxquGYYlg/eEsMnKP9iMOhyTrNodw1DsA4yVgOGwxPXIoyxd0pLCWn6HfZ+cSYkHqB8+LTAMlzN9N8W5wxLjd9lPqboB7Gui8Rx4F8CwoAPSM12EFMpEOlAiHGovbp8i5kjcmBB+rcmEcDKOz+mHB5Hxb2qLENDjSjzDWiWOD6zEwUeWMB47CDdlFX0wDwIwCAbpTnklk7RmcRy6MXcg3mHizBAyi2DbY3YDHYr8IFC1H7BUCPAmJF1ciPapxvqAk7U9SRQ8QCFOMig73UDXA3ByMCKLh6TB0XuNoLRO5OYS2FNXoQNVIXcSbQfoZY2wagVQIEyxoDOG3u8gHCJO1VvA5hOwPnmLcpDiXjf8BzAe7LYqmNY1R+fUO1miWWuqf7nDEeXbCGWFHMtGEgsJ+1wCXWR+87SpNxwO7B7G1AC9iL0xfcs4D2pm7ZcQQ7xEHU6O5GYZTPjGY0+ImlrCvnBgf4kzUia4MwlRtEuRPqXO6xXva7/fInoORqltaFAPYJAOgi7rMOCc+1so66zEGABsc2OnVRKdBqQKMNWIgzDWeUrwV8RI02Np/wkqiAopwqhwekn/If1OmQ3QRCvKRZ+tksey3RKKrGAL65HTt31uu25hyrBe48y/rDbNAXEQu4ZhJti7IUlDcw+A0NAbf6WKMm5i5lTlvAGho42ewph+pZm/KFjeMknznL3nks9Roz04/bADbQJVvUG7dFR1CaOnEUJebRTKxuy1BvSlRhcViVi9wqqwTASkI5qwEg7FAjB4aB1i1esE7LL85UcUkEe6cRADhmWQsMxmb+vv2z+u73gBkZG3ue2KAdD2Kfmn0HguvB9vB1LD337XtPK5flavfOtUpJSAXqGlZ1fQt7+akobhWqtDwKZV/s1IlFly6H9EPAuRs3rurZncXa+XA2c44LRbtJ/elffKChkTFtf3CNfu7ZhdhDEg94HqMnsXcVxpYyrP37enXm7BCK0MzFSYnYgMaroCAGW9AoLVzoVToKbberhwGTGnSzqp+5IAWLWuw5sUkvKPRq1eoYQKYo9roiHCDv2mWffvcPPgCYyteTuxZg5+lVXmGE5vi85ragTp5o18EDtSpjvfcUinNpKWZjWqcrVcNaj+3pY4/dqTdTsbMxff16QG++UQXoc1K7H92s//BLS4HaAOda5vRX32xQfX2ytt2bqt1PRamY/TcPh1mDQKQzE1JncxAVsgFdvootPP+OQ5U4GZCzCGBsCe23cFEU4FgkNqB+gKAW3QY+c4eSlU77pWd4lVMilS/0aOVyFPAo3+gQ4NyZAJBUM+WZ1q5dBXoIu1mzh41A5cvq82+/M6TqGq9WLkEF7zNe51HxO98bRFVtisNuGfrSr8UDJwLO0UXDHNKtuRXUD18Y1qVro1iVJgMG3lGcO3EcYPD1STW2XNeOh0t51s9TGUCdF5W8O3uJBgeHUG0D3DzWB5BYS1szp2B9bO2TlRWDwjr9jzLmFEay3grrwDtdgHYTTm4qBog3CzVPa8MSVMUqy7yq5H0JwJKHj0/oe6+Mq6V9UI9sSdeTu7NUDPzooU3sceytNwf0gxfqlJNdrGeeBBRd4NapU4P64ctNgM5lqCimaNvD3CvXmgO8b2sP6vVXa3XqxCg2uMV6ancGqoV3FOf+9M/r1NAerfvuydIvfDZW+QUAbhYOKGSQPjMyFibP2q0jlK+3j3MucaimpSaz9qePVkaqEuAtPy9KbbT16VNDunqtlbZ2ceAkkzpIVFGxxxmDCxgz+ViYxlF/Vy769c2/PA0M1sF9rtHOx8uVXwQ4R98ZAxA7f9pyJ28yDhK0c9da5WenA8bO6eSFIcbQlPY8g53wKp4fsbplVtXtGtpqb0AHjlzW5nUp+sLPlWPvGwVUHcYu+TLKfbe0ekOZPv+FNdRxnM3Kzp5wmHBunP70FMqDx/v0wZEa1beyDgAGT0srxN0nkXoX0GckiuNRgKsAgse6sfEFQB52ccCF/YDUJGVnhVVWQX9eSl2UYM3Ouniw06WXX+xBvbKGWJKhZ54uB46LBZyjDdkXeecds54lhs92AM4Bza5MAJwb1suMtaSEFAece2BLHGsfwEvuc3g8pLdeHdfet4axt57W//Ifs7VlSyqKc2G9y7W+98I+wM1cfebZFVq/Pg4g3oXj0bT2vfMGP39TJaY49/gTgHMozkEwhmyutjHAs6sBhPyVmG7/s799vNc8OPfx6m3+t+ZrYL4G5mtgvgZ+ag18/Inpp1723+APTaHBVJdMcc4UG7785S87lkgfJq4MmDPrpNbWVkfFwYARe58BJB9NQn20aFMkVI4fP67f+I3fQHr2Cf3Jn/zJR388//dPWA3Yc37fIA9A9SENcyKpugE577oQSnIhdfL9SfYAWBn+ndc/ZQTaw1EKDx4bVrj1q5/3aMs9AZ07d07f/OY3HHvXz/3i/8FD0RotLOfEl/fOR0zycNTUOsMprWN69eX/W9nZmfqt3/qqli5d+v9SB/s7NzT/j5+5Gvhp4NyHoJwl283qLTs7m4fMLDZKFjqwnAFzFgf/sRj4j1XWvxY4Z2WxOG1KOx++PnpvH4XqrGxWpliSRp/k14d18tF6+vdUH/8WwLl/qL5u1AW17wPi/PWgero5yYmt6ySwtJ2stE0lC/XpiS7t2BShHZsj2FRkg7Xgzuldg+vubHr9Q1ee/958DczXwM9aDdgziMFzpghs6kF2cMfmXXvZfLl//3798Ic/dP791FNPOcrYJ0+e1PPPP6/BwUHt2bPHec7IyCBpS/C4du0aliuv6sqVK5xMvltPPvmkA9fZdS3mfxjvDZzrvHVTL/3Ofwacq1JxVq4237tNiwHL4lgP+hOjSRKQRJmdUP/xD1R9FHCOOXbV9gdIlAJ8DHYAL7TL24pyxhDzL3NwiE3aBLNvXX+3A5cNnkW95tBhJbIRmrJ1o7wbUX8BKjGrxZC8DgwXjEDJAS/swM1q9e59U/Uk/0s3rlbRnt0kWxcqADjnJyNvcdMS2JGoA4nkd7CjUbWvvKqEZhLgy7Hx2/EUSToSyKjiOW8m0RUOoIQxM6yW117W5LGjyi1A6e0xlOxWowRl9AeJiRAWpsMnAedOHiRJNq38+7cqbtFiEo5Ypt2+rYi+XhRbZjVJInESyjlh43plrVuHih+qK2dPA869iwNZpiq271LCGq6bnKwwCVA7uk56BjiCnW+Aw/FqEgDvHdTs7TpVlgMsbQOcW4kFKMplDuBE2VweNonZnBfgiR9wb2bvXlUDuORvQ3Fuy2aSgiTQo+LuQA6WvwzPkOBsIMl3VsOdt6FISpQODBGVkMMusyXBDJxrBeo5r97WFlR9SlFaoq5QBpm6flRRM+Myy1NP+XZcS0mko4ZjAl52Cx6UFhQcpQxYWo2jlDVYrcHJTlQ0ANPSsABOAF7zoPLEm80+Mjh8QUM1b9OWgyQwARqKUYiJNnCO+0XFJjwIcFK3F/u1PkWaxedCoLJIEtqDVRpCmSZE/08uLJO3AHAuuhyAME0R/jYgtBPATMAC3e1wkHPKKsPms4wEbuparmvgHPcZAJwbQIWqE8taAIL0JY+wMV8GOHdZM80XsQlMRT0MaCdlOXVCu9sLcM5PYhFjTZKxqPGhTjLTATQ10EW5sFcvAK6hHAEvZQCCIZ2gSBRIJhu4H+zykjLT5S1C7SptFQ0XD9tEv5zqJBF9FEWsWyiAZctb8TAJ6wUI2d1SoPEDgK0hoIwVgHOb6SdF3AP7CKEp8vgnsRo7hLpPB8JPJLbKAKjyDcijfrlDaDZcx7A9busEvIlAvamQ5Icp1dVqoHtQiYUrURZClQZgEpkWfgdlcmx7rX+HSaST7qEPk5Tuvkqyv4a+i9oDSeaoDNSHSE67SU4ji4Oa1RX5USfsQ20xnp8nlWB7lgg440YJinjhAtry1b4tH2ookZn3yAtMYeBcmLobagTamQScywOcKwWoi1/ErdP2IWykJ4BoWi4xbAEhogEdTIktbTsQAonsSNbsQWCiPhTrABKjI2dIYBcCzqWorw3lIjLpqWnYIAITuhKWkKAHYAXeCJqqDm3iskUVYFcAK8qxIRQRR/oBl1BFyS7DpqgYHogkOqAdlUj5BtXfBDgHkJGR7uE+AaNI6AdDKQzXcdqvQ2NtVdQTFoNlBZR/DXaGi+i+WLVW/Qhw7iYKjaXEmcdI6q+gXzDGzHoQACOIPeIM6kTjM0AgZj8HiBAFeBSOBTwEjnEBZoZGsG+svwnAh2V0Kf2jOIVxi1JY24hSASlTCwBlgLlCjKsgIJDNAsbhhC2GhBhDMz3ASGc1PoxqGmM1IRkVL8BMT1Kh/CiuucOY4/lQDWw8rv72XkQEc5RRAVScXkF9UQ92ndluzbbfAiAEnIM8S60oR8wsU4Fp1Pg6gKb6rpMERg0nn/6UsIU7sLZHRWqkU3OdhzTYdZTPDWFLugFY5lGAvHL6WTRtOMkYPIYS6Bso66DIU/ogfWQzsCSKc23viqkBSIkxmPAw/RqYi2QbDcjvsTAGaLoDxFg/qMPertrp3/HMadEZS4BMGQsAMCFA6GCAvtxx0QF/ExJQ1ikDgMvYSuwEGKVFwj5sPntQsOw4RhIUy1DAOXfqGsA5lA+BVwPcjzurAngX4C6pBJALpTWeCTV2ScGmfZpuq1EUYJ8nF1gvh7EdC7DnjeVWUZhC7W2wAdtlVHYM7okFQNBEnWOvCVVCH90OMEv8whYy6AZUt7ELkeq2eRd5rRCQySwqVZOMs2C4H7WaSuxygaeJVTQi9UJQn6tFZfOQugFNlYxi4gLsMVHgDNHvw0DBkVM1CrcfweqT9yUDd/JzTwrjl3sbqAaqHPKjuAPEg1pkRFIx17SxPUzfMVvGSyhbXlPUSB9xJhXQfBMWmqh5oVjn+LsxX4VHAJ5uH0PpjWcSwNj4vFLULqsUajrJuIsGnCPuAzJBcdPulJ/ktcPqgGG5zK4RJacp7ICHaEf/TDeJcuwL0+4FpGUcALWHo2YpZh996YjCgHEh+nlU5TaFswCbXcD3QE7hOcCzrhOaQ8VxBqve2ApsdfM2snbhmr1HsDtmLksoAEhDeSsJyM1R1WO0+G8wX7yrztZaEvKxKBUuUgKqei6sc+lE9A+AL8CXISyTp4ZalJmHvVwOc8hwO9aRPcSNXNSu7lFM1lLAsizUXwEF+a1Ipw2n6acApLMASYNXNdZ7BYhzmnosB/IBtksnzrsNEjcFNxTpuq5gT43qG/0rBWU8b3a5gl7mZUv+znbeAeeaGrlmpJIMnCuhL7J2mexu1VTtOcWSwI8j/kRk30ecJK4D7dB4/N5FDdadUjwAWYwp0hUTRwHNkXvjWhxCGEdhtnU/ynqAlanEv9I9qKpNE59PoP42rtQSrNCz7pU/uoRrAgbSbm7AXJcfOMfP7wNYzg0Sy7HX9c+hm8h6LR4w243yojzAtwCuBj8FBuvUd+OAvEBoySX0tZyHAPgK6e/WfqwHmPOmmg4ryjWsqEKzaL6XH8XJN3CZGHYRAHwYGJZ5iK8oOpCtAYMTwLujPsUDqXnjQ+oCbkzIwOozn0MDCZv5XGtDCBeS+VNdF4hXl1FMAngBtJ72ezTUMwKgGkG/WERcXEHDlTFu7hxCMXVLA5BdKLwaaO8DuhrDEnkSe3QPc00adtdxyawpXMQz2kTuIewqAYzbqwDi+7A4LaSdAeeiGKsWvFgbBYfaNNRBvBrsUsHCBUCm/Jxx4Ud1d7T1rPyjrSj8oDIJYetmLeZi/eCexgJ1bBSFPNaGHIJILtrEegC4N7GYa3NAxNaMjGV/6ymUzVoVmY0dbyUxAkVc36WDxBE/0BzgXOE26GXAOVrQXjZPMBC4d2K1r1fTI6i6dVbLPdGtZGxmo7IBTLPWwFfmOgrObhTwZlGUm2l6n2qZAEBGeTVrM3AT+Y2AF5CN9fOwAXzA0sP9wIn3oAbMmi4S9TWg0cmeK4yhUdqXMRTLmoM1UzjAHMXnuYi1ZjM9B9QVyVomvhC4NwEAPczanznKwMzpdqzde+oAFsNKXVTswHfDDcy/E25llzMGsSl2MQYDzqEE5k8bN0wVbgBEF/Ogf5D1cdclBA77OCiS66zV3OmVjBUUJFGzjqSfGEzce/0s7QecirJfJMqe7qQ04gH3ypoqjBLuELb0M3NeJdK2ZhEfEYVV5yDrOA6iBIdbFAtkEsL21a7pCUzKM9FLfTDuS1HeLt2l1pF8VV2aBnDCHrTXw6F3NBwDI4BwMyg4JaEGVqAM4LkeTlPevD6qWvIAPYMRHKxEHRI10nL2f7ZtSdPadcBmabEAWlN67vn3+Nkc6mr3afu2PGWZpSTdtxPbwzff6gWuO6Bli7P0+GNrmFfT9OKPhlXf1IKDTBoAVYGKgYC8XtY71BlMjl7EJvLixWt6ZnuRdm/Ppq+7dLtxQt/4q0OanJjWww+txY6ywrFpdANu0rmd352eiVZzPTaeV6dV1zCt7n4f5Rt29lkzMxK17u4MPYD1pDfWreamcRSvOKQEsDYwhHogMccO9pRVpmnL/QW6ew19HZDoOtf6w6/zrOVGWWzXIj2yPQrlL9bilK+pzafDh5qxF21WOcDlUzsZ+0lzqOzV6HrNmNbdw/sfwfqzHLU3FOesy9+o8mPzWa133z2pXTvu0xe/uIS1uSnOzei7376tpnpsZjfnaudTAHolPM/yjGUAOA8YmiUf0tIUAvibol186u0NaGQcRUvWwmlpPlTD0lARxCo9MUqtXT5VXR9SK/Ux3BuhCfp3AHg5Oy9Cjz+arXvW8kw759LF8wbOAUiyLtz1eK4e3o5qeZYJbkyrpdWt7/8tkGN1lJYtjNGzT0UDS4b0/R+gOs89rFqVrV/4lXgEPDB2txBDpdTeDurHPx7X+cvDCCMkYeeZLLNqPXk6oNfemiafU6XHdhRTL7kqzENRDHjNDpi5APYttk9NR6HcNq2qG4NYoc6po9Or4ZE5TXFQIiE+rOUrEnT/1myVlMSy5qAurs6ojrbsHpxkLTuFVegcys1RKAnm6EmgxTI++8iJMT3/Mu9BeXHH1jTKn8rBPmA/2mQGGOytt/v00svNrLELUI/jd2ivE8f6gT7blMcz6KefQo3sASBG5jgfB0Q6uwJ647UGnT4xgepeIVBWKsBlJPUV0J/+2S119SXrXtT2PvWMFyiVMkIVskxw9lPneMbp7gzoKmPrNoprnT1+yjfNs8gUB/xxYlqaSR9FtTgjikPNHG6+NUZf9nHvgFvQknMA29kZISxCM7X1/iyV5Ufp0qWA/uKvT6KI1qKHH12DNeoiZWFvbLDmNAqS50/O6a//+kco98Wz97JJxfnpOnQwoLNXBnnfpHY/XeiAc6nAYYRFbExR1Ntr8OVpbbo7TV94drHKi1Ct5xn79//4mq5W1+ruTcX6zM+tBKiLc2x/bd3B6tE502Hrq+7uGcDbEV27OQuw6NHwmBsnAsDz0DBAWhxtmKfFCxJwLQgDsg6prnoWIDFCY+NzlHGUnPWsliyP1/adJSqpiGNeC6FiR7+7VKc1q1KJNeWAhjHOuDIlx3eB5vbu87OWb9OzT+cAziViOTukH79xHTAxTU8+Xqktm++Ac8w4Ymmr/W9M4mg1xJbAmL7yaznavDVDvR0hHdjv1/dffBswD3Du51fp7nVxjmr1yOgkinNvar+Bczzz7cGqddNG1rOsE+2MAuGLeYX1JTXB6oR/GjZn3/x4r3lw7uPV2/xvzdfAfA3M18B8DfzUGrjzIPZT3/Iz9EOD4/bt26dvfOMbGiKJ8uu//uuOUoPBcWaVZJaXJ06ccGyQTA3i0UcfdWyTDCb5MMFl1WFJKbNNMsWH73znO46dktkSmiLE/OuTWwOT7Le9/UFA/+mP5tQ77CyF/05lfJzRZvu8Ochnb98QoS9+HtnvxUFOXl13bDDb2rq19aH/VU292zQx69Kf/w4nrfJIMbDpcPlGUM+9EOIU1QwKI+/o1Ve+g/XXCv3mb/6miovZvJinNv5O2/ws/+Oj4Fw3ah4Wy+zLVG/S09MdlZqcnBwtWbKEk0NbeBBc5diy/nPq5F8DnLO429zc7AAGdZzwtj78YT/+8E97j33Zy+L6rl27HJtiDxvVn8TX9PS0AxpaW5vV7r9Hxcl/q+DcR/vT9fqg3nonhDpTSA0dQTUPcXpwFqVRyxVad2SD665yt556MFKLF7uwb2DTn7huzoqOoMpHLzb/9/kamK+Bn7kaMEtWewaxucrm34/OWRMTEzpw4IB+8IMfOMkKg+Q+85nPsKn5rl544QVOxyfrs5/9rGORbod9DArvRFnMwDk7EGQK2vZ+U4U1UPzDOdA+w0Cljps39T0ODfVg1VqZnqOtqzdqwY6disZHJowFlivA5jsqTX1HDqnm1AUTz9HynY8paSVJLSy4QkMkkttRI2judaz/xtubASMSlL0Z29L1d2v65m0N738PaGRGadvuU8IDqBhlGtyTAgznIVlyZ1M0ms3rUEO9et9BYe3iORWuXKzKp54mOQXUAjhnKmi2NHWcC9lRDU1igdXZqPrXX1dse5vSl61RzGNPK6KYRCnwgeUPXYBz7iAby74Jdb72qmaoj3QswGI51RxFfUSgqBeGZg71dKv/8GENnT7JhnykcnZsV+zGdQRolEK6UQjhGPtUT7/6GttIxrQAbCWr8t5NykPJ2VThrr6zn4R4rBY+sANg8H5YJ1R1YqNwjGWT15LMJMDCJCCmaurVdOAwinNVqigqU/I2rFoB8FzxBhfZtjATgt03UJADzt28pbl9e1XDZ+Y8sE3ZWwA9soA4UAayBHuYnfUQiXzXDMn3wXNYiqGCQ2I+GeWxGNRLDBBzMb+EUR0JAHsNtDSTwEbdaDHQWhAA7CYKe6jxeQtJwpbtkj+mGLQImxYUYLzcStSMJdIHuPcq4JdLGh7tQ/kGhZ6sciWmkLAHejJIyxIYYRdqMaOXNVG7TxGos3izlyvSrFrNus0NGGg/J0nrb9jrWAlGl2+Qt2wHJ8spxAB95NZFqimAqgngHPZzZDCxBUYFbKaD+j+jWZLQIwOtiMnMYk9XofjSR4AV19MhUJky1a8A9pUdWAJ3nMCqNV5Jyx8H8CsjcX9es23nFMdGf1TZ/YBza7lX6oX51zKRYQ9JXNcIUBkKEah4jfe2wB54UWTgM/JR80HFxIflrYu+GkES0IV93RQA3Ew7ij8oani5pjvDku2MLSAKvEYB644Bb9TKS8I/smIL0E4lEEGdQvXHNYEVXTQwTnQx95JaRFsy0QfNEu2YfM2Ac/3dACZYnZatUwxwYcidy33SjwEI/V0kmjs6gAeB+gpRxo8lcYd6Vm/PsGJzVwN5bcaSlES62ebSOSzJjOwF1ze1KYAagJ2RgV55XawxcgE1UFgJo0hgMIIrSCLRjvhPXgdoOayuNqxaaefUctRwUgAmXBm0D315AiWw+nc0M469Ila5Bki6sYwNoQ7WdRu7u7EhZednAVWtp0pQ6jPlHnWh1HVVI/VnNINCT3xsBHWLAhEqO+EYbELpG/J1oTh3lThyTjGAdHGMDzfAUh8KQrMoQqYz5mKLsCJNWo69YBoABs/RrlkHFHMhHRIeH9BUdxV7LthXMjbisrDNA7iKRAEqhPKiC9U+0ky8r1uDAH6T/ShfpKHKgjWqsKkNCUgT8Cs41qaR5mu0U51SKwuxoF6tOU8F4BxWrdd+rCggzwQSf54ywLkkg7miaXeAMRSUTK3Mjw3mDPCJJecTUXqLLNhEHQPYhQBUAIqCg00abLqlCJQekytRJCxMAwhtBZwbBpwrUGr+MoTTioD10oFYAaiDpIoYjy4sTMNznZrh9/s7aunvPmzQ8rAPtD5aBiACVEY5TWXHNYcCZdtJ4Md24lKCMiuwBkZVMOwxaAF4A8W5aVT3htqAAmKS6GuAc5k52ILNABTx/Z7LysgAPCwEmozn/kPAmxZzh4DaGGODnUdJpKICVmqKbI8QKMoBJoDWSBQHuj8AtHgbKGaWsYGlZ8b9ADhWvkNKS+YQFgCFK/kB6g1wjjBnY5DOQz8lzqCQ5B9q1QR2xHYAKim7AFCljPhInwZkC9GvA4BzfoAPA+cmrr+PFWsUKm+MpfTN1EES8BxQB8p+0x2nNQN4kkAiN3rR43wc4NxYuwJN+7H2a4T/ASAuZ2wmA1rQn6gcgLFzWFy+jtodUCIKX3H59wJaAF2h6uQChDPL4cDQJQCkK3DQKNHkAUxh6RZEcW6IcRmBtWdi/nZgVOz+ABLDgHPOi6ynyxT7gI79A8AsWF3PASfEZaJuQz91GYjjpXwGV5JldfkasEY9pr6GS0AgKdgLooJFXw5H5dIWALx8XrDtGOAJcT2pWImL7kOlEHpo5pZ6q6+ifjeFSk8FYxfoCiU0OiPlGwA2qgVePa9JrIKjx0eA8VIUyTj15AL/JhUphDVy2OZ7wKapqgOEDqwGUVyMLSrFwhcgrekUEFgs9ukPw79sZp4CxITDdIgHVMscq+QAfYQxMtbPZ3EiNAZgKIv5KBJ1RgcC5qEmgJKaW0O04QcKY+1tykbeSq6Xu4nr0xa83ABGwdajiIKhROqLgAvbiXvxBuYa4L9u1PhaGol/+UoH7I3CFhxqizISE+ZQnOs9qJ7mGk2jhpfBXJGEAqsb1UfwHN6DkhRjfLgFWGakS5mFSYrJo9zDXeppAr5m/kwjzsRkLlEwOgcuEFgG+CDCsRBGiQ0oc26kRaNdzBfjwyjRpmN3C4iWvAhlykziNYcNUH2MCBn4d129dTdo66AySlAjBESGjqN0QPWzxPPuy4CO1QwDgJLCBViolzC/hrBCRgHt9lkh+giQtlruHOZ9szam34exAre+P9xwXjFAGzEFS+SpeAR+fAmfSx9lPRMeqcYSfT9/1ktYV3oqtwPOjWu46YziIieB+IgZqPsFoy2us05h1rd44mKtFJoaRaUMO1jimJ81WSyqNonAYJ6UIoYIBwJQ4AN1Z12IjWk/kHLV+4oOA24XUwf5WxgrzPeMazus4MOKeML5zAl5UQZ0Z61jnesB5kLlDKvslKR4xk8x4wY1UOoojLWhr7cTWKwH0DJBMUAdfaOdikEdKbkAcM7ihpURpTdIeubgKxptu4GaoRdlvnJNAEoOsE6LZN2XUQSQCvyINChoNSqNEfQNOqvZ0Lp8QwgWt6MsexslPlR4Y+PgUysoK7EgkjYI0E9svenu1/QoMGr7dUCxTmUWA8Zi/+qKWkIBWXPQxoH+Bg0Arvw/7L0HlJzXeab5Va7qruqcc0Cju5EDkQgQAIlEkAQJJlAWqSwq2ZLHnj17LHvstbJG2hE11ihaoiiJpAiKOYMIRAaRM7rRsTrnVNWV0z7fD2EOV2e0tkRpx5S6eBoEqqv+cMN373+/577v5GS/VMzFkrQMW/Z4Jhak3TKOJbgLlcBsrErtnjL6NvcYAd4FOIwMdGBBjh0xCoNu1NjsFXczXUMxVL0LmWSnUO+NdO6lLFChRiktCztmUyos0dMv0TdCYqVPmVTBjz5gAJMUuonYThDn+pnP+FDKw4bUh8JthgsVRYAyi6proniYQvUP2WbGxEmJDmPj2glMHAGsAMJUJUmzvZIy5DqAdhL8PkbMjAB6u6pRCVZIE1vdyKBadp9jHcNJDGsApOb+mAcyASB2ob7awzg4MSmxdJ0jLQK4u5H5tvZBNvf8Wv1yGqWsiYFuFOeAt+dV0QRQRWzHgtxnkVJsYdMBGU3ED4VRdSMA0QjAk3EoSt+ZvIgyI+PEyAh2mm7mPw206UbKo8BQbYQBBQmdQqCVjQVnDgPzmYCpUFYtayDmc60O5l3EqiQA4Ah9NBzFlrp4Du2Y9shmFD+bciZoixb6fj55HmsO8cUGHDjNXAaIeLIPxc7S+eKYs529AfXctoU5Uwr1r7i0dExJU9ugdHTyXJKbCVS0SDaszQQ2YfvCJG4EfK6zKyAXm0cNe1UFSFcvK5fbt9ajfJUr+7Ds/Pnju4D5I8Bsq1GDK5HsLAVGhI2YCXkWNa+XX3lL5s8tlO13LDTO8eTOUbl4pUMWLcqSu7FqrW8AyHKhGsci0+kzKXl8Z0TOnzsr92yplPvvKAHkMqFkPS3f+h+MI2wK2rJlGTaJFcB+zDcszPN0cYo5pAJmytpPct09WC56u8PS3jFArmGU50wL68Q2rrEaJbgMwyLUj/pXN+tc3WwE8HqHpfnqgPT2x2XV8nly953F2L1a5fJlwLmv7uHaKuWBu+rJpzmAz8xAyiJtwG5vvN4uB/b2AxbNl3vuwKoVgO3Zl5rl+Ll+Wbi4UbbdUcH92Q3FKqZGKOrFAcua5c29h2X71lXy8YfnSk6xWXq6A/LD75yXtqs5snVDpdx5r0NKqpj/8BCpz9lmHfOB51RIeZK8TF9vCqgsinXtuFy81E/9tUhRgUe237kSe9N8cTE0jY0msAkH1OoIo443hn3qGIDZlKxbWSD33FUrBdlONq7FgcpagDHH5K47K+W22wEfi9QSG+CpwySP/2xULl02y7zZdnnfvRkoLKdQBBySo6d83Fe2fOyzgGa1qDIzfSAsy6XzMWxPh+XUBZ+sWJwvn/x4FhaoFjl0DNjsBZTfiT93UiZbtxajrAaEzkYmtrJQdwWdsrEAAEAASURBVADRPP8pmI7oq/h9OGwN0P6A0bzeUWnvHEUJbRDV3rjcsqEOJb9KKQSQ84/zOTb7dg35pb1nSM5dGKLOp6W8tFQ+8YFaWb3SLcdP+OVnv1Rwbkru2JQrd9+ehxIc7QUQTh2VXnxpQp7ciRI8m4d23J1nKLodOjwiv/pVLzAbbfDuTMoFqExBu6gZQC4lv3rai1VrmHIpArbLoK4tXGdCvvXfz0vvoEduWlUoO3a4jbJhHx79AbCX0UiX9TW35fMn+Bxtr5s69I7RzgakrW2UZ/o8uePW2bJhY6bk0O59vgTKdLTTrii/H5FLl/rokz1SPysPW+R5snFdkZw7r/a4B2QIcPbW21bIndsbUN9DcQ5gz09bOfZWVH74ry8gKOCRe+5dBeSZIbt3R+TA0V5irV/uurdWli5HcIB+q2sWFy7H5RdPR+X1vUdl05o8+dgHG6Wm1I6aG1at37iAkEarrLipWu7fAVBXlSYu5rEm5v6xGP2Rfm/hvMbjGZsI+rn2HgDanp5p+tc4/ZuNOMT+dTfdQDsAmq25pt44wGc6OoLc5ySqi9f6osXml/t2LJe1N1eitmqSp3cOyckzV4HisuSe+2YDzqUbCog8wqDiF5OXX4ui5tkNyHgNnDt0xC9PPQvsDAx+351VsnEzG2xyLTxz0zcol+eempYXX55ic1RYPvfpPERoMrFqTcnu1+Py0ydeZI27SB54cLEsW8bYD1Soapdq1frqy89LTWUZinN3yeo16wDneIZV9VQNevQDVTPVFRKF5mbAOcpk5jVTAjMlMFMCMyXwH6kEdLT683ppQkmtkhSe2717N/LKmxnk5xjJp+bmZgOyKCkpMeCk9vZ2ue222+RTn/oUEtskkDSjw0tVI3784x8b6hCqTKfqdao4N/P68y4BNoXwMByXf/ivUTnPA8K77V0KVFSy0/mBzVZ56IPITFeItGLnpJDmsWMnZNWaj8pY6C/kpQMm2UT+75GvmCU/m3VVnof3H03J177NNZCr+7sPT0pP59Pyy18+xcPrHfKZz3zGAKaut+c/71r70797Vbr56U9/aihiaoJeFeVKeTiuqqoyILmVK1dKXR0Lin9AZbY/BjinSj1vv/22fPOb35SjR9ldSjxWGCHEjjr9sdtJrgDL6WKJvtRqVi2K3/e+9wEosYD2Z/bSsjl16pQxtml9q82fApLvtdd7AZy7XqZhdkXuPZaQ4wdQGm1OybGuBHYfrDOzWqi5bVL4UogK3Y5brLJ0iUWW3WCWEnYFp6Moqmznux0zrl/HzP9nSmCmBP5jlcB1qFvHMR27FH7T9/TfCrS/gOqYbt5RRbn777/fgOB+9atfGaC4KsC+//3vZ/PDfGOc0zFON//od1566SVj3NPfb9y40bB/1divL7UfS7A66r14QX74X/4vmUL5pCEjV9ZWsut40yZxrVGFEBJFkQDQz5i0vfSaeC+3cw3VMvvOe7D/KmaRnHkkoBmEAFAA6letKAu9+IL0Dg9K5tIlUrYZ26ehEfG/+rr0kejOXbFYim5bj7oZClbYrEWBkdA3Me7ZFSXh19cpQ7tfkzMH3kIhLFMW37NDPPPWQLqTyGTSa8RATY5rTgdFk+RQj3Q897RY2lskr2GBpGExawW4iaPakQBAMJP8tJJoM3FsHwoNYcrQzCTYybOdcyvXlpfB/aF019Em/bv2i+/sRRLplZKtv1uCeoydwMtCrRmozRzEQqy9W9r27pWuq5elZsFCqVl7E9mISbnE/Q0MjEg9qncVN99Orhbox6MQBPMQc5Qj8ACALVest0/63zoofQcPShlqd7nrN4pr01YYQhL3nCnBvenTgQFPhkhMX7gsERaUr3Z0oDa3UQpv2QhsVEIOkZ3q3FsKwM4MdGQJewGvUBLyXpRxksfpVfOBThrZHc9nWQBPkARPtOzBxrRLnHUow8y/mU3cAzJxEatDknaucpSjqu+WoB3VKuNKolh1YS3IQnhyyoutGkqDgXOGhZW9YLnYckgeO4FwgCi0fEzUTQr1rxSqYtHWXSiBdAgUF5ara0l21/ARHjZQywmjAhVsexUVA7945gJ7lGOnyndtIy0yce44yV4s4fKrxFmDMgrtLA4IYwFES3SdAJIkkRxBdc2G1V5GPmpoN6MoA2gB3GCACCSko+0AO90HgYWyJW3xDlRIaiXYcUgi3Yf4rIJzmySRTXvCxtSi8hZABmZbv0RDWK2RZJ4GNHBYsWDMxtLLw7GB/lIowsWtbCihv6idXSrI5wE+Qt6DRjLDWYutDMpcnAw1GcAhwBhf80EUY7pIsgNGNawF1kOxbgTLtqbDAAkjgHMLJY0ktDmXBC6AjaD+l+jbj/LLWyhRjUvUxf2hFpUN2JOwVXJ/SbEGWmAFVFWwm8bPxpa6WoFzAV7oINk3hYXcUskGVHTmlFHlNqMWbQpJRbBsQy0wNoqyCPWSAqZ056EGWLDAAJLiFmAjFPrMJGnI7NGvWoCDDkl323kD2smtWYCV62z6ErAaYGAKVa5R7C4D4SQ2ryskkwS+BcIj6msX74UTKLWMSGlutngqAecy1tJGyDYmAIbGj8hg61GE4QaZ01hQ5pkjjqoNqHnN4WpROPEBMnhPATydJQGrNqANJM1nAc6hzDM+ggIaVq3YmEr2IonY0kmk0D5J4FvjY2JC9SeObWUQhSuTg/4NcGXNx4rZpVCUh3hAGpF+aFX4xQ+g19ssU0BHHhRZXPQVc9ESBIZQuor6KKcuwLrzgIFeyavHIg/L2YgdwATFubGTT4od+0UPClq2erXgXUpcSieRH5ToAMBh527UaFq5NjQMUadJwybWjpqQKRNYM0k8ALoMoXQ10nlF0rjn7DkAeJUF9Mte6e/EbjctDzhjLuBVBbBeNglPoB1srKxJwFtAm8g4anZAXxGUwDxZpVgkAQq4rkGNdE7iDfEK9U6oBZQIT1B2V8WPilzJrDrgl/mQBMQDFLdSUx2oIp4GrOuTLBQss2uBDvJLSSLHaV8XZbD7KGCcRYoqAJ5ybuQ71D3QZAKIw49K2PToaXG4EsCN84EpgGPTZtFwaIyUX7B7N/3tTY6VlPS6zdyLgnMKJe2RrAwLaldAunk3Ey+Ai+lPBrSTRJ0L+DdM3wmhsJQMTosHaMJaQKzycGytb/pfnL4Zpd7jJKAFRazQhV2SFlbVrcXEktWMKdkcNySBQB+KkMQM2lKO2wk4t426Wkx86gZGeRk7xyswdiUoCq4HbgWusRfRtrEbHqN/tj0r02rpSntwF60EMlKLwWpiGP000gGoehi446JEUrmSgyKOO8NGm7koI709gD7YApdsYVyZTb+2MjZSdLRStTFNTQH2jgAAAyBHAF/s+bMMO1C7mxiTAv6lnSaoP43rNsAQGX5bxtrPMkYDG2Ir6ywjJqaX0Y5IKKK0FGvbjzJfNwAbynJzFJwDhok3S+/lE6j7jUoBcbEApUFrEZCPPQdYBTW1wHnUFo9xn73iAUAGj4YZrAd+xb5RQTFXMW0ZtbSeoxI+/7LEmEtYG7FjZjwLAIskOg8T89wolm6WBKCiiZhgI2aYNMtq9tPuBrGgRc1xgHNEB3EUR7kqd7nYaf8GnKiKaBwzRMznTzEPYKGIAqDfT/yqQmkR+0mTk3ahpQawncBaNNx0jLpPk7S52KtXEEvNXbArBwAX6OtY5hWWLUYxrh5gBiVW2n5yHEXNngPAn52KkEpOSRlqnKjSqWKhtrnICMqdzTLedhkVzEnJb0C5sxxIFXCuHyAkgdJjQcXia+Ac4G4cmBETO0C4Edo3wA6Q9/RQi4QBRNwAla5CwF/gspSdY6hFtsKrTFQs9IUEfXXcC5hDbCwAzsupXEwzAlaOE+CC2IT2YZ/e0so4XSjZNUA4tSXAbFFUhIGxzh0WDOqlsBqVrNJbuL9y6nfSqPtI1xnUvoBnUe6yAm+mNQIgu3GMIKpYIozDfD/W9DrgXDu/rxLLnHWAcyhmdhyXNGsQeJWYV7BBkmn19D8b8T9K3QHmoqQXHW4BvGxm3WQY0EjHWYA3bHaTCs3pWM+fjIbMqbDlxMpysvk1IGAvoHI5oDxgo473LLMk/MPEWMA5L+o0WEa6qjlOARByEE7eyzgx7ZXSynrmB4CjNsBc2kMCqHqyG1XVkU7JK8LOEuhgYgLYmL6XVYbqYsFGYhxlFx9iXkK8aLsAp97KnCEbx+9FgHNZMkgstcb9UlRdTf8F1AKES6C8q2pPIA8AfxwPCM7X65XQlA9I2yVpKLrZGaPFwYYOIYYo5GxnJmYdAai/JGPdZw2l1OycfPo3EKZ7OWUP/IiqW7jvPPESFdbYmFTMB9YrmyPxsBu4uU+Cg63YdDolE6tlBjuGcYA4QK5IH/aiqMdaKGOFIax5KL+Wb6MdUS8OhVyZhU2gzgpIP4XSm734BsmcvYX7Dkr4DKqn1Lu9Biv0ihsl6Swz5jNqb6zKtaYIdTh5VaaBX8PY+toYRzLy1sB80Qexm+cP+ir1p+AcgHpsnDjqfZ3NDL2oZzJOADabNeayuSDJnDXRd1AirS8S/ymrOoCGSqBZwLfgwH6gw6tscEDBuRqo0VlBnwW8BEaNom6c7D5O+Uwxdrppg7WSrhAvFuQJ+qBZ1S+Za403YWs+Osl4Wih5Cyn/1LgMdqJUOWnBFnGtZBYDubnctDjGNVX8ZZ4CMYcKIBa9o1jbj02jZF0OvEofZL5oSqd/sxFBN+QkGIMVnLMGO7BYPwyQDcwBgOmsaMTlEIjciX0vqnfxkWPS03KesTZd8oHWM0qLiRH0lS7a9hSW8YzFeWW0U4+2jTSj/8WZp00O9DJvAFadtVGS7kpJI7aZmZfFUV71BUyGwtSTO9koA/izalkFVpZuqaoF1qJvEAINpax+wJYXX+6RI0euoEjnkDu3zpGVqwpQrYqjILcPcC4o996zSrZsRL02GzCSKxgGXnv++TF56bUTKM4VoDhXD9DkNmC6A8eapQS4Zzsw2rJl2eL2EGWZ5+7aHZXHn2XzUNc5+eDds1FxKwWcM8slwLlvPnIYRbKY3Lp5CTBTieQBFlmATE3MO1RNNMb6VAjFsRT0j6Hyy7OQifs8/BY2ji+GUL66LBturpDb7iiUymo2lzBGxwFXU0lgGgCq48cm5Ef/Sj9gnqGWnavXWIGVIvLFL6lFd4XsQLXqjjsA50p4SmI+0A6Y9+aubmxf+6UK0PieO3KAxJKya1+H7Np/SfJLUcy7Y64sWkx8QL0uFEzJvn0ReWLnOTl9/qy8/6418slPN1KX2H92+eQHj5yR9tZc2bqxRu68x4XiHM/YAKzGc3CS588oSnfcp25c4kGUsRrrUO750LEgz89vSx+qvbdvXiO3YrmaWQZAbYmIjfhgAbScnEjKwWNqZ9mEsq1Pdmyfj7tPpjThOPHzncwRgL/vpi62bC01LHDjPN91tifkCSC4S5ejMhcb0Qd3YDfOXGvnM5Pyyi5UnjMT8ld/u1DmLUBJl2avininToTlRz+5ikNRQjavqZBPfTyXDXFmFOfUwjYkHT1nOE+13H5bMW0BpUGeO1HW4kdbDNXI2mM4oo4XuqZw7T4jAFsjg0l57PFWOX/xqtTNLpCPfGieNNTybMR3VHBTAfcp6vDIkbC8+EI/sSgiD6A4txVQ7nJTCBBsEihyVO7aUmAo0ZWiGpgC8ArT5l58eVp+9mQXzi558v57cuSGG6wAhT559DGUHAN5gIzp8qEPOZk/mekvqBACB+4EnDt30SzL5uajcJYmi5aZqMOEfPNbZ6W73ylrVhWz1pEjtdUAbNh30jr5oQ4BrIKMe3GAdTPAWYqen6B+rl5h0/Kz3SjRBWUJwNb2e7OkfiHPfA7mK7pmwWahaVTbzp4CBttJvGWuvGl9ozz0QJ00NSXlOz86JKOA27duXS7btteiVo/6LIUzPqTgXEwe+9k+gMhMRFbmSyNKbceOh+XVXVg7o0y8cUudrKdfVNIfk5TH4aMReWxnUI6fPS/bN+bLxz9cJ5UldtRvU/Kl/3pBzje3y6qbZ8mOB+qxKLajNgiAHAfbZa6gfVGh5Bj1GNf64xoMrpXb6OxIsA7UJMeOXsReeIHce3edLFxikzQ2X6cojxTrPdGwQ1oA9555tl9Onj4iN66uxc55Hht1HPLsMwNy7OQV8j05cs/99TKnwWPAgRH61a49Me6H5/bpbiyQy2XlDVly9kJUnvzVIJvoxlEazJc77iyQ4nKe/+g7nZ0J+dmjA7LnYEDK89PlP382G3AOZTva2ZuvAc49CTg3r1De9+BSWXIDVq1AkxPA46++rODcc4BzpbJ9+zZZvXo95aybOIwJNHGH6iKe6yZABi9+rrVr/vI7v2YU537nIpv5wkwJzJTATAnMlMC/XQK//8D0bx/7P/YnpqamDJBEFeZUBUIt/T760Y8akIUu9E1MTMi//Mu/8NDwIpPBG7C5/D//J3Rw4sQJefjhh43ElqrWqULTzGumBHS6pzt9vvdoXL71REwQVvi9X+QEpZHdWR/YapEHPmCTUgA6TZSqldcj3/625KMSkF98txw5nY4EdkJunG+Whz9IIoWLCEdEziL7/dIu5Lv5++aVVmSsx5i0PiPDwyPyuc99Tj7ykY8YCdbf+wJnvvieKQFdPDh06JChtqlw2aJFi4yYVlHBIpgmYf4Irz8GOKdxeWhoyIDnrivnqWLBvn375K233jLisILQCszpS2E5tbCrr0cxhZfGeYUV9J7fCY3qcbWM9D390X+/87PGl3/9fX1fE97vPIZ+Xn+uf19BCIUV9d+q8Kbn/F+9rn9Of6+f08+/86XXpMfV3+vv9Jj6+s1zq6Kgvn7zXPr+L3/5S/na175mqAn+8z//s2HVrJ+9fr+/eW2/ec7f/LceU6/lndegn7l+bb95DXqud/t6L4Fz77xXLzvw3jyYkLbTSTmLhH/rKAtUgRTWDyQc4EKyWFDYsswiS4HnVvJTTbxHWIpdfgpKvPNIM3+fKYGZEnivl8A7xwn9u770/7oRR9WrFZq7gjLJ2rVr5S525BYUFMhTTz1lqGAvXbqUXdg7DAt1tWLVl46xqqD93HPPGWPEQw89JLfeeiuJBpLz74DzVHGu9cJF+f4Xv4SKy4DUQ6zMB+Spm9MgOcsXiqMClSks+oI9/dJ08BgL3AmpXbhSSpfdSOIH9aaIT+zpJGvZfaxbvmOAGCN7d8sw6ibZy2+Qkg0bsd+KSvDwEbl47DDiOR6pvIHk1Gxsv7JVzcUmQeQMYmwfzwFsUktY/5kTcunAfvED4DQsvEEK5qwQW3YpyQ5sUTWBw/zXnuNB4cWJOsqEdDy9U+JY02ZhM5t1yyYUMYCU8O5KZpPodaH+wjK2OY5VyduonLz0uoy0AkfV1kj+TasBBkjYksmdbmmS3nOotIyHpHzxEvEsmi3RHDtQM2ANiTi7AmKaROrul55jR9nt3SlVzB/K16Mux2c6Dh6SppOnpAgYrmbJKnHPxv4SJZoEi/sxtZWyo5UBHKfJDD+gYscbrwH7TGDl2CD5azeJk/pMMXaGsIkVB4mPonyS6Sz+e3tk+uVXpfnieSmY3SjFK1SZpAoojaQfanmSDmhhj5NIHabSm0iAXpDBMdTHACxzikkGAxMkkTZNDDZJCqWhqVF+Vw8IMnczIMCADFx4EeW5SZKlK8ReuQ23RQXndGd3TJya5O3tJ8l7hbo4JQ7bsLjzC0lQLgLcq2Gs5/wWXdhmfpJGktXBv7F0SQAD+AFTOALKdHXUFQlHm4fkgR+FEBLQPcfBCOOStYjF8YotgHPMhcZaZfT0UdS0pri1omtqYygVJO2oeU1hqdbdBECHwocbCyyS8ONwM05PlWSSiLWnFzJoOiQOEBXvOQRAyO573nMteZDPNwDQ7Of7ByS9KA9wboPEsgDWUMmzGuAcanNRlNhIMo/2o5KCBWReIUphwCRmWwNr9EXcH3bC1L8JeMeE4pVh1TdyQsLdh1FwGEaFAqvMUtRcsCpLotoSGu6Sqc7LYg9PSEZ5jTjqb0FZ7ho4F20G6EDZwJZdIp7SRlRMUKx3Uzao/oVQ+vL3NdHxaaucz0GiMRe1M1VdIxhI0ueVYO8lmeQ5zcRu/Pz62YBzKDYCz3h7xjjeYsmpBaDBqlUhLc2umEmkp6YHJDJ0miT+WyQiJsSVWYSTGXbJ7mrqhT6i9ajKhGYFmYCvTCiqTAMDdJxHOQJ1v5w8yVYoQtWo1A51qkn6AB/CEdScylYDDy3j6w5AiVbpazrDefolz4V1G7CdNWcxIBvQFYBBCDBxHJU3UwLQU5P2Gdi5AnA4sqpoi6pmNki7ASbByjWNJHJaJUlpVy0AwriE/aOSl6/gHKAa6nARrNuoCO7PR2K8k3o/Sd+8APhBPMrHfhL40owKWwq1PhOWrUlVlwGCNZuYD6M+mZzAYpr7M0dGUfIqEltxHSo73B8qzFGgX7WBjKJKpGWcUbcMOIG2EJqQkVNPihm1JHdmNgDBSkAAlMWoq2QwgFJhCyBJq7gtwHkoRfkDWNm6UE1CjceWB7SGElUiMALw4gWyQnUP4ihvHv2jtIT7HkLFZIgNSnnYEC8Ue2EVkBgTPpKC5lQchbhx4CzUg/qPUTf9QJ2onwAKWAGpuFliNNCaJZtOgZqPQ59hRgBLrqKChiLUQAdll4WSHXAcbSMJRJyY6IBTbEY9ZVxyi7CHnQV0o/AP1nyTPReB+A6J0xGRwlKALW0rduoQW8H4YB/QHBaYYdoIan82YrinfIE4Mio4Z4YkSDyG+k8AEZ4i7gJHNgDO5d0kweFeLHf3SybtNQs4ypK37ho4x/qECTgASSMUoLBNpH3HfSgfEUvcWC2a0mdRlsRGyk7B6QRKm6qgpypgFj+KTVhPhvs7xJWRBzw3Gxgum3EpLr4p1AeBBs0jKJ5kZYhz7m1AOyioTvcAgbwqY95LBqCSiQqWM78W+BC1PuJBFCvqMeJTCvAmBgir5ZtX3Mj9ldM/qOdQD9DVWRlFichkKwICXEJ/cvJeE/ED8CQDJc7yW1CY5Lpp45C25PhocyEAkoGr9HGAsul2cWalwTASM2j7JjP1l9D6AxChj2lbtQCmyEQbbfQiMPG4uBnHHCiNmt3EROJqEvuwEDauYcZZeylWqvVY4qJsJPFL0g9oNuLtl2z6iI4BThT7zOlAmABjwQmsNVG7S1c4mGfgaT8QMnBmJv3bVVjP+VG9CwOwD16SYCvqoKjsOetRfqquB5w7LxHvEdpCuqQ3AiDnYL2I2qdNgQNAQAIR7QoYpu8EYqlXSIjHsEaspQwXUgb0RcASE/WYsnsY77OwQwRIn2xmjCMm9qOqRYLdXc7nPaq0SF2j8Bj1MlZQVykA6qwFqM9Wrmds8nItBwAxm2iHVpQoKzlPpViB/tXmPMxYF8LmN0KZRwEVnMx5PIxDjmzgOo13qoQ25AXq6qJMopKvfRB4NTbRL31tA8SLQtTKllEeQFdaHgpdAfQIao8KIKvddGrSawBJLtqGOZM6dHBcxpSUKvvxd43PJtYDkiSXA1h2D1LvHk8QiK8WBrTRGK+S/i76CTag/UMo/5UCrxILqwDnAH/HgaGHzx0RN+WaX1wlDkBrczr9wIDxWrn/TorcLxFVF6PtZ89irkZbYsJDLAIgGAHwJCZaglhYljAPmLuFGDolE63YYAPOZQG8mgtvYRyn7ZtZ51FwDig9PnRRwiiFTfuwUvegRA90aM2qZJxQa09iLp9Vq16TiQ0VCRRWAYHDvYeBQS8ZcceFFa09V8crIFecVyYH2lH2BILLQgGU2G0pXMl1m2WS8SOCCm5ucSkxBCjbDqwGABujv04MtmNhOQJAnAPwBYQ0OozaFZaCfC4DONzqZC4GPJWYwqrV28LYTVsvLmRDwA3ii2Pt3d3NiB2QglpgLaxJxVplwKgmEyBjahBgqxX1xGbG2h7ghDSU7YCg+REHcCn3mEQhVVVszcyFTXY/RQMs3I91cf9F6jQKaNwInL6EMmB+FkTZcoA2PwrcbwlKWWODuFCpjXMdYz1eLHcvSlYa9vOVQOp2zgFYFuP8qnQXwdLdqSAfUd5sL6b+lhD3FYTOBJYFQBtHEbEPkDwAGIwiXQaqgQpoB848wYwrIM5ayrN8FfGinHkmStM6VkSHaZtNMq12zFh5O5xYnheylodKKxKpnEkhNKBAYFCTnXJ0AHVMt3EPBwCyWphzuhkPZjFPr6QscmApud5+1DUZdxTmczesZ1MI4BzgXmDgoPhGrsAPAv4ApjqcRYS6EOMnYGsPts46FqYp6My8HakuhRMdhcSiNNbgsA2MA/FPdHZKYIocS0GF5CysI04wxnrbZHTcJEWVqyWrpJF5FZ/XAVUfiyJx5nmdMgY4G8TqGgNc6gPoMF1BZeqNuXrKyg/zTY0zZlRpdYwPtDIv6W8HtgmzwYDxvohzOanrwAD98yLzmU7mJLlSCjDrLi8nHvsYi1p5jsKGNt1DHwSuddP/UPuMA0NGsZidmPSLvWyZTHmWS8849oaMv9nELivnx/kU29SIvL4LNeDhuCxZUo1iGXETy1H4D8lmvmSHoBscSsib+3qwCW2TssJ01K7qWZfMYXN7Qh57Agtr7HPvufdGQCXs1vMV9EFxrg8Y6PlxYK1j2DJmo3I2h/9nyb79E/LKm7RTxs8Vy2fLjSsZ13N5vgoCrewPyct7mUdR15/8C4XRAORyzXKlJSDf/O/M1/0B2bp5oey4H8XbIuIBzy0WoKso3+0fiKBaNUKfsKOYlyl25hcEczlxNCx79wexZPXK2pvKZcFilKeAvqysV7qcNpqWleenpJw5NQgU1gbQVSYP3FcmNyx3yNWrQcA51ETjlbJjWx0Kcqo4B8BGFbd3hWX3Lq/s3dOD/eUClMxyeLY1ycmzg/Lia5dkYMKOJWo995hD7LfL+FhM3jrsl917T4uXzRMf2bEBcK6edmzBOnZCfvzdM+Jty5FNN3Oeu9OkvJZ1UwXnePYNh6wyiEVo/wDjF6BfZqbaVKLOxoWcOB2WfW+dlonxUbkFJa8FS/OZk9EHCH3ZacD4tD3fVBIVvJDs3t8medl+wLl5UleTheUr4NyTp7AsBSq7cwHP3swjCgG8aMMdrUCRv+gFPIsAztkB54qxLrXLvgNBefq5y9LDZrDt96yQJUsLUIrESnQsJKdRonvhtR4Z8jlk2/oK+cwngQkB544YVq0BVA5Pcp4axA/KpKLMAThHQTJT0JfCc0NDPKPSHgMoiLvS7IxFAFm0pRHef+7FVunwdqM0Vii3bamVDDeboADsPFkuniWw+WSDzOlT07J33zDlFZX3bytGATEHBbWQPPbUBHmtUbltQyFtN0/Ky4lBjOsR5lLPv+gDyutEXQ5w7t587Ddt2IZG5BdP9Mn5y9PYkZrkwQeqsAZOkwk/5cg97tnrRekuS1YvLOR3LrlhhcKPCfnaN05JD4pz61aXyIN/kSnVNVYDnGMQ4pnNYqgFNrXy7AXAn0EdutOZIyUs0tpCG93bjaiEXxbMqZQbb0R9uZT1d+o/PQ1FYGJmaNqExfKUvPLGRaaYWBZvnCX331WDnWtKvvPDgzI21YuS30qsWmtQ1GMuQNmOkss7tj+Cve4eKS70yLa7FsvihW5pBQh9/uWrlGeHVFcXy/r1c2V2jZuNHHE5fGxaXtkbxO63Re7dVCIf/nCV1FU5GPdEvvDl03LmcousWluHe8B8qeZ9EzE+xb2ZiZ9xVOemASi7Uamc9PNknW5jQyRzYoC63m7gzUOdcuHiFcq/VG5eO0tyWdZwpZvZgASAx6YhJlTSzrW9/kavXLl6Cveg2aj9zZU0uwPFuT7K/qIsWZxL/yeWzMF9hvWKKEDfrjej8sprE1y/Ks7V0Oeypbs3iX3rOP3jHPeWjgVurczCijlEm2lq5v6fH5TTlPucsiz5/Ody+X0aMQurVmxff/LEM7JwYak8+MFlgK8o29G+xyfIP77yjLz20gtYtf4anFtzE7kU5hcoBhN4mKMwSgLN0ZQpF/54F68ZcO5dFN7MV2dKYKYEZkpgpgR+Wwm8u8Hptx31vfK+Jvy7urpEE/PlPEiVlfHQ+46XQgKvvPKKofClCS5VLnKRPFLFL/2sqjgpcDfzmimB6yVADsJQnfvnb0blVKfulvndX8xzZdU8i3z+ozZZd7PZ2FmkKwptbW3yjW88Ii+8xCJWIp8HYY7NRFM/X1ZiQhJaARsjtykDw9ha9rALBXBOIbxyHlgrigWLm3FDtURhmvei+tTvXpoz39ASuJ6o/004649VOn8McO5/da0Kzj3yyCOGgqiq9CjgrLH8+kuBLoWke3p6DFUfhQo0diuUoOp0Wh5qZ6q2d1pGGs8VZFBVUrXEU7hQvxMgQaY2scPDw4YiUFVVlaHaqKCY2nbr+3o8/beOJ16v1wD3ampqDHU/hfiul71ek37n+jXp9/SaVQlQ/64wm45NAwOoxQBwq224vtfS0mJci6oDKjyh96UwrV6XXrva9eln1dZPAUl9X8GLRx99VPQ6FJi96aabDChQr1HPpTHguhqfQoF631oeWkZ63woo6nnU0ldfapGrYKIqsOr3fCR19N5VnVVfWl5qba62sArX/SFe71Vw7p33frUTC9dmFOiOYxd/CoWpIZFJFkl0YUsHiXnYe9Q1WGTbVrOsXW6RgvxrKnQzAN07S3Hm7zMl8N4uAY3TGtuvjwWqlKoxVZXljh07xgJ0tmG5euONN7Ijnl2/Tz5pgHPLli0zwDmFwDXu6jF0bFArVwXn9Lhq5bplyxYjbuu/9Rz6o5+9ih3oI1/9qlimA7IM6KQS+CEtQpLXgK+wOSQQ9fUOEJPCkt84XyrWA0Mwsew9ekCiXSQMMjyS5iHhz6pmkDFnCIAgmZUtRWvWSB7XagbEi7W2SdOeN2Ws9aoUot5VUkSisKBIVIutlyxFgHFj7i3rSXCTwAaAHzp+XDqOHBa7LygluagtYJNkdrhkFCvBJEp0uUsaxbOYhCtwQt+zL4lvD1AQCf8CgDhHaYmYGdNs8wAlGG9U0UuTQKbeHvEfOCSt+1HK4ByV+QWSX1JAIiuK/VCnTERNfH+elKxfz3s+Eg3nsRQbkDJAjVwHYBGAQBjQpH8Aq62iLCldt0ZylwINARf4Wtrk6p7XJXy1CQUMF1aVtdh+lUgwBeRF8tuclyOlSxeJZ1Ylal4jMvzWm9Jz4iTJP0C9Cl3oBqCiPvonRiVegOrUTSuBoEgysrtl8s190vTWfnEwN9Ax1FlWjRJglTga68RUmi0xVJxs5pDYsJCL9F2VgbZTxvVnZFIv7lwUFQBvpgaAFEg0TydQDVkt9rrbSc4OAzq9Qhp1SrJR7UkD+BBnqQEtgPsBlqF8xrEmABei/n7J8KQkDbtME3BPCsjDUFzBNs2Ecocll+Q09qIWVOVSPqCcLlR5gGVU8SwdG1ozIEwYdbfYFPDbFNCNFXuyhWvFXLkOAIH0Beoxw4Bz06j2uV0ZKGkBUbpQ78D+L0riOOJDgSrDDWwCbMn8ZrCvl536U5LFM7c7DcW0qANxk1HUdvpQFESNBuUk56IPATUslGnAuWDXXmy48rAdXC/xLOwnuX4r1pMpvcdB7FG7j5DABYIDzMnEytdOsjbFvaVQplOVEVVBsmaUAWNVkVQk2RHqlMgwAI0X1SJArAwPCbcMJyADu/PpPxHsZxxkyTxF9Mm6jUCidSSSWwB93kLRq9WAuFz0GQfX78BmLBaMiI/vRBnzM7LcJEpQluHHCSDpwOpMc8aGdS/2ipq8Iosr2bM5djpJFdpyd98IFrcNgJgk3xW0S2aQLEHpCquoOOed6jougYlzJJQSQF+AZVYUtoDluADSa6gXWrCUzUK1JY3+YqOPRekrgIQTXKsZGNCNeqLbg+pXPITN/JgMT5CgRw8pv+hGLPRQc8KqNQU4N3L1tEyPDYiThLWT+3K68zhXGtcMUOUfp+wwJaQ9REmy+AOobnH9btqphbJKqGLltE8kOCV2Yoqjcg7tsVKGu4co0zEAJjcqdItQNMKq1UQyHY8u4/5UMaflDTEBIzhQxrYC2SITw7moP1LrqjgHNQM8QAxJ555NXCv3MN3L94D0EkmS6RmAisQ0dSiL+qLim8TOkSx19qx6cWOZa05rBBwjqX/2l5JCyc2F4mMqvZT7LqIt0U4B58YnJlEcw960qICYlwIW60J9a5xkHKoagG5C2wqh/hcmtk4BQznZHFHQAJhWXIkq4pgMto9w/UUoCaIGCGCQBCYhJABrBFDW6RB/12EAh+OSjl1jOrHLwjFTmlwCdkzFAXXsgFWZAHBZhfwd6BW1kygKfIPtpwE2fJKekQ+gk4f1V4h/DqE+BkCEKpwnD+B4FvawuWpHi5UcClHD3kOsLdCuAdFMLoBTYCndaRenH6pqipWEXBi4IwLIbAX01TZF4QKDsMgBwGoN9YuNRLujbgOqiisAYntksOsAzx8OwDmAw9z1Bpyiyd1UdAIo9AQKUFhYo+Rlwx4uk3uz0nbM1F0KYFWV91JcgyW7DOUwFKSs+bTLMRS9LgJfnEUVaphNJSlJzwLK4rP+EHEDMNlGX8zOpd8v3EwM5v58fVh/qs0ykDQKizZPAWWSSX2hexcnsUw9hoCpMoDtEsSu4DQWk9xHBtCS1mMcBaUAIGo4gaYjlp4ZKBQ5aDsx7HOH+mn3mTXc33pAnBrqm2c1E+omSewYx9tlohlFk6F2SQPecOcBuugmLlVTitBGozxvOgG/FBAHHDbbeY/xLKAqiv0XxBH0YrsHaAZUE8faMRUiJk4CowJo2Ivpg7O3ASBxvNgZGbhyQHwD/ZJO49FnwTRiiM1JfTF2TqKwluD9fEC5tIwiYt6oTAEqOU1BQDdsW7FFjIdApynPafp4Eug1o2YtEGsjMNplCXsPE/Yd4mpcL8k8lORMmdQX8wmA2uQ4QBLKN8P95wEMRoFhrDzr0RYTRSw8AV6S5DUD65gz6J9ZgIXAimrZmBi8Sh1epN+hUuvGZo65Q9QE1EesS6F0Yg4CTlsAP+ftACC6hRlDF/1gnwy0XsDmDFVDp1s8rnRJ4x5VkWSKuU+YOvRkU7ZAlFHmUSngN5eHcqAPEkwYh3wSxUY2BYKUNQdl1irUb7Ha7GsZJNACqVSsxCaykfhDmfKsmkLhLEJ8i42cx6b7hKQDIdpp8+ZcIBvKDMKOz9holzyPA5HaUIO0AvuppXaUcpnofpP+08L4ovaCjPXAZ8mAX6Ja/pS3mbrIqQbQrCxH2S1J/XXK6MWT1PsUczbWDjiXlbmdQv5xvhMJx1FIK6QdkvinETiIJc50FK/oI1HUdyLA5rZQn9gBpy25teJq2E75hmS0+QjtOYD9K/29+CbiIlAcyrzgTSh+0taaUYoE4E6iOGVj/mfhOV/jqM5xVPUvSR+36TiYVkH/LaC9YVHtx/6796T4OafDZWWcRrEmoQqkCcDMCWIEltTAEZ5yFMfyVwOoMk4CYwWwHacy6H+5xD3gZuopNj7B5gE2T7DZIG8WawWlwCq+ALFjAGjKLBlpKMk6gQromzHKZnoSRTjKJLuM8b6SsT6SjZrkgNgBu3JmMQdTWNZSTZQBoLJiQRvvANwHOG09xTyEcY32aaU9puhvFuKYWisnTFmo/jHPYgOyhTalFvOxqXbC2imgdyBd5g451LEVgiIcJoaGJxkTUcujzIob5gHOLSVeFRjA4HTfEWNO4mY+YQcAVvIpAATuD2JDjFWzjXEi00O5+pifTgM5EI9M7gwJsd6SCDFnQwEwzKTAVbZcPMwjFKgLnH8axbYA1rRAhBVriCHlzJOIz9Si2X8VpbdDMt78NhAdn8lOR0kS4MuZyb0xftNOEygS2xx5xD7Ubt2MS0n6i7+JORUKWczPMu1Jypl1IgDC2CRzNsYKU3RQYtyvG/VcOwprJtRbI2w2GOs+RzyMEC8BqDWWAWWHfaMS9A/T7/3Ee9oOmw2CwJJRdrzYdT7D+G+0Y607xvwoltfpuZWSMY/NCtZp5ptYmU9i6ApgnaHgnKuQOgDyY5xO+QZkivqboFytiXZsupl3YeNtwONswtH6M6GIaXZVMBbW0E4BMhmvEiNAjCjgRSeaKfMEmzmIRwDKMeJo1DfGOlaY+inieWAJsa4K+C4KUAegNwgkFyKWuR2MAcwzURoLEV/izGUmglZcb9fKxZEq2XUAlb0Q0Hv+QsCkTMYQlLJ6x2VgEHA72ypLVtQzbzVLW3cP9xyRCp4NnCjVDgzH5Coq1jbzpKxZWYyyWTnKX2myex9OII/v574n5e57VsvmDcVSiIWqgnOqUvfcc6PAK4dkXiP2infPx6EgV9o6IoBlrXLoaBstwSPVVTVSmJtB20RVvC8ql7tCzLN65JMPNAI5YdWaCzAEwPZ/A875piflts3z5L77a9gkwUYRWBtdX/IBNZ46OSgvPX+OnIIbcKiBua6bNmSSq8TKoZEAa5M2WbGiEBWxEdYIiZ8p+gdWuOpSMj0d47qwJveNyPq1DYadaFmZVS6c88nXvr6fflKFUhmKc7c5pJg8RDRJbqIrKvv2dGIT2SpVKMnefWchEBk2uEOAP2/2y3OvAjkCYTfUVWLhmgW455euftZZAWgHhpvkA/etk098AsW5Yot0dfrlR985IZ2tbtlyCxavqP6V1YD088yhSlxDWF6efHtUDh5ksws2poUF5ZLFOi9THex2J2QIxdDaqjSgqBoUlkUuNqH+CBhdBqSeDlw0MRmVlu5hGSPObVqbJdu3VTPm2QEhsWp9nFwMm2fuvmupbNxUzjOehSgk0tEWlyee6DEU2xpnOVFWK5HaGjvtBfXB17rljT2naKMlMovnvxyU6AI+Nmv1AMf1uWQ66pFbV+fJpz+dQf2a5Tjg3HPP+aS1/QTw4Sy5fVuVVAB32VCKS7HhQp/vrcxPT7wNaPniCBAnkDxzuuwsYh3j9vCIH9irjWtOyvJllazlpkvzFWzIx6NSUFTJs1AW4FiUz/TKyKhPZtUUyod2lMuKpairAdM9unMC+94R2bK+SO4CnKuutIkVcE4V+557bhL72RbqqEAevL8YG1GH+Bmv39yHst4bl2WAZ+uGmkYU2yokQIzr7B3GvhhYnvmCgnPvv183t1t4Nkdx7r+d5zxuWbemSB56kM13VWieKkjFfxE8QtvawgB5p7i/qOQB6+foXIjFU28Xz7j9XmPD8eobGeeBObsYS1RJsrCAZ2jGywmUY709I6i8dgPX2VFwrJTVywvk7JmEfPeHQP08N99623IUFetYi2fDxq/BuaMHFJx7mbUMj9x190r6QCZ9BBB27yAAYLP0DcZRgGuUqqoCw/WmG1vVDjZMT062yb0oEH7oI+Uoy+k8zyxf+tJROXupWVbfVC8ffGgJawI8cyphy0tV8xlesB2Os6bTAvjWz1wQpV3KzUzcH+gjrnS280wyIQsWse5SmC1Nl73UAZtUmIdkuJmf0PK66Ju6zuEG4N9+32JU50oBac2y88kebHcB5xbkygP3NkrDHM0RmHgGTcmeXVF5+VWelQJelC9nyfKVeUClKCBS98+/fEYGB4YpxxKpqa0HWqS8u8dRILQYgOcsQNH/g3a6EXBueOgaOPfoEzvJMZbIA+9fKUuXolyMm8ooa0avvPSc7HrtJamurqS/3Ini3CrGAuZdxG4mKNQyiUpmfroernHp3bxmwLl3U3oz350pgZkSmCmBmRL4LSXwLken33LUP6W3VbnhyJEj8vWvf92AK1RtSKGLL37xi0yiVvwp3erMvfwBSoBnQix1kvLtH8bl20+z80xngf/Oly6gs+lYVi22yD99woZkNQsE7/j6lC8INOeVr35rWFrGeOplqqmvmlKTfPovrLJ2xbXPs+4trx9IymNPx2QMhSOdiS5uMMvnP27joYBFSICZ2bNnGwuuxgFm/pgpgT9wCfz/Bc4p5Ka22woz33PPPYY1qwJk+lJA7cKFC/Kzn/1Mjh5F6YRFW43fCospBK3WpQqaXbp0yVAXHRkZMZLWXqA3hc70swqaqWrdcZL8ahOrUJnC0zfffLOh2qg232qVp3Ca2qHqeNHU1GSoAemihfYzVTLdsGGDAeIp6KfX9Itf/MI4ngJ5CsUpmKZWe9u2bTPGF73WH/3oR7Jnzx5DLU6v7fx55N+3bzfOq/et51TYQuE1PZcmUPR6PvaxjxmKggoUquKc3o9e88KFC+Xv//7vDWjvscceM46rn61l8UavYXR0VL4KXKEgh0J2et8/+MEPWHA6aKj5KVR3+fJlefDBB0XVjfS1c+dO4/5VCVBfeg1qb66/1+P+IeC5PwVwzigc/hhhsbizIyVvoUT39AF2wQ6ibstinqqTaqSehY1rfZ2ZBTKL3Ew8L8JSw83CA9Uz85opgZkSeI+XgMZp/dGXjiWdnZ2GapzGcYWudZzYhIWqjiUaj3/+858baqpLliwxgLq5c+caMVYhZ4WvFZxTpToF5HT8uA7OXQfzroNzTSjZfZ3YbiVRv7mhVupJUIW9gEH4SNtJ1FiAnEyuNKCy2Vit3iiuhYsMSG3ywF6JXTordsYt3aEeZXwjt4DtFMoxjDXpKLfZq2tQIgHsYFOG//IVmTx5WmyMnxAJJDRRcOV3PoUuGAurbr6FBCEblGJs6mAH/NTbxyRw7gIghk/SdAEVNYkQoI+zGvvElYsNK1Ubu66Db5+R0J7DqPO0YW2CUgeKAdY5cyV3Azao9dg9ouili682zhnrROXi7ROo2p2V9JFRcSh8QxJl2oZSLGo3GYvXiGv+PIlPdEv/+cMyxGJ2uj8iWbrzm0tIkTAyAfq4b1yKFSiqcqUVwAWATT6/BE4fk9BJ4JYebClRfVFllhgJ2RSJmKz6eslbsUwcs6uACFC1akEZ4uxpGWvuxFZtWhwsCltZEQ7w42ignDetFTcAuiWFasO5KzJ4GDullqviZN6SBCxTe7aCdavFsaAGZTwU/7Co0yQ5UiYSRvEnOoJdTJikOYl6K7tjrCR/TSRrpydR0CtfBUC2DaiDhHTnAcAHH4pD9eLAytCEwo4m0pMkpk3hIQm0HyUpzrEASNwodjmA2eAfgGeAPAFUrIAt1sxSsZPghPghScN4hGpbFMUcVWFKjHcBcJFURoXDSnJcn0zMCgywyO5AIcak0JUVpV4S0742ErfjwEfAFQ7qVVWfwgGS3SzUOzRhng80U1QBzJCJXWUHVmdXgCrGQKMAYBRw4+Cm5BiAwjDKVIVin/N+kqeLUW05g8LQEUkHuLSVr5QEMJ0Z4MiSIjsGABHqeR1oBwUcoC0byUwHx7eaLfRFksbANarm4vQUiqOgHjWz+Uxk1PZwCrUdr/h6LgHNtGGtOAFPR3pex2MrCVyt/xjllTlLbBXrACOAfVCAifZi1TjZSeIaRQ8bDQpoNEnSKMl5ktgp2rJRNkEpMRka5tK8xAHUy0j22EkiOPihMIBCUFkAnPNUcWyS1YlREnxDk6jelUtGFXXgKCFPQsI4TsIhPIiKYrNMYO0XQIHM7UqK20kyPQHMSpuOArFGmcumgO08Jags5dQBX1CQtImkv4eyQxlH4TLgAKdes4NfOcAjufREMoskzA2SnrcAJSzgC3+bTLSfo+1j/ZtmJvnCZQD4JogPMJOGCpNLoURghQRKJIERks3BYXom4BpxwAKgpPeXIO5YMqgDLO/UnnBymMQ+gEJGrkvSS7Bu8wCxYfdmgHMkJiPDKHt0vi6WyVaOQyLKmkcISTMAL7X3TFozSKSXSFrxbEMNyYQSDRWEYlEb4FyzBCcBYbF9RssMWMBFG0aVkwR5GJUHV2kNoOpC6qDKAOemWl8Ts+8SbYQyQtUmSoY5Tp9kxwnn4fqw37QXkrBHBTIx1Q2U6QWKoF0pEUmMSbq0vFF1AyRUVYecGux2s1B57ANq6sd+L41NO6i8WbLLIaJI1JM8S0UmUEID3OjFynjkIrGQ4wElqmJQAiA4kUgC+RGj0yvpwyigkTg0A5dQmIBHQEZYr4a5xzhxFfqGtmThPnX9IQJ4Q50CSXhQazNnAXcGMM1VNaaBk+K0Yc0HIBilw8cB7FIk4B0ASNbMAiA7AFbqMDRJHU6PXOv3gGVm6tCKSo6ZPpgClLDS3yzY2UangS27TwLRAsWVYQGehY2vVTeWYtQdA56aPM79vYUbrpc4C5CA+h9XSdyiXXHOMIpDKa7TBVDoyp1rwCKmBP032EMcwPpwtIl41aehi3gOXKbqWCHKnLbnysKieTbno2+lprFpRHkzMdjBnJoFFMrQDNyWSjI2YBUZV5U5oBhnDrAX1oth1HSiADaMOlwPQIGZerZFKTfadioLMIe1EkCvFKDIBFC1zVNB/QG0ZFA+xMIkVll27EuT9EEfIHYEaJrIRuITpVAszRRQMGwh6Y+mtHwcY+nTKAmqSqPaEiZQtIwNnULi5AzxHMBQYwDAh5U6TCmEE2BcylgMNLuJc5IgjZzDZpiEPVCdG9BOlWdSwDYK3MQYuxLUkQtoI414b8ZGNQkUEu4/aVhuahu20sEtADIpxpPpEBbt9KWM0tXw1PNogx0SQ8UsAUzkRIUxBsBqA2ix04Ygb7jHFtrxeRkDXohYp4CusDKlPO0k+83AgUnan8Lv9gL6R8lCsWTSxrW+ADhjAJMxVO4sfvoimekkynsa98xqVwb8G4i5Jb32TnGUYjmZxGp58CCAFFaXnNpFoLEBQdsA1NVqOEIfVjVLF5vNzLSRGABgyEcbS0QBVBh3jEZCnwnyWfq4e1aDOCoKjHY85J0gthdLdhHje94s2oeHdqoKPIOMndiX9p2VJDHfEwUC5rk5jNJqFDDomq0h1+tCzYoY4C5C/VDVHZNu2ihKqMMHucfTDIU+2htggyrS0aKYMtG/UA2lvbuJNQ7mdoi1yTTKjoE24kwctTFijQmYTtXGksQaTRyb0xkrUPRMAi7GsB2NYcmK/JlRvhB0HDuEZecQIBNkpaeKOdNdwKVsju08Z4BzHlVIRb3NBCivnQYTesD6JglfeV1MqKvFOUkCBb8YoKGZsd6i/2YukqRvefJp8yi9mpzMZVCmEq2PkQuo2XINqPlZKOe0FOOrKlAybwuhaOsAmE0vxqY2ZzldHkhu/CpzhAvEXkAikvq6mY/tg8BW3Bz9Zoz25ClFcQ4FHchZVARH+BmmLKeJXcAg2rDNCTb/BhhbIoBXgGBFDSTv82VqmGtADTmzHDAij1hhAUbkOyYbUF6sXcLdp4wNAXY/8y5UykzWHOInqXkdvxmTgwBjycxa7GyXAGUBshnj7gBljGJqF3OaqRDwsqo6Mc4zZNEgCTOUIIrLWTWonxWiRscYHAewjQ4fMtT4UtNRcWHja7UzymCjGGPOGaWek4wBGaiBWiYYj/pGqTPKk00GiTTgQBTsUli4+31AXYVsuqjZwCCKbWbLLuZYQeZcC7BhJs/gKKbPMO4ppDt1hfa5Hxvus8RrNj+oGirqXyGuMQEcrKqPEN/E4RJsS1XxspZ6ZC5nYm7IhocAc6rUVBdquWx4wHrbTB2mqGMT4yWRTOyoy1qBSs0KHgWx1UaN2Dc0CNyN6TNtxEKHZDpBvTP+mVUZCUgPZbdEgHJFoS0aB9AE6sGpnTaFelwgCvxLvM+uAspjbMcGc7qvVyaxOs1B/S0tr5b5HsChtg76QhKQ1tcFZM34ZItgja1zIzvzb2AtRmEdTdhMk0l7Yz7LvMTC2MoAZwD2seET9EF+dDMB95RiPLZTh1bG0EhY18cL2OwyRxxlVZQJ4Ahzk+DYVX5aiF8+roBnCJ0AEUN1k9B4kLJkE0bTYIXsPTIs3V4FhpjbCH0eyNRGmZcURHF0cUl1o1taUKk6cmZQBr2MAXEdJ2kLxE8nc4KGWTZZixoumg8kAABAAElEQVTWgrkONkiZZe+BmDz1zNvU6RQ2kUtlLcBQPqAbxQoclJTde6bkzT0nZXatW7ZuqcPWMw9ALoXlZlD2HRxFDWzagBU9aajAAd7qnLQbu0Q/c8AH78Tq9PZCKcgDOGoNy08eO8t3fXLL+tmci83A+ViP0zbpiagdJuXKpYC8/HybtLQy9jHn040juiZotoSxhU1HqcqN4pQdeMsnZ073A/aFWV9lVk4ZmBm77faAzK63yPr1xbJwvhuISeTSxbD8y3eOoBZWItu21MqmDXbWOak/yrVvIC6HDnfLoYOdbOqfJ7duyQUIYpZIBV+8GJWnAcBa2hhvmF87HW5UbOkrlPUEylWtjH333bVcPvqxOYYV7TBqeT/94QnpanfIJsC5jZvTpaCcZz3anz5qj48mAKRUWa5f2jumGAK1H7GRhM0VZp6dCvJSsn6NG5DJxTljQIko7I3yfIlCMnrk9Cf6HKpZJeVOuWNLhixZ5OIYKTl3Lia/eu4ocy4UzDYtAgQqAuBHvIDY1tsTl2ef7UWdi3KpdgEGFgNX8RxL3790lWs5MCSnzgF8R4De7WHgOXUySTcU14anTLJmqZ11XKfU1ZrlDJtt33gVu1T64aaNNbIOwLK4mOtjvNDNT0nir4X7OXsuIq++FJQr2I8GmLyYmZeY6H82nr0y3ClpbMySRQszmGcn5ehhL2XhIyYSp2n1MdqcPqOVlDhkxQ15suGmdCkrMstR4KlfvuwHEBuXDeuwXt2YS3tAkVTHCspg95t+yqBD8rPzaG8FqJXx7Ei5t3bF5MARVM6O9qKGjB1tWjFQO/OINEDG4SAwIqAp/eX+u13AfFY2qiXkBz9qZkOxW25cgTXxXQ4pAbLUOEOPoD+TV/PG5MmdXUBlKHdjk61jlcZKE5sU8nKTrH1nynK+O4poxPFjY7TlccomlxaeTswApCQmlRTGZM0Ku6xamSalwKAXqYNfPHlKhpkDrl0/VzbdOltKS3TuBVA6hpLiiZg8/sSrqDmnoSi41FCKNDOWNrdEWRefkrdPTsv4FOMaDT4ThT/EPWV0RIHaS3IrZfWBD5dgQUx7mbbID753hu91UEbV2CzPk+IS1g3ogNpGTdxnjGfgfpQmX39tAKvVbhT6mLOq6quwdiLMi11xWcCj6cIbdM5skQNvdYq3M0xcA2YmjhPhKS8/6n58blGWrFqXDwTrkMmRpLz0woCcOdkiC2gDd95Wx8Z35sCMa1quhw/SHvdiyx3qlc23VsmSZTncjwlgDgW9IxNs9kZ0YJBVH8aJdAD4NJ4XNJ57B8ySnxGXv36YfMg6J+VOzHpzWp546ldShSLjbdtQcFwIRE2cHUNp/I1XX5a39u6R+tl1AHr3YOd8I9egtQsxSP2kmAsnqVPNedK8jNjEL36v1ww493sV28yXZkpgpgRmSmCmBP6/S0CnzTOvf6sEdEeHKuqcO3eOSWcYin6pASj8W9+b+f2fVwnoBDiI9PHVtpT861Mx+cGzqEDw3r/npRPFHI9JNqw0y39+2C43LOSp9h0vci9yCcWi7z2akqd2pSSgC5m8tAevX2aSR79mYeeW8RYTeZGfP5eUL3yHxXsW0vRVVmCSzz5klv/0UZOhQHft3Zk/Z0rgj1MC/7vBOYUTFPL6xje+Ibt27TIU3RQ6UIW2s1i+6U5JVad74IEHDHDuH/7hHww4Lp8d2KrOpt9XhTcF2K6rwKkSjP5d7fQUqvvbv/1bAxB75plnDNtvhdtU7U1VgVR1Tb/f2tpq2KP+0z/9E4tK69nFdcqA9NQ6V1XmGhsbDeUgvSaFzv7yL//SgPr071/60pcMVVMdf/R69drUYlnhCFU7VfhOVepWrVplVKICbqpip1DgX//1Xxv3o4pzai2uSnoKZOjx9d9aLjqOKUg3b948Y5FsYGDA+L1eiypSKsTxhS98gZ2TTxi/V/hOz/epT31KbrnlFkPNTq9BYUGF7LRMTp48aQAdCnH81V/9FYtFVQbU8W5a2Z8SOHe9HHwAzT29IqfZ8bhzV0yudLIzdJwlBBYyNLJXsFBXVwVAt90iW9daDEVRl4Okw8yU7XoRzvx/pgTeUyVwHZhTmE2hbh0fFIzevXu3ETtXr16NDcvtRszUJKOOoQrOvfHGG4ZFq8bUxYsXG4pzeiwF61544QUDXNbYrL/fuHGjoTinBaPn0c/prvsrTc3y1S9/hcXgYVk+q0ZmZ5OwH2WX8cAYiaoI40u61AJxzWExM2MeEEk+iSxNjLVj2dcJtADsFAWqQNyFnAM2MJWAB3VAX6VlrEuzuKsL2GophspL3Nsj8Q4UyYaHWODG6g/LG2shCauqakljbLWg/kOmkqQcyX6UXiOA2gE+mwoAgJPQd7hZJC5D0WVWJVLJKKKQhEwMjEj8CiBVl5fEEBZ93Je1okpyFi9DoaqSJCarwMRGKypBKUCkZH8/4FoHyhx97Lj2k+AjB16QI46qWrFUoOKG2hCedgBoHTLu7QJ2AAIAwtD5uh0QLhO43jEfizasZU0o3aRQmNPEYXIESKkTaKC7RyaGUDIjeYC8DMk/rNSqa8WOmowFeIvsMoDKqKSGsUFlR7yvDUWd6SD3AgCWmUE5VKOWR+I9F0WbBEU9PCoxr5dkaBsKVj5BX8YAVwoXzhcnCgDRDKxS+aBCgCYUYFJBL4lp1K6mxjgXx2Qx2yyo90yiejSE8lTJDeKq2kwuEkWIUS+57wiKW4WUPbaWJFqNBwfAOWRWAEdIjk5g+4gyCFgM18jbQDdRI0Vp498kyIF9HLnAHijRqAqLWes6NEadAUFMA9aoBRsJNCd1a9OsA5aPMaAFc141ZYjyEAkcM5ZnsckBA7SymrlekpoKa4WmuW6S1C7UwtTCT9VYkAkAxkKxhYRGfHIYZRXqm6S0CVuw1DTWf1ihxbCWdNVvB3JcCATJ9fu6SOCSQMyukYS7ksV3rl2hSazvYuMXUBtq4zqxZYNaMHGTpAWBIXTXvcItwB5YHTpQVbGjCJXUe0YJzxSnPWMfG0NhK8b9JlKob7mxVATwVOWylOYTHMAbaueJhWYSi80kdropgBxVJDMBpySwMQ750V0kGWsD9rBlk6jX9hqaxOaPPgK0QxMgwcsP0FsSNZdp2kCU5HoGbdXpxkoQq6FpEvO2bBTsAAjMNtTykrRLgAyTQkmBPoCkLpT7erAHi4mDxIoJdaQY9xoGEAxpnVCHHuxp01BQU1jJRJswARPpNScAM8KAUPqeHVUvKyovYRLWUYAYp30WUGOV5hoBvIZkesjLedlAAiBlsajCINcKvGTAIgp8ZmIlCvSXom8kUW0Jo0SSjKC+lk7S3UYiHYW2wCDWhahiOLETRO6N5qLQSBgYj8S1uxj1HOoAMIq7gDpR61isWicuAE51US8AMbQXVV2KKeHJhaUs9Dk7MG9eBepg+STu0+kWgGPUQ5wyCWsdAvuZAT7sJIXtQCwmGwqaJIPMbtT5gCeSqPOpTW1iqgM4adBon2YgtDDJzgSwLvlQ+hlAotoGq1Uj7YOMGP18kn4A0MPkTW0kTekk+gCTp1G6MVuAdnIL+V6uUYdqjWShbBRkU+Uoo7OR+ITqkzhWvjFAjNQ07R01Jb5sxEON4Qme9xMoMpiBQKzAUA5UJnW+TWPmR69hhHZGPEKFS0EdBxk8G/2LCubfmhUlkcx9mrDcS0VRzUJ9JoVinBkVSxOfU6gkSvY0RYxzkky2oIBoRlVHLy1JnIxyj0lUO9Uq0MaziRlwTq9TQS1zNhtkPCg+ceKA2j8CizmxG0Umjdoj4Q2NnOK7qWg/ykbd9CPU2bgfE2OACeU5tRnjQ4bakapCGf0Q9TcranwKTiospEBZCtBUrUgTXKOqSRq2ytx+kuyfCbtnCwo4aoFoUvhoqp86maI+KAMFIQBvEsRNtZ41k4jXOtT/p1CPTKkyFGOEifakoBNEDscDnAsAB2Db5wCQ0zEpBRASC9FnqHsrimIK5CUo3xTtwKr1xbVFUbRM0hZ4m5fCKoyZOqOnLjGrpgEBmaE4ZQGKMqEOZaItmhQEpy7wnaUOsZrkyzrGXqs/4hPsYBI7aZu7ijbJvYRR1BoFdEblSxlStUONYXUQC4aN67ehzmbNLCRWFnONCueFiSlYDGMLG2OjFp1F7IDnqnQ0OYatbSqT8W4l0OkCypgyGvcy3lHHwJkJoFa1EaPb0g+BCFTpE+gmEGDsEcrNGhIHZWSNZqJ0dA0osABx27JQJkWRTxUTGb0ZULR8GA+nGC8APCO0NR1XnViRm1BHS2KRHEORz5qD8mNmPbWAzaPvMsKUPQAQCs5QdtRzEnXBOO3ZjDqsPYs6VLW0lI614wCIKAkGic+cz+7inLRR/8i4TAGG5NTOATwsM+BC31gU8CufOKg2yIXcK0AqC3AmrFrD1EEEi1wBSnSiApng/TBASox4SkVyLwALKNTZMkoQuCwBjCUOAI9QwLSjNmP8jTH/iAG6WhgPrYwTWn5JypzRG/ARVVOFMOk3EWxVNe7aTcRO5gVQqihaAVdxHjubGCyoMZqIZWoJmwoAg460E399tCHigEPVaoCURluJ/RPMOZh7VNxO3/CghDpMrIoSolA1zGR+BpxLdGUsoGyArFMDFyVFHce0zwMNxrg/0HHKnLGK99Qy2Z1TTrtnvLeXctP0VZTckmHqDXg0rlA8cwXQd+6fMRk76nicmEVi35rNWJ9eT7+iXsLUN3ODCH1CoTMLfUCtHXWTBgOQ+LgXKyqnOneyGPdIG8bGPc54YgbismBnazahuojSU4hzujwk9IFBE45KVC9pP4AtDgBfi7uCPoiKIlWUsvD5FOPZeBv0UYfYKU8zIJlhvwmEakGlM8GYFcYiO5leDkCE7SoAq8I/DNzEKMqF2BEGcrMAWtlRcjJjmcet8jsU8LgPnR+YUVNU+1ITqnCpAGq9zEeiU9h68p+VuG5S5UD6MCGF62JTQCZKmsyRkkPEB2z4TEAyTLr5PyqHvmaZnADqRsksq2ID5ZkGPH1FTwh4Wg0UCeyMGqNJY4jWBfCwTCrs7qVOdA5HzVHfEStzMsB3VQyMoUbmxFbVwz06gDtNCspbuVbaeIQYHJzsom78kg7c5VA1Ra6V4EOMZ8zJY/5Ee2LoghfUPstcmjl3DKVOE88SNivtmHklhcY4TL1q+2GuooKGuvkizBwrDrST5mDMtId4xsCeHWXCNMbmzLq5hEI2iozzuSgQei4W57RztefWNmoGhjQrRD5BfBnvoA0NXpsDcE9JKoHZnAHs6fzCmQYsStmY+b4+N5hQ90sFuS8fcY54EjTsVrAPZq5l414SSoolGDcA7C05jE28n2K+kwr2E2u6DXVWCo6mSZ/VcYRxI4zKccJRIUPhfGCgqPT3hGR8JAEAxUhI8M3OSpdZVdcAJzcwXNcYVoaowg10MxZMEPN48HAwn80vyAKCshtrOkz/iQtmaWpJAN546V8RmTe3VBpmZ6B4Rx8kdPkAba40h1lr7JHCPLvMn5MHSMR8j0nIBJsvW9uTcgkb0OERFA2JUfm5KGL7TXLqfBLlpybZcSew2uY8QDULam1xOXIUBWjiS2N9gTTOZcOCh7qjRHV41R/2PMnlCwHWKwOoeUaMjZxW+mhWLs+FNW4U8lRZFJgGKKmtFYcOVKemgEvjqGE6ae/5+U6ZMz9NqmtsAETEUq5pCBjqzb3dnDddFs3LRjnPghoy8xGi6IQvKW3tU9LeMgFEWMTv0qSkTKFP1KkmknL+SoLfh4HeaLNJm5QWpesjhZy+0C9NVw6jareUDbwNKKZZGCNicvRgD8qBFtYyC6W+kefTTIX+GLOYWihsOAJQ2NISlC7vlIxPhnguJRYQi7Jzc3HqcAMUWbQ7Su9ASppbE0BDwGeoQ5tQX3YyJ80ocEtlTTr1YEF9HbVP5oTenoScOUcfZG4zpx5FuVrA6F/f+wQqjqfOjuIaAnhI2Syel0VbUXVt4h5zwG6sN08B3o0MhxlRIqgYohIZdwLtRaVn2CSrgckefNCOIp1FejoTcuk8my5Qipw3L5+6cKMSzEyFeT4NjP5AzCbujI4AdF1OoBIYpY34sNBlHsIzY4bHLmWoltfWpEkxMFyItceWqxHUybAEnYrQloH26MM5edQfkF9ttU0q+ZwTqKmNNv/2xZiMo2Q6r9ElCxrsWBBjR8v4pXPP5qsxOX16lM29Hlk8Nw1IjDkUoQRxR+nuj6FsF5D2ZsYs4pEbe1Wdq1y+Qr13hGQRdqF33eEGtLSiTJ2U/YeIE9Mu7jmd/mDFMlhnY8Q7vq3PbH7a/VnKrL1LrYODtAd9+OL+Mp1seM8EBsOit8yC7S794yoK812TtFGAZ/q9jflDbj4OMgCVs2rNlLduYmOI6EvSB/vobyNS15grc2g/WZnMyfhPefSBboRTjl5k07td5swtk8pqlFYdXAvX2+VNAkYmEFQJsckiiUuIbhwwU1fUV9NFWb8uS9730DVwLhWyACv24UYzIlVVGViYlpErIP4YNUgopV0w9PKMhzsA/bq1fRx4c4rrYpMRELo7DYtpQLu62awFVypkJpQtKoydqHeOA64H9Fgmycy2SHUVn2twYJFOHbKOPE0ZXDjrl+7OUako8VDe2YCAjMM21KUZNNtb43K1mbkH1toN83Oloop5GcNXhOLtpx80tYRQ5meuyDNsVnau5LJ2dOFCWE6cjUkxwOJffjwNkQ8X6/4ip05PyMuvvEKcmpTqWRmAmB7UG83ElDE5efwEdrlXZOG8RXLfvTvkpnWrGSupBGaSWg4pCPXr4BzFwZzk93/NgHO/f9nNfHOmBGZKYKYEZkrgt5aATklmXr9LCegi5nU1h9/lezOf/dMuAV1c70M96NDxpDz6eFyaO5IyQrLn39PDdAJfxs6w7est8okP29iZ9v+eMkY4zoUrSfkfP2EX0x4Wp3XR+dfFqYsb2/jeE4+wy5hFVH2N8PD+w6fi8sXvs9DLYobxWf5QJbvv/he7LJzLw831A1z7ysyfMyXwBy2B/93gnCqyqQqdKqbNnz9fPvvZzxrQgYLPP/nJT4wfBRU+//nPG8o/Cs6dPn1a7rvvPvnYxz5mWJV+//vfNz6n4NpnPvMZA7JTBbXvfve7BrCg6nB/8zd/I/v37zcUSfv6+uTjH/+4PPzww4aqnYIRquCmMJmqzn3yk580YDM9rsJuekxViFMVOlVu+973vmfAdPodhdkUXvvxj39sAHUKq+n5FLZTkOwf//EfDVtVvX5Vv9PX008/bdjWKmD3la98xTiGQn2qPNfATt+/+7u/E1Uuevzxx43rUvs/Bef0GnR3qR5XleYUEP/yl79sHFePo/CG2rbq7zZv3myoIZ05c0YUBlTVIwUQ9X1VTFIgUKE8VaDT8r/+/rtpXH+K4Nz18oiQXB4eEblyGYXQ52NyqhmLXv6tO1N1caQQWG7RXIt85CGz3DAf+0MU6JwzAN314pv5/0wJvGdKwIDY6NT6fwWcFZpTxTi/3y9rsDzdunWrMVZdt2JVq1YFn3VsUJVrVfrUMUvhbX3peKMx//XXXzfGKwXn3vn76wWj49eVS5fla1/5Kou8PSyMV0h1VYl4ACDsSGK4IlaSBi6sOCqkfvF8cZeVk1wii0CSLkVyXuGmJElDVcchw8b/gSUAM0xkGpKoHiU0qczSJ5gN69jsgOe6Ncmo6jOqmKPQhi6YW0jeGcoeLIebSFTqLDYRZeGfBXBTjFVakljkAUiakohT8AVFDvwgyTySIECSMwXIgG8byV1SPiz0Q1gAkgDXoM5zzfJEd9nzuSTH4TupoF4Dh9VkMNdhRtXFTLLeRDJb7UFAEoxkdYIPKVuikEhK7437sSg4oMlEFuz1+lMAGCzGkkjhuMAFyTAJWDJchm0UvzPrPSrUQKL12vMh12okKrkWkmwIslAunIBkjRklFTMr0zHKXItO7UTNKOlAU1IWlB0wEMZdxnXasLlEtILcqKrQgM7o9aG4o1BASgE6VIY4AscB6pu8KpGBUyxST6Isprasa6hHICTNKOtnOLdJM4gkzAw/KJW24JgpBSt0Iw5lRf6Vj/J3FtVTACRgPte+C1JnsgAWqfIOijEmrVdgH7U4I9PJ5wCiOAcZGL5O2VL/Ck4pKKKqXZrAAUHgXCRzeBvNEI7LMUjmpbSSSIySAeV4JABIxCcAD81AXRbqO6XlFueBRbPQJHID/ftJxl8gqZwvntpN5GT+H/bePN7Sojr3X++7pzPP89Tn9EiP0NDQzCABRUQUEfXGId7kxmvuTfKJv8RP8ocaNahxwCGa3EQTLxrQa4xTRAQVmSeBpud5OKfPPM/77Pn9fZ/aZ9MNQeJtcqOG/cLuffY71Fu1qmrVqlpPPWszeSJ/iIJtQTiPcbICePIA44RU9zg1AthbQGe4/Aaw8FEpZIK6EvgsgF1E98r5CuhH4CdqwpXcF6sBnsUs+U6pndAGQoBdInjlwkyuAkBxeMkBT8IKICAUrGagWniGd/EoKC3+AahEWDv1HbWlLEwRQtyFeDYETZtkEQAiAr1DmQmvObzXJscBJcHAV9e1ntCUPeQR5zAgOSURUIc5wBgCUql9ql34ALV82AgDHPkCFHi0kUAyAxSSob3DS4hDHeAegKOwGMNouyF9VBn02yCjukFGCFEMDoiDUIoZBxwKA9oJ+zjuyamYBdMASjwc42K84A20QXUyzSnJHB8POUpmAZ1KjH4IjfRh64MNSOCLxcETNjcyDLsc9ddzNqDQ9eiQaoqGTNUuADpkaG9Z0qGGKJeeVztXvS1whravcMG0M/jTuR8AHDCODDL2BXClzGpt+j9EPYuZSMxlAd96RtUix5g0lhcCxCJgldNhPCD9jPxcvrnPp56z6E8VXeGUZCfn79W3GrI+6k88x58CNqDAOEXe1A/0LvqEQA/qdlzlHH9rAq6LhUMXuKr3CpygMaJw3b2Cq+525ZO0fFWQTrgE+Ud5kMy5Wfcrr6cO3cThnuF5AdV0E1JymSIh/XLv5Lx7Vu8pLBKoLC5P+fIIDCj9oTBh7n0etYQ+z4uDe4QwdP1b71G74FunpLdpc3qbsqA86D/VRf4GnIf8pZ8CBQqkpGRUZ04uupe/c8haN3nUhdLOqX/zn8Lp6hx/8pGO5I/T5aAy6CLnPFc+7tUbpctcmvwsHIDX8unSv5C3S7dwzbXRvHxceqSpEuTlX0iLUrry5R8qiJKEyLrem8+7dJQyrPHJAczUjpRHHnCXuOrywZjg2pGQ6wD0giy7I3Gc+1H6HvWRQzcojCgtlOKRG42PMIJlGDx8sUkRqtHLAZZUfxTYFF2Rnt1vY/2EwCUUWFXHJYRdheEVBtWAcVvjWCBWRoFI1N7IkhhnVUbptoBQwuYBnAa85QGcswxAVMI651y+0WfoGIGRlU4IneHx8QGyql4CAXTUzlV/Aimi9wSsC7L05dAK8o4upWxeDpCRdI30CXNLHZKTxl3VtQ/YSuMKhgHlIQ3Kn8sCRhIgPACoDjvl4MBJQt/51rp6q9U393A/uh6wkAcjl+9CdPNb6SKSECBSUEKUn7Gbk55Oin1QNoX6lw4qRUA4BnHKTrvgQym4ID0Oexh6ncrgG1mpNlw7w37QWiDgORQUZWaMogPoDiPUpUDlXCD/vJ7xJgeoPGD8kgwRHmVOoq9nEQlllI2kAzlkCIc+d4JwpDCWlbUqFO21yK4NOS73XdlYjPkIHt3I2EW+StDbAnuJEZcX5N9FmaTP3TjgMgGASbKVbgTwEMg+gXnKA8Tm1AzyyyFTn3pwTZXQm0AMGDdpExHp0zbqFwZC3SxbjLLn1H6lb/WAGhPl0piqIU1hBmntvIdkYJiCEpJ3qX0BsgcENTM4ANBrzmpgiCxvZ60C8HeOMVvt2KPtBhorSFvsjwLFwaNI8rDWAXpxIYbVt9WvVHbYzhAA7bqM8Y0xLVQPoyKgW1UB9eeFJp2Mc4DRjHCzVATnyJjqCDnI3hLoLQCknXbtD4Ak/cpX36JvujaDXaExn0K6siosr9IQm5pYG1VoeBr5Jr+Lz8C2+iAgEEJmN28j1PzV2LMA8GkXFI7X15BP6oA+QGr5fii5AdT06VMhZCzwv+yqwNe4DLiU8TMuBiIfgCfy0SiqtHwVkvFd7SlDvxJroFi1fClj6Un+ppFYprQG9rpaWiSjKumK+U72gYB1avdicHI2D/1DY4DsYk5wD88rDfVJkLaeAXJeOm7jx2E3ZVNKdXO3NfRsZB4AaDEBvDULgIvQqQF9O4MNmaEfw+dGCF5ag8ZgbMO8jUnfoB+pvzn9odFdY47qT2x52E6OPZn6BUJM+6GMtE2BsGS+CRDqyTaS3YwNKUY+hZAOwstMVLQTH3kE2AhON+ohgPkYEpSFvwHdJOhDCRihcjABa14j4JAHsEzlLmc9RlOVgH4EBBZWQl6j+QoAb3WBtOYG3FvKPRUwHoZofwIBLqBu5uYATSPHqrII8zoxZapfZi0JO+kcoW8TsBqX8GwFjHVRbEl1kSXWhubpwksAwjQWqotrTnbv/QnCnBJ6fLHX3kHYzKtfUUKfyfeNRbFXcW8puzNKYAoOYG8TkFa2cZjyigGWZUiAx9Kv6CqKpmYRBlQvxigx56rrqstLjGmAzxrLM9iYYuIMcV9FFXnhXjEr612KojAxzXt5rhr6waoKWCfDzMlIKsU/KXSIsI0h6lUgrRhy5DFCMRPqkvKlmT+EJSs2tqheHn8sad//4V78G4cJl7rdbrhhJYBEdAAPJdikQ3EA6bDxig0rMgPydgezPDKgOWOaehOrF1MTmaS8C1ueuZvYPkuZm4pFjmIhl3wzzlKRPjeinujr2pzkWyV1ADbVyQgiRcIxs1GBOi4njaryfD2rvAIlx3mRxqpSnuWyaws5dDJTXp4jz5RLctJMWNO/PbtT9o/fGORaub3yqlp7w+sBvHWpX2H5Io8Uc4WqSoHIpTOZO2hcwuiSbs0ug45SyG4hLttTcpetRIPhKAf8p3JGqFjN9VK0Hck+zX2qD5qtkwWYWGTBGiP5kkpYTAFkBOiXplCKfFFTAlgZmQUAbLnFMQQuacMZAi9D/0Kyqwo1mhHAaFjledZHQMojew7swJGk/ejHfaxdJ+yKizrsumtqbGUPOhYZz1GHaDMH2ipBf8TQmyHmmnqXbtD4AHbX9UPNz6glNXyXb4VnVcRtmaZiwlM9SvaqU21u09AbY24XZT0hIocbjVtmAksVgOtIDf1cVQtLJu1UY1GO92rup3cusbEwxJwrop1NCMUHCK2NJup/CULIqq7VnmR37XwqYXfB0Nd7spfwpY32+ptaCOXKvAsbagkQXIrKF7NfJeHqtYnOHc7Qy8tTbYf9Q5SR+TT6VGXJMp5rw0oJbVQbJVT/eiSFfNO0CU3Dc9RTVnlG9jHKyD4SNwTJllc/WgQ8p3ernVcCmg0rzG8IYCgyzKbLKAdjOvUURTdEomSCPinG+CXaElhx/lYdYtdQDjHL/eNXjzrg55Z1jfa776y1c84JOeb2keE4YNKnrY+Q4AlCf/vUT5h1kcW5OKDOwzbQN2hbt5xvN9/8Fta8Lnb6BY2C8GT1ygKm0VE3kqUshjM9isC5M5Vc8bmiBIoSKEqgKIEXkcCZD0wvkmjxUlECLxsJyGcwMR2w0ytjX/xqxh7fEdgkhqoMeNjzHXBNfp+fd+i+1ewM+W83hu0tbw5DH/3cPim/zK69WfscoLlv3osxrbnCaYlV4N982/Vh++yf4wTlfTpGJ2CmuyNtH/47HtbENn/aqis8e8cNIfvIe2Ps/Fo+WfwqSuD/gQR+2cC53t5eBxQTC9t73/teF8ZVwC4tpij09uc//3kH+vrzP/9zx6ImIJrC5gn0pZCvctx86UtfckxyYpr74Ac/6EAJmozfcccdduutt9qaNWsceEyAO4XyFmBNYDIxtYkxSCA9McMVgGu/93u/54AOer9AdALCVVSwgMpx9OhRB4br6+tz7xRzkN6hPCi905nhxJr36KOPMhFOuXCrSkMMRmIuEihPbHUC3wlE8a1vfcsB8MS2p7wJJKc86fr/DXDuda97nZOnQs+Khe9Tn/qUS0cAQIESC+FxJR8B9L73ve+59wlIWFeHI+QlHP+ZgXMFsWjhZYaQrQcBzv39P2XssT05whYQ+kRr1dxUx6LPxvW+vfudITt/C+GGm1kcQd/TnItHUQJFCfyaSEA6W2DjO9mVK309PT1t27dvdwBj6Wjpco1RbtGfMikMuPS1xhKxoyoMtvSpA8PBfCrgnEKIX3jhhQ70LZC4xh4dhTSkk/ft2WufZGxLAPa54qorbOv2bVYH80kMx3pJGp4M2ass5pZWim1BTlMcW+iWDAw9cnBqOTkqMJeLWyWlg/OIc2lWcLXwzloyDhicdji80izYBzjuFT6mYK3KeSUQS05OWP7yAasoZE+Ch3M4p8QSw1KtA135LBR7cl7JchWjj0AFjG9yfHkymAUAUxlxUGqBW0deD8rBLdYdHGc6gZMvCxuMc6CRJy3iSpfqCYFTHPhLi8EsYAv8l1FZKAf/O2dOCDve46PE5SRzb2JlmyV38kT5AQaJPUNOIx05fnvKu+z9MAxSsJ8ojF8IR7sPm44rEwvZzvlPsllsDK2dh+SQxDnpQDekh2eRNyBrl1vSxWmbpg60ih/BiSBQVBr2HqjQSFt5xHEIu0ASRrX49H4crYHVtp5tpYR0tGgrchAIUs4H/pfTWg5gOTi1Sk9+cso/zkcf54b8jtyAnwKnGs4EXBf8ktQAKuB9kCND/grnKZCkqHgIIZCdYIiUjVP4vxwwiwe5V+/jGyePq0tVq7xa1KOARVocF3hEoLAMDjdVm1jaMpkZWN7GCH9JCDMHIMCJwQp+Bgab8cHHaGf9hHnrdoCPTNlaEq3BSS7QBGmTSKBKoH3JqewyRX0pD54vcNg053BMyzmag/4DR7BCRQrUEOD0DXA+C2Aih57nnOAAD3DgpSic5IFrAWc59SqAHGxcvpyrhGD0AHF6AE1zi8MQXMF0A/AtgDkrAKwWyPFDWbOkK9AXqAeYUaYtAmNMuET9jXqAcUyhU5NjR2yOiV8YoEc14XFjlZ3ktZ4PDivJG7BJjvQkPVWB2qMYBdW/fNjHnINbABvKbwBhsjj444ADBC6QE11sc2HkJMIK7sgfzvODfHBsQg3IswlsDxgZcZhEAQlEYWfT3QoluER4YDkdS9ATMQC2IXm90A4OGIK81TcCHP8ZmLzEehEjdF0IsGIAKDAOc9zcGKxH1GVtY7uVdWy0ZPlq5+THvYSTjTKSb4URVOYoqms6KqhCGQqkaYHADXxcw5LjHCc4oMsM+ZBOyVI2hTdVEupf9A7uxZmGM0r9PkbbUBgrAf4caxnycW1G9ct1geNUPpVDRx6oxS1KUAcyf86xfPo558h84XQeRLN89dmT+n16QssXCtefffpUqqrnZ58gf6eO5z976spz/nqBR55z/fQfp91bGEeem19ull5cfkZldH/znB51f6szL+fzWRnoGZ3X4W7MP6Cmqt/LV1za7tllmT/7vHvw2WRdWvnXKLFT508ltPzAs1/LL8rfnj/rXrp83p15NhfLT+Xz+Jw0nyN/3bb8zPMfLZxfrjlXdiWH7nNPFWSR/yWRuqNQXt2fl5dkjYzplwHhBbOEy8RtCrsXOhmGuWwOVi3AYGHGPKn4/IHuxhmaTqFXYHyMwA4nALtUY3ZhAsDObpuCbbQUZrLaFYQvr9nAvBfmRkICa2yls+mNvDOvxh0AnfoQCAjOFe6Z4hoAJ3ev5rHoCMBy8L25fpijH6n6Qnj+pZtcSGJSVPak+9ES9E3G6wQhfxdhssuQ99gqxvYWXi3A9Ah5R58RhtGDyVJMXALeuzUwdJjvxizAXDARBoRmlHPZI2SzPOK5RB/hPA/a6OQUurnZWldtt8qadqqJfKInQqKgZcxxAH7ymMHGEEubz3udXtE44owB1tDIowMEqQawWzzyIwoyscbp8KTTBWTjOydQgcYDyc2VD30K4C1FmHeftbgIoG6P+mKFkDoj9CfMWCJgi5ZWUB4tygEaBqmRonwUxOn1RHwEfPoUwARALjK0sDNyMGMuEQZ7dugQoSyRPMyd4c6LsGWa0YfymqOrdStjL2aGy4uaLLmjHiiPQNI6IUSBGzPlvKZhUGcCY7gweICRxRipMK4CjnkCPWKXZRjwVcL8CyhfACA61ctLAGfCbpeJAZyDAU4bIELSw+h1MQY5mSAnHkDuyIw0BUqUXROA2knD1CiATBSBeIScC3IwG44fsplhxllYMhtaCeFMGO8c4X9zAruRCxeqnHagQ/iKDI2b3PNO2Fllc8IG5cI9C1XDGO46B+N64MOoyrifpc8wOjEeUpfKh097I2++NolgH2gsUgjTIAvzbBx7jk0TjlGurI0swQzH9RBpl2DvafOEZBkI4C8bh1yoPytHScZSo52HvCkA51Q4wKjsYhJWq53MAXZgQwZW07zdKpovBwxLHco+olwZ5C2QkVoamAeeU7/BDkJ2QBXoabxXZdSmAPqYOrfGwgRAdKBqyBDZMLaHyYjGQtWtspekTjUuxmjzEdqhQH1Z2liOesiW1wOea8FEA6yFbRqSLUEZVJI8YI7NHPQhnSETrv0bjI/BPIBeAfhhmPYUxnwJtueJQ4RmHABoWGV1HesIj7yO7idbRm2dj9u0QI1hc6SxyQQNgx+ObzJJWd2OFpWbfDubnjyo2znwqtIAtCvAmkCRmnsIAOZsCEmMNRO6HH0dQF4altMktpnYSsUCKbssXEs/w37Hxg/LzleBCsqTVwfYuhrzc2ykyIhykzwILKowiQoPL4hmBtRKVOd5jydDmHapvu3qnXPSdinpI+7VE65N8k4gOugm2MX60U8809pQRbhJwDEl2EnY+inSIggudUY7J1/6kJwLwTk2kSLUKfYL6VTChBXh4sxMyu784Yjt2O1Ze1POfuu/NNm2cwHdMT54AkOr34IIU38MIZcsAEosWtqAbEH0oPoSfc+1NBWGQ+JXmV2/ke253A4FJOMqvyUkniVNic7tXJG8KGUOwc/CnnX4GO2KjtnaUGItjRkrL6WNUp/5aYRsRuqCh91wyLeAToMTGdjKAL0yVtXBlhagq0dHMvbje6ftiSefZuOub298w3l28cWNhHDNjzHKsQB0OgQUd2OVJEbbEMddxgQSEuul6kf9Xg1XugtbHxloXutp/sE8b1mtIxFkhcoQA2xK9iJpE5SW/kJKgIEnZ9kANwRrKDqtBUa65kZCO2vyw51IhzaieQThxTmjWYPCtafSERuAZa63fwHG0zLAdmwXIb1BgGSPPDpm9z542FpauwntusIuv0zMeUkH9Aq5OWHephXYUQBqhUFWO6OzUUZ0CvlVHt3IKp3NL8dQSbl1SJe7TVToAz0nmeeoL5nvVCEyIt/09SjnYpIF/wn42AfD4hIArdaGiLXWYffDIKYSybbIqfIkb/4LwT4Ypiwac8ZhmDw+uACLcs5a6qvcmD1KvT76s5P2xM8OkPeY3Xj92faKy5qQndqkdIByrlmNPnoD+VRl0FgCrSVoDOF1yqsrt9MLXBaykLbrcz3PRswsDXBa/iaNDUpV445qhV+02Zw22fDMDL67o4TvzTLut3VGrBHWPecn4O30HP4jGR5y/ZmnZXskWMOZgpF0mj6XSNFnmQNFUM4z02m7/94+wtPOEM40Zte9uoOwurW0C1oNfU5lcvmnPhywl1wpYc0fpcMkTwEQBRpzGxy4z71/uQVprUNmQYjr+Y2C+XpVLPHAgTBPZVYykuh0JAH4jY6w7gSTZFVFhPDGsLziC/RCsiGxCRkvtIkAaSMbjZOSUolj6+vthbkwyTgAKjHKZsnFxawdODBn3/nuzwBoltkrr9wIwLPOenq0FsKoSz7miBywCDNxmt2Cbn0GnTw7Fbd7f/xTe+iBh2zd6o2Ean2zXXLxhQ706C2Py87u4M2SlP4jh8r+GR1F4NwZia34UFECRQkUJVCUwItL4MwHphdPt3i1KIH/3BJg/g2tdGC7DuTsa9/J2HfvzuDskGnOOh27rtas8Ozsbs+e2AnbB0x0L3TIR7am1bN3AZr7b78dcTt6Tr8PUgvbCYDis19O2zfuZSIgA/70G/h7Bc+/979G7F1vYzErv37EQkVgX/jHtN3yJS2onHpCf20FfPHR/y9q11yen9w/L7niz6IE/l0k8MsEzgnoJjCbWOQee+wxF5K0vb2diSmLZkxMBVY4cOAAizQZFw519erVDuSlPH/84x93oVAlBIUhLbC/Ka0tW7a4Z775zW86oJzAYgplqtCmAs6dffbZ7p3nnHOOe4/e96Mf/cgEztNkXEx0AkI8/PDDJgBaW1sbfTbfaQVGE4ubHHcCyQmoJsY2AecUXlUfAfN0KN8C2Cnkqp5ROFixDylU7NjYmGOaU34uvvhiB5zT378IcG54eNiB4Hbv3m233JJnnNO35CBmO4HgamtrmVDPOpY5sdkpJK3Y7ATWk7NH5ZE8jh8/7kK1KmSsZP9SjpcDcK4gH613Se8fPApzKeDnRxg/jhFOgKiGUv9WyWLRxeeH7H+8PWybCS/R3JgPva2FneJRlEBRAr+6EhC4WTr2iSeecIylYp3T2KPQ2xozBJqTbhfwTQyfYp4TW6lY56RTdY9CuSqUuAB4Asw98MADbjzTeCF2T+ljjXH6FJzuGof27Nltn/zkJ9HPIcI/3+AY7hrrAPvgQJDvUmu4bss+TkOPsSqHceoWX3HiKHyjwjD6LPwrbI50jQPwcIMYZuQ/8ViMF4OFFnSzvFuMcA4QJb2E4pKTzmNR1oHVtFjPQrjcm7jT+eY3DgY5lmKkF5LzUV5QKTyt7iOTzAxOWWKH+LDteez68AAbKYM8lr+PNAOxE8gpjMNCDGI5nKNywzmnDM4NgQO1497lAUXrGHTkbFEeWSwOGLuyPCdnKavTLMTzrfV3nnNl5pmMbAhdo4wBzgQt8upe5zkjw46NjSTFMhIHXEDQIxxTUXbwl+Fgw9mo9+XXy13+PTlBAM3lFgkDChOIr9B1hM1VWE8B7SRbHwcJsB95CEQRAOhhyqaGd+AEGXYMCFGAjwqdNg8zYIJwXSVNrYTeO5sweWeR7xoWxpcnBjxO4UiHWYqEokKRdzFiyCeBfxDZaPmccmkxG5kIUCgWDWU7SlsJszDuHFRqNHJ+055SOFi1xC8HmkQhR5F88GKdw4NGswLImIARhW8PhkK/AqAYi/ABdSavjRhy5DRwzEa0H8dqtgDIauiwhQhRF6U+fUADGfrPAsws8eSCC/FXveIsCzdstmSkFfmU0sZgY1A+NDmTs5d85b0HZF6MMGmcu7lJ7oApiRCsjtlGYA+AYT7h9xTWUMAIheWTFKJyHrn5E20DMJ9jHqJsGXn9CNuaGt8L+O0E/ZTwi03nQHwGe8r8ccKsPWlzM4SWa9pikcbzXfoCfErGOYAvRkhdLzVm8ZEjhJQbBfhA20eWWdhsEjwndsdwWYuVdZ1DWNY1+MVqqC/aMcWgSeD4Jj/8DslJjLhcXQLE8zKEVUv300YAlQGuUPk9WFVAxFiW8smh7sG04+Oo9WHoC2D8oKrdobTVzkW945iDcDRPDx/E1pu3SspV3dABJga9QDsdHRxwbAs1DW1W09IFQ04tD6o/UkalB1NMNtEPQG4/chiyCnAcpTjSlwjNN6eQnwA9K8rbrLZpNSJfge+b0LWE4fVxvMlvoy4V4GjSt+t/OCldfRqOHsB3UPJxLxSO1JIYkbwwIQ0pXy5WbUuAHXAX49xGPq49AjBwTFe4rGFeVPPyBQBRWFPKEiJ0bogQvWp7sl+lK/Utxmp9ZI879jP1lZfpoXFEMil8JIbC2CI9qCPvROeb+yQzHflxSM/mz+tcXor86/7I3/uc67yr8JwAiw6suCx7pVvIg97/7LuVgNJevs/9+BX9pyBLleX5ZdDvwnW1Q92jeZXsglKcs2F0iBjKgoXDNnvyMMw0i1bCnLC6pRvwbTNCzoPeFHLWHSgHAX7i0ydsYeoIumLSylHyYXRjZokwbRpz6Gc1bWutvOU89EwX8mb8EaMaut4BwyRadT9k7KoMPewppGV6FNzBGH1ROlWdCs0L+5NjOI008Rs2WgdEVl70LM/xAULET41n6GaBVsQ2N7PLUsM7YQ2Csab5UkKZwiDqERpyao/NThLCPUYI5mbCRaOHxEQrEAhe33y7AjSXHD9M+NheZBMH1ER7AxKVBAQUJxxxEpBERfM5VtNNWDDCL8qu0ZgtsIwDxjDmCwTjGPDEtsmoDWIboBbA5jg6Js1vxnAH2Cc0LzFakRPlM4XKztsgTkAIJ4ezPscAqI0AKp9GxTA6OQugfRxbz1Ihq29ot0hzG7ZKCBaYacKx9VK3nG9qAzwH0xhMZDmAeRnGIYG/wgEh0gi9uDgKUBxlWCqwB+xfaeovrl216LsqAMgxQEm5mpWMUeQJuTMS54EgMNHQUTSUqxKRmwymQcaKk9Qh4ETGcww2zvMBUOQRQtoj/Grg05YcsIl09KwqH5shy3jIyAKYgzoFsGWEH83GBwBh7qN9AgZpXEXo5rMIu4xOxhIQQDtf+0pEUhH0XGc0RisvtHPlg3Dl88PK0yzgQmQOu10a2cUJQxskyugDq6yiBbayxhWWriAsK2O98uVAReRdzGvSF9q8oLHUEwshTHzB0jjy4m83sNDmCMHulVBGPlmY48S2GBF0TrYWaWSwdwW8EbDDAcFpExqvFd50drgfQtlpa2hbYSUNGy2N3eHYgBjTIwDnBEwpAE7zQqNf8L9s1IWpk4DkCDWeOYbNBlQHRrsUbGNzAGAzgMaqaROVTduw2baQR2Qn+xJ9IKCGTESZSmHJnDahMOkBDGqexnyYCAMAYY6mSAYG/cMra+Cb0OAwBmbF4qhxkCelHp1JixiWNBCSOdlqUVl4c8csNbSTfE5brGmjxdq20xwAmcLC5hgc6asg8Llb/ZbaI0My0VUBBFokjPBBy47swXQDCFrKgE/6CUJlJ+hDKQCUZQ1rrKIVe4bQwYHbyIF8sf10yG4PCLOak+wxIHwlzAYfhW+2FPpFYFYhmAA1YWwgYoAeoTpkUkf5qrhOW8DeZvR2NoQntBz6Ksv9WRqJws168WO2MH7CbUAqrajGNgPAV9JJO8J+QCjC2cia0IYXkLxIC1nCrIjkKScbVWRDcDbEHEi2hfKcBnQioEkUeQj8ZIBxxGZHw9JjTlTqK2ltxKDfaX4TZh7iAxYTgOtnT83ZXXc/Qf3mCHV4rp2/rZH1Nc1fYPWlLjOA64CRkg+SWm4Dk4B19uwdt4cfGQS4UkW4R/Q+DWR4ZMyO9g5Betxil1/Ubje8upywlMjT1RH6AP0skzgkvc58IglIUeDVGO1cwDkZllk2SYhBUwyfdAUVl7/1rXFXP/WDNqhxhdI7ZlL0T76f6BJ1hP6TPhXL1eGjabvjazv4u9ouv7jbLr0wSrhMAJ7s2BBzs3SVgHOSv9SB+nOcEJg7D8zb/Q/3AvgJWW0lIenRQ8Oji3a8DwA4tvtvXNVhr7iqlegbpwBJpMD70b/IQvaa8LnOfpC+p/6yAOeYvVECAOaAp9OMkfl+zlgkwCwdTeBSkDzUV95mcQzI5E2wq6TS5nwM3ap50iKbXA8dztq9DxxhXjJnF1zQY1vPqSdsKAKnrgWilu2iPizWawHK6JTYzWY/I7Tp/Q8xt2GcrCKUecC4MDI4af3DJ6n3Bdt+8Xr7jVd02KoesQwy/1bnlz5CEqoBskNZkT/zcv3wtbGIdqIRVqJUTYlVjmw60LTaHfBN5EuZ6Ys5be4iId2DmBxwTnUsAJoghjEuRtxcP2QH9i3Yjx+ds4mpBdu2td62b622unquw8AmeWYpq2N7JB3Z7yH6QIb0j/Qn7IGHj9rwyZQ11q3hnA8Ib47Qr/vRaVOsR7fYdddusA1r6lxIZxQZ4yZ9DXk5pnDao+x3gbWdvSAAMJrGZZwyyuSQGKguDkqugvORLeLBzu5pg410Fu2LRMkc8mDOqjzLhlRaAf37KBuV77p7H+HcB+28C7pt27Zu+hRzSR7VXFZt0r3L9Qetb8N3vZS2Q4cG7Zmnx2HwZ+bJXEZg6KmpcevtHaYbROzsLaupwwZbvS7C+M5IiJ3l0lN2eX+G9qYakg3rGJp5UT5f+Tp0zMyuE6pPMh6pH1N/GlM9p3j5Tf91UkA2GmOUTx8Aolh8uZHUmVczvk+yAfv++0Zt7/4+61lRbxdf2knIWUCepcxv0GvqEwZQ3AHX0Pda3xAguffEot31gwMubHEMoHgJIb0nJpfs5ECvjRHOft1ZXXb9q9fbeedWWEMdeo02JRCqRJVFL+b7G/mlLicn5u3Of/kO6X3Xerp77PU3vNEuuejiPOOc9D86xW1WUv27dq6ynvlRBM6dueyKTxYlUJRAUQJFCfxcCbyUoennJlq8UJTAf1oJyDhdgGb72MnAfvJg1v7X19LWNypDHBp25i8roA6/4lzfbn5T2Dphkvso4VLv+C5OAwzbQm/Ttwgzzurx7fdhmXvHW+AB0KLAacdzQHM/wZlx2rXT/zx/k29feF+USQ2ToeWbRsjPF25L20e+rMnSc58s471vvCZst36ASWztc6+dnm7x76IEXooEftnAOTHNCbAmkEJPT481NDQsT0zzpZJTQg4JhUutr693wDmB1wSUK4Q+LQDnBIgTcExsPgI2/DzgnIBquk9AsoIDRMAGge60mPO2t73Nvv3tbzvGO4VcFQit4PxRruQs0bnf+q3fsssvv9w+97nP2Ze//GX7oz/6I8dQJ6YhTa5PnjzpWOQEwiuF9UNh/HRN79izZ49j0FM5fhHgnPIrUJ3yoXTFhHfo0CEHBJQc/uIv/sK+/vWvO/koX1VVVQ6o8fu///sOBKj8CgAoYGDhUB4l3ze96U2m8IHK30s5Xk7AudPlpPWdXYey9sUvph0DXS96nagBDj9Rhbhfe3XY3gro+pwNvtXWFAF0p8uu+HdRAr9qEhBgW4DuAkucQMYCw2lckY6U3tQYpFCsCtW9bt06B4ZWSFfperHOaWzp6Ohw49B+GOc0zq5atcqxzWmcKrDNaSw4/dizd699/FMfZ4zw7cbX3mBXXHIpY0beuelWz3Uzi524efChEFqScIcyOsPEkBHgTQFl5BhipZUVZW51Dmq5UljoZW1bzGyOGU4r+1qkdk4DrurbLYDL6acd8ig1mcsszgesemvp1y3/Mq4qtFcUIJYD3MlbSJpaDQ7iSzZ95JCF52ZhHGGRuqMdY7uSdLjXOcBJTmvkeDSyLARrMTi/pJwPZ6ndyz5gqhh5URjRQHFc5EVSQeRBUH60SC1gAmWXl1IOD623OwvZXdc/yhIOFMZKx+DBDXrMXZG4eYaiu3MpFoIXcMAlkV0EB2EZYJ6YnP28Vve42lHemRgEgInS/UdtGiBDKSF0yzpaLRzDOagFcd7hXNWSK+1DId9yc+M2NXAf/vPjuJTgpWBHeHYJlz+TimxjvZW0rbJI9VqIgzphoyiFiY3Fd8lRGdWiuxzoKhn5RYSOgUS+c7lCSMl9HKCPe3LIZIn6kzxLcIyIkMzVIY4JD29jhjIlcdpowVzr7Y4UTC8ibbEEeoDBctOThJAdhn2E8jU3W6yjDYCgQAiyGQiVx8K6HB4+D6usAWCFYA5g0+BuC2YOg4ME6IYDO0PbScGe4eMcLG1Ya1HAFOmyDthVxOCTd56X4gDyeS8dhDxQOOc5xCm0dIzwrifwLw/S4HCow2QUwqkQwYnrERotVN1l4doe2BZbAQIS6pX0wLPRbiQrPgodKAcuecjKQb84YPP9DwHu2E9/xfmz4hUABrop6y5bbJx8KwAAQABJREFU6v8RgIgZq+m80irab8BB2+TaEhWBWBSOjPKkhyw+uM/io8fJ6zRNDycn+c3EI7AXNFl58wYAFucCROlw5RBDoGpBYVrF8iiHY0ihGdWGAKoFcVjuAO1lE/sA3gE24ZwLnwlQwC9vIIJeGyCxFiaesL3AzmeENAxS1JtzMnNKB21ATkbH5rYEY9MJAIBskqgENFfd0Q0WBnDB7JANHz4G84kPgGC1VXedBaAO+46wjs4ZpYZECLmAEG3zoztgfToCUw4sPbSVJajX0wKRVrRZdf0mK61dDUijmncC4sORrFCLcgB5QkMgKznl9duStNPUnKXihCJePEzaw0SCAzigvgEQMFSCc7UevUA+ExHKBhgyChDPXQdkYDjlBewLcAa59k8nzC6MAFIcML8OIAtyCcnBSpsVuFmMoLKBe2Gtlk0uAHPBniehl92Rd+hJ/VAfTokgUvVvPoXDqSdaqPpxElB3ljBT3ECbpkcLBCZ9zu0uxGzhIb4d6Molo/S4Lt3KuBhh/NHftAI+6jekfdr7C+Ob6kdHIT+n58ld+BX8R+U4HTinPOu3jkI5Nc9U2TQ31fje3d1NqDXaoVg8ZnfY6IGnbGpmwarae6xp5RZAZSt4GIYp9SHCh7m0HHBuytIzAGBH98DCdtIi9M0o4CKFV0wTly3S1GVlLesBM69BD6MbxI6KzhT7UF74qllkr3+VHoya6UUAKDPHIYUbw3ebZ/KRXlSo7XBlj4Wr16FzAHQA4skyMGtEkS52uh2gnGOfExCAvusBjMmN3W8LJ+4nbGHG6le9xmItl/PufksPP2xjA70AW5krd2+zkrpu8oCud8xOjBkau8VsMorOmwI8lxxlbjSH7sZmUWMTQLgCkFPzhRau30Z+yAsAHIH7Q8jbAedUNslfgzL6PQtoLjs/iArsBQQ9hE6CAUqOeYVpi1UCCiK0Z9VafvfQHsWkrvfwP0W0KOMM+lOhF8WwFsGx7af7LD1GfR0/im5mo1P7Kot1rVSLtrnxUZiGDsL+EraWzh7wzZ2MxwDWAPwL9O+2FcDqNz+83+YHTlgsOU/4UUDoAAQFOssB4otVrwHoRP01dlgawJugIfkxnBEMB76ltKGNb60Dqokhn3R8J2xnbNKLA54RsM4xpHEfz4eqsT0q0YmV3ehf2hNjs3uO2/hBMoDLAN4oVKDCg8t2SU0dt/4TO2DVSVl991YrabkAMD5gaJcXkqe0WAruX2rNbY7whWwReEDgOVhlczNTjJv7AQT2YleMU/44dakxT+Hpuql7QGXVZ1kAaG6JeHXCrjsgEvaPICMCKfIKxgnqE1svSDIeLqC/YajNMXZwQRWEjVjl2ma4ttHCVY3UaT3vEBiTa7JzNM7KhqBpqcieQhEHAPimAYEc32cLADl71m6i312E/NcgD7GjCVwmRlXJGjCQHkZGjCYyp+l3QCAZa+YmHiOK6E5GokULJyg/c3mx1oVqGO9b1pKf9ci8i7cCdlMaalPUmTZbiCk4zwarh2YgGyYU8fQgYfpGaGOwlolNVUAvn/JVNtMHGQ8r2ikyZXTACL7UiUmWogKcE+OYGgT2BpkMTe209PF7bXpoyCrbLrLK1a91IMo80BLZAaQRtbDsXeUr62xt6g+QRoQ2mpt+xoLBJ9ngMuJUgfSZwEnSJ9GaLjZzsJm0ehPyriUbkg89lPFdsnYVJ2EICKR+iZ2QgY03hb0mUGbO6NPYX2LaVXjVKGxxpbAORkq6sRvYrCCgr2wFpYxY3UYgyiomaQHuvAw23+weG+3bbfF4HKBpk9V2baWbrUUG6DyAPs5uJy85MR3aDEXkHJtdhCrKcT4lwCLGcjioQHfQVshPRvYgjY6Riveq3amvCDzJezmkQgWkzWD75IFzAG8EaIJ1LASQ7e4fzdv//upd6Pk4QJKr7NpXdsI6R9uLoIeof2Ys7p3aLCMsjBrkzDTAst1TPHvc+vqxuBROGTkvADJUqNSN58BUdmm9bT6L8K8V5FmAFAELUVCaB8m0EkAqIT2Mz0GbeQSmU56lD91LKHu+nqkjyqCzArWIsVs9mRRIj29tdGADBj9Vba7JCtyo5FLk+ekdi/bRj/0Ye6DN3nD9Rrvx9WXW2o5tANCU0Y20y3mT5MUb1MbJW4LQnsd6l+ypZ4ZtYZ75YIj2jITjOEQENm5uKbVzz41ZexchWZmD6hkdaHDyRL/X3MZNBkmWPDm9rDbGXMzZLgI90gFymtxx0AzJN9eztD3d5+Sel4eTGHlT9hTuVY9EEIjaShzShBPHs/b4z/qxbxZty6YW27Cplg1vPMu7lA2BydXWlUeB57hAeM3ADhyaI5zlIHIRGB6QMvWXBFHnEWq5CTa9szY3Et6zAnY+sRsynrjSafxUvvJlJleUA+Ui4VNeXdMbNJbpP5oZMnXFcbqDl7gTmmPk26fGKHcLzyM312n45pxsNtfGqchjh+P2yJPz2BqLtnlDHeWEObKOeTRAsDwQTcA56TqSUcXTl3KMO8Ow1D2za8SGBmAO9AUwRt0xtqayU9YAw9y6DTW2dlUtjHu0P/Ut5Y68ORAsbVHhf9WLZDPkFTtvoK063cHNqlvX32lvz64puPkAFwHzie0PBUCi+fZFkZRB8kpGlCaVGGRKrO9Ezh58CIZvNmpt3NTNWgybzqrR6bw8pMbB4d6VFwzzBJUjayf7pm3fvlkb7CdHPkBs7osnsYUYZxqbqokOUm8ruqNumULvFhhN8+PCkcEeVntUqGBns5I3x0LMO3Nqc5RPzzmGTHRNiIm3JBLAVOqAjySkqa1k4Bqmkwl54b4AZmG1Ac2tMzDiDRwN7P98vd8efnwP60pN9oabNtimLWzGqNCcVvmQfaD2o7kt80YymoPh9fjRhH3/+8cBiCYAfFaRL5hMNYx6M4QQXrKLLm22C7bDGNhIX4zk7Sq0l3ImLK9uRDbkA5FPjcft+4Dm7r7rW9bdBbD3+tfbpZdczkYH6kjtj8Yq4Lr6eF5X5UugVM7kKALnzkRqxWeKEihKoCiBogT+DQmcNpL/G3cWLxcl8HKWgOxmGY1DIzl7+GdZ+/K3MvbgM3kzMYbt18Jk4sqzfdg8wnbj1Vi0GI1Eo7EHH83Ye9+fsl2wzqm3yRiuYq55PmCH3317xG6+Ln/v6bJ1oDnCs37m7zP2T/fmJyWnX3/2b9J65SUhu+OTgODYBVQ4RoYD+/w/pO2jX8UIdm8tXJFrA8Bel2fv+d2IveNmTeBPXSv+VZTAv5cEfpnAOTHBiTVNzG1i6vnwhz9sN998s3PIaLI6Pz9PiIR+55gQe4/CpL7vfe9zoIWXApxTGFSlc9555zkQm5xwCpUqxjeFdX3rW99qt99+u+3cudOFj73pppscSEKLjElCZCgf+l6/fr0DQCgvCtN3OnBOaYqBSMx4YiN497vfbTfccINjo1MI2A9+8IOuHLfccsvPBc7ddtttDiAo0IbyK5CGnFQC3QlIODAw4NL/ecA5yU+yFQjw1a9+tSuLWP4KziCxzY2Ojroyn86qd6Zt6+UKnDslr8Dufjhrd347Yw/vzlkvjKIFAF0jlPvvvDFkr7oqbGfDJlpTzaKWhpTiUZRAUQK/UhIQK6hAcHfccYcbfwSQE2BO3wIeS3+KSU7gZOlVschJL2tcuOeeexzgWuA7gQYEkNNYIL17zTXXOLC3nOwFIIHuUXo6tDC7Z98++8tP3eoWxW+6/jq74qLteeAcQBfnJCdcpxfFlcbCqUIe5nCqawE9AvDJk3OThewAlgznseTLK+W3bEstxOsdWqmXM5RFUIHSHEMaC6i4ZtyirNb85UiSp8J3TgRu1eq0yyNLtlpZZ2xWqJyAFWGVw92HQzA3OmnH7r/PIowpTWvXWInG1yYcgrBPuAVfHD5k2bFUiU3BAyijBeAci8F6v0KhyGEkIE2e8Y1lWgH1tAquPOnVvE+APbeFXWXiT8eIpoVst0jPKc7JgRngKMg7fnl2eYGYK/JikYY4VcQyASMFjrUcbBMhFqYVEtftoOea5hIIiPV70p6HbW50yOafeJTQNset5cKt1nLuZnBIOOVx/Dv54g2Uw9otnDs2DhhnZnYCoDsKJginMCwNKqdXV2uhFthGKptImnBbfjMAIpWdOhSQTLvsBYBcduwpyypvIOcef2vxWvvFFVrP0+SFhWzJNSOHJRUYZtHeWIhXHaqt4HXnPP9RnyqTYwpxQDPS5b06GcCelj1xwkaxeUaPHLR2bJuGc3GiNtbRhkgPdp3AhWaj7nF45J2nLP4T6jSYPg5I5BjOeUKAKcSvwJyEEfMJuecBvhIgLQ2zShIHiaoxDKggggPIhZRTm+WsJ+8Cfy9NHbTJ8SMWJ/yrB7OfyhiGYaMEJ4hCSUVgHqvqhAENJpKgBHZigS5IwhPbGWU0AG9smSJJ2onOwFCwMPiIJSYPEA6r3sq6f8NCtSsBcOx2wLnJiWmrab/UyjuuB3DWxDt4TqKjfTqPOQx4WXbtL00DrAD8IZaRaEzhgyrAmwBeqF+Lk3kFMsExKi+KAGDkWwCODODOEE4RAc0ELg3ihBqa6bf4xBFbhFklAyuV8iiQmOomWuJbZV0lAIsu8gigEqYcGgldFSAOTjvnS1KfpG17ONSDNPJeABzT9ySO5gXAjj1W1r4CVj6SnOq30WMAENMA55oB87QAfqN8HqHY5CRXaDjH9pAdJxrtcUtMn8QnOUkfoi/QRiM4zkupv0j1CufUF9jAeVpd4giIZkUF0S4oK99inpQjPbUwbhPju2x+5hikMDAOom8UMsvPUo/Ucbih2SJdawHQUcYwbQv2K4qDfKg3HLXuPWKIUvvHcaZwl5m5IcvWADCobKVekCmTYdnfYnC+++67bS9gY21M0eaQArjJOV9J4eV0qMwqv8YifTSu6FOQha7pozBZi4uLsEtMAIIitCHgOd0noKnulXwFnONWzqtyqBopDg6d0xECNFdaUgrLodh0CL/MuCjwtb71bh2FvBTAZTqn60qrMObp3K/qofwrnwW5KZ+n/9bfaoca62UfXHbZZXbJJZdYndoh7FfB7FM2sPdJAJ6L1ti+xupXbkR/wbqpUJ8CSqXpU2gIF5YbkE6wNAS7FUyY8ZOokVn6TArQATqzgs1W9d30X/RBiPCQDiiiQY5OSB40Xrj+ospBjUo3ZBeGAcQetqmR4wxyMy5EZZiOprpPoStLYLesRF+UNK0FoNRJd65kDEQnMNb6pJuByS0ioFGEPArEIN0w9iDAuQfYBJCyhnU3WHn3VagA2HcAh4/3HbdoRZfV9GwDWNTJM4CcpPcAJXgR9WeBrHsBufUCTIN9LjHBOcoXA5BE+Tzy4JWgS6M8C4gmx5gsgIVjtxSwR81PDlscyB4KP5GYo3wnHBtoBDYwH4CQoAtijZLeKIlFrKZps8VqLkDddZAPjflcQj4W0niP7cGfAs9FSc/PDFhmfJ/N9AICSvqwA66wWGc3rwXoPjZsQ7372cAXtsbu1YCbexA3wF8hm5CN8qXxMDtDuSaHWYCEqTSDU5ywnAqX6Fey8aF8JeMo4yFsMBkY4wQMCCMTgbmcbZSWDSE5kUHGilxyxKbHnrD5id3ICznJLuFdjIKALcgVbF4VDT1W08EYTbvIh4znWScfCYsx3wGRkaMDzk0DDDxmg727ATJlrG7FeQDnLiYv6FRuF4OYbKA0eUIq1D2QJMZrMfO6/GmkEvB7iTEMsGKS8SsbDCH3FOYUoK4S2HdLkEtsHUUA+M3YtxQlPxh0IcYHN4474AbjquwaGLBy84uWmuyz+PhRxtZJ8HkC3ciGkR2F7RbFHoOpq6KZ0K/1K0mTUN+ybdTe1TfpHwFjRoj6c6HdYefKMO4N9e61ybGT1rN2A/K5jH0ksJapHYqVmVCnLhytWLRk+zgEFy0Bw0rMrgGgkczCAcp3kH4zRVPBVuE+vwq2W8YgD0YfA2AS5LDfBGRQH1R2VCTZYWp/7qOxa5a2029jgyfQExNsNoHxDtl6KcrHWKmQxmW1LYD71gE2XEU6yE0WktO1XAdckQPMngfOiVeLep/ZbekT99vk4IBVdGK3rH2da1NUDNUm24M8SUaMuSpbjk82TH9GJ4eo22iij8jNBwFADiNzWHPRBx4h6sOEsA/XdptX2QWOAzAt47z6i9iN+McN9c4AAWzkysd4nl1MAVAcg23yOGB5gHjYRoHaEO3N9UHqr7apg3Z6LnbyesqisLRqXQDTaEsh2bpqCwDynQ5MA76b3Wljx3YCbknDxtwBEPdcstLNM7Dlcq/aqUKNsjrIu9jcIKYqmNIEtg8A8adoC2nKHgJs40LBSpwC99MvXNh7QKAK/UiJlBH6IeJS96WpZmO0KWSvtXawtY5oUa+758cJ+8rtP+K+JXvjjZfZq65ptZoqUqDeRe4nVj+BbfJzFxLkWeeDGM7ZUzumALTM2cIcOgYbqRwW5zXrq+2sLaVsZA25TfwxgYqkv9SmedyZVXzryEn+SpuPAHH6KTm4aRjvUefVl27SPbrsk5GAeRX/8oxKypgrwCBNU8/RNalzbibvadrsU08v2l986KdEUOiym1+7jvCjhGsFGJaif2l+FGaOWYJQ1L61KUxzXx1Se2LkchlUJt3bdYU8IBOZ9DqtK7/4sVzwn/tU4bpSfOGUdUfhiutKnMgs2zEqtuwUte1T+S3crXPLB8+oLeSBRcvn8oJ2P1Q2Z+Y4+epUIV8vkJZ74vRcuRMv8s+L3Vt4T+Hx/PtkU6luZQ646fKzZSzc9/znOC/gF1+OAbRw+fQy0g9URr1BNtCpMuovtatTh96vtlY46eTOXfnv/PPOjlo+ob+dHajf7hzpKc/L7xKzuWu/nFOirq1Rh9r0J/syTCEFxFMaLm9692npqGB6XvMqhRXWJXff8nmK5TpViLSkuyUJpcVld+jbd+sErNNi62odxz3P+Rx5EOOesyn47eQutURfFDuiSDAU+lTvVvYFXnTtiLmQHlMhnV4FNKjfWotIM08bOqboOUP28GP77Zyzm9jQvs42bCzFViMt3cej7h/9oXGHTyYdtimIy48cStphwJN9ffM2v4jurKqx1o4KO2tjyLpXhbFJPSsBwOqUAI9rE5x7+XIbkF4JkNPEZNx+cOe/2N0//KZ1r2izG15zo1126RWM77qfcYv/ZPdSMsor2LLLlXJ2RkcROHdGYis+VJRAUQJFCRQl8OISeGmD04unXbxalMB/DgnIOJ2eCWznvpzdAWDu+/dlDMZiFj7MGis927bGt2sBwL31prBVYYyefhw5kbMPfDpt3/4xCwIYkQ3lnr1im2+//c6IXXOxZoDPPZLMHfWez3yJ8Kw/wQn03MvP+YWfxYH0/vrDMatlwl04RoZgnPt7GOdu/9fAOd1DZAi7/rKQfez9UVvZ6Uz9wqPF76IE/l0k8MsGzg3DcPL+97+fHVPfd4A1AcwEqJND/r777rOvfOUrjnFODGtywOjel8o4p4nwn/3Zn9n111/vwBAKnXrrrbe6PLzxjW+0t7zlLfYP//APjrHud37nd+xd73qXY2tTngSmE8Oc8vCnf/qnjoXohYBz2jErIOAdd9zh3vOHf/iH1t3d7RaexE73N3/zN46lQM9eeumlDtymvxUaViA5sRJ997vfdaFYFQ5QrHwXXHCBm7R+4xvfcOH8JA8B834ecE6LXF/84hftM5/5jLW2tjpg4Pnnn8982XOhYj/96U87pj/JQmEIxdjxUo4icO6U9P7pbtrvD9J2LyG8RyYCYy3DLYqu7/TsHa8L2/WvClt3B3voywqLeKeeLf5VlEBRAr88CWhMFKD7pz/9qWOSKyyYns6eo8VDAQbEOHrFFVc4cLTACBrPxFZ38OBBB0qQThUoWQx0AlrLwV5gmyukW3DGa+F2z9799tFbP8OCq29vEXBu+1arLoO5gxjQCjmaLcHrUYn7rRSjElYOeW/wCcHKBbMIobQsAZhpQY455CcHSQVLmwC3fYAlYpQRgMUXi5vATziCxNwSJPkN24YcwQHgwFQljv1YiZVgB7sd+IDyAkJ9BUlCHmnxmjXbgHiqOQxbhUaLyDuMoyt1rM+O/sv3rRKwex0A9OjF2y3UgbO4HLaqEgAyUcYX2CUE12OrNH/iyMTxmQW0pbCXWS0sy0kJsMeDyS0g7J1W7pVfH0ebpXCNI4cccsCtSBkoGwvo4VLuJ7SoFnMFLNMCfFgLy6lFHNB8lnBOK7wR5Zcj3y8jFCYOezKOD0qL2zj10wAXyEMKJgUteMPZxbo4i8N4mbwwju44QMWDR23hnh/b0ZPHrO2Ki6yJupFD1RSSqowQljjWiZPKk3zrI1kDYAgWhmAzwUkJi4Gsf7+C+qjDcVsO+5cHkwFh38TqgveR98Sxc1hoF+AKWWgxPgR4Qo5pEwCBsVuL7q7NwDII3UO+XmCWCSKEuwTg5gPisoC2ofyTjAMpcm/AvZIfnj7SIO0octOH9xCny5IHj1jfQw/Y0J6nbdX6tdZ88YXUXwcESdWAK2poc+SZZuc74BzvppE58FtSoboIP5oA/CQHtNhcwsiYelH7DHDM5kobACgSPou2FiKEq0d+PRy6gYBa9CVfXki+E4Ram4dVIAeYJwQTUlm0FOYlAByzgHzGh2AET1hNayPMcVvMq9kImA+GYoCinuSnhkmYwwC2J8e0oXYGSGMJRrWkgFzkoaTzcoAPAOdmD1hq4EGYQabBL54D6OxyJlyEEwNw4IA/MKHRGagt0gB4IuAIaDRkSR/D0elTFoVXFfNQJod8+A7JsSAAmIAKaue0HVqvA705RnHaYmZ21BYBWKRpcyWEVI3RthQaMLkwAUDsKL6JUYCVTVa26nLLVK1zbYO7cPpSRnloAOEGYjMC4uApfGKy3xJHH7MFGOfCrautcuVawDn0BQB6IwcOwIyXtrqGVittXUlboK0qJKxzlpAzwCXqhwEhiC1H/yaUlatTAdfClN+1N94r8Az1KcBPTqha8oBrCBmrT5Ef/kZK7jsJQHR66gTgk1mrjZaTc9iR6Ac5MccBkpilj2dwptes3mhlVbQtq0acvEPOdoUClC9dgBuBNpT2IkxcizDzAZyIwvSk9qQ3yf6WjhbDpxiYtblEjF/St84hR1t6uR0FJ6LmN/pb8wPXt+QcRG8I5CW5jQFsPn78hB07CjsT45XOh2H5EhBOAEQHDMUpJpY5OQnlxEwzviQAkejeBOGclV5VVbW1tbdbT0+PY1dtagbUCpBO4LjCoTFT8yblR38XgOI69+twFGRayOvpv/W3gIdiK5ecNQ9TGPYmwrKK7cMWn7K+vU+hY+LW0rzS6lesQfejL+jLoSx6l/CMYtFCwfPRyIBOVcjDBIBYAD4oDje+eTHpIfpfAPMbTFUKY6nx24OVjBfTG+ifYlcSYEdjahgdz3iTnBxH14yTRho2MDiD6MfZZA52ScJrw4RVzjhU077Bwk3bGe/Vt5brDT0q3RJgH+CO56MxknEGEHi87zHmjktWtfY6q1h5pcVgnEudBMjTdxTgL6GiO7eAb+pweRWTigYgLyJdQn9ELweEjM3B/CMmUY/3hGQ7AJxBMMyPGLdKCe2ojQEAY6RnfLHnZigfYBFaNHJA5/BIBntkbnwYoOEEYW0NXQJrHjo3jvwWZg/Z0uwALDmdhKi+Fr2xmXGLdHBgSxYGM5ZC42ZgAtKbIsgylASgO9Zrc2xGSzNOV7d2WqxrBe8N2cLIgI0f2wMrTdTqVq4B1MzmM+rBA6TkOwc0+hYgmFjUNIZDc4rsGAewbXzsAcOWEbumyih2v4x+o+9CyMJXOemfWZj9MsuMc05fJmGwA5CUAWAYQu+WoLsjANdzyXFbmO4HXA4YkpCxrWu2W4ywqwFsOtKNjj1NwCLYsYIg7vSqB6gviFNe9O/kyEHYY7JWAeAuQrjdbAT7jLHWZ9yiZTBGq17IJoAkP1don4wHqid0AYhMhE84xWCEnyOuHfoYBT5tMsgAbEsqdCxgb0JmeNiIgWxNUA8CJ5B5/hTYjXqAqS8AXZQCeOXAhtRFuIz3oX9yCvWdOGpL84QaBzReQRj0+m4AkLQvDFrSE6MaCWYZM+gDgcJZMm570lWgCCZGDtn0bL91rjnLytsupl2sytcTNh5Ua9xP38Hmkb0oEJ2vDQEAspUvBkHXB7Pcx+BDN+OjfgjIzWM8C7DXzGuizmRj8b9sDYUcVP9l8Mq58nI/dhsK2FLYFzNTE4DG0oQKjFpZKf0Ve2lhetRmAe0Lt9fUsQaG5q2YgmeRLv1YacA46VEu0IW8hrRkE2PX5qaxW3ofsamxMStbeZmVrbmB9oXcadMewDmFNvawJ2jY+bqRWUW/T8JoLLsxBljfJzxyANiUzHFOwDnqLNxI+WqwWbA9ATxKT/sCMTJ2iyHPz6rNMh4A8PUElueNWcB/87MztsQY7dPWYgDPQmK44+/03AlbBBAZod/VdFxoJa2XE1WwjREde1r5wN4I5ag7VFaQkU3CeUD8wfwemzq535YQQVVrt1V1bSJfjRQHYCbt2slDbRvAqazpUIBNk66n7rA5YwBQ6G/q4elFLDls+RS2fFZAMv4TC1kpzoByACRhZJmFbW0pQUr0d6YWlB3x8xHApIK2S3K08cB+8MMF++rXfop+ytlNN1xoV16GfYbKkmYUcE4RbcvLfMBlAonq7aQFo9fiEpzFk7A4TWZhKhN4lG0zJFoBiEXmVQg5lKOzKkuwJ3mnoG6LFFws0gKuqm2mBKJFdcbwV5TikxAQR5uixPiWFOOfwG2Mpdp0FGPcjcbIh1tTIm+UPbEEuAabW+FdBZ4LYWtFmE/EuC/CfRoznoKp7EMfehBcbI+98fq1dt01hJWmeZO8zDAr5b5K3p0g7PTQ0JBpY5lsi8L8Vd8aCzUG6iicdz+K//zaSkDjwC9yqElKN/Hv8u35tqA2IdtR1wRQ07xKm940FOiaPmJ0k43pwr4yX9V97qBNFQ5nP9K2XDvjpObnSlNAPaWhO13LU5r87cBwy8/rWQEgdUFgTq1X5FPWv/nz/EE6+pXPk/q5niljLaSlpcWtnWvTpHSiDl1DXbh3YhbbwiKb79TtNa9y70GH0Ke1EUpkHcrKEv0/yXiX1VyI/GvrWxg9G2MOBjGr6zujvb59/fYRe+TxwzAGNsL8uMq6u9H7VITSCGG/xJQuetbNnzUu0e/17gWG5Uk2aovpMk3fFNiuvIrxhjWJMMDhEGyaleXodXRojvl/BsWVSkpvSB+zloOeybCuMgM78j33fN/u/cm3rae7AxDtTTDOXcZGKik6DSqqL3QJz+QZ55briytnchSBc2citeIzRQkUJVCUQFEC/4YENMgXj6IEihJ4IQnIWE0w+R0YDOyue7P2z3fB+LOfiSrdppIJ7kpCsV5/mW+vf0PEtsL280LHIhPRO3+StQ9+NGXYuPYaGOLe+dsRU4jV5x8iedi9P2uf+1LGvg7QDjv42SnD8+/V75Z6z979ppD98buj0Hqf6ssj5Pfzf5+yj97BwssLpCBjuavBs//5XyL2h/+dSfHyuuYLvaN4riiBM5HAfyRwTgCuL3zhC/aGN7zBAdcEkBO4S+xun/3sZ93EWmFDt23b5sIwCSD22GOPOSDbH/zBHzh2NIHKBEr72Mc+5hwVKnMhVOuWLVscw1ohVKtC7QlYpvd86EMfcqx2YpXr6+uzK6+80m688UZrx/EjMJzY4eR0U0hUsQjdAeBNADk5gsSCpzTlJBF7m8LLykmiexXK9SMf+YhjnHvPe97zbKhWlUvguM9//vMOIKfwrz09PY4l43vf+557ZzPh0ASoU5mffPJJBwrUpFRAPeVNi1S33HKLA2KIrU4hXSdZiVNelWdN6gvAOYH0FKpVALtCqFYtKuzYscOdk5NRzEhiPdICwL333utY9gSoE4DuwgsvdPI/kzZUeKYInCtIIv+9xMLs7d/O2oP3ZOw+xqPJORY7WXHRAvbNl4ftpuvCdsn2ECyELIag20+NDM9Np/irKIGiBP7jJKAd9RoXpX8FNJAe1QJswUGg39LTGi80fugjnSpngu6bgMlHHwHpBGIQwE4scwJA63A7pjl/enr6W2k64NwnP0Pom7D95quusYvWExoMZogFwmcqTEwCh0e2roowoW04FFdYtIzwIzhE/ASAqwnAKYMjMIvgkELRQCplQV2JlbXi6GnvIARkDc5QFksBiIkNIzM1AGPEAI5LnG5xjF5AV5mGJkvhMK5obbcamMPCKjdApyyhr1L9J21hZtY5bNIKpwbArqKxzmoom8ci7OQzu2zwzh9aw2A/Ia1azNt8lmXaWi3a1mkV3QDp6nF8Ap7LaQEbIJyXJJwd4UzjI7CC4fBMshtFzCAxQFqlbZStlbCVWrDWvYtiOsFJNzxuiSnlAbcVO8LDXK/qbLdSwAohQrpnBXRDxt4izCHjJy0+jPMV514OQKGvkHdldbCLdRM+FKaVCjxeyDyX4DpMMXHG1inSzqaSQBFYfAaMVwZjXqyxGYASzq/HdtriPT+1YyN9VrF+jdVsXo8TvRm5NlnNylWUk3BSLE4r7J0LA0cefdhqMkPHbZHvtML84JiM1QEIa60hDzCAleKhguErB6hN9RcfGbbZOUKu4KzMIf9QGU6shlqrbWnHP4pM5cDGQZuCYSw9PmqJoUHnfE7hqPdwykbLCUMEi1CsFmexygdeIKeQqmO0C+olRTtK0zbEVijgXil5KIMFzIuzaL5zn5145AEb2w9wrq3JGjdusIB24zfzbgBZ4XZkBgDDdwvoOOXFkCfGnMQU7COT+NQBDBCO1iMUYQiwhxcVyGuecHDlFq7rgfUE5/MsLC5zIzgBmEThBEjhSE8zoQrDcBKhbfhQfbD0jwtDYBLSwcHuCWiBIzw1fMhmxk5YeTXMHSsBLTaeh4e/a9mJjkMgSXi7OUIHxkdxOxIajvoTACQ+ftCScwPgTClrz1W0wzXIer8lTtwHyGLKqprOsmjdRldvmQxgB5w3YZidQtE20mcCCZuLypfG2ZwWEw5VEMMJHsYJk8VBugTALkYY2CjXgnnuo2/Js5oGWJpC1gK8lgCQKwXAKidFBk9HFn0RxqEdAliikGABIWWzIw9benKHLZYQ/mnzqy1Tew6yKJOrGYAKgEPaaWZhEjwoAEvEFyFkbiQ7YVlC5S7FASC2r7fynvVEKiTNmZPWv/cZnORLMFbW0eZacGgDLiG1gPL5AHCj0gfkyzGRAERJEc4vAMwSBpAbJhygHDZZADw+Dhu/DJZM6iMOCDWXmncMVhH6scqWxuucQZ+p/4VwBqW57uH8L/NhgsnilKf/grix7BBMQLOTtlBBGKm1m9Ff3byjDogpbQYAbw4HfGYOpzsgToWqCtNXPMCvGYB9ucaNAGC6cQDlJ8HSy7KB77zzTsf4dd111zkbXTr55Xo4J6IWDTicc0sLIxw6r7mL2LvFinr40CE7ceKEG6PUFsupt8aGBuYTrW6cKiunzSHnwrin8XAB9upp9Ogk+mN8csLmSU8OzPLyCtpXLeGuum3turVu44/GQ411Gvs0rhXyUPjbnfg1+Ucy0FHI+/NlrHFe81SxzoltTvOrRrGsinFu4Wk7eeBpm5+aZw2oDRBXqyVp0wuwPEUBzpUCwonGAIoRopnOyDOMafOAhubHYfWhPxBONIzOiAAezmUYVxgTwgp3TP8JAFDlYJukg7lnxbiUAVCS9uewCQT6wBsLi4nCnXtiXAIsIVCO1HUaHTo9tBPX7ZxVtqyzWOtV6KvVvJ+1LvSxwF8ZQgqmxS4GwCsHIKQ0Cvtn/CjjDX2Y8Hdl615jJSsusjIxzp18yCaOHwRwUm/VhOX2SuoAfKHnAMOESgDGVjI2C0hFiNAUgNol7KL0IoHDsXFK0YNilBVUPQUjk49OigLSSGfRo3OEPE0zBsLuKcxWWsB9mlOMthUC2B8AahHY3bHkqd/j184Abk5OP075niIP5VbfCsCPELBeCQB1zbEIsxekGYsJmb2YACiG8hOZSjl636ax2cYnsC0A7bR1WImAc+ihJGPy9LFdOLlZT2zvtFxlG4AuwMyEEI+iz33YOf2SVsrAOwCVCpSURlfL0ewDtlMdGoBFhdvOYa/lagiJR1hSf+YEzFaTjNtwz6GH5wB/ax2wlHG/DFA/jUCdF/kAigtT74w3BitXZvyAjZ7sBRuZs5Y151oF4CKMiXz9CXwJyDsZJ4Qm4Ekf0HlMIEu+g/i4TY8CIEcOFZ3nWbjtCvR2CxsuAEnC7BlHJknAWlnaVIQxqRSwXwwmMr8Muy0GUJAsCRCYJSR7KoEMk4RZBcQdDcOKQz17GdrpPCVQ+FqAjKC9AW4zFoIuCEnIGtkB3qapt3BJDR82UzB+GMxlqjyPDQ0kxPND9J0nbXF4l03Abhgt77aWtZfR7ruoD8YTgSjRWwGsiElA9hnyo40OETZDBAtLhJs7bjOLw9axfoOVtwKc87uxVcj8PABszmeQfRbAWI66E0QpVloNWyL2CnLnBeDxkoxzbOgg5G4EMHksypgPyF2qIAMozYOlL6K8MCYnZw86gFuYCTw1DMMr/9JIoyXoUIeIYDwU/RjXfcoWIjyfBdiwM702299rS+NjVk8Y8gqAgX4T9gwlofNRh7NscmATAJ8cG1ccKxtt3kuctMzIbpudZQxeealFVmInEAo4F0xzH6FL48w5sB/FlEdCbpz3YHRMlzey2aUMvQNIdJ7yL07zGtKm/YdgTwzHmhFrCVy9ACxL04BEyStQrsQC4NyFEG0BsCBVlSD8bI5ND5FS2LfLYJCjrBmlCcguDChNbTQIFthjgK3c/zj1g/yazoXp91rzqrqxn7CBDOa93Dg2Z5+lZ7Gt4oDiaGvhMDZl8rjNwuicoLNWtK2F1XIDfb2W8Qxbg3adFisn9a42JDBhqVEXQTPlhLUS4GWSfjPLAv7J3iUbHkqxCQPWZ8qoEKw11WXW0VZlXe1l2GKezU7lGBOTNjK0SLkAtjAFigBga22O2aquMmtuEBjN7Ps/jNtttz9AvkvsN67cYutWVQLqXIIVb0n7Cayupcy6VpRYcz16sZSNF9yZQZfNLYStf8jsxLF5m5uC7Y85Ux2MwmXVUZsXaG9xwVZ38GxrCfYj6mcxY7v30J4JD9xYWw3QL2YjY0mbZ9NOe1fUVqzGLkS3Y6rZ0GDWBkfiROVgDsD8LIq9VltdC5C90jpWwABJXczMpm1wYNrGR9G5yCEAMBMGpNzYUGc9KyutpY1NSpG4Pf0Um3w/9DP69Aq79ooeu2ArNi16bBodHUY3dXSW2KaNEZuDpfDhhx9x65EaB2VraVx8/hhJw3v2nP4uHr9+EshbbRqNfrHDhfqkSwpU5mwkzfF5VDrTHbST/G/Z9qdSdWE/sSN1WwE0J9uVhuXu198JxoyMwHXcp98Chrl7eEbvUnqF9+q3Y6NjjqPDge6Uuv7nmtprwZbT9dPPiVlfvwXk03cDawqKSqN1d63VF+xZ5ZXu6wBzI6NJO9kPe/N0FmCp3uehA3xrp890dJXCTIltg94cGEwSDnfMZgD6Z7DJfMbMUnRoU0uFrVxTbRX066ETvn3jjlGAc0ds04YGu+ySLu4hVPwMYx1gvpraiHWSZltnGECcQG/yG8IUDehtcjawvhMJ+vsSbOZxqyCiSR3rMrFYNbY6k0VA4Getg0myOYzeMXRjhrnABH24xsrQm0tsqJuZweZDt+7afa/t2nmvrV/Xba9/nYBzl6LbGT+Z3zLYIWvVn+TIWOhqSZI8s6MInDszuRWfKkqgKIGiBIoSeFEJnDI0XvS24sWiBF5mEmDdzIZHA9u1L2vfujNjdz3IfkV2kVWwjtYBIOGyjb698tqw3fAqFuIx5l/s6O0L7B9vZ7GStYs3vDlk67rzxnfhGRnMrKfYTliE/u4rhHf8SUbRl/5N03Hzat/+8j0Re+VV2sl3Kg8DJ2Gs+7u0ffqbLG4WXvK8b2xwuwbmu/f9WdTO2yRDtXgUJfDvJ4H/KOCcwG5/+7d/a7fddpu95jWvcaFGBTrToR2MOv/P//zPDhQnR98sDBqaHItlTWxz27dvd46yT3ziE+4esaSJqU2HAHJ//dd/7Rh9BLATs48mzAKoCbwmR86f/MmfOHCagHO6pvCpmgjre2ZmxgHkfvM3f9P00f0KA3UH4DkxWggEpzBQAkIo/KnY4AR404RaTiUBz775zW86wNvb3/52B5TQOwRa+6u/+isHtNOONX0Eruju7nZpyYklhjmB3gTQ+8AHPuDC/Ik5Q6C8c88914HhlI8xdhhX4OBSXsRapPeK2UH3SQ5izBND3R//8R87IF4BpCEH43e+8x376le/6liQtACgRQE5fAS8k7zkdKyGVealHkXg3AtL8MRgzv4JAN3Dj2RtT3/ORmFFFfh6NYDqV18dYmwKsxjrW2sTOzNZnygeRQkUJfDLk4D0Y+HQGKSFVn0Xzut3fvf0v16E1T2F+zUG6NBvfXQU0nt2EfZ579pPqNZP/OXHrQJb9c0XbLPNsM3FB0/a1CwsFTj7ZCkKPNKGU7fl7G0WWwebCkCt7Mk+myeE9+DRIyzo4viWE0+Os+qYNa9eY21bzwNg1o1TEhfjNACjE8dsCmDNfD/MEIBrgC7B1lBiyVrYvFattXWMuzU4WPDiwMQCa9ee3TYD+HpqdtHiYkJAUXkAg9pWdlvb2jU4VkPW+8TjNnrffdYASCxUgXOps81mGxusmvev2naRVXcJzKVxhgXqecB4Q4TAPLTPhg7us2lAa1r8pjA4a8oJ0bTSmjdtgdVmJUIjD/2wVR06YhNH+yxBHuQWTMPWwDZqa9q41lo3rbfK1k6cmrCCsAicPHzQEnt22Ghfr00yZqbJbxagkkB57WvWW8vmsy0GKIwB1TInyceBPTYMc9Xw7Bz5ANiAI1C6uHlVj7WesxkfMICH+x+zmQcetr4JGNGQTaSrzXJ1zYQ867Z12ynf6k7Kh/NVO8/FGjcxY7M7HrLZgztx5g5bArRThkRLAZu04phv2LjFYoStdIBFwHWTux93ztQZQGyLMK2kcXR6TGTqkOPKzZusfs1G7qX+ABGmjh2x8f37baIPcCC2kpgjwjgDMRKQxQXWtBmWj0Y5OzP42nttnvLNAphZnJ5xwdiWAGNkqb/Gzi7r2rLVygEBjDzxtB154iGb6T1gHTgZq7GD0vXYC7S15vMvscoNmy2CQ9WFjML56RnggPSEpRRadQJg24JYWmCyAHgVoT34IYAOaZzbOHYrui8mOluzZQYOYfAdwCEJQA52mTgO7TgMYyHYTqpr26wMm4SHab0ABz3Y89QkxH60MECowMM21rsTR3bSqnvOM78B+y/c4cAQuWw/DERHbQYgaArARylO8VpYYkK038TMEMCTecBjXVay+mrCha6y7OhuZPhjWwQw6QCJFa2EpgIIB6gFHwXNuQICuibClwH+IszawtSQzc7Tr3B4RHCwV8DaE6PXZKHkSIdrrby5EyAITp3xQZsG0Bgqw4lP+RzZIeUrBzxSWw+LUWUz5RODDwXDESR0a0CYviDRjz/9PurqYZuBvahi0w040nGm0y8jMMl4MOctTh4kZOxJALQ4kgRswP6rxNUdnhmEUDFi6c5zAAYSnrAcAMNUrwsT6QH4q4OhMlxWZXFCQyZciFakCzNLeU0VgIEqx0I1D/vNHMDHnA8jCl7cMuURB1J2CYevWMga23G81+IgAlgCe1wJTu5SgKs5QgWlABPkStAbjSutHOCtwo453hdYsPDC88HYSQwC8NtrM+P9Ns/p+jVrrLJxLW2I+ka35hZwok/A0jkxZUvgDMRUAwbVwrBe+QIvdl5jkTpCvMIOpEPzCQHnfvCDHzhw85vf/GZnl8ueL+had+PL8J/TxyqFCddmm8OHDzuHt2Q2AIAuBTqoqhoQAXOwVatWW09Pj5v3VAPyjiH7CE5+6WKNYVnmLJr7zM3NMg+ZsKFhWGcGBt33BEC6+XnAWsi9s6PDzob5T6zWa6hfgcUL7KqqBo2NhfHx17WOCvlXeSTnUdj7bmPuKpC95qpXX301TtYm+i06eWaHjRwGAISOqQVoW4pOXURFz+HoBIUK6Aw+xqpmK21awXjZDFBmwRIDxxifhxjfAJnRj8Iwy5ZRHynWnbKENS4FQFrW1AY2SoyZjyNUABGl9eigKsBcAJVg8ayorrM6QiyHIvQtMVgphDe0tIIJifUrB7BoqvdJ9OSYVQF0K2+7knGmhxIBrqKfZnj/7OgIega9i6NUocbKYCyJpoYgGxuyBdjIqjbA5NZ+Af2TvAKQmTy2H7JVykNY0gDgVJxwp4uUMwqLYWUt4adrYSAlrOc8Y8T8VMKSiwLNlVp5BD1K2gIdLQIOLO/oBMhdCdAZEDsyjTBRKuE+VuVsHnBLEK3AgdxOJPBOdCygc4fm0jqdPhwecl941Gb6HwLkmbSaxsutBoBYCABYAKubD1AtPv0UIVD30qYBCZFuBeMNPIAWYYNCcnHJktxX2dFtMT4oH4CGIzZ36ElAaOg81gHiyHsR3eZTKeUA0kphKo1QhwqXmpZtha22lAIQFEF/AaAuBSwZRpbSgx5g4dyK83kPTureJyyWHAK4VWJL1N80zHoeALXyGpgbqxs4DyoHMBNoMsYksaJRf7C8BdPYeH2HbRpAX13POivrAkQe7XL91fxRwNdcGwUYCCAN/IxViu3Mx2ICgLwwMcaaB2zC3WIDvhyZ1GEvDVh86jDhtYcALGGjAITy2TxQhQ1aVt5E3QFeqlwB+AvbFqD2Ikx4s9MDgI1gGsPuEGtOqUBdgByz87RrQqJHtUZBrS6MD9CmJmHKQZkjyyXsjCXCpEbqOtD/hBZnTAkpZLGHXmc9Jktb8IMxAN97CKW+y0aHCHtLmPf2tZcAfF9JfgGYAvTLzfcCkISddhoQH8A8sYSVwhLmA+Sbn5u2eUBNbRsYD9u3M5jwnAB7o3vZLHKctgkAHVsagdB/sjBqlVl5XT3pA2ClPcWnYImbBvwIgDSMLMoB+EdKsL3pa4tJ2NIqNlpt6wU8ygaHY/cA4IMVEJBcig0CCQCVKZghywnrWoP9FVIdskmBP8g7dSgmQgMQt3jSZk8esJmTRwB01Vnlim1EuD2f/GAPwNqaw95IwxCYBeiXhVUMNCwATWyBzASAs36AkRk2M1xu4VWvtTT2VSolJtt+WAXHLYw+jggdQV8Xw1K4opb66LJoAxtcAAKmx0+4jSoJAOpZWKZLBXzUhhbU0qJAcNUVVt+FDsNmmh3A3p5OWHWsHn0HGAxZpxgbyihfVQMssNS1ACcodj4oN/VH5JKb3cV4/4glhvrBOm6yklWvBPvZSf8VAAZg8OwBS2jzAgDA9BIgEsChZVwLZWBUpAxpwvJWtm+0GKBQH7bF3FLaluYGYbij3gVMRSa8jR5XyVypwcrqVptfv9qmYcbbdzxujz7cb72ELUzQDgJsJI8Qs3X1ZbZ+40rbdm63ldFm9+9L2I6nsfkBtIhtEJOdIqRtZXeNXXZxh23eUEeEmLDddU/avvS/H0fGhD5cQ3jk8sBm2ZiwuDhB28I+Z1PL1nPqbfsFAPM6aIPIYxFw8ZGjSXvsiQl7ZuegLc4uWFUZoWuxp8sq621yEXAa9fjKK9vsou3Uf41nfYNx1vt2gN9NWlsDm4GokzE2NaWxfzeR/nkXdsGiWWInj2cYyxfsRO8Qc7dh6m3ezQnr65ts/YYu8sG8jTwcPABz+jOECgY4l9EAAvJROqQDBvTLrlhjZ2+tsZKKlO14Ms0a5C6bmmm0bRs6bEXrEgAfWDYB0ghY2rOyxt75tk5srj42Dn/XMaxqPbSD8V7rjBoHdRQYuQrjvDtZ/OfXUgLqW3Rm18d+kQKoDQg8xwiRf5Iv/SV2NR3O3uOEm+LzW23EMbthX6YBkhU2HWpdW5s5xKKob+n2OPOSFGOnAGe6HmdNO813lncKUKb+xotdmpp56L359y2zy7m8nFrDcQB07irkQaUU43KIuY8eVln00Tr71ddc4zbOd3V1ufV2lcUxOaIsjh+DrXHnsO3dN0CkK9YY2KiQY34VY0PT2vUNtm17l3W0ltvEaA5w6pQdO9ZLf4WZHcC11n/E4raip8quvnajdXVVslaRs6/dPmH3PXiEPlpqa1fC0k75p6dY76C8ldiQmzZ12IUX1djKtbDVaYoGoHhqPIs+mOYzDPv2DDJLwBgZsWrsiFi0hecZx0vi9robqm3r1nIYUAN77NG43XXXU4zdtdjpLch1Bvu+F2BuAt/AboB+TwHeWwFw7kY2hFyKfCRUrbpIbtgjAro7uUoiZ34UgXNnLrvik0UJFCVQlEBRAj9XAjIFikdRAkUJFCSAXYuDMLC9B3P2RcBujz6atSPsumAt39rrPLv2/BCGYtguOt+3GsK0/iKHTHxn53M7yfyrQ6x0Dz2Ws1v/V9p+spuFFx74RVK+nDzcdkuJdfeculvvOXI0a5/4q7R9+R4mtP/qbadOKGzsO18bsj/9k6jVnxbq9dQdxb+KEjgzCfxHAefkgBFQTB8B09atW+fCAhVyLUaEXbt2OWeYnBH/P3vvASXpdZ5nvlXVVV2dc85pcg4ABmEigBkQOZAASVEUV8dnV7Ysr7TnWPKuZcqUbB/ZR9L6rI4lHlIkSBAECBJEGAADYDABkzA59/R0zjnnrrzP9w/aAmEQBAGYJoUqoGemK/x1//vf8P33e+77GiS2atUqJwljN7B2I2y7+q9everc1K5cuVK5qCTYw5QUzBrPlH3MEs8Wc+ym2JIZ9nwyC6MrVqxw4DYD5yzBYeprlgwyaM++y6ye7McWhBYfBu+ZUpuVq7+fBCvJITu+fd7OwcA1u+FvJCHd0dHhJIuqqqr++w22lcESVidPnnRU7gxOM1DOdrHZd586dcoB4Xbu3OnIwVtSq6GhwVkIMAW4ysobgJ2Vwc7DypPHLjgD9ywhZnVm5bHn7HWzrLLztJt8uzFffNgigJXRLAQNCLRrYcc2Zb/3X4fFz3ycv+Pg3IfXWmtXTMePRfTs3rBON0U1zuKqzQMrAehuvcOjf/6VBNUC0JkqqevDJoQP/5r4q/EaiNfAJ6wBG7ttjDVrOoM0ZvDCsDHbxk6zs7O5xn5sTrCHvd8e9rrByjZX2Xht84ONxQZNGxxtc4DNG/Z++7Fkg43P9rC/mxvq9Tf/6c+VDNB1J3PMshlUZFBjm0/BZi2XpCGqJMHBMWUCkOSu26iMnbtIapI4O31eoydOaQBluBiwjysT9QWSoDFUT7Krgb823KT0mqUkplBzaQImO3FCw1cvk4QjqUSZYpzLDOWcJLmXVFyudTt2Kr0Ei6TJIY2fv6jJI8e00IUqVhrzY0aOAgxQYeCLvNJiFa1ZifJaqnovndHwkcPKHkIxBBu/+apKTQPOpVZVq3L9RmC4GpRfUE+BowkDXoTPntDgyWMa6O6BVEJZ590kzBxgGJWsYgD44ltvM0dVzV29qI4zWG6OT5PoRP0kneQ6zy+YGk5dmUo3rVNOFWCeWZB09GrkwGEFL1/RjC20Z1IfqCIFgAYDLDRnFpWqbsutSq+uA25DneWdE5o6e0ZdACZB6tKXSmIXYssS61lVpSq/7SYnMTx19KRmqIcBFJcSColJSopRBctDia1a1cQEGRUlnB/zLu0mOjGreebk1n3Pa667GVaK65xJmWkmARRBMlASK1u+Xpk3bUVxKFtT9afUfGQvoAAqMRkFCiWReEYdIsiCdVJ+tirXrVHBOuy8aCMLzSivnTiq/mskH6GMPCi1+FCmc7NgP83rWavWqXjL7SipAU6MjmnsxDsaPH8W1TuU3qy9cq2DwHtTgFFpObmq3XyzsoGlRk6fU8vJty5NT50AAEAASURBVDXV3cQ9VIZSiCPC2Vh4lVQof+MWpQDOeYCyLH/vNrU5oInwZLPG2s8ogEqSLawb8JSAsp/BjgI4C5PkDKLyl7t6N6JKVZpuPK/gtdNYVHEQ/KAWUAoJebPJKxeSSK8BDCknd4zyIViaqaOZoVpCBGuxacCqUWDBjsvyJ85jCbhZnpxtFIRzjJEInjzNjv7LJMUBvQAC00lUp2OVFQXQDKAC5wPUSy5C9bB2Fza5VYqSHIi0vKEp4E0X6jCuVBLDJJDJhAKS0UDZIeUn0ZmUm+oozU2iBBVEMc/tLwAMRCkOtRa3qb/RnlyAFBk1y2jCbgV6OwAwux3lHJ+1Zw/qUCT+kzNKlUpyNzEVuBP4w2XgnPXPMEkflKRi812U6RDJ5iNcFxLvyx/GSu8m7kMBd+cHUKZB6an/PGo/fSRJqV+sZBNIsqegmpQwhhok4EW46maU+DYqCZg0RtJ9oOE0QGO7svyoAqWkaSEhmzoFSkRFLgbQCNsI0JdGPyYWoU0GDDRMRoWQMcnLjrQQdRfFuieV9pJcBeRGknxwoAvVv1Y+O61UAFdvQgawHcp1yVXy5wLt5ZRQR6ggABlw8ggUAFRY4n8OcK4L+HYQUNcfBpyrVmruUt5DMh1lpNDQeSx1L2oKpUizM0wAxPDShrwo77g9KHMt/yrKNWs4b7MwIxXOeGxx8d69e53x1lShLa7+LCvO2T2SzSGLc5HNW3bfYht4Tp8+rWuAtrYBJwmlmApg2JWrVpLwX8c9yVLn/sOsqewYHMCmI3oeSUiLQw2IsKQoc9sckLXddwxwj9bCvYTdVzTxd0cnSUGuial0rF+/3lFfs/sUU7RenO8W5zkn0el8w6//H1aXTp1YNfDvxd/tOZvb7Z7HwDm7j7TNXaY4VwT8a2pumryo0cZ3NDfYBiA2S98CDKKOAx7gmgXUaVEYSsT+MbuoUn7URMNs4JruaGJOps1jtxzm/THGCQ99cZbkadibq/TKO5RZsUTzfWeYB14n3iA+QLEuBHgXYVxxM5alphUqO5253sd8yxziGJKiTuJlfHZjjxgauq6BtrOMc+PKLqlTSglqXslFjEMA4WOXNNPTDPw7CWRBchbwzQP4Jaw8fWYhiwLWAjJLmat2K7HsJgKeIZjYMxoBgPdxTmmofkWxIg1iCb1AfUVRjExMANzKAMzh+6cYn0MLHDOWB2icymg7CwwELAugO8Owm4vKaTpA+gJKXL3XG1GjmmWcYQwBTgtjSekB5EpJr+CHe1zGIyY0rgxzboyJ0uw7zap79qimet9GrWwWMHCLsopvRd2sBHgaVb6ZLg13vgY8d4mxlY+jpOlHadcP2OYD6ptl7I+mZ1PPS+Q3cA7Vp9BAnyauHEfVdwwbbObwtHzAHgBylJ68wDOJnKePuTZKbDWPLW8YeNxFvUVIxht4aNbkvpkhriOqfcWMeavuBXbHUvbqG0qyZDWbDEJJqNh5UXfLKJIfJeFEviORedPlZiwHLjdlL48bWAi1vOgYVrSdDYBS/cqorEKZdy1jZhXnDqoWZJ2g+yzwzTBjMJv1ktLkx743GXtVFzDfPHGJH1Vab9Wt8pQBnjM/hUbrmSdMtRTlWDZhgFM50JSH9mGWrUmZq4Cu1tC2aP+BDg1jpTk7h7oZQFkibdQDmBcBUA9MATNMuwEOSokdqoHIUMIhlghOdwMBWV2bFSixR3IuzqA1zBU1Sk0owD4Um3uXD0VaNpRpnjYxrMR5YPzeeo30Ms/ia1lYx1yYwjli1RqbQVW2n00GgPSI+tIEUCk0aA7FNRdqcnPYXQY5XsHKTUoqRcUtAZVAyhvovYg6L/a3qO9ELP5gfomgrJjAPJyWkQnMh2og5z8xNApEjpWoWVoCyd+wLEXtGGW7GWCzTOblwuq7UQRkI+XZ79NfUU9jfg0l0QcT8wAGme9zq5QB4Gr2s8jV8ZNMrZp6LgquZkUL+DfZdQF1tUYU5wDvy9kIkMd1BB419WO7vqGxLsqDeitxhYfjhMOo6AK7eoKoTQIgeut28vMoGxIyURHq1Vh/E2BgL9fagD+AMa53NABYC7jnz2SDRzHrZcRSs9Tb5DTP+wD7GFN8pjAIOLHAOS8Qj6QUsqljeTXtG4Cw+ZqmBkeVgbqc28/YlcjaGu3Si216UmYd8QzzPXG1bdCIMd9D+PFvFN7GL7LZ5W1Fero4rzXy1OwkXiOW9KJoOIeVbvcpAMxG+iCwHNCcj+vqB3pMmAfMZIMCNC+qiOvlBZ5jBwhjI/MOFvALXPuobaQwENTUMbGiTwZCTs2olLd0m5rGirX3UJcOvd3M+9KUi+J2UpK5wswDmsQATgqZ85YA4/j01oEh1V9lfRC6NCeTmI7YfWp+GuU4j3ZuLdVNm0uUm5Wk114P6e/+gU04wzHl02aLsvnORLMqZv1wBEUo7ImX1SXpy08A0G1kkwGgcVtbUIcODurg250aGg4wHvuVn2VjlVdjMxlqHWQDTKhZv/PFOt1/f5Fy891qaJrRf/7LQ1iojykvvYTvyhH7GpSVF9OKlVlas7EIxUav3npjnLVD1HkZk3Oy5xg/uCcEKrINDnV1BYB4hajRzevoYebn68POGJCVAaiDmnOQfprBZqpt25bo5i0FQM1RXTgXYdPuFbX3Z6muuEDlBaMoYNHWUTHuGzbl7Tn9+dfXIzwwqGd/9GNnc6/N7bZx2NYabV63H5vn7bH4u/NL/I/fyBowmMxiP/7/hQ+mP+eaG5DmwJNsWoowVplNq7WF98dPBoPNE0fOsJ5iMaqtq9iG9BkUGO33IPPkPJuD5vm3bRSfRo09RAxk8FiA8WyO9weYp21udWxbLU4lVrnxPfYc5bZ5F9rLNoAsWrfaxjw3ZbN2ekMZ09ZfeQ7wzjaL+HyMAZyDxXWTqN2moLy8c9cu/c7v/I7jWrMYt4ZYgxkdd+ut/UP07+vqGZgg5kpRRjpzE2NggBinpCyF/sV9c06GThydor8O8/wk41GY9QvuSymknU9BCU5Yj61R3RI2JbHB+pmnJ1C4rGfemVdFKXEIG/Gi1OPERMCJZ0qKq3X3XYXaeTe28XnYQeNicvXinH76Ygtr+yPcHiWiaolqP5voZua8fCZHfeMeVeZF9Uf/IlPbdiSjsBnTvlfm9c3vvs7dZZKqymqVkwV87h9TVjZz9sh5cgnHESAo1UMPPYDi3BbmCWsGBs7Z/RebwszDmYfTRj5KI3He/T/+EQfn/sc6iT8Tr4F4DcRrIF4Dn7gGPsHM9Im/O36AeA38etUAeSl19kX11tsRPfVsSJc6YkKkgZtZqa7MpcfuTNBXPu9Veemn12+I23WKG8w//6uQDgDNGfDwUY5uawp3A0V8+98nqrjwHz+BsIGuoJL3n/5rSD8++uHgHPfhWotq3R/+7159/t4EBw789boi8dL8ptbArwqc+6j1YzetdrNsN7eLUMJH/ewvet+3v/1tGTi3detWR6nNlN0MjHB2f7276PNBx1gEKGxRwMq0eAP9Qe/9oOcMprDvMfWF9wJtiwmuxYWFD/rs4nO2AGHHsTL8st+/eAz72xYm7FgGfziLHO998RP+Ow7O/eIKDLNQAxupbz0d1OsnI+oeuKE+Z+oqG+tc+t3f9umOWzzsVGQXpK1Dxx/xGojXwK+0BmxcXhxvOzo6HFjbgG8Dug2KM2DbVHUMPDao28ZR+4yN8QbMGZxsAHRPT4+zKGyvm9KnQc32Y0qf9px9h81zi+O//d5Sf1n/3198ncRgr9YAsWxmIbSmDCgFVTVPRT7JOnZeN6FOdgXFFCwdc2/dQoKpWsETJNIbrpPQylLSmtVyVxUB6JhNJJZggC8e3uMyu8buAc0eOqHW4yeAi9wqqCLhBEDmxl4uyu/TwHDhpGTl1FQ7u4xn6i+p7dARRTu7VV2IYtaS5XJjV+ZKIMEKWBMDUvIUo06RRoKZpH/v3peU1tcFaFMr7223SRyfTI0SSEyaVayL8zFYaOH8Bc2//qpGrl0hgZoPWLVOnqJC6hG1tdYm9bQ0O3VaRh2npCVr9Po19TW3qowyZKAY5yoAXENlRB52pmcDuhXlyItVUXR4QoMow3UdeUf5fE/B0uXyLFvC+WWRomPpl/ozFRA/8JwH1bWF682aOLBfMcCHhPwCZWxG5QtojFQASVUsbNM5dhVAHHN36FK9gq++rl4A9RxAwDSgEzcJ81gWdpN2Tc0a1QNggi1SuIUE87Gj6jz/tvIzUAhcWcf1K+OmAS2i+k6NX+0iiepV3m275MUqtf8CSm+NJ1UFNJC8YjPHrSHhi9IO9ihhCKfEAixjAfUio8MaemufJk8eUQKKPrlLVgIOrnDgRzL3N+zs8gDRqPdEvitAPXe9dYCE+TjWMeVKI9HuNlUwrMlmSXayQg+Yl087BNS4Vq+eE0fUU38e+5ga5W2iHNSTG5UMd3El6nr5sAnYO5GY8JB81kwTVrjnUCeyzQl+wDeuMXGFPWLBcRLfqP4NdSuC4kn2us/Jm1Wn6eunNX/5BCouJKWLK0iqVpF0LiHfStsGSnMloliEcksEEMIxrkOlz4PKjmu8Acitgf7Vp/RM1PCKNqDuh5oMKkyhiVMa6n1DkzO9wIIFKL2U4QILnDYOAjA6RIKdJGp0EnAORbQle+TJrgFSQxGl5TVN9HQAbOagGFfj2O6SoQZim1MAO8NZrIwTEkn+AJ+6gI3SiuiDKRWcHW0f69hAL4DJKG0kJVPZtDNfKmBMf4c6mjqwJyRpW1pKcplrznm5E60/Aol58x3lGTeV6DLrG+DFmMEa06ge9ryt4NhFxfz5WAE/IF/WKl7DEhMFoaHOs8TF3Zx7VOn0lwSSybEgCRiS6wuoPc2jUOiquVVpNZuVmAFsMQY4B4w5ipJhXjrqe2UVSsispL7of0B/ptYzNYiiE/G2l74cBUJJLDGLX8oJFBmaQPmlG3U7wKAUgM8UoFtXar6GeW58sEVpWJLlYnfszaQ+fEA3nnLOkzHGVKCwZMTEDoiV86MPuk1lZ7ZTga6LAB2AH9RNdi0bZzJNCQiYYqIH5cvjAHSt8gOuJuVVUlckocySbawbyATVqaW/TR9YS2LoRnLWkm31qHOaIrSBXI8++qgzHtsmmcXxlAv1mXvYPGQ/ds9im2lOAEgfO4ZKZmenc99gKjGr2ZC0etVqVVdXK59xNBWI1u5NzPLSWaF49xh2HKtLF9AcDYemyroHc5RzfNrNLMnO4eFhR3nuMmqn5wD0bL6019esWaMdO3Y4atg2R9o9iz1vf3+S+5df5QW18tpjsT3Z7/az+Lvdj9k9z/e+9z3n70VwLg9Y3BUDnJu4oNGrRzUz0KKMFOwP81DXwvaaDsrcghLm1JimAJsNzjHAyay8Y1iKJ6WlAhYBewMlmeJrBJXHsbZeLbiygN93AHWtwLb6hIJNr6HMsqCEIoDxIuZlIDAX8GuCBzDKQ790M/a42dzFORgMBRIEDIP6ETatAwBNBlrnoXiaUrCJ/gY8P92icC+qqsCxZtWcllcM8wO5QTXEAHQjADdzIx2Af6nA2SjOVWxxwLk5FOeGAOcSAJaymCP8+YwhKGy6mAut/y6gcDWHdekC452PsSkDNTy/v46KBaJnXDZlrdEexhoS0gVrVmA3XYyKab96rzbCJhFvEZukFZYDlZWyyMfchW2pi7pweQ1AZjwz23HmQrNtVXAIi9cTQMbvOOqa+VW3o4K5jnrIBOwKabL/qsZ7jyrdN6xs5kFfVhVAKMlpVGRDve0aQnkTWSjlsiEtmYSygb+h3h6NXD6CLfYM5SDey68m3qHsZqc71oEVOGOaqckCBLiACtNKahlrAbpRo0LqCZipi+t1HehwghiHOWTlI6h4ofB5eZ+SsOhOyyV2yF1Jndn4WezUHQOxcywuDG3O+h+TLspSEFUK91/WFKqwZqmaVbuC2GMZrxVz/EFN9x9CtfMq1xZVwPwVSk7nWhgojYJUcBjlPBQjUym7v/Z25vTbmcoAoebaUDRt5TtMncvGV1pMxJT77NrNcw0yiRFWME9irT52DTW2PvmzgDiLuCZ+yhcxpb4RTfYNoYQ4DTybryRA6yBA0zAxYXC8Wzls+kgDEI1haRtgjo9gdxti3vBh1Z2IZbGHc0R7iB/AueggczfqpJ2o5I7PM/9UKLeSWMtHbABYFh5pYm46AUQ4hsopIHs2bd1AOMDO+SHASDaWBEnsl63erKSyTVwH+lwIiGycc5zv5Xd0my1M4fpFUFAMj6LiR9zrMciU4wc9xLRA/WZh7wEUQ4KPvtqqEcC02RlUDItvUl7dHup1WnPnv0M9jWD/znsLVgK8Eltg8+5OBJ5NIY4E3jDIABNP/gYqA2D0xPoUmWrRWHe9JlHqLS4uZLPKWmK45czNMwp3XtNk+zVnw0hyIVbOKMO6gDpiC6zdDDZxb9BooZ4Sl+1UwpJHiU2yYcjGUG/j3IDsE12ztB3rC6hLMjdGUKCbw0fQA0wRoi04Vqn0z4SspVw/4L4wCnejKFAD/c/j1pJBXJaxAugW0GOm+bom+gHnMnOVxNjlySJGA15VEpBhIvcBHvoic4JBbM5waWMk3xUZAIRvO0q7AtQsQU2v/DbOj/4aG6SdoFBJP/TGJlA7om2hXkfDp6wAhUNs6uH7YimFbETZhF3yGkIWYqZpwJnpHuoPxcQk4jEv54cSb3SCzRm4J4QCQPxFu3W0vVQ/ePE6+QGP1nNPccvmHKxJabcwdmwFAOAFOOSe68L5gA4f4doDF27dXK0lNWwYQvVtfBZo1FTnCGcqyv1KQRXq1ddC+m/fPqXG9iGtW7ZRO28r1Io6N5fWpcsNUR05gxXrVIO+9HiVdu1EQZR4/cjRce17vVXdWDQuqa3SzevzAPJc3A9Kx85E9NYZwJn5ev2z3wJOeSTfgWCuX5/Tf/iP76j+2oBqCmu1/fZVWrYcAJQ8Rm6BG3tGl5obg/redxvV25eqDauKdesWD+MYkAznx/DJ5izemx3VwcNdOvo29xixXG3ZWMsGWVQC2YAPZ8hYGETpyosFK2ARMOyFc1F9/euXdbUrU5trCzi/BK1cbhC+dKkhrI6uFv2rf17Erc0gtuQ/djb8bt++XXv27EGtlvsUHjYX2ry+ODc6T8b/+I2tgRsRnxMN/sJzICJ0+j7YOm3gxietPYTt593Nh7ZuYnG6/Ywx943RZ4fZgGbrJc7zKL1PY+scWAg4SnKmchxkPrd4M8x9hIF8ZuluVNyNtuYhZmVjlSmw85xBcga/2fq6RW1WDlurT+F+wP62dmmQnYfXbe070dYtLN7l+QSLv3ifPW/HnCKutc3yk5TV1nm+/OUvO4Do4vsDKN+2d8T0w2c6de5CF/NdoW65uYL3cG/M6ZsqqY+x1vrs+FBU3/9uPfvXcrR2Za5uRtAjh3EAAU3NIMSRhOLj8tV+9iJ6NIIbyY9+OK6XXrsCfMu4tGWFNm4oFuGgRoZiOnZihOP4ANp8jBlJqqzxqqMtotdfGdPbx1qVk5ejmzaU0yfdKJBLbe1RvXFgQqfbQ9qAPfX//Yepum2rT0PkTvfvAwZ+8jCxU6K2bFhD+VNVUcb9JDbcp07upY+/yHGK9PBD9+uO24HsLSxwwDmL41FPRVnPHiwxUIfOPz/WH3Fw7mNVW/xD8RqI10C8BuI18OE18Almpg8/cPzVeA38xtSAwWqzczFd4Wb1mz8MIRkeVef0DZW5giyXtq1z62uPe9lRxg4ybpA/rQdrKqrnO//fb4f01H5WLAhKP+rRTSHuaw969Cd/wA6R95TJ1vhOn4/oPwLO7TvLQsAvKGwmCz2f35Wgf43law0BbvwRr4FPowZ+3cC5T+Ocft4xFsE5szY1i1NTW4s/Pr0aiINzH70u+9i9fOp0RN/9cUinG2IaRi3VFirrmC9u3+HR73/FqzoWQIzF+CQLEx+9RPF3xmsgXgNWA5Yct8VZswV5++23tX//fketxxTkDNgwCM7sre+//35nJ/IiDG2WeBcuXNBbb72F4gDKFiz+2mKrQQyWVDCV0i1btjg/BjC8FyKwBVxbLO4AJPtv3/hTEtHXVYOy2o7SWq3ZuUepm1BtAA4zNQdTaxt89RX1dHRhKVaBctk6RQzq6uhRIpCd7zYUVmoBZVIN+JkHeEHpIhX7HpScFs5d1fBLr6uz/qpKV2JZuuN2B5xzpZIIZhE6RgIsgo2VF2o3SpJ1+NAhXT58VBkoqKy6C5WZtSSyckmWsZIaC7EDmTK7TBKO5eoIsF/vj55ValerklZvkO9z91EOlKpIPlmAa+NYjN3TZPs1fvCwpl76KQDPmDJu3abMHVgxAljY6yEgxd5DB4HarqkA2CupqJiE9hD2If2qXrpCmdisJhST1Me2yJ0EiJFO4txPTMw1C11t0JW9+9Tb2qvldatUsm2nfEsqeQ8BNParlsiGzOI82RU/NKbRk6fUd/SI8lhQz0Z5zX/nTidRaXRYDNsqxysU5TEXi/PhxlYFXnhJ7U0tyrv9TuXcvg1okASp2bP6qANbReZEo2Psmj99Wd20m8GpLlXetFQFN6HYUFzKy9juXcLq7K0z6m5sUeH6TSSg81EHO0cyuFElS1HMvWk7NmrLOD+ghSQvCWMW+P2sXANBmEVr44+fRK76oooqq5S2/R55V2wk78jqOhMIZn1kVClLKuAZMOAwYNEganq+nFyVb9+lpBUcNw0lG5SJzE4zzM58N2otHq57GHvb3qNvq4E6WbphncruvAuxlnIS+ZkkvFFkA66z5KiHunCFxxUevqCJtpOo3k0oqxTLuGIATKBLawuxwKACLScUwlo14s9Q6sYHseoCnGs4o+n648ATKSTvATwBPlyAZVwg6sbUdQCfTLXEks0o1pjSEQci6X1a/Sj3BVDgyShcqoy8VcBjgFeAWcH+Axppf42CofVStZ4k/xrgNJLJ01xnrNlGewEHA61KLShVeu3n5EMpJQagFmrdp5GuXmz/liqlbCtJ4RIS3YAAIdSdhlBZ7DqnBVN/Ai7ILKzGwvgW6rb8Rj1jsxXsuKAR4NgYSiy5y5die0ViGkimrbkd7iFfRbXLUWGrpm3kcW70P5RoYgIIQ3PG7XjQ0n+AIaKo9c0ONJCYB3hwTysTMCO17HYS3SSnUbMJD11Vb/NlrhNHKC/CzraSZgREEERJBSW6+etHTCBP3vJNqDtuAFLlHCY6USS8DDg3rAJgwuy6tSi+1FEOYI65Aa5dk0abrwAOjJAwSgAkrcNO7VYgySrKxPlPYX3bflGz3ddI3GBnWAl8CvQwgprjOHBCVpYPNcZlAD5Am16gDRK0ZMa5BowLJO1jKLx4TPWGRFhsYRA4DvXHAcoadCEgs0bppajJANnFUJ6YHrhOOc8qxTeBLfESjrmW64CFIGCE2Q7ODKCSVnmfEvkuR92GmrREnKlPvxecsySXKc79U03e2pxkD5tT3gtwOU+++8fi3GXKci+++KIOHDig9vZ2R+109erVMiVrA+eKGT/TUQW1RCEX7r2H4N/0uxv/O88vvspTN95pA7k9yRM2Ry6wGaenr1dnz51zID27Lja3GSR+7733asOGDQ5gvjhP2mvvfSxeL0uS2sNe/3nn997P/Sr+beWwx2IZ31suux79KPp9//vfd/42cO7uu+9GARxwTuMAr6cB505ppr8bG+okpVZUMwYCOaHyhNwc3awTy9TLsDTAIFheBlArcWeUKa0Ma0NAbAcMB3yPDDBfXL1CHJGKdfkOJVcbOHdcoeZXFQKeSq7bIm/ZFvqnKbAx10aNCqL/RIF+sJ+268RgyljajxIUwH3fZY2QHE4E+M6pQCkTJUwbXMLD1xRsO60pQL00xrukMvo26lKKMp6gzBXuOqLprrPYnicre9WD8jFmCghorue4BlsbUPvCEYE5IKW45gY4xxgXxV46yDg6xJixwO9ZZYxFhYz7icsoFuUNY7s23KnhJqxnUfAqXF2j5IpsYJgu9dY3o4aZoHyOl1JK38+qZCwlTsFK1VTYbL61sdSy1S4gIXzIFWHcm0U1b2KmnSGkkHlpi/wZgIW02fnRDvUwbxpgVFyUrmzK6kqtZBwl1pkYAJBDia+7k7kzCSAdC9vSJdQfFpwo7Q5eO4ZyXkBZKKkllBIDpXINY3PUZzuA12VNj7Y71zQtt0zpS+5wbEWdsZZ6D49jQd16GBi5R968WqUv/YKCKHlOXHkTxngAS/RaeUruoM6Ak72MoWbPCrzlbE4AKCPlzwW0WGsIwTDG0J7zqKJhD4rqXlbdrSjUlXCtgb4mW9TfSpuYb1NWbrkySrcTA9Zw6RmDAb3negDPgbIyiVeszbiLuH6o3CmCYlqUtgEIFgvxXYDTNi/FpnsB44c13o+tc2GFUrPQURy6DHwVQvF1nRKLb6W8gHehacb3Xk12XEFdsVk5+dicVy8FXEzRUFsHrw0pj9g3tRTbzeRyp/2bKmEAJVE3iXv0f9hEQIxMbMvIztdjg9l33AHyQQNRZNwEL0lb9BCzoUY83d+MBWgzzgJA/8S77lyzayf2Yb6Y76UOujodq9yqleuY1zdRlxXUD+cVQBUXwCzqpT6whHWhsmZzYbS/VeGBQdoPkCDgnCunVP7KtYBYtI9E2j8hVWT4siZbUF1GFTKlhE0TS3bSL8Y1c+k7CqLCmISSoL+C2DGVawi4GosCu9mEjVqjQW5I7VHHDJqAY7HZei2MXtQYUH+Y61tQXsucyPkllXF9OwBiUZ3t74K3B/QHhHdnEGPamL8ww8aXswow38dQZ0tchlVr3ee4BtbXiVUtVjIrX6xgBTwXCxKHGThHX5olDgqwISCCInMqx0stB0REIdbFnE6gTz+9Qhu9qJkJlJYKCpXJRg9XACWoplZAtmmuQR0xwFJ58ohJsIaNutmkQozFbI7S3xznBpAHkIXnOqAvm3P6ASxpC8m4OWSVcg7M+S4U/BTsVXfDcTYcDKBylKb8chwasom7DBCdBEDlc91t7agQc92r1gLPrWPzQgHXlv6OGl0swnVCQRGZPyqV/jfNZ7jmU8NR+P37dahlqZ58HlBlKlN3bV+pu3ZkqzifurOq53JAamJDGkMNbl4HD3USts/rgd2VWrEcFc0iIBzamIfxJAU4L417C1AYwLmg/u5bh9XW1a49u7br8UdqsFEEEmPoud4c1QuvDOr4qYO6565aPXD/Ctb50/T88/166/B1RJXT9Lk9dbr1pjRlpXmYK2N6462QnsF1YGDwvH77i+WoOqEQmutjs9Wc/vK/nFZL04BuWrVMX3liqZau8tPvCDsBcSzGO318St/8+wvYEWdr1x0VzDd+VKs4vwTKw/l5gJUXUDJ94acdzMXdDGWF2rOzDDcN7Khz7D38sLEhOTkqPzKQEcbFc6xD/ftvnEVxLkd33lyqLzzq0+rVN+7ZLl0L6PyFXj16H/F0qBfXjucdcG7nzp3O/a85aNjc+P55kpqOPz5rNcAYa3egppA2j1KsqUJPTEyy4XDAiY/6+/rVz0aDURT0p1B0s9cXgGwda1bWEcIAyhb72UYei3ENfjOozYC45OQkwM8U/p3owHEGuZkinJfXzSr6HyE5fuc/Gy/9/kTcZLCJ5287boTjGzRr9wj246avM5A432UgXQLwnSnODQ4OoWT9shpxi7ENII8//rgDztlnrUwBxtXWtpj+4R/adLm+n3XaIt11Z7GWLGU+SuHejr7oZtyPEe80XArrv/7VUWK3Su26PV+7dnlVUMhcwHsijEkJqLYaX21jyUCHKc7167X9FwDNvXrk4dW6/Y5CNksxNcHM733BlOtsH+KMHv1CNiqUSVjAhvTcs33q6h3Rzu2luu+BXDbIoazPPN6J9ev3n+zRgZOzqi1I0h//UR7OJqkaNtGR18P61vff5N4uSffs3oBScobKAHRNZfK1117R66/9VFWo7T/y0IPO5heqhkLa/MwVZlNbXHHus9a54+cbr4F4DcRr4DeqBphh4494DXyGayDE4kPPQFSHj0f07e+FdbGd4DyKojtrTCu4iX18T4K++BDKHkieE9t+ag9ibdU3RvQ3fx/WT/ajhkEy55c5/NJyl/797/mwhkQ1gmB68bFADHr0ZFh/8TdBHan/xce0G/+aYpf+9Vd9+tpvIbvOfW38Ea+BT1oDnyVw7umnn9bf/u3fOpY6v//7v6/aWhIG8cenVgNxcO6Xq0pEElhQiurJ50LMLdg0dmHDR84imTXiteUe/e7vJmjnrR6VoT7HOlD8Ea+BeA38imrAkvimIHcIcOzo0aOODbapdZpyj6nz3HPPPY7KUV1dnbMYa+83W+4f/ehHOnLkiLN72cBsU5ezHdWtrShWsMva5pyHHnrIgefMtnUx4WCp0SiLx90NV/XNr/8bdQMf1GTn6Z61t2n1g59HRQ7LImwjXVGSfhMo1ex9UU1Ys0aAjJZs3qrEnkHUIZodFTrvclTIqsoBbNhZXYHtIUpgLqyWyJho9MA7Gn3hVfKHYyq4c7uydm+Xp5BkGEBSlNVRS0C6SV6KBGX4ersG9r6uK6fPqLhuqZZ/8Ql5l9YpymJrjIVoVpFJeAZRsSGYxTI0AjzR8+wzSu1sU/KazfLd/4g81I8LK6Qb2UeSe8B2MWxY+l56STPAf2mJScp44BH5d2A5ywpxjEEx2jegcRZvQ0cPYmuCnduSFRoHeGsEAEkh2Z+L5Wsq4EcyO6z9KMl5S3McqCgK2Bg8fVJnX3xVMyQqV27drYJt2xEcIcntJyEKEEj2h+9g8RerqkBjmzoBS4YuXlAdAGL2XcB7t99CAjuFDBSJNkptSj22o92F/UoMYG7m+Z+qsaFVpTvu4dh3A7ihpoRcqL3v3Q9QD+ycP3RS/QcPaJpkc9U9W5S2FjWSDJLUQpmvsU/BN9/R9SOHlVNZjnpJuWYGe9TXcIXkVq7SlgEGYqHnQ7knoQRFFexeTNklMhvTPDBUw3PfVArJ0DLU8fx3PaCEGtoGCjOcoF1ifrhZiC1onl3zHU99X+rqwFpvjbLvux9IsoYipJEQ9nMNuRzUiZv3elBfCTVeVQdt9+qxM1qD0l/FPXvkpXxmkxdzY8nJ6r7dwbhi3Iyh7BPsO+2Acy5U6NKrtigxfxXHBcjjBsws5cKtBxVpPk6CFTWQDfejcFKnqaazJGNPKgv7Wf8SEqqotMRQA7Kd71Y3+BFyjaxdcQoo+CAhA1gGNGKqctjbJQC5pZbeQkK2CusoEtQomgTaXwXoOKDkrBQll98Oo4YSncfAMhb4UXYZ6foJiZwLJN8LUGv6nBLTawHnLinc/jpWcAaJ3IS60H0AhaV8ztqqKQmd1kzn2xob7Ke/FyoX9RMf6kV0FspH4SKoxnSfx/avVSG3B9AC0CQZSIJkcWd3Dxav5djLoaKYWUU/IfOJqg4egJwUQCA3cy6sEx04IUQ9jjST7AeuQOEwOx+FFCDExLQ63s81woIt2HdJfS3XlZRFOWpWo4zIMUnOQ5VQTuC+6/tIlmLjVrwBkADANo3vmGpTLwqCUyMBFZHUz6jbRDGsLLQTrN4ig43Y5mKxO4JVIDbCiRV8rmqHohmV3D/T/xdQpOs+o9n200pNDAILAJ+gEDTWMaQJEv55+ZnAA6j+5aCS42b8AJIwmzmqAi4VpUssGN0R1PJmxjk/EtskiYOBPhSQSrCCQ2Uxcw3nQHJpZkCDqOlNj13HFi2iDMBHT+YmmkAen58A7GvUwmA3Kk+3okxJnbjox1whU6U2q9aXGEcMZn7sscccxblFdQne8k/isTg/WHLaQGx7LIJn9twi0LV4sgZz2T2dWbP+4Ac/cKxUbZ4xdVRTgFuHSmY+iqkJzGHOwwa5T/Kwi8EjzAJNX3+fYwtrc+aVKwZ6hbQKSM/sS+37bS40eG6xzIvnZp+35xbBucXX7e/Ff9t7/lc9Fsv5/rK8H5y7Cbvuu+9mzkEdFAkzwLl3NFZ/TrODI9g2YrtXZcAVY7UbVTHPFINvg6ZRZZpDecoHmBZDdS5aulG+6h3YOqPwY0NRdAI49rpmLh5XZCERgHQbfXUpqlPvKNK+TxFLEK8AcC7ezhwAiOY8WBwyOy0Uv2Cj+ZuFMsaK2DTKm4OnUB9FTRPgKRnbr7TiWmBnygtAHei9rEDzSeKeoNKAVhJLUMHyGyDGsQLYeHbtQw32MBasXsCahwHndhGPdGEbe1SDHc3EDVnKKVvrWGK7kgCOYqhQTaOM1n2CpHAjoJRL+VUAWCXrATgA5wy2pY1ERrFCRTVzdmpAuUuLlFKJIh/j0mBTJ+NsBuANZSkELEpD+ZVyW9wBuc2ZotZpc5HNvdioxkZRAQWomgL4CvsZS8qXM86gyIY1qMc9x1SC3XvDJeamqEqq6lBAWwHExdjFuBVDrSw80Mh5XMfy1qWcaisn400wWQHg6kHqJSkJG3SgxYQiyp9IXWMXGUMxbqrzNGPYKSxRsSouW6ak2t3U2xKn3vgDa0ogopaXUOtrQnEP9bSaJxjXFjR27SDqaqNKr1hFHLGTGI3P2DzI5XIetl7IdcFvk7+w2J3uUNCUvIYaAa4Ssd68CTCMjQ/A6hGUs+Yn6jXQ+hrWsf3MIcups3uYu60cAFRzWOr2XNJIy3Fl+eaJD1HWLdnOdWA+iwAcAjSFAJ5is3PEA8y7gEkhQKU5lGrHsaLLIUbM5JJOYPObgDpPavk25qG7GMOBwW0emuhDVPSoZntOEsMAYtcsxQUkE8U5wHKUifPKASKLgbUSq/gMaoqACkHgAStbApO9C4ASAo1ycp6T17EdPc00OwLUvkRpBbfRRkv43LwCkz0ap63FgDszi7G1LakmrkIBzazPsSEOAK73t6NOOz2impWrgR83ElvwnUBrMaxWQ3NYgaJ+GAwtYEc7qSSsSwX0GAVudaN45EYlMAHAL6FqO1M9fdX6FPFI1ED75recuchbuFZJQGtRrt10/XewHkbNtoKNIpUPoMLG/YCYCyNeLqHN85wi1zOGShHkBueI+uDgMc0MX2L92sNcWInCG0AaACcnosAgfbCBfj03DnyIPW3lXXCwKL3RuwxEj1K/wfpXeR2r9SVbiP12UUbiCtalY4Cn4VAPyo20f9RaIVXl4Z4lNtUOvIhaJPSFl1grLX811wfFwezVhFwoOKIkFpu5jPXzaeKdQUDaDGWvAIgCip7gHmB8JKi8KqD8CvpgFrapwPdhzjGKSpzH/iN29AARutyoFALJzwE2jqLCiP4x7bASS3YD9Eo5P/orlvTtF46h3BdWMcBoViWqd/RPSBPARqyih+vVRRw6T/vOAOrNLycWRl1XEWDZwCgxcBf9AHXI0Aw1ElQC4GnMrGfHMcIteUjnR+7Qs691qr5pSrXVZbppXYGqStnYkJugXACzNNrm9EyMe7qA3nijhVimB3W1DFXX5qi0NgsILUWFQCbpZkPM8GnA7SuvBvXNb+/XBMDt41/YqcceQqkV1Sj2/wACRfXTlwf04suvaNN6QJMHUSEnhn8KAObk+Vat5fsfe6waMI9WwTFt2Dp2PKQn2fhff/2MvvBIsR58uAS1KL8a6uf1X/76rAb6p3TP1lX63a+WsTmGWA5VbYt6AvMuXTgz6yjOtbeHVV2RoQ2bUlRW6VMh55hfgspkKvaN01HAl1EdPADUOxHVquW5Wr4sVSUVfhWXJ6moJCJ/om3qog8ylp87G9Y3vnFcY1MoTO2p0BOP+1VVzYYvulRja0jnzw1pxy3Y9M71AAT+1AHnLJa47777sIZFSTu+OHVjvP6n+OdHjA8tFgoxXiwqy/X0dLPRDUi2q1vd3V3q6+uXbSy0zYcGXptlqsWCBrAlYcWexJySxOa8ZGC4FGypb/x+4/m0NFRcAXBto4e910vsagCd/dvNRgGzYrW4345lG0HoZU7cmPAuJOe0T/qxbUy02Nne6/45dh4W6/VT7qeInS9y/7xy5Uo98cQTzgZJO46Bc0HG8p5e6Yc/HNTJ0+2UmxzkCmyOl6appDSZ/pWM8i5AOHFE09Ww/v5vLxIb+1RD/1u71qeS8mRixHTlF/qUmUt5GBcYvtXXjoPWUx06fOyKapbk870rtG5TOtbqxFEAz6+9gMjGq9xjRnr12OMFWrM2XUcOzaF810O5EvWFLxTo7gf8ys4EyiPuGBuL6envjeilV6aVlRrSH/7LAm3bnqHBXuDdV8P67g9eVXlZjj7/+fXaui0VVVEXKnuzeoXNdq+8+JIqKyoB5x7CqvUmAEYGQ7spN0Cd+o3Z4sG79UyU8rEfccW5j1118Q/GayBeA/EaiNfAz6+BTzI1/fyjxl+J18Cvew0Qx7JILl1tiurvfhBit1ZEfezaslxcfraLHVJu/cFXvVq2FClzAtBP+zEzK720P6z/568D6hliDWVxUesjftHNq9365p8majUqeO8t3RyS+PuPYP3610GdY/fKe1/7eYc2YYuHt3n0Z3/iU1WZBa7xR7wGPlkNfJbAOYMXTBHIEjhr165lxzKL8PHHp1YDcXDu41WlSfbXY9v91/8Q1rFLUQ2z4EFuR4VJLt19l0d/9L95nR3ObJxkQejjfUf8U/EaiNfAR68BW0C1n46ODvWykGrQnNmK7NuHmhm/33rrrQ44ZzvuDaSz1w2w+9a3vuUsEG/btk27d+9WWVmZA3aYXZ4p0dl8ayo8DzzwgCorK52F3MVSxQguu7BG/c6/+zfYZV7VkqIy4tvtWnb3/Si4rVAMgMpltofjg1o4+KYa335Hc/MRLb/zXiWRkJnEurAbBZwgcIwfy9aMXCxXq3OVUlcJMFWFoFe6+t/AOu7lfUojUZWxZ6f8224GfslXJCGZJAoxJYujCSRNPQbOXWrSwMuv6ToQROWGzap87BESiuUKsfM75LbEIHE47/daBml6VpHODnX/5DkU54BxsMnyfe4RubEpMqUJS3CbBZLLrCnnptXx7I8UeONN1HkKlXLfw/JuuYO8IVurbdf68JDm33xdkQNvslBLYnXTzVrIStfV640aGhhWMqpg2UB26VizpjGX56xbhkIPENX8jEInj+vCG/sVTMnW8l33K+fmLUA3QFRkwVjy5vRIdpPAjOGVstDQrJY39wM3XNWyJcuUbaAYSmtid7vlC8IGSNEGbLHczbHD1O/4Sy/qekunqqnzwq2Ac4BtAjwit+p8xm3n1zmg4P6jGkHJbqEiT2XUc+JSbN+ww0SuAysuwDnsby/vfx0lM3aIo0Zn39F25jyAGFZe7KZ3p6TJh7JfWlmpMoHXfMXlfEeaJq9f1fUXn1RWYFwVd2yVH6tXx4Y3kWQtpIWL+oo5ynrzmrtwXt2oISVjp5ixaaOS7r0HBblKoA3U6bC4s5uoAO/3oH6UgGrMAnZ7rYePqPn0Ba1B1a9sz93y1vJ+rAWRUqEqbPGcUwD6ipGwDPafBS47SwKEZEfpHQgpkWj2AVmQ8HeFUQLpOgQ4h80vAJh3zb1c31pNtZzRTPsZVIewnjWFs+xVvB9gxEUbIeXsiVCRVLspEVkSNgJQtTAGKLDQg/hPEmAYyfccVEg82JWirBSbxhK25WUUat7GvjWXetqG8NktTsI7ZqDRXLtGu4Grpi8BWhYqrRyLwbRaxUauKNb1JlDAKIDpFsC5e3F9JTkPmEWWHDj1pGa7DmNr00OSsRRYk/ZccRN1BmhgCYQIloC9F7FQBZyj/efVVKCKgIIfNmr9AyT8UcXLrgIMzKzg/ajyAJ8YCOc0FD7vcvEd4WEFJtpQm2skoTROFWcrm2RyalYFiR0oBSQJYljEBVCZGejoVDJ2stk1m1BSKud6ELtGsXacRkGp4TUFp1CgKdoMJAqsh2ptbLpR3fVXUMiJqrB4uVJrqLf0UpKwKLKFxulj2Cc2n9X8cDNJKjcuq7T78jsUSC7jKpjN6hB1ekEzKAqmAp2kllQ4kMVI1wiQ2wQWnwVKXQpAgr1gxJ0L5EAyKkT7I//tBnCIAR+6AijboaAz3duKihXQXCrJcGAEr4GNiXYOtCOUoXpaz2h+qkMFeQCYKOG40laTnOf6xmY4/07H7lHZaxCbqrKDO/fKnzVwzuaIDwLL7HmbpywhaK9PoyRmim8//elPHZtWu8cxlbmtW7c6ScCcbCyt300u2mc/vccNe1iz4TKg0ea6s2fPOnOfKc6ZEpuBZXl5eU6i3cq7OMcult/+Xnz8ukBzVp7Fcv1S4BwqXtGxkxpvuAgwNY5tJkB05SZ2etLPYnmMv4AmUdTfug/Tx84pcXIYYAWluqptipTdiXol9p0QVC5goBiKZdP1R7B3dTP+ASijUBXsP6Vwx1v0oyQAZECtAsArDwpQNj5z7+BinuFP4GN+QbUuOouiGopyYcAUF8qcrpxKxsqlqFMWO/MK5l6M5adQez3OpwB6am9mqKOsqFnR8TnOKMpO+xH/PKRZVCMzVgJhM+a7oth59h5jfGpjzCpSbjn2mIxTrqRMxi9gqPkmRXqOY7PeYKOKCitRcivbACRWS/mYs0m2R6eGNN1xDSXLDuXWARiiOBcb69JQSz8lyVd62UaE0QDwzZ7a7Fmd/5wZmrLxHVZHjB/hHlR3sQYPoJDnzSt3QMUYtqAudpm63Fg6DpxRb2MD3wtkAlTnLwAO8jOeAzLHAoznQ9jBt2MPy/yVW1mhrCLKCDg3341yK3NWClaHmdUrYbIpP8AYK5PEEf2oa53TYPtRJQEO5QIu+5hjlMRcaH6gKM5FsWSc6XwJwax6xvgyYPlHYdUC2M8flY/PpFUBQwPAIVn27me4iMRBNs/csPNGJQ1oZQpL9BnqyI2aZ3oudreFm+GnUWylJCFUX2exaB1t3680z5iyS62cuykHcQf/GfgYHmnQaNMhpWkMC0xijpId1E22QrPdmmeOnR8mAT8XVKLFEKiHzWNNuwCQNzMyzYbkPJRuYgBK7UrOzlJS+Xbm7h20Y64Jmx1iqKYGe45opuuYUtJjSkShdBbFw+EurmFgHqXUOsD1jYzf1XwmmSYFeEQT5U/iTVSRDVbnGCEg6cmBVr53XGnZaajxojSYsZzPMW8Bm86NoCDb1iQP82VWRQ3gHNcQe1uDsJm0ucYtGmi/DDDUo8qVK5i3NvNaFfslApxjM3NXJ5buqGQC6HvDc8oGfkvE4j2EYp6bfpKcmSEvKnWest3EGStQIEuFFQAWmEJpr422390KMLhGyct3MueNarb+SebKGeZPYo6Sz8HLVcjHf24LCK3mDXw0jAHL5dgs4Nd4o2b6zsGxDmETXIyKG6B4egXfAwhPTB3qRdGx8RVimyl5gRsDJTsV9hXTThLlMwXkoTOO0uQMim1JtVv4uZt6ASqbpf3SZ6Y4l2nUgCNsNPEF+QwQrHu+WwmBdtTOZoE0stl4QH/CZtmVtRL1PWxl3SgOAnfOoaA73Id9e3qqClaiMIstynhju6bGIiqoQY25iLpORw3SnQS4STwD3GYWuwlR5nrGu1iwR3PjzcCVrVgRuoB/y7CrX66EFOb6GPFpDBW8uTZ1A86FAonKR7kxBTjVrGZNqdYVw6J1vF49DeeALl2o0tYAXKJqm5Ch8BzWjVyjhbFWHHbpD7yeQLDhjw7INY0qLWso/rJ7NOx7UEcvo6h0+BrrKB5lJGeqkPumooIEVdWlqG5lBnNQgjqxLzx6tF1Xrl5C+WoCFb4M4NBSlZQVaMWyAtSqAWCwT0xkE8peFOe+89RbdMkxILitKDSVKi+b+RbapX8gphf3DuqlvW9qJZ978N7NgCsZeuqHI6pv6WLuzdH9D5WqrhYrWNooDBxATlhPPRPS2Yun9ND9JXrw0TLK5EPV3BTniDsn5nX/3ev05ScA6hAEDLOxBTkA2pBX/T0uHT4wStm7NDqO+iGgXEZGgopLs1RbV6ilS9KBcjxqbsaR53CPzl/sBBL1KDM9ic0OfpUB7axfn60a6sJgQlMLP49t7df/bD99rlSPP7wEWMbPZgvuDZg6mtqCunx+UFtvStX8TLee/+kLOnPmDMpZuxzVdQPnFkH+xXmSBh9//IbXgM0pN4KJDz+RGMGGbZqYnJzQEPeZfb2on6H4ef36dfX0sBmM9YgZYtIw8H4CsJofOC6dmDQ7mw1wbOCwvzOzMpXJvX96ejoKcaiVcp+XCASXAhCeCJVmynF+YLdENnuZIpyLe2XuyLkVuHHTbXGvtUGLfVy2uYvXHYVR5xRsDOZ3Ykt7n43GBsDdiOXsNzvTd/9+9z0G/T3LBkgD50w12axaKysreZ890HRnvpuYdOnokTkdP9GOJWqXs1aQmZkKdJuh8opMLVmSo5pa7pmJlQ6+NYq7CErd48R43AKmsTGvuAigtyoP2C4JSA4b5VS3BroievrpLh1755pWry3XY1+o05IVKOwlGczu1Zt7Y3ptbwjXrXZ9/vESrduYqcP75/TMs0PKyszUQ4+m6o67ElADt7lnQZPTbr30fBD1yTlsv6f1B7+XrR27MtXPBu3XXg7pu0+/DNBboi99eY02Y9UKrwg4N8lrewHnXsGqGnDuQVOcu5mNNtwXG9HvgHOm9Huj5uxK3PgXf32MRxyc+xiVFv9IvAbiNRCvgXgN/KIasMkp/ojXwGerBtjEopHxmI4jJf7tZ8Padwp1BKoghZzPymq3nrjXq689wS4yLHIsaP6f8SDWJhkZ07Mvh/Vjdmk0dUeFmrGzrvWLvs9232/b7Nbffd2vJTU/W8AZ4L+9QIDfAJy73vfRwDn7vpVVbv3Z73l13z0J3Ej8ohLEX4/XwIfXwGcJnFu8ebab5sXFng+vnfirv0wNxMG5X6a2fva9ljOcRlHoH54J67lXwrraxjzDxn+mH92zyaN/9hWvbmMuyWFXIOs+8Ue8BuI18D+xBmyusB8DNMyOztR+bCH4u9/9rmPbarZsZuFh4JxZeAyi1PD666/rySefVE5OjrNL+Y477mARM8uxfTWYwGAGU+Oxz9pirAEFtkvavsdZ3AW66rpyUd/70z9RD2pRtYUl2nn7Li3ffb/82Kq6sgCegJzIoGr+7QO6dvg47lIRrb7vIZRjihQeHUYBq18jfag8TbFYSoIpmwXUdKC55K0k3LEBHT1+VnMvv65UT4JS7twu31aUqACKYl5sXEn2mX2nDyWVBNSvQpeBgF7cq+tX61Vz8y2qeORhJVSUKYgyTpjP26I5+U8SkCxIz2IJxYJ5+3M/UkZnm1LX3CzfnkflQbnFxYBlx3WhOOEicxTDMqrt2WcVeH2fCnJRmLnnQXlvRgED2M9JNKMmEgAMnH/rNUVC80rZtROgbQ0Lz5PqbQL4GQI6mATOQbXFRYBeuGqp8jeukT8du7LzJwHS9iuUnqOlux9Qziazn0SRhOQ52WN+SLSTVIwFAOew1GxGcW7w8kWt4jrm7gEsW78e+IvAmuDdrjkLu0BafBZLmdAVrC9feVGNnd2Ac/eoeNtOeQqKFcJGNUw9mCq0yxIEvd0K7z+o0bcPax5wsngPYCNgnim3kZFWpBfVjGNv6vT+fUAEJVqyazuwAYBl7zB2Z72aGu3V/Di2bAtT8ntjTtI6c+1m1EU2oRjTosuvPKt84MaluwAsbsZitLDMASjMsoYv4IeJA0Bw7sIV9T31tPxDA1j9blDKQ/cqobyCt9COsNgyJcCIXRMALLNJm8Uat5X66Dp9TitWr1HZbpSMlgBB+NM5NxKlpmDEV4S9qASRNA4NoBSGIllCzI9121aU8VAWQXGOTCrgHKAbUEik9Si76ZPlW3M/rFc14NxpPnMKcK4AO7SttOm1lAOwjLLzKcpB0QzEDI4oMn1O84OXNDc1yo53rA5LsPUqXKJ51EtAGEia8l0z2Ce3AM71HFR6Hip2pbuAQm7jegP7RVBAAcwa6TyIgGKTUrB0y6jcLW9ajaNsEus8ADg3jLWIeIAaAABAAElEQVTgJiVV3AlwRwLe6pDEc2z0BIprR4A5+knkFKF6tEGJVRtwLUQdxextSfLO9l7VUHsrgkBRFaBukuxnnOhDtahvDHWdtUAW2K2iMIQEHBM56ndm1Qa0aTZqLnVoAQWhcd4fnugnaZFOG1iBfSLqMz6DMtglBTgRQ3Et2HVSfZ1dSskHxgNoScgqpZyce5Q+MHsVcO5N1ILot3n0OcrpBlyIzZGAvo7q11QIZQPsEytNscmUecjcoNYSHelBPeqC5kaxVSZhkoKSXbTsZs36StH9i6IgM6X5gWua5nqloh6XiiKjKVMNGTg3MoXdUDGKcyhooQIVUjr1QXsymzsgAZdrivMc5byAMYYaNIM1kxvVvfRcbCFRDFQS18+BN01hqEN9bZRjopuxIBH1JcqRvoT+BOCFfbBresABOjx8zpdV6fRLavEzqTj3QeDc4j2N3c+Y2kcL44NBa2+++aZTRzbX3HnnnVq+fLkDrRnk7SQNbZj4lB+0OkdlzgBzS66bxbn9bd9pc52VwxTvbH68YXv1j/DcjUTmzxbog5772Xf8an6zOrbH+8vzoYpztP/YxGmNAIHMDGNHjgpdRjUgbeoGAAgAt4Rx+u8VTfQyNrWcU9LEiJIzslGbA8gpvRtVryKGGRK+QDmxqQZNNh1GCS6qNGwik7GnXOg7D1B7BNYGlc26PXLn72TczbKhCaDDhihbE+MX4oXYbAvDKfDWYAdj1RQQX6F8gL3uzBrm/Rz43BQANeaL9iOocB60URi1q9uYVzYxNqOKy5DoDgPO9RzWQhMKeUgRpaxiHEX5MwFwLugoynVzLEAP4OLUgjqmF1PBon/PNqEgexIltBaGdR/A2lKgK1PeK+egjIvAdZEp1Gja6zU93AaADGBYnsf4jL1bcz8xQ7GygfH8hcxDwHimXuqyAtk8wQKhWeJGgySuh6/gAnkVWDcgTwpAYA6KagVAY1i4u0gaIymmKGptI4zXkRi2iSUoshVxzBTGcztYsE8LqKl1dwIqA0/nVVQqj3HTHU7CQh1wru2UUtNcKGShRpa7GQA5z5k3oyhmLgxd0FjHcSXFACSLeL38Xsa4VVwE4gh0t2Lz17TQ9RLQD9AiqqYZlQ8COS0Azp0AUptBDXQl4NxGhRkXqRjOD/DK5nGuvSsCUBbowmUUKK6/jTp0A5SVACBjaQ1IHMVq1WbOaGBaAco/3HQALHBcmWWAc4U7qbPqGxBBAPVOAMwx2lEqryei5OUuvZ3X/JrGfnWK47ux+kwCgveZ/R1xR5C2M4+a5/QgQCCAQ3pqDIiqS0k52SgfGngFLAY0bcFPFPgo0G2w+SlgHsA5oLZZYKmR3n5y7abEt0TJubdyVhWcI3OFgXOEVR6hHOaiL4QHOMdWzfSgAgY4lJaB1Wwh80UuKoNYvTuBQQwFvOEWjaI45yUmyKAf+ItWco41RkMQ16CKCIQ+isW4gXOlK7CPp79AtGlhaoT6O40yay97BlB89JXJTx9JCQL2ochmAPv83CCKgKlsAriFDRFYyzMXG9DvJobWNO2r7W1Nd3ewEWOtUpfdTZmxG7z4fZQGF+SrvUVRILcY8GgC7dP6nz1ctokBS9/o/AAqik1Ali0KzIwCF6KwRFtx56LS680HRqOtoJoXo4zhxpfkQh3Pi+pjsHyrA85h2KkEYofYCOfQsc9RdEtDATdjyYNOXB0BqJvru6hxNqVEICASkzKUQlvyzIehIYBaUfGbm0ZRjxg0GXXZxBq7fqvYZFOAHSnQ/nyTxtqvaLh/QP60dJUAziWgJDhBvD05FqY/oGZr/QWLYtvoEMPCOBKkbmimbqBHBTqB9AEzR69hEwiYCSyXCSiZjMqwy+yVrc+inhmbAU49/zYheDIKcOuVxDhEw6AOEulPqAuPXVZvwxk28QDnVlQRd1XJE3YDhaKyPdJB3fUDs2G3iAVpIqqP7oVuHHNR9WSHe0LBFkWKHtHQfJmjONdwbZqNDwsoWYJlzsyxp8OtZasKtWtnjoqA5wYGp7Fj7AB86dHwaEgDoxko/qeqrDhbW2/J1ZbNqcrP82jf/pCeevYIbXdEDz1ym3bvKuF5eijDRm9fBHBuGGvHg1qxtFgP3LdRWUBqP3huTM2dPSiLZ2nPvSWqrUJxzm6WsHm8dCmiH/7EwLnTevC+UsC6CqA2D1atM/rL/3xMAVQfH7pnPZapqGATskXpIzG7N4smKjjvVV9PhHvQceb6EQ0MTGlkdA74DwvetDKtXpGj7TtRnULJyspWf20M1S/b9BXS5MSCZjh2WWkeClMlQH1sZMh16dLFBX39G9xrxSr1xKMrOAegxkLAOQCh5k6U8S6PauumVE1NtOrHP3neAeJNRdYU50xBfXFeXLyPXfzd6QDxP34za4B+zeRKv/35xTeLVbNbHWajW2tLq5qZ41uJPzvY5DMMRBcw+3QOYmpxBskVcs9XiHJpAZtubANFLj8Z6WnyO2pzfgeM83KfbpvIHDCOe3BTOrP2ZAp1Zr/qgFoOjEzh3ls+J0Z7t9CWDHRe43fnefvFirL4N/+2t9pri8/Z6zxpbdjWeX703HPOJntzDfjiF7+o6upqysUYZu/iYwswzIPk7Vpb5tTQOKDunnE2RYbpIwmAqgHen6U77ijVqpUp9DkXfXVcnah19/dPamjQlPkYzxPyVVPj012701W3LBG795ieeaZXx041olRZCjhXraXL2QSAbbRtuHrzlRDg3AKbJdv16OfLeA/g3IEFPfc8EHlGuh542K87dgHOcfsYY0wfn/Lq1Rc8euEFNjey+elf/h8pjA0pAHox7eNY3336DTbTFOmLX14JOMd8xMaAce7VXkE9c9/Lr6qyrFIPP3Q/4BzKprZ+wkhkAz53v071WW0618Mq5WM+4uDcx6y4+MfiNRCvgXgNxGvgw2rg3Qn/w94Sfy1eA/9EasAJTIm52zqj+sm+sL4NTNALQJdI7Jaf5dI2QILf++0EbV5HYP0rggjYVKNL1yL6+++FdOpiVO2DJFXfBRt+XrVnEog+vtujf/tHyDPn/2wfngKSeH5fRH8GONc18tHBuWTyKffc5tG/+1c+JNhZoPnZw/68osSfj9fAB9bAZwmc+8AKiD/5qdVAHJz75FVpkv0XLqM+9y3U585HNMS8F2TdpBAVmd/7aoIew5K8sgyFGNbZ42P/J6/v+BHiNfBBNWALqGY1Z7uT7cf+bYqlpihnij6m4vOFL3zBSRwYBNDd3e3YBj7zzDNatmyZvva1r7Gzn8QQu6dtwdVsXJ9//nl2FD/tvP7Vr37VAQlSUG9bTMyzJVodly/oO//2j9Xf0KCqvALt2HybVqHIlrqWBBtWlPgUkSga1NRbb6r+ndMgUl6tfeQRrDgBs1jUDJJ0nB2YxtKLhLIlqy9jx0j5krfdrtwdOxS8jsIV4FwU26a0nVuVeucWEswAQyTD0P5gUMEWyAXktoDCWnOr+l/aq0unT6HesUpLOV+z+oykppGwJfnJOqqF324S2DHeH+nvUfPTTymjrVmZ625Bce7z8lRUAglYkMogxnGdD0TD6gSwm335ReWlZCjtXmzftgBRoSJnicboGAo0B97Q1ME3qLuIsu7drSTAP1OlmOkf1sII3zWKRWdrvwYbSEqzC7pq220qWL1E0cYrurhvr+Y8SSjO3aO8W3egvEKilDogM0w5uLEwu1bsYgPtnWo6eEB9506zIaVShbsB5zZuArqywdUU0Cwu5z+DBAMLCgM/jrz6EuBcp2ruvEtFDjhXQmLTB0gI5MDRXQYTDqD6B/g3YOeQma9yFAMzVqHOxbnavU20B5Wyo6/qxIHXlY5iwzISUMm1XF/sWIKjU5wfdlsoyyx0Y73bhEIO1ypjxTrl7XkCxZRxnfvp08qaH9UyVKSSt6N6V1xFjp4yWwnsC+yH+l640qD+HzyDwh3WuWuXK/fz9yuRBEAMzRn2vTuL4JAGFMgAQdKfTSQ+DuxX76kTWr56lUp33y1vrcEFlNtRz+EcOc8IVpwRLM5Cg6c12XICFQ0ULVAr8xesgfcC6DIYL9SnEMBaqP0EdrDpSln7MHZkNZoCQJgGBMtGKdDPZ1zpgIqO/Z7VNuicqaoszKIA06J54LWFyXb6HxY+QAJJJUuB2wo0w/FIeQPO0bcCM1zH1zTV8SYJmBSlVO0CIqEteSlHeAjoBHCu44TmJ7vIz5ZhBbobZ7NaRQG6oq0HNTbQB7yIvWANdnAZJZSddgKwEBk6rkmsWufGhuEoSfBgW5hYtxFFGdSiKKkHpZrZvssabGsFGOO+tBJwjgRHeKBNfV0kxos3KqNmB4pOgKPAHo6qERZFdBSOj1XqzCWNDwPEjA4qlaRENlaovryV1F8FZeDaxCxRgToiynGhntPqoy8noxLlKM7llNFfTc0IFZsplGLqD5G0DikxD7WnMpSE7PLOt6DyhErO+LRy84uVUg14m1FEHwKcC3J+tLHJlktYtXZjjURtVi2TgF4C/jIbBVDfmSQRj31s+wUU5+aVWlaBUFAuSZdRwM5ZFeaVKp02CykCfJlGYovENwCFQZjQC4wvnZrqu6o57M08qAWl5ayUP3stQE0h/cnaCEkoFwlhQIbRniuaHGpxNgVkVNk4t4JjZtOuUKNCqW4eG2NT+UnMBZAgmWajyWdFcY5T/cf5wX7h8d7EtKOi4fR3kvi9vTp48KBeeeUV598bN250kts2D5mah2NR9W7yz4aKT/XBRbF5zH4MKDNbclOcM4XWepQ6bQ5ds2aNozxnMJ/Zxy6eh82Pi3PgYnLSzsteX3zPp1rWX/Jgi2V7f1k+FJxzVCvPaOjqKfr5kHIKUJNk/HCnA/JEC7iII/SVSxrrPITSUwOKcxMAPWlKqbgViGo77yunlDcg19j0VY01H0LFKarM0vWokNZgh3hFga53GK5QCqndgx071pFe5k+uqzUHU3Y11U68SxkDLgOlAdhimZmciroLamuObTPWyzHG0ihgs9uFdXmngXP7SPyGlYyClg8rUFMb5YrSt0cU7TiKKukJ1KBwd1gNOAc85cU2LAw4N8RcGnXnA7kBIZuSWwoQnymyYR8a6T2pUWDzMOBcNgpTfgPnMjk/j42LWISPdwDWXcWGs1+FNfkA0mwCoP30NvTD2xQqD7XP5GJgPGzbo9SJO8oIhaKVo2iHktbc6GWAqkbE57A9TctBhW0zgA/zOFCcywd86xqlzlEAHLyikdYmYrkEwDIU0FDuc1RgLTYJoWaFolsX43kQiCm/sk55KM65wn5Uxrodi9OU5DDqr0BuBbcACRZSdgD8SdTaugB3+i8xH2HZmb8MUbX7UUEDCCKGgIBiLGzQXOvLzO2NWMeWo+b3EPNbUKONp7CDnFVmLapaqG8F/SXUNQpnSiJMotyAeNGZHuIBLH0Hr6JSBtiWWYma6WoA6wrmCqBH1PXA1piPAozpLRq69iZKY0Oo9NkxtwGVlXONmaAWAN2Zb8abUBD1zAGtA84V34zaXETjzC8BwPKsHOBtoGyPj7nFGwHcH9Nk/4DGukeUk5WtNOaJqZFObMSBr6pRKs6/2YE1kRxC9G+EergIOHdJaekJ8pXTRpmjR/p7OY85lMMMnNtC26yi3pgrCFeifIcbWErBDuoDQGugg3l6nO/PRe0PhVzU9IzqNhFYZOv4wBjXuVVj2JonTPcDyucBcwHeZ6y+UdeRIcD1ek10AImiXlewDEgTkDwazNHEYCs/2On6Z5VeiDWufykwAZAGanuRQWLIQeoYVcZYks8B6ZPLUVTMZk50s+BK3Wr8goKth1D8AwQsXI/a6t0AsMTa534gP9fYAedKt9Peip0Q10VMZe3D+WyIjSCjgGuA6AtTEyjTArXlV+Jyy/klV9LGAUJNKclDXDp4RhHAufA0ym/YAgs4Lka78MWSiKmo5+HT2Ke/AQhFW6/ewRz8sFO/oS42mgxe0AJqzEnFNUpkrudfdC+Ly6/RB7GG5bvDXNsU1P+Slt2Kpf0amMcC2s4k8UKrhlsYZ4ZGUF/LUeHyWsC5OU20AfePBlEMxKHB1P1SCogB2XhBv4gFmcNRknYBH4YmrrLJAEXEhQGlZAHA52EHm0EsAkxrMYepIDuKcpP1GrxwGHtbLzHgasrKfUsafQlYUwH6++h59TeivEzfyaioVUYp4wQw7FRPt2bGe3lbCCW7XCWm1HA7wIaf+TZitWOMb0PE+ag9VjymKODsJLBKH+DYUH8QeC6ippYpXbhu0KRHe+6u0T07M7CGR0ERoK4fgK5/IMQ9WoLOnFsARBvX2uVJehiobe3qNL11BFvDHx7iXEf1yGO3a89dpcpG1SnGYs3AYBSb1mG9+NIhlOpK+cx6ICE/oN2QrqDWt3FzLuriFVpWS53gIGD+pyfeQXHuOTZHXjutR4Hm7n8QRWMAvYbGGf3VXx8n/J3Rg3ffAOdymCrM9j6G4lSMvk7lW3VoagpngsEAIM6CBgeC3JOyAb/F7jWnmfNztHUH1ousGU1MxTQ8FNUIPwO9AV2+OqLGllmtX1MIFJOppSsSdK0hoG/8BeAcUOsTj6xgU34iwB763FGptTus69cmddtGANvxNj33458487pB8O8H5xan1ffPk4vPx//+9aiBxXjGSrN4rRY3ZSzGYvYeg9fev8Zoz4cZ7+cBMIeZo9tYE7lCfNdw7RqgZi/AMjbYZvX+rqpcfn4+KodlqJeVq7S0VPn5BcwPaTesWFkb8XFfbkCcWSL/zJcRwzgPnnYCmn/85cbzlM7+/+8PyvXejzjH+sDn+IRzTDss52j/XjxJDmDP9fT26Fk29l28eNHZGGmbHKuqqpx1HPs+qxn2CqLCGwMcBKAbCWloaIF+FlZrY1SnzvUD6g7q5pvKnL5fjdpkkPeOYXk9SH/t6QyovU3UW0hTs33avQeY9+4ioHWPfvxcn46dbEbxvQg4rlrLVvqAhM1mVtr/akCvvgrMDjj3MFDt6rVZOnp4Qc8+h2oq9/8PP5yhXff4sH1meAZmHh1z64UfRfTCS8RzvrD+z3+Rqp2Ac4M9Ub1BXvXJH+7XipVYwn55hTZtNntcF3H7sF4Fmtv38j5VVZRxzPtuKM4xrzjXgXnqZ8C5914Dq5xf8hEH537JCou/PV4D8RqI10C8Bj5KDXzC2emjfEX8PfEa+DWoAdZLNT4Z07lLUX3v+bB+uJ8bc55Lx4Z1fY1bD+zx6CuPJygX5Z3/FY/JmZgOnwjr6R+GdeBsVOOsjfy8R3WRS3/8O1596Yuoe7zPRnZi2lTsIvr633Bjz83tL3M21cUu/V9f8ekrX2KXGWtc8Ue8Bj5uDcTBuY9bc/HPvb8G4uDc+2vk4/8+wTzz3R+E9dLrEZ1vZc9xgIUhJokv3+XRbz3q1U3r3ewytMTix/+O+CfjNRCvgQ+uAVtAtcT4YiLfwLm2tjYHnGsAajOr1ocffpgdwySpWLTsx5Jk7969DhhXXFysL33pS6gNbHEU5wJkOkxxzsC5F154wdm9/JWvfMX5vAEN9l32MKvW9ksX9a0/+7rGW5tVlZKpW0kerbpzt9JuwWqxABUXkqcRAImBV19TK+Xxl1RoxQP3oyhV5kBNbgN/UGiITQN6NTZqCtBrgCRb0obNKtpDgh21qOnX3lR38zXlbFylgrtulxdlB+ScSIJis2kLo4wpbjLwUaCiEQCwywf3KyM1WasffESJq0hUZueQEyYpx/eYnaSjMJYQRlBiWK1PfUdp1/5/9t4DSrLrvO/8XuXUOec0PdOT8wCYgAwiY0BEUpRISlqv1+dY2tWulvYey+tjiTJlHUo212ctisHLIFIkmAAQA4AIxGAGmJzz9PR0zrm7qrsrvre/7/YUPIZJwASGxBCoIhs93V313n333fC9+/3u/39a8toAjQDnXC1YPrHjRdXmeLPYHhbKgV9GntslsR99TwLYR0Vuu0vCt92+qDhHMioJmDb82isSAyIsq6uSgltQM1u5hDJyHMpH1pqEGRYxHYMy8vpedrq3Y3e6WRpuAQKc7JdTz+9iN/i4tGEXWwtY5mlsIuGHPZgbKA88UJXyVCkjMzQq/W/skZ43dktdJCyV27ZLYMetJJtJ6BOR6/ssD8lVtW6lLaQvXMKq9Rk5335BmnZsR3EOtbeaOpKfKHAAzxlVDRTnZJok9eHXpOeVn8rwbFyabtwhVVuwokVdTy1k0pdQAvv5Ljl38piUrt8oNfc8IG6UNSwAMctYhQJMoSCT6ekEkHxRJs6dEamolYZP/y8kC91y9qnviLvrnNS0YUV2z4OowgEb+UhOKsDIdVkeyk8iMtOF7dmuZ2X8+AFxysOcZ7tEsC+TcBV/xx6LBIgqElncE1VFiwFF9ez9OfWxS5YvbZS6O7GBbUWZCPU+Vu+xE9P7yLMKyVLBui81ihVj9xvYqAJW1axC/QVVM1UaYne8JMcAQlDc6z9GuyyR/FVPYtnVimXdfpSPDpIYL8NaC9WTfGAqN4oyLMc7gGBWCmWWaew9UWKLAXUFSXRGylCaK6QdkWB1AmHsZcm6k3l3p318kVQc2S0zXa9wOUkUy7aKD+URl9bHwgi59g6ZGTiG+NEwVq11Emy5l3aGkgyWqun2l2VqAAvUumZUm9aKu5B7iS2YqvUlB9+UaOebkkZJEaJOwsAgkTaS2IAICio4cazPeo8AFQCMkKIubW4Uf8QLEzeE+sA0FsLrUZi6lXLXcU8BYOhUDgouCr5mgAKTY6/L1HgX5YxIIepHgStWhDzpUh/AtArbpXm4JEmSHj4uo+1nxcM9KKKsvqp6gALK6ZBIH78g02f2S5T+EK5dK/mN67Cipb0CoYxcOEEyvQfowSsR4AxXeSvtVO2DJiUJSDAOlGlhu5oPkBrAXtHdAigDmEHjM5+PDQD2DV8gCcY9aEBxIVCCDd4kCi4pVBDrJK8BtZ/8Om43EBQQgqoz0iiMCl4CFaNx1Pwsbz7KM80SLOP+BcoBBCibtiNgCBeKcg7qO/Hhs9TjYSyYJ4GCUIuqwhLXR8YYlaQ0QN386BiQzt2UkbbLBzXk+aiBc1mQLDsfZeeMbMJT62Pfvn3yFEoZqvKmSco/+IM/ELUMVzus7EvrTse2a/7SQ3L/bb40Gavl02fLk9hdKch36NAhM58qcK5zX9biXN/rYYzNXo/+rNeYvd5sYveal/dXOODVZbv6Y+8IzmEb6UwdlsHTb+Cs3gO8GsS6uQ0L0fWMMYwJmRHm6OOMW0clieWeR4cGrPjCqIkFsTd0018MGLUwxximSmlvsIEGNRHU1/IA35IjALU9h+kOqEu13glYuhWgWQFnVWchbgF8tWcHUfG6gPXfJY49jk1YIWpwWMuXcGyU5sxYwzjjANhYgHOZkf0GnFNLsXDdKgAbYo4Q72NKc+aAfXveYDg6Lgu8P7xmp/gab0MVcgCLTmzJ2wHXUn6sx1EJA7BxFeocYwO1HJdU336ZHQamxco5H+A2oGpnFUBJPuZN4OaFiXbsms8jzBWVumWNQHL1jKPTMnAOlTgsucuaUa4CDncCgHMANW61DE9QqEQ/KrtHZHqYcW4O3cuiOq4Pxc78FuqYuQJgF5kurg0rUBRaMyNdMtx+QmKzw9hElkphEyBQpJaL0+vrRznvjPR3dDI/YiPXugZoh5gDFc3UQJdMn30V0dqYROqbmasZJ/P4HHCbPXRJYhwzMw3oBOjsBW72LbsTmKuZAY4DoxaVQXkzduEVuBvewzgbXPY4SqC2THYcNscsXEKZgYhS/ho2LQDOASVZqpZq96DYeVqmOEeacbIY6/ZQOSplEQByvX/ce/RtTXfWdLY9iU3mhRfEFe2WcFkNync3GkVQodwOylxRQPwZFPcK/T5AcSy9q9dLciaByu1h4HzmCeLA7H1RqNlGqWyaDP8kcUExqr15BV7sZntNewg2YBles435PsR8iTrsTI9Euy/IwlCfFBQXiNoJz2dCzAEjpp5KgT0j5euoE+6NKs6haKfnUMA7M3YMSLtL1IXDjdJiQU0bttzMs4Cg/If5AoDDQ0wBlJ0EnpoeOA7wfRqFV9SM6lbTpwAldULBkjTFPZzuRaFvISUlK9ejQLsWADAs033nKftRoAIbVg3QDvt1vfEO4Fy6+5QsDHZKMk6fYSzyEG+Egch91LXlKiOOYMYfw8a461XsKlFPBU6PLL8LmAHFpcPfR6E1LYEWYMoa7nugHnCOsuoGgMwUnRqL5KkOmUUNMTk3BJygfbCNNkr78AGMuUv5og51DASitMePi3P5FZkb7aYJN6D8iB2vgmX0fS6KmBV7dWyBE8ApYfq9r2EnYQDwae/P2UyC/TzgaLiVTRqqLku8kWGTTbLnjNhcP5QkNsRerqtCgk307dK1nBuwTa3bsYSfpg9GJ4nHUaQsWtZMG4zKVA/WuCxylzSsB9pdyXhQTRSvbZQBX+HAzDRAJpbT2BXPxaYQmiySgqoG8RZxfQq22cQxPCtkiFvdDoBejBj6LBsZpuYAaCqwam0B4kVVEDDDifcCtx6T0U5iMzbyFDSskghjgV73XC/2rzPAgiXYN1bx/mATbQNLZzYWpPt300eGxVu+idv6iMS99YuxKsdMM+bMzthAaRn53jNsDBmYlRs3NMuT9xfL8iYXkCZzFbcrzvg7OJCRHz8Tk30HLmPvmpDHH26S7dvKZc9BwLl/eAlIdUoef/wWue/uOikspMfxrDLKBvofPz0iP3z6VVm7qlYe3bleamoj8tSPR+TVvRekqiokDz24VG7cVCilOONo6PzCiyn55g8RAxg6Ib/7ZANKUZWAcy6eK+bkP/ztIUSUF+SB21fLk4+ibMeeJkeBSmLMFM9bibjDvUd5kOFPYyFVMY6xVnT0UFp2/SwlHT3tKOrVyMfuK8Iy0mJ84T7xzGRCSTbrHzowI1/9RifqviXyqccrZdtWn7RfSsuf/yWqxZk6eeyhpfKgUZxbBOc6etJy9sy07ECRKsomlqee+oEB59SqVcE5tWrNzvfXw1xNreRe71IDGs9k40aNtfS+aSyjv8/+rLGcthsN6/Tf5jN8j6NqPMJcfh4rVt0QcY61kC7miGlUhr08A5ewNlDD+seKlSvNOofGoWVq8806R5CNTbqR8NcRfr7LJf/CP+s1Xd1m9Wf9UsU5Bec0dlXFuSw499ZBqK9Ugv4BdM4TGEsgqs5IHwOO6+uw5ZnnJmXfkRPS3FQuTzzWKmvWBnSJgA1ExLX0/7lplOoYj3789JjsO35CdtxcJTsfaZXSgiDgXJ8cOHhe1q1Gce7RJbJ8ZcAAbXrsl1+IA7XNYtXaKQ8D1W3YVConjyZRqRuTgdGo3H1nlTywswDbZsrHjVNlym9/Y0ReeG1eakqC8rk/KZZbbwvJyMAiOPfN7z4HoFcsn/j0KtmwERVZnrWnUD5+7pldsuuZ56WxvlYe+fh9gHPMQR6euXUO4lo1xtQ1Im0bpn28VTG/+j9y4NyvXme5T+RqIFcDuRrI1cC71sD7nZ7e9QS5N+Rq4AOvgTS5tcFhR557JS3ffColhy6xkMJDrT5wbl/vlj/+Qy8S6vziA35pbmzPmxn5Z59LyEXklXVNx0sXjWtu8KrXhmUu+dLnfLJtB4s6b3spHPitn6Tlz/5jkgWjdw9AiVWx0OI8fOkOlLtuc8sXULKrB87LvXI18F5rIAfOvdeay33u7TWQA+feXiPv/+e92JT/zX9Oyb7TtkywQAp3IesaLWPd+vBdHhLYWLd+8FPi+7/Q3BFyNXCd1IAunuormxDQn9W2s6urS7785S8bq9bt27fLzp07TeJArehmZ2dlz5498vWvfx01iCm5DXW3u+++WxoaGswu7DfeeMOAdceOHTOf+d3f/V2SMI8bsE6Pr+eyWbzuOH1KvvJv/5KE87AsDebJEpI/TcB5xUBu3kbAExLpyfbLMnLkmMT4W/VN26Rs7VqJRqclHouiuJInHj+J8GRKUt3dMvMG9lIk2Yo2b5XSW29DYCwjiTcOyIWDe8QJe6V6bSuqZ9gyoVCRwW5xHiuhJKoUpSyaejNxWThzDJAKpQ0AoVreF1qzUbxVCstgixpLM/YA3VQWib+uyMBeff/4LXEffBM1jUoJ3XqPWK3Lcc0kkVqIGgLgj4MiWYZkT+rgQUk8+xMZvwgUxo70optuFH9FJQIdbEa5eEmGz56TEMpjCsSFGuolDsyTSC5wnAIALJJxBNupnlHUK05jDzUhtZs2S+V2VFCcORk8fEAuvnFEigHRatZgMYVllwd4JAMMuJDAPo4BswirSY8q2J09IwOv/gw4aRBFszoU6naID0ULvScLMayt2JkSbiIRmIcqyNCkRIEjTx7BarS5kbJtkkBTI9Zt5dh6VYhLwUOeDQz01nlERt94Xs4cO0Oytloa122mjpq4x7YsAATa2DRlsAAuuBn4AMhwDvlqhwcgP4kFN/WkrxRlir0JMNF9GQWXJin/vd8nVx6QCSxeE9w/GwDRt+FGCaFm54kUwHslsO7FsrOkRvzVDRIgwZY6Sjn2vigjwGh5jcVSvoqkNIlpAYJK0WY9qKkESXS6S5tJStvSt/81OfP8t6S1PCQNW7ehpEKylPviKS+WFPChi8cNL4CnkwQMwb4sjepcDEUwCRaT1KcuImx3Rxkjw477uZEjKNl0iIcEceHyT4o7uFSi/QdkFjW34upSgLUbxMnX5LQqyNHnVE0xdhpllpMyOzRAPWLXpSBJyRI4uWqS2nkkYEmGhgJYx9DOSVm71A5w+iDt83WZHh/F7hDArXQ1uVoAsYUxXFc7AfAukASeImHdIH7UmSwDzgEjtP8cqA54raRIvPX14i+uBWTDJnABO5yBoySEz6EmY+OuRX8ioR+uxwY1QqKQ7Kc9gwLQ4EksYCdp02FUg1rES1tJs2O/dyAqoYq1gDI7aK8k6FW1hiu0NJEdHUXBCJvF4deAJKJcWyP55c0k8smQKiSggB3goxWsoF5IwcdVheUCUMIZ4JApCWHxFsCW2cX9tmkv6ckemeoECgAoCTevkuJGLIsBbh36xNCZI0BpF6QolJBQbY24qpZKBuAuFR9DwaiTaxySAhLG4ZQX8LKUv2PvV6xgoNon0vZGLzGuAEIAXeY1cd2eMsC5CZmn35cDIUZUOSq/iesD4tMkG23JnkexaXg3iljHGXf8iD6tljwsZt2o7NhcXxpVhRQPsT4/1kxcnxWLG/hvbuBN+nQXqoRltDWUuVCmE8qZApxLLiTF3/Y7gHMAD/oZajMHzi3OUTpOaRJ0cHBQnn76aTPHzMzMGGBOVU/VSk3np+zLzDXmh2u7bkDKMXuKt5KyWi5VINm7d69Rnjt9+rSowupjjz0mN6OWWa99Duj8anAuCwbqwa5Oar518A/gH1pn+np7eX45OAe0AiTizByR4TOvywQqlJE8bFbLyrEmbGMsoS/GgY1GUYEb7RMv0LIPqFRhnwzzu48+7quiv4VqASUSWB6fwtL5OAUIovx0ExDrKtQ+US0FurPUWrP1VvrMDQy72sdI0AJwWSg5JofPYzcN1IJNZCDPJYVlDczzbYyjjDWodSp050JRzvIA67qAM6KnxB54FXvNLnEAfMJVzRKgP6oNc2YaCBnb7PhYuyTc+ZK3+jHAuTvF66AIydg7ceGUeACfCgpR3CpjDGHcdoDW0hNnGEdPAShh5c5Y4w8wbgPwuiqBYIF47dQQIFsn6kn9gHNxqW9j/K5bQt3MysAFrBu9pVIO5BOqapSMXyE/YBfNPs9jJT+FstgQiqaxbobOComUbAC4Ws5cwTxBPJUG5nXR3lQR2K3Q/jT2Zb2nZRSQOj8cl2LiGA/jl4WCno21ZxqFzYnhSQPylCxdB3TVBBxDvrgfC9GzP+MfU8yVVWIBibvU1luhbexT433d1BEKcSnmW+BEb80a4CuFsAH1AeDSU5dR5TstXsrsBVz0tD5hwLmxy4dRDp+XIjYXWIxtGR+wGyptBGAMcCOoiCoUCCg1Oyn5IbVVBKwLM95atcA+QGWqqsrc47piLa+25EkFyFHWs7A2d5UTe5Wh8OmdQWS0W6b7e4GQp6Usvxhr7g2Ac2sAyjOo4R2R1PglCZYz/5UqHEWsoyppk8w7w9jGj89LaUU19QuoN83PM/PUG6A1EKgn308SfZDjd8oMynxpFHNLiBeCzctkPhHE+hOQmjm0lA0eeRV6bxppa1wf6r92ckrsiZOSRjkxgTWvwxzoL1+PnTfXqDA/Y73j9lMvQPLYtbr157kZ5qVjAN17gYGY04sbmWPWEssBns32IHZ6WWZGgLnSfilbswWQag1gWYSYApvUAex2wzGAfaAw4h+HBpmZQd13iL40PQn4iNoyUGEcON8pZr4vW87abgNwN9c4eRSVWoCv+Ky4UeuLrLzDwCYzR58WP40k2MwmgKo7gDsbaKGA4UpIxQeIE1DAU0XCyT4UadPMpYB1+YD7LuICi3mT2MfFPbSCfvovGwcAy5wBlHwBoFJcuw+74FARcyJ1Zs9OEacCx42eo8+6xdt6l7jrH+b62CQzsFeSgFgJYNQQduceADYBnl1gI0lisEN8U8Pi99EeUbdNI1fvBnj3FuuGCyC0BBa3wPzzQ73EvAD4FfVsdGhlxJvFgviSjM9i1Qq8WqgbI4K1PCcAzqmNegI79tlu1OCIZ6ZQU3T7sIFGyU+hUcYih59tatXBYlmhVw/wqov+Hu86JTMoyHnsDIwq18ZY4WKOsuf7YTBPowIMMIstfGHjJvp9A/1T1TAvoyo3LL6ghzGU+CfERiEsl+1p6mn0AKp40zyH3CTT3q1yod/NGMcYxXE9PMvMLwgqa3H5+T7U86I+2bS2UTYu9UqedwFFPFuKiqkPwMXBAeCU1yaxXxyS1sagPPJgnaxbny+v7Z+Xf/jHn3EtMUCS2+Tej6EQV8KYwdgwNGjLj34yil3rq8Au5fLxnWuldWmJ7H5zXp59oR0r1UGAugbZdkOdVJe4iRdteZ1Q5+X9aq3eLr//OzWy89FSwDkPMNIcinOHUOlNyQN3rJXHHi6Tctb3LZ8CPQCA5An6+hbkAsp5iaQbmLVIIvk+5Qrl2JGEHGIj/8zchGzfUSJNrbRlrs8fYHM9CuFe2kt83pZTJ8eYiwdkCc+ln3ikSjat98jFi2n5iy+8jCpWtTz+0DKAuIBU1WJQDKxzsROr1jNTsuMmLCe51zlwTiOB3+6XxjMau+grC8plYxz9ncY5ZhOMJp30yYw4TjcOzs5Gpbe3B6DshOw/cBDVNGBbYk0ap5RgxdrY0Gg2RChMqRsKS0rYMMV6hI8YT4N23TSm/czSh8jr4GViYXONi4XRn/XrncA5fbv2xUnU5c6cGpGJmTnx5keYhvN4/nHLELDa3r0jcqmzl9i7QW7eXoWAfZS+mED1OSRhxvl0wi3dwKqvvjaOomO7fOyeRrn3wQYJ8Wz7/X/slAP7z8iGtbWAsytk+QriO2XTWZd5+YV52fXTKfKFHfLok62y+cYqGep1sG+dQcyjC0g3KFsBfVuWhAzQ10nfffGFUTnekZTlrM/8iz9BifLmIKp3try4C3j3uz+UVasiBpxbv4HnbDYmTs2My/NAc7t++rw0N9TJIw8Dzu3YdAWc0/vIPLvYLEylvd87mQPnroOOkCtCrgZyNZCrgQ9fDbzf6enDVyO5K/rw1ACxqsyzk+pyty3fexprVnZjjUVZF2KDQ3O5Sx68xcViq1duWH19EAKjk6gBfS8t/wGgYZxdZw2lFokmSw51OTLP4qFKnJNHkJvWuuQ//Ss/suj/fbkn2XXyVa7z/wKcU0X/q3u4LtNmY9MA60dqpYPghzSiMKTnqqlxy/obXMBz7ODFDjb3ytXAe62BHDj3Xmsu97m310AOnHt7jVybnw+fychTzDc/2ZuRvjHmF9bkC5kP/vhTqJk+4pF6FjkD5CKuWgO6NifOHSVXAx/BGtCEv36pvZy+dDFVwbluQLS/+7u/MzutFZxTxTldJNb36UL0pUuX5Pvf/77s3r3bAAJqladwwNzcnLF3PX78uLHQW758udnFrOBcYSFJJY6vC9h6zktnzsiX/u1fkEAal9YwalTzMckD9KpsqJUIKl12Mk6iCssigsSqNWul5rY7SGYF5fQbb8p4ZyeQTEjygiFsDrGanUH1C2upCJZW1TfukHxU51wKHnV1Sue+N6Snk4QaycICQLFAQakksIecYT3dw/vXbN9BopTE1NSoTKMg142a0fToCInNatSxqtjRjP0I8Fp+SZnUrV8hxatQmSFRPPz8szKFWtxUdF78qIy4K2sBjuqlcnUbCfF6o86WTrOAzgJ8aj9leHOP9E2McU6swIpKuT4BgCJ5DYGmO56rb96B4kRCutnZ3j9I8jESQL0Z2IIgOzERRSFjVgoqa6T+hpukYM1yEmoZEoj9cvGl3TJ2roPcK2pblZUk1AuBi8hJk+QMUeblG1CxqWuUDFacE/t3S9fRw+y2jgEX1KKsokonlAOIIVxdKXU3bkadBqUZrPJir70ux3a/ShIrhmpaFUpr3JfGJqlZBaDHZ1UVjwKTc+yW2LkDcuh5tfealkLq119cYe5xnARlIeomdcuWSsH2O0hPBuTSCWy2qN88Hnr8mrhlgTo+A2yAYksJydxylOny7ruPJBqJT+xop/e+Kt3Y/8ZQKwlW1Bn7tDSyEpPxlJS1rZH6TVulkPOxSi5TB/fKxVN7ZHyql+Q7QERlHcnoPEmwY6ecxEbD5u0SattIAtYrw0f2yeGf/BcJL0xJBfcrXNMoPlQDStevEk8jiXUS2h6s8hybbF26F9jiAuqIABrRBZKUKApESEgDeNjz41jndfBFghQILG/lJ1D9W4p6zWGZGMKqVa0LUU9yF5K8BRxR7sYhKR0f3E+i+Ri2UFhvkXgt4Bp8WITyYRK2fonT7lQBLw/ILRABVnST7Ji/iAINgCDJ1XQCgzIs7ILUoUVjSmM3tzAPhIe6jSatw823Y+WHpS/qQ6n2N0nS8p02lSnNF69CHagppWIL2OuNSdAVl/yCsMRRgppd0GRssbkW7TNuFHpcMa6f5LsHhcIgiSI339NDE9I9QBK3epWUtNyEtR5twgKo1D6OrWx8okvGLh2QhbH9UhhxSIrXiB1oAfLAMpHHRNALAJEikvZtEub6PBnqGfW41DhgwhDWy6iwWXkkMGgTNiqOrsQswmyjkqQdROpXA9ag/EcSx2b8GDp7lMR5txT4KGNBnqSxT4yjHBBP8hnub4gMSAVKk+55kqQkax2SzV5seS0vkIlNWedHSO4OohKl4BzQg7taBjvGUDxYwHKpBnU7lKPU9hbrNhdjFz7R2BECsgJvREnuu32F3CdAzTB2xqijJDlf2ucVGwikUBWMAtixJlEGjA3RD1GymjqHch5qKp4S2hJlQQ3RoZzGjnL5E4w9y6nLjyY4ZyYi/pOFt7Q96b/1ewy/KAWyVSVD55hKxjuF0+69914DZptE6JXgUN+vL1Kj5vu1+g+90Ixtei6dK3U+y86Lmoh8/fXX5cUXXyRJf1F0/lOoXNXwqhhDFWzSuS9bzuy16c/Xw+utOntbeX45OFdGsaOAPCdk8KyOMd0kTzOMV2HAYsZHTwToBOVYYDQ/96OgvAnAtklSCzMoL2JtmgbiAVhTcC5Dn3GlegFvOgBRglhe3oDS4ypgGCCXzuN0h5CElt0ivqrNCLECjIF3uGwA5EkUvHpPyVg36qZplJqwT4wUoxaFClTKKTTwupc+FgJ08+c1o2bJA0WacZJ+OIXS5CTWsTrGhINFwMOMJyjDugBbUvOAclgzF658EOWu7eLDAnW+d6+MXjiDap6b9SjUaAH+Eyh+JlEQzSRGGFEnJYzqrM0Al0IZc14AsLFRdYhFLGcWSGuC8Yi5DnCwCnAujFpZfDQmvReHmE/LpKoJUKkC6BzQTmEqVxobeSygp/sPoz52UII2irrBKsbhpSwaVkma4yYAlzJ+xkS1qS4oEx9xgJMkNgCUGu7B4h7Iys/8EvCXgF9zzOQMsPe4xCgDnpRS1LQSkB5oBzom1Y/q1vndBr4OAaukC1DSUrU8YgAP1qJeQD4vsVIiiXpbLIXVa4hkehFQIuosxAJulE8Ds30SwPrSXQo41/i4zE+xxtl5lHqJSwkxJN6YzMkA+MydVpy5hvePdit0eZJYLi7lRYVYkRMrCrGJFDHmFgJWYb9cVCWhYs5FzOdwnx0gxXjfKbORIwWA7S9gHPUnidWA/lHsSs1jm8qxChs3omi2ic/4jdVstP+UGaMFqNMKMRelowDLqKUB+9mzCalgo0E+QJwVj0l0aIoNCNxPbxGbGMDEgqioojoXH58Qa3ZOSunT/iUrJMrYPtI/SnltqQDkz6skRkSxFYqAX2H3x7i/AKCe7D4AYDZO/6hF+BioX0FODQi410naYCoSIYatY8MwNry0oST267MjB2V6DFAOFRw/kJVCB94U6rConsVmF3hGdhMfY10OOIf8IZsCBlHb28dCcz/KsHmAeECb2kbi08RBk2zYiEuEuSlAfc1k0jKLF58vWE7bp13FgbxT3cy17cyJQBAoJoZX3EU/smX0xLPiQfm1sJGNH1W30C5qGcdUqSnNEACcPnhA+i/uRyxvWooLAhIp0rZTLmxloQ+igsy870clzs986MknZqHvO/T1aA9xFUrRKZTagmwCyCO24kEC8L6b2HeQOBCorvk2cTc+CFDIs8PIUZQDj8k0c7OX43kBRtFEksQ84CZK0OEkSnIRWjqqQvOUdyGJ8jOqrl5iJS+bTZCwlIXoDIpSLkDEeilZvZLPL6B02SFjKMyWL1lHnM3981cB5wJr2vNiR7F778fete8Ean8DPHeEaYtAicCuNs8SugjuYNPqRX3SX1zJuQFZ2QBjT/UR410iNhnQiQj+k80wXI+VniYW6MfmMMr4UCVFDRtojqggEhelBvs5D3Cfxi15wLB+zkFc5UoAZgKSzGAd6S3dJBfG6uSlN/tkNsbYhd21108/yXixU5ynbwL8VzYAuKFoCW3WiT30XHTS9IcAYObwmEe6eydxcbHk5puasGStYq3dKy/uHgece8H05Ucf+RjKTtSPKs4R8qhV60+eGZJnnt0lq1coOLcJC8VKYkALCK9XXt8D+DqfkLrKCqko9GNjGZSh0SJpH6Qvp7vkM5+oQXGujM01Cs7NAs69iqh4Rh66a4t8/CHi7griHQ9zK3PP1LQtJ46PyrPPovCpMCsxWEFRvqSwzO3sWgCoCbMHqUA231AC0IO6XgebGHQ4i5Ryn4PEclhKDk/x/oBsv3Glub66WhSsT8/Jn/+7Zxmfa+TJj6+X++6NSGW1y2yi6uhMoio2DPhXxCaK3hw4x8j0YXhlYxr9rl9XA3QmFuMiNUbUGCzO8/cYVsgXUZk7fOSIiTUvXSIeIWZTNeOGRmxDV6+RtqVLzSa/Gjb2hFCX02Nmj8uBzPst7czXR1hnrvvqGDNbF+8GztmA9v09UdwDDsjZCz3A4PkSIY5i95jMkBscQu0+wjPaunVLUd/Ll5OnAXsnh1gTivBMx7hllFgZO0YT9N+0PPjwMrnhplI2NFnyve+2y5tvHpMNa2rlE4+tk2Vt+cyvzCaAc6++OCO7nhumn3fKo08sly1bAeiJuU4cX5Bnf3pZuvr6OL5HqmuYJ1kPnqQsPf1eGRj3Skt1WP70j0rllh1YtTJm/ez5uHz9W99kfMvIx+5vlhUrsPb2M8awHvIG61eHDh6S5W2t8vijatW6BXAORVCU/i2iJYdnMJqM3tL3fStz4NyHYTTJXUOuBnI1kKuB664GrpNI47qrl1yBfttrgDVWGQIGOHQCOABo7sd7Fu2GClGZW7nEJZ+5xyMPPOyW8g/ImvXt9atqc4ePZ+Rzf5GUvedZECqy5A8fccv9d3nkxz/BVu90Rs4NkmjlgfVelOb+7//dK60Nvxic+xqqev/qS0kWEwDjsB1ATEIdltjpJTI67hgVu/X1LrnnDreUAUcsaaFOWi2pYrdb7pWrgWtRAzlw7lrUYu4YWgM5cO7X1w4mUZt7+kcZ+Q47BU9edmSKn4Vp4DMfc7Mz2SvrV7mkooQkW25q+PXdhNyRPxI1oAvHmvxXJRxd+NUFVd1xreCcKs6pRcm2bdvk0UcfNYo+mvjX90SjUQPIqfKPggwL2JqoNYkq/qiyzjxJy8uXLxvY7hOf+IQ8/PDDJLNIorMCqefS815AAe2Ln/9LGbrcIQ3sZK4LuKWQYxd5SXyjIqIrlWmXLa1rV0rjtpsltGIli6ppubR7rwwexRZNbd9I+ukwYGFDVNJA4rmtTYpWrCWnXIPSGNgPqmvzfb3Sf+iETJxGiWOK5BpxrYu/ebHwKWXBdNn2rYBgWPyRZEtPTMr06bNyft8BGR8bZwGccYbEuJcEZA1J38bNG6RgGSoyqEHETp+UYaC8vvMXZX4a6ykWWQtYUF9y121SuhrFqDySsuzY9s0BFvRhRXrmrHQCe4wODZDQ4WHA0YRXkdQubUYNb4WElqI+g21b96nz0nn+DCooYyS5F7B9ssRHnRSgbNew9WYpbFuJVRPKZQEAoCTX194pA0dOYUlLsm0SCy7UubgZqGlUAKytkPp1G1AeaaSW4hIfvAxkdFIGzlCerjGydNi7YXGkO8krVrRJ7dbNUtBUDzTmxmb1olzef0Aun9KkGzanAAfFTa2yFoCxmOO6+Aw3gDpCTQV1lqH9J6XvxEUZ60eBjQSaD3nqYqjnpoZSqdi0UfyohM1MJqTrMCojAHFpri+D0praf7pREyqrKJP6FSukYAPWWk1NZkOORMclid3r8AmAxos9fB6rLnbze0jUuwEEGzffJI2bbgQ2YDEf2C8xOCAjp/dT5n0yC5CWBAYACwUuQ9GwiWTH1lskuHQF5fbJfOcl6XztRRk7f1riJGrdngAcQY0svfd2Kdm0FihCE7KAW5o1xI4QokvmUS+bQVkjPgv8gI2e5Y1IfhBLtEwv1rnAXlgIhtY9apRWRgbOydjgKRIuhTzTrEVdaRmJdJLCJFodwLL57oMy03sSVTMsW7GoUvjATZ1Z3G8Hi7e0A6SWRxK/ChCzHKUePHAshzaJ0tncGLAIoGFqhsQr7/cDGvjYeZS0Udyx5ySINVgeNnteVIZsbGATWLTNYrHnAJXEIyih0T8z01HxAnZqGyykbfmA9Oj6QIcxVE0AA3gADAHrFQVQB0TRJYMqokVy21/fALSJ6t/IvAyOoGpT3QpstpokfTUJV/otIKu1MC3zKDZNAAbGJ88Ac9COsTGzbcAU1GXSftTtaDsSwiqNchaXLBO/ghQkw534hCTHUT4CvIuiVpV0FgDSdOMU4wb9YSEVlGDpKqwSlwMaAtVND8soQGNqfgo4A5s0xoI5lGmi9I0Uyos+IJ6K0mqS3ahYwhvER0ZROpkA2sHWMIiSIbazfEKSKM4FOV64DnDO2yjDvShbAv2UVAAvojhnh5oALvxAbjzHAlosoBgz3nuQvo96HApzbu69hR0k2pTALAAhjBsuwNQy1JyKsavzCJAR4IkT7waca8fluB9Vlmk+Sz2TTA8HUBdEzdBTdwf2b/RXAyVQXqxJ1f76mWeeMWCygmKbNmHVxjh7dTKMDv6hfum8pIk+tUPVL4XoFOp+4oknUJNYZeYenZuydZL9rlzKNX0xL2Xnzex8lj2XWpWrzflLL70ku3btMip069evN+D5li1bDDyeLUv2M9nv2d9/kN+1/vT19jL9cnAOxTkF52JngGRPyPTkOH3Ib2KBOHPvAuOMUrI+F9bLgMGhMqDbCEAN4NMMAMws/SCBOpDLVYRKVVgi/hj2yNhPouRZjL1mft0KxtxhiXejPAUsF27ZioobylqMJRlVPlOVs3GsMxlrNtWIEwAAQABJREFUJ/svAAWN0S8YxIgfkjqeAH0xXbDWBGRfyjilFofAYm431Hoc9TD67jhWsHNzIwQbHvEB2oUBkUNerDIZh6ZTqBg1M78wfgcsYNm+4zIBhO8CqAmHy7CUTUl0DjtoFNkCrGsVFaOCVYQiLWPcAnOVKsQmAZXcxDG67hV0o0y3MM/vUGBdskSCDS2AgnOA4UPEGUD/KGhFUOa0vGpNSjKe60tiIzqBVfbcyCnG4XHB5JY6RXGP7wliqTjjhDuvWPIrGWeqmsxcYDEeqUrc/ARKOcOXgJjHGJOTkuf3SojFN5txPQrznvFgN1m5hI0KVbw/DVPUJ+PAaB4sX0N5QHnEa/NzcQBrPkt95mNF56urAJFLM36NyuzEOIBzHNE0LFFRaQ1jwRnEEtQCjHTyW8XX8DigssgU808IlbHClgZxKhqBpFCr03lf1XSxRB0fPAqAfJH7Rz2i0uPTxUHAcZ0HMwBXabVAR5mwsAp14EJgM4tNuABymUkgsZFemZkdofhx2h32ltxrh78v0MaCeR7JR6XNX4KluVUIhEgMiLX29AQgG81EbTO9wQBzBkAhY3McO+9SNlcUVLXwRxRLUQOcGe9nrEHRlToJY+EaClHfs9OI8qHKh9KQr6lNZrGdHQUkd9PYythAESltYT4nJuH+OLTTGKqm8/2opPUdoa9MoqoGvIlVuiboaSycC1WuILF1aYEUNTLfM2973EUAVjEU4ri+UebC8RHipZgEmYMLw0AGxJsx4pZ5Yu8KYsJQ9UqOWcIcyVyGWtvc8EUU86Y4tItrpE+y0UT7cWIBm102BeTVVtAO54hz6V9Anh5H719YCuiDbnvY2CpblWsl2HYXgKTIYPvLtEfgxypgulIsfIHitNwQhoBlzNcD+7EePY6yH/eQDTZuL4u8KEKjoUo/AWRjfoyU1Eo+8UIYRUiLuMoxaorUL0qF09EeSaLEGKRu8gFH/djuWhnKoe276kZx191loCgLtcG5ofPAhH2MLzOmCH7GDlV2hZwVD/NECKVePwqBNuWdIUaanYpTTlUkY+05L2VsGGdpl0FilrI2VBUZIyb6BmRiljbaQDurbOT+YV3LfG8RT9lAqJMoKc4MYvnO3O8lTnMpUEpZ7QzQDQpMHsDecAkKdtVLmb8BL4knHYD49EwPbe6izEygrBhPi596CQXYAEgbTRKrax8Mly8hFOKcjFs2alexEayIJy+b+20xFnqpjwJAETxqZZL+6CpYKV3RKtl9YEgutc8CgYAOpmjDbBYoLCySJc31snZ1g9Q3h6VraF72Hzoql89fZpMEYyFlzvBVWJwPWNcoN2+tkrZWYqqwheXqqDz1o5/TLlNy/323yY6tgLXUmc4KE1OOvPTKoIHC25ZUAtVtlJVrKiRFf73QPgcwfplnwQ42A6FCTP+twUraBzx6ecAPCHdRHt+JveKDWLUCyF26HJWvf+1VlMPjcscOALa7m6WolP5OcyLaJh5HAe7iDPP8kUXVuYQLUNpD+2XU4zmypbVFbthcx/cg5+6UgwcuSnfXFBa6Gn6iUIgqY3FxWNZvbJWbbqqVpUsigMMi585G5Uv/6UVJZ4rlwXs2yJ23EZ9hHauKc919caxcB2Tj+lLaTH8OnOM+fJheGtvo+JcF3PTfGsd5GKv0pXH24OAQGzKOyRt798qp08D4rAF4GcObmltkLWr369etA7xagU1rMXB3mDYFEG8WIukh2kl46Xn0uPp7FxsBr4fX1XFxtoz6u3cD53T9Y3w0Lj976QS2qu2AsGnmG+2HxDLMJ1Wos2/Y2AJMWE7M6AaEO4xqIyA0z/npNPEXG+XCbFCqqauW9RuqZMtNRVJR5WGudOTZZzrl+NEzsmZlOWPNStTjgIqJkRSce3PPjLzyCgq92MrffV+rbNzC8xuuAZNjtuzfP40KYId0dTOnqsNASb5UVtWzoa0c1Ujmp7yk/PE/KZNbb44wtzny2s+n5bts8sxYQ9LYFMBOF6V7NkzNoqDefv6UDA31yZYtG+STT36ctS7mGZ2zzaoS47fCcwxKSiW8X3guB85dDz0hV4ZcDeRqIFcDH7oayIFzH7pbmrsgk5A4f8mWb/0oLU+/kJbLCovxHNxSqSpzbvm9Jz2yuu36CLKzt2uMB+Vv/yAlf/v/pmUIlbybN7rk7z/vl6XN7NBiHeJ8uy2v7M7IqYsZuedWjzz6oJsFgf++/0YBH559NSP/z7dTLPqIbFniklVLAARRrjt92ZYv/n1KYjy43rfVLX/xr33S1nR91UO2PnLff7trIAfO/Xbfv+up9Dlw7td/N05fsOX/+05Knnk9Iz2jLHqxorq8wpLHPu6RT6M+11THgvrimtevvzC5M+Rq4ENYA9nFZE2UZ5PlSWw1FZz7yle+IudRP1NwTsG3rBVedmFYwYUL7Mw+cwYL0AEUXFiILSJprOo7avX68ssvS1NTk3zyk580ikD5+aquAbTGgrIN0HSWz/31F75A0gzLnXWrZUNLrZSzfTgPBQ0v9jyWjwVLEp95zShwNDUjPoIaF6BX/HKPxLt6AedmSZ4CfrG8aZFUCjaUSKCqAqCqlJ3RJCYBieZI6riwgHO6SMBdGCJRPg27kuLvwGLFJOdIHgbrsE9CiYuCcTzAFpKns5e7UbADLCMpqeV1A9+obWSwrpbcZDGfx+xqEvWY/j5UT1CnGUPJg896qislsgaIo6YaOIjEHYvJfkAba57zooy10IOFGIvzmXnKgAWst6gEpYkK8ddRZhTRnFgCW8kJmQMAc7Atw7/OlMutdo8odQRbV2AxyXs1znbzN9KSDqphyaFhmesFyBsZI3EKuoNyhwewLAhQ5iOR6yqizIBKdnpK4gBl6V6+X6LM8wAEjKHe/KAE6lHkQWnNnRcgcc/ufKzCEoMclwXqFBBAWhNSHKdgaRuKIyQG80hQo7yXcWKAEMhmD0xJopeyj0xJcg5VNsL4AMoToWosbKhjK1IuqTkPx+xDVaPDgHNOnDpXVZ5APvaUpdxDoAbuYSqk4CTPSKpChuVnamAEtR/qewxgDZUUF/dPlcVC9Y3cF+4fyjGseAOkpSU9CpCELUwSy65kIkr7UEUVwAzsrPyowbhLqYsgSbooqm9dl7knKALOkHFjfnGjahFZ0Up7q0UBiIV/7Hl11zmySfwdqGtuFMszEthxlGdow5oc9XoAGFEds0nm2v5i8a+9n3u0zNiNLmAjGASgCqCoJB6s70hYgv2R3cM6bfy8sbazM9xnP9fp4RwW2XwS+BaKc3amkEQo1rEk0d1FDYBYtA8LCzAbhbs45ZimHZGER0yGJG6hsV5DB9zcDzfqI548LBBJbjvYnmYmUKwBNHDyUbHJo2+hQuJSe14UBTwonnjCWj6Oj0phfCGGIssICnBYKQL0eQG7nElsV4FKHcDZcEMzFmXALwuAegukGFAYtIoqaAvFJCRJkFOP3kyMtjUo6WgXMEQniWXaB0CdjWqOJpRsH4pI2HKlUYLxFQI2oETnU3UXnjMdgDZuNKpMtGdUnGx0Ct3Ae+SUOeYCSnuADmGs+fIbaaecLI6l3nA/56BNAEjq7zJqhctxVJHJInETQvXJhc2qjRSjPYt15MIQSW0oOhLYLmCWxCyWrqO9KJ6T6Kmhj4WXAc5QTu6TF7DOnVcP7EGfpi0EUPdzJYEd57phU/T6UIIBoDEZX4Ui6deacM/w3Q4WSBCrQ7+/joxaCfeXe28DXSRU4QjLRJSI3KjduFHZcQNhsq2MulwDmMt94ydN1nxUwDmdP/Tr6rnITBhX/qO2rKoy993vflfUCrW6utoAabfffrtRnst+Vt+e/bc5FvV4zV4cSm24bIU+KKu+solY/bcmTBUa13nz+eefJ/n3igHFtYz333+/LFu6zCi0apFMcvXKvJude/UYH+Qre01vL887gnOM/xK/yHzMnAyU48E+1IWiVYaxV0EoB/tTlwcloHAl7Rp4VO2ZXYCrSWDXeCd9SAG4IEAHYwOw0yQWlHHGkUJgtfzq5cyrqtw0TkUzTxQCl0foSwAiOia7UYsUFD8zM4w1s/1Uqr6P+YgKdgCvLN6nluFMGOKjD7vz1sKrM3eR0HbTt5ksmKtQ74z3A6EzFtiMm/6I6ZM6Xs7xFh+2m96iJkCvCcbBS0C9o4wxKJ0ES5mLkoyrwMzAUW4D2DNmR4DaURBLAxAtzGLBynG8QDIeAHUX83l8dIKwIi2R1mUGnMPVGlBZwWIAmjzAMKw1LcA7hjOAHsbRJOPgbAfxQDflVaCZOTPF/AV4phaRDole5NbMOO7Dht6FopQBmFUJDNWqBGO5A4Cu8wBTJEqAwEAsPiZTjIXAUt5wOcpd+RwTkACQKQnkZTEXeRkbba4rrdbqWKq6LaDeAuI74qY4Y6bNdWeiw3wGeInYwAck5WacT2Obm0bdylW8XIJNjyO4FpHURB9xIbangGGpfJSxuJfgQOIDdlfF0zQwVIb5wklx7wzccIVC4E44qACKu5x7hyIsCnKWEjBqgcflOYCq6bkhYMRexnuAL8BlFwp1lhcFsnQ/9ygJXA3k79tIeyFGSFNmbLKTjLs2qrVUFE1DY0WURPkhCcjsJ9byFTAfOsxpxGMZjp1OYUWKWpkC+y695yMjkiQ+9FEfvpYVkiqqA0gD7WGO8zHmuwPEqi7uIxsQmCyYx4i1ACBt4Hq1FaXB0M2ZKwCuuIGUDdCazRzQ4dQvSm7+GqZL6lrnNLUnjdGO5oa5DyMG8PNgD+pis0FGITnUT4Mob7oLgOSpJ4sxCC9S1Nd0/iW2ADlS9WUPsJ3DfcqgRmjR1zwFqMAxTyeBRlNxIHLK4eH37uQ43Yg6wqbWVcmGhyV3YWccZLPMadpjHMAV219vE9fFXKX3wUV8nejD8vwcY0AfZWQupI3qGMc0yHyowAqxFPfFE0BZtgBbUdo5wQTjAzE+5c8kuwAmOvg5TvlRwwWWdHs1rtR+SX0X0G/LtlKfKEyjVJfhntvRQb4DhCpShfKjQrIWkAPMF9cLuEdIT0Vw/2jTbPzQenCHiI29czLFvYtNuthg0IQ162raTIh2v0CbAaQp4P6xMcDx5uuR+R+bKrBqtRlfMqjEOUmeCyxtfBoXaj8knkKZ2AKeVJU6L7GUxroaT1kaDKFQmCFOSCp8mUiZ5yMP7UJRMIdY3XaC3JtyY5tsoazosJnBJj4xbRrqVPuli/HAy2YYHfOSSfptsEEmnUrpwXJ1eMBmQxBPAqyh67gWDvulsSYk9TUAeog2js7Z0jEQk7HeGOMk8RrQCwQvwAnvawhLQw3PXDi9aJh74XJCjp0EIqYel7eVSEs9m0noHnqf1Smns2cBdbcBKS0MS2tjsZRUcoOJxaewZe3qSkhf97zMAs1ouy0piKB+55M9RzIyNHKW/EANz4EVUoKzznSUvMEZ2h0DbFNNMRvlC9h4oWM252Lfj0M/mprMcK55VK3YBBEjXuT63AC/BYU+qapHcY6yKQg5zvn6uhdkZDAJOEed8lmv12Wur2lpUKq4vnCYe0SXGx5OyWu7uQ9A0qvbCmR5KwBpHrAOf5uO2lwHipflbulH8e8HP/iBHEF17I477sDS9QGzCUznxWxsoa0r97q+a0DvVfZ+Ze+dljgLzWV/Nw+A3MlGv0OHDgGIHZT2i+3M0fO0tUJZiWrw5i03yMqVK6Wurs6scXiZP1VtUwN0jdF1HvmvsZP+m9/p33R8vA5e2TrIFiVbL+8Ezpnxmw6p9shdvVHp6Z0HorN5rl2s0yB9qhQIrakxLOUVwHR0ot6eGONRVKbJHbJvgnjDzcYsL7Ac41FjgE1I/E6htQm3nD6xID2o2dXX+AHvIsbCWTfxpejn/QNp6e1i3iRGa1wCoMd7AmyISiUcoyLXh+rk0BCOBKjeBlj7iTBIHT6WkoPHAN8LbPmnny6T7VsjjLciPd1JOXteN2Ogjh5iQx9rGxbxyzTqpAf3v8ampCOyHpX5xx57RLZtvZGxloGQhzkTXzE2G0VRfmOmuvdxO3PgXLb15b7naiBXA7kayNXANayB9zEzXcNS5A6Vq4FrVQOs+8npcxn50ldT8vxeHs4I/tioIutbXfI/P+KVO+92Sx0Q2fX00rj/DODCv/xCQp4/YGOXZ8lnH/LIX/8Zy13Ab9lXlCB6cMSRUtToSvj6RS/WwWQcu9Y+1Ol04a4K+KGEB3ovkeibhzPyz/81gW2XLQ2VlvzxZ73yR79/1Ql+0QFzv8vVwHuogRw49x4qLfeRX1gDOXDuF1bLNf9lH3PLrmdT8mUUTs92qzqWSBly/o/c45b/4596UTMi8a/rHLlXrgZyNfAr14AuoGoCXxeSFXjThWRV9lHVnK9+9asGjFNwbufOnQacU5Wj7CKsfk4hO1WfU6hBfw5jm6QQnVrVqUKSLjZ/6lOfMspAqjinL/28nu8cinN/9e+/QELWkscfuE+2bsEmlOSehcqLgz2WAjBWhMSqn4VOktIKKeEZRS6MlVxN5mqCJU3MyeIu+SsUuXif0lqoaThqV0byjDQ+EA+LtXMAcTN8oUhB5pHr5Lt+BhUrSxOWJLrNIroek8SiQ4KLTA2JVoJ3IAkdZCygGvyQ+LcumnNerGQdVLgcLQvJLWSmeA/JSOzELIXm3CS4eJsLwApNA47FF+81sJomr0gcW6rKwW4SK8BnSVo6JJIdtXBkJ7W5QE2CskCvCTFVSUN6i88QH5OMJM0LtEFZdYUbOzA8xUi4sVrNy1KLMA22XcADJLRNuV3YdbmwnUKFw6UqnjP8XROcSvxoDhClIAuA0OJ9iwDXPOWgPECMnIr3aln4HElUF9fpACZmqEuHZCpvEi+3xQFMckjsmQsnMWi5tZ4BwXivVrhjU38KGWmCnIQwnqD8njoAAFDVQEgt7qEX6FBvDogZdldk1wzQSLae8uiTCeXguvV9ZLn5HMcEcKMyKAD1wiThALbpORR204QgmVX+tgg0WQHqmfPobiajcAYcyeo4b9J2xO/DvJfko8M9dlHPXBF/0nbA+5JTXB91DAhBus2URaHB+UunxAbY8xTWi2/pbcBPrYtlJNluEixpEqKoBJHtvlJOrj3ZTRkGOdasSfTrMck+UBbqwdH3l/JVwd+KqSKSuCS/bVMObPMcktpq75miXFQHF0RzWSBJTOIaGMTlJfkbWUodYZczPykOii5pkrCeYhLuJYBu1Le5Zm2HKOtoElybucUxHLUzw+7VIiGOJAzAxSRKOV3AHVgYotZUiH2iH0BDYRRNxGt/SHG8DAp5JgkMDOBDhcZCiRCKgmvq5QtYgLbsoD6lCWftH/jRkZ8tRVGmnposRe0Ja1ySnqbd83mHhLVLuIf6ZVEmQLc0ikgZLNas/OWAAkCeqroYR9FloJfq85J8BbxEcUUAHEyfMe2Cc2k5ObaCFlAIOiBwzHnqls8Dt8wCwE4DzBZgbZxX04ZiYBPXRb1oxyCJrNCF2rSqjZib9msBoGpCHJMg/qYgBBAe92Mx88s4p5a8gHqOxVgAEKN17JCE0ZtlodAlqFdRsfRF6pexyKWNlLEhzTjlyltG4h5og6vXYeajBM5RQYtjK991ntBX9nt3d7eBsX/4wx+aOtm6datRQlUFEJ13rn6v+YH/UH20Y+7fNXxly6OH1H+bueCq4+vvdD48AeT3jW98A/u389jK1cmDDz4ot912O5ZRQMSsfSiAp9CdfpmCXnWMD+qf2Wt7+zW9IzhHv3TmT2Fd3M2QCmqSR1/OB1RlvFK1OSfDdyy7aNT0KR2rda6lD2aAUOhDli4QKUTEfJqe7MaWtJ05H+vj2nUowbZRV8w1jPsOc4UNzJJSME+Tm8w7XmZ4F5Csk6APosamQJ646F86Jiq1A4zrMDYyafFjCacpY85Se3DGeu2LjG9gH4xV9F8FmGz+xjznxHpg1C6TqGVKqsBmuxjrRrsf5vUUCmDjDH2AXNgjWl69LlqZzrWq8sZ1aFIWr0m+6Nv0b533LZ3HmCcy44MS7UZ5yg5KwfIbJFjNOEMZ7fRiGSGUqBvmQsZUh7lVj2M5jDX2KJ8H2MkwbgD3mDkZMBe6bXEsZ2xyUPRyAG8sxnLIGq6JzzPXO4C5Og9C8nEu5kPGPRsb7BR2laoc4+N+GVtdYDybzRBJlL/c2O2qspsFhIe0LJ8Hqk4zpjHnWl6U6ag3TwZraR3LUlynyvgS56SjY7LQdYa5bU78NWvE2/QAdVNKbMT5IbwtYLw40EGKumK0JyJgjsV21EoBhaVG+Ik5DQtvfe/iWEq9YD+q4JzjLuPc1AvJda1nV4oYT6ta68jS+871ZYgR0gpPsUkBO2yPB/g7fxXzwRaunbFYlQZ17FXgkvKaA9D/HI3vOLPOp2a413gEpS8TL6I2a1lcpx5f7+EUVp99KNwB2QRRFvM2rBSrfAXFvVLnBo5mbsNumJvBF/GWwqV6D7GyddRm1RRcr4N2rPCc3nNs8CDx+FUx3Yakf4Z7S2zrJtbR82pbhRajvjXeovUSn6awGE/Fh1Eso78VtlL2Wo7JPaLNaX8y8cTiMGpiG/NPrVuNd4hnTD8ENDX1rZMNMVFGobnuMzQd3Qhyk/ga7iA8QMnOovwu5j7mQou2oBsALEAmE7dgISypIb4AvfSeoH5mYiRVf/TRNhzaDpatgqKjicUU0jfxINdNP3aEti1D5nqFuEeA4yVJH4yeABpFWbp4o7jLbuYagPU0lta5VuMwhewB+01MrAApL+2/lvYdlVfTuMPEaVrXWjAAuFi3jF3uZoMAasFl66Wgfg39jXqn3hyeH7QbE13yKcB35mWNZTwO9W6ukTrQjQ56TAMH0j5RRYQI5kvneGI14FuCL774mykS5bD1M5Rby8DGCC2bBYyXnAQCZKOQB9DOU9Jg4jWFXi0TS3BOjQ90fNR7QyxDb+HWK5RfIPOMG2k+q6CghtlcsfnSMB1xJglie6rzjIbj7C8CZGa+4X1UNmq/tCuGR/hmuDfGQm0YvO/CpaQcOgKMyO9WrSiSpS2Ac8Bp2jf00Uu/bP6jl4UbLUVysZmA43PsaFRjb8rC3/Q8ad73yu64PPdaCpi8Rz75SBUQWjH7n0AG6RdJQBt93tExQNU69XnPzD9aFl42BU+lgHe5PnW1yRC/KXzs0OZUVVjDfr0+m7asDCzu0aYCLH6mSOY9Ck+6vRrH83nK09+XlFd+Ps77grJ2ZURWLEWRMp9j8PcrVUMshqp5+8UcOKc34bf8pe1JYxdtz9kNDvpzNs7RtQ5Vhb3U3o5q4QEDznWzkUrXQhrq62UVyvGbN22W5cBzFSi4Z1WeVUlOe4Xplxqn8qX/05eeS/+1CNaZX33g/9HrvTqm05/16x3BOUrt4loy9MMF+qo+mqXoZ2YY1Uunz2jYo8sRHo2BuGrDojP+6CM+fDn1yDF0Cud9Gvpp37U5UBSFzOOH5qWre4569qPkBzgHsOpirSel5yIu0S99efidnkc7qarRmUc4IF49l47oqkA/hCXr9384IMfPRGV5S6F89ndKZPN6NnExXqTZIJCg72s8ZTFX6Dipd2iKTZTPPfMjeenFXbJkaYs8/NDDos8UbsY0JiDeoREiawbEhVoShhrznX++p1cOnHtP1Zb7UK4GcjWQq4FcDbxzDSxOlu/8ntxfczVw/dcAcanMApYdOJqRr3wrLS+8yc52fpfHGu/mNS753Gd87HBwSYgdW9fbixwcD71p+bPPJ+UU8MKapS75m895kVXXpOG1K23voC1f+npK/v4f0litiHz2Po/8+b/xSQRQL/fK1cC1rIEcOHcta/OjfawcOPebu/9zqNrsATj/868n5TSW4bgAYiFnye/vdMsf/p5X2lAwVfYl98rVQK4GfrUayEJzuoiaBecUhuvo6JCvfe1r2OVcNIuJV4NzZmGY9+tn9ZVdkNVjzLFze9++faIWrqpEd/vtt8uTTz4py5YtY4GVxJQGxbz0s+fOnZW/+uu/Mj8/uvNhueXm26RYVeXMaqgmSPhTkJiZhJMuUitYgw8lSUhNJPE3EpQK7uiXZQAnIBg+rEnQNCu2pIbM5zSpZOnCKYu5ZtVU10UVnFP4SscNkrEs8S7+SYvH+wwIRzbJKEZwVLNKTMKfD1E+PRa7oadJFqJm4SaDY6F4YSA1VodVEYW8pqkX/a4JGTwxWXjFjlOvzWRotNxclR6TxV0TUytYpIv8popMIRfrS/+oC9MKVOm16J/0pXWpv6f4NtevYJKlmSsWh81Lj60wgCbbOFeG32dInGdI4rtR+vCmUPDRJC8fNclGkoYOiW2TDI1jEQZQlIyT2EKty+sjqasntxl8OaeCTw5ZtzSFVbUdLZLXJKw5BolgA2HxS3MsvSeazTOJau4JQKPeSr1QXZxWlRCtAEsTiuYmce8VKOSvLo6t59TjCQCGQ2J88QeuVVflFTgy4IkuhWfrkeNqshFogAazyE+weu+QzDW2K7SPOOdKzAB8qMUw1+IFltN7YXFtNu00o1Ae14s+HmXgOCRPbQCRBGpzLmBKDzfS5SLhS4IzheLOVHePeGib4aoVEmjYLOlQPccjWWMAO8qYDnEdXBPXqQoseH3SBDtIbgOCpTR5CvQILKlqQm6ACLcHYM5XTxlqqVhgCmyMUyzoY+zFdaJexHcP99OlbVEzGiQPbCxfZ0cOSSLWAdtYhNrgFpRM6lBr6pHMAFZ40QkU+lpRTVwJtEaCVxs/cMBiG6EvAc3Zs32UCTAEhTcH4lSvOT2Jgt8IdmpJ1NiwVc2rWoeqDp/X20YJU7Q57XnMwvyEchSpUAwEqW+U7lARsuc7eBOJYwMF8hbsb6wwik2FqCOhrJNwyrFyUwUiLlXvItelTULz0gqcujTBr3AO6n1xrOBis7S38uUSqcGWME8Vi9ql/9x5ZJtcUlLaxvUtE6uwxLQhy0AMtGnO7QA9ZlAvVIBQmROXL0Hb4/qwepwenuBM2EnWtmD720z5yrk4bfPUPffNUaU8bWtcsGMS38AGqpSDApTNtdkOykvYqPFG7p+qVik4VE1mGvUZlPh0bMhoG9frUWgDWFLHhnnGHXASTBdp+6hCLaDq5ylcLoGCOj6jyTmS0h8hq1adH7LzC5dOf12cZ9ROXC3BFZrbs2ePUZvTOenee+9FIQboIAtNXJlfssfQ+tM+95t+abk1Ifvtb31LXnjheZmZnpUbb7zRgH7rN2zAOhoohnIpAKbjxPXyys7P2Tk9W65fCM7dfbdUlNFP0qipTh6QkfbDMrUQl0hdi5TVMw6qupwqKNJ3DEyt7R8V2RSSQpkkQMvMKPNQlLiDwZ2vDPBbAgvqBLabAVS3AoBzVn4L9QNwk8aykyHdBmzWL01pWig/enU+Ugg7hl04KlsZAJyMa1aHbsYNP5xXCX2dsdQPdIWS3CJUr3M9CVXukUJajPa8l/GB0UYVRq0kcNTQEUlgpT3H+l1+y310402cvFNSw6/LzFAPtthVjPWrGMMYoxlrHWPBrfML9xSgLAUoYttYrTPHaVylAL6DSlZiCKvvESAooLuS5beJD1jGAYIx8Lj6YWqymPag9eSo+qiCfUBXOs5kUqiMolzmKCDPZbvYSODGotKDWppFGTJYe2YAdN0ANe60wlPMC1ybzsEO9WtzPD2WhXpaZhxL9b4O4JWUFNVgg4ZdqyQiKNeOG3txDWcK65thuJbBtjOOAc4JKoAKA1jMafNRICmgLb9HAWmF8gCukJhJAJUlRvuYH7Ceblkn6ZqbKChgno6f1I3D/Bpn/ktr2+cLtod7yDViyebMMU+lGFMBpXWMtQCE3ADgHiBiF/VtY9dqMyYqZ+YChnQxn2sYxw/EFJSB+Vjne4golM86ZKRznwQB3vIBHz1lt3LvKzgZdasW4Ron6bxpoj4+o9A6x7Gpe53L3IzMCl0lgc4tVG/cnMgidnIWiImwPl9AIdALUBgux3q4vE3SBW3cR9TQ+Ozi/4k3mC8WoxJViaVNAl7ZqLtlYqq2qLAYESobHLRO3cipukMAtf46fk9719iWv1NFi32DudlNLEQUt3iNxCTxyWGZGDiDtXGX1LY2Soh52XG3cF6UzrQd6ZcSQXQv06+pbJdPg2C+uFfpGDa7qQlONU9srmdjYxoqiWqTnhy6BCQSFn/NNnFV7KB90c9104UGsMQNLgI4yxByV8qj83OSeXCBvgMAniFuRJuIvsEcC3jlAf73WNxHhSCJrWzdJMJxFJsy6p2igBt1TWzgIR5wJYBlp0+gzvwzmRkbAmbfIeH6++HT6CvEB+bGc8sUvNKXYWS5Bv29AS9pB/Y8YwH1rSq6FhtetL1Johfu7axM9KBGGGiUfI7rR1HSgHMKv9G2GJT0iIxTIF3cGw+xn2UDBCZR/VsgnkEFVxVmM2bTCe8hNvaEi+mL5XyqjL8RN2jcDVyjzzC0dg6nbQx1OereIp5xsbnBinbIVE+H4KCMhW2d5BGXuQKqNIjlO2OTxTlMTKwQoH4pAKyBM7FEgk5ArzEgGcK9i/tcTNn1PqK4pp/XujHwCeXQvqdtSZsDXykF0PhZ42rlfBU20705P3slJt/+zsvckxQgyTb52B3VPJMRd9IfMxpn84ygp3HxQbQLeR96fKzPjIwlUIACBiRwi7DpxE2sPzk5Lz99YVxOXPBLbbVHPvVEqWzeHJRQPp/Uc1MbLsrp4b2WPgvoeGfat/Yh/dLxhkBNg0Etv16T9s8rEI+HvswJeQ/9hE9yCea76df826XzKh/lEs3vk2yIOnp0Wv72b37OuZvkkQeWy713BrFqpeUzLumjhXm2Yixr57k3pzhHxf2Wv7TNaOyiL40Rs+sdOifrv4eGhuTkiRPYDO+R48eOomY2StwSMHasW2/aKhs3bpTmpibJQy1fVSJ1PNCo4b956Y/aFE0ro6lm/67tUsec6+Bl+s5VZdGf9evdwDktvw0Zm9S+p3Mn/S3b0Uy/o19qv3urk/E+utxiXWi16w8afzB+OYwZNn1Zx82hLlt+8P1+VOIuyoa1ZbLzoVZZuoxYJsSYwnipM7BubtC61HlJX7q5YHY6LX09k7IACayW1z7i6JnZFIpyc4w1qMcngnLXrc3y2MP50tzIvM04x0MYsd5i2RerYDFOm56Kyk+feVp2PfesNNQ1yoP3P2ysWn3Bxdhr8T7qPKzzuLnFV+6wluZXf+XAuV+9znKfyNVArgZyNZCrgXetgesj0HjXYubekKuBd6gBYnIZm3Tk+d1p+Y9fSUlHL+kOmnZtqSV3Y0n6zz7jlVXLeQi9Tpt7V58jf/PllHznJymZo4z33OyWr3/eL2Ul17bAaYLrXS+n5X/6lwmZY91r6yqX/Is/8cld2/TxPPfK1cC1q4EcOHft6vKjfqQcOPebbQGa4zh6MiNfZE7ac8yWMSxHIizK7Njslv8V5bmb1mMJoBv8r+309Ju9yNzZcjXwG64BXTzVl35XQEPtV1UtRxXnnnrqKbmMfcm6devknnvukZaWFmNTkoelqC5Gj6v1KOCRAnFZKK6H3dovvfSS2bldXFwsjzzyiNx6661SUlJizqOL1brzW893/tw5+esv/nuzgP0Au31v1vcBzqmVkCZzyLUCMWkaLM7aK/oUuvOXhJHyYAqoaHJRF2KVY7FYlFWsyCGxlyEDlOIcCs55+a3m5U0+xiRK+bcu/hrATBeD+TyL4Wk9DoOHrrMauI0kiyZSFZwzCUJOqtCVyTZxPhvoauTkUXGND0l+ZZX421YgKqXqUiS3NFGub+W8NuXWZKxDslaBILZHkwPT5DPHU1BNF4Y5rwsoTmE9Bd8s9SrSv+visYIVlEnvklmYN8lw8wP/4Xd6bP6XIvmk0KAm6jmryTUZEAz5DJtjG2YNGQg3imVG4Yqa8ah9FteidUMx9ZQmL6dDaHp0SEbPn5TR8WmSsmukuL6Z5CQXxedNckkVxkhGmmVtoCS9Eo8exKil6T3hGimbOSa/zwC+qAKYS28ev9fkt0nccTI9nybQ3JrgUFBOkxO6TV1BQE2Emy+tf7asG0qQ85hkCJ/UZMgV8NDcUlbx9Xj6WUNP6M1X5UIFLDXRqVlDjh8DUBq61IF9Vbc01NZK5dIlQBDAAUCUNtvbk5RT77sq3nlIejpcdyI+IWM95xAYG5cwFqZ+rHLt9KzE5qIs2FtSUFQl+Q2rxV2CzSfKRnqBHhKQFhk5kwBULIz6UWDDKBLNn5TE5DkS36Pk+lGPQd3Pw3nDmhDwFoq/sAU70iWo+9F3sOlKUi5NvWqPoGuQDsCOjGPpMZGnAZzrkYne12Ru8gwqIUWS33Qblqqt2K5dlGTXHpnC7rCgcS3w3GbUBStpp6i+qEqONlTanM31xXuPkzjvxBYMy1tUPeKoCSXnAGOAaT2RKslr3AbsQVtHykOTp9qmMdGiXAoZaon0i36KulGaxPXCRDdOipcMZOZC3cHNfXEUCgjnoSYFHFPWijxKC4nUfJMbdiEjoOWx2UWVpg+7SGK6uAeuDKpSMY7VsU9mZ0kZVy2XwsYlgiMxqjQXpefsOXFNeaWsdAXgXBu2Z2VYwQKGUudu+q3CD05sUOIAAVNTg/T7DBu1ODbKS0kAgiRjS6CkUQpqlqK0VCkprG8toDlMgLgW2p3J9Go9UfHGS0zVHackNnqJ+sYGKI0CEcl/rY8ACfg8tezNr0O0jnaFtaQVCDMm6V2jPanaDLCIjgtR+ieoHeBcSnyq7DeJGkp+M4p+Naad6uk+KuCczglXzw/6s46NOtfMzs4atTmdk9QGXJUhHn/8cdmyZYuEUL/MvvQz2Zd+9oN8KYC+d89eefbZZ+TgwUPMnYVy/733yQMPPWQU6FS1xDSoD7aY/00VZevv7XX3zuAcarMT+2T43H4ZxxIxv3mJlDetBp6qpq8DzADDqEqambPhPuYArWZGByQ92CsRlEGDKgkLZJMAaF0AQvZilZlf0yo+VLzS/irGKWwbVd1S2wJzsvY/ZhidnoGntB+inMY8PDd2WaIoXMbdUeY2+jdKqWFvkelLXvq2Wy2lAc0cxlGd8zV20PrX/xqVOQIJjwJEALD24H6szPfJHCpKea0PiZ9xT6xOiQ++JONd7RLwo7xZvxHrUcAybDlT9Hkz1tCXXSjDTQAqL0y3G4AkABRlMa7ZjDNprLUzjJ15tSsl3HQD43A5V6IWkwA4gNOqlqqquRqXmCAIQCoNZDiH7eYsAHQSuDZDnGYzV7gZHwMhbAgLsJEvqwfGa2L+qqIEWM0mUWTLdgX+YZO8jjPZu1D68yf6xB47I0M9FyTBuFjaUC0FVW0MTfmS7BmR8YHjKnDKXIGqZ9kqrq2KOSaP4wJM0VZ1fJ7Ban1+uF3ysD/zoILmME6nsNmcB7zyYWMfqakVX8NybFlbGToLiN20Xpir+LyKkWnYod1T4w8vKmX22AWsTy8iJowdOypbaQIWF1CZH+ttf16lBEuWiL+onvgkCNBEXGEzrqNGZemiKlVl+ZmLdIeEqpfNYxc7fl5GOvYwggPO1W0G/roVwEnnPN6sEBKzqIkFtJIUjFdA0dRTgOMzPzmogQE/jvSdo64X2Kils1ocEGvSgPIe5s2ikiYJlbciZNfEfKFtHWCI0VzjTgWnFmElHcPYrIACXjLKPDHeLfGpUezuiGuAoAKo37o9SUBanwSxIfYUrWQjRjXluxIgaR0pXahQEdJbCoGZGAyYa2GyT0a7T2B71y5Nba0Sqd5CZS6lHMBltG3Tl018wJyrcRGH1HNpP1RF5dgYKqvMzTbtNQ8VdzcBVJy4doF26kWZr6S6TvyVAKORVbTHQo5LHEfdaRxKsWgHGq9qzHYFSqeNxkYHJTE9hxAcGxPYHJAEnKN5c/wSXEtqJJBXjQ06lumoT2aoL7ZLm4gGs1GOGOVWBmAbi2kT9PepY5Lo2yXTQPt5NTdLqPEhrg/lV+6d3mvdZMDNoiCUh3DRgiBTsC/DXMqgIAlij8RIhySIz3hAMc8oHmzVM7O9xDMZNgCsFn81fVBt6oFN9SAGisdaV5XUbOZmc62Abg7AamqqX+YnuujDALqMO0nidYXJXJGwhEqLJVSEJXuokbLVcBygL9qaqalsTE7JbBo8+k3EM4N4g9JG209jgZqRgspGKWoCFA7UA6lh90xMp7A/Dd20S24O9UOd6EYHaonw2uQT9EYYQT925GsYaKk6N+fwakzHZiFTP8RAppPwGY05FBBDqG4RhuFDekRtYtq3f7orJl/75gvMuQl54tFb5d67q6WkWKN7YjLeoF+as9BIVDX5bMozPGbLkaOD8vqec/R/t1SUAivTn/r7BqWzLy55xevk9lua5N67QlJXS51wQo1jFSLUjRKKMZu2reXXL36mlFoq/kr70GumT5rBg7/r68peEfN8Zbo05dLnIC6Rz155E9ektaY/6XNYmg8dPxqTz3/+Ze5dozyxc7l8/MEANpOLZ1N4TkM0i7Hs4oUcOEe1/fa/dPy7chXaDjL6/MHvNM7UNdzDhw/LntdflyOHj8g0Nt1lpWVmvWPHjh2ydu0aqa6qZoxmk4OuJ3AAPspntW1eaWf80gCa+kdtafoG8+/FH/V918NLr/nqmE5/1q//EXBO5zGdZnXu5upZS2B8pae5iaMUhn+rk3HpKVRQtT7Mcz5jiz7um41ium7A/7SHphm8ettF/vG7A3Lw8FnAuRI2lLRJ2wrmeoZhh/hO1yjUJl5HAp5y+Zw+v/ulp2tKnnl6t4yOzGHPWiUBNmANDc1IV88YrlYZWbt6lTz+8RbZuA5wP8S4rwEGsaOOJXp7Fv/DMxeXMDExI88/+6y88PxPpb62QR584BHZsf0GxnAKzXzrMPaa85q5fDF0MIfQw7yHVw6cew+VlvtIrgZyNZCrgVwNvFsNvJ+p6d2Onft7rgZ+/TWgblB9KKk99Vxa/vbraRnDFom1JFnZ5JLPPuCRRx/2SDUPa9frS3NOx47b8n/+m4TsbrelHgvVP/20R/4J6j6BX4M63tFTtvzpv+NcABHVgHl/9Emv/Ok/J7GnT8H/Ay99ANCFXd2V/stemqzVXUZm9+8ve1Pu9x/qGsiBcx/q2/sbvbgcOPcbre63Tna5z5ZvfDMl3381I53DLMMwVy1vsOTP/jef3AlsXYjtRnbd6q0P5f6Rq4FcDfzSGsguoqrKnKrEKTQ3ODhoFH7GxsakFrBILVdV2Wfz5s2yfv167DISZtH50qVLJvZScE7jr97eXmPV6gN82b59u1GcU+AuC9ZpId4C586elS+iOKcLug88vFO23LxDivJLxKfgnK7NKrTmTZAkIrHJwqcqcVmkWkyKhHMtKm0RWOvKOOozFgkv1lfJJGFlxKLu4qIn8BK/MuoLJLrMm3UxVZM1nFfhMk3DpzWZrjvKdSEYuM1D0koTiaoswXI7byUY1cyjJlVV8WB4RC48/5x4+rqlGhuX0PZt4maRHWm2xS/iTU0tadE0jPVo2YCoHKxGTMJPf8tbUwTbaFpwbVyHWY9XMA3IR8vBArOxwjRH0HGNn8lw2SzoqoWU2rnp+Oci9nXUp8iotXEyPaspLv+hPAoqZcwCNzuwqUtNrFkkbZ0kf7sSMhs7Qy2sPpagDpfu7JDB116Vzt5+WbJlm1Ru3AAQhk0stqsGfMN2MoMdlRvQbDEvSC1quQGD9JhWmlpXdQySURkCea2LRU9troPzwLJxffxHv5sX91o/iBUY2V2+s9itCQqgNXNfuSCHHeeOHlN32wBh8QOCYAFJknxmrV483BdLM2qacCT5pYvmqvhhromGYVoCu+fVijfZ2ye9x0/I5ZMnZFlbm9RuQ5WmHmUikiQmWaf3g7KbJB6V7FCOJIpI04Pnccy7jGDcCOdCvYeEgIs68AJAhEsbxVfWKEk/CmqovvBbIY9pkqDadjRpYFOgDPCegxpeavwAYNlZoLkocAL2g4BeHr1+lFvi2B97sTMrLW+QUOUKYKomlHqwb6VEWpse6kohNGPHx73VqnMW+rEc3S2xqVMs/hfzkdvFXww4NwGU0PWaTAG65tdtkFDtNkA82irnpPB8aSFpm4lhWeg5IsmRC9Q9SnokxTkLz0z5ABrYLxbViReFHYU9LE0qU9daPzZJ9rSBWbgHCqNoWbBPTAMExsZQqpudoG2kcU+l92ryHTAihk2ZQiqRiiUSrr5RXOFmzqn3luv//9l7DzC5rvNM86vc1dXVOeeERo4ESSQCYKZIigCVLFG2R9Y+Hs8z1rOWvdr14xlLlsfjtPasx6PRWMEKVqREiTRFgEFgAAGCyDk1QqNzzqmqu+K+/wVbQ9ESJVOURJl1yQYa3VW37jn3hP+e/z3fZ6XkWc1JkJtSIZdn2mz44Wq6bZ8mgKg8FYtVUNsCNIUd89Q1dV0EUpkNkLQ1xbkm3AVzAWg4H3fB/vNY34iMa26gg6TLNRTusBl0VP9idEO3gow7pjQXQOkqEcgDMgHkI4kfIGnrxjqabJnTnig018MF0TZjs6Ma7z+P6GSPYzFk3mWmXOWNcH0RtOR8AC3AFcHKFq6nGpUXg/GozxR90Kz0rIvTxiw55DP6d3JciXEUpQqx+yUJT8PnN28vcM7mIpsPLLFp39tcYfOKzU2PPvqoYwFuz/APPvigY33a0NDgvMZayMKxMJ/ZeV6dLFz4/S/qb7uOrs4u1Ez2cu2PkeDrIym7Fuvyh7R+3Q0KYi/rXJ/d5LfIYddsx2vr7ceCc6UAwqY4N3xYo5dIPs/OKFRTo6KGZYC2NaARKDHRw6w322xoOkUR2vrMWK+ifa3yTfUri7HGY/bJBvR4GWuKAWLLGGdyKpmT82A/GFMYx93MezbuuQxGtrnRCBAW21Kozc2N9WlkvFvzfuDVECAS4IgXeCcxA3QPpZ1dUKqc8kUoqDUh3FTozImUknNR+UacGGRi44GPZGsaVakBFOdQ0IuwazRn8QPy127h1Zc127dHIx0dCmXVAdmi6FnYqCQqWAl8Dz3MTV6D+7FTnUHVanLoguYYq3x8hsUfpgCXFcgGAitVVkU1qpgVgDcARAboUkazgHb8zQz0NYKDYlvyeBZoZ2y4V7Monfnj1xVS7drj8zNAT4OMblOcsxoFvA2OLanL7CLnme8oEl2A8xBTeJOMhzaGzSkw16ckkNpA1xVFOH9hXZUKUZxzodYS6xrAKvc00K9H4boWwLmljO/YRmNzbgp+BmTZ2Bfp79bswDnquIvxcYoy0F9NrQ911OwiLO1Kq4GXKwHKyvkNCqS0K88r63NUkfN6i13SVu+ouEX7sfodwyKUi3ZBQ6egYVLA1/NAcPNk7XOAkEsbV8ufz9xqECWl9iXZFmFxBlCYJbkdLtngsigKfWOXNdn2EopzEwrUrJG7fANBR7lTp7YzwoGzqWCn65EkNyVQLp7fmx23AVQA1fFu9XUBNqGi5wUksrgD/I+13KByc7D0zq8GxiznegvZRGA25RZx2iYCuyYU07AOt3toduCz2KnOTLQqOnyFnwOJobzmRkHPDVSXiLY5c74nWI463hqFS4HvDVQz4MnapjP3cM3EkDbXWsyB+RxgZp/GOlGcG2xTdUOzsplH077F9BWuxeZZJ/lv0D8gGPXideZozkc7TWHLO0+/mRg8y/zVSvlQh7ONG8RkXj/KsoBgOcU1zrwlHypqWAg78BLlccYIzmWRiLOZhDk6GRkA+m6nzfc4UF4WGx9S2P3Oe2eYS1FYRIEuyHxaVMocW7yIGKKeuBAIkrpKWPBC/ds98NJXfEBsbvpLatTAuT0oNPYBmm5XsGkHYBh2qDSclMFtfK4pSTvxgW2eQBEpSYyB3h33YR7QrVPzjDGzE0MotFk9WHziok14UZAqVpBrceVazG4qdiibGdHlxJy0QYMo7XtO7qhR0g5nB7GEx2rZQ7v0oUqUsD4FIBZFCTKFnXIwN0/55etQi13HB7EBiPtnYw8dgAZPu2Tcc2ycvbQle8/4JY1cuQ7OhSsB5xrXEgPW8ZGo8lEGUxa2jUA2alosQMujDRK3E8OmKKttGLGyeyymxbrQrtZcsa3jMwpxT6kf6xu808Zza0oMQdetVfne/u2jvigK9cUlUZe7AOe++s1nnc/f+cAm3XFb+fXN8vSv6+Mw94cXm9qxRbRJxoOxibROnxvWs3vOM98x/lJmN5KYNm/nF1eoZeUy3XRDoRY3u5XHbkdn2OYTzGLV4kE/57LyJhnTDWg2MNTN/fU48zczBzFzgl1UCUBom0UMxnFgJd7lvJy3WCUnzdOVPujE+TZOUVb7vfMzysk/depEVH/6p88pFqvT+1C52nF/lvILGL0AEz1sDPLwrOKmn2cU56xO3+KH3VDa8I89aAuvjmfse3uLtUsDxo4dO6a9QHNneAY0ReDy8nKtR2HuFtYhVixfwXpHiQJsWGJkoe9aG+XjaJN2DqdxOX9f/5lzIa983vWYjgt7vWt75b2/qL+s7K+O6Zy64Gc/DTiXYsw0cM5GIkc1judr62O2+c+qxbqYCT5brBEnPrG/3cz5tnnOhlDnEZM+e93Wm40AzDHdV4P69jf6dOjIea1ZXeiAcy0tPANff2S7PpcTq6Rt3LNnTWcedKm3ZxqFuMOArcOanWGDG/B0jLnLlxVUfWOTbryhEeAxRxXYQfsZZxOWF+R528M50jxL2OjAUMC1oW86OqPdTzymp596TPW19dp5/7tZs9rAxg27WMZonlmdtQACFhurvLzx1XX4r713GXDuX1tjmddnaiBTA5kayNTAT1EDzGqZI1MDv4I1YAE1m0F18WpKX3kkri8+wgIhAVo2z7url3n0n3/bpzu2sYxoweFb+JjGnuKxJ5P6+F/G1BNJ65a1bn32kwG1YNdqQfGbfYyNp/X5byX0n/97TLksBrzvNo/+6I98qqtwnoh/4seZ6okle00dZeFB6dVvsmA3xCK1JXDr6+tf/avM92+jGhgfH9enPvUp54H5wx/+sGxX2Y9qL2+jKskU9Q3UgI0nQ0ND+tjHPuYAJR/5yEdUQ7LoZ3mofgOX8bZ9i9mfP/p4Un/1RZRce1kwZXGmPN+lT/6eTzvv8joLvayRZI5MDWRq4KeoAZsDDVQ4ePCgoxZ38uRJTUxMOMpzpppjsdPC1wOo5dx7773OWffv3+/YstpYaIvRlly3MbCiosKB60wVqKEBVY5XFIHsdwtf9tqLQHp/+5d/CW/i1Y53PaB1m25WTnZIvggLs7O2UM3uZR9KINksnoaQQAkADqUsMU2iI2LqHyT7LGlE4G0KTm4sltwhrBVDACokqC15aNflpFuwkEvNoByB8sv1BBbJnQASlbzOZaAdgFmC63ASTSineHi9G/WUFJalvIGknL2e15piDfBbFLWy1n/+rkI9XSpfslTBbVvlqqlCSQu7rlAOKmE5pO5IULLonGWJrDTnMnuuWRJolC/N4nMacChBwsbnz8GiBHsuA31Qt5BZlRkQMMXrUaO4Ds+RCLdrKCDRDigWJzGZRt3Ox8Kxz847Pc557X22vE2cbmu/7NZxG5xh9Y89VppklovrMJ+3NIlMxNK4juv3zMA5Nw8mLhIFrEQrduqsxnbvQim7S/UbN6n0pnVyl7AdnIS6JxfwACWfNHCR2dc4iUFTHsF+MkWSPwX0hdwIJzcbqyxc3khG4/2WILMdZ/HZ2LusCPdhcpbrsMVpHioAAl2mDIdVrHMPSapyM/gNC9gkVNMx2hdtwsRhHOCO+2pvc5XkKxamjCzrB4DpLCGeps2mAZfMTpd/kfADVyLZ7vWjCsQCegqv7wSgVc/hQ+o4c1oNzYtUectN8jSTKM4HmMpCOdBPWbEEtGSr88WZUrSx5GwH1qUX4cp6yIXOwAKiMJZDPbD73QUwlw7ka46kd9yVzb3hFNSlh8V/F881Se75HAopKaBDP9k/V0+1ldIAAEAASURBVKRVidlrFIdzB8ppA9jsYTMYm24DyGoDgJtQOaqNhY2oouSvA5zjMyg+6QLW9Km7GEkKS+jzUxf9Ik0yNjp4SJGJC9xzwLm62+UvbMFG8Tzg3LNYno1iybdaWeWb+X0l7SpIqXgvSVh3kO+SwySrL6Gm2E4dTcCwUccow3iDtViR1fEgW0SyG8sz+oDbFAHpB+mY9U/GDyMhaUtmpWxJDhdtLIV6VHwa+0X+7cG6L+AHLON+JFF9Ge8/qfnpDlT1qlWw6C5AsVW0I5LXnML5g3KlgMssM+MAGSQxXLF2TV0zcA5FKSCYotolAH0BxSa71HXhMn3LrQqAm1B1Lf0wqCj9ziBFH9caALYyi93E1Jgik93chyGuBBiP8SOI1VowXEQZUaTi3ie4f1E04NLcq6AD0VLrTgLb6po6oV0bwJng/s9Nd1Ns4AxUZ1yMTy6ASDcAT4z7F8G22pVTiHLTMkCSFiASYBLahQvo2BXn/hkAZDALV+L026lZ2heWdyUN18E5+qTd77eT4hzFdeaIBXDO/m1z0AsvvOAooBrYvWzZMr3//e/HVmmzcgEkF46F+HthPjPobuFnC6/5Rf9tbcDszr/18MPa/9JLKEGG9eC7HtQ9d92tiqpK1oLoR4xPzo3+RV/cj/i8hefh19bbjwTnKENZmYFzjPlDxzR28TiKtRNYtZYqv6FF6ZwqRcy2kDGXYZ3hFIAbQCWGGlMK2+QE4Eiasc4THQN6JvnJHOXCWtqN7SWDFG9jngEmjgGnxE3ZzOBbrtkgtDRqYwZ+OwA4Y83cHMqfWEOng9i3hkDRDMaIzmq6uxP76j5lc+7C6hUKVG1B4bKakxjcxGADqJsGlDCY2uVhQswa53b0KT14TvPXgFpm0ZxcguJc3S1000ua6XsOm0fAuWCD8stuYC6sQNkSG1nGRT9zjMfiExdzD0Db7Hgb1t6cjzHGlNFcBiCHS4G/KBeQbTKba0B9LsH46CaT62hPUkYmD+rHoCTmVsbmCIqbs9PAiYzJOcQLQWILg+ZjKHrGJky1lDEbCDpcs03B4g18PnGIKV7Z0ML86UqOMIaimIqKltmU+qOo1g22a6ivE42vpPLrqlVS2QRf5wOc69VE5ynGMzfgHD8rQjHOQzzFeOd2ggqLUZgXJwGjJ65xTW38e5rPZz7zEruFAYaAygxwguJDtbTYmWPcc7Mo0zFfmTwUMYMznxuoZZAg1tWx8Wt0dNQ2g6gLhssoN6DM3JCmhzs02j9MDBdSZfNaIOtGxXwopTILcvdl+xFSnCdp8yzX4BSa+C050aFo+yGgHMDF2hVyV95ArEdbtQkGWDBNrGBzXwq4yM3Y7rH4A+DMsUllHnK5iaU8QwAVqAwCdyKj5vD4Puxwg1gQB5kn3Ci2urxInmLt51g9MqZ7mDk8QJTpBKpqUWIYPsMVIopJT6Dkxlw/20XRgI+CtEEfczBqb3MjB1FlRa0LK/RsFFPLUSIMhAycJm6z+JbyMWkSh6DEBwxgGyusL8xhdTzeeYk5v0sVjcSg5ZtoT4uJC2lJVEzSjRIj0FoSe14PfcTPXOp1Np/kUgtF1EOCufMi4PkZ4lyzIuWWEXcFwiXy5pZxDwG4qGu7rymD/YnVzFramqVhWg7QBOzuQPQEkwls2ecn+53YJouY000AlEijlohd6lR/P+2FeKasHrD9ZuKsG7hODMqB4BIGtRNbpYlpXMzRPj7AtrOkJ4hbuvYB46GIV7tNWc0PEp8Sd3rjzMIReWPE5nPM4dxDh7RgLE2hghxnnPFwXnd0ANU6rJ9nUL5lrk7S3z1saAhih5pN+ZArIs6jv6LwS2HpM9S1wWYWh/PaFBCIE/sbfQVUFpuY5P6hqEgM6w0Tb2XF6Kqozo6f1fTAaeoD7UjUDbMrt6EaWE0LDThlsXg1TYyeol3aphV2BtEnJ5QG+J9oZ0zh3KHyWhR02Zzir6ce2NBAv01x72I8DxhYaBt8AsCr3lSQcYH4g/6R4nkgBaQ2P8V2FZQx5zjPHDGP2SkG6eu5bCoIZtH+mF4IPWlfaQBODHTtdfbMRDFDDMz5qA0GgcZMlXDX7oi+8o3nLCIBKrtZWzeXKJtmnAJKSzPmeKDsQvZ6pi17rjLVznnOPTCY0Nmzo2q7MoGdIjEQhF5RYUhVDWUqrslWQb5bZYUu5YaAaajOCGP4JK4B1KLyrPqpU8TOefQAsMSuMRQGtzX1T4LvKFawMzy2zEUZE0xlEKgxi+vNyXVzbfZvbg91bHFShOeKJICdgT0G1wW51gBfPtST7VpPHp/Tn/2XfQA+1Xrv/U0o6vmxe+Q+Mv5a2bJR8AxTZ22XM4pzNMK37uGsWVhbp+1Y/7RA+bXHK+saC2sOtk6QoCOMsoFo794X9fwLz+vsmTOa5nm1trZWGzZsRMl4o1auWKkCnrt8tinslYXElD3bcFxXnuPDONcrf9gFON9an3F+btfkvNj50777pR8W1706prN/29dPAuesJHHGRQQtGV8YO3h2jsUYr5l93bZJgO8CiGlk068DTh+zWAy10QixRNStOd7Inkqe0/g9z4n+IM+LrOsMduXrm1/t1b4DF7R2VTHOBEtUVR3UHGMUo6VC9O1gGMERLOudOrfqZ5iPTKMGeXlMVy9Nqr9vTlOs6YTy3CqpKFTzolIVlQDuM22WsB6cw2cmGOtmWW8hRWg3j/8ZDzl/gs8ZG5vWs99/VC/u3aVFTQ3auePd2rxhA+sE1p549nSe+YhVKafV1XWQ943f0ww4xz3IHJkayNRApgYyNfBm18Abn5je7CvJnC9TAz9tDRBXEcSldehEUp/9WkJP7rPd+lJxjktbAM9+69d9une7PdC99Y/e/rT+Hijhb77CfmHWhT54n1cfxz61pODnc/WWA3vyhYT+45/ENDSadpT5/gDI8NffZYuQP7m+TBXlscce065du5yHg9c+INgZiouLtXPnTmeX+k8+Y+YV/xZrwGCAT3/609q9e7cjx97YyG7xVx6I/y2WN1Omn08N2Phiiy1PPPGE3vGOd+h3f/d3VVVV9UMLEz+fT86cdaEGbL793FcS+h/fjqu9g0VT1nJsMffj/8Gnh3b6VFVBsp4F/syRqYFMDfz4GrAFQYMUbEw7ceIEC8p71dqKMgYrjQYe2Jcp/BiAZqpx27dvd76CAFamAnT8+HF1dHSQHAMM4zy2AL169WqtWLHCAegWlObsd3Ye+xz7zDiJy0tYtf7tn/+Fctjl/+5779bqpYtIds9pbgjVpWnU0cj8+kj0hqrK5GeudheT7CIhkholSdfdr5nBAdRYouS0WNQk8R5iATdkMEJ1g9yFpbZOSrLPLB6nUZrq19wgyhOM24l5EmhBEnuFxTibVSivBuUQgDezanVhmZnqG1S0owtFj1GSgUBgXIcbQCentIx8fhULuF4NUO6e5/YoD8u53FLs0ZavUgxFPi8LuHmNNcquQmGKRHmSxKIftQt3lCT3CNfbM4LYBIAZiWRLLHqCJIBLaxSsxhYNe0kngUiib25kUHO9vSSAxwGIGOxI5rnYVZ29aJGCtXVACSjxkCD1mArKGAnCtlZF+rCi5PqTJNNcJA/9AGXZldhHAfS5C82+i+TrNFZxo4Oa6R9H6IVsFO832IzK5prLldVUg/INYOBebOqe2qNeriN38WLUPuqVKgzKU5avwmUrlQWglDZQyJQdsI1LRbGeHGnjmi9jjzVAHbP8TWbOl5OPYppdQ70Sufkk0rHiIguWaiOJzK7/OeBH0qWAi2woyqF8JQUkxytQbuNek2DmBqDMgZVpPyAC1xyfYgWcBJ+jOIG9WbixTlm11SQSr4NR+P0pOYxaTB91iIrAPAmxedqdQY9hlH7CZRVkAFDxQW2g/eB+jbW3qYr7lk/bSwMQWHvIqV+iAIlMV4AJxQ5LRpKsJgtLFQILTLWTCB3hMgA1aTOeLB6SUA5KkSgwgEFFjSRVUYxBqSY11kMCkHZFf5knuT8Voa68BajVlJD4s5OT+KWP4fPLl2EhBh2i5jN4Wb3XLiqPpGVR43p58zeSZK6m/5B8xUYuPk0do+TmmrdEBAlI2oLbNYsNHIl0YC438EG4/m75ClqwMTytuY49qNthMZuH+mPBSkd1aZ5+YPkIXw5gWSGqUKg7miVZetYsBSdfqWdAOT9QJ/Z/SRR+onEgGNqpL4vyTgOOYf/qAbLwvJKkgB0g+Q5AkU3bsIQ01+YKAHGY5Lp1SEtOo2w3S0I80necNlSg/GX3YMO3muwu4II9NANSmK1qYrYf1R+DBVAXQgXAj7JSpLuV2AtFqbLFJNOX0saBFiZ61Hf+LG1jBmgeJZmyMsVpGzOMWaZKGQKMzQJmNRUnB6qM0z64l3b/fCShvW7zBwIsJckCdQO4g0oSsMj02JS8AAVZ9FM39zCOlWKS8xmA4clhfAHq8RlwgfIMHY4L5wv1LLv2ZM8FgJ1+M2UjIb4INahlAK913CPqIYIV5BT3D9gngfocBeed6BQBHRqg4Kq4AbvYWkchwR5/3y7gHEX9wbEwL9k8YbH217/+dSfeNqXT+++/Xzt27HA2wi3MLzav2Jcd9l77evXPfnDiX/A3ZlFtz51PPblbj7JGMcA4dsP69Xo3Fuam3Grgn5Oc/SnWOX4Rl271ZsdCXS585uuCc6g3pgaPafTsYdSrBpVXmYu1YpXmUFicNrVG7ks2SdWQAV/AxaZg5jZAfO4qkO5lpjzUH20O8oNCGbiK4lIS4M6XxVyUi8LYHDbIQEceZGj9jClJAJ841tjpOJbWBrzmMz7RxxPM0wwv9nFAaChzGdjajapT22nyoDPKx945WHenXOEGCyPodlz3NH1x1oAYgFXGl3QwymWgJDrRpVhvBzaKzDFLzap1K29o02zvC45VazZW3PnYTLuy8jQJtJFibMlGrTSL63NjGSnAK2gozj3PfAyIxdjp8qBeAyQP1cNcAFxSkIMlaq7iAB9z48NcEvbfgCxplFriKN0lUFgNAPiYxayBQabYYmO9I7lkYynKpOmJY5TxiKaB7INV61HRAuYD6sM/lLEI2CXaCaBzmWQ3ym0G9jEO+1H3c00Okjge1SznzamrU3F1o2PvGuvs1njHUcfGOqcCxT/gx3kDmmkXfu6hj3p2A8FDZAGnDzMP9TLOA/kTn6SBltwBNg3Y7wAblVUI8F3P/WIOxhbUn5gGYmH8IzaJzgCrJLl/qJcadOy2eY1xFnqc35sSqt2fYSUmAee62hkyJ1RayzhauwxFV2IDqsEHEIZPNuAQUBpzOiMnalio/gE9pVE7jfZeIqkPgFO3Wh6AJgZVgMQZQoox535bTGp176GtmJOGFygLqot6BipDfVCuUeq4H/s57D2ZM2xThVl3u035lftpEL8B9m42EiS5H3MzAwCQI3wmMWqcjQIRNjlQRl9+PiAaABrtl5UD3mNzoamKUc4UAPckwGnvCY2MsvkjVKXy5puwF+YeWiwBPJ5mvkhjYZsglo2aWilQVRAYLxWd0ngviq7YzlcuRsGyfAtQ2yLOy9tixI4JAO7UGF9T9IU5eC3AIO6PJ5sYKEAca5UYvUo/bIXLY85FCsxN+3QHwsB2HspNTJFFPB2u4V4QK2B7yolo835mLNow9zhK/84GlAwVEMyYVSrxiNviJeIxR7UH5dkUY8JkF/NhX5uKieuCtZvlKtzAuEd8hQJymnaRJOaIEvcm2PzBpyqLh3lXnDKgRDg7jqIi4Gqg8QHuD+qO2A3PxYaZZwflmSEGAnDjCYCxhfjUNh9gnewm3nInqDc2ODhQIDcrRSxIDdCVing9wL3FvsR4gWLuN3d2fgLlX+JYvwEz1jYMECGW9gEQelHcNctoKo5qo6XxXIKsLOWkfOOngTSJl7kP7so1PKvcCoRfQ7xCWwQEtHuXwt41zkaCBH3BZbGMl/5P25oixp/lGSFY3gLAup6u3cB4iJoz0Gg83kP8Q50YcGrdibgq4KF8dv+A/CPELaOTbrUDkQz2xYDLwAnZ7OADOCvMC6i+OlcNjSG6Gxs9h5Pq7Z5mwycw8Sy5CU7ooc2XFvu1qC5XNZVYGwKO7XpyXp//8j4+16U7t63SiqV5PAvOAqRRVoCSXBICdXXZqq40yMwQF8Yq2tXEWAo1qIQ6O+dRVsQmm/tRWkJsRVw4Tj+Jzs9qSVNAzQ30B4aT4dGEjhyjH7Ixv5xx0A/AODgU0RxjU219lpoW5Sonx6eZSRfW7LPMm6gUo0KQAKxLch8LCgNqbM7neZPxlvKNjqAi1jWtwUHOEbF+xrMrY3xpcZYWLc5XSRkbZpgXzpyK6c/+6zHKVKJ3bKtBoco2V01RjwSuHr8qKrO1fm2OBrCi/va3H3E2Wd9+++1OzLGIZy+bFxdiCxpD5vgl1YBBngazLdjd/1C8wn22oc3mLPvL4gELayyGGR4ecmC5bz/yHWe9Y24u6ijq33rrrc66RktLi/LyAMXtTRweA735/l+Ac85vf3X+eG2btX/b1+uBc1YFKTpNlHhrnNxm38CcurumNDmGapxN1TzzBABYS8uz6LM5Ksr32eO3OjuiTn+dnoozbrDZgXEmzIa70gq/Glp8KmCaHe0N6ytfGtBz+y5rWUu5bt1eCwSbQjV4hrl8XoXFjF8N9O86rOjDxPbcBlOotb1UI4xl3R0xDQ7ME59hKc9YEMq73r/HGHtszWjdar9qa3heZRrv6Yrr7Ll+npFDjCm5XGOUZ4opR+n9/NnndOHiXi1b2qR37dipTRtvZo8hfRwlWPtMEF3q6fpCstXH9Vbxxu57Bpx7Y/WWeVemBjI1kKmBTA28bg38LFPT654488tMDfxcaoBnbI2imvb03qT+/p9iOt7KAgtBVw07rB7c5tGvf8intYv5wa/AYSIDp8+m9Bd/F9djhxJa0eTW3/5fft1+Gwv77Nb4eRxWfxeu8JmfjuubTydUTqD82zt8+oPf9yqf7zNHpgbejBqwxR+DnQyyHGHXmT04Zo5MDbyRGrCFFQNBHnroIQfGLWSHYub4RddAWt/andQ/fjWhYxfZwcw2atuA/FEg9X/3Aa/qa5DrJ0eROTI1kKmBH10DljxcgNoswT86Oqop7MUMUFj4uUFyXlMbYLwrYNUzn0SgjX82n05OTjpgg4F2BtdlkTzOy8tzXhcOY71hUNArh73Hvuy8CYCIy4Bz//2v/0ohEp33rF6pRhJKcyhTzI6gLoPSQDaf5wE6K8RCJbT+BmXduB4RjhwlTqDOcei4+ns7NYplVIR1TT/Z9FISemWNTcpZc5N8LYtJlKKOgVXkfOtFjV04qxlALdsxnQRQ8pAcjDBm+xc3a+n2rcqpwI4Mu9NYe7ciB45r+vRZTWH5FWe1Nklixo3KWzlQYMES1EuAwXqw+ex/6XmFBrpJrJE8LatTlAR+cFG1qresxd4Ne0aSwmlsp8j+KN5+RkPHj2iotZNV6KiTUDN1ErK88qD2kr9muwpW3egoZUSuXtDQGSCEnqvYTxrgY9oo6DtkhZW7bouKb75F2fUNwEGoXo2hpnFkn3RiHzDcoAMqxFnsNUWwbNT7ipsXKWcjIM6iGkc9L956QbPssh+4BrQ2gXKJAT/swic/qIJlzSrYvBoFQOz0nj2oub0HNDJB8r8ABbWSQsULwgph61a/aavCi9colQ885MhHoOTVjbLP87sVaT2NYtoE9cZub0vOofJWXFat3DU3yrdslZJhEp9XLmrm2aflou6m+Px+7vMMyfRs4MWyiipVYO0UXreWRB26I8ODmj9NUvnMeY31kkwEDLRkho/fxYAAqtesVdGNwALlJSTro+SBr2r2JCoql7pIZJNsQ6ljmuRokqRqeU29am5YR1KPxffDL+nqwZeos25V5OYoGygyVgSEwWuqNtIelmKbRVuzENFl0j1ukuiJq0oMncSqtVPJGaAF/vMCC3gDLK4DXaWnI1xTibKWbwdOq1L02hHYkJfpE7wutwDdvCCQBXBBTjl2bHXKKaqkjKZKY5MUV0Xf4CaRyB5RbPSquq4eBjiZUXEd11ywGcUPEtjpQXbyX9JYP5axo0Pyk5Akhc+OfpK0JLWjqAnFgQ0DxVi81r8DGPM6ODff8QycxTAJSspI0jzBDZ+mz5qtmRdwLlSMqiSUXmJ6FsiBc8zOkYAAfKT7emijRqtEYl5U74qVW7+cxD2vRclppKeDvgRUkUMiGp+wRJwd/6jlhIobAFqqyTOTJQUuS5NMt+Q2HYmE8ZBiPS8o0rEfBIDxYvmdKCUBzqWob+Cx5DRJ5sGTtCMgwCQwIFXj5R7mUj/JEfrkPImScuDNqqUAbCRMUJybvHJayckBc+4FBAxqFoBihrbpZawJ8r4gsimeEDZvwItzAIdTSZL1wKK5gBrZqGClkU1JInniAqj1VTUzzlRqAihhbqpDYf8sygcBBxSYR/FFhQ0KVC4HbkCJz1SHLJHOGOaQFEAK6XmS3X2XNNkPfEuyrrC5BRgUJSAUhiBKAUwvKzFwUbMABVFshtyMMSEUsGgZNAOUjBY9KF8Ril30DXv6fbuAcwvJPJszFr6f4Z6YkvwXvvAFHTlyxElyfuhDH0IZZJMzDy3MLwt/U13OYe+3ueaXfnAdcyQCTxw/5ljNWhlysfTbgXLr3XffrUpAb6+jOvdLv9If1Jt989q6+5HgHNdfhp2Zozg3fEKT5w5jj9itbJigQDjI7ObTLBCVWeh5sZjMBZzOKmJezqJtA6SmhlBpQuEyGWceMrgWYMXmwykA1ThgUbgEW+mKFs0zFs53HeWqgH/ow3FUMOcBl5DyAtipUKC8BvU2s8dG2RRlMvAprFqx+gOSSo9c0UznUSCbIccC1lt1B6BTA4Mav4teVnyYMWaoA8gNoNzANIAdUzryR1HEmwA6SucptPQ+eau3kEhFqbJ3n4avnOETAGKxd06hLjaOKqypsIQYLMMh3p/LIMS22dQkqp1TQCoxVCa5ZlMaM/jNtM5myDyHGqqVAyQeGxvTCApHXqzAwyHODLw7l8ymDoDI8xfJX7SUuYjnSxuMUVsyZSxHZYcxLD1+QnPtL6E+NqLsOpQta1YCNgG8JQB9UPZMDB9SmnqeBRCbR23LA+yLISsqtTOaZbFvHhApp6aR+Acom/E9OdCnyY5D2M4CDAMbz6OwNsdGgST2ogEUz7KzUNYrL2WsY34fNbWtCQAV7BdRFI2bwi1zpYu40A1g7C2qpYw3KjoZ0cTVU8oGJAyzAcFFe5+cAzJHdS2rbLVC2GN7mJ8dgBSAyYGgDEYG+kpOd2qs45wiAx3AfcwrDWu51wAspsI1h+X24HmNDxMzsoHCxoAQ83HAEt0kx+e5fzkAa4EG4oOK9YAzIVTDejU32skGCuKaeTYZoFhmNqU5AFy+IPappcux821xYGhHVXbkKjEuEGVqUkHKmYWdp4/6SAAWmau4HwDeV9HIOI5h7vBVFNO6UfECyGKeiGF9a8puWQXYqJYsod3RV2jjacYls7N0AQERiXCtl7AUPYMKK9+zGaFkCXFgfjP3GkAPi9fk8BnqmmuenXFUw0yxJww4B7pGaDmr2WhKZUtuxv53G+16kVOm6BA28BOoJtIGMEx1NqGgEaQcIH9/HhspcuqpX8DysVbgUZRvAd5MRdXUj13AjdFEjPgAOCp/tXKrNlHfzJutuwlPBgHTQqiyhgC+AooQz+SXob5Y3cT0XUKbNWQKoAVlNQMSDdhzNj70HgUSO63c4jxlVW+injfShmjLWLwmJzuxWqZvAc6liME9zIVB6slLG3R+Tz37GrbI23QfYwd9A2hubAI1x6FrygJWDBAjJIiRbaNGFqq4Qfq3r5C6ZrNKYog+znxvFoPWdzy0EY8X2J44YJwm5ilBpciAKH4/0t6pmYFBFRCvWOwTAQhLY+OaU9CgLLt/plRp8BywPQ8m3DvU/9LM96NA+9cOKsGzS7pyFXHurTCPlfQhXjrdRyxzQokRXhOdRD0SIJDNI2F/nHZkz1qoLQa45uo1ysf62eVmM+8Yz1bDpgR4nns0QLtlawnt0+DbLPpjoLiFDTpr1TNfopdPTer5Z69pYMDGBdoWY62PvlOYH0Q5q1YbN5Q5UOfRk1M6e/yaxgcYdxMMs8Q5kIWqqQpq66Za3bgOZeMCj558Bqecr5xABMCr2sp65Zmib4SNNrMTmmYzTA5qmTetL9Cdd4XV1Ex8xbPC0FBKp09HtW//lFovM3bPofZEGfNygRe5j4Oo4cXjV/XA3eW6644SVKFQdGuP6h/+1yl1d8ZUSTxVwDPlNONDIoWd+dp8bXFAmpDOnJjTwZeH1dNHvG23kFgJ3SsVFmVr1epabdpSAACZ0ImjE4BQgxofYYOLjSAMASnuT0VZnu68s143bQgrO88DOJfQJz55Tn0juVrZWKrqUqBAVJ8N2JmLBXCOCOgPPtrCs2yvvvWtDDhHZb41D8YObjMxgF2e/fHKv51//nDcZ33f1LNtbcPsWZ977jkdeOkl4PV51dXX00c2asstt6iFZ3Zb37DNGAsb6hc2+9maxfVTX1/DcP7xK/THa+Phhfj69cC568VzMb9IJ89F9PLL/Wq9OKbpSfogY6fVexDYrak5VzdvqlZtRUiXWxM6fGQcIA+oHsV3i4NskxvLQsCwPt3zQB3KcNka6vajODepZ/d1IsgRZhwiliK+m2JeHp8cJr7xMX4tZpwp0PLVXgeei7O+29ee1P79kzp+YgpI1s7tUS7Qtj3fMaRrYHiOuCyqD/9mnjZuCgHZSS8+P6uvfuN5rrVclTyHJYG5IzPdrFPFUZQ9rf7+Q1q1qgFw7l4HnPMB+hFc8GXjKU/X9nxnPyL8+lmODDj3s9Re5r2ZGsjUQKYGMjXwY2rgh4OeH/OizI8zNfAvasCCQUvg2bFgD/UvXvQm/4B1cWTKsTZ9Oq7/9oWEOoZZMGL9bxE2o791v1e/8SEPO6p+xojrTb7m1zvdOPLpD38voT/FNnWMqrztZo8+/YmAmhp+vv1yBvu9b/xzQh/9K3aOUqfbVnv0sf/Tp1s38gScOTI18CbVgNn+GCBgyajXJgfepI/InOZtUAM219iiShHJdoNFMscvrwaeP5LUF/8xrj1HWfxmB7OBER9GrfT/+KBXSxd5UBOwJZ7MkamBTA28tgYWFlBtLjRgbmBgwIHKDYgztR+D4OpQJClFlcsAOnu9vdb+trnU7M/tPfa3zam2wGzjoYHEZl9t8JwtRC+ADQtzrinxXL1wXn//538uD8pVNwBm1bKduJiOWlSMPVAeVim8Zq4fuy6uK91EcuX220g0V2ruORa9Dx4mLUcGqLlOySLsNUn0hQjGA6jSeZehMNKARWUERaeTR9T38j4sv/qUB9yTV0yiHVvNRMqvfkaF2cpitWzZqFBRgeJ9PRp8+agmDhxTYXQelahSlNoKUbwitwmUFeQ12U2LsZkqUeRKm/r37FJ2XzsAVJkCzauwWERJp4bE1rIaucuxusoieUeyOtHdrvGDe3TlwH6UJ1isL6lRQR4JytiUJoCP+uMkUauXq3HzHVgm+TR87qR6zhxSEaonlSUogGB1SlaQ8pLArkE1Z/mNCjY0kNRPa+LiGXU9/V0Fr51D3ZokMUlMDyo1aSy+3CQLvCgte9eukKe+DJinV2P79moKu8M4gFI5Cd8gIJulmKJAgp4a5rKbLEEYUnI/gNj3XyQpPaCshjr5muspX6G8gBLZi5fLXbVIKSxx3aaSNTSiyWOH1bX7u8rFCrO0tJCEZBHARFKTA6xcoy6TVYOF5pbtinJvZ4+9rMhLz6qIeg3StmZLqE8SlWnKGERJL1zfqOw1K4HRUNFA2W9iP1Bgdw870kMAZ6j+5YdR+ElplERyLq8Nr+R+2/0b7AVm3Kfx05eVizVeSUkF949zA3nNUBcBlAQKli9DDS1Lc6ePq/fgPhTnrrDgX6ECAMN0STFqXzUKLFklT1UTWXGb123mQMEi2ab4xFGu4ySNIcJ9wjaLJLSLRHGaJH1qAuUYIMNkiLaw/gES37WKXXpW0dY9wGo8ExYCyeXSVkNAB6iFeAE+vDk1JGBNeYYZy5JAJJwh14A8BjQPXNXfcxYVkDkVVK8GQtvApZSgMnMZJbTDJECvKCuZUB7AiR+oEyKMxPoEifQR+loSEGKRsgDnXPlLgCuAGa89DQjWSWIcBcCwgQpAJsQvMdr1DAnmOSx88wpIiM8isoMIXk64EAtawFfAVdc8IB0KPtORJCqHpSpctIYENZBc71UNtKMEhcJOuIi2EQJe9KIOSYLZg2KOyxLNphCFtVrSkZJDBSQNtBobVKwbcK59vwPB5K26C6BmOWUn5Y5K4GzfBdjB03AiAyT5SWLRbxMWb9GO5ronuRdh5VSvVwDrRfcr4NzM5aMAEddQ1ImhnkebBmpJAsW5IqjRMH6YspAPSz1TqpgDWEjkhTh3PoAN6jLTc4CKgBSAFr5ClK7qACdQ/pnt6dL0EKpF3mkAjDzKVwbsgfVgbj3wXBMwSx3JlAKS5wC+RlGY3B6Wkopx/wZaNTHQYw5vKgKcy65s5l4zlnB/5nqOKTWMyg+JXS+AkQ8A1oMNUmoCKDDJv1f8JnDKSu732w+cs/ljYa6w780GfN++ffrmN7+JWsiwbrrpJn3oQx/SkiVLnHnF5qGF11sPWpifFuYZ+9kv+zD7vz7G3qeeekpPoIo/0D+gW0jUvufd79bK1ascG3RnnHkLBKlWf3a8tv5+JDi3YNWaxKp1hHH6zEEgtHbG6YSC+SFgYRS+gFQSzHPzkSFmGdblClBdzGpkPAFcpb/6GXe8OYyjoTB9BwtG+s7QSB9wSZ4Ka7Ypmz4e7zml+OV9qJyg0pRXJigd5igAU6AvH4pzyi3HvhNwTqh8ogjmgHOoNPlQdLR+Nt1xhDEatc2yFvmqb8U+uZahFqBl9JAmGWPTWKCGSZb6uAZTkUwAYaVG+7APBWDGLjZv5QOMCbcw7qDo2v2SRoDAUgCw+Si6BlAwSfmZ462jz44yH2LNCniTAhxzo0TnBgB0ZaNiFiwCqkJxCpWsWRTuIiTzC5ehoAYMZvPn0Hmz/55SXiiL8QsgHfAmHSjnehpRqGq6/n7U6FKmXso04VAklC89BmDceRAoZ0LBRsoHhC+U0xJzWZQPu+ju/QpEupirgPtRj0NSD6BoHItO7DOB0JHLVUHDMmAmFFZN6a6/U8OXXuIzsIVFDc8H5O3KDjO9oKwF0JxEfcrGLAZ0lAA5HWCRN4yEDGN0BFgqin1oepRxGxgyi1gkb+mdqOlNaeTcAfl5fx5Jch9jc5rYKJVN2fJWygeA7PYR41iimmo061xTeHUBlcUnLmv4GlD0xCCKc9WAgat53yLGWtrPGPMxqqUR5j0XsUE285VtBEiwOSKKlew8MWkR43YIBTdX9WYAolzuKRspaHcu7IGNQzSVP1eMOQWwkr0iWHyjYFu5DMCQWGsYpbtB5n0P9qtFAaBBxmmkzVOAgNEp7iFvsHgk2LDCgW5Guy+gKtYOHMgUH2I+zKat+quYN7h/OcwpZo/uM3t04674E3UtJNWANlHp7WWjxiDKi0EApqU3E7fUUxdAS+Otmu95iXvJBg02Q/iCpc58YXFC1IEMsCBlDitbvkHByu2cmDiGWC4xfJD2dBHSCOjKgdbN6h6lXyR6PI4iLHMZ8XJyto82OoHIH/3G7gGqcwlsaaepD1Mmy8P6N6/xPua4OUVPfslRGPSFUWArYu4LEUcDjvvC5YDe9Yox38fcAJCU0MPrA2lgRrNIZfPKXPcxTY5cUAGxYaDmZrmKb+Ie0j9HznMPzwMyjikLRTODNd3YHqdRnk5M9VEGwDruUlbLNjZ/3IelfQ7PJSMonLVh9dyuXGyis7GvNTh33jYusGbtQ1EuiEpjivPHJ4DZIMX8odzrsJhZwGMnbRDiOH3Wx/NEyXJiel4zcvkq6ob9KiaeymUTjCuHsQX1Ok+Y9hluputggey+Pr5YzExDdfp0augCeyoOcyv75Kldw/y9jVgPcBUV7MTABeyiDwLQXUYUE1VD1GzTjImi/Zka3jTqy/EQG4NqblAe4Jzb04QQJpqCwxeIAy8A8Y4DKlIo1BXnaHepCM9egSoF6m7T8aEyffvJKzp2coznrcWs0VcrLxxgHwxWxJSnqjIMDJSlrgHWRp7vVl/HoBrZhFSLKraBypPERnm5Ca1dGdSqFdkqLvA54Nw//ONRdfXNqrp8iVpqi1Rddj0OuYKiW0f/BKq+U/rAB2q0+ZZc4lM/UHhUTz3TreOnRoh1y1VfWa7CMNuCGB86e5M618V4Gj+n33p/kx7cUaXSMrcuXojoL/7qmC5emlFFQRNqdNU8j3hVXB5FIc6n+qYsFKUSeuKxHl1oRVWK59Cm+hBAICqHjL0mP1VRlYVtu1etVwb04nM96h/wo7LXoMqKbAfUmQfadAFLrl8X0pp1bMzJc+kUjkCf/LNWne0JaXl5oVY2A+6VY3WLNXY7innRuU59/D81EicPYa/+7YziHGPVW+5wYpTXBks2cbxypUY5veqwTXqjxPdnzpzVM888oyNHecZnbmhsatSWzVu0ZcsWYK5FThuzDYIMYE4caaewOMi+LC56K8aWryrm6367cO0LL1ooz+uBc1aNSTY+DI2k9Z1/7gKM7WDOL2BDci1jADEAv59n41Z+UUJLl6OeyzP8ru+NAM9ivxz2q7E+W/nAqmY1HUFhPy9/VlvvKFXjoiwNd7n08NemtRvo1wv81lxfyXoRG9hyUGhm7r7YOshYUqztWwt03042vVV5NNyX1MsvzmrPnnaNjSe4hjI2nqBqC9DeP5hUa9usrg3H1FJB7vD3crVte1BD5Gaf2Z3Q//rHp3m2CjDOrGScLFBZSZzNX3O6euUFXbz4lJYvr2Zseoc2b2KDn2PVimotz3UpgHhWvEyQ/zo4x99v9MiAc2+05jLvy9RApgYyNZCpgdepgZ9hZnqds2Z+9W+/Bswu6uWXX3YKeuedd/7cC2zQXE9fWl//Tlyf/npC/VPs/CcH0tLg1kfeBzT3ELvILeL6FTnseaSrB5vWz8f0d9+Kq7qIcvyaT//+w14V/JyV30wVf//hpP7z38R0GPWgWnal/cEHffrIf2Cx6leHO/wVudOZy8zUQKYGMjXwb6cGTraiPPcZVFL3pzSIrYDBc+++w6Pf+ZBP61Z4SOY56zz/dgqcKUmmBt6kGljYTX327FkdPHhQF1CCM7jcVOfKSbTYwvLGjRsdeM4gBfsySM6ghpMnT8reZ1CDgXZ2Llt8tvfdcMMNjmVrGdaJARI1tlhrh70/zeuunTur//GJTyqCXVEjCeNFKEWtXrlCletukJf3pPEISXX2qZOYfgA4qYif1y1bqtg+EokXz6E4Vang9s3yNqLkZPsr+Hyh/uYqJbGOGlrs4iXNPrlLl44f4Hzsat60QoVLVpJ8r+PcKFKRBJ0H+inEVs5Donfy2CGdfvZZklZDWr1itQrXrAOAA/5hLd1sstyonHkLgKu82FaiXjfw6MMKdl5VsGWpfNtRssFC1Z0DiJNDoonkn2O1hfLI5JGX1fvUd1B5u+SoW9RvQjEOsA6aQFNkhTpOXyTRO69Fq9YrB5hn8OolLFGuqL6lXmWrl6BIV065KCD1lyRx6M4rIRkLlNA3orY9z6j10D5VWv3ddLOysVF1s6CcJmlPVs5W/h34j03TmjlzWu3PfV8erM3yAMXKtt1GQhglG16TJGnvIjHvqyTBz/2Lnbio2BNPAW91q4B7n7+ZZC6qbi4GUjcJdSg0lDCwoJtGtez8Jce2tu38caxVKlS7fq18tdwTwLmZk+c0e/QM8OOsSrZuV4Sy9AAzJlrPanEzSiXs9vc01DuQmsEGKRLfnpwC6hmljMFhtX/ve5pAqS/H1OhuWqfwiiVyFQFGoa42MzEJEJCnYAkJaRLCs4f2q2PvcyilzKkK69yyVVxHRRXlytK8baiiDgKFABfAiYnW8xrY/6J6Tp9W7fKVKr1lM0nPChzuKH825QSGoKHyOZaQmSGBexLI4oDGBjqVG65SQfkix65TKK6kJwEK+o5yveeVAIoL3vxu2kGNopf3aO4SO925d74K2hIqK658rgfozmx309hugZLwEXwOKkIQV0B5nQjtnED55IomuX+5QBQ5gHPu7CVkMUiKDqGG2IvCEBZ1OajWZZcCcFG+dBRQYKxLY93nSOZyfyux9G16J3aAgHNjp1FE2aXBrnasAVH/q1hDAtdgjkLU0VAD6j+okcFTbATA2i+Vze7+GpU08RoUZCAsSfqiBtOPyt0waiXePBXT3nFqxWrX7GTb6XIFKsBGL8C1uLIq6C+8D+W0NNZ4SZQEQfpIPWCzSALFg5pUeqpX08Ae89jK+sKUb+ltwBMNlH0a5qxNA20GppAMLg06oKyLRH0COYHkECAb/T6FMkgOyfcslPjcJIrTU90avXQUxZ4r2AV5lFcPPFrayJiAwhyqg7H+Xk10tpHMB/ogMEgBvgbrl6MiAywC/BtH1XC2tw31oi6YE1SsGlCgCZZpugsbPMDYoB8QsboRlRfaHtCtY/nno32Y+ozZvDJAuDkPUnjcQmzZUMqb6z+FotwkfcUU+pagGsX7sF6c7u/kWk4qgAVbThHqhBUACEHOA4wZA9ydHZ1R1op3Yd22grZ3PWH9dlOcW4C2bD4xtbmHH35Yzz//vANhm03rzp07HRtwKv4Hc8qrk5v2c5tjFs5j//6lHTbn8X+MfnT0yFFUbL6FYsY+YPR6vfe970UN507sC7EWdMaaX/5a0cIc/dq6e11wDmWw9NhBgO8jGqcN57IIVlCNVSHglFkrQgEDT13RGP3XC6zrA06LoMTmTWepoKhGPtRaXah9IfkIKHRave3HgHjyVFJ/BwAw833vEcWvfB/4FeW68tXyV9xMFzSgC/Uy7F2ZDBldUKIl6ge5YjxFZZJrcs30A+ie0wR92ObuPKBzX9kaPguodholuq6XNdrfzdyCVXcZ/dtgF+z6kkDCsa5jxCUXFaefF63ZiWrZVvr3sGa7DzuKc2kW/wqLS1ESMztZgDQCkMToFYBZQKBxYiGg/9yCQsbnxUBG9GUU8fCg539AsH7GmmRE5SsaFAacS/SPYDV9jeLH2TRgEE0ztvSMX37G0TRjNNa1UD8AMyBJgCBegGlIOca9fgApwLn+swB1gF1Naxl663gfQDRQ1Vj7eUUHLquQsToHSN6Vyxd6c6nJTkV6zmt4YAhYsEIlzah3MmYig0aZO7iW/SjqRVRE7BMsBaQvJFYxVa+hK5rqwWI9MunMC35A4hAWqA5EFeQzsdiO8Jq5TuYSLHAD2LmHl+4AXprUwLk9WFH3qwD710AlCnr5LZSJGMHNF/faZRSbAV7WX4hd0ozVLmzR5wdOargTOBtAq7gRKKkUaA44OxXpYw75PrEagDWTUbAa+K8Ae2uU0aLA+5P9HajJXlFpCKi7iRgDYDLlKkeNbUJJIE5PGlgsgDqe1+bcCQAu1MaGsK6NYEdZ1cj8xlwLaD4NTB3gmrMbFwG1MdZHDEobwiL1MvBFLxaQgJWNBs6FNdx2mc/uoU2juIxSoifMe7DVTPtzsaIP08fzgNSIC9kg4HLxuekxBoYOwLLjtPFWRaa9TJsrlN2wgffmoYwzi70p80jfYeACFBtR8AvkL6PdM1+gzjbRewW4qIOEftyJYYKVW6ibxdQfcXDkGn8PEGMABrIGnUZVLw3UONfbhXUq8xKDUpw2nCZ2CJZX0KcaiEsA3+f5ObDeVM9x1HimlV++QfmL7uVcM5o69QVU0FB4K63H3pc+WMi98ADb2RzIPTTb9YSB6PQ/A+7donzzXbTvC8ROtDli+BLm0UAllqQo6qVm2oHIX9RYTyv3P0ydLQGgr+aeEM9MjwMTUvZ+2jftIkyM4G9+gHoElADKSxL/uKJ9CqAE6AGWRz5Q8bEhlAkBYolvvZTZ+FIXSrHBQsqHqpknaOUzkI973deh8cisgmyOKV2JOnQyrpFL1zTUN6yiwiIV1qJax9hktuy2uSEJ9J50YfMK5METEP8ZnAaIiF1yvA91OGBaNzFoAEVEP+3NlUOno51NXaEeiWmyuA+5ZeXUM2ArbSAx0UE7Oqcx7NwT2FPn1WPbXUM846+njaKIx9glrGo9vhnKQOzK2JgcG1BsaJi4EEXNqnu072qZvvzICcAWVOO2bNZtW8tVUQ78yvAQ4T5SdOfe730pCjjXQZuP6J13tmjdKqBms1BlncSDcl5ZSQRLUx9KiUE9+WRCn/rM87p0rUcb1tys++9q1prlKN3B+p06nwR06dS1jmN68IFlOC0sUiGqcrt2Del7TzHuoXy4aeMSbb65TMUAbuO48Lx4YF5P75/W1MQpffiD2CE+WAfw4td5FKz+4m8A51qHtITY8Z33NGvF6gCq3vQ/3ptig8PRIzP60hdOAdIU6Zab67X9Fr8qaygfv2MPh+NY7Uddb/fTl7Rvbwdga5XuvaMF9aigQrlEnDxPwNACFbKJBliPoV0njiX0yT89q5M9Yd22pEg73uHRmtXE4Px38mxMZ8+367c/zNziGmCuzoBzv7S46XU+2OaI65GS/cl84RzXf3L9e1Nf5DU816Z47TiW5AbN7dnzfb104CUsysfV3NQMWLVNt2xBaa6lBStR4nk7eM+rj4W40n7mfC6/f21s9OrXv1W/X7j2heuzf9vXTwLnEmzA6+hM6R8+fxVAt0MNdbW65456LUY9zs0YG4lyHjYK2L6H3o6UPvsZnvdnirTpplLdtt2nciydE9yiKWyk0SxVdX0QeN7LnI5QxtfG9cQzF1EXden2rc2oSBeqtMqNwl1aT3wPYPaiVFfr0Qd/k82NS4K6gFrkY98Z0qnTHWpZVKRbb63WIiA8hDB1vjWu7z7ao5evJLSmukj/z+8bOBfQYH9K32dM++yXn7YZT7dsXKu776pyrn8OsPnZPd/TC889pkXNBs49ADjHepJjU2I21LQfxlKiBCcHacp5P8uRAed+ltrLvDdTA5kayNRApgZ+TA38bJPTjzlp5sdvgxroRyr9j//4j51FyM9//vM/1xLjvqFr7Sk9ym6G/4lV3DDBXlGOS+uXufUb7/bqAzstk/er1ZbJaTjw2p/8LfDapZTWL8Gm9f/2syvHyvLzPezxp48g9zPU5f/75Tg2XS49dKdHn/hP2G+ZDEnmyNRApgYyNZCpgUwN/JgauMICzxc+wwLKgZTasQ/BcUl3bXLrd37Dr1s2eGRr2ZmZ5MdUXubHb8sasMVTg93MZvVZoLFdu3bpHIpkZsNq8Jwpahqs8J73vEeNjY3sAvY7C8cGze3fv9/Zwd3eDjwDGGeqAHY+e5+ds6mpSXfddZc2bNjgQHcLFbwAzrVjGfrZP/6Ehi5dUh0qYrdglbLmnfcpb80agJOQY9uYAigb/d4utQJ9uYqKtQzFIc/50ySqSLZhcxYC6PIvrsOSEtU5U3ELhpVEVctNMm9q7z5NPfIdjZBgLLt1rYof2IgNIwlp7LOUQl3GbZklkookapOdHRr8/i4d2f+8iiuKtf5BkuUtK5EOMYiKZKDZVVoYDOCUjqCg1d2rvoe/pmBHq0Kr1si/Y4fSdSTnfCR0SN6alRneqpTBrZ6nHtfQE49gShlT+X33KO+2rSTbSX6iMpXs6tPAMy9q4ugxlZKgzq6qIfE+onZgn5IlwG2rV8gP0OUuMGDOEl4GIHINqIHEgdvOPPKoRvEjWXrTRlVQ1976WpL0ZLlIXrJdnC8WtmfR5uvq1QhJg57DB1QFPFZ0+63y376N8mU7yQVTJXHz5eKy0yhRxM9gh4ut/eWOTlVx3tJtt5KvRimG3dge7JfIdJKMJcE1NKW5A0fUvWePRoDZGm8FsLxpLUAeyX7qNn4WAO/J59R6/IRKV69BObBevW0Aje2XtXjpYhXdTJIYtRsPwKDHlIcCDNIeIIFZ4MyL13Ti4W/Ijw1s45pVyn3n7fIsJjkO4JdEceK6hQogH3YqqdarmvjOtzRx9oTc1EHlzvsUXLaM8tEeAJuoMZRWuH9YjqaBnBKXL6lr34u6euSEFq+9QdXvuBOLzGolscP1oMrjNkstqwuS0unkCCo1hzTaeQBFk5iKqjYCrQFgkMB3Ofd4UMlrT1DHz2s+CxWO9e8mcVqnmbbnANb2Mu9gj2bqbyV3UT4UiOw6aCcpQI8Ulm1uS6ST6EGuhoQx9oWDz2liuIskcTH2XSRiyygHn2UgabJrv6JdLwH/ocLStAkLuhtIjtLO7L2THQBkgJ+oseQBrWQvfhCRs2WOGlS87XFU6npRUVwBGLYViAJwATUksw5NDO3RWNeTWPAAFASKVVSzVuHmzbR7YC/6sSmjxLpOabS7m+QxqiCLSH5nkwjBjrSvrZuEfoNym27ArngxZSvmvpBIx5owSQIc4T9U11BWI2mSRfLXNULytx/7O5R8vAEUe6pMJWkdwB3vi/A5fSfVexUoNjdbpY1NFLuBspO8RrUrPUpy9vSzJMaTqALdBOyxHpCNtjIBxIri3ORYj8qrc0n0Y1eYd718aYDJBHDMeOsxbFc7UdsDHakD/KujfLkAKgAaqck+RQFspjvOCFE66sdgxFKNtfU6iercfOBMVPbc5cud66TCKSP3kCS62alSkSjr0E4Ae1LTvdjuYdE7co7682GfiKVrRTMJeMYz1CZHOy8BQ1xSQSilvDqglOLFlA+4yNRpsOObQ43MW7cVOIGfvw3BOSrTOWweMZvWo6iDfO5zn9PVq1dJiK9C6eYDjk1rDmOFvca+FpKbTqLU2is/WwDnfulJTq7HkrcGXHR3dep7gMAGAsahG+6552792q+9H6urZmdefSsEp1Z3dry23l4XnEtjhThxQIMXjwOiDasYAK0IZQ9PURNtGzUyA+tmWjWEdXVksgfohPklEQRQwQayciugdwPjYi6fifrS6Evqu/SUo7xYXHM7YnJYTQ8cUrKdn2HV5W+6l/GCsTQbRSg/cxx2g4zsDNSMpcx5SXeEagSQBdJKmw1r+yXgCcaLUmw+qwGZgJ7xxgW4Yiy5dghYKQE4vx5A7CbUpYCmWXei0zPOPquZKy9iwYk9/ap3orrKXIli3EzXccD2C3DsARXXct8MFkZVzCaLNMBsovdl9XViD8mlldfWY21uY9TSH9RDbOwKKp2n4GT7Vbm4QmEUr+L9Y+q90A3UwzkZL3JQMHOhCppAIQ0aiXqB+KBUSaC5FHJz/jgKadgaJgeuag7L53h0UIHyeuIEYiNsOF3uGear8+q/fIZrmlZpRbWya4D3AvWMz4yXM6iodR8BUL6GilmxCupXKsC8nPYkGJuwfbx4RGFfSiUNi+UrX8tcVsrHM7+MXdV02xng7MvyodxVwHiY1XK/M466eL1cKIhNtmq29fviZjP3NCtr8XuAhCY1dPlp+d3YYKKK560BLMpmfEsz5ieY721ngo8MNXa+Nk87MQvQUXLkJPDUCeYlrMexUA0zb3kMXAa4TE9e1fDFR4EjrwCJ18nfeDt11kh7wDYXu/IpVArH2w6qNBhTTj3gXPmd1B2qr2mgMgC/FG0yoWmUZGYYu8evzwED/RodAjpHRS4Hu9K5nj6ggCBAIkBTLfMsimwuJFmTqJiNdR7RaO8ZlRRnU3/LcIbN08g15mygwiKgq1Al8YG/mS+gORRn5/Ht9AFMeePEFYwJ6DszTg0CAKI213OQua2bOqhFaZQYp8jeS3w33YM9/TEA8/MqZENDTvkq2uhqqot2gU3vDPBjf8dxpvBB1S9fhKrpjUCTzDVYlCPHRkxAHI4EmwEALkBN5CBRHQZsHOxEOXBUcWDdLOam7MYNAIlLUaA1UJO+NImK39WnUEOuWLbiAABAAElEQVTupV+sY5PFPdQb8NOZryCqOsr8ib1u4z3wcvV8FoEMcQyNi/tPH0ShyOJOFyAa28qZD08qCvw3MQH0GqohdloJaEmcALQfGz2F6uwu4t0ehcuXAZHehopdpTOvQmfSvl/W2OVnNMv34cXA9fUPUPZSlOyInIhbXEDqyflRSjfNZ1JOs/o0cHOQfs9GkIQ/RJ0BpwKzeQEOmcCpE+491rvTrcdRLRpQqDRPRSvYBEFcOHqlXaPD01gCN3NPDe4kHgR2TLC5AS1rymMt1XSfUdJDTS+FuuwMqniz1t/7u4DIUPOtpa1YvGYg6Gybhk4fUGQiooLiGuXUsgkmHxiPsSMd7UYN8xj94iyqfVnKr19BO13NOFjNpwAjEs+lbBxCwRKZYdoM989sbYmbRgbnGXfu0ZH2RfrG4xc0POrDbnWdtm4uRW3NQ8jLfSXO8WW5NDqW1q4nY3r+xWEguRHdsb1YK1YWKr84gHoh1sZY/uaGpumbQCKpHJ7/5vW5Lzyrbtr+e3fcpg9+oEE11YZZygFoHvnnQT2z5yltvKle79q5VqUFecxnw9p36ApQDBbkO5tQeAOOAVxjn4T2PBfXV781r86OQ3rovZVA72zKKM7RhXNR/TXg3LX2cd168wr9x9+pU20z6k5Us5vnhlmgviMHI/riF85pciqsLTdWa9PmgCoA68JYygZRj7MN9VNjCX33sW7t34fSaaBQt25tQMU1qIISKxtWw7lu+bHIZsi3YUXHAef+5OPH1DNcqAfvqNaHP+TnORXol9zHhUsJNoLN6M7b/YzP7fr2tzNWrdbm33KHE6MwzlirtO9pL/zxg8s0a1YD5+yYJYY8ywa9PXue1Qt7X0CBbAjFw1Lt3LET8Go7axooodtzJ2+//g47HeMlXwvx5UI8ufABr42NFn7+Vv7byvDq614o208DzrWzrvr5L/Rh19oLOBfUrduqUOhD9ZtNgrn08yBjDSKxOn08oX/4n630nSJtvJE+u9WrCsaOQLZHAcaaQLZLAcakJCTdAOf8xteGteeFy6phbvvg+7DM3cxcm4/aJlPH449i4/psgkesfr3nfdXYMuexNjOP8jSbi2aSeuf9VbrvgWyVAAoTommgj2v8zKAeZ7ypLsrRxz4a4v4G2PiX0pO74vrCV59UeVkWm1Vu1r33stELmHZiagSFvEf15BOPq762Fhj4XWwS3c7GT+Yzp11ZmyB+5z+nhTnPYww6b/DIgHNvsOIyb8vUQKYGMjWQqYHXq4H/HQC93qsyv8vUwGtrwCyjPv7xjzuLpp/97Gdf++s35d8WXNsui8MnU/rHfyLZeCCpMQLBagKxd97i0W/9Jgo3y994cPWTLtISkmaj1dXFIo0tUDgPDT/8Lls0zs/Pdyy2fvg3r/+vMXaIfemRhD72dyyKkZx67+0e/dHvI39e9Yvpk7hj6ft7k/rjv5nXue60bgRC/Js/uA7uZVTnXv/eZX6bqYFMDWRq4O1eA5NYfj/ycFL/3zfjusyiiS2WrmHX4p/9kU/bgefgRF61xPZ2r61M+d/uNWALqBZTmqrRadS3TEHONqCYevMZwDazY7399tsddZxmEvxmT21Ha2urvvzlL+vAgQMyRTlTpVu0iMQ0seeVK1d05MgR5zybN292FIIMejC4zg6LWc0mquP0GX35E59Uz/lzaiou0p0bbtIydv0GURVzA86k54BzRqYUfRY7jSPHFAVGW74V+7axQY2eOaYx1LJCjQBC9TU4KhWRpK5C2ataKWww3e5sjfG+yKOPOdZk+fdtVvAdKNmhPiGXqVaQnCKFb+ujwrIscemC+p74LrZDB1W/dplWvPddCKK0kA9EhQYABiENrpvFZxbk09RNsrNTHQ9/XTkd2IKuvkH+Bx9UnEV4L4GqD3Uhsm1OUtHK0P3db2n6mcexQgorb8f98m69EUUyQLg49UD5pp95QTPPvyBEMRVAKQ13EOyHUK8BFirH7janplyeYqwzK0sVwtLEDd2TnsJe8sBxnX/sezADWWq47S7lYTvjRdEtGTRQzJKXJKJZ0U5Po/l1/op6XnhOYxfPqpFF4vDdd8q7eZOSKMjxCj6ZxK59USFpkg3xsxc09fijOgs41/jOnSrfdhsMBGAkNLIHRRtSC0ZEkYgdVWzvAfU/v1eTZYVqvP9O5a4g6ZhL/dKu4q1XFHsGcO6lfVjCkVBeuZRE2Bj3/pSKggEV1NSTC0XRpQwIkoSGDytXd16RY0k1fuqiTn3nERXOTKl52y0K3buNLeilAGWoTnDFRp9R24jPsbSNat/wP31Zvl5gAGxe8971TvlbGrHRw34UyA/TFYA47guKI+bJFmu7qmv7Dujq4VNauuZG1d5zh7S4BgG5HJSQ/Kj6UEbzxCOZn06iZDL0kkbb95GEy0a56DYE99bx+UCVWP4qgc1n/y6lup5SlHYXXPMgSfwGzbQDznW8iJpBofx1O1Hu4TNIxBvnQaYW0MNrVcR1sTCPrVRqGovPYZTrgPRsN3ywbCnJcOy7gPDSaezyZiY533Oa6zqgrELs0ppQDCzbyAmAIRzorl0TrXvoMxdxg60GWrifz8RSF1WiRNtuTQwNKrtiNYlyEtR5i7kI4CMS0OlRlPG6H9cE9oTenArl12KDWsd53ZbEJYEUxXIUSGCstx1LK+yUm2sUzE6iOIdVa+cwSiqLUfW5mTZKf/EAQ7i4HgqZpJ8nXczBgCw+kts+VIKSnRc12dHGzz0o+6BKVWkqbrXAHrxnlrbSe0gDHVeVXVQJZGFJ9gaug/aAeqFrkiT8ud2KTWHHWn6jAgYz0IZSE9iQAcZFSKJX1JnCG4CBKfS5AD2xa7ZkcwQVqxlUr4LhFOMEYGfVrVivoQoEEAJ9ghLPKU22HcZuCNACANaVVaZRrM1iY1NYEaEi2EhCvJhzYh9kTd+ywi7KZomVNO3Ejb9tikRMfPCaJnvPY8U3RdIepZtaIBjs2Zx6nBjXCCpQM6MdKikEquN37rxlvB+AkeS4KfvFh/ogbtahkNNMua2fcXmMzTYWP/744864bBDz+vXcI8bTH/X8z1t+ZY+FhJ793YsS6R6AXJtnTP3UIOyHHnrImWfM/nvhtQt14Mwr1qFeOWwu+qUfXI+t03igdm3d5tDBg/ril76oy4zva1avBgR8SBtQ9DRLcxfwgpVpoTyvLt/Cz37e5bHPtOO1n/e64JwmUMA6qO6LqD4OjKq8sFxFjahkYm9o9tJQdTRiLKaxLJxEHS2JNbTblwugezvgDqqYubX0K4Ai4JzU9MsavEA7j3gA2rbjyori3ODLgHO7uSpsqRt3oiZ5N4AJQB5AkgPOMUxDuDAX0IeAQ1xpAKGxdtTmLjrgWBorRFM/85YAlWGvzYsVBciav3YUJUC/chu3MWxtYmzOYzygz6WGlRp4Bjh/D6Kt2GEvvx9I91b66BhA1lENAX0Huf7i2mUMB1g4mmqa1VvkAtDzSxrsBkwDIioF0g9iLYpXKPVAcl6olQFDDbaf1hTqdBXNACyo3SYHJtR3uZ8xDlU1AJrsquW8p1pzpubFu67PtqjN8p+pbfnmB5TGyjvWcQG7znHG7FzlNK1WugA1MpRg3a4x2L+j6mk9DQToVlmNQVWMQ9mNjM/AvjMAfv1HNNHeCawXZhxehtV8DTaYCY31tWkYqKgAu8qSplWopN4Ip8aczBgOWQy0fRog7aSygJlyUC71cT8UboGZctNmgJKmAYEu71IaoM9V0Ihz6HsAnWeAyp4FNhxmTF8qb9VWlAhbGELziCFshOPwEjB55igu4CPKowlsUueYc5KM7W4gSX8VynaVqN5i4YtcH3MkwND5RwGoUDisXS5vLW0ih3oG/othBT7Tf0wTgI8lKHaFmMtcJXfyO6w246OoBwNKRwaZY7GnR4XL7IIVGVN0AsU5bL0LiHNysH+bGhhGzZZ5tGGzVHwDMSCbM5Jz1B82t8BuEz1HgD9R6qpp4Xw5GunoR1E1jos3YHYZmy5cdTTZHNs/oXk2FdDCKSKTP3XnQg0uiaLe7PhVRQA8s1EkCxVR38W0Mz/zIW3FFFknmJcScx3Kt9gPWF9B4g438j7MLzEAsaH2I5qZblPt0maazFreW0+9Eg+h6prAbtVA/wQwnIfr9ho4NwacDWieRinNz04yPyCbh34YKCXu9DHP0o69kygCdz7JZxNLlSxXbsvttG8s3U8/TEw+DXR+kwL199LWgMAWhls+I80mAOcAqkybTXKUeZd4JjJ5jTgMVccqYPcCoDm+dzEezg8eUbSN+HV+TEHmcn/F3cSy9CWahPXlFCqW01eeZM4bB17cRgy1AxvXIkKyCblnmGtRnYvNj1Of08S62DjPTwNUXgPwxMYVZcAUirlBrJ6z6m9gYw3wqqeMuplx5tkoqrbDgx3MswGVAc45Vq0X2zQ+MqfyhtUo3XKdKEkmUJVNOBtsbHwhqgG69ADMuRKoSs72oqJ5TdOjo/KzoaAMSM9fDFyfbXHTLJ9zxrEojkV8KFquRCVuFW20AgCT56AENrSjwJetqGlSb3k1i3mOsf5L/MQDQGp2UrEJrKGJlS328bJRxAtIFyOGGRmdV07VO9Qd3aIn9/br2KluQLU6NTdWYzsaVFlFlmobGH8qr8eXp04ktOfZPrV3ngMaCqi+sVJlbDoqrQih6ORVFfaoYRToTI1y9+45fekrz1PnM3rvuzahKlelslLgFJ57eoFTHn9iWLt279GypaXa8c51Kskr0Ne+Pqpzl3q07mZsFXegAtXsUy6QDNWll/bH9bWHWYu5fEjv2VGmBx6oVklJji6ej+qv/hqV0uGI7rtjrT707ypVXEmUDoDrjOSEZRfOJvXE4906d56xJhRUQ0M2ClYeVdb6VV0X4Dx+xus0c+qsnn/uojo6xgAHqwDRc7F99TllbWzi+1IvkA+bfLik48fj+i9/ckAzs6V6zwMNbAjzq6rGwLm0rrYlibOi2rbJQ5+6lgHnrvfmt96ftKsfHDbvcl+dw35u3/Mzi2MibACzDRe7n3xS+/btUx/xpG0EvPHGG52NgC0ti4Ers4EqeV42UO76WX4Q+yxsKLQ4cuHrlZf8yv316rjSLn4htvxJ4JxZtfZjd/rdx6b08uE2RYG0q7FHra8vZzNkHv0soOoaxrdCj/q7Uvr2N/pRiosrnJNQ0yKpspo4ohS1+PKwY61cxFhiexMMnPvm14e0d/8VrVhSqvcBzq1aizooU5spZu5+fFZPPQkEH+/SjgerHHDu4N4IkC4wNzHc+95brDvuI3YrRPuTjYqThJjf+UZU332ceTQrod/7SLa2b8/W4Cvg3Oe/sttRqfvAr6/Rtq3Yt4fTgNNd2vXPjwHOPQUQ2AA49x5EQrYDztmzBU2J60zbBj0Wc6xZWSRmM9P/bnB8+684MuDcv6KyMi/N1ECmBjI1kKmBn7YGbIrKHJka+Mk1YFYe3eyCtwVJO0Z5gDWlOQuC//AP/9D5mSX6CgoKVEMC6s04ZiLIjx9K6b99LqZjZ1Ik86RGZMDffweSwh/0Ojatb8bn/LhzWCLz8OHD+tSnPuUsJr92gdPeZ6ogZq310Y9+9AcPAT/ufAs/57lYV9sp1z/E9bnHE2oBlvuTf+/Xu9/DbhGklH8Rh11De0daf/f5mD793cT/z957QNl1nue57+ll2pne+2AADHohAAIESIKdBLtISbaV5cSys67kZF1nrcRxbiS5xNdWLFs3cdVyUy8kxQo2EIUEARAgescA03ufOTOn1/v8GxwtRpIlWTJlijyHHAAzc8re3/7Lt//v+d9X9aU2fQrL21//VRcWPD+fY/h5nGfuM3IRyEUgF4FcBN6bCIRRpnjiibS+8PWkOgcpXLK4v3UFEPhvAs/dCDwHu5CbTd6b2Ofe9RcrAmYB1SwQm8K4UYozX1FUmoz16hNPPCGjJmcU4x5//HEtXUoxidzafJ05c0Zf/OIXLXW6rVu3WkpAK1ZQNOJ3Bqp78cUXra9Vq1bpE5/4hKUS5PdDrfIwi9AGnBs4f0Ff/sznNHj+nFqBTO4Cjlp2/y75lrdSjKWTkt9nJimOHTqiziMnFAzHteL2u5Rf4KZ4e0lDfdhAQVC5KHYbJK+0olQBjsG9CnWoUhSj3jik8IsvoReBkdvd2+W9BUUHbBqhiSiwo/pgrDfMAikL7KlL5zXy3JPYzp7Ukhs3qPUh1LqwjsoYS1KO14Bi1FKt52ewxcwM9akTcK4QyKcEcM7zwDvgHIuubgpdWQox1LkBd2IaeuKbiu99UWWVFPIeuF+OG9einAIogQ1KZo4C6b43FXptH/BNQl5iYG+o0vA1oKRO7CVjCfmxfXVRbHMVB1TGseWjRGdLokhy5KguvPIy9f6Amm+/UwWozjmAGFNeipj8Z4rsDgPxzUcVw061e/8+zXdd1ZL2ZSq6/Q7Z16xTmuIph0HB1GAJKPrwtwHnUtjvRgAJL/UNqu7uh1W1A0ir3Kj8EC+XGT9R+mGFOz0ypcTeQxp8AyW0+lq1oKiXj5KcLR/VMd401d+n5IF9OvPaKyrClq5u+1bqvn6NXLik2SFAoUgKqMpF4Q6rrpIiFTYCUy2lEF9YrCna0QUWtcvYmNQGvOm/dYsy7E6HTrTGbwPNZTNOipBZxU6f19Df/50KUU3LR8XO9+C9WK82WKpnECkseQOnGVUSA5gBXMa7iMfBY7p29DwWUVvUcBdqfUspeOZh9QuEYWPxHoIChR4WzlFUio2iAte9X26fHwWTW7Ep28hnV/DZtFMKmtnxV7Hv3cP9oE++VQ+jcNKmeaC52MDrKsL6y9Nwn7KB7RTfsfsy78tif8bAeRQmbAaawnYsMQFURjE8jU2pF5u/AlSCbBSa7Q7ingSWXJhRHHu60MhhoKwybMF2SsWbKQijkIcCTXYBa7nON1H7uYp4UpVcS2/j9Y3AmcAqXXsB5yblp7jua7kFWKWJPgCMRzw0s18JwJS58SGUEKtV1EwRu37TdSCEQoEtCWQwhq3YEGouFM4rWoAcvS5gOtQSB+co4i4HnANswAoQMpV4gQiYtm/6llGEwvZK6VGcDq+i1HgeWAA70lJgDmAIO8dnc1ItQZEnM3sJZzdsmdkUll/ZDmRhbADrrZhlUMyxzwPOdb6iGDCLG3UdX+0aObyAc1N9Gr4IOIdyUHVThQqbgQu8FMoBZLMoF2WALOL9WNyauPBRec3AiCj7ICnJtQCMTFKQxlp1rueQvK4ooEULinPVAHyTFP0534oyVKBQXipshc/BVpP2RBHEuoZZo2CEFW023M9YdRFruQFFsCXyBVCTqqUgX8b5AW8qAxhJIXyq6zQ2lQNYh6GuAOBnywfGExAQijIZLGfjE6PAKihYljAGGoiH335YwLnFYh6nbM1JZo556qmnLBVUs35jrE2N+mlZWdkPrGssroOY91h8LP5s8ft/jb+t47GOCYgUVdcugLmvff3r2n9gv4oZt3fdfz8wwQOqqUENC+jaPN/Mj+ZvMyebudl8vwisv9fntBi/7/+cfxqcYww0VtbR4+q/DBg3NaGaihKU07AMz1/OeRioDFg51gPIdlSzPW8h3DYMtIPF5ZJdctU+gMoUADKqQMqikrVwSjOXXgFgyqJGtxVVxg6Est5WYmCPXKiR+lrupbvcAsANwIpFGOkGY7AZo8xgA0Bj66MrYcMK5BsdGyfXDwAFAVaVM2fmBxjzeC42pNG+Y8p0HWVu9wIY71CykvHcHkAoitmI8SA79hpzxF5LkS5/2YOwTDt53bBmx4HE+64pD4C2soHNABbgWs1BcJMRvggEdxBFOQBjG4qZtUtRmQKGKazk2JgnzDUNzWmqH+tK7B2rmgotq9bsxCwqdkDF2FWWoHTlq1oKrFYH81bAu5r2jHo2XwYXt2HfmZm7DLR8HF4b+AbrSqNI6gGASvuwGOT8HFnm5FHAuc6LcmFjW45VtBeL04yx0SZe9uhVlLkAy7oGmLtQjqlcJg9jehp1ttmJXi2gVFfIZgGjRJcpvwGYnLkEkMkeG1B8EHCu85gKgM28KIU6mh/k+gGsYe9uszGPLVxgrnkBWPkSUHCDtPQhrmVU04yt+U7U2Jo7UBG9EWiZ16TJB808ax6Mwwa8M/NEeq6XcfSaItNzuM66AfSAHqtQRENRzgFMLOw+kmOXNH7xeflsU8xXKIrVAMb5mslNyBl5jxgWrrPXDqooPcc8gTUoinMGwE4GLyo4i9IZimvpdxIfFyp1duZgA1slQiiOlQWUj8rw/OQsdt7Y1TZuA342wBrjNPC9UTaL0ZYXBo5hx4uSDsqDkaRfUwPj8gLxl7QsQ6mPcd0JFAn8mAHMyNBQjUCiLcbEaJTgaKNRjmV6hvOljxcCmwYqV6K6y7yFXTq+nNj6Xsb6+CJ2cVjcAjr5K1Yxr3XwJga8QiVvArCu/yzr34OqWgZ4VQcw7mDjRJTNJii3xlFWS5BjpFB/dQEYeRLzdMVJfj7KMQSVV1TAPMp51d5pAV9pLJQznJ+LPpgaABod7bVg+MLlO1A/iyt0/HkU88hRAQkdTcTTY9q9mf+4drROS/qH3AavVwBKlIEmUQEGYE2j2ptXuQRAk+sAGGfPkK9wzinaaPLai7TpIGpy/K7+DsBWA5nzAKLNTh9T/BqKcygw+xrI31vJyT2o2gVp/5Ns7JgnL2P+TRuwjCPwZMglsVl2TQMGhmPk+jUAlZvkBISzlzVgMw/4mOF9sdMNA/CPjfeSp7lUBzjnADacxC45NJ1UTQv5TwU5KFbElt27USCyBhqaAPasWeCRbBBb3onzCs31Y+cMuIr9cAGqfQ53IwdPXpWZklDhnLz8BrBeMeDjJmyi17BXAWjQTX5kwLlZVIwvHeMeho3uKO/6mugT9LvEQpgNFOSDU+RMUfICxg4ndsROxsf0fLeCKNcW1N+iRPHdOtfl0qEjfRoYAhiNk2eoUH5y+tYlPq1cV6CONvKjWEanj4/r+Ck2B9Gmk2kfanNtKmFDwLJ2vzaudVnWhQXAZS+9nNA/fO0Ac09Yjz26RXfdAQRden0+Gh3L6pnnJvX87gNasbxED+xai8peib7+9Sl19o7qpptLdM8DNWqsd6L+xkBDUzDWqN9+KokF6nE9eG8Fak81qijN05XOiP7kTw8CqMX08D3r9ZHH2MxTymjnSgDWeFFAd2gS14Czp8I6emSE85tTMsF1QHWzAJWrpuaA1qzl+LFwnEdw8PSpCaxde7HhZAwyoCjXK7/QrY0bWrTphhI1A/N5fNix8n6f/Qy2jdkmPf7wct13nxfAjvtR7lW6+9I6Dzi3fYvTglFzinOmI74PH2ZK/N6Db6zvzToGuZPJofjbrGEYKGw/97wvvfSyenp6gCfzrbWMnTt3avv27ZZCvtmsYFIIq3+/08fNtyYPMrmX+TK5l8mHzJf5+ffnRub57/fH9x+3+d58/ThwLoMaephbxJNn4zp6fFRXgIvnFyLMw3kMVeUKFHm0YkWR1mxgbaLIpc4rMb311qgG+kdQjluwctc8wP4awO9Va0q0FiW6AHPm5KBNT3xrXIePdjL+lOvhR1u0lL5s1OuMWOnLuxPk/Unuffr04CNVWsf7v7k/rCe+3ccGxDI9/lHsm+9woyBuUgi0Y0M27X46qaeeYZOWfUG/+ali7bi5UJMjWb36Qkp/+9U96lhRo1/+leXatBkr53wsfGdHAXOfB9J7WS1NgHMPPoJVK5sPud40Ie7zuKrcl9ISrMt7vaUwz/2Ujxw491MGLveyXARyEchFIBeBHxUBK435UU/I/S4XASsCMzMz+su//EsStbesJNCAdN3d3VZiu2yZWZBmLcXnsxQzPvWpT1nf/yx/hFCzeQ2FuT/486ROXyOh5s1aq+361Eec7IBAlrjivW+7JpGfmuJGFXsr8+8f9jALreXl5d8rdP6w53z/z1iL0muHUJv7fxPqGrqu9vZnv+PW1k3mLH9+jyALpt94Ia3P/O+EFvj3zk12ff6/eLQaFb/3Pro/v/PMfVIuArkI5CKQi8B7E4FoHOW5Z4DnvpIA/qBgxVS5sd2hz/0nl3aiPIfQUu6Ri8CHPgKLC6jvXhg2P+tHUc3k1kbpaPPmzT8Azl26dEl//dd/bQF0mzZtssC5jg5sIclJze9effVVvfzyy1q/fr2lErSokGQCbi1A87yhi5f0D5/5rIawUmmpqNZtqNOtePB+5WE7lSr0UKSiUDw9q9iBg4BzxxWiANRx9y4VtTfgbDivuYFhRVCFSo5TUAI4cYdmlccCaP6OW4CXVip0EkjnlVewFUNFaieFt5tRqCrDXtNVxIIotkCsgZrCtJ2CqQHnRg04d+6k2jYDzj3yKEW3lUoDzqHzApiE2gSLqUZ1LoWyBVumdfk731ZeTzcWpOvl3fWQMi0AN4Bz1PMpIvIFWJZZCGnwW19Xeu8rKkFRzYeinmsrRUIW8a3fA85F97+phb0H5GRh3n8P6h8bVmK3Oa3ZSz0UaClyzqCSMb2gBZTu7B1LVAN8VsR7Zc6d1tlXX1KGe5z2W29TYCtWZpVVSqIIZ87NbkdlJAnUQyEudumaru1Fce7SRS1fvlIldwKWrVqjNBQxJpPXC7sk2MZxTaEI4BzF5d1P62r/kGrueEiV22+nJluC2h1PotBsiqVZQJD08KRi+w6rH1vcGMXd1nvuVuFyYAFzfsQqPTyoxIG9evvl51XSWKdWVKPcLQBtXNfpbtQ6BoEUphbknJuXLRpUQQCr0w3YkxKDeRQCzr+wR6XxjJpv4ZreQgG7nuvHtWMjODE2BT0oPuIdPQM49+W/Vz5qhD7aY75R9qtvpFjq5ZpxKOZeieK4AZiyceLRc0W9b5zQtSPntGL1VhTn7gI0q8d+DEDNyJSaCcPccFiFFdQ/RvdooXcP9qJu4I0dcpdv4peVvJefWvp10CI5tBe8wK+85Y8C1i3VfP8bFNcPqIR7MV/T3UoWbgXUoGhJcdmBYlkGH1MbcEw2No4SzHktAAOEaYuegmIVNbTLU96iiKtCTiRrXGmKhiGK6QOvam70oApQ9/M13aps0Wbi4UMFZR6LtQGFLh9WdqKLgj+ww7JbYMcAz6YuoQK0T0EDzjVugC/YDgiAwpAN+9B0SLbJN1Dg26P50SEJdbQirOk8TZtpQ0AvBMGWHlNk+LwmUZhLY1VXidqA30Nxk2s/NBhEVWkFdm6o0KCyB5FH4eo6yGLZzwKpZJJXgAQuawEo0I1FoT+vhCLyDUB6GykUA7ICR9icgGUzAG59gCn063xAiZKmrYBz1RRTDSBJnwOciwDOLQCmuYDuCoEA3UZxbqJPoxdPKoRFX1VzBSAF7+tfRbOgn8eG4XHOYQ38tsKow+RRHPZhn+iouovCvwGBaf2AgfHR0ygwHcLlOMYYgoUrUMBQ7xQF1AVVV5XznqvhGZpRyqvgvp/2ZwpoVHiyKBbhRQjkcFKJsdNKo9Ti4Hk+iuSuMuA3f4npiDwX2JYi+1QXNnhYxpZWuFWANZsKOU6jgMl1SM/38T6oFpURf2Nz+SEF58z8YBRPjXW4sTU1Sqg7duyw5hijFmLWdBbnKxro+/thrdGYAcgMJjCtqD7uRUXvO0Dpk5NTqJ9s1L/91X8rM3c6gSbMw5ybmUfNl/m3eZg1HfN4r8/bzP2Ln2P9450/fig4d9ddqiwHnKOPp8On1AO0uwDMVV+FGhuWo7YC2ne2gTEXWDncpSSg0UI36nEoXnkALrzt2K7W3cdcAQgJYAtpgjjdeQUvotQZYS6suRFVxqWofR5XbOgAsEce4m13AgMDIBv4HYjKINEWNJcypO4UUNN5RbH4DGIlb2PMLAZgcpcDqBY2M+97GX+BfLJGufMtZa5yLIBy7mVblajeAFRXAnABYJGakIZfVqJrn+JMGb4lD8kF1CN7P2DZGxoGnPN7sEUEVPKh6CmnAYgosIYvKDP8uoZ6AOeAnyoZFwubGWMYz411tbG1TEF5TPYyHo5dZRwtAh6uYvya0UTXIAw2KnwtHfKXt2EZjtoVVq0Z5oksc62HWdqooyXmxoCiTgGOnZef5uIq67DGC2M7bcA7G8S3wyjuYfE5ePWi3JDCZYBcnrJmrDlR6yKJsSWBmcaxqrzSzVQewC6WzQbNAFeMfQaciwHOFQDCFaJ+l6rYpATgnBs42I7yaGQA61QDzmGr669HycvYZ/qIgYEL7AacAx68asC5i+xPaFR66QNaAI6Z7jmmImfEaheuyi2cK7kSaq0GsGYS4gtoDuXWNO0kis21aUdOzr+gpEXeKt6/qAYIvvj6uAv4ngQqG7/4snxA7UUAjA7AOZuviXmEhcwMyq3T5wHnDqkAe10PdrP2yp383I8CGsqxAFkOF5a3HuYeAEcHG4ON9WYCxbl5NoyUlqLCRx6wMBOUu6BC3totQIDM927Gcor0FnyGVWt44G3gMzZtkPeFM3lsBBjHRjej4kYsfKs7gNzqENRj2wZqZKb/etjlYYsCmYZGUQ67qIW5ToXIET3EqZB26itqRImxlJjQ55PAj+NXNQ04l4iPYP9ay5wPOOdaQntgLDAqt+PMqX1XWCflmNnU4jNKhbYyVFKHFBw8TIo2LmdBKcddao0tLuDzzCxqexPAXijs+fILUGRDKbYWK1ZA/7QZf2zkjCEUanv3ko+g9lfZpqJlO1DHSyAq+aI8XCp3G5s7GneSd5KL0S7MBg07uZ7FF6CUm0GJLTF5RdO8HiN4lPIqaWOoEOe3k3OgHmjAplRCmZGTSl97mfxnVo5GIMym2xkL6EsmuSOfyUyifHdtr8JT01yDbdgJA2miTJgZP8hcCwiPCqmdcUSMCw67kxyJtj85JrySAduYl/MB55oA59oA51A1Trm41lnyJKC+IHEbmxiQK+BGrQ9wjo0o01eA3qdS9EsDzi3l+gGiAtuZtm3lgkTeWORmUCNLkS9EAOfsjhBQZhs5xY2WsqQ9zVhmJKNRrczOvs7mj/2Ajwac20KbYDzMBwxkHLKlBlHGw976IgBv0om6JsBsE/kqAOkCqrPRUWIPZOvxslCRR86Bta0dGM/kSHOMId7KTcC8D2gu2aiunhD1gHn1DaQ0OubQ+Az5hiOs9mX5umdnpdYs8bDZII7K6bSudM2odzipsUmgkmmAvYK0tm8u0F23oSLV6Nar+5L6h68fYNwJAs5t0513VCM8cH3tfXQ0oyefntAzz+/X+jXYrj6wWkV5hfoGylFXuge1aWuZ7seKtbkFNV23yRsN0JbSt55M6sy5t/XgfZXadZ+xavVxvGH96f93EEvdkB68e60e+UgrfZ37EDv3OOTIYJDsm0IVajLLcxd09cockE9ck1MJTWCny0DH+QUA8epQ0PNrPphR19Wgujm/kWE2fk0kNBcMqQSV7+03NWj7zcWqQVnu4oUFfeazz9KPl+jxR9fo3ru81EoAouhyXb0ozp0Pa8dmF3lfTnGOIL0/HyZHMWnKO7mRNfdCOpncxcY4G41ELCDs+PHjltrcJe55zWaXVStXsVFhl8x6RR0bMazNCLxmsY5m2bRf7+jWeS/mQou5l/ne+gw+dzEve38G6AePavG4F3+zeC4/Cpyznsu5pugc03NZRC1iuto5hbIj1usTKU1NuC2xkvIKO9Bso7Zv5d6ryAmkGFbn5UnsmYPkuEnNomvC9KHaOunuXXXcbwcUn/PoO4Bzb719WZs2Fuvhh5eorT2foZzPo9/veTFpgXPhaL8eeQxwbn2RDhlw7ol+NpwUI+hRrpuwVM7nFs/MF3MMCc89GdbTz83L41zQpz9doR3bizUxTM32xZT+4Rv7Afyq9fFfXqqNm7woWLKsglX3C8+8gJUrinMNgHMPAc5tu9EC57Lm9p8xwayjsHhwPRRW27iej1s/+Gf+kQPn/pkByz09F4FcBHIRyEXgJ4nATz8x/STvnnvOBycCRn3NqFuY5M8kgkZ57itf+YqVEH/605+2fmYsPRobG9mZdB2k+2nO3uTooRA7F94A6PoinzmInD0JVXO1Tf/uAac+/UkUE/J/sdutAda+hsrbf/g8MvBoKT+0DXu730XWvebne15m/fPshYz++M8TeurNtJY32PXfPunUow855TO72HKPXARyEchFIBeBXAR+TATiqD49uzutP/qHpC52s8Od9Y91bTb94X/2WLatPu+PeYPcr3MR+BBEYHER1SwGm3+bDSh9fX360pe+ZEFwRlHu0UcflbFqNYvI5nljY2N6BSjNwA3mZ+vWrVNzc7O1CG02r5iNHSY/v/XWW62NK3V1dYiSUFDikWVVMguY1A8496Xf/V2NXb6iZhS2bu1YpbUoshVsXKEsxRVhX2lDcS744iu6duoCKiXFWnrfAypY1kaRlGNFjS0zgXoHEFaaHeXTRw9qjvct3rZDFbfsVLqbQu/LrypEMa1g21YV3rGNIhert6iw2MyxAEUZGw4oPJ7bqfGXntW5t4+qfkmblj7yuFVwUx5Fb2OZyPuawqCNwnMSGybbxBDg3JPyoQxXuQIbpnvvk72lFTEQBhXiYeArG4U8G4v4g08+pfhLL6rIB1R13z3y7sA6DFWIbIIi4NSMpve8rtnDR1VSUqLCu26Ve5MpgBKnEJawQGWZcewme0Y1cfmyuqNh1XEuTZtvkA147/SLzysInNexaQt2qrehstaI3ZUbCAAVFWxGLVPTaEpxbNkGXjugMTYZtdc0KHDHPXJtu4m6sR91ieu6NnZUfxyouWVjgHPAjMHnn6GYNKCGW+9RxbZbKU5TFCTuRnHOQHHGBzs9GVTk0FvqAfxLoP7VevOtsECrALYo4vN+qa5uJfa8pjOHDqhixUoANZTdmuqII58TNSpcFHOxq0339Gnh9EklpyfkIf5FH70Pq7GwLnx3t/zjM2pcv1FeowqH/arNHKMZzM2qNsqBVNuUpA1NPvktJXs6ZW9vU9kD2OstbedYjQoH1xoAwSiDGbDA2JOmBro1ePC4rgHPLVlxg+rvxrp2eQPxIHZG1ca6AKat81qKpemJfYoO7qF9p+RtoAhasZFzKAW6Mu0U8G14D3DHISUcgAgdj6ECuJyiNva1gwdUCnjla74TGBSlHTsWvQAxTqNUlgSIRAEmPQcsMH2Rgs8EfEUJdVIUhCraKAiXKoraiB1VPY9RcaPAHBl+TTO8pyvPo0DTNqCQzbQ3bAATKBGh7hK6fJCKQC+FVZT7OoAJSlvoIyjOXQX8o896a7Eua94KOEc7cWC9SzFdqCQlerDnmkZJJa9UBdgsetsoBDvqiRtxjvcrNHhSU8MDxAULrOZG4CU3dflR9Q9QgOc9i3lPf6CBY6FvGU8sAwzRfwUUmJh5C6WmS6gHzFNYwa64lv5b2EEsOAZUlQwYR0eRfeGqkiPHNdLVy3s1UUxGSaishmsINGgUaLA+C3YCAIaAHmo2AChskNefR/sZ1MSV0wAEI6qsA7ZoMepwa+nbFL2x+klPn+J1JxDZGVc+x+2twZ6u5lYK0su5ykA9WMhGsQWcGzoJ1JdWQSNKdJ4aFC3HUZyaxx7M2L+u+R44x9XgHE3/plqT4fwWerF1PQ0/NIZgYQB7P6MoCTjhL0etif5FX3Fj8WbHPm+u7yxQSKcKsTIqqEOhphxgx4kKDYBHehZoZWoc+HUL4NyS6+MOEf8wKc5xutYcNIDq4He/+11Lbc6cv5l/HnroITWgImnmkcXC5eLf5nXvu8c7hVajgGIdJ/NmCEWrrqvX9I1vfkNHjx5DZbNY/+YT/wZ1jJtVit34orpJCijZPMy5mjnZgGvmsfh765v34A/zWebx/XH9oeDcnYBzlYBz9M108JT6Lh/SPBBrbYXXUhZzFK+nDTczvwLBoK4V7Tuu+FinnBGU5QC6XHUotdbfBGCFfaGDMcBAKYxVkQtvAJvQTxnfXPWtCo+fVnjwTVyuUftsvRNYbAeBKGGIMcATYw0QThar5HSkDzDolKIzF1E7cSgf6MpXuR7lKMYl1FHwZGOqMBh8EMANtbmu1xlzo4x1jOX166znGGDDiaJUZvAFJVAMTaRc18G5xnt4/Qhw8esa7r4sD+p01XUr5CtDyc4F7EPYMqitZUaMVWsf4Jxb5XWtykdBzgbAA8HEWJRRYnoK29IriI6NodwJlFVvwLlpjXT2A6WXqLSV8bm8VUmgnTSKoDbmLQdQl6U2Fe5TkPiFAJKMulagrJpYGHVXYmyOgedzFJwfANqYUZw7jbpUAmW7JlTb2hHmw/LdzGdJwKnRk5q82sP3pSjLrQacA9rBpnpuGOvQS0eU782grNpBvrQZyK2KGZQYAzzOd59QtP8s4Bxxq14hRwvgYwFrq7ytzc58BiCdvAwMNdkDrNQs2/L7FQHamew5oTxnWKUoETort7MhoZHz4kWmnRvoO808GAb8nu4CghkwKZSKUCPNYxy052OxiupqFtDb2GPbkuRrM9iUAoM7osMq4nNctTdznRs5Bo4zPgGAfEkzfSdUhIKVl7nCHgBaj9qAtw+gCHpOJSjgeALYhLtQc3WiUsfYHcKadHx4CHAu/x1wbg6ozsCNAIJlBvKmSs+1yM4DL/afU2jokgqKfSihtSqS8WDRDahGTheowbITW04Ie2C/PNoCeSAUkTc5Axvaj9pfN+cJXAZQ6gxUYJ++VO5icw0DXCMDDfFRCXOO/SjznAW0vqyyOuagKvIqr1nHJjjYx6eA2WfZuBEDMi5d2sE8jEJgpghF2qvAV8CN/gg2tjwfgM/ksMiY4RAKbAcAHwPCdwOc+auAtFHjswXI3dlwYeBHG4C6yQeCU2Oyc42Ll90GhJdU5NizliKxq438oe5m1JgB5zgUGxmxjXOxYZmaDaPeCrgfx7I8jG2tu6wWRdo2NhLUc27ArsojgwAkJAdL06/TPa8pucCcByjvbtzBdQIsQ+WXICs1TlvrfIPhIwSkuUOuxvs4d3LiwVfgBrHxdRdzzqhLo+6KRy7z+IxSg8D9vdeIM5th3IVy1LXLvWQ1ioWoxXpQnDN5RGSATRsoFpK/5BXnqw4FZyd9c+7aoBYAPcqxV/bUsPnDV0ebow9a6SD5GlBhOsRGENTuohPXsL9dUH5ZAccOZBdYyvmhppcKAKPSrg0EHD6umU7yqrkkaoJtKmgAnGNzgc1JvmblJW9q5PIZNiOgTlmNEnAjmxkA1uZNH0RtztgF51fzniavou1lI73KjB7V1Ng01rNsgGh4BBXJdsXZQBJcyKJeiILUcAbb1LTeOkneTV5/9y2N2nVrnuoqHYqjzDxHHWN8OqtrXWntf2OMDVJ9Wg549tGHW7RubbH2H0zpy98+QHudY84FnLu9hvsSo+IFSzyS1lPfndSzz+/T+nVNemjXapWV+PTkEyO471zTEiwXdz3YqlUrvOQ3tGKmrMOHkvrat9O6dPW4PvpQjR55uB7Y2q3LV8L6whffUggI8L47V+ljH29RSQWIMABtNktOzzhsNmAk2AAZi2Q1D7gzw/lNT2V0/MSCTp3Gqhc4cdcDbdq+owxADjtZ4Jz5IEpS06gHAtxduICi1VvdWNhe/9z1G73qvBbS7//e87y2WY89tFr33+tXGYIHcJO62pMCrJvVzTf6Aef6clatXIX33YN5FnyNP8ipzEhp6CbzeCdvMXmKWbs4wn3ugQMHrI19bu4vli9frpvJsW666SbV1NZw/+LnpbyW15lcy/zbbknKX18DWcx/Fv++/hHXcyPz73f/3Hz/fn+YvO7dx2y+N18/FpwjxglA7phRded2LhTOWLaos/TD7msZHTx8hXHhilaubMTWuUMrVuYDnjHPzmc0M5XVFDavXV0JHTo6qBFUy++4u1l334faOPeJ3/n2hI4eu6KN6/MA4dq1tKMIUJh1WobaPS+l9MILqJyGe/XYx+otq9a3D8XokyjzMhY9cH+VbrvHz701rYBrNzGZ0Te+OqPnXw2prDCj3/oPZViyFqCIDoT3UlJf+fYewLkyfeyXV+mGTfny+zk25uDdzwHOvfCqmhoYz9jUuOOm7aSJ5KImr+RhrniW+zfTVKz2Zv30p/sjB879dHHLvSoXgVwEchHIReBHRuCdROhHPif3y1wE/s8ImCTQFPM+85nPWIU8U/RbfLw7YVz82U/6N2/LDq+sXt6f0mf+LKmeiaxc3Ei3N9r1G4869O8+7mL3wi92m2Vdl11rGX3+L5FpBzSoRgr9Pz7i0n/+bRcLtT9ppP7lnjfOTe/ffiup/wnwYNaLH7jZAezgVgsxzz1yEchFIBeBXARyEfhJImB2Lb/4Wlr/9YtJdRnbVua6NS02/fF/detmLDkQrMk9chH40EZgcQHVBGAxTzbgnLE2+bu/+ztdBtYyVq0PPvig2ttRwfJQ0CApDoVClrrzM888AwBw1II7iouBzHgsoNJknmcWqe+++25L+djrNcpf1xc5zd/GqvXq+fP68z/4PVS5BrXEX6jNgXKtYDd46bZ1crWUAnChDHatT2N79msKi6GS9lWq3X4rUAxKWRTAXcBnDq9L6Rg2Wz3XNPriixoFRCvfeKPqbgMMm8LyaM8+LGHPyNfWorKbb5SPIqfNSwEUGCeFxVMGVTt3HmAONpjBY2/q6sGDslNEa916i/LXbMRyi6IjVk0pCuzmvF3FnAf2TtnZaV3+5pOyn7uAGkijArdj37kUsDBAcRBFpJQ5V4rYpgY4u++gZp98mmLiBFDgepXebIAnCsFUa8I9vep/8yh/96ulY6UCm1cpRZEUTgprOieqK0BiCxklu8c1ceyErqAEWLdxk1p33ixHYkEX9u1RN8fQVl2r5m075F++DHepQpR00pwfUBr/uX0FqOpFFcTudvLVfSqIYse1Zi32taislQDDMSYmwyiFsDHFWR5AAIw8u7dXk8++oMvnL6px9QbVmgXlJop4BQBcfsAhY3VLCTQbiil69oJ6UVGa4tia2peqYuMGuVAlQ1hE4dPnFN8PTDA0qIqtO1S0bYuSwGk2t43zc7MBiRMFgkv2DaO894YitLdsdZUqf+PjqK84NPDsK0qfu6ICFNR8O26Vbw3QU76P9hMHauDmwAAYfNlQb4oe2Ktx4Mmgx4FC3o0qX4fqVz6KKxRqkyjvZThRV4CFfdNmRgY1+sZbuvLqG2ptWYpb2U45VzSgYOJTFiWWDLZlNi6CI8NJoHCTnX8L6AMVlslx1HtWKq+Kgq03QHEQxYLYkKJDB1EgOQf8UIld8C/BSXQo1PcGxXQU5yoNOHeLMkUU7220PQoRjgzKR6FOisNnsZXDtg2VI3eBF2iuHegOS2HArTSxSVLwdqCK5CTeNqzWIuOva7L/daDUiEoplBdVAImhipJdAAacRsGv54js4RHl1VG8B1qwlWJxN4H1WueLWK+NUKBvANZA3aW4jWJsBap3UWC1Q0B/RzgPwBUXu/4rW+knAHm+RmIA2LBwRRGgshmUHTOOMpW3r5Sv0Ank1aOB4SksytaotOkmeYsoYHOsls8Nyn4ZLO2S09izjh9RPDwJ10EcgE0cQAIZexFfQI0oMaXx/81SlPaaYjJqQ0NXLlGE92CZ1wJUYIrGqDWh8pgGIJgbxJIVmKWgbiNWjoB1fqzwsNwZv4zi3GyPSkscgG/LcOVbSxvDmjExgDoN9o/dF4EsYvK5vbjJNsldt+l6DAysMzuoyEingsAe+UVuIDlgFHeNxrEfi4XnsFwsxd4VEA9LwrStHOCDpIG+m01is7eAxevkaYq1kyrGXjhQsxQuF+gtW4XCjU9hLP9SdKd8gE1PFBtswJfg4BnUcaaAbhoBGbGNBZLJxrDhHceecHZG/ra7sFYEePiQKc5Zkwd/xFESOnbsmL71rW/p5MmTam5utuy+zXxi7Lbea3hs8Tj+Jf625lcDJkMe2OnLpkgbZu58fvduPfvM0xodGdU2zuvhRx/R2rVrAS8N6EsXAjw1r11UO1kE5xbh83+JY/th72E+0zwWc4HF5/zT4BxAWhZIZO60Bs++qeBojyqKbSqubwZ6BZzzNtNXZoCAgFcHL8jJXO0hDnHG1YwfyLSGvljexLjJfM8clRm5gDrqUeZ3rzwAOi5sRsOTFxQGdHMBwuS33I6A1k2MGwHmAI7TjE9YPaaAUkMTF7CBBWrTjFyFtcDHQHMGYAXWgaoC0EVNFFUtA3hp+jq0MzeGbSzKnv4aY5NZy/iVDwSIBfzIbgAgoBYUSwuWPQzkBzjnmFJ46E0NXTrLuOJUJUBQQRWQrY/Xkc+kZ7GQnMJSfhIoinm3oLSMcbiDOXYlryUvCE/znn2aHehTDAC+clmr/EDk6SmUOy8CO6NiVwL06wSaTqGGlkEhC+NGa9zPYK8axCY2BDBmz+SrtHgFcC0AstcoYjGHMi5jVs8Xc6tzBhbtLIDeSVS/ulF4ylcxx2FztwONmzkbRbyJs6ixjMrjrwbWWyM3dqMGGI8C7cyff0UeG1aetY3weIyjheRAbDBITw4q1EuM5wZQHsOuDaDH1XTLO3MJ42GGz52+hsXt23IsAPfUYLGKqmAihAJU/wnLBjvQuIbz2wkXA6QOAJYFtoLi49p3Mbde1tTcJHOeXaWVNQpg0+3yVjI3kFcmgQINxORiMM0yl4e6NTOAYtVkp/L9RSoAPLIbQNFkPCgPhzjvGRTbigNO5oPNzOs3AKgngJKAIWOdAHxNQGvAbcDvpv2mZq5oauiqZuamACKxEQ7kcTxcr7ida7xcnurVgBWom2KjnsJye34QK2BygZKyEvlbsWrFWnKOeLrZLFFU04K6mJl/2mk/RnEOqAvgyxXGJWT0bcWHehAbdssdqMNeFTAQVbssgH/S4VXUjtIyVpV+lHSzWJHODhzX/NRblj1lIXOCvYD8B3U1GzGLDwJ/9aHqhiJv7ZrNXC/g7kSeZoY7AeeOKhCIA5cDhHqYb6ERMgsAc0Nn6SeDjC9Y79K2PXnApWVsBKhkzmPTRzYBVDqHKio5wXyYPB6L0eL2e2iDccVPfJt8LSEntuyOmu3ki7R7AzOa/8hfMuFrWKh28toBOVHWcwYqARpXWOCXjbwiS26QlbkPoL0yHmbnLmFv/zoKul2y+6uY028ABOUa+gw8MQMsy5jRdVxOcqzSJTvladxFP5sHaH2VvOIyx9tAnHeSV9A+HUCBIaPQTP442GOpTMfYYJEpRsG4YQnntwqQvZLXB7Gq7dLEwFXNTgVVTB+tW9GB4lxSwav0zekUQOs6xoMVFoiZyZp2h7Kdjc05iVEtjKBiPUY/Z1NQCeBqXkU9bQuw09j40k6zKDjasl7+jVRlql/B3sOapk/lseGkpB5Aj35LQJjvgVdnjqFqe433rlJV3Tb4xgbCMoFS8UX6+aQKi/IRzasjn+f96dcJYMHM2DFgFOyHK25SuuhBTUbKsb/1kI+RE8tp1Squ9cb0zO5B9Y3YdOO6Gt17sxf77KQFJHrYcOEgJxkEsHv55SGdPo1ldIlTH3mwQ1s2VenNw4Bz39rLMRpwbgfgXK0FzmW4BmPjGT3z7KSefnafVnTU69EH16mtya+XXx3Ra69fADL16qbt7bxPhcpLDTyT0L69cT2zxw701qlffbwGZalKVZQ52ZAVQXEO1cZQUrvuXI1jT4UCVdwH0E8yjOCpuJPXp4Ab2XCAqqiZF82mHpqhjh2Ja89rYQ2NXWFjVo22bCtljHNi68yWBsZ3hjHcnLM6hbXkV752RTVsIvn4Y426cZsHcC6s3/295+h/TfroI2v04C6srKvsQJ5ZwLkE97vjgHMBhRcGcuAco+n79mHyFJOqcJ99/R90be4H54Kzeh3185deeom2fYr+lLXyqttvv92yZ62qBjbHOtuMWddfy6sZi6x8x3ov8/3/mQMtfm9i8f15kfnZL8LDnMO7j918b75+HDhn4rQAEIGYSAAAQABJREFUbDswFlIkxqYx7v093EMRMvV3p1D0uwjEelJNTU269+51amhyKa8AtWB2JXPnqjiv7evO6LmXhnXyzDFtv6VVDz7UoQALr088OanjJ69o3eo8PQY417GqiPk+xr26U/v2AM49GwII7tLjH2/T2nUlunYhrae/G1RnzwgqdfkAeKVqbC1QLJFBCS+mb6Jg9/q5jFbUBvRf/mOhdt7q0fhIRq+iXvflbz4FfFeuj/3KOmxfSzm+tGaZx198Ybd2P/uqGurqsZ7eBWx3M+sSjN8WOMcKzvWmQM5otZif6VLnwLmfKXy5F+cikItALgK5CPzwCJhEKPfIReCfH4HR0VF99rOf/QFw7p//TtdfYTbNT7HL6XmSuN//X0kNzWZZeJeWtdn1aYC5TzziIAn/xW+vbCbUPixoP/3ZuAbYIbKk3q7f/g0XN7pm59zP/xHl5vjQ0ZQ+/xdJ7Tub0RpUgv7gN926+w7k+KnL5B65COQikItALgK5CPwkETDw9V9/Jan//c2UelgwNvP66lab/uS/ebT9BgcFq5/kXXLPyUXggxeBH7YobMCFq1evfg+cu/HGGy3Fn6XYQRkgzhT1Z2ZmLDW6J5/E3hQAzvy8srLSUpmbnEQ9hE5nbFoNcGf+9qNsZqABAwIYFWjzHp2omn3xD39PtuCC1haXq4XiRRkFkvJVKHC0VgPyhDWDkthUV7fySioAw25RXmOLJvq6FJoCQqNo6SkptKCo1OCApk6cVJgFz8pNW1W3A8tTio/JoyeA4Q5oHkCoqLWOwnSrPBT00kAp81HAMlQ26juwySr0Kz7Qo6GDh9QH7FWIB0gVCmn5TS0oQjkUQs3ayUBRvKSJQmk9tduEOp96TuEjb2OH5lbFupUU+ikko6znYRHWXoBCBCUfo1iWwiZ1avfL6jrxNtCZVw0rl2K7RWGcYuZwF0p5wxMqzQ+oCbDMA5A0FET9BFCosBDlEz7TQfE23j+l/gtXhB6DWgHI6rBrdQDHjZ09pTMourmmZ1QLwFe6dAnFxxJFUGYJJpFgAFisbW1XIVBiks+afQ3FLuC0IMcRuGGTCq1CgltBrGFT2JMVc2xFWK46KXaPvfCyTlOEKAQGbFmzBgGVWiw2AyiwANAVAvd58mRHVSTZO6jxQ4d0Yc8elRCr+hWohxCjKLDa1KVOpfuHKfSjprPtZiVRWBoa7ufcIyrAJrbQVwgc6FR4hIIhkJ5tdlZFnEPFLz0EkIBl0lsnNfvGYSyb5oCdGlWyAlu50hKUKRKaRJHOgw1YeQvnh4JACiWnoT0vqXvgGjanxJPrmoc1L7ic5oAyUl4/13SFAnU12GRNauzNwzr33G7VF+SrYj0F/aWoWVWhRFPfpExJpVXAdqFqZwfSQ0YHpZHDmuy6RGGuXHkBoAIvdrQUH9LxUUVnzyEg1weEWa2ijo/zO6xae9/ivA4rUIGtast2ZUs2As6hesL5GtWcxMiR6xaic31y5KH0TWy9hU0Ah0Bt2OqBTND2sBL0Y2+WV0ZroqCMstPs4Juaw8rO669QfiFwgR31wnhaieCoQti3eVAtCdS14US6i1ruUsRpUJABnJvD/stG8dhf2i4XkFsGNZ0kXoSxKeA21IzgGYFRABOwl3OVNgMQUrRFudAVpcjLc+aDMaWcVSpdtla+AgdAyzX1A84VAS2UN1P4LaQwbaSCgAeQkUFpbpjCP/DJ5En5jPJPOf0Ca8E0dnRJVPRMwdcBxJoBcHMCsnrsKFEBdUz10D5RVfEDEhSVYVXrRtUPeDERHmTc6QVGKFBxg7Fq5Zr58wBWBrF7fhsltz4F8rOAfAbw5PgBCtNG8W62F1U43o+KKldTMYA9V0mTvMVc4zSF2hDQJfBdZHYClaMC5Rvwzl2t8f4RAOEZVdQUq9Co2BUsp54CaGrgE9T0UsGrmhvB/nj8jKW8U17VxDjVxCSBChfKMykKyWEv5SNgrwKKTS4Ke+k5YoJ6X3y2n8I/kGIZajxeiu7RBa4DCpn0p+KVD9MWlhNLFIB4tw+L4pyZXc28MDc3ZynNPf3009ZmyJ07d+qxxx6zXAPM3PGLBM4xQFjqo2Y+XFRrzUB8HT9+Qt/5znd0+PBhlObK9NGPfdSCzCsqUB+kgGu+TCwWH4uFz8XfLf78X/rvxXzg3UVW8xn/NDhngK0g4ynA69lDQDtdKs4zamhlAMDNzD9YLQOnxugrkdlxBYCcvHnlqAjFNR8DisjD1q+4AgVNwDLmY8dkH/PwVdTPUMNaspV5BMv1CcaePsA5D2qVrczrgD5ZwDmr0wHoGsXI8NhFICIK5ZFBFEdA5VAhYyLhebVKJ4HlgNCcjKE2FGQdxks9eg3VuYOa7EPlMs3m18IqebADtaP6FaOgGpt+CwW7HmCbApUsfxjVsttRmJxUpP+wBi+eUSKaVkVlLecJtEP/TRuwaB7gDog6A1SWNmMgu07dQC++0qVASqhMobSXYYwOkh9FyR8qViwDBGxElHMCcK4PsKxMZc1GAWwp8FA5MBWwdAaFrXmAHaxVp/sPYnE5osKCWuUZ+1k/4JK5Nlid2nit09uEACuWjm5A7yRAFaprQ9dOoE43D0BWIxcW0gzgjPVjQCkDmpkPM2ZXAwphD1sL7Ms8khgd0OS53eQcqGeyEcJV0sxYCtgL6J3CBj0G2OZ2YVGNyl9cfuAvxi/GfbsL9dw0cBEAY5px2AXY7cZi1YlCYAzLxHHAOY8npjKU/Zzlt/FR9Vw/c/ALzBUDAMVva2oA1dAIAHVRKbB5vfwGemeszqRQYUsDzniBlsnLbG5Aa+bchYnjmh0+a6mhBYDQ3B6gJVSq0kjjBLFhm2WsDZT5Vd4EkFW6CZA7g9raUdTesLllzvcWGfVa4sX1imBhOo1y6UI8qPq2Six+UXvFY25ydJaNByWoF5LbAfKnk/NKRoG6sFxOzi+ooor5tKUNy1WnJgcMOJcEylsin7HldBrQmnwZJb8kUGF6EgW1bqB3rDjzmbv9lWxyKEHVDLVFuCRFAfOSeYXY1fE7IEG7UXlDqXV++DDtbYJNAGVAYo3yMAZ6EnNcX4DxceYuNoe1bNwGILiBMaOIuPQAmB+RF2vPQqBU0wfTzFfpedrEdB9z/jzzLZgV81PCKKS5G5Rf3ogNPYApeWcmjDpsCCvZeIx2sU3FbbsAvaKKnvgGNvNc17aNsteTz3gbrVzBKMxmowNKzp7RLMqraSDyAtSG3fQPdpUAq5bwPNqy2YhgVOCAZO18loHaM5PHANQvIE6LbWhhA/l8OZbBKHMC8EWnulAu7LU2j5S23QLHfy8Nhrxp8HWFR3lNqpCxYzVxQdkVsC0WYf7EftWBsmwRm2EybACJGeChgL5BXmXzmXl5nvaIciPquvEw8BsbMqpXoaxIm5nsBLYMplW3HAjRstolz0cRl6OhjQ1bYOd0P30fsNaPhWoxGwzcheSSDmBb1BMzypfLWUlcydXM5hPHvKLjF9hQcpa2N0q/LcLauYV5oACRa6Nme5Xrx0YMf4NqGrainkc8UdoLDXVqepxcDfvq4vIyNnugSkleFqUtOLD3DbNJwlu9XT3z63TobJD+UKyyamLnKVQYdba+gVmdOIOKbqZEW9a3qL3epqixgU9GULYjZ0Rlb2hEeutoHxDekDraSvXQvcsBjEpRoYvoH7+xG1WnWebcO3XPXc1cFzJPCJIplOqe2z2hJ7/7itrbqnCB2QT0EtCpM7N64aUL6ryKimZVjdataVIV9qdjYws6fUY6da2IzQYj+uQvGTiFvl3q4N4xhOIcGzYiST14z3o98kg1bZW5EvVHO5Dg/EIKe9YJHTx4CVjOrbq6JgU47lTCrrNn5nXuApB1dgZQj3y0IopCHEWMDHkhNo55+YBwFBEunO3W2XPTWr96hR5+oAE1LBdKdyH99889qVS6Sb/02EY9cF++yirtitCHevpj/B6QfWMpSniDOXCO3va+fDBWmvsu64s8iWSJtsD9LveOFy5c1HPPPYfi3BHNMz4vQSX/LizljdpcK/92s1mLlIz5jNzK5FnmrfjPynfMe31AH4v54+Lpme/N148C50w4Msyno6Nx7T1wUQMj3OuxTlNJHzeW9oP9bHB5+7xmpke0HNXOVauXYN06wJxNbsR9d0EB+UTErt6ejE6cGgGCG9R9963QzjuZ++M2PfHUIHDjJW1Yg/XqIx2W4pzTQx4Vd2j/vphe3j2r4HyXHn18qTZvqWLMlg7ujWjPfnI3jdOfi9S+vIl7poyudIb09smwLk34tKGhGHCuWLfeDDiHvfQrL4X1j1/7BnavFfrYJzZrzZoq1qMyjDETevG5F/Xi8y9b4Nz9u+4HrryJDRrvtBFaR9ao2vP3dWXVxej9dH/nwLmfLm65V+UikItALgK5CPzICHxwk5cfedq5X/7META3oZ///OetBcovfOELP9P7mbx6DIjsqReS+vyXUhpFCr2QjQgblzv0yU+49NH7WNT+gDRV6k/61vMp/cbvxUU9RPdscuh3UORZu9Qkjf86j5HRrP7qy0l94atJC5Z7DGjOqM5VI82ce+QikItALgK5COQi8JNGwCwSfekrKf3Z19+B54DpbuhAee63PdqyzqGcbetPGsnc8z6oETB9xDyM4pyxW/3bv/1bFqIvyIBzjz/+uJYsAcqiaGee19XVJQPNvfXWWzJKcxs3brRs9AwcZ3538eJFFq7nrQXre++9Vy0tFCnNQjVfi/ZzVy6e15/84e/LB0C2E9WvRnY0h0exoAJmy6Aml6QIGY8EUS8oUfPadcBNgEfAJP0H39AMqlQuCtNp1DcMXuAE9nMBJxTUN6p4C0XElShzAHGl+/o0cfJt9V29ZEEwRrkrH6WbNAu/Yc7FV1+n1TfvQNkBwApb1YXLl9R/6JhmUVzzoszgZWBIAqHFUSszwFXdho0qWI2aCFDP+OuHNA3UNY+Ch98PIFPAbmxsQiu2bsVSrp2CVhFrrlC5c/NAYRfUfQRFHhQm8tMxdm07lKTgHUqm5MOuq3H5OhVv2Exdb1xXsJ0c6+uRm2JeEemuD3UeG4X6CMpFhR0dqt62TflcC8g5JVE9GTmMRS2769Pzs0BGwEhAcSFApChgTkFDk9o3bKGoiqLM3KziZ09o6sxJNsgMCyNcim1eoCYskTgWf0ODarnWxa1L2CvuQoHvlC7uP4CK0DAgoR/IpwiQsNqKgbdtmRJF5XLRZOwzQcWA1vpf2g3g1y03xcqU36d54IEokEg+7aNt9WoFlq1m93pcvWdPUgxGFQRwwo+6ig870mgcO1m8nYqqy1W7cZ3yt2ygqM3C9fCg5lA0HDlzkaJ9nKIxEBUwYIy17ZDHpVLiUYPCXXFVLQBmWMETJwA43lYIAMBtB5IGPEtilxlC0cVTt0RL2VFe2dJEITSq+XOndHX3c9zoDVOUR6mntFj+5hZVbNkhL8fqoDjqpL06sFW1CXUxCsrB3vPY7AFbAXlmaHtZgCi3K4qC3CDtZwjBn0oVLvkI16EdKO4clnFnUB4KyE3x3oZSnVFYoxyhNOpjC71HFOw7QxsHgssHnCvk2jkKAQVQX0KZDm0XYA8AkuIWoDNsPY1qC8XmxPQ5zY91AZJhq0bR2uyGt7voB0AkycgkxW36QQVKMg23yg68kjHWhb17aafDqM+UUkAGmgMcTQAEJjNAEPTnvDw/RUfUVbCSTMzMoIDH+XmMdY6da0RxFhvZSATVIVcFbRt4EeAlMt6nIcCCAmzpips2I/5HwZg2adSXsijOxOkXk72nON6zWKQmeE0R0FspRW5AP+BBB7Fz5lFoxibOX1xL4Z7QpKYo7vcDiVwDRJwCprOjcuOQB/sfaEOsxlCMsdGua9aipmOOAxBkrlfD2BJGUAsq9HG8AJIZoNl4kv5hdoUB0+ZTxMnjKwUoOAcEa/qeHajNiSSk04H6TYRrOjtPHApQqFlGp6vRNGNRENglUAE800B78PNzgMZsknYJYJBAHW+q/xUsbs+jXOtWHv3BzvVDUpE2c121MI76ZB5t018MlEPBPJtAuWHqsuYnryoaJq60LzeKluikAOQRcwCbgiX30tda+awPHzhnoG1j0/rNb37TstpyU8T62Mc+pjvuuENVVVhGvsumldby/n+YOZX/FyG4RQU5U7B8/vnnZRRbDYRuzu+RjzyK5RVWnYxvViGXs1uck8337/734u//pQPw7s9493v/UHCOgnRlOYpzmseiswvVx1OWVavfHccKjfHIQOPGgpXxKAm442LuLS7DVjOvAdg4gpUXdo4xQCzGcg9QkoPxy4udc4p4ZLDQzGNMcTWvtMaZUP9xABWfAs03Ac6tAyorZH5gsSob5vmAdahTTfRdAPCZVkmBk/7OOIOqZQK1uQTAjt1YKBtYFmjYwzhk1xQKXNieonYVYi60x+LW+GrAswyQWBpALZECjgKkK2m9G8tEQPXopBIDxzSD1apRq81DmdTDHJREjS2CvXmCecKNIotRirIn06itRIBVzBzktdYkPYAwboDvaDgE4JJQ6XIAeubc+MSsRrpHGeeAy+uxfy3H/hWbZ0tJLjWt6FyPggOnUHvDSjo1C7RMXgF4lQE+QryUMRxEH8WuvJLl1jm6mP+zjJXI9KG0eUxxACQnY7IbGNGBOh3DIrpOKcGmADDlq7y2RcXVqOUC/sTGRzR2eT/WlWxMAGy3uwqAglBKTODTlgxb+YWvskaprIf8LgTwBN7NPOsCxHIDvTgyC1ySScYy8rGSNux27wLaQXFu+BSbjqMqRiXVVbyDC46ymLl8aaCkSLeCWIPOD5zlc7A0RZnUCyxuYOoM7T6Vpm1wHHllTbwnKqgFNfQPAKoQqmFjWJnOoNZKbuTgfNzkWw6+okBCwfgc8F8esB7WmyWoH3LMcWw258fO8dpJYEayHNqU28y5WK6bazgfnVZFUzFgNkAU4/HEEKqj5G9ewAw3eRJNmkeK16NyyFdJBceKtXconc85TspJLltez3xdiWKZC2iMeGdQaYvEmD9RdQ11v0GegpqYt1Q+wEcDdKaB0xO8f9SD2i0QaSkW3j7maTt2vxnA7dgoeYeZ74lVhjVoL8paIFpsWkDljpw1SKxrV6ylLa2Di6xQAhvgSM9x8s4+CxJNkduYh8NspGB8zaLS6Pai0AU8yCljpQt4idKdGyU4Ul6+plB8o+2QX3irbqT930++FFfk3FNYBofkblmvdN1WwDnmbCAnB9RfBrW3MPDjGFa+9tCcAmwodxXkKUb+nAAAtdMPHSmuIdCntwJVXdQEhR1oNmKshzuxPp2WK0R+AaGQYC5NoRrswL7cjT19BjW/vIZt8jfupD3aADPPaQHV1nBwxsrxnZxf2gkwiaprKgwUiHJuoAg4nvwiwoaCSILXiLbMtXbRBx2AkjHynCS5dwFgfnFHu3V+U739mmWdv7b9BuVXLCHXARjF/tgBzGbOb6r/NArCRrl2gs12bq4ffZTNB2nm6ZSDDRSo7/n92CKXLOXHJeT/tJQwmxlGL1tKs5kY58M1djLf2xkHkqkw+XCMvKIKW/pVgJT1tAfUkecmNAv8GAoOkXcmgPDYoEI78rLZJI94hObJ2UrX63ywQa8eGdPIBB3aRh5lN1a0HkB71ASxPGxoq9aalXUAoDFdQhV6dGyK/kt+ggpiKIq6JH26vgpl8M212ra5XFXVbh04OKVvfOclnHmDWKPfrbtub2MzAuAcc9gIinMvvDyOytSLgHOlun/XJixbqzU7m9LhI6g9HezR4FBEfmJTXmRyK+5DYuXqHmUMjnXrE4+26P77UNUsswPZhfTXX3qT9jfPZ6zE5rGdTRLcW3KvZSiVhYWEzp8b1+7nz6u/L8H9UoCx1lxDNtPQLl1spliypERr1xdpDLjy3JlxTY6Zvo99L+oGadS2Q/Tx8gqUp25u17Yt2LliOXvhfEh/9Hlj1Yp96wPrUckqUDngXIzxo2dgXpcuj6DSV62F4HAOnLNGjffZH+RTJqV654/rB8ccYVR8L126hL3nbh05wkarmVkrX9y5c6fMV1s77auA+5t3Hj+Q5/AedLYP7MOc77tzRvO9+fqx4BzA7vBQXM/uPqkz5/q4T89jTQQ7bVQ1I4DoMebY+jqv1m+oU1FRQEcPX9TwaNBSRXWa3I/cKxxBNZ8coqXFqzvuadCyDj9qn1k98/QVnTp5WmtWVOvB+9epfRlzvjuDlboNYHZee/fMaCHcpXt2NenGmwCDGTd7L6e0d/+4zl3k/omNFvncs7rJd9IZP2qxReofwzGrJK3f+r/KdeuOQo2PZbX3tRl96zvfBswr0QMPrdfKVVj1Mv8F56eA817Sa6/sUUtTsx5+6EHUn7cybpm6J8kE45A5bgueM83jZ2wiOXDuA9u9cif2forA9w9276djyx1LLgLvTQQ+wNnLexOw3Lu+EwFjH7Vv3z7rO6N08dM+WGNiR1ZWTzyb1P/6e6A5dnEFqNfctNahX/9Vl3bdyorPB+RhbkL6BrP6n3+T0N88hUw9Msufetipz/x3ayn/X+0suTfW7ldT+tznUfZgp9vG5Xb90W+5SIYpuJi8NvfIRSAXgVwEchHIReAnjgDw3FdT+lO+eoauK8/tYE7/HPPKlvXAc6Zwn3vkIvAhjcDiYqqB3/oAzr70pS9ZanIGnPvoRz9qgXMGXDBgnbFn/Yu/+AtNTU3J2KAYOK6+nmIYj2vXrll5+KuvvmrZuxq1oK3AZMZux7y3UQwyj84rF/XH/+P3FZ2e1rbmVi2jGLIwOMKid1C4bwAueQFWyrXmxhvUfMMGFMfqUMOIauHkcUCtKxRoze5koBcDGKF456cI5l/SjkXlUjkqKWoZZZnwLCoqADxXLmsGqCsF9OTNoEBDVSsVKJYX0K1u/Tp5SwBieHoGq8ToVZQnzqMghqKHsZzK2CmKFflV1FgPuLZK7uYVFMwKlKTQFqYgtYBCln1hDEWNuGzNgHvEy9uGNZoXpQ0KnOaNE8A6we6rCgGNObH5sgMSwN9gg1aogqYlyl+KelYtQEFwQmO83xzwoQPbWT/KNC7AHwPEOVHx8W9YB5zXalnCJlmYtrF4nBnoVuzcaUV7uwDvFqz1/xhF9SRF2MLWZajQYVlaifIPRd3s5AAWX1iSAQYGh4cBeXh/doi7UZvLRzmlaNVa7NlMUdOLXd2EZgAnpzsvEccF1GyAkmrKVbaBa9G+TCEK3AZq8lGAtBnYCiAvfvkicUOZBhCOzeZKl2NltrRZFai/+QO1FPEjmruC3VWvUZejWJqgeM2id5KiWLKuVN7lzSiaAQ6Ucv0ALlwUCtOD3Qpd7Fb82jgWexTr2dWU8lGsLMGizaj2oBjiZ4e7jUX+9NiEYhxDrPM8YNMYyiVxxVA7TBZVyt24UrXrNioAnEcll/PrA8p7AxUY1OS4d7QDwnnrmlS0EVva5Sj6EBOYNJ6KzSJFXltqVKnZaxRuh1EMw8bNFHDyC4HLgLDig7StPmCNMhW0PYT6zwosSoeBOoAWKFA7UBGyFzXQJk17wFYVZZvYBLZm073KRMco4M+hOITaBoUGZGdQGKEYgLWZE4UdV3E7tnirgDVRJssahZ4hyy4vOTNkAXjQGLLl0xaMIk6a4jg0hQs4zlm6hqJstWxhjmsKizgKBXYP6ki8TwIIIp6YoqCcRgEG6ALloHzOxUY7Tk/RRuZnUFvBJAv1F5cLxb0FlG2w3spgOehvXo6CC0AFz5mbichTTp/Dzs6JbaAVL6qsWSryKVRdItiPJrGo8zrDxNeoaHF+QKsmqjbibfejXlDMawH87GYCxhYtG+caTnUrgepHxqhSZbA5pX87GQ+MGnmc9uIqXoLqEio5Pto0inhzoyjRARF4iK+H/mpel46ihpjlnLwoDKBY6UBSL4PdbQxVpAQgQ4qCuJ0CvROAjgqvUqNYoLmBYQAXzLWKLEQVRmHIy81/QUkr0EQT4wbwG+sDBoBLo6IVxT4xG76K0gznAxiUQXnIKNJZdm1OQAjAFBcFcSfQDr7PnDN5R2xYkXms/lCuyUanGCHof3yuEyU9uVEVqrgRZaXq6zGi/X2YFOeM2twJ4NevfvWr1jxi7MF/7dd+TevWrbPmDwOemXnKPN5dCLR+8H78wxAHPBaP2fzbgK5RFDCPHDliwefHjx9XU1OTdt2/S3fceadV8F1Up1usAXw/OGfe5714LB7n98f2h4JzdwLOmXk2i8JZaAjoHQtKVDyc7hT9AZyMomoqE7Yg2TQWqH6gGU8h/ciNEhXweGQGC+hQP31lVl47MIQD8AqgOToFPIedob/mBvoiCpXM4UnULQ0M6ynHUrVoCeAccKzpc/RhPBaVGGdOmQZeBi6B2QMeNqq2gLMUPuNmzEXpzhMAnCtZBTdC4ZfPy6DIZYswFox3Y70+Qt8los4iy/7Z5k4qCqgUBYD1VWxCVA4VVSDa7Djzm1FVAeo2Ywf0FDnNHOAFimxu+jpKngWouzqwH0/PAfmGAN+Ig92eBOgABgLAjxiQfiGmkrZ25Tc0MY5iEzk+z7oW5wwY5iROeDryxVjCMSYifdhNX1N2rF+OyBw/5ziZi41aboa2ZM7RBoDsCbQAiLVail4Z4m1LAB1NX+F4Af0MKJaNccwAjajuxclfQilgTCCsguJybD1rGWfzmNfmFBw5S86BVasJJIBgCgAwg3qsg3N2oALnqlwCKFSCSOakYrN9DMjTHGoGYNlsjCCvAdyKMRY7Aq0qat4FDIdmF7CwAcx9jNt2/xouEIq1Zg5OA52Hu5UcO8q8Q04HkG4DALIBOdkYx5nsmH2JNYqyrtIm2ZkLofctqB7fSsZybGfnmAfnJ5g62JAAHOgEdsoAMkUApBzG/pyYuPJ5HWBTBpApzpwcnekmX2CjAQC502Gg5lY+x4NS1TgKgU4gpnLaKQDe3DSwXb+cC+M8j8PhZzZek8QONgbglEcO4gF2THhqFZ7j89gsYABqp5nr3bQzaHDwAsZw8z5ncaU9acFgHkAyOwpZHBTXIM51xKaS/DcDfF5Qt4x5jc8Hj8uS32YYE5MzffQnvpzkpMDyTtTEnOQaSWIUwnY1r87MMUuxe0X9L85ci01tFgA9nRgB/oxwGG6OH7U+8go+zIIGHagsZ1Iozi7MoKIXA5ZEDdo8z72gFP1iOsh8WrqZa3gvs7Ud59H9zO+cY/UyxcrW0AbKiBj5gdllHhlApfEsqmydgGXz8pnNBq60Yh6gWWA3ewqYPFGEimCTXFjOZoEq+QH9d4ThYxw7+WFlxsc4R3KjPNouKq1u8lI7x5dCnchOLmOv3ED6xLp0ZAy4H9tZFNuyqWHaCUkBapTWRhUAKNEOvOQSTgMl8toY7TeNkquDecMJmGb6YAKwJsY5e8qwYecewMaicmQSKA1156KqZahPolRJG8+gwuhEsdEW7rcAuMRcv9yJGZO9EAtjj+rheVwjFHXTPNcNOOcrxvoVhcesAT9RtEXmEBW+HoDGYca9MPc/5FxmlwD5QoqYZ4HvXCgPulHCttEvjZp1fL6PcQJYMkr+S/7v4r0KySWcJJ2xObK2wjb1ZKt1/HJC3XTBhSCvS5lNKNi2+rDubbJpyWq/6qrzUXtM6fL5KQ0NGmCX+zc24pgxpKTUq47leVq30q/aKtQxOaSzl4I6eOg8eWRMmzev0toVlcrzkIRwlzWO+87RU/M6feoc93p+bd7UpNZWYsTxDfSnUYILsWFqTvMzYeA5h6orixQHljx5KQG0d16PP9imXfegEAc4NzIe12v7ehRnA8nalRV8FqqHRQBvfA4jAFBOUoMDCZ16O6hrnQlgOQOfm/sgswkioboGnzpWlqimxqWBwQnAuXkN9Tu5pvQrgBenk36YH9XKNfkoTAVUX+thTqDW0ZPS08+R7ycLtfmGKiwf3ahoAWsyT49OLwDtz2l5c4kmxvqsudnkIrfddpt2YeNoNo4tzsPfPz8SoNzj5xEBpgSrhVhp1XX4y6wpdHd1ay+1vxdeeAGlw1FVoERu1i3uvvtudXQsR/0MpUv6uEkdTf5l5VXW8fJv0+IsKsq08w/mYzGPXDw78735+nHgXJY+F6Tfv30SWO3ClCamUL9PML+iGG7ueUrLHFq+3Ku2Zcwr3HeePhFUT69RgIwABifph2x6IueorgpoxSq/2jpQES82Ns5ZvXV4UNcu96qR+/4bNzarto65m/4di2V18UJcZ04DnDN/3bClUO0dATZfebUwy/oS48Hp03PqH2BzViKl/IAXVfxCjY67dfYyivX+BX36k1W6+aYAjmHG2nlaTz/znDy+MMdQiZ0ssDvz3BwbrY6Rg184e1YrlndgF/2obtqxzaRdxIZthczzJCqMb2YFgOGa5mGayU/7yIFzP23kcq/LReAniIC5SU2woGkGNmNxknvkIvDhicDPMDN9eIKUO9P3KAJmDWAYtbOvfDOpv/pGSuPRrIqB5nZucOjf/7pLt20xydQH52HO99DxtH7td+LY2GXVVmvTf/qkW//+l8ge/5Uf5y9m9Lk/SejFo+wX5Br8yq7rqnNFhbkx4l/50uQ+PheBXARyEfiFjMBf/WNSf4b6XO8YJRkW0u7a7ND/83+7tGEVizympp975CLwIYuAUcRZLAaYtYdewCoDzhnluC1btnxPcc4oAAWBwMwGlb/5m7+x1HGMGp0pLizazA0DZO3fv19f/vKXLTW6j3zkI1h03MeO5CILnDOhNWDAVYCsP/rD39cwn7WmrkFrsXZyQ8zNYUUUoxicj2rE0jVtWrFxJUozzdQ9sT8FyMpMUmybnaDgRgGJQhdVKwpOQDjFWAKVUXAOFFEoR7WHYq6NwiyVZ4CnWWAjLGRRMrCxU9rshMYLFUilHFYFwA2pDbOAbkflLgswk0S9OjM5gTMUrwdIshX6qGWjVBNAsSsPCzEWVLOorWSA/lJYbxmwCCoHVTGKpqje2YH4IHJ4HgvzgDZUiAGk0HkZRUViapyfpa5/Hus7Dl5jL8byzksZkkJhEuW4FNarAgy0Af/YUPQwtpY2FocdNVj5BfI5XpRukD+xAzHajVrW1KSyvG96AXCOa5n15mFVxw7u0moK1Rwv4BH+NNShUYSJzBCPoNIzKAVhuWaK0w4vFojAao4y4B6K1Ealy0B1BiRM8N4KU7lCDcKeTxxq6mQvKUMh5XqRyoA/doqVohiZnuA4KL6nWdDmEnItAILqsMoKYKNGsTgzTxymKZDOUvBE/UuAc4pzbD6OtxrwCltMGxa1YaPmBmCWpwjwHFaAAHPpMWCHOdbFKOZnDSyGuo+zithVck0A76xratrH1DTPNddknqIj37NIjtQJxdoqiu+o5NE27Az8mQViMIK9njk/ADsqKtjSBSgMNygDiGcseh2slNu4gHYbccUaioYB7DRNOyR2ZhUdu1bKAIoPoWIDJCZ3hfKWPiRH4XqAKuRcAKNM7I1NmI0C6fVdP8TGWAymOa8kMEYcgCM9Tf9DQc4oFFIAzSbZ6c7CvZ3zyqLOY3PV83w/hwjd4QSeA9bIhsY4HhR7KDbYgDKQzuGaEVMgBy4+P2vm+IhNcoxjvmwVbY1CShYlpiyF7SxtgRZBU+a6e4EHaE+2BOdHIdsoqhFQjod4c35poJiFCSAYAIH8ptVyA5ryBvAvPAfIKw0ARyOloExRmHjZULcxqnNpYmXLjvPetB9TrKGvWg8DFNF1bS76HrZ9WY7B9GGbURxBLSQbp0KCulIGZSrTRg10Z6MYm6UwnhX9yltBbGhTqPnQ4KhHcz2i9D9T0qYwTfmF1xNnVJyECo9R9iHIPNdUYHku0ETGxBxYh46MKt6s5vsnKfiiTNWwgr4ASAHkZ9qPUSO0G7s58WXAP44A+o02M8VnDvE5tAv0Em20KVNoMs/JopLDxeb9uZ75tGljE4ctoCnQGzu2TGqCc2N8oQgP+sL50X4pxNP4iQdqleY60QdNE/uwgHNmvduozRklNmPTauYkM6984hOf+B6UbaBr8zwzVy3OVwT6/fugnS8eL62GNkfbZO4zx97f16e9e1/TU089hcJOSBs33aCHH37YggTzAUd+GCT4Xp+zOVbz+P7P+afBOdq16WsJFDmBxPAqpe1ybeiDBhIxYI5FSAEz2X1YpaIklwUIMv0nGxu93gewenU4eJ4BiIJT9MMhekQhSo0bEX5EuYtxGVqEOKIKioWyBV0B0KQMsIJ2mpvPtkUBbxjPzJhkQNuskWIzczTjgTUPAZ5kPYwx3iY+p4T3AgBCKQ9tI/owxw2gkgUAN59hz8vjOFG/4tiNkol8qLW6qrFaZg4ID3Dc9Flzo2AqqQDwJCEooxkAhvEZu0Q7840N8CQLvIssKEAvfwMJOdzMp4xr80OoUDEPlrSuZegD2mG8T4VNu/CjFIUFrZknsEHlRVwIziljch3mEeZ5M6byppzH/8/ee4DJdV13nudV7FCdc+5GNzJBgiRIMIEESYkSlUiJVLLsbz0rz65l73hsf9p1nrHHsi3Zkscz9jiMJUsaW7assKICKZE0RYI5B4BEaDS60Tmn6lTxvf39b6N2sPxoUpToNQjVIwvVVfXeffeee+85593zv//DvUX3KjsNECYAaBcqQb7S97Dx+QG6RH2R5zrSrgboGaWp9LAVVlpFfQHWA+6TbMLYmBj9g6HkPHQ6DKgewMIQDGsIANnQRrSU2NM82K+8UvoQ1qoA26MUlOTo5HxAefS5D0vY+uhJQMcr2AiA+L23MBYAa69h49C/YQBhoMjpN9qn4p3vgH1In6Zt2Gz8v0C0avhOcM1Rp03mF5QwfhdjTUx7jCEMCpef0eVpQN4wouJ0OH3r0TaQi9yD/pKfEAWQFG6nDZSLPALSd/sA2WgY90Cf5xsZJ534i/gnWUCOkTRjAMHEmbuM4QAWV09jhLFFI7k2ZRv4oKsLUwTtYamF0c4DLOXnmNegLz0A7x4MWT7sqgE20YddLADEF8pgr0klHMK+GQDrIIc8peBlwxjLVMDJNVxBOzUGYPUJKDPQLhLk6vnIJzTKtciINKVm2BXmmoB5IdiU/Fg7PiG2HJlGADOCWseO0vch7g37Gmhz2so8AIzHQN8cQxHmnb9Eu5EdPofAAj5zKUXq49UkKUFrrwTc+XbGNeNj/Sh1ZEwlWi1NelE4iplhFIuelg0MsoBPeffwQfRSu4IYegA/HFQp9cZXitH/pI71S+kTxoMnUJrPmIZ9NVgDFIq9BbHJeSoZucAcGKS4vrTL8uW93J/NBDof3wAnD7ky/sRaqLkXYe4AcggAyUms2kgQ4PMKiAaCi9/4jjr5bFrInB7CpYUFt6EV9mZSswOs9dnJ4tgNmYOaQwHg0wBgXChgLshfgHEy4NoQTHGBnHpNHl6BfAj1IX3NAwVvjDVS0OfEiE3fx2BhFIg1QMfoWcSNI36TvwPajkpxn7jS1yLjEP2EDAL6z88w73l+cfaDXg0zPgPGjVIOG2x3yVidTSfjtrwEY9oKwEbSmFIaa+UeKYoZHU143DAGQ8BnS7N5NjkAZEwhGKkNqpqoDsHmHYKhE2CelvuZU/NJYh6zjCcAmXV1pKNNsLFFQ5TfNgDtTVHG/FzKEuUe6VjjAJJg/OXSdcgF1pZJ5zrLrFvCZ+K7+tqIvXjMt2/+U5aYykv2oVs77O1vg3GuEZAiZU1PAzbkOaGOVLBNzSXoRwFUGJf8l4XtcR1wTZIhsTCDll+THaVQALFlCWIyDdS7AfAOw2Qd3bkwlwfg48HeDLsgXS2Qa6IysKY2eDRhvysF/CfZrCR9gHYZ4uu0vT5izY1ogDgbcRg3G8yrdZi9edSzITY5iVG9CJxDaOfiwWBQulXNjampKTt06EH7Hhv1nnnmaR7rK+1K1iyUovVSGOJravXsif2SzsNOS+f+Tz+HuaCBxXfyy87XY7PtTOQzhz7r9WrAOZ0quckELyySHHUOltokVnMDOSEyMQVXo0PqmM/lVQHs3nAP424szjOPWJvJ4TuE8JEEyK2phR2e86JleGTM4QDw9hzzf2kOpvWyqDXXw9hdhu4GnO+z3rG0FKBneEaHIbS5BcbRCvoOkD/q1FKYfaVgnUcXZbCNYXRcFjt53/1L9ujTSWttDNv/9tP1AGPLANYZoMp12DAftckZ2PBhQ66sgnmUei0vz9tLR14g5exp23vRXvvg7R+0aw+yaQ5dSAvOjIcSnkPQ8ciiCJw7M3iKb0UJnIsS0C7uyclJp9guJMVF8ShK4MdHAv/TuP/4tLnY0nNBAnr+n5oJ7OukLf3kf8vaFEGBWh6i3nJZyH7+f4/ZNfv0OHh+HcTGeLDN2Uc+ThokmvbWS0L2G78Rt0thePvXPmYXA/v7r+fsT/4ia6eh0L/yopD9MSlkrwDEWDyKEihKoCiBogSKEvhhJCCb8l8Axo+w0IuJt/feELaP/0LUdm1T+p4fpsTiNUUJvDkloAVUBcYLLDcCzind6mc/+1k7duyYA87ddtttjj1OwLl5wGL33nuvS+WqAP9P/MRP2PXXX0+gheAPi89auxCwTtdXA5gScO7d7363A9EVUtYpoHX82FH71Kc+YaukED24b78duOAScFZ1CoUrlE7sNGSdbaTSaQBcBtuCBQRbtVhOwDYgmOUAc1mCvQSTiEYCpGLhleBLPkKwkoiKQooRAvQhT4F0Be9wdrMsgRLkCQC3eAJUARhTgE0MDA5MpMAdC7ceQXTyVW5ep5QjOgdwnRZvuYB7C5xDWZQZAK4LSI9EJQhoEQQrJYhJKhAXvePbMEGgEMA5bkpMDAYVwHlEU/mF+yCvEHVW0MoFT8WWJfAegT4voAxYYbST25VFmwJkAkUcwTqu1R2Rh8fvLrgKYGqzbIBl7lyCcErPRnBPmCF2PHMFMkB2AYErLULzLf/TVu7rITcHQCM4TIP4jZfqTPo0yV3tC8QSBjOKx27zPHUX443gCwpiQDJEgJBzJTuuc2UiN68UeUluWeovoCNsZ1C/cA4XKJCroCqsQF4Z55DyNkWZhFG5H6m0qEOZAqM8i5G/i+AmZXE/16e8e7CIkVMX/AABRaV04z9Pwfd16g5wSvVzYAFAVznq4hOwVCAwrPoRYA1IPxqQIlhi0NgRIFHjIq9Feu4EPwmB3RwtJ/oI0M0H5OYDcFNgfpO5ABkAjlsbeNLCi8MWr+m2aO/NBLL3OjCF6w/kGuLpyhNoTJ3mDu4PK0ZeAVcYYzxSoAlARQiTz8iBwAIDknvwBoiEqDEipS6yTTDTuX6EXcl1opgUXKSY6wnyQwNEPwFqLO8i+E8AHpBWkB5AWgSAlarLgVeoj+7FGAsAbHkwDHoAD/wUwWiC34RhuQc3BziQT40BDDxmy/NZ4rUwf5EmNVIpEBhjiLGX5x7pMEFrxkOY+QMfj5NvSABHACQgO2k3ddMUEKBMQWeuFSOkD/NjHoYhWWEFZyV1hiJNRz4uOE09FAmlXHiLkP+Cep0gM8BA0poqEKY5lWP+eQLOCUzKGPIAPfgA2TzSwpLbDxkgeOQcCMgioAGAwFCIKJBAdwAQVwHKLs+uWBVA08qOPQAh+javQxepzIB6CmShKUR4dlP+6MxAgW+OgMkUiPmG2+h83c/jd32GRoH2ohdgUeIbvlI/E0yHPdCxVErWkg26yd0HRi6BOArBrh8X4JzsznPPPWdf/OIXXQrwvr4+ZzsUAK0AGCJ5FGzUy4Fd6oNz8hBwzmmSzdoFfJYNVEpjMR6dOHGctLT/4NqbgJnyRthbBTLv6elxLIuFNp3d3rP/Lvz+Rr1Lxjpefo9XBc7JVuRg44QNzWeuik3JAb0FfnbzgzkP6ApEEvpM0mC8o7sEJPHQL+SQdLrQZz6kpodsYXQAew8LWsd+UhZf4OaCs/swOQakxESh8z/MJzBawQsHfmydmYlucwaNiSRbq2CngHbYsgCwsdjN8ujEXAjwELpYaWGjmn9KaSo9Q70D7H4enaF0kIHAtQK7AOLyYl2oyUZ0EzYgA4soLJQBPoEAZyHpTdJ8YnBpBzch8At95SboxYF7CNTjtyjlovmwnS2dtNnhCc6ptYatB8D/buXaqk05cb2AS4FAtOhmMSypfgJti0nP+QyMnQBfR/cRbscBhQkaY7hoKyBr1QP/xJWD/hVA0QHEAMIJBKbNBOoHuTkCS4npTClLw1FAWmJWATziA9oJ6b4CW2EbnBydgyT/h+IIZouRygfU5NH3WGy+ZFNAnvTV6NKVUVjH6IvyZlJpt91EU9phYuNCgYpJCaq6qmoKkqtfwpQR4toAcBg3c/WXn+HJBji/QwBIAb9g+BLjMa3W+Ny0Feo7QMvIHAOryrl2BAIn5wHk8X2IvgMN79oBuh3sFyA7RcFj2BBY2ARC07hSulLnk9AeL0x52AexCXr4KnBSUR/GEWlRM4uzpE4dAxyQtLqWFuzhJeCd93BvbCpgf9XZ11gT853kxngPMcYiyMTDBjnhM9YcyNr1OXZE/pcDSiMQ+Zfy/ZATHU1xfEe5oTD2KrRAeznfzQX1IwBNgFxh0pNbvBW/BcSUfEr6IwCwpc0cCIe2qTzGheaj/DP5woDg/DCbB2DT1UaCUJ4xx9zJwhqn1MBh5lBl03UwxL0VNBb2PoYfobkOQC0vuTEAReqsa+WP5wSqQ07yATxSFcuflq2Tb86k4j6ws/HyAIoBhaC9XIw8BXjkH/qYlwCEmn/YZ88HLLgBII8NKiGA/QFgPWcf3ZiWrac/6CsgG7w25RaSDAHAaezkma++L4AdgE38TDodVxeW3WUYjE+RHhagaAJ22UQvMdaSJvoKhroz7dG5Ts9IZs7jkJ5wkwa5ygPhRO7gObtPnZ2DjYzdXMefQd4pfgc+gmcjf4gxqkEvX1030WePjQVsfPFhtfQiAB5jAv9RrDZH0K8a1wiRsulAAWTlr+WlUxkr2jjDKyOQfoaLEGGO8wQXlV8WByAbY2xp48emekPrco6uZXjAXIiMKE7Y2AjV4o0643cDHFuAOVFtriS1fUV5BHJL7s/dOc0xgau75L7pEUe6UpjEFYAySiEr8GgYJRFhLmos3Xn3un3vEBtAbNZ+6oN1du11lVZZh8/IeM+pHRQqzyoqp5xKoN2YK9RNNeA3XDv2tNCX3COsOcs5jvkS/06PFPJ79ZvudUaNI1PpXNomGUiVqislB/5UesmpSeSOj11XQ+pwAD9hsRwzd9Vn7mDc9p84UQTObUrj3PuXOSE/JQfIew0qwkcff9SlD36GtJ9rpEHfv/8KeycM+Pv373eb+QpMc85/FGCVseA2MDCc3PODRgbfvdznOfca/sPXSG0/u336rNdrAuckF+ZjDr9VYNa8nveYb1zqdIbIfKMAT72I0tvju+SwV7B8ymzzvzukj8IA2MMA/X35W8hbqdGXF7LoDPwE1gdqq6LMVSa3VA1HlvWaPHMas+HmsSa0WO4yrAMkl2BJxYzqeUzrJxvYmtHRFOlYh+zkUNgu39tqP/XhStg0mfj8vwH5yfTMos0uzAK02wA8x33Q5Utsvnz4ofvtmacetx3bd9n7bv2gHThwrZYi0CvYTuoSaLMfPpWqJTV4pnqujq/3nyLj3OuVWPH8ogRehwQee+wxh3RXWpSf/dmf/f8ovNdRTPHUogTehBL4UUzTm7C5xSqfExLAR2MnEruj7srZ5/4hZ8dn2a0Es9lbL1d61rBdve/MQ9U5Uds3rhICCv7XL2bsD/6anV9lnv2v7wzbJ34nRozw3JiHzx/27bc+mbF7n82zeO7Zz9wett/696TlYh2teBQlUJRAUQJFCRQl8HolkCVI/cd/iq0HJD8I8xymxT707oh97Kejtns7i78suBSPogTOdwkUFlDVTqU8UYBcQI1Tp07ZF77wBTt+/Lhdfvnl9r73vc8EZBAD/gZAsQceeMD+/M//3P0tUJyC/e3tMJGxkDk4OGjf+9737Ctf+QqpfHr/X2CddoLrfj6LsHnASCdIn/rJT/0eC7CB3Xrzu9iYcjUkaU0wmcUtCTCLMy0Bk0kFqd9iYuLK8FJQkkB7EFXwigXNLAExFnMdiIdAOn8SPAIgBaBG/AURFkhDCqgqCMsRUrSZwJoAPC6YwsdAKaEc4weMKURrctQtTFSJ8A/15UVZinX5LCIrNq6gkZhFXNBPwDnu5BbgFYmCYSpLIJqQF2UQlKL8KMwuYuByq65QO2QJ5gvHR1EurAqki8J1BYFsbpAh8OQpKMl/aCIXYHVBIe50hsiKskmHpwAlslTaWaWr3ZStgogscnPzNPWWjOIsfEOgQ9s5l6CcCziyCO2zyK0jIPrJqfxBhSU33qCdoX0Ej7X8LeAgYIiA9uk+qpMWnhWmU8BXElCIlGRP7jcVpd5zq+ta8OaleoYkcwek4GGLPvSQu4LyDijG9YoP5/mOzG626iokOFWacCagCAciYBHbCYBFeM6jUMpWtZGRgiH0lXo9rMAbi/y6v+SsLlcMOn0GWKHkaWHAiR6L8P6G2sICPOlk8gCuiEtScwKtvNQODR0FeEMEOn2AIRuzQ6SAI6AbY2xxbpbgTZZt9nnS/VWGYeJo3w1r3gGiir00q8RS6gMEytB0AEoXvKBMmsvvWcsAtsozhsSSJDCfAp3qNcd+oHHGfIJjg5M19rmIsgSaVCCBTxzqbcLVKjMDKxOpBL2l06R6g52wGdAJqd/81QmCxS/aWoq0o7VtxL1JQ+aRkhAmoAAJKzCiuRJkliHIGwTTMknQVaXyLQwr6eXTpBycYCDXWGU7YA/StHkEeYm0Uz/GM4CwlBsbyIlAfYxoaETjiLkUCMxJsN2xyyADYSUl1BAA0DxR2xxjlyv4MsZI1QiSaBibAnHwN4Lnf87X/ICBZWPqGGmAN6yEdsWVfhXWw/zqhs2OTFgcIZRXkkqQ9MBeOYFlgRIoMaRgPXOQhtB/sD3BDqlMaaVlBHpJk5taOE3ADXAEqVJrO7ZZWct2xN2BuGGYRO4ecnfSZkAIhCNQggAQgVIdwgKoAYbmpC0a04xNSQ6ZOrZCgVBholHAH0gBASF+p+8isBopuE8YivpRugC1pF3O8AolSCtcWu3uyXA4rxnnpLMKAT1tGBdTqYBz0wAZDxw4YB/60Idsz549jtm0cF7hXcPjnD6kggSyoA/VTo1s1V3AOX2Sflog5fPDDz1iX/3aV21waMh6enpMIHWlNheDayGt+ebp7ipXxr+UDDbrSU1V6bOO1wTOpUh7PT0C+w9pI0kFmqhrY24yhgFeCQSkkS5GRkNH5gEJ59dmwDCNkaqbNJKkBBVgZh1WueV5Uqeih6pJw1zTRmrGRC/zB7DcGRZHMXA5OQKKEHAOblpmeJYXdl4AKvSPgLQCzomxS6BV2Td9zANyA/rL9TBLU6OoAzGhlJjR0q1Z+iMQgINScwDcNmb68Ymilmi+iFStvQCJSPu+eIKA6xTVKbdEfZuVlbdi2wEnAzqiWALI2F+Ayqnlk7yG6f8oPlMz9crBKnfSVhZOkHYwS3rUHVbfexBm0i5kBLBXQCkBjMSSif6TccFkYueQnDYJCIAsfYoMNXqycgi4YQj0SFhgYflHaFBnRIVG00F75M94su9ig8P+hQC9eYCV8lA5JUllngaElKitsHIYcgWE2lhM2ursKMzbpEQU+2yFwFjoQYENsd0ONOxxDnZihUC0xCXGGDHjra7NsgkC1jEAkfWkME10X2TZiv3UGaZXn3GgTQuyzeoap0vRn7yHqaNScgYw4tFQ1wRn6vgUcnIphREKyyAfBL9GkChK4Td1KifJt3AILsqXbED25GDNSy0cxfdZJIUtfmnFFtrHOCNlZn5hCPxetUXqdwIu7+Aa1U39zkvzVL6iB8NYbgyWriF8FlLMYqtCGK88rH/rpG9d3VixaEXM6lo7sEO7GcIcxHoAAEAASURBVA/b0f3YCrS8EDw+QL+cQNuuL7DrVDTAZwzJD1TfYc8EDvdwEJUSXa8QKAH5nZKRA9U5IA/jQMLg/xBzB5pZ/hRygSqTXn59ZtSlxq1owGbUb2fIw+bHmBBky9ldxpIDcnMVpsn5ro5VGRvqw5y4ujZEqtcpl04vxljNwXKs9Lv51VFARTVW2nANoMArAOUBjC/FlmsTigCCnOtGGWU6P47v8Waw7wKk0h84XmJV9vATNS/IZ29p7CAQMmoUsxLNTfWdWA/x1TzWm30ADTn8Dt3BF+ARdr4AFt8ApuYY8y9Wt50hDoCaUhAg/yFVxkAen8hpVOQZQW9F3QYKQBnzp2wteYJ60hsw+MFPxkYJmCnnj5JmGGY8xnaiY6+VwGqZh/FWnqZP30QBUvEEgcTO+N/8JS9Lh9iO8+pHjVnNJ/SPngvoHNoJYFh9zniiRdIi1JRnFz4BSaRMDXw+yQfOQ6W2PmwrsC9nSGNbUtEJNrEJAKSExViRXAQs02TRg4Q7CvMbmfP8k2FDgsZGGBCKpOozGXMADn38q5jzv+h7fhdzrYbhZn9pbCNj+ZF8o5I95qVUj/zvIyeSAJGOAGrL2aWX7LYLd9bDWse5tFnAOqV2Vt0i8m8kEgpYYFP7sf5le/ElWEd5pqivrUSGWTZQUdYTAFaSnXbRrlr74G0x272bjSvlpCV2fYgvxH3DVE5+rvSd2qtxKt+TD9wXOdAn0k1uXvCLpoiYAzc9X3qKusixlH2RXgi0GYfGyNyoknqUwIPUULOx8ZTd8c2nOb/KrtjXbXt2RayqAu8N/eRzD4Hy1L8nisA5N+LOuX9ko8+8lklhfeTIEfvud79njz/xGMyLSWvvaGeT3nvs+oMHrZP1iBgs7gwUDR0ORod7Z4YR8JOPE3ILfW7gnXNNfSMrdLafrXILMnxN4JzTEcgcBaHpJEWiWeZkKrExs6CL5Qe9WI+B6dZj7rlzmYDunfmHtaIPeDFPNc+W5srt6Sdn7dTJcdika9A1LdbcjI+BDpMqDXiGdOlSuVZlyHbneRabHF+zRx45YUnYKCsqSLkKYHxuYdVO9E/a089OAobdYre8Y6u99YZSa2mhnu55XusA8LKC8FWcV/oA5J4t4L9898477N577rLenj675T0fsmuugnEO39IP8YxP27gL/wrau6k/zwwfPr3+owice/0yK15RlMAPLIE/+7M/wxh81xobGu2vP/vX7uH55Q+yP3BhxROLEnhTSeBHMU1vqoYWK3uOSIBYoQ0O+/a5v8vZF2E4m00H1l7j2ftvjNjPfDRi23uc53eO1PaNrcbTh/P2b0nTenggsK5mz37xp6P2C/+GJ9Nz5Fjkofwrd8AA+Cewzq0Gtv/CkH3mV2IAGbXAUDyKEihKoCiBogSKEnj9EoBwyD77N6Rt/TtsCwDyOGb+9gMAx/9D3NrbWMApuqKvX6jFK95UEtACqmPAIXAxMzNjExMTJgCDFlS1BqHUeTt37rRrr73Wpcvr7u6GAa7BMdF97nOfs+eff9703cGDB23Xrl2u7VrMfvjhhx347rrrrnPgBzHnl5Rs7nbQ/RSYOXb8hP3BH36SNEERe/973mfX7L/aKiphsxCbjDZusLCtlGclLMBGRcUgZjmCSQqEK46q4GJAwFPeucBEWYGpaIcgXJq6m0u8LPNyPxrJdwQnFYjiL8VG+Z+wFiEVFoVD1EdBasRBDJV/CFoKKKZVVheAIarDVZsxLEV41tctNT5hIVKSRapIQ9dImrQyArCkFMtzjQAyjkGL24W5d6CUdRSUJ1CaIoAoYFCU9ineG+GmjqlEAVcW8nPc14G/VAa/u3bSNMVPs44igupRZgSQgAJPjs1HC9o8yCjApYt8gm0bnK9UYTHdi+v5RNBaTBcKPML0RYAxTwDQ41ylkORWtB/gGWWHVmHrmBgnUxYp7epItUrq0hDBTwV4FZxSWTSKdy1wK9BJ3cQqwqp3SO3is76UzFxAS/JTA4RsVKPUh6KbcOxqnIfMBFjzKVPyy1GW+iJKn0YBzulUsaL5up4PLlDOfRQgU1BNQb8Mn1kad0E9QpjISPfTfagL5Sht1iawiToDUMrPrVpueoVd7bDyNQK2qoclBgCdmhZRHzMeNoP0CnoKR7hmCyMvWXLxFPckZZeYdwTUYEG+BLnVA+QqawNUVr0T+StVG/LVvWlXFDCHAGCSiAvSq0HhWYB3BPgJ4mZJGwZixGLcswSQQ7iEAAQp2zxScim1YD4AdAo4QmAsASgkV0UYBAz0JX/u769N2cbIIfMADJRW1lis4woLVbaTRQ1Ay/SztkQK4MqOXVbecgHdX2cRAVKCzcCSgKA5Uvgmx4/b2vwQTRfjjkAgMMmkV2EwIeVVwxYrbbsMEEIf7WAcEPz3HPhNaYPpANon4JyCxAJpKkWav7oASIaAKmAYn3S4YmELwZQSrqok9TEpE0vr6DcFwwm2OnAGo4sOc10n0SELknbRpwAGNqZtaeB5W1lescrGTqto64LIjYD80rQNvnjM4jAdNDV3U8duABFKJ8h81BxmNIpNLw9gbmnslM3OnqL6a8TyASKkYcmifVFAc9X122BJ2kXd2tAvMOoQTBWAFdSGCxpnCVbnGYsxUjxGxeYDe4G/BhBoNQW4bdUyYgXiDDG0xEjlGALcFwJYY6TGVQo2gQQVwEE7AFrUgD5TN/pO1Cp50tWll6cZPwSEqpr4XXPt/ALOSVecfcgWaG1bL4G0laL129/+tgNoC5Ct1KUCZAtApnMKAb/CNWeXdS7+rfqqDwut3tSdfGauSBQZgDRKNfaP//iPds8992BW1k02813vepdddNFFgNBgZjojn4Ls9Pm1jrPl+lrnnv37P3ePVwXOwcgVLB+zqVNHbI7gdU19izV0b7V4AvYrxjz0q5hf5iI21cdgrcGeJnDO2vALFl+ZtDiUR25mZXMuRXtZfStMXj1WVt3B/AewA8gmENBGc9FJkzdNS1g/87BcxZTuEZbN3Cqp2Dc0n8UWJ3sPGLcEAHEVKVoTdei8SsC0lVyDHmUWbtpddKn6glTjusKLC/iyZumZJ21p+BE2BvjWuOWglTXtBWAya6nJZ21q7DTA6Bpr7N5t5XV91K2WNjKXsWleBAYsAL4rMydsYarf0tiZUrGBoV+z6FcPoFpJotEqW+jb5r0AiiqoDzYHA6Y6OXuF8pNK8LAFLhU1jKs+qUIFNgxoX4701RkAyVH0aJSU7BGlF69qR7514O6QsxhSsNMMHIpDz7hyZHsEyQNgQrrYzOyITY6cgi1qw+pJ/17T0cu9S21lctqmsHEVsL/Wd/WR6n0L7FSw9BG0DoMqxzOjPOzg2HGbHxtEfwKUhE0mgD0rQ5pTH+BfKamp6+m/aPNW24h1c36lxQWaR5ayEY4NFH9NM0N2P+TSx4+BCRvBTqxaCkBjDhsXwVcrBaAd1jgiFbhLd43OVEsQNM3SIFCrJHfmBO3Up1yGVKoA5JZGnrSyYMkqW/uoC6lUkfE6KdXXxo9alD5IdF4BFnwrZrSM/mcyClmjvtCDH8C5jdSgjY8eBhi/aOX4JSHGp7D3AX5TFLtT0QqoqLEFhtNO7orNgFVRjDqyzSEx/3GecwDkQGIbgiz9tzZqacZ8JoVfRf9E2SQSxk8IlcIQSwpUL045SqWOXCiF66gLbdSUD4mJTKyHDqQQWHp+1mZOA4gHzN+xtdfK2i8Fs9VFHYCFOr0jP0HHmXKoxuZGAGxqmjTKqzOkr3vB5jcG8DuA0AucukobseHlAM/rmjssXAP4nk0AYi5WkwTKckB7jVEKx0Ogbvii2EQPEHpmcQJwGmySAoJrQwBzNAQjcCTBHKxsxSeqpE74W2JVpACBsFyHyt8TizEiwxLyG/7n0glbG3qS/QBJq27dY+VtVyIn5hpHwD01fuSm+YyTnHwG/o4zrvDc+WXNkpOw5E7Qf6SdF3jMAfCzMFRmVqy0vMpKG7cBNtyJbmjm0QJwJnWQ1KOAAOW/oKzwG5AdHYC06UvYavFB0vgzmY0kDMv4OvKH8Ysj5bQLfy0Uof+8OuYfdaIuGY0b+lKWfvM5gvP1B8x3weJx2CePM29g9mvotETrVrB3YsRE36ENcF0phzkCM7HAYyHpUm120TBH12TFIqnnAIBimgvyzfPoV12j5wc39ijDHcjV3Re/U6ynGTfG9ZzEuZsqwumce74/an/3D3dyP9/e9c6b7YZru6yuWqMI0KRAq9zDx+mMa/5JSXH9DGlSH39yxO6+90mbgbW3nE0bcm+XYa7L+OXW2bvPrrum1a65MmpNpGsU6E+AQs1bPf/ocUBjQQC4MDbCVU31p4/zgHz1yOYJ6qyNCmoMc90DNCgQMT3OS/XQmBGYTuWV8EIefNA5iN/p9gws24ePLNsf/dEdnNVk73335fbW66ussVn+M7aJ8/UsFLCppgicQ0Tn4qFO5dDmvoGTA/iK32Kzxf2sWcyy0aDJbrjxRnsrrL3bt28n9Sd2V4NJl2gIb/7DZ/pbfie/bfpSGmv6/fw9nB96lt+oz3q9NnBOGgI/wk1SyecsQbmu0D/MPSkl5pyHTpCekX6WEtjsLekZaVb9B6ideTx6Kmrf+PqgPfv8i3bBBU2AHXfb1u34CXpk0134RxssffSQfEMpgYC1gqFTy/b1rz9KRgT8IV8ssaWkV05ZkvWK0tIysiNcaDfd0GnberVhQZepDqqf9IvqsPnMTLG2uLBkd377G/bdu+6wnq5uu+Xdt9vVV13tCMKDkOysbAxM4fiROja1nfvzh/qnCJz7ocRWvKgogdeWgB6cf+mXfsm+853vWFdXl33pS19yCwdRUm8Uj6IEzn8JnGWYz//GFlv4rywBgeZGxwP7G4Ln//3Lm6C51irPfvLtEfvov4lYX4/cpfPzUNu//3DefvIX0rbCwtG1e8L2m78WtWsu4Un1HDqeet63X/39tD30gk8qr03Wud/8xSLr3DnURcWqFCVQlEBRAm86CSywc/HP/yJjf/WNvE2QGpxsj/Yf/13UfvqDBA5qtbD2pmtSscJFCfzAEtDiqRaRdQgE99BDDzlQ3MLCgmOOSyaTDijX1tbmUq0qJatAdLru/vvvt2984xs2Pj7uzunu7rYsqYdGR0dtid3gW7ZscUx0Bw8etKYmgFcKupy5lxZFj7x01P7g058BFBG1999yq117xZVWXQmYh0XaPEEnD7CEgk56KVinAKKLbrDgLSISt7BKajGttIqxJsMiaYiATpRFVgc8Y9XWLRDrFAJ0bglYC+kshWpBXawLwGkAEBGwIQgWFgpJO9ABkRHuAujCfbi3GKKi1HsT+MQyMalB/JkFm3yMwOz0DMwq7Rbdu9vCTQT1YBlzzCZC/vA/TeZ2CngS5GIhWWxzhLz5DqYYFnKVBETsXJ4C3G7BGRnpM1EzL813yNmtSVOe0qPmQPcqXqy14DBBagek4nydI7YaKsd9YPogAJSF3U4sLQrIK5AldjhfASdaDWyA2xGEVn+wyK0ggsKslEhgkdZPAup6/FmbmZyxigsutMpdtK8aZhx2hG8GGzYX3h1yS+wi9Kf6LMM9PYCQAiWovygWkdIWfaZ8Ad3Uv9zGVTmg/0KKEHKiWMmIWlE/6o1kVKYD9cGioXRb+o4v1FDqy3cOXMd3oNOUwTVNO9L8F+NepZQRUdBT40Yv5MI/XEN7CWZmFucs2T9kK4cHAEy1WMVFOyzSCesOq+1iaNFCv0B4CoRr4T4ikFPKJxhLMHfuMJm0JqgLgD7Ac5Ew7EOJDktUbUNGW8EY1BA0JVigpkqqMAaFAExIwq4vXVUID2eOw/Bz1NYWYF9aBdTHuIoRYE9QbrgUFhGAB5HGHbCntQOCbKTvqgFl0XNK4QWwQOnsHHBOY0VR7Y0RWxu51/zF562MVKqxjmsJCveYPwdIZfxpW1heBji31yo6LifQSyBbk4iX5EyE0rHLrQMqW58fBpxBkB8WoQjjKBIpo7xGKwFEEbCjPxsDEEZ/RAGjielRQ12sgo45jf5zQFWY+HLojjSMOBlYlnKk//PFLiVmPdYyAwCyUUAHJY291LGb8UDQOQc4hm7V9BRGJlBawTDgNMAPYViOQqShXTh62FYWV62qtdcqO7sBpsH5snLaTr90jDS1nrU2byF1HjIrbWEcArrRGKF+Sgfnr83BlDRsywAfMwAWA1tSDNbKAC8kEu0WJ91eqJz6RElnS0pqN9EkG6rhk2YoJR0DQKSE+pT4sPtRn8zkICCUcUuzbkv30d9ie8lZHBamaF03zbqIYH8H8xC9Jpo7+k+AQ/UfiAQ+A9pQY7k4WF0CpDFGFsd6i1W38jvnUvU3e6pW6WAd0huFvwvvBXsgYNahQ4fsy1/+sj3zzDME0y6wD3zgA87W1NQITMB4oBy9dE2Bie0HAZG5i8+1f1xbNNTRm2faLobWp556yjHNCTinFLWKARTW/l+prQU56rfC7wU5nf3dD9r8s8s7+5pXBM5RvyZY8cRM6i88Z5MnnnLAuarWdmvu3W4l6CAGPXOP4GOwyT6XZvyvoQOyy2OWnXjSPJjZwqSaVPrIMMyVXlUXLFCXWUm1QCi6VmNGekrBS6dQGUiqmdhGsfdiOxGYBb2VnBkA/DuLHgUMjUwjXBuDoSpW240e7aPsNiZ7E+xShRSa1I65HQLgw8STwnFANqX9zM89aMnh+20lmbaGvhutpGE/7Zy2zPiTNnt6CDBXs9V2XQzwpg+zVEd9sORn9JVSeqcWR2F0g80rOQ1mDuAObY4y3ysB11RQl0j1NvCE1AW9mcE3kCKPw1YVAbykUYGmYfoDGsH25WASXZsB5DZ9wqKrlEe66QyAtRiAq0gCYFl1jZW0X4ju2orFaqTMBHKhPuhuxxCGTZHIxEwn4FwoTTum+21yGL2MLWlsbQWEvAOzWGbr41N8/zxsLqQy3LIN4Nw27Hot6kpgfuwiMvewTxsL47YyPQzQdw6djewA00WhtY2X1VpZ3Q6LVsG4Wlpj68g6AktVCaAVW6evuL9XKl9BdhgrKxudmrb87GHzZ49altScq9Q3g7wkiTJSyEZqmi3WQF3qd9Bh6GZYbRwgU62S7+AaRznITa8A2aRmB2z+5KMA3pJW0b3TItg9zbXVwSdIQ/o8BGqtVrnlWoBz27BbMNlhrwSKk572YA7zwqQNzg7b1PiLloVhMLIBkBzgX8SrBAjZYGX1dRbH3wtXoJuwy2YA5QWWwr/QxoEQY88jRZzSbzt0kA9Qf+2YZeeegmWMcZrCT0EuMWyhgAZRgFehup1sUrgYe8+Y4jck5FwluUV6CXyn9LGSPwPKsvgN06eftyXK696xFSDgZditXlqJrdHF6El5dJsXax5hZxwQTPUCCJWatfmFI7a4dtxC2OY4TlSMVwS20wTg1XgNNrRcdlRtdHB4J0PZODHJyebLn5XUo5lJs9mX8D+Oog6W3XgXw6EYGENsRogBII81bEe83Zg9gGVKIS8HSQgGDjEz0mh8GoE08O8ApgdLR2156GFbnZmzurZ9Vt7+FsZOnTtHGyDkyzHCaSatFBMx5SnNXgjGOAHvsslhGGbFOgfLbFJAi4yVwlBbUVJviZpui9RupS6t1B9Au/KVguKSxQ0zphwrnNvUwX3w3YIsKW3Xxzb9NVjscuvLjsU5hl8YJh9oGPBqHF0QriLNe6yHsgAh4iuLxVJ6GNfd2S75t6qfByulv3Dc5gYOA5xjXbsF9sp2QIoA78xDb9IQ6Qbn68NqB2KWsYd+zDFWAZ4IxJbX8w5yiLhdI/jgqir9oZSmmhJ6pHBfOqAZf2pQ8KVAlY71Wr4kc1DgNW4l18S+ddeYffbzd1DnkL331ncCLOuwRlKrCgCZB7gpwJtEH2c8ODggY2qRNZSjxxbs0EMv2NBpGHxJ9+qv8xxGquct27bYBfu22I5t5dZcz+aLUsqij+QCqx5oXF7UjM0JTvr0P9Xn/vxAfbMAe5382JC0yTbqTAJDRb43Y1rPBLILlBUwP/RMKNZGB1w9U478P7lxaXTQM0+v2Cd+79uU3GEfuOVie8/N5dbUyglOV0pGvAB9FlO1Sv7n4EE/5/AVxifG7dADh9xGixP9/azV1diVV15pt9/+fkBz2yxRnmAKMTY0kLjGTYjNfzY/q2n6SQODdwc0Pweb+0ZVSX5dwT9UmQUf8TWBc05+6DGukbouHNIXOtz7GRFvipL7SH8r/fGmQWVOkc5ez6eUpVmeZS1m8GhgX/6HcVjijtnFl9TRbztsx64EDIGb9oBVH3wOdByFij01xPyU/ZqZSsPQfMpePDxhUzP4IaRejsIqWMumt90Xttsl+5qtu7PEyknlHGGznDZDqpJuXlOmgHNinfNJ57y0uA7j3LcAzn3DOjs77L3vuRXg3FVngHP4pGgJpWoNqDtNdENJ7z/sUQTO/bCSK15XlMCrSEALAs88/bT9+m/8pj304IPW0dlpv/mbv2G33HKLVVfLOS8eRQmc7xL4UUzT+S6bYvveSAkoZjQ+Fdj/+HLW/uwLOZsBPNZAutL3HAjbr308Zj2d5/dYXFgO7Itfzdlvf5oVI9YdPnJjxD79RzECCedWu2cBNHzxKzn7zJ9Dr0x6pQMXh+w//V8xu+ocA/i9kWOzWFZRAkUJFCVQlMC/vATG8AE+818z9qV78jYHw2kbsZnf+eWY3fauiFUmFJD8l69D8Q5FCfxrSKCwgKrA+BNPPOFYbwSgWyUNlAAb+j4GiCRCEFtpWm+++Wb3Uho5MdM98sgj9jRrFpOTk+58pXtNJBLW3d3tQA9imtO5ccApulcBKFEAzn3q0/+ZQI+Ac++26668bBM4J1AJwQuilwShAJvwuxgcWJJFRLyEFlLAjZfSPLm/WSR1MWfHWCA2BM7nfkqtpACLWy1VQIyF2CBD+ZznAW7JE9xSJEhpMhVXEkDPpWMSCoaJLyY2BwgSk5t2UWvXMoC2/DgsV3d+z8rGSGnFQn38wH4Lt7cQUWKROEqd3SozTrU7AE1FCHay/KzgT54gtM+7AmBqe1ggHRcIVF14aaFXuTc3YGUQMNC1m7IUUSohzEQqEcf6odVgBY4lLwLNSo1FJ1B/6kp/hcoB5hB4VkBKbCsetHMBAe4ARhcFrEN5fqd/QRq4+wdck5dcNgCLHR+xzF332eDQiDVec73VXXG1hRqqCHhybwHyVKczijFQf6gcFrgFzlPbXaCX+ypNnKO7UxRPjBmKmnGtAp4BY8W1U31ILUV9ISAWkdbNc1W+RMhCvIAVihtLZiAHOI86R0mhAiONGEfy3GcT5JYGMAGoiyCZWGQcUM8F/CmX6KUCrgFggdWRQRt75DGbfuI569t5oTUdvMbCXU2MCYBzSkOK3LTLPi/gHGNDoIMwzCAebCP+Wj+VmqEOBDSd7EphUWum/FbqC3sJqe1yjFcxl0SU1hO2NCiRVHnqTv1hDVGgPrUICGz+pOVhfymNJgjqwpMHyMIAW2yQOjQPq1Fpx3aLw7aSBRSRhckkxhiMwvAkIJjGicSmlwuKb4zaKmCP1MJhWH/qrKzrRsjOtpOajADy8KM2szAH+84eq+wWcA6wh5tHCJg+3wQXKkC8SN0W+LxI+QTpXTREQDLY7wh45wl4Z9nlH6E/I4w3hULcvKBZrtPVzzxYBxsZy8wDgpgac8AIyAAAVlAs7VZ7lzaWAYGSDrCp1yq69lOfPsRDkALWOB0u+BLVnEEe1MMArNn6nC2+dNiS8+tc12fVXb2kOgTQtjpgowMnTCRwrQ3dAOdgkSljLsIu5cG4yCSjHM0pwKukqw1yBPltgt+W3Xj0YIOzAHl4HYgBQCEAHijoVAvXFjHOKZ1zDhCAD2AzSl+GYVfKLozZ+mQ/QeJ5B74LCRjH3MuR0jdNYD0XqbJo0wUw5Vxo4bI6RnyZAwM5MIPmqBvcAPv0TjTHX14EczNlQUMHqenauT3jjbPOJ+AczXGHdHPhENhatuRb3/qWffOb33R/K/W3UpZu3QpzGfNCR8FWCYB9dhCwUM6b6f3s9utvgc3vvfdeu+OOOxwQXcBBMe4dPHjQrf0X2vxK7db1+r7wKsjqlc59LRkV6vXya/9Z4BxZcaByBKwLE9uxJxyrZXVHm9X3kFoR1sdNwBv6w69EjyZgko3BuApYg1SF8ZWjFk4Pcw6K3TFPYefL2mFuAvgK4MrND9lrbJuzccxBsbBxgZuaGBf3tw8wJjl+krSawzBArcOWRopvbFmwTtrvpRRFA2KtJpUljE6RRthAAc8JcIG1cmD5EIxUnnSqdAXAd6iuAMYfsuTpQ7ZGYLZ+69ss3nwVdZyw/NijAK9GXIrVRPtFgNbQ+SVilBSojOo4H4H2ACYLHAPbEtfN8zt19iuofw02FJkBPs5GSemI/cgCUJb9jMMqGgFkyAWb/gLssAx6W4NNZWFswALShlZ52Aqq6AHGD3IpmK9IkQoIKtbQh55Bj1bvoGwxlMLQhL/hBeuURxpq/hXQRWyXHsxnudl+mxrsx6xmraG93RJtpPnMlllqeMKmRwW8DlutgHOwcILmoh5n9KFkL19DupTU1z6AIsshP2yhJzAcqc0s2oENbeGSMkvj27jU1DD6oRRpv4BzSB4wj/wSx766MmPpiRfNmz/t5BQCDOgQNTAHbsASug74LA6jXlXv5ci7y+lo1Ufh/EAgZEqSfyhZOfkjlxw2b5HxWAYbYUnPBRbu3EfZQCIHHsT2P2PlDa1W2nMF/dhDObQN8GTgHMh6yqAPZNsjC7ih0xbamKXu2EWBs/KKw9XCogaIqQxfD3CQSzev9JyMAQcgp1mefDFsnca1vg/WAeLNv0gq28P0wjL2HhCjfFrseBZbkUH/Z+OAtxsvgbX2MsBrVYCj2PJAk5zvqk0RMK96IcYT7Zbfl19cgPXvJZudOW5d2/o2gXMlW/m93I0njY/Na+QrUCkxNwJicMJVuQKn5U8zL0cBZGFr0xjoLKBBgHIe9kppUT3YeAPmLI3gnM2+5w/KYwxQOR9/Tv0YAryah+FtAzBlGNsWBSzniUkS5toNGF1TKQBd8TqY1bZatL4X9jnkrnLluzJSfTGL4R8pda2bSPh4AenlV0YesqXZGYBzV1tp+zsY20ovz73DSjssFl/aJnCtGC0BQDofTz4Z8wSqXcY0wE7effk08sFCtIlNCB52HmVAG9FR+PhiYjZt0MDvDgBV8AP3OTOmlJad9MNK6b660k9rpy3BBpYILHUB4EilzE0LyFrejK/Gc0DdJfhKzAP8zU1WtE2WWSrCPTb7kR0FAOf6be7ki5bieaSmrcPKe/biHuODBNQJG5dT29xmIKXnlXzUr+ga2B+1qSfNsA0zpkP4xJ6AqU6S/EP1NTsEE9GYF15S4DH3yMTtaSH+qb7bBPTJRdepiMG+c/ec/c0X7nSAldtvu8Hedn271bBZPYcezskP5xkiztyNUaak5MN2t5HxbGUtsNn5HIypsImzRu/zOUyBFfVhWBnDAJk8g8QSZkPqo+nGtRnGU5SKkQnWHXqEyDOu9bwWZaOCe25TbVErAtEwJNzjkdsPg64Jw3YsVm9VX7EcNcIBeQS4QWfIdY3qWYmK6u8c+uGFF3L2O79zDxsnWu39795ut74zbo1NPB9JRpSlfTnSrUXgnOuSc+6fPHpQvuKjjzxqd333LjZZPMv48O3SfZfaO25+B2y911pVFXOE0SkmTw2tTf+IP5yBLjSJccN48Bl0GmcCzr3c5ymceT68F3zEQlv0Wa8fBDiX55lV83VzwiNP5qkDHOo75p3SG7vfmGOIkVnInNVmQSaunr997KrA5E6d8ruAa+Mn8/YPf08/PtVve/fU2O3v77HtO9k4gAnSnFY5Z5ZbXNma3qqvloXmYbhMzufRxWw64Bk/DBt6eaVnVeiaipoQz9HwU6LUoixCiGlU9d9Mfb6paGRylAZ7cSFld37nToBzX7Ou7jZ7Lziba64+wzgncDqjR/ZSdkBXFl78+UMdReDcDyW24kVFCby6BLSAoDStSoHyEruxtUv7ppveap/4xCdcmpTzWbG/umSKv/74SGDTuP34tLfY0n8NCehBa2o2sC99LWv/+a9yNo0DVg9o7h1Xhu3nfzZq+0gJer4fx0759iufytg9h/IELD37t++P2O8CSDvXDvxle+yZvP3aJzP22BHfPcj/zG0R+/VfjEELf67VtlifogSKEihKoCiBN5MEZAs/+V8ydsch31ZYDO9t8eyTvxq3t13Hoi9xnOJRlMD5KAEB2fTS2sL0NGCwwUEHglOgXN8VgG76W+C53t5e6+npceA4/aZrlN51cXHR1gD7aHFTwLn6+nrHllNZSTBYIC6thJ45dI7KP3rshP3hH33GRT0+8M6bYJzbawnAUD5pK4M1Bb8IjFQANsHJC5URzIQtwosS2VCKRNiZ8kne1wmiiZmNRdJQgiAt9w6VAZhRzg8XrCbaQsQlT3DLX+eV4nyCOgLLhKoor5IAKGm3AlIriuEuTL2CFdhBFpOkRwNIREBP0RgBxsKkYnWBUphHsicH7fSd37Gy8VGr2kIqsMv3W6iZtF21LP42wh7i8oQQyFOAV+nbwrBwcP/8ggAyLMgq3iuAWSmMcJWkeiV1pVcigCD3J5VdnjRHPjui82ssWRO0dIv+tD1ST+q5csBdBXCegAWwhfhKUYf8/TWCfArYsgIdrkAWFZRdVg5AEJAZDDZhgHNQdCE7+NSWeAiCXc4F52IE9xIwZ1APfz1rmWePW+quf7JRgHN1l8IEeOkV5jXWUVfqW1cDixb9IRoNBR1YSVeqO6Xo8leQ2xrMD+qSGP2eQKakelIfeqREyys9qICLq0naSOB9lZ3pCvxJHOwSVx+GCHx4Al3QXsdCl6fc5XXLzSi9mAqmP6iv5BWuJT1ddRWL9wTSAAkoABwkJbsN+hygk4LdBEE9AIdh+tqBCQHOrcNcdvq++232ueetd9suq7+StKYtDZDH1MAS1mTkG3SBa4meJlI2dVT0gCB6sDbEOCeYTqospSnzAAd4pOPzAWj4BNW9WBeBTcYAwdowTCxeGPACAVCl0oWKjnfqA/tQDnBFGiCXAnvxOAwJyCsgjVhAurP0HCmTCYyXIfPqrZcjkwtJJwbTGwHUCCw6QR4wGWntlCpPgMmQGE6yANUmnibV6gDpAJst0fV24sK7CNAO2vrwA6QonbDq9m1W2QWzicCBaWQvcTqWHT7rA21S0DkADMMJzB31AanjjGAULDgZAhseY4lecoyIPuBONwYAAhDW4HzNWy4ldBukmSfrgDQpN3IGAKtYsBiGkrPP2CpsUxHSmdbufIvFa/cQNSUdLYwqAmbQgdSBoLfqAdsJwiQ4vGxrg8dtdTHD+dusohNgRw2Ag9UTNnwCZqCkb22NPVbR3uPALGKx8WEfUfA/JDYrUj07IGpAmjR/hDIBfZCCjgnCfdAXgAZADCAb9AIMJ75YCsVGxbtLP6RB7QaDwtLoRliIMqR6FStUXCl+9RsTW2kVl2bGbIFxGCqrt6YeUjrW9BB4Vv8BhhD7oFi6SP3mZwG1uMg11ycBfCRXLWjcDT6gk/I2AzZvduCcRoN0/tlHYS1b3yvLisDaX/3qV+3JJ5+02tpa+8hHPmI33nij+7vALqdz9SqAyM4u783299ny0N8bGxsuVe1dd93lwOv6bv/+/Xbrrbe6FOiyqZJDQRav1t6CbF/tnH/ut0K9Xl7GqwLnpI/mn7eJI4/ayuocKT4brRoGj1AUFi4BWgQYZV6FAAdnYglbB+ASBUxcmhoC1zPG/JKNxSaKKQi2Tj9cjy5BfwLYEvuXAGhG+lMvxLwBOOfSNAp9AQMTxpB5C0BuVelLkwRRU4AlYAYTQHsVcMnkBIxcg+i4LKDhrVbShh2r2JxbKFuuRdcJTItPQUVAdjCzQ8zBhRdsZehpSxKcbdj+DivpuJqfR8wffdCWTg9DfCaALmyS2Cq/BN0uOxHQRgCCDhwNQxQFoz+wh+kFdBC6gZSlJjYw2POU8jAoY0MCaU5zkh9tiACK8TIAlQQGk52OSd+U06444GD0KHa+FMBIhPYJBBRkANqSVntlZgSQbuUmUx8pYEOljbQEJktsYQgAtAdwT+k3Ba5WCNwDGJhfOG2zI0OWAmRSB0FDRftWp/8yo9id0ResLBGC0bOH9nUCOkE/Enx2AAP5HUqb69i40F+AkjD4Tj9CLcc7v6FLgzznRdGl5QCI0JsBLGTkuaRN+F8l+BcCXWFbwgD8otTLJ/02OdeoH7pX6SrFJpYbw1U5aXMzE8gwsKate6209WLLxzqQI76FANX5RaejXTp74meEyakfdg0A8nI/qYCRZUk3AMeeq7iGVK0D90HM+jhgHkDo3XvNL2+ifvg+gA/5wHjqZsw1O9/Gi9AHAbLbGKMrJ5EhNg/7Ti5tXmIyRZfDdiN7KNkqGB8gT7GAGWDIAGZSgbpcn2ciztZnAKuRuRSAJ+OX+8q+5VdgJ5wfs+Q6qWmreqwegGCkqhXXEVnIpnJOwNh2Nln2kMsQgvOdkpOnbG520Np2kmYcJlnD95DN1tiQPwBt2+ZY0gAFmOlAcQC6obSljsgpmKSsEc7n73XmDDLwmKMeIHBfeVPl15AO2G0swee2rMBn9A02S0AjIRw8tQWAoL/ORhuY9ELabIL/6QFu8wHip1d4PpiexBdYsfrmToCBAMtqeTFWtInFAxAY+NPUZWKzbVnmiAB+qRO2PvUwwMAZUglfi99yG2OjlqGI7wj4MMzc8lLMX/z/IM8YJcWt/FIPX0op+hgElImtz85THrLgPgZ41/PU5/gapJAGoQhelvqLETqL38g4C8FoKDCg24AiX1MbVjjypHnN5pZpH+le6XP5FAHsappLG6PHAXUAKmu9xOJdV+HHNiMjZKDxSKplbUbwYdb0zoAOcVLRTxO2fHrAkqw3lDV1W20v4xQWSvVtAOBQYzvgWgTBi0736TePctGLOe69znNVjr5OJfOWXoVtGfCc2OsEgKsgllGF7x0rBYDHF2nGZnYNHkvOTen5hSYJeJZgY2BVGZs8AL9of8Fd967a5//2bsAtWbv1XQfswOWwAqNzstrAw/CIwURZVc11ZWJzQgcxf9NsBEmhSgWaW1M91uhTplNpCfMbhjkB/OSz18AOXFHKTficwb4t4Md5KWY8ekNDaZ16beAzxqmPeGLigBOld1ZWfHxFdDx9E9bcoq7REsAy6KhyAL4Cu6W49zrty1KGzvMBzklfJSpDTpfFSSUglr4XnsvZb/2H++jLFrv9nX32lgNR0kPKc0UHII84dayrhRHrVL/zR7QpTH6IGGAF4pddlI18uX2khcXjDZTAK8lY6w3a0Pfcs8/ZXTx7P/bYY7a0nHQMc2+76SZAcwcBQHUxlhmo9JObtZtT90zN+EDfFQ7dw92HcSKGxc0LCr+eX+8vl2eh7T8IcE4plJmWm/NzBf2heSYmVb4Tu2WiCnAsczEiAB2H5uEG7OwZ6Rl0El1hJczlEs4p41yJenLIBzg3Y488fgp26QZ7z3s6rb0NPlzsCOqA/YfMRa4pY95GQdZSBXQyB7ZnA7O8PIeuWYYLnvUPuXryVwSgExhXS031tegawLo5gXp5Lkwu4V+xhqPsAtKTsr2LrK/cd9+d9uChO6y3r9Xe995b7ZprDrB0IsgxCk02wAGoN8eSWrfZQlXk9R9F4Nzrl1nxiqIEXlUCBaPw0Y9+lHQpD7sF6fLycgBz7fa3f/u3pp3b2vldPIoSOL8l8KOYpvNbMsXWvTES0O6j2Tmogr+RtT/8bzmbYoeUQHM3A5r7Pz4WtYsvwAn7MRiGTzybt//ll9I2COPOnu6QfZwUdR+GZedcPMSO96Wv5ez3YAZa5qH82ovC9uu/HLXr9uNlF4+iBIoSeEUJFNgcBGzQ31rwUeBJYAalANKrpKTESkkNJmYHfV9YFCq8v2LBxS+LEjiPJKD1tKcAZf+nz2Ts3qdZpCVecdn2sH3qV6N21b6wxYuPXudRbxebUpCA1h30kk3QS3/LThTsQOH3wufCdYV3/V5YlC28y24UFmYLZSvgX7AnhXuc7D9pn/7DP3Kp0m57y7V2eV875BCLtjI+Z7lFbBUBn5LaCqvt6YAdZIuFWzoJrAOGAVSUGjhu8wND4JgAVAHeUVqjksZqq9myhRRcpAqrh1WC4JkPuChYWbCl4QFYW4YtNbdi4VWl8SDI3VBvZT2dVrdru4XrCdYq8LsGO8bAaVs+StBuZppgE4FvgjUCXdVv6SZFZAfAGLM50glOHLrfEgR1o9UwcXX2WRawVWlvszVevoMgdisBVYLGSvPhEZDLkMJohADp0VlLnQYwBbiMWCsAmUqr6KbOO3ZatKmecwF8LZFaamjMlgYmbH02yeIzgUHkF6sst5qdPVa9pQvgkAJoClDDhDUN89XwMVseG4PBjKB4mrR3pI+Kk76mpofz+7rNa6hjsZjANOxYacB+CwOTtngKEBggu7DYM8qjBCZbKJtgOUGy5INP2tr3H2Qdas5izR0W6dhiubpqgp4N1nLpPivjc1BG0JiBECKg6MN8kxsZs7XjJ6gH/ZMmaApYLVZfRp/ANLK1FxAX/QubSQAoMXniqK2eHKA/CG4Ka8UDV7giTgAf0AXsKZGePlbPAVLATpZfnLXkqXFbPj4GUYeCpoxXAnChqoRV0Hclu3eQAg/fRaCzJdp3Ggai/mHYzhZZvJfsqCfnlrOrvG4r7QMkMfPEozby0EO2duqktdU3AIzotBwpIUvoi9qLL7VY3zaC/gCuWGjXs6DS+Cq47M+PUp9TsIpNErRkIPC4FANMGGHhfh3monXAXzUtV8G40mKpmX7adwTbQZCRALtIYzIwqkVIBVpS0QShSyPCI4DtgswUJNAkafgCpfebOW3jI0cIVBIE2AoDTc0+y0VJX+iYVCYJgI5ZbnkWzAIBWMBdEfy3KIHq9eVB2yCdX5zUo4nOd5DyczcYNECQI9+3+dkRK29qZW50kJmVOboG0BCGlSjsNiXVgAaJ6PuklMuklsE1AmqkzTFYg2JKXSqWE/7bIB1fZX01AI44AWNS5cyTzhX/0cOPTJPuOMMrSl1KqwAmAH507G1hGmEE4gViBLxm/rhlp+621fEHAJPFrWrX20kDu5e5QmCY+wQEmLNLgDqWAITAVuMB2IOYkVSGGguMgXXGa91uKyW9YKSa8Z88ZsPHSZ8LiLaxrgWQWj2QP1Iiw5yYBTAQAbxRVYm8QwTdM+i4tRmCL0PUdYmgeQXBXQArBEn8FOnQAIaU1sA6x9xdgwHOXyMtKww2YYAEedYJ0mnaUFJpsSrSB1ZyXZzPChgREHcpWMVUAJvW0sQpmxhjrFBuB2krK0m5mwu10cWMXbEXASZYVwq59WnKzsOQVWYleYB9ORrafsCitT2UKaAGQWFYJA8fPuzY2AROvv32223fvn3OXy/o1II+PlffZQ90FOyC6l2wN2IQEWDs61//OuDOWbvqqqvs/e9/v+3Zs8exnOq6QjsL1+vawnf6/c12FOSheutvMbWurKzYkSNHXLpaAQkFOr/hhhvs7W9/uwOsKx6gdr/SUShP75LLDyubQjkvv/4VgXM3kaq1qYE5i66Ye96mjjwCcGIGcG6CdJ11loVhbW0DUDEAnHKYWMsAJXvYypSAuoCcQwsjAL9GAX0ABiJEGYOWMgJQZwNgGVs50ZHbYM5sADx+GpD3MebCCjqlhHIBGoKB8fPlTMVW2NGwtbEq9DxgVkDqYh6NCCS2QeAUPTo/9jD+/JRVA6otb78OAPwWFDpGx4eRam0RGztnadJKAz8B0+YT4M1ZaZr0y9MDpCEESLH9XVbWeYB5jd4fvR/g3BA6g/uS6jFDsHYlvEQW74yVBbVWHe2yWALUB6A8H0a29OoCZQMaw47ES0hFBqhHKQrXAIPHGitIGVlCIHfB1maXSZOJXQDclAXEtYZuCgD0lAJci1f0oAqUflX2VkAl6g6zHrRxpDd9ESDY87YOSLys7WLs8l42ArQAagIYhD201dMwZWFnAS3hFbjgdkl4DUK1WdjP2OwABLqqvccq27agwqLoZfyC088RayKtZV2T5WINMEoBxILqJU5bS0gpGqkkfScMazn8qrXlcUA26DzAU1HA2HGY2MJQyuS06QDdWtLchX1PgZUepXxS6AJMWQOosozfEsIHq4Clq5r6hmF5C7L4MzlsTQ6bSG94kTF07ov4bcdtbWrKmjq3WynspDn6z7FlBTDnMTY2aEeGDQY+QHCxYZXQhyFYP9fGh0gpiX3u2WeR7mspEwDRwN2WHX4EuwFoq6XXVgAmCtwdBShUEiHdZvkubHkffqaA5GxcAOSdXhpwaT9lwGOkaY1yXg5AT5o2xLEBcYC+mQy2dwEwF75aCX6PH1ohs+sMmE4kVd5u5bw0Rn1k5NGPIu/aZFWmn1MDtgbj3uzkInWvAyC4jw0B7QCVZDfxB7JKETpu6/g2amME4GmM8RDGXqwnZ2yZV/OOHaT63MccwD/lnHySObM6Alhvik0q+FjUNxwljS7MfTH6L1TCGGWzSWqFc5K0jw0XlqZc2HJLsN9i/t3AjzRsYaQRuwVaISANawRAqMZlFtsEBxrEt76Vk6a0BOZFj7ESYOs9kAxOf+D3iu0tvwoocPSwLU2dsGrSkCc6rmJzwrXoAPoAwLDBgJhJ4SulAZ8BvvPSNTC4Iucofs7KMzYDc21Vy3VW1fNh3CVkHUzb6toIvt6EhZOAUtno4QF2Iz+wRWlbDP8mzGaRAOBZdmUYnwhfSSBCahxB98RLm9AdJfhiAlOUwj5Yi+1Wel9SGFNWKbLJYafXAcYHgNdL0VmlNU2UWcFnBpSMMvPGgUTxn32YBLOD6L5ZQIgtu6yklxTAlW3cCzlq0458taVTlM04ZQyJlTAe4z6W5Jo5W2azQLxpm9XBjBilfT5lZlfHafso8pjDbwLEAUglEkbOZc2Mzw6wmF2W5PPEvNlLL8zjK65aks0lAqFHYYVrborZ9m21tnV7C45TzCZm8jZyYt4mTs0z70mFTCMESOnqqLALdzaSqrAc8Itn370nbZ/7H/czZrJ2zRWXWndLJSAVnp/Y5BIpjVpzZ43t2l1jfb0e6ZxZiIdFeiMTh20ubyf6F63/+IzNs8ElArC5pbHWKuqqbHGDjSq0fe/uOtu1tdbKq0I2s5i2Bx7oZ1wDiEW2MXzgqZlF/KE16+mttj0X1VptTamtoDqPHlm2ocEFW17ZYArio0F3V99QAdilwXZdQJ/TjuHTG5w3C8hyHRsESBgdFOf71rZyu3Bvs/Vsxc9DGTz9dNZ++z8+aNl0k7316h7b2p2j7jwvcN8SgMydPVV2w/V1lDNkX/nKVx2behE4p/H+/98hBjk9ewiAKya4TXYzNligKwZPDQKau8vuve9eh5FohlxI/tFb3vpW29rbR5+jW51veMZPcnP1TN2dC3rGD9VX+pPf3Sn4TOfzUfALC23UZ71+EOCcbN0swNWT/cvWf2zaFuZS2BX1EY9CAGN7e2ts9wWNVluHRgPMNnAyaSMj8zxvwsSJPYwCRKtgQ1xHd4VdclkTaVXjNjns25e+NGUPPTxgfX11bBJpo2qr9Ok8czxtDfWa3+22fUc5c521KTYe5KnvBgjdmakcOg+dMMBGKp7Zqmoi1tTChjc25s3Owr4LOP/aA7W2ZUu5rSwEsEdm7fHHTwMSLrFq1gGkY5LLYpNdt6GhR+zkwMO2a2en3X4bjHPXwDyPLpKPJjClA1BTM42Owqsgw9f7XgTOvV6JFc8vSuA1JJDG2R0ZGQF5+x4bGOABTatsHHpY/qu/+ksTorq+gYfU4lGUwHktgfPbgTmvu+5N0Dj8IXbyB/aVb+Ts9/80a1PrgdWxRvKOqwDN/VzMLtmNk/ZjMARFSnHv/Tn72V/O2Dy7NN5xedg+86mYdbWceeA4B/vyiefy9quwzj30HLwO0Md/9NaI/eovRdlZ92PQYedgfxSrdG5LQAE3BWC+/e1vuwUgBWYEYNAGDKXdq4CNRrT2SqXX3EzAn/cagscK2Oj7arZ9iulA4DotRvxzQZtzWwrF2hUl8INJQL7BQ0/k7ed+O2PHT7Obkc+3XBOy3/143HZtA0xPjLx4FCVwPklAi6cCsinQpb8VANRaxNmHbIbA1WeD37Q+ofPElKPrZBsKIDmB7PS3gu0CY+vagv0o3EfgvIFjx+xPPvX7AEaydtOFF1gvO4IXh0dsaRGADEFcj1QfSgnU3knqt32XWc1lpCIDrJU+/KwtPP6wDfefslWxWhGoEydDSW21dezabZ2XX0P6RwLBILLyBGyXTr5kp46/YKOTQwSk0paAaSIGW02+osaqtu+wPW+5ySo6YBkhwLs2OGxTpPCceuEFgvNJgs1KpcaiLQvyvRfssi17LiLoHrOTjz5mc48/ZtWweCkYmiK94mJVLQC4Dttx435r3rmVQA2BUhghbJXF4KEjNvHUszb2IoHKOQKbsFP4YrwBHFbbtdXaLt5vdcggApNMinMHDx+x8SEAaADstLk7L6A77Hude7bZlssusZruHcSBYZJbXLa5556AOY1UnOPjtsJCdV5bsAEblSQqrHPnTuu9ar+V9XZDqgEo6jgB2ueeQR5DBIxWedaBbUJsWbC7tW3vtS37YHXB3s/c96AtkA5nemnJclU1lq8B8FCZIM1nh130lrfBEia2lnKLkEI1tEz7SMk5/fCjtki9k0sE1BkP6+wOF6NOW0sVdd5nFbuvZGt4i60MnrKBQ/fYCmwbWYKXPkH3gCBJqDxmjV1t1rmP9gEMwvGwABBe8siLNnr4BIvtUwQfWRcjOB6wGJ8pj1vb5fus65orrKGp1sIAndL9J2z6+Zds4jjBaAJtWQAUWUBeaZj96nu32O4DV1oZ6dEGHztkQzAWZCfGrA0mwVBVta0TAC+Hfa7z6oNWccHFBMUBeABmCwsUlSWIujwDAO2opRfGYTlJEsAFHAFDUYyd7RGYjhYBjWyQZqvjgvfCdNFri4P0y8D3rZRAfllVo6W9CoB1BEkBzFU3dFlFNWNU7C8EZwNF0qHpUAA3AGyRmwV4BfNPtCRt9Z17YaC5nIB1I8wvAPaSh21xvJ+APiBLpqqW9wVqiMU1d2HGCVYcw1lpxzsBfO6EPelFy4zdY4uASHzYGPOkafWzMKawc1+MjSX0UUVDOQCAcoLlG7ZAny8vKxgN2FCAFxjfxIKYYj5nAWQ2byEtHCxuqdEpmx6fBNwHCyHzcoNUtmkC0uWVDVbT1A6wpMkB0/IEq4HFUAZN5BUOpgDafNcyI/8E4CRi5TtuBkAKcM6HJQnQZn5l1pbGjgDKG9nUKcwvMRuUwZ4USk3QnwS/my4BOLcHhkli8zDXTfYfBoy6agn6ORqvtFSuxNaZCylAeOF4zpoByVaWNQLwDDEdlxxzRVp6j/kXZvz7MBNlYEWqhM2xDqalcFmtTY0BWJgZt1raX15aDWCnzNbSsNSUN1qidZuVNQOG4H4KhCt1bgimvJAT6hxkMi/aFOu5Impp691pFc3bLR9uAqi4atm5fgi0jhKMnoNkjhA2AZoa5l8VzC4ewf/I9lstVrcdcAiBQ/Tamx04J1ugo2AjCvZG9kRtGxgYcJvDH3jgAffc8eEPf9ixvOh5RM8qsi+F6wv2StfqeDnAy335Jvqn0LbCev/y8rLdfffdDiQp9teWlhYXEzh48KB1d3c7QPvZzTu7/YWy9K7vz/7t7Gte7e9CGS+/9hWBc28DOMe8EgDNX3je5l96wlaWxgHrAjQHuJKxGkuuAoIDRF0FOLS6NmEJWFk97G4GEPLq5CjAd8Aq+BCof+YYDGzYjCTsaj7AmcrW/QAMyI12AABAAElEQVRmupmDhyGme9ixL5VUNaBHE6RQpVw/YbUNveiaPkDXSpGNTUBf53mVYvujBEbzcydsbuwxQDJTViU2ypbrYIHsIWgOyHodIP3suM3CspYBsONjU3IwaZWhR2u8acBXU7YOiKt6+3st3n49/gFxmbHvAyw7QTC/xKLl9bbGPedhnFxjQa0sU2JtMEZVw7abB8S8Afve6jI2CJ0DfNcwb1aGvdgATLMEGKamFyB3e42tLkyhv05bLBWxWtKsZkkZuwAgKYceq4Elr7qZFLNRWF7FsCe0PQyeXgRFmgc4t/CspYYfA1S1bmWwsZV2XAzgG0A2LHE+oJvMxLO2PA57HuAwHx0mf6zCFi0OC53Sg6ZiPOO3bbNECzIhpVpuGsa5U48C7OIcAMcbIeQNxCcLgD3GX5XQbyeaWyk/gh5dJG0afY+tF7tWiLlagR2Kwgyah103BFi5aut+iOZWbf7kMwAZkSnXJxM18IUBIkRW9TVbrb6uAzsHKEko9CxgKlioqAy6Gbaw1DFbGiWd5cSU1TX3WKzrEssBbFOK21Ca9LyTjLsJAZ4ABWJLiM9bWVR9vwL4eg7QWonFe0mf2XmQPqN/T33PcqcPwT4FYw0gq2UPOwYLcQhZlMDwVgYgsrp+C8BzbBZsX6vzE4xp2NJg9A2BFKgAUCe2OAXs1/AXawBuVHX0AbxhMzYMfuGVeTJiALIGXDaPvYTvzmqbeqyxZRt+QiPdBzgQYKgHE45Sn3rRRdp83DbopxnABhFSiDZtBzhXw8aIGAH8PGnbF4/QRpjJ5jDYgAvLsIdlWpNhHGwAjszDsNeydYeVtOAjhHrwcQFszh63zOIQgEr6mXrmqEuYNMQJ1nSq6kgpWglYDIO8MD1oC7KzGKooI6MsUmHlyC8Po+0ybYy177GqvksBf81b9vk7SSeM/QI8mYQ9Mg1YLY/j3VDXaTXt26E5A7QHONDDp1TZoQCGNTfPmEdjz9j88HP4RVUA564FOHeQ39DrADszC/Tj0qAtZwH5pQFo5soAoFYwVmFLzA9aci1llS3Xk3r+J/CXa7DpYzYze8zWJ4YtTprbUmdfwsqozvNBNba200rZKOKnFiw51W+LgLGVajSEHyhm2FKYirWxYxWwaaKyzlrkq+N3zff3w045z7oXPhJ9lAQkH5AathLfvpLNI6VV6Dr8almzEGx5Av6hyNgI8gwpnO9no8aw+aSCjm+9Hh+vHV3GeF6bc6DI1NRLzCGAJMyiDGD+UtjaKr1lwECAUErqrLzzQqvtJKUw8suspGDkPc0mmEHku4wMaBhMTPIvKtngUlHfY5GmAza23mkPPblg99x9xCan2ARB6WI5DIWSsKaF7ZJL+uyag1fxbcIeezppzz16xBYnRygHHwu26Bw+T093vd108CK7CuBKY0PU7ro7a3/5+YeRTcZ6unqtgs0US3PDtrAwjdzRTw0tdsUVrfaut9db71Y22SKP6VnfXji8aPfc+5K9+OJp2pnlunLYYtssRlrssdkVW0uetNtuuZjr+qyhOWLHTi3ZH3/6qzY5krIO0tlXVdago6boo0XSbnbBHrYLsE3Cnnt60Q4dOm1j+Jl5xl5OKbXRFI2wQl98yQ674cZugMY5e+ThUQhnjgOIwW/Flw7js0WQcWtrhd38zn125TU9gLAj9gzAud/93cdtdgEAYA8gnsQSzFPHbYFnhyxA1J4tFfYrH0dnoVv/8R+/UgTOvZqz8C/0m48uLqwluHS9Gv6wao+NjtmhBw/ZN+/4pp042e98RQGdhJkQsVAFz7sBawaFtKuyIiijf6FavrmKLfiEhVrrs16vBZxDgLAImz313Io98OAJO/I8z5L4OhHsvcDFIVjsd+/qYB5eYu2tDfy+Zo88NmKjI8PYHYDkALBVhljc+rbX2vs+cKlt21GDrQzZl/5u0u75/nGrqS6zbvRQJjOLXj/Fs+cCAPhK23vR1fa2t3XbRRcTryAj1jqx2pHhdXviyWF75olhGx5hfQbQfn2TWDAbWd6os9GxtNVV5eznPtZnV13TZLgzdt+9G/b5L3ybdaiEdXa141osUdYom+3ysOMNcM/DsN51223vfY8dOHAGOAdYUHaswHSnUVR4FWT4et+LwLnXK7Hi+UUJvIYEJicn7Wtf+5p98pOfcqlSCg+wetD5qZ/6SfvYxz6GI3TJa5RS/LkogTdGAlooOXuB6+ULKW/MXV6plKKj80pSKX73o0tAz+kLS4H939/N2e/+cdbGk4GRWcneBmvZv//5qF124eZi7I9+p3O/hPHpwP7sC1n777xEAvCRmyL2F58hvcI5fCyTNuPvATz+FqxA6wSMrrogbP/nL0TtpgMs1BTVxjncc8Wq/WtI4KmnnsKf/KQLxIip4uzjley5AHKFFHt9fX1Q4G+3np4eFp9aXQCngY0bAtwpmFUE0Z0tzeLf55ME/vLzWfv053M2NCM2LrN/96GI/crPx6y1UYsp51NLi235cZeAnvP0EhBBQXulXRUDkJgL9L30vUDVHR0EjmCiKAAWFhYW7MQJQEqkai0swup8geZ0joAAshFtbW3uWgGyCwBsAUV8FsL7j7xgf/p7v2MhmEv2knKzEzBdFYwzVVu2WbyFIBygitRpAmqTEwSuKefGt1hVfZ2tHyLd1nNPW5rAYrRvu4WwTxkm5jrXx2Cbqtu6GwaVbpi7lkk5+rgdffBemyOAVdJSYfWtbVZZ3cK8jgNaIXAJQKb7YhjU2ImcGRu18Scet+lnnjeyHFlFT7vFW2sJxHsERtOcU2tNfb0WgiVuHtDfxPe+Z3Wjp628BZaZK66zlSaYXmCQqelusEQjQTZSlfkEmG2g39buu9sGHif1Gwx09T07AU4R3AaMNcc9FwEKVlY2W++VV1sZsbi1Y0/aiyeOU4EKa8D+lhGIF3hvNZeyUpjvmmFkq2jfQnA6ZLNHjln/A/eYTQxbNSw/lQRQPYBu6wQGMwAb45TRsXsXaeUAFhB8Wr73Ppt76SUCnzGr3n2BxQGMiQVrBdaWUoCHzRds55qEZZ97web/6R4bJMVW7R6CWLu3m19HsBVwWVU3gfyGToLPgKQIHLL125afeMSO3PNPFqXf23q3wODWCkgsZcuD/RabGrWGxharuOrtXN9j44efs+GnD1lzFTvAt/TChidQGQAh2hcmqFnd3Wn1F+4mKJm31YefsqUHHrW5KQL09bDTMDZKkH8OxNgMAeJEF74JjHOJRLn5/QM2+8ADNjh0mnRw5aTI7LXy5jZLASSchdUoVp6wnj07CdKV2tyRp+zUoUMw0/XbNsAptbt2WdBFesEWGH7a+syDucxgE4sova0H69k6DCCTR0mt1Y/KIL1UjdKCkboUoEcOdqAM7GgZgHOReAsMhrcRMNxiKzDbzA3dBygkbeW1WwC/9UGm0wBjTi0ybrLKaDvBZR5AAV75EeQYIbUeAVwBLVfGjxEMPW6lFTlrat8LOO0KzqsDVPesZcbvIyA8AoMMrCwwjygNap60rz7pDFeSg9RvxaoAk5RvuQVAIIxz8y9YduhbNgsLWr4UgFwjc7mMoGa2AUaWDEC8AYKz4y5r6TIBgzVYRCpJLVoW73bBeWj5LA3L3noKQIKABT3UJwpT0v/D3nuA2XGdZ5rfDX1v55xzRg5EIiIBEATABBKEmGQ5rHfGO7bXY1tjz1iW5ZHofey1d9aSJduSJUuWTEuiSErMIEhkggRIBCID3UDnnHPum/b9C7oeDFfJEkWTYl+pidR9b9WpqnNO1f+e92trVTuwQCgOMJB44vjkYuC9UsxRhWwXBgDamx+g/Ymlpc3c3IDHUMD2zgCUdbygYAcAA4X6+Pm7iPO9heMNoDpqheZ64gcbgVuBQbC9eOKIbyQCNsJ2TvZdBUoBwCkBgCxeCWxJpOogBsXr5zVIsdkfG1YSMXe+5Eo+m2t61gx1DbTNqJIBDiLTAJ2TQC+J5fQVFFFo8+kZ7EtDGCEBQDMomqdXVAHXZKizkeJ1R4vSKcQm0We4AQNCvlx+LcAyWYzZKeVGAZrj52a/PLZvFHc1gy2v/YwGsTeFADMySrn+csqdfQ1RHB5peh3T1hWq71gCk2ljjH5ei4DDTDMLJRu75Nc4Rks53tzXcjR/EcA5GyOi9wzR8cLGCRtrjh075oBztnB8+fLl+vVf/3Xn1yhwHQXv7J7l5q9fhLE7+pw/ui8GrhtI+Apjy6FDh5zxdSF90913301RbxOGN8xHtFv0FW2P6J/tV3vPH3R/d/P3/LDfR7fnnT9vY7vVKB5//HHn1zVr1miHY5xjnLM4y8G3NXTlFBHFTdiswIUYg2LTKhkjDFYdkwvrVGiS6GnAmViu6fFJ4DEsb2aVSWKRVmwSEZGYS4MGKnW0039nAV0ByxRV0R+8pYn6gwAS9I2A1zGpWEaTiGQDJPZhj3InYIg0SxRzDzgeYrKBoLA5xQW7NNWFsQoAOUSsY3pOFVDNJmDbYi6qXqB6oKu2JqwoYYCidCySvIfFko11yzV8iXkJ2+FPVfLCR+Qr2MEbY8gDnOurOwewNKMkjK7+rFyFEjFcYtULYjj1Yx3zKYCZc0qTwHMxvkyG8Sq4dPpCIiODvOcwliwCLZXDuJVcmKFZ4L3uq3WaHZlWKnBbbHqJBHgdYV/dCalAgcm8D3Hk9CUR4A64EfoGdC8BbFSDx4G5jwOrBDHOrVIMUa0uYLiwxTIS4zra9hY8djvQfSrGV8YKLyAqY0RwoA6D0zRwWxFGXyDxQmA0wLmZzmb11ryKlXWA/Uvn3ysReeUrCMAzCxRpkfA+Ihht/jZlDw45TmbjMshrhvF7lohU1xBAE9BiLEa3+HnbmeaMMuc4xOeyHQBbwayFQjPKeVyk2JgCJVjUOtFsYWAXt0FlZiYFvHIJYG34qvobr7Cfo8pkXPaVLmHBQynjwQgmvVMabDvDGAYaFFvEHADLMEa0EMbVwChwNkbWBCy9sZUYzkq2OcbTUOPLCjUcxo7K4oWMUnmy+KLNgyzAmODYTfQPKDOV8SXVzkeAvwHaGNgtDiNrDHMTN8arABDZ+HAPix/GlZGXo7jiJZoKpgCG1WF3bSAm04uVr1gBM+MBDvk4t/wAWl7GI48Tv20QORM9bHQuVy/WwzOc86c10DEFAF+u7GoWiGD3lYeFKZgW+7pOAIUPYHnFaOynDZzxHuiacW9krN8x+pQsXKq43HVA3MwxxjG2AqNFsOJ6LKocE0+QYzNlENdED+/D6cW80AXsPYLSKwhoauNsTCzGY4uSH2vWeC/GZeZy8YyzaQt3ImikbU59g/MXC1wykEMO80T20R8ANvRn37AJc77OMie0McsLTBpDrKwbAM8ssSOt5xjTa5WRzTMkIFRX+gbG+17Musc10HqWuS3WXOaviWwXgl62gQUbI82ONY9RFRvyTvmKPsqcDLtzgIjTkXrNMF7H27bz2DqCwXF0qBcQgghoDGtJGUmYBEc0hY3PTZyzn7lXjD+HvhE75BTjOsdvCvN0ShrA2OIFwPwzGrxmc6525vmAGik59GHz6VdKObczuA4xC8ZzngKcRRyPrJk0WdhBfx0ZeEOB1n1EpnLuE9XqK98GVItFkTnqTEcttuuzzOlaWciC0Y9rO8zxCI1x3wQsOMw9SgToN6VsJQDmEo5VKjbhANvXhzEX8JNLzMf8NcznzAwxTwu0cP1l0Ia7dbp+qZ545pIu19SrpLgUQ2suz+8ssrWf6FTguZwMLE9Vqm30ad+hfg12Xdei8kRVY3TzJXk0MNxLhGJIi+cVaNF8fjbNp72Ac3/35ZNqbu9VGdfbknlpWOlYHAIAU9c8rZr6aeDSEf3ax+YB5dHHA4ieBsp7+dUanTnXpJSkLC1ZWKqcDHpg9uPKtYDO1wOzBdv06x9dovt35bLIxc37jOmv/nKfaq/2Kzu1TPOrilVexb1mvkulWN8KC1IApQJ66omLul4/xD1khhYvMegT8Jh7MVuQlJWdgmUsRVeudOvIa3Xq6wnw51JMVcCfidw7TAIWh0e1bEWVFi0pUHyC14lq/fRjb6mmOU7V3NvdMj+oinJ6bLJra+sxBWMn/OQn6A99g5hf58C5HzZP+Ln8vRXonN7DoC6uaR6+Recy9jziKPd2L7+8F3DqlPPxy5gr2rzIIu3t2XT0+cLNc5h3zmN+Ltv9AXjTaDtGN9X+bF8/Cpxjus0zIBffQ9rTk506deYqY/aYFizIpyaQST8Y1DjjT3KyT/Pncx9DlPTTTzczfwXkzvZo8VIWIWVipGRx0AhzvYSUaa29rVhFJcks/IrTk0S1vvjqFcZslxbOz1dVZbyS04GGhyd05hTzxWA5Brgi3f9AonILsVT2hPXG0QG9tPcUC8SCKuf5TCk2+xD23aaGIV256lZbT7zKcmP1B7+Xqy13JKunK6JXXwzqS18jfpp7yAX0M/PoHwqLMewnh1VT8xrRv6+qutqiWu/Vpo0b6OMZn4HsITDnwLnoCTP361wLvN9awDqwy5cv61Of+lOo+dd5kMoqlO+/7CZ58eLFeuyxzzg56zffNEe/Z+7XuRZ4t1ugo6PDeahlD6+saGIFlPfmZbddc6+5Fnh3W8Am4gNAcy/uD+rP/iaglgFumnl2sX21Rx8nonTd8v/5MPLd/eT357tdqg3r9x+b1XHiWjPSXfqtj3n1qd/xvT839qatOnMxrE/81YwOnQ4rC+vcr+/y6r/+HsXItLl+46Zmmvvth7wFDFz47Gc/q89//vMODPHTNoeN++Xl5U4h65ZbbvlXmM5sEGajm4PoftqWnfu592sLzLB0/a8/F9QXnw6oi4hwalz6y9/36T8A0KWx8tEeKM295lrgF6EF7NmDFcVtvLA4wBMnTlCIuOLc+5lNzvr5zZs364477nAA6ij8YDacp556yvkZ+1l7LmHvYy8r/hvsYRDdunXrWDW803mGYYsA7eftK8z31gNQfenP/kQT7a2qZJxZDmyzdA2FunUUOgHcIlhSQsAUrQcPqAOLWRqrykuJpIq8+YZUVytPYbFiNrBCuLLciXANYASJYMSKYfWxUwy9Xqex/ft0+dQxILhsFW5eRmwpUAzGrwj2FoQaFDBjnFhJFza2oTMndfXoQXlZYr147VrFrVgkdzYRrlgVgtPsGyaYGCxk4XjMWi1Nan7mGWU01iuperF8d+5RpLSMQjurq4kv8aDKC1rBCxBp8uhrGn/uWY22tCl26Spl37ZdscSyUvHVWO0VNZ0iaq1nTFWLlikpOV6TTZfV2NWuTIC3XOxyvgKAPCtMY6phU4iCI7aNeMnZjmFdePWQGs+8pSoikeat36DYxcsA7gDLWLUfnCUujRX4cUAMLqLWBk6fU/f+A0rCrpNAW6Zu2wZYlcIBA/6i2Opmmz0UHA3kCtXXafyVvaoFiiu8HWBjw61y5wBBcJwifHaAFeFgkkrEYhsGYmx/dZ/a6hpUBMiYfyvRosVEr1DUnD53WoHjhzXeN6TENXcQj7YA6955jTRc1LxFlUqxY11QyP65MQhhNgNE82LW86bzGR09an36ZYXfvkwhGxhx41bFsd0u2sgisCYBMjyAU3FpSaRjEo322nG1AeGMGrBJMSV76XIK4znATdjHKJRbt53Az3owrE1iIWwBnOs6e55IqwXK2bxJ3soy3juD4ixxskCPEYqFHi8rdEKYbwavaLrltCYGegAbKPYDLrqA9SIo1EIzmIUazyrQ1UjxOU+Jix8GQiMGrvGQBpoOOpFR6aUr5ctZifUnW7PEckYsrjUImBDknsvAOaLxTAHnJl4sAvDQ1XAZ48ug0nOTlZGPcS6e44qJJdR1SDPN+4BKx4kmXsb5uYLiLjeyQBQh4k0H284T7Yv1JhdoofJ+ohEXEOd3VsHGl9Tf0UhxGvNa2cIbYFkon6g0yh/dbxFLfArjxrgmiReLySpUdvlaCv3lrM6nMDx5RTPtJ4laG6Y4VaCk8pVOPGuwo1nNTXVEvxJjTBEjLrOan6lkP/KpnGNH4vqPYCaYJCKQI4vRhqg7gAqNXAMq3A8odpXiNJGnFXdxTS7icyaIVgR+bLrG+TqqNOJ6fDlF7B8RcLYdAICj9a9juvFhaFmvhJLVAG7Aqf0dGq49R4RQM8Cfz7HfeNI5TzhHUT1iCMR200zUI/1SrMfH9UPBPx+AJn0e3wNkQFF7qK1WI93NSgeYSKuij6A43VlPgR0jYQ7Re0lYmdzZCzHMFQGLUDTCABUy64IdO6YI5qEhGRpIhfjHwYua7jiDZWUCQK8Yww4RbNj3LP5neqSBQvox+WeIDsK05OecoAGd4xfsr8GyNypf9SP8PQa+XxBwzsaY6CLcaCHT/s6+GhoanHHkpZdecu4l7rnnHn3kIx9RaWmpM0688/vtfWxcif69M+B8gP9jbWAv259oO42PjzuRrQbPHT582BlPDVSztjHDit13Rcfh6M86b/Iu/Ofm7bn57X4sODd0Wv2X38KM1aRkovjSS8rkzwSQ8jEOEE0cHrRrvpb+bhbo1q9xANEZYksT84uxwOVzvVlsI2NJd50may/B0ALUVt2muPIFmu4+qalr+wHBZ7lulwCIrad/pW/yAHITSWrayQhRyoZazXI9khUqv/rlnb7GtXYS4xRx6oDiGVi/4jJWcg1nAlUBjzW9qV6u74T0PCUXcX1b389kPzSB2bT9dcZXgC0fUeqLHpE3f6cDfwTaDqunjv4US24GY1xiMeA38LJBZ4GuZoXpu8aGgP+Anvz03ak5Cxh/gdncqVhKMdv112K4a6bwG1CuAd8lRNFixe26VIshjmdxeSWYNBczDgGOx+QyLgJFY6N0ATM7UBlWNWYtjEnWL3dj4zuuse7TgHUJ7MMa+ndgH8aXUP+IBpuYV/RfJhotwnhl20mbYayL0P8GOgHjgdFdgNAZlavkKwacw4Q13t6o/tqDAEnjSi8oZvwE5kmiPyfCO9yP9aydeEWAG9zExOTmYvZdCTRFn8iiuyDw0vRAA8bcM3KxGMKfD7y9cDcGrRH1XtirmIlOLILYPEs2Y567hffIB1hKYu5Gm2PQiwAbGljmZrGBY14FcBrvuKReFk8QPM8igmXyFlRg/00Dhrb4z6NEoV/B5IcdP5vtAOSCQlOYiMvJzuuaxACWiKErtpw5Rul29i9GBs4Frh9kW8PyE2vrzV/E/mXRnkSPDhJT20hMLeCR34u5iHPV5nA+jH9+IjhdcTbODtG2xMDTDtPAVynEUfrZrulwFmM9ix04r9IykoHYOOaJqxUBoIoA3BnU6aE/Z8/4H0bgEMV5TGIWwzo7+Dpj63kgL2yAyUtZeLFGnhS2FbBsoOuchnveVkK8CwhhPkA+7+sFXAQeG+26glGnhXlTQGWLlsKUrmc+Mp9tZCCabeO66+VcYX5BfGoEM09ovI15WB3gH7Ad8yF+kH1ijCWKNY7zjokN4yVzwb7zGm54k3N0ljF2E4sx7uPvhzX19leAw7rkKsB4Rpv6E1ngQrRuJJLMGI1FEgh+CgjCzTUYSxSyF7OPi6jYqa46xtfrmAeHlVVQynmxgbZhvB+vB0Tdp8HOS1hw05VYuoxrERAWaNJMbbMdFzWGedZjVtqyO+UtfPTGNorIWqDYMHOEGFeYS5Y5BYBUgIj7yV7ac6SfbcCOy32Jj7E7GcjPk7qKc5R9DHcz3p9XP+P95DCgakY+iybmc9UHNcIiijZA+RSu28wS4NxURCXecvhG5oPAFG7Ge3ppzk1AMJtMMl8VUc+BnteJSz5K29M/la5TTNHtLGpIZS4woKHrb2mkvQYD2wwR1kTeZnANMi+JjHYw77rEnKwNyWKGUkqWKRmzsNvibmeJwsVOFyLaOAYDpscsk1zvwcELQLanAcJIas1/SK9dWaXHv8s8cSys2zYu1ebbMrhHM9hjygFDWcWAQdivg6+FdeDYGFH3Q7rvjlzdspR42gwik2fNUof5kgjDtBQ/EKtbe/cF9PdfOa1rzZ3asma1dt+VrwXzsBljyqy9FtKLrwzrSu0Z7bm/jPGo0FmwsndvJ+YoTMzcS61fu4CI1wwshCxQYKH7/iMBvXAQSHrkuv73h4Fg7seoCDhXWz+uP/+LQ6qtGdDSqmV68P6Fql7MYh4WJyYl0h8wPpw5Paavf5151HiiNm8E1NvMYhj2z+Zb3N5wz8nlzj3f3r3X9MbxNiXGF2r7lkotWw44mYZ9NIS5nPM+NQODYVq8A2mffTugzzx2QjUtfm1aVqpH7kvh3hTTJp937lJQl6626X/7FfqDSMeccY5T/L182dwj+mjNmRbxH5vvmZ2+ta1V3/yXf3EWWfSx2KKKOfrOO+90zMQlJUD0jD/2is4LnffiIo3++b3cj/fjZ0XbI7pt9mf7+nHgXDDoYo4e1pe/2gag26qq6mRt2QKcipkxNpZ7R0zkLvpgf2y8Guukv/sCc6NArm7bkMP3xSgrzyJWBTzH7Mw36dgm/Tw/6W336zv/0gWoewW2IEl33jFP6zckKzvXTax8SM89260L5zH45kf08EeTNW9hrK7VzOrZ77bqzNnrWrqkmIjeclVWx2JCD6rmyoSee2ZIb12WKnLi9N8+nqyt2xIYH8Pa92JAX/zaEbpDt3bcvljbt2WrqNiNDTygI4f26eCB76mM50MP7DZwbi33/H76FTuZ5oxz0fNl7te5FnjftYDR1EeOHNFv//b/iRJ3wJmw2kZ6mSAbuGTFyU984hP62Mc+5pDV77sdmNugX7gWeP755/UmUSZG8j/44IOyycl784pOnd6bT5v7lF/8FmDexoqHiJ59Jaj/++8DqmMVAqkQ2rzshrHsdoxzH6YX82Wdejuk//jxGV3DPLei2q3HPhGjnRvtodz7+zXKzfi/PBfUH392lvgEae0Ctz7+mzG6+w5WeX64DuP7+0DNbd2/awvYnPLP//zP9eUvf5lV0wPvyrbYXLS0tNQxHxtEt2TJEucBRtREN7eo411p5rk3eR+0QEd/RH/xF7N64lBQJOIpnTi+Lzzm0/3bPURKzc1R3weHaG4T3qUWsKJ49BnEvn37HHDODKU2blhc965du/Too4860HS0YN/S0uJEgNuCPwPnDGawl8W32sNYswfZ3997773OcwszCUUfbts4YcaSxgvn9OVP/4l6rteqFNvL1gVLdMvuh5S0BpNUKsU+Cnfh4XF1P/usas+ecswriwHrvLVXAZ+uU1cGYFq7Qd4KwLm0BH4mjgIephBiJCPhGE2dOKmBF18gwrRJ+VtWK2fXBrkLuY91GxzGQjAsMRHLd2JFdIiCWdfBl3Xm9SMYvvK04qOPys/2uGL5XmAbW33sZDRRYAuHAdI6WnXtye8ouf4aRo6V8t8NLFWBqQxwjkofLYGtkjJAmAJ5B4Dd6AvPKpmPSrv7I4rfshNgzT4fq19bnTr2H1bv25dUlJmNEQdTG3CWLVzLqahQ6rIl8hYVygUcZj/jxqBidfMI1oVZiu2nnnteff19Wrb2VhXvoLhYDPxFvKbzoo2tuBfhOMy2tqvh8BG1HT+u+ViLcrfdKd+mzdRV0XVQNHIqgXYIY4APJ7CENGD3ou1qMN8V7dipvK1b5M7F6EIROAJAGLCHyuymp4eYtOPHAOdeAZDyqXTLDiWsXE3BHQgB8Cx4FUPa/u/q+qkzgIAUhMuXq7ee9wZcq5hXqWSOp6eoBEseNpZELB7YWlwUO8MUS8auXtOFb35PiQB0FUvXKO6uXcBtAGsYb1wx2AgcVMKaGrvO9SYNfPdZDddc5TPKVfTAbvmJ4XX57XxgH4l3pVLAvk7SHgBijdfUcvR1dZ44A7C4Uvl37pB3fhmFWeJZrdjM/jlVHCIGI8SlhbpPaKzhdSp2AaCtVfIXLnW+1+wjrhARrtdPaBYTUQSYI34550Iq4FwD4FzDfkASCncLNsuTs4ZzD4CCMq0Fo7lCnFsGzvFyxXCsiNCLjFkx+Yy62xoVASTNKAG8yLb9KKKgSjxyw6uabDhIBJ2fIi0AQvattAf7F+7BLFev4XoAOMxAScSnxVXdw3lTjdXlPMfzFQrwbYrDLJRQvQowwyCKdCcaNcy+TTYdAfjAAEMBPLl0qZLnbeQ4A71gT3KFsCE1YIZpaqOoDaRQvgggDItcV7NamlowLuYTL4dBKJ339BhkAcwCpEClkiIukKnb2h5bHYV092iLxtquEP9Vr4TYEBGBRNjlYdPz57PvnZhbzqmr6boSMM+klWLpI+YugmXGOY8H3tbE1X0an8JwV7gaIG0F5wzGud4W9V+9gAVoUFmF2Zj2KEAnVbMtXCvBTmA84AMsjmaSSgKkiC9YKHfx1hvfg00ugqForBVjXds5pSS6lFyOVRLLTGdDhyb6+jEjpvCey4iXWwLIkgdjEceZRx9mx43/ergQvNgVXUQYRcY6MECd1swA8B+xq/48oBQMS+54tnOGCNwebI8955TmH1VSMbZM4EcDgND+cfyATrqIMyzchskK6Ii2o+U+8MY5dsEpgFoR1Pr+KCRmY85bb73l2ObOnTsnM6s98sgj2rBhg2O+tu+34l70vsL+bGOVjSMflILoOwuW1hbvfNn32Cv6q/3ejK4nT57Us4x958+fVwKx2NYuDz/8MEW+Mgeei7bLzT/3s7ZL9L3e+T4/HJwDMgjTb428pe4rbwJjdSknO1UZwORuwFQuYCCeEbiWOk3VnlIIsMUzA2DFGOIpmk8ftYJzvdA6QCYPgHMGj14+gQWS2kfpWiVUztdMz3lNXwN2YpxKXLhZ/qItvC/9RYgxlLhIBi1+tT6VQjvAjtszDjvXrMDIOfVhKwvM+jGZlCsJC6c3kc+inhLsuUh/fQzj0zgw/SoiTgFrYgGAeA+LQA21HNRUHVHemGkTF+wB2LqdaxRTZtsR9TRcdSxW6RWLgNwAleLoJ81OMlALoMyco71BU4x96ZhTk4pXYKuya5k+cRbIZpBozMar9FV99JmAJ4BzQfqvrkv1iokQb1i+VDFEULvii+k/DdihiEtfY8fFBSRi/c0NqGwIo1yzBnoNsO4kMrRE6flA9rYtfFeos009AC4z2DSzyoHOSxezf4xvQbZzAngKsHewpZVmSwVyXAL8VUrbhTTc2Qw4d9JZVGymTA/9kwtLKhQQhqwGjQIN9rdcwwY6S8xsleLmbyEals8EMGPDOM5EpNfuJwK3WR4iJZMXP6QZ2rjn/MtKAO5OryCOvnQH4xkR4gBXdtwccBwra8SDcdUiWBlTI7TVDIayYcaK8ZFJ4JdqfhZQOiWDuUeE+NLrxL8eJN62UxlZQJpEeboSSjgveJ+pNsBl2qXxgpKYJ8SWEdVatJXPYd7S/LKm6g/RtWKiK90qL8CdwWNO/Dsw3mT9UcyyzGknsaXRwScAlcWU8/P5BjhzvhFxGx4k4rfpoiY6GgC9EhXLPk2GktXXzDHE6JZRXKhY+/7Y5exnBhHAYIaMERa8zfSK3eUYAKgpALQ9hvmv5zgmuBag+0KlZbNgg77f5cNaOMy41nIB7rRJWZmYD/P5+8QFbCsTtTARrJ016m6p4WcHVTzfrMKMo+5K2pT3dnUzf+aLqPKIjb+MTwxmCvQRj9zLmDgC1AbGl2Djr4F6Wfyc364l5sKDwFm00eTIGADgRhad3MfxGNXkxa9i5hsARFwmX9kd7Fsl8wF+hjhjF9evzQmDzDc9QGsxHE9XCDvd0EUNA6BODGHdjcVWCxQaQwy5QXqhfky4DXsBBtswJWISrriNOT5wmZFR2PxCmCbH6w6wSACAr4z5WfGjHKsUPquP/cPYxnwiwvdBa/JZzOtm6Gf6gQO7AN65fwkw70vJKwZKY/+SV/MzpbQN5uKJqwCSbxOH2sWCjERlLACU53/D9c3q6OpVVmmRsis5dglc2xH6GTM90lqcdvQ5dv0ZODfKH3ppq+vApJexFbYy58pibsg9RjrnKcfIwMDOK8f4t35lYtpLrWAeZ3CnmznpVCeLILB00pdMh+M4tvOUwLzLE5cnOhTalD410MuvYzZ4cq3cMO6G+y4w359WTM4uvdW8Ut95sU39gylau2q+blufqhLMTCmpLkzFbC9Ty24sTa/sD+jA0UHadVI7NmVp2ZJ4ZRW7MAa6scfxvTHAjnyqTWhefnlW//C1N9Xe1a1Hd6/VLz1SqCKD8fjn9vawnvper1545ag2rSvW7l0LsTcn6ztPdenk2XpVVOfoPoC65csA13hfukAdOhLQN74dVB398C/vKdR9u7Ixxbl1rX5Mf/k/jjB/HNaODWv0W/+pQgVl9CFMZe06GaVe8+bxYT3+jfOaGMvQultLiIiNU16hh8jvG/tnz/uHR4NYruqxkXURX1ygOzYXatmyGKVlYYVm/5L4ctv+8b2zEDxnTk/pL/78DbX3Zur+O6r0H3/VFgYTGT8b0dXaoM5fnNIdW7xY7Wxh2NNzUa12XrwXL7u2bIzjs8L0XxH6AGeBHed+X1+fTrx5Qt/4+tfV2NikTOLht95+u+666y5nQZ4t7o7OW2yOGH1G4bzfHDznHL1oW0QPpf3Zvn48OCc1t0T0tX9u4troIL45XxvXF2Bo8xGx7ubellGEfoakb106F9DnP3eO+5UsrV+TpY2bYpTP9UriOzHJboydzP25DgMc247msJ74ZrMOHrmo4uJ8+pkFWrc+0blmg9MITp6b1Suv2nynE3AuW4uXJ+rUiUk98W2s7xhm99y3ULseSAPMs9kC/VxHSI//Uz9904SygGb/y++ma8vtSdxHh/Uy4NxXvrGXKNkk/dLHVnPecA+b6lb/0KReeu5VvfzScyplv3bvvksb6Yu8MfTdtI0zp2ML7H7dzsvoV7QN/62/zkW1/ltbbO7751rgR7SAPYR++unv6tOf/rST220XrQ0ACQmW/VzqqNFNSfrRj35Uq1Zxgzf3mmuBn3MLfOpTn3ImjmVlpU7cmxXK35uXDU9zr7kWeHdawG74EFfo2MmQ/huF8MtM2GxxyjJWUX3qt24Uwt+dT/rgvMvUDCvBAAL+4JMBtWEcuHutW1/7vJ/V9s7t8/t6RxgWdeZCSH8KOLf/VFiZrMT8pZ0e/Zf/7FNJwVzf8b4+eHMb9561gFl//vZv/9YxztnNsXMj+C5+ulkPFixYIDMhrMXOY/MDi+VLouAZLei8ix8391ZzLfCet8DpK2F95i9mdPC8rXqVVpS79Tf/l09rV2AO4AHQ3GuuBT7oLWDPGQxws1XdFy5c0NmzZ514OCuUG8xgcMO2bducxVMW322AnI0lBsh1d3dreHjYgRnsYbX1+2bLN2v+0aMUIBmDDITYs2ePE/UdbSvngThQV8ulC/raf/+kWi9fUhlRnnes36xlu3Zj7MIERZymQUphiqYDRNbVvvE6xbuAFm26XbHEaQ3wM5MU1JIKS7FlEGmWkyp/SQ4FJYwVqUSHEa81cgSTx969QC0zStmBqWc7QF4WMa3CUIa1yoMNzorREeKdgg216tz7PZ09d1rFq5Zo8SMPy19I4cxizwycsxcPfF3YMMKzFCMpTNc89W0lNNQpG3AudufDAFtlQEzMoa1vsKkoc1WUD2p68glNvvyCcojDSryHGM9bMW5gPqPCSzG6R4OHD2noyDERmkps5TIHyukiBteDFSQlO4eiPfF8RL968rMpWmJsYewNE30SeOukzr+8V7PxcZq3fYcyeF+P7Z8fAM0e92KpsI0wyG6aGNNLhw6qlzZfUlGlvDvuUsxyirtYNMino1rFBrPds37K9IANEQC0MYC/a1jkiu+4W7lbbgecI8rLD7SCmYInVBRjOT7tnZph+/uOHtEU8a2FO+5XLLFh7rREPpsYYExfs0ef47MPAAUsUPLSDSTWDWH3O6U0zpeY/GKFcvI5btkYU9LlIz7Jm0mcG0qJwYtXdf7pZ5U1Mauq9VsUe9sWefJyKWiyoWwnGI1T3Hbx1H760jV1/su35OacjF+xQql77qdAX0oRhWNn+8b/I1b5DFtBd1Ij9TVqPvC6ut84i/lutQrupjC7sMwx6rmJgbIkPPuZEKYNV7gfQ88hjVFMjgFkiC/byvZikzFAk6PlBpwLYy8KYhcJhBPlv2U3xrwyjHNHNdx42DHQJFRskTsTcM6LHcmMKhzrCDGt4XAs70+xBxgzSBxsALuMqwfwKjCNrGmJYgtWs78cUzc/Fx4HSniFYvJRpbOg0W/F60yK5QYSiSL5KNF8dacBMTD0EK8XW3UnRdwKwLJLnN+vkgDbCyCymAL1OkDFYraBigYF9XAfdpnGIxrq7oQ1s9iw1Zxnt2IVzON9McdEmjXbhE2qpQljlJeoXmJOib0yU1J7K+2N3S6jCgtdehHvCQxh1U+uLweGASgJUYx3WYTpRCvFcCC2jhaiCr0U0Usp9i/CNlPO/hEDDXQRaH0L2951JXEupQGReDJKFYwBPqC/iBk/x/7vpahKpF3uLUoqWoo9zkexvEk9l7G8ET2YVcR7YgBClcPnG+hAobz/mmbrsVYN11HAichXgkkyf+P3C+NcKURITnZiQWx/U4lxU0oA5HRjuetu6KXgP6acTAripatoywVYdbJoEWwFFLLBU0Asw/JZIZ0ow8g41j+2ZbztMn0L0EF2BVbMLXwO/YIBepPEYAPnjbE9WWkRzH2LABsxAorYXyJ6I8NcSxTzXVnYCTMAGagycQr+QoFz1vcblGVfdl9iC3QNDjPLmo0zBoaZTcQW6djYZF9Rw1z0ubiNM+8Eu2im9+Uret/147bX9tO+177P2sjGX4On33jjDRnMXldXR7RUMsW93U5ka3ExJkPGAWsL+7no59jP2iv65x/3ue9stB/2cz8anOPcHyPi/NpJ4pIHlE2kdnrFEiCnCs5hjK1AoZGpRuDmkwo2X5bGgI/iEhWzcL3cFRu4hg0WAfZmXA0PX9TUVaI0KZ7GAijHl1VjnLuoIJCb9SexC7cRp30b3Wc214yZQRi/GMedL4B1l9iWCGDQGEBRz1mglSFgsmosUmtumNF8jI0xGHFb6a/rX2Po8xInynvmrqMfBU7nWnaHiPnsYBuuv6YJnh0mESXtK9lKX9mCie41jJgNGN7ylGa2tZxqNp0xic+OjBAJ23JU/a3ESALzpWOjSyzECJrEPIJr3CCm8EiHRtpqNNrfrMzyVIx1mCuBmXqwuXjdmUotWcGYaHYz7K4O4E+70IfaWIq79kY/SoRjkJ8f72pS7/SwAtiicksXce89j14VG6HZxVqvq/c67YZ9L6O8GFh4Ed1yOScG8zegkGA34FxzA9vkV1pRBf9UwFgXxM7Xpj7mVqnMIbIApLxZmC8NXI4wJxhv0VTLefU1nAftmSJacoF8xOm64uhrnf6eMXnKIGtgKKI+I8RZJ85/WFMjExq8fEAJkSGl0Td7C+7gPTk3bJyweQSDrQtDn5myODk4X4hhHWrFNlYLeNUNy07cbcGtmNGA+IhPDxL/aZHdvVcPMNoMKcNAwyzGwrhyji0RrABbM73A2dfeVAoAVyzjmSdvE/ObGE237NVE01H5/VmcX/cyT2F8BRiz4wfFhUn4Fc6N09jkLHLXA6BdrhgD1HPWO2M3pBTnehvfd4Hh7KrikjF1lREnzDje19qGSXRMGWWVwKC0W4zNH4nzhpabjgCTcQR9GHzYQDp1LIwjnZocrAH+wYYaN6vUvMWKz8TsC0TOYM4+Yv9rZh443qfUTCJh8wCvEhlLHHBulHG+AVjvksYAzguqAa+YK0Q8HIvZKY5DE4a5ZieGPAzk6Iow4mKpDY0AXA71Ez0/BvBABG0h43bJnZhpaTtiNyG6OM5varrtMLHLfcSZr1VK8f3OtTlR8w2ut1HmBrRZ4U72rZR94noi3pWJEP/nP8CTLgAtO4ahiVoiX09oAhOuCwg/PXsT1yD9Qjw2Nj+LHTAlBhjPw5h1fRh5IzafAR6Mwf7nCrBopp/FCE3PY4brVyzXn7/iUXmwEynQoiD9SWCSBQ1TWBP58vL9blsUwd8FaNcx5ryBRCI7i4G7Cllsk7SGbS/mdBunL2KMbj2D5e8arGxEGfMB5bkuhgDlewcmlAsQm1LI2BxXxvmYzhjEYhHnNLX5v+0oDwPCzGUmmXMA9Q4PAaUxF8ziZ7yZzKk5t12ERoeHz6n1ylHug4hlLlzAfIWxHitayEVUPPHJ6mcbuEa5TWLBTLniSgywx0wbAMofa+WriWh5ooKZZ5tx1xPoY18B5cZC8uTerutTm7TvtaBOngZKZFvnlaepmK60AGtTQbFXOQVe2t2ji+cDeuVAP9BRt7KJly4pJqqwnGfn/HtRUQJgHIsJuG/xeVzY22b11W8A+02N6JE9t+ree/KVTTwqw5N6+8J69rlefe/5V7ViaQng3ApsdQn6lyeGdKmuVavXsMgKOK6iwgeM55wKgGpBffNJbOb0KQ/dW+SAc5lZgHN14/rcF95QX/eodm5ZoV/7lVLmbuw3Bme7EZmZwUp3ZVrPYZi6zHMYg6PKigHnCnx8uVRQEoO5jsUjdI/HTwzp4KvdamkJqTAnS6WlLuXkBpWdF8u2JCsnL0aJAHQhDuLbZyZYEPkaYGWxHrirUg/t8asA2JDkR+C+oK5engZA9ALrmQl3DpzjYLx3Ly4te5mN3kzpdqnZ8wcz4b/44os6cOAAAKRb69ev1z0syFvBfV66xYl/f85jP2vzlOifbS5jv/+3zoHsfX7RXtYWN7eD/dm+fhw4F2Ju1dcf0Qt7iUM+hml1jOuPBXRlxYnY4LzKzfMAszLXycQih93t6Sd7dJHrNc5vEciif+Hf4O6z8+KIR01iDIjhGILDca0++e16vXHiIgtmCvTQw4uJYsZ+GosDcyKiAy8H9OqrAc1iZH3okVwtXpakY0fGsUDWYl9NxkpdrK07YjGMs2CCIXV4MKznn57Qs88zbmOu/d3fysI4l6zO1oj2As59/ZvPatHCDP3SL6/Q2g3YTVkIPcBziBefexkj3SsAgaXa/cDdGJ2Ju+f5BgMYoxn9ns2X6I+YlnPXR5/2M5wYc+Dcz9B4cz861wLvbIGjR1/T1772VTSkr1OAXC6LSLEV3xUVFfqjP/ojB6ozE92mTZucB9jv/Pm5P8+1wLvdAh//+Medh1q2CvRzn/uc8zDr3f6MH/x+P8vQ9IPfce5vP5wtYPNwg+ZOngvpc98I6iVuMu3ZwAosZb/zsRj90gPcWH4Im6a9O6IvfD2gr30TgwHFpEd2ePS1z3IH+gF5mT3wOy8F9Zm/JX5qMKIlpW793q/H6NGPeJk0f0B2Ym4z51rg59wCJ06c0Be/+EUHZDCgwQCJaDHKHjDY79+NlwEVBs/ZA41ly5aprKzMMUZEDUPvxmfMvcdcC/x7tMCLh4l2/7uAztdYgdyl3ds8+mOiwRdUGij077FFc5851wLvXgvYOGDjghXgx4jdHAXIMuDNoLhvf/vbqscOZqabhx56yLkHtD7dHrjaz9n32a/2isIMbW1tPK94moeerzoxr2bJNyAiBWuT/Zy97AGuPRxvv3JJj3/6v6v5wnnAuVxt27hNi4ijSyDC051KdBvgV3gUyOn1N1TzGhGNZBQt2XG3kiiejddhc2iiOAg8x3NYilUeCrnZ2GMqFTsPqwmRT8OvvalR4u7iMQ4kbd+o2K2YqLJyHVAlSPwTtwL2aJRC4bCCly+o86XvEdVzUaUbVmreQx/BgjGPqDYKZwAs9hT/xoNTvn8KO1RLg6599ynFU3zOwlgWu/0hJzKWjKobEhXbJovMIha18Ylva3r/S8rNJdrtngcVs9rMKQl8wyzF9j6NH8b2dWC/fEB2cRu2AtuUEP3ZoIHGej5rmoItBSUf7ZGWoswVtyi1utKxXAVOvaVLr+xDY5emip33KHXVWuCeTAWxwlktwROhYIoBJAx4Nnm5Rpf4jOF6Yk4A3rO27pSXeC8XxjnnuNCIIbZ5Gs1KDAVI95WrGn3me7pe36Ti7fcq5zbipyhKmQ3O5TQcxTzgrhDwy+yBwxp647imSuYr764HaX/AxyTAKQ53mIiu2UPP6uIBjgMgQe7mO4GTEjRh4CPGmxHaZ4ryqxcIJBGrSnpZkZIWzFckOVV9NbW6/NyLysE0VLV5m+I2ErmWmwEPRXtgcQlz7+LB3uKiiabOXlbH448rDvte/Lq1SnpwN3AUpj421kV8GJVRGoTj4YBz0xq9VqMmwLnOExc0HxNewV2Ac4tKgbiw+tHPu4EJI3aecu5EQhSBuw5rqukGOOcvoS2yMJgQ42dntCuI/aT5GCDGcbYpQb6lVogu1kjTcY7jcYoK6UQObiGmy8A5jrsVXu28w8pm55Y7iMFwvFdjXdexK11XPKaR2IwMzGMYztIAHmIAJTlTXUBg49deBeB7XRk5uYqtvJN/X0exnPaLECc70ayJOuCUnjpi1HLkj4JzmF1CjfsAtvoolGN4qmBbkorYP4ABIMLw0AnAOY5hZ4d8GF9SyigeYKGZxupk14cv0qbZ5tPEnRKPzA6nlmDhiTNwrgtzQL+SgTJSKzn30nJoDM5/O0EiAIEGwwCmUkEGNu3VVHcNpiNi2eg3fFkUirHPeFNKNe0BjOMQ+SeuIXXimGBxTMouB5wDdAVADHiIVqSQ5qNQPVH7ksaGJxUDRJFMvJkfgC8y2KCeq5c0MTKubK6dxApsMcQnCjAxgtUnTLTezHVggCFAhxRgtwrAzoINAC/VDiAZmRoCnAPoazlG3NckJjvAOX+mupv6ec8J+rEcIgk53smLNIsd7gYyh2EOPIarknkBsMZwuwLEzE71NHMRjRFBC0CZO1+ikO7yZXO8udbHWzFgnQVgpM/IiFEScJErdT7XnYFznAPD3ZiagBczlwDOlTttSbN84ME561+ifT+74/T/9ufTp087Y4wlW+Tl5emBBx7Q9u3bKYgDCn8fCLPvj75uLvzd/Pvov78ff43u94/b3mgb2a9W7LV7M4s77+rqctJorGhs8ejV1dVO9LktoI/Cc7bf9nP2GdHPuflz7b1u/rcf1U43/9zN32fjvG3L4/Sx9qstmNqxc6dysji3w8Bqk6fUbeBc7yCgBfGfdo1h3RRRnBHsSSi6gOYAguvPy4XZUkkp8i25Xa6yzfL4MZkKyJVovfAw10fNqw5MEle4hjEDcA4DWKTpDcYd0neA3MJATC76DLBr+nYAZMAUZ/Bycw0BtAToR8Z6aok9bqfImkYsIf1ZGn1CfAZ9KeefexjD5mEJWNgsYr6q7Qpnb6RvTqZ/o/AOeBVuIxK7jojxibCS598PpMy2ulo13X7EMabG+AqVYbGMmfMwYgKUY7oTEZDhjoNYRgHniG5NA7yPL2asj4JzgG9hjJTDLZc1RB+dDTiXXML8or+dtuugDbBS0c/E5i8A2jFwDjCbmEYXfZ/L4CcX7WxRoQPNmuxgH4lMnWbcjCkuxThXjeUrD2tdDIAN8xk+YwA4PBjjUTpzorhctpNtpjPiWLQzRhBXWVdLfZhY3aJSxoR8B5zr72tX/5XLSmO+kV1GlClR4M7+AUNFJhgH2i+qDwg53jWhRLOHlW6jXat4XyA4oJfwJAatppecSFqlAjGXP4gdbkwD1w4r0WPgHPHpedvoe8s4N9gWgECz2TlgtWeCcwmT2jDR2e11wGvEu/vjlcy2ezOZqySUMoYHWE8xoCnAub4rB5QYGWH7OddyttNmxYBzAfpnxpoe2vjqCWUAzsWVAs4VbOJnMUm1vMi84zXGDcbPol3Mw+ifibZ1JnezmPiIDh1vZjwcGVEsiwoS8rGeFnPss7bQdCnfP9c7NNMClNlyTvFJXvmx/I6H/Bps75KfxQ4GTPqIapUfqJJzKkC7gAUqlvmK1zImZ5g/YpOb7SJKeLCZYQqIOh8428yysZVsC9bb8CCCuHoNtdTLSxxoV85TlgAAQABJREFUSm4h4wkwVzzXlDMfZU7Cv/c0nWfe3q6i+YBz+bcw9hYA4HN+d5+jHYkp55oKApvEMK/zA2e7+LcAUaaaHEUwFw8QjrG5aDfzNWs75rnExWsCyLz9EIbaHri/NUSM7kL4OgnQ+s8cLyLiS4EIi5l7cD7ZE3Q3u+RMhOxxUoTJGIBXcAywc+AK1rpr8sdMKx5gLjZzC8edcS2G+YF3DIvvGYDY/cxPRxQDMBo0EyHHMCbk4TADzwwBtzY/r3Gi2GOKNiquYg/gnJ/r+zJ/dwlrI/thQD19UxyTV1+Qh/2Y3FwTHaxXmVYgKRXwlvhX4mbdccuZx0GV0QZhzv+JljMsFKhRfHJY6YvLAd0jGrjeosHhkPKqVykhF3gViC+CYdcm9FyCDOF2rtqc2qBATHNdzClGmoDsk5RCHGxshl23HB93lnOehEZOqe3SEc7wROUQw5rAeTwbm0e8O31OqEMewLnBmrPEskY4vsyHyuz4J2uWe7CJngbScZuZstInhpnjMoeN9YzKPdupQebzXuyCk5k7daUpW6+/MaGWVqKq2f0Y2j8hNqDi0lgSITKBUlKIuHbp3MUZnb/Qrq7ucRZlcMx8E0rJdGvhvEytXpGvqjKusyS39r1CXeDxg9zXDXLPd5vu3lmoTINTsLX19EX0zPPdeubZfVq8oFQP3LNG6Slx+uYTI6ppbNetRC3u2lOgsooYxZnhGRjt3Nmgvv0Uz0+4x9t9VzHgXC5G0hvg3Gf/hoVNg6OkxSzXL320Uul5BOYaoEvPHqYfGx506RI/f+J4vxqYi00DU/u5/4iPD6qgKF6LF+Vo0aIEjU3Z903o3Pkh9fdbPCsx0T5MohjI588r08pV2URM+gBwXcQ/Tuqxxw5qZrZYD983T/fdF+tExwYZPxtaQ7p8cVyb1vg0NjpnnOOMf+9f3F84fQn/sfmPzXkO7N+vl7mP7+nphomo5Jjt0uYtW5w5483PmG2OY/OXm+cwUYjuvd+R99cnWptE54a2ZdF2+nHgXJh+ZxKw9yJA6clT/bp6pZ+aG/1gxA+si/0xVfQxaVp+S7pSk72qI8757XPjampijgJ8a2Crn0VuaRkJWrAwRyvX5CoL4HW4F3vlk3U6/uZ5opWL9OBDS0g0SALmvgHOHcaS+eorPLeYbNKu3flasTJVrx8FnHuq0bkfe2APFsrbWcRl0lrGoOFhADksdc89O8a9w4R+5z+lafPmZHW1Iwkhfvqfn3heS5am69GPLdHK1Yy1rMvsH+jSC/RlLz9/UOWllSzw3AU4B0jugHPsNO8bZBFdBHjOzeK776+/+6kP7Bw491M33dwPzrXA/9oC1oG99NJLDlG9cuVK5yH1d7/7XScms6ysTH/zN3/jrOo2pb3R1fYQ2x4qzL3mWuDn2QK/8Rv/h15hsmLn5Be/+PfKz8//eX7cTe9tjwrnXnMt8LO3ADU1nTkf0l99MaCX3uKBOc/Xbpnv0p/8pk/3bueW/0N6ql2qDev3PjOrIwCFuawm+91f8eqPse99UF4841B9Q1if/ceA/umFIA8UXNqzxaPP/JFPpUUf0oP6QTl4c9v5nrWAFTuuXLnijOO2am+Eh8FmFjLgwRZm2IMJgybMHmS/twUb9mV//mmgugyKvVbYseKXafQXLVrkWBFuvmF/z3Z+7oPmWuBdaoEXXwnpD/+fGdV3RqxUp1++16NPAs+VFhADwpxi7jXXAh/UFrDnD9bX3/yA2f5shpsvfelLqqmpcYBoA+esb7cH1fbvNrZEYbnovtt7Wazct771LSdm7tZbb3WMcxbp7febXYSa0vcvmFAg6IBz33rsMbWcP6eKjGztWLtF81hJ7ltWjWnKR6GLcuPwmEaOvqZLx9+kQBLU6l33K62qGtsDQF13D9aVHo0C+U11tMs3NkiRtUAZG2/DbLZco+cuaxCAz4vhK2PnJiVuuYViPkUtPCUEeFEw5UG7C9uJFRMxsXW/8JwuA9AVr1qq6ocA3EoXKOSnsG3Fa6xmFsnookoYwS4VaqpTPc9pElualLbYolofkruaArKPog9TUOd7MeRFxicwzn1bkwf3KRezWtIuM85h/HIiV/n3QaxWB7HbHDxE/RgQZ+cd8t+6GrhoSsP28LmrV7O9I+zjIA96h7BpYXTYvFGZRfkKXzqnsy+9CHSWpiqscMmriT3FTBf2U5hkO91WkKaw6IBzl2pUs/8AprVrWjSvWhm3E+uKcc6FdSRolhDvjZXbQcp83ulJieL52HMY5+qbAefuUe5tWylOAzhgonEBk9EgHB8D5zoccG7gdSCxgirMbQ9gDFxEWidQFhP1SMMVzb5ixjnAQEx3+ffuVhpxq5HRIY01N3MM+zTZP6YxgItAf79SiSTMv2WVEteu0Uhnly49+6IyJmZUedttir99rTzFQAo8XOfNeaBtc33Oq1mPps5dVMc/f12+nk5+dq2SHyFeD5udU5zHYEJllHozRV1AsUhwRlMY/ZoPvaHWU5dVvWytCi2qdR7FY56oO+9rNVIK7WGelocwIQU6j2m6+Sgpf15MNRhMMrHfxFA1IKosEiAmtflVhYDnQphHfMu2K4LRbZQ4sBm+UjOA2Mo2AoFhIyG+k4ajEAt+hUVEHoCqyV4FuomlI+IvNAWokVyAaQ54NCdPM/5U7lMxnHJc3AADE0S/jjS+oVQgxrjyHQpS2A4DLfjN8jKOEYhI1WDvdQC4XNr7LrnSK4kQPI0J6QXAOYxzwCj+4p3sJ+AcxQ9XBHvi8Juaaj5IIZnItPhs4IZbiGJbh12tyCm2xES6NIWhZajFwLmAUotyFcN5EOzsVWczRf2iZUqpWgc4l0GNGRCXUobbhQ0rDCQYtP2rJ02tjojSRowpQ8AGqezfYhJKKRQTiToFNOPiOvRNXVWw7Zg66mqUnAUMUk7MYUYZYCAQCYVr9zhwXC3mvBGiyjD6JJUul88gyqE69V27CPTbj2UlW8mVpCNg5nGgnWnmsv1s//VT2H2uUsDHXlKJDSh/A6BIGX0DhfBpzsGOCxrGQpXgmwKcK6UYnqkeoMDxcYxzGBYSi7lWYhdzPmSxPRR4McoQGAhUSHV4akxTXZewLLH9AAlxCcT1EjHrJaowEl9An8H+YfyJjLZja8JUNEjcFP2bxSe60hZy/DjGWKLCwHcWo2eWJ3/mDUDCznCbm9v83exsNm9/8MEHneQR61M/CHNrGxeiLxs77M92n/Hcc8/JnnUbpL1x40ZnvxYvXvyv9ww3/5z9/AdhX6P7Gf3V9uEn2W77vuhXFJyztrL7s+vXr2s/xeMjR4448elLly7V1q1bncVK9lw2OrZGP9N+vflz7X1sG6Jjr/2bvX7Qdv2wf/uR4BzwkiZOq7P2LWAb4pLT85RRiVkK2yVEDn0AgMtMg2aBVaaargLOAfnGpRLzuRnoawPXFUCL2dUMmgA2Gb62j+g8LzbZTYrPnw/Ec9HpWz0xsfLP36aI2eEAkhixQCyAWgKMdwH6eMygsxMYoPrpCxhP/ESf5hQsIxpyIcAO1yE/T0dKvz4E3HVQagTacbPYsvw23pP+mb7IaScgXwPgZhqOYl5yK7F6j/xFwFMeA9YOqAfgzOMrACxbh4xtHkNQOj/H9T152QHuBhuAXjCQpWKg8hcTpZ3I97At8owpiHVzgD5gtK9ZOZU5Si7KAITrVFddB/1JJjGfyzF40i/E5fJnxhbGUBsnXG6MnRHmKUBSE13EoWKGMz7Zn18CaFclLxGtbCntwXEmTjPUdkmDDdeAjr3Y/yqQ4lVgSGX/3JCDM8Srd2Jsu17DWeAmtpGo04oixq6I+nvaNVh7gShprzKJjrQoUzpb2taOTbtmOoi0bj4pv2sc2IsY+eK7gbkAxDz0RVjjDJwbb3gJqyxjEBaz+KI9RI2Oqr/pkOL9Q0otA5bO3cqGM1eysdP2z8XYJWI3XQPICZsY93kP4vn83gylZC1QbNY82gP7qS+RzWU8CdIO1udfOaxY4l+Ticv10p875j8iToORAU0CoA/WnAGcCyreokjzN9oJfwOcaz2KaSwTAGwHjNpStp0xGZhJM82Mcy9rtPkNziPMaixASM4rk69gqyIZzH9YROHCJBoe5xi0nsJadpYFIUQKl1RqMhgH7IcdD3AupbAUayBQNBBVyJOKzYo+D/NXLNCVh+jNEO04S2T3VPd1Tc7MYgOs4lxfiPCQGosbEsDMuK5+5kXXNYSxF22b0gqIXs/hWMQtoa3s2TXWwd4adTVjSuX9ylhskJC3BAlwOkLHLg2wfTGcLwlcixBrWDuZQ4QA2AeagLLqNTvWzziVoOTi9WzrPex+kTN3dVnU7cQ5zbQduQHOpa7iHL3LmTfNXPxnzq8J5sWbAOe2cwwxPdOmzKJvgGXMkc3oFhqv1eTAOeyFrSxqAUzD2BqbjeU1hfHTIHjHoMgcuvss4NxhzhksQUDwERYkuIlz9Ya4y+Y5VaSfRQCNL8MZ9gPWrWcOBayHLXK853Xe+7KmuObjAf4SsLT5gTA9WPRcjPEimn5mAniUPiYBiN5fuYk+ZgFbmUvbYdTD9jba8LaGuY4SUzAiLSpl/B0n+rdeI+Mu5c1bp/jsBexfId9v8zXgfOszmTfyAbB3LFDAqNjfcV0xGB1Tc8qVmGNQI/NNrJF8GD+HmRBDbtv5Q/ThfoB+onQB6YPx2INZPOHFUuzqO6u+q7QB+5GWS+xxeQXXdEQjxFkPMBcLYy9MS04hwhe7JnbgmGCvNMR13U+ce94iJVTv0HikSq1tXjU2zGJxmlJv25Da27inmAmpCDPw9m0lmJz8zB6xS3WMqqFxRO2dk6pv6VVHd6+SE2O1gfudHVsB3spjdOAI0arfekWhcB8WqNu1c0eJMkmiYf0N4BzGuRcA5555VfOrMM7dtRobb5y+/eSgLl1v1qq1qbpvT7Eqq/yKszk6P2Pg3LeenNa5Cye1++5KPbCrUNk5btXUjut/fPYQZrdx3XfnCkyzVcC/zHIB/zibaL8I645igIO9amud1PVrg2ppniTCfFbNzMumZjyqrMjRXXcXqmpBvGanw2ptmVRr65C6u/qI9+xRVydWxkCubtu4ENg8A8ueR5evTumxz1jsN+Dc/fMw6vmJjr2xaKipzYxzE1q/yqfRkTlwjoPw3r+cQ88zCa63IWRBR44cdeaJZsLPyc0BhtqCbe4eYK2FiovjHvMdRTyb69jL/j769d7vxPvvE2+eD9rW2Z/t60eBc85e0I4h7rOHxiLEPmPPbxlRS9MYpvEpfnYMmJEYaqC4TRsXw6YwXqe46SdmnGu0tWUIUHdUvd1T6huYUWqaV3ffs1Sr1+YwZ5Oe/E6dTrx1EVlUsR7EODdvXgImX8A5ANkj+0mU2jep0fEGrvEirVqTqROvT+o7T7YpPSMV0C5d67cRx5zEok+sp6OjLj3z5AxQ77RimQP87m8lEikbrx4kIYdfDeobT7xAP5ihh395EabCTIxzQQ2PAM498wpxrQeJaq3Sngfud4xz/xOcowtzwDlmmTy3mgPn3n/n9dwWfUhbwAqZtqq7tbXVKTTaarsvfOELOnTokEpLSx1jiDVNF6u87GXFSZ/vgwM5OBs9958PXAtYxM6xY8eYqGzW1776VSWwsu29edmjwrnXXAv8bC1gC/uu1QNXfTmgJ18JihKFlpa79V//g1d77vVSAPxwnmd2///22ZB++w9ndLoDW1u1W5/7Y5+2bfhgwdgTPGvbexDr3OcDqmkPa1mFW5/+zRjdfScrQOeGx5/t4pn76V+oFrA4vX6K0laAswKIFWMMnjNAzgpx9m9WvOrs7JQZg2yuaYY6A+3sy37OeXD4E7aKzVFtgYcV+NatW4euvegHFnd+wreb+7a5Fvh3bYEA9qG/xm76/2JoHRynvMVz8d//jRj95q/E8GDbHtD9u27e3IfPtcDP1ALRvj36qz2TsGL9V77yFV0DMDKo4SMf+YhjnLNnD/Zw2r6iC/js5+zPFqvy8ssvOw+6bfww2M4A6uzs7P8VsuN6CTNBb710Sd/49GewRV1RVXKati5coeq771bsysVAQywJxsQV7u1R78H9unwBKCUhVWsA58y45sRVYUEIj01SSAagu4yF4gQRQyEKdZtuU+62O4h97FQPwMFAF0aOLWuVcwfgVQYFaQ/xce4b4FyIwqrbALDGOvUSiXeZBYoZxflatPsB+RdiqUjAhgUJ58I24ZhfKC5DolGYblbT099VUkOTkokm9d27R27AsAhgVYQVywQDATqBEDG+dgHkjb66TwmALqk77lX8xs0AfCkUHIMKdnVocP8hzR4/wT020Vk7tsi37pYbABc2tsgQgHsnYFlti9rePqsObFZVt29SxRoi4NrqdO6lvZoGtpq3erMyN2ymkG2FUirqFGmdL1NlYGybqW9RC+BFz4VzKucZUxYGt5i1xFXGJyDr4SE603+XFTRt2y3qq7YGcO5ZzoNGFfK9OZsw4eVSvCZC0WVWPS83WBT0w8CLs6+9oZ7Dr6kbA1vJ7XcpCxuSC3sc1VaFLp/X7L4XdPUUMZgAlLn37VZyNYYP5ugWkRsaBkrrH9dwTb0G3sRIRDxYWvUC5T/yMMzVrC4986J8nd0qXb5UyTs3KKaK4rIV5ZjHcBD5P1YeYj5nr9ao7zvfYj+JNquuVtb9tHPlfPYHIIOH3wb6ubwoOaxdqALO1tep5fCbauDB/bzFRLXu2K4YA+c4BvaE3IA5l8W8Aq2Fp6c4Bm9SLH+Dfwpg2gGIyJlPIR2IKkShGxAh2LiXc+INCqNJir1lp8JAA2MNF0l/u0DMWh6FYcAMrEcOOEdRMMy57Q73OtBdcLARUUqtguOYVyi+xmatwjpW5sQVT7IoiL0EVABunRmisH9UQ9iX/AmxgGMUhDPXUPinEGvXymCzxuqOA2M2KY7YVF/VLsA5gInBExiInqEg24N5aAVxrTtvGOfcftrRjGxAqc2vaZRYPK8/RanEmyVUrgekAJyz/2E5mWy9oKGOBooUQcC6fPlQCQQ7+9TTOgpAsUxJfL+Hhb1hrH4GX4IJAAnQV2DLCfedwc7C9RscV3xqsuIyC2kLisQ+4D1sawGgBQ9GHPfUFQU7jqmzkTieJOyRRJd5MdNBb9AXEAs8BMxZ95omx8Fec1ex/yvkdcA5zDvXz2pkuEO5BclKAdpxJQHtKBWYBStmdxeWvrPYbxr5/DDA2kKi5tYDtZUBF3BOTHZpAtBktO0CcEcQSK6c7iGLfaMATVE/Ky9LScVck95FHDmuAYDbsGkOLX6WyL2Z/jaNAN65goATxCbHAr0ZNOICQgzFmJXQ7JYAH6NAvk1XABjrlJTIdhTPp80WOv2RixjH4GCrpoAbvXnreQ/AEmALm1p80ME5dsF52ThhX3bf0dTUpK/yXPHkyZMIM9Oce4UdO3Y4Y4VFs37YXtG2sV+jxeBoEdju0QxgP3z4sAPQ2d8vwBq6ZcsWB6AsKAC0pV+Ofn8UkIu+z83vae1qf7aXfd8P+7fozzrfyH9+ODhnIDomtNFT6rwCOAcInZNG5Gi5XbuV9HeMA0AiYaJTx5vPaqYbexTQZIg+KzZ/qWILMbKlAZwAL92AeE+pr/aApgOAZyXbiOhcrMm284DqR7BAxit2/h1y5XLtmrkTS6MHm5eL+MvIDMDUeCPQ0CmNjVynv0jAgMLCrZw1XIdEqdr3IxwI8+UGYAt1vc57AuECbiRY5CpRoG6uVzp1+oNeBTFuTQHSzgJEJVU9KF/ebYwHLXQV+9VZd4Xvy1RO2UolZmOtjMNkh3UyPHoVcO64BluaaS8fABUWKoxzriTgZcYplxubL2BYdx2QLbas/OpioKRMILNOdTZ00qdkArHdgnkMCAcLX9iMbIDvBmtBPzvGyuk+AOTBbs6RkBKI647LLwP+AqJ3pTNWmS2V8wfjXKirFnAOgAnYJ6O4UAmFZcwLAL/oUyLjjNlttepubGJ48wEAYumtKFWIMc/g6YHaM0rlEkzHIOvNod9jWxxTFwa/cUxroz0XAY7HaNt5wD5Y2wwMxHrqck8B9l0Fzge8YSyKTQcIK3lQsyOjtMlBngEMIKEDmM7ZynlB/wYwbWeiKzxEexKxOdGgMSxgI9300zE+JaczzqYDqMcBMNF+FL7YXmgcIKfwUCPb+bpc410cA2xstBsNBxw5wzgzpPHuZkCoy8qgr04q49gaWMdruukFjXMu+ROI8QSY9KQtY1vSuA6YJ2AiG6p7WRMAXRDQjIVu+VNzlQh058lmzubnODOGhTDiDTSdZN55CeNiPKaweZqajgd875Y/ZOBcibzEx5pdzcC5IOenAWZei3kdb9Z0LxG0/Q0A/4PAZESw5i5nWmAQuc2ZaHjOaXmGGVrqNdh0gbZkcQbwdkIuY30i+wn86ApjJyWOtpvo4/GxPpUCkiQCUwWnkzTQ2UQc+lmlpYSVVbmC7WBew+dHpjs03XpRw0QFT0/0Kz4hnrZjUUvWbTfG41jgRwMTRzk/W49rpAd4EbNucgVwZGBc0+e+AQw/IW857VEIOAfMaHMlly1iMKiMBReRqR7GMKyRI5cZsmeUlFaESbiU/SxBQottLWpR1CTGWqKE6485YJy/sApYn/l5XA7nMPMSe1bVcYb53GHmrwDqFRv53NtBZXkm1X5QY0TcuojbTS/ASplcBfjGvBuTXqj9FPMwTHXErwcALX1F8wDuACeTmQsC4HNxs41tHGfgWhZZpKQlKmVBMULiIeZVDRoGnMutvAmcizB+Y5xjB2lzYL9p5moDLELt7SBSNaQUFvwkZFXJk1TK+wPNifOU2Y/cnNPTXGOXDmtsaEqZ2cVOtDG5t07f5QLOVd95ddfSBgBimcQzx5XTRsxH+lhAMTzQr6QEoK68YiTFJZwT6UQENxLBfEL9Xc3MxYl+raY9iKEO0xdNjLqxWEbU1TKBVa1Xh9/EuhdM1PbbK3kmnoCBzsOsjJ56NMwCnLAu1wzqwKFGYLNezedzH7yvEklGnF57M6THn9zHdvTqIw8Bzm0vV2YK8AynZA+WqGdf7CFafT/gXClRpytUSATj08/06cS56yqvStB9u6u0bGkS5l6XEFLrrZMBPfG9Ee4jTwKqLdSe+0pugHM1Y/rLvwZOnpzS/Xev4P6yEgMei7LcEDUsLAlD1gSCMZxDXPP8b4KUmd7ekHq7ZjDs9enMOY4jYOh991fpttvTlU38a4T7tAnmhv39M+rpHNLpU506dGREC6sq9eijeVp+a4xqazHO/RkWQAPnHqjWLgPnctlWbpMaWmYwzg1r09pEjY82z0W1cr68py8bDJib2DxmgnvDK1jPbXHF0aNHMb+OO4sF7rrzLq3C2J6enoZAlL7yHS97tm19XfTZxDvnMe/49g/NH3/QPM/+7seBc9Z+syEX1jlSBuxRAv3A2EgYk9usLl4Y1GvHGtXZ1a1bli/RA/eXaN5Cv3xxuLixaA71G8w6poZr4zr6OlHKnY3asX2x7tnFtY4p/OmnmnTi5CVSanIB4RZq4aJkx1AX4LMOvzKtfXtHNUpc9d33lmv16kydPzOjJ54ihp1Y7vvuy9K2exLhYdgoljP39rj0rX8e1Ev7Z5Sd4tMf/OdEbQCc6+0OA+EF9dVvvoBxLlOP/soSLV+RRv8U0AgR5y88+4r2YpwrK67U/bt3a+O6W7He0d9zn2+nY5hkAjPPWzvwt8792E970swZ537alpv7ubkWeEcLRIuY1oklsALEXn/913/9/wPn7O/t5tW+78P4YMH2f+71HrUA59idTFDOYQK4h+icf/zHf/zXicjPfwvsUeHca64FfvoWYN6tjq6IvvhPAX3pO0GNU/iuyHHpjyl2P/owxgD/h/ccMwvfwSNB/dGfzurqSESbV3v03c/7WTXywWoTng+ip4/o778xq3942qLGpAe2evXJ3/Wpqtxutedecy0w1wI/SQvYHNQMEFagsbi+wcFBVow16+rVqxh4LjuLOnp7e1nF2ed830/ynlbIsfglK4bt2bPHKfCk4nWPFnV+kveY+565Fni/tEBHb0R/+NiMnn0tRMyHtLzApT/7pE93bjEI//2ylXPbMdcC/7YWsOcJ73wZOGfGObvvq62tdYxz1odXVVU5APTNPxP9vcEQBl//0z/9k04QEW6w9G/8xm84xvKoEccePjrfz688kVTDpYv6h//+aYp0rZoPcHQrNrgqbGNxKzHDFVPkIjI01HRN7cdfUzuAXOKi5Zq35XbFMvmLjGK58RNpRbSRMLuFmhsw0+1TN3+fuGad8rbvwEAxpUFggytnTiqvuEClazBpEaPlSqXg5zJzHsUZICx/RjImrAmNvPmmrh48TDTWlBauvFWxy4h2zcVSgZUmQowqT/UpMFKcpqocHuxTx9PPKRaTW3wBVpKt2+WpANYhotGdlkC0qpviMs9rMF6NHiOSCChvDAAsiX3IWn8bLFAObcC429Kktjcxa7S2qGDBPCWtssJyMqu8KYATTecmpjMySdGrsUdDb72p6531ylu/RsVbAJWwd107fEzt52pVkVaoAkx23vkVzjZaoY8MKOqwwDfJxOXNzmjs7bfVdfioEilKps4nOmvjVgQnKdT5KDrNEONF/KY7Ow12gIJVa5smXnhR1y9eUvYCgLd1bHNJCWAZbZ5CG8TT6bkpcgDWz567oI5DR9XY1KXieUtUvJJotHxgBWCh2UvnNX0cixjARMb6jUrZCOwV53MsgG4+l53ks3ko39qhiTeOq7exUZ6iUhX/6q8isfGpce9+jV245Kwyz9y4WnHVpc52muIkbDcBAJeRdGL7+gY0ffiAuk8c1xiwW+4tK5WxhPMonXbmz0HAJHcMNr30BGrwxFp1tavjyEnVHXpL5UXVCMiILJsHhJCFySg1nrhbIAseldO7O1Gqgf6rGmt/G0NGJ1BaCmZDTE/EvYWDaUAhRJMR/xbpuaBZ7H/xK+9jm+ZrpIECdN1Z7q2IhStjv1OJC/RyLJzzf4IaLkVxiqeTxHKFMKTE0ra+DIrAVkSnKIy2Q7M2uABz4EW6sR09RIo2nSAibFipWEoSgPjcCcQgTwNz9gI91F+Qe2xASXnV8s2/l/0pc8C5QNMz6rco1uQyJedjmUkrAESIoe05ft2XNQLYFjb4w+tXPG2WUgy8GosZIMS1NoaxDbvKSP8Am+LH+AgYRhuGWGDR3Y7VJm+pUio2AYFhU7FrxVrODGtAc8FurEftRLJN9vCZQIXFJZxzdk1RCA4BCrg4ft4sYBa2ZbKO9jhN0R/whWObmJ4lXzbwXhzfw/kZxmI02kwEXYAIvcK1ii+hII4xJQxY0F17EvCviQK1V2klgG9p5ZzXgHPjU2wD8Yit1yl0D8gXCyjLvvsKsNUBp3ksug3gbgwIdaqzVQnJsYovn0e0WzbGuU7mxAPKyU8HAAFYwDjnWGvs2rT9o/gdHLwCFIj9ZrRDyal+YBLAugSK4y5ijQ2ai8NuSZt6+ZnI+DhmwToNd3KMsGMlZGLKyQYCBjiNYDUMEtk4NTqD/ek+/n6Bc97afewHHZyzPt++ov2/LeSxYugXv/hFB7aORoGbbS4+Pv6dw9GH5s/RdrJf7T7JCsj2zN9+b1C6wexPPfWUzpw54/y5gvHGzHMGttt4G11QHzXB3gzSWdvbK/oZ9vub/85+Hz0+N/+b/d5ePxCc20FUK+ZLB+oaPqXeyyeBhzqVAXycXAA8lA08mgBMEuoHDgPY6ajDqopHDXgmCOji8iYqDujJm1NEX5RCHOAkRqUrGN1OA6wlENu9Q8mlt2gaCCjUfBioGOtSxU4pB0sdZs8wFjS3i3FrFpMW1/AIINPYSA2mMmIIU0oVkwxcHlvNjgIExQDj+ICsAfZcwL2Rscu8J7GrHc1Kysgj5rIKmJg5BxB2aIwI1I4TgFEY5NypSpv/iHz5m4GKWjVj4Nz1ixRvY5SFKSw1twxumb6MYm6Y/ieCOdTMnmYZSzTTFkCQJx1wCYgnAqQ8xTXe2dgEC4QRauF8gNwsjGxdxFMbOJdNpONK+K+FbCuQltHs/IzMaDpSD0iEnYrPCPKwy0df68sBQE4BmPOwkoftRM/JvvJ7xt3wYAdwF8ZdDGNp6fTZhQWYaTlWjIWRkW7NtAMbd9GfA4NlEasbV1ZOr82Q1NGqnsvHlUC/aJYzb2417YJJlMUF/x977wEm2Vne+b6nYqfqnHs6TZ7RjEZxRmkkMRISQVkCkX2xjY3Ba7B3F9bXvl4bY4Pher1e470Pz7Xvg7FlgwUCgSQkoxxQGGk0OU9Ph+mcu7qqK5/7+3+twmOtBFpZ8GhQHamnu6urzvm+90vvOe/v+78F0rgmSBGbSQGKZ0mJBqhW3XuJRQRBoziXp/3SM8dtcfg5/LMJp8ZXver9sGYJm0HdLxKatJruzUA/QFdhfCWB2+qWuRGEwFATHdtpsxODzi9qamOOrmFuBN52ALTSl+LruXSiqIQWmG8XTjxvmcmTAGBsOFgB/IS92a3g1tIFwKapgQGrLwfAXHUR6Ugv4bxY5sR9tnj0Yfwi0kl2nUdKVZQRo8y/pAfNx8dIw/s0gPoYakaYn00VSamatQAptZ2LP4KvBCCWmxqxiQFSzU8PWFtHI4p2G20pUUGq8xG8BZR1WH+iwOR+GdC6R5ldBJ5NEHOHWGsB7ugjnlJq1lUDbFJHgYFG33MbOgD4WO+lbJxdHLMEPkdmfBeqaiw/LfgGtUDf+AT+En9jLZmkvbL43ivO2mwVKM4V0jVAgydZy3ZZLEaa2lVAmMBoyn1cWBhCvVHpYUdoqyUro5IRoMhy7BxpR+E2ht+pfjx1hP7PujYTx66XWM26d+PnxC35wt9aCHCubCXqsp20ISqHBfy3ABCnR3p5n/SWmZFDqCmeoFkXSJHcCPjYS7tx/bI61345+qdPCnW33tOXswPAthN9+DxVqBN2WjlKgCHauhAnG8IggN/AbmxFmtLVl9NlrqJN4pYYeQR4DXAuDKDbdj79kD6Cf1ugXoXhvZT/MO1LFgX6ZKaK9mlb5xTkpG7rs/kgvzAAPIjy3mzSavF1qjcyTwHTLgyesCmgkEaA2Go2RnhR2sVn3OLHk98WH4v+j5rd4sQ+fs9ZTX0P1eqB/5QKMvchQdqO9mbS43z43rlTbJ74ESl8+1CyJa0hQH5QMDF/95Nj+DP77RQbdvwgqVy7GYPdvcxpWZvlXiDB/UV1LEo5AAllP+yWB6TMjjxvk6h6lrVvs0zDtTa80MQmFImpSOGZYiV8/PC8ffu+YRuaTNolF66wHZdUogLK/A5nFAVq4ZbAxoDg7vnBPClUB6wTBbhbru/iXq3GnnymYHd86yHcrwm75dbLAed6UJwDnMMPHOcZyF3fX1ac28x9xi3XnWere8vtgQdn7IFH2PgBmnfpJWvtoq3NDrabJ77w6I9Sdv8jUzbHvP7h926wmwHnWloD3FfG7QtfegSAMw04dz7PCLutugHfkY1J3CXi4xVsmtSwp4ZY/0hVWx0jMTflTi+R4v35vD30eNpGUFzc8bZOO/cCNkuQJjKKP8t+Ege4ZHjfi7sX7Rt39llrc72957ZW23pphDU0bf/XHz5KP++w228BlrmBVK3tAeZz3470pWzPrjHbcVkDft9gCZxjVvq5Hvg9NL17xtzff5IsfPeYUtSPkzK6p7fbPvTBD7tnES3ckwfViV86Tvdz9LN8nCI4V3revGwk2aTo7+mVoh/4k8A5uYxSXydzNFBpHvXtgpWzUYnlVmgwynN5u/f+CdtzgOcPK1fY1Vd2AsWSEpk5pqKSOZuxmMPfGzxZsO/fN227Duyz7Zf22HXMNQ01IdSmR+2p5w7aOWdr48x627g5xv0ZG52yQXvw3iW79/uLtpgctJtuWUX8oM5OHs/bd++eBPrtt02bAOfe3mmrV0dQpkQk5WgOJcxRe5qU1Gvbquyzn661yy+vsMmRgv0Lwil/83VStW5pJ1XrObbl3EruM/Kk5R60e753r93PM4aVqMbecMPNgHPcG7P5kUlHXZF5ECPIN+bn4hc/vq6jBM69LrOVPlSywGuzwKuBc6/t06V3lSzw+i2gBVWB9MtJjTIwMIhqwG32l3/5l6//hP/bn9TyVDpKFnh9FpDvPT3j29f/OWd/SorWWXYvtMU8++A7gva7n4mwC/Wt3b9GuRn96j9k7atAhVM4nO+6Mmjf+G9lxvOlM+5AkMMefiJnX/hK1n50qGDruz377EfD9t5bQlZR9tZu5zOuMUsFflNZQD7AIoE+Kc5Jhe7FF1+0J554wgVvhocJRBL80nt+2lFFMFgBsfe///3A+O+wnp6eHwd4ftpnS38vWeDNZIEX9+ftN/4gY8+z1mj35XXnB+2vvhS1LiA6Nt6XjpIFzmgLFB86K0guFXwpzgmeLqqHCpxTqlbdIxYD+6qwHsgWVXEEQ0hNaPv27fZLv/RLbr7Xe4sPsHVu9yAS8OkYUPZfff5zlgHA2UjgZz3Bki5gmUqUwgIrVqAikTRv8BgBwkHLta+whiuvRqGlC95ov2X6+wkoxRBZqSO4zAPOGeCe/n2W4mltHYBW/SXbuaZnqT277eijBEpZr5pq6giUthN4byMAF7JkgQA6ClhtqJlFCWKmTvbZ8KNPWWrvYauXqgdKLYEO4KEokN0iQEtVpVWt77Ho2g4C5Smb/M59lnv6eaoDXLVuI8FEAs097Va2tpsAWQ2BYtRtqG+GB8tJFNmGAMzSIFB1PaRlApzzeMg/cwr1EoD0OoKWnex2LgO+SRCgngYECxHoj1C/AEF6Q0EhARyYJiDXfPnFVnfZVsVXbWrvQRv4wRNWNzKNeghl7SVlWxNBdOYnj2BpGGiucuM6Upk1Wg6bxX/4qGVQEVwKl1MPylxVg2oP4BxqIWE+V3neBgu3oco3t2CpB35oh576EcGsqLVs3GRRwLlQE2kre7sAewgWlpFOl6Bt7hQpKJ99zo4+/COLkRa1rasH8Q1gJ9RXMoMnURYZBVaqt9rtV1gB1bppAoOB+VkAB6A+yhFQqAwoK4XiR3YpaRWopzSjVhukLWef2WlTTz1Nms1pgAKu3dFmoVgFNvctThA73NNt5Zs2WI0gMCDPqUcfsdGTQ9iuyuoJVIYaUe4h4pbMcN7qCqvftMbKSDXqL87a1JMv2OHvPWJNwBK1qwEnulegetJm5eu6zFrpV4CBEdRGgkCIClSnJknPNQJw4c0DWEV4+E7asTwqPqR182YPIRIzYGkgiorzb7FC3Vk2e3yvJY6/YM11qJKsBJyTug1B0oIewRfm4E1+aMn+J2hv0nOiBFTZ2GqhGmA8FHAKCh6HK6xQUU1guInUqI0EK+hni/0oMO0BRDhq5SguVKLyFmDDbQFbFxbmLD48gmJgxmKt6wDnSEXX2Elb7kRp5h5Siw1glyarqiXlMoo7Xgj4EEW19Pw0ceAlgtYVAB+o8bG4VVCPCKnOCCFAU6BgNDsBHEDKQALgles2oPSGSsdIP4oFM1a14hzsdzmgQw/lJsJPvw7mSLs3B0Ry4jnLDO8ncIwKHZBNvqkdoIDUpBnggFwFm4BbMAkBZdSQ/Nw4QAtqPMAeyekxF5CPVAOilhEUpn4eynDJ8QFS8VWQwnErgWbUnPAv/dlTNg44l5hH/bEGxbhGAuSVLQBr1JENIbm5SZSepoEhCPAT5I+jZOLXdFpFQ5dFUE3MkwouRWovmyHNKupn5auAE1CpGh8YRnlkgkBvNapFKC/FAFpQhfJ5pmD0p7zaYvhR1GdQm7MsAA6gQB1Bbp/2y6KmFCGQDgwciNXR16lDhnEG8JEYQVlx7ijB9QDX4++o90GjAMfMEMBFsW7jh4AkNtH/GB+Y/xcFnFNXktL1AECLlES++c1vAl61ODVTbbDRRhutL2/lQ2urjuL3oi2K92NPA3crG43gOW12Wr9+ve3YscOt0UVlb31WX1rPtTYXQbriebUWF89f/F5cn4u/nx5k1edeHZwTGDfJGAR6OoiyJCBPVcQnJTJjUIAs6xfJB4GS+uDg55hXWghMdpExO43SFOqKYe4ha5lry2OM6yyw+5gtnDpJwBLly963W2XPFqBdgLGBh0hHSmB19btZWy9j3RBgBJziz7M2nwD02YNC5B5UjkhrCUQRq+8GyF3DtZlPGUWaQ8Kkrw5WKmUrkd/MKdRSd9n00AHmKiAiosFeFesmQF4unWAOOgTQNggQU2/1a99j0Y4rGftDlh19GLgKeGYpC7PdgKIWc3MZc3WalOgLMxZkLQDvsLTAWrqyFxMMjY2AZwUvLbGuTo0x92cjtmLTFtJCt9rS7LCdOg70Yy2snShpAu0o/SsVxLYzAMj9KMQBiZ182sLAusEa5iCgnUIFcC5AjocqKUQ4czP+BzBbAGVbpXmMAy1Pn9ppEdqnUuWoqoeRx68BBs5Mkc5zjvm8Er9qJbDeih7m7QBm6bdJwLlwdgGQBd8DuFfUVj69SBtP8n2eawQtiyotzAsgNnAgqmkevorWjQJqfT5qcIGlBYq0jlSZ76fdU6SnfRCh2EngIdK/tl5L/bpd/STmZanjAIkP2SJKYQnSXkeBz6tRaQ0B9XmkuPWBlwqBGtZ92o828sKsShnU+0aPogR7jLrOUSZUPUkZJzVQfymF4hkKYlKvrYpZ3aoLUeK9GFui8Xv8QUsde8IpTBnKoOHGHoD6CMF61PIS87TFONVlLa4njSxpPacnUEylT5TXKiVyOQA0XQ6SYBZIPZ0gLTEbBMo6N5LaNWazKO+gu2z1K1exPp1DHVdiU8Qw8B8KaVKrjj1jyZMPk7a83/mSZU34uPWCyMsZq4I61Xad9CdSoIZYR+lHOYDTPGuMn+LcrPP56hbsAkS/xLo9hYLpLEAXq3Tz+i34ZJu5Xp3lJocB2J4DcmM9baSvoNTs5VHcJeVrep51nHlDqsjlFdgVNbyQx9howncETBWwFFgYxZ9hrY3nAN0vsdj6d/Ksh/60++usl4tsArgARb238X7S+wrop9YBUjHnJp626SMv0MfZvAIAHWsBzkeNEUkQfGh8Nq4npclQtA2Aq402VLrZvShCAyFmZ/F9mDdQQC5Hec8H0shPjPIwH6iSVKll+E/h1dfQvviU408Cq+9jnIZJtbqa8zFuc4w76gedy3jRGIlYGjhyjvIFmYNidfjkpHf20jOAlSjvscEmTXo/KeLFztpAn0o5xbnJhQzgHP4MaorBMtoGFUdk5hzotkR62JmhFy0XB7alPpV1+IwR4EGuU8CeUsEMlbcD+HYC3YGWoIiWB7JbOPkiZRshPXAMP5h5SWBdhnTKyX5gNNQj8alaercAKa5mPwjo5cg4EDAAKXWqqif9sjZGcC9TWBxiOkCFcC6Pf3C1nUhcYU/uy1qmECNlLeODzQc5UlyPTeTswIkF+lLALj6vxdpJJxyfjuNHLFmMtKtR5ozp2Yi9uC+HAt28bdng2fXvaraN66vt4cdyKM49QorCKVIXXgY41wl0xyDl/1HUm+68a9i+fde9tmXjKrv1xm22+awql/70/gdP2YHDpH6trrV1q+qttT7I88OgHThu9uKxJP7QMfvo+wSqkaq1OUiq1rj92ZcZ8wspu+Ha84kz9qKqiF8mZoWeH0fZ/9D+JXv8Ue7rOE8TMv+KUSAsChhYsP4hYJbgPCAV94uxJGk9UTElJW5tFeAOm5aWUOE+3ofaFalpz97UYddf12hrN4bs4KGs/eEfPUE/b7P33NhrN95YZu0rAqw9wDd9aXvxhVN25SUo+GHrf/7nO906e9VVV9l1113nNo5pXdQa+fL1kSKXjlexwKv5FP/L22mDAtKG4+MT9tRTT7lNAoePHHaKxFdeeaXd/t73Wm/vSlSdNTcu+zhqB/dMgVfkwxR/L/5caqdlK7+8z+p3ff00cC7Pmjk2XLAHH561PtK0lrG5rq4mDMQasKlJs117ZyzJPftZGztQoaxj0/0SmzoAtitR46zW/VaAa3i2b3/WFoGWr7m6k80etRbFZ/nHbwDOPXvQLjgXcO69620Dc0kkyn0VSuUPAM3dc3cCf2DQbr1tlW27uMniqE4++UTcHnzoGOM3ZL09zXxFgLcLNjDk2979KWN/oa1uDdpnfosMBm+rZGNXwe67J2V/8/d3kdq5wd59w2bbsJG5pCKDmt0pe/ihf7EnH3/M1q5ea7fc9B677NLt+KRMQrhUYua0N4//qcfyvMS3132UwLnXbbrSB0sW+OkWKIFzP91GpXf8bCwgJ2SBG8PLL78CxzduH/7wh+1zn/vcz+Zir3jWf8/S9IonLL34FrLAAjdc3/p+zn7vy1mbYPdVIz72jduD9qn/ELGz1uiW7K19HO3zsU3Gvv9Izu0K+aWbQvaFz+pm88yzixzakTHf/r9vZO2/fx3HHJDu7duC9se/E7YtZxG0LDX3mdeopRK/6SygG2wpCk1PTyOff8CpRSjFklSJpEKnv+k9r3ZIIVnpmG6++Wb7wAc+YOedd54JqCs91Hg1i5VefzNagE3X7HhErfUvMnaSdYdn5faF/xixX7pND5N4kFtyXd+MzVYq00+wQPEBqh40K9CuL90DCn4TOKf5Xum2lXZV4NzpO7n1Xh36rBRJFdC/44473O+33367S9NaU0PQh7WhGLx3D7kZKIQf7DCpWv/8i58n2H7K1hOIWpnhYStPK6sEm9SgIkYgvZxAbRMKXw1bL7TKSy51D9ZPPfSwLQDdKWtXAdUSMB1ikykCm6QXW0UABsW5KECBFyEgOj5mM8/vJCsSKZFIPZXnQW6OQHCW4GO6nFRV3Z228YorUe4iAIaqz9LhPks8/YJNUe84we98JUExoKw86i/NqGV1biU927kEtnm4mngCtQxAu3HAsYwkj1FvK9uw1qSMVr2qm+AhgTSul58kQHjwsE2QavXEYR76EmSvKK8kiB2weDJBkLbBVqJu13DuFgClERvc/bz19fUTRAOVIqVslGBRNJ0HlApb81pSPkk5bx3KcrFyFGiApZ4gpdWPdtkkgdR5gvWBmhhBQlTKgANr21ZY22UXo9SG+glBsuzuPbb4wvM2CGA1R2qjMO+rcI4yCl8re6z1bRcDeaEgQ9Nmd+6yo489ZgP9A6ihxQjMNqGu124rt14AKIVSSgx4AfiwQGA8w3v67/+hLRw9gfpIxikCSKmjQF+qr6m1xvMutArW/flE3I7vJIDM+0MJ1HSwTwhlPemUVTCfNgBXVW8hiHjRRQQfCQQC6Uspb2jvflJMzRFQRimmLIpdSE1F2Lbx/C3Ub6s1APIFsEUSwH94114gijHOyIRcVmEZzpPhQX0z0N1qbFGDspyHulpiz2E7djfqGiOTBD6BAOtqrGItNth+AfAeQXX6SFjwCcqARD5JOzqKWhjKOAByedLFeUBiHio/5QTGKzP9FlwctAwAWPk5N1muZiNp1o6iKLjPGmobrbp7m1OLIYrrxleBQHFu5AGUzp5xaftCqKNUouxjjIMMfS2DSlKKr3AMe6AaV4VySxCFpmCONkTpZr4PNaTFSewGMAfgp/4YJLhKTiNgTd/KGnpQCEQZhlRz/uIhWxp7jFReQ7wNeCIEyEG9QgSE82mCmvSTsgbKDSiRoX1mp0lzBhgSAfaKAR1GAinzUqSOS2GHiMCyDaRUCyOEdMpOjcXpExutpvdCi8ZQWhMAQJ8IAXhkURmZOUnfRGEnhgqBEdxdAGhUatZAhvOSkq28os2qmtaT2hSgJUJHz9EWpN9bHEY1h9RraI8wnwAwhknPSirAHPBNDpgi2gTIicKOgwaBAaZJiZtJjjCueD/tnSTNYYZ6BYE5grks97dBguQAhqSInptL2CKKVwH6Bvo3gCVABShOhuILwCWoAHUTSK/qtMmxaQCMaRQDAee6NjCuVtJfUSdKAdwyV2RRgZoZfMSljQsTpA4DKIQF9JBmspCOWA61yEI9sCeARA1KOkHaHoIIGIL2GD9IU5GqD9AjRN8MMe8FKJsAmLJ1twJ0rHVjQy7FmQ7OaY0orjNK4a17hzvvvNNtxtm2bZu7Jzj//POBJugXbi7SJ96ah+yk+6LT11b9XNykJPu9wHz4L6QgFzyne6+1a9fatdde69RY2ttJo8y6p8/oPMV1t3hOnb8YVJaF9buO4r3Yy393f+QfrdujAO5f//rX3fetW7faNVyzpYm5BEi2MLsLSJa5cWrYouGshVlbU1ZlKV/r85KFvTnWwwhwMMpWUZSV4ouMAdTLEiPMzUz8ldwPhlF/zc+hWjbOzA081Ym6bOdm3rcPmOQJB5KXrXy7hWovcspxAVFMoDH56YM2N/AcKQyPMPfErRqwKwp8nMspzTqpoHlbEGininlUam7BKuZZoBZfwN0ESpOjJ4GtAPBYC7NRYFvAizAqb/4S8w/nqe15l4Wbmb8LI/gQT6NeiRIWc0oIxa4Qn9GRZU3306jMAehV1DAHRlArA9aNp1MO9FZJw/gzHuq4SdS08swRLaizVqxotxRg7/BJ0tn6TdbScTaqlaztqKvpMx5pR/PzAxbvew5l3p1WRVrTUHUDvku7LTGPKn18iPnRI7V0kPTUFaj8hfFDfMDyfALFq9FnyQh6lGA0KTjxJSLANVFgYYG/SZRd8mXMT+0o3TZ2A1cxZ9HGC307gfVnWfNpO2iVLJNQBphQql4VSDqVtwkOBoecAuZnc518DQh/5q4IaoIpC6PWFEgDk9cq/eZ7YIJ9mx74EXPtrNW0A123XE552cDAnJenn/qJIzZz/Ae2NIGyIGtHmHUwDHQcAegKQR9mAWJyoRr6DpA8cLlAIgpL+wFEk9Z1carfMrRVEGAzBGwf8PD7gOoT8VmUqgC4UPMKtZ0HrEg/RuEsO/CiU5LJVLZZhjU162dQrYm7db6K9amqoYVlDuANRcMF0nwvLMR5j4ftWEN4lhGm7ZcWUVEE2KtpRTW0lRSp6Vabm5oBYkKpr6uLtKpr6UdA4gVsw/qST0wCdz5r8YEnKPcE5gKiwlfICKhE4SYv6CnQBJC+yuoa1pOyDtiNNQtHyQrjz1lyCsXXLIqF+KJl+D+VtHsANeNMikoBQ9b2soY2baSjN8OSooSKMlkBhbIEPliadU5pDaPA3RQUKIq5gQe+lTyTyaGknJkHVmCtTFMGyZFVkYY3kpyypTgbL0iFWrXm7bRL1uKHvw+6D2TavtnCrdsA4NvxpwU6s24l8HVOPWGjR7HtUs5iFbXAEzX8hXrhn+eDzEmAhxE2AcQa1sLT4j+ynheAzxPTh20e2DKdgXqAVChjzIVJ4xumzwVIKcuHrbznIgv3vg2fkH49s8cW2MCwOE06XVKphiSlRrkiIcYkm218oNFwLXAsypdzEFGLSVTLcmWcE38ukKQd49wvZBw4Vx5rt9hG/AhMPXNywKbjWWteBdBKXxO4SeNQJnw/QKpZ0tzOoqjnAfxVRStZ67lPYZ7KMn+luBnRRodYLYq+jUB3lSjPkfYW54e5ay+QJ6nqmQsKgINSiAyG8JEDpJ0VaEs/aOhYT8reVYwJfAb5J8xJiZkBMiDnLY8f6uOHlXv4KEuTlkxQ35rL7cXRDXbv48M2PEGdGR9BvuR/JaDLtElk/eY2O29jlc0OpGz/C4eB9EapE/dK7LhJptn0YdVs+iuzS7ZW8lVh7e0Re+jRrN3xzce431mwd7/rInvbFW3W1MD8zP/DYyg+fW/QfvCD+23T+l5gu4tRi4rZXNy3Z55bsEceP2kn+9jww3sba8rI7FNvS9lG6xsLohR32D50W4/dcF0rqsBBO3ps0b76/z6GQmjCrt5+NgDbKqtvpY/QDnycMYdi3AvzpFLczcbdNGsa4zoI7MPKkMZ3rEQ9c/36JtIvlgMKDpKedtCGh5ewKSnA2Sd4woEAAEAASURBVOgj1fQU9u7l/u7y7d22bRupu+vNDuzP2F/8D1KGZ5rs3desYDNvBNVI7gm55vH+pB3YO2wXX9BkC/PDJXAOm7wRh3wKfcnH0Ner+RhaU7Se7N6z58cpWrPA5hdccCHw1G2oGV7snhsHdK/90nlOP5/Kevrv+rl0LFugaP+iPYpt8tPAuRyL/6nBAuP+uL2w+wQpyVOMReYw5sUM4HIGZd01qzrsnHNR7S0L4ZsOAbGNsUdgAd9zyflHyRQbpEhjvWZNk+24qt7WrQ8DfPu08Zg9v/uIbdlUjQrdaluztoZ1jxSpuaD98P55e+C+KcC5U3b9Dets6yVsaGOp7OvL2g8fnLDdu4HGFwC7y4NAwzxnYDPVQrwRFUrGc92SfeoTNbbjiiob5zntvzywaH//j9/h8UwBtbl2a++IAOglAP7GbO/unXbk8H4A4LPstltvZ8PnlQ6coyu+BM7Rd+lGSrWueenf06NK4Fyx95W+lyzwM7BACZz7GRi1dMrXZIElHggeYuf2Bz7wQfcA5pd/+aP26U9/+jV99o15079naXpjSlA6y5lpATIz2QOP5+13Pp+2gXHfarh3voZUpL/9m2HbtoWdmaWuZfsOFOxTv5exRw7mbdUKz/7wExF7383s1uRe5Ew8eA5pO18s2Jf+Z8a+93TeOkg5+3HS8X4S5TnBDKWjZIGSBd44CyggI9UIgRUPPvigg+j28KBDQRUF+IoBn1e6otK3CsL45V/+Zbv66qutqYmH/XoIUjpKFjhDLDBD+pH/jpLtV+7M2tyi2aYuz774XyJ21RUEJhRDKB0lC5xBFigG5YtKP3qgqiB9UXHu0KFDbs6+jQfXRcW54sNpvVdBeL1fKV0FzUkRZ8OGDfYrv/IrdvbZBCwJ2BWD+P8GiuDB5H5Sd3wBcG52fMTO6+qxLfWN1kAQsJwAlEdwJ0gQrZIgdlsvwaizgXW6CNSl4ja9b7cl+o4DMqVQPiGuhmyJzzpSi+Ja45rVFiVw6dUvK5MohVoWYC5+6JjF+wdRpQIAIoLqR/F5Ueuq7eq0NtJcRVBxUcjGJyVstm/IxkkjuzA/TgBWQUVABoCmpp5VpC3dYGUEYzxAq9wgUNeBgzZx4pglUTjRk9UIZW284AKnjBdFSUpphpQmtRAnqDo8ZKdQ8FsYJoBF3YL8JyWcespbt36DRVB6y6GwMXn8iI0OnQISSjsIKkhcMoo96lFcq1+32sJdHYZzSxpIqXZRl0HAvQOHSB920qbmZgh+hgnqS2GmxmraSLWEQlhYwW6ChQWUvDL9x1DTGiCQRpCLclThJodQeqrs7QEKRH2vBeUzgpEFyjnFeU+hPpgnVaZPAK+ctEcrzzvXKjt7EOMgWK76AScJ2EocPWxzR6kfvkBAYAGB2DABxBpSylVuOAuVkmaCsTM2wXtSg0OkPJtFpYaOQNA9RGCsFnWxhs42FE16LYD6lIuHZgCNRods5sSAjZwYJ5UlD+4xdDmwUb4sbA30C6nIRfks29BJ0zVtiZMoeRxHXY+2lpKET0C/QLC5FqXBFVsAE+hHHqlYcsMTNvfsXkv1k8KMXfNZ+mqkg8De1i2oBgIgQGIGCQx4qCBYkF05hQXLohiWJg1eKj6Hag3tR3C1ErIxsrAH+T+UEEkrWr75RsvUbrBFgZqTQ1aN4km50qyRwlMygVJVKKDok5sG9kJBJJec5xoCqEh3RxCWWgCdoP6D+kg54FxNQzeqPj2MNZRKlIYPKCE7h/0A9XIEUrNAo2G1N/5VkMC4RzsHpEbUCDxaieJLetSypBRNzAPahVFnIdisFMJhUiEzOHkPwFdTG8FewDICuUsonyUWgdRQD6xEla4iykIH4LKIWlIe0CDWTRpYUqD5i1mbR5UmUI/KSlMP0IIUgugT9IdQboZg8ylEYI4RoD5OTydQTEAzg5Je3gdXy6EOARQRBhCoaFiBog9BaIL5FJb/UXxCvWlpYdZSS3MADajhlZEmiD9DSGC/KNAo6kqkQBQt65PmVqCdR1+JROnMpPtKoiCUxs5BgIAogZ5yqS1xLaUFTgG0JDOoYHGtKBHzKHNYADAwPzMEfAE007keZZh1jH/Og8pedUWNlaOUp5S4ghR8IFYf9ZY8gF+cNk8nh5gPUPjibwF6ZzBP/VBEIKyDbQGAAOfKSSnMoKQNAUUALjKoTMUXGOOAQiGPdxJQD3uo66G2E2ghLWAVAARn0zOLMx2cK94TCL46ceKE3XPPPXbfffe5deGGG24gRdIN1tND/2H8vZWPYjBT66tsVlxf9bvWWH3Xoc1Ku3fvdvCcIDrdj60GZN6xY4cJaJPyXCVjuQjIFT9XPL9eL/6svxX/rnPrdR2nv6bfXxGcK6ZqFayygIop4FoG4CZSwaaSinL6LYBGEnBcIG0UTAOAOlrN2kVqZl8QCQqR6TjKXZqvWFtCzDVK+RgHhPJRbawGsCoH0skvDQOp7gUMAX5pPo8xv4EZkrWVnSuehxrUHAqVpO1LoiDCZMA9JUon0sr0eQAIgKNnRD7QaiUqdOXN60lZirqlxYFoAfRQuBMALLW4HBHSDJBRCDisDFDaT+k10oC2XICCFbC85rRJlOhQhIW+IQWmlMJyLKspQAwW6SBqkaR7rQBcE9SXRpJlARXQAO8lWTiqk4A+gH1JAG9ulQHy1pHtEvU9IOXZ8QR1qrdqAOkoc6FTudUMAOCdZ81cRF0tj0plmSBF5pFMWECg5ntszRqu1N7BGKkjSW0dANjPsz4GBQfGjzpwLoWaWob3hZmjBJaESdkK52cpQKxIXaNVoTwXyMTIqhsHRNyP3Sg3ymvyq1KAOFnmUoH4UoOLSMUU+Cc3N4Uy6CB2oQ1YuwJh7OZhVz4fSE2b17AG2OlmguvlQHYnUG9NAqWRMhalLi8MxUIbIXAGlH0KlcFnUf48SpCazRuorWnTQJT0lGEpcGXQ8yQVZnljO8AVcJkU1ADukRekfpOoxLF+L4ywngApAtyFsU2ANKtSH4yWUeeWlWxqWImfgk8zdpQ2BO6nrQpljYA92CDLWo6KrPyVmmrW2mr8O/ojCy2iX8vwtFTawgLkadeQz9oSH6Zvj1usuQH1r7PpJ+sRGqAvMH6qGoHQgdI8gWiS1KOOeeIqmcnjrp8GSXMbQmXOZ/3LyBetBA/ncmkDfop2WkPTWtY7AE5BxFn6IenhM/gd8yipLQAmllO2GkGQyHMptWoGqK2yDbvW9HI5lAdTQKDAaIXZfhS6UpakXaRgV4ZfrfbPMh7zrEPl+JAB/JvsbNzSKc5DG4dJUV8RmGRMnUANDACt5RzUVnfQvuWWHt6HCC8oHHBYoHYdYB9gHGX0PHySpVH6+0HUV/vxP3zWVI09riNaEbBTG08KBdKg1wBYNKASh+KrF6Stc6RZBpjLzPdbSmmIge4C2oxQjpIjcBmSdPjPKRSMAddbz6XLcD3U97LztDnKdtkk5wc4D7FuR2NsVMHYOTajBIA4wjXAu6yt8QV8O5TkovSpMsCMEOBcepFNFoAXlfgxsdVnY+sYLs68JYA8a7tQ5AVODVBnCkP7aS4gNS51W5odwwdhjuGeIIt/FFCaZB6ep+kb2sRTWQ0kj32C9NGCA3yQZWTDRYH+n0LZNsM8HqQOUewcKgcpxm8uCPbCl4mifgcRx9zI5iWlU8YmmcysJYFgPfzXOoG63PNkFpm/KzfaUKbDdh2Zs76hGZtHoa8ApBxGHVHzYksnym9n1Vt3S9hm+5as/8AAmxwAURhL4JCWA7qtRxVaKlEb16C6RrrWClSEd+/P2cOP0nfoI9u29QKUxAyz0sbGNfSsfc527dpjPZ2NKEWtts4uUsgyr4wA1e3bNweYRgplQMyqskqntJxIxuyFg6hWjR+2997YjnIbaVy51uhE2h56GAB5MWmbN3TbhRe0Ah3Sn5eXOOZHznkqbTufHQCWwQ9kswKaxW7cl3Ff1cF9wsazaq2VtK9DqHEfOjhupwYSzFX45Mz+Hu1ewU6c887ptY0ba4BlsDebFPpP5uxbd50CrKuwiy6s5bpsLqrP4SsWbHw6ZaMjCVvdVUMa634H9wtOLynO0S1f51H0M/RdPsW/eQbAOYuv6/R5xnH/yT67//4H7O7vfc9GRoati3vjd77jnXb99dfT11jbmPedb6JNQqXjNVvgdDvrQ8V2+WngXIEByX47e/7FcZQlh8jmleB+U3M5SuzMfXUo9W/c0GK9vfgcuEAHD47ZwCD3RYLNSWetNTUUKXfK0med1WorV0dQpfTIZOPbc8/O85xp2Lq7Irb1wjZrb61ijFI2rnkA9bg9u/GTMjN2wdYOW7U6xiYrjzVWaVn52545lKtRDeXZSm0DIC33WQMnKyhjwRqqk/aJj1Uj/lNlyUXU7vYA2z30DCvDojUzX7BnkX6UsURyGnDuOTtyZD+qlJvsPbe+1y7bfvkyOCcbuS/u6/heAucwQukoWeDNbIESOPdmbp1f7LIpBZsC4p/5zGd5ALPCPvaxj9mHPvShn2OlSw7Rz9HYvzCXSnOP/fTzefvdL2XsOVKp8TzILtsUsP/0yYhdg+JcqVe55wv23HN5+4+/m7ZnRnzbin2+9idRAp08HDiDj2l2r3ztWzn7k7/mAWTStwvOCtiffipib6Pdz1Qg8AxujlLR3yIW0M334KB2oP7A+Qx6yDQ+Pv4TFegEyq1atcql8ROM0dPT4wD9t4jJStX8BbDA8X7f/sufpu2eH7GbH7/j9iuC9rnfi9oqIDqJZ5SOkgXOFAsoQK+guObl4sPVl4Nzl1xyid1yyy0OnIsCqujBtd6rQz8rpbeAOaVpVVBf6jcf+chHbIXSrfL34jUE5xWD9rrmgUMH7PNf+lOCdgm76tJL7RJ2/baQiiuK8kmAeJhUzyIECcuaGoi11xCoBrIuAMsBtGUJ2uZJyVXIEpgnKEekjDSnpA0DhvNQtvGoj0eAKseA9JGc8WdJ7TVJKkRgHIFGUieJoHoaJcAZriHdJWmWfNQzdJ+QXyKlGuoMeRShvBSwEOoePoGgMtSjogRuPQLT2n/sE2zMzs3a0uQYEM0sQe4swE2jlXWgVkE9XAAHVTc9ei1QIQ+YJzVB2VHnU84hHj2jMEKgl5RvgXrSogEb+JlFgog8MCZFegE1ECnUgBM46Ki8tgZwoHk5dRoKFBmCCAHKFgEm9FDGWpohkE1QXoFED0UMKetEa5qoJ+cW1StlMoLL/hLBOJSLktgklM1jb1LkSbGskVSarW0E7UnjSmDdE5g4NUewEAUyYCYF00KVBG07gKwAgQqoiAREt1FHwVI+tsrOjNgSyncBQIKwgulAR4EYalsKJJehaphPEBQk/RnXz8cTBKxQIxEoifJKWQVfqPUEULKTIorSaiK5AmSVID1WCiBvyTKAWlIaLOOSQQVHW2i/RtQ9KL97wk1/LiQXLT0txRTBEQrEEk4VdBED0iLVbLhakUBMQdAuj9pcnnRseUAOX/ahT0Q62w2ZDRfgVBE8wXNBJvoQO7MArgqLQFcEZwuAFkoZGAzRR4afsPzoC5arIFXr5ustS6rWXJJrA+RFgTYCwHNeVIogVAk7+gTlITCw2TSBUgA2rkHEgDIQIKSCUvkh/Az0RZujJuShJEPeMz3Bp0AUCkUQPzsGtDHB+JXCH0FxmWAJuCM1S1C7muA2qb+A2Zy63QJBX+xRRuq0ULSRgDxKbKhVaXx6pGX1BJMIqEEpgKgysIkC6agBYv8ogfTcFKpA9Ns8563rWg8c0kNxCZxr/AFQ5gH3PMZgwAM2xIxBKfKRmieb4rP5EfoHykScLSCYBeCjgCKc0rxBJ9JP6av0uQD9BQ1E15/y9KcsQZEc9pZKWzRMuBfbFKSaSFN4Fc3ArkpViBoLaQGTSnnGeIwwNgNV0i+kbZXeVeouqOQFsIOfj2FvxiMqTj5l8oFDlGbZo69mgC8SE4ApVWjdSF2ubhNATy0qMVI3jDl1KaW2VeWkQCjFGiYiAv2k0CPtbsFDVYh25Q3UspwmQq0JtaEAoGCY8ecxfwg28eiLKA84EDWTHsc2Y7AA1A0lu0AeoFGSK8CSwXL6CtageQgap2zv3r129913u5TY8psvAM4tzsVc9E19aP7Xl9J5a5349re/7dTmNhGset/73keAfptJmdT1RVX4LXgUA5mqenHNLK6xLw82C5ST8tyuXbucyuu+fSi9YdvubinrbHOg+7p1pMkEntO6XlxzTz+fflabFG2u7zqK7yn+7l7kn1cG565xAVEmGOZS5hcg2QyKmNE61uwa+nKGeWEJSAPAxwc0jzJHBEhVDfnGhZj/gOR81JsKKF9qlRQEl1s8aZPDKMcBkdU2bwd028TaDLSdH2LsMFbLOhlrrQ4SUYpSKa55AFJKpV0AbDOUp3zOzexB3bElMItS+BVQcgqVkVK0AhUwVMXyoMkFwLIw6lpB5nOfNUPjrYC/4QFkBwFWsgvjQFUeapzrWDt6WAdYT6YGWOOmgGhR/6TPUgCuyzqpGoSAZcobYeqklMZcvojPwXm1joVIsR4IMDcvDQHODzkVttqVG1Hv6mW+K4PF0Y0D6RYFH7P25YD/wLCEiuA3+ABCXDMzBuhEikfWnTzzrIeyWkjwDhAyDgT+EXAvsLzPQy/E5ABygYRQ8PQBD3OocAk9ocCsy8A+nCORjJOyHaAIwCgWIT1oqp51GXAZ2M6L4FPEuAZAe450ngV8rCDAHtKglK/FXT8IWOgvDrAm4nMw6y1DxWO2hIpqkNSk0db1Flx5E75PPQB0HH8HX4N50JM6ncrA3JtzoDHz6EIf68UoxaLedEU4JNqG+bMguI71W0qAgMtBvgdQ8hQ4J98HxwS+DeiZdZRJ1SlOBQL4AwCU2cU++sA8vgdqwpXdrs/4Av2mh1kTOEdVh1Nak7pWHkVEj/UnWtZNO7VRNmyqPppbdJs1cgDbQYA5tMXoYsB6U8eBxoHim4EO288H4r+ANZPU6tQnCuEdQnHQC8Sxi/xk1ij6UQHAO4faXADFP08+E4BZAX/MKlB9I404yb7dOl9R2Yp6GmNELgF2gIyj/6dQOcTHQ6VMafIqANQDQFBplBKTrLOxVoBC6pNDAS0IlF8grbwH2C3F3Rw+GE4u/QFfDGAqT9/MUQ9BclJk9cm5W5AdKVcghM+SPmHJMVKuJhfwvc+2yq4rHZip/kxjUh2tu4CPQBFSNgwEGf+s77kMaz2ge4BzslQzZ0A5BIDYUIIz0u8VsoxBAM8A9fPK9Xk2CQA4kVgTP2kckJY0q5ic/MC0MWumh4+T6YOVxw+vY5ywEQBHFmAQ+6G+V8DvzQIJcnGKAvBarnbH58+wPlOuMGBaIbDIe1DVTQAFYvMgKVkLqDZOngLUi6etDkXe6u7N2L2NtkFdOodPWaexRP90UKAaAbVFFBiV1r6Ar+ZzfjwGoEf8C4D5IPVgKqXPRGn3Bp5n4asBcGbp4+rLEe5ZQulpVHwZ/2zECQJ1hlDXpCMACzLPAHSGAG7LagGDgvgp9BUfYNLw7XL4KkuCD5mXYtjEFoHLgJGV3nWJsTSdLNjULL4x6oB5fMgQm2bCAK+x+qjVNAKCch1/DoXEqYTNzaO2m2JDBr59AVXQWF3YWkhvXBcD5GO+UJ8/ciJlO5/v51xB23J2u61bXWEVbFrw8Hsy+Cbj0znu8WZJiRpFbbQKwBGfkTG7RBr6mZmCjY0tAiSiOAr0WlNRxuaogt3/aN7GJ/vt9hub7B3X1qM4F7AE9x3DI/RT7m8aa6usuYn2w6kv0G4+c5OmKsFz06R+nALmXcKXLgDRUhJ82DKrbyi3xiaBkCC+CwWbnkIRca7AvST9Gf/MowNWVQVtRWcM30Lpaakf88WpU7498C/4sSgQnrMlZpvITFMZY67gM0sZNlWkQlYdiQJwHSuBc5js33vInzjdpzjdryi+rmvo5wQQ6Y+eesru+s5d9uSTT5IyuxIFsO1uY4UUiatIN7580D/4v3S8dgvIvi+3vV77SeCczi5gTOlap+bTNjaZAKLj3pTpSMtaEEC3mnvp5qYobaPNHYzF2SXmGTZfMd/mcEICbEwKsZmpto7x2lIBEK57GrW1Z3v3zADFnrKO1jI31zTVc++mhuXcUpycmuEc3Cc387kYc5Rpsxzz7GKiYBOAulOTrGKA4RWkaS+gXn7/vWP2zM6UrVxRZh/7aL1t3aZ7yRAK6p719aMQywasSBlrh+7zmUnibAR7/LEH7ekfPWbr8ZdvveVWVA0vZQ7Hl1ExlovCVOTuxCgb9uDr9R4lxbnXa7nS50oWeA0WKIFzr8FIpbf8TCwwNjZmf/d3f2df+cpfm5yV3/iNj7tAyM/kYq940pJH9IpmKb34qhYgTmQHjxXsi3+Zsbsf5pYW72bjyoD9n78Rsduv0x1h6ZAFJKl+1715+7M/z9gxHNPLUeO78y+iKD+d+WNu1+6C/dc/Y2fJbh6Q8LDg1quBGUij19Vx5tet1HtLFnizW+Do0aP2PXYJCrqXAp0ACgVbXu2Q2tz73/9+B+UrgFbObtrSUbLAmWKBHz2bt//w+Yztwe/Qg5U/+rWIfeKjKCDV6sHQmVKLUjlLFuARoov6/Nvv/f39P07VetFFFzlwTinhlAau+BC2+CB2FIWxBx54wL761a86AEIwxLve9S6XnrsI5GktEDinz+jzgvMOHjxgf/blL5CWK2g33XC9Xbr1IgJZBNZyPPBUfjU9vdT/BJiIERP808+sKaTQ8lF98Ak8CsLxCaYpoOYBcimYrmC2G4MEtNIEifQgNsB7BaE5hTZGrM4T0Hu1s0LvQQlM0I57WKpLo5jmEfDyCcYq4OJTAA/lDqciok5D8KywyN+BWgRcETNzSg9EIyknyl0Cz6QEw1t1ugLBpQAKGx6Qk08QVqCUR8CMCBgf5F0qhnb78D4XLeWzxAN5L/9QGUEDenCsoHpegXHOKRPpo1Giq2GUxny+CqKKgHpceVEHE5CGRgppwPiAPk/QDg0ZApvAOwSKPEF2Aoz0d3KhZAiU+wQYw6SeswUChvoO1OSr7URE0RYBPVgmqFjgoXhANuf9NCnFJGDpUmURDKbYkEJck3WdMjiFGIAwD4DQF2gBtCXAj5MT6CeQzrko3XI5VE4+SvSQslM2wrwK2OYJavnqGzQS4To+w1eEJ+lAkG6rOhCBT+BUqiiCOQrseNd31+eCAEsEFR24RL+AnkThRkpJgBYEKj2C3R6BZIFYskMe0FAmUXMsg3PYN0iAHfUQD8U3wo/8gaIICgGyiJ98AVXBAYKuXVax/lrLVZMSEAWQILbx1Kkc8MZ3xgHhcsqhshH0JeCvALAvWST1DXbCe1HsRNBRwWkvSNDbB4bI6TuBI+zu2hFbQy1wLtrI/az+TZAV5aUMSm8hAtORlvMB9ggIJ1HJm9rr1Bar60iThnoLf3B90AWH1ebYRm1XSAwTlJZKEm2qTgE4QK5EWxo5DqxD0LduJeDc2SgrdXNtygT46BN1FaghhbQgDccr2A1ghGCzT7DfJ8WhD/xWoC+r/YJK1Ur5fNTvvDD9gzGTB8AkGR5tRV8RaMBZCm78ANUAt6B74M63NEpqN5TuwlUrqMcKguOUGxhhfmgASCdnVYCrEdL9ehX0EzUQ53J1dfAC4xiQMZ9BaTEM7CGwgUhMfj5u86MjBGumrba9w2q6gXWqeqk/ZSwwhgjY8mZ6ocYwtQzQJ4EoyKtMPyKlY56AOIF8HwDVp596KMcFCXL7qCp5AWwEeOfGAXMUVeLvOhvjGwUEBhlfmmsI/sBPZpVqF7WbsBT9KL/ee6aDc5rvlVJUQbrvf//7bqPNPGCwYOybbrrJent73dqAIaiv2uytd8hGOorrY/F3rc36Wa8LgDt9rdYm5xdJTf3EE0/Yzp07bXp62oFsWq8VcNZ63dAgkEOLJ9PNS+fSmqzzFc+ln/Wlo3jd4u/uRf55ZXCOVK0tQCIEJP25YzZ/CgUhoJ2K5mqrlIIlqQzNjzHbaVxzTeZ4BjrzLfMA0Ck0Lq8zDgFvIEpYS1EMmukH0B1hXDMOW7dbpB6IlanK532aX9x44pyad93wlu+S5TxK85gHnkLBys8zT7NuaH0IAtMEgMIsiOKqr/FcDfANuMs6VgDUQ88M34DyA+lycuZe+QCM4/kjlhhHxW6pgGrreUDz69y588yvi0C6QcZneUs38xiwE+0iKJaBa2mpggI2BgDHgqilsIBSRxYRlMLI8wpzdRJuCwCOuaFp7TaLoBDH4kPZmYNJbWsAUVpncwBMOQAhLYNBtT/1stQwNgISpM4FoDPQINwXbKvUaapfgC/ARF9tywfdPIzKm9YJLu5e15rqYZ/s4rjNTI2QnhOABuWpGuZTL11Nk8TZYHAMrg0VVdTUAsDJPpsKHLQtX0XQI75QFh8mkAQC8yc4H/MoZfRZz1MLYxYHfAyzvsU6t1i46x3M78xlaif8MhqacvOfbE2fUB0CzO+GaqelRpf7kiBkrZP4R0pvHQBGh17Ez2IuRQuwAKCtzxe0tlMvZk7KwPmlXiuFN8qST45i6124BpNcfhUKxJscOJcdO0AzHLYIyrChVoCpmPqGyrREeSiTW2sFaLAWs37lgAKlASuXwUH8QGpSJo3TRxfZTFDXjkrvii3AzmdzDsqXZ63gvZ4AO8Av1/baXKCq0+fzAJCCojxU1ByBwO4Q+Y7Imrr13gcaUx/XOujWVsbLcmPynf5aCHBOxkoAX6ewsGgL0/0AfIPW2tNmERT+Cl4z6wiA3YlnLcy6HW1fi/LsapZB1iIATdkbY7m64MzAhDEOAeWh6uh3jE1ey02TFnbkAPXwrLLjQitruxT3mn6gNlSPpI+6dVUDEFtD31FmzmWAlEuMQZ0PoKEAaOqFF/ksdZTKZEB9vZHzArjhm0rNMK1q0YYR/AwPuNPVGYjc2XHpJEvsC0BhYygmk4q27RzOw0YC7Ol8PI1bruH8IW0qcBsLgKvka2F/PztCtx+k3vgQrK8Yh/MuWio5aaODjKVcBTAHaYrbNlLOFsrPOi0/hA03vr7cfQS+GAuzBxBsbOCQr4YkL3bi2oD0Pn1HPnAAiA0pPuoG8Ae86OPjZFA6RuCWVPDUj3EorTc+yHfNd/huKdINDg8AXJKetg6lyuZ1AJ1AnkCGy+9j7NLmPv5dgfYJ6V5Hjpb8EQD8PPbTCFCzyJX25VxQC5VfYJ7PHBehf4U4vwM3ofhyjA+6DmWj3JwmxNwVEnyseZm/Pb97EXWmPdz+5ElxepZtPR8VTIaD/HFdS7cjWjcizDER+ouOFO+dBaiZnBUgj6IT19cNRzLh2w8eWLDHnmN4lC3aR95Ta9svqbLqOsrMW7TWyA+SRpz6Wl7jkDkbD545T+XhWozpHGNbexIoJe4adzMAO8tQtNZELsW9neqv2wGBpnqP5poIKoTiT51NsIPSDR8/UbB/vAMYM1Vrl1/abJdeHAHqoS2YrwQkF5jfAkCDx3meqXTyJcU52e/1H0V/Qmc43afQ68W/6fUM/qE2YTv/8L4fkJ53EEGHjc4/3LFjB8qCqKC75wfuRK+/QG/RT8rWr2T/nwbOyS2UCmyWe/msQG/GlzuWhz7zPjAyc4PWRwe8ch3ND5qTpFanz2ssagl0Y5a5TGGBmemwPfbwsO3ff9hWrayxKy5fy0ZL5nY3p2geYxxrPOvjDGxdJ8/8nUjkbQK1ytQS2t7sFJNCXXLJs8GhlH3zzhft1HDetl/Uax94X4utX6sNoXgH3HO6sqscrFX8y3+UYSJBf7ub+5Hv28relXbzDTcBzm0DnMMv4b2aULTi6f068A74ev1HCZx7/bYrfbJkgZ9qgRI491NNVHrDz8gCA6Rw+fznP+8cmOuuu85+8zd/084555yf0dVe6bQvrciv9KfSayULvMwCctAGRwr213+btb/5Rt7iOG6rWjz77Y9G7OP/B15V6fixBQZHfftvf5O1v/smu6twB99xedD+FsW5Bm5kz/RjEtW5b96ds7+ifkcnfFvb5dlnfiVsH76NVBQ8ICgdJQuULPCztYAedj/33HN21113ueBYX1+fC/oVH5C8/OrV1dVOgv+Tn/yknXvuuewe1YP50lGywJvfAgmeiP/9P+btD76SsSmA9BX1nn3ljyL2zh3sWC+tN2/+BiyV0FlAc3MxgC6YLct2YoEaeoj9ta99zaVg1QaqG2+80SmFVqAupUC8gu869HmpIX3nO9+xe++91ykhffCDH3TzuRRv9LC2eI3TP6Mg3MED++3P/+xP3APQm2662S669ArSbhBgU/BTT005HNglyEiuvJ7OKtAotS4FrhQd9glYOWqNn/VnHnjqP3m0CiBneeDqHn5yCj30lNCDpye3ChZxLhdt0R/cBRQsUyCKv3F5BbB9R6/xZwfEEPwXEYb6R2ExbqkTQEbTKNaQFjbU04EqBhEmgmgKavMursE5+KbTK0CkkxYAo7IEhj3OFyJAFiA47BMVcmVR4QgiumArD3x9FFd0eQrlzqVvqkCO82Sxu7hBPWcWahQmuCT1HQV8iZLxWdmGiQjAQCmjECfhcwo0KeUaClfYzkNdwz3hlryfQAIKKh08pW8NTBM0Pt5HCjQUx7pWErTsBEYShKR66Dq8n4s7xS7VkJfVbAVFrqiPgDF3QkXcKJ/6gB6eE5XiH66n7emU2QVgBddRtmKbu0CoGgBb6DrufTq/Aoaq10uHgpYFvrKcU2BhkL8pABig/G5XuewhdRLq5ggEV0h9mFacnbfUICnCRqZJ8dVo0e42UsvRfqjYFVBSyVOeEAZxxaW/eGECyqiDLY7stghpySIu0E7/AIDMkoZtjhS5YRTzqjvPtkjrRZaN9mAqVNRc+6k/0i4umE6d1CH4vbBEqk6UznKk8PI4h1LeFTxAiyj2kwIXKVq9ig6UfgDBUD9TilUHeFI2zMk5VT/OJ3BOMGly0BaGnrYkEEtZrNUqu69GXW8VYikHbGn4QdLlTVtd6wUo5FxBjBmoRSCLOhD2UZ8roIC2NPwCnMUg6WwIHtBePil6CygEplAyTAdR0+u+lED6ORSDADjKkA6GYZwUZCj6RUDt7KKXACwE7nPAhLnECcSAAA6AA31Au1AQlcWqVlILUjdSkkrtLucJzmA86AswQ7BnwVWSviJwzkfRMTVo8yd328JcBpBlPWDNGhTmAAjmSc17eD+KLnlrbCctavdq1IlQTxRxw3kEaAQkZZOesNRknyVnTpF+VqoovIbNMygkLJBmL03qvroeUvlyXgNsKRRoP8qllLIKCsFXuoBKmGB2wIAClwZhIY7BHwzRtCg6MXc6CBNQLlIFOFTbC4DXRbVQ2AEOLRC8lypM0AXb1Q/UOWhDfQG85kn/m0ZJM0DKtmhtO/ZUqIZT/wIozs3MzDi4S8Ho/fv3W1tbm/3ar/2aCfKql0qoxv9b+CiuwUUTyB760utal3UUgTc3l75kLynPHThwwB5//HF75plnCCpOoAJSbZs3b7ZLL73UtClJtq5CSVOf09fLz3O67fV3Hae/pt9fHZxjHiH9aGFyr00e3W2LQCXVHU1W27kGNqaL+QwVNoEugk4Z1YKQC6S9zJN6MrdwgvmV/g787LHQ5RKLpIckbSbzeHXzRsC0SwCbehk/BG095iHKgcYQ87xAPOZAuoyHSpYBBhdILenH+4FRUC3LoizL/JoH4AjGWkgLuQ5Fp7VMdczxeeAm5hetowVBsJTLA6hwCpL85gkKZ77JTz9lc/1P2mI8a80rd5AC9ULmihHLjj2HIt6gRSrbrQYwLFK3gkLJB9FXGeUEBFMa7ol95sWlnAeQJQ4G0NZPoYI6N0k6SBTDmjZZbe925gfmGdYuD8jaY77xBc1onQIGF8whAMvPobyFull+6qhBJ7r1MIefkWHBDaK+FGkAko6toxyr+WzDS/O61hydSvMMlnP9hflecx6KYumZXTYx2m8INFlD6wqra8POqGQmRidtru+IVQAk13T1oELbhYOBuqxpMwAQG+0UKCSA0vosO92H76EUp0DlwPc5lL3iqNSmoGsq61uxz/n4RhfQTrK5bCB4T4Fr5j3WdxeIViPSXoWpw1aYOcZyDSDI/JpmXpZSYQil1lBtF4p/vRas6OGzFZwDHwtfUHBkjnaOMK+G8vgQ2h0gKAwoKTN7yGYHHreq4LRVtG8hzedlDkhKDTxiqZFnmV+brbyHdbB2I3ZDGQyTaykMAhe51PMofRUS/TY31U+fI5U2klUBAFGpG+Zmx13aygBzfC2QdaSll/0b9C1DZTGLjyLfR/2Ktjf6LY4ElaesAO0JUnb6pHiNpFBgBS6jBbEvqrV1rQgOrmC95zwo+qk/SUVH645zFzilfE90v6gi0B39Pj8zbtNDLwK0H7HO9d1W0XE2bb0CAcAxmzvwkIUpa/Xqi2Eqt3INxqmKJb9KKneAYH5iEkjyJHwfICegvNLTSlEtMTuKivS8VQG/Vq3g83XnUQcgd/wMNz/IMRCAJ59ZkJLz09gAkBlA6ZcxvThJZnvgVQB86D3ajTWY9vNi+A0o+nkhAFbAuTzrGz3HnZMr04b0VSAvT34v854/t5ch9xgqRsMWRRWuUuMl2kIfZKzg58kGghlpNdqMZ0f6HCSF248CFJ+Z3WX+zAsoFwKjO18YXyGTtHkA+YUl3yqZF+rbtsK7reIUGr9QVswZBdZlKZ9pS5xweac0qPS3KFAWZgfYbDFIOZmPqH6WOhjqzmEA0zDpZwPlzAdSxFSKaMAOdckgG1nC9J0Q7/WdE4ytgWgL8wds+uRe0rECrza3WWwFYGDZaj5bjQ34oJx/waECykgRLP9KqaHdvIftBUFKhU3pKwWpqCXUh9XOzv/VJgx8YA1/X7ScbECN5Ocvyffgy0Fq+GRcxk0RP3w4YXd840F83SVAksvs6h0d1ljHB/hfp+BTrhu5M3FeHZMzWdTD523nHpQjmcMaUJDLoWg8PJIlxSPzXabKLt5Sax+4qdw2rg2hIs6HBGqqnOqOfMm3ympu4P5Cq0VIc5vqwt/c7dpLdZL6t2A5VwgZgvfq83L1udVbNhkv6C9B3U9RR+6UXFvmsOe+/Xn74p9KTbHVbnnXSrvhepTzWrT5SdsisLPGI3PwsSMlxTlM+IYemjvcus330/0M/TyHHyOVue9857tu43U5z4KLKXLPQolezxwEqIf0DMB1gDe0aL/wJyvavlhR/a6vnwzOaYxqLmRDHnONRiavFE/x0vfl8+hVB57r7y+Nad3ea7OgXtBc6kB3xmOe+Wa4v8zu+tZJ7gn22qbN9TxfOtvWrSfbQNQ9KeGDmhv0uZfmPEBhH6B1aCBtjz7KujfjWzU5pKuqKkgDHbBjfdN25Ng+a2+vsRvevd52XFFjTY1J5gTVQXEFzb2UlXK450uM86nxhH0PFe/77rvHerpX2k3X32jbLz2PRzisJ7qfZeKUl8KM4+rt/JWXav16vpXAuddjtdJnShZ4jRYogXOv0VClt73hFjh8+LB9/OMft3379tuv//qv26c+9VvLcvxv+JVe7YQvX5hf7X2l19/qFuDeGulw3+64K2v/9/9kxxPS3m3Vnn3s5qB99j+TLsIFot7qVvrX+h/t810q27sezVkDm+s+emPI/uC3kYCv/MUYc0eOF+zL/0/W/ul+giDc4V91UdC+/NmInX2Gp6L91xYs/VSywJvfAlNTUw6e++Y3v+kegkgZQYGXVzoEV0ihSCnhzz777JLy3CsZqfTam9ICAyO+ffJ30vbgXkI4PGd598UB+8rny6x7xfJD4TdloUuFKlngNAsUH6BqfpZK6NDQkI2OjjpwTuqhw8PDprRvAhw6Ojps9erV1tvb++NAvJSE9D4BEcePH3c7xG+//Xb33hBBLJ2/eOihua5TfO3YkcP2F1/8E2LYAQDqG+3Cyy63GsA5pVmVwoAguKCDyggAEWjJAzTpCCpwiCKcg8MUMHPyKow5F1wiIM4l9ZBdkRfCTPBYPAAlmKnACrFyYik8wEclwUWU9IBWcUC9n/JJ4SDPud1DYJVDATwFL6Wuxv2GJwAnS2B9csomnnzBQkOTAErdFt12rgVXtBCbQlUKUCenoA4XVJ1D2ECqMXqAnCP4mBb4x7kiBPqCPJBW8EgvuFSQgqD0xet5gmJOwUoPoymuQCJPSmBUIkMAjQxeKLuEQAkon+qkcxAs8yk/J+FLQfjlIHMOW+T14Ji/B4DJQkT0Qhn+zts80jblpQTHz0rXGZSa3uApSz/+lI0MT1j9BRdbzWbqVwe4RYpYBfAUABc4p1St1BIbUUTsTJE4APlkK0WjVQzOqSrKzgpKClRwadaAKFwbunIu219toFik6qh+onM7yI73LkNznMsFa3mojtJZnqB0hrKw7x2LoXZGOwf5u2zhK5oGpKjgmbOvDK2C8p409Rt7fo/NHuiz9pVrre7CcyzU1USqPdLHAT+osEEijEEFaAm4STklmx0FpnjaQrNHCXxP0x9UF/RoAD1ypCEsb+mwqFRsKjdgT4LfRFbVzzzXmQnyeqiqEPh2ACgqKAoyLxK4zsVnLUo/K1OdASUyqKKwiR6huVaraFtjlaRcDZFKzAQguGA61VE9FFh1X/QXzmfpAVsYfMzikwcAKVqsetUNKJlQFiCJ9NA9gHMzVt1yoZW3XQOsRoAe+7kWE/Cm9k+PWWroOafIEyB9XUGBcsZrgKhmGPArULvSgh2XUK61FIDoJ5GRAIFmd3AO9QsF911AG7WezNyQJaljFqAlBEAQ1PgiIJqj7+XSJDStaKR+1K1tHQwegX1aUEo6SkFIo7kqko+OvobKjj9DOjfUdY49ZwuzgHMNm62mfQOAGoGN+aM2cXifpRIZa+zotkpSICKVQ78QOKdeAVhB8Nzn88mRg7YwdtLKKU+ZwvYoJ/mMnUIFwAmwTrRlI4KRBL6laqRUq6on3/WsQbxAwQV2GUMFUv8A68yP7qV+Q1YGrOqmEfpDBkrVB5oMVa+wspazLdgAtBMFFqAsrksGUcCRApUbLXxK/RIFzUJ80oFzXt0qwI4uN240fM50cE7lP37sOAGqe+0H99/P1J2z7Zdvt1/91V+1TtJ5S4lU4/6tfBTX4eLaqHXj9BSrbi48zUZal4oKdAsLC6YNz9q09Oyzz9rJk8wpwHZS0dA9lTY+r1u/zinAKr2vPqdTaQqR1XUtd/D99Ouf3h55JH1Gx0bt61//uvMPtm7datdcI8U5xi3Ki4WpF23i4C7UoeatfkWr1a1EKS7WCQyBWpjAVQEo9Hc3BeMzFOZJWToE2LJwktdR9HqpPDnm3QipiqPNG1hvzgFkbmfcKb2g1jTCmCwOYQdM8KvKrbWUeSYzchgu9gjjcpJ0XLzGsE0yNy4C9+bDQCm1KEsxFwbLuhh7gmSoPPOtABMHx6MWqTnH0/oGpJuffNTmTj5s8fmstax8l5W1Xko9USoaf9ImhwYBujqBA8+zaH03azIKeJprlIKatczLA9mMPU8m2l22xDrBgsr1BMunsTe+AGmmywCYQ/XnMj0B1Ko9BLILos9qPuB/DOKFtW6RRhAAKjVxHIjokEWAnaS84wPrLDE3J6WGRbrE+lYgvioAqTDlkZ/kAtYymRqZOVoGZg4TsO3ljyHM94yNDx4DcgsCG/cCufXIuqjFjdr00SNWhQJ8Xe9aCzX14kI0MqtTTpTx1FYBlP2WRo9aEoXBEClyI8xlSlWdoU9mmDcj1W1WicpbpGE9JgFKAroTCKWUvM4OUiiTG6GisYZIpSw7vB+bn8B3AcRDcTXP+VKs4+BGwEe1tN8ap/wXwHY+7ad5OOOhHsp6KT8qROpFT3kxddICinqz+236xEOUehr460ILdLyd14EN++9FuO8JizaQDrXnKivUbMatAO5kXZL7qDSjUn6lMIBlJ2x67JhlE3FmboFdSZR9F/AJ0jCGgFIot0aa1wKENbLUozxIPZHs5TqciP4uAFBKgwKfCqRAjwPNzYxhM4CyGD5YGT5cgPVCgFyeOvnA5FHW+zAKcT6KpXlWKE5Cq6hM2Oyl9dWleafd8ygGTg/utPmZw7ZifY9VrBDg1mWZGfyUAw9YBBXGqtVXWLCJvksKUS9Ef2I9VR9w6mkAdin6lA8EKbAvCDSaZ95Iyf8tr7CqzrUo6HLO8nWcVwD5IoAV9VHjSVlZC6ID2PDpl0YA9g9Tx6MAe7NYIoWbg4ItXyB+bG5oAGLdAIy3GZC1C8at0UFnaS2IHGHaLQRYuczf01+B6Ar4LdmTPySl3qiVrbzAKlddzXzQin01VuQk0peBOtwp5BdpY4U2iTA+gijg5WZ3Wn70KTYCjAOvMffQLll8MY1C8qLSR9ms2bgVP0igIufjf80JeQeKUCfKLc81JECPeS09Brw6cxw/ZBggBKgVmCetPsrcWECxN8ZcUAYQG8D/81HGS3HOPJ2qTP424KxgOFdGbVjIjqBqCfTYt9upsda0sNGh63zKtZ6xxJyJj6q5ogi+SqFYPnoO39nBY8AgIYF+9H2MzHmX53RVQdVb3hTEnOb8XfVHziY1RJWBeTNDX5Av7q4DkCLVaExn3/9Bxv727+7j3Sl7z63b7dqr2q2+hhNyL6Pm1r4VfmM88BJNp+E2NpW3Z56fsQceHrARIJYgfUVjPZHEbmySWL2y2d5+WZVden7Immr5DPOdTqJhonOpmlleysj/5aS6H5SIdIB5Xm3rhpPeKLCOPiL1KS0BggXlp7t7BN6vYaL7Nx1B3XtRXvcZ/Fj5uSl8tOdfyNof/9GLKEm22e3X99rN15dZU+synOjWA62B2OPokZLinDPkG/hP0YfR9+LP8j3kH2qT9bfv/JZ7liA1YsFyt912q1207SJS8jY52F+fCQDOuf7zBpbrrXCqor2LdS22wWsB5/KsY6x+jLOX5g/agVGyPHZ1QiYBN47xDtzA1GsMQymGKy248Df3HuYbAXSaq0ZPltk/3TFiTz1zyLac22C33roWhcEqUqVzB8nbNUUKuhP0Kv9MwHE+E7L+E1m7555hO3hkGpVCnp2g8LmYrLQ0Kq51dXHbdlEj6nUNtrY3RIppgFjO5QMM63mO5oDl66ucIdI7ozh3970OnOvt7iHrAeDcZee61LKqs6BBdCv5zPIcQ89zS4Sq93qOEjj3eqxW+kzJAq/RAiVw7jUaqvS2N9QCehgj6f9bb70VSdSk/f7v/5594hOf+HEahTf0Yq96suUl+FX/XPpDyQJYQPeDs/O+/fP3cvalr2atf8q3egCw268K2n/+dNh6OtwtZMlWp1ng4KGC/c7vZ+yBfXlbQ3D/jz4Rtltv/MVRZFvieekjT+Xsi/8ja89SVynp/cotIfutXw1bE4pApaNkgZIFfj4WSKKgohRCd7Oj67HHHnMPRvSA5JUOpROSuu1HPvIRu/DCCy0Wg+otHSULnAEWuPO7OfuDv0LldFgPZcz+hDX1Nz8aZjdkab05A5qvVEQsoIeoCrTv2rXLHn74YacgJ4hOD1UXFxedIlBLS4tLw6oUrNdeq4A5wSYOKQkJmvvud7/r7hM//OEP2zve8Q733mJAXufXod8Fzum7Xjt86KB96YtftCjqardefx1w3larrqnlbwpe6DOCA/icFDtQb8gSEBOUptRHUq5RGtOCcj2JM+Mha4Boi6A7qb4ZAJvie3rwKpUGnoFyTv5RukgFXASaSWWDYGEAgIhCcRIe6GMHfYmUcSnluI7+pjSqnh7aUyale8wNnLKT9z9k0VPjVr9mjZVdTjqrzlYUQ8oJxBHE48mvoDA9sNUjW49AsCAkBWULpOly9aOAAeUSChNsV84RgkYeaSAd7EXMSXUrKJBOsMtFj6hbkAfL2kCdI9AlFEGpMUPUz7IEEPXFzyq3njwHIqiwkOpMhsgBBCBsQ/kJ4JHiSjmyEI7hvLRHGPsQSPOiXEtfyZRlDw1Y6r4Hrb/vpDVvf5vVbbuEACxpqEgVK5t52pUte8g22Epwm1KjSj3PTYRKD4W9A6RVUvo7CD8HJSi9q+faj1SqaaVT1RN26gcA6OwLIEkk3rIK4nLu5YA1AUCkccSG+aSXdU/W9bAeyM3KCeXTjpp7gwRUhTqpLEq9J3u46B7tqmCbUxPSw33aPn34mA0+9oSN7t1nvRs3WctlKKtIda6KAHEEVRtSiOoziggotZZHSrs8KemS4wfgKg67VHzqUx6whEea3yCp1sL1qMTFuij/SsreuByEJ72XA8kEQ0oJUPZWd+N7ioBwcp7UbSiOlAejKHEIolwi09k4KjJTLihbh1pUDangApVrMBNp3FxwAhugGuP5pA8jmO27QD8GIGXoImo686QYDJY3WO3qd1l5/VoyGe6xpYEHLD4zRyrSCwD83kY9gfBIBygVGVcgdQ6AvcLUSZSE+lHRm2Cs6RrgXqixKHWoBxDhVXXSx1Gb00EZfCojRTYqvfySABRFU0kbnI7PWBz1GkNtDjE+UuDQmoztzNIiZRwghp+0WMtKQLfLyN62hrpxDo03Pk6lnN08oDk/iOJckLqi1BM/sof0dGkrb9hksa6zUCIiEBw/amNH9lsynramth6LrQJmqKwnRMNFSX8YAGpR0NwBGig2peZO0i1IM4gyEaQHXa8KQZ1mFOLaqVsdfVVgDWVB2cYp2eR0Ho0RID4C2AoaqY9l56nf9CBBWNJTMh7CpFAOCiChTRdmUCYC24g1nwXQcDbqWx10UOAIIjmBICo5ShOrFJf0CRfkpj/48QVgQ5SAajcAznW7MaxZ6U0NzqmtdKigLz/4m4CZadYIqYl861t3ojJ60FYzX9566y3A0jdYBfOl1gK3Vrg5+OUneev8XlwnizV2Nin+wvfT/15cR6Uep+e2AthHRkZs9+7dDp7T98WFuNXU1gK+r+W+aivB6I0ocrSzOQnVWNZTqUXK5Go6wXQBrZ3uKDamGnf5DYIdx8bGHDg3MjpiAueuvfYd/wrOzewGnHvRpYNu6Gi26p7VqKkBkivtohTV3EKudQwlSa2/pDFNTqASudDn1i6B3xFgnCCKtqEqxqBT3uphLKJGKKhXKlmacAGmNP70LwOKl5j7F4FzxictOTtC6i7GYox1p4J5JpckHekkc1CCdIP11ohCXKjhXNa4Vj7LPCzlU4FEmj+V6pxXtW56jM3C/As23f8M83CONJjvJJ3jpTTAgGWGH7epoX6miBVW20Uq0lrmRfkn8jlMvgtkCNCIH++z3MwRW0J9yxc4x2ygpS2Eko7UNgMogXplnc42zsaanziNW8hUNdcw+AOUMZeeRyVzDNht0qKcOwzUFqQNs9Rvfu44apnDFqvpQU30ShRGSTsZYk16SaFK/gCFWraZIDXAr4CNM9Ufs4kBVOOY/+o7lG50BdcGxDs1ZBMnj1slSi513agG1quMUitkDlQ6ey1e2C4bn7Y0oFt+oR//gzVISl34ZwGAvrJq5tJylEQjbdi6i/czxgGuyBmOnVhTQthH1A3Ba6fmSd9Nj54iZTbKq7R1pAoDAA1mUJ2bW5glBfEi60eddaw816KNrBOkAPZZm/MeKc5Jdw1atwzpyydUIypgvzhgU8cfR41uxmId51m4awf2DVvyxH2WGXoKaKrZwj3bWa/Xci7me3wswYsiLkEb+Z35OTMGDAaUiUKZT/rRAOWRlnCkDPg0Vu/S2Hqss0qD6hPAp9DUUZMec7rmd9YutxaSIjWHGmpibsLiC6MWZY2oALSKyD+k3+WSrNuz2BMb13auQ9V1MwB/I8Cg1q5l/87j2i7dL/6bzq+KZmYmaau9APHHrXv9GkC3CyhCJ0D+KZs5dL+VoWpWvfoiFEzp86oj/dDTjgZ8LQdbkRo5MzMI9woQRppUpSEX5CBfNIJ/EEJN0StfRTN1sf5MJRkvAABAAElEQVRSt+wYNkzwM/bBb3UQGGCqxrRPH12cH7UEqmwRHLUqXg5FQOZQo1xcmLHZOeB31tS2rg3A5Cirla9hI0wIiI1UyXyFsXcQWF/wpkk9EDDOnz1o2b5HAeemLNp7sUVWv5Pmke8B5AeEGnBKcthDPq98ByAyD5XZPNCaU8dLDODLHEZcb4iPcH1Bo6RP9SLV9M8m5pleykH/Vppl+XhFlSGgOI3X5bUZv5F+yqUsMYmSZXyCHpJwSrdB1Ikz+DBLnH9pfpi5p8oqW0lt23Ip/kcrYCdzE/05ih/lwH2NRW2mUCfNTMJ3HkEV8bCl8INrmjvYgHOus3fex89GpU4pZl2bB6ij0sPTv3z6Ur6AT089lc43h+/sfGjO6SARSu6mDporIPgWc2Kc5X80FehLfYfJSLcVHv4YvUFCe7zu2d33LdnX/v4BzpG2225GcW57m9XpcZzmJEzsRQV0UxLezy2XO2bmC3bk+JLt3s/Gq5EUsDB9iAtpjallnK1aGWPzesg66lHapFxa17QJaAmhA/G8YQQOxLwKntPmHqWAVSpYpnpXJ07GPKV7VOrEz+q+DuTjMwKpVDZXL9aQPONPP+t9TnFOk676NV02w7jZuTNpn//c07RDp73nulX2zqui1tzCKTinYOUg9xKCvI8eOVJK1brcvG/Yv0U/T9+LP+vketYg6P+Of/gHp0YsBeJrrrnGbrrxJuvt6eG+KAqMSQPRH9S3lzv4G1ast8SJTre3Klxsg58Mzi2bWviYvKWC5mSNL9ZXhpc7mDLcfBDUXKNX+EfnloJlnnt7QXB6v24vl8fj8t9H+sz+6R8m7cmdfXbuOU1Aa522Zm0ExTmdg7HLuA3yHGBZOVPPVjW2gd0meU71/KwdPTZrCwv4CFk2RqByGauOWndv1Das43s3Pk2MdRMfg0cNfKlkFJh5S+VV+eQjzM6kyaz3Q8C571gvGQNuueV6u+yy81m3tApKyVSf4p6NeVdnYKZZrqP+8DqONw04V2x83TjocM6/WzVUzdJRssCZaYESOHdmttuZXupEIoEM6qP2gQ98AHn/dgfOfeiDH1xePX9ulSvN3T83U5/BF0rzLOrxZ/L22/81bQeGEfDn3vfdlwXtj/9TxNatlItTOk63AH6i7X6xYJ/+TMaeGMzbhWcF7Gt/HLGNm7ij/AU6pud8+6fv5OzLf521U6TQO4u+8NlfC9t7ryeFnm64S0fJAiUL/NwsoGCOUvjdcccd9vzzzwPkK1r/vx5KAbhjxw6XGv6yyy4zKdGVjpIF3uwWSPFw6MtfIUX43///7L0HmF1nea797L739N6rZqRR79WWbcmyXHDH2IAT4CTkz5UTDoQcSPIfQs4hCQkkIRdJgITeDMbGYHCTLctWsdVl9T6SpvfeZ8+u//0uMTn+HdNiUGQzC8Yzmtl77bW++q7vvb/niapvDMVbEnZf+ceANt9gifSr/epnrm+mBC4vJNoa2r59+1hE3OKAc5aIn15fs0SH/Wx2KXfddZdjr23gnP2+v7/fseXeu3evysvLHWhu3jySYySILyvbXH6es/dPgwD23RL/x0+e1N/93WcUIuH67jtu0dqFWKqh7jCGbUscuMagswAJvFRU6LwspkfTyOCQ2PZhAxQfGNZoJ1abQ8AvXCtyICjV+JWSkytfbjEWkFhLWUKbBJDZiEX7BjVOcn1ykNejeOUGVHJnsMO5IFdpfHmAehyFMrNMZI4aAw6Y6hvm3CTtDRBLDXAdWVhgAhyFoxo+eUbN27Yrpasbi6UKeZeQ7CzKlTcvB6CnRP4skl8G7ljGhmVny7yZvWu01xLOWF9GAOhYbPZhNxIiuRTMA7oLskALOIWHHWoZXEMXdqIjqPNwv7Zs60kNKaMoG7WULCyhsHMC7jIQKzmBLRYqI5MkICZHUdYyKTo/CfoMrjcb9Z6MXNRM0liMpg7MVm+MBGDvqEbaB8jxowTGZXqx5gwBxgUKSQCPkvw6gL3nczvV3tqmtCXLFMLyL8m9ebMyUKcp5Zo5J55L1CTwAmUEFB9HWXYKi9cJkqSRcbRigCVSMkmk52NFlkN5pFJ2XLOmUFWj3Yx192hqmHjAQD/K2ENWLYUsXaAwT+FswCcyZEFLwJldaN+Iwh1DivRx/dyzKZ250oLYsAEhFHItQEumXmM2swlTB+nuwhaP1wPPGWzmAdAJ0jaCWbnkIiMaPXZcLXt2a6D+rEorKpS1cL6SxQVy85q00mraBRBVKmlywArMWMm9WTqdpAHKKrGxi4oAcSWADrxuEsyACh4/mQIU5eKAHp5MzuXJRdQF+zJe7zPlNu4lBuRh7KSHNmzKT3YYTOkAY0BVLsveoSSTAJwL9zRquKdZaek+pVUtlytnCfAjQIKBHSRU49igJqZaaFo9fAFjYVlsqnETAyiqDTSiiJSrzOpNuOzVosR0QuON2zVGeWSXAB9gn5egvGIo89h7fMEMeUNmxcYdTlK+KJ/FAD6s7xg76Pan0dawsXWbMg6QIBsb3AH6ILZ6E0CWXhR73O4MyoOyB1j0Aq16Q/RVEuWRMElu2oePtu1MiDyHJsPdGu84pPGuM47SXM7sW1GQ4x5N5c3JdACUAdslJvtQJ+snwT4GB4tqiynxNV3U2ABaPHkLlYqtqhc1lOTIOXWcOampyZjyCiqUChwUAUyIm0SKMgH/snk/yXQr7wiQR7gV2K2BhMoQdZpJQgeVSweUJOFLd3URixoYQDOlnIExkySpvbRxF+0J1SGXK41z5lPmIZJJvIc3uQ04pT/jLwdzcUmTXaeoh3HaRbEyKlGmQkXI7O4SJG/dbsaVRD9ATL/Ck1jQ0ocCQENu+nlkhA/NWwE4V+2MdXbNVyU4Z/VIpt5Jl9lFOqVrBcwfXnWY9Xd9fT0baH6sp55+2oG8brn5Fmdz7uIlSyg76s/yN5xjRknkVQX3M360udTmays7m2On52n7nVm3njlzRnt279bhw4exy+twaqayolKLFi9S3Zw5wG7YaGZkAqlnMA754bZ9CjCWe+kzHpvrOKeNM9OHM18z5nYx1z300LfV0dnpgHObN9/sgHOuOG1+6Jh6gVongUmzmZ9Si0ro7xkM7Qa7A1QFQ8AmzFmeNP5tYxXw+Xij4hNAO8wdLlQavfQ5L1CSozhLwtKUoNyhTMY5+uxEP/2LRb+AJXEvKzwngVD89Cm/G3BpivGJccblo3/6OJ+P1wL5THYDpzSegvmIACDPk7fkOgTQqi4nZAXcFEWZahyFuInxy+oowDweFM9ckw0aZHzqH0yoEHAuuxQwPtmuSNtO9TZeINYoURbnc4c4c4xxnmSv15cHBAJsZBCdKVyOAbuNMX6h3mSqKT4AeU8QCI37jyeZj7GGdmO1akRIZIJ4h9dQ9DbLMG9ZqtiFRShzC2N1nH8b5GIAk4t6chEzMeECPh1WX9srhEABwMAblVqwnLGIsvBZP2TMMiVLoCizUY0xj9r9BgCazBK1j7YRp35ySlArYx6FngJga1VX6wWlpAFxFVY4AFxsyuBMhi8rGxvXgRAN2kmMY107eIlkOoryXJM7mEqMiDIp152I0n68qJyl1zBG8v5xFEMB7Fxe1NoYz6eAFQ1y8qGE5qNNCKtsg+JdQeY3FPSc8dZA9YFWdTQCIVI/ZZV1Si1ezN8rnTYiYOpktId5iLE0jN0ri8IuIGkPKlcEhhpsQRGYOk7DAjNQej3TpEdjF55VtGkPt4C9bRnzoL+U4qcfETfY/OMGWHMzV0A6cS5ActpdDFjK5vIksaGfecKdynwIKJ1Apc3FXOdJLaZ15QGEh4Hlgapoe0mzCkWZL0Gs6fcUcB151Cd9lVc66sXM92Z1nkR9LjmGel/7GQ0Tm6bmVChz7nIlMvIBiQC66eNeVFFNkTTKfUZRcqV0FUDhNoK4QQ9tcbC3RTUoNKVXrqWPlCrcDzR49imFYp3KZI5USg3vow0BA7gpXz+xo5dyt/k5OTWgyGgX1z7M/WMRT7X5rS7pi3EgrSQgngfAzKDV6NBprp06JHahNTvnjE0CO/ix2AuaIiEzGZslPMwL1mZNPi4JDDdFHfa2ngRsbVFpSZnSKgDLMlYTI6HuizKcy+ZMm5MnW2krjG3YKfuJJ7xY2cexjh8bHpKvcr08tXfS/20OxSYdi2cXao6uqSEgM6yfLcbz0/4CxPKAsY49nwGs1F90pI1m30F/mODvtA9Tt/VlEo/R92gv3jQ2SZgVNOeKAjJ6KSOCLPogv7PyJ6bzoiCciAJAAhBb1/PSRpEfZI8Lzyr9jHv0wXFiWXfOcgDGW4nRq2jnxmfwO/ppgmuNTl2OdcHwiFPN9rdTA+3tKNYllUlfS69kPgxV0B5pG9aeJ/qoA8YmA0SB73yUid/HuOrl/nxZ3LNXY5yyuzuqAVR4x7FJjgLcUX1Ky/YqJy+VrxBhlxtr0rgGiPmHuomNiX/N6tnN5pSszJCK81OVnWlUnCnOTejbD79AHYR1683XaMVCxmDOOTEB2JiSQNkyRcUl6cpMZT3dgZhdWE8n1TuYVA/Kcy3t4xro57WUX05OBna4qM9R8gHaTWVBQEW5KFJC3Y0Bkp6vHyDeS7LBnfsFjOobGdcEm4oKclNUUpBOTocYlXh5kDiqu2tMI6ztW3xhipSpGV7lct15vM7irvHhOP1gCpA4DKjJMxzPFMFQEnDPo9xC5rocNsNQZUcOT+iv/moH91euOzfN1prlzCGoSU8wNrq5hryCDFVWedXcNAPOTc//b+T7q5/7p3+ejlcsrojwHHz27FlnDeHpp55yBFuWL1+m++9/h9asXkucAnjP4SjN2fOJxZy8z3kweSMX9hv23umyn77t6Tr4eeCcDQq2Sc/WOYd47urvmaSPEUsQs5gjgI95MJM+npcfoq5sMwYgZPekevgaHbbNdNQXD5EBnu1z8oMqKuM5POhW66U4inMD2nuoTQsXZOvGDYXKzgGEs1iPeTU9I6g81jpy8nz0Y7o9c5OtpbBcoe6OuNpaI+rrndQ4c64HGLqoKFUZWcRiAPp+ntVmVaQolzHNnuH6epNq7+wn1mW9gLgmgh358PAgMVNUBw8c0p69W3FSKOGZ5G267jqU9Vk3smswlbukUBM2xTprg/zHvv9nj/8ScM4q2irLHsTsy362TmcLfPZvO2yxzhYkTPrbbCLsuy322cPFzDFTAm+WEpgB594sNfXWuk6bRH/4w8f1kY98hCT2Rue7qQtc2eONTE1X9kpnPu2/pgQs+dDYnNDffT6qrz5DYoUH1DUAYJ/6E5+uXcU/Zo7/UAI8K2v7S3H9yV9M6cxAUuuWuvXw3wVUVfXWi41OnUvor/8poh/vJJlKc7jjOo8+/j/9WjDnrXev/6GiZ34xUwJXWQmY9L4B+V/+8pf10ksvOSpGr3eJBsvddttt+tCHPuQkZqaTy6/32pnfzZTA1VICrV1J/fGfTmnrK1hDscNx03KPvvwPzK2outpiy8wxUwJXawlML6Da9U2rzJmKnK2p2bqZKdrYd0vK2/cKIKOioiJnrc3+Zq8zOLqnp8exgisoKEBFiOQpr7VzT5/DfrbDWfTmu4FzJ0+d1t//4z8qyEL4PWtXaVlWGqIWWMW2t2kSgMcS+RkoexTNmqOc+YtQnJhDgsWvWGeXBk+dUjPg3eTgAIlnEkEsrKYBfRXMnqP8+csVKq0guUTwFyZx19OuXj6r70K9hntJtJFwNzU2N0BeZt1s1axGhSufRCKKENGBfo3UX1DrocOAaNhxogZmG5aT6SRzamepCPDAC2nWsv+QmnfuUkpPl0IkUiEINJ6JXefsWaq6bq2yamaTpCQBayoRJK0TfS0aqyeJX9+g8XaUs0gERwlOXSiV5VXXqmzhcqyaKvigSawymzVwoUHt5xs1BvAXsyQ6SWs/wFLZojoVLpjH/ZVzfyzqcu7JlksaPwO4cKlRQ/0k3FChSJLI9OfmKbd6tkoMfCsrp5yA8Hqwz2yoV/e5S+pqaifBNEUykUQ16m25dbOUt3KhQgBew8/t0ej2veoaIEFJOcUL8jSFelGoolx111zLa+vIzl2GFdyWuKQeRk6cxvq0Xv1Ah6PAhwEU1LJzM1RQhgLR3LnyVs0BRstSuKtH3SeOq/fiBU1yvWT7AN9YhGcBPr+G9rVksdxz5lF0rOca0NDcooEznPdcI3VJAhuIwwOoFEtPU/GKlcoj0RIA5LPzxHuwKTt/Vv1nTmu0u1uTrBGbgpyLBExWWZVKFy5WCnXafmCfLvA10dqgUuo2lfsLA/f5KqpUsmq9suYtJQmaqqiHRL8lLQH4PJRfZLCZa27AkoyyA/YIojaTkhpUyE8dTwIvorCUOvtGx1JspO2kIj3HUF9BMQM1kokYyWVUZYIB4MPsAnLAWHKReHfIRVP0Ye5IxskMYHWWwD5tsOk4558i771MbmCqeBAgFJAvNtahke5zjgWqi8Q3rQibVwAxynAclZXJSB8AaRmJ2JsAy2qwdAOCbMF2sB/llvQqBIBqSDBgyTbJe4H6UrMylZ6HHRJKRklTb8LSdRzlMzIIJMWB/EydyvBBkpwT3hAgQomCaezJH2hWF9Z+fhLLAeCz6BSJc0Cy1LRcpeSWY9lXSrbBjIS5MbqigbBkIwAkKLvelzXWvl/jcZK2tfegjLfK6SsQEMB3A6g1XSR33KjxyLBjb8zSvtK9JLJR6YtEAgoUAaOh5OZJZWwaoD2fPwmEEGO8INFGpmUICCNCP3ChdhRCYSY7u4iEOOoBqEWGUREan2zBYncSpbhcrp22w3iUALbwk6z1ofLnAhDp7x6grgEvAAz8gKVgiRpnvPN4GTey5iiQWUn9WWKPxIrJo5h6XxKQMXGJ/n4cB8kuEtsB5VSuoMwWkj0G0DOVvslmYAxsXlEgGgYKsKfSLKDYVHIWBhq5yzYBec62bCHXT3MALDpx4oSj3mwbT97xjndo5cqVTq5jejzlZVf0cOYNBxo0VZufPFczxk+P83YxNmeMoqL34ovb9fgPf8h4f0pz583VfW+/Txs2bmTILKS8Ls8L/GAN5PLXFb2TN9eHOeX+qrnU6t9+Nw3S2XdrI60trbiHHNHhI0dQdj1LWxtmnGKuYU7Iz2de4HuJqVlmZyudcTxE37fnLT8QnSnS2bOY5c4sj2Zflvzsw578kUcfUR/Q85o1a3TTTTc55zJwTtgO9l88psm+TqWneJSankXCNYSNFnpSXJ+H+SWzIJ92XebASQkA1lHAtDBjSALQ2+PB2tgNOG3KpwBcccAiXy6KZ7mlADvtinaeAyIBikMJbYJ+MWaQLuBIVjbQc/Zc4FfmTgOPAYHxUqRPQZQAaiX6z2q0cb+YzJVSUI0q7AYcr5mbTWkORbHYcJdGiV8So2OKoXIS5/7hoOXBljuMOuV4JKjCmruVDnDnjjcCzm1XX+M5B6ZJZ4yL0++Hw6iDAtmE/JmAKGWA6sDnjHmJoX4g8iFNGthPT7byD1CuUxOM02G30osqHVjdoLLBdoN5wwoxZ8YABMaZ8xOUSV5uCTAVmxQMLsMq1axdL/d5gPloN5DuKxppQ2UUFc6cshuUUrQCMSogYQOXgOQjWGVPUsaT49wf52S0VAaqtR7AnYFBs+NOVW55LaqYZbQj4p9u4q/Go8wLUe4FiAzbyDB1GCMDHQKECmRmy1NUxZwKuEXZjfdSf9ikxcm1GrCXQpvxocIVt3tGNdALnBbDCmKi8wzcY6/j8DlpZQYs5EE9LB1AOCOzBEDM7o97A/phoqW9MQ+iqBodaVd/43kszXtUyPydUsK8HKqlPBgrEqjBDZ/XOGBWeIzNA1jMe1EL9QK+eZMTGsEaNT2UANRa5QCTCeK+qUtbFW3ezbxKXAbUF1E20BjAJeO6i3jDj/JosIDrZkODKYKGUfob7+tmrgUsox2n0De8zC8x4PUY86dtpAgWzeZ+MgGT2ml+vUpPZRwENBsDeItGA0BJNcoAYPdZvdh1W0yIEiokPJ+BIuHUado3tp3tgGDebOUtWC1XXrHTByy2ctH+Y4NdGmK+GAOC9HKOrExiSyCSIa5tYmxI5QsW404OOOetAlRr1MDpH8jHHJNeUIl7bCntGHt6FP9MVS6D9pmWXQoohFk5yoEjQ8RJkwBetFGb62GiqP84vyN+z0QBr3SFA24NXdwCPAroDQAbdqWz+cNn4bVyUGrNpS27UYp0YenuYg6DpOQ+6WNAX4mJdg03H9RA6zEgqRxlVl7H/V1PeRuYwFwfbiaeOkssf5E6NGNXYiraRgb17x7EThggzFe7Qe7au+mfabTjdj63gbGmnSYygPUtGwmoGx/1kp6VrxAW6T7izIQ9F6DkN04dTo700X9RqgUU9tsGDhqawVvuVOLCqjLud0rjbU3YwA8olTo2BcUxQMkYzyAhLKdTMuayiaeE8qPuGHNdpkrnMsv2QcY+bJlbdmucZ6Bk5lKU495G7F1JIdJHUTwMD3HeHvoh14pXrEKMp+zJYFsNMNjwiCZo96a8m1FBPBMCwpx0EXfwDNTf5MRCceBPi1WCSE1n0af8qFW6M+doLJGti01T2vlSpy41AqpA0UWitI/AlAqLg1q0pFQrV1UpCLB77tyEjh4+q5YLl4j76JvELW76bGVltq5dW62lC/OVlePTM8+N6yvf3ObAdYsXLlM+m296uwYBYdrp8xHNmlOga6+t0JKFWcpDQc6m7CE2qF9qiuvgkWEdOwkM2N9DPBpHfANVztxCsccJmK1eN19foOvXYeOdFVJTS1gPffcVDTJulZeUEncF1cZGmgni2aWLS3XtNbXKyw6pvT2sQwebdP58l4ZHeK4hTnN7R4FqfFq8pAJrxlrG/qTqzw3p+OFedbQbFMMzlD3XEJNX1fi04cYFCARU8jqPjh6J6JOf3KPhsUKtWVyuqpIJNn81qotnvQT1UFdXqPf+dgVrlM169NHHnM2+mzZtchwxZs+e7Yy9Nt/+V8VddKo37TFdbtMxjJVhX1+f40jy+OOP6/jx48QkJbrj9tt19913qbSUfuyMETbUXV5LMKjcKXtreDPHL1wC02U//YbpOvhZ4JyVsCmEw6bRD+M6cZw+dqKTPmaAsQFraGqnJFWNFfOKFdWqqc6kn7MBc2+nTp/tYL5ijnSel1gqYAPY7Lpc+mKNyitTAKkTevR7A9q+u1GlxamaOzcHlmtMnZ0txK/9xKcZWrp0sVavLVBlDc+gQQOq2agJBHfiOGPZsV5cbPpZR5kA3EtDSZlNccF0tXUMkvMb1f33MvZhAWtD7sEDMT359EHir5CKSohn2JDV0YmqLiDwEOB/W9sJzZ9f4YBz69cv577sOYYkM899STY4JEwZl8J404Fztjhn1j+2u7WT3S62wGfJGLORmJiw4ITFEf4XYlehBfxm9WNyj/ZgZtLUafzO95OHgOmG85v83TqNlal9/005bAHXQMo3wzEDzr0Zaumtd41m1fOlL31JX/3q1/QHf/AH+r3fe7+WLVt2hW90JiC6wgX+pvo41gV5yEnqa9+N6J++EdcICzALKlz62O/7dd+97DqbyVS/bn0Oo4bz/afi+vinIxpg98iGtR597ZMBVZS89fpbeCqpZ7bH9Refiehca1KluS79Hpatf/h7WLZi3zpzzJTATAlc2RKwDU5m1/SFL3yBRNqLyKyz4vo6RxpJnPvvv18f/OAHtRCVmzdLzP46tzLzq9+gEnhya0z/62+Yb7pJrpCz+osP+LEIRzEJa5OZY6YErtYSsDUgg9hsfcTW2EZHUfngu43XloS38TcD6MgS69ObUZ1E+qsWri1Rb+O5rSlNJ/ANqrP32lqcvXf6sEVv+yz7zNOAFH//D59WAJWq61Grm0dy1jcyrDjrePFMVK2QG0j0D5Ew8qhw/lJlrt+EOkSaJo8cUA9f3STFHCWzjFScPEm4kUDJBpgrXbBc2dV1JMoSijVcUNv+l9XVcBGVirCCqemoq+Xi0OjXGGBQsKhYC9dfC3RXgCrKoAZOnlbHvoMaa+0ADkKRDlU6UmQKc705+fkqnj8fNbJ8dXPtLVu3KqMDRTasxpMAe+PZKGVUlqlk2RJUy0pRT7kM1cQ7Sfwf2qPmPbvVRxYpQOLO7NbMOqgPKzIf9ma1cxaqaNU6AAJs986d0IVTJ7BgG3UgBj8QXIxyQ+BMWbMqVbJoIVZ4VSSXAyS6u9QCwDdM0iFG0tENCOGmzCMseIM+osxVoNq165QFIJgAiAwfPKD+w6+osxsgDuAvAIjgxSppkMRpqKJIZeuWKys9WxM7KYNtO9XR16OUcpRnUJmLkKwNkMyoWLGS5GCV7VSGFaLOB0nenj6lC1t3KMzObtvAnABGi5J8TWBzlsd95s9GLWbdBm6gSN0oItVTFgFAgfRslEWQCw8ngCWAHzPzs5Q/d57Sl60iSYkt1cUmDe3ep7Zz5zSC0l8IyCsEgGC6Q320m6L5i1VBuaUCbCZYJR85tF/NRw+TqOxWKtcRxPo3EUrVMAnLQHaBqpet5P5S1X38kC69vEuTl86rHIvV9GqUPkoL5eW+chauUkpFHXl8P1ZbliAdIweKYshgk/qaTqEONEqLMGtBs3OlbZOsDMZ6UClCuQU4KnPxfY4N33DDS1jfvQRMEFcMa7CIrxS1kwKSt0Uo3pTh8IatnwEO9BXQEae9ko0HhkDFhIX9vuZjJHGnlFaHGlveUl6Lak2si3s7pa7mM47VaRpJUT/t2G82c6ihjY+2cG1jKBPWKKXqDhR/6hyIK9a2FbWRNpTYUAtKr6JvpgE3AHcAOZiKTCgVhY4UYMQJNqOPsjkdJT2PH9iMBHLQ1F0mSFxGSKaHCpU3ewHr7LTTnnq1NzXzzJ1CAgX7PCzSIPVIXGMHmT0LqA31JZRrXD4StSSZuUlgD9ZgoyjJ9G5DcW4vWkxpyqy9F4hwJdcPBDQxCLx6CeiRco50kl1G3cbLWABskRInUT7SRF8A3iu/VkGgDDcKVQngje4zh4HR+pQGFIJXo8ZRwYujQOMGcHCjXplC30mhvSemWDMgbxAmIZfAdjgFcNGPokLUxi/UodKBN1Mra+gbeeoGChwBNA0lIwqhdpkAwIOsYFxAcTGzGtUZADtgGVIq3B83B+zhcndxn6jZ9R7XcAtQzijKKFXLUMFbSFkUkPhFTbIDhSj+PoLiQcTKDLgwlfIJ8bc4AGWg7gEYO8DRqxycI6P0/09iMpfYfDKdVJ5kHqkHVrbNuDvZOJMA9Lj11tt0O3bfl1VJze6P1ztrNtSbzSn8f+b42SUwnbeZLufpOXx6brV/hycnnXzZhfp6oMuTjupfFyDxKO08wrhpEGtaGmqnKSiCMk76gDkMnAuhgJbJmJllMC3zSAp/t/nb7NLsvTtf2uXM89dff71j2Z7H3OeKo/yFtelg/T5UYBuUhjpkqsFjwRLG9Sw+D4WSaB9wBNBOQZm8ALWm6jrSASjDnGtAKxOtExMETYGLfhyjX6VUXEMfn4P65hlNXtwNmAs4l16kKGNQNIBdKTm9DMC6tAyAbG85qiTYdgEGJYC+/KjNuVGQivecZi4/QL/vUWphNfPYBiDqKsZaxtiB04xBTRrt6QdlYhhmvo8AZBug5x5rgfnB0tQNIDL3fgWKNwA6nWdcflEDDShlMo6FGCOSKMhNMn9OAem4US3KsnGGOZWB2oHxxnmdC/VPU7T0Ac0aMDgyNIlKHZD4XGCnMpRVR1rVXX8O1aQxpaVgz8n8O0USOBnMdkC8dCyy3T7K07HbZDy1scasa4H7kiNHNdl6ELhrUunl18lfzFxBWXI3vASIpWm3JrCrtLEkkFnEWI3qHWqeblRFx8ZRFE3NVzaW18GKaqfRhTub1XF2F/c6TDtAQdcPUGeqK6hl2ftMScqHoqAl1EdRPIvSf03Fy9SaEoyjLhTtfIDfSUBCZQO6zdmsqfEh9V/YzhzSytiZqggWvpEA6qlBxlEgoMysUgd4SwIzmUUkyBLfsUpLUH9DQOKoqo329im3YjYbBpYx5tZw/xPc+yu4dx9CoXiIqYU5wotKoVk3GtSEdegk783Bwi2t+hq5rf6w8ow2bFG0aQdQFRsiQuW00WJ+D5ADnBxDsTCB2kx6cQ4KqJfveaATFd/xKNN0Kn2B+R6b0BhlESYGcJF3zsovVLC0TqMAlj2dFxQFig4x2XiBseMq4Zpygeaw4cwBxkM9kV9Qzvbl5/4Y7BLMF+ETKP0BnraiJufKVcHCa+TNL+G1xB+jwIEd1DFQ3rixntxjAMWuENadyTAWvqglGrxZNH8Jsew6To01+zDA+YmH5OojRsjBMpT5PsL1Jy0+BxxnxqSd0X8A8EbZeDEOUOYGyPQbTEUc4QFcT0ywCQCwLKV0tbJmb+JextR74lvEDFi4B1F/RhnN4hE3VvWZqONl5BUy35vKczrlQtkBt9NAqQtUxcLNmiCWGWw6zbiSzTnXsRmDL1P2mWhmEwCQOaC8ywX0YIqTKDc6/Qu7+NBwC8BEQv66G+WacycbEIgJh5suQ/VAbgmeU0L2TGF2y8STbuDXIJtRQtRfFCXmcSCuCNCg25XFGJdBr0DRCGA2zj2PIQnnB0rNrwW+ou7HLrFZAXgxzU9bImaMMvbFULz2Ac6FAAgDaShf086Z5CgPUxUE8k2Oyc3YF23aicpsF6KyixWsupE4vIT7I6Ydb+GcJzTZc5H30Y6AOj3ERD7ivOBUH0Az43GwgL67hJh+KTApIOfgFGXS5FhPB1Av9KHuG4mx0cbiG8o0NQMVwKKbdKF/lrbsaNPLu8/R7tmgUsgGhhTadRxIkE0GNbWF5O8WMa6EtGP7sM6fPaL04LDyUaIzu9oBxvNsBqxr18zW2tWlyi8KasvzI/rSV15kI9SEigrYdJOXBXxKHE/5tnehUh12ae6cDD1wH7DdEtsg5QLai2vv/hEgGGLWgQnlEEPlZWKVSPzeP5yiS+1AwJGzes8DJbrzjhI+P0fnzk/pM/+4S40NQ8rPog7y0oVIOM8DYSAarhsoLoki44vbWrX/QAv1lVBRYRZjK+p54V4guKhqZhdqHeBcF88yu1++iL3qCPFyHiBnOmMSqqRYC6emT+j665do+QrgYIjQo4fj+t9/eVgXurK1oDRDteXtQD0jAN5JdXRHmAOn9BcfW8O19+uRR74/A87Ri38Vx6tjFvs5YWsOPPdcvHBBj//oR3ryiScBuQeoz7XOxpD1669zgH4GBfu/dbnLoSE/O7/4VVzUb9A5rMyn40W7bfu3ff1McM6mJ57Jesm37nhhQrv3AdWz6S09M6icbNRIUQudmuqGs0qlf9UpH0j2wJ5RvXIYJXkA2Nw8F6/lHMRiYWKH3IKQbr51HZas+RruTuh738ONZusJAFePqstRSGfMcLlHgIkHUdEMq7hooTbfNEvXbAyiLklUgOLk6ZMRPb+1VRcudRKDoCiZbWqnLlgwVDeHQqheRlG1jOkP/58yNufks/kpqW3PxfS1h7YRx0RUXpar3Bzb3BVmXSvCps8TunTpoBbMq/iJVesaxmdHW55SMnDOZktDypmO+Y99/88eV0xxzirWFti6gOUaGhsdItXsfkza0Xa5ToJCvrpBTC/05bBzsLqqGiJ5MXT0tZo1i52i7JLNYsekl856xToe1/9LHzZK/JoPWxQ1ANHKy8rvrX7YgGGLuKUsZtqi7dV+zIBzV3sNvTWv79lnn9WnP/1p7dmzV//8z/+ke++91wGPr+zd/vrHvyt7PzOf9qsqAZuqBkeSenxLXH+JolgHgVQZwdkH7vfq93/Xp6yMmbbz08q6Dyn1Lz8S0yf+JeLY2r7zZq/++s/8Ksh5a5ZZBypA//adqP4FCz3cgrR8tlsf/n2f7r3N7D1+WinN/H6mBGZK4NdVAgZWHDhwQJ///Oe1FejANj+93vOHgRrvf//79YEPfEDV1dVvipj911VmM+d9c5RAGAWMj30qom/8MI4CRVKLC7Bs/UJAyxcZzP/muIeZq/zNK4HpNTZbF2lpaXES7M3NKJ4BWRkQZ2PxEiz17CuXJLm9ztZT7H32ZWpI7e3tzvvMks/GdPu7AdC23rJ48WJUDSqdpLytN9n7Daozy5Wzp07qM5/8K9TKerUE2GVZLKEKQLZsPstXXUWSL6xxgKk+Ev8u1DoK121C+aUMG9GdGq0/riSbY9NXrSQZXkY6CTshktJes6ArImGbTyKdRd6JXTt1csdWkuxuFc+rUfasWvlRnwLRc0CsBJamRSgJ+AkKxy8BdO1ECav+ksqxmsuaj7JbcSFJMqxKRrE0xc4upYwkL7uhp1oa1fLjHymj+RIJrxp5r7lGSaAyJDWwGi0gIQ0MANhmMNAUyj+Tzz6htuNHFc8vVuGytUrjvGYL1V1/Rr0NLUonyVS5ch1gX0gT586QjKpXRnEBSfvZDhQWo9wmUcRzobKWVlZMYjcPQZ1xdRx8RWee3abMsUmVzJ2v9IXz5eEaIqivmLJEAgWbXNY/g1igjpw5p4Fnn1eM+jW1veyVqxzLVXLqGkBRJcmCeGYtKjgk7+OHTyn8zPNYL7WqYPlyZS5dilUriVJTZSskKQiAmCSJaMnjGNfa9eLzqn/5kPIy8lQ2f6ECqHiEgYFGTh2T5+wJFqNRHLr+VsXyK3TpyFF1nDyqucztpcuXcr5sAAvsW1Gu8ZJPDpCEDWDLlhhlk8+LuzT00suK0s588xYom/sLcS9xym5gBGANRZ5sEuo+xtjwmVNq2/m8ejvb4POKVAjMmE49xgEPh8Oo2pEczkR5L0RideLCKbXveEHDJ46qvKpK2WuWy1sN7JhPQp/240pHDYuBO2kqYiTwXWNYhAI89TRdxHKQNkY9hlDjMIuw8EiHkt2nhRQRyfgCZa38bSCMWg1f2EoydisAH/ZSxahV5CwgcVrOGnQJtldADCjVxQwuBEawNoa5KOopqCT1dCjccg41jkbAEL9S56CykzWfwQnAcOgs1nz7HAgui0Rudh7KbqgnOol/1NiGe8+RRB6h7cymDu7hPuYCnx5TouVprPkalUjJI3EN+JYF1BYHbgTcnBo+6yT8PVghxmKoMQJepBSZ+gnKPvRx9/gFxYD1BkYAPlLKlF+3BOUd1InaT2Gj10zfxrorF7gyuxw1GiAPgDK3QQlYpJoamStAAh0rNPPdSmL7lwy3aKJjC4nhw5QDSXcs2HzpS0hoUxYDbRq8eAgVHQCELBIcRfSpFOoQyE9DWM5i3QYWg/3p9UqrvJZrxAoMILD71EFN9DUDRaLsVIwSVA5xY5BMLOp70e4LWC8DwNk6MNBChOS1q2wuZVrAmehTKD6NtJHc5zyZQJQZtVgwphSou7WVBE+DMrj+1HxUaTKqqfty1GxoG36gGXEeAydNSQ/g1eWxzSCo6Y2fxYr2GLDHMEpKucoqRy0zx2CPLBwcuzV8aScqNSS7UdP0khyCGpQHW7bEQAP1AMC44LcVLMQaGWDYns6vRsU5BnDuh4tzlg9etYbAuG4HPA0KZb167rnnHKU8S9DNBzq+/4H7tXrVapJXqC3ytsugtVlnW5DEL151KudEM/953RKYnn+nk6H2bzum52b7bsnpcSDRbpR8bE5vaGhw5vguAA9HeIL53VybzPra7HITVmkcXqDSAFCawXI+xu2ggXModNnzm703D4D81ltvddTB85kTDJxLDB1U36mdGgaGywTCzWDc9eXOo++XARphDziAohTqYQHGKk8qsBgAawzwKoi6UwB1rSRjahQfLtcQUEx7vaYAd9NrsR6tAvxoP6Tw2eeBabDKzAWIz8OuMyuPsYPr5H1uxtO4qwiACKsu5jKyWkoFaPGYVWobAGvnCebicSzVmScM2gkVMbc0KNy6TyOdgL8oi6RnA00DnceIA8LDwEGM50lUYuMAuNkL38X7bmQuN3BuG8p6p4ByiHGyUN7NKyUOIUbgc2OMFa6eRnghxjr6gVmzInOqQBHlAKiSnMSauR/11I4+GB/W3lCZTa8GsEL1pJ1YaHIY1biCQsbnKoYK7gfbySD23D53PhWLxStqXkliJw/zKuaEwGEokfUAVTUfIVYCpsIC04eVKZJafBaxU/cZ9bW+jE1iLxsDALzyTDEJ8IhxMkIZD2INGgBay561RIHyWVwzOn3tTeo887z8qAhmA2/5bc5iTE9GsIvEAjTc20pZRgDmmK+BsQL5s5SaV0EdAC2y8SE2jHpfzwWsKZkz82uUtuhO5sd+dZ1+BuvNVhTQiI2KFzH2AgZ7mW99fAawi9nbMvoz30wwQlN3WHwiTYsoYIOGaLtRxv/cGq6zaBFjLTAfCfto+zZgpUO0W5zFcuYjogVsRtnEwj2ItgKa9bUC8aA4WnUtdu7UH8hYvOlphS9tw/YUS0vakr+g7rISKYuEEezJh1CTDaWgpMP7ErxmAPUqX0ohMdhsZyNFHChwfLADm3mgRcDgPOKMYMU8VF4D6mo5DcjWAAiYAhi/AAhqJSpopfKlAWyhdGZgJwgYUJMtQDLm2dwTR2lw9IhG2o8y17A2m1Kt3DlraYvML8BK450naaf7qesRFHmJu7JrsLZlXgDsjNDehkcGAMQ9KlqwFHDuGpTE5iuGGlv4yNfl6j0jFZVKBm2nFREXoP2KlXOsvx22HmVUZtIprssFzJZKvOwPFjnzc3Swng0QfC7xY2r5OmXMeRvXPaL+I1/UJLauXkBAP8qG3jSL69MYJ7CUBX5NArUlUaLzYLWamArSnlDxc3XgmovqY8tZhfsAGVHzCxSiqJdJOwWmiKLIO0gfjWHfmwaAkWLPGLT7SbPb7DgnT8dZyokmPf8meerudFQYzSI4AdRqdcgHCOc/lP4Q0GEDR3Ski3iKdp3ORqBxYHhgVrNVTslexDUCY7HxIDx4DCXnVsA5t1IKS1UwB8VK1GrH6xs13AQ4B8gWLCoH7sOeFfVcGy9cKWx6IH50EAqDYik7obiUxD5eva8o3LiHMXCA/rAY1eL1wLnEPokRVDWPq7/5JCBvP5tCcmmnxDPEDAkguCRqkDYWhAMoL1auUjbKwl7A/NgolsnDQLvYwAZ9xITGWPJ8M9nH+GKAYYzxuOI+7WlcoG/98CTuO2NasXShVq9BfZN8SITxeAKoMg1oNAvQ+MRht57fNorSZas2rE9zrBE9QSATNiV5PWHNqs7WrCpiPlR1t2wb1Je+vF0XGwZUV7Nc160u14I66gWg8fipUe3cB3A71qb3PjhPt96CgmjQq50vj+rZ5xtQvxuAtSjS2mXlKsJicZT4fff+Se06Qs+Ontfv/3aR7ronV3l52SjgTemv/2YPylRdqiio0HVrsBJfyjNeqRvghviOZ5FL9RF9+1vE9O0xLVlQrrXrcpVf6HXuLYyaY0qa34F2du8+q507L/EZuVq9Yq4WzKc8AXbGUQKNUQfVs/J5FjVVZI9OHIvrE391Soda07UG5atN60c1fwEbtZIh5/4a2Ajy0T+ax7yHuuoMOEfP+xUcP4lNps8UN+6EL3s22P3yy/ruww9r7549gPoZuueeu3XX3XdjnYlCJ+OkxTWXYxwLDF+1MYN+P3P84iXwf8vx8num48efB87Fkdtvbkzo29/o1yvHe5ST6wFuLFRNDYC0N6yR8V7ANaBWbFUH+lL0lS+eAYBM09IFqVq9LkR/ZjMW4NwwMYAB7XPnVwH4ZmmgM6GHv9usJ585xGO2R2uX12j58mIVFHnYoBHWiy90AcTmoD6ZoXvfmeEoR7Y0JPT8ll69sP0cUGVQy5aXak5dAX2XDcvn+rVr96jOt7k1rzRF//NDpm6Xrr7OpJ57Kqp/+9YOrmNKq5bM4vozgIoZy30T2r9vq/bsfkqza0tRwr5L69evZ62Cudp5EEFd1KB6frbmZi3ujbS6KwLOWcXazlcL+B+mY+3YsQNYrtO5AVt8MznpbGjDDBbKpi19bLfsAFYLo8guT+9+9UIPmmqSBfobN25kACVQYwFs+oHjF296v/gr7dpNBW/6QeQXf6fFAXiqM2BML1D+Mu/9RV/b0tKsf/mXzznlaw+ub/XDdlcVspj5Z3/2Z87uqav9fmfAuau9ht5612fW1w899JA+8YlPoB4wikzxI7rhhhv+fWy9cnf8RqamK3eVM5905UvAFMZf2o+S2D9GdAg7zvRUl34LAOyjH/DiaX95V8CVv6o3xyf2sGvk89+M6a+/OgVs6NYfPwhs+Ds+dh6+NfubhTWvHE/oEwCWz9FmMll8uPsGjz7yQb8W1s20lTdHq525yrdaCViccezYMZ4//kXPPPOMA2i83j2a5d9HP/pRve9975P9PHPMlMDVXgJNHQn9zh9GtOdc3FGwuG+zV5/5S7+jcjqzzne1195v7vXZGpCNyy+ziG2bp8wyZZAEmyXXzbnhjjvucFRAa2oAxFibsjUqW+OyDZgGy+3fv1+mVm6LsAZ52NqVOT/Y62+88UatWrXKge5sE6wdHg+JQxLLpqr2uU/8uYYaGzQHWGsjajSLbtiklGsAYipJ+EVRbGCtquWpJ9XR0Ky8klmoyZFwO3tc8bZL8lVVy3ftDfLUzAJUQzMGZS8X6jlkKh2gZerIMQ08+bQaeH350vkqvoXzmrKKD6AGUM0Sxc6CIveU7O1Wz0vbdWTHTuWSVFp8083yLzWLTAM8uF6gHrK75F4t6YklaBvg3OOPKg01lLR5C+XfeDPXUeeouLmADkxFKWmKIiTkBrc9q/EnHwOWGVLqtRuVtuk2uZnTXCQWY2fPqJXPHLvYqOKyKqUBjY10dbKzu0Pl8+qUvgLrEMrCBWTlIkGqAElTYDjM1hSh7A8//oRaT5/XYlTAZt1ymzxYybrSTBkFCCIObIQCj91jtK9brUCBbXxWOUBE8br18m/YRIIv3amTpEF5AZK5qahAUYfR49joPb1FTc2NKrh+g/JuuI5rJoEYMkUQH2CMqaTx+jHs4I4fVvNzz6qvZwhFtzXKQ+HOW0GS18TFjh1QZOtTarpQr5xl65UonaP2s+cU5rzzlyxXxvpr5EHpzcWucVPogBzjPkMoi6RqsqFNFx75gVznz6myarYybrtDnnlACCHKAOAMMoR7AyZA8SwO+DSw9Wl1Hj1IYjdTlRs3KASo4zL7MEACyz4msdkyxgk/MMUunlHnzh3qpO2WY9+af/MGeWsuA4EulH6cNsSLWckl2YqySe9RjTbvA6AESgOECJUAXpnlF+dMTnZo6uJ2RRuPKIwaS+YqwLmMOo1c2KbR888pI82r9Lmr5SlBVc0NhJDAGhQwxOzA0HYD+iABy/8wPpMXG9ZE+zmszbDUooSzKrAJQxHIBYiWxGJ2rAvFwJbdJMGnlFe2RKECIBIS10kUA+O9F3nffhL6HUrDos1ffQf1Czg3cEKJxi3qaWshIVyNgsxa4Moa+nA21qwooHXuxa5wP4nVQcopDeiuRql119PWZl1uQxOXsLd7RQOAqBGsaXPn1CkUBJwAnGu92IrdY6WyUUzxAEq4DIJAHSkpVBjZre9kGlCWcbnGHTWiJO0lPnwJqx4UpBLtfFY1IOgNqPHwWYCv0bbz6jh9hGR8QjkVhfKX8nsU3qwfJUlQD559AZtVfoUaVcYslHmAIhN9Teo9Q/30tSmvMAPwbYHc+XOoQ9r2VBfg3En1nT6KKtYgSXEU3Urq5KuhPWczFqBslBzv1OClE3ydQuEppEz6kjtQpC6gwJHeNhXlkMitng80CrwI6AEVSSOi3aG2QyXyRdtFGQZSAdgCBSuS5YPtpkrlQSlhITZsnI92gd+aprpJltOOUtyox5RjRVlKmwgCSWAfZHaUk32o7sy6A7tAPouEkj2dX5XgnI0azAHOl12lBTj82yAsq/RJFM/OAT1/9zvf0d59e1GTyNSdd94FbHWrylGwtKSovcdyJPZWmzMcSQc778zxM0tgOulpL7K5+NW5rOm/8Vurhst/o4wnsLw2x6ZelLv6yZEN8HNPT6+jFDsJsBZGiWqcjjVG/xwDyLY2Z/O75dHsMGUhixHC/K6qutqJCW6//XYVANEZWJwY2I/99i4NAqjm52O/zFzoyWfc8pcwbmE9bepuzSeA0vp4PfMSkE0IW9MQfdFTWMnrAMvCQMPYSg/UH9RkPISF4vVKr1qInfY+ReufRw0rqUDFGrrgtYA3xc78Yv9JJtOZkTOAasBgnXk3rpApgA5f1GQjfXG4H+tN4N4KVKPygGKZDyJdJzRRv0sTqJRmltZiZc54nm7jMnMbAHK0ZQ9A2jGUZoPKXvyA/JXMlckGWLVt6jl/GiiG/GPJHN43h3EBGAYoI4GyVOTifgAogDbG7mzmtWAVtuN5wLFu7g/4KT7UpgFTtuoZUUHdHGyqge6wFG07yXjBEFJQXQssOBeolnnCwGNsK5NRU+NDpdfmMAtzNAxwBWg70oS9JIp6qFOlAEallFA2AMMuNgnEBlrUUf8Kddmkkop0ypLxECjLVNcSg4BzTcdQ4W0DKspTXi1gb1k18xLgXEez+ut3KtWDBVoZSlyFKL8y/iZRvEoAII82YscL3OjCvjwIbJVWsx7hMSA4VEBtfkoMXdLU+e3cZ6vczDWBhXfw+gHG3xeUinJpdvUsVF3XSBnzKSHAJGBiU9zkhE4CHAwdm9Up4CCsEwfOK9xyEgtUq79S7OHXycdcE08A9nPvo/XPyD10DigKq1dAaje2r0KxLjmCzWXnWayyUThjjTjI57mLN3LvATYNPKuxCy8CVzEnz+J85Uu5dOqGTRtJ1M0GL77E+1uoTmZk4r0kKmWpWGj6y3DewcrV1GXjgHMjzaeJ05qIa1HBqlqgcDRVPSgRRibalI81fWYZtqmBdVQWMRMwZtJlKksIwaDoRkoaWIA+kAA4DTdqdPAgFnRAmZOZqPasVgaqeqakGgNSG2w+CnB2AlW3ABAb80g28S9llRzF7rvllPq5hgksZUsWEQsAhco3G4AMyP/IQ/IPXVCgBoCv6hraUwVzOaqAxKAR2vVoyyHa1Lj8hbSbSmxIC4kb/QZI0o77jinSyAaSAazLS1YprfZW2sYIbsxf0RTAXVrlfKXNugm4nHN6mONNRY/yjPNlYKA3SbwFGGr3F588T384AejfJR9/L6CvBQqAH4NlfE6jphpeBkRsZr7LV2oNFsoo5MnDpgwW+2PYu0bO7mQenVJw/kb559zFuEF74brxq2dUoq5pL6ItumifccC5BEq9Uc4XiwH523gBjBOqWChP3nLqopxrwhp39LT6UF/rH8aGvbhYxQvnwsChqXe+kWeRIdTz5ih11sLL12KKsljRE+zRVnn24Q5d2Ka6AM4IHIB/e4mdTmqs7ZRCAI2ZpUvZJLGMmIw4GovavvOMBz3tys5hPJs1G4XsKs5FXxlDuIYxqKeR+k+kEgssVf4soLsQ/Z1NeBKKhq5h2tsIZU9MjKplcrSNMjlBDBhWoPQe7WxerG8+DpQ4mqHbb16ijRuygfPgB0zlnjZmse5gb0JbAEdeemlCGSmTuveOHC1ZitIuzis0RyARoYDMhgK+I+qGAtSAvvr17Wpo6tXbbrpJD75jFgpznIjjwvmEfvCjfr28/6BuQQnq3rtrUJ7y6/uPd+j5HWcB9bJ11x2zteEaHP8IvZhStGVrVA//OK5uFHbf965i3XEXNuF5KVivTuiTf7sPRdoerVu8QO9/71zNXcB1ZXItNB2L8Q7uG9XXvnoK29gcbd5QxbOk1wHrrCzs/qxPjY3G9IMfnAR+6VVuVp1uu7EE3oO+jSCAPX+4eS1VYV0GsFc6eSymT37quC52ZGjzuhL9t/cENW8+duJMc8dOxnT4aI/uuzMDhdSWGatWp9Z/Bf+xONEOCxf5ZkC/rTmMEmv88Ic/lNm0trW2aeGiRXrnu96JQuD1sD08g732GE6yUwAAQABJREFUsHGP97025nnty2b+/R9LwOLC14sTfx44FwOca7yY0Fe/0qrT2CHPm1ekW2/OUd1c1PqZW8W4YcM/j0A6eiiqT39qL11zlm7fXKhN5Gbzi13MZfyRIcSWQ6xvG4jW1RQHnGvT1m1HsaNP0TvfvkQ3bMhj4xJ9EfXHH/1gUjt3xXELHdCD78llrSBNRw6F9ejDF9TUOqAN188BsCzQrBriIj780qW4vv7Vbj23d0o1hSH9yR9nwHuloAKb0LNPxvSv33gRViyk++5eRuyajo20W8NYgD/z9I+05ZlHVF1ZrLffezfg3HWMiYzddkM2/xs4Zz9SpJdHwf9Ytr/ob64IODc0NOTIZJpt4JkzLOIQ3Jtq3OLFSxyZb7NgNSlpg+ZsB6sdtnvGYLuenh5nd83p06d572mnk+bl5UM0Ltc73/mAFtFBbUfs9Pt+0Rv/RV9nDxz22WMAfDFbvPolDj+LblVVUJksrNnPv47DElfvfe97nR3FBhH+uj7n13Htv+w5p9gFEyGgL2dX8lNPPeXsoP5lz3GlXz8Dzl3pEp/5PFPwNJvWL3zhXxl/KvWVr3wFj3EeKglWruxxpT/vyt7dzKf950rAcm7nGwChPhvRYy+SmGbKXzXPQ+LNr2ULAaFmms3PLNiujqT++UsEtt+LaH6VW3//R37divqas9H7Z77zzfvHycmkfrwtrj8Hnmtsu2zZ+v57vPoD1AmLWWCYOWZKYKYErnwJGEBx9OhRffazn3VADXvWswf71x5m5/Txj3+chNudzrPea/8+8++ZErjaSuBrqLr++WemRF5MlWkuffL/+PSO23wobVxtVzpzPTMlwCKmAzugeMWYvHPnTj3//PNqampyisYU6AyU27x5sx544AHNno1iC9CUHZZQN6W5J554Qvv27XPOY84Otm5l77HzmcWbjeH2ZWp108+StpHRrLKaT5/QV//3/6seHCRqs3N14/wVmn/X/QqtWoG6WRYJKjIdJF1btjyjxn0HsEx0a87yNQp0YDl5CVssYDvvPFOnIxlbkkOSL4v8NNCcH5AogYLcrr0aePoZjQ71qnDjOmXfAjBkEDaJduRq+LJ1QxKkQB7xxiZ1AHIf2b9HlXNqNP+ee0jUzUf5xRK7AFck4WwF1QXwF2ctMtbeoPofPaz0pvPKW7RMwZvvlquapDzWj26AFzuSlpnCwq39iUc1zlcWqj1pt98r18Zb5MnMJlmH3VNzi3q3Pa+Rg/uVg1dRCJhmfJQNww2X2N2N2k0diig1FVh3YRlFmXgKsNqkXF2o9UxR7ge+/wMsvJJadP1NKrzuRnmKgexCBojx2W6Se0AKyQnWwBoa1UD9th8+pFo2DxffuFn+NevJmqF+EaMMbGEdmytTQ3Gh5hA9eUojT/5Y52gLlZtvUfGGDQAAQFEG76FCZCvmCexPkygQRfa8rI4dOzSI+lclybacZVho2mt5aewcid+tj+v4nn0qmI2Fbe0ykvM9GjpzQkX5JG0XoWzGeoOvgPvLIqmMYpgL6HIs5lHP6XqdfeRRFfT1qm7lNUrZdLvcFVVcgyXFeCAEkkBSj3L2aIo21P7IQ4q2XlLa/Drl3rZZQdqFK2iJTyw6eWmUL0vturFam0TVr4XyaNp7QHUAfGW3b5ZvbiXKRyRtDcaDUDBsyWUJbpL4sS6saxv28DNFRoLaT3LU7AVdtrg+RUKz6XklmvdoCsWQ0Ir7ReYVxbmXaae7UKDB7rBurVxFy7kO2l+C9ucCguChNUHyzyxkEuyat89JoBLjakMRBXDVnVMJNIeSTC7qaR5UXCaBWpqxuW95SRnYzaaUszs+exVlAYSHAl8CpZiRphcdNbMQaimB2ltQk8Git++s4hdfUH9nB+1rAbADAGkm4Jwrn0Vz3tfzksYvPq/RgU6S4XnKqiQJP/saEs0lXCvljOXVVNNh9bQ0UYYkv+dUYIFFYht1u+aLncrKWQTgt4FrqSBhjAIbfSuBQlKMRLMlHQw/9KAC457sRLHpPMp95zU43iV/PslkUwtEfchjkAhqbFHUWXouUoc5rO+XA7Pkl2K1i7oiBe9FcW7i9DZNkCw1xZu0aqDSVEAQVFh6z53UCGVWUFIMOAdMmFnFB5MxjXUDkZzV4JmDimJ3l4n9bgDIz126EagS0M8XZqzqRvEH5RuS3FlcfmY1AKynmKQ2gMRAH3BQGgAPKknYtbkClAmgh0EVTtYIoMUy0C4vio0xlPSAdfpaSfaiKJOOQlB26TU0k1peywEcN9p2RoNdp5WdFlMawIU7F6gGOz+DAeL9QBAAvL4ixrjc2XzOVQzOWbzO/53DFl/49+W5hPZCv2lra9W2bS+QFP0hm2MGndzLu979LuzdljhQtc0BDiz3k1M4mSn7D/+fOX56CVgZO2P1Txa8bE599b/tZzsYuf79JJZstvk4Ti7KQMU4iknRcFTj5MoMhrO82SRg3RhzyghW6bZx2tRmx1Crs+8mAmHvH+XfF7BWS0eF9pZbbnEU5xxwTgNKDh5Q18n9GurpU05xCXbOjOs5jD1urMOjgMfD9cxBB1CaPAdU0++Ms6GylSgnAV4BQzHwMI5iozh8Qu0XXkLBi6Rr+bWoNc5RBMWt6KXn5aU/eGs2oQh6A2MTYxe36rKsLfByAvAyQZ+0cd7FvOoeqweieQVFOWwrsc0MFM5FfRTFSOxVPVgsxlpQrrywm+EPi+TaVfKVsdYNFOwCrjJluHjbCyiTAbSzhpS16B4Fqhkvkq3You5QFxC4H1Wq3MplnBfgKLWID0WxD8Ap3vSyBoD6w4DdOVWAZdg0m+onAxLjKED6aA8Ks+cAiTpUAqidWYl9cz9WrecayBV6lVM5B1VbwDlAOCYA7tHG9oDhOthn80/U3gLqZKy8hBrZeWB1VPWo6eLyxSh0LuIeiJvcAEs9x9V8HqVbdNyKa4GliwFx/WWchTFxvBVAGWjnYj1jT0j5VTWMidWcHLW9jlYNNuxTEKvD9HLG5UIgN+xMXSjMJQYA9aiL/rZXUMIaAw6bp2DNbdw/Y1gCGIgLSZh6af2TSgK9uQtRIJ13iyYHsXg8DSycHFVGzTwHnIun0DaAAoXSk7VVpzkDQLmBhIwgTKLiGu0Auu9iLCX+CgIYByqxXGXOjdKOo33MIaeeVWq4XenA+F7ARnc698D9JrH4DBu8fGmfMgOoGlYxT5TeQD8BRmvcrrFGwPOULKXN2cg1LgZQTOPCiXsmGlGixcq1/QjAV5gEOjbG+cQmqIF5CpnvgcohowEWuzXZdhAlw2MAbX7gyfnAPVkaYn50RXsRGUSRrZh5OWDvYWxnsZoQhXnQaSaAkICBiUHae7smhk6rdxh1t+SQ0lPqlJe9UYHUOto00Cog/FDrcWC1JmWV5qMCyxwUoh6Zg/kjwPtlOHB0sldFi+bTbpbzvnIU3ho1cvQx+ceblWqQPHMzsngUDe0Ii9lo2z6NE69M8b4UQLYUXuPLZr6x2IS4KzF4hE0A25wNHD7m2fQ6wDmAwcljX0dscBiweyUbIzZzf6WXr8XGJA9KZwY/c5d+25CCcmpyEoBxiHrAKngy6lVW7ixl0188QJBUFnHHOdrKiyjk9cpLW/ICwrtMNRD4ztpBonOv4vVPY6E+wCYAIPlZt1OeeVwjMS2QbTzWy9jUyzUM8XrA/AnGFVQiEz1NCo9BbQWIkQDz/TXEo848S59CydDUJgfpG329nUrJy1DxAp4fsGodutCkoU42JGB37GxWyGBDB/a1DsjHtgazJwbroLXycE99JSd7EO5rVG93K+DxCOIsZfSzRWyaoKwNnQUi7T+1H1vfGL+vIF5D2S6znHsnfqDdJroPq6sZi+ZEEDXMucoHSPQCEBtgn4z3AnoB14VRFCSGd9NBXCgiqusiYP4Uzzu3a//AGn1vSwt2lxPkQObrmnV5KioD3gNmy0a1LS3VDZSZ0IvPR7Vjp9n69mk5Ck4LFqSpuDqgDDYEZGawyYnN8+zFcabzLVtH9I1vvYBq1ITuu+t63X1XGWJDNvBIHa2Ac4/36qktL2nN8kq9/e5FgHMBPfxYuw4evaDZqD/dc2+Nli0mriOOMj76pZei+jZrImcvHNbb7yrV3feiWFzgA6qf0D989oB6ugZ18/qFet9vV6uwGD1g2FQP8f0E8MzRI2E99K1LiCd5NG9Ojtau8aus0ovSJ+rV3F8mkF0M5dInnmjXtufZvDKZoxWL8uA7PIyHlAOqd/mFWDUD+Fi+KMFz0pFXovrLv9ml/pE83X1LrR58MEXllW7HlebM+ahOnBrRzdcHsWdu0ve//9iMVSst+Q0ftB0GX4fDMQDfDoPzzTnye9972HE4szWDW9lUcQdrvaZMPC2G5bz4Vf95dazzql/P/PhzSuC15Wb/tq+fB87FUSntZEPwd77dqH0HmoBj6WMrTOkNu9Z8XL5yfcrOxcWJfnsO8PRfP39e3Z1Zmj83hf7qUkmZGxCZ1/Da9Ewv4JwpITOWYO/88He7UYk7q9mo173rgdkoyGWi6grQy7LP1icj5AaY53k2e/eDeVq4NE0vvzSqx76PAimx4p13VWvTLVhCk8ezZ8z+PhTsvjWuHz0dQ/E4qj/6QKo23QTMzrU/82RUX/rmVlVV5uvd71wEgJuq9CwX1q5jeuapx/Xclh8CzpXpXtQOLyvOEQMSSNozikF+dn5rtZdb7s8p6J/x5185ODddifYQYF8WyO/du89RQHqOnYwGva1evUrr1l2jhQsXqrq62tkJ+3rgm53LAn4DQSzIP3HihGMPZD/b39asWa3f+q3fch7kbDft9GLez7jfX/pPZnNhigrNLIoZuDV98PE/2cGD/L09BNjWg9ccZo1x1113ca8s9L0edfua1/9n/ml2SfYZUzw0GBxjO4PfqofV/8WLFx3FuQPsLs20hdCr/JgB567yCnoLXt7u3bv1b//2Rb3wwgt4fd+rP/3TP3XG2St/q290erryVzzzib/eErB5s9sU074V1d98LWqbHDSbnQx/8js+/bf3kiT69X78m/7slvNruJTQ3/9zVF/ZGtOKudjI/W+kjlfaQsdb97B20wYw+KXvRvW5h7FsJRRbMsutPwKce+e9Xmfn51v37mfubKYErt4SsESMPYd87nOfc2xbXw+esw091113nT72sY/p2muv/amLKVfvXc5c2W9aCURYnP2dD0/pBzvjjkjVDcD9n/97v+bXWqL4N600Zu73ai+B6bU3+27AnDk82OZTc2/YsmWLs45myuNvf/vb/x2cM+DB1GtMoe6RRx5x1ttsl/g111zz7+CcJeTty9azbB3LIDo7ptfbEvytHcW5hwDnOk6fUk1ekW5cu0Fz7n5AwcXzFclOQbkiKjcKGV27dujizl1yj4xr7vUblRIh6Y+1WScKWIlQpmNrFSrJV868ahJeWGEWkej1p2jghV0a3rIVkTaS3zdfq5QNADOsJZKd44vrcTqkJbiGUX67qM5nntapE0dUvWqJankG9lXWKo6qWIxEJXyTfHzZUmoUyCDaDNT1+HeVQuKrZMkKpd72AKQsCW8kHKyb2zNJ0oC0MBDcY9/U1NYnVAhMlnLvAwqv20CyOVMpJPhd2BmOAM6N7diGhpVHwaWrsRbNUNPZMxrrRaUD2M6HtVGwCEtLEvGZSxfxMwlA6ieyc7sOP/EkOfl81d16pzKXr0NZA2DNVOm8fDb2mAYRJEZREEKVroHX96KWM2dOrfI33Srf0lUI0ARJYNuuatvND+DEl2N7dfa0+p94XCcbGlSz+VZV3HijvECHBq1dLjcD50hGtQAxvviCul9+ScOlpaq843ZlogrowgnERaHFms4qsuMJHXn+BdQ4Zqt02Q2AeQm1vbJX0f4BRdNJ9GP9mlpSAHCRocxyLJXY5DpB8rTzFBufUSQopQzn3nCTQus3y10CnGXeXNi+mOKECwUdY87CxwAFvv11BXtRW1u5TBl33SJ/VTXlAKSGwkeM19pKrB91Nx9g4Di2rg3bd6ll/yuat2CJKu4AnCOBGg2iZkOC2U2mG0E46hMlvihqYO0kmwHnfCQ9A+Uk6YtW8jcS6VbTCYCwtq2KN2xXGGWS0PL7FE+bpZH6/Uo07cM+i3siOR3PXYwqErAAlqu2Gm9txLSvMQKm7Pux6gPyQMXNixKLC1ggVIF1F4lrT7BQbrvPySGNXnzagedyCvOd63BlrSWZjMKbwQbAFBNNzwE4HFMwE5C09kas3WpJJKNmA8A3yIbyVECOYA0AAgpvSRLlLiCEZO8uFPO2aATFNl9mqdIrVtD2VyrhN8iPpG2C+we06G2+yJwWV151kVIzsOsD1Gtq6AOcW6HcMuomC8U+NmIbyBKnf01Zu+J/fu7PGzcFId7fdFRDWLa5AEczqmYpgGpRhGS4KSj6R5poLwB6DcAQhfOATpajsFOsGFawcc7mB8qInNmB9R+J4yLuAwjQiyVbYqgee0FsXFEQKMJyMB3lFrNWo2C4fizPgHZGzh9C7a0Z1RU+pwIVpaJN9EGAFi+QaLJLo90o1p3frQxTWqqoJWFeql5UV0ZR3cwvwPqrGuUoALgkEI4zCtiAYLUXJ+OMWo2w3JsaRy2rux54iHImaZ9XOh/wAmgjUEj9ksgfR9EIqG6o6wJWpV4U85ZQDnOBYchDGJgy1K7JfuBFrCiD2bOs8Tlt5GpUnLP5ApLg8njO2GGJUbPf5hdAWJOokO7TI48+qkOHXlFFeblMnextb7vNET8wayZ73U/eRlu391Oc1iHslzPHTy0Bp9z56/Q8Oj1/v/rf9mbn35Tp5dK0wnWqyClfp4ipr5i1Wyt65+fL87Xl3mJA8VH6eZR5NgJYa/+2ubyXOf+Zp58BuBt3VGFuvvlmmVUraUtp5ACqj4c12DeizKIqVMwYu1AAiwPIuGNDzFcXUfncq6nWw7g2owwFoOytQvWx8lb5M6u4XmCSKKpm4yfUVv+CxgAnikvXAiHVYre6H7D9BeBavzyzGGcKNjEZMzZZG7R2ZNAHMBwDNW0SFarJYUV6TivcdgQwjrGneImCJVhLpvMe5mcvSlFxxuXExZdJ5qPQVLce8gy4xo1NJCCXooBnHVuBnpmXJyLMZ7fLX7mePtoBMLUXpbYG7DuLlVe1gvELcI7xmQ+GpQGca9nBOHleUww9OdXVwGcAealz+DsKOmSEo6Mo/TWjEtXToJJqrObL8rH57lTPhVaARpT6qlAzBQQze0hhE+sAaSjB2REH4k4mB+VBpSzWdwYoEBtTbjmYV6ZCFNF8/mpntHW5AG669qnt4klNoUaaB6yWkcs4gzWqm8Jyo4oW7Tyu3kvnmAO8jN1VcFW8F1h8qr0Z4OwgFriIwlWjIlq4DqE2YHzAXlNki3YfRC1mJ+paQ0DPKO1W3c0FAJ/FmWepguTIWcUuPY49+Els67Ekn7cZlcEwwPMRBeKTzD/YIFatIa6q4rOxe7N74302BLhcZMxNOQxYKAwgFOYa3eEuxlDsToHmYjl8DvUdQ/Er1gs0dmKr0pmbM1El9JZtpMyqKCWLuYgrULgauLBD6VxnsGop7q7X8ie/phpfYv48hEUtlrizb1IydxHnZKMFc7I3jLJqwxPU+8tYoo+xSSQd0BygC2jLnb+CuYJ7pB8kRjuxE97DnHEQiBRFO6DMyGSuRts75YuNKLWskvlpCeAWkBtKg7bQGKON2mzoQdYriR27UKmNDZ9Hae6sBiLtCqHSWFC4EuvitbRB2lOSmBQF0qF2oLpIN4qupdiKMq/55/E3GlcMy9Vu/t5yTMMozJYsALgECKexK4Ji3ujxJwDn2pVSC1wPJI7sHvEofYxNA4ku+mHLk4DmbfQL1NXqbmTeBwpPohJL3JMcPQpYis18JxtncpdhFX8z78XW+eg3uf4JeaqB8cpvpt2XcEcWwVDmbsxLqUr0f3jNOOOB2RJf4DOwaI4AxmGXmgucGqSvJ2jXpq+b6Dqm6JlnAeqxoa1aLdG3k8zbBlb4RFDXv0/xiz8Gvuumfa7mNbcRwDH/oq4bZZ4cRdVwBDtOuUeJzcfkBerzjvcogFKfUKxzAUf6SwzUpG8XLOC99Cnm4cRoqyZQDOztalIg06fCBdjMo9rXe57+BIBRgrVvqNgUhW28oM5tIwXxCYXDzyjBoVpnNsJRVB0HO5o0HI4omJWvPDa9BDMqmbbp6/Y5A/XqO7bHiSetrwQ4pyuVZxOA/mS0DWD2FXU2ntQ4ZZ5eVgMojAItdswJxpypkVY2MlzC0hkg3yAO+kkQS/kQbW98OCFv1S1qCt2iHUcj2rUDBecxP5uTgsor8KisNFu1c4o0e1a6stN9gHVxbd/eBxQGqIilbXZOqkoq87A/zFXd7DTNqgoSjxAXEds//dyIvv4Nxr5oXPe//TpgpmIVFtBymdq7gVAe/3GfnnxqlxbOLdbdty9VdlZQDz3apXMXG7VqTQGKcpWqqw0o3VTvKK5XUKH6DuDckVOHdfcdFfy9FJ7DrTPnxvXZz+9ng8io3rZpsR58oBJ1XsrYNu/wLQI82NiY0Aso4O3e04F94yjPkShyFgZR5s5SzRyeeeakKIfNNidPhrm/Tr4P0IbBcbOjQHhuVTK+LllaqprqFIBULK4ZHQ6/EtZffnIrfaoU8G+e7r03hJsx4BzrNvUXYzp1elwb1/rYvNQ4A85RXm/4uBx60HdMbTPOOH95LjP7+G089z722Pexj29FOWyWHoTLMXDJNt/ZGoPFJdNxzRu+jt/wE7y2LKfjxp8HzhkszFKDdmGd+uLOel1sHmZzHeMMqpFFPK9XzcphY2SmyipCjEsubd82ov0HxjXEmJ2RPsqzkxuINVO1dbmaMzdbRSXYTPvcjHsGzvVp74ELjh3r/e+oRnEyjbiDDQJsVti+JYoSHOs8xIr3P5CrxYBzu3aN6geP1TPnZunt95Xp+s1B4FlrT1GsYJN68jFTxGSDlXtMH/zvKdq4KU1dCGaY4uY3vvuc5taV6N3vXqC114SUxnPjwOAQ4ByKc0/9WLMqy3UPFsHXrr+eMZjJmjHXYkuD5px1Bj6FUfgNtaI3Bs7RGRJ82Y7Vy7tg2P1hgTmBulGntqO1s7PTgea+/e2HnI7zjne8A6W4d6I2h2TuL6HCZo3Ddsxs377dsQXas2cPanTdUNR36z3vfQ/kJAte7G78VR9d7ML80If+yAH3TCnPDi7FIW67u7sdCWyTLs/JYSZ4zZGVla33v/93dSf2GIW2QPZrOKbBOTv1//gfH8RX+p5fw6dcHaf84he/6Ox6s/KeAeeujjqZuYqrqwRsnPza174mU/e08elv//ZvdAuLIjmoBFz5441NTlf+emc+8dddAraI9viWmD7yqagGxpIqYrHid+/y6o8/7EMSfKa9/LzyNxny/a/E9defierFU3GtXODW1/8qqEUL3/plh8OPDh2N69P/GtVz++JK4cH8gU0e/a+P+lTJbpifbID6eUU48/eZEpgpgV9xCVgyxoD9T33qUw6EYfZOrz1SUlL0nve8Rx/+8IdJ+GOL48AOr33VzL9nSuDqKAF7zj9wJK4HPzKlps6k8rEK+e/v82Anz85nklEzx0wJ/LQSsOew6cMWjKcXN+3n11tAnn79q/9mv3v1v6fP99O+v/ocNh5PHwbR2dqJOScYEGfgXG1trbP+Zuc3i9bvfOc7ztqa/f7BBx90/m7js63R2Y7xILap9rO9fnrctp/tLuOs+XUAv33v/3xc7adPqqagWBuv3ajZd9ytwILZSgLOuYCB1DegkZf36Py+vYrg37MAu860wjxNYM3ZdQEFlP4RFClIuKLikYUKVx6qeOmrV8pXUaE+FOeGnyW5GvIra/M6Ba5D4SmfpCQ2mUoCLzk7irHiHOEcJ9kpjbLdWQCc6tVLVQ04562cpVgwXRFT+OCiuRNDcVA0wQaqqV7nHvuWA84VLV6plLe9S+4qEuQk5v89qrYkKSphTY9+S9EXniaZU6TQ3e9UfPV6JVNxy6C83djIjaPMNL71WfmAmII3bkZFbwGqVO3qqT+HugSKbmOAVVMky1irLFq1SsU4ZwRRlYsd2qXDwI3enELVbb5L6cuwJLPNwAbOkchMuplPUfZLsrs/fOKsmna+qP4L/x977wFnV33eeT+31+m9d/XekZBASBYIAQILjFsSf1z2k9ibOLvJa5JsCs7aThwnr19nszG2kxhjOxQbGySaGk0SAqFeRhpN773eub2c9/scMV5Za7AswJD4XnsYzcy5557zr8/5P9//73dB5syZJVmbbxH7IhRKsK2zkKnSelE1IkmQIYtgQdXUKCMsKp9uayWfebNU37RZHMBaCs5p/RlYtVqwHUt29Upsz24ZRnUugHJcBYCMfzYWqQCTVuCiZFezxF5+So4/t5t6q5Ha629FjaUYu9ELMtLcJMMj0xKKEodz0hyXQdKvQArYzCvl1dimXZSz7AgvQOWlgc93r0VtqAw1DzeKhiRWWUnmSlAc4/kmcfyEdH/vX8WPYpdvJcneO7aaSoSX4DYX0AHH8GVDFsdOW5k+f0Za970ofa+fkNnzFkn5VpLHgHMKdKTi1DTJTFVHsNpogyjBxXpfMcE5Jxa/7mpsygpJpNtYO9Z4xFB71b2SbN6Hwo9HPEt3SNxXL8Hmo2JgiZYFrOmoXws4t5jrQGFHgT+u3obKjU1X5lFOSWFpNgHsESSh5GWXvy+/UlzlJHszaqnHbNbKUcAj6R0ikRzCdjWzCBivapNYcteSZQaco54tYcC7DgC+oRPAKMCW9TcAztWZFnsKzk3Ql3x1S8RTdx1J/WoSAkCkCs+MvIwa1LPAHL2ov5Vjg7oMEGGFJLFuU/UfG2BgCru10U4UeUJxyaspA5xLSXQUNZAurA4LVwCJ3YiaCrZ0LlXSA5wDRIijPKOWdI5EjyQDlxSS4iSr1TbXVwFUVjoPliFPMIyjPBIkhS8CtByR/hYAt6J5klW7iusv5jqcnC8sjgnAu3MHUKgBnAOGcZRh80heOxVoll76bYiEcwl9NhPIDQ9I2oYSNcC1482o/wHRDHYyFtjFVbEKcG4zfq98Popz8eQA9mcnqK+XJQNlvAzgOyswzHDXOK4xIazFioHcGDtIiietClmAmtKOVJHAojACIGgC2CBAon9idADoMpe1swXiA/7DixawBxATpRtjuo0yPIt1XSuJalRTalGIQm3PsACPJgHnpvolOjYsFgBLV3Yt4xN9jLt434Jz9NmZsW5mHqFQ2KTXKjt37pQnnnyCpNu0fAAVSp0/NH+jc4L5Hp0HABEQQ/s/zj/6B36ffl19CWi5/6I53/ydlvQbxamcp9qj6dhtFrNaDWj9mT+8cdAVH2vWqXl+hgHycwNYiH//+z+QAWwX16xZI5s3f4AEKvONAbwyfRhbwhPYaqIqBgyb38AYklVLf0EFNjkplqlWwLlDAMhHUYYaNBXnLNUbRCpvAV6pYq4AuAXKSYXOSE/TXtTvMPIsWS05ACXJviOMC8+bCVtb9RZUwjYx7Cs4R+OhDVkA4/kQrp4+hspVcgz7Q6Cf6MSoeHNLUARjrFBwDGVOZlyzLybbXxSjZb85ftsa1kuqGJjWkgdcQZCu1rJ9exkTmZejwP9zgcwrGTOTqBr1vi6DnR2Mc4AygM2eYlWh1DwdZTt1RpKdz8tge5PE8D7Mq6lDDU3BuQb+DjANnJNQxbkewLnBFimpzgKYKYa7HiKW6aUyUJitBvIzwTmARLV3BZJScAY8mHOoylWnRLD1jGKbGkd1y4JNqQ84yJ0DBGwwnmudSg/nPAjg1ogCKuBc9WzmE2ITwDmd5y0hBeBOyUh7K7lYgBvssN0V1XwORqm9XVjJHhGP1ypZdWw0KFoHOFcKPI0aIEBWfPB1LKyfZzPCBDAX4Fzl7YjGcY8prlXjnSkAyfYnAftOi0HZO2ZvkegkTmCoCztRRfVjQ2uvWYHoL9fC+AzHzvipdacxFu+PdAJKMUYCFkqQsdiTARA2SywFqLoxJuvYm8DGMj6CnflJYrsU11GDylfJesC5Sm6e/gBgnhg9S/yA5axlDGYM9cMKwLkUCDfzYKDzOCqugGh1N1EtwOzAnQrO2cLNIp27JIJda2gMGAulWlUes2NHa81fwSUy9qO+aDBOhwHnpoATVbHLXTwHxbk8me4eYq4DDqyqQRFsIW2UMZwJykDuKkGAYwccscQjQF0KkVOHKMGOBVAWy8AKHMW/bKyNCTppZ8Sm2MOrOuskFqAKnuWwmcBbBBhngnPMa6YK7gVsv0/LJEqu5fMaKCc+01EMnNkjU2eeAiADyKtHXZa5WVyAczHeF2OeGXkNy89dzFVdwFwAd7OIfbKB/FIKd1IPwRMo9+6W8EAX8cUSYgbAOdpu9PhD9I+IOLB+tZYzf5pzLHOgOYZQd6gsWowQ94dV8chFmexrllBkArtUN3NhnXgLqUdnEZEbwCT1nUQRMn5ul9ijk+KoXS2Wmi0q58tYw1/1PKNHJNEKqDtOTAfs7qi9mfd76N9tlDX3zoaXKPGYi/jewVYLe3JCHME+cU+h5obinM2bg60zlqsVK7CNXsCYQ3wCOGYAFEa6zwPOtasYsRQuqKR/TcvIxRYsrFNSVrNOfMVzaU+0N9qGqdJIC+XGaKPDfOvkGlCZw5I5iEJnChXLLNTiPAU11De268SAFgBKVQAePvEyIKBLcqqwQ6asLR5iaOIfBhjgUhTnsJIOEmJmlNYwnqB8Z3VKiDhtrL8DaL8fcWereIiRiAgBIYkNJ3nmmQzSh66TaP0d0hXIl5PHuqXx9BBKTxNYpLNdgbE0N7dAFi+slRuvz0clyiotbdNy8lSHtLb0AYzEJBBmYxJ07Kz6bLl+TYmsWJKFepNNnt4zLf/20LOMCwm5Z8d6ufkDqIdin6hD7eBgCmhuVHY+9YLMrisEhFsq2Zk++f4jQ9Lc0QWQko9iWIU01KB2BzhHc5CjxxLyg0fjcuLMMcCUatl2WwkKVVY5dz4o3/ingxIhvtq2eQmQXiXqcLQ9m45zCscqsGOwkSsK7NYlF873Mu+EYCbVYtaNHXgFlqyFzENebKqtQHbTcurUgHR0DgFvj9AfQ/Rfl9TVz5b1a2tkxTKstbGnPXUqIvff/5zEU1Vy951zZdvtLhOciwLqXWxLyrmzIblhNWrlU21pcE6b/Nt9mWsaGuvRdd6ImePA+c3NLaba3B6efXX40I13Cs7NmTvnUozIoGLGH/xN5yx9/aI4x/xD+j+/tASuLDv9Wb/eEpzTMqfs44zHfV0RwNshOX6qR1o7RwFZldlysc5jYz2hQFavqTNB3RD52ZOnxuVcY4f0MX9EWNO3sNksA6W6uXMLZP0m1gCqfTLSi0Lcv4/KkaMt9OMsuXtHNc/hXtY1EE4j3/vC7rg8+xRAHOq4d99TBACbKQdeCspjP+pgM2aG7LinBHDOBTinFxmRyYBVdv0khvUv84AtKP/193Llho2Z0t9lyLMAeN9/5FmUDIvk3o8slJWr/DiOEk3hRLALx4RncKKsrqiCg7pLrl+n4JzGIpyXZncJnGNeM39U9PbaX9cOzlFRCsjpjtZhdlpOsptLwTb9UntT3ZWqFqrHjh0DdvqpadGqktBf/OIXpbq6+mcd6FouXRcAH374YXnwwe+an//Zz37WtCvVZMw7/ZpiIU534ioAqKpu+tLdWJFI1PRzDhIQzpkzh8Hihv/ro/1+n+gO30XYyaok9rvxmgHnVPXuy1/+snzyk598Nz7mfXHO+++/HyDoO2a7SoNz74sqSV/E+6wEdGFOLdEeZYdoXl4+0sdP4AFe8itByu/cLV0Kkt6586XP9B+5BBR8Ono6Kf/9i1F59bzBA5nI1jUKPmHROpvFv/Trl5YAawjy9PMJuf9rPLAMGbJ2uVW+db9b5tb/ZvS1YNCQXXsS8sX/FZcL7ECpL7fIH3zMIR/dweImks3pV7oE0iXw3pSAKhx873vfwyL+f5vS/frzlS+NRe677z5TKTzfVDi48oj0z+kSeP+UgO6W/su/i8s3WDBWldOl7Gj+3gMumd9A0i4dsrx/Kup9dCUzC5lXXtLMgueVO7AvP35mYVnfO3P85b+78py/6Gd938xLx+C2tjb59re/ba7BqfvBPffc8zNwThPwr6Le/0//9E/mcQpErFixwtwMq2t7CkcUFBSYa1wVqA1lZGT8DKDT92pqxACc6z17Vn7wF38hAxfOSQ1A2/Urr5P5WLV4FmFplIP1F2o3yaFRmX7+ZWl8/XXSVlZZsuODkg1Yp1mZ1NgkqjIjZsIyeBG1kKbz4szJleybbpSMNasleOacTDy3h8+KSO7m6yRzEwpWBYV8OOtqKNyYVq1YrBk4VMTPNwPOPStnTr4u5fNny+wP7cDiaZbEPZnYbJHoongUclLVOYNOnexpl6ZHvitektMF8wHZbrmXZNZsEpckJ3klKU/STfwjIt0//oHEnv6J5LHK67n9Q2JfuxGlqSxy/UBto4MyuXuPRFB6d2J35d12q7g2YFnF2m1yZJB7Q2GiY0iCF3tkqKNbEgBRdTfdhEoGQFTLCXn9qZ0SR6Vi/satkrdmA3k7VMRYzCbTzT2SJEQxjG3bEuP+egHnBs6ekvo6krSAc87la4DQgL9U9UVfLJLzH8DAaUk0n5ehp34q50iuN6ji3I0bUZ9BGYyNJ7oZ20x0Q6Kl+gD79u+V4YMvy1hRkZR/YItkL1gMzIf1JqdNtJ6V2PM75fV9z0tO5Wyp2XSXeFBHsbio25EBmewel6kerAE7UNsCOMhIRaVo4RLxb9ws8SFUJJ7+sXgjU1LDTnDvxlvEVsV9k5RTBTKzyaLekeT6DRTnBh76rrh7ulDtWyQZ2wHnauupNMgqtd+jbGH9uDtM70jgB7H2bN//ovQcOS6z5y+U8i0kj1EstPgVVqBtAM6ZH2Cn/EhiJ/oB59oPkTi2i6fqRixzl5LEBjpUWbpkL8AEwFrri1j0+VGcu0dS/tkyDaxldB4VX24+tqkbJJEPOIcaG5lbzgL8hcKPYDWVwDosOnEECLQZdSbsfL0osQGVWXPKyfXncMUZrJtjYwXwF2/dKZH2neLJYud+3UYAvutpcyi4mMqCbRIA9kgBDbizc01wzopyWQrLtyTqSqODwyj+zEdpZ7XYUMQzBDgDZRFj+IBE23bLFOVtxSIwq2aJOEmOJ+wVABGAczEUjgaOyngXVqQhi+TV12LVirLYcBuJl3HJKl0hueUkHFB0sjhJiAN8pYAUDOrFDuQgAbVMPIWiXa+4ARtySkkQFywikT0LZ7gsSkLbX0g8oQsoGL4qvU2tkoGtWzaAhb2gRJJAKNAQYkGRL3b6EEo9qEiVLRZn+Wwc30gATbdI30WshafCWJVVSA6Kc+Kv4JyaoAZSIIE9fgFwDqvWDJfdtP2zlpGEzwBsc8QklkC9pvcYUOJByUCBLrOSdoA97DD3Nj0VQ1GvWLKBT2xZ5ajqKP2uqveAivxLpaVSKGFODx6S8PQx5TaACpdT/usAZlDP8bJg4UjRbi6pbo20n5bAcDuqKCjOVWHtnDuH7kdfod0bEyhajYyS5EfJCuDx/QzO6a2bySX9ZnZE6gHwVje/7N27V376xBMowJyRCoCPe+6+RzZ/YDPqZAU/D1CTE1FYV+c2iwZG6Udxs1jf6j+Xz9O/6LiZed+MA3Qs/1kl0RUo75n3q1sTaIP595n36Pkuf9/Msfp7nbcHgbgf+v73zbzWqlWrsHBHca4AlTYZBiY6KEP0sdGhScksLMOWG9XKnAbmCsA5FNxEgapWbAt7TqAiNmSCc/aq9ai0YhmeWc456CeoRiWnz5qKc6GQFdvlNVgdM371HzOBNAtKJzbsGq3FNzGuA6IBzTHwMW0x1xn0H5S7kqhtxlHxmp4IA4ag0loJ/JVbfUkVDutNTJ656wC22vsl1foM42qKsXmtGKjb2Wy0T6AXVSxLde8FsnoBi0lDMuczV1YDzjFORLDoHOhop0/nSgGKbD7UxgzAOZ2RjKlzXCdWru0XUR51AM7Vo165HOUqzTGiHocSVwyoZ6jjrARH2qSkDsW5shLszkelr6kP5r4IcI6+X8y8BfRjWJibmUS1X6kVpcS6AJLOAQUCIQJceUkgu1VRKx/gyl1BcZANpp5Ic6PmdYjrOCshiqaoul78HGdxlmsNQ+J24r59UgaJ8SD4JZ9xdAacC/d2As69pg7ukg04ZylaC3SFkijjs0wA7XQfltFulFcB57LLAPZqb8P2lLlQwTk+KwUgGWnZCQt+Viwo4blm3yyxyYRMs0lCNwr4a1GcY34x/MQSXIrOxYbOQcx7BvaoyUmU4kZfkyj2vlmeEvHnMUbmUX++ApRJUeNi3OVsTMkXZezEc5LB3JzLnGYrZbz1V5v3p1a7USD0idZDjOdTzNeohVK/Cs6F2w7IZPsxwPIiyUCR1Za/FAU0YHnahTXULKn2XRLreBXFryDQPOqx1IVDrc2LUOdVxTnAMyMwKCHAufGewyheEQ9QDgrOTXUOAwfGxU9528sAGd2Ut8PH+M4cwc3aUCGzTANljmOVO9CIsGEHaqrAB+UN1DmbAgEDTYVGjrXGsQvub8FCHJg+MkhZA/oVojjnYs5SxTmsXpMDwF8dZ4ALhqRi4RyU7hScA0xDUW3y9FPimOxBLQ4b1oobmQorTXDOiBITDlK+wPfTgFnu4gbxzNqIshxtCOCbC6AbYtXa8ayEAcMsKKb662+hbiMSOf4DFHeJW1Bks6iKHepwjNyUub6ofBQVjSibACbPMdeexuZ8CGVbn2QWV4gznzr0EntbciSK0qAV1C01dFSS51FMxkLUDoRqrb3JVIpV9VlVbDMGiPmbnwOuGqdMV8MhbiauxVaaPhjo5zNSWO4VotCYXQQsEUHJeMRU1030onI3MiE2TzZVsJDrBcrT+6M9CWCjAVA42XZaxgbaxEv9KThns07JWFsrqpopKa5aIxlYrqrqY5JrZQsF8zNxICqA2gdTY+ck2t9FXw5gK+qnyNlQVEK9eEu5PmIx4k1rEnAOpduRUy9jIYridSnwaiVxr5eYy8EYBISbQnFugOsIxhWcq5V8oH8bc+lYZ7eMACnbbaj78lzky6yhLWJXP9lNvH0cYJMNDtyXseAWibFBIxp2SG9HTNqbJ6S7IyCtrQFAOYDS7BK56/ZKWbseriLbkIlxi/T2hKS7C8jsdAAVuFFJxEKyZlmJ3Hlbhcyd45O9L0fkew/zzMTT1o67rpctm1AKZI2cYUWGyR3sfHpYnti5D+vUIrnrtmVsAMiUHz46LGca22Tx8hzZfmeNzMPGUcE5alEOvZKQ7z0Sl3MXjqLwVoHFYgXKcoBzF6bl619nfJ0Oyx1blgHO1Uoe6lSmkjTvTLLpQvfwJHlGmgxEZADQpr01Kl1tIcCcoPT2e1Amc2INmysr17rFy1Cpylh9PEt0tWs5TGIHOyJ9/S5Zs2IWn50DlGWTxvNhuf+LewHnKuWeO+fIrbddAucil4FzNwLOTafBObNXvyP/YZzXeceMLXjGVEfGVw8floceeoiNeY1SySY33Vix9dZbcQZEXVuhfn2HGVcyZPAe/dIYZObf5gHp/1x1CcyU/cwb9Gf9eitwzjyWcleINUn/CJBHGxhOSkf3lPR0MsY0hxlH2s2xYt3apXLrLRVSWwtazXg2MBiXrs4R6emeBNaN0O8CHDclH7x7sdx4UynCshYYqVF57WizLFuaKTt21KD8jjU64FwiYsh+wLmnd0ZlcrodcK4MG9dseYWx6ZHHurGFdcmOu4tl0y1uyc7R+4gBUVvlx4+MyRNPMFd44vL7/7VMNtyQI/3k+3YrOPfwk4BzBfKhjy4HnMskvuFqAOCf3vWUPP3E01IFuH7n9g/K9WvXASuzWUHbLFObAfCua006y10a0Wbmu5mSvPrv1wzOKRynamynTp0y7Xn0wWp4aBh/aSbcNzqJz+c1rVonoMlVJexrX/uaCZKp3cPlAf7VX+6lI3UxUD2VH3zwQROgKy8vl898+tPyyU990lS5+1XP91bHawdXq4skgeLMfSX5nQJ1H/nIR1C9G4Zu3C5/yaLhlS99gFSVBd2pO7NL98pj3u7PaXDu7Zbgu/v+tFXru1u+6bP/fAnouPhnf/Y/5MiR10xrtIce+h5jIsE9k+av//VefOav/y7Tn/jLS0Af1jp4YPrKP8bkX59MslgjsqzOKn8FNLf1Rn0sS7+upgQm2Ln18M6k/I9/IElAAHz7BoD5P3NJbdlvRl9TIY2unpR8698T8q3H4thviKyZb5U/+V2HbL7BjnT01ZRi+ph0CaRL4N0ogd7eXhPSUICuu5vFQR34L3upCrkmahTuv/HGG00w47I/p/+ZLoH3XQk0t6Xk7s9H5WwzSlHE0X/3h0751Mdt4vf9Zsy577sKeZ9f0Mw6kX7X5y5Vf9PvJlTwxnPYzN/09zPH621d/rOOnZe/52puW891+bnVDULBuW9+85s/U5z70Ic+ZFq16rrUNKCZujj84z/+o7nwqnCcAs2ajFdlGt2IpcfNnz9fNm7cKAsWLJDsbFSV3rgv/kHSFDUB1v++8+d/IaMtzVKLtee6hlmy8OZbxL9yCUonWSSwwyiW9cvInj1y/kKLWIpLZemOO0mGq90R0BkElxEGoAvEJAlsM7nnORlCXc0LNFe8CTtN1hon9wCLdbZKwaoFUviBNShpVZGcAZxDPYRsGQk6gkOSncn+ARTqXpSTB16UDDavLrlDAb6lKN/lkvQkqZvU5DVv4R51FTU12C3Njz0k7ounsaWbJ64PfFBsfLd6UaDivAnuz27V+wQ4AJoL/vQRcaFWkrHxVvFuuAWATxPHUYl0tcrA/uclfPQENkSFkrFlkziWLSDJTQIzQZI2jNXqRAI1swEZfuVVucBcWX79OqnZcJ1YsYo6+fQukmwjMnfBCindsAl4rw74CbgHpS+1rVM5NlV0Sfb1y+grB6Xn8CEpIeGecz3zKPanFh8qKljKkqkioQdMQb0JyexEa5MMPL1TzqP6Vg+0VnnjDWLDAcPi5P51R7bChCjUpFDHiL3ysvTu3yf9sYRUrl0vRavXYpFVbC44J868LsE9T8i54yelePF1UrZZLXA5j4frQ3vEYAE9hc1KsqNPJg88L5N8rpPd38X3fsy0jL2463ETUiybN18yt9wu9jmqUkbZsMvcrAgUSFI2wLiWVhn/8Y8kfA57N2zNCilH12yS116SwaiE6UsVwgQFUAsL4ZHOJul44QXpeOkAFjLzpILj7XNqTHDOtGQjsctKP/dJpSdJUg+TrO04RG44Qr4YVSAU0SxevwkSIkdHee2TOAn5mB0L3hUfAbJaCDh3FCDtsGQAazrqbkDdhiS9qo2gpmIFRLDFUMZBTSU8giofSVb9nSejCNs0kuhZ1cAeuWay3gAuM9RCMBySeNczJPaf5rri4qm9DkUVgA43ydpwAAW4NhLCh8Ua7BF/QSmWrCjOkXQ1xhRaeVkGe7pJ8mMf2LAUwLQWMIMkrkKgQIHTnQdRMhmhfrPpX3PFDyyQcnMNCqdMX0B96TWZ6O/ncwFTG2bDA1ixQ2yTru5RyS5ZBrxwvdgzUWgh6aDohubR1SZYuJbYwIsyPnieJIRFsoFqPCWoNJlWseUmKJBAkcCiIGEQcAF1qf7W8+JA6TG3oh5mowJwg/ZMUj6Fwt30qcPY4qUARlDOq0FpKIMkx1QHik1nZAzQtLggR3LqUIfLq6PeASBR60kMoU4DWJeYGBM/D3vOovmAMLR9oDV8DyXONQa6TgBuHhe/OyV+gBe1aevvAsoFoMjDOioHRRlrBupXtCULiRqbhfandmxY0oX7O0hmN6K+MA3oUUg5AJI4OLcrh2Q9bZPuYgUKkukeGe8EnMPONcsTEz9qWnZU6QwXYwF9PTGEygwQjatio3jyZ/Om905xbmZ+0TH7TV/0D4P5xjyGw4LMC00okT76yKPy2muvkoByyE03bZKtt9yC2sQc5gSXCciZZ9Tz6vvN/1ya85DKedOPSv/h/5TAlXUzM3/rEVoXM3X2i34/896ZY/X7zO9m3nfl7/TvGleoEMT3WBvu6+0zn8duZq4uYs5SxTlj4qAMNh6REdTosvLzpJA+6MIeMWUtYNwaFRlvYq47hVpaB2xEACjXKy76oQs1KHsugC7KUVBzWDCeloGLB4Fi3JJbeT1Qbj3g3EkTSLM4UEmq3yYCOHfJDlETmowxjKepyLDExhinRk5i99rL9JQtrvx5KM0pAKNzAMpfNqA4HRMsQeSTUL8DHpqcmBBf2QLTCtIK2GOBrjbCgNJdByQEQBYBtspeeJu4sJUVA6Uxxsl+VFttxA8FgK8egCfBftFsyePnJNF+UEZQww0BDeeTjM2sAZzLYCxQldvkGGN0iwwyVsWCo1IypxzYqVSSgyPSe76fdSjUmKpXIqbH2OzOYqxxEW9QnwokhQYQGTsDFIiK3PQk94dqZ1GZ2PIUxqni/IXcG2AXdWUxAP8A14bbTmCV2Y8FJIpY1cxXGYyl2udCHRLrOSk9rZ1ideRLUe0SlNUYL7n3QE+bDJ5/CavpGEp181BOY34BkDJBJgDsqY7XZHr0PONfAIWtakC4zVjyAmwJcQRxRmICu/AW1PoAAx3FVeKbfysgF/AL87LbiCOgiq1qxXJJeoEUgZGsqAwaagOrKnHDPRJCHXQ60iOeTK9k5mJdmdnAGJpHzIGlPGq+3J0J7htYKQ6ffEYcqOBll1TDhQFzZVZTt3HqH+vtHsC5rjOS5zPEW6tWnau4PxTnOrCUbznMPAfQhZKcqwSAGyBOlf3wOcfS+1kxULOzog5nwnQohLmr2BhRsZLxnDqMTVGNgFddRxmrzxP3AtcxX0Yj2TLVpYpzCWy9FZyjXbhKKFKNxYgfASvVdjap9psDrQCCQ2b46Sgsxda1gWvnWKD7FGphOjSqvXFqrJP7OAPg1iaZKDt6i1BP8wKAcT5OIom+c8S2zRJAibZqMRAntsCXrFo7ZfLE42IdbQecwx4V0A1anPun/QdQV0YFN9xB+wZYcxdSRw3EEcULqEPigRjlO/66xDv3SWh0mJhvpfgbbuP6IwjR/ZDwidgeq1Yp3US9lFMbbAbTWtE6DKGSN96Kkt5ZxCM7UVq1iaugHMaU41BvIyhivvCzeSCLecBF7HiKDRpPm5anjiI2C9SupI0SGxLPISNN3HcMpdiXyaNHJRPLWReKdKlkQKZQPIwBTjqYozMrsELnPQqlKtAY721ENbGJtwM+ArW5UYFzAuXZGIsME5zjvOOdMtR0EjfZIckqypFCrFq1vMfb22R8ykq/vg7QlHEDEE7BOVWXtXJ+JGslyaaExNBJiU2Mi9WVKS7GLltBrQnJGwDwhtorW320H9p1qF1GGw+wYQAIFCXobIBKK/bFpuLcdDf9/ji2zS2AhB7JBL7MQqnQQnw51oGiHnFFJpuHsopKOB39G/A2NUK76ULVlucaG/FPknqJEkfZaDPK9UUJMSaxmm2+mJJdz6LQNhSVddeVyLY7fCrgDPyH8mEUtz9Cl86OpDy9e0ROnm6Wcqpmxx2zUI0qlJcPA7r9+z6zH915B3D0TUVSlMdzFm2yfyAlT+walid37geOK5AP3rFcKrBN/fFP++XA4Qsot+XKbbc2yIqlvktOQDxbPcuG9e8/HpduxvCP3V0ht20vBba2SVNzQL76d3slDBR359aV8qEdtZLL71PAgqpmneTZLs5zSQg5Pp5eGH8c2NESIfOs8OorSdn3QlKGJ3qAdQpkw+YMKSllkwabepLxFDAenPaogU38lPzop0NSXpwrH747W1ZdZ0cpPSL/8ysvAUxXyN3b6+RWFOdKSok+Exa50Eun7uYAAEAASURBVJaQUycCctNaF1atacU5Gt0782JOYoigTenpVK2+SXahSPz444+jLBuUtdetlY99/OOyTJXU2XRn0efmN4k73+z378yF/uc9i8Zwl5ed/qxfVwPORZkTosBsMeyM9akdzh1RekNaWdt8dncvSnSn2YyH1SljyLwFHvgl8/FPYqjkBwPU9/mk7HpqQE6fP4Z9arXcvn2W5Pjc8tgjWLUeuSjLluSY4NwcVZzDqlUV5/Y9h+Lc0xFgvXZgu3LcQbNR1owh7jMow9g7b72lXLZtz5TScit9N8FzqMijP+yVPc9PSBGbkv77H5TKxpuyUeFUcC6BiuaPULzGqvXjK2TpCjYcuAHnsL1++sldAHrPoDhXK9tv/6CsWwMkr4lm2qr5iGKCc5CA/OISOMcawjW+rgmc0wW1M6dPyy4Iv91I+4+PT5g7VLWjKCynoIbCbapCNzqKFCDgmcJy27Ztk4/TqXRX68zi2zVet6lsd5Zdrmr708XuuduwQ/2TP7lPampqrvWUV/0+vX+9t1tv3Ybk6YDoQuRXv/rVq37/O3lgGpx7J0vznT9XGpx758s0fcY3LwG1aX3ggW+ZCY9PfeqT2Ez/wbsG7b75Vcz8xYyuZn5If/8NLQGNtUfGDXmQ3Upf/iawE7FLTZFF7vsdu3z6kyQf0q+rLoExyvFff5SQL3yDxTB2dHziNqzj/sApRbm/OX1N7WpVufCfvxuXZw6w4Mqtb19vk9//jEMWL7CZi1hXXaDpA9MlkC6Bd7QEVBFcQYwnkU5XNfIr4TlVIv/Yxz6Wtmx9R0s9fbJ3qwTCLFD/2w8S8j8fiMsQi703zLfJV//CKcsXWUmGvVufmj7vf/QS0MVMfSm8pgudpjLMFYvIVy6AXv7zDDh3teUws4Cq3/U8Ct3NKM4pONfY2Ghatc6Acwoxj2CFs3v3bhOsa2pqkmKAqsUk7hRuVnW55uZmOc1anwJ2W7ZskTvuuENmz55twnR6feY98nktrMV9AzeJcHePzEF5Y6XXI/XzFkjmqqUAYOVkgiISvdgqPYcOyQjxW9F1G6QaizgjBMiE5Zkdu1OLn2QcicQ4Kg2De/fISBBrRcCtivUbxMkOieChg3L+8AHx5fmldDl2SnWqHFWIipVPYsBi6ICgWkESl+TY9JlTcv4FknK9AzIbmMq3hOso04QmiRxsIK3IwDlJYNqAc1JTY9L+k4cldfKIZOSjCnEd4FX9XPNv9hySbdyPhePxq5TAK9iYPvsToLBWEvCzJW/lBnGVoqSFitZIS6N0nzortomg1C9ZJpkL5krSb5cwdqkOlOPsSCnYULCIdY/J8OHD0trXKxUoAFbfsB7rrwhlc1DaDh6WUsCDomVY1AIpWvJzODcJKEA9K4ONx9x0bEjw9FkZ2L1HnMNDJCGx9gJqsedko3zGYjUAlQXLKyd1afdijQkcMbT7OTl36qQUVFdL9XVrxFVVRZIShQ02NFuwNFXLTYW5Yo2npW/fHmk7dV5KsZwqX7Gasqgk6Yji5jFUhl55CdWIaSnYuA072XUSRvUjBezgxG7WAchgxbI0BjwQevVlGW29SJlXSuXvfJJEmEP69+6WyaOvidtFcnHVdeKZCwzhAyIDCowiR27PyQPQQOmDeg8DBg4dfEEC2DTlzsaydxa2P8VAGUAIUd0GTzl6irFNJSEZBeDofukFufDsM1JfVi7lN7DTfHYNkAP2XjkoqfiBnugLZsYnNUlS9pxM976K3Sk73jOwv8Ja2JkFRKVjeagP67Njkuhtx1Y0V3zLP04ZLZbAxUPYux4E/igmKbwRMRIs6tQbTNMPBjvwJ4/ianZCpof7gAST4svmXjSxClRgcWaRRPdxfJbY3CS0rXyhFpMcfYn+sgcgAvsyrM88JQtQViHxH8Kma7hbxntbUKYLourWIO4a4NFMFJuwhUuQbB7sbkF9KUcyq1GZyaNcBDiDeoliwxoEhkglsXFDBc+F5WpmNfZ7fspDh6Px85JoOyWBiWnuq4JEOgo/PpKwgHOd7YOShW1qbs31YssmgQ3Qob3KAhCRoo/GBlA96j5EwnwCezygQJRZLJmV1B99N0k/QaFFbQ+Rh+L+UIwCnBnvPs1a1Ij4c1EuKaEuAAhTlE8cpbaJ1tNANVjbla3APnU5jCPABso5A+ePyhjWcAUUbw7t1UrZKLSQCvajXAiM0ddNchwUgjZr+LEQ1IQ7SXW7lXseB5zrvMDHt6PMQuKnAZUjXznKLP1YtY5KcUkWVnko4viALIALVIWP1Cz9C7u2gddQHjqDgkIcFbkK2lcd90Pboa2Z1n4oKzFQXWLCaJfhoRb4uePijAHK5BaSAAeqwerNiqpivI+E+ei0+ObdDSShgMR7A87NzAncpDkn6Lxgjtlv/GzOFbQebRoqDmCjnyjo3Y4S166du+S5Z5+lOwZNFdJ77/0wuZuF4s/I5B3mCfS/P//SOU/nOPOAn/9T+qerK4HL6+fyd7zZ7y8/5vJ/v9nxmr9ScE5VYvr6fh6cgwICnAN4PfsSFuPtWGlZJRfIXcE4ZCGBolB9BEaaZsxVoNbGWJ6Ma2U7xJdTAMhaxliTgSs04FzfGRlvPUP3ycEq9CbsJ+fzO8C5rpdgpzLFXov6VeGNjItYxNICrSgjWSLYhY82oogJ/Iq1l9drSFZBNUDXXPoeY40qiKp9InO+xcMAYaf/hlCN6nlBhro6xI0VoipC2VDpVEgjoRAXSl/h7mYsNjMlb9HdwEc3cu30964Xpf/CKcYAQ3KZdzzF1QC4lSYsnOgHGOo+BcAyRWyBemxmHoA1581lPHEy3sWZJyiHse5OWP0ESrWzxVWJ+grxVF9jr7jsJZJbtRKYtprLzQQiUfEM5ooIyrrAv0GU0KyBLhRXsG0srLwEHGJZaxgK6zDmuLF0dHKvQF8GFvAhym2q4zBuS0EAPeAglNu4ecZl7F77GlGDGWE+L5cSFEh9VQ1mnBPoaZXec3sorQkpKK0SZwGKfT7mV4Cl5ABjV18ToBvwGLGHAbDtr1jM/Av4BZSWYg5OjHZItO80DOQIY32N+BduRTEsIsNNx8XLZoccVOxUHS6OGpuNTQU2BZ4ibRIfR/Fz8CzjbQRL3izsvwGeGE/xrOaaHdQzsSnwnMXOz/zdCLMp4uJulNvOoFLjR/ELO1YUQS3Y08cZ88dRBA6gmFaUncF8xdxUguUs8E2sYz/z8n7KP47F7XzcRlEtddEmmBuS04MogB0BDpuSDBRddU/FFJbB7qxKQCosW9ncYYmTtx7BzpYND5HxYVQRiwC45xEGuWUCdWC1gs+prRMXNuIpJ+UGUGhBcU5j2cTEaSDrw9iCt4sLIMtfUAewVoMqKTAZxxjAojZ7DlMh7RRATdXbQr2nmC+OEqXGxVvQACi5hP6jsDjqq/2NiLCMSATQqGrRKvGWr4YNrJI4luZTR3+I7M95YoAacaC4pxahNtRSk2PjtPsLqP2iQgusbc3IouzqiJMoB9TqUkgUGmMnUFQDkEToxVWO+tqc7bRv1H9OP0w8QFsC7rQCzhmeCnqgnf8lsZEfpI9fZK6nDlHUc6Lk6gM4txfR/1yAjwBzkuC+AEgtTuZQJ+17Grth+mCAvmZFRSijpMKM5xSUTwUDEqONhlCWSzE3ZM7aSMy6jaZNTN/OJgXiFauPTQoo+6mynMahKcD/8ADwOcqtLthCKxte1G5XwVwbVsVWH4QYbTcBUDjc0YJ6LnXFZ+ah3mwNT8pUWzviNjbJr1krbuIqix/FX1WYBZS3Rkdpo3p/rxFPNRJH8CyAGqEtmz7l0ViRa056uS9V3WU+tyGaEx+Qic4j9PfzWAYHGWOIf4pR/nMQG4yjONd3QUb7sOp1FwDVrQReraGdTLOJoRX+oV+8WVbxF+Vy6mL6OOdXcK77GGJDU/CRG2Q8G6vWiVyuhWcYPwrEqBmz10haWsPy3N4RGRpNAqWUyLIVlG1mmL8nJMuH4i6bX4aHU/LcPhSdz7RKRbEbZbr5PL8VygsHgUy+/4L2NvngnUtRnMuVogLGamCm/v6U/OTJYb5UcS4Pq9PlsmB+nrzwMgDK7vMyOeXGKa9erlueIyVAcFHUlJ/Zm5SdBxI8XzTJpz9aDnNRKAUoyzWjive1r+03wbk7bl4pd99dDVxIu6beeESUOOsno6NxaTzHWBy34oSFqhxQjYPreP31iOzbz4aksUG5cUOJzJuPXasvTl2LZPmx7UUFOsaGqhNAcD/dNSnlpTmAeZmybJVNzjdG5K+/fBD4p0zu2V4j2z/oBpyjdSes0tSSlJMnJmXjWg+bANrTVq2MXm/7pUHiZS8Vzzrw0kvy7488LIcOHgSixPJ3+3a59957paJSx5NLr8vXNDQm0S9z09pl50r/8+pLQMvvF5XpLwPnDNYGmAak9WKIdZ+IuP02ycp2mnXR05mg7w/JqTMXUJYukhvXo7BL7Od2WxhvLNi9E6HRl1uakoxHw1ghn5MNGyvl1m1AshkeefRhBW7PozhXjOJkPVat2G4z3EYRttj7TEyeeyYkoXCH3LmjQlaszpWBLsaSneNy4NUOqavNlJs2Fcks1C1DcGMXmmLy7DOjcvyCIQ0lXvl/Pp8jNwHUjgwqOBeV7zz4KGNThdz70RWyaIkfe+8kyrad2LQ+KXue3SsN9bMB/+6RtavWXgLnePTXfXZEBfxD1+I0AtGHfV0TuLYHlWsC5zTY/u53HzRtAAcHB3mYWmxaOZQhQavKcuorq4uFKuP4LA9euvimFa32revWrTNBszUsmumxb+elQN79999v0q4NDQ0kYj5vwnlv55xX8940OHc1pfTOH6N1nbZqfefLNX3G//gloJNpDDucz372c/L8/udJFsw2bbE1AXL5JPvrvdNrm5R+vdeY/rR3uwRQ7TftNf/syzFpBfzKwRrm9+62y32fd0gmAVn6dfUlMMruq++QxP/Tf45KFTLp9/2OQ377t1i01PXE36BXlPXSV15Lytf+OS57jycli9zfR261yx//rlMq2XWWblW/QY0hfavvuxLQDT1/+7d/K/v27TOhiysvUJWNvvSlL5my/grSpV/pEng/l8DgsCH/7c+j8jigdowFpD/+kEO+8IcOyc/RHbXv5ytPX9t7VQIzi8QzAJw+h+nv9KX/vvy5bObYmd9f/vPM8eYb3+I/M+e+/BBd3G4F8vrOd75jujSoVeuOHTtk1qxZqCq5SNYNydNPPy3f+ta3zHW6JUuWmE4KGzZsYBOsjx3lF027vj0oxc2ZM8dcFL/hhhtMqE4/Rz8zRSK+CXDu7//mKyiNDMsCztsAbJFPAi27tkYsWPypIkmYrcQpLJEygFlKNm5BxClb+o8fxpISW1YXicZsIDESZNExLF17evh7vpShepa/FKUXPiN+4by0v7hXJkmievwu8RUU42xZICGLU6bISdtJls++bjXCZFmAQH0yfOy4jBw/JTagO1cBSf2CPFPpJBLGgiszR3IXkGydR6IYe7j+Z3fK+KEXccxA/asSS69SLKmqyyWbv2cUFZNrQuGDmDPR0yGx116S7ldfkQkSw9mocHiB2aKWGMmtQYmi1FLC78pXo/zhMFChaJLeXlRXyP75UL9zkU2KDgdkBHDBkZstFatXSy7rpzbUrCJtbdKxd68Y1Jcd9TgkILBzzSTNqckGi2kTWrF4EeIgqGf0YPv60ssSAhAcIhhOlVVwHdniY+11OhQGJMJ+bik2p9WVWIpFZOrFl+QMYF4URb4ifucBYlLgr2AOFm38WzwkQkm+Jinb6RPHpGX3S2LFRjNL7WJReQihqBfl3rMnhiUTpT/v+i0SwYKu/QIw1SjKRKh/ZaJCZQdgDKP8ExvuFxekVjbWqdnbtpPQdksYqHDy4Msy3I29GQlmPwocvkyS0cBnozwkZrLgXbQIezsUDVN9XTL5ykvS3ogVXgzLzTys+jje5s2WCcAmly9XylaslIy6MhSKRmT0yCty9sknJBPAMKcGsKCyDPWXCsmduxSLL9RB3A4+BwVHIbmPRVd09Bz2nY1Ye0bFAfTnwobThrqgLTomtskOsU4MAWQCzi3+KFao82Wq+VUJohCiCVN39QbseZeR+OehQ1/AJtH+PTLZeVhCKKG5sYvzA6zZASagTVDB8UgMhaQUaknerDJxZFAnumgfBvgYeoXraGUNn2Qp0JWdRKw9GQPeCpC0HjbVcnJKZqG0g6qav8qEVhIdL5JEaJMk0KGT+nHzOZa4A0gE0ADrRLUq83BPCZKd8SgaMsB7Cpi5+EgXSmkWkgyRMMlg7Ed9dSgRZXDsSId0dg6h+LMIxbnrABeAxLgWTTcYUQADIIrJdmAWlFX87PB3Ac5FMmokacfeMW7DwdQNBFIEiFhLUpvkug0YDZWW6EijjGG3mDIm6LOZOJtlk+TnrKFRCU70Y+/qA9ZbLtlVS0x4NkUSurcRdT8+L9sdNftWyl8kcZRYEqjqGAB8TgsmjU4S2yhBBVFeCaFEqLa8PmQkneEpPrMfRbtBgEJsfAADLb5KNtcPkEAFnCvKlNwy6s67mD5FklwvJonN2fQ5GevZh7rJBUCTDMnyYtnmAx6kDadSKB8By6SoO1XEcblUuQa4JIwyVv8Jyo4kPONgUsE6rstFm7ID4yaSdvHOB9TBAvK9AOfeag6ZmSv0uz4tawijTjr6XXM6BwGcH330Eelob5fqapQlbr9dVJWsEIBCAd5f+krHRL+0iN7tA2bq+PI4Qz/zcnBOATpdI95y881AFYX8kfFx8pT0n3tJAkOt4kNN0cdcZPWUorBJv04AtYT6OUeIvklfAHBKhKKAUt3A32HmY2AaIPhkMizGeBdwaRd9DTgZcM5duVBCqKMlscl2Agg76z8gqcL1ABaobNLy7IA8FtQ6Q91HpJexJoKqW1aBV3Lyqkh8omgZo78CZtsZSx0ZeUA0zMs+xvUUkO7ESRltV7XPKBbOjIcZPglZQedQi7JNDnAtQ4whOYA1O4Ba1jPXoZ7Zc0CGW09iJ8Y98gzqyMqTJCB1LJmiT3dhK46Klw+g/A11p4RF7w1VMcZFpzGMe3ovY9i4RAA1ctgk4K4qQz0Lq9YLfYxRBZKHOpZCcVoeBiCMBWXVGONXACAv0P6aeOMj2EsCyAGVpexANQA1DOrAunmo45aKIxN1U+YOI4INMqpc4fb9KJC2At/wO1TNLA4W/WKMM9h4jk1hko0KXS6QtK+sBnDOYkJdfU37xZ4YJddKeXmqgcMzGa9R3JpmjIxOYa0L+AiYH0T50wBsc2WiNqzgF4qzVsY3aCEcNaexN60W15wtEiLmGWw7KV5HAigJiKsIcM4FzES7ssYBmQJssuh7QaZRKotFMO11ER9lVdF+cpjlXdyjBdAME10/mxZUNc8NkJbifSNHgPSAvFAXSzoA3n3YAtuZvxJTsIbjEiKXnJ8FKI5amb18A9cMONe2R4LN2PTGgrRD5lXmXFoR1451fDwkAdQGfXke4kTmaSC44f5Rrom5HutuD3OnA9A6gZXvFK5hiSCbAUrLxVM9R0IRl4yx4ULVSHOrUXErnQVEz3WKlzgQ7C0wBjSHam3fQWKffhT9ComT2GiRQSwKHBkm1k0BsnuBtbJyaS9uOzFmCHW0JmKo1+lXKJQB1VmyUGFlQ4U10kO59aHIw7zP7FM29w1wzgU4h+pb4Pi/i4151EoMG0PxLYVCosOwU+9TCC4yr6K0anPbcCBJoSUHbIQVrpPYxYryli3awfVeNGNCV9kqyZy9DXyA3PzZnzD/TaNIuxQokj5IPenGExuxsB1gNd6LVTNgeyjE+YmNPLm0U4VRiW0sUWKUOPMc870CjlbuW4whYLdT2P+iRDk9BuxGTEA7tbiBxGhLSdqbMQ0UCxjoq93AppfbeAgCbAU+DFLvYeBTJ33aAWhoTSSJHbBMDwDPJSYR3cG1jQ01yVBSoqrW6+VzvbRnrt+YxhId+9cQcY6/QOM9gETaw2Qb8QqWf4XY1LuKAfxVSZK+RGfiPagY9mEj3PUazwYA9owVHsB7w1FAu7oEVSTY6GAjTnNkAX1y/1bLhESpvwmUNlOo3DkJpqz0KeR0xYZCsG0C+HM0SIxXSixD/WHVqlB+bBDFwBHiIPqgO8PF/SnzADSKJS8krIxPYhVfdKM0BVbIC0fGgNQVnCOOIsagSHD1m5L+oTBlUIJSkyr4xYDeULZjw1EOzxNORIpGxpPS3DaIGn5S1iyfLVs2VklNjQer1qj84OGXTCD+rjtXyM2bC6QgTzUFsUFFce7JXUOozj0nc+rzZMf2lbJ0aaG0tE/L3uc75bUjxL+pLKksK5TCXBVCsklrt0tOtvP++AX5zEfK2URVQDwAOIfi3Nf/P9b5JkOybfNy2QE4k10I+QZjqq8ALjnnG8dl9zOA1OMpyWHDh8fLeVCi6+gMA9V5JReb2BXLGfcsUVRQB1kzjPCMmcUmGyecakR6+yZlfMIvK5eWydatHqmss0rj2Yh86SvY58ZLAefq5I47sWotidKWHNjbpvj7hKxb5ZMA5fzYYz+So0ePyqZNm0yRJeVFdF404x++p19XUQJvrF3okVpuqlb71K5d8vhPfiIdbLRYunSZfOiee4CcNlHHKGy/cfxMOev7dC1Ev5QR0teVsYn5y/R/3rIErmyz+rN+vRU4p008QZzS15OUvbu7UJPuZV5yAMXpM6pdxseSKL2xgdFmyNx5tVJZnodTwQDjf8zsq9pfWYKR/t4kYwcK25kx2bK1WlauzmQOtcqPHtMx44wsXlgsH7xrLlatGcyzKeZbi+x/LizPPDWFImGnbL+rRtasKyD2MFCyjqCm2SXDxEyFhV4pr2DNwzohg4DAra3Z0jGYLTWFdvmjz3ll02YPkHGKMWRa/vV7D0tNdY7cvHWxzJ6bw+eEZWKsA+5ht7x66BUA4AVYU38Yxbm1AORsWFI+jlEvSQxg8IyoczhP+vzu2vv9NYFzqiTw9a9/nZtrYzBfLp/4xCdkNQtABUzsMx3CvFQq88EHH6SAkIZnMeXkyZOmUtxdd90lv/3bv23uYtLjrvWlu2mfe+45ExBJsCB0ScXg8zSiSx175rzaOXX37czX2+2saXBupmR/vd/T4Nyvt7zTn/YfpwQ0QdLOItfv/M4nCKz7zIUuTVyrasB797r2iem9u+b0J7+TJaAPfyfPpeQvvhKTvSexaOV5atNqm/zT/U6pqTAjmnfy4/7Tn2t4yJAHvpuQv/zXqMyttMnXPo/VLapzv4nOKKNAmD94PCF/96249E2yO6XCIn/0CYd8HCjThxpf+pUugXQJvDcloBunHn74Yfn7v/97U+lIn5kuf6n932/91m/JH/3RH5kQR3oH4uWlk/73+60EWMtnV3dCfv+vo9I5IlKD5ck3v+KSjdeTIHhjgfj9ds3p63lvSmBmwVg/Xcc9VfDRtacZxTldONbXzLqUfp95z8za1MxiqP488zvzTb/Cf/QcarWq4Ny//Mu/yIULF0Q3rOr6m4Jz6hAxs7n1gQceYPG7w1SV++xnP2tas+panibWX8AG87vf/S5qJ34TdNb3q4OEvvQzUsAnjefOyt/8zZeA2vplQVa2NCDxkc3fTHUiVZLSRTkSaJWV1VJOot67cD6J8Ih0voqy2FmSUcBedpSqNHkXR2XFy6ba4oa5KMQsAxQjwco6p4EqwWTjKRk8ewYFkj7TPkST6AnUS+K+TGwZa2TO+rXYngGwoOgV6+mVsVNnZOgclmgkRlk1BWxxAMF4JLusXMqWkmRegDqH10Fy8igqcAekv5vzkihPulGrqquWirWrpLgOkA61GgO1BIMkcrL/IsAfqjxN7SKTyEJQxxjEAr+5USSrwLYNpT3U1JIk/rqxN21tugAkBezFQq0D2MeGgo3Nw0Lx0oWSM3euOIpUmQxFMq5x/OTrMnH6uIwDNAZ4eEqQmEswV1pJvudVVkn9CpKf2ECRpZJka7MEAcvaOjtlcBIFMerLb1PNEEN8wGOla1ejClMLiIUK3elz0n34iAx2tlMX7L1mwdyL6lwDIGVWLYpe1JEF4AdJGUkN9GMle1QmzrdImORsBHgorildp0UqCrIlZxGQU8MCGcVWt+XUCWyxWsWNCouHKrbT1nVrekaGW/KrSBwvWCR2oAJVz0mNjEqEuus/0yij/aiaaLKUwVMt7ILAOCWLl0vJstWo3qBiAngRbQM6pG30oigSo30owGADTEpi25UDHFC7dh22dZQdSkUx1O26nt+HKhzqMbq3HFUsZ4W2tfXUxRJACw+JxTgL5qjxGVO0t2EUlZokONULRDZpWnA7Sax6rFFxRgEtSPQmAEX8i7Cj9c3C/vMcqj+nAfhQHirHLi5zLkk+TcTSCbAii/Zh49UN8DU9jngfNjXYk6aSVnXXBYTwSEQtaIE1s/OrAU5QRSH5bUmOkFBGGQX1nyCqPgl8t6z0dw82hg4UyhKomqk9sA8VJGcFGyBRK0lh4Zrgc6ZRZjOAEROoBXKIJJEnMfgwu5M2wC4iL/dvRBISGpmQADBCir7ucabEC5xiD5L4jwCAA6z4UaxzZbpxrRuQ/sEJks9zKdvFKBWqGhCdjrEqSWI4Mkxf6jmPys1ZLFITqN5kyjSwhyb6rSgxelBk9KHs5kKRya5qQW4mTLV9DKEAN4xCFVZcOk7YAUhsZDLsaoHMpBrHCs2LDaO/hD6GSmJqvF+GUDOQ8Lj4gTMsHMfIwOdg90Ubs2H5lQsA6wC8NAAFQtgkjkXGMXkEmGEc8Cv0AUyQBJZxAYW4a7E19NUwjtGOScbmk9Dzo55lcdOHVEmO+zPiYxKfPAfMigIg9eGxAAVIEW3CS/WS9AcsSaBklOK+vKgn+f3FKBmQcAImNcY7APVaZBS7viDtQEE7PxCkH/s6PcYFJOBAJcfgnvWJVMdjVfDUHEoQa6u7777bzIMoxHyt47yOw7/odeUc8ot+ZjDQ/5vXpucIMgYdPXpMnnrqKXkexc5MlJk2kwi94/ZLSqMu2tFVvdKP31dVTO/mQeacywdc2a7eGpwDwg7QnluOSAS1Jx/92MHYHCPpGkdZLgVYY7Wh3MX84acvKFxiICsy3XsWyIc+zuxjoS3bFGJNMW6YADLHVq5hHp8HKIwiGWpyOo67sKdO5S0CBFKYBCAO1VbLKOBc5+syhAJXUMaB57ExRWFLosDUMWBZ2pWDucSTWwKQiv2nH8DKGiS26AHq7ZJAP0BbCCCKOSVM97bBvflQRLWhAhlOZAGSb0KRaxnzFFaUg8dkijFNn1XtQP4p5k9mQOB5IDTyifnAVb5C4BjihtjElAQAQwhhGJvRHnMCXFtRnWRsnQomJXvOfM5dLdGJAHMstpjYMeYVAUblAbsApVMgDNIhILAhmUR9K9x/WtwAuw7G7xjjRDRFmaG+aVNAH9UqC4BURkG1+IDTVQFOYR+1g0wOnTfHkDgKnFadZ5wghyi2RhgXDZeObdUmTE2aXGLjvTLKXGFBZdWr41cql3sjD0rc5UbNzelWVVqAIqCkadTLggBqnIW5yYlSmgtVOYUO21ABnDQV7uz1tzBXshkA+0w3Y3OOWpjmLSZGAWTSEQTb3mTgAqpjrzDvXGC8pu04c6gDIArG0gRwtRXFOa8Vy1xAIEc+ip4ZQPtYOorCY6NNMoXS6jTjt0LsduIebpJyYZ4IohxHOXpQF7ViORqjTpLdB/k6zKYK2g1QulqjJlSNlvpTK1g7cai3hHE8S4FErOJwBwuMonBMLKZqV16F1mjLEcCrSHAS1UEU5ypmo/qWRfw1yntQ1SsphtOqAOYDHFO1PG4zSpuepv5CKOQluFYHAL0TeC5FbEnUQrxEH7AVMk/US24+arGZgNbcCnKwAJDnZaqvVaamgaqY+2hy4gJ8U7VU7V+JuAtV4EW0UfoFimeJyS4JXXhGXACTFtp6APAxDBDHgbCACaA9OzagGWZ8EyUGHGO+j7Ozy8Vc6KP8XHbaebiPNhoWV9ESyWrYqD1UJlpeIl4ISQYqfapEqNCYwaYVxaqsWI9G+44BSJ6jrUWB++h39IUEsFicDmWPucQV96MIVsZ8WI0CnFqWYmca7wGwb5cg/TA5RVvSicXhof5pZ2wsscXZYJC0oUq5nHhm/c/U9iJ9ZwEfiGeIMtUS3EZ8rhtFIoCYKcs0EI4fUNPHHJ0AggIupJ/QYdkIEEFRkDbHxooAUJ07O89UJ1b4b6qrVyam7FgsrwKgB2JDyc5C7KHqxnHg1Yn+8zLWf4p21w2chQ2tT5WdGQOoc6vOicRn1rx6bNbn8iySzedEiY8HJAIEF2TTQkTjbyBA7Scenu08xM7hILGCHcXqwnnwoACz2I0axHWhcWI8rHpV2dGqcQHW1m4FFBNBNt/QRwqWS8tEuew71AFIFgW4J3bTDRcarzKOFaBUN2dOLRaKWdLTH5CzpxtlsK+f+JExhdhPBT/t3ENDTbGsW4nF4qxMyUTh7pUjEXnyqQOcb4rnuhVy/dpSNphfAtaGxyyy78VB2b//eamrzAWqWyoLFhSiCmXImcZJeZlrudAMaBoF/gdE9gCbJiVH2vpsqBc2ysfuKpfttxVg720j7xjC9htF5qmgbLhugdx8SzUbilDD1H7AyIBYORDbiOzbc1o6OsapP+qBv2jorPdXXFwo8xfMkupKP5uMxuXU6Q42OozSPlEZZBywUA52Bt36+gZZtbKUY4lxUcFqQpnqgQcOA84VyNYP1MjmTXbJy49IOGaXzu64tLYEZNki+jJlnwbnKOy3+dL+TFMzX0me3RRE/PGPfywvojqnLM7WrbcSz+6gHQGRK9yt/Ujfo5M2L/2usYd+6dqC/jzzN/OA9H+uqgQuL1N9w0w5Xw04N9CXlD17OuhjbcxBqhavdaPzIHMF4/useu1fWHKzFvPq4WbpoB8msHVVlbZ4XFcW2CyMUv/8hdmybHWelFda2Wwk8syzbXKGjXFz5xTIlg8skLoGnr+dWDPH7fLKgZi8uI+NYME+uWlLhSxbmSsslQDhGXLo8IS8fhy77ZEAsUISdVjiEjZXjY3WsK6RKfnZKfn8f3HKxhtdALaGvPzihDz82E+B0uMyZ14RoKyPa42hpDeAwuQRabnYKMsWL6Ed3ivr1rIBTIM1gGIda3TZDV1cxmBGpTfapJbftbx+ZXBOF/0+85nPoCSwH6q5Rr7whS9gWXrrm362dhKt2G52GapdxGOPPUbB55uw3ec+97m31XH03B0s9n3qU5/iezs7om6W++67z1yo1F2w+rl8gLlg6eGBzw9E4sW0Vy0qdCHzWl/6uWmr1mstvWt/3/33pxXnrr300u/8z1wCaon26KOPkqj+BynhYU/HxE9/+tPv8S3PhFnv8WWkP/49KQGVx+8dMOQfUAV78HEsWglcZgHL/cOfOGQrDznp169WAhrOdHdTnv87Lv/407gsqrPIP/+JW9bdcO2xzK92Be+vo9kYLKcaU/K1b8Zl14skGVl/u2GpVf78D52yHjgz/UqXQLoE3rsS0JjkS1/6kvzwhz80AQ3zeeyyy1EFoz/90z815f3frvr4ZadN/zNdAu9KCQyxcHM/GwD+7bkEO8hFPrvdLn/+BYcUs4v7ba7DvCvXmz7pe1MCuj6k62S6KBxGBWpgYMBMzuoYp8CZrj/pS/8+A9PpzzPvuXKc/FUWmPW9M+/X981YtX772982AeYZxbn6+noTnFPXhv3798s3vvENc43utttuk9/93d81wTpdI1Ow7tChQ6Zind7Xtm3b5MMf/jAKAZd2lOsxKRKb5wC4vvg3X5Tx4QFZxW7+VWUVUoTahBNlDHJEJMCsKDf4pGL2XMmaXYMbGaAYqiCTbW0k8dqwphwjoZskaUUsizNFRlWl5FRjf4XKmBXIDOktEpmokQAmRQDFJts7UQohOYeatdphOdi066+tkuy6ahS0SFTz6KmqFbE+tQHD3rF/gLwuCXY7yZgclMTKSiWzpooEF4pqLBgnhlBwA0Qb7uwB8gmRjEmKs6xEipcsJPlWBUQDqBPlpCzSJrGqDA71YDPVI9FOkmkk1XVlNkevobJa3NXYRuajbhfFMqr7ogy2qvIWn43Kl4X783I/WSg3Zc6t4zjUsjQx6eSaOUkCFa5w70UZ6+yWicExQbyEhKlaVOWR5CyXgirqLZP3YJGKtJskxgZktLdf+rtIyqI859albZTf/EBrGfOw/wT0snOOZP+IRDtQcWOzc2BiUsJAUQ6SyhWLF4uvAns6VrKt1KOVxKIBsJXoAwRq7ZAJ4MMw0Joul2YXYLtWha1pJUlBrOumUcKZ5O8B6kNtwzRhrUluBTKzSykLFHjsqNqluF4D+MJGUtIYp/46erHSIiE8TBKb39mB56yszeYCSnqrUSzzo8gBEGBgdRUZ6JOJXpQ5BgAJR6dY8HaKFxghu7xGcutrUZpBoYcEnYHSW/giSfuWZmymAiR4OSf2mXkLl5qWvg4sa+0kP20qG0gZaXI0FuwFdGsHDgSU4+HBDcDhYsd9chJFtjGsWgERshZsBcCqkwiqenFsy1wAWw4SsfjCmVaoZAdZlwfVw0I1PtqKytsEbUTbCbltEtgGWU07ZRthl7uBgmMGyjhuXzV9X1VweGAhmZpAwSkaaCdJ3c/PBjBENol41FooSzqzaW9qza2hTEioTqPsAjyXJNGvXjhxILt4PEweHbjBQt1jhepGXc5OstMACEmMY/821UMimCQzwJsTUEDLKoCdcILzZVbWoPDn5/1Tl5T8sPvzZJIMJ7mrCQbNNiRpV9HpgFlOlkArQMw0YAKQAJAHLQaYTbWEgOfcAGX+WbRXVGhQwBGS6VA4ptpMdAo1RtRrLCTQnSRLHVg1psSPzRaJZFScnKhL2rXOQyP0rW6zLTqBB6kl+qKikKhAJdXuEAgnt8i0T4bMgIfokmCkBXUWVG6sfhLSwG7TanXbRc7eLp4q1GayAR9Rn0mEdUMV9r7UgQXbNy5Gh13qnvtBHS84dZb66+V+SOomaIOoW2l6V3NLCcojhT2cO69SfBmAIrYsqoY/ANKqEt40touhcDfHR1B9zKEtYacHEKOWilbgF0056sf9usE5vT+dB3Ru0XlBv88kLfVv2r74v/ldr+8iY+XOnTtJru3BAm5IVq+5Tu66806A69WXNuEyjl9V7oRzpV/vbQlcHgdcfiUJ5vGB/ktWrQrGX1Kc24LiXBH9FVUOrDNDw218R5HMeQkuSmA3HovQ1hnH1GbVjZWzw48KF9aoOgknJppQewOKQ+1J2xFvQxUrIpNDAyhwuVCVXMCcC6DNeZKjPeRqGY8L6ySRgXoqfdYC9OSAALZMYX06eBEwpVPCNuYHN/bBwhgPqGOhT6rVltWBahnKj57c2QCwBdyaqoioEt6kxMeGAWNQh0oA2LgsxBy82wlMhcpaOAY8nQ0UnAmky5htTFy8BPtxwRbAOcXfY/RndW5xMj6o8pMd2JnZ0zx3ZGoIwGqMoT3KWEc/4PcB4pYJVKNyGxZKZkUNY6WB9fY09+NlnMgHjEOlTuM9HUtRdI0zZ0cmUfSaamM+BA4BDAHTM5W4dG6zWQDMAc2s3hLU6AB1FZxjdLVQrmojnmIOUmgnrvA9MLaLGMtKTEO3phyJsTIBCZk/TagXxbXIZAfzI2M/AFMS8DCMXamF+nc7mZfYbGBFNVDtp5PARCGFsgEMmXxMW3mPoOgFICZsALDkEavUo1ZmoOAEtG0DdlPFwaS3EgvcLO5ANVSB0pnDYqjjxSc4F+qECj4bUGOpJJ+fQsWLcnEBFnsBcezZFQD7BSTtCS6IO1KRYYky94aCXDPqhTauywKc7QaGTsUCxAl8Rk4DbaZB4gxadmw27eONlDVxBTB9ijKJo35oSnUBWjkBzu05RSbApNBi0iyPQTh3Mv3MWW7gAIW4o4FhmZoclAxV7C2fi4JeKeUEjMe46AUQdQAPJ7Hv1CFN6zwZolzHuoHagJeYmxSKTgBRpVCbS9iJn1AaBgeiDdWjZNjA+zOYC+gQtEEDKD+hZU0bjQAwQouaNvdOp27K4Oxh4kPmDFsOMRlqrCnmw8TAcRTeKHMvgGUS5V6A+Lhas1IemtN2AXbZ2OiRZI6eHh1FgW0SThMIwaswKnPhKHHfNG0WYDwbWNUCdBZGTVJBe1c2iniZCi8C+rMxgZogjkOdceICfbeTKZatLNRXAlYtzr3F7SlxJDziiqGqi+KcAqw2VJ8NrHsNCVCuAWIOypi4I0X8ASJDH0Bpmfu3JlUJkMBIVWlz+AI0tBC7JCeIMSe6mHuH6Su0SxRkU7TXBMArO1Ow7kQl0MH1BbAMnSI25byqAuu0A0CiwBbCyjjA792UQw5wlQ213TA26UGUA/25C8TlB0Slj5gDEypwifA05d/HVwd9oo+6ogdSh1rBtCDangL79EOu057DBiPUEu3ELQ5QXksM+G9yWILjwPbAQk76tpcNDA59DmJjTSpFXXjKUKMjRldoQ9s142k00AN0OsVcCzpqAq/0PSDC6SDxXUaJTAJrXmwPYJHOM9EEG56IuSw2wM18v9TWlUhVLdBvhk2GBuPS1jws/T1jEkJlUp/J3Fgq5hdnSX11oVSXqlouYzT11Yr94onT56mbKROGqa3KY1OGVrBdpkJOaWyelAuNTaxfZMj82Wz4KQHwZJyaCKSkpXNSGi8O8HnE/lxbXk4eGzA88vpZ5o6+M3LPbeWy7eYi5gy7jI7F5dVXuxjbYjK7rlhmL0C50s+YaSPe5olE1eqG+iMo0w1LV8cYynFsdKGb2oiT84pcUs291dSWiB9lv8FBFNJbRqSrk2csIEmCWABf7q/AD5BVIRVVxNA4Fmld9fcn5eWD7XRlvyycC8wzl00LPp7dEnYZQUFraDAmVeV+7Mjb5Uc/SivOac2/2UvjQn29VWyn8YSOgQbHBgHGn0Kp/nHAuSZU6YtQZf/wh++VW265mXZUypiusbsGlpdeGoPqayYGnfmcmd9fOir936spAbMe3ihPPV5/1q+3AucuVRwqtKg/NjUFUZMbkcGhCaBWxljOlcGaSUFhFrapeVJWzrzDo+eF86NwVRMyNQGEz9pHko1PWTke+mq+1M9GCRjbZ7cvxeYBu5w5PYhdfA9Wypkyf145jBcqnTbWKHi+a23GOvlsnPhrSmbNzZSKOjY+EKKwhwjHgyROCAFpa2dNgtgjK1/7cJ6cO+MHqBNUZm3yuU+6ZP06hwn1Nl0I4zR1gs2VasHOI7DGYcSN08REpwDnmpvOyuJF83FV+KCsXbeaeZc2yNitM3cKcD/JM52GG7qe8XZevxI4p51LFwI3b96MrDuye9vvNBUF1NLhrV5aqbrrdmRkxATtVB1JYbuPfvSjBOaK4l/bS8+rC3u6kHfs2DGpra2R66+/HnlBdoYwqelnasdUi1jtzPPmzTUXBNUmSBcwr1XlIA3OXVt9vd13pcG5t1uC6ff/ZywBHY+ampqwqv5DVD1PMS7fIQolq+3Oe/u6FCy9t9eQ/vT3qgSCxGSPP5WQL389JhdJOmezS+gP7sWi9fd52HSn28avWi8Khp1tTMqX/9+4/PhQUhbPssgDf+5mUfttRoG/6oW8j47HZUNePJiQr/4vHt7Ps8uZXca/dZtd/voLTsnNSrex91FVpS/lN7AEDhw4IH/1V39lwhf6THb5S5/99Nntj//4j3k2m3fNz2OXnzP973QJvFsloBsBnn85KZ++LypdqJ0W+C3y4Ndcsmk9u+gvsVDv1kenz/sfpAR0TUqfx3StTL/U9u755583LVGLi4vN9aeioiITQtB1KVUamnFp0Pfq18xL/63rV5d/zfztzb7PfL4uTOv7VE1FlcjVivXcuXOybt06U+VIwTkF+HQt7iV2jKuDRBsQ29atW+X3fu/3zOvU948DOik4p+/X+1Kw7t57770CnEvJuQvn5Mt/92WOicrtN93EBoYVKLagWqWqCCSUdOHWimWVKwslBxSuDBK3mrhjsQ5QiWTbFKuoUZK6JHU0iW1DRcMGpGTakdn5HYnASwuglC2LveZ7sLZSDkqVQGwki21AVFYfwIsmKbl/Q1d/sQBNAgcqNJcisUM2iutABYOEow1FKosHrRSsHw2SmGojGw+QyFNrNGyErCwm2/JImqEKY2XBFZ8o4CXsRxych0SsDYWF5DhgUAgyDYjGAchkJUlqQfFL79XgGIMkawxIKRUlkaDgHYkzG/ZKdrV/ymDtk0u1aJYLAMqiyQZUKAzsQhPINCSCJGF1tzdBrYU1VhvXoUAUJ+CetTx4wOL4JHYqsQDJRGA/q655ch6FBw3K2kb7Mq2vKAcDJZ4kqjlqrWcAL4mf9pdLYp7NxCmF17SOsDMx1UxQbjF4gEuQIEliX2uhLTv0mrO0zFj1VrUzVMbweKN8UQbR86N6o8lmq0J5nNuSQXmgPJDgelR7wg5BaWPxPYXdWxK4IIkCmCZhVNnMipWtA5U0ZMvM+lO1DnOneJjPBxKLs8qeJGFMVpoksddM+ttRQTE0Oaltg3ZsjKM0RntN6M54LSM9Lq9Q1O5W781Bgt1C+6TC+aLgE8N8YR+cAPpLAF+kADTpL4Hu8ySge0goF2H9dROgVS31QPlGgyQDuGfU5JIk9ZEoYjmez+Ge8KblvWPcD4ADiVhDATUzF8XCPvef0rKinm02BTGATUiAc+H8n2tRQCGB4goKbVqAFoM2DDRGVpcv+oh+Fklqs42QuDUAKAz6DUQfkAMAF8lesy2Yt4WyijWXa+PceixWdGIACtgV5lCoII64Tx+AaxAlNBQBsaZzUj4GyYUkfdUCPGjwZQF41WSyFpUBkGAoUKMWf3ESzUmul/o0VEVP8xIkhzXZbAWgEFspv0cxEOU7C2WgoIu2aYUGUqrIhuKdwgc0ZH7v4LyUiwPwgrqyqPJQgjoEfIOF4Wfty7wF9STNwaGjyC85FogNioayIVEf7+XauylHQELAOpnGzo0NG9MjQwCAfhSE5ogtF6u9OKBigrbF+9VOlULSrsg3xlctv+Qw99nHhwCz0BYMEj5kfvmb1g+/1usFhGMAI+HLd5T2LMASCjoaqP8gBcUlAz5aSDQngfKSjH1AMFYfn8f1muVEYf46wTnzwvXa+XAdu2fmk8vnGz1G71CFBoL09927d5sqIqpOWl1TY0JzGzdulDJA40vvv9SedW54y9cv+fNbvjf9x7ddAhpFmPXF9yvrKk5dKzj3/YceAj54A5zbAjhHXEInZcygLQPs6L8tPKNZgHt0ojUYj1SVykL7t9Bnoaf5nY5VjEVhQLtkL/2QeVyBYBSYUqhOjfQACgG0ZhcvRJ0KhTH6kY5rhsYnAHhxFGNB1AGYFJzjYmP0P9RAkZelvzF+2Um+xunXhoLGvBeQVvu+Va0u3di3WuhndGSdW3SsNLAfNRhnUtqnmUOswMJWYTyJA+HFUQh1z6L/A/aiUmagrGVEx02FPAuxmCb6DT0H12EFlLfYtA9znWoJiWWokRqln4xzPsZSxrs4APo4iixxri2nboV4i6s4HtUzxg8duNTS1WJKjTHQaC+jPFN675xL7aEhhhgvda5gTuTPFuY2HfsNnSMAxsxyBloiWGJu0jmY8ohNmEC0oRsSzOCBU+tAxvUQtFAm1BcQ1qX6Iv6I8jmM7apOZxjAZRBQ2jXN+QU4ycKX+X5gqVSUeQXQ2VQaVdBbQcH2s6j1oQZTNFtsDTczVqM8hcKXhXnK6ibBbs1mdCYu4JykpilXyio+yBQ4SFth3Af+MTQoiHNfKcoUYJyMLPeidZGFEiobEsxYTWeUONAymyGYC4V5VGFKgiSzrPHJNO/D8JTL/8/eewDKdZX3vt+efub0qlMknaIu2ZYty022sY0bzRUbHgnFgXsvjxsCL+QCNyYh5IZQA2mEFxKKIcTEtOBusHHBVS7qktV1ik7vdc7U/X7/NR5H+BljuQp7tj0658zM3nutb9W9vt/6f8lYKwAfpyc6CZN5iHtgb8ZBJibUO41VSgz3kA0FSKvOyHbY2yf0JwMM3wFMYp6guWRiGCXjqQkrJ+x5+eJVgF1Aa0BNAdV1zb2YSwgQZ8QgjYyDpM1PYifqGpnlXgDJqvOAc26XAn9bro4XAEmohTQwxsnogN7cMJ+PFHNYpYG2JQDOzUFR6PMJmetFqdvAA77GOUBOfwq1OUJ9etjLx94+Y7vqt0eDCWrB04GTXIeNKDng1hw/iadKm2JOO9lnI9173Uav8pYT2PhwKvMn1BsT1A3NcZlDeEBpFCx55m+VlY+tUFfLAuPrO6qXOcZmn00jOcbzIGXnoYoWIKR5AEBR4Uoz5D9HuwxSFkHm0lKh5IKqEcx/aEMqA81tM4yZ0RYKD5jN2YT8Md917Yv5sub7HhsWfMotxzyKWMHMK8gLBY4gJXnjOkB62nDg8YbyO9ENZJvIorbYYpVLllP0nJsCFmccDoQF8VD31D+w+caVn8ZC0pLzqWu0aTo76obmL+Sd5FHMvJTnBqZpbKyhXjHr5yOAWs1doL6yqN9Kudmj7kpdkwuSP9IIHMqODfLAT+Z9Hp/5bPhhIk16dA7lRNvyqNc+EGqO+Y/P30nsPjsfAhplZo+SrsKmqz+K8HxSURl1gIpA/QyKkfPMcRI8c+T4Xo7refhSoqUoDwIYwkqyOYTk8OLRxEbHMRqAYTWb+6pKmZdTPuo35oGZJ+cEvSRR8wsCQRLil/5S2WB/j/tscjZtSW1mUltgPrpxU4bwrzOAwfvs3Ve02cXnNfAcyGyONjc+Th9DestQ7y5B1S4D0Ki+Tf06DxcIgIMncuF5XknUjjVlUr2PlaHWR/WLQfRhSU3jgZPJH+p9Uk7U84FU6WLAgJXVgMIlpJG8adN8Auiwt49xhnlrQ03Q6uvYFBCkjnGdFHPBJCAqgoy2f/+eIjhHlf5Nh+rkkeCc5gt6T0dh7uD+5i212SQqlH19fXb99dfb7bff7pSTzzhjg73jnVfbKevXu7Dn+cb97HfUtQrXffZvFN99Lgs80376W6/nBOe4oGzO8gBtkGUXtTGUV6Uip7E0EKId0gYrKkBd4/kxdGZS/QPtlXWZNGsXATqWWEnASisjrs0GgailiplKRmx8lOeHaVS/6WNqCe3MEgsXpT9nPjM7Tb/Geil3Aqjl+oCv6voTautzvoP55lEMdaFU6YPGxgL2sxuHgH6ztrK92v77u+OEcc6PobMzPkqijJ2oyAXDdAJcSN32JGqrd/78Nrvn7jts+ZJ2u/yKy23DGYx1UX1H4wi9k886B32R6jDd9os6jgqc0yKbCueyyy7D4BkXbvXaa6993o1AjXPTpk0OaGtqajIBbAXy9IXkQtfTwt4HPvABe/jhhx0gV1XFZEyDjqtM+auqwuhhMcLq9sKFC124iksuucTa2trcwuXRNmI9hBYV515Iib24c4rg3IuzX/Hs16YFRtllJNWAj370o+xQK+XnR+wP/uAPnHPm1c2xhsfi8Xq0AEOk7dyTs//nUym7B9grxgL4hWcG7Z/+ImKLml/krOX1aFDyzCY0e/CxjH3my2m7H0jspDUB+8afR+0UVNZez8cIk/If3ZSxr307Y4cGc3Y+9exL14Zt1RKtghSPogWKFni1LCBY7qtf/ap97WtfIwwDjpVnHB0dHfapT32KHWJvt6Lq3DOMU/zzmLPAGDs2P/SHKftPxmHWnOyat4bsc9dGrLFOzo9jLrnFBL3CFigsYuq2BbW3f/u3f3Nqb4p20NLS4tagtPal3xV9QZs4BdEVIiEcuR5V+L3w87dl58j7a51KoIbAuW9/+9vs7H3SpDinUKtLlix5etPq1q1b7Z/+6Z9QDXjErY198IMftOOPP96BzFLLu/fee+26665z/bNCC2rtrAIlDt0PNr66AABAAElEQVRL63e6z5N7dtkXv/Il1waufNslds6Gs60GVTIfp4tCnHpyXCnEIyG+fECpJI4YqY1Fca6F8IJImQtvC04WXiyeelGt0pNbkTOClXCH5Fgsde8JbGEXiftTHmcuDW/HV7mPpnyCkdQY5czkM19wi+6hnSdyBAqqAhrEN8YCLGoTwFQBNWbisAkS4ub5C+JQmnfSOUBdOK0UJlKsTJoFZFwz+Ejx/gCVOWegPpADWyAXYZPESfEhacRZxmK1+Tjms3qTz6VuwWqvJ0erHHFKNAo8Wk7OomAiZ6xco/yS9xAJokJ1xaVLIKKUrgAPPRxTeB15CbgjP3LYK4/k3cPGDoZjQVxgk3PaEVLTQ43BwYzado2D3eNzAkzhANfCOA4ymf2phXh+kH5dk/OwoSAj2dl31+c8nH0eIJZU5niX/CltyoscgyxUkwb9zOmifK6wo8qXs4eDk3hbBfpUllRuvmyrvGJL3VPL7c45LVu4siRvKlvVCRxqaW6nMLJhyi9E+DMfR58+81jvRZKEF05d8iqnnpyveDy5H2fgLE2jNhfGSR/SznjleZ4XjvTxfkKO4eSOL+hANe90IMs20knoLGcDksF9s6rPVKAQUIPO9VBGwcPN9XEYA42ockplD76Bg/QAUTnHfhaHKgCDq+f6SDYEbPBxMvjURY/zPDnjATuyxMDJodJmETyLFa04SFEzSRL+dPog/vcplIVQXpPqk+oy6cmpHuHkRXeIcgG8HN+PdwKlmhjXC5Mm8pTD6TDbN0JoMxyci060ijYUmFAecsZVvSStCseTTzjlp/KRveX4Jk2WG+Yz1VHeUgNyZYHN5Tw2HP05nPsCamhjKm3lTzCKapmgTLUHOb3lBZdSW5Z6EFK+gEJd3QL2S6HmIiAtjAM8UKb3cXzrQwEXKBZREKRlEl90H+buJ/s4aHCYyqGdGSHk4Og0TZkQj80dCAMu4xotnAtIShs0HMZ4bKm71B3qG0XJS1jENPUNWAhowFM9QZ3AtVe1aeya7y+ihEWToxuQBVgkKLiQz1UvzCh7lF3kpPHo73zU7VwZA85JTdK1Je75aoBzJO5px6jajl5HjhMq3Rng4l27dhG14of28CMPu7HozW96sxM4EGStsUu5LJyvaz7noYsWj5fUArL/0RwqY9d89c8RxzMV504jdPpFF10MONdARRFQ1okiGuAM7TVYJkhZEBkKTNR9p36m/kVAGHCCDxAuaDg3tYfP+uijNKbRn6GulBgdQ8krAR+12MoIrxlCwUkqmTmNTXxbwFSO8cLjumF+SvFU/acPXOT5UB9qT6b+VP0baRBEpgbL/z5hLqV0lsUxG2Q8xIfLm+QXlTM6PTdtUNM1wVvzB1FN62S+AaBXfhyURjvf4/qofEoBLkgfE5R8CddUP0GsVC5FP6G5B/bIzh6my+vltuM4a7kebd2fI/TzAEplw6h4Vq2xiiXnwrotYMwl3CPpUWgz3Z6Luv6UmRL5I5FKJ/0hF3Bp8IL07yHGDqmi0OdAbdFX1/I3QBlqTfl+WM5e5ib0TTnAY10Tsor0MEaSD38e5VmU/HIAVSHW/0MuTB5jOvBSdmKAYRUnM2E+kZ/lcpq3oeBGHy9TBuiLc/TDuZle+jIgQlTFXJ9NXzpP+c0NjVpUoXEXrbLg4tPo0mrc/QXfqNwEMzq4infVQXga3HNAbln68KD6RI2tvCcQWfAjUHa+L+ZvxuksdSGDrTR10fgf0VjOOKNQ7fnJCOMo8Hd2oofcYrPSNhTnVvItIKCZ3YQV3UsxUR/LWxknG6gnAiCppwIL3XxLacJGaal+UYbUrxDAWUBAN5saMmMonI4R3hEYrKplmZW0dHCtRk6iruFoVx1Q/tycR2O1K0CVH+kjBKZgMAYKvsO4TVMg5irpxi4AbuZXcx5gGikSeA7NxfU0P+F7zNOkPqZQsdB/jM0Cxgn/CRDvNmqUkxfBqQB62dF9KPmNsWkBCK90Ed8nXZpTsWGAWTEzJGD4GZQQmSuEgeZUVzT3JP46ynjdNopyXKQMddlFJ7swxV64gbQzjjF3cXCi8kgq3cE8wlMdoy64sgPYdPVV8zDNGWmzghLZdcH5QG6a/1APsowpfIM2yseq58wbHYjmrsccODWEqXqYfjFvLu0gC0vyNiQErZ/oxU6khc0BXpT5jIBVgFpBiFnK3EPhmRjM0BHMPaRqGNOcCeAUReLs+CjzNepvtIr8rXShdj1C0Gu+jUQsdmeupY4gTPkIuFP5qZ9QvtTHAFS6Pk8OC7WnEOfRtrgJL/oqQquq7QrU8IGHQ5wbdOM9ZUo5ayruqR4AnmYovxz2CKLCGSqr5p7qMzWP5Tvc0t2XuZ2DlAL0MbIjc5Ec8/MMcyrVfo/2VOg5NM1RqTj2ln4sS7hsZDTdnCP/AWbmc+1LUmOGQ3Pwse6XY67f05tDdU79aMJa2yusZUEMNWi+iw3YjwPwy51IH70L/YjS6APKAOAMZK2rD61fMhcHolGRj09m7e77R2zHvllrqc/Yey5vsQ3rylA8lAHycyCqgCt/MkCbpl4KpOS9IPfxea4TmMmf/O6qhx4TKGPeoS7LPNqspF/0mKEiUrtxH/AZBe6uLXvr0GPJ2EgOoRCARJ4bl7XFrHUxSG6ceq9xhUYmKDFHu9u7Z3cRnMub7Tf+6+YKfPr/mxvK7rxUZ2V6Pc9NskFJYVp/8IMfOJ6nko03V155pdt0t3gxKp88jziuRxWieLzkFlBZqUwKR2Eu/9vAORWgQFsaCO2L1iUYWX2FLsRnbpmB52+n4Mp8TAqabr2Ez/Q99QP6njbdaazTs3maNZDZyYj1dKVtGLXI2uqwdbRGCR/ODIFQrQL09Ryl/kjNW32DOgV1t2MjWdu3PwHAl3PQXpTNc+NTPqBryu64ax+KrFG76OxFduUlJbakjZP5382t1G/opY7OHQHCMc/YLTf+zG65+WfWTrSCSy+93M4E5gxqHUlzEA6Bcz7PrDLdEeZznx3tP0cFzumhT7tSr3r7VW6R5ZprrnFqAUdz02cW+tGc+8zv6loTExMuBOz27dvdQp8AOS1A6iVFOTV4AX+CSzo7O50aXR1hFE4//XR797vfbcuXLcsTss+8+HP8XQTnnsM4L+NHRXDuZTRu8dK/kxZQ/7Zlyxb75je/6ZwjUgz48Ic/bOeff/4xkB+NlMXj9WYBzc2GUZj71+9n7HP/ghoCfx/XEbCvfjJi56DOogej4nH0FkAwwn5+X8Y+9YWU7SME7uknBe0f/yxiJ67WbPT1e+hR4EB3Hp7buTNrp50ctPeibFiph/ri8bq0gJR2CsrTCh0mx88LVZh+XRrwJcy0gI1PfOITduedd7pnsSMvrQWW9773ve7zFStW5BdcjvxC8feiBY4hC2is+cb30/b5r6WsB98CIkx2w1djduE5RdW5Y6iYXtWkaF1Kh9aJtDNbKj5an9Ia1NQUwAt9XlVVlTUQrlMbOdvb290m0npCfQoe1nil7+h15ALp88lUYRFVa166lzZ4at1Li9xauzv55JPtwgsvtLa2NgftCYAT0PxjQq789Kc/dUCcNsZu2LDByggRun//frcpS8qhxx13nFObk2qdxtNCRAelS338F/7mi1rbtCsuuZx5/jlWU4UDGDUCvJw4SFisxCnrAfBk8QTN4yBSIKcwSg8xHFEBQTiC5rQWyg8Bdu4PrdDyyuF8wdXpFl/drE4LuZiZdV5+8o88U3Js6k23KppXbXDOZS3ekrIMq79ygio8V5DvyJ+mcFAezsYADn5jBzYSHPiscbxJGQcnaAJvlKAyku+ETZRBnUeKuC8gkLZwAwfmHdx8ICeQuCOcmlK3cI5kOaoBdjwtVuu+XMsZiu+6v5R+PnMOCnmQWdB2i9iCkZzDju/LY8SFHWAoB5SYJUGIATljdX2cbixOu4tzPVdvBM3JLjj/MwACafIXRpklSFgz5wR1mWL5m3zkZDvZ2hlftpIzS9bjHa7LlZyj66mkknvtEBcQBbD21P0EXMlh5RKv5LpMcgHWKORzffpQXvlDi/GypbLGr7woH5xt0nELsNguH7MSoGQpNT7tSTCdTnbn81kKG86SjhAPR7Ex1L4EjlFvPeBFgXNZyk7+RoQCsQ/Xo755qBjNozY31rcZ2I7wq1JBI41SPMxhozlgyFhVjZUBC4RqVnKtFtKFQ56EuvQqLJkcndg94JzHygzO2zQgmBSPUDaUgonC8BFnDu4LtcAQSjGEGTUcuQE5ZZVp8oT7kvdwplJWOQrdA4QMyqGL8tJM727ghV5CmdZarOVkhNmaYPMITdX3OCH4xixWR9jeBatR9asin6ji4OyQseSstPlBm+7eiGN5Pyodsit5w2ntCy4kn2TOYq1nWKh+jasPOkV1USoqOqTiSAbyBSPDAQr4hHPNcN0M6ily/EuFUQp8wTKcESXUv6BgD0KqAX3kAMvANdy1VGd0/TC/BNhwL4UX4jHazOB21AimUDCot9L6JvIBdEIo5sGuTgdAVtfWW6yhBeADlSMBcw72EBQBAAgQOD24D0jusJWghhOQWo0gEvq8NO0xTEi/+KLjKT9gAynzkR7Vbadwh52T9AG0HKoXmAW2CQkWQCXJQ/0mhyJKFiJTDp8QAGAgQp1CyTInlUX+FpgRdFCN8BE+J3fo3PG+6h9KQFKCQU0xizKHp3CNZQArfEfd0isNzhXGIrLK/SlhyuDX3uP9vCrpQbvt1ttc+K1Z1C5POmmdveMdVxO14iQ3JrhnJ2Xg+R5H8dXne8nX8/fUUxzNoe+rnFVk+d72v87OAPk8Haq1r99+HZxDAWriSRvv2WPTKGzGGmqtasEi+jCUOwWVqXem7woBjXiA0j4KQplJwhAObAZw6gISZZEKhSipcyaQFJG6VUXT8UR0XU8TXEw/V0K4R2AplyEB6Kgq0RZd/8szu8AYB88R8jqXQH1OABX39OkzpR4WpG8PCL4DrAFtpq8R1M436FZclROgJHiVN8XA+yjEeRNbLDW09SlVqlMQTVtD2qd4b5dNjPShdFpt5U2tAEtAO7aA89V/aYTnVwDd2aEdCJ7tpl2PoNgEYAvMEwAMzE2j8GVVwEhnWmTxBZwmBT36Auwe1hyDsUrgTJY0CjtQOh2gIvXSOfrQBKCNN8D4BNwWSSLuQRQsQU3RemwoW4tyoR9W/oHss6hvZbg3FuN6qM+SxghKmZnxgzY62IfKUoAQ8E1WUd9I2YMcjhHCsnu/lQCnlDchUoLCaCZcw4gT51ph7M6/qRFLDOxBpZMQ3CiyBXAyaz6QQTVsDsUpjzGrqoGQ9guXm1/VynROIBhzGfp+AYuubmlM5n9nMP2Cup8/38/8l/FQCnb0iUFAoWCQc6WmBhjoK9Q1tKPgGmkCgiSRJhSJGb9ijPuCKD3SkiPUaIbwvYnenUDu0xapW2mBxlM5J2SpgY2WHtpkEZSNNT76pUsYBxl/sE+ItAUEb6Hw5zE2Z+c6bbh/L1zlNPYALaeO2iyqeMBmGUJRBgnfW9oMHAj8KEVZKhp5ZDwjnyQsf2gu4ioZbxAqzk8Pc13GfdTHJOWjDRkKPxpARc8TpIitNW/wHKRF3WaO5eZ/1AmBRB5ztoCD5alLM4dteuAg49mg1SwinHtjG2mogu8ctdlDWyxEGLtYy3GU4SramOanMjfjOmNhhhCiM+N9hHTtsTDfD/GOB+SlNphiHpEByq9saraSxhMZj1aRl3rOo34JetMEC1uREPcrGeHCjIXKH+p8BWW9LBsgPaDQIDC71NQURlbtMb8hgLGVOq5y1KxXVUBMQ4B/pMYWYGzNAjhODz5hM4SJr6w/HnW4UzgX6GnicZiznZZKV1pJPXOcmhPIN+I3zK2YNnMfZhFAfKn+7Zbp30nyAN2A4LT5wSd/PsB3ji9GFiwFejweXnExNwc4pA0bMKSvjSIkyeMcqepqTu4mntrQAjiZBUj0UVDUxhoZNYBtg1L3RVlWwL6b8PFJjjbk015Up6S+rHlWDqU/7RUJSg1weo9NDh5CjQu2E+ixlD7TI8xymk042sgQ4HlH839BpL5AS/rPNC8x+lJaFBSqeQSBerEbcyluwVeVdG7FOR5wnnrLDMq8zEs0yZN91Mdpz5GgFAmDgjLzuRJs9sjGtN3+i02uDZ1z7lo7dV0jz3Tcg3bHtj/OYz7GvcJMal2/xPljo0nbtGXK7nlokpCtqEtVCIzM4M+Zts5eVHwJU//GDYRpPa/Mli6UyjY3pj67DkfPGnqW0O21cYKPVCPyetPYS3N6PlP9yNJnOBhVU1bedF0InZbzDamdcF6ADzTDcuCqOjT+cv9yO40fe/cm7brvbAOcq7Lz37DQzjo9ZtU1GI3Pc27g457MU/cUwTlZ/Xkfmjc4uJMzCmsQeXCOguL5oLu7x2655Rb72c9+ZsOoO69cudLe//7322mnnQZIKVCV82Bv3Ear533X4hefrwXy8zrXktwp+luv5wbn+D6diuBf9T3q69SuclprUDujzeSbsZ6KmD/Rn/tsNFD/5z5T0es++sl/ursURlOoQo4PRuyeX46wxtUNNFdjbzynmdDSUdQyGQ0E4qkH4F6aFykUtNp9htv0dCbstp/vs8N9k8CWMaJ2lNnYhEcI2aQNjxHWdXmLXf7WZjt9XcSqKjWyUC/p9LTZT30xaK+7tvIyOjIKOHej3XrLTQC0rXbZpVcSXeENiLyTUm1Kc5ZSf/7UeOD+fuH/HBU4px20AwP9juabnZ11i2hSC9Bu2VfjUEFqgVBhCgXQ5VXlIk5FTiEwCuCcHGhaHNy2batt3PioW+RLMsK95z3vsXe9610uLEVBvvz55OOVAufUWSmPeuBVB/Zsx8aNGymPS/k8aH/913/tOrBn+95r4b0iOPdaKMViHl5KC8ywyCWHx1e+8hUGzl7Con3aKQpI0eDVPzS8Fo/XmwWI2mP3Ekr0019O2ROHctbI7oM/fm/IPvhepIB5cCseL8wCiCHYT3+esY9/PmUzSBxffHrQPvunUVu9pGhTWXRWss/It1cAzJUUQwG/sEr2GjhLsIBUdDZv3ozs95gLQXPKKae4cKClhAIrHq+sBQRYfP3rX3fhALu6utwzzZEpkPrRZz/7WadkVCyfIy1T/P1YtEDvcM4++b9T9qOHsxKpsqsvREn3r6JWxzxH67XF4/VrAbe4yJqNDq3baH1K8JwWNQWw7d27161FKVKCPhMkpxCu2qktBTr9FFAnsE5Qm9bWtI6l6xau6X556p8j3y/cWz91L8Fs6m/7+/tduFWFjVVfu2bNGjcmrlu3zinLaZ1J46XgOqVP0SDWrl3rFMu7u7vd+pq+c8EFF7hd5bqG1svUr+vQ2pTUir7whc+zCBuwyy+7wt7wBsA5lPQEeskJ5OAngBmPXchISeG2EVjD+zg2wnhNwIvyDkp5XeQUiuL8kwdI+WbxNssrw0WkbKFVW62G6aWvyGEkJ13e+8OaGe+z6utUCXRlyCAWWuW+0fv8yVcldCCoLCcnIUoRcwe62O0zRXjOGot0LMH3JgcvMBPfE/vnLq+08buW4hyQpvPlCAPUkaqC3Dwun4LncE1JnSTvqENHBdUc3ZuvuetpAVpclcABlZcWsIMsKjt1BhKq67vQlQpDqgzxkoKCgxWUEaWJkLE5ORC1CC4nu9LHtaTshtsin1BgBB8YKdm1j7nQtFUsZKMwTnSP3d14p1mE5v4Kw0UGcXFyHaUIp95TznC5ugTkCYFS+pRumTSDM1g701l21GVIjIwCwMfPrL5EOemrTjUOWEqhYORMcYe+r2zpJ6eJz3I1CbvlgOCC5D3kvILY9Kny1k55OTdlT/ce6XPlx7kzQAyz3b2WOdRvVdWNFm9rw4GK8xtwTqpici4KnAs4sJDzgDfngSD6e59AGOcAYmxjKLUAOqBShscUh2cZoMFCnNcdONEFMRDaDCez8qUkB7G5Rxg0D4UfKa4EyEg6sc8SEwdQ7BB4RR2USh+WVCS1aLwCXzxgGMpnXoQwpjjl8yVEoQKGODUaYFJfqoMK34ZyXS7Za2MHHrGZoX1EZG20yqUXWKy2w+aGdxAV8Zc2jQO6qnmtVS5CEa8URy9hU51ih+yJHbOEWxzrfNiSI4BzODiypEeKiyHsVgbgESP0XmjBWsuULHKqKpxGeskd7UY/8yFoqQ8COVGA8gEsUii8TU11029N0Ya4lgBOvhkl9HK8tgpFqSaEbJpxmtZxMTlddM38lVWF1YYC1HNPinPznTZ+4Fc2MTpMiLxFVt26jLZHSLTJATu4a6cL59XU0mpVvO/Hm3DyCjiUk5eanZFK0qBNAM5NDB/G/glAwxnKmFBq3CTsQMM1QBaUX1QQRN5R5JwmnJ+lfqlNy2ktbZIQ5efN45wnBFmW0HYZwMkUKiZSSRB/GScMdAQI2KsESAGIwOAqWcAQ1UF6LjoEtXfVXdXPIDbL8AwyNzkOCNCE4lYTN1L/8OqAc+pf1Gb0Uj+ufltjivpt/T04OGAPP/jw0yFal3R0OJ/ChQrfyVjk2i3nKmxavj0qJ7/leB5f+S1XKH58hAXUfo7m0Pfz5a42+OuF8ZvBOfo6hSUd3Wm9u56wYSCc8rYma1q8kpB6i6jZqJUJFKU+hQQNq2sAjktN9NpU3zZU6jq5E4BHECiFNqZ2Ul6/1MrqgY9Ll/I3IJGAEdqUhjC1GrW9oOqV0is1SsJdZ6cGLTEOUEaIbqlVygGvcQXqm3aIOm81dTJeZyn6mRyqTXnFOTmA1eeSKCl4ckE5ar0M/fHgYzbd9bBNEyK8bukZFl+4jj52jDHjCRvo7qK/qLf6thOspGYJEwPaKunO0Lerf/AIszg9sJ30PGnpuSHClE5hAeZt9JMx4JBY9WIE4k42r/ZEyxHWlp6DXAkGRLiDTsaNhwz0Ko8g5+SIk5adHrH0YL95M8N8f9Tmg6izQYvFAHyjsTqg7TbgQPXpAFIo/Qmey5GnDLbNARbSKwGOlAHnBS2aGQIgY5534Elg8Iw1tLVbQ2s7xgzaJD7PoT3brZLwiXUd9PeEyk2H60lhGddCLRV7RYChpwC05/r3W2x+FFXWKfjxFIrAjGFAjuXkr7JxKSByC2p6UviT4hV9O3mTQpUARTcncAbnPTYhZMYOWW6M8XCeMYPQ1ZpfRLBBiFDlISDocEUHfX0H12euQX8txTmQfl6MwfwXY+wXZO0DVqUIpZkaP2CT+x61OKFDSxnzIq1nk46wTR26y6a6H7J4TT2hcs+hf17FtUgjsJJw5pDqldT9qAOZmS7AOcbnqTEAQVQCBQcQ9jYERBSpEKgGNCfoKloGDAUoDagdYLODL+pehwrQ/Uq6qBPp+V5LTu5DtXbAtQEBVSH61CD2kOpfmGsFKikHQtu6uQvnCrTXfFSbEhQGNqSxjLHLQ6EvBfw4dGC7jQ8csvbVK6ysDcANwDA5MmjDOx62KMpz1csY6xvWAuXRjmhDUmD2BSqx+WNuatjmeruIOI7CLOMjE29sRHjRCAHO68pdGNMgio9+cCFthDCh1CFfF3EHjUVtR2lU/VUbJGyvoHTIdqIvpxCr5ZpMGUOMhUHmMwFgxWglirdhlNVQftR4mOZ6KkuXS2yvpij1YCnoZsa32kTXnQCeh61u8XrK6zzmhLSF4YdtsvMJm06UWd2isyzeciqTtWrKkOcLJYskhlGDTVDH53u3McyjwivlPl1c+cMG5TV1VtIEUFlF+5VaI/BZXq1Rc24uwuHCxrteh/aTRpF2ZhA1vj7mEahlJpk/aCxnch4ECoxWVFNHG+ljFnIicxnNiQVgsInGk1SSnnso6yzzCj0fBGmD3tg2Gzy0k9CDaUKKEjK2bSUqwU2W0LwF21DaTz0jaAzV7Xg25JWmLvC4g53IE31hgHwHyXSA/ocPKQ8+ZP7meSz+874vVUp9Rp/IJV32UpRdoT8VUivDCVa5/Y55++Z1tzJPGbe3X3WBXXzBYqutBHqm+ebnQOp3VV58W/Nxfo6hvvj45kGAlkNOsS7As5oU8WZRpSytjNkJ604AUmuyNUtDVh6Xohz3IimaR+i5KMe8MKV5ITeROpl6Pj15MSWlvvEF7qGXmpNMyRfcW24uxpfccxy9E5lXgvgmGxYcZMrvGIrc5fPML5s3zdhf/uWtPDsttHdevtYufUucZ2fahKo1DyZO7ZpnjyI4hz1+y6G5gg7N7wq/F05x77nnLY8QwXO2FaGW/7jhBqdEH4lG7LxzzyPC2TW2bHl+47Obd1A/3VyxcJHiz5fMAvl5ndpR/tDfej0vcI7+3TVBeiT1d3rMFzjngG5dh40HWncJCK6jIWmzmA6NDe4+WnChPfOkzt+EXIYcHuyJ2fXfP2gPPLCFzZVNdtWVx9nq43jGLGNE1wZC+i337MzNxCmpR3CKmF1zdvMtm2zX7i78dowRWeDrLOMLwHLLQoC5U9vY1FFpCxt5Gi0o4WrcUofDVfTMpb5Zfdjo8JjdeOOP7fZbbrTW1la74rKrAefOAcjT/TQfVP1mPOC7Ov2/rMdbL+A4KnBOhhM8p1ANO3bssLPPPts+85m/dOBZgUx9AWl4xU5R+h944AH77ne/64jZKI3+05/+tF1++eVWW6sB9/kdrwQ4p4FIC6uJRMKBgFpgVUdUeBVSWgTnCpY4Nn8KaFIYzba2Nuc8PDZTWUzV76QF6M92795t//yNb9h1132XOtZq3+D39cSZdztEX/VMvdjh6VXPQDEBR2kBPQztPZizv/67lP37nVmL85z39nOC9oVPRa25sVgfjtKcv/b1KTap/uDmjH30c0l22Xt21XlBu/Z/Ray9pWjXXzNU8Y/XrQX0fPKLX/zC/uEf/sHBAto0IwDhoosuso985CNOaVpq1MXjlbWANjdJdU7qS3queebxx3/8xy7UvMCR4oLLM61T/PtYs8B3f6qQ6SnrHmMZiIXoW78eYyc2jpOCL+JYS3AxPa+YBdwCI89mhUOKPurztNlUqnMKf9rT08PO7W4Htg0NDTklIo1Lgta08Nfe3v40TCclOgHFWmPT68hD99JR6DMLfyvsqhQ+t23b5pTnBJArDbpOeXm5U4x729veZm9605uc8pw+19qYXkqbjsK99P0TTjgBGO4NtmrVKqdEp891L91Xa1VPAs596Ytf1LKkXXbZ5XY24FxtbQ2eFOam8opw5FAM8ZCGCYSlFEOoIs6VdIDWNN0MlnVQhQ/R+mY2KphLagU4znlPCilZGprCUSrLeYcMP3GgSHXO0y+osrgd1e56LOyy2pvF6eKjxqWQcA7o4mtSfnHgnBaAcaLlhges+1cPW7C73yrbCUu44UwLNDeygMuiLN8neySMFMr0ujZvkgwXIilFYuQQyl+TJVy+JmcwHlfyxKIxi8t4nMm74D1+ZT1aDkHl3S0hy344zOTklxNK6g8Fe0lVQ3nSISenL1BLbi4cU24hOAQ4B7yWkxIdzmySxu8Cn3BjkpCAbM8upgxwwOwD99uh3gFrOvVcq1u73gJASoLmJNWTV+MANNSZwEm4gMk3a40kXdyeC9Oqi7vP+eGmT1pgZxFeDhJy4oyjfPGfHMV6T1CREuXjaPRYiJcjy12GMpdTEBM5O6owcrKZW6vnhs6BKNvzfTnhnlq8dxVFoWl1yE6uYLIIx/RZ7xObbXDTNutYttIaTzud8msGcgImowylGIhoBy8lhhfqI0mc8jMz+3CcdhL+lDBnSZybGWBJILlgGQo0ABpeSQXna6Geukp+VDVJGtF4VS9xJAsqw/YKhZsc3YiC2jZLzk2Sbxb/US4M8DAcTBEuD8n1SEWzxZuAF+qXoaBWjwoM1+U/QV/5EH5yZGBYwVfaIZ/stonOeywxvAtFpAVWvuRtwAsrbW50iyW6fm5ThCirwslc2XIuzl5CnOlcOVT1g/xlUGZJTh5A6aUfASjgyRT1gfRHABjD5fQlOHX9koWWQuUor4hEWZC5IOVASwNmoUylpAhsp1CM2cmDNgeENzl3GH8K4AggRIByTUsGnduWV6GUWb/C/Jq1RDps5ToAhYIrVTdoLFhK1QeTqV6iRDW332Z234lSzqjF65cACqxE9Yd6PEmfCPA7M+9bw6KlVtO60rIlDQ72CGPXqNRWRKsDsGRQ+0ni+E6lUDgCHhBsESGsoBTeAlWUP+HbcgzITkWPeysUmpxBUlRybVT4CHVL+ciM96JqtBXgrZN2QXqBZ7JSCkL5Ko46kcog2ABcUbucelGPqVE2UqbkZMJmznWNfQVzKuxzZpowhxNDlBkAZlULH8jB8+qAc/JXaD1QfbV+L/ytflsbbzdv2my333ab3U8fEUNVT4qkl1x6ifPr6G9OJOW0a4Grau/ub733HMdv+fg5zix+9CwWoGod1aHv58dnis/VvP86/TnBOZSmcqO7bGDXJhsF8Cprb7AFraushBCRWdTVIK7pJ2j7qs+0N1/qaQnmFoTSzM4N0o+jVEVYwWAYtcZIIyEil6PAJfgYBSd18NQfRWRXFVJ/JEUwOWA9DcioeEpRM9m/x6aG+lz6pfjooBdg23lCYPvAaeX1zShULqfTWQGoW8N4o2sJuBZ4oj5ZqkeMKQy0IfKT7d9oswcfALCet5qVGwjniKpVdsDmDz9howDX4XgLfelJQK5LiNBI2w6VocKIU5muL8S8IjPbTR/ejWorfeoscD0fRAGdBbqpnzH60Wyslj6D9kU/JJA6TKjIgDKqIZAfGq4UnjU1O2QJlKkyvQctjN2MjQRZwttmGOvTaUAgoLHqpjUWrUVdTOAOYTl91NoUUl5ha33GHSmqZYHfBIeF6ZtTA7ut/wCwMUBRXVuLVS9uJ/dBm2UjvWCssjjXbFtuobrlRNdtABlDGY+w15pvRLB9ahIYCYgoNDcM+DbJvEZpQs0O5d1oKfMg+l9PsLDCZ2pckMSVxgWpeIVxeGvuxf08xosAQFK6ewshs/cC35PeEjCqoOZwqI4B1SndJXXHW+nCM8iaxlvmRtQJQXP06hwKgg10SLpy5DdJeFyppk7secTKSVtZy4kWWng2ZR4i0u7Pbebwg1Za22Dl7edQFmuYW1VyHcZfACSnOkfl8Jl3+oL4ZoHwZlHVY0wOErJddwzECK1ZRf4YD3OojeWwdY6yk5qfmz+5OQPJcn09FY1dF7nkqM2N76SObsIWUnijnjH/Ahtj+J501w0BzUUaUVCrXm0ZQIAMoJznxnzGH/pg9mTAYVFLpb4K2JRRSNXO7VzzkDUva7fSxccB8S2wFKFkR3c8YJH5SeB5bLYASDMucI50AFTRSLg74808oecnRglpir0Ukh2LKrxoNFYCEFoLf8kmlkA176P6p40kZElTIU7md34BmNCGBSng5QidmkR1cqxvP+M0CJoLWSvFw2nyARhJm4xU1wHudzDWttFIsB02p/ZyvSC2oC0yxnvMexXuVPNWf2yLzXfdauMjXVax+GQrW3o+bR716aFHbKpzs03Pl1rtog1AreQPxb+Cep3mjWgLomxJKNSpHuY040S8zW87CDB/DZRQhwmzHCylHQKFKoRzfi6stPA/adHh5qOUDB0M7bgPdbg9hOjtQU15AsiV60g6jrJ2qrHU6RIg+UjNaQC/QLYh8iBwTvfTpJXyUweWIW0yYTDTZzayzUZoa0pbRe0iK2lfY9nyRTZjhGqnPsPccTpQpUsL5mYelGbuoWcBHVLK1q96CeYP8t2AoE3qnRtuKU/XiWhDiQ5H2/EWJ6SoR2otml1LbTCLyh49g91yW8q+dd1dfDYLzLIBcK7JFtTSd3MJptWcoWcOnaN6qL/QvWNefrgvCd8xZX29YMDMGxWKPkCfsKCp1JaurOG5NGKVCIuFaNc8YXE2/YC+Q5vTBFRzPAeukRt16+oPlTY9l2jOpOclB+0oH0oHL67A+XzHQYJcV4qJ7nsoDWuOzVd9+hkpyenZKUkRPP7YPODcL7jeQnvX5avs0rdGrX4BYwlzHac4x1kCZ/ey9vijH/3IhRdVJC499y4j0qDmMYVnWCXl9XrIBnrpkE3y87u8NQrvFz7r7+tzPMOPsedu7NrBJosrr7jCMUENCxr/61wVWPF4WSzwzDpbKL/nBudIimtr+XUJmptah0uf+kaFb3eK6mqnqgrUA1ocbTD/Lfcnb0kRHrkyXvl+Ncvz2OFDJYBzh+2Bh3fYiWtr7MqrVtvKNWz6jNOOWZ9wN3b3yo8PPHHRX3g2PZmhDo1Yd9e4jYzMEiEIv2YpIdN59l7UVmutixgT6K/iJSRIGxyVCCm/u3FGqVN/oquhlDk8YTfd/CO77ZafWEdbm11+6TvszA0ozjEPVc+m/lX9mw7lzOXH/fXC/jkqcE63UCFJ2eyGG37oIDqFBvzYxz7mdslK5e3IRvfCkvTynqXFQ8Fmf/d3f2f33HOPe0j84Af/x1GFNtTER6oWb3nLW91urXe84x32RRYNX8ojr+434HYCV7NzV7uRtYCpV4SHh4Kdi+DcS2n1l/5aRXDupbdp8Yp5C8gh853vfMeFaJXa3P/8nx+y9xH2rGUhD9zHxKEhqni8Xiyg+dbEpG/fvh6n8v+bZle/bycsDdhXPqEQrXL6FOvDi6kLk2zm/d5/pu0jX0haQ2nA/uBtQfvoH0WsqbZo1xdj1+K5rx0LKLTc5z//eZ5PbnCgQiFnUvD54Ac/6MKYKzxe8XhlLaDnLsGMeukB/5nH6aef7spNYQCLYOMzrVP8+1izwOi0b3/00aT95yNZHHpm735LyL7+WZx6RUXdY62oXtH0HLnQfOTvWq/R3wrVpxCqCnEiQE0hXB9//HE7cOCAe19gW11dnVtPW7BggYPoTjzxRKcSp82dgtm0/qRDvxfWgY7MpO6jDVWPPvqo7du3z91T7+n7egmYUB+rPvfUU09199N7UsaTcpxCugqk0xqUwrVqvJRKXXt7u/tb9yzcW7/nSM9ONtL+zZe+5NSXrmBj7QauXclalZ+kceBMcukNsYhKaB9PoUKB5+Rc8QkfmeM+Up5TCEmpYuF1JsoS50RRgGDHsFM8wtsjFQot5LoGh9KNoSYg9RulwZMaRAzHDRJR+O6cU19OGolT5AidmQG2kWKZrhXivkFJgcmrg5JHprvTdt98m0W7eq1+5XFWeu655rPjOcTG2gDhqTyUbpy3CdvlPT9cFAdahufveeWP+zjVGxZpgyi5Sc3NhSgBvPGJSeIneTJKYidCOErpIatyw3Ma4BXEcetpcVeQma4jR+0c18e5m8tIZYJ0SzELgEvqD3rxBwvY2AmAyccBjI+SPMr5IccSIA/5cnbDZh6wQPrJvZa44+e2v7PHms483+rWn24eimxeKXYoJyxwqa7PNVSWPKP5OBE9PFMeu8BVPj7Pca78UHDzYuQNZ7g8v3JySX1DqmQ+3/dx2HKyq+ceioQKcekk13C0kiDywlWgz3zKzp9AbQQlE2Vb5e1FsAdqJl4pQBnl5w59d457zPLCAebLCap7ombh7OA8gAQq27/Huu7/lQ2ihLCUsEFNZ5C/lkXm1eAsLqMO4jxWeF6t4ctBmFPYuxChNHO9OCe7nfJMMEm+MqiLAM4ZoVV96p2AMi8iYIA0UddylGWQBXjBnEEcexiMNFGPKIAU4QAzM/tZmscBjQM+oDBm2CY3MQzYN0KVDVj5gjYcqmsNuRXCFQokUXC6eZz42BgAxU9RpnKmC0RIofjS8yuAvN2ovKCi13EFYUePt+TYZkv23ExIrUGguXVW0Xw2dZpN34AeDnSLUjcID5sLALRlx0jDeL4+IfMkF4OcirK1wqyhjWJZVISkApUFBpHaXYj6E8I+AQGngtRECypk2xRKbFOo+nnjAA+4ZdV2qP9ZQt/NjAzwO+BazXIrWfIm8ytWYCPqtRSksId7YSMBoik5OiiI8HyPze+5l3CCk4SDXGklbYSMrQScQ3mnZzfKLfQFtS1tKCR2AF9UkdY4jucS1CkBPuXkwbbI/JE+0pYj3Bp2FDPjoZLkxVDGoo/JqdxURzhyfJ8ahm1VZnLm8CK/QQc+cj3UtbLAHsSczNdxwu0hqWf+bD9RcwEJ0qglVS/HsX8SoEUraklACDiLPYX8k+KdypB70ISc4ymHUs/85AgiW0stimqTOiVV/1c0VCt9oyBWjRdSCHV9NfVff6v/VkhWPS/9nL7hvl/dZ1JBlQrpJW+7xE49DSiTsOFSUXLAq/pZGVIZzP+mv579cF989o+K774wC+R74Od/rr6vMd/Vx2eUV4by7+/vt3/7t+9Z/6+FagVwI1xnDsW5kd2bbVLg3KIaq128lLBbjdR3+kKwpgBjkUfbEgWeA5byCAXuA3D584S+BFTz6XsCzC+8EEAvClcEL6TKAGAyjvn0Mz59QUDUnDy0tEGBc/KFBjTWzQ1YdrTL5qekBgbAVEJISAANCCIUHIdtijobLCkH2jkOrmg9sFMz9+MWCnmpgTBDWwSw46K8qTZH2ka22vyhjYQeTFj1irNRJD2Rzwcs1fOojXcR3jJKeNOWtRYEciXuJ30nKkuChQjDLaVUZDC55hB5nAFKAfrl0lKRcuE8I8DV5C/LmOHmLfou8xn1VUGFOlNbkNNWcxLguAzXSI304OkF2AOaCsT5AqbM8ntCqmGAQREgxYqmU+lPUepjLJHyWw7lNo+QlXRUzubZdJR70JfS/2VGCdXauQ93dsZqFi+0soWL6XsDluw7bENdO1DMDFrlYlSCFWpVfb7PmBiUEif505yG8Ts4q3FimG4RyCxC3wo458JyBhSulHLHYR1QuHGgOT/BvIRy86JAk5EEHB3fB5hROMogIeH9gX30m/SjDPmm+Y3Kj3qVmkEFdZTwq+GFVrvkHEKNtzF2afzhckhegfDxPcZPNwwyHkplD1ArN3HIxvc9ZuXZaYs3nwQUfy7GDNt81y8scfhXgMnVLux4QCAl4JTUYZHsY6wmzQDcPn2zc7ynCO05T/pSlJGU6CgcDzDYi2MPlEmzAUGK5FUhVJUOxgkXZpVxhYvmq5TmQACemZkDgNFA7QD4EcLMeYCUUHlEOCVk7sSIzWUrLVq/1so6NhAWXaB8ft7GxV39DBK+VNCcgEKNIdmpIZvo3m0TQz3WvJywowvXYtJGxv8Bm9hxr0Wp/2XLEESoB/oMUXbA+znqlQsPS8hQlz7KxWc8Vn7p+bGpFNQA+qLABiiV+QphGpZqJF+fZxML8wzHOmmMVohjxnqFVvUzI4Dfh2iDQ+yriDFHYa6othmYQGmPdE5M2jz2aGhdjpIdgF98KfUJlUa1N/IXpJy8DHamf/BznEtb8bFVpgeFQFTs4m3rLb7sQuofgOaAwDk2A6RKrHrhepT/VtMGNfcilZSd5wH5awwC/vIJO5ubA6DQfFobKpiDEneXr2F/5Y105qjTgrh8Qe+kyOMemlcL6tS8Qf2E8jcH6JtEtTaGOmY0JhiY8pOy3kw3bbCbOVwJGwHORTDwAuoH80KRb8y3PQEcmpdTf1J60GA+EfIBFicJbd25h/MpbvqRaPvxlqtcbHNeOXMpxlvmQgIKHURG3ySFvix9okJXZwEMM3TU6qtTzLWTLCj4wPcBbXZgvhQmn1GehaJEUdF3eIywFF1cig0Z2nAjcE5hj4kSbCVhPdsx96Zgb70tbd/+3gN8J2lXXLrOzj2rzioADTUu+ORHYVZLYjlMCFjPvE/PIEk6qpk5FJyGfBsfyrGGSh3D3CV0h1U1hG4lmks46rPximKKMGfkvDTPbAmePwS4hZmba76QZONDhmeZOGnWK6w5LRdKUXaJBLqGeh6inOhIeTRAVw5/RpTretTrDHPtNM9V6RTPusy7qOi8r2k/ow9zzxDzV7LOc3PGPvtX93AdVK7etsTefCFAXxWgMzbLkQbZqwzl8v37iuAcjeE5Dz3/5+cL1DmNCRyunrhxNH+qFIe3b9tuP/3Pn+Y3QM8nUfU6066+6ipbT0SXUiqFC8+qwqKeFo+XxwKFcipcvVBOvw2cc/N/jQz09QqxmuSZXcOgC9lKH6R2GwUQ1rOdlhwCwLmuP2JKpe9T/MwBGJxZxwnRrkroS2i9dvhgzH7w/SF78NG9duIJVQiRLbHF7XQQeuBmPGAIsghtMUK7DTJWSeFOg1CWvm5qIodaXMbGx1hHZa2hhH6gojJCP4NGJ+kAKbPSMvVtjGn0A/NscFNfozUOhX3NcIsUz4pTEzN2150/tbt++RNb0raYNFxlZwmc47mHmu36D/7VbV3VVBJeTBU9anBOhaVdrP/4j/+IVON9DuI677zz7Oqrr7YVK1a4EBPHOkA3MjJid999t/3Jn3zM7cD90Ic+5Bxqz1el6ZUA59SR6SH7n//5n+2JJ55wFeD444+3k08+2S2mSkUjTkelEBtSzJMEYjFUq2rnsXUUwbljqzxeS6mRU+Rzn/u83XXXnU6dQGpzS5cuIfyPHtKOhePFDE3HQvqLaTgaC7Cx3R55Imsf/z8pe3x/zhqQBf+T94btA+8LWU1FsS4cjS2f7buw+vbdH6fto19KWkt1wD76zrD99/8RsqrSom2fzV7F915/FvjJT37CmPg594xSCCUnKwgUOOuss+zP/uzP7I1vfOPrzzCvco71cK/nmI9//ON2//33Pw1/FJKlDUFf+MIX7Pd///edw67wfvFn0QLHqgW+86O86lwPmwWIYmK3XRezU9ficMqvOx6ryS6m62W0gPo5HVqk1O9ax9FLf2vdaHp62oVqLajNCVJTeFSBdNoQqbCtgue0KUqbM3VI7e3iiy92kJsiD+g6OrTAfeQit3uTf3Su7iP4TdfQ9wuh+fQdjYs6T4CeXoVwsDpPUN/ExIRTItLf+qympubpdT2dr/zofOXJLcbyt1Oc+8LnUDoz+7/edLGtX7EMRZV5m+kdwu+MIx0HdLwalZSFCxCoaEGABjU6HGL+NM44FMMS/cOWGJ2GhcHRBbgTr4oRlqvJIovaLAAApTA9UqTJAWBnegiPdaCH76PUIRgOx1mguspibC6NLmoBvtKiLfZHDSs3OGEZbJweJ6TZPCG2uCd0q5U1NQPs4BADPJvCGdB56x1W1tuPw3qhsV3aUvU1Fm1EVWNZm0X4ruGsl4Kdh1PHT85YDsgkhVpNinTngPKkjBEGNAmjVBciDYEK0iCgAAWy7NC4pXtQPBmZYiEamApFqwCAWHxhPQ7SZtKOLaRsRrH6qBJmDvejSNNF/oa4BtfGKR+pqLJILbZrXow9+D7gj5/CiT5GuLf+MVTIhmCXcMQDoUUA0CKN9fhdyR/2SmzEMXn3fdbb34dyGc7OJcssW1NpAfJfvWK5RZsXsDqN00Mdl9S4WMDO9qOCeAi4aQBgANBNi93R2grKAwWaFsKOkh7BcD71LA2EkezutvlRACbyp7oRYm0y1rDAIijIWnNz3rmJM9qfRrGkf8CSBzotOYjtcPIGgB0D1ZX4X9st0rYImIDVctqOj2MmM0BYu4PYeZjywyksZaJgZS2hRltxajaqstvY5set8/67LLF/p3W0NFoVjuc0tvKaF1l0BUDWYsqQeoyPT5Wf+qU6BtSV7CQy4V7y1+/gQ89DQQi1EwMOzAFIpIAnorUrcEoCGwDAGSEEQ6j8oXfBIj42AhTwAe3CUqaLEOKQMIdyDHiE2JNKiWCqHKpj8/2HbKQfpZUKwOql6yyAQz0TJNwgzuggYVAzhABMA6Vl58ZwQ1B+gkoJ4Zoa2uNCh4bLWy3Uzhpv9RrAsk0o+txEiNNO2gf5IuSqZVDWm8HuhLoJllbD/hEOtkRlifoMSi3zU/QjODHDKM0EBYLiVPWBQgjaSj2sd+Xuz+BwB3rwUFuRCqHaVdIpJlGOQAW4VHAaY78w9REnLgkljZRRctik3DRPKLVMDABlzdupn6gC4RQOSB1QUFviEK/DVEXgEMwfpv7HAUFSfQdsfgJVnAYUlhauBuLE7gpZt3ubvCFWRRuMEAJW6jopqell49i3jjB/KARJvYewh34Cp3UaaAeHtlRM8KxS14AfcPdLJcmLSSULSBDgxs+iNETZy+5S5ElJiQ9wI1xCu1JIxDROcVShXP5w3nt810dFa+7wPpzIUoeqsFoc4iUNy8h/PQbgfoRPFDCk8Isp4BMpTIaBiqTGlcH56zUCwFR38FU5/18CcM4NL/xDe3z6KLynO+ht/a2xB6eV3tA4FKBtF/pqKcdpTOihzd71y7vs9ttvd9C0QnC/5S1vcc9Jzc0tRMaUk0vn6XJyO/ELzqri8epYwBXzUdzazUUoLlduR5ynsnfg3PcA5+i7Bc9fdNHFhG+nTqN+lRvbZBNPPmGz9HmV9RWE9m6incSAGaSrGKIvLYPdqWesaaRecI7GucR+1CL3MBaiuCV4nbmMwLKcD8gEeBWkjQXKqoGzJoGLAG2lVoqTVmpFaY2pAkRicV60a9qV1OICar+C1oGn1MazAGejPV0OqqgiNGMpYaqRLqOdK3Oo1SVRnkQ50keJ0qmiyFErVTT6ngShSMenPcaGi6y09QyAlD5Ldj1g44f24pyttPLGVvrMOP0e8IyDVWpJC3MJwlJ6zEdyM8BEXDdNHy8l2DDtWWAfgwXjMIBWJX0Sm2ZSSVQw6W/R5CJ79Mu0Nc23BBUpDGswilqYG4foZ+jXPMUQB+bBKPzfaRO9+whbxnpp8+nYHUgsjpIdsLznYbfkfuzbAzzE2DXDOCigRKqzAEWTQ4R8JbRoVUsHJhE4hwX6umyka4vF4jmrZC4l1bF0ErQ7BeASlrJqDWXCeEcD9xUCc/wwRTnBXAfblzKOAVxJ9SmTZpxnvIqVraavo+fExh6AYDCKzYOz9M2UJWNJhPoQJNSpj/qZA7EEzQnqknKuES514rANd3VbYjJrC5aexPzneOZu1CHV0QD5yw4CZ1N22M+j/w9onsS8MYda3GTfbqJspJirnGqBxgs4ocQyXXdYtuseN5cKNq9GTa8e7g67kJ8gcJlXyjwFm+dlwBmzqR85QnELzhQxoHCSebCf8RalOS+COhihZLOMozn68iBqdwql6jNvyaoMgSG9eCOhfeuooYIggTsBlQLAdOp0NR/zJ/oYDw/Z5ChAPCqztas2MF9p4frcA3DKF4QOVJglDSnCn6sdRMupR8w3JvqA5Ag517xsDbZZz0DZAjiH8uzOWwHneq1scYcFypuxEzAA46iguWAp87hy2pWUQQltnJtA8Y95YA6wLMycJ6C1YZTdMkASgdhi2i2QG6BkjjDnaleaT0nlWQwTzZBzSoFVuZZwA/X5jLNUBP4WjMq8l7RPdB2wkYFea2hZbFVtp5jHvIQGLguQj2maYi91tAcVPObbjNlh6kQoRZjjoZ08k4wDzp1u5YBzAipzqEFOH9yEr9uzygXLCXHbQFsBTGQeGkRtMRRqYF7AfCqs8sNuKOppXkilc31MAFVEjfM5gAqBcwHWkfiC5QDhkYrMj+WAXRkmHgozHYxLnY75GknNMccK0T94zl8G3MdcOTf6pCW6H2cqMM8c+nTUKQHnUCPUxhyjjlqSvNFOM7STedqfT/2OkbZQetBmmNfOz0StvBLwsQ2gkD5BikjIBtN/9JFmPYuRNgovyJwyVEL9RJUuF6ljX00cYM2zrp6sHeqkLoyhzpggxDHz+ZrqiC1uLeeFFiMAyigq972daRs4nOQ5jf4Ke4XiPv13zNoXlVtLU9RBLbfcngCcu498B+yNb1hrK9qrqSJpYLgEcG7QGheWWHtb2BrZeE9VoS0rUC9KUFM56+/MApxlbWSYegwpXFcbs6pqtP+AXRKE7W3viFljU4i+07OZ6Yxt38EzSCLI91CUpP/vOTxv4xNT1t5aAhdSYZUVbDehm+jpJn9dCRsZTfH9JFANGyTqYtbaUW6tKNkJ5hsdyVlXZwZldlTaZ6gLL7+JdgAAQABJREFUtI0Y7zejeNfWXmoN3NfDDps3pe2v/s99tKNGe9O5HXbK2hD2GLfxqRRjFNdrL7ET1nhs1N1bVJyjdTzXobZeWKfQ85sOvef6APpTzRmmWEu495577Sc/+bFtZpOS1PEvu/Qye9tb32qLUMnXMyL/M24zIOuX4vGyWKBQJoWLF8rpt4FzFArzG55egVoH+rPW2ZVE6Y31AcJLZ5mnRNlw1dxSYm2tcdaFeBKlk+yhP+rqStEPZBCvZ94DVF9WFbSmRVHrWB5HyMujrzD7j38fsfsf6rLVK+vsovOa+V7Q+gdQCaavaGqK2dJlJda8kOc+xqP8xsb8FGFyNGsH9+Xb++T0vFVUhRAJi7p+ZXQcUJkxYvWaEmtpjpJO3w4dTBJ6mXkpay3l5XHWqzKIl7EOBLS+e/c9tnPXXShlt9gVl19qZ5151lOhWjVYFjQvNRvWf7LGC6+jLwick3rAXXfdRWf0Y3vooYfcQslJJ53k6FOBXe3t7cAbyOWKFHeNiSTyU86rws6nQqG/Gj81kdXC5Xvf+152+u63d77znfanf/qnzztcqzoRLUheeumldO6DQINXObWElyMvd9xxh/3Lv/wL0qk73eKldv92dLQ7qdEVK1a6RU4pTR0JzsnWr8XjM5/5jH3zm//qdj1vJBRJZRWLIsf4UQTnjvEC+h1MngZKOTW+9a1vOcW5SaSo3v72K13Y6WMLWn5t9kO/g1XmZU8yvjPr7MnZZ/8hZd+5mcVw1kvOw4n89c8TSrQ1PxF/2RPxGr/BJGta37sBxbmvJK2jMWCffn/E3vVuFjK1blM8ihZ4nVtAYVm/9rWv2d/+7d+6cHjPNIeeSwTO/d7v/Z57Pnnm58W/X14LCMiQGuC3v/1tHthZNH7Gcc0117jnsEIYhWd8XPyzaIFjygLD47794ceSdtvGvOrcf7syaF/9C4AYtxPzmEpqMTGvsAX0jKaFaD2nqd+Tmo/gOC1uSl2us7PTgW1aSxKcJoCtra3NhcfThkj1j1Kj27SJcFAcCjX+vve9z61RFRa49f6Ri6ha9yncU58V1t4KC6v6qUXxAkSne+vQe/pMf+unrlE4p/D9wjn6fuH+un7he7uf3GVf/PxncSNn7fLT1tuaChQoelHNOgxsxMKs1v1C8RKUszqsfO0JFlu5AqcWwa26D9j4ts02vAdlvEkcXThmdM3yygqrWbbcKk4+w8IAHfLdOYil65DNbN5mwV17UfaYwmlEYMkoii81QGjLVlk9YTojKPXpyPQCaG3ebvNbn8CRNowSASAfbqQ5VPTqli+zOtTJgjj4Bx95zMbuvd8qhgdxuJRaCiBrmvSXLFlszWefYiWrSGsFDmypRiVQFcFBNrNjl01u2WWB/hGc1Kg14HTKAp9Flyy1inUnW6wDRzxO1+xAp82g+Db35EEc/4RBU50AYslii+rjl1vtiScAo7XhCALMQ/FiZv8+S2zdYsmD+13+JNSQY9t1CFisvI2QlutPtshSnJ+obaW7Oy2xe5fN7OtEfYUweXimWGLlWmErW9lu5SetdovDM3c/ZImHUNcZH8WJWw5wVmsJwXeLW631zLOtjHVEqwC4wt/hSzVlfMTGHn3MJrduA4rrswh1WCCQV1Vq0aWtVrn2RCvpWIXKTpUlUE0cf+wRy+zdjTDZFIp51CfsFKAOl+NMKWM92NadhEMaaAsoc4a6P7FthyX37DUP0A7vJWUQtiShPitOXW+Vp6y30gYpqOEO7OslDbssseNJHL2oGUmFD2dxprwW+O84qzl+PSI/PuX3gHU/fKfl+vZZOyFoS+vrbJ7y8luXWPmG8yx84skWrcApLpBIIKKH8xKn9/wQgNbwDgskcMjiOPAIqRYsxYGOMzWVnLL5YI1VLDkfEKTJ5ntIQ+8OxFASOOZxPFOPCHwMN9FkJY0dFmrE4SuQEMAsAEQQpF56gFoKMZYdO2h9+x+zssi0VS05EWDxdPIBlKH6gaLZ9PBemx/HkY2aC35Si4erAc8IN4VimeF8DlYtt0DbJdgfiG+cutx5kzsnVF9pmWpsJWUSnJdBVOVCJbWE5GvBaQ+0kpFKU6/NEMJNbTUGhBIV7IGaXAbIIgg0Fq0HVFTdHRqwzHA37QwADycsrmmbRoVAYF1l1TLsRxsMU2dUScg9lyOvPNengC0G7wWcfNBmcwCpK68mpOlpqNbhtAcKzM0dtOTwQ8ACT+LgnOc7UqsDIASKyKISkEwSSq7xRMxIXQXW8FEXGiNMZGBuwkoBDaWGmEOZJ0V9SKM2F8DJXEL+wiWVOPEBAiaAHoHnpCzl+igg0xzh/5LzAGzUz3B9Oz6TepsbGkZhph/gZRZnLX0cqU8AWeQov1gdoShrAFIBVMTeBdTopH4EzGKAMPODKDr1AoYC6jUIPG1cBSiQD0vlz/QAxO6x+bHDgALUf+CIEikQqfxQawoufTPtjXLjb62GvWjFOeo7SVfnzj868n8LWlAZ8wFvOYSBHyqnp97Ln+TOyCDTMEbI8Htwgt58y81OnbS+ocGNMRddeCFO8Q6UaIBEgOTc+boeL/f70/d1lyr+c4xaQFXElRk/83XgvxKqcV7A3PeeFZwDoBp9xCYB5xLDA1YeJyQpkHUOtaA5gGqFJg8BuJVU0W6qVwPlLKGPk0od/dLIdvocIPl4mVMaU/cwLxglUE506pUoxC0mZCOqlYf1PcAQAJ0U0iZy5mqALwU0KalvQyW0xZ0jBTOpHXm5cTIDgD/WZeN7dgEZT1vFgqWojKEsWr6QLzDoAKVkxrst07fHFC7UJwRqJkYPRch3LzsFzDZmUxmU6pZfDqx+joXTvYyZ99rEoe2ISaGkxLgYRLUpkZlB8DVtsRChxkpbUGlrpu0CUjGHmwWcRi+YfjBlcWCSKNCM1MlS8xEraVlCX1MKONNv44xbQWxcJkgZKiyBIkqaMaGcfjxaTQhW+lRuxkuFhHM4C4xnAFVzhKjt2gSUlLDaptOsHHgqAKBIR8Z9Bgnj+RARqhmvZucsCqQSwW4BVJ+ygQRhaBU8rcYqAQrji7AzbT/d32nDBzbSF89ZZXUFczDsDfyYmlMwdsJeVxHCc0GDKggwHNDaSC9hvVH2kqJsKSAS6qUpVFZT9JPhsqWATW8EXGJTxuGtsH79POcA7QGgJ5DA8lA9jTM+RRo6SC/jIuOglGGdEhsqUgz2qKp12VjnkzaLsnFD2xIraSUMZozvi2j32SiAKlkCyFzzgjCbEcKofnlSEEOdbW6ml70L4IiLN5jXcJGDqLOHbrPsoV860NtqFlkSOC1L2YWArjzC7RITlDraypjO71hnDuXQ9MhBxnvmpIzhIcBAJh+MLQI1sQjQVqi2FYAsCLDWwyk9ANUo/dF3ZoDNkgrjXrmS8WKNRaRCRgEK7FMYODdepIGoCR2e7NprU0BwAcai6pXAj6qjKNmpjuamDsCmog44BUgOpIqoGmksZzwK2vRYwuawbxNzhHjLaTSJVuaAh2xqx48tNLOHeQ2AXIQwpgzOGRS7FJI0zLxNGwgCccLBOtVUFO9maFuZMCphgPFlSeabzANQQYxVrwIQPZW5I3Vjz/2UIRAa9ZDVesoxStsGAqteQP6AYMqayJ4U+GjENEKp7Em1z59BQffQDhuhHGt4ZilrI531J5I/yhr1vPlJwNZJ5jJzh2gXzM+ZQ0SxQxR40gM+HSN/8fYNDpyTum2u/zHmvI+R7wTtg/Co9AnztIcE/QgygIzVCyze0Az0Cbg4AwA7BvRInyKF5yAbDDzC/UolMD3Dcw3qjCWLmzmPzRy9+4Ewxygn6iFzuzRqvlKSjFSxCQFVOw8VXwZ6qj5jPfM2KpGDKXNjOyzT+YDNDfQxj6Eclp1PHwMUCyBJB8d8YzsbP7bShsZsHvtnaeNR5osxQsDOTLDpJcMmo9q1Fm89jrQBkfK8ICXb1Ng+uingUeYyWYGm2DYKTBwhfHKAsMyT6TrbBUBy3/0ztvcAYYh5ZvIZp73gnNXVB+z4tY0AzmyGoU3u2jVn2x+bsP5DtE2AwwxQrReesra2uJ19RrudtLbaaghxeMvPJ+1b37mbqXTQVixdZZVAoRNAaVP0nz7AbdOiuJ22vsJOPbnUmhZgC0Dmafqq/XvmbeOD07ZlS8YmgdBKmQ9WV8XZ+FHC3wRQBiB+43l1tv7UCqtGHGGwP2n/fv1OGxjKWhOAbjnhFvv6UzY7N2onn1Rp55zDZpbqsPV0pe3Bh6Zsz75ZlENpDsznY5E5wJqwnUD+zjijhvxKmGnOHt04Z719bJjgOUftX/Pupe3lhF9caMevK0fVL2BbtqbsLz9zr41NNNnJa1ptYQPzmpF+GxlHTZqwxMuWh+2/va/W5ti4ccMNxVCtVPLfeBSe4wtzhcLPwrwvMZcAWNpjN998E6Fa72btYtROO+00u+rtV9lpUpcHZJLyvA4Bz4Xzf+MNix+8YAsUyqRwgULZPRc4p2m7YOoE6nHdgHBPPDFlW7cT+YD+IM34LjA8FE7YipWVcFwLrKMtZr3Ar489NkXUghTtvsTN5QKA8pWVKVuxOm7nXdRii/necG/Obvj3Ubvrvm5rXlBux61YwHNOBOguyXmjxuM4CtY1XBeYtYMNhkCvOdZ5JlGZ27pp2jY+Mm8HDnrML7Ns0gxYUyNgP+Ni/8CUxUrm7R1XNaKEXc2amG8P3j9tP7vxAdLQxCbThfRFaRtFfb0kSlD3ic02OLAR0K7JrrjiTWwA4jkUtU5Gb0ylUVwv+jn30vMYY9sLPF4QOKd7Seb7YeClH/7wh/bIIxvdwmB7e5udeOJJbvFPYSYU5kEgh4A57aJtZuehFgi1iPZqH9qN+4lPfMLttjrttFPtD//wD007rp7PoYVDwSof+MAHKMxheyvE7Sc/+cnnc+pRf0fOQKnO3XjjTQ720wUaGuqd6txKFv4ihJyQyoZs+ud//ucm55N2LuvvArh41Dc9Rk/4zGeK4NwxWjTFZL2CFkixG6u7uwfFzP/lFCelSPC///cnXeidwm6BVzA5z3ErLdoVj9eDBWaQ9P72DzL2qa+yk4hnzI5Gz679vyP2vneyO6hYDV6SKjDBTrPrsPEf/33SVi0K2Jf/KGpvvZwnzeJRtEDRAu7B/stf/rJ7JpHizjMPKee8//3vd3P9tra2Z35c/PtltoAcNr/85S8dvCj1OT1HHXksBQiQkvkFF1xwTDwjHpm24u9FCzybBW64NWPXfiZpnWxGbwaY+8n3o7Z+DQ6VF74m82y3Kb73O2QBLWSqb1N/J2BOIVO3sEO7q6uLRT4ciLwvWE6hV7URUutO+qm/FUlAm061OfXQoUMuJIqiCihU6oc//GG3y7uwcKp7FNTjtN5TuK8WrROskEo9Ti/BGgq7qnU4jYEFlbnC9wX3KV0C+7TepPe1dqR06h5aw6uvr3cvQX2F++unDn1n75M77G8//1cWwlF0+qJma2MhNo4yRAWKGJUoRkidSovt88haVAJqNZ17Fk66Cpt+/GHrf/QRmwaCq16A2l41zkHgGtzdFqlvsrLjTgKIW4YzDIWPA9ut64F7bZLNrjU4hitRPQuhsDWH024cB2iO77effiaKUAtwdKOch83HHmZj79iIVaKEEK+M2xyQ0DDDTgnrkw3YPYK9Z9iUOnrX3VYBPFRSg5OM+yVrCLe5kPuvXWUhnHEeKhVyducAyZIPP2rdv3rQZlChagDiqgTym8ebOQCglMDJXb9stS1AxSdCzLHkk1use/s2FDHSKEOXs+O6jBBNhElkCTfY3mzVq1dYfLHgnrjNosbSdfcvLb33SSvlOxXV9TgDYzaHnZOsJMebUDABRIsyd0kDNYw99ABhvLaj4MJiNg72yjKUfVhaHZ2bsRjKfrWnHg+sWGap+x+12Xvus0HOqaBsygjnNk9dyzY2Wf26UwAQOjAIzmXAuxxhN2eA8Z68806czEO2IF5KuuMOehonfF4OlZy6pcut5oxzLcvi9QDQY8/991h9Nmn1pDeKQk8a+ihJHYxQ18pWL7fQehyrgAap3Xut78EHbBBIMowCWn0Vn2OPHIBOnxy7AIp1gHM1WmlHYW6ITdndQJLZyWmrp5wqK8kL1x0hXFxJQ7s1rzuNdhS1yc0brfeBu2y+c6e1oFBW1dqGqhsO1EXtFj7hZAsAYMp5jnuAeohySRoYagIIo2cLwBlwCCBVSJCAOm2F+0NBLAkkkYsvsKoTrqSOtePcfdTm9j9ocVTIoign+bEaywCS+WU4mhu4X00L5wsbVWgz1rwpvyDh6fy5Tni4J62vc7OVxRJW10Goz/ozsEcLn/XbzOAOG+1/EqczYegQeokAfQUIjxYAHMgBMwRSCYAMQp8ueQv3WAqgstmyB2+x6f59lkXVMFNXjypJqcUBOHKEtE2jRhTEERypBAAlbOEMKm8eO6vCqCmFyV8AsCCJ43QO0CIUqbbq1mWE0aGt9+GgB2I1bBGpJTwaKjZp1JGC5YCBqNSEStpIM22AfGEkXjzU006Jd2iZwZ8Dw9xvCZ8oKEuvxJF/Bp/yOQox80NbbYpwbEHrc3U/g+M/x3kBwMTEcD9OcurX4jOsfDF2KSMPQIbDOx615FCfVRGWK4bKYQAVKtAM+kTgBcAPqAjXf0LcAMjl8xcFzAxJDY0+a24Gx+z4JI54waY4yVFCmuw6TEjZTlSLEgAeAHjU6xThav04yoU1qy1W2YozGVUgLgHyRnnguE9LQRH4dnifjfWhcIntGjqWE3J3Od5BQNo0igo9m2xmgDB0gEBx1G4iWncHNslJOVJwypqrAXlRasIjzKVfAnCOizx9YAvXBesfrk7b+PXDfYi9NR6hOKT6zVsT7MDbvHmL/eD6623rtq0oN5TbhjPPtAuB5o477ji3QV/9/5FHoa8vOkSPtMqx/ftvKjON678ZnBuhj3nYxncDtPT3ArgClRHWW8pkGQaXJGNEEpg3AExWXttO3whoxLiQHqFPT49aAPDcj1YRNRrnKOFbpwBKcyg1VgO5lRHyNTO4x1L7AVNmQdjL6hwkFy4B/kHVMlaJWisAazrSQPhPoBiooiDjfBjlTC8HIAtgPLF3F3DSDHA1Y/dCoB2peWWmHJw8O7CfJjsI1AYUVQY8y/ibAbqQUlUKJaJUqAaQ6Sogt/NQiGIc7/6VDe3fwuconQh6p/8xlDqzSMKl5xgn8LcGCfnsE+bRJzYgvDFUs0Kmw4YB4QtqnkERaW4+aA0rj7Oy1npApx7re5I8oupUQ7uKMVbkFBYVNbSY+tGyZcBiwM6M6xgRIA5wjvCoUgf1p3fYZM9jhLPMWHXjmVbWcjLzDhQ3Gf8zCeDrTsbk2T1WDuRUKeVNwir6QIXzwL3TLLp6kWarIcRnDHAOOTrUcA/ZwJMPAlRPWFV5JbcEfA6xoQHlGCnbkkvAHcqVnwmUYD369jhQpEA9hYBMMybNT/eTNkKoNaymL72CcXPORvbeb950F+Mx0BDXzaLo6UWBgCpRRKtpBZLHZoCQPiCWyLCgwDkfVc4J+tGunfTD/VaPEm3pQoD6+BI+I5z8FKGB+x+HUx6xeAqIDfW3EIbOpemGk6NAOD1WUQP4veRMFOcupEwY+/bfZukDd6MehjIo9cAvBRoXvM4GhAQvEE7mfYBXtZpTpm0Qn0kKMDAWZ/xgc0EUp3qKufE0GxqyM0mra2y26OLljJuEqkT5Lz3bBThKlWDuFwD+TANtByqXMz4yJ0QtTKF8XY+rfwhL52VGKcODqP/usIl+4Dzg7prlAueYPwJt5SZR8O19An6uC1sD9wEnhhh7c1S0GWDImSmAfmy2aMV6K2k+BfZwIXZhXN56vdnYTubGKARWMLdFCTcHLJ+Wihr3jpQwtjC/SlKPIQBpSyg1AvUzwgNLAOYDrydQzasizG3NkvNIKlDTlhvZ6DAEYCrFuoXMjwAoqRuR8joUUilDoFEfxUjfbRIgpDnAlyfFuSnmLD27AAP3Uf6oBLcAd9ZTjoyZuenDNt69DQWybvJF6PYYYyFzIR8ww1OIXNRbE0CKlR1nWdnSiygdzulD2XLfRvqJKStBhTrONX2UIlOkMYHqG1XTStjEEgRWTRGuVgB9mDlElD5FGzQyhDWdm6OsZwFVmX9Wr6F95aa55i6bHhxyDEIEe0hpLgcMGygFwqvNg3Oe22WissOIakuEm/fHBc7db7P9hHBu1Aafc+n7eH4BuE/Ndtp0z1Y2GRxkngZ2QTvOUq4CcwWSTvHMEQgzR6fsSlpPoGAoAziJDLBmeroTJVrNVairbJhIEHLWB+YLxalzTW+wPUNNduvdw6g2jQIaLremFsBkgLUESpvh6Iy1L6mwVatbbHw0aA/cP2AHdzM/YmNAY0MT10Q1e67Xamt8O+OUxbYOcK4aAOX2uybtX791J5uIJ6ylcYUtauqwauqy+pPugX4gxklbAvhy1eUdtvZ4WA040v2d83b3Pf320IMD9NEltgBV6logPKmCjowErOswz2FAx1dfQWjUty6wBXWeHdibsL/9h0dt76ExqwE+lWp6TQ2bk+qDKFDFbNWqEsrSCKXYbxsfG6Pc4qhP1VM29LUoEkdi07ZoUQVwTIMd7p2y++7rRe0O1Bg10cYGFOzias+DVhZP2hkbWm3tOp4bKgK2bUfG/uLT99uBvipbhtr4koVzVk++51HI7OplAxBjxl98ahXPvYP2H/9RBOeea7Zy5FzhyN91juYLw2w8ueeee+ymG39me/buo3yr7JK3XWJvfvObrbUNpXA2P0mhTIfUjV2bcn8V/3mpLaDyOXIerr/1+m3gnNRYx/Ah3nnHqN37K1TQxwjtXtti9YyPrCS5NtbUHCTsbiPjXondcftBFNym6bcqYI6a6B+ArBkH0oxzDY0Zu+BNrda2lE2egHM/vH7Ibr1zD6x90FoXLUR5sobzIjYxNYua9UHWjirsgjc22nkXllvdgiDQXM62PZGwW25mE2nPDNEzG1DMBFwn9PsE/WjP4TkbHEtaW0vMPvKhxXbm2RXG8gV9SNL+9Ts3Mw/1UcZbghIdc7oq8WWIoaEgvG/f3bZyZZNdeeXFdvbZJ7MeIliuAM6xRsaYmAfnBNC5gfsFFc8LBud0Ny3a9bDj8HoewKRAp99nmZDpoaulZaFbaNNDmQbY9vZ2t6NJsJcWDV/tQwuKf//3f2/XXXedS9/557/xeYNz6iDmWJgU0KbrrFu3zi677NKXLUs7d+60Bx540C2+HnkTNR49DOulxdZrrrkG5am3U8kbnO2rUGQ7FiDFI9P8Yn4vgnMvxnrFc18LFtAAKQfHj3/8Y/ubv/mKg2Tf8553O6WWYwuak7U1MBWP17oFWL+2LTtzds3Hk7brUM7qkeN918VBu/aTEWuoLtaBl6r8h4d9+8Z1afvzf03ZmraA/f3Honb+m359gfululfxOkUL/K5ZQCGHFO7zETb0CBR45qE58vnnn2/XXnutbdiw4ZkfF/9+BSyg0IRS/bvpppvcs+KRtxSgIYXm97znPQ4gOfKz4u9FCxyLFphkw8A7/2De7tmOgw+e4JqrQvY3n44SWuRYTG0xTS+3BQqLmFqbEfy2f/9++/73v+9Ch+s9ORUW4zTUSxtJtVajTab/H3vvAWfXVZ79PudM7733rl4tybJk9WLLXbaxw5cAgS+EQMq9v196CMGQBlwI+S6QAAmY4hp3Wd2S1WVZvY+m9977nDnl/t8tH65CbDBgiHBm20czc8o+a6+99lrvXu9/PY/Nk1n/F5zTsZ+2uPMY8JCNZ2aLYs4IBrAZqGb3evZdNqlt77U5tyBIZ3M+1s8acGerxG0/BpLbfJA5QtjYZ/uzz9g+Ojs7dfToUedh77XPW1mC+7cFsAsWLHAWZhWgImYAnr1mm8vKQRlqr1zQNz7/19hLtqsEeKoCy8TS4gqlsagrguNkQNZIY7OqT6IuQXlLly9RJspGg4BUI1cuo3yVrqQVyxVBwsiHvdEUCh+mXBOegboUCTFfS4tGDx7QpeOHAWzClD+nRAmFJO5RFpsi0TsGmDeFwkkqq5BDySaOXalU9dFD6m1pUnFZqTJmo3iD0pqXhNcYUJLZu8YwYRwCMOVrqFPzc88rrqVZMajWhdy+AugqBwuqZBRIgLhQISMj69zOegC+h17apuaLlxWWnafsZcsVT5n9JNI7K6+q6UqNwklqli5cqhjqbaL6kq401SmlolSZc2YpOjmNyVsUTSZZ+p0cpYhM7Cjjk+XrHFQdqneVhw4pkzIWzJ6ruHISfMCBXhQ/zJAphE4lhglsN7awQ8wJ1r2+l6Rcj+LyCpR5ywqUgYD9+Gz/UD850CglFOVR7FBNnT6nkV07VN/arIwli5W2eIECKbSjeJSAaI9mhWWKI2Q85a2sVCtlOA90mA7IWDp3nuIzM1A4ITkHtDBx9SJlByBauYEEdqHqz59UGwphs0oLlbVgGWpqeQ44B7JAHQPPJWLTxeS2SNK17d6jnuMnnIRn/JzFAJRYdCYAWQT86mbeOBxgLg6wL5qkt+f0STXu26v2/mGllMxQ9uw5is5MASAkqYoFZrgrRrEpTLRjLzNBuZoP71Pn+TMqAr5PX7JcburEnYY9YGo6Sm1m9WUT5rRZrMS8o1UkVc+j7NOAslC0kjPyFAmYR46W5C62qS2nsaqtImObqpjFv6GwuDIN1xzWwLX9CPGgJ5e1QK4UEqPRtEuANOSKAO+SuA4Bv0jAmmVoCMlwN0nrAKvg+5uB4/rbSPzHKiV/ESpGi/kikq4959XfeIikMRbBKQCNwIChgBq+caxzgOYmWq8oBJuvSOo5bNZmWK0S4BEUT6r2aLClUX7UYULzUEtDqSUSOzQfc9FjrZXY2jaSaKb/sWR9bIqS8koANigf8FRgGEuxtkYN9I+QbE9Qejmfh5n0tl1VS20dxxKLUE+B4tJKACBQYYtAzQZA0GW2i1zrBghg3scxUpserv2xJg037gScOUMSOEsxRQ9xTQJKGswyVMWxnyUB26LYhBDaUSF1lisfyW7PALZtzSdQlgnF/nSFkouW8Hns0rBqbb94SoOtLUqNx3owr5hEN3AFdmzeUVQCe1s00EVSGywiDHm+AAnxiEzUp5LpzyiXn7bf396k/tYmJSXFKrl4BgnuVPVh7TzEcSegdhADEBsKJDGFalAgPI/EcxEPFApJxNsWDmThJrfBxQUAV6nxFs5h34BzvlOwYItOygUGiEB9qVPd9SeAS+pRb8L2OGc2sACgC0ornq56kuMe1Bzvw4p2FvVGu2Dfv7DinBXQ6Xr5560+2OmY6FPfbjPlKR8TNMGXbRy4fPmK9uzdo/0sYrFc5y23LHZyM4tvucWBt2/MGdjYEtysz7/x7+Dz0z9vzhr40Rh9wzm0kv5kcA5YdOCEOq+eAP5pVhL9ayJ9aCh9kwulNO84KpHdKJ2i/BQJZBYGSDY6irobY158YgbW1kVYizLGYYXpHbiANecbXLtx9OGrFZsHlNRFf1Cz17Hgi0wHTElFFSQ2l2sUxbco+ukoQC5A+EnKiXYY14xP0Vhmhk+h+NgAhAvcGwrYFgu8GppzKyBRJjaIDZpsPq7uThTCwuKUlF4A0AXYzKenRrqAsc7Sb9TIhz1i6qwHgcrXAzm1a6z5iNqrzwOCYBfIuByXhg0n1zFSmaiTNWioBytpIHQ/sUVUdBwgewGwfwn9IfAJMNoElqXtqMuNYqmYN3smx8g42dWopgsop1EnaalZKMDRj8YZTJZBn0w/CoBF0MF4RP/BOA5iTj8DVTJJOTtPqr/lHJd1ghIz16LQN5/+F8W1sX4URi+oreUocVmfUji+mPiZfJZ90MdOdF0EbOgnsZ2n1CLAemI0oYI1RsK688JhRUyNKCk1l2ObidJWFoDDOONII8BYPQzyMCVABYtjikNJMwZbaevrvQZhmzJaG9AzifKIXACUsgcdVa0u7NBCgaNSMrIVk0MsEV1I/wl4BZjnI65yhxFXhhqsS6LaFMewI3VP1sNRX1BXc5UmWQSRXlKmuHRUucLyr1uxNu8HQD6rUJ9ZXVpcUMbxAX6NoPTXW41S1gVgDb9iy4gxAB+RxdNE7V6NVB2i38ZGPn0mD8BEVMd8kygHokTa312pmLAxYmsAN+7NulB0gyxkCJpBPx3FGDKKHXGbhupqFSCnkolVcSSLAiaA0DtQjRvDPj0tmfaUgZpgZCEAKMcIYOZi3HCHMJ5SVqQYr1/8gHABbzPj9hsaajmjwQGUuuKLlVIKWAbQF2A8megC/Gw6w2fGFEs5IpIZl1kQ4QXeHyIW6e9F0YzdFQL7R+cyhoZmAc6hyHzqKVZDVFHXpQoHfsOnnvZHsxkEBqWdIlPI9cY55NoJR401ivbhBnqH39bUwFX2fY72OIqN72IUF9c5kH7/2aewcO9xbMQjs1dQ30CPqKA5Km6MYa6wBIYV4nzK43egwAHiGWzZgch722o0jnpcCv1CfDpgIG3R2qi/86z6mgAjOffhucAXKOFF+IlLhgHbui5ptPsssR+2n6UsmsnfTNsAnOs4r/Hqk8Qjg4rkHiA+z+DSdGJCYmTUKce7azmOXpTrxgERXYrm+CJSZnMJ5XLNYAULqNrdwljL8SWh+ps2r4JyjqFMWaXONhbMJALTsbAhIoXzF8qCB2IeKoj+hqCHstAREDewoUoXmBhm4ch5TdYfQTlygGPATrj0ds43SrRqUUfbefXWA3CycCMlKd+pswBW1AEAS1/fBfV1tbDfdBZuGDhn54/4YhJFOCx0A0B/Bv+5I2mIQHgT3ajU9tYChYYrJvsuHa8u0lMvE8e2+HTrkoVadhu291iojqGA6QMsjUngOqLvOHvKq9f3oayJ9e2a24tVMZNjYbHD0OgwMaBPBVlxqD5xbxPt0q7XRvWNb+5UdW29Fs9bgV3rDEA5FlBwfV6u6teRNxq4N2zUA/csBmopAK51A9T0a/uuStTjBjSzohCVuwLuA0KJfyb0xvERHX0D9cfxBn3wAzN179YcZaGGV31lVJ/7+8O6VNWqsoIirV87k3LFKxUluQzgObNZvXpxVE//ADXN7kjAvhLduhwYJzNEo9jljqNMbPebCVgWH3j9qo4dqeLaTNHypQsB71CiTjR1YBZ7sFAhPz8BO8kYVIulM2d9+uxjp3SxkcWK5VnavNZNmaMYMcJ04coA995V+sNPVgDg9gDOPatTp0458793332349hnscx0THO9+7rxX6uTYL1YHU3Auly+ckUvvfiiw/lMjE84MeODD6E2h+pcAmrefuJMe6/NSzgxIr9Pb7+cGgiem+Deg+fr3YBzzc0B/eDxBp06W8t1nYQKXDHzNMQkqKaOjg/DZgngNUatvO/b/8oChzGUJRfkoi4Zh8IoC6zovsbGxxjjp1TGNZ6YEqaeJr+efKJd23adg0cIaOVtM7RgkYmkhWP77OU+ox6LZjeWzZF69DczVVgSobprPu14ZRCFzctYPkc4QGxxKYu1WNhWUz2sPbs7dbHBpZkAtn/8h8latTpKXR1+7d7p1be+twtwbhC1zAqtWVuiokIUR1m4dPTwTh0/tkulpblwUFt0+0oD5+jjmT0xYI4ojnaKEjzPME3i/AzW4c/68xcC5+zLLAgfY9Cqqa3V/v37HcDr6lVW2LXhJc8FaJCcwXJbt27VJz7xCQf0uBkAD7uB/OY3v+nYoHZ0dEA82oDCCol3udmx2SSjwYM2sWiKer+szerYbrgN0rPf326zzspWExcWFjoTnJs3b3YmSJNYwXsz1PfblflnfW4anPtZa2z6/e+3GrB+4OzZs/qDP/gDR4HSgsDf+73f05IlrFC66TYboqa393sN9A8G9Lf/PKWvPMEKRubWbyt36+8+Ha7bllyfiH6/H/+v4vgs/GthZcc/f3NKX37aqzlFLv3zH0do3cbpOv5V1P/0d9zcNWCgnFmXm+JcQ0ODc+/x4yW2GHnmzJn63Oc+h5T3A++buPjHj/Nm/tvuu2zBkSnL2UKrH98+/vGPo577585Cqx9/bfrv6Rq4GWvgGz+Y0mNfnlL3WECzmex++gessi5hQdt0+Hsznq5faplsXii4meqbAWzbtm1z4LQcVsVXVFQ4czQGy5m6nEEKBrDZ2GTgm/20h20G3lkfacpzNsc0d+5c56e9zzb7ruD7g5+3+SF73VaH79ixw7F7tT7XFrQapGfuCPfdd5+Kgdbse20z69innnpKr776qjOflZeX58yHBY/FFr7a4tCVK1c6CQdzNLjxu82ipeb8aX3zs3+hoYZ6FQGz3JZdqLkb7lI8MFxITiYJJA/wW5uqX92mutpqIJMszcTy04stqae+TjElpYretFGhhfkklpncJKlMZh51FsAk1Esm3zyj/pe3q7H2KmDUPOXcsVzhhSSkgfQC2E/aYlYrR4gPFYO2TvUcPKDTRw4CkEVoyb13K3HhItRpEtkXNyionSGTQmKLB4ovvuZGNT71pGIb6xQ3b4HC79woDtRJ4qO358ywguWRY5tQ385dGv6PF1HXAERcu15JzLOZTSeeTBq/WqmGPQc1erFW+bnYhKZnarSjWVcAfXKWLVbuimUKz8xh1hZlG7MMjeO8oxZChl+Tl6t14rmXAHHqNZ/50rzVGxQK1OS2MtvkL/uHgCEBy/G1d6sd4LCG40tHOStn9RolrNvA8cU57cHDxLblBENtFpw25CXpMfjqK7raWKtC1Fyz1q4GhKDM4Xw/7c+xogP2cvUOUM9vqn7XTjWh8la2bIVyl9+mUEAjmzH3XjyryT07VAuQmbZwuSKLZqih+rK6Gio1F1W5tFWbONckvE1NJ5TjCyW7a7aXTIZP1dXo2rP/IbJZyqpAvebOBxU+Zz7n77qC4aTXbKkAzgDj/HVACy+9qJ7Tb8qDclzunfcrYSGJSCzNvCi0hHls9p4D5DOmVuNrqlbTEZQncD4pm7dIuZvuVEgxwAIKf2Y3adCjc0nxa4AJ96mBUxpoO47CC6pkqLgl5sxzgAmuPrnGWuQBTPPXnwSqAmpc8Cj2bOVYzB4AHNzHPC9WsCUbFJJFApoEuo9mOkWbCqGNhprPJ0Uj7URyehxb32r5sdfrxubVQz0n5BYCfMxDxWgG5cBareWg+uv2ULWTSipaCjS6FAABRTaUjPwkjIdrjsmFOk0UkGv4rPVypQKldKFudu11xwouDBWRqFKsjFOAv7AtC5g1GqDKWM0b5JRJuEalkYCfpYQZt1BW4EXqyz/UJA/J7V7akJekb2pFOeAccFX7ZfqBBuDFHKWUzyfpDXAWVkDvwOdMPYgKNCUmUpgkmkkAA5cFhkdJbqMQ13wUMKGFBDX7yroTpZxSabwN69cztOeLADEh1DEJb8AMV2QRfQG2q33H1Vu3XcNAdHH5yxzgIyyKRH1fPSDmBQ12dqBmAkBiCn2pQIqW9B5v11TXZXXWnJEPwDEKcCcqA1vRwltRqirl7GHlC4Qw2FSJBeNlxaMslFgE8APU2FvbqjEA4WTUa6KBNkJyqEuS3H4Bu4hEvYtrhS3AMUagNhdq19pEB9aBKG+10kdxbmOz52CfOAdIElUcFIrGu2vU1WbJokHFA5bEZC3lmsrgmuuUp/Oyxvp6HEgnKoXz7aYNsv/3DJyzccYe1rDfGi+cA+AMOc/bt/G8jQV+jsU2G4+uVl4jKbULSPoYalnDjCnzUHhej8rEEgemDqNvD44vwbHl+n5tt9fHm+Df0z9v7hoIjt/BeCJYWosR/oviHONYBlA+Uq0KDJ1S65Wj6gNEy0CdMSWPWCFjNmMVKlQGl3RWaqzxPIpXgMFYd43TCUbEFSkxF/jVINFQxhb6P1/3ISzYd9K/YUteuBZ1KgPnAE3r98DR+xSTvxaL5jvpmwrYD0lNxn06Uq4V7MMAj9FiozVji4pNaegg9pdc9xP9fYphkUFsNmOjKV1FJaO+CoxX9xpWXQNK4Pn4fBKmMcBFPuKUEdTmWvZppIWxhMRp8sz7UGRbx3F2arjpsFoZv0L57kyAnWj6kusWlYx1fRcBB4+TrEUxj9giA2A9CUvpEIOCsWmGkALIA/JtuKbhkUHlAJmkFAJDdzaq9VIVQ3WYUnPLgHTnMi6XAbIBzQEDEajYFerEAD53GH0pALO3HzU2YGagqiFUMRNTiR2y1qAERgxiABrPdTe8iaLVNSVnhwMDA9TFzef6BqobvIw16GGswFG0ishSeukiFGnpY0N9AFy16rt4VLGopCbkoJKWsRzwi/jKhdoVUNlw40nAsVqA6BH6Uc5fBbbkcTOpG2KOcfqOrtMour3MWNsApFguVWyFfx9V79W9ikThKg0QMrxwE3HMXI4vAcVVFAmJa0JszOc77eyF0G+GAb0EBiqJDU+rF0XbiIxUpZXOQYGvkP43jfG4kXbyClKYVUAyBbQT1MgS6PNB5wLDqCB38DnOVXLcKEplgGh5q6nHcMbkfeqvPopiFRa8hSvoevkM9SxgbN9QgwbrdtGcKxmXKQvgsi8ySeElBtWvpF6jKFufJvtrNHLtPDbs9ajvoBDI2DBBvXY21gNEo7jLooEE4EB3NO0fuBrpVsZ7VGEZL9xTxIZGuhmVh7Khd5y20AUACKxm8GMSUFlcFjEOcJwH29C+tiuo7VUqETXY+OwF2KwC0AP5BCzmaL6gTuxFRzwjKmSRSky+tbMMAPMa9Z1+RmH9jYotR8U4n7gjJs+JdwND3fI00q7rj1KOYd6ey/lYAG9XwZjHOATg5aMdjwGCDXWjxEj7TShbS+jZr8FLP9Tk2CC2vgsVU3gfsFo5dURcam0URdeALQCwlkpIY6pzDOacixrAzgvq6e1iTI9QRnYhoCVjeijWyl20I9rhcG+jAkDp7nKOAfXBcF888CMqla2HGe93OrFDUvHtxDrE2ADdPoDIydpTGhoeQ2UPYL8CheDYIuqF/sABN4859TYKJJeYngCcyvGlr6CYJfAQKCBzfjurTtMuu4FNk5XGgho3qnRd1Q1Y/Q0qLbNQadimhhEfIIcIVEXcZfGMAT7YJLtpG6Gm/sf+vdTnBHDnWNtFlJOxX2aRQ5gtkiCp4fNWYWF6DKXfMeUAwqbmzIcH5powcG6khu7kCHHHVQdAjc9H9ZHPQjlynuACzJoVAI7Rn2ZLlBmCsiCWsJ4OrumecRSL79GhazP0/efOcp8Wrbs2ztf6jWlKzqH9A53ZUG585mCnX/t2s0jvtUaEcvp039YKzVuUqOh4AFMCfnpPRbNgIoJTaEpLO3YPAc7tQOm8RfduuUu/8XCZiguZl+CN1bV+Pfdysw4e2o+96ww9cC/xe0qknn+uU68fqeRaiNRdW8oBWpKAY9yo+KJUtXNCz784ojZgx0cfKdHd9xUoG3Cu9uqoHvu746qs69SKW2brYx+dqZIZ1F88Fs/056PDfr15tE/f/dZxTY6nauOaCm3aHKfcQiJJ4vNJ2pcfQnN02KuXXrig40ewAcYe957NS7RwIWqlfIfZyE7SD4dTHwbiwWXr9EmvPvO506rpjNWdt+Xrfz0aiU0jC8ioqwtXsaM81aQH76J/8bRh1ToNztE83n4LxpFOTEmTtv/ees5ihxHudbdv3+6ItdiiC1u8t3XrA9qyZYvjfGjzARYz2iI6t8Wi9pjefmk18ONxuP1tj58GznkZqxrq/Pr2t2p1+WoDcGsO80FFmokqpNmnWj9ja4dsocC5Mx59/f8cxvo8RxvW5mrTnZGAroD+9CsWDxmFFBXL/TLXb0eDD3CuDVD3HNBdFPa9c7ViFcAyapTWb7z8/IAOvY7LQkyvPvihbM2cHauTxz169qletXb0acP6VN2zNU25BSEAcAGnjD/8bpf2HnMpH/Xz//tTkQByYVhCA87tmNJ3nzpIDOhD8XCO7r4nWzm5IepFsXTby9u0a8d24OB8Pbj1bhTnltAPMnYRSdpixYAptQZsMQZDgR2r/fJzbr8wOBf8Xrsp6+rqcib6jhw5on/6p39izsaj+fPnO0mqe++9V0VF3MT+IqUNftl78PNGxTmzqigqKnRW5L6bXVsj9SJdevrMaecYs7IyHXvad/PZn/c9dqNTXc3Ki4HBt92F1atBcjYha7Yea9eulcFzZgFiNPf7YZsG594PZ3H6GH7eGrDgxNQnn3jiCX372/8GWV3iwMgPQf6bYsHNt/0CI9PNdzDTJXqbGmCI175jXn3iTzxqHQooN9Gl3//tUP3R/8YuxhJT09t7UgNc+qqp9+uLX5vSv28HnCt26f/8aYTWriPS/R+wGRhlCWCb9LVFDpZIfrebLW4ILjow1RXbRpDQt83287MsmHA+NP3PTVcDdtNoCtLf/e53Hdu5dyqgKe2Y4txHPvKRn6kNvdP+pp//2WrA7p32YgP3V3/1Vzpz5syPknTBvdgCgK997WusdCUJOb1N18CvQQ30Dvp11yMenar3KZ7h+A9+J0x/+SlshchDTG//82rA7tMsTrF5JYszbGyyfs8sUg1Cs3jDoDWD5uz5Gx+2yDEItNk+LO4x2MKes8WZQWjNYhr7XFAdKDivZt9t33vgwAHnYd9t+2hqanI+u2nTJseVwOxhgwsqbV7p+9//vnbt2uXMH9l7DLKzfdn3Wrxl46ap5NnzwfLZ6/a9phxRe+6Mvv2Zv1R39TWVJ6Vr4+yFmnk36i6oGYWkAr1gveTv61XH3t2qPPkmZZfmL1ui8M4m9VVij0RyMWXOIhJ5hSRNUfMwtTDU3tzYoJI10dDBYxp4aTu2pH1K23y7ku5YQuI8laRdHJO4lvDjbayadgHleK+R0AYaPH36GIpaxVr0yFZFVKCKYdJaTJy6LbPirD8liY2Kga+pXnXPPqv4+mrFzyXJdtcmBcrLyGny3SimOBG2ZWqwhWx64TmNbH9V6cSNCXfdrfDbV1FG7r0NFMAaqnfvIQ0felPx4dEOoDMMBHW1/hoKI/HKKytFgQZlLCyp3CxyDcFO1YWdVWB8UpPnLurY8y+SoCO2v22F0m5bxeu5gAEAUVidBRzVDwo9RpuoalTD/tfUehpQzM7JHXco/FagK9R/LHlhtox2fC4sMQNYxnmvVaoPcO5SfY1KgRNz1gDOoa5iKmw2g8xZpl44j51YmB45rEbO0RjwY/G6jYqbS4Iwg4QxVebF/mpqzzZdfv0AqiFzlDD7FpRu2tRy+SwqFymo6gEdZgIypWIxm4byTBIPQDP/MHZ25y/o8vPPKZYEcv6ttytq8wMKLSkmwWvXgFNYErrUMZCP5/JVdaHS6GpqUMT8W5S49WGFFaO2FhEGqIYODRyQy+A5rDMDHhTkGqvUcPyYqo4d14y5C5R/x10KKycRbGpwFNxPG7U7UWfq2Wu2grQlEvF+4K+4zEUIkMxDCQTbVdqZa6pd3gZgtrojgBzxCp/HAouEUo3XHgJIO0AyMFaRZai/oQjkCkOpj4QlTY9r4Hoduk15wSbqsc8zdThfO0ndoV6FoWoXmz8bdRaSrV5Uf7AW87a+hjXfXiwCURIsuR11vCUkhLEoDJhlWL1GqlHT6a+iPtM5nlWo3OUDPFzlPBzUIDbBkWbLV0o7iUO1h2vAjSpLoOsUKocHNAB4ZvZricWAFKUsqrSkP1BDYLyF9n4RJbgWB3hJKeN8cVn42q8B6LYqKhcb3vIl2JnaXD0QAlaqfJjjtLQsm2uC4xujHWK9hzXiAI8hFIliUlGHyyxXWOw82iBKK1j5eVtOqrOuSrGpQMKFMxRiyWuS7AESM4Ghc9ipbdPQyJjCSUInFM7H2Yy20EfCG9XDkf4B1DnTFYeCkstgDjdKMf42gDtUi1ComUSlLw571nhTecsFhADccQGABDxYybVd1lDtecWGYPfF9eGAc/VYrqJemJScoqgSzkNaAXWdTL3ZOcSSjrQz6VvOI0p2tBH35ABqPvRN2Pl6xzuBAFDQAcgJR3GKC5Kkfr8GWi4DCgBCpPixMSThn7AYi0raPkCGr/8KVdSqsPTbULuhL3lPFedocdaBWqt2GrVzZvjn+vM2Lthr1jdbH239wcjwiKqBVg2ofu21veqj/LcsvkWb79isJSjNpaM8GbwPts/YZuNDcFyxv22/N/5tz01vN28NXG8H1kSs9/v/t58MznENDb+p5iuH1IeVciaqJOl2vaBWBi1MDhJwDphnCuvqEQC6KexZ/VhFJ+QtBUjDRjORPt0snQGJ/APHEMraqUlPGFDdGsDScnnbT9Bf76UtAUCXrkONdDPdS4EzhtMtcA3zNaCsAcArUyL1Y7voHkF1CtWs0Y56xnuURoFhIlEXc8UU8+YwFCGPa7z+NdSbphQPgByVtYyx2/o7wDkPSlhte7HPPITtIaqX5YzZefSlbsA5nmtjTIum708vnou1OGUPBTjC4jUwxvXbhSJdcw19oFtZ2QBdtt8ogDVgW96A5Wa9+houYTPfqoySNOBZgNqeJpRnGxhWE5WQb2q7gHMGBrqtHzXlRyZMOUgH4OEaDZtqwfa0GkWuavX3tAEZBpReuJDuGqgqDPU7IDd/+yV1VaKCinVqShHW2flz5IugH3JjGzveqCnsXbsY40UslFo8U5GFjJWc8qHmeg1WHsciPID6LON4+nLqmrK7GF8G6oEf31QPapohoSjSFc5UdNl6ecOBB31pPIDfBy9oquoZflbKDVQYwIZ7DMBp8No+RU11KSl/BlA95y9mNvAddu52hIyHbjcgkimCMk6EGDSH7fl4Z7WGsI61VxIKy+mXZ5GERxUNW/IJLHh7r2xjn9hP5s6mTdxBXFRBOYlJJlBVA+Dra3xNCRH9imI8cGevcaDIEaDzoTrs07H4jS/ZjDIpsJmLhRQo3Wm8ASDrJYC0NwjbBp1YJxZYOoI250ldxflAKAWbSLNWnWg8rQng9th4VL0AKMcVi1VrK0PdEPBSHu2JMS3CADFsaUPDWEJhWnjAc5PAZbYIwzUEG9WsYWw+e3vOUuwhAMASwLmljMGFtMMpjXU3Y2l3Edi0QeksJIlJX0KZKK+VNdCF8to5ddVfUD+gYP58bH9pA3JnOW1s4NwLihhpRm3vdoDv9SwoyeFzjFWTAJfNx+WpRjmU38NziompiIuxkuWEEWcA5QFWehoOaLCtiRhkjuJL1/DRPo1eeoJiAUwWLlNkwb3A7oyfxMZ8kAdFAibz+wDkgZpcQKz+cWxmO89pqPMq6l/hXIMVjPeoeTkQHwtXgM18KACawmMYStNeAMXQaNSiUZzD+5bjO6IBIEwv8Vpiwe0wZXdwDlEC6n9TE00ozKLcHpPHgorS26iXXAoA3IbFrL/zkHrqTqNyNKrU7FTFOse3jPZNef0AoLxnsAqlRhQUY5MjlWpWrZyTrmtN6u4dVXbRbCUUzOI8EDuJ80eMh2k5+0cRmEUXIcBz1y1omzXSzb6wEfZ7xpXBeY/MQvEZy1oD3nwTVagrH4F/C6FPRA06aw4wJa9xXQcmmgAH39Aw58+DolEcMVRUAf0E4Jy81A2KeQGshv0o0U4xhvqJucMmW+XuvYKS4jjnbbPO9izSMzvqUFsaQwEKxaYF+coqiFRathulJzf5PZc8wwEdOzCufXuuqbuvTRWzc1Q+OxfgJFFpGW6lkntJwO0HF2KuQZde3dGrbz++S0ODY/rAg+v14L2FAMC2AEJqA8J7YVuXXn51p+bNytL99y5VenICC6jadeZCHZbt6XAbRaqYhR0zXZe1ijePePXUE6iPVR7V1ocKAOfKlJUcqprKMf3DF98AghnSpnXzAefylZbDtWGKwCxQmhiXLl8c1ZPfP0MMMqn8vGwtWZytfNTvElNCUNCk3JR9CuXPfa81a9++K6gv+jRn5mzNmpWudJTrUtNDUJoLURz2rriCcvoCDjj36c8eUf9YurbeWaiHH4xQQQnwDcd3rdajSyoI3pwAAEAASURBVBd7teG2OIC8Rj3Lop1pxTm7st9hs5iRh/ODK4Xo0el/LRa0uYPHH/+udu/eTbw+ogUsePvQb/0mqoHLnTn0EOJEZ6O/tlhjOk58hzp+j57+8fq1v+3x08A5H9die1tAzzxdr6MnqlFrj9OcucUqQ+ktNT0UBToYImC3CJQ966p8+sF3L6m2egrxfWyUFycrNwflYK7DtHRUiIFpI1G15JvV1oDi3A/bdQD1ONvXI49UaOGSeEWzIGuSW8WdL09p724gaX+THno0U3PnJ+jg/nHKAUTPgq/7H0jSui24BQDI2v56evx6+vtjevFVn+KIXf7g45EIhESqA+GQXdun9PjTu3AHjdfDjy5AiS4REN2FonuXtr24Q6++vFPFBcXa+sC9KM6xkAOQ2BZfOLEEsL0DhNO0nRb7n8Pin+nsvGfgXPBbq6qqoHufAZz7qjPR9sEPftBRmyst5ab5Jtr6uXG05M2rrAYuryjXww89rNw8Bux3sVlnYit4P/e5zwOy9TsrcT/84Q+/i0/+fG+xGx274X3qqaedlcvWOdlNrgFxdnNrk7P296pVq5yy2KRoYWGhA/O9n+xap8G5n6/9TH/q/VEDPdiyvPzyy/r6179OMNOs3/3dj8v6VwNlb87tFxiZbs4Dmi7VDTXAHI+aCGb+8NNT2nHcy2onacvSEH3lC+HOSqQb3jr96y9YAyzO44bVr7/9qkfPHeSmsgRw7s8B51aTsHmfbxZvmeWYrXqypPDChQsdyfXgRP9PO3yztjY15AFWJJtCp8VT+/btcz62AQUOU1h5rzcrZxD0M6jZbEJv1s1iWSurJcitrD8+0X6zlvvGctlinS984QvOebVFPO+0GXxgCq1/9Ed/5Cwqeaf3TT//y6sBUyT/67/+a5m17tgYd9Y3bKaa/fjjj+vOO+/8ERRyw8vTv07XwE1ZA499ZUpf+o5HODTpdtTmnvhuhHKZzOZWfXr7H1YDBrXZw6A2izVMOS44uWkQm4239noQXLP4JjgZamOvwXUG9N8ILtjz9rgRWrPPBN9jr9nfwX3ZQktzUrD+1eIfU72zidUVK1bo4YcfdpTvrHz2OZuz+8EPfuBMjJs7xMc+9jEWZZU632X7t4d9r7lH2JyTfcY2OzYDtZhEVPPli/rOZ/5GzZcuqhRQZvPSFZqx5X5FziVxDGxkqgsBbN66D+5T5cFD8gF0zVu7SrGhY+q6eoYEdKuSsSKKj0qSLx6XhIJCRQA/hZeVoPKSoJ4DKIS9shMdCiZT71il6PUkM02NLWBgHbGVA4CRlvVMoLBWrfZXtunshVMqXDJXsz/wgELYnycCwM0VgRUjdWkBNXyIDz8rL4mBmmefUUItii7zZin8XsCo0rcAAJLvMGskwEjmMwtc89xTGnhtB9c21rJb7lYEgLeb84XEGOoZ/Rp67ZBG9x0EvkFR55alGk+NV8vVy8BKWJuRKLQ6dCcCzlGeqIXYqqHERYZSXiDyE9teQSUsWrOISVOXLiPRjZ1qONASQCAlRQWLdoIqw8TFGtXt2a2+q+c5j+VKBIYLW0QizzJmKDxQGN7OJLQ9RiY0xXjbt32brjXWq2QjinO3r8RSFegwItypN7LIlIGEWnuHJg7uVysPPxa5+RvvUMQMoImUBM45ZURdZ+r17bq4dx+WqiTNlq3jq1xqO3sSi7p23OHCse5LBmpIUzjJvOjiTJL8RZQlRiMXrujCtpcU7x1XydoNilxzBwwVyV1bXEUCk7Qg5QVYoJ4nLgAK/OBJRfd2KwrVu8j77wciRGUFqQxTIjQBFJaO817ePzmiKcpVf/Qw4NwxzZozT/l3blFYxQwS+nEkgIGHqD7ywLRjO02o6rUDlpFMjggNwT4QRb1U2hIWXgE/7Qg7Mn/bfs7XYXlc0YqcvwU1lkKN1x1DNe+IIhIBX0m+u9KAENzxlMXKz8cAD61YbtLubkAys1DzAKO5BwAjzBYOS9XQdK6FsGwU5QC1ABC8LXsdhZrYjESFF65UIGEBid1IFHsmWNnTqFFgPS+gXFRSCoAclmEouAS6L1GOQxroHVI4dm6RxWuoc8CTAJ8D+nINnAHyO0Div4kV/mkOMBFOstkfikoSIIrb08qxnQdEAJwwi8KyQkUDrPmAGpqauoBfSDSXLVV0HKoxJJnx3uNzJH+58uw4HSgQtSVfDxBl65saRJUngAJFVEEpqlO51CP2xtSCG/s4LwpDXXUNKC8WAQIAJyYWcvzpVgwS+tc0WYnVHi4qoaghxQHnhMaRLO9rViuLQw3QyMw22BDFFwNkSFC73K2owlzEXpFj7OpQInBqYi7Wt5kk22MB5MKAenxtWP5dQI3ojGJckyg+5cmNKlV3Q48mB8ZRUskCaEWxCWUj/uGM8Z1AEPScTiNhZh8YBcs9bBoHOuqAHlG4SuB7srHJSyWRbuAJ7dTf36mBxgsa7ruGvSn2r8W3SnGLuOxSOX6cYQavAho0A5AuAZwDcnmvwDn6WjsPzvZWP+z8bs/bxk/7LTgeBO9DrwEjHjh4UEe5T2rvaHeUQh555FHyBSt+BM3ZZ4Ljh/1u/b6NKbYF9xf83Xly+p+bugZuPHc3FtTGbYsPDJa3n7ZIaZOjOJdO+wFGGTyu1qvYnPf3KD07AxhrLnbVwDgBXp8CEhpukBdVy9FmQKCxbq6vBMWV0Q8ZkAasC2HEe4eAa1EKq30N6AXlx+zbHStQX8spAJt9XEfhCqm4XYEsHhEF8vq5DrkEyXPSj6EEBaBrlpb+UVSgOlGl6jrP0OYFRK0AzmZMiqUf5frFuBvL1aPAQft5P11+0W28h2sRdSnknxi3UaXqel2T9PdjWHjGVWxRRN5KXm+j/IfUUVOpqIhUpWCtGA7sBBHNmESbn0QltOuw2puqgevdgDQlQMV8Lhogxg+Ux/dODTbRB6AShQ1sWkkcKmHYNPe1qLsKy8YAIF3OIoUbfGOADZbgATsml81NoH6maOayUGcbxpq1+zi2jYwX9OERSYWMrQsVoE/nQ8DQPfK1Ao5VXnZUTWPpZ8OyKzQVkUmXQh1O2OtX1FdX6YifJRcVMOYCzgHwjLY0a7D2BPM6UkIxY1zaUsYBFiMY/IwN6WjzabXXHwe0GVMKwF0UQNOUA1ABzvmjcZRkvK/5DwV6L4kKkrtkq8aHxoCSAcgDvcCS2NoDkCsSIBLrdDh9YwLpKwCuAAvdvk7oPZTbGFuGWTQRYKyMTsZKNAPgDqtLJHEdWGa8HYDoyi7FuNlnAYqsWetoCEW2I6AjVFT7sDRv2KW4UOKBXBTnUCvzA29N1u3QSNMRRSZiv1p0L6cclS8bp2gHAVTyvHWvyN98GFtR4EkWKMQUlsGHbVAgeQ3nIhaoD3CO8k22obzXfpFwASgnqxR7zAQUabsc5dGEnEKFZc5imChlzQV27sTBXg4ykvHePU5fy8ILlxf4bvi8unqqgTOHFYcybnLaXBT1UJsz9UXgwXEU2XqBsKcmG7Brz1Fs2jIObw51QkyjbsYbxuSGc4ynHcqeg3ppPuBcaC7gXKOGLm5TBAp/0QbXZ6zl2kHBmXsGpPoAy05qqn4f4yXWydgCR5YBryaRDwoA508yrjFeeppfRymtgcUSsxVXvprPYjN88SmFUtYwwLnQvE3UN2AZMKvLTiDgqN+NUrFvVKHE7exck9jljnUBWU71AOpjjZ6xAsv5GcSnxDJcs2KBgO/qdrqPHoUWz9JU8crr4BxgZMAzKX8vtuzNzPewiCA+bw2w2D20Fa7vfq7LlhPqHwfGQ7HSoDqXKTqyECEwUQdTeFjdQLqTkxNKyc5UdOEihm2rG+ZsUYvzA6TZWN/XXg1ACTw2G7U9rtfeay3qHfIou3wuVqgVhA9YOgdYZGN9EyMkeBWxG6rAU5Rhsk1jPVjHoyA7PhUKgFcA+EU/g60xnRv9CHEFansdZ4/Ji7VqSu48YErAzlgUOkPt/qCDRRJAhbWo+xGTxtFmYgvoo0KJ30e4F+y74ijMBcZ7sQEOpf1EKhJ75nDO2QCWvpHFa9WdtEFHzvt16GA792uJKEDRfyTFoOonQLpQXPsilZsWofaGSb1xuEFnLrUCjCFOEJOltKwk5jKlWeURPKKUnRlGnyZt392n7/1wNwunvHrw/jW6c1OuUpOug3Md3X69tBPYZPteFKdSUKS7RckJKY7lYmV1Iw5W6Y6iXFFJuMIBaey24sJpr555cpj7qkO69/5CXq9QRlKEY9X6pf/nmPoB9O7cPF+/8Rv5SgbCQfqPOndz6xRCzBHAhrVNR4+3c53AFNIPxWLNmp0RpvKSMM2aGans3DDVN47o0NE2nTvfrwkP9xDRqDPGAy7TJG5dEqfZ5dHKBPIJp0Bnznr16b/ZzTnL0sP3zQDAiQCyC9EEUF1N46QqL/dq7bJ4jXAN/Y8E566HbrT3n7bRj90QV9rvFu+Zmtjg4JCOHT2i7zM/cOnSJeLnFCf3cj/3ZDZXEBpm/df1LRgj/ujz7GN6e+9rIFi/wT3b3/b4aeCcn/GXdUo6cbxX+w406ErNlCZ96VyHAHHJLu4JcMyYF67SmSx2ox88+nqPTrzRqc4OU2xNVBLqwzk5LuaGQlQ+K0pFpSwOjA1RZzPg3Pc7dfjYZc2bk6QPPFKuufPIeUV4ULl267WdAe0GeBsdr9WDj+Ro0S1JOvDaGOAcKuWJKUC48bp9YzjzXjaH5dEAoeNL/zGl514EvnePA87Fa+36OKyy3wLnnngO5iFRj/7WIi1Zir13bIC+p02vvLBTr760T0X5ZUC/92vVisW0T8YRoi1rijYXEGCwst/5N1h9P9fP9xScs4DckpumNnfy5ClnAu6jH/2oozr3c5Xul/Qha2R2w/DR3/5tncd2YOuDW51ETmYmvfO72Ow4BwcHkaq8C5CtQx/4wAecpN27+OjP/BYrq018mpLGV7/6VZRThp3JS5tctdW/ljw+j91FGCt2/+RP/kS/8zu/46xs/pm/6NfgA9Pg3K/BSZou4i+lBmwC7PDhw/rOd75DgmMPlnMz9PnPfx6PeVb6MTF7c26/2OB0cx7TdKmCNTA8KuTFsSn74pT6uVmZme3SY38Rpgc2MWE1vb2nNcAcGyvBfPrslz3addKvuWWAc38ZoTUr3v/gnKnFWWLXYBubzDf47TOf+YxjN/ZuKrmSpIFBVa2sIv37v/97J2n92GOPOfv6m7/5m/dc3cpitra2NgfisgUaBufdrHCzlfUYCUcDCW9BeWDdunU3NeT3dufbjuHJJ590zq1BWfb3O212c//ggw86i2ZMDdv+nt5+tTVgCwBMHfDf/u3fHLjjxm83wPQf/uEf9JGPfORdK4Df+Pnp36dr4L+jBhqaA9rwG1hzdgeUje3BY58O14ceQJfA5umnt/9RNWDwgT0sVrGfQbVciz/MetXmcwxos3mk4HtszLKxyAA1s0U1W1aDvINjmb3PXg/u136/8TX7Pfi3vWZgnu3f3m9Wr2ZjfgXLzltvvdUZ/8oNSiN5bZvFR5ZE37Nnj2PF+qEPfcj5aXNLBnpZORzg6waQ4kcn1MrB9zRevKDHP/uYWi5eVAW2ZpuWr1LFHYBlc0gcx1vyiaTx5Lj6jxzQlf0H5Rmd0Ly7tpAoSwIyakGtAcCoESCna5DkISpiAHpxTMwnr7pd0UB8fUdPaXTbblSkXIq9Y6XC1wO/oNxmig7ISZDAstXKBMljQIlXq9TGArNz2MeWLJuvGQ8/IFdOgSZZYW0WXRHkHM1NzBKsk4CMfoDCqmeeVGLdNaWh9hFxN1ZhxYUkvlAvmUKJityhy7L6QF9VT/9AQ/t2KC83R0l336vwRSh8JJrCDuhNb5+G9u7XxGv7ea9HsRvWK2TRfBRVuoD5KuVqaEShqldDY+OaiIhWwtyFylyyXNGAab7zJ3V6xzbgqWTqbYuSge5C01BkoQPxm+oHhQ0JAPsNeTV+AfULwLnha5dVUlamhI3rFb4YKzED4QxEM78V3hogaRUYwxbz0lX1vvIyVq31Kt+4CXCOBGwm4JxJNzjhD8lSJtV9gJaT+/eq/dDriKLlK2/TFkXOJrmbGEdd0KaBCKb2bdP5XbsVkVWk4vX3ksjOJzHeghpbFRZXtGvgpFFU9gLhHiXnpypzIfMTObOwHW3Q+VdfVhJKHyUbNit65ToEVUg8hpi6AbP51j5Iapoa2cSZc+r+4ROKHUBV7VYsee+5RyEoh1FgwLe3OlQrN23bEteT9ZWqP3RAVUBBs+cvBJy7U+G0GX9cAodl7YKPcvo4SqA2FGzasTqt289q9zAS1NRF6gLeYOAcUKgfBSUHnDukCXekoheYlWAeVoDHASzfQHksGXButXxJy7h2AQppclAfTrI5gNIOcnZAU/UaaqWuAE9iuZ4jswsBDGbi6lXCsfI94yFYpg7KSyLZrN3ispMVUbBK3sRbyBfHUE4SumOAc9Wch47LikxIVVT5GgDGItoYqiZ1r6u/bxDo7xbUYmhjlM9ADGhUhaDgMt4IGNjajNMxyVeANUuO+0zFxdSApoDCmsz6rhblGJ9Sy4oUjZWqr62JvqkP6GIedni3AtOhnAa4YfBJAHDOFGCoIMRXOuUdbeKckwxGDU9RjHEFFdizlQMWoDSHOl0YinvhQ6hENWLVWtukuIxSxRZhq5iQB9yGsh/7CQyhWnWFhP8gi76zZ6EkNIeEPIBeH+W4dJ7+YVQZLGiKKQA2jCqkfqNJ5LXBIABV1pxHwahdiagVxGVznaDqhlwN4ChJcD+WrIBzo7UnFQkgEpPP89Ep6mzo1UjvuNKx/o0ruoU+qZR9AtIa7eHcL5AcMsU6F4qEvdeASlCT6+sESsQuNrdYkamAtFhVOsp3WDb7SdQMY6831ANsmxwgoX+rFL/EAedCAoMAq4CBXfRpScCxfPY9B+fod3+0Wd/DtWBjxI2b9f+9xNqXrlzWwQMHdfLUSf7uRT0rS6tXrdY9XFcFBQXk/rmu3tqd7cfGkeB4c+OYYr/b2DK9/XrUwI3n7sYSW7v4L+AcKrPmVuSAc/3H1F6FRSL9Vwpwcwp9SHgc8JAP+1NgHchceWuPaxTLZx9gG+SSYsqAYejDXFFvgXMAyho/jVoUNqljKJRkrsRGdTYKWefkr3md90UqdNZKeTOW4qyZRxcK+EshwWzo/0bp57mGRruA14CuOq/R73QogfxcCBaXrqRZXGeJoC8AFPSVky2HgIf2Gn/uKHeGZQDShtAXGfQ72Yfa1T7GiH0anwBmnwE4l7+SfriJ8eoI/UIt+bR0pQIqhSWWc31DqlgbH8eKFuCus7kauB5wLhsoCTBQMUA72HAGgL19wM2DwMEj3dWAcwn0NakAMm3qqmrnGFDZzDEF1hmMH1kMbZGU2RZQAJTRD1vs4cHi1YNCqKfrJOPGIGqWedhpzlVoSgXvJbahLkJ8HdTZSQ2iCuwGTovJLUORDYgrhnIaSA646G1BoampCmdOP8pxOaj2FtFVY2Xa2sY4dxL4xK14wDmlL6OuM6k14GBUmCZaz6IIelwxKM4lFFbATgFvA8H5AymMk/T5gHO+uhfk60VNLDEfm9MH6K9ZZFl3UNEh2FOjEBaSQZ1EldGN0j87XYMNiGYVj9LcSJ08bec1jF39FGcqFvgulnHCgZxDgA+J2aamUFTuAJy7tEvRrj5Ab8C57DXss4jjo5KmGK+xteyt2as49WA9joJxLvAb45EHcG646SiwYfZ1cC6Jc2PgHEUIjNXLV/+qYx86ATjnZVV3dEG5Igs30H7WAuHT97PvwGAt9fAmbewCoBJiJKgijk8mAs51s/jBo8R8wLmsmTTMQqDDWHBJQCD7FsawEJSCA6iI+VBPHQDuGxjrd8bqJFR3o5MAuAxGcxE7eQcZr64x5p4hLKwHnMtWjIH3mktBqTdsg3095zSMDergaJvSZ5RhV2uKZbk0i5a3wLl2bNlXEzOtA3ID6LKxGCUzf9ebLBwAQB9oB/pD6axsNe2YmM3AOZQeA8MXUXnEPratFsVaVIJnrGHBwZAmzv0QNTjAuEKzvl1P7MjCBGeMBykIRcmX68oPAO629tUFgNnBdTDRroSkKIWnU8+MdYoA2GfxgQVBgdY35Lv8Eip8/QopXSB/4WoWDKA4B4AR8FJPAyc10bJDgyyGSMxbq4i8hxhv+Y4+VKya6WumorBYXqYkoNZQlC2vqwaaBerrXE8nsOb0KSknn3NIHBFLvQEVCjDeP9GIXe8Zzl+N4hLDsRsuZXFAAJvedvURK2dgOx9tbS46kxiJc05MzB0LZ3GS+hvgGmaxyACAfP81QK0+hQDkJtHGIhOI2dyxvJcxlb4sMFij7nMAugYB587iGuO6jqevM3Boqg1w7jxWtBex2uVbUKdMoq2FAPtPdnZT91cRjmxUFApsrIKgHcUDIKKk2d+g4W7gxTwU9Mq2qnMsGwhtDEDJp/oW7GYHJrgX6kVlza9Fc7O1YXWO8tJC1dc1qsvVI7pc5xXNVO3dgxod61NRbpQ2rSnWyluTUZZza/e+fj0OOOdFLfvhrTjQbeCeBTiFw+dzfr24vVuv7Nir2TPTdO9di5QYm6ynn+xUVV0jecV03XFXngqKsVxFxY5LVecB5559CnAO5b277wGcu7dcmSnhqro8qn/65xPY7Q7pjk1z9NAHipQKOMedL19l4AoQL+F9e7sXq/hhXb0yoYZGHw6Fw1g5spghIVIL5ydr3YZkR/mqo9OrK1dHVVXtVXvnqPqAbicnBlTKveKG1YW67dZEZ7HCuQuTeuxzuzXlzwOcm6m77kZxMJM+hTilqn5SF893aD11MTbcNA3O0du80/ajOMF5A/MItHrryi3nXFdfp5dfekkv8rAF6TY3cQ/ukbfddpvDn9hHgnFhcE7ixr+dXU7/857WQLB+gzu1v+3xE8E53myzCF7Asd6eKVVeG3XsjGsagFq5xvr7Ucn2diEiFqFV6ws1fw795WSoqq8Nq6rSrldTghugDfQSL42pbEa0Nt81QxUzkjTQJT39wy4de/OKFs5L4Pov1+xZccwvjWtkNFQHdguLVRyjRhv0wENZWrQ0SYf3j+npp9sB55IB5+K0akOYWB9JHz0BOOdCpS6g51/gPtg/pE/+TjzW1YnqBr7dhdvW9554hnwayna/tRC1bNRbo/zqH2rXy4BzO17ar8L8cj0A2LlqxcK3wDm7b+G+xlmASOfntG+LNp2AJViNP9PP9xScswTnv/zLv+jLX/4yE23Revzxx7UcOcebTW3DrC8swWYrb60z+OQnf09/8Rd/8V9uPN+pJu3G41cFztlqZUtsXl8RvAf6Op5BIxlwZiZUOFLAIW6n7CEhofq7v/s7Gaj4ft2mwbn365mdPq6fVAM2KBqIYeDsCy+8iJJBiP74j/9YZn/tTHb8pA//t7728w9M/63Fnv7yn1oDBnKdvujXJ//Mo3PcBHE/rd++L1Rf+qxNwk6f959agT/jG6y+T5zx6a++4NEh6n1uhVtfIzm/apkFgO/vzRK+f/u3f6vnn3/eWShgK51skYAtWHintha8mbDXLTFssZHdWHzpS18i0ZGsl7gRtNds5VRxcbFz42G1+Hb7s3293fOWZLBY0F6zhHfwPfZ+s6E0AMgS5RZb2vfYZq/ZZu+1320f9vuNSQ97zh437tP50A3/BD9rTwWTHDe87Pxq77mxTDeWNfhe+55vfOMb+sd//Ef95m/+pv7sz/5MSUlJwZd/LX5aDG8qrF/5ylechMBPK7TBA2bXapZ0pqAzvf1qa8BAkqefftq5Jk3t6MbNYI2Pfexj+tM//VMVFRXd+NL079M1cNPWAJySPv5nk/rhTq+Y49f9S0P1718PV3L8dCx00560X1LBgmOzjb1Bm9Rz5845izQtHujr63PmnWzsNdU3+2ljs20GqT3wwAMO1BC0S7X9Bcd4e2/wd3s+OMbbT9uC433wdwPo6urq9K1vfUuXUXKy+bitW7c6inI29tnnTM3X5pfMqtXm6kzR12Ik64utDBUVFQ74b+4Fwe92vuyt7/NT9gaO77uPfVZtQBozUjK0afFyVWy5B6tNVClSI0mkYonUPwh09pouHSOJRiJ14UMPKwlrJVO4CAxjddoC3NLYjqVTlQauXpQ/Nkbpa9Yp/daVKGQAs2zfQyJ8TPHrblPMRhJ+6STyzSopLIoknCXEuAhHUSCrQnEOhb1zWMIWzioHnHsIFYxy+WIBncJMmYt6AhTj4AHnKFdri64980MlmuLcHJRC7tykEFwbXFi1+k21jUVBboA0S5bXP/VDjQKAZbHyPu7erQq/bdV1xTmSUz5UsPqpw4kD+1htTaL2rjsVuXolST5UJjpI4Ld0kETrJoEKQFlV56hflK1Yrex5gEe1Z3Vu+0tYpcWqYv1dSl6BLVdWhqOUEnARY5J6dkMGBJC0nLxaq8Y9r2ng3FkV5+UrAcW58JVLScq9pQboBszi3HhJEronAQOxPu1h3uBybZ3KUJHLXbsakIukXzjZMNIkBpdZQjHQ0SkP56f99dc0mpShgs13K3YBSVDAPrfZLtWiwrPjRRTn9gJCVahgy0OKnAWwR4LbT0LUg5TESHe/+lsACirPKXx0UKmz5iht0z0a7enTuRdeUNIoVlSr1ytqzUZgOEAqC79MhYf6c5Le1Pfk2QsaePIHCu9oknvBQkVvvQ/7r2ICXd5s4JyV14ruqM5NoQZ3VQ2v71fd0cOaBTiXi3VtWGkJyV0UgIAIcajhM/zP+XZZsrzjdaz99vM3tjSoCIVlUncAXTQkskUksRt38zgkT1gsqoB3yx+dr+EGwLnWE0pITVF4wWogt1uJzxNRrWFCnmvMFcIqMv8ISfRGwK0rGulp4fVIxQO7heegfheXJw8wRwjJ6ZAJ6hJllqnmVzTUsk/R6azOL1xHPd9O0hooA6Ul12CdRq++huLKVUWmZKL8txHAr1Te/jNAILvV34k6T/YtKLJtwqaUBD3XgJ+EuHpQ2OHYRljQHRYBOJe/SGGo2fnDCh0Qwe2r02TTMYCQGnm4ZrLKShRNO/DSLzW3DCgacC4GcC4K2CMUjAVPQMpDvRtg5gMsG0Sdh+Mb7+9F9SMS+8VChQJRTsWmU88oS6KmEsp14h6qAvg4geJcHTauqCgVLgAIyaeeEzkZKJEMXdLY1d2owE2hXrME21mu51jAOZLJ7VdPaXKkE9WXRKzZUEqKp48giU3qFQvUy+qpOQeUhuJcWISicm4B3liL+gvW0ygnydum8fZzGgEYiQhDca6wAHAuVW3NAKt9Y0qnLpMADl3xc+h3AG7N18uRJKTOXUAqHlSkWi7RlptI+aJICDRhCkmWeFcI90WmwmdKWCOtGm49r8HOiySjsYQtoA0lrQB8zOQ67QEMRDUPq9aQ1FsVmWzl5/6QI7Dcw4ULFxznCFMefeihh5wFS9YX39h389a3397q550Gbe/gbxsTrB93+mbOqT03BfTR3zegiwDNu7DYOnXyJMpf4yoqLNKatWt1G+NAOXbUkRFM3Ni1xFhlm+3LNiuLPWy/9gj+7bw4/c+vRQ3YebPtx9uVxRr/FZzbzFwyY4JvyLnG26uxWe3uUCpqRqnFC3AtX0DjMJvlHqCyixqvOQKYVU3XTZ8TmYTlI7brBfSlKD9CdXEd8vzIG+qu26WRiZC3wLm5ALqXUJw7COQKYDtrFcqm2BtjSekDzjW4GRwKQGyQCwXLbPq+MZSkJlhkEBWXBDQ2E3iYcTwyl5E+EtiO/ht1Tl/LAVQ4sXHHqjW6FPAmFytyLFfdU/RbY1hmt26XByvXSYCa6JnYeBs4F2jRSDuAYHM9ydV0ZaBsGZk0h/43l/IzFg6dlrdzl7par8lcwTNzTcltNQHarbwOKG8qYaNXAANRnMOGM6MUFbFcQKeuflTs2oFmkgGEZisKO0tXNON4CP0zpfYT+YQClbmwup7qxBqyi/54olexibGKySzEYbaIPhJwyPkOYhqvKcqdVV/NVcdiMSGvkL6SfQIKO4A8iltTLecAAOlvsa1NLSqCIS7g8yHYcwJdXUN5MxJwEYtTf4ap++Xy/ViXDlUCHKL01XBCse5hAPhyIKr1KOrRL/qBmx3oClvwhhcZcy7zVIEiS+7T+MAIixwOwUujrIbdakg6sGQ0wCHHR+Nw+ka8XVHxQimwu1JDTRfoU8cVlYYKFzBYWGIBwzdjHOOKxWxebEynsP/su7hDEZ5Ox1YzFLgNCVIOD/VgoKTxrssoiB1XEra1sYBTIdkrOPZwTdTv0UD9UdTrUM0pRXU3GagSOMrlBdYarpWvxhTnTsozgWIaiwoiDX4sIu5IXUtcABQFJB/orwWAPom66GUsMQETGavHx+PV39pFWDqF3W6RolDXdUVlAiZiR0vbIAJXFKB/yAgxXV8DSmwXgQKaeE+C4rHmjUoF1IrMcFT4XORnQ01xzuxom1CHG69Wai7J/gzaYAjKgj4D+Bh3Os5yPRH3jHcoc1YZ45qBc9mAc40aOf8yVq2tKDCvlTv/Di6SHNovsBptyNdmaot7UHLrJqabobCyjYpKYUGoG2VXDx37yFnaxx4U5+qxSp+HVfFmvm9YE2cfN5wJa1fGz9xViMradRvnKIS5AeHMvjTgRX23g7GwBSBsZEBhKJLFApG7k2doKrSAuBZlaGIxN2Ccv+2YfNUvoPgMfIbNbKAAQDEyD8U6i58ZLweOAzluB0bvUwqgXlTuBzmHk0CZ+zTYdEQ9qMTFl3LfAUQW6s7n++kPRitRwt2JNftpYhtsBDOwlSeecSUsog6IHd0ou45XYe3+Bm2kDdUkIPeyQsowrqEGbNZHXEorvYW6ZvwGevf7aRuUx7hVUzn2A75OYrU+0A1kSX8VCbgfnU7dpwEemtUzvRGdJ+O5KRNeU885FlOM+OjLABuLZqOETV/BQhxNNQI+vqG2OpRu7ThyypSez7UcmOLYmtRPXBxJrJKI2m1EPNBjOErIozWcO1S0iblD0ojRyh+VP6aM4wFqawmorplFeK0DunqtA9AMq/jobN2zsVR3boxRWqo4Nr+asVtt7ZzUpatdOnqsBnv7YS1fMF8PbCkAXAnT/mMjgHN7AOc8euTBVdq8Kes6OMdQ384Cvxde6dKLr+zjvZm6755FSkuIAzDr0sWr9SzcStGWuwsdq9boWMA5PnPiqJcFykB7V0/ovvtKAKgKlJkaologt6/+v6gPo867aeNMPfJouWOtiga3jT7cYdg1w+bzozAcQDnKpeamSRayD+jUmQHV1vuAZny67/4CrVydoCQU5kYGUTFv8vGeUbW09rLoq1O1tVNavKBMW+9P08w5fuC6EX3u8wc4vmJHce6OuyKUkUWfQlkra8lLnenShuUJGgdanFacsxPwdhvxncW/9MV2f+RstHm7p+/p7tEbb76hF8i7nDx1CrWxHMSitmgz91dFjDM2VxCMMexzwXjx+k6u/x38ffrne1cDwXg8uEf72x4/CZxz3st5tXthey+3AurqDeAYFlBry7Cqq7p1+sxZQNYeLb11MdbNczSzIob5KxPuYt+tXjU39DFX1MP9Sx2waqfue2AZsCuxCIsLn8V29fjJK1hMJ2jrwxXwSfEKY/HcyLBbB3cHtGcnDkzjrbr3gXTAuThUMydx0Oxjjika4bIYbdgS5ti/+rg3ZP2hXn5mQi+9ghpn2Kh+/3cB5zbFA84B4L2KVesTL9BnpeqR/2WKc0ksDHAxp9ajV17cqe0v7VZRQSng3D1atRJwLhxw15k/Ybyk47cWTo/Og3kEfv6823sKzpl629e+9nXnpnAtN2eWrDTb0B8P3H/ewr5XnzPP5ueee85RqDAA7VOf+qQeffTRd717u/H4VYBzdhNrq5T//d+/o7Nnz8gU8cxqY8mSJc6Epq0CPnv2rDMR6ubGfBqce9en8Ff2RoNITc2lsLDQSVD/yr54+oveFzVgg5wlX773ve85qpMG0K1ZsxaI7p8ciNbgipt3Y5Se3t53NUCTdIKuL37No+8+69MITywF5PrGFyI0j5/T23tfAx7ySsdO+fTnf+/RiSq/5s1w6+ufjtDKJe/v+rbE744dOxxQzuIhS+g2NDQ4CwQMfjJr0Rs36ystMW2qVpaksPfb7wYdW99p4NycOXNY7dblxKWWGLb4NPj+NGyHbHGCbRbnmb2rqcaZwm9qKiodvNdWYvWiHGL7s9ct6WH7sfjMLNYsNjRrWAPS2tvbiS8/5cRoliS3xSWm9GKxm+3D9m3fZ5+15+yz9hmDwazsWVlZ/8k+1caDkZERp/xWZqsTK5d93pLuljyxzb7HXrd9WvlM5ca+z747OzubxRcpzrHY87bYxdS/TMnvE5/4hJNUtyT5r8tmN4xf/OIX9fjjjzuWsz+t3GbN+4d/+If68Ic/LDvf09uvvgZef/11B447ffr0f5p8sfa7fv1653wuIGk/vU3XwK9DDVhMdOSET/d/alJ9w1geJLlZQBjOxC3p95s5RP91qNxfszLamBx8WCxiSm7W35l1qo31wbHZJp5tLAomsu15UwCyBQEbN250xv8gqPZ2c2gWC9hmr9nvwYfdEwZfs1jFFOf+9V//1VGcs/kjU1w1K1YD42xrbGx0FhKYornFT1ZG26fZt9t+rIyrVq1yAA+bPLd4wo7PXnPbdxEnNQBofOuzn1FPdZXKYxO1uniGZq3fqOjlJMuzUVjAFklNqLHs2MXkK8nq7CLNfeBBVEsKFCC5SPYalRmOYwTFuKqr6tz+gtr6+pW4eJly121WCODZ6B7U0OqrlbQQGGvDcty8Ckn4oSoGDMdMKPZgLDbwY43U1qqe/ft0HmvCROLDuXffr4h5KFVgC+IiBnOyOHZrSqLZSe5gXVj9H08q5toVJZdg50W5Q4pLnUSoo8rG3BqOWky6etRpK++ff05RrGSJXXcHlrFAdqlpKPFg3dZQo87XAOfOnVRaLpaQm1CCWzAXGA7YBiDMTUJX48B6DR2qff2ImgEWi1GsK1xJMrCvRlV7tqsDxb2iubdxzMBwpUUkyMOvT/yS/LKMn4skh6elXa2vH1TX0aPKwcoocdUaRW9cB0AFXGTtgP98nBdPZAT2qVgCX6sBnHtZlyqr+K7Vyl+NKkkOVm9RHBRSEl739UlkF8p4U4ePqAt4rm3KpbwVa5SxbJkC2Vi3hRP3clxj255DXfCiUhcuU/qGexVq8BuJeasdKoH/gaKaSRbu2qEhACF3Vq6y//dHnaT/pWefV2hdg/JmzlU8anbh5QYAhDoJcj9JNRfqLJYwnapGDe2VFzR6+ZTGUcdKuXuz4mbNorzE+lhdBVCmc4WyQAuXjQDJ+pGGa2ra/5paDh8ABpqhHBSeQ2dgz4V6X4C2YfZqxkk6vB02qu6uIwABBzUBxBcKuBEFgBYSm8ExoNDC5L6nBnCu6Ti2uQAht9wPwFmgAcA5T9sJLNhSydmukT9hOftLoL2REiRx7MIidGq4mQRuJUIwdYATUyRgCxSOZRvZfpQDkzQJ/BXqwl4LFRa3BxWctlexmtttX6vwPCxjM4EwSdj6UTb09tRo8soBhQw0KDIzX2Ez7gScKyORjk0pSjq9XEsJqdRj7joS4rnUDUksbAk97YdRxyOZPjjAuJMMCDAHWGA1SkLF1C+JzIlKjbYeoy9qQtUR+y8UWmKsDXDOWloHycnPgUEDnIsj8QuEEPBxbQGOBgCxTGFpqn0HtoI1HDsqlMlYOKYDs5BQD2Cb6hfJbheWqpaIs8Rwh4Ez2NVSj3HAHtFAdgYX+OkLDADsq3xN3pEQJWTQfrMBCaI5BqzzuoAkxrge0jKwTSszWKaCOuJ+xNMFMHcV67aLsBlYvtF/RaTPog2ulzuOthQCfAtgMNpEAgiYJDomgIUqQB3qeS2tfSRtx5UG1JsMpOeKnU/bobyAcwFre1gP+ieB3QYuoq5Th1odto7YCoalAtNEFTvQnCsMIMiODdAzABgy3H4Bi7gzigsfVkLOPJTvVtDesmkLXEeoJA1xHxeJDV506gw+896Ac9bnOol/+mfb7G/Hto/fDUSx3+2+tweQ9fy58zpw4KBOvIlij3dKM2bO0vp161ALWeHcVzqCBtd3w4evg3LWr9tm/f+N40rwb+fF6X9+LWrA2oZtdu5u3ILxxttatWLjGBg6pdbKY0BjjcwtxCoTeDXMbDBDAFqw3/QNnQTkegPBpm5FMIJ6UeIKAHxF5y7kmgEa8gOYToygyngccBaoB0gqGTgnPm8+/cdleQGv3PTdETMAhTNuYfwH8qHvsEHCFEFdHqC53irUms5jkdjhgK9R2Vg9J8+El0rn++JJABOD0JeGAfmo46h8DdtQmALuyV/s9KVRkdkK9TA+AOB6G3bAtmAPT7I0avY9uDyu4ljaAeeOqrGmin4rQTmAc4npfEcYcKDBPgNn5evcr66OKiy7fUrOzFBs9nKFJAJt0e8jxaZJ+uL2hiv8HFBOWRlKUyV0UWNqr+kiLsKuM38GfR7QFeCcD+ibqJB9M+ZMoPpJv+EDQB6dYFxH6Sk+l/42mTEonLmnAH1dwBYvGqQ7DJAP+F6DLTSwdUoO9tvs182YYtdsYLRe421nOI5aRx0zqwSbT1RgLUk81Nak3stnFI3SShL2r8pa5ai9hfip4x4gH8a4iU4UTYGkwoCbw0s2KCRhHmVEaXcSIBf42dOwXV5U2dwGLZXfJ8/QCAsbDgEsDim+AJXQ9HXw7ga5UVZs1uQDJpto0QT990hnrTzD7YpNQo0vF9AoEdXCUGIwoEeQR+qRcZmYzd9fg+LcboXQ90elFxDXrUYVjtgHGH18okEDzSjSVV9WJvFKAiBUSBZtkcF8sv51rICxzyQGiilFuTd9NjC3qXmxGKW/Xp4qwHMsRgMAi+PEC76YDFQBV2JpSj0Y6Ecs4O+rQ60Pi1SsuVPSE6nbmRofi1NfCzI6xJJJBcWUnf4/MtVpcyxfoOx+hY11ofZWzRgOWIldri0AiEgtug7vRRfSpqPlRczEHcbiGGioqf5G9QHojQ6cA9BLAMimLUUtoa6JaYDlvM3n+M5LGpzqVd7CuQDsi1BwzWacrNPImecU1t/AYoBb5S4m3oxFRZWyBVA9nWx5Q5O0ba8HO9G0MrlReovNmA8wgE2oh/G+74TGGrGE72lnrFyqpIp7GUdHWZzwHew3R9nnYrlzGbciCzgqA+e4X8Uq2TXBQgigskDbaVH5LOCIJjaYC5w3R/6oDK4J2ikqg6EExiHEwoHeUywMeZm4o5UFACUKyVuDgmIxVxzxGna7E8Rc/SjjTTKvmVGwGfgTcI7zEug/jGrjYbUR98cUFisDkC88tIhajmYSk4UarcCRLfQFky7FYwUfS72EAtpD3vF5yjh6WS3XTgFp9isjOVXJM2k3QHBD9XXqGRaKurcqlnYRElFI3EFd0124iIkDKON5GaMnUPAdBxYJJ7aMyWC8TylkvKcPoB0FACWvj9uAvKO2mOcIitGDikvAHrBgBu25iLiEfU5i99t9WE0NgL6uKPq6OY5abhjxWF99LQC7j2ugSElZFYqwOJO2GOh9k8UZuwA2O7iGliqQ94g84cWsZSEG5T5ognIOTgRUWeUFXKpkAXakVi7NBhqLUkEhAIwtViFGmcLSFmFJvfJKnU6/UaccVB0f2FyoZUsjdOT0lL7/9GvECJP6wIPLcT4h9sGqNYAFZ0eHX8+/1K0XXt6P4lw2oMxC5WVFa9sr3Tp6olIpqVG6c0s5rAE23FjgUmXat9ujZ54bViMg5SMPlwLPZSsr1a2aylF9+atHUZzr0ZY7ZgPOzeJaQqmb/2zzEjN5WU3oQaHRxX2QC3VRPyqe3DLpwOEpbd87qraua1q/IRdlqQzlZ6Mkan0b4wFhJ/PdAZ18c1jfe7IDaDBVD2PtuGL1lGpqR/Tpvz7IIoESPXL/TFTwIrHUdjtWrddqpnTx0rDWL4/U6HD9NDjnnIl3+Ic4wf9WHGlCTDR6597fFni8uv1VrFqPcv15tGLlCkesZR5OLTZHEMw1BOOLd4o33uFbp5/+OWvAuQe4IaZz7gE4fz8NnDOr0kmuqQk6lynoUgNafRbrcH/XUO/Xq6+e0psnWIxXNBdr5/maMzdGdPs0B/ob8nBjo3at++C76nC/OqS1a2exwHO+EvCCf/bZNvKTBs4l6b6HKjRjVjxgpZ91ji7tZzHxTqxaR8Y6eC1Vi5fG6sJZbJ+fHlZnzwhzXfG68z5yYNnMJdH8Wlr8euI77dp1YEK5KSH6k/8rRWvXxaoDyG/nq6Y495LmzM4CnFvInBSL2wDn+vuHtP3lXdrxyk4VFhTqgfvu0koHnOP4sI53xhratR0zMhf8a5OyFhf/59iYJ97V9p6Cc5bgtAm6s2fP6fd///edxJQl9H6RzRqFBfo2AWg3esGL9OfdpyUkDzKhZmogF5nsM4U2S6CZNca73aw8v2xwzo7bJjFtlfBAPzcE2CpYItWSpJbUtASo1cWJEyeczmwanHu3Z+9X+75pcO5XW9/vt28zSx8LYD772cecxMecObP1/7H3JlB2XdW57tynP9V3qkaqKpWq1EuWZMmWbcmyZIPtGIINNg6Q7pnHBcbITQMh4WW8kIDBJBAcSLiEjAx4QEYugUBIcIeNsS1ZtmXZlmSr76Wqkqrv+9Pv9/2rdHKLzkZugiRqw7GqTp2z99qrmWvuNb/1zz/5yJ/Y5i3sGmdB+MI+Xt2kdGHf02zp8KHt0Scz9tGPp+xIv2+VyH//v+8P2R9/gMWT2eMNqQE209r2nVn76KfZSYWTu3oZoOJfRG3DOj3oXLqHADf5lHq99a1vJTC22L7whS+49Kof+9jHXDA3f/fy7WQrf8gO++OktpD/JP9TD3k7drAjEr9N4Jz8p/9A/UL+k9RXFAi+//77XQBZ6Ws2kcpKhxQBFPAW+C5pcqnGyS+TWrF8XanIKNgtX6ye3cbaLLJlyxY7ceKEU0BTIFqQm4LVUpHRdZ999ll3PflyKqPgO72/fv16ayQd1YEDB9xmCNl9AW5K83rDDTf8lwqcgu8q09NPk2akp8cFsPU5KckouK3AtuYFXec///M/HSAnuFBlVV0K3lu1apXbOabv/Qj1EEFz2oChTS4qv1QQ8nWQr9sL9V+1h+7tk5/8pD2Ayoza+JUOPUf8zu/8jlNtVX+aPf77a2A/wfePfOQjBPa2uWe7mSVYRBBCqQUvlj44s+yzP//q1oAWeN/2voQ9tZfACnPLe98WtHs/GbECUrfOHr8aNaD5SPCBfAsBDFLUlC2Tqpvs2rXXXusU4OSnSNlVgJyA913s6NZ7mr/l5yhtuiD8lwPnfrJG/wtmC0z7hCpLHpz7p3/6J7eWpOtrfhc4J19AnxG8L79DanjyHeQv6Fytra1OOVfgnfwb+Uqbga7yirT6rmCNHIuqpw4fsv/16U9ZsqvbFsfitoJA6qKWhTZnPSoj80lhRiA9ebLNup7bbaOAT9UbNlvdmrUE2gj4ATAVoHYQBuTJoQCXbD9lPU9vtWGCyhVXXmtzr7+R4CNJPJ7dbi9tfxxQK2wNq5Za+UICUSVzCCLFbIryZoDvyuvncg85mzy034499gRpK8/agoVLrWgFSnI1rEWyjjbFA4zPfcZq60hvhcoO6V2Pfefbltm90yrLKqz0yqsJwDdZAKg+SMBcQFomNB3kTTz3vI0/cJ/1o2IcalhgteuvJuBGEI86GDp8wDoO7bVQesIa16+zgsWLLEEgYpzUpYUEd2OsQgczQUu099upXS9aFxsbmlG/nb+B9GPZIevds8v2bn8OaCduzWuutMqliy1KmtQsSnMp1j+z3F9BHenqaLch5s/urY+b39VJKtR66vNaskCV0e+I46HKly1ixXtBg8XxfUOdA9Z//yO294VdpN1rsuZ1V6CY0QiQVA50hXxFHPBBfWYMBY+DgEPPbAe6OWgltfXWcvk6AseodZBSdWDvThvbtd1do2YjYF/zWpsg/a5PYLiQlJ2xIgXDiQO3EezGP02eJV1oyxKret/7EOYosM5HH7dB3g+h9FZBCtfSJaQLLokSYE7aRIrEeyh4xKubLESWjdTu56zryUft7FCflTbPt/pVK6yEFMBZAoUTKKnkCquseD6qBxXFpII7bWe2/tDOPvKAtZCNY+5V61HwAJwjEBqoqidlK2n9uL8A9xAAGgiOHrD02edJZ9qGws4cAquowpWgCkSw1h/vRcHoeWCMg6ioVSIwdDtCLPNt6MQOS7U/Z5X0IanDWSkKYwoe+/wDCJYbQeWsY5eN9h4j8DxiZXNQkatpAgxsZuG+nH30McuiNBKKVFk4CGiZ4YFu8ClLdjxGmrBu7p00ZEBowYJyS3D/6SHgkdP7LDwxSJu3AM79unlVS1BuAaBAXam/vRUwbJ6VzlluYeyIR/3nSD022X3CMn1nCWgDVIG+BUlTGgfq8rgHgQD+OFAiSkCDY6QpixdaDeBcEfNTprMVuGyI8UA9L9xgBUXNhBYIHkvhT/cHKDBxlrSEbY9aPDCGilw9fYc0qkpDiJoKxoSKAPYIEBhWClSp1aBUNHz6oE1MTVi8TGo8dfypiIDpOAFrFFza9gOBFaNqdT1QylUIwBFsHQY+BdqZHCSlHWqtBXUoR1UBoxUAy0yN2Wg3Sm+ArkV+BvSCwCpAXbwegA9oAWqDeusBDCStHKBCYQnjBRsUAFxpayON8HjS6hjzpY2kVy5agY2T4hzBXWyOP4kCX/92Uu69RNskrKy82mJzGrEXKMRkAWVI7efFae8g9wpEaiglJYEuhs/sQj3qLOOsGjABoCVa7tJZpvpJtTuasOIld9KnV1A3vxg4J5uqQ/OHfpYdzs8Bet/NLepz5/6u93TodwU/J3jeVABtz57d+NZPujVDnWcNSjTakCK17epqQFigFb3cYHYn+PFz6nz5Y2aZ8u/N/nvh18DPazc9I2tz3o+BczdJcY5NZACk/uhO6z78vPW2M4aYaytrmEeAcoIAMz5gz9TwEZTSAHoZC4WAsOPJjI1iW2JsxqsCwAoZ9mgyAXx2BMAXdUjsQXnzFituXo2CHL+f3oYdRC0SBa0Qioy+bAbkkQfAapOtQMOkYj7zIqJ1pDeMg7tVN1qwEpA9BhCGmp0gOz8GgBU/B8IPHcJmP2F9PaivAekWzmkmbaNSTYcthW3NnNltqe6jwL0lpEdHvbQRcCqE4lzHk9Z+9AAwT9hqSSddXg3wAyCVA/LIkL7RhlG5JVVlCjgvxAbEWCnqY1WrMAFlpDE7beODR6wfpass91+/dBn2rQWzN0Ga2x6Gezng3CqgbAH48lEYaz4qV0kU5Pr3k1p2u4XGWvkbqTxrAK4q5wFnKUrNGqrHfQHwWQiAzkvTHmdtBFs50H3YSuIZK2FeC5E6ldqy9ARpNFEz6+4iLXhxnc1rWgWEzHcp8xQpUrsP7eZDCeYJgLTqyyxaig1Oj6IkdhSwDcVAwDzJXGUBgARZhysWUMcRYDCUasdIQQngJ3XU0NwlFlt5uyXGxvF9tlocVcDyJiCmupsgxhq4HhM/84Sl2y3ZvcvGOl8kFe0gaSajVkAK4ABpui2idsaWSu0tQh+JFSJkCjU+1mEJYO8MynOGMlyoGoCois8G09gzUm6eIaUlmwXmMs+VLUaRbB5QGTefog5HTzzjkooGUfkqoB4Lo6Q+nUqR3rWHuRAAGlIqShkmsJkDk8zwqLOWAVtH+RwUIJxfB+qtx1Hj7LXKWhTVUNGbSpRafwfKiswnlY0ostYABkZRAZTirUBNwCyfNpxq3WnpvlaLFsYA5KuZIpgrwtxnCGgNcC4XLWTMFLl+mJscRakVdcKeZ+jno6jCkf6b+TMYZD4c6wMobbXB/jM26U9Z4+VrAPyu5jzzAbD5L5F3AABAAElEQVRbbWTXdwAdjwIMMg6ATyMV+IDMC2ng6GTPadKbt3EegIhYqaXL59M/8ItQWfXxczOUcxywcorxWDhvE/XHGmYGIPHFb1g4ADi3YD3ZiTfjBzUAYgF0cc8BoHADOBw8vtOCKN8WkgJdm0MCFdQDfoQPuJoTKcicGAhoDALYM3/muh4HFj8CPMaGDcpQANAfDsQtO8l834PCLKC51K2qFt5k8aZ3UleApAM7SUO8zc6ibhUjtl27YIlTu5UyXIo5PNFHOuipEUtMoREUBMDkvFGUd+XX+ECakyOA9vgumZGkg+LLVzLevFEbaztpPSNkwmnBz6wB8Aw3g0jQ9/DT/alB6uUIMN8uQPtjwGp4SmU8N5SxAaGggnpnA4p8GNrRw1/zQpQz2WlKbTx4ljHDOC6hvSPl+ND6LOqYKeq5E98kRdrqyvlr6DcL6L6kWm3FF+4ZshB9vWpuI+OvwtVxknTy1sGz0PAUzxibrSP4ZjveXYhKbhHPXyj5YR+nAFqOnBy1rU+cJGVr3K5e22hrVgEcR/G58ShLy0ibG4mx2SbHmvBJO3Gs01q4xjtuabTL10btyZ0ZwLlH6ccTdsftG+2mN89lozAgCe+cAU759//sse8/8KitXMaz3a1XOoWpZ57tQ6H2IOecsMtWLATAm48SVJBnxAnb8XTOtj+XpC3a7Hff02xvezubxQXOofx2798qJfeg/fpb1wC2rbDyKvUPng9xIyYAADs7xuzIgQ6GVJz0jHNQugUkZgw/v2vUnnk+aWOM82uvrbXmRWyGCDNm2RRRVlRMvw7xeJbieaTHHn6U56Um4MHbq+zKazw7dmKKzCFS1Ftg77p9ORxE1Grnefibnh0/qbSwk7b56jAbJk7NgnO0+csdUpiTvyBfU/7ByZMn7eGHH3ZxkV42168mZnDrbbcRY9hkVXou5vk5f8hXnOmPzvQd85+Z/ff1qwG108w61u96vRw4p3GYAcgdGMryTMB6wGCC58dinnNYY+J/7a1Ze+zxfWyyPGJLFpMO+ZqlFolmHXdUgvJ/PK606p61nZq0J7aetZNte1AeXMoLRW6Ux7/7bz2Ac0dsDSmXb3vHIsC5Qp4hAe4A8B//Qdruvy9to/gqd7yrxq7aWGq9XTn7wYOjgLMA8XVRu2Zjg7UsKgdOz9jJ41P0vS7bezxpy0g//f/88Ty74foywLmcPfxAGoD2fsQv6uzOd68hdbDAOdawhgfsoQcesUcefATwrxnA+G3E3dZi93mGUlpuDvk1GHVnk+ix7n/uD6/iP68rOPfNb37TvvKVr5oU3ZT6SYuC+UW2V1E29xUpeqhDSBlDAT8FGWcO2vM5rwKp+/btdWmC/vVf/5VJpNo+9rE/t5tvvtkFV3/Rc8mw/HeAc7qOAqVSDVHwN79DeGY5Z8G5mbVx4f08C85deG1ysZRIaZoFYXzxi18EBnmUXaK19q53vcvBviVSWpqxuHVh3tP/WXy7MMs3W6rzrQGee+0MDsxfAnB9bysBHdZLNq0N2je/ELFaHspmjzemBgQrPrEjax/5FLAiD72Xr0Bx7i+jdvWaS7fO9TDwwgsv2J//+Z87SEzpqaXA9pd/+ZdO6e3DH/6wvY+gnPxBBRQUeFa6zscee8wBYwLD5KdJeU3qKVIXFjingPaf/dmfuYdEqfRKTe6v/uqvHDym9/Pp7qXQ9pWvfMUpx/3Wb/2Wu67gM4Fm//t/k9oLX1TglXxUBcabmprsj//4j5GOLnA2W2UXAKfUZ7fx0KkgtgLYKovKpsC4fFLZeP0scE7lFYin+1EgW+rCSp+qgLe+p3S1gghVNoFu8gkV4Bb897u/+7umckr9Tv6twEJdU+pygsUE60ldRtfQZhEp2wjAU2Bf6WwVtBeAJ6hMaUwvhkP1pwd8td/zzxPw/AUPPZuoXwlqFEBwoR4/+ZB8oZbzfMulZ0SlMFbKZI2RmYf6qVIH/hopAfK7Gmf+ffbn2Rq4EGsgjXrN17+dsQ99LkXwxez65oD989ejVl/LLsdZV/hCbLLXtUyy1Zq39ZLdkp8xnYXhS87G3XLLLbZlyxbbvn27CarXJkjNwQLq9DmlSlWGAc29+qz+riO/MP1KhdX1859XGfS71pAEvilVq2Blze/5a8p30GfkI6ismktVbvlTuqbU8lRWza/yRWSPpVYnv0M+ijs/gags1zp+7Kjd+7m/QXmh35bjizTjE9WgejK3kRRmNahoce7x7j4UBNB3WbXa5rwJVTegsk4UztJtp62iMO4WabNZAr6khRodIuUToFTVpjdb2VqCe8RhMm0H7fgT1BG+ShHlriHtYmlJLWBSoZF1xJJAYC1brkP5hsDlcI8NAaf1Pr3TvK5eKwDcKZYyHOlMhxKkZSwptUoAsuJ1V1qQ4FPb/d+3gW2PEswesxrgmhhzUKCF9GRXrLLI/PkEQUsRxEEVD3mHyZ1P26nt24Ck+qyGQFhVaRVgHylNAHpGuOfSZYusjhStQea13gMHrKezzUoK0O+KE/Rip3eKIFRH34ClUfJr2rLJqtesJIjI+51ddph0tIMHCJAD2tQAuBTynE/Y3hI+6V5LKqz6iiutCDW19Ei/DT27zTqfBzLqH7K68korB0qKBD0bTU5Ydh6Kd9ddZSUtiy04FbCRx3fYwW1bLTM5ZnVzUTeuJ40W91W5gkBsXSNBeymLESzv7yAT3x7bd99DBGZHrH5ODWmUgLlITdY/eIag26jVLQFavOJNNpkqtrYX95BSr90qgBXLSgADAQzHB/tttL/HBZTLr7jK4r/+TtqvACjvkPVShgE2noQACSoqyixWErNkKGM99KPiJWutet0mADnUXQZ6rO+prXbi+WeArPpR0ijj+bKa582wdadClq1ebI3XbrGqJfMJzPVb/47Hreu+71jF2KCV0n6BhiaCwkus6DIgyKbFgBZ0IBbllYIsQDrWTD8KIqTZSyUIapLOJRwFFkWhLZhMWBjVucDIGUsCYRSsvw1FoEYbPf6c+ad3WzF9MiJFHKULQyGPAUMwEAWXzsds5PRTNjrSZpFYFlChxKLFBGGBz5BoISMo2isFZRYtB44rvgxei5TBk0eBKHbY6JmDnAO0jucGj744Sd8OANZlyE0TYVzGudfw4reYh+KSD6wxfJo0vb2nLYpySVEBMJ4U/0IoHgIxphOkhgNGiwlKAOBMJsc4J8F7IMAA2/kDwHUZgIYpQMVMtMjKlgAcFAOg9ZxAuWSQdHYrraR5o8ULGwkscH8Eb5E8AiQhkH4KaPTsHiuDNytSmmRAmpzwNcrrYTOCpJYLFiwHSqM/USQfhYEUqjwjPaQRBAyNFEcpK22HMlGGYPgEwC7EiJWh5FRSR3AblsEfBgI5sgugtteKGRPRYmC6AgDfaJiUhnA1pCgLUZflwMbkXiTgOg5wR6Ae8DbAmPHZYZYZpU3HBwAmCugDBNILau1sK6kDx5IAMnNJG4v6ZAnpkdV+fN6fHEZ5aC8KVI8RyD4MuxEmdSIbkAoJzgO6SrDAJ61rgIBvqJx7VLvyey454gCfZBcpDYHtQoX0/wgqQ9jTLAo6mUCJFa74bQC8c+AcPoDsrJ5T77vvPrcx684777R169a55zfZbtWlbLdscP5n2WTZY72vw80HNIvQSPczLSXlprHRMZQjTrvnoG2IA5w6DTgCjCyF9S3Xb7HLUXDWc2eE+/OBV/Vd/nPunPyovjx7XDI1oP6jw7XzjLuST9D1M8E5/A1ScNkEaVqPvwQU2gWAS1pK4k+REoA1rUtkAduxKT4SnmWozEWLmoByUPgHcMn5gFKAQnFgIEw69g1YiDkyEwFAXrSZNKrLgbV2w8Y9gcBsDGVL4OsK0kiTqtVD6YrILgpUe4Gat6KGthfOjNTRFXHgOiAg1B4F7JCnFVuDzSPlZxCAhgmEMY/yPiDuSNcJIOQe5kAgvmLsKzBxCmhX6pQ+UHgGtbOyZbdZpHEDaluooXWgKHfiIKpVvhUDYscJJPshlMywK34qYVHGVBB7lWHeSGJ8UtiCMGBNHDstJbgENmYM6D9HHdUuW27FdfXY80HrPdZLbZMSGmXLyFwU5woBisUZoKaXHm3FZjyHz7PTCpiHgvghyNkxP/Ev87yPoipyXBYoWepSQQajBHozpJ8fPAm0/AIpvNuoO6DXwgrsCwoyQFwTyWHaAJwwOpdUhSssxhwFwcb80WE9pIBPkpqzoBgoByg7zPwS4uHEQ50vgz0JAaPp3yT2KszcHSlmnsaHmWD+CZFiM4yfEUqhWksa0MCKdwCyTVr/0W1GXgWrmE+Kydo30y/UftgRpfKePGST7aR6BwrymWNK8aPC+A/IjmJzgGVQ8soFK51aV7hiLlMjf0P9y+8lJWgnMNp4H7bed3OF+m2atkhNTFmCNJTl+D9FgFCBuevph9zhyW1AmDuYW5n78MEChcy5+CEe/TMBbJPyYwDs84G8KyyVRQkQ3y03mUSplL6H7yufLYcvODlB2tzcKO1XYdGG5YD5lYBzA9hgNoU3AM6VLaH9NKcBdwa5xykAza4dtMfT3HKfAxDCqJB5wIc53SfpylOkXIe4A5BbiH8xF2ONuvHIEeZZ/EaUBn38tGhZFfMFvgFQW4ZXgvk7R93XLmGTx9wNdIdFAFntKPd+3wLMS34pkCHqfSF8ZR/gOzkM0AbMEAdcC3GeycSojaNoGMHHLYjxHov1GYDA9Gi3pbNsFGnYYkXNt6L+NWlj+//FoiinxhpY/6rdTApi+gx15wP9k+ecDRl7rffQc9TlEGpppFWuKEd1mTk3xAtwNcC8H4wB05WzQaFsEW1NGw7sB5xDaXW8G38ZP4o04AHQRQUJQhPDbk7OojRW0HKdxRbdAhxG6uLe52ycTQlD+LCRAkFjSvHHxgj8oSQbWnKo2UVYG80yLtJAGJqvgvg9Mfw5j3E5iT87OcYYxzcoq6qzQsA58wDsz5627rGMlbWsBZRcwRrfAmZJNpakab/Rk0BwqP917bJAop1U9RHmdBSYsVMeMLAUsHOoA/sozYbKlJKVtK0+4xzlxdGOPfgtR/FfKRe2JxrCzjD3+9jA0RHmfNRti+evsqKGpUCpcUv1DHCdw8CRrU65LYS/m8uxPoCPHBoXEIgtqXyTvdB7mf3w2SGgrzifAxwF0p/E3rTzTDFJuul59WW2emWDpVgqO32yh3EyZpVslClgQ0HfYNLasNUF0aRtXNdoN7+52lqaw/botpR9418fxjcZtNvfcT3vN8I80Hz4SR2dWdK09tgDD/7Ali9usLe/7RpbB4TS2jlmP3zkFM99+G7JCjZMsTkDfndkdIJ19Iid6aY9c132279B2s5bAR2J+Zw6MmZ/98VHKfuwveWWy0mTuJI1dSAV+jhuDN/17cWXBtmsvg8l3KhVzmmwIvpwJpOzs+19+EEBVM1jduUVFTZEyuHjJ7i/ZIT1dfkq+MMT4zxDodKJTbpuw0JSztZaQ1PIDhxM2t2fQrU4W2933LYYxbkQ/k0O3zbMmnqOjWJjtumaKODcrOIcrf7yB76C/AVl+FE84+mnn2Jt9j57/oXniU0Uu01zykizaDGK7Dzf54+ZPkbez9B7+Z/zn5v99/WrgZ+sX/2u1yuBcwLf2tsy9v3/POnSOwdj5aRUrnXPp729vrW1n7KKyhGeR9jMVFVtL+7uIeYUY40FoJxnrCwbG3pJRd9L7Kmyasre+vYWW3sFay0TIfu3bw3YMztPkEq50m67tdGWLI2hWo5LkPLtsR9kULJk/WfilL3jznm2YRPQPO7CSy9O0MeA9VqHrLCoGhtXzxzrYyNIz9yVtp7BlDXX+fbh/9lo12+psL4u3x55KGPf+Ob3rHF+AdDeYlu2AqivMAU412FbH3vSdjz1LADwUnvnHe+wTddd7eZwZQqQA5bDH8gxD+raskvTTz6vrl1eV3DuG9/4hgsqdpO3W7CHgn0Cvl7toQdFLTA++OADGO0Bt2i3di25uLH+Pwsi+3nXUafSomBrayspWr/rgjWdLFK95z3vsfe//3/YkiU4Rudx/HeAc79ocWbBuV+0pn45n5sF53459X6xX1U2RvbqgQcedApLClTcccftDo5YTtqSi+N4LVPTxXGHv2qlFOPwrf/I2D1/l7J2UpI1Vnv2qQ9H7DffwYLxbHO/Yd0hwaaJx57O2oc/mbTT3aTGXRmwv/tE1NZfdukudkvx7bvf/a594hOfcMEFQW/y5ZR29Tvf+Y5TThF8IzUUbaz42te+Zl/60pds6dKlDv5SgFcPE1Jekwrd6tWrHTgnX1Df08OdwDn5qFIgluqa3hdUpkOBY0Flgt3e/e53u78JhpO62alTp+y9732vKfChz0mBTuCWFOsUXJbim1KgaoFa6U9vvfVWF4TWdxU8kXqdAtEKWn/rW99yO7sU1JDajJQBBPtpI4q+r1SvupbSWn7qU59y11PwW/61/GAp0AnkU6Bd4J++L0U9gXOC5LQxRCozNez8FVSoe1IdKdWtYD2BeF//+tedCt0HPvABBwMKtrsYDtVTHm5UW/+ihxR3/vRP/9S162t5RvlFr/dqPydYUuCD7lN9X4sW8g3Uh6UMtGDBAtfur3Yz0ast12v9nvqlwEX1W/088xDI+vd///dubAlCnT1ma+BiqYFTp3176wem7Fg7YA9KPl/7QtRu3AxUoMDZ7HHJ14DWrPQS7CAgWHP2P/zDP7h5WnO2QHgBcpqzZecEuivbgXZ4SzFVKd7lFwhe13ydP5fO90qH5ge95Nfkf9ZckU/VquwFUhzKg3N6ptTfdQ3NH/k5RL/rHPqbIA/BzSqz1t5+8zd/092DgunuGnw2S4D58OEj9snP/jUBxw5bU1Jk6yjHXFRoCknJJnWBEGlRBbaUNzVbCYpu0ctQfEKJbXj7NksfJKUnAcwEvlCO+wzw+RgpMeNAX/FVV1oIH8ULEykFxJk8edh6qdOJU20olaRIM0a6KALpKQC2COpStZs2WHSeAl2oqHSgPvXifpsAoEsOk55MFYiCjh9DBRJorGTdespxuQVicRvft9uGn33Kxo4fsyBB5BDBs8AS0itdv8GKV6wkKFtDgCZiAf6W6261kb0vocC2z7welDJQqAkBEgRQVQs2t1hs3eUWX7KY65+xsd0vWM/pYwBNYwA/BPQJslMbqKCVWMHGqwD3VqFUVUM60RD3l7QpQLtRlOHGTp5AHWwccSuC5EBdoeI5Fp2/0EoB0aJLF3J/BItR8xo99KKNHiF1ZmevxYkmR8DssgT/g8uWWMH1G4EEmgnYo7xztM36du22AdLgZlOkyiQYEkKxreUaQLWFpMMkOCm1EfLvkUauHWW4523qGAHUYdRVCKakACa8kpCVtlQBPpK6DzWd1IBnPYBzkycPoEzS51QBw6jiecBDodIiK0ApLr56rfnLAZVCBFr7SLV5AMU0Ni9PtrYRbBzXejZBVlT5AKEqr9hglSgMxgHn2KpuyVbSXKGcNUpmEGNHObWGalvMxlFTCTdfbvUbt1hZ81zqDoW2Uwds/PEHLXXkgI1JcYZ0wUWAGnVXv8kKlq0BXkAZJMD9kYaMjoMsXi9QF8o/BEMTKL5kCVIGonMIQodIb9vP9VDOQRWnYC1Kb8XNlmg7bB6AW7ii2oJz1znwChqNc3JKwISkYI8O0qJNdNPHCPwDewUpsUfQ2AMYyAD8xWlDpVELVq4DTiilOAAW40ctjbpdZqSLdhqj9RjDlDUi0I/29xIADCgdBeo300bNBG9RMuvajeoTan7EJqO0tUfKtSxpwdLpJEFl0o0VAUuiBAUdSBrBNlRZ8K8Yh3HGe5hu5hOQTk+RpBgFvILmRRYuQWVisJ2gyKBFa0gvWL8ayKIWG8b9Edz0Ex0wH4cJRh+yBCntirm/QAFwgBSdUDHAEKDmR3otVGsKq9jlD2jgASdIuc3V88Bhgs2nCIaO8LkMIE4QRREFwulXfgnqQiussBrAAFdPQe3+oyjTALwUkpqPbkOqR9QqAWqzGeBalJyKUAeMAgz6KMUlRlEWIq1cJoAtCKMKBUDgYXczgJchfMfIAspSNM8GOscBMEi9V16NChTvkfZN8JsPEONPDnD/B6jXp0nrdQqVAvpqCGUY2i2ADQRnARrA3hVXWWFtk0Ur5qNAVE1hsVdDJ4EoDtvUaCd1gd1A4SDuMQZQRkRy0EJNt1IfjEHGsQI2rwTOyfbK7mozT96O522x7K3mgQDqTrJlYlUw3g70UbDzGPCynkF3koWmjfFVBiy8du06p4SuOUbzTZh6D/HMGAhQHvd1bD0LN7qGKyDvzR6XRg2ov+hwbTvjltS/tK7w04pz2F0fmzTxnI2RgnSKIGkMNZEw8FgalaUpQFyhmyHei2HLCkihHEBRMwcQNtHzArbmuBuTSjcYQ3FWKRzT5EvMAcdGFrBW0UwazGGddwfgCXDPPN4ruYp+LHvPpUkt6Q+TwrRrG1P9EebMScY5CmQAqaBbAGwYPFRDQii+RasAowVzAbwJiPanSL04fJZUzsex6fyL+lmgGFAdYD1CwNcfp/yZctRC3wwofqWFfVQpgZgmzhzDxgHeAsZ7+DFT2OAEAF8cqDgOBBMSNcL50yOkegbmn2IhLk59FBTKHwPc0XmJy5a3LLHCuaiSMV8OtQ1zrlps/RpEq/BdAII8UitCv2Hnsd9nUbpCvSqW6sdnQBMN+CUFcJUjjWFOqciBlgMVK5gH8SWYFwUICfbO9ey3DIpemfFRS+FHBADxA5QjG2I9IIm9yFUCuSzimqi1+Ulgwk5gQtT4PBR9i/gsIFVqImGBybRLCRksQyWtmrSU1GlmCFBm4CzlRBUPm5stCGLH8GMFN09xfamdLrqNewXQRXm00IZRNgMqQjEwF0aVS8aI/mEThxyQmOzb55T4olFSpwIXCcB2sBtzZoZ5NlKzzCmpGsCSlPgCU2dpj8MojJ1GXXeAIHeSPkLbYXcD9KUMgGIoUmhhUvYGa0lxCgSebSNFL+qwPnNKrrCMOqEIwGMB7LDPZBIkVWgR81mYDQW57JiNo8Sa7O3Avxu2mGws59aRSQ5Sf0PUN5Df/OU2mau2oV7mTCC2ippG5u3FfEpBfbJ8BUYABShjz3Mo+r0AiD7qxoPStkPB4b9Sd9jWVAg4oLiBTQCkjy1o4rvMNSkUt0Z3W6J7PyqHg6j80ekD+An06yATncar1JuLUc8LzdG4WEg60S6bOvEjCydOOkFXfSfJBodJ2jFIXylmno7hn3iActkJNnWMtlIH4xbCnsdR7/NCtCfXSgBWBqs3kbr8NuYXwMgT9+FbMmfWrgfIx58BovBCQOS5AUgnxkbHXtLTA3zRphHGUJC+lmXG0MsjPStJj1FgxNeoW40y80repfJRZ0wPo8Q4jFLjVCuKcCneZ5wD9jF7soED+Az3K9RAut2WjdQlSq+9uyzNtdKMowD+iJT0cukRQDn8TSC2YJw2AYjzpUZNH5ga7MFfGwb6yzp4zsfvyKEkm5kA4ScNe3RpC3UKpNHTZf3024LGJVZaycaDIOlttYkB/9YfIxsJaaCngDX9yQ7OBeAV1vhHcZifswBfKdrL8KNic9ZaVGOY95AXxMagnEea4xTQrI9vFKWPRgFOA9Sp1JVycXzTWnyqOZQD5U2VLTt0AFeM1M/4e36IZy2Br6jyBfARUhP0zcJr7MDQYnti96idaktRd6jrUpYcZQhEEtbYFLA1l7Oxel6BnT6esf17J6z7LIApIFmQ6yep+nhhxFYuLbYNpC5csjjKXB+wp1Cc+959pDIGnLv55itt49Wo7ZZkeM6L2OCwb088OWCPP7bdFjTW2Ju2rLYVK9jITb0ePjxh27eOoUKO4iN+YkEhAGIJGxNQZG5rD5IS+4zd+fY5dsvbKq22OmAdbRPMJU/x2VHbvGm53fjmxayp05+pMh6jAON8O3Q4YQ8+fNaOHKc+ctgi/BifZxj2Q9j8uRWszRezIStiJ1pHbdeLPXa2E/8/BRzJVcGDrSCestUrKmzD1dW2dDFqVoWeHT6atS99mc0vqQr7tRvnkeqVNZdqbGE6aqfbAnbs6LhdeXkYtbxZxTk648sc+An8X9DcyMgwG+wOEM94yJ4hRevY2LitWLnCxZ2vuuoqFMpQQ3Rt93/WGfJrFHl/VRf6SZ/jZS4++6fzrAHNEzPrV7/r9UrgHI9Q1kPs8JEfDNjul/psYBS4XpaZjVYBNh+WFGWMPY22aq18m7A9+Xg/a0ce61jYRHyiAFBbOJKz6tqorVlbYFdcXWRzalGAHwiQInXcdgHaXbasyG6+scoWtOh5ECVzbOKzT2Vs+5OyJe32pptrUMMstVjcA5DLsD42aM/tHLRWgD6tr5SWlhDXiNjQiGenz45adfmY/c//UYddAaolu9m2JzL27e8+yPPxiC3mWtVsSg6FSM2Nau2BfQes9USrreZZ5847f4PsCJuYVzFC7qla4hrETgC3eXxy67E89rzq43UF5xToUxCrvf2M/c3f/I1LMyVVjld7KDj06U/f44KbEaRLRbxKcUmLf4LntNCnDpR/5a+jTqRDA1ovKTfpQeHBBx904JwWKaVed++997ogodQ4zufQg4eCPW95y1tRQul2C5Gf/exnz+cUr9tnZ8G5160q35ATzYJzb0i1XtInlf0ahOr+ATv9v/zlL6NmdMzBDx/84Adsy5YtF9G9v4aZ6SK6y1+VojKV2vFTOfu9D6fsiWNZZPHNfveWkH3mYxErKZ5t6zeyHwice/SprH3o7iSy9r5dAzD3tx+P2hUAdJfqIRU3KRcL9tImBynMCaYTCCd4rqmpyYFigtUEtP3FX/yFUzuWMt1dd93lHvDk+ykYLeBMgWLBd68WnBOUJn/r7rvvdoFuwWh6KR2s/MzW1lYHMSlIoSC1oDwpawkQEhD37//+7+5nlVsKaXoQlS8pBTup6AmEE8wmBQIpwyngLgBOPq/guW9/+9sO8JMaze/93u85BTm1vVK+CoZTylKp5f3BH/yBU4kRcCioUOe84YYbXDfZtm2b6X0FcAQJvuUtb3E+u8ojxTqp2ynAcrEcUsq75557HGCpdv1FD/n8AhqlENiA8suFeMgP0HOL2l1QhZ531LcEz0mJWn1HKf2Uzlcg4MydgBfi/cwskxZpPv7xjzslJEGBMw8BgRoz73vf+9x4mvm32Z9na+BCroExFok/8/m03fvtNAoVZp/6v8LY47CVzvpHF3KzvW5lk02W3Zatlq+yZ88eB97rAkrXrrWrHaSNF4gmGyjwXkCaFN0E3yt1unwFgfJS15VfofWtPNT2cgXVdfXKL6rqZ82JAucE/x86dOi/FOc0X+QV5/LXmAnn6Rz6vr6ruUeAsxR75SNIpVXgnA59TqlE9wN8ffozn0bxZdDetHK5XdvQaDUEu2DFWJRnEVUwHLBNcQPpmxoBT1CwgOCyzCmCdGdOoXgyDjdGABzALkqgWYpWIZRTvArSJRE491B4IGcY8WBUHbo6gJQ6bYKgPHmhCIoB1ADOxUnTGp1PuiVUqjylOJtCwaMPoOhUu0329qM+g39AkCtaFAeeqbVww3wCxyhNEBTLDaPK0XoCRa2zlhgRwMTiKmlapR5XOI/yElhXYFtpHQMpFHcAuSZPt6NcwTyMgosWlQtQVonMI+jN5wNFqI+gdJPtaiOIB/A3Sno0VELCHiFHAoJR1CIiK0l/N5egM5ARWn+k8KLfoJaVRS1jHPBlcpCgNUH/IOBcDGgnVttgofpGgtUK5gMDTBE8GyZN1FngofZe1EFSpHwl4AxRJdgwtGQhdYEvx83khrmn7l4bZW12AoWOBPUQrKywOhST43NqgQdR8iFQrHRr/tQ4QXqC/J2kt+wbQBWD4Csh08LKuBXVEzhB+d6LARZNoujTQworUqUmhwgqThFoJyCoNGgFwG/helK04RtnqRctVgcIrPn9pELtIfh5pgfQYtyp3XhEz3zm/OImQAH6RqAA+AnFHNd+PQBu+MKTqK8lCQj7WggvZeM0wWV9PlpKgFMqM2OkwEM9KHH6pA2PTpIesMDipNCtaF5q0ToCn/Q/TkpgmIfYILVNqjmfYL0/2WnJsQFUWHwLEDwNI5Xkk/rPSGeaJR1gdOXbUP+5DCU+AqVDnUBYJeaR0otKQzmHQDntkEPNhlyuliWgmEpxXie3RPsQWPV0XnDGHCo0EeosToq9QEE9cSr6NECD+QTox4EiCDpnkqgi+Vl4rkIgAYKrAI68SYo7ZEKKBKPNof+PAE6cQaFmmPFLAJLAMdF4BOu4f8oRJsVfoGARcQ/q0AcIPHd/hHSBELAjjKPcACom/YylMGDg/GWoqqCsxgPmJMo+wdIagINaBxUEBAoAW/jACDngwtQY5SRlWSgIlAoIQsk4Kw3L+CaRGt8BdhW4AajGQMN2KcwOPJeiD411o4o0QL1PEdA3VN0oEQo7GYKcHunzQgThYWkYz4yVrnYC0Rlugb+FAZCpgyTgRZA0jfEAgfQ4/Z/+qyi8T9rBKRSnUijtRKkL9F9oByCXzpNOeSc+H7imfCGAC8p1wCVRUqmFSxhzpNFj0NIfKB/goz9FP0CZJM25XNl9UsyhDgnKQmAGW0jhMkAfsTJAhUJSx6Io5GkMAnn4Y20wI6TNo/5VrCjgHJ0MgFR9ZQ19BciOmpCtfDlwLm9zZY916Pf8e5ordAioC/C+C3RSv2PEIc6e7SDYfdil+z58+JD14U9XVlTaFTxHbuSZUmlapeLMzMD/6I/MTQLn/is9F9HtWbU5V72X1H80f+tQH5p5yEfRc+VPg3PYGVJZ29RhoOI2pgJUowSiYp8zQA4J7JGUCiMATFGUwjwAZg8bgjym5bB92dEOgNwxWCDmOWwylJxl6Js5xmyoHmWrxpXApQOWRHkkyFiNFrUAwc0HsJAqJz0TEMSbOgHkRppsVKtS/O7AIuysD5GUxc4wqLDThRZBcS1UhrJobBpedvMWwA10FBk4UZdEMU4p3QuAfgLYhhywTTYFiDIHxbJS1Ny8dm71OQeX8Qb3yL1zb0nSlmKysU9cQ/eILYYAwvXgXsZQmAT+CQKqxQspW2YE1dl+G8dMl7csJ9006S6TWQA7gBYfNVWUNwMA3B6gjCylodrqow6XGuLagNlBfvawyUoxn8G+5Li/rCA2Upr6RfWkoQcEAhRX6vmQj3IottEfIoU3CkxpKDHNEUEC0B7KXVkA7VwahU0BzwSfkXGjHgHCAJk030VItwZhh0oe5ea7YXyOIApuPspWkDzU+yjQUxc2e4K6wEZw3iDXTLYfNQ9QL1S1zLym2ygncA0pdMPeOO3HulfhQkCjUj5LOQAlvRSqbmMHaY6TlIlrEazW3CAwUmYti3+QBXyMMIdGSucCF6PsC6wTyAI0jXdYdhj/AFUwB4HTRyKU0/lnE7Qt/kkACDlQsRR7DGA48BLz8gFsMfYL9dM08Hta7Y7DE4gB2ZXUseFAKV/l1wBwTg7xD1AefQGpWTg3+hXKZhl8vwRKhfE5bL5ooo/G62HW6YvYykKUWiMxyukL0OSazF+5VI9lAawzQPdh+guYPPcm6AqVUNrZx9nJksLTKwDSLpmLP8Z8pTpQWtx0B/eJr8lcL6U8HQJN5d/7Gc5FGvNgOf269DLO08TcIh/yOXxDVHDLgamw/0nmgqmppEtnGcdHCarvArP6QGE5KcyhuIdxZ5yqylDTA+6eHOF+KzaSAvbtfLYEoHUfvgC+Jepm6mu+IDXGsYfynj/JPI/ycHKMtgBq9IElfIGfgKW4A5SFOQjgIopKULC8ybKF9bQh2wTkq3BfyckufDXqiP4O3om/i2/g8bcp5ko2h3hzmJPr2Mzgce6Rw4yFk7QHNiqKkiswWTaFb0KHCWB7gigxeviGXoz6we9OjwzhS+Cn0JnkzwSA1zNdtN/IlNvUEFtMPwV6TaE4OInvHCqn/bBTAWyQh3cQAAT0Ur1AeD34M2xyAJj0sIeaE7ko98eVaT/N9V5sHoq3ALpAwjQ9B/4a6sbZiS5UAvHD0vjb2NYI38UVoHPjC4UZT4xbj1SyXgCuAGPiJ2lzYMsk381hS0Io5oVRAaYxMQn4CpF667caO91lKI6niP1JBVxASNylm6+rD9v8pgDQW8B6AV/OtGVJES3FS/x02sRDlbK8ssRaFhRaY12A1LqMRW7n+Omc7dmL+h8Q6soVqNCh0lQYx2OjvLh6drJ9EtDkrFUiIdzcVI36FHYg6qHi61vrSdJ1HkPJaUTAas6qKopQfQuyITxrnX2nULCrBoKptBoU5yYYK7tfYDyw8amludoWL+JZJTo9FjzqUkr8A0AvJ04SuzgzBYjDph+ehXKM0zLSzTY0lrIGGyTVdMD6hsi00joB4DPpzqv6UjuXlUZt8dIyVKkiqIMyMrE3nV1Ze/QxANR0hBSRBbZiOY8GpaQwTodISSnFdFJo1wkoOj6bqlXd92WOLDZlHBXDVp47H3roIdu6bSvpv7vJQjPX3nzjjagIvoN2aqTfyrel6zL+Zvqn+Z/1t5lrEPp99nh9a+An61e/6/Vy4BzGj4bxeAZj3J/IAsBO2gCQ/zhj0UcZsxD1zDlzsDMLglbXgD+BMtupYxnrOJsFPJ0C9OaZmXm2GNXwOgDeppaQVQLNhniOQ+Td9u3OsFkniZp9EHAtimo/lp9xK3va0Z6z06cAvvHPFi4ustq5+B50I7l3g4z3k8dTrjwTlEVr/3HWL3bvmbRdL6FuX5m2D9xVaddczebIpGenTuTshd1HsB1SQkVIIMbzOv7dOM/Qu1/YaQf378UOLLc773iPXbtpi3te0pyMI4l5Z17Gz5Q7LGjuggHntPAn9YoXX3zJfv/3f98pdyio+GqPURabFNAT4KZAlx4aFVTUAmQ+DawUCRQwyu/Q0rX0kKkFwykWznQOpb/Q4t8DD9xPiqtBoLnLTMHVTZs2uQXAn3yweKXy6sFjFpx7pVp6/f+ugO9Xv/oVl9bsuZ072V0jh/TCPmbBuQu7fS7E0gn2ePTRR50K0LZtT1pz8wIHB8heFepB/qI5NFvPHpdKDUxM+fa9B6R6lrIRFrJXzPfsy58iXegVLODwzDh7vHE1IHDuh9unwbkOHkI3rgrYvZ+I2rrll2bFy4dTEFmqYEqPqmCtUp7q/WPHjjnFFoFwf/iHf2gf+tCHbBtAmH6W6prgOAWo84dSlAmc03nOB5xTOlRBbdoMIsU5wTydnZ3uPSneycecj3KJYCap2SkljtSLpdYm9Tr5rjPBOannCZDTfQgIlCKe7uef//mfXfmkNCOVOMFuWthWsFsBa/m7AuKkCidFvRUrVhAMWfNfD6hSJVNQXLDdb//2b5v8pMcff9ydU2CVwLm8qrIUCe655x53H/KBpXwn6E6Ke/quwLlydpVdDIf8cLW72vbJJ5887yLr3tWmAhdmQgPnfaI38At6htGGH21E0twvRUBBD+ofUuPeiR+8YcMG++AHP+j60xtYlNf91Go3waE9BNJnHrpPPT/qpbEwe8zWwMVSAwosPPNMzt7+R1M2SPzi+mWe/e1fx2z1UhaSLs2p+mJpmje8nFq81JykQ+tRAucENCgTg9aLtOnp+uuvdwucStUn4F0bOOU/CB7WDm/N4VKs1UYBKc7pmLko7d54hf+oDPn5TM+SOqd8iZmKc0oxn/9M/t/8Aqx+1886tMlUinNK/S6fQ2q88q205qbPqGy63v4D+0nV+teAI77d8Ws32zXrriSIUckZgKIIECEfQvCJpUvUyBTR8wkWC0rxgKGQjVKcj3VdApAEET2iVJ7+roCuVny1SK/FTymGcR4F1aTU6xMAVhpFpXkTCBIgQO25tJWUXcF8fCtfilgEacg1SsBTdyRIhP+iGuMB9inoKuUoInIAQrz4aBaFlAxBL6mkhaX8RVCREKMri0cgh+VnAmWALJCx2Uleigpz3wEegoJAfJ6nXdx8HtU7fTYHQJVl0dd3aUJ4nwBbAFhPgF+AdHL8gpoO5cB4BADfiOg5kEnfU3k8wAGPIFqAILILcqvMfM03QDLBhAlqhBSdAQWvAY5cf0FFjxVofuaWVQ/keFJ6L6dqQ4BeqcICCrxLtoxr+6wiyz65++IeaQjqi9AWanpa/KbagMqAl4AiDJhJf/dRHlGl5lDpIdpJ4JfgLan0iKRTv9xbIX+nTXKcWyBjkNRvPmnCnLIQwa0cSnjTKSM5H/0igBIWUTLXJvQqVx/uswmdGxBAwVfURHyAsSwwoRdBVYZCczf0A+A+QIEcqTcFIPgo+LjAK33NI3DrIonchnq1hyoYsjOUc4D6og5RcFE5uDh13wu0uM28wUPAmPMssvg2lIvWoo5IGVFAchIZ9BvBDkppl1XfAfKL0R+kTga9wP2qDfhZ9SZaQGkIJeMjBREC3O7Q+2pz1aW7V+6P77rAO/cUoL48IEZPwXWgOx+FuizKSCz98z7lJSisPpFTRJd68bxhrqH+RupCKThxO362j/eA1YLAYSJYUeKxxKAl248DfIwDWcyzwobVACgLKEOxahEAgbEZ0cjkHnhHo81LU34xEAT2PeOcgHp+UPfJ/QEwBKhrQ0XEAzazMGuypHPLEaDIqj/zVS7u7k3lI2TLOQleZwcJ6J9FHYfzE7iPFBOEpw/4pM6dFFxJ3UVRQYwUlVqaeqOGLSK1KerAjWcC+twM7Uj9RABbAhSQdsyhmDHecxZ+5jQphgu5PyCZCtQfPWxRBliRdD2eojaUXYo3qjuMEEWk/hl7PsH+nFPRcwOMa2BLNd6wB2m+a6SLDAWQxkO5QM0b1HdRXxKAl2OwyQ55tK2f0LinDQUVOHBCY+Dng3N6ltWRt70zbbHeV6BS76m/O/iT93p6++zA/n2oNuxBHeaAs/P6zLx58xwgvX79elvY0oJKSBnzkeyqWlRtQbswJt24VhNyTgfOaQjMHpdMDagv6FC7zzw0X+v58cfAOVTpaxCjkI3wJ9tsqp90lNhUKZ6FSSfqASIJDBLgBqrOOTWwOb9ga9kxqcWxHuKT0tALQWWgQpdFnTLZdgx7Ww3ovMlCqIVlGeNp5iz1tyDzZAjb4ZHGUtM7mD8/A676wB4EQ9OoioHPAoMznwCGBRn/EHvT8yF2R4pUTEhcX2ODsYst1vzso46CeeQaUsaiTGOnKU4rfgr2o5Jn/ZKFDH+A96E9lkLBKkD5QiULAI7LsH3MSVgpbpb74xrcnwdso/lTPohSaXrAUx7KfFLXGminntBfm7MEldnqRubxAsAmICJsRQTVs+lxpZvjpfrivH5agBS2mH9l331sPdQxNhX4SopsgHyyebLtQeo2yDXDXCUoBwZYR7Zf047ziYB5/PQQipf4DMx7kSLgL9ReSbrHRoERlHZbmVJzAL81rh1ylFVl8KhbLdpmcLyyzFVSww1Qh5rPPZwqH78lMXbGRk7utAiAYDFtF553G7Z2Dtcfd3UAEWZp0l1nmZ8cPE3fQP+K89CGOeAkzav4OoIelSbc02RAm/l8z1Oqa3ePwExcDRyMPsO8gk+lWUf9TAfKLvhOQ5bqa6P9gXgqGthM0QJfBWA1tB/4kbSZgJTB4hbqsIr6o0/IXvN3+Y/yazz6kofinPwqVZzPPcvOe/ycZXPDRCdp2UldW0KsOjp/hWXKmlC/od9RhJDOhxqP79TIuKYmQuoplKMOSCXs6Vyag+VzqNPJd3LzOkqBpG6FLKCumHv1wkfIUie6X95QM/Bf6l3zPiBWEtXZLAB9rKoWtm0p9zCXVLBA/T17cGdJ6zunHiiLFz4mXhk+Al6prqW6Up9Q/3D+gsogn5lNJkMvoRD4AlMj2kKVxLvn3gqgXkc1UBeqGwDyNAqrOQojTbkAc6qPcq3PJgydy0eBMMsLlItRge+hohswJHXiAQfmuMcU45jWtTCAlEc5ctynPu85YFH+CnMkym65kWNA/yOo6QHO1ayimnh/9AR+wGnmXGAyAHopOzMgXfu5euS8HqBqzucZgfTAOqUPpEhDM3Wj/jfcaVMnj7O5Aw+hup40sJy3gPtTP6ewmA7OR/uIypcfRvuAV063m9pOmxtoA/mVAvkEognekxotBoG/MSbpt9ooIJ9IPHBQD/n6LvWh+TRAH/U4j2tQNhtIFVmp6zkh7+n/3KfsqgYtNRWgHFKbkz+uvyP9h0IhQ5s6yKbZRCOXgiYUlOcxRrUpJxJDJRIYTXBoKkGJsXE+G210Ah9ARQCuADNhqiqHinjidML2H+ziMzlbtKjWmhsL3AY+zf1TtFVC7UV9RPk9THk9NgiohAlUolh2BMDU+Skjp5RP9PiPRu3RH5FSGB/53e+aaxu2VFp5GVgstzoFqKi+EQfii6EgLU9jul4pC42gjSlSn5LqFVWHmywLxbiXS1XAvzLjqmf+llZ1yS3jJpz9pVxBBqMefwL4y7Kpgm66ezLEATppQzYUr6y0JQulWMU1KKuUkFX9QcadlHi19r5r1y6XjcWlHF20iL9TSrWdPngpHmq+Hzt4Q+/9jPudRIVa6wTP7niW7Df3UWfH3Qb6zVs2k373FltHHMFl/zj33Tw4l/dXdZlLth5/rA5/+b/8ZJ/V73q9LDh3rn1k51OMwxTjL8e8o/iTAF7B7RHGVhg18VAU/wwrmUlFgH0ZxXxWzwxqX61bhBjfTBmYVXkgvo0g5HFg74idaR8GmCsCXCuzOdV6ntcApMsx4FPIYmZ5homwPiMVuBw2EM6e9K2k5E7yvImZkZ0TMNzVkbVv/9spbFfWVi6ptPf+VqmtXoVPhY1K89w1gWKu5tMA0J66o/zZUTZLPfKD/7BHfni/LWxutttve7dtuOb6aR9AtogPyuXRM4++Iwv1Wkb966o4d/DgAQJ7/2Df+95/OChNwUEFeV7tgJLiiKA5KXUoQJnGaUyxi0GBFcEkV1xxpVOMU7opKdtpsVKVqEVKBThPnDjhAorahTU0xE4Pdotq8U+7fG++6SYerJHLxwif75G/xqzi3PnW3Gv7vALCX/3qLDj32mpx9tsXbA1g1DPYLzl4CnRs3boNArvIQb43Ya8USHk19uqXd7+vZWr65ZV69so/XQNaEzjemrO7P5O2b2/LWAnPlX/wzrB99ENhfp5t55+usdf3HYFzDz85Dc51syPtWoFzd0ft8mXn77+8viV7Y84myE2g2Be+8AV3Adm+fHBBwJqgNgWFb7/9dqcAJ2UXqbLJFxQcp6BB/lDwWupverA4H3BOi8vaCPIv//IvLkWZoDbZX4F7D6MGKvU5bcpQWVSmBQsW2F133eVANwXFfxKcE2ynVK1K13bPPfdYUxMLdOfAOZVP4JxU86Q0o2vnwTndo1KkCXDTSzDRIh788w+vemjK/6x5QpCdFG0EJgmqEhyma+mQMp+upXJ/5CMfuajBOQGDAgvVR1Tf53voWUCqe1IszCvonO85/js+r7S/6jfajSWwUaCf2lsQpFQQpWIhsG7z5s3/HcV53a6hsSjlSD2rzTy0QCNAQ9CsxtTsMVsDF1MNtHf69t4PJ+zJvQSsgE2++smYvfNWdt2zODx7XNo1ILus+Vh+gvyT/KZNKYQuZyesVGW1HvbDH/7QKeFqY6dAdQHSQ6TFkvqp1qe2bNni7L1q63ye+XRtrU/lD80NWhDXBgDBFfKLlKp1Jjin78h/Ubm0hqbr5d+Tiq0Ufrdv3+7KpNSyAvcFzuledeh6h1j7u/fevwauCtjbmU83bthoFZW1REWmg3IKEjrVCsIlhPyICwFtsaAZIZjkEcRUcNUjiJ4lCKkFTrEr4nIUpPKBodyhCA3nRwiKgK2Weh0zw/f5oAKHijbxps8X9VJITCVkuRdlEM6fViF4g0ARb3LwVwE4BMZTpDUTxBcsRlGtah7BxBJXTn1Ut5njGlps1Q5rATsKOAVQlspJ5UTBUoqmc7K8CyDGdVkV9hSMA+qBJmKdl5Vm7lGHPu7KRUCbk/I5hZlYPHbXoHYUwOYTCuC7hV4CSkHBPlxA/JO7BwC2nEfqPILcOVJK5lAAUXLQoOqC/+ucun+BdEZK3NwgCj6k/QxXoOSHCg4yGQTlOB/1mKVAAgJ4h4AegU4uoeClLsX+qOl4IJ9TW0gJTFiVwLsc8ISCwoIWFNw3BZcFw+lLagcWyPmJv6vF07yoEwd9cQXBVgLVFPSl/yFJwu/6lVKr0jn0doCOoPPpTF6Ieglxv/wtoyAoFwkpcMx9pUdQOkHtUGqFYYBNQXUOStNYUIW7Ak2f11DQyOZQ4wEQCRJMD1F+BQp82i090WvDnS+Ryq/fimoXW2T+jajiIGUhaEN1SSDTZ3yoK0kNQ7AHf+DzQBmAn64aBM1N1z7fU+fhH66fFezBTUmFLaDvErDI8R0F+RUgVFXo3nQ+1ZULVAvQZPD4KYKZQ6QwQ6UtQnAyXIySESoBafVB/hchCK06Vto9paQRpJZAQS2X7sS3VUsSBE+OE4gn3aBUAhOkeZuz3OK1lwHLNVDRBPm5L48groLEhDqoI8pAu4cVhASizEmVKNHONQAcgAMVvBcZFwWqCQEyBkhPlwuUcR9KM6bRrfum7LSRaj7EvQeVgi0H5JdodSn/RkenLFbSbMU1zSjD0I9JddvVetomEhmCLw1WWg/0RspTksphK4AkCfr4UtQBzMmhDpcGYAgWEFxmLPnUzRQw2XAfajdAKVUN9ahALTFkduivqGOhrOCUbSgL5AP9DIAFcC6nwDbBfVc/E1LMUX8GnKAfCmAMEgAKoDzkx2qBWrAPgD5qDzVNiPR2wTCKVtyj2lJWKSglTWQYkqQvDpbWoRwFkKQ6oC5/nuKc1D916DN6yf7q0L8z7b9s7TiQjEBrpQF/+umniXEctOHhEdKjldjiJYvtmquutquvuYa5pB7BAUG8jD/aQ+eRzdY5gvRjB/W4y3BNmSauO3tcOjWQ70PqTzMPtb/WF34mOAfsnRs+ZX3HDtgIylyVNaT/bGxBuauWMQQs4yZnOo3rn4wZzd3E1bJjgpOw8WHm6sAkKqy9qLySnnmoz+KljVbQcC1c0wpMfgE2izFMgdTlgpxP4InSgcsumofCVoaUk8D0CJhi57HxmP3oFIkCAcuC9HGlFRc8lAPecVCMuz2uy3l0r0xh2C6NI8A5IL1c7wukZN2lTOhW1HidxeouZ4yieNr5nI2cacd2VQOGrSC9YiPQTSm2DRuKzdWaZwD1s9ww0M8kaVWl/IYiE/KU2MIem+o7Y0ODI4Bq9Va5eD3McK2lqKMM81qY/2k+1iyM48Gdyo5z18zPOeywFEMF6kut1JdN1BwQIYYpdSzU0zLAx7p33ZqaT1C80m/KJ9KU4hgcLLDnk6J24KT1tJ/UFayibp4V183VVclu3m2DZw5ZKamxK+ctJGU0Sm3AZUyk/JWDkwh0nJpCRYv0pVHm3hBnEYCVwsaOkPp2fOC0FRcFsaWkGK/egsFjbnVOCJ+jHTOqJ7Wkm/vwP3QvacDHJCpggOYZ5o4cFRnBd4iFSEkKnBgoKKccpKPEpqN9Nw0HuXthLtVcA4TtHEDKI5W+xFCrDZ3cA8I0ShrxxRaatwqwMGBTZ3dasuMlpwoXr7uSKWwhcz4gHz6s7t/5JMwPAYA8KEfU93rcPYeZPzH29Nsx+mi3DfcPYcdjVlHfYhHmi6wU2GjDaZMoW8xMLDBONp760f3LbzDU4CALeI+ZkE6XAzRTKvIQIi9BALBABMVoQYUC9+gDVAExbOoIAkhQnwZBQNA7IGIaMHC44ygAZI/NaVqAuvBKbmAO6oQdNnZqu0VRuStougLg7HJckQquqC/LV5z2n3NJQFNUEckhiouMfRe0APA40XfAhnsPo/ZcSArY60iLfj1NVeeurRv0mQdgEbhHPBQ6VlB9gg0OOk+Oe0xlxm3K2cIhzAAAQABJREFU+eek1eS+o/TTIOl3vRgqbCiq+vTTNPdIq+H30g3UN6l7V//4iK7Pc//+4GHS2e+08ZFe2nCtxRdsoE6ou56XSF18kGvErWTuanff7ryC5ziJ28iBb5NApTKVbHNwRzDA9YGxBBWmGYOTbLwMkJq+uH6JReYtp1yoHDtFX05BX3bwFf2Fs7mX2lE/q4zaMCGQ0akeYn8EzikNsHx7GsDZEn0uDbGXVh3xaQen8R4dV/+lGbA7wCW6cU5HCl76iepUl+OQtdMcrOqQ/dT+HwF4rm74XI77SwfYyMFYCAKievQ1fYbhpS/zf9qYzzjfk6urFPqu8yW5FwndgYA6kE77WqT+J7d36/Z++4/vb+WeEggNbbbNG+ptTiV+AOVAGJO+iTnmVCxRMOZVSs9GMLqdpEnt7hY8E7Y4a3FK7Ts0OGWPP3HKjh5LWn1DIc+QjbZ6XYkVko4xhFFilLgTepyULnWudhnP7ia4EOWS2XCbFuS76zlFzxA8I2UBkWXkZK/lYgtclC/tzsNXORkvRh7zi/wsgaqyO4cPJeyLf/8YgF+53XLTSrv5+mKrruFCGqe8BOJISe3IkV9BcI7qdf1Lk4c71AfV0Krqaf9SH9GhjRhSvN+xYwdg5KP24p4XnW8oxWLFHiRWNad6zrQfeu58P8+3mD7j7H/fyBpQ3c/06fS7Xq8MztHWGo/u2VnDSuOTAcfzTIZ5KcCY8TBMei6Sk+E2/7lnVp4xZSzyh2wbP+tZPQMw3N8ZsPvvOw63cABlywa76abl1twiVWI+hyEL8JJ9mP6fs7bYlJD1D6axJyjmp6Q2WUx8L2p9Ayk7dmTUfvSY0oRX2I2bF9odbyu0+Y16ZsFHEgRNUWQ+VCTZCHVrPf88cL8y83zXmpvm29tvJTPpVTcA6TFvq9z6HPeu/+lO9ESq/73a43UF54aHh12wT8EQwW1f/vI/OqUQ/Xy+hzrC/fffb//4j//o4Ld3vvMOt5NKC3kHDx5yspKVlVUOmCvCkY6xM1QPh/qeFiy121cAncokeeXm5mZUQdYQXLrOBS7zu3nPt1z6vB48ZhXnXk3NvbbvfOITs+Dca6vB2W9fqDUguyWwt62t3aVnldqSJsfbbrvNASFKTZ1fXLtQ7+Gny/XqJ6afPtfsO7+sGpDjIfnuf/t+1v7s3pSNsXh9xVLP/tfdMbuclKHn4hy/rOL9SlwXZX57eFvW/ujupPUOAs6tnlacu1TBOSm2KZXjvn377IYbbvgxVTD5X4LXpMopiExqaQpAS3lOzrXgM6m2uEVUHgoFIt9zzz3uMz8LnFNqUp1DUJlApPe9732uT2njxuc//3kHyQnkEWQlv08PmfI5FWRWKjNBe9u2bXPfV1nlpyhAonPOVJzLg3NK0SqoTZtBflFwTjCclGvkD0sp7f3vf7/bATY9b6Scmp5AMinLScVGwW5dQ0p9Upxrampy93QpgXN5EFL1ovY/30PAguBBpWyVSuCFemhBQ20puE8wmRQL1daCI7/4xS86oF5jRUDmxXRIbU4bo1pbW3+s2FIX1/jVOM4rJf7YB2Z/ma2BC7gGEgTVv/q1tP3pl9Lszjb7kzvZYPAnYatid/a5dccLuPSzRXstNaD5WC/5HvJTxkmnI2BNcL3mGAHv8h30nlRS5d/oM4Ia9IwnsE6wuwA6+Rc6l/42c6H05cqXv35+/WtwcND5KEr5furUKecbCNBfsGCBVVdXu3U6rZFJWU5QheZE2V/93NfXR2qeZ50SntbU9CyqNLJSNMqXTWXRZw8d3G9/85m/ZiFTz6y3snF2s1VU1RAvQWGGx0B4N54TtIBK8INlSynJeCzORgiGCO7ypfKA4oJTtSFumgaEYQmXQB5f5vxa7pSikyAvDSIBX4QkCdbxF6AedxAsFKylBVW9dLXpbxKSQt0B4QoO/oCPyKX5UdDMqAUmBq1/124LdymoR6rJZevIvVZNQIpFXoqlc2m9WUHrIEpbCvArxZdHwNDnlaEMegYS8OVelDnLbm6i7gSF+BxRtGCWVFgEY3V5BT3TCra5NWABeFrG1YX4Cn9TPWmBN816ZoaCKuAW5n4dYKDCcH6PsvsoivgsXmcBlVJABAquB7E9UuWQqoSe2+iE5veROuvAHqdQVb50pRW3LCTITIAONTgpsihWJfU0hfkV0NN96ob0nlOl4l+uKhE/qowP8HcvStloVF1fanMh3buAIkXxVL5z9ebKoMpzpXGtSDCSs3HPii6qrO7gPJ5UPbhIhooRWKnKUiDQtTF9QO3lBXXfnI0ApJQG9UoNjNrA8VPWT/+unTffypcuI52bwDIVQoEBvfhZ11WQL5Jgx32nDXW+iPDcGSsgOCz9Q0PVaALljCmC4PHKcqAr0phWrSJtlxRM+J4AO/UJ9V1e7p74rE//9YDggrQXF6Cc525KTXXuR6nupbm2T1k0BgjJ0h85F8p7Dv4QEKoq4bsBAvACAqVKQkUQ4KdegKWGjisI3k76rohVLltqYRRWUmFUX/hMmP+4wCwVrzbzMwM23o2i0vBRi4YmXUA/zRjOkA45R1q0EDBaIQBJtHwx0VMUplFUc52YMmTofxo7dGt3zqgUBUnbluoHdhs84gL9Shcq2JVujFJc0AorSi1SySb5wkW0YTXVTRvqPPTXAO2pcRzU+JMyC+ngLHHQhtofI5CesJKyFcAeK4BWKfvYSes4ccxGx3JWW7fIyprWOkU6wSjTfZOTAD1CTFiij88NnmVdDDUWOooAnuTohFN8ipKmr6SRQHrFfEADYBEjZTH2heg3P1OnAmBRnMsRvM/6AIWkp8sOnEIl6yzCRKRj5HpR2gQ8AwgB+1VVD7SwxjLxBXwbSFCKMRrDBJm8AOpRBPqlcuOC3kimZEjHlyAFYLCclIukJPQ0xqmznwfO5TeF5ecOfVY/y7bmf9Z8onnkOM++u17Y5TbOnO04i00K2Vw2Wy0nlfbaK9YRvFrpbLtiIrpuHpzjF/ezzqn209/cwc809+xxidWA+o+On/Qd1I9+Cpy7CcW5mmoGBsDM4FHrObiHNH0DwETVVtm83MJF8zkRwDXzeQ57p2CocA2llsyNkF757AkLkoowGMbWAjcnUFQbYw0kixpYWTU+j2A1AFYpPzngGHugc2HtsA2UUxJCzGeWIf356FHS7J21JCIZgkAiAM1RlE/ipPkMkwI9gF8RQHkrG2tgDmIdhv4bxP65LsypNOc7BVFnQ1G+69pmw6eftNGprJUtvAl47lrs5YBNnXneBlpPA8OXWXnDcovOWYD9qsB+AZti33PMMcH0IKmj9wFlHwNmGbc40HWA+S5LrsPEGPMItqBw7mVATdipwhKmEFl2YXOA1G4upEAiipwiJn4DKnCpQVLHD56E3erGhkzDJfKJPNKgxmJVVlC1yLzSBvNJD++cBKLDbq5jjlF9YUHdK4gf4qXPoEh2wPpaj2DaclZZ32BFDc38PWojnV02cPIlKyZ1Y2XTcgtVr0EYFKhOfgu1FaStA8b9dR+2iS6gJOxW3LVrjvTXCRuV0ivpMouZZ0oAyvxiIGR8DTcfy4E6N1/LBxB+5OS9En2WHiBlOqnLJ1HpS6uNsfkxRE+KaI9ICalkK/A/eCEpyjwh5VYh4tPtFqJfhCUTpsi4lO+o8wSpRnuOPGWF3qiVzUf5rmE9f/Zs/MRjNtH6nJVU1llh0xa65yoYgDJLMdfLXYhQ50Egb09pYElrP3TmBEDnsBWqDam7LH13Et87AfBfWNlsJXXLAJ0bqaNpuFA1rT5FSaizcz9x31IfzDAXJjuPWAgFV5LQcU3O59RPk/TRGDZ/KUDllUBm82k7+YzaMDLdNxwkqTNzYqUu13w2wVzWfxpwbrjPGlCDLqxfwfCo4N5PW+/hRywGOFexcDMs3SZuDH8EP0T+mdPtAn7MAPxPdJ6wwPAZtlAk3HyYY0PIODBdEl+0bC4w3tz1zKXLqXN8I92bisB/NBQ1BJkR8GNYaE50od6Lipv66QTpelEd1oaKCL5UjDYMFZAmGVg0WN7A+WrwuVFqdA7f9FzFXbnxF3B+EH1faXKHDthU61M20NfJvHyNlS66keuTqr3rGRtt3WPj2WKraNpoBQCQ6vdSDRbTEZS/EJy0JGl5J4d20R8mUB4CUMdZSk+x4YU0wgKpCmuWWCFAZaC0iXZAsZHvyxsLa/6UbXDNp5ukDTRG1V35Ve60ZkG8MHeP0w8GjA51IFdLqiM2x7BpR65DiEKFqG89hgQEEjMmnb8syla9mF+c2+b8ROqWs8tj5EquPAL+3AMRFS6FN1Vbhn6ekXIkpXBq1ZxAw1D7aly5+avqSmXNKW01gJlTyqNutN6d1Ut1rfI53xLfnOs8+PCgfeNf7mcsjNod73yL/dqbeeYrV61QBM6t8+G6UT79orYLWs9Azna/OGTPPNWKr1LE5q25KNDlrLe/hzXmVistj5P+vdmuv6HG5jagUiX3lfvGQnEOVap6EU1GPXjugYEb0SYROplTHqfs+gS1pOqlnXl+cUQdFkTPOxTMp2B6vtSzpM6lG3N9lTpLq11wdOXK79kzZR//i4fZJNFo73nHCnv7rxcAzgFKasMO/VS1nsWJPXr02K+e4hy+qfMlqQNqzNWj25CkbkI9ygeU9ciiXKgNe1K7FzQnvibDpq9l+JLahH/DDTdQpzVsiFPfpNJpk9njl1sDrl2nDYMriH7X6+XBOcYSz2MyqtpcpednB+TyHp4A/UF/x1ZxXj0v+Nh6qaljWhjH2kiAlcAPYyRi9/Qso6HO9iqUhrvbgvZv3zphO3a+ZJevqgO2XGVLl5WSzl5wPXO4ntU17nVuSqxzZFGWO906QWxtv3V0Ml+xeSFCCve+/knr6ukj3jxKH1xgb72x0dZfjpJkCT6njCUvnYMu6v7Vs5l6ZH/fEPDet+2hH3zPmhdMg3Mbr7kBfxT/Q/MkL60o6buyyPrfa+nJrys4p8bTAt1nP/tZFtIO2h/+0YfsXe96lxuE+QdDyv2Kh86jwNC9937OqddFoxF3ztUEBPft3+8W86Qi0tXV7Qa9ZCZd6gb3IIvjhZS/gkxlZaVuMXLevHqo2bUuxYQUSbQw+FoOPXjMgnOvpQZf3XcVkJ5VnHt1dTf7rQu7BhSY0MQnuOKb3/wm9ixjW7ZsAZr7PZcCcOau0wv7TmaW7rVMTTPPM/vzL7MG8K3t4PGcffTTpBB+LmdlJWYfuytsH/y/w1bEjqPZ442vgTw494efSFrfkG+bAOc+94lLU3FO6ivaNCGlNG1wkHKboKD8ArAe+hRwlsqW/EDBbnrQk8KxwLC77rrLKXMpQK2HQkFkSpmuQPHPAud0DZ1LKnJK96qXjq1bt7pzCqATqPbRj37UBbulciblFqU2raiosMnJSXcNwUsLFixwkJN8TIFzClYLXFPQWXLxUg47X3BOqVoFBcq3llqcUnarbgTIqU5UB9/61reccpeuowdepVf7RcG5r33ta66O5atL0UwB/Hxdv/E9+9VdQX1AcOXdd9/tUpnqmeF8D82pAiLVfwQ0XKj3vGPHDtfugiCUZkDggnwFgXMCBtU3lUpY7XYxHQIe9awoCFbtmT/0rChAQ/1X6kazx2wNXGw18PTOnP3WRxJ2hrTqi+d69vXPR+2q1VJ9udjuZLa851sDWh/SfKT5RHZNa0UCJhQMkGqo1qYEqwmqlxqc/q4AiOy3QOG8uq6+OxOae6X5KT8H6l8psO7du9fZVil66mdBF9pooGvIFxJUL/uqcjz00EMEqwddqnv5LrqWVPIE1MkfE6wtZValpNc96MiXR+U8wLrc5z77GQf8vOPWt9m1+ChlbGzNEkzJKthGcJXQE4uwBNtZ7UwTQBdzFSTlqkdgTmpfPik7tAzqkS4kW4CCB4PFl/IEi6VZFnMzqg++JOWqAL+HRfacE3NxqZHiBLtZ1BUMplSPbk7RZwnsBFngDRDccinmuL5Cp1LG8Qmgs2XaWh941ApPd1hJy2qLbLyRLFmNKEyxYEwQJuuCQAoG8VKaVqfAwaIwMBwJJTkHAJcWnlV6B++xqKzIF4GjHIvPiqcFMwTYSG0qlbcMdZuhXJ47L+XjHrWQK6jIpX3iPEKXFCRSwFbpGQkv8wnu1wUuVE8E3AngURiuUWipXKELXEl9zwUB8wEOUpxmTp2w0cd/aKfb22zexutszjqUS/BbPUW9SE2nwJsU/ATOhXUfLujCgjcBR/7DtQR7cShwj8IeFcx3WWwHnlPwReBjgLK6CI2D4visrs9Laa5UcfLOuGX9V/+h7viX9ghyfrecrigp9eUjf5EJRqkfFve5Y7W1SwFGP3H3S1DYRRsJjPJhzgOGdqbbzgAStb2011qWXWbzNm2wYH0tMX71HbU5C+aUmYqa/j1MCrNkp4127CbQfNLC4/1ADnwOsCrtEcxXitY6wLTKBQT3Uf8hpZxScZJYhjKr78WpK7edXnfi3lPPcMadenTg3Dl3RvdJ96df0n+V3lSdXh/hRfwelUN+UKAfSGs6EE5fBdALKkigaxAA9VEByXZ1Wdezj1nfmSNWUR232us2WKxpkWVcGlCgMlF3BJDVv1w62ySwZO8uoMKDdBX6ONChgqUB4IxQwRwC+wssXL6UsTaPr0VdgaaDmfR3F2Kg7yuQy3/DSVSgCOBP9ZF6cawV1RmBanwHdcEMqkITEwMWLQSem4dq09zruLEF00CExrrGnwLBXFt1p4CvoQJlqQM22vaIjQ8BzpWvAVJcDQyjPx22zsNHbIKqrgXWKGpcRzvWc55p6DQgMAfYw1KAcz2HgQNPkL51jGxnjAfsrhQrpfYTqlyCuuJCvktqQaVWRdHGR4nPYxxKLc8HwDRBmIyfLIo4U8PtsHj7LDTaQeoyzgEgInwzN9kLKDFsmUJSyTZebcFaUj1GAHdQtFHA2Q8oRSKFdaNOqWBpB6W6m+izFOo6Vj4fxbn66bahaX4eOCcFz5mHbGvenmtzlWywVOZ2Afju3vW87X1pnw1grwVjr0DJVPZ56dKlKK6j0leKchUBI5c2lpO68zh7oz6S/336X13H2fG8vZhZiNmfL+oayPef/Dydv5mfD87p+RHIpe+gDR7Zg18ygOIj4hQLAIgLFvA3lMKYiXLMPQHsNF2JORuVJsZO4swe0i620ucYV8x9aYxeMlYM6NNsBRXN2Idm7AzgHfMLcmR8UXMDEB0BVWcbBM5hF/wEapMDhwFs2txcX8gcHNWYnsQGAsVqLITn1FisYZXlKtdYOlKJ2idzF2ZBIL7mKgWHp+cUfrUBFOe22RDg3NB4GmW4W6ykYQufJyX0md3W33bKIvEiq2hcaOGqRuY1VDNJPYnh5zwCdIYsObjfJlHuyox1o7pFkFfUDGnMfNJiB0qbAZiXM5+2YPdItai0kti7EOqWnuZKPuWhSikITMpjudF+ILUjpFA9Rk0OAFYx83OP2RSpVcf4bhIIqBzlMyCuUEUjxcDX4v5dfen2GMJuKiMQHGA+CiRaLduL4qQU55ivy+obLFrfgq0rQNGz20ZPodTG7ZQuWI5i3GqgMFTQmFfk/4T0fX8Q8O4w6m0nzcOOh4ECNTfJ20gGgRQrF1lsDpsJyuosE63g4igA4ne51JTaFBBWe3N/Khi2PTvWZVPd+136zQxzXaCwiLbgWgTCQ/i5UgILlHHO5vWk0AYAA8xTa7EXmzKh88a1o9hxpzTGXC9wLgU41338WSsAAC9XavGa9XwyRIrOHwFj7QByr7V48xbAOe4PRdA0aVb5AJsNSBvrAX1ngasSAzZ8llShQJ4RVOicCpz8UXwgv7iB9KjAoaUL+ZLGwDn/VtC9/DgF3tW3ZEP5aw5gK9F1lP6zD5hzjDbkviPS/BqzBCnIk/L5oi1WVHk9qq5XoXrD3KXvyxHAlxM46mAlZ5Px4QIDNgm03XvquE2gmDp/seY9VG6Z2xMjJ63r4IMWTfdYzcLrqbvrmGqYs/HZ8LrkOjDnAe4BaqfOMn/1HgM0RUWXulPK1CxgqaGEGKtdxHe5v5jUCJkb+LvmdtfYOs85f0obDfzJ05YZ3GNjpFrO0S9DQPVK5RnAl/QRoUmiEBSIVQL3LSXtKutmpHwXYZFz/r1ADA75d9SIqz8UGv3hAzbZ9owNcH+lDRutpPlmPocyZe/TNta6yybSxdiZa1GDvJJxVEwdyWdgbnLSvtikkf1AhLtZa+2jafGL8dfkcoao2xj3F0fNMgjYmQI2TEu5Gn8sTLupL2E0XH0L5POZ2wW5y6/mkYNiuicBPs04pMh6ScnNSTpiR3JAXVI3zqESqNTJHvcS8EnRq895KO6h9qzPB9Wm8u3oL27DgOpUsAh1LVskcNL1H2BaZxc0v+OrTSs306cEl7lPAiZTMHU7x/ZTRvmvSp8s05lx9YynrmcFOXSULcsGGLmfGrdKTeqJhOSNB34wYf/fPz/AucfsN+64EXCu0apKpoERSkM5qT+6ZAgfKCD/m/L24yrufmnCnnisg+c/+SRltDf7FEgHPWfOuK2+vNjWX11tTS3AptgVqbqpPfT8phS0biMMdaMjXx/yKPmF2///2XsPMDuv8t733X16771p1Lus3mVjjMGNckM5SSDnkockJ9yU+5yT50kISUjOzQ3JhZCEQ0ihhJoAtuUqyZasLqu30Ugzmt57nz273t//E5sIHweMY4Rl5vMzntGePd9e31rrW+v91vtb/z/3JfUSJQ6XbaMeDRQvOW9XlesCaA+alvdSc6of/Vb1y89SP9ZznT4kxOaSs2fnAOeO89nl9sFHa+2hdwYsv5B7T2X6fjwj5dAbP4/gnCqVutIzsOZ7baSS+tat6qR+VM+8LqWuc6yjP0fu4/Tplx3uZgHg7p7de2z79u1WW1v777yM2kdda/74mdaA4rrbYzr9W1+vBZwTyBaLin/SfagxlJuNRnVzTzkrGNyPzvjBABd1FFT1e96rMYXbWKs4eq7QvatBKszaTXerx7759S7AuauAcwWAc4vIx6WicsrAwXsEzDrjoIA7xhtNDbKB7uoM2QsHOqzh2pSNTxCTYG8f4Zde/5xVVfls3T15tmpZmhXnofLJHHsrBtKamjNzUQBKrnJwvsFBFOf2fseeeRZwTopzDz+G68FOzsX8zvjG1TjjjoBaAXga7/8zXfkNBed0IVcB5gR+/P3f/72VlJQ6iSkl/rRgp4XDHzyo6c2vcqgDyDZC5Osf/uEn7DILcko0KrknKXItJOohUouBeo+SLlrgE2gneE4LkFIQyc8v4KavcRYGtVCoZMztne1VPvo1v6QBZx6ce83V9Ya9cR6ce8Oqcv5Eb6Ia0GKAxrP9+w84sIUW13bt2umMnffee++bqKQ/aVH+M1PTT/pZ8+//adSA4u/xibh9+dsR+62/CrHYY7Zllds+84mArVhEFDR/3JEaSFi1fhxwrl+Kc1i1/sUfBWzNW9CqVbCZ7De/853v2Dve8Q4H6lKi9/ZD46XU1wTf6D0f+9jHHDsxAXICiz784Q9bdXW1kxQWcCYITvDdK8E5wW2Cpv7u7/7OUe9SMvk973mP8yDy4osvOgllxaS/9mu/9gNwTiCfXnv/+9/vKOFpvBbEJHhNijGC4zSmC+Q7dOiQY7MqezQp00lhS9Ddpz71qR9SnNO/H3jggR+yapVNrCA9xc9Su5NKjWAiKd5JdU5zg2JaXZvqSvZrUlDTNQjSS4Bzt1u1yt5T8F3CqlXn/uY3v+mUWXHyL//yLzvXoDp8Mx8CzQUHqo71HPB6Dy0OyIJ3586db1pF1+OAc+pzgizUh6UKpCSarlsJN9n4yt6vpETKFnfPnKvnRAGrDQ0Nzv2SaEMp6wr+1L25fPnyxMvz3+dr4K6pge6BuP3BJ0P2Lwcj7FSP21dR533fY+zQVgA1f7wlakAxgI7EmKtFSK1daW7Sz3pdX3pf4kvrU4n3ax1J70v8TufSOpXAOr1Pr9/+fv3+Rx2J8+i82kBw4MABJ+bQWpUAa8UkOrc+Q3bYUvXcs2ePEwv827/9mzU3Nztl02dqrU5xjd6r+GjXrl0OnJGXl+f8TuXQ5+nQNVy5fMX+8i8+TbLK7Bfe8YBtvAc7Ju32lT8aC65a4BSw4s5EsSSL5BZJdZbyzT1FsnwM1RosG+NTt+AX2Zu6EVuxTJJ+6SRrsYJUUlgLucJ53FNYnI0Dz0wCIsnlhwVXlxTUckj0pgM+sdHWUWCgHdwkOeO8Nz7FGwXtKImJXZwrlQRuMuUPjVqoudE6n3jOMtr7LKWy3nwbtpq7vIqkMbZkbL6NJ7PQzIW5sWByk+xDTgqYhs8G+omPAZghRUEeAggJC64sykC5434WZkmix8PAeZN89gQL1qiZaZVXQKA7RWVOJrGMakUSYA91EUcNzKXk5Az1gWVkHDUUrTS7vCTgqEvDhtSVhrJcgPIoES/QAJWYGGrgsTHKQM5IBXGhjuFC9cSdhnUq1x+5dNkmSYy0d7Zb8YYtlrsK+Cc3m8/nXMCNlpFJUhybKBbHpSYTZx01Po0V5uQ4pA9WdyFgQVGDKVwbX64A9acykEyMs8EvPklZJ8ZJTquObyXJXMAALjJrLmxhXanp5PfoC8qIRSnkrGALVMdERylRLyky1OHc6ZxXbU77YH5Kx+LaZ1THJL+lLBLEikzAoD4/GfAPy7A4ycNQ43XrPHbEei+ftypi4KKNJOXLy8yVnWnubN7HBhqXs9JP53SSm7QJCe7IwHWL97agyNdNmQQh0knT88xdssg8gHPulEJeo+wkBOOzw7QHCkG0STxM2YDn1C6GvajTl7D4g1ZS83JNJFxn1J/5nGmAO4EiXKOL/uaiTSydPkX9yfqWLD/XpfqmTqYpA/1FndrFfedKw15UtnbTYYs0NVvXC8/aSEejFeenWu7mDeZFsdqVzXsyCkhkoxrn49yQB44qXxjga/I690kTwEcP9yhl4Y7zBbJJMKP6w7XJts/Fv120jdpBim1kJ6krJfdJTgj8UKIBqCU6i50cakkuF9aEAaANP23P72OTQzaFHaAU2/yAhin17wSAW8q9Tl0AC7oEmqLsY7I8RHXIUecDrnHFOvi7U7jdhS01C3CuDEUqLiE+1WB9Vy5aaDpmecW1llJSzzXl8FmcT/cCNnsuv5LQE0Aa3dyD7bTNAEl26o2705Lpo6jNuXwltAfXl6R2ScLilqsHjHQBbjAocL0jJKL5jv1jjN/PzmLbBwAkqCIzBSs4wTMoC8UAF8KDzahn8ZG5iy2zdgOJ+hr6s9S3aNM49whwjUVobwf+4WU+Jc7YGSEnEc9dgvJQJS8p9/Efg3MaZ5Xb0KFxNTF/6GflNgQ3a6OQnklbmm86Y3NVdZVtWL/B1qEyV8cYnce97Gfs0+cn5h5O5pxP50nMO/oMHXrNAUJ4v1tjxl307HDrCub//6NqQO2r45XtqvigFxD3h6xaHcU5oCHUiWJAYiPXzmAbOWxZpVmWWs44AywajzJX6R5jXnMRR5iHeU5J2LkBiw1d4Ja8ces+0DivOTaTGzqnhLmwEOWPCm4B7kUUrDSWaqh1JclulX6nqUxKpvT/ONalc8CqMzMjAG1x4hTAOsaS+HQEsLXbxgdQRkvxWEY11qGlW3AH57yMzzoH3o18cS9KkRIAyJlnvdPECZdsEqvWkfGI5dW+HeBnhzPvBwXOddzAKjpg2VXkJzPymD4pk8YJAGO3FN+wEoyj9hUebkYprov5EStMgSyxDOYs7Ot0bycVME5rLAV6wVIxpnk8Sq4SJTMmC+4x5i6ULTUvSyolOMLYDIzsB9x1pwExcY1xxte5gQGb6h/i/RkocC6zJEBkV3o+Y42gX6y4UVzTuBfDSjMOnCvVWXcUGG2k2ya6uomP3JZRWmF+gXOAwjPdPTbbcsqSsOpOrqwBVKunvrkmYg2puzpzouZl2iQ62kmc1MY8yBzLXOnyA+InE/+gEujCOtaVTIyAGp+a1sNGUU9E8zZfqG06UDlKWFLaixAvRFHw1djpYT70MPe79RnYakZ6OhAuBWREqSurfg2QtcBtVIkZ1yOMxTFAPh/t78bK1pkHZXHLdUcBFvvbrvHsFLfsMsC5AuAqIJxQ2yELdpw0f1aWBSp5TaqIYc1LzBX0GXcy85pIHtlwo1QWHkNJbbybsXmQfgIARUznzSA2SAdEC/DlY74HvIpH6NtcnwPqE7MKyNaXlLBczBUaX0OToyiKDlgSCX1fGgN7gLlTkOV4h40P9tn4TLqlZK60orqd5k3N41y0vQI0rlNzYRwrcZXLqcMAVp0zwzbSivVvF5tb6pdYWtUiYosMQPUW623Yi2LfABau2NGmraQcAhg1zwM2JacC7REv0S/xVzbXUCMfMcr51d+Yc4ifXdmA3kCPrmRAxZQiZ8x3cc9qblY8LAgxOsedHSLOBGYVaBiZ7rIg96mHevRrTsWiOI5y3OxoD0qGg8ScEcstrgNgXcU9vsSJj9EA5Jzcb7o+5lNHRVJlUFg0BXgPXDs6PGbpFYBz1fdTz7R533ELtp7FEjZA7L3W/IULKJOgM+IP5swwMcYkNsJeI77ElnhydgQlRMpBnOynrPncs7koRSYD9satELVgvwWJVWeJg8P0wbkJ6iI650BYKcRqKUCyfmBCYaMKv/SMM0Xc5QGwSCImT6FOAw7owX1KrDNKXDPJ2JOaWoJIAPe54EDBxbTlJETZFM8YipeTuWdSUHj1ALGF+LwwcWl6cpaloLjJCgDvGyfsneGel5JtAEVO1JoEvnGNqamofBPTxoDqpidiNj0OA0E1uoFIfMRjyQwjqfSxJL7EN87SNUOTcfofsS8bL2QjK8Hg5GQXyky8H4VJgWZ7n521f/rKPrpu0B59aKvt2FDkKDgTnhK/0WU5X2a6i+sCpUQllJveprDE7uyI2uWLEdgKWbQSN1BMhD6tdoHLymuxfc53oybOWJNOLdK2Utgf6EMdlPKnJxGjcYdMTaNnxbOAyp2R6WYM4h4HrpsmrlM+yQm3eaOGRD9dLi0VVVG+fNyTM7NxG0ccIMpzFY/TziMFS4JOWdNR7U9CrCEE0Hj65aD96ade5lYttvc8UGlv2+Onr9LuEIG6t5M5X05WzFpu/hxatdIGmv7jGre+H/sJntPhvM7/xsfHWHu9hkrXM3aCNWZtnCsqKnLyCnv27Hae+5OYo700kv7GiSn1HDh//ExrINGeiULciuN/HDhH+9F0Ak9DKKsHgzxXcB/KqIdlIWdZRFb0ySlu7jM33xlreO/sDO9hnNH9qmlQfUD3fFom9zXTkIfn2u5Wt339qz129ESTrVxeAsBaZWWlbI5k/BJvJ50yjRUpGi8YdxRDRLi3JxnnWm+G7drVMAITEcb5COrjPvJ3fqurdVtuHpuxuIcLGW/SGUdUzmmUyEdH0dNlPvIzzs0x9mhMmWKN5dCh5xC2eMJqa8rs0UfYvLl1I59H7MEIHAdMVnykSpCyHhGOM04l6vAn/f6Gg3OC15TQkaWQbkopvCnJJzWLhDWEFuVe7XAa6vtQ3KdIJB46dMi5kT/84V+2j370o06jOX9Hg0qOXguBegiQR7PTeb5/UkeGnCBcC4DOlx5I38CHQn3mPDj3ai34033tk5/85Lzi3E+3iufPfodrQOOWkuB79+61z33uc45V65Ytm53xTpY6d5896+0VeCtQu/2V+Z/vrhpQsHKpMWr/7Q9CdvxqzAoImP701/32wQ94sS24u67lbi6twLnnDkftt/5ozrpRsdkCOPfpTwZs7RJim7fQofHw1KlTDsykB7mPf/zjjprWK3fky4rs+eefd5TklASW8lgWC2h/8zd/47yuGFMPgUpiK9hXQltKLoortfFCIJpeF5yzZs0aJ8ksqK6xsdE5j8Zdfaa+S6nlF3/xF+13f/d3rb+/3/75n//Zgc30GYpvdT7t9lF8+yu/8itOeaVCJ/BNQJ5ef+yxxxyllq8BC+nzPvGJTzh2bIphv/71rzsAkWAhKb6pnNoM8o//+I/O5zz00EMOEKfEyhNPPGFf/OIXnXLo+vSa6knQ1Ac/+EFHFUZglRLhUvPauHGjE4urnDrOnj3rwIMCD6WsJxWZkydPOvWguF0qdnpdymZv5kMggDbnfPazn3Xq/vWWVVCAwDmBjf9ZJerXW4Yf93fHWdQQBKl+rj64atWqH8QM6mO6Z6SaJ7VBqSzeLYeATYFz2hyl+yBx6J7bvXv3LZVxFI7mj/kauNtqgHVGNhBG7KP/zxy2R2b/9R1e+5Pf91thrpLad9vVzJf31WpAcIMOxRH6WXO+4GbN3YnfaWzW7/WVeK9eu/3QHK7XtK6ksU8W7gLXEzFP4m9v/5v/6GedR2twguAEz0ktNxEDJcqhsil2EeQvEFu/19wvdTmtayU2oAqyKy4udoD86upqR11XgIfOk7gG/ayxu+Fqg336z/8SKyc2FOzabmsrS8w1ir0jyWBPiLEd8CsMeJaEjUbywqUWKym/ldzs7rPwzVaba+9mh84kwlhziGyRnMohgVVVbv46VC0KSfZpwZ6EcXR80Gabrpm13UR4ihtrmswLSe6YVHJrKgB36syTm0Vylvoew6atBfCFzbxRQK0QC7kRKTulpKKOVYL4RgFJvlmbPXvOhg4csjSS1oEcnafKQkX55i2vseTFK83Hpl9XKqAb6jBKzkelJgV4ELzWbq4Ori9Icp0snIv401OFklftAvMU5tIxWLAdRQmEhGgYNTsbIYHOAm6Y5Fo0E+XBWs5PvXpzSciRkBVsEx3utUjrdRLCbcBlk44aT5zfudMySIhXmq+mGhUfyk3SPDYzZMGBHgu2kwhuHyXxCeBEojdGOZMqSjh/pZNlC584a8GDR2yA2DWzAgWg0kqL5mUDiBVZytLlKGlVobpCMlHdeY5E/8wkZbhh0bZmC2PRFFIyXLauuTkWKCsCPKhAja2UekcZZAjwsIX3AuVFJrFEI86WioY3I8V8xVj8VNZRjwvI6pH9I9kZGu5HJabdojfbzEvcKqgsDigZBThMqiMRW0P7AVw4NqCTJLs7sM5sa7P4MLABMJ92jsfSc81fVGf+inqAiajNnD1t3cdftFlBZQU5lsGO81hBkVk59mTLVpm/is8PkAzXbUf/dMmiE1WW8M1Gi1xttHg30MAMIBb0RRi1Lje2dkn1K+kL5STguEasO8PtvLf9MgJGQ3ymdrwDZyaTKC2kTuinvgoggBRgKu6F6NgwbdKCAkwXbU6Zqb+4kpsZKLegCJS0iOR/nlSduRZgtLnefovcpH/09HONWAeTfI6qv1HPgep62sUL/HjduvfvB5BotsJkwJHaaqfPuyvKzL9guXn5fLcyGYBz5lN/BHyc6rDIWIuFxwE+ADdkfeXhesjkUxUBko8BoINsYDqSyECSYcrqRkXQzRgkC6cw/ckLQOFLKqb9Svg7MiCcW0AchQQO4bNmKXvXGQt3XSTRDGSz7BGubS2/FwgmEHSQftFEWVq51ehXwiuASgOuGaxWu7Cp9logazXg3ArzZjOuAM4NYBMZmQhaFvdiEn1OUGqcBEkM5Tg3VobuNIBIzhEPkcCeAp6b7QHUAwIg8esWkCugZxYIAMUkj8BCVI2mGVuUcfZj9+j2Bkl2Uy7ABaR46G81cJDY4jGW6ZKSAUB8QJvuCMn62RaS+xcBdoZtDugkq3Y1dnKLqYd8CkXeYQaYEsggOtUD/MBYQN14pPITQaGGBJWVYQuJ4pYS5JzeSdRLLV3PcXqG1YaXtWsBBhhXNaZqPNX4nACwpZje2trqKIaeOXPGmgEoUxiX9dyi8Xsd43dFeTnPBkC45Dv093zYv08PnFP/OQfffjBuM+foXVFl0flpHpy7VUVvpf//oK2dPvHvV/YjwTmgGYFzQ1cAEgCWMvKTUONiXMEYPa5xj3nOzfziydF9WMmYUEpyfowx7xzMHSAyULQLZVV3APAqLdkm+eywK9tSspYAMhSZC/AtiuKa4FeB24hXcQ8xXwS5d1GHdKcxbvu5fxkbXUnM7wAdsiCXGm2Uvx3vOM00MmAZhSjhVG0HGl7A5zOnB1FPme3lPmxkDmPuBKwIA6j4UK91YcsdHGoB2HBZXvXbLbl4Kx0fOAhwbrjrCjd8xLLKM80DJBClLHOzlAf71qRMxgYU08BUiDuA55jLXWHmZO4nF8qkUPKMA+mMaT6Ut4gXclJQUhm1WeZE71wKYA9jLeqhIWC+GMorgbQChl4AREYYXuC8xIU+XSdln2MuHGuz6e4mYOI56n0p8/RKYooS5l7qA5UrJgkLT6Oahm22BwjHxz0ssCk2E0alD0AeVc70sioLlNVx7mQL9XTZbPNB3jcBkETMADAdFPSNPaxUQ91JOdR3HeVBZY9xNDrewFw1xFhAW2CbBhHGZ6KoK5AwI9eixZXAP8RnA12WBIiWJIAZmCoaAhYU+CPonHN6PUCFqHGBwtAObMLQpYYAlYfbgaWbsOfut6wK4sDydcBcC5mPVaPEGdj0xqZ7oG4AK5kXZfcoJZwY5x9i/PUyh2eXryEeXEfchiJe+xHipJfpLrRzQTV9Bch9ljIBNNGYQPMlgI3EVsxndF54LuC9ceZ65iw3UJ6bGNjDWBpDXTQSZi5MpvzpxUBkqLlNMRfGxp33yaNTgD6BCvVF3JRFXQJVRoDn44y5bvVRlOfcaudxYMXOm8RaKNElF1jJ4q2O/avagwqmHpivmC9iQKkOHYVyo5vyR4kLJ3onbbJvxgrqV1gK876LTRWhKc517WnzzfRYblEN11JKfXAvCrhnjnVlADVmVzPXMd9Ntlt8pJkm4dzEukwqwIPcG5AOUaAmNza0ngLmIvrkXN955mbqmM0YEc5DtzbftJ9YQPd1Ln/LZ6DOpr6g88SYM8OGWuJ0G/Xe4sQqWaiwJlVtsFjxeuLtCDBCL/cHMcwocOII7ahYUgCtNp6gWBZBVW9obAa+cbNl1D3I/IpFafsJCzedBqKjmGU11G0mfR2VQeKVaaDaYYC+5t4x9gRkoFxZaJNsvOifGLTxuUlq1G+lWP8urVhmJczhfqkNctlDXFcnY1d3X6f1cw/IclCdMAs7+YrCMitD6TCH/i3gtr9nyJp4lghh915cVWrV+VWWw3gVA35rn7xmV7rP2djIrNVjD7yw7B4HxAhigdtPTNWExXH/KDAmMGwqAH4+sYYf1evZIOAgoP7SysVWlltCmaesqbfZ2nragetSAFMygVGwsp7E1pdnl9rqpWyGqLKpEY81X52x7jZgEKAWgWN+IGVBapVVyVZdC8QISNLbF7XW62ygb4/ZXBBQj2cBV2DWikt8tmxJBvEAKnyAL0/vmwOce4nnu6jt2Lza6sqzbbxfoAmoZHLUCkvcKEP5rLYKCCaD+4H2ng17rKc7ag2XI3atIcK109Wpu1wsXrPzQC0JGkLc+8uWpvBcmOSAcYPDEXvpcD8W2x4rII738Kw2MIT57NyMVVcHbMnSNMvO8tg4AM71G2wCaMcGeoI+T5/00G+ycrxWW5cEmMc4Snfr6ohY0/Uw8JxAu1t2kIFAlLV2ny1ekmzlVdy3gHFnz4bsU396jOfuQtu9qcKWLwSvnOIemkFvkJi2uMRju7cmY+PYjJPXv6LWe8bZKKa1bcVPiefYWzETY9Rb6eA+4EbS//ii0RQDKBbUxip+npyccjYsayPGgRcO8FgzRJ6i0DZu2mRvu+8+BKeWOEJVznyv8VmxqeYbxjudbv742dVA4jkhUQL9W18/WnGOd9PuIeDvoZGwtbUGeZZg1h1g/NDwCATv889YWaUfR4I0YDUAZB6pbjbNWVtLmHHAyzPJLSA3JSXO+1y2fK3H8vLj1tfhtW98tccOHW+3uppC27KplPO5UYELMwXMWE5uhLEi1eo4b04u9y74F9MdNuCMYy3T1nglbu0tANJsLMzJD1phUbIDIY+PCrSLkcPymRToZtgg2NzEptCzAwB1SQgIEF9Ohui7M1xahPWrk7gvHbRF9aX27kfvt23bV/P3rFsInHP+08/EXPx3K/pK1OBP/v0NB+d0c45iQ3H8+HEUPD4HSXjT2eW6ZMlSEwiipI/kxfWgqMW7xOClxTo9SEpBLqEQIl/ld5E0lF2hFhN/loceNrRAqC891Mpq433v+z/w/R4gKfqoo9ih69HCpwaXOzUYK8msxKrkE6Uk8pGPfORnWU0/1c/+5Dw491Ot3/mT3/kaGAHK+A6KSN/61rccy706Fo9/4zf+m6M4p939d/cxH2Hdze2nkHuMXQF//9Ww/Y+/CTtqEusXuO0fPhOwRTUKPeaPO1UDAuf2Ac79X3+MpfNg3DYtd9tf/mHA1i17a7WDHgCU9BVIrMTt/fffbzU1Nf9bNSu5IOU1PfhNoQKxe/dux6om8beyKlNMWU5SYdmyZU4yQjv3peqmRITsUxWjCZzbvHmzE88dO3bMjhw54uzwF6QktSvBeDqXgDKpruhvFKMK2pNSlhLNimUFsUkZeefOnY7dmT5bCnNSolNiWucSQKekiZLiguR0bl2H3ifVOCm+6ToEP6msUofT71T+HTt2OOCUEilHjx51yqnYU/VVVVXl/F5J8ISNmj5HdSNgTufUZ+mQ0pw+S+WW2toSbH50zkOHDjkLC6ks1qnOpYr3Zj5UPwIIBc5JjeH1HqpzgXNSGXyzg3NqG21IWrdunfOMMTg46ICWshgW2Pnrv/7rTnu/3rq403+nmEdw5zw4d6drfv7z7kQNHD0ds/f/etC6UYMqJvf4+P9KtrUA76w/zh9vgRrQ3J04NN9rPNZYrDlbv9M6kOZnHYob9KXXE6/pdWchmu+Jc2n9SHGG5matk/2kh86tc2l9SpC/oH+tV+k1rU8lNgPoZ51fz5gql0B0fWkTgK4lUVaVR7GD3qv5MbG2dfs1hFGSarx6zf7qzz9tqSzMv33pIltAwjM+SFKJrcpegB3y7RZEkSmNTQG569Zbyj3rneR69Mo1GzuNGkxrm6PA5eXv0GuwOEosGXU1vHejJVfVkTAE7kGpZfTqBes7d8pivR2WHOZ6PDmgOKk2lU7Cpq7aKrdvQqkFFRMSXyFAwMljx232EtZhhlYcAM0UZYmzC7mE9xaSPJGl4eT5Szb4wiHLoP0CJLVCxQU2lpvpQEsF92yxlDpsrjIAAUjW2AxKI0Bl/WdO28DlRguglJdOwkkXGKIePcVllrNitaWvWE4ylaRPW6P1X7hsUy0qb5SEVZIF+cwpvrKWYsvLemhqZRWJT1SxAJdGL2GRdfG0TbKxQYoTXhKiUb7cKIukoxqTt2atBWpqWXgO2kzLVeu7imVjc7f5xkOWgpylEvQzJIWzgeYKVy8ltciC9/GzNnvkpNM/k3JR/8nNtyCKcz6Ar8KNmyy5brFFqT83gKOLuCra221Dx16yqRvXYBX5N4mzIGNWiOtPLci28mUrLWPRGuCHbBLzzdZ36qjFAfikJiYLrFuKeiTxS4otY8kKS1uzieQpqihAeMOXz9kAkGYEG7lU1lRl+RfxAp0C/mQvX2E5a9ZbGrCmaOMg0OcglpTj9A0vCiqy+o0C5c2h4pGOElnh2s0AWCSBTp+03pMHbabruhUD7CUVl9g0MGKMfpNH+6UCP3pwK3GkM0hyCsAUEDhy+LhNnwfiGB4hKYSiERYxE7LmJTFeuvwey1+yFkCw0IJtnTZ87qhNNZ23JFRMfF7s5QAWZoAJvUW5lk+SKWvlJpLu+QAkYzZ17bL1X8RWrId2wcpQ/T/KeSMoNwTKK63s/ndZSnkF1zgJXIdF2/kLNtFE/x/FYo2Mgh9rv1muM1peagW0dwaqMZFLN6xj/4uAc62WjfJOJs8bszkk8atrLHvVPcCoK7ELZLwgAWxY2kVDQzYzcJOvNsTQSDzz+T7K4efL6asMXRFfMgpFxdzXgAJ9vTbc3gFvgGoHcabuV+XeU7imQPZiku7AYrIx5LNZCacqSZQz1rgjffSX40CIR7Fbw75r6aNAGutvwRBhQMrpdpvoEwgH9BFDOY++7Of+S5WV3BjgZyTNAvn3oGq12nwZJOimrlt/A2pXPJNk0SeSUegL0a8igHwCHDzeDJIh/A1ljKOSN4vNbjiKihXl8gDKeGT/SKI4yn2ZnJppyUCvbhQrh3uwEBwZ5nNBgFA/IasPDMB9lV4F2IayU1aZBbGII5XNPeO2ZMYxbxiVweBNB5ybpH6moXyyapYBjn4fnIsEAYNR9Bu8TlMC0qI45OceSQHg8AKcxFC389Y/CFRTT9+7Bc7NokR46fIl2/vkk6iyTNt7UP1czUYqjathxukw+QW9Z2R0xHGh0LNnA+BvG0D2OH1LTjqr2EyynmdNWbTmAewkAQM780hiuU1TDveFM0bTRhrLnWQnryuB5PxbP3ONep++nNd+0slm/v1v6hpIzNGvbFvlsAT3S3FOG+j0rH///W9nwx4wKBBcbARb96unsGbuIEHJ/cKYCt0F6ADwjfKr7Nb9zJP+PADx1FpAo3Fgr4vAN61YhzLGCFjlXoz4gzYQBF73ZltO6SYspoG0uq4BTR/nPMDB6Rk2y70phbfkcKolA/Z4cxYD1JUztzCXkciNABEzAwLkENcMtdpw61Gbm+q0zPxKS6vexv3LOfldnNglBJgzM3yZiQqFTebXCPAeAk+A+8DrKI3NhZOtoO6dxAfbuU5Ans6XsWpF3RLoNj2fe9UXAO4FDkFlRUBaemYGNstZ3DHMf1OoV00BNTMPgWHdGiUAdaVuNAvYl4rKSUop8O1Evw22dJob5ag0xiopWYWorxhrWSlZQNNAtFLvM+zVKSRjNRlk/yygMjDc+DWbbrtg00OUp2i1JRUuQ3U0n3oAnIv228QAcxDKfjGgoVTGrgCfL4tuFwpNLDExP6djQwuMX06d0F6R3g6baNiHihsbAnDhmgNMnEbBK44CaKrqFStQX/YSYGHigSk2Ocy0UybmN1S33MDHDkiPEp6b8ddTwHxVt8pZl5povUyRhywT1RftZwij5BJDjc5LmyTn1VA5KPcZwDOWmQIEQcwpHGtVgIEznVdsdKAZcA4Fr7KNdK6l1Cbj9XQjMNEF6o+4h80MPqCqAFJXPr6i9Ncx4PXkTKD4CtY+ALPjWHiGAOdmuk7RN4FB0lFd5fpdxBSKOSkyMQfAZhEKfNjSQzcBDTGGA+0ZsZPGaVlBavgLz4YtHIxYJjFDMnBaKIzyVn8H83MP86VsPoHINHPQr305ReYvAKjPKAXMzAcKZVynl7qxw1Q9u6Z6LYSKoeYbQWOFS+4h9qiiMIK5mSvHsBwdamZeZJMI6r6qQHeqH4g9Djg1BxRpVrB4DeAcQCEyX6GpZhu49CSbUNoBBYrpT1jmoqgXpsFDitOB9ZOxGPYIXJweYt7ruwWv0jdc9GE3iokRqLg5JvQAwHdazUrut2kbaXiGMGjIfMyRUQGUUZTYgqlA2UAPOdV8NjEYSocuSKY4oGwES/JQnDk0AkjeDiDb2kr7Z7EpYZvFgcPjrmk2FFyzOWyWQ31dWPMC5QGmRem7EerIHRnDGb3fxqlr3bdpCwDnqLdo+ykLXzsFQ4gybR73RTr9ls+a4XwDzIc3RqbsXNMAr2VaRkE9UF+6jdF/J4DTojMhywLs37x8va0DaC9CUW+OuL+BOflcyyXmTFTu6AsuYp85zedAFOUFJbaMzRkrKqgL5vu25i47wfNEy0ib1aysxU1nm1WlVSOGPGrHGl6w041HAewCtm3lO2z9knvpr15rH+qyC40v29UbxOmo8iWhHpzuy7T0lHSbiQDEEW+l0xfftnanrcAieRho9FjDMTt76Tz3vdvyuU7BxXMoJaVgNb1m+Q7Lz1psTVem7fjBdhvuF2wKyCnGFYgwNSNu9QvzAewBeXluO3du2C6e64oDjpwAAEAASURBVLUJNtD7gVtjviDtO8S6MuXcWmlr1+QBqPgB54L29/90iLghaNUVKNISR0ZnPIBmIZuem0A1KsZ7M+ze3QW2oAZrWzZdDKEwd+HCuL14sBt2AyAP1cxM1LnTUOuLAjL2otoZDnfbww9W2s7thQ5M19o+a1/44hnr4XtuZjHqUBnEbAJTg7ZieTbgC/cVMeali3AhJ9p4LkVV0pEUR7E4PgM4F7DlKwqxgM2xaTY3nDk1aJcujLFhh/HDCV0YfzyzVlLmA8opszXr2KBBrHjuXNT+6I8P2cBovi2rK7GSPMZolDwnpue4PuoZqOd3fwOVzWgP+dWfM3CO+13z/w/P/bfWBoIkk7Sh7kXyAIdZ978Jo5PLRp7NQHN77r3XVvLsmsWGmoTSHCcilmUWhDFxVJFpk/njZ1cDr2xX/VtfPwqcU5Pp2Xxiyuw899bJl3usEfh2NphG3CP9NcFnw1ZTl8yzRamVFOVZy40wqo5DgLQog6IsLLBWj49eoHv2pNnb3pFtCxYl2XBvAHCuz/YdYl7PSrOaahROUXUbw0lgcrKfMWuKPFMpa1o1tnwlcwwqlxE2QnV3ztrhl27alUtBwDwAa9TrMrKGWaNKZn7LY5zw8+xr9qEPZTE2pGPzHbcjh4P2r985wnjgI+dWwByHwiRzQRrrRSPDjdi/nrbFi8oA5+7l81YB2wvKJxjgkLLqLXDOGVacRyLnF6/jf288OEchBJkJglPS8JlnnnF2S01NTTtJTKlpaBerkohK8jkPjAQASuQpmG9jd6F2vqoj3HffvY4ShBKbzoPh67jA1/snWjzUgmPiSwuRUvZQklFl1b8/85nPOD9vYsBR4kpKH7nIpWdBzSvxqkVHLTbq+w8PYK+3VP/73yXAOckPKhks26a36iFFmK9+9auQ0Ol2CpWUzO8ng9/M1yvruBdeeMGqqqoc9Zk3c1nny3bnakCJDCUqtPNUtnrXrjU68MVHP/p/OlCFQIyf1phx565S0/X8cbfWANO4Xb4es1/+7aBdaolbPhsQP/pun/3h75GO0drz/HHHaoBnHdt/JGq//SfsAGHX2AaAuf8PcO4eALr544drQLGb4jR9TyR89Q7FlHpNQJlgKcWfsvoU8JY4FO9JOUYxm0Cl/+jQeRLxoJLR+pwMJedecSQ2W+h8b2QMqzJKqVTXpM9+I5TGdE06n0DAN/uhsiquUkwokFDt9pMearcHH3zQUXFT8uCNbJ+ftCw/6v3ahCT7X/VXKRJKsVBHb2+vo1woSz5tmJEqt56t7pbja1/7mgPOCUDVM2Pi0L0yb9WaqI3573drDfSxCP2RD8/ZvuuyCYjbN/8sCcuUebvWu7U9X1luzZU69F3zj0A1bfo8ceKE82/Bac5C8/f/UO/TOKfnusSX3qPXFSfoNc29sp7X+JeA2r7/5z/2m86j50qdJwHOCebTepXW4xQjVFdXO+twidgmUY7bT665VbC/xmXNMdrAUFtb6yjh6mfNk/o7HbeuKWLNvPev/+xPzUNyeWVOvpWiBJZNIjoLeMqHulkMG8gI63vBoUEHGiresR2ns0KbOnXGhgCHZqiX1OpyFKIySNAA+gF/BYBEcpYClpWSjAWECV+8ZM1HSJRgrRnITCIRX2qp2eUkXwI2AnDkAgZbsA61jgzUVkga9wLkDZ05D7wUQemOZHJJmc0gbTGDMlphQa4Dz/nT02z82g1rf+p5y+/utVTAt8iiRTZTWGD+0hLLWViPKBUqMWSxnPQBCcPJo8es+dQJG0FpLrOk1HJR35HF6WBvH46iEyRs8q1qw0ZEzvw2iTpe4zUANJZpi0rLLRVoLUqiPYjVWnI5NlP1KMDxrG+Ucbyx2RoPH0OxpAdrJCXBURQmBo0AKckmLJW1xcIlyyyAMkAIkKf/yEvWe/WKTURiloOqWgbglBdFtUnaILUw2wqW15Owoi7OXrSJfS9YOyBXVm29pdTWWQxFPB+Ke7mLsaYtLAVyAL4BVnPx2VOnz1kD5w6hHpTD4nRyQT7qKlGb6u5EQW2QHeHFlrt+B7BRHjZiF6353AnL51ozgcIsm0Q/C9QCLAIkObPZ9JK2fBnZaaytzgIbHjlsIyixCbzIZAOJJw2rMZLao4BRqVW1lgeUl0WbxNvbbPDoYWttQqkMf6o8AK/knAwABC/JtTngp0zUXJaT6PXb9JUL1gXoN9p01cqB5lJ5fQ5lNzJtllW3AIUh4CneR0YeAEP2YCSAL1y0zv0o1pCsTs6irQuzLJwc5LlhAPu+HqvKyreKdVtQrFtng7Rf+5mjWMb1WxEqfQHWrqPU1zRkmQu4q7C61rLrl3MvAGGSkOo+xjW2NJPU9AF4FFoAODWOktskUGqctcva3WyOyeMcAIHTJ05a09kzxmZ6oMQiy+K9gttmQkELoshUtGSx5WJtHL3RZG08twx1tFkuScxcNnyEi0rNhXpgNgBoMorTrjQyrSSdLYbVIYngsS6UAFGfScIyzO1HSYYe7A6hhsd1TNG2cV4von7SULwJA+S1XG9CXiTJ0rjvklKzSDoAjqRT/nRgjyTaFqW6GElo9JFIhNOjoRNckS6b6z5owa4TqB1mWdrCRwBqgCoBY2Ko08z0N9pY703AAmzKUAqMo8JDMcw7exNrwgZHtSejGLixciPKPCnYy7ZY15XTNgF0m5PqswwSehHKIVCRDolqEHZ42AKnoewi66kpgSDAJd60HHLaPAujcjI91Gcz2OXl5mXR/iTbkzOs/2abTfZ2AbkCtKAMI3Ur48ubDqyTWUXdAeBiPSz41Qvw6pMEDglvozzR7qs2xrg1i31rzgKUoKT8g5pUBGhvsh0oEIs8RizUfzKBUUgM4UXkZsxleLDkZY8BwCwBLACu5cJnpmbsypXL9tTTTzFGjyMmcL+zeUkKf2Pj5BiAOPuYR5QE62RDUAebw0YA/jQvaMPV1i1SmVvHeFxnmYA9SqTrYEa5lQ2ibp0K5jo0NutwRmqN19//N4O387N++4PfOe+c/9/dXgNq89vnZl1P4t/6Wb9XrKFNd1/+8pedjXQOOIeoRSHjvCsGLDp20oYaTnLfdpC/ki0XkDXjQAwALsb4GZkaRFFpBhAsjzEesHhuFrAJu9Y4Y6kvG4WiXKCrKFNaq/WPoDbpz7T86t2WW7ESu87LNtf4AmomWDdnljJf1KImlQ0snALoWgCIx73lQ2GWfj0HOIecBtgSoBNASgjgqp/7LQSMl1Ncw/xIXjClBLh1xMKMMxNAyJFZ1NVIBLuxCBdwFgUSsslrqGnyO9SdChY/YoHy3YyRKMh1HbOBpssw2iHLyM7F5Zl5HqvOMLGLICdPdJC6Q9UJACgCyOIDtPExFnldKFJSD3EsMce5h2cYKvKXLbaMSqwih3us4yowLRbuuZl5xCfF2GMyfjPW+lEH82Mj7QUsY8Lli/EKtVXzoUQb60Uq6zIw/HngMewTuTY/qnOOZSpjS3SqzfpaXgJ2agXmY1zOAWTDltIzjjrbIBavjOdR5ols5vfU8lqAFx/qdUA4lw9g8zlmacRk0Yxq1G5R9MLq0oNCZpQ29GGlGgTmmUZRTTbsAY37AMpUPPVGm442wfZRp6W15l+8B/BswgYbjzPN9Fh2Ou2WXgZMRHsxrvtoz0BeCcAVnYbxLop/ZBzYxoPamzvYyUaK6zaNKuzY5CCW18w7xSjO+RbwWVMWxLJzvA/lUmDkZOrL56eeYyFzUdYg1q+TWIJn5aRbOgpnrnyAOyDOUMcLNtVxxGY13mbVUS8AZHy2oLI5rFTDjM9SpUnLRf2O/jjYM0BbolOGEl2AONaFXWsEFduZ0T769LjlE+elVi1xAMtRFGbDEy3YbaKbl6FrrLY4lrxexQrEBi6gQ0epjlmV0Iy2APRj3nVj7R1qu2qjg6zNYcGZv3gtvB39HFBKYOd09xkLYnfq554IJPE64JzLNYXV3KBNoEjs5rpKlq/FWncJsUW2hQDOh89/l7qjLDlVzFWoNaPoF8HmdA5719jcGJtHuD+Yl0O0Y0RbJSib24v6Mf00jgrj1DDW3qEw/XONZS3aBlQ4boNnv04dDaIMzEaKnIUopWFVHOMeSCFe53odi/MY6lKCC2XTim1p3NXFFAyMT90EAblzuF/8lTvNXbSF9gCMHDmClfx5mx0PWhoW8AFBnwH6F/FajOvwzTQzJkQsfeEuNqM86MRjsW42tlw7hiUoC9zZqOcCtrnTUBPExrhtfMDOoGZ87GwTAJzPSoDdKmoWAfYDuBN79A50Wk8fwBubg+9bt8tWl6+0UfrnixeOWWNnM3Ob28oKUZZmEFPs1dsPuMhCejnx866NW626pAqIc8xOXT1jhy8foRsn28a1m2xF+Qpi+R47gopxJ2NKLRDj9nUPWH31WhuzCTtx/YSdevmIjRMTFPDMUZxXbCluUNTZGWvoYDML8GQJqna/sPM9th6gbyDYbQcuPG8vnTgKoBqz0pJaq0GNLj0JGDBQZgurVttof7bte+oqFqkdlpddymbxXPou7UncFAWeyy+UMl219XS57dChG9bT2WLlJblslCU+S8V1hGeaZCxXV6/Ks+XLClEHT7Nn983a331hPxvMu60U2/vFPMNVFqcyNoSsuW3U2roHLS8nYr/w3tW2ZWOBwcexsTtkz+1vspfPXkJ0MN3qeCbLJQ6dmfWjzBe2yy1sUoi020c+tMre+UC5FRS6rallCuW3fdZwjevOq7TlS9jUUuwDqotzHZlWU5WJ6lvYnni8gTznCOvVmbagnmvHdnV6Jkg/cFllRQEb37PtSkMfMNcV6+8NWiXjWFlZKuUQGDyLylyQ60NZblUBz0duOw849/ufOGxNPcm2uDIfxbkZ1PbYIIUN5Y2bs8CPg/aH/4ONRv5hnFu+/XOlOOfEfooF+E9rAjoULytv0Ibi98FDB+3QwUOOKIAPyFwbMd7+9gdsA+vgubnYOjNu34ojeWrgXlMbCZxz8X3++NnWwO0xnkqif+vrR4JzNFuUiaq7J27f+ddOO3G6gTE0xnoQkFwxquo8wczMjPHM5EWFknnO8uzZJ/uA62Z5znBjfxqAtyEOoivN0ocys0O2fVeZVdWk22CX1775L/22d98l4LeILayvQuiMWAL78rGxCaySe9m4lUeercre8c48Ky7zAsHF7MTRMXvmWZ59JyM4JhWjECmV0zGef2ZRnPRZa2/AqgpT7bc/XmLbd2ZZL2Xf9+yM/eOXv+NsvFjE59TUFgMi+xx76maUSxuuHrH6BeWO4tz27VIIZe5yghk9/WoNh/mWOkt8vd6W/KmAc4nC6CbVIuKhQ4fsPItjatgx7BPkm5zJDggt3mlhUYG8VEMEkmhhUcmf9evvcVQgVq9e7SzcJc750/yuAUaLhvI9V1JSiiYq88DAoPNvlX1iYpJFyClnZ+6VK1cY0EMO8LKEBY50dtDc2p2b4yyeCX6pqqpyFhy1aKmEkBZK38gjAc5pR55st2TN9VY9tJv7+PFjTqJ4Hpx7q7byW/+6NN4J7Dh8+DC2f1+C/m90dpK+5z3vdqz+lCC/PeFy99aIpqf5426tAdZ47Qv/Erbf/UssTljj2VTntk/9kd+2rlFQPX/cyRrQIvwLx6L2O4BzTd1xWwM497lPBGzjShpm/nhNNaB4VDutvv3tbzuLxrLI+fSnP+0ovb2mE8y/6U1VA1r8lyWtAKw2Ntzo2SEBPuq7s3hwW4kVfysOVxJKcbjU5gScCZ5LKPLd9vY3xY9SANLmI1maCsz81V/9VSfRJsU92evp2nXdAuqkFJiw93tTFP7HFOJLX/qSA85JwTGxuKM/UfuoTWRPu3SpdqTPH/M1cHfWwG/+Vsj+1/MoGrD2+F/f6bX/+Qd+QAiWM+dD47uzQV+l1Jpn9Eyn9auLFy86Gz8FngkwS0Bmeo/GOH0pkZ2AtPVd47e+9LrGPqnbLgLe+klh+MT59VlSnNXakMZWqc4JnNNmVSnZyeJPa2yaAxNJdX2+Dj136r1SOtKmLn0XCHYvO9EFbAvmS/xd4m+lXNTacMU+98nft0lgj9rkdFuOLdnSFeusAMjDj7pIHBWlSHOzDb30go2jtpHHrvbMRUts4vRpm+L1dOw1M7ZvNm+lkomAYrJlRRHLiyWoJ6PAIg2ANs/stxunj5uvtMgKt6yzzPolgHYkbVkQnSKRTc4R1RA24wLXjJ09aVcPHbY5kpGL12yw3A1bOHcNiUU29Q71oKqC2kN+NvBMks21d9mNb37XclGFy1m03Pw77zU3yTh3JooHfMUduw8qh7XB2cMnbHLvMzbc0W6+JUstZ/sWSyc55w3FbPhGs7W/fMri/d1Ww7yVlp2FtVybXQNYy1u02GrXbyAxyPWhUhNHucbFbmp3JuoutHm0u8eaDh63q2euWAkJyQUbN1nWqoUom2AfB2QXYZ3R42X9ECWAeBDQ7Ox56wGk8rIwHahfaAX37rFkwDoXi+MCiywJO0wSa24ggshVlJGfeNJutLRY5ZatVrAJSKmEXeHO9WFT5sOKC5jPN02dX0Qh7+n9dq3pphXWLbSqdaiZlZEMB8ibPn/epll/i4ygNoOSm4vF7puNF6wHVb3lixdY0cbt5ilfyEWhToR6ndI2XqybYnnAfz1d1k0Z5mjvVBKfWWw2TmNtV6oqeLRh00J9ZJCIp695gY7mjh607qOHbCzitYKVG/hCgaeE60v1An6hgkd9JGWwWx4FkPDNBut8CYjw3EWrWrzCCnbdZ94FWJ2xgdmVShsS98VQjolj0erB3jNy8bJNPLvPRi41oRK0zDLX7bBAHRsOUqZJbLdY23P7LbW9E9CjzlLue8g6blwH5jpvecCaNVs3W1JtFefFlo+6jkZIrJMg92TmWrB/0LqOHLFuEqRsqrcSIMcsFPe8wJUuwLk5nj9CQFlpVSSpSUDNHXnZxp96joRAlyWjUphLX8osraCquSbiviDQRiAnywIktmKdbdb54n7rb71pBSjNFW3baYGahRYDiPBQDx4S/C4UPsiwA1m0kMC+atMkltOwrMomoezy15C8jwEKNGMHdwlAaxglqAwrWbjU0uljkd4Wa77WxHWhLgnAmInCkC8VuAwIhsw6nZ97BXgkSrk1dbk1XqCYF5tifGnbj5LJdWDAKkupegegWh33CuUYu4Ia4kVglhnLABhNKcTONq0G+AbNupGjNtG6j1vKg3rgNsC5rdRhJgn2Tmu/KnCum+sMWB5Wv56sWvozCnMkpGd6sZFsu8HnB8kj+AEmsLMtXU8/q0GhCHxtotfG2+nvndcZC1IsawH3UFK2DTYDKQICZaE2l1a8kEsirkyuBBDIRZlItoQBrhMIj/Yx1BfcJJIYPCzST7K/9RpKObyWi8Ub6oz+zEKn3WeHUIFrwypwBpA3t8iSypYCn3DPYbcb7rlhU1gtJy96CPAU5SiSjqS3bJZ77OrVq/b44487yueLyRsUISYwOTllwwByyjvouUYAnZ5XPcCA2owvcHnVylXO+n5peZkDA1DQ+WO+Bn7iGlCOSzGBFOckVqGckUD9/HzNH4NA6i/ZQANgeF83OS0ssYBzk/J1H2HLCpgWAhSd7AKEAS5NCvgNp1ALo3iVlA+0DITq9uSjagb0NvGyDXYcY8xLsZyaPZZVvdpm+y7bdON+7FC5f1FUSy1lTswCEMcaEmqOawEqizIOEAsFfbOotU1hycgcF0StqrPBhvrbUN1yW17ZIpS2UGoEsIpPNgObH2XM66S8hYBStcybQHCopoVR1YoOHLG5/svc0UmMx49ZoPxexlggoG6u80YD75ENYa2lFTFnCKIljRwZuWDhvpdtanTQpkgjJ+WgrlSy0JKy60gOExOgnhUeumn9ne2AXWErWrbUMqpLqJtu67iErfd0BCvECstAndKVUYsipuBp5kIUojzUhxsbZzlIunw8GPgHAaAAxHsv2RxgmcVRyizfwdDL3yr2AHCa7rli413nLTU9wvxBOfOXAtUw/g22WgR1rT6gYBdzZw4xSWZJFbCDm/e3046HLQ0VuVxe8+YDNKcylwOqRQcYn3pvAJlhO49tdoyxPqNqLXaY9ZQv3aIog0VQcJnrOGRulNd8xdieL37I5sanOOdBRE37LbuQsbeQfGPaYuYJYDIfgBpAGpS0ox4TZTyNKNbFqtIzju17J8qq3e24WPstC4XUANeHXBnj+qBNtj0PWHYZ2IprL1jBnCYwEFtYFPymUU4dB4zMTvewuQJl1+Lt9BPU/rqesbGWFx2V0PSyjZZSsIp5FmU95uXQGDbgbSctFbXB1ADQB/HLFEqC3vzFDPvEP8DUUPMWHkaVrxuIYKTLslGPTa5ZCjiHQmnLFVQMb9Iv0oDSlxErrQHaxyYVcDIO5CnbQjdxtv6TJp3QlKQICsudgJ1txBfENL7CKqB2lIeZE2IAQhNdzEtd57ABD1F3S+j36+jzrN/OdaJw12hDvdi4ohRVSiyQwlyCRzLWsq02ATjnGm5FDY9Yomg7mymYl92AktPdAKOXLMo95aau4rLKza2nTegfSVI1BF4cAShtPwnMhsJYGeq19buJ9cY555dRUETdpwDL41Ji44x65j7mQh/KgUB9LoRYuLRbh9rTNUJ7tMGSA5G34eYxCmQBAOYv3kH/XkUbokTXs9dGu6k3zpFRvMr8udU0E4D5zBTz4cs218PmA1Ri0xfttqT6h7gGNjFwD05cP4rlKV2IOslBES+FmCeChXHzyE07fOWIvfjSWdQQPSis3mMb7tltxUXV1L/PrvZctENnngO8GLP77tlpu5dttzY2fjx9hD6BkvEKFIxXU//JPEdMAl5daWjAFvCqxVhM37Vtq63Vphzu5daRDtt3/gW70d9keWxCXlqz2Ppae6zrZoflYs27ffNOWwo0F2CzxrXRa/bcmWfsOueqKa5gA/s9VplXjtpgwDqxMN53dr81Dl6zYsa0D+38gG0EVuybbbV9l56ywydQ2gwFbGn9etu68V4r5D2pvmLLIj45/uK0ffVLxwDMZm3HtrU8pxVZXiGgErhAEMDTg8pTPJpixw7NATzdIH4ctrfftxylNja2ZKAkBb3L9i0ryIuwkQd7bVSbntsftM9/4Tm7wTPchnWb7J33r7TliwBHgUmuNszZs/vbcHc5b489tNoefKAeZTk3iumT9uwLDVi2DtqmzQtt88Zq7jvAvGGzg4fH7dnDqEhN3LCPvH+xPfQuCSC5ea6Ytk/92SG7dKUTNb9F9u6H19miZcmWmw8ATCDMbWIXzkyQ6zxFW2XYts2LbcfuVCup5FkTtcwgao8Bv9z64nz+NTZCX6T82Xbvrs3EPBmWxvXN8p6Z4LTlFyRZAYCjB1D03JmIfeKTJ+xap9e2rCi1R96ZjBpdphNTnj4LxNjQZh/9pVLuzl775rd+vsA558al3rUWECfu16E1CTEtR48dswMHeJYmF+JmjJajzf3M/5s2bf7BuoBufrWbhgD9vZtxSuCcE/w7Z5v/38+qBtSmibUXlcFpY177ceBchGfV5uaYfeHzrXalscmqF2hdZ4EtXERMQtNOTqGOzriblu5l7PPY5//2EvBpHmNAoe3c7XOsncGMeI0Ndt45q6hOszSex7tbYvb1f+mzp/dfdCDXPbuX2uYtxZZX4LHpybjtf64LoC3GpjOXfeCDrG0sSrHrVwBpvzdkZy7csMWoae65rwr75AAbxuawiJ6wJ58YtNPX3LawNMP++2/n2pZtyQ44t/+ZkP3jV75HOeds+7YVOC0twOYZS/O5IH36STv44pMAxmX22KMP8vtNvE99mEALvXokIfhiTOVfiS/V3+s5fqrgXKJAeki/ykQjUEQLiwLR9FCoAD7RCbRgqMRQZWWFbdmyxUmcaJHuTgAkKoMWPpWkkpWVOuA1dhleZIerdt3q31GoW4/nltWFn92ntxZECX7521sLn0j9s5gj6E4LiznIA9fV8bCLFYOSP1VV1Q5VqdeVwHujrus0dl6PPPooi50sTlB/iZ3Eibp/o7/rerVAm2i3xALwG/05r3Y+LUxrUbesrNRkqyb48s1+zCvOvdlb6M6WT2OFFi1Os4D82c/+tTPOFLHT6bHHHnOs1gTN3T4p3tnSvdGfpulp/rgba0Dr4lebYvZLvzNnl/hezMPLxz7gs//+cZI3BFnzx52tgTliv5dORu3//lTILrXFbOUSt/3dHwRs8xoWOOaP11QDUl956qmnHFtsKbI8Stz0sY997FWV4l7TCeff9DOvAS38y9JWgIBgOdm26jlDlr16vrj9kNq1EgSLFy92Ek+Ky2V7qpj8zXgoxlYSTUk2qXcr1laZ9VykWFhqQIrFdQ1vRzGgBNWRu+n427/9WwdcFfR4+6EdwrLOlSqk4Mb5Y74G7tYa+MLXIvYnnwlZzwQJkVyXHfhKktXXSmXgbr2i+XInakDjc+LQz5pvtK6lBWo952mN55XPcno9se6ldaDE+onGcb1fX3IrSCi7Jc7/yvMkXr/9u8qgL32+7Nj37yeJ09jolEkbtTTPaZ54+OGHUQKocSC9xN/o8xNl0RyqOGnv3r3OQrsgu/e///0OPKc5RuVOHCqXFtZbLp+3z3/i96yfz6tBiWZn/Upbdf/DloZ9vAdALU6iPNrRbcFnnrB2rCzdKDWUoMoWudZgo82NJNFJILFwn8SamSstiQSvnyQs6giobrh8KTZ97GWb+c7jNtzZZjlbsH17YI+5y6oAXkSfkbjE7jOOmpQHJY54F/ZW+5+zC0dPOPZbqx7+BZTD1qD2hX0YKU43alhgS/wtiV2sYWM9/Xbjq9+yzOvNlrOMBNk7HjJPTTUAEFZCPtRegGmcRx6SNwPfe8pmH3/CktmtnfK2t1vyvbuB94CLWPuLAMj1HDiAstopKwJSTAEsk1LVNWy7SoHQykmI+lF3cyWncV0kvXNZxEXlQbak4YtX7Pze55jvJ20Jtp9lu3abr473puBWgdpbHNU5JzsLdDfX2WsdB1+yEdYQalGZSd+63fw7Nt9KWpM0jtPHHMEA1NlYUbYICZKxJ59EGeKmVe7eY4UkDT0lJLo5t9ziooCP/JG5h8YtfPy0dTz7gg2ihlOzdTfWsLRfDutsjFeRa1ctvH+fdV08Y5nLVpi/phZ7pkaUcBpsyYJqy1m1hfMuwm4LACOFtksGCkzBqo7EvGxfm775r5be1m7li5ZZEv3QCxzqEjChBE2EMviBlwAFIs1NNvTdb9v4lYvmK1tgxQ+825IWASUBeAkIxOeFQvOA6lKdoMzG53ccOmKtx1+2hcCaZW9/p3mACZEwYoH/1lgbwQZGSoZuLOZCR47Y8Hf3Wnw0bFkbHrDkLYB2pSSL02ZQW8ICCzAyymbvuJcE5Dvf7dgZdl26YLmonFXifpJCH3WjouhGcYdMP/2P/upGXfBGk7Uc2GfjDZessrrKijZvN1890BSwo/LQ3Jys3wPOAm/FZyZt/HvP2eATe1GeSbHsBx60lF07btmtAj3E6V/OHEH546wpx3o6Uch7zrqbrlshgEvp2x6wpIXL6NMAACQc3Si0uFzYv8Wx3Bu6YX03LhlZSsspJDlbC9zgq6YA1HHohs11n7bhbsARFIvyaxcCY2CrCDjXeqPdkrHby18ApMh3CDH+RjAL0Br4ChlxAETAORQCXWFUksZI+JO07wdKSUqfs/wK1BDzN9MuJPaD7YAnZxgPgCEA07IrFwEtLKG+Kpy2i48fstmWx1GwwZq2aANwJkAEEGV0HNeZxvM2g6VeSQGKhSgAuTPpU17u89luiwxctjGsAmcnBlBY8QMDLAWk3UGfqydBiooeloxBVG8m+EpOi2MLx3gCRDDY0gWA0Wd5WalAeqtov9XUSSntQf9TQlDX5czJ9Kn4BFWFZaWgjXZsWFEojAMGpVYsAmig7WUlBAg80XvdplDQyvKg8Fhab15ggZgftQ6s2mKDAHcApu6yXQAi2O59P+koYEhQ87e+/S1HAUUbXdx8sBx5NHcE57B/5TlGVkfptG05kFxdXZ3VoTBXXl5+C1yWh9v8MV8Dr1IDms91JOKFV/5bv1OuS+sfeqZUPkUgvTZd5eUBzgDe2sRR67ty3EbI0+UX4KJUu4w5gHvXg3paCPvl0QabRhktPNLrzLdhbEH9hUDGALoeoDUGMT5kivehIta0DwgD9f6q7YBZK4CLL9lM0wFg8LilA0ElFW9jrgAqI8Mpe1EX0JxhlQnHjaonMYONYpncgxpbE2AcimVzKEbmFyNOxdyRuoB5jrl/GGvnjv1YmY6gcLvCkgvvYSwHhAYAi8+gmtb9vAVRtpoBws50wLndnBwIqOsgyqBNxAPZgD4A84XMGWnMuYoJRi8AQR1FBa0TgNlr6cVYw5YtB+Ktpx4YC1Hmi4y12BCKWFPD41jAooBXU4QdeZf1Xr4OIAPcV7kQAA5oOY2/ASyLM0fEOJfQYzfza5wstMs9znjTasExbFqBtmMCnKSeVryR+Y4xGLAuBqg4fB1lS6DgzBLsR2kPpDQ5F3nEiVYLtb5svTebAKcyLYfNBBlljPWo5E11d1jf9eOWjvV3bsVCioCzQoAxD8Wp2GC7zQL9zqBy5/aiKFcC9LxgN1XGeGvMaVihxscBylr2UhcohuZWADs9bOFxnA4aXzJ/eAjbVM5ZQl2m0DdczIWUxxlDgdRcWMbHPXwOc78L2/BY71ULosQ2zTiXBISXXr+G2ID+FAGsGsEK/drTqJC2WQZqnl5ALlc618D8rvab6wPWajkNBD4HmL3eGVNFFIU6nrTR1gPMU2ko2+6h/XR9wGYEVfHpDpu5/jwbEs4CKWKj6nMzheSZr4J5pmQn9XoLnIsBpM10nLNZQMI0LFWSUBcLRlJttKOJcbzPsorzaUOAvIzVxEqFnFu2rcTtXKOU9KTexXZN1mFmuW+Ye5poJwCqWAagOBBYgH4j6CQ00mejXagtjt4AUEIFr2gt18i8zHVYqNPCA0DmXU02FZywMuDHlLKVlLEIcK7DZs9/D/Cwgykc4K+MNkot4h4jfg32cn0nAScPAzpi855bad7yzSjArWDOA0BlKJDt8lwb6mgD/czNqy2jbg+vT9jMlS8Dd44wHa+y5Or7Oaf6EwqNLmIw2lHDiKZDwhXmeynL8llTV20G6H6a/u4n7k5n7vXk0r+Z0yP0ozDz+dxEO+1Qa8nUsyuNuR5lVh54iF+O2uTNp5jfpmj7ey1pwaMwg6jS9u0n/j9mw1MBzrfRcms2oh6bTT2btU1ct8NXn7N9L7wEKJ9sm9beazvWP4CqWQ191G9XAS2fvfA4IPoF2wLAtnXRBrt49ZodvkhfKcuxdWz0qWBjjReAUOdr57nmyvnLNj44apvYELIVdblyVBInUXU7133ZDp5/iVgGhW0UfWeGgbSo580rt9jGZZtx20FNEjjxaPMhe/Y0arGAg/dtuM+2L9tmecD3PhQk+1Dy3XvuKTvZfsKIFux9G99nmyo22EC4zfZdfNKOs6kmM63IthFzbl65i/sSe2jel4yS7eEXRu0rXz6JwuyMbV6/xrbtKLH8Yo8DnqVmY/9OGDY1BoTyzIy9cKDFiU92bF1oCxdno1IMUIp1KYK3gMNY8hIeuHli2fsMVq1ffB6VqS7guLfZex+rwYLxVpzT1RWz7z4xYM/v22c7tyxCCR+l5zQ/ccmoHT+Hml2l3x54V5WtWU07p7gBbOP20uGIfeO7YSxwz9kHHquyRx6ijIBz15tn7M//AhXu5gHqdJn9yi8tskogGFx0AdwI24Djzp6asi/9Ewrgw2m2ZlWNbdiUbtW1lBuwLpCkDWO8D/jviSev2UsvNdI3clDBW2PLl2c4cF4KQ5IsHlNSeT/gH2LPDjj3x398yjoHs+zBnRX2Xz6QbDULPKgrxq2hMWJXr0zaO+9FrzTY+vNn1aqJHghX3IogWLEtba2t9jLPjAcPHXLEWrysJy9essT28Ewo1dlSPdurIThurV0oVL+10U/xhBNTECfPHz/bGlBMl4jvVJLEGs5rAefa2mL2pX/osnOX26y4nHtsW4XVLUjFEhXINQ1Vey2ncH81Xgjb3/z1eRR1M23NykLbtDXJyiqI43hPcgr3Ie8L8D1OoNbbGnWsWve/1GAlgG7vfc9C1OXS2USBciiw+rN7ZwHa4Ke8/fYLHyiwJdzTp4/N2Te/zWaIiSDQZpG9/cF0KyojJuK+7uyM2te+PGzPvRRlzdRtv/2b6bZjZ4oN9Mbsub0R++JX9iJGlmqPPLLGdu/KtQLGyfHxSXtq7/fsmacfR82zHHDuIcA51iP0/PxD4BzrAc5/t+a319uSdwScU8NqYU4BuxYOtauqF1sDSZDr3wLNlNSSQpuUH7SAp8W52zvH673A1/J3GlRUJiXbtGAoC6j+/gHnT/Vgm4adQGVlJbKoFU5ySjt3tbip3yWAOz2I9JLI0qJje3uHcz4tYOpLMMw99yi59YBtY9FK1/nKhdHXUs5Xe49AxA9/+MPOg7fq+KddZ7peqV3ogUuQnuA1DbJ34kgMGBUV5U4yUfX6Zj/mwbk3ewvdufKp/2qcOETg8vnPf95ZOCvDUuQjH/mwffCDH3QWxX7a9++du1p90nyQdWfr+437tEm8Y/7qH8L2yc+HHbW5HYvc9lefDtiy2jsz1r9xV/LWOFMIBujomaj93p+F7OUbMVtBe/zt7wds6z3z7fFaWzih0qVNEYrhtKlBcd1ba8x9rbXx1nqfniMU/547d86xb5U6sRJRtx8LFixwVMze/e53OzHrnYpbby/DT/Kz4gUBcu3t7U7coNhb16hDfVbPIHqWENgg2OxuO6Si99nPftZJTN9edl2Xnml+53d+x6qrq2//1fzP8zVwV9VAU0fMfvPXQra/GXCDRaG9nw3Y/Xu0u/quuoz5wr5KDWg8vrXAfOs5R+O1jkQ8oX8n1mQSSqiKQQSx6XdScktYyWvDpdahtBamsTyxuTLx94lzvkoxfuglnVdgntaF9KWNoJoHpU6qz9i1a5ezSau+HtCDz0rMJ7oOrbvJ1lVz6De+8Q1HGUnzj9Z4PvShD9kDD5C0YnFdf6dDZdJXjHpoBZz7yh/9gXViHVqRxw7ljVts1UPvtnTU81yCe1gHiw2N2dzzz1rj8SMYsEVtCWXxjwxY76VzMD6zlltchf1ajXkKULWrKCGZW2IeLC3xjLQR1uQmWBj1RoCdsDdM3QNklF0AUBZwyhBHVURwlDc4ZtFrF633ye/a+TMXrXzlGlv63v9ivnLgG08Kq8IsOvsIppV4jAMxRoGwAOfa/+Vblg74lA4453/XoxZfiNIVYJ2jPMXCKwulJHHnrONr37a5Z5+2PNTRkh/ElnLbNsAeVEiwqYsN9tsgO/nHUD/LoQ3TUJOaiIatGQvW1KjPinKxVcIG0y1VtdJ8CywqJdlJmSawoTt+2s49/jRKGQFbtBub3i3bzFMEhAMko9wqWRCSzJQBJZvpG21244UXbeTyBVvO/JiNGqBvNfUsoEZdUckNkrVUDEogQRTnrtnY089YS3urle9G6WI74FwxsAJQW4z6cCN944pQ/h42NL8IgHYYVbn8cqu9712WCuTmTqeM1FVESiMHn7fLL+6zrKoay129FlWhQVT2jloxKkAZhQtokyq4qQqS28XmL8PyJTfNQlH6PP3i6r89bnmT01a7ZQfA4f2o05HEZxeW1FuoYAoODEciOnjpCu3xVXMBd2Wt2mhZD7/PfIrTNWh6uCbFQFyf4Mf47LhN3bxmLcBwLUdOoMCx1iqB0LyAii4UfhyCkHqIAFaGlIgHnJt5YZ/1P/GUpbkzLHfPeyxlPW2Yh3UbinNxkp+hAwdtbu9Tjo1U6sOPkUiPWveply3OJo38Euzoyrg+oEh/WaX5K0hWZ+ZQrICNXUBt8sBz5LM7rGrNKivYtotkN+VQ1lMNIxpEsJ8bNeSJYev5xnetG0ivvLrech551Pwb15NMp8z0Nee9/JUmjRguJ1EUDrsA5/pbblopMV/B295hvoUo9mTQNqgEuAUoYKdm0WFAg2bs9BpIGGOXVV5HGQG6vKg6oeToijVbpPtlG+kEuAi5LKeqGsAMWLGb87cNAIgst6walB9zaBtfgGKo81HvqBupWFQlP6ue2gDjLqCSc9O5l9NLyyynBOgCtTk3kJ178rqF2o9bPxBkGsozmSi2uFFrMh+QKYo7eNRaiER7UEqDxbjKlK5B4Q7b3rEB62i8YrNTI1gI5VkmMJ4rpZ7PBIIRgDEKrNaO9fAIkB/KCJlVKwFCNtGX6Xvcp/HpAdRtztl053HGKewkUZl0pZRZbxu2qqi4FaMok14DtJGNfaonD1iA5CKaQdIjcOBYoEBuZM7ThkJdKwo6PbxO3y6oooyLUcUrof2oZixwhzqAXQabrCAjDjQE2JmLjZ4rA1CE+hkHhhjpNytAcTMHeM8hWRkGgyjf4FTzPTbDnDx5Ehs77k/GtLAgGqduOT33byAJm+tMrHSxRc7nWTWTMTSAupfuEkcNSO+dP+Zr4BU18GoxiF5LzNV6u+Z85W9aUCDVs9Y25rA9e/bcAudiWEyOnwH+OmOjQyMkJQstd8FKIC7GGgOIBRiLTTdwb6MehDpajPvUk5QFjIpyXDnAU2Y5HVjzBffAKGqy15+DC0f9tHIzVpGLsOS8YJGbL/J7jyXX7zJvwVasJwHrPUBgAGmuCOAOU7PmA5ePc0jFa4yxpOuSzY4NA7aVA6QL0K0BoiY2QHkr1IvCVPuzFiLuSacMvoLtKK2icostp6GoFu15DnvJF22aYSd78SOWVL4TsK8FBbQXAa9aCC+KLL1iDeMI0B9KRy4A/BiWm7HOs4yTvA+QKLsUxTUpSqZVMWdxXjfqrNM9Noj65RgWkIVYDWZXYmU7hD3t1Zvcp+mMrSvg1FbBHdei1CkVL6VsnUtz4olYPAhUhBX0+AWb7r9uk6Nzjo1rXqVgdanNMZbYOPMy8cy18xLitayKKgswprtSCykDo0GQcbj3nA3QlhEAnLxKrNgrGG9AcmYA53pbTlpaStxyK5eaN289f8P4xTwUI+6aaz1uk52HUXfC5rGGeBCoKpIEgBxHgSYGkDh5w2Zbn0IJ7jqKZBWWVvsuwLmgDTYdM79rzDI5p69sO7AwbQF05SgSaXmCYgnacLkAkOOooo1hX9pN7DDUD9CVSf0DlJWtgMNmvAfMCA002cDFZ7G4HqQOUZsr3QlcXklZGc+xFo8MEb80nbBUbMiTKphHKrZzbnRBWx9HXfQg0HYWan/3Eivew/wCbGbUW7CLJkaN7OZLFhwfZOwERC6tApyjb+TvIn4gZgtSt8Dfc11nbbb/CtbDCKNUVaPwlcS4T79jHM8pob8BTLqYX2IelBBRLUSblzpivEb1lImAa+232CxgNopxk119hqkv17iEz0JRNxX7bhTegsCPIyjuRYOoGhPfphSupa9RXgAwJlKm7UZg9qs2PN5lFaj3ppXyO2+Zo4gXvLTXvJOdQPjEbRXUjdTkuEpoPNpe8+gB7o1RoHc2hFY8wPUt5T2QRsSk8YmL1P0xG+nuJBZbZdl1u50yT13+CmXmb4DxkqofhEoCfqPh9BXF8lXNKJW+ZNrR7VittgCQn7I5YFEXCrQpqAL68+ij6of+ZGIO6vDGM9y/Q8CNKEGXr6H8xBwxaC4sX+PD9DXm+3HintSaey2t7r3mA5yLDz5v462oz86mco9tI/5nDAmwwQXiqpMY4qWrj9sz+5+3dID0PVsesq0r77f8lFLiGK81jF+3ZxqesOOnDtm6etTl6I8nzp62M+1XANnTUUSi7bgWlzZlcD49Y/S191poKmwb1qxHpW6PLcoDliUG7ZodQEnugB09fdj6h7sB1tJtw6oddu/aB60eFb9U2nQ2NGrPX3ranjv/NP3d7JFt77Ut9ZSZvhQAnBuLodbW8LS92Pwiw5/L3rXmPbaxapONMIYduPSknTh10oqJI+7d9pCtrUXJmcjCR/v7XECAF8ewMr1ux04MYplaa7ULUHMrcmPRalZT77XqGjbacE++fCxkL+zrAF7Dyp5NM2XE2GUVyVZR5QFEw862yAtcBiRK+fY+PWf/8E8voCQ1hh3rVnvkXWWWz+Y9hc+9WCV+5/E+e/yJ/XbP6jrU2rDxZSPCV785Zpevd9qK1Zn20GOlbDL2I8rN3wCjvXw6Yl/7FnatV07au4HqHn240rFqbWicsv/3Lw9ivzhlD+xeZ7/0oRoUQdl05AWeJViaA8Jtapyz73y7CedB1EPZkFFZVWhVlQErrzArq6TcJf8/e+8BHdd5nuu+M4PBzKD3TnT2Ijaxk6JIUZ0qVLNjp654JVm5Jzk3ccpNcuPEzkmWz0m173KKk9hykWxLVqFEimIRu0iKpNgrAKL3jkGbfp9v03CYLDtR5FgWFWx7BALT9v7rt//v+d/Xxzgc16HDzdq7q5k1Ty/qfzAXM1KxqPdwnQnVzvGjWgWPYZtnmNMunI3pM390QgMjeXrywTI98zG/Smdg4UzMXHcjqovnR7VxdTL13ojLzAsfaqtWZ46mzzkDm/20w8ZR6vH9HnHG1hD3/OMT4w6Tcuztozry9tsozV13Ns0tXDCf9YBNjuJsMfcUyfAsZsU6FS/Y9976b5u//tX5vd8Tm37fj1QCt9aJfZD9bo//CJyLsemuvy+h117u0Z6DNzTIWkIxavKlQMaFBV4253jY8IVCeUGSugBrv/PcNZ07Owok7Yd9ymHjTjp2qi7GVTcPH1bLNxmt7pYYVq29OnD0CupxWXrmmZlatBggGLAuCnC76/WIdqEUN4mq7mNPlOqOpZk6emBSz3+rUxlslnr08Uyt34wKbR7jFpsWBgGEv/3NUb30yoQCyaOsn6I+eU8W95pAeNuj+oev7dLsmYX62CcXau26AMCwSwNDXajUvaId3NdXwWk9/thjgHOsobBWZXd4DPL8tLs8Wze42a1+hK5l8Rsz4wd82MKeLSRO7by1DmmKcwaiTe16/SBOyZJttoh5BgsC25174ACysSPDDsxnN6wLFy4guboE1QNkTFk8nILlfJyrnact+FiDtRsRuxaD5Gxx0hYfrRFfuHDRWYRsJDi3583K1VToTE3BbMJKSHgZQPejDEajLMSeYwebLeLaufy4j8bGJkexxRZXV69eTYLr5z5gmyuoWHZsLF267ANtK++3XKfBufdbch+t91nfNDh3x+s79MKLLzhqlqYA8KlP/aIefPBBB+CYSkh8dK78R5maPjqlcLtdid1YXW2I65lfntTl1oQK2Y3wy59I0u//38x701X6E6lOA+eOvxvTH3w+rMPIHi+ahVUr4NyGldxETh/vqQQsTrO402IlizftMX18tErAFHY+//nP64UXXnCSA7deXWVlpf7kT/5EH/vYx26L2PHWc/+o/dviIbNi/dKXvsQmpe5/dXm2IefXfu3XHAXe0lISuNPHdAncpiUQYxH3V349oq++Zclx6VOPe/R/mLcziammj9u7BCyOmEpIG3g2tRbkLDBzaRZv2BqTgWu2odJs4u2nbbC0taSFCxc6i9YG8dvfbe6yvxvgbTD0v1V2ey+lNQWQ2xqUPSxBbkq7Zk/f2NjoJMlN3dwU56buN+18bT3LrsccFmzjqCn622Hvt3Wtp556yrlPnQLnpq7bXmPgXOvF8/oGVq0t/CxHPe6ujXdr4dbHlI66a4zFUTefweKaQgcP6PKhg9i1jmvRli2sifk0UndFA9giucZQjCP5G0IyIbm0UDmoq6YvwJ4KgKRnP4oyqNVlktzJfAC1sg2r5UkDwkmQaCcJH/Viz0Wi1R8hGXr+tDq++4LOX7isqrUbNHvbx+UprGKdzSQQ+AE45zLrI5KfZoOawN6p6ZvPKQNLozTAK+9jTyo6E8sydit7yeK7saSztdf42IRav/k8qmtvKpdrDDywVd7lqNtkoGxGVjs+1Keh/Qc0AtTmp94z161WrKRIA3Uktxs6lITFCd+sSZTYlJ+jnJULlDObZC7JvcixU7oAROVinbH6vgeUhQKAG8jHgcXglsgUkitlrTEY1viVRl3f/5b6Lp3TQtSocu/ZrCTWKl0B4lmuj1dyPqgXcvZxwKTo+csaAASrb2pUJYBE0V0bUIYDOuQ84ixUewxkCmN1294FOHcQ5bYTcpXPUvU9DylgVpeAcwleE2vjOo68pdO7dihQXKrKDXeTpEtW/7l3lERiNjGB5lckRbFUgL+CHJKjM5Q1B4WubB8qNVd0CbW+XIqydtP9Srlro1yFnIOXhWyD57g+F4oeriiJnjPn1IiNYCqgU/aKNUoFUPSwwdBRpHPfhD3j3Ih6gNBcoRENA87VHTzE47CWoupX/cDDSsLG1JL0ViAJ+mYM664wieDERL+Ce95QLxuks7BQy9nylALLUUfB3iqaDHw2SMKc5FIYVcF+VEWynuD50nINYXc7cfEybRQLVYCyMT8WuijJZc6sVcFsbJWx4B3EArbx0FuAbr2qWLFMuWvXYw/M9SOXkaCOoTQpR1oA7TQOBNLy/HfVsWu3aubdoeytD8u7DPiRhK3ZFNI0HZbQjfqcA841NwHOvake6rDUYKotDwDOzVYCBTU34JxLpjYHOEcyPdTRQHlfQz0CS9zK2cAN87l+gE1U6dwxAIuO46g38ZNkaDZAoh9lxWhHi3raUDsqXopKm8GgRSSwSX5SglaGaCTRz1DYiWHrx3eMo5Az2XmWsWECUMWgsvkI/1QAYmaTouAdqBVFG49yvs1Kw6I1o3IlynvAEEmWSAc4GHtH4bpX2PCNyh9wSTpwn49yiqPM03blIspEgyopKwTOWAQgM5NyA76NDPJ8HSpwWP7130DRAJvXKtTjcrC8A66j1KAp+rGvO63x1oMkYYIAbZWO8k1HywiwXVAl+UVAFigIpddwrllAMVFFAFi8dHCMaOnnAKRAc6EeVI46mk1IUpl5JVjozqFMqmmD2LrSF+LBDiBG1Hf6mlSUh25M5RJeOI/yyoL3QG0L0GGyvwMgk+vKrqbO6ZjEvJGIqWK36tixY7p85bLGGQc5cXvKAeYcNQR+t/HYQDk/7cxLe6NJOPNJPGGb46kLfp8+pkvgh5WAzc8Wj0w97PepjWL2N5vrLSdlsYdZw99xxx3MxcyP8QFp6Jy6Uc0c6h9E9agAkHYRcwCAidmoqg9poEuIZB1DBew8r+2h3+cz1m9mTtlEX4P0wEaRDgA4h7X3lT2Ax4CggHWp2C6Hus4oduMAYzf5vplYouetx2IdtTLGdbe9D8iCDkn7ZkwzlSvgLVMci/TdwAYsDcvQxahcMU6iBuXC9hSiSONNrwMWv07f9CpQyTnkbuKzirgWRiIbMwDnRm/sRs0rgRX7I/JXbOTjGf8ccK4e8K/EsSn1YNd6E7am3IK4TLWdVXfDdWAi7MIZx30OxFvOd5pVJ/PqWJe6m65pBGv2IhLLWaWM89iK9qDc6fFSbtg0JhUtVBwoKYIKGiOAMzYSqTBSoSIaHVB4ECCw8zRqcijjMUYHgG8zypYrFjALel5vUBaqae1XL8kHfJddMVfeQiDyFMZR5hEk4RTtBWBmc10Upb7ckpmofQEoo7I10d6ijhbAOdxC8qpNLW8V7yl15pbEMOMkYPNIwx4FfAMK1AA919yjkB8VuQRKWDYUUvZjzTsV6r+OQBu2sxUPcZ4T6mk4hqpVkLllgbwlGxRO5nxQYjPlTGP5DJqjgVH23XzGRQV7rgAHYguKqqAf4NGTD4iWgcIZMUoY9d9JrC373t2pDOavHKBmT9Fm5m7KmWgpHgWcA5Yern8beGkIII28X/k6ygb4E3BuvPWQUrPzsV+lzrMM1irkXbj2TLYjXfOmxur2aZQ5JYlNEJlVQOQVm4lHNjJI0ggAAABAAElEQVTAZgLO0Uax/w51nAIyPCMrUh+qnhPYbvZ1DgBt0uIpy1RT0U3GptVtMBoAE7OFKc3hQcpnUD/YlU8MXsVOlr4BdJidi204inFxLMYTxDbeWI8mUUrtb6Xth9qAG8uUiipiIgmwjs9KhKl74MReAMnewSZVzQOcK7G6ot2YMiDgnHesTclzDJy7i5Okj9l8TKwb6Tqm8RtcZ3+v0guxeK3YxnxIXzXZMdcY/nvnAUuPaojYLSlnMUKFWyjToEbPfgUQcxRobg2A3/3EgqUKc01sJaFk47R5U1kOKxWo29WP01eHQeKoxyaHAPSY64uImQIzKEfKIpmYlDl3DEg2KU4MUUP9EkfcBOey6GvEYMNva7IZxej+bs5xM/Dc00rG7k99b2i45QhwT5qyitYBxa6lrFEsTvapDUvQAxdf1Ou7XldeZrEevOsprZq7UVnefMrepeujDXrj2k4dPLZPi6rnaH7pTB09dUIXUIJNKUsnfiiQL8yGE+KuOOVl6kixSdBHAN1FMxdpzZzVqqEdurmG3ggKnGffwor0Nd1A2bKwokRb1j+qTQsfUhnjQyqBUAhr3+2nX9LuszuVjCrdE+s/ppU1a5XuSZc3jGU11rO763dr9/XdKO/F9cCix7W6aq36o11669wOnTh5XOWls3Tvhke0qHSBUtiUgHYhQJwHoCWsi+eGdeDQIHG6jzjagw015tIpE6qsdGvJ0hzNn53N57p19nRQZ871Y8+MIif3P55koo6cqOZjuXjnslzVVKcoPc2NomhYX/n6W4oCeD61baUeuK9QeTnEwvTPzp6EXn4NcG47ZTevQo8+CDiX6tc3nh/RVWxqV6AI99BjxfAKjKk+0BPG5UsXUZb6Nhv3Tx7TYw9Wozg3A8Ejjy5fC+rzf/6mxoMRPXr/Cj39dJWyYJpjLsY4rtAeBuycOT2i40cHgP7YJBbys/clxnmOq6I6Ax4iT3Oxdx0ankCdjs1bp81OnPsT4s2kZCDdjAktvKMIJasSzeT6AsB8589E9P/+4WHmsUI9/ViVHn7EpyLUp+CDKEMAv7P92rAqTaMjTR9qcM5ZN+B+0VSG7b7NOWwctaCQMfUHxnr2vDND/PDnJ1Av7kMA6gb8yfETJ3SM+5qGGw3c93u1BNGATdwLrl2z1nHws7UG+0g+zflpn/5v/+2ciz0xffxES8Dai8VzU8dUjPcfgXNxxj9u13Tp/KQOH+sDkA0CagPFMgbZvvviwrjmzQ9o4VLuizMDwK5BvcPrbjQGUYM05slU6cZRqktowaJcLV5WAtzqU09rXM9/s0/H3rmmZQC327bVaM68W8G5sHa9EWJjaJO2Pl7KWJalg29N6Dvf7lNRYbYe3xbQqo1JSkOhzmKa4Kj0+stRvfjCOKP2gP7HL+fQVrPV2w6Etz2if/zmW1o4v0wf/+lZWr7Sh0peQv3A+a9xz/7G9jdVWV4NOLdNG9axzkFbZwagLdv4z4N/TXWpfynBqZJ87z9/IuDcez+9H88rpxKothvXlCkOs1vxGgsNI8ERILk5DOLsUmXRrqamxpFHt53BU4uM7+WMbPHSdhZ3dHQ4ahG2GGmg2ZUr+M0D5pnK1Lp1a7WFhUO7YTGL1ambmvfy+f/qNXSimJEOH9DxNoPvX//13zj09sc//jFHFeKDtrq69SbwA7rs9/010+Dc+y66j8QbpxYpBgYG9MrLr2j7a9uxlatTTnY2ft8/5VhSm+rRrUmSj8SFOxfxo0xNH51SuN2uZBR53S99PaI//muSvUTTa2a69f/9FWpztd8L6m+3C/oInK8l3U+dY4fV/0G95t24Fs5y6Yu/59Nda2w5bvqYLoHpErASsJjewLmvfvWrTgx+a6kYhPV7v/d7+tmf/VlHLfnW56b//cGWgAGsn/nMZ/TlL3/Zsa6/9dtNSdqe+7mf+zlHkenW56b/PV0Ct1sJ/MWXI/r8lyLqG0toRrFLJ14MqDDv5gLO7XYt0+f7LyVg60h22HqEPaYWMKf+Zr/bRkpTuDUQzTZomvW2gWw2xq3B9tGANAPlzG58yo77HhTEbGOlbaq0daH3ujZk3zcFzk2tkRj01tTUpL//+793wDxTlzGLegPnplTt7LWWSDd1ub179zrgnH2nwXumdmoLslu3btXDDz/sKM5NbThwrteuH5ijHRWlZ//g99V28YKqgMo2rrtb8x59TKmorsUAr9womiGpotH9e3Xp8DHKJapFD21VZnU5Sm4kADtRO2sfwPZsBHvTLqwYB5TKOlnRmo3KnrfAsT8N7kRxjoR61v3Yct2zVklkZyxhi+SJyDOR6ovJGyIZCrzX+dJ3df4cEB92sHOefAZLsUoScpbwtsVZqzMywyyjJgC1Ylxfw9eeVTo2mJl3oDj3xDOKVs8k8ccOaFKIbq7PbRaoQGitzz2v8O43lMeaYIANb97ly0nYorAD+JUIDmto734Fd++TNw44d98m+VagrBIcV6SxE9CoH3hnmA10gxpgrTG7slRVAFbpBYWKX7iky6jxJVJSVXXPFmWtWotyCAlSNne4UEuDeOICSQAGcbG40qDG/fvVi33o7KrvKc6xkdSV4if5TnLQ2iPKAUn8T+OTigAQDrIb+2pDAzDc3SreuJHkdDGJzwDJWwoDxblEFBgI5b3x/YfUdBhwrKBENffcp4z5c0k0W8KYpGpLk8IAe6f27AY+qAZQewjgqUSuIZLHgFhjHQMa6RvHZnNCE8MDQHUoRCxZqPQVCzTc1qyLL+5Q9kRc1XdvUWAjyfEZxSR4SXRRJwbOuSkzFzDH5FmUEqmPANaaqUuXA849gnJbFWURoCxuqm5RHU67c0fGsQC74oCEVw8Bzi1Zqpr7UZyrMnAuldoz5Qv6EIv+9h6yaBrbs0ODqBemAGJk3PuUfKsAxUwZD0s712C3EqwHR155Wd3Yv+R8/JPKWEaSGwu9WBPqNJ3YywwMkgjsVT+OKaYMVgsAm7tilYY72nXj+BFHzacCBcA81ni99DOrU4OdHDtaxzqJfgqU0vnCK+ravVfl2HBmbb1PSXcuUjwT+AKohCZnmUfs4EisYgsaA7Zqptw7cUUpor2UbHlQAfpWOBNwDvgRs0+S1sBzQG2T7aY4d4nkM+oB1TVYjC5Gca+McwiRLK9DgQYbxpZGxek0OdRjMmo8McDH3o4gSjcGztH2Msl8ksSz07Ckgz3oqI4S2/hQswY661Gs6VVmTj79FMu2zFr41RxeTyLXVBxRi4m2mYUggFs+G89rVsqbU8lnovRjSj3DwG2XtytIn0qqWAa4go0x2ZtEL/aCl8+SPBkARilURo0pNvHZCQPnblqgjjdgzdjfhD0ZcBmqTm6zZkyhvi05wvsmOg2EOEgyaIIEeiUwTBkKCsMMP2MkelGXqkJBK7XcURAKU9AgRKR2w0qOk7GZHARywBKy8yqJ8nEFUN5LL5opb3Ylwwz9EbDXrJ4TWNL13eA8UUvKz8bcsGYFYOBS2nIe14/q5Ug99n/tqCAtlx/7Sju3m/NFAhh5XJ3kBgbYtO9YaRlUx2Egr1PvNi59PzHK36kEA+bs/TZ+0b05D/vP9DFdAv9+Cdwak9icPhWn2Lssp2W5p7y8POeRZHNBbFCu4QvqvW7gXA9Wz1nKwRrUnTGHcSmfdtwPfAzw2QR0hfWmByDK489RWvW98pbeDxBVxFxF20xg6doHOHdpj0KAM/7qFQAztSh7ncVa1KBW4IdqVEfzNjBuANfYvMyRiNiIw3iWGAHSOo+167uAVZ2OZWR6yWzmLaA5UyNzAbB6mBs9EyiP7WD+eZn5IUkBAD7lbWKeyGfeRmUExbJ4K9Bcw16NTbpQnAOcAz5yJbCSbtmn/nrU1HwoOlYCuWE36+JanKFutJm4AGCt7qqi9M+iyhqlGBybWs3zgFmcX2SEsaoBC1LOrxgFpVTsvuPdzSiytSjJj/U8r/dgTx1D0S6GupqNokn0Y5dGGMt6uL4m7J6xvQW+DQA6paDg5ctHcS2jknjGYHWgZAGfdbyrlsvYF3LNBYDE/mLGEwecY5ydQEEUxbnexgYGCR8WtrUo481i/PADzrWqE7vZtOy48mzMy17HuTMPMN/HsX2NtZ5g7tyPetwAZYId78yHgN4B6AwOAyCOAS5NNL2JlWq9vEDP/hn3AsCNom53hHkvqOwqNjUUb1LED4RssQaHbTLw8kgQh0WH6zTeeYZYrt1RM0ovKEf1jPLz88D2NAZlNwlgOTlwSb1nXlMmaqn5lYv5zHt5TRWfxmYEVEYj/Six1R9RuhtLbFPjm3EXLcRDvW/HZvUQwFse4JwBccwVQJhRN/HmZIsSLW8odP2gxgb7nLaeWlkrb+UWuWgfCaBpKpDYrEMTHSc1Ql1nZKGVynwxFg2oh1gGopPyng3ERnkmFzAdUydcp5sEvCcBgQC4ZoD0RM95DQP32ebmzLw5xHNAiplVKCBj5456IVgUr8GKteWcIhOdyq9AvQ+r1ngybdn+F+5QDFC7EyvX/tEOVaMElV68mm4wA3CuUWMXXlLyeCsKxetg6TZRdsRugKbCgjbaCZDWuFNjAyiV8Zn+iqcA52jH7M2Iu0ax8j2jKGU0xrqYJ2eJUmfez7wPCHHuKyidjcsL0Oou5TP9ZYCBvIdSR6eKSHqcPjcib7CPftCq4U7ATOb1TJzhfNghu9LnMoBAGhK3KWmY6zut0eu7qPdhLHyBxcvWcQ70UyBGZ8PAwAFFm19iDu6lrd0jX9UzgBi0w743Ua5EgXo8gM3yKgD8NZxLoQPOtQzXa/+Fl/X6G6+pIHeGtm5+WqtmrVVmci7jREKN4ze0s/5N7T+6X/PKZ2sB48OZC+d1qaNeBbNKtHjRPOW4ma9twwl1xsnSh7AbJO4rzCjEphXrc+o1wtU2ENPsOrVbx88dVe9oF7BpulbccZc23/EAqnS1ynb72SQwqFdPfFdvnAGc83v1xF1Pozh3E5xzh72U2Jh2Xt+jXdf2KRKM68Elj2p15RoNxvq0D0XFEyffURUW0/dveEwLAedSaUtJdl9BEdoGu+BwXPWNcV25HlFz56j6BibUTZw5ziaCyvJ8PbilVgvnpgC/cO1NcR6TAHBjam0fVFsH6k1wz+tX1mrT+mJVVSZp74EJPftNbLEB/Z/ctkb331uknBzuD7iV6OyO65UdPSjO7Qa4K9OjDy9XLuD0N54f0qX6Nq1Yk66HH8cmvgZ4P5m4nPK7dD7B8wbOndK2hyr1KFatBY5V64j+4q9Q9ASqffjeZVg1Es8WEOO6iPNk8bpX7BegjyTU2hxXQ8Mktrnj3AuPcm/ZS79J0sxZabr/wVJsI1MI0bm++rg62mPq7ulTDzGh3Tcne7O1DlvZzdgzFqNCd+bMpD7z2X1MN0A022brgQeTlY9KnwPONUZ1/mwP4FyGxoLNH35wjtjO5geL62gRzk+bu+2378d6FhTyq3PwT6LEf3ne/s7f7P7fYsrJyZDa2tuce/5Tp07p9OnTzoY9u3c3tsWU5034qLKy0hGs+t6nTv+4DUrAaRe3xP9TMd6/B845l8V7aBoaYZzp7E6ooRFlRqC47l7Gmh7uSbClz86KouI2T+vXFrO51wPDlECZ2DZ+jqura0AD/axhcI+Vy/rlgw8vAYIrAhp36/nnenXs1HUtX5albU/VaNZs4jtslaNsVti9IwxnxZrAZLMe21aGKnyWDuwPIW4wiCBZqp54KkV33+d1AH9aLnkbl7a/GNZLLwNpJ42hOJepuzZkqAsRl12vRfSV5w+w2YM+/8laLV3mwzY2rj7s5F+Dr9j1+h6ULGtRw9ym9Q44l8x9E/2Ea7feQhdxupN1F3u83+O/HThnN5+m0najsVE7duzQ7t0sRnR2opqW7QBzGzZscHYBl5ZiFQH1/l9x9NPYzCrDAL3jx084Uu35+Xl8z2rsL+53FlANPnuvC6T/Fef0fj/jpZdecmwmzY7213/91/XTn/wEUokEUNPHDyyBaXDuBxbLf4s/2oRmiZMuLD5M0dLkgi0RYbbUjz76iJPAt37/n4Fyb6+C+1GmptvrSj8qZ0uMwY6ruD6B2typBuyk8LT/1MeS9EefZseB3XtOHz+REsDJRecvx/W5vwzrlaMxIEaXvvD/+HT3+puL7j+Rk5r+0ukS+JCVwOgotgFYgH7xi1907E1vPT3bXf+rv/qr+qVf+iVnDr71uel/f7AlYJb1v//7v69vfvObjjLSrd9u6t5Wf2apawpM08d0CdzOJfDuVexafzmk450okBESn/uuXwvmAkRMx1O3c7WSnLRlOFuIu3mfM/X71E9bxLa1n1dffdVZ+zEwzeYgSwKYCp1tmvzEJz7hLGQbOPfd76JABVDx0EMPObaoWVlZzr2hff7Ud/x7BWbfaw9b47K1JHsYoGyWbAbO2QZOWyw3cM6sWs0SdupczSbWFO9eR4nLlPHWrl3rJNOPHDni/N3AOXsYfD4Fztn1OSUA8NSOGtezf/iHQC+XVZmVrw2LV2r+I48rZekdJCoBh0ydA1WV9h3bdeXYaZQrMrRo29NKn4NSFDnABMBUYgTIC1vKCXbFtx3cp17UvfJXrlf13ffK3dyo4I6XKc8O1LbWY995NypypZQL4BywTpwPsUSCh3KNXsP667XXdO6dE8qdPUuLPvYMolUkEwPYbJE4s0QOuTIOMlYRoKTOFtU//zWl119V1oLFSn7kSanWkqUouPFqq123ddzJiNq+/YJGX39ZuSjfpT/0gPwbgYyyb9prxnv71bMTq9Z9B5QNGJn58Gb51pFk5HMSYyhZAb1F+gG9bnSo8eRpTQ4Nq+rOlSpYtMABzy6j5DYRjat2w0bl3YX1WMkMEvmoTZn8i6nLkJCOY70arruhzrf2q4+ESEURajlb7pNZxuKNQtIYQMyDWgUAnSViTCEtStJ9+NWXda3+uioMnENxzp1P8tVnSnkGlrGgDrTj7urTBFDjjb1vaQj3j1ko2eXdicJPAaAACdIo7WJy5269g7pbxkKU3bY9pYxZlXwPCjospidGxsglA841AwUdBb5rbQEoqlTRUw8qMhbUxee3K6VnBDW2tUp54AElzapCKcxLgtkWtGmvdsZYu0QuXNDAc18HVLuupLnzlP3IY/JRjwbOIR3DuViSnvfQzhVBoa7eFOf26xqbehcvW6Iq1NiSWDyHPOXMPIqgapcMFAJfBuSHws1+krWvf5u8fFwp92xTYMMWrjEXmzrqeBDwij4Q2QnU5fIo+6d/UWkrUVexcwRgiw+OYh3XrQnAs8GLWIY21GGPBSSBYpzZD9efwJKsoV5Vc2ardPMGVGKACtIMrLyZuHWuEYWRGLbFfS/tUOeuN1ALyVbhY1uUfBdKQ5lYktKe+UL6JudMYjWJJFgUC7g61Olar15XSUG+qu5/WCnz5mkyDdCO+jPIwu/C6jk6rDBWrT1XAU6A6TIrS5RfvRIGoNo6mTyRK5psPUoSH7Ag4ldu9SwSZyjOUVfdHUNYpaH+Vr36Jjjn9CnrJtSLjS1AKNGB0+rvuqrR8VESHTnYq6Guk0M9AkKYomMCiNUFzJiYALJEXart2lVE2sqwe1yuQEHNTXAOlUfk+RS8RD1gFxSoXKH0isU4wwKXdddr4PJpkid9ygVGSceCzYXSG9okQH+4wXQ1ANydRFivFTATNR8UmDwkp12miEW5xocHNQYEMdIBMJIWxtmxCmWqYvUCzo0CreYXFTjgnNm3QoMgXEP7IHPtpbQ8oS5F+82eFQtKlAn9aRlAgXMBdAFVSLBHXbbOzTpIfIgs8A2NtgIXoYaVmRIDNlyORfFa2jL2xAksZVHcG+tql5e/+bF6owM78JupitiYa6ByjHHTOWz+oG0aOGevc6YTZ2C1/9wc+21s4208x2tNOdF5/ubbp/87XQLvpQR+UAwxFSc4Tk8OwTEs18gFVNNOqQ+1q+z8FBQ1sfnMnkO7A6YFiEPyVP11x1HquiH/JOMhfTOlZAPQ0YMAyMDQqHsagJfoPqPgxX0oWWEfWLPaAeeC3ecYOw8zHqfRZwCcc4C5rF9ZDGDgqEFXAL7RyUaguSMK9V1E6smDGkk1wBjqkmn02yRTmrU5l3ncw7zUsU+xxhc1wVyQUrsecbANhAQG+fG8gVc3dmu47qDC2CnmLHgcqOcueJ9mFLD2qa8eS+ukLADjO5RcPJMYwcA/PhrwNdQK+Ntc5zjHFlVgI08fT6QZQJjBJaIWh/pl17XzqOH1qgR11QCgbwzYu+taCzBhgXJRtkzKnYkoLmB8cprTr11gOu4oCmNDVxFku4hyZg/3BC5l5JXCCwGpp6FS5oxLnAagsgeFv2jPOTVf4vWTfhVXLABMmsN5UgYWE4zWM8+eVm/DdeZ8P4p0qKphKa14KgB1mzpa31IgMwyQBuSWv5HyIqZgXkiM4oLV/A5z1SklR0fkL6GO526jWOfbiINK2CiQXJ0m6/dQlaj9AT0nAzzFEP7oaz4ECDeMghs22UX3KhyYyfhs8RenFB+XD4vWKCDSeMdFjXddZ37BJaq4XMlYubtQNlOCdkSbiTEhTzBrToxcxqr1FaWHOlQIWJRcAtwWYMwk5khgoTlBm+lpeBu7zjE2WqzGwfQextJkgLHXFawH/EvJAtzcjPLpEsoOyB9bTmTYFL4GUNl8EkvdMQQNkwEjsVWvArAr3sLYT5sDxI5hqT0MQDjUfo5416+08pkKhlPV3dJPPbkA52YpHXAu7s+nnvxcJ/dtDL7eOP1knM0QWLAGsVkNA0f5c4Cy8ygToDIXMGg8hp4YcKALW9sQ8OFAyyU2FLQrtwggHJXVRCoqwcSgrjC2sp1n1dZ6VUPYxxqIn1YEQKZKmm+9Ri9+S17qKzALC/WKe5mC6ANu4ljbcNJ+CMvdNzXJphQfinPJlY8x59CGnNR1UB7sksNXsZ5HecpbsELJsx7iukeJsb4KcDQqD9C6u2QjceYMyhswkPjYlOoSiX5U+zo4r2tsqGgAemBTSm4lfRCwMov53ocqnDuZ10Z5DKJKCDjXsFvjQ8D0hZw/kKUbi+IEn2kxWrxrL/3wu6g0DssHvOgp/zhlRNw4uB/lwEOA8tih079Ta7juVDZjJPnUhIrQgYs7UJx7Q4X55XpwE4pzs1YoB9XXBAviN6jjN+rfwiLwsObQbpZXL+HepV7niOPL51ZpE2rTMzNKiaYs5mfetXbNfJ9EzEstKgAEn4xhan9oQEevHdOh84ccaM6XnayRwRHlMhasXbRBGxasUWkq8WFoVLvPvakdJ3cAe4W1dcPDunvBemXTX91Rt4YAKV86t0t76o8SZ3m1ddlWrSH2GgKk33t2j46/c1KVtKUH1z+uRcCFZiPLXg1nXHB7MIwnnA1OuESIqX7gsc7uqK7XBXVof50mxty6Z0MtgFumyoDGxgFSBocTGg4Co7VOau++62q43qbaGTV6/IEq3bncr4MngvrqN3dxPxjWE49v1H33lrLhhziEMKOzC6vW7d16+dVdqDeV6PGty9nMl6nnnh/QO+fqNG9Rih4DgFmwIF2pWKi6Uas6cigGiBfRlWsX9Qy2r488UuiAc9frg/rCFw6gODemB+5ZymawGuwegWb4Hx3NiR9NiTJOvwkT+o1wbf0DCY0MxXTqxIiOHWdTw3g7G7NmadM9+WyWRZUb5m4CAYeR4agGBkaxjOzV/n0Dqiiv0BPb8rVoWZIuXp7QZ//kTdpYhT7+xDw9/BCKcyVYw/K1129EAP36tN4U5z7k4ByFRJ1Q+d8L6pwQ7+Z/eIZ4zw57jodtoLDDUR12fvJnG0W4F4pyD21rDCbS0t3T7TgRXuBeyu7rbb3Vcs1zUYBft26dlixZ4ohC2fqqM/c7nzr9n9uhBOz+4dZYzn63x38Eztl7onR+lm24//CwgQcXPGBWA3abgFn37D2npqYGLVq4GNB2FutSTCKsJwQZYwYH42wWA3htGNfBw41qaL6gzZsXAqsuUJbPj+1qnw69cx0lwww98UyNZs/9F8W5vbsi2rljnHvGJm17YoYWLMzS20fC+vaLw5xPXE8+kaUHHgsoh35vm5gQmtc3/7lXr74+rrwMtz79a7mAc+nqasGqFdvXf35uj5Yups//zCwtXuJTIC2OvWuTtr+8XTtfe1NVqAM//ugTrGGh+G6Kc3QZ604Wn9hjqmt9r2e9ryr/bwXOWeMyJbipBcJnn/2aU4izkby///778eZ9xkmo/bgGEltEtQXSZ5991tl9HAqFsRxdol/4hV9wBrM0FkE+7BDNF77wBef8I0jg/O///XkZaDid3PrhfW8anPvhZfNRfsbGGoPmzILnwIED+ru/+3vHqrWqqtJJXPziL7IgS3+/dQL86JXHjzI1ffRK43a4ojF2M331u1H90Z9FuOFL6M4qt778RZ/m19rC//TxkyoBu6+63hDX578Q0bNvRrWgGnDudwDn7p4G535SdTL9vR++ErAFhL/8y790VOfMGv3Ww4Csn/qpn3I2fFjMP3385ErA4qLf/d3fdaASg0huPXKxqPva177m3JPdDpuJbj336X9Pl8C/LYEocdRPfTKkl8+yOM6i1Z+zCeF//Dx2lKwpTx8frRKYWsS0qzJora6uzoHWDF6bWrQ+f/687GFK42YbvmDBAlTIrztjod0r2lqUzVNmizo1/r3X+8Rbv9/eY4pzNtYaOGeL6CtXrnRU7mz+mwLg7DzNLvs1YLOTJ08qGzX0J5980jn/l19+2QHuDLYzxbkpC1n77KnvirMS24zi3Jc/80cKNjapJi1LdxaXac7qdcpYgepKKSougHMTHS26vo+Nqq3dKgV4mbkZlS3bdEmy3IPNqCXiEpxLrLlR7ahMtHT3Kmv5GlXfv1X+4SENv7lTVy6eUvb8GpVtWaW0yhq5AyTRSKBHUKCIosKREkB2oY+d0WxSPc8DiQkt3IRq3QKS0lmoViHFEUfhwAA6N/CbCzWFGDBe80vfUTIWkRkVVfLfjRVsLaAA8JvjYULS1awW3fTjvr171P/yi4oN9HBt2HHetZaEcB6ZD6zDbqDOd+CoItew00RFMH0NtmLVKJWQx01CUQa9C8C3uMI3utV25G21NzZrxp2rVbYBxbNgv67t2aU2oMGKymrNWHuX/DVmk0r5kJw0uyXEx4CLfCQbhzRw7Ji6DgKw8alpKKJlbtyMKhCKN/weY5BxA0N5MtNY90aBge8Z2P6qrlw4y/ctVelaErBFKM+gWuMCtksAFEaxFEtCTSZ8msVzEpUtne2qvmORylYslbfclP1iWIadRzHwsOrrG5VPUrJg8/3Y5mWgXILKjCn4WU6H5E24sVWDb72lAaCppBmo6v0CVrmoG117YYdCF68rv6Rc2XdtlH8B14dMRhz4YSLCOaMEkuTjilBHmaCu+04fd8CwPCDOrLlYyLG52eXlXLEvjVGPXsAmjy8Ju7YGNR3Yp+sH9mr+rFqVb7gbpbdZiBJlY5OH6hzX5yEBYPyhgsCZ7x7X6K4XSWQ3KzB/Bep+d8tXXk4yGIiKhFP79leUuHROaWUVytz2MYRfKgDCIkomIW8KcIkxlMLabmjk3VPqOnPKcQ/Ie/IpRVEhbCY52nr8uPJRcipfuVzpi+YALAA50F+sn8RJeHuxGHaR3Rs9cETtO3doFNWmwtVLlLtxBdZ3AGgGIbDCD+aGclGKvKay1x9U/Zt71IKKYklaqiru3qQASbBYLjaHJL8ifD5uS45qWmygCYWe0yRgb6Bi5Fcp9neuJMA5kxuYuIYa22kNAEni86q8mfOxA0NXsRMYs2MQxTkSIdiqejIAYEgqkzlBIY7s7uQQ0FwD4CcqRST/XSlpAAW12JCWUm+0UZRdHJgFq1LHljbUTVleVse1CwASPuWUVwELVNIuM1ARAubsu6Khq8ex7PKgNrfKsWpNCpDU7a3T8OVTCg51KaMoQ+k1s4BxqoADASImRhVqR6Xp+iVU+PpQDORrC8qUXG2ASjXnyXUMD2u87bKGOs8pAyWCDMYIV6BYfa392NkNKQ/oMA2lJBdJfQNwEvQP05xTZAAo8iqqicAeQx1KSQPWKSgC7KmkULFNdOdSd3adqBmhLQMRwvVd12DbGfmBKALFtYAZy4Bt8xgngGr6rqFGh5JTxQNKLZjH++i8VqnUk/20sdMZ2+13DmcsZQxx2+vsT9aXnP/wi73G/uZkfezv08d0CfzXlYDdM9+ML2hgMdTQhrBqvXJM3ahKpmPtV1hTjYXoTOAj+gDPR4DKepouAqmPKoO2OTHJBJdWBRy1FCvOIrmTiQ2w0I52Y7F55TTKYqlKn7kWQHamRoFpQ62AVyiwBaofpH+t50LoV8zTNuIlgFJDk+3Yn2Ix2X9GgThAUqpZmKJ6lz6LfgCIg+Ibb6R34NREYjQx8C5z3OsaQOHRD/yWNsOsZfPo24wHE30KYUk61vQuTuepypv/tHwolkFUARDvU0/dOa79plJbSkmlPFjA2g7hCJar463YNfe2K0SyObsoX1kzFgLJz2Ms4nsjvQ401X0DK9fhMZXNxSK1sgzb1B61Xmlm3C5QAeNSMvavSsZakuuFKAZWRhGX8ot0voPaFjAcYLQ3v4xyqwCaK+XaGJMY6+LEQm4HuhplXLqu7uvAyn0jyi8oBm6uxhKXzwQsiw9jJ8159qKam5yKOmDlLMA5YpdYQJNtLWpv2k3MEFQulqOBglUM03wHypoxxvKJZrPbbpEP/zaDorxzAJqyZ1IPzFVhXjNYj6rbESC7DvkKGOtrHsDGeuT74JxZtXoK7kFxrhagjBiJAcqDtarGKbuOsxrtqAMMGkU9rUD+0irg5jI+2mJBVEfFeI4Fdcg2dwBADVx7Q55gHZaM1HXBcuYfXm8g5WgP6p/XsIMDUE53YzW6lnjyHsZCr0L1gHMofOHlqNSqVfIVUz9sBrBYLI4aYLDuqJL46TXQmHE+jNppUtlilPPWMIybVeswwFcLFqoXFOxrJPebgS3xLI1MBtTd3Me1eJRfbuAcZeIrIE4JsEHDrpJ4dbQFiJt4qP0KgmqDQHMgWGXVuPjW0n6IJaydco5ugH06g6KjvajuXqZdX1EamwUyi4lnclFJtWbPXBnmubb2Zo1GI6pZiOqrKa+5AecG6zRy4RsA/1cVoP0ll2PXmmrliLk4MeM4kPh4+yniZuoQS/BA0Ur5Sig7JscE8GO874wmr6NKBwgWKGHDwqxHaBtjil4k941qoqd8KSKEG2mjnDMwo8UepiqJRLLCvZcVAeZzj3SjJJknbxl9MJsNKF7q0AMk71gWc30oi8WCzJ1th1GiRT0R4CybsSApq5LrS8Welvm+8zgbKvbxudi/1qBOWf4kn0F/GDrMRplDGhoYU1oeMULtEuL0YjZz+NVI3ey/dFhvEnPmF1To/k1PasXsVcr3meJjVPWjTXrjyn4dRM17AXbSdy3bqK72bh1E9Tcp4NXKxXdq9czFyvDT3oj/wsSYUWQBDXxMZVNFij9dkyEU3lquas87b6oDZcTiigJVz6xSC+rCTXXtysey/p5VG7WY+5UAn3Gu86J2ntzlCO8snLVAG5as1YzsInnCCXVgCfzau/t0pvOysonxHl/9qFZWLlc/Gw72nkGp+fQZwLla3bf2ET4PW924V0m8j8bJBoIJbBPHGSt9QLds+OBv/UAt9fUx7XitnthsVCuARR66L0szSgFngJM9yclseHGpGwjutddbdPKdehVnF+jph6sQ40nXkZMj+upzO6mfCODc3dqyuQw4lNgZdYQurFpferUHeG4Pm/gKAU2Wqqo8W6/t6Ne+gxcZS0K6e/NsrVpZqvysJE2OxfXmm2N6aWcI1alG/cyTlXrk4XwHnLt0Jai/+eI+TbKO9/B9Bs6h0JfP2GvgK5suaNKAfxH2pBiQCejCZiBOygHj3jke1u49I2pms8Kmu2u0bkOuEAVmfqBsmBPcvC4EJHjieL++9o0GFaOE/czTRbpzrZd765A++792cz9QrqcenaNHtppVq1sTlOn1BgC/y8NavzqgIMqeJlpiymubN2927mNN5X3qHvbm3Mcp/aQO2qNtQrMDtNM5L+ecvh/z8Q+LGXk+bjE8h4c40UBEe59tehscGnTu4ZuamtWCOEszG2EaGhq+vym5qKjIsWQ3YG4hYK5t4JtStHc+cPo/t00J2L3CrW12ah3mvYBzEyj99/dPwCYA7XqZs7mnZT+Vmm/EtH3HdSygL6i6slYPPTBHNdXJzn2Q12tKnQzdbGq7UY9S5fZG4NqjumtjLfDsIuWnp+lb2K4eOXENIDNNTz4zU3PmpqFKy5jLe/YBzu14fQx18Rt6/IkKLVuerWuXY/ruywMAru0IiOXovgcLVFHlJx5C/fNGSM9/o0mHTk5qZnG6fvd/FmvjXenqxqr1zR1YtX59uxbfgeLczyzW4qUBOAqDiDv0KopzO159Q1Wsn9wE59YxhiQx3jGVWheyB7Xs3E7xb/7/vo//VuBchEUdU3577rnn9PWvf92hEe+771598pOf1F3swvRaQ3Iimfddnv/uG62B206zISTa/wq70507dzqDXXV1lT732c85EF0mu4x/nOfw757gf/Ck3WD9xm/8JpaTL6u0rAyP4m8xybG4wKLY9PGDS2AanPvB5fJR/6slLBpRtXyZwdzsyGwnwKJFi/TzP/9zDjiXlmo30j/K0H07lOBH/fpuhzp47+fI9KTWzoR+7v+a1KFLcaWzFv7zjyfpz//Q5wQe7/2Tpl/5X10CVjctbXH91d9F9DcvRLSgyq2/+bRPm7aw0D59TJfAdAl8vwT+8R//UX/2Z3/mzL8Wc08dPpLOZoNnSmemvDN9/ORK4Ny5c/qt3/otZ1OB3ZdNHXbvY4ta//RP/+RsJpr6+/TP6RK4nUvgN/8krH/6dkTD7LjesMStnV8NsHh/O1/R9Ln/oBKwNRKbcwyKMCD43Xff1Ze+9CUHQnvkkUccixRzOdi/f79sMXsKnGttbXXU3l588UXdd999zmZKA+f+s5s4pxZRp87N7kPtsw2cuwjctmrVKj399NPOGGvrNvZ626FuSYUXXngB7inigHuWYLCF929961vORtNt27Y5SngGztk52ThtD3t/lE2UDZcv6Quf+yyKK91aCIw1B9imMgcwZ3YNAhkorrA62wnAZYp6vvxSVa64C2umcoRB6pXo6iA5jg0nG8ksQRBD0asHiC1IciAXu42CDRuxP0xolHO8dPiAIuNDKi3JUcEMVEyyikgmuDXIJtRQeoaKlpK8B6iabGpS49vH1AssWJYSUAFglDcXwA2IJzLJuaNS4ptZBiBHAjo6pu7t2xUDePIA36XPRp2F5LS7pJTEYjnqbKjXmPIbq8chLDCH9u5Sy+lTfJZHpUAFWSiIKB5FFeWGejv74e0KVLxijXwZAE39XRob7sFOJFleI32Y6sJdg+q6Adzk8qhkw10qWYNiFsnd/rOndP2tPfL19agU8DCdJLjZ0Zoa3DjXp6x0ZWGd6i3I0SR107N7j8brrgMGpapwzgKUZYCoWCSOobqr3EwlL+a1XEMcIG7gjT26/PZhpXNOFST5fSVl5OgLUP+o4vooQ1Me4DuizY3qP3RI9QcPYnPlUmkVCfUSMllcX/BGHZZfbfBWOcrZsFmJwhka4Pfk4BCqG4B6QG22HB2mDfTx2uDEmNI539Knn4AbSFPf4RPqOXiEMhlQHtmxXCzLPNnAjIBz3fQVV2kFCoTzlcYO9higQOcB7Ggbb3Af6lMJAF4yLh1mv4pwn0KBQmUuWAKvUaoJ1Mm6Du3TjZe/rSp/kormYh3KrnN36Qx5qgGnikqc5LqLNmR+arG2Ro2fPKR2rjMexsauuFLppYCEHq/GsN2ru3xeaYCcZctXYBW7Ql0dnVi0dikHSCMFiy70MAAPOtXdRH0P9KqANlD66NPYCFZo4NI1tb91kCRxi2wtt6C6EqAE8MrKJTSpaFq6Mpag/IL6Q4S2P3j4kBpOvs3as0dltVXKKrfzQHUvGtYoCnsBQM502iJZavUCW7YeOaRUbIyLUbRLrkZBp6JESeUouxSWKgokmESS3jOJvRfgWH/7cZjLbuXnFKMQRBmgmhgbAwQZIEE/DlyQXKqcWQtppwmFeq+qtQMLaeCDXOzbkrEyTcQBWuhbUG7AGygvtbwLEHaBesbWNA9ltawZsAFYoZnCFJZ0CbNH9AO3ON8FADvcppHGS1j3tqE6hGZcHuACoGQsSuJmEDimo5Xkahr2gitQ+gGwTTUIBsW3q1it9rXDA7qUAuiWlFvI92BRNhrWeDcqBagepQC8etErCtFvvICrgVy+G2u9aBBLIPrP+FCnUjNTUCKiHwPRdDd3oRCFigxjQArfpYwFnDOWcE6HwYZ3BKvh1uOoxF0iaRvCbi+dc8XOz42iH8CNCxtaoRzlSmUs8BkUQfmNdmqMMkkMNgITJMlHX/UAOjrw6FC/Ric92Ol+TKmFKAsxltlYyRcyXt2cIzzMEbYGaOOozRv2mBpbLcNzM1Vqb7l1HW3qr1Mj/PTP6RL4z5fA1Nw99U773VocnQuraRS+Lh/RYE+9ktMjKGJmKC29kDxPOk17TCNjXcQ2qFkCh6SixDU5Os7f+lH+cis9G5Vcxm8XAHyEuGKopRcKIhfgaT0WjTXMfaewjjwA9OGXdyaqqvnr+VbAYiAjR4FqDFCo67T6Ws4yH/UqD7svP2CKG1gokZRH3wGmSML2FOW5aGoVnwP8jf1lrPWUBm9cALSYVFoO4yDQdBT4YnwUVTGUq+IDjHsGCs/+GOexie9rVrhjt7obTjv9Lj0zSymM195Ug+s9msCWLIxCpc3L8UTYXNuJUfJQ3qsA+MLiPIyl80gHMMggYzVWrvOxXiwvRSGvWy1XWyiDPBWiOBcAnEt4GZuAuwxYSwDnhDsY39pOKBmFNQ/fl1RYyfUQhwjYKU4uDfgWqUvgLuY7fk0AXU0AaI21X1YA2MmfxbicdRMMjAdRSOU7h5gUPdjk5lbOV0op3xlLRhGwUW11bwAZsQEhLxu4qxKgj3GMDf0RwP8oILHbhRIbUAxmjCjuEdvlGCSP5TsKYxHsXENYkLpjw4D2s1E8e5QxeExdKIQFUJzLq5ynpLx1OMRWc5J2fXzKBCBfO9agqNlFidPSGO99hXwmdqpRVPcSCeKMSIB6YxxNKUUZjbkxMkh5H9VE71nKdRJh2WwFMnKZywycG8IKbkCDbIbIzM7BahQVmWLaDFnwaMNrmmhAEY/68QAeJueWMY8CdE0C7zNHjDNP+lOTiAcB3mJhDQwFqbtUZaK2m8ymiMQEmzn47OEBwLXJMWISLIeJhybCSQ5o7WKDRc6M2UpBKc+NaimDuzVTcrj0EexxQ3WHmBpb+CwvaoHUVRZzLOCjRBxmUBlgljvNIDPALQCXyR7qsO2oYuPdCqRn0a7ZGGMVHB5n/u1VX/8Irdersvl3UoeraAczgDKBsy9+nfZL3WcVOrbfSXxmAqgyNIK66ki/Yrw/GdjTxVydzPf6bC5My6ZdRqjDRuZ75rZxgLXidcqiDl0x5t8LX2fKDsqL9a2rbDMbSCooU9b5bY4KN9BnUCNsOi03CorJzHVJ+QVMl1yfHyW9uBHrwKMB/pZitqrA8mEsN/uxo227BKw6CDgGSMg1uhgzoqjK2nw/OgxsSByTV72JPvgAz4WJK97GbfiQhtnkmoo9ahrqvaLuQ8SS9cRWh69fA6C4ipIYFqSbn9bSWauU5+e7abGNKIntvrBfh4++rQWz79B9d2M1THy37+hb2BBed+LIRbPmKpd2I1R5x8ZRa8YaPg01u5lVM4GHZqizb1hvnzym81dPY8ecpzV3rtDsGbOwQO3R3rePqqGlXbN57Zbl6zW7rFL9qN4euHREh4+jqMx31dTWoPpcLD+wVh9xx3ninU76a2lBqbZteFzLKpaqbxKr1tP72ZR0WlWltXoApbrF/D0lTuwEWMKeDF25NKwjx7HljaUAKxc7Y+goIgat7VHuv5oYf0JaurBCpXkpKLth0Uubz8mnHIhJe3pdOoUi20Av914zM/XIfUW6Y1EqFraj+so3XyemAJx7YhMWp6XKxarVqtisGl96tRdw7iDgXIG2PXaH5s3L1LmLQb2x+5ouXmlTdg52twvYpFSQgvJbRKfPBnXq6jjtpks///QswLkC5WPZeOnKqP7yC/uB60a19f479MxTs+ljgJ3EsdZPg+MR1V0L6uihNo2PZfOefKXiZBSmzK7weXWoWLlcQSC9AscueZx6ItgCMMWGEcg0Auxz6XKHzl/s1NI7KvTYo8Wau1C6dnVSn/3cAT6nQk8+XKutj/i5pwaQxZq37gbvuTiKtasPcO7Ghx6cMyW5m3Ee8/D34kQKgbHb/sPfnPmZp5w4kbGZ+/SxsXHn3r2djUZ19XWoLV5HMaxJvbTdIPeAbsYEczezDXuLFy92NuiVcz9rG8inNuQ53/G9z7Z/Tx8f/hKw+4ib8drNc7Xf7fHvgXNWxXEg1u7OmI4c7sIqeUK+1Gylo3BOaIMCZUzvnm0gjuvVHGyuZ88sddQebV5PYxNRBnGYwWfNrXGdfLeV19Xr4ceqtXET9uso+X7rW90oR17WsqWprC/N1Zw5rD8YOMcYZuDc669P8J46wLlyrVqXy7yV0MEDg4w15zivJM1mnWjmzHyFiSHqG0Y4lwE1dkc0u8Sn3/k1A+cy1ENuetfOSf3js8/x+mw9+uRSbF8z2CyJAHiwV7vhqfbt3adZNbXatg3FudVA9gbO0YcccI4L/X534pq5nPd9/LcB5+ym1Hb5fvWrX8WidScDD/KlWAE99thjDtBig8kHctDAY5yL7QI229NXX93uWGnMxEri05/+tNayiJbFjt8P22HlZ4p5n/rUp1DNO4kM4hrHZsnLzcmtnfjDdt4/6fOZBud+0jXwwX6/TWCWMDFA13Y57N2711G5NGXJn/3Zn3VsmfNZiL81cPlgz/CD/LYfZWr6IM9z+rusBCa4X3lhZ1R/8MdhdbDLZ16pS89+waclC6bhrA9DC+lml9jf/nNEf/zPYS2oBJz7DcC5+6fr5ketG4ttbNx+L8dUrGOvtzF86vf38t7p13wwJbBjxw4HnDtx4oSzUeXWb7Udd5/73OccCODWv0//+4MtgX379um3f/u3HbDk1m+2PrVlyxZHMdCsDKeP6RL4KJTAawei+t3fCesqu8gL4YMu7ktBqWg6Pv4o1O2t12BxwVRsYLbhBqT9wz/8gxMnPPooCgQovu3Zs0eHgHZsUXvKqtWU6V55hR2zzF2m7PYzP/MzsvtEAyn+M/CcfbftQp+6vzQ1OVtM/9u//VtduXLFsWA1NTmDkw2cswV4+7t9t62PmfqdqcuZIt3p06f17W9/23ne/mYWsmVsmLTzse+x2Mce9hl1fMaf/+n/QmWkTQtRAZtHIrI4ESH5kg5QQvKMlcsulFnSi0tVeicKYsuAxVjIHT50QBMXz9t2ZpTZ+FwyBS521ZtShx+wKm31GvnnzyOXnYwlG0DduyhpYZHp6+0gsUr/QaUsAQw3jkqDt6JSMzahxEVyIBGKoEZyTQMHDilRdxXwzsAe22GNYpsnDTeoCmXcuVDeO1HQIA84fvCwxniMYInp5jVuS2zWzlXW6nVKngl8A9iHqAoKGX0KXzqvzmNva7ChST6SZ37O28N5h4EU3ST5cxYsV8rC5dhR9mvi3Gn1NKLaYUCTl2tDMMCF8kWEc0lauEg5a1YrjbqAbHJs3roP7EH17QRWbij7uFFKC5DgJYmY8HHOVZXKW7NSfhaZ4yRNJ945ocF3T2qwg3NGcS+FMvLi9+Rhc66/BmDrnvWoyMx2gKmJd86o4dBBDXc0KQMozAMk4AOqK16Nhd4syhfbTSt3s7qMkITpY90iwc8k6jZCcs52nFuGLwnwLoUYyrd4BfeLCbWTDIw0AreRjDfo0JI/8VhEk6gOecsoi+VLFViFnS0whVrbNEBM1ncWlZ/efiAAygMlPrcX8JGfqXcsVjblnUkC3zU8otEzKKNR39GWJvlpH2Ypm+Bzxk3xB2W04o33Kn1uLW0rpLFzJ9ULOJfc3MB3UZbZ+fJW1Tr1l7oAhTGgSlboyXpH4BeAKdqaNHT4sIKXr1BPgAFcoxuVnxDnPo5aSR7AX84ylG9I8rUdf0cDFy8pQH/2Whngr+VKTIr0JVBengqWojy4iuRzehFttE9jqPb1nzoDCNCL2g0JB9ZITU/CjcSoj93w+fdi8TsD1ZixIXLUAIJHaXtAl76JEM2ARoY0XpQyiQMX5ixfpsy5nD8qK+Erdeo/ckiTqLiZdW6InW1pNaj3rURJZskqhTJzSBAkUJ1D2QMLv/H2I6g+XZE3jEKOiwQJSQQ3NmzxEG0nzDkFAAZrFig5jb6C+pEpPKajjpZXvoKEZSXlRUKcdqVwu2K9qOs0nnJUlvwB1PsyUPPzZJOUoE48JD49IcBXbNHygO4y5zm2gYnJIBAsifueCwoNN6KiNElCn3PwoqyGfdgkEEYUy8X0PCzaShfKk44aJApK/dfOYPnbQ+KbbkHyNAx0NgkUFwZkcJNsTgN6CJCYj6OCNzrUTf0bbOGWDzW/JBScQqMo3FBXvswM+avp3+k52Pxgwwo4kQsM5y9dAsixGKChgGs0CrMXmPMqapnHACevUwf06WxAXurCFTXFPS91QrIxpQThoRqUfUp4DhgiiioTqnqRzqsKBVHlwdSRoZF+YAAC4xVwiHvWE4AMs6gvPud7YyZN4fv/tvHT/m4PO6bGVOd3+twUNHfr884Lb7P/3O7nf5sV9w883ak6sCetnU0d//J32iBtOjF4Ub0ozo0Nox6VEVEgGeVLkpvxMFAC/TbE+OcFAM7KI4ZIqVKMfjjcfZokaRPtlT6LklgKc5YLMCLYN8G8CxCBUpYpYUW7zyjWfhCAKUleYDp34Sqg0gLaP6ARc2R86LyCLUfU23IZnnoctSgUOlMYuwF1YlgrmnpzEvC0DyDNBZCalFYsL1BU3JTh2i9ia8pcG0MxCzXZKAB/BLWlJFTQYqPDmoihyFb7pPxlG4k92hTBPrK/lcQtNHsAOJtbTzomCrSct1DR87mZI3OBbujXseEBxtQIoK+f8Qu102T+hvKkAe3jMb+y5ixUOvPdWH+v2ho6mSNyVFS+QCmmlulFYRQYzxUeQN0WNbaWdwCbzygZe8+kFOb3FNTMgAETwBM2DHgYRz0585ScXSUv1+rMWVhDRzuB9QHZItRBmL+7gHSTgETinMMY43kEiClzxjxlllRSllixYaXbW7+XumNczwD8J1YKYz8aGwvLy2YHP0qA3uxyLo+5YBh4DKXMuA9lXQ+22MDCHpTEEqFWxsgRufJQsqt5HNvDSbXfeBv7xqAKK+dQD0sZG0uJMUy5BtANVZlQ4x5NomoaY85ORo1GKKSGgY3HmV8tSZ4CIJiZYRsZsIXNmOWUeXzssib7zio40IZC2SggGNA0Y6mXRDrVS1uMKTUb4K18JSqjSwFIuO5W1KbaDlBmWIOjcpxIpgwjjKcTwPERriM1ByVQ4tUMYkRU1ga724AdmZOZu/3WpukDBqZMTtCmmXsz83KZl8uxevRqCPVVUzDNKqkBfqyl/QF0AVwlkqIAWD2oGB5TouGovIAGSam03TTyx8QliQSQHlcYR/nOncXnFTJfpJUzP6KsO9pE+wQQRIkxHqVsiTnoBYSVAONhU27kXGhfOainJRcxRyUXKww4N3T9uyisAoKlorRG7EqERz1OOHC9gdp+gDovP2P93bQPbIBtrgFwSxAPSrS5eL9GwjEF8lcpG3tkU6cbvfwidQxwN+NOqWQjcTQbRPifJ45V8ogpER7QYOO78g0HuRpiI8owAoAYpd9GqsKyJwAAQABJREFUEjfBOX9GmQI5c2hDlI/og+MdGgfAHwfySw630X44U9QTPYB2Ee5JxrnmEOWTXcyGhJK1XAtWrUNYyjejRjcITAg4n5SarHEsTEeJVdqB/c4ST526goVv6Syt3bANZTnUlWnHXKE6xtqwVz0McHZMc+l/WzY8CNDp08Xmc7p0FdXMtjbmb9SQiS0T1DWtU6ko2VXml2jZIttgk6vLdc16h00wUWKmZSsWaNW8O1WCQu0YmxzeunBSB04BgNK/Ni5drbu5Z/EQj1zubdAx4s2r9dec8TCVOCGDe4BUvmdwHLVr4rpsANVH1m3V0uplbOoZ1IEzB2+Cc7Sn+9c/gFUr30+5uADnGHL07vGgXnn9khq72JThAaIFzo0RQ9A0lZoyqXmz/FoyP0+DXdKFc73q6O6hORIj0qfGQsSGwLsVJUnasCJFq5YHELRJ0r4DI/rGt01xLsQ9HeDcxjLWHHgPfacDlbpXtvfp1Z2HtRDFucceXagFd2RogH524h1U5/a3qakpRD/xY5cYYONDusYmkzk/1BNDLfoE0MzWh8yq1aVr9WP64peOoBLcr/s3z9fTT85jbiAGhwkl4tYw4+b5s8Pa/kqTWpu57/ESizKHmDX3JJuY0ojJ5s0OaN58P+cV0sVLnerpnuCdpsqP1SJ1ODExogIWTjbdXQYjkaH8QsC4C6P67GffApyr1BMPzwXkY4NUIQp3zBENTTE2io1o9So/oiUfbnDO5l/bTGGq2EyGFhxy7bcc34sRTbHa7ueDo0EHjjNQqoH7hvr6ejU2NSLCxLjEPaiX+/oMFK9rZ9Zq9pw5zj19bW0tm0ZyHZW5qfv3qW+4NRaY+tv0zw9vCVh7ubXO7Hd7/IfgHJsO2lriWJp26d3zPRoNsw5AnMZATLviHis8pNqaVAS82IjEOHr87WG1dzIfWNtkc5ttIpgkzjAFy+qaqO7fWqDZ81KBnhN64VttOnHivJYuQnHuiUW0u2wcDYgXWSPYv3tSO98YY7NWnbY+PkOr17FhjzjjRn1Iu3bdwFI4CDOR6TjweQPEEcQII6MedfSGVJQ9rv/5K7nauCETZc2E9r45oK98/RswUrh1rihT2YwM+ZJR+x8d1LkzjMlsuFw4f76eevIJ1Cs3ABwz9lt3omvZ2pItGdk9FT+cx/ut5f8W4JwtJHZ2djqLh7YwOUGEsmXLPc7i5DxoXLsR/6APa+g24L3++uvOTl8bAO2cDK5Zv57dOX4a9IfosAH7rbfe0mc+8xkmoiBU6VP64z/+4w/RGX44T2UanPtw1suP46xMTdJ275vdjSkHWNIkGo1pxYoVTn+xpIkFL7dOej+O8/jwfKZNT9PH7VACFlzYzdSvfDqsN0/HuLmRfuo+j7785x+ueeh2KMsf1zkODiTYQRbVb34xpHnlbv3Fr/sIXlldmj7edwnYjaapoHR1sSLxvePW8dnitKnDktFmr22bBUwevbS0FOl7dgdPHx+qEjBo/U//9E8d6zmzS7/1qKqqcuJWU/qxepw+PvgSsHuJ559/3qkjsyi89bA6sc05pkZXWVl561PT/54ugdu2BAZG4iwohXSIhSs2b+q1Z31adycggCXrpo+PZAnYBipT1jS1N/v3vffe66hoHsVa6MiRI45dysc//nEHUrN7RlN8M3jNNnQ+88wzykGVymKO/yw4ZyDb1Pvs36Yc93d/93cOALeBxURTj7OFdBtrbTOkQczf+c53gGVStHXrVkeNNZ01san72GvXrjl/N3DOxmRTbp2C8yxWso2g1wDn/ow5t7+9VSuwm1yBSk0+oSnpdgeCi+PmEAEcKp8zV3lAZR4SSHESrWNnTmnkMvaIWLGGAbdY2mQh1KvckmKlV1XKUwO0ZkpjpvYxAVDEbvrxusu4gV3WMOceiqCGkWIJ4Dxlllcpe9FiFNmKUIAi4WKfeeWShi+e1gigVmgSuAeoxZeSo2xUvNLnzJR3VikJZfQrmps0BsDXd6MRFZJhVGY8Sq+oVSHJMl9VBeooUDwkglwxkvt92LQ11msIcG6kk4QzChAeFLfSUWLLMku0GmxhUXFLDPehDHdFPS03NIHifCwMWMfitQ8lsQyscjKWYr1VyzXyvpglZVFAiTdcV4TF30FUBPpQyIqRRPQG0lAbycNGDHW7uVh/mp0n1q3xjhaFmoDcmlop9x4UemLyR+Mku1BrqK1WxvLF8swocfpWvK1DfZcvY/nG54+PKYbCWXJBnipR2MjgfEXi10VCDfqPxCm2nCjrTV69omAXqlrBcdzQvChhZGEhRpJ29hzAtUo+B9uXK/UK1t8AthhxEvxUH6ADFi/FWUqvngHAh/pN8QwKDmuYEKBUa5OCV69rpKGVJCuJekA1s+n1AIlmzEO1Zy6wQQ6QkgFufaiEoeo2Wt+gYHeHkzxLkKB0oX6UXr5Q+YuXy48aXoyqSXQCTrxzDBDuIrvPgwpTxqaqV7hsmTIoM0v8OkvlZOETJEWR7VCkCYiBtj2EM8AEVlleLPvcwBap5SXKnD+TpDJQAYowQxevYCUHANY/jDjMCFCEgVoAItjUZqH6llY7C9WzmRAP2L+N0z66uzRZV6/hljbs9QZQ2QBWg6gK5GQrE5Arb+mdvB5lGqtDEvlhvn/seoNGgbsmGCfigGIektSBymLlLpyplDJLvJOM7w1SJ/UavQIQ2tOp0cQESnkFKly+XGmLlimGapJZkrHHHuCxjzq5AvBGfbMmFSeLSRMjiQ/IMtGh4ZGoIr5yZWFb6odQG8eObQCrMF9muVLzZrP+TJ+TQRuoCEV7ASsaUGOr1yQ2rQhZkHDBAjcGEEvfjwHjuVBp82QUoqSH8mFGLeADairUoSkzJUawBRzi+kIGZqAQR39LTaYzAaiEQ9RTSg2ACsqHljRBPWq4owEAYhz2xcY+oAmUNcZJ/JO1VgAVpjQDBP3Y1wLnhkZ6sAjrJRk8Jj/wnJ+TCweppz4UsAAN/FW07awK9feNiYwj6oUBBJ2w6Uqr4fMMpqQtYKUWHWpBza4BR9rum4Cfn+QU3+0G7jW7sQTgQwLlrLSCSgVMKQilK6NgE2bZyntDKElFOQ8XSjBe1PF8Pp4HnFPRak672EncWwbHxkt72F3lratjdp/5g+47p/5mz9vDxvUPyzF1bzx1jj/svOx1Nl/YYXOZbVSzw67lh73X3mPP2U972Gvtpx0/7D3Ok/zn1vdOfcbUc1M/pz7j1s+/9RztdT/s+/7t36d+t58fpvqZutZbf9o5Tl371N/tb1N/d9mYFGSM7rhC0hOYJQ3whH4QQTkkNApYzf88qHilIO6QnDoDgNTmF5SchpiTGSMmGesYoYF86GcukvyMhZFYjtIL18qHallsFIB26CyCQoBLhfPkzpxJ38plngOGYe5yoZA12c2c3ddEH2HeMUtCkp9xIBLTmDSbQU8ysFAuaqIFjLtAZ15T65lATYwxLIrFahSr5Yj1zTTAMkBdjxuAl+dD0VTmJc4j9w7mcYCiAbPobGZeR20NQC+Oymd4HBgW6D4ZpaxUoDyPqbtxHTaXRwaDzEGMrbRhP7CxyzPGvD4ELONSJraNmcw3Fuf19QRJ2ALp5VbID0ydYJxxGWDMWBgbatdEzzWFhhq53iHGTYAnLKBNiSvqBj5yc5XMb8mZWN8CJnk5D4PgElhJC8WW+NB1wK0RjZnFK+NbABDIw0AViXixImW8yyll/gQ8By2aNLXbrnPUwzDjOZ/PzDCK4lYc2N9gyBSsXT3YYkKjKxHEEjXYCpxMjMS4nwwEFvDbdV+lPXShMku8CHQ4SbK9t+sa9TKhXFROA1mVzDmUEd+fCBm01gVMfBK1vqvAPJPAX9QZmwTC2EpOAvnYWOrHZjQnAwjZgOL0SgYCGlgMa2+sxccYR8OjzF3ECqnAU37iOBd1H56gjgK5cgPwuTL5ToaQeD/tqP8C7+c8AQkjfHaY+ZeKJubEKo5x2pvLWA0UmYhPAKF1o0TahuoY4zTjfgBAPkA7DDHvjaK+l5GTT8xQy0aBHJh2YHBaXCpqp940a+Os8wGixaif4YkBLHnZjEE7TZ6kXlCqTbgJCIgfbJ6NI0sXBRQ0q/gU1AQDqCN6AN4SAFQGFkZQgAtjIRxhzqLCgUypB5RNTREoQpzkRznPnU3chDVyONimse63geHbUXcEygu5HchvEpU5GqJSgfN8ucSwPlQQh5gHDSBFATKGpXgSr09K7kexq0XDkxPMe8uwG97EtWNnCbyJZh/lM1uR7IXMy3lKttiBzS7xILEO6n+j3fWoEU5g2E6PBgqbYMqeZENLDLjPxfmahXIa0JyfNkBzI1YIMp12MhZgRTxKP5wc57oCzMu0f+oxkogSr6POl058kTWXzzDQshHlQ9rKWB+QPO2A7xiZBEZlTBrll/bRkOpaURvMqdTcees0o7BaqYCrBs4Nhvp1ufkianEXNaOsVovnr6Rd+zUQ6lJHT7M621qwbm3DDp6YFogvCcCtIDNXs0rKNbtytuC5dLkRQKy1kb6aormzq1TF92Q6/dGlq71teucS6psdnZqPgvGqBYtR503VAHBxS1+Hrt2oQ7mtg7ieOIxzz83DThC1uaYuVJjDPj2y5hHdWXunxnj9ueazutZgVqrlWjZnlaqId5Lp17aRxo7WhpCOnWjVtaZ+DTCUhbBxdQPPBVCprijPAG5LU2WpF0vmGMAYakwtHRqZGOOeiToDksxFAXj+rEwtnpukkkKsFdm4cvbiqA4eBlQm5l2zepGW3IHSG4sPBur1DsR1/NSI3jl1UdWVWVq9okLllcR0tIqOrqjOXwgCsHWhYofCJfNwIerS0WimzlyJq4fx68mHyvTQ/Vi1ovDWNRDCihFl4dERLV80A7CtXCkZ9BUCHANVQqG4WlsmdeJYtxobJjUO2BqK0n6ZS9IBXisqS4FdUoHeXDgeRQHeOrAb5X6Nfmzzkm3ayUj3a8H8ci28I5N1d+5dAjE1N4/ruW+c5bzytGF1lVYu86HyTB8CnOvs5TtbR1HOSga4aXDurT+sVq0WD9nDY7GhjYVcM5O00y7sPw4wh9DTKPFrdzdlCCT3/7P3HnB2XdXd9rpteh9p1KVRsSzJ3cbGFRvRgsE2JrQkEL4QQt7kDfl+qYRACDXgmCTkBx+BEEhCCeEFUmjGgeDYGINtbAd32bJ6H03vt37Ps0c3EbwEsLGxJOZId+69556zz95rl7X2Xv/9X1se3BIPPfxQ7N2zNwax72fp3wXmfW7G62fD2Zo1awEibkqRAWWJdz7vof2l/q/bAHW9n36c/3NcSOB768zvvn4YcK6GzaAP8du3jsf9Dx6KfcyJprBnBPW3oCu6ultgcOsG9AZol7HxtluHY8f2SeaKMwBcXVOqwlbYRv/rivWbWmL9RkKO97DpbbAWt9x8EFbJHYwlrYBV+2PpMsD3gO3KbDy6766ZuPMOwHHYk+de2BcbNsF+i+6dhOn2oS3TcdddI7FrB89graoVgH8P6yH79hVgmJyB1Xgifu3VnfG0Szpo/xF33zkS13/162y4GoqFgIPbYXt1s9g4dti9AJ0fefgB2DYB7774RYSSvYw8CB5HPvxlW8IR4JwQceYeP0Ztn/DAOQckwyR+9rOfTQuYw4RJPf/8C+J//+9fB1l5NqhIBfvkHTqOZJ77yEf+Nk0ur776BfGKV7wixaE+ViZhdkpl+I53vAM5/hOD8pr4zd98bVpMffIkd3w8eR44d3zU04+TS/uHAAwpc7/5zW8mMOy3v30HRmorjpKLkqNCMOyTPdb8OGV8bPf+OKrpsT1x/q7HJgF8SvGFr5bjD99UjK3jtVi+MBMffXdjXHYBhvz8cUxIADKI+Cjh3n7zz2dj3ZJcvPU1hXjxz+EQOXbW0o8JOT2aTAiY0zb8EjTP9cmk99c/O7bXDx3N69evT+O6kxSZW3SGzx/HlgQGYPoQOGeI9EkW8o8+ZPERlPXqV786uo9BZuej83qifrZ+3vOe9zDn+ch3AVYtrxuGrrnmmrSpqQtWnPljXgInigRe9MrZ+NfbYP/B6fNrv1iIa36/gR3VJ0rp5stRl4A2gy9Bazt37gxDh7vOI1jNUKkPPvhgAtQJur/88svTZirBdP/xH/+Rxj9B3YYUNwrC0Yvc9fR/0LvrXQIAvM9Fcjdzbdu27buAc0ZZWAugSQCceRE0d91118WKFSti8+bNYYhY05AFz3wZ6tV8G75VxlZZ52Sr++8jQ8idB+Ot73gnIJbJuOLSi+PpgMIWtAM2ga0hHbI34aRspkw5QifWGnCm6bobOhzl4cMJVAbsYM5Rgle0kfgbORb7M+04IHGGQSOmyx6WJ7w+AGPKw/twPuKoqwDMI+0cjsoCIZxysEtlZWmRsUtZTAOeI0xUifA/VRzzWRzUeZx5Da2dkW8HoNVq+C1cpDh5i4QcLY3CVgMTFh4k2C9Y4IVBLks4E1BVOEfJBmnWAH6DtIoSTsUijvISG3GreKMaYVdpxBGUgxEjg+xB1eCEH4lZnl2axjkC+CdLOK08jtYGADx5wkHWOng+i7slAFpVmMEacTZmWGsrk+4soH+xbDKFZairBpgZCsgkixNTtoYojeOoJn0YtiZZ3M6y2F3AaZQDIZVHd+Z70Z8AAnSQgmggHOVEzOCVq+CQqrIIXoPdo4WwZXlkJrCLyEopJG5NR+M04chgtSsj6xKOrmwBXhTAEDkceVlD6sICVMLxVxmbJozYRFS5hspgaRoZA3hq6EDOnVwLEK0KyMpYKVlChIUsZADxSsPIbhygAPfkcABnYQcrAIiqIsMsSA1DcSUGImRbGh/DmU5dE1LXwTPf0I78CB3I9SyqkD5tHcYYYp3BHMaLNlKF/S5HG2pcgBy6YHshTS35DG0ri1cgA0iiZvuR9Q2HXxnmj4DNDO8TYIIWHNcs/NuuAHdVhyfIQxniHz4LBsvgEG6E2QEwZQNsOlmAjcTwo7653+V5QF9Q5BAmjTLi+C1LGwJQLE/Z8jDf5QW4wcqYw0mdoQ5pIAAqpnDscy3MM7UMa9JN1Ec3bEvdXEfdZ3DGZwkhVx0hBOLABM5p+gAsLwUcB42A8PKw5ZTJbyU5xSgGrEQ52kaVEDbVqVFLjvMagGfzQQCA9+CAAxzZBLBw9TnR1rUcECtMMqSfg1mkYvhDd+p7Fwx7OQAsVBrthldpmpRwO1QBI5RayKugMlmlAAzC+FMp0P9yMDVxTZ72ngF0AucI49Ag7CwAJ3GWV3OVaKG9F8qyFIHmy8LMVgDElhdQBdNiERAmR546pMMDFoDdifYuW2QWAFsOAAaDAnlinIV5p0L4tFppkLLZBidicv9OGI92AwwhjA8h0Wo9pyXARx7nY55xIdsMwBEGuYptBLapLOEPBWpUaMMV8lzL4j7OCeItKrX0r8YziwBMcs0w1lHOjM9neasCe1EJNqNsiTwDVswCZswC8s3AjgWVFIBbQ+8qJy5GJ9TnlamAj/KPOuXHuf9RPu77Xl6fDzu2mpf6+w8DwXld/dp6OepO3PqD6mn73c9Hf6/7Qo4+V5eF5/x89Pd6mvW0jv5e/3x0WqZf15v1shz9e/2eenr+dvR19WuPzsfR9xzrn82/ea8xHhOfE100SJulXcsk6agOOrkCKwlCoq/SZRl3MxnGdcBa+F0ZP7wHYBh6lD/YB4zHMzvj8Pa7GTPao7PvsmhedCbXM1YEmxTtPrA2yiRXhQ2zVKVPVXNRQE/VGDsrjosNpEU4x4y6DqdvjQfVUMI1+m0GXV9tBsyELs/JAgqgPSvwXj0/DlCZcScDkDbXwigGIyXxMdGljJ+NsNsVFgC24/w0IDk31mnLiA70OQDHZTjKopOz9HMGCOoZ/YnuqeI8roAGyQLCyWYJXw3AcJhxxtDhvevPiI4l/Y7IjDWymWEL5NE9AIjTWKUiJ4+JpZLxWAa0muM/40gVBi+zU4KFqYi8s7DANMDK1ChbF2x2wbjMHzKCvq8MA5bBNsDuyQkOB5TGSEvayEcgDmCbLPrWMbqCnVIF5KUezaJ/U3hTyqZxkwPAnBW8zA5pAS4MfuTN9Bn7qqSZ9M2hKO36BgDsPYCPAc6d9BLktIjyTZBCGR2M/oPpjFrjXoHMGEjoh+oM9VtEPsqNtlITDMJzqtg9DppEGUWHI1vAWjXKqN1Ho2N8wOYwDCp1nyvNUCqv5R6AdjWY9TLKkvG3qq4gzxlAa77S7aTNEIMe4JnqCuzFDHZCRlmQvCC4KgCmKmBFX7UaAM8Cug2dNs5a3tj+AUJfLoON8CxQOmvTZhAaCHIiF4DeAmbUKnVVRI7T6NYC430DQPAc7Hip79O2BNnXrAfzwcSujI2XbekEI4qNIP0W8wEMjSTnahWdnAEgiCxyKbyrz8B2gB0MVAE6mFDu9K+a9TK7n+4yyou2rz3ChpMqzHC2W9kXs9i+6mEQctg0lBHgmQBF+2UU2dhw8HZAVoAnF5/Jxg4YlGFArGLrInzqoDumGmCORWcJnAN+OWeTzNCXsdUy9PfENsyfMgCFWdsR9ZFFvnl1cQF7XhlbH/RDGgH5RcawK1YpKwhG2hl2oHkhbbOdyRnedSHXMmZjV9Sowyp6PAvYC+QegPxykr828RRteoQQ7TnAp50A8ztpbwXB4/SnEiD1UcKgDrAw3uxmmA4YunkQPGYAs9hkwFgyNTnGZ/JlEwNU30p/WYh93kq7E+J7GJDsFHXINAAmZliP0dVN9F0qkLCps3EIpsnx4RFCRrdFH0DIMvYnQeipmyrThAk2OjAu8LwMQFI4leKWe78d92x7KDrp+y+8+GfjbMLCl2HWG5o9FMOT44D+FsYCQhS3A3ilxdLveFHs4kw1RkfYiMKGhnE+U83ICRsQ27izvSF6OgErM2aWp2rY+qUYY9CZpi8XsYEyzQAoseV7vI7PTLPEY8YIoJhDg/RVNhf19rbFgi4Y/RzjuWeaejjMs4aHp6KrHdBfd0M0MlfgUsCNTLGmqoR/ngEYXEzzig5s6e/cnYkvfKWK7bg1XnzF4nj2M3tiwRLAqo7oBycSq/cCbP6uTjKKrAXN0WRSG5+lTMODgr8YOWhTFfSM/TXHmnobm0S6OrMwDrKfhbCsg0OEIeU6ATEIiOvYTMV1vT2tkBwhE5JnAAcQaeQ+5A9D1eJemPFIowAImu4RM4wXs8ynWtkAsW3rQ8c0cG5u/LCsczbMnC5GbgjIcMAjIyMJMKfPYcuWB+NBNvs4p5dhzms6mDsJmDMM60knrSfU5nrAiP0AEWH2Yj3Vubq6vW5/abcc/ZkHzx/HkQTqtlo9y3739YOAc17rTIYuATNkhSh4RVjdCKFNSPYsbaORcaaZsMjdvTk2JjEHog8NHq7EBCFVp9k04fiUp8+1ttFfAcW20NcYihmf+A17Z3iQcRRQfgvz1u4edAmDED2QJ8IeiqobHQZEx7y7s7chWtvdMEwbJM/sFWTzZolrYLanLWdh0SzO5AHiDsUtt5Zj+SI20v8/7YRxRodwz+hwGRY8NsbBFN7cyjoRaw2yzw6yCe+rX/kCYWi/GhsAQL+QCAmXXPK0tOGP0ZC8zP1ltEx5YqRI73UZPtr3Ex445w6Q+9lpaRjUbY9si7POPit++Zd/Oa688spHK6sn5HobvGFbdRZ94QtfTAPgy1720sS6IN3msXAICtrOQP2qX351Wgi+6qor4/Wvf31aaD0W8ncs52EeOHcs186PnzcNEFltduzYkRwfAkvvuefu5Hxwh/5LXvKS+OkNOYblPH8c8xJABcVB6HZ//02z8ekbMJqYeF1+YT4++V7YGZhQzh/HhgQg0Ii//3Qp/t9rZ2PFgmz87i8U0Mn5aGua72ePtYYEO3/84x9Pmxc0zLXHnKhqkwmU05ksy5yHwGeBcx46lX/t134tdHLPH8eWBHSAaHdde+21cfjw4e/KnMAs7X/nA/0sLswfP3kJGBrwjW98YwJryNx49CEj79/+7d/Gc5/73LTgc/Rv85/nJXA8S+CNf1GK/+8jLPbjT3jqplzc8KmmaAI3MH+cWBJwTlhfzByH/erLX/5ymhvKRu6aziFY03xpV2zatCldKzjNeeTTn/70FBJVYJv2Rx1cUAcF/DBJ1Z/rddozsntu3749AedcB7vooosSo50gPu2Zu+66KwHnbrjhhvQsF+AF1NXtIBdj3TQpu67gOTcKXHjhhQnUVwcaOH+4jzH9HX/6Z8mB98IrnxebLz4vFnZ34TjRNsWLgyMFL1lypsgcwzIs+dNxhpMTRgTcObzmHKs1zqUtz9yaARTFzSRB6DXKk1LD2ZQpHXE8k8acV06HJc/A0ZpAP8nRx1cAMFWAP5ZH5FuO/ORx0CdnM++sueI/xKnAo3Hr4aidiUaAV1k8SLUMzlcchMkJRNL47vmdfAouSA4vnNPkqcI5YTYZnulybA7nqeUJQohWeAn4qwKqcsd3jhdrzcnRWOVDBUccrkmcgCwk49DiKsBvlA8Z1PQw64AiNSKsIS+BPnzA4cRSOXnD6a/DmdKUKUCONLKk4UK4XtMyi+GKH0gUeeZasl0rURBd0oi0AlKughcqARJ5FG7U9MqksnExpcLDwoU+nBt4Sw/nczWXj1mctWCgwDYoE35KvysnwXuUgbBmnqzhhPRIecDZya84j3kau9EFP1kaQ/dlKGvCCCBH5WfeXfKW0TAB3nAq66bL6rgF5FajrquU1bJkdayzTglqDplRmeQx446iBHg0GB9shTimkRCOUfKCc1bGtxpoQ5tNKjl1lOoCuZcpEG7waKRuczgSobThpjkZmFrG8KSUM7Vv6rQm6C6hQRAE+czIgmPYOAsEWDLtbgKcUSVPhi22PoG50R5w3+JQUB41AAu2X53gZdpkpcH+IlDNvOD0RI6ZGdKgmFS4mSIfZA5ng/IrIwvTth3mcBbnAT9CA4UQ8U5Q1xnCtkV1V4wP3BOHAANmCQW4qP9snCFryWMvzhJBpITtIR1lLzgT/g6ebf/0haz4x2NpZ7T0Mn0D57YgsxJ1DU8TLmTd+40E4yNLOP0EmYAyoKSzpIBzhE8CRpp4b8RJWi1RT4R8TaAIylLNAgoEfCHANZcHlAHgIjlf6agpT8jG1pGYWmQkoQ4zhBQE3chYAaBk+mCM7N1F2LpBHECLo41QdLWuTVRBe6pPikXmKQl1ab3nGZNyOLxtDzVC9NEhOGkdGkqMevafgAjaFPw8FJ1+RgkzDgb8L9NWuDLJKQuoJQdwLk+IZdtmjUrO4KDPSPV3Ah3qNwHZ6qa6DlBXpTGWcnpOvaPfwM8e/l7XZ+ol7/UwHa8xTQ9/86gD0LzOc/XzXuer/vvRDuH6uXr+TNujni/Pm46OZNP1vPd4zt/Mhy/P1X/z/vo13uNnj/rv9e/pJH++93v9/PHwnkZiZVKGGasEuAiAUNax2PYLQM4xsOa4o37wSP3CvoDmnKL/0fYzhGFWJ4GMjllYxwb2Erax0JeAcw0LzgTwRr+jn9YYj3weIxXvTfQtQKZ08IYi9wNetf9lCIfKIMZ53tW5PA/ENroUsJfAXlloHZPpp1leOTv0LNcAPrYPZxoFwDL2CVISMEXdlQHMVimLejoLwIisMyajS2B+M9Q4eHIbMOWlbTNW1xg7c1WY4ZBJ0lWAtwRh1aZ3xfSBB2Jg/wHAeF2xaP050bRgFcCiNn6FBQ5NnncsVK9TLgHituysgFzGGu2P1FbMc30sIc+z6I4qY5IllZEqW0InqEPMFyHJfMl6pi5Tenn0nM/QVEg6mGepPyh9uifZMdSpXznNwWdsjgzhwKvovgrKZBrwUglgYA6dVeDZBeyiPPVcGtwNC+odUQBc1rzolMitupwmsJSxE/AYadkTCozZjTwzi/1RI+xiRn2OPoMCkB9pS7zmrqQM1hXtiQv4bMhPNSAy5lQKo0aZqCHOCD8kJC/d1zE0ZdzCcbvgu/QrhWm0P1tXytd+yX+tJSotydaRnRzRfrgGEFcZQBfxdun/6Iwcur8CSHvoIOF6d8M6Nxl9sCA3LT8PfXEybQu7DuBcTeZCcmPbN682LSXfhFLyTC4B9ThjMbWR/BF5q+eLpFGlzGhtZEq9GPbcISlVFu07z9qUdRPd3K7G1OYkzDi/F7E9i+lO6pj+ZKkb7XfJFkkFTbKc26BBXRUBjJbUYwAOLb9ZgtWrOLIlDu+7E/BWGZbeM6Jt6XkAK/sgUVQnAYbF/ptEHxreNc9zE1stcs0j12yS45z4uRgzEFssjX+UMzUm+jIysd+SBLpP2fMfdJz1aCasW75QH5TXfsyJWgB6tbxca+9Pfdtn2T7TuMAYz/OVt/VdRnYCVnkK/7xcUBXQB9pS1v7CvTXs9CxtSns+QzkMZY8AzDXP0sb2uTRJ/ja4AYJxpgRDV5GNEl6Ro66VsRZ2Pm2A4EqeUSTtCnaMJlaOdMukOUWfmQGEX8HGSvqpwDl0/86BHfHVb90YOw7ti/UrN8ZV518V62H0qybbiNC6yCcPSLiQAfyHHAq2S5GE9GcqjqbgPIBWxW/1arY8dhnnDRbOcUGZpTGFU5rmVepWVkxyTjtDDtjCVSpkArt8BIBMhT7ZwQaLDljxvF/dpUnqM7h9rt55vizbMzTwMUBzowLmaMMt7uyj2ooA9r78b5PxlZsZm7Ij8XNX98TFF7VFFwQLVcYtNw4RhBUZzvUBZZ3qnraknZyAu5TB8lgRNpX02QrlsM6y2l3It8L4Q6LIm9xRj1XrkXFS5k3biH29Sjtxr8zBQ5SP33rZqNPbZnugHPxeU2/xIDdaPQTQTDb3Y5VxLglAgZBj270b7qZAFI0z7x4YOJxAcvobHtm2DSa+nYAmARcj1yacc87XXSvQL+GcftnyZYAgewAn0c6YEyD8VN9H2yt+1jZS/9RBdSkP83+OCwnU67KeWb/7+qHAOfuN469NjUNAqq80snIujb/YBNksDOJ0vRo6veoLUDzNLZ1zap3BfmAGwAdn54xBzAPHRzOQFDAG8HtnJ+BdJ9msTziHrDEm2XdJgnT4S/fOoiuLAF9HRpk3ArxL82j6toDXnTtK8Q+fvBdmxUKcf87KePlLWSfbiKXIeobqXjXIZWkDhzrAhAdg+f385z4d118nsdfKuOqKK+MC1qkKAOvS2MqzLakzNW3OIys1nOX+x3Cc2MA5WsjWRx6Jv//7v09hWpcvX4Gz83/Fq171qjSJewzyekJucaDcTyjZ17zmNXHnnXclJry3v/1tcQ5hBo6FiZh5+9d//dd485vfEosXL04yNK/HQt6ekAp5HBOdB849jsI8xpJSWen4NRTOJz7xD/Ev//IvGDoDsWrVygSq0Pmrw+Gnt588NqV0jFXzCZ8dSSGuv7ESb3jzbNw7UGN3UiY+8MaGuPpnXGA84Yt/3BRwFD/IR/+pHL/5rtlY1J6JX7kqH6/934Xo657vZ4+1Eh2/dSzLDOM47ZjuBFuWGBmvtBVPPvnklLy/6+x2M4YhzmQsdrLqJNT76s4DWc5cgNdJ7sKKh06EiQkX7QA6kkb9fPrxyB8nFaZtnprZrep188djk4AsOrLO3XPPPXOTtaOSEdD+hje8IYVQ/371cNSl8x8fZwnYLwSS/NEf/VEYUtd+c/RhOPv3vve9ce655x59ev7zvASOewncfFclfuU1s7FltBZr0N/fuL45+ha4bDV/nEgScEyrj2uu7bigqU3hy7CnguQE1Omw18YQqCYT6plnnpkAw9obhk01DfWTdsePMofUfvDlPb471mpLuKlLMLLAOcdVQ8Fqt2hjaPfIkm5I2dHR0XSvNor5FtznznaBc/39/Qkwt3nz5ngKoSEN/eIz5kAHtbjnvvvjmj//y7Sa+cIrL4/LLjwXBghYu2B4EHSSZKJDLTlDyB8LuAJicvxJbiLSkgHDsCGGm5RhyqXOLE4lFim5n/P+zm9+x0vIzyze6p1RheB44WY+aG8BdGEV2MvwAHK+mGw0fU950tfp5vKpQLsqTmfv0tFXxYueHDg4PDOcT8AsHfSmM3cRj9GhCCgqnQPMR3riv/zqyQYWogXOeWRgmqj6ctEYR6iLvpZW1gf9eopCIchGpTMaHx2OJ7OsLLyXVLkogQMolw7ytF5t0XUOCizQq2VeWeCekwV1Mvd4HLcAASiLwKwMTlSBeyAXSBNnPG1PmekIrZJfsn2kiDr3uI7fdGaT07mycm9ygCXEkq47wF1HnuPCtR99rnVqObI44fHgeZY8G6KNj9ybkemORXND0yGU5ORjKzrn+b2A04tFeTxmpCNsTQAAlyHXDG2HIGXJMSwwMkMoMttDukDpA0zIEL6MEiF3y8eCP+04w2K7y/xF2pdpuWDfSPlyOBNrtKEKyD9BcpZAsJagPMstIwae9QQkSO5a6i+Vhb9zDxVIxmG7tL4BXCWnBGllYFtKTkBEIHAusZN5qXXZkCFkXao1nO9ALAghl0KaJnChzmQc9j4a4FytwdIQApW/cMsgPp6D03dOluQ4tXkdGDrIBEBSjzgSdPZXJg+xlX8r7In7wdQJ5MOBAatLCQaiqfGhmKYdNK84OTqWbiKE2UrS7kGeOKlJSnAAEkUeONDJY0EQBCxHGVh6CIpD8QGqCcCsIGeem8FbUsIBXYKppWjIRc4BaaE6Bf1RGLqk8FVywF/bFNAHGK2KB7bDOFAiFN5yQj/2JRakSpmQsYQ/s1m3d/QRenAFYBGYXmjfFQGJ3itQbgqmwMOHIIIaoXyOedb9OEwlgzhxkGkGhprFp0Rz3yZYilYAjJEFjqzbVI687AJ56iubmeAkABsc4bYJYCGU3vBujjUyRsk8JUiHFw5dqPW4hpv53S6pEzpDW5dFKiOAh7oi1hqMlIATZbaEnTE9nFRPhEO94pzX+a46QN1UB585zxUgbkgx39U/HkbfMPS4YcPUeYK2BdepY4wC5GfTqOs5f3f+LOuq96qTPNQjPlv9VJ93+5vX16/xmabr8+vpmrafvc7nmxfn195jeQSte4+6zu913WZ+vMa8yBCrzlS/Jn1GXky3fnit5+tlqJ8/nt5rAMczM9sI0U148ckiDD+LopnQkVDFgl0A3AZzVgZAhmNQKid9sjIOo+vAw4A7YOAihJ4O0/LMCF2UEHKED2/twdZYeGFk29ehfgzdyfhEX1RXqH0FppYZR4Uq5QTRy1RFfzQUauDElQ1NcJygucgtQW0INNKDy3/A5wK1ZUATXJIjFKSDmOcZZLgHwNwYoVEHtyQmoqa+02H9XAPQi3CbI4QBHwJMle9LecwTBlqQYEVd57qNGSzuh4X2OzCTblV94YjtIp/kdPJgjB0mFDeMLc0LN0bvugsI/7yEkaOZIZqNB8gpi21gHs252iePkitkKQu6rMBYApRUNevgmJ5VYxyTRVTIEC0pjSM5AMoochNgjOG6Rscc2bYYq9LYdUSG2lNHwCXqGZMkCcBn6k7T5yXKXRsH3aKeqDKO12rjMTi8M4bGdnMOFjWgzx3oxhYoqSYJFW9fau9aSGh7wr73nAWGrydGKdeE6ZGpRq6HnzWVpYYhJE5O4FZWwLcAPY2CZJ9gA1Aix0t1kUcZGVXVaZ6jvtRbHtpe1CB6kd8Em/GWsS6xpSq0FQFlQoggzRMDSBnmxuB0HXkSTEdq/GjrsvyEtCTc7OG9DwF4HCRcdzVamrG5AJVPDTNG0UYbYRfuWnoyzK2bYqapP9lOJcoh1NtcCV1XoOSYlxscYOmhnQgynNPD5pN2QT0k7CBlqqA7tVW1OfOcT3ahHn/KYwfKEEI3NQD6VbLLSLuGrSq4HPgCL9oQl2rBFLB1G1A0tivtphoFt70oYwoFPv5gjA9uo10eiGZsx0YqPUfY3RnofkYJ7du0cEG0rzoVsPwG6rkPc6mDZ7n5AcAD7Tw1EfKKeYLuzkQT+WN0T6Ats5s2i9gflGhq1+SFz3NADOqKi1JVk4ZXKbG0sQDpcRXnBCBa77RlpKetnzaSmDiHSWvKc/GRsRUdQ+mpzdRmFNsU4/4IbJJDhOVrI2Tgko42wqrO2cwYCUn2TiOSOUQ6Gb9oF5KxCi/zlSd/GRmXSVBWwFls5LJ2HhtcCoLP1GUCtwS+Y0Nq5ZVSf6VN0T69du/ogXh45yMxxjqvzNh57LSxqUNstt4WW7dthYmpMS5+yiXxtFMvjcXNy6kurSDbJHkgbe3Mgm3CvDoAaCfTX5AYr2Q5pbKUNFQ4JzZEQK9tSfCeLcOwvsreNl7F1pYHz3EJ/mE6Fv2Mtnjvg6W45VuMe7SDc85aE6dt6kqb7B1HtFlS1zMJcpZFTs5ThseqseWhsbjrP/dRfx2xZGFfAsTu3j0Z37rzYBwebyMkbmu8+OoWNn3BLN3KOOS4omypgxzMpDSdBG6ZaybaUzJeWu/0W+dC/F6mk6g/wCqTV0ZHb6IRuOkn2dvcNcfuyXXKiDafvpNvpSIz3q49pfjydVtJq50QsQvjrNMbmJeiO5CJbVvZ1aCf2/LgA/Hpz3zmJwuco64oIHn4n4+5OTF2MQIpA8AtY9PoO9C3sJc1g527duKb2JE28R8g5PkoLNPOhboJHy6Wxch/vvr7CY0JS7ybjpttj7RpD3tisk/Ih8/y8LuftW/q349neyUV4qfsz/famKkdUadHA+c2bNgQP//zP5/aRr1+fXeMcXnBEVlGR7f92E9yDAr2PMfptEmKpmuTSWOMwDc6qhv7kr3FPQLuc8556M/jo+1xx20TkEkMxrIlrfjFumPJUiC0jS5U+MKGUWuSpmGj5zREYxzcPx23fHMH9zdFK7ZXI0DQQ4er8fC2w3HHXXdHT/fCuOr5G+I5m1tj8RJyzhhSZRwgg+SfZHlX5/A/hgg7+7l//Ux86YufjjWrV8VVV14dF1xwkUS3XMBcbq7EvNdBc2nrhD/yevQHY3EanR/9ncfBHToh/+3f/g0n2RvTRO5Xf/U18YqXv5wYvhvSoH2sFMGG70RQgN+HP/xhBs4hwqA+Pzn+XFStN/wnI78azjfffHO85S1vSWE9XvrSlyZnsqC++eOHS2AeOPfDZXQ8XmF/HcLAuR1nyCc+8YnkBHbBZePGjYnRxv6hM8SFmp/e47EppZ9eeT05JR/BkfsHby3FR7/EIgETk0vPzcU//kUjO1fm6+/JqZHv/1QiLcUnPgdw7p2zUL1n4qXPyMXrfqch+pfM19P3l9ijP6stZthWGcncJPCud70rBPPUDxfWDW/mYr+TExffZW0RFOf1MpwJxHNietJJJ6WdYOqFO+64I4XylvHslFNOSU5yF+/rh/cJ8tKx7oK/YUTVJb7cVTZ/PDoJ3HffffHWt741MQnqYDn6UKaGaxVAoENk/vjJSUA2R4FxH/jAB2LfPhyk33O89rWvTXUj0+P8MS+BE0kCsziBLnv2dNy2rxaLsbP+5gON8exL3Fl9IpXyp7ss2g/1RWklUf+sfhe4JlhYsJrAOfWS80PtAMFygtpcCBc0lxY5sSHqwLkfRao+S1vDewTBueFRAIPjrDaNdok70zdv3pzCurjgrv2iLVMHObiw7v0CCczvTTfdlPIr29xznvOcOP3005M94nX1lwv/99x7X7zz3e9Ji5s/e+UV8fQLzyO0EmFFAc6VBSfgqMrhHMnT2HWOpFVH/vCR5VoWNj2hw4oF2hq7hIGxpAVWGVBygtVYMM0ATnJxV4ecv7p8mrBzsKPIeoWHMjlNdAUlb1vyxKWlYpwSLNj6M3lJTG70OZaLSYWTM7NEWTwUGZyMeRwP2QWEkWohvCeOnuS841HmWdePzDaGY0zrrYRjlIVK68K0ddQVKEfCi9EOMgDBdFbrxNLRpbOoKtACj5JFkIXE5WrLIsuIq8A5ZUI6GZ10OPI8J7BAIFkW0M5ceUmC6zMNAgNwFIFCqAkY4JnJk5zkg3QAM+kkywDgqVC+aUKGFgAoFTq7EbNAMJ89d1taiHYcQtbpHurJRXOrxcMrdeABE+QaPlBX/qYjXJKuChnz9gLl9N3FatxjvCNjw3GKlNBJxrWJcY00TBOyDkKQ8kzKmynQDnCOz8kHZzIApVQ/LvATNgvqD9oA73rbYCOTi6WGF8DQssUxHOAjB6O5g/B1Pb0ADFp5DovjaYs8rgDzqrSRJ37OOQwB8ioCTqziuNOloCMzr6z5J+uhTBc6+XUA6o1NeeEaqyqBNy08BZoLw4lDgvRkZckRXlDAZ3qobEGCy2zo5gEGvAnSm+VWW08L4AXZBZPjnOt0JlfRE8npyNJRRQYAgI/KyDC7RUKK5hpy0YTjOA8IZU7YArrmQHfWfw1gWXEMUNru26n3h5CSDALUD/ktzfJwWU8WLI/mVWsJB7uC3xbRrmCUsnZJElhaymueCm7EY5sl5HAAdKtMHSCU7G6Ye2CkgrElD5hN1qdMGxuEAFdE41JKxFwFkKE9J0fYVNEmNdo8IxlpIn/rHRBeTO6I0W3fJFzQVDQvWB9dy9cRCph+MPZI7Nu6BWKgaixYuCpaVpwOgm4tYL4WXM+EziOUa2NtiHCO+2Nqz9YYBWRnD2wkzKHANdnqaoTNbeg5NVqXnANIZhkywsanL9gExWHYTaxDqgLxUb+kWYMhqzxziPDPAwAqhrgIZy+hYXOZTgBiPciaPtMMYEfgKSFYhRdQOF7IG5lnkhd8DrBBkQmFNwHj1iHAJguioWsR7Sj1Cq4/vg91TN1RVy9JXQ84BxakrY4TcK0Ocp3Uw/YvYO20005LwGsdvTqI9S04n/Zer6m/BKs5nz711FPTfFkd6W+m5xxCnabeMj/OlQXkCbJzfq2uVW/dfffd6VrTqoP8fHc+7RzctHU2qzPd8Czrqswu+o0so89TF5qmG6AvuOCCWL16dQLyWSafbdm91sPr68fRn+vnjvl3ywG7Wm3iztj/wJ0xMlSMvr6N0b0awE3nAvAYsLIBClMtqYdS86fPlQcfjNk9t0d+aifYCEInE8q6iL4tw8LZ3A1oru807j+F32C6Isx4GZCULFK698X9mFCZZ+cA3GVrgAOmD8fsJOHbCdlpeM0a44VwqTz9Ot9yMv1wNf0NMKtOX4DxFXSVYF81TwFllAU4IoKrChgmUx2IysA3YuyRb8Y4Y0rf+udGy9ILKMC2mNx3YwwATigUVkXvyvOjceG6xB4nExpkS9zL2DCzNWYO3cDw950UyjlHyM4E+iEkbUUQWuuCaF12bjQuPo+xk7CXAJtGCZE4OSGwU+YwgdK0I0J+thI6tr0RRts09qOnHCcZD7NJkfIsxtoqzFxHRhGkkkM+DMiAlJNKdwhRCfNFfk/ZUkk6VYNjN4jElLcK6QiMsVWqj9074DicAZxTY1zTAV1R1wCwysLGd+jAg7Bk3Rsjs3vQNWOxiHbdR+jyMqxkmZ7V0b789GhjjM42LooR8rMbgPEA9WvY8iYA+UBJsevURx3RQf12oPuaDYVKPujR1gqZUUcJAVPfAqYhf7LUWYXmMwtTYY36ShskBCYzWKe2Ue9btCvZSMsA28sCrwAIzbF2cT8JCALiQaSJHHmlBprGZPQPbXoCcOC2+26L8vC+WABrXzdh6Sqz1KF6DN3VtvwkdOLaqAB0Gqr1xggGwwRx7aYAUs8B2bRBCTVH5XU3NRCKk42tgIQaAQXIylijrFpAQrsFeZmpHOWUwS1DnmXcETynbkjDBDLGMOOl7gBsJNDHglC3giwFaAk2t1jU7JztjI1nSFyFV6FsZSo2I5gc8OPU2K44tOsu9OEDkDQORw99rIOxqZLFjm1aFG3LTor8EgCBfJ6JBYQXbo8pyj4GmH4Se7aCbS54op34ez1NzdHBuNfCw7V6rLUcY2SSLXlJE9ZkoKRfVIHYSsIhyC/lcWNEqlPvS9AzW7Rl4l7KKSNtVrsJeXh42qP+7gftW0qXQB4J5MXvQ7Dq7Ti0P3Yc3BsLli6KTX1LYykbI1ps37QZmcdMElOZ8pBf7O00Y3CM1vzgN8+leIVKlXnGDCfLAOMakGWB8YeaRJ5cqB1G/zRgcMm0qLjKEVDjI4ceiW/+5+2xHR1UpE1UbSNFbLOJ8WgD/HHK+nVx4ZkXxdpFJ0cbbLoJMEZ6VqZsefzl6bZxcmeb5dmGxU7h6Hmm7IMCRBKpMGXL077s57JdZ7GPZdit0tdlg9JQrBDeGY5Xcjq3Kada1kYpxJe/MhN/97GvMfcZi5+9+uJ4zjOXEfKVUZKii/9PfYZ26H32NevjAGETb79tIL74pQcImdoaXYCBa8VCCsU6ja26Zu3ieObmzrjskjz2GX1S5kYHJ/KRWDaxQ817yfkeBpa2sOBcawL4PE+i3yGCBJyz7FxTc8yz35ITwcbOtewKlt9yOxb7PXUZZehvGHL/+Z0iUUVu4FmL44XPWxdXPq8l+hYjG66pMIbLIFrFXnjw/gfiM//02QSce+YznhHPf/7z2Th2ErLnQhM+Sm+TybmD02Tovw+/exx9bu7MXIbSb/zo76SZWLK9nApOz/F3n+UFR9LQlpkBX6FPYfDwYLJb9u7dAwBqbwLL7QY8dxDA3Cxz8QL2axeAuVX9q2M9fgV9CzLNLSIca3t7R9p859zWTWE8CFnRD30UZavbKHWbxnPfz2Yxi/PHsS+Bej3Wc+p3X0cD5/RxCJxbtWpVusw69+VQKbjb8VrgnNBwR1rZHYWrz7VQR0HaD+NaehYb7dxQZr9TP1Uc89AZOewP2SEP7O6KT//jwfjWrQ8Dzu2Lq1/QHxs3oRtb6MBoMRQC95m2/+bSLLMmsO2R8fjsp2+DeEiw3lLGwU5A/E0xOU1I69bBOO+pi+M5z1oSG08ihDZhWe2njgEVM8KYbr9S1dudRkcn4nP/8n/ic5/7TKwFOHf1VS9mo+fT5sI6M//VYhIYbJhxx6A5aL53HumMfHo0xwkNnNMR+dGPfjQ+8pGPpEnS6173unj6ZZeBynXie+wdLmz+6Z/+KYwM16cB8a/+6q/SgqoLqU/G4cKuO5E/+cl/jI997GNp8vi7v/s7KZSIk9T544dLYB4498NldDxdoUHiwop91YUWHRIyznV0dKZQNlfiNDgfoEUbO/JddPnpPh6bUvrpltlPtvQaIlu2VeNXfm02vrGTBQXWmK/9rYZ4xUsLTCZ/snmZf9oPlgCb7eKzXy7H7/wJjiIWL597YT7e+gcNsWH1fD/7wZL70X91fP/CF74Qv//7v58W7mUtMzSZh5OIrVu3xvvf//60KG+oVh3ef/mXfxkCtVy8dwHeibA7x9yhft55LKhiR+kgqIeuccL7K7/yK2kC76K8zu3Pf/7z8alPfSo5BNxhr+Pa+1/0ohfFVVddldJ2Ajx//GgSEJhg3b3vfe9LTpmj7xIs9+pXvzoEafX39x/90/znJ1gCDz30ULz+9a//vmFaZXOwL/3cz/3cPNviE1wP88k/ORK4/OXT8dU7YGpg7ec1r8zHO38X5xnr3/PHiSEB7Qcd8doKOuw91P+y3aiTdPprG7ho7rU68mVvE1SgXnLO6H3qetPx3ZeLnj/s8Jm+vM9w2DLJ3XvvvQkU5xzVZwsUcDFVIMIzcCIIiBM8531HH6bh2o8s6qb1rGc9Ky6//PJYvXp1AhL4u3kyb2UYw+69/75415++G6dMLl6MvXLZhRdEFyFjMoSLMaSnO/8bcIYJoXGJtsau+Ll1WL7jwMU3haCQF4usVVhVpnAMesqukfBw5E+wSwojKgJGr4CLnpwTdJcBYCcDh8Akf7I4iaGNRLI61HCO8Qf/Dr+TkO96ZXTYVA8PxOC3b4/GA/uiZcmyKJxJpIdFi7lOsI2LyHPP0vrKA5zLENrMBWTopPitMZUoreWaRxyHruYaokQfRvJjcF8VL5Clz1Bu3WZeJhjQMrhwXcbxmiNPyYF1xGNW8Vk6zQAG4DXmpaMMGaWHWRZAZKIYcIFvh8MAAEAASURBVKLVUugtkiMdMsi7mcU5h4O7sndfTH0HMATOmZ5TNkXnug2RayM8JovIPDwdstT4NYWoZJG5hmNdR6TgOX5K/vqsQDLDbVmf7EKfY+ZRntQVC+nJ2e2itOVOHnFlroNUkBEnqcgyeSuRpk5mqgrHPmkbmo/rZWqbi4uqZ09H+xFwn2XR28aCfXrhXKsBasooB15lgDKDD90bB7bcE8vW0LYBd+YAP5YLOCGpQ2XGY8gLZeQW25Mi0lksiM/CkSKyp4aSd9emohy5WJnM3Z3alEC9OWggmbJO6pXMR0VplepMdNd+Do9vpsjz9VJy8Lj08EkKLmEb3EQwu3Btqk+ZzXCMky9DuiVnG8xMgiNAu0UMT8XB+wi7NrA/WrtaYvGpG6JlIRtvcBwLIkihYvUwCxbAWVmd2R8z+++K2cMwchCizjbWgLM/V22NhubFUehbEblewW5dCKKTZ8LgR8mqoApLAB90XhrSOIVfKwKAGqMNDT4S0xPbuRaWJvp/geel/tHWQl76oqULZ2TLJvIsKxPtH/BEQpHSIGzzWR3F7MyrAYipTW+J8V03MiZORWvvRsAZGyPfSj8Yeyj2PfRAZMZlvu+PphVnwZJzMmHV2pE/kD6ANYUYpZnCJnZwZwzv3wXABsciwNks7H1Gp2poWwZz0FmR6yA/hEqlgsmHoWwJhZY+U99UliDQrEASHMtlgHhjlG98dDesfDLHwWnFPTm88I2ANJs7+wDWrIp8GwwsOTc8ka7jSgKi4MymTmWWoRJJj3F4DBa1of2R6V0MYd4y6vPEWAt03Pdw3Fd/qTf8rD4TdCZgTWZV57rqGuey3rN///4ElHMzsfNZ59QC3CQLcOOZhwA4geSmWWc/lYVVhnBBburFAwcOpLm3G8zUZx76R2RQFZQn8Nz5tAyr5sW0zIfzbA91sHrLObjpmr6/uZ77wQ9+MOk8n2Ne1MXqbskDnJv8zM/8TGzevDnc1FNf2zUt5eChHDx+FF2dLjzW/iCrqABKHf9m7Lv/9jng3KJN0bPmNICxfQk4JwNjVqZJmwHjYxXgXIztjPKB70Dutp3TAEhgQCqjs/JNAJIWEuKzaw2dcjHXt9MfAT6hf2U5VddlVdaIrcaALMNcZdZxBlDt4b1RngZMify1D3LowjzAvcbWFdHSS5j5TgC6sEjKflVDz1USq63jC05gspYTnEc+sjBY1g7dHJNbv4EdBBBww9UAhi8lzYdiZu+/xeGd26OhaTWMarCT9J0U5aZexglBWaQjULu8Lyojd0eFcWlqYiZmwPxWGPcaKV9TKyGpu5cwjp4clZa12DlNMQIIf/9hWNwGCBuNPVMkX1OUt4ZN0wbmrhfgyqLWjujNtUYb4z2SZJx10MdmSHJgWOFc6mVUh+xXGV7aPgmgjbgcZmqJpYr8cW3Sazi9HdHMmzrWDdAeBS7O0z4F/Mv8NGcbGLy9KY3RWdjYJkb2xNaBLbEVBr7i7GCsoV5Pxi5tauyN3KJTAf9ugKS2B71N/5ueigdGBuPALJsH0FvN9Hvtq0qBTRLtC6OnvSVWNGWjDyAzEJrkis9ps9BudLLr9K4IKAMNU8A2SCyqlC2FdU+h3WBOhWVPc00TTTvJsL5VmPrK2XHuFYomJ5qgNQCxtB/1qqyD6kytKUZlSs7GA4B4FXRGmVDcozAEPvLgXVEcOBgEVImFsIIRuTIaAaQ09C5is8TyqLUuor56Yt9UPvYOTsTApOHFqTv0cJEQr2X0TAvjwIKu7lgOQ84KdFIbeTCT1QScy8Vssm2sCdjb1Da2azSWGx80J2oCirQ5LBgAKNm2BNbr1Gewom6pOC6Uc64qsJGSsJVlzqwDHSrjXIbxqYy+mVGJcY3A+PLsIcJW3h979t4LyHNv9AEKW9bWyv6PJZHvWMOGiVVRbeuKKYDuo6Xm2HUoG0PjszFJCOOZ3Chy1S4vREtjZyxkw8tiGmsv/auFShDKlad9pjakgcI4Zy+rUhcC4Axb7hiYmIFtqNStqfk3AQe5x/oxVKmbSGz1yTb3Psp3xAxO96fWr52msEjDl2xx4DhjAEbhR7B/tg3si95li+OU3iWxiry0OYZgZ8kgrVFZou/PstlAVuFm+l8aj/kpPUf7Tboy8p8hnyVudf6QpT/lqY/UPyjv3KYR7X/HGMYSbF9BJlUAdAcnDsa9Ox6MB3Y8EvtpTzMMCk0FAJXourXLVsWpa9bHqj5A/zDl5rkfjuNUXm0KoLQ8m7ToC7LXCqjPUI9udlCGOQGtSMhWrAhS8zjSXJKuZXOJYEkmSbQFr8XSxPYROJfjYmBptFPyzG9fvK4Uf/uxG7lmMl7ys0+NZ29eHN2djAc02aq2uJtCKHuBgcemJKPbyKRr3FPx9Zv2wp7HGDZN3gGICkJburItTj2jLc44Mx8rljEGNkzS7mXItFyOUa2kxVyE+jA8bJZnJCZetkEl5irGAPuB6rLKc63erBSHzGEgcOLd0ZDea/VYMsYuxzobCVen1uC7h8C8O+4oxtveJnBuWbz0qjXxwqtg8F8kQ6RjDBzNMq8xPj3IBjCBc3fecWc869nPSqzua1avQYfb13w+L3VfOuY+K+u5dmMGOPz9SCbS3IDcJBud97nvJjOXlpclgCQf0jm+O8/UJioS3mkGwO7U1HSyX4aPsPPu2bsn9jFP2717F4DFIdYIppLtYbjVNGdfuQobZy2b9TcmG0fwf0cnzL0avBwpv7alI2UxD0lgvs8fx70E6u3Ievaof/ez53zt3bsXrM4n08aVTZs2/Rdwzt9yzD8cJlOId/WPX2wrdC773pzO9K/nUwumXaOEPUO/dIZsg7KnO86ndQfsCafuex5pi09+bAB2y4fizNMWxEtfvDpOOa3JvWvcbm+0HzPekrQh67VjqhgoBw4U45av78R3Bnj/EMGyZ9ELMNr2dDdDbpaP089qjHVr89HVDrMlY7p5cVwpufZE33ZdxGUBTtOXRuOLn/90fPEL/xyrV/XDOPfiuOiCS2BTp4/DWpyAc5TB9ZoE0DVP/HusxwkLnHOyZrim97//r9KOpt/+rd+Kl/3cy2L16tWPVVZP/H00yL8H6PehD/1N2rn12tf+RrziFa9INJxP/MO/+wnKz5Aixgb/1Kf+T3LovvKVvxivfOUrE9PKd189/+1/ksA8cO5/ksxxdp6+WWISr6FzN4Dc//iP/4gbb7yJnZWPpEWbyy67LO3I1xFRX0A5zkr4BGT3sSumJyAz80l+Hwlgy8fnry/H/3rDbIywRn/62kz8w182xYZ1GBzzxzElgUnWqr50Qzle/65i7Dpci4vOzsVfvKEhztw4X1ePV0W5ePGDgHMC5N75znfG7bffHn/wB3+QGFje9ra3xVe+8pW0k93xXxtTh/Wtt96KoV9J9psAO4F13q9D+oUvfGG8+c1vTg7sz33uc/Hnf/7nCZB9ySWXJH2iE+LrX/96cjz86q/+asj0+2RtoHi8ZPuTTMfJok6Zd7zjHQnsePSz1c/PfvazE4BLB04d4HD0NfOfH38J6IRyo8Gb3vSm5Pyyjo4+dI695z3vCW2pumPq6N/nP89L4HiXwO9fMxsf+lgZ50XEM87Kxuf/vhkH1fFeqvn81yXgmOYrOZKwJVwsl1lHML1g+DqIzoVPQXM67V0Y11Ffjy7g2FdfGDUd9ZXf6+fqz/p+717vM2W4/drXvpYWUWXzqT9bYILPEQhwxRVXJD0omEEd6HO9X5vFQ8CcYbUF3W3evDm0Tfr6YCwj3/Wx23dBEw88cH/86bXXsJCfw4nxgrjoqRcRarEbxwosHLxyLMISGIk1WRxHOLqJ8YMTinIKxsLZrWesOs18kYXQDKG0ioBoKi6OZlno1KOMe0aHUYH8ZUQdzZCHI84XfY6sDLOYyv04qHWd6OjSdSdQJ48DO4fugRaCRVa8MJQ1w64gHVYVGFOqe3fEji98Ltp2bIuOtRujYfPlkV+1FicczilkUsPBJlbPw5BZmQqADUFcOIoSKAxn9ZwqY4kYlrlKgxwl5JPy8WTyQJuY8dn8DgtazfKYIHJ0d1INB5ZsFi7r5vitJh2ZQDtXhblM9g2dx7KSyOCHUDjpTnEmBJQ2U2YAmW3miTjNceAa4rWA00oGEXYNRAlGp7GvXg9Dxp5YfvEFsfAp50W2B+ABrD2JkU0HEjJMDHk6c0mzdsTRXaNsMuIYKlXQXJaQmLJ31WDxKsLqkLB2sO8EcpxzeHHGFXLryxPkuQZgS5mbrzL1L9uEIMnEtsM1qQ3ouBX0Zz4EGSXHIvlSHpRLR1QGmQve4wKuQw7JEzAb0zu3x55vfSN2335rnMRO+yWXPi1yAOhKAGqqeZzsXEttzzkheYQ77REaZURW1IMOV9k88LLzIt86f33ZDwF66ORLgE3eZaLzMAsJyKBzzvxywmJXua8I45K8bQXSztJO57yz3sU1TYAWZF3i/nyFawTO8U+gooAB/aoCr7gUOVPmBspbBACwbyh2fO3m2LttKxtqemLlZZdG65rl9BXaEwAMQ9cKDrMvVAVVwBxUBQxWmxkgXWRO5vI4kWWJyxlysQXgF+NALYt8CHUFFQ0F4n8DDtz8FDJCYhXqTXAYDDZTQ3tibGgXfpChaIKxJ6986PfF2THYpWBq5Fx7z/poWrSZtFeSD1zmOFUFBswBIJChtFBkLWSRKtMmd32FNCejreu06FhJCEVAJdUpgHMP3gc+DsBTp8C5UyPTtRrgCaHlqP8qrCd5mXYYS6rThGudGqbaWMTI4SivMj5Q/izMT9EAWC0P86B145ijMxcWljIskTWcMObL8aSBdpuZoY+M7o+Rw9th9xgGvAXAMEvIWZw8VRjoKoS3rRVgz1q8JloXE84P8E7FcJGkl8MpDMyX5zB+0coSEIixpjpGKNrhAyBl+mGcW0H9kI/H4bBnPZmH+sExv66P6p912jkHdl1UfXDGGWckUJr6Qp3nvPdb3/pW8ils3rw5Oa8FkP/1X/91uJlGMLfM7oLH1V/qHJnr1FMC7QRuq58E5amTBNapM9WNhlhVj1566aUpXXXthz70objtttsSw+pZZ52VwOnmVSY85+aC7p72tKcl8JzP3rVrV5p7CKCTzcUNb863BdqZd9lXZZh3Hm6o8joQTzmYrkddJk9m/fxYz7YYVdgkx2+JAw8CnAOs27toXfT0bwJo3cswBvAB5se8oYtp67QExnNAUABhM1MA1KYOc17QHHpVHZvviEJLL++yWQLUApCm01K1ILOmejEd6jb6tIxixUnC2A8QQnXwEOMjoBjsBMH4RcAps1MAbmF56+om5OASgA/da7kP8C95EoSSQo47wAiiTbSvhBMrHY7a0B0xs/22GBmcje6Tn8+YcjHj49Yo7f1aDO/dSR7XRMey8wDA9ccswCFBeI2wqeVK9G31LExXic2SdjyrLifbDTifBS3kYOeq4iEuAq6dAiB0aHw4Dg6MY+8RiLWpB2x9c0zmAUkAPp6enYiGxoZYDVBrXWtvLOI5BtgGSUya6CJlyrjLQ5Gs0uVB6D//qbKS3BBYUteyzaGnpZaTyU1GM9Wh2CHBzGVuwKoARwTECZ2ZwqcmYeO0xgaZ4ckCeXLo1QqApK2EvP5PxqvxyaHY1FSNp7QDooJBLlqWYc/0ko1GwEgzsX90KLYOYc+St8ZmgFkA00vk8RBiH8QaaOH5GwHPbWhtiG4GYyBeCYRtCMoKeVTFWlLBYym0Luo0MWkJEPdlOcibOplieDH1ARAKnVqClUsbh1KRbgMhYrE/Ehib67y2PjjyXsGmmgVsN1UciwkAb8PU5fad2wjZOx4LG9tiZU9ndAMug7wVBDt5a4TRFGBmhTZ6aGw29gGcG8asyTYD0GuyJoZhaSvGhJsXAGMvb+mMUyBLWICeE2BQ4SUooYTNp/wNc96ATZcFqCX7l+BHD1Oa27hBSWinc4y46OFUh+mSuXJTSm08zYAETku2jml5P4zQ9C+tDFtKA9dpmx1CV207vDPGDu2JxdgY/Yak7liKHb6cdtpFn+Q+2sUQ4YW375qI8RnqpgmbrnU2AfEmQZGNT1JTgJuWYrv3w9zbS3ttRZ4JZC7wkrZaoYy0qFTvefpxA31PIBxVi54zf4KytNoA3Fkk8iwTLyNDAl00IENB+cnuQSwynyUAobas5aasqa0rAvU28iwi50NsIHgI0Nz2oYPRDePcKT19sZrxv8O2IlUu+RY4J6BwBltApuom0XI8X1B71TbHe97rU32QQ5+lTLERctgQGZkPyavtrAq7Xw0mO++vYQswDDGWVWMaJt1B7I59Y2y4mZRVtQxwLs9YBeNZ24Loa+7BtMcmx07mBlprLm0QSczCyKDMGFbClhH8Yf07J9A+sKzODcqz5JWqTnYTQk2bGJRtMjWFqVEHsMCVmScoc0kIS7SBHG2+WYEj30opF18AOPfRT97ANZMwzl0Qz7x0EcQItE03uvAyxGoj+RY4lzom0i8ir1E2LezfPxuHYZ+bnaIeGWMamad0LihEV18WZldAwIQ6bnSDBs+ULbTIuJjFXgRmRyrkiboTjMnUCbuTwiB7Ggb5mvst1S+nssgzU4BplOsy2KL2+wo3C7CRPS2N64wtlpOpK9d7E1qG9G//9kS8/W1f4Vn9zDlPjiue04RusFykRXPIOQcinQeZx/7TP/1T3HWnwLlnJxth1aqVyZ5Ic+r0HPLIUdfndbumDo5LbYBy0nyOzI9pvXz2SCBA3r1HprmKYyq6QrtnVqAc8/7xifHEkDs8NJw2DAwMHEK+g+jYkRjBlhkdBcA6PYOPGX3KvLATO8iN80uWLI3V/f2A5Van8Kz1zQhNjFk528+R55qPubIcyVQ64Z/540SXwNz4Ncc4VwfO1RnnVq9endqFgOw5i4B5L01kzl6iozjcOr9NgzdjA6dy6iWBarZn9FfFAYZ+yYkkSue9c3N1+ikd+cDOPMC5A/H1W+6PMwmZ/KIXrY/1G5rd18CzSdtxl5f9pSRomQ9Zvs/CeDpwcDYO7JdFGt3JOFNAb7W3w2bpOEPEs5amTLQCrlVP+/QSBsTs7NzYKfBVIL79bJiQ69d/6V/iK9d/IfpXrYkrngdw7vwLotBsgVDkSfcYfBy7x+dz5qieksr1aP6csMC5USZG17773bDN/W2iuJQxTXru+oTn0QjpJ3mti5Uf/OBfxz/8wyfSRE0GBsNj/MQcSXSWMotD0qDr5Pr4xz+eJppnnH5GXHPNu2LDxg2st8zt3vpJyuV4fdY8cO54rbn/zreLRUUWL/fvP5AcEgIr3M3vTkQXU2RIcff+ypUr//um+U9I4MdRTfMCfKIlgM0R+w9W4y/fW4prP1OOdtbJf+sl+fid32yMDjdqzx/HlAT0vd34rUq86Zpi3PZINc46JRsf/KPGOPdMzcD54/GQgGP9DwLOuYBeB87JnOUud8FZOgwEY/32b/922hH2jW98Axr5a5Pt9OIXvzixmznpNe1rrrkm3BFkOjoG3o2dev3116dNEjKh6RBwZ/3f/d3fJcbkZz7zmfHHf/zH0d/f/3gU8acmDZ0sgrSuu+66/6vMOkusKzemKO/544mXgOEADX0sG4RsE997/NIv/VICo+q4SotA33vB/Pd5CRznEvjaLZV41a/Pxi4WpM9mceimr7Crn0Xo+ePEkUAdfKZzf8eOHQkwoMNeh76gABf6dPjrmJf5zTmkdoSsOUezvwlmqy/gK50fNiZ6bf3ZAhl8pkw/Ahm+937Xkwzb6lgr453fzVM9Da8X8GcalmM1C7DuctdeEXznUV+wFWj34AP3Ei3hT1IYsp+/4qp46plPxSnTlJ5dJO08DpZ8aTItZGYIOQOFU3IAGmYzYPaoTuCEGWNRF6depoX89mDTIp9sM+AXgVeu8MJmky1jBI+zYDtMWfGeyFRThu0hw5pUQ2sbDn5YImA7Kem01JmHcykzDDPLBMw4sqS4+kq+cu045PEy1cqwTW3fEts/86no2L412tfAfPX0yyPTvx5SqQ5CPZpmS3qGrpUCnp8MrCy1WYBihL2sTJPPaZw1rgfr2OoGyNRNHnDg5QRO4RXMUKbKKGHnWC8gu8iNPDUAJOiB9a2dtTSurQEyyuCAS77jSa4fB8A0jWz0QBF2LoMjOkt4zhwAERlHZL8AZUQecOpTtOokwTFxMOFeBqOQi1YmczrpM6MAmwCgTF33RZyl22PRU8+L3qecC8MK4Tlbu6NAKMlsG/IlDyncre5YgFYVQIzlKco1y/MJWUbcNtghcOowL8wikyrhK8sAKGSLy4wO8nxC7JEB18VlASpAo5nHuSNAq9YMWxgeNCGNVZzvVULGVkUOT+MAFICI86wAA4wghCz1mBWYZz3hgKrNsnA/RX5sJzIACZzDYZQl3RztsAagYnwLTCtfvykOwhq4Zg3hiy69JHInr4upTuqvozcK1GMDbVvZzzkIcQwYto40K7Sl8oTl4xyNI4OzLUsotiwAtwwAqsAZqqehhlOrQvjNGmxkNdqAzm9D4do2cq2w7TRyLQvyMqyVCU1VBUySpV3TZFOb5smkjVuRdpcD+FHFQQClEs5GwpnCxohY+IrwCPOXkV6FfGTaaEPd5APnW/mhPbH9S1+JAwCKFsKmt/SSi6N53Spoung27DAyCGYBwiVmF5yoZBTgif0FdkvyW6Rx6jRGYsmZT+o4T3HWFihfhTaobwHHdKaZD43IBkBCdZb6A56QwQFdnIVJanoMx0qR9V+AsNRZTUDe1KGYPfRAzA7vYnf/0mjtvyIKvWcgN/o+YBjlkatQTsAZ1Wnrk+cLFszA7rb/JkLozUZr95mEyjszlbU6/nDsuuc/I0dYxd7eVdG4FFuwWZY3Qao4sHHE5Kwful3NtoAzv0RZcVHShGlH6QccJLAXCCa0r3hNCumLDASA6OwtAsBjVQ+QAOMSINUq+ZudgZmPNBubOkmfMQoGu8r4tpg68ECMElI207U0Fqw+I5p61kUp10e6OsVhv6sQMhRQbhk0pAHLZOapEfa5ODEYseBUgHP9lBcHzuN0PJnWQn3MV8+oLzzUS66Hfvazn02At3POOSfNhdVnMs55j8A0meX0b7iJTGCa4DWBc4Yu9/sLXvCC5Cw2oofhW//5n/85zYOf+9znxtVXX53ScZ5tpA9DvZ599tkJ8G26vmSPcz7n4dzZDWqC42SK0wltPp1XCyj3Zd7cwCYgXKb49773vYmtzs1vgsq9R53tXN7Nberrl7/85bF58+Z0r8+xbB6m7evo7+mH4+lPGnNlnPsGwLlb0fsTsWDxyuhaeRLjYReAD8AgAIvyCYwMnxh9SUYmgVlZWC2DMULISUm7xbERUNUc0xD9AvByDtB3Zoa+4VjO4iMjgQJM9wj4zjbqiAU8Nz0YJXaJCo1qBjArwKTE+Dt1+JEYG7g/mgCidS1bH43Lz0fXrSMN2hh9T3CLzE1lFSLhXTOC+mpjsHXeF9M77oixUSJqnHx5NK68kMF2Z5T33BRDu7ahp5ZH56qzYVZbCjtcE2OJbKAyMWFrEIK0muslnCVgM/LMSJKcy43UtSG/DeVdxNZwTJElbGJmnFBjOJxLbQDLtHMaYxImtaHZ0dg1NBAj6L7FLR1xzoIlsUaALkqpCIhbh7BbC+SMcoyTsXbuRRYARDcgA9lZ7XF5fjd0JPAaVCRhatlIkJjcqAcB9llALlXGdn4FrlYiRdm0SIcy1AAXCbefQKeVeSW8Gk7s3TjI7xyZjsOwyZ2Kjj+/tzHa0a9F6mCW+wTvNAA0HqN8w7DqyaRXaIJhinoF9hI7sREeho1uhjn+emzEs7FtlgIazJPPMjp+BnBVKbG/zo1eiaUJ20boRxNpN1P/spOhwMgj7USnOIVVFRYZ1ydpF0DkKZONlKCQgDg7ebWgp1L4TWSjcOY0AmoUHVigcLt3bovtu/fGeLEUhwGnlCZLhMttjQXYBq0gk7QnDJGJxuSZ6EPGtGkWWiexOWbJc55dTQXsrxptapr8jiPvWVjf2qmPpdSt7WQCcO3Y6DD3AerGPsvym8gnQ9VWea5gIcE1jpm2dm2HxuYG7O4W7LRO7Gn0FJlXJzuG+EmQA0oryV2t4riSxlxkQWdKAL0Z6kzAZWIVpM9NUpbDgMCnJ8bIXy66yEvvgqWx7lRYNbGX3DAgaGoWm20QVKAQrEKz+hSAFFkeBNi+d5j2OzJJmNfGWL9kSSzHEdBBngS0lbA3udLWxDfsTZ6dRXc2IY8G6jaPjsvTlsgudWE9UQTuFB+L5UcbADBWRk9TZ82GQ0ZOOWyJhG0jVfUmHMC8eBryKmNzlgCtTjFe1JpbYoyiP0Toym0DB2Cy7I2NbPhZT7j6RfRZxwpaUQJNTdDfJmHCrBC2vTKObYBd7QaYhpamaKXNNjMOyYxWBVxWxMadmcFWYoNCY4uyELgpCBVwIOF6pw25jA3a0kB/0OaEKr6C7TjN+DJUhkEcMGwJ+62BftDR3IE9QdvFjitTNw20sVyBvkM9VugbZZ/DXEX6wClCu9ovRHi1wADcIsiWBj8NcHFsuAKISsnxM68mgCPtsC618bJpFam/idFMTCIQTXMZ9moNlWgltm5XG0yYtAXb0Re/DOPcJ26g3RTjqivOjQvPXUg+bVcAMgGkNLcCOmzLRwt5ytE27ECy+9FkYxy5TIzTVqa4lnbb2sL4wyYNTVTBL50+C1yt4VinqWBBdtki8w7AmTaBCezYIiCwVoArHR0AkalnyzJOmmO8ZmaUEbYhc4qmVq5ra+QZzvMYz5hzTE/B9MoayewM9USWqT4AoMih03oEXEh/uOPOiXjzm6+jfKvjxc8/NZ5xCW2KjUtF8itop70jF52tMOg9PAec+/a37wjX0p/3vDnm9KwoIY563zJ/CC71Kc/xPx1+do5r/6vreD8ngBznZVz3Glnlp6jnKTYEuClA0P0wNk4Cyw3Cig3jtzaP2IoJgHTFI/Nyn1mgztqYvy5a1BeLAcstX74sli9bnjbbu7HNMK0tLa2UzxGTMYJxymean/86vuvzf52d/3CCS2Curc4B54xapN2rjf2yl80Rhbm24zWCUTEC0hx9mvn91Dgg4wm+q2T538SY0ML40dpORAw2INq2xhljJsa4bob+wJgM/g77D9AsfbCdjU55prH7d1bjEx/bHzfLOHdGX1x55dpYshTbwQUJxpVmxoC2NsZ8QtWr2UzDtFXZJcbYMZ4xNgrQncsdg5tYlykwrvibFy3oAjzHvXQ1yCWq9CnWAZzPsqZgP1TvjgJivvGGL8bXb/xyrF29Ll5wBcC5C89nTopOBxDtOlHFjWmM7T7fMeqonvOoW8gJC5zTaXbtte9OdN3PfOYzEqOHA9CxfjhAG15Wh58D7yc+8Yl4+tOfniZtP4m8+3wni4YW0/HrRNeF3Te+8Y1x8cUX/9eE8SeRlxPhGfPAueO7FtPAzIK38cMFkgrAlYlRh8f5IJoNXbxu7dpjNvzzkyv9H0c1Pbk5/2l4unb7TbdW4rVvmo0te2uxbmkmPvInjXHh+RgX81V3zDUB5v9x172VePu7i/H526pxxoZMfOCNTXH+eViC88fjIgHH+0cLnHvrW9+aAHK/8Ru/kUKA6oh28vL2t7897Xr/vd/7vfj1X//1NNkVxCXgTv0hU5270bT1nEy7KC8DmpMcD0PN/M3f/E2aBL3lLW+JCy644L+cFY9LYU/wRJSp4Vo/8IEP/F/hWgUlOLE0JK8baupOoBNcJE9a8exXsiApb5kUnWccfcg0IZD0F37hF9jRisNh/piXwAkogUkWg5/y9OnYAvBnI4vPn/lsY2xYM29vnShVre52AV1g8N13351Ac4IKdMwLmPN3dY3joZ913gsCcHOkjDmy43jOa9RR37Uw/kOEZHq+vMf3+hhbP1+/3YVUD/Pg4XOOPurnjz5nfuoAvHo4PdP1vO8P3n9vXPuut+MQq8bLLr0szl7WH2Uc78M4KyWSy+OkbMBR1LsE8N3J6yO3bBmrlyyGHjgYpR27YnzfIRxBhqfBCSGQqqc12hcvjvZV66Nh4WKAXeSmOBIVHBBj2w7EzK7DLPriFGNhtghIS/BQ5/IV0bd+TeS7DCMFCGgSJ/ruwRi9f2dMDgHsol5ky2CFOLqWL42O5Ytx08zG8N13xJ4vfz469+2KRhzalQ1nxtSiFdGA82Iha1+ty5dHFdCEm64LAqdg9KgQWmdi/2CMw0xS5jlABXCawubXvyRaN66Jxj4Af+yMrg4OR4myje/eFzPDhMTCyVbD6Sz4rH1tf7St5jk4mWuwnwQMIJX9yGP7zpjYdzCmxybS9YHDKgtDSnv/yuhYtSLyPV1UHunA/lICkDK2eyAmYdNRdjM4+2ssVPf2L4/eNbAqAKybvIlNfl/99ziA07F51eoorOqPEmwi2cVLYvEZp0frihVRA7iWnPEArarjY1Haug8mt4MxSZ6LOi5xaDa2N0Tn0s5oWtMf2d7l5LkragMwfsEQVkIeowATR9HrVeqjxfpYvIj6Wx1Z6kUqsByyqxw6GMU9+5EHIa4AE86S3wpOtQJeuK4Vq6JjRT/MY10sauOYArBZ2gMLzs69MQ0AsKQjFcdxFqabdpxL3aSbxxk5cNedseub3wBA90AsZ3NKy4aTY3b1ypjtWxSLNpwWXf1r2ckO2NPmbpuvUB5AUuWdsBvtGKAehzgFuAuQVxZHbMfCvmim7vMLlwJs7AHogfN/944Yf2RLzAweTHIu04fKAP1acC53rV4N8GtZAm/SGACA0j737IixPZRxiDYKcFIGt0YcX020+84zNgEuMzwqwEdAhyU2ZI7vG4xJQvwVcVTWcEYL3mtc1BpdaxYBLAD68J0tsfvfboj9yLmV0Mrd9KFK34LI0457aaNtAD0Fg8o2l8LmVscAnx2ASW0fbBcAdmE1acBB2graowDgrYJTogooxJCHgiKqY0MxC2gi24qTpRVuSLAtJYChjY3d0dKxKHLNbi5hnMDxOsc2gBx1ts4epC/cFtPbvg0LSHu0nPSCaFhyLoA1QDNZmesAEY7spi73RBEndo3nguvAoQHQYOxhHKOwmSw8I5qXnUFoVUBtw9ti53e+HYWpaYBzPeSPkLuE+i3hkJYdJk87asBWzCJ72f9KMzAK8SrSjnQYNwPYM2RwEYBEA8xCBYCT5qWIo7JSBLgL+4lDwJTgD1p1EyCKJkLY5QmfiNecMgkKJoPUV+B8r8GANbv7gRim3ZZgkeldcwqsc+shTFycQBe50l6AlztonzBKAXiV1a8F57FsgjrsY9mlhGoF/HfESUviP/bxZC/N1HWXBVHXqNcElhnWW11y5ZVXJl1m+HF1g4dgOHWi7HDa94KwBZUbHlVQnWA1gXOed0PyPUT1MD3vEfhmmh7Oqw3TKqOGOlNde8stt6ToQssZp1/1qlclILg+FBnrnvOc5yRw3GJ0iXn1evWxzBwyzxl+/EUvelGaH77//e9PwDlBfD5Pf5FzSOfhrv3KnuecUaCdIDrLpixMt64H6+X1+3F3WFUVGNrGbwY4900AHMPRzTpFZ9/SNFbMyoSKxtQZ2dDUDiAZvQxYFgURNcIcV8b2osIA6QIKziGfJvpdGbCsLF7NhDRtaAd8N3yA0Oi7AGMAQAfdIAtZCYbNIkCWlu7epAszAIBTGEP6PFSTNDJsJQA3lUH64fabwIvvi47F66Jp9bMA12/kGjq0Y2lioDzEx0H63iQAJpy2TYzpUztjZt8WwnXmo2fjldHU/zQQVvTbnTfH4LYHAMN0ROeyNaTVhQ4TWASQg/G3DRB+rnVxDDf1x65SRxyeLIL1htkJIFUL438rLxlQp2HQ7AYk1glgzFCJwMYBogCg4hMjQALbjWIXbRk/GNtgRmnm5FMXLYuNyLDKeDQEiPfQ5ERMyX4q4oMOXpZllDLrrG5irFvY2kn4U/JEqjp9BS0xisNkV4L1CkgZ13uPzU4dlgHw1YqdI6sd0HXdxQkMNIVuOQSoYxBAEr7wuV8YkIf4vI3yCezoJ4bpqV1s6AH0M8WdU+Shg/HwJEDi+Na5B/s1lQ37jfIBL4x99IUHCK17cM+uWMIYcBq6dxlAEEFdY4CL9k3NxBgDbwNyEug2O0V4UYDaeWTYBShpCcDzHsBXjYL5MLRq6KpRqvUQY8sw6U4ARJ8R7AwIybBz3dTZMsKML8rBCMfzlZOsqkOMuQdRXqOliViJHbLn3vvjka2PoPuoE/Jl/7RvClSx39p/6+OZ5+v9eO4c6SJnLrRKUr0c+ZM2wOgnHWJziZtT3BRo2o559Y0ofnfcczzzJZjG79rRzYCEerHBHFMcZ1wPdNOMtnU9D/W81vPp9/pY47v2fX38SeAI5UB+kWDSPW54qWJnLrvoYvpqa/SgO5fQL1vUbcjLdiTzD5AF6rkWg/Sj3QDvDsKGVQa4um7J8ljd2RPdPGsGm+ngzGiMqNd4gkBY66IKqD8LCKoVxsUe9KhgvWZsfXAPCYzIdJd7YFhng8Mkm02KvAog5ZpoC81sJOgify30dwHvQKTISRUoOtdjR43QXwZhgd2/bzfthk0QgHf3D43HTuylLG1mBZsGTmYzyWnLV8ayrpXo3W7aAO17eh8AwF1J1xweGKGuAFoAOOpgw8LSvsWxApkvwJ6plfJxcPdw7Ni5FdDudCxe1R192MTNeUHCAEJkt9u2jXnJTKxbtSFO6t/AuJdN4W33De+LbQd2xD5AfEXQay0A8paQNtDUmKFdyGh30rpTAF8vYUycip17tuFD3EG9wyZGvkewtSbGRgAatsXa/gtjyYKTCVGdiYfuG4n9ewBuspHI/i/rYE9PY6xe0xvrTmpP4JM9ewGpPjQWB/cDVKUTlwEK5ppmmEc2x8aTemP5kg7aYCGuu74YH/q7mwGwNMZF522I/hWtPPMQoLwhbFHGlMW9sXZNR6zrh0kuLbkBxmMsPjRQBGw2TvhybKuRceqmTFvtifauvhieLKArAdduambdFOBnWyEODZXj5q8fiumRWvQwTmlnHmacmykOx6qV7WwSh+mO8WSc3x/cMhM7do/AagfgkDrON8ACCki3v38p69zMSWgX+ynfDp59cL/9hTkz7aEAo/GSZa1xyibK19/C90zc9Z3ZeNMfXQeT58p4xsUnx6knAWKdHIzBEeSBHdjf3xqXXtRKOg/Fpz/zmbj9ttuxS56WGOcE9dvPBLQWBbeit+xfhqG0X5XcCMG7zHElfp91fD0CkLPPuQFOwKX9WsZBo5FN07cFytnvxsdGwVGMp88yvtv3Z7E9qqRJd0JGAGexg3pokz3MWfoYAxYy7+jv7wc4t5jz6F3BvcwZCo4J0oyqnzl8fv2QzSuFd66fIP9psPIh88cJIwHb5tGH47+H5+u/1UO1aiOL2dFWtT3VgXNJXzD2Dw5X2RBJ/942jP4C1M4CjYynqB+Yn9ti3UaYVNn8Z3/durUSe/cA+GStwXaXY9NEM7bFkuXNhFLtit6FBVjQK/EPHx+Km761CyxED/7jJTBDzrDxcoCwxJO05VY2hS5ho2YLczrtR8xMDIcplMPePSWeMQqmgj7CpoLOrgLjgSGIW+lLMLITbv38p7bFmn6Ye9mEto38fPvbe5gHNjP2dLA+AkB14jDpDcdDW77OhpubY9OG1fHCF/xsXHwR0YQaMCLSjjmfKShaW129d7Q0H/3nExY49+EPfxin44eTIfh7v/e78ZKXvCQZJo9eRD/5O/793/8dFpI/i69+9avx5je/mQ7w0lgLOOeJPnTiChByh5U7wXbt2p12JPt8Kco1CFU288ePLoF54NyPLqtj7UqNooMHD6bwAFL93nHHnQnocDKLls997uXx/Oc/j50By1lcZNF5vl98n+qbN96+j1COiVOaYaNjtfibj5bj997HvkIcU694dj7++PUNTOLm6+2YqKTvyQRzPCaz1bgWhsAPfrEcZ6zLxF/9YVNccPG8Tv4eUT3mr04OHi1wTlCbIV5e97rXhexyLn4Z3kXQ1p3Qw//hH/5h/OIv/mLKk8xyAucECgm406FuyFcX6NesWZPYz8yD+sRzpmsIS6+R1fR7ndyPuaA/BTc6UfwMCybWgyCG7z1k/VOusg4IVpg/njgJuGD8vve9L7E6OMf43kPAqPXku6EA5495CZyoEnjKz0zHXejxxezcfMMbCvGal8LOxPrO/HH8S0CdI1Obel87QoZaxzMd9zrHZGxTh7vwruPNzZE63tT56iPXWQxz51pLfXFUqRz9+X+SUn0RVdtBgEA9PKufTd/n+nyjLvjZc/VFV515zne9ViCEh+loy3i9c1wP8+H5o5/liv6DgBD+7F3viDboNZ530vo4VWOVHfajALemRcro8IVJoKt7QXSfc3a0nH9OYsgq3X1XTN9+Wwzv2xsTOAEEwVVx4DW1tEfPylXRc+bp0bR2HVgWnLSHD8QQdtXA3Q9Edf8Aji+chDiVZ3FCzOIQbFu9JtZdenG0LDb0JGAhAGhT37wrxu7bGhDdAMhrTiGfpkhr6ZpV0XfKRhxOhRi449Y4cNO/R+fhfTjoOqO8ZGWMdC6IxpPWxvKLzo92WPmqOP2rbFXOAaCpfAcGNzbmDgP4E4Cj87ERJ2LZF463tqecG31nnUmkTcA5MPGNfeeemNx7ENAQTlrkXoI2otYGeG/Duug+i/KtXAvoCmDe8FiMfPPmKN11OyHFcP7jHzFsXJIJ7aFj7ZpYdP650bxmNQx9MIkhiynA6EM7AAEAzpJdyxBluKSiZ8OaWHrROcnpMn7916N4w80sRBNSs2dRVHsXxgROmexqHKPIq431jHIrrGWAAHKC8R7ZHuNfuyVmt+4AyIWzk+mFAMUMoKvOBeTjtFOj6YxzYETrjuJ9D8X0jTdFDSanCa4bA3BQ5CUbXA8OoD7qL/cUrnVr+q69UbztjpjEmT0+cBgXKU5bAINFgAhTLMYvPPX0WPyU86IJGcrsNgnr1Midd8fktu2wWSBbBslpHKAzOE1tG8vORhaw9w3cfmvsvJX87t4RS3AQ5wHsjS1ikzTXrDj/adG96QwcxzLC0QZw2gfseCUcmSM3fiNGtuwmpOZEYsYhkBkALViKCFPWu+HUaD3t3MivWAvoYn+M3npjTD14B+xv8PWwaF8GxDAJkKoA6HXRaadH79mnAVyDmZA2WrrvP2ME58XwfhzQMh4CHMkl4FpTFPoBd17+dACMiwGrAd7b+nCM3XEXdQiwclzmIuoQJ1mVdtm8sDUWn3ZSdOC8Lt/1YAzcdDPrUXtTv2kHgDeBp6MJYOKy8y6I9tPPSkx2kQdsActSFcBXdR+Axv0Pw9gCuBMmvzyyboWNMQtr0DSMJpFdGC0r1wMqBNwy8HAMw8wjk18WsFmRPHMlAMiF0dFDeNK2kwCW4V0VOAewVa97Yqwr7o/YS5t9+Gb6GACZU66KwuKnIGsb7wggl50xu+fOqA5tg+GFTgiLT2IjLMDoA7PUDCwx+aVnRNuqMxO7Y21oX+y7+7aojRGODZO8gbGnxD0VWG2qgE4N/dMIC06uA1AWDs3pkaGYhFGlhoOkgb4i800VwOrM+BSOlc5oXoqDtKkvRvftidnD2winhuMe9ppZQrsZtrXQtSCa+5ZHHna7an4RLlrCF9sPYK6SDbBGSLYybWX0wG76I44d+l4zLHi1wiKQAbDGjNxDuvRvQm6BRQEU2MQzYCoCyIc7PXLrmQ8u3ES+Hz8F/2Suzvz/7L0HuJ1Xeef7P7333qt6lyVLsiVZli33bgiQkOAEkhkmz81k5pmZDGEgDENyc5PMkDYX7hDA1BiwcZUsy5ItS5asavWjco6k03vv/dzf/zvaRBgSYuIHbOd89tbeZ++vrG99q7xrvb/1f0P9RdAZ8I//tlP4u9/9bhCqtYBy6fGU1eDcd3gM6zlUO4/d3/hlJTcDcgbmvvSlLwUgnB18a9euDaA6jxWqq6sDJTr3m4bbvGjMDkADKu43fbz7SC9idohYv6ye+thjjwV93Ne+9rVAhc7HPvroo0H/G0qzxQB+8IMfaPv27br55puDsbnTZcU5X7esrCwIx2o4zn25+/Dm5mZt3bo1UKKzmofvzZvvP9Q3+xz+HHqFrveeeedeNE2/OrBfHRcPqbetGaiFEH2AzEieAkLQBwO8hdOGuA4lAHtExi9EXZH2puVN4NgrhNSkLbmmHhoJCDbSBSgUnaLk8hVE/SzWeHOVJquPEE0VuIE+ZwrFy4lp2k0lKy6zVNEAcWFJwHjhhIWl/QnkqLjmjNVVe89q4MorpK+OOlup6NL72HchDwHAFQh3vO2yhoHaZ3DWEqOPdJqRBxACBhztowxOAw8tBJwruhXbgX687gAg3tkAAEpKSwlUYAfpR0dQQrXiSVJSjCazSnUZOK9qqkCdAcRhO4rzkjZDYVOARmO0p/PyilRB35qMbWRLKRoHtUORUiSCW+jju9MDnarCyRtFX3YjSmBLgb8cyfsq6lNVHQ3qRmE23KAEbd4UsM80YJnbTLeXRUkZWsir0EAeTckg9lU7EFLDUL+aR4doe2mvUH5yqbQC1QRgSXpqmpbEE+4PGNhaLwPYpi30OQ3UyV72H6edGqdPHaNNHcJuG8AmneR6mXaMA9Y4+3unwaq4iSIc2bdlFiqLTOX22WahOVuKNHtqB9i6RFvZ0FinVMDJypx85aK4F4Ut145S6emONjUBvidkprGAgq7B4BywyjT9RCJnLI1OBswjfC39FKJegc1xFeDqokGuQfpJYG8r8E0TEjWMayUDxpQTErMSWCuN69nc7CedV4FVLgP+DwJSriIfJ+sbcbgD0RUXB3NrofmzUJ31nbx1C9m2/t62sPd12xM6xm2Z2x23FW7bbDPbPjY056gSfrfN7GPdNvrlNstQol+2+W1vOy1uY3xMHgC+F9D4swE62+k+Z+iaoXYm9O7v/fn6ze2PN9vnY6jmnT9/QdU8735swUmguXIgtZWECc6ij4qnbAbP0TYRddZhXzuxpWpRjazvaEJxr1/lecWqTM4SusgAicM6h2JiA4paDvNpsJThK+UTaAggPoY+OhcIvwj4NQclviTSZ2iumfJTOzKgLqv9Ub787By+N4Ly5lmeHOzqvJQMpaFK7FLay+919KsNo21q66lWU/1R1Zw5AaYZq5SkPPItEr8FKst8E0+6syjwG5Yv040LN6gwvZR6MKJztcd0puaEmttZUEIBdfm12WfAKD8nUysXzdPSogWAdqlquNSqw8cOqKX3skqX5LFwaa0K0uZrgPs8dvogfcgx4NgowLPbtWb5JqAP8qjrit48d0znLlSpn/oXjT2aRFuWlByvUcCtQep5MnX79vV3a8m81eomTPP+k6/r8PE3eOYzgFGonwJhTQyjthubo7UrHgA4W6Xzx0Z0YA8LHbpQcwQ+nEHZbjpsUKnpUVq0pIA+NZ2HG6HDRwf05nEWnwC2xHDtSKC/0ckWFeZHasvGcq1ZnUs5itGOXfgJvnpY3X3xKikoQ2WR9mycxTCDbSjeAS8mpmvFshTde2ca0Bo2OwWiqXUMJbcOvb6vRbW1tEeUk0Ta0LhYFiBE5aqpKwbg5ZIeeTBTd9/Dc+M61ZdG9b+/RB/ZMKZswtKnYoNPYftFxnQCt6do/c0FhLKN0/EjIzp4CJ9qZy9tMws7org/2vWU1HhA+HJAlywNI8B89HA/gHwXCxG8UILFCFHYjYRqzmIhydZbi7VmLYuOULA6dWpSn/ncXmDKTC3GLivIAmwe6VRnt5U/Y1RAN/Jf/0Mu0HWdnnjiB9r/+utaumRJYEvYTvFmFS7bG8Ms1DAo578Ny3nsbKDOkcb82eN1/+3QlNOoCY4yrh1lvByMsfnbdd2fRyg3wXfUb6sOWlkzijGL4edE6l9yEpA2lGIabXM2NkzONXB2VlWOtoy2IyaONp467DrucLWhDUSKyjprc/g77/Njm9sDG4ccF3zw29z2ns+B6/uAn3Yzof4qBM7Z1+G5JINz7vdCfd4kEwojLFQ6ilLjvgP1uniJRQaTjAtR856aoj0HUpu3IF7rN+YqPzNep9+c0JFjUyxeaWbhTxv10IWPeSlss9KySN3zYK7K58eqoz6MUK0jevG1ZmzFGEBgAFhg3r4+FHb72imO01qycD72c5ZWrgbkRzlyjPFnbe2EXnutk3pMne0iADv9YWIy/X9sMu1TnFraB5WVMqhP/GYW6nEp6m6T9r4yqW98h3Y0KhVAuJhw0SwwmGpHrZHw2V1HgawPUsfzUKi+H6EvFOc874ANYnvFC6+CjbfrqtVPy9Kf+d37E5yjAfkDHGJPPvlUQPT/3d99JQhL8V5xyHig9o1vfDMI4fWRj3xY/+6Tn9RNDPB+YuM+HVPbjbqNrAg3pEGj+RN7/pNf2PCzMWhnr2E9vwzNLVmymJVXDwbSpg5tNbe9/RyYA+fefp79Mo9wJ2QjyIMbk9sOC+AVip7kcSN8MxSzAQZP8pSVlQXGzS8zve/ua89Zbu/W58MYQVU10/r9z47r1RNTymVM9r8/HYOkN2Fw3q2JnkuX2ltn9L//bkKf/+aElpSG64v/MVq33+X1pHPbO5EDnnz6ecA5T6QZkPOqeQ9UQuCcV71/+tOfDpS0nL63gnOeeDO85T7HzoHS0lIG77MT8HbC27az7eVwNV5l/xOD5Xfipt+n53Bf7slNg43f//73gzy+/lY9ufmxj30sCNlqp0togHr9PnOf35kccH2w8qIXBXm8cf3mcdnv//7vy4qNRajfzD2H63Nn7vP7LQce/eSonttLeBfmoT70QJS+/MfRrLB8v93lv877MXzmMHN2yu/duzdwgDmEu1VNHRI8NAflvt37GiywjXDoEI5qnGoOEeeXx5Z2zoecY/+cNtF2g1928lmpx4Cy7QuvgPf1rPTj89ppYOdcyJaw48Dwnm0Yp8fts6/r372fVTHcLvt4p8nfX58ur6I/D0z2V3/8eUUPDmgtq+UXco5CHCiJdlBkZRLGE+csKmOGiSJYbFawdTOO8jSNHNinkZNHCV+FSsW8CuJyoPCFwlYUr2gc9lEVZcH+MyhgDAPnXXj9gEZYbJBHWjJyCwFfslEhC1c/joqJVNQ11qxWLKGBJpquqO3IMfW9eVYZOL7iUZSJAJCZYgw/gGM9Pi0Z0K4Uha0YjVdfUPuuFxXXhpoX5wxfskKT2XkKA8CKryxTJCp5MzgzDHpNV13Q6K49nPsQSh3hSispxRmMUwkQoK+lQQ29ACI5hZp38y1Kwvned/Kw2kh3BiBgks/NdQ1GTeKkisaBFl1GulC3mwFKGDh/UdW7tiumuVa5wCWJKLCF4ZBz6KERnkdYRppSAdGicbKOXq1V185dgHkXAJmiUcICgLJsBMDAIAotYTmoTqxYAMyVrMnDZzSxY7e6mglxNW8pqmAosmUBQzDoS1yEUhkOnEnCxoXj+CGeqwbeOKT6vfuUxER7Vn6eIrIyuPcp7g9QsKsNQCtdmVtYxIFCQtcbRzVw5LCyUZeJ416mUU6YMOSIgzsiAXWT8lJFLF+KN39KQweOaPCVV4AnWpSQnq6E4iJYrCQArXH14EiMKSpT6mJCW7KoZKz2ihpe26uOS5eUipO0AFWRqKwcoLw49eKIjkTRLh3QLhrQYPTsabUeBAy8dF4FeQVKXrxQk/mE0swrVGLFIp5fMRwGDSwD25lJnF/NKM0dOai6l3az7N3FM09JOYTKYhJ+qAuH4dUGoIl05a7drIQ1m9R0+qTaXt+ttMEO5XI/UdSHGRrsYRzNYwCPCcWlSlq6iOeA8/7sGXXu3YMCWy2LMXgG+eWKRbkunGc0NTCkwYRIpd5yI4p2mZqqRVlw3+tqPHqchKH6QToTgQanCeM2bhiMcDlJKHFYqWmXn5tvAABAAElEQVTqEs+F9qSvtVFxVh9cuERTnDcyC4WUskpFkIZwwv6GRVqlEPXGFqsAnlXUYDtKgYRjTbL7G/BstBNQEIW7fisnAXrMWwJUCOTVdlZt1fUAnDyX7ApU0gAPPadMqOToBMDO6HLqZSIOBytFMXmAmpPDZWm0SVP1B7iX4zjUAfkW3q3I7GXYcEAfw6gQNp4GPDynRJTnYlNIQxRKe3ixZwjxOtzeoBFg1piKNdznatKBNlJ3i9rPHdVg2xWlxk0pCdW5cIAR4uJyDEjoABCF1QFJG15J8ol2IxVlOoBcO+Wnh1C26e4K1PMSklOUUAa0FlekTp7HUGuVMmJHFE+55aFwrxyTmK1wQrCGJRZqMoKyi7qdVSgjCbcbAYA4M9RDWayh3Wqg3ZNSaQ9ickopSynAH+0oQx5GResCxwDSJGTSvhNeGkBkGthjFDgvevGvKSZ7Kflh5807s/2yx/pu+0P9g/sb9y+PP/540N+5f/HiMYdr9ZjW6nAHDx4M+iLDIu5bvBDMY11/tqK6IXMDJB7neuzsuVeHVHXfs2XLFvwP9wb+G/droXO4H/Wis6NHjwbjdYMpjsxjSM796le+8pVgsZSPNchn0M59qNNbU1Oj559/PoDnnM7HHnss6Jf/8i//MpjzdT8XSovPa3jO6bIKnUO9+Tdf3/2gzxk6r/tYpzHUr74zT/sXeRbq8yTgXP8+dZw/pO6WJiWgLmfVuZhkAITwBMALFEhH2piXGCGkVwFOzhIUF1HZ7L2KA3ZIESiHOjw3BQTFWQCthhYURVGiWniz4ksrNd6AMmXVXsA11ERScwG5S2hnqI+R9KEJ1MOUYqBhrkW7YDDHRFQYYRFnJlA2bT+BUukBIOoO4PiFqNbejdJsBb81aaLjoPoaLgVtbBLArOFaw7szTu8AdR8V0CkAvvSFDykmfytwHcqhdQcB584A24wBBycrnvaJSsw9GqYArkZxsjM+S+fjKlUfv4D2Ik2ZqJzEUBYcRtIhOnt7gKOBJZaVztPijAKl8/xjuHeHH42kbaJJotUFSqJsnMSRfGGsJ4DuDM4tjErUEIBMDaDQqbZatQMghgMkpgMfxQNa2089BBTXNzygJFTYVqQXaEF8OmWPRZVc41J7qxqB0gYZSEQC0KTS9lj5tnccJzIqnNH0UStTC1URYzmpMLUBo9ewQKEf6CMIa49aF62pWmnj+ocAf1FZisG+ywcmzopN1OD4iJqHUHoFrJuHQte9GcXKwwntdFHq+Qe1SdpEhGDUNDOqCzjJm+mfMumTK3MLgODSSE8ESmWDOtLapCvAWHH07ZncYybhSR2ydACoe6S3XwljYZoP1FWGbZEMnDeAPVCFalM1CyAmyaPEOJRBIXsC0IXzTQDepaNSWA5UXsQxZLU6+K66rUOtQIfxCbFaia03id9mgsUN8+bPD+zYEERwvf1K8n9scxsRsrdDn12nXb/dZrlNc5vlRa1ecDmfc5fR7hmYsx/WbYNfvkaovXJb6GMN1djXagDY9r7bF7d3/t3tnUFhA3R+t93t87tN8eY0hdreULtzfcJD9+TvxoHHj7K44wT53lJZBCQZhjobEBM2cEEk4Uon6TfZD1OUExM6lHPDJeg8z7CuA/iUBQQVucUqT8wkJG4Yyn+jOuowqSgjRpKczOQEJTN/FgPINUoY8yGU4AyYFaEOXYmCcg7PCuxc5wb6dGkQ1WVsyRSHSaV80YpTx1DXHULNjO+zAecK0wChqfMt5M+Fvla1T9PvqkWdbcd09o3XNdg1ChyBEjAgaGx8mpIpo5NAar0N1Sqhbd+2bpuWA9/3UMf2H92lmsYLVOV4FWLrIlWHEtmI6lnoMk4ZLC3I1qYbNnB/84HeZ3Ti/HHtfXMndu0MYMdaIKwb1IIS9rHDB7h+qxbNX6x1q7eqOG+BBif7dOjcQR0gTX2oMReiAl1QgPIx9bUPNbeLAHvd/c0oH+fr/s0f1IqKdWqb6NaeU3t04Mg+2qJBgI9sLa5cBCyHiltMgcqK1qnlarpeeOqyqs+MqjS7jGumKjGVULEAi1M8n+y8eBUXRevy1Untfa2V/rFP5cWoOFUAaSZOq62rlZCuo1q9Ik3LlzJOSI7W9l2j+tL/eUOXUXUqzkOhdUEmxzB2wzarvjqq6ivdSown0smH5+uWzdhQ2JvHTvRp954anTzVStkr0MIKYFkUvAf6I3XhMipvNSzqmLqkj304Ww9/IINQwCwSPzuqP/2zU7pU3aW81DzurQh1PJTUC6dUWobtXhRDyOQJfeebV3XxMupyRWlavNyhHil31FVLAGdloHRVHqnzVSi87m1TR1sE91ao0tIYFI8N/Axhm80ADyZr+bKoICztKfxWn/38IZ2rR7W/qBAgEtGHPNpmQjJWX0G1sK9Bf/QHhMGNaUFh9gfa+dJO7PrkYCwbR9kNQXAjqCEOo4jpOmjQzW2M663ru2E6K85NUHf92c0eyQj2maKN8mY1uFCf77rqNjQGADge+DWe+mt7wkBsRmZGoCQXqFLSDqbwcp03JGs4Oor2ygqcNjTd9VHt/C/X8wI3X9eQvr+f/d0ckJU2/SP/zh7DODo4ZvZQ/zu3vcdzINQXhdp+307oO392H+XfbDM//fTTusSY2Ta2wTkL+7jP8/4uru1tM/rek/Xa9wYKwSxYWDCfBWgFjMkAnx1mODFlSpXAcBGMt559qp16FKvcrAjmryaxA8Ppv1gMQ1jVhJRhbdqapBLqd09TjP7+26N6epdtwClUL/MRgUBxHhHvfsa8p0/VU0aTtHFDlu5/iHDEeRFqbZ4Gzu3Ti7suYq+HB3WyuBgVUEpxfdO4zpxHNKJtRPOBgf/L72dp05YkdTbP6KUXJvX/fvVVFjFFaikLWBbMT0fpc4a+gX7j4is6c/r5QLny0YfvBpxbCwwMWQ0UyMCTNLjyOMfYZqvJ7Oef49/3JTjnRs8KH6+99lqwcsiy3B6YXV/Yfo68+oUd4knPHdt36BO//duk/4ZAyeTBBx/80fVDRtgYBrDlQC1/bsdfSCL4Rzv+Ex9c2ZxPNuQ8wWpD8MCBg7wfDyZRV65cGTiBb7ttK51X2T9xprmf/qkcmAPn/qnceXf95nplJ4I7IE/2OFzxiRMnGOBgpJaXo4ayQXaCuFOyMTS3/awcCPVSP2u/ud9/0Tkwgo9k+65J/canTexLt64I1//8fAwrpDySntverTnQxUD/K9+a1Ke+NKay3HB95uPR+shHWNGEbTi3/ctzwHbRLxKcc3/jUK22wxzG0srIToNfV65c0eHDhwOHu8O4efA9t729HHD+Wj35i1/8YgC/v/XoZcuWBQqABh49gTG3vfM54Aljh0H6x56BJ56/8IUvBA4yj9PmtrkceD/nwJ9/ZUKf/UucKKyGv3N5hJ79thW93s93/K/j3jx5aWe+550cZs4OdytoOvy0wbOQSo379mBSnvGmx5xelGVHvqFirxA2eGCwwJPqIef8PycHfd7ZyfWwYD7H6XDIOzvofB074zZv3hy87JwLpcfQnME9j3X92bB+aK7MjkBPvlpByKHxrIzhidjQJK7TZ3Du6pnT+vLnPqNh7qUEZ8GS9AytXLdeWatXERIU0Ah1uPGrTarf84raUZ8oWL5EufPnafjoIQ1XVykBwC5p80ZF4IgKi8GYxVkaqJSh/hWOw23kQo16d+9VzelTwEh5Kl53g5IrcWYDvrAzjsIhVEoIsZaZqnAcwT1HDujc/oOEa2WBydqblLQUhTtAIzyaOO2GmTfF2Yzqm9Wzppoa1PrUk4oj7XGLlilq6x0KLy0HuAGCAZgLQ0EjDGffzPSYBg+8oYFnX1BXbZ0SUTLL3nyrolBWIxakRmouqPYozv32blUsWqEUHIidly+qvqFW5YsXKRWALDIPYCcOwgrVjjDuKwxHDjIzgEetuvzyHlUTprIQoLBi3U2Km79w9ncr2aGkMQ1JEA1YGIazsfPQEdVv36kUVGpSVqxU2tbbFIZqjieEp8ZR5GCeODKDCBE4WaaqajT+zPOEVLmitI1blEaZDM8lHb5/VJvw2AR5ONONQ/oE4BdwW3XtZRWwSryQ+cfIIiBCHOxDrGTvOXhAbQB4RTffqnhCpdadfFP91ec0f8lipa7bpHCgQeexFckcfjYMwC2cexxraVPdM09r9M1jqCqguLB+HepkC/nNSr84pFEnCktIRXUMmIny0f/6Xl3dv1cj/FZC2csCqLNangD8xoGjZnAExJBP4ajNTJLHLfteU/3RYypZvFw5t25WZCmhU3HIhuHwD+OYMBTJvPB8Bqf75NmTan/pBTVwrxkVVrnbgOofoRxjCQULPFn94i5NXmlQTtl8pd/xkC6hGNjFcylLSVDOJhRxK/NRDOQecfjZSRdByL0oYIexzi417tunxtf3KZUJ+6KVNyhpGUpwuZQ7vM0zOMxHUTKLK8gIwv2NHTyq1udeUkdTqzJROsm6abNiCoDscHJP87wNH0YmAatxnan6JnXi+OtuuKqMQkJfbbpVkSVAYfHcIzBhGE7iMKR8wsJRDBy8qvYrp0hPPeofgHpllKN4VPwAI2b6UWFsIuRhJxpIhDbLJKRPbDJOwtazqrvUgJJUgTKL1yg2r4xiiT0cSfkAKIPaJPNIB8ofEagJhEVQxoBSxnoIYVt3RuEDOG4BaWLLtqBYR92Zoc3pqVL9RdTmAN5yCCsbj1JbGLDeDGo4053nCH0LBAtakVCxXomVlDMg1pmuOrVWHQdUq1NmapzSSisUiRIVsR+B2IA72muBXS4FjvcYIMTo9CJCDaNWh2Icsoua6kGxpbEacPAyjmdUKCsX034UqwVwrr+1RrlJU0ouRtUqvUI8COo/5S3KSi7JwBcGlSmzhEcLA5wLm2wnPORVDdedJ8SZwT9CR5fOJxR0JgAmKomdpKXuhKInW5VE2xKTTz47fDGgy0QrkGn/kGLmP6jYHKBKnuE7sb0bZtRCbX/ofkLg3Cu0G6WlpcEY1guL7V+wCMD+/fsDWM1+irq6Oq1btw5FiIcDKMQhVb1A2ZCIx2JezOR+02FUfR33V/Z9WEEj1O/4vAZP3Ge5jzMI5zlZLy7zdX0dh4D1eQ3OGaYzOBfaLl++rGeffTZQI/f+jz32WACnGJwzFGMwziHTPR405GL/iF+O+mMFO8N27ptDkFwoP/zufjPkRA9d773zjvt9mpCT3QfUes7gXKvSDCgXlyo6B3g2FgfrGKGley6qv/kKUQQBrcBvEAajnUBtkvCj0bll1KssFENR7+w8r05AkmmAu/RKIOSSeRptOKTRi7tp36YIW71CMbnr6COKaNcB7oDWZiITgbGo6/TpUQYEgBio1BBkDdgWx1AqrUaRBw64EIXPjE2kKYdw1Gdo015WV3uzYhNylJ6/KAjxDG9C2HGUPFteV2/Lea6RqKxFj9Bf3x4AghMAvx015zQCdZUOZJ2UV4JNQDnhGfZ1NagdZanLw8zDRBVrLG+1CrPKNQ+IOZ509dI2NxHeurYZKG+gX8vpK5agyJYB+BBLObA5H8Z56dY0BPjQAOx9rrsNFGhMWYB9K1OzVBweowGmW+sIIX2uvU4dQFQGwctyi5SHQlM8bUYngGEN6psDqGfNJ2T2YoAch4TvIHRmFXbF4NS4ErEJ0hJS6HPoB7h2N+HLmoc6UTrrVwWwUWVqdqC4VU96r7I4Ih5orRxFuGjAuavsUwNI3T2MIhgPMoEyvwC4qiQuNQiRer6zhfSPawHKo/cQnj2P9hHThYzlxmjSprBJrB1bNdyt80PA0CycqAR0XgIIlQagZaC4lTmYoy2NKMH1BopLCzNIE+kN87GAc42o0bV2tikVSLo8txioLiGA46v5rjtsXBnkVW48EHoYCnk81G7yqw3AeoJrZQEyVgIyz2CHNpHWpgYr4ySpLD9HFaSvi4WTVoaqqKwM2oDrwTnX1VDdDdm7rquhehz63d/ZtrZd7HZg7969wYJMK0jbJvaiGNvThuVC+/rY0Dl9PrcJfvfm9svtSkiFzuf0AhcvWrE6ndtAt4dub6xu6eu8ta3xeULnv/6zr2Ggx7CPwbkzbcBn88sA1Kc1D5LhRhZv5NOnW9XP/HsU5S+cY9CM1FUWMJzqbGK80AX8mKh5WQXKR/3Hs2It9K1vNDdSXgaUHIWqEAsk8lgMkohdM0Go0gZAs0bstmTs9YWMM0qwSQYom0ca6tXKe3Iqqnr8lhABxBHYe8OE6+xSJ22129nSLJR4CcXaBIxW09mIAjKqdsmE+2s7rjdeeUk9HUOEjKZs5JWiwFbAmohSVPXadfYIixQ6u3Xbjbdp4+KbgeMua++Rl1DYG9MibO4lC1ahRJtACFIU+C5dUBXjlMmRIW1GiGbDkpupr7mq667Ts4efBUS9oHQWrJQVL1AL9nhXXZtKMgt0+8bbNb9oCRBGjM43n9FLb2zXRRa5lBZW6KZ1m4HSyhgDSQ3A/q8eeUHVLWeVC0D2wKYPa0XZzdx/u3afe0mHDu8nbPS4VgHi3bHhTuUmMyYMQ/GWurbnlX5942uHEENO1O0blmrLplRguXBgMSvmBc0rdr20d88o4FwzZW1Yd99RpBUrk4BYWDjEOGeGxRZZwC3ZhHa1ytn2nUP68lde0+X6Ht24ap3uuaMYhTlsJhqn8xem9MJL9bpQdUwP3btC999TiWpehJ57sVcv77uosYl+rd8wTxvXFyonPQL7Z0Y7d43r2VexewbP6bFfzdODDwPYZpEnF0b1hT85rIsXENspL9WjDyzRilXJLLqJ4Jw8bWylI28MMgd4Uv2D6bptc5m2bItWWrZVKx0elT6T+4ykbr/0Yp3272vEn5GtO7eWaOUqQk7DM48wfrRqeFZ2hLIzHWo5TCdPTOrTn3lDp+pitHF5sT4AlLNksUNd89vpMZ0526pP/BoLL9So7z3xfT1LOPkR+qVYK7q57vMfOzO2GgcaxuZzReIrfz+FWpy/8HcWJ7LKXNAueFxzDVZzmFSLqbi++uXxdDx2eBK2gQG9ZMZWZjIcXSaZMU+CFxdd28/jardFEYbu6E/M9LiN8XV8UYsgzdoRhv29UIHvqadhfB9qW4L9+Z6vg/sJ2R0ONxuEbOYenRdz2/snB0J9yPVtf2jexzax7WHPQa1Zsyawwz3v5HJmVfxJjJErl6f05b+7oFNnG1VeVqxtW0s1fwFqc0y5mGGdJqxpdOy06i/jZ/zSaXiIHNqAHMSCIpVJfZ2kLA4No8yICmR+IQsrkybV3Zyg736zW8/sPE0Zl7bdtkg3bUDplvbLAu3PPtOsUycnVFQYoV/7KOGIK+N05sS4fvhUs85U1Wrlyjy4iiKVVcRSxwn5fnFMzzxdp0NnBjS/IEn/+T8WaOMtyWprmtauHZP6P19/jnua0q1bbtBtWzmu2DbomHa/8qJ2v/wDVZZl69FHDM7dSJtNn+yBf4CJU8/4RGtzrV78/HXjfQfOuRB5ovBDH/owUrVVwcDJg6j30mbDyipX99+PhCtG02c/+5kABAxNthqss6Fl+XBXEtPQ9953X2C8hQzDt96vK1xgVDHJ6Tyy4eZz1NbWEoLyOJO2rzCBW0cHEMcAdmmgxOEB7pyz9q05+fb+ngPn3l5+/TL2dn2wY8ETNqa1jxw5guPjWTU1NQYTJA6dYxUAT5q8NYTOLyO9751r/vwd03vnHt97KbWh3dI+oz/9yzH9zVMoUTC4+e+/FaWPfwKp3Li5Z/ZufqLdPRi0T0zqv/71GDL34fqdhyP1e5+Mwskw99zeiefmvuAXCc5ZheZP//RPZaeDF3tYdct9jCf/HR7coUY3btwYqHWF5OXfifv813IOP89jTCB+ATDrxRdfDPr56+/dEx6//uu/rv/0n/5TsHL4+t/mPr8zOWAnmKHQf0xtLqT6Z4AutLr6nbny3FnmcuDdlwNHT03pjl8fVS+LFzYXhGv7C4R3wwab297bOeC+xqptdsrv2LEjAM4MCFgJxxPloQlv36VhYu/vyXbP9xgucPg4z+kYFPDLcz+eDL/+uJ+VQ54j8msvzj2nwUqfhsftlPME/l133RWAeV5o6Wt7TsiOO9sfhuzszPN3/s3KPZ6MdfrKysqC8e/69esDZY3QfFKQHvZvPX9Oj/+3T6v53BnlY9OsX7FaKx96hFCkKzSNg9awlzp61QNQWA2gF5mcpMobb5SuECKy+jxOcML+rLtRMTg4w9MBBjOAynAiT0US7pAwjf37Dqn/2e0aZL4re9N6pd29WRH5ZXgYkpkM9cQoalpgODM4jaeBi5p37tCRA4dUWFSuFR/4sKLno4KFelsAiVHVwnASz0xTAVE0mcIh2EAon0TU1hIIQRp970OariAdTDhH4tQgQ/B2Ev4NUKjlxZ3q3/4iijIRSr3jfsXfersi0lMJ4YnSF47s9t171X/4zcDZG19cyursXtU01Sqrolg5SytRGstFsSwluP/weFTfIuIIPUcYoCMndfLpH6ob1Zkl629U3pZt3B8QIfBSwNzgQObmUOtCuaS2QXUvv6p27q8ShY/UO+5S5G23aAIHpCeGSTHp5hPZEo5zeercBY1TJi9drVHetm3KunULgFs2kA+z5eHsZEcQ8MNUC1DE/sPqfO1VNROSqmDbLcq+EfALGDHM8N658xp5cbfOHzyknCUrlVqBg7HhshqrT6uCsLPZqwiniCpNBI7uSENtKbhcHTpzkAW+587q1Pe/q4T2JlWsWqnEO7cpsqIESBIVGeexnf7hOL1Ix8T5arU/9T0NXjit6MoS5T94H6pkFdwPYIWRBPb3IRQqEj1CSN6LqgVYq0HRbtGqtSq++w7OTd4R/kszgPio9RDDEyiDjzhrxw7sVfsLz2isf1DZW+5WMhBgOAp9YTHUSeCC7hde1MDuV7kGIWHvfoSFlB2oBp5DrSxWeWuBtBbnaSovESW3FBx8QFeolFl6p4d01+zZrf7z5wlhs1AFt25V1KJFUB5AWXgGccfBzxHq0GpsXGfw2ZfV+NwuVJtYhf8ACzfYH6kQbszhVinPhrjIP1bVEFawVnXbn1NT7SXlUIaK7rxPsZWrgU1c/nHSGzQl1I7QjRnvvaDW6lOo8gwoE4AgZd6N7FdEGpn7HbpKSNs31dlC2NyZaOXMK0M5CiWwtirVXUYJMHWRsspvoYyWkx9exADMRpjGGRwPLlsOBByJTlH4TLem++rV3XRZA53tABcoHgGIRWU7TYAzo3Wc8yiOmguKQ4ktt2QJoONC8orfcOrP9L7J830exZEJwsWuU2L5akUQfmum65Iazx7HUduD4wVFvLKlqEpRz1GLIi4qYON5dVUdUX9vA2lFabEUx3jhFk3Ho9xIyEYN1muk/pQGrh5TXCKhaSuA2eIK1VzbRDoblUfYs+CcGYsAUQspQ4CVM5RTO1doovwKCwcwnOpCveqKhlDMm8KxHhGD0zN/MfdngCgWiK9PPfXn1dd0QWmxEwB1ONYLlnFO2mzaggmc6COM4aKKNyk2ez5tOOd/H2yhdj/07lvyoi+LE1hl1WoqBr8NvLkPsX/CPga/rKy6a9euADaxGoYXHxuc8wIxg2qeX3WfZ/+GQ68afDNI7sVk7ncMWLgvsn/HC8ocDcT9bRl9k5XgfA6HgfW1rGR3PThnEMX9qPtGf+9xtftc982/+qu/GvTHf/u3f8ucb1MA37n/9djcfafnhB2Fx1C54Tyn0+ezr8XnDOWFP3sLvQd/vKf+oYYDvM50H1HLmcOoW3YqExWwtMqlqJICmwLOabIL+Pashi4f1XDLFUUAIodDssWgABdTgsJH5mLqAGGoJwBruwgTePFlnhlqmoU3A8dXaqQZcO7Ky+QKYSortgHabUNZk/1R3JmmL7KajrsjA3MItgJp0e+NNmscCG+g4Rzw0rCSslGCBZwLj1/ODrEaad2vwcYXA3W2lOK1is9bS/jpLNoD2qyRZk00PK/eq69rDDsia+EjwMl3cZ+dhGAGAq+pIlxpkrJKFykxn3oal0c/Y1CkXo0tpwGPutXC/cSV36Sl1P15tMdxtBG99CVXSct5bI3O7g4tLi7TUkCjTMAgT6Falc2swyD30Yr3+EpPt5oGUf5FCa0UldBK2lZ6SA2wbwPQ/SWU2vqGR+gqUrUY+LzE56Fdb8f2ODvWrbp6bAhCpM0HarOSazOLA6qba+m+o5Wbk6d04O1YlDzdxg/i/O6YQimxu1MFhEstBbazWlNNb5vaOzoJ+5qtFSjqRgCdH0NR6/IQ4a6BhcGn6SbDtQwAcElCBuFOx3UUVdB2nnEFoNu9OSXiKDpormPVUPqcUfKhjoUIJ3pbBJpMiEr8h8CHC4HXEgDdHB6taWBQx5pR40XRpjA7V6vTs1Vmhzbdch/5U8v1jzUC6yShWJZThlJdurp72tXY3aYJAKCCDNTX6IcS6e7HAWZ6sYWaB9qxVbtQnopHOWwBzyxcdU201yhxFaGyZ3grY2ZUDRcuoKQzEggfGJ4NwW3X11tX0VAdDsEn/s77+GU73dCc5+QMJniBicFZtx0G59zmhY7zea6v/6HPPkfonN7Hm9/td7Iv1va5bX+3hb6OATqrzTnKhIFiL1gJjSFCx4fO4ffr02r4xuCc/boXaEv7F89TP/BjGeVmLeBcLsqEMdxXuME5p4M2sR1Q//RIjy4PdsmacPOBLcsJnZtMOTRu0Eh9fqOpQY2ItWTHJWo55acoJpZnTAFmu9yNcnZnaxD2dz7lsQJIqI/nffhKjYZjYgizWcBxCaiskw9Ap6B9GsD2bqDch9GOlmVTtlCTawWCa0A9LSkd2DZjSm31B7XnRYCniTAVLlqlLOz5SGye3PwSmpA+nTy+SxePHdeGRet104INqjp7WkdRhEsrTNKqZSz0oT5HYudMATU2Y6ecPnVCHeTzjTes0da1txNeeT7hYYf0Rsth7Tm9R81NzdQBVKj7UMxOKNSty2/RplUblYENM0J9f/XMLu08jN3C4o07brpXG1feqhRUeaenwtWFiuWOo0/pjYuvMj5I1APrf1WrKm5Ry0yrXjr7gg4fek15wIH3rduq21bcQSB3FiNYDQmoZcdr/frmtw+ro3VUG1Yv0c3rUfkuiVJSajiKa7OwW3/3jF7dOU5f1AHA0qiNwHWLlmYoMzdVySmE1E4KI/SrQ+G6zHLOnYP6ytf3qL1zQA/cu0mPPFSkklKALKpwY/00fWC7Xtz5sm5aO08P37dCqYSm/vYPyAsUosoqpfseLEbBLpHvCVXMY9u9e0LfwPdwlcUCH/mVAhRYc+gHY4DNR/SF//sA0GKrbrtpqf7tby9RUUV0sJAjGrtnGOju2OFRfe1rl0hLtG5Yma31m1CyKiLNacB1yai0kaYxgJzngWxefbWGLEkG2CnTkhVxSs+eDGy5lIx4FAfDFU24W49Hjr85RZSXQ6rrTNfD28pgFgjfXIkCImbw+QsTPOsh3bMFqGaiUS88v13bd2wPxrNBgeUfF91w2voAiqOMG2BzPXL98n/UfmxB/+26SgbwHkY746GV//C+BucMy8W6D6Scx2IHx1IvYrFVoilz/s52UCSfg7YgOLcfueuNX8HJgjJgIM+Qnr/z7+4LQ9u1vfieX52O69Lpv0MAno8NQsTy5ewlfI257f2SA6G2PyhL124qKK88b8/X1GL7ui+6kbkVKyQbnHOf57Iwicpobe2Uvv74ZR0/1aj8vBTdtL4YNdYkoHXqIgBuPHU9DDK+6tSk/r+/PYaibrJWU19vujkBcI4wq+yTnBLBoiQUS2NQb8c+a62PR02yQbteOaWS4mSU7paxOMaqoJTFsXCU6wa0ayfj3vBWfivQ4mXJOrBvlPDJdQC6U3rwoWLd+1CCMgzmUZatRvetrzcj7NJDAIJI/f7/lYe6XYpaG2b04vYJff1b3yftkXr0VzZo2+1FjBlQvesZ0POed2NcX45K+wcCxblr4JwNDfo012jXOVqz4G8q3bUcfPtv7ztwzobIeSYuPvnJfxcM2j70oV/R5z//+befM7/kIzzI9OBtEBnsP/zDT+m3fus3A0nfnp7ZQeO+ffuDgahlRR3myHLkHjT+1I1KZZlRD3BtkFka2CGsbAQ6dJjDsnoQ6BUOd999l/7Nv/k3ATA358T6qbn5tr6cA+feVnb9wnd2p+N64QmeAwcOBLDCyy/vxuCJViWT+HZ8eOLGEzhz29vNgTmj7e3m2C9if+ZWdPz0tD70eyNqaJfKs8L0d38Wo1tuskExt72bc6Cnb0bffBrFuS+OBxOLj94Wqc/952gV587VtXfiuXmCy+ovDivpyao/+ZM/ITz3zcGp3VeEQrAaxnKIVU+cfe5znwsm8B2q1XaYbSnDQj7Wdpy/N5zlbefOnYHCmZ3YDiHqyX5P4v/VX/1VMMDxZJz7Gq+it33mQY/tsY985CPBZF1wkrl/3lYO2PlvxTO/7Px46+ZVvX5GdvjMqc69NXf+ZX97MP/Xf/3X+pu/+ZvAKfXWs1nJ9zOf+UxgZ82p+L41d+b+fj/mAOI1WrgFZeuBGa1iYvxbfx+jReWetH0/3u2/nnuy7eDwTQ4Lbke7nf22B6yG44lOv0IgXMiRZlvBznzbFVapMwxg57xV6mx/hLbQ8aG/f9q77ROfy3NgVvoxCGcnnJ3/tlfsoLNyrfs5Q8q2LXyMQQTPmRlY8JyPYXL/5v1PozLmcHgeH4fUe2yfON3efB9TXK+16py++9/+UHVnT+H8zNGmzVu1/OFHlLx0iSZRzQrHCRvW1aORXTuDcKvDHLN48xbFjgyoowpFrz6ULPIKCcFWgHpUshLKcxWD0p1Ss1HnSlD/nv0affZ5RRBWLf62jYq7A0grt5jJUOAog1F4MwJwjrBQkzjrmra/gOPwTZUvX6HFH/yQolC7mcG5jAcj8FeEBw48lMtGAYAa63T1e08rCZWxpBVrFH3PQ5oAnIuIigjCKXOTzDoTeGq4S7VPPqWBl19RDs7v1HsfVfhNqOQBO4UTUmm6o1n9L72ioVf2KS4KFcnFKzVI2NKqy+cDSC8FIDA+gxBMOVmABMWErpuP0FUG4BxqeQcP6sSzz2gSNYSFt29VutXbcJKHxc46mIlPxy0CVOFc7686r8s792jw1DktxMOVfOc9pGNdcCzz0BbrQ2WDaWE7WAzOnTmrseee1vk6VOTuROli6xZAMZR1onDa4dgK4/5mxlA/rG/VxCuvq/PA6+ouImTYvbcrZdkCID+eH0TRVM1lje16VWd27VFqaYVylt2gQULCnT97SPFcLw1FmvikQsURsi6+KF+xAF4O6zrDUvbe0yf15jNPKgNlwMrNmxS/5RZFFqP4hfPe/v5pqLbwcMoUg9OxN8+p9fGvKaaVhbw3LFfSQ/cpqryEGwPgmo4Jyl2YvVdTwBmTQxomf68yH1p76LgWr1oDVLZNUfNLgSCAyqaBolCqcNw/h4abxtk+9MpOdex4VrHcf9o2ALT1GxWemQS8QeYBRI3v3auhZ19QT/+w0u95kNCzsWo6WYVyUQdOPECrYsJ05hFqCed8ZlapolLIS9zNnafO6sq+vai3Nat0JU7eWwDnKisknMjT9lJDBYZPo+Y4TThf6lPnky+oZRfO1OJypTzwsKI2rJcIgcaM1Ow9zgDbAnuEEQptEkCjdudzwG3nlZ2fpbK7HlDCvBs0hRPbagJ2qFE4yMgWjXdVqR2Y0UqQGYUlSiwBUI1k/oosm5kkXFkz9bnxCk6LGWWXEx42AXCz7aIaajuUnLMalTfSnUp+U+/sWp+ewrloh0ME7sQwoDngvJlBwLjmKpSc2lBVImwbakwJ2YsIfQywCMwyM0hI45aDqC2izAh0k1VCKNakCpz0wDfAnxo6paGLz6DWNEw5uVFJhFmLAB6fBpBprnoTJ/WockqKlVS0gqwF4kMdz2pYk51V6qs5pMHuGsoaYSRL1xCW+DaeXSXh0qwS16yxxhMaqn6D4k3IvrIK0pSr5npUm1DSyc0mhFDpcpQWF5HuXMp/EvWKwksVN4cZ0C6EiZsauqKB1gvqa6tVfNQkUCDhOa0cl2RFKvKBsGw9V4F52i8rE6d1XMUqytBSwhqimkhbMd3ToPGOFtK2SjHpQEf2Vr8PNrf37jP88mf3Z/7sRUkGvw2AGM52X2GFU//uPsmL9PdSrxxC1fOqIXDu8ccfD8ZkBu38nZWb3B95nOyFYz7WfajP6fGBz+Mxtf0WHstZHc5RQDyONnTnfsvwia/j/s/H2YGYhUKS+1CPBZ0OQ+VWyrsPwQH3t76mxyjuL7ds2RLAdPanePxicM4wvPtQA3pWsLtesTWUH368/5x++l1dDGZ6NNN3BFD4iAbaegkvV6jkihWA0LRjhFx1HdTweY1VEz6x7jTqrt2EWCZ8KdBcZMkWhaXO10S4QZJRRfYfVe+FF8lDoRaFqmQeinPAtJN1u2lLCGc3H+A7ZxvqUNkaw+6wOptriUM5Rs2MAL5Rn8cJHQ+4OtgM/A14Fh2fo4SceYrKBGyPyqU9IkpM426NNrwUhBRNqtissOwNNPdZtFwAzeOtNInPa7D2ZQ0AZmRUPoTS3Z1chRCrza+r9cpF0Skrs3wFQnnzae7y6TijgfAIg0r46tONKHZFAxBVrNXSVEJXEiIsHu9uX0yYrk6iYtVUr46eLs0rKNSiTPoD6jk/BX3vOPWilRCDNbQ7zQN9tAGRykZ5q4yw0pncr1W/+tm3gT6xhpCaDluYwW+LM4BmaGWjSWU3ps1ZQrnWNNYqConq8jwUZFhYWdfTqrqWRtYBxBA2k5Dr5Gc07Xk4BsAIYMkAcNkAYSkLEtNURljYEdqsi1bjJGRlZUqeVhKKfJq6uQ97tZG8Ju4memM0gKiBLuX31ai1jtBOH+luVwNqdIWAIffQBubYXrB3m22UTqcd5bsLhG+/CnA1mhSpIqC4xcDkBIhjfhKVG/KgAUD9uPMJX+WColItQ6ms0N0hxxOlTQ3c3/76KnVDJOYRtj4nKoN62KwmACqDczncXwoO+MRR2hwyd4jQtH1AVyMjg/RdiSpByZRI90SZr4FUnNL8zDJVpKUS/nVAdRcuBv4dzzNcD84FN3DdP67DbmvcXoXaNP/s+uzN9q9BNMPBbmM8J+gFrRYWsS0cagP87uO9hdqCwEbm3P7bv4W+D10zZLO7rfHCFbdBVsV0O2Zgz+CcFZ9DIaJ9bh8TOp//9rn8d2gLwDmAsktdnRpevEC9PKcSbMu12Nc5qBlGAjTaZvBj6KGMXu3tUDUw/2hkBMpwmVrI88/HhkX8lmvxDFGcO9LaopbpSRWg/rcyPRPlOtRiOd5XrcanfJiy0skzKc/K1rx4wDme+wls6yF8aWnYuamcL4IyIGAL5CpRUhtX10A3CoqxKsm24lyGWtvaWV/TjS8bFcW0STXWvk54vqdRfk7SgrWbGA4UEbZzBlgMdcv4YZ09t0cn97+O4uMyLcdWOHniTZ27fFKJ2XEqL8L2mUK9EqhtBkmlQfLXaslTjIduWL5Sm2+4VfNzlxDGlrIzUa8XT2/XG4cOqKu5HQgtTZuWbtFdq+7SAqDg+IhY6lOXdhx/Vi+/uSMArh7Z8iGtX7BZceHYEJR3NAQB5J7T3gsvYYpF6P41H9bq0s2EMW7RS1XbdfiN17QQ9dwP3XyvbigC6phmPEKj5+HL8UuT2v7SJR09dA67NF2lxdmEUCSkcU60CrA3C4oTUPcL14WTU9rzEiEUCTGdSP7kFWYqp4DFJfkpqgC0K8oLUzILBiIYBOx4aVBf/eZuDQyO6IOPbtID9wH2Aqf4eTU3TekpwLnnX9hNXpTooXtWKiUxXt/4/rBOX2rT2g3Ruv+RXM0DgIujEBCBUcePTejb35vUyTNH9Mj9Rbr/gULAOUK4XhjW//MX+9TbNai7t96gj/4ai4QKqRPQduEs/mBopJqLKNw916KjJ3pQt5pUaXmUcvIBKrNSWGCWinofAFo8gN2Rbr2y5yLj6AHawjzCvMaSB9ipOXGatyibhVCxpJMbYPx2/PiUPvM5bMHRHH3w/mI99GgM+RSBctYMfMOkzp4e0e03Y5tF9zKGPa7DRw4TcrafejJbP23HzdZV58jsZmDNim0O42rEJlSnXIdtPzssq/cOWkDXddrx6BgU/qgLs6qQwVG09bN2kWuH7fNgf87hc//D5u9n/w61C/4rCBd73fWdBu/nQ4MqHryTilCyfRCfg9+Dk1/7wt/9Q2pnd7p2vWC3n/jHJ/SxP7nNXuof0us9fKeh9P/0I2e/DdLM/tffeujY0PtPXnH2m5/9+4+n+Pp0BPl23UXf+vc/ds136/du40NbqFz6b38f6kvcXxi4tgqq4bnQAg/vN4F90AN8u+PFVr2y/wI276CyMlEOZYFSFuPaosI4FZdGKheluO6OaYC3akKfUl9ZwVAIiJyZSRh5XgV8Lq9EFTUP1f6oaRZzRRCqFaXIA2e1dFEOvMRiLQO4jUfde2o8TNt/OE6/6VDl9frAB7A9VqcQjnlET3yvHsW6ZOz7TN1yVzTwnhFtbJ7OaT31RL+eeb4fCHVMv/vvMrR5a+osOPfCuB7/zg8I0ZqkD390rW7amItfLBLRo0698PRzLEJ+QeUlRXr0oXsAiw3OeZ7KpYL2KCivrr/+LzQe829vf3vfgXNeyWsA5g/+4L8Gq5M+9rHfCMCyt581v7wj3EifYcLyNz72MaTN6ylYDyNnuCpY6XD69CkGf7OSvl71YAPuf/7Pv/jR6mRPmnoic8IvVmc5PwwGeUBrWO706TOQ2KfU0NgYQHS+lge4nlDdwmSWJ3ld2dwJXF85f3m58d6+8hw49+58fi737mSsMmfngCdGPDDq6urGcRCjbdu2BZM/BiM8OPKqgbnt7ebAz9cpvd2rzO3/z88BGyb9OGy//veT+g//i1ALjCEcLuxLfxuj/My55/XPz8lfzp4DQzN6ZteU/ugvxtWI+tyWdRH6m8/GaEHZ3LN7J56IByBW+3V4T0/Ef/zjHw8GIT63f3Oome9+97tBSBfDbJ6Q86p5T6x55brtMdtOtbW1wX6eAPP3nnD3Zie0V8N7cGOlLfcvDtX2HDLyVn3xBL5Xqdl284p390Oe3PdE/Zw9FmTh2/7HNrGf6Z/92Z8FSgbu96/fPBFqRVmDkA7DE5r4vH6fuc8/Xw64TBsQdf67TF+/OSyrnWVe+OOV1SEY4/p95j7P5cD7LQccEmHlXcO62DSjotgw/dFno/QbD0U5mt/c9h7OAY8p7WR/4oknAjWbO4GUvPjRCxJDE5u2DULtnL9zX+N32wt2yhm6u+222/TYY4/hOCkIbIlQlvys/t/nCb28ONJOOPd1Bgxss9gRZ2efHf+GF2xneHO7bEjO7+4LnT5fy3+/+eabwUICz6lt3bpVH/3oR38E3YXS4/mmxjOn9N3P/qHqz50hdFG+Nm65Tcvuf1ApTODOME8VxOzo6dTovldUtf8AoYVGtByltKT0ZNShqtVcXQfUBFiEclc4K55T8lCgW7xYMSuseJatvr37NLl9R6BMEH3HJsXcvgZVunycegZfWFFtSsxgGYpik4Tba3lxh06jPlGyZrUWPfoBRRZWomyThKMQCAhTOYL90SbAh9el6forqn7ih0qpaVDqsjWKuvNBTc1D/RSnrR2HZCrOPmCY4Q5d4fkM7HlN+Th4U+56WBFrsfdSCUc6w7n62jS4+zUNoQaHz0jxGzZrphS1q0ZUaa5cQgkM6ADHdBxKLGmEdky/Yb2SFjoEFEjV0QN6c8fzqO2lagEgSMrqDXwGnLQiG2O0aRzhkbzw/KinClBs1ysaPntJCyrmK2XbPYpcu4YQeTFAk5QpTwtzjN1D00BXEzybsed/qEvAV/l3bCOU6S0KB8qcRhlhBieS12JrBLd5A+o8QH8t9NVDgGr5d20DzipVNOCj82sS23f8lVd1jFCmCQXFKtt4WxAOtunyWbVfvayZHu5tFGcrih0J3Ed6RakSly4GnksBoKjSm9ufVwbOqvm3bVO87WSH8AVOtOLDNA62COA2OSQT6nsdX/87JaKQE3cjIOMDdyqS9MwQ+m0qDOcr9xWGY0lWNZoa1silKtXu3avGYye1YPlqFdxxqyIXlKEwCKQ1DYgWmB04efwsARj692xX646ncI6ipsGzjr4RCDMtSZM45A3OTR08oIGnnlYjoGfeBz6gpLIF6ieMafeZagQKgT+nh1BCdCixZByE5UqbRxjJgiLuEfjs8AGgqm4VoKqXvmETMAnpIOSvV/EHbmqAEuTKUCRrVf0PnlXLq/s1bzFqH/c+qKjVQGKJVukjweRH2CT1c4IJfxT7ploaVL/7OTVzr9l56Sq64x7FLlitEdIQgUMVvI0yN8glAMfaz6uj+gyZFKaMgjIikq7ldIWc1yWjXqOtKJs01qBKM63sMkKdxhGituWSmgjplZR/g1JKtgJLlgK3JFIyaJ9Qe7EaRjhOkpmIFsKkEr4UaG668RL+b66djppc1g2IPwJ6AjfaKT4zhJJTKyokjYQ/zpmvjPK1iiUk4wzqbmHThMUaOovi3HbmIgidlX8j11yO6CAOy46L6rh4Sv2ou6QCzqWXYI/HLOGc1HPq6hThX3uvvKHR3mrFx00RXnE5YYe3UdkW4MREcW4KcLDlJODcYUVFDCuec4ShntTW1KXevn5l5GZwTiC9eBTngGuspEdmAzTy7Cd5RjjzZwavaLiDefa2etoXwjhmF6BGVa6IJOojaj2B8iNlsw9wbrCrltDDwDSo/ykVcE4pijJk29+kye4WzWQsJ5xsBY/i/dG5h/qXELjh/syb51CfeuqpIFpHaWlp0IctJAywYTf3J55n3Ycq5Ne+9jX5e4Ph/s1jZ/dP9j94PO1xrvsih2H91re+FaihGoz7APXQY2aPnz1Xa9Db+7o/c1gqz9F6vGz1J6vGOcqQ53MdinXTpk3BWN5pNtjuc9sX4j7QUJ3H4E6DQ7X63cC7x9w+l/tQh2/1Ijb/ZsjOxxjCCfWT7gdD+eLPoX4xyJj31D/UgRkryh1Ux3lA+44B8rRYyeUrFZ5GGUYJDPlF6jbqYDUHCJ16SpHDwPaJqYouXq/I4luhp0s15lCrQLwxg6fUW7VbQ4MzALn0dYULNdl2QjO1uwOAPXLeFvqAzZqIBuyiHoIU496cBHcbU4x6qO91muq/qn7CZw92WdUySwlZqxWdtgSgnLRE0O9HYOM07CHU+S7CeIYrtmKTpnJpd8NRdp1G9WisHXG5HRpp2EVbM6m0svsVk89czEyjJpr3qb2+mnDhmcoAvInJWEAbWcwrSYOEpG2iHT1V36SeGELtVSzTopQSlQMRx9NM9GMY1I0DjbU0qbu/V+XYaguAiTKp564Rk7x66J8uY4/U9Heg7ikVoVq1gDCm+YBC0UFnhOIanXQdbV015xkbnyDMYpYWEeY+l07fHoc+bIPzE4O6SDs6zoqbUoD7uJRU1dH+GJyLjYhBDStFMbRTsdhONNHuMjRKXzY+Na58wsIWpmSqh3btXA/XmJwiZGe+VsQCf5Pfr9ZUqy1iUlEsVhgF6nZfsxRFuhvSAOewZ48C/dWN9CkPePx2YKUMwEDbFdwOKmVjqhns1pV2YCTKfVZWpkoAroqAjJLdlLLXGH1uA/X5FPfXgx01v6hUi2LjVEgGWSXVMzENAG4HGy+oG0A5F2WyLNLW1tKqJpT8xrB/0rm/1BGAIINzOPHH6RbHZlhIMMHzTExAzawQDdRx1dRfZn1BpCqzS1XsEIkjQ2qk7hpI8zyd/Zq2b22nh+poqL66/l7fpvnv0H5+t0ql7fMLKNi5fbDqZCnt3E+bK/L+3nxut4/Xtw2h7/0eukbouqGxgNPr9tJtnO1v2+FuM92OGeb1PXhOy1uoDfI5fHwoPRMoubqtrAYoGAGc66fPLQvAuXzlELo+HKCB4kIo3EldGmpTbTtBhAErMlmoUpKaqWJAyTSfj2tM8n0j4N0RQuq28VQNzi1PTqFMAM65yeB1aXhQh/s61DrSDxiarnnRSYBz/Sg2NmsYNckUniE9oyKx9Sih2COoFVECJ8irOICt3LRsJYbFq6G5hXCeKAFnAkumTanu6kFtf+4Hik/J0or1W5WYnkuI1zH68VzCMo/oXNUeHXttrxaj+FpZsAi46yihSS8oITNORfQPCTMJCNyB4lJuxyn7YcCnGdi9ywnhvpyFB/kAuJMsCGidatWuMzu0Z99Larhai42Vq7tvflB3rWTuNbGQcUAkCnqt2n70h3r55E7GTtF69JaPaN28Tdjz2GBhgHka0G5A4b2XXtIUC9wfWP0hrS3ZpMbpNu2sehHFuf1aDvT7kZsf0OJcFlNNYX8D9U1jgrTSRp4930dI0xpdrenFN8/zBdKNpB7nl6Rq5Q2ov6EuFzYarvMnBwi1eFV1AH5D/D0dngOAksJYIAG1ukQtWxJN3wo49/KIvvrtl1mEADj3gU269648pRHK1Q+stWWacIiden77a1qxKEMP3oNyIqF0H//7XlVdadWmLcm675E8lQDAxVBPw4AnT52a0nd/MKLjJw7rgbsq9CCwWk4O6bk4qL/4X68RwnVC9962Bl4hjzCtjCGjXDhoABiT9AHsnDs7rIOHULispUygYgl2Q3lNRT2qXDeuTgCoiULMclxnT3bqxNFWIPkJIMlJykkY9xelxUuztP6mPC2eH8dQh3CsQIR/9IWDlKFMfeCBUt15TwwgIQA216uuAZw7M6bbbgbQy5xSi0MJYxe4njgMKrWT68/CpkGdYTzhcuzN78Fv7EMV5l8exbUfA+iOz7O7+njqCHU8tN+PTsBBs9Dajx8f/H79P9eu5a9mw7D6+m57rqXt2olD4Vpn6/dsukKnCe3vVBn8C7UBQbvjewh2nL2jH0/V7H3M/h76JZTqIEWhS/DuO3aezd7PP7zPfprNTf8WOpvvw0eRJmc322wW+ndfw4AiYGUIYgy+9bn+4RyzKb72nNg/9HewE//M/j17hP+dTQl5N3uhH+VD8BvfuT0O8uTa5+BI5+97ZHPa/7EtdF9+97yOfVfmeiwEECoPrhfgQKq6NKBDR+t1vopFAR0O653CPijXp8Zr4YJorbkxjjoZxZzUgE682aQrNa34paiF2HVRLKZKZ2y/FNW4DZtR/syLUFfrtJ74Tj3g3DlCQRdiqy/AZxaL0vg4ao/hevGZWaW40fEmPfoB1G5vSNLe3YN64vt1KBpTdz+YqZu20pegZuetj2ttf3pcTz4zTPno1+9+MkWbb8UmaCRc9Asozn33eS1dkqEP//pKrVmXokRW7XWjTvv8My9ox3M7VVZaokceBpz7UahWThqo33qubbbu/Esf+/sOnPNEoeXAP/e5zwVGhR2fv/M7vxM8kHfzPy7wY6yyGyUGtw0Or276wz/8dDBwdAVw+C4bUyYrx5h5t3HmVVa/+ZuPBROh/tsriz3Qs/PV+3p1ll+tdBqdnR38NhCozXlgGocRVVpaphUrlgcrGmyYWdbRSihz2zuXA3Pg3DuXl+/ImahnI4Ck3dSPCwyq7Nj1AMPhaiKYwZ43rzKAHExrO5xNiuvDv7SVfUcS/l48yXvHKHkv5u7Pk2bPD19tmNZHf29Mh6ummXQJ07/9lSj9j095Anlue7fnAH43vX5kSp//83HtvzCtNcvC9fXPx2rp4rm69k49O9tPBuQ8QWV1FS9QCG2e1PLEvG2oQhRRPDBx3+EFCnZ0e/LeA7TQflb49X6hkPde5W5QzhNqtrds13nz94bsamtrg8+2wzyxX1paGjgYQtefe//5csDP9Bvf+EagKuB8fuvm5/a7v/u7KFV/MnDIvPX3ub/ffg64Dlnt2w41l++3bitXrtSnP/3pQBXi+jr21v3m/p7LgfdTwDpUhwAAQABJREFUDuAf0wMfH9Vu+nGrU3z8I5H6i08R0mPOBHtPP2ZPytvJbkjtIApihrHtZLdShDc7u0LOtNAkrt+9OeScVXu+853vBNDBb/3WbwX9kG2Qt7OFJldDzjsf6/7Oaqt2xBk0eOihhwKYzxOsnlQNpcHH2o7xHJrvxe8OS2V1H4MRW7duDQAGOyHtsPPm461sVUcIpG995g/VCiBVDJS1DhWv5fc9oLTlS4NQlZYdmOnp0OBre1RFiNGh8SmtAKxLm1+Kc7xHA3VtOMD7cKz3aqoDlRiUwSIL8pR0y61KWrpcQ8ePaWrHjgBsiSNMa9xd6wDLcpmYNTiHMhfOHeLD4fjt1STwYgsQ2punjqtoFfNbH/ogjv1FmsGZZ1zO4JzFzZCb0+Rou8Ka63Tp2z8AnKtX+vIbFXX3QwoDSHOoVqtyzHoAkE0Y69GVJ3+ggZdextGXo1TCeEZarQwJhDBAgRlUO/oB5/ziSJTg7lTsutUaB8bqv4wDlxBR4+29eMlQ0ekf0XhBqfJvBuogtMh01XEdQwVtmrA/i1lkkXbjJoDBfNJMqFOgq0nCe8aEE152iDm8SzVBqNZeQLH5pZVKuwNFNgARh3U1T+YbhE+zB4JbHCX06VmNPveUqmtrlH/bVmXfSpjbgnwU6uLYFZCTXcNxQk818Qz27lc9kMhAPk6zu1CcW0RIXyvOOVQrDsXxPTgqgefiiys17y5C1RJKdRy1mR7AudH6boW1j2iqvYtQV11MnMcqffVKJaxdjWJQg469sF0pwEkLbwXI2+Q05ADDMZltZyr1IAiLhGLO2NET6v7G1xXb2aKYNasU+whgYFlx8PwmCW3rZxLGQDbcBDKhWkcunlEtSo2NKAwuWLZKeXdsUdSCUpTeUEgStvsEKANlm/8pH0TcAJxr2f6kkpMSlIZyW+wayhLg3BQO+RlUjab2H1DvD59VE/OlhSy8zli1FqGlYZTtmjWCrT/S1aIJ1HAGCL8XieJPwYIlSt64WUMd7ao/uE9j/FaIbZO5EYCjrBzIA+Uj13PUWoQDWoAKk91danzyadWjXjhv3iJl3He/oghrHJYEOIdiEIQafBbHBeAc870oEtXtfl7tNeeUl5tOyN27FAVwN8w4IczgHA7HaA0D2xEureW82i6dCfIpo6gsUHRTRGFQHsJmgN5aDqmjqSZwauSQr/FxpKflipoaeglzuBqI7RZFJZfhEAScw9EwY0lAnC5hESOwZYQn7SZUY+NlJRAuMCk5X5GoSYWlAP1FOb/dnljJ8ZwmWt9QzdUaxWahOFexjseBwiBITBj1TgPnNHB+l/pwtsbmrwWSWy4EaVCcq1Y30F/nQK+SilE0KbsJRauV5BsA5HgH4NwZdQPtjPZUK5FQQUn5ywhpfCf5tph8ABzEOTNGiMWhK0cBOEcVh6pheEI6oCBldGAQlcAcZQLjRcYvDMA5Mpo0A2HQdoRNjmh6tAelvLOEdb0AsIB6A+BQYg6qi2ksXCL8IJlAIgFMAnDujIa6r+BwIsQRilVKWwlAQqjWKZSyCGM70dksZRL6GXCOzOQ674/NfUvggGYMG3LK2d/w+uuvB0puVkzy2NbjVyu9uZ+x/e8+xIqqXpzkBTNeOPPtb387CFFoxTlD2QbSPHY2gG5wzsCKQRVDdQZIrAb/AqoSFgOwz8IL/UMwia9ndSaLBHz1q18N+mCP65wWQ3fu2wy/WXXO42svStuyZUsA1HjcbdV395XefzHQtsf2Hjd6vO4xfllZWQDUuQ8N+Ul8/36F+l0/4VB/+t572tSF6U5Uu1BiMzjXDmiaVkBoY8C59AWU/0zqIYp0g6hJXj4IjHZWEagwRSSk0b/eCDi3ScooQv0LIGRmUNEDp9V/9lWACtTCcm+iPi+hbwiBcyj0VNAH5G7UREw+CHv0NXBumBClHIty5iTtzCggbT/PwCGrM3KWAemuIwRgOfUJuwh4OSwCFUrAuZkrACJA19GVpKEANdNIgooC1HMCTbfvIMzryxoamQGQe0AxebfxaAhV3fiq2uouEgo6lfYJSD9jIef1uVNRp+pWY+t5ncQu6wG4T6lYqoXpZSoD/kmgTeynOtePjulic2MQXr28oFDzcFZnYF+4RekDVKvv61E9fWMvkFBKUqrmAf4tIG9SKC/eyUxJDx+votJVRf84MQ44hpLt4ox05QDCOZymwbrzhIm/2FirEeqQwblEwLomzlvLMfGAvA5laigpHtjaoqlwQii18c5FMqNilBYdq2bCR5/satAQdWheeqFWxVtxLkz7Ll9Rq9u/5HjAqRGFA6osT83VDdzLMO3+oa4OwLl+QkzG69bcPKXQ/nEZDaK2VwcgfrWvU4PUqxzgvJI0QnLTiKcBQ8TSP9NKEjZ2RvXYLOeArnqwE0sA45YmJKuYnsDZMMR+dcN9OtpwUQNAQnnAeTlxKepq66D/xU6KJ5w40FQenXMq9zbFOUeRv5rmvDF06MnYRzGAXC20uRcbr2AOzKBeVqYSQt6mAc41XbwQtCdevOK2JQT6um66zrqu+v36Ohz6zvv4s9szK0jvwfbxPJ7VNA3kuk1xO+jjQ+f15+ttb3/vc4Q2/+5X6Lvr9w+dw7/5HFaAdvtnX7DbRLebK1as+BEA6P2cbr87HaG/fa0QOHcRv+/Q4vkaxH6bB8C/Ni9f2SjOTdOf93ON2rFeXRjClhkeUhbKiyWpOcoljHA6zxDXQGBHTgHONVCeD2OXN09PKS8hSctS05VPn4ZIWLBVc/zh/jZ1UM7KAe/mxySotx/FauyWCWza3IxsZbOgIjbY36FaXTqB/fG5xUaz0AJQz0qGVxqa1Dc0rIwsFOrSKRv1h/Tsk3+vZOzt1RtuJ0RgDgD8CCHocwkxP6aqc7v1xisva2H5Ui0AnjtOXl3FtsmvyNWKpUso+4Z9AbK4Hy/OSMDazYmOVyFlPIOQvvExmeBuoyjuVWv3sR1E4iFEdX8nfVasblp+q+5Y84AqsuehKhelgYkOQrE+rd0ndqJmG6WHNn9Q6ys3KyEihbwHnKPN23nuBb12/mXsG+nhNb+iDaWb1UxY6B3ndqE497qWZJbrV266T8tKsPUYt8x4VQwLRqwI2U1I0+aGIV2+2Mt4cIiQhSxIYqwwNN6r4oosbduyRKvmcwe0cw2NwLZX+1XbNMbihEgguhHuZUyb12YCyGXSV0Vr56tjQCa7ActGgNlu1p3bUMS0qhN1rq15msXaXXr2hf1aOj8ZcI5+FGj3W0906syleq1dn677CKFYWUG7AqwKKALEOaVvf3+IMnlED989j/CvqEMCzl2sGdBffPFV1IPHdC8Lmh4mxGsmQE0YiwJ5xB6GoDwFPAdMeLV2RJev9ABIDgDtWKGdNmA4U5XlM7r3viQtXBpJyFZa56vDqq8dIh9GUSGcUXt3P+3lkDZsKNMdW3NVTJjXqiqDc/sAhLP1wYcrdM+90coHnBvnWjUG586OauvGGBUWRGockHR8gvECqfE4w2niloLEud74j+BvvprdZn/0ft6CYsv7tV2D7370z4/tFNrz2s7e6bqvgmNC+//Yb+zkk4c2J4a/Q9/4zwAUu36f0L68ux3xFux/7bjZ70Jn8K+z+8zehP/2Nnud0Mcf2yf48to/QQI4l0937ZTBWPPaV97LZ78+eTQvfEnbytj8WvK8W/DZYzi3cQYP+f9Hlw12uP6f4KTXf/FTPl9LT+j2DOR5c9v41s154pefefDc37rDe/Tv0PN38n3f7ks8dxS6R78bvvRCoP6RKTXSXtRe6Vd93Yhamwin3hSvptYR7NoBbb4lU7dtzVBKahTA6ZCuXMIGpt52d9IXdkbwGue3YUI5lwDZpQrzRN//XoMOvGFwLl8ffGSBFi2JA2xmfmU8Qi+/QKSl5yc0zNjskUeztWxVkva9MqDvP1kLX5FGyNUc3bKNPiCZNJL+AcC5H35/TD98blQxUUP6vd9N0sbNSWoHnNu1Y0Jf++5O7PwcfeQ3lmr1mgTGEQDg3a16/ukdgHMvq5TFag9bcW7jOsBjDCHu24UsGMteK+5BOQ2Vm5/jmb/vwDkPqLwy4Atf+OMg5MTv/M5vA5f95s+RNb+4Q9yAeJLSg0WvaPC7ZcZffXVvMOD0gPGtmyvGokULg8lM06UeFNq4MjxnZ61DvHrQ5wHr0BCrZblGGisBvOqisLBAZWVlWrRwkZYtXxY4dt/uBO1b0zP390/PgTlw7qfnyy/6W5d/16Nh6sZ56pgHIwbmzpw5C1DaR30oDwYmG1kJvXHTxmC14U/reH/R6X5vX+9f0DO9t2/8XZt6/CN6/fCU7v5tVrFjpdxQGK7/8d+jte1mZlvmtnd9DviZXbg0rT/9IrLpr07qhkXh+urnYrRi1U8OEt71NzOXwJ/IATvXbcvZMT2ncvoT2fNzf+H+3xDAn//5nwfOF9vJ128eWDocxqc+9algYtTh6ua2nz8HPAZ5/PHHA1DR6g7O/+s35+8nPvEJ/ft//++D1eBzttb1uTP3+f2cA5M4gP7LH4/rb5+YEJyIPnRrpL7514BzlpiY296zOWDnlcE5wwBW17E6jUPFec7F7VtoEtM3GHKS+RhvdujbOeew7XbMecGnF0yGFCWCnX7GP548dTs7O0k62976/IYCvvzlLwfhYO30d+g7OxJ9bqcr1Pba9jAk4PbaCzA9d2SVOgPQBput/GNFXYMKPjY0Ae3QMrUnT+jxz/43dVZfUgnOxfVLVmjpPfcqZeUyoCQWn6EGN82iz66dL+rCm6c0g/N0OelIAZwLXLyDwEG9qFB0Elq05rJ639ivrqkJJQIk5W69TVNXajUMMNHb2aS02zco/cGbAXYKgHo49yR9NQ5dT+jPALtM1tUG4NyRgyjDVaDUQGja6HmkIw6wJyIaB4wnzT2BzxhoEhCAsGB13/qeEi9dJrQsijb3PajIink4tKiQ05w3mNhnDg5Iq+npH2rw2eeUimMt5e77FQ3wYXAOOTpCdDap7eX96nv9OBPCOG7vuUMxG1aRNBy9fSjfoHY23c1kcnWzOk6c1XlCpRRv3KLyDWslVOlOPP8kodT6tfimdcq95XaAINzLhOKcBhCwGzqCdyYxNFQLyIGqXefeA6pEuS71rjsVfcvNgHOofaGI4/BPgb8Bh9cMqjgGCQcIA3yp+ryKb9mknC1AeUXAejhSpwhdG4FT8P9n7z3A47zOO993+gw6Bp3oAHsnRVEiqWrJsmx1Wa5x4pSbOI+zu4lTnjjZzd3N9TpObK/ju5s4m9w41UnWVhxbsopVSIkqpNh7AwGCIHrvwPS5v/8Bh+HDOE4s27Il4ZOGmPKV853ynvOd93f+rxTy0gOE6np5D2p2z9gIIWKX3wlUsnm9+VCj0XWTZ9os+fwLdubAIZTymDd8z30WXtZMvgMszk2jVES42nHU2Khv4/sIoQRUEGklRNoH32tJFgQf/voTFhwet2VbtlkhZepvbcS5BrZH/lAa5CH/8jZ5us2mvvpVwgGeMd9KwMD33kP5tXB/xahzKK8X7tEjhTBgp/j5k9b10gt28cA+FOfWWt07ASeWUa8IUwe1yf7UD2UIE+iZ6VmbfeUFFOf+CfWTlFXcfqdFrgecqwBAy2PiHlWyuWdetKmnXyCMnteqf+bnqMMbqFcoXzCmyTKnmgUKTHNvwydP2WD7BZz9DVb50Hstlk2Rjl021d5mDSjoVr0DgA/w0IOiimQDszL8ap8BnLhzMRt9/Am7CExYjrJVtasrqCjSVjyKswsIkU0CQuL0zaI+kkaB5+LOpwHnUHSsLLWqd73T/GtXWZxQN1mfF9YOtUjgSm9yzOID7TbcdoxLzVsUALKg5TrCnQLO4a622HmAl/2EI+smRC5h/lqXAs6hLtlPm+mbBBLbYEUNUlEkWCDKQar8crRaCgwiMWjzqFGNDJ6CnIihBBS1PMIbKuyphZqo59Q/7Ak/UojngET3Wdu5MxYurrXqxk2WTyhfr1ehc2fJ51Oowr1kM3MZFKCus8KmTRaIALVOdtjo2eM2DLBRVotzu4V2XriZfKDcCcOanjhso217LDZ20QpwzBbWrgGwvZM0rMQ3Qh2c77FYH3N63UdQJUEVsJ50hSpsDHBuGoCjpKqCsLCAcwWAtJ5KHMIqE6kA4umOoVY33sW+bTh8pqwA9aQCwrP6CnSOYtQRUa+hTngF6E4M2cRFQsoSFre0ENW8+pXUIdJJ2ElL4eQdbUf5b8C8S261YDnQkezTW2CTzc+9dDtX9x2Cy6TmplCq8j3IpyBoTn/Vt+g7AXbbt2930JqOldK6FvcLRBFoLtBNz79ScJcfR+cSqK0+Uc9sEkXYv3+/6+fUD0m1Tn2pXoLmBOBp+8Y3vuFUrnWMnqP1e65/1HHaV32Zzq30qd/TwiqFOFf/qQVt6uPcnDHn0H3cdtttTvUpB47n+lrdx9V9u0vAm/If2nl2DM/lHus5/ZpN9Q9bFZBWSQtwanQthV2NCRnB1gPFAqbO9XZZZn4OcK4E+HWVBerWmae8nrDJ2GlCaPvGCJl8crcl5gMWWYJKbP06Sw0ftkz3LrrhkPma3kE48lsAoistQfnQbYG1jAMB97tQyfPdJwg73Yu5j1gB4TcjVQC0oeX0N8AwQDvqtDyeEUv3vGjpjucshk0Nt9wAIwzIyzm9KM5lUalMDjxlo5desvlUwKqXPUxaAG0NNTO+G2g/ZkYfVLG0xfIqV5KAVs5fZPOAad0Dp+xob5+NRKKW37rRlhKutgHVFfRDZeGA0WLW1gNYh/9gaW29tVKvikhXDLioZ27cLhK6MgZgVoDiam1RFaBRxKqA2rGS4HVkNfc7irlsBxw71UNIS/qYyspqW13Ovuwh68voxE7HJ4GBWMgZS1orYFkedqkPG9PBd0EU5xpr6q02WKSezoV/VT4KEaF3NJZxGt2a9QDnHRrtsQGA5LroElvLogMP9f4Q6e9NTMOWo+SGDzLE/msJz7seX+Ec/es+wo73zE8T8rXQbgd6y6OcZoGeBgjR24ONngIejJKextJKgKQwaUAdiuuFuXiMvxPY104WDpxi0YQU6qpRNNtUVGEtyGsjZOpU6Lo412nCrKZDPmukL61FcW5iDOgQKC8WCVpDea21AAhS6ihSIY7r0XgoS/54gCzps7HJA6lpwuZeQK1mDli3zlrKqqyKutl79ozzi0oE4epQrVePkdWOr23DshXa1P7lK9LYXAtmpYypV+5cOlb76nxXn1Pn0/f6m9v0OXdeffedbEbuXPqrsbvG4Pv27XO+KtlQLTaUQqbgXoG92q4+r86pzwLnDh48ZCcBFifWLrVZxibL8wrsBtT5ylCcmybdfahSX5zoJ4zrrBXmFVpzSQ1lSEhQ8lNnZvTrFlVwqHWjXvgqyo+XGE+WEx53DSrQNUCZUpzzAH11zEzZ4bE+IuUmbDkg54owinPAdAcudVqSBQpNNQ3WGCrQMoaF4TS1U6NULTBRDimsbzqRtvN9fXYJYDJUwfi5woda80F74tH/YwVFlbbxhttRHqtEdHnOljSg+FyUZDHQTtvzHOAc4OvGFde5Z5zTF05a05pGu3X7TdZYWMdIKkQdYZzJK8LYsRiATqFXvShLJrnTfpSk95992V47/jJjhGHOi97lyISVRGpsy/qb7fp1N1pVQRmLBmZt94nn7LkDT1uMtn/XTe+2batvtsJgOffitzHa6dOHv2V7z7xsEdSC37/tfbatcTttdcSePPa8HTx0wFYCdT607W5b17SW/iVE4bEwhrFBnGFIHHCQJoLvPkP9B24bQIXu3LS9tPc0cGrSbtu+zh66i9DL1bRVymQc0G6I/TouZGzn7gG7wMKWlU359r4HmmzzdaX2wp4U4NwuQNc5e/ChbXbnHbSJCo3nDEVHQjE+NsJrt61ZXmYP3rPBaquL7f/844DtO9JmLcvL7b4HWm3D+jxCv1Jm2K2XX07a33wVlcnzR+3DDzXZe1Cwq6r02PkLM/a5Lzxjc9Pzds87r7cH7l8KOEd+syqQJu64lRSPTwoVGY8D0zL1OjGZsSHub//+hB04mCY/L9l991bbbXcWorqJWl3KA4iXBR7lHodQuzs5Zs/vPmvNjYA5D9bbluuCPM+k7Xc//SL5Vm3vB5x74KEwClge1OxYLNaestMnJ+12gJtaQsK+LTfsAJX+yq3rOZ2mzcY/+lo/a5/cLrRB913uN/dB+1/egV3VZ+XsjYPe+KzDtKtOp8195i+Xc0fqmi4p7le+B8jO7bNgN/nhysE6weUd9Z1e+nz5Gld+0y65Y/T+qk3p0345W3jVT1fefifbe+XHt+gbZQtTKoxJDIgta3NA5tPYEIVGPX82Y7te6reunjPA2TWEdl5qK1flu2kVtcOpsYwNA7BewNbsfrnfLvYctzvvarW739OM/QvZo1/rRU3ylG3aWGUPP7iCY/MsUpjksdFru57M2FOAc7OxS/bgwxW2fnORHdhDqNavdVOERPt5oNbuuof+nTDS8m8qPX/9F2P22DPzhLb32K/9BxTnbsm34T7AuScXwLn166sB59bbps0RC6E8PjkOOPfYM/bEY88T5roVePdewLktTOFgh1RRqA+6f07vqtCVevc6y/otB85Jte3JJ5+yT33qv7uQiz//8z9vWr3747xpQKQHSq041upeDXi0AlkPkM4IfIfE60FOD3cFSBVrgKVzyJAFAnK4htyDngZW+dD+xUg71yA9q8FjbpVWDauCF2G575CxP+CvFsG5H3CGfo+n04ON2ocmUPpxTmjV/Ysv7mY14qtupWNFRSWyoqud9LaksKUwtLj9oHJAo53F7ccpByYYBP3+HyftD/6Cld7A+O/f5rc//l9BIsQsltWPUzl9t7T09Wbti19K2uceTdr6Vo/9v78RQsqY1d+LRfjdsm3xt7d5DggGePTRR+0P//AP3QTbtdkhBQSp8fzGb/yGm6C89vfFz//+HJCS72c+8xlTiD8t6Ll2kzrEpz/9aafgoHxf3BZz4O2SA+IQvvbNlP3078QJz2T24HofoQ70zL7Ygb+Z64CeM7XwUQ54Of0VflqOLilTaJPz/lqHmuZ4NImn0Kpy2stJ9/73v99+8Rd/kTmbmisOvX/PRK+edXNOwFw+al5I4ekUuu7UqVMOWlCoVs0FKT25dOmvFm8KfJCyhpyEUuZRn6l5JAF3Cj27fPlytyA1d6yO04R854nj9uX/+t+AcnqsBZhnbVmFLd1yHWpdqMTUolQDeDN3qct6X9htQ6PjVrZirTXdeguqZ8AJhK/MB6QOoYSRnUUZ4Fy7jQFIDLPgs2D7TYTefLf5Jqdt5tnn7ewxlKSWV6O4tQn1mFYL+csBkkJMBMtZHLAiQn94ceKNvfaKHd/9POJQM7byxhtQktuC8hUKbjiaYzigM+R5uCiMmhWOV0LIDvz9Vy2AIlIA0Cbv7rsswKJSTx6gHQ5bjwA62qxmXid3vWCxb/6TxVBD86EQVnzrdosQ/jHLPUy0tVvPXhzxlyasdtVaK9qy1mJLCnDOxS0vmCGsGo7TWVT9zvcTDvUgYd6GrXHH7bb0dgCCaRzhzz9FqNtDVltVZnU3bCP65EoXpjYNIDAfAzYChMtnLi87x+rvV/bZ6NPPWxH1x0eIz/x332rhaClqFCguzBJ2Fce0H+AtwFygp6vXRr/5pJ06fsTq1q8B1ttmwZYGVMKK4cpQywAIksJZZoxwvYeOWu/zz1lbb6fVolTSfP0mi9QJBkrZLLBf7OXXbHJwxIpvvJlQtDssHgG64w7DwF4B2S/UuOKAc5Moqc0AYAZQW1zysY+Sh2G78Ngumz52jjJCre/GbVa8gfDsJcxfogw2g5M1gOM9UoB7fGza5pij6Xt5t02S9ZVMgFewwDdUUe2gjNg8zuoUTlBUfAKEckv3nLfuV1+wk7ufs6XNhJB9B5AGwJ23iHOhKJIFKkCbBa8B4Nx8nNC1x22UvB5CjaaIcUj5li0o6AESMAE/y731PPmS+dp6AMiWWsmDj1i8OI/bmqQdMM1PGSINZPHBIRs+dNz6j56wJdFKq37v+yxbEbXB/a/ZEMpb+czPlm3dbHnXk+5q1X+coyhyyIGbV4UaErDi/EsoOxG+dgKHdNnmtRbdQToaUWVjPJQglFoc8iAIRBCSUiLO4q4Xd1n30X1WAWDQcMfNlr9phSUqQBUo4wBObD3/eRIzlhpHteXiaaLOdlu4kFB3TSimFQKZAtdlJ9ttvus4AOoonvGoRZcTZpUyTAxcsv7eCUIqotLSiKoTiiwO9nIPlbSX+QmLj5yx2aGXAWEGLZxXbgVlhKgtbHJgmlObI4xuFlUZrws1i+LaxBHrBH7MED6vvKzRSirqia7IvQCwpSdOowJ12OLALfn1Wy0fsM5HOK7MdCdqV0dtFFCworjIovUbzB/djAOnBL6tk3Z30CYvnUQZaYr5C5QViustuOQGCxJmMUuo2vRkr80OnLXJ0TYrKg5ShkBrfkL5daOMhBpltLrE8oD0vIWAQB6Vi+oS49MkClfjKNUBBU6jOhgpjFoRIf8Chc34GSvYN59QvqjeUa64dZFJYUH6wHHU+/YCifQQ6rDOwrWcN1wJ9IPi4mCbxVHcCS99kHqLGp5An7fAlnOa6lbU36lfysEhmmNVnyGAbmBgwEEfOR+G9gkEgs4no76nqanJ+TYEmku9XWrt+l5KPym87HHs2oWODtcP6flA/Y6ufYG+rJfz67w6Z64/VX8kZfcVK1Y434f6UqnH6dw5H4r2VV8sIF0qsALo1LfpHgSoKLSrADr1g7m+OudTEdAn34nSqXPk+uOrz6080efcb/r85trUyU0Qh/Qg/dAeGwPMKi/AftS2YEbX00/U08kO0K6PoVB5DkgVn5NX/RuAGuE3I5VVqLk1WxbbkJgDLRsgzOS5ffQJBRZqvhNFuvUWHz5kyd5d2PqIBZveDSh8K30s6meAWwKovZluVGBP2Qxqb3FgsjBwc6ii2YLVq2mzqgNAeVmQKaAvhfn2ZlEyHzlk6c4XCa88BOCHwiYwra+QNgusnJgbsvnhVwm7fBwlvAL65fcTdvndsN6jFuvaAzh3kHHDnJXXA71VtxLCmZDu6QiOWPro4XaAoHnro01n6tZYTbTR6sJlQDcBFNcy1g/kfgnQZx4ofEVDozWXV5B+gBfgtM4JVLwIrV4CkKRQqVX+AivE9lNzHJQk5S2Z1nFe52PTdqKPcO7kWT4Q9QrsZANgT4R6OYT80pmZYZtgjFKPEtuKilrzh/NR/pomvOslxgVxKymOEpq03CrI03z1xsBqceA9CAWrBBQq8QdsBMWwE9PDdm6oh/aRZ/XAZaH8QuueRU0GpVwpjqWAc/KATJdX1jOGK7UJwLPT5OlEOm71LAS4pZJQ3MDX3XNj1s1CgBjtpDhUYvWAUhUojOXjf1QYW+n66m+S8YrAuQ5CvR5BEeYScH0J8PvGghpbyj3Eg17rZTGCzjUM4FXK2GYZ0F41Y67ZaRS1CB86ipJqZUmFNaMqV0m+q8wThI2PadzDqqNCH4plTGRPsgiifbDbegZRKkZBsLaiylrJ49FzbVfAuVyoVrVJtdFc2722jebasKA52TEpXMpvJN9Qbgws/+u157j289XX+dd+y137X0uLbKz8VgKIX375ZTe+lxrmbbfd5tIjm6Zza7/ce41VE5Tl/v0H7Mhgrw2sbrA4qmarKNMbqVv5LB45PzdhF0cow/lZKymIWC3gY3WklLIjPCh10g8MJ5gtiD3zqa5nUvYy45R2xtZ5+Jhb8ZvVUCb5QPsJaK/ucUL6CnjLC9uKsiXWCiQ3jYLhod5LNgw8WsFYrAXAspQ+IEiaGW2yMCAJyJGwMPLPhYz9/BCfHSjxHgWCmC4A3isB6hg4aS8/+QR1tsRWrttOXa9i+IVyIeH4iovSdubUTjvw4gu2tmWN3bL5Vusd7Lfd+3dbOpy2javX2dalm6yYMZSX8VYCOi1Ne84DHonSZvKAWSe55smLp+z5vU/bJOrTrc01thwor6erByW1bgvmldoNW7bbdcsY60fyUZg8Yc/tfQpoqw01tmV24/XbraaiEVDYa13k9e7DL1p77zmroj1+aMfDdmPTddaHKudTh3fZ4eMoRBPy/Z4dd9rqxpXm51lE+KdlQoBstIMebC9tNQKk6iNPZvDVnD49a48/SzjlsbRt37Lc7rgxj/YQYxzF80shC19okd09GXv629126kS71VeH7eH7lqPMVmG7X0vbXwHOZbyTQGU7gNUJ01vKOJhtYFDg3LA9/o1dtnpZDcdstuUt+fbMcyP2zAsnUbhOoPK0wrbfuATVUUDhyRjPhSi+7vRji9rt595fa/fdU2GVgHhtHVOEan0cpbiYveedN9j9960CnMO5FKSNUZditNPRUe4PRXGBgvlFhG+nHbOOyA7si9mLu+OoWHWgcrXEtlxPmMnSrEWwZSHqBL2pC/V94OCQfe3rlwi/W2MffqTKtt5AHW6L2ad+7wWGcLX2yEMr7N77CdVaQ0Qxxs7t7Uk7fWLYbrs5Cjj3b88v/mvtL9c+r/6r9vZjv9FunR0j//R8jpEgL5WbC4Bv7n3uvt0tiXBjH3bWQMbdYm4/fdDR2l8/8SRF/4XtoRPT51yWXP3enUDHuWNyEDFH8hymcyms7MJZ+XP5BC5nL1/evddJFnbTu4X3+ruQPL1z59df3ZsD+/gxCx16bTnlPuvvP9/3lavoFG/pTcCs1Nw6u+dsYhZIPj9oedj+NJDqBSDUb3/7POGiT1prS73dfdcaq6ohjHYedjhCaHkWkdBVWMf5FLbmkp06dwDIdSnQ22orRen+a18FnENxbvOmMqC1lYBzhdgx4G/mZXY9kbInCbE6E+uwh95bYdfdWGwXzpl9/R+HCRvbC6hXYnfeXQWsW0zbTXAN+WmG7aUjZquWFNknfzlqd9yWb0OAc888mbK/+LvHbT1w34c+sokQ1nn0DZybsc0TqM09/o3nrKFuqT1EpIebbtpofp6VnWyqV8qwzI+5iqO6p6e4BVv4egr9LQjOxZ0c+H/5L79DfmSB5n7GPv7xX3o9efOGHaMHNYFyz0HOa3CkyddBJmYkEawBUa6RX50grQAuKSl20sF5rDRdAOQWPksevbqq2pqam5xcuh72ciHBrj7H4vsffg4sgnM//Dz+bleQ01YrHdvb251D4Gtf+xqrGEbd5L8mQt73vkfsoYcecjHB9eCxuP0gc+DtMyj5QebaD+tcGtT2Eo/+7p+K25mujFUjjfsffyZgn/wlHnIWtzdNDgyx8uOP/iJln/rLuDVVe+23ftZvP/khHrgXHe9vmjJcTOgbnwMaR0tNR0CXxgFy9Fy7aSXxL/zCL7iXHDGL2/eeA1LM/uxnP+tUHxQ26drnFz27KPTSr//6rztnWW5C43u/0uIRiznw5syB02cIs/4BwgyyOvruJq/93aNMGBcvPn+8OUtzIdWC1vSc+dWvftX+6Z/+yX2p0E5aoKjfZOf0utoe6ntt+it4TkqdCmP30Y9+1Dnpv5dnUp03d+7ctQQYKE1//ud/7sA8qf3oeXcBUvjnlfg6To5CRTrYs2ePAxY0/ySnnc4hNVYp6G3atMmpsefOr7RrYr795An749/9lKUJsdUE6FCeTOFoLrElK5ZZQT0QTiJm4xfabXpwwIprG6xu++2o1CxhwvYMz+MDFmURaAmOXE88hWO9x6aYwM3gjK/YcYtV3ISCGI6kxJHjdvyl521wZsjy6oqtpnaJFUWAX1DOGsFxlQC6Wr3lRhaTRgjD1mnd+/da26F9hFnyWlVjqxVLoQ6H9DjOZh9w35LlrJZuUtrmbBzloxiL6WbJh/CG9eZvXopIV4PlNzRZCMe3U99CdSHZ1m6JF5617iN7bQzlk2JU04qqypl8jltfV7fNjaCGUdJo9TtIM1Be5xDqX2P9OIz9VsYEsy/phdEZt6ELXTaHc6jp5ndYzdZtzO0SQuXEEet46RnL4AAuKotaQfMyC6BAM8+E73gMZR9AombURvJxWiY6UFd55nmbPHvKRoHHAhtXWxHhrfJwBM4CWWVLS6x4/SqL1gEW4fQa/fYuO87coh/ArY4yCQGYBeqqrWTpcgsBSUH8uKXoqUvdNgX8dRAIzQMEV89++dUVvM/YTHuXebr7SFulFb3jXTbDPGN3Rztx6cYtGg4CG/EcSRjSOA7vmdNnLEhdjq4DxPrAw4AOEXiM09a/e58NDwxbgHFW5TLAx9JCnPzzQJLTKIyhNkPY0xCwQbKj03pe2ImaRpsRgwrlwCbuu9pSoSLCe6YtLIf92o1WWF8D6DFoA6+9ZAdQ7CslCQ1rV1u+lKTKqy3SjBISyn3eIGFrRCzjUE7jDJ8FUD2381nAijmL1lShXAZgF0jayNCo9Z7psbq8GqDBmyyy6Trr7Dhr030dVpovR4KUS3w2MzFpgx1dhBMcx9naajWoK3pr6xB0a7eBZ5+zsQ5UdqOAi6tQMsLZ5wX0i0/GUUYrtaZtQG8oOaUudNjUqy/amT04//wZizbXWUl9HfBGic3h3J0hvRXUv6qlAJT+PKC8g9ZGnrD0n3wi71bVmjVTT2hHxdWN3CNliKfCE5+y2NAFlNNwgAK5FVIXAkVl2J6k+QFJYoNdALExoNFqK122FqW3oMUGgA56CGFZvcbKmq6HgxMsxvwNYYIhwQhN22fjXYdsbugQTk0AC1SAvIXLCLWLppEcnDhWsmAbAsfCgAu+AOqKc+02Qti08d4hCwBwRGlHQcIeepmTS091MS+HGo2fMIj1N1gRDmZfMRDt9AXrPnPEZgDnosCfJSWEgi1tpmzyifbcb3MouWViKOPhzBEIOR1H6wWlnDyUrrxANhnguDngubm5XiuOsmi8CRUsX6sNXwQOmRm0shqu17zBgXNZTxn1AZspKHWqgzLeZZPDJwDtUBzj/kJFQIxeoNgManOeAiDfEguWllH3CM2MOkx6pstmevYQwfkkQo8hi6CK5RPkCFSXQH0nkwUIWPt+lKxWk5dvnb6dVuT6GYXoVh1xEDV2M51CWVP9SAr4mT5D8Ib2kQ9D+0nBLRLWAgHUxlRf3DGUGbZFv6mfFKiWBvhR/5KiD0kCVuicQX5TWD+dW6CSoCNOyjlwhfG70uAHOgjSznUuRR7SK+c/cf0i59R5QkDafvbPHac+Nk0aNFc8D5Ss6/v4jtM6N5ufehgmzWH6Iy+giBy03oUEKBHuPLq/t8RsJyBadvKIdZ/eY8PdFwiFSZjRaAUQi9ogNnIeBbeJDmdj8ug3AkV1qLPNE7Z6iHKl7wSiyhAGNEmfGhg9Z+lLZwBryyyyjNDStLvZgUOEed5JOeXR9wLEC5zDhric9jAYjhGyc/SAjXWeNM/oqJXj2woA7iWLmywZrsC8UeYe4BvsqK+AMMCCZ2dRvus9bNOXCBdOew6XVlgAKFzO/RgKUnFg3LnZQWwsIa5XP2JFtbebJzZi8Yuv2uAFbFpy2gqxFQXcpy+vknrnQxGJEInxaZukv+kNVtgI/UEoXEofTvsnHOk80iijQJmjUiClnqxoaLY6FgvM8V1HLzZskj4uWmA1pKUqiNIW/bJsYJh6VYDTNgrsXIAayqTfa+0ocJ4YumgDE4S9xZbWFVdahYf6xnhjFOhsMIZ6I/VrdSVQOGFLpbY3nJ23LmC0IUDVJG2gBAXfctIa5twp+oAEoFyAdDUBKlej/pUAtOkknOlZ1HUngOSC7B8Emo950kSOHyc85YQb/4VpO/VlKEnRB4/PzFof444EedyATd/G9/OEbT01wNhqeoT2AIAXrbcKFAdDjF/89FcRIKsK0lfBffgJszoN5NYJ5HxgqNcuAGsVkBdN3iKrx+7H4Fl6MixyIK9D2PKlgHlLi8qthLHeXDxp50cIEwvYJ4iojP6vFBurvkoLCWbotwOAUNXMJSwpLwX0SVn/1CjwHBAediiCmu4m7mEedd44dkDgrfyisg2yLbl2m3uf+6y/+k62QX4jqb1J5VI2QurRW7duvRKmWfu+EZtsqYRVvv3tb7v0KC1Sf962bZtT6NQ9yc7JBirt+k+29zXSfhC4cmBZHaLMfltZUmbXA0VKPWxvP3V0ZJDyCFHeZbSzYhAuVM+BKATOhQBZixjTOTCRz8PkxyvUnTbUdn3UtyWM16O0zSD7zTJ2m5yaolyCQGRV1kwZVvM+BvTYBkR6AWXCNLa8jPF9EWB0CNvpRcYuS7l4WDkWBaKsIW0FjBH7YvN2mDDol9KMYTKTNtZ/yo698gqikECjK7aimMa4gzre0FjPOD9hZ47vtkO7d9t6FKXvufU9QLAZe27fLjtJSPsg9Wbd0lVWBrQXYtwfn4lbYoJw7bSttatZqFDbZBdHJ+zVgy/ZcUJTNy+rtlt3bLNlVY02MDpoz7Lo8zTw9ZKaBrsTtbtNKG/OZads79GX7KU9LzGemLKGZU2EB21gjOC3ASDT812oZdNel5QQPnTHfbatZaONsOjgmf277OCRw9Za32h33wI414yKpycPG8UzQSZgx/YP2HO7jgPkMk6qXggtHkeNt7Nz3M52YFuBGq/fvMwqClCjG+50YZWjVZWMpwJc1+zYiV7UgEdsw6oau/c9ywhhXmzPvTRnf/W3T6EPOW4Pve8Ou/tdzVZexDgEuzBAmNRvPtZnj32dBV5LUXF7YLttWl9KNK55exJQ7/CxNhR3i4lKh5pyGYpTkyi4nY3YsXZsdbLLfuaRGnvvg8CW1T4i4EzY5//HPwLWzNk9d+1A0XydRatQ+WOoTM9P2OuEnT1Dfj6r6F4hq+L+CkuC9BHMx55LEL6V0M6RpN1wfSn3OUhoboi6TL4VohgdZmHV7FzKzrUNuLCu121otQfvjdKefTyrTtinPv0kJd5gjzy8iVCv2IIlqNohZdl+IWZnTvTbrTeh3lkjncO34UY5y45l3XhqYfyZs3f6Pvfe5Qxt3ClL88GZx+80XtU+nFP1Rz8zZOIYqdj983hIv2s8J6hOAyL3Wf9gMxbs00I56Fjtxy5u32svx+7/PGTWOXldGXLpIF1bf9ncH/2jy+gF9JsD53LzJ7n7zf3VcS6d2NK3y0Y1IBR02p7ZecnaL/ZbqCBoFSwk8mGrJcZx9PBZ2s4UtnG5LcWu9fWiSo5EXTGK+oVFLI6g3+jtztjJU4M8Rg+iErfKbru9gWcov331Hy7YvgNHUJyL2iOPbAScK2O8xBg+lrVdhFd94okYap7n7OH3Vdu2W8qBbAP2yktT9vgTB1GInLLWZYDuy5cwbiGEbOekHT9BGx4stDUsPPzkfyR07O0FNtgDIPzkrP313z9qLa1F9p77VtuqtXk8N7Aok3Hp7l2v2ovPv0bY5zXAew+hKL3RAmHVQxgqFnNpPEjNoL5oDB/k9foXM73lwDlN/h0+fNh+5Vc+4SYif+InPuzCL/04Nw41Zg2OtLpXk6da7bubwcAXvvAF95CoVVEyAHoQlLNP+y9bttQeeOAB+8mf/En3wKnfNYDSSw+gekmiXA+p+pszID/O+fBWTNsiOPfGl6o6RLUnOW21yn7nzl0OSO3r66UjyDDoWmZ33HGH3XnnndbU1OQcAWo3i9sPOgcuj2x+0KddPN/rygG6RtuFfPfDv8iqewaY2xu99oUvBm3zqtc/gHhdCVk86PvKgeHRrP3vv0vZf/tSAmeO2U/d67f//ImglSFrvLgt5sBiDvzrOaBFKgIbcqpzel64etPDvVb0fvzjH+cB8BGnSnD174vvv3sOSG1CkMbf/M3fOPUG5zS76hA9i9xwww3227/92y7kiZ5tFrfFHHi75YDCsLS8ew6HntkNrNr+4z8P2qaVi6qxb+Z6oHmZPhRIBJ5pAaQgONk758ySQ8vNSONMwMmlfbVpnkbvc9/pcy4clKC772XeRufRK3c9nV/PwYLFv/zlLztw7sYbb2Sx2PvcgkrNDeXSoX313KxnZqlsyGmo91rEqfk0vb/99tudQqickHpezt2PJs/beM7+/Kc+ZeM9vThZC6ySCcooU5NVKKiU4aDFtYdywJiVob5Vtxnn48atqMyErG3fXrvENQIAc+gs4FwmekJyxrz5aQCeRkKF3mB5S9eYF4dbBqfowNEj1nP2GCESBwlLmcYhhsIVTvhpnCyC3dbfejuAHA7+2IzNdHZY+4G9NgTElYfzpZRQkh6c3uMhVA+aG61p03orBt7yBnGwv7bHJl4mhOelHpQSUJnD2RZknqByx43AccsI81pEwTG+xqmTbjtpg4dfs/7zbeYBthCEIZUXhNAAblCLWQ0ItHET6lf91nF6P3BZG2HWYlZE+QezTOQmWflM/kXXrrGK67ZYpKkVWCdCGLtBGz+816aOvYZKDt4wFLz8OL3jACFx7r8QxZMGlNryW4EYcEjGj1MuRw+iwNRrMRzPYdQ8wjgNiIZEnrVaBWp4Jfz1M8SZO3jCLryCilBnF3kGtFKBo7SV8HLbtwE2Luf+UJ/DE5FF5S9x8YJdAg6cIt1egMcMjnApZunpoqQIB/26DRbetNmGqVsX9x9CQa/TojhAgzjJke/i9gAgAoBgdRUWRZUvvGkL90e4xqFhGz9y1LqPoYY2Okk6gq4eZaWoR5nUrNto1Vu2olxUYdkZFAZPnbRelFZGu7pwyqOogmpdFtWvJOHySmtarB41wsJlzaRrxqZPH7e2p5+wRC8qRSgESpkvXNeIitsOCwEbeoApCQYI8EScGsoi2dtv/S/ttjFCuaUmxri/DGAF4WTUDvLLrWnVDVa7FoAM4OzSgf02cuqghWZRNEK1LcX558grAtMCTUatlXDE0c03AmNFAUdHbI57HMZJOohjeop5WS+AATOxOIzDqKu1WNMdd1kB0GIWKCJx6Zxd2veyDQOVZrEXEQFYqBOlgSrj0UKr2rTaqtbjAAAUmUUBpffFV2zu+GnSgQJgMSAGIbVqN6O+uIEwobQz1/75DULF5ntpJ4PnqbeoP6ES4/ejBBSU6gptYyYJ0EKoy+YVFkRxLk4I5L7+ScuvWWvFDVssxL04OEhhSefHLTnYaVNdKA5OnANWSZm3oIh8KMVZDuwE9JdVuF0Ak7yyDSjRUecU540QZYkZQsH1ALSMDiAohZuYeh50XrJ5m01MAG4UAkRutuK69cAuXiCc89bbcRzVQUKl4SmLBJiboGwEMyVwvjN9B6BZahHgkvR82iYA+hI4s/04WIM4ohWWLQ0IE5/tQ9kEBSZUrjyBVTiMUJyLAc5VB620DjXJ/OWo/5WZL0F7nJ0nqadssudZoLtT5gMAChAi2esDkMvidE2hiugjJG5xtRVUN6JUA2xLmDcjPGB8DKhy7IxTs5Md9NNOwtyfX3YY2CNYtwOgrsnZKBf4Do9gGvDPg4NIDkd2cnOSGe7Pg4NHTiyPn4d6jnX2NUXUGaeIhyoeMIzz9UA2yJGYBBCR2zAAeODjmCxQURJgwgOM4wd68HAuCp6X+hrKkfNkgASJm8z1ORYILK0+SQ2b+qyvPISP86Me5eX6hkqPlChdmG3qUDqJ7wFHmgBUOZ28Hn4HItXZs9gUL31dFlghKYARu+Hjen7gYoEcaY6RM9NHW1eflqEeZH15ABRcnLCeGeDMLPXWBe/DjsnRmiadWVTDfIBXCqvnoe1lEpMozKBxBhQD/UwL5Hfsr9tfaotACl4AUa7gHLK6N+UV1YaNNJM+L21L4T8zABQ+8sDrzhtDAYnrAUCrLPyq15RlFptGSsgL8lRqiV7SJhAwxaIrQaquLmNPOUZ5Q44CkWIrAVKkiqZMzaDIlCG2ngfHo1NZQf3CfEAcnAc9NWfjAzQYL5BgCjvqwU74CA2sRuScwJzTgSYpwnxTfh7yFfza3VGW+qNQle4OqT8UHPenfo66C2AGBkM6uXuOydKvZqhPpNCVvV8QoBszkElelPeAmjyEe85OnbDBtoM2ASiTx2LM/HyVFeAvsGiGNPqpd/m0/7zqJlQ9gZkBSOaAxQQwqzjTAIaqeuH0KBB1j6VQlMtruYN+bp3NDB8FnHvR1bGixncAvd5EFQK+JY1Z6lh2GgXC/gPYmrPmQ4W1mDqFFJnNEGZymj4wxbV99M/5QKrh8rXAW5UWIoSiZ7Qb+BcQeZj+G8uM8CVlLEU8lAl5xk/OS/cVKGvpXdi4TahbDVuyByW6iyfISUCrAuo7EFCcNhbX+Ez2GlglE22wYUIxdsVDNkXH6iG/pUDroY4mqU9SaktQxs1A39Uoas1MEKIV4HCYeugrJMQscFEeZRJUPSb/QvyNCpgvKQU4y7Nx7FsbduTsOMD69Iyzj0X87iOempc6k6TuyDVRVgxwXVpjUdqmNpBQG2OMMwAsNgK4lCINPtqi2lYadTDV2hLKYRWhLWsp1yz2H8TOLgGi9QDoTVCOst0KW58iXXEBqXFsDOUQxfZFwwXkl8dGaJPzVK+GSKHdiIrWFKpip+gL+oCD/CyQKCkoR0mX+qpGRr7loQZWDzTXiqJXYTFjF9LTPTdlB6lL3ek5K0YltjSGMh2DpdkAPhofynGkthFYfDk2vZKy1YxAnHrZA0R1gT5ynH4xzfhNIKAXiI/GQBvIWCn2qYHrNJaVOis3nYzZBQDANvJjjna8GUW0VDf9Awsl/i1w7upxsOyu5okuopz72GOPuXmMtWvXOn+RlCo1Rn8jN43LNU+lBYmvvPKKG8dLLfO2225zYVtzoijODiph5JP6gdf27bdjqKBNrm5Fpc+D4luRrUPtNkU/vL+3A2ByjMXWzBszXg+pH6JuelmAIjXEUurlEsqvEvWzAtr+EGnYO9xnnagGhsmfCgDoAGOpFGqECcK3Bqh7UeqHlOvKGe/IMstCjVKfFHp1ZHqcdgXWJlODbZc9C10uwyX5xTwnlFkhQLoUCttRYLzImGNwvIfFKEes/cRxlA3LUZW73moA/1Q+VTWVlheO23lCSh/b+xog2kq76+Z3WV5JkR3rPWMHzx62Sxe7sSmMi2mvIdl6EpSPTWwsrUIpDpVaIPiDLHQ4dHgP9mfWtl6/xq5HmTqaX2mTtIVXzh203Qf3MiaYsx3rttm7rn8HY4o86wI6PHB8nx0/dwzQbYa+iDENbc1PnkwiyzSBPSwOFNmDN91t21rXo9w4YS8f3A0MctQa62rttm0327KGjdR7FK1Z9OMH9Dq6b9Aef+ooKpK07FAlbZ5WABiaTBBGl92Wr6i01SvqbWo0ZieOnLaeviFMPX0EdluhFzP0iQ21EVTplnAfgLeAay+8OmFfffQp+plJu/eBO+z2W1FQZjGPFukME3rxqae77clvfstWLK2xB9+zHXX0KmwQdePQuL2wu8vaO6fpZ7yEovc64Z3Z+XLU9FAYnW+zD9+/xB68vxooDfD3wpR96X8/5Rbs3HnrFrv3vlVWUhU0qpKqIswCgOOpMRaTnSbUI30riwsCVLI0dSfJvRcxrlq9qhyF9pD1D6B6dazfRgbRJMywyAL7IdX0FIqS9agx37y9HgVAFkQUeuzEqXH74v/8NuONamDBTXbXO4sRDPK6fqrz4rydOzNg27dWWU31gs1U03hbbcp8VwL6Q4+gcRttx4071VHnfnNvF37XEQv7UHjXbuzHadxLQ1c3psMu+DTe0TnY+Ei5ahyr4zXyy11FY0Y+Xd5P9lYLLfSdytg9a1z+TedxSct95prqXlyKcsly6dCYku/dOHrhGJc+HcxzsLuPywnT9XI2/urv3bXeJv8wLCf0dRo7021Hjl3keYixEc/lDPwYa4doi9MAcyjCbq5HQb7A9u/tYjHVnCtnL2C68lSPz2Eg7KVLC2zHLYRQX5ZPGNesPf7NDjvCs/ratSUoP26AT6pgkQptcS5rL+9M2fPPzgG7dwG7VdkNNzEeZLinN0oAAEAASURBVAFaV9ccYmFn7cjhfuyOlDYZKxVSbox/R0aj1t3rY/Flxn7lF6J2521Fxno82/n8KOGkv255+fO2Zn05iyc1hp9D/Xbcjh89aWdPtdvG9VvtfQ9/EHCOcZ4D53j2QUFTNXChFkptTvMZucr0vVeAtxw4p0Yr8OyjH/1pN/B58MEHnJPse8+aH90RmnB98cUX7Sd+4iNOSnzr1utRxCpzA6eurovM+8StALnwe+69xz75yU86iXIZg8Xtxy8HFsG5N65M1Pa1anBsjJVH5zts566ddpzwK5cu8VDNZGkVKzR27LiJVUPX27p165C6ZjU2ExyL2w8rBxZt0g8rZ1/PeSensvar/z1hf0WYsCDzUR+61W//3/9i4PH6xw+vJxmLx3yfOTBOOX6FMvydzyaYQDN75w6f/cnvhqyuerG9fZ9Zu3j42yAHNCH6J3/yJw7uEiRw7RZGkUBwl+A5heYoVkizxe3fzAGFRfrKV77i8lahTa6FEnUCTTz/2q/9mlOc0+Tv4raYA2/HHBD8vuY+lJb4uyzfY//t/wnaB97tZxLo7Zgbb517lg0UPCeAeGEC2ecm/nWHOcfW1ZO4csLJMaZ9FyaQvVZfX8/K+SVMUIbdd99L7uQmh3PHCJyT4tyf/dmfXVGcUyhYKc4J6svtr/kjvVda5KiQ4zB3rBQ3pHYhoFzH7gBGUvg87as067gzgHN/8JnfQyFiwNY3N9s6wuFV4lwuAhQrzKICRIhLgUnlrXVWtHKt+WoagRS8NtZ23sYuXCRc6rT5USYQcJKO4CRtzEOBC0iljvBpRRVkHhO8coADlE0DAsY4JobalyAOL/mUKK+0QEurVa1aaRHC6jkHPk7USSCw0aPHzYPKW0FMqn8+my3MszyAq5KVywkthxoYp051AfecPorDnsV1OMrwjJunqd6KCFUabmkRreMm7+UstKkhi3ddQBUPVbHeAcCiOIonHFJEqKuG5VYA6OdjriE90c/1z9rEpS5Lc38BHOF61PISVjOEOpwU4YK11cAHpJfwaVmgiXR3B6FqCQHZ3QvQBSCBAkUGZ7IXp3yY+Yo8VPIDNfSbOPjTqFolu87b9IUei/eOoLglWAQAAVUWpTl/I2piS4B8cIjKgTx9BlWtC72EwwSOAw7y15da9XWEYl3SisQIYCB1QMpyGYCF+NnjFj9/zhJDg0x+M/GM8zFAKNjCeurl8qXu/qaABibPtFu6o8fyyTOBjPKcpENAAqg85C2ttnBzE+H4ULTT/CSwSZJwXzOo7c1cIN9GAbo4RmGbkE6xwhWUHffnR4WOmXzLACHEAP1m2i+geDYGVEL+MalOPDtUvBqsYNVqwKRKgC2AKkJ0zZw+ZfPtHeTbBBAVsAJtKI/yy6xstQxwWICJfy+AgZc65qEs4px3tu0sIYQvojw3RR0jT/MLLECI1sIVGwEwG4EdCJl1+pzNdZwGzqBNzwPnQIUkCNuVJRRuQatU2xotxL5UEiAsIBnGlDHa3EQveU64zkwc5xLATT5wQKS+yQoB+QLl3COATzY+CuDWST6eB/oDBp1NWTgFRFNEfasrsfxVdRZBXcUXBgIYj9n8KdJ6gjpC6L5p7yxwYKGVEX43TGjhNHCFXFEZnM8BnKVewrKmx8iP6X7c2LQtQmcFcHpkWJw9QeistC9qJQ3LLKxwuXPjpBWIoQToLQpMB7Dpd2VGGmfHqL+EMh29BPTVQxkAYAGZJABCMsA6HoVmDZCvYepG6XUWLALExNkuvMNSkyhU9aLA1g1EN8M5tbDbZz7C3oJq4NRFUSp/NaqOTcBvhBhMdNoEAEwQ0CUPp44XlDE7T10hFHKGcKkWrgKwIz8ihE+MAdNNd2GnOmnv2I8AinAAVimgyznSGkKVLq8WcC5vHSqFqKClRyxSlMQ+1AFgYn88ABdp4CFCG6fJq8TYq5TvWcAYABJNigg8ynLNJOGMAPyyeRXAOk0oNS3hd2AfcjWTGLEY6nZzUyhzEi4oABQVIe+CgCeCzjJe7o/27QMwwUBQr4E2CXXmQbHBFwJcBshLzsuJiIqaP+UgKi/36Amg8ijACmdWltCI2bkRwKcJYEfqLlOWcWCFiQnQTa5RSkjZSFETansAkIRL8+axaL4ICCqg5xY5jbFZNkVdG6Jt4H2awVYAtyrsYUpOxwWrhHmlXMLkIffnJ3+V3mwaAMyAhdO9KP4NkOfzDoD0ZQG0UAFUHmVwgGZQz/KX4OHHViWxyRnC1Xqne3FPkR7aSwY7lqWPg1jivslb1MMsWEV+0H/M066oV1nAmgwwXQBIOY1q4vwY0FUiYPmVW0jTGvJMAA1l7acuE+4xG6jj+tgD1VNqUzbWR13vpG2P8Jm6LGPLbxk6A+UTepF8V8Cx2OaCKkLvlrk6HhC4Rx3PkMcZlCuylIUfwFqwdpYySQE20YFRT1u4XdLN75lZoAzATy/t2hxcSR4AQ3rloAMc8qLQ5dGiIIC1FCqIGdR4RBLIXecJUKe9QzYO/DznJeRxxXIryo+inIa61zTn4Br+QuwJ9UhQMVg3YBLHxMYtMdlnnrlhQGLsFfcn+DwtSgxbAmkFhMY9Un7eyBLqD2nIAL0BMxEjlXymb0PdQ0CLAcyEKAepimZktwPV5suv5zycNI4aKnBIkn2DhMGUbzVJHsSAOIgXTtiuKGHOayyAXU/Tb3gSU5YZ7rIMbS6DQllW9j8iIJH7A5ybB6MJLdkBoL2WrKNMJ45Sdwj/VQ7sG15LHhZxf3jcAfuyM4R4Hj1LP9+FKtwUMBjfc644rzlU0wR5+XHqhqLLqG8oRwaxORS/l74og6JkYrKTuiOAj74FJRLZPD9pTs8Ihiq1YM1W7FsLOOEgY46DgH2d7JcHBIZ9paxiSSm4UXd8xYxx6ui7Gm0CcK4b2GuU8ck8dTiD3fPTX6excSP0UTPcc2P1EqsBsk4T3nRkcsymqSMpAFYKnDzNAHZxDM0wAjkUBU5twL5HqScjjIvOJCftPCqZGQiYSupBCfeXwl+RpY376dOl0lVOeMMigEgf5bPQYoFksfOTiXkbpVymAH3ifBZEqbGPXMSlqK4tBcAsJ72CQDEzNkZ596EaNwjYGgcqUrqCDIASjONmAM8EM+SzfyH9fZI63wOoOQVkVw+cuw0blorHrIfQkyOcJ8HvDmZnjCdm3ke/WsDx1CRAO1RqCguonz7rBWQ7gKLpAHZoCfkUVR3CTs3SVuaArouoCy20+Tr6duWPsixFWY8BWPUxzhkhT6cQDElpjEx7Vr8UYRxXxjiqinRVMo7FLLox2BDjhk4AWIFPTYx7ZjpQ5yIv/y1wTk3i6rHwMOPMAwcO2FNPPeWEFaTwtmHDBqc296PwtWqsLdEUwXNSwdP8ypo1a5wAhJ4X9ByhsXjuPpKMb6Q4d2powBKMM+PY+QYAr5UsShDKfA6YbRzlTlXRMCC2WBOwb/4Smo8mV8H+Vewf5ZVH3eln3Lu3H3AfkLMCGLQJ9TgtqpgHphamWYTdryKsaRl1V6wCS0ncWITlFDYBrDsIDDcK2BynTAXr0vNYhIpaTt9QBWhXTHsKsTACzsKGGaMN0U+OTA3Y4PBZGx3ooU1UWEv5SmcnQf8pB10nZsOERe1hrFVPCNp1KzYwjM23/sSYdQLSXurvQ8URmw5w72OAXkBapKzXiFLmsuZlNk/9Pdl9ERWlC1ZTGLQ1LSgeVzRi6ktZBJO19ulLdhSwdpwFH8sql9qW1k1WiN89RpvrHrlkp3tOWfco4w/uNUw+KcxyH6pz3ZcAjulr7r/5bruuZS22c946us7axa42K48W28rWFYCCyygF1NvSjIdQMRjojNv+Y0N2ZhB1wQT9Dv1imPqez9i2ocFvy1fmWTVjxvHRjFNvu3RpkvrAs6PGKCxSKS0NoLRUyLkJ5VqBImfYRzjFWXv1tdNAtXG7butyW7OqwvIjGuOz/gdA7viJMSC8U9ZQVWSbN6CY2UB/T9vqG0jZydMotp1BiXssZvkUaE1Vvo2MBwEGsSGjJ+2D99fbPe+qxqdKOOmRuO184ZzFZlK2bmUd4RMJgV6iscWCHUolMjbYl7KDB6cIv86CD6DZFDbWKL+8PIVYj9qKlUVWVUk96x/m/rBPvR4U7AB6sH9a8FES9dqKtaW2bDnqhFXkGUaiuydmzzzThkpWvm1Yt8TWrwvTVukjuL+h0ZQN9E7bitZ8wvvSR78dN9qSa9i5e6fdqUT0v9r7lc19zmL/+Env3T7uDe9zO/KZ/7WPADzZ4Su/uO8Zly4czCIC3mM/9LvOomM0v7Hwu/tm4Xh+oDa67x37z746yJ1Xv+l82l2b+3Lh7ZV/L+/rduEfXYe74Po8A+tZD3t4JU36kS33Off3yrneBm8wqyyAzNrhI7N2/gJqnjxPzs2xSAA7HCHEdllUdqbA6pt43qF5njmBqu2FJFAbi8X4wsfYPZKftdqafPYrtNpG7AzjsnlsybEj2NyLfVbXGLING5dYdSXPPYBzdBd27jj25BjPX0DGm7cWWPMKFodpPIeqbEf7uJ08OofN5GkRBcq8Ah/AfR6hsH127OQMoaVT9vGfiaIcWWTwyAghTdvefcewHyysKmNRBwqcfr+ehQbs4L4Ddu5sh21mUeb73/sh5qlQMXehWhlL6fmA/k2vHDSXq1qvp+jfcuCcBj+CZz784Q+7GO+33nqL/e3f/i2dIZb2csN+PRn1Rh4j1bldu3YRsuNDboLz537uZ6HBN7pB0/nz7ZCaXUzO9rpQHg8++KDdd999DrDLDZzeyLQuXuu758AiOPfd8+f7/VXtXZ2yHo5GkLPWg4VWyB87dgxHwRlnCyorK1mZs862bNni5K0bWLmtif/F7YedA99P1/TDTtvb6/waN/b0ZezOD8SsfThrdTxg/PYnAvaxDzIrtri9qXKAOSd7elfKfuu/oiYCRLdts9f+/vfD1tS42N7eVAW5mNgfWQ689tpr9vnPf56Jl2dQi2CS/Zotnwmy2267zX75l3/ZbrnlFqfcfM0uix+vygE9c7300kv2uc99zl599VW3UOGqn91brYz+yEc+4sC51tbWa39e/LyYA2+bHBidyNodH563Ex04uHAS/ep/CNhv/F/AHeINFrc3bQ4IOtMiLUHDUmXTnIy+k+NL4JwmbPW62kl39XvtJ6AtB9np7/cyr6Nz6Xq6ho5VOjo6OuxP//RPnXqcoLeHH37YZH9z4FxuglnH5tKidGgTAPjcc885yFxhzAXOKWSVbHkODNQxp06etM/+wWcAkObtXTffbDdvvM4qAEakdoZ/FHiCCdogyhSlqJMQstGDwpl8J+kZVjSj+JSZY4YVgEVzdJ4IAEAUfzbqET5UX6A5+F5eGPINh2B6HEc9imUZyTXiLCajkXpDJQmnlA/1DB+KLVnRcNx7inBjKcHx03Oow+mCOJcBy/yEV/UJ3Ma5DFYBVzLOeYeAigAUZrQchesBc/kIU+pFHSZNmQiOkzqKTwpehGPMDI0Df5EGgC7BGV7Cn/lQ3ND+HkIoZRPTDshITQG/THNOlObceUmvD8e5v6KYe8Wpg2M8jRM4mwYMmwaSmQByIF8yU0z2MpnswbHvwbHvBeD3kiceIDC5ORVCMwNUkxnjL5CdIDS8hHhCqT9RFMPK0fzDoUeSuT7QCWGqMtOklQeIrA9AowiHPgqAHkJ2plGYkvoT4l54oNkHODBDfjA4AraQ04EJZ8Lb+krI4xKgryDKNhRgehLYYRxoDkUYERlSiEJKDuAA+CsKxMEq8qzADjlMAUCgGyxNWaQGUUBi4j6Ll8sD4eJB8WchzaUAK3Kdcy/kh8ojPU74QMpRDlBqBfmBOhIgmReYwIeqGLQl+QTMAqSWHh51YJicg16pCBGCNVYOsERbCOMAltqJnDVS53F5AoCWnsS5CnwAikDehc3jyhD3P8oYHlS8UsOUCVAUcmvAR5qEBzhF+ceDopCvAqURVEiyKIzIienFCerBwZ2eBJahLaRRDcnOK91c0wGQhMYBuMNLjAOZ+miAJjhiU8PUJ0AoPMuo6ZAf+SgpVrAPzgyBkD4P9wzElxrhPnsoR66RAWDz4qAMAGF6S6KAXQBw1FHpWIH70fYoPyASwUCQSwBhZApwTxKIYWyQfI2gYNS4xiIAkRQ6TYNyDkYBPqgTKJkI0YDb4RyoUAEMGuENIUXJWEAifpB4opS2pETgwQntCQBbhVvIC8Av8he0jjTjVgfezQKXZLhPhYsVmOYNoGKXEMgjCAUQM1ILlAR0mRq06aFunMk4jsPUd1XfzBjHkYdAZjRargNoimvaeUjTlI2nn89T1COcyOT1PLHLJob7LQ8Yo6BmHfVknVM4Mw/7BMhjgLKMr4rjIggjkmGSbEkNUbbnuL9ObphQYYIBKU8Kmd8AmFA/yfrJZ4AyL8frWOca9FAvs7RzoIA0cIBPDjupjZF7qRnU0QgVGaAe+lFVUdqhBy0xDMjLvfuwjx5grCyKdhmADw9qgFLP9PgA0AQJemjThM6EtCNttDPCX3oIJ+zB4TwzOkjIY+wgqnjVDYQmLltG+8AmA0l5gZ18KOBgFKkM2E6nVDfBuYapN4Pc7zjvaYs0djkauSDvgdtQv/EobGaYuu/FAGfI7xRt10vZe7AHqHQhB8qLj6or2ijflGAp5KACUkgFnqCJ0V6od2Pt5BX1LkJIXAx6VpUT0+XFqQ8ZxsGAfTpNmnPHAPoA52QrvEDS8yMXbar/DOeKWDHKZOEo4aw5PJsGHOP+zU9d85CnUIRCy7LUK18awBLoC+PC99hl1TVOn01jizMKiwdIJh3UMIAT/UoCBTmFwAzIuwcglE2Rz7yUDvU3mAtgxVHur8+FlvUWb+ZyzZQDdmDqKL8Nk23kV7COmyK/UThznR1pSnNuPzYd6pQ0cV6AQ4HiHrVDLzZnHsWnPoDnUB3KnJsBQQjRzOHwIpQxdTzE/WH3s8CTGVTgCF5JPQBgjFEGhF/2pIAo1acCOUkNTuSSBwiLzKYe0f6CyhvKL0P5q99x90a/Qh1ygAxp8/uwIwnA6Gnsa7DWgiUrKXfSzDWSKFBm0igGYuP8+ai9kpcZpV3nIx+9qMh5gKMyQGBe9YfkkcA1Vz8cqBWne7pok/3A16QjVEUI9eoNgGykkTyVcluadCYBB33YU4X5dDBnAls1D+gLHIjRJ68oG+yCIE/dayZBHfXSbgADLVwLNFhE3ijr6StxzArUzQoqJN/NL2hTtod2MjZmiVmiL1VuNU/xUu6jjzHBEQeSeiJV9FOAmPRDWdpcVqG9MqqHxdSTcpsEMB4GcJkjjWmNkagbKdrMBPnRDdQTB3hcXl1n9YDLQjTm+F77xqh8lB7/pfkee0btA2elVXqtGHsp5a5BfjtHuXYAiXrp6xuKqqyhELUWGpGXsYqXcViI8UIYelF9lqBo2R0/72WfFaY1xndz9JVYadLFPajOc171d1XkM75uBypwNpunYxxn31GOUbdOLXAg2hTnGqC854HkSgDZSrEH81z7PGEzpwD4GlF4vBFFuAj3j6XjBcjFdeKc03UbvBfAWMA5i4BdSvjrA+ZKQGb1AM4dAZgfpa9orm1CkQ7FWNKtsJpx7GWEfKhgvFYkaI6Tgd3CZyqtaMlynmnSoTxNKE+4hvKS0uE4LwpiHnoCbA9jFw9j7Vl+n1SekIYI48Me1IxjzE38e8G53Bj49OnTLkSrfK3yG0ltuaamxo3pudUf+pYbh+tCOchD3ykqmXxcO3fudM8FWuApNbwSoK3cc4LGNwrVum//ftQBCUu/ZinjlZAtRbVxHWNehbzuAXCcVRmRd4JTOTU5Sp0iPwUhFpOPCI0xzFNPlgacSwA/9tkIdaceKHgd4VgLKTuFYxWSkEd9KZFaLucQmaWxoYYOUr1MMVCYpvwmpdar7/iPlkyZoQbHdfIotwgvem+GP+zLFWnJlOEcUNQg44Q5hpS0LYDdOLYsBqyXD5RMtE8W20wAS0+ipFuMChnh6IOoQNK3TWNIJwAohwg9mCLNMB+0vYCVMs4twzbnA7ZPoXo3SDjmFGObGmDfcsbNIT82M1tE3fSS3mlUlQlNDYRZjApwqcB8UcSkdZ62MpwYRvWRUNBcT+Hdk4wZDh46Ym3AG2V55fbArffaOvpmtciZ+KRNAWeHsTElLPyIcC9gH9gr8pxMSUEMDhAOtZ1C6afbl7piJYatiFcZhRHlpQUHDKUIi5sCZqH9qq+gvNLY/nCe1ypKgejzZRXU/s3Od6btyAkgYi6wak2FLWsBgswj9/mRtT42ybPA7BRgHLYoWiTFJy7G7SFEhbA2oahZ1Dc7TW/BCYsKvLbvcNqe3DkPvHfGPvxQo73rjkorA16JMSQYGBSAjcp4MXlczhidstGjAP9zczwT0zCnpzyEfOWcAuf0fMkYMoSiaWGh30pIewjIeZ4FE5MTtHdMeAIDqjEpyQOmwWYC1kVIB4w+90ed7EvYnr2MI1nYsGJZgS1f6rcClOhyQ7oEisQl7B9ifuVtudEHuc3dPv+okbPlnu31Pmcz3Hf6nd28GvjI0Lrd+Yfv9ZOgSv2nr3P2KffXqb7pVw7j/4V/dAxvBdrlwDl3PU4mW+321Q7uAB10edNxvPS72y7/FZCXudw/KBSsO4cqmM5xeXO3jP0XOKe2kbOdud/1WWnOpfs77ZPb9634N8bz6AiPAjPAbnOz4ibIJ/IvHAFYI4xzcQntiyEg5p42mNG0CPstLKiEpQfIzdJe0cos47kT452gzc/hg2w7A1DfO2k19WHCtBYydcIiHAwHwwybHs/YJLaEUrGKGh+qclQvFl5JsXBuBtVwFOsw4ajGkRDKM8lEy2PfGra9hyYJbV1ov/DRMrtus2Bi2aw08B+LEuhfPCyg8SEjGgAcHiIU/LNPP2178D2sXrXOHn6IBZ7bblxYpKPFV+4/xrHEj1YdVvW+Ur9eR0G/5cC5XB587GMfsyeffIoB0zL7h3/4BxdyKTcZmdvnx/WvBmuPPvooanK/5WLZf+ITv2LvfOc7ncHTpKygoEOHDrlJTQ2YPvShDxFrvNpNhF5rKH5c7/Htkq5FcO6HU9Lq+NQZqz1MTvLgiXNADztyiB86dNi0Yqi2thb531U4vm92Dz5aZb+4vZE5kBv5vJHXXLzWd8oBxAzsseeS9vFfJyQ445ObWrz2V19m5SNS24vbmysHmBOyAwfT9qufTNjenoxtXeu1v/50iAHrYlm+uUpyMbU/qhzQuOFb3/qWffGLX2Ql5EGnrnNtWgQHKKzdL/3SL7nJSYEGi9u/zAGpE+3Zs8e+9KUvOchC47FrN4UF1OSzQrQqXOAbHe7k2vQsfl7MgR9lDkziEfqp/xSzJ/csOIN+8QMB+/x/ZgUnE9eL25s3BzQBLXBNWw6c03OqnldzgJp+y03g6r223LyN9tN7nUMvnUPzVrnfF/b+1//V8VKK07VkYwXOXby4oLCqsKs33XSTPfDAAw6c07m1/9XnV1pzk+m6io6V2pyUROU4/OAHP3gFnNO9Kl065gzP3p/9g99z8NVD995vN2+7mUnYcia1mTxlvOrFSeIhpIZzhONV8eBkc9yQ6BucwAqplEZNBi8eE66CC3DLMvHqcdAcjUL7ucl83FA4z7IOEmMm1c2ac27gKUUO5Gb4h3MzQeo8d5poJw80E5xVeD2BZSjhCW6RMlBcwBr55WHm1j9HiFjJFNHPZ3kpbKFXAB4zxnLkxji1Jn+9WcAfHHMePEYCq/wZfhAUoX0FzYh+lbcJt7KUj/DcAU9wpDxJKB+5UDlyHHBOAWhZyiHFZzn4PCjVeABd5DD3cm6pxXlwSma5OYVM9UkihDxytw2R6MFpJ3Ypw2SzV1ILAluYfZYKX4bQUSofD/en8JxxJs2l1uPBAecPch3ACZIDKFWK/w64hwllBzqSZ5CB1A0AKZ0zzfWBthyYRJ2S3INTGUPhiOrjLknS3aR0hnLyUAZenGBwDi4/fQAzHlcJcO0De+Hth2vgPuK8cIDIcaLwksqPLNBPVhPu2l83yc0pPKAgOpUnZyL/2VcT4C6/5YIlpBVjkCTewABp9+Pc9JBHGTwCcBaWQGUtRBkS+Bbg53Lekd+qt1LjgG7D3SvwhvIUxKcwleRHVplD3mbloSSfTRCHPDPkBxns0iylNQ+OuSzgjMpHSfZS3k4xj/RSiq5uS8mPX8gX0sa9uurM9dx5yUSF2ZTKCLWYY3hJnihMuQF/yOXgl2IWWSeSwD8HSCcvBvnlFMjUaeAM9/DKct9ZQWYozqUAbDJAKgTJpemoPQDyTffBpXTYJM6QYOUaFyI1BIDIJXkpz/zUWNJIG6IgqIe8OJ0HIEvAkCdxnvQAYOEk1rW8AjhQVfPgjPYAEaVwCKdQ9ZJjS6oyHgA4T5Iy5RxyqHtwdHj8grjOAMi9ZrHpOE7wFSiUtAB5AhwB5w1e7DLfvAf7UW+hJRXArZzJpU9ueQBZ0pVFrSCLQzuToU0HUUFDfQ9K1RIAZZMDhMuV07S01gorV8GgtZI+6j2OFqkKQulRrECG5Cm17zKgIjitl3T2UGGGqa8AOPpVkI6AQEAkAUlZL7Ar9Sgr7yw35ewZ5xU4q/MpmYIRsvOEGh64AAB21grLCixS3czxlSjHTdlkxyuEYuy2SFUjYO42zFU9JcxRohQpA6cqxbmzWWBe4McMIJA3TdsHAFMZCnYa6enCCY06YUmjlTehOFhSRw0GZCINChcrhUMP4ZdUr2TzPCGppuEFQ40nDQCaQb3NAywqOCeDzUorVDOKiBCYlG0JoAHqVilgsITaGfcZoPJJiY/67cpShFeC9FGfpsYBsqAtClF4DBIaEmkJiw12wWyeRpkMZbGqlcCfS8lz6ohsuPo0hRWlTScFrAFe+WlfCq+KNw3gatImBs/gkGtH6BNlzsZbgai3kj+EMBayA4DhzSqw90IbVVuSOp8XRUFPvId7RnnOM84L/IL2HkCRxkdYXgvUUo5L+Mt58MZLZUsQjg9gSTaag7l3BajENqpzor6nx9pQNj2Dgx9trpq7EPBbCyCGatrgcyjwAXhGGwGgt5J3VQAMtD3uIUv9zKDU58Ifkt9ZQgQHpMyovhD1oUyyz+KTbTYyPIAa4VIrrb2OfGqk6AmpKduC7VHfw005R7GgUQ9UXZp+Jw0Y5pkDiJUCIUp5gry82Esf3kgPYIxHcKlgT9NfzkFdpQJRaNyPQmir/AAkZYA8QIiJyeOk4yIweR3p2Ez4wxLA7VGb7WwDYphE4LHU8uvIMyA5AqPT12H3EtSLrOwfloXTp6gHSJ5aMI0CrK6Xpb8AbpkdP8Zi9hMo9VVayZKbLK8U8BDARfCZlNGE96I/iLUBfMJuK/y06gJxncmzYfajDIE2pWrpxd4FgGcI3sV9VVGG3COAl6BBKVh5sOFZ4ByqP+dlf+qIT/VW7XjihCX6jjsIpKDudgtUXke9ANod2WszhBD15qNsiy30E2pS58tSL7gIqQLcAiQcQCFtjHqCtVMOKAttkn6phzY+CExUyEXXVzWgOAe0z28Czia5lynancIWC0jPAwYtpUyjnFuw1wLLkbFh+rkOgXPAwl5sViuA7cqyGloyoYHpa3QtjXvUTZIA1S6XMpcazqtNDmU5oaXwlGFf2coU15cdKmQfQUNcko36RP8moA9MGyVAfYOaH+MjKYldBDwcBySqANKvpo3MQtBcHKMPob9fWlJhmwipiVAocB0hzbmvGfJglj42xTUK6OxLsDnF7FvE+cPkgcY0U/yuUK2niLM2xXcttY22DOicUmQT/kJZkZaw0sk9KJkuPDL3IURQ4wkFQI7zg5qpSkWwK7WMbzUWUw8rm+lGBuxPHig/qBNJgKg2xqUxoNXvBs5pDKyxrMa9ej8yMuIW/+3du9cpP2vhpI7XYsp/7zhcd/f9bkpLbtN1c+kcHx93c1byeVVUVNjmzZtd1DHNsWhTTjhwDsW5o/29No4iruD4jQWltgnAPkBZdVJpJmkvHo33eclG+Phe46Q87HMR+Q9jxTWBG/lvAHt/gkUJ42RuMyqT16ECHRXISZ+e5IVFB7hTn88x9GMJbKk2v2wN4xp1/wwFKFM2dXPcG4dTiLJNhDDmHEFsQJpzzNN/xV2FVzum3+KoJCD5GOcaGJ+w0Ykh1JGKrBlQtZzfQvQZAiI04BTAL607heodZmwwyDhWsGmYNsrSDCui7y8lrHmAdj3L99PsG6atlDGWjvDXKXRm6EepVVLIjgNpZ9nPT3/vxkG0Y4U1HkNFdIZFIJkQ1oYxzRwKqf3jvXZg3yHs6bitblxtd+94j7VWtmjYQ36o5QoWVJ4IVmWMxV/lBZnoMmaCynuShSiXCPdayvPHKpSXK7geo3in5kgRkVqyS3VB0Ds2jrcoR6qPBj5kPMYIAdvLldhx74GUPbPrNDZh1m65dQUhTgmfDFimY/SoleJ63LK7L3hDYHvqDePh8al51OXom1M8E5BPGk7OIVf57Atj9uqBOSsIT9hHHqm3W7azMAGIRu1fY1Yv5/NT9l6MmwN4uQ69GQWPIjfPPzJiqtFp7lfjFKVZwBV/rrzIajcuV+hZ7ZzJMI7h/D7OqS4xAywuG6AyOHt63r7y9208J1fZ7TcvsR03At8iCKFnr4wkMKlXEeydTOfbcuP+pb6WZMFRTkFemSowTrZC89maA9CmZ6HcpmfgDMemgG8X5iFoUbQxqUjrODd3S1mS05xN+Uz56bmDv9pUpk51jh0uW3TKUWnhkytrNXzZqYXN/dW+/C47nDunrqmTemnTAdKpNGV4HtXvAnxz9cedRSfR63Iarpx84RJX/s3Z1Nzfq+c6ruz0ln0jpUdUdTEOaZ4nsihMu+c78lLzM17ajB97k2HMnMKG+ChHGRzNy6hsMa8ue/U1Xf1CO2dYOYQPcudzY6hUdtvK1SV22x21VlePAirtUG1Pj/J0NRxLJIEwNp0FHlna9SzPskP9wNGcw0+/I6BbwswXCeH66DfOWE+/z7Zf32Qfel+xLV3OM43qGJYmzQn1vMX/FDPpJv3j46P2xGNft28//bi1NDXb/fe/F8W5WzR1wyZwjvvN8szHfWujijub4z68jn/esuCclA+kNCcj8bnPfdZuvvkmVrAx4H8TbJLilRPqK1/5OxQv/pP99E//tItlf23SpfIwAiB0qbvbhZ6Uo++NHNhdm57Fz/8yBxbBuX+ZJ9/vN+r0NBCQg1ZhmU+cOGHf/OY3ndLcLJY3ymrrFSuW2wc+8AF7x+232xIAusXtR5EDGsksbj8OOTDOSp+P/VbcHt/JgyaDiUfu8NmXv6jVo4vbmzEHTp/K2Cd+K2HPnk0jMe+1L/x6wO64XU7WN+PdLKZ5MQfe+BwYGhpyzwgKY6dwdnpov3YTLKBxxM/+7M/aSoVJk+N4cbuSAwIzBB7+0R/9kQtrMkGYtGs3PZNoVfRv/uZv2r333rsY+vbaDFr8/LbLAanG/t+fidv/fJQJaianPnqX3/7sf2gy822XFW+pG845t/Q3t1AzN0krO6gJav3Vb/o+91vOaafM0O/qi3LOPH3W69+z6Xw6LgfeaY6os7PT/vIv/9LOnTvnoGXZ4JaWFitAnU0T57LhUrLQsZoYz6VbAJ4Wowmck5rounXrnOLctm3bnGJ77lq6J537c5/9fY712sP3P4gT4xYrKka1yIFeOC+YLEVPgIlUwRfMWsqhJg+vZtJ56dc0nrQss7J+KQVJPUZeJQCltGZs1TXz2wIApLxYyA9NpQoMgRLhxQysvDBAC1mc3UJY9LP+8WRx5GWZlWXyVhCa0iBVuhgAXxwltlhnh0Xou/LKcc4311uauTQ/++i08vBlgZTmOTU4I2eN8ZKTn3IhDG0QB5hX8Jx8D0oWb6VAIRUkF8pSX+M8lAPIXVdOI75z2UB+8RNn43B5jXine5Jj0ccOApBwobtrpXAaKEyZnBTutpVPugZ7ZBQOl6N9cj7ICcK8ZxqATyyOodg2e4GQtSjfFTS2Wn5NlYXyBVPhNsRzlia8pRyTPtIXJAt1bUEyWR8wEt/h6idxl/NMKef6uq5CA8qNkWSfFHkgXR050VRVlUQ9ikgECbaMa+l+USHyCN7ghoF65P6Ww5uK515STFrIRibHcZoovJyKUxPuGSbLVUeUyQ6OI++z2pn8SxJ6dnRg0CaGxqwiWm6ldYTfJRwv2JklOaeAljBlHdT+SV2P0ygJZJgUb1S5BJcFBeTwybn7yM/sZceNJufxLpMf0lbheOAWj6tffFI+6BwAnnIecvs4ezkF36k0KSHe853KgaOpFnxW2ik1wt94BbS4+g+8BLSW0W/sAnrEuaR+BMTDWXyoJY5dHEQhKWFRFLPyKmsRlGK+F1DBZbjLRw6kwKVElZofsclBQhoT7iwPiY4gYdDSqJalpgjvK+gmVEpduMEigGW+kObFVUhcV7aDc3mAblS31Ua9ON4yMULzzQyjokboL8CkJI5pASY6bwRlvpBCTZYsAcCpsgSAk+qtkEap5HkAJOWg1fmlRGJ+4J/kERvsetpmx2astHglaiKrKDMU01Bo6z3bjopM2ipxOue3tFq2sNjmyBtqJyF4w5Q/dQHHdWJsAEFEoB2U2oKEZZSS1dxEHwoJBIiLVFu0armFSgXNAdoopCeKe8obtQ1y1NVb1UtBTYYq2PxouyWnLiH0BjgHoIQhpR0TdpWwnsGSJnikZq6FTZP9UF1SW3UgnmpaiPNJU4pU0o6zsyM2Rii3yZ5DVlZTYkX1KzBlNQ6c6z/+bcJyXrSS+pUWbLgDpcAVOHqpP+SPVCcDQFJeD6qFAFfT44QanB+jWAM4+AHF4rMAIf2owQBP+UusrG69FdUtR+mthLpM5pI2XEzYLhzzIhVEm6hGBaURhToOYXtjhDVKzU6YD0UmKXVlmBDKFKPcRdvxoT4pSDDkKQaipV6IFiaPPISMQ57P3Z+gQQ8AXzbRTwTQNhvo74SVylhVc7PlRZdw7wmb6DpnM9x/SQXhhusJyVm8ARMCBEklUBsJSGkxBUQ4AGCHymZY4ARtWQBbfGoMRYxunF6EY1pSD3R4A+DdWgAFwvDyHyYOuyJ7h+2UU49CFCTlQ10xPdEBlNaJAhd1AWDAw++FAAARlM38hS2onS4DpAQOhBRI0lCDssWicmSE+ZyioXpp014Bz7STxPBhm+0/AA+NE77+HjjKLZaZvER458dtZpI+o3qZ5TfeSbTWRkyLbKDuUPUDewPgPTNEGxw9j8IRtpv2ojCwSepXDPhO4Gkhioj5VagiBqkbLpQvx8roLZBVODfVH2A8gDhi9Imz0yhqjveZR+qocyiZAqgo9G2IOe9wWbmD1KRY58A5tV/ZJk4nG4fR4a/6EuqJ4OhEJ2l7jehF5whRXWdVLVsRbuXYkWEbP3sCZaMxy6uvsNKWFSiCVnJ/xcAxwKsJ+kOB4hizOH3AzARhmMe5x8w4Dm7qTApbQ9jPydlzQGTd1IFGK6lhsVQe0CHhkZPkdZp7x4RTM7G9pDFIofrot7JxYMWpXpuf7La5xBBOXhAvvvfTTxcCrublNRFW9f9n78yD5LqqPH1yz8rKylqy9kWq0r5LtiXZkmy8gsEbNtC4hwa6CXp6ielh/iGCie6ODqaHhl6YiYAYAqYbppvF7JjNK2BblmRZshZbkrXXvm9Z+5J7zve7RRKCEE0HZrNdaaeqKvPly/vuPffc++757u+0APIBsAoU+rFEswffLd+cZa4ATo59oGbE1XqyQL5jB22x7wBznIxVtr7JQo378G8oxQ0dtLG+HsQCW1He3I2q4UrKR4ps+hBmwYeBizhX+/ycDQJ/aDwmoSRvFGwCmHsUICyDqmgbENHWmjoroR8tAuhP0q/GAVMneApg8tFfylBZq0YltRaF22qg9gg2K08/Rrk7UIDsHOo3WHZbW9Fgm2vqLcJ3FVUdZd9ixOUf1IL6nCAVdSR3FoHoak8NugDrjsNhTBWQJpFRZ5RujOV3IASl2V7gUKFNensABd/zM/hdxpoZlLTKUXSNAxilF0hNy9ywHAXdNfEaayUKnUdxdWJmyqaAe+aIhrMVAl/qtSjjXzUqXjWoE9bSLlFsWCAfFmp9gLDnBklbTsB+XUurrQPybPhx+VTH9ApnpoJm9Ytcq1T9ZLoe6qFAv8owJqldde1YsPuMbEezAsF1AjH1H27IGKbdHHgRMPjc+XO2+AvAOc2XNYfVvFc/X0Cp7fDhwy4TgRSar7vuOqfoVpwXU2W/sUfx/uDKL1Tsa5D6VDmVvnXTpk0ubavm83qoljR3P3L0BXsR+55bvxpV0hW2gzSlmxjr5aNOAcWOSCkUOw5go27OyFgeRrU2zrMBGy378WIySX1tCHDuNOtlU/jntdjxbtQHK+kkaoMMcw6Bc9og4b4fVcmUA5A1ZGie7xyQm/bJ9+PC1YWw1aW3pMoYZILg040w58wAb2jOpgcohWvlOcbvYQCx3kTCugdJrYpS8paaJmvAzpxmNEbvZs6MIWnG3zF8Vx9zoBGBt/yHUCswJymIQ1GLA2PHSGEsUFDv4a1Q2Esyr2C2BhgslVM5zYLmjj7mOZxPM3a1hXzMAOlvz7WftzFUiP2AYwXmH1OkPx8Y6rOxwTFSg1bZjdfdaLs33GA1zIc0aubk07mP0JYEv+4b+E8zRFfvXLM2IEgF8tjYgHXPjltdadR21rVYI31R9szEemmaLL8ht66NFbqXopNkKK9gbfkZKR67BubtH/woY1/61iG+f8ruv/8Gu+O2epSg6FfuM0yX1AZUOadjvsA/qL9NITF1+kI/SnWMUZkKq4ihsIvj6R+ct5dO99s8EPrOHfX2zntrbPM6xmDmnxI0FSTjZ/yUN5D/kU9QLw1SZ37NMej7guYFO+n1HG0pQMtNW/luuSfX3zkXM1COWaA/yga49+D76eIcL8wWP8/LWcYQpZz82Mee5fdWe+De9Xb3m0IWr+VA7KrAfEJWJzB+yZL443X0kK0uoNI9gdKq1rkTiXGbn0dlGl8XRuFcwG1DQz2CUjWojS3NW4vrDkl8vODc0ZFR0vIyj2INQX4xiF+vQIW9rk4peuuslKxtgtmSi0l3/iy+oYx7kkpUyaU4rU4uixBIp4xwCc4l3xVDOV2CTz5sQd+peyG9PgUUq5i+/LXOlcFutAmkjOOrq+mzuicH1tdaSZb3dD2K92v9Qo0sW+ILuUbmC8wLwvgwwc5XrmkUTeA/up5SPP618FP+JouKb5p7S/UOP/crAfyqwLOCbtCYU+i+z/U3DcVyWeqb+l11y9/6nZfcG14mF5p/D3R47BtfHbLDx87Zlq1xu//t60m9rHrnQ3xmyZVrpEafFmcm35dLR6y/N22HnrtsiYlFbLCcuV2M3/N2/uKQXbjUB7TeZve8Zb3dchNzizpGdy1wMD/XmoV8iEcF5FzaNDg7NW2PPPINe/SRh21lS5Pdd+8Dtu9GNtwwH1oC57Q9g7kdCp+6AJ1rqVy8/Us8XrPg3KOPPuqCOdplK0W2D37wgw6o+SXq6Df6ETmohx9+2D760Y+yGHnJPvWp/+OCTXJ0P/soTvg0SZKT+G1M7H62TMt//3QNLINzP10fr+Qv2bsCAVrkv3z5sh06dMgt6Ot3Ee26cdiyZbNTZ7zpppucyqReWw52v5JafyWf1aC1/Pht14BuSnoH8nb37yft/GjB2io99uG/DNi736pbpuXHq7EGLlzK24c+krHvHc1aI3LpH3iX3/7rH7NbcJmFfDU253KZf0s1oF27n/rUp+yhhx5yCwxXK0Zzc7NT2nnve9+7DM9dUUG673jxxRftM5/5jFPv08LI1R5Sw/7ABz5g73vf+5wy9tWOWX5tuQZeTzXAuqD920MZ+4t/SKP4YPYHN/jt/312GZx7LdiAC7Aw6S4uzhZ/6v5Vi7xa+NU96c8eV7z24vFXvl98rXjM1X5eefww6Ul7e3ttaGjoJ+lWtclM8LcUPxtRB1q/fr1TqlAQ7tlnn3X31npdGzBVPilvKLPB6dOnXVkF3Om5giCcyqOnvlPXpQDlP378HxC+8gHOvdVuuuEmgi6oNbHQLehMCglZYBQ/UIhTW3DB++KqKouqbqEWpRp3Yaj+OKUHzi9wjsCp7u+lIkCIyQV7/ARTtOC7tPqpIJKgOTqSbnYEeREyUcBV+gR6CHbzCJyTKo0LOGvlFNCLAE56JGE9zx6w0MCgVa9ZZSU3XGeFxjrKyXewMOx2NxNEzBLoQcOMADpBHZae0T7hfEoaRpkIFCg4JIhPO7eleOYCPFp5pgwK33hlE4oAEeSi8lywF802zsS5OE6L11obluIUy8MEAHmN39GnIAApWEEwHedx161lcK6XawYT4GulYMF7nEBn1LeC38BbULG9A5Z47pC19w1Y3fV7rfGa7RauJHCllJs6Gmgux1ML2koXSMmJppHSD3BLr3kAdnSdCgAusY5qB9WsSq9glj6hkusYrpSPq9apBtdWYpX0YiGQ5vOC7fi0ICN3bRwnSI6z+ETZ8cE8fyslj7senUifZb1b7Z/FUAI8XUo36oOPWXJwxLpPn7Xui+22ccMWa961w3y1Uj9TYFZBdYJ62EJQET1dgMqlH7zvBLn43UfbBaT0p7pVZFLtREmdPfG9sl+lQFVJBXApui+lFsKGLvDjEZiFDejUAv0cBKgAD+eRHchedI2yAwcD6FJRgJJyn9o4T4pFSqBLdZBdjrI41UX6gdQNjVSuXc+dtMTAuDVUA7ls32UB+qqH9GKCB2lMghjYAvIgPugBgXMTvWdtZqQbNkEJ9agqglNebKxU4EZ1q5U0X0MK0SbekU3KljiGwuYxIg9PKSUR4QCQmgNkGbbEWBeBqk6+g9S8sjUqOEC/DJBSLRbl3rMe+K1ml2UAXFR/UgNSekGibjzVwbl2/IOhdGjpMzbc+ZhNc11VlRutqmGz+UoBw1AwGzp/gaBf2uoaWy26Zj3gXDVhVAX3CPRgY36UOD2AaYsouo0N9SBwJqVIYFYfmAZ9Mxgqs/KqDVZSTerJCDCfVOMgTzwC5ATZAkGmiZrwG69RRnxCGsBwvP8U0FUfilAE8tVOwDIFnohCogxGRpfqjcBlawB2KvkkwXXs1YMiINKTtC4Bd6AipAJ5mb+BrqYGztpEn8C5SisHkvOGmlxd9p78PlXQZdUrN1qo9XbSTm7CBlAbU3UzMfBLxZBzpudHSKnbDlw4SGrPJNct1T8polEuAopRQJ+Kpm1AfQ2UBQvDBpzyILYupcoCvo1YvLNFpbKdm++x6dEOWyQtZEDp7+hUUqMqSMWONlFKznDDatpwC3XYzHuolqWwL7VbiD7J9Tofhr+HcEIVbNiSwxcZXy7BkuatafUqFP5agBoBsbsv2cLAOYvGAeead8Bx8STNrfx1js/6UL6Qulii77LNcY0eVMa8TpaCfkTHFKhWVkX6OM7njQJcBTgvSpBSONJD/UgPBVhdV6XOC9PdtjB0hky0vUDOKFASNPMDQ5SRSjuIuoYfcDFcs5HUxq0uzXMG2MdHHXiSOid9njrN6VqxWyl+FhZnAOeO2tTQfqoVoKj1TgtU7+T6hmwB6HMq0QGstspirbcB5K1CxQ6wiP4riCHH9wW5npnBUzbZfxJ/hY0CRdK7KDv+E59SBhhR0rgZALaVtif1NACER34eJ5CnYTz4HA/9TxAf2l5AMhM2MoC9I5ZQAsgaobw0NV2LdIn5WSslr1+McwaqtkCJtFIQ7FEORUaATxKkpJrTtRY0AU21W2r8uA32XbRstAnw8XorQ5XKEoOWuHgKcG7SIivqLb56K31zJeUiFS/Au1cptagjD4BMkv42N9FlMwOnyeBM+mHqM5+hHvC1Of8c6VkJttevshBwrCeA/ecIXlI3eUALxTCxIgcTh/DLPiDBHEDhwugFS4y3M/5hk6QGC8mHkFLMD7Cnvl1aRV+sXUHqcvo26leuRimS/O+SlxW8ytjnzHTYCqNP2wLg3Oxc1ipX32mhJtL+ensZN/bbcFcn4FyL1bQCEdes5RqrsDHGOx5qh1nsoZ1gfuf0JPUPqILTUJVm1Ea0ZnlJqa0FdGuORfEpZoMLUzYwTnpTlBRx+nQd/CigjFPKoYOXxyqsrZZ0rKjdKq3pBJDmZdJN9o6NWAkA56ryOltXGXcKbB4Zk+xc5+EptlOvaFz1yJ+qLXlbXcLN/dS3OE4Piv3jsYSDBdXpg3qNepcqncY9+T9xte3Mf84khmwANdhZwE70uKyCAHqEk8XLK6wBKLMW3xyiL01SD4Ooek6lgK6YI/oCpJmmcCl8vJSwqhhbVnONTdRLECXUJHOYwTnSizIPneV6Vjc1W2u0zGr5fs1y5H/5lWvA3nnKByplrt7VrMKrgZ/xUSOnZnlcKK/xGX6qInS8IJ2kcwKopPFyUBfFd80LnLvwi8E51Z3irprPChj59re/bZ2dnW5+LIVmzXd/E9kGVA49ft5cv/i+NsXMoSAscE5zfG3uFNynGLHuLQStC9g8ggDLad4v3XGNtbautDXYXD21OkNa+4Oj/TZECl2plJULGKQi5/hd+rYVwRJbG4tbA7GzEubvmuENMYd4aWQIxbm8rQVy2M37oPLOHwtA1rxcQJh7QGFlAGr1wJMzT8AusVGZoexYJurckuZnam/+E8Lm47qkyqyNLYK61P/8gOk6Zg6bHGLTQzf3JB0D7VbfVGPb8QGNqBSVygjwNzIB7YuYZIzsGh6w3ukhW4wwnrJxwCPFMxSXpZocxsfV4kMaSeVeRbnRCsbHLG2GgYLjXNp4w4n4n1JzambS1I1mgJrv9g3027GTx+zMpZdJwTpLv88ACAP4Ycu11bW2ZcNm27VhlzXFmphLoOLK5qSsPs81URIHuKnIOc7r7hl4VeDcGHV7nDrunk5YPeqhN9Q3Ac4x31FH1YdVHs7D4ZSDeRljqMYIzR2l4CalOc21fRpDcBaPPZ6xf/vKM5R7yh542z578xsbHVjmpZxuswvXKF+iuyW1h8aSxMQ0AEynPf6j89bdp1TXbK6ibLNAShof124ivnrrGrthe4nVkj5VMJxrKxUNgNJHe6meUs6WBVMCXLHhx4dcnOaU6stcJv2NcRZD0JSFr+b6+EnZVQ45NG1IWeoHmotTd1y2l7nWElQXBL4OknVnzj784cexq1Z75wNb7N67QlZHBiXN62VT8iJuHqfzv44e8hNKT33+3Hk78/LLLlY+OUmqcvqF25BEXVSTsnkTbak01CvxDRGAbkGNi4C43d3dbsOcUkJr/UBwrs7pw44rgKjXsW6wl81z2nxHI5Eyd2n9QOvBmzZvtmt2bHexeKXZFVinz+qY4yeOOxBvzZq1dt3O65hrlMqdk6ZzxnrwU2J1+vg5MwuQji/I8dQ6RBy4bzXftWnTRtaPGwCsShwDoM167e0dzmereR0ARrvLl4dKQs4ftrS0mJ6VAH9X+vCf52Nfy2aiPpHmnhMroNm0roBCKH5D44VcixtfcKICa919MMfr/yXXo46p5taR+gXfhc/U5sHBzlL78peG7MDhs/AXVfa2d6zHDsqwKY5VH+fpcQqkGsF1HubHSMF3XJ6z737vmF24iAo6uxJJrg40yeYDFkTr68vtxn1rsbM6a27yW0kpc/klV0B5cBauGPoHv4/fmBwXOPdNNu9/68fg3P120423GO6TB/bLtRaY2SypLeN7cA7yD7/s4zULzindqVTnvvWth1261s9+9rPW1tbmdtj+spX1m/icFjw/+9nPOWBOVK7UMJTH/spO/5sox/J3/GpqYBmce+X1qAFZwJwCsxpcDx48aGfPnnVqcyLOSyDmd16303bu2umUTdTPdTPxEyf/youwfIZfqgZ+PMj+Up9d/tCvqgZukjE4AAA1IUlEQVRQ3LdvcRP1ob/M2AiT2V1tXnv4i9xEVy+3z6+qjn/T5+nqL9hHP5Wxr3yPu2puFB+4w2ef/JsQu/eX2/Q33RbL3/fqroGTJ0/apz/9afvud7/rUrxf7WoEzwkaUKo6LVIWd/de7djXw2uzs7NuHqYUfj/4wQ/c3Oxq193U1OQ2Lv3Zn/2ZW2hZnpNdrZaWX3u91YCWj547kLM3/pekEQO1B7f67QtfBpxzCz2vt9p47VyvFh8FksnPFX1d8adeV9BLwa1imlQdXzy2+LtqQ7/rqUfxfffHL/hH36GngLf9+/e7+2QBcAqwKdimBfPa2lqn+nnXXXfZLbfc4tQrlJ1BynSC5iLsJNeCtXZ/a8e6dmxrzHvLW97iMh9ok6auQxs1dS36vtNnTtnH/mkJnHvnfffZzTfstfIygkZaNZXKCQutaYFzLH76UTPxZglOKVBC4EdBfaeYIoUcLrlAIMoTZF5LoAWdHwJnBO9YzM0B9zDVdTAbQhBLi7sKsupEEF8eyatp+kvgyE2K+e6sAmq87wMO8aCm4o5lNdcFcQirSPArMzBq5x/+rgW4/nqUUaNvutU8bc0EhQlz0Y4K5ik4rCiPEod5cjM8CaIpyAYAptelTqHAGSFCglEsTLMMXKD8gpC0YO3Ra2prRYAUCeRMeQKZSheoz3mpHw9QT0DBLBajBWmluR6haQLnSHhIqbl6Rc8VoAT+gSQgpkTYGFgnl+S7dH5yQSkAJssRUOBdRO3h7EWbfOxxO8uaaMutd1jzjfuAgGgDH3VCMAQqgvKj7KDVZFdGNQxwk+AYzkMeR/4BhqCssDgOYhOSqOuEwOCLqAt+dSqC2rGuYDcfpOZ4kbIqSqgIHRHAHAvoCoJnUGRSvUqQ0K+gOaXFnKhvjiPKKkjQRcpdH+DzOiXXlqdOBO84sAUoULxXur3bLh86bBdOnrLt1+6y1ttuNn8rMBFpX3MEVqVCJHDO76KVKidPHvoupXTjrJxHykC0LccuwUcUTEFK2Sfto3RfeUCYpSACdkpdCZzzCFbT+Vz9UUgeBYxK6S25TM6sWtCyvX7jNcrhwDvVNWFqPQvYlgOvCAYKlnBBXk7qUTpdpchE3SfbO2rdTzxrw+09qD0QUL31jRZevQb1LCAPZEqVYjZDGQXZ+KnnQgqliHEgqUnSSqZngVUIaFJfQRRXoqTTDJZj39EVwEwoqOlBEFZtL5i0gLqTi4/QtkoFbFnU2IBiJgQk5adQBwBfUx9epA8vTFsK0CWUTVikZoOF1zyA+hYAimyAa/ERyfV4CLqSJtkBq4CwLhKcbrdE11M2OTZDeq2NVtm4CUUq0gRNdtvYxXMoVKQs3tKM4tw6K5DqMYkfEKwmlQQBSF6UqTITBNnGei2dJB2mg2zwDwRrSiLVKOCtRYWtFeCrhiB+zLVAQMaCDxCsmKW/KsUcDUIb5lHmI8Xr+CX8wbhVkkowoJSsGZQ2ZvtRGBtw9hoAjipp2YHQWyvtDYyn5vZLxRIAVdHavABTXTdQGClnZ4cv28TAGdS2SDfaDDjkb6Fdpq3/1CPmXei0+AoU8VpuAZzbQP+h/ynyz0nFbUpmJosS1SIKekkAgAyqbIXkjGujAIohodJK4NeVZKxs43iUhlygWTanviI/So8iHZJS7zqIMzcL9Dhgs+OdgF8jQFekqAui+oHPzS0M0cQXLJPGP9cSI1m1B/vYRL+M024R/ATnBHrN4w/kx9T/kJ7CZFAEG+my4cEuWIOC1a9GIbC6CcoBNZCey8BK51CgI70jqnieMgCxEqAs1ydw3vKjnG9xAnWxCRTPZnu4XqXyriCdU41FgGtC5STXi9TS71dxdI2rGx9AhtflnlSvAlHFd8j/Ch7LAnAlR8/jEKYwFdqCccxLatTg3KhlWTtOo55XEsOmVm4xX00bwqRxWp/6BqwjLMd5lrSsnH8FvimwtpyaOIJd7KdbzFvFqtstGN8B3Dlsi91P28x4F23QyvlutED5CvqNwFl6OraQRc4yQB2npy/Y/Ohprm0C1UZABDpWgDSjYeoiXLOKzLgrLBWqpi5KAFAVwJS/wJ9qDORa1f8ESub5mVqctHEC2XlS6UXpf9FIGUUm5ey8lPteRlVpCKis1kIr32Ce+E7eY/yVz1OackEWBBCdH5bh4usKyW7LjL1Iqq1Oy0WaLd6GkixQiWeKPnjhJAHsSSsDnKtatQ0lO/p0HjU6ly5YZaPvQKaRzJCuTeBz7Lzl6Lu5pPykJrGkdIuGLdaIr6kA1wnWc02UB6gOF4U9AVzwebwOvhklScBvL/4pNwf8OIVaoSA8ThMpA+YkXWB2BsVLlHNSALMlZSErXbHW/PU7GJqaGE4FLGKTPJZaAL/D2KKxtpAatQKKcwu9h4A/SN+85k0WbhY4122pwWdQvezEZzQAse6kbVdRP1wjEKsAWAHzSWDIIYCbHsCRxCxqLcQiFGgOMT8qZfyuxM7qsbMoc4U56qQvOU06yVE+n7cy3gspnSzlmJpbsGFAxCQDXmt1nW1DobMJmHeepujBJw2jyhaC+mkC5mkIM//iejRkav7hAs9cm7yVurd7nXVcRgjGZPUkvcffHKth2A21vI6Lc/CgiCUBLjpWYJ3OUxxeNcIPzy3aualxu4ii6DRAbRxFsWYULKtRIGssi1mc+V8YH5DDjiawtSFUDxcYy8IozJUKXMTHDJO2dmCeVL0Avusr4rapssbKw4LquHYUhPqmZkiNaVZXWW41+C8tU1Icyqweo/bSpgHGSPqyuy6uX32BF9zfBXyja2G1M3Xr3BxvS40Pr8S5l2YbAudKqBDND34CzhE7+vdStVIM9xAgIoXlxx57zM1/laJVSvnFtR7NddUev45Hca5fvAe42vdceYzsUOCIsi2p3IoRr1mz5ifqShnSZQucu0w8uXUP8F9DHalVUT4EmJ0kxfmJGRRpGUcraIsq5oACzkaxnn7s0IM9rEBFdn09oFXpUvrhMdrnIgAkCc2tBRW0za4NNavROMoclXOHNHdVI9Gwmu+79qJVxTmkeSqdsEvNiQ3KNjUF0jGqUbW9T5NIzpHDf+IKsDBsGmU1wcwLHDECodU1Nm49oz1W24SNxZkHMV9h9urmzDqf+sIYGwQGJse5zmnSMAvCBprH4JOLKUtQV1N8R3W8zjZX1NgafIdsRqmwXfp5N3fVHI1ScZw6i9KAa1jX/YePOcMMYFt7xyW7yHOcekTzCgFlALFoia1aCeCzkg1JjDMRfHoQGNoLQCrRPaUYdGfmvBKDRVdLp+ebhB16SEVbsJPDk9admMEHRG0vSrWNKLoJhnPTUMpARbvPONUlN8/UnJgxnBNpAwqzar6DsYxNDd9/FHDuy8/Sx2ad4twbb0dhrIZv4mSacctfaGiXsqXzm/im+XlSoF6etOeO9tmljmnUxnS/yteye6ChmUxe21tt64ZyawSaK1V5OI9uR1zb0qg+6lFXlcYm9Jofuymh/FIiVSJZuqXzPbIb2YmGJ4oBSM51MX3RKd0tHZ+Fo+Yz2BTH5TlfAbBXcxltUklDXR8/nrL/8bdPcL4We/D+jXYP4Fw1RO6Pp1FurA3zWf5/zT50762HfJMe8hG6N+/lXu/hb33LXnzpJSAzoHruE0pKStzGvWnUuebmZh2Edtttt9mb7nwTcFKzG0MH+vvdeq6g3MREguxUjM30dUFskyiyCx4XoKvP3XHHHRaPx+38+fP2uc99zvmhO26/3d56/1vdGsNS6lV0xvBVL7HBWuvrA4MDtnv3bpfFReeREt2ZMy/bwQMH7NSZ0+5vqcRJ3U6bSCYA/jJ8Xsfu2bPX5JNbW1vdJvfH8dPPHX7OrdkrzeeSf5YtMkelPioqgdTb2mzv3r22fft2J5wlv3rl01Xa6+Qf+do0dUPWZfoE/+GP3L00v6sjypQ8OHAv/k6ZA5x35li9rpTdrjeq/njLwwa4Ajiz3hvqjtuXvzBiB56/xPpQtT3w9lW2bi2+j40V2kDoZwNLgLUKOVMpmnvxuVpIGBtO2pEj+JlL4zY2mgSaY/xnXlsVj9nmrbVkOKiw2nruNTmc6ZW7D5G/yOLLdU9CUZaKSBknxmfske8/bI8//k1rW4ni3H0P2BtQnGP6xkPgnDwOczGemuvzaf775R+vWXBOE4t//r//bJ/+zKcdnSqI7nY6tTrg7+pDTvCpp54ClvsXp6R1zz1321/91V8tkb2/q4VeLte/WwPL4Ny/Wz0/901R4+rDSQbWvr4+B8xpgNUgrbRqAukk37px4yac9Va79tpr+Z2dq+yOutpNx8/9ouU3fo018EqGpl9jsV5np54iTeuffChljzzD7RITiXe+0W//+r+1uLX8eLXWwPhkwR56OGufAJ7rWSjYzbt99nXatHoZhny1NulyuX9LNaB5t+C5T37yk0457WrpRlW0ciTjdQ/x/ve/36RoK8jg9fhQ/QiW06aeI0eOsNhGYPQqD91rvetd7zJBc2vXruVm3K20XeXI5ZeWa+D1VwMXSbd+ze8vsMPSY3ev8dlXvkE6KnZWLj9evTWgsUTPoq8rBsKKV1T8W/eo+v1n/y7eu/7se8XP/6Kf+m7dOx87dsyeeeYZd78s/6z7ZS2oawOmoD3BbwLhNJ4pTcr+/fvtZXanC5aTKp7OI3hOkJ2CjtqZrh3msVjM3V9fWW4de+blM/aRf/h7CxHIf8+db7Y9KNuVEPxIAf2kBDcQ8PASLC8ldV+gsp64OkF0LcgmWXwlrVI6MQnAQaiMxfI8kSKlQApWKv1jHSBOuQv6inLzsB7gSZC+bXSSVGJJF7RhpZcAGYF61I2CpInxcm2KNhdI/ZQnaJBGxWEBpZQ8yl1exqAAAe8Qqij+0nJWYdFjuNhhXY8+ZhHgwsrWVgvv2W2ZxnrzVsUsSlqaEMp5eUXxicgotWZBoA7KJNkpQmazGUsTgBQ4F6QMYQLDPtKEetxKLxCSrgeF/NRUghR9KDvxt3ZpewlKhxqbUTACmigBfBJopVRRsxwzDhREkF2pxhaAlwKs/paEAVAoh68M0InAuktNl5sH7qC9JjluElUiyqEomz8MIBIvtwAbb20O4OL547bwgx9Zx/CIVVyzy2LbtlGvJRaK+ixCuh4f7eFBRQSj5RppqxTKDjOkAiT4mKT+iHtTb5RBSoSVpOGsRB0pDFxFoK2wMGe5cVR9qA/isU4JQnpdfgjgUBkAEm3oJYChgB8VB2xDIASFgQXqbnGG9mBBPhRg5zvKaeGqOADKUjlcmlQiZwWAgxypZtMEepKANhBb2BH2C6wQIoWn2m/29MvWceCQ9Z09b+tXsmly53XmbW20AkGdUF2D+apqgDAArug8BY6X3RElsjzKBgWCQxkCoRkCqk4hjVRmfpWZdSRvKcE+Fv0LqD7lp6csybEp0h8JQKMFXTkCbNoMo8Lgq2KjZgjFNLUvcEF+ehwYiDpBeUgphxQwVgDXV1aJ4lUTnJ3mjmALGcpAQDc/tUi6Q9pqntcYD/yRIMeR7rEasAebz5ztsr4fPGtjvf2kQqq2GjaKIqUg6QgrwUbDcda1UQyTZoynwHwsNQ6oM0D7DMJxTXLhBC4I2HqCKAv6gcJ8FQRyZU91/M01Jqc4HnCJvqGnglYZ6p8mpr8oaElAFPgmj7KYlCd8WkhIE4hYSFiy5znLjbyMwFWDlW7+T+YDLuIkfKdAK1TgkkPUyTCKd7SnN4JtELgojNns0EmbnUlbpHKTlaG85SfInQfsGr90huLOW6yhGgU00iYTxF+E+iiQRi1M6tSAkxEgRI4tKbVrIT2BHcyI+UOBD2iM77AckJa/FrCwHlanylIE/ELZWcyQuiG4mwMckiqKN1BOullUpvBh+QLQAGp6AUAdPBXn4HpJqZofvmCzKLGkfVVW3rYD4AkITulfZVB5fT+KcIB+eaLhUnqTCiGN71KiJkaBkZsbrXzFVuq5jXpI2NCZ78HbXbby+iYL1KJu6WuCQ6OdgXW8Uv4gcCif4OxjkdScs/RF+qMgRQ99xaP+H0QNTiAS1+jFNyhdLZ0VJQf6VAB/gs8gHm5JfI3g3SAAj2KrSo/qyUwTnCaARYpmAX/5ZB+qXCdoj05Ll1RbsHUf6UNRbfRxjahN+qizQlbtN4ZNcJ2cOMjnfaSizEyOovYwQnmBiFatwWejKjaXsvnui8Bzp620EnXDxnXQLCvhpeTnZE+yQykA4qNRCyws9PKZXmd/OCauB2APG1NaXQ8Ao0U2ATqu5PtRwUz1cC2LqCoqCI9CiogMgm0+VJJ8qP4wkFBuAnbYDJ2I8gOLAQymBjuAy8ZcauHYKmytfrPlw02uH0uR0UgtmlnE12Q4HmjOL8KIHafZBdKNzp7iK3IWXXWzeeNb8BmAc50HgMV68FmNqMbRtoxnGcAipYwM0Me8pAL0MN4VUv1cG2Ah/cuRAPRBDJ3yVfCMA1xUu/TGQWzau0hd8FQdqePlgBPTAKc+zqPgNYZPGQVYA1PIRgGW+Af7A+gaPGa5YQA9+l1o9S3ma7iJ/sD4iY0XQc7CAm2YpS05v9hKTxZUZuIy9TJsFl2Nct5NFiSNYWG200bPHbNZUhFWNJNWrXEVttYA/Iffx7r1WR9jHfmQGRex0wzXNteO4h/XCaiNMVB+gBEinh7GAU8AWy1t4eW45fHnhZmEa9t8CUphBGrz+BF/imA5fdsbEEjM+I4f03xB47UAuPwiulcoTM4OneZ6ZizauNoCLTdbvmwN1wMuorEhA+yNepsHUFjjh1N7I2hqc+dtcfCMTc2iprXqjajo7eX7SS87sN9GUUYMRqqsqglFSBQRswL6gXN9foAh/HkuUmkJXzlwW9DGGL+T+HIB9WHaIoKNllC9IeIU5dSDH/8+TzkWAG6D9LkwDkk2rn44y9jcDpzWQ4rkcsjY3djNeuwkSX8eADIaop1FlFSgzBThM3Q02orNCZhCOOQHzFt6HSvAZjFrzrmkxJW3SaDoeakJMaYJGAgDEUSYh5XSUMyGHDwn3l6p5hmibZF5xiJzvhQ+Psc8Kc1uhj6AubMjfTZLP6hnfFnFvKuacQAdTKBGIDm+sIIxWunkZ/GLKewuiA0GuZY00ewhlOo6sJcJ0liuYGzbAJhUS2rQINcyz7UPUXeznCOKfxMC7eX7s8xtZIsl1GUFZSij7C6kzbUxa8JfCJjKk7KXfpjWK7zB/0p5HGYeW4qPC8n+Od9SWF71AjiCulgGP+hn3O67fMnNfX8ROOdgE+Z/zz//vNtAovSnmvMqJWFxLq+fxfk5X/krf6jselz5HcV59pVfptf0lDqe5vmnTp1yStKKgdWhZqg5vlOce+GodY4nrGnfPtSfy0yYcDV1l2GsG8Z/Z3E0pbS/2lgAJaOMdc1Nk/J+FPXDnK1varEm5jZCngTMdWKPjLRWwVymkZ9B7C7NU4p0aouKQMSdT3MGIZyMSJzXxxwWdIF7kkXGjgw2J0gMTNYi9BfZaZhyhACjwL/4D3gLo5jHlmdpw0XGB0Fe+WDUpoHB+gGgh6cGSP8ctTbmdfWMa2E6mIe5vmytBJtFnBJxOWwLhU2l+3TqSby2SBm6pybt8uw0/jRiWytqbRtjh1T3FklDPslcTHXjF+yKTecAOZSKEBPD96JmiD+LAZT4mJssoka6yIR3gf4+DUQyicLpDH0gggJjHCA7RsrnqC9CutsIcxZ5Ta5C8BdgXg5/Ns33jOMnkvz0sgEH72HT0ItH2xPW2Tdj3MnYtnLEDRgrI8x5q6pKmOpxHADbzByfTQhkAuwGkFTf9vMdFSiONlT7rSoWoLh++y7g3L9+6RBjUtbuunOHXXsN9w7Me+Y112X8LOectRwfi+SBgKV0y/iCGx0Zy9vlzox19QEuTTO+cv3RMtQka5lHQMsFmAuubvBbUxxEjyFodiGPYhibO3D9UeBB3DGAYpJbj2lAWb+trEUlFGiLLPM2OlawxCQpnvEzGt8EwkWjiluUojwMbE5bzM+QCnssh+po1l2f2tBPqviSClSIG0pIhStU0uzFkzn72488xTGNds8dq23fHr4YZdZFfEWA9o3Hg7a2lXscOZzX6EPz1uJDfkN/6x7++cOHHcw2zVqtlOW3bNnqVNd0rz8wgGLi8ePUb8KleH7Pe95jO3bsYANnyk7gT7700EMm4akWlDZ37dpJ29TQtmk23/XbS/gapX5du3aNPfjOB0nNuc3OIWLz8f/1cbcx7142l7/nve+1pmY8BEWjGwPBzzq/+uUvf9l6+3pQArvJ/vOf/InVcF7F8h9/4nE7gt9NpdJO5GoNa8XlKJ3Kx126dNFlNRlhjrJ27Tp7xzve4eA5+T5t2n7qqR8xX/JaS3MLdlTqbFh1MDGZ+Mkm+FtvvdVtetcYoHWPK/1rETgs1uFr+af6jKbLMzjIKeKHk4mMzU3TD/HJcnLy2VXVAfoY434pYz3LCRP4gokEG1y4v8nyAq6fTR8oD1Z5jez0hru14e6ofeULY3bwSA9Kc3G7443NVlXBRoJ57p/wNxXcAzRIeZhMZz76sR8fKlg4xXRtoD9jnZdzAJU5wEuwtlKv1ZKWtRLfwhSK8+dt5QqUbyuYpzDITyQK2NAkZSihX4d4Teqrs26d6ciRH9rRI48D7bXY/W+935RmXdCeu4/i2pbwYOZkfDd376+oqV+z4JxqRQuHCu488cSTdu+999hf/MVfOMBGC4e/iw8FpD7xiU/Y17/+DRYJ8vbRj33Ubrn5ZpdX+nexvMtl+sU1sAzO/eI6Kh6hgbIIzEnRRMBcd3e321Vz/PgJN4gmceBLaWbWuZQzCmBrV9CVg2HxfMs/f9s1oEFr+fHbrAFNXLt68/b7703aiaGCNbDV75P/M2hvf/Pv5hj426yrV9N3K73bwUM5++9/nbYTTG73bPfal/8xZK1tr2xC+Gqqg+WyLtfAr6oGBOlLyVZpW7V5Rengr/YQLHczc3Klbb3tttvcjrurHfdafU1p/Z588kn7/Oc/7xZttdv5ag9tYLj//vvtz//8z51K0bJi9tVqafm113MNtJNufdvvAZAseuzGRo899NWwtbBo5BbqX88V8yq/dt3HFh/F36+8P9Vr+vtn3ysughcXc4vHXfnZ4nmv9lPH6/5ZD6nMyVdrTUkgnJ56X8E/PfUdSkGle2m9ruOl6C5/Xjxe4JxAOaU5EUAnJbpiELH4/Spbnu88DXT3sX/8uMVYW3vPrt22XSkkSZc0MzFqUwqS8J1hVIRqVq226KYdFli1HriDUFkfCjNnT9nE5cuWm0K5iYltxk96tuqYxXTstussiIqDUjwWFoHE2BG/cOasZS63A4tNo26A6gVQQSEOQLRmndXtIHVhHeAFAfksgYHMufM2xW73MVKhZVnE9RHIDwKeVLeutnIW/wWidL9wzEYOHLBKgr4qY76hyWYAuAKr2qxl7/VWwYK9pwRIRapSRH9yIz22cJ40mBdROBqZcLvkCV9ZSXmUVHBtFt24zoJNzZSBxWdgjTnShU13XEZ9aIrAPAFgVoS9QF+VW3dYxVbSpjYA+AhOWpi3dHu3ZQHAZnu7UfsDVCFQ7SftWZj0bRVtgItAbwHarUBQMcfu/XT7JZvo6CNlZQJYQME5gkYRv9VsWG2Vmyk385qFHx209HOHCTjOWram0XK1QF6xiJUDJjUSHA6vAxpAfUPKgFInygMZLhIgWUTdfwobmgW+8xIYLccOKloaLbIRNapWQAjAsuwA7XfsedLBdgFakKKRfHVp4I0gsGMl5azaTNrHbahNhVEemeP6Oy/YHKou0939Np+YcrBfGHWBAnZWs22HlVMn3jgBPtQ9suNjlgZqnL1wyeaHhgmkshAPsCegLU791pAWyAd4N3T0sHUQeEkTEGpBIaUUkCzLpspC80qr23OTlW7ZzmeAjQgWO6UlgqG5uUVbxGaTZ89YmrJn5uec+gY5YSyEvUU3bbFQ20qUsoAnJsdsgTInUHhJ0k9ySSWH40GgJtLSZHE2bEa3XWueSiCY6TnLtndaFgXG+V5gnRk+qyA+fakU9Z7gitVWte9mC61ohQ4AQkqQ7vIywf2LnZbsG7b5WSnyUdYYO/ZXV1n11iYCuwQNjp21nqcPWmJk1KLYbykZFeaoMy/t0HjNTotvuwZgjU2jSpEGtAUtCl9y2bJjgD+k+hSY6SO46ieo7kUVMIcSSpJEZZHGNYA+6AeODZDGcxCYEoglEmZMAlxIksYMqEQqYt5ybBTYk8imC7LkCW5L38SXGbfCwAHLdj3H+aIW2Xy/+ep28n0KYgLFAu5lRlCjGusA+lE6VWBK+qAPtcPUzIAlUaAK1m2Dq9lGG6EcNdllY+dfRPVmwqIx3isvA9IgkE6/KWRJ3Umqxkg5KSMJnBQEJU4OWRp1vQJISICAtx9gR2nKsoA2fuClQF0bQnD1wAAjLpVkKelcvSgXpmjDFGpr/igqacBQvliDuzYBsBp/pQ4J5UN/77fM4FlLoPS1iNpWzSoUiOo2cyyBQWy0MHvB0hMvw+skgNZQjOSzSu0qMC0N6DVJEDPahq9ZvYt0o6h2ARqOnvqmBafOWKwcCDkKUJYhVXKS9tFiDSnaAvTRYE0t3wFUhFpaGqAlD5zqAVL1E0T3AEgWghEC1vhEb9zKGlYAyaLKNUKfAmKTIp2AzkXAkVn6v/ljVlax3kpRzPMLKna6PAJ4heXQJ4DibPpFG28/DWiHImHzDYCMgHOAkC51XboLNbdjQMLtNgtclqT/RIHzoqongMR5YF9vrAawbCM22IKfRkms5zyKc8cIVKbxXTWoEpXzWb4TfxbFvkIApF6UrXAA9MVhQOFBwCyAOGA3fwlwrhREUSjN5GjTBgJgVdfx/YxnvfsBkqawyzI+WmrT1EE+UGHR6hbakVSFAAHyu9BoDrT0SJo0gw2OXbJE9wVU3eaAH1Era0A5LkT/FtSdAXCd6iflaDfs1RBCnhkAEFLkAQXlgbSyyRGU7wCB195snurNQMLjlr70vGUAuXyMiX58p0CLBUAKKWqEIjFA1lrgbwL82GaOc2dmgMsAPLwADV7s1AN0lkaRKIlyYaga1TpSHRcmeyw13E+loi2KnSc5XqBWMFLKtaEQWcU4yLXyD081HW3n0g8Dlwy9YNmeYzaPHYXXAPg138hpgEyV8jkFlJ5Y8gULcwC1DGOBEsZj0ht7SZGbmuQeLrbZwituQ4WSa0n2Aq8etbmZfitjI2YpKRszSaAK6lrAcEBgNv7VX0l5gA8LABK50VP4sku4NMByUuBCpjhXkQPSygOeBeoZt+pWWmqs29J9pxie57FZ0pvSc+en6TDJCiuvBrwEJvUCtefxeUopqodPEBxtkp/ssLmeg5YFsiytbrNgy5so90bam3rIALPPoIhJ6uAcbSj5IsGZgZDCpKTcBWSeTZdYrO3NVtK0j+tg7OwHRO58Gd8MECPwmHpdWADSYm0tjFpaSXkl84pWG4q02blUuQ0A/S0yhxC8Lfg3DPgTpI08M3PWWlphreX4SpyH1M40v1I6NM3G+ITBgthFVCMvAJr68D17STO5CTuZB1ruwY90AtTOAqz7GWcDXLt8ZQrFQw/nKmOsbsbnrQS0iwPeyD+lqLIZbHcIeHUAtbdJfEOKuZMfaE59swLQro4+0oo/ieKrpW2Kp7ZJvmsceG+CudMCsRQHSodJfUd9DcxPYG9SISu1epQHK+RjASagjgGu/La1qtqBUVL/Lej6HObG21zbKONwBz57YKjPyqn3ldhHQ2kcH4FyMdBcO3D6OIH3EuY9as/kHIALapA5VAerGHPbUNBsAo4qo85we06hixa1foDC4akJW3BBeOqa13x8dwXX1sgcoxbQPYqdUI1sYMjbCGNtH+eeZEPGGuaXiz19+OHUz1Wco1rcfFfz3xeZJ0rBTSpKe/bssdbWVqfgpjnurxuaUzn0KM75i79f+XfxPkCv6V5BT212efbZZ93vUp3bvm0pTaLWs44cfcHOjo2aZ+d2TDtgq5hnbY5VAXT5Lck18T9AJObMU4pz4PXWjR31MQ9Ostmmtb7RWlnHKcPmErTdOTbBDGH/dCnAMD4EaJ8CIsvTYWKo0DXhO+pplwr6Rgg/DwLLOT02ymfGmNuOz7GBBNDBBzgWwi4qKU8tc6ha7KyC+4YwY9E8djTA5oZ+xoIJFAznAY4ZNRi3qoA+Si3B3HQGWD5S7rc61EyraH8vQH6ODQfl9I/V8QYro90ZAdyTWwkqFXfAU9DoZTY1nAKeS2ErG8pq7FpsLximjCh8Xprux1bJYMU9gJSqUyhUZhjLNBXwh7yAYnFbwfHVXHuQOw9hfjOA2QMAu31sYhnlGgt0zCDKs2EUlcsZx+rZ7NJAf6gEXmM0wacHbY45cg9gWS+bXmaZ16B5R3rhoA2PZu3wiT7r7Jq2AGsCjfSvStSfqtlAsWFrPVm16pj7+eziJRS8TgLYdU9TP9QO7REklfqq1rDt21VlWzbFgMuC9tgTGfuXzx8FJPPY9i3rrKaqzMZGpm1sHB+E72pZWWPXXRO163b4rKGWTVVsDpqf9lhHR9ZeOJmyk2dTNjqRcnBcPF4KzBI09ujYAuPwm/ZV2htuoP5RnuvqXbSHvnIOqMVjLQ2k/WTe1jc2QRrbQb6XlIt7WgBfotwiZFGJS9tlrm96jo1HzFODKPXV1cUAbhpRIgM2p44unl200y8uWj/g3twCm4mo73DJojW3ZW3fjY0oUbEhBiN88XjW/u5jh2x6ts52bllhzfWAnRNDNj4pIK/K1q4K2/v+EL/SpLnoa/dR9Acac3TvPjAwYI89+ph95atfsTo2Lz/wwNuc6prWruUX1P7PHTrs0rhKUe7e++61a665lk0sM25D9Fe/+lXmYT6788432113vcWJ0egeP8HmNqlYnjxxgvHNZ3ffdZdtB7hTFri///uPuQ3U9917n733j/7QGps0d1yaoui82mD9FcC5HsC5GwHn/vj97wcCDTl1zydYT17g/msX6wZSlFu5YiXzOzZCUAYp4D39zDN2mPtXcTtvfsub7Y133ME4vWhf+OIX7Nn9+02g3W233gY812wh7g/16OjssGf2P4O9HcdetrpMMQKptC5drC8dtzRO04FeBw+aAwVJ6qY/a2dOJ8kSMA2wxvxWG6Q0JwzMk4a31K7f22grmspZUyjYCfpre/sEfYzjWBtgasJ8xG9r15XajbfWW9OKgI33ee1rD03aMwf7rKGxwjZuqHHg9tDgKMDdNOqeZYxLjWQEjFrLKqB3Bh2lap4aLdjZ0ymnHHmpA0CeTSHV1SGUEMHncdwjo1MWCc/bA2+tsZ07Kh00d+xoCtDyBAqK1fiNRuY8CzbOnDIYSGHXL9lg/wu0d5s9cP89pHnd5e6XmHhjivyHoywwv9A46u7fX0Gbv6bBOS0EPvzww061TYGbD3/4w/b2t7/dLQS+gjr7tXxUnVnO5SMf+QiGdALp3d32ta99DSP7aUL21/Llyyf9tdXAMjj3i6tWtq+HaHjR8iMjI0Byl+yHP/yRHeCmQHKtkmKtqal2Qdi3ve1t7samvp6d68uP3+EaeH1MSH6HG8ClAfvmE1n7679JkUbAbEuLx/Z/q2Q5pefvcqP9B8omj3n6dN7+24fS9mxHzjat8tjHPxi0O29jYfaVzgr/A9+/fMhyDbzWakDKPNps85nPfMYtRiq13dUekpLXbuQHH3yQDTn3mtK4vh4eSg3y7W9/2775zW+6FIBSA77aQ6DF3XffbX/6p3/q6kn3MMuP5RpYroGfroH2joJte1DKS2bb2Y35+S+GbdtaLez89HHLf726akD3s3pqge7Kn1K1ENimZxGSc4t4TNh0nF7Te/KXSo+qh97X8z/yKJ5Dx+u7tEiue2r91HkFwgl+0/mLC8Y6Vu9pwV3+XGUoflbHqBxaO9Pv+ryeeuhzxWvT592u84/+k5USCLqrpdnWorYVSIwCuAAEVJS61EszswTZAD4qt15rTbfcgbpNzOZeOGzjR5+zeVThYqUE+oGCpKBQgD4JNrVYbOf1FtmwhW8kdU9Puw2/cMQSp1+yUiC0CIEpDwFlJUBKEPTJN6209bfdCoRTh1LUhE2dApg7iOJCXx+KZqjIoLohhZwFgvCVBOhrtm8xf03MRl48bqNPP21x4Dqpy2Wb2iwFqBZoXWnx3deQLnIVikJAHgp9AaYsvnTE+g8dsPEugsQoVkQF29F+M6jEFaJAeZvXW831e1j4B145e8H6ThwjVeAogTS0OwheCApIEYgOAGjFd+622OpWAkYoeg302sCBQ4CBL5uPuYeftgoALRApdoFOb32dNd60F3BtPUpBEzZ75AUbZ61wYnic1I3lpLWTIlAW0G2S9IGksdp3DQBbqS0eeMFy+4GuEhPmqW0xVruNLeEWrq+1+LXXWhCwxxtD2Qkzk7LawqUL1vnoE9Q3gALrLn4ArRx2scB7QYKidcB+sb17adcKmzp9zMaffsIqaW9fNG6LpKADH0IhihQrtaSGAvSL3ABIRXtm2y/b1MFnbPzUaSC6eYswj/IBhci6R1m8rwGSrN9zswVQwVP7jZ04akMvHLdk/zBBUdT8uL4ctjhHoDbCdvemXbsAcMKWePGI9Tyndu6yBhRSwvWkHZKSX3ObVe7ea2Hsx4OCm4fgJcQhQNmczXd2Ws+TTwHAXLIYth8hUCMUbBpwdILfajdstnquMbSi2aZQPRh+/rAtdHdbFXYUoNxaeU8TsM8Cq1Vu3mTV+24yD0o9SWxifv8hmz92FFgjQRsSkShDvYggELFay9Y1W+Pd92JTKCURBF5AqbFv/9OW6h0w9JYsEAP4Q40K7Subjvu4xtVWC0SVPX3ZRp561maGBq2M4JAfhYUk62GBlS1WBRgY27wVsS7a0AsEg+pWdhq4c+A06Q3bqd0kZQZyAzDxZgHzAHwW08B4/mqrWrsVAIQw8ECXDfV0Y+cANeUoZqH+5PMC9USbLASw4y1rwg4BvVBbgoqDAcJfcf2+3IgV+p+hbZ8FgqGNttwNJLODY/CBmQT9mpSdPWfNQ7rRADYRRMFJuBbyegikkUITuCfctNtKWq/BBgEop7pt5Cz9ZXzQYqigBIAkc4AdOYLhWcCQPGCO1Awj2HuefjwDsJZFLSYEXAJm5yiODJBFZmEGUCxmkdb15uUaxvv7bAGoJpqf5LMoA6JIlgNC8pUC61ShwBhvIyUoABvqJl6lTpYaSg6UIIta2RDQ5OAAKik+q23bZLF6YB1/A6aE6szgEUsOHyfIPuMAnJBgHZSUvAAcc4AvxN6tfP1mwLnrLRXkc6SYHTvxRfMPH7MyYCwD6suTUlP8UwFYYI5rDNPesSYAIgL6i9TDwugQzZoEGBNQxVjBwYiiEdRW2tIqq8P+InXlqKBdstGuLtSYAAKxkTyQo9Q3vXxHuAKguGwzTFkZ/UANwBfSpyGSABIHoSZO2NClU4BqZVbVvA8lSuyJvpwHfMpMkO6r54fACd34b/w58IAfsKCU8cIrABW4IAgwF1lDPZICtUAuqMW+l22m+xCAxCz9thzVv0rUQQMoQ6RIKQcsgFSFIOUC17u4MEvfABAD+PPhn/ME6KQi6M+Oo4oEsLbmdgvV7QFa7bDRi4+QknSMLgW4FSJwho6ShWstDEgVqmmmn+NfsUOP1BBJB+VBCcgyffhL1c0Z8wGyOWW8Jmw0hB9ErS0z2WlTIxfwb4wTgHJlQR9nwF+QGjA1D2ycmiXlbIWFNt1unpolcC578QVL9QGK0dY+ADk//iMrG0WlLgNk4kMlrJRUs3kU7JL0c3X+ADCcD8BEUh5pAGwEVi0rdSJS9pbJ9inDdGc7dQ5EghJknpSuBWzdz5gUrGoDVAOEDiPzAXxXwEYL1JnHg8JkDrgEcC4DODc1nwYgfwOA7j5sg/EDNbj0KMBZ/1nLzXQDB6MBhVqc4LkgqXl9QFS5eQKn1YwDrXcCyBLwTvXa8PnDNpXoACLB7oCeQ/IHgH5p7DpFfQTxgbH6DYxPpNHF9y/0UbcAhgIz86QZ1Hih82cESwdQrULlraR1A2141hba91NPU/j+GOOylDdJD+upJWs0KnK0oyfWyGeYBzCua50riM/2os6UnwLk7vgR39djsZrVFmq+G/B0PZWIWuYkUDOQcJL0uWiduPpXNFiqRnwAkBZVPn/cKtfdb6HGm/gM6Y/7D9ho+4uoYAFKAkgr6C5Vkgx+RUpaUjbNYs/dkfV2PlMHXEY9SFEJeGUBP5qhXHwYyHfettW02HYA41rg+gBdS04OXs0BSZTAEtTHJeqiHbU9Lzayh+PXce2zkLYd2Mi50X4b4X06hgOsA5A+UsxdlCIvMG99tMI2ROLWhk0E6ddz+Nce6r2HtKmjAGh5xheBBUr1nQeI8wIo1ZVV2I7qRqsD0pTqVw910TWNKpzUb4GNvNgrF23zjH+TQFBzgiw5RxVAaxUAsvr+PP0DgtVW8dqtdYDwANFMRxhnlF6cuSPnFZA0xnjQvjBpvYO9gHs+VKZqrQFfHwG6GgUaPTMyaAN8h8bwEuY+QT7PrJTvBBYExG1mfrUOOLuBfgQ27srbDRB4iXMm8K3cFeHXpASmADuKfsClzdEqayUtbCObNLBobH8eVb8J6wT+SgKKX4sSZ75vgL0AaVSXNhCUr/4JAKc5qx76OTY25lTbTp9mXsJ1FMUYBJponnvl033oN/iPyqOHyqC5eXHOrd81JxckcxSYRakVJSCh9Il1bByQipAgl+ODfTayjrEEWHkzasXXVzdYA2pcXJX2ljhI0ad5CW06iU11M+/sJv42i09e1bzC2ljLKUXacIT7iJeAT7uB1QvYRRnzuRIpQlLvaQGe2Go8XGkr4jW0S7lVM44qQV4Xm2Zk82O0SZZrCAB1ehk78vQBH88q5sQrAdKa+EyM6xlnc8QFALtL0xOA3yl8D9+BrSygwjsHyC+1NHBii6FiJADbjw90ir0AVjXAl9c2rQFUk1IiXYnrYmqAn6SdGfPQoaQPztlpqTlTPxvKa2w782MvqkSdWPHp8W4bZc7mpQ8qtWhQ6riAbinKmaK/x/Hza4FpVwtGpaxoMNkgyr4dpGsdog+n6Bth+qcni+Ifm0082FYNc5B1saitxoeWcP1TvNc/nbMLI2xUQqHOw/1QOemZJ3oW7fnnuuzkS8C/2QprQgV6XQlzaGDaYEnSWtdzbdc0A/J57cDBQTv1EpshCmVAdUDJ1NE0QiM18ZzdvA/oc3ecWGnInvxhxj79uedtZHwRYKgJ9bdaK48AVwE79is1Iu24ZaPf/uDBJtuxPWQlQLs9l7MAR+P2wwODNjjGZhk2LlRVBnFz4MfzHusex3LSl+2P3rHS3nZPvdVXe+3shTk2TR0E5EtZbVWrVQI0hsqABWuTtnVLxCnd5VELfOrJYXv+BZSV2QBRVVXgfkX3pygBU/61a6uAu6pRMxuzA08P2vmzSiXfzPczZ4TsFKBZEpuwW29fQey3ng1dXjt1MmN/+7eHrXMoZqvraqwxjl2g2L+AnQyOMYYGx+zv/241UB6w+WvkUez/upzi7/qpp/yB7uO7uU/5Duu03/jGN4DQVrisH1J5K+M+Tn4kS7v3AhR3d3cxd1tKY93ERiTBw48++oh9/Rvf5J42imrXfYBzdzm/qT6YZ2zp7+93sXmBzBs3bQRyqrfTbLL6p4//k+ub99x9j7373e924JxzXfiYecbHI0eP2EMoxPX09riN5zpGKWS/+MUvkk72Radm/87f+z3gyetpW+Ya3CPrerSRT2CelO5UVimBbt28BSZg0m3ePnTokO2GlXn7295uq1evxl+wsYrPXe64bN/5zncc/9PW1ubqoJhatlhfqkMd+3p56P5zfLxgTz49afsPDgPNMVMGPK5Ezc0HqJZKTZDqOGW79tQBoTbY4f0Aj8eY7zG/r+ZeNIIyZRYbSDPfaGzK2Vvua7N1G6M2NZx34Nz3n7yEf/dYcyObeVgH0caiKea+CTIENLPmcOedDfaG28MWr+a+hrztZ08t2CPf7bLzF9isUKgGWGdtiluiOQDkCZTIR/W5mqx94M/b7Laba20EkO/Jx5P22X97lPWWoK1sZnOgFK3JzR4rQ9V26Ch2/RyQXiub9u+yvTdcx73i0kYDhjsHT+u+nS6gP1/R4/8DAAD//+CK10sAAEAASURBVOy9B5Sc13mm+Vboyp1zzg00cg4kQQIEAeYkiqRpyzoOcpz12WOftXf32CvN2B57bMmetceWJStLpESKYgYBgiCJTOTYjW6gc865u7q68j73h9qj0aE9Eq0dMXQdlZrorvr/+9/w3Xu/77nvZ0smE0l9RF/xeFznz5/XX/3VX+nYsWO644479Ou//uvas2ePUlJSPjBPbco5OzurP/7jP9GbBw8qKztLv/3bv61Pf/rTcjgcH5hyLhXkp6+Bv/mbv9Hbb7+tiooKffGLX/zpL/AR/0Y0GlUwGLT6f3d3t06cOKl33z2pGzdaFIlEZLfbVFlZqa1bt2rHjh1asWKFMjIy5PP55HQ6P+K182F/PNuH/QE+9OWfnknq9/9jVM/tjyrOVPLwHQ49+48e2Zaa5kPfts2tCf3hn0d14HRMuZk2/e5TTv1fv+uS64OztPnQ1/HSA3y8asCsw9955x199atftfYMMzMz71kBbrdbtbW1euihh/SJT3zCWpd4vd73/OyH/ZemTi5cuKDnn39eB9mf9PT0yKzb3uuVk5Oj++67T7/2a7+mzZs3W+u09/rc0u+WauDjXgMd3Ult/aV5jY1K1X6bvvFVj25dZ2fP83GvmQ/38yeTSZm3eS3+t5lHhoeHNTExofn5ect+JhIJmbedBrexIF/8t5lXzJ7X4zHr9J9uob54jcHBQbW2tqq3t1dTU1PW/bKyslRfX6+6ujqlp6f/i29penpa7e3tMvtvU86FhQXrbXxkeXl5KisrU3FxsbKzs2XmvcWXKbd5JfBftTQ06h//7M+VGB3VmkCalimhkrxcZa9aKW95iWKxmCa6e9R0tVEJX0D1t+1Uflm5po4d1nTDRfnSU5W1fr1Sioskh01R9v4Jf5pcpdVy5ZcoMTKsqVNH1XTyiJLxiCoqS5VTVSdnbin/tmt8PqY5X5pKVq6Q1+NQ6PpldR59S8PXm1VWVKL8FavlLiykVA7NzQXlcHvkLy2QM8urSGeL+l96Sam93fKX18q5ZZfsReWyZ6fzM0e2VD/twKI6add842VNHHxNfVcvyxHIVNnKDUorLJctIY02N2mg84a8brtKt22Xl/LPXL+h9pYbysnJUuGKlTxfsZJOhxaiMcU8PnmpW29ujpITwxo9fUrXjh2XayGissoaZdUuk4vvKR5TcH5WUXeK0utq5MpIVxD/SN+htzTX1S13Vo5Kt9wiX04eZYxpanxISnUrvb5KrtRURS82KrJvvwb7+pWxZqPS1m2SvaBAtkz6AHVip71wiIpGV7S9TUPHj6nx+Ell+VNVvXKVUqsraL+wRminmZbrclPugp17ZM8qVP+lMxq7ckbV+TnKXLNB9uIKJR1uheMU2+WmzvPlrC5SjDXE2KF3NHX0uHD4KLOqUln1dUrJzqHfJzU0NSs/bZleQZvS/uGWq2o//Ib6TZlz81W9fKXSikuVcLo1NzunpNurQEW53GluhdquqO/IO5q8fEFl1dXK2rRFjuIS2XKLrPIkMrNlw1fkSMZliywo1tmu0RNHdePEu0p3BlRSu1yB0mLamD402K+mc2flZ/hWbtqqwOat6rzaoP6L55Uhm6rWrqU/F0ouKRaPKuRwypVXIH/VMsUjcQ2fvajRt96SY2xEWdRt+rJauYvo04yVhbl5LeC3Sl+/Tu7UdEWvNWvy7XfUeeGcvBmZKqKuAxW1srtSFYqENOYIKa0yV5n8Ldnep6k3Dmm2q1XpGQH5t2yVo6ZOyfxCOXLz5MykdJRJGlMy2K25/uua7euUPRpWOvd3ZWTTNyKKB/sUGu3U1OS84s4s5S1bwxrNpVhfh/roS1GXTxmMl7Tccrm8BZSFPuViDPDZpM0rm4MxT/uIukomYtJ8l0Idbyvef17ypsuz6m6l5CzHKMwqMdWpUcZDaGxSAa4byC1USiBbyfCCEpMtmhloUNTmV2rpbfKVb5Y93aPkdIcGr53R/HCPsn1JefOK5MhmLHrTFFsY18JIh2axBR784nYb48julSe/Qr68MtmTCYWnJjU33Kf4WJ/SMv3yVy+nfxdprLNHE33XleYMKSO/TCm5dbJ5CnnnSu5cJX3Zitt9Sthv9hN7IixHnIk51KbwwBVNjk5qwZGtnIrVCuRUU89pWpjq03T3Mdlnr8vl8cuVVSGnO52qmVFsYkCT9KUwfTurfo3SqrZpwVMvW3Raw2e+Lnv/SaUHUuXMX0HbVcvucCk+N6PZsUGG+6wCATvXEn19hjlE8vhyGcuMVcZpPMQcMjGmkcEJuV1pyl+9Qp7SbM21N2ugpZ3x46CPZMmTVSBPJm1oPWe5Ein0cdNxrbkpQX+P0I7zSs53KD54WqPdLbR3kdJL76JKqmSj/sNzg5ruv6SZ3gsKeONKLSyjvNhR2tA2MajoQJ9mJ+bkw0an1q2jL9YoGaLc3Vc12XFMXgXl52+OnGVy+rMVCYYVHBtTeHZAzuS07HH6gjtN3pxy+TKLGScuRWYntDB6XckpypPiknfFbsqzhbHZopHmA3LFppWZV0m3XC+Hr4x+z/j20kd9qUqa+sFW2xJUXtJGG04rOdeiYG+Dxgd65U5xKJM+4S6opd3p07P92NDzmqFfOTwptG2evKkZDO6kYtikmcEO2rlfmdkZ8q+5R7a8lUpOjil246TmOpsYQ3Z5GF/uvBLZaaNEyKaF8U7Njt9QCuPXrAKSDmwVtsidXipbShrtTD8e7tTkxKgSzCHZ1csUSPMo0deqsRttSiR9Ss2v4/lK5UxjXPvoU6afevMlp7FCTsZegjdtoFns9qBCPWcUGm5UOOmUv/J2+YpvFV2Zvtmp6fYLmhrrld0dV05+ltzYk1g8qcR4lxL9rYpNB+Us3Cjvsgdlp22Tsx3qv3ZcY8Md8gYSyszJVnpOlVKwBdHQlObGOxScHlEa84Pfm6UQ82koNC9/mp+xWMJYyuJz87JPtCrce0MxW7p8ZTvlqVxDnV5UsOOg5ubnlJJeJl8u64O0Uto5k7rhzfiRK13xlIDiSTdtSTMluVZ4TJHhixruPMV8MaXcIub5wr38kftNtfIc72p6rIt5zKlANvYwNZO+zTw0O0ZfatTCRA+fzVb2yifkKtyNfRqhbMc00HpBYfaymczBaTmFchk7EE1oYprxS98fsQfUk1KhycByZaQXqoT5ys09xsOzGpqb4HMTii7Ma01xjdZml6gg4ZSHRjfLtoQD80H700IajMyrfYoxG5rDBrm0PqtI5Sk+zdFGXVzr2kA3c+AE/dmr0sJSZaV4lWJ3aCw0o/557BDrhSpPppZnFMrn8moSW35jqFfj2Ac7c0pmZqbS8D/YsMmTc7MamZxkvrNpXW6BKlKzFKRAN+ZndL2PfsDzFqdlys8acCoWVQ/9eXh2SgvYdLfXoyrKVsqYnJieUvfMkGLOhOrTcnVvboVylCK6PB2LIcKPCM86x89e1gfX53i+0UHl+/yq5b75tKOdv/fz3A3DA+oNzingD6gsLZ37Z3Bdp4bDQU2MTbAmiKmCMVKRl6U05pgp1nOtrGP6w1PYVpeyWcekuwPc06aJ4IwmsPMuylKcmasq5j47fxkaG1bX0DDP4VRWFnWV4tZsR6fCCyF8NXX8Lot2ubnONv6LUChkrXfNuvfatWvWutysvU2cyXzWrH/N5xfX8j/tWpxq+Zm+Ftf2izFh8++5uTm1tbXpe9/7nlXmvXv3aiXrTAfrkjNnzuj8QI/6qpjDfW6tSc/WLXmFKmJ94aSO7KxZHbSPaaMQ/zfI+qhlalTDI6PWWqmupELF2EJ3Iq6BcEgXx0fUSd8y66jC1IDy6T9+Z4qirGvG6G8zkagVm6vNps95/ZpnLXplYkidM/RFPpeXnqN0+qghDsz4n54aV4S2yaGtqrLylefwaJB+2Dg5wv2CCtDmhWbtE0+ob2KKv81pnjWqx2lTUWGetT6NhKY1w3wSY14pysjRxtI6lbg8CnAPJ2tQ84wJxmKYsThMH20bH6M8M3LQtssZ78sCfsXoyy2xOTVN9DIGR7DZNuVl5qs4kKdM1heh+Qj3ntBUcFrl1Mcq+raffdCk4mqbNN8Z4/nc2IdcpfqxXQm7ppnLjW1g4awKv1dr6E+pjNvehYSaRqc1Mj6jjEBAeTxflt+jsydH9PSzJ9XSP6GCug3asa5OdxS5FWDch6NB1oc25oiAGq9G9dY7HawLJrV1Q53Wrs7Cftrp+/Ny2MOqrfSoptKr1IBTBw9F9MWvHFF715DKS1fo1s2VWr8yVT6PTeevzOvouQFFw1369C+t0N17i7AfKTpxeFqv7r+hlq5hVVRUasvGChXmuzU+FtaJMws62cgaOtqh33iqQo8+TJvl2nXtRlB/8V/e1fXrIyrOK9XmDeWqX82epNqt3Dyb0ihfR2tQX//qBfUMeLR2VZ22bQ0ot4DZKxTWAmvy9DSHCvLcxH5v6MjbbfSLLG3duJ6+HKAf2DRN+85FJ1W3LE01NanyeO26eimmz332pBq63FrLmnznVofWrmQOsafoUkNIrZ1t+r//qEorVzEXfERei7bIPI75b2OTFm2U+WlsQn9/v/bvf13f+da3rf367bffoe3btqmIvXVGeoZSGVc24uomvm7YE4/Hy+dcGhoasnzf38fPa+zKOvY6O3bcTn3XKD8/n3YIcFf2MaEF1qgxpaalUYaEzp07r7+Fc5iamdZdu3fr8U8+rjw+b8rCUGL8YDsu3PQfm3vsunOXHvvEY2rFbn3rW9/SFPPMXXfdpSefeELV7N8c7I/Nd2P0ffMyfMDExKRVXhP7N3Od8Wt8/Rvf0Kl3T2oTfuaHH3qY/lrBs7itZ7vBnvv111/XW+zFVq5cqSeffFK33HKL9ezWRfk/U18fp1cMe9jTndA3vtWvk+f6lANntHVTsapr3GJJotn5oGKJaeUXOhWdT9fT38QvMuDTiro83XqrX9mM9XAsoSlsW4o7pDXrcmlnjyYGEvru0+N6Zf8l9h7z2ryuUuvWlSs336/p6QW9c7hLw6N+rV+bpseeTFdVTYoGu+M6uH9Eb755iTVBqjZtWK5lyzPwi0TVdH1CJ8+Mqb0votpSv/7ofy/RHTvT2e8k+E5M//TVH1COkDauW6HtW6tUVZnJ9jqi06f26/TpfapfVqZHHnlIt2zdRDkdtLNpbMYLP5LMc8zovM0i5v2/bB9lcM5UyxiTqgmEfe5zn2PgRfXAA/frN37jNywH4uIC5P1X37//m8ZwjeFs3H/ggP7+7/8bRmKOSexu/eEf/aHlsPz332HpCj/PGlgC536s9pns40yKZtNiJkQzyV+9etUCXM0GYHR0jA1MkE2LywpGb9q00RqrVVVVKsZBkcZifen1YamBj9fC5IPWKmahMDic1Kd+NazDbXFlsAH5b3/i0qceXQJOP2ht9X7K0z2Q1Bf+OaLvPB9ThHXgo3c59NW/9BA4fD9XW/rOUg0s1YCpAQOKHT9+XF//+tetvcMkTsH3ehlw3zhYd+7cyUbtEetgTiHB0o/Khtw4L4yj44033tCzzz5rwXMGsjB7lvd6peFIeeyxx/SZz3xG69ev10cVJHyvZ1/63VIN/LQ10N2f1N2/ElJLT1J5bpu++Y9u7b0VIODf59P5aYux9PmfcQ0swnJmHjAQ2sDAAEGF62pqAqziv41T2oBklnOYn4vgnPme8Unde++9liPZOIkX//aTFNF831zTOMRPnz6tQ4cOqaGhwQLnzD1zc3MtqNmAzeXlQEcEf8x3DGC3b98+XblyxSqv+Z0pn/kZILhjwDnjdDY23Vxj0W+2OM8lmQ+6Gq7qG//xc5psayVQlKZNpRVaufMupW8D8CkATCFAmxgZ0eU3XldvW4dKgKDqVq9V4spFhdua5a0giL77DmCgCiABvLjAV5AiFmhgKKWFK5c1dvBldd1oVP7aepXeeavc5dVABTk4RYEtgOcijhSAGpdsQIOTxw7o2vF3CMg7tfrue5RqYDEAMwgViC4c+gwyW4qdZ5wj6N6hbua3QEebUus3KGXvY3IAMNl8KQBaQAo4Wx3G7YrneeTNfRp79UUlCL7l3nanMm/fK0dGgZLRpKI3WjVy/JAWOpqUTf36AKpmCOR2Up6iFctVgIPfye9tAR/Pxx6Mt40gAROqotcb1fzaq+ro6FZt/SpV3nGnPFU8H4E9i8oj6CcbDl++Ex8b18DJd9Vx4phyXS4VbdmutJ27AfxMYMNECqM8G3tvHxuBOIAe4NfMK1y7q0clt+9U7o47AAKLFSfALb7voE7s9JskwYnwuXPqI9gwODGtivWblY/z2VkGtGGPaeHqBc28fUh9NzpUuH47EE2N+lquaaynVXWrlivr1h1ylFbKRnA86eTe1D0NoogzAizRqZZnXlRKU7vKKqqUdtftcq2ijgHIwgTAozjkvQROHQAvid4ezR56RYOXTyucla2SXbuUsWI19Qz8Za5rtR/1RjDWRrmCXQ3qPnyIOjlOn1qtor17aD/KkQbAAWwQIirgYCwa5NM5G1Lk3Lsa3v+SxvuHlLd+h3K275SzJJ9uBMg6Pqi+l19W7NIFZRDM9e19UG3tnRoAwCzKzFLlTqCUWtZ4abQdIKMBfSBuaBsCrB096njnsAaB7EoL8lW6nT5Kue2AJxZsZuqY9rYRKLURfAq98bbGXtmnGP+defvtCuyk/ugzdAquCyvhor2BeZwEkBKt3Rrfvx8YqVlZBRnKoE87l69TEADCTrDIBcyWAqRkUw9wyjVNAFFFpkIEtwqVWgXQ6APe0wJQU5NC/cCO/SOAKanKqwNA8TkVA6Dp7ugHeitXTu0GIMYa+hAAkWhL4DabDaiNUWD1RYMvQEclF6YVHr2h6a5Tcof7AFlKlVIJsAOopvCgYkMNGmrp5BpZQISr5Smuo6wZ8HuMuanTmmo5CNRgV2rxTvlKsBOpLsZVmwabzwKQ9aogx6e06rUArGvpSwZm4tmGz2vkxhXFCOB7gF89ecsAGXcAcy2njAsKzwBydV1VlD4R8Nvlq1zGOM7XUCcA8Uif8rI8Sq9Yz+fXc03qmjaM2+O8HYTBaRe7R07Gj53AjC3cr/joBUX6LwMyEqDMXa3Ukg1yEVBPhuKAf02aHjilDOewfIV1wGGb6GBAColxwLkWTWMH5rAT2bX1tMEtCvsoS2QKcO4bcgwcVwaQlqfOAKhraG9scYjA1Xg7LOIZgKce+kxcEQL8vvxiufPXMFYA75w+Pjek+Z7rGu3qktdAIitqlVKWBdzWrv7WDsAWp4orqJf85Yy/Eq5jxg3QCc9mB/61M9YMYCvbHEHwMUXHmxTuP6uFmTFgu1W00z1ypOXR90YUHLmk4W7gp5kpFZWWAZiuUMJbjL2gL022WdDqWN+A/Dm5yqhljObXUj67ZrquaLrzpALumNLK+X3hLZSF+o7GgSIHNdV3AsAMYNoVUaB4BcDyDvpNhTWOkpFpBXtOaqHrCOVMyL9qpzyFWzTb366Ra28CHsWUWYFdKtrFGCyjfzL+rHFI4Ji2TDLWk2YM8V1nCIhy+JKm2q/S1+CJAeZ8wBVOxrIZk0kAvenmoxbM5iutl7dyO4AaIDPzX3h6jOe4oHnaPwtAN7DqPtnz1wJqjSjW/Jamuq7JSfAxwDhMoR9C6TIP+BXnuSY792kecFQAUIG8KmVUrZc9tQ6Igzk9PKx432WNdjQoxBycVVMNXBBgHuoFDAR2dVGXFZvlKlpGnTBeUhh7pn/Q9saK2QwJYtowPk99Mo4n2zXVc555f0g+YElfCbY1cw1lAbocOcecfFbBeFQu5tzsEgA45swk10iONSvWehIgbUhMqgCKD8gBMJKcbtVAEyAl4yUDsDy/crm82aupL54vNqX4DDa//awccyMAxhHmXv6UBQBOOzuxG9EUA5/hwx89p2DjESUiPrrhPXKVr1V08pRmu1/VHPWbWbFT/mLmTw+2lza7+eIn/ZfOpyjjMc487YoBoADazvWf0+gEoDiAbU4p/TRtK+2cpsTQWSV6gIqBbtwAdZ4S5voANlqMlVlAqp599KdT1B/wVf0vMOzv5voTCvUeVX/bJe7hVCGguoEVba4y+o0DSKZDveOX1Tw2rVE70GPJFtXl1ajGE5CPMk1jAzsBIG8MdGkS8K22tEarMkpUDNRowDnDFxvob5b/HmasdfPZfuJ8EeaA8uxcLQdEy+O+MxD3A+E5rtOjccADf2qa6jksUES/MeDbYDyspvAIYMygcqiTZbll8nvSNBqbV0d/N+CpVMA8lQ+M5mV9YKfWxpjzO7mWAd9qWD/UZuUpxHhomp5V98CwCr0BreVAhIf1QTMAbiuw1ATXCzmocebsVdxjtS9PkwCB58cAjhPzWk6feYA2zqNueHzaiK7HO8zzTTAfNwMR3giOAY8vqIZ5ty4tG7voY45Jqh/oqHFkSAPAlLn4CNbl5KiGQw4RyjQci6gLcK6H+ShgILiifGWyZhifmlcPEN4CMFBhbrbyCa6nMv9EgaUn6ctd/G0aYCsNUKmqAHjSHlX/cJemBseVS9nr8MMU2OLqBIgbZd2Zw7rVrKfN2nXxwIfx8xjfjokRm3W68d0YcKOIOJOB5sxnzRrYvBa/Y/3j5/h/pjzmbcpj1vrGF2N8NM899xyxs1GtBXa5887dwPYZFtDSQL2P1hdrHihylT9DOwqKlJ/iBzpnnUz/YmnLOElqiv7csjCn5nFgKuq1GKi7GtA90+2jqQHnoiFdoh5754IAjG7VAY8V04YGWXSwLu4AZLwyOawY687a3GLVBdI1R18409OpGexsYXYOMF2G0lm/RrnfHGD66Nyk+ieG5fC6VZVdpGJHqgaAiVuwr1G/S8XZQH4AtNNT0+rAFvfHAXQjrBYBW2vKKlWWkYdtWQAGadX85LSKsvO0sawGMNAtP+bRQIFMARY0N0Ff6KAf9gFXhsIR5aSzlsvOVxEHMJgW1BELMhb6NMZ9zIGlag4O1LBWKQBEDlPPbcEZNfR0KJP1Zz39zQN81G+LqHmkE2hvXlUBQLs0oFpOrRtf/Ew8oqGZcY0zvrKxtesLiuX3BdQE/NfE4SkfcPca+nsp0KvPZdPBN6f0xe8dUyuwXeWmTdq7vVr3V7pVztRmbEmSpebEjHTgjQUdPjrAfaZ0/91VWrsuXalZNlqIpTzLs1SvTV7AQge2YP/BoL701XdY9w/p3t279PijFaqrob1oj6brcb3w6pjOXDijvbsr9ehDtcrK8OiFHwzonWPXsPse3XP3Cm3blElfYm8FAXzgjYiefZXnGm3WLz9eis8zX9l5djUDzv2Xv35XrS0D2r52pX71l1eqpt4N0Meygz3WAkbiwrkxfeVLJzUbzNOu21fqjl1Ag0U3DwdYWyHKHpqL66WX2nSC+6cC2e+9az3wFuM+nbWXmRJYavs4YOgG/KNZdfFcTH/6udPYwVTde0uFfvUXfapdxpjlj5cbwzpzcUi/8ESeqqrY53xEXov2aPGnmTf/+9zJ0pO+auyaAdWe+c7T6uzq5GCMX6UlzE0lxSrIL7D28DmA4jms2cxhOAOoOg2szDhrbGzUK6++ogZi8iZOX1JSCphUqUre2exjMjiQkc2eLA97mo6NMXH8M2fP6m++8HkNDA5p1coViNxsuwmoYacWAb32tnadZW9p/ApGuOo+fByXLl+ygF8Xe1DjNzZ+iUJsrzkUMDMDmI5dM74MDB77w5t+khzKm829B+AGvvHNb+jo0aMWF2AOaefzbC7GX4IO0NHZYcHQ5iDgbmA+41MxDMGPHvwzdfijdfcR6SL/6mNQhersiOuf/3lUl5uGtawuXXt2FzBmUjj0gM0wS2bWI0kDUDcl9aV/OK+F+QLdekuB7tzjUV4xPhMH7cHb9BefH1CffdNwV1zffWZEbxy6yP4qric+sU637IDVyOSgYSipF1+c0/F3E0pLDerJpzK1co1PV8+G9YNne9TbN6idd5TQPiUqKWMvhs1u74zr6e8M6q1T8yrN8+qPfj9bO3f5NQw498brMX3569+l7yX10MPbdd89VfTnFM3OhPT6/ld08MBLqq2q0CMPP6Lbtm/D/mBYzIu1GNMdb3wj1j/N7zGs7/P1kQfnFhcWRnXuwP4DyoBW/cQnHrXU3AxF+/NUrTJGbnx83HJy/t3f/R0qWzcslYZf+ZVf0YMPPvixGtTvs/9+4L+2BM7dbCIzYZpJ0GxQzAn45uZmK5hgnPZdOIB6enqtU/EFnIQ2p35WrKjXapyg5m1UTMzkuvT6sNWAmaKWXj+vGoAT16GTMf3RH7LBQ3mujBM+x7/LicbipXb5ebXJz/K+M8GkXjoQ05//RVTt/Pfu7Q698Hdu4OKl9v1Z1vPStT5+NWBUgc7iEPjSl75kqawZYGzRWfHjtWEAMaPiY+A5o0Bn/tso4n5QnK0/Xt7/2b8XnS8GpDjAgZ5XX33VOtVsOTHe48vG+WAcMMY58Tu/8zvasGHD/+CgeI+vLP1qqQY+9jXQx6GGJ39rQaebE4LP0dP/1aOHgd+No3zp9eGuATNXLPp39gO8GAevOSVtbKXxOf2ow9bME+bfi38z88g999zzL6pwP6mPytzT7LONM9tkWDD3NHtr8zujEmqCgcZGP/7449YpcrOnNmU0cN3TTz9tlc8EDY1fzASPBgcHLbtv5j6j9P7www9b/ikD05mX+a4JK9r42XetQd/+7J9oqLFBZSh87Vy/RSseeky+TRtRsUEJCMWKJNfpfPtNtZw6A+SDOgBqXj5UsaZvNGiBa2Zs3ypPTRWAQSpKZLn8RCEE4COJVPbcuyc0vf9lYIRR5ezeoYy9QHYEAZXCtQHr2OoYkSCloFySBHIa2f8DNZw/rdxaFAee+AV5lq+CAcoErKCuCawQV+JN4BH1psRAm9oA59I72pS+eqtc9z4pJ4F0uQlO4l8lVAnQxR3wXXS98KymX38FNSxUXh54VN7b75Ldj3IR9EAcgGT6nTc0c+oo0I5XPgCxKdriOnWfBkhVjqKYrxy1KwIQzvQ06zltBLESs0FFz57ThZde1AwPsXLXbhTddt9UpwMitFFYWyJiwYfJ4IIigIfth99Rz8VzqislIHLX3XJt2wGQBVhhlv4/9BUn+V7SnCKnfSdefVk36AvVe+9V4R27ZKPuIrSxCbgRagDOCitJUD1y7IQGDx9BvS9dZXferdT1a4BFAkBUcVSWAD0O7FPDsXdVULsGxb9VnALvV8f1BpWWFSkPODEFcM5AKSkoG9hQILKhZhYJTWq2qVmN331JGcNTqt66Xb777pSztlRx1Ovmky5qGMyB/usMAn42XtPwc99UFCDPuWq18nCCe1CFQw6IRsNQLtpH0/kIUE51XlUH4NzgiZNasWqNShk7zhoUpVAHiQAbRLi2qRbjQXJMBbVw5KAm9j2PUiK6YXc9rNRbdwOsoVqFwlZiclRz+/cpuv9V/FVJ+R74JIoiQXVcbQTDcKhm03ql1RbBR5HxAF+yA7DFgjwXkgo2NKrjyBGNE8itQOWgeBeZTWpXAjwBgJp+RCFMX0omCT5MTmjy+y9r+OV9+Lfylfboo3LvvE12AAiTBMZAnTED9lBmO4HZREs3sN8+YJsmAhjZyn3gQTlXbNKklzpBPQKtNJSZpqiaboXHrmjoehPgl0/ZRSuBx7ZwPdSqbEGiyA1AUqhJ9fbBgfqVg9oVy1cU51rU0wEABpCSvWwH4E05ZWZsJag1E2SnY1nrX4LFyGohNYQCBIpVE73XFUJ5LScdsKgKMC6r3gJ8FCSgPHAR4BC4C2AoswK4zYApRlEqjmLg7EnUC19UaDomfx4wYtEWYBeCnqiMDaECFZweV35xLnW9mUYC2rEThU30KDFxThNNQF6jA0oF/PCWA6OW7VEysJKaYhwBSgW7gYhunALaivLs1bRrroa6hzRHYLAgn0AhUJLdfN4J0IssDSgXb9NDCATRS5wJNJQWCN6Pt2qm5yLyOAPyosTlLt9Om/MMAGgJfJgT/G1uokG5GQsoaq2jv28GjMqjOSYtAHCm46pmUL/LrKqhbrYr7qUPL0xq+Bzg3MgpZRKAd9U/IlvGOu5NPUeCXPeGwtffQH2MdsKu2hhLgUpU6Uq20Y+wYdxbCyjZ9TRprA31RyCSjGUV1EEO0EiX+tt75EXJqqgaUDFnFQwQsJPdKHg5LXUoJ2MsBYjZBrhgE2pSE22aBQAMASR5AA4ChYzhvDuA3Bjzji4FB05oCLthAyAoqlwJVLeaa6ECSB82gNVC+3n6Urd8GUAU1csZR9VW95gEnJtDBS0DwDRQsQElutswNEBZBhIeG6Je39Ls4FGloWiWWr4Vu3Ef7GnFzeeLT6EGdhg7dwBFnHl562/nvps1P9CryabDiGmivoOCnwPgEhqMQQXcbAYX/0sAiNw08JSPfpYYuaaF3ktaADxypZXIX7YFlVGU37zYEfTAYkBxU42HQLQi8lZzzcq7lHRRTupnAehqvutdhbuBI1Hw8a0iJpOznj46ADiHne9rlssAESu2AnfStgkAnjhjZvo8oPDztEcj10pXRslawMktzD/LUF/LkDM2jK29oMkbZwULASSH0mZGKmUd0HhrH2BbMUqTtzC/0ddQ0UsAhBulVIPGGqUMO6CKAdEhZJk7gBe5zySgqTcDNcvSZUrJAjR1ocQaHALGO67xtgtA0mny165XgDFodxpVJGzi5FUlOo4o3NeleM4K+eqxm0BW4pqDjLHx0WGAoyzl1BjQ1PS9Ep5xgvHA/VqPApQ2yz3PoTLAPm/FRrmqbpMtqxrFL2PDgnKNn9JCw0EAWy8Q4N1yFq9SePwE6n+vUNsOZdQ9SLs+yPcB2gF30MCRjf5sYG8zicVRjDOBXM31KTJwCZj2OqqPSaUVUT8Fy1CNqqM8wFT9xxTvPqgIaiVu7ExKEWA4KotmPCdDtFXvDzTf/baiQNlpdZ+8Cc4lRxlGxzTYeY3Ppim3oh7QvZ5nLOPt1WykU53jF9Q4AIRvB+aqYD2D0mQVwEuAK88xB3UtANMAcw4xpiuKqrUScK4IsMxj7DfwigHnRplkOsIz6kVFa2F2HigsVbVG9cuDGiZ/n2EN0gc41wwEF8TnkJWVq1UFZSphnjNQ0yj3aQQcbOE+KQtx1RRWASVkapB67+rv4Rr4c4EdM1jLOQEpDThnZoFeVGuNGl0F828tgEQIsPTGzLxGJoKqYl7cmsX87LTr7GCv2nmOuZQkdRtRCuNoTVaxNmMzZyn7qalB9TB/V7EOeyirAtjvJjjHOQWsJtMYnxlAAKEdAGqM+2cCaSzLyFWZ2y83zxAhCN9nVMToSyNAcqXEctazNqimz7Dq0DT2qGt6Tuc7uhRmLVBWyjoCsHxsHIUxIPZYSkxFlDUPtS4v0G2Yz4P7amh6UlPUp9ebqkoOUMQA7HoH2hXl+WrTy7UMteNMguQXTp2y1rcU1VIqMmtXc8DPKC6b+czEh83cZlSYjA/D/M2si83vzMv83azPDUS3+DvrDz+n/1sssynTon/JrM9PnjypI0eOyO/3W36o6qpqXbl8VdfHRjW5ptwC1Zaj2HcbfS8XKNMG7M/sYq2ZI4y9YWD2pnGU3gAuMzhosJx+aJQBvU4DM9BH46hGjfL3hbByAS9XGajN6RX8ArZTgGWzehd1uXE+Vw28Wws4NzU/q7M9XQoB/xexJyimD6azEEpQtwuspydCsxpArc0OOFeRi1oj6qn9o0OoHBqAO0sVOahcM976AXK6+N0kipmmzEkU4CqLylRFP3MCbXahljsxzr4AqGdDaZUFzhnFOdOCYcyHgVN7I7OozQ0CWKD0BgxYhbphAWUMYGfm+WAf5WkY69Yk4Fw2aojLsrk+4Gce14ny93YAvdOdrVwwpvL8Iqt8vcmgWkZ6gLOjWgasmecKAGpQp6zbgsmYxlCoG0NVL5NnXltcJhegfxPqex1AeMUoda5HfbcM2+5i/Xbs3Xl95eVzOtnZrrSKCm1ft1z31KRpdZ5TmekpcnMIYIYYzjuHIyjJoZI616VN6/O1el2hcgpRCc5MUW4WSrM+A82ZKTCpffuD+so33kKta1ZPPna7PvloKUp9HBaiXrq64/rBi6N67eAJFOKKUI9bSdaagL79TK/OXrzOIal8YKZlWl3vBfhjrmCtfOJYVF9/JqaGG5f05CfK9PAjhcrIBpxrCepv/+sZDfSNc/BvtX7z16tVWsWhIwB3FlmagR6+eG5Mz34PxbmeBMBOhbZsLVBhqQdVqhQy7DG3co/QXAKVsD4deusCwE5U27Zt1KoVeayPU24+X5HDAucM4GNg7YtnI/qzP+UAxnSuPnlPuX75lzwqLmU/xQNeuxHVmXMjumdvFjaFNfJH6GVsgHkv2qPFn4u/j7M+GEdZ8Sj7uPMXzquzs8tS03RyaMeoyxkbaAC0cvrZ6tWrVI9CZRaqnUb52/AoF/jOKQ7etbej7swcYrfZrUPQfr6XkZ4JTFdiqdGtWrWKPu0G0D2nz3/h8/gauq3rFhYWWL6GmzaKeRwbasShhlGKNnb2nnvMwcDdunD+gl544QUO96SiCPcL1u9yUc82jEBnZyfqg+9a0HOMOcY8k4d7rVy1EjWz9XwmpGee+S7A6UEgfeaI4iLr2nacZ8ZPPcbzm/suW7bMgvIMWGdsuymTqSfzWqzDxfr7CHWR93wUqlB93Ql8PWM6fbGTOTCpLZuLUHFMZ53nYf5D0d7EDllj3bgW09e+fB4fkEN1tZnasg1bVeVRWgYAHNmt0lKdqLmxb0dds78DcO7pYR0+1qhllV596qkV2rwNZWNgPAPNvvJSFIiXjGeJIT3+RA5Kdak6fWRez363g5WKTY8/VqK996Wj6kzbcPuhoYS+9/SsXnojzB4vrt//vVTtvNOvob6E9gPOff3b3wNwT9cvfGo7YgV5tCvQ5+SsXnvlBe175WVVlf8QnLuFPacB50xzc10LCrTAOeMPME4S40jgD+/j9ZEH50ydGFneS5cu6fOf/wLKVudUUFBIKtTfkpG1NYulRUne91F/7/srxuloFjzG6JjTAi+88CLAUI1+67d+yxroRo5y6fXhr4GPKzhnJiXTx83kZcafmZCNI9448LtwxJjxaOSxDeVuFvvGWW/6/8aNG1kwbbNU5pZguQ97/39/k9KH/ak/KOWfBab63N9G9dXvRa1TVbeTBuzlr7Bw/ugcwPmgVPXPpRxmg3jmXFz/2/8R1sWhpDattOsrf+bWulVmQbj0WqqBpRr499SA2YCfwvH6ta99zZJ8H+HErVnPvNfLbL4N9G+Uecy+4rbbblNFRYUFICw6ON/rex+k35n1mgEGzWEG44Q1aVkNPGgOOiw6G368vAbqMLCFSVf7qU99SsaZ8qOn+n7880v/XqqBpRq4WQND40n95h8saP8Z9kkslb/31x49fv8SOPdR6R9m/jCHIf/hH/7BUnMzihZGtc2kPV30OZl548cdt4vpVBdTtS4qvP3P6sXYb7PXNqCcgfTMXtv4mIzanElZYk5w346y1Sc/+UnrcJqx3eY7Zl9u/FCmvKZsRlXO3Nt83swDBsAz1zSnwg0YbvbqZk4z3zUvnIjqudagbwHO9QPO1QKJ7dl+m5bf+4Bcq1fIlg78hlKEUFsYP3mcdKQnNT8X1oY771IayjRjV85xmnhEHlJJBVCBcHGS3V+BqgOpXFOKgRxQapiiHLOo4iFGotS77pT7DiAHnOwieJcguBbF42pmZtd8WIlrlzT0ynNqarissi0bVEvqGGd5jeLuDJy3BECJzRsMwU5AzahmxfvbdOO578rf1aHcNVvk3vuEHEBvNiQcYDIs36pJgZZEMaWDz4UO7Sd9EUpeDzwkJ/CfHcc/CwOYAoJ7hw9r7O0jQjdN2QDkwYBH7fSB6PikFZD089lAVjagEGkUV5C+kBSnNoJEkVPnde61fbIT/Ft+z33K4NS+g3aIuQiQUgQHyjNJAgRJFD/CpPNrO/wW6fSukJakToV77pNz9UZgEANR/bC8/DAO4wSBj1jzNc2+/rKu43cp3/uAClCcs9GGUdJ3xbA76I0RgCTtX++AwoeOaZT2CZPOsnjvfZRxBeAjZJUBjADCIu8c0Pk331QGweqSTdsIWnBo59JFC4p0odDhRnUjQJA0AwU3D22YUkxaR9Tygs3X1fjKPqWGo6ratVu+XbdJfCZBHRuIAhpJKWxonMBqC1euqufbX5UPwMa3EfWCBx5GUYv2cLrNU/Gi0NYWhwdMLgCGXFUnSm+j1GH98rUWSOisBTohSJNEYctSoOIWdFkRbVfwzVc0A1jpRcXGc98n5N62E+CLwBEp6QTgED1xRNGXXyD92LTSH3lSSQDO7stNKDf1Ko1xEchCJYS3lwCRv4JUwoUocJEebPbKJXW/e1QLpDEs3bBWObfsAmhaBoBpwDkCvhQb4oXik57SKNs994KGDhxSZU29Mh55VK4tm2+q9RlHP8HUqAEfDV4AmJBo71f/668DlTUpn/TBhfQ914oNmjGwJNf2AUw4SWFoi/eg6NWgnuYbKI/lkQ55owIlRi2KMhhMJ3YdMbiTKM71EgP2Kq+yDgUj7tULLNM9Cli0Tqm1gHNARjYHjgLGi6luk5oVTzpv4DuUrMLTXfBk10iPOExwgkBcIUpexcCKPtQXTWBqhj7fd0aDKOWlA3ClGkUxwAtLRRJgKRk6qznAueBkWL7sbShPoRJFcDg5eV1jLQ0oFSyg2lgJNLae8c/3AEQgi5SYJgUtwFF4sF0BlPLc1dQZymMxP+ASPSkZIw3mICphzW/J7yD9L0qWdtRLBnpIbzoTUT7B+NQK1NsCXNMBjEcNgxtTZgZ6AtDTPG+kn/58VcHhFlK0YpdQpclApcxVRD2S7hEjAmA5pGGgsTlSYeYXurgm4Jx3lRn1AC+kqQ0CeaFyNjN0XVmVZYBQW4EXV/PcgHOXvy37FIqGRaQbrX0Y6G8N5TBKKbOIwLUo0nRA8c7LCsIoOlF/Sq1Eda90o+IB6teMk+igIqQtHWsF3EIJKLO2TI7SLI3Spv0dA6Rcy1Jx5Rq5s5bR5xhjNp8WCCCZxJ6MeHliC6gETUO+AMIOXCWN55BlR9Pya2gL6sYAfgQgbYlOlLqOa6QX6BrgJ79qNVCdASML+Bu2c65XC91XUW7sJKUnwe2KSmxWmeILpLrsQeWy7wLqIKgGlq8jDe8G+jR1B/CVmBjRfN8xTfSfINgJtFK+S44ioDQDwXFftHVQETuGGtp+0hvPUEeAc0WbFRka1EzzSYK4BICrgfGKUPhzA4Qa8JZxzg2ssQ66wJw0SvlIPd5xEWiuWy4AEB/Kdq6CjfBUKGgyZmKAdeHe85pqeEPpfof8NbfQl3YABgJtY1cSIZPO85Tm24+TptZGGuJHpZwtKLz1KXH9dQVpA1chyqLL+V4GbZNAyS0OrDVzBdjuVU0ONJNuF/izdKvcJSgc+qkbUn86YqQDHL6q6Zbzmo8mUTEEvssAEpvs0WRPn1x+AL/y24AQsXnARAb2Bnfk6QB6jOFDDTEJCJKY7wEgBSgbRhHQ4VNW6WrUCZcraZT9bICP8ygm9r6LzbhK9tNiQLXbAOOq+BvXZIza5wDn+o4AFmLXM5bJX3cX6Yapm3ng1SaU6kYnWAuQErEatbx00yeYbxOAcqFrmus4haJiIyDymFL8mdiM27DROyTuEwHuTDIGXBPnFWp6i1SmKGIWoRpYVI9C5RlAtgNcC3XYWkC9gj3Y3WzGjXlh6wAybQYKNLN5kr4C1DU/0oa6YyP/ngduo66Ax1LSivk0aYhp62DPYYW7DvIdB5DmXfTRncwVpHi0M8vHSH08uE+zvW+TEtAGxMqapGAX/Rc1M/rfaE879Q1kVbYaWBi1RHsh3Yg0k/EhtY81qmFgQFMOVHaqNgKElaoMGAzGw1hB9TP2rg92aYB0sBX0g9WkGi7AfriZxxKAc0FarRsVr6apIU0CFmU7/Six5akcICkAtIaJJ+WqLLDu2kCXBf4V5BRpFSBcEf05hXvM0tzXIzNq7GmzlK0qUKPzsT7qRt2rq69XfiC/bJ+BMSNyUXdOclMuAChNURcR1gvVfrcqc7IA/VLUhJLYNClQ64C9twDGxln/HAc66gU6i6L2ucDCyE6O9dWp+dqCIl6IMf7u7IjaZ8dViuV4iPTN+awZYQMVplwjrIm6Aa56x8ZZzwWVBhhUxoGHEuZVoyxmpg0D//bNTesKymdjzH0V+ApWALuV0Tbm8IRZQg4sRMmI0qpRZ9JStSz0BzSCCmI/NjaMWm0GhxAyqNcU5gQrxTCqQmEApgSqn5kchiitqkIBcE7tXa1yoHBTTznLs1DKom+cOQo8cv68tcY2wIdZu5osAeZt1tVmHW7Wuuago1kPm3WtWY+bte2ir8f87oMGzlG1VpnMz3A4LOObMlkB+vr6LEWnLZu2qK21TS2TY5pdUwvIFdEy1srbUWXL5gBCzOGiDUmViR0YITX4AGDaECnVnShblqPGthwl2xyjusezR0w7A8ufGRvTIIBZIYcq1tA/ClBMTKP/eihDO2uUU1PDQKSzqgS+rUKxbpw+fwHYOUi6vHTWpumoZPqYO02727lvmLVgCADGS/yvGBgtHSCvGzXSYZTo0tkHlOYUcHbFTsq+Po2geBr30DbYjRD7iNKiUtWwznRznd4h1kbjQyicpWodc2oZazEzsxubMskYHAG+bmcMDqPm5qHM5SjVVZM2NpP1pAszM0N/7sXWXBlmvkM5rwIFx7rsYsBAlJi4CGZDA9TBaWzFzPSM8vHz+VCL7UMZtXO0lzETUwl7C5/J+QrkYzq+GVshDqOEAO5yAPyX0/eM0lsranoj80FSvpZqhScHmBB7QTk7OqN65USvvn+iUQPzdtLa5mpNXgaQrVvVFamqqeaQQ6ZLXa1xHT0yooam64i3hgHXSPVN3ZWWZmjFcrdqqx0oxzmA5+3AKhF97ZvvYAeCeuKT23Xv3kJ4B+YS7NPgcEIvvzKmV/Yd0cr6bFJdruEAQBbg3DBgXAd+zFw99Gil6qpIlQ4MaEcK6vLluL79XFTnLp3Vow9W6sGHipVF+sbm6/P6f//+rCbHQ7p/1xrAmULllxhwmHUVqavDVGBPV0iH3+4DhupiG5gAhMthDJKyuTRdlZXAr5Wk8iWta/ONWfacN3StsVeulCxSuLKXMLBWcSrAjddK85idZ6BqYLzzIf3lfz6hWKRYTz5UhcoUqWHzY8wcDrV2RYgxj2rXbTl89+MBzhmbZVKnmo2G+e9h9u+dHR1WStRe5oox1rKTxNun6cNBfAIBAoFr1qxhT/+w5bc1B8DjQOuTk1MWuNbW3o6aY7flHxgBfJubY+/EIS+XywBsq3T/ffdpeX29rjc3ozj4V+oGnDO+4IqKCnmB3GyMXTs2NsYecWBwEJ9Ej2VnzaHAO3fdaQlGvYS6t2FdnvrFp6w0r9nZOZZinlG+e+nll9QN9DcbnLPKa3iAnTt3kjXyQRQXXXru+98n1edBy+dh7muy0dmxaXN8fnh4mFEl66DfE088YSlxGhtvXou+7PfyvVgf+Ij+n5lrp6eSKFZO6/DxdtQIh6hHrwU75ufmM04CpEt1qbTcSXrdhN4+1Mvc2U97LFC3aYzPdNbzASDFNFTq3CplzBoFyCEU4r4LjPfu6XatX5GlJx4v16o1HnOmUbhj9OYbgHMHIuzpuvTIJwsBHzP17pGgnn+2Q2n4dp54vEh37mVfDWhndpkTYwkguKheeC3I/DOr3/vtDNL7pmqIDB379wHvfvtFrVldoqc+vZFD+j5rDzEJoLyPvvQ6qv2V5aSRfhDFuVsB51zGgcMLmwwmz9AwBzSYR7ARZr/wfl8fC3BusXLMwuKb3/yW5cQswLn32c9+FlnJrZaj0Cyg/le9zMA1husi8NB3vvMdpAxfwqB49Qd/8Ad6lJOHxvAsvT4aNfBxA+cWJyXjwDdB2EUpbJOO1QShzSbGgHNeFtJpLHQrKyvJn32r7r77bisXuYHoll4flRpgtlp6/dxqYHSMwOx/COvlS3HIfZv++DNO/f5vu3Ai/tyKtHTjn3ENXL2W0O/9P2Ed42dZvk3/52dS9Luf/l+3lvkZP87S5ZZq4ANVA2YdYxR5zN7htddesxwKxlH5r72M89WsabZv364777xTW7ZswalVajkMPqgAnXGyGLjCrMsWTy6b036DODsWncfv9bzG0WJO9BkFoyeffNLat/ykkMd7XW/pd0s18HGqgfHppP7gTxb0zFsEaXBqffsv3PrFR5w42D9OtfDRelaz/zVv45Q1J6cvXrwo4wMwAJtxGBvFN+PfMfPEj7/M94y9Nb4o8zbzxeK1fvyz7/XvxWCfubf5noHdDAxngmkG/japYs28ZCBno4pq7rH4ObNXN/cz0PNikNCU38wDL774ovVds0c3tt7Mb4t23tzLRD87rl7W1//TZzXAYbi6QgPObdcKntezajlBd8C5GE5L1B5mjp9Qw/FTCgbj2vDA/QR+Ukk72EoqvR7Fh2aVQmDXKKXFAigdVAGcbEK9tKpcU8ffBZx7g7RGKUrdQ4B/x3bZSTdjIwCYBJyLUXaGEOoPQcUbAedeelaNDVdUdetm1TwOCEeKOguci7lEVi8rjZPZnSZJsxZHzeXG978rD+Bc/ppNcu95DNCuCnUsBqIZi+YZCfomqaP2576n8FtvqKCoBKDrQTm2oKiXbtJZRVCLGtfQ28c0cug4EFNCxbt3ybWsghSKfZq/3qpk7yDqYaSw429hnsO5cbWyNq7D+ZyO4lyDzr/+BgBGpuoBDjM4PGgj4B0hAEa8zQqMEwEDBpnXwvUWtQPODTRf1fIV9TfBuZUbgCyMQo/pGTaryBZ7MDNnpYFd2P+Smk2q1j33q+D2nSjO5Svup96oa3syjKLeguz9BKrfPK7RI6RRzK9UyT0oAhEYsWWYlCzYKNQBI0cP6tSB11D6IpUVqgHpwH3Tnd3ATG0KD88A/pBSh8BcClBTanmRAiivmfSjka5ONRzYT2q/hCp33yXfzh1KFqL4xVaF3sr1TZ/lvyH5Fi5fAZz7hvyjYwps3q7Ag4+iLlVB/2TMWG1hHpEPGzd7MqTx9iZSpB7T+LuXCeRsVNGdqBvVkGoUaNHyxFMndBEgG+7BOAy+8ZJmAef8KBC4UZRz3rqLZ0SBhgqzLYJzr7yoUT6b8YknlbpsFeDFuCavdaBu1C9HECwBZZUwoIATsDMHlbvA8joFSSPcfeqIwkFAg41rlb3tDpTCAE5QNDIpbawXim02+wIZFknr+jzg3FtHVYcaYvaDwIEbgB/9abThzb6Mxh9fAYijzeMEOfpfP6D+G9cBtQi43v+Q3PVrFCL4ZaeDeGwoiCWAoeIDCg03qftGO2OUFH6lG5RaRBkALGQDzIrfIDXjGY309xLYdamgolYBC5zrAJCakLdkHcpUt8H6Aaw6+I7pUATdZUdlDnAFmTlFZ4c0PdKCeFo7dgwFJtSqvKQbdQRQNzPplXnG5HSjIj2nNEzfyMipl790GwBmOepShLQdC5An5wDnXtbs1AKZMVGDAAxz+AApxps1fgMwh8BNZnmd0qpQPLTSzBJeRoksPt2BUt1ZbEULwB8cHmloVXyHogGekWC1gdYiwxfpjwcAXCYAaYopV7Z6u2n3ybjys8oBvAzMCyADQMT/3WyXOEBIDN8jAeLY5BXSex5HIQrFLoK33pwauXMqAWSLKDvlACJITvZqoBXFOeCx7BK/sgDn7F5ANXANG9BVfBb1MMC52cHLyq4sVrpR/bOvpQonNdL4XQtayiipJrXtgyjRAdSZaqZ9HDPNija/CaR6laCTQ05UrvxlgEuodS2kV1C3cXkBr6LARKOtKM6hUpcBOOcsydIQym+VAEeSAABAAElEQVR9HSOoPhEEIzWoG/UvSC+CzB7NA6TNM3ZMYjp/fJYIGoqDI8cVmQCagoRx5ZKmtBBlMW8lwW6jAAYAEO/SQudxDXNdJ/0yHxW7lFzTlwCoKHBynjSYfY0a7Wsn7aOdOigF9CsBpo0DzjUCxl1Ubp4fKAmoMBXFMhL7CYg2MU4Krr53NTZwTn6U3FLLSNdZ9BDjk+ua/Ho2oLehE4q3HFAEIMdZA8hZsJXUv4OaazoDe0k6zBrSvxahqAeYxhd5m/n0h2OMtHvJYJsiI4CL/QYgJd0r6QNdBSuU9NfTpiiPUhc3wbkzmr76OqlY6cc125XM364Y0BVojuwRUmv2MVe1HAUeQNd01WOkNL0FlT7AuZbXFKIPurCT3mUonGVQ10nGLuqoySnUAlFAmx5sBThECaV0u1IKURX0FwJmkQ44iuLcaKNm2y8rAoHkZy5xpzPXT7Vpog8FI8aeAU1TAOfIS2eV1RgwA87RwRiDk4oH+zVPqtX58UbUWCbkz1+pQMF2OSw4j4FBv3aQajhBetMB0mm7AmVAYztRjuOa9AeMNMqFDZiLt1GAA9ZMRRWxbjfjmDaYb9JgowHnJpULyJVdY8BHns+k/E0acO66peo4hfplYnJcHsCztBrSw5bsAJwD9mYBm0zOKGXysuauHSY1opO/kYq6uF7xkfNKdh8CGqNv193FPHQHkFkels6sdTAP1LtRM+QB6V+jKNShAjVMPyQNaEZuNrasXs60MpobLAoVSge2ZgY1uWD3m7S+E6B7r1zZt1t2hltwHfawI29quvew5gE6syv3yFt0G2N4gL57UqPdXZQfxbbStUqxwDlATIDh+eiEOkZbdAVwbhooJgt4tSYDmAZwDqsI+osyCrBa23APiqRjqiSd8hrSTRYwlmDMGWvAKQbYmZlQJ2W3s96rS8vXSi+gCzYTbg4gEkU5xn0rEOS1wS72AAkVUt8rU7NVhKSbh/pYoFvfCAHOdbcrPB9SGTCxm7mqnTHUx/48FdjIgHMOfBIe2pTlAuAiBwkAoEwdV3hTUNbyapQNxmXUxGaR4FoObLYZdSzwXx1j7dPrDCue6qHmCSovxLSWVNRbAItCzOPHgdc6sFmlzAUP5FYo10GqTep1CjvUzdqphRS0o1w3m3Tey1NRKQNeCND+1uqWshiFuD4DUA0PkLI2AjhXojpPukqNLQIONODSIKkzD3Xe0BCpyYtKKkglm4rvARhqGjvhxaKSgj4NKMmFip4JeTsBN8w6wQuQk8Pcmsl8OBCfVlMHYz1s03IA4BJA+AzAoqtnzqCc1G7BGSZVnzkUYmJMBpYzrx+F4sz62fzbvM1/G9+PWeeaf5vXBwG0MOt18zb7BFOexbKa8pp1+oULF0itWIXvaTdqTaNqY3zOrWZtQtvWk2Z0G0BaFiB4kLXUDH10iAMTHcFhjfI5V9ylknSgMhQJS1LcQG7GilAPQGMdzGmn+Ew/4EEhdb6ePmrAuVQzZnm3A6ucBeYcAnStQOGwkvligt9dHMZOkXY1HZAuA5VLN/sRFz9N2zs5LZbCfOtDAS41Mx0Tl6IOwLkJQMt0AN4C4DQeUJ2suWboYw5UU5P06ZnZGZWQNrwG+NFj+hff6ULtzpnm0+qCSlUAgzDTWIqIPcyP/aRMHZgcxWbYVUS/rkAhupDP+E3/Q+F5Csi1g3mxYbhbUQ5W1GbkqwZFvRwCJQgtk+YYSBTlxHNAphOmr+cXKDW7QH0LpBge62MtSL8FHvYz9m3MfQYqp2l++EIhE9XHArO+BEztnBzQ2Ow04FyVVpBOuoB1oYtxhviernQtAM4N6mzbPPMes2XSLTdzWU5mRGtXZujWzSXKRJ1voCesxuZR3eiY1rAxx9EM9nJJVVWEdcs2pzauy1F+bqreeDOqb3znKFPGBHu+W7TnrmJU+W7a2YHBuF7eN6pXX3tLK5fn6JEHN6Bsl6Vnnp1UM3uxrVsz9MDDJaqpdClAKlkb9XT5alzPPB8BnDuth++vBpwrYTzZSfsa1Of/FsB8NqqH967TE48VCOYRSBwLg5EzqlTzcyhLdnLA/+KIblyf0fBoCvFhxjFr16wsN0Ipbt26ww9EaefA16yaGsfV3bGAchgzQigArMW+oSqu7dsytX4zh48ypUsXZ/VXf3mMgznleuLhWj30gFt5hUDAHIJo65nX1cvjumM7KYVR5Psovcz4/9dexg6YPb/xURuVNpP2NETK7cnxCY0CvholtgFUVI3aW2tbG9BRQE8+8aQVf88jfbcB52J8z1znR+P35hCdSY969WqDWlparHW/UaC/e+/dGgJS+8IX/loGrtu8abN27dpFOtcsq4gGngvjR7iCD/34saOo4S9YynJ79uy1Urz+4Pnn5QVoe/yJTwLi3Q/YXMBnOKDW0anjx9lLAwQP9A/oGr6LiYlx3Y1P4Ref+kV5sd/fwydvDvUVFxdp65atzB2s9blfCHV7cxDQ+DsMlGcOCu7cudOy/caem/qz/BaUcPHnv1afH6Xfm24DS489jeryFVRgr06ovzfM4R8Pcx32lwNdy5fZtf3WbFTm3EB2qEs2jJEZcBqQ3fizUKhnbHk4RFJbK922KxOAzquZ0STqcRM6fbZbG1dm65OPFat+NT4sZD8NOPf2oRhKkkD7HAp6FKhu44ZcnTwcJFUrqneBNBTnSAW7F7VrlOyMkuTkZEIHAOR+8Arauaio/4ffzKFPZWpkIEmq1qi+9vTrWruqVE/98mqt3wBwx+GbMQ7/7Hv1FR14fb+lOPfwA49ox22AcyaVxw9fCWx4knWLjf01XhV++y9GevEjP/HPjxU4Z5yXLwGpPf3M01Yu5yIknz/zmc+gELHHkp9cXEz9xLX3Pj5oFjwm7/JppDANxHf69ClLBvOpp56SeVdWVloOzfdx6aWvfABr4OMCzi0u5s1ka4Kwhvhu5pTxyZMnrOCBUZyLYrVTWPya0z4GWDWqLCtxrJrNjJGPNU77xU3KB7Apl4r0U9fA+5+YfupbLX3hf6gBYmY6zQmhP/z9sE6TEiw3y6Y3/smD4oVxPSy9Pio10N6T1J/+fUQ/QMKYA3T65Qec+oc/N47kpddSDSzVwM+qBjo4uWdSlhppeaOWGyQA/m+9zFqmurraAufMOsco6RqAzqQIeS9o4t+61v9ffzMOFnOwoZtThUZZzkASZ3A0G7Ui4zT+t15G9t5AGOY03wMPPMBpNJycS6+lGliqgZ+4BqZRBP7sfw7riy+BSOA8/+Z/8uhTjy+Bcz9xBX4AP2j8O2YvbPaxJmXq5cuX9U//9E8WxPbggw9q9+7dlurFezlsjbPa/N68zTUWX+/12cW//ejPxT24uffiNQw8Z+z7l7/8ZQsANzbbKMctgnOL1zafM99b3H+b5zDzg5kXzJxn0nbv2bPHsvc1Bkr6YWTIfD7BZ9sA5778p58lZes1gk5ZugcIaO0D9ymwbhVBdz/iMajHTJIS7PBRXTt7EVjDpVX3o6xWV47/EhBlCuCte4x49hiKUQMAIKTdAnzKv22bcm/dRvrO65p67aASKDhkoDgXuOt2wDmgCSN/QqAY2SugBHY2KBQZhbWh117Q1fNnVbZ2tZbjV0upqVOSgGyS+9qNqhSBaaIEN8G5gS6AuO9binOZqwH17nlIDgLUNuA2KsQwIuyZ2FBx747nntfc6/vYT2Up86EH5doBEJROKD0BEIKTf/DQYY0cOQXg51bRvXvk3w7YEyWA1I9abR8gzjCKWIB03R2tmiW1XvmdO1RYvxIFozad37efIF2KVt55r/LuAMYgIBx3o8+Bs9dOQCMZ4R0MKwyE1/b2m6TFvaLldTXKRxkuZSNgjudm6l0KTbHNLo+w5+y8Yk2NCr72EoG2TsC5e1VwBwpPxfkoE5k6M4pbBrrCwz00psjhUxp8+6jm0/NVBmTnW7UapTBUzShDrLNN0Xfe1Ll3DspXXaOaB+5VqukLUUCh4VHe08AtUwr2DZI+tQVlrAWlr0BtbOediqNK07DvVflCcypnPeRDlddehsIUKm921D4oMk1JYJR2CV9uUN8z30EBr0+BjZuV9YknlFJq+okJYPM2Pwn6GpjRQExT1KUB5wZPnCdV6yaeEQU+Uy7Udiya0vRV1GygMugfU5p/83VNv/oCgVrSSN5Pul3qwyjO0SHw2o8rfOiQwq+/qikC+ulPPKW09ZvoNwbqmqEdB4C2jOIXgX1S804EF1Rcu0wld+/RAmmEO06+Q3rOXpWvXaWiHaSjrQYcBYzEIljtYXNQZgIUMRz9nT94Wf0ozlVXVCsXcM69ZROwnwHncPYbkBAQzCL/ZgDnuofVd+CgBltuqKiAoPI998u1nH5DQNoEH+12AgLAKhBHqDo1a4jPIdGinOLlpF9dCwwCDMU4s6E4Fx08DbTUr1AchY5ywCzAuVhfF6lwAHCK1yi9mpSkqCsZ2M48t43+YYWho4BrE+2aI8A8M4PSHKm40gCSvIBzdm81fSmdewD3APEh+UYKydMa6CT1GEBdFukqHUBgNqPYZgesmz0HAIfy32xEHlTAMgD8UmivBEDYSBPA2UxYOaUVlAW1NVLvWYpzkTHFxm+QJvIckexOUrHSpGWAdaW7lUhdyXWp2+gk9gOgrfMIaeZmAOdQY0srUG/XJGlVIyogTWd6+UoLgksaVS6ejEqh3mh71L8SIeCSnrMoRDYoBbVHTyHpqLJJt+sFqAEKtPoeqVyTU10a7kDta7pL6aRTyy4BEE1Djc0GOGdsAfUz1dVIX2gAKEOdsmIb5VsH+GrAuaflmLuijOJKOSvuJ4XnGr5He6MYSC5SLbQehvdpRiELaCO9VGk8oxM1sZgBhmjnlNiQosMdGm5ljIUiyqqrkKsMxTnA1kEDzhGQzwfkc2VWWu2eRI0sRKAIKwuwMCN3qJ86uo5i4CWl2FDxQoUvJQ8QLcOUn+BpAhCY+rDF+hXtPKnh7hbZgYhzSZfsyl3BNYmKcy1Lca7/qgXWmeBnFmBKCuo3CRTDxnuvabznPCl8PcoqN4plmyg7/SNG+tRRANw+0pQONcoNLOktATAte5BxYsAy09cGycp7CnDuKIpjMTlqd6JMthUgEvULoziXDrxUTRsWrgTUNOCcATxNvyMFLYhI2AB9Q9Rj/3VsfYg+ioopQIUNWCHiMKpvqVb6QXuCFMgDlOMqSpikAfRXb5GKbkFxLp3aApwzinPdpxTqOk0aW5p/1SModW5hHuulbK8p2E/956CuUnuXHFlEEs14wdYnp64o2vWWZgY7mJpyUZQjPSypPm2BEsoKsAM4lxi7qrnWS4pG3Iw5A0Nhq6eua5Jx6EgrR2VwF22CAhqABzpgwGSMcQAEq2/Ndys0Rv2OdPDveVJnpZFidx2A6HIGBOptwCVmvrKR0jcxQrpkgN4U0hJnlm2jvFV8hmSjJg8oioOxvnc03d+hWHqt0gHZLHAu2KzBhrPAseOkyStUVi1jzAB3wGoUEhm0Fs13Aqf2AYMDEdtdGQqgqucpIdU0xEQCuwolL9sY4FzTEfoxAEjJTuwQ0OLwBSU7D1rjKGXZbtkKdwLOFWJ5mZNZAztpP7sBgBeGmTM6sTUdmg4C4aX6lFNWA3dbx3eZu0QKW0AgO7ZmvvcdLXS/YQ3h1LJb5crbznglcA8sqijpiIfe1mz3CYK4SWVX76Gct3KNQYWAIgc7gB+BY7PLViFEuIy6oW8D1AZDgBsjrbo6NKoJXw4pn9eoirFoFOcMOAfnosFYWJ2kYp6cnFANSngrSLmdR92zjAMGC6ltYlQjlD2BYlMuQHxVIEvlOOm8gENmNjBLkHHercA9zcwZIcZNLkpM9cy95UA9Xq4TpCqvz08DvbdhNuOqLClnXKM4h0JYL2kt0wCUirNImcic6GVONGFiVgvUD8rV2JQs6sDH/NCLstd51McmiGTXoey7CTjKBJDPDHWqKzajELYuzJg3oNB6VAO3ZORoDoDi6Fi/elC8K/ek6t6cUmUCSwWZlwb4XSfA4MA8MDaQXmVqjpYDD+UBPxnhVgPPGyh+gfJ0sSY7MzqgAeCrEtRZ6z3UA/Xkp087/z/23gNO7rO8932mz2zvvcyutJJW3ZJsbMtF7timuGEIPbkncIHPIbnJ4dzPSXJTCQmEE5OckJsQQgs1IeBuI7lblmy5qWu1u9re++7M7vSZ+/39leHoEhJMILaR9w/jXc3+y/t/y/OW5/v+HtrLKP6iJwdP22IRajdNbVbLsyanUGSNMKYo9VsjCnxVQKNFAEq8Cn0db0k6pQZWRnm4GMeMABidZANGgn6jtSaM8h1KVckV62c8uohifiugv9SUtdkvvzEkP4bVuDn/IbnO2FZ/03jcGd/yU79ro0n+Gp33ah/nplHp0XHuBhZtetQaTho/23XXXgcwE7czrO8sbF5LqFbAOUK07q6mPWOrZ7EQE5mkDaKWOAbYmaFuNaE0GKZu1Pn8KLYBtdEtetnokGNuMMDGkP2EMQUtsxpCmG4nTGqDlzLBjKjO9aNG9ZJCtTK+W1Ndh9JhMaFKI/b8xLAlK4qtiXFCI5tcAtQpH0kHH0Ytjg+/+4HHckBqy7xTF4DlLGF4C6nj9dgSH88YBtqZo40JdtZHIY2bWgilWlFJ+gDnpkZtbG6S6OJFtqWessdmCpWa5Z1PooI3SbozvENtZRVqetVWQ5jaUtqXnu0FCFukfRJ0nFCtqPCixtWKQu3a6gar5tmCV6PUuTHGmi+hyhtj3a+Bv5VUEP6dEMxDM2OyXICabFJAxVFtW2nSuML5kF5ElS1AO4yzOaUbtdJh2k1jcYttLW+yesa8anN06zaOEtsjp5J2dBB1Z5YXC2k8M4z7hmn7pShC3rBni119aZmVo8w0z6a7kXEULUey1sv5R4+PsCZ5xnZu9ditb+u0jZ31tveRjH3lG4+SkGm77fYr7PrrWpi3YHB4pyGu+949k3Yfm5G2baq3t79lF4BemX3923N2tGsQIKXU3vK2FutcF7AyRBfc5NPBQyjOfZv0nXjObr8VcO4tgHg1bsCaqH36z56m303aLTdeQNjXJquo4jkab6vMeLcM16fgoZdI99hoFiUxlCj52d27aKMTUj+L2lvf3mw7Liy1AsCcZcK7jg/zjqRzcpKNWX0TNsR8YMf2RoC+RmsH8DlxfMX+9FNPc98Wu+Ota+y2W4JW38SmJIDM7oEVO/LynF29u9oaG85/cE62QbZK8/a5OWDt/n5b4GddXT2wdYsDmsn/nkjEGXNHbe8PfkDZ3w9EN2o333wzsNMdVssa7ghwnK6vqkKpr7mJ6FQKTA6cyVrGwty87d//DLDlfcBzp531gNtvuwOFt2X7i899zgFatTnwl37pXY7yHEly7GWM9irb9O1vfdvGmDsJrHsb850TJ4/b1xCOEih3PZvy7rjtdlujeRt1RiGVBeQJAJSindJ65Mhhu4554/ve+15ssZ9Qrd9w7J3CsN7OZkBd62fOrhnt3n177Z+A8rRpUGnSZkFt8hbjo7zKH6+lPc+n4dX6qdemGVqUdriA8tzsNG1rjL58JGe9fYLkuunnZon4t85uvKHOWsNe4Eng6nH6aPyMo5w7NEwUhd4xALxhe/Nb2uzqa5oY83pRj5sFhDxj2wF877yj1TZuCbHZhnrDsx4D4L3vvqgtRk7bne9eYzt2VtnTjywD2w1aIeC/wLnr3gxcDYTLcAAILmvf/27E7r5v1gK+ZfuvH661PVdW2TTg3L6HgIG/vReVzAb7pfdssl1vApyjik5NjxOm9R7AuoetLdyGPXs7fEkenMMAcajP1DzLxdjjZ/WCv6HAOWWenEKPPvqofetb33LAnoaGeqch34TspCAeOYTyAyud//M6ZNBkfMbYwfL4Y49jfO51yN0yZJfVsBXmSIO7VwPe+3m90+p9fnIOnM/gnDogLa6rXsuRPAPRLmU5fXp7zzihXgTRqbMWHLd+/QakYVnI3rDB6Vglr6r6/3pxJP/k0lw946fLAU0dVo/XIgeW2RL5l19J2f/8f4EjGLxsbHfZvq+ErA457NXj/MmBOSai3/p+2v70fyZtmoWOWy732Nc+H2Qx8Px5x9U3Wc2B10MOaPfb008/7ajPaSFA452fBJhpPN/W1mZbtmxxZOMVqq+FRT+FDNFmgTyo8Gq9nyaPkUjEGZv1suNQO5QFAmrMpkUGjef+vUNy+YLkFI5W6kMCMQQDni+HyjO/KJ1Xffpx76ZzBJtoDKz54n/GnPHHPXf1u/MnB5bjOfuzu5L2qX9IsxCVsy//P0F737tXwblf5BLO9wea02perFCtX/va15xw1wKorwSIkgPvXIeX3ld2JG9L9G/1C1rU/WkWdmWTtM6Ut0X6XenRmlcenLv44osdddAOtgyfm4Y8OKfnKR26VnN3rZVJyUIb3xSmVR8B4Of2W3JK9Rw7Yv/rj/8AsOeUrQXmuXbNOtuJ8670IqCJ6jKgLxTZxmdsdu8+6z6FsxpnaCfgXFEHMEFQu4BxngFJZAHoUqf7beThvTZO2KSKN+20MACaF6hr4f4foLYwYJWXXGi1NxGutUEgAgNdgXMeFsxxTrl4TgZQcHrfw3bimacJJVRjnbfcZsELNqMqxgorzuJcFgqCEEK4dHAKAk+MjxI2k6gPrFkUbtxk/psJIbe2jVsTBha3IP49VL0EeKVt9O77beH795LkjFXccI2VXEMISqlJ4PROo641se8xm3/xJVQpCHF3w7UW2AW0hMJHLkVZElosu4BSWfegjT39uPWNnbHWS3dZ22U4+mcn7DBg1OTUknVecJm1oObmW9MEbMG74dDOUo4uHEKKrZokHOPgE4/ZtMBAhWq6ao+FriIdKMs49Yi05VR/6KuNPM2c7LbIPffaaZwdzdTBWuqgB2jNxdjEhcOcG5MnwEyzi5Z87gUb3PeEzaLmEt59pVVduAtoBRgIz14StYE4DpeeIy9ZxbYLrOEG8qmdMkApxgXYZ0mBfThnBgip+MijCBedMG99s9V84JcBPNLWRT3yDA9YA3lchLqAD5U2x7mG01uecFeAlXbgk1R3r818759s6cRxczF2qgM4LUIhUe+jUF1SZBOI5qLfFSCyPNhHqNanUOHbzxruNmt5MyDhuvXmQoGESk5eCEKTF4cyXFmy+FNP2/w9d9sCzpmyPdda5TUoA1YDWqCIk6WeL973kCWefArurMDK3v1eC20i/CHOVTdqWAo5nJ1Hv2Vo3KZeJiTqkeM4I3HIEikkjfO37+BTNnnqmDVTf1rJ58CmToAk6j+VKIfjn2pnHvI8u7xiE3c/aEPfu5dQdMXWcNP1VnQ5YX8JP+bCsSs1IHywZ9sZoXwzI4BzD+8DiDtl9UB+tdfdADgHlFoMzKLJHqopgoYsB+w0dwq4DHgnwvsRUrgABTniXVLncSDEUIIbPWhzgv9AQKrWrAcm8AI9UidHZqykbgNg2YWAhJQrYFkWYMEtII12IhW15PCLFpkaIVwnwE9t2IIosXkKawiNSBvIgZQoXBkqUJbtt8jMSzbQe8yKfIRiaySkbS1lGABAoy5kAedmex6yJepnQeNOwDoU0nDS5VBwGTsKkIa6RD0haaU456roJNNou/ERlNaO2FzvYcvhKC9CXslfhwIVKlOusq08l3DG0XHAzZctOvoiDtg0oV47aBdNNjIwa8uLEWvAGV+k0KGFYeqQQDhgZQ+LJQYQtIJa3/QZmxnqdpSsymoJF10HuFe4hoKgDAF1lMskHvBv2OaGD/OOJ62wIGZldUA91aTBC6CYiAIYjtnSEHDawhkra28lHZeRRxdw6ZxNHvmyeecOWWldk3nDN5B2FNkEjSZQEuN+UaCx9Hw/TiTqDGpX5YTADRBu11H9EgCaYJwOUDSGHXHHAdo2rLFQuAwAChW6/kkrA5wra90G59RK+6qk/hcT9jeAQxloARWy3NxxoDlUw5bmraSymrDK6wAJ2wB8BDCVWRJb7KcP8AIqZgYO2dzgEZimJStf02IBlM3M3+y0lRyOsPjIYZuibgaLUSZbtxFbUUfotGWbHTwOPPu8lVG3Klt2IVa2B1BVdnIWBbATlhx6CTWxM7w3z2kEVl6LSmQZ9g64k0pm6YGXLdv7EqA0ykSd18HqXYSS4pAtdD8OtECIqDbyo4b89rFhJ4fdQDHQ3Nw7M2wL870oRPbxnvOElqK8G8MUHw4+bxPwIIpowJ3q74KAPF7AsuixB8yPiluQeuFtvxygGLgTyCGHKt9K/yEA0CMos/gB527mmdsA4wYt3X2/LQL7+EpbgIhvAArF3tAmkJDDPrxEX0BoUECMtKvECuq3WKDjUspv7dk6lxgDnHvJFk+9SBmjntOK2l8FwBXtdnF0AOWkMBAffR4qgBmUweKofPgyKfNxb6nNJVFym5t4EbgrijJTO228k3RQ1oRjNqAWEkJe4IGkvWRnjwBYHgWC9Ft57XpCy1KXUT3jJCiG4xYfesoWAE9cFRutYv319Ms11G3Uv44epJ1PWUUlIQ3bN6Aaiv0IYCOTC6T9tMVogwkgELSECOUYAsjrBJ4DzKsjHfR3lua8iRcI0Y2qYQYloRaAtebNqBw+j+IcyqqMj7wd15jVXUWaG7Ad2HLqnBvHrSUGsGFnAN6A54GnUkSnKWgGUFQaXM3YYNqYyhzgBjrQ0uNPU18ewskbsYKWbbSVHdhF3oO+IouaVnr0KZQTD9EVeqxs/Y0WaMEe+qYsiurhUA/hZsF46ls3WHH9BvpE0pINAniMo3rWbSfmlmyU8NNGeOc1ZW3ARwVGQGebJq19KMUNESYyMR+xzfRzHYw1imk7As5OzU+hLjXFvelHaWMthBssl4qXS8AQj+ejY5ZiOJMQODdic5RnEedtqiYMJWMa3hCtqJwdBWwaGOq3StQKO+tagAeLbQhop2cMdULKugmQp7mo1IHnqIGUCQfFz6WOAh2PtGGg/5dJzwRgUBOhYrcA/viBD07MjqKKB+SbToIOAda6/bajosV2ldXYYopIFqj9jRLKsxUo7obqsJUwrpjKoAg0R0hNoCzk36y+/Ozz63g/KZAFebaLvg4ql9qRs9Oo7h3EHg7R+mqBCzejULiOfl44SwY4fRhbf3i0DwquxFqb2qySccA83/UT1jJW5LEWQuA20YcIDuRVsFFnwaQQj1AeZXjBSdrradrO7MyClZfVW5g1iioU/8ZRR1KoQG0U0bqFxq35sXZ+bK1/5w/ZBR15n5TO0Rha52jtI39N/vxX8+e56fzR5ypdPT09jiCLQiZeefkVwME5B2ibWNdqESDPTsCuy+nTKlHcG6Qv6Iss2AT9aIb+vQIVtjbCr9b4z9ZR5bPqqMrIQx89RP3YH0XBDhixDABuC8qULUAPhapcwHWDjNWPE7Y9yRhjQ029dTjgXNT2j/TaSkkh5dFqHZRrMflPq3XqqEd5ysenPOazwBjyJLZodGYKRr2ACCrNViGIkjGZQrjOa4MG47gcc+XW1rA1lVfTL6VtZGLIFoFCqwrLbFsTqoiEas1SjiNxhUkeswT1uJbNC61ltfytwITL67287NdwwLmAwLmUvTw7YItz3AdFvfW0s6ZAkXMumDzvHbWjgHMhrtlQ02IVtNMZ1PrOAHOnsMu1tKcWoEOBpWqDyj9ejSvPwqx6xwjjy6OEjO2aHUcVtsK2oi7ZhD1Bzxn7nAacQ+RgYAGQ0UsaSq0FAHka9bmHHjhj4xMxu2Rn2O68rdrWtOkBjHzZACSwZXAoaw88NGrPv3AUqC9t77pju138phZ7Yn/GvvrNH5CCKbv19j12/TVhq6nCIpE4QWv/fPcEQMuDtqWzGcW5CwmXW2rf+d6MPX2IeUIroZnf1mEX7cBuFTFTYq1i36Mp+/p3gSiHj9h73tliN7+V/Kz2WHfPsn3ms084ipi33rQDaAXwm3CxbpTqGNYQUhZojnC2KTYmqPQzjBEFxkX5PPtszB55AgW6qVN2w41r7apr6wHdUCjFdgi0W0apTu94GKW6f/j6aatHvfqOdzTbhbsDKEmm7Y//6AD7mfjurWEHnKtrBABmmNtN6NtjR5fsqt1F3E8lcn4c59oA/a52r5/6aD6v9Vut4z791FPOpr11zIMEqq1d045Noz+kfaaoaz9gHvdtGJjT3T120003OtBaIBCwp5/Z74hKaY6/Z88e27xpMwpjIceGppMpe3r/fqIkfp1oiS+yCfBawmzeie8/an/5v/7KUYW8+cab7H0feJ+j/ub0QWS7xHQOsF6udRCFjN2z5yp71zveaeNTE/bFL37RsVtbt21lPflOZ41cDI4aT1J2lzWNF196yb71zW8Rse6A7eFd3vue95jSqigw+0nvpZfsRjXvTlvbsZa0Bp18OMC5iuYogapdu3Y5ax4S6zk3op3y7rW05692jSRLGYux/k8DUchzLDtjdTZ40sZ6e1QnehA5OmHt7Z12x60dtm07YZrV9jVtpA0uEle+/0zG7ntw0F4+9hxtda29/ZaNVoAN+6d/nML+dNmOrSjIvWOdrd9YjDg3axXJnD22L2X33xezJVSg3/meVsRbKuz5/XHq3wTQZdrecmO13fS2YsI7o2ZLIocG0/b1rw3Zw4/NMdf123/7eCP9WYVNjubsUcC5rwDObdxYDzi32S68OMj8C8W5+XG77+577UHm7uFWwDnCD1+2+zKGJxoNnfVpZDWfcgl+l+Lcz3a84cA5GZhxJI4fe+wxdtJ+z06wOCMH1gUX7IBQ3O04gjTAKiwscKjWcxcIf9qs1rO0AKmQF3KynTx5CmJ3v0PJDg0NOip32sUryUs51t5Ijfinzctf1PPPK3BO9ZmOTJ2zPuoQ1UnLASs5bO1sH4VYV/vS39RJhcNttnbtGqQ9OxziuwMqvB5gbvV4I+SApg+rx2uRA0tLOfv134bO35uyEP6T993kts/gmC1lILR6nD85wBodk520ffQ3kyyA5WzPNrf95Z8EbMOan3VoeP7k0eqbrObAzysHNJaX+s7dd9/tLD4IjNBY5ycdgrDq6+uZlLY74yDtktMOOAF0UuDVYoHO+c84tGtPKteCHyS5rzRLyv4UO/mkpKe5SX7B+N97vtKozUXaoSiAQovRWsA4Xw7N1w4ePOiMX7VwvmPHDmeDRx4wOfc9Nd5VPgqO0YanjRtZQGAn9+rx2uaA6rE+Kr+88+O1TdG//fQ4i/B/+4WkfeLzKQec+9L/CNr7378Kzv3bOfb6/4vgNdkRrRvlF7G/8Y1vsM50wrElsvuaC0v5Qus9+Tqq6/RvXSc7qw1lsq0/zZqQnpu/j3JKv6staF7+hS98wVGc0+KxwmorDXq2rtEz9DN/jX7XupXm9fewi1eweFNTk6NUd+mllwIRFP8w3bomxVpAz6njdten/sgWhgdsDY7uXQA927dstdqdF5i/ASd2DKU00jFy4KBNE3qyfvN2IK7dAEkAWakIoeQKCdWIGzQJ/Hd6wKYfesQmCYtUctEO1LyuQSUpYcuEQT12iFB9jXXWcAmwTbjd/AUodKEekUDhJEF+laI04mYhP/L8Iet/er+llpatecs2Qr5uMl9jpeNkjcMfeIFgQqUs7haSB/R/o9++x3xHT3JOg/mv2w2ssda8hHj04ESjIBw2T6pri08CFNzzoM0PDlho4zqrvgylHZzBWRxDK72Eu3zxBVR1xqx200bU0rZZFvW9ZRarvTgtg0FCqqEikerD6fzcM4Qk6rLGC7dZ8x7CzuKcPkM+n36522pRtmm/+CIr2dQOVBNgkTuNag9q/V6czSi7GGEs5wHep598nLChixZc32EVe1DaqSp1wnZqB38amCqEIz+AszI3RGjN7z9gJw+/ZLXr11ojdcAXBn7Ege5BrUiKLfL05SJRS548acOPP2XdZ4atPtxhrSjlhprr+XPKIjgwYigOLKPgUr37SivadZGt4F3NoBLlDwLbEFbQjeM9NTJt0R88ajHAOVd9i9V96KPkY4BQo/da9KXnLUD9Kb7kMivavJld6UxOCfmWxrHvKa8C9mkkjOQCz3nCJg/ut1nqYjmbLevoX4MAii5CVcVRTsgAAjnvh4M1MT5iQ08+Y6d+8IitA7BoxmnsY2zlAjBzV5QAwKHEh8NVoysPYYtSR1Gh2feoDXSdNk94rdVdeJGVN1QDRiYI70hI1EeeNO/AMCE911jx2wHigH9iiQXqKKBWAe2W8Irp8UWbeemIjRw+gtO00ppuvd1cbA4dx6HUt/9p1M5c1ozqXNnmThSQKDM5tah4cZyhJS3N5vMELEpdGr/7PpSdUAnb1mnVF19gQaBaF46IGGM2qooVolQUClGGM0s28tgTNgpUVoFTseGiSyy0thMotR4oqALGjbGHR4pbqHVFe2yFUJ/RiT7qHKGN11yI2iLwGUCDLXfBhx22+RkAFX+JVQE7FVAGyWkAieEp1NM6CJEKeFLe4gBXOcI6ulHyyhCuLwZsFRt6Di8KkFFFAyEPBZWhEoYzgkTTRkhnDnVCQXyeGTZonLZ+wpl6AUPqKbuS6lacqdgCyi8VOYZSGWEwUdEqbtmJutxOQiaiCAmQMXwUNStAkmpCeJU1rwM8JAQqKlm5GOAe4SkXx3rNSzkGZS+B4vwNqD6WobaFkl0mMmlR1NpiM93YKa8VtpFHwVbAuRmctXPWUFtFGMwdqBF2kBkC51DmcuMaTyt06ctAYoRIRSGpFJWy0gagu4JW8gFwRmAg7ydYUypXClm7grN3YewlQj9PEva31oI1G3EWUdYAMMkpwDoUxxKJWYCyDituvwLwE2U58nH8pa+Ye+IAzuUK8zXs5v2AeoFYsoBz8WlAg0XeL7vEM7FrQAJ+QKeCSiLRAO54AgBWMdRlpocJzToDeIDDqXMdbbTYIqM9DjhXUlhH3QWcQw3tLDgHAIkiWwq7kJo9QXt5zpKLQ7TJUiuua7dgJe8IYJfMFRGGkbDRgE+FgDsBYIEsebkCIBiP9lthZQF1eQ1510xdAlKM9ACVdaH2EIHFWg/AR7jLuhpLoto3O3DYAecKPRmrqt2GbeL9AefIGEsBByane6iL/I5ty1Tzbq2UP0pbfmSOclEULUa6oH16cdoR8mnzm8mniy0yMWAzPY8Sfstrpe3Aj9U7eD9AzIzAZIozO4Cj/bBNT57gPYECsRGVAJvecsovWE34ylpC1lZRnjhXAaKFp3qACJNde0lPv3krw4TOBfJDcUhwZ3p+DDt/hHC23VZZBfy15c08k3CvC6go9T5EKN5TQGh1hNK9AqYMtT1sbg6wJDNHHhMi1Z1cBPJhflRIW5FqoFTpAOlUfhnq2sIguAZKcKVrttLOcWoTpnhxdJBQre2Ac9ebt2YT4FwxdgC7A4zope6k5/toG6jBLZyiXhRaZf0OmjFt2w/cjENQALnsB4mho5l2ILi5gWOWpB8sprwDhBR1oDIBDIunHcA0sjBnASDEqvXXUccInx3ptcmjzxBqdsxKS4qstAl7RT3JAddAnwLEEdiQspGGoTi9BM5XvUeQ9wvWAD8W0I5TS5YjZHK09wVCt5bRDm9A6W0zAnDPW3Z4H9ERCAnfcTWhb6+kTWHzgUBc9JNQbrzjYSIJEy55dtgKieXpq2ugX+b5oWY4OcovVc17Av8BbbmAfnJLnDvyqM0C//oIsRhqABIAAsoQXjQptSlCBmfGTlAWISvfeJP5WwWxTvKMAzZw+ihF7bJ64MoK3tFD20kSi3QBR+zCzJD1E9q0D5guCpzYBCTZSR4UA8dMAuz0LdOWAXpckYRdgGpgO0qgWerhCP3XKerOAiVXwziok/pX71Odc0gax24VA/2Repuj3+2Lo5YG/DO5tIDqZiFwXKuFqaOFlOckcM6p6BRCqNO2tqTGNgHbeOhnRwkV3jWq+g60C8QTZgxSDhih2bgfwN6Fw5okMu4AXgm4AfCAkgBlB+hriumDWglhF2C+OkadGiDE5ixrGFg1C7JZYHNpg20uqkZxdcVenB+1Gcq5pbjCrsFOeGkXZ4AKe2exl+ksfV+jNdMXlFEWIdYi9SlnDBuUc5p+apn++zR29yD50ZeJMrYttfXcqwPIU8BQnM0Zk4QPnFicxebWWCvjhVLGZhHS0z3DOAlFxcqScmtFra+SsYWODLBzDlgrQFjMQkBFgR0L2KMBVGeGJqep+8AyNYQLJb/nes/Asiac8a78uufO5fNja42T9dHYWz916Pdz/67vzr1W/34tDo3N82P2/LhdY3Wld3p62tkEeYywiFsJH68R/VAyZsPtjQCMGdtcUG5XAoQVBP32MnWtF5AygTJiBeOx+vIaoDnANpUbz5AaYojfK9h0ECDE8TBw2IFFwmzGlqyATQDryxjzFJcyzsEKUE9GGUNPsCGzmO86CbcYpg9ZQOXwmdF+lO0SVk1o1zVcU0m7F0SW1jOYNyhUa4h662Z8KEXUM4vT1j8xBn+fI1RrndUAbEaiy9ZHXRhdXqA/JHQ9/wuH26yGNCeBOMdoB0lCrDYDe25raUdh0WNzjFH7aPsEeXX8kO30LXVexuCkXbWokPZXwEuqvkYAvMB07UXCQk8y7iikLrcD7TURArkIMDVKQzqzPEf9GrXGAPMawNYy5iqLfN89NmrzAIhSXG1kzFAOtOeAcMxH0rJn1KcQzyoJ+C3OGPQkfeNR6mmG92grQ9WLmKMl7oAtoER94syQdU+zeQWb34xabAewX2Q4bQ8/NARwFLPtmxvs6quKrbJygboJZ8qGVSnyjY257KF9Y4BSXdZQ6bF33NoJLFRnTzyTsq9960GSgOLcHVcDzrVZNX/XtRMTKM7dO8465gOoyjXabW+9xNatLbO9j0UAVhjnMb659JJ2230RKpMlAHr4Fh5/Imv3PwFvHT1lv/JulN/eWkUodq+d7onan/3ZPosDLt9y00677W3rGNuw4ULDQijJGMDVzFQElTnUNzNsRikvZlMDG5coi+eejQPkoS5GWOwr94Rt6w5A8kLmxZRhYUEAWJV+ijpy9OUp+8Y3B6wOSPi22+rtTZcFUFXL2O/+7pOWitfaO29Zg9Jd0GrqmHeT9z0DUtBaAZyjXBpIyHl0/KgNyK/d5sG5oaEhe/CBB+xu5u4N9Q0OGHcRc76SYsYe2IplxoHaEPfQQw856nK3o/Qm4ag0dfLBBx90vhcTcy1sylVXXW0V9BV6puaVTz75FLDlfayRjnHNzU60Ea0f/+Vf/KUt0O/deOON9sEPfsAaWS9wujuuk6iO1lW/+pWvUI9H7Oqrr7b3v+/9sAQZ++a3vukISQVo//r+iiuusBbCMGst1YXtUdhYbe7+zne+Q3jZFxwI8D2Ac1o/kfiV1iZ2777M3vWud1r7GtSz/+U6rbfo7/ejVKf12Xe9613OZm+tq+iQvdSRt/HOP87z/1AUthCJW1fvHBBbEu6pmP6YNRDGl8NDKdu3twtwrov832TXXtWBmmSGORmC2EXAwABo6RTnDWTs/gdH7ciJZ+3qa9bYzahbFtNv/OM/jtlzzx+zXduL7PZ3bLK16yoUBABY9qzi3P33R1AO7iUkb9h2XVRtI2fShFZdsBcPDyGkVGBXXlVrbR2FWOGk9Xav2Pe/P2jPHl2xdY2l9lu/EbY9l5ehjge8+0DKvvj1e1DUrEG9bpvt2AXcXMR6CGHd77/3ASDjhwGL19gtb7+FesE6DmOQs7q3mGLmQOjGMn9j3vgzlvUbDpzL55ccHo888oizm/YFGmSUzrmtLew0zM0s6CgcRT27pwoxIDJIamjOR72OPj/mkHHJD8I0mNHCaV42Uw354MFnTc+S862zc4MpdIfU5hTSafU4P3PgFx2cU51WXc6wwJFkAKRwx3K+Co5T7PHunh6keuWA7XeIc8UUl7qciPXOzk4nRNn27dsd53DeSXB+lvTqW/3rHPjxdvJfn7f6zc8zBzRAGp3I2Yc+nLCHujJMfMz+/nf99tabkdHXdq/V47zKgcPHMvaR30raC91Za29w2W9+2G8fehdbvVaP1RxYzYGfew5oTCQY4cCBAywkPOnsahNk8EoAOiVG4IHGR4IR9JECXR1zDcnj5z8aRwmg0AJCfpL/k15Ecw8tUmiMpnmHFjTyH43XBgYGHMVrqV5rceWVplfpkEqwJqKXX375DzcXvdJ0/aR0v17+rnHuJz7xCSesgOZ873//+53QhAIbzz2Uz3fddZezKCNVphtuuME++tGPOmDMueet/v7q5oAWEF8C7tBn27Ztzk7T/ywY9efxZvjE7R++lrL/8zNsRGI39pf+O+DcL6+Ccz+PvH0t76H+QYfWf6T2pl3VWuDV97L7UlrXpjLZmHPhNV0jmypVOEG7srv690+zuKtnyI7l76U2IchXinMnT550bLeUQgXOSUFDtkz3zz9D6hqyaXLC7d2717GFeg9t8NTCdjgcdtKcT1cWJ0UKaYAzPV322T/5I1ti41x7MGRhnLZhnGjNLW2o4sjZnbQozq8YEE0hO4LrL7kUNadawo0etUU2kRZwjY9VWpdidUwDcrPAjmfISnZfaKUX7mRxF3TrxCnrP/CMzU8TUpL8Ky2vsALAoiTLoAu8Q7a6ytp37rACIKLU+KjNHj5m08dOoToTs8L6SkAroBDOi+IMLyRNtRvXWsFanPa888wDj1r6AOEjUafIoPRmLQ2AM6hXhDcgalQHDEAKUF1L9Q1a/JnnbBTwPArMV8o7FOEAS7BAHJmZByKM4tgqsiqgOQ+KbvM4GKbHpyyAc6wEyM+HMlIcFZuZqUmzkqA1AEtVXNCJgyluC5RT/36UwgZneb+glTRWoVIUtBjVaRkHXiEqfY3rCSkHHJDmvgvPPm2Lp0/YCiBSEZEzgqWsVeIEjOCwdAEIVQFuFTW0ABImbfnxZ6xr/9OACWmrayJkGXXQD8gfYpe+tx5oDLU6Q5Ukg4LG/JGjdvypF80VJbxVVTWAIWHdIAJWcCp6CRFXyHUlb0LRBKffaD+qQDhggzhmgzgNfTje4yg9RUcGgXrSVg4gWXHjLSiNBWz56As2e+gpwoQCd3FtMUBEqCCI6kfcAR+D4XVWsvECCxUTTmcE5a9D+22kG+Uo8ramqgY1HgAgQLA5HC5ZhRS74AIrb24EFFuw6UMv2GkWzcsxqmXUOU9ji3mbGqxo01pCwgLX0Nb8lL2X989OgQIcP2XdTx5gF/0yoUQrgLRKzA86sIyzdXJi0gnrVbvjQlTxNtt4b7fNDKOSmItZEQ4eH3BYYiHOeaOI7EWtbWOHNVxzE6pa9YQIBQ5F0W6RawqBV8qrKyxIO1YoshXWz5KAnY04owqoU+nBYYs8e8CGn3sWMDJuxVVsoBBkR7tcJJ0xoBUBgzU4AjzAENPPv2jDB5/B6znMGhugFmExi5rbzN+B6lq4jVCUgLYeVIAI5Zme7UWJ7ziRMacBcSosW4DajuxNag7uDTW0RYJ3Es6wGnAuVIjqwGQfm2BRK0N1qTJMeytuJs+AxXA0uAjLl8SpPIsKWHzmZQv5koBiQKVFLZyDM46d/DQOIBrAR6AqD2pPinEWSc3a9CghRyeB2FCgKgf08bsZz1JGaYCy+SXCyroLAY92EUYS6Io6n10YstFTKMZNDVkZ0kyCy1zcNw00kQWwzKwsAifFCD3Fu2IqBMEKAHOHyhAdk5MV+DEywX1GrKQMAAwnENKNNgVkFo3OWU0t9a4VUK0Y9S8gJhdqjpZZAiI6Yyt9+21p+BjsUc5K2dDiI6xbOq3NENgloNc0H39JBQClgDvCYsVQTECNbGWmx4FvgsEqwBOg4oxAw0VCExEWGPClYs06K2t7E0pmwH3xRZs8/G1zARAVBgvIK0JTBZvghtiQTCjcGIqIHsCWYmAYX4HPYgAlK5FF0onKGvYjUODF+YkTCqh4bj5mQRz5desB5+rKaHMDNjU0Td6gtoQim6+6nTwBMnLhxMbGZeamgeCeBZx8ljLgPKBLF+pMOYACywZAYwOWAQzKop5TUlBJEQaAxFBXnMaRPnmYfJojnyk/wkO6gACyiUWLAWDMRQEQKjdbbQdqX9jgNHDA/NDLKLIdIBwewBZ1yVPQSX6DFPD+RghUCEK4JxfOU6BgwOc0zjc/c51AAG8bin3ZxTFzzaHmg70MbL6OcK6XYl/ZlN3zJO0Vlb22HYBlF0IGUddw6OWoCLnoSVueOGjzgHNu7Hh5cRHlJcgKkItwojHqajwArFhUa4XFhKf1sTAWGbHcOMpow8B8wDgu7JInUAzc4ad8ExaZB8ogtG15dRG26VpCkQJxUm/jvdixAfoWIB1f1VrqIJFTWFsTOJddQb0QRbBiQcGsmy8Tws6NelEQ8MmDfcyg5JeKDtpCFGA40EAo2+309dhfhSEeHqYdrLXC8PWOip0FvajWEa42AQQ31w+4ipLfVBe3nQNAoD6UYP+8FZi3QtTHpMYZAiINw+exOd0NSJtGtWuyB4i2n9Czc7RRnsM7pgCzXDHSsThsccJNh2o2WyVwpxe7msPBOX3yOVvEvoWwtyFC+Ll4loBKF32Ll5DrXjq8AHbFW1pBXUetjHogp7wLQFyhqdEaAeo+Y8u0/QzhmYvar0N5byOgJWqRQ0/S1xPKqwNorvZS6hJ1ME25A2LlVtiMBFQ6NfgyMcVmcNoTLhHgKYW9kRqiL07ZGLasgLoL5OZC4EKhCFPzR6n7AJPkexBIxV9IXwHVl8bX5l5CZXF+nPwotvL1qOo1EjbXNUE7OGRjZ1A/TGZobxVWxPt4GIPESUcsjk1QGPBguQ0VtNm4H+jXheoZ8HoQj/CKB4UsFLAW5ued/nULdrgeldk5NgWcHqUPkN0tDxGitdJquaaQ8ZCb+uXGVhYB3jUWlFoV/WWU+5wBiuzCps+Qhz7yuA5QrBSYzw14s0AaZoHHfIwDN1YSohCYX2OvRezW4OyUTfH8FGM4wWN+ykpjSn/CZcGEh7bnsQoAliLGEGkg3DHGcYPTU0BpEeo+9yc09goqWHP8W2HJEcSlDgesBZXLFhQ+V7BjQ6lF0ph2wLXLUJZbxq4dGe+14Shh1MjjhqomKwEo9vBeCsEZAnKpQ7WsivINMf4BS7ZBbNoLM2PWT18ZZGxWjTJdJSqKXs5Nr6AwjPpqgcdrzSiV1TOWCkLcxKhLZ7B9Z4ATJdZQSh4W8z0sBXVcNhsgie8rKa+6SkLaY6WnAJ7PsOYxzSaDYMhnW8nLBAB8HF+vfKzapKgxb/7Ij3k1Bta4Wf9W3ur3/L/1nX7XkT8/f/1r9VPpzY/bld78eF8bJOVnlo9Z4yXewiYgBMY3tFqKPmUL4NzuGkJpUw8fGxuwYYDGEOOy6iLWmQA7QyjdSsHQzf0ZrlgVtqIee1VC2U4wfj9EaOBe8tjP35vpA0u5Zw5ly8hKFFZ8xQKAqi0o3LawdlXOc5cZp/UK1gSoSXB9AfVC0J7PA6CoeUqc77h3Jf1aOZscPCjZzaZi1FFCvvIcDxt7/KRLKkSz9KMztBP57XPMlWsY/1RpbMDgcG5ywjIApI2VqEs3tVPnMjY0NmyDcxMWK3ZbGWOuWmx6AW0K0s6ZS5RSNxq9AIPY+ThtYYBNE0eA0aeB2oJuj1XS/4aA0gRYJGlrEfoT1YL2YiJnMS4opJ3FSNcw62yj86gUM3b0oHYVAJ72MTagOyedYNPAYdXY/hbatM/nIeRrCgXGWRuZmXLmUcUo6hXkSmzoxLA9f/CYjc8ngXqrUAVsIe+rLMU4c3pq2orpH7ZvbWVekMH/2mvRxWVEEBi3Y9NmFwutb5A+B0Bu15YSu+G6JsJWFtu+x2P2tW88hGpX9Kzi3NXNVkWoVooPBbus3fMgKk0ozq1fU2u3vvVS1kwqrbsvZY8+OYGi2BlnvNjaWEG4daDuDKDaQJG93MtGnnSv/cq76uEXSCeKic61WAAAQABJREFUc109EfuLv3jIYox3brpuh91+y1aUvkF4qUPKswjzj9On5uzRvX0oQhKSEVtbUIi9568DgxHyPGtlpOuiixine+dRPp5lrOZmfMScDQg7yXxtaJj53EzMLtq5hrCe1Rbu4LmnUvaHf/QY9rvB7nxbB0p3AQec0yaqnv4k4Nws4FwF4FyQZ50/h+yR2rxsvWySftd3+rd+1wbpp1Cck8KbNky3tbU5m7S1tpwjbxT2WOqUOk9rEbfdehuheS9wwqJKoe273/2ujTL/rmOet2nTZuYaVc695+lrdN0kIVS1CfwtN99sW1ln09r35+76HIBrxAHntG6q+ypN+qjNCpz72lfPKs5dzfzng7/8QWd9Q1Cc1O90X8F6stNaIykFsHZjJxRetqen25577jkbZ96otdY733knbcl3VnEOISqtl9x5553/vw2JWp+/9957HXhOawXaLKjN3pXYgnPt/OvFpr8atVPtfnQ0bj94pN96+maBbsvJ8yonPxbmo9Z75jQ2CcB68wZraaqzwYEJbHsSm8aYB3uZRS59diZuw6OLFgjO2A03he2iS1CpRvX6u/84ZM8eOmwXbCm3W27dYh2AcwFCLqeZ6z22N2EP3D/ngHNvu6XdLtldz5jdZS+9sIxSZhf1cBlIr9IaGpk7+ZdsagZ70eO2/lGPhWt99t//a51dfSXgHKGp9z0Usy/+w7fxm/jtmhskxlRswQIJA0zaU0Cdhw4+bxs3bASuvR0htMvoQ2SBhGSrX2ddAaNEk2FUcvbzH833Nyw4l88wqQb88z//sz0AoTuAxK2oWqkqiHxVgxStKiqzkEGiKFftAs4vGubvIeOgQ4uOMhJyXikckpxWx48fdxYgpVKhBclqdlXsZHHvV3/1V01AkZxoq8f5mwO/iOBcvsPT4Fy7yZeWmFgx+NViujpJdXZyTimOuohwtQuF6wqHW0072uVclYKinL+rxxs5B9Q9rR6vdg7QJO3Zwxn7Lx9JWDehPBtrXfaDvw1C6WsauHqcbznQN5y1T/11yr59N0o7yCN/4Gav/cUf/+8FovPtfVffZzUHXg85oIVKKe4qbOu+ffscNWn9O78D8JWkUfMJLRgIkNMYSgsOCuWnRV5N8jU/ULjX/EeghRZIdGiRJD/nUFoEOCyw01bjNC1uSP1Xmxy0eKI5ieYfOv+VHnpWOHx2M5F2EkryXoDf6xlGeqXv9uPOU97ccccdzo5LlaE2Nf35n/+5Aw2ee77Aw4985CPOvE7XaFHmk5/8pLNAde55q7+/ujmgNvBXf/VXDiT0wQ9+0H7t137NAWxe3VS88qdp1eC7/5i29/0B4AjN8ku/CTj3X1bBuVeeg6+/M/NrQUqZNkhK0fNLX/qSs3Cs+ik7Lpt/rh3XelLeAabvb7/9dmcnt3ZHa2E3/3klb6vny3bl76lnCpz7u7/7OwecE/wsGydwTv2Izs+vZ+k5Ol9pfvzxx50+TfdSOG7Zf6mlqp9SGnXofIFzGRysvd1d9ulP/RGA0TAKK5W2npBI1TiqKnFeVxJuDl+UraDUVHbBRqu+cBdqWWu5Lgs4d9wigyM4mHA2IU8gGMbF/QKEYSysr7XAunbC9AFHyKFJPxYHTFrAGZpZiOI0YxkUR2gKYmEZp5wXxZeWTZ0WALhTnJ8kgNRiTx+qQcM4rdM44YBOmJImWEQtQCWsqiNsoTYc8HyfOI2KTlcPa3azlsCnk9WaBo7cmvA6B5xzo5BghBXMsT6YGSUU6ekeWxidQOQLJyOO4Dih5hI06GB5kVU211motRHlHtZNBgdQpZoyHwvIjrIGC7cKlxLnfUrCTVba0QbgQjnjacugsrJ8ZoR7D1gMFSCfF0cuClMJHMZJ6kyI8Fi1bWuBHLTwTIg64LToYB+Q0QxhHqWyyQZfFopj7Kv2kxflhEINov7hJl9TAyjHAE5GGRdIkcOH+oCftchC1my85K8Dzim8H+BHmnXLyaPdqBHh/JejEeef421DPaoYGKAo3AaUFkZhIm1TZ/pQkOL9qOtyuvoIV5jIJHHU56ywppwwlWss0LyWvMNht4Dq12C3TQ8OWXQJGIB0+akYfpaz00BmwaY2K167HnUtgNEUTtKhPlvoBVScQBGJeuanTmTIkyWc7C7GRjWdnVZST93AGRmnDk2+DJRBqDY3Dk8XcI8P4KJkPWBZK4pTXCOnu8KD5YBZsoTXmzpy0ub6x8yDc6AIsMAPoBIDvEqhalfZTlragZqoxxNdp23m8AvAfH2ESwRiAaBIAanEUfMpWlNu9VsB/jp3Ao3gwAc0WjnZZfNHj1mKthBcwAGMozqHQzXJfV3hdqu+9s0oUBE6EmdsenzQ5l58weZ5hs3MAefREdBWEjitM61NAJg7UCyjDuK4iLFRdRYlxZUjL5uHNbkc71TRHLaiCy827xZCIhO21+WL04BQe4oDz00SSnPqDCG05oBX4PECQEjkXwoncySCs81XfjZUK8ps8dkhm5pCpYtwqiX1KMmF1OZYmwa+yK0A2KAIsjh2jHt1AUgmHQDBBUCYzQC0YQfIdPOGgPkqCddZ2QHgiAIaNiK1PEE7PGzu+W4LAc36cCZ7OVeqWFJ2SrtQ1qzeZAFCnbqLgLGWFQL1FEDPFI78NHYAG4PjOkG+Z1CI8bmLgIXKCeVWjrpThnaC01xQESEbc+SRPlIwzAJqFABlhhqB5ILrUOnCuQ8gUlKD0hHqVemCBieMpo/64EkRWhXFvVj/s0Bi3Yh1oUtQBMRH3czgpHfRtrOAXSnAmsL6NgtIFRGFNigpFC37AOe6LYbCmSsGsEEbkEqKh7qaos4uYx8K6imjxg2UAWpcQIgLp/eRH6dwRlEonkqguyDgaIwQqctAbChNla+xAj4e6ld6iegdU4RtBXLwYbn8QH0Uoq0wYJhbpM3hlK+lnoZqainrOZudAGwAVCpvXI8oV7MlPRVYO6BGgLUsYEBy9AWnPLy5OWCoMlQ6CcOFnfYI1AFOTMt21tUDLBKW1QNAnACMJERvGmAyudhPWgHfKBe31LGBtVJUrGgCCKoY1cum9ZQNIW2xIbHJ47YyfhCbsEh+lsK5lAOMsf4E4ObHjvp4jod+glkMim7jFNkU7RPlJAAZN4CBKw0AGhlFrQbFo3VXWGHjbscxP0mI11AIJaSGLYR23Y7dRA0FG+XAdoQ6TY8eAvLsoQ3EgAxpSMAY2Qxl6Smg/wEMFCRSAzBNaFJXAHXH1AqExxCKc70wjcOAV3PYB54pGA2wIk0tTmWmgZL9hAFFGbSk3VENTKLCF50coL6RD4WowqHWkgUA5iWBj1CnKyJELPVFSqKx+WFLLKr84AIC5IGfsN6eiM0BFMRRM6xsBC4WnDxHXzE6iZIc5dmwh2eRvgDhyjz0dUlg0NluWxk4aTEASFBD8oE+SfAht3QDGPpQ1HABW7jrUeOrBNJEbdSFWmR2BdBvCns014XjPM4zKXOAvxDQlg/FpvQyKk4lwHoNKE0CKli8z5YGjpIfMwBubLYFckii5hijT0UqygqBLAt5P28ZiqjBZifv09NAawt9gMQowNG2/YBLPjegJrBfAgC3oOUq2sAWQgyfIZr0C/TbQDptF6IKupl2BOSTBtIUOBfpQnXxOeoxan6AmY76ELZwBVunUNChBP0ENsNLaFp39QYURWuAF1F7TdO3EN41OXHaAigZytYjt0YZkn7yKku/nUoDPzZfBujIM7PYjZmjgH30XwwK3KSHHorv6bsowwztzA/Ynq1os6lQB9BXrc0IQEd5S2FmBf656NNWCP+cWEQZpYEQmLTBKWxM3/iQLQHQ+wDABSb56O/V/7uYr4MhMyYK2Rrg4voQ9QYll37ay2kUKlfwHRZhNwsZ0+RWYqiwkhbahAsAs5J6G0YJsZJ30iouNZ5nxB1YbHYZkBaYOEaaM+rHaGelqRDjLtSWKgDOCqgX9Pkx7NhsbNlGsCVzgqE1zkHZi6ZvMex8BJBN9h7ralXSruNv09SPCH1iM+m6uKIetal5OwZEPQUQ60N1q0jQMjdQ+EwP/YDCp9aQl82ML8p5P409xpejQEljNkbeFgJIBYBuPcpHYD0XdUqaN2HaSjM2vYRrPdhdjaUmKYch1PjmWLdIrqhtAR6qD2ec4sVul9Kn1haxZgKcGOA5Kyx8DwPi9VHWUeDbrbT9NMBNIhb/oeKcwDmNeTV2zR8/+m99f+53+l3Hudc4X7xG/1F68rBffi1I/9bR3d3trEcp35XeCGFS5y/YwHjMY530FRei0pYCVn5qctimCGUaIj+KUakUP57F/mdVt+m3yhgnNKDI3BgsQRHNB9CWsMOsJY2QtwWcU0ebFMC+Qgj3FJsqClCmbSpEQQ0oqlg2A7tDL2ERkjVB+Y9TbxYA31IM8J3hAtkfoN2V8szaojKrouxDlFeKsdIM6ZoSGB6LAKyh/MkYIFPI+IcL4zwzsZIAwgsRkpy2QlpiCwK4I1bJRp21DWGQXcA52tMEmzxSwOdBNoUEqfsKy0rjJpdcVkbbWFNQbR1AnznazADvcZw8iQJ/lRdIHdrPWBB/PnVQNSVIOuuB5ptRoasB5tPqOtlkETZDjNLm+1EXnabNpxhDurE55D6bFLKoQAITohjbBthfpHkKJnuCdtvDWHia/IgzBgqkSmzmzJIdf37ExkfpD+MlqP1hq4BXC1BzbqhL28YtxSjCVbGml8T3OmAjhHTN0calmpsDCJcyVEe72Zt2BQFfCmEW/Lb/QMzuvv9FB2i+9prNtvtNFVZeenbONj2Dgtz+WeZ5z1h7azUKU5utc3OJLdPwTpxCXfDANBDmPG0nTb56rApwOp4qt5P9fluKnLF331brgHNV1divoWXU4A4AYEfsios77ZqrUS2upE5hqNRyllHIPn1q2fY+MMxclHkO/YeLuUSWjSheaNuG+lLbuKnK2tpDNsic6thhrV0i1kK+eNxs3uAmHs+ird8QZPNuLYIs9K+EZ+zqQn3qS0eZ6lXYdXsa7Ko9PkA+QnzSIQ6STz3dc3bhBeVWW33+gXOal6vta+4ue5C3V2rzWgceGBhwNvYePXrUWQ/W9/m5u/6udYhwGPDpoouc8KgSu9E9tWYsQSnBt1o71iGbqTVf2Rj91Pq01gSkni8oTRvy/v7v/9557tVXX+0AaoL08jZJawqHDx92orRoA7e4Gq1BaEOyOBml8dChQ866g9aptb6Q/zg2jHO0GVycwbXXXkuI2Guc9xWzIw5BQld5ASqtrSgvBPkdOHDAgQD1Xlq/0DnnKs7p3vq8UQ7Z+IkxFCz3Tdrho3NA/0HmVIWszVCufubj/nnYpzLbso3xGOHqDx0ct74zcTbYa9TChhk6X5cngV/CZZu3hmznJWVW1+hHmVdA24QdPdZrnR3liI+1WbiNNopyJFlvzx0g5PsTEYuuDNvV1zeiKgkQi0L9zFTKDj07yQf4eFzQP31ROX0Sf5tfrLLeQTfjnhX7+IdLAedKgfbMnnl6xb53z/2MvxatfV3QqqpVV5LUo2lUKY+yDtVn27dtJ1zsO+zyKy7nnrQNR7s9D87JMqsvVy/wHz/e8OCcjMgizqbjkPpqiFowXKAjVmNTuFapQSgMj8IqtbUhD8tijIyFU4kgy3XIQOg+UnmQkoPoWRkT/Zyfp4NjMKDdwwLyJG951VV7HKdYHsL7jxff6pWv9xz4RQLn1OGo3gv+lBqJHK5jUN5nVRxedEJTiVLXQEYdjup0R8daHKoX0onuclQa1TbUQauzzQ/qX+9ltJq+/6wc+Fm6pv+sNJ3/92U9wr7/YNr+j9/CCUBbvYzwnV+5K2gt9avlcT6WfmQ5Z/98f9p+6w8Zy7BQcetuj33+LsJWFa+W9/lY3qvv9PrKAU3+BSc89thjzkfjJYFr+YWDV5pajam0MKFFDf3UR2MojaX03Y+OqzRW06KExmr6qX8L5NJH85H8dxrX/TSHFmO0YWjnzp22Z88eu/LKK52xndJwPh/KN+1cVLgC5YEWiD7/+c87KuQqg/yh8IW///u/75S58lyLQH/4h39o4XA4f8oPVf9Uhpoznnv9D0/6l1+0yKNxtcpXC1i65kcPQZJa1FJd0wJSfh6aX/hReWvcrmtVTkq/DtVBLUbp0P1Vn5RmfadzdS+Blrq/FrI0J9Whc7TxS+fpe0Gd+Xvq74KCVL90ve4jYFPvIeBTz1G69Hctvune536v68899Ay1F+WRwMw8mKNzVHcFfOqnAFP9FJyqeyqv9F0+D/Qen/3sZ+2v//qv7Vd+5Vfst3/7t5159ut5HvIg/fZt//e/gHP/F+Dch1bBuXPrxi/a73lbm6//gmzVL6h/UJ09tw3pHLUz1U99r7aqn9q0qYXp/KbKfP1+JXmh5+uja/RTbTAfqlVqFAq1etttt/0QnNN5+Wfr/rIxUsfTbm21Z/UBioywdetWx67o/Hx7yqcri7LEqRPHAef+2JZQNLmic5NdGibUByprPsCHUBZHGg7+LM6OgksFzcnhjlOa903R9jM4NvGlcrAwqx8sdUody4PzSuflcFDr0DkubHSa87PYuyyOVF3mIs9yykPOV/gxObYdlw1OMt07iV0k88/emweQOw744QOsUYgoeWNzqF9kOS/F5AmXjm6KUweHgRSWZI+VBiWOtTw88M59pWYjlRz9AXcgjiKcZThzvdzTDVChkJRpFHoychDjZTubX5yn2+Ac1vOlSqeQjGfvTdmRjjRO8wzl5ryDypG0KA/cpMcv285P/S1HXmQTcedcqVs4h96PjxvVHakLKQSknpvDwZfh/ZRmAYcUonMfL/dzkWb9+2wh8DfyTWlIA7k4KkDyEPI8rXl6cYq6sflOaEnePcU9s9xbUICyR+ECnVLhnbykQe+nc50XxGGbxWmYQskpTdrledQ1aCo6+esmBJ2H/JbiFn8kzSjgca6USVzUFeedOTXLM1z0FT7ez6O0812OPFZdygEU6fVUXnquznFRL0iY0x54BSf/VR5SukmitqT35WucvVpk52LK24ujViHx9M8k58VpO6kelJ6iPANAxY2ykqcaxacwnzogmzIgIwARqEGgvDkADmCjoWGzMZxPqieqD6yXeRpbzb92o3kACJCRoy4tASVNWmJw1LIjKPEtAgwBebpKgQebUcVq53zCzjl1mvqUHByw5GnSMakwaynCZ6LQs2GredZu4Jqis/fEUeDKzvFc+klCcqYIX8oWC+qmqhpwBe16cYYxI9BR5RoU53CKZnFWL0dQFyiqBxhpwVuGGhdqLGoKAlgyS4TPXBoGVhumvFacOqWgZ1kgI6dtUJ9dftS8itcAwDXjgC8iFZQ/ymIQaXiY+aD45iIv3AoniTM5l8HpCwyjcK/uUpQPgkA3ADDJefIMB7lHZYgDPodDOk1IuQyKBz5CUnpRJXIrBDF1KLPMODsxRp1fAjISOMT4hrFIfATlLpzroeYt8H/rCVVJGwXS8EmVEWWaGKFXeVuUEikawh7mAPUyhA/NLY/T9KnPOFycaq9mlQGcw+mdYawRRLHHo/C0bik10G4zswCvKKMBumapf1xGnQTSVB2mzqmae1Co8ZYB+Ci8JMBJilC5rvgwbQn4IFdKmyDNwCkZD9AVSlQ+lPw8AdRXSIclUN8EnpOKmSAvKXkhXwk8ELWZaWA9nPjVhKcL1bSSF8AxizjuUVcLljWg1IcaJ8piapHeJKAlYfCyC/3ASYOkY0mx+ICOsS0AM540eQ3QmKXe5ypLccQ30N6beT/gNkJp52ID3H8Y+wSkS8hFx65Q3oIrU6kA+cPYsYyxG2HmctiCLM/ILh3lvGXyEvWvFB/6ArVTD2XkAdpzAzpgODiPMSIqgVK8FGHtBjzMxqdQTSTULfUrSGjNYO0lPAfln8VBIFIU7qijnkAr11PnZYO5NhcZApg8jfLcON9HuQ91R2VE/yPlRNVHBoyEFA0DeYZ5Vh1tjWsztDlCyGYiw5YG1hOQ6QFAc8LuBgAyczPci7CupRvIe8GEgJakLYMimotwcshb2QqAj9RGPZSZ30deFLcCbTE2zQIsxsctTf3K0Re5vfTxhUrXnE1NML4GGitv2oGyGuppqHnFZyLOe/nKtvAs2qCXsbuHup2LQCAAgqAImqGeuUg3aCZ/516AjLLvLsHnwKuucmxBUTvtEaVL4DgqD3nSi23p4t2ANQAJM/ytAAelF8Awiw10+QnhWtJBWZFHmQkguwHaF+8jpVXyO40t1phflFWA8vQL6guhaof6Gzfn/6jvRRnj0C9L9c4b4rrsCOGdUUTNohLXtAcwbxfqbNQl7i24xE3oZkhU7lFMNaCfB2zJxQhjyH0Sy2OklbwA2IN5oR6TDagu+uP0gzh1PSX15qnhHQldngDGATvFnM6bl1C2UpiDuqCNMn+mbNyMB7LUkWSMTQslG4EDW0gbfgwU8bIr09h7YCpsEgMLx9ZkaYs5wFfZilxpi815WmwU8HOOPF7CLjl9N32DlLtmUYOKL0YJ09pIGMRK4LS0zQPSJIGzc4KE8FZLwUvCJznGSQoTVo5dqkcBqwIILkL5SHGuF9BH9biWkJaloULaCuORNO9E+wzQf5UAI5ZQNpSOA+WQHdRp5kY8ZwGoaR74LpJj8xx9mD/ttXKg5iqg2EoUKv18pw0DoPUozJFmzp8FFMLiOGCe7HGC9hEhjVIPLeRTAgCc4Tqpks0DMDUDDu9G8S5NHg0nUZl0bCROa9oWkly0ERJE56rZYynjsVr68VLgJBqijUUjdmxq3Ga4XxWhA4to/17ezcUYx0f6S8n/BsC8EuBIwfQerpfZjXLTJdK7xBh5iT44Qf5jrZw+LMCzSpirlvGOJcD6sFXONYuMCceUJ9jHJmDrRcbdCeqt/Lf/1tyaR/2rIz+G1h/0u478eNf5x2v0n3xa9Pj870pXPm3yOe9HhWk5go3AlubYQBHdtcmSdEcbgGV3lVc7gGMPoc4XgMKkaujn/TS8SmP/MxqX8u8S8rea8WkZ/UsAGzrNRo3DE+M2RVlUFxY5atIMjGyZ+sANqdOo4wIal9LWvFxLa+ND/6I6R0c6z3nzqMnFAHfTtANBloWoG5ZQ96QmWEgZgtM77SNOYiKAyHPcf57nxilbSCnauRdIj5EMtogeliEV1zBuXUKZcJkxTQX1bV0DG0EYN8yjcrjAmCauhkf6GPY41zjjcN6vkI0PjYFSa+X9kvy9j771xCRjJdbSmmnHRUChastSXVSfXootaQiWOvkRIu3UeqfCqZ4uUPPGaE8TqFQyGgfEIw9Is2DZchT76sj3aq6X+hzdLLYqZ+O81zhQ9WIMtcgsdmaBsMxDCdZBWK+J0AeiosrMwyrYTNDa7LJwux8Bbi9i3Bnr74vZ+IjUkRkjMxb1ASlXofzW3u6xtlbA1zK1EuzKQNqOnmBcQh+6rqPU1oR9gK4kjPdn74Sd5j5dXSiLVRfbho4q1NqAo7C1cwtZG+hP2+muuM2h/lTIZvx6FOSmprz21CHaF+qld95SbTfeRHSxWi+QS9qeO0S/yXh0fXuVrVtPCF9UjGVzeJQzBpqZytjp4zHWbrIAwnxHvibJs4qqkLWFQ9baEkT102XjU4RcHkgQbh1kfZEeLoVNoDyrq7PWuclvTS0+R3GWImBtKWMHnkUZNOWzzvVB2jibrYqYW5AniHqj1Jew1sYAyq+81Hl25NcS8u1ec+m8zdJ3WguUyI2EobQRTuuLOkdrg1qzk2KcoiquY1OO1vfyc3qtF0gQR3ZkYAAFY0J7q9/XuqDWALXuqLVhRSDR71q/GAeGk6Kc0iQ7K85FHED+0Dlas5N4lAA4qcpJNErnKE1aZxA3o3UK3UtriUq/3iMP0On5WkMUi6MNfHqWgDsp+utd9My8fdc9tR6qe0qpTvcSXCcxH61NKp+05qHPG+rAxq/QyZ88TYSAviTzB/69gm3EwBWV5IBOs7a2I2ANTWzuw+j2dRMxoY++lL0yKQwXXQNq/jnU3jy2Zj0wfy19BfYkijjL6RMxWJEla6gNUq+KrIKQ0OxldMYTg/0Z6wFyTTCe27iVfqKRTRMYyAz2cW42ZaeOsVnzDDaJqWJJJeqThIY+dixnB18A0itJ2sd+tcQu211kMYbCvT0pwrt20w9MWmFplLGRxlT0FyibvnDoBTtx9CRlvYV1fMC5ywHn6PfOgnP0hVLQZows2+F8fobCf8ODc8o7NSQtzqvhysiItj18+AgNb5CKhSw8hiYkGVYarz4yPn4Gex4m02qkcrjEWWSS0Tn7idNYWdyjk69lx6ca7Q6kMPVTRkcGIG/wfoayW730FyAHXu/gnOpuvt7KgTXFRPDUqVN0uF3OYr86MoUxPhvWi4Eg4VHWretwOjB1gFJIUQeqTquYRQnHIv0ClMtqEl+NHKCHWj1e9RxgnGhf/HLKPv45dtKzKel/vNdnH/8Ykv1ax1g9zrsc0OT0mecy9v6PJ2yMQewlKAv+/m/57cqLzr8J43lXeKsvdF7kQH4BoKury9npJsl7KfMKRNDCwc/jyE/283MHPVOf/ALqz/oMAVBtbW1OmEApEwmy0CKFAI78wsrP+ozX8/V5cE6hAzS21QLOhz70Ifvwhz/sjHGVdi3Y/M7v/I6jIqUxr2At7WT8gz/4AwuHw848Uos1Kn8t6ggG0xhZk/gLCC2nhZv8ob9LrVBAi8bZgsB0rhalBCtqoUqHFp0E36g+aXOW7iGoTzsvFdJRz5CyudKtMAc33XTTD6/Vgtn3vvc9Z/wuKFBzTy2KCf7Ts7RopkUvLT7dcsstDrimOqwQxFp4E6in91R+6HkKLay6oA1m2tGpkKh6hs5VXujvSpPq0aOPPurMJZRnepZCJWiBS/VMh+YbWuTXe2mRS++h61X3BOxonq1zvv71rzvp13d6T+WX2pQgu+uvv95RQtT9pDZ33333OXmvNAgQeve73+2c93pdJHvy0bTd8GuAc/jDv7QKzqkYf+GP/AK26r3qr+BRzZ/1vT6yM/qb6qT+rfZ0Liyq+bQ++XaSt/evJGPyz9C5uk5ze7Wtv/mbv3E2c8qm33rrrc6iudpb/t5aaFY7VnvUjnMtnud3cktpVHbi3EPX5a9VH3Ty+DH7sz/5E5SCUnbbVdfYJdt2Wgkh23J4Ytx8XKiEuJAjcTeigEYEBx0MW3Hv/PjDeQ/9nec4hwa5/K5wcDqUd/rbv3cP58TV/6zmwI/kgOrWj9adjL7jPIENP+7IoiiWRekpi9MWcgIYg7ZDaFFXEfUQEADyi++ZbOPoh/YDHqK9o37jgHYa/9HG3Tj53ThfXThsafDAcHzvA1AUqIHqXS6Cs2iZj0A+1pndKL7QiAgRSngs3NBKm4d7ugDzMCgODOIGwnCVVQN4EGITYOvsATwFaJPJAJQlp4ETAHaAvyB4gOkAvcZ7bAlAJ0Wow7I121FQAwYi7RnUT1yEW3OhwuJywC+1NT4AjzkcvdAsDlgiVS0nm+Qd1t/lf8L5TAPnnQCtgKlyBkwF4uEGHHEBMUElAU2RJ4CTjkdVz0E9TmCTi3V0FypqUBikA1AJQALpJG7KOAEFFy7k3+SRTIDAHrwhgj+leKMwq44iF4CTS6pzAGYr45OEhCR8MGENCwjL6i5bR3kI6OI5CpXJ+CmK41whnotIfwi1NxdgXg64TM+BqnHeKyfPbwboSlCZQLoQaQHEhILj+ZQjkAdoMucAkkm5jDwS0ERBOXUpR7kL7nPh9HfhQ8gCsVEA5loY5FlAV8CXyq9cDggQp3qW69yAoG6U0UgE+aR78n7Kd9RryCznemhIi0wP2Dz2OsTm/rLW9Rao6KDqVQMbkWcoikkp0cU4K4PddetNcVQT84x7kUeUAzfm75SHV/WZus97krEAk9jUoJxk1D1XtWUII5cDIMgS7g36y1HncwuWUAVAWQ3CiLQBC0AmSK3MpbLXvaTcBmzn8nN/n9qGoEFUw+gfBEMi4cPHRyoStKllVKsAJVGkcqNeBL3mhL9VCFw3wE5x+Erzlb+Jcwn9CHjopq55vYgXoPzo1DPao9q0G6DNHSNkHfejofBcnk3LUV5KNTADdpQFQPIC/LlcvF82PxZH/QoY1FC5y0m9UMXvEdjKOai9QVTS3yjNtBNPNffHt0MoWT3HRZjBXIgQ4Eo3eeR1xziXckXpT3kDBUqZzPJ86r0gU7UXmwTa6rapURTuQo1W1ryT6KlhzgFciHGOm/QpxC42w1DayblJA9iHlPCyqC25AGjdagyUi8BDF20kB6CaU76q7frxL3lQfAHMUmhyB5wkDLBbgCnwaJb6lqYd+AA13QLIAD7MXWMW4P3U5nJSrJzhJ6BmiH5b4/U0oWuxDypDF7CLC1AGApBzZPPkWR0kKeOUB+eAfEjZLrNwxBIjLwJnVpk1XYfS20WUAE5ToB3VnyxtKQtQ7KItenBqqqk7qn0Ae6r7aRQdGS3xd96VeuwB8nETts9px4Q5FniZwf5JnYyzSHnSQkBdxNEmTcpn2WbSjr0A1SH92GtXOd9Sd9Ru07OcR10BwBEM58rqO+q2ypJrstjrJBDqAlZiHhgjTp6nMspz/kYdnAcCHB0bpTyy1tHQRJjRUiAj2o+IW84VQJrDlitHnOZMG0nzDz+AUiH3EJA0g5U8TTjUgakxlOKy1lpaa00oqMn6ufAfehnnBLELUuPVPXl7/gayrObHT6YNTsjIBO+boj6oH/Mx3gJdRFlW1yoXUHniuiT3iJM0J1f1LnyvsJa8EYAQalicKRNDjgH6oVaGveidYeyKTVyLKthuAGAQQ2A/Nl6JROLwA/gQwZ47UZ5ql/yd3ITJBZynj9NzRxaX7AjQ1Tx509TaYnX0bbQu+iUU+PgEqOdBzlXb0PvpXbkr78s3tEXnHfmLQuhiiPg7z+Dd/HwUFtPHfXUoRWq6sEgoVaboWldsqOsUvtgVZ06teazS5PT9vH/++NF/579/vf3MpzM/fsmnT9/njwFgl4MHDjphDRUO1VdfZysozllxyDYVltkOQh5LQY5aTwsgD8hP2RLlBlaFrpm6xr/VA4XIX0GMrDIBhsXtxckxFAgz1oTK32ZCOhfx2KTGBhRVIfcsofQFdqq9JumzVbfAnrknYDhPSPMs6dDpp1PPnDbAGfxdQqouClt9apb7wVhTjsyfqNdYTsBZFAW5SIp06l3VppKkNYKNGJ8C2gIUbCost42Eii2hfQl+TnJ9AjUl3U+jO6wBNpb6JViQLwuBd4t4vyX+fjq9bCdnRzAHK7a2otpqUZeTTVCYWL0Tvb2VYZ8FcfGraiehhvkPB02A/ATeoP/CcgCjnk2fAMQgaaHHoa1zGQ0NM+a8+4obqBOF2RhjDB821JtAOS8KKMrLSvVN58GeOaBbKcpNRWw6EiCC6UfBLWcrbJZPYS8EgojSkwJbMRvli4DhGU7JVNj0PKF6p9R3G1AvYZsB04Iap5EbSfJyidjM0WjKihh7CS4LAMGAGwOwZAjnCbC6SJtm7OPYGi579kDS9j4F+LYyZe95RxkbSoEeK+kXyJe5OSwwIExZIfdCoY6idYZ5ztNIC9NQQDhdq3rA5g3qjdRJpQpcSjjdggKNqSnzGCq9fJJx2jtAbhZQ24EsyYOyCmxFgLyh9PWJkl/9g9gwwP+6GjdzZpTKA4wnsH5x4GE9U6Cghufny/GjbT//Xvpeawr6qfUE2QQBYxK/0bxe63haM9OcX+uHUl6T8I1+11xac3gd+l1rAVq30PqnrtXahbgXrUfqk4+QoufonlrP0HP0bLEy+ns+DUqH7p1Pi87XM/Vsp38gzfq7vtc54nB0L/2eXyPRswXZ6RrdW//WswTcKZ3599F6Sd5G6l10D6Vf76Nr9c46R9fqPH3eUAftUNMACW0sAdCprVF0jjHz064KCANdVJJh3ZXNg6hgx5b8wLnMWGiH6oelZu9F3RehTyBXNvjSl8qOxxlUzEyyqSWSsjKFxi7XhmypEypQOyPrFdoqtiRDuy8hhrdCuMoep7AXUaDbJaDhKOmJkxbHBjA8vOeeMXvpSNo615bar36giFDCzCdJQxQJ09kF9S1sJvEuUl/VH3gQP4vYoz94xJ587AnCVK8lasNtTqhWLxsPsvR0zlwNO6V5lord+fwMhb8Kzp2beTSoFA1YTgBJ3mqX8CBhBSYY9E1PE/aBcJUyImrkTu9J8ecP7T7TQr/CuirEkhwUjY1NOJ7aHKpXjod8fOX8Nas/z/8ceD2Bc+owVHfV0emjDkVKD3LgCRpVXZcDb2pq0nGCqbNV51LPLqGWlmYLh8P/8mnDkdbuLO6rE1s9VnPgx+fAG2xg8uMz4VX9Vms2o+NZ++M/TdnfPpxmsmT2zU8GcTBrMP2qJmX1Ya9iDhw5mbWP/W7CXjiWtfoql33s/T77bx/WEtjqsZoDqznwauWAxlea/GsDzvPPP+9I0L/88svOnEKT/PwCxauVnn/vOfnFAy2GaOehYKO8dL8ALC1SaCHljXJoTCy4TFCYwq8+8cQTznj3rrvucjaKKB8GWJhWCFApigsak7qgoDiBc4JL7r//fvvqV7/qwDJaqNduTY2vpdik8K4C4rQIJDW3L37xi/ad73zHWWxSaAPVG91f0IzU0q655hpnjP6FL3zBvvGNbzgLRoLFtLAksEXP/+QnP+k4Bb7//e/b7/3e7znA2ac//WkHQFN6NZf92Mc+5iwgKZyCYJi9e/fab/zGbzjzVZWxVLFuvvlm+8QnPuHsMFW6BOEpHYImBf5oriAQTe8guO0zn/mMA+NoI5gWsLQwJXBOC1Xadar5rjagaVFL8wj9TeDcpz71KQfS0zxaQN+Xv/xlB+zR3EKQj95f76U0X3XVVU6eCH4T7NfZ2enkpebWmrdorqKwD5/73OccOO7Xf/3XHbUszduVxj179jiQo+DP12s9fvapjF310RiLU6vgnOrr+XBonp0/9LsWgGX3ZV/UB6idyy7ob1pglj2QDdZCsr7T4q5+1zWy0flF6Pw9f9LPfB+jn2q3ecU52SxBqT8Kzil9WiSX0tzDDz/stEeFchWUqj5BtimfBrUjpTHfdygtGfo8RWz47Kf/lBB3HnvnzW8lZNCbrAgHMLJjeHjlkMXhgYfVJbgHp7QO3Sd/6H7nHkq7Pvlz8n9XHupQes5Ny7nXrv6+mgM/LgdUl/J1N//3fL3S96pP+uS/y5/j/ASAyAGMOa55x3nLrwAN3FD/B5AAIJFcB8MlFzCSi/AxWTmQWewXuqTa7ZaTPwtwh/yLrnERpipHiJw4zl/hCnKCeoED8DeQBtoMdTyH1y9D+0nilJXTV+CCF3hDDgkIFtoVf8dpncLjgB9CPlHADP4EqBZFuSlJyNYibIkP9y2eWUdxLDvVg9MV21PXQUjhbYTZDHMhCnA5pUt35n9yNhKWR65u7sYHjRfSleM650E04RygW85F2uW8AKRwCZAFnnMAKfnhnIzhFzmbyVcHZsFWQBk5aXdsA85glxtvMRCPQyxhixwYKweYwzOVn47kC15nXC/OOU458C7pBdQwUMNzoYIQQDEhB6AUIxzp8uSceQCQSuraYZ3Wo0rVxD1YCAGUkupAEmcKiJzuhtYVsA2Qj8spW95Nz3Oew19xrudwIOUIRytHcM6PWo7kZ1w4G3F255wYmXIB6X3IC+4jN40cvQLmkqggJJfnUfFjo30JimvYwhwO+uT0AFk7R76jnEfIyRzwXBZUIcdDBB14BCmmUfEi/FsmhlIIUJ+LdLvdlCHgWBLlt9lxyhblqMpaVHdbOmG8Okh7He9HGE7KUAqVVB/qHK5wwEDntfRqkDs5nNHciGfNcwLQlP6pchMiwPN5OX4qPaU4qAJnYQDKUjpQAeAcH05vB6akrDKABjHCJLm8Kyi5oQYYrOHdAT1RF8xEUdWTUltxPbejDFAgzclzRz6pvLOoic0SBjiNWpYgjRD1yAOAGJ8fsYWpXlQpp3DMN1hJyxXmKdqOWk+xU2ZSwqKmOoAPuUX6BOjQgqhbPqnWqT05SmW0GQFSnO8ib3Hrwq4BlikvAcCcGKfOu3MJd3GjBYTsG3ciiZRxGngzs4LyGaFc/cBC3tI2sqkWdTXmcTODQG6gRqX1ZhUtVFnuiXqbW5AoDjrDHkjJL41KYwwVOx/0gQ+VRMhv6gVlN4uCJSGFfTXrrahpu3lRacwB82UA1ASjech/wZoGNJflviAYlBihkQEtvOS/A0IK8CL9Tv3D3uR456zjGJRaZAkAByEveRfuQghN2RVwGRYIc4RVTGFfPGmpJJ5BZWmS8qsjsusa+me8ogovCxiYIJynr0TqfijSuAE8gU8puLPPlO3j/xo7pIENM8t9lMIsaoukETAtEV20xPRLqG92Aea2mqflBhQ3dziwrOyYzEMaiCMtIJHyAUkBIMF+yFyQVqVa9lMfxxTxbxftwp0HSEWHYOgSgtH0grwrbnOAGMYYqmN6a92HepXDNqb4OzkA3IGiGZVFWQslyXWcy3gro/EEkI47x78Zd2CcHQWuZerNOPMSqbSl8R4LhvNwP4FqM4STnJ+bBRYpsQ1NLVYLCAia50BAam8khQyiTpNXqmaYV1Sz+J7ftTKnXgEU23qAGvsmCFdPWMT2kjrrqKkDuKF8OJdLHHCHUqOG6l2VH/QDlKOHtAm21WN0Tx16Ds2IPDh7reyJPlkyNsE9BQsrbRLi0jW0ZBTllmyScL0rhKNMAwupEDL0d0uo5U5G5gm7G7RNFVW2nVCfILlAlwKZeA738JFdbtUp7iNwTmZeaXBR9wX0JElvP5u9jo6PAQq5bU17u7WST2CaKG1xLYWnoV9O55JwwX9Z7hcgj5EmIYGMn6nTErZzSpXbUqIOLKf80YN1vZMH+hsfWCkHIIzTR3SdIrQxc8284lwenONKbKWerZv84hz/1hgm/y6ay2ve3XWKMO2AI/5aQrNuXW/lzY22mTLciKIpQbKxAWfLX3VAH7JNpgH7dbZu+vld9ktDmjTlMgrw/Cxql5MoBYYB53YCUVYCnukawZF+8jFAIUjBTg0s4xGWqT5DLYJ2zXlq26p8KquU6iTtTJCej0qjtkuhOd+J/BLAp/PEGmt0NAfwOwH0vcxPL+MdKVILqptB5XOWDQ1lgK0dFbW2vrzcSmXb1Ix1LQ09w0upnpMEp90FeI4YfadVcs4c79gDqHucsUOOudm6sv+PvfeAk/Oq737/08vW2d6LdlfdsnCVjKtsUwzEJuDLBRJI4vgNLzcFAjeQ3BSKQyDk5Q2EFEKABGIIBEKzsY3LKzfZlm1JtrpW0vbeZ3dndqfe7++sxxH+UAwmia3M2KPdnXme85z6P+c5/+/z+6OqFePBJSB9naMSYFFd+UiatZbu6WRNuM5aKu5ayqvKrGupX2nsqj/ysfteY9LLtQXE5rF7GT9wscqCvfcAJScIDzSNatsia4cwqqtVhFiOkPfwM2k5e6Z6o9xqbw02V/9arJExjXMvaXtZR3qxe31Dq4RdHXV5Xd/VYK1NUQBVhiUDR9NfSg1CGwRJx5WDay0m0jY0zv5SP2sD5huFEFeak2OEWr17xo6cqrR1rWF7y41R9ppYtQDK5bCVrrx0KqesSdYopltDkg36Gvdy5Dev9RSp5qmIPGFaWYHwn3T1CkaD7zhA1kRIMCXlaM2HfM9fPuY4pSDAnJUyilkZe/CRUQC8gG1cH7OeLpQES3QeayBaR3OhoD/V/9ny0phRXf8wu6U+oe91D6NX4f5Ze9J6F76TDSzYweempb+VjuZ1N7c/c67ut/XW3sSZ992FaxSuqesqDX2un/pc6RTS1U+99L3SU7vqXkcfy+5LbCoNHarv9darcF3l+cyXvi+kre+Ub711Db31XaFOCp8V0tDnehU+PzPds/Z3ipxjPqV2GYNuYLoRtlYPWC09eORZZWzpYQWpAHO/hFFTTdEa2BRZZNbueliDtYJbkzKmF+N59pnnbAIF5braEuvoqEZgifk7IMvNsdhmbp7U0rxpU+yM7jMnp5M8BD3CHlUYXwNQI/dHi6gfHz8+bXfceZx+gKrl1RvsF69HkbKdc9RJOE92XQ+JgP2qAclvxOZnl+273/qOfe+271gHe9Kvf/0Nbq/Lz/1zXms6+qG7vyVH6oFKSj9/1lcRnPsxNaeN/CmkKk+yEa8NfTkQ9MT/Ak9QpHgaQ8ZIhiGIJmGUxaAcJ3KWyOkkx4GcCiU4Dlwr/ZjrFL86e2vgvxKcK0w+mkD0lmT1LE4uOZ0kwSrn3SCwXF9/n+vfWnRr0hKZvQZ/rsmybtq0GZnTra5PF2KEn70tVizZz68GXsjU9PPLxX+nlJiS7KF9Wft/3r9qx0by1tXssS//77BdQLjW4uvsrYGRibx9CpXBv/nHtZA8v3Kd3/7XR7TZfvaWuViyYg28WGtAay/dP2hNdfDgQSdTL2l5wQsCfgROCAIobA78Z5VDmxDaPBDQpPsTKYAJphLYJSl73b/oYQhtKPx3exXAOamtfeADH3CqbFJ7+/jHP+4gEj0Y9S//8i/24Q9/2NWXIDaBclJxEjinY3WeFJ5+6Zd+yUGIAlcU+vWOO+5wymgCznRvKJW3973vfQ5YESSmdhBQI7hNwN5b3/pW971ANKneqR+9+93vdmCcwDnBeVKHUojYN7zhDXb77bfb+9//frvwwgsdSNbW1uaaTw+A3XzzzQ5cu/XWW12+lZd3vvOdbv2vkAmvfvWr3SaDwiAIuvva177mYMDrr7/e9QdBa1J9U/l0PyPITnWiMLYC4FTWK6+80t1XfOlLX3KKcLoXFoQotSrdb0gNToCdoD1dUypzKo++e/Ob3+yup/GiulKdCtj527/9W7dRd+ONNzoVOfVXQXQXX3yxK4/yIDBQ8OG1117rjvn85z9vgghVpne84x2uDQQHvlj7896HcnbFO3AWs8dUVJw7eyxOYRNXP7VPpBAlAtgEdWofSWNdn2sjWmBaR0eHdXZ2OgBUtvnMDWfZ7OfbfwsbwkpfdkLjU+DuV77yFRe2ReNKCpmyQRqjGhs6TpD3N77xDffAqNQhBc3JlugY5UXXV7701vxRyI+upznsMCr1H/v4x9hcNXvDda+1yy5+OeHnUCxCmSsT0Ea2XCDa1KaN3T9sfj6zyf3DWl3zo+pMeVM9qQ701rXOPK8wfxby88PSKn5WrAH1Gb31Uj/S7+o76jd6SylBe1vq3/r+h72yOCzxJLDvLziDtPQ3fgABT0aoQefpVf8O8j1OviwOQyl7rIFzXIf/Ajj9fJIokU8Ih3QeZay0pEjkpAUckdNfTl2+ZLTwxsnAF4AMOFkJAahQxTzrD0jEnIYikiBUqUrJRYHf1Z1PBEa8ojM2P7Kf6KincIySEmBUXrAuITGDKKuFy6os3LrV/IA7FmoEsgGKIa8CSuTg5dIo2OFwkxNF6SpvvJ3aE3/LoZ7m4ByggxSApCnjASTzeHCS4ExBRozf5WSRkwalL8KyOfcs9SaHvJwncmjLkeL14qxB6SkLbCPHtc8vhTagW0AjKdHlVU9Urv7LA8MIHPIAZi2P9LEfP8RxhJIi/GUqPWeJOEAaoFtVhAc/6jaan/CCThWMcHAEqsKhCxCDo3nlGWdsCTUXQpFPClf5DPv6qD9lksgsAGD5IF28qHP5/KhUARx4AOAyqG2BIVEW2kuecv5XE2WBglddmXEnyb4BlC2MD9ni9CmrqCMMYVMz9UlY1hVUPE48aQFgpbK6Ngs2nYvqWBMlY82tdqWkUsRxqmYou6zMnkb5YRIYTW0IeAbUlAZUSqESFiqNWSVwYKh2Hep1qHoBB+ZRgcuQb9Wt7v2dshbl89EODkVA2jZPWNE8amlZwpPmUCZUKEt/uJw0UIUjvCidi7fU2IDHyBE9gJ+uqA488OPc9AHj5Vfjlp4ZRW2ij3ZKWlVri4UqWgjz7LXlKcJ0Tp2y0hIUTurXA5ihekTIOqcYCtgmWDKDUt/I+ElLzo0D5CXVOsBcqJMAJKUA6ErKAxZr7EBN7xyArm7qr1S9zMEUoE7kBZSIPptW/yJ/AdrOl0TNjb6fA7JKA1lIa8sLLeAPl/CmjgjBKjBNfVVqqK7x1BfVjqQuoEEhOAUfZoFFMjMnLTX6NGDfqpW0nYcYXCdh9eYs3X/Q0nOAYjXrLNj8MuBMwDJAWHRy3DiRQlse8Gh54pAtTB3GpgB84CcSkCSo0FBGrCgjpK7GYD1wJ+FdpUiYhmjw0n4++qezTQB7OQC5rHNWCmii3ldQseP8rBTvaFepariQ4gqNiZIcEpS0HyF06aMrjFnpeAmcC1IeL0BSHqen1Jg8q/2I7O23+MwAoYVRvqvdTqhllO7ooxODx20B6LO0vpzwmh1Af83UG/Ap/UG6RB76u/5bQV1zhXDQK6NPofA1DgQDaJmLOD9ChnC7QfpFWVO3BRt2WDrcY4tAe3E6U5I5fQXAbZXzVe8lRHAqJWxiGc7TUlk42oP/ecsKyoKydgDaUbuJQMvRudNQGhnKL+AtwFvhGzWGOJw3FoMyeiU3RP1IOSqDXZL6rWysj/ZVr1b/yCF3JTjZne1sHdfgL2rI5knn5NK89cengUoYM1qzAQxp/sjiKwmy4GlinbMO2E2Asi6dor30lupZFnsdZD4J8V1ID0RgP5gB6OuCPz2ghlk7kZyzvnHgTKTSugXOIVQQIc+C7mRbFPpSdSGgRJZQoJoPG+rKKjKFL9eKzL8a95SN5ln7UCdyDcEqRITG6SyIGuwEXzJFpdyEY0Wx69j0rE1Rr6v00RzH5rBlqkM/Za2vrLKNvDvoS1h3oBsAN9JycwJtSRO4l8aQ6sVlSdclawls9QA+08Pcv8ZJr7uzyzp5yKqOPMLUcq5yQZo0Sob2JKfYCCBV5jWBgQrnJsulDsEp7qXW4fIO0tHvedpegKVTCOWiasc8c3sCsPUoinNaAwuc0zr7zLWrS+wl9s9z1y2F7Otz2Qv5p7XPJNW5ubl5QlzXWdXOC6194wbrBoJqZD0uSE0KaQnsQQJgfJW+qqr1A0CV+VAAo58yGzCP0h7Uq8b5KGP1UeDOCdpmHUrSFwCWVXPcWr+jn3BtB/xj39T5PALhdd9AT8fg0MBcwPUTAFDaEiEz124+2l/gnKyKXgJndBOhXqEerr4lzrsfdcejhBqfWIrT1rQx9kI9I0UZwNqtDXXZLh7UqWOPBgYTGDvr+t4S/XgZuyHFO0WIk32JycawBtBaR/2HiKB2GkXQp0f7LM09R3dNg3XVNFopaxbZIfV5wXYe5iYPHwgQzQjK4T8pQvpJS4Acf7qxqKEnLkWvHGNY+dR9kcI206GxYbKfHEzd5hhznjx1RKvM83Xv7IKNI/NUATTXVVdtlYw/rSfcukzpk5ZaxNUlnztwlosJNJTarRRbpa4qO//wY7P23dseok4ztuuqnXbR+Q1WjjJdwNm7Ndupc6TaKJuhUTU1s2L7nh60+x88ZvElFOVKUE6l/qameKhzcMUiFVts16Ut9uprQtbWgi2CTHNKk7IHpKD20gMFEoekaign+QXGWQN4Wb9wHanNKQy9VKCyGT6DhFF9qY7UDQQGZlmzBKhnt+Yh5bX7PsLmuvrGznKBo8eW7LOf30MfqLVrr9pgV1wWIvSrVDf1EAm9HFuo1ZpKdra8BJe58qi9+aUw7gvlU5+kW1FobCA2ofBSH9TaQ7Wh+xvZisJLhzml7Wc+0nm659ZP7U/opetq7a29AL3cdTU5ca7YAo0tPRhUeOkzvXS8FFjVFnqt5VnpraX1g/kgl/QNKZMpLbf+4aeuJXuuNNw1SOq55xXSVR4F4uml8/Ry1+bXZ79zBztj9Oyx7sCz/R+1FUqcemDFg10R2qsaUsu4mRfATPdz+tuNOyZ019xqA/eZbB4B3rGpWUh2PYrIWfcAAEAASURBVCSVZ706iVDLPfectsNHjhHJoMEuv3yDdXaWWjiktat0TJnLAYSVhtuvIVF1j5OnF+y22x/BH4I6bKiG8R61+fg8wi9TKMit2tYt2+x11/XYhajNlZbq2tgpzsVsqDczznnQBdsg8HhuJmHf+fa/2e3f/TbgXMsz4NylKOTpXp8yqR8yD6nV1fwqo94/66sIzv2sNVc8r1gDz6MG/jPBOTfBPDNZyECtEC54cWnRKT5oI11OKsU8lyNX4Vj7+vowYNxQB9fkWysqyjF8PYSU2u42zeVIk7pD8VWsgZ+tBl7I1PSzXfG/+1n46e07d2Xtl97PZjCv63d67WMfCltHU7Etzua+wR6dfe/ejN383lVDgd2uu9BnH/5Q0DZ0/PvNzNlc/mLZijXwYq0BrcWkuqWHb7T+EjwhgE4hJwVEaWNXGw0CtwobFj+PsugmUxvFhbeUjQQfKRSo1nZSmdPvUueS47iwKfLzuPZLMY0zwbm/+qu/cmFBBWW98Y1vNKmZCSL5oz/6I/viF7/oIDWBXlJM27lzp/tb4MlnP/tZB3YpnGtnZyc36R631v6t3/otB7AIjJOSmtpeaWszX2CY0lbbf+pTn3JKdFKGEsQ2NjZmv/qrv+r6yEc/+lF3rlTg9u7d62AXQWgCyQSbCZyTYqAU8s4E5wTeSfFN8JsAyQI4pz4nZTuBa4IGBOJJiU73Cn/xF3/hgEDdG6h/SoVO9zK//uu/br/3e7/noDnlVWpuCgMp+FLn3XLLLe5YXUd1IeUqqWypjAIC//Vf/9Wp7n3yk590aezatcs+AGyo0LOqK0F6N910k1Ns/Na3vuXOV/7kDPjlX/5l+/3f//1n70l0nOpQIKMgOZ0vMFFpK7yuyqa6ejG//h2c89jn3h20t9+MWw0Oo/h6addA4V5cNkVj+MCBA268CmTVXKCXjlGflX2WuqPGzJVXXumgNgE8+k4v/Sz87j74Ef8UrqmvdR2FNRY0p/t+hY/WTz3UKduv0MwC4wTsCogV8CqoVWnoe4WVFkTtNr75THOHxrpsmmyVNtQL+dfPw8eP2Yf/4qPOgXkDYO3VOy6zWpxrGRw4SZagAd5EamVDln+eKdePKIb7WHnVHKk9CoVcUR0VgCZdr/ByG7GFP4o/izXwI2pAfUZv9edCny44d/S51kAaC5qHBMg/96UNdz3jngeokmPWKWvg5FV3lqKRoCznAOQ4L15OD2pGORzIchALwHBjmJ8CrrwCy7hH87IuBLNgPPDTwXxyNJM4jqM1LyLH8ZULhbkct8WBU4SwnCEcV4lF13URTZKQjqE1MFVKRtwCOidvWJkCIFoefNxWpo6RAOoeOD+yAtZwOEaAiEqqWyxQux4YrAV4ohytrTWHs5zfUgFioBIi9hkwhXwGnDNLSjB6u1wDLHFBXvouiMNWIAXkliub1As8foWAlMsatSygIFAJwD+dQX1xmPKsuvcBDvqAAhd6j1p+aICwldUWbFtn3po6oDe5rdec96scS/YsrHMAh5JjfYBpvVQfYJmHv6nLNNRGebTJYuXbLRheB4AHYAhJmAPeJYe85TyRE4d5FjsUEfiXpn6AfJJLJ205zp4ooFpQjlucQlJECQBc+WOt5qteB3OlMJgAfYIl1VY46vTOAjgRPFfNyhnkj7XQ7GCvzY7ss+oWHkTu3EAI1AYU5BZseN/3zb84YLWtPRbuvNRWop0uTw6ao518OfofBc2vTtjK9BFbnOkDRiMMqfoLwJoP0DDI/BCuAGiq2EBkVvSbAG3gHmiMKLCDFFfkFJOrSX0U2BJYLYfCWWZhGBWwPoTVpgGvABXpp3Ki+SJlKKo1mK+cOot20ZFQIKSfSuVB6hSqd3q1Skx6pA7A5yF/aZThpkZ6LR9KWx3zQ7C61dLLOcp+0hZHjlp1VZlVtG03b+wcIOoqnF30LfLko63yqCLOjvfaysQx8y2PUW61DqpwKJb5woQEA14IV7VQtnpYKYA3wDDHTZEJacx5pVxH/rLAWJoH/IlTlp89QKTRfiDTFR4UB9Ig7x7gPYuWAfW1Wpjy+YKC5+RQw4HMYivH3JKlX6u2hL5lqasMdeYB7LPxg7Zy8hGLAvpFN15GRNNNgHOTlj6x11IT5Lmmx4IdfF7dDthHsFDGsp90/ZBFeZS8VqaetMTMk9QlMCBpCgTyo3wYQcGtpKIHcLUTcbgG2gAoEyefgDBnDjhO1kL1rXGbo397PIQgXjxtqfkRIFOFjRY4R11CHITDjJmyZhTiWEdHGugHlSgjhR0giquUMUP10Yhe9VvKmxa8uHKESLKP2vTwSUCUFqtt2mGhyibyPWMDxw/azOK8xVrqrb51g/lL2qjxGPmRshsKiLSfAmYlFCJ2adAy/cCgs4PAJfQ4oEEP8CsUsvlr6yxS10rMvBbWAdU2TB4G2KCaZ6NyhXcaKAPMwUpRjytDjbaBftiE6l0ZgJnQGL+c9aoQ9T76jgDTHFCEhOekeqalhPBOv+pC44NBIF1OfuFTWpQ69QJlZQnDmqHzeIFU5HyHyXBpKlBpVrAk9Sj1TqdGqPZTCiQ3xzV7UbU6AiA6MjtpqyjPlUYJHxwEMAKsqUcppQ4FrhpCxwpemSes6cTKgs2iGLnEXCHHscKehqj3CqDGWgDVOsIPxyif1NvmKV8foWWHpicsggBeeynCGzW0JRnMij4hn0wN1Dpv8qI6cAZYtlzziPoLP8gyLSKnuGAR1pQYaZkm9xKo4sA5LAJlDCpkteBtXnns1vD0iD01PWUnUPRawH4GIjxYB2xaCmDUEI1ZK+8WgLrYKnAi1xE4JwhPDnH1dUF6yoqmDYWmdaA2YFCO9zLXnkCh8fTUDABTzlobmq2trNxi5M/Bf5xDoZhDyTvpqiya/4QHaX6RxpSuJLBDv8kCaVTodzcncVH1CtdYfK7KyqoPcG+bBHSUn0v3r9rnONvAucI6prC20RpDZT1+7Lh7AG10ZNQqiRR1Hkrv6/DrVYUVKJl6wX4PMl6mUORbzCRQIkw5mDTE2KgCuG3AzrbQV6uA0zROBBBP0I4H5qdtmrZpZf1xbnmMYwXlaqpaU19T5Hn3QAHHeoCl3ODENsvOFl6uXVlAyY4rbdeGGre0ovqy1hbuPP70AG5ovSAgsx9V0v1LE3Z6bsIWeADArwcdCNNcCQTcFCm3DiDkeh4k8NL/FwGvpmn76SRrCkDjZSBqp1JK560AoK8PlVsTKpEVzHu6J5HG6Ai2+RjjILW4ZK0o87USirYC2IMIhm69pz4paFZKbl7S0Ty2Qj0qrwq5HKAfehlbenEKH3M8eUmLEMFSBujf0nvMYYdkmVw4dh62yGkgAX5DeNg0J+4fn7R+6rkGSP9lrEXrWN9hsSTip2Hi1ku6BlXurkNCbizqKn4MVkBQjOTkgMduv2vGvvjPtzN/ZOwXb7jartnVSshW8s/6QWNLEC1WAnvBGkHpoQo1O5chROKU3XNvnw0NA6Yrj8A2GdKoqIrahm0dtvPCStva7bdyidTSlnknTyilKlTEqA8WA9hbGlhtSVl5ioB5VvMZDy+QNwdG0fgCnNJkRGCVl7/XwDnZavZC6XNBbKSHPqYxr/Jq7pQdEFCXBT584vEF+8if7yZv7faG1222664LW0MTdoP+lGaOCJA3ZlSXDanwqbNyO24LS4QeRq1umZ9ZR3DKouSB/D1WXeO1qmpWqsTWVXOCBZJ/1S7nO7hQ+VYZZXf0k694uaIKhix8rsZyX6r8mlf05qi1bOgUV+eu3vnYgb/YaM11ys0qN80T44QQPsGDY8CtHR3l3LuHgJF0fVLmH91nSPFZILhKquvQTDa/kLWJyRTgI7UvhWHaQg+3BJH4jFG26lr0pyP0WTURfTiriUL5VYFd3yFd2gBpbNYWyiifawxzTSnH68ECpyCvv12RqQkaT/OBS0b5I10XQp6cqYfRchpCtL9+Y35iPKgeR4cR+Oglr4T5bG+LAD0RllXsv6qKS+otcFegpI9x4iUfmvqShGyfnFkFmGINscIKQgaFfIYJAxqjjxP0keUH5VatKI8u7/xUeXi5fPLT/cm1Ci+1gzobveiZnwK2OB6lcKwV55F7PegkBWyul8uugYUUic/4X/lWn3HdhjacyNnTh8YsQXj6tvY661xXBgRGGqpOJcybIcyL+nYTNmlyjUXCEE9OZVG0BSjXGKHvSDktFE0BhyJyXBe0EuKw695J9ZkXyKZVC6Aqs7MSJD2NHT1OQCm5jpIXZJ7lYS3ZWnBg970u72ZRsqAu7OqETGV5mMjL2siLjVXwbnUJ3c/ltD6mvv2sD3Ksc4dPZ+3rX++zvU8eZh+41V73C+uJDlKCT0H5WrMJHmfYOd+VldJynf7BZfveHYft6HExKiiBsyZJcy8WCOXYa6ohmkibbT+3kjDMQM7uPpKSKt+koRZCz52MU2bWM7PTAue+bXd875vWCTh3ww032KXsoQucc3Wr9qdkaha1kbLxTFb47ad/FcG5n77OimcUa+B518B/BjinhbOcbnLkaZNZT2trc15PuGvzWTcP2kjXZvQqTxxqwvIxI4bDERdKWBvl2rSX80s3F3KiavNQTrPCZvXzLnDxwGINPFsDL2RqejaR4i8/RQ0kgPC/9KW0veMvVq2MhcstNwXs5t9A8puIScXX2V0Dj6I0+PbfXbXTY3nb2O6197wjYL/yi9oCLL6KNVCsgRdLDQgkEkQnSEjqQyMjI06lTJCDfpfKjoALOXblECr8LGyQFsrhNnD4Qxuoa2s6bjDZaNJboIEABzmC5RQWMCdYQuCEAAj9FJxRfP17DZwJzglwExwiUEtrYkFiqmepxKnttK6X2pk+l+KcVOM+8YlP2N133+3gNYVvFWyil9rtM5/5zLPnSTVN7aV1udpbys96UlyKbI8++qiDbK655hoHrylP733vex0op81/hTEV8CjFKLVrAXoUkPbDwDmBmgLndI3ngnN6KEblFDCgPiNYUMCcwq4qLKtUp5VPlVsQjlSrpFalckrZTYCegLgPfOADDmbTfYegNcFr1113nTtOQJBegg8F9ynsrMomxTsp+11++eXurXsNvdRvlYbqVkCelOSkaKd7GeVN4V4LYIOuqzCtCh+rdlJ76dr6TCCdwDkpar2YX2eCc//wrqD9yv8ognMv5vZ6vnkr2GrZcvXze+65x0HTGktSdCs48aRIKXha9+ayx7t27XJqb7LRUv6U7dfY1HnP56Xras6QHdE1n3rqKWdXZFtkSzQedH8v2yRFSV1PCpn/+I//6EI86TrKm966rvYV9FMQ3fnnn+8gXeWtMAZlH3TNg0cO2Qc//lG3IfyLr30t4NylVl1Rw74mYfvYsPSyIxtk59YpLTwPMlR2SyGzjx075sqj6xWupXoo2CX91KsI0LlqKP7zQ2qg0Ef0VeF3jSv9rjEhZUMB3ldeeaWbC3/YnCEllCyQRy6F0xaHnxQW2PsnDW3ha0Oep+9xVMgJ4pxtUuzB65un38uVwYF8rs17OStwJOHM0nP8LpSmUyKRZ0MncyyO5zUvIjftHJdPAnbMTtv43odtbpBQ7tVVFrvoYvM3NgE54YwUPMe8KUetgD4ERSAO0DIiHGR+qR/nJypV2ARBEwbs4Y9UIkgFABYVYAOwj2uWUoGb4AxZ5WR4JHnj8uwfyC+k8GLOOcee4ZrCiXNFYJuUXZx9aeAQZ6eYwwURYg88crgE8dzJcYpzI6dwPXKeELvWlT8AvINdUb14KW9+fsaGHrjP0ocOWB3rmrLzLzR/eyt5ZI0quIm3nL1yZwVxMvlXlgDcxgGIBrk+4TTzs5YGjssBBYbCjRb0b8QWNbp69BBPMI/3THAU7i7aSdfkK/4OyCEF4LI6T3jQ6cOWoL7C2YSV0W5yiUm5Ji0nMHUVqN1s4VpU7CKosDwD5wjtgGx0AIqgKTlmVP/e+LItoNo1PbLXqltjFuvaCvHX6VTaBvd+ywILp6y2bb0Fu6+wDKAauJXrRV7nHFRIT+7bCeGoEJipxWEcVEvUtZziKOGhaOgHMPKgqmf+Ti4GHChqQW/qX6E4pb8lJ5lTR5TdpZ1WCR27PHEClbheDp0DgqFdKZqUOKSe5QVYitRttkA9ITUjjc7R5votUJ4Di+ivsvV0KJThgL8dONdrU0O9QGOrVtvdTf10AAcCzvX32tLAYRx8MStv34Yi20baEFU19XrqXY4wD+BbJjEOyHfSPMvDpKvWKaMuWXeGYvTRGn7iASXsqHl5Ew5V40kQkAutC0Qoh23eCyhIO+XnDltmci9++iHX7aQsl2OcruKkXaTf+VHUi6EQF62lzgADIeqoN5yCjBt1TQE4qgs5TXFXco1Fy40csOSJh1GcI3zelqvNU7eFvjpuqd4HAedGzVfbZaGOy81X1YHiHPCbnOTUvY9wzBSOUKjAoImD1CX9Uyp9dBBfvpou3UHk2m7qmTKGpfCnscX1aRPN9HKy6z86tmqdfM1aKnkaUPQ4sNs4sOeyhVzMUZTEdK+44sUZXmUlNR2Ef91oyONR3xF6p2wOfZl0fIAH+ksURg4Q0JMiranHbbKf+ve3AXJeRlhVwLnVcRs8vN9mWLtUoyJY1wY4J8DWW8n5co8L/gB8Iy2QFAtk582PMp8fsMVRLjgyKRTVT3tXAvKhWiTFyUXO7AOiOrWUANhRXujzjGsf7ZljDOZTqBay5ukAWmmJVrsxSIuTFv2TFhF6IkBZY4XTNfJIAyaPT0IaH9SDG5dSZqTPKhSs4K08zuW8bKqcrJyvJQNfuXpWa2cBBKS0IsukcUMjMPbX4Dl6OVBNxo6jEtgPWLOIilk1YFt9JGY1qHPVAevE6EM85uDA2ZHEgg0uTNg8YXGzhFv0YJelVJVNAlxDAMUAeTvKURiOVFiVFwSRfEwwpmdQgQzzfQ3AUnloLY8ONOD6WjMJPQgw9qTQJaDIVYBKoHUh1S0FuFU88QqBKUU53U3JZtOlXKmURooPUuoH1IMPoEI9XiGNZwkbfRRI8uAy4grko7Ki3FrYO6ii/9SiQFnH+Krk2mFCRvuBURQ2ldZyyp0YHZzt2n/QfKZrUoO0pwYgVtFNJXHWkHOArFlsWjXAUhX3aRHmIxfWUy3I//LYq/+TOn2fvALGJOinq5pr+U96pZovOZC20xjDh6WyUBdrczDtRz+RPcjR/hpoicUE/q9jwGRJB85pDav7y8IagMR+4OVs2w988uL6Q/lWHvXW74VynPmZ7icG+gfcA2one09ZY0+Xveptb7OWZkB57Jzw8Tn65vE5IDjAubRgHeZ5KY7lIdl9Kfoovac9WmmdVdVWzn25QnUucM0+oqzNUyXVjNk2PmfWYbzQ0rQfNY4doP01BBkvgm7cS1CYxhSdQ8fxDbZgzUxoNEp1UEC4gBUXnlU0icpGn6JL0E/XjkUb1Y4w55yYHrVxFD8jPOjXispnI2OwkXuMasDOKOdpjTBN3xkAcp1eQNlTAx0oV1zNKjBWPgWky/HthHVtL2EMy5ZwyAzXHZUSK8fU4BeVMp3AVjFhdHn3chAIdShQRS+shsuz+qUHARKBNGttxJekJ1hH9kUPUQhcF+TCqOE7WTM9aMH8lGZtQx9XaPUZTts/NmeDs3EHzm1vqrJ6YGKFNA7QzzViaXz964au+8E1BP7RfMBIrC79NAKFzbFmue2OJfunL90FhJYEnLvCXn1tM+AcdcvaTOs4ralU3QKXlf88wOkq7T88mrEjh5OEUqR8AsBRu9UqtqYpYG0bgtbcQBhZIDOnrEumFL4+w9qYkUcfkxVghcaEupZT+hegntofVIuMUt5nIBp9r+K4cUd+dIwzWnyIySC9tRGvhFzZqXedo2WjetKTTyTsjz+4G7Co3d54fQ9RBkLW3IrdZ+xLXVS2yofN05LayzVZcjBXmk3O5+14X4r7YgA75k0p5Al0rKtBubDDZ/WNXgsxJsSYa/3kei1zgutEmpfV/Mo3P/hL/6y9qH+pKSu0tI/fZXMFkGk+cSqAyr/qS+VUmfiKplibMqWeir0LsDDVunN2ymtPPp63r3+rnzZJAD222K6reAADsI9qps9wPoPOixKun/Hj0ZyLYVbdjgNc9Q0CcE/lLblCqwAkRaiLGMBVYwdQWT3KiygPSpna5+Y95g3qHGvq8qm5RQ+4+XiKRmDd2oqEg2lbAXOCRsnCs8VW8TVeXTn5fW1ZT71xrpR7wd85nnbhI70F7IUpp6C4Rx9asq98CzXD4UW79oqYveaV1dbUsFZBPvqVFtMZxo06il/QO+XLUMcLPBQxNL5iYwPA3hPcGzCf+UM+K6/yWWMLeyzNZuUAagohLQVvjcOM+jxJK3Wahjyv/S4ToQ81rap8LrQwD3FoxaEHExga9CHpWvKWXeK+zusXKApyml67r1F7ODiXxFRa1aHUcffvz9pnv7CXPjdpu6451175qjZraqRn07d0fXUvrcG9bu6jMzAGMxjIGfrowEDGRoeYA7VIIHMC4kqr0tbQYdbUFkINknUKdtZPnnJprVjoOVKDle0l8VxGczNwOznVGFAxdYOY8rFy42/BdvpeQ4/qdW2j3yV26yqJdGQrPByTRcU7TbJezhewyekkyINI3C+OnM7xAPOYPbK317ad22Q3vL7DNm9iTaQxxIGFoc0laAVO4xo6fxaJzaPHFnmwlAcpkP1MJNkjwuCWVXgAKMOMxSB70Ng/+rl6kfKo8MuFdNROrj1IbGYqad/5zm2Ac98AnGtGce76M8A5DTTOY/AVru/qgjz8rK8iOPez1lzxvGINPI8a+LmDc1gPyQRrgezewHIrPCk0MTHlHHOnT59yT2nLITc/z1OYDqRjY4jjtFne1NTonG7dbHJ0dna6DXI5WPWW6kTBgfU8ilY8pFgDP6EGXuj09BOSL379AzWgjZbRybz96UdT9ne3Z3jqGojug2FCq7FCLb7O+hro7cvZH34iZV9DcbCWxef/BJr7k/ez8VEchmd92xcL+NKsATn8BTVIlWhyctK9BVvoLZhKP/UQhGA7gUkCGeT01UubwHoLKtDaTu/CWk7OX63ntFmskKKC5qS+9XwBjJdmbb6wXJ8JzikU6qZNm+w973mPA17+9E//1BQ2VbCYwo9KUU5gh/6WOtxNN93klM8efvhhV+dnqjVpM1VtqQ1CQXAKYSp1tltvvdUeeOABl67W52obtbEASoVKFCgm8ExKcFJfe/DBB11/UDvqc4F0UmHTQy/6vgDOCRwTGKmXwBlBfepjzwXndL6U5ArqdFLTU3hU9SnBPYV7AeVL+dP9xK5du1xYWoFzAugErUmlTpCd8i74Tsp4KuMtt9ziPlc+9Pd3v/tdB8NdeeWVLuTq/v37Xb9UXa1tOLMpw7VUV7qe6liQncA5wUWCD6+44opnw0foOqqjX/u1X3OQnPL7UgPnHn0wa1f9Tx52YlPqc+8K2duL4Jy6y0v65RwBlED2pL+/30GmCk0seyx1SqlE6nf1ddlzKb5pjGs8aCy86U1vMo0RAax66biCk8x98GP+KTjR9KCcVO5OnybUH3sAeikdQXCacwSZ7tixwz0sp/Em2yI4VcdoHOm4ArCtz2RzBOwKstVcUlCcK2Tl0OGD9pGP/ZnbBH7Ta19jl198CeegWsRms3NYs8npxVPgjfA3AITbPS2cfMZP5V9lVd4FIWteLADCAgkL3+uUQj0/9/czkiv+WqyBZ/uJ+rTe6jcad+pnUmLVuNO6SXOb4NCKCoUk/MGXC3NK+M0c+27yQHjk7QDUkqNCoS4F9ihsjRwF7h9BMihF6M0/vPmCa3v8JYA6aJJxDn+SB5y5qBjZCo5/4CXcF3zIeMWZajngKA/HJlnzjY3Y5O67bKHvuNXUVlrFZS83X0cbTFEpYwo7gUpMHhCDfyifYAPSSI1w7jjgzhIOYzYH8NZ4UMXysl5EegsoqQwVsBJUUXB0QsgF8Sp5uFZ+iTwL4ImQwRD5ID0KClglb48KSFo403OrckTz5qc86B4UYDwlpE14VTkdBXJ55Cnj2nJYC1BzZaUCPSijeQIAYvJaoQiTmxqz/jtvs8z+x6yuvc2iL78CJqwLYK0cLgxbGQGYkfMDh5AXR5pH4VWlmJaa4vwxskR4VcqGJAPXpv1yVWSbMtLGzjtJlqWoonLg7aKanbuZagZ4pLZSi3MWJ+xrjnCvUZ4yDOOIlz5dJjFmS3OEGeRynvIWq0E9rSTWRjoo2bqqkG1VWpQNB7kcTnI05YA2EkMnbQ7FsvKWWitftwVIqps8L9vw49+wYPykxdo6LdC2EynODs4l7+pXKOIgA+jspgfg0TLz1Blv1B8UTlawmWMRZEO96qcAYLQfBaUuuTi22vUdvpE6neUEL6qjRbl0lrKMEYp2grCRhJyMKCQ49YF6X3q0nx/zFqxstZLO81COW0f9SVJG6mG0oVQqCGcnj3ZeYwdlrTyqWukZFE2He4lMumIN3V0WAUpTBKV4H+Dc6YNWUR+zktZN9It2qp75AMegCuDCZ6kvoVhmgFp5wsYyMfHmmioXajOqS+WbzNBX8PpSjjzX9TgoQ+MKByB1Tefgc+CI+AjVdZI8LqA0Rshl4CR1zNXlWZudHLeV5bhVlZdZZcdmFPA2k2YjSarulBb5AADL51DSc55UwEM8hZmxI7Zy6nHmO+Csja8wT+1Wkkcl/NRuS9JfSuraLdp+IcO/mbwLiFV+hFHpoRmNk2H65wC/zpFXtQfOPgIh5j1c21NPHQt65Fh5UdXeeFKdbVE9ie5EuUmqMh7aMrUwg3rdFPWLsxzjESqlD2B/UoRujY+eNi8hP6OEWo10XmKB6u34sQnd6ypRjnXKCDDjydMffFzHr7YcsfTEYZs9Tf4AMGPtrK2rGuijp2344F6LLyxZbUu3VbV3A85pPKlvMnadx1fQDFA86obKeyYdByxGDZFPnSNfndkr+1IJb0C9cN4q7T2+tGiTgEyC4KLYrDCgWIrzZ5IJmwIOzqDMUo/93dTQjjKbYBjS5r8kcKoAfCFUck3LnU8JxBUSdpEapS8EkNvxMMYVQjkFhJDB6c4dswPsFBJRZ6s+pAAXwOlLSznzk3FwgCAYqoQjNKypEI4kz7wXuMYYYEg/alfjc1NWjw+lpaTGqjByeiynnOurxbH2qHjFbQoIWE70ENBvQCElyeQc9/CTwGkEOrMmQixvqyIN+qiE3+ZIY5k8qGQlXJXsmfhlB93wmfbvGDGgNlkgGkqPUz7PAR55uKEQVhney1wvwbwmkFHHKhSlwJ8A0IuU6lRYgXNJ5iHBnYKHpRykwbrqWbZBQoEfJsz1BBB5G0qmm8vrrQYVxCgAZAnjNcIc5gfgUIjeDP0SLNCWSBeUjhyrnfxWRj4RweElHb8VSzJHpJg71q6n+lYoUM0zqNBQXsFSQuVkr5zpov5Uao/8W0DCSc5M0Gc0HEvUXg5K4XCIEQ99SgiT4JosfSMrW8C5Cu8qoEGQUzKRtWNHeln/rhKqdb27Z9a97Uv5VVjzPvd+oLAu1pp9fGzcvvzlLwM/HXU2+TU3/RqwRiP1r5kui/JfwkbmF2wFJbEwc2Y0DHBDW8xxzz09T7hy1jmVjMuNzW3WgJpimPrX8mGe9sZK0tpqD4FcOfrp2njRGCzDBgfdOoM2FMTt2lZXRWuNNNRH9ale/EkruUCuYjkctKLxw8xMqnqpdenjvNVHEGAiXGzGeqd40BQbWN1AOFVIoDrmL40/kuZIxi+/L9B3ppcIZ72cBERFAIQHC+CHbHoVMBQbGkfVqIZ7k62xFuvG9krFaI4MLTAe1DvK1B+VBXd9zTp6g81wjKZCgTjq5k55i8GZpYOygnFlW8u7jl9LQ31XuK9CScv6aP0iGyNLmQSQwWK5fKukixx7bHTFRqbTAMR+21gfsAYGVAkFc2NZF1Uh9eJCCgMLk2srnKdB4ueNUCRzBXnC5tx2R8r+6dbdjPWE/eLrd9jVl9ejEgdYqymYd445RMuFIGs9hXLXONTstJygL8yRR0CWnKA53poTI5WMdPwKIeyqbIyWh/occwSQxjqYbLDyY+zxGddX/UigRQCUbJrU6JTVLOnlNJa1ZtRJygTpKE9UiypWJoT1Bce595oNxByxLCA9yqj+vu+JJfvDD9zLkqTDbrx+vb3qlSFD4FRV48A9qdMF1Wgk5+V4qXgqA8tcfzquiGzkgUlEy0EfYFlpOX2YZZ2PDqk52Mf63k9Zda646xydWP1Mr7Wwn8o/CZJ/fayySBUvQ13InnF56kI/scMO4qNe+ExjANMIZKrGo8/zAyaMuZL8ck2JLM/gx3vowYx96SsnUTlN2Ote3c5DqzEUfGkj6k5Lcp9U48hrkLbzMO4EVOdpU56zsThtOL/I2oB5Is9F1S8iAsm05A5xDf4Oq3wUCDOALeD6lFNwrereQxvoYTef2oW3FEGdapt6M4V17efahvRc8wGUcZzaRvc1KrfqIkUHXTvPJevOVTsImFWdPPBg0j73L8t2YmDefuHqCpQDY4CZ2u/g2qSr+S1LG7iHPBgvmsvUl6R5u0TdLc1iz6ZUgZQFmDNEGRF/5eEZ+gwZjXB+mPM5jdDwLMU4VMm6vqj1JnUplUo/deHuL/hSfStFH0mT/0I7Kv21dlwrnwtVT3/IyrCRBim5tlWZ+J/0hQt6UfrP2F/+3R7s6oS94tXn4YvtQEFN5WOsqIyUw6ko0vf9GBfB3ynGdQpburjgJ2QpaZMXljM0CtVB+wU1Bll2BZn/g8zFWs94Rf1xTI6x5ph4LpCTLeYz2R0tbgKUEaPr1mEqD1Mw93DUjepE5VaeSFNjgcPXADrOhSFkjADkuXFCeqSjh03SHKS1zfRwzr5867Q98KjAuXp7/RvbbBOAbYQ1s8yKj2NVRqWvc3UtZ7fIn2zNKu9ljQP+DtA3A4zZUtQCdZvqbq1Vp7I1VIHykKF+1uY/8qm1BHmcI8T0bbfdZXfd+XXr6GwEFC6Ac1xM63tdX+sCUlE6er+QVxGceyG1Vzy3WAM/oQZeCDgno6CFsDb5Cm85lLTRLaUSOZMKT6tPE8JBn8tBNs8TGYLq5EiVg62hodEBc3KGFRRItBktxQltJBZfxRr4j6mBFzo9/cfk6mxNVXvU+4/k7Ob3rNjBgbx1sAf2hY+F7fIdxTF+trb5meWKL+bti9/M2G/9aYqwEMAKV/iBHILWyJNUxVexBoo18NKoAd24K+RGAZgrQHNa0xVgBpVEQIM2ggU6CJ6TkpAABwEXBYWel0aJXxy5fC44d+mll9qnP/1pF471NYQflDLNfffd58K2Ctb6+te/7sAtHXfzzTc7UOyRRx5xCmpXXnmla4tCyXSu1vBXX321U7cR6CbIS58p7Q0bNjiYRiFcFTZRDvyPf/zjDjxTP5AilUL8SolNxzzxxBPuHkCQzR/8wR848EXgnKA+KbYVYDipTim0qqC2W2+91UF2hVCt55xzjgPZCpDdhz70IQe+6b5AsJpUp9Y2KNisIJ8CBwXbSQVO1xA49zu/8zumMLQCD54vOKdQtQL+BPXt2rXL1Yn6a+ElQE9pSbVOeVEZx8bGHOQnSFHQjl5nAzj3wH0Ze8VvE1aMvZ3Pvztsb/sfa0BFoS6KP196NSD7XbDhUmrUWNHYUb+XYqLUImW7C2NLtn7Pnj32b//2b04ZXmqPAk11737mcc+nJrRnoJfmDNkcpa2xq/v8wvX0vdIVvKf5QnsKUqQUYFeYU5ROIS0dr/IIKNI4L8wt+l5p6rujhw/Zxz/6ZziIcvbmV73SLkK9PptI2cIs4QHZsdbT5eHygMW2brFQQ7PbyFS6z30pLaW5b98+2717t7MDCikrNXzNb3oVrqnr6/jCZ+6X4j/FGnhODaiP6K0xoP5d+Fu/a06VEqrmfs0tCnuucXHmWOEEywyPEbLxFIpPUzimAGpwUBghIEO1FVYBSFGG4pRXQJyUr1YZc6gOLRPKUaBLCqUeOaaDQG4lDa1WRohOP2s0qTkQ1xLgh3E6OmlLhOlKEMoz412ySJRwgGUtFo21r6lSHD9qY7vvtLmhXj5HbWDrBkvWARQ11FpN5waLNLQBZlXinJBGkwA+gJulEUuOn7b4AEDYgiAjHFbkuZRzwq3N5kVRJoe6itSqVuZWbXFoHFBo3PzkR86XHA9flDQ2WHk9ISArqwHn8OjhlM4r9Bn1kBoasWXsWgrozytorzJm/uYGK21vsVCZPHTYOML75aYBs0aGbWFyCHgJTAQb4QlVWHnDOhSytMbIWPzkURv8/u0WPP40zsEq82/cait1TZatabbY+nMt0txkfrx9HskoCYUBlMgBzuXio5ZfBJwjrGZO+Yvi+aRMCiWUx4sXxmb4aedkHFgHXigE6CPwLg9clQOGyQYB80pQIsLBnkVRR2FNfSGc6T68bwZktHrSEhNP2xT2ccVTZo2d2628Zj15RglNL0Jw5lcJm5lAzQ0wRp6iEGCHnFmp2QmLz5y2aAt10onqWmQD6S3b2JNfQ3HuqFU0NBHi8xzauwEHo/LLej5aYUGUulwepFiCkz+F2lVKIABwkp/yB/GseijrCipqOZQwwih6BVEtSyUAjFDEkvNSVZFfQbFuZZa2IQwv4Jo3UIuTWx449AVxUOWDQFsAKZ74hGUHUSY9dZQT6XfrtlmoeQPXEPiMyx7YKpehbxDCM7tCJdKmTu2J62SX55k7hi1TkrG6LpRIq9qoNpzvAydtpf+gRcoBF2paqfg6+mYJ+cWdiEqQrwTgk7fA0lxiwjKoleXkFcOD7EfNyyupnxT5hyzyVnehPtfG9VknzY/iS005B5tC0CVX6A/+MspPHchjmV0mDXAEtSFjSJ7x3DJheCf6CB97DKWrRavsQmG56TKUGttxoJGffNwBmJnEME68cdeGAYA9oT55YLz06FH4OvQ8el5FqNZtll5AFbz/blueOw1EWY8YIe0aRLkRgiHHwPHRtyMl7K0DeiP7yDxI/VB3dBTqEgwEAsATiNFHS7mfixBdl3GDAqPaazURdyCuT2pxQHNJgFCFwS0FdA1lAdES4CYoiQiw9EYZcziFLTNLWOY9lho7wDUIs9m1y0JNlzPJ00flYRfkmJyGDRzHOTkF6IE6W3iVsUC5qc/loUn6Qo+VtF9pfsa5pY/Z+OG9gJSLqNN0WBm2xSk+Cn5lbvYD2/hLKginW8+YqwOICdkEAO0M96d57FmAdwQHbCSEPSLscJaNyRLgnCASNxm8sFLI8zEmo/SDIP1vicXBBGuUAfwXo6j4yfl8bmePtaHOJoQ0DvA3Qd9Pc+0w/3npE0n6/pKjBwiBiqJUHf2lUvfC0BeCCJKoHCX8aYsDjSyQ7yQO4gyfe7A9Ueq/BhCwBGgSv7oDPLSKkI0GI7M4cGsCkkAh0jLkL8+YiOMgHkGla3R2nDqpsSbqKQYUF+bzMOnXEro1glKcyrfCOioo2wPcKgUmjbgpbMvxOcIwJmaB2vy2g3rtYvyt4vieor8uoGSqENkRQCDBFXGlA1Ath3MEkDcGlBwjL+WUWcpeAqHz1FOC1CcBkmexEwn6lsC8MARHFVB0tUJZyubLQU5XSeJ9X+Jac7qXA46ajwMRzQ9hP7AIJSE7ji2XH6kBUG5zSaV1VfPAXW0bocEr6SvAP0C7q9TNBOUYoi4mgB1nmRsFsjWXV9n6anxNZfQLwIjF1Tkbnp2ysZl5i6Iy11zVzHc1tDn9nPljEuWwYaD1NG3WANRVz/khyqdQbUuLcRufHnOQ4jJjphQYu5k5IaA2TxK8NRy1mrpm5sEq/FxpOz10GgWiGUJJllmJN8Scu+TaoL6602bG4259q/trPZRytoJzdDH30tpGbfiNf/26PfX001aBn++St74FtakqN5aq6UsB+lQSEMJH3yhVuFL6C0GFbZx/+/EdTrBmyTJGu1varQ31xzIsYYZ056j7JSARwSQB1gcpbOsy9ltzShWgXQshzCtlT+Vu4CEZKcgJDUkAfszSv2c5NgUxJAVGpicrZfxUMD9HsH1MUqheYXr4HBQfuIn1CHNZiocJciToYSwt8/0ofXRsVm1dZZ2xKkI7M19ANS2xJstCV0Sx+xQLcCpt5YzNRjp+gHXfMnmaAD7uRaX2FKHYA/TD7dVt9jLstp8xN8m4GWfd5iVvFdgFhRxeZX5JUzZBmBH2HGqYT8o5VsqQgnIcBQQUsgxUNEk/nUHBb5X+6KM+o4y9MsDfEvId4fqILzHPkC9+gtdjL7M2xtNyS6xB+Ii5C7U3Hl44dHzOTvfOm4+HERoAo+pZ89SXRay9vsLamqIWwtYtEIpzbGTBJsbmeQATeyrwjQcc6mrLrasNFb0qoH/Ke9sdafunrzzCui9lr7hmm23srnBwfJox7GduitVWE4kiiNIa+dP2i2wh0+kClOLoWNoGh+M2w9oNA0740iqrqCkHusyyhpuyda0oZzaUsFYJ2cJilj2pIQfXxAASPaw3prjvi6Ms2tRAGN32SsYwyCZ2aGxsmSgLCwBBhPpm3aiQqBWxEmtorrRGQpEGKfPiXNpGCOM4McYDw8BfUtPyhzIckyfkcIw9IXBl6v2JvUv2gQ/fRx/psOuu6bELzvcDy47Z4jIQdaCC+qiwdZ1BKy8DwqHDOXCIYsaBdHqHyMfwgsVRndOcXV0T5S2F9xV86sMoNDZaz/oqiwJipVnvnDgCfD9BXgHvpWI3v8B9AGv18spS6+5BIbkcHVaFD51IAq7GbX5uiXU/CnIodsZiqGe1lVp9A7qZQEHzhIgdpnxz2KbE/CrjCFioFEC5utQ29ERpZ4CphZw9eH/GvihwjvuHq69ss23nROlfcewj/ZQHH5raKqyjo9rqKgkpLHSL+SND39N4G0WB6+gJ7PsCCsWMw+rY2r7wCsck6Kd1lR7bQNjQCu4lJlAW7O1Pwg4sWE0162H66fzsPOrLQOx1ldbeVWG1jaw56R8J1najY0vU3Tx7GswHwMlBjePyKHa81JpamHMqgPeWczwAnCQc55QtJbAupOkFmiojvm9zU8w667EVLNV3P5S2z31t1U7SHtfsCNulFykcK3M2aw8WYlbTUGqt3eXkg7kWM+HHDglwW6ajTs6u2tDpBRs5tYjFAAasDHF8zO1bJJaSKLKFUR8rsfpqAVoee/rINDChFyiLeYI5bwE7t8wDM+XUQXtnA3OKHmhAsW+cfA/Q92eW3d6JgNSy0gDtF7XurpiVlgWZN2nDIfZlR9mnjyvkPPdAQIyVlah1tqHY2lzC/O934NynPrPHZrgfvOyqc+3CC9qZm+a57iLPF+Wsljmrex0KtrWsRYD9ckBepOYepJ2c8NnA4IpNTcSdmm5VjHsmKo1I9zYH5N5UH7HuDsLaA5nx/BWg9CTXSVpJVQXRCiI2N71gS8TrLWFMdbRXw4EwXjl2ibXDzEzaxoaA/MexWSSoe2P19bp6FDyb6SsVwOk8SDU6QkQEjde5hLPFAtlU/qq6UmumnGVlPpsazLG3PGf3P3rKNm6usF3XtlplOWuvmQVUkpfoe1Frb60l3aiVlHOfw1oiwyJnFXswv0DY8MEF+tScU4YtQ+22GrseZP27yD2rx7NiPRuwD9g+TDyhfVNEauGBlVKuzXuJBzEWFyZZ263a0zzssf/J+xi3LWvg3KUK1Yp11YNoa1aWfkIBnvnL/fIz/lME537GiiueVqyB51MDzwec02K3sFH97E+AuQQ3YAXlEQFxAuP0lhNtZGQNmpNDSaCcDMOaykgFEzULyoZ65zzTJr3U5bp5GrCsrPwlf+PwfOq8eMyLpQZ0S1B8/WfVgB5Yv39P1m74zRUWcmbXbPfax28J2eZ1rHiLr7O+BvQE190PZe0tv8uGBvvHF2zw2u8T/u21V2kno/gq1kCxBoo1UKyBH1UDZ4JzX/jCF+yVr3yl7d692ym5aV0uaFGqRwLMBMAJcJPimRzu73rXu+yv//qvnfLb6173OvuTP/mTZ+E1ASxf/epXnbLU2972NrdOF+x21113uZCov/mbv+nW6LoPkFqcYL2LLrrIgXVSjlYeBI7sAjJTHhRa8dvf/rZ99rOfdWCd8qHQhlJ+01pfKnKdnZ0OEFCedA8i2EXgnEK9FsA5pSkVtwI4p3Q++MEPujLqiXWBdYIL9JJilsAeKWYpH0pTanu//du/7d4/DTinelXI2+985zsuhOvHPvYxB+ToOoIEpYwn9SuFWxUsozCvus9ReQUp/jhwTmkJVHqphGr9/h0Z+4X/twjOqe3PllcBzJGqpOBQjUHBa4LmpKAoIFXjqjC29J1gWI0vqawJpH3729/ulNYKxzyfuilcV8fKXsmeFfYTCiBQAaDTsfqskH7BvuncAphW+F0/lUcB2noXztHnemX57gT5/9+Ac2G8I6/Ddq2PxWyePYq52TnOxUnHZmkYL3XPjf+XVV9wMY6wNfh1LYV//7eQrwI4J3unMS9bVFCcK1xfedbvhbL9eyrF34o18IM1UOhX6k/6Xf1GTnSB6ILYBZpqbhOkGaPvntmn8jzhvvDAHpvY/aAtoXiyTPiaJdQBVoENytqarW3LOdax4QIcH4QkXADw6D9lC72HbXzgNIoIgGVSZwMcyePIKW3vtE1XvcIqmlsAZdDUGR+1ecIRT5w4bXNEjVghXFiGUDphINVY/Tpr6tkOGBIihOk+O/XA93GY9luUcVTV2mRzJcA96zqt56KdVrVxCwJPhBDFaYlnCfEsHBHHn7IJzpvuGwCcIQ+Ml4DObWm0um2brWTrJvPVAPDg6Js52Gf9+56yzPQ4YxgFVMCXZRTJqrt7rPXc86yqez1AE6HF4vOWYv0xd+SozZ88ZUtA7jmtEYBpUjgVA0Bz3Rfv4BodGBJC/EzP2jzw8MKRp2xmbBiHEEp8HJ/DsVzbscHaUfWNlJXaKN8P3HuPRfp6LcYaK1dXb7M4a7Mt66x957VWvXkLdYJT3i9oDpgoNWcrs8OWYH3kBbQIAr2k8eZnWOdIWC4FbOOP+K2ytgGbVYEqwpSt4kgqxwkfwtmbBpBbAQLIohwVrl1nZbFGByzJw5fPAzxJAsQWcB7huJx8wqbHgSAzhGZs32oVtYBS3lrKh1N/qQ9u6bgl5mZtUUp9OAhLqbsSzFsGB/Ui0FsJCnqlXRfQB1A5W43bxBNfNv/0AZxTgGml3cAIVZYACsnigA+imFdRWY+6C0AhajNZgLyFSUJEzgE50Q9DEZykqNFIFSIeR0EMR1D1um6LVjdafJjwdMMDHAMcRCiurABPHPA+IK1o9SYLVwKgudC3bA5hj134XFLwoECXmzhgsycO4LgLWlnHFkBF4EAAMIgp8jBhKzMnbCU+hhMXrIGwYCHaMAr040UNJZ4gHzi/Y50ok6Hik0fJJTmI0lP/PiBCQrgB9qX9Nfh1gQN4QsAP4FCKqlWQMtJQwHBApgvTTvGEDFoI0CaocFLkS86yUMfFFiaEbHJmDMXFvVw3gapIKfhf2OKrUlCsIYRVO/AqIVCpGxqQ9qENCaEJPUpDoNI2N2hTx/fTVyasAsU5f/sVlomsQ6kDECcxZJk59eVRYA320hmvYZzBAh4gShlLjAkA8+CG62j2bYCuQyjO3QUYexSxuxLzVzQxZoGgICLSwFYBIKpywgiWxABw6QOpGWwG4wbCiCEB9IYSBxQSSi9SBion7204FEssuzhqs2NDXB9AjD67igc7jtPPUxGxmsp2Qo91A34xxqVqR7sprJ1H8keoAWbGHrJU34O2AqweWnelRVqvBGChfxF6MAd0mZoZIPIv4OrqDG0BwEcfKfUSPHV12lZnl8xXjj1o4xzZkPQpmzn2hC1NT1oJYzNawThkPEjBKwOkItghHKu2AABptrTHCDIPkIJS0goOckKmhoBYozibw+EKoFpAL7LYVgXwECMtqBsphEhlyrkw+U7g3CxrkYHEIg70ARyqKdsGONeFg7uUthxbnbcTwLELdIbSPKGYU0EHzsUhZHKkVxbKo4wVsrbymFURYlHXW8aTPEnbjwEMLuBcTUm1E6BAKkYhhVitbLIY8F8ZREWYvGqHjFq0cQC9YcKqKcwnDQDUihoXxy0AjMzNz1J/y4xb1Nx5l3BugI1WH8f2VNYBd9U4CMkLnCc1LVrbdUWpt8RJ/xCg2iGgHT9p7USpcCNwW4I2HsZWDQKZJQE+QvRZKWSpDpZwOmfoixGghHrK1kF91pF3rZxIgnC3KRvnmOFE3BakKsqYzmIjBNuVMoYagPvWARKBsq0p29GXplLUyRJlnJy1geGT1t/7BKKeAHsNNTYOXBwnTGQUQLWJNukCTtt+zsW2rm2jVRAvLbMCpDA2YIeOHrahiXGAxrQt0j4rzKl1XGtb13rb1rPe6gHPV4BKnjp1wh556gDDMGwXbr7ALli/HQAUhzhj7MHH7rdDx3rp+9V28UU7bENLFwyoB4hjzHo578jxQyj3TQAH5gjdRqSkulpLk6/4PGposToeLLvY2gDQ5xYW7c7/c6f1T52yBpzzlaFSWxoF/MZOvfzcXUSiDQGwpJxassC5wvqXKnTrgDPneX32Yn5p3aLXmXkufKb1jH7Xe4E1yF3fu9P2U/dWVWlt118HD11i7di0rdS3IEwBbbqzlyCXVJzYLrYZ7Hk/iolDrHF079Le1GJdFbVWzTy+iCxV78ycTWLkpJQU4cQskGMSQH8FcLMckLcdtctmFOpKWQsQcNCBrmgU2ujiMuFV5wmXrXC9mjPYR+GaUe4lYqxzqlk3lCg8Mf8J/JwFrBwDQp9ZALiXTJRgS8iuJGVc4kGgBdo8yjzZAGRRg4J1ljG5BFwUxUZ3VDURZpUHGiiP9DbLpRJGQZcxNmj+W+/KtB1eEOSVsW2VLXZBpBa4NWCDrHmOA8Uml1awJzygI/Cee5kM91Ap+rkfaqehRiGkK6zGyxhdawrKREjM5KINY/OmGVcp1A0DwK1h6riEPFcxbhuxYzXYfdkYKefNUP7TlGGcMbtM+sKeLOUFFFuxx/f22eBpQl8z71UB05WzzmwEvNq2uc12XthBukHuFRcJU3ocoGUCmIV5HIMAzgYEV21XXLLBzt3WCLjit+/dlbbP//NjAF5p27i+22KlAFkoxC4vA7Swlqmua7WdO+ps50XAMoQnVWjTxXjejp1I2mP7J+3gEdamrCNDkLpV1TWskRpsEtBsmTDzr7mmw67Y2eqgsP6hhP3TFx8iXOIiME8zc3O5TeGvTrI22L613V6+oxNAK2AD/au29/ERO3V6mDyg3gxs6KN8NQBuW85ZZxftaHFrgyOHpoHiBlnrLgOK0X7Upz+wZK3tWdt1zWY777wOB2E/sTdhH/rIHiCiRjt3c4s11y8CrDF3MZ/lPRXW2lzBtZuojwprrPM5sD4BNHdiIG2339NHOYexubqvBaSurgB6imK34naUteg1V+9gbw67R+jP6Umi+Hz5mB05mEMtrAZoR375U6xlZ62tvdFece3F1EOIe4k4sOoI5Zzie9b2rAX8AEA11T47/4IG8t3C4A0AcC3Zgf1DrCl5GAFYU9AXizqrrIvZdYTyvPiCWvLhsYcekOLcKZvmHnrzhirC7AYc7DzDXJGhv7W0Vdvll223i7bj74+xn8C8t8jaqxfY6tEn+3n3AdmlUVCmfICmpYRflkrb/Py0be0pRd2tAxtaZgcOZez2u8cJm3ncQW2lSAnOAd/qQbotm2m/y1tty7nlLmToiSNZ2/PosJ3o7bf44oKD4gP00wrgzg0bG+zCi1qtpanUTp5YBhqbtBMnB5irKSM2wwNMW1kVtZdt77ZrLwPKbQw4cO4f/iVtxwYStoX27WyKMwbngH+B1wnvXdcQsQt2dBB2swkIk3HNfLlCvQ5OZO1x+ug+xsEw9e3jwZdoRSXH17NwtpgeAABAAElEQVROYZ6YWwQUbbZrrmzgpx8wK2df+uohk7huJfNJNTCdoPFUcor8VrLfus261zcQ5nbFHnmkn7oA+Ftm7cQ9glDeyoo8Dw3H6BdbHLB17PCqPf74KTiMGaA85l3uJ7w80FGBGtzLd3YwrjqtHhhz796MffJv99go91XrN29gnFUDo6JgPT+lTRWrYW112c7NtuOiSsBQ7m8Ya0n2S04TavdJynfgKQBy7s+kLFcPmF5W0WCzcb8NjZ7ivHr7hes6GHPcZ6Ha9oUv7LcDR4ZR42wCsCvnGqxjlmaADiN21eVbbBPtE4r6AM1XefB6wg5zzzczzVjhftEHEBwpyVlHRyl9tQ1bUk//NNeGJ08NMl6BE1n7yZZGeGCjq4e6eCX3b53lNjmE4tw/z9o9D56wOsI5r99Qje1McP6ULXL/W0nfOJ9950svrbfODYJgUZlb9dj0TNae2D9ljz95CkBwDJ81kbKYo6srG5ibqmwMKDEaXubB6UY7/6IawE6zRx5iL/1rd9MGdYTjbWNuF9Q3YCVRHgiYOwbMuM82b+4AnPsFt2+kWwD3AJR+cL8grUytnRlxL+hVBOdeUPUVTy7WwI+vgR8A59hAlxkuLHYLC11tSmuS0tPh2sDTT70VuktOMj0V29fHxlZ/P6El2GBgQRdkMy3MzYwcSwqT1N7e7lQrNm/e7FQlFJI1zE1K8VWsgf+6Gnih09N/Xc5fildmX8G++e20vfWP2WDiXux9bwrYb72LRS0SxsXX2V8D7EXwxFrOfv/PU3Y7AF1ztcfe9eaAvfe3tdVWfBVroFgDxRoo1sCPqoHngnOvfvWrHez2x3/8x/b973/frc2lBCXoq6enxz73uc85cO6SSy5xYUX37NnjVNAEdkmF7bzzznPr+scee8xBawpxKJU5KdpIBU5pvupVr3LqblEcmr29vQ56U+g4Kd98+MMfdkpVgtnkzH/nO9/JpgAORO4VvvnNbzolPIVO/Ju/+RsXck5QnsAAQXxShtMDNgqpqusLjhMM9+PAOcF3CiWrcLO/8Ru/4fIm9UKFkvzGN75hKp/Cub7hDW/4mcE5hYJ9/etf76A51ase9pFqnRT2dM+zd+9eVy4pzf393/89DuDo8wbnBAcqVKzqTepe7373u50Cn9I409nwo9r/v+Lzb6MQ+6Y/BJzjidHP/y6KczcXFef+K9rh53nNwv19AZzT+FTflj0RNCrV94LyhY6VuqLGvMaYFCtf+9rXmsayjlO//Wn6bmFPQWnq+vopeyFVOdklQXsa0wX4VOUWUCM1U0GryqfsoI5XHmWrZHvkmCsodp2ZH11PzqVe4JhP/9mfWYhrXYStqcfJ7cEpEOEp6QBvH5BQemzSWm98s9Vd8wqAgx++N6H0lL7AOYXClj277LLL3J5GQXFOeSmAfsrjmfn5ebZjMa2zowbUpwov9ZvCS/1Ie2uC0NXvFbpYiq3PVZyTEtbpL37FZh/YCxCDgllj1OKhFZsBkPCiyNLSvRGHwPkAAmUwJ6jfPPyAzR47jDMXZS/GWqS+znLREJDDIso+UTvvFdcAYHUClQG3Pb7P+u9/CChjGUU6lDl46NUXQqUFVYUUiktN6zdbQy0qCn2H7fT/ucvGBvodfNCwfpOl6xvM07HO6pnrS4HhPAAXAp1ywDPJ46hGPbDb5siHF+9BuIYYVsAQSYViIxxiWVO9NV9NOOXOVks+3WfTu/fZzOgoCnDALag8rAChTTEpBYFB6jZv5A3ghuJK5uhJG7//QRs+TnhOHC4R4KAylE5WvTguUHnJojR3zkUXW3X7BvKRs/ihg3ZqD0DP+KCFcapHANmkcpfAiR4qLbHW9T1WWd9kC+xzDtx1n3nJdxUP+PrWr7P5NkJZNrZYw5YLrLy1jTBFOG58hNLMTdoqal8Lg8dR6VsAACIPJajnAVmsAKEkEigQJCetPBaxcuA8X6jJJvpGbWVs0GI4t8KoGyVR+VoNUdfljRYFuorg9HfxgKBcPPJEo+RmuXmUHE5YauIJoD+c1DlAws5zrLR+A/AfoStxnC4Po/QFdLaKopUv0oCDtxRwKG4BAKUkDuokTveyHtqne6etBs8FpJq1mce/YIHxvShFlBGNt4e2RnEOp1QqE7cc8FEsUmEl9CtvKeGQVsZxDKEGmgMIitThX0UhcBUVT9R4FlEIKcNhWL1ps4VR8Vw8jbP51GHAmyRhuVhHoFxnYfoFIUmj5V2oAHbAk7Ex5MLoASTwv/7Jc43c5D4b73ua7oMSSPs2lBE7ncJglnlgdUp1fdApouUi5Jn8gTJYaXLWfPE5+jnKKtUtVkE4Wl9tK4pSOVvk+IWTe1xdBClLqqSZ9gmhMkPe04tOxagMyMUHGLHCXLECpOYpLQN6JCQvYzS/NAmEOoSDK2Tlm64mJOrLUE88DmR6J9DFvIXKa2012mqpAHUeaQY0bOMdQwUNxUXNmS70LaiCQvjmRygDDvXjRywI3ODAubadAFGoNKJUpbInho/gdKashM/0A9+FUSvzZsjrHN/jIK6sBWY853rzVG9Bpa0fUbbv2erwIcoEBgZQkAGoSAOYSEUDDzMh/VZhVnhQHTguPb9iK/QpbxTVPcZ01gTI9jkIKxCot6rmLYCEUcLM9tnY6RO0bY6QeoBppTWWLkGWhbKW0nahKOAc4VdFvCg4oEID54HnDDgxM7LH0icfJs2URdZdapH2SzgW9SUgpeTEoC1NDCDKOM1nHK/QjCgXBVJjeNoB6rAHkdpzCCe8C2U/8pgcwX49bvOo9AVCpEe7hEvbCM0VQ30HEFEqesBmVrfJklXbbSBTYqdxMs8ThrMEqK6ScegBplkEBp2aXbYyQJPtTW3WjfO+nHaW6xJGgzZa84Us0V7TQF+ncPKeHkWdD1DinLZuWweUWJrPWD8qOwdmEQqgv1fkUL/xA0gGfbboS1sChcEsMG4FcEBXLRCzVKGonVmUok6gNDhEmGUPa6AK+lWEsbJC31ymz+XSQM+lqKAATDRAuSLKYlOEmj0Sn7Jh2jyMHa0kNO0KMBc9EYUoFFeAPcpYQ1UD7ARVhzjU0/QNL8o8L2taZxsAzSqxsRI5ITkczVQVbylgLfPBU4B3Bxen6dF5uxAlzY3hUkKRmvUD/hwZG7Bp4FsfcFMdUKlCk2rsLTBnJIFLYqgzbqpstHbUGBX+UMBaH873saV5oD6F8s1TzzilqfNF6l6hKcsBCbZX1lsL90UKhzuYQc1qboywsUsovSRtiBDLh5/aTX9csGgd9izM+CPnZcAJoWUc6ijpbN14nu087zJrb2i04ZETrFOfsD7mTD1AEQOmCQBMzcUXbQrIqRSlm/O2n2MXb9iEAlnQjo0M2J17HkK5aty29Gy2XRddgWO8Dsitz759x7edGvE5W7fbzvMvsVaU7ZJAgE8dPWCPPPEICp8o+9WW8UBVqRAsbEzCxiYBeBdT1tPeQ9jJa209gO80feKr3/2aHcZ2RSvD1lDNeEKBsoI5ZMe6l1t2DlU+2k0K7PKTFcC5wjqTZnr2deY64dkPf8gvhfVm4fjC34W1ReHvwqnPPU6fF44p5KNwzHO/K6Rx5udnnlv4/kxwTj7EPQ/tscNHjtgyCk+hV7wcuxi188OVdjn9rhI77GAdkVz0S6mggYkaaK/1oYjYP8lcyZhZ19RqPagE1uZRhGQN9BgKun2ARQHsczXziMIoY/JsGXsnOLE0Um7ttY3Ac+WEMRYEZ/S5hPUC6sSB59CGY5wFnFqph/TS2P1AKGC1AtIAzKTmNsO8doIxOEL4cIX8jAKxK1QruK9Noca0CmitmIs15TVWXYrqJvYoiWqUIPG6qlo7t7HLGgIhK5WZ4/qa6hDoBLpT+VDWAqQ9sjDswLmtsWY7jznV58C5ZXtqrN/GAP09+FYrCKcs5bkQKmLLAIJzKB8FeFCiC3BlPaHjSxjnUsYbBAwZQn1sHoheYTJD1FWA+VBKiMuo85ZWVtiGWJ31MNdLXXMUOOTE/JL1z0ySOQBtwFbp9A2jNLfn/qO2/+l+lmlV1sWDFd30/SBgSMC7DKBSa9u2rAdsDdru+8fZKzoCkOJF5QvFPPw7M0DtZaXYlfO67IKXtROyNGp33p2yv/vcHhufWLa66g5rqSWUaSVzE8a3fyhN+MigdXWG7E1vbGDdi/If606BUXfeM2F79p2wBG0qxbFYFW0phbypAGAzD1tlRu3tN24B2mlzQNrx3mX78//1gB3vHbUqbE4DcFCsTupfIduyodE29dSifGV21x2jgGUTPGORssYWgNhKHj7B9ilUbnNLi51P3kdH03b/7mPYmEnuVRtQwMLWBlBeXZlAPThpL790i10AQBgp8du+J9P2wVuetN5R1AcbKlDlm7bGemZGFLWGRuLcz47bOZsa7LpXbrYLt7MOZ+IZJv37Hl60b93JgwCpRWtsCnId1rsJL8pewNb414dHTtlb3niV3XTTZh6S8NpQP/X46aP28N4UkGcU0ArFtnrCvtcEAOfqbOsWHn5Y8Nq995wGNuxjGekHQKvBBkVRLmV+yk476Gr7ud2k7bXb7uwHhpoDhMpZcyMPlUFhLrDuy/HAxa4ruh1omAGkfOThjH3+i8fsRN+gNZHHro4awDNB1XOAYJPwAQt2wXk77PWEcb1gq9/NqaeHsnbP/dN2z0OsSel/tYB+5fRlgeNTU6so0SVYgyXtqoub7B2/3oUiW8QefCRjX/7GBHb3MUAuj3W0xiyGLa2sCNuG9fV27vkxlP7Cdup41u66PWN79g6z9phEXS7LXgL3G1xH9xRNKMmdSxnLWbPcfeewPXlggf5tVt/KeqpUKmpShktbD0DbtVess46WsD3wUMb+/p/T9vixaauLztn6lnnqBduCyukQD4JMTk9YbUOlveVNF9C3uT9i22AWePOBR9N2x70jNjhwiNCuHqtifbaaLmO9gRLcyCQPXGRsx/Yee+P1TfaybUHr78/YX/39cdt3ZBX4dJlz8I8Bi1ZX+vhZCVjaApBdanfdd9oefOhpgNQge6bMGYB+KamC04YNDX679JKtPARRYQ/ujtvTh44AUQK00feiKNJJDTCVmmGcNgPP9VgnKoNP7MvaJz79qB05BdBW204dVdLfeEAGMnYaMHR6zIPaXKXd8LpWu+yKctIxG5/M2b0PzNvuh5+ykfEJwMtaa0FxMku/np4JW/9YhHDVg/bGVzbbr/yy6pG1CMDkxz72hN394BHuZWJAZcCUjIXKyhVgz7Cd/7Ie62iroSwexvcMZTzOQ40TTpVR8yLmmnU561wAzM1bWxjHDUBqc7b/wAR7P3Frpg1LWSet8vDBMiGwuS20V7xmg63fWMWDPUG79Yvj9q3vPY3tzFBvwMIo0pUAyMVRXhwZWKRfbbBdQIxXv4r7Fep9njbc9+Syfeu2Q0CAIzygQVhq1hg+1pCz0yh4TgRoe7MWblt/853NdsWuKvazzb5/x5x95h++SXuEUJPcCEwZZY2Qoc+lgO8etd4TDzLWzlCcU0xgk3qh9gAEjGvfiYct+feFvIrg3AupveK5xRr4CTVQAOcEtn3yk3/JQoFJhk3qNO+UNgyYMBWaqL9/4Bk4rg+1hUFI5mEm/iW3caxFsRbL+lnJQkxhVqUkJ+fdJp7Y1N8KueKeCOfGLsiitnCD8BOyV/y6WAP/gTXwQqen/8CsnWVJyzcwNcPi6S/T9omv8YQEC7DPvC9kN74Jx9JZVtZicX50DczO5ZG+ztjvfSLFk/tm//dVfvv//jBozbXFXvCja634TbEGijXw370GBIwoTOK9997rQDcpP+mhFim4KWyqlOMEZQlqEUwiCEwgnMC5W265xa25BXtJOUqQx7p16xy8cvToUbc+F0x344034vwMufSlLqe1v9SUtOkuVWmBLUfY8FZeFKJUoJ6uI8hO632t+7UxfuAAT5PzEhz21re+1YEuCtUqFTepvynMo9IWIKOHbpSuVO+2b99ut99+u0nlTvcOUnErhHVVWQXvCPSR4pvgO0E2/f1sKI+NOZhPEJ8+Vz1I2U3pKA8FxTmVSXUilbiPfOQj7rrKp2A5Kd2pLCqX8qZjBQCqPgQECvSR8pZgOinZKVysynr99dezoTrqgDpBNAXoR3Uuxb+bbrrJAX3Kq8LuKWyuHBEKKav8qh107/Rie+Hrsq98OWO/9hHCdbAh9fn3As7dVATnXmzt9NPmR7CXxrPGsGDYT33qU07Zatu2bQ6K00/BnHppL0BhXB944AE3PqQwr/Fxww03uDGlfvvT9F1dV9fXA3cHDx501x/nyXZBcbIBUtUSpKrNUu0X6KVxp/EluyOQSMCtQrcqNKtsmyBUje9CPnSNwu86P8f1Th0CnPvAB4GBJnG2ofwAUNC1jqeuzyfsX0s93rWErZwYsPDLLrSyHTuBCNbKr/PPfBUciT8MnCvUmfZCdJzyoX2O4qtYAz+pBgr9Sj/PfKm/S3FOcOmPAueyjNHev/6s+Y8PWC1Pzod52j5Vgqo3ZEQW0CZaUccT/o0wOqihPPqYjd53rwV4Qr+sq8eim8+xSEsTQBDOAgCPJE7c+u42wtehULX/sM3c/n0bPXzMygFFKs672MpaWnGkBS1B307Sz0twKpeUBi030mvD995hU339OD+rrP6iS8zXtf7/Z+89oOO87rPPZwa9995mUAmwd1JsEimSYpMoybIs2Upix3GJs0n225Oze9Yp36Y61U6zZMeRJduyeicpiZJIsfcCkCAAovfeOwaY2d9/6PHyeJ1YthLH/oLhGQKYedu9723v/f/u80gpmSh3YR8VQ5jYOCHInWmUSNrfe1/dZ08BnniVsX6jwl0uApQhmmjtUPvlS+obHVAWKgu5pUWaLMdW8zyAnSkwrVqksOJULEyxNwOs8gJAhBLYicogwIy12MSBw6p7F3ssvktZtkYxjCEiCZLMAu+MAwvN0qklZaI6Arww3dCuWuDX3sYagmHYLS1fpUiAOiTRCKKhOAISEZWYBNAVr7muPrUefFf47SiR/SM2rdXsApeQpFE4SgtBAIeOYNPFIaA+jQIbygJjLbVE/SMB75ahRgcE5ZzSDNaaY93Vmu6vR4EMhT9gLoXnqaseeKqjDvvCWViyPBTtgN9icgB4Uf8EoDPrS6giMhBAmDLiIADu86BRM1apyfZL2A6BFYSk+qGyiFSgR8LyMwBUI03H5RypQdEH5brMJcBqHM+sR/uw9e1uJZiGCsUC1LwA5yZClwOfYRd18V8V1gE4B9zhSFshX1yxv3zMon4x1dkGtAJECRwj7Mk8XhQ7IXGCkvIVkohCINfpGe3VcGujhrt7lAgUk1BSrBCAlKmWNnU1VDFXjB0pQcxwLESdsQtIEnBZMAAdqm9kEsnk5jns5oI2YF/nG8OitPuKuihjjsgkJbpWUO6w8gWAmgYKGMHidLStGpiAxdpZefKh1uVg8XZwX4NmW7ECHsKaElvEeBSlgjJcKFMBUTVdQR3uNHAFsFFGjhypCzh2NLZowxrvA9bq7FL0ODA34x484OTMcgPdUYdQL5lFrWq8o1pTHTUolQLolO1QROpqLFMrNVj1GuDRCGlDUTlllRwxxdw/LM1DTW2OCQ+CzwZagC9wjTbnAeQ1U6v+VkC+5g7sLgkIAz8GpQPAhQNLjjRrqvY9TXTVk1fJCspCjQ74MwhLVy8KcKMtfN/bg41bmqKW7ZcjeZF8A6T7xpsoL1YCq2K/hiqiIy0bIBTVOMr/XD+KOkBJYWb7C/AY7ExQSCb3Ii4f29VY8qBeE31n1dfbAiiVBkS79BY4N1Cr9toaBE5CUPhxKTID1T/ATl8o+RMMJBuawX0LvQW/OQCbfNw7lNUcqKSNosQ3SxqdwB6RlLXQrKXcW64F+HCgGQhisBclKEBX0uEkkO6lnHuHq7CIvuS3h41MW6i44q2AvulYH3ep59pZFDDrsFabUUxquh+sc4TkAHONcMybzDV2awhwcSBhiVqxkB0OjqQMZAKhxSiBNm4GYKaVNrAGaCoaCG1NpkuLklMAV2+Bc9Zrc3l+qGyY29SGRV5VbxvqOliZApksynQrC+WqcGwGGwBXzg10qq1/VGnOZNRsUVQhUD1psC6gbO9gD+NnAuGoY5XQNqbQZnXx99XeVvUDnqQCLeeiwBaFneQUZWIYqKUDdSQP8FxmcoKKAGyjAApbxoZ1FaBllAvLAvZMp50aACC6SVvZCazqRREuEfgunzY6iue74elB9XYCdwJcrsopVhltcQptiLnnWi8D9+MH54hVa5AxUjkwWMP4EMpPUVqWmKZ8rgcMETBwUtc7GklHv8KAigupL5mhKC9yHV0Tw2qhXTCFrGLKgjsWC2D6B4O2b3Q2ahAFuQiee1KAtpNQM7J9egHnWtlvhjq6MDpJRShbmp1j7WSXqjuaGTMFY4saqlbq9dnTB1GG6lNsVgYqqG6AXdSZAAhEeak5V476Vrw2rrtTZQDOVykTFVevwF2GaRHPa3npeSgIRmh8ckJnblzVTeDMLJRCd69BITjdhUIgykf11Tp++gy22nNatnCJCkoKdK3+us5dOIeimVtb121TUWYxrucxauis17EzHwDPlQPWoCoHhJcLnDfGPb7WWqULFZc10j2qRYULdc+WXSrOLUOVbkjPvv6cKhquAM5GcI6lWupapFTqcmZItoaw3OsGPrCxrj1X2njRxgD2tkUXgd9/dFzAbfk3XwFwzTaw/WwsbD9tPGqvwPeBY9pP+ywwbrVtbB/72/YJbB/4PXC82/cP7GM/f9zr9mPbopfam7XqRCV1hvI9cQcqmaR1Lcqc25NyKMO0k5RJIzwRD8WC/AdKaJSpGmCKjt5OVBiDtQDYNZ8xQRy2eq2A7qcGsDmdHFEEhdsFBJ9BfoaRnxP0+53A3f0sPkiOT1ZJUrpcKK6BHes8Nt4tgCdRqCtmMSaIB4ywiIRZG/YN9WsIlclYFOcK2SeTutUOiHa1DytsZCNTElOVFoIqIzHTuvEBVOFsIRAW09iau9LzlBELrIy9dTdQ/jR9ZhaAyVosxvMAA6NYiGZtjKXTQ3uEw6S6aWPqUBVtGkZBFDhuYSJKWqYSDLDXjA1xeVeTmnuQWSJ/cjLylIc9diLtxQjpq0X1sgv12Dzq0xJTtWOb0Tme73o71Asg7wSSio1KIH20vyhGjdHvN3KeGfrZAvJwSQLwM3WlCaCnoqdfA8B+Bu1msFgiFgvzC8fb9ewzJ1XbNq6CBcu0dW2+1rpplzCPnvMCNjMGDA6K1YWzXsA5oLu5AW3dmKzSsgQWXGD/SX03xatMFj1kYDkZgTLtoXem9Pg3j6mmsU9LFixH5StdS0tQA+QeVAAQHTuHqt9goz62P1e77kmjXQrX+4en9MbbANw8ky5ems6cTwbAjxMFtSkdOTat905No/zYpM89WqL7781Ahc+JctuE/vQvzqmiqkUFOUBRdxObXhiNglYkKmnAh5SRyooZPfU0/fdgEIpqHPeOEOA67o1ZtbNoIIxxdDRj4vff69HxE3WARTG6c1MB6cPyMtKD1TbtL+OVzMxEYLdo4Hanyq/O6g//52VdaIzWWuC83Xd5WWAVy/NvECpqgywYvU67OaQdW1dq745sxWJTef7iINBcuxo7vFq8JAF1LRThUsKBdTw6fWJSx09Vqgk1r19/+E59/gvFSkwLAs7y6O//plZHzswpNzWG4wVr+WqnUtIiANqxDmdEUn55hrm3G0DE04BaWbpjTRL5BqhNvfFwDfHYqUbRr31wbFgvvlpHvqQA/8Zq8UJb4ADaQ3mZARLPzoxVTmacpiiwZ8/N6l+erFJdU4tK3eTrnYCspYD0QFc3qod18ADPDTEu3bs9W3vvZmzG4/yxk9N6/vUm1TQ1auuOIi1fkaoEFvv0d0/hGjGKKlgrdqWT2rPZrf/tC5lyu0JIM5awz4/q1KWzcmeF6c6Nbi1dHAfQhe0qQaTEVAcgnRPr2HHcIDzYr3q0fPGs7tgcprQM2lAWzIwCxlqZi6FOtpNfL71QC+QFqLs+UUvXUI4TUFIjL6ZsPIwNaImL/iouhPyY1eNPe3TqWpuK0zzasSFcK5YyhmDc39wyoqMnGlTb2KO9u1ZxD1F1Bbqqq5/WMy8O0R8AYKaMUg651gxUYUeDdB4Y64MTzTjzTWvzqnx94oEkLV8arMamOf31P7bqZMUk6nzTpDEapUWeSUhjXFQokGAoZdOrf/j6Gd1s6Kd/K9KmDYBuWRGA/DzXsIgiPJw2iYVCF88H6cj72JQzNt6wjoVCi7ENTUBxm/ZlampEyYwnsrmHCaTPwLmvff2CzlypxsK1VNvuyuee0FYAvTbfnNTZ44x7qut0z3a3HnyI8QO2ydcrPXr5jXZV1t3016GNG4Dy3ZSJUdTwgDcPH3eqlr72UzuT9ZlP51DnboFzf/wn1/TG0XLuWQLWzKWU7RigPuA5rHOTgXAjWFjV3ubVU99tUznWytkZEdq0MRMbV+x5rZ2knQ4GNIthEUdXW6ie+vZVgOcYgMU4rd8QwZiUNDJenxibYqztUX4x7Tn1u78rTN/nmK8cQNGZBVebNhSQt2mkBUvbgVkdOTyEEpzZEAfpoU9EMQYAwK2f1YE3e1AcrESFL1obN2Uj/JQMaEofVEU5PTygygaUYmlDfu9/pGvzVpTtunyAc5P652++6h+/bly3BiW9FEA9s1ie1KnT7+rkiTew003XA/fvY8HlOhYyWZ88ydt+muKczTvR9/L/R3nNg3MfJffm953PgZ+QAwFwzhQUTFnBrCHMfqmLwbxNUJuqnE1q22DXJtr9UB0/fQxi46H309JZGcrKc4PjjIBORbLagnYWJLK3TW5bkG5+Avkn3Ij5r/8LcuCjdk//BZf8S3pKe2avafTq1//HtM5WeYXqs779F+HatdX/+PhLmqr5y/5pc4BuRAeOzOqzX55m5b20ogS71t8O1b3b5svBT5uX89vP58B8Dvz3yQEDTgzsMtDts5/9rF/lyCaxzW7RYDib2DagJWAbeOzYMb/dqsFsBnfZuLyhocEPwJw9e9YPh1nuGcRmwNfu3btlzwE2yW0qbnZMU5gytRub1LfjbN261X++ixcvyu1263Of+9wPrVlv3mSVLIEcuyYb+5sqnYF+9mxgL1NrMwigCdDNnifsvAa+2DXZJLwBZbbtdSCXZ599lgmLDD+cZlBM4GXgjoGDZhlpMI0dx5TcDJYzxSyzkLW/DVA7cuSI7rzzTr9tbRRWMPb8Yp+ZBa3BOZYnli57Wb4aFPfoo4/6VX0saGHQgoF+prZl0KDlr+WPWTOa3a3J9hs4ZyCfff/pT3/ab0UbeNYxEM+AQrPKtbw1sKa1tfWH4J+lz565LM2WZ79oLw+r4b/1rx797t8zaYqF1JP/J+Dcr82Dc79o9+mnvZ4AOGf1vLe31w+sWr2wslxcXOx/W/21MmnAjm1jbY4BbgbVmaKj1R+rZ7bNhy27VsftbfXw5MmT/nYoULesXlvdsjpplrEuFwo2BP1te5uDMCtmq/fV2Eba3zY3Ycpb1t5ZuxGAa62O2j52TYGAn4/PGq9X6PHf/30NUKddsdFajcLHcmxpk9avlTMtGRUb1KjasN5CFSjE7fqh4pwdy14/PBZ/2+8BcM7OF1Ccs/pt29v3P7qf/yDz/83nwE/IgUD5CWxmfZDB2wHFObNqtTm12+vcHIHbpq8/odhGIJB1dyi4LFPORMo/qkMOfxA6FAtAgJ+6JqC5o+q8esFf1zI23qWQZSvloB6ZHISPAC7cCNaS/I696MjB99Xx+pt+hinzLuCgLXdhBQnMw8Q+F0BUnhAzanE+wBFvZ4Ma3zsEhFYHnJCiNLYNLQRGikUtIJQDo5JloIaTAOfIdWylXnlZkwAz+SWFynrw43KiSGbH9bKMv+vIe6opvwyQFqPiRYBCzVhGVdYqFMWksM2krwQIjSCqgz7dAbziwKbM1Ow8rYAt33pSfRUVigPaS919r0JQvHVg3WPAkp87Q2KJJb6a6x7S+JkrOv/OAYKcXi28Y5kS128gfYxVgoH8LI5hb2A+g7nmuvvVfOCgZq9cUjLjnpgd2zl2CddBkB/ow2E0ioOHW4CmuZEWjdSc8gNK4QBlke7N5IObgyFrMtqIeli5JlsrUMhzKAJVAkW61NXUieod4GNUEH9iQQ9s54i4dS0eIAEwCq7azBVpbwlUOwiW+yY7NNl5SeNdNwhUA/EkLVZ42iLAqjjgJ9xBOqsBvMoJiA9hdZiv4KwV3AfgNJTYZnsAyppuYPnYpfiifKxa12gyfAnqV0OAc08pmOPGEjwNdm8jT4CcUHvBD1Uz7dXytnC+kW6UgFASQ7UhjDngsNzVciQuJw+i2K5bA3XX1I8lcEIIi6mLXApKjAdG61APoJepeqQUuRWevQiRBWAt7D0dGNchseZX8SGJgIEMPryAgRzL09+k0c4a+qJRLh9b2dxlKLoBSmPgNw3M0ttUg9pWP1aIWYrMIT+jKUsES329tZquB0gDgguKZ156AeAgwIxnYhZYqxIxs0uo5/kUlwfwZ/kNbGEKcNMdtRpmP2dPG6Es7GezSxVchEJaIveDsoCnrsZbL2q8+byf7YstvhvVxnWkrwoL0deBMKYUlbNcjvS7gcAKAR0MeqQcsSvGdX4gKwgwzMA5n6cFlcUr6mi+Jm4Z82KFis9ZBCRHGrC99Pbe0FDFQYpWn6JdCxScj404lmZ4X2Hvih1l4w2NAcHFY1kasWgf94pFLgPNmq05AFBZpRAAq7B8gMHMIuJysaQPGKbvpoZqTvutZSl8CMYVKaqEfhDVPx8p1gxqjX1nUI+8phn00ZLzgNawD5sjP7sbTLEmXvHZyxTKdTqigM4dNoaORdEohtRZv4vCGMgVSAfRThxoBtvVW1eFbBTqbsBrEa7lQHq5nAd0hWvpQoVwdtZssNIVmV3EMYHxUMDzDpRroukoUGgbcGwJ928z4FwKSmPYg1Wc11BPM7B9mBLysWlOXkw+Y8M7A7g6XEVw/KbqpyPUGp6r4aAk4DrUhzIKgc2iZbplSAOoATj2QlMrCkwerUjL0EIgqCTG/WbT6CSfTFnOoLJu1OFuYM9WjyVxmM+rfORMiqAqYp2zbGfg3JzOAJV1Dk8DoaRqMfUmITzIr2g1RjluwtKzYwDVtKAwlaZmKC2M54CJSV3t6cQeO0g5CTFAOYAFyGOBe6Le6ETxCeVHVD4TAHIXA/XGobLW2Deg6yiYhSYmyM3Cgnjg/3aAoFoC5P0+2mDGMNHsW0Z+JAPDDGPja3anHs61GGvjRQC+YG1+G0ecHoFmuCWkdYizdgLlNTGmmgJsy+W5rJDxXzIN5gjbNwEUVHdg/zs6piTUYsqI99j10vIB+6CCN96u7t4+JWO9607OUUhkBJarHtV1tgDFevwxoYxQgvcG7bGPQXUN2ML2Aya5gIhKgCVn6CIqR7rUgm1wBmOwdOpMfW253nnvZfkAU7IWoaSZXgCgE6/8tHiFoPp1Hvh6EMhnxarlKirO16lzR9XV0aVFpUuBG9YoEdXOUJPTw2L67I2LOlVxDrthj/au3qYtJWsAouPUir3rBxdOAbJcYrtZpWalqIN6H8b5N6/YqjuKNyk+lLaLf2crTuroyQ80AKC0ecsmrVuyWhnhSWgrTgNBXtU7pw+rGTiniLzeuWWnigDnegCtnn3tWVW1VyqnMEfbNmzTCtrhmGCgrhmAmLYBLC07/GPvwEIRG5/bOCAAztnfNqYMjEHJwh/7sn0C44fA9vZ3YKwQGBvfvnPgmIGfgXPb4i/7zBarBb6z/QPPtnZce9l39nlgm9uP/W/9bttbTHGG54C5pAS1ABIMAawsJi+30v+mUSaCzdeR+4awFaqHKD5yviYUcm/29crDIhcXQFcJoGsq5TAEu8IWVOUuANW1AHmamuBCoLZsIFkwXu6O118Hq6m/DgY4JViAmirbAKDT0eZ6jaDUlkm7lIMNdwLnst52mmN2U0bbWWgTzPEKUMTNQ42znWehSs7jYzFAXir2jKZoSJ2sHQGspy6YOmkocEVhDjaTCcnkDe1DG+0t7UNaIra/WUXKp12P4uHaSfuCnJwm6Fp7rU4A37UD8pkylEF2RfE5ykKV0xQvGzlOOTamvf0AxrRhxRlu5QNCE0ahpfWpCqikvK2Bv4O1JBl1KtLey7jsZnc7ixzmFE8dS8Ci1FrqGKrEJOdrRPGyGwvyJD5byvdxQNPNKH/V9VLrGSeVpCQpBwmtaPq8Ix906BtPngKIcap02Wrt2ZKhzWVBWFAbxwcIDuTY2+3VoQOTOoaiWEy4R/vvydCyJaTVFsWzDUNF6hU/SbZZUh86NK0nvv2B6to6tHfnZj32oEtlLsBRuv6a+jm9dKgXUPWE7sI28/57S5USFwnwNKBjZ+tJT6R273Nr3Xrac8A8ipLefsujZ17A6pmxy688bHNeOSg8Bqu6akJ//pdnVNsASLRmkX7jM8VyFTJeinYCwJF/+NOeOTWmbz15FeW4RAAwN4uxQpWWieopYzTEQLlXDN0G5/Ti823AXygDx2Voz84cLVseomhU0GyYGIY9cAjpY7jGfRfzRoBzf3yOtjNW+za59JnHsAstoV8gfderPHr77Tpdq6jW6uVL9bF9udjmEhs51Ka3PqhXaka29u11adkKgFpu2BSKXe+/N6tnn7+oK5UV+tT9m3BdKFFyVhCAsUdf+5sbOn0By9XSLBbOxmj5GlTUuHYbwvZ2zgHsTDMnVgUQGqaNWHVuuzMcpTRGlDTioXbdlMHRYS+OUD169qUGlN4KsUpNQB0wGCVD2h6GMEGMk23BRAjlcRjltNOnZ/WNb1cBoPXSrqGedj+QUyG1h2PV3JzV099uRp0M+GlVtB55MBKY26HnXhzUq2/Rn0fM6Td/Z5GWLkN9kWuYGPL5bUP/5ela1bcMasdGt37rc6ksKg3WSc7z1PNjukyfv2Ftth68r0hLUbCLIX02HHOGYq1Mvr///oiefnqG9jsMS+BQbdsZil0s2CDPMxRnxi0M3VBFu3BuWM99n7nKiVzde0+i1m8KVizwnalT+lCqhWGmLngVzrHf/2BGjz/l0aUbnbpzWbQeeyAVa12UG01Fcdinl8ivV96s4bNiPbI/BdtVJxamI/rG0y0aGE/VTuDDTz/KYgiex0axob1AmXj+hUZdxw73jqXZgHPxQIDBagCc+8o/tuj0jRktKwrXpz8FvLmavCGNDKGpMw411Hn0F399Wi0dc+TDQu3anqTMPMowQ267h6zpwFoX1bODHh1+D6VLFFJ3bU/EnhbAEHcnWHS2cZigL3lBmaVNvXBhVv/0jQu6cK1Sa1at1K98aqEWooBHF66OJiDYd4DkXj2iZUCqD30cEaTMMOaEp/TqIZ5jZnq1dXsx86u0jZk8g1Gnjx/36NvPe3T+eqUe3Jqgz/5angpQnJuj/P7PP72mt09dV35Brn7j15drxUrGnvFoNqNoeAuMI421Xv3rt1tVWdOLEiRqjLvSVFrMmIwyYs+mobQjDJt15SKw6N+fAmzN0rYtvLeFoSRJeSavgjgYXQjl1hYGeBkLCjeTVpTsyoFQY/TIw0u0ZRPWwNSrMe7hgTc8OnHC1DV7mP9NUumiKADWKb3wAguyu0d0N+3Bvn3JqGoyH0a/0Fjv1TNPdQB5DikrOVi/979nasu2OOqZT28f8ujr33wJiA+r4Qc2as+uBFQBqS8jM3rr0Hu0jy/K7UpjofYuoMDVtIkkhvHXrRfP0n69Y8bQP/jkZ/0xD879rDk3v998DnyIHAiAczYJbkGss6xI7e/vYzBxy5J1msGqTWDbZF0iA78UVoUnp/CAyUNmCj8teGRvC4LZ2yaPA4PrD3H6+U3mc+C/MAc+avf0X3jpv2Sn5tlfl695teOzkzw0SwszHfrWP4Zr9SJGOvOv/zY5ECgHX/6rab170avsZIf+j0+G6He/xGh+/jWfA/M5MJ8D8znwb+aAKS0ZLGYwmY3ZbcLaFrMMoIRgE+Q2TrcJePvcQBiboLbxu4EwNi63CW9TUzMwzo5lnxnAlZ6eLoPLbp9kt2ManGbHMEjFFKFMCcr+ts/tuGaxavsYzGJQmKne2aS7PRNkZmb69ws8D9gkvEE4pmBlaTCQz54ZDM6xyXRLk+1r8J2BaJYOO29g/0Cm2PYG8XR2dvr3tW1s8Y5dW0Dtza7RYBxLdyBdlnbLE/vcnlMCcJAd167JjmvXYAt9LP9se7sOg3TsfJZOS5PllQF3to1dt+1rAQdLj+VJ4GVKWXYuO78pbtv+tp0dz/LJ0mf5FwiYBPb7RfmJUIW+9o8z+qMnCRsZOPdlwLlPzYNzvyj358Nch5VhK6e3v6zM2tvqldVDU3Iz21EDW60eWzm172w/K6/2tnLtdrv99sgGxN6yz2Aylm1s+x89x+3nC/xu1xI4ntktnzhxwt9m2DUYIGSvHTt2+BXtCgsLfxg4tHbKwDmDeK3uWL0ySNcAPNveLKJdLpe/vgbOZT8D12TgXNO1K/r2H/yB2oBy3cmJ2rx6lZbeu1+xSwEmqLc+LMS8fQTdmV13EjAySz172TXb64fH+kF+zoNz/myZ/+8/OAd+tL5+GHDOS//f/PgTCjqH2k5+oZwLXArOAZjB3s5vQ0w/45sO0tTVSrUcPQJI1KzMJajebNqqkKJSlM1iNUuwwQIS4G0ExjwAbKg6vfS6Ot9+h/4uSwnYM4etXydHXMytFHuJMgFr+VBr8k0Qmu5qVfWRt7ByvKGc9ATlbtsKE4UiFQCHRR5nAduMnHNijTh87rIqXnkeW9AhLVi/Usn79suZAIBjUM/YqIaPH9WN48c0zu+ly5crAeWvmcpKTQCoBC9czDXTNqQw/slMx4IzHjgJtTcUWqbrUMT6p6/JiWVb/Jp1igJWD8ov8p/fi4oJldgqNM6Rc0CEqMIdOa6z776p+IIMle3doNjltAVRqMsRDA/yGUBE2ixTuG4vAfuag69q/NJp5bjcStwJlLdoISp4qO35gISwZWQrQCHUHwCXJqpOKGgMJSHUuhzuLVjE5pG3o3KihjLZdlmTjRdRzUFdzg0ohDJWd1ufJnq7lAqgFJUHdBUPrBYCpEjbisCX/9ppmfzBLgcqbwaUzfTXaqjlCkpTPShKYAWasRn4qZibiC3bJKpiKI5N9DZhkUYQNa8MGG8RdlsAZygtzaGQNVx/RaP915VYgEJN/gp5wgHIsCrtvfiMgnuuKiYtTyHF+2gfuRaLUM5iJjdA8LX2CPe7GpieaDXlIdKFolzOJmxdUadzoi4406Wxjhvqrb2u6NkxJRRmc7/iALla1d3SiQJbpNJKUA5MW4D1arrmQJmCiMw6UT/zAf74o5yooWkWAKwfYK6ricDqIKBLgiIBD4JTTcUtjPIzqJnmCnWjKucD/EwrKPIfUyHp3DPyYKhBM00VGgEecmK1FLcAAA47qll8Iof4bJjvErl/sXnLaPMBSEMpqz4Uy1CqG2s6A5RXrkiAyAjXOgUV7IQxy+FG0B94UOrpBDxrOQawN62o/LtQSrsDS9xa9dQdQCHECzi3CdvU3QScUWKzyCcAkQ+7T3Sf/G8T1fNRb6f7rmm07zzjQcbTMdSzjLVY81KHUUjzMV6e62TRxrVDqHHMKLKE+5C5Sj7KRRBl0jeC8p1f2fAiajVBCi/Z7YccvUOtmqk7hE3wTT9oGOnGCjHJyoXVXazLAMsm6g5rsLOOY6E8AogY7UYdLyLXD9jK0wR8dll99Rc1giVwItaT8VjAebsbNNTYRt+Ypei8DQrOLAVYTSRtYVQRQDeUkSx1Dt8EZ6Gse/s4DgFqwJGBrjGFA8TFZpdRrN3USfA17Irnuq+pvb6Cv6OUCvQYnm7HzKIc0F6MVaGa954GUfdzJLqVtGAj4Bx9NQqE3dcva9iU9mgHEgsBDRPLNOdMAUYcRFHxptqxjq5A1aXOEY/SXpoKgJgKU/OVhHJQBBaO07QDtSieneE5yAuIs5Q+v4RxQCLgXDj3yfLJAxg8DNBXjw3nNQC1YbOHjI3SUp430iEugkGmZqE86j1OnR0aVu+ET2UoiS3hGSyWCC74E1sQDB7tVw0gnJcx9IJ0VFtQimsaHlE5Y5kg6nsq9TMO2NCsEh0+rNfmUKQbH9EQge9YxiHLLd7CJTX1DKoOVZf4zDTlmE0iZagJRam2CdoOgs9cjEKAdopy3KhtoeKG2ltzVyMKPmOANvkoUaUoHarBVN9mCJyP07T1ErRtoR1u6+8kH7DOiwB+S0lTMoqeBo2iCa4GU9QCenVgd+sCXCzF6jCR+uYknwbIx+vU95ttLQqejpIbS9hwIM62kQm1Ae1E0MalAbxEQ834YVHydZzc7SJtI6NDcuFPWAxMOMnxrg30aGBiTEU8Z+YQoK6pvKA33n1Z0TlAUndsgasFJuWq8hNjFIOa3uVT77PoqkrFS7B5dKfr2ImjGupn//xFys0sATgI4mxAZwBxTV0oAzXXyDvi1Z4VO7Rz8d3YG2dgcelRFcqR7158W+XY/3pmp7BcDAG8uEtbF+1UYVSpQimLI9hiv3PyoM4A2YUCYu+5Z69W5gOxe+M1HTyl+qlqHTj3pm5crlYWbfGOTTtVQLvYO9Dnt2qtpy4uW7dc96zZpcLoEsLjOC/5UG4ln4YGh3644MzG5TZGtnGAjcEDv3/YMbbtby/b3t62v70Cz9C3H8/OETiujW9tX3tGt9ePQnv22e3HDFyf7We/2/vDvmwfO5Zd0xgyVJcBW1qYk8jHqnWdH0YLxgqY9h9YkUdQyqBPzcCbVcNj6gbsSkYtbhmiHbnQHeEca45ztxGnvNLLczr9b0Z4pJYBgmUw7mGU4q/rTdgsX+tFKY7tCoDzCmlHejjmSer/eEwk1qrMRTCWieVYQUBls7QNQ5OoRTKPEITKYj7gXDY/23k+aqZuhgHPuhKIh1LCuju6Ua8cVh8n8wCpOegT8rOylUc5ZgChesZbPcM9qGLFaVVavtykO5a+04ldtwfiyuqQQbwNQ12a5jpT6O9L0rKA0oinsuChn+7jJoBuxWCbJgaGAAKBV7F9zgTow/3Ub6dcx3nOtNcpaHRSZUlZKDglqX1qzA+8TlMkkpMzULREXY08jQAbZpTit37uQa0yjnq8CHA4FtW9lmHAI9rNdCC6JfTtGSFAKwArpy4N6MkXKnTs2qCi0oq1fkma1i8IUVFGCPajqH4lOVHV8+mDI1N65+0uDfahCFecqrKSKGUXYTeawTZAJjHRgLukz/q/Qwdm9I3vovg72o0DwEY9DCiWk0B5ogi2A+E9d6BLrx98C0WpQt2/bxX3KELPP9ely9frVbIoWbvvzVfpQuAiABiadJ054QGQmVTVzZMoOuUAzuUrHcWumusT+uu/Pa0O1CDvvmu5Pv0YiplZzGMAEdmwxsC5y1cm9b3vV6u+WVhYpqBYFo7NaRCCyeHAtKjKmuXnuBcwZkDvvNugQUCvJWXYWZeFc6wQBGTCUZoze+1b4NWsAVoX5/THf35C/aOpQIRuPfpIuHIAnXDK1U0gqLdRzjt5vBZAaJke2kubGwcs+HoTNrSNWrLMpfvudWNFiqIysJBV6zPnZ/Xc8+U6evycHty9kUWrZUrJDlJz07T+6atXsOYM153rUDH7ZDSKerR9NuRmPwMayy97AOfq1dw6ik1rCmqZMcrOI13cu8w0+ngAprlpwJ93RvW9F5sAkmK0dnmUlpQGYzMajEqzU1QpVNiwzKWP7O/FjhQb029996YmWbRwL3as9+0DCsda1h4NGuvn9J2nBlH7c2pF2Zw+9Qhq0UBg3366Xe+fHAOeitcXfzddBUUoDNIP+YDKKq/NoWDXrMuVfdq4Kkdf/I0UVOEB57Bq/e6Lw8Bm54EVi3X/fS4VuenHrA/i/omxtxPI9tKlOb3wHJDbNVuE58W9gnuYDwCJJW9yMsrHqNLZ/a6pmtT3v9eghsYoLSiI1KIlTn9eJKdTTnnHoYAWx3grnI2PnpjSN4Hxqmv7tGdTLMBistyFQX77YbpLPfvykJ58FtAwN0efAoJbBBh55myf/uV7NwEzC/XIvYl6YA9W67Rx4/S7NQ1eFio26PQZxhzFWfrE/jgU54JkFrZ/+c9NulIvrV8Wpc99JgkAHJie/tOJKqSDPqi1yat/erxa5y93Y3OarnWrUwAVg1DcC/arEpriGuuTdAaVvDcP9ePkAcRdmKCFi2IBCMkDFAiTU1EL5l5HUKYQsQUi9OgfHj8LrFgPfLZSH38YRcx8jgPQNdDl1ckjM9yzN5n7SQIEW6Xc7CgWH1PHj9YrOnZED3x8odauj0dlnHtBub4MGPjMyx7seC9r5x0J+o1fKQAiDgXo8+n/+fNrOoHF8pLlhfr1Ty9Q2cJw+loykQrvI69n8azubAsif3p0+ly7QnimW7o4kfoRxjwxNs4skkhO5b4D9dVUYxMMtFnX5OSaorFgZy6aNKYmYYfOtkko44VR3kxhubUV0A1w7uiJchUXxgHOLdbK1TbPC1RNuTsEaPjm2zOoPzbp0U9kAkHGozQ4rudeuElbFa3792Zrx3byLs0JyI1Naw/A5LP9evPNQcr0nH77t9J157YE+gHprQMePfGt53BfSdInPrVemzcmoj7sYB55SgffPKxDb76Ggl4G4Nx2lO9Y0GKNIWNEG2+ayrL9dJAX9slHec2Dcx8l9+b3nc+Bn5ADAXAuiIeMIuSuz5+/4N8jOtqCPbEEruL8wS0LrlnQKAd1BPN7t8BYCB2ofzLqJ5xj/uv5HPjFzIGP2j39YqbqF/Gq7Hn87Pk5bfrMFBNE0s4FTgbJ4UrjQWn+9d8nB2x+pafXpye+69H//IZHiQxef2V7sP7wD5FGZ+XY/Gs+B+ZzYD4H5nPgPzcHbKL79kn2ABjyo2cNTIrb97dvY5/f/rftF5iUt98D4I39/uNeP27/H7fdv/dZ4Hw/6Vz/3jE+7HeBlfeBAMSH3e+XebtRAoF/9lce/S0rSGcJZH37j8L12Cfmwblflnv6o3XX/rbX7fXGPjNQ1axTK1BqMrVH+92ATyvzBswZ3GpKdGajbJbFBpda4Mu+DwTAfrQt+HF5FAjM2T4G6BkQZ+c2mNbULZuamvyL9x555BG/7XMgeGdgnW0TgIZNEd+UIE0d39Qcv/SlL8nlcv0weGfXcnv7YlatLRVX9T0U51orr8mdka67tmzRwn33KrK0DDs9lvpj8eElKGUrzp0oujgN9OEVyLNA+gLHnQfnftwdnv/so+ZAoHwFjvNhwDkDbDqee1Z9hw4Dk85qBoUipDZQhUpXWr5LMZlZqEUkaOhSuZpOHCPSOqzsdSjyrMdONTsXICFGHkAPtGUI6LPmHdmCWepY24uvqBeg1l1Sqti99yh0FYpkQBH+if45lCV4mySMWQR6ezpU/d7bQDA3sMgjqL1tmyIKUWRDpYXIH9aqgKg0Pw7UTAZPnlX5my/TtnhVunm9ErbvABRD98RgoPExTZw9o6ojR9WOtefi9RuxDEvQ9M0barxZo1GLBrJtBEHpFDcwjduliDzsY5MSNdVQr9bH/16RYyNK3LRFYTt3ypGVw/kjgWoAe4gi+hUJUJiau4GK2eEjunDsMBatJSrafydtAbah2HXOAQIZOOcwGSSCQT6PQbU9qj74ssYvHCegmq/Ee/YrBPVNJ4osGJ+RtEn2ASDALna2FzUGgrchWKmGFSyTN2ejpqKyCVpPom7Vrqn2y5qoP0tQjOBcXgF5lHULnAOuSQUEjMo1Ra4y2qJEMoz8tQgXwW2CIcBIBJk8bfIM1GgE4GMS1bFowMHoTJT5EtaQVmxKscn0jtYCf13T+GCn4uKjFOEG4Es0UI22ew4wEpu2nk1/NwAAQABJREFUIcC54Z6LSgA6iec650KLAeeG1XnxOYX0VyoWha6Qknu55yjJWVAHNT3f6A3N1qK813aVtpv2H4u3qMJlCs7eLG/EEuCpCAXPoRA41Kju6isKGe5WojuT75MB+ZrVhipiWEyKMrB7C0su4rDJZLEtVAgFCyRoZNAIYB+yQoiqmWLedQTy+plrRi0H+9PQNBeKcgBSFqUDnPM0lKurFXtHIIoUFA5DUCbzYhvqBHzwjTTKA1g3DLgVhLpMHPPbQSiPzSG1NdxWj03idb9NWGzuKsrPasgQlNx8WK4Z+Nh8HPvXs/5gb0T+FgW59mJjmuu/B/K0o752Up62w5ocm1BY3p2KSNqoSVT8ehoOYSmHqk/OFmxTdwHjAU8EUy6ck9Qb63+Z40ANz+HBinO4U4NNQJTjzSiYYCOWUUaeLAfUQPXZFM+wCZxpu6ixm+9ih4w4X/FK+dJXY1+Yit0mATfqymTrGY02HBfurQov3Qs4R3kbatFU/SGNcz0RQHgRLpTvElDic2IDZVMsI+Wabnobe9gq7GCTuf+rYQKBNcOwbPZS7uc6AN6uqL/mPDaADsA5N9ZdgHOoSo60dig42o2KInmSWkp5i/PXLX//SPLMFs3hA17z9VMXOjTTUYUqYpNmHai+ZSxBOQ7oEbtdi+r7JlpQ6TuvtvpyuDksTAtWkn6uMyiN41D3pmrJ6qPY/lYCaOYpofg2cK4S6BMFtkSsNxMKqYeJxcC/idTBYVQTsYGsK8d6dFS1gHPOSFRLsheqKIV6C8RrPfskdamO9+kOFJ5YxLIkOV6lgGXxyLYEU9doKTTKPWhFia0BsKmTVSRRKJSVJcRqAWMDU5sjgQAoc7ppkMTQqIY8QVocQ8CbmxFDvSUjsLdzqAY45RpleBbQvwRAJYW2sLF/UFfbgdUiWdSE+k4k8G0IeR0EsGDw3Bit8XQwi3Fob8uAdKKwUGzt6VPbJHZsWenKiKNAcLwmVLQ6sWn0AHMRXxcejCrKxibS7G6xEG2lXe4dHsU6MkdLqavpgKlBtIOTyD4Nc31t0+Oqxz66f6hPaeEJKknMUgZtdhSKX6CDgHUhqgXwrO/uUgje2EXYFi9IwfKR4jdHLg2RvqpZVLhaqGujwdjEogoHWNhA+lo7WkgXinNAnUEARbA95EkwsBD2mcBJXupoAcosbpTwRpB0qhjAdhnwaQHQTzZjvsryM7hTvKGU0hwtunMH15xKoNuhfEC8ZNQKK85/oMqbl5RVjMKpK0UnTp5UbztWuuFYVken+0FEJyf1YmU3DhA8jUpjUkSSdq/cqc0lGxTJQqtxwIQurGYPXz2k906+pf6eXmUR2H54zye0Nm+T0pVFWXaqb7pXB46+pksVF5SEXcqe7Xu0JGeVogDnPE7Gz556Hbr0miouVioNAHbrxu3KR7VpAKWxV996RQ09ddjJ3aGdq3YpOzSPvAW+ADZ1YJtpaos2JreX9f+3jzkDz+hWvwJjUP+G/8Z/t+9r43NbVGfPrLY4y/YPHC8wfrfzBsbw9p297W87TmB8b5/ZWDywKO32Y9x+zR/2Gm0fO4e9hwD2j2OvfBMINhvb09XAjFlhtIWUf7N5HuU6WrDBrAes6xido22Mlxtr30WRKGYxVrf2DDEjtTJmuYzVcDcwXC5KbGsA8TOBEgxFGKOsNYwPAp51YW086S9v7ohYLEwndZ7nkEEW68VSJxMATyPILzoIa8j8CwmmqC+RBukx3kgmD1qgIzrZJho1uRxsl+M4dxvPS11T4xqLYJxj0N3ImFyMudyoxpl6XQtAXwdQnC2gW5aOxSrXHUsfTINL/QlW+yRKeoM96sdGMY7gSCHWlQUo9caSH07alH6awlrq0dU+gGjaogIsn8uSGdvRVoH3+0HUJsr5OayRZ/oGVZCIDSHq3a0otN4AaJ1GiTKRPpeWHcVMbMsBm6c4ntkwT9HWpSD5VYLddRjyY/V94+oa8KgARc1lgHOp7AM3p9ZOjw6d6dJ33r6u5gEAKsYcRanhALkxKitM1sKSWGWmGtwzpw+O9gDQ1GkcKDQKKDgLG8dcd6KKixKAYDhmCopWpPPAIcC577yHyuWgHnp4o+7bhkIckAnNinr6fXrhzW5Asre1eJFb9+5ZhdplOFBNt6rrmrVybYq2786VqyCUdvnWNVZcQs3r+9O6cu0UquWZ2gN4lpYcodrKCf3tV89glTmlnXcDqT1ALDudZQgQW3A11BHATMClo8d6deos7WXfMADSnGIBqLLzElWyMFsLS2OVlYgLQc00lqBtunQZ1wQs36MYaqelR8jlTtEiFMSKisyKFvUtyu3FSx792VeOaWwyQw/sywcOBK7LdmLHLdU3Tusw4NyR95uVn7tEH9udjCLhrF56ox5wrE3r7ijEpQArYjdgGYAYl6krFXz/8g0dOHhU992zGcW5xUoiHa0tU/rnr11VTU2kdtzlBmaKUi5AlYNxh5cETk06AJK8LI7rxl61GdXCSb9tamwC0F9uKoBWnJZiOZuaFIZSHKqB7/VildnFMGUUxdM5ICSAPxcwXkmiVi1J9oNJQ4M+nUAJ7unnUDSd7tf+PdnasztTqUBqs1jyNtQ59N3vjLEoz6lFxdMASXGop0lPPtWOMt6sli5N1Wd+M4ZyQdtkYxLuQc2NOT3z7IBOXejSysVJgHMozrlvgXPPvsJiubpLlIMi3tly5aC0Zs/qJgVI+8uSPBbzhgMievXuUSyN21sAI6exBSWNgIIlxYwBFnAPs0O5b6iyvd2jU2cGNcCi1/Aw3C/SI7mHiSosTuQexvrBvHj6rWMnJvTk96aB7Ia0765YQLgEZblovYGy4Mj1/KuDgHMdlOl0fXJ/LGnFMvYk9eSFKmVkLdSvfixZd2+8BWlO0d82dXj1IkpmxwHyinLTAeditWYlinOts/rLf6pTVWu4NmHR+tlf5RoA2EIcjLFJmwDnYLD11tv9pK9GHV3ULa7PHpuSU8JQ5ktXaSkgoitcXe0+HTs+hGoaasUjgyywdgCcxaGYlsU8DiBacaRygUH91sBnPPrqP59Sd1+77tm9UvvvLwCyYwxEmzPS7wXCQ3HviVfJNwDA+9cpLyuKa5jS8bOtSs8c18OfKgbyxBKVemvjj6oqVBlf9+jg4Su6a02cPvvJAsYEgHMAan/21+Uo0TVo9foSPfZYEfkMPu6cAG6bYXxsz2dhGhkM1ulTkzpxqkNVtYzNUM1MwqY2i8VYhfTxpWVxcrki+Jx+4+SITpxBxZp2PzRknDU8wVwfysElAK0LAEMB6cKjnCi6eoFi23Ty7HUUMBP18MfKVLY4FmiPIRRN/SGUKl8D4p2ebNYnHsoAKo3XkaOjevbFWiUzNvnYvlRt2QJkChw8w7hocMSn1wE5X39thHo0oy9+PkVb7orz5/vbB4FJn35BCxcm6pHHVmvdOsZuWDD39YzozdcO6p1Db1Gmc/TA/p3asHEpC2QMnLP+1sA5fqW1dVjfaL9+hNc8OPcRMm9+1/kc+Ek5EADnTC3hscce80+a28S4WZ+YGoLZp9gAd/41nwM/jxy4/cHvP/98NmKbf/08cmCCwerLr3n0K3/IZDGDssfWB+vxbzFBPX8Lfh7Z/wt1Dpz/9No7s/qdP5sWcwzavNypv/kDVpeUzfczv1A3av5i5nNgPgfmc2A+B/5b5sDQqE//1x9N61/fQqGHWZ2n/yRcn3xwHpz7ZSkM9ixl70BAywJeFiiznxZMs+d6+9u+tyCZqSCakqRBbabMaN9ZwM0UHW3RnC2eC9iz2j72vR0/cKyflC923sC5A9dlxzCr5m984xt+q+R169bpoYce8lseG7QXSEPgOu1vs4194okn/KCfgXNf/OIXmUx1+be1a7BrCxzf/vZxjparV/XMl7+szupKglrZuuPOLSrbfY9iioF7jDoggOkl+O2zfEFNfx6cs5ybf/28c+D2cmvn/jDgHJVKY1cva+DCRdTM2pnYH9AwCiiUaNRAMuQqW6IobOV665pUf/KYQkYH5V69SonAc0HZGVA+0cBGwYRnCEDSzgcBinmwvGt9+XV1A87ll2CTuHe7QlcZZAqwAeTkJaDsYLIfagbYbQIFMlSH3jqgjpoqudJSlHX3DoWVLMDFEZUrVEv8PklW//uH1H/6nC698QoWSkEq23SHUrfeiaoZ0BLn9o1iMXr2vKqPniDg0aMld25T5tISIKoe9dbXqKe1U6NDnI9gMIgWCheoqyxbTvpKNQXc1vzdJxUxOaGUjVsUuXU7dpmm+IUiFsHHOeCNIGxVg7ARnK1q1uhbH+jS8feVtqxUhfdht2ngHITSLEEcC8U6AAONWSOy6lecqz/wimbOHlO6K18xgBMhS1D/AkrzOJjcAKIJMzIEW8SZ7jqgtFMEvYYUU7hYvkxsUEOzCFx7FIbl5hRKZmP1pxUBTBWem4/iHOBcM5+jWpWC8lUUVq2OuAI/OIfpEBdARMWObQF9YBjPYAW2rlc1PtRD1hKQTC9SSBrKgVH5gGsoC/uG5BiuUX/jVY0NdighMVIxpqoWvxAYAVtUQBLvaAfQVrmGuq4oAeugxIKlqI8VQCEAKFx5WSED1ShbAc4V7oPYQunMroOAu8ZuoGb2pmZbgP9QtwqKy1JMwWoU5zYAzgHmca8dc53Y1Tapp/qqQvu7OX6WQvLSNdbaotbGHmAu4JzCpQqLc1OWkkgd2lbBBI6YCwo29b7pAc0NtwN+VZDGVoJIqMoklmCHCnSF6hwRU8oK1zILfFZ3Q50A10FRKMOgnhjCNjOAc8GUNcdoszwt11DlQ4knAhWRIpQK0zLlRXFwsB0VtNZrSkRBJd61hrxZTxqtDI6jVIe9XfMxDTWdUhwKOZH5d8mZs0fesFwgGgKEPoLKA8c113wAOGCUtAFdpmzRRH+LeurfAVLwoWK3HnBum3yhqeQ5kBUqP/4yZcAbSmDeEQLOXeUaAHbwocAXl42lb1oREGUOsTMsa9ljFmBkqvW8xqoPA8Y5FFm4DiB2naYdyTjfkc8AJ9MtJzQKWBeNyl3YIiDHVJQDh5oB5w7K09tKXpcqLAegLt7AOQvKoXw3elnTjW9rANtdZyiwWN5atllGvqYTwIwkjT3yDVxV/03saAkOJ+TkoeoDINpVr1HaBWc84FzBncCGpQC3qFVyLWH0uQi/UEYB5+ZQcZuq00RfIzBhFwDqrCJTTU2Ocm2qYVgiOgBmfJP1muminjeWo86SggXyGoUA+tHI8EbNkmPMth3zg48UMsUXblFwHPk5huIc4NwI9qBJKK8kFGNtnICSIFahpnTnGEf5r7ZC5b2juumgPqB0twA4IR9wLj7sFjhnFqWNvE93tQLODWkpPnllACfR2DRblTdVtDZAlvqhXhS2J5izjJYLuMQNoJnKtYMHcI88ACizqqF9uAj0Og7stxjFuVLaEIzu/ZaExHd1HUvSq9TV6ZlplXG9qZT1FgCXys5uFPSilQyFFgvAFYLqbagnwkq7PGHYgkH4RXG+dMrHLOBXa0+3upHgSkW1LgOrSgdwaX13m9qB32Yo/wYfaHRaJTn5ygYumsM6sh3grYtxXR4KVosB8DIMgCXdw/wwla6WwV51o4wZwlgwPzFThUA5SSgHhloUF0i3l2u9YZaPBKeDx2ZVBBi4IBWoh4lbRn8apj7UzvarCnBuDhWm/KwCLKPjVTcMONfZim1mkHJJYxhjMIv/c+Npo269yUalxxCQRv27B6W3cq5llJ9FQEmZAHQVV0/r0NGDyljsph/YqTHA1ukJFPUQdUhDBbDi3Ae6dvOyMopQa8pLAXoBIh2YIMheqBzUBSMBk0xd1Po0D+2nD+XH1JhkLc9fKlc8qoYEtCeDp9U23qF3LxzWsVMf+NXfMrLTUHT6uNbnbwROAkT+ATj3+vuv6Wr5BSCERO3acY8W5q4EkEygFEyrfaZR71x+Q+WXKlEQzNTWTTtQOSzExrBPrx58AWXARuzhNmn7qt0AXdmUDvpP+lBTxXNSd+xlfb+9bPz6s77sGDbGtgUppixvi05sDG+K5zaeNlV1O76N2e13U28OgHWB8bkBcvZZALyzsbdBX6bQbscKvH40LvizXLeprR0e7QGc61Y+EO66FMo24BwjDIrzrNpmxlU1ShkeHKdeAGAlpMvFGCSDwgP34O+WMLBRA9blFwYA57yovQHKb4xJw+bUoASqBHBN7cSQLgOLD1BX8uOTVRgerQHAritY/Y4B2qUwTkmjbEYA3kG4+q0DnYyJZmlRwllYkMw5wxg/WH3rmJpAFTRF2YCosEBqamsCvgTMBECj19H00Ihy07Mox1hP83c7ypLtKDpGUkeX0M5n0kdGUB6naTebAe2bhgawTB3ARjGY9MUpn/KdQTsUAbTqRM1yiK6X2qUrXQDRgwPApskqBpxLxo/Q1LoQelMbINwlVFSnAIEM8kuin2tF1bEO0M8JKJ4dQzvJuCYCcC7EO6Zp0jLJffYaMBgZqlRU9n3U5WrazBbAqjwWIiwDJk6jfIbSfc0CX1V1z+iZ03U6W9NNm+lVDAsLwqEWE4F2l5Tk6K6N6Si8hai7B/vmahR663rUjtIo2awZymROWrQ2r3XrDmCS9IwgoFiPnvzue7QiQ3rowY3afVc29ZMEkZ6ebp9eeaNHL715HFW3LO3du1RJ8eF6DsW56tpWFlilate+bL+iWAiL763KXAece+F7Hiw9z6HWlqPd9+UqHZW7mhvjgHOn/HVi9/alKKNhB8z5nSE0SKSZAZ/GyMTWDo+uVQ2oqqZLbe1AVUNTALe0zQnZWo7C3rYNsX5lrd7WcVTLelH0QuGwDyVSxr9THCMluxgL5wJtXBcF7ONQRfmU/vQvPtDUDPai96Katx87ySzaHPKzoXlC773TrPcPt6BWtlgP7cG5LXEaEKtKV6u6gGxLtQsVN7MqNcU5j8enK+Vzev2NSr3FuHX/ri36zGcWKQFlrc6Oaf3z31eq9ib2tduzdS+2qNluMhFZP1OApeHDltTpB+wqbwyoumZILa2DqObRf80AbkdPaf3KZG3Z6AYOilJj5zRWsn3YZvapr3MEsJ+FJj7aabiAPTvSdcd6oGzy7QL5/dT3a2hj+lEEdGNLmQlEGMJ4APXE+mB956kxVOKCgMlQnPtEtGBdEcvo0tnLc1qxIlm/9oVIgDzKF33RHGBVTeUcNqbDOn2xWytQSfvNz6fJ5QoGMgOIfHVSjSwE2Q+AuGdXKipjKIpRvZm54D+eB6gf45MoQrYHU/aGeHdyP/sB46ZxiAcwpY4vLE3Uxg1pKkYFsa/fo+uVw6qqBmptQ7kXKHeGOYAoFsIsXZLDeVCpdYVgy4v967NTam4eBZyL0yf24m6BEmEwoNgY/e3Lbw7oO893sDglXY/cF0dag7jeNn3/pSrK+EJ96mPp2r4ZdVrowGmKWmMbqoIvNQO20Q7x7PXoA3FYsgLOtczqL77G4qCuaG1Zn6JffTRK+eRNiIGBDEioPpqi72+jjNbU9QEkDqipZUCD/eYMyLNQZAygXYHu2pyKuBEqlShHmjVxQ2OnmppZoDFIn+tDCAnofM0KQLANgHbF4bqE5enXAOf6GefsApzbc28uwNmtOb7hvlvg3BPfOICjRyJKfyvlzolCcXFGJ871+MG5hx7NA0LDMpoxrz2pVNX49PxLBs6Va+u6eBTnXHIB6c2g6vhnf31Bl6vqtW5TqT75yTIVuA2cY9EVsLGP8Z7XFDhpq7pRSKytHVN5VTeAaY+G+iY1PQrwT/uc70qi/OViJxvtr0fXqwcAKjuZt+rUAG3X7CROIpEpQLqxwGxZKlwQBXAIOPdcp85drNHqZako5xUC4JnaHfWKe3jwoEevohQ3NdWlh1EUXLkiWh8cB5x7Gctk4PoH703X5s0oEQLO8birAexdX39hVK+/PuoH5770BRwMtsRgE+8DjKNOPPMKanrxKD8u05q1qVgUO1n42aPXXz2gdw4eBpxzAc7t1saNK38AzlkdtfvDmNjBGJ10cpqP9JoH5z5S9s3vPJ8D/34OBMA5A+W++tWv+ifDbUAczANTUJA1oExSzL/mc+DnkAP2oGb2O1bm7OHtZ3kQ++kuk5HM/Os/PQdsPqCXFSp/9JUZPfEak2gsPv888sV/95X/7yH8P/0i5k/wC5MDiCKwKgwZ868ywD47p4X5Tv3V74Tqnp08zH7UEeMvTCrnL2Q+B+ZzYD4H5nNgPgd+OXPAxmy/+XtTevWkTUtK3/tKmD6x79ak2i9niv57XfXtEI79buoTFiizZyyzI7a3BcXsM/vOXhY8C7xsH3vb85jNCQT+tgDa7VbF9pz2o0G0wDF+9Kcdw1720/azazHluG9+85uqrq72K8g98MADKigo8AfpAue167Tf7RnRlPG+/vWv+7ffvHmzvvCFLzDB7vIf045txw0c338u9m0tL9f3/u/fV1dNpbKwIlu5br2W7tujxLIFwBiE4FA/8bKKY86JvRMqNk4C7P59f3C9gWfRwHHnFef82TP/339wDgTKV+CwHwqcIzjmRdnG7ERnu7pR6WrRaHOd+usBV/pH5HIXK2XfA+rHUvXmyeOaBWAqWlSm9I3rAaMI4mOB5WOuz18zUT1yECSb7R9VJyvk2995GwuqNKXuRUFuPYpzcbFM7gPRMMnv/2cPbIBqcy0dqj94SF3UYVdKqtK236OQBaVANnGAcwQDbBrRX8dG1H/uos699gosmEdl69fLdc82tgOWAdTwjrGYCiuqG8dPa3hkSou27VTGHUsAtAB+CH5PtJE+bE2nGjo0fqMeeGhO0YsWKnn7nQAZwDDPf5986FP6mg1K3L5LQVmZ/vP7Vb9MvcGUymhDZms7NXb4tC6+/67ii1wq3r1NMcuBh2jb5n6QFxa+8M9+Ah94Cey3vPGadP6kklz5Ctu+W8HAiL7YSCCbGf9xowiGO7B38/TVqa/hBG1QL2peWAZmLte0H5wjuEQaxtuva6zxLDagk4rMLZAD68ve5l5NovKZglJgpGsh+ZxLPDeBYBSTJRatIa8gojQ31qmJ9nO0VbUKQjUqNMmU5uy+5GkmLBlFBIKEKH4FA2b1GRjX16S4WOy2cpawHekLMmU/oOjRFg0ClQ33VCsR5Yt4YBJHKLDe1JC6ykln/w3Fp7qw/t2LwtsSGlUUwoDVfEM3gLLe0mwH8B7BzqDoLMW6VgLOrSUvgJ64177ZDo311qjvJlatqP8l5ediHQw419aq9uZ+1ALzlA7IFIp9rc/H9VjQLJT+CfIqDHtP5yhqLp01GuipgfWaVVRiLjAOqnZs7/d5cxJBJA3Qk0B8tepqrOdzrFqLFyC4VwLMlQ48iHLSmIFzlepvblQw7Xl8ISAgIKV3dkZD7TfU0wI0GBsOsLUWxcMNMD0o/M0Bzg22a6z5FOAcinME16LcmxWSew9gYB5QmQW2qGu9RzTb+IYmR8cV4saWOG2rJoAUe+oPA+N5yZPVKPxtwPY3hc7OkFT0HG1fQAjvWK/mus5ptvuif1zlTAFuQzEwKCYDuM1U4YDG6HNNiWOq/YJGsGqNRp0xusDAuQ0AYomoVVH7sKucaUHRBnAuMgJwbsl+5G9WaWaEdBvc2NOisHhU7LLvv6U4R2TTEcQ9HL6kce7hCCBccHAy0N5qheatxIo24xY4NwsY2As413AVZQ+AQxTMIlH78fXWo3LYCeQKOFd0Jz/LNAWoOQfgEU0KgygPjplJbKGBK/svarC7CYiO7+IyFJEF3BbN/Qux+02ZttZmuh6475g6m1GcC4tXEvBeaJKbbQDZrcGYrMOK97RG25sog8WKLcRaOgHgibJrVq0jqNAmp6cCRC5UUHIuaopxnG9EGq7l2m+gODeumyBsDtTHCoATXGmFgHORfoUbxJTVADxytrMJEG9Ey1G1LI1PVSg2zeOkxUCu6v529QAvxmCb6yYYm4/iXBJ1zmAZSjn/zwHOeVWD2tRFANHxWYcWmJVpTDyQiuGeACNU3WsAYVeBdmYBXxcDMqdzjFbAuRpAuChsWM3aOpl4SzhNU/hMKHBLpB+cG7Og+Q8AobEpAuVAiB2oJiVibZqDolYoEEwddqjNE6h0AsAg3aRgFOlKUTrLBkSeIw0tnUCGgDzujBxUqpKUTr56GON1ILVSS5/RA9QXHOJVJraTOUBl6QA75JiodhRbr/oB+K4B61ahSOMcmZYrOkml6WmK43qNrRsmjXUzA1i9NSkU79cCrFpDUeVrJN8auW/JyLosIM2JWLXieszrB3Pu/GCUpkhTtkOJpw0FuXLsLAcBBPMB59KBpq5fO6+3jxxSammeFm3ZikIWUDPCjWVAgRlAX1dOHdF1AMkcFKFSUc08de4s40lp7YpNWlqyQvEO7GwJjIcCNM5C5c5SryJIX1JkLHa9KD8FT2CT2a9LWBIfB7praW5HCJFyR76vWr5Kdy7dqmLa1xCUuAZRD33rg7d08fIZ4Ipw3bNrlxa6ViiGNtrAuTYgz8PnD6jySpXS4nN09+ZdKs4rBtDo0QuvP6s22uE7N28BnNuFhSh9LveBpFOP+Uldtpf1/fYKjDX9f/wU/wX2NyDOFsA0N9MO/CCekgQAkAxQZZ/bZzaWHsWG1MA6+93OaWNr+2mQnI3v7bvBwcEffmYiGm632//cYJdl+9nL9rFzB8YuP83193GfD1DPGrFCXoAS5QYsSJOI/XgYC/RRr2pHe9U0DQiO4pIrIlMlWKmnco9o7m6VJNp9U067Sd06i4JnF31CAWDu5th0ZTOWt5wd55ZWsZDgAlano3MGZqZoAeDc8NikLrQAeSPblA+Mmge4Fm39BvXDoE5/nlDInYCkEZT3OepMVWeLmll4EEp+ZgP5RdLftvTQr431aRwCyEdeeABt86hvJXGZKBI6UaRDFRJwLpaxwmJXoZJCTfttVgP06ZUAcz1YbgcD0aVh8ZwFwJcJEIdOJjg52DzleZTrb6KtudLeioJsH3BdPBapmdSpyB+Ccx3U9avN1XRfpi5Jm5iUBhgLwMS4JYK8K01CCAV4CMwfcI4xCvkySZ30jxeQioqmDs5Q3yv7RlSL3XEKQPlClCUzUO2Loi0InQ5SjydYh1v6dQHQyDcKcDeOamUDbdC1dsWERmvv9gJtvSsGmAtFVxrYdgCsxtYJ1dZPYC/Zr5GBQa0qS9eD+3JVujRSb5326Dvffx++q18ff2Czdm3LVlIseYi6fU+nV6+90a+XDpxWCfvsvXeR34bRwLmr1xq1akWK9u93q6AMpVr6aKs6l1FAM3Du+o3LWCHmau/+TMA5p6qrxvQ3Xz2pGUDCffcs1v77Cuk3OA9qV07seemkGRMEkX6HhgDounqm1dIxoc6uKV28Oo3CHdBMtAflqSRtXh+tFNTGDATq7ZpUa9cw6mbDulLdj81mmJYtd+vj98f5LU6rAfb+5E+OYXuao4/vLwLmC2Ohxy3FuYbmccC5Fr0HOFeQh+LcHhQNAedefP26Llxt17KVC7UPSKy4mBEgj6Ie+pZz52b18iuXdeLEKUC1bfr0ry7ERhVwrh1w7qtVAERR2rcjC3AOZTsXKl4oGnvp45zUBx9qXrNQ3MNDXtI1p3bgv26u/3LFNBDWDZyfpvTQ/tXacBfqwHFODQIHdbfOqIfrbG/B/hY1uLqWGcAjpx78WKZf5az8OpDQs1Uozg2gqFek3btQU02zBRDjamoM1lNPjnKvgrQEd6lf+yS2ytSPf32qX8fPj/sVwT732wnYgpI+boEXAPN6xZyeerpdVyoHtWF1sr5g4FxesI4d476+AjjXXAm8BTh3D31mjoFzjDdYBILELH0RCq1zoYCAEZqkrPb2mQLdhDpaJ9XSMIX6mhk+D2Gdmar7H3ADfYUDPJJGQLTOtim1NU+q4tqoquvHlJYSo888lqF1ayN19uKYnn5+TK3tY7rvrgQ9vPsH4BwLGVi7o5cP9KC610zdBoK7H5XGhcFYtXbp6e9fA2YrALrK1gN7w7AXZXv697omFNleaNDJ0yMqdmXr0YcS/eBcYzPg3N9VoGgZ44faPoVqYH4+gJ41sZRtm5aBnbT1fYzJuO4egNumcXVw3fV1k8yFjAPWhWvb5jTdvR2wODfIv13/oG3H2KFhBtiT9qumT27Auj335GrX3Snc+zn9wxOnsRQf0M57llPPcBQ0cI6x4lAv4NwHM/qXb72tzIwk6s1SymokyowzevcY7Vn8qB58OF/LVwEWJzAK5FrLr0tPf29Wh49d1a6Ncfr8r+WzD+0Mef0nXzkFFFqntZsX6pFHlgDhhbEGxvoi6i/tn0HHNqawPtxA1u5Bximdk+ppm1DjDcD68n6N0r6tXenmOgGoFwB/k6e9fR51dVlejKmphvpaOc0iBUDAPW7ddTdj2tlgPfNCt85dqkUxMQ3FSRdgWxj3h7aa8cGBNwHngOempgYB5+K0YnkkaopYtb5SjxphCO1VjrZvp11LcbJIivPhmPXiM4N64+CoYlmw8jtfApzbHKPuTp/efYs68f2XOT5l5VPLtQo7XVhMINwOvfHqQR1647DceW7tv3cPEOcagEoKP22vf0TJ84EfnCMfbo0I+PhnfM2Dcz9jxs3vNp8DHyYHAuCcy+XyT0Z/mH3mt5nPgf+MHLAVUm1tPNzwQGfqBrGskopgNZqtlAo8pP3Hnvejdk//sVfzv+rR7KGmEanoj//mlC6zIiGFVUW//ZkQfflLt1aE/a+a7vl0/fgcsGFid48PmXbsWv/Fo4x4h35jf7C+9PkQpSbO18kfn2vzn87nwHwOzOfAfA7M58DPJwc66KMf+60pfVBBcItJsWf+LlwP7GBB1a14yc/nIubP8jPnQCCQZT8tKGb2q2bFaopy6QAERUVF/mOXA5WZ/akFimzB0u3PWraf2TTZKwDXLcGisLS01K8+99MEyfwH4b8AnOcPTHF8U5x7/PHH/RP4piD34IMP+hXnTN0icC2BYF8AnDPFOQPtNm7c+KEU55qA7Z78wz9Sd80NZQHHrAWyWHHPLiWsWqagxDjsATFdQxnCG5MIJOFCocjC4///YGYgT+fBOX/2zP/3H5wDgfIVOOyHBed8Q4OAZeMEoAB/xoewVWz1W7O2XapQYjzWmB9/VKMEZWtPndLIlXLlZ2Yoe+M6hS4pBFqLwtYJWMdWNBncE4miBBDM8FvvqRlYjJCxsu/eqNi7UKhLS2WiH2UVi3DwctJemELLXFuHGg6/qy6CcHlYmKVt266QsoVYjgLOhaKORXTM0mb2kkMV13T51dc01t6hogULVXj/PmBV6iCBZC8KCoMfnNKNC1fkDY9R6fadSrZrjEKxKxhlAiwTvaOTmmvq0uTJSxqorddUdpay7tsDkJWoluefV/eNKqUUL1TOrn0KpY1zRoXB4bAvqixIW3D9wEtYxo6fvqJLh95WCB5SpRvuUNz6tQpKT0YBDIiQ9BF8QBGAto+fc6jZtb7+mpxnT/oVuMK271Xw0tWAdjHYnQFGoWgXAQnh8AARjtZrsP49ecYaFZVK8Dx7uXyAbWaR6EN5arT1BhBZJXCCU9G5xTBp2epu6iRr+pWWhYJc/iKUuVDQUjJQCfcG+MiHupp3FEWZvkqNdVUrIhTrSKzgnAmlUpgb+C5Z0yGxqFoAh2hCweMo82Fz2t91Q5HB40rILMYmEzgqBJBrBtvIgXoNEGgfB1ZJdOUpFqtWR3gJ8M2guq++rLnOq4pFgSvCvV1BKUuJ6wDOYY0603NTkwBnUHeABKSXexSVnq+QrEXYZeZzfFMKQ/2BIGdvY70SGSskFdOeZiQDPWJp1zysCMCrZKxBg2PzKEEoyDkJtmJ55HGOARa2Unavcx7uK4pa4YnxKLHlA3cWQJnYtpQ3+inCa3Qk04BjKNs1VBMAG8CWOAcQsZRyk06ZpLwMNWBlWqX+th6Cz0kok6FIl04gDVWhkbZrBIXPY8XqBCpcqpDkO0gLeQN4NdfTCHiKjS02o+GcLiZ3KfmwyX4B4AAgIx9m24+hOPceeQAMWbgTsbYdmhhoU08d4FwCinPAhI6kNcBcgGIGeQDPIZsIpDWgWZTYPN3XEEppZb9MOdOxGo1zkXf0OVZHDJwDoAOFA367rsGKQ4rguqJyy+TMWgMIlkSZsHrQrcnG8xpvuoCiFMHZpfvlSFsBXNmk2drXAUzrgAqA3nJ2ocTHvhHWj9M2DFyh3ryHuKApjcUrNqWM9K0CHnT7z+uYArgB6utrIl+90UrIXYBdoYFzNSj1MU5A3S26aDP3eyHlLYFyGiEERwDnsEwb7tDM0AXsdS9hiTaqqPhs1OZK6GMpG8HxpA1A1mdaUdQxD5a3AxfU2XiRvPEBM+QoIsONzBoAowURhynvzRcB/DoVFLtAcYW0KYncv3Hu+fXzGqa/TkQZKb6gWMGUQYUl+sFPX3eNelvrVQtk1cB9nwQoTUhxKxvrYbPqZDaZGuJT/cyELrFdGOVhDXb0C+KxiA2KUCeAbzMWZ60oWFF5lIsKW0EowA5tAbFWMEG7eooi92qa665DQfASlsh9WD/mRidjd5qKHWuoH5wbB1y4AXTWCABkQdjFyajyUEdaAJL+X/beA0yu67rzPBW6uqo655wRGhkECJIACEYwRzFIlGxLXn0Os7Z3Pf6+Wa93x2vN2mPLceyRxuMkSyPZkilRlBjFnAmAAAiACI3QOeccKof9/V+zLJg7XoOURHqWVWShu6teuPfce8+9753f+58O0qhK7bYBBbcaX54VkSI6AOSVQ/rGKIpuIIiKmXMc0u0lFq13esK6SU/rKyTtGn6lCEh1iBSP3ahQzQuQ4NmHQCLH1je1AkIUETwGYBjqB4IAVECFbn056oKkZpxHMaublI/DAINuzlONQlwdNi/L8RvoIeAeLps+C0NkM1T2fHwZdTxg1sUVqyIF7DrSX5ewrcK7oLzWvTJlI4ztinSera9utFwU5sZTEevo7QRSBKZj+9pAMbAaQB710UsjmHC5UzcQFhtNLNvpRZS5ZsesljmjAgN3XjhJWrrnrLyV1Gt7r0ZZi3MC72ypIJ0nSl3HXnvZOrpOW9vWVmvd2GoHjhy0mWnUA7futCu277ba/CrAJdlTkJ/gOVoegC4AlMqUhDbhnHXOn7fn33zBBrpHrRzYqppUbwMjfax16RNbr7Krt15npcCQoeSSvXXikB16+wBQEADGDdcCAV1l5ShKRmmp89On7IWDP7C+jgFrqVtrN197K6n41gEkTNujjz9sI1ODBNf32Q2kia32NTjAXA5l4WrKsYdjE+xz8Xpa62T9ffFn/7jxP/OL5tgpgNKuri6AiX5njZ5RjKuvr7ehoSGbBZLQwzKKtwiQ0/o+A9EJmtO1gX5mtpHitNb++qkHVQTgOXP5RWXI/P1+yqt9xkix+j387Qgw2saiattd3gComgOktWwDwKiji/OWwAlXMgZb/aVWD8QIWyUO3nGVNK2TqrWHFNEHAZeHYgtW6/bbVfiJRvypBzCRVRHtPGMds6MMJretBypbmxuwRVKkHuohLTdAmVSo11DnItYGepw/0yrSlQQrdD5LsM45NzpsFxjrUpqtqyZ9tK8A8A31MT6bASzXushFCvO2BlIUAtN4mSOH+gZRMgTyLS0Atlxr+fiGEGqQQ0vz1rUwjc/1WG1BJapwRVYMsZLHGXX1gbYvSo30UwZLD2uXk+OjNo+CYSnKA2sYU5Wkm9UoRNDNhqKM0f4L7AM4W1tHu5bYOLa9wD4p6BKpSbaQVUy2k/It3oJ1i/ygxnvKyoA3ovi9jlnSu86SqpS5opWUxDWAr0WA+z4KATtkByfmbQDprGpS2reTOmixO2VPPgq8zpfX7Cal6r2l1tCKxTiPnsUKAUSNTKaATObt2NEeayglDvSJVtuBqtYLhxL2d99i/QU495kHrrdbbm5EuQtVJ5ah0+MpFJ2m7JEn3gScq7C7P7HFGmsL7fuPjtmBA+fx5cV25+3r7PKrUAJkkRGJkp7yOQCYR2I2MHzWHnqgwe68r/KH4Nx/etOirIvvvm0LKVNRUK7kIaw0iDT+IR3nQREI5yj10rI2Qb+UKhzCxE460qeejwNhnQM0qrHbbuK6GfhN80CcNXKE+X8JYPng8UX782+NOWqUP/0pQJq9pLg+F7H/8AXWCakGe+gTawCPAOdQnKNLWHdfxJ57pt9efmHQSdV63x1lVlebtOde6rUXXh/ADzXb3Xe22GXbSUVLR4CFJX1k3L79yCFEBw7bp++/2X7h57YwZ3oA22L25f901jo78+3uW2rt7vsA51pR2OQBgyTGdLOWiEWAyrBRCgBP61oYUD4zex148ekfnOEhhfN2961X2q13NVthLf6SPpWmfmkAyMWZNNBe0p54bpp0mNP2qU81ce+gwE4BXf3dd7por1lHce62W6p58AMACgW4gT6Pfe0rgHNnvLat3eWAc3l5HvvOdxftqReHLL8wab/ya+0ARn5jee6AiEcPx+0vv9ppPUB9N1/TADiHshzg3ME3UZx7FECs95R9AuW+O2+vQFVNPlxrdBoJypsqoQ7otpUQjp3Ox7Ts9L8oANbcONkSvjlpp8+ftk3tufY//ex2UrJyvUGfx20A/jHGUEt9CzDxiWeXbBkQ/dP35dvNt5TYaYCtrz8CPDiySCrhIvvk7aRqBUrzAMLFOMkjj43ZV/6+x8pKgeDur7Vdl+XY6dNz9jdf6yCbUimpc2vss58ucdIJLzEWTmOz736v346fXMYutfZTD5Xarl1uMv0l7Q//+KQNTRQyjqrtM5/KR5kMqNpxRFx/MFcIKFtCcZVfnc81vuDTraczSbrROdLrTgNsltvdd/EQwFagO6BitaNSES/xEOw7pA7+7uN9dPBR2399lT10XzvKgGn78l8fsjHmjFtu2W733NuEot4qOLegVK2Ac3/5109bfa3AuZ22vi1oh49E7LEf8FADc+Itt7bb/hvwE7Usc/FVB4+kAMcSduD4KbvvhiL7Nz/bYmua0Q+lrL/zxTfs+Nke2331VvvkQ5sA+KSYSQmZ4+RwVdYk8GoYhxbnWDyz4dQ5jd2GqeNTTy8C4HbYmpYqu/e+BmvfhionsUO9VMcwsOcgqYaffnzS3j71FipwQKSfWIegepH9w3fG7NCRC3bZ5gr71H1ttmVL0PwZcA61OYFz0ciCPXQ/4Nx2lPhORu3h7/bbBEpxd9zYgkpdLWmbc2yZdN59ffT7r43bc68tWH150P63Xy2zffuYB4B9X3wuYX/7949w/Er7zM9ebjt3cv3CgmoeBe2nHn+a9zMoTLYC0d7JQ6JX8tACjtIB51gFpVnpMV+pRqu1cqr2gf7JgnMfyGzZnbIWuDQLZMG5S7NTdqufvAV08dbR0WHf5uanUgI1Nzfb+vXrraWlhadmC7n5hcw+Fx0/PhXEH3V6+snb5P8PZ9DTEhd6U3blJ8PIJpttrnLZv/+/fPbgTSxys6+PpQWWWRw/8XzCfuOPojxZQrrWHW77o9/gia3NLCSzr6wFshbIWiBrgawFshb4yCwwSOqBe38ubCe5uVZENOHrX/LbHddyMy87RX9kbfJ+TqzAlAJZCpwp8DVBoPnxxx93ALX29na74YYbnGupRx99lJu9p51gmK6vtF9m3ww4p+MokKfv77jjDp7AvckJpGXAtksJ7umYel38U8dXME8KcirDbhSolKp13bp1DsSXqa/qoHNoe8F/UqjTzww4p2vEzHG1Xab82l9P3Xdx7C/9zn8gXeCArfcH7TLe69ett9JtqFnVc9c3HrFwb5flAMMU7NptXoJoel18zMzfOn4WnHPMk/3nx2yBi/utDn0p4JyUgWbfOWEugqo5QFweVxxxsimbP33e5s53k/4ThYgHH7IEDyOOH3vHpl95zfIInJesabbgljWWS+o5F0/8J8OoVDAuclvX0v9Ji0fwY/LpZ1DRumB5rfVWsmszEE4DUescgoqkOyWqE0RdLq+0xFxAGAOvv2KDR9+2EvxEzY6dFtgIOIc6khfVOVcxSnVSHSJaFx1EteXFl23q0NuGdpY1oHznbqxgrKX4bsIWkC2YmV+2wk1bre5KlMy8KDsRFM8hEJQD1CbgIsVFY5i0eMuDQxZvabUaUt8EgV/mn3/Z+l970wGsKrajCES6WF9ZEQATwZUoKArKNPnAUzmBfIv1Dln3S6/iE0astrTcilHhyyWlaJpIXoQgudLLBlGfzCkrxz/EbPwHT1nipedRx8JGV15rno07zF1RbT5AAi/KZU6QS0pocZT+Bp63KKlG04JhyoHWypopd8wSwEqL4wMox4xZUVG+5TcByeU02Xj/EEDGkFU2BgDZAKkC6wmioECWJhQODJMMkT504nVbGj9qSdLNlZRXWn5ZPdCdIIZCUp1SnwDBQD9qSqSo80QjpHTtQVntCBHFPssF6PGXo8zlA0qKoEw420ea1lGCRQkraW23/DVXkLpwA2kup23yxMOWGj5CsK/IAg27zE350wQpk+FpW5oYI3oXMT/9xU1gNpWYNw8BdC+ggpv0hy7AhvTKHGBav00x3xSizlS+qRWwqRibDNlk35LlF6+1Io7rKWmmndC3QuEpnRNCVYV0lmMdFuk5Zh4UfPxlKIChQOQm/WUKhSASh1OOAvMAQrk92JtPErP088FT2OW0FZf4EE3DzvmVgIaokCz0W2hskMBvhEBti5WuBZIGukkDeIUGT6JEdxAwLm755c2Ufwvpt0oJ8M1afIb2mx5F3QqwiL7mLakwX2UTindVxPcKJYdhCcDCFO3rBiL1tt8DsHaLhVAeclK18vBfUStpmCoupw+RipYgvEC+dHjAElOUdewsAN2cBUlx51N5i5tIAyuojPih4EsvqVq9NdiyEmVB+krnqyhA9povj3ug1QCYjLc0EF4U6HFluBfIcMSKS8sssPVuxhupgZd7LdH9fZvv6wBGLCZF6pUWqG6DyxMkAPw4j5of4J4nHTFfEiUYbyU+oJ32aKUAnDs+aIm5d1DQGCRIW01/vJw2Q2USFcKpkQHUDVEZbNsLsLAJ9btKVDjyAfsAFZcnLTL0DiqGKCLG+lGaC1qwfA0QaAvtB9RG34DKZViVAqRKYRLId4l+0k/q2IVhKw6mLQ8w14Wdk6jHJOcHAEzPAXeSEqt8s5WuuxFwDrgt3GnTZw7b/OgINiTVbk29eSuaAO4AH0mZmBztsYW5GZshresY7TYG7LjCefPon2Wo+gVQooqxFhkGNjlPPy3AX+0mtWF7SQ2pR93WQfrdwekpxlXCyspQgCLwWgnMGOQ+pqA5v+AW4LcA26YJpPaQyvTt+WEbAI4p9ZNOkjKW4Ec9QGkLIZQ7Z8Y5X9Rq8DHrgIPKgVBHiQ6fBOSZBbQt4r52NSpoZTkFABk8uMA5QgnWbPS9IHJDJZwLzMMGgYvOoGC2TLC5nFStxZxjMYEaC1DvHH3aRXA6iLpKIxBQEZBOKM65sVF0OW5bGtZaKz5D/WZoesy6x8YtzlquEp9ZJ6AGAimIOlY+Z8oleBsEpvJS/kXK4kA5w4C2wIRBUlbWcfxC+qiH+s8hATe0MkP7LdjavErS3ZKekjKTNM3ODvbaDPCkHxCuHDCvJMeHSh6dXOtAKQhTv0LUtwJ8Pu9KWE9oxnrxjT5SDOamUcrpO28H3nwdRcwaW7vrCkuiRCgccXMlqjgAK8def5VUe6dtw5b1tmvPTsCIE44asYfU3GtZ37bWNtMe7ANsG1kh9WeYegH8VVeW49t8Noh/eL3jNVRyTqEiVWP7tlxv9UDYHX2n7NU3XwYSKSWt5DW2ae1mJ2Xu0Ei/vXn8AOpSJ6yW9NPbNl5mDYV1qA5GSGOJUtTJt+iTC7Z17Ta7ed9NpOJba5P4p0cf+46N4hv2XXO1XX/5LVaV20Q/AvyAgMDKWHj1pXVlZj2tdUBmLaDPL/WlfQTMXbjAGAeQq62tdR6Y0fq/tbXV+WwaRdbx8XHnARoJFAiI02cC6aQmrQdqBNPNkPZTa/41KHVKvU7HuPXWW03KczqPXplyqtwq56WWVfuleA+iivbIZLeNAyNuYA7ZxTyZg8LbGVKJDwj6gjKqRl2xprDMyhg3AnRh3xwlJreHvspc56YfDqJQd2AG1VvmjULKsoE5u4qUrSTDtWWU88ZmUSuCPqoAtF3DOKxhbl/BZ51gHAwBvOYFAFhR0y1B7U1qhFJH1LpK6Yj92KIgF3UkPh8krerp2RGbZXQWo1RXgV8LMQYGAeLHUMZLsC7yMg6b6XvVRVVAOaQ7RuUvAQBYSzpoqUFq/TS+NE363jGA16iVoLAo4LaCtQLINPO+IHw39fVaCdDaMuPwAoDQ6dkJ1JW0xnNZLRBsVbDY8tguBBw1yDibwDc0QlldxhyXz7iaA9a+MDlFth9gdebPCtZgRSgcBVkjavSFGYvoPZGa2W1N2EMqrZ2hGKmlF20BP1Fawj7AecX4z+XeZXunY8w6UCKO8WBFS0WxbQbWXuwL2xuvjAIeuUi/SQrFnQHWjqRbp7yFfilK5drkgsdeeHXSzp/tQ5HSa/cDkW1HMe6FNwDnvvEkCngC5260m29rtoISlN+w/TTpGh/7/qQ98r0XbMOmYrvvwW22aV2tvfnqvL3wLOvaqZBt2dhsl1+BWm4NfmgpZW+9AQh2IG2h5S773KfrAecqHHCu8zyKc3/8moWWZh3FuU9+EqW2KkBaFzQaanyxsNcmx1FjPouSMSlN84vzSR3OOgYlrOPvLNibb6FMiX/fdzVKee0BywPU8qPKHAB2zkFFLQRhd+z0lD320oy1tFbZpx6osJ2XeVC+ithvfeEgQFEt4FwLqVoB2oDupBrW3RsHduohVWu/tQLu34ea3caNHkCzBfveUyM2POEhPWyF7diWa5XluYZYH+WI22sHj1kv6ds//9B+Hhjb6ijnDQ9E7Ut/cgbQuNDuUqrW+3KtrpWxqAdNGGPhsBvFsZAN9gPN43OKCwNcx/tRQPWgqBe2gwf6SAU+b9detdHWo5QVAnyT8mZhgDrSN8Ko651ku9ffmsF3huyB+5qsuTkPUCtpX/uHLq7JZ1BWa0DFjOuMKh6qcaE02ONCcW4eUAxwjmc7Pkuq1opKLyBi2L77RC9KhD209wbbuqWC1PZArHMJ0oau2FMvDdvcStJuv64RJfkKFOc8dgglwW8/umRd3Sfs3jvWA85VWxOKczk8rMIoZU2xep+jfyDCvQ3WRaT7DObnAH+yXgTEmkOd7alngXDxJ5vbUXS7toV9U4xV0g2jpMv0C6wFmMoDoq8eSAJtxWmvoN2Iytg7Z6L2376DmtnYjN2zv8QefBec41LGgbu+870x+5tvXOAaqwn1uHq76gofCpNhUs6OY5+w1VUl7K47SZ1clm+LK17skWBuGUWN0W1Xba8EnCuwy3ZxD2TQ7E//+ATpZUnVugeg7tPF1tTMvTa1IXNpnGuAWQDGI0cngDXjpM1lbZVHIVhbDvSH7M0D8/j9hG1cVwOsVWDFZax/wWIDQZT5WJ9HwznE1mP20st9zM8zdvONtXbv7RvsQmfa/stfHLTR6XEyPl1m99JP6wDEYMtJfQoQ+mrEvvK333cU5+65+yrbvrWQNL8xe+zJfuvoHLMa1C2v2dNgba3AwFw/Hj0BxPcGfX+oyz57ixTnGpkH8Wqsk377i6/asTOkaiVF6UMPbQAu9q8C0GpF6pnE/yyQUlbjdWpuCTVr2oc1pJ+Hh8b73PbSS9OMm37SsNaSylhZAZa5jkuyDSrbAc6B0uBwT8JeeXHMenhw58b9zXbrnWuxQT7g3KAdPHjGtm0CGLx/g23ZjBot601Bk089lSBVa8Ii4SnasMSu2JVP+wCKPj3FmDtjzTX0hWsabc36Ultk3dHJOZ59FoXJzrz/aFIAAEAASURBVKi11xbZb/zbEtu3J8j1TspeIKb5V197mLFcaZ/6qcvxc0XYPwo4N2DPPv0De56H0da0rrH77rvX9u7Zg7Kf+jCzjNaSPBjA1QZzKF1ab775oK8sOPdBLZfdL2uBS7BAFpy7BCNlN/lQLKDgSGdnp/3e7/0egYkT3FgscsC59vZVeE7phOvq6pwLPSkj6O1hYefMMh+ohD/K1PSBTvix3IlrbxaMKdv+QNh5cmv/Grf92V9wc6OBtsu+PpYW4L6gnTmXst//csy+/UrS2ptc9h9/OZenZbggYtGefWUtkLVA1gJZC2QtkLXAR2OBvuG03fjTYesnBUE9T9h+7S9z7forsuDcR9MaH/ysCn4p6CWluWeffda5xlJwbN++fU6gS59JvU032fVQkn5m3gqE6Xe9FBzTA03XX3+9s68CbvouEzD7l4JmKofemQeftK/SQUllXApyGXBOinMZcE7bZ4KJOr7qkQHntL3AuV/6pV9yHq7KlDNT5kx5BM6dO3Pa/uiLvwNkMGFbCTivCUWsgtRMxQSBPTUE69lmaWTI6h/8tNXcTIrHrOLcB+9w2T0/sAUy4ylzgEsB55IoxZ3/7qMW60QhCwDBy834KFDI8tSs5RKwaEJpq+DWO1C2qkGhasgW3zhgc6dPoTASNW9VqflJraf4k2QaPAAkVXv2WrAOmGdi2kJHjtno0YM2vUR6LgKpeaQ09DBuwvIRBYVWu56Ur+1rAdrcNnXqqHW//obFAaQE8uTUN5m/eZ0Vr1vLz2bAFqAuRXoW5mzl9Fmbe+UNi3T2AtbkWpK0YBB/FuacOcsRK2Dfkn3XoMhVZeOdHTZ59iQB1rgFUZ8hru7AW/GJGQeEyAPSK7p+H2BTocXOd9rswcM20dsH0IC/Ki0lXWWRJbBJhECyl2Buy87LrbClDW4nYsuUY/Lw2xYfnTAvwRd/JSkOAZoWCKp7pcS0eZsVUUcPAemlgwdt5dmnULCYslRVs6VIS+jFTnWX7bSCBnyIIvrAZK4kwNHsEYuOvwP0AxzkKjZfoBS/x0UvaQ8jQAIR4LIC1H7yGoAL/S02PkwQPTRoZagwVDSi3uZfBefSqSAAF35yAXBl6FlbmX4HpQ0PUFIdSiSVBNqxHWopEZffkgBmfmC6PCAVD+oFODsLjb9lK1MngQaBnv2kdwR4yUEpwkivFVkhyIisQwEqbXmtu4DuAORCkzZ7+vvmQnEsgK/3lq4hbWQZaaKisIMLcElRy0fRLR8FPW9u2BLL3RYPT1LHHHiAQu7HKaJI2iwegp0CFsjJQy1oU5vl1lYAOY6iQgcQWdBqhfU7ANIagAYBlLkNl3KRNnB52Bb6jtNH37EA0E9+FcpMhZVkUiO4TZPHBOsFqwC2Wi2XtH4+AARFvsJTqMr1HkKNag51QZKGEux3MQ48qOclUPWJEB8vLG2z4ubN5gGeSpFjK0Ka2vnBw0AYIRSyKiyRC6hGustEdIYA3DRYC4BUMA81lZQDUer8HgLlflTTfHRh19IQ/bibYZNrng13o5h4ky1Njtlk/+tWUuwiRe+2VaU+tnfAOVKYphY6LDxy0GZRr3Ex1gpRglT9EqgWxWhJKYn4/W4LCNAr2ACQSFrSMHUA0FseQoUvhBIQQVAf6m9J4NgI0FUCFVmkobhXWmn+9bcC67UD2/Vaqu9J9jmLchnKdUVrzRNU/0vQVxYJdM9yrkXSTJGWPCJlN+oDYEfnB9YDNvFig1SvTQOfJTy1KM7tAibAFgsAa6ODgInANqTm9ZfRRwE3GZXmw/bJuT6b6z5sC9NnqM0sanD4imADilxlFiMFaoq0f8g7AufU0n4EPllruBkPK5O9QKGnzBMepk8RQKSsOqaLYH5ifsyiITTtKjdZSds+UjYDfkY7be7CUZsfA6YFagkWFdPmpaRYpi8BjLoWZ4BTACOa1tksKnMDjI/BuZAzRoKBEsA3ApT0Jam0jcxNAs6RmrO0wtYB7owR/D0+OWMTkAoF+IFSUhYWcaw81h453LTyAJvk5+aRNaPESSnpodF6gO6Pzg8BYk2gApcPrFVI/YEUgSBDpAdM0vbF+LgWAOZ6zl8MpDZDkPhCeNaGUJyKo54WAFbLBzLJhb4VkBZDcsUDUFZRjJpLnoLHpI8F0OtcXgAyWkCBJgGUghYoPnMOCG85RpprZJLcjPMKoKEAoGMcJbRFlOXIXmkb8VW1QEMroTnrHx2w4ZlZ+hhAINuW+OkHUVI4okYUdPksH7isJL+QcQ7EwnknUOY7Bzg3tTSDH0SVDvBIqngUwVaAeAUglfgCthnwsNVfYH7WiTHqN4LKVzcA9xT1F8xWyDvAyPJgbzf3+nPYrhJ10lLOlcSnTQHrDAMTLgDvLixMAQJ026kTx62UVOHrd+xAaa8UCM5n7WW1VhF320n88dlzHagWreMhkGttFlWXIycO24VuxiX+vhTfX4LtfMBQkUXSkMb9VgvIvX3bFgvQ9493H7fnD79AO5ldvRk1uE37rYw0sP2k2n78ucdQARomlWCb7b5yj61BQUyqx8e7jturR1+1GSAcbVsO8IQpbD5JW2LX+HzMNqEYun/vflvTtNZmGEOPPvaIjU0P2e59e+06wLkKXyP9wwUciCqQ42lwLry0Xs2sXVc/Wf0s8/ul/pSC87lz57DhgqMeJzXpUCjkqEQr9apSuepvPUijmEoDMLwgu0GAdr1aWloccE7bav0tcE4Pt2jtvX//fiARAFVembJqzXLxGt358hL+ETjXDzj3faDgcdRW1wCV7ihvcQCKw+PdPFA9y5gQVFpmRfwM0t9zKA+PAhH7cQFl51k5UAQIhI0AuxwAdu5emQX2cTkQHFyEA8eEQvheAPGi3HxrBhStRXm3APhUaMkgk0MfdV9cQe0Upx9kzIH2Gqw+c0ucI+PLyXxUgY8pRH1tHqC+CzivH2g5DliXj5oXjtVmgeemmVdjMVLfki65nDmlsKgM9TJUsOSjmYdqAeXXNbbh6902MDFsQyhOJgJulOgEBQYsCORD1ZwxnYevrAPmq3cHSPPusW7G00nSvc7gl/wsgPKZm/OZF5ltAWRjnD8KhJFDuulS2wTomwdUiNiWDaFUMDy/gLoekDuAEaKX7M88QL0iqK158DkNzHsb8gKM+xwbpbwXeKq9D+Az6cXv5vB5oshG3hm3N984hcIl6U3zyq0yj1S1pD2OTS4yb7uttgKoDDWnZWQve0e6bAXAsIiHE/z4w5mQj3sIKGV6o7Z7R53dBLTT0hi0l18FiPrWM+ZNTJMG9Vq79mbAOVKPCpybQKXuyScm7fEnnrEN6/NQfNqG+lorak8pe/n5YTt6sBe/mmPVpJDMr+D6FrBoYqzUuvpYL0W67KcfqrM77kVpmfSKvZ0r9p+/9CprsRnSwW60+1CvK61l3cQcKnXX5aW0nT09a88+xYMiwC/5xcylAMJx5ofx0SnGSwpYtNC2X16HPx+3vu4pi5CqtpAU4FIRXVpZtJHxCR4kKbA9u1sZI0UAuC6uU0P2+188zPxfCxjXbHfelkt5AeeY+/sGkvbCC732yss9KFRusbvuqCJFpNfGp1P2gxcX7TlgxBSqnTUMtcpylIRjBYBfpP0d7wc6e8s+++A19j//G+pRxWcAY3/xpePW21Vgt91AKtN7AoC9buZx+hP/z80k7ciRUTt8qBOAJ4ICFvNTLmsOIOuxqaitoOC8oaXWNq+ptgXU8870AdJR90LNO4CpERQLx0iFGWURdO3eMrvxeh6WyfcCUibtGw+fo89P2z23t9itN9UBxwG8M+P395p985tTdvZ8EvDYi0JeGepxuagBoqr3ygSA2jH6I3atrLBy/GgMtb+x0ZR1DaWZU1y2n5Sqv/jzpfgiNwqDMfv+4xPWP3Dabru5nXejNdWjJPpu/RgarF9I1Xt82p55ZshGJ2KsdQHiyFaWTLESIWX79BRqqVUFtmtHlRWgfHfuHMD+1DIANbA6ArgRwPOJKRQUVwrpm+V2/71B27w1xw4dW7Fvfo85d2LW7qDe995cSAyadSjOIg5E9f2nxhwblJXVkG64lfSupHoXxHkwYi+9NmnDI+eAl9OshVgnJQttepax3LuIql2h7bkMuOpTebZ1Z5TUuWZ//mdcaw37bd/uelKZVqBsh+K+0u3STxOJAOBj3L75LRR6J2edh1r8+CP53llgVphn1iuNpBivBeZEiXZkEMh2gHYCnAv4AQODtGHKlvBFWzehpncj226vIB16yv7ybw/ZJHD/TTdfxkORzVbTQP3whTNsf/jAiv3d333fqrlOvOP2vXb55UWsTdKkmp2nfl1Ae/NWycNTjfWs7/Bdo1N51jFUaKMzA/bQ/kL7+c/WMQ8KnDP74h++YCeAZwW5P3DfRoDRAOsdOigvPZgTZ609Qvs/92yfdVzosSSq2oUlrLdd5bbIcYeH8CmFOcB79QCaATtzAaXExVmguVweoECnEyXj2Yk40G2YNXgKMLPNdl6JDyYN88OPdtuhAyepe609eM9m5sJCwDn8BuDcs8/G7YlnuI5YGbH776lmDHMNiA86djJkzzx7lpTFY6zfAqgpci3CwwazC0D3Q7moA7qtpTLH/t0v59u+vQGbxme9/GLC/vobD9PmOXbjzeusfWMR7bHogHMHXn/Njr51FPh2gz34wIPcu7rmXXAuznUEKuiUnytw5lUuI7HJqmUc87zvf7Lg3Ps2WXaHrAUu3QIC51588SVramq0L3/5y5e+Y3bLrAV+AhZYWVlxAii/8iv/ixPg0YWIALq2tjYm7V3I2l7uXMjp4k2S4XpCSk9HZZ54en9F+lGmpvd3po/z1lx/8sRRwu745ShPiJl9cofH/ua/cZOIi9/s6+Nrgckp0rX+fdz+49/ErYDA/E/d5rX/89dyrKos2y8+vr0iW/OsBbIWyFoga4GP2gLd/Wnb9VDIuAdu7UUu+/pX/XY5irDv3uv6qIuXPf/7sICU2nRt1cdNcQXUFFTUQ0i68dpNoFFBMgFtmWCYfuqlzxQwzAA9+lzBNr0F0elhp8y1V2aff65YCsJp+0zqV20ncE5l+qu/+isHiNvDU7if/OQnHXBO51a5M8fXTwXvBMxpewUHM4pzzc3NThl1zPfWQeDcWcC53//d37EF0r5tRbFnPcEnUB1ABNI4cmPZxbm4V2qbPvMzVr3najICKiTFDV3so1embhk7ZBTn9LfKoPS1QW7UZ7Z3drpov8zf7/2ZOd57P/8o/nZqSn3U8vo9U2enLKtf6sN/uWirJmPbf3nTD2uL99o5007/pI4fVmH+P87z3nJeCjiXYox0PfaEzR5D6QmIApESgnjcgqdv15CerW5DuwU2bAV4KQaqWbQ4gfW5kydshkD5MtsLcpC6ho8gdCFB8iaAtbyaepS5SIlK0H3pwjlS5fSiAjDL+EVRRap2BCq0Td1GVC/WtwEA+Sw82W/jKM4tvXPKIvMrKKXkWlFTuzVesdsKNmwkxSNBEA/9ixScqSlUis6QzujEaRsf6ifgCjRAMMoDnFLJvZ3KrdvNv2ULwU2zkdOoX505CUQzZy4C1woqeAno5ZFitbKxxQo2bTNfGylPc4A5SJuW4HgzAHST/aMERZeBvoD8SIkjqCq/ptradu2youY2jgJ4Qmq/JcqxwHuOYDaFoxxmi9ybCDajlrDzClJ8ArcByshukUMHbPDMORSeSNOFQpRSf667eo9Vrm8xNyolLkXc08iDxFDtmu+z0MwUKQ5JYUmwyc0TYVLMU8rO6OI0YBrQD2n93PktBMQVTJu1wrKgldS0AiQ10iYoARIQThH0jsxRp5E3Lb7Shf2VCg2IC+jMj5pSIpq0MKow3oJ61PTaSIWK+htKSy7XisUWLtjy7Bne0yhSAc8xfgPcgyH+g68ieA445a9qsUDdRmxEmwPzLA+8au6Fs9yfIeCVW2mLKDlEgYPSkrQCyCsqXWvBkjYUH+hroS7aeoD0tGHgG6Ue5GFWAWj4+UngEugQq6B/BKsbUbhbstmRJSsgrWxJRRsQFKAWgXn5lBQ2WZkds2WgsiQKRL7Egnnxp26OlSSYnwQAjXNuL+nvcqtRkSipIyAItEBnSC6j4oeKW3h+1IHJ4FHMT/BQMJKbYFyMt1Sv8lF3c+c1Wop0XvFR2mbkFABGBIWcfKAq+i91TEWB03xJCwIsBVDPShHFXwasWCJAp+BfADgwj1R53sQYwNkFi1KunDU3A3heZ6H5JdrotJOCt7h2DelXWwHFlGoXswEYpQDPImOkgJ3uI0gIiIVCoJt0goIYlPIzjvVygTqKUF3KRx0wJ1BJ5Zh7I6TxnQBGIBCqVLrc7kShDsCMvuRlbnMBi/kJhPvqActIt5paBNwcfoX+14/SW56FA9UWwSnEKUMawEMKgQJgiwAsXKiRJScWAPCU1o1OAXzhDaA44yf9JwBa1FVG6rbNKAEVw3yO2QKqlt4Aikbl22gLKR76LcW87I4DDsx22TwpcCPzI6hpRXgAkiA6qVyj6QKLctyExiD1K6lk7YDymxcQ0YPdoRAsRgriOO9weB7YD7ANyCUPf+BnvCcJ5rtRDMyr3kL70V9i/bY4eBYQYpagJLAr/SROO8UAx5RuOhcfE0BdzVW3xuYKmmyCMTSM35tboY4AVzmc0wUctoIdJlAVDHCOLdxHbgNknAAMPTeN4hJgkZexnJvDmKE/5eKHBHt5UGUrAIyrQwWrKh91JcZiF0qWxxdHbAawtMqHXUmtHAfWSSTpMMykBZynivSldSiYlQHC+GnrMP5uHBh1NLxg80shQCbKpvTC+GJpaubKd6L6WQ2wU0l/8zOnLvLdFLDz6IqgVAAo0k67BCNSnwQAXxS1KEF4eUAffhR3pPwTWkIdkeBwW1WTVaGkFGFMjqEuMwMFEaPtcmVjgGJBgXK2PlSvSgKo7BWXkxLSR7o0ygk41zkBFLmyYP68PJRuChBQRAUHlR5BsvmMw4aSUmvOI9UkY84ruAmYIUTfHWCfAaChZfooizkHPMz1SimMPogTqimttFKO52X9FaV+84y/8YVx0nQOAkr0kUKVNHjMB20bgXtR+MrB39WhDFnAqS+c7CDlYQ9qN60Euy8n2B+2Acb1WRRSB8fw/SGAJWBrrRtzAUJLgJma6ppRQNqKrRN29OzbdqzzhDW1Ndm+jftsM2POj90XGWPHSAV84sQJVBJdqMbssM3rtqNcUwA4NGgnu9628z3nnXW0m3VkAMWk/JIAIMY4ipzL1ljRjHrTtdbeugH1q2V7/pVnbBSob+uO7bZr0z4rzUHxFJ/nw6/hof7JOutHXZdof6nHSXFO62qttQXFae3d1NREyskxfD9zJ+2oVKzK6KO1vLY5e/asozKna4Mgbbq0LCBjHkU0FGUpr+ItV6IAm3lgRsuYD7p+0n5aJg4BPj8LdDoGYNlUUG6byxpJARizU2P9tiS/RnsofbGbMRMEvNT4cPG9n7m+hnLWkra4kAl7lE5+iH49AKRZwJxRA5QOCeHA4Wkg1BJ8TT0prmsAZQqAnbxArVr3CC6bZp0zCeQ9FyFNN5956Ltu5muNCakkVqPuWAqQJlCFXmzTwL7DqL9No8ymIe6mHydwEiFAsyjXSqmVOEAI8Q3KRufgumvZYsz3VfiudbUCA90oWI3bKNBuPJCyANcffj7zMk/puQI9GVDAuRrwlVKQS1GGQdrv5MSohVDfrGQtIpAW6Vn8WZg5KIFvBbgvrbC11LsWGMwHJEdJUIxM2pSgoIVl5+GHBD7HhZyem3EKBYIyXb41UK5mlGEFzi0xvsZZT/TNyB4LzKf401QB6qyLdupkvw2Orlg4Kh/mtSL6mlLbttXX2vb2RqtFLbd/JsrY6QPyGWHOU/8AhKOtcgtySI9ZZrt21tnGtUErZRHyNkpizz/5FjDkou2/drttv7IaQBqwjKpNkCbytdfm7I3XXrf1AEQ3Xb/O1m1qtsWYF+WsFTvyBqpSnfMWou1ygmEg9EpGUoP19AHPzJxD3a3Obr8LSJLjjQOWffNbxy2Ez7z6qla7/oY20pOjjEYfEHG1QvrLC+cXSJt6gXSgy8yVzFuMmwT+NeiJoJhVjEJVizW2FeNvBu3YceCsUfylFWBHqaCHLcA6Z/u2NlT3qm3NWvosgqoXOsP2t185gupZld10XRNKYqjvllM/+tYoUNKRt8fsyKFuqyXN+PXX1qJSpXVAys71APO8PIKC3BAPWSzysEgB6n91lDXfxlD8PX3mkN1/1x77+Z9DhRVwbnQ4Yo986yQAnZ9ztNk11+dZZR3rA0oof7AiMLBjhlSknah4jgP/oarGfJHG5+biB+qqamzPdhQSS4LWMxK1w7Tf2Pi0lh6Ov3azgAkWkHK7tdJuuKYZ5UqgbL473UVazGcvMPcu2vV7m23PFRUooTF7YbPRUZc9/fSodXatYL8gaW5rUVDLtRWWkBdQKXzraJ+9faKHIZpk/sgDUEdFN11KmmzUxWYStmdnnn3+ZwuB7dzYO2Kv0t5TpHTeu7vZ9l5F6u9qxjDzvPPSEwe0w7kL8/bqK312rgvwmjqnUnoog3mQilRWFKLgh40BpsI8NHfkaDfprEl1z5pBnsiZI1hHVKHAuWVrA7A0oBRtdZSUqo+/MI9fXLLrrqyw/buxbTnYM2M0ygMtrx6Ysqdf7EK5rpBUw7T/VtYnvEZH1L6L9vbxM4B8w9xnyONhkzJ8SJkNDiwDLXMvbVuNfYLUoJu3xYypw77z9+dseiyXVKEVdtttpaT1xZbuJKOY9kr4bGwsbk8+edIudLEeRBlR7kIQsfpgOfBa+7o1KKoV2TRj551TvdbbpzS6rNspTyzJ9QEqv81NFXblFZW2bTPrilIfPj9p3/7uMdZ6c7Zn70a7+hqg9CruA+EelmbTduZkGOVH0qWj2n3dtTuBCYEuWXIMjyWo3whpW7tJj45iJ9eaxXoAxV1rfajm9Q722c178+1zD9VY+xrWeJT1K199w873jtiWHZuBL9dafTXrGt1M5H8uRZz6TE0mgUkH7Pgpjsu6IUol3aSAD3jKAQPzaL8qwDP6CuU7dor0xD2sBWnrdBJwjodY/KxnqlD23LK53C7bieooAJtS5P4ASPX42x2UpcZuvXEDypDg1n5lYnDbwbdiqACyBlsZpX/X2c7tRaxPWffMoCT59qidOT5qY7QZz1RYXjkqn6TVnpstAYAkxXVBzP7XXxA4FwRQTdsh0k8/8v0nWdfP4e/y6E886OVe4PphwjpOn3TutW3jYaxPPvgpu3rfdbQJ9510XaVeyPWNADq5dv533vz4QK8sOPeBzJbdKWuBS7OAwLnnnsMxsij+zd/8zUvbKbtV1gI/IQvoAk/BEvXFI0eOcjE65SxqlCIoF7nsQCDA0xz1yNHucAIWW7i5Krl5wXUKXLy/l6an7OsnbQHu3dgf/NeY/S6AFA832mdv8NpffYknorOvj7UFuLdoLyHX/tt/GrcTfSm7arPH/uQ3fFxcZ4PzH+uOka181gJZC/yrtoCAF914Fwhz8UvrtAJuTgt6+VFeOv4iwS6t+fK5GawbW9nXh2uBLm6MbvtkiJubLruCG7R//61cW1O/emPnwy1J9mwfxAKZIFxmX11bCTzTT40njVGN4f7+ficAlnkQSd9dPN4y+wm4k0qFttM1mMC5zLEuJYCm8mh7nTdTNkF5AveUerWjo8P27t3rpGqVyoUUxbV9Zlvtp+0Fzkmh7tLBuZR1CJz74u/aMoDgvk0b7XJUNsoIToFsOCmZFIBWoKvmqj2oQDVxw3rV32TOnamf/tbvAudeffVVp3wC57Zt2+Zcm8rWKrNe2u7i/ZwP3/1cv+tYmePp78y2+v3DfqkcKrdTZk6eSnELF9UK5wqZeuh7/lEh/9/l1OfO6907vuyrT9j03X9Wv/2w/3XK7BSBVn63bpkyXPxd5rP32j+zjb5/73eZfX7cP3XOi891KeAclbP5k6dtEcW5GBdVoDYAOwQrS4tIs1gDcFLlpKEkiusodClNZIxgTgiVtaVZVMQIJKWAunIBLvLrSMm6vt1Rb0sDTLhQOEqSmnJphFRuqC5EVkhfSUAxl/sweZV1VlDbTDpNlKUKsXEC1TBSukU6u0mdOUdGTwCW0garaN9kgeZmZL84v9JyCmYgoEOE32IDgwRlhywEyEEUAQAIFciqMgs2NqPYxRP+BBKXx4ZJSUmqONST4pRVIFIOMhH5pJgtwA/llJEiNEiQ2umsyGcRyIuhXLcyOIVSgYKr1EOwBnBMEFsUNzRaLunRiHg7QFsSGCg6OEBaUwCw5XkCUQRUUJvyExAua17rpAuVMloakCA52G9zvf2I5oUBsvCThQVWvbHNiuoBdVA+UP1cbqKKJCtMx1ALW55F9Yy3UwYF+wjI8tnK5DAgBgBD/RpSZDYRlAEKRvVJMFhuQQXACqlt3fkElfFDBL6TKwQ8Fy9g43GqSUicU7iJHuXiExXgjJNy05sLTFbYbN4i6oaiUhp6JZ3ExpFhQL0JSBZgNFKo+bCzl3WaolVxApdeUsp5ARbMi3pSdMniU++g9jVASlTqk1NKoJzgN4SAWzCmJ5/yNaLcJ4U26pgcok1GgciWzA0w4KJMLo4dQrVmGtUoF8RRRdsasmi2ARAApy0IKiDtJZCNG+Uul1TjCF6n8etR7BtfHDZXaMw8SdSOPIq840uxQZqAeSpJ+ssgaVpLUWxSmemDipmmUV5LAkDFUBuKRqZxU8sO9OcDLnQT4I8DOrhzeWgTSMeVC5AYJu0rbZ4kXa3LB66GGlgC0EAKbmkADD9Pd+aQGtCDmpTOHQfQiqC2lWLuFLjjI/2V0q7GSbsaBsoItF1rgZp9TtqnMAppUvzwEyR1odRHBM3plmlSFKdRtEgu9tAetAmQUgqISvOMxoPW0UkUJ9yorQUL6iyQ34BKF32awGIqjbIcaaTiwDfplUmn/dPMV27UF73qCPFl2gblu2Kl+AV+XJkATD1KW9JXUHlMkkIzDI0gcM4FJOXxF1tuUa35gABdgLNpgTWAI6k41AYps9wofbjdsyiODAMUAkPVk8q3lPSMjNko8JmH1K45wWbqBx0gEFa5CdOTAIn9gJpDgCOUB7ukGB9YBx4LnUn6PZnhGKeAV8VV/MS2BHJdwACIjZibNMVStAsDjypFqdtPKjJgEB/ATJo+armo8BU30l5QgwlSDANWScnNix/wYosEwFgc+Ez+0wc4k0NbpwtIU0kq2SUCqgtAkQuRqAPXadwobfQMQ2AAoCWXvrcRNahWwLYI5pwECAvTHokUJRFISx/0KB0cx/aS8zkI7FlKatVibmbKV3fiJ99ZBGoBVFsTrLQqINYUql6CRwUuFdJOxYylEs4ZxAgewIkUYG6Y8TRH+83TrxYof5iHxNOMT1gtUsGSghZ4sIT9CpmHfdiIolmEfWZJDThOStp5+VCO7xNoCHgTBWaLME6lCKoUgrFkGIhkhv6WtLbqRiA81BhR+FqizZfoRfQG4BmU65CO81I3gbRK2V1IKuoq1KKKOE6INhvCJ3RNDnD8KAARinkAfYkY6cU4H9wP6yhgAvxWIW3j1bpBynw0mYDKBeozDdyzCAQV5s3XTE3Uh/kmiD3KAoCoAJYaJYqhx9l+hpSXQ/TziVkUM1HLKqQPV3CP30+fcOErCoBNcxg/U2OTKPgsENSvQCWomn6JkhpjYRoIYAxgeYa02KEo8Bz9KQj0psB+BbBuTXEt/SVufRMDNro4zgMk9bauvM0qCc67ad8kbTJL6s2+AeaxyDIqUTVWD8zsp94h5rhJ1Cb7JnpJQUlaZyoUwO/n0EBdZ8/ZWO+EtaJyuO+qa7lWWkOFEtY5eAGocoYgPoqDFetQKSt24Eh6Mm1NpX/ML107j0+gjNVLymn1Q+IpWkvrOlpQndbtukbXWGlpaSGNYJkDAeo6QPEWrbPzAbZi8ZgD0rmxeSlqhQ31DcAvTYwv5hb2zbwuXq9kPrvUn1KKe3522KawZXNBmbWX1gpnt1EUbiPYll7hrM9TABQCud2AbS76rNKnlgP9VDAHBCGaR/GvR/FjI/RlgZ/NQMFe+mscqJUZxsoB5yq99FH8kEcP7DOOmB3FcwBsMg7xD5P4xzAgchqfI0XQIJ+X4b/LuU4Q+MLkTVpqbZ+wBcbfwgrXxoDTKY4pfwwv66jQpUjPqrGUykUtjrTFk6gOhlGjrARQawdS1/y3uLgCoAmklSPyDr/IudwaHADO0iEMUtcy2qkC6DbB+BijXh1Aj/HFkLUA7ZbS51JprUEAsjmXm/VJOXWuZswGgYjd+Pk0CqMJnGuI486juDeN+lwI6BChMQBZ/AbpRotQjSvJ5Sdl9zFe6SnO9jOoss6zZlkCbGS0SsgTcClMit+whRc5Pj4lQJkED7fVlVtzPX49nzG5mLLu4RDqYagKAyEnaC+4FyuuBtBrKbG6moCVBFFVpaqTXUnrOTPq+J+2Vh6WIP2nlwf0HAXHEA909URssHfQ6pkG17JvSU0p6nsohS6kbLg7aoM9KzaHf4DYB9gqBeAptAOHkyg/dtg99zTZrbdXWV0FfnY2CQQ7CUActpamEmtegwoUyq5SpcQM2BDYFEimt2cZJakQwE4MoFLXyDHq5wZ2DFprS4UFC3NJubwIFLSA6hfbUEb1FboF0GIuQC4wdSXwDvuoDiPjMWLrPWyTbzu3VgLfYmeAHDhtW8CGw0CIgwNTqGgV2VrKVElaTznOpUgugFHEertZc0/LU6PaW1HGw20BO3SkA4Dxbbv3zn32Mz+z0cqAnBYXuSY+Pg7U7rY1LWXWxnnyZUctnVggaX04NwvEPDBnIyMznDvMPMecTZsXlZNavI5U2DX59Dm3Tcwnab9lyj7HepP1F+tvP9R2BQBQc2uBtTWxPkGJNBRxW98Y8NLxCebhuG1rL7P1baRDL6Aze+g3S17SaC/axHjEyrDbxjXFDlQnhdRFHuAYpR+dOj0GbLQCfO5BdQ7fGiq0VwCY+kZidvWuQvuZzxRYQ6ObtJncI+hfAmxasNbmImttKkS1GORYFdS1Dv1fpNcs6V77B+dpwwX8XMwiUc3PrMX8LquvK7a25nzuXQBoMxZ6+9iObRfmuYcpSBafVlhGKl06W11jISl+GR/MoRf6AAlPsXZYiNq2daRBXgswitqeA4RyfdM7SJruPlSygXHXA4Y2YidcE+dIM1Z4oKkP1dW+CYqIQibgeipdZMeOjgBqhgD5muzue8tJRcx9GUC408dmLTTn5d5KwNZvwEcjAqz6OW6BeckBPC/MoSYHbI+yaZTrhTS+Mg8V2OqacmtqLLKqitXxMTiI7cembVnQLnM1Tp2UpoVAj5WMAcZgMXM1ZhsaTNmBg8NA5hHOCZS2kQc3SrjuF1hMfG4cFcAzJ7uZO7j3B3RWBeyGgKvzwMgE6Y17eiaBBFmrxzxWxjXV7FKRHTjmso7zvXbDnjz76QdrbW0zszvnOvL2ACqizKf1VbamTesIbEwNmV5Y+zKW+D0G5NbXF8Kuc8wJrHHwd1rkl+HbKsuB85vw+RU8mMDGA2NzKBhOs8bBFiHNDW7geBQ0a6lnSx7zGp/RFgu0xYkzs6QPHrOGGgDKLdUwA9Qjh3U3a8H+ITLd9bJ+jC3bupZC5nrmc+RKtQacmY3ZSHfYRnvCTln8pXmoJZfYiWNx0tKGUMx12S99vghwP+goW/f1Ju3kaR6QQYE2r5BrCeA8CG7AuWnAvSPch+qwzRs3oqAJOLf3WtYsWhTTjhgIT8KbNZhs8u6bHx/olQXnPpDZsjtlLXBpFvjzP/9ze/TRR3Hyq7LNl7ZXdqusBX6yFtANY13EKVhz8cu5qOMCUKCcgLlWnjbbt+9qJ33Qpk2bLt70En7X9JR9/aQtMMWTCz/9b6P2wlvIPwdd9gufyrE//D90OZt9fZwtoAuCvv60/clfxO0vHotzU8llv/pTXvuFn81x+snH2TbZumctkLVA1gL/Wi2gp/H1wI3WZxfDLbopryfWtTYTUCJVK63Z3s9LN+UFxzz55JPOjf0777zTURZ+P8fIbvujW6Dr/LvgXMRlN7d47B8eybVSAIns638cC2gsvTew5QTpFajnPQDo8sYbbziBtZ07dzrK3pkHkLRvJkCmcS5Q7c0337T29nZnbEt5InPszM9/yTKZ42XKpaCervUEzknxQuDcJz7xCUdVXNCsXplyCObT9qdOnXLAOW1/seLcxefOnEefqZ4dbPsHf/xH3IRN2idu3G9XX7adIAZ3prnpnibQ6+KGvpR4vKVlwAurAVrtmzlOpn6ZcmcU5+T7lPJW4Jx8X+al7TLwYeYY+u7i4+jzi7e7+PvMcT7Mn0nZgeGtW7eql8A556UPKateKv/qb6s3d/XZD+v3rm9gW6eeqwfTJh/Z6+K5Se3xw7K+W5d320Dl/e+113sLnmm/937+4/pb5bv4HJcEztEgyRlSMAK/pLmoShE0dUEhuIGnPMBqafqlhxCFi7ScThQtKeUp3gReklH0DCRdgG3cBF3dqD+4ubeSUPQHIMQdW2JfQBPgqQRKFSkF3AB5iDsD2JCaE/UlIsEALQqykRKNFJ3pBcqC+liS9KGeXALYqDG4i1DWIhDlhLoIePlIpekiwJ8OoQhHytSUZEb43+PTOCQoRtAZQgmzeoBQCA6jvKFzpwhyShnJDdzkQYlJKVTTpB91EViiYAQDw9QdSIngWIr0fAIkkoAxUm7wEHR156NyRTDbRerItJSniNoI2koDkyRClB31Kz7g3GyLkpBX9QMWIzrrKPClgDIExSSJjCo9qYDbnGLqiTpTWhEg2UVkn2sZG3PvCggkLXvzFgTmQiElzL3WpckpyyupsLzmdkAwYCcDzENZx+XC5wEMkUQXcA74EYO5CExCDeK6RvBfk9SfYxPQFDDpUmpUAKy0G/Uwq0BgQ3AY0B0AooJLXmzhApNJxwFIAMME3TmhGfn/xQVSwM5YTglKTmUN1AWFM9o7PHqcwPgQ7VbiqKalvaWMG/qD+qZbtlMqVMrpQlHOhgGZSNUq8FEnpExSNZsbnbSZxSUAtGKratmAQl0rNkMpIk4dCYS5gQ04GPur3VUifGGK/hClftEh6jjFJ9hMMm8p6piiTqifuQD5XAFBaUBXAGpSE5UtBHekiRCnUa9Jp4D4aG+X4LRwhDSNpJ1zL1uwvBrQqpl2RKEMKCcx30v7EjQtLYEpLWTc0Iec9qM/KzoIsJDGTmrDVGKZ76QIxt8pAIupHhTguilPnhW07jN/xS52RTGCMSOYzA0UBP3FPqrnu6B4bIK2HOZYc5SRcmsw8EoDIEBz0O6Uw1eBbbC7UpWyf5pxGHPgQXoE/ckV1/1Qba/+zjFCpPpcGnF8l7fkMsZ8E4AnENvkQb4bM1cxQFHZOkAt6sc5HQ8NkIlcHPWgLUjdmV6ZoV5ESUnhB0nH4RlrKKhNjY6jWKe0rKipCk6lHs44Nfqah7lTUUnUMlxeqAoXucKSBIhj9AnSk6o/pFVB1Z23oc7ojGcvD8EABDrlcaFcZvQn5mAfQJIb+6TIK6oUbi7q7VaOUdIixoGjzEtaW9LtugWnJkYsQiqwBP7AhxJUDj5AwJeaRuNUfdNF+ts4aXJJxmbo59kSZQnJ16uvYAWlIB0GIusjXWIhfWd7abG1Epz1cBxcoixsMfqy/ksDzeml3qpUrV58pY86efGx2q4b4OadhWGLAT1sIn1fqw8wAz+86svZlh1xa6hEkTqaPonr40C0O04Uj2YrfL7CuZzzOf4/ybaAu+RLoxc4anNqbZVCoChxYJun7ZeoMNbHHQL08F2Mc4Ls8J/GVBpAjpSzI8OoeHptQ30LqneoErJPgnMIh9G2DrDD3++ORI7C+ahfnlNH0sDySR8pc7vGBnRUQJRqlLPwF9goh/306EEB6wSQT1TUdFoKJ9+EnVyqHwdmVAMb8ZOxJH0fLQsEyXn5JUAfUrpbAZICKNVVIgAr07T9Cn4khzYNMg6VllnzotrDTRtJ2TKOX5QSpQ9FrgC+AEQIq5DWjraOsU6MCahNS3GH8qiv4vP9pNbMIy2mm/lmGZ+8lF5BvQy1HEDlPNJi0yiUkLMglxXCF0aAEz34+ABznQs4dQU/sJAgLpEijTBHFiAVx28NDffZ8SNHUb5ctMs3XmH7rrjWGsq59uVoYcBBdMEI1APV5KC6ClgpsSS3YEze6l/qujKd7CIAACs5bcjHzudqF+fFD6c+/EtvWP2MfzMtqK10XzUKSLaAIlqEOVYDwwcUoyB9jPlQ9hP8Jlvno2rmYz6MA/gtM3evAC8LmHMDPCeYxwThUTznoRCvYE/8m45ZQlrwSgCuXGz6bmkpocrEm4o4fZBvBAQwmp02ERwhn6/vVF9hH2MAa68BIQrK3FBYbZuKK0nP6iYVPG3I5k6/1vH4XSp9jHKnPII+lNY4Dz+fSxuPAXgdmQEMoj9UBYttEymdC2VMxpUXf5JH++eRss8BNZyxJ4vquLQiFdTMuIDhNFvysbO+YQVCGlj6n2rIuRi6q2q+bJBgvzh9Nk77M/MBGK++nXpr7PNZiLrOMCcNjg450HoTqp3rSWkaYG6QH0pTtiTnVgpVpa2VnTRuNBZ9lN3H/gHqF+YTgXPnx8aYs0MoY1ZbHQ9FeFG4S+K30arDbwEQMifk0SW8GmxYzpVDelZtQy9MpHOAAknpzlcxToN3RUVTqZk1bjmnfK7OzzYUy7E7qzJHBVJjVjOHUlPGUP9Mc3wPdfdRz3z6VAHrtVzWgDJ3BPsvc5IQ1xJSixL8GOJzN7C5Pw9fSAMolS6zgHmW6fdAX7n4Qx/HyOH7FJCTpkz54BVSpCaB8cVi+bUuDJA2lLZDRNgWSIsYX8YX0LddDKY467+Dh6P24pv0YdYu9z/QYtfdUG7VxZyHjhRaktPFX5GmM4e1I8VijtFbdqfd+ToUpg8AaU/znmGfHL6rIpVuQx5pfrW+ow+FKVg4zlyCLaLOeoftuHYM4uRLUdWDd5QJHfuNAk498wxpT+N5dvnWCtu2ESgZuFD21XIpCkAWBbLMYWzmq1xA/EpXuRD22RxgXYRCpbG305lYmxx9O2XPvHjEFplr7r/3SrvrnhaAVmxG3wmx1sUNMU5RwARU9ADAqz0FVFNB8bscjzbhHQd4ZuWg6Z9xDmgJ9FUIzMwuTnstUPewwGiUB10YSks1P+XODcYBujX/+bCxx/qGzQ4dZm2Fv9ixqdQ2rgN2p7EoDdC2bAXMTV+QOmIBNhcjpLGyxKBW/cLAmZor0pwvBqF9vjNt332yE7+bY7fcWG/33Y0aHg9qStV3KcR6FcMV0B5BYCSl+XbJ0JzN8SaMzzigfoRjhaTAjIpgAh8vNTb5rQD9Kw9YElfurLukuqd3EsA/zfH1AIOXY/sov247SLWWHmcX+lN28EQSRc4I4JzPdrajVC9oj2/VjiHOt4Q9MQrKeagca6LnJQBsfp6xwDk0F2nOlbrZ+a6Evf7aBRsbX0Y9bw3pRKustY2xxNgNz2ELllJBwNIg8KVHkx/9WyURVKX6LFEv9RmNRT1UJN+VA4Tu5yGcAOX3MI4TwLcJ+miMczttrYLq/gbXd3pYJ0h2Jy5vnKX1cH8S2HTCgSnXbUDxc0sB6VGZTQRzMabiES8PS8XoA6wL6Cde5gU9kBOmzosLlHlZ14S0I0Sx1mBvn0raEy9FbHxq0u6+ucAeuLPS6mvxTzTVEmM5Sln08EMAf5FLmbRO0jpB16xUk+rSbwBSl9VX2VaKl7hAfKb6qcqPv6IsmhdCjJUQtpBP0vMjuib1ayxpDLCdzikodg5FundQqRzon7GG6oBt3aL705oDaX/6R4h2kSqdGrQIG1JNZw1KlwNKRQVS7YI0qZT/wvjLsSm3PfbEEG0Zsi3ry+3znylGbXL13k+Y46wgTaeVDk+D8EYRkIrNz87Y8889QyrXl4EG2+yeu++n/a+mTRgU2FOLIlw/7azWdjzxu/863el9/5MF5963ybI7ZC1w6RZ44fnn7bXXX4e+ZnGXfWUt8K/EApOTk07qnpGREadEumjOqM5lVE0aGxucFDnXXnst8rNXWD0qAu/vtbrIeX/7ZLd+PxbQBe84KTmvfSiMDHTaGiH0/92v5tgvf4bVSfb1sbfALBcX33gkYf/3f+aJW/rKbXs99uUv+HhCSBdF2VfWAlkLZC2QtcC/JgsoIPTwww/bF7/4RdM6TaCc4DgBKiFSHEkVSg823Hjjjfa5z33OWaNpzXapL8EO3/ve9+wLX/iC7SKtms5Tw1P/2deHa4GTx1O2+3MEfbgh+8AGr/3dtwn8/JAN+nALkz3bj80CGdU5/Tx58qR961vfcgJjd9xxh11++eWOMkXmZBqLeo0RtHnttdeccSlY7aGHHrLq6up/hI0uhn0y+/73fmbApQy8pzII3lPq1fPnzzvg3F133fWPAJ/8ihMwpRzaVwoaZ86csa985SvO9krt+ou/+ItOuiltp+3fC0gJtusgddUX//RPOZbbHrjjTrvhyqt46hoIg8CCrlF08xaShwgOd5QJ1Go7vTLlzdRPf+v3DDinegicU6pWqaHrO70z2/1zx8gcV99fvL1+/6heTpkxg16Z8q+Wc7XOFFR3dp2bu/r5w7Ku7iQ7KoijGjjQ3UdYF6cS/KP+q/aX8o/6RQakU9n11t9667vMZ9r34va5uD/9sM6ZM/x4f2bsnjnqJYFz2tgB4Ah0OQElbtorEEGAIUmwJEVQyUMwRenHViOGBMNRRVNMbfW2PgGmdwNsaQCeODfzFUB2ERz2oi4nSMRN4NXFMfRKs68UWcjLQzdQBIZzAb7E51H7WkHdiwCKG3UfVwEAHlEoBSfcBJR0CrE07EHRVoMlCh7zqwM5CbRx2CUAFA8bu1KcU8ExB45SmbWh/qYdCYYpvWyaaJxUzqTipD6ZIhiSZn8FW1yKEKtTUj6BEC5AAKmhKQjOQOcrwWkK6mMHZzv6AREY1RszOmySjp2Osy1BztQ0YM8cKl4oTblJH6i0k2mgDSmXqI4CgASFeTlXWuDXEqpYK2RM0HlJm8oHgIWo202TQpXgaKBmveU2bDBXYS1lBv5Tqh4nmkkcCsUH4RQ+FY4gVTo6zYcDRDYHHThJiIzqmyZg7ib9kwtVMbevCsMCXwE1Sc1M/IqHtnLLr3EsRuVqvxZghhJFdKzH5qfOWgFKLnkN66lXHQDhok2ff8Xci71WUNFkOfV7yBDWyr6UjVPqGESKOTXIDgBTKtoFjzdC+6Uc+FrQXBy1qNmJKVtO+C1Yuc4q6reREqyOwiIdg6oapXLaUZCngro6pKCtNBBLOtJLhO0cbTdKnwqvljcNvuChbv4mwLBqilJOwJv7R8qpp3oJmhTIp84DoKJUuByE/VFWnBu2mVFgNdq3vKnVcstaAMrctjzeZYsTpyy/kLR49esABNdxjFKOJQBLx5P9IxYDQkuHJoGEALsIbCuyGQHmmmPdmwD2KConrWr9bhQdN1IegXJEPp2wniqFnQgE6nMBJEY60/RKr6Uio0w1i9iPbdXnqYbLjwJfPjYKNtKG2EkBRQKLUJ4WBdiRMgbYAGNHI5M+zjhIAbFEp87ZyvhJUhqS8q/pepp/IylhBy3R/yzVHzQPSlq+xuuRMGukPhkfh0+gdC5SgyZIG5siVaIH6TB3gDYAOowvT1lslrS3BDB9ZZtJybwLiLKW8mBjSpFGqUg/5ehdSjMFnEnF6B/Ac6TNTdL+Aj2ltMEvnEvHBuDLp26Ancgbsk8RYw8lOtWDbuAjgC3wVupuTn/Qz9gQ5TtFSt1e9qmzYM1O6odt4j02PdiBSgppSUn7WlzTTFpbgZXqA0BzoC7qr1HKuYIf4RYTilRxWyb1qlJjqeAh6j6FguUCikV1gHc7uIZpQNEMt+LYhgM5I0agGr/y4bvjG3+hpqR0DsSmkdUDbHtydojYcYqUr9W2LlBKmmB8E//JXa2qqRDgpmJ+3gIUnEbHLykcG2e9EWV7ehd/aS/SYdPeYMEO6CM1OKWo1CtNOTSSEXhxFOvARG2S8TwHWKP9BYgpmJ6QyhuKayuTM1adW2qb69pQeNHZNYiBIRgzAtZUmZTjV/kMuwgMlIWoplPHGX7vBZy7MNJH33NZU1Wdk/ISVBHohnbDNugCOyAYluHQHI/jCwgSFEbBnboK3okxjwg0kjlXW2G1qfWB4BkvPpxNHOWuRfwfCBx2lDqVQs8cBhskmAschS/mGH0m2E4Qml5Jt1qDY6itqKmLuUW+X58lqVAMv695Dpyc+VBgGMqCgKIaZuSyoV207uNg+Os08F4cUJZ/2V+aT0o/zToYhbp+fOMSSTBzSXWrY82j4NpxvgOlnF6Uu4rshsuvA57bZWVBVCupkNNXVC5gSG+aNhBkoDKrAvLzjF2Sbjrt6sPKUjXUXKJyOy/90FuNwv/qM9SWN/WVm9EcpfGoOvOb2l9AXxT4JYXNPIxRt8ao1gZJxojq6fgWjsFnAsApHaaiDyEzNLc0ZROoVs4DIas9lA48hurp+DRKe6O9rO1ctm39lbaj7QpUBrn25z+mQMrM3Em5VD71JiUdBnFxwK8l5tVl/BU8hzMHR6nLMuebREWqZ2YclUPGYFG1bc4lFZ6Ownk19UthcZn+r+oLYMuhXhp/qr1eOnoOgPU4Ml6H6OsjpGJuzCu1nSh+lrOWWW3PGCXRuBNw6fTS1Z0pIcnKsQGACRB9mNZU/9RLppZFfZSXzKZstzoeVCb4Fef8TrPwvf6eZC5QGuUUBlPf0w7LXBtMkv51BvW/Muy8EQXdRsDAHMYoze4c10P76D+lBBaY5/hoduZX5+WGslWK5hFsdW5MiqVhW1taZ01lqIYC1miOkv3BZgAIsY/GkJYGHsDlHB4GcONTqb0QXIHd9AbGIWtBCi+/opbX3KQ6qA9oLImfVhXkF+TTEowhpX1dLRtl5zwavyq7QEaBic44Y199Eqc/LnPMBfZbwq8vcgMhrjUc34rzD7AmLSFVdhH9Ko9KB2lMHY8hDeyslY0sop4ssEZtgD20DqKTCerqH0xY3wX6Pg9zlDF/q60mSPH46uFR6x6NoRjmtXvvr7bLduShPoidtf7XEZ0+rzWQ5hx2oo4a6ylnnKMsizVCjL0hILT+eZRVqcMaVKvaAnELas3IuBHj7rQTnSPTB9VaKr/6i/qmzCevcY4y/tf/coBxWGV33NhGulYfimeYim2oFuVQv6aW7KT+ya8ohKFc2Z+03mHGLAbJA8hSW01NkgbybeDJ7gvW1pK2e+5ttV1XFKHwxv7YWX3OTeeULVQnl9MJaB3qquNrNnNAdtpGBUjRB/gK/0arcnyeH2D/1X4A7+aUT75VJnIK5qy1UEPlM/W2hXmugU8n7dEnOnlIZs5uvqHerru61krKWA/TJxPYMQ1xJ8U7zXfOGGKiV7rLnsEYinorAGqoVgNyJanz6HDKzqLCduTMeWtuLrO77mwkJSsPapZRduqk5T0VoE0oA35PZXB8mcoov8+a3/E3zPnqO7o8ke9T2wieUz3UTQUCeukHsr/eemGu1enYuTBRbVe9l/C4Q8dj9shTcVTe5uxG0rTeeX2h1VTyDWWSXXVJFJO/5QP5QHWxJGDpDO11/kIClbKkFZG+VuDiFHHQY+8sWldvnxWjCHjrbc125e4S0qyuAl+6PnPTz7mF4VzrONcrKhx1SHP9JZuqRnrJJqrjajm0vcrDyEFRUj7P+VyjExnMJP1C28sIMqOHBpeCrgjaMyeT9vAjp21ydtz2XN1uN9zUQHpRrn9cq0qHaWByl679dF4ZjLJJfXpsfNH6e9O2MEuGESmuYot5YMiDxxft+IUIKrBuu++OQrt+T8AQK2XOwINtbb0gAABAAElEQVRorFEIrQV0KGYhZ/3gNIB6ok4BVJ6kzPJJsq2207WcQF3N+LhO2gqfRGM638v26uMc11m7UC01o+qq/hLFX4yNkar1uSknfe36tcV28/5m7i8BGqMql+DgugaOsa2bNzyf49+kJj4JMHf+LAD9nABhnyE8SsrrtJ1Dne61g53YyEO/b7J7b0PhtIn9GRyadx2lb8eLhSi/5sY8m0ap8sknHrdnnn7amhtb7d6777W9e3Y74JzaRO2XfveaT7ZWfRyb8+8HeWXBuQ9itew+WQtcogXGxsac9Csr3PDKvrIW+Kgt4CwmmcBeeullRwmxs7PTuSHn41GBkpJVJZPa2hpramqyjUieKmChtD4lJSXOQub9lf+DTUrv7xwf761pSmSZ07b1thAXUGY7alz2B3+Sa9ft1KVG9vVxtwCiB/b6m0n7378QsxPjKdu8xm1/+Gs5tv960k04K+CPu4Wy9c9aIGuBrAX+9VhAoMHXv/51+63f+i3nYQbBcRUEkgWRZNI5vvjii45i8P79++23f/u3HRBGAMKlvHR8wTy//uu/brt377YvfelLjnLdpeyb3ebHYwEF3Z94MmGf/vdRbj6ZfWaH1772DVJnKWaaff0PawFdXwlWE9wqyPXw4cP21a9+lXQfefbAAw/YlVde6SinaQzqxrDGrMZ1b28vT88/Yz/4wQ/spptuss9//vPONdiljmkZTOfWS8fV/YY50qbOzytlyqAD4vb19ZlU7wTcNjY2WlVVlXNdJxhOyhfafpZ0TLom/M53vmNdXV3O9p/97GetljRYmRRUqoteOp/OpZuvZ1Cc+90/+zNuTnrsgdvvsv1X7rUKrhkpjTZdvdEqtR0CFYruXCo4JzsJJNyyZQs3YqVUx+1O3he/Mte0mfoL5Mpsp5+yYebvi/f70H9X82CzjN3+yflVJb5XffXK1MUJxug2L3fqpUikv5266M69Xu+xxeqHH96/Kqf6egacy5RbJdDveqs9VC/1c/U1pTVTm1w8BlSnD+Ol8lx8rksG52gWik/wS42kSJCAFsYuoIggGw+BAAWOnJeitgSgXES0kmyjdhVEIaE1RSYigFFCj3SF7iMAoFCYmyiFi2M4w4UIoW7qSylC0RsptqWAB6bPnrYwSmolKJXltW82D7B7lKf/ifc4gSmdSCsAiTJIbccJ1vK7YnKK89EYAGMEaXmDGADt5TgBE77hxQaK8LGNE2EBcBUMkdL44SuPAqu84hxMAd8cginu1Ug02xOUcRS1OD+KBI6EBgE2QU0K0BBrVwzJKaMT7sdQUm5QYJg8aUBrfLqAOtHZdyzWf86CFYAxmzeaB58jklzQXMIJmBOgZhevVLNWBi000WFLU8BLFNCL+oA7hbpXeA7TK91ns+XWoRBWsQZoisiZVOMIUK1Gw+iPijZiFA+0RloKBqiKRWfOATX10y9Rj3PakOASbeLOIc1WWb3lVrZxGAAzKyYYJIUkBZVpVIAkhQJdKC0psAMFgSoZqbhGz9n06GErJv1WUetm2LMm2nHehk8+bZ6FbiurXWe5rTcDQG7COMBfiuGpfxJRTbnoIdFBUtCdIR1uNynxQo5qBtKAlozMoG6CkkxRvRXV7qJs6xlXxdgXsBjFJwrMgTA6x5LahzpmCrgjFJlCSewUINdp0uxNOOE1BcWScWATYCtffpP5Ad88RY2O0ttqx8FOTqiMhYmjikdnUH2B5qRMFpvuscnhIc7rt8rWdvNVNQAfMJcMnwF+O26lJaTQbN4G6LmTItU4inhOLjuNjRQpsQDswlNdgF2k40JVMQUYtyKVRMiOfFSXSmo3mK9kC+VpXO1XGjmC51QsBamBFQRFKsgZWxkFZDpFOlMgJFTsfPRJj/wONpBqor+k2fzl7dgbMFAElxPoD9K38un9jAUCxoIyaXXakz4HlLEy8rYtDx62AgimvPbbUZfbTsrTXot1Pg5U14+AXbv52+7EZuvo6wRAFdjT3vStNOpZkaEO0iCfxYTc/w+iRONescQKABwAZWFBtflrr4Jz20GbV7AXNnYAAtWNZtN4VJpWAXKkuE1FVkir3G+RuT7G5BTnIA0a9khBXHpyK0gfR58vbFsFMQXQMc86w54jeLGBA+PKcFRd/smiF2AND9jCACClr9EKm65GHbCM8dhpo10nbHYxYsW1a62yZQOKOOXsKbBPcJh0ogTOAUhi+FHUS87OkBYOeMgn5SC+DZOGMB4iraM/31pIP7qGIH4F9tbylt2om2zM7wrM4kcS+DohV258o4Kc6rYK3EoZqx9bdcyggkcq2C2lNbahiLR+yJ0k6dc6hoaNo7RGf/fhKwTQuQWvorblQLyodgl/UbrgtOMIma84l4/+LHhBMGEKgDktQCK5ujaBKQaoTNksR+8OLwBzLaCGRhpmUqZKWTBOim2pdBYDE64tbbI1pMDMxfEKEiGMDmQDooXTcyA76BQpaAkCE7BHMfCh8osEwyn7AOBc51AfFUlZY0WNtZRWyLsAkwj4wEbQJApmgybhb/D39Iuo6k59lXaTmtH/5SOBz9jKURjUmofzKPWlpiwpa5I5jT6JihyqNmHmLbWAWkuordLyOeqSOg6wcgZwymVfwQaydJp6UCT8tgA56qZBp0lRL/ptHLKBJnPmC/U1uYuYowoD9JvKpU34QOUiyJ90Ac1iE8F72guPTfuSlm+k0472v23DQGSCMDQqw6TAnAKI9gKBbGndZNdvuc6ayhnLUrbjP41VzuYcBQ1RujbnkH+grAKQ/x/23gPMsqu68/3fUHVv5ZxzTh2qOrc6qLvVyoJGwsaMjec9+3nMs/3xPJ/DN8/xGbA9mMGeYRiDbXiYYQAZBwFCuVGr1Tnnil0555xuqHvfb51W8Wmek0CWDExd6Xale87ZYe219znrt/9rDaBzFXuzsWAons86l2JHKLsNBbeRFM53Zpdmk/yIvJTNaQaxIRtF+9PuHONAEzSC0/J2DprAlMcECGEHeqPA3gBAFqSPGMzgnNdgWMrEaU2lbxB/fqX9ijqneug3w4mo47ylwB1hNC+opr5a+7YcUW1WE3aOYh2fMHACi+QcVgurF8AGx+GxOIY0itjvsI1BVO2QLgLO92oBeHWFexGbp4pSc7QZALyMfjBYjc7AIFBNAkYwONTaMAH/5XsDAjG7CWNzXqA5r2seMDaoS+PTGoUoLU3I0o60bAecs1TLa4x7Q70MRTX7jmDHjnIt87KbdOBRpx0ob5S5ydaw1IVmoxLYH98YrGclMIDMZIzW6AQrov3OoFiz/D5Sy/YuzJDeD5tBCckUfwMGCzIvmuJWBakoq0j/ne5PutefnMqm+FhAFxvz5u+pGv1lPtrW0VyTARLmb3O0ZD/QYfvwAOx9SFXM9SUQWP5YQ8ysdUlxD5zhY06ytRCFos7ct8fMcTxgIe0WC47Ip8wY+LMZEf+zbnOWf4y1EGU0W2H0s+bA4lnIWNvhGZw3xeWrYXLmY2lL2t9GqAPiMk4cMMssyNJyU/4J5oMB1AsnDdBn/WLndl52Qc6Uwr1SHvdf2cDK6XzewC3zGySldyA9W1/a2PHS1l78r1MvhucI6TivXJnS6RO9Wpi284BZUt/xqUUNTs0qObdQBw6X6PADiTwzAjzDfmIAGmOcvuPS9K+9DWgx/2KpsW0MGo61Svss4NS7pwPY/hLzfYw2oVBVB1RuCpy2kYOqslaxU7BWxSdZP5naralb2drP7MFZD3KpK9eW9fsfewV7KNYHj9Xp2OM+npPdO9aFL3LmFq5uLxd9Z20/R6rUU5em9NKJbq0s4WNjQZQZr+MTKOHNJLJJVXr8sUTt3Z+o3HzAalMlY5Q5ODAOzfyfA59TuwjnjNDX1uQuGpAS8zvqYL7CLsqHbQ1uwFAsdXHjQMJ8OIy/Mb/ggGD2OV6GF9kocHBlg4oAwy5cDrOZkjVpYELHHivWE48WA4HhixxAyjwBY4ULmcWYgqL5vHlUAs+cp36vcq+AAnYc6siRgF/TEwZeBeUno8HhI3k6fCiV1KMoh2GyVkMza4rK9bFZfvKY8w5TF/re/LX5uDUHLsO30R9m4mbkZkd2nM01zkkYn5bG3EA3x/fxezMNZ+1Bha1vbZOF1d0UZY+fXNIXng6RznNGxx5I0r95byqpbekr6z+zWcwp7IwV8++0Je81yMPuu0G98OKgWjvGUINLBrr2UnevRsdXBaevnTvS9dAj6SqvvLcZ1drHwwndNi8xWJwUwEz+bsaEFd38syn3Wi84Y8mplNOFzt/5CH+hHdgkY8rNVju3rbXxaWt0sp3DTB9zvTdHcK0oquBXLob151+4pJHJfh19qAkVwwoVlmDLzIv2DCQKaB0O0uv4B2sPO8cSIOztO/06c2qcepo7z3TG6cTMkkZQ5otlXXxwX64eOuxXbSnqeYwftOO4NmMNX293d9Za5lu91uDWE9gnK2HegI/ca9AM+BH+4c8G2DFFOm1rP5sh2doqRMdZP5pVmA+2JbP5VLs1ss/Z+tSU+Xq6ovrq1wZ17nKbGlF+/LH3V2lTQyKqdLQSwKXZVohr2bra7k1tg9gqde7qXQV069FQ7yL+I479lLHM86QOZuPTSmBRTY35jOkCbd8cSxp4Q1DtPysL5eIeIGobllBdd63Fa2piGXDuWc73LOBcqZ583zE2ibK2Zj1p/UnzMg/Z8Qaf87Mz0hwj5qfv/bUBzn3vbbZxxEYLbLTARgv8ULaAPby2gM4nP/lJPffc807w1dRLDIzbvHmTdqEsZyok1dXVTtD27VXSpqiN1zvZAtzb6m5PRE2PrzgLlKMVbn35a35lsuNi47XRAqwZ2ZkV0e98IqRvnw8TwJV+6gmv/vDXYnlwu2EjGxay0QIbLbDRAj9ILWBAwZe//GX9zu/8jgO4fO5zn3PAOCujQQiLi4s6efKkPvGJT2hgYEAf//jHZXCLrePWXwbBDA8POwpSltbVIBknfYw9wOX8Tz/9tH79139dpij16U9/+rvgnAVjTHXKVKoWeAhvSnR2rMEO9nDHwD17r6eMNWBi/WVlGxkZ4YGSWxkZGex4JYhhTyw2Xn+vBVZ54PTHnwnqo1+wQID00/d79YU/3QDn/l5D/RD9wsaOBf8mJiac8WNj09TbDHK18WJpUmtqapyxZPdh9nkbV+vgnKVnHhsb01NPPeUozhUWFn5P48fOZ297GfR29epV56upVtq5JycnVYRqeFUVwXB8gkGzpoBnQJpd1yA/g+bMb5jim9XDALs9e/Y4PsA2UzU0NKisrMwp93cBNQIBzS3N+vin/pgHrW79BODcA3v2KR1VCufJvt2k8NDS5QcCMDKUAO9bAefMx5mv+v+Dc+t1tHqu+xdrd/OL9rbvzS8lJZEWJTnZaXs7xj67/nk79t1+OQpQb5SDgvAAngfa5h8dF3nPT1ow18DLZYLF8+RMWiXFpaVKS8a3J6ck871BMfZZewhs31rgYv0c9ot377XeD9ZH63Di+u+sFPa91WVqasoBOO25g6kGGgRuzxvsGHvb68398ubvnT/+C/6zbgfrp3yr4JwBSA6E9IZykxPEJiBqqR4NVCLREvEJ61N6hWChy8AUB87iSs7v+EqA0uCaFYIbAR7oxxIdNdTBC+RBbBH+jKgJwQgDp5wIHQE4C/BEAUYik+MaOHsKyKVPeRlZSt9NGpryCkVJd2pRVhti9rLAi9eC4kQgnQCkWYf9aOnFMJMICmkhAyBQ6XITMPQS6XEsz+A3AtHOeLXfGODKlyighqmEmcqQvdYINJuygR3nkC3mb+w4ghh2bReREfMBUBb83dYGtBE2Gv6uvRPuJyjjBJ7MhgkSRsnDtjY2rZFTx7XSclWZRQVK2rdf3ooquUgXazIFaw7cYIiBlYUkZ8tAUhMdKLr1siiaJ7C0RPDQ1I1oT9Jm+pIr5U214/MJmBANJZjkvKysBE84DX1Eo1lwCGh4bXYYlbR2BVACchMhcrF2cdMPQVJ1rgLOxMSlKTmPlKjpVfBI2ZwT30aFXQTQXaQWdaLyjiIX9QnRlkszWhxq1cToRcAxAvrljdhKOeof0xq4+S3FLN1VVkGVYkseJDJWQ3tYQNT6wtqZfrcIcnBEKwsotwGnBZZJ90nZTbHP1Cw8XlIiArn5MwEoDeYjOI7ROIEzBxay89gcY5AB9mGBsLnFac7VSprQDsCeRUAf0tcC+ASWCacDOnmA35JRUEvIaUCdLY+uI6RmfXmv67kGZXLetB9tHg2OKzTRp6mhQeezGeU8M8zOJ9PtiqYNnBu5rszceKUVbwXmaqR4lBMbd1LmUtwIEOAcqnyr0+3yBEapGwo+BJfXAL58SRmk2gUsSy2GFQC4Q63OgUYMKAS6uReBNfsCViSAGSDCGlgY1uxYK+DcEPVbVSJTTQwDY4X0vUuLqwR0U5SeXQ6sVgrIhyKb10AHxg9BO8fOgSDcKLsZCGp1jgQXtTJ0Q8vdlxTPmPHXPS5lNZKCtkehzueduvtQ/POXHZU7tYJjzAfQXuYHGBaWujc41q7l0TbgRFKi+nhGR9pVNyBUHLaTlFlOhtTNVIFjTfXPosR2YTvWvrNopgdoDniFhqHJQ5qmrVeAPL3ueTY+EtA3ZUsCuatLzBP4lpSMcsUAU7oSqzjWj+uxOYbxbnJe2AP/cB1AEi99GOxSEPuc7b/LGCtSWjngXGo6ICnBzLYbmp5bUVZ+pTJLWbMk83t8gYO+GQBDu5uS2xLg6yip2+5O9aPKtiivKcpSeUvzGYvKVEFanvISkoE3omgyEVQ3/2b+ie8NxjK3ZYotlv7MTft5Gee0Hv/RjvwfYHyOri6qb472YxdoJUHjUvouBpLC/ma4ggEg5hccyNzGNGdw44MNNnRSDANYhVDKixjMBIRoduSnzw0ctnWIC/WzMOPBgB+3BWJpxwjrlQBOeZ5r9JMqdnBhXouMaxfpQF3AAS7m5wRstRA7LUzJQ+GJB2uObzQlJUPcAAwwIwfuYx4gmRwjHGjcAXcwDcakhXAtheUUKmQDY0P8Mqxc0jrnAxr6GeP3PBZwj9NnBl8YsEZQnCYmbEyZqSu+2BQ8jVxw/sb5DF4ylMeaYoXPGp5j5zIIDqk859o2B5mhGX6F52F5RtswTxmcH2Y+M/zPfJFBGWaGDrDyRmo7a226hvPzedrAQcLoa4PE7fdWXOvYNUi9AHCn9bMPpRsvCpkOLAAMGgbkXeOaDsyIbXjp+wDpQO+Odup69zX1DBnYQl8ZlEC/+VG+yinOUUNpg+qzN5H6MJVjAKkpr/lKQ/DMYEBFAJMMCzf7uRdgDzJGVmgMWom/+rBDaxuzOfw0PteLctM9TSs+QDsapOSK2IRs56TRTNnJwArGtQfbsLnQsTqgAuuDCHOApU80W/MClbqYLw3ICllg38pkNuWMbfqCuWuINMhXO66qg7ouoggXYfzY+HahSJkISLSpoV51eU3K9BZTUlA/6mlpdyOMcwOg7eXCbzOI6SfTdAN+47wjKMtNk+o8xNwdZJ1tejyxlCWb1L+FidgV4yANQNWFH3B5aA061rLLGzhnfRTnXM3sAoCE+d1+66H/SNypcfqmbY70tPRjvi9NNfFpSmGsGjgWxB/b+DNwzGzPqIw13hHGVQRlNutrAxUMLnObbVm/c3ZcAXZh9sr8Dtix6oAQVlfamN9ZitV7Y9yl4ZUFDQGvLgRW8fUcjZu0zQFmbzkJSaT7TFZuDL2LTzLI3sBbL7bpIe26ow5mAAhwp4d28xjcZLCujSPADsOPJ4Fsh0gvjiSailDmyybtsqk4rTHfWK/G0a8+fJ1BI2bvYcq65J2j/ZcZQwY2WusBclJ28+M2diy1udmT+RIbg7S8WQPnMJ9jv7HRj8+hrm6nPwFW6dOw/Uzj2Cg0GzSYx8Azu7KVhSJogjTHfTNjmlpk7YNv88calMgRpFacnpthvRRRRl6GylkrFgMOJ2GbQXyZ1dVGrGFk96yeFqStrC/4Nenfw7p9c0Knv9OisUHKZ/6GZxVBwMz4jCRVbq3Rrn3ZKi+nNwFwzT/4GX8ex3fYOaiRzZ2cz0pu/1l5rTaszADnpN6ZkDpng6w/vKpFQa2a85A5kzrTZlYOe/E1wpiwk73RdfQj7c6vzGfYnpIrV1b0Bx8/wbgEnHuySk88HMt9LW1LUzlmyGmottOWZk9m2YtLEVTOxvXC8TZgG/62lobfZ/2AVGNKShL3t8k6dMivolLah7WsKbC56HSzbi7Mie13VkCKRl0cO7RfUCYHaGMeM9uyenBJ5xBrWzLkchzzPz4yBIgbtvNwDlMltLqay7T6GVBm6/WJiYguAs59+a+Yo1FcffLxQj3+SDbP1fB3nNcUFOkWO5R1L+PIjue9gIrtJcDHV167obEJA/nMH5E23tSJSdVb25CmXXuT6b8YJSXidznWNsdYeexkBkc56dadcckJrfPsfoRxYjC1KT46H+UY+zzV5s04dDrZ+oo1OJ+P4M8N7GWoAzLzIftq5bbDOJbbHweGfPnEsj7/tSBpaRf11AMJAJBJKgZYtNsqgwIZcqhfM9/iQ2hZCsO8F/Cpvy+i46/26PbtUTaepvFZU3sk/TopRCsqSN27PUX1m2KVQiphG0vmPxmG3KpQNoaltb2VwarhnNa+vtHFjlriG7+2jrQ62MtAsRgDweh4A0JtvWJ+2AHI+bvVzcaQ9aOdN8I916VzYX3u8xc0hm9+6JFGUoiWAc7hA2gw59J0JlMyP9OP1vl8XV4NqrVtROfOjKu1hbktxAZEBtQSdhCfHKvyTXnauztNDZUxyiLFr9fam9HlqMXSyBGb2ymMTdcO98lpDUS1WdLgQNvceC8VLZejDTDze7eqtI+1sMHiTpp7/EXU7rs4l9k6R5oZ3POB1oBcJsi46e4AnHt6FMXGDm1vytT731+h2mrStnNilgXcPpr/srWFYcFcgfERgMzuGwqSWrVf3W1zgOOMC/yY6ZtGmJuKin2wCFnavjVROajrxdj9tNNi1mlWX5vDKIzBtoDy05OrbER+WS88/zcqK83Xk0++l+dGKFVjSI4NUkfzCdZO5pvNE9u/3+9rA5z7fltu47iNFthogY0W+CFqAXtobIGFdtLqfPSjH3UAutraWieAYkGdVB7O+3mwbcEce4Bxb1Xxdir4/U9Mb+eq/ysdaw/MW++Szuh9pLhg0fXULlJ+fdECU/8rtcJGXf+pFphECvlrz4T1J58NqX8xqr2Nbv357/m0qQ6D2XhttMBGC2y0wEYL/MC0wJvBudLSUkexymCXN78MbjOwzpTpNm3a5KRWrKiocNR/DJj55je/qd5e1CmASAyos40Q73//+x31YIMV/iFwzj773HPP6fLly45KlYEOlibW4DmDVw4ePOj87Rvf+IaTbvJnf/ZnVVZW5qgIWdkMEjLIz2CVX/iFX5CBP+tgxJvLvvH9vQecH/nNVX31ZR7l8Czp54959ZnfB5y7Fy3baKIfwhaw+6v5+XknxenZs2d169YtZxyZ0ps9wDNoLRMlAwNYbYyvA0d2nMGoNv5svL/vfe9zxltqKipC/G19DP1zQJF91l729dKlSzp+/LiTKtbU50x5zs5v4JIBZYmJiXrkkUf08MMPO/7BVOm+/e1vO4DdOhxrSmIG1RnkZH5g69atTrnM3xgIaMDfvYfELjW3tur3/9MnnJ9/8tH36GATKWl5gBudIywHlOFwNKkEz/KLUQ5Bvdye2L5RVvu6Xjcru32/nqrV2sh8j13bymJ/t+vay9TL7GW+cHBw0Lmvta92j2ttbH7L/Kb5RTv2zZCvc+C7/Y/1j3UR9XPqadd3vieoxoNx24m/Sl3Mb3d2dmoISMLqkoDqQ0FhgSoqq1RcVEj/JTt1t3NYVGm97d796tyzt3vVuFcnK5P1mZXJ7G1oaIgA1xUH8Hbqws6d8vJy1dXVOVCmqReul9++rtv6O1UXp925zvrrLYFzVqcFlL4mZoCOCEqiRGWwhTcBha8kAlOJqaS4BKayKIHZJuolUDxOWs7wypICtIN1VayPwDIASgjYYo3xF0dw00PAG0IAFbAVrU2TYhS1Mkun5fb55CMYHBOXeE8Rra9PfSde1VRvt7KZz7Mbd8hTXK4oeYhiAS3c2ISLSNA92MYCrgTzHYCLYN0kCmqLFi0iAJJA0CYjGagsk0CCpRfjGECQKKpHQQKuYWBNF9CLA3GhHhebAKSSSFpYUr25gFWinNdCotBIAESLpNqkXSizBadcCQS8U3l+lJJIQM5gAcY/qhwimBsBggguL6BsSV0BGlwo4sQlJdAmBPUA50Jd/Rp49XmtAs5lM24Tt+2Rp7RCkdRExRQkcV6C7sBiJBakbpR3jaD18qiW5gYUWhmhrjOcC2zCeX5FWk4vObvclDsGBS0BzhHgcVKMWtopwidOh9hgJPjjRNSAc4Lzk8BS1AWVLLefYJWXPllu1dz4HS0sAf3ElymraLvi0yoJCNF+HE5CPE6HZo21x4opdRCoM6CS8yyMdKBkgupaGQHPsm1cp4bzTWuw+RsIkLUBlQFGZO8jBFRIiWhbJCI8wA1eILFo1BLqzQNcoUI0P6DIyjj9gt1hXjG+dHnjSB3rpV4xpLSNS3Iip2tRwDNSSMYQJHMDihCdIngHtOAlXEWdlgiABTiPAmOwiISxDQDkOkFgwcD0bS2hWJQQDwSUu10xmcB8XMdSmRL5pO0APEKUB8iHxqaeBKBQ/gvPTGh2eJjgmAc4sBwFthznmjMDLZoeuIqqXjKqZfWoXJUDqrCxA3swhR4PaRgNSAnPT2Ab3XKtDKAksgRzATwVn01qVmwaGMlJsZvAfAHkFg1butIp6mb2bYACwXjUBUO02RoQoCnhBUmDGiHAaFBqvM8UYlCbAk6ZGh3TMnNgJuMptbJO3twyxhnnJeBqSlguIIe1yCxKd9P4r2XmClMuA+4Z79Rqz00nyBpb95Ci2VsUmBtUuPtVRYDFYtOLsE/61gBNymPKNm4gChdKURZtjKKCF7GUwqujwDuzCsei+gLg4ffiwzkm5KO9SLPq5RiPpQdASZDBhO0Br6DkFIzOYcMkOXQBRiz6FUDNb22NNF8xBgIS0LcgJ/VeAFQMzfTKx9waV35UnuxdnAM7xiZAzHAH+CBgr6gp19kY5liPSIeLyuL0YD/lL6D/duNLGC8LIxptu8OaYVlZ2QB1hSVA79iQPWwE7HDhN1wo+Jl60BTFXcCulvAJcyjiLbPWiJgyG3N/WlK6CjNLUOszXIlDsZsIkM1qcJZ5bd6Zt9eMsABGi8WHxsXxxt4suGqKZktLC6SLBV4k8hnlmbQfgDwbQCTFS/JIbGV0GR9AEDXdD5gHFOnFPwXwLXMhAHr+FuD4CKBzDIqRCYBDMbGkOASEJVStXMBQ/1qSs5ZYWMU2oihVki7MoNtlxvEK0GTIoCD6Yg3VpiBAzqKp8pqiFxuEUilfAbnFCtIylYyviKFdIsA0IWxnlbZeXAoS6AaeIvhsMHEcSnwZ+PMk2s2Ctqb8NEV/LC0ZDGWAAapLrBFT41BG9JE0mDlk1uYa/GosfjLZl6AEbN4NvLGKwtMEc8ZciPkC+MsA35SEeGBM1GZQn4oNeZWOf/Aw7ufwYZMBlLEoczI+10N9FvjMfNACyIYarDkpZuPogxUA7QDvVYPEOTaFPknDtyTQJ04gnVIHma8WAyv0HWOVz1rU3+oeT/rchLgMxWMnloIxRGrNqdC4xrD7GF+scnx56HRmMDbQNEKFbIE+WkBlzeCYeCt7YgoBcyBC0jb3T3QRVO/R9Mw8IBD1R1U1pzBNqRnxpC5lLYqaYjqwcCL+wdzcNOldF+k/X7yf+RRfMG8EBB4VQM9j8B4AlNlnPMpEsYzPtTkUsKi/USp+rp2UmIzd2dxiSmeGF9CP+O9VbHmF9VcIX0clAX1YC8fnKM6dTD1YdzF/L+Jr5oChI/jTdKDTRMa9zQEB1geLQFtz80uMa9oyFttOpLz01QxzVd9kn7rHu9mwMqpVUsUksT5NyWejR74fFah4oLkCZceUUF/WFvTUqvU3qntGQsTSniEAmQDAXRiIbM1AVOYOgzSX8c/TrCEXGBdhPpftTVI1tp7tS1I89uXi8xF8QBBFn0XWMXOk68Y6nPk+CR+aga9NIp2r10Au/Ncy/bewClTOZLfEudxxmcqMSVIGftrNeSZXSBkOdJoUn6Q00soykmkbIC3rf+a6qeAULBrQFeuBTGDHRMoxG8RPMKekAryl4Ne82PRsGDgvPE1/eVFbBCJmmllATW6Bb2w/QZSxZuleF5nPFrH7FUA3U1TK4hym0JgVA0ju9B/qfXxdAHxcBhRfmRvFmwA/4U/j/dQP6I9RiO9D4RAYegzofWppnjGI28V/xANhp9PmPtY+S2vzmqV+wdWI0n25yohJY14xEJK2YO4bB2yfCuBDWVv4PYlKwQ/4gSTdKE2Zffjjgen8QCrUf2EVtTz6JYZyxpBGdYmfA9hBDG2V6c/Gp+Uw58Q7MRyDy9YA1WIAkQ0yDvL5AKqqRkxZSs4Y1vwB/PrkyiR+is0o2GM8/s9N+5jvGmbD08ACKdDTSIOan4OiW6oyqbH5nEl88Cp9aVCzKS+aSw8Z6IiNMfwc/zA3RSrV1nHUYEkLzzjBhcFge5SckwgET3pW4DSGOWX3OL4hia+m9GmAG9XheYaNItQOmWdWsKGAKW+yScMUCRdXomonDWXLGHMYn69mXVeXnAgAREpKICcvEJ2l4lycDWkeVboQaqamoOmlPyzte3I668s4IHna4fq1gH7/D06zRsonbWWZjhxARRTV2hX7Iz4rPokNnJn4Xqg8A7YNvLJNin0jAV2+MabBPjahTiXT1nFsSnKroMijPNTO4uPnlJtHPTPwJ4zxMCmZ56YYM8uci76wvl12xh/wH8vRrBw/Xyk37TRPOs1ZUlDC6uObaVwahVsENrugU5zMehJSbREabGqGcvLZyCprGPyth9/HJ8QqK4MNX0z1s+Q/P49a2Re/0stYDenJR/K1f28S7QBgSp+b+mdiCuWmPRLZWGLYmLVnEChpYDSgO/RfR8eyZqYMk/STcjeNzW+oAudx/nSXSC6mnCw0ZFlGLa3ikxY4L9BoInN1BBBplTTtUe4DEuLIRJbpUwJKdeafDXq1uW0OiH8JdbvgigFpMay1LGMZsGbGAn24xvj0awY/PE9bhJa4D2AQewCdGILAbDHKoK9jmBdfMcW5vwqqF4jqPffH6bFDPJ+gLe0+2GjO+HQ3drfG2iCIvRlsZhtN4zQz61cn9WtvX9LwAD4dmtwPRJZbEEc9UTnm/BkAV+kcHxfHfEbfTE/gd4h/xbH+iudeaXkR22S8+FgnpqWzkYE2iTLA5hcinJ/6LQELBvG+EFd+v5v77nvns7VhCJtYpP4LKFQGUdYNQeGaorXBialcMwXhEoMJLyFW8d/+/Bxp66fIJNCoo0eKHCU2bglpD1KUUub0TFON4/Pm5+zuhPE5ORVUR1tAra1hTU0yPtmA5o/3KKeIjWelPo4LqDArVoUoOvttKcyBo9RvkTnBVHE9nCxA2yN67IzVdBuzCGW4KbttIFgifS97q7WIDVo/x3Bf5GdNmMA9XHIK50zELjnnHHY+T1ssUk+WHfgfxgfAqwGJKaT5xbjU3xPV01+f1NnLHWramqxHHyvHjvFZHMugVwLtEc/nk6hrPNenW/E52Dj21nU3qM5m0pT349e5T/QmYxs5cYCrMahVR5SUwN1Rll8J2Iop+yGwqdk5dFaZJ2LoE6YBLS9EWUus6tSp4zp95hs878khu8Pj2r+PVK2sFaOsU21s4D7x89ynOv/hq2jr7/e1Ac59vy23cdxGC2y0wEYL/BC1gKXjMWUBUx+w4EM6svw5ObaLgR2xBEYsELH+EPtfplpvZ2r6lynBj/pZWGNpYipKmqSg+thB8zNPenXsEVYrG6+NFnijBez+7fI10rV+PKhTrREVZrv0K/82Rv/X/8ED7A1T2bCTjRbYaIGNFviBaQEDD9YV50pL/2FwzgprqR1/7dd+zUmvaN9v2bJFBuz8F1ImGsRWVlYmg29MfcrUpixF42/91m85KSC//vWv/z3FOYPt/uiP/sg5X2Njo5Ne0qCfVqCYpqYm/d7v/Z6z2eI3f/M3Hbjjz/7szxzwxiAaW1saNGcqeKZY/Kd/+qcOKPQvu578gemit12QWR5YHfu5VZ25xUN3Huh85Gdj9PF/T1IeC+RvvH5oW2B9Y5KNw7a2NgccMvVGGwcFBQSnuc9aT1VplTQ4zWAhU2c0qM7GsKVTNejL2S3LAt/uy9YhsX+qYRyQ6o0PmHLcjRs3HOU7G5sWOF0H9Qwqs/PZmLa3QXSmCmaKc+Yr7PPrn7XTWdkNSDEIzaA5AwDX7xXtqym93Glu1h996o8IHLn0gUOHtaughCfV01ocG+HhNAF7Ao/RRL9yDj2opPIaHuDeI0TXy7zuJ9bBpnVwzu5TDZwzf7QOzq0r3Vm7Wft1d3c7Zbfym1qeqZzZ38z3WYpXS31bWVnpAL3r1/mn2vEd/Rv3a/Z4fL2e9pOpzFk9l1AN6gWae/3UKYJC12i+KX5PcBtoIAPbMNhsHwqh9tXgZAcys1ts2vxf62X1+J/qwtxldTFozmBM65OTJ0866oXrtp6bm+v056FDhxybsn61uljfvNP98+ayWpu9JXCOOq12dGrh+k0tjfQzPoAnCLC4CfjF5+WS4rBUCUV1KMYAuto4Q8oiONCN4lgfAbcpJ/WkgZHJwG1J5ZVK2bpdnixCmWFglgVgGsbISu+w5gdGNUtKsgASEh6AwpTcfGUWlztAROBOizq+c1wjfV1KJpiVW1apMGoiroJS5dRt4rxV8gBYmPIbFBJsE3ozY+Na6e7VxN0ehZchXAj6upPjSB1Kis9KxmBOAcEwoIh5yjDUq8m+bs2iThkg6meBZG8cPikf2Le8Tn5AI1Nhc4DABQLSI8Oo3w1oemyC4AUqS0BQLoPcUAXKr6tUEkAJERrAIeCjQZTJegc0OT5MkBrYxcBAgsrZhUXKKSgDsovX3O0W9bz6gta67wDcAN4VVymQXSjlZytre7USywsA+NIJLgElERR3rTE2loa0MkMKx2XKEJgHKADgwJY8nC8aIrBPgNyARheKcwHqGCHlVYwHkAhAJhDmTUDFQwTYR5v4CDY5UR6CyZBbfAv4BFKg0G1Swl7W+Mi0lgNpyi/fo5S8eqAu6mfAQXCIOvZraWaOYO8KwAHBZIDKWPydlW1yukMpZQVKK9vJeTcDQc5opPmv5Fm4SaAxC4BxK7BDhpOCzlL7xXLtpHj8KyprLgJZ0fC4VmcBtYAzIIFQYCJIHZ9BuTNpd9qXesTiG7wZPtZmE1qaHSW9H0pDBOVCpHwKWaQYgCompQAVOWA7AlCm2GfqDy4PqmuEXKOBIaDNyxohPaeiycrO36X43E2o9eXQDqixEeAPzfcosDhCQBPYhahXnAV66cc1wKb5aVLk+oBKAJRjMnL4PGAO4NxM31WCiAT50ooBV/JoP9Q5GEtxAEyJQDleUuuRDxWQBfhxvh8lEWyXssbGZwFQAMoR0LNAcEx2CYpnGdgw9jx9l6GHyg/tFCLgvUia37ApDKbmAEfR3xaVw2eaaoYHe4c2JOA3izrhkCY6OxQH9JNZU6/Y4i2oMtEeBOM8qClFUDRbWgDACVlyTgBzAvUO2Er6xDCAixfbj61/WK7MzZR1CHDuNYVItehOYMynAeF5SIlmQVb6x29pYVFGcwPKivNFFkedsi8B0kSRnokH3vOR+i+ACsy8G1vPKGBMA5DMT5P6dpxhgw0COi5j5wuhWczRo9TEfPo1n2AfUXjUioSSkyluGFQAwaTg8AWFBi6JOLDiKh+Wr+gwcB1jARDQgR7nJgBoGdvYkAW/qaASYgAzVgY1wzNhJRUqrWoX6X+xa0Ck8bZm1Aln2UydqdTsHIApYA36w8gIL+PHw5qhF2i2dRq7Z6SlAkwu42NGhwGBFpeAVPwqySvRptqtlJ1xC1xhqRingdQGxro1MjTA+WkbFGA8jNkkVK3z8WVFqAxmWvo8FPK6+nvUjU+yKpYxfxcxb6SgoofMiboGe3ULH2s+qbawQpUZBKTxV9OgNu30V19vF4qHPBDFFhKAMDPzULGESBgZ7SVo7tbumt3K8uYBAMzqdt8VjS4NAvLin7H58QlAy3nGHTWLYkOZwIPJ2JdBbgOD9D3jKgu/UZmVoariMuWkF5ASD3vkiHFUD/sG+zQwPEUA3tSxgJBhkDKz0lRdWKbyzELns8vAyu19Perp6Xdsq5w5pDQzR6nY/TLR4M7Rft2520ZwOFZVNdUqycJGGIvmmweYL9qo++g8KZuxXR9QXmFuFvYK0DEzrWQUlBpLm5RKOs1+4J6bg23MK/QlcJMfYHVqdoG3QWsogmE7RQVZyk7P1Cx9OTM1p3kgZzcARQn9UVdSq3z6xFKeLQMITy9NqmuoX0P48mU2iURZa5nPy87IV1lBnUrx6YlATsuAkW3jbbrZd0u+xHhtKWxSbSbjDgB2gVTY7YMd6m3vYyzHsKmrTnlsDDDppFlAq4HJbnV0tmlyjHkJ4CI+2a+8MlSFPNj5/BqAVrZqSxpQ58uDOV/W9ZYLGlnuVXoO4BJY1PII4MYSYFUcfgrgYwn1yRkgsaKSclI4uzTZPQ4IALgDaJ0BWFTO74ssFTdwZAiwaI401BYnGR0Y0fT4JIATMDVTX1JSCvWrVXFuFUAh4CvQ4CjtcKe7Bf+2jMpOjcoyixyYaBlAdIRxf+d2mzzzsaoFHi4qK8UHo7qFXQ1M9Km9v00D/QNMqUEgxWSUHVMVnx3D8/xJgLJs1ec00e8lmPCaehkLzR03Adm9rAUzGDuLmp2aB6IxFasg0EcCaltZ2HtUA+MTGmfNzWSo8tQ87SzAfrAFH4RFCCU8A+b6BnrUP9yn8TnmNersAapLiietMjZaiRJqVlwW/iysnuEWdQ2Tbhp7zSqsBCyqAcIDLANMnJyi7r1tmgfkLS+pUEVWuZKA6iwt8dDksNp624lJkG4cyCwVyLGA+ScLPz6+BEwH1FjNGqYstVAewJ228bu6M9oiL/aeTdrq8GxYY9PjmsbmVoGfc/OLsedC5hBUN1l/TM9NAdyhDpeRraaaOpVhC4l0kmn/TUB49EyPkgLwLj6gHx9JUkturFMY3xWZxYzbCqUnZQECkmJ6fECt2FoUKKekpFjlOSWAmcz9+Kre6T619LYwr6+qrmir6os2ATXGAgUxBmcG1DJ8R/2TPdxvhICDU5XPGPABCK4B0hhUVl5UptycLGfcdA21qmegFzA9FagmU+OTQ/iHUdrLr83F21SdsxWQPVmT+O9R/OjK8gz2NQdgDirEuF0FnIU8Bw6MKBMV3gTglRDzoN17xbCZIMY2KkCpBOn/UfzynTGUYEkHWYpf2MrGhxzKM4eHGlxb1gRzdizKgeCqrJvAROnbIDbmAWpN574iBR/hWUYpboX7ElvPM6WGWJ+D0WHl2Jtdl3LAwFD+ZGVQpzRgtjggMf6nXGQt4LPjq/gTyrK8yPmX8dsrKZoYCelmd5/axgaBfdZUwaasxsxsVZZkqaKWTRps7JgYRZGuGYC2h4wJwLWmsRcX52Z+SNOmxlKVlLNuYX119WpYH/uDC8w7WTp8X5G21IWBgfqwS+6pYzO4T03WZhSrDMLBBTnqatyaYFcR0qDOqbWZ8o2ZIlkc9sV6NB/wHai3t+eWtmwt1d49BQwhl6aBmC6c6RVDXbmZeXzexf0M63rAxSwgrT17K7mvZ+5BoKCtbV5dnSj7ApatAld5uXfLz3GraXOCqitZSwEo940F2Wg2rIlhFMUX6V/mQB/gWWZminbtzlJ1RbzYv6JTZ8P60tO9+LKQDu3KVUWJj3SkU1xnhHU74FRJrjY3YrPFeD38k0GLdKfGAaZa2xdRrBtk3Ur7sVGhMBd4NiuFud8FOL1EmlaPdu2MwxY9zGkB3WxmXTo4oyw21NjnZ/BBS8wvhYUZamwqUkVVAqvde23XfneGeWtcE+OLDnzmBRZNT05ATd/PBjcfgLMPeC+i5jbuZ7tnAIMNzsI68VNpGTGqr83Rls2A0Gyke+XUkr7412vqG2aD3FavNlfc2+gwPgbgzj1WYWmSahuzVFYBWBmP4hubCcKs+6bnYtXdtaY7N2fV3jxN3T1KyaKti9MYE8CAKAvm58Sj3ulXYQEgLTTqqdd76DcXEGE65/JqbHhCC2zsy6Jdtm7NU0mFT6uMg7a2Gd5TmprG1qHHMGl8T6zKSjIoewYAZAwwW0R3O6fU1z8MtGWwPGtbxlNGRhzPcFJUW0cWN0DPq5fC+vRnUZybnNPu3ZtUV5ML+MU8Oj2Lz8WvZSdyL2z1A/REAdCuZu/FObOlkK5dX+aZBiss1uwZ2EcGcHWI9cvE5KA2Vydo75ZM7pdYW2FDJ09349OBmDPYFAAcODk8r2U2Y2UCY27blaOi8ngH8JwDDOzrW1UbNjLQv8DYXJDPu6jUpDU2s7FOqSlgPKSiXuhWM2Bid/eYJrF3g1j9+Ng07nvLypK1pSkDqDKGdozoK381odfOtGPjidqzq5TnRygFD3Nu4OqM3ATGdpbqapnHgTYdsJ5OWsTv93SHdecqY+4uID0bLOM5X15xipKY820Dp9e9qIN7MlVRnCiWMDyfWtOly52MS+wsjc1A3F8aEGnp7Ht7z9Mnr/F8JFfvf+oR7T+wmzbG76Awyg2Ps460+1rrT4bK23ptgHNvq/k2Dt5ogY0W2GiB778F7EGuPWi2twUE7G1BFHtYvv4Q2XlA/v1f4rtH2rkt/ZYFF+zhtT18t+u8cy+bojZe73QL2M1A71CUXSxRbh7YWcCDmo3XRgu8uQUGhqP69P8b1F9+nQcnPDd97IBHn/+PBEZYyG68NlpgowU2WmCjBX4wWuCtgnMW9P+pn/opB3b527/9WycNpKVtNeWoD33oQw7UZvCIrfc+9alPqaWlRX/4h3+oD3zgA85nDLpbT9Vq68Ff+ZVfkanJ/dzP/Zwee+wxB5wz6MfUiS3t5G//9m/rPe95j37jN35DzzzzjH71V39VH/nIR5yNF9NAMqZAZ6CE/f7DH/6ws778wWjRH7xSmArsjqdWeNAVRbnCpf/wH2L0f/6EKT3/4JV1o0RvvQUMDjJI1aAhe/BnilumAGn3cpam1dKdGri2DhzZ5w0+MzDNlCEtjaVtZDIYdf21DhWt//yPfTW/Yee1zxvAZ2PS7vcMhLPXOuxm3xuUZ77B3lYeK8d6Sk27T7TX+n2nfbXP2/2ildHKtn4dO+can79z+5b+5D/9IaooET1cW6danlAGxwERuHaAB722yoyiQlH9k/+bcg4cBvy4Vz87j73W73XtZ/vewLmTAFd2X2xq6OvgnH3W6mmfsTJbgNOU9U6cOMGDUwJTAEdWJ1N3sPoYqPjQQw/pyJEjjurmO3u/a6X7Z15WXaszDbJed+JCjkqN+VhL62t1sXpZ2U3tbw6lpFnSVCUBhhymHg8ePeoAZ6Y8QkN8t+3+mSu/I3+2Oqz3mV3Avjdw0eYN67/XX3/dmTusHgZoWgpxs0mDRI9SD4O5S0tL/yd7X7eFd6LAby6rnf+tgHMGw40+/7zGvvOKoosoyaGKsUJQZ9lUTHKzVVhfr/xtpIUheBtBJWfl5k2NXr5IYIy0itiqqajEErg0pie2qFAV7zsG6FbqnCvY2amJi5c0BeC2tLBEEJyd9X5SuTLeYgkolm1uItiQrXDbXXWefFVD/Z1ASwRPiwoUSMtQLAHrwqZdSgOes/STjtIcymRroyNaunJNU1evaxS4LQqYs8a5wyi2pKcmq3AzQMu+/XJzjrXuLs1fuaye9lagBhR6CIKChAEfeVFFK1BRQ6My6xthyVJJGYsCWuttDTHex1lXhAyiAvKxFLSLpnhHoHbL/fcpp6KcCCrPs/oGNXf+qlbbCWIvke6QQHDA6offSCWtZ0XtJqVkFmiyrV09J1+WC8gjkedfkZwiLQCkeC2YvX+bUmsrHLW+KGk8PSiDuZbaFRq6qUUAvjXgKhdl9ZDix+NGyZOAehAVu3jUc/z5JajHpWp6GPXd2W6gmSWAwHRgikwUUUxND0AxG6WgdIPEaAsX8BXqFW7OKeA8V/QugNQtTQ2PEyDzofbQBNBYgwNNxXoIIE/dQMDtJoFSU8ExhTNAMeD3OKCmNQLHCyvTQH/lvO9DFa6J4PG0Jm9+SZ6JiyiioAiWXK2FsIFzqCAR1IbaUkZKvhII8JuC3xrwxcI4bQ60Y2kC0WPjbb48FhU8SzWI6khZCfVM0/wEcCL24SNHZBxQSAjVryCBWjeqe4kpZYrn7SXI7URY8dOmIhVFKckdHUHG4ZZ6bl6iP1HRKt2rxLwGwAvUx4BiQrO9mh+64qRADQMiQY1igwTNLU0oSn1zlgY1NV8pVfWkHs1D2Qy1JhTQFgDnYgl0xgJhLLvzCUoS7EctyAP0lYIvS8C+TRVvCUB4FXUwNzZvSlfIAwHUAIah5hREISqhbLvicmopxwDj5AxppeZQ30hFDSwV9RbgMBTbkoE2rA/dpmRnpBXrqAj1s2SY7gjKXTPDwGB35JmdVmoFKWXpx4gfEC1MHwI7BfvbmS/HURICBAIw8XOsl+89S4wdVOzi09Pka3gctcYtQHYDCncd19JYG6J32GpyFkAKSIPlPSRA7MMnJyXwDDctid+hmDYNfAOwFzIbZZ732TgBSpkDkFiMI8BaWqmsFMpNSt7pHlKzAW+6E3NQH/IBrLCxAmAnK6MKJZBK2iiVcWzpIVEMAzywFKwuQI7I5GVF+s8AY5CytfJB2uwoY4Jnu4BTIrVrYKxTK5NAOviyEEBdlHeChzR/IeZo4DFPRrnSa/Yq1sA5FPImW26RRneM8YICDIFm9EhQKjJVFVT0AIkiqNxcR6HyzCDtih3mA6/5UD2anyVYO0dCQMZ+NvDsgb37VVkOMIXUzDxKObe6buh2G+eenqB/AQtI1RwC4gwDUxYVl2hvw3Y1FdQSvPfo5t1Wnb1yiSD5pGoaqnUfG3JyEtIJ4o7pHL7tJn4rq6BQe7fu0Kb8ckSGXGoBrDx964y629vlQkUnOQHQ0NYagLoeYIuZiRHlZaXryYPvU0VyBepMI3ru4jfVPHCbbCsJQGLlgLhrgFbTmkHdcHlhBfWaXGUB7XhdKF7NoYEFGGhqW1mAwo2btmhH3TblAJdNoE51vfu6buAbZ1BLsnFiaVYNHDK1uxpAml11jaoqrMJEqV9Ls85cuYhC2IK27mjU/rqtKkONtA/fffzWVYCwOyrKL9DRPftQjioFOgMWGOvVZf7WzKaMVQBVWwP4ADSTSeEWQiFrCQXHgoQcPbHzUZUVlevObKdevnoS8KpPCSiZpaA8FMK3GQS4CKQ9PTkKMJKmwjwUEwMR4NgACmqMXwiMjJQs7Wnap201TUpLSdYEcGFrT6uut97RsAGeAFeYMoqVgKaArNUldfTRHpXiu22eudF7W9+5chIFqKD2btqvw41HlBKfouHlIT3/6nPqae7jsxW6n/VgSWkRaTNX1QVAdfXOFd3taAeMFjBUCmqEpMKECZtaRhlzLqxywLX7dx1WWX4FcMO0nj/xjO4O32b8s/bzA+POsj5l3o1P4TgYqAnsZ2B0WIXFRSj+JSkwuYIy15LmLVCfTnrGugbt3rJDGcC8i1y0Gei3nfl2bGgCpR8bZyj3ACganJGDvTVt363Gwk2kH07Q0OCgXr78qu4ydisry3S4ab9KMnI1R19c77mtc69dQJ0tS/dvPaTqTXUKJkRJ+zuka7euqK0DKAswyA9U5ifAD3uLImxQI8Mjyk8p0ZGm0QL4YwAAQABJREFUh7SlqtEB9y5cO48604usm90AM0X4WGYfUinPoqxotupnU0phTh6qgkn4Y4OjlqlLWDn4yT31Ddgo5U1jXYyv6B7p1YVb1zSE2p0pA8JbsgRlc8DyCnBSvnZv3a3t+Fwfc21z321dBEzsZ64vq6xTE+ucciDVCADn9ebLunzzGjBfnHbu2ANcVo9iVRJQ2aAuNl9SC3ayxHopBsgrAdtNijV1JHQugS9jUJ3at/c+bSnYjB+I6FzrWZ28znofX5CbUIAyYALqZPOaAXafmp1hTQ90l1nmzBGrplgHwB1knvHjf3dsadJ99TtUwhy0gHLodcDZSy03NdzdqVj8rqmh2X1EBAimICtfe7bsJM0h6cMT/Grp6daZi+cAGQdUs7la+xt3qgpINoxq5KlbF3T62mVAc68e2HlUe7BhV1ysemb6Of9F3Wi+olkgwDhkx1L9KQCjiQ7Iusw9TwrfH956nzaV1wKsTevUnZO6cPUS4yVBOYC4IdQ5wyhBpgHF7d18vzYz54aUoo7pZQ0BUoYAImMAPmNQegsAoi4B8EYAHjNJsV2VmaY85LF8tqZkhmNPBi+DxJmr+XkSH3/dwFrU9EoRxtiakKks/jbB+doCM+oeHcPvoizlRaWO+SWMMlzIAHbmrzTqXgyYmcd10RW15QhgJcp880ENobi8iAqgqSa6UcILMw+Y2lg680NpToYygb8YeU4ZpmjvVq4zDYhr0NTyZJz6mwFfWuexj0ktMPf4AFtzWEdUJwA41eIbD5YwR7jJajCi29d6nXnETzryeFTkDBxK577u/oON2nEfkHSqR1cA5/6fj13W6Eym6kuTUXUboMRT95TOlpKUhOrX/gP52refdXQJaxbWngvzEfxzWM98e4R7lEnmeFTNbE5mQeNmzRQgBW43qaIff2S3PvjBWmVkeYC8g/ryF69zT4oCK2sMvw/gODIFiDqvsqp0HX1gB745DkGSeV280qvR0QnWzvYsh/Uby6WkuGXt2pau/XuqHCW2V88O6/rtXjY24H+4N6bj8MlR7u/idexYuQ5S5lUA39fO3EvVOgKEXZGXBtzswy4WgOcAvrCFhOR4HTq0Uw8cpP0LuUdnIE8BzV27vaxXT/WovaOf9SibSVDKjDNFXgC3+WWgZvzxzsZE/fiPZwBK+XT9RkDfen6Q+jUzZwNnpaLEGpzGh06rvr5Q+w+SOrohFVgrqstXwjp9rk/DowOUG99hzwZYZ5rlVVckkZWiBGAxU6cuYfPnAPC597A097GsV9ZY+/mZo7c1lumxRyvx+Ul69WyAVK1htfWsqCB1SYXpc9i9KZoG6UfS0QM8NjRW0C6VqgQu82H/K6tewMAoG8ymdet6txamFrjvBwRNYeMpEPn0jPl30rVuKdSjDwGx1Xg0PBLWX3zxjjq6uV/BDlL8bK4D4DaYr6yUjBoHagEs43TjzohOnLzLswRgNfovHlgPfUnKHQEWK9CBPXVAd8ko+a/owsWbmmZN6sMne3DeQVTnYlE4tRSjhw6XqKo8UzeuRPSfP3NZ3QNTys8rxI+x9ooCTLKBZhnF2Vg2WTQ1FunBhwtUU48acyxgLr79bktIr54c0cVr3GMFAFqB4+Kpo01ooygPj+DjHjuYqX/zZLGK8lCoRUHuz/7ivC5cH2ADAKr0KHG6WHvGuhZYV/l18IFKVdXZpk2/bt1YQJltUC3tE7Qz60sAvhiXtcUsQGUqz4Q3q5T5uKXdpZOn+jXIZgOPZ4m257aHNaCpCRcz3h56pF6V1Smsx6L6ytNDeunVO0CFPtYsKGJzr7nK+nZmDhVnVkE1zH2PP5yvHcCRCUCwK+Ra7h0I8YxgVDcuo24JiOhnHmF6Zm2KSiH3IdMAyxnJIf3sTxfrvt0ZAHLS6ydD+h9Pv4ytrykvn80pzF0h5qIUYMbZ2TsaHDpHuuVcPfXUg2y43Ak4hwEBCdsNgfPohc8zHTDi3t5rA5x7e+23cfRGC2y0wEYLvOUWWAfkLNhgAQDbmb2IJLgFWeyhsn213fH2YNnSpZqzt0DKO/kw+S0X/nv+4Nudnr7nC/6rHGCBAnsg/+aX9ZcFpN6tl9nSehDu3UhJtA552rXejeu9W+34o3ody6pw4lRY//fHgro9FlVdmUt/8O9j9OSj7yQ4+6Pamhv12miBjRbYaIF3pgVsLn8rinMGhhw7dswB4uzzBrYYyGbrkc985jOOOtQ6/PLpT3/aSflqAMknP/lJB2h4MziXg+KDwXem3vTEE0+wez3bWU+MjIzod3/3d52Uj3buX/zFX9RXv/pV5xyWAtGU5QwIMnWrD37wg85GjC996UuOktXGuuAftw97CFnxCKlo5qUtqS596r/69MAuHoS/3a2Q//glN/7yLrWAjb911Ta7p2sniGuv4uJiB4yzewW777NxbvcIBnPZWLGvBqLZ9zZu1+8p1sfwP3cPaJ+3c9rnLTWrAbM2fg2gszIZuGRlsBTK5ivWQbr1a1lKV0t1atCTlduuZ8CTfd5U5izF6/pn7Tr2ss9YkLKj+bb+83/8GOoas9rBMbUAERnkHY7JJyCbnclDXCAa1GgyHjumtENHHEUtO369jut1s5/t+3Vwzq5j4Nx6qlY7xl52/zGOOpZBic8++6zTxgbM7d69mwfcZQ68aOlqrS6lpaUOCHz//fc7fu1fyy85dVu/TaSO1m60AG1AcJf0SgYd//Xf/DW7sLuVT8DcoOby8jIe4vc5fzP1T1P9e/TRR3Xw/oPO5jdT+nrbT4GdFv3e/1nvOzvSsQMqYs83zHYMmLO032b7Nk/s2rXLUVE0oNT6xezM+sXmmgMHDrxRF6Lwb5zL+eYd+GfdvtZP/VbAuQhjp+cLX1D44lmAoly5szOATxjD7HL3JAEuAcOm1ROIJd3bciuA2Kkzmm1pRTEM0Ilx48vIRCkBtRegiwifz37kiOILCc6i2LZ49rwGzpwHPkVNhMB3LIFO8n6Rjo/nQ6T2SydYnWrpE1HpGHztOxrr70DBCaWchlqRv1degjGJJZXyAYi5CJZAtWgNGGIOEG7u1FlgmkG5skjbh9plhOBrgKB8lICmDygz48ghxZeVK3QLyOnceVQumIwqOE9RtqM4F2XM+gjsJpVWKIlyeEiVGLpxlXK8qvHBfoAeUj7nocqRATAoFM+w50ASAfzGzUrNIwUlcMXMxcuav3BNPpQdcH4oyGU6bRAgouglAJUO+GfHr2APAydeULTzplII8HtqtipcACyH8mZafQVgGPAg4EeIAHBsYFiukVMK9VxUYBaIJQk1OuAWR2mPgFSAQPsiikpJPENLrKxC3S5J4wAfC+Oov/lXgCWKFPTXKOCjbwzgSkeRDbgriMpPCMUiHwPK6wBJRGqCd7U6eRsoDYW7EOBwQb3iMmgjUrNFg+MAV6dId3kLgIpnPai6uSw9Jup3btK+BlDzCaFKlFRVK3/FAdTGtgPOTWrqyl/IM3KeFGLAPNkAYf4CBxhY47glyh4HsJCYSqpSgoBB0jUuA0KsAZD4k/J5JkgqPJTXtDhDOilgM2CftLoa+Qoz4Z16NAJw4gIWSE8jHS9ruDC240LBJ94HeBMLjGfpXYHvzO8IiDBKANsdGlBo4pZ6m2/yc6oKKvcrweBAAq5BFJ0WR7CPgUvyR+cpQyrwQBoBZ4DvFdpkBlUgUif5siuURp95UTEkaqnVflK/9gAKhEjdlZyraFI1sBXwZmAakbkJlNBCBCOxJ1KCBQniexIyCJqlGa3N31GyWqSOpvaGgltC9f2Ky98JwNit8daXyFY4q8RMzplcrLAX244rQOGt1FEkREYKO8CPIIcTtgA/qU49wWHAMdQUe7vlIwVjYnGlPIUAG6j6RUn3uzZ0jX5sQekLTAAgIZY8Zm5AxujCjIJTqPEBeiRmAirUHwOc28rvUQDsekHzg7cJ8jHGAUBdlJ+Ba9wqUOeUA9vFJlkqPEt3iAIOtu5B+cdjamqofIXmUGzBT66ixpZTVaNMUllFxpo1fhd7Iw2iDxvzJFG+GMB2jktMpP/iiwGuEkldSKAdwMTwVh9Aj5v2Cg1dUGTwAm1JELTqiPzFBwF9LMV0ryLjKGIBzoVR8/Ji7y5SN0ZIaelaHkbZqZMALooj+bVKrztAX9EHCz2avHNVc4MjAKAoUaWibGll8acDn6D6NT2kcZRvTqPqdBJfMjIbwJfX4jo2A0SmOOk+h/DzcyjK1dfWaveeA0pGTa2d9r8InDkJ3JWengIchF0CpRhQ24ecj6UJ3VXZqEebDiib/u1DufPUtYu6eecaYJAXaHy/ioGyOu92AVfc0kIwoq3bdmrX5u0qwC5nCfa/eO24rnRcYpyFUJYqRA0ug7SVEQ0CTA4O9BNkn1B1VZk+9OiHVJlaDQwyqL8+9TVAn/OoGPq1qXSbCvLKtEKguHu6U91tPYwPgsHZ5Q6IZpDIKlBSP2DKFPUvLSrRg/cB5RdX6EbXLZ25eQ5Fo0lUXoDtUCdyAUZM45OG8Zdk1wWQqUY96BDQXQ7qd2N6/eZFXWy9hrJnpo4A7WzJKwWYa9eJO9QPRbhdgNMPbNqpTOo3E1nQa9fOAIxccVLZFeQXoiLEfATcOokqZRew6hJjsiq3TE/tPab6shrdmmnXt8+8yD1iO0F9n0rLy1lbFAPbeAHaUdS6fQM4J6qy0hJANFKDkvZ2GWhukHlinGB/TWm9Duw8CHhSpNb+G7p25zr9PU38Pk5pKPUkAoguAlAO94yQOjNGTXVbgeR2Ud4M9UwN67Ub5wDEOoBxSnT/jvu5dp7ah1v1wovPY7exOrjtAe1uQsUzIwGlLYCrO+eBIi8A5YRVSpsXJRQCyAbVv9qjtsEWfHtQDWVb9PDBR1WFzU2SAu/rz35Fzd1X8eVeVRSirgZgmhiHTye13jSpqVtQ7uvu6VU2c2tlUaVyk5nf8H89o0ManRpXJuP9/r37UG4q0QRQyslLp9m4MElKvSTWiyhZAuMGANzGmc+GJ8dUVV+jB7Ye0Zb0evxzQK/efl2vtTAf4XeP3ndQTVV1QIaTQFcXNNQ9op3F23Vk62Fg93yNRad07s5F0tdfZAPBnPJQlS1A5czSAY6TIrmbOWqwfwBQpkrvOfiUdtD3SwAnJy+c1LMvPAMMDThYhS3mVVCueA0Cfrbgu+Y5VwHzaG1eDcpE2eDCUvfgkGbGx1SA7zp0YB8gRynr4nGdvXpZnRwXC6iTwzogGVjZQDRToFsEdKsuqtLRpqMqySlDNWxSF9qvovJz2YEutu/YofrqaqafKZ0/fxYgZVwNm7Zqe+Me5aUVAVpFdOrGKZ0DXl0ir14hYyAf2IjtNcS2bEMR6WlJn5qJetNDbJzYWrgN+HqNcXBCL599EUgwhOpdLfWrwS5dGpnp1p0bpKFG/aggv1rFrLVSUoAhmKnHUNbrHxgGas3Sg7uPaFd1k8ZQtH3p5lk1A0L78JF1rKkS8NHLNmZ7hwDqQyoHVL4Pmy4CRp2bntfl29d0+vpZ5nW/9m1r0p6yKmdTwPEL59UzMkI8rlZHtx1RTXGdpkiferbjis5eO8f4n1B2broKsnMBPIGSiN2193XSj9PUOVc/duAJ7SrfotG1Ub10/WW9fv40sJQbhcdyVXIflsr8kBKXrKr8LcpO4ZoLKDwNk8YaxdyE+AQAfDYEAPnMAsWNA9oFZodUALS1JT9L5Sm5SuBz6KWhCAf2yHS+xrxn/T5IH94e7NY8AEoF67PN8emyVK2D0JbXFsbUgV3ERknbmYiCJ8qZMaj/hVEoXiB1ZQi1ynzu3ypT2VQP7IsolEZos77xeSfFs4G6CYx5D3OrqV8usj5Yww8X5WSi+peoDOamFTZNdS0GAFBJvcstTjLwykS3R6/+3QhqbKRjBBJPKQdCzUPBjX4s4ZlHCQpXpQ1puopK2YmTvagTs8kKBcayQuZ/P/eOrClM2XJbY4HqtzInJrt19VpYv/Oxa2obTlJdQQoKXPMoH3pQePbpbk8UVcxRoCE3G0grdfC+VIAnF0BuQM+9tKCTZxeBqqPAvm7SdMZQ94g6+0gb2deroZFm/fSP36+f/3cNKJV6NATg86f/pVknTi+j7pWkepTVyssjqM2xBCd1ZmlZrvq73fi0AWCgKXxyHH9PVDLpZ1ew7ShwfikpmDfVZqutK6Knv3mXTQ8e1ZbHqKzYsgygMIZU7Brtdt996dq5C8CVdcSpc2H95VdQfOwaUhHjaHMVyoFZgOYA/X3DqL12DqJsVqsnHwf82sOGCsDw1rY1ffN51L/OtmKb8aooTwds9GlhZg2VuDCgKGnIQy78VIY+/O/o50qvznCdL39tSmcuXSEtZiLjuwCfhEpqZlhl5cmqrMmgnn7dvhXSN78Z0qXr4yjRLqum2kdsGsgfm1tGXTXb1ORqUHxz+fV3z/fpRltYedke1XGNVNTUVqnUKvUsQ1Hs/oNZKqTtXjsT1Oe+gqLfHQDu+CVtq4xlbMQDrMWoG3ittWeUzRCx+smfaAAoTOZ5BKp3YxG98PKqvnNyUquso5qqUT3MIPUyGyZ6UdhrbhsDEMzQoZ0l+uD7k9h050UZLqxPfvquzt1mPc5EXFfmUUN1IvO0h3vPRFVVsy6mIk//Db7uCus9lKprqzKVQflDgO8R1oMZ3Cc01ueisBar115bUWvHHZ6nxDh9HQf0ZTG2IMBrVQ0w785UFQM7Xru0pj/59GXd6hhgTcO4L8tUKTbn862xHlnl3j7AvLygY+8r0gOPYjOsvwZ7peMvz+r1Mz0oxYZYV6FIR/vDyatzwKPrXTHqnerXv30wTR/+mUJVlsawTo+ivnhWL7zewVqpEAEVlEOLPICea8rKd6HUyOYLbHBxNl7femYMNcM+1ooeNjWgzIjSIluZ8P2o0qICWFfPfQYbNL757CSKcwGU3YL09b00tba+XcFPJKdEte9goYrLEnnGhE1/vV/ffvkm5/GqpqKQuZl5hfl4bGIFOxhnM0qODh/I1rFH/fg/5jqef546O6fnnm9mLsF/5ALslgAfsimsb3QVMDKAzwmqKs+nX/tIng4fSkE9MaIXn2MsfvHbzN4Bba6rUFWFKQC6UBV2o0B4jkwrL6m2NhNw7oE3gXM2X5CylX9tT43L3nz/dl4b4Nzbab2NYzdaYKMFNlrge2gBA+PsIbkFM2xXvH1vu8stwGFv+96CoaZMYMEBC27Yrux/rYf930PV/oGPvt3p6R845Q/YrwxWe+GFF5y+XA8mWREtwGTqEaYaYGoulnrpnXrZdXt7e50gkgUjLFXaO/myOlsqKFOwqeXBWH19vWOn7+Q1N8799lugg5u2j/5xUN86gSw7mzB+6jGv/uT32AX47vGdb78SG2fYaIGNFthogR/hFrD5/K2Ac6ZQZOpxBidY6lUDSUwNziCG9773vQ7osg432Fx96tQp2frAADmDNN4Mztn6xGCGW7cIoLKWsLWoKWfZ1+vXrzsg0C//8i/rl37pl5zPGUBnqSitnAakfP7zn3fU7A4dOiSD9AzE23j94y0wPBhV1eOkMVmRDue79eWv+3gAz/7xH/0l8z/eKD8Cf1kfb47CAePYgDUL2hiUZipwBrsaWGS/s8+YeptBbLY5ysaMbZgyxXEbw+v3E3YvYT/b+5962bXtbee9ierV6dOn1dzc7Nxr2vUMhj148KBzP2kw3Po9pX3eymfj+fz588663n62lynMGfhk8JoBsgbPvbksXI6HkNx/oJ7y3z7621oc6FUNKi9NBHobtjYqhUCUB9goinLHWl+/opU18m3Z6qRns/Ovt9d63exn+349Vav9bKlaDZx7swrfPLvRrW6WXtrgOVPsszKaipmlxLX763Pnzn1Xvc3S35papinXWXuvX8/K8K69rLHsbS/ryze+t765dfu2XnrxJb128jWCZvFOPx194KgDAQ7jg1977TVHjc76xe7v3vPe9zhwcjwKe/9aL+ubN/effb9uR9/61rccW7LnF0899ZQDAZr92ZxiCoH2tnFg0Jz1i6UoXgcz38n6WBnf3PdvBZxbQ0Gk67OfVXJPt1J2kf6lBDjI+ByLGKGm4jZ7IjXjKqkBR84QUL10WSmM/RyUXuIYA+70LNoJtaZFUm0C3HkqAaw4fuX8ZY29+ApQ0rDz2ZQduxgrBltgGoBzUdLlmSIcyW201jOgkeMva4LUaHkEtzIOArlUlMtNaiUXilQuH9Acqi5RYIvVrk51vfSCFm83o0xC6s0DB+UF4DNpmcjQiGbPnyM4OKT4mirlb94stXcqcPWGfFwrZu8eeVF3c9lNIQFZIqRypQBxAc9EgLqWnn1G3adfB3SKUxGQanw1aUsBYJB1Q72MwBH1ijHwjfRlgVsE/V55WdHJKUc5Ka5xq9wlRQ4QFiWC4QJIcJGizsptaWWHXnpOkZsXlF6AosiBB+Th3C7U8dxpCQBiqG0AH6L/I89ir4Kdz8k1eoOADEBSvrVxGZ2xShuPaWUIqGWoR4kZiUqtqqYuBF267moelYbMJJQ9Chs473ZF4iqArqgn/6+5gLpE2k2SocWiqmXpO0Ua1rWZG5pHUW55KYxCHCljczYDZGQ6dh+Z6SJd7TnUcUjzmJNP35GulzR20WXgMJSh5lB5CqMqkQJ06K/cC6i1hbSuE5q8/Jfyjl4G4CNdadkOudJqANoI7KCwszzWQ3ZPS2FoUBtoFIpcXqCs2HRAAt48aAGa66GOpDJEbS+OwHd6QzX9m60lANuh1m4nW29uMemDSUEYTcumX0jtGmE9tmbltrmFjkK1zBUDzBgd19rCXS0PXweG7CNVJ3B3+WHArWLaE+gLsGh24JZCAAzppKFKAOwx8EooFUWmB7Q63KWp8Rn5SCWZQVpOL+l1o6iFLKE2Nd91keB+UPH5wIuZ20n9itpdkLSvBP1WUWgK01cR7NyXXgxIiqIhikEYBSniAL2B9ULDXDdAmraGhxSXd59WgL+mWl9QPCoayQa/kd5OccCAMcCmqAU6+RTNebhRdCOwH0GB0BUkDSnw0GxfK0HDedIlo75XXMvHq81QSavcqqX2V+RGdc6Lapg3vw5bJ90sAdAIkMzqyF2tTKFkmoIqVO17gVAbHXAu1PmsZpHQ8QK3JpE2MgbFO6G2FA0BMUze1VzPFdTsFkgvyYBIzFAKMEpMKmMQZbooSoShiR4g2F6U1pj7K2pQBMSXTtzWFD4mTJrgeBT/EnKqSQWbj8oSPoB0www0ZlogHuoWJOWiF0wiHvU9F4DhfNslrtsNQMgYBtL0ZG1hpJCueea2FvuvEdhGGQlQMwFYxANkGA36SUHbrBWAyJXFacVS75Q6lGBT0hWd63TAuVnmnZQEVMrySJWbjY3GWbssUPa76kEZ8oWBLp0mpdsiAdgd9fsApg6TEg41JQL7d9pv68o1wEkC4LsP7lc6Sk+nT51Tf18vioY5pFCrVzEAKwlwNU0q3TbOdwV/mIs64qPbD6kJ5bkgvuc2oPDZK6dIf9qhwso8ZQGpjA5MkGJvkVSwVdq/635VUifyMKsVIOuvT/ydpgJT2lSJehRqXVmoiM0DE7cND+gcylKDXL+8ukQ/+fj/rrKUakCoQT1z5qu63HKOuqbp0PaHta12FwpPC7pCIPbSucs4ag/QQ5N2bt0JQAfIRQT7esd1nb96Hn+Lwu79R1VfXqOT50/qTtcd5RTnsWlpm3KpnwsoYjY0x/PSO+q+2a4koIIH9gFdVWxB5SVOzUPteuXSqxoltWUZ65ZiIMz2dtICAiJU1zboQOMu1acXOuput8aB4E69REq9QTWwFtu+qQmlu3zAFRdpP1t04sZxjaKAU5lbqffteg8KYLVqn2nR86dfcBTU0oD+djB/1TFOQwCLN5uvs0Y8zvgJqYn5977Nu5WTDMQM1NqKv7xw6pKjMLNn917Uaip0ASCqo+ueyl91/WZAgEzUjIBrAH6vo4LX2XFXKSjiPLzrqBpLtyrAEqd1tEenAM4nJ8ZJ85mngpI8dfV2aLhvWI0V2/VA02MqykKhLnZZN3uv6PWLr6PO003wvkF7GvaqJKFU86F5XRu+iJLgKc0MTpPytkEP7n9Y1aQQNzWyp7/5ZaC+W6ijpmovim+NRTscVaBITBiA6rZOnz/DZoReAJBK7d22V9Uo1ZlCadtIty6gKLY4OaNdW5tI8VZDsL4fFcALSkB1qa5+E/BDGQBTPGlLQ6TI7dGLx19gunLr8PYH9GDtw6SNzVDbZIdO3DqBvV9EeTGb9XKJs9a/C+BZhDrbg0BojcWNgMOmiNihb73yLDBOP6AACjxNO1QIIIjUpnpQ2zt1/TW1MG+W55briQPH6OPtqE4u6dTl0/r2S8+iHEtWmSbgxM17lY76YjN+7fUbr3O+QcCuKh3efEBVZfVOCvbbne26fO08KXjHtP/IXm0Gtu9lbfD6qQtKoi9qGjZxHdSJALWXgrPqn+5lU8NFFDWjOrTlIGkA9+HSEoDsehmD5x0AMiMHiKYkB186qeFeYBCg6KP7HkbxitTvpLwcnBvRt77zDVINU4eyMu2p3aFSgDpTquwn/fCpC6fVNdCu9KJUPfLQw2rKB5xbJs3g7e/opXPPM396tX3LAW2v3YfvcKmt/4pOHj9BysE1NVTt1J6de1FYYs3jIgYx2KkzVy+gRDTjKBDu37JXd4d69Qr+IozQ5JbaatQrUSK1+qG014JNt7WgIEtaw52oBu6q3al0lKIGxoYc1cCuiS7U4NJUT//1dvRooHcU+DVfh/YdJtVwg6Mod3uiUy9ceZW2aAeaLdbOpiYVA8nGQK4NTwzr+MWT+KG7ygPk/8kHfkK7SrZqaHVYL9/8js4yDsIrLuCizTq096BygNpjeRAfx/y1FPKrd9TSFM8xXeQBHFnaZi/AvDSMUl4XKm3zI32o069RpxxVpOQ4/ebhdsKAOUvBuczadpL5pwuF2kGA75h0n2pQt6tmTZLOZ3pQiLsMtNiBX09AyquYtNEF3M+RURJVvSBKVkF1od4Fr6iatDj8I6p/1KudVJQDk6ZwhuoZ94M25m0JYfeNE8CRwyOARvyuIQf4DahuhrFygzTLo0suFbEBIAe1s86rQT39hVaNjCUqvyZPRTsBjqrcqM25VEUdkuOAwYFmvvEy9zpnRlEvzdXjD2ZrSwOKadiB8LMe2iE3CwgrE1gQIu8K4NxHf/+aWgZ82re5RE89Eaf6BoMkpdstYZ27NKAJ1lIH9lfrvY/lA2+hWPX6OAAqaaZX03Vwb652NKKYl+5CpVE6eWZFLxy/pLbuW/qZDxzRh3++QWmAU/0AdX/+GfzYhYhK81J17Ik0bd+NYl+Gi/tOgDAc3bnTQe5FsS1Uxw7sKyLNK2BxGop9lJlJ20mj7WPefvnENP6qmxS+ZXriaKoaN7MRhAYP8Dn4RKA7l3Jy3ZQnSjrZNX3hv1/n+sNqqq3X40eL2TjFfQRt33Y3rL96poX1KuPoSLaeem+co+Z2/JVV/c23hjTCvPz+H6vTtm3cKyShtDoV0aWLAYC2QRT6wmQcytMv/UIqYJ2bFJ+Ac08v69KN68CHANwHy9RQHwvU63ZU+9ibQRpRF/P3gr7yPwDKB6LasyMBmCmelJ731OSDjOF46hHPWroF6OmrfwdAOp+pw/utnb1AdbQF9VtdjpA21yBYg51ciDmE9NmvBnT2Vpcay3x630MFjH9UboGeBwbX9MJ3hlG1HNWR/ZXExVNRRHPzfGJZ//3pcXX0eJlH/frQsTSen6C0yCOLq82zevHlLqBhngdsLdKHPpCo7dvc6ulf0yf+6wCALiBajktPPpytfXuSnD7yUW4TpR0aWdZnPvuaevv9jOstOnJ/urJJ8wr66DyL8aGym0rZT70W0iuvkHB7aVoPP5hDKtsE7g2o3//H3nuA6XWV59rP9N40vVdJo957H2nUreYGNsV0SAgkh+SQK0BIgPxAOCckIYQYU417ka3eexu1kTQaaaTpvffe59zvFp8vhR/+QDAnhH+2L3na/nZZfa33Xs9DXWHfgkIZqtkeIX/UNa9eHta3/+mScu8Wavrkedq6aTJzXW+gbGFjO6pL53t17cYlVBGDtfNJLFKjYlFgG9Put6pVzXxyypRELV8SCXDnAYQpnc8BjDw+oryKUr1/XYA+/qF4bI6ZO6Dg9tdfuap9J28DdCbpPY8tQN0tEGgSBWSgxeAI5p2+bCAo89JPf1SpvDvNMAaxylodCSAIdEm+DfQz72F+6u/vAYQ2qu8+y+aX/ljU3sjDFWzWAtpk2EkeosZM35rAff1RwqskbV96pUoHj95ETc5HG7JmAnJGKZzy39o6pkMnO3SH8hofPaT3PzEBS3M/wLgh7d5TpdybJZo+MUnZq5KVku6NMrXI11G9vadBl271AM756s8/E86miUA1VD8A5579ySEUp92UvWaO1qyiraScDjAmO3PmsE6fep3+N1KP7lzPuhHzaU8K7wiZS/tHE8k/5j1METCVtpnCf/oYB+f+00k3/sHxFBhPgfEU+M1S4Nq1azp06JByci6zcNzqWPoMMtD08/N1Fo7Naubxx58gOFlFQCDXAa9efvklOjOjpv+7HdZF/WEfFigwBQAL5FiQwGUFZNCjBXos8DSfXVqPPfaYowDz8KL9u5Uypm5hgQqzYzPI8utf//q7delfeh1755dfftkJmpvKzEc+8hEn2PdLTx7/5e9NCnSyK2X3gWH99deH2N05xkKZu77+BW8Wm1iMHz/GU2A8BcZTYDwF/stT4NcF5wzY/4u/+AtHpdgsVg0g+cpXvuLAOqZa7BqL2AvZeMTGCaY4ZWDbhQsX/h0451Kcs37dlKpsnGLjGYN47Gcb35jinIFzNq75zGc+48B6n//85/WBD3zA+dmgm6997WuO8pyNY8ePX54CrG/rIgtg6z5ulkjS1okeeu1NH4Lgf/jj5V+eIn84v3XBRC5Ax+qyqb8Z3Gpzv/z8fAees6CDnWNfTQnONkoZHGbKXAZ+Wb0zYM51/DrzBrue3c82thw9elQHDhxwYDi7h9VfA8Y2bNigRx991IHhrH2w69p43tTMrA25irKEHQbW2bVsY5dd18AzszydMmWK82zWNrzzfKjqlN++pR9+6S/VREApGeW3JZOna8bm7Qpl7uOOzd0YC5uj9fWoaAXIMz7Bsayz+9i17XC9n/1s37vAOXsGg6tc4Jz9bH835TKzNX399dedIKUpzdmGM5cynbV1pp5pc21rJy2NTd1sy5Ytzru5YES7t+ue9v3v9LBX5f3euZ9Vd35nwPKRI0d06DBBcWCY+aT1li1bHTAunI1XvWywMxDS8seUP6KjIlHQ26KNmzY6iigeWIba8Ytp6fzyd/w/u6crT+yrWROb1ayVPYNEp0+frve///3OBivrEwwktXrw5ptvOmCn9UeWJ2vWrHFUDV1l8lc99m/7ju+k/c9v8OuBc4MqQFnVs6RckYAHvhlJwCeobgQG8A8FKfrDsTFvddwqUMnxI2rHYjAF+8y4ZavkPW0mYB0yCaMoSA5iq4hijBtKF6PYdDXuPazKfYewMANC27QZWGwlbAwgmRUKi7YBUz0AqgY0UlWnmqOHVV9yz1E5iV2/Tj5AqDLwzMvaCoInBBdHUQDpBMLMfftNlKGwOASISNi8VZ4RBu/R5rDm1HLqqAquok7l7aOp1M/gBkCl3DxUwULlNW+xvKaiLheJrQ8KIm4Ezt2wIrMd+4NlKH799Dl1l95XCIG8Cdnr5cUzuPkSkTTgicDnAConCNvJo7JBvSdylHtov/y5TuaqZQpYvBiACsgJsIkHBjTi/fg6hnLJaGOTKvYf0MjVy4pISVVA9kZ5Ade4YUFnIKEpiI0B2YwOE6DqQLHj3h4AuvsKJDDukbiEdACGQL3EbDW7Ab0ayq8ohGDWhPSJDljVUFoNIFFHQMsf0GkBcNQC7h+PoheW0uojOObGG2L7OUpajvTxTFhVAZT1N1xWT1sh54YqMGaVfAGnDPgb60XtArir1SwCA/sVlAo0FzmFNABSG+4EWKpQR3GB+rBdDDVVvQwDx9IB51rUdP11eQJJmVqf5+QswLlppAeAldm3NQMzVZ50lMAGCDS6+4QrmKC9T+xi4EyAPwI/poLXXZWLih62tlimhaPI5xEHzEAZqSts4D1QMUufJb+EDKJ3RPGwgBsbAtgaIDrGx91McA6rI7k18zyViOBxz6Z8Apv9WA9PA3RbDRgZgahPG2BVnpqr7qFqhW1SapJ8YyeRFgBu1n9hWdcHPNZSW0VZCVbEJCw2DYLB/qm94o46S685gcbgZODpiMV8DriNtB1pxJa46py6G24w5qH/S5opz6RV2JOm8FwAoIPdGuDvg8XH4fOwnZ2+nvdfpd76YrUVHFKwdx/Wt3PoR5Zjm5zMewBzk28PmlcsXnk3KwvooJAPAHr1WGqhwDNIEC00HgACm0V3f/J+xE9D9dgk39mtQK9u1BdnAj8uQjiOqKeBge2N6q/JA1jIR4HEG0h0k9wNnOus1FDRHrUDe3kDwwWkrUZpj7LqgwohfeFYyw11Fx7Dnq4CZUogOKC00IlzSVOgQsqYG3Z5w02FqFLeIRmxMEyeBLRK+UbZsK2qDDgmEhXLxbwzUKs3kOIoY2nqF567wJTuAGWAc7wbOmPyGwDObChSc2GJvEc9FBwPYBk3h/pAugzXAQZeUEPZdexfsftCVS4wnndHoc/KwmjzTVQbTziqegqfrIDJq6kX1E/AzMb8a2oDHAxHrSoMq1XPyKnACgB8/W0oAd5XedkNvVlwQ2dR9TH73+2LH1WWgURAtr2UlfyqfB3LOa5mVOCmLZhNQDVE5w6dIWt7gBWmajJAazBAqBvlHuNF7PvKde44QG4n0A7wz7rl6wnWh6O01IqS23Xl3DoN4FJFXTaFJNrLCclaOWmF5lMOwhjXNHfV6zxqZXvOHKAMB2vTitVanjhTIeT5wNiwygDTTl1FrQ2LyrDYcD265QNKmZCJYleV9p77Gco2uagepeiRpY9rJuW1ZaBO524f07kz5wFmggiyr9SCaYA2wHWDXO8G9f4Q0Fl9dZ1WLV2p9PgUwIJTqM3Vavq86ViYTcdGEqteVK+GkGmpa6hU/uVbcKP9Wjx7kVbOW6nY4Di1oIh29d5lXbp5icA/FtuMbQb7UKNGcSsbAG0WcNgE4JpWlBqP5p3RkSsnhaOgdq4BOkqbTrllbDXiBVhTpTeu7QM0zFdcYKJ2LNqpqcBMRZT9o2f3AgFVE3yfCASUjSrTVLUw8bh865KOnHgbq2ofZQMqL0tfBAg2AbvuAWCUIp0+c0pNDXWaNWeaEqckoXrG+9FXTMMWc1rmXO4N2I2l8pj7gG4X39ANlAGHO4a1ZcFGZU1do2DAoDYAVMuXs3mnsQ+tlz/9pgGsE2m3s+du1tx44GvyqIO26PjVQ7oAWDXiNqLsrHVaMGmporxisRxFQavluo5dP8QGjSqAn3StXZrtgHPN2Dy//NaLKmko0JTZU0iz9coMn4ZVnDfQ0YiuFF3WsTPHgE+qtGL1Ckf1LpF0B0tXaSdjlZyzqrpXgspaCkqE6SoGBL5VeFtxqYlYvs0APGAjC5S7afK0dNbrwME3UXdrw+5zhXYueRp7QcC+4TbS/ZaOXToMZFYMYEofSH0LGPPX+kXrtSgTNTYAWLNvvQ4c+Obbb9BemZLyQi0GVgwNpM5Rv2u6KnTqxgnlnM9RPHbdG5ds1pzps1C2xTb02nntPbpPQXHBwAJZmg906Ovho6uoKx69ehQbxUZAt0XaPHsjQGoqaeip4poKHb90DMWf65q3arbSJ6ao8PZd3bySj/0heci1o1D5RMMU/Z4urIlR27M8L21B+XG+Nq3aohjgqs5hoDNgtxMXT6sa0HSU5xlDhSkapb6lU5Zo7cx1WANG8fkh5aIYu+/4m9jYt2vdilVambEYO05TsMUuESW2U0Btl3IvAJ+7aVP2Ji1Mor2lzh+7dUiHru1XKMqLaxZso2wv14B7N3a3p3Xs0BHEZL1Zr16nlQtWIFobRj+I4lNjgQ5fO66Cu/eARBajPDUPtcY8XUPBMTItEnvMWUqh/zNL035A8TJULfOAEhvqWoEIZyp79ipNCUlDYbFXl2kjT98+q9o2U+LzUkdLp2L8o2mLVmn5zKXYx4bDbg3rNOn9ds5htbe1a/PS9ahELlWYL2MkFFhb+lv0Vs4Bnb9/Rf4o0j6x6kktSl2oBiyGTwBWXrp8SQH0EcvmL6ccrmXcF0Y5ZFxFDlT1YFNcVa4BlP9SmHslhkfTtz/YKFA9Mqq7AKKNgNYxwDuzYmNRhEPRibGAO7DOADBuP+1GE8pvFV29wJ819H1dACVBygiOViL+h34jHqpg89I1VP+KaLODAaLTY2KU4gdYRf7Ts6iaviunqYH62arJ1NNMwNgOrp/X3qHazn5FTIhWDPbyoPcAKIxhGU+2DA2gVNgqN8b1s2NDNGUC6nK93bpS26xBPyxUGe/FjXnp/pV+/fSHeSqv9VLkpBjFL/bRpGneWkDbM9fHE+AWwBiL7Jd3VwHa1NMvxGh9VgRQkheqX+6oV3koFIvHANZI3IF7hsiL69cH9NW/ywFYjNDG1el631PAfulmtwqwe39YR0/UAdfd1czpk4HqEugnhrVvX6FOnatCSQ0Q65EUzZiOui9lkeUxnbkwqBdfvgxwd1Xv2bFWH/vodIWiSFZVOah/+24pkA/POyNcT78/QNPmohCPUicjbuxoAecAwN7aXaju3mEtXBCDDTG233Fcm7IUBIhEtqmvZ1R79zfopd1lxHUTtGF1KEqNAHgTgOewivUFysJRnL04DyxXLwMG/uj5XLWQJ+tWzNTjOxNQDPV0LOiLiof13A+rVFHlq6VzgcfeG4LiHJaZLzZig93CJgdv/clnAeBmoBAIsGuw09UrQ3ruByW6Vz5A+5mgP/5EGBuz3LHtHNbPXu2l/b6uNUuTtWN7Am08zw285owVKWc2xTx9ug1wrlWlgFIL5wWieh6IEp29Hza4vp6ofDI2HxjT9RsDev61YtW2Yk+7KIB/WHvGeToKZH6+Ho5QA1+cPSBHTw/rX18a0M2iMm1cGgxYFQPYjeUv+dwFPPjqG0165c1yQPEUYDCgK9bKcq616bmfMp4bjdfO7FC9fyc2reRhe/+Y8osG9OprBgyL/hpw7vFgNssNoUDorm/9ay3qrtK8Kb76xDNhmjPLlymIbUikuvCvDIW7//3tEyosctOs6TMAsyKVjFqeP2SpH2XPj/ej69fh/X06cqiN52sFrgvX3HkoZ0d7UI7IwwDO5dkp0vQY0jXAuX/6lwvY0xdqxdIFlNFMZU7zsakdY4dRXTg1qJdeP6yMiT56/MnJKLvG6cJZ6e39VTxTu7ZsRR1zWQgquOQFA9wLXO+nrwCFUq53ZAXpYx9AKTaBsRzg3N/83XXa6BuMPRL0yU8u0dxpQK68HwNF1JcZepOPNeXSTwDnbt1uBvIOB4gLB+T1ZpznzhiXZ6cM2n6pmzeGeO5bauuOAJIMo8/xZGOn2ayiABlg/3BUA17jVFWUj+iFl6p1/NQtJSfS7j06Q4sXslmEPOljLLP/4AB9cA/pUaf3P5nIJoQgXbjUq5deuwd82K/tG1FX3YqddBxwINerqR/Vz37cqP3H+xTD1PlP/yhcWdlBbBQaI+2x3P3JAeBSf2LqC1AtBZqMAAzFnvfAvn2s97yhNKx3d23fDDi3FHAOa1zaPieTUWAeA7h+kN2WOz/PeL77TY9xcO43TbHx88dTYDwFxlPgP5kCZmFi1jIWQDF1AVMBsd3yq1evIaCxy1ngt8DJSy+9xC7z0+wwyNA//MM/OMEK25lui9MG0VnwoI+fR/jZmwVIXx9f53s7xw4LvPw7q1AWuIdZ7LKgil3Dgp8W/PjdHtYx/WEfFpBKSUlxAtMWAEhNTXUCGBa4scCB5bOpCJrSgym2WGDM8u7dPCxPrRy98MILjlXRJz/5yXfz8v+va3V1dTkWbRaA/8QnPiELnlsZHj9+v1PAFpZvF4zqC98YZMFohEVON33sCU998X94O8GO3++nH3+68RQYT4HxFPjDTwEbn/1HinM2tvjSl77kwGs2tvj2t7/twHDWF9v48HOf+5yjdOtKLYN3DI4x9VtTqTsMpPGw4pyNUex6BviYWt3cuXOR7Q91+vXvfe97jnqTqcx9+tOfdn6/e/du/dVf/ZWjYvWpT31KX/7ylx0w56c//akD17wD1bgeYPzrOynAOrj+178N6a+/azZl0qMsLL74Ax9n1+c7J41/8982BQzOcQE+BqUZjHbixAlHudHmC6asZRCbzb/s76ZCbueb6qOBbaawZkDRv5u/kRoPzxtc1//F39nvDZQz0MrUI62dMFvYnJwc5z5ZWVlO/Z80aZID1tr5BjudPn3agdBs3mKgmoFy1g6ZBbMBuXZNA882btzoKOO51Ors/mY5WomF44tf+ivVo2yZhFqCBZqn7XhMQTOmIycA2ACUMYpN2igrxh5APO7MT+34xfewn+2aLnDOfnYpztmc1g57FoN0TTnTVPXSsB/bsWOHsrOzHZVv55n4nCn82Xtb+2Vwmlmf2kYfg+tsvuJKO9c97dqu39n37/bh3IfnchZseUdL32E2zd1gp7+1pxcvXsQCJVjbHtnmlANbBzCQzM6rrq4CFDyB/ek+Ared7KKfx3rBY1j1THHWDNwBKuz6ljYPz+t/l+/jSh97PjsMirNyZ/C1lXmDsTdv3uwoypmioqvMWF4Y9Gjn2VxyyZIlDvRofY71Xa6+4+F8seu73s/e6WHw0f726x6/eM1fC5wjjwp+9CPVnruowMgIBSfGobwVjmpLvAJQcvM2MA5wru3KTRWdOqyBlmqlLUBxaNlKuSVlAH+FELtgzg/LwyoMgT+ssYByKl/drdqDh1FRSFXkzl3yASxzI3I2ZioVnO7mSbTNnfrV06fhyhpVAIFWFxcqMpaAzuYNWFhmatjkIAgMexqMRuB0FDvYlmvYDRGc9yBYPH3FcsWt34idKWpfSD6MErzuBBYouHBejSi1zFiwGLssLL5u3FJdMxaTKB75pqTKNyFeoWlJ8ktGmRIlOhGk771XoOpn/0k+Xe0KX7pcftkb5JmQRKSPUCpB3kHu14MkgRcwhde9KvUeOKPrxw5hAZmuyds3yH/2HBinUIItKOghWTLG+6EDgPVoj0aAakv27dfIlWuKQiktaEO2vGZM47mB1AApTC3Poj9j/dSZ1nvqKj0IoFauQOz6PBIAqPxQNeNao6hA9NQRbKw4qyCfXoUmocoHfNFY0aReFA4joicAahGs95/JNbGe5LpDHkRaLTiIJIHHCPfB0nK0vVxDBOZ7W+6SDz3yjUiTVzSAVNBkyiFwVAe2dtUAWajUhKB2EYzF5FjINCw0CdSDAiHzpa4SbIiq+HtCovxRmHL3TyQv29Scu1+ebUUKAB7xyszWKEAcckQEn5GU6Lqt/vLd6qu9qZ4BDHADUUpJWQCrthzgEpDK0mK4WP1Nt9RSels+w90KywBEjopQZ1WDGkpRhvFNUUT6AlTC0gDLyHexNjNE4I3gmnMAXhHGRRmuBOU30hLYY2ywSUEoBfqi3ucRQvp40tYOY59ZcomAfSnqP76aMHGKvIED5Q44R0B9rKdC/YB1TTWlAKSeipw4Ud4RgHMsQbYB23VV3FBYAIHhJODpyGVOemsYIA3Ip7/yOFanZ2i7WM9MWwD8mM1jZnDtEJ4VgLAWeLB0HxBRr7wnZQOmrQbuK1H7vcPY22E7nrZIbrFrNOqdyLtgF4bNmdXtUbd+qkwfyYSaXl+T+rGvHGwsxuaqX17YRfomzMGKNY10RBlw0B348SL2r68RVMd+aiKqgBErgNNQNDKr1p5mLIRz1VF1leAwQe3J6wE/UQ0k74eL9qu3rkSeAGe+KY9g4TqVJgDFR3cCc53XNFC4Xx2oiQ0DPgYlz1VwKuAkwNrIGLARVrNjqFR15t1wFIgC4lNRFCQg3pIPjFcGlIstegZlLYoy6g7ACBjlxPisDlDHRj2GaEW6UEUEYuEznVUFWJaNKiQiA6U6FPOCU4H4gCUHygH0TmEXeQNByGhFpi6lHM8APsMuGZU4SF+snI9g81qskWDskNNXyyMMUK+/XA13rqoFddCImFiASBQdsTUdccfCFhDOrese4NxVvZl/BXCuFnY3Sk+v+qCy0rJZh6b0j/XpXuM9HbhySGVdlUqfPpG6BeRz7DKQZocyYmOwPQOawxp1GGBoiPfpHkLxqqQMBT0/LZ+xTFvWPqIJIbFoSA6psrtY5/JP6PjZY+roAthJTEZhaIU2ZqzVxNBUeaEAVo7a3rHLp3TiyhnFZyZqe9ZmLYycLD8sCa3NbaZduHD7qo4Af3miprRt4/uUFjnNUZzbc+Z5lQBIZgK/7lzylCZHT1EjtqFngXlOnzyFPV6s1q4AYps4i0A0lmy0b/ntRTp08ZhKC0sBAQHqULXMuXgO69A6FPXi6ceBoSx0Tp65W7vYidNMVSNwo48Wcf7aJWsBwJI0jH1iVVuZTl4/pbO3rwMbNCmBdmHTfKCcTCAhwBfM/HieBr127k0AtHPATOH6wIZHNR24DpzTaTuqUdnbffeILt6/igZROO/xuKYBuZY23NThU2+pDqh21tS5yl62SXGomdUzhriYdxFFnzcVkxqm7ese0YLYeQrECrgb0K+4uQy7v5NABPmaOD1V0RnROgIo2FzXjureJOwcU2h/6c+A7Nz5pr6jBuioSj6835aFm7V+ejaKdABqvH9Ra4n2XNuj87dPY6PYpQmhIdq6eotWU6+TvQGeGT40Dddr/7k9un7zioIBrTev24RK2wKMsyPAKrERHSzRkev7lH+tQNFYc69bsRFltUw1Mb59fe8rKgVEXbx8sTYt2Kwk3zSqC7AI/128f0Enzp1wbD23omy7DGXEMI8QYKURVfTV6jQgVzEwWQxKi0n0ecW1pbrFOwcz942KiAby8KXKsSGe/4YGe3S/KA+Aql8LUWN7POsjDsQ3THlvBgrMKbmoNy/sBsYsV7B/GHDbXO1YtkspgH7+pGtDX4MuFpxjI8c+gLwgrV+zWvMmLVCgqZRy/eaRBl0tzdGRw4cV5hmhTYs3As7NVu8winM8595jBwDColCe2qppcQCwjIMuFwA/Xj2AYlyP1s5fow1T1iuSuj5CX1aNZfuRq8d15sYZZS6apOi4cBXl3VMh6k6xkcmUo1ggTDPXo+2kDvaOdmI1WaZRbCtnp83SI9k7lIJdOiNKnr1ZZ/PPYWMLwFpRilqUJ3an87V+Vrbmxyyg/0dtrqcdOPW0jl88RDvkrh1rN2tp4kKFjtDPQuA0oZx35f4NHTp9ANirVzs27UCRbRHW8kM6knsAVbYDip+YhGIhIgN8rn2oRVfKTugwG1+CaHvWz9umxbMWKjKYMQ9pXtxdCEx6ApgkR1PSpyszZSbKh7eUX5wnvzBvFCljUVoDhCctBmnu2gH06wH0e7GMnD1jtjZTx2YDBLtDpFhZOJx3VGewQq5vQZGN+dmqGVl6ZOZGZVDWAwF8u7Ad33ONDS7AjR6ennrP6se0avJiBZKGY4yturEy359/XMfvnqMPHNOOxbu0MHUxQB3gXO4JNr1cUTwKfxuWb9QsgEKQJlo3D3XTrN+nfSitKAFGHdXkhDjFYuHtO4biHKMaMDIVYEFbXV6OFaqnpsXEKY06FAwY6s7YuxcV0k76vqr2PpUDknb2tmPR6M08LEjxKJpG2YYAVCmrqa83sGMspM2OCMGekrWgBC9vRdJ3sudB1TRXZ9vraffrNZH5UQZtdDPzwTwgwZaBEdTE2HTAONAscK1NG8ZyvJuxagdwjCfjgZlYXpQxaucAAEAASURBVE6NAJyjz75cTx9HXzIzJEIpXL8ahanXXy8BEG5Ql2ef/JNHlZAepCUpiVqHgl5GFPFIFNlOniMNj1SjOjWIumiAA4olpngyRvbH9jtY0RE+wGXW1w8q92qPvvwVAPzeJBTXJunxx7CfjTMrUxT6Skd05Biw9Km7WBRnAs7FY4+NytXbd3XtZp3mzs9kXpIMLAvsxPWsg716Y1gvvXydtbFznJ8FODfTAaJqq4b0nW8XKf8OIOyqSD3+tJ9SJmGSC6Bv8GBnp6cKCwCE9pUpL7+YPhD72aRgbGJpU5JjNCk1CHDMm70fbthTdutVwLnahh7FAvykp2CNGx+kBNrglDRUAONRI8Oetw37yktXzao1Vz1dTfRXM7V9W4Jisa4dcUMBu8RNP32+VQUFHigrjuj97wnjc9IPflwF4DwA9ButT/5JqNLSAfwo+3SBupuPgt0PgcdudWrp7Cj9kYFzWNsaOPfiG1i/oqK6BVvbHTtjlAGAaBayJAtzYsoZeX7n7pD27qHfyGGM4tmhBGxKY2MDlJgQgWVmhJITUF6zvrgcNTwU067c6ga06sNq1BOYKYh7oUiYxLvGeCsMa01PoK4TZ4b0vZewyGXsuj07ADgumE2EtEn8bQi15TfeaEN1rwKVyQS9d1eopk7xoL636EcvFdHHZuh9O8K0YyOtM3nYRVksqxnV66+VEA8dxI41ls8EI1zSB+znqW98p1b5ZYFagVreJz7syzsybmSK5Trq6gf0s5/d1JlzlSghhxDLxSI1LQA40DY2koeJKOEBcOahnngYcO52/h3WccjrZGznea/EFBT8MsLoXxHBAbTzJr+vXx3SP3/3gsqrq7U+ez6iOGlKSuG+pG17I3l8ZpA8O6T4BC/t3DXVAedOnZBjQxsa1qFdTyRq3sIg+kx6CJ41L39ML75Ce3nyJkqfwfroB9MdcK6/a0zf+NY1XaYvmzs/Rc98eJ4mY6NKkWD+xFoDZWCYetHaMqaD+5p08ky5Wjq62YQVyD39yRdfNlNijZwSrJBgL2A47FdRkbuR38W6ZJ+TJwlxvGN8qCZmBGE3SzkNtvkpoFsloNtLtVjL5mnG1DA9+cRUzZoJtEeeDDPn2L/f7JF7GfpX6KnH2SA6PUQnT3Vji1tMevrriceStG49aRtmG0LceS7p9Re7tHvvAJsWevUnnwrRmrWohteO6vDeYf3g+f3kHfd5er5WrwpSEKp6TU0d2vf2WwCNe3mfeO2gr1++HFV2xphjzOlMIXOM+mp3sLEBLR4pYxXD6v5vfoyDc795mo1/YjwFxlNgPAX+UylQUVHhBCfNssRsPL/4xS86i8BPPfWUo+bhUglx7f63oMAHP/hBZ1Ha7LUsGDAHeWg7zKrGAi+2wG5KAGbVZaCWLVCb4oj9zg5bTLfgjAVH7RwLpNjfLYj6u1UG+c91Ss5D/zf5nwXCzMrIgjr/ip2LgXF2WJpbXlkA6J//+Z8Z4BY4wYFnn33WgRZdr2eLfZaHZolmATXLk192WHDCVBYs0GBBt7i4uHcUZewalr+mDOOyhrXfuUBLu64pzljAIiwszFHBs3u47m2qEgZS/n/ZyRqcZ2XHgnkWdLL3+Md//Ed97GMfc1RvxsG5X5Zrv3+/a2N338tvD+tvvjXIQoC0BrW5r37JW/OmMrIcP8ZTYDwFxlNgPAX+S1PA+noXOGfjvx/+8IfOeM31UEVFRY7C7L/92785alamNGuKtmapaiC7AQw//vGPtZhAvGs8+SMC/6ZqZLaqNtY0FaaHwTmzzvvbv/1bB4SzccwMLNysr7cxpinNmdXrn/7pn7KT8ZMOOGfjV1OaM4DFFHUNsHnmmWccq1gbg4wfvzoFhljEfO+fDOjt07bfXFhNeOm7X/U2AZ7x4795CtiY2g6De2zMbPM92wRlUJT9burUqTJozeqIzdNcY3+btxnYmpKS4ijCGQxr4Krr+EUAyuYXdrggI/ve7u06z+YDNv+wZ7B5wc9+9jMVFxc7dqYGzk40yIH6bW2N3dvaG4PMTCHs6aefdr7aNe1vph5mgJrNbQw8M7tTF9Bl9zdwrjbvpl7+4hdUeydfKQSHVqxar8lbt8t/CjAE6gVsRSdy1c5uefQtCMi4A9vY8XB6uX62d3CBc/Z8BvKZlafNUex8mwPZM5k9tc1pTAnPVPTs2V3n2DUsjeydf/CDHzjPb3OrnTt3OoCdfW+AmV3P9QzOu/w8/+xZXGlp378rxzvXZl7M9/0EpdoIdJlC28EDh1RVU6W5zO23b98BGAdsgVq56+jsaGehPh/A7i3dBE4LR8XvEQBnKyexsXGwS0AbXNPSy1UmXM9vv3d977reu/HVlW52Lfve+gQDRF955RXneysvBjSatazNj+257Dn6UNC7jSqalUlTn0sEPrPzsrKynM2ELvDvF5/bPmvzWPvqOuc3fY9fvOavA86NUY4asMqtOH1G/W3AZTZVQoLBh+B3FPU1NgOAISZV7SjOFZ8/piEC5GnL5itqMbBTbDrAFHajBorR7rOKz3r9CEBLHeDcK2o8dlTp2O6Fbt8lTwLRbubhAwDnLOhbpMTqF37eI6VVKiWgXll0n2BHpNK2blLglEz1c75BeYRiJECr0XYAjctXlLNnN+oHPpq1ZrWisRJ0LFEN+OrCWjE3R/fPX1I1waoZi1YpPilRg9UVqipBBauHNsPS19OLAEqIYhl/hFKvPJJS1IeCY+1z35UfKkITVmHnuS4bpbM4RwVqzMMT1Rosnwlo+mCN6H0fsGzfaV0/cUwxKAFl7Nwo36kopgUYOGdKKpQFHtnD5A1QfBlrqFfR3gMavoxdI/cycM4bpSO3IGzySI1RQh7uBH/VC3QIONdbeQxVHyxSsd30TFihUf/JpC11HoCgp/Gm2kuPK4jgXihgn5t3tJqx+Ovt6FFEZDgKYAB8AZlcFVtKgMMxosamBIixNnfpk0dXqUZRzOqvQzGM3/pgw+oXkwkYl+lAR24o8Iy2lzlgWDsWpkERHgpJm0s7Nxs+Dfu10W7gItZqyvLVhX1kcFKcAlOnAzOhONbXBTiH3R92mIHxGfKctEGjQaiVjYAvABerm/tW7AHYus76zQhWefEKSQWcS1jKMwMhW6INVqBcdQ9bz3x5Y30aisKCR3gIlr/12H92yT8gHftPPhORohFUbQxaoMg5NkuE0EhMFIMG6+ADc9XZdA+Vmy7WAUMVHJPugGDySSctWRwY4f1LLqiuskKewKERGdNRWAMQk4FzXHCgBDYtT/WAc27ebopJnyi/8GTgxTGguEKAs1sKCURRzcC5iCWUZYPiulAFrKa8YbtYdg4AQwpIXyL3hA0oF6aRJ0ARwICjDac1Urlfg2wE9kxdi+LZSvK1VG0lhwi6olKXskju0Ws16sWzkO+mxmEEzphBc6PtKKNVarilSO3lQIEW9MeG1TcWiBU1JvlEAFP4ofrI2lv9RdUXvgXIMko6LiMdV1MmgNWwrtMA4F3NdRjIHPn7sNk4E/vgqBnAqRUaKTyMkl0VNsJTsW7eQZ9GefLF1su9T+6Ac2Yl3FVThB1wlAJ4/8DE2YCBgETYc3oOo2bYdF8d925roGdYgXEAfWHU8+Z8dVAPDewLmLiONAOU9CD/nBrO61ldGYVKdAMKHAFCb69Ub81dbAxRGQxJxYJ4njxDEwBZSWfaJ7d+bH8bz6qu7A6KNgmakLwC+z6UER2lI+pT1y2NVQH6Vt/XGIqNPmlr+Hykk691d68BUHRisZoCEDmH66fTdwdTTtuAOwtVinLW7juo8zRUo7QSo/dlfUgrk7NQWjEFpX4V8n57r+xTcUeJ0jKBVwe9dOP0TfW1dgEbRQPFMUcwVTDUiEax3Rv2AJFDBS3cP1xzUEtcPHc5sOIE2okx1Q2W6+y9Y3r76B7UdwC3JqYre/EmrUsHvPIDRgXiKK4vQl0LO8QbF5Q8NUmPAO3MjMhE9cuX8jEMTNLmQDt7zx+GmfTWzk2Ac8CO9S212n3mZyqpvYvd6nTtWviUJkZl4shQqTP5B3TmxGklRCcDnGVjCzcdcM6fFqJfdzsB53KO6P6dIs1Dic6f+1xCLa29p4EgOvU9IPKBeiVAuweAyxA+a2NU7/CAaM2cOsNREosICqe8Dqm5r07HAGsOXj6pirpq7AZRF1qxU0uTF6LExDgRFLceMOulE8/rUsEFJcbE6ens92o6bRKzBvLEQ/WAh/sLT+js3QsKHArRo0uf0NT4SSig3dSBk29hb9ug+dMWK3vpJsVi5dbMWOzS7YvAWK8rZmKEtm/YrrlRvAdWwv2Ac+UoNZ48f0J37uUpaTJlJy0McA5b8ZYepUROUowf7Srv40Ywfoy2gtVE1BV7NQFgbMm0ZZqTPE+h2AaPAX7X9zfqrau7sYzd57jsJKUl6ImNT2hJ0mrFeFhb4qaG/jodOLvHsXwNiwjSpnUbNSNxnoJHw1EL61fDcLUO39yr3Ms3FY4i5IbVm1HQI+bR3Ko3978KOFmkZSuXafO8rUrwTAFScQdI6kL96zQwzmk1t7Zr+45HtXTSUoW6BaMaOKiy/nKdvnEaFbY7qFkFoNAVrdKaShWUFjL2or3DgtzXE9CVftsDxVh6F7qfTpR2vDQ9fbbWzt4McAcMSy/Rgx1vbm2ufnTkByoCGg4PjtTq2SsdJcbYoER6PH/VdNfo3N3TgHEHFAWMvnHVWs2fDBzogHNYDY416VZDLjZ1exQ0GqLNS7aSZ3OAkzp17upZHTxxUFGpUUCF28jbOUBT7rpCfh++9rYGRnqVteCBfewE2jcD52pbGnXk2lGgzJPKmJeusMhQFecXquJ+pWPxGwacxZZpns2D/6yHHdYQwLDZeKYnpWvJguXAPdSvMbQtabNySnJ0+NwhIKE7CgoN0Lo1a7QesGxa6CwaXy9VYxl9FIW705ePsrnAF3BuqxYnLMK2Ppy2w12twx3Y8d7BTpDNH6TXjo3btZB2fKQFhexrB3X0xkFloJ66aSFqifFzOb9F50tOAiTtU7hPjLYu2aUFwJ+hjGtopFTWV6bjd07p9NmzysCWeFLiVMCS27pXfldewR5KiIxWuDuquTSb1rOjec370acyVp6aMUWLJy/C8jTDEcBtHmkFnDvk2BpXALtHxMVq+9JHtXXadsWSnl6U4zbUId+4vEfH8k+jhhmgJ1Y8rmUZCxUMTOaO2m/3WJeOF5/XsYKz6m7t044l21Gcm0f9rtGJa8dQP8tVauJkbVz5CM86F9zNj7KD4B7/ClAHNmgxDK/LjPg41O7YtMgYidG8mhhb3+trU2V5hUIAASfFxKPqHSxEnyiTTKvo+xpQGq5rbFFHD3055TMuCnw20M85P5RcRhRSVSNjug1UV0y/FU35zmTeEMccFF6FNsTAuVFd7KpjsxU2jig1pYZGAMwN6k57C1DSMJay9OW0Rr4MgNEKZh8G9Z7PjrK5xw8QKG2Cr+IBKqvZ6JGDb6b7hDjNIvY0ifFkP1ahl2916ez1GuWVlagRZWAGmUoOjtfCuMnY5tLuT/F2rDxzb3SSVm1s5OinRHId3x4FBEnTZ6Sh5harKZNADrGdvJXbry/9zTH1DSTpyV2ZzJt8FQGwOcR7llSM6OjxOp08gWVw0hTt2hKr6HAD5+4rN79eixZnasvmBKxKUXtDkW2UZ7x5e0hvvH4DVboz2rEFxbmPzVQ4SmK11cBP//s+6u1BQKuR2va4rxIZqngADDPjYo7tyTzOQ7dutrJhqAh1y2rGbhQ6N3/s0aNQEAxCtSsaJbUQtXcJxcVW5d2uUlsjYyHGtJ6QbSHhQUpORVlrJQpgk3wdpa6LOcP6yYs3iNe2afuW6dq0gXdAlQtiXsUl0s9eaFD+3VHNmDim9z8W5Si+PfeTGl3IHUGpL0Yf/GQAwJM7wxLGR9gT3y8Y4XrtunC9Q/OnB+tTHwlReqoXc2OAwTf7gA3ztW0rFtGPRAIRA7ZxK3fAZzf6VwPnWtvcqF9D2DlXcf8ytQNejVI2/Xz9UUQLx1Y3RbNmoMwc4K5cbDav5DaqorIOp4te4CsU6agzcTHhQKvxWrbAF+V0d9T/BvSDl4ewfm/W9vUBWGwCZyUyXoVrGgSce+utdlT3qgBBgeB2As5NBfS71KAXgK4iAbif3jkBtcEH4FwPfVEZcNXbqBaePdOLgmyU3rMzREsXD6m80k1/9+0KFdWSxkuC9OH3eZE2tuGICsTBR4G6zf63RTmXK4nRd6Eox8iLRPBF4jUmNggVvgSU9kL4jBsQZb+uXCviunUA1YCjPl6MZX24Jja+c+JQtAxFLc1d14Afv/tsjhpb6lmnmK2t21IADoEZAQPbAOdyAOd+/NMjikWR75GdWIOjHH38qHQuB6gtulu7nozUrLn+qLcN2YxJhffctPtNlBOP5mnlwmB96IOpykii3hDX++b/uqS8eyVauGyq3vvUNKykfamtNlcmH2ko4AqxYx1DjbEP9UvUN+/WqqXNQDLU9JDImxAGlDstjjUIbKIZI90v7Nf13AaVlQO7drMhhXYkwD+IPJzA+wVq5vxARcfSN6AQ9+ILdTp/KU+zZ4UCB05ByZFNOCjvDXO/gwcGUWpF15v2+vFHEzR3dohOne7WK6+j4B4xQbsejdLyNV5swKA2sQnMYpT7dvfrrT398vbo0Kc/GaLVWaGOQt/hfQaT7lfm5Ag9+dQcNkMGOYqRzU2tQJ1v6fCBfYBzgLTbgeTZgOppG4AYX7BowBdrbdnYwtjgATj388x3SsBv9r9xcO43S6/xs8dTYDwFxlPgXUsBW/Q3wMmCFabc4ToMnLNF5dTUVAecq6mp1ve//5yqqqocWxMLjNy8ecsBpqawgGoBUTv/1q08Fqm9tW7dOj3zzDPO5SxwYtCdKRAYPGcL1+npGewG3+QEJFw7wV33fve+2rD+D/twgXMGjlmAzNQRHj4syGMKg2ZtZsEDs1S1QIIt4NtnTfnFAt4GzhkQZ0oTZv1qQSA7LBBiAbW3337bsV1ygXNWLkwBYtq0ac45lrdmS2QBpkeg7e08C0xYYM7uZ4Ftsy568sknneCZBdUskG5lxoA4e367ppUjU5twBVoMsrTguCkl2nn2DnaeK+Dw0Y9+dBycezjDf8+/twnCZSZWn/3yIPLY7Ppit/ynn/bS//jj8aj973nWjT/eeAqMp8D/D1LA+nzru21ThYEgBnuYYo+NGexvBoMYyGLA+65du/SFL3zB6ZPrUW0x1bj9+/c7IIKpQxmgY+cb6G5//+Y3v+mMD2xM8jA4Z3283c+gOwPlbPxh1zcQwsYspkRlwI3dKyUlxRkfmMqcwXs2fjUrRFO8NZDDgJzx41enAEmpxbt6lVc8pki63c9+xkt//mF0JWwT5Pjx3zoFrI664BybZxl49p3vfMdRdTOQ1cb2BsPaONrG2AZ32RjdoLV9BIhsfmd12sbptpHFzrHr2fHw9y5wzgAiaxNcf3fBX/Y7+5yBczZWN3jMNu+Y6ppBti5wzkAku7dt7rG5oSmE2d8NZrJr2fPYPMHqtv3O2gCbW1p9t3s490NCsfZ2nl4CnKsGnEtGtWrV8g3KBADzm5ICL8ECJcoko+3dBBoMnPuPFedM5e4UsJLdwwXO2RzFntc2EJnVp8137P7vfe97neeOjIx0YERLJ/tnn7U2zCBhU6ezObPNzyxtbd5k8J8rv+w6rvR1pbf97l0/HmQlwXw2OzH/Kyq8x/xwny5fueKoxtvcbe3aLDZGPbDrdd1/eHiQ9ruBMnJAR4GtOlGoWLp0GW35Vk0BxrT8ePhwvYvrd/bzu3240smubflikLXNU628WH9lZd3WN1LoL1zp63oGm+8a/Ghl3voWyxcr965NaK7zLA/t+q5/D9/Tdc5v8tU+/3BauOax1odZ/bS5cgjA4r/Le6tHAJo9RQBbVRXqaWpROxacncyHfSlDEwHCQhdno3TWrFLgjY7OCqUtmYlV62p5xbN50Qc7VQOeLO+5FpUBlZUGVb32IuDcQeyAMhS2w8A5FOews3UzcM5OI6DmgF0822h5jUqPHFVVYQEBlQilbd4o/2mZGgCc8yS45UFgTH2AcZSppmtXdYkggi8k9szVwAlZQEmBQC/c16xC27EsvHvmktoaujR92XrFLSAIj+paDzaDLcCbnSiSDNWgc1LTiHpNgMJIE7ORHelsV8OPvi8vrLhCVq6W7/qNqL3F85xAmwQ8hghMGHThA3TkUVyl7gPnlHP0kGJmTNakbevlNw14LBBrQTfOJ1RvijnmCzQK7DbW2KAiyvZoDuBcMgovG7GBxdLRPSjQUaejiZHnEP+jTR3BOrW39CjgQC3gHLayCcs0Bjg3Qjh3GEWznoZbagdsCvVqV5i1Y1hfNlS1qNexG4tQgKn1BE3k3jamogPmMdy8UPHy6lA/qj/u1Sh0lecDsfUDWk2SZ+J8gKo0jaAqZXaZHsMEPNsq1FkOeNaOagLKISHYO7qHzAUAin7w9w6gpjJsPpvu4poZg5Ur7+6dgppXtxpvAs61YDMbi+LcRPIGpTpHEmKIQGT3PfWVH1EvSmf9QFV+QbEKTgNcSkANLZCyZL61Q/UAa4WqL7otP4CZ8HSUKaNCgAlrec9O+QanKDxtPs6oabxTMMFnT4JePDpFxH0Y2gVQZ6T9Hs+GMtpAPZZjIQrC3tBnAqCifywnAWwD/2mkDNW38yhKVGkMi8qYiTPlEzQN9tP6pSHSrogyc4PAMmAd9SAudRJKP8mUQSACYKy2qpsoN6EkkTKP9EPFzh0oFABjtKtCA5VX1FyKklsQVl9pSwHngDt9kigXvoAO3RppOKvhioMozvXJOxX1wYhlWJ+W8hnUmELGgPGoKxFZqJZF82Y/P9yA3dx47iHerzVPPbV56mtDLdA3CVhyhrywkR3FdnMMdSR3FNDGKE+dWPHWFO4DxhhRZOJSwLI1XAOoCwAPqToN1l4jXa9Sz4exXV6Dch7rbe0ovd0/qmHqinvoNHmk7OT3ABio0g17dMu9I1cDRYe4PzaxPpGoUqLklTIXVcRYyjLKOkAaY1hLtpJ/PcCRoXFJCppAP9+IQmFtNc+QIT+gMLcJ0xGbQx0L8BHEnTpO3gGujQEZjdHGDDYANgI1uHuCT6FK6BmJQp0v9QtYy82dgGgv4Fz9ZTWW3yNwHYfl7xJgykkAfKEAfAbO5Wqs+rD6agqBPoEe0wDnACrG+opUdzdXrcAPEwhOR6Kc5RVM3qCa5TbWwd9LVVhyVW/nXdLlplqFhMbqPWs+qGUpa7GP9KIe9qsURb29QCaFrfcB5wAyh3x17UQuapSeWjBrLjBBGlAS9sGUTcf2zyysKZ+BXoGKRakoPjyRADUWhEMDKmzLx+oQ9bdrpxgr9SoasGTprFVaN2Wz0sMm03a5o6xWpjOXj+scVqapBLi3rN+qSUCCAcAL7g4416wrBde0/+JhhCaxRdv0tNJDM7H5q9PrZ15SSf09TUufrccXAc5FYuFK2p28g+3iybNKiUoF1NugGYBzAaguGTh3r70A0O2QCm7dwxJuCUpMwSgEnVa/R6fmLJqr1JgplGSURoFMzAhzjP7Rc8RXgZ7YKEfGEDAOc2CCvtEelaKIeIxnv3yftVjUhRLiErVp3gatylylaNQHvYDZ6qmzb5x9QTn5ZxylsKeyn0FRjjpr7SiLeg0oRh4qOKazdy4oeJjg9bLHlZkwSXebb+jg6b1qqm3TwmnLtG7RRsVHxgN6tKI4d167T76m2MlRemTDLs2KWAg4F4S1bTfWtvdRnDtBkB0FYRT8wtLDdPLKCQDAYcC4FZoaNUteQ9wdeGuMxtmAVXfvflSPsNEFtowIjGEs5g1Yw/izvkT7cvZxv0sAXv2Ul2BtXLNJq1FHS/FLI989AHRadBBL2Ws3LqM846etG7ZoJsBp0NgEbCgHVTtUAVi1X7eu5wHOTVD2qg2Ac9OwKG3TG4BzlS3FWrpiqTbN3aYkd9Sa3T3V6dYKcHlCp86fRQmwC3DuMS2btAJcGnCOPCzuLwKcO66iuwXUf9u0DjhXVYklcB3KQfRZ9C0T/FBZhDhw5x3daEBHobC8GLvHADSnh6Vjt8m1sOmsQ23v7L2zeuvyG2rorsWiegKWvzO0a+WjSkPB0I82taYH+1/OOXBwj6JQ4trM5pL5k+cphPaXWQOAVL2u1V51xoPBtEGbF2+jbM1DcQ6Vp6vndOTUIUB9wLm12zQtYSH9prsu3zmjo9ffVD99UhY2tdlTNwEBoihJvtS0NgDOHdYpwLn0uenU5TAV3SlUbVk91qYLlJaQrmDAQC/AS1Mc87NNLZQlD6iVIBTNolF5CvANRvlpRFX0ZydvHge2BKSuq5EfVp8L5i1S1qz12O0uwYrSB3irDsW54zoGXOcf46kd6wHHAL5BEGn3RtQ62qobJTdRnDsCDNiljes2a1HaQo21j6AUeVgnc49pEuq0mxcR14ifqWasws+UntPefXsV4Rejbct2asHkuag5+dAaAj464NxJnT9/QRMTpygTG24D50rqilGuiwVona1oH9py8g+uDXgPRIQxhw/jKoMi47DZDvWKorxzrfZSR7nv0q2zau1qBD4P0qopG7V9zqOaGE37xRirrb9We+i7j94+Lg8m67uWP6oVE5cpxCMAWB6eGOD7aOE5HQec6+8AnFsKOEib0tyLWnTuUeXeuIk61FQg3F2anDhHfoyDbD2eHkuFqHGXouzq70t8JTFJCagUBzk4o9TIhod7PU0qN3AOC9i06EQsgoNIczZnMZ6sxd61rgV11K4O2iFPxUeEK5z886JdNJPhAOoXgwCZ5Ws+1ympwfIVC+IZ8UmKod8O5Bms7a2mT8/pqqZtaFc87Vk6sFLn0IgK2pvUw/wtCggu2tdPAaNejPEYUzDmHLYhnPcYwPQY9ZLem3wu7urVpRYbO8VqdpCfpvKcXjArDQD1BbV9unqnHuvKBrU1DeEGj2bmQKwy43z1WHaw5mR6A9tg/1gxqMqaPtQbu1SJSmB5bQubEsKIXVE+slFSA+y6kz+gL//tEfqIRD25E3Bumy9qzLS2gHPF5Q/AuVMnCxxr6kc3o7g6ATDurQJdB5xbumyKtm1OAHTykjeAjw2Qrt8aRBXvOvPKc4Bq6/RRU5yLwqqVZ/neP91nHS2Iuhetrbu8lZCGQrEnL8X4dYw2fnjEW92oflUz/qooa8betZsNawLg80AhvFVTJgdq4/oM5grBDNNN5b1H9WUAwRVDqq7tVk3TMGNPd21YE4jqGxqBAd4OdPX8S7c0MNSpbZumokIehUUlQDzZWVyKZedL1YBzfZqZ4YXyWryjcvbDF+qBEwc0fVaEPvRHwUpJAYqizHsyNLp7Z0TPvdCiy3ndWjQrRJ/6cIgyUjyBzIb16lt9Kqsowr42mbl0mOJR7rMyb1Ng95+3e8O0KZ0U1kaU2YpLO1CdRYylHmCTd6lt6HWguLVZKVq+lPkT96znvPLKDpVVtqu2rp+NYUPq7AH8TI/V+7FQnTPTG/W6Pv34lQFV1LRq27ogPbYzTAnYv7phOzsw5MbaY7t+8kIlisCRqMeFs6HXA0CrDrvSIk0AjH9qB/NMoCsfbEMNnCtBcW434Ny5s51KS4zWU4B1ixeMAH9JX/uHEpU3hKNWGqoPvceLWCvAsI39qX9wxk4b0dY5gioltsllPeRjlxobaPsqe9TS3uNAXpvWA37NC5AHG0XqePeS0i7yso82sV+V1d2MT7qwBgbszk7W/JlBun1rRM/+6Jqa2mqUvX6GtjyShoId4zlAUwPnLp4eQDnwCL/z1iM7ZgDORQFWj+rspV5FxvRo1+PhmjXPT6FBNopHmbIAcO4NwLljd7UCcO6ZZ1BNTGXMRd36xjfP6tadMi1cPktPPT0NhTwsYSkr3MoBY+liHIaso38E6/hBNj90AzZSDmuGGcMPO5BjaJgXKm4Z2PBitYqFcFPToCrKSYvSfso2axC1Y2prd1d45IA2AVguXEz/1+Oml1+oJS/zcCoJ1ROPTwP+xzoYIHWA5zpycEgHDw0xdSvX9p1xbAoM1vlzPXr5tXJHyGXHrkgtz/JGadZmKQLQHNWeN3u1Z2836dSlz3wqXKvXhjng3BGsWp9/aR+iQLF68r2z2MwZALAIdA5Ab4pzB/fvVVpqsnYCzi1ftswZgzBZ4apAyKwlWEPnZms9VuF/i2McnPstEm/8o+MpMJ4C4ynw26TArwLnLChpAUtT/vj+97/v7MD/2tf+ztmpPWnSRCAmJKd7e9jE3+GohJjqhwUbDMayRd+dO3fIrDQtwGKL2WahZTZcFtTs6upmgXEIO68/Z3D4EQfY+m3e4Vd/1jqsP+zjYXDOgt0Gnj182EK9AWcWRLD0//u//3uZ5ZlZKD2PysOLL77oqMZYIKuVxQyzTcrKytKf/dmfOcqC9lkLdpuVjwXBk5OTnQC4nbd27Vp94xvfcJQpbJf/X/7lXzrBh3/5l39x7vm+971PhYWFmsyOdru3qc195StfcQJRdv5PfvITJ7BkgTwD6SxwtmjRIkf50OxzrOwYMGeKNmbBk5CQ4ARoTFnADvtq1m0WgB9XnHs413+/v29oHtOzLw7p77/H7gvGj1sWe+ivv8AuvPTfbjD5+/3W4083ngLjKTCeAr//KWATfBsbGARn/b9BEa5Avo0nDPgwlWEDSkw9zqwHbVxn8IIBcNb/XwHEMKs8g/ENvLfNEQbaW39t/bjZ5P3P//k/HZs8g2YMsLGxhI0VXYq2Lkjf1JlsHGAQn40FbCxj/b3d6+Mf/7gD9Nu4x1Tu7NoPgwm//6n9f/8JB1gYzljdS2BjTJNZ9PmH73hr/RJ22Y53v//3M+N3cEcX3GP1xza02DzMIDoD0gxmNcDLBfBYvbbv7TyDu0x9zOA1Uxm3Ou4Chuwx7TzXv4d/b/XeDtcGKNe17XcucM7mkKYa+YvgnAFDBqgZ3GcQnkFlZhdr4JMdNsY3SPe5555zbDTNxnn79u3vgLy2KDnK/asJWD3/119WDeBcUliEVs1erBk7dmLPCNgSbpACKk0NqHV5segcE4eKAEowHPasdrjaDNezuxTnbA5iUJVtCDIbT9vIY23b66+/7mw4srmNzXNs3mJtkqWnXcuVTpbutjHJ5mb21ZTWrc20a1q76krHh9tX54EeeibXz+/K1wevy6VQ0qmr04Xz52lz96iajXFTp0x1FP3mzZ/HuzywLHXS5+dp1IcFkrW5dv5t0jshPgFbnR0EgZY5SuWusuRKQ3te60tcafKuPP8vuYjdz8qR9RGvvfaas0nL1OZMBdC+Pqyc6Pq4waJWHg3ItLJvUKaVeTv/4f7OBc65PufKW/vZVWZcf/t1vj6cNnb+rwXOWVibdZXRTiw8sa0a7u4FbKpR241c1d+/h31UpBIeeZKghLdKc06qtuK20mZmKHEVqmmpwCwoyjgSCk7eE4AnmDGKZVbj7ldVs283lnCRinxkp7yXrkTByqAl6rOdC3gAGfRAca66XmUAiTWFdxRDADx50wYU3LBPDAh06q3bAM84AMzUO6DWvFu68uYbcqe8TFu8VHGbtwFnYY0I0jKG0kfzuWO6ez4HxRBvTVm5XlELAZuAFcbG+jTAcw2iqjdcDJRzCQCookpuGRMVse0RR1WhDnBuqKFWYYuWKGTrI/JIAXwAJIDeITANBEfg1B3YZQzQr/vERV0gkBGBdeHUDVnyn4vSW+gEgmKmGAMARkDXKdrAFCPACiV798v96jVFJqfIfwPg3CyU2ADnhqnTpkRgdmBuvNNIe6F6io+gYlUlvwRsIOOXYr2aARCFtdlgN9DWDZTJTgIQ9CokJZXni1J9RQt2iYOKCo9G/WsG4Fwa6YxFHgFgJ66Cfdioey0qGoBrxTcU0NmEzVQYinoAcRGzABqjsJm25waiIEA+1lmrzsqb6kL9LjgI69BEYLzw+ajXxQBcdWGhWa4ewLkuAvLBqShQEUB380lDMIxg3M19cmvMx1oTm1+sWt0B1pz8HuzQSFeZuoHVeusKCCAPO0pwQcmT5ZnEZsbgyQT4UJgabFZX5T3Kwm2UrghoT8VaFyUPy6sGApV+AHNhgHyeE5I14gkkhY2wDS08CMC7AV2NttxSfwMWnz3VgHCoSESloCiGLa5vOtliYCPtooFzqDwNl+egWIY1LJ+PnzxdfhNmkGaAcxaJHS1QV/1VVD9r5B8UoVhAFh8DrLpRcqssUFvFFQWFEJgH/POMWMC1UZMDnhgFquopy1Vb7T3snAiYAdZ5p64G+krg7/QVWPqN1J3TYNkRDaJ44ztpnXyilmHzWaGm4mMoWbkrKBGQMHwNdSTyQf5R9iAweOYGJw176oDSmkpR0giTf/hMoDkrSygMMg42WMtthLxEiaK78aoq7+1TkO+gohMMNlxG+aQOupPHvailVOYCzt0CaECZZgYwW9R0x2p2BFvMoapy6muGvNM3o36XgVokfaHngNzaUVZDIa2/rlwDHiEIzc1QcCbPa1a96Ai5DTcDyeVhEXtD3QNeCk/OUHA4EDfgXFddDYpzQGwp3CtsigYBHw2c8wEe8h4GmiPvR1E57K8tVF9LA2Nyf/lGY9WbOAt1u2SqFLboLLs6sFZ3OeDfdWyL77EpA6cJnsM7hucMwDrRSkQnVrEVx9RdVyaFTARgXEv2MzbpLVL9HcA57K/CI5Ox/Z0lj1AUxtyR/RHqkAM1un3nvPblXdHNjibWJRP02KqntSR1HYAh9qu0I2WATHsvvqX7LfeUOQPlN/cQXTp6BctWL61cvkJzp87D3tTU1GgHSBUDkiyY6WHvir2WjwftEVna2NGoS/fP60zecbWiXObtC3RL8xgL9LJq5nrNy0DRinra0Mp5QC8HTu5BATQC+ABVp+hpgHMGlA+rBTWss9fO6HTuWYDVID2x6SllhE5WDW3Zq2deQYWp0FERe2LR05qM4lxNX7mOAc6dO3VW6YAzaxeu4+/TFeIdpH4UGx1wLueQ7uYVaN6UhViXxugCdpqdatbilUu1aPJqRXom8L4WpAUi4WVMscWL/PdElc8b8AadPdV0VOkSlqlXsL01xSpIFw10DSotaqLWLuKeqbOBYSLVOtSkYzf36uSlQ8BpqF1teJ/mUG/83agvtOuVKFweQLHr2r2rCveOQrEOG3fAuXwsnQ+f2a+Whk7UupYpa/4GJWKh2oHS7fmbZ7Tn7FuKyYxGSWmnpgPOBYLr9GM5Wdp4V2cundbdogJAxDQlTo3TGexke9p7tXJWlpZNWQnEQ/6Rnx5GHfAuY+7sCKJY+XsGotDlQ7kdJN0bdTGX97tzRR3AUn4hvqxzt2ENl4b15gbNj1uIklWAOrAJPZZzFEDsPADeCODjZqxOF2mCexQlrl8Fnfk6dv0I6nD0t0FRWke/lZk6AyCuXa9h1VqNjbOBcxvnbVeix4N073Rr0vmCkzqTcxGFvW4UelGcm7hC4Y7iHHBEH3AgMFhxAQpxAKMJQODltdWqrK1lY8VMLcYyODGIa9FOuFHowN15R8br9EWBgEVhwHAeQEQtWEJfL7qmY1eOqryTuTYbRZjNYaPtBXC5XgsnL1d0YJIagQMvFl3UvgPMsX0DtRawfuG0+QozOI9SUjWIsuL90zp5/KQiA+IA57ZrPvWkj/4k5xpKZmeOAM5FA4ps1dSEBdjgoTh35yzp8gawUA9Q5AatzdzE9QDnKHc1WI4evXpIp1AzTJuThpJrrErul6jiXqVWLVujRSgQRgegpsa59o7e1D2zK3eyEykmd8B7G2O1M4/JL72tY5cOY/FbJw9T1qS+htCOzJ+4VNlzsVVGabC9vxmrwAs6cOptjQUNUKY2a1naGkUAk4NIqXGY8e6tCzp16QwW22MOODcvaS7t2ZDO5p7UhbyzSk1L0uYFmzSNPrp5sF1nyy9pPxtfTEXPgLoFU1AyZB6ANp5Keot1suAE85MLmpYyU7NT5gMS3VFB1R2lz0zXuoVrlYran6kijvCOpjRkoIwHYxQPd/KHPPQaC8JKtRUraLv/abV0IyJAn9Xd2gN0l6zV09drxVxgS0C6bvLvZPFZHcoF/Ovp1qblm7V66mpFADdbD9vW1669N7B7vX+RcuGGVesWLcqYQ7tVq+Mo/+XeuqmUuEwsFndoCuCcPxA1DBVYrZtKGXPdqwBUpyalpKQqhfI4geelCVQtbcfd7joArmruNQFwLkGRWNjb2n0Hcbwy4OWOrjYUdk3BbYISUfP0Yz0IrM0ph37AyjbOsuvcIv8KUdSLRNlzGuB+vJ8vz0Gi8Pc6AMIrbeXq7uhUin8EVq0x6hgc0d2WVnUO9qIUOoHnCgI79UZV2MoMXSjPZ+M+D+Aqfxv7MR4pAm7OAXjrp0zMDAnWdNS44JrQjgOeQ52roGxA94sZb6GQ3FIB5HaDcVtHtR5dh8rfWmwvgcMGgfK6gW7ae1B7Rr0v50ar3jraAOwUo6d2xaJI6kVMq19f/X9QeB5J0hPbzXrVR5HRKM7xskWAc0eOVrHxE6tW2onHtmI5Djj35p4CXblRC+CTqW1bkpgPAhbycGQXQNaQXnwZ+3HGxo9uW6uPf3SGwlCcKysZ0LP/UqTSkhCUMCO1dSfQVTrbNJyNjqhHA7xReyhnWALzTn1seOjsHFJ7G9e8OIwd6X3mR5Wsv83Vhq3J2MnyvsCcfW2Mk5pGmQcO8n4DKDW2KCm2EbvSDKWmhuvu7RH97GUUiIeAfjejVLgxkjW9B9qQRWUjevHVOhRB+zQ9zVtPb4uhPnro+ddadORCqxKTffXHf57ABjlPtC4fgHO3bg3rH5+r1o37vVqzOFqf/ihWrYnu2JoOky69gF+F2rIpURs3RWC/SlvAPMUKjocH/TPfWlkdo3EYgZZsBzDr6sQwvmNYVeUDemtfNYqHTVowP1FPPZEMkAcUClfYjYpbI6pmNfXDQNEDupDDHGW0T+97LEQbssJ0M69fz786oKq6Nm3NCtKObaEAX0CZPkBXlIG393bo+RcARgE9n9oVAbTmpcvXGvXsj++xhyVdT2yN0nu3Ux79AFe53/2KEb0KkHXhUrNmAXw/vTNSC2YDGpaP6ev/WACEGUq7G6UPvscHRTbaOKBOst5p8/g48By1hne05+5s5/1Yo7ub368TZ6l/NS1atjiN9ZlIZUwGYCT/uxnrdgD+1tcNATp26+TZYspTt9avxS5+c4aK2ZPw7I+uOuDcho2zAOdSFYNqoBcNUSe2qRdOoTj3k4Oofvswl5+p1KRoyuywjp1qln9wj7btSNDipQFAn4xaedC8m1i1voBC57m7Wrs8SB/6cIompqA3jrLbN755ATXFEi1aPo3NhDOUkoriHG2EZSNdF+NO5ohcBCE/DZKZPX3mVIYbXSubsVEjPHKMORht0Izp8djGJmsOwJ5Z5najANeBYmRT3YhKioZR+OxQMRtjsrKBu7cCV3sG69WXa1Hgu63588L02ONTUYSjTSQPe/ns0QNDOnRkSAOD5cCBAM3zQ3QVYPLlVyuc+e0j2+KUvYV3jDEAlXaomjx8GQXdwz0KDRzWn306HKvWIDXUjOkw4NxPX3ybNe9kvefpWajjMVYEfG1hTHFgz1s6dACr1pQH4NyyJYBzQNJOQ0mf7cw1SQpn3cAy/bc4xsG53yLxxj86ngLjKTCeAr9NCvwqcO6rX/mq9u3f5wRFv/Odf2Zx+TbQ01cZ5Jx1FsmXLFkM0e3rWAAZRGULz1lZa9iVUo6yWBlB1eVOwMYALQssmL2X2QSZIshlrDxyc687QRKz2jRo63dz/Ja90+/mod7Vq/5H4JzdzIIKH/rQhxzlA1No+9a3vuWoJXzuc59zgl32OwsMWZDKAg9m62QqMxbQtgCS7cQ3u9XPf/7zjs2T2ROZnZpBcRbQWrNmjQPgffazn3UCL2bLZhCcfc6CTKZiZwEvs1iywJkpEFoA3YJqdm+DM9va2pzd/xb8yMrKeifYZ+eZIsXq1audgJkF7Y8fP46E8luOUoC9g/0bB+fe1WL1O70YG890NmdEf/zFQRWxQycjxk2f/aCXPvlhW8QcP8ZTYDwFxlNgPAX+q1LAgvumVGVjPRtfPHzYpN8AEgPobcxgoJr1ya7D1HtMXdb6aBsLWh9vCjpzsf4z0M7GAgbIGLRgwIxZvmdnZzvXdH3OxhWmiGXgjo0NUlJSHCjCADyDIWyThv3d4AeDbAxIMbj/mWeecaAH17OMf/3lKVDNrs3ZW3qF65EWR7jrjT0+iuOrrV+PH384KWD1w+qxqT0a8GXgqo3Vo6Ki3nlJq4tWRw0eMgWuCxcuOCpdZolqinMuoMv1AWsb7J8dzgIgX52dtHy1a9nfXD/b3+3aVm/tGVzg3OOPP+7M+azdsHG/KYTZPMI25thc0doJF+xkm3ls/G+KdQaxGfhnAKBBuXaYVYtZ9pQBzD2L1XM7Njyp/oFaBBw3e1WWQpYsIqgfg0oNwEtpqVgdxZ5wsjxow+z4xXexn+25bd5z+vRp5/ltzmpt3YNNX13OhjJTzDQFOYN4bW6Tmpr6Tjto13Bdx565nHbQFOrsPQ3+NVU3gxOt7bM0+8U0dqWr84Dv9v8s66jno6wQF9wrwF5kr9MOexGkXJu1ljmWvUsaKjos+P78PfiGPH2Qr+XlZTp08LCOHceiCChww8YN2rxlCzuwpzjv4QLN7B0sDezn3yU4Z/ewNDbA2/LEFOSszGzhmQywdCkbPpyM9hkrl1Y/DJyzz9jvrOxZX2SgtgsCffhz9r2dZ//s+MV8c375H/zPPvtw/v5a4BwBx6E65COwKHWzyRPPPtIIvIRae8WVy47NW9zOJwCnwlV15byqCHJHh6Eev3iFfGagjBWE/SFBTScqgDKHe5gBRAPqPnZE9bvf1Aib1qKXrVXgSqwgseoh4u4EqiCJgOwIexFNHSEoVX38sKrv3VQkKl6Ja9fID3DODdDODRUkiFROIs8B53pY67lPXnSWFis5CWugLVhJRqMMR2BzrLlSTWcOqwT1Sf+oRKWt4L7pqQTJeT6CJG5EWsZQ0Bkpq1P/yYtqvVOgIcYYsTsekU9EqFpee0m1t3IVlJyiuA3Yr04CSMLSFQkG8oW0sR39qJ6MEcjovXpdNw8d4LKjmjQHAGTpQnnERwMYoRiDyo1BUm58DvJCY9TlmgMH5XbxvEKiYuRDXfCcSZkOC9Gwvw/KaZzLfx4AuOosBm4DnAOg80adyit+EYpZ6cRHAPe669VbcUOddblCtAVoaxJAWLxqS9uxah0CckxUIEF2d6wtR1FXsoQ2KAKaC0W922qvA4YD4gvFkigoJgXoN5n8oL0GBsFL1Ardg/MHsMKuu6ueGu6jVsA16mzcfNIwirauQ6ON5eoEWOolEB82KVUBGQACvhM1TIC7Lne3VI9yUkSU/CYBS0ZMJh1Ig94WDTbxuZoSlAFbCcUTnCXPfELDUL3DEjQ6k3cknXsa1VNeqAbadbNIC582UT6xUajDVaqugmeJTMd2FDvtMCA2T2zyoHFMMcnU7sY68yhLF1B6KdEgdmy+0anyiUyDdokjAA4sDQwz6gYQQpq4Y5U3Upev1pJb6kExIzI1QX6xMwAAE0kLVAJ784AUr6m2vlWB3DOSd/SxdIW566vMpy84Dxg3pEBTBYyeCziG8uJom0aqC9RZVaCBzgbSk+SNy1AgAJQXkIOGAaZQ7Risuaj+qnMEMkflP301912qXmxBm+6fwarVBxW/5QCHa8gXgFDqFJFD6mUlanAF6m8qUmcz16ZdCgNo85qQiZxSLM9NdNWxP7YgN/cZwVa09RbKfQcdFbgJkRmkHZCkqTOiXDdsqoEAIwN15dhIofAzE0COv4+2ARQWHNJARaE8gqMB5+jjyH83VGLGqKwjjUB7BeewdAVApQ57RCQDzi3l3MnUU8oc4NkowF4z4GOvW7gmZMxCcQ7b0qY7aq8FZgyeqIDk9ajZZWrEJ0iDQB5sd+YZGzTcVoZgYKH66ysohyjvxaJMGkM+h8VryAMQFHUyL9T03E1lqK8eS+jbpPVd2i7yITxKXnEpvN//Ye89oCy7yjPt795bOefcFbs65xylVqsloSzRAhlEEDBOg1n/+sdpmN/GYIxxwDaeMeMZxtgk2RYgBIqtljqpcw7V1ZW6K+ecw43/8+7iMm3GXsgIli2mrlRdVbfuOWfnvc/5nv2+OW68iIzUmb/tNO29jza4DNG5fRabUUD9MUYAco30opyVkWLZFdVY1DLWAN9FQgAPqJ9dQx3twI2rdn182DIB5/bf9UHbXrWPYS4ex2igpAHAuZPPWxPKiuu2rLOi7Ao7f+SC9XX2WfWypbZjw1YrV1+UFSb9WvUsG0XwWJRXU7DoQsEnCFzdWmPHLx2zZlTLKrFgzcnNwa66DcXPcVtahtPKpr1WCQwYmkJN6VaN/cOBv8f6OGQ7tu+wdVWbsH7F7jWEohQqVIeOv2G1zTesqKrI3v/gU7YYQLEL6OTZo9+yRsp0ZdUae3Lz+205ee2c6QCce9WOHztuFYUVQGx7bXX5assCqgygqFk30mAvA87VXLtuW1ZtsjXMv8cvnbCGXhSOFlfZntXvQvFupSVi4wjpSt4wsiaPAgPj4gQGoqY3O2PXmi87YGwQoLlqcRmWcPkAE02oukzy7H+T3bHuDqtIWwwmNGMX2k/bi0e+b5ND47aX9GxdtZW5B2A2FLAm2vzL5162RoDa0pxye2LPPDh3g7b46pGXURcdty2rd9mdG/dZSVaxjQPqCJx76dgLVgQU9+Ddj9vy7E0YJacAzo1Za3+9nTj7pl1vuoF9ZrWtBDQ5gw1uO0qissTcueFOW5RdyvgD4A/BE6TuAswbdBynypdA/sb8w1aLwtixk0eAYFAAYhzMLcnjvTpUbvptI/bW71q5z4oBgaaQ7DoNfHb87FEbGOrEJnSL7QT0W5RQjpLQlJ3qOm1vXDyESlMXan7ldv/eB2w5im79I8P2ref/0boGW9ym9fuwai30MtdR1rO+UTvT8KYdPnUcS94xe+TR/SjO7QJ4SwWukWJZE1DZG9aAVWsOY+yy5UttAFXVS1euWSGqybvW78IWs9pSmF9jmdfmyN8k7T8AJCQL3TxfIes4r13vvWKHrr5ml29cscKyEiuvKgdkGrRbqMOWYfO7dy1qhajnzXoCdqUL69yDLwAUTNmqpSts6/ot1AdwF/81DNUCwR2w67Sp8vxqe3An9rko0s0Czp06d9xeP3rQ8isK7IG9qAwXb9TwZmdvANRdfJ7PTNk9qAnes+why0xQ/0b9aqDTgXNOyW1LtS1ZtdS627vsyqkaW1yyzHau321VxSg/ch/gbj0Z84O0JbLq4NWE2ERnaXuz/ZadvnAKGKnJshflWFEl1ueTg9ba1G4FycQWUF6rKlnsjm3qrLcXjz6HClqLrWdD314U8EpTKmhTfqDCZjt0+pBdvXHN0nLT7H7gyLVFa4Gm/FgsHwcgxWa5fJE9sOldtgJwbghw7kTLGe6LsOeOy7L7AdE2AxJKHTAIbnZzptEO179ux44ep69vtD1r9llrW6sdv3rcknISbTf527oI+2HGGGGrgmIipCOCx3BMHEpcGs9mE6wRm+eXTj1nnWPNVkD7rKqodO28q7mfcavU9u26x1aVLGXqwIKy+6pTmmxtb7ONazZyjV1WglV1HGpc3SO9qGy+ahfbrjBHZdrju1DcI13DWLy+gRLfRTZalJUA4e5+gPOto68xBzHizTHmdUxO2A3iOgMTU1a4qMyqsnNoX0BK1EUbGylqAd762TxRnJAH2Iu6WFqMTVHH/ayhuwfaAddwjwGYq8B+N4cxR2suAbuxHqnDscbit3bWE1eA5Rs6gXtQf63KqbTSjCwgUNXPipAMAABAAElEQVQ+ANfcDFbBHcybc7aMzU8VqCKOon5cNzBlfazP8jOxb81JsUzaRbw2MqjVkD4JAfvo9yn0i1jGuk6sOc8PDlk/kFw1QP3KVABF2urgFLbUAD5dAyhksnk/B4h2pD3CvDBnPc0Ndv9u1M72ZBLL9LIPgwMob1A8wLEItp+T9qWv3LJM0vvU/nzbvSMete4Z+9zn32BcLbP3PrYU69V5cG6Oepbi3OtvtAEhXbeqslX2xMOllH3YXj/cRT9iPk/Ps3v3lvNcLNGyM1Erm47YG0eC9q3vnrXrrLE/8MRdgHPLLQPFubYWv/2Pv2q1WzczgEHT7cHHY6x0cYi5Tr0WWA6AdXaccwAJ6ZUAQI/wqiF+bMdPBuz5l5rYXADotHeD7bsPEB17S+0bQ1SN9SzLWY47eTZo33m515LjW+wX3rME8CjP6uvYQPvMDQCyaXvsocV2/7uymf+AKVkztbQG7Zt/P2jXav0oofrsQ+/JpTxjUN+csucOoBAM+P/LH19pa9clAVSy5BgLAzbN2V9/7Zbd6g/ZQ3eW2q/9Yga2ll4UGwHnnpcy3GXAOcbWBxY5tTK3kRP6zcf6StDp5ISHjSDcU5Jn7enx6paD/RN9PWHsRtutpq4ZO9pCe+o9laj8xbn2oc8Euf4IgNm58wF7+cAkoiHD9p6HMuyR+7Ip65B97VmUBbsHmYNS7d2PpPNMExAMOFfw5PPfH7GvfqMF6LHQ3r8/BzAvhjyP25e/1mz9w5m2d1uWfezJFKc6NgwIdh673e++0GB1jaPMUVX21BN5tmmdD3W1kP3RF69YW1+q7d1ZbB9+b/I8OCcajXrQc4wZ6mtohE0/XtbdiYJsWc/NYY/aELLvv9SNjXi7rV1Zzj1qvlUDzknljlsd5j8AOtQGL52fs3/8LvdLfkRN7iqy9z2xFmtVsy99GXCbseH+Bzby7IE5XlAi150ARjtzBDXDL79ghSVxtv89a215dTF2vyF78dU2G+N+YPt21h5782i7UnbF5hcQ8+v/GLDTAMJP3pthH/0P5VZRRHmR9z/6k/N2pa7RtqMY954n11p5mdTIWSLrnoyBJIJ65FzAZ2PUhZ+x3ge87NWzY+yCWxqVxyk7exHHgEXZpLPU1qyNt4QkjqUPyp7WD8jaejNMWxlkvL5ou+7ItEcer2ANkmfPfbvLzqMGu2E9NvH7VzGXZgC0UYf0q4Mozr3K16y/zR4Fet2wOZ0NB1J37LK2ziE2MaKi+kieCfRj75c1Ud7PfBO3kwt+q8pLsN/8RJrt2Zto/djw6jx/+/XvcJ0Ke+qDG2z9ugSnGDk8MmYvvYBV60vfn1ece+wRLHpZc4tuBfJ18Bz1rB7KUKXhcP6Lbz/JawGc+0lKbeGYhRJYKIGFEvgplMC/CM599rPOcktqIgKoamqu2Wc+81lntylb10fYAazA6te+9nXuEUPO5lUBD1lnSSFAO7f/4A8+ixXoa/bpT3/aPcyWikBODrLZ3HEpSPKhD33IBR6kMPKzeWl2+vl+vRVwTkEzQW9f/epXnYXaF7/4Rfe7FCaefvpp++QnP+kCzgomnD9/3gUQFBx//fXXXX0ryKMAhBRhFHhT/QlwU7BNqnMKKskmVnawsi36yle+4sA5BbkV1FZ7kcWaAnEKVijIrUCY1CXUNrTDX9eWbeuv/MqvOOUaBTSkQid1BtnnKLCm4IwCMQrASWVO514A596Z7burN2Jf/ErA/uxrAUvj2d7ju3z2yd+OsyXlLDIXXgslsFACCyWwUAL/ZiWgOV62qVFI4PaEKPAvsEBft0MA0c/oWEEl2jShcwic03pPcHv089HzCxwRQKP39Z7WMwL49b6OScY6TtfRWkB/E1AjaE4vQUHaECDFq2eeecbZu/4kMEM03f+3fP82uyY/9sk5m+BB3d7FXnv5+QSChT//a+X/W+o32mfV9wRtST1S8JrWz1q/L1u2zAFc6nP6Uj/VvZxAMQdDAaPu3bvXKURHy0z9KtpPo304+rfod72va0evr9+1rm8GWNN9YRSck92q7ifUr2VleuDAAXdvIhjugx8kAL4dNakf2FVGwTltxtF9jMAm3WNE4T9dS+rljdh0/tc/+H3s43ptKVGBlTysrV5UbunrNpi3ogqugYBj7XVL3rbNUlCc8TGu6HV7WqO/K91RxTmNLVFwTn+X3bTU03WPq/FJ90/aDKT0aszSS+OYXtHfBXXpfkmq3dpQJPBXZSC7VoF0P/rS9fX1M3lRXnpiOzc7R8DiuP0Dyt/1QExLUM57N5ulduzcYVmZ2Ev9k5cAuPl6HRsbZWPVaXuOvNzg4bmsRfcDMureX/WpvCvtai8q258lOKfz60uwttqW7mfPnj3r2q02fkUhUaUlWqb6fDSNyqJU9aV+rnpVmxdsp/6h+199Vq9oXeh3/azv+vpJ5proOdyJ+eetgHNSVOw7dYIA77AlsVnRK3COfjOCivs4Kl9F9JvMR4DTirC2un7Nuo+8YQHaXGZJhaWsQG0rE/CKthma9VsoNd7S1y632BQUYukP468dtO5LVwB2ii19JeBRWZGF0hTyDBEQJOiVVQqUBGw2M239xw9bK/BCLOoyxWtXWcrSJRaHbWFcDoAtAUoHrwHOBbt6bOTMaes5fRK+acLyATDiSsvJrQDAZhurv2xT/jnL3bDJslathQecAzYaJlgE5JNGv6S+gl2DNnnhms31D1sc9ZH/0H0Wl5dtMyeOWcuxQ6gVzFrukiWWDpgWD2QQAXSZpk3jrmRZXCshORGOqdW6Tx63wYY67jHJ98qllri03MIpCWwmVIA+0TI5Nj6/ACgnxgZfO2BzB19BjYE1yQasJ8mfByDMVwagQMBUqnY+gsixsy0W7jhCGi8TaEGzCjAqNr2M4A5BL8C5ub56oKxWS8iJt6RKoLT4MutqHrdplCHygZhSK9dgI1aO8gK2uJSJF0AnjFLQTIeU3q4TCPZaMjBZMnXiQdFLKhugBFw+zXyJQFVArvQsC2KXOdN8xrxAaB7UVbxY8jnFthlZufbZNIpgfoKe6YA0ydhdhpOqLTyFbRXgXBjYIo2AciK2sb4MVHpj0TOaAsbEAjiIlVIq4F5cIiAgAfM5/6xFqOPYgiryyBg32g8QJauzIbcWS1+2GHgq3wLNHQ6ciwWcyUbpJi6zjDLLJO0EAJmLwqQpOHAKm+Dj5gngGpBeCey5EroQC1FUvtB2A6DMR+0DGAvFr1haYRgLwrm2C7TzC5aYgmaWlNWkKoc6lmcSSG2w0YbGsOfNW2o5izdbfBZ5nPTadOsVwLkjlkAAV4BmBDjQB1BtcyP0jVZE+QZQbMHUM+i3aayM44Fh0jJRdwokcc0xCw7VoCx4A0iFgPyqncB1W7Hg7bXB+jOWmYT9ayngXO4eyhsFQx9qV6hkRUiPv/scqm1NzFVhS07Nt/SCJRaJBwaLJOCGy5hIv4tNoB6BzJBo45ibWKoet8nBJt5LsSTaRUx6CvU7ZjNjXTbXjR0rY24aUEL8qkdRuVvtrFqDTSjKddURlcQOi7THAP94sC+Utai/H/Cxp8GSgIEiQR/QSKIlFC63uIwV1AXteLaN9kGZArbMJSyy9KrNlpwBcIbi3ADwRCKgVEbpvSgG0nZpf37N/cCZEX+7DQNljLU3ESgdw7I2w1K4LkSczVG2M8ClAueSsbeM99AepfEzfYt01trMcBdtB7vZHEBD1IYE5YZHqdte1HCANeJRo0tfAribjOIcVoVDtedtDFgtKdFvqXm58KDVlHWOBYAiZqf6rIU6PNnWbLVjI5aUkm/77/6wbavmeNrRdGQOcK7FXjn9AiDZDduwbZMtA0prqWuzK+cuYv0YwGazAninDKWqVNKJPh4R08mJaUuhfy0C5snKyrb2sVY7deU4qjrXLQlY+I49uy0/Kx91lAaruXwN+DaEmsxm27xsi+XHFmJX2GffPv6cNfQ1uLXB6iVrsTXNJ2CLAl5Hi12tuYp6UR/WsZX21EPvs2qsNruHeuxbR56zxm42lqMo994tTwCmANRNd9kbWDIef/M4KkBldveOvbaubA3QSIabv+qGsKI9/QqQ0zXbuWmL7WHevnTzih2/dpy5PYjV5DpbXrAKxZQ0IHjuxyZpT7PTBPhTrLSE/pmSZwO9A3b88lE733DW8kqzCfRvRt2uwi4ATp26fsV8jJk7Vu+wXdXbGCuSrR3VoleOv2wN126guoYV6LJVqBcXO2tClfe5xjM2CIxaVVxt+/dgB4niXANt6pU3XnHg3NZ1u20XwFtxpsC5ETsNrPTikReseEWxPXLvfluavZFWkQRUNm4d/TftxLljVoPCaTWA9rbtW6ypud6uoirjJ0hfUboYKLsMJaBUgvPcH04BbJPvlMRUAvlllgvA0zkkUOoAtr7YvRajELh1p2UX5FoN/ebw4SNYyibZPSvvsPWrZeWcbo2o751AFfDqlTPOanIDY9jijCra06xd6rtmFxov2MTguK2oAJLZ96AtwfZyAAjjuy88B4zTZnfsvsPuQ60s31fEwpL504fyFQDrweNHrHtw1B7b/4RtX7LTMrDvRSfKOoC7jlx4w65frgGcy0C1GBU3gIajx08CWU8DZJYDTZZbXkY29p7kEdWtHqDlGMaQSux8l2GlPA0MdOj6q/Zm02FUfAJ2x9a7bXnlKusdbbFTlw/ZaN+obanYbnes2meZQJ9t0x12FDvhuut1DlhbVr3USrmOIvqyN77QeBbrw25bWrbSHhA4t2Qj4/+0ncCq9fXDr6HqWYhdpMC5dbTDiJ2tP4ma2UvMx/OKc/eteBjLTFm1ovIEBHXwPFatWKCu3LHMNm3fBPgybWePXbCp3lkro59VVLBGxzrXF4vKFx6Z4yjOxgPeLsLWuhRL0NHZETtz5TQb8GWrnWYbKKNS4I6e8S6nfjyO9ea2Fbts85otlguMNDk3zjWx5W08yvgea1tQpFsMlMvKwFqGW+xi7QXrZBNCfkm+PfCuB2x9MSDyRNBOXDpuJy4CqVZW2Lu2AM6VrLIRFFhP3jxjL734isvTgwBnm1CcSwOcE/h4c7rODt044Kxa1zGGPrzjMZvgOcWBM68BDbZjxVqECuZWbINRJeUZxyRrqOnZMYtJCNqiUjYDMNZPYBd6EmDvRO1hSwFe2bh+oy1FFa6bNdSJc6ewAh601UvX2N41e1CBrLBuxr5DVw7Z+UsXnGrkymXLATmxBoYO6h7ssdP156xtssMKc4ucTe/mso02BABz5PwxO3fxvNvwuA8l4tXM+6n0NgZVkNg4lNiCdovNBs2MCTHJ6fTRLGxUWSOy7u9Ac/HWzCDzxagtYgxeVVACXB1rPazburHqnuT8WWlJKM1lWz5zTQrtwofqZ6x3BmhQ4JTiewBgAEFXpzuBhJtRnvViN1yALXY2oKvumeawiaZ9A3RmJscD0WZYCXPKBPNXC2BXO+tAHzB5FlByJvNcCoqJXoCUAAprLP+BEGNQsWONF++1UQDTGtSZm9lckoNiapEv1cYGZ622odcGxoB2wijtkefM1Awb7QyjgIgKGe13z1Y2cgAVhZl7Q8xdqajbxSYkk78gCttDdvzEuC1bvMje81i2bVgby8ajKfvMZ19hbi/DqnWVPfRAouWhWjUDcHWrPcgY02aHD10DkFpp+1H6WrHMZ9drp+z5V1qsrmnMli7Opb7TLS83wQb6jfoJY9faiPrZDfvg/p32Hz5WbdkovHW0zwHOtWHVmsa4AzwHOFdcKXCOUgVMHB1BMbAOpT3SMweclJmN1XFanM0yTl4GELpw5RYz3Jht37oSJTjKgvVhAgq2WdjUJyLbNzLmsSs1YTt/lTRVTtl7n0DhsyTZrteEgMauc85pbGWr2aSajaKelM4i2GcG7RvfHOT8c1i1+uyjT+UB+8YjiOC3b32/x27cakCprMTWrcmzHNrKKLbrl69O2BunR210LskevrPEPv5L6UCiXjsF3Pfsd7oY308TV2ZsfbDaXT+GzRzaVCKrVsx2ARXZlHuR54CTYfIYZymZbPoDnJPV5xuH2sjHqK1ZXWG7thawhkW9mnpLy4yzOO5nRqaBxGsmsSuesgRUW596vMD27kq0S9cC9rVvT6C43m8P3ZNljz+UhVUrinOArTOAl88/34/C2E3szYvt/e9BaXBLnHWh0Pfs90ZQnhtnLThjj+wtsJy8RNQigddqx4B8b5EWY75ZAmyXa5vWx1pbR8D++C/omz3xtmdHmX3gvagnLvKxXmFtz9gbRI1tYDBoR080ujEiMzuZe9Ac8p1szTelcg/UOjhtWzZUA7ij6hszRd+csTQ2FiSggjs+Sly0dpw2dAvIz48tb4U9fH8VdRi2v/zSERtEEfeBB7ZSvhWoy5E/LeNRGzxz1G//8399zwpRIXz3e9fbhnWlWKJGgNi6KJt6NmzIErvSqstZ36GCd6XG7PDZGGtiffn0Q5n2sV8sB86mIQK1/dGfXLAr9fW2864K2//kegA4lF+5jjQKufsEcgzb4IDXLlzqtx7uXxOpl4xs8sh9Ji7RdvLUIBaug/SLEpTjclhLDsGbzTLus97mvjUEYNd6K2JvHh/GzrzJ7n+wzO7B7jiekew732q1s+cuIc6SxcbKtYBzWSjOsd6mL77xqp8xnPXBTDN5LLHNwI4DwJYHD4zYkeNNlOGcbdyca9VLCt1Y0tgYZg5mvOiMsRVFSfZbn0iwPXsYIwHnDrw6a3/7jW+hileC4txmrsd9EhbReob2ykvftdcPvGTVVRU8S3k3myd2Ma8BBlK/+tJ9HMOpbm95jsB7byPUuQDOUX4Lr4USWCiBhRL4tyiBfy04J4WAT3/699xDZtltfelLX+JBdZb99V//d6cm8qlPfcoOHjzoggOCpaRMJpsaqYdIcUx2nXr4rECoHlLLsuZ2xZKfbhlodvr5fr1VcE7Wq88884yD2ATRCWrTQ3upH2wjmKQH+nopcCY7VwW8ZEekAJV24iuwoMDbzp07neqc7FWlmKAvBRBUx1Kcux2ck+JcHUEZqU3oevqcziOQToEpqUcoKKXgil6C6tRe1DYE16mtydpV5/kq0F80gKH3PwvYqfx8/OMft9/8zd9cUJxzJfjO+YcNyHbwzZD9x0/7kdgO27JiVOc+EmsfeQrrhbexoHznlMBCShdKYKEEFkpgoQT+tSWgtUN7e7uDHl599VUHz2kNIJBl4fXjS+DXPjVnf/MdzGbgex6902f/8FcoUPDweuH181ECAoO0ptZXT0+PWzsL9NIaX5tQZBcqAFX3XYLr9JlawDMph+s+TZulpCQZBb8EqkphctWqVQ4o0jo8uhbXOaP3Dlrf/+jvOv/t4JyguCg4p/MLktUGHa3vZSGrewOBaNpMpWtorS8YKgrOSWFSn9EmrOi1lM+G2uv2hc/9gY12tNtiHiQvjgRQZwFSwBoxkp1rcaTTNzhgq9isVXj3vQAoqP/wiqb99vzo54sXLzolNp1b9yhSvtS9kVQxlda2tjZbv369s4peAsBzO0SsY/RSeehcup+R4rrup6Top7RrQ5DALgF3t39WPytNOi6aJveBn8Y/nDf6Ghsds+89/117ljTJ0ldqc7pHWwqkFEtERupdHtLvHvq6tHAkhwtSqwWYe/67z9uhw4fcZqhHH3vMKZRrA5TaXjTft5ftTz0vJEflrGvoHlhtSCrkAhO1aU8qgGqvSpNe0evr8zpObU/vqZ5VL1I11MYulcGePXt+aBXsDv7BPz+NeomeI3retwLOBelDNd/5tvUDxflCwCeod/kAz4gaooCRZsUoKCXLZhVII9g3YONsQOvFlmuC6E2YPhAG+PIQKVEgOa6s1KoevB+BL2A61HICWL32nAZ4aO8kCEV5Enz0J8dYkD4fg1JdOYHi4lWrLSbRZ1P1NdZ2GjsxVIA8PoIAqdh5lVdZMfBIxipU1FLTAaKA7acmLdDVaYPnTmO5eI2ABSpRcYm0DWA1gnCpSV7Lq6i0nM07LS4123prblg3jgYBwNhQDG2OG8C4IKoL01jl8Lwoc8NGS922FaAIaAS7sCGUU7pqUVsbHqKToTySMq9650fewJORbit2bLfMijJAH4Jn7S3Wffa8jTQ22CTl5c0i6I26nZ9AVW4+VlnrNlr68pWo8qXa1MWzNvzGy9iNAqQBN4VQPYnBSaF4xzbLoI8nCuIHyPWFes07cZ3g/zUbpLyDQRRWsPtLpL8khrnGHFaY/j7Kj/IurQIUKrWujkmbwV4qH2gkpXQZPFkJCl2C4gBNA+OAg40oq50w/+BN7H1QwUouJMCeSv5oryGgM9Qn4oCK0oDAEgEEvNglGoo4gQFAO9SZxrEnm5CyC0pLSZSJkLwAoOQUMGFGOVacqFV54gAMAZ66ag9aYKjOUoELfUBMsxwXAjayEKqckIdpKSWWmlPB34BMATDGUGCaoN0h/wAQRR4BOWOBj6YmJsl3DKp6ZQBtBQiZ9ZHPYdT3CiwPUCk5s5R2BzgXRokUaCAw2AEkdhKLz7MoUIxYOG2RhZIWOYBQahYRbB/jUJ5LylxKMKvYYuPYIEGdhUdqsSs9acEpID6UakIxWAsilxLvGeXc4zY+A3yItWAutpIJ2UsAIXw20XHNRtuwxYwjUJqWgfpTOoAPvQ4IJBaoI5FofUImamq0ycHJcdpGgDUQQcgIeaQfxPl7LWauA9zCawlLd1ts4SZU5Pqtr/GcpaFYmF22GY5rO0ygADFU3lQXKDZNtJ9Fta0JOAulOuCd5PR86i7ZBdOD1IuPa2ZkFxEILCWoBkQWQlVvssEmsI0dQ+VrjrrwoEoXlwiMB9Lg4ZlYDDBqOpaGiYtRgaNsQtjmTbcds8m+OsYw1OD4W5gA/wxj9lRwjvFhBmvBEKo2zHEAorL9C4YE7nE9yi8hBkjVaOOzE8BuJdj8brQU2r9/8JYNdvfQJyssvfQOILcy8kfIE9VBb3jA5oabrPvmJdTW2oBb5iyb/p+UQJ8kdDnLnDFHMJ4otWWiWJeaVimOllbYj5JiK/kjIDzaSR3zHiCFFwXF+Ai2qzPDQE9AFgXLLa1yh8WTF+/4pA3WodqIillC0jSWYYkARZk2E0KBinEvEgae8wbsaj8qLKxdvNiwPrb3A1hS3kUgOhHL0aA1AzIdPnvAbnbX2bqN620d4xgEIHPXNWz66pmTx52CU4zmNtTxpIwke8/K0kpbvXIdoGIqalqngAlOO6W2TdiXb1y9AQA3nQB2P3Z7Z1CruQa4lWY71+62jRXbaJM+u9Jx2U7XnbI27k8igB2pQDY+xjT4SwAEQBPgy6IygvAP/IJVZZRab1+3fR/VtZtdt4AyVtij2x9x1qy9E732JtDeyROnAKMXEWzfY2uqVjv1sSBgSH1/kx04fdDqGAe3b9pq9925z0ZnRuwK9dNw8xZjI6pWKGrGa8wnrh1gXE6k/5ZXVJG/tdRYstVeq7Pz10/bbOyYbdy+jvLbaIUoI97q7bAj107btRvYsUvxatNdtqRiscF+2nXsn69cBj5iQ4bmz3gAFsEl4UTUp6Z7sDbE7o9jHtv9iFWXVKMu1GSvHXnNhgG4Nq3abrs27rHirCLmpmE7fQkbzCMvWmF1kT10L/aRBesBXbHBBMzuBK45fuaI1QFgLFm5xPbceRdAwbTVAMjXNTYDs8w42CoGpatY5IfC2PfFA8ksXlSNveg65g/mdoDAN68BJOUkY6243TYt3gCImWSdU112GPW/5pomK04rsp07dlnVkpW0YeCDthq7cBXV1o42zhFvWSiNxTBWBFKxtR1m/BqeALhcbO+66z6rLFoCCIA95ovfR3Wrk80DO52lbj7jLCth2vqsnbl+0l4/ftR6sXR95HGs44EswZUJoAPO0S+OX3zT6mtqUXHNIhay09Lzs+0ac9VN8jjSP0q+wjzbZi5mTgxyjB81xfJKlKqWbray9CpgGexCrxy0QeakdevX2baVu60ovdhG5vrtEjbXZ5nbU2bTsO7dYatWA2xnADABnV6suWS3WppsbmoWGB9gl7bhSQnZpAflp5stVpZbYQ/swKp1+WbmSj/qfyfs4OGDVizFubsftGXAQMEAKlJ15+zQedSHUQu7c+terEPvtiysi8OMD939HfbmuSNOzW3ZeuBH5uWUxHQgiQ6ruVrnNs0EGfhjE4DP42mkjBYxjGeLcksB9tY76LSW9naq5oSNoNi3dT11uAaVw+w08jdkl+sv2VUUimKxvd6ydqutA/LLYA1S13PNjl1/w5puNloM4146fdYLRBHAgneGOa63px9YL90eQHFuY+laVFjZRHLhNOk8Y5WAc/dsuQer1hUAPhN2tvE8Tj2vWQ4Qy33b99laILY0rhGk3bUCPR+rOWQnTp6wVYvX2EN3PEI5JtsVVCqvsVYa6sba2Y/1MwOhYPcA67Z4xtOS/DyAqQ2WzvqqobYZKBFg3jdua7avtA3LN1h+UiGQ3aSdBkQVNBjCenP3ijvs7g17Gevjra6/wc5dPWN1DTeAZ1D0Z4xJjWFtQjn2TAGHUfeZqHo+uuvdtgn72CFUDI9fOGlXUOotZT2zZ+du8rfEUjzayAg4h1rwGGPgAGv75oFBG5hEXZS6SJWsF+mdjA/bMCCcfxT7WBQhlzMexbJ2aGb91dnfZ7PUfTpQY0YCGwWYZBPYXOBjHZQIjJ8FnFaC5Wo8678OAJoalGxv9nSxfkCpljVWIu06wjzgx+Iedpl5OduKsjKtPDnBMpFN85OSIdYkvUB1gyit+gOsqxhM0Uyjj80rmzJ6W3ZaihUzZueQBj9/b0MlsZl7S/8om0xGsZK+NWLnL7RY/yCQHMfNtzXWG0A7uai2rlhcYKuWo3AHPFhb38g43cM6LBZlM+Y1EjaNomhhdjVKc9W2fVuq5ed57dr1MfuDz32H8aoQmG6rPXR/huVj8zrL2vJW+5QdOdxoRw/fQP1svVNsW70yDiXIEG1tyF58pd6GhwaZH8xS6RMxMQWUYybpA15svQRot8k+8vQSy8VWs6sDcO6/X8Sm0mv37au2hx/NsKJy7gEYrzyeGCzNjX4wDmTdDGTX7+ZYuYEFw8C20yivxQRs47oc4ndFKO1N26kLjUDiQI2soWJRqJ2eZb00CwSfkUufSLPdO7MZb3x28dIUNqWoAwewmX5wk+27u8SycrinZZrvBAx89tkW2tSILatMtA8+WYVaYxLlFmasGEflswFwtp+NrzEoNLKe4XxTU6kor2agZJZgd6L69UsfS2UDnc/OnJpDRQwr2tYL9tDDKwD0qrHLZV6h/Qi6YprhFWeXLg5jhdlqjTdZY9L/4hOBHJlj52b4AnIrK83mnrwUq9JYu3IeS+jmIeZk1i4JARTO2CiK+pgvJsPWrl5qj2LLW13utZPnxuyb3+20Lvrq/fsq7NH7SngGwgaZWGw+WQe++FKH/cO3rlp2Vq49uX8lm/pYJwFjXUFZ7vXD3VaHdXcCZZiBwreP9fpsMM7auoYBaH22ZV0lQGU2z1NiWA8E7Ev/4ySWsWHbsaXM3rO/FPhbdcNNNeu2EHXV2Tlj3/j7Y8RlmVtRbYxxKtOJlBvwIP10UUmx7dhaCYiPAiTwfltrF3XNZqOYVAd8TbHWgylFFS/f7ryjhPVEBhA4aoX/66D1DXSx6XA7rgPLrETX5f5tkLo6e3zGvon1aD7w20OPbiAWXM79sQ91wHE7fKze6hu63b1jRio9Lh6129Aia+vNsFuoRu6/J92e/liZg01DgHN/8idA59iOb929xPa/dwuKc4CnDOs+aDHVY4g66sFu9cWXaxyUN8OGnIQknn9E0miPrF8B88rLMlFxK3Wg3IWLtYzVvVwfTUzaMLujbHqGZwzBdADQRLv7vjJbtoIyR4nw28/WY497wdatLbR37wc+XgY4F8c9KnV1+OC0vfIydTJ1E8W5Ktu2q5D0+Jg7/XbkaLtdr2NzB2N9ajr3cGyYCwSzeS6Va629uJ7wSPk//WqC7b4zwfp7UK87OEifeMbSM0O2FQCyvBLj6LgAtsEDdvniGavnfnf1yhU8R3iS+XyPG0NUv+qUWg+G+a61ILdRGoJ+4tcCOPcTF93CgQslsFACCyXw9krgXwLnBCy99NJLLohyu1XrJItqQU3aaS5wSaBTYWER4NTXnTrIp373U/bawdccOPfJ//yf7c//4i/cTntdRw+l9cBaSmHaIa5d4Xr/n9t1//ZyFT36bcxM0VP8O//+VsA5fUaBBKnJ/e7v/q5Tndu3b5+j5BUQUxBND/SjLykkKFj2F9SdQDmBkAouqN6iCjJSh1AwSVa7Osc/B86pfhUw0q5+nUfXkM2r6ryVBy+y91WwO3ptfRe5r/Ygi9abN2+6gNnTTz/t0hJNnwI9and/9Vd/5axef+u3fmsBnIsWzjvku5pbCztbvvDlgP31twOWEe+xx3f77Ld+PZYbMa0qF14LJbBQAgslsFACCyXwT0tAirmvvPKKWx8I9PmzP/szB7H87DZg/NPrv9N/2/vkjL15NWypTLO//LFY++z/A2CkuMXC6+eiBKSSJjgoqvb25S9/2SnKSQVSgJegNAFF+lkqbro/mJ6edr8LXksliC74KfrS74LHZLGqv//o63ZQTH/TOl5BVX3pfkFqd1HFuR8F5wTGHTp0yG2u0aYqQXG6VhScGx0ddeCcNt8oP7Lg1Gf02dvvG+pQn/zjz33Whjo6bHVBnq3KSrEs8kcoDuUPguIARAnc06wgD9lr1gOezJOi0XMorbenPao4p7xJTU33OoKydE+s+xmNNYL4pJqtzUO3H6ufdVy0HFQfKnsdJ7hL5S3bXG0cKy9HLYh0qrz1pWOiabq9DtwF3uY/Lj2su5W2LsCmr3/9G85yVWX96MOP2Lvuv5+d50Xuea4+63VBLdLDg2+PnvbylzAATVtbu6uzF158gSBCwHbt3mUf+uCHfriJ6p9Ld7R832YW/snhSqPaRHc3dnfPPuvswXXvqHKVErrqRfext790jPIfbZ+635TFq+5vBTjqPlkbtXRPq3rR56Ov6HH/XP6in/lx33W+28virYBzUpxreR2buPNnEcwaokV7XFtOzc2wvGXYMC4mrcVLAZvSsPYhQNHdaxONjTaAT894fxcKPdO0LR87/On3VUutaM9eVKryaQgEI1CE87e12OCVqzbCRroZnu+EeEYTk5aJStUiKwBCyFmyGDgNgAalsdHaqzbEZ8e4xiSKFhkFWKZu3mrZGzdaJAsgBosuHyARlBhWly02jF1cd1OjzdDPAwTg4zNSUa0ps3wsVhNLUUhDXWbkRpP1A85N9HSjljTlggpJyJCn56VZTgUglexYiyuAAJPMNwmkNDBio2zIG7zOMYBrIdpnDIF/XzZpRkmmdNM61FtQzqP9eqRQ1UQ6aq5js9cCKIAFJg4JXgCKosVLrXD1WkuqAG4j4hTsb7PJ2nPWybOpEXzMAwTBYlCkW7Rtu+WwaTCJKKcP1RSP1MW8/TYz1WqjQx3O/tQLmJIAEJEI/BUDCBcGfPHGk67icmCpMiyYSDeBu7Rk1LhQr/AACIUIekk5LhyasNmRduCiGqxMO11QJ0yAOkJQVD65ESDgEMF3H8o1qXko1qUvctCVLM0igV4LoyI0OtLLNfppGdQJ10hJwCISm6cJFIlSAQST84DYUGaIUC/DnVdpJ90EqWMdeDg1SeCa4LeCePGJuQRQl1Pf5QT7GcPmemxmlHJBSSpIMFp2ccmAfb5QxCaGhwliRyylohRFsALzj0yiwIR6HsGwDKxDk4H/vATFIvL2wsovON5rM4NXbXaUfIaGLUh79aOypr97CaopGB6bkG8JGQT1sdb0xTPGM3Z7Qt3AV1dtbvQWFoOz9HnGc+o7LkmQmQ9lEOrECyCRVwn0VUl5xHOdZtToUKlLBsqKR7tuNkzwGzgw7AGeSbHE7GzzAWJGgMKmUNUbwwo2BDgQB7iTEo/d6OwwVrztqP8BdSy+g/xtIoA5bkM9NTxrgg3MR8EuZRmKbyheaUwBQpON6XT/DZsebgUqAeyh+mKxxQxTj3NcNwDAGgtgkJ5VDBiIel88fRBrORq0BcZ6CdoP29g4dUFbSk5HDQZgR45xYQKKCSiNJeStM29KEfUwZFNDV6iXVgLSzGsx6S6YO07dBuh7KSlJzlIujiAg0UhscYexSgNMAKJUwDkxhXzGj1Cn2P1aAZwjNsZAHOHJIdI+RttERbJwpfmAWiMELD0obnlDgzaLgtd4bzNqWAMoCgFGYNsbQx+xOZRfUE6J0N4tMYM2ugblPMYjoAs6LIoiw7RvymboJtwiEC0EVmxsqiUT+PYBwE1NzlgwlWvmr8BmDnUtAsBjLTdx9m23hJQZvmIJmEaws9OGAKzbOG1CBkos4yNWAzgXDibbztX32RJUsGJQvZugD3SNDti1uvPWN4K1Z1WVLS1baqmC3miDNzsbrLmt2foAF6aBej0ENDOAFwoAPKoBxKrKaEOU47makwTBmy0f+H7bum1WkFnk+uZMYNYaO+rtEnaxYxPjtgoFyc0oiQmoGQsOoqh0w2pu3rDmjk6gQvXHFFTDgG0AKq7fqAfYzceq9UkrBzAdY/45V3MG8KqbzQql2K5u47OFwAVjQGP1Dm7LAr5cu2o9SniLyB9dif+6USO8BFzYAfC8bPFKoLRNQHIxNoIiVXNni9Xy7LRrgLEBWC+BKG0yEGpBXgk2rsusvBgVNVQa62sbUXK6iTJpIuubFVaeWck9AWADQeTr3bV2AbjKh5LV6sVrXbmkpQAX+8ed7Wxt03WUkToJOAdRVkRRvCATxZ1+6+hqtbykXHvszkdsMYpifZT3xWuXyCfAGTDSeuCm3DSUQ1kD1WGZehIwKLM407as32llqEUmAf6GgVkHBrsJaF+29p42K8E+cy3KwT76UjfKOY2d7dba3cV4N8C6dYQxCXg3Nt0KGRdXli8H6lpsUwNDdr7pnNWPNFr1uipUkTZYAUASKy2bi0xaY1sdlqj0IdanK1euspWV2BUnptrwbD/wUIPVNtWgvDaAo7kXZaNUyypKR2mow/rbe628AJjwjntQg1sC7Dplp8+cwtp2COeV1bZ+6TpLB87QHO1l7K5vvmEXmDMHAWC37dxly1DhS2aOEDinsrlBO+lp73D2n6tXrbaM/FwbYg5s7e6wxmagbzaazwD7un7L7qYsIKnllYttCe05Cfixhrm4vu2GpeYm2c4tO60UFbN45pXZyJR1MD+dv4ICJtB2OSD0ihUrLQswL0C6WrCWvdZyxZqZG2cnef4KBJtdnMaYPGfnT5+3wpRie2DXI7Z28Tr6gge1LvJx+Tw2j1kAbJutOLsEgCGMNe8tu1h3FagvZKuWrbGVKNUlA0mhA2xDKEfWNVwHvmhA5ajIVqJQmAc0LPi1vqOR+r+BclCnzaC46oPGiWfszaOtLy9f4cA1xYouNJ62+r4aSweO3w4UWJFTzVrGA1I8ZV1jnVbTcA24QiqHi21dxSbLR+VtCqvf+r7r9IEa2mMHoMk0QG6ipbNeEvB/sxY4HQ/HR+970NYuWgWI7Hew4o2WJiviecLGpRusDBvnScD7hs4mu3D+kmWgUrVpCe+XlDEuJzMveWxguteut16zq6gzStFw0zqgyORM7EWnsWTssJZm+ldbkwM+Q0yBCUnJKKwV2NKCKp5xL3cgXU39DRQRGy2/mHsWrOFLs8voAynObrp5qhkVx7PWiT3xsrxldteau5wa9DiweCv1d7WB+mN88gPIpwOwFrD26Z/s49qtzKRJ9sju/ba+fIuDTK831JKeW8CIObZmxXKseIsYF5g/GdHmAFJm+FnWqwOslzR2jjKfhSlnHwCWh3F6CnWrOeDFbNpudU4x43SadQPk9QxhQY8SVBx1HqO1XxAwnbFT1uxJ3HPlArOVZmVYIuNPJ0BOzRjqm9hiZjM3lQCqIA0GdMm8RRuIpe/nA9XmUU4Z3I/JRFtbkGa49ZjgfAOo+Y4Cvc6gLDk7w9xB+08kfWmJug5jLBNzGm0mzLGjzBO9wIcjQzMA737UoqatsXHYhgb8TIm4KbAuktV4EZa9q5eX2lqU0XJzBD2NA8R1WtOtfuZiFG9RFoxFsTOXdeuerWuBYrCwBTLyAFY1t07a3//jBeafFGCzatu2MRXbcBTQmQZ7Bibt4oVuu4a6VjEqntu3FwJlgiOyJugZDNmRk92M7e02ghKbN5gAFFXK+iubttCHKh3Ka4BzH3hqqWXn+dg8hsXoi7XW3REkrlpp23dk8D4XQcFV92Qz0zHW3jpjly+0M6b32RDKZUGU+rxAfwK6qqroO9tyLTs3Hlh2ys7wue7OMZsDGI0wfnvYcJLOuLNibandsTPFCgX/Md/W1c/awUOXmXPnbNeOlQBNeazpaMjUx9BA0I4d7UYNbcxKihLtLkCtwuJ41tZYpw6h0Ify2olTzcCxk7SLoBXko+oLUHyjIdkptm1Zn2Qf/lAK85HXGur8WID2WS9A79ZtqNStJ61SigN689GmqCZe8S6Pp0+hZlc3aINDs6zpuEcFSk9NS0C0JdNWrCokrygEskHlynnqsG4I5bdJB83FsFknPScJuC8fVbpiW1OZbKkJHuDwMXuda/cPjjOuFdvOjbo2Zccag2UlqnKDduTNBjYWpKAoWoUVbArrD9bVk2Eg/Ck7f6bNGm90u00eWbmsu5nbbjaPsLF3ytasLLXHH8mylatirZ/yeuVVNsdwH7ASNTTZg+blouRLHXpZg9LJqLcQynlN1H83+cNenaVhhI06Kax/KquIt65ahGhJOmU/Z1evtVr9jT6UCrk3YENBDGBwRlacLV2aYxs25qDkx70I669bN7Epfa2GNjRoa9ZXo2q6iOsKnMOiF9vb+tpZAM8LxIBR59xabstXssmQDRADw0EU7MYQOOkmL4OsuWeA6wq47yy1ppZEwOAmu3tXqn3gw2zEQsGO5SCqb9fsZnuPLVmxyHbdWQlcmuCuo0eLPoFjQGwjYyHKtAO7W9pg9yRravIIFJnEeFJYmEHd59niajY3cb5L51EhbOynzIYYw2lHPsoiPdMqykv5XAHW61Llw8IXFcnjx7oot1bU+LLYeFlmxYuSaW88r6CLXLmEzfzZUTYR9QO7Fdjy1WxoSWRNCQx78+aMnT/XbQ0AglNsUMnKS8YVD8AUwPNaLfNiSsA+/otptmNXiiuvSxeHKE9AcX83KoPcUQGix/jm6IMj1oAycUdrm60DUH/iiffx3Ohe1msUtO6HeYUjlARrzflnD+6tn/ifBXDuJy66hQMXSmChBBZK4O2VwP08LJfS11Pvf8p++Vd++Ycn++zvY9X68jw4J6tW2XJ+7nOf4+HylH3+83/owLhvfuOb9j8Jyih4+dWv/p1TI/jMZ37fXsPyYiMPUn/nd37HwXWy2VQwRAEI7dSXpYqsUGTLqYfUsuP62bz0sP/n+/XjwDkpAEjlQAoHCgx873vfs4KCAmeRKwBN6g+qGwUFoi8FExQcUNuQOoKCPgoaCXoT8CiAThCeFBgEzkntRYGHH7VqFTgn+O3FF1/8oaqdVC0U/NK5HkOpQNZL0WsrqKBAmV4CMxXQELwnVTzZ6URfSs+f/umfOkvYX/3VX11QnIsWzDvsO2479tKhoP2nP5yzXpxbli/y2v/70Vj78Pt4vPXz33XfYbW1kNyFElgogYUS+LcvAa0ntKYRJCEQR+tKrScXXj++BHj+bOv2zNgNdpuW8WDpv/63OLv/DgUwf/yxC5/4918CWkNHvwTFCcyRMvjVq1cdxKZgVBT80QM8rb0FdunnOGAygUb6XecQOKT31bekAidwLofAi/6ml77PPwTkoegP3tPvekW/S6FM4JzgPd076DxPYO0p1TvBZ7onOXz4sFMu1/UEomljjZSudQ5Zuaqv63ilT1CUNmBps0/0pTzUoZj3p3/4h8Ap03Yf1mybVyzG0gdFBaAMIRkegSeoEaRUo6jGPY1H0XdeP5ruaJ50j3PkCHaMlIXGF9mqahOQbFqlgCerWeXjdtW46Lmi6RLUFS1r/Sw7XN0nqRyk6qd7X6mjCUyMllc0Tfr99vei53w7313eqKdZAAopDErFr66+joft7MB/5FEeqm9FAS/DBSb/93XmQTNFS6RAFyEgJfvcCxcu2vPfe95uNTe7uvzoRz/qFN5kxx19Kf26ZrRMf9r50XV0D6vy/OY3v2k1NTVWUVHhVM/1/EFAYLQN3143+lltRnWj76pn1avU0XUvrDa4Zs0atxkrelw0L/r+dvIRLYtoGb0VcI4CtJmWdpsFGAjxrEb1IAuY2NREiycgJRUrT2IWilKUPe3dQ/2GsS2aQzXDjxyF4AnRJgqCxmYRGCkuw441xQVGPUQW4ggwBgBQ5mTTSf/RznifQDQU1+LzsOgEUkF+B9kiFMkGB7Cj7EXJAyAHu60YQJeEomLOmW9zgD4+Aj1xBBo9qF5FpoGJsJedJmgTGicQKyUSoB5BS3EZ9G8s3SxImBVYx89mPT/3/i6tBJIQjrI47JjislDCSc81f0ImoFkialoEKmYZs2iDftLrHwVSCxF8IhLkw1bIh6JcPAEsD5CbQCWfQBtgpGAfwReeawRIU0SfpywS2LQXlwlAxRdyRUTKxoEDCcyiKOVHGSSCQpVAoKSiRaSDz6EERiEQNJ2hPEkzKlwh/wCKccPwTzNAYnFYWJKl4UGbGejBehaoqbTKfCh3+QUYYesaQ1DcC6RjWJhFCN4LjFNgMuwfBr7qJQ0jlJ1gVWwvCRxToXyGtPDZSDzqVZR3HHZpHkPJjXZhnnG+oxyIyt0c6l3gWShMoWCCFWgoIKs1yougVAyqQhbiHCiSBab7OGaMJqGgKelljA4TpfNITS82i2uUkT4B0gouky7yGJobIPClZzPSgPGiTjZmI739DkhOX7zE4rCGIxsof3BqVHd8ScAjXgBsbNFASLg2SoTBSfIoQK2d81Bv2IyGUdIgNqrTci1+RqHOm5gPCJlHe1YZUabeMQLxnZy/B7vhAMEwng+gyOF1UCxKTIA2HgC+GPqA14stagAFLKn+zTQC2wBuMa+EQwDUKNdE+O4T+Ac85yGYj+8xn0c1cK6P70BUhP1jmCsCAB1BVMA8vmSLq8SGOn8jEFQYwLCbthagbnMt6MsjsMm5CAr6gijZzA5ZeAaQC7AtAmhAw3BjRYQobIS2GAZQ8AB2xdKWY2JRfPSg0KjMe4geUjYBKSSiJiJlRro3ajcq6VjSxXERMIKEAki8VPIC/BnooDxHCUrT5/hbmDYToG4FfAnWi41DsRXoUG06MkO6CDyGQzxwIb/emAmA0h4gwH6b8hVaZtF6QL4S0kAOZ4BpUdGLpKNOGAvY6RFYBtQAbBQGJgwRIAwDOWD2TbOkPGfn2zVDBuXMsbSzmJRK6qaIPkLd+Die/HmCWL0SuBScGA6RZi/BXvLHCoS8YFwJmBJKwhYZlZUEVIsE+0XCgP6J9CuqKUgUO0TZ0FloLJQNdsvjPNvs5xlkJJSE7V+VpatM2QEyw9Q+QXseHeunPMcsKwU1JGDSWBq7H3WjMfra8MSIjWDPNxtgTCKgmYRiU0YSUCNgY0oCCne0197hdpuawx4QG+RCFBAT6bN6nqr/xjlvL8DlBGNndnIuKl+oLlJps6CIY4FB60VZqR+wxA9QlATtFpoDtqsjKF7TAJRdhVXrE1YCCBuhPAZH+2wSUCURYCQHsCae6wS5zhjP2keHxrGWS7asjBzAGyTf1F6BH6cCUzY4MUQAfxrLwWzs8LId1Kr+P44VXyd5H5oep44CADKs4WjTKeQvOS0HMDSdsTtMIBhwcg5rTIajjPQ0oJMsgJsUzuC3Yfp8HzAuFUjZcUwS9QIIE2G8Gg2MWC8g7egU+YOU8DH2zvFfbX0N6sI3KYsCe/yuR60iv9xmmQcGRwEn6bcZiTmWDyyYTLuSbbiO7xnrYayIJd9FluLLBMYU2kazBR4bxW5xAlg1UfbijL8eynGasXscyHdwcgwFoTEH3ND1qZtky2ZczKMu0mmDAVQMeyaxt4sMAYamUYf5oERAD7Q4D31E+ZZt7iTwcTqKlLnJBahfYXUMkDUS7LO+iT5XtuEg8yn3STP0zyuXLwFfd9vyChTwtt9lZXmL6QusUYeYi+i72ZmoowJ4MWs5cE7QxzhgZb/yQX1m5+ajxpZGGWssAP4BGhonH7PM6bG0wSyce2KZHzHGpo2isDUxaiP8fYZxKcK5EmlfGfTtbNaLaUDHPsaT4UGUdCiHRBS6CrMLUAljXGMN4Ocs4yCkQ8y9gQnsjlFEzcqgfgHGQxTYEOCy7IDHmD8DM6iEAaX7fVN2EyD0/KnzqB4uQUGNOgRIi2GslQVpP1bEcdRVLulMYuxmurBxQNi+UeYs6i0rI9v1IZ8e3kJSzKIcOEYf0DP9lCTgw/QcS8YWG1TchpjnBLGOTo/yuWnXp+NQ+8xAwTI3LQ/FTOZk5rfu8XYUx/qAXFKtKA1rXpRDw5SFHwW0yfCUDU8NkLYplCAzrCARNVfOHwBkGA4OYP3ZRxn2u3R44gH/GRfaWlqt9uINS2cOfuLBx7BFZn4OePncuA1MTVhKcho2xKSTOSJA3x2nfw9g+ZnIOFsAXJzMvOFlrRChT00HJmx4ehAYZJDjaH+slzQvsXqxKcbakfFBGwbu89PGmOWBlZU/rDnjcp0qX4A2oLYxSh2nsl5x1texKHHSQsBhbDQyQh11AlSPWS5tuwwF1wTgST99bSJEu8K2d4AxYI6xRQphsWw+v1p7GfiqHpAuyx7es99Wl25ivKWdsB5UrCgFVbpc7B3TeHainhYG6A6RtwDzLFw/ynOMO4DBk6R/jgr2sO7ws1lgcBpLVgDFYtpvdW6hq0f1xSnGZH+YeRvwUZsAPIBmavdI2PJzLBAl8BbwSBygWQ/t8trwnLV1D9gigK5l+emMTYBmWjiwOUC2o6lx4KBStYUlYvbinDiLMyAIPZlRnTPnzUCmBVnbCLSPY+xJYg2TwjVSIdUTmItBrWyWJjjGGD4NXOVnA8EcINg40M4UUFUYJTHJ7QvoSWPzQH5uomXnCMYHuGNjxsBwwIZGgWEA9JhcmDcitJdYqyrS/KD1A+kBFB/js01Nk8xTPlRBE60IyC0JG94Qn59ifB0GIBvqD9Jv47mfjQds0hwZQt0N+9W+IPbVMwDMjOfAuekpiQBXXnvjmKDuy7b/3Vu5z61G2crLnINSV9ck40SE++9Ey8lFRRR7SErhByWEaiD5G+qfRX2OPI9pTuVPpDEJ6CoHYC6vQOp5jEcsF/sBCcfH/Cw5WKezYUHL0PhM1DULEykLKYsB3gE5DQ15rLObdRqKWUUFqYCFKPTN7zfDBlXqYXNAakFLZVNNQX48Y9APQEfOOTzC5qz2Gfom6z7m7Lg44LH+ODvwGnNj3ywgXo49+b50bFG9rl4GelH1m561gkLSAQCWqE0nuo/Q2tfN+eQRQFCg2JDSz3Xn2CSkQFESdZIN9JhJ+lLZDeqn7Ef6/TZGWWjjCHrNjOG0KTYjZHFPkZkBgAxoRpN0G1qae8nHVMiK8+KshPMkMN6r3UU498CILKdR/aUd52PHmpE2H5sK0QZHyGM/ZTDUrzUOyx3mx8HhGDt+uoPnLn3YnlbYQw/m25LlMcxTiIa0zbDGi5C/GFcfsXHaqKGNRmrnCbRTr/WR7oGBWe5rWROpDzFWxyd4icNS78pfGnMt4OcwYN1QP/ajtOcQfdhLZhJJm+o6NyeGmDonZb07RhrbO2XhG7Bc0p8LzJaQOK+Ay3IRy/SQ9fVOUD9cIwcIFYCOjsiYRSx2GFi1l3Icm3XPQZIYM9o7Y+3QCaDKm812z50p9p4nC7CcxSWKvt3ei7OpWQAAQABJREFUwtpnmk0fGaShIJFypL2TszjuVwWSM6RRDsZ6gDIb1nmpn3GBkRjS0g8zsuLJYyxwHM8Z+NzIQID5DdVsNrwEuHeMQWExjnuqrEzWSbS3hCTOyf9+2mJv1xzwOnN5Jv05HxAvmftBN4DQp4dCrh8GWAvlFsQBu9EX6Buqw3Ggu75u1juMS1p7xzJPhNnMdfToGIDfJNBrvP3SR7KIRzNv0twGSVN7B/e/wMvxCYy73Be7NRdj/bGjR+zsqbO2fMly+u/7bdfOvaRBqxkyQzql5Kd7Om1C1Ltv57UAzr2d0ls4dqEEFkpgoQTeRgnIWlNqAwpGfOhDH/rhmWTXeeDAAfeA//Of/7x7OP2XX/xLduhNmyxY9WBZ4Nyz33rWysvLTSogCoRIBUzHySbl937v9xw49clPftIFcBSEUOBGMNYnPvFrtmfPXSy48plUmFV+Jq+f1Xl/Jon9iU4aBecU3NKue9muRl8qbwVI/vzP/9zBirLKlVKLwLOnn37a2aXKXvUzn/kMi5f5AIcCVVKHkFLEJz7xCfeZY8eOOcBt7969zlqptbXVBYD+5m/+xrUDBYOkpvBWwDmp2Ql2U7DsYx/7mFOLuf3aX/3qV11gRn+TvZTgSgWXnnvuOQf86bOC75Q2pUsWtL/xG7+xoDgXrfR30HfdDzW1hO1P/tpvf/tiyDK5cX3/vT77vf/CDrLMn/+++w6qqoWkLpTAQgkslMC/ixLQg3itYQT5SAFLa8qF11srgebOsN376Kw1T0RsdZrHDrySYAXsfP2ZLcHfWrIWPvVTKoHoWlpwj9b/UnQTEKR1t/4mkEiwkL700uf0s77rb3rpOP2ufqW/6f3S0lIHWAle09+iQJg+f/s59LO+9NKxgvdk1ap7BaVD4JyAMYFn6r8C606dOuXuOZRGqbgJSKuoqHDn0b1GVPFaCmK6T9V9SNTiVHnSdeprb9hf/PGfAmqE7fH777XtuzfzEFU2qDysJMDIE3YlyaAAHTARTaOO1+v23/VzFJzTfZBgKqVXtp4C+LT5S+994AMfcPe50TJROnQ+Ha/yUTnqFQW0rqOKp/sk3fso/dpYpM1JGsP0mdvTEk2PO8FP6R93ftI4ytgpGPErX/kKD6Unycud9jAKeEuWLnMwsq6tsAxPkCmY+YtLdS5a57OzM1Zf3wA49z1Xd2kE3XU/KVXx24HG6Odvz9e/JivRsvyXjtHftaFLedHGKgF9gv/0TCMKZioNyo++VD96RX+Olrk2BWozn9qZ6lnt88477/wn1t86Jnrsj0uX++C/8M+PHvuWwDnOFSGoFNFOIwIaRMznz668aUe7+i19NeQAHepObZoggexcPTpG9ai/6TBgFi8QRJAAbMjlCbsmghJegq8R+ipROkVx3Oc9ACRh4KIwgVOvICssLr30hzA784lOE9BSZARTUOxdI1i5+gm4+LhIrKIefEVQtIvo3AqkEjERdKn0hhlDMMDkMyJiFBUlOCa4T9dWMROog/4DrJsHuyIxcWAApAUlnXiCq1LhiugYP9FByTIAKIm+kgqIVK7CpCHEMQIJ1fN9BAE9wAERwU2AHA5WkuQCAQwF/D3xUHqUXwSQAT9QV3YuIQSkFNDyeIlCxbgoDMcAmMUAF3qBkVCX8oRGOQ2QlEAp6iY4PIpyHOpc4zOWnFOILSsKOajg4GNLormOvlBtEcxL9J2fNdYqD8ARNk2wWUFkEo3SGQfxnW+qJ9IXJsIpy1ZvhDIPcx4VljuWNANJeAgskwGOQfEHFS5dR1a9AQLRaus+ZG88ggYpW5dewvNuWKTcXXDS9Q/SFgeUDLwj+BH/USJTI5wLAM1DvkNjtJNhm+rsdmoucan5loktazzWsxGUL6gAorDA10BKoKmkkACWrq/GR3DcA+wgaA5yiy/yqLGZcuNPvFCuQHUmAAQjWopqVhW58vHSAryUjZoStUb6Nc7OtxtZd3oFdpJm/F85F9+DE3x18WGgLSCyEFBVRG2V4BUlwRm4phRbUNGzIHn08B0bRCrbtanxZmwMu9uxm0UBr2q3+XLXUAekF2DBtTeAuqAX+BTMQXlU/rwAbZ4gfwfO4hc+R2JpQzpOAKA+xy+8T9sHHhPQEKGfeFBWmYfsVNm81H/UV/SdMhI0GQZCCAPpqYwErRFuduXioZ1EgDJluae6dOO2AD3aSUT1Cshns91cg/6k/kEwOuLHwq4fxTXAuQiWqpmlGwh2lpAy6g5QCrqVdpPuxoewa1NSERJESVrVT9XmlD+1UdRyIlgl6srzbZFzoFrGoIBaHUF/n596xGqLCmaEcEVCx6S9kViXYPKspgHwFgSokm1qDGMMkyfgH38jWCpYDHKOz/NdfYYSIKrOeEdJUL6YdGLFDqQJTCElqDkFRTm9R2VIn47hnD7RASpe0hP0oerHuISeEE0PFUDy7aO8YoE/1NUYPdyX4DmNpQKhMD3lHPNjC5XNkfPgkwAoWYXGAQKGaY+TtLtx+ss4efaT9iCfDTHutDe32Kmjp210cBLbvG323nsftlxgQadWSL78qmuuGqO24tqJ6pzr0Te8SpfLp36m3ZNHB3HqX9IntS5BMHRx/uV6wB5TjL0Cf4QyyKY+Vmknb5QIPAWqmKprjVfUS9BL36Ldx9G+YoF/BSYF6etB4BmBmxqrw+pv5EVWl6O0cYFyYRQqVR3TwIXNHVhOnjyF8uWIrV2y2h6+4yErRjlP6QwxrshSL5brxtOvlVYPUWgQE84CIkS5CdDx0o7iSafGd4+bA3TcfD+iKBzwpSFKgecA7SNA2sMCjzmP6jgJW85Y2p1P7Ys8h2KBSOjXc7Qf9BvJMX/j/PyVv1M+9HfOQp1zTcb4CGDxJOqf4xHAZwBIN2Zy5DQgkRT2zp05h+rlNHaz220H1q+F9JlY+rJAc1nbSwlN7Ydi4hr8TOGo/pQ/gTyab+fzR4oJ0juon2PCBPBJjFtva/6Cd3DApdQjVX6qY4FaSjcIJtdhHlYp0nYFPqnPe/l7DHmLU0HRVQLMiSHqR3WpMV9LBoGj6rPCuHqB0Ya8qPUxF8c4uNljLV2N9uaJQ9Z6sxXlw+2AV49bEbCWj7mPJAD6qawAiZiLaE1urufK9CW1WIAifamRkQSpYYUYJ0K0Q6fwypuxzMcxQFZKb5C/yz4xAGgV4LtgVPXDOMaxWPoiBqeuXQd0PH+Xhat7j+YQIP8h6jSk8ZWEuTmGtYgsW33M55PhCeDHIZTKphiDqGE+P0Uddg/126njp6y/rc+WViyz/fc+iupZAWUGvE/Z4PhNOTIeUI5e6lNtW2fX+sQLJBxP2Qp58GiNQV8LkvYQ46PaqpvjyFuYfhSmDwepjyDrpTDQsepPrTRCHrR+SUAxNY66o8JImyBAYXKM6RwTR97jUeSkuAHXArRDyoa+kkDfSdAYxntTzFljc4D7KmNgOa2X5pgD+7CuPkXMpKejAxvIJfbA3sesKm8l52UuVR9kPBTUJpVPMHJXTRqHPfR7tawJ0tlDXmcZXzQNKveMwjbIhol2NhRMs/lgWWahLc/JR72TMYH0aqTU50KkQVWv9uDKiLdhegFjdGbsSJkSxkhrHYpzraSvPC3Z1hbkWA5rBZid+bRQ4JyGz1MOHK9mpLMLRHPLRL7PMk4GVTH8VddhqKfVsPmDfKlMfXyQknZjk+vdnMDVIWNCmLyqrylrmrHUJ/Szjx9o0syzjCucW1ydn7Lgf8qOauI7rqbUy/zvES4Ki+cUlwf66csM3Vk8S8khZpEAnOanvgJcS2ONhq8IYwDTm4NsAszJQ2OxNsJyg6qnv5AA+usMUNyxN4N28FgN/a3Lnnhyg+3bVwIopT7HhwQnkj+lR3VD0bsxRz9r7BbcpuVzRDA70yTTm6sPlh1cmzxzIa0bQqy5paIbYE0cw3WVNxqD9q8wL1LulEWcj3EVIFh2o8OARRpz01I8AMb0NBU4L5WbrqEK0ryjjZeC56WqOgoMNTLK+EZfEjCp9wYGI3bx8jTWpxfZoxKPeMYye9eDQLKodgkMDJFuZTOWNOh84o28KiDqVvcNSmRE/ZRrzs3Np0ljvMZFuhWAEs8pyIPqURkPkX66ims//Eh/5H3OLXtbvgFYUibU9ST10IPymtpqDo8L8jOoN85Bklh6MTYrTyRDTU55pMu6fE+Sx9ER0kF6EnUrw2fGaetnL0zZ0RP1lN2A7blrle27p8RKyjQ2sxYiPcpSDGXijVXiNK7Nj+N6RqH60/ysuVY/86OrawFbrh2SHt3uqZzU5gTj0SX0MbfeUYN2ZUCZkEzXDqZ4zjdM/pT+1HSBlKRF19cHOI9bUvHNnYf3GD5dfsdRYxsbRjeUcuFW0c1pc0Cox8/M2itHp21qZhSV+gx78CGULgE5fW5Tjuad+bJmScQ51dfU/zVCkx4KVfWlcUNlqrlRt3DqZypztS2lS198hEwJHqT+Wcx5dZ/GexpjNE6qzjnMfQUExQ6FUa6kLtgflJbGWiaRc9Hm3fqBAnTrBlLiowJ1HEWMqiIAN+2S22QH+amtqu3ebAnZc9+tZZzCjnX9Ivvg+7JQa+W+i2sK/NN4HFHHZd1ETkiP2vywvfi9F+yVlw5YZflSe/zRJ1F53Mb1GKdU6Uo7iVfeXR5Jw9t5LYBzb6f0Fo5dKIGFElgogbdRAoKPtACpqKhw9iTRUwm4EqCkh/qC5KRKJ9UCBQOkBiarHwU1pCgmxTgBW3q4rPf0pV3069evd4GTGzdq7fr1WvdgWxY7K1asQLa40j1k/9naa2n2/fl+RcE53YQLVFTZ6qUggQJRAs4UqJESgAC5j3zkIy7oLNDxC1/4gqtLKQkqMKaA9Msvv+zUH6SwoF34Un0TKLlnzx771Kc+5RQnFKD4u7/7OxfskuKCgmKyIPpx4JzSJYUJfV5p1TWkHKdrKx8KXMiCtaSkxATQKf1SuZBaw2//9m+7IJPa6htvvOGOU1BQ0Nyv//qvL4BzKtx34GuSG8bvvhq0//IF5NR5Pr99ldf+8DfjbceW+QeB78AsLSR5oQQWSmChBBZKYKEE/t2VwN9+J2D/3+8HrI+HYFvLvXbkBXaG8uBv4fXzUQJaH9/+0n2A4C/dt+lnKaZoPa2fBcYJXtN9m3sIzbFR0Ch6Dr2vv+s+TV86Tu9FITt9Lnq8vkdf+oyuqftG3Q8K0mrAsklgk+wwBSjpHlLHSDHs61//uoOwdE/w4Q9/2N1z6hxXsNPSPYngKB37C7/wC/+HspnyVH/9hn3xj/+M55MRzv+gbdu7wzJyMvldQTA9nFYeeRqtJ7Q8AY2mVdfQ6/bf9bPAOd03CfzTvU8+G7yOHj1qzzzzjMuXVLB1b6J7l2iZ6Vwqn9t/v708enp63D2O7sl0ryVoTvkpLy93x9yelmh6osf/NL679FEnPdh/HTjwGrDZP1hySjLl9ajdd++9zqY1CiHrs4JplA59qZiieVO96tnAwddfp25ecvCjYMd9+/Zht4JCGe0kepzSfXu+/jX5iB6nY+bT8H/WlVQABSOqjQhGfOCBB5wNrupL9RA9Vt/VTnSeaP3oPeVFVq96DqI2qPwL3pSy4aJFi37Yzm+/vtKl3/WKfne/vIV/bj9WH3+r4Jy2u6v56ql/hMg8LY22zX+K7CgtClzStBHUwnqLuiIEo/BrrIIrOs71DB1P+0QxJUAehFV4FCTmwb+P9z0qH0VT+FmBljB/I2zHoQSlABR8BLHx5iPew7uCm1DoCRGUDRPwQATMxWEcEkY0xENi58/BfRyRKgVR9J/SGqRevASKvFh8ubS5oiRAwjEegs7KmSAmnyJ77qyYvxJQjnAtQU8KIinwQUrn+7QiZAKrSKeP84cJ/gqyCwNSeYnmEQcEoNG19QU8BzzkfiafGgu8RBcjisiQbgF888AOp6Q8ofwoVCZIV878LutK7ySBK6xqR5qxu+qzZKDCOILR4SAKGzwbmUJJIQ7ltoySpdjdVgLrZlFngHNYi0EIzdeZEu/SpDTonBxPMEbWTLTQ+aQqEqlK5R0FdaPB9xidQ5FkdUoVBgH0iFeBdyJABJYjfvITAELyxdocyhoK8uos8QIAAA29Ijs4XF8aLwUhqkDnLZkJMANTCIL0+FEAHO3EHriHcpxGiYSaQU0rMNVJPgepx3RLLlhlKcWrUWAr5DwAawLEUOdwATQXVhdUwShMvtxlgQ0g7yirWeAR0kMETHCOK18BGgRVJ3mWRS44juCrInsusQJ4CIxTvwqN+6RSg+JPCEWhSb5CcTNYKmLzGY8i6iz5ltrYeBuqEATWsyrMn7wUSAElPtJCjdLeKW8gmcn+VlQRewBagMOAwQSqzKAyNNXbTVXPWFbpYotbtBEFw8UUFukEJuIfTiBIi7YPaMRsSL0RkCRvHoGXBKipUJItoEaQhYAK5iD95zoFtaEGTJ92aAx9x6PgvSsj2gCQmPqL+qEqTv0lpPO5M3AdykSaUpyEz5AmqRlKlo22rGYskEefjRD1nBusRUWxgaA9EFi86oVgJOpBwaEum6P9JZdQf0WrCTQCb7uS4TwC9IAzBSyAvpNU+qIrc2qSOcF1WlUm8ypn4C/UjfKjcnHRc2WINBCIDNGmg1xTEJEL0iv/7ouPqP1zOr0oHtgAQRf8DOQy/948iOKAW4LMtGTe5ryKmvKlLinkRO0iRn2U8USn9uu6nC+GNPkAlKSGohCuytyjgDWKkYJu6HV8XpAOQVFgAqUxRJDTadRQ9jEEa3WsoBmPgrdKnPoFl9fcGEK1SC8FYwVzzQKvtPW22a2uWzYI2OLNAE+hevRss7WJ5+KNHVgDltqebXfbvg07ME6mHyuaruZA8mQbrP7oxj/QDpWrLqlXhLbiU36UD9KpsVTgoNLioFQ+QxN0LxUprciVherLQ5RaAVwBXIKxBAf5KCDXFlWGbvxUu+I98upeGjTVVwE2BFupesOMc819LXazu9kmgWm8WLqRKFTFhgg238LSDfsyoJo7Nu+27Su2WCbqX/NVzUVIqMZuH+2D31xbkPKZxjSBQ8xK1AFAEXnS8Ky4tGvySo7mDtIe4vqaP0Gn6SMqAUFRzEe00xgK0Af9IZUrzu4KwwN9EwBIBmEkr+QZIEkWnbr+fAkJJAEO1lwEODfNQNk+0G5NA7XkbwQVHkGVPhtnDXu9oQ6lpH4UiYrsvl17bTnB8Yx4FEvpKxqR1PqVDhKgn9w3p3ZMoF10j4ZrKR/RfGirzNWMfTGMdzpW84CgAreWIOmCPwUkRRiPPBSC2qVmBc3DYaDniINZKXvardSupJ6sv6t/MRUwxmtu53i1S52PsZJtHHzRHiH4erG4vd5HfCfcjYIcsKIHa1DskVtaG1iLX7VU7GDv3naP3bn+bpT6cikjXZ8Sc3lTHtRWBF6ppTIakwbNsywqlESXXyUmTLsKsm4IMSdpHT4PvilBqlvSx/nUHoPMW4xK1I/OJshVedN4qfb4g/PyDm9wKJ/U5zUP8xY4Hm2FOlC71fDDe53Ypzf01dsICqBeqRLRDkYm6YOd2DfeuIkCJVaJm3faXRvusGyU7FTLYdYWfso6TBrmS5sWST6VIlLM+RlBNF7xnsAp103IrGAk/d0NOCjXaR4Q9aP5T0O4j3arNhsgv5wCAIvZjj/K6lzwL82S/ABvoHSo2UDKg7JZ5UT87rLENXVZrsv6SONxT3+n3eq4ZcMoS3pTGLto8hOALK3NWPu2tKIml2TbNu20zet3Y+27iDTQRricXpQ65wD244qal115A5V6pErHwrF2eMQmSFMCa0RmAqBRnsvPTdkACpvsY7B1uSVWnca8L4KIk2pjhIc6Vl/USBx0R7kSwabXb2fPtXMWvy1fU2IplSnWgVJme0ezVaYm2cbCAstHhS9OYy3HSpk1qDGOvHrdWpREk3f9E6Csg4wNARq0yiX6iqVs42n0Pn2euXieIOKvAFIaLzTiaewSWK6RQ2OeThnDNTkdjVqNRuOK8sO5aQO6jkArdxzHakTRdTz0WR0cgV5SCXagcvXGG9iPz8bb+tWFtmEVdsBAOwH6t/qMAKXoJXzMK17G1En62TUsPi9fG3LplT20B4i+txur1Sty3unGRSHZHnqswpYvR+UzQequmuul8MiajjRKoYvUupeqVdCu6kB9Sg0qTJ/RZ9zvGkgZX336O+cROCeFwRADEm9R9nxHYc1Pl6P70ibYCkCbnZn2WmtbyM5dbEd9bo60FNqK5dQ7Sl5q4uqiAkZ176BCmV9zAACi9lZT02kXLzXThnJRo8v6/9l7DyDJrutM82RWlvfee9PV1abaGwDdaKABEoYgAdCIFEezWjJ2qNiIiVUoFLHanR2FRqOhOKPhzs4EudQoVoYURS8SBAEQhrANtPfVrrp8VZf3leXS7/ffwqN6MBLYAFcrSszXSGRW5nv33Xuufe987z/ARuk2NLJOiNVRHjq7Sbtstcef7LCtndlcfzJTkY42jSo+AW/YWuVT/v0aA91f7hu3ZOb2hXbmd16qDzfosCYiT5Rm48eNhZD7/e2hgbZAeyXtAAemMy9q7dnft27HL6Cmicrblk0Ftqsjl/C2G/1YY6deDl4mVaWtsTjCvbNbwyG7cO4WymlB4D/UkYGPJ1GfO39pzAGsDY0Z9uiHCd29B8g0n7Ee6s7NfZjLp/U6/Vg59fMgjRRvZUWaDRWgcVMfNHGoDbL20/zE/Kb2+na327gW5G8Hh7GvxmYpRlM8DuJBAI7THDk0GLETp/tRQAzapo5qa99cht+d3qb64zTuhR1VBcqPTL0GUHazZ8GuXiEkLmF/C/OKUMHNdGF3z3ZNEkY8wUN2JfbkE6W2d58PqJLDqGPN7G64pjSqG1J1cxLDJechb1xrax2QYH3vzSXKrgqvGt/oLfytcmF1PYCikN9SgRbcq/ah8ZnUdZTrn4Iyl2YThCyft8HhKUJt59jWbaVWinJhKhCr1q7UGMdovuBhID3QQRuTmt/4xLqdJNTuEmHrc4hOkMkDVNOo093oWbSz53usuKjanniswx68P5fwxRqfOCfZElxIxVEvUIWMyYK1F1BdffqHz9izT//YGoCWP4yi8F0HdqP0vdFf3LEqp2zhyueK6crxfv5HH9HwlNySFkhaIGmBpAX+/7aAQCYtyuVAud0ZIodBlBVKgJvgComlBYecIHoXFKdNDhjtpxvRXrhV3ZzWd0rTC5+lm9MK/yllO33nPWH/91/WjQn27/88/3Bn0E0ZOW+k3NDW1vZTJQY5OVS3UmbR0/dyVilcq9T+VEdSP/jiF7/oVN0aGxsdyKg6EjCpcEy/+Zu/6RQRFP5GcJq+l/KbnAkC1s6fP+9gtd/5nd9xoVe/8Y1vmNTr5EySA2IC54ycS3KaCb6T0oQ2nVuwpaA9OaaU94aGBpfPGzduuLb1uc99zuVV7UhhfgXpqd1t28YNWtrj8PCwczx5ynPKw+0hgv7haiN55vdqAV0sdPfG7Y/+OGJ/8VyUUA4++x8/HLD/5V+kWgVPIyW3pAWSFkhaIGmBpAWSFvj5LfDPfytk33ueG3fcAHocdde/+CIqPvIkJ7d/EhbQOlnbhqNl40axd+0mNWmty7XO17WeICet/XU9p2sFret1racQl3pXGrenpb91XeEBVH/b7zqXXjqH1MD0kkKbwCat29vb213oU637dW49JCOITNcC3/nOd9yxR44cceCcrht1/XHmzBl37ahrmEcffdTl+7+Fn2LWjZrdF7/wH7ixbfbokx+2fffdbYVFhP3jRq1c43LKOZiH26cCGry8324vlVV/6zdd3wiU0zXI/fff765xFSJaD+1I+U7XOfpe17JeWs5Y7/I/2VgAoK6VpL6nh80+/elPm5TAdf3t2U7p3Wma73K6/+4npa/y6PpLsNmrr7xqjU2NXOc9yfXZwZ+qxenc2lf1rM+ytf7W9b7ajW7ZzhFK7+zZs/b973/f1e8999zj6kbXoB4Q6WVAx76X8nj7612bd6z3t5euriVVTwrTqvC5mzdvdmqFyovasJdv7a9jvXar771Naahe1M6ktD6CGsaePXtcvSg9Dyz10rg9TS9fXlp38u6Vzdv3jsE553jgKNUNTo8oHg85AZ3CixKTjbmZH8VBx10a5yTKZN8M3V6XD0bHOW8GjijKH8PhCU+BowbHs/oDx0hlReoWzmXLu9Q35LaSilwK4fHChH0LLy45Z1tqeTVO2yJUlQh/SNpyNGq0UOitNJytgvCck4RzOuc/Xh/BXzqle+qeD37CFnFaPtDeUYURICN1uRh91k94M7+ADlSfcBeRT8AYwXP8pRPJQaNiMdrwP+2zyl44nvnRJ6cR4FyUlxRVFEFSjht3HuSMEoLUYjoaVy/ll2MmtkropymcmYSuSyUMpF/yEzk4KyXZIfpFHg+lgWNLanjx9XGbGrtuS9P9lgOckS5nPmDhumKVAskVlrRYFqH8UlC08aVJjUqqLlLUIm8yjezFuxuwZBXqDW8RZf+bvu/AFo1bKifvCt/owvSqLp0h+UHll/2gTXwoIEjBzEI4iSPkHS9bBJUXQRt+QiP6p6cImzsHLEE4uGLCykkqg2NdhlQ2Eo3j4Isij7KidgB4EJ3ss9XxAZzGQctCdsVPuNY4aj5yJqYXNVhm9S5C+TbT1/LdOSF0qK6NcwpDU0tUrUtZy0e7iIfmLbo+DXzHO9ErpNSTHgBoAcL0I0vjI2ziCuHqpE/mHNYqvBxm/OVACpmfeg6g8JcgNG1oeoA5rR9wc9XKG+ost6jOEss408YGLDjRbXmFOZZTu91C+Z0o9BA+FtsR5YzzglyhDDY7ch0lnQFgBto1TlrYKwtSh3KzFxYQmq2m2VIKGnCWl3Ni2gr9xtmL/Cn8qhzaguLknHXKirRXvxTZ2KRkhQ6Ocss7v5NzNR/5t+UlVV+LY3/1HdWD+pkDqZj3XDgnORlxfIfYT65WQX86owuFxzfqsw6cw/HuPLykI3+xIDuBsT4ghdXRC7Y2dpG0CbOMw185ixJWMgDkWVBcDji3zQJ5jdTfBgDkvIJSPJKiodoZ3nUpR2lskGKhAFM56dV443LSo7wnwNZBO8wXbowWNULu1A44Ecerl5J7jlUzc12RNumAVnnrtbcKxv7COvwuzC1FA04Mc5zaPlozWJzcq//wSZ5fqYUJ+pLDNl2AK6GVZc8wDUf9SOqzqUiEKKSl+p1OnEjFkg6c03k1NgmNAj4BHFHorijjQ0xtQ+MYMI2ALL8zKrtziNhJJeUqlP31nSsn9bQeWbbu/ht27vJZG5gashicbBQlnGXkbFaCa5aF8uGubXtt75YDtrms3tJVdo2T9CvBOIJP1FfE1Gj8EuhBLboTb/QhyqSGhj2o3o1+jSlcu2EvbdpfWdILE2BzPug86ufAJBEHIigdOePZz80l9FN2DlAwzScOoAU6cSMs+ym8sTtfesS6+q7ama5zNjI1SrvFloylayjOrayscu+52LZ27KB8e62B0LYZgtg4r+qEnPCPc5J3tX9Spa2ypqAdhQA7lG8e44C1VD/g5cYjDlP+VRA6TlSAMYOn1OXk/KZgfIfyHbYXcOSH3k6QX1dJ9B0fto8zrzDTkX/qGrDGKfyo3C5ZxiNUyWQEP2qUy2s8xDF0w07eeN3GZob4Hqc4edGYPr+6TLjYQsCKTrtr6z4rZ+7LABoNqJ7Yh6bEuZSuMkwb5v+CHTQ1qW7DtG8N9+SO/KuNq1js5VEdKq+KJLJOO7lNc7HqWj+QDgfFmWsET2knzSfCejSvO/upmqk2QVk6RCOv2quAGcEDggdjq8CB48N2ouekXZm/CuwZQnE200LLQD3LC/TDmO3Yvs0ObjtESM525jWFqCMPpCfYQu8CWwLUm+CGjfypjWBfzWtuR7LFgCBVOCkaah7QyEUQRDeG8KezF19iMwFRQgpp+9hAY6ROpE8CqqUgRoxOxkNXZGxJuQjFykjNF8oHKnNA4q7TKF0Ovznaa6e7T9oAc9caMKv61yr+kaUgIXEZU7Zv2mYHtu+zNqD2bIBjZxcMpXWMcqp50D1MwGflx9UlZfbrZrXWTuzihji1RfKtOcxJdrkvORxgStlWnxEMrSQjvKtfu9UGbVcgnvoGzRQ70bZZP0h5L0ABZSf3QAQHMoS5Pqo0oqwvBJKMjA7YeUKy9owM0hIYX7GzVB+Xg0FCwWba1rZ2O7jrHu6fN6Eels85NYZha84Zox9pfFN7oLfw4tpIsBv1N4edL94asxliOArqVDtaZ64IsrZIZAesAmBuM/FGq/HBSIFNbV1zsY/0BLZqnotpPlQD5nwvv7ps3/n2q3xcsQc+eI9tOlBp86mrgH9DVo/YxvbyCsBFyspCVFOFn7UHyarUFJY+K0pFtsVIbiXBTlHXNthXBpEJaTMC75w93STC99oY6+LqDKSlNDf6C42H9qkqEXzvhiXAZSkjcwCnoS8xFqjuXLckKe2ruU0jsWELZUch5lcZTy52Ldp/+uKPyWqpPf7ILnv0wQIrKyX3HCfLuCyq/ngFqGMpqAXpZ8dOjtlTz14B2IlbVjrXqAA9c7PrKGClW001DzI9Xmm79+dbPip9fmznx7YxjWtxxkcUhHXdoT6oPq9sSW0OyWj20/zL+hnQ2ZWTQ1gEsJdmUe2ruU1dhTyqn6IuG9C+rAsiGEPWSiUNAZOLiBicPRe2b373BL7FWR6s2saDUY34BrXW4D+9OEKopObjhDom36ytx7jG7bJnnjnBnFCE/7gSkDCbcMYA0sDk1QBIDz6wyQ7eVWElhLb1i5KmdpU3PYSgNYbmCMF0tFTWhdiNetW6V/1JU66GSk7sNuVDleRjnFG+NGpgHfbFPi4NzTMbdbJR7g1YMJVEUmhvJ47P2DeeGrPR8UU7erjRHjlaCXwFSksfVjNW7lhluLGa3HIe5gP6SV9P0F58/qxduzFi6ZlVtIEsfJgJQqyucX8ik+vpSjt0pMoqa1HZRFFYMCgzEimoXFKyVX413wqcUxt3rZ76EUqudYDeWcdRR1qjCCxVkQXOuY0qd12DNh5z86eMonFTbWMDEFXmL5xbtT/72o9tZn7K7n/gbnvwg+3kDztTNoF2gh91DazU9dCYjl8mTOzFi5MoIN4kFDHXC4SJliLrEtd+C6tBK6bfHrmvxQ7dk2/VVaxweRhB45aqQtNyzGubyhGZdGtDSqO1juaSGA+cqDAaN2Rh1Zd6mL6U8qD+FHDsQ+WaRT7vuu4DetvohXz2Nlo2sO/EsNkzPxyw0+ev2OaOMnv40Q5rbScMdCbnZISUrQXOxQXOM8/HaGMC7oaHl1FAZK3Wx7VIVGrOqMsSnjnIei2FB3j27tlsDz0AKNxC+NisjZld+UvRHE1/pkW7dqZ7THMzs4BzP0Jx7jmrr22xxz/0URT49xL+GZtqEcb+ar9aZ238UzFV7ve3JcG592e35FFJCyQtkLRA0gLvaoH3PzG9a7K/QD/KESJFuLU1hen4m02La0Fm1dXVTlWus7PTpPbnbXKCSOnhKcLtCKITFCmniBxaDz74oHsJclS6b7zxhttPTgU5s5Su4LojR464J/MFTZ4+fdo5vnbv3u1C/gro+8pXvuIcaArN2tjY6J3apSH1CZ1bYXLkuNCiTU40Kdh9APUDqQfI0SHHmxxWUgSQg00OEanqaV+pDSg8kBQCboc+f3qi5Id/FBaQTPZzL0ft3/xfEW56xG33Jr/93r9MtfuPcBNB12PJLWmBpAWSFkha4JfWApM8VThP2IHGapxkuseS3N6HBRJ2+NF1ewtQvZgbV3/we6n2mY/hTNIN1uT2T8ICznF9W0m0XhcQJ0BHa22tu2+hvKB1tAAhrbV1jTAwMODU3QTMbd261V0HeMphtyXnPr4T/rn9d/2mdbvOJeBNgJiU1vr7+53ySlFRkUtbD/AoHKYgJ4FWurYQhCUQSuetqKhwN3RnZmbcQ0HKkxTapH6u/bXpGkfnU+gMPXTzR1/4gjv3Ix8FnDty2F3vKMRTml660ewcX7rpyQ1wjtXm2ev2v/VZ+Xj11VddHg4dOuTU0qUUJ8BKDwF94hOfcMCb98CYS+xn/E83qKXaLkDw2LFjTuHdCz2ra7N35uVnJPeef1a96EEqhZyVrVUnBw8edNdwAt48WNKzq5cfDxZTW/Kus3RdePPmTac0LmitoaHBPZylh6tUx549lcnbP99JpnVe7xgvD+/8W+kEcRTKjn/1V3/loEyVRfYUiKiyeMdqX++z0tHLO4fedS0sW+gBrZMnT7p6URheKesLKr0dtPNs4eVHab+XzTuvd8ydgnMJUW7yGuGIkI9KIb4Eg0hQQs4ctWYfUESM1wrOCL62TPaVsoJYKk+VRuG6nA1wmCjklRx8emJe4WScI0XOFOcukmOAVHEkKj5NAiW1KR6emx8fs4LcPCvp3GWBimpbS5PDGQfP2wUCEQOYQM0EFTOFxdwIBcZkg8fH97ZjVI4POSSRGuID73IgB1AO0tgBqBYlPYV79ePUT+CsdHCQwnDi2JDDRb4VQQpyUCosoVRfcGfxK+1T6UmlDaDBqc7hBZX/1J3PnYecSrlIDlM5jAC9EigdxRaWbfL8VfP1DlteZZVlbN9sVl1CMpzzbfvKLA5E4UMCtbOlGca02T4c6jP4RXDy4LhKIRxdRnaFZRe1Wko2sFUq9Az3VVyGcQCq2C78ojLqDOG9CRXgN0oiP9rb/iP5W8SMOOdrQgAMh3khpNgd++BeI39+VHUsRcEncfKJHEIBQuOcnPhysSIXYeGuizYKNJtT22BFWzotUEg/RYVMxIPajpQuQtgoBEQlNCOA6pJ/bsAiOLqJPQsDBXxB+NYUHIGBnDxLK2m2REEbaRSTB8FHZJy+pbqRA81TlRA+FsDe8eCCrc/12vriDQCOMSA8AC7qXM57gXOB4kpLqWiwSH4NVViINbMpO45atSMKvtHnsBDtS2FpE6vMDWN9NkGIQUsPWXlLo2UW13OemK2O9BAy94bllORZdsMuixd2EgK2mPNtQF5qMwo7uwIgs84rvjzlwIR1gIF1IM0CIL6iojLLyCPEa4CwpQL7yJFfnQmgwCkUqiO+rfSG2NCGU5qyytWqHAfU6KTwQoXyybVBfSXnp1PzVF1RRwJFXcPmN7/6J05dgXP8D1viZpXjnP6Ky5x+ITvQT1xDZD/1T8mryT4AFKpLISUK/CvVxvhct0XGrqIytwAQoLEDe2LvdJz/ReVNQI9NnFT3BTleJJsaHCBeAie9aDapW7pGz/l9UibkXVI1DvrDoRvFLgpLqFRTsI0csaovhRn1s59Cowr4UZi1GHupt0aVFmVIl5Nc4nwci8F48Yky+IBgdc4o6a87Nz4Pb3Nkiuvr7Ig9BWNJSSzs+j0wpOqG9BSWOAxcIRto/3T6uNzOCnNHU2M4A9RKkXXk7mfsw/Yp5M0nOIzxNSrQjwFDznaFjJTinAuTqmpVRlV3vFyFalzho/IueCsSXaWbjVrX1Yt2c+SmLRHuFNzYOUtzcvKtrgoVoabtVkf4y2IUO1MUE5CEpIy2oSJKgpRTyxS1qzhKUHLcCwCgR7GOAT/S2CYgi8/qYxSXKhOqstEmvX5CE9Ww6qpzY9zgD5qJwGplemNMZGzAqy1FQIWTFFQmUNCpCgIzYVDXthIAaWp/sbSYDU0P2eWeq9YzNGCLqFzFgH38LK7ycvOtCYfxpvoOqwdezQe4Epynpi04yuUL4zl8gHOpnUhRTecO0Z8FCTBbYXPqg9JqXKDIG/1MxBb5DAvgxCIBjTO81N4FXSkN4Y8b4eI0nukCUccwTrOPlPZcbVPPSp0/3SblH/NLDEBnzESZOcUGUGK8PHjKekduADsSDll9kXvTWUC0NY0N1lG/yUGBAq4UGlR5pjGrSjgP/ZI2p4w7VTZ9yfwjNmWN39T21VM1HrrQnhwtpbS3uzK/sHGIDiMhPqqfUNf0eT7SJkkhoDGesYtfYxobAcc8cE6jDEWmDt+eQzChmovAG/UWP3mRGmcwOA8gdcHOjp+1aULMieoTdJiZlWpVtaW2ddNmayputQI/859iONKQ1N7UbpQ/dSSBDYJm9J1Sd/2djDvwku/Uzzy1OVoOeygQ623gHHklEV5qf4JvVNlqJbxxro32S88FHNEY4X5gf9de+UJKYurDLqyrB865fuOzURRELw9fAiy7QfmmWGawPqBdZWVkW311vXU0bLLG0norANDWfECi5IOxjPFROdko0cZ8rPyoyO6hAvVD1TWvDeafdqe+R78xwkSrCPpdoCTmdJvqUNUnkFnZS6U+U6kfn2SB2U/9QyqXDgImnxqrAoy/Gq9IyuVG441rAoyvgm5m5iftBoDu9YFemwrOAewCRdPOC/JzuT9Sa5vqmqypotUyUpmXAfY1Z2idGGMNGeaD4LkAjU4qau6BIs5KRyEEbMIGp+dtZoVRi32jhA9OaPGRyfhbmAk4l2WVaemGjqQD8TZUEtU/BKAy/mEHRinagsYQsxeeW7avf+05yhLk2uB+23Nfva1lRbiPM+1CtNYByedpfUU5ZWdBhhtzO41Y9qIPYCTs4LA1+tHGWCXDauxmd7gwPmkg1N+yk9oUP6idgM5Rd6yNZDxeal3qndpUp9pVSp4Yhi+oCCXApnWl5l3VMx/d8eqzvhiQLQWLAV+uUMFnzi3YH/z+s2S10T75xC77yCPZQEmcW8erHfDBAWqc1DV1kl8HLOvqXrIfv3oD1bIli/KQg5/yKfxxVXmpbUEta+eeHCuvoE8qD6ybdH+GpRdjIdexhAn+aQhTlUInY1O74KycE/uznsV8bxeJ+VT1rJFHNmUMjTIrqcVp3gsAE8U5v1Ysmsc1cmoun5uJ28m3ovbn3zjHuDhlH/lwqz30SJOVleuBOllb/UMNnvmNfCnkudTwIrSZG9238Ble554CIN1aDuuqLNS/sq2sLGC7dkgRLN/KywhJLGFb8qS1gOAm2UpqyqpblShBqGO9SxFa4WhVVcLJ1GeUh9s396faw9v/XFmVBj+4+nQ7Ux+qC75MY4DR/PjaazP2598dt1vjs/bIB5rsiUcruR+C34n4wep3uuyK0O/0cJH+qc0pavzY6IqdPtVtV66N2uw84a8ZgwI+rotK821LR7517sxlLCX0dKaAtLfbm/LubPY3rdDPQzlqoypnnL4WQyGNXLsCqh6jAufYI0BmtRRTW1DdutaszLi1FfMb45hWAc4O/N9dy1HwMyfW7D9/+Uc2uzBtDz9yhNCqbVZZQ70zDmjNLzXCKAVSKPsA617VKs9IWV//CvcJJhBMmbAVHkTRsjfA5FJE/bduqbLtO8tYz6RbJo1FD4qo5wvQVIvQzODmW7516rYY3T06QsYjbo1GmTGK3631BHVr0woG5U9Rq9SPH1soXR8vpZVgHe0ewOIMGvudETgyyv6jfan2nW8O21unLqJiWGgf/cQ227SliBC6srPmCc5AParhKD8boYENNb2wnTw+atevBm1qhr4JXO1nfs0vSLfWTeW2o7PCmhszUGBl7Y36uWwc00NUND431sj+LnWfzU4t2NNP/YAHRH9kjfU8jPhhgXMHUJzTfKH5R2uQjV6jVYBbBylf7sXbe9yS4Nx7NFhy96QFkhZIWiBpgTuxwMaUfCd7/mPdR84QKa/JKfPOTU4OORAEockZ9c5Nxygcql5yUGlfOdHKkB721AJ1jOA8hbMRxCYgTupu2keKFUpbi1+Bd1KZkKNBjhOlLYULOSUUNieN8B+3b8q3fleaOk5P9stZpnQ9x5j295w9Or/2U74EzeldwJ0cV3ICvl9Hxu15Sn7+h7EATcH6BuP2xT+J2H/9YZTwAGa/+nCK/avfpE2U/NPvw/8wVk+eNWmBpAWSFvjFt8DEuNmXvxq24bG4/U+/mmr7dukG4i9+vn/RcjgyGbePPL5ul+YSVotD/cUfcYOogZttySn2F62q3nd+tF72Nq3LtUb2IDBBXwKnvHW6wDUpnjU3N7swqT/4wQ8cpCalaD3A8l6gMO+celceTpw44dTZFGpV1wze9YkekNF1idbvAuH0kI4U3ARl6QGeN9980z3Qo2OUf11LKKyrHpBRPnW9cbsKmHc+KWL/+z/8Q/xfKfb4x5+w/XftJ2xOvnN8y6EeEHSk+5wZ3LTk/A5M4E+dQ5t3/aC/9dmzmfItwFBgoVTadJ0kOOvJJ590AKBAKu9Yl9DP+J+nvidwTXbQQz8Kc6prGm3e+d9Lmj/jlD/92bvek7qfIEDV0+OPP24PPfSQU87Tjl55lI/b86LP2l/50j76LPVCKQk+88wz7nu1G4GYuo5TPWvf29P4aUZ+xgcdo807/vY0bv9Ntvzxj3/s6kX769xSJGxqanK21b7esd7735amyiK4U3Wih7TUvlS/R48e/e+uXXX8z7N5+fDSuGNwjpCDCZ6GR3YMhw4uQYFXUgWRU0H1gmPFh3dOv20o7OBGwfGoUIXOwUl9OOe3ToxdfLof4KCxt/uFXB6kI7eHcy9SxxsAAkAJIUzjeBYGaDdjPd1WXgL8et8Dlt7cYrGsbKdQJtBKKk9CYzIA3gJSnZOjHhUoOe7kPZICGp6LDV+BHJUiBHQejsGTrIzxD+cEjjhpjPgAf1zYSqm+Aei4/FDGiOAieUJUvwrriANGIZ2cEpc8lHLc+qWmhlNQkAV+FtkNfw5tSkCIvvDGSeyCUkx8ccVGfvKGJc5dssKaess+dND8m+pRQQOMQIFNThcBNHgX3XkZ0HB6ziNCMsbxE6RHSC2p3wCMBNKKLCWnGg8kkiEOytg4nepJnjsXjtXlc8Mu8oapXZGyqyt6mvvHX/jFqFuyLgDxbQpCwnvUPWlhSykaiU/aIGEUOghHkUJ3OsUwxjnCcCVQQYsPD1rojZet78JFy2vbbhX3HLVUxl0fSjKQduIvHGS3jgMtjLqDtEQygAHT1m8BqI1hQ8pHncp76Qe+8mfl8yonSmiFqxupZiifyrP+CZpzIXHxuMmN5gvRLucXbG22xyLL3bSTOZov9aaDgHAjKHVE/VmAc8BchA9NzW/htzLsTcaULuVCopB65R3FPylRJID5wuNDqMb1IUgYtiL6fWpRrcWXCDUGuLQ8epUouVmW1bgdBcGtlA8QRMot1IlAUXSICLe5yA2uGZTw5ly7WqfNRAJ5hG3Ktex0QruiHpgAhiSnNDc54rGvvIiYX86vOFSMj98T9CW5xtSypL0jN6a+UUhiOf5d7b7d1RQCUGnEFXJR4AUQjgt1puZAv9hwsFF0PimkqP7xEfUnqfCJyJB9BTvRNsiRPMNynKtt66WetOrOFbOsyLSlrU7A0AUB1sghdZeCekiKPw/HdTVdqpjksLGMDFAGxkSWcHMSQoxGZmF5mmln6ZRP4fCihH9VWwswzyoE7TowjMLbEREYEIPzk4IcuPjPKTtOf/YVABAljwpqqDFCe6iNp+MkT5fjmLYjuEbqYwGAmwDKiRgEW6bYMvsI4FMIaKkcCTLQGCGmIqSy0kalPZdNHlKxdQQbrZCe0CJGLqGXvOPAdUgArn7OscY/nV/KXwIAUxhv0qVowvGC5qIooUg1JcC5HCzpxiMKKCtxzAZMiM1dtSgNOUdVS4B86wu2hLrL1PKsBVG7WsH5r7kwJyuX9UiJFWaXEdo514VpDQDCOgDxbchIKagmBQ0phCwIsvvGNTbKqPIITHKqlZqDNSbxT0OJWkUcL79gDTmlBScKGpCzXd5/F/6SOWMD7uJM9EfBXRQRm2kAUXl5iEYQEW0zBvSn8zr1GkAe1VgYQCYIMDwLeDWzuGDLhHAUpplCxah8RbnFhIYsAZrLsUyc/mr7CcYpQUGYipyqHvinPPGt+okUA3Hd6+z8ooGNZshfqmVBggLTdJzguxD/1E7dWKM2T11F+E59BG1A6p9xSTAZbUbCkHFA0Bh5VpnduSmf1AdlD9nZBySpsJVqT37G7Tjw5CLj3OTKiE0tjllofZXyY0TsmZmTbTlIQBUz7uUDFpMS+WO8V8ukLjiL+6cxQk5yzoItNfcxX5H3Nd4F0+j7jbFBZdM6irPzWxrwOhmhnPQTzRH0VYkeaaxQF1QJBEOkUB6FglYfk5UErDhIR7/zjdSVFQ5U3wkZkIXUu9RHUoD0FFIvxnwwsT5qAyuEqSMstdTi0gAHM1gb5xZkUI8FVphSzDd5tDcgWvIki7l+S9n0lwvtrnrD9hrzNMprbJLynxv/+TZOuxLYrvmOK3bGL9SXsLHKHKUO2N3NEWHmflo9+9DbqB8ftlD/izL3pTIWKd2N0tGGMKAUMFXv6i9aKUjtTX1Y2mmC85djQPArkza9PGmLAMPrjLN6OCCXMb0YVcQS6jAXaC4Newi84+oAuwPbYjM3lZKXjdLqDDrHRpuUxdW/pB6nhw7U+6SQqD6jdY7mZx9zudZmIfbRGC+4SHOhWrhsqPrBCppyKT9zCoep5YQ0F7BHGmudVMZEsVyCt1w4ZrUDWYC/E4yxIdro/PKijROOfHF9BWCXcRcQJpc2WpjHOMO1VQ6wd6qvgDyinku70gykhybW6W9hvpBF1avV9lT7PuwcZm2zgr9mcY39eFiCLLN+oE4IN5gK4JZNCNpsyptO3TBkMSWTP94d0MkYoNDAsqN6Ok3eXvjRun3tq8+xw4p9/PHDdteDtRYnWJXqLos+kwesmMbEIRtstBHmV5RQ4xoXmVuUM5XcQUHKCzBNnBOTBTd2iekzwDktDTQWq6KoAvJBPTpbbZQb4UnS4FgO1AMJ5JQ2rzRIk99cmHF+0/pZ2XdtT/tgG80z7Ib91Bc1v2qUx04kdPlyyP7w868DltXaxwCSHn8kHZ+YeqFswnmYk0iCPzhWNtNHvpgF7B8YQ71rnjUPw6zGiHSUqQoLswGvUHwvAgTPYHRQ8cmA6l420RJWyaUABWrdtAHMkjb5Vr5UxigEf4S2qLJhXloWJ6de07AJzZD2xDeSYKZtai2UQr3rDBHGONck2V2rgbnphJ06GbWvfaubfhgkLGcd10PlVlTMOE3ayofm9Q3gTXmgRdE5Y+R3aXGda0PCn46jjRlS/QEGAlzmF6TgX0yxfEKhStFNtpbyl8op4JhbAO4hN+VdRVfuVVsaDygaf6mOyCC/uwcXOJdsrd80l7lM6bMyyKa6ExCuPq393Ld8cP2BLzGJHTu+DDg3bf0jk/bwA9X25IcquRbXgxwbx7h61OTK0VJi1bEak9dX5d8M4r+kL85hUxpeeno2ZcygDlNRnKZXZXIMRlfWdIyUYd1gLovrS9XsRoOklviOB1LUsdRGIlw7xWjbakDaUzZRngJkWuCXNizryiUDaP2o8ro2o/aAAZXXs6ci9l++/JJNz6Ea+NAB+9CHGq2ihoQ4UmvgAC/NV1oT+zUHcaDmm6Ul/LOTEV7LgHQYi46eBgSWUwQ4W8bMUJiGWiJlxNaMnu4aQ21S9tKDT+SYsmqu1wqKcUYTMvMBIzptkDmF8msece1QhaGcKpGOc5+Uf2ynNa6b/0hHJnPrJY2p7OnGA/I61p9q3/r6uL156qpt215sH/tEm7W059DGNF+qfWmmlU2UgErOvETbX2ORPDcVsamJmAVRRIwIUs2IWE5empVWZNHWFW1P7SSCcqcAVEBGxuYwHYXm7mytmlCuZ2dWUJz7oT337A+ssaHennj8Cbvn7oOAc5gBVVvNJxoBNGdvzBwagXXk+9uS4Nz7s1vyqKQFkhZIWiBpgXe1ALNkcrsjCwhw0wJDN3n+rk2OF720n6c88Hfte6ffu8U+K+afdW6lJ+eaWwS51eGdniG53z8GCyyziH3htZj92/9COIiBuHW2+uzf/Ms0++D9PP2jq9bklrRA0gJJCyQt8EtngUvn4vbP/td1GxxL2G9+KmD/8+fSrGxe4g0AAEAASURBVIKQGO7e0y+dNd5/gf8SKP13fy9sQysJay/325kXCDaUlVwjv3+L/uIdqfW0XnLmak0vIOib3/ymCxGqtXNLS4t70GYOBalt27bZr/3ar7nwqVKH+9a3vuVUnJ944gkXilSqcNruFBa6/dwCgqQ256lU67pC51daWsfrQR4pRyt0q9TW9Lsgv8HBQaeOJ0BNm0KhNjQ0WGNjo3uwx7tOcM5qB/q43YDtrtoXPv95y0Ci+BOPP2r7Ore54B62iLOV0Cq6ievjLqi/rtL8RbncdN24aak8a/PK6NnOA+dCPIUsm0mpT6rXegBINhNsVlBQwA1znD1vl2sjJ+/+f9ld4Vq/9rWvOfBMCm2f+cxnbNOmTS4dnf+9pPfuZ/tvf1V7EGwmOEwvAYsCJwVQ6mEo79zeUZ4tbv/b+yx7qb5efvll++53v+serBIE+ZGPfMQptgk+U516aXj29Y7/We8epKfjvTRkF33WpvSknPe9733PwZaC5RTKVwqGgi2961PvWO8Yves7L32lo7+lZi5ldfUBPdCldFQW1YsgT2+/29PR5/e63Z4fHXtH4JzyC3QUn5jjnRhK8jAJQsuiPecDlOWg7pNDmEvCODlfRBhHNE7/BGE54zxsBg0rLwSQVLb5aLMKh+lTmXT3Xw43wKWE0l+Yd8fJC+mjPfjyijmGB+MI8xkfvmW9Lz1vk73dVl2YaxV7D1h6dS1AUpn5KlGH5OG1KI73FBw/AcA5X2jdfCs4z+dXyQN5DuPhoP6gkcxfkEO+yUMm+SEPceC3BGHiEgtBfKx8lgcPB5/Cj/qzyQfObl8moVPlTEVuVvCKraIURF+KEX7UBwggKM+fCWSRz0N0eTnsh10AC+WQiRMeKzG/yDuQFE5nTuj6v0+hQVFKkuc2NDZlIz9+3gIXz1kRY07Gvh2WaK21RDHwVFkV+xXiuNoItSrvUEIqfGHyGpwg38OWWMHOAD6+dM5dUIZdarExaUvJCmWpBA8fxgl1a6iuJSQfomkXT5ifsc9XqrCp6n98pzEtSrorc5YgXlZ8ic/iReRwoUx+wGF/MXWXy3im/oBKUmIFhxXKQfElAD5ACEMJiRixrp5TKgpwaoUsevWSRV56zka6rlhmXYeV7jliKVXlnJ9+Wkp6xdQJ7SmKElY0BjSFcy6VsIApiSHy3s9LthPQxP2hVNLOQCETpzyYJHkHsJBaBeFu46p3oCqpWSRQmonjNE9BoQVpOoutoya4ChAdXmD8R90hi3pLoZ2uoAo0OWjz06gf5VVabv1uwtxuI40abCE3LvaKzWI31fUizRNHFF5BQSoRHIKLY6M4ieOW3wgwi5oXP9s6KnTLY5dQigLSrmown0BGYDG8xsBgaQBZPlsCakrPV/izNR4OBUJgPFvFMZaRVWllOZVW5CsEYsAWqB3GaKcrwA1BQLs51OlC2FnOzDTKnZ9darmZRajGADqpLUVCtrwCsAGslpOdxe8F7IezkEqXfsYKIWrn56bIc8wKaa+56YSVJj+CnVbpu/NBQl/SvqL02RQczBmoAsrpjVQa4XJzLY9+nIoDNYQa4xJtWnBbRo7GWvIPBCj4IUxbVNuv4NhS9l1bnbcl7K6wr+mpRcAVdZaXWUeXlHqr7vlJxQnoCxW/xZUQaWzgPeqzOfSRksxCFCwBkshfCOWdlbUVWwOoDWQVANgXWC59LdXBLCFsFLZZ8uVjnKrIJlQu4QMjnHeZelzh2MXgoq1ivwD1kE87yqWfhBkzloHyspAwKSOUsGC3JRQJx7FhCE9lCecQGLeyHMKuzLmcL54JQEOIXgF6xakZlsawqLzPAQvjxcZOOQ6SyRCw43AAAXWAIVKLpH5Ca0HsGsZZn2Z5ARQGAdvS04WTRG2dcIXrCk/N2CdlnoLsYoZb4CrqT7oqQUIjLiwT4gtncS7jSC5hWGUx1UEktmAz2HuSPrxKG8tMQ8Ews4C1SY4LZafQY5XZhZYHrRrnPLPaDyXHVMqeyvoiuLxGO4haJgBnaVYJgk+MmRovaB9OIYk80Jj5D6AFZ3eAd0Z3YBvGJMYPH39HABEEGMt5rl+lWKMHBviRv+hOUBhuHpQTmrrRXC1QUC8BP1GUbyLQO4L0BNQJvBNgJ8DRgT7cB44w7sbZVwkGyEcKpIbwJcFTGbQ/fRfl9zWNG+wjoG89tk7IzEXC3QWpCfQCGWMyirItG/tpt9VVwBnqOwNIp4g6L8qkToDaGOGouw0Ibp3fl1YX3WstuozNAjxsW2jFmcXUI+Mj0F48hNIyMOz0+iRtNM0KsGN2KqGaZTfG/zVCr86tzFCmMEAjdZFBODraiSCrhTgPkgNdLTPHCGAKAE7n0k/99CNfCICA9IvU7gEgYwAOc4zpK4QXTs1mvsH+4TXsQh4F9qQyxsd4RZif0pjvNHeHl5YBL0L06wzLYu7Oo73nBjZCJTPSsVwFR6RdaIxZYUwK05/85CGb0NpF9P1MbCN1J0GCwbV12jwPpjBAFKBqmJOWi+2l2spUA7g5uzTNuBvmHNnYAFgW6E/A4nxizkbD47ZA3jkV9gcUzgM/BMpZW18j/Hi+laZXWU4q8zrlWMJegtACAD6ZQFQRQvPKhiEOVt9OzyV9zQOAIEEeegkBVaYz9mQDc+UCVWYARUtxLkCeBdcsMn5IsTAYXsLmhFlnzsjleI0HWfQT4XJh2ldwbYlxRnCfAZ2WUE+M4bTDMPD0CnUfxEZR1ha5QOqFhL1MhVSIAFgvxhep/2lCC6pvEU2HtphHG8kBmIsRehcWgjrMswLanWiqRfI7C4QmSD6da5MIY9EyaQvr9NN3ClC5ziFthe0NYp8Q43MG65E8xuNi1l5p1J/wjARtXOG+1e+DhBJfDE65NBJcb6QxDhYzT5QEWEuwLlCfCDJfLlIHTGyoNjK2Uz5p84WBOJbiy24cWWe+KMkqttIcFOQAXWLqU7TTqbVZN3aEGZNTaV85uTm0KSlz0WiZK/KB2nMBowXMrdDfVteXeZYAHw/rtFX+DodX3bmKAOALU0tpy6k2E+SBmHnmqFXWIaxzNO5k5FD+Ir+VFABg8UVsnd/ZZ5EH8ELct9e6JY345zl5fsAh4Br2134v/ihsf/rVl/EZrdnjjx2wnfvLjBGXsrFyoJ0VZNPHkb7PzGatkUp/0QzJOdfXAdNWUmyB5QDN39V9RgbpblQVbUvzZgrjNTalv4SIRDA5tYGIpnJuqewucM23uoayfrbPKgG2FJVefWuWfWfnmYuW+Jt2jymskGMKyXd+ISAk+61y7CxlW2Rfqsctyehyls9UWVwKypurgLmGQnuEh7WOk58K+8iDDfbAIeBQ4KIV1ARpbi69fOxGN3djsAA9zOkuRyeBreZRdaNLOci3gPPn8hJQtEShC4vTCftKf2SREUMJcHZqI8+KtiAuKQSZHmaeT8tM8HCPoFfWD+pX5Ht2hvzBYQoy1ZI7k4cnioDxJNTuJ3/qzwuAQkvAcUxjlI8vsBNN2MrIbzH2WKR+T58CnPtmH2NR2D70SI3t38O6j3pVqFItrYuKfZZHnjPJQwAlPG1MC0BftA9sNzYcc+FLBSDm5rJvPusZ7LImu5Pf8gr9jb7sSpz8MO7QlnL5XmudNc6h69UstZESoE3Wuxq6owCE8wuUk/ytcR9LMKGAtizuY6kOC6jDTNJgaQKYuFGPq9SHWDPZPoPlZAngXhn5YYlgr78VBJybscHRJXvw3nLqsIQ1G+sEzq9pVnVSXKmxW6cnv+SBKgJqo4ycf4aHUQUI6rtM8pBL2qqfMDbIQG0uHzHfjCxBcBv7Ruk76ZRZ6axS3tB6hOPUrgDR8kiESQMhdZuhblZ416WC8iGAq4i8lND+5A8LUaZlbLyALbjUc3aQ/bOou+IyH2M59Uly589E7ct//IZNzS3ZA0d3c61faamMEescL6gsH7tWsL8gP+VbHV5tRuddXoi78i3RX5gyXb/Oog7j9K9l2qSOLaNdZfJ3nGsc2WKV7wOUm+WCrar/MKTK5uUlccYn+jhroRCQofq1QhUvco4oAybDprH0cvWczzmY5lTdjAHAbbxWsbUAOsGgMJjOVkXcAxZEiui0ffsb4/bW6X7bsqXCHn2klj6Ryryx0bazadtFBRtjk3ueiodvhA47yJQ6mJ2O2ewk9uQcKp/atdbdajdqNBVl2J6xR0DqImPS2ETc5U99fY36XAcsDKJOe+zYC3b8zeetpbnGnnjiI9xD2O/AOT2go0oU1r4BzmFr15r4+n1uSXDufRoueVjSAkkLJC2QtMC7WYBZL7klLZC0wC+8BXTtNjAct//zT6L2f/91hAsos499IGCf/23CdpVpCZ3ckhZIWiBpgaQFftkscPFi3J74bcC5kYTtb/bZv/vX6XbPbp7w1F3T5HbHFvjc/x6ybwDP6QHShw8F7HtfTnc3D+84geSOv/AWkDNUm0AfKUVfu3bNvvSlLzlY7sCBA05JTsDR2bNnHTj1qU99ipuNWxzAI5jqpZdecspdClUpRWml471+VuF1bg8MUijP1dVVd/Nbx+uBG22Cn7x3KVzrJRhN+whmUp71ElyntOTQFYTlgVjaT9/fDlHp7+vXrth/+MLnLYsbsB+997DtBObhMWuLTQYtlZvgCkWiO9QZDxyy1C3NzhGpfOhYbUpXm/7WZ4Fzr732moPDpHKnULAKTSr1u09+8pMm4E2wmfcgkVcul8i7/E/K3KqTr371qy40roCvz372s07VTrbQub3XuyTzvn5SfUjV76mnnnLwnkKaqiy7d+92IOPtNtAJvL9vP5ns49lIUKGAS8FrFy5ccOlIwU5AplTAf15wzjuvzvdO+6qtvPDCCy7srepFoJuAT4X0lR117nfm3/v79jJ451B7FRz5l3/5l67u6+vrTX1D9SxAUufXObUpHS8t7/g7ffds5+1/R+Cc+sXlqxY6d8F8E5PcwQd6IA+hHACBmkLL3tRk2c1bAKsEueHkQB0uPDxkkUE8ChO3zAcAgOcY2Iqws/UNlrZzn6Wg7E4SODxw4mvf7h7zj2hfQCb+xXG0B6qbLLWh1cET0RvXre+1n9jC6IBVE9KtsLrO8FJZoIb0tu+wlJYm8wlIJWyfD8c13nOL9A9bqHvQYkOjpCvVIRxdOTgXa8otvbWZtBsB83IA6+Ys1NtvizcHgOGWLBXni5TzYjhjs4C7Mlo2W6CSfQFXEng6Qzjm1/r7LdF9k/KNIyCHWg0elQShyvz0+4z2VpTLUBfDmZcAxIiMjNlqz4CtTYxaAI+iwqvFaR+pVXWW0dQG4JZjc9T92PPPWF5flxVz8RlorLbl6iILV9dYwbb9loEdUrMpHw6PBI76xBKKN5OzFurvtdCtPvMRCkkevwAAgb+mznztHZZWW8+4A2wDPBgaHLK1gW48t+N4vfDGssXw5vrraiyT8TertpY6wrGCxyoxPWDR3ksWHhy08AzAHaFX46iaQSGwX7Vlbq63QD1QM478+ELI1oZmbZV8+MYGLH01CFCRCQBHQLWycsvctx3FGBx5p96y6IvP2uzAELBcvWXVd1q0BKiuDKhnZ4tltjYCNOIdAriLh8kHzk3cpwAGXQB5lwh/OgksiAITYJ2llwLLleKIB2TBiZ9VWmFpeaUWmQbcmp7FCQdElQp8E57FmbsIEAR8mNUGlEAbIZwV5qeNMO6i7uAP4MEODVh85pJN9hPCNQVYpmaPZZbtIC/1WIlFHiBSfOUq+UD9BDDMgFRSgGX8aVk45qK2wDifjke5kLpMKWwEBPXbGmFag7fOWnZ+zDILBEcCRKHEFwFWmiM0YS8AVi9QYm59pWXk40ReHrOZuXlbpmrysqpsS3mHbSvssOJ0oEaUikLcoBgKjtv1mZs2NNvPvDBLSwgDvRHCLqfcGstbraGsAegj31aWCGM9dBOF5D4rxsabmhuBDqgvoKQZIIibg73W23MTJ2iRdW7ZbJXYTipRUyh59d4aIsTgENAHcAcL3BzA0YJcoBocsevABKWEwWyj3xQBOc7PTFpXzxWbBUAprqUufUs2Nz9CWWhvAD0KP9pQUGH1uSU2tzhhE2vjDsZITymy+rJO62hElRWVMCk1haLz7NNrg7d6bBQgdQGHYBiHdxpe/3Lqt6my2eqKGiwTz+MCwFFff7cNjxDWmHK1NG0iNGANjtooCkjzdpWyD02OAMHk2O6GDqstLoXjCNk45++f6LOBwQHst4SKXYYV5pYCzpfaMkpDSyvLwALltrN+G9BHro0s3LJLw11M4zNWX1EPQJVh0xPzOGRpc/SzeA4AVApAJ6BpDSBaKipqU1Mo6q7ioAVSKisvw/atVppbDkSf68afaWw8OD5ioxPDOEqpQ5yo6Sig5ABBtda1Wn1VvYOMVsIoUE4M2eDwAKCNYfN2a65r45oj0+bWFq37FuuB8ZsIYQastXGz1RcS6hxv8Wpowkanuq2HvnhrcRr4kfbEuFWeXwrfnG/j9I8AY+vu1k6ryChmrbNkl3ou2cTcuGXmAZAB4c3NATAurwDKVNjh5rssNyUHyEUh8RhB9eKfNkEMAoLcQM5XcdQgXShuvtd3Wpu4uZPPmg/dfhrB+Fub5rMYZJhTddG8zvHqm1L/0TncfEe6fsr1N/OvfhGI4B27sV5SKDuXK9qMU9d5Ww1PaUcY06ReLGHDW8FbNjQ+iMILISaBf5YAYYsZy8qKq211Pkroa1Qkma+kSFRbUmkdtZsBtWscELUCSDULJNZH2xqapI8szwHhsC9wSXl+pbVWtltbZYsVMPbFAShvTnTb2f6zCH9mWDttqqm8zTIBWEOAkz29V617iDET2K2jfYs1FDehgpVNv5uxnhnmutEe+hKhFIGqMgCyyytLKF/UQiiTCpLsaOywinzmCAC4c1cvOIWzgrJ8IEyAzxmgwFVCh0NHZADtCYBdBqQqLa90APn8rWmAAY2LqVaGV76Rsb+mhPDgjPECa6dQLZwCCNdDL7MzwH0KpQcMIgC1pbreGiuqrTCLhwMA5MYmx+1GTzcAVtAa6wjRWd3Kb4Xo6IVsaGHIrnRfsehy3DbVt3KeRreOZoVt/fP9dn3smo0T8QUpQdo8SnplubTBFB7oGLf8FEK21u60Gvp8mLV473Av4UGvWjr2KmJMC84DnzJvrwDvKoRuQXGBlRcDjDNOzU5MMwYuAEykWmVZhbXWt1gl5ctOAWoHXA5CTXSP9NjQzLBNBwm3rXDLXAPkZxRZLSFU6ysaAJfL3Rqgd0b97AoQx5p11Gy3TZUdPOyViaImD9kQKr1niDmDf8017dZIePRcxt/l2BK/9VnvWLdNLoxjU3QxGccKGGfzKefC3CJzpM+2tnRYXUE1w0fYemiTN0cHGXNRQoPQWmedPrcw60BfKRFVlZZZOWDt+nrIFpYWLQg4mIpqkcb+juZ2rpWqAEgyHMQ8A3A3NjdhY1ODNj474ELFSgkti/VXY2mjdZS1Wynzp5T8hsbH3LozgQJfS1OzNdU0ce8718F+N2a7rWv4si0vLltnbaftqN/B+AP0HQ7arblbdoMxaHRuDAAk7NpGUTEPIAHArEaB1ZnD2ys6rb6kDVg1hL01tvfSlgMs27KBPWcY4xdRjcqzLdU7rTl7sy3NZdqpa4vMQYQ6X2aMgL5MZU0jOKq5Jde2buE6DAhrZCRqN6+FbIz3GKFGBbylATDXcMm1tT3TWpsygWRS7OXnIvYnXzsOwBK2/fu3WyVz1PhiFCARLVHWiGUl6bZtS5Zt2oyyeD4gOmNIOJJlYyyRrl2JWfdN4LJ55iD6dz6QXDGQToi14Qq02c7tObatA4Uvyjs7EbZX3hgDaAdo4UEHOpZNzQPuUFeb6lNs35ZswDHmVyCdSzcWrX8ARHwhEzgKoI71ahUwUltLCuVjJkwjssFQiIe+mK8mFWpU2pcCh+NWWW62Z3eGNW9izGVZcvZsxD7/+VNAPaV2eHetdZDGKuPBLOsKgUOV1dhje7bVNacDXgISAwVKYW0CEOj0hSXr71235XmF5QUWL0VxDmAnAvU/cqubUJ/1dnB/pYOxlsj38WOrNjIA1JPPuoe8LNH+wtFFKykPYNsqwvSm2DShJ292L9vQIOPoEmtGGl0qIGhxYcLaWlOtpQ0wWvU3FrXrN0I2M8qaIgjYyxjtz1mz3KK4HdyTa1s2ZbrnLzbAuX7CfK4Q5aGaes0D5MXeQH/gXVZbnyBkZ5E1NaZZLqCW5qOwADHAvZvdYbt4jrXZgq6VBKvxQoVNANss43wNynN33Z3Bg3E+1Nypl8s85De2zEMGrL1pB4uLjGBAtbW12ZyjwBpbAW45eGwsYlevBG2wD3hukQc3tM4Bos3NDVlzUyqKY3lWjCre6GicMKNh6loAPNf+ZCIOJJ+bHaHN5dr+HekOtHvl2KL9mcC5sXXb3VFiW1pzAYl9PNhA86etlVXGbEtnmm3qYHwSjMa8FKMO54Dy+vsihPlcsaF+5lfuL+QWxhnjCVcLTBzkYZ3Kimzr7CSKVx3lmY3bqy8vEAIXW3ENo360tBi2FdZLFRUptmt3qZVWpQBBh+0Gfau/l9U3dUjXJu9AmHkxQoAS6nZXOsqSPuaGNeu5GaStAlSuAJYT0lXqn4XkYTvnbN1M+cjvhfNR+9JXTtjEzLLt3LnNWltKGLsAvZZYX7IiKirKYA2aZs20jYK3gTE9TDXPdUVfd8KuXOISC1v6WRMUAaAWlUiZkL6EgdrbMukPuVaSzyKH9eIrL9Omb61bDuNLCoSkIM51YLQS2tXevSnW0BRD5TjLxmd81kvb77vJgxFEEQmHwKSBlbOzY1ZZlbDNWzKtpiqVcS9h16jDwQFA6CXaKXOxljECDWvqfLZrbzrt32+T+A2/861xe/34iNXXltvuHeWkl2Kz2Fdwdy6AX1trHm0j3UqrtabVLMs4Crw33M89sq41zkGLJiRtfjEPNQDdBSBlZ+ZR3GY8uPdwvrU1Z6EkzH2mqzE7cXKCPGRSJ5mMRQDWPCSS8C3bQP8p6+s9ae2bau3Jxx+xew7vdf0NxJNKZH1EP+dOlKrT9Qm9v98tCc69X8slj0taIGmBpAWSFngXC/w8U9O7JJv8KWmBpAX+P7cA95rsuZ/E7Hf+Xdj6uNDY1OCzf/0bqfbxx3gGj2uf5Ja0QNICSQskLfDLZYGR8YT91u+H7Nk3UaHhxtsXfyvNPv1xVAB4ajS53ZkFFLLiwx9dt+evEQAKG37hX6XZbxD2ViIUye2fngXkMPXCtH75y192IJrAIoU8VahOhUSVmpsHzkkZTiE3v/Od79gDDzzgYC4vfKgHCt0JLOSBQT912nITXE5XPT3unLhv/327xb109bugOQFechRrk3KYwDrBUJ6KmBy6+uwdp3PduNJlX/zCH1gON3gf7NhsjbTx8BQqRSQjeCVA2lIBqfrVj1vRvXfjiMSLy6ZzavPS8vIvEOy1115zoW2VH4Fzelc4UCmRNTY2upu93v7e8S6xd/mfYLOxsTGnAqh6kENZdaDQuFL4cw7u2/LzLkm9p5+UT0/tTiFnBwcHXT1Lpa2trc2VxUvwnYDY32Yj7as6FWymdiOIrbq6mlAwH7IjR464EKcebCbb3Kl93pkH728d7+VD7wLdBOwJApRanM6rl+pFbUPn9o7x3pWWlw+lcfv3akNSOfz617/uwr/qeMGj6gvl5eWuXm4/Rsd6aXl5vJN3Lw1v3zsB5xI4rkef/pFNv/QTy8IZqHDEuvW/jDM9WllolThEanffgzpbGTAX6jeXumz20gVbARRLwYmaikMrDjgXxZGfUtdgNQ9/2LLrBRit2PpQv42dPmVLPT2WihJOuqQQ5AcByMioqLfSzn083Y8jHjC1W+DcWL+V0XWKcSTHCosto57wybt2W3rHVpTQCE2K8he0moXHbtnkG8dt/uJV8y+tokCCkwsH3Xps1TJQtikEbiu864ClVVRaqA8H+olzNn6zX0JzhMnMAnBiLABqya+qsLIdeyx/807Cn6Lwg8rTOPDnyLmzFrt1y/IAmjICAGhASSEAWx9hZGsO7rfcTc2UA0h8aNgmzp6zuZ5eiyLhkE7YsQDeFwQSLK+iziq37LAMgIZRYNZRFNmKhq+hCoPDqrLUpstQSSPsTu3Be62orQOnNAp1hLWLU57Y0JitXL5iYyhdrgCBEAAPkIH1CXBFAtAyc/NmK9291zKBlmIDEzZy5hS2G7BUACE/dZGgja7j1EypqbLSHZ3kYwtjEjDQYtAWzr5qSxeOWWh8EucoanAohMVQaoBvsILaUqvas92yt7bQhFJt5Qa2O3fVZnp7LQXVvjzqz49C2jo4VAh4teaD9xH6C0f8ccC5l5636ZFbFkHVzVfTaiHqK16SYQ0Hd1jpFspXRP0pNCmOxgSOsERoBNW7ExaauYiTGaU4+kQAiAypOER6AKiklAMgV9EMdFJSZ6sAfFOMKynpcRxLNKK3AZxAFgp+he2WyislFe+z1HY07KagaJUCOBfrs/jcKZsCFozF8q2o6qCll3eijlNGn0fHaxnQapx8zPdg5Q31pQA2TCM/YRzVS0AV2agjFrVss0BJswPnVka6bG7wGEABvwFZxVA3W0cpah3n1TChxE4Oj9u50RnLqcfpXA5wCikYAlZbBfAMEeq1uaTRjmy92zY3dKIwU2SjwHnne7rsYv8lIC/KjbPWh6KTHxmMNFTqWoCqDmw9YM1Vm1BxidjZq+fsdNdJp+KyZ88229yyiXks27rHhuzYWyeADsatY3O73bN/n9UWUU7goss9N+wUoXT7R6kjJF3SURDKwUGXSb2vB4HQgSGam7fbgd17UNEpsOGBHnvp2IvWD2hZWlcMUIKaHqpCS4BHC6jWreIcrSuopCx1OI/XbC4yZ/MRgFYcw2WFDXb4rg8AFLWjbJZpkzODdu3mCbvZd82WADtjhI8NM89GUS8SIN5U2253dx6xakLhLgfnrOvKeTt1+gTqI3Hbxvhzz/5DtH9AkLFBe/G1l1E4mbYtzR324I77rRGCYDY8YRdvAexfOm1jgLSp1F1Obh7zOqApSplTqCuukMe21nb70P4PWWVhpV0ZumQvnXnB+gcHrApYqCC7CFCSPgb4mFuYb0Hfgo0uDWHvRWsoqrACeuEyZV5aB/wAbs1FGXP3gT3W3oSqLZCa5owrN7rtOnPWHFBbAngjkEZDRL0nRrr1VQ22Z/se28T+oGF2daDLTp07SSivWeto7bAjdx/FMV2COs2QvXX+LQfmlOGo3bfzHmsvA1BlzOoZumDnu960vlvkCwd0BvN7BlBvFmMUE4aNTk5bMWPaR44+BtRYA5wzbS8cf8G6bnY5eKiipAYnONAH4F4xoM/H9jxpKat+N8d5c4437whU8CMTo7lLc7a+1/yify40K11Mv2nTGkq/6YEE7avv9VnrEa21vHfN6fpNaXnwueZCvby/lZ7SUjp61/f6Xelp89ZpSke/6zeFb49z4XFj6jqKVmOo5NBOw4t2a5YxvCrXqgEW/euAY/SdEL9FUAbNo23s2brXdm/ejfO93KaWZu1qX7edBwabmJ8GnAVUTpdzfgU3eKaDp/Zv22ftwHMBIMqLfRfshTMvAr2u286OvXZX5yGnDjYGXPryq8+zBhq2mvo6O3jgboC0NmwZsD5gq7cuvW59Q4w1FCc9kMs5eHgDYGUR6DLE2F9XXmv3HbjPmukTguueevFpuzly3QpR9yxGFSyO0k6CNpWVR7haQtVOoeQ6AYxaVVePahmw7yxqbbTFZcCQTKDYtrYW2t0O+mQ5SmURuwgM29fTB2RBKHDWwykBaRkS0DYEjIE66Z7tO21LI3Ap6mzDrNtfP01+x3qtBgDv8J6j1lTVTD+fs7M9p5wqL7pqdnDHIevYtBl4EUWxpXE7c+W0XQMeXA6uMEbmo/CHKiNARhwV0FuDt4AC6+z+vR+w7c27CNsZslMXT9jLr72ImhdwQzWEFKBGBOmoIODnHMp+qdionPwXoPwWYwxdXURJDgU3hQ/d3NEOzLCLMasKsCZmvf39dvLcGQfNrYPNKZyvpoMw4Q/LiyptZ/tO29Gyk+v7HLs+ftlOXH3DBntHrKNum9217bBVA48vAGWf6jphl69cND3YcmDXPba5qtNyUHztm7xpx7vesBuUT+NeRlY640wm8KUeTAGoAF7Nycuxo4eO2ta6dje2nuw6aycun2Zei6GiVMb4BLjBnDe7PgeMO4laX77VU++ZgNJSAJQa4TrKkOrXO6i7ndt2MmZV2SqyiZcGrtmV3mvAj2P0pRXqjn7GOAqXatUAhHs37bLtjZ3Mkbl2s3/Ajh8/TsjHcWvf3Gb7dx+wurJawszO2BtXX7dzPWcBhlPt3q33292b76Ytplr/ZL+duXTKrtBOVpB0ygGizkWpLh0obhFV0cXokhWivnhf5wets2EXIOGqvdV13E6ceYv7JxGAYiBupmd6Oqqcpbav5X6r9Lfb1ctBe/74oI0tcM0F1JyFIlUaUGRWTsxaWtlvXz21lGqnTs3a+bNDwJGEwgaOlspwPDbH+Bmwew9W297OCispzrAXX4zYf/2LU4Bka9QZCqd5qE1hnzVgS4HBUvfctbMYhahygB6COPLgwPiUESJ0xl5/bYwH6fVABGGteZAhXSq2/BPQEw+P2oc/1G4P3F9uFcBYQwMr9pU/OW59w/OWzbyRi0JxSNB3WsR2bingIUseIABWOn1x2t46M2hcFtKnAMYZl33RIIBRAuCvyA7dXUr6y5z/ll29JoiePsHcKPgszvqgnNCNR++vsb0H8gndLMXrmP3+75+zsZki2mY5oC8Ajc2jXLtiQdYlGVnLduCuWgCaRmtrlOoiIVBnE3bm8rL96MXrNo7KWYAHCaSEmMaDAAptHETNdmT0gj326GH71Ce3oFiWwn4R+/P/p9/Oo3BXUYIqYx6OEh8Adtqa1dYVEkK1AxAr1c6eGyNPgzY7yxoJu4Htkm9A0NyobduaT/1VAPqk2Gtvzti166hLA4RnASxJ+TbsX2QsitjDH6i3uwHxYqhrnQN8+4u/AnQfGrdaxuj6qiLG/ADg27LNL45ig7Aduner3X9vjTUBKMpHI6Ds9IUVIKph1hJA6ahHIjINWMnDO8yHwRXAWdQM91Dvn/qVMmtoIN9no/bMc9N28fJNVP1ygOkhoxJct6Qvs0YqsQP31FjHtgKUwGPUzZKdeOsW19C6AsphfJbSKYqSgQWUvnLsrrsauK+RZ8dPTNvxU3PYgrDAmbnMrUB9rNsy0tY5d7U99hCwMnDW62+t2J98Y9Eu985Sf8BdqMv5UadfXQ0A2KJmmh60LVsL7LEPN/DQHKqZXHOtkf8r1yP2xuuTduXyLSBDlIChuTKBRVPSY9gAlUseYNq+pckefbiCB8nSbGQ4an/8lWvW04eyJjC1YDOFpFd7qa3LsbsPtVpFdbpd6Bq2N98YBfzTNSxKlhjVz0VRVva6NTdm2ZF7q2i36Xb+3AivfsBSzhlA7Rg7RADR09OBHPeX2eHDdYDU2Q6c+89fPmO9Q5PAvVzPAXdLM3cduHclyNzCOr69rcgefLCcdRwPFqKat4pS+OWrk/bm69PWdYl914GOs1NQPOWeC+D5XBBYDSD4/nsr7aNPNll9JXlEne1LX+q2N88OA86hxsrDSH5IeSkj11TF7b4jBdbewQMgqLqeubBux0+OM8cBTaOMmcrc42MdE0B9uq4ulf5VY/U1edZ1cZkH82ZQ31uljmmnzINaT+jBrFr2++Cjdda6KcsWUGP89jfH7Mcv3eRBkxzgOcZvwrSuoPi5xFo1FvEBvrXb0QfKUL3cABxD6ynMKSEU8wd5gHESiJGg1tQh2SYfrLdRXJ6ZXQH4TNhnP9NiB/eVoiyYsFdfDdm3vvs6/QA1Zx7OSmc+NaC57Owo8/VVu0Xf3bK5DnDuITt0mAfUAC15MgibK+886ER/ZGnFWoPD+OX9bklw7v1aLnlc0gJJCyQtkLTAu1jg55ma3iXZ5E9JCyQt8Pdiges9cfuD/xSxH77KDT7urz92b4r9x/8tzWqruepMbkkLJC2QtEDSAr9UFsCfad9DhfS3CeM9wY3HT9+fYv/Hb6eh9CBnzi+VKd53Ya/yVO9n/zkKUdxk4sFRO/ks6gUNGzdx3neiyQN/YS2gG4xLKDMJAPvKV77ibjh+7GMfcwpaUpU7duyYlaE6pVCdUpwTGPb973/fQUOPPvqo/fqv/7pTpJPT03PY/qzC6pzv3Dwnr+dk1d/6rE1pa9NxeglMUyhRKYgpXKb2lVNMAJ+U3hTS1dtfTyjreB0XwZHVC0zzpc//W0tFcamzqNiqcQiXELqqoG0zj/7XOEgoPNBrxUcfsOJDh1F94eY8m5dnL5/6W5+9UK2CqYaHh50in5THPvvZz+KoOPpTRTXvOJfYHfxPzuR5wm0+99xz9vTTT7s6UnqC8aQ+5zm4ldR7TfvdTi+bC4589tlnXR3r71/5lV+x++/HaYayoJzfni28d51fL/3t2eX2PKksUkV59dVXHXCm31QWqc41N6PqR/3oPF4675a/d/7mtRHvfHrXd8qHHPQTExMOPhTcqLbw0Y9+1J3bK4t3nJfvv6sM+t7bBG0Kxvv2t7+Ns3TK1F8ee+wxB+NJ8dDbvDS9c3jf38m7d6y3752AczHAhut/9qe2dP6CVTc2ABXlwx7FbY14YyEcqQVVhKbZtAOnaLYtXbpmky+/YsuoWqXhHM5rqWP/Uq6lUL2RkiNqKnX7Dlo2IVZj/Sj/vPG6dZ8+jUJZqpXU11t+JUp0OMDW6T8JlHUKmzZbdkGphXF297z2ks0M37SKXJRytm1BkQ6VqQpUMXCKBioI24qSUiLKOQhJOnHhrA28fswSqN/VNrYC4dXiXI3ZOupsyyif+PGmVdx9wApaW2z5OvDeiQuAQVHLb0XVqbYGJ6IUMwGTSDMf1agCVOdwFtjamdN25ZWf2ARgXklZiVU1AG3lF3FeVMYInxb2owKzbavlIn+ifAwB9wwCzmUQzqysoQalNaAxbLaCIlEqAFgpSjyZlcBAKPmNPv+MZV45a/lFhCHdvs1W6xk3UAUraWuyzJJSzo9XJYQzcppQg8dft+lTb9j4/Czlr7XixjqiLAHqjQzYDIo9KYwvtXsPklalRW+i/HP2NKoeKIu01Vua1DD5fZlxK0o4u4KqSvLRwPGEA0X578Zz37fweC/ONcLW1QGQlbYQAg5FhNUJoEMAhXaUk2pqLTqzZLdeecsWLl3FmR9FBbDWcoFy/MBWIephBVW3qj17cCYDHF3osjChaIe7b5gBi2Tv3mm+2kogv1wrqq8FIiSEtULBAo8Q647QuWsWm79ioQmAu9VbFkeZJpBfhQMSlU0UkeZR1JkBPlJ+aslPDnDWWi9jJfAXbnErLi/AwVuL46gWe+PIQvnMh5PeOXRjQDyohyjkns8fBG4GUpl6y6ZQ+zNfGeV4wNIrNtNGcAau4cAev2oLt86DAq4A4JVbAKBIYXIDhHiNoNS1gDM9G2CuZBMKD9gqTvil4PB5m+1/lbCFi6hIlFmgoA5b5wNuxu3a8Ii9ePGKHe8ZsEycX80tqBXVtqHsVoDSzoKdP3XaAXl7du6yuw8etUygnTOXu+3ShUsoriyhsFJjVZUFuMLWgG0nUK/qo5yptqt9tx3uvBdVo0K7OdZjb1w8hvLcNatvqQSI2I/iUA7qVJftLG29GKW1u/bfbdsB6gpRtBqbGsaJe8K6bvRYBqFRqxobcUAWQT6FbGpkyPr53lAS3L3vHoC3Q1ZG27jZc9Wee/mHKLx1AUfm2eZm1OtK6p1K0k3UiPpu9qEclYNyVDvOS/oobS4YRYlqbIh8T6JMst2O7DqKElQ+juALduHqcRSkFgFjqlDdQC6HMWMMsGlgaMAC0Wy7F5BmJzbOBWKfGhu0N8+9bpdHrlgeTs4j9z0AyJhmXShjnn7zNGouxXb04AN2sO0uQuEVWNfIGXv50vM47K8QPi/fWmubCf9XSHhW1D5GR+wGc/MqZd2DnX713k9bXWGdnUG94wfHvos6DlBSYSnOz81WW17v1LbSCYF7beyKnb5+0uaQKNpS32Tba1pwvJcAZvgAUoZQm5qy5m3NtnfPPisrKLGBm/12kX4QXMIhWlFk5ShKMuwBXgJwDYxbEFWWLe3bgD/ut1LGx1EU785cOWGXLl5GPSzdjj54FJWkSuu+fgMn/wWUpFZt5+5dtnfLXahw1qOKMmcvHHvKunrPQWb4raGhERgeKJJxb3pq2m7STqZmZ6ypvcV+9SOftCbUvManxuypN5+2ExeBLIFSOpt2sC5vYWxHaTIRtw+0fdBWxlfdAwUC6wUxevOO3jXHCnbT3OjN4fqsOVFzjQezeXOxgK3FRcJYsgYRcK41or7z5iWl6X3WWkTpaO7Tux5e8D57sJw3j+mcegma02/adG7lTWBeHpDkEgpJIytDKDziBOfWfC+qY6e7TtOsUdvCsd1Rt8PBYGuE5x0eQSGrd5Cw70126K7DqHG12nVgpHOXLto0kGVBabGVAy1m4tBfoiz9vbfoKn76wBY7AkRemVcGlDdob115E+WhLtT7yuzQ7kPWwBx3ZfiqvfTyC6gQZtjhXQdtx7bd9J9ym0TB7sTlN+346VeBnMIoELY5lUWFiB2YByztv4YqHupfbVvs4fsfthbU8GZm5+1bP/y2XbhxhnJl2uamLdZSqrEkzzKBI25NDdoFHuroGxxFta4aEJe+WFgNBGCmPjoyPswaN8/uOXgAeKgV1axF1LNeJzTjrJXSZqtRDc0BJllFjVEKj0Mjg6gDtdAXjzpQLLwetdevvGrHrrwGjLgOJHS/bWvbCXg1BgD4is0x/+6o22uHtt/n4MX5yLSduHTczp4/Q8jEVasmT1WlNTAtPpsMjlkvKorDAIWNVW32oXsft31bDzJmrgEcvWFP/ej7TiVQ52+u30Ro1RwbnB60ywPnmQ9mUc2rt50Ne6y+tI66QO2pr9eGJwZQAUu3ww8cAh7GXgCEJ986wzlGrRAIUHWYlp3GuLPCdyOUG3XJ8kb7wIEPWmtNs80QQvvklbfs1Ansy3y9n/pqBkgZXxq1k8zt89MLtqNjt+3fftCqC+oR4w3ZK8C2x7pedpBWc3WL1ZYJ+PJRV9NAe702MTllZVWl9tCDD9kuAL0Ia4HXzx6zF9/8CfNmCIW8Vu4ttADKp1Dvw3aJdVdoIWzNlQ0oWLbRVlAgZj4eQfmwt78bFaYSO7L/PusEMpwFrnz5zCs2MNGPcl2OtdU2OVVIrb2GmXeWCUdfgxLmvQfvRR2wHeW+sJ3rOm9vnHqZ9VrADuzbD6zYaQMo+7529jXqZNK2btpq9225zzaXtwPFBe1NgMHjJ990YF8NEGEtanfZ2GZmYcYuoF45vjxOu0H16N5PoPhJnlan7JULP7E3jr8G9LNu9Q11gGLb6WsKsY1SI8p0U33p9tTTlzl+wSobmoEsi60kF7NhTzStKGMmbaWWOvIDmwzY5MSIbW6tYJ1fhPos6l9A9Hk5Cetsl8pnETBYqj37QsS+8qeoMI2MWX11g3V2VKPaKdUuHnTqX7VrPdMsg1bsU5/YZofvKbN0wO+z54L2woso7V0eIbRnmW0GQikuSwNsMetGDe/69TmUwEbsn31qLzATDz6gONXXG7Q/+PyLdvk6wCdtZ3vnZqtpyETN0cf5UHwrz7Gx0TX79l9fsOt9Syjf1dvWHVI6JWQtYzYBpK0OsLe1oYB2Ngsgehn4LWatbc2oZGWTr421qEKFb99abu2o5CFoCrgUt9/93fN2aSzfdtUAFm5bYxxDQYtx70bfJOuCK8yl+faBBw/YYanCoY537QYPgP5kEEC+FwWvXOa0KoC8HMZkP+D6kl2/ed2Gx7vsf/jEw/a53+i0otIUGxkM2xf/6Kq9fmrFagCfdnemWFMr4clLpKqaY9WsYWDL7ekfXbJh2k19PcppW2pRys6Q8LMLi15fmwN4VQJoGrG//iEqqoSg39aWwVwDJJlF/TEPu/lsewn9ifU+qr0C2v7sa9etG4hX4NyOrY2ATbmUL2K9AyN2rbvPmls3AaF12L0HMoEbfcyLUfvBMzN2/OxV4KYya6c9lLOMWWKdfPXauJ2/AlDLwyAPHdlk/+IzVdgrDXA0al//1qi9eeokYHAewFmjNar+yuNWV59lDagdKhzthfNB++vvAnNdmUGhT0qFrM1RlwvwgIxUCAvzKU9jGUp1qbTlLuvuW2etVmEdKMwJCFOI+AhzS2N9kR3eV0lbybA33orYl7+2bm9eHrLawpDt3pYKcJYP7JdpQ8OL3IvoYz6M2see3A+0VuJUAcfGI/bMjxfstWMjqFXO257OGub5bMZJn/UMTtqlKzdRgfPboQO77FMfrwUOBXbtj9kX/v1F5ruwFdFPdrTnAKyhjgf4WV6RYU0tXMMwRn39GycBJIeou4b/l723gK4rTc90X7FkMbMssCxbki0zMzOUy8VNSfckmU7u3JmslXVv1hro0GRN0rczM+lK01RDdRdXmRlktoySZZAsZmZm3efbzsl1ejVUVzJzuzPaVcdHOtpnw79//p7/fTV7VjJthidtEdg0IHkI7UlWRrSjMnf5sin31nIv05WaFo6lLParWNsPDrXSvQ9Szhzgs7hg3UMV8a//5jb9TsZtUUmaOzcF2Aylf79RFl70qqQIhVcWW+zalaltO2OZ13Dn+CywPfMQq17yyViQA3anJjKmGHIjfw4pvwhFTVSXX9yeqi99CSXeRBQZWWjztT8t1LHcfEcNeO7cWdQPEcwxedJnEqqu03hWPvSD3PThJ0DP96roK4h8Gsn8jg8LNBApRwIuMMBDKamRdB589dGHgGh1o8T+QtkPS3MmLkcA0vvxYJ2GOt2SFXHkMT91YZ367o9rdfjkfcYvk8CWqVwztt4hjERYiFRYADw3kqQli6jzdwcpHsjTrI+vXe4F2CygHRyi7WWBCfXWCGrWtbVdKnzYjmL0CHV0kP7w37JoYl2kmhukUyeG9eb3PkaJdpC6IINXHPfoTv9ojPx9VYUPLihjZpwO7N+uVasX087bHJcpNHqSjwxkNSX8KXCORJnaplJgKgWmUmAqBX79UoAWamqbSoGpFPiNSYE+Jr1PnhvXn319VA/pEKfEuumPfstLX359Sh3nN+YhTl3oVApMpcBUCvwzpkD+wwn963+PLc7TCZkbx7f+1Fdb17BK8P/jGf4Zz/Yv71D/9UdMoAGk12OdkE6bevck5lVM/k5t/zJTwIKfFhA1IO7NN990QLR169Zp7dq1ysvLc1QODJx77bXXHKUwU6Azq1YDkgwYMvgpLCzMCYK6ArW/LKXsnPay7fnv2GcWVHV9br/b3y2Ya5sFV00dzyA/u7bKysp/COZaoNYUv8xmdv78+c7PzyuK2XHHCVRUPH6kb//Jf8Kir07pBJfnoU41G3AlFAvPsRTUTIb7NF5aJLf4FPll5nwqcM5U1KpQUCpDzam9vV0ZGRn66le/6lyLS1XF7uP5e3Vu6Jf8Y3atZnH68ccfO7atdlxTnVsE5GKB8Z+Vhr/kkL/0zxbEfvjwoXNOgybj4uL05S9/GQuVxQ4E+Pw5f/rn53//6Xu1wLvdy7e//W1UM5qd4xmQl5OT4wTb7cJ++ju/9GLZwZ7r89+zn115yNKvsLDQAdyKioqUnJzs2LTavRhcYPnKdc2u89vv9rLjuI7r2sf1biCggXjvvPOOk1Yu+1eztDXr2ee/5/r509zL8/u4rsH12acC54DYHn/rTbnV1Cl5FbahM6KBo0wTCWgUOyYPLCu9g+KxIu1R48XL6rp2WUFYzYQtmK+AReR1ILJJJvAHOc4o9x8YHiNPwKVhU4Q7fkztzU0KnrdIoatXy5dAtpsXIAb2ZWMoaXkERAIFYTcKIFmZe1ZN5Y8UF+avuPWr5ZOWInfAVrdppI0fNp9Yx00Cv/YRMH986qRaUWNLIKicsWWHvABlWPqv0coidV09pxYUmYKAzaKA3IaqGtT6oFhBWEKGrsFKeTb2sBAt42OYsKH84gFA5B0O/NeBneR776j82hXHVnT62lUKyUbpCUB2EiW48WFTURqXl9VbKIH1P3mku+fOaKQLW6FUglAEsrxnAASh+DCKus04wUFvLB49AHkmO7tUc/yoJlGGC09IlO/GzXLLzNQkkKA3ShJuFgAZJh0GfDRa06yuI++q71GeIGwUtn6DArMBdLm/oeJHagbu6yFIHp0yU8GRcZqoa0F9o1hhGVjWrV6CDex0uXHNz1RoRoHRsDa1SGxHr9quXVfhuVOAVQTwgIj856wGnEsDMDMlpmoUjLC1CwkjxOatzkLgqFNnFYhlbIgF9JetlA8QnBtBwvEJVJtQufGLAKTz8tfYUxSwTpxWcQEKNjnpit2xjueXTFqgMkf+8fBCrY8QshdBQk+sASeADgcb7wHt3KdOwmIJkM0jYg7HQv2ot1LtKLo1tVZj7eeueECgadgpjqIgYeBcv0ef4tISASMWABjMRaSGyJkfFomAWOh5kPdQ0wDUQGMI4LEHBb8i1PVuYHFYhxUgtsOxm+UVThr59qqvvUgdgC5DKJxERWJLHJeJ5TAQ5iA+S53FAIZF5F8slUJmKjwDBcOoGaiHoWxSc1ed1RdR60F9JxFwM34B4F4EYMUI9qZFOnbzmq6SP4PjgdpQaVoye6NigGeGUHy5ePEoQfgHwIzRWonipAcw1hVgt8aKBs3iWEvJs/GRIXBlQyzkaNSNB3dQfKpSbGCcti3ZqoykDA24D6ig+pHOXj/lKNSlzU6jLfVSZTnPoWdUy1ApW4pCXZQ/5Wayl4DyHV2+mqcuvGIXr0DBLGsOKjmBAFPdKi9+AIyWhzrUiBbxjA0kCkNh7yng3OlLR7EFfYylWJTWLdqu2YmLUJZr17XHF6mXbwLOBQLNrNTCrKUKCI5WPxHKwtLbunH/HOCav7at3INjbpDu3LulujZUq9Kwx0QtMT4ckJLn1dRfpxv3rqvqUZPSIrO1acUO4B9gkAnqpNpCncg/qYruaqVmkMbksyoUEEe7RrUI5a/V89cqOTidcjyhiw9O6ULhSeD4Xq2au0yL0heiyIVlIAHvu0/uo+B2Sx39XVq8cLk+t+pzfC9JN0uv6/0r71B2SgiAp2vF4tWaiUJeKJaOY6gZXn58Vbn5F9QJCLMqe4E2cM746GSAGU8VoiB5reCWEPTRspVLFBEUojtXucfqBvo7iYATWYAgQMIAMH0onFUBAty8dAvoMUwbVm7FBhCgxsOcD0oc1blSYP74pDisbUOpu5o12D9AwDpJS+cDPGG96A1QW1JVrI/Pvq/OsVZlorK1KHsJqnHh5LkxwKlyAu1XgBYJds9M1qu7X9WMkBTVtzXq4xuHlPeYcwOybJm/hXyxSF3819jRrIXhS9VR1em0pQbWB2L3aO2gqy20foj97GpXXG3Z8/CagWvW/lv7bP273t5eB2az71k7Zu2lgXH2cvWtrH0ycM5+t5f9bC8XOOfaz75v7aa97Gc7hr3bddjxTcnOIL2hQcoKSmfCSi4xJZY8MeIoMp67cVbjqPTMR9l0/fytAG8JGh7vAyLJR3XqCm2cCJCvVAYKpTfuXMOerkxxgNrZc+dSH9A2EXw3Fat8VFaLi8pRZ5ymTSgD5qTNwT5tXFXt5ZSrXLWhuJMIIBaXHK+nzQZPNWpR6lxtyV5PnknUgI+H7gGgXrp5UVXYf2ZlZWjlnBXYHCc7il13UEvMBRRtx2Z1DgDY1rVblYJKXTvg3AfHP9DDygKFArSvJM8vmr6cMWowIDJ2heUAozz3sopagIUsrV6A8hvAFBymnlB+rt+7pi4sT3OyswE8ZqNs2Yx2q7itAABAAElEQVQV3G0AgkAtyJoLnJeCqhcqdfTly2vLderiOceC3JTlNmTvRAkoXEXdj3Sp+CzWiFyDXzSqSTPUiyJbZf0TpSanaH3mFs2JA5SGZyzF+vTI6UOqo31KTUnWskXLgHESqYcnVdb6VFfyL6voSRGqdYBzq/dpEUp9A1jbXr97GTDhCPX4JBZ5C7UkZxVqZaF6UJWv3IIzlCsU4ZLnavv8vdjrYhtvgDKQ7LX8S1gWV2r51sWALrNVBdB690Y+6nRxlJE5jnqcJ+2f2avWt9Tq1o1bAO/jWjd/nVbOW4WqlY/KmsqUR11WXlqlMPp34UmoT/WRlyn3cQC2m5ftZEybLm/URKtbKnU49wOVtj/CmjKNemGdUkLSnPxp1stX867oaVkpKqOUtU1btTBtIW0J/T4gtdNXzqDi5a7lC5ZRxy/RZIC7Cmoe6cL5CxpsGUQFL0ergZ1jAd8RXkINr0gXb19wxgZrABaXzV6FslM5wDTgJWBHDnX4wunzHYvpflT7nlKHPAaiHEalb/nSpVhDrlS4dwKAZ71O3TxCG1IMSBPuWEY31DcCUNUqDChq3bL1mhczBzXZQBU3lurM7bMqLinWjBmpWrxgkRLCAdqB3etbGnX2zgU9aXwCdBmjg4Bzizh/60CtLhac1fXb10kHdxSw5mnjsm2K9QU8HQ1XCGqcNy936rs/uqH6bk+tWb9QGwBFYrFudAfeHUe22wNl0WH6Ptcuj+rS5VoU6bq0Y9tMZecE4e7OooyRUdpD1PqCvFEMo84grx079QycK6us1+rFOdq/LRlgiP4U6l8PS1gcf7YOYPsu4FWW9qIgFwSEf/RUqy5eKqbuGNeK5ai4Lo+mH4HiJkpmVy726PwZ+oCdT/Xaq9natTeeOsAd1es+/fl/voiSaIvmzspG7Slbs+b4Ady5A1LTz8A+1JSrvvtWnlp7AgFa5mjDdqyFY4COh1AApn4OYEEAfuI6frQDy8ciYDk/gLcMzVmAChWWl+Ms6phAVSsc6DcknAWbWLzm35vUf/iPhXpUH6QNc6N0YJen5i3AEp6+TcGTLhYUoa5Kfbti6TLt3ZpGn85duVc7derCEwAdrEqXpWrBPOAvVOV6sKW8cm1Qp84/ALp7oDcOrNGXvzKbxSEeLCYb0X//RoWuArJlAEXt3e2veUvcnfZ7mh/qaMBxN6+PO7DYMEqTa9bO4B4TGCujsMs192P/GTiNcQJ9rzPn+vXRkRpgy1htXR+onLnuAL6eGkS5eZA6OYpriYnAxtKU426P6Xvff6jqulrNB5rbuikF9UgWVwDIFZV0A0DlA2sFatPqFB3cGeqoVR89OaIPT2D1DvD52hvJWJA+s/Lsae9jsV6DfvJhCcqhPtq1IUtf/Uo0oJunbt5E2e69bmC7m5qTEaVtWzI0b14AtqdulH8Upf15MZ65fLFTP/xhJ/bpw1q9HDB/azh2qKYgzaMD1DVNQi+UssvKR1AFu4uqrB+LqTK0YhXQVajBjyysoT0KBLaeDtAVQJrkXpnQ3/4A+Bilt0XpHtqzIwzozFTvPNXcOIKSehnKulWcb4H27aFuTXZHtbZf3/9xveC6lTMb94sX45gr8VI7Vp/3H3ahfPZYlVX0cxZn44wRBzgnbDwn9V++UaWrBYNKY93M/u3RWrokRBGR3sCjbg6cWV8HIPk3tD+1LViyLkTdMF1JgI2IlNFeY2/sMQGsjEpebhd1Uj2wYK82raefsSRUIRFoXVP+RrBxnoa0YXgQ83u+3rrLM/z6f7utu4+KNGd2lnbvzdL8Rf70BVEHLhvSlQs9unb1Oop3qXrx4GzKk7cKUQw8cvSB6pF/zJ4zU+tWpwFdetPnmdS16wM6fKZXT+nrHNgapy/9VipKr9gNE7f7kz8v0fFLLApBqfvll2gjloQpFItkf4pWKHmfpXn0fyf03e+3UD4aaH9CtX17LG073+cZDvB83Fns4+vjySKHCeatbgBBhqH0m6wNG6cpLIp7ZBrJ4Dl3zzHAWHRmyf+tdRN658c1OnH6HpC5pzZvRJl0ZQJwNMhv74ROHWli8YMvoKT08quhmjnbV08fjzEX0qV7hZWA8+FcRwwwKhbFlPPSkj4d+qRatx/0As4F6w//z+k4AgQDC0/q9IkxvfndQygPTlIellJHAJ6jtNkPkHzu/HFduvgJAF6MA86tXoXiHOPGZzNiZvnsQ7/InBIM43724u0zbVOKc58p2aa+NJUCUykwlQJTKfCLU8Cap6ltKgWmUuA3KQUqqif0V2+O6t3jrOCn17lxqYe+/sferGBhBDG1TaXAVAr8i04BG2gyfmXQbGpCLD5j9px5eSaknr0zJ+9srgGojULtZ8RGkIZ/7oWagBdL200WfWr7zU6BNiYWv/FfR/XmoTF1YZXwl1/10Vc+58mqYUCI3+xb+19y9b/9b0b0zjkC+5Srz+3y0Lf/wpfA7P+SU0+d5H9xCriCpxbMNFvQt99+m9Xjdx2oyOAzA9OeEFg2yMgUwixwavCT2W6asptZui5ZssSBuCxAaoHQT7vZuV3nd33Pfrdrsc3OZZ+7XhZcNeUTO7cp3hkIZfalBsvZPgasGZxloNfWrVtlMJMBfc/DUQbOVT0s1Hf+079XG+DGTILbG2dkahaWlH5LFklx4ahFDUhYV457Y70VizqUL7O5bD/rWu28pjhninAPHjxw4DkLGq9YsUK/9Vu/RVCVwCD72Hdd9+Ec7FP+Y4FsU7H78MMPHbU2u9/Pf/7zjl2rKezZZsf959wsgH39+nXHivcR6nxzCTz/zu/8DooABP25N9fmOq8rXVyfP//u2sc+M5U2e3bf+c53nGdnwX0DL5cvX+4E+Z/f9/mfnz/eT//sOrft/7N+NsvZ8+fPOxCg5R3LE6YMN3PmTIIQ05zDPf891/HtMzum67iu3+3d9bJyYapzdvxElMSsLJgqXwQAmOv6Xe+u4/4q765zur7zqcA58kvRN9/UOGphiTnZmjaDwG0EAaxgoCcsutwA2yax9el8UqnKc6dROCtUYnqKIgDhvGah3BUeal5JrHa3+0QMiajvBGnYdQRo7uQxLMP8Fb5tt3zWrJUbtp5uHkNOZ2sSe0si5QQeCZ4SpC4/e0IN2OQlYRcVv2WjfADB3AIDgMqgACABJrE4mgT86USh6e7HH2q8o02z5+coYc8BeUTGA7ehYoeC1VDuCVXczgOK81TCvAXyAhhrIbDjh+JX0IJ5wG1p8gzlvlDVcsN6x82fa+K5jtbVq/Hbb2qAoHEQoEHEboA8FC/cvIBNTZ7c2FwUByxSMd7QpB4gtGtnsF+NCtPc1Svkv2S+3E3+AojLDShwctjqNgJTBPknULisOnpM46jvRaYAb23bKc95c7g/wj1uQ47CDPJ+muwhUFlSoboPfyCPuqeK4PqdfVNTOC92bHVV6gRCbryeh7IEJnkJSdieUkZKnyoUUCcqK1PeSSnAcHGkNXkKmzV3oA2h4jFWUa7GM8Bt1GWpAIEJazeTFlwDYB+kEvfXze1h78T9TbR0qQ7IoPryVSUDGUauXCfvFdj1RmHD5okHF9fs5m5gjC9p46Gx0hoNHz+rIqwnpy0GGtqPkhpqTu7eqOtNemmCzrMF6Lywe/LA0moCJaGe6nz1tZUqPAZrsulL5BY6j30ITxKI76svVCOgiY9XH2qHKfIJT9RoWavqUTGcQDkmlnznF5rDo5jJ9aCQiK1iv9uw01fzIZ+QA+UG4DXRWw+UUKJ+Xh5YeQVEZ8PZrXTAQje3RvU13FdLdQlpi8122gxsdbM4HvAjyhCTfcUabigE4sO20SdO4TMAIyNTgD4JmNXmq7MGq9ZpowCac+UVBQjhE8pZx1GOeqSj18/p2qN8JWWma+PKFwAcNikURbpxAs+37p7RhZvnNTJtTIs2rtSQp7eu3QAiRJFsUfY8ZWArOc2DQCDwVv/ogAoJIppi2jQUCXcv366lsxdjD4sC0EC7ztw9pztP7zoKYqODY6Svl7J4phsALdIj0hmfjKpzpAG45hwqUI9Q64jEpu2AUhNQPCKaODaO+lZjMUHni6iW1GlOzjytQREtLChaxQCApy8dUX1fOYFmYIgFezU9JEt1PVU6V3BcN29dRTErUXvX7FZ26mKs1IKwghzDSvCWzuehHoWN8wauV31eukN57Blu0sycNFRlklF3AtDieQxipfuwOF9l+SgATURp76YXtQI702AUWdpQQDxffE5XsEXs7O8hL1BXDLspGxvJNfPWKS06QwHUSx2oER279YnyKi5Rj4ZrN1asC2IXOkBWF5BUYXWhzt68oEqsjOfMztHnV6LGBhBzteSq3r32jmoI3K9etk5blmxTPCqGPih39KFQdQZFrQuFF1DHGdDO5eu1CTgwzD+asaOnimuAjICyGknbuYuwvKP+uHnhOupyvQAn6Si8UMYD/J71I2hqO9q6dP3iDccGdtXiddq4aguqJtOA4Dr0pKoQaOsKaj7VlCd3AAEv1FxStGLBCiChbIX5hKMc1afbhXk6dvkIcCcB+/UbUPpaqEDSfIJ6oQEo9NL1XCx87wPmRujVna8qPSQdUKpR7159z7EVtWO+uPxF5ZBfm0bqVNFYqczAHLWWtzl9ImvfDKy3PpOrP2WgmrUptrnaNde7fWaLJ6wP5VLONWjOjmH9DOtHmYqu9T+sn+eC7Vzt2/N9NdfPrnfXPvZu12Mv19/svK52zhZFmHprW2sbAE8L0FM4lqKxqMt06DqKjCevndS0GCzoUCdcmYYaMOqUI5N9Kq17CEBzUXWod2bOz1ZSRqJy81AarWtCqScTVatMx6LbjfrejbJYWl7qAF/jQFDrV210nk0QKpl9gLn5Rfd17/YdtTW3Yp3siX4W8EL6DG2cu1YLouZpGvaenRrQufuXUPK6AtTSp+2bABhnLlW4e6QGJoFEux7p5J3TqnxSjqJaInDeBs2Ynukozn1w/H2VtzxVxpwZ2rR4u7JC5wPwoZaJmubt8ms6e+US/b0mrVq3SRsWbFCSP3a8gNDl3cXKvXNOFSg3mWpYGu1eTUO9HgKuRQG2ZLKgIgLbSW/ymzttVEtHi3KvX0VpbAAluJV6cdWr2IMmqH2yRQ9b84F7L6O8V0UdS93kOcn4zl1b123V4ukrFeObqC6UQu9W5emTYx85CnNrgHBXzFuhaNT5rM2s7q/SlSeXdeXKVcUEJGgHUO2izAWAcz2oXV3U8XPHFRQfqLXrNmpB+gpHsfVeRZ7O3zuO8nC9ls9drd3zX6QOog7meqvqKnTh9mndfnpDOWuxkwemfvqkRMUPSjSL/nlqSpoCbUECzd8ICj6Do326d/OuulB6WjBjEXDNLseiuWe4RyUo3V4HnKxprEOZEigcqDUikgU12au0Nmsr+SYM8ILvF9+iDH6iQb8uraU/syJ1taK9UI4FAGvtawJypk9w5wb9Jg9HcW5hykKN9Y3p0r2LOn/rPFaG4dqKhfXCGQvV4z6om5W3UWg+Lc9BT62bt15rF60CLsKOl+dX1Fmks7QVTx4XoTq5nO8swWqxQPfKbgF/T3NAlemBCbjXewH6Sg0sUHha9AS7zmasXanHF29SKjC65a8H1Xd0FQi4rqGacsgigNFxRaJMuHDOUtqTpYrzo1yMDOpq0XWdvncWdctubV612YE7rd3yZJ6sa7BbJ/jbtZI8lFZ9dWDFfi1FBa+VuuRc4RmsKwGp/bFxXL5Fa7I3KNwDK8hRH4BDL+VdatP3fnIbGHBcWQuytXJZNNCINwAeqt+AYzZX0A1MdfbcgC5erkL1rRt1vBnKyMSeOMkTVUE3IE5ANbqMvgDX44BYRwDnvvvDG2pqadWLu5fp9f1xSgZWs1n8CmCXd4+06OTZXK1ZlqaDe1HAA0p7+/0W3b5fpvTUUO3Zk4Z6HCqXnH+C+7uWO6qP3htSBRbe+7CI3LEPS1jAuSIUsP7665fVUNOj9cvn60tvpALnAvpyzQbpDQD83Lrdpbd+mK+GVpTWsgEOlwcCJaGyzPxRWKCb/Nmvu2OSZ92BlWk5yoNYRi5OBhwC4ALOsn3s5YdVpxevCSCm+/cn9LWv3acOD9WOdfF6/RVv4BssjBnqlmJTeu58GRDsAyDsuTqwI4m0HwbUqlM+6mZxiTFatjQedTgv7H+BjPlO4cNJXbhczmK3Eq1fgwLYNpSBgyZQAOvVB+9WAku6UW7CtWGTD4p6Yw4YPEG/cQD70+oKHz0obKMYjwOkJgAjhQKZsrzi79PA19IBFb28W4O6dq2dZxqM6qA3imVegE6RLHzwlA+QYQhpHeDjpq62CSx5x/Q/fljgWI9uWgvcuDtGiYBc1t0vreDZvlUKEC5soEP0pZciaf/d9YMf9+vE5W7Kkbv+j3+L4lw2NuU88HEWquTfHwI6KtYjvrt5ZYp+/8sRSk320HUU5370wQDKnHeou5O1b38SEDHpwloSpw/L9z1oZ25e62NRE+3x03bNnR2s5auilIC9aXiYp8LJp9NQ9BtG+ezhoyG9+0E+tuheLLZL0fx5gYqLZ66QfQJ5hoGAasH+qKNS71wABH3zh2O6XVyrrasC9cYrMdjFsyCXfDwECHfkUAvj9CfKoM/w8gH+luGBWmCHvv39p/CYCdq3NUKvH/DjWt1YBIAVefm4PvqkTHfutmETnKLXXojCGhb4sWZcf/3fUMkE2Fqc6ad/9YUIZWeSR7lmD8qLDV2qKof0N//9igOAzphBGVyRBpxPW00e5XEBhRl8Jp093aXTp2rUzkKixQvTlTMvXDHAW0EMbUJsP67Fh3kWsoLu5o3pv307j+sq09rVC/Xy67OUOhMVWfJwa8O4rpwbQeXurLKzwnXg4BwlxAbo5o0BnTr9lLTv1/admViZo87L87Rye+cO6oAfjABS30M1MFxf/OIMzUhiwRRp9ad/UUQb/VDps+P1u783zyk7ZpFLdUa/lwqQucf6+gn94O163bpfw/xTGIsQEzQbBbxQYNQgno8p3pkF+5PCcf3d396i3+IPgJzIfv6oN3o5AKSlRwD51MqhHba5ehxwrk7ncx/Sdw3SwQNZWrIsFBU+uqNM+5w4PIBtNLbP7i1643NRypobqJtXh/QO9q69QKW7dyVo5x5/RWLTa0LY9TVm/doGANlDP9tN/+YPIgH3AgEpEfUgJvndHxxmfipABw8uB5wLceagO7sGdez4EcC6j2jXUJzbt1OrVy3n2TK+JN0YtPBuL8M7ydd//7K/fJZtCpz7LKk29Z2pFJhKgakUmEqBX5IC1jxNbVMpMJUCv0kpwOINJpLH9LW/GtW9qgnsR9z0B6956g9/h4EGMMzUNpUCUynwm50CA0xw4JiDpD0TFNCxOGQ4k2O28tk+a2aSrr0LyyUmvfqpD4b4+xArR4eZQGNO1tkMiGM+xQHjPJhR8GGQHkjshsV2rCpkcoSJoEgbZDNRgouQ80JYw5k0CGAf3I2oT6b6CL8JOYm4jI6dHNe//s/DTH6y2m+hu/7Lf/BR1kwmk2w2aWr7uSnQQxl647VhnXyKMgTp+OE3fJjwMyWJn/uVqT/8BqeACwKyoKapc5manNmzVmC3aMFRC6Tay6y3kpOTHfURC6gaSLdr1y6tBrgxcM2CqS5lkV8lOSyIaud2BVdd1+MKprqOZYFWCwDbdZnandmX2jUZqGSAn12rgXQ3btxwrFLtugzKygaaMeUTO55tEygGVBUW6Hv/8Y/VRNBxdlS8Ni9Yq5m7Dsg3e5bcw6j0CYxOdLUSsPSVB8pCHj5EFNhcx3j+Wu1nA+cMbDPYrBG1renTpztpYyCVAVW2j+t+XN91DvgL/nGdy9LH0vvw4cM6dOiQY532yiuvOMePwTLI7vtXOebP29d1PrskO58p6Nn5DKZcs2aNvvjFLzr3Zc/BNtv/+Z9/1v25juk6pwXG6+vrHavWmzdvOkF4s/pdv369YwFr+ednHcc54c/5x7W/65rs3XU+Szu7fns2p06dcvKBqSNu2bLlH1nOuo7h+p7rWPa7vezvrn1cf7N3U+U5fvy43nvvPefvZqFrz9zATRcY8Pwx7Tu/yvb8Oe17nwacmyCNK9/6kTpzLwJjYRMVg9pVTKg8yYc+sUmoscXSkYlSW0GRyq6c1UQbFkqLshUJSOURnyr3QBTKDJyzx0yRMbZsDGvSpo8/UNv504pPTlHw7gPyXrIMhTUrFyN2aexHPgS6mmBANt7QrKenjquh+InSorBI3bpFPpkZdLawLyUy4jZJmhIxnOweUsede7r/yUcorUwoizJrUN4kkN8knTz3oQ4No9JScemyOpq7lYpiUzDQXfvjx1gltskvPER+kdh2RYfLA4tm30QCz5QJtyDUtyqr1Pjm38q7s0PBK1bLb9cOeUxP5vy+XKfdHMpLKBa4mcISCmu95wAhLpxRXPZMZezcLL+52cB4gCycz5170wh9P9rDSZQ2JoA7qo8c1wSARYTZv+7YLq/52MOiTjEKpGWLMjzGgctQC+t99FilH7wt/84WJaxeL79N20jnRM4/jnpah7oB2upOnFYwKz7CUUqCMlIx6neTWEJGTwuUP8pHikGBbPpMeWOb6o1CgxvB87En91R/4rDKmjuUvg5oYA3Ka1ivuqHAwAWTb7k/rnWyf1BjVdWqvnJFDYUPNQPl0HCuwWsuYFuIycgC+rmPONfsBGzoM489rdTI8VN6irJYAOovsXs3yzcuFSUZoEoC52bLaXCbB1iJ+1izxlHT6QSMG+htVNT0OPkmkTemZVEmSGvgspGmYjWX5cl7vEHh6fGAaUkaqWxXU1ULnFykItJRnAwif7hFo3boA3NIP35ylOAbihJcoe8kYF9vsYaanqi7GaCZjn0QtrZ+MZkoDM4CxiTBJ+o0WHWbY1Y51rfRwBbeYbNIC9LEjTw6VqGR5gdqqaolsIqyCTaJXgB8k4Bz/fUP1Vl3S0ERvgpEdcc9eKHGkR+bACYsqnmiY9dPYnl3S3OXz9PWlS8pM2IVkBcA5lgfoNh1Hb98VO3jbZqzLkftY4ME3vMB53qVnJCoCD/UpBinuBuQSLYzi8dmrMjCgBD3rNyllYAOkQEAjGSu2wT5j6IQVPDkgUb6hrFmw9ps7S4tT0cd0zOUktat+oFyHTl3lIAwAfX4dL2w/VVND09wgqTj6sWWsEyXgDrMBtVUm9YDPkQAZDwpearjFz9W21idA8CvnbUdGCJRlR2lgBLHUWm6qszpWXpl0+uaHjGD60GBRgAfDQXYCR7lmhuwkF2jIcZZt2+ifNVXr1js3IKwZh4f82NPAvpeQ2rrxMa0oV9hHtHas/EFrZy7QhFAiEOUi+LuhzoLHHP56iWrWpQMFLpn5V4tBawJ9CZySTmrBpb9+Pr7etB4Bxurmdq74IBygmjf3Q1iGlJZZ7lO3jipB08LNCstXV9YcRC7y0RdKb2id26+r0bqqn1rX9D2BbsU4YXdL1BDH5DMCVSUTj88Dxg6ppeBbNZnAPujyoSTl8qbUCC5e0plvaVKwe7Pj3Hencu3ANx6UVWJUkBgEOUDDR4gYk+uY5T8V4MqmB8GnsvmrdTO9XtQqSMdSIWGAZSqHl3QsTNH1d/bh31cImDWaq3N3Kz4gOmOyk0DeTj3tsE355SYFa+dW/ZoZjiQs5s/z3EM2KkTBZM87vOMfML89OquNzQzJAMrz3q9ff6HKq5/rMyZc/XKitc0M3qm6ocquQdgrIBsNWNpaHadBs4Z5O5qo0nuf2gXrV1xbdY+WTs5gPqnKcxVUX7sZX0m68fYy+A5e1l/y+C757ef1749fw7b//n9XH+zd9fPdp3WP7A2tQNVtV6UFcPjIhyL7W5snq8/uKLjV04oBpWhHev2KSecPINt8PBED1aXxSg/5WK5W6qkmUmKnBmm3LsXCPa3AouhNOVPP4nyZyqO3K7a2zhHRzPAAJa66zdrOXatoShpGbTY0tOiC7dydSXvKup3tB2oje3dvl+rZ61RvDcW4zyhjrFOyvwJlBevy5/6ei+LLuZNXyDwQg2TC+pGq3XqwQkV3nkApMXCDOrmjNRsrEe7AIDeUXXHUy1duVTbF+5RmvcMQuSAKu5dulGWq/NXgXqaOrVj1z6ty1yvKE+UL6nvKwYA5wrOq+QJaqQokMYBy1XRl3rMQgR/AxtR1PP2wi4Xsoy7BHzpVyULLmiyAOeW6dX1r6Oil6hhFoU0DzfqVnEeqmknUR6rAwwJwlZ5DuVmP4prM7FjDlVDT4Nyiy7q5OljigyL1PY1KK6lz0dxjPaH/5onmnW34RZqQ8cUMBGibct3aSlKgMPUiZfyzun4hROKTo/U1o27lBW/mKvw1p2S6zp/54i6utu1Yclmbcveq0g/7MkhgRqBQs9jm5p775wylgEBAowWc69lj0qx74sC8giVtw+KslT1NjCdQLG1uaZZ7ijBzk2Zp92b9iopfjp/lFqALq8WXtHFvIsoTlUqMCwAOGKF1uVs0VygaB83L7VhUXr5HukNjOkDoLVz004t5DrD3Ky9QKCU+vxB1QOdvHRGveMD2rFlF8D0Ao10j5K3sDK9fxFbyGTtXLJDWQlz1Dbereu1pCngXKBQqFu4VctQ4AvDSpvVBCrpKdEZ1PbusugoGzW6zKRs3X9wHxC4QF6Ochf1zARgPBaObvSDBwDo24E3x5jMWpizECU57GixSbX+U9tQsy7cP6Vcg31bahwb67UoMG6ev0ep4cmA2l6AcS06cf+kzmJD60H7uG81dfEMFqhgx+1N/2sEuuw49eHZwlyN9Y8Czu2hnZmvRtqH0yh+3si7rZToTO1a8xKWugD/pJkX6mhuqJKVP0Bl6XSZzt1r0qBnpOIpp6lxAbzcsa4FCEs0m2Z33UYx69ylSpWWNqAMhWpbXKwSsNJMTHVTRjL7xfgA0NGXoDo6dHpUb/34IoqePXr54ApUybAAp0mA+MLmdhKL4xbsf6+i7pekF3dlA8346H/8BCtOVEtXLQbkOZDI86C+8xujhLorP4/vvD2qgqICbduVqO37Yxyorbi4T3/zN5fV04rl5LoF+vwryYBb1Gm0zaaaavOGT4pHUGSr4fkMcHpPAG4/JSYEAL96Y8vrDXADQMhXbt3rwMa5BkgN+/dArJJJg/g4byXHu6Euhl1tvK8CAHzGWJB75/6w/uRrV9XVj8LUzlQdfBFVK/azOcrKqjFdzG1Cne8Bbf5s7dkSwjxkA2qWDairD3P+UIAb6l76XCQIc5io9gEftbb18z7A3wIcyKsfIL2utpH0RuWUchEdHcp36ZOzwMHgyT7GdW48Rz+/cIApLHIhrEJCQwCi/SlbQEuULZvn8eR6h1mN3NvL/CpivWNIdw3Thvb3YB8p1HoBpuJ5frPSAdGAhTragQ0B537wY6B/1H33bM9GlYuFH7HUuRy3gvv79veaUF8T0KinfufVMAc2+9ZbKAbeGtbsrFD9qz8IUmoqaqbkSw8W35Q+ddf3ftSqvIJ+2thw/d4XsWxP9dAVYj0//mhAxWX52rUzRbv2xSs5BeNyFouY9SYFxJJIJU9HdOpkl65eK+P6R1AQw66bvnNCnC8wtS+qaL7A7ljb1o/q0JFS3S/sp30PVAzqwAlx/liYuislGbW5RMBD8rXZt1681Ktv/WRQj8obtBsI7rVXY5QEhGZzy2NDkzr2Sad+/BMWLLFY4tUDkQCJHrqc1wxk+hCgbZZefyEWNUFUFIG4eljoXVY7AThXqRvXelC5jtOre8O0ZIEXddaY/vK/VupRpZfWLwvW734pENtw7svGTjwfmmsA8zG9/wHw2aUa6opIZ8yZkOBHu+CmFNIpJdVXkeFeelQ4orNnarHdRfnRi3YjLl4xsb68iz6CwZA8w1CUnUm526j5vfm9a/QzKrR5y2LguFnkUepcykV36wTjoxFUBXOVkhKo/QBnCTGBunh+SJev1QClDungK+moSvqTr4AwyUNFT8aBEkd0mkUIa5ZH6ItfnO2Ue1O4+Nqf3tON/EdaiJLib/+rBSzCDHSen/MIRxi7UA8yBAKWbaBclKq5BVX0sHjKVwj36ImFtJtmZADrxtOnbnDXofcrgC3JrJN+Tr6PBUZOSp5k0YO7klJ8aB99yNfuaqzimn7cpMvXi5SFfe/Bg+mag2KhPd8x6rbzJ0aZXxrBsrhSr70ex99ClHu+nzkEs0kPBtSMxErc21GSpBlUO+ly8vCQPjnCGMWtX7//e4HAqkFqagCsRXHurR99wr2F69XXl2rVyiD6Zjx7+oXHjh7WsUMf8VyTUL3cozWrGX9Tb9uztb7SpL2clpbfeTb2+qzbFDj3WVNu6ntTKTCVAlMpMJUCvyAF/ilN0y847NSfplJgKgX+p6ZAHas7vvmDUQY1rKhipcrqee76sz/y0bIFFiCZ2qZSYCoFflNSoIsJoTZWc3YzaB4FihtmZV4N5buC1Wd1bZNqQk1skIE3c9wMrvk7kByxQQ0w6EWhXqN87qjOMQAdYyUri4f/YdRpE6LOIJR/bHKI+LAzAeZDoMQHWM6fKJ292+SEG++BrPCLZNVhYqSUmeLOBC4TO3zmTbwxGPn6aD73Z2LIjju1/XqlQEHRhH73D7FrrZhw5P/f/4aP1q8wG6Ffr+v8dbuaU9fG9Md/NKJCJkN9KReFx3xZ6WvKX79uVzp1Pf8cKeAKXNqxLIBqih9mbWmqc6YOVldX56i42X6m0GVB2aSkJOxBFmjdunVKAFhxBWhdsNCnvS47psFwru+5gqv2uV2Lvbs+cwWACwoK9M1vftNRdzOlO7OPNUU0+7vZpBrwZSpgdl0vvfSSduzYwUQuEITNSLJNEqSrQenhrf/4f6vu8RPNik7SpmXbNcvAucwZcg+i8p/s5/9O4AkmxQE7PLx9nn2X67HNdU2u6zNwziw7c3NzHchw4cKFev311x3gzJRaXJt9z/Vd12c/792O7dpMAe7ixYvOfRkcaMp/Bw4c0GzACEv7T3tcS4OfdX77zKVKY+8WQD9y5IguXLjgBLO3b9/unM9APdvX9Xxcz82u05UWrmv+6Xf7ux3bFAFd4KMBeqbMZwCmqRdacP7T3svPOv5Pf2ZqfZYnfvSjH2EldNvJEwYdmsKdTYIbdGjX5br259PG9ZnrmD/9u31usIE9l7feeku1tagQoHJox7d7cT0X1/c/7budx7bnr8X1XYNGDWa08y7FvsvsZu0+XGXD9pu0NL50Xb3YsI401qmfUP4AQaUJ1NgiE5NR95oLgDVX7U8rVHbjvMZ6qpW+LFtRy1bLIzYVVTMCvV6mnPaswoe/0mhjk2o/eFcdF08rOWO2gvYekNeixRpFAc1sEC2WTS5EmY28QWdsvKFRJWdOqan4qVIisEjask0+QKnjQf4oxhCgs1scxs6qvVftgGP5Rw9Rt/gqa8MGBW/cqolgVOMmAOf6OzSELVpZ7mU11rRp1nJAgpR0rDpZ/V/6VMPdHYivjXCH5Gt/P0WiwhM8f4F80mdoqLpGrd/6lqZhrxWwdoM8d2wRGQBlBB8nsGqg0rgbQTpsm8YeV6v/1CXdu3JBMQuylLZ3i3xRe5vEAm/c7oyAsQf3ZhHeSTqZ48AdNQADE3fuKiLFwLmt8p4PpOVvajEopbGrB2Vtoqtfnfn5evrh+wrB4jFxI8pU67bIMw5VG448OdCt7pt3VHv4uIIHBhWxaJ4856aoq6lRg0/L5NOCbSGqXG3jKMGFxiqY+4qeO1v+2PyNlz9U48lDquweVtrWVxS9Yq18CAxOWlXF+d2caB51KCtJRktLVH7ujJqLiglcJSsChTwP1CMnglkR4m6B5hH2p64FXEKsDXCuQsPHjqv4bp4CUd5L2rdd3vHkDXfyBkCgG/0CYvwEunoA4xoAKx+qo/ox/fJ2LGexro1bynXMIg9hzYvazHBTidorbshnvFohKYDIABxjNV2ow3XRyY5TxIz52KfOING4fsrkMItaBunge3Jd3oAenkPtGm0uAMgqwTZ3GMfYeAXEzsT2LYEOO1bEKPpookFD5XkExqqxIg5TVBpqdEAgk+6okZJDNF6hIcC5pqoqBIT8AedmyTOM5zCA9XfdI/XU3VEQyhD+0xdS5y8GmgsFYRrV09rHOnbthK4U3tD8VQu0Y9XLmhW6zAHnxhmgFKKQdPzqUbWOtWj26ky1DHbpfn6hA76loiAYgb2t+wjKGKMExbi3AQ8ssrDJijE1n6yVmh0zQyHYllqw/17dE314/YTy8m87MEMOSkMvbTqgxYAhQZP+CLT1qLqvVB+d/gRL0jolp2Rp/5aXND00jlyK4jZoWX1fmQN1FBQ8UDrw1LqlmxUVOB0VwzLAuUNqHa3Tpk2btHbWVkV6JKmys0SnCg7p5t1rWFbO1+c2f0lxKLVRoki1URW3PNQ5wLn6jhosIhepvxMY4e4drJzbFTs9goA9wNg4inPsP4564TiQo+ewtyKw9luVs1bZACIBBKRHUO6qHagAnDulI8c+QaFuDBvAbL247hUtTFwKxDSNEjGhMoK2H1//QI9bCghsZmvvnAOa45/DOM1bQ+7YafZV68iNI1i23kZJLE2fX3ZAKeHxul5+Q+/d+hilj2Yd5Jg75+1UqEcYgK4HC6pQVyo8q7NFuU7U+dWN+7Q2fSnBXdKUIH1ZY7WO3z+upz3FgB1AVuS9+1fvaIi8lgD8GAz84oEqkxtqUKbQBYng2PGF+oUpc0aWlmatUOg0oFLKfDPpe7X4gt459BN1ofIyY1aaNi7fyvmwFp0GeEXZrGqq0Hmgl8v5V5VCed8J9DMjFNVF/vMkFYaxu7xXdE+f5B6mSHgDzr2ujKAMVKiwr7v0tkqai5Q9M0cvLX0DaDBNtYNlKsVycRbgXEt5+z+Acwa5ucB6a1dcL2qIf2hfzJbV2mFTFjbI3Poa9j3rP1k/LxIg6/nN2iVXG/X8566fn2+3bL/nf3ftY++uY7jebT87twucG0AF09T2IiMi1d3VhpXuZdTBjio+K057UDLMCsFO2C0AAKhfVdiGXqYte1L6RLEz4hSaGogqWK56USdNCpuuGJ9YlDEtLSwwbVaxBLnJ3f6+2BjmLAbABMRBcW6StqwTaPH07XM6ffmcWltbyOMxennfa1qZukZR7rGO2lPneIeO5x7VPex9wyIDtGvzDs1NnK/AyTAN0aY0jtUDaZ7Qg1v5CvMN1WbAubTkLLWSH94/+i7gXAl9w7XasWCPEt0MxgNf9uzRleLzgFyX1Eq7uHffQa2ZuVbhbiGo6g2ptB9wrvAc4FwReS1IUQDplXVmAUhAHzWesAgWrwDFeCAFaq9JYJdR4DIfFn1kJmdiTbwBa8wIp33qRhnxXtUdvXv6XZWhzhYeHKE181frhVUHlEBd4evur5ruWl0AAD1z/hSLkWO1Y/U2lOPmYxHMCkK2FpTr7jffATA6Kv+xYG1dtkuL5yxGmbJHV2/nApydUFRqpLYZOBe7mLT10a2iKzp/+zBN/4DWA86tn7XTUQ0cp7w1AzOeR1EvF9XNtIW041BTxbRXlUUVwBJAT+GoVBnVAzRn7TAjUk0AWAQAT6bHZmh5Dmp4ERHWAVLbSJtuFN+kvjuukpISRQJ2rduwQWvmbNWsoHnUtZNq6a1BGe+CLpBv/OP9UMfc59RDIW4AlPzXOdmmhzXYS+eeUs9YL6qCO7QwaZHGAOcuYl19OT8Xy+k07QR+zI5D6ZE0vVJ9BfWlkwoFdty+aIejJhoaYPTXuMqBcs8+QMnt5jXa4EzNiM9QfsE9ldShmh3lixV0vPwnWMk5Rj+KSmLc+g+jY8wvTQNQylRW+gLFBFkb7I5VbQfXcEZnaHcq61B5jY7QjuUHtAsFv4TAWAeC6Rhs1CHS2sA5P9QwD6w6qOWplBnsiX2wYB2lY3e2+BJ1/0WszQe0f/lOLQXoq6f+Oks+u3u/QGmx87ULUDwrYRZKYKg20W9zY+6tv3lMT8oGdKGgC7iQ+gPlYE8WOnjT20wBqlqck4LlJGUTCuZBcQ9jNWzZq4c0NOKjEepwX/8hlFT9gSdjUDILVGCwu46cGQWEO8/CgwEdeIExwcYYxYWM8ZQ9VdfKAr6jbeS165o7I1ov7JgL7OOrt97tRB2rkXkdLEn3xgHkobboS58Opc9H96R3iQfcffBA23cDzu2NIZ3cVVJq4NwVDXQAeG2ch8pXCpa3UHBOp43k5x5bUVArLhrU7buDKqukD9eFFBUdI2+k+hLjQ7RoYaQWLpzGnOO4Hj3qRb1tWBaDGAQum0Dpd5p3v+bPikUFLBEbWBQ6AYnu3h9Ece4s95ekl/bNRrnKB/tIWwzG3GblOGOtZiDDRwBdGdq+wU9RYfXAbJPynwbUxhjPhgV/PzRwyp9drr2s7/9MlbxW5WXl9BeHGO8GOgpdAVh1j7FAZQj4sg8Czmywe3vps/E9G2uFhoY69bvV8aYk+gws5o+WYxlHcGjaAkBxgOaa2gYVcA/3CoJZAGAqdiG0W9HYsk7D0lSA9wYJ3QK469P+PTlYqEY7oKJQs6yoQHnr+5168Nhdi2ZN6Csvh1CWJ/StH2DtXjChBYti9cXfnYa6qBlUYsEJvFjy1ENvvzuga3f7sKH2B5wLVEa6B2q6Y3rvk0GVVj/CSjRRW3cCryV40qezvEK7TBttfZGebp4hAKTZ7laWY2ZOuR3BQmF8bEix0X7Yfcdp4aJw7EE99fhJDxD0oCqxue3v5f6pP319+gHtPFGgiwR8DmCRAlat17r17XdpK+s6tHNbJEphYaiuAdMbaAg4d/xIF/MAlahMxqI4Fw74766L12r1k0NPqYPm6I29kdqyGnARSKufPkRlwwQL4upQ7exXclyUXt4bzOIET4DJMf35N0pU3RSEtW2IPv+qn5KTeSj0H60DYXPaQ8yLPyTv3crrVlGpuzq531HUn93duhVFPs/OjteyxdGMq7zIF/0s7uvGlhb4cdDsgW2hzKBSknyxAOYZzgtSXISn8nmG3/wO4HZHuTbvWkobNJsFZoyXyAfdLRO6iYrj975/WfFApC/sz9T0+ACdPoOiXF4jKnaDeulVVBcB54KZM7cxYUnxuD78eBRL4VtasThEn/v8LGWkkJdJqz/987so6hZpGda9r31uAQBfgGPfPEm6uJP+triKoRlqjKgLFnTq4UOAb+C5Ib7rRt8xKLgflTpvLaLsRwNW11fSZ7vbz3gfVeYO0oJ6xHdaNwriI1gGh2nFGpQl46apBYW4995pQU3xKfcdwUK3VOxYWRRgAC8w40UA3mPHWPDUX8lYPhbV5hBdONenjz+qI99Eac8LwVqxztTs6O6RzzqpK86dHNXHuJtMAvF/lXy8dkMghgWTOnMamPTtj5Q1J1qvvIEa6GLGF1Tx3R3tOnrkkE4whpw+PVn7WcxnCz096Z/QfFHHkwXJhjbk5H+nf2Dvn3WbAuc+a8pNfW8qBaZSYCoFplLgF6TAP6Vp+gWHnfrTVApMpcD/1BQwYOZ2wbj+8r8zqXFrXEF03F/Z4aF/91UvJTGImNqmUmAqBX79UsBgt/JaVt5VTBIsnNRQj9TYPqFqBqNN/OyoyzF47mEhmQF1fQya+yywx+CSORLXP89uzDXCZGLBhyLvw8jdVr3xv7Ora+LJvsZclQaZIRvhODZQf7bDs8M8G6ryM8fw5hjMOyiQFYLhgHIBAHOMbR3J/Dh+T4t6plLnh1JdeIybZqa6aToS7gbfTW3//6ZAN8ppf/jvRvTe9TEssqS//H1PfeUNb8eCwSYOp7afnQJ//Kcj+rsPRkU8nmCKhz7+lq1InUqwn51a/7I+tclyC6R2Qy1XV1c74JwFUw12ss9tAt8m2c1i016mpmaf2eS9K0D7q6SIBU1dMNc/gn/+/nM7livwavvaPgZA/dVf/ZUDKhkUZ+BcGgF028+u1eC1v/u7v3PU70z9ywA2u2bXeQwsqnpUoO8DztU+fgzYk6B1S7YoZ/dLCsxBvQgLII0DzvW0MjEKOBcKOPdLFOfygWMMnDOIytJu7dq1+tKXvuQo4bnsQJ+/l0+bRnbPrjS6d++eozp36dIl7HUyWSl90IHOAgmOuNLolx3XdTzb3352bZauLrDQ7NqKi4sdlTa7L7MdNUhvA4FHAxBd57Lv28+u313H+kXvdg4Lwtg9mNWuQW12L6YCZ6qBdvzPmo/svK77smuzezJrVks3A+dM6c4AS4Mp58yZ4wSOns9zv+i6f97f7F7y8vIce2NLq0WLFunll18mqLbQCUR9luM//1xc9+Q6v6WXWQJbEMwFzpl93j86D/c+jtXbWEmZBmuq1A0A2tLRpE7u358+TwbWdYFrdqizvVvlBI1HW0tRnMtS9Oq18po+gwX7ZodmgQvyhOUTysBEa7vq3/uJ2k8dVRJlLWj/i/JYvlRDfgTN2MNwEj/6UR6j7D+I4lxjo8rOo4YC5JkShjXQVsC5ObM0EhiAegF1BREBN4KOE53d6gCcu3/kY0dNJmv9OkVs3akJs14dp6PX2aSh6+f1FBXMxuZewLrtiluynGujnmqs03Bzo4aweO1FZaqnrhZ1YD+FANNGrVmr8a4edX7ne/Ls7ZY/v3vt2aLJxHiCQMAFdO7MWnKU43gxeJworlfvqWu6d/GsIrNnKH3vJvnNQ3EOSHjCghko1JlikcnwTWKhOc75qo4c1sTdW4pMm6GAnVvl6YBzKFsSJCdQQfCH++smWHkvXyXvf6gQ6tCE9Rx342Z5Tgf4onaZ6O3AqjVP1YdOKJRObdTqVfJZt5hOJuoH9dQ/9S3qaeb5NXWhQtSiSV9PJWdnaDowm0cnVqdnjmP71630TfsVu2q9vGIjWfBBW21DXvrBTqQVSa1R4IHyMydV96BQswnURFJvemUBtpnVLvsSen22Lxa2k0BeY2VVKM4d1yPsOwMXzlPqCzvlMz2V66IDbDa3lC2kt/huL3VlvUZbHqm15iGLXJoVnYASXNxiufllUyCxdsU6b7DxqdqrbxIUrVVochRKb0kaq+pCMagLdcE4FKKAHcPIe25AxlwQbB7h03FyHoHAkVqNtVVgBVuhMeBCv4AwTYubKa/IZE2i2jaOTagHkI3bRKMGym6osbJCnigyxaZlyTsig+PFcG9YtQ6XYdVaoCbKhJ9fqMJTZ8szBECsf0yDtQ/VW5OngHBUSLApdY9cSsQ4nCDssJ6WP0Bx7rSuPL6rnBULtHPVi8oMX4g6GHbAQCL3n6A4h2pR+2QHionZ6hzq14P8h+Rxd61asAyFvxR5jwFfjpCPUIMaQ71j3Bv1DJ9AgK8ELF/9LTdqkDbnYuE1nbx1BVCgEVW9ccVFxWrLio3YC65UrG8ksAMB+v4aHTpzhGBpmeITsbre9hJlDDVJyuAginNV3aW6eu8SQdUnSs9I15qlGxWN3WNFeaVO5R5XC2pTG9dv0PrMLYrxTMAC8qmO3P9QefdvaEHaIn1h028rPjCJK3IHXhzRo+Z8LGQPqb6zmvpzngYB54oePHEUXrJzUB2JTpXPBFFEoB1TxjIVRx8gswD3IE2PTkFNL5Ln6KHukS49xBLXVKXuPbwJ4IA7QEysNi/dqRUz1ynEBytiSk9Ne7UOX/9EhfX3GE9l6IWcg5qL4pwPFsOjXgAo3WX6GOvYO1gczpyZrs8tfVVJwYm6VXpbn+QdBpxrRVEOYH7edgV5AEMRhO8a7EABDEvd4ssOVPTy+he0Jm0FIESghsfHVNJYruN3j6qs5zG21ahWApDeyb1DCrhr3mLyf0KaYyPrOYoiFHfjZsFdrjYQf7gILJRjQmNQ+vJGvW5I5e3FuvjgvC6jKDcItBsVE6nF2UsBl7ZpZiSwJvm7tqUSxblLQEKXlZA1XTs37NFsFOf8UbBDTx0b3E7dfAT4cxU1LPLk53a+rln+MwHn6hjPvK8iwLlMYK+DgHMZkemqHixRScNjwLm5DjhnfQKDt00hztUWutoVa4Nd7YUBF2bLakC2wfLW9hocb9CcAf8GV9hnru35tt51PNe77fOL+gM/vZ/rmPa5veyazMrdrsOsWgcos9HTY4EXYgAQ23SlIFdHLx1WHODc3i0vao7Bq5TBEQLkVSi45d44p0eljxQ/M05hyeG6St01ChS7bPYyZaGc5QOU6Eb5G6We9bA5AuBfb09flHIAkoPCrTrTEKph5Y0lOn8zV/mPgV8BDPyC/LR+7SZtzAJ+88FunCffPtnp2MbevY+dJX6Tu1HwnA9U5T8RhsX0kGrHa3Wy4KQeozgXExChLWu2KCUxU03tnfrwxAeqbS9HYWa1ds7frXh3gylRx/foA5y7qLPXclk42Imyzctalb4aDTvAOSC/sv4SQKWzKqHPGo4CYmxcNIpzjapF3TUtdaZmU5+FUq9NAghOjputODmVm/Lx8qAMRqBKiZIiC0AGAXtqOytQRbwKxIYqH/3cQL9gZSRl6sB6FmNEz1KwZ5Dqe+p1+fFlnTgDAMfikR2mODd7EfsGkkelJurbvLobOomSb7AbinTL9mghVq39oyivYVV85tJJIPcIbUPdMRuQmkoPcO6qzt09jKpXH/XSFq0DnIvwAcxEXbSlpRFly4sAj5eUNj9NYQnhKiouUlVJpZbQN8ycMUtBvijW0lTRhXfaSHfUH/2oZyJRFIwNj1EgixhHxjuZP6pCue4GUM4dIMQm1En9ASDmamXOFs1PWIk1KcBDd5Xy7nKd1y/JM8ofBan9WjJ9ObBtiFOPtU8ABpbf0/krZ5y6eTt9j0XJSzTWNarzlO9LD3KVnJ6mXShizo1bhJJnuy5VnSc9jijMK0w7l+7QslnLUVSLcNKrrJfn9+iUrl6/olkp2AeT3gX376myFpVEVFjnz19CWsQ7inPukDGmjGnKYr7AnSGoeoYERqHSFcy1DQG2lGJzSD36JA9L3U55AhUvRRFxx6L9mh2XIX/6OD1DbTp254RO3z+Le4KnXlj3gpZhJ2zgnBf9mBHq6nM83/MPWGCBBeE+wLnFM+apcbhB5wsvOvB3Suw8bQHIy07KcCw0Dc4mK+LsjrsDi1XLmicA/1CRbuhVa1OHGmvbse0cQokqVVs3xyob9axRylp93QiWhkNqacVyuAa4paIetc8BrVoUr63rEpU205/8Mqbv/fg87c4w4Nxybd8YpZgwYKhJT5HN9dExwLkjN6j3GI/sykEF0Q/Lzm6UVLH9XeyvXXvisVT2kW+A5U5ANRTQfvLOqB4w1tu/jzp2B1atWEg+LevT//M31wDA+4HzclAGYyFBJGM+yr+9bG5wBIBmELW1WhTBKqpREW3qo0walNNJvqEPht3kvl1xysgA9iY/NpEONfVjKHV143DQrPrqRk32emr5ktnatCNasQBfBYA/X/uTM5TN6XoJ6GjPTh9U0J6dsKpyHKvHRp3MfaKUtFnavs5b8VHN1MX0U8IjHJDZ6sifV7/aoh1b5GZ1eRSKwqYSau+mGmpjevuujVUMkO7oQM2TdxunGqhs43drL2wsb3W/1fmuOtnebbM2oxIF50ePWRRQMA3YlsXKQxPatDxUezfHoQbpqdsPx/Wdt28D7nXrxX052r41FoALtTv6A2Vl40B13XqAvezC9DF95RWUoukHfedHtVgOjwJbRegrvx+ipGS6XeSXScYRjx9OYJeLpfmDIS3Jmabf+woqqmnuugE498EhA+eeaMfeJG3ZEYXSoQf5k8Uf5J5xFJFHqefHWezS0zuBAjxqluS/xnrUeOt7VF/bo4b6DtLVG3h5pjasj3LqyTogttqaEfIxqqPNw6qqxS68owe4LFSffylBC+b56crtfn33wwHV0h/fuTlE+3eSbvHepKEHbf8kYGeX3n6nCivYKL2GMlk2inO5N2r09uEyBUVm67Wd4doLdEUSM/8sZzH4J4cA7a8BnCZGRKjAygAAQABJREFU68X9QVq0GCvdKhTn/vqpGlpQK10drFdeZvFqGm0klt7kAhs6mIilensmVN9EHgUGq20cVgvOADU1jWpjwU0Q9cX61TNYJBboWJXafmWVg2pqHFFLYx8ga5v6qPszM0NZSIGV/PwIPSWG9rffuaba7kpt2L0YNb9Z2IKDrnLKnlY33biIHe9blxyL1v37skkbf527iKXwjXrg0369+HIaY2CgTUBRHruKHo6hODeqs5cLqO+D9fnPpWpGqjeQofQXf1mg/KfFWrlqul55lT5W/DT68dwY53Jn/Gn3aYDgIPO4Lai6VVMOa2pH1NxE/VcP5FhTzjx8B0BcMs9wFjbRQHFWDkk7q28aWzpU39ZBmWxmLD4MqDpfK5cmCGd1ffBOE6BnsRYuiMDGeQb1zzQWw3M+xsdnsYw+fpwlKP0VeuWlePq8wbp0fkAfftSoMEDTAweCtGYz4FwYdSjX10G84tihIX18ZBB4c0B/8HtBz8A5FOfOngIm/fEHypwTqVc+v1TLFoY49tRdLU06eviI45qQND1Zu/bv00r6A9bHtWdrxc7AOVOopglwIER+/MzbFDj3mZNu6otTKTCVAlMpMJUCPz8FaKF+jTbrBFvH9tNsFpyx1SNT21QK/O+aAt2ANR8eG9N/+GsmYfh5VoKb/ujLXqzWoTM6tU2lwFQK/BqkwKSqWZn5qGSSyaUJtfFzKeDckzpW+HYyF8BknE1A9PMaZHLUJrEYQj67bkbvwahcRDEoj8b+wGxV7cViWrkzCU7chgE3A03iel7s66jJ8TNjcOcQzmCUQxknZ8c1i4QRgirMT/6Dep0NnIm3qhm1rRazf2XyrI0XC2pZaW7X8ayPYDHJaZwrgIlaPx/Ox6A7hmuZnejGJI4bNiPuSMS7ad5sN4UE/FOGvM9uferfXz0F7Gn9Gfbd33ifFYg9k1qT46Y3/wKbBibfnDzxqx/yX/w3DDb8vS8P68N8wAQmcL75f3nrt173YvXtv/hb/9/yBl0T466bt0l2G3PYyybaDaKyny3AauCcTdzbJLuNNyzQ6bLuMoWSz2Kz6TrvL3t3QV12vbdu3dLXv/51RwnPbD7feOMNB+KzYxg4ZyppBs7ZvezevVtf+MIXsMyJd05h1z9pQYDCfH3va3+s+idPFBccreVzV2rJ3lcUuSRHbgRkiCRpsg1LQAJQnlFxuDvyGZsrvew4rt/tZ4Om3n33XQcIs3QzK1AD9kwR7p+SLnY+1znNptPsRj/++GNHZeyFF16Q3b+Bba7rcS7qF/zjOpbt4kpTe46u79vfDTazNH7//fdVVVXlwGavvvqq5s2b5wRwbF9X4N32d333F5z2H/3Jzmswm93HHWyrLFizZ88erVq1ygEcLS99lmPad+xl12Qvu0aD5QxmtGdjmwGNZqeakpLiBJX+0YV9hl+sTDwhDxkEaOeJxT7Njr9582bHytiVTr/KoV3X70pb133ZuR4TNDeLYgMMTDXPFOds3P/T55nsHSJAQB7u7sJCiCBZa70aAU77AKdSsV0L2/eKerAgLb92QQPlBUqdnaK4DYBXmdhr+puCndmukpZ0pibcgS+6e9T24XtqOfyRc76IF7Bq3bReQ4BwFq5C50M4ZAE5ka+AmyYaG1CJO6smFGNSDCzBqtUbxblRrKHMNgyRM8A5wDGusQu1xvsff4j9Yb9mrViu6S8chLdCKQpliEmue/DiCRXl3VEXylGZW3YqZvkKFMH8AbyAofp6gNM6NVxRo+7rN9VRUye3jJlKRsHQx9tPvVjWDtRUaRpAY8BBVNZmTOf8IEYGBXqAqAAseI6gfFcBfHcuT3dOHZM/6jSzdgLyrFiMTTPgjRPEIT3QU0FSC4UyFIuaO1Rz7BCKc9dQLkvRtO3Yr87LdCSLzSL1WY+PHsjAkPoePlHFTz6QD8qdUYuWKGD3TnnNTCFLANahstOce1VVJ84pyidAsZs3yXcd9wcMOWlgYe+Axjv7NNjUppKruaotL1Z0fKTmbEMRjyBaA3Xdw+IyZaxYp+mbUL1LSmZVB+XH+sNGghCFmURtZKyyUjXnsd8G8kwNx1qN73uhbucege2ec7EWrbEfCI2PAs6VV2vkBODHrWsKAiBM2YsVbcp0jmudbFsVQofA1OlQ5nRzb9EoSl2tNfka7EBtIzJYAVjeuQfPZ79gzt+j/oYilN5uc82NqALGAwMBzpV3q7m2Q5PhKM7NnCefUKAUAIxJjsvBCTCTs8aqiQ4CsDQXq68ZVRvfKKC5NHlGJAE1AiQAPo0CGxG2BFRrVD/naKwq5d49FQ9M4gsEAk1IMpBXABeGqu8BzqHkEoSNGFaXXqHTAedo87CZ7a+6Jl8W3PnNWCAPAz28DegYIiCdr8NXz+la0SPNX7Mc67p9KM7NwUIWS1micPexPDxx5bTaJrs0d/V8oD833UNparJ/QttXbdHyjMUKQb3OY4K0JRo2weBhnBePSP4EyjzJL4PqUQUwxKGLJ1VcW6Mg1KQmAHz6AEtTYgn6r9yuOQnkL7aWwVadvoSd66NChUQSyNt5QDMjUoFBvLC37VVR02OgilxUbKo1a26m1ixer5hpsaour9LJCwB+qDFtJa+sn71eEW5RKul+osMPPmLh4S0tTF6sL278bSUEpTBOwg4OcPBh012dKziiuq4qVIS4P/qIT/KLqR/cUStZofnpC1GuM7UrXwdEtdzvzrUYdOZLGfQGbhkHjKlurUK57qzyS++iFAgU4oexLKo/KVGztG7xNoCPbILp3lhlNunUrWMoRd9inIe60PwXtTh6ufx8pxFIRH2k7p4+vP0R11WImk+mXl/6RSX6p+puyR0dvXGMMWWLDm48qO3zNivIO4A0H1cHSoinC07r0uNLDgD70tqDWp2GQiN/H0SVqxglw5N3PlFF92OlZ6YD6IQr78IdjQHVLlu9WEuylikaqGXaZBAlhEElRdvgWLMwMxDY3UBXlMyaOlp0h/xwrRDFz4kuArteqKZgtRcQpVVz1mgJ9rChgMltXc3Kw/r3xJWzCk4M07Z127UkYbEC3a0+JfCLjeT5POwg8y8rJC5YX9j+urL80gmS1+q9vA/1mPIwGzD05SVvKD0qXTVDZSqjjGUAzjWVAELRR8vIyHD6Z9beutoQe7fN2hbr15m6mynN2SIJg9ZtAYL1lVwAu6tdef57zgH45/nP7Hi2uc7j/PJT/7j2cX1s+z7/mf1u1/QMnGtFkWdQMQDGMQ441+6Ac0dyP8ZK+xk4lx26BPtIFANRnKvuKNaFm2cdcC4xM0EJM5NZYHFXA1gZrp27WqsBT8OxJ7QQtI1rPGjXvKjHJq2ds2A176OTPWrFnu06wGP+w4eARRMKpC5r4ZlGhkdp64ItWgF05Q/o2u7Wq7P3zunmrcvkcoLy27ZpWRo24pNR6nFj4cHAUx27jdrZvSdKpn7bwfNNm55N/ujSB8c/UE1rpWPNtnPBTiWg+mg13rBHv66WXtbZKxfU1NYKOHfwGThH3TECwFo+WIHK2Fk9ffSQeY9AJVMf1wBwl5bVKjs9W+tRjEuLSOPhBqC2Y2XwGYRCbgUKZAxH2RpHqad5oEp3S2/oCtbMrdhG+2DTOEmEfmLYXeuXrteqjGVKQrWskzJz82mejhw/wjwH6mAr1qMktoT5DOBmtpqRSp1/DEB26TKW4gnauXy/Fs1eTHnq0zWg0DO02zGpUQBQezQnfgl9ai8HnDt7/7B6UOVcu3wb8O5uYLEIYFagj6a6Zxa5BTeUPn8myoHxWGAWq/RhMaqZy7VmwXLFBmPzPQEMRBthamYT9PFA51DIw4aPesaD59L9/7L3HmB2nuW57rvWmt77aHrVjLo06rZsVdtylSwXMBiDAyQh2Zzk7GzO2cneyZUNh8CGKyQhlJAQajDYgI2rbNmyLKv3PhpNr5re++rnfn6xiC7v4IaTEDK/vTSz1vzr+7/e3vt73ulmO99w3vYdOUG7n7KYpCjKfAqAMMaWLLzBtqAQlw3w6p/stkt1Z+yZ1/bZVJwHeOQu21B1g2XjEhttKutjPDmAqtyhw6+hOB3GfSauWoEjvcM+XFvTNi8fsOLKSuDHB60mby2uuofs9bY99vwLT1omKoN3rb/d1sxfbykJueRWjDVP1gPOvWCHjwLOAb0uRWW1Abi54QpzMuYlW26+w8pzaphLRSHSRZslT5zBmXoaJG1SKw3SRkZ9fXb6/H47dfqQTaJMmZKbAkwyCCScaRtWbLLN1IO8hExUwKZtP3Dk7mMvkd8Ttn0LdXTZektjzIwFLJr0TdgLx162gxePAApH232bdtqaipXWKzewp/ehLHYel6PL7Nab7sOtLIpz1CcP8UIwjvrKG8aHafbPJojm2HgQNSivNTbM2MsvddjoSJJt3pRvd+1ASa8UeIq9NnmJmARkam4M4N60EyXANivFZeTdt1XZqrVZtLugffeHr7Iu8dl99+HG+LYclKToV5iXXEW96amnB+xnKM4tqcq1XXcvo1+Itx/9ZMROnufwTTWKgTvLbFnNNfU6iRLu3eez7/5oxlqazgAxVdi9dxbgwtFtdU1T9ldfPmITqOzdecsKe+iBQsA5Wsm1rtHZbdSvDBuOiphcoo6zGTkGpHTwCHl6aIZ0tKN6V8I4motSIKA/0ZzgvkHmEgPjs3budL+9/FQnClfFtut9uHu8Icbq6qftLz67F8WzEntwRzXgXZzlcfBWAFQHalm7X+1mPKjnYMpCXH8LnOtlLpXmrO+03lZfqb48crBI77XG1Rpdh3e01snPz7cFKAnroJiAON0T6cP1Xd2v9anW8zoMJ3V59bda82kdKffegqYja7BIH621h+DqgaFZVPaK7NwFt+0/2GfF6RP2wR3zGTeS7FxzyP7+sdOoCo7Y+3YutTtvzwfeo+xJYjPg3Pcfm6BfDVhNhc9+96EMAFwP5T1gLxzEnXZhjP3XPy5B+SsWsWtmc9SVkydw7/oPQ3a5JWxb1yfbJz4Rb+Ulbjt8IGA/fRJwDiXgu1EL234XB/nIxyQtPphnhOjbvUDsISa2JBnFPSkBmk2OBWwEcHOox88BtHpr79JBqgr7yCOKfzx9RBj31tzLXuFgX9BOnJ60Vw/1kF9e+/B9BXbLlkw7eQlVxKdmrYP5/w5UxR68O4X8wj12HIcM2LP+ybPD9u3HWoHpc+2R+3Js9ZJoO3S6F5W6KxyYKLcP3J5rH90Z5+ynaf/5fIPcrTbzrBlbuqDQHnggzZYBzqk+/PWXmhjvU23zjcn2gQ/GWSldu4t+QHNIN+nSGormCChM/eTZI8R9eDRg9Q0Tdmh/J/M98npJCapx6bZ4Bf0H5TCGSt04942QB5dOj9jeV89aVOy0bb2lyt535yJrv4Tnpn88Zm3jXbZxB2qTOys5CM6BSfrasYFoFOcC9p1vC5xLoQ0uId8SUHqdpe42W2zCmO28j/nmhgwrwPWr0LczwKvf/gEH5U5esttvSrGP/VYxYGgsblBd9pnPn8eNch3jYKk9InAuP57vcDGn0hRLamt6jziz40VGSnNqg5yDstZGH0qoTXap/oJVLaI9PbAM5dhk9v2B35mfTqoMR1CLbvfa7n1dANhn7ZbNC+gDFjEPiLUnf9RtR09cBlZGce7+Kg6YJDl7+NrjfxFw7rkX0EeearYH7y+01cvS7OghLwDgACly2S7c6d6+I4GD8lLtBJoFSPz+t/vsmRfHLJtDl//tk9m2ZUsSgGIYiBrQ8AeP25JlWfb+j6wHTsTlMumb6uu3Z5971p5/cbcVArneATi3nkNTWtM5nYnWCYTtgHNkAp863RMfvatrDpx7V9k296W5HJjLgbkcmMuBN8+BdzssvXmo7+avmuR+4QtfcE7kR76vSWxkIqvPIpsOmhjr9PnHP/7xyK3/6X5qQaBFgC6dtLn+lOR/usz4T5pg1ojW0Eq7+brffvBswFF92r7OY3/8R8g5L3am5P9Jc2Yu2XM58O+bAw1tITa4QtbdEuIkHxLznOTsGWCjAsEKqc5NsgmgDW3nYhhOglMoyHDZfIFoRW5OhGOgwEVqMpBcFkpvAuay4MTT+Twuho0E7TzTxGUEdF6EwXB57fefB6sf6iOcn/qd5/3ipc9//n6KTZsBoLlBXnh6sSEW4dMsxmEpONGHvD4uY1t7OOk3zCJdXLu+y7Ng6OSpi1PdwHyAfRWc1FtWghIdm3Vl8zEyrXbbvCziyn1z179NDjz7asA+8wW/XeBEpkSjXvz7OE79eYBZ/m2e/x/tKS8fDdqf/bHXTqH+KIGdEz+It1W4PWeKOXf9huVAZCNeydIaQmsOATkdHUAonEaX8VUgkFyz6or8XRvy2nTXKXdd2qzXRr0AMd2jNcp7Of+OGAgiBgC5Rf3qV7/quJMVPPToo486cJf+LrdML7zwgj322GNOPORi9MMf/rBVYuDS36+tnzDeXDhvX//Mn9poW5uVAAqtyC21VaiK5K5fjbtKVDtwueO/2mEhVCziKqotCsOFrsiaS+FE3ut3xUmgmdTwlF96rqA2neK/9sx/Bswi33UCeIt/9LzIM4c4TS446zvf+Y5jLJHantTTpC4QyZu3CO4Xf74+3Mh39ZnKT4YUwYeCwaRstmXLFkdFrayszEmLArn+O3r/TtPU2NjouGtVehSWQEPBZqWlpY6xX2G+3SsSb8VBYUXeqw6qPsi1qdzOXg+1aY0YScM7ifsb46T8ksFq7969jkKf1qFygyg1QBmgIkauN37vrd4rHIWtNqVLcZSbpcOHDztloza4efNmk6vi/wOcoxx9V3vNBcAGouMAZoGRfhs8e9p6jhzBRWSS5T74iM2m51j70UM2eOKAzUuNt4L16ywe98tuQDda8DVwC2O7B9BOEr8TrwLCPf0TlDqGLXfjZku/5VZH4SwEOKIDBy6sCG63jB/U2eE+a359rw1cumTFiYkowG1G4QzwB5dyblSQXBjyZRkIY/Sbwo3opWeftoHmRiukji28FzewwKrhIG4fe9pt5PWXraW+ydzzSqxy4zZLKauwEHLCURggXLKqzExaoL3Tpg8dtZ4ruG+lPZTs2on7uhybeuJp6zxx1DwF+TbvjlssCZU1N2pkMsaSM+b3hIBbEsw1gkHw+AU7+9ILGMCmrKpmieVvXG8xhTkWjiZFWHGCuJrzROFqFiN4eMpr3S8+bz7U8NKzsyxx623mWbIIWCwJoCuWgxyCyig48sXb1mlXn/ipTZ09Y7FAonn0DQmoxvFH83a2W/vhY9Z96qIVF1VawcaNFlWN8lpCHM9yzNNYb9FeGxyxrpdftCu153HrlmYrAE0TMjOt/+hxO7mf8svHJdjGLZa0YCEua0kP3xXAJeNLFL/TYdsA7jUb9r5qKcBA+TUrLBXFwGhc4Ml1bjAgVMBt0SlyS51goY5u8734stUeed2SK8ut5NYtFlNeDOxEWAof5ZswAIM/MGNRMbgn83baSNcpXKmes9SEsKXNW2weFLSQ7wQ0nLDxjivW23nJ4uInbd4CIK8sXLW2jlhf1wguaHMtc/4yi0uroGOhroEOuDjNEvaOWnDsggWGjgNutlFmQJeZQHPZAHwJwBsAAC6UkbweoDTyMgpXqbO9l20QBR8vz8zJy7HEvApzJwLjUZfCg402016HAskgwFyxzUMdKVaqdajtTLefs8nmA8C0PksoqTZP4UoU86RSiotEwIZnDh22Q/XNtmbbVrsHtbLKtAXUAtzOAfdcbDyHO7/dNoDBeM0WwIScDDt6hDJtxjBbudQ2rtpgRYCCcVFScgGw8uOqlfxGE8rSEgDLollvzPbaUZTrXj7wusXhA6oGVcUQaTqHK7+R3iHbum6zbQKISE/MtGny/PCpo3bk5BHzeXy2afNGwIZqSwYCE0hxvumsHTp6GNdk41azZqXdvGYTIEW+tbe02fMvP29D3mG7+467bOOCmy2T/xrH6uzZCz+z42ePo5q1CnDuo1aUXg4457Zx/7Rd6jntAB9XR9sx/G3AQJ1sZ06fR/UD17QY6dcC01SkF+GeDnVG6tAsMhw++gvV38SEJJS/o1AnQvnz0kl79dg+mwYsWr95Bff67OLpSzaIEsyqGoCWNdusMCOPQ1MTduDiPjty+aB5J3y2bdGtdnPlFkvLTEUFecgOA6y8ePYl65kmf4E/H17/8WvgXONJlAGfxTDeb7soo9tXbkfFTnEK2BDqS6+cxTXqxdewwrrNAeeqtrB2TLJpqI4GVMZ2HwWcG7poS1cuA1oAxDt4jsNdnVYK1LJh1c1WjkvdxGgUUYF2gsC2WrBKuSwGFchoTmlNoaJ1qbkWN5UotvR32sKVC3ATlgG4AXDQPYRhuMxuXr3Rqgqq6JNx8dd62Z7c+6RNuqdszYrVtmnhRg6GpQEeea2lvw3lsVfsCqo6BQBEj9z+AVuSOB/lo6v2xNGf2uXeehTGFttD61HbBZbq8rXjbrbeqhKWWE9Dv6N2Kmg/oh5Eo3LGEI0jGh8FTmjc0vgoBSK56dMcSdCc5n3/0tio70VeCifyUtj6/Jdduu/6641hXP+3iOLcMHOdaaDV3OJC4JFsG2fMOXz+oD2198cozgGnb3/QFmUAQ8tVK/Bu68BlXG7usdpmFJuWltsS+rcL52qt7XILisKU34p1VpZfCuCA8iP1MewD5vQzX6UNxtH/xzKeTtB/XGo/bq++9hJGc58tWrzM8kvyccV8wRpp+yvLcdO86i4rnldqs7EBO9p4zPajctfb34Hy7zq7YdFNlhdTZBPBaTvdfdJeObHHepqu2oLS+YAwd1p16VL2FMbs8WcA51AclGu2u9bcaQWeQqcv8Lpn7GDD67aH8a5nYAB46H1AbCjOueWq1WctM22299TLduXSedTVgCGAYvuGx+zosbPsh2ShbrnOlpQsYu8EUItxGMkleGHc7pL98ShPCTydRcXvwtVTKB0STiNQe3k5ajtVqGhRd2sbLCdtnm2r2WyrypcxRALq48L66ecB3ThNuHjBUkD91bj0K3bKu2Hksr18fLddOl9L26i2HRvuR3FuHX3iLFAerlr3Pm/5Fbl2z7adthRFNjpq+jja7umnHWhr043bbetCuWql/wYS6+tvBxZ9DQWqY1a9arFVolDbDkR87uhJm8/4fSNtZH5BtQO1MgCxZwTcAATkAZwTIKsyDLiGrJn0HTh60Jrbe62wvMJKKotxuXvV6i/XW1Jqjt0idcf8ctzqAmn2ddpP9+yxZlTClqxYjiLdOisE1vUD4HRNd9krr+2xSxcuWHZOJipnu2xF4XJgTFyUosh2APW84opKR3GuJn+NDQdQRWx9BTfUP7GsxDS7Z8N2IKF1xDePxhdnLSgG7qsFnENRtLpsKf3mZvqFbjt5dB9xd9ualTfiknITqnmJjIAA75wi9dPNBMOoPiJR5QKIn8BteXtvnb0GlDg02EPfhILTwmqrq6u3litN7C/lAuFuJ7+XcbAzgX22Wnvh6G5rbGsifYtxMboad6/ZFk/f3j/SZy/ijvhi02UUBecBGz9gq8pWAFb22r7Te+3UubMoSi2x2zbdawsLFgFmMxdluuUGMPKi0jo57iWWYMTEi2HNxtkjq68PoMwEpN8VtnWoyd1+R7zlFwPeMFeLp3y039bFXszzu3twXar6FrK77lho63DZevR0yB778WuOyuuunWtQrMtFkY3+iklML/ttT/1M4NwBW1INOHfPclTfkuzZ3f22d3+tMYwCsS2kHeZYajquuQGEXnyVA4C7x2yo74L91gNVwGrFuFp0o1A3ZX8JODc1OWN3bscV+v0FHHIRkKQ+kmkp8MzMtI9+cYa4xLAe0bwPJUPgpNfxXvDcy7PW1X3ZbtlIW9mcheIY6pUeqa7jWJ59khny5+ypEfv2N67gbjXbHnxfka3c4EHpbsq+8LlDjP8ldv898znUBThH+gTOdbYGaffd9vz+OoD/BbSZRCuZN4BqWCxqg2nOS/2o1gXqmyN97/T0tOlAldadWmusX7/eSktLnUNB6mcFvF2/BtH39LnC0d/U30qgQ+C01n1aS0gBXGslqdVFLgHNbW3tNjaJu9q4clzvRtnPnumylKhue3gX7jer0+0C4Ny3Hgc49g/bg/csJG9xr5x7DdhqbQ7aD340YWcu+m15OYpz70tHvSvann5hyh5/vtmmaDuf+GSNrVyDEl282waHgnbwwKz98PFu6xuJsR2bs+0TvxPPmspth1Cc+wmKc024Ud71wHzbfrvAOQ7uiOgEgg+TRh+w6QQHXMaA5QJAxB7GZ7LHQsBXU+zhfu87TbiN5cDJgnzU3KqYe6v8UFZjzsJ5GhT5wrjp9dmze/pseGQIqAxXyLfn2cV6v337ySnr6BmyndvS7aG7M62wGNCVPd9p6sfjzwzbd37Uihpinn14V5ZtQHHxXN2Yff2xRmvvT7DNq7LsE+/PYj/bbRPE5diZCYDQy9bcEmJuUMl6PsOB3Dragvblv21FTTTJbl6faB96JMFKyoVtsa4QFB2gLsxw8HxgirSSMNYcAdI9Rb1vawvZvheBgs8M2oLyfLt1e4ZVLQ0x9vFtxj+maOYdZQ/+5KQ98VPc0fv7WeOX20fuX2VMj+0rgHPtoz22eccKXOGWMW7qYBSHyHGTeuR1v33nu/tQlMRV6z3LbMniZCDeGXvy2Vr2yvtt/Y0LbevGIqsujrYgioQHD3rtsWeCdq6pwXbdAjj38SLGHObNXpf9+WdOA2DW2eaNlfZbH5SqL32cGiEDFkmUx2pUlwFzh73ss1OPYzmMgfq3INw26tPzu3vtxNlTuPdNtztuX2LVC5OYu4BRs16juXJgAaVD7Aw/erobV7KHbPOGUntg5xIUYBPtyceZb5+6YsuXZ6I4N98WLEq0eA7NBFmv7d5Nue/mmYBz9+EK+IbV6ay7UDn8ySB1rh9be6bdfne2lVWS59C1jU1e+953GbtOzjKuZNif/N8o/W1KYK5GP7dH4OATQH259gHAuRtwiZtG+qYGRuwZ1rzP7n7OigHg77p3p63bwHqT9Kl/1fpUB1cESsuewRKNsn/31xw49+7zbu6bczkwlwNzOTCXA780B36VoemXBvqu/qCJ7aOPPuoYZBSAJrya5AoO02a6TodoYqvNdU2M7+aE9ec+97l39azfhC/JqPCZz3zGMbp86lOfck77/yakay4N7ywHBLK8zImk/+dzPmvilFpxpss+/kCUffJ3oy016denfb+zVM3dPZcD/7FyALuJXWEz4ATKVVdbWcCiLnehDRW3fp3+A0RjQ1Ay5ywRjf0mywWEW1zmtmVVbsviYHgSbTUbcK4Y96cFbIpoY0z3aY9ABlOdLNNBcSmHvdetWnFiT8nZtFQ6gmykYcdzXDLIXWw3abjKS3CdTii2s0l4qQkYg5+C7PQdhms2clClI54xAH9Kx4oKt+UXAgNW48JpjcfystnI4J65618vB3oHw/bfP+WzJ0/irpUy/Pr/iLGH74+ylLmx4F/M9M/+FQp93/OhNmC2uNxlz34z3kqps6rPc9dvXg5cb8TURrqMqVK0EqSlDXRtxOfm5v5io1736JLB9eDBg0AIPbZhwwbHZaiMrwpPa5WI4fW9yjE9N2IwkELK008/jTuN551T9TJAyjWmTskrXoKLdAJfaniCsQTOyYgs2EhXCGWYKxcu2l9/7rMWxI3HcgwjywAZKrMzLX1+qUUVZLGByqZ7Z5/Fb73LUm/eYlHAIrqUPl1Ko65IeqWgJheaiptcpAk2kztQQXS6N/KK3O98+S3+iTwrcpuMJFKCEzgnl5033nij46ZWz4mkLXLvm/1UuJGwI/HS/VpLao0pZYHnnnvOXn/9dUd5RgDg9u3bnXrwL4UbyYt/6W9v/Czy3AEMw3KpK8hRdU7KaVrDLl++3Mkzfe+dhKu4R9KiZ+i9fkZc6KpOyE2rgDbVaSnqvPEZkbi9k+fqO4ILBU5+61vfcspfdVFrdyk/qFwi8XIe+Db/Ufz1itR5hXEJCE0qfaoDgiWlbKcDc6mpUoj754lEmLYy8sJui2kBNpOveVRmQpOjNtxz1UaHBiy7uNgydzxk4ZwSG6+ts+H9+7BmtltyWqolEm50Rhax5HuopXmT0yzhxpsA5HLM19xg44f2WhcAlkcu7orKLD2PviEWhQdgrNlolF+KKyyxFFgU9Yd+FG36Dx+xNNzOZZSUWVRZOUAS6jo8w11QiLIdk7oAcNxgrw3QXvsOHrGooWHLL620hNx84o07qL5uG+1sMW80ivo3AA5VLjIvLktnOtoNIRmLEsiFKlZodMgmW1pxHzVtLso59647LD4r27wHjlsXcObUwCBGvHmWWgD8lcoEE8P7NBaJqcQYy1iEmgrwW2Co3zqPHbU+yjKDPMwpRd0mLxNID9UoDDiuWJTUSuZbTGk5vyfYyGuv2Mye5ywKd4yJlUssqqgcuA+3T4tJJy5TkXIA2EJxA5WhycOv28Ch12yS/Zt55EFqgYzpRnn04VJoiHkwyixrbwY4yzP/8IBNDvcC44TowwAEMaSEUOYaBzbtpn3GL8JV6223WhyqZNMYPOvpr0OoqOUDJaYWF5knBRU2DDBTMrIlp1paeanFF+SYFwNpD3kxjeogplfLKi61JPLERfomvbM2ClCSueYGSyF9SFOYb/9Ba3hljxPPfPq0BCASdy5G/TLKcF6JhVBw8mHciYrWZHnc/IMoPHUeID96MK6mWDRKOx5cu0rxboZyHUK5KTYp2jIXVSHmNs+87aiddI0S31xU6FCcyyjFC6zUjOgvgxMWGu+0yavHbLrnhEX5hywZd4FRGCRVDgIRIPgsgJs/byqwbUK6xQCxBcf6bLq3CYCvEfUyv8Wn4caVUzgOiDc6TBmP2ugEE/TMSstcWGMxuXmAfdO4aQVQaz4IbIAr2OwchPKK8SyIKlHQZ63k8V6M/MeaMQzett1uBzAoTl0EMhDP2sBvtVcA517ZbWOzY7b59i1WtrDCLjRedPqEKVyi5efjbpJ8TmB89NBOp+XjLhBCoTqLNc983DO5AX5q7QiuBScAdlYuvcGWowwUBOo8ieLTieNHeRKu+GrW2ZpFKEwCz3V0daLisd/qOussJSuROpVnKanJzth2tbPLmq80U/fCtnrtatyQb8ZYmG+tTa323B7AMiAy9ek3L0Dh052F69NGewaY5fiZ4w4c9OFbUXBLK4ORAEAAIr3UfRbXo7utZ7jLtq683ebPWwh8UGenalGXwb1sTm4GMA0KMqhPyhXtJOkL0HfJ+F5KHYtjodba2mJH6QuGUIecv6DKbgOA9fhcdrkW4Im2wU22buVaW7twrWXSn11GNe/gpdcxWDai1pZv8wsXk84kx4VpO25769tq6Z4mUMBbaQ/fJNeypXaqGXAO+K3vap/dt+VBu73mDkuOFjjnt2HfkO1FIWz/+f3mBpx7cCuKc9WbqLvxNsvirqW/xfYcfc4auy/YCmC8RVXLrbutz86fOQ8gMm5ZtOvsIlzfpaC0i1U3OAl2i2E+Iy0DuKQYCCANw3izHT17lHxuQ0U137YC4mfg5r2BdnsKaHlyeox5yGJbh/pZcVKZDU/hPvbUzzjUc9riUMOaX7TA0lHz8jIX6R8dsMbWRsfFdinqaR/c/pAtSgac6++2H7/+U6vvbbTFqFY9tP79VoXiXJe3y+qvNlpl4kLrbZJrskRn/IkoFV0/tklxSEpzF1FV0zxPiq9SmtPhB92v/WRd13/H+eDnn+nzdzOmRcLQz0gYkc8i7/9ZcW7QZmzWcormOQehx4H7jgDO/WzvTy0f2GHnnXKXvAbFuSQU53BPjOva/UdftdqWOqtC2fSmGzZaZ/tVO0tfPkbfk52TDlycaXG4VY2mv5tBVT7ojbMMVCsryyotFZi8tfeKvY4iYld3hy0oXgzksA1wZ55d6am1F1/ajUKZC0W1DQBOuNScl2k9E1126BJzzdrj5gGULi9cYHlJxSgU4UpupJHyu2QzYzM2v3Kh3brxNluMYuTo4Jg98dSPrW2ozTaifnvH2jst35MnlsG8qCkeqkNx7nUpzg3Zffc/YBtoo+muFNBPFBunOzDOv2KXzwPOsUmy7sY1HG6KwX3gCZSg+i05PY4xLov2AxgliJU6OgPkJYirorTUChhbBqYG7bXzuKFtPGsJzI+3rdpsi4HteidH7HUUJ9uauqw6D9WgFTcBqeAi1z+IYtIB3ExeACYzRx0nL7sAeIO9nIlOwITL1t3aYfPzF9uOm+63mkVraU8+3KS+bi+98gIQUJ7ds/Ueq8kHyGdv9nDtIXvu9DNAbxzIuGGbbVt4q6XHJTOG40JwtNP2oeB34PRxq1651FahMudDdfUSc+vBriZLp3/JzcnjsKQO6cQBicxygMRnaYxDVbjuLsibZ90ogR2+tJ9533nLnVcEkLWBg5fzGWc5dHD0iDX3tlsp4/PaZWtsPrBvFEPBflRVj5w7RX/nsyLciOfmciKTAhkaHUMNrsX6OSFZUFCKsucuW04bneE05d6zr5GPBzgQWWH3rLkLoG4ZeTVsrzfuB+76Ka5xU20HfdzKqhuBYXF7DXTfMtUEOMe8+sg+q6pcZbfc/CDjgN+OHX+R/vEiYGO0A6+mM37HsdnlRTUYXgk7D6435+M+NzsDd9kdjCvHrfFyHapP+dfSl7+IfuEqwO6L1oZyZkUx649ld+H9YLXT7x0kP46cPUz9QsGxLBcVt3SLpQ2PjYxaPePEMO4oCxl77994v60sq7HBGVSaz+62k2dO0rdV2K2b7rRFBUDmtDU37nZDzIXqa8eYh3YzXqUAR2egNoUCINOAjqvTdhm39yqfNSvnWUF+FFDmKCCan/JLAxJHUXQkgPJYO6p0gM+LUuyu7ZW2aGG6vcq+/fcf309eTduuHWvttlvmWS57ZETVcYX69DMDuGp91ZYuzLH7d+G6d2GGnaudsD0vtwJFTnImA2CnEnAuFeCbw691rT47VT9rgZla++33V9oDuFbNzkZxrn7Kvvjlw4BzEwDkuLu+v8Iygbi0Gan9xVlO8TY2jtq+QxzamEzibzmWlAD0x1zucjOu2wGTomNGbc26JJ41aVNjs+aaQemOvjuG+eg08E0jc4XLF6/aymX5pCXPqqtQ+Lo0bp//3Gn2FktQ66rAzSiuWnExGqIttQPOvbSvzV4AAiwuX0SbkZvzIQArgXPpDjinOb7WBLrU/+p39eM6fDQ1NeUojGqNEwGfdU+kX9Xvke/pM9kXdSlMjQdSnJNyttZiGjuWLOHgCAfitIbWPQKt5eq9sZWDdVdLUPCKZp04aqsWzzgua7Oz4+zM+aB974kGwKYRlNhK7fbb5lkmSn6CoFpI3/d/OEx5+WxpmcseeTDDSgpjgcpQj3uh245faLaKaiDQ+bmWnOLhnAcwewMA8rkRB7LasTEHTwjJrHvc9hrw4mNPAwy2nqEeAK5uz0e5EHVbD2lk3a76EqI/rr0CiIxC4MAYUF0yAGQyEaETm2JP8OzJbsph0lbU0HcsyLThQcpx0o1LU+aLQHCT06jktU9RjoOWziHoh3dl2k03ptImJu27Twyz5ws4hyvi99+da0WFzNEFzrGv/ZNnx+07T3RYFvDto/emoZQZBZhIGl+ibzjFAZMwKnBAVBlZQMwUQUvbkJ0/14Qantu2rq6wh+/LYp3qsjbg0i99rZED2km2EcW5Rz6UZKWl2HpRJ5Zatnlj6RP99uKeNuscZ78lfR5rIdomZdrXi0vTK92QrJO2fnmRZeUl2niANQgPTEimjmo9A+Ta1jJp9XVXrDgvQFnh8nZzseMe92++cZx+5qrdeleN3XtvGQfAgQ4Jt5+D7vsP++xb333JCnLj7MF7V+AiOYvnBYhHCwcvUOdjPlleUWrVRXGo9aJM3Dhrxy6jEIcS9fvuSLFHPlZIn0Jbol/7/KdPACU32NYNC+3D75fbVw4cUYZMIZ06M8v8dZADvEcPDOAueQwWPAX3qGm0U0BabHsXL/VQN67SHyRQb4CjhwVJxqFkGMVL63+3tXejbl+LIvlsJ3PeAtuGCmYs0PzjP+yjv2y0pcuy7b4HAe0Xx1pSIu0C0cIXXvTa07tR+Z5stQd3lTGXyETlLmSvH5i0l/a3A8Ya/X6eVVawDuFgSXvHDHUfldmeKFuQl2z//b8k2paNMXYV97LPvgI4908/wcVwkt27c7EtX0gaABgnmM/s2bvb9nG4bP6CMtu16x7qFweZZOTgAo1UJSYN19T3XbzVevHdXnPg3LvNubnvzeXAXA7M5cBcDrxJDvwKI9ObhPpu/qTJrQwLOg0Sua5cueIoLWjT//d///cdNYPIxFgb6Tp9rkuf6YpMlp03b/JPJIzrb3m7YbzX90Xi8C/FSX/7ZZ/LRZOMVVo0yLig/PhVr1/2rF/2+a/6vLnvvzc50NMXtq98329f+LafkzJmqxe47f/9RLTt2H5tUvrePGUulLkcmMuB63OA/R8bHA6xgR1CKSHEKdeQXW4PGTZLDPIAcyw4tbDXCeh0VOMqS1y2bpmH035Sj7sGl5Xmu+nDWTgCx0koQ56hHDju12Ro1tAqMM45CcwGGXYzFuxhTiCGHQW9kbEwGy4hNioB6fg5hY3MT6J1Ak42XkF0JUB0i8vdVlzsYnPRjZqBx9LIDw48zl3vcQ5on/F/ftZvf/e0n9OyYbt3k8f++n/FWgmnKH++l/geP/E/bnBy+fD7v+uz77MxiSiK/X+/HW1/+HvRbAjPVcz/uKX61jHXBrzmtIKm6nCdIQBIMJCUs/SSOpfWEhGDqjbdBfEIrhKgJnU1KZ/pME9kbqz73+76461jeG3er7B1TQAjCJATPHfq1CnHgKCT8trk1wa/jABdXV3OwSKtCeSqVS5oBNbpChH/WuL/hf/9BZSQ2m0lJ5TX+CatEIWN6GRgiHQgOQw/XtzulHz0/7KsW3DDGC9Q4/9cW0XSK3Dq5ZdfdvJNMJNAMOWHLhkeInmh+/X+7VyR9Oq7+l1pU/lI2e7QoUNWVlbmKMFtRKVK6563e0XC1c9IvPRd1QMpDSotP/3pTx0lAwGH9957Ly5NahxAKxIXfff6dFwfztuJh9wLCTZUGQoEU1rkelbuWqWwoLCvD/+twrw+LfpdLxmTDhw44OSXVBkU9v333+8YhVRXFOdIvHW/Lv18p89Ve1D4//iP/2hHjx511p4C51avXv2LPIs8563SEYlD5Ke+F4mb6te3v/1tx8C1EmU4uc8VBPhGcC5EPen+zndRQzuAagk+iqJIG8psASC6xMJcSwO0jF+zFTGwPFyOjtrsufM2c/IEsFGneZnQyB2nxx0NRBRt0SXllnL3ToutLAMUGTN/2xUbOX7CxlGAC45OYMghD5nchDjE55lXaBk1ayxl2UJgKI95W6/YEPClD0g1gPLNLAb8+LIqy7wROGvVaguiIBUKzZobV552tRvFN1x9njpj0919eETFuALI5UdVJgH4KW3xCktaf7OjFjd8/JQNnThm7okRskk6aajOABmp3JKKCy2O9pewFjerQKuBjk6bOnfRJnm5OrtxBxvCjRNKGgAzIZS9XNw/b/0aS6ouJh1Bm+3CzdiBoxaqvYLhBGMT4XoBm+gALKe40nJr1locxkkXezDei2fN++pLNoTaixejahigy4PKYNYtG3BvutiiMKS6wiiZAI4FrwL1nTtmI7h0DqCI5WYSKTexfgxF0UA2GUuJA2o37pkp86KicPUSUBKKazHEKYZxORpXYXJ76SqvsPj1pG8JeQwQFgLaGz8BsHbsuPk6OwAscCOL0XAWA0wgAXUDoJB5q2oc2M7F595LdTYJHDjS0GgBFFdcqMiEOIUyy99CgIbld++wtAWLr7lHunjJ+vazB9XSxsERFOCSUcErSLeCDastfVENLlYLLESc1J25qTee6XoLDBy30EAdeTeBQZY5L4byGFmjsfpMjk+jGhFnSfTFUYBz01eHgJxGgZKzLadsMeBcEXER0BqE9Ry22aFWXO8et/Ges7jnGwd2TcOomQzUTHgoAIbdKOMlAvPNW2SpuRUOoGXU98Bwh812UucmKO/QNIqBQUCSKIshPhQ+KkXx5sctbGo5MDWKYCHq3/jV8wCaJ1Ft8wEBJLNWScAFnZ/H4IJuZNhONvbbha5Jq7n5Vtu08R4rSFsESJBA/fQylpyz117fh1LhtG3evsmqUTUcmB6ySy2XAMxqHVePAeAsrWliY3ATzEIpIyUDV30LbQUg6NT4hB07eQwXcg22sHq53bB8C8bMKgt4gtY0dsVOAmNdPHGOzwpxl7jNFhai5IcKz/m2c3a07igKS43AKrNAXbEY2lOJV4yN9GFwRhFoxfIa27Zuq+Umz8ONXpO99NqLNhoYsVsBL2+oWo+L1UxrG8Vgf+o5O3PhDId7auyhrQ9xP3AMuyfTpK+u56K9dvZF1jl9trVmu62u2mDDY0N2BsP1pebzNjTSTR81C0zAOCeFTMo7HVfLCwHvy4pLUeqetnOnzlkdLheLikts/RqUlcpWWbw/FrWubtt7Yi/qa7WWyXi5eS3pK69GWWkUpTsUIIG72lv7aLc0wSQPLnbRl0qJsgG+N4V6Vw39wvs3PIqKUqGdaz1tL5GO3qs9tmPTA7Zt+W0cXKKNkPcjsyO2/9x+O3zxEIoyUbZr8y5bP5/25o61Kayl3aTh9eMvW0PHRdz94fZryVqMxTHkWYM11NdZ91CPTbjGLRiHxhJqNjEYaJNQO6wqn4/7R+ou8PDJ2iN2ofYs6kPxdtNqVLtwyxoPuNeH2ucx2v65K6ctkXF63aKbbXXRjRjy4+zKEOpYV17GfSHtEbWYhLgki0FtMiYBNUfiffVqJ3BFnn3o9oesKrHUUZz72eEXrGWglXxaaLvW7AT4r7Qeb581dKOullRpvc09DvwggF8QhC6NIZF5ngB8jfFSFs5EtXLx4sXOHE8HsSNzPOdL/4r/KD7Xj4mR978A59j3ngHKzC1GKZSDGxMcjD5+8ZjtPvC85aJiJvef1bi1FDg3G0T9GFj2KK4rL6GiVgUIfcvNt2AED6LyVEe7umy9gAAC7MLR9FNsQISpe6n0HYsqltnSKtrTrB8Q9JgdpX7kAYxtXrHNVpfgVhuVtv7Zbttz6GUgyiuWiprb+htussW483XF0j6HW+wYLoIvMi+Dw7Bk3HzGo4aaBHs1PEodHZ0BQKuwLRtuscWopU0OjANW/QzXsh22gQMft669zXJxl6zDHIGoGTtJe37t8EHiiyrkjp12wwLGd9zRBqSANgUcdeYAinO1lsN8V2rLGYC/lzubAW8vWscgyk2oNUZDHsTQB0T7+emLsQUoaNYsW2HZtK8L9RftwPnDNumaRP1mHUqOa60QNcph8vpMFxDr/sNA0x5cE6+1FSuW4cbVg+tJ3A8zHl0iHydwHR7tiXXU3RLSMOrHe60VuLUotczuxqXnioWrHNfHB04ftn37X7G8snl2x6bbbVn+UvPQho/XHbfd9CUT3gnbuHYjrlo3AZbFAfJMWt9Ytx06e9wOA5lWLVtsa9ehtgd83dvTCQC5z3qYn8zO+qijpE3zA8bFeMbxioL5tpK2nJOTa6eaTqJIecSmp6cceHLlAubhhKFx//xl0n7hgE24x231ipW2FrekOQnZ1tnXaSfo++qACUemesyTCLAOQBpLWwwHYmywewKgNQeXonfbMuYAvrEJ+pLDuA4+ivvEUrtzzS22rLAaF6qjdrSJ9O153tJR2rxtPW6s6WPT40tJX5S1TbXa/su7USncbwv5fPuWh1EHjMWDwWnGiSPAMg0cKh1HdQgIiPKTMmZcTLrlofa2YuUGp38/X3cK94fHnfFsHW7fa3CrmxFXRD89hfvdV+zgKdyyAktuWIya39r7UHFF6W640Y5fPmrn6k8DQo+gsEgfqrlgfDz55HNclqYxDtx7w324al1NX4mL2vMvMx6g5kTfs/mGTba4aJXFujOA+OOAXlDYPjGOS9oma26TeipwPHOZMBMBMT0ZaTG2aAHjGypOg/1BO322F0WzPmBL5pWUnc8h1GattCQWKCjXVtekc1g2xl7ZH7AfPHGIMXXUdt4DOLctj7CYX9IPCdp57sV+1py7UT7M5O81tnR5gY3SZ55GOevga5OoQnJAJBhwAJiEZNztorrY3I+u6VSdfeS+Irv3jhzWZKiSN0/bV75xEDhrGDhvIfAV7hvTfq6uxsNmAU4v1uJS+dlaq2/BXbwlALRI60sKU4kcMMlCjTLOFtd4ABV77NyJqzaAFw1XKAlgEDELKBeBriWliahp5aBYlWAZySi3nhmzL/3lWepCid19Z6Xdclcs3ig02zFra/cBztVx6P+SldEn3bkxy/LShohvirOOVD8eWQuor9RcV+tgrQd1wKkYeFsH2CLelnSPLt13/fcife71f9d9Ght0mEkwtdYygt4F4UmBVId/BNc1NrbYgYN9qO4lA0ymW9G8VFTYYlC15QAM16lTQXv8x1eYko/YfXcU29bNzGuzWHMz72nt9NtjP+m0C5fHrbo0zt5/X7FVlMXbCMpupy6g4HigF7eb04SCS2ncnurAmdsVb109PuZHuBJdnWq/+2gqsJKHAwN+e+L5MZTVzlCmFXb71iIU0FiraJ6uzNTFryfPzNozu6ftSus0br+nWFMw32WeHg0wnRIdi3Jhkq1aHc8BbpcdOHIVEG+C70XD1rGviwaolOuSOJ2zckUWcF6ilZZG2ZFjA/bET3pQ2x9iDCoD4C6yfFx2RgPOednjfv7FUfvhT9osLTnLHr4nG7U4YDak0y42BFBEnbRTwF79UHkJgNrJqenscXusv6ePNPpszcIC+8A9aajvuqwLMOxL3zhvbf0JdvONuPl0QMMw7qhRXqNoQ/44vLcAIz7eYGeaRmw0xBjBnF/lzVBh2eThGhQZN6zOtMFJt5243GuNHbg/nwG+JG0sVpiSx1luBkp3a3jGDWnUoVjafND+7lscYhpst1tuXWn33FVhpeQtyxTnoPiBkz773g93A99G0wZX2M3r8p341F6aADQd4eDALEp/HsugD81IT8GddrI19rmssacJldgk+9BHcdVaRN5OueyrXzxkTXUA5OuX2n13L7DSItxtk48OM8YUnyGZgzUhe/GFAQ7gd9nghJ+DYBorgHS9HNIiUjULYm3dUqnIJtiBU8P0pV7OYrEutWmmoShA+lMsNpG+aAluyLelAPXF2kRfyJ78MYpzJxtswZIs27mrnD4lgXEbbWIOc+zbN2W7XxnlUHw7aaxgnM0B+AXCaw8AteO+/MwIB+RDzNWwVaA8GxebgtpmGvNwPMqkue2/fTwKt9uAc4MhQFhAyx88YSmJk7YSdbvi/EQOSbCHNTaI4uFpa2qts6UrFtoD9++yTayLo2X0QJbTQx/iDvN7gJsF06kAnCvy8+dv3+aPOXDubWbU3G1zOTCXA3M5MJcD7yQH3t2g9E6e8E7u1WQ1MsHVTxmpPvrRjzoT6W9+85vOicJIeJow6X4BZJJd1kS4vLzccRcTUSNQGPqbjBZyIaSfUi6Qway6utpRFJBxRve0tbU5xhidRIwoTih8gXza3JBRSBN3wXz6jp4lWC3yLMVLz9MzFCe5ddJ7GUh0wlEuBCL3aCNHJ11kANCEWc/XJF4GKG3mKHwZdDShl7Kc4l5ayilqwtBGj9xGSVnggx/8oBPnL3/5y45xRJN/KWLIgKKFReSZyhsZ1yRNrY0jLTZkKNR7xUH3KQ66RwYwxUGKF4qTPtfzlCfKm8iCxknM3D+/FjlAUdnxc0H707/y2f5TIU5ou2wXwMQf/ZdoWzyfFdXcNZcDcznwnuSAADK5Mb1YH7JDJ4NsPrJB2RDiVCILX06msrfpgHIcAvkSTV4AAEAASURBVLNMlONWLsOtOK8C4LEc3JaWI72vzTHZW+jKWfiznv/1GobfMp8EDLLvLc8ojtT74LA5Cnty69qBW9fDQHSXLjPmSKGO/NKWltbBCfRL5ah5qU9agSvpzetx6Ypb2oSEOSW6t8z0d3DD954K2Be+7LMGTi/mokC659txKEoIzHgHgfwnuLWBU64f+ajXTnSidsTu3J6vxdu2jbiuoF3OXb+ZOaA5eeSldcC5c+fs61//urN+EMgk8Ox6V5AR46vm4lJ8k8qaVN0effRRZ5NdYWlzPrJB/17lmsKNnJJXmFpXKK4C57S20LpAStyavyuO51Hl0Jx969atDjgXcZmq74b5++XLdfa/P/9FG2HdcXNRnm1AkSQXedAg9T6AYdUNPBSFy8L8W3da8sKlAAFsYOq7xENXJH2R9MoQrYNOiqNANqnARRTBFS/dp5fWaXq9neuNz1J6BAS+9NJLjhtVgYBSaZOKWgTSe6twrw8zEqfrvxNx0ypwTuumzZs3O7DZ9esnfU/pdzbp35Af14f1Zr+rjOrr6x1wTgCmYDnVN7mf1dpKYUfy+M3C+WV/Uxy19pN6osJXfZFqnvKrlLWj1qlvDD+SH2+3fPRspUOXnvXYY4858KRUex544AEn7+Su9Z2Ep7AiYSp+eum92uaPf/xjR21Q9V1r44cffthRHYyAhvquLinODR04hGtQIK2pSUA4wsFQGpucYKkleRZTWmJRuB00IKQwBuVQ/4D5G+ttvIu1LcozQdTjoti4TwDySCiqsISlKy0qKxMIDMMkKkl+AK2Zjg6bwOAzOwEtgGkxCkNQPGBPcmWVxQMeuOOp75PADw31NglYNjkyjuKrx+JyUUBYutwSqheYPxVIFTNqNIpxcQBgoc5e80kBpakFCAnXmrSbKAzL6fNyLami2jzF5SjZReFSs9nGGi+bdwDATgpzTLzkKjE+PRk3rpRtRZW52GOQkpoLl52hoUGAP6DAZuKMi5xplBdcKNjFZGShkFdgqfMrAKjSnHwKz6C01tRGfrSiUtOLa0hcNsuMFp9kOSUVqLFVmqsAYxESyO4Rnt8IOHQFJT4AiWAQxYTMLMtevdQScL/jiidMwDSQOnNPj1qoj70X1BzHW7vMB4Dg579o4Le04lJLrFpmUTmAaBNjuDCttwHybXpaski45kKaORojSmpWjsXPr7ToilJzpyUxiaQfcWHswZVnoKnVJlpa2ccAbqScAkwwo1HoSWevJBV4LoqycQFthegnA20tNtVGWfdRPjOzqLzRJyXFWRwwW+7KNRaPqk8Y402ov8dmWygPylrqNNMYx13pSVYE1JBVBjiCeoULA2SIOISx1kUHB1kMdAFUAq6No/KAil1sbBiQCBgT96Lj/cPkhMeSgSdjAN18HDIZH58Fpkux5Kx86lAGsKQM2KhqAAJ4cf00MYp71clmjHOTGJxQeAiiZMG6QguLsAc1lNhMi0otsqRMvk9coCmA40ZQq+sw/xSuWwERgi4fUB2wk4v6gPE+EAawjs23mEwUxICXwqgdzRJnLwBZXBQuX3HHO+ND0chLncSt6CRqep2jwFUAJQXlS20+bvYyE/JoIxiUga6udnegTCJ40ot6RKXloexECdgg8FxnXxcKVl02hnqR8jQWkDAWIDWLelKBYmMeajZDqEDWN19BCW/cFsyvsaK8akuMSwesDNh4eAg1oTZrPHfJkkjvwsrFVpKH6iEAYv8MsNTAFWu8esXGgADctPPk1BTsbEAaTYyJnWO2HBBv81pctablA5thrMOt7IR7ArBhoVVmlFsaalZy3Xq+6zxGx1YrziixdfPXWhrPDwOY+QAReie7rQ6wUH3D4sKlVplbTfYHrGeqz5q6GlG9AWhBwitI3st1VSzqrDkodRUVlmJATnHAwDba0yALo+JSFEyoOznALDGhKJuemUAVpIX1Eko1KIlUVy1FpQiAMoq9Rl83qh3d1tHaSx5NYvgOWHJ2vEUn4vau7pJJ6apm8Sq7/6aHLTcl364CI11qP8E6a9RWANIsKkKJFTBO+T4FGH+lu541QAOKcR5bU7nKyjLKaD9AprTvCVyz1wM69g234/qrwIryUb2MTQIQmLDunk7i2Gq947gvhv6JA4yLN9SG4tPZVyx1lHlmg7N2pe2SoxCXlZzNwahlKH3J5TX7stSR9t42a2irp34abuWqrSprsSWmJtqo9Vvd4DlUDenT+scwjAMA0E8L4B+hPly8cBFXpTn2KIpz5XEFGFxH7RRuhbun+1Esz7eVxTWWk5RrY4EJ6x8fxK1nhrXVtzrKcW/cn9TcROOI9mMFrWsMFBivsV1zFY01kbHqjWMjjfI9vSJziEigkfcRcE57rYJWc1FSzGaPNkA/1Uo9Odd81pJykmxx1UrLTyyzOACAENDmyMSANXc04QK123IK5rGmXmSJ9CXDk0PW0dvJi/1c7vGGNF4BP3pQksosQcmw2goz82xmhPqBelo7IFwxLqmXoiCWH4+rdSDQiRDKXFcbrLm+k34pxkpLK6gjjKOobE0Exu3qeBcwTiMulVHP9EWjbhRLXwqgg5vdHlQ1y4Bgt9xwCy56ga6mZjGYM2ZM4bq3Yj5A0mJLDafQB6GUFO2lnjRbXQthMV9ZurQGdUfGX9xFhuhfh1FyauyqByboRWUt2VHKSyFvhqjbnYNt1glMOYJB3kdeCRCJJ/2pccACqKWVFJQ4KrGXUIps7Gm0mHSAkAU1VpIwz5JwBeoDEO+dRf0IBaLp3hnUrspwDUj+psaRWyHA0i6ejbvt3j5HcSwhDg3MjCgbC4/YSdxSFyQDA928y5aULyEtYepzC2pDFxizUmwJYGIekL4F3SgDtlttR63NBmZxu1plVfQlyZAEIdztTk4P8gz6us52lAYLyecFloK7bB8wdHsX8Cr96PDQqPl9uP5kfIuKCqEcn2iF2VVWlrMQJcEku0I5tQ41Ab6x1wuYnE+/AOYHgOCyQVRpz6NU2U/bKS/Gtfm8YkuPTXYUJzupb41tKG4NNOP6cNqZK8WmpqFCRbu+gHqpO87u2X6XLSlibsH84Qp18XJnG4po83BvvdiK03KZ30xZ0xB19BJ1FMBjKf1cUTY2AlRUpVY2HBiy+r7zeGKoR7FpAXV0neNCd8LfYz0jTdbZ04G7Wqko4V4XMDwWcCg1pYDxhHBQkVN7aO5ots62JrwxaCwosXkAi9GeDAey7Z9oog86Y/2d/VacXmXLFtxgCcAr40BDV1Hza27FjTkAcBAgN5q2n5iRaF2jXfThzRYHYHn/zQ/YOmDRqcC0NVytA3ZqYW8uxRaUVtFGioyjFIzejLkBNy6s/Xbu/ABj2xTKfMxPKZMowOkkxtMClB0ryhMdTxH9gHONDSN2FYWuSdx8+klXdDJgdSbAYylw9fxEFN1Q7KI8LzcE7dipdqY80yiBFdmi6lRLYS+MpYBz6PHs+QmeeRGYLxaYqcDyizKAhdw2AJjTfDmAOum4o54XGxcGws600akEYFs8Q+DW+gP35AM/ZaLI6AZk9uHetcm8HFJYsmgeoFIeinJSd6J7Zl/OhypZd/+snTjTa22dE9QBP3MO0gdInZYEYDQvG4Ur1B3Zu+zoH2csGqRNThlDGfMLwCzG30TmtvNXZllVdRzuc4VgM39om7FXXuikHaQDpWbbQsC7xJSA43ljABW+2vp+q23oRRUvH8Uu4NAw6tBZaY4SqGxSkfWJfuoS+Kx1rvpKrTelEqf71HdH7pV9LLKGiHxPP/WZXpH+XuFpfakwta6WZyuBc3rJxiXbX0tzG641+62tLQmgmEMWxRm2oCbWMvJwjcq+Zjv7RydRIZQK16rFWcChqdjjWPvyX+9o0E5QX1pxy56TFWvrV+VZDrBkgHncyEjIWltm7ey5ARsZZj7IflMmymLhYIqdvjhjHZ3Dtn5Fmn38Q+nUKw8AWBDYbsJGBrtxbZxJHUph3e+5pq5NOgTUiuDs7Anb+Ys+a6aO9mI/nKRexzFnTncnWhnqzAvKEhzXr3KxevbKEO7ax1jzMiv3cViF9X4i85jcnGQU6ehDq6j51MXmlgk7f3KE+dykLUD5cPFS3Ohy1iOKfQM/z627MGunjw8BxCY6QGhZBekHuhybCFlTG/1GI3BXBwdnmFulJ2cCdyVb7eU+2vWILVmQZ7vuyLDVy1njcMr8xdc6cX/qsar5qbamJonD5bicpS92K3mkb5j95KNnBhhnRhjnqEeoGEfxeTJxLyP/lhSkcig9GtVElN86sJ92DJHX08x2iK8OsKAwXlSo/jmGOSfu5jnU3d6Ji9wjbbTVYcqvEEA822mfclUs98cNbShknqxzVAGXLSvk8HsKeQXEOQZE3uK38wB03T0TjD+zKH6m23QoHWg3bGfqmgAck1DOQx2wECiY81wH9zY5ap4VZflWsxTV6YwYoHYiR3unu0bNWoAZeXqR/qhpkL55ApiOcRLQMZY5elF6qi1Hya0Uu4KXL9R1TDFOT9goqsYBDrFoHpyYkAYEOs+qF8VaURnKg0msOUbdduYk4CUHMooLE5y2mDtPtmT2mKjHDSgV1l5i/eEfteUrcclaAXxH3zaLnaOF9NcCf7ZfHWfuOovrbx16z8SOHm8nz4csm/OLf/iRWEDEaMd19SXcQr926BAdw6hlob6YyAELTB2sh0eAFI9TH2pxub7E7mO/QAf7HHCOEtKa0cUawqW6LHCOtQZWAb5JxryLaw6cexeZNveVuRyYy4G5HJjLgbfKgXc3KL1VqO/F3zXZPXLkiON2SKCX3PXIFYwu/U2T53/6p3+yV1991dkc0Wa7ILTbb7/dUSWQYUWT3z/7sz9zYDcZu3Q6XlLPMpDIsCSXRpJr3r9/vwOqaWJdWVlpcn2qU4maTP/93/+9830pODRySlKbMALbMjIyHMPHQw895EzeFSe5AdD9irfgOH0mtTzBaB//+MedUy2arF+4cMH+5m/+xnmW3kuBQPd99atfdVQivv/97zvulwSsyQCidCm+MoJs27bNTp48aV/60pds7969zv06hS+Dn1T5vva1r1ltba39yZ/8iZMG5ZfC2LNnj3N6X2oZH/vYxxwD3D/8wz84cVBcZZCTAUcQnjaVlK9SepBhSRtQUizQokJKGytWrHCMdgp77vr1yIHh0bA9/mzAPv1VH6ejzSo5FfQHD0fb73wMQ4bmn3PXXA7M5cC7ygGWciwaOYGGu9L9x1lon2FDoCVkLR1sgrDQlcJaIovfpFjgODZV1tcAyy3F6ITKVyGvYl4JLNK1BmSIeZdLwXcV9X+zLzn2dDJqnE2LNk4P9rCJ08bpuf0ngTpqgxhlcdPFzgB7HSyWzYEI55e62Xh22V2bo9hEYYMHV7Wx7DH8+s5K/s2y81d6UCty/f/1D7y2pxYjMyG9+A+xtmk9G0XXWJhfKezfpC+/9GrIPvSpGYw6ZsvL3fbYV2LZnMeoPlcBf5OK+Rdp0Xw88tIGuubFAsC+8pWvOJ9LnUvgmeb2WgvoXq0r9LsO3DzzzDMOOCcg6dFHH3UO6Shw/f36n86bX/EfPVtrA10RVRQdphEwpwMuWv/oufqbDrdorq55ukCsRx55xDE+ROKusC5fvoLi3BfNz+bnzs2bbCOuodIwMoXZlQ4Agmh3OioaxYcsXB+ilOXCwK5L39UVSaPe6/cIOKf3ch0rI4bWDXofAef0/Ighwwnkbf4TeYZ+ak2l9ZHWf0qzNlu1liotLf1FnN4sWIWhS3HW75H3ke+oXAWaSQlOKnY7duzAjcgu54CR4h65X9+PhKGfb/e6/vsCwAQBqqxUtqpHgud0CCuiDvh2wlWY18ch8l7rvqeeespRZND695577rHNmzc767rryyFy//VxezvP1T36jl7DuF5RWtQmVB9VB/Q81QOV//Xxe6uwI2HqO3opb7Tu/O53v+uErzovcE5rbalMvBGcI0LmxcAcQJGJRT91mXB4yTAQhYHQhUHIJWUvWSlkKURVKgQgEpydwnMYIBXvMU8AnmEcxwVmdDyuYFEKgZzD6IdeiB+gCKgooPtRgMEUCn/Cxn48bgwTcXWE+1MXygju8IyFWbMHBGvRdANABy6M2x6Uw8LAIX7al2wBMgbF0lajkOcNjQKUTaIOgZHYxcTIDRDnwbDuwp2bO0HKJkQB+MM3BeBHnKP8ir/wFABDlFQ8cs0JVIAVl4BJN3Ca0ucCzAuPEWfAhSDGWxeAigvjtisl3qKSUfMAkHCTb1jwAf7IA9wcKS8EL0HaOJM0VxL9AwanAO3aE4UxFDUzz8yABYDEeAQGHWA6/h6D28YgYfsBtcKAc0qmG0OS24/iERPn8IQPxQhwPDdQXAzlApRnCajVAepGAV+FgRN9AIlBH20Ut0KydLlpe3IX7SGuJrdhlOW1OkVasFqGcfEZmCDfUOgLOV9AnQGwIBo31B5cqbkAKJzMxjVcCINxiDwM4OYuoBMfxA82A5FNVHbo6zyopkBfAlWOO4qFfgz505yQ8dIHBmWMQtUsibhECURTeQCmhXhFUYaqb2HfLMZL8pDP3NETGNp7cbfaYkOos8QCc6SXLrLo7IXkFy6xMTTTQMg3oEPKzKUCdiIk9RgvxshRDGgj1EYvxmegMJQ2eCDv1YcRcT51gTF5pMaHUZTS4zPAERzVhimfkOor9dZNuKAWjmE7hBHKiwtZOnhANqF81H/KWuqMquLMuoHAgFXIm6B7EpBiHLgC03cwGWAN18WoiFFrnPxXPRUwN4nVHNMle18AiAB8SoUgshm/z2ZxKRhiUaS0uTFsyvAZQ7kkYlwVMDDjRRFlRkBPGFdhOeRDItWQVsE4FMLlcRg1KC/uAVXXE3FXFY8inhtVK76FehxAAsqEQcBEF/cLgOkc7GGMQLkRtZY1i9fbhuU3WG7aPCc9wyivTXmmgVUSLRUDciywp5TtRvwYlnHLmgAUlhmNSz2AM/nLCqov4b+JIHWcMk2NSgZIIu8oeB/PmwI4E4BAp+G0zVnqHySHxWNsj4+Va1oMxxiiZwBFfXQCsclJFotxP4p2Ie0TSXaHAHVnUGGRjTCWfb8o8gbHz3qqo+hHsWDM1tydOoFsYHt/ix06cMB6qU8b1txsd6y/D3gsm3UnkCMwoZ/9yJSEHNRAIuO21llAiKhajaPIpTLOjE23RIAhagbwhbqQEHuUlCF5rb296Fj6MuLIrexvzlI+uBsEkgrTH4A+OvUwip9x9GNRqMN5cU854R1zFDsTUblLBgaVGqdirXT6/NQR+strhm1UyQDvPBhRZ4B0RkK0dwDgMPHWISwvTb4X0PbUWVT6mKeUl1TYb936PiuIznaUH8dCI8CPhBUdB/iDm0TKTLCsn7wU1HP5wmUOZSU4B3sFcUfGEe3Tar9XwLrGkLKyMme/U+O87omMf5oLXutbSPy/0qVnXf+MyPvrwblo+v15HH5OS2L8oe3MAIgNeYcYZ9wAVZkWT3uMpo66KNBg2AcQPW3T/HQLClI7JE0CPKfo+ydpYz7aCDWRMqXNA0LFAuKmMA7GA6SGAWPGgQ8nqXOx9K/pMbRzVHwULy8+RieAonzADJ4A+AztOwagm4fTf81Q7+gjeLYfd5Q0QAvGUkdncXd5EpW05gFbiarblnW3WFFuIU+WG0DqNbB4PHUsNQaX1kHGYQre5aGPZYyYIK5sr1CGaZYSBURJBQ2rfOkLJumT9ZxY4pxAG3bHoJBEm/cChcz4+Bt9UZC2IlBL0F8c8Hg8L4EGkHko5aCuSTo9CWGU0FCABV6NUR4ySNHb0n7oMwEk4rk/lvE2xOe0bOoX4QPYeombm7AFePZ4e+1IwzH6mhNWjRvaHZt22fy8CmBGgFv20CenOczCuJLIfr3qaoAynKU/9FIeAlviAMPi6UsEXpiLPhj33NNqa0Dz0fQRsYyJLspJBxK9wRnymD6BMSssQIQ0u6NQv2OM84RzzTueDtTQR/bTR+VHM98HGif+ccTTo/EeGErrgVEvgBPqnMmJsQAmzIdUGxhHJphDTKJU5KUf9BMXP21wmFI4fe4sSmFnLQsXjvffucPmA3xHkb5xxsNRb9ASqT9ZtHVS4cwVJuhjhlHCjUHtKJ15UwKqZx7U5pTeoIc6CoQ5Rd7ERKUBUWheBVjrnrXpINAOfcQMYaqf9ODNAP0l6ih9PX3F0HAQbw4A4eRhBm5F8/EEL3Y+WmMFqotkEfMr5RF9EOu4GBeuB4G8tIE0S/qkWBic5hAALw35Afq2Uer6q7Uv27mOk+RVmj248QFblbuSsIA+mN9NkldxjM0pzAsSND+ij/Qw5rtD9HfTgDQoQM2Sp1O4rPZSJtEczoim74+jTkqNKZ5q7WePy8tp0dlZRkegFx20DbP3F8NJWrk/TaMta97F/+wfhlBu7Gae6rf587OtuCARAAlYiCkC3ZqNjAcBeqaBtvwOrCSFU/XdPuaVvsk4Gx+m/QhwY0xX33HkWMie2RO0YYDSh3eifLgtw9JzUAajzPsBiBgQycM4S03mkBQDdgjAmqrCKM7+JukZG0flm/B8jF9e3wwgWzTjX5ylMW9IAJqlaXIf0PXUtfT5AfqZ0jl9vIvx2TOPkYKNUH516lmQ/mOk2+vUhzSUHBPTNUejzGj7s8yDpmdxc060PITvAfgfBXJMA27S2iXicltrSPXPstlpfSMbnFyqCoDWfREQLvJT3fcb+9lIGNd/rt/V1ynctrY2Zz0rO6OAPNnB1HZa+Xx0xAsoVAg0TtvS3Jx0GCqsAb47S1lPAlC5yMdU8j+J+XiMNo+YB00xNo1ykGKMth/Fxls643ws6XQBQfFl5vR8F6DRi7KyhwmSDjhcafTbj59psu6BIGpcxfaBBzJQKQR013MmgPQ1zque0d966It1OIiMdsJT/+djzjaCG9Nu6kXfGP0i9TEzyW1p5HUG9TqJNhmYYFymrvtSom0WJdsAnY2HcowmXpq7xFNPEwE73cmkkfpPEswP6Ke6E8VBu+hkDoygCq2RXuOKfxw34MOkm3Ydn0FbQR9EdSrAPzOk0Udb8E+yHmG9IMi6s8Nlz+9rsQttg7Z6bZndsT3HFs0nXPrNfvLDR94I6kwn3rHMFZU8spe4IEpCXzAC6DdNn+inX3XTuGLoLeKpp4mBWIufpR9mHeFnfuclHX4aUih0bV3gYo7vZv4Rq3ZIHY1jr15rsUlcHI+Oqo7i1lVjB/U8GteiOnxFVaetA55NEX/aXVxSDO1c4wzZwd+8fHdqPMA8ijkd+RNHB3CmPmw/3uMHiu1y3A9/6L4M9v9JHzk2Pqa5Mf0Gz1EbVPrcmpzRGFn6MBrp0Bht32n3KA8yroUoHzcZIAXIFOaoKayPXKhRTrEmdaGCN8mwPMtcT3VOdTqafjiR+VlSBv1MLPMrjbHkDSLPlAV1OS5gaQyNbtYPQdbDYcppdpJ57RhzTco7gbKPcfogEsn8208aZ2jvM5Sj2r4v1g2Q77Gnn+qzEycDjIPp9nsfTAJ+JAGsgadQGx8aHuAQGPGmI9ChDQ8qchOD47iYfh5Xra9YJXb8u+5/v629aT11CdCVesQsgpKmsnEoQLikC3Vrek21Zl7v/JoD5955ns19Yy4H5nJgLgfmcuAtc+DdDUpvGex7cIMmtYcPH3bUEzShlcsWTZZ1yeD1F3/xF/aDH/zA2fQRNKaJy4kTuF9BFU4qdZ/85CedE4YyhAiMKy3lNCin/KU0J7U2nTQRDKeJszaOJPMvoE0KBwLqBKDJICWITqf4pRqnk4qKg9TiBMdJ1vlP//RPHQUEPfd73/ueffGLX3QgOMFsumT0kWFBwNlnP/tZx6ggqenf+73fc5TsZGzSa9WqVfb5z3/eSfMf//EfO5MgQW5aQGjBoLQJ5vvbv/1bB9z7xje+4RhgdIpS98lActdddznxFSAo+E5GBl1Sl5MRQnFV3nzuc59zlAL+6I/+yDFSyNCh5+j+T3/6007+6F4ZqgTJSUVASnvagFK6/vzP/9yJy/ULEedBc//8u+WAJtxNrSH7y2/67ZtPI6fMomA7sMRf/PdoR23o17el/7tl2dyD53LgTXOANauzmdTVhRvxowE7jDvWiw0AYT06cYoxiLVlGiv7bE5br8UF6c2rcUeKglpxIcBcnjY43jT43/g/TnBirbUTiA7ls4bmkO09EsQNENA7J3bHZN9lzyCVU43VZbxKXXbrjVG2dgXqfAXXQEPtQc1d7zwHVG9//w9m7Yevs/HKhtX/+J0o+9RvYxzBPe7cdS0HdOr2777mt//5j36MB2H79G/F2h9+EgWv5Lk8+k2uI9fDaFpHyAWrwDmBQFu3bnXAM60VIvCX5rgywGpO/eSTTzr3y5Wn3EZqXaIrYnh9r+fD2rzXOijyDK1x9F4HghR3XTrcI/ecmu8L+JMam9Y8UqLTvYqbwqmtrQOc+0vHuHj/zntt44a1lsoJZllp2NtkG5zTvphQ5MrNzYblNUiDfcyfPz+SNr3X7xFwTvkpaGr58uW/OECkv0fuVxyv/13v3+xS+JFn6Htau+hwk9zp6qfWX1o7aS0k2Oytwr4+/pHf9Xz9rnyR2oDKVep5UhBX/gloezNXsG/1zOvTF3mmvqP1lNa0Aud0AEuuTQXOKe/eiZJ3JMzIc/Re5Sx3tgpbqgwyAKmeal0pBcXrL92v+OgV+f36v7/Z75Fna92sw1Z6nspFa2nlneqC1rORPIr8fKswI+EqHWpvapc6hKb1s1SD5KZNqnY33nijU7d13/VXEApFygtUZVVmCpgXhgDD4CwFBFVy4WbXLt3AC6uf6n0QCCSkvMBwhNkA91wCjrhXYchCImiLe8MuJi0y+IcBRIGq9J0Q8RDI4EJRBskvCwOmunWMH/DNBSQQEnzFcxWcbGbwQXxXDAJGbC+mEsYeQWwO9IYhRQkQCMafiS/gphMvjFEYFcLU11jBeLKecvm4P4SBx41VR/YXxTeEcRxshm9iuGAiAOfE3wWA0g8QVlBGe+WHkoWxlKQ5xjzBPrJQOcY4DG6Kp5e0Ce5RvGks5kJlKwa3YjLsQiIAPsgCQxvkA2kG+Ak/wEu3S60PUx6GLww4GPVCskIpbjxQua/DJtHkdwxl5AbG0GQwjIEn7L1mLHEB2IWJq57tFIWMi/wuoESGZ5esc/zUwxzoMezlOYSjjMDoIrdM3H3tHozojuWLPNBHmIhIGwZnjEzRxM3NQ8IoNtg0CnEY2lxADD4UBYMYjYmdU5djeW6UCkWxpx4IKHJhvAurH8aAL6ORC4OmuUeBBa9aT8sVDNvTqEoUWXrJEgDAEv6WxStaQkT8VG3kF7LFKXQVHsBiwDXrQFQgbFRd3FgCegkIUT7wv1Oe3HDtwoIn86Xqi2NIJV4aIwiIMiWPlF69pfy97BMJRomZBaTCSO3cQ/2E+uTvgCo/f0A4xku9miRUlQOkAm3Bo7rKd6USRXZRcKpdynXqhP5GPREcpzbsBENeYWa/ljZN8p18J07859RttRe+4HbalAA96o/qJJ+p7kaTt9E0IZdP3yENSifteARobsg3yDOoV9R9F8bCYeCtC6gxnjlz0dIBSrbU3IKy1ApLE4TCc3w8Ywq4RFkOknMN2uG7XhqGUiDDbDQF4rR3pyyINh8KCnRhgJVBWWlTHoYpB6m3yqLqAI9EOcQfg7qBNuDkN0+Cg+Ae0qLsZ5Go8pY6HSM3MWDdSJ7KKKlgg/zdz/dnUBicQmFLfV1YeY5RUyrIIzNDdkpuT3HpHMM+3fbNd9j6hZsByFLJA57B9651FoCUTpkRKJfqhQzfPtUNGk4M/Z+bNhOmbJRMp0goO4/TXvT+2ucqI+UbESQM4sLPKFGmgZ9bxGmXUncMEva1/1Q33NfgCX2mPpLvSIVLYUm1yKP+ko9D1O8h37CNAoe6yf9ohUMoo8BDlzta2VM9jnvjWVu1Yo198KYdlhVGIZRwwvEAKNFKiZBlgaPqz3gMl5dx6ALjncZPHXqWApHGBtVF7btqz7K1tdU5eKyD15GxUPdE5lOa60WutzNeRe59Jz8Vn+vDjry/HpyLQ9WzIL8AN2eCh4gfbU6Kf6pjbuBXDypZUZSf+kFdTk9KHtJUqJ/05YBmyhjBRurAVcMDtBUyzAnPRb2SelY0IJTalcYKH3VcdT2Wz6JQSBUEEgDKCFBWasMaazSuqsoGZBgHWp2aGabeAH8Ds6lzHuOz06gCHj57xIIcJNy66ja7afnNKAapv+M5Tkw1NgMl8ByaJOV6Lc48QJGjP3ZGLP5O6VJXHOUZ4qH6FyYOUixS3aWrcsY8F/ETvOoMYNRtAQCC5V30E0GM7zQsgqWvZXwN0E6oxDydVPM1NVeaM324KhEvzQ1o5yG+L4R+AgWpadyQ+m3cGesEjgZmQ6iuXbF9Z/dbBzaCtctusHs27ETprIi8IzDGUuh64kMt5a0z1iqJ/OehfbjoVNSu1fCcvgPIno6YF21SX9AL6k7YqWbiP//fyQeSBHDGP7ibddFuxscT7cJZs6ee2WupABIbb1liy5eiIpYIgET8lcYw/Yt6T/XHSqcOEbhRuQsDWk4Azgwz9wi4BcEocEGU09Y8fNVZSwz39qNeV2U7tt6JwhtgsxMn4GpwOXAPi6cQoshXVTOqDPnEM/mdHoY6RDTVn6qDUZzJj7DGANJ+7TPiBHgjeM5PAKDF1A/lkUYccotyHB6U7afVzpy9DAyXads2L7GlCxJo44y3jNkCwfXsMGCah35V0yeHOKFOak40wvxrGmjXw5zMw+/KCS/htg522ctn9ljHZAuwzkK7e809Vp1cTYQpX+5SD62gmFJRXVgL0S40d5Kbx5DKTo8hH/3A0kE+17is8lUVcuoVyVU8FIagdrouwhQOwi/ES/uFdJ/KMDVTO3Jiyl7Yc4SymWbeXGNrVhXg0pwwCUwQjZ/CU1/s1AratlrDDGPP+LgbYAsIGheyMDiM/WDzQErP7h6wfexTJsXO2qMPZNnGdamo7JEu2rlfDY5nquVHUViaQ10D5SkD4qUiIiQnPYKpAszJ5D4XpNqi2CtRfBV59eEqN9XvWeqAmqASOUO9nOC5mt/FUkboG1sCeYRnR+aMPJfPXVEcYKAOCmIMMj9D54w0qV0z9UIleYA1kaDT7OxsRyxCfaQu9dWy18kOpsNjK1eudNZq1x82ivTnkfv13WtzoWvfV9+rzyL9buS97pHtrqGhwQlba3qtZTQmtDBujLPRm1lYwuEBYGPqEMK8FgK6Uq8WQ9kI9gQXo75QVuSHxnf9LYSa5ix1fJb2xp2UI65YdSdhTAGIjQ56cQUM7EylIPtZG4bsyKkee+qFE/8/e+8BJflZ3um+VdVdnXPOabqnJ+eoSRppBBIgFAiGNaywjWFx5LDG59j3+tp4F+MLq+tzr70Yh10bfIFFCYTySBpJI03OqadzzjlUh6quqvv8vp4SQhYgZOGrg/ov1XTF7//l9D7f7+VMSYndeftqXBunObeo+lxZQfXm5sIcqR2UA90Wc2TVPT5QHw21Ng2c1jU4jRotisUc+KgsSgGcY+5D1xTpBqg7P2j9w/1WtL3M8rblU4eJN0puHvptdV+6hyeBkT+X1sB+YQIVLB4Xyqq2qkdBYNio2hxf1AxGELSXObv7XL8jXkEiFcA16SSqglHgr3SAQ7oamx9Die3MpD13HCVIxvxD711j+w7kIygC0MZeufYt1WaoKtS7pb/qojUHcGAZ9VwAnUaKKAdiPBxASAS8igfMCqOqFm5jf/nsdea6IStYXWJ5K1BnVuax2IgC1XEOYqmbppDi2efzq20wxmic1dxe6wDNNT3qE4HZovTrUS0+yKMQ/btb29COVRgBDohPAU3qAJHcmnp5X+3h8Mtj9sizAMkAqB+5I9fuOpRixQyDRJPf6X/XQ7uxSus5Rc/DfNr1PwTNaEsZEF8VuHoS/XHfY2QQpN6GkugxVF8n+q1iV7Fl1DPPVdekPoUhJQropyFfLtojKRyM0fmhjFTcoiss8gtF6TgO10Toyxc1fnNHjcc036V5KmnXf9Ro4NyIjQ0BDUo5HUgzQp6MAFk2ti7govic9fbjDnZ7rX3yrmxbWU1amKN5GLzdHFDjvjpmXF17Q37Ut2fsB48+bE88+ahV1tTYHXd/2Lbu2eoOZKnyuHQz+moEVosAmyRuak18+BauZXDuLWTa8k+Wc2A5B5ZzYDkHflYOvLVB6WeF+nZ8ronDG4Fzel+b6VKHEOwlkEzQmybADQ0N9ju/8zsOmNOGvowrH/zgB516mp5LTU2TcgFyf/mXf+k2/AW0/fqv/7ozOknO/7d+67ecsp3uoVPvAuekRiDFus9//vNWWVnpjGxSu5Nim95/4IEH7PLly/a7v/u7DkT7oz/6I3dv5YOAsz/4gz9wE6F/+Id/cBv/MnR95jOfceHrFL0eipdU5e6//35nyLnvvvucIUKLA6ki6N4C9qS8IFBQaVDcpUYnN7bKA23C6N5ycSuI7/Xg3J/8yZ+YwhU4d/jwYRdfGQzlAkf5qZM2Mrh94QtfcEYYQXZ6X+8pDl/72tecQUPp+exnP+s2qd6Osl4O4+3JAWw29tzLi/Z//N9BO3c9apUFHvvdj8Xbp341jhNr79y2/vakfjmU5Rx4e3JAa9VhNu+u0YaOnFi001fYNG2LWC/vSaI/g/VcIQvvskqP3bTdZ1vWem1FNcAcwFeKlOWWr3+VA+OcZm3vAppDte8wm3unTsq9LS4NgOvwnoZqheFixWu1FQB0u3y2a6vPqqs4ZZfKJtVylv6r/PxZb/zZ/xWyb3w7xMnTqO1Y67H/hRvSMtQQl6+lHGjsiNjn/vOCvUjb1p7SA/cn2B23ymC5nEO/zDkgWEqXNs4FoOlwjA6h6DCN5t9SrNbhEEFourTe0Kb9k08+6RSeZZj9+Mc/7gA7wVX6XJv2rzWKuh++Df8obK1rBBK91gigoAWsCZ7TOkAHe7ReESQleElAluAlXYqbvnv50hUO9dzvTvffe/c9gHO73LxelV8br3LvFsZ45sPo4fNqo19bukvp199Y+hQnPY+Bc8pDwVKbNm1yB5CUv1I303313dj3Y79XWD/tiqVZ39dDaxqVkdYzWtfIsPEbv/Hrtn37Drf20X3c5vJP6NoUnj5XWO65+/JSumYCsxjrX7FHHnnEAXQyyAjOEqQVUzyIxVu/1SMWr5+Whjf8jDhMz0xTDpecKtx5gD0dSPoAKuL79u//MfWEN/69ZiVcr6Zj6WUsXnqlOvqd73zH1VetEQXOCRaIpWXpF0tpf8vpuBGIyr2trc0dXhN0qPaidaQUD3U/hS+g683ue+tkvRQwVHfmWci89NKL9je4UNY9fBixdqI4JyVAKc9pPerKPZYg/kaodxGMUj7VB1lfVO4OuMEgwOc+jBrYdGgQVBRVGQwwYd6Q6gyvMIIBvPB5FGuUTrwrn52hSuHwfWeswsivl87QTjuJEr5eItWGqhp6Jn3dNodhL5k+IqWk0nwYncMYD5YgEr7HvWVYFLgl+4uPNAsEc9YvhaPsklUfg60gJfm0Eswn9YMIxm0ZcrwAAQ660deIZuRGnfRi1HC/lAWEIBUOvyJs0o0BU3lLCEs/4kbKBmzaBCEjKi8cIcfnyj+ll987SIq3osQzimrMVGebRYd6cdeTYfGFZbhPXQLBlvIagzYGpBBxkgHMg5FG5qB4KfcRbxmeiAbB044IXspeuk0c5cCvyEO9SUeEEcxdZJBsVoKOEMdZ+i0f6DcqF7JDwWKA50GA8Ri//BgVZXTClMWXlqBagTlSZ/LwZR8GPYrVGfsXsBbJaJjATTzELTI+apHOFguMDlt8Qb75KyoxOuWQQcRHdVn3JCxX/oogFuPo7ICFxppRAuwjXfO4g8KQhhpTYHbS+gdxJerLsZKynZZezIFPP2pOGIIEGbpJhjIDNScsyARGIngplQbVyEUgQHp+7gHqhdHe9aV8S99U8dKhq1BINACBSlnWRf2emu5V3SdNXoEc7tJnjAEYvaMoP4W7mizc14kKCqpIJasowxIKQZY3LsKIJIHRYFwT8uRDIUnGRK/uhflfwEAE1ZQoeas2ppaiNi5Dp9TfdEUEShB3Z2qlTYqLMdTsuAkfqg/mqQqRtHqw8Km4IoALyhO1x0WUnjBxmh/wwqv8UYGRvlmgyGt91+xqz1Wbw7VdqpQI+WxgDPetHc02gaF4R9UeO7TxdqsrrkbdRfdUFqOs5kXZkTBxeoo5Tu8LTlF+cVvVPyLlwDniIjhHzEeY9icDqsAFIRW6l4z8gj9kaFQAym/YAsAD1WnlEz+ksoodc8oyvFTbVbUOUgfRQcEdIVi6QDJZgsmzCGpBC/wdWcCtW18zKkSdGGBRL2FfT9k2PNaPys51m5OrtOoaO7TnNluZtx43ttQNl/EAn+5KgueT+z3KRflLfAXO0TMRa8ygpEswktKsklJ89XOvAD/93tU/lQ+v9D6dodz2RqiLgol9Yeov9UoKtVL84ucqFtJMuPqPshXUE5ViIBmgfJTayJIBVGATcAWKiJe6L1nrUKNTU0tEwUYqm8PTU9bU1m49uPksL6kG+Dho71m929KBRtUvqXpIRUYxVd9MzAmfRBLXudmAXWQs1VwsBs4pObHxSRC5xhTNiTS/0/PYXCo2nqt9aY6kv3rExnyF83ZdsXvFwou9/jFwDgVTzQkySIvaShjVojAufAXOeahoUvIix5YKUIXoytkVF+VHq6O89KEXyEcKRCr/RcpKaJzquIPSUOfxqWKRf8rCML8L00g91Lk46q66f2hdwhIYwb3on+j1CCvs2mDncJdd77oOfDWNsk2qaw+Dw73W2AnUgru7Ffkr7H07UJ6tWM84KIBZvQHl5cpNNVF1RdGk8qjxE676xBDjpJqV6qTGJX1HEByEBPZ+QWfEhc/UbQoYIUHEVRVQ7xOqwlSVIC0RCAIPLq4drCbORJe6OL6qKYnahp67CuwC1TiuNFOf6Ge6R7usgX5maL4XFSFUFj0pjAuzKOi3Wmtvs6trt+CK9sBa1KlxrSrIQI1KOaX0kGlERBHipcpO93DQXPJSXvCRF7jPo/IiQVHUptwcgH5YALHGO4FMqiMaDQXoaOyRGo8m64PDcfbC0ZD9j29+H/ePPvvQPTtt755iS8lUBgAv8dtF7iekx0FShLE06xC0FLSWoQ67TFrGp0ctyY/aHiDQZGASt5Ld1tHWbYVZxbZv6wHbtW6XU19TOqKgLFIBcvCjUkF7URsUyCaISuELchRE6+qXaCxFRGWlcmYgV5ao/KLxgHM+1HD5KETforrhY3z2KxwG8yG8FTz1TDOHWc4wlyywe+/cbLu3okaYRV4yL5C7Uhcu4QueVho9jMdSCJ2jP22lz7yKG+qpuSFU/Oj/qF/z0/PMJbute6DP0vOybd/Og7a9ZpcVxhUQJnMX/pNKnfp3P3nsgH6libjLhbeH9/iItNIeqZOC91SplCaNIRqzBJGr8NxBBuZjugTEzGpuRRwlEAYOrUSTJR57+vC4fes7j7PummaufqvddqgG0J6bMP8L0k7DGlsJhrtwY+LB8xGUx65cnrZzp8ZQ0U3FfXUW7QAoENewxy8yf0GVcs/2YvuV9yMSUQNoi3qWhlHVbcVVwJP6EAem8lo30BxSsK3iLVeiYY3tjH3xpMFBnrxWLV6ai6EgRghwpPTb8zaB2tYMANMMyqWz8WPcAEUrlAfzU/MtD/XAHAo8mYJmakhbELjJ/IZ5RUiwIvfUfYFcbJb510hfP21LLqVz3NpO+ac2oIfWmx0dHe4zHTTSd17fT8f6dfXheq6HW4fc6NMVzmvf02tdWrMKshaYp/fkWSkVELu1vd26NK/PoD9mzJYqHASd+VDty+RvEUBYLmFLjTeOzBEUp/mXa+8AZgu4DNbYL6k+MGOekL+ATRdOD9vRF5tIYzUuWvGMRZ7Lden11lbcc3bbjpvW2L3vr7ONK/wcLFZ/QuaRT4qtgDF1klHW7YLINKtHW423OADEPSaZgLf2DVvbwDAKcPlWX15gRczZkrnv4nkUWJ/rtv7eLiu7eYUV3srcj3nIYgPHcppRohuZRtkauDGRufGWdMvaTJsrBHxayibuyXfVQZNH7mAG9RFBM9e2NdGLAk7NMTmaCyZbX3fETh7v49B0wIoKWCtEE21sAPe0567Z6HiX1dZn210fWm9rNuUy11mq50sthj6PtsEQ5OqFSzT9gsYpdSmqg/B7S+2BOW4cZJxHhxCoeqGr83bm8DEL0AdXb11vZevygM15n/sGukMW6KeeUl8XSF8mLk/LdrOOKVDlhH7T+kRzcY0Nmuy7OTHlLdk+2pdG0SjjkfqCefKyqWnSzl4aNRhjy8wu5Ot+gMRxO38JF/ejYVyxltpHPlBg2zagG+2GQY0JShRxp82p/MKkUz279lqUx0qWm0PRR3vpDz3cy631VKfoM6HLLXgVt8rPDFn7AMIvB6usgPxjam5h9tLne8l/3I4HRtCRjYxaWlmcFW4usMQNeRZOJU1AbepLdBAmQv+yyD2kAKoZFdXH9ceC6TXUat7e3z5rLz7falPj2ofPx3VvhvWTtmstvXap6YoVlhZST9fYe3anMQ4x7xCESDkJrlXKlFhKkoB9uHUPoB7/Q3vyCcC5Ktyc332n7dizDeU7jR83soUOSrMAzRGWBmoK+y1ey+DcW8y45Z8t58ByDiznwHIO/LQc0Cj+zrw0gX0jcE6T3C996Uv21a9+1SkrSOEtdmkTQmpxMoJ9/etfd6oQAuekFvH7v//77ncKVy5zfu/3fs8ZwgSiyVii9wXKydWMIDYZA3QvAWsyTMm16qc+9SkmSCyymJALlJNxQoacBx980BmuBNnJeCAlt9gpRp3Ml3qbwhOUJ3gtBs4JihP4J/hOl+IvdTy5F9BGj05Ryjim+P7hH/6hM4j8d4wJAuYE0cltkTaMHnvsMbfhIgOf7vVG4JwMT3Jbe999PwLnlCf6vYDC/RhvlF4tUOQSVhCdXMdWVla6uMltrMIQMCggT/HQd5avd04OUIWtHzDlmw+E7P5/DLG5ZbZrjc+++Jl4u+VmTuZoE2P5Ws6B5Rz4iTkwwum75paoPfH8or10NmLNXREbBj6SYEYOB7Qq8r22CVW0gzt8uIZBWa7Ua/mcXGP/f/l6Ezmg5XRHb8T6u6N2+nLEXkCFrrkZqA53AOxpuD6qvBAFunKv3QpAt5fH6robrm7fRPjLX1nKgadeCaMMG7TzbGDlIxzy4sNJVlPx1jcifpnyVXXw2efC9rEvztsoLgz2rfHa3/xlgq1eIcPZL1NKl9PyRjmgub4uzeM1rxVwpIfm2VKWlgFWbmA0P5cCjN7XifRZ4JGDBw86l5RSPovBaa/fwH+je76V9xRPPbSG0JpAbmqkwKZ1geKm96SYpgNDmsdLefqWW27hBHcR9RhDFb9V3JbWK1cdOOfFQHk3a6L9zOEzszG68HlsFRjGaOhlkuhho/3NgnNaMwickzK1lLv1WsZp3TeWzz9P/iiuCkO/0RpKBnCp6glu02EfGbg/xGGeW2651bns0XeW0qkcvpGSWIJ4R0CgbFVSI9HnAqxkBtL/g4T79NNP2xOPP+7ucetth9zaR6rkAiRjaYjFP5afutObvm7UNd1baenoaHfrSdU3AWF3UGZycSr1cr1+9Yr9jnxQXKUOoUvlp2Qqn2LxUp6oXmgdKrezqrNa96o+CBZQXXnt9ZbS8doAeK4wRkZGHKgnxT6VmQ6AffSjH33VqBWL3+t++oYvBZctpXFJZfCxx37o1vBKl5TiN2/e4qDWbdu3vSE4FwXqWbJLUO81WSPTPKQ7HCcDE/MKDAGCRNxFHy+FJalYBQE9tMnvl+FLX1Tmqk7xuSsC1R2FxSl9Z1rQm7KgaqDQQz9BPSE82G/d58/YcO+A5eZkY7DYYYlFVbgOQ6mCnzjjk+5LEUckv0ClVJV0ameyrskipAvjhvNrJCkPpUOWZgwtS7QOFhhBN8RT1UEqMjLsOhgAy6SMyk5ZhK87RR4Ze7iPWrj+CoCQocZBLdQrUuyMJjLSyB0cX+BSJG9EWAYknkvZJTI2Yf0nTtpswzXLLSqxtM3bzVdW4fqKKHFVXnsScf0kUEjJ0HvkmSBcuW+lehALxZUwSbhrW8RG1Vs3drwBljGpC7lLcSA9EYAvhMdc3JVfPgyIFJvss0vgHNkjBknmdz8KcM4gjHmIzFkysmMBWgRaigMk8YUAi5RGGXhQx1BuOBAFd3Fh+vjQqZPWc73Bkun/8wA044rKgHaYPLk2qDzhxy6ziRBKNtHZTgsNnbHgyFWMV7irw6guF5Iz3CQo14fZay0t7ybzp5XdsPZh4eKS21yBIFFBggIhlR9qohjHpNYgAEF5JXBO9JQXw6qUB2VgdmUjS5mey8UsYKWStEg9kNsnp26kvOE3/EvU+dTVOeI7MWTTp1+2AHJFyYm4ztt6q/nK1zmFPQFeikMYiZgFgA657UQvZil/8LO1CIwSpkzDKk/i5APMkHKa6pDHFS734veLWCrDkhdzsQJqULuS8UzpFPGitnOjPntQggtTxwVl0PnRLnAFhTtWzLPcGb0rNZalCoIK1Iydbjpjr1w9xgGmPu7ldEece70wpE1hYbEdWv0+21K81fISs5eKSu0BCaQZnLwKHpJ6TqIyQxAEYStP1aaljuhgB92P+L2azapoVExBNaSOFAm6UnYugWjqcNSHCITV5aOteqXOslRhl6oKSZZSh9SdpOAmyCNeBnjVc8prkfhJY3AYF8jX2q/ZacpmkMOsIX1GJZ9bmIHd9FplYaltW7XR1leuw2BfSv4AsjgVItzGEjfwF4yhagUyQqvcl9phiMQolTQjlycyilI9eI800I/4UBPSF2QUd8lXWlR1KAu5+VSJSCnHt5hI3SJ80hPCWi6wVzUkTmWq5NMuKWQyh00n9WF8JoWzqAAQAXfEZw61oQutp+wM7i67BzuAL3DpSzbMcDBArg+zMgts85rttqVmm63KLLcU1R0BAxhzHSSgguFmAuc8oukoq4Ub4JwOEEspSHMQjY1Sd9UerfZENZ8TbJGOu7zYvCg2x9BrXTFwTnOXX8T1+jE39vq14FwCinMxcE5gQoR2FFI50IaltUcHwX+UL/mtvkAl6wBNV36CJGd5R30a/Qv1K0g9EMi8hLoIHFXdpQxREIySt1JMFYUgSA56Qyyruk0Cp/y4r9qeilZgjbCMWcCkhv5WO9Zwypp72+DFmadRJHOAwouLM8w18mxH/U7bVX2TFaWUACwpb9XPyhyuukn7cjEUYEJr0hjBJ1FuKjffeqXbS/1H6RDkow47itJObFyg6ToGzYFAN/rjJbhH0VWcVfcEe5LaBfJBg4OqDQErue4G9AuuuhJ5jYtuLsBzp/AH+Nw53GpHG16xq/1XUCNn7EDlNSRXiaF5oHG/rVu92rav3GG1WfWoawG4ki6lUcHrZvQ6lAtAi8rNERHEixYrF6MhxiFV4zjatsByxV2w/hIRQpnoKQEJTHLjk77rQucOrh542eP1uAPS3/hHPOHkJNiv3rvVDhzAlS+eSqW2t0g/JJfg6o8SSDTTBNcnL9KPBxkPrw222LHG89YBRB0EDPIyPi4inRQEyklLybCN9Vtt++o9VpZTjiqW1IPJO3UOxETjpUacJVUkfSAImXpC3gvu1/ijfluqR0SbesRDlyqSskGvGXvVh6ikQupQ+L2DsNU5MW6NDEbt8cfb7fBzF4CLquzDH6zDRXUyLjOp70RDcGFYFY+/XhJHjnJfgXPAP4wFDSPddrzhhDX1XCIfJ11ZRHHvOI+bg/zMUlu7mj4GMLcsowxNVcB0zTP4T6qgCjaO/lmp9VG/nMIo8VPDUJ3ROKLRQONgm5JmAABAAElEQVSHWqXSpXrqpa+ktlGvGed45ua+pFVQv9yuatpAdPkEVSWN/fSlTzwxYf/0rSfor4J2z90323sPoQKWx5e4g44dqH0q72EB9Zb+tyG8NZw8MW1PP95ng31+oEegLu4+PoX78viA1azJsvfekmu7NyTibpf7aAwki5W1ur/mCCoCD3Nhgd4KX32R0qFLU07dzst8gCGZeyq9yh0UvXhorFPrmsC9cNfgqA2Rr4hvMc8G2EyaoB5x+ArQNSMB17PpJVaNm/Uc+lh5xpQ7eYW+ADinMHRLPQT1zo2P2Xg/4Bz9uA7hqD9XHsr+NTo66lS01UdrbVZVVeX6eX76Uy+XLr7hyuLGN2P97ut/qLWZ7IYxl96FBYXW3ddjjRPDNpBJHcHVcByuzudpzzrWQDdgtWmJVgNEl41yVwLufNVlusxTptIeVB8lXxjl8xAVgHdR8Yray0eG7OFHLuLyErArPgsVQdTL5inxxHmrYA/qwKEi27Uxy0rTBeVpfUJ70ryEAJZUAikn6mOQfFPJoCfMN1j/8O8UAHTn0LC19gCNZhba6vISK09EOXCaPvw4NqFnhm2UdOXdXGkZ1JPFMbx/HMUtdwPtZxyYNYD7aB8u7G8usIJDuH+vpP1r6NGlvol5glvLaC2lwtNkQheJ90gVlvY3v5Bk7c1he/RRXOBe6eNwQD7tCNfVcxyRwSV7ZWnYbt2XY7sOFFh2cbKbGrogCM+1OxqN5veuwlNZtQSSApyUfZUHLp9j99VnPI+OMtZdnrWLz5+iJXqtYuM6K6xFdX8S1bSGYZtsYi4z5KM+RWwGUC5nTaat+miB+StROCPf3XyZvtldGnfoaKKstQwFPxcPZBOdojTlPDvtscsXp+3JF7rt7LVJ+uw8vp9sswHuDEBaXZ5qtx/Mt907Uiw3jzkSidLhI41l6mA86pAZGyNqYJSump/GdD1xcB6AqYd5kycoBW7VJf4HcI7Osxa6GrbhwyPW2ddpVYeqLH9DDmvPqE2fD9h06wwdBNlBPzca6rPkMvLhlkpLvSXfPFlkkl830Xyc+/JUUVK/6yJwo3/RR+ozqFzW3TRrjzx0zRoacYcdQgXXk8WhtnTAyDnLyJ+y7Xtzbf+efKstiaM9kgiVEdlFFSRc9csqK8Ii7ImheXvk0aftsScetsqqEvvgPXfY7j2sIRMYW13LWMoDla36Sj1uvM2Tn/9aBud+/jxb/sVyDiznwHIOLOfAz8wBBrV36KUJ7huBc5rgCnR75pln3MkQqQRoE0SXNkGeeuop5wZHymtSRpNimmAwgXR6rnBjSnLPPfecO/0v9y+6NGlezaJYRigZNrQJIxU5uVaVUeLAgQPue/pHSm0yUkk6WhCflB/kalVQmcJQPHUpbrq/lOcEpH3729924cndkNzpCMiTC1hd2vCRMUynJOX+Rr/RffTQe7oErMk4IXBO99Jm0KOPPurC+GngnABBuVi9774lcE75J9Bv27Ztzr2sDDcyuAjCE3wolwYy6ChOupQOqc6dPn3aLV5kyKqsrHSfLf/zzskBLXyvXIvYV78esgeOsFnMibM7b/bZ//7bfqurZiH+zonqckyWc+AdkwNjwHEC5l4CODpyBhduTREbAqKT/akwxWNVqMltwx3rzQBz9Su9Vsrr1GV1uX9T+Q2Tvz3dUbvSGLHDAHQNUvbrZ2ODTVedzi1hE3Fznddu3+djnPJZbfWy+9s3m+HjbJR96r55e5qxAG0O+8E/J9iOzWx8a6/mXX5Nc2L0/r8K2V/8v5w+ZT/py7/ht9/6T3FO3fBdnjXviuRrTquH1gvajG9tbXUKyzpw0tPT4+bBMqDqoc/1XcE7guXe9773uXm7Nvb1eWwj/rWb829HJirc2CV4SNCc1jaKq+4rGErvy82M4rJ9+3Z3+EXK01q36IrN3WUdkavWr3zlv7mN0ns/8D7be9NWVIgxugQxA8m6o41KKS5wut6TADRG3uiKxSOWvlh6tabRYSDdQ+CcTunHlM303df/zgX2M/7Rb/SIlY3KR8+lBqDDRlL+1hrnJtZr99xzr61hneVPlAH9Rl7xhxB+zFDCyx9dihfh6Vth/krN7OGHHrbnn3/e5ec9H7rHAXkCD2NQ5I9+/Bafufvrn6U8GRoc4HDUi/a9B77n1pv79u1zKoG1K2otnk3kH83QYxFfmrE74I90eunA9Y4rW54ojwRWSpXvu9/9rp06dcoZnrRG1vpQAKjqyy/iUv17HOhQKndaj952223ucFklLm/jqJ8/lvc/MwJLZb8Ywp0QefQA6+3vfOe7uIiJt2zSsHr1Gjt48wHbwnr1XyvO8duFWUCmOf4yedDOPWXt4bdRVG+iGI58sv7wvuC6Jas53wEEiKBwJfdHzuYuFRFAN28yAKjqnowMWGpkBLc51DKANPD9xu9pGwCMCt9U/zAihqmXzc89a8Pt7VbKwbuiPfvNX1FnnhR85fgxPvkBXRycpBqK6c2poxEZBqAIqrtOaU1GFfm1QhHBfZc0yIIgEDBKP2So6EQBWKCBCENGedIKbCX3ql7SKFkPZxaVRQSDfxQDh1OuA0TQJXUno44597LEX7AQGcC9iROGO8VFth3XnqgyHsKj8nBP+suBEet++rDNXbpoeSXllr5rr/mqqkkffUUSKBF/DdWFiGAJGbQjhIcLU0F32PT4S8AYt2UF9pBnHj9QmOolX+cDvgMwFuCBldkZmQBzPH7aQxqOypKAKghTfZLKI16yz+57fJ92HKbwhC04excqbPpdVACwDFTAAlEZMBnzo1OUPz/1YEDyJBIflYfARiSPw82tFjz8nHWeP2fJdaus4OabLb680pWfK2fS5cH6qzyMEg/lqyc4gFLddVscRMVtdMB88zMu/WHS5ysEWM1dY96k1cQDV8mqYIBCUQ5HIt1G2cgoLyiBfEB1wYPhSU+d5ZCwlSVRgI0oLtgYkPgt+SlwQhIfuLpTH21x1C0U/dRnhoE3pQwnd2GIuvB95SvhqB0kksnke2Sox6aef8qmT74COIcbt73vt/iVW1DWSyedxCVZacQwDvgkM2yQscFBt0BfQaAn5/oRqMCHS7AUIptM/KVk5uomdTcCnRrA/eAckJ1wMiUCzRtc+yVZAuphHsFelBTVybnqC2MgFK3lwyDtBcgTfIE2CGuuOQyJAn1wkggc6MWIOE8ZNg4227mO8yiztVuAuh2iXXpxdZZXnG/VFStsXd4GK04oBoZIdsbVMFa4WQ8GRYAiVTMBbw5C4JlUnuQiUXCEV7CiAB3e8woK5H2KmL/UN+UfvxMkISh1jjJUJ0LL5yKVarN8z0//Qmi8T51WfVdbouicLz21O5WdjMvkSQhAZZH9NPXL3Iqcwp0sBteesR672tJgnYM9KPlMAX2QD6SvqLjAaktrrBY1r+KkYsu2HO4SB4AQAFoYd7+Xczypu6l3ISFqgg68kKoPb6CapdhRpjSAIItaH/BmEmlNwtIpqFxKMlIoU7+idIUAcHAIT9hSs5G7NsobSMgHVE/sKUMpZnEvhYVxNMGXRPgCaOgLqS+CvYLkk9R0pDaTGJdCKkOksd0aui6gitNsI1Mj5KfggnjLyMm1iqpaqyKNJUllVuhLsxSiItBjDrBnThIoxEvlqDSGiTs1y7y0j6sXLrnDBDHgXbCFDjrITavAenkb0R6rn35B46Uu7WfqeWxe49688c8bvffaz9/Kc7XR14Ybe/1acC4+yW8FqLikpAJZMc6EKSuBnypCp9xC7VJtjKN85drSlQsuD1WWdLiUmcA58og2JeWoWeUbIEOUdiWtsHTqQDzAYQS4ZxFIQBAVKyB+i8tBfpPo6jCpE0ilT7hP0MEKQl81tiyiwNRvF7rZm+5qtskJoAHGGg2D2bmJKMQXozi3ivKrtkxvjout1EQFE6tLklKlxie1Ix+PBN73EzchZHMCFYibZjce7q/+TKOcn8AFrHilPshv1Ucs0v8FFTfCdvVdZck4ACPvHur3qN0Ab6gkArIu8l0HdZGP8SiISqFLdTRIR7nAvSJSdXV3Jp+J0+TciF0Currcf9GGgXY887Q23IinZiRaaXW+1ZRXW2lquWX4cskX4YgCA6md3EetTW6Iw2ofhCUwQeXk86bQ7hNtXgpPdOV8CDxH188jAeCAoYi+QMgwswOq+oyGT/UfPOL5bgKfMcy6fZEBPB88+8qi/fd/eNaKc1LtEx/eYHtQ/YFxo/TJB43b9Fs6KJ1AWvlD3Cg96oLKs2N0wi62D3AgqNOmRgeZ0qDyyRwiOT3FCgvybHV1ldXkl1gySllS4gyj6KSmrnDEKipimkZr+I5nTIwQN5XEPHMJDVM+YA+v4Bc+l2Crj/Qprfyv7kLdIw/6IjINz3/u0KnyQN+RW+nhoYh9/5Fme+a5BsvMWm2/cneFbV5LD8detqYsHr5LkZFOfkvGxZEm9avqR2eIaMdMwK50tlljx1Ubx6Whl7aUwg8SGSurK+oBbNdYSVaBpZJhVCWXFk2ZuLUbppkWuvRKtJupm+sflXglfYF0Kd4y76gc9Zt45hMJ8XPktRBj5mf0h24KRjnO8xCbrMQrvsm4UBRQKjD9sR/Oohr4rLvZvR/cYQf35aE2SR2nrBygpOko+etX+ghDdWycIbOlcdHOnghYWwvjxhT5DxWXTGeZXxZvVWv8tqY+zipyae9ERVNVtT3VKeWdKwcSom5XfDtTNtZQfEYZRigcpXFJ9ZB7UnekzKw6pRxeGr1IO8+nGX/7aRszKkDG6Dgp+6XMkt5ZGxoO2FQgzvKScmwd9akCIJiiYzxkbKdtzNFm5skphcnbbuwNjo/ZbP+ApSQmWU5ujrPBqY/W4RzZwmSbKy4udrYngXWxfpyfvy2X+mONGXLrrTFDSt2TU5PUpXELlhZYSlo+QKXfRpiHDE6PW3hmwsoS4m0N41ZxOocQtHZgjhtyfYsqqdK1BD0LgFRPoDcFlHY0zNnJY8PW1c5eZ4C+j0Lyp8ZbfgUg3nrq6Cq/FaI8mE5YYOCuH3W/Jo6ajwhC17SQ2TVlS+lwH/XOajuyOw4MjHJAa8ySEvNtTWk+cwb6L4C90Dna/5FZG+7vseybiyzjPZlAVhEbP4fyZTf1WAfEhwI2MTNoCTtzLB/gKrGKMUfzXpIUhb6XeqHgJo27UdYtr841mT+4OawOpfClUfZxT56atIstEzaIa+Ew9GY67a0kK97W1sZbPfU0u5j9SNTmYOqWLvUdhO3mvESJpBEpck1tUHvtZLEagoPJ5virBqGs5qMIe8mhhpA1Hb8I3GhWvL7O8mrTCQtwrnXSgl2AzON+C4wFbWRu0jKq06zuV1CWrgKIS2L95zoW6qTaqi4dTpoD7pwkcOr40vycCNGpRejDBwnv5NUpO0v6xgNEcjaBPgZQLZ86UZdg9ZRhTj59LxVfw7PgbApqKU3q2JU2NUbeZuhhXcGDvwLnyFzAVuJCG4LjdpeqF0OVhZro416gfIc7rfQ9lZa3OY9DTRwApU4tdMybf5LxfWTRBsZ7zYDlyvdXWvptGTznnglKHHXF/SF8+nV1KlHykkZNDSIOymOdsqCfmxoM29lz49bQMm6D4xyemUvkMEMO+0Z+q12Fd5j18ZZXBKJOH6z+2JNM3aQsNTYwDXSPpUGM8Wx83h596hl75LkHraK2yD5493vtJsC5+LgkxgRqN5McugYexIK+SPNiN2Dw561cy+DcW8m15d8s58ByDiznwHIO/Iwc0Kj9zrw0kf1J4JwU2mTkkiqbThI6IwLJ0MaEJo7akHrPe97jHvfcc49TZfiXf/kXu/XWW5kYRR2cJmhMxhi5WZX7F10C1KQ4J0OUVOakOiDXr3KDpFP1cuEUu/RducKRIedP//RPnQtTqcfJECIDmwxusSu2SSNQ7jd/8zcdOPe5z33OGboE28m4oUtGMLlDVbz0e232yEigzctnn33WAX9vBpxTfKUOFwMCBfcJ0JOy3H333edctQqcE1go11QC6rQYkaKGXNz+9m//tgPnlD7FPXYpn/VaCxjli5T5lq93Xg7MsKA58grw3N+H7OglyivHY5+9J85+5zfiMQC/c9v8Oy8nl2P0y54DgrdOnIrYseMAzsByTV0R3P9oE9BsNSe2tq/z2lYeUqMqL/dYMe6Pk7QLtHy9bTkwxWZgT1/EQXQngecuXAzbi5fDNjpplsKCvJQ8X1vpZfPUZzcf8NqG1WzyxjZb3rZY/PIF9B//04I9eJR5CJsiX/xcvP3ep+j/l112W3NrxO7+HOoJHVErx6b99S8n2qH9AqV++erAcop+PAc0/9ccVg+tE3RYpK+P0/scWOnv73dgmiAkzcU139V3pKysOb3WGlIviSmXaL2h8HS91ij643d8a68UbixsGVeleifASwpzms9rfSDlOSmU6KCODhDpecxNpu4a+71iGAPn4tn4/egdh2zn+mpLAzwIT0wB7oSWDBFsWMatXmu+giI2ypcaQyyMWPr0Ws9/keCc4h4zjKgMZAiXGx2t1bS2qaystE9+4hNufaP0Lu348oeNaf3ngKAb6X99vIm8PnHuby+cv2Df+9737PSZ0y7vPvaxj9luYDNBkrp/LK2Kz7/pcnVkac4wgYHozJmzDpxra21z9UqQm1zdat3pfbUTWqpX1KylW9+oDzGgUfkio5aPcpJqotbDSovqidaZcmuqtawOgcXy8t+Uhhs/fm19ULkI1NN99Te2vo1BlK5c3uxNXR5ho6Cuq6wF471y7BhufjBaUc8rqyrdenkza/5/Bc5hsA6jQhXu7sBogAsrLb21EY+bNV9ernnzUXbIoKOXAhJWzyiqA5EpIKfxAYvOjGBAwEqDDcObnGKe/FyLKykFuEPBSW9SDB5c7EYBasMD/fwW0E5LYiyqPtbtXpQppL6y2NRsLagXTna0WmkGqhnrN5kXt4Ne3CX5+I63sMS8qFNggadEgbmCgHiU2+LgiEUGpzDwyPRF3U3D2FKQQrwzgcZQ3QAEi6BkEGUytIjxIcycCH9IRApjjx9Ft0xAgMIci8NlV1TQGEb6yCwwwPAISgRKH0ChSCUsEV7i5c1BZTKH+q12Q1uICuKiDwgr38YnMBbJrE1eCMbL4ntZuKwWiNHaab1PP2XhxquWlVdoias3mo99AE9BlsXVlBLnPAw/IPqo5zklLxSIbHaUcEnfEIDFrIAj+hngEE9uAd8vIX2kUcYjAYzcfxG/R9Fx+t154kCZKo4KP1KEO7xs5HQgI+QKy8eEMYxxMDLCA1jSwYxYbjyJKaSN8svLtkgWz4HMPCh2RSeAiwZJWz99HRN7wWEeGbQywUk4MCiftKGL1yz0xGEbvnbV4krLLYt65i0p5jsojRRQzhhzvYATUUAOKV9Q4zC6M3GdHCZc6kZXj4uPrFKeNKCNYlQ/i2r4TQWGIFx6Y0yPUufCg0MYusiTWVmWsFKhaOcFYI7LA3zKo7xlrZcxC2t7ZAoAbZhywR1pFEU7KXl5EskDxbuw3DypZQQBHIKVPjI1iroG4Y5Tr6EunIIG/YOX8Hzkhzc7nTi22NQTP7DguZNU32RL2LTXvFX13Jc8KyB9hdQNjGKzyH0MokTUN4a7L0Bq+AkOs0zZ7DwgFWpqyQB7hSn5VpSca6nxxJe2KyhqKjxt/QsjNkS5zwGyysiZhLE0HwN6flKupSVQV4DoZucXbCAwYGPzgxj8+SylCONbKnmKsS46YZO4wJ2m/mbGJ1tJNuqG/mzajNcG5watdaLVuka6iM80tr0QnGuS5VMfU1DeSQxirEzOsrzUHCCGeBT3p6xvus8mPYLLSQRtPzQDcEf1CksZju9kZCYxZvJdxvv5GeoLxvFEoMQ0/GvlpmHA97L/hvlawNkE6RsODNkUxvQwfYZgO42T6akZlpORZ2nUfymCBUnfFFDBFOWdRP3JwB1bKuUsV8GLqDD2TPTZ2Ow490m0wsw8S+Z+yr/J0KT1As91D3aSB2PAJQt4+PXj1SLPUj0ZlhRMIc9LrCQZKJN8H4nikm0OgGl+ynJTUMgl7gH6lDBER7xgPcYSdYUytmfIbSZzhgn6ssACDhKBHZRXJZkFQB+owZD3QfJzAUv4LOFN4HYzQNvRe4lAkum4Hc7PKMbtWAbdBsZ+LLoTs8M2NT3mlAeLMkpw0ZdJ2we8BLobnR+1wdlBWwBayU0tRoFsSb0qEBwHuOi07mFcmk0MWJA6HZeaZOn0pVm4tsa7HUBLmtVkFVoW1M3i4rx1jvbZSHASt4vAM/TpMwDMAeTJ0+KB7YDsWq+2vArO6dCAFIo0T9JfwXRSXY2N6erbYuO65gavHc/0ma7YnGHp1dvzb+yesdBir2Pg3Ij6a6CDpDxcGHopg1nqIypgobggbTDVUpLSaHtSd6OvXMBATj3LTMm2XPI9DXVLzdakaLjAbwKAQ+PUgwnayAwKgDLI5yVkWDltMCuJtoSxehaVzRHKb2Cy3/yAyYXpeZabmOMAUBMcguvEPuCQeciuTA5R5yemg7vg0jM8YR1TfcCd3TbOPFT9TBLlkl2QyH45kNhimiV7i6wojT6UeuWhHxigTo8vTtEWUO8EGppkTIow706mf0gF8pKm4TRDVFoW4zT9+8wEEA3ue6XOmE2/lY9yVWp8NmmM57uLYLCzNkndHKPNzgloB7JLAAJMBh6X68PERPo21N3iI2kWwO3G8MyocsayU9Pot7ItEUg3BLQ7sjhsvdTBRdpNRmoeeZNDnGgnTCB6ZjqtZbTBKRH7UB1KAhpNzE601ALmBYz3KdTRvORiy/TTZzLXmEJ1b3RiFOjGh0s72qHyfm6GeKSgvlVAP5hvk2N+Gx0GdgLi1pAs6CsT9rko34fXAqcDxNye/Se+M0SGzLFuF0eQCLiSm+mzyjyvZbN+H2Z/6tljgHP/+AK/S7e737fK1q9KsRlU8UKMGzrQmZlBmLm0e0CsOH6v2eQCeTsLBNLZF8YN5QJQGeMLcG5iMnBOCkAIYJmHNldXnmS1ZYwbShcwzRAQTBC4Ig0wIkL4MwDogkCyc+Isv4TDjMRpjIN6PSMcchllLOK59hwEnSWz35xLvHNInx/gbRLFtH7AuCnyYI64wKzT/3qMaYkVkA+ZqGyNkL4HH7puh59vtZT0Nfb+QyVWWSz4hjER6MSP+lJhXpwVcLAyg+874SbWLkrfGPFrH+ZQTNesjY8yZkodiXhkQh0GAUVSsqkDFRn81k89RalpLACoR783n4B6G3MtoJiZceAS6mZBEa4Oi0if4B5eB8iDvgFAI3lHUPw1bSSMrJwQwOGC5VG/E/hvbgrVPPJhdAzIhWmPm1HRBrX3Xl4KZJpFTWbceOzxoP3Pb77sILs7Dq2zTWszadvkCelkyEGt1mvFqDllp1M+9JNRxqsZAKQR0tfbtmjdrbgYBZrRCiEjy2sZBfQCHPJNQEWyGs8YpcBzNEGbYM7T3j1DXxolrylX5mgB4Eu5nMwkzwsr6HMzNNelLVLhZlkbT3OwQ7CP1r/uP/pKTGvO5Wq82/+kn19AYYv8SqCD8QHbeOPB3qFf2ibYWxlm3gmtt744z+qz6DvcnGnWpgHrphk/5qGh5IiY2RKQLGlg7uljPpfGmJiNYrNsb7Kx6SCbHhJy0Nr7bT3UFOuMb/yVsvmVK1fc/bQeU8ZO0enm1FRbOkqMmsMNM751zAzb8CCKXoxBq/LKrJK5byrtXnsK44zvU9QVKob4H2BZFCAhnrRWEyyZSn8QN5dg87i8XEAlbYE6FSRvwwnUsVR+kM0cJBVomH5SjuWTKPM0AkqEWAWJ4hCCDhpQR+kHxxirJyinWUFWVJgU6pSfsWEMaK6vccoSF7NtBXP9QuaMwnsj7HstnlnEpWi/5R1iLHg/nQ91JcQeeHSCRy9lf3HKHf5JWp1lBfuKzV+lAxWspaT6yvi0ANkWTx2MmwSAZs9WSm8OdKN+a+3iADfatNI1TVucBqibod6F6Uf8QJ7ppCWLdpBIWiMiweir4hnzBJjBVTKPpf/gu3poXql5uoe66cOGpYeHkw/hUfrFYe6LnUtuhXXAx8O9oj20/YsNNpewYIWbqixvfRZudYkn87oo66bIMO6Fr07aQPsQbSXVqu9W+vhtCveib3KHfoi7wMAo/W8Yl9GL3YSrqWyS5vXcn76UYmAsjtokdXqCPNFyJQKnFof3igzyKhMPNImkKwJF7KVc4zI4soEbbR1QjJDPEbqlCGtQit2Bgd406gb9mS8XoNbBbepDqRPcPzzA/fkdfDwRI0zeC50dsfbpTst7b7Vl7crTuRvC5Tf0SzaCruO1WWu91so5K4+tuKnGcg4y7pLnnDWgWgLlaYG8QF83yVqG3yhfIpKNpAtgWHPpZLpr8+Tvgvo9HUBhHhieYX+oX4A9fQ19bzL5obxQX+9lDFD8PfRVYQYuhlYoX14Luibes/Oz9tjJH9oPz33fytaX2PvuvM1279rBuMp4GyJiklolCu6ijiyDczfyYvnPcg4s58ByDiznwDspBzRyvzMvbSz8JHBOLmGkCCd3NIK+YpsfWtBq4ivDl9zUSH1B35Famzbi5VpV35WRTODc0aNH3ab/TwPn5NL1hRde+DHFOYWhSbaUFnT9/d//vYPhvvKVr5iU5D796U+/GidNpgXXSd1AhgypAEjBTuCc4vPlL3/ZubZR3OVySap0Mor98R//sTOGacEg457cqmoz6I3AObnn0eaQjCZy1SrwTqBgLH46tSNI7gc/+IHdd9+PwLkvfvGLr4Jzuo82cZSvH//4x53bA91L7+tSOuT+QKc4BcwJuJNBY/l65+UA1dPkcvI7Dy3aX/w/nLBhj35dtdf+4Dfj7cN3cspHu2zL13IOvItzQOu9kxci9szTi/b8ibBd72QRzEYSezhWzMJ3/zaUzm6Ow3UMm1dFLPhZ6LOXsXz9AnNA4iRj9FudwItHcZN7ksfzbPSMTrG5Sp8l6GsXJ9127/TZXe/3WQWbqctl8pML5A+/ErJ/wm33GJsiB3Z47Z+/mujAz5/8i1/+T4Js4j3IuPgb/xU1Bja1P30raqz/m99K2SB3G1m//Fnwrk2h5u06WKO5tg6jaJ6tTXmtM/RZdTUbkTfm25oL63syHAhoysnJcYdLdBpdIJKMq7HHLypDNedWvHQJUpKql+bgWg/E0iCISAdvNCeXsUHv63eKZ8wArPcuX7pqX/s//xuKB/F2103bbTXuZ+Z6O2y0pxd3Z7h6kSJDUqLVfuQ/WOaWbZwg5gg0V+z+sbD0Ws9/keBcLF91Lz1UTlIJFzgnhTOV3X/85CfdwaisTAyXsUGATXT9F4PLXFxdKmLpWCozKYPMotL24osvsa57wKkMbEXJ7CMf+YiDIxNeo9j32rgoqFg+3Aj2zf0hXu4i33RfKXc//PBDwGanHYR59913ceBqPwbGPJe2pS/zmxs/46Y/us+Np69CgtRFre+kOi5FviBu73RATGrhFRUVTjnvLcX5R3f8sWeqS7oUptqS1tq6rx6CEz7zmc+4daeek9s/9tuf/kKJxSCEMVyAqNbrquurUQuS2l4u4NI+1tubAJpeD85J/Wv87Cnrfel5m6aNzNOxR8hzH0BS9ooaK1xdb7l1KzEAAXbMAYt2jdgYCozDbdctAJSxEALEQp7FjxU7vW6FVd980FIKizGUoW4ziapjW6sNnT3L3xZbRGVPIJogu9SaFVawbrNlUl8mL1+xlhdfsJm+TpQVgHIzMm02JdPSK+usbNdNlrF1OxAVyl4Y8AW9RWdwf4Oi/PilKzbY0mVzwE5BKY0x0cmvLbOSzesslbh4MfZHRsZt9lqzdV5qwM0SAJ3AP6wVXpTp8msKrXjbJstaWweUhrreLAo7QGWThDsKBDYFkDGDZScKKOPPy7HUqnKr3LzR0ktLHFgjsG6hodGGrl6xgY4OmwW68AKzSUkut6baiutWASWl28DZC9b5wnPm7+8AUMDVVkauzWdkW1w1cd2/w/I2rLOEtDwMICigST1ucsiCnddt6MplG23rsvkpgDjcm8odYXZ1reVt2mHJ1SvJRzQ1cIc1fOWS9V25arPszYQBj1QX4lJTLGlFpeVs2WhFq1YCAABiYNgNd/RZAJWpoetNGMdHMIijhoD13AeIV0A5F21aa8m1lbgBwtje12MB3K+ONbbZWBtlPQ38hRHQh8JHKupOpbt3WWJ8kk2+dNqCzx2xKSC4AJBKqCDfgtSHONxZ1+/djbLDJvNghFzAcBpBpkYGcz/5GuzosWHKZQJD1UL/EG1iDsMTYZcVWiHuq7PWrUeNLxsjX8hmrrXZwMVLNtHbg3Fd4BeTWqCrlKJCK9tcb/lrq82fngFUgqEPaZnJhjYbJu4T9NOLGGuxUmOYwmUdZVi6ZRdlSZxSACaBRxbaG6z/0mnKsM9mAe5kjPbTh2dxIDIfsDpj3WqbvHrJBh75XxbXeo3xAQApv9xGuX8idaGC+la8daP5gTB7UQI7ce28nW2+bHkVQGmAeiPk8wTw5sIsgEp8iq2tXmPb12wEbAOCZIwUKNI22GnnUflp7W23iclx125TgN+qCypsQ916q6+stzTgnPHpSTt+5Zhdar5gpeUrWGPttvycUgd4Dc512eUrJ63pwhUrzym0PVv2WlVRHfN8v3WNddiFzosoYFyxkQkgQYz02YzLBQXFQAA4qhybspqqCtuydgMGzHQAkQ47fum49QNwFVfQnvnOWM+ITU8CzzEPjGCArqguAZouwO3doA10U5dmI5aK8lJlTbntpI5WZq0A5MpAhWrKmkZa7UrbZdYnbTY9BdDoxlm/lQFaruOe9SX1KKvFkfYRu0o5X7l6zbJRL12/CdejlWuBnfw2BuB49OIr1tB+3XIYu/ZtvYn0VeDKLWR9U712rfmSXb12CYCG8qaepgKAFpUWOfWTOZREynOr7eb1t1hefoFdHLxozzQ8Yd39XahE4ToeVa7+jm6M1vOWlokFlHY8D7gWh0G9gHxa5P0+0jkZmEXpw4+KeoXt3b7bivKKaW5y+Ba0/rFea2lrtPaOdgeeheiTkgEC8wG01tVvtpXV9ZaZmoVRfMrON560q42XcKvG4bLVW2x93QbmICk2NTdpF5rP2dmmU7TBONtUv8M2lG6z9KR0AJtBayCNlxrOWRflI8N4Sm6m5ZLG9IwcYJBeS/Gn2Xu230Te55IPo/bs6ZesbbjL0rJTMEqnWX/PgE3QPkrzqu0Dm++0UcpNe5GC5DTvkWqQ9lL1XAcKND9yyon0KrH5i+ZwGstir/nIXbHxPvb67fr7+vvEXsfAuVGgkVHg2P6FPsDCLhsbHQbUCaAqNGdFNcVWUFjkVFtG6dvnGCMTaHNV5TW2fdMeKy+g32D+thies8GRfmu41sjhoDYbAX6c8+GKDiigmra+rWaDra7Bo0pqNvuDk3a+6bydaTgNsJZKG9xs66ijafFp7AMH7Ojp43b2+jWLz85A7Z35YkkN8EUUCG3IGvua7MLVizY4MEwvDSiA6llOaSr9MAqIGPDTk3ArvH6PrSgso00O23MvPWndE11WXFNiCcxReym/aaAmQXM5tLU5xupRgNaqVfUY3YPW29rBeDhNlYxaCfViI3Grr1gLXJvmwLn+QL81tjejwtxhI/S5Rp+bSJ+cnpZiqzest9oVqywnBaA9mGSd3b128tJJGw2M24a1a2xH7QaAtzSUGifs+tA1O3rqOLAZKl2rtti6FZssm/6Jgd+udly0M9eOWVdHJwSBz7KAcbMrAQsBrlq4dzruoLfgVriuDPCYvuQy+XH69ElgAkBe+pNxgNJ+8ifdX4ib5W0AeRXWeG3CGq+P2nSA/p325se1eXkJSvBbC23LugIgAy/qiSN28lyLdQDU09Wi2Ai4lZxKu8u3u28rQ60plT0SsyMnFu2vvvEiUF0cnhBK6O+YI3fRLkbHAHFJT12N7bupEuUs1EFzqOtU5IkZgLL2RTt9st+uXhqkD52xONRI80vyiXcWsC6gCwcJbt5TaLfjxpFqY1cvh+z5I93Ac8NWCli9CIg2NIAiFWD9xs3VtnN3iWXn++1iwzR1BrC5dQSXqCysgbkTcPudX5xgu3bV2cZ1hU7I6PLlYTt+Cji4DzAU5dhF8jotNWy1Vcm2d1+trVpdBIzrsYd/0GTPHukE3K2y1XUALp5J2kSvBegP/PSLW9ZV2oE9FairAUkCq9CV4+4ygrvroL1wYgA3zR02PzFtWcxVCnPyHfA7SZ2y5HlUvXJtz54igF6vXTzdiNp4J3PNePqJKgdHDXKQKsE/hzBDGfaacispp44CtDW3ztsLL7XimYIxhPGcMwf0o0BzhUEOvmTbbuYpiZ40a7q6YKfPdVljW48DVB2QQ/2srky2A3sLbcP6Isukrj71dMj+8ZunbQEIZzXllQHAM9LfD5A6agh4WllNru3cVWZbNrDGU7VkjjMx57H2tnk7f7LLrlzoAsQEMGUakZOXawVllcDowImAqrfsKGPPp9iygFlae2bsW995AQW1oJXQJhPiMhjrAKjJj1XrM2znrUVWXsV8HcpuFNB0gIMG4wEOdUDPJABoJzE/imf8DQQWaGOpQIvplsncKpGySyTj/QBlbq7P/CTAvKgFkOb8yLRNEc6a/HSrp88WOBeMzFo/Y2M//VqA/svrS2G8ZU0+E7ZU5nxZgKYeoO5U7qH1tvpGHRLTlZ+f7x7q59/Og0Au8Bv/aI0t9946uKO1k9bTCRzeqK6rBUZNxpUx/R9QT2do3HqAz0JDM1adVWw1RQWWDqg+x1jbPDFkvUHc1XLAIhkIdYF8DADJg8Ginp1oBWklVpRaaDnAyylM90XGBmicIygoDzLHGFkcAdbjEAljqg9YXsB9MXBsgeByAKQU+g0dsRlGJbI9QB/DemR2AXIqnGZZ9NBZAaDH9hHraxqzhQmgS2DYAuDnQmDizGnmev2JNoy71Lz3ZFjWB7DfZWhMpPFQDmHAuplXJpmHA6yu4HDEnjLAMubc7H1LES3MgYoFHnFTcRZq81j/uSEb7xqDs8bxLuWZzpovmbJbAI7Uo6AWBcU1GSB+AIfXAdb4fgruRzMyUN/0zHA4gzwECCtaXWlFlawdAOIGG3Gr2sOBG+bp6kPi6d+SGJ8yK7MsA5jPlxZn880Ag4Q3yoGcyTCu7IHp8xKzLHs23fq7OyySE7GS7cDyWwHKqf/cHriPvrQ/ZL3HOCB5uQ94Ncuq7qo0/wqgQwAwx23xPS9tkXMWNtkWsNErkzbVwnphClAdherUbPRFqX/zqO3pQEr6Sg4UbOTAC/k3/vK0jTeOc7CH+VIaB3GAAgeZR0dYzxUx7uXV5TLnX7BRFOpmgHoj0zQSAHWphsdlob63gv5oTb4lFIiCg2vrAdC9TBopxxnc50bhy7JRNsxd5PBJJ+7EQ72W/YFqy9iXC1gPnEdfJGU8uW2dPDVlV082oqQabyt31aKezeGaLMrQD9yK0qqHfI1MsE5sw6U948BQB+rGAcJgDZzIvlByDu2SNdzs3IIVr8y1HPpYL+p5gW5UkR/vtPg5v6XlZqMSCxxMGw9xEqWwOtfyV+RYJG3Wuto6UPljTT3KXhn9kR/QjtZvJ3qO2amBE1a1ucIOffAW24nXgySdymH8pXMj89FElo2FcnACgPx9q9ey4txbzbnl3y3nwHIOLOfAcg78lBz4N4xMPyXUt+MjbSy8ETgno9D9999vcsUq90RSYtMEV5c22gWISdr5r//6r537oBg4JxepBw8edBslPw84J2BNxhqBbF/4whecGyQZJ6TM9tnPftapPQg203cE2UnpTspuhYWFLk5yM6S4drApLVhOn8fAuQMHDjj3rlokyDj08ssv25133umU5uQGSe9rc0fA3CdQWFD6/vZv/9a5qn2tq1YBcZWVle7zL33pS87AJOhNrmkVroxcv/Zrv+aUNe677yeDc7qXIEMZXaTkIPU7xUeXpLIFCAoilItaGUhkUFy+3pk5wNzYLqA291/uD9kzp8MSW7Dbdvvszz/vt/X1mp0uX8s58O7MgSYguZdeDNtjR+gbG1g8crqNfQvLR9Ri/xaf7ccd627+1lSxUcGmXMwu/+7MrX//VGtDcopTyx2c9jvCyepHn0UyHjVAiZZgkwRg8dihzV675dY4uw03rtkZ79x5zL9/7v3ojt99YtH+y1+ErJET3rVsfDz5nSSrKH1355VO63/6Py/Y4yhMwj/Y1/8wwT71H3zOJcqPcm752S9rDgj20dpChlQBaJrvai4vg6mAI6lKS1EuZtjU+3quzfo3Mrbqc12xv29nvsUMu7Ewte6Quphcc2odpNeKk+bqWgNJXUzuyGLxjcVJ4Vy7dNn+6qtfs3gMFHvKy6yKk+oG3BBi81OQTDw7luHpgNV87Fet4OAtnADXEWdtqi/RW7GwYvnyiwLndE/dK3ZfvVb8pQgkFXCpm0mdReu6D7I2qaqqxFVSgr6myOqfpV1X/rq4ul3Y2GeEy9NF8m14ZBhXSY/Z0888TT1YsDvedzuHre6wCtZRMgLE8l75G4tPLO2xvCCoN3dhGHY3xvCxyCbzCEbyx5943J5i3TgFQKNDSHfccYdVVlU50G0p3gR9o24tpevG69d036oDiqcOZqkOHz582NVdKYVrnak1qOL/dl6KWywfdH+tb59++mn71re+5erlJwEab3/v7U4ZQvn4pq4bdUzpHeZA2vdIy0MPPQzEmmm7OGgm5YlEjMFSUH8jV60R2nTjAw9az4svYOSjnWJICWJ0D6ltYCCpqAemWr8ZA1A6CgJDNnbinPVfvIzhcNIpJPhwUxnid0wvLL4E0OW9hyyrvBKlL4yLl69bE2p+o31dKEDg8gvwy4tBArwMkCrPqtZvsQJUbgKAGo0vvWDjfR24cEONBcWxYDoKNsANBZu3WcqadQBVrJfV7lDLmcXlct/LLwG3NdCWKeoUwAOMaHMoDSUiIVCwAndwu3ZaKopos82Ae8dOojLShTtMjHnp2YB+wLSoxaQUZFrxpnWWv3Ed4CvuEJt6bfDEWesn3BCGUQ/5Fk1Jtjmsq3OAJAk5WbaecHMrqgGJ5m2+pdV6T52xUQBIqUtFyIsICkRzTHozUZ6srVtt2ZkFNnq90TqOPGPezmYMvChkFZXYDEp6cRzYK926wbJrcTWM8T0iF5ioBYWvXbGxk8esrbHBAihgJatvIn3BmQBGOKC82lVWBByTmJEGCNdsjWdPWN/wEAYpDI4Yp6QGNqd6gRJa0ca1VrNmLQbfZFzGApccPWEjQHbzkxh3MeBC9gHDyZUYBmRU4iq2bLKMVaucIWbklWM2ghqiAIsofVy8DK8Y4xZIazQ1yeoP3oyiUo7NnrpooWePWE9Hq81R76JlpRjgssyfnWN1O7Zbdv0qiwI+4liXuGEKROVosR2g4PgZG77cYLOTqHj5UYxDGWR+AcUkjMTZK8qtas82QDfyunfY+l8+bV0tHbCFuANNSsUblOoprrBQQyteR3mvRwUUUC86Th9/FVeTLwJrDo5hvFaeoA6B2sYEahEJQH0rgTFzaqlTKLrNdV6znuOHbeD6ZZTYWK+kAT0At8VH5EITeA54tPimXTbX3maD33/IPI1XKIMEW6BuTuYCXAPOla5ZaQU84nIzrAWXrk+cetYOn3rekvPkvq8EwzeAI1VXalYjw6NWnFuIYsROoKh1xC2JuXq7nQf4auxqxUQWJs7UO/q9+SmMyLjuq8OV6tbN220FCncL+Ik7c+WEHTt9lG/6gGH3Aq5sxTjpRQX4nJ2gHc30j9m2NZsAu/YBxlXYNGP1iSvH7eT54yguDTDeJbpxLw63uNRa+o1hB8Vu2bTeDuzaw1ouzxpbGu3pF5+2pv5my2Jxl4+qVgIbIQFA8UEAEx1+zUXdZkV1BWs8WjfG49lAEHhkwhKRut4PzLyrfhcuLjOtpbvNTjScsbb+dmAWFK4wMqp/mwKsFbVRWV5tu7fussrSMtfHX+cA74svv4JCUNBWApXesu825yazqfW6PXfkWeAIvFsABR/cdcBK8otsCKW+sy2nceF2zKYAoVMBSlPT6Wuo34v08cMDqH9hYV9Xv8k+cvBXrKyg1E60H7cHXvm2NTc1Ak9VWFE6/T1lHk87T+a3k/Ql3f29NsnJo1KUO3NQ3aQJovaEGtPYJACk3/ZShmvlZp29vCEUs65cuQiY0YrRdI5wllyDBjHGL86GASZKbPOGrbZ29QbUpPx2uf0CZfgKIFuf1VRW260HDqH+V2BtnS32ysmXraOnBQCxyvZs3m/1RWudu8Ez10njxZett6+TOuqBG02hPeA2lD5zIYjSVt8IB9XK7GO334WaTwl1bcAePvKEnW64YHG4nCstBaKh7gaBS3OTi+yurffY1MCUqwt1dXVuv1P7pRozpE4kpeCYQvBrx67Xj+n6TNfbPV66QPknNma+/nUMnBunPrYPd1rjWCNKiePMSWZtDDi0Y6ADI3Ua8E45cBjqlNTfhdkZmxrDhSjW/q0bd9m2DbusqDCfOt3jwI/G660Y21EtAsyKomATBGxA+s/KgDy2btxGHVrPOOSxS61X7YWzRzB+j9ialWtt3449KNjlokjUZ489/aT19A5aDTDi3pv2A3eqXgfsKr85fvEEQBrlB5SRAawcB/y46AM0GB+wBdS2KopW2a373gcouwL4kgMPP/i2XW+/ZukAdrkAn376Ej8ASDLt3SMFxsFh6+gdAp4oB8pE0VDgMP2c0i83s7UA1nt37kd9thSluUk7dfEMgFabzU4s0KYIJwG1p4Vpm0QlMwFYfj2w5MY1wNyJxTZA/3nk+POAqlepVwV2C31DXVkFKlzDdpL+5MyZ8/QVZbZzxwGrBwaOA4Bpp94eO/2iNXVcY2xmLEdZM4m8DyfixjWC+8ZO3LEXVdmhPe+1jbi6hlgEBjtmjz/2QyoQ0EYFys+MM5INS4qWW9zCSoDWMAdfUJGEl0gBjvar36IfyUoN2bZNpUCn5dYL9HX0eYBAYL1kFMRSAUWDuEql6wP+SrQPf2CFbV+Pch0s0Qvsi9z/t/QvQKo1VSgCAl/4QV+nAC5HUftKSSyw/TdtsYOHsnCp5zNEaO1KU9iOHOmyi2cbUNcEriWvElAzpHtGfc6Hi02pf03bRz640j7xsSLy1WsnXgkCXQGiMrYUF2RYcX4y6mcztPMU27S5ivaVz/7ZvD11pMUuXEc5l74wJzMOuA3gBMAoAXXXnYyfG1aWAfbN2JNPnXPQXCIKhmmZrDEIy2MTpC/C4ZGNtnlLtQUAFX/4ZJd9/7Fm6xvPtvKiYsDqeUCiIaDncRsZGkcBzG/vPbjKbr+tGriS8Z89o+stC3b4hU47drYPsDlgeainZqD+F8LN9DjqSn1DfZaWE7Y9t6y0j95daYUAM88/ccW+9/B5xp2g5Resoo9LZq456Q4YblhfBniLwmdxirV1ROypZzsA5y6hBMieExB9ciqATGiYsWASO1CB7d68g4MYqXb0xSm7zHww7JkCSkUdFpXd2blxp5C39yba4OZK1ADTAecW7Rv/4yTrgSnU9sqoh7i0jg8wf5i04YmAA+vrV2bbh+5ZZRs3oFQG+NJEPF58ccCOHaWPG5mwNMbedOIhFcm5xRxrQ6EqONNun7h3o917e5XlokJ3pWnSvvK17wN8T1hBbjn3pj7TZlJRNqtbl2Gb9hZZdingJPshbUMovk5N4JZU4D1jHGsWCpVDhiHyMGD5ALz1FUVWyO/RK0MwDPUxwDkGckDXiAPnGgEizwHZzwCPrsnLsLqMZOdudp5xv32gj/nFoE0xbUvF/WlihDnTAopoqIimAJ3NTU44CFrgmta1GmP1Vw+t2fX6515/xTrdn/JX/bPWUhK4EDyn17pnAYca6lbWovjppy1y0A6/mh2orfYCBAeHZ6w8s9BqGPMy6TemOPl7abjXmqdRIeb3uczxEjmkFQ5NUz9RG6afSEY9tyi3zCoyc4HaWB8x7Awy1raPTdgoKrMh3K97pErNPDq8AKoMeJQFXFzCXLSY9prKvFxAY+fMlDWgqDpGw5YibjalkTFBH9A2Z8NN3ahWEgfGyQL655LEVEufY+4/gsriWDKqcfOWdwdKuB9AQ5Q9bzcWwqktssc6c3TGBq50cWAlySnOJQicQ8VQy2hFNhziyE63WcdLQzZ4DcAtOg/cjzttD3PrBfq8WT9gPqrKuC5eubPKcnYW8BzI7uyotTzThlJdHIrVqKDnLth04hSK1olWtLIcl59ZNtE2a71NjCGkLQk4FibVKdCFUT5LYI5fvLocqCvDRoDPhtonWR+gyJpOv4BaZsYCh56GUm1wqNeSilB23FdmaTszzIeioiN2gd2CPRwMONppfReBzpjzV31olXlqyU/AQKnUM702D+B3sI2951ND1t9CHpIvGRyS8uNiOYr03CL70vPjwOLpgGK7si3roHRYUdz8/pT1nu6jnTCvyqL/y0EdMoEDXNnxVlwFOFeTYwMc2ugBDAxNwO9Sal5A93nayDxtK5u5W9VKDmnUsbYir0euB2zgGoq9HPJguATuk7t1v2WOo6SK6txowpQVfpg07s3EzS+HjFQ6DPPhQdQ8T03b1RONvJPAOqHGcm8BnEM4HGlI1Ar5i7JfqCVs86cnresSh1wA5OhEmFOjdK5VLoPUKGC7XABv2LuSdKajzI0qLCqGnX9Hv4bKZTzw7GIuazTU/cLJYSsF/MsryQEuBdrjkOY0apv+SLr5gxxoYv44T7u+0nPZOlCMXrGBOeGdO23V/nrASJQnUUx00Bx1m+WL2yqhyhCXG9WOvz/vtQzO/bw5tvz95RxYzoHlHFjOgTeRA2429Ca+9+//FU1c3wic0/sC4+TeRi6WBHBJxU1GpJiLUsks/93f/Z0zHPxbwbnPf/7zqAM87AA5uX2tr693yg9SP5D6mlTb9NAJR4F1UryTy59du3a5ybdU7b773e86mel/+qd/ssrKyjcE5zRpV7oEpWkiK3epcsEkFTmFIaORFg1StBOAJ5cEAtz0uaA4Kext3LjR/uZv/sb+7M/+zBkBZZDRKZpjx445JTkZ1+677yeDcyplwXn6vVy9yu2s4Dud2FS6pGKn68///M8dUBc7xeneXP7nHZcDk2xiff+psP3l10PW0BuxQk6dfPbeOPuD32EjHinp5Ws5B95NOSC3rEdfCdsTz7HReSFsnUMYSlicy4PWgW0+u22vz3Zt9ll1uYdTsCxGtRpdvv5/ywH2mTDMRzHIRez4ubB9/5mwXWPjEA8QuEgxq60Antvutbs+EGfrcd+KvXz5ek0O9OBW5JOfWrBX2iJWjHHj0e8mYiARAPSaL73Lnl7GFfBtn563QQC6KjbOv/kNTsRv4hTvuzhP3k1VILZBrjm25sY6Xa657izGZClAa/4t4Ci2Oa/v67keeh67Yq9jn8Xef7v+xu4bi2/MwKo1h+b8irvek6qKVPJ0Il+KKyWANoLnFK/YpbVFiw4Tffm/WqC319ahuLEF9aZyVLWS6qpxTVjAJi4bxp29lrB9h6XhQiPOGf7YxLyR5lh4sXj9IsC5WHz1N3bf2HOlVevBBx980ORidRsKcR9ibbJ9+zY2fDm1HLsUX6VdyefpUnx57vJjqQxnOaXf1t5u3wXQOnv2jOUBj/wKa7adO3c4VUF9N3b/mCFdr5fCWqoLsdv9zL+qMrF6o3ApCwGbOjz10EMPWlNzi+3AsKi1ZczFaYSyeLVe3YgLv7yRrh/FTfERRBlzbXr+/Hmrqqpya+M9e/Y4BbhYuf3MeL7JL8TyReGqXqkeHj9+3L75zW86N8cC9u6+626nWJ7Apvibulz+oCxIupubm+yb//xNe/65523jpo22/8B+62jvcKDoDspH5Z6BskGsXVF+qgAAQABJREFUXBR+eCFol7/xP23+WoOVoayVXFVoi4x3QVxLRgEG0inflIoa55Z06izKZkeOWhRDVQbAUGIdqgO4k4qQnlmUMBYAcgrXreIUf4aFrrba2DMvWcvli8BK2VaAqlt6OS5GWXtPozY2j1vU7KIKgNVMCwOetQPEDHQ2oc6UbKU7dpgfRSdPVgFGBlS5iEMU2ETA1UJfr3VzSK4LIC+Jel1JOv3lpaidAI+gxjOCAtx0cMFKNmy0wjWrgfKu2/DxY0BQfktdt94SalZgscRMvohxFVWpuCIUVYAO/Lj0CT5xxDqffQ61IozytSh8oZbhL8m1WQxRU/MBjCFmpdU1rP9IX3uP9R592fqbWy2NNpS3eoXF1xTZHK73ZoDd4lEgyc9GcQyFKbkY7XrmcVu8csZycE2VRLsLAqrEAZYlZ6OUiDIeHQ8qB6jb4CJ2+vHDNkWcx0hHcn2dZa+qd8ok862dNtTajoEFwGAz6oG4go20NVgTLmDDuBTN2rDaMsvKHdg1qT4OY3FaPm4iC1EkQ4lm9sxVa3jyGUCeeSuorLQ0jG5xRbjRwmgbCkxiXMYgXEqZ5hahCDJhLY89ZQso03mBFbLXoJ4H1BAHnDeHikMgtECZrqasARGvt1jomWft+uXzFl+Le6Nd282v+pGJa79cyk9KcJS7F0Oyl/q2iDLe5MlTNvD88xaPco+/bJWlAn/5UnDr2Y9iF1DGMG4pSzfWWvXWzRZtAn4EsptiTpsHQJZeu5o4Y8TEECmIz5+XZBkluONEHWSxtdUGXjpubRc6cblXZsWr1gG3UT8o8/EA7jOJfz5AUhoqM/ODlOErT1vXsWcsJwFXfgAwCVVrLU7qf5wGCqPkhD87y1i/xiAjbBJYd/7saYuixJO8e6/FA2/58nBtmolL0ixcyibGY9jussfPPmVPnHgMxUOvrapZZ+vLN+AaMsemZ6dR0zmN0X7Y6uprbA97bxmom509d5b9q0byFiWzFbW4H0RBCehjFKWi5itNtMOQrWB82r5jJyp4GD8neu08Cnmnz1+0dCDMTdt2Odd+ly+fsa6GFluJm+N9W1BjK11hEfqRxr42DtE8b83tTbivK7TV5F82oObE/Iw1Aro0NV43D0qMu1HHOiAACHiwqbURoOMpu9Z9HUgjBRWsrVZLeAHa4EXqmwzkid545sT1tmol9Y48C8wsWGPzVeCvJpe+23YesrRoqp25hAJf42VUEP2o1JUB8WD0ZwztR8Gt4WojLlJnbT1w7E7uX5iTB+AxYacunLOTly+Qp3G2c98eIJIka0AJsunSNSsjzfv27LVVgIQ+yvxiL6DdpReshbBqSsptfU09Y1EG7lJn7VrTdbt4BRdkwbBt37bbPnLgoxjRC+1c1wl75BX2Ia9dx+VrMap+m6yqrApXaLSHuAiwUpOdOX/Wejt6rb6mzjav2wAcUsSaN2iNTU3WASRQXlJA3HZaJnt8l4hbw9VrjD1RVPRKrbQExRmUhgTedQOJdrX2WEV5FYqiB2xFZa2DmM5fP2enqU9SR9uycytwUpE1NzZZy/VmXDum2M6t22wDKl9Z8Vmo2fXbIy/8wK73NQCepNralXWWiyLiFH1ES18ne4yNuB6ctmpAhV99z72o4pYBuPfZQyiWvXzxtGvb69ajmla/RtgyqpZR21W6F8W5MQdbaOyTQpHchmvskYeP4uJiB1z8pHEwNqbH/r6p8eotfOn14cdex+Z1Y4BzbUPtNsJ/Kbkyxi9aW3er/X/svQd4ndl53/nHvWhE7x0gOkgC7L2DvbfpI9lyZCmb+LGfjR8nm8TZx0o2ceKVvYk3WVcVayRZ0ws7OSTBBrAXkCAJkGgEiE703u4F9vd+HCi01mUkj9ZjDb4ZgADuvV8553zne895f+f/v3LrClCwR5lZOZqVPQ+wJwm4aYgyfqTy0geKjUrU2jWAibNzdbfypu7cvsuz0sXia+5B4G1/+sNh4p2HxLkdbZ2ayTNp0+qNykrKQOGOsnp4C8ixGEjGX4sXzlcC4HFlbRWLrUuB6GK1BkXE2bTFAJ6LzagRXuZ8bt69DswXQT0UADQl0C5Rm2yqAK4qw4JwHHeLhdq2fpdmpmepDSvUD469p3Je8wO2mT07X3Oy5wKPxBK74sYCiH2z9J4qeRYlAJ3OnVPgQHpm01pV90gPK1HHRO5o9ao1ygOCbKI9nLtwEZE5yiQhQ5n09RGocA5gx1pdX6NbzFPHpiRq3aotWjBzORybn+5Wl+pKKc851ETnFszmXs9S32Cn7t65hbqpV0vnrkJxbwXPuVAglmadoT99wPn6AmTk5uShGMfzdswNxPhY92ruqvFJgzLT87Rt7U4tyF5CeY/r6u0rOnwQxV+em7mz8oBLUfCMzdZYd4oe3QjU2VN3sM2c0PzFqMChqhbECsABFLxcKDOlAKOFAuZdPP2Uuf4G4PwArduWpFTgFQPRurtRCZrg/pqLXeXMUO5H6XwJ4NyfX0HBsZtjJdHvJCsHi9VR9nnnVrfK7vUrJWmmtu2MVOEuf+69SX18blzHT9I+sLxeMicWYJb7OyqEebAe3bjaqdu3gNh4Ln/lpTn66i9jrc0cccnFcX3vLztUivpgeiJKkBtmosyNglV8AECZ2dm7VXShQceKgMcnY1FeS+a+Q4kpdAAVUGBfIOXU+HQsGsN0+Vy7Pjp6FegwmVg7Q1nZQDn+xENjPUArAwCACZqZEYeam4+OnWrVX757Xw2dUdjQztSq5ajCp1rs7EEFr0vXb9xVbmaoXjowX8uXxuPIyvzGsTadPF2GlXMwfTF96Nxg8iQ+qnviBbb06Oqde4gRTGjXnrmAZSj1ApWePvxE33/vju7UDZOjWaiVy6K4lybo5/y4twD8secNQzWqpGRcP3jnnh4/adPsggwWcCQSB7no91Bh5SsaVazEiHg9uOVCMRmb3pEWLV0eruzZofLj8919xAj+Y8qaGaj0VJSIQwJ04uNx/cm3LwHltXBvzwe8TtCsHEATYpmKyhGUB3uJ79v0KvWxZVM0alfUOyDjyVOPuW8a6INjuGeTGL+4URP06urNUV24haXicK3++Zdm6cVdyYqOQ8Wwsl+/9wfndf9hB3WRQ1tJU34+SnxpY4pK8FNEcqCGgFgf9fYDcGGJjfJTWijxEjGC2Wn2AjK1Aeq28loiz4UCvlIBx0FcUdgDADK1KOJKm0LppUWUsZimDLvlST47LzZSmUBPbuJrdF4B+1r1ENB6kGMkJKA0FopNO5/3Ajq7eAYE8Ox5piKLhTh9uI1vLe9lX9ZnTvWbf1N//jN0zT/er33WgOtKno0G0Nk4OjMrU3NmZ2Fn7CJOG1MVsHIlUHNrD7EmE5EzIxOVjqV7OOfXiyLd3c6nKu9vpY8eVRr9ZzKLdWZAZHmA57qBPnuxDZ1BHJUODJpMfG3a0TU9barvRmnZ7VFkaDCW0agooh43Pgys1Il6GzFcBNBcZhJxPrHE4MigHqGmWA1U5eMbqiQAvxziGP8qqf9ijxpQKu5y9yhsVpTysogfgVR9sGfuL0MhuX4MkG1UiXtQgwOcc0VBS/oAWBMmjj/CkvMCipIVbcTA2DAXYjGdRcyLTekkoDW8Hrae2DHfG1HZ6TrNAEhNzAklBmdcQ9l5n8BklY+pvumJ/EM9yl6TqYhVOFYxv955C+D4ZIN8OlEZBdIMWUDdMn/rBjAL5HnnRwzUUT+igQ6A7wAvcDGKnACqHu7dwfJBlKB7AC0pSxYjtaM2C8aGAiigaRatLoTGV8cT846PntQ9YTHRhNIKExWyhnEiVscT9AEurGM9dSyYKCa2vtfAuCNG6a/M03gOTDngl1ksB/LcVKNXvSyqfXylDUVMYFr6mOh0QD+A6EFAye6KHvXXDBN/pShqLUqK20gS2NjxPZQar7UBKPcz/iCWnxMmVyoLDwD3AoGeA0J81d6Eglw7IJw7UOGA036smBqi7+2pYgFXk0uJ4Sko/QHAssvmKvoQFiBFJwQqcmYQi64oeyykx8tQ6axCVS5gQEmvxSiCc3CxuN+Z7jDFOSxs+270s9irknsyULkriQe303FgJ20Aow+248KmefA6+sIlXMvTLvmmYTE9O4znE+M3oL6uyj4W+DRyXROaB2QcsS6UBVIuDWMB2/PHTQCjkHcoiQbOQ5E7kzYcO6ngOBZ0oajo6fGgGNuKUAevh6AYPkaZYkvcU92pinsP1Mx4JXs2ffP2fMUUMs/m5CFRpPMFQkbNEUbS2bgkMEHq7dmvP/X3aXDupy6y6Q9Ml8B0CUyXwHQJ/N0lYI/bz+dmAbJNyP/Kr/yKkxT67ne/60BrdrYGyX3wwQeOslsNk4xZWVnOxHpLS4uzwvC3fuu3nASYTexbQsISCmb9UlhY6ATJpjhnsJtZmpqCgSnX2WZBsyUvTA7aVOQscDcVOVO1s2NYksJWodgqe0u0GVT2b//tv3VsUi2BYCpxv/M7v+NYwdpqR9vMCsqAtq997WsyJQC7LgPhpqxa//N//s/PkjW81/b7u7/7uzLAziA3m/Sx41giz1bgnDp1ykmS/fZv/7a2bt2qP/iDP9Cf/umfOn87cOCAvvnNb7KC7qZ+/dd/3UlixLPK1DaD7GxS6TIAnZWnHdP29W/+zb9x1A6+8Y1vOEkWe68pctiE4h/+4R+qqKjI2bet2DTFuSgmuV599VUHDDRrqM9y8GLHnt4+2xKwfFQTK9DeeNujP3ljXO2susnP8NG//uf++tJ+ovPpbboEvgAlYPdBdd2E3j7k0aEzXtU0sLKOVby2mHJerkv7NruZ6PVlIuoTYM6WOk1vn5sSIOfmAHR3Hkzo42KvDp326AmS9DaojgBwXDgLBTrq77V9vs5K4c9vVPP/b5ES/mjfayM6fQ9LKwrlv37TX/u2oEbBRNIXcRthYun3/vuYvvkDLCdYgb9rrlvf+3YAk+PPJp6+iGXyRbxmi8FtbGBJS4PQbGxg44gcVJMMnrNY32LmqffZ5L3FuqawZX+zMYC9bpv9/ecRB9v52bFs3xaTmzqOjVcshrdxjr1um73Hkgo2VjBYyhTzbOwxdc72HrOUq8fy74/+A2MTAIM8rJ+2pKYrf80GzcDm0QUUZLYoE6hzeKOj5J+ZITfXaJvt37apa5w6p58HODe1bzve1HGnfrYysIVFR44c0dGjR53E/ssvv+yom5l1J1P7zyaPPykzTtjO+lkZWlbnx78z8Y/XVSmqNKaS1tDwhDHffH2FcZEtigpgQnvqmM/DWVPnM1UOzps+zTen+Ozb/+xzJ1EhuH3rtqMMbou90mamOePUtdSfWStafdn7rV7tvO395JOeBSzO785OnTqxhU4GFP7oRz9yLFttLGtj3rlzgW0YQ/7U5/sprun5srB7yBaN2UIya5sGb9qithWAY88Uyf/ndf+Nu7Y2xtcA42vbx1tvv6WHwGI7WQxmsNz9+w+csfcyAEMD5yIARZ+vGwPnKv7kO/Krb1QSxw3ITUHFi8l4OzRAgIsElY8blSIS3s0XL6kN29U44JbElavlDzjlQs0B6S5NeACNfFFsCqfcgBP6Tl9U65GPsZPCdmgNgND61fJLBEhCnY7bhS/UNlD6QBRHEyziazp7Ws01FdgohisZADcwMxd7WNKLJCANtpsk4JwkgdoHxFBKOx588oTkb7bS9+6R2/Y7ho1sK+jEeWA9FNv8gMqyF6FoA+zaBbgTDsAVtnyV3Jk5gGCosqFCNoEl1CSJdjeqchMtber83hvqZr4jKDVTMZt3KwBozUUCeALlFw+KH5PYpfr78RlUaYavlunOqdNOG8tlsWHkqmVyp8XKizKB55P7yI9ycyGBMInKY+Oxw/ICTkTSXoO3bpObuRIXSnlQbrRPCpsEoRfLyRGg1Cc/fE+TDx4qPAM7oU2bFYhimw+JN8/jenUwr9N4/w4WcSR94xJYrdeiGqCHsJxsxa5aoYD0dMotSB4XdcICCXeQH8pCKHA86VP7yWKVXS5R4kygCaDKoPwMuWwVBRZEE8A7VuU+AahYocTRfoNE96nzihubVOSSZQpevUJ+WJH6cFtNeLB3RVHBj+SOdQ3eqlqNfXxaFaU3FYJqWerOrQpIS0ctBi8rX5JD3IsevvzszSQsh6prATDPoqpXguoIeh+rNisA0MvUOyfaHqv+4nGUFy5TZ0HKp1y9j7FfvIv1YQh2soXbNGM2ClwkNCdpnxPAamaH5RvIfdDZpoGrxaovvkL7ClPawkKFFiyRy+Zb6JtImz1ro9Q7qSausVgPTnyEPddDLcaCO379BrlTMoTnK6AotxVKYRM8v3yB4iaeYttLffdxj4nrjti1U8FLF3NsVF9QppM/12hWay2Pdez2cZ2+dQKIMVxrFxRqZSYKdwGx2JEB/d68oKuoCc4ID9S6zetQOgzgvi3BTvup5s4vcICasGASetwjBnKU3ritmkfVTuJz/cZCp81zV6IAVK9j506rvv0pVk9pJGdRHCOpHhkYqq3LN2pxzgISqoHq8fap+M5VwICrdvXavGGT8pPnY48Wqi5PN/atpcA/l9Tb2qnlQIkbV64n+RyJpV6ljp49rpq2WmXmZGjr6i3KTcxT5zAqVHe4hqtXsFmOALRbqyXYLgbzmeHREd2ruovi1HnqflKbVm1UwKS/rtNfN9BOc+fN1iz6uHDKzJcyG6RvuHHzhiqo20istLZu3KS5WfkAef5q7mjTaernXnUFkCM2acASHU2oyfmEafX81VjKYvMMrNM71qWzpWd1+cElcpWMZVZu0JL0RQrxDyYRO4Da2m2dvXJBbRAzCxcv1YtrXlJCWLxu113XoUsfqBbwdSHKVxsWbyIhPxO1KawFAVEuYQl7/tIFdbV3A0ttAERcgxpUPDylWVFW6NKVc8DKQ1q+ZrmjunjuzEUU3tqVAxA5Zzb21tFxAA+ogAABtD1tUdHxs44t67o167DQXeeAgM09zVhhXkdB8Cbt0we7tnDsOgdR9gvQwtxFWkbbTYyK1zgQcxkWre99fFCeGQCAyxZpUW6BooLDnfOp7qjXuYvFqq6pVXJ6qr68/WXNjpiJ+lmj3gG2u1VZBjwbp42rNwErzUcdE9sz1OlmhRSotfap86yxuMcWUz/EAtvmTOfPB/YEsrbt5/EcdHb8Kb89H9vYR6Z+nwLnzKq1ubdVgYlBAGSxqE716xY2qqfOn0KiTFqMmtWqgkKA0AT6hUHgtgcq5jkx2DsKxLNYWXMzVFJahE3tE2VkztLc/KW8NwoIhlEySkl3H9zRbfrciWGvtq7ZzL5YHOE/Qw3drbStIsq9EnAMUCghFjXAZsuZa/l8jjlnJapokWiZjetO5V0V0zc1tj+hzaxAERIlzoA4DXlRuGkuRQWtCNvxp5qTlq+dG3YDzmWrhf1/cOx9rFXLlZiaoHUrCjU/DUVQKCCvD5aT5dd0rgR4qL5FC5Yu0Vruxcxo+n9O4FELVqpXL2IT+lT5cwDegN4eNzXo5i1U4mLjgN3ma2YC1pP0W+P05wZmHjt90lGnXDZvlXYs3q+4kHg9HWjRjXuXdBFA0Ov2KjoxCtXhEfW0tmvhnAVat2CjMuLzAOEHda/xjj46/h5Kbv3KX5TPNS5XMu3QALzazhpdvH1WpfQFKSg8bl27C8W5RTy2WdRxs4TY9DBA3AwWnBdqKeWbEJzOMcJ1+cyI3n7rHIp7wdq4vUBLlkYT5xn6ydy8C5vKAJe6Wjx67y2spa90K31miva+FKe0TFPl4108Fnx5vkWgZBQI4NTJ3Mf5Yo/+8M+KUa0b0/atBSweTFBaChaKzPHeuDykg4efshDATc4jTHtfDVdV/bjeOQQoebNai+YE6ZVdacS70Bgoa3aisnnm4wH95Q+rAE8H9bXXCvT1X41FkdJH584AjL3ZjZJorZYtitev/nKqsrPsvHjcEys97ZjQ+x890mmAlBnhOdq0MQ11NH9FouwW4O+h/0DdG3Kmp8lHxw+2oCR3XQuB/7dvTVVOnilj2vjFg4qdh/6XdwKHd7HA9eCxBr35wV0WEKRp59ZshAFmoCxnbVkAGP361l+U0KwHtGsnbW17DiC8W//jT+4BcJfTPwON7ZsDJOlPGwemYb6vqHhcP/rgKnD2iHbvma9f2h+neOKcoqM9+v77pXrYMqjNG5dp7+4YAE2cDcKIA+n7/C0epApOfdyn7/6wFEDNTxvW5Wr1ugjFJlGHvmim0qYcQBX7w3MnxskD9XGcPlSk4zRnHvAe0myTSD0F8HwPAxQJCkQ1jf9OnBzTn367GBivEfW81XppX7pyAIEsxKh9PKFDx7t0+eotbVibod07AeXDfPXh0V6VXKllYa+vdmxO00pUtcIjrI+e1InTI3rzIDFB6wN9/ZV0vbAnHiU7VFyr+/Uf/8/zWGR3admCxfrqL+VwjaiPhXvkonzGefQ/AdC+DdTWjmpaIoshZpstJIqkHoKIniGUqTqeYh/8VFHklPKTsHZHzYzWQwzC2Nj5InylbtpQr7rX1qIG4LLwiBmay3gmmfY9yUUNMo55/LSN+KJN4/4BykzNUmpQBM9D1JMZk423oATGc9XyTJaDs/HPVL9t/eVUn/l87M8p/L0326+Np+1Y1h9bzs7Gt17+lpWZqQIW4/hRb/0a0wMWbDwCmuvnOmMYR6ezMCYxIIRrwOaY+7SU52c5VvSm7DWbssoAHg9yEz/TB3cCWNYSg/WymCc+hjKOitU4MXl562O1jPQpPCoCkDAWS9ZAypW4kLh7hP66uaHZUXTLmYlyGYtauvq4H3mmtxHfxcYmKpexQB7HGwaGajvcpUZA+f74EUUXArHOT1QcMfwE1p+DJWPqvo6VMvamiXuSFXGAGC2KD9LOEVN7pjh3ESgQpTPfPB8lAs4FZxEfY+hFqPzM7rQStbvLqAbebkE9OANVbJsv4L7kf28zQNeVAVVcf8R97FbOmhyFrmZ8QLvvuNmtho+bFDIYqvh58QopDJRvBm3dhtsGdPHPKJKHHsgpH1b3UGSE9CzOYnH72INx9Zejwgy46cGu2APQGJYdotilwQrIIZiHsvJUezVawgLB0hr5R4wrZU0SamxYu6YAXFofyn49j0f0lH6q/UET4Fy8UgBSvRScJxh1RWI8vxHGdVVetR7vV/v9PkVgPx63AVXOVNoh1+cxAOz6gBpLWhTnHw84F6bArc/G6CMfjqvlSqsm/VDCXRxDXI1aIrCZCzcUH67PoMMxLLrHAfiQQhfdHbaw7LPXg/XsiIbv9itoDGW75CD18mJr24CCgO6SFoUqOJPxATGAQW3DF8Y1dnVcrROtSngB1UL6d1fYs35xkvnNiU7q4EafHl2pAGj1Ve4KYtVt1HMMjYW+bGKE8kOx7um5bnmuDiDWHahgAF//RWBqDKMmAKz7b46o9nY9sfuQ8tbkKmI9sS51NXJ/SAN/hJpgDyqH8zmvTajNZvEhQiy7RpOLm+xjvU4HF0YLdrm5flScJxHwGH40oro7j9XI3FVKdoKyt6QrBCDPZwadBvfOJGPqYcbHVjzWFoK4Fxm1Oz/z60+9TYNzP3WRTX9gugSmS2C6BKZL4O8uAXtEfX63DiwLLClgAJvZtBjANbUNsIruxo0bjo1pKwG/JbVSU1NZvb/8xyv37W+2st+AuLVr12L58AwksxX/t5g8N4DOVNVMUc02A/IsMWNJp40bNzqwmCnOWXLDVNgsYWbJG9uys7Odc7J/p4J7A+sM9jMFAAu+LcBPQ2LfJvvzWdlrgwEL0C0JZvu0yR2D6uz6bLPzra+v1/nzrDytrnbOw5J49mWKEna9lihZv369s7/HTFBb+VhZWNLHYLqpczBlCis/s2+yZIYl1qqqqpx92TGtzKwM7O82wfS8epwNHiyRaOdox7B9WtktXrzYKVsrr6lrdk58+tvntgRoUrqLYtM3/58xnQA68RLnb17m1m+jOrcCpZ3pbboEflFLgPkQ9TBoK2EF2TsnPbpwY0KtDCxtUiMpxke7N7m1i6/5qJXFRpOoZvw5vX1+S2CcyYbm1kndAgT7wYe2wtarDgbmNsmSgBXpC4VuvX7ATwWzXWKB/M886P78lsBPf2av/fqIDl/wCpcZ/eqv+Onf/RoqORGf77jvp7/KT/eJdlZarv3yMEoGrFZloufXv+rnPAcRSZnevmAlYJPlHvx9moBSTEnZ4miL+w04Mssvg7FsxbnF6/Z3i+Xt/fa7vZ6eDlTxycT+1ET+ZxkT2/nZeMQ2i7/NUtbO0+JyG+fYmMCSwlMr5G28YeduKtQ21nkGLT2rVAOx6h6U6c///b/Dzq9CWVGR2oyyx9wduxW8eBHgCeAZYNokK9u9QCJuII1/SKtWO+vny9LKwsZGVldmC2oKgQHUzQEWIr388iuKT0DdiIlWSzBa/bjs56kvpwh44ZOnge2nhcTwecZY77zztpMo2QTY8/qXvkS5AlxRz7bZMZ/fpn634zx/bs+/52/8+a/uyjmV2uoaJ9F6+vQZTnvSAQB37tzBeJEk7SfnYBPP9mY7ttNjO9fEn/jduSJ+t/HisWPHnAVg1l53797tqIGnoAph+5na1994bj/DC1bG9mXt38rTYAWziT2I4or9zRaUbUd57tk48VMEVVbWfNl4/OChgzrK9QwPDetXsH3N4V6zcahZ9S4hsb6EsbQl1p6/Li/3ycM//ZYm7mAnhkLMjIwkEgUkfOKigUJRUzQIdAir97sVqr9wTr1PqpVCIj6hcBPKCgBMwCuWkDXSzkrWh8TFZD8A2weH9PT4SUViSxm5bYcCmINwYRXkg5qTrbowFQYyi5oYGAKca1HdqRPYqT5UTFK80lF7D8rOQyUsBHCJJILZHhrJ1N2vLkCiGx99KN+RURUsxVJu527OF3iVe3CiDwuiSyWqvlSsnkFUUBYuUdjYMLZI90jk+SgmF2tMgARkRlABSAKIigAyY/4A5bmRx9Wq//P/IXdLs6IXrVYoqk2+JP4cqyV/oDkUMyACyWxgplnbroHTN3UNkCoqK12zdm5T8AJAuAggKvZl27NWxg+AM96WVj05eFi6cY3352jGjn3y4/0+AHvWGM2WE489rOh61cP8R+VbHyoEkCl9KeexYYfcgG72Pm97m3pKzqv2zDFFA4LFcX4urvtRRTlJ9AglANj5A+a5SSy6I8Mpb84nnOtDTW8cG6fmo0V6UFWhnNXLlb6pUP7pqBagVkFFOFCgc3Mx5vU2NevxyYtquXpHWdhpRW/ZLr/l2PVGW7qXJKlVOMSjj60fQyXIU1Wt0eMoDd28rpD5c5Wxi/pGpcsHsMoHCArxNKxVGSPYdXaijIGFb1XRCfWW30b9LEuRG7DiLJhPeQDZATj13ryi8vMnsa/CSg8lJ1/61qcVVZqgCcTNWuBY+CL/RV/LF3a0PhGoSpgySV2Vek4f0xPmZkKzFih14175Z89iv8wd0R/4EOzCcJB0G5LPQK/aTh1H/QMLXeyVFu7YoUjUw1wJwH6oWvhggTQ5ZtbdQNhc6wRwztDZ8+qifXmCgWT2GTi3EOiSMnZRxjTncTJ8tai7Hb91TGdunVLWggztWLlTi+IWK9QVBrA0piv3r+vjs1hc05ZWbl6lEQ+qk5eLsXjrJVE7C4AjhbTZszbhwh63srxajysfM38GsL1tq+bPmqcoX1QMAVUvVVzX2asXgMxagT48KPfEAGWt0sYFhZoZij0kyeqWoUadvHhS98ofcC9G68WdLysvNh+lmmANkKkrbytlH0UAZHXYn87VhpWFigvFMrT2sQ6dOqQG4K61G9aocFGhErH3bOpv0kkU9a5ev6qshCy9uOmAspOwjSJDPEZZPWws06mSI4wt2lDIW6JxlGVu3bqrnn7m2VDuS0YxyU3zMfVQSzZW11Q7SnGBJKj3Yrm9DIgt1I0FIpDqnfoHOgOwU449LtlZxP+iVLhwuzbM3YqNms1Hjqqps07Hig/qfv1dVJeytGvlXizu5jnw3RiJ9YeNlTpWcgy17fuoVc3Ri+teUVJUqq7XXNf7Je+hBvdEO7HG3Llsp2JQszF1n35vj4puFunstbNOub649QWtK1iD3WcoylAex772TPExlITqNQcr5CBgxQtnSjQ0MKhZ+TlYgyYBrJJkBYSdoN8aQM3x9qWbdoujQrZSu1bvRB2HxD/9f3V7tc4AbV0rvc6CFACfiGigOEC+/K3KS5gN7OML1FSni9eLdab4AtZladq+ZRvwY66CgATQSFLbULuucM8UX72ksKgwvb77Veo4C4Ux4rOid3S/4QHATo72r96n/MRcB5h80livrBl5aq5udeZWLR4ytSCLl2xe1hZCGDxu20/9zHY+9dl9c57h9vz+ZJv6/X+Ccx3qGUVhZma8ElAQ7RnowBK1BCWvIwpLCtMm+tBl6WsVxn3jAfCqa61kbht10boG5czOVhyWjkW3P3Ys1nJy85WWhHX2uC+qNgbs+KIqVqf7tfc11D2MUtoWbeL+igqMw+4bBa8nFbpw9ZyqaszejWbKs2rpwsVau3C9cmJy4cT9NYBn3dnLZ3X1xhUsc6VtW7doXvoCgJEIDeH99nioWkXXzqiqtErJ4cnatm47ENAstXd26K1DLExprdLClQu0Zfl2lL5RNqWz8aBBdfPhVadN1DW2aMv2HcCdWDP7x9EiPCgxVeniTZ4VldWM72NRI0tTXXOT7gPVx1FGOVkZqGDSL9IlG7jRO9wP4HoF29FeLc5bol8q/FVlxqRjgW7l9Uhnbp/HbvmWusew9gWmz4hN097CPZqTBGAJFNyNxeD1R5d0+PiHqEIFad2GdexnOSAv9xRn3DbSqisVF3T6zCnsW2MBELejbLYQEGVIxTeLURA7pvjkWO3asktz0wHR3fHqawvUtZJhvfXOJXUDc2flz8KONAE1OBSdsPFLZB4qEriiq82jD95vVNHZpyjzhWnFykRlZgQoMRbbdiC7KN4XhGOIC9irDTu+C8Ue/dG3ip15jpf2L9CeXZFA6C5CiEndvjGqd959Sg5iEAW1ENSUk3S3YkA/fJ+/tfRr76Z4fXlfPOrUqDHRUQ/TRi5c8Og7361XNcqSX3opR//0nyYpkGfN+VMAZ+8A49BHbduWol/6UqJSE/gQn4Fzxk4X1bazDTp84qH6hmMBNtM1H8W5BCxCY+N9UDBEcYk+shPHkzPHmojvrpKTSEcpbRaAW4DgHlHvxPoU+CsIAIahFvDupD44Uq1DJ1D9C8zVKy9lq3BjICAvlcxxax8OAcldR0C1Q2vX52nf3jziQ7d+9/evqrWlXlu3LABCQ+k1w/BsE1rFseAa1/eDa7SFfm3dNlev7knEtcClU0cG9PZhVIhRunxh32Lt2RtNO3ORkyHOtzCPz9t8YUlxt3745n3sYD2alZunJUtilDKTuuMa4/iCh8c6WDp7ckRnTj3hfLrJlaQg5AA4lu4LrOkCcnJhMUuoxnPcCzxz7PiYvv29i1gcP9WrL6zinNMoC57vHLeZOn7vYJsOHzmjZQuztH/3AvJfAXrrI0Dx+/XKnxWh/XtRf5uLVpbNddk5okL4fRbJV6LS+KXdSXphbxx9iYtz7qdsTmPb3afN65fp61/NYaxDXEBZ260zhOptdR+gLjmhESD4DMZTeVQazY37kEXGxJ+N2HhXMMY0Jea85BRlYP9JhO2EzYyQ2Y8PYZEHMK5bjeTboLeBSyOVFRKkCGB8LyXZB31V09qmxo4uYo5gZSenKjUgAMEwVLK6u7Bnb1E0OT5TULd+22L8qX7bxhk25pgai3Pan9lmffFUf2zHsPyc5cb6+vqUynisYNEiFJ791OEd1v32TuKHAaBRlGejo5USzPlyn6ATRiwiyrBXFRB0oSyWmY+1cSrqwIRrlA79k2eEz5u6XDfAfIgWovY6Bhh/50md2omrIogHY4NYFIRzphdozsfN89frUntbOyXsUnZKEvGLv3o627H1xsYT2Cg9JU25KMinYunacb5XLUc71dHZr54sjwK3xWrWAgAxIHZ/5r3HaR8DZzqxMe5Q1M4UhR8gvrYVtZzdJMSSpwqbz0tDarjfKL9Zk0pBtS00nfdwLziLMIByh2+jgHuhEtB3gLHLbMWi0Ohm7pUTZP5AGigdUFXxI0C8SWLHPIWuoZXQ5jtKO4jH6xU1Ga3klcmasR6oNY3PcS/Y2G+C408AzE50oKrGuU708Rrl4NPPyy0oTdYP0ke2O+rMQQnAlStiFLJshgOn+XB+Xtw1xq56VXWxHFBuXOkoWoaujpA7CeiN0JZHpsbrRtReXKeO8nbmd+KUuB+1/Vn0ZaSVXR7OY5ihzn1ysO81QoDSf82PUshW5kMSOBcA00ngO4PK6k81Kmycel8WpuCNLCzitcGDo6q7VCdf+vKUTckKWsZ1RzNmCLKiod8mbpro5anWTg6uhSLvtP3xL/21FyBvsLaTsQJ2sXER6nePqh1F7Mj8YCWYFauBe9zjE+QyRi9SPmfH1DryRNH74hS1BigunPvPFi4xtPNSdj03e/XgEgqnxLKzls9R+EbGwNHcoQbOAbcNNXhV+3GjPKX9xHn0g4UR8ptHWdvrvbx+d0zVAIZ9rd2avSJfketRlktDXbMM5b8/fyofIMvAwnCF7AKco3/3Ia6FAGWxEtfUx4/UhV3TJPuy9xoNN948hhV5qxp7uxSXHaeMzQkKX8sYJZjPMjaZpB2MMKaG/aOl+6A8yZfTMvn2M2zT4NzPUGjTH5kugekSmC6B6RL4u0qAh+3nfLNA1rbnJ8qnTtmCaZsksQDXAl9LdlnC6Pn32uftteeDcPv83/R326dtFrA/YUW4qddZEG0WQQacGYxmr9kkjQFvU4G98yG+TZ2TWQbYa3ZOBsw9f052PnZ8e/35v9s+7DUD+0x9zl63QYRBbfazTbKYsp3tz5Ik9l77myXY7FyeB/BsH1Y29j4rE0toPH/Mv+0cnj8Puw7bvx3TrsUGLtPbP64SIP+CZYlH//G/j+t+3QSTyj56bbtb//I3/JSSYFMD09t0CfxilYApStXS1t887tGRs0zY8DNdpXDRQmHArf1bfbV6qUvJtH/miZ2JjV+sEvjFvRpyM6yeZfIYIPJHqAg+qmVyj79FsnQ1b5aP/skeP21f78YuikSnZfC+wNu/+i9j+t77TJgx6bN1ra++9V/8nXL5ohUJoZLeOujR1/+DxVDSSiaUv/2dAGyIDC75opXGF/t6Lfa1zeJaW6Dy7W9/21GBttjaYlxTU7YFJ1Nxt8XettnnLI7eDhSzceNG5732958cW9jfPovNVNbsmAYUmUK0gXMWf5uimCnL2TnaApjLKAgdOnTIAYpM1dq+DC6yzc7dAeewIHzj//jf1QSAk871bUbJq2DnHgUXzAUcYuYYeGQS9RwPAIWbxLcbRQDbpsrq+TKwn39einN2PNu/fU0d2zkRvhkkaIDjX/zFX5Ag7sSibI1ef/U11BOwgWScY09xB2bgs+xg6mP8S31blfM3Gy89QpHm0OHDOn/uHNZNUQ5otnPX7h+XmQUDduyfPL7tcOrc7OdPvdmx2d+zIMPOjXlzIJYL588Bzx1FXaIWhbYVTr3ZIiZboDV1bDueszn/2DfnQpx92HsMqLSxqYFrpkpuam/WNm3caJ/9yfHls539/b7bONLGudYW7RwMarNFXT/60Y9UV1enLVu26EXaaEZGBvXyrB39XUf0AqVWVj5yoMgrV646i8p+4zd+w7FlPVtU5BzD1PQMnjNVvueva4L7pPW9d9VddFFBZNH9w1B4C0fhDcs21+wchXAeQag8DdwsU13xWQ1jE5m6eJ7i1m6SOx3LTIAelw37SWI4xcsvE53Nan37HXWd+ViJWdhw7jwgf2wYXQBOMnDO3uhlbgFrswlU5Lwk/utPH1dDbRVWRAnKAIIMzc3TOOpPHpICvjxkfI2a6h5Qx2UUmqizYD9f5a9dp8hNWwCnuF8BbSZJuI7fKFEdVq5PUafIXLIK68VQ9QOLdXAMX1/ULlEAmgQyC0CxLQzILBCgwAfwbhjo6vG3/m8FARDErdykGZv2obiQRpaD+8I9jOLcCJa0IwAVo5qoaFE/tmk3AEhjFs1V1v6dWC6hZDcDhQgSdgYGTqVFJ3m/t7FJ9YeOyH3jJuAcyozb9skHUNcnhOQHCReD7Eh9yDPQrvbSm6p475Ai+0eUvW6HQqycE1Nos8x7MJ/Qj0pbA3BCKAnZaABed0yM2iprNIpdoVln+gTNkG8EtkwAZQHZqQrMIbsO6OB52KRGVK8qgGlyd6xX2tqVCoxj0aOBfnZb2I1F1UwC+nlqa1V56KS6y6qUjdVe5M5d8qXOJ7Gms7N1m4QGyM4k8aEPfd/4oyrAueO6f+O6whcuUDpqbIHpOUgfoEbIOVleaoLz9zFZFazzBrFJfHj2mPqBTvLnz0b5oRBIMZuypt3Rj47Tx1ScOAzEWK+UeXM5zyQUBpvU96QZAQ5/7gsUl8JpdySpAzLSFZibI19gSG/jY1Thjqj1fpkCF65W0tY9CkhLR5XOH6EKyo+zNuVCPxKu7i7gzqNHdP/iZZLqscrdupNk4mygygAWyJGwxaqJBsgHaKNewEmDFovOqYM+1BsK3PAi/f/iBU6du4wAo7+YoI3WttTqxI1jKrr1sQpInO1etUcF4QUKdgVjDzym6+U3UIo7hcLKoBatX4xdYAf2mFexOSXRPJOEGNaJk0Bx1v+YzWIvUEI/KmQx0THYk7LoMzMfcC6a1/xV3vxIh4uPoIR0A0DNCwiwQPs27kNxbYkw7qWGBtU0UKuPPn4fBbka1DnT9dL215UTPVtuoMAx15Ce9Ffo3K0irF8fOJaWhas2KCkiRbXVj3Xw5EGSjB3asXuHVs/GwpXj1vWhqFd6FJjikuZlLNRXtv6yoyplKc4R7sHyxlKdvn5UzX11mrs0H8ss1HhuYAE4MIJqVqrCsbGDwKAPHCdpip0aAONgVx/J8hDtYvHq4oz5CgGcM6CnfqBZH185ixrQcXlRI8nJnaUX1v2yVqajJOjGjo1keh1lcKz4Q2C2BwARBdq+cK9yIwtQhjRgc1JNqK4dvvSRbj28oiwgzZc2vK4EgKCr1df1AYpzraj0vbQJ9dUlOxXux3Gpw77xbp26cUpnb50FAHHpta2vaO2sNcQvQSiEjasSkOnM1SNYIVajxpdFXczQtYs3NQD8mEj/FRYVihIRiV5sQk3NcQxL3L5OEv7+IVoK+Lll8UbUvAyQ9FPDaJOKKoqooyPq7uhVWkYmizO3aVPOTiz/ZgIAkKjtKFdRyVmV0PflzsnXvu37lBOXBUuIkiS13I9t6K0Ht3UMhcCA0AC9vu81ZWF1WY+62Y/Ov6OKlgrHMvSVFS9pTkIOCj3NqmmqBsyYrRbAOZv7tBjA3DIsfpsDYGgLk6cWBP/4Ocr98w+x2XPy+XOY+n0KnLN55UGst2NmohAUH4fyF+DcnRIdLjqohJwE2tV+bA+X8xyjXrgnGtuqVXzlAsqs5UrLS1UElnknbx5XN8pNSQnpignh2Uci3k1/5Qtg0TXWqU6+ArBQK1yyTqtyVigG1ZxJ2nwLgOjpK6d14RJQuX0+MUEv7nhBy2evAmJLott20eX1oeZ2TLfuXAeICteOLVtVkDZXwRMRnM2wWr3NOnvvrO5dKwPuC2OsuU0F2QUs9O7UXx4kLuiu1arCVdqydDsWhalAqkBefO5qeYlOXTgPONuhPfv2a03BOkW5ue9RFHw8WIv15DlVomgbAXGUgFVhXVMLClrVCgsPRXUu0kR0eAzz9EFGdNRUCPvaHUh4IYqNr634JWVGzOQah9Q12KwS7pd36Wuqmh4zDxShtYuAlda9pLSQHAXxX2t3iy7fP6vjpw4DocWjbLlVc3NQx/MHDuFB0eVpV1nDTSxBD6IiFaotq3gdWLrf2ws4d1Gni04BSSVoz+a9mpe0UKE+URrtCVBN1bjOXGikfTejLhmA2lGYYsNnAEkFKwe4a84sf2yNfYnlBrGh7VN9Qz9tmRxDmL+iw4OB64KwNfUFguF3VM7agSNMce7Pv3Me0NhPr724gJgvglgJ9TAea3fujOr991pZ9N6DwliovvKlNJXeH9D33m1X34CPXt4RQ/2GACPaw4+YmLjnylWgq7eweC2vdICsX/16qmbwXC1Gce7t94Bx+mu0c3+KDryQrCSOCU/rwPIIb2GnO8y1N2MFil36OMBVuB/qm36KiZ9URl4AC/eDFernVnVFr4rOYONcy5jbnaioSGCyBOwuU/yUlxOinMwgRQMJdnZOYOlaidrifQWHZ+mFA3lauXIGinsgFSxUeAw492ffuanG5nbA5lzt3ZcLtO2r//T7lxnbtwDSLdSu7elYwvo70FsfcN+dUg+KcdcdIHXdhll6ZV8GiqW+OnlskGdEBYsUBlCpm6fNWyOxAndhw03szNPWoDn7qaaKazzTposlvRoawcaWODAsfJJrnFB6lpc+zRfIMUyPsbssudCECnMLQyp/xohh2C26UHJ0seAqCCXPZ/UO0or63rje+OEFxqE9LAJahbJeIv26RSdwO8CD7x9q1sEPj2pBQbYO7FnOc2eGfvBuF+2/gfFfJOOWZBZQWSxBXEA3XXp7AsjRHIMq9NKOJB1APS8eKLG8qk/f/MOTAHr9KP0t1Zdfn8XfA4gjnvWEgwBRNbx2i3HmBJbm2QkxyiJsj3SiDRfPCxweANvuP6mXOzREuSmpSp8Rxh1j97DD69B3YW+JhWNLB3kr4pN4VMyTKaM4Fo0QEcH0YDlMHFLV/BSb0x4Fsp/Z9M8p/qgOEkd3dXeqCzAviryaLbiZyplZP2mbjTfsZxt/Px/7Oy9+Bt+m+mP719yVLOdnY+sELFVnL1uBla2/GojNG3i+eVltEcf4KoV7M4brCyKmDkCPrp/rK20dAZwLVARjikVxwUomPnDAOfqpHsYptzvaVNXTrXAWLixMiHCeq6UNDXrKZ0OiYug7KVXoIbKDxKb+xFte3jOqIOjIZMolgWdcD31lSzcqdIR2WUkoQAZEKRZ74zaguNZj3eoDYGydNSbPziBlYwudSdwW0U38ehmnhZO96mWRQti2ZIXtx8r0E7cFA+fGa1BjLwEyflAv/1nYna4HrpvJONLie9rIJPDXIGpmZdyXo0DA+SvnKnoxfWMsF2fvYXHScBWfv1yjwcYRZSLmEY6qmCt0Uu13gP1OP1YUUHTKGvqWVdybSUSzLvogIEEvypWj9COjVQPqbyMiGEKRzhOgYObx3djbjnYOAKYyzqJkgjLJRa5jbLAIqA3Q1AHnWBQ7fsur8rOlTpyVtRSltVWAc9gpW2hr5+55MqqOkkY9vd+FYnCsEgBP/Vk072LIM+mx86edlhGr/KhKwUNYFa+IVuAmP/masiT7QFhUA3eASE828XqQwheFKWwjC1649p4PhlR/o07+wLFp21MUsjiEGJv8rB9qdsRfvoNmddql/geDxPjkarGTdmP/7QMI7GVhxiDtAtNjzYhBbdZvXN0eLF+Xonhn4FziM/hvgn5s7Brt4fSwWvpqWfiSqMi1gI3YftvNbHCjwYfdN3p0j0UO/qhDz16Wj1J6ulzWdzKsY82EempGVHmqVpMPvcrIT2WxTZjcs0aoCnLKA8TAlcyLXahVd02H83k7hi9qn4P3ybV/t4sxkFszNocoiGeIm/7FBWFsz4MJeFlPw6S67vVrqHFUvkOoWnpQux5BSRAl0yfEFk8pxKgclBg3xWDVCthMLtLAOdaECYFBZwhucZKVhXPf/Iz39jQ49zMW3PTHpktgugSmS2C6BP62EuBJNb39tSVgAbSt5v/N3/xNJ4j+8MMPHeW4v/bN03+cLoHPeQl0EnS/+ZFHv//HSEqzIiqTAcc/e91X/8tX/BQaPN0PfM6rb/r0fooS6OplBe0lr9474SFxMKE2JhqZB9NcVpK+sBNb1o2+ysv0wRpiut3/FMX6uXurrXYuq5jAvsLDJCsTd9Qz8yzKRSJ/xwqSRdhRLyhg4u7T5e4/d9f3WZyQQXO/+9/GVEfZLAES++A7rARO+uK1e4Nm93xthP4ACxnmYl/b4as//j1/8txfvLL4LNrVP9Z9WFxvX7YZOGcKJW+++aYDINliE3vNkq32ZRP0BgcZKGRftlmC1sC0nSjLGJz2/GIU5w2f4bepY5qa3HGgDjtPA6K++tWvOkrZBliZCltxcbED1dkq/b179zrnN6WibYkxsg6qv3dHPwCcq793V5moHG1bvU5zdu1TAErYrlDAuYkxVp33o/PhljvsHw6cs+KbSjBP1ZX9bl+mrG2J8Xfeflt3AMYyDKIAFFuzGhtNEi7OZtc7tX1Sz+QgmDHmi9dskdUNFMzeAbSqqqwC1CggMXcAS9AVz4A1523P9jFV/lPnY7t9/mf7/VNtHNuuhal55xz45rS9UpIzH6HSZvVn6jim1LZ23TpnUZYBgHZ8F4mi54851XbtmoYAdOyzNja1clmEMoKVh/1rUKV97vnPfqpz/RRver5e7O2mxGj2eGZ9WwLwZcrkr7z0shZg/xkagsLXc1Xy1+8eJQMU5m7fvqU//9a39KjykRYtXKx//b/9K8pp3Gn7g9T9MtTkDZz7SavWSRJvo6Wl6r58TQPcK+O041GzWkUJIiAZCzMsRWOz5mmookZPUM0ZRnEqZSl/A+hyzcwHSgJgInnsIqngtBPkpLzYMra+g/0twEty9myF7XsFxTLUvACOyN46byTX5SQi8G2St6FZ9WdOqhGr1jiAr5k7zY5ztiZIoI07dQhiQn36dA2o88JV3Xr/HVQ1XMrfiPLP5m3yCY1GMQ2Qa7BL49cvqJZ6bUGxLWfdZsUBVU10d6irASWTp10aBeQZJ2NtlkbRWJ1GL1qoGQvma7ydc3jjTzRjdEgxa7coYOMuuZJQUSR554M6GGZGqBWNkMhB46msUQNHSlRaclGRiwo0c/8Whc/KxZ4SeQgXGRY3X7bRbifHMMhsblbtwQ/lc/Oq4gDnAgE5XKh7TYYgP4R6q91efqhjTQ6gtlZ6Qw/feV8RnGfqhu2oSwDOoS5CJ+Mo6g0CWTWeOqYggKPo+UvkDyw1ji1QH9fXR/J2tBfpFuuLgYZmpCUobtEchaRlyFuH2seJs6rqalXOro1KWb9KASjxTzjniq4DkJaVySQ2YeOAho+OnVDPPcC5pCwUNnbKd2GBvJEke0jsuqkLs611GidSxuOoGo0ZOHf7uiKWLNLMvSjOzcwkKUffaCkc2jB7RgWDz/WNAc6V6QFQy2DLIxUszlf0xi3Y3Obx1hDqEEiRNlx+BAXGhmogzcUK4p604/XXN2sIu86xjm6NDbPgk6SRNyJS0QuXKXoettkk8EeBhzor7sm9cKXituxGVS8ZmBAbPtql4Ymk8+U7OiwXoGEbwHTZhcuKjk1S3jYURJfko3QRCDhHwgq7r0kPiUm7SqQfJlob1Hn8tDovXwK8DMPqaZ/CUHD0CaCvME9c6tFL265prtGJm0d1/k6R5q7ECm+ZgXNzUZcKRl1uSNceXNXxix+T3OtTwaq56hroAkwgYcm9Opu2GhFMGZCI9AM4NODE5Ppc1E1cRLRmZaJ2E4viiN8MnjVeldbf05FLJ3T3/l3AuQkUevK1Z8NeB5yLIXU/xnk3jdTp3SNvA7nXAJBk68WtryonEoU4IBSPL4nMoRqdufmxbqM4mZmdo3Ur1ys+JAGlKpTcio6rAzvX7Tu2a23eOkW7o1XTX63Ddw/qyvUSLchYoq9u/ppSw9MBS1FqGxsE0rqjC/dOYGNZq7mosQ10D+oOtr/j3O/pmbSlcJQsaTo+XKOLxK89Znz5bCTqMwuAwjLiU1FSCwDkIb7o5Rwunda54iKUL6VMrn//yle0fOYahfqSXO75EsQAAEAASURBVEW2qbb5oU6UfKRHrfc0O2e2ti7Yq1nR8wHngBsovqbORh0FnLv58DLXl6UDhYBzUWm6VnNFB698oA5ghJc3vq4dC3aReA9BpWZUfZ5eVPVO6Fwpx2Unr2wEnMtbj1IO4Bz3f9VTwJRrh0lmVmIJOZOMpR+Kcnc0TkyUnpWmqGisgQ1iddsXNTWByg7gaGxgpHJiM5Wfng8cAOhJmVQNVOt0xWkdv/Cx+gGDU2ZmAGdt1sa8bUoPTaPFerH6e6SiK+ewjr2sbFQJD2w7oNyoLAcinQRi7R/r1dV713Xs/DEFAhV9ad+XgJ6yVPf0id4qeU+Pnj7U7Mw5emXZK8rHVrNpAHUs7r3s0DlqqwGW4j9baGALIQoKsAsGnDP4wkCLZ+3/73wIOe/7eX1znv/PxSZTv/8YnGsHnMMvLy4N1SHU1fpQeLqC1e6hsweVOCtJ+7a+qILo5YBqQcAqJOkBHy8CZN6vuKPUWSmKzI3EFhiYtW9ImUl5mhmRiV0uUDPjHh6DIGoj8s7w0HYjNCttlrJiMhRCX0Xvo8b+Rp24fAor0wvq6+0DFonXgZ37tTJ3rZJ8k+n4gJlGu3XszFHuseuKiQvTrm3bHcvVYA9wGJ5zbRMtOnXvlEqv3VIIJMKmtZtVkDtPXZ09ehPFuXoD59at1NbF25TmO5O25Ovc2xeA7U5fPA/w1qe9+1/UmtnY1aNiNwIEVz/8GNXEM6p4UAZQEwRkFeuAc48bmgCMElF8StIM7jPrmQ2D9nBTTjCn449qVnZUttamr6G9RtPPDADP1utS5XV9ePGoHjfXK4yYd/mCxdgev6zsMJ4vGE82dTXpwoNTOoydcFJCinZs3I3tMspx/jEWFatrok1lTcRshz8AlgnR5pXbNA/b0X5vt0puoaZ49jQW4nGAv/u1OIW68kZgjedWH/Ot1XVjOH90YFnbhb3pqEaH6TfoM6IjJ7QQZaNlS5O43331uI57s7pbdXWt1MUYIBjPnokQYEYXNq/RWoDK0fCwj4pRj/rOd8+wCM5Xr7+8iPFAFDaxWHYClt29Nw44186CDKw55wfrl7+cqrL7o/rOO/0aGPLTy1uD9cJWP8BSYksah/cTcO6Nd4jBymu0b1ec/snX4hWEBa6Bc+9+gBricJV2v5iOIluK4oOxMKW8zZ4dfhgRXRTLqsd07/6wamq7UMQaAvYBtgXcCATqWLEsQ8sXA/ahLvS4bhDAuR/71DHcILA8H2VxP1B/Zlqo1ixLpzyBiehbj56sUdH5cgVHZKMElwUoFsACAs6V12qxNvzu98oQNmhEGRFL0v1z1M91/cffv6TBkU7Av4UoOKegDOonQh8NMB9Ydtujb33/ujp727R6/Wy9/EKmQv39dPLjQUDJcqx5h1F8m6dCAMR4FKZmAKO5iGcM6rF+cKjfh/IcU+ndAVXWjOlp17D6ECbwYBcbHMpzb3YCC3noN7GNbwYaeliOtWkNZdFP34bd5shYP/MuWJuvztSKpdzjKAmeOMWixr88S0kOau8eoNJNCcB3PN+JYVpQnDt4tE1Hgf/zub/37FrOvoP1g7c6UNyt19rV0dq3P9VR1cPN3CmX0lIWkgLOld25CxyZyj0cz73qUnnlgP6vPzqt3v5B7di2xFHwizL4ka7RRf0PAtc64Byqwl5WFmclRiuH2yqK/n4S7K2PB1x9V6fKnzTIHxAwB5eitEAWcNCzU0LqpZxae4HKWvthYLD4jI5UWjTAIO0ZEUHehYoV7+tgP49aiat721EXDtGcuBTNBOQN5sHagRJVV3OTIli4ZuPqnwTn+LgzhrJ/bVz+8xrX2P5NLKKUeMbcliKA1XJWrVAHMWMDwJtnGNgzPEqpKE/HBrjp61hAREztOzmsHiKz20979RCILYIFM4tQx50J6GZzz/b06WScdPVpmx6iiBnB4oJFsTyrhwdU1tymdkopIjIaEM9fM6BffYjBJ2h3Hsbu9uFAwL1QYvNg/t7e1aOnPY0KmDGh7HjU/+ifYlH7ajvdo67Dg059Ns4e0tAetzJmJSiTfj6ql27kooFzg+rhHMK3xAPOAUwZqMn+J7H69VShVlYM+HavQTNy3SxMSWLREbGh3exUtFmBdl/t0d0Ld51YtmDlfMA55gpoYwSbWHgyjqse1uOrteoF/s2dnafINRGobmMNWvpUDcVPFIPKadJK1CxXAs7FWWlzfCZtx+o8ajvfqNHHwHFIxE0CSvsHBCkY8MqNUttoI4v62hgTEC8FZgcDrGFDvoj40o5NF+k1S9ebqKeeIw4NnFDG8lmKXhHuQH2TVgGAf94GFlhdbFbbvW6UH6OVBngWMIf7Owwo05HepD3fof/9/kOFDkfw+TjAOZTxmE+m+6cvB8G+N6KmY40KGQCcXRqhoK2Ac8R/ve+PqPl2k/zop5N2JqBaHaQJ+jtb7OKmPfg0TXLsWrVXdbOgJZL6j362MNOKwPr5xi75jwKksYBqBNiul2dWGKp10WvC5QecZh3uJP3YGC4ro6cGaIuPFQ/IHLUWcJF+3ynHT8C5rps9KisBnGNBUP7SuQrfgPq8KQtaIMA19FUOqfpMnVw1viivJyp8dRDWvIwnWagxieX1aA3KkYDendXtylucq8jVtJNk7uGHwxp8o49FZjMUuDlAgVsQjkEF1Oy4GV7KU2/j8DFVXasF+JxQGHNVkTyL/Ynrx7uA/Vt61A4EGZ2VpPTNsYrZADiHJbYVoP03wYDaRjjWY4DjMWbgtZ9xmwbnfsaCm/7YdAlMl8B0CUyXwN9WAj/7g+lv2+svwms2sWFJqW984xtOEP3GG284lqa/CNc2fQ1fvBKwcPTxkwn992+N63sfeix+1vxsl/7l1/0YYPsqgMHH9DZdAv+YS8DaeHPLJEkWJvtOeLEmQYkMdalocntL57n1ZUCqLeue2bIy9zK9/QKUgCVu6p9MkpTBvpVJw1tVrHpkAiGGfP2ifJd+7XU/Fa5xs1qXyYtfgOv9aS/hQaVXX/m1Ud1lJWABkxwH3wlUetoXrySusBp73z8bYYJ2Ujm0hf/2XwOwLCIB/Syv9tMW6/T7/5GWgMFIU0lLA37MluXmzZuO3aS9NjUhb+95Hpx7fsW7gUlmixqIutDUZ6YStJ9lsdj52TmY0trVq1cdRS+DpdavX6/VwGIG0RlUZ1aWFy5cwHop2bHq3LRpEwpEBnw82ybYj4Fzb/yHb6jpwT2SirHagprR7N2oaM3HntHsJ0lUTo6gdkWS1A0Y4GvZGDYrB9ueLxf7+eehOOcciG9T9TN17KnfLclsi5nOYG96HDtPUzbYgb3X/n37UIkALuH3Zxv/ct5O3fAHx37VJmH5v43kUFHRGb0FfGcKg5s2bkLBD6s+wDWD1Gz7yWt1/vj3+UYROtfgTA9/co78U4M95JEjR3Two48AFaK0e88e7dix01HKMRTJEmh/Ezhnk8xW9ydOnHDUCC3Zs3HjRseq12zqDKr8eWxTdTJVRlPt387l3XffdZQPExMTSV7uU2EhlomATX/lGqiXZy1qqpz5nb91tLfrHAqA3//BD0hc9WjL5q36F//if3XavqkpDgJWLUcl0axaTRXS7osfb+xwohtldOw5R562aJzEUG/jEzVWVWmkr1vZwC6pm3bLQ6L2CZZ0ne1VSgXGStywVe6MufIEsD9TNrAclZODMMCrS50fvIn10YeKTSJB8eKrCli7Xsi3AxkBuNGePABBExMASkPYGQLOPT55XC2P7ikZ5ZFkwLkAlBB9uA89JCQ8ZJZs/+6OQfWWXNfdD9+W1zWsvA0rlbx7P8pmsWR/2O9wt0avAAucu4ACzqDyN+9UElCnO9BPYyhejKA2MUqfNVxbr74HlfIZHFE4oEjstq0kej1qfvN7qLp1K2pVoYJ37pVPShrwLwlyUhLQMcAFI3IDinlRvxk4cVllxecUPCdDGXs2KBJIyIUSBhVGQTAgxMvL4EBbjeAFaKv8iHO+fhE73FyFYv3ozsealASXl7eT+1TAOLoiQ6hvlZWq8e23FMK5Rq1cLf8tW+Sbncn+KFfUPLqvXtNjFDTDUNNIXLFOM1auQxEBSArobrjrqTyoCo1gQdiKmlLX+KBi87OUu3aD/AEQWk4WqbKhTlkb1yplS6H8UT/xokY3yfkaxOTiPCaB7ryPK1Rz5pRab9PfAWrFbNsiv8UFmsD+1Q1U5iJwnAQymLSTJ9nrqXrsgHPlt68oZMkCkmqAjxkZcgcAEhqAZo3WvrjQCQYVQ3du696ZD9SFveZ8bOcSAAl9s5eSQAyn/Elelt1R+bHDCmlr1My1WGSupQ5RGPGMkERvBZprQJHmSY36AS2fcL0hWQXKWr9NIaiZjV8qUtf9O9LshYrbul3+uanAzNSg7zj1iAqZ+TaRjIfIUOvhY7pddBarzRAt2LVf4bQVF6p6UGvUI8gS12ibr++AJtrq1X0M4O3SVYnjxO7ao1DUVHxm4ClF4tAUKTwk6KpaqnTyxnFdKDurucvnaPuy3YBz2DJCfo0Cy918eE0ni0+ofaxbc1CkG0Hq4u6dMmBEX21etVHZiVksTiDxaQlQTtPSYAHIo4RST6HAYDOA0if9x4FxOvQxilIX7l5Wz0g/sMWYQrAMXb14tTbP36CMsJmoUIyp2dOkD1Ccq3hQoeTUFL2IBfHsKOxFJ4JRnBvGZu6hTl4+jsrPI80pmIOS+FrFBMSovrYBNavTDji3BTh1de4aRbtiVN2Pyl3Zh7p+5yrwz2J9dePXlRqWifobCAPtrbKzTOfLAeewIF1UsFBjXcTPt8sppwAtX7kKi7lsbGJncI2BXCOWcrQ9P56bdl3BPDcDA+gTSH62D3eqBFjnFHap9ZR9AApSQX7BXNtGrZ+zAeX/WMA4P2w0ARUB58ob79BXZVLeBzQ7ZiFCitQ1SFB952OduHRId6tuKSs3W/vWfglFvZm6Xlusw1ffUXtTu14u/DLg3H5F+IU51qo9KM6dLAWcow59qdsX1r6oddncLwEhWLUCmHVSx1c+Um33A6XnpNP/BepWyR1nYcCy1UuUN7NA4a5Ero+2BExpql1ulOdC0RmKdkUqGMUuXz839pM9ugFMdArg6F5NmQNwhAMB52cDoczbpPyEXJL9fiiP1evszQuo751T8tyZ2rdpr+ZG5imM8jMVmacDT1VcWqxzNy4oJC5Ur6MqmBueSx006UeX31VlR6VmZc3Vywtf1RzsQ5tRI6tuKlcOQOfTWsAenhm28MHgegO3M7h3bfHDVFw29bxyboZ/gG9TMczUoad+fx6cG/IZBpyLQ3EpWr0oMF3CqvXIuUNKmpWovVte1vzoFQBewcRoI8BfWI+WnFBFzV2lzUnBwjtOF+9coX9BTXvOGi1NX64wtykjgs/SPidRSfUBvA1AATYYtcsgf1RZAc0GPF0qrb6lj68VYQlcJVcgiBj1umTRUm2cu0mzwrDO5j7uAxA6BSx7/eZlFE99tXfHDi3KWKYwbzT3oFd1I7TRW0d179ZdRQMlrF9bqFm0ge7eAb0P9Pqko1qr16zQ1oXbgGUyEShCgcdnAED1nM5dugh8O0Dsuk+r8tYA7ESC+aHECOxWdOuUHtAXRkcGKSk1ketuIVbv1NzsBVqZtUIzw1A9ZF/jkB2jfB8D/jWQOtovAmW7OKAVwDV160HHXUd98WZFqYaAf2dgvZiAMuHetahbJq1SDApoLahDnq86w/m+q5iIOO3avE+LspYrOiCWY/iiltOkqzXFOnr0MCp0ESw+2cnCvAXY3fbp8t1ifQw4n5Aar/0GziWtBPogBsdq0UusMMSEaw+AREvbMPMVY2pomNDdMmwrWxqA4oL10oGFWrI4TDg8Ev+MA5gBlz0hXnkCDFIxiiUqdtPrYrRrT5bCUHm9ifLQt7/L8QCtvvTqQq1di3JpMNADcx9l9z16732D77q0dEEgCmPJjhrcX7wL8MUCuhcKUanb4a8UgBCDYiZRPbpY7NW3fjikspoWvbwvVv/k6xEK5BlSXDTOvtrVO1KpPa/kaOeOZMUDwgSayhA9u5dnDNwdEDiqUtjzNTcP6WnzqJqb+gH42nW/sluZ6el65YWZtAlQKx63bd1jXPeIGpoMtutRdeUTDaGwuHLxHJTiADWifXWqqA4L2AqgtDxsTNO0aqUfYxq4D57VNQBs3/vBQxYp1Grl/CS99kKBhkcD9e+/eYV9t2Mpi2opgF96uh/PHcqza0K3rnn0p9+9pAHGOOs3Y+XKPmcEuHTiZI9OnipjEco4inOLtGFTJOAcC22xS7R4aoLnvctlfby/OdzzyPWqBfCxqWWU5zYQ3aM2lZe3UW9JqDBmE4MHc/7YkvaMqq1pmPlIQJzHHt249Zi/9Whhfja2qwCC8/1UdHEI+9iTzn34wv412ro5SbEo7o0BKDVjT3nsRIeOnygCRo/Wnp2LiaUjAOPadaushvszClXnDOXmBWLVymiBuOfa9XH98O1Rld+/oVd2Zuql3SkAei7dR8Xrv/3RRaDJYe3YukgH9qUpNIy5D0I8N/fLEOPDxwCzNxkbmVVrVlKU8mb4ArDafRWoTp5t9YxBKxsaARmjlAtY+8yC1Ee9kx7AdRY0WNw94lJieAzjS9Tm6EdsBGJqpxZUg9SphX7+UWcTgGojQGUA/XgmUFc4cSAqVQbOYdUaxoIfA+ds/GIx/lQ/OfWzjcOn+nTrS/+6ft0+82k2++xPvtd+t2dIRXmFyu6VEZdjybpysTpYQNeNIm4ccUUGsapZpoYBRaGDzDlY0OlVJ4vPrj9t0qPRgWfgXCRgoz9PTI5jw4p2yrm4pV0PidsiURteynPOi5LrfcD3btRnk1G5zGBhXpRJhxnNZnEFn4WvBaID7iIesAiwuYe4r/MJqvCjCCAkY5sbr3jURXvODKv3o3FUFbn/8vo0ss+l9Pw4zSSGDcfy1HMGZbMjKLqhHhixLVYR+1lEEsc4E1lFg9e8ldTnhSE13mtRcOYMJa2J04xMAEujHylSU2Trut7LmIFFDixOmbdyEVatcc8U53iLt4so4f6wyi+j3gxAP2d+niJWs7iQcmor7VDz1UbismglLEsg5sfiFGjPNgPC+u8O6uGRewobC1F0Hkqx+UFyRzJmGiM2r8NC9gaQaiWqaFC1Pml+Cuc+DV6GOj5Kjla4Xu6XsRte3T0POMf8XsbaPMUtC+XcGKPRzn3on7z0pU0X29RShnJuaJQyX4hHWY8ANZxFRfYmL+V7F3eaNx4puGOGYpZwnli1OuCc1QHjj+G7LNQ43qCIEVQ0V9JHbnNauYY+wIr0ZhsK0oCjWCQHz+X66MutGidQjxy/M6bHZx8CNY4rfs5MhaWHMlSgM6RsPHXjGrjWI782LKIZXw64qUNUTCMWhSgW8NAXW28fFEgnKCdTnBsq6kfJuFHJ+5MB6yLlxmnF2YhfTXGu6zbgXPFt+bFoJn/ZXBTNUTOnT7Jy8sF5ZATIuf4sC8wqXErIQOVwdaD8CoDfAEEngbKHy4HfgBj7Hvdr1hLs0NeEPFOcQ+mz//sDqIKHKmAzfesGzgsgj90+s7m951HfhV5VVVQqLJN6tvKLYVwGlDleP6LGW83YtfYpJjVVaYCbkUCJNq551rhog+b9zRyc9Rc+9qB4fozvXOCn/zYNzn36spp+53QJTJfAdAlMl8CnLoFPHrif+v1fnDdOBdC2gr6FgH737t1OEuCLUwLTV/qLVgLE7Lpy2+tYtpryDmNXJmnd+le/5q9li53w9xftkqev5wtSAsMM/CsfTeiHhz36kAmCxlZLyEq5SLXv3oQCGdasCwCpIhhUT2+/eCXQzaRGCZMK733k1ZnrXj3tM1sHco9ZPvr6Pj8mNX2Vxqo5FsF+obYRJmE3HxhBnYLVqSiLfv97ASQDv3jA2D//16N6A2VCWyG/Y4Fb3/+zAJIxX0yY8gt1A/zExVpcPwXB2cS5wWkGplnS1SAge82U6Ox9pi5ninM2aW+v2d/sMwbumKKXvWab/e2vgDw/ccyf9Vc7F9vs3Jqbmx3lrevXrzvnaJBcCJOsPSRjDP6zbc2aNQ48lUny/Zl16bNzM3Du8f0yfet3fodVxJUkm6O0FvUWg3KCUFdyxUYwYclqbiz3xlgp75eQjDMis75sds222TXaNlUGPy9wzvY/dYyp4039bnVlNjp3Su+gvvcjkopPtHjRYn3lK19B8WE2iSAmcu0c+W7/WvlZvThAnU388zezAz1yGFjt0EFsWqNJPr2oXbt2KZpkNVfp/M8Pn+32rAid67JrsSyXwWStJKoMFnvzrTc1ih2Pqc2Z+p2pz/lxLfbWZzDgJ/0UdeB8nrMbA1Iym9YPPvjAAT8NotwHQFhYWPhXLOo+2wt5Vv9T9WFtYgqcs3Z45swZR/lwECWO9Z9cSybQWgCqCz5cr1M3XLsBgfaLUzf8YPX06NEjvc+1nMFyNgbLygP7D+jll19y2v1hbHUdcA7FucUozv1/wDlqdgIryEmOO0nSapLE0EgT6lqoRjQBjMZxv6bsfZ0EUJjqr13Uk/LrSs5MVnrhZvnnLwSGo+7NJ4aV8VbePkEAPyhB9GLz2HD4Q8TXSIbs3KOIwk3yiYzgjJ/Flh4DgwCC3MSek41tqgOcaycxnxwZrPjNG+Q3Jw+IiXsL5YRxEq8+ACzunmHHMrbi0HvqRjUibVG+clHa8QlPoMGSqO1pVfeFk6q4cVtj2CLO2bRDcSjmuYKApUzpbnwYcAtIsKZegyjXdQFf+qRnkEQ5IL/QYFTy3lRrTbWiUHFLBl70z8gl6ULyywGoAPOwXrR68Db0aPDibd0pOqnJaOy5CpcpfvlC+cVgTepmLEjCc9JjmVUD6QCEUTerOmTg3FklpM1U6Ibd8p2DQhrl4SVJ6iBdtEkX5T9c8UBP33xb7rpqhXBfBu0AWsvP46j0va3taiq5oqoLJVi+xQKVbVLAgiXOfhx60cv1dfdp/EmrOs4Wkcgvx242Vsux8QvxDdXTsyV6cBeVpfw5mrlpgwLzZjkQ2CRgg6nNIeD27D4jsdhQchHlvkvYgWFLugaQYcVi+cSbkhb9NqoeQjFMgEz4bcoLiDh24qQeYJMbOh8Fvp2bFZCZzqXzOqoofIjz5/0oKUwCcY5UlpNcOqymskvEmKlK2oAVbP4akk8YnLV3qRUY5NH5c0og+Z4OOOi/ZC62Rqh30F4mgN4mevuBLbFlvXVJD67ekCcskTrYiTJiLmqAt9Ry/ZKGI7G1Wl+osPkoAcYQzPpT/176BeAZS/JNksnvvnhRd7nvBlCwW7phm2LWFHKc+E/AuU/6Cz7qDkZxDgip79RZdRdfplpDFLl1N0p4y+QD6OKUCQpDXuR4KztqdPLacZ27eUrzURXZuXqvZodh1eoTxKKUPgCumzp58Zi6/l/23ju6suu60/y9iPCQgULOBVTOOQeSVcw5U8GSWm57/MesNdOzZtyrZ61e09PtHqfuWc6yNXJ7UbIoihRJiaRYLLJyzoUqAIUqFHLOGe/hpfn2BZ9WWS3LNs22ZRGPfAXghXvP2eecfc69+zu/HRpxUrX66Xfnz18gleeUdm3drU0rtmhRSiGfDwBku1FNMZ/nptQoaqB+Zt1rxjUGKHZDPzj2I4CVIZUuqwS+AJJr7VFOSpYObn9AG5esd9KVDsVIO3rmsK6gZJjMOHj8wUe1pmgtqU7TNRUldVj3dX105mN1A6+uXb9OezbvQxVpkVqbW/X+kQ9JUzmuBw8+CJSzQ/nuHN1FIe2HdW/p/NULDjj35fu+BjhXBXCG4ghqbbeAbA5deYv0kW3atX6XUoLJgOJ1GgqNa8O2TdoA0FiYkkcAkRrFCfBHCeDSl5L4vpeAn4sUp7OgAg2djaR8PUIKVsZjWR6BVI8G2gZUQPB4z9pdWosiV3panoamB4Hr3tWlplMOOHBgB/Ur30aqxGT8fFA3Wq/r8Jn3gIZQwFuzWk/vflmlgHMXW8/qnfOvq79rUM/te0WPrH2aVKqZxPAJvIaG9f6VH+t4w1FHefC5XaiZLt6DL05D2YzAKPDwj8//QM0jN7R0FcowqYUOODc1OanVqBZuWbVL5VnLaTPgK8ffsT7ANikEmA1Y9AD8zbmCau67C5x3XJdaL8ufSxrK9GyN0w98cb/WLV8DxLVZxQCCIRTFLjZechS/fAXJum/HXm0rX0/qtnQFAQ5a+9t0DMWz63duKL80Ty8efIU+t0JdpJX79sm/RmXytlbUrtFzgHNLcmvUN92qlu5bqqVf9rcOOUC9zSOmtGuKcw6wzdyfgCwS6xcq88/ySMyZiZMn/v4b4Bxw4qJylFiY/8bGSEt/9YR+dOQdla0u0VMPoLSXs4W0hswlqBu1DzTr+LnDqm+uU8WKEpWvqtKZaxc12j+hzUs2a+/qvcCVZYBjwBFRnwNgxpzANP0UkNvLfDwbGyP1IADeaeC0rib5coAi6addzJ0e5qrda3drV+1uIJAc/DvX1/iqEyi2TpNa8sDB+7Rt2U7l+Qqd9qvru64fn32fVJUtqiqp1P17DmhJ1QoNAbR/H8W5LlIC72V9enDDgyr2VaDQBLDjntSZxhM6ip/ux389+eQzpFPepyx3FmqNQbWHWnX0CmVDJdnSlS9ZWasuoPjG+juoyq/QA6sPaGXRSgAvS2kMNEcpx0kFRwJlByDNxlYmidjKeD9264hOAn17AILyUNQKoQA2hoqrjeX7lj9MnwLyAyI813lKb77/BoqSHm3ftFvbGQeFmeXMLajBTWDz6x/qzBng8ewiPbQLeHD5BlQ4Z3Xy8nF9dPSQyqqBHPc9qTUF25UGOBeDerFrTgTYWPaxTuHn9BTqQ/1xfXhsTCfP3WS+mkJVbYv27c1FrdaUdvCPAA7jKC319aKm//Es9zauaNkyt55/YY3Ki7NURyaFP//mB6SrjQPG4fNQh0pJ81EvFOdIN/j9NybU2j6qDQBaX/pSke62ufTqG2FdJV3gwY1+/QppGhdX44uZ2ixV5weH5/SNV/vVDfDyKy+WAM5lKhlY4/RHbEJ9A7Bn+pYefaZWjz1aphJgB78R0ebbmRdnuMdg95YtZS7TnYKUfXQorOMnJvXWe62sy1P1wtMVxFACgOGkk7X68Z1J7tncagzqzOk+XTh/if5SweeoX2USanOAcx83cZ21VC8/W6YdO7xKQ3HObHmnJaS/+GajelvbtHdTGeDcSqZzv377D67oOv1v3fqVev65Wq1aYYrytlkmRvrUsL716hFA5Wk99OhmlJlLgX89eve9AeC0KwrORFGh26b7gEzyCqIASzNOBngX4GM8ih8mPaGpKLm81Jm+Ow1ENEgd6+un9e6P2tQ34EfptAzF7wCKnfNzXNTqyH2ozi4guEP9ZC5qJ91mpp57okI7dgV06iLpc7/9Y0XZtPTcM/v0yIMoR2YBq/G9ARS0fkTZ3v7RUa0AnHvqsfUqKc3h/taAzlxoQk0vBXvWAltmAcFhF9agh4/O6btvjqqr/aq+/NRywLkyfIlL9U3T+r0/PKYZNgo98tB6PftUpTKyWBOyDLJLWEsH2gEsdp441yhzc0VBtpZmpABO+zXL5ol+/EZ7/6jaUUbLAihdxnqsnDWfbd7pDE6gVod68dSMChkT5aSMLwEezwF4T6KPgD6xavJpgn7Szd+3h1FFZl51A/stLahWDdAXCUM1hqLdJEIVWdCRiVSt5isTftKuFex3u9746Wtt8+323t/2sPdtbrCfic/e+3viPInv2/V2G33r5KmTGgxPqXDHBkVQy3WTnnYx6/Qy4ONM1Lv8dHbTzfXhSzGGBlHeOzfQqkbm3gCbBNZkV6g2OZcNB6zV8MC9oaAuAv12AX8V5uVoXTaKuSjc3uwa0ADrumKUqpexlirg2B76mKXRNig4it24KwFAR2p6/Ek3KUs7h1ivalqlhYWqSCtWGfNyEBXKEcC5XtTLhksnlPxQumo2LlIpgR4/wOzUByGNAYpG50LKJY1v5pOsXWCq7MHlvyLcP586GdRg/bBSK5gLtmYqib7sYtw5Tol+OcV4bTrbqJ6WNq1ZR5/cVv4TeC1Gnx27jKLk2Tqg0zBg3Qpl70BJmx4weG1EHWdbHXCueFuJkregNpbLcfnfgLuRc6NqfOcmqd0rVbCtQN5VAFeUDdeo6ZtAacdIEdoSViag/VT2rAKbSZO7M0u+UlvccgzUzubORXXl3HVFgLmq71/ChqN05rR58NCAsTCf6T8zpr6b41xDZrA5Jlv+GvqNgXNQpDGeUTKY9HyvU7E7c8qtyVf6fUBjFQwK/jcobYIx2wNUlgfcncH5kx/GgdJes/jWrguAcyjOFT1l4Bzlx/dQeUXYzD93bk53jzY5G5NK96FIXMX1IHsSbFPSFLDh8FHg25EUpQPHTvqmNQBInFmVgi0WKakc5VhU8wzcC7FBfOQkZQj3qfKpQgesc3Nf1zYBMfE5ZRy7NqHG0/UogPu0ZCPpcqmDAHLtssdS7kYpT8/xfk1djbH5MkNZW9kAgrAApuXaBtAYVc2WU52aG4jw/SrS4gbkpR9M3ZrW6F9OC/ReAcC5VJ5ujotgnANVBi+SYvbjUfWwKahkS7FygSZNUS4eRInu1rjaTrWrr2Wa9V25Sh4oRGkdxTrb3wl4ar1dtj7hfwY4NqVR6fuf9rEAzn1ayy18b8ECCxZYsMCCBX6OBVi1LDz+VgvYIt0UHixgY0GqRKDsb/3CwhsLFvgFt8AkFxBvH4roP/xBWC0soEsyXXqFtHW/8a99DljyC178heItWOC/s4ClIT59IabvAcY40BQXuJnE/NYvd+uLqMw9sMdDMJndXLy28PjltcAMN3/auTny1+9E9M4hbtL1cMHOBrbFRS49vterF56w1K0oQ3Cv4/P02P9sUKfroyrmRvh/+o8ojAKRpnLj8vPyaOfG+RMvhXSzLyY2s+v/+rd+ff1FdvMu+IPPSxf4ST3tBrvdRLdH4ga8re8tgGnwj22S6UddydRKiklJY0/b/W7qZJMElQ2ms78tLWrieiBxE/4nJ/mMfrGy2tNSkJmimIFJlrLVVNdMUc7KYrvjrewVFai4AE7t3r3bUaKzgHGiXHYd03zzhv7g3/97hXp7tIwUZmtI+7JixSrlrF/Lzu88dpwTGB8eVGQpgUjU9HzU0R6JgIQdK/G3/f5PAc7ZeRI2cE7OPwZltbS06LXvfldXLl1BHSKfYOIL2r5tu7JzCG5QzERwBevx9/zNV/s9OAtAfOG83nn7HV24eIEAOwo2zz2r7Tt2Om1q53Dq+Vm6Rgvo8P/8w0rBnwRZ3NAj46TsuXbtmr4DBNgMAFW7ZAnleU5btxIgTwWO4WHlMdM7tuD2sgW/zSazBHdOAM288cYbjj3Wrl2Lct4zWrdunQOWOfVwjvDZ/pPoD/bz3nMYbGrpYr/xjW+orq5OK0lZ+MrLL2nz5i0EKEln40AMBL8oznybUic7Bq+H+O6pkydRm/sr3UElbuuWrXqBNt22fZtaW1r1wx++w3X4LOm6tv1scI7+HbyOIhSplIj4E0CYh4QmAbg6SSGbQwqkome/wM79cvVduqz2Yx8rlWB3yUpUldaZ4iLKTyiPxUm5Gid44l28mCAZQcPL59X9ISpybXe1CJW10o2blVxe5gRPLS3lLOCcj3RxScAiSOuo/+hHGrx6nqAK6Wi2bJJnBcAT/dONSodSAVAIoGlmTiGCcl0fHlLnjcts5EjVkn0oQRWVYJuYIp0d6rl8DlUTVCGWrFL5ZtKR4oviE+ME/Qm2AN/EUaSIdfdp9vxFDQGPIpc4D8mR4m/s0CE1nT4NaBfQ4s1blVWD6p2lzkSpRXwvStpFX0E+tk9SkCBzw9GPNUpa2tISVETWLVdyFcqNfuAsVCsMnHPnkeYHxTYDEDrff0vh4+8rK40USlv2yLMM26Eo6CnMU4RFlZcgohu7htvaNfb2u5pByTOMsk8eih2pq5Y7QZHpjh513GzQUFu3KlHrKeLptvIRhDS1IxdgQxwJm/jQpCZOHdPNdlSQKgu17unHFUDNZIIUqTcPH3OCuhXLVytlzUrHxhZkiaK+50JhyENQ0oJAI7cadPfwEfmB9bLw43m0dXJVFX0E4BE/YhCcp6rS8X+xvgFSIH2s+pPHlAx4Vrljh5Jrawj8oEaYCfBGnV1cQJg6j+WSi/T0aODMMXUf/1CIWihv5Tr5VwHmpaYr1tOtljNnNNDVqYqyChXs3U8bmPranLwWySfwaoM6BhQUrLukhivXFcsqVNUewLdVazn2PPTXRSC5bHGN0y7eogzHNtFZ4ErUzrw5BcBwaaRVaqY/H1fH5StaWlZFWtvN8lRSxyTOA8SAwwAs9MtXm0Nwa1zB0+dJP3zUSaWXu3mXApYedhF9FAUUdyZQDuoit0ba9OG5QzpK+saNBI0f3fs4KTFJz8t/ISStrt1GoerIu5oCoNlzcLfyy4p1GtXTBhTfshblaC1pg6vzq4nVZTkAzjgL8xAwVyYpwyqoZyr17x7p0MnrAFf1V4CFCrSB8WJ99MalG+oEYlxStVg7UZisKKp20s5ebrimU6YWOdFPwHaVVtWQKjQtRxMAMHVNN3T5KvDDdFibtmzR3i37VZCBshypuN8/jMIeMOfDDz+oXQBFBd4MtU2264dX39fpS+e1pmqDvnD/l1WKuh2YFSaLqLH/ut479wP1DXXrwJYDKksr08XrV3Sp5ZoyCjO0ZsVyLSkuIThu6m6sDaaYp6ZjwDoBlRDI9qH81AkUewpQp+5WnZIzkrR97y4gE48a6hrU0dSq6pIyIMNdWl6+woGozxpAdOWwA8GvXbVBa1dsVDZz/Ow0yk3114D8zmp8Fihq00a9uPNFlRGUP3/3it648BZgyLCe3feSHl7zqLJR8LH+OY7a3fvXD+l44zFq5dWLu0lpvng3vgTVQCKst/tv6z0U5+4M39TaTWtUVbRUDVeb1MiYScpOdtYGNeXYGMjPRbA+hpRVEDgigOpgPrbNzMrQ0MygLtZf0sUrl1EtCmnzzm3KzytUc1OzWu7eRdQwiTSNm7W6aiXKZOnAce16g/TGXSh7La4u16blK5UPvD6Jv77d1qrLHKevt1tViyv0hYe/guLcKkCpPr12/NuAfo1asXS1ntr0nGryaoAt29TW1aylmSvV2dTjpAq3NZnNf7ZxIKFYlJijEj8ZEf8sj5+eLxN//w1wLkZQuQJwDuUhU2A6dfW4fnj4LVWuLiW15guoLG6UnzRpIdTYOoda6V9HgWJuqHZFlVZvXqurt66rkf6VgcLQmmUrVVu5TDmpRfgBv6ZnpzULMOHD9xXjz9MBgofHO3Wl/gR964y8+KfVG9eroLSQVOn1unYFODmvDAh1j2rKlnPNnKMm5sGjwHqNbddVs7xSG9ZsUin9cA7ffaX5GkAPypH9Y1pZvUoP3f8w/miZhlhTvv7Wd0g33AoYtlv3bXpIi/ylztw/55rUxaaTOnL8BO0+zPrlRW1fvR9VvUx4gll1z97Wscsf6GZdI5ucCrQRHzE1N6ETx4/gV/xaU7tBS8uXO6mTbb0eBFzpGxtgmmWTHKmMy1NKnU0n55vPAHceVjc+ezP+cWntUsCELl0DoLfUkrtWPAAESBpj5p6WsSYdOg6kyrpjEXPbhnVbVFW8HMDFq6beRp1vOAGQe1uLSysAig9oY+1mVN4As86f1ZGj76ustoBU009pRSHgXDRDM0BytomzrZO5CYAoE3WgFCCIMe5ZHT0T1AXmM493XPv2rVXN4hzah1SEqMtmAHXBE+APYjp8bE6Xrl/VOiC4Z55bSjrPDF1lc+CfffMw97VcgHNrHcW59HQv9Y3r5rUI68IgSmeAzevd+tJXC1FKQ23+vTD+sA3oz6NH9hQzngDLYCp6UUT6+MSE3j3eR1vO6V+9UqmvfJU0hJTzzBHAudcH6Su39dhTtSi5FasIWMLH2g1uCoBMpAKdUd8gcynXI1mkDUzmGnsayOPsuSDnQ72JOfGB+0q0aqUfW4WZmoAuSPeKoJ1zv+bMhUEUSy9qaU0JaVdXAogBzh1t1YeHGwFxV5CKtgylTwPn5tOYNpOq9c/+vF497ajwkd71ZWzi5Zivvt4OAHidVL0lun//cm1cz7Ua9u5ojenk6RDqo6eUmTGLsvMavfh8OZCyD/XAfr333iXNAs49+/QO3X8gl5TAMaA50m8yR1NKQDYv5QR8aR3HV6cSC2L+5d6BwXN32JB4+PBdjYxGtRWIb8N63gcGSULhMR1lRlPA7QMo+vDIqG7Woz4L8PbM46XasjVAauSg/tt3AOdIg/388wbOlbFWZR3DvD1gqVp/OKA33zpGOxWQRnY9abozdOTYkD4+fkvjU0GtXV2jbVtJnUu6yjHUqA4fn9Whk8Msf5v1qy8u19MPFwGhMZ+jOPe7//Uw4NykHn94g555dpnSc1iLsiREdA/YEViIa8jLw0NqY8NIOoqGFdnpykxJxdOQAhTf0UOaxeGhGRUsKtWyEtLZsoQZD03qzuiAuqdJP0nq9ZrCKuWxSQFNZuCaOdJwz7FOM2CeVLc8e+gwd4b61UrK+DnWoSWLyjhPEYA9UCLn9vX1Mm+T4pXrUYu32WP+WoHrJY5jPjzhN+914vaaPRLXfQlfn3jd/rb37OffdQw7jt0P6GHt9f6hD1gL9St37zZll6O8hUpZWSCTOmE3VuveWJj09ey7cfsAyuMaBiw/N9yputkh/IqHNVORqtNIeZySjCov8BegbhdQeRxYvLqwQNXJqI3FZnSLjRZtU4CGrJVLM/3KZx7zI1cW5Sy2gSXCmtqLvTI8aazASBk9TjrU4T4NhMdZE6c583FFMik26mIafn9GvfV9CvlmVby6SCvW5CiXAsfxI+NnZjQNFAWVp5wnspUOOOcitXWcsRobslSt3C+9PKcp+rm/EDxqNTHPSlSc8xkHxIkMtAwPxdR3uVe3TtSpHKC4dDWb9CpsPcvynvtrIzcnUci8IT/ryNW7ub4CLqPIGgacaz15W7mAhCU7KpS8eV5xzi7P58Yp23nAqh+1qshTqqylrBtrAL0AwgxWm0Zxs/PGsFyjMRVzDTDqHZWLNK/5q3OVXMk1EeBatIV0oNdjunW3VRGmvPIHytgIlMI1E/WbNaVrPoPK58QVwMCWSQUAQwv2AZXzWRf7ptyMoTj+JUpmjomjkxo8O6o01tA5K4HzyqgA4z0yEtHwjXGNNk6pIDVXGbsDSn6UDTvm78h20nWxT17SXxc9ma/UNQbO0S9x4tEBwNZr3I/+uE3uMOlR1xQppYzvcT82Blg70jiukboBZ42UXQPUnc881tFvnV8FBu+Vsfbndkx8jMtHoOihejbMeAdV9RypfbfzHmWz45itYgCSM42zaqtro5lJt7uyXKnbUJcFUnTTWe04TK0aPT+lsbMoCRMPzCEvc2A5dsQnxyZIm9w4qL76XvmCfH9TtdL2Ac6RuneyaUaD35oGxE9W1kEUF+9H0Rd/ZZvNYvjC4FXsc2Ranbd72AgD1LeW6yb6QQwAfOZ2n9pQOR8fiaJyB3y936BE+gDdw4W6p+14igOLQjbbC87TvO+nfSyAc5/WcgvfW7DAggUWLLBggZ9jASaqhceCBRYs8LmyQD83Sr7NDpk/fjWiThbai7n58rXnvfrXv+JbUOT6XPWEf/mVbWCX3BsAcx+ciqq+jYteLgSXlbq5eenRgzw3rHYji78wz/3Lb+m/fw06e+I6dzWqHx+O6IekIBme5CYLuyY3LHHruUc83FD0coPy89MnHv9XQR1mN2YG93F+HUD6f/maj6Dc56f+/+4/h/TH34lqgoD89sUu/fVfpLBr326i/v371MInfzksYDfT7Sa6/TS4zH4alGYw2s2bN1EQALbpJBVHVpa2EITfv5+UagTQ2tranPcsOLt06VInLaqpuhmEl7ipn7hh/1lZKnHj3+C45ub5tJ5XUdGy81RVVTnBhaEhbsaTJtPKsW/fPtIUPegolhlU5wQJKEyc+t6ub9Dv/cf/oCBQYC0KWpUESBYDg+SXV8iPWpmL4F9kbEqZzzyvrPvvkzcV8sS+i33skaib/W2//3OAc2ZnA7QMbHyXFKcff/QxQNW0HnjgAT300EOqrSGARtA0RuTJaRM+T8md8htwNtA/QGrT9/XBB4cI6A7rwIEHUNB4VEuWLJWPtrSPJurpfOkz+MexFyZ0rPiJ7Qyfw/s4wKP1q+8Dvx0/doxgYiqBySeoy8MqAN5xyoKtrQbzv88Hhwyk7Kcu773/ng4BSplCwsGDBx3lPAMoLYXw/8iHjZ+fDlLZ+UwV0cC5Dw99+Ima3zPU5UHGDypf3Ax3UrbaB6mQdSvrl2afERTi3n33XX3nO99xbPIESmnPPfusqqqrdJd+/4MfvOXUcfv27T8TnItjj6433kExoYnzRFC0CpGSZlBTBO3nyL+YX7tMuU88KzdKONO32jTyEeobjHUvUUNfCUpQGekONOMLEbHMBgrZd5+SKsrZmd+lsYvndPv0SY43TfA+R0m5eQSbUFhDbSUcyFYe8FjW4lriIwQHLl9Q76ljRKI7lUxgLFpaguJbmTKXLENhoJZACmOKoFx0clxT166r++gJzXV0KgOVN48pvQHOzdEvgwCV3px8Ldq5T+mllQQsmjTVQHmJJ7gNLLNo7CSpmAC0iLordf165T5AOlRSz80xzjtOnNRQS6syUP7IzF3EdwLUjwAHBwgH/MresB5FiSpFp6bVT4rNXtJA+zlvei6BpALAGz/jBggpNW2R0lZtkn85cC0BzXGAhekP3lQYu8ZLyuUGHvCVkfZrw2r5gaf8qQSKSd0VG6QOZy5r4sRZ9XW1KBklo2RgPYPOplG0mZkNo46TRZpWwK0M1IU62jUCLGbUqzsZJQpAHReAYbCjDfUFNsFsXKni+3Zhe4AK+ljHu8cUb2wH4sH/Fi5SdBFwGwqNBvekZmUrc/VKJS2pVnh8TP0nSM92+aqmSUUWyMlToKCIfkikycA5vpNzcK+Sly8hmBTU3OmzavroCCnVplElKlUKygheVEByltWSthWAENWjmPkUU6oDspy9Ua+xwx8p1nIH1T38LZCkpTvyjAxpCGjOS1mKNlHHFas1QT+eam+zTk/UFSgCCM81Pa4wIPPw5DTBwpXYY59SATSjM1MAmFd0+/hRJc+GADgAKoAlYkmWLi6GsiDKRACHARR6yJmFguE1deMLk0gB5gdEihJ4dtH2nojBCR75KosJdG2bD3wCoQwdAvBs66BdCpXktCEplYAWMpaUKZaTrvrhLhTePtTps8e0acd6IItHtZi0miRZddSlrjRcBRZ4FwhiQvcf3A+4A3SFDc5ev6jOkW76c7oK6Ds5MRQ/kLmYCKHgSn0r8su1DojK0ppeAZg7exXAE1ts3rIR0AfwEBC1vrFRp4Fop2bHtGb9cm1ZvwNQo0aDI4BElz7WNdLERl1h5RcX0LdR90Puor+fORAg0w0MupnUs3s271VeWiYqlqRwpX3GQrN66MEHtIdzFKO41g7U9vb5QzpHv1hVu1YvHHxZZZkGzoF+Apc2dNcBDr4LDNKjh7c+qnXVG9TYjjLXjaPqBjbKzSSNGX0h2Utw242C26wboMlLCrsqrUFhMeKN69LtqygpnSKaHAZwWKndOx8gEO5XY3MT8MNhQPwJNvGs1f71e1UOjNA60qITHN8AQC/tXFhUhPpOGgpVQRRxh9Ta2Y4K15w2r92gL+54QeXZlaSBRXn17LvqBwx95r4X9Mj6h0hRyVhgt9D41Ig+uPGRjjQcdQCcl/Y+j3oYKYNJ0RkxcK6b9cSZN3V7oF6btm5C/W4DamXjOn/9AopAnYDzPtL8oZySgrJuBJ9DCq8YAeiCvAItXUI6RfxVU0e9zl04p+GeMS0Dlrpv10HlAAnfbWnWWdTJ2gZvq7oWdSVU+5blrXTgyY8uHdEFgKUZ1PsKCoEz6CshYOcRxsAw8OrkAKm18U1ffPhrqgSK6+jt1vePvIqyXYOWr1ihJ7Y9o5qCWnVPdJDyskXLslfq1pXbKMnedtZptl4rBF5MbBxIrFc+63kdZ/0PeiTKkfhS4u97wbnZ6LQKKwpQvCoA7KK/Xz6m9z56W2Wkan3qoee0atFGxqDBLCG1DbTpOGlvG2/dwC5LtXffHvXQXy9dOksKzFZSIfpoK/pQMrbgW5Ns/AgDTOTz2vqlG5kb0oHUr+rk2R+TwnGc1JFrtG3zTvpclrr7WllXkIrZ2mJJrbYC2FYXr2JMRnXu5ilSwn6o6ci4Y+9FWcztXr8GKW8bPiU0GtaKipV6+L5HtLSiRqOoIb+J4lwHyoS79+/W/m0PKTOpDDOwhoiP60rjSR07dpI+PKannwa0X7tXKaSCA4VXPypnx1G+vEb75uVWajcAchr3ck6e+UCtwNle+nImIEUGAG2Kj7SH9Ozh8QHg3QytJZXrktzlQKUDpGj9UI3d9c6a6mHOX1tSq56pDp1uOEY6xhsqy6pBWRI/VlmD8tAMvum8zlw6rSFUf7Lz8kmHWg3ol+qklu6ZaGN6bwOcK9OjO+/TFtLFhkI+xjrpimmr0po8PUka85VlbMaIp2sQmOTStVngwGbmPT++I0NpzJPjk2xcJhXrTKgX7j2FFNMVwGZhdbYPM0V4AcZQg8JPTkx41N4JeBEZZp2bpf0H85mevbqI8tCfffMQbeDSyy+v1v59xWymAfIycO4y6VVfn1JT85DWb3Dry79aDqzmxbdE9dbbXdiOtgOMKitOQgzXfIVXbd1ATV3A8vEJFOfK9Cu/kkNKXxTnjs7ptde6SRV7V08+vZI0n4UqQMXJw7qURqKPxFDE60PJjjTrKG8GLEUn34PD4joK+LF3BOW3xUBzOQqGpnXndhdr7XSlp6LSCVRi6V17ByBCNAIEVgFgV6ZAmod1+i3sWc/96JV68bnF2roDUANgz5SV7jTN6s+/cVUdnX3avqWGVLVLlZmTpIvXZvT2203ApFFlsVarqmBtxLHGxlLV0u4Gzq1HFW1Kjzy6WC+hYhcI+PTeu12Ac+dR+IuhcrxfBx4kxSD3BjzAHHg8liSo+AV9KOKNAUYyh0+xlgFS8nINNUdZhtir0Qv0mUMq4W2bih0lx67uIUC8UQA0NlYg6TY86ldzGwYBtNq8LofsByi6LfUDRIZI1fpDFFlH9dKLXIs8UqVc6mj7urq7o3rzBwP6wdsfY78SlPjWa83qDN1unkFZrl3nL3fS3zNVAXReQDrEWVRRmzsBs9uArGLt+vrz1aRTXqRCgKCmOxP6vf/ynqYmRzjHBj33/AbWQqhYuVFVp5YwKxrlpDcB5OpHBlGUDCqddWZqAHVcr5uxHmZNFNHMKEppzFU1xenAYoBgrBOaB/ud76azeaQsCyDIVprMyXEU5wIo6ZUBjRUnpTBPp2gAFcs7g4O629cDu0Mb5Sxi7DJfAa/6J0ZUjpJyUToKWFyPBtjclfDZCejNrukSD/Of9rTHvZ9LXIv/rGuUxOfse/detyeOZe/b0747zHr4x1xbXervVvL2TSqpXKwc1u+wkDBUYXxqjHTFbuUAKefzzOOVSQDic6N9ukY64AjKjwWeLC3CL7mT3ZpCHW5sYozrbK8KMwq1GFXRfC8zMTbqmJ1R8/iUJoKs30i/mo6qrscTQGkOP4g8oweYNZNrjrJAjvJZI0VmImrhGuLuzBjKvRG+41Iu6bdTekg1eoV0mddHFOmfARzOw2/jbzJoE9snAvwpIM6gL6TsJxjfqCPStAoCogZvhxQlpbDawwqPTDopND0lXEcWkuq7PEnptQElFXCNzGP2NimkP2yRC7YrNSObjUek3MQn+SYBIElj3DPSqXggrtqdtUrfwaYY1kOD1wZQMbsuc/NCAABAAElEQVRF2utsle+oAZxDtRao06CzCOuV0M2gBj4Yka+bdbFd4+RwXwVf4pthrT7sJtX7pKPKXkYq7BBqkDZnedNRMM5HPZXrGu8g/bnPrw7WiZFiNgmjHJmzEWCMtpztGNV0G1siukkP3gFYNjwLpAiIW42iH+MtCVA3sAa8s5qNMWanRsBJ1ECD+K807gv4AA9dQHBR1nIhILjwoE859M/ATp+SH6cCXJeNf5cUrqQi9WOv4kcKlEaKbDc2oMEBx9iQ0eJS85EhzbTOOP0oOZNrJa4/4yGUX4dmFRqcVjrryfQVQIMrXRptHdNoC6qBjKhAtgGAcflCtH2/n5TGXH4kDajqmQxlb2ODHeqooQ7GKMqS4X7UCXvCGunDMXGfIaswW95q6ljoU3o1oN9iRjx2DTaRHvZ0VP23hpWUFFEAZUrbmOVhs9QMcOcEyohexmXhlsUK7Mc2gHNjd2bU/q0JZz2YfxBFwv2Ab9xHd+Mj4mxaDzUA3R2fU91pNtCwqaewlM04fuw2HtQk1zn9Q2PM0MkqX1Kl8vsKlbYHG2VzL8SLUjxp46kho8r6GLZx/mOO+ZSPBXDuUxpu4WsLFliwwIIFFizw8yzw6Semn3fUhfcWLLBggV9cC3AtwYV8TH/4rYj+P6S92XCmzahz/bvf8Ol+lJm4x73wWLDAL7wFmlvi+vNvc5PwUISbOCjzcJ26dYVbX3kSlbl93Ggo5oJx/lr/F74uCwX8bC0wza66u61Rvf1+VN9Aga6HG0bcvyLFi0tfesynL77sZfcsN24+29P+Qh7tq/97UK9/wA0V7pu9hOre7/wf7Gq1NAmfg8ft9pi+BDh4mZ2k6dwU/U//1qevv+IjBdjnoPILVfyZFrCb5vY0xTiDji6hjGTqXQ0NDY6CW19fHzcTk7Rz50598YtfdEA0+8x7773nKM8ZnGZpUU3VxG6yJ4K0P/Nkn/JFu5mfuNFvKUlNac7Ob+UyUGw9sIyli7U0rVa2jz/+2IGVHn74YdR1HlZODipXPBJBglsNjfqd3/otAJVuLbe0VwAp+dymNOWRlORUUhNx05IJtPKLX1Lunl2oR8xPnPZ9eyTKkijXPyU4Z21l57egiJ3fVOfOnT2r73//DdTNrhNQQjnu+ecd1bk0IChTdOPjVmr7x3lYatN6YKnXX/++zqGOtAi45MUXX9SePbsdhQNTgLv385987R//Y958TrlNYc2KFLO2/aRswygtHDlyVG+99QOCA0MotG1Gbe15Us+umAfgrCKffG++fAQ+gigbADt97/XvOUo7lZWVehbQzBTZDPi0fp1op398Bf77I1ifT4yfRL+wTxmAamlVTQVvdGSU9tjmqAGuWrWC4A43yS3wZXWxxyf1MgjQgNXvv/F9Hfn4I5WXV+oF2uX+++5Tdm6OWprvAs79AHWPae3YvkObNm9C7YjkTvcE0WIco+3NtzUOQBQLsiOe3Kk+AnueJK8yioqVuXyVUjZsVjwTqGhoQpH625q5Xqfx7g4Uq6YAcVCPAKZKoYxJFZVadP8DSqmsIKI0pXBXu4brbmiKtI9zgAQzBBUjKQRS0ggeFparcPlqZRs4RyAm2tVBqqLzmmyo1xRtNA7AmETblG/YiJLYGnnTCHIA5ohAWBRFuemrjQS5GjQx0AsKQRCNwJvP4yJYn6usWlKcrkM9DCWPYVQJh1FjshSt4K0E1AhwEXgMoBKUA/gaAKLyA165CebEUN2bQe1pEJuOdXYS5SLJHvaxIJ8PlSgBghVs3qj0xUuIT6AA0zekycvXNdt4CxZvQFOuWcAf3gJKKy6v1SLK4Fu6ipQ6WShSNCl05pgGUKUaBYYKA+R5S0tVvH2zcoFaklEscxEMt1SmUdJHBhtva/gWcNkACiNmNxQF3KSwygZey61eorRlK+RCHWu6qVEdHDOIur8pRFog100/ScHXZQKsZa7j2FXFcqOcRkQSuzUrfOm2prpQfaONpn0ueQn6BFBIyS0tUS6pbZOWLaePEVBCwXAWSHG4pUUTgIJRyuChvS0VVhLKdMWPHFT6shpnNEaa72jgwmUUE9o0hwINeUEB1nJUs3UjyhOoB+ZmKE5awAjgHNmz5CbgP3e9HtW4axoaQE2BNLOmBphm4SHA46xlK2mbLfKQ5mukrk6jgFzTE8PAvpNO0NrHGEgG9E1fhD2AqFIWL0XBIRv/BUDZ34fqxTlN3bmLgsyU5livwGeRSjVJeWWVKlu/QelV5Sg6JCnWS7D7yk2g0NuOUtUEAT4DVVNRl0pLTVPa0hrlPrSf/sFnAfqmLqLsRduMjc2i/OJTUlG+8laT7nfDKnmBXO+ODQJQnVNdw2WAjiXavmE7SiiVzBGkrAN+bDRI7sIpfp9COWczSknLAEBm1ETqzLr260BufSgkAkfOElj0cU6rY36ulpIefEnpEk0DJl6+cYnUo3e1ZGktsNlaleaWOcfvGe7XlbqLqORcVw6pbTes2aLlpetR/0nR3Z4butJwWk1ttzRHoM0PmJDCmPJ4/Niffo+yxVpU//Zs3q1FBOA7Olp15txFjeP7d2zdpg3Aq4uAbfpJh3wMBbeG23dUW70cmOcASjdFjkJHmP7V0t+si5xnYHhAO9bs1drajfR31EFJCdvYVqfJoV4HpIWVRT0mQGA4Tbmo0axdvFy1qFUOTY/pPPXrQFlycVGJNq9Zh2rXCgKDyeoFxjl9/YRut99SXma2dq3agTJXrYIEg+8O3db1hhukWARAAhBP8nkIomNDYNe2gS4NTY5q9YpV+sK251RBe9R1Nuv9a6eoOxD41gPavXw7KeRQ+SBoPw2Yd7r5vM62XXBgk4c3HtD6srWO4liEAdZNyrzTdcfUNtSsFSuXa031WoCcZIC622xAa+B6qYs2nXXURPxx/BhKZ2lAh1U11aqlP/nwrTcar+t2020CvhnasW4PoBR+nptH49T/WsMV2uoCEKBH60jpvKYKWDeA6txgB8poV3QHiGWalHbuZOCUjGSC00CCqCn23upALaxYLz/yFVT1lqkfMOPwhfcAMu+oCiW5PWv2qYS+MjjTT7rLPtICVujs0XPOZoclAH33MW/YHJh43Ds/3ft74n37mVjjJF6793P3zqOJz937fuI7f9vPxHfs/Xu/lziugXO9vb2yDRARVJpKyvEF+P/haVIiN15BVe6YCspztXfbfSjtrUAhKs1RBesa6dFlQNW7rcCJ1RXavnUnfoPgfDsQYcdN9ZE+0VIfu8OowhCMjuEjU3MCqqys1bqaDfLMelg7XdK1mxdUWlqoLRu3qJZ+aBDCdGhUl+su6FpdveOvN27YqtWLNwFEodZIf7l094ya2m8CG5GSl3k4CXXTFECs3n7SLfZNqRqw8cCuB1QLxD81PqpjANd9zHHrN5HmeA0ps/1A1MxfMdIZ37l7TVevXtMou9v27r1fy5agduw2PSfSDQeBUKhjE5B0ekaxA8XmF6Sps69et1qagPHwZTMh1q1WxwzWGEDXvohKUShdVbNCJaklQFbNutx0WbPAIpvWbdTG8rX4gCyNxkbUMNgAPHtRoZGYVlWtoWxr2VCWDHzHee/UqaH1Fj4ApVf6f0ogi/kCldakaTVYqnCg48d2P6AN1ZsAtpN1/UojiphHaKts7d+1n83IwNtobQ0PulR3cxq4rA44MApgQvo6fHMY5U4vqbHLUWtavwnIODdZd2+Po7zWpeGRKeY/JmHAOQ9+z9I5V1YFtH1nKiAq8DT3Nm5cB4L74XnWQmE2gFRpC9BWRhpzE3zW3cawPj40obaOES3hvu4jz6DiBFg2hFrU5csz+O4+1PeAFXBgfn+M9KnZANlZau1CBZm6v/BUgb70xSzWQyiAXg3rww9ZJ6ESuX8/gObefOVkcM0DsIPjc+p36tyQLtV1q3sAKJ728FBur8uPL0gG7MsknWgx1yNJ9NURzt8AVAZpEktmDWnHIFNiVjLXWDnasCmPzS8pTtrXY0ebdOlii9ICi3Xf/kogauCdTGBLUqd2dYT09ps3qMMAqtGVeuhgDRC1X6Oo+F28NM76fkSdwLzRWEjJgL+pqfmoxgUA6u4wH47r4IFK0rKiGIoa1dkzPTp1qp4yxYD2tqFsl60cA3ps8wVzuYt10NxckuquTgA/tqi9jTTtQYB9Cm5LWPODebnpWr6UNJs1AcZyVNevoxjLujGGApnNTeFYlvzJWcwNOYDg9lk/9gBKvEg92AQQiYyx8WWbdm0vB1y19f68St3hI8PAeqdRX83TgQeWa+WybOwbZ16e1Olz/WpqmQFQZLMA2QMMII3IVCGlidE7euWJUj0JOFdE6sj2jhm9+p1TKKOOa+/O5brvgSVsMGCMsZ6gJZy11jTrrW6um1onJzUE4BUNTwO7McGxUSxmimkhNiWjArwoi75YkIPSWoQx3aXO0TEhWIaqWqqyvMCQXKvRSqypWZ9SlVrWnGVI8MVR6htCTerO4ACb9QfJNO9RJnAjF0Osb0JKQ1G6go6dyfrKzxrHrnPvXeNTzL/hP+3vxCPhV+/1tXZ9Yn/bMez9xO/2HfvdrmHs9XvPce/3bbPapcuX1MSaMYm1Vk7uIpBljgcCx4UBa5W40oDb8klDXgj8l8MGBXvnAuBhQ4RrEMZGoQe/gQ8OsU6a8wSd+wTZSTkqYVNBIXBcmm2iYL08wmKyg/sQw8Dus6ER+hYQkbPWYi7nOBn47kWsfYtRrS4kfTvic+plfdAO6DhAqtxZzuciiJMZyVT6GNcALSHNNU0qexSvyMYaP/BogHViMotID5uIp6DosnajKLkzoBDA6NilCY3eHFO8f04p0/QI1urhpDnNpZAgFgXJQGWuitcuUmY16/sArTvIMeqm2Xw0oYmRWawRI618mjKQM/MBgo0AQU7EJ1W+lf5s4Bz83WDzIKpxLaSST1UpYzZlWQbzPuPM7sPZuhaFxZkrIdTSghqlTwXjaBSiIpqBrw54A/inMNeApDKvzhFmdcAwS7XtYRNRMuvcdNDFlFiaRulHc4uiDjSXDpCMcTRyu1tDtyc008/9g0kUxbkE83OdEUshqXcWa6zKdOVsACRcCvZprgnltsmbcxrDJuEeasf1k4d6e7GFfzZTwXYX5UpRynYUOx/CT6PMOf7RFEp//fLjO4p25nIswM8Ua1/aEn8RG/Fo+GZII4zd+AAynawzDZxLCqSgTsm1MfdmzW/6q1jTrsfnDDL/1WPjboB3Nk94k8LKSMlmzw3nH8NPe0dUuC9JGWs4djBJ47eC6rnWD1jHfa0pD/d6aSf6qCuJ9K5pAIBA0sWrC5WxIpPrEbBP4iWhhihpecfYYAasTR9NYvwGUtikg0LtZBdtgKpl4fYKpZvi3GK3JgEJO9+a4H238nYElLWJMZrOeQBwLZV5rBs7XGHjyYkmRdjwk4oKqG2kCgdnuLYcVS9Q4xQbNKtXkEb7wWUq3gNuym7uuJsJza4bmTFdcesQdneE//Afn/axAM59WsstfG/BAgsWWLDAggV+jgX+ETPTzznqwlsLFliwwC+2BYIs1M+zA/F3/yys986gRsQ1xmO7PPrffs3HDmnUIX6xi79Qus+xBcgapWs3YvrO9yN6+2iEHfJxbupK+7Z69NXnfNq9mZ2A7IRyYuGfYzt93qse4b7fAOqaP0B57pt/HSboxQ0zbvKVcnPja8/49KUXvMCVv/z95P/8fVNcI30Tuykf2enWX/w/ySpip/rn4fF//79h/de/5CYY92aWFrh16HvJKiN17z/mpsznwW6/zHW0m+aJm+emXvbaa6/p9OnTTuBt2bJlakMFzFTclqMe85WvfIXgzConpeabb76pRhRxHn/8cQdUygZy+Omb75+V3e69iW9A37e+9S2dOXPGSUX21a9+VStXEpgjuDAxMeGov7366qso04w4QN8rr7xCMLTUKYodx1LP3KLcv/Vbvw24Maad61drY3Wpsgk0sp+a+74Ertn6zf1PLWJ3fWpVJczJ/AowUY57gxP2+z8HOGfntafV5zYqXK9/73ukc/rIAQafeeZpAmIHSPNEKs3E4Kbu9rAgikFdx44dcyAsS4G4BQDLYLvVQHd+Ugdafe1mrf3/mT+sGJ+UxY4dJ2DilJGb9zNAgA2NDXoT2MwCyDnAYpaudefOXU69PEBwzuOT75ta3cjoKKDdW05dLIWwped9+umnZX3X+kSi/omf8wf4bP5N9Af7aUGnxN92dINQLU2rjaeLFy+qBGjNgRNJ0ZaVleOocVA4Bwyyn2bqWdQWDqFQ9z3asqXlrvbt2+/UZQ3wUypQzN07zXrnnXc0RZDewEADC38anItb+xLkD97tIohA5JjQE/FKUqiiDoIKm6+gkI6dB3REOpoginSo41iqzdnuAc2OTqL4QV0gqT1p7OxHNSywpIbgAoGdWFBulA2iBM/nOglq9YwQuCQIRjDCBRiVRhAxPb+Ec1hqUNQrgfDC+I0Q4MsUk+2UjancQuUAEKSXFKLiSLDSAikGzwGMaYhjdQ5opqeP4AIBHwKtSakeghwESABuLE2qBadnuylrR7sikxOsXwhEAEbFCBhl5GUovagIpTnSq6YShCQgT0SGtDkTmiJAOTYwiOqaRcomhWCB/MAp8VxSNFWUKjmbXEcEhePThOn6SO9F6tdp1DKmUJwJA9W68Q3ppF1ML6lUHNWdOMCbH1/j6urQDGWZ4neEIhRPz1R2zWJllJdSPyJaFgxk4WWwXpyg4xzptya6OzRH2SO0uZsgrKngBYpKsXEmARuUIIYGNIzyXphAjZvUVCzbUDrxAuKlAdnlyb+IdJHYJUagCaJPLoJ10TZUXQDGJvFnYQAAUy5LRiElQFskF5ajZrGI+hHMoZyxHtoEe0zhA4JIbLAKdKBKUw/MWLIcJTtgBs4amxhC0a5fw539mp1AgQN7uggcFwJDpZWiAAkcacR9yNJnGXhHcMo9jLpId5cmOf4EaTXjBOvhnJSG8lsytnPlFRM09gIo9qNWQWpWA+fGSTtFG3qT6J8Aiem5qAgtKgBsI90W4JGN2ziB0TDlCHX1K4i6SAjYctZNPTl4Zj79rhglKRT4Yox3N6CUG5WImfZujQ/xeeBRYmaAc36UhlJJA0z60BVLUJqgPxKcjQBPT3X3axzQIUQj+rMzlYVyTRYgnocUaRP0xS6C4j2D7covyFZpUZnSkjPRgUhmk51bA6QlvtvVhmpMSOVlJSrMLAB2IWAPcNM52oqKUDeqJTPyzxAIdJuCCABfXhbqN2XAHXmanCIAZ9BLeIwUwaXKzyzEZgBoBNanwyHUq/rV0X2HoCqKNajUlWVVK0CasunwMGomd9XWBwTJWCO+J1+ygQ3MB40tGu6d1pqVG7Rr406VAE9NY7fWji5NMb4rUDErAS5IxWfNAi529bRzbTCIvy1UZUWtAkmoY9DxolxcjpF6rovUkpMEpYsKFqPoU+rAtSNzI6Rvpa2BgGIoGEb4bJRgoyslDWWkLICuRcrmvCOMv7s99HkA0kqOX+Gkxsyi33k1E59T12QvAFkrscK4qvMqVZRN23ujmkR5qIf0vQaETJJ2mHg5KRv9pEGd0jnS47Zy7jWkwX1myxMo+ZQCV43rTl+HgqRuW1ayRBUAZUkWeKQeppzXMtahO1NtjtLjikJUctOKGZ+kLEPZcQYwopv2nZgeQIUMVS3UgixwOhEcV+9Yr/qGe0mLi1+g3yWRNi4NFbB0lJZyAW+zaUsqQ/q+XlJvTSiTgH51yWKlB1BF5ORRzj0wMsAaqgNFmwiul+Pno2oIxBlk904/7/VQ/9HJPuzHjSiggDAdqLsVBaPzDajxlOuVJ79CmaoceO92z02NzPaTij1LlYuqKEeGpqOTQFOzKNIE9ON3PnDm940bNzrzg20osDWePRJgRGK+stfs98TcaL/fO4clvpN4zX7ee4x7/04cy37aw46ZOK/9nvg78Zod597z2t/3gnMeVKzKSvNRXMqizVGVBdBsA47MyA6ounwJaXNJz4yqooEpE4CR3b2dGhrrB/TJVgX297KOmwAkHpoaImVpryaZ52IoaRqw7wmgIJUb0CL6WnFmKb6ZFJb43B6Ua8sqylTFOMygHxu1HGJkDwBQtXT0oPIT53qpkj5cplQC/KY01zPTqc6BZk0AxbmBvT2kGDSw90bDLfW1DWop6Yf3bdurSiDm8NyMow5nil6lxaStzi+ln3Gz0WqBrxwdpa8BQM7i+ytR0FyET3Oh0hOlzEEC9AOoIA4NjbOuSVNxSTGQF0kgo+ManOjDFzBGgSmCqBLF5vBPQLrJqOkUoLJTjG9HPxXlrm51jQ4AuqdoCQp4RUmZSmZ8BN1hDUXH1NqLf0CdqSCdVNn4sgzqYsDVEGOrHV9q9pkDbjQQOpZGuvKJLp07dZpxXa4n96GqBxDrgwzpQ6GnFXXVVJTHqlCrzQUmBOPju371D7tQ9xsh1SW2nU0CimUa80WVne1TUUmKSsoAJ5i6hwBXOttpvyEAb9TJXMylXiiRvByUxSpIA00mhRTgjeBsXL0o2TXd6VcyQMXimlTaKAO7Au8zFzC81H43gsLVLOOVjCIr0xh7HoA01LUnXWppRTGqY8qBDpOYU9MBmzo7/Dp2ErADtalnH88mlWkqwJkLHxnV3bsjzCmTql6ch69EaSk5RM2QGGI+CE4nq7OXe3C9k+pnXphg3ozTGfzALFmZKdQvwLUIadyB8PoH6QuM8clxNmvzGYYI7eVDyTddpeVAQYV+yomSG3BFa8ugerqs3XNVUZmpomKwCn+QseplcxPpRxt6WQeT2pj5fumSfOZ+q18EBTiyqHTNMDYm8QnMsaTTNjXCIaCZHx+6DowzA6S2VE89gWIp4rq9PRPYYgTI1AXYVoSNSUhOGawPuuLcNMBnx2hfUw68c3cSNcdpAB4KzsWS+WyWByoEJCsrARwDBBoZpo/QhgMDrBcBRlyM8bg3lTVrBupwpCovAiKHKPPg3Dv7uS67jX/GltVsDCgryuQ+JvfeOfwU466pZZZ+06r8HA9pbLPZ6MnYoDyjY4zdbgDvDtRIR2fxTyiwshbs60/R0dMx9QKwv/h4iR5/aBEqi6hXTUR19UqPAwDVVOUyzlCCpT/AIDLe8X/UNcS8MM2xx5nTbB1va9k5+uAMa5RJYLZR1sFDrAlysgJanFtKenpSlLN2H+H6w0lCjO+yJNJGbuJdedJXAYEKWH9kc4wwcwvdEnCuV4OTw6jmJuHfFwHa4aeB/5KmUb2aZN3C77FPro/MPyZU4c3vJp7mQ+1p79trib8T7ye+c6/ftWtGe92+Y68nPmvftYf9tNftaZ+zDT193BuYYmNBGhtHfGwSMVVpN4C7C9DH8J5UVCizWBdlsKZlySz05HQJCcK7rO0yUCCuYd2TRRryCOmlkYFlI0gKwBUpPgHu2ELjAFx2ERRm7E/Qn2b4bBC1uhA2ZNbFmqY6SRYg1jnZXHMEWEfa09S4ZijvGGUcxb9OsWaaA75LYY0UiLGRgHSbns6gUgBl3Yz5KGBXKnCqH//gQjU2REsHykkHWwoACsM10xzUTOcM1wJAaHgtW8MHUcObheaKMG4DgKE5lSjOFbLuTWKNPsc8NozPb6F/4Kts/eUHmvNH8Z39pKZu6Sf9+piqt1QqdxdrfJYLExP0lcF++rfXAcOTssgZynLaxbzPkEbRlvYFWAv1cZ+StVUIGNBLO6SZkh59iHcZo2wAIo28m2utaXzbBJ+Lcz2XxHVMABVLH+vdSJhrD5Te/IWMFBQ4MYxmUaubGJzS7CT+FEU1PzuDPHT+KOOXhY2SgHpTUdfzLwL0AiSOcv0SG8NO+LU5yhNF4c8F4O/lGR/yabQOZX4XfmUDNtnGNQLjea4zzLpw0tkIkF5Bf8jBv6G0Z/NcPMZ1H40cHqEs/fi+YcBExo4Lpd1k0tknMe+6KA/DDVuhvmhpXAFVwz2AiwOs+1nnuKD9Ulk/+ehrsaDL2WiRUsP4WET74ktnu/HtbQDXCM35SS3tXF3Tr2Kk/p7y0l+Jh+SUAiGSKtoDaGuXoDFA4xDlGR9kAxAb01Lxc8lsghD3zPvrh1GZDKtiFxvPdqPmx4bz0ASblurY3IVyZEolQCaXpy4/9WHN5waUds14NdeFemVTL21Fx0Lh1q61bCyNc/139eYtNTS3qHJ5jXY+tlPL97ABw3azA8nb9Yo7hv2B/+nejEdeTjz59R/6WADn/qEWW/j8ggUWLLBggQUL/D0sYDPTwmPBAgsW+DxaYJItUh8dj+h3vhHW+VsxbmC79NJDHv2vwHPl3KRZeCxY4BfNAtxX0QnSVPy370V0/GJUfeNI4nMD7LH9Hn0BGGoD0GeAa/KFme0XreX++cozwg2Cj0jl+1evhXWiLsaOepEeyUU6X69+/ctelDDYIc1Nhl/Wx1++HtZv/hcLZsW1f5Vbr/5xMlDDL/8I6eiL61d/PaQjt9iFyY2Z3/wNr37zf+JGFfcGFx6fbwvYjXMLWhoI90d/9EcoBfTKUkGaktyFCxdQRriM+lW5vvCFL2gFakqWCswAHlN2s7SgX/7yl3+STjNx8/2ztGjipr/9vIbi1J/+6Z86qWL37dunr3zlK1oMzGHnNXDK0ssaONcG8LcJVQ8rc2VlpRMksCCAHcPgu//8n3+b36N6+MGD2rZlrfJIJWjKEG5uJrtJO+Th5r0PVSU3QcZEncxO9rj3b/v9nwqcs3MnbJEoA1EPAl69TtrVd1A4sxvte/fuAWh8wlHic1M+C1g5Ho7fg4Ag7aj/vEX7nTh+3NnJ/sQTj6PUcUDFxShZWdTS+c58QMbO+Zk+5k3IXfHELxzdCsef1gdHSVVqaoKWQnaM9JIHDhzUw/SxGoCkZFQNnZvgBDPsS2HUk5pRFHv12992YE+DNx999FEnVWtJSYkT9LGyW7uZvX5iM3vxM3gk+oMdyo5tfyfOZe1kIKoBpj/64Y+cvvYQ6oymgFhVVUWQkbrQLqZ0Yt+JAY4MDQ3qte++ph+9+y7B4hgpq17QQb5TBgDpA1K6c/uO894MENaWbVt+JjjHwRQdA1qyoC08mgUr6NiozxC8MFgOuCYKNBRGzc1S93gJCrpnp5xAUYzYaJxAgIv0pHGC5qauASWkCOVEr4xgEIFJxgYCCooBmcWA9KIEvV0cy01w2IuCgqXndCVxTgKQprYWI0hkAewoY0sE/D0AXQbmue0cAG8Gv7kI5LnnsB+BrDgLkniQYwPVeVJ4ncCPAVtxlBaiccYnAWGIPeAAg77mx0OUzyYFKCMBOr5AJWxSo1NRb1BZBy4NGSRG/3LT//2U2406iqUUdVMeDwogilJuC6AQfDJlulgEIA2Qxepnvc3U1bypGUAtBH44dgrl8KLEYWWJ0obW3nGCVd4AdQQUcAG1UTmnGJYGzPkvCASBikl8jjoyzOJ8xku7eKxfs+gyiNTUFwyasyCYi9RUMSDWKAF1l48kg3zWUvxaECZGkM1SpXoBoeKktYuZ2hB1pFGwgQWy+YHKnwtlLKRgqAEP7BwHgDIQzWk7zhfnsxaw86CI4wKksNStluKX3LWOvcKTfJZyWN2QYJIfJT8P5TEiycocAnidwx4eyuHjuG5gTVOlCBO044SsJVlvEDSNJ2VgS35iErdduISADeaAIuYIhFkfAEZ0ExR023mALHFEzvGR83HsGJtinJAyN05glPATym3UETDEwzOCbaxvRwHTCAPSR7At9ogCzcWxJ43jvO4ycA2gMZyFTWgeb4zgNOBYlFS4EQCAWNjsAD4NGOLhSWGcdg0Bj8xGSB1mqhj0GZsnLGhv4EIQsGEcNbJZU5XB3ikGX1PHKOTZTAw40cYXUKF/jnJhEhtbXsagjwB6NGYKPIArUYKH/Gepz1NQmLJgsKkBRoigRYBxpkNj1HkGtRGUTLyZnIGgHHm0ppE7mQyPa5YoY4jz21Ha27t06fQV6u/RNlK7blu9XvmkFraxPk3/YwQTnLTUqqZMxYN+HEUhL0K7eWmDJPqvizblcBiRFqRtgtEJzhem/syHKLFZ/49RmQhKUXEDE2l367vWF6IcNwllyFT6qqXmCxI4n2LMuRkLGUBCKWY78zH4S2KDBKRRE4kAdAINBCyIThkMXpiNT2kcICqEekyUwWLdIA68W99UpyNXTpAqcky7gJAfXHMAcCGH91AmpP/bGDbVN0sd67LAo41ButMEbT2C4zKF02zUwgIAcKZKFqWfGeA2Rz9EGgTVwyRAF4Ne7X/qxDlnCcjPELyeA3Tz0odS8DFJ+DsjBiwg7OW/CG0cJcDroc+kMJa89EnzS+bfIgbEouJkw9tHsNxDAD5OhRiNKPDModyJokl4FEA2QqpWqQMA7OLx8+pr6NSm2nV64fEvKjuNdNd8fhwVnlAMdTOOEwBC8QC0cnT6ErDBSEQ/evtdJzhrKry22cGUgOfnF8aAwUT4vnsfifnKXrt3bWFQhT0S79t71mZ2DHs4/g5bOu3Ie4lH4vMJkMNet9cSP+33xHv208pjx7K1mc3/NmeOjY1x34JNPWWowmRkmqcBBAJKmZ3Erqj+AC0amhIHXDVVziiDKIgvmYuhzMPclkz7mlqLqacaRDHFd8PAkW4gDFocX4PPZJ5Kor955vAFgEJz+GS8s5MiPsAYT6J9GILMevRFxv8MPouuDHgUAPhgkwW2CJGTbcKFSs3cKMNoBrcMyI36Y3Nni06eO68xFOe2rN2unRt2qthgYJRkgqj+xEnjaQCTF5VTD2QQLp6xxPGBxSP4TgNWU1BX9KMYxwRDL6TUpCoMk55uDr9qdkvCh1AEfFSU7AL4AkDaOXxFNMI8GWE+Qb7IZr8UYFNmOHwcn6N/h+ljlio6E3XlAPbzMP7JbqdZwAUDFOP4iFSgwwDHN99t/wWp1wR+dAaIKGZG4PVOUjyfun5GdddvaD2pYJ/e+5TKgDyT8F9hjmnpcKFTAD2ws5t2wY42L8+FkzkOfo/7DiF8boi1gR94wcC0JFTPfIAtFJv3QaimgStIictAZq1kqULdqHZa2kxAJD5nbDzDDmAwzroxzFqWdHxZqF8ZFM3rcXxHxCAN4IoI48PDuoSuw/GYs1jHhJnzZ4FUplnLhJkbvTTEHAPwxPFp0rh2cG2cqZeeydcjD/mdFI/2nRmbK/C3Zv80IBG/x+Yv5hggm2gYXIr0k1MAOrP0tTA/LR0l3o4+CTiditKt1Y85NwKQMoON5oIGKNEO9DdTOPTjP1OAYdxGjJkh+DE9he0pI0egHKinBkytj7UHBojSlyYm8PycK5m5y1Kimq0irANmQ0DBtPssQPgMfdyADoQ6deVyhPX/GSA97vs8sQYFt2wAVVtPM4/w+SjzYID1n4GEsEl8j34IJGX+ywDBSNiPzQBWrU+RwtDqE7c2YtJIo78FSL9ry58IwJvBpqE5s+8n6yhbiwLEWRrbNNrLh81tnE1gh7Ex1mf0wwxSK6cB5rPa4dy0MWcdpx9Ms55M9oVJnUo72noBmCpIHSfoJ5M85+hLDC6nzx09EdSPDhlI2qUvPVumA/uBnElFaT53nFSrBmqnYWeDS82PGzgXoR5RxjQrT84JgGOHY/Ql0972vQns18vP3pFu1AVbgThTtSy3UqXeXMY+15+MW5ZK2Ap/xrh1A1oz29OEjEXA1zigZjJ+Lk79hljDNfW3A70OkHI5Gfi0RJnAuLZ09tH3XYBzYZSqbO4wH2lj3p72MB+a8L8Jf2qvW/uZPzZ1Mnvdvmd+1R6JzydAODuWqVLa5+2RmBfsb3va33aMxPetHKaQHCC9tZs+autZg51cHhvXVBknYhsDvDR8lLUgwpW6MjiqFnxKHtcQa9j4UWJrQa4h7FrCw/lpZdZgeCc+TzZb5zg2V9u6IGZrXNYXpigdxu5zHJuzMG8z0syutLP1DdvkFWfOtfYJMo/M0q5zjG+XrSGxPst3BRg7PgBTXBnL/Pl1I7VkjWR1BH4C8kS8lI5G2QHWoiYbiJKy+Rsbf1H6qSlP25rdz/xjamvRVNZFjMEkADnfNCDxMGW26xebC/E57LnRVP2sbtbdYc0Z0dJdi5W3OUuuXHwM4zPIfGFjJIk5wNZ75n9cVMiUD2PYw3xGnHFodo/R/1zMZTbXe21A0j4x5jxbf5u9DG6zjQsx/JOhhLYGcdqVsjhmsK9gb5qTsuEb+HwU32jprw3OctZJNt/Z55zxyWvArOZHYtjZBn4c28RDFI5zcAqsTb8ESpzpYc2EEqivAIXFfL6Df4vZ9R7zlH3fY/6O4843rv20P+bPydKH41q56UOMGZvb3VxnmRGsfNYHXMwNNlZdXB8y4dLH8SP4I49dp1B9O485ex/pXsOmascc40XdGZk/efD7TMfzD+pnRNoc57E07ThufDhjgI9SHcfJxJhHwubf8Ve+OQMrgRJREmy5wQYc2mLZftRKt3JdUMRpMGacNnfsx+Wjm3Mb0OcozpnRo7ZRiXE0DjRHH4tzveL0JX6MAWR/8OFRfXzshCpqq3X/0w9q/a519AX8Pv6GYeXAjG76gXVyq6dTSKrwaR4L4NynsdrCdxYssGCBBQssWODvsMCnnJX+jqMuvL1ggQUL/OJbwC4qhoAp3novqj/6q7Dqu2IqyUON6Vmv/uev+dhdtuAffvFb8fNTwhluSP3wcFTffC2iy/V2Eyuu2hK3XnnUo6cf9ZJ2h5uMn8TLPj9WWajp38cCxN119UZEr34/qjePRTU2RTCJm0cHd3j0b37VqzXLPAS9/j5H+pf3mdMXY3r53wRR+Yhrd7lb3/0rwLmSX37f/rsA4b/Hc5C2LuEm+YffSdbSxdyo+uWv+r+8TvpPVGK7+WcPu8lqKT8tzekf/uEfOjcFn0Ppy1StDh8+7EBJ+fn5eumllxx1N1Ogexe45/XXX3eAq69//euMoZKfHCtxTOeFz+CfRDnt59WrV/Unf/InDkBn6mK/9mu/pqVLlzpnmQRKsfe/8Y1vkOpzUPv27XPAOSubBQosCGABgQbgut//vd+3+5h67PHHtGP3Di2ifoTnucfJjUoLwHCDlYgfN0PtJvz8IEmU496/7ff/UeCcVcrOaU87z0+f16k0RR0HMLMymPLahfMXVFVVpWeeeUaPPfYYwS9ADOrOl3ly0xaFNvvMd7/3mtpaWlW7pFavvPyyTKEmQMpN8wdO+lS+kzifc57P6h/qYg/nxyf1srIZyBMGwLAynjp1inSlb6IMcUUrV63WU08+ru07tisP5SyDOvg2T/os4NIp1BG/9ZffUk9Pj0yZ7amnntKWLVuc1L12HquDtbnZ4F4b2nufxcMCUvZIHPvePmJjytIev/7a9wD8mp00x6aGt2PbdoJ4KHtZEM2CEhxjhp30V69e0be//R3V3aijDav15S99UVsZg46qHPW+A7Bq6V9nCCBu27GNVK0oziEV4rTvPZWJhwgIESx1ymVtTzDEeThQFbfmgQaIKTgPC2R5gUNM3Sz+SXTIgidGqsQJToTpEHYoCyWSnJKfPIBLTTXAOosFUBwwzCI+FrAiiOGy/Em8TjTHYgYEEagnwa+YBYksWkTzOXazAIYF3/iQm88QKeG4nNuCM/zpIqWTy2O79ekfADthgiIeiDAbmpa+FDJiPphLQM4NEGDptFxRAi+Uzx5xAiVWdwtIYWXOw9OCUAS1DaqIA4GYApoFpOcDSHyHGA4DYD5Q4bMTWXDNgr6Um+AdJaVOBAB5eoAtrDIWwIqan6BsbouuWR/lEE6aZDu/2dqe9hp90Sm0fcQCtbzulI1gl9XJSuooDXB8owLidiwABBqZ8pu9PzkO3zMTWKpVp7wW4PqkPaBoKALfcyIuZov58phyjKEgVi6MZSXnLQv4WBmBxAjCO0E0gxwAjYjOcQ7O7eJCgrYjasc7ZgkCdPQl+36EIG3Y2h1TeQE3fFYHO5YVk75tQXfH/oAskU8+Z4Ehp4wGHPC+BS8t+OkywA/7OeXj2I7Sp1NE2gqfHDfKjDIZOOgE6LD5HPWn9edVQvlJTfmMUzPsZbbm2GYPi87TqVy0echAO0rotbReIeppdkUlxInYmk2snJSHX5z/5wPr1r9ofSpqoXCnnNjGQpzWZbGq025e+i5xVqLfwDgElcNANxYOM/UzJwBofdQBDYCYmGti9Bnri9YPPHQUay3rI3YIa3PzxQavWT90UALOOQfE0zParv7pFkWSURIKEOSnZw4OjampnhR6tzq1rLBGBzbv0YrKaiAdzs1x4pzLAtBz/MQQTvtRFEM+2Exh7UbL8rTuYd2KIvEOQJjLdHRoX4PmDACw9+wQHCEO7OO0CYanOnyW2tJfPGZDsyvBP7O/m9+99E/rJwz++RNgI7Ja0aeonxPEtYOS4opUsL0T3dSxj8JhO2BNF20+QArg69cvqbm1Wek5GXro/ge1t2an0klL5gSBqQwYEr9T3/meQAkJwNLHDYcgDG8lRr/Ggvbz49SgPKuclyJ5qLi1QZwnPceKwj/zwWMDiOYsYE/F0S6kLQjW0x8I6/MbAW3axUUdnDFvw82xB9CptZ2VCp/kwsda28eA7WZJv9aFkt0IqoegLcBDjHUKMQzIWX/3jm6cr1NWPE0PbN6vh/Y87sBgViBT34pbH6Js1nbz841TUFRRpvTxRx8DkMw5mwmKilDgNB9nDcbDfje1IZur7LXE64m/753D7Hd7mJ82ECMBUtgx7L3E34nf7bOJ7yR+t/WWncPOaT/t/XvXYYnv2jnsdQM/RlmfGECXjdJcaWkJqmoo5jl9yfqQ9RPDdAms23yCkb3WfrSd+WioXQAYzoWqjZun+X6mFOA5xj/t5OP7tlkiSn+IUZa5uWQ1XUWxDYWcnDyPVqwnRV06Ppz36JpOP4hY+Z1eQ992fAj9GPtHmTOHp4dQTOxxlAn90LIWhJ+YGiXla51uN99VJqkID+58mPTGG1Bdy0aJknnT1peU2VKUcgJ+zvczK5f1Dd6lLvzLAHQZTcLYc04LYBID0rCx6fQnfLH96vRvbBIG+LEub2kNY/T/Tzwhfc6FeuG06q4MsL5L0+IVmcov9ZqYp/w4LlNhMhuFbSxyPPMKNhasj3FU6oiyJemcZ5Bj8gNmGbwxiSJPw92bOnv9PFN8VA9seUAPbXqYVI2oAPKfjQGbJ8zGVgcfEBFex2k3rMTBDUyl/fAljsIt7eSx3Vx83qkf53d6Hy9hFv6w/jHff8w+99ptCrqprSem6zc6WPP5UBomJWc+YCN1shoZ/GLtaf3P0vXZoOFVJ5VpfQNKtKOW5h31IYAva4s2lIqOHWtGLbhDu7at1/NPlTtZRwzqMzjV8czY2T7szIPU1NrKaQxqySxA36Rv8Zp9zIP97afVxzgIs7E1vX3H+Y8XnPU2deRrfPaT8WWv8wWYY7WR1rWrc5RlQAaKetko1wF8kVrROajNq8w59l0v/drqZzWcRfGo/iaqnd2oc7GuzwIsIVOmGhtmde4iSmDNd7R5U5GefLJKa9ejogm46LZ62JTJD7OzldmpmDNPs0a095kD2EVAmZl76Ts2X/C/Yxc7s5e1mtfsbUXhyUtOOc3lW1vbEtMMYnNDEsdzPstnWjsiqE13O/5tSW2BKkrTgONoKWxlGwQMeTW42mfwlQvAjv4zNelGUS9EOm/6FkpbWRkGLyISNRTRj4+2qPFuSNWVyfryC6WkXw4AHHIM2s02qVg9neUn4JXBLjHsj2dlrANg04cnKd8QoGgEn59K37SNJ6PUt4s1/fBwH74khPIkCn8oxBa7AMD4vM3h8EXM104LcBQD6FAyZpIb6g/qyskufECGylflyl/uUTsqqKZmmZGZqpr8SuUAQoN7A+pxPuBTmycTfharOT7SfiauORL+214zn2mK6/aaAfj2sI1k5lPt84nPJgP32zHN9waYV9PwCebTE59JXEvY5+1pn5t/Wnsyz+GHMJUNwXm4isWOQeCYndd40eqPbUdp68soD7cALeehdr0Ole1y5gP09ujhpvxpq15sg2qorXfdgEm2PnIGC/7A6XzWaWgoa7OwvcaLBiy7aAenAe0lZyxxUmxtR4zgW209am7Dxpv58RQKZJsXOBDzMp+1tkJJbLYVqB+bpS0GCFzM/Gy5UqfpH1xy2AGsD1s5YOxtuefUlyHu9OEInSfuwbeNA6c2scYbZE3Bpg/bRGTijDO9cxq8O0o67w7lL0/X4l0oFdew8SCd9ymUrXnoekCoqMqNjbO2GfhJW9l8aRCtrUHNpDaGTHkQYzltwSv2Kw/edN53/rCPOX/aO84X5z/k/HnPO/w9//lP3nD+tDb+6aM4PsqO4ZzD/vmbD/PzcfM99h/1tmeiAFZeu651/C8VtePb+LX5wh6Jfma/G0Rpf9tnrNx2XOujDjRqffeT/uvMK9jG2fTDayHGp21asv6ehQJvyfIKZaZlc53K+swW5ZyPA1E/DG2OhA5h80XjXVOoHUPxNUNLqzJRzLP+wPEG2TwC8Ob0d4DcucG5/5+99wCT67ruPE9X6JxzTsiNnCMJEGASSZGUKFGyLFkeeRzH83m8a8+349Xs7Mif9VlOux5bsoe2JCeJkmWKGUwACBIkQCLn3AHd6JxzdcX9/W/hybBsjyUuJZJQPaC6ql69d8O555573z3/+z/We6kH1mGA/DCfL7qt1nJbaONS0iM5TAANKBtFXjJ2bhLJbwhB8940wMAqs8Z1p4jMuyXjUZgJn37qeXvu+eesYV6j3fvIA7ZuyzpC/GoLBnNQkkrDmKRJgdF9zbVdv+DtnRwp4Nw7kVrqnpQEUhJISSAlgX9DAgx+qSMlgZQEfmIloHl2FwsNX388Yl/5u4iNMDFeUJdmv/rpoP3so+yPYm09daQk8F5LYBqQ3Heej9pXCDl55ioPfKxaNBGG4Jc/GbBHHwpYNWEYeQZLHSkJ/KsSgLDFOq7F7e+ejNpj340SMkg7tdNs+1qf/ZdfDtr6lSz5scZ9qx3dLLJv/0zIWrsSto1wC9/6ZoYL+3Kr1fPm+rQxpv3ar4XsRQC2WtT8zz8btP/26+kmUpXU8ZMrAW8BXouWU4CQFFJSoDQ5PB999FHbuHEjzF8v2muvvWZlhHkUcE7hPNva2hzjnEA8YpxTuNS6urrvCdItgH7v27vzwXO+tra2OsDe7t27CWdU6cLEillODoG+vj576623HMuXHAICUT3yyCM4YZNhZFUvpXP+7Bn7o9//AxbvE/bAQw/Ztjt2WBEhAlm61fq7MAp8wnHBIjkruiykajGUhdIbC9Fe/TzHwo8SOPcv5esKc9Mfhc/qvHaNMJ8v2VNPPUUfj9uOHTsAXn3GhakNypBTNdW9vbXNtalY3XTudsCHjwCSbG5qwkGevC5ZL+r8o3gk1sqxDmTpyU+L5XIci21FZb108SIgwKdcOTNo17vuvNMxtS1avNgxr3n39sK0Jx389t9/G6duhmOau++++6y5ufl7TAnSbem53vV6tw/JVOl7gIJk1ZIODjmtrnV22jO0yR4AqCHa6d57P2QPEt64sbER56fYDHFaMxh3dXXBcvKkY9pDOHbnrjtdGOSm5iZ0m+tYQL96+YoDR87AcrZl65Z/HTiHM0OMBAJmJRnKyEQeaMojXY7h4RcUBlVn/R8nDW5JuZrkEBMAxTlB+C0BAEZOxDCOLeA94EgFG8N1LMAQTih1Fp/zbMtRSEo4SHw43H2AhSKERI3BYpUOMM0PzUsiiMsMx5KPa4RHi8npSkfzEw6IT6oy6cobIZYvyot+ONaONOjtcEzEcNiG5bClUwocI+iT0kkTEAHHZgLmKgdswvEBCsClpzQEvHGOCzWMkgXsEgfs4yA0NwBEclbL8Sm/ME2pojhfiwMBqo46IycRslGp4iEYtgh1aoSq9YkNjpBds7Dz+WDbEShHjkCH2ZLzRJ47lyLZIzLO4GBVjcVzlQRlCJihlqD2vABeyaGMXBUOi8tcvs57ApBRotFpOQ/lqxGgSAxAotpyDkYZL04FcQIq3I/APDqUQxwgF08LtDGloP0EqnCyURpQriQU71N1hOmDuFMQcBFuCXYyfy7xpWDiirCbYpbMgzjFxDumskVg3lK4S1IjXUHYaE8q6qSmOlCuJIgK8A15+tFNVyLuJVPkQ79HywSOUUxhyVehXyUnXUfStA3ahe5iwDjDyzmicKbTftNcjzuVcF839EEVlQyc117C40WjJoFzpIsDLSyHJEwe/rEh6IpGyBc55ZfCBifwHPUXkgR0g/qcKwBiiZJeFAAB0CMc95ROnmP0EcEk24QSJ3B2ieFCxZOjbA4PmEK4Umu0FiYMJYfuiZlKTlFa2dUbKTiwhdpF/SxNLHmck2fVOScpi+QiQI3kMkV8w9Mdb9vJtjdtJDoEa6J6cNxGCf83PjCLLPLtrmWbbEvTYqsk5K3udmyT6GgM1phZ6gtHI85b9STw4a64ahfpJZop/ZBhUrkoKzxolIvnAcBI/u8B51QZrqWvC2Ljuq6u5y40mOthF0QWwtA6HC0yATcEOEztykm52l0Dq45qJuSNh1Dgu17Ca57vPW9n2s7a+MwYJEekS/8eGgboMjJMyLIsW7ZgkW1ess7mpzdS2xzGBdgK6esCdwq4mRBoi3ZSL4uglwJEyNLokOXT47GTKfWixwJsBDCF854f6SfkJxVwlyMRCijQjcAVAmr5eamNdU6sgE5SFByX6406odlcG8GzKukp/QCgpzQBiHUj9mqSfydbT9n5jvOE7hpkvMIaoLejsH/2KKz0RNRWN6+wnavvsJXz1mA3GQNof3VVBwAmK5k3lU19Vs01C3Og2IEF2C4kTK4cynJA3zz+acz9l8ZB7xoPXKfv3iFmIn33xjrdL/343vgtWd6Urnev5lv67L10n87p3bvGy0PnlIbGNn0uKS21iqpKWLpgZrxRb0pBR8Q2UdcYCxxR2jmAPsr8IWL6FPBRQDUCAPtiWegbLY3A1AbSTPVW5atvund0yGfPfrvDLpyZs6Z5AXv4k3VWXZd0nAsALZuGEjmtwT/OPWpzvWAAg2KobaDdTl29YJ2EbfQBogjgOJ8mrGkfIAg/oN5ljSts17p7rKl4AVHwYCKVjqus1MM522WbPTGjF0kwFedo1+ShH7lBAzPjjzqKAEv0KO5Hn9yl6oOSMXqGvdAYIJ1U6QVWFZPmwdeHmTNfIBRzvd3zQKWt2hgkTCBjL+B3Oei5GH1P1lO4dskqiiEbx961DXXYmcvHrZdwzbINaYwpM1MwA472E5pyhJClzbZz7U5bW7ve8oMKn6k2l/6r72kUEHCOeQD93Y0N/C6ASiImABaWh+yTE24JRgCe5P3OfOsnypYQgBBEu3RO6bu5B/1VbTM0Grcjp2L2+BP7eUbJsfs+tNxWLc91DHaIw8lU9ZFuJZCNG32QYQ/rAM89fxWW7TH0rcSyM6CiI/3e3lHCcHYCwIjaox9dbbdtKrOSYmwW2alebtojG0ba+u7YstRg6oC0k+ZOGsukK1xCadE2yq5NCTHsm1gulZbq7eTp5ET5aD/XzpIP0tK4rfRHAXS8ur/djhxuBdzXYLt2AuRbnWE5bHR0aXCN5OJ3aEfYCbF1aYBKx0cy7Nmnu+wwkSiCwVI2WAQd42hn9yThwwnHWxK3e+4pt9t35sM0jW1HzjJNpCo1Q1584Eh+B5hGG4hh7IbS8Ys2IakM9Bfkocv1ojq8OM/LvQv8wcGdwpgw/vAbecmOSQyunbnmjYNhe/KZQ6QZsnvuXm1bNpYBhER/CCHpNnBIHqhIOvUUgDbB2Ds0ELMjxycJqztkU7B+FeQWOzB6L2HXr/W1A67OsZ07Kuy+O/MJPYztZsKoMU8WWxtF/KSJOUI7YJmSzlMiwdMVfHQQOXaMDtk4/TlInmnMfaaYu4yBPkwwvgFvAQAAQABJREFUr62DoXxBMeF6GXcKsUGuX5NWnPqJHCsMkFU5BZhDx/j90tkp+6s/O8R0otq23j3PmrcEbTjSbUPjg0ngXEWTlTI2ZyHAdCoqhlbNm3Qkx+J/fJcu33zILg8NDblQzAotK5ZR2YRx5qgC0+l3pSHbng/7rOyrvuuZvra29nubjG5OU5+9fHSt5iFx2Nji7YCaRmGe1BwlD90qy7BgOTYawHEYJlV3H9eOMO4c6++GcW7KSvJzbUV+hdXB5JqLoDIYF7WpQcBaWQVag/ku46rrXIwDnHfKJN1RXZlARNkYo7mYz43T9CP1P7Wd1AmF9dN/ZB20OUBWR3ZaFkW/BQA/Qt/HDWicwP+wiUXOEw4enevp7bNSQqiW7CT0J+DIeD/jA2GDY4TmJJY8QkdPq5n3lDE/KADczZpZmDbW1NDNA7tgFt87YL52dFMbe2izCBuXJicIV8rzXqwkZDW3FVjpKgCKMBQ6tBxl1/zAz3xlilBHHR0d1t7e7trMGzO9cdLJExl4OuAE/H75gwz+yYG4dGgcEgtejDl2QMzGmotRBz0P6136p5ezf2p9Pmueoo2PYpuVzkqPdf3Nuiv9VRvpeh0Ci+qzNgcWlRTZtnu3W3VVLdNFdFJ6o6dcaO30zAhtOesLPuvrjdvzL/XayVPttnhRlT2wvcHqGPSiV5h7MRakh4KWCbA7Duvc5NQMYZlHLYpvpWxdiZWvKLD0CuwzAFwNP7JnAsjJpEkfNN8UMFO656M+PvUR2QT02dl+DWbUd5h5+jNPPWu7n3/aGprq7IGHH7INWzfAkkz9JB9eejbT84Tud2YgWWVV+4c+UsC5H1pkqRtSEkhJICWBlAT+bQkwSKWOlARSEviJloA2eh05Fbff+0rYXiEMJo9Ptnaxz37z36fbh+++dZmYfqIb/QNU+UkYo/7+2ah9GdDcuTYe8nl4a2lKs198NGCP3BewynItinyAKpQq6nsmAa0t9MC89gd/EbFvAsQcJIwrEcxs80qfff5X0m3LenY2y6t2Cx2TUPivfSRkV67FbVOZz74BcK6pQYt3t+ahNv6DP4/aH/1l2PoJQaFQzs9/NctWEab2xvrTrVnxVK3+TQlocV0LmVrYlMPyzJkz9thjj7nFy127dhHyc7tjzHrzzTddKNafgplMIVsFsHv22WftypUrDpz28Y9/3MRIp8XdH8UCr8qoRVK9a1FV+X/rW98iLN01a2hocCHJ8mAmUZivS5cuuVCzK1eudMCj22+/3TmQvQVbOZrPnQY497u/7xYoP8yi5ZadOyy3pIQF1uTCposuIpYFrYbiYH0vgHMqrw5Pnt//3f144492ZItJ7vjxY/bEd590oWgrK6vssz/zGVsP+1pRUbFr47GxUXvr0FsO9KiQtvMXLLD7AZpJRnJUi5nDHXr/URjEZJXIgg/us/4oL17UN8m64rPhoWE7cOB1B55rb2+zliUtdh8hWLfdttXVRTIROEBhe5966mnAkodcXQSU3LJliwNKJitCE2LkPL30ZOn99m68u53xOKa0oK/0b24n9S/prVjnnvjOP8Bicspali61jzz8EcfmWFJaQvn8zul1+O237HF0WsDQJdRXLIBrVq+xPJxOQZwPcrh4wLnZWRjnNm+5Ear1nzPOyUspp7WAIi7cEDJWqEbJWM0apr8nudLk/BGgADCCnJHcIZCH9EBOdxeWC8eogAu4A3AQ8DTkrmOSQL/AO+JeulIhGiM4rfyc88GaNNLWZjOjw5aXk2X59Q3mLy6zGcAf8gTgMncOU+docKgHAZvIH2dBGk4PBw4A/EmgHLKBEQxndAQn+xwlFSRAiFY5rDNwtDl2NgHnCJMTw0HtF0AFx5h0So5yis9n1Rtdc15doDTYAHdGdaBmclLrJ3V3XSaHjmOrw/Wm8GNyKFM8hCrvBU47QmiNXrlk0e5rlldaYBnzm2yG/pNG/TLwcjj/ssTNy/lKkJ1LgAyUrxwpKkGEC6Lus9LlPid/BQojtKfKw8vd6hwoVAQQIj5YHCp8FO5Iv7sqUCexX1GJCOVVLgSLBJ/DPQBAdBnZAD6SE0kOXbWvUEySN2VTPfH0J4Fz5D45brHOyzba02WZFVWW29hovsICC8M4pFCfagW46XBKcidgINUnyQjkICnIF6eS5KqcAVYFXB/MdmwgUiHXJDhXHVAAp7QYX1wBkZ2gd3Juy+mp5tC1AhEGXVwnNYIcrjg26Q9hdCUEyEAlyiYJRTbUnCYhEBJy4BMv6ZbS0me+Udaw2pvxjs5mia5OkGM42hYuNF9ZFW0oUAdClWdWifFf/tYwoKY4+iXXelAACemqYoqJpYQMBMiUJ1dhSKUrjlUEvZxhHBEcTrBIOdiCONP8sIY5tkmUQ62lOgts6KePBnSzdvDpUKNJRymHkymnxSQ4h5P43LVjdvj8Prvac5lIkxFB1wCjBghjV2YLaxbZrvqlVhGi8GMThE01y64CGK4xGqd6GD2NAq5IuppvyJj7JX3XD2WLKY/TU5QtCSkAzgGbo8CYlIJyIE9eYnlEMrxLvpTBVV5AQTkfuUS6Kj1TfaSrCCFNJ6UIXK+Trm95Ok6RB0Mjdr7vkp24eMqu9XQS6nWKNhO4IWoF2MOlzc22Zt4iKwN8k3Zhwopg9sqvqTV/CWFmFV5aoCKADnJgqmxoA22nHke78RKUSvZMFsa1DW2YLqYRaDhdPwEA4JzyqqK6LleKGVG6EMSgCHRBEhKDu06MeqqDwiv6AJG4NLgxggDUL1V7IF7cJwFwkO5MGmHlr1+w41eOWWfXVdhTx0gShkLS9KOPdRUNtn7RGltW02JlOdWkQJ2QIyqV1Geuk/TcH31QXSlET2+PYxrSRgKNfRqDdHhjoPty03eNV95L13v3aFzTec+B712j9Ly0dE6HN6567955Ly3vu67VOR03p6Pv3nmx5SnP3DzC4WqnaiDDAWk1TGSSnZzRCvEdBemjVgwA+HURTZF9WoA5InolUE2aGOfUPlzrVvFo/wB9SSOZxjNZwYHrcfvrx1rtzMm4LVwUtE99rtqaFwO64z6Ngww7qDh/OMABSAtkTtGrsE0nRqyttx0dvWitne1sPIE5ULHO6Q8K9VxbX2erFqywFdXLrCS9lHYD2ElZnQbIRstGyTmuOKnKg8xcd1d2N+SqfCVhJ2XKn7TVugAZ0j+TAwBfpejcI6CvAL6MhFyBzlHm2LQPB/2A/dlfnLTyqiWEf6+yrXcQ5pcw6H7AXKqfGKOk0i5/MlMfiTGIjQNw6Rq/bqcvHLHLbad5LqCOTBzEmBfITbfimlJbsnSxLSfduoxqQhgrLHAS/Jzse260Q3OT4HxKR2GT5U9gSxSy1rGKMi45e+ABkSUXyq46CWDoAwwZYxyRfvkc6ozCCuhNnxocSthrB1kP+8uneQbJoX6b7PYtRYCBlI/KKulxL+lo7uBe1LmvL2779nXZsaOAl8BPh8PYNWfbjDU0s3Vri237tnKrq4S9iCZyaot81YU1NEj8ahgNM870kZ1rH353wDl+lomUlgrsmSBthTuXRHwMnLIpmrcnmWGVlmyUmAy5AtssAI6KPkg5n3/usr267yJ1arFHHm6wdZuDllNAfVQ18tf0MA2gY4L4kDrnB6Q5OZpuL73Yy9yTuRisWHOw4yrstx8QbGlxia1ZmWsbN7P2MV+hbykTN6pK0gEBtGU23HfO6YsL3y67rXJj7xw4WZMr+pSATi5UK7VytpD7/aCkZa8SAPw11Esf1LEkYpl62TGN7RKPmJVeeHHWvv63L3EiYo98dKvdtasKMIxkJUhbkkVM8tR8VbRy2rwxPpZmJ06O20t7uqy1A0tPiGKxB8exu/mExGxZXWrbtuXZknl+y88RcyN2g1zVBgLNBVxDau6UDAkaI03NbEKMB0PY0raRQRtG5wUS05AcYf5FnHXLJ8x6fXGR1bChhRHVsiL0Zw3QmuwBhhJoMiawJ7ISCDKGrp4+Nmdf+u0XAf/V2f0fXWKr70m3mcCgjU6MwPqWDXtdNcA5wipTRm0UcOy01EZ20bOrEpeOm+2pfhPAqLu72z2PC3gk2y/Akdg7xTqnZxMdsuliqBYQWmmIkXQxm5GKxX5NPjene/Nn/Sb2w6mrszbwyoiFuibYFEPo66KolbVUWfGqSvPXsskGVkbNm8UmN8F87cJQn3UBWMsn/PuCvHKrQHa5/J4hBCTy1ZyBluQe6Rq9GbsieTljhDg5JRGiYsgfNlHZyATjtGwHg4QDt0vsfhonyEt2O43zDnjHZ93udFdzRxgzXUaE506E2GhwlDLuwb719FrVnYCi7s2lPxJ94yzArYvjNtcLOBDwuoDuxSsrrGhtqWXVs3kHZkYRWlMU5qWkfy1ug/uHLHwFkNd0GBZG6RL1gz05qzjX8hYCrFwDA2Y9cuFcAl1Qwdz0koWG4eFhuwyTuDZOaQ1Dm/wkb728Q2Pl95/zfns/vXs6o7JK11Rugd106LteGts9fZSOCiCnNR0BP7XxUWs4AnxqHqD7da30WWBQjyFRn3Xek4nWrmRR73jwbmusb6QHs0nFjZc8P2L7eZqi6bNhqAtYZyf+k+902VuHz9vyZZX2qQ8vgjUSNvOrcRs4PWzRYZ4hZ5lzwEoeB7SbUYKtXZJrBWuyLaOaOaK6Et1cCiDSdeWrjUQOzIvNdgzS5OqBOenI6K62iiTHHi4HODdtzz39tL34/JPW2FBvD37ko9jiTTDWJjdPacxysqRjuHHGKbak+M6OFHDuncktdVdKAikJpCSQksD/UgLMhFJHSgIpCfzES2CKnUb73ojZ7z8WtjfOJNidabZro98+/8vpUL7/4wPNT7ygUgL4sUpgDNDPt56O2J8/DmtOe9KFuAJQ5y/8VMAeujPAglhqDPuxNsgtkll7Z9we+3bUvvNMzK6xw1mLp5sAVn3+P6bbbRv8Jl/UrXIQZcbWPDJrl9ritoqQpf/zsQxbswxHgBZDbsHjYnvcfvM3w7DN4f5kPe83PhOwz/+ndCu44Vi4BaucqtIPKAFvoVOXa/G9t7fXvva1r9nhw4dhIah2YVgFTjt16pQDzin0pw6FbxWzm0KgCkynkKlazNSiqBbyv3+h3930/+OPFlC16KpD5VQYVoXzFBOewHsCJylv1UdMc8uWLXNgsNWrVzvngPeb7pez6uKZ8/bHX/pDubLt/oc/bBvu2Ga5ZcUs4otPSDuIo4TMEdeWFn1xALnVS+4lfR1e/fRdn38UjHNe2i5D/nx/3jp/8zUKW9J9vRtn4D7b/cJuHIpDdu8999iHAMY1NjbCLpntmAJffOEFe+nll5xjZdcuWM1o0wUA6MR+5vJQHdWGWqx9tw8nP9J1eSQTT2Z3Iy8WoGM4QLSgfvnKZRy+u23Pnldw9GTAsLYVtra7KetCByS71nnN6eGr+/c755B08EMf+pAtBAAjx5DXRsrF08t3uzpKTzK7uR28z8rfyZNrBOZ85ulnTLLXbvydO3eamPHmz5/vynb27FkTA+DevXsBqmc65ryPP/pxqwUMousd+I32EHBODHvTDGIbYINcT6hWMQvJgXDzocV8wVkSYtXCGSgmJ0lYDqooXswwaTnHDecESArAjiHvr8BItLzcde56i8vhIO+A7qb/4WCM0//ErJYG05jSx4MmdcGZRI8hn8AccIa+QWt78w3r624jvG6BNWzeYFmAXSYDOKe4Jpsb5HRSj47hmBJQLICTXt5JOc7kZOYP6QJECExzj8LFAvQhDJ0Ph6yDP5C133koKRasZfF0wmrhsPKLFSvMA5vkD0hHblcBAQI4MNSN1YOdi1gICN3PCbH0yJ9F9ZEXt2IjEgLOcreAcxKWA1MJFCUbMTZpV9541WbOn7C6hlor2bTZoo0NOIVzuRlwBOk4gIucfuiHczrj/JczVKGNQMbwwvGHzOVkV3kcyx75+QAR+Ki7oFZpDtyWLLQAdqqTw+6pEmJuU9pkprYUOEjAOYUJkxXLxAkoOSlMmJw2fq5Lhv5TUFEnEaSitAUM5IVnO00h9Mggdr3HZt44YFfOnLRCQjnXEhY4WFcDq0gWIC10CX1QyKx03rnVlcWFqwO8mEb5VTyXNuVRaEUXKg/GB4EfpUuaaknO8qhHKad03H/D3qhscqwrWapCCcVfpjYU0EpnaRvV010Hcwj1E+hPTH8Byi6GP4VrdGF7uVp5KSv1CDGpqX1QM0sbJCTkwbds5tgJi+fmWMHO7ZaxrAV0DoAv7vpHxim58gmZByBHwAiRmqk8Akc60Av5OiAYeTqUJnWMC9ShNqGSgipGpT+UVz63TKej1EftKb3jjTtd2dK5zqfCSS/dwbtTJspAnZ1vXuXnjrHpbusbboWxZsBCACJm3dmg5WWXWFV+pS2MZtro8QvWfeGiFcJIV7dyqeUIHAiIOkHsMQFc4urXOAAleqdeAANd2FXyEuhUwDnZkqTc1Wbql6qF5Km+Sr24JiqgC/9khgQgFHOMA4mpYtTXAUpcY6KRTLTVfg5od6OaYH1cn3MNjoxnE9PWB5tVD+P89KSYR5QuQDeG45ycdKvBIV0W9Nn42VYbfvG4VRXVWtmG9RZYgL0U2ErsbADnVD29SN45swWoUHn82BwBPaWjybbG0aoYgnOqK9fyUQBV3ac+pDCSTuOwBQqm5cCRQoLoAslKNwEqkY7JfibD/Opn6ar+yhrTt/lNgV7T8LzHaLPh8JhdJyTtCOH6QiEYOrlGTtZgZq5VFFZYZQFsVxn5gCkABMqOSz+UpfJzh3Lm5RqP9sJuyBHtOZxvHgO9sUi36bz3m857Y5bmUbpf46fGTM21vHB/OicHthzWSl8v3ad7NDfT73rX2K3PcnLrWi9t5afP3vH9573yKE99FkZFjmtc17ywhjQkUYmT/YP2CNP+6jtB2kGMhrKtoiB19prziIIXf9BjscDQTMiN1iTtKP1L1mSwJ2Z/8eUuwDd+5g3p9ul/V2gLlgEOpU9IowPkL4BHgn7hwqui+67cKGwUBsSJqWnrHxq30fEJm4GdM4beghK29Lx8KywqoQ1hMoIdMYMxSyx4cdodtUQ/cMprvBNQhDFNbauikji/0atIPwkAlLTob5RZpXfsk1JolECn1K9cP+ScA3NyjfRWUAH9TvRYEGFphIUbsj/402NWDQjzU5+stNvvCAJARfNlL7lXtkpsigKOxAE6OZ0nPPgcfXk6Mm0Do4DLeE3DsCojpFCXftC4BYDFSwEQlTL254Mo8DFrtgTAFGygzJiyV30C9DWhaDUaO2OjCgikJBSa2gcYY9yNt8zIYYSKUy61n6ujqzl6wP3Sfr+AGGpMyQ+A2WC/z/a/HrOvfPU5Ky3x2yce3WA7t5cTthM7o+FCcgJlLqCvxkKNtVLDaUL2dXfPWF/POAAsgiWz5qs2zqTeJaUAV+dlGyRGgI4FeKVvqTxkLeCviq3mUrU0juld+ejlZMl3Iou7izL4Uay8srW6x9lb2lf1ibLrNQiYSCHeJSxZEo/tU/OBKG0xAEPSC7uv2P79rZYPcO5jH6khrJ/f8gplVykIGcqeJZgrJQj/LBC5z/JsbpYwpl2zRBWYtZFRALmSL4XNzMiyooI8q6tJtxI22YJpcvoWp98q7Ps/AvmkBcjKDVCcRz9UTDfOq27SGzd30bu+qjwCgyZtoJ+2cT9wv8Yt1/W5342JAMf9nMggQZ9A6rTT7hdm7K/+9mUkELYHH9gK43IljOKMwzAjhpGTmMmCjIUBjcOqC2lG5vw2COtcG6GI+/oBjwMUDwJeSwf4XFCWZaV1WTy3+q0gCxY3wFdUjqEVHaDAvhvMtw5QKbZijX2qCLZG4caZddpwaIbwtuid8ud2zZvTADVnZaRbHu+5KGgurZqBjRCQXveKrVRlA56P7KgY/UJsU6eOR+2//9fdtHmNPfyxFrvtQcD6hdhbAI0BdKwIEF4e9ROcHYk4XVQqnl3k1D85PFuq32WLBTrSc4YASN7zrjYXKeRnQUGhs8sC1YmF1LPLesbX85L3DHFzmvrsbB3puzxoxL7jo3bxm50WJCxpYppxOWfGGtbVW83tNWweoeS5siUaVQGRIc8h2PkmkF0gkA64PcdyGFO0D1ebXpwdQXZ0daeX2tCR/MizBZtfXFtwQlIVvA6aZ67TXAkYrnSLtrzxo3uWSDI/cgr5a24hE+HSUKfTc4psj85jP+KAiWfejtrIK2EbGhyy2p3FVrQ9yyYHAUMfaLNxgHNpEzz9z5EX43rlhlqr3UGYzgU5zIHV/5V48hnGYOGbbePZYxB4dgRwqjYEoOd+YmFnEqI2s5znlTL6ezYazJxLm3qcvVDxAc4NDA64NQytaYi1XxsTvfbx2l7tq8/edzJ/Xx4qn+YAennllQ6p/Dc/g3vXST+vX79u2sSnDWP6rrmD1nAEIhQ7YiljjD5rfUnMcpKNrhEgz5ujKAJC1/Uu23bPTjZBN6FjstmyWYBl0SsHI2ecDSP7jg6zxx/vsjfePmmrVhTapz623JbVA1aEQHwatubIOOM9GMs4i6U+gKBZxbCIwzhobLL2ATB2m6Kwy1LWGHZGYd8D6KZP4xHjfIznHPfkxSVpGgQpgZ7RZEY1/1PbjwxM2nNPfddeADjXVN9oH37oYwDnNlMnEnUbL7jIGRHuR6ZO152yv7NmTwHn3pncUnelJJCSQEoCKQn8LyWgKVrqSEkgJYGUBMwmYOd57uWY/e6fRexMZ9wKeSh85C6//R+/FLT5TUxwU0dKAj9GCQwTSlOgOQGcLlxLgubWLQU099NBu3+n34rZgZo6UhJ4pxLo7EnYN56O2je/E7HLfNaj+9YVPvut/5Bu2wAN3yrgORGNbHp01s5eidtibPqXfi/d7rotwIL5O5Xc+/c+LVb/3p9E7P/9K9jm8HlUFabZP/x5hgvDe6sCBd+/rfH+LJm3QK53LVzu37/fXn75ZRYYO9yCpRbbtRivhUsBfTyAnRYvBVS6kzCaYn3TgqYWRAXi0fu7eXhlVJr6rN3IA4TDugjbh17apazFWZWxoqLC7aIXGEyhaLTAqvLod3c/C6qXBJz73T8E+OG3Bx++39bv2Gy55YVucTPM4rpC6WXK0cciqHPCa5GeQ3nr8Oqn7/r8owDOuYx+iD9ielC43dNnTttT7GY+ePCgFQOSuOvuu2wDrHP5LD6//fZhwGYvOwDd4sVL7CGFqd22DcdJAXJS+yXrKMeowCxePX+IYvxgl0qMnorc/JnTqod+Gx4ZsRPHj9t3v/td5xAqKSm1nTvvcIxyWkg+Abue9LSzq8vELiggmkL2aqFd5b65rb4fWPaDFfKdXeXpxM13awf92wBNH3/8cccGKOfInXfdZdsAA4YBCojRUXXpxomwaeMm+8hHP5IMw4rj4EajUGWftV696tgCp6anCKO86V8FzuGl579YTnAyygkFEkQqnGBh3rGh8C5gjJ92DuDsSYsBUGPEVxhFt14v769ukIMKZ4DzEjjHNs5ZHAqCKTnHd1QOrVmcUTgGcJ5HYQfyz+IsbO+xNsIGX287D8sNYbLu2WXZSxYRzjQfrABAAlKQo1FgNYHHAni4gyoDZSERnBZcoewDOMt8DNjoYjQtByCFWHv4ijPOTVAAuihUTlo60KYgoAWK6ksj5Bph+uRQlgNNHCNy2PvpzyhFsi6qF86VhCgDpIdy6OIQd47jMA421ctBK/SOM9h5hnWPZAFYC0bEs7uftskTh2zevCYru+Nu8y9cgpe9gOKTL8kicZgISF55JpCRwYqA7BJzVEwSwMEn0JFjOBOwQp9hKXFgQUA1aQKZARZ0iXGHwH2el16zM9dHSdUPuxbNSNoC6PDOZXJc+qEGck5uOW5hunF2GdYe54CUbHBaO1CkHN0CN+GV9DkwJHdfardZQKuXjr5thSuWWe3dd1vGvHqzXJxP2H0xejkQA22B3wj9oUTOYS7GJfJw5VB9JG/JkIsI0xX1A5z04TxE5+V7FBBIPk188rSbruXQu8BVeuka7hVfQ0KOUzm3XS35i4LgDueapIwUmtCBfeV1dsAUObN1OQLhv4BPAmz55DjldKJrwKZf3GvTBw4RcizfCh78kGVuWE3I1lzKKUCL2NWSjq4YBYnQNgK0BGQbXb8iDcceQj0B8qQBGnQgN4CbDqBB2R1YjzwFg5QzTUUTO4XkrTI5EBnqIKahBMAFnxzKhJNKKNYbF+gfCEVLy4RlTsrEvQopzMX072lALjC/wIyhMIsCe0WE7KB/BZBJXu+4XXvpgLWdOGqVRZnWuG2z5Sxbaj7Yhoi9SxqELBNwRoVy/Z6GEJMP7DoOkKN2cC/e0D2nq0JOCNGkA30TqMKFbQYEw03uPoEnkBqVUsJKQ3qITiBDB5ThfFyZImP9zm03XrQKcnVOcmSNC5pxnt/C6gfZ7ho5rBW6Lz1txtIIbzp4+IQN/MOrVl5cbUU7tltw5SKAgfnJkLtid6EkMmUSowOvSYfUEpxTq9AKvMQ8BCMMYN20sC5WW2MxBIAgP4lAZfIBjBQA1oHXAOA4xkbpnBJRekFKjPNUOgt0gzxIj590RHDghuJiPwH8RVhZf8RxbgI2jsFcNkMo7ClwBAJTYbdpQ7FupnO/ADFBKpDJPEROd/U5x5apQlEqejF/0R3S1TcCfbk8XKb80TjojeGa/9w8JnrnvWv1rnmUHNlyWAtgofmgGHEEhhMrkRzYSkPfPXCG2GLkKJdTW6HWBLzT/QoBqHu8+Zc3Jt9cBuXpnffek+WkdsgsTOjVCPXWNgYF/81CHwSSk7rNYa8F0c1AbfRyoGT0NAnuoW2ZRwh45Nd8lPPJ8N+yl0o7Ofb0dwO4+uNuwrZlwfKaZZ/+TIY1LgIaicqp4QWu9PkJBwcoSbbAr3FUlp22UdhugcyUh8KOi2VIuu/GNIGtAGCJvT2I7vsxRFHGpCggIY1rfnQ4QR9mOKAwtBlpybyLsVAt69gcSUtTVrWrO6k25juZ3KijdBotpp6yc2Le0yE7EOIahiF0jQME11NPj9rvffm0VVUvsJ/+RJlt3wHAVpFJNXBIYyQ3hwRGoshXui0mXGUs8KFCVEewDRGYtYLYCunpHLJJI+S7QnzmUMeg5hsxQKvxfMotcCV3k7y6uuqWnBdxjwMGcBnyVPZ6+RjUBXpX/wg4sCW/yRZyuFqRkPRCv2sTgeTjD4pBLA1GtgCsagn7s6++REhVgHMfW2t3bAdskZWUm6ohhjdnfN3gmJSxko8CngoxXwlraHH1dkMPpGL0JZil1N8F0/cJ9cD1Avk4QK2+Yh8Yodz4h4JSB16SDWVWs6ISblxkaHBzFteMjB9pAD59NFicDGPkHyXEyfjEJEC3kAMFev3U9WkSmp5OMF8fhylpxDKCJQCdcq2qVuEJyckJVuWUpNQxZKOosAMlYsM4FaYgEV4J2TKuQ4L0SeYm6Ilj80OWqpxjv1Mlkxe563S1bLwzN7qG2nkAGLWn0z3XvzQW6x8FRknVRo4Z0LVbsu2cPpBQGD2a0zyPEwE6q8btOGNJa2vEzl245u5vaKy0eU0AZwrRQ8qZnZ3L50L3fOdHV8QX6ZPhpbixCAxxM0FDfCZiMarmGPh8tGEcHQgg8wzqpD5IjdEz6kS5hHOXWiTbJVm3ZD1Va7W1WJkFEEbXkAHF1tDs5omau/n14juWAButgvCfl0DkrgzSBI31pEOr2DGAc1/4v1+xCMC5jz6ywO58COa6Cu7hVjVjFnIUyBLeZookTddcM3koeVdZV7IbJ2+8qeQq9/jEuF3vvm4D/QOAMpNtIJsqW1xUVAgAOmS9fb0uBemY7Ls2vlVWVLrPanev/rrI5ckf2Qb3nepMXJmxoT3jbIwh+HJ3yPpnBq16JeFe7yi3DBg744QPFtuvJKd5bxQG3Ijm2CSgjRFit5S6ScVcR+Ezj+D0Ca71M6Zz2g/A2GZpdGTJMIq9kBjJnMZ1m0SwDxEJnlOad7r9Cu4z13Oa6a6MbvJ+2k792uVFX3PzPwFfQ/T7ozEb2csmPORVe0exFe/IsdmJaRs5y4aKXuanAOdiA3EbHh+xwtUlVru9mo0HzJkAzrm+RGld1wFwGxsnbdJ0JzR26DlE8wcq6gPMncjSuKB2Qp81/1fdVGYM9MDogAONiR1Q6ytaS9HaisZUb4x1+ioD8wE4PPsl3dNL3/XSZ80F9FkMcQK7adOjNmaq7lq7UdQCvUpg//eAc3r3APtKQ4e3xiS5aD1KwLtz58+xjrPd6qsb2AThmsCNaWIp1vw9iMGLYrxb2xL2d9+8bgcPn4LRPc8+8fHltmR+CWtBtCF9XOB0mVL1BYoLeI5zGT4L0Z7KPgNbyvSUXkfeAB+1mUMzFJSdm1BmQKJuw4l0m9NKzzUd90pfZHOGBybsuSefsJdeeNKaG5rsoQc/DovoFnSFno/u6HlCg7o2XcgSJAevd97+KeCcU5vUn5QEUhJISSAlgXdXAslB+d1NM5VaSgIpCXxQJTA4DJgEIMkffy1qHYQxrITR67MP+u1XP5dutZUpe/FBbdcPWrmHAM19+6kooLmIXejU4orZRkBNPw9o7t7tfnaQpnTxg9am78fy9gwCznwmYn/1zZhd7NZih9lt6Nl/hmnztk23BnhuBt/u9k/N2okLcWvITLPf+nzQPnm/Qtfcen2oj/Hrcz8/Z6+cxzHBAs7//qmg/davs/sXe3Hr1fb92KPe/2W6eaFTi5A9PT0uBKbCoV4FqKPvcqBqkVKLm1qIr6ystOXLl9vOnezwbWpyO4Q9cJK3YPpu11z5K229q8xaWBYbipy1etehBWcB+LzQHvrulUv36dC9F0+fBTj3e6bAPh+/9x52Hi9mQTNqEyyST0MtkMmqaT5hMzIWrzR/Za1zfuheLw1vAdcr03sNnFM59FL7eWx8AmIpBIxYBbQgr13cWqgWIFJsMHfdeRfgrTtd+2lB2zm4b9RRkvIWp1XvH/ehuojJpre3F8a5PY5FT5+r0LsFMCRI/q1XW629ox1wYJE9RIhWhRXWjn21vQ61sw6vrbx3d/LH/Edl6WhvJxzVi44lT44CtYsAf9JdLfwLsKBzH/7wh+2OHTuskjBKAiOo3J6eaVf+U0895frjpk1J4JxCL3k67qqF7GIAIKKdrRYiVOpcWLYfew9LRg6hYTPLymFAqMSzgEdAnlUcbglCKoVHYXcixGoYBgzcPZYDA0ZmeZUF65oIfyiHOI7qKOCv8UmLDoxbZJBwRtOTOAkB52XhLSott0BFNWHzAHecu2Kd6F/PNdiuCmGIWrPSYrRNpLKGkIpNllNSYT6Y9YQLUxi9QBhw3PCQxXthXuobIqQOTgO8DVnZQFAK6KXlXF9aDRIAIOH0rMVxds3xio6PQWQDYEVhWrOBV5QVWHZ5owUKa/A1CBhEOnNcD/A30deDPMZtWgAzQm35snHI5BVZTmU5zB8AQjSppv/EYaCIwd4RGR2CZSQZAkgsFelFZZZOucW0N97aZpdeftYiF09YTVmJ5S/bQP3mm1HObELvZJSXAL4ARCiPiQB74VHCPpHuICCU3imLTAOcYADWNemlZearqjYfgC1RZ6RFCJNKeeeQb2xMjCS0H0XzsXMhu6TQskjbT3kNYIZjh5mBuYb2mIPlb4oQqwKwyUmZlYHsigDE4ojylRZSbgBLs1OuftHhcZuCMSQESDANh086jshsWEkyYc+jEBY+cdZCz+22jgtnLLOx2QrXrrF4dbmFYfMqam603Ko6nH7ojzyVctyqhCGc4ZOE8hvotTD1DJG+wHDC2QULAAtVV1laufSOcH4z6NLIOCwd44TznbbIDOAU2ioDFEYgj9DE9HN/RVky7KYc3oT7ig320O69FppQ+DFYJPCoBrNwphdUW0ZFDaFkYeWCDi4RZSyYGbVof5/NDU1QDoXwRa54s9Pzsyy7gvDIRTDyXO2w4WdftlkY53yZ2Za9Zp3F56HrVaWWWVtt+ZXoPsxfAsnIAyYgqk1TZkCTIUDb4QnAkFRZDD7pZYXmr8ozH7pqAeQCiCU+xrXoXHhk1GYiMNbgJQ1kwl6RA3RK7VIGwBemO7VhHBsQ57pYd69FRyYsjOzEPOZHxoFiQgE3w6RG26epHgAtEox58YFu0u9GdtMOOJegf0NzZekVtegVjGuAH9te2med509bKWEVGwipmIZuzqGjefTBrPIG0kePBBKk/yTClAGAb7x/kDpiC9CVMKjGAOCYPBzvQeTh592ysa8AaOLDI+Q/YFM4m1FBDK3YfnDUU6d0118JYpeJ7AA/WZTyClTVgyxwGkbR6YCAZaSdCSAioxKAOyxyYo5MCIADADw2PGqT/aMWmqS+hLOU0zwLBpfsCsJeIubwcLddP/CWjb7ytpVmFlgBoMB4c53Noev5zfMsE31L0K4+5gMC5fSN9djA2HVnb/Jz8+jqsM1gD0LIMxNbUVlYbI1ltehgDm2F4xon6Bz2bnRsAPbWPpudw+4B7AuC5CjMKbKKkho2UxYDjiJEHSDV/vFu0u+lNxDqsbTKSgsreE4nbxAX/eO91jPc5ex0TXGjVWVVEzIxE/DRrPXCHNg/fB3gg0J90hczAIkUlDLXyoIpqs8BNOZVzLO8YKFjJ+qi3Sdgp8uUPSe03zRynZydBvyQZ0trVlhRNoyCjAHe+Cfnuw6d8w5vPPG+e2OMxiHNZTTP03xBQHyNVXJea56ncUbzQc29PIC6gHPKQ+c0RusebV7Q/LCxEVtBOjePvSqXXt6Y5eWtd++zyjUdhnFwtgu5jWCr0iw7UGF1efWWNptlAyOwI6Or0+hsOiChPMaygrygVdfCMlWGJ5tnna7OuPX2zNrM9AxyFaiNvpoVZA6RZ5WEFcwtALRAGMwv/w8Y507n2sLmHNh5sfM5CZuUTY3PcL1ZXW2ONdf7DaJDAJYCpABExkZqTaYHNq+hvlmbAzyRwRhZUAATDvcMTYUxc7A81gStpoI+i1Z0dsYAtqA/6EtOLoxHtOHISD/zLh9McBVWX5fnzkcAg08QWaC3L4TeYR9p2zjAnYDq6ObeWVZWgW2gWUdYk+zsYA48Mkn/BQKqhzpo5nIrC6yxjk0kBQCHAGk89eykfekrF2BSIyzcvcXWskibZCZJG7BOOuyGMHvV1qcBdMQ8O2CSgEKwbk2kAeZHfwemkCP9EoBfOWNsVk7QxmHjGp8D7DGv2OZX0Y9BE0wOpdmFi4R0nEnHFMFYB5ZgDDa+eHycuU2B1dblw3zsR0eQXW+MzS8hrp0CMAa7M2Nffn4QBtdSAHACHydslOfX/n6YoYaxRaBoBcbJgH24pDTdmuYHrbCEUK0Dafb6gZh9+bFXrbAgw+67d4UtWZhnk2Mh5hERy832WUVVFvOpoOXDMO/nmV9dQYB7HmsAbsxZd1cE8JVAgWxAZb6SlQvbInOjaVgv66tyrbE21wFhtB7c1jlnk6FZKywWs17MJoZG3BhaWVlE2akfwJoRWKi6ehMwC00zR2KcwP7TVa2gKMuq6nLY0EPjMXROjs/R1v1sTLrOfDf0PWCqyucAS7wLuBWlrFFsLqUnHQFQ1NAcN96SXzQO33QIIHbTV/ejd+KfXOhdlLzfu+R7ibsT+qObks9g3h16p9e6r+4KPqpfq38LXBtinJ4FSBtCXnNzYthDzszr1ZaaZyZBJZpDJHEnDqTGXEugtCx2MvppD4FJ582bx7x/EXNJ5kr8ls44QTZ0c9pwyk8/T4NdL0p42hDz0YQVFGYwtwvaFNOSSHjaGuoyrIaQuwwFNjbJ899FbMIs+p4LNxXmYng8TBnHrbYCgHmNgDr0T0DUPURf6OufoR8AbmZumMacK4u5QwlpVVXDTshQOE0fHOgM28jgrE0BwoxgjzIAS1WWpGM7Mq2Ethap3rGjUfudL76JPCps1856W7UlHfgfjHZTEa5HL8rS6bM+YyrBd83AtakAoCFzjG7W4noHZtC7SaSdDLeal5PlWLFGmI/UVubTvzJg3yWUKn3lQuuMjU0F2UxNP0SOcXRrenqYMTRiTXWlrHVRdhQyG0C6f4rxC3sTQ8cyMhVinTnUWNwmCVUaIRRrycZc5qhsssCOxEdp1+u8OrBRJ6esvbvDypcAKttRaRkL2cQAcM4hgwCcitEvMQlocJASD8/ZDMyxYZg+MwEWZWFMBRCaA9QLLtqyawAMlSgDpk6kHe9Cq9DfNPrrLJOsqdkJN0cpYuzPYA4XCcNKO4wO0d6J2eT8QCzYmfmkXcUc0vPNYEvjECBER+I2BzNhhGvTQTMHmWfGugDPncAmD/ZZ9a5iK7lPzwDIgTaPI494P3bs3Cybf7otZ3Gu1WyvtJwFyJIpk4BzLvw8z1UJ0o8NcU9vHDkK8M8GC9LPZr6uDUXkbHMBxqlS5omFAsBTx2Fe2NVAnd9GskestaOVMNhj7jndY5zzxkiN1Xp5gFXXWd6nf7yyeuO43nWoLvpNNkGbMLUuIZb1ETbHab1G60lao9Dzr55nPaCc1iRkT7x0vGrrnJee0rxw4QIbBs9ZWf1t9P8KCzGHTrChJF3PQIzDRUVBa2xkfIV5uY15wd9+q8feeOuCLUN379413wrzMm2C55CwQP/075oaQKWMsZrOpzGAapMRwwPjUMKGugBTwm45zXNlGmNFYUmGZWOTZkYxNuj9/EWEwq4gDDOMpr302/a2EWwZ86kcAL6zEZuZ4pl8etSOH91nJ4+9agvnN9pHH37ENgCc8wPEUx9VHxLIE8gd/V3zN70n7aQngx/mPQWc+2Gklbo2JYGUBFISSEngB5RAcpD/AS9OXZaSQEoCt7gEmO/b1Y64/flfR+xvn2QBh4fYxoo0+3ePBOznACFUlaVsxi2uAu959bRA+w/PCDQXtbPoIusNtmml337h0wG753Y/ISlSOvijaiQ9lOuh/V96eP9R5flepyuw1Te/G7WvfZNweSwGaXPn7Sv89hu/FLBtDjz3wdY3Aefu+eysvXUmbqyl23/69aD9+0dZVIeG/1Y6wAfY8y/H7Jc/H7K+CbMmiEa+/v9k2uZ1LI6yWJw6UhKQBDwnh0BX2gksR6jAaFrUlJO0s7PTsY7odwF5tLArNrfGRhzAgHsEwvLsoxYzZS+/f6Hz3ZC0yql0lYe3EKt3vXR4+d5cBi02q2w6vPMC05w7cwrg3BcJL2X28fUbbQnO3bkeQqYBOJrGqcP6pRVS15JPfMoyN7ETWOiPG2no3aufl+b7ATjnyUTtpF3chw4dciAtAelUTslC18gJvnTpUheGV+9aqNah33WdrnmvF+m9ughUpkXx119/3Y4dO+ZYBvWb2lSOetVl7dq1dv/997uQQ56T3tMT1ev7P+vce3GoXymUksILC5SqdhHrg3MwsmCvcEl33HGH7dixwzkKBV5QO+jw9MwDzqmfbtyYDNX6z4BzyGfmwAEb2/cKzq/rNgPoJ8zCPplZcV2dVS1dZlnLN1haQTkAlRmLAgKbPn3MRjuu2vjksHNoBmDJKMjOsdyFi634znssSD8HzQO4Z8hCFy/b+JmLNtMJyAfnuZg84oA8AuycL161yrIJyRQ7fsZ69u8DVNBu2ek48mF+HIPVy99E2M+NW6xo+SqAQAA8YE2htJYGwC9y8pyFjp22QcCBU/RBhQHLBmhUBKgsb+kKS1+5DnBUucV6e2zu1EkboA+HRgZwkODgg2IggVOkqLHKirkut2UNoKQC6jdnkc7r1O+cxc5fsimcjPg5LQTIIQFQLBtWpKaN6yxPbGo4KwQGCp8+bzOnztp4z3VkAfiLNojh3MnD3pW0LIOsq8D6Tp8CtLPH0ruvWq5sA0C92QKAYfUNhK3abEUrWiwAOMoxZBDqLj7QZZHL5ynHZRtrhREEMJeAgUEAh3k1NZazivotmG9pOBTjgHQmzpyxsQtXLDE85sKUMWWxNPShuAl2uxXLLXPRAmSRw1nYci634wA8b+OXCTFF+4ASweEMExB2WSC3vNVLLbC00Tl9o53dFj5zwYYvdxCiirQBFvjpR5nUMb+6wip2bTV/TrZNHXzb5l7aa8M9nRbOKbA47T+bD9iI2HWLb9tmZStXm7+kFPCen0iAMDrEYRwaGbPEFcCaR0/Y8NV2m4RFJ0gds3D051YUW+aqFstcs9T8BSU4YAEUnLpko2cu22gvYEKc6iJUy8KpmQFgLncFerRyOQ7QcioOZwRgvOkjhwiNe9amsdEhyh2Duc5HWNLimiVWjA3PWNII+BAbh/zmLp+zmdOnbaxzEMe0GCdwQ5N2FuCswpZ5lrNygY1fumjXn30Rdr2rlBN5lVTbRE6+BetrrWL9Cqtav4ayVCI3QBxy8uPYnLt6xcZw+A21thOulzBoAMYy0wFz1VdawYpmy1xIGXACxgHrzJ49Z9NHj9tcVzfAOZy2IGoStElGebFVtiyyvBUrzVdBv8L5Gu3qtblzF2zi1AmbHewH8EG/lNMQEJr6Xvn22y0fWy3nWRydiOB4DJ0+ToiwKziD0S+x2uAIj5Fe6er1lldda7Po6LVX99oQTtlc2KQKiwttClsfqq2x2nUbrXL1ZgBu1dRP4yOhEsdGLXzpCn3wjI1ebbUJWC3DjAdB0i0pLbEiQL4ZgNP8gNzisJBFzp6xSfR0AKDpqJgUAZplI8dawLYFK1dZOmBLH2GaEwBB4wDDJrAb/aev2lTngPlCMTjEqB82KZM+WLx2leW0NAM8pK9NApYD9DeNfvS3tbs5iaBHAoMWANyrBpCVWV9hU33X7eK+AzZ98rIV4TTPKi6xECEIZwDO1W7fZiUbAL2jo35ACZMA4N4+/6YdPn/QAQIbGhtgC0sDrNxr46BnZO+aAUvu2LQRIGwD40shcF5AQ30ddvHSCeu81g7gAlAxQEIBn0oKS2zR/BZrmb/UqoprAd9N26krJ+zEuWMAI2ZtxbIVtmbZessN5tno1IidPHfCzl05bTnY1A3Lt9qq+jWAMoPWPd1px1sP24VLZ8AN9+MoxVZmF4K/xSYAEGi9dslyizLs3q0fsurcOhuaGLZ9h/dbR28Hzyu5gP3KbQKA4eD4ECEzK+2ntn7W5lcspM8kxwwa9nvzIo2bNx/euKh3vfT7qVOn7I033nDf5bTWhgmBXMQet2jRIgeKO0Ob6/qmpiY3Pmlck6Nbc8auri7HOKd5oUDhixcvduOarr/50Him4+bzN3/Wb9dHOuytjjfsfOsFGx8PWUFGi82vvMsmesrswuV+68ImzAII8YO8yM3Is+qqbNhzK5nbZAN0idmelyKMt6PcO4xUYX2Dqi4HAOnSZXW2eVuRzVsYAKwQt//5let2+Jg2E2XbshY/IMQwIJVR6g4LDrFhVy2rsofvY/NBY9CyxIyEGEcB8h05OWEHX++y9iv0xzkYHvMJq1uKnQP/3Ha904rK8u3OHRW2fTOgcmzyi7sH7dX9/TjzAWkAupqdHaDvXAFglm6btyyzzVsrYdrx2TCb186eCduht3th02XsmuHhDcBeOiFfq6prYMmptVVrspn/mJ05M2eH3uyHwQbwFmOVQssm0uesdnGt3bWr3lYvwjbxrPfUcyH7nS+fB7DC/S15VpQ1QWjHARsaw4YGi23hPL/dfnu2rd9IyNU8AedhQRsL2qXzPlfmy5cBqs9o/p9j1ZUwL+VlAEQVUPSKfeyRtXb31nIrzU7Y1QvT9pd/3W+dA/lE5cgHXzsL6K4DEFg/TLrzbfPGBgD5foBLUTt6bNzarwnAPcp4FcLOhKhfBiHr5tnK5YAfZnx26uQMm3iGABECJmaMEONnNrrW2JBl9324wpYszzPU3147ELU/eextbETA1qxqsnzAvde7hmxsdAYAS4K+WoSMy6xlRYYVsn4LFs0mmAhcuRBF3weYY44RqjVC2gGrqgAYSD+dYu4wMnzZ7ryjltChDfT7gB0/GbFnd/NMxBykvrGGuQ9AkN4uywL0t2btfNu0tQo7CxvyyZAdfGvWujqYTwBA9gmczjyonLQ3b6kFVJoNKDfGxqRB+l0H87tBB4arYq7jzb3VH9RPvLnf9/cXff9xHbINKo/Kpnm3NweXjdBnnfMOPX/IFohlWaBbfdZ8VRthNNfVS/foOr0rDYFnlLbevc0i+qxr9FqyZImtWrUa4CibmGDPzS8EOAq4bAI2vtarUTt4aNpOnBizMQCkucw3xDSew1jQP5ZGO7baPXeW2e3bSq2Itmm/FrKvf/0S9tZnDYDM8+ggPQD4Z2e7bcuqYtu1rQ7wd7pdBeh68MisXWofAtQMCxkbR3ykXVJVZkuXV9qWzYzl2IPLp0N2/M0B6yHs7yT+gRg6GEwPo6PZdgeAstX01UzYC48fj9lvf/E4fa7Ili6pttIawnQO99sI4P4AtFbNtQHbfluxrV2DXQf8K2zkzFQcHYrbK3sn7PSlHhubGQBAywb+8horzme8m57E/py1u29bah99sMHJpatvzr7299fs5BU/oE9sEkDRmdFr2KguwLk59tBdq6yhCsA+gK+5S7CUXwJMCPg3Dmg7Jy/AhjE2fAB4GwV0PFcftdpP51sGgDGhcn0CugmIdiVuo4dG7WrbBatYWGV1t9Vb+jxsidjYhFsG9OMLASi7BvDvVMgmLg/aEOBbAgUzvuXx3IaRpB59jB1pjPn1WwoZ/+HuY2/C6MsAyI8yr2K+5APQO+gbtt5QH3O7uM1fPh9QZL6FBqM2KJvUN4mQmFvBUh1jY0JWaS5zdDZ3rM0ilDTlACQ5B7B4+PyUTVKWxAxzUeZr+dgvP3kJ8NaHHS4HTFzyEQDeKj97eOIAe2NsUB8/CjjwAsC5eTlWf1slz0PphKflmYVnBaB/yANQ4AAAwPNhmwVIOAnL7yx9ITOYaUW5hP8EaDkdGbWJwLiVLCN89HKeCWjY6fOzNnZg2PI2YyNXh62tuw1bNWbNzc2OcU59wOv3ng3w1li8fvZ+fffK69kIlVN9XnOJ7u7u70UJkE3QWpLmCI08W4mdVvX26il7c/PajScPpefZRtkd2Yfz588znpxjTrDF+vuy0Pd+CAon3Uac3PxMm9+ca/fc1WCNzVkAJeP2198esFdeu8Jcr8haFtQytkQBuQ3Tz4ewEVkw1zfari0FtqCR0MkZ2ClY25nm2pFTc/b24Um7cm2KdaIQzzWA2AGSZ8DePNAzB7h+klDo+bZuYwFrFQkA3XP21DNH2DxQzrhSDXAOGzV6lXFmkvdzlPUkc44GgHMP2dYtm9jwwsCUpiDRmk/D4Oi0TCzCgtH+0zmU5PCDHing3A8qqdR1KQmkJJCSQEoCP4QE3vnA9ENkkro0JYGUBD5AEgizAeTC1bh99VtR++sncS6zWD6vKs1+6oGAfRrAxby6lN34ADXnB6qoY+x8+8YTgObQvUvsdFL4jXu3+u1znwjalvU+dkqldO9H0aCHDx+2AzifFQ5Qhxb3xF5UBLuNnAKi0xd45J85rX8UhXkP0uxj8f6l/TH7s7+J2MlWgAqUYUOLz34RsOb9u1hc+wDrnUJ6PPBzITvAbtMyKvYrvxK0X/1MEMaCW6svdXQl7Nf+zzl74QiLS6zH/LefS7df+wXYMlJsc+9Bj3r/ZqlFTc/pKYY5OT/lCNUuYIXNELhHC5V66RDAR3ZP57Ww6S10amFTC5m6Tvby3T68tFVeOWUEqvIWU728lafKIyeM7LWAVN4itK5JlithFwDd/OHvfMFyIiG7rarG6sanLGtyit3GhOxgAdePQ1bhGHM/+SkruPdeFuGhEeFQGjo8WXj5v9fAOZVJC9XeIdkI8CiQlnZ2iwlGjis5lLRQLbCZQHNijfHaSnVSGpKzznl19NL8cb6rDN4h3RRT3smTJ+00YBgtvuuQbra0tNjq1att2bJljgnRK7P3ruv02WsnfX+vDtVJzgOFqNH84hM2a9EAAEAASURBVMSJEw6YKlmrr8k5qLC5TU1JBkfpsHd45Rdw7sknn3SOyH+NcS6OI6Hja1+3yX0vW5FYOwAtxbK0Yx6ZZuVYOSFF89dvA6RSASvddRvc96p1HT4IeGnGCgCd5BUXOSBJDFawSGmJVd7/gGU3NQIcgvnn1Am7tG+/Tff045CDeQP2ugBOpAnYm2YBopWvWmmFpRUWv9Rmo6/utUkcQmK3yGicR1ow3VU3AIIDuLcQ1kAcm0IeJWJTNgsgqefZl23m5FnLA6gUBKAlbR4HXBiGSSe/eb5V7rrXsgAETaIDowfftBnYtvJL8umvhNaEJSckhoHSIstfvhJQEqAdQFBRWMW6X33NegHlleAYzysBiAXD2xwT6WGc2ZM4yltu32KliwGbhKZtCoDdtdfetFBHJ6CXTCsAxBXAiT4Lu0ACRrgyQDvpsCLMdLTZ0KsvW+DqeViJ8NBVL7RYKYCb+hor2LDCMufVAerCZuBAjE/AznfquA0feNWun221TB/pwGyFUmLHhmwaME5uHSGzbr8dxrxSC12+ZOdf348HEZae4lIAXaVyo1C/sAUIYVSCzmcuXUL75eEYHLe251+2wbdPWBbtVQh4KIs5quZr0/T3AHIsBgSWBVCMktjw6zB0HTpiYzB5pcNQVQSLXhBbH6PuIagwqu/dYZkAo+ZOnLbw7j02BuiKRrbgfO5HFhTcipYtt2yAR2LIS4PBKUTItFAEJpSLV2z8pdcshKzjANuyC0txIBOqCxa6PgCZweZ6q71zqxU0z7P4tT4bfe1tG2hDzrmw6FFPF4KPOkYAS6YvWWw5sBRm1dVaHEa1waOH7dJzT5sf0FwVYNkcdAz6NpvCOevLrbTSNWstexWsofk+mzp32q48/yzMJTBrEpI0nzCe/rxCnF+w9wHkCDZWW8X29YTj6rJeZBe6cBHGqmzyWoSOwnQHs17uEvpgy0KAV+w2YJdBYgzgxqVWG9j/ml2/eAEWwyiAqXIcpFk2BVNc3/SI5dYWW9PtGy0f2zqOjbpOv4oRErwUdsN00o1nokcwX8UA6BQvngcQbrn5cYDHYXIao026XzvgGOQKYL/IFhsdjDJT9KsRQF11t91u5WvWA44hROKFy9a9d49Nt142giJaLm3uI5RdGHDeKGNezuJlsMC0wNrSboOvvQI74mXLxVmbi95GKyssUVtjectXWC6gLx/jncKyQoFoc9il7r37bfCtI+ZHl8qwSYH8ApsDADkOUC6PcbLsrl2WC4AqdOWijQOMjQKsTAPAFae8DLgw56HLWbmWtXiJZcKgEcCpGOvvsrHjb9qZ1+mzwxMAbAqtpqgackVAlzAxTWKjSlavsJK1gFKZgw8Beuw48DbtQvvBtlEE0FAshGKWFHtR5eJFlt3cYJGpCet5+6gNHzllxbDE5dZho2CkieMMzVvZYhlLF8DMqPDrARsJjdmrp/bYy289z/ym25qb5ll1OayJgSCgRpjnBvqIjDVn69cwnqxYb0X59YCaRgA4H7KrV84DqsUGYRczsAUhFoCGegdxfubaZuzo5lVbAVCkW2v/VTtwaD9Au0tWh47de/f9VlFUYVfaL2PvjzimlfnNC2zz6q22oLKF/jxjr5/ea4fOHID5rh8AVa4LrZ6AnmgaJqoRAF+Dkz1Wt7jaPnHvJ60pf571jvXZ0/uftWOnjxKNLmCL6ghHX1BsU4SBzcYef2rdZ21h5RLsTnKu4o0b3hio7zq+3yGtc7pG4+zRo0cdWE6AOYHidHjPvHJ2awzS2CQwndIRU46+e2AZndO8Qs/HYorSM7PS1vin3/RSObyyKH397pXR+37+2mnbe/YlG5wCYI7cs9KW2WD7Ajt7JB3TOG01tYRXBlSoUJKROYWenKX9Csgzx65enLUnvjsGS1cu4BHmCmUJ1u0iAG0mrRrWs223l9qKlZmMx4T4/NNu2/cG7EWRGasqT7PaaliNGBdG6PPdXVcBeiXsZz+x2u7eCUgXwM4YwJFTV3CI7z5rp09et5xAmdVVIYtAro1MEA4R4G7nQI8tZpz96Y/X2f13ZriQro//3ZD9/XcBpDIOVZZkWgNMdkUlflh2Mm3FihJrWQ4QB5DLiWNzhGPvJPTcEHOaHNhvchxYRoxkQRh0Fi0s4/oC2HZn7bX93WySwG6X5Fm52NTQ0+GZfhjnsm3H9nrbtCwPwGGaPQlw7gt/CgAxWmyLyhNWXz5t5Yydw2OZ1toFECrWaRvX5djPfHYB4A3AUfEwDD7T9tKLE/bamz2wJGVaU205z8iw/Q1JN2etb6TT5mJ99h9/6Q57+N4qq8hNs3OnJ+0Lv99lR9uyYW8EhFZOXSsEGEtjvgkjG+xtgyypPL8bNsyOKTbdwFZVBcNYOnMjGL/S06dt4eICW76swS5dCNuevW1sMIhZHXarqJBQ10yJZrAxmVmT9qH7y23txlKjmWz/a1H78l8eoz6j1lRTTJqVhGplfJiMWXdPH8xck7Zla7Pdcx9M2WuC6LfZubMxe/rJK4DcupjvllC2UliB0tHnEcB+s9QR4EJ0xD750Sb7mZ+p5XnHb28eCNtX/wZQ0sWTgJcKYBQsgSEsE8avXMpcbM3zs2Gam7TdL7QB7gSkh62rhbkogznSNKyqcFLZqtVV1rI429oADR48eMRWroihy7AbMZ4LnKrnlpv7g9dPdO69Orwy3Jy/d87rt3q+8J4jteFKIZ71nCZgi+yDnsO0ycoLvaiNIXrpvOqsebCuE2DOsxMC3mm+r2samUOm+SrRTfUJAHZsdLzcGrZXX4WVeh9jzDgsUTwTVAJEDcMWNzCOPevlmSZyyT5NP3zoIcb9Cj+MiDP2pd8/bW3tk1acU2plMExVVBXQnhFbvjDLVi7JhcXOZ8++3GOHTkJJCAKrBlBbTgEsZwKI82hbDwPhFpjY+q6P2Bt7Oq31wpyVFtUDlmWzC5ELZmEq9gWnAaMCsAMUVlTkt5OEav2t/+ugne0n3C6bFCorwlZR6sN2JbAzIdgHL9ualTn2wIOLbMOaYuy5z7oB7+3ZO27PvHCduQ55VAYB5+YzDsbpR5MAZgHeDVy3z370NvuFn1mAnfPZddiwvvSn1+zpt+ZgRM22+ZyrL4tSvyiMdkHbsYa5JvO2ibNj1nt4BPAZYFnmJjnadID9MNacfaPYc0CnaQsSVvM5wMBLmSOyyRT8rgVg14xfZi55YADmsNNWMb8WUNk8y5gH6AkbwJCtqa0lJuI2/da0Tb8J8LAbcCDg5iwYMwNseIhhiwfpZ72wURZWF9ti7EfeBhjrAK31fIP+ij0OAoZP4GcJ18C4XMz0Aua+kqZCN28Ya520QYCEeYR1zQzkYed9EGdj3zW/g22ufluZFVTnwBoHY9zRDuu7OmnZiSoryWeuyVjNgGlzYqubZttOBrbww6VW+DAbUdhDprDMiSnAXu1xG3tr0trOdDFnzbM6QNlZgAPn8gScA9jHdT6AeJFWnreev8qzRgjgVAHPI3kAeGEynPbbDJsrBqZ7CWGbsMatTVYAUE5MexPHpq332X4rurPA/HfFra2H5wmQWU08+zU2Nrr1F/WFm+3Ae9n/b+73/9ZnrSFojUblVR3U/9WPL1686DaMyTZo/Wj9+vVuLUJzBa3T6FodmifoftkXzx54tka/K33vN70rfTHlvn7gjO19cwWbTkqsCR2oKOaZCWbCcdZ1Kkqj9tAD82wRfbtvPG5f//tBe+r5s47luraMDUM8Y+XQJ0ZZB7rWfc0qq0vsQYB2H9qVA8idOeoIYOjjYXvi+Q4734YuZxcwjyhk4wfgT5gg+3voq4DQl5TG7Tf+Q4HtvDPPevvNnnl+zh772iuA7YMwWTbSP5nrFoeZ64Wwa4cA/b7GWFBvjzz8oN2+lfky4Gp4DqmfgHP0E4CmSNHB5vRM905HgBRwTpqTOlISSEkgJYGUBN5lCbzTYeldLkYquZQEUhJ4X0kgAgvzGXZn/dHXwvaN3TgWeTBcUO3jgRXw3CcC7FxJ2Y73VYPdAoWZZFH36Zdi9iewHZ5A99ggb/ds9tv/9rNB27ZeVPC3QCXfh1WQU/vLX/6yY7nxHAfeIoAAJXrQlyNBjgPtEFy3bp17KYThrXQQkc2eeTlqf/TVsJ1rT4bxWLfQZ1/8L+yQXwswhQWgD+Ih4NzDvxiy/QDKiliN+MWfD9qvf46wL7cQcG4ah/LfEG73v/4pTg4WAbct8dsffzHdViwBVKQVmNSRkgAS0CKlQmcIxCOQlUJoyObJiVECaGAhABeBeQRMEmDOOzzHhregKfuoz3p5v3nXvhvvXtrKRw4ZOXfFjiJWPJ1TPbwyeAur2s2sxdkVKwCOAPLT4Zy2gBcunztjf/TbX3AMWi2ADhbiEFhYW2+VsORk1ANqgTForqPLbPU6y9m03gKwMOm4ub7ed+X7XgPnbpaPJweB5+R0EmhOctICs9pQ4VAUSk3jlZxU3vXeu9LSZ73eq0NtqEPtpc9yyAnErvqo/XVODnmvLjcD2L2ye2l439+ruihfT29UJrVLH6FABWyUE0HOAQEaFWZW9fEYHL17dL/XFgLdPfHEEw6At2XLFqffN9dd18bwCl/8ylcsHYazCgBWGU31xEKCKUEhv1iCzwAslV7d6AA7I28fsa59ewjROGqlgNOKVgBqg/VEYZIigBMigIayW+ZbEPaj8Nlz1v3KHmtjd39hbZM1rIGprQbAAMwRCsU1S1itbBzcCksTa+2yUcA94x1XHSCkYCOsjQ04PosIG4qTwleEJwyglhFWMwJopOuN1+3avtctNxy1hVsAkNXX4UDA2dzZYddhlxuDVatu5RqrXLQYQM0lmzh+lBA8mZa/bpUFFzYrtpVjLYMeALBOpaUDZEjgaJx5/hlr27uXkFBxq10KY8mSZYQuBVyDj2YG0Ow0YJli7ITAb5G2druy/3Xr5b0IEGAT7HK5DY0ujGcEUGEUVpksGNACOLhj/T3Wu3u3pZ0+Yfll1bBrbTNfYwtAqCLz1xJSs0BgYxgcYRqKdPfZyO4XbOjQAUKH4cTcsNWKl6yApmIW+bRa95XLhHiaoH6rrYhwmHMAvs6dOUnk2wqrWbGSULk1sKwgqhggQ0BG6bAa+QGaJeZiNnn+oh199gXzEQarccFCK4OJTmFicS/ZHMCihEJh1lVR5lyb7eqxqwDFEu1dllNeaUVLlwP2ov2y5MiEXQTmvpxF8wAvATi8cNXCz7xoA2dP4BBdaMXbcOrQJmmEAvWRd1ou9SNUpGXg+EWrRsf6AcK9ZUPP77FSwCMl8xfBIAIwC0a9SD8sE8fftn5AfrXLW6xpLSFRL7faKKyLYfSsYvMmy54/DyWHcQQgVRSwYRqAq2ANQEvAi2IfawP82HnhHKwN5da0tAWmwHryJ9TldBg2OcKf4jAPVpUQumvAOg+8bhf27bNmQm/WtKwG5DcfljvCcBECdI6QXwYwLWch+j80aBN7XrWJI8fQWRz6G7dbAIZFXzHOTNiifIAw09BlsRPFO2CN2/+mdTFGzsAQV4HsihfSL3gIm4VN8OqRNwHiddIGTVa6fQdMi/127dBbVoQOVrWssoyFLbCxFLo2jONYD+L8DtIPFKt4BsBhO2DUMQCTDU2NVrhkvmXQ5gJehkKAl3Ay59UD5IOFLAbQtY/Q1dcBEubhbBT7WjaAMV8OIErGwJBsN8DvLOyJIffhV1+xYcLtFpJWMaDWAGAmHzL0lVXBWMg1GbDg0eficmK/us/a6Idi1mtqWWKlgDgCBfkWHhi0AYCU/ehxOfpYRZ6x6/Tvg68ZkCDLwWYElwHkBEgZpb4K2eoHVBiobwaMhh4eP2S9e3fbNQBX+fObrXbVGisoa4LBMgO9o48nCI1OW2eXFxIGutcuUOaOsxetAMDCvGWrrLgZnaR+Ceza7PSUZQLmDeKcTsxN2djRY9bz8h4c4jDirdtA/ea5dlPbgWTAu50J6xUhE+fGbN+ZPfbC28/B4NNpyxevtHXLNlpFbQ06GLfLVy4AqDgOpjbLtt22HUa5Bjt++LSdZ56QmeMn/BbsurXVzCMIX4oj9iLlu3j6ojU3zLe7dtxjzdXzAVXM2lmY4w4eeQNmuEFbu2k1DtgquwzT3rVL162qoIbNbttscTOgQmLNdg5es6f2fNfaANyVobvLl8FeV14NQxWO9Csw0Z08Sp+5bs0rGu2T9/yUNecBnBsGOPfGM/bWyYN0vUzb2rLV1i5dZTN+mIKnpuyueXdbQ2GTs50aPzTeaAx1YyBuV9lUHd54os/edQorPQBQuQP7o7mCxly9NLfSmKS5g9ISeM4PmFPnlM444bLnaBtdp7FMz8hyhJei3xWMB/qsQ85w/e7mYJRN+eql7zqUlsrrysrni9fO2Zme4xbIhUmUMMVzo0327OPTdvpoFg7oXLvr7mL6C7ESsRehOTnrw4DHmC8Aan11bx+OdJj/FjcCcMm2unofjFGwJE2FLIMwqY1NGTDUAYKDReZ//HGf7d7P+cCk3bapxDasL8b2ptvkdNSOHG3H2X/VNq1dao8+WAcDTQDAWMKeeOmavXzgEvdk2h0bF9jKxWWO1fJ867S9fvCSnbrYZYsWrLfPfKzK7rs7QGhis298Y8L+7jtXCb84Z1vWltvWTaWERyW8K2C8EsAtxbBh0d1t7yvj9o1vXgD8UwDDe7WtWw9jFYCUWRjXovEoEQaw6djdffuuwyrch+5X2baNVegigYEzzAZhqItlxWG8yrN55TAx0uRPPTdnX/iTS9Y3k2k7AefceXs6oWnzbRKA0PH/j733gJPrus+z3+0F23sBFruLRe+9994rO8WiYrnEjv3LFyd2EttfPseOZdmWbEuUZUUUSbGABQBBgOi9977YXWzvvfeZnfme/0BXgRlaFmWFRd5LDmZn5s6dc849/TznfU0xC2vuQP92Pf+l2Zo9B1XKPpcOHCnX/kN16u6LRMUtVTOA9WIIa0lpt86er9TVW8DV1Kn/4d+t1naAtETCePeWgXPluvggUPNQ/lu+KAIYLIyxBOPrGJSW4JCuXHPrzbfPozIXorlzxnDtSOxNDfQmD9EeD2EcbtaVhw7UowZXBfCTqtXLk7F5NbCK87DDdPO7I8dgvzksVK0o5J9Bce5vX7oA7Nas6ZMytGxRlrIyaPOAfG7caNSlSw+Yt4nV6rVDtXZTHCpfbAw82Ks97wMJo8a5YP5olH/iAbkAjyrbde58i05dYQKEvPWVJ4frxRdjKftYwp50oahXyVzwFZ9F7ZrVEzRmRBTgBSqU8ej0scn6yPEyHTycx2a1LC2anaFpk1AjZPhg4e6mfowHmgygQT95qk5nz53ShrWRyhkZSh8w3reZxSCxRw8rI1YmPuvDqScsLFZmrazae1buDZ61DVf2sPGklXUbW1gdYeMMA+RsDOY8LI72t9UVDjD3aD3g/F0HKG/AjV0vlT5dWwcqcnXB5Klo+v+BjFk7yKdFKqtowfIQJcmZQ5WWCBTW5ta5yy4dv4JNbPc9vfhUlrZuG0oaBwCk9ujP/uKW8gqqlU1/a9G8LE2awoZY8lc8eTQceDUXaPMHr95FwSoEpTsA1PlYQKfQ16E/109YTNUuNtJfZ04U6MKpUgUpgboDRUyAyGDyci8Qu4v+5XDy57AMLFHD/HXzllv/6b+c1pVyVP8B81cvC9PEsYxLgb7u30YR9eotLMWbtHzFFBTyUMKLDGBc2aX395aoFLh1woQUYN9wxaNy1QRYfe5Kg86fu4mVcqm+9vgq/daXx2ARHqByLED/4u9LtecC0CrKlusWRwDiAXcmA91RttKCAFCrgPUoW7Wo6UWjWpo6GmvwFFTbaM/7S9zqvtauTmzVQ0cFKunLwxQ2LpTmmvxNNgymTHmZj24+3Yii5y0lo9iVsQi13RzyLcp6vlaGboC72qNy4EPXnW6gJDYwjI0BfqPPyNCut7FHRXeLAQ87lMR4Y/R6YH4D55ibq3oNJevTXQoGag+cADAN0Bs0DLCSejuAOsZDn6G3Dmvdug6UHsk/9BFNPbYXYLCWfNAG6J+KBWdSCv0t4NSiewXyAtGmoUgcBbhrVr/9tfSd72CDXdQmdwhQ4eahit7KjfNNcfAbFkfEEtoutKvidqliqEtT5qYpJCdUbjuHa5jasKeOc1A6vPPBVSX7JyhlfLbCcgDw+NwsbVtz21VYUuhrw8YuGKXIeRE+a9q261i772tR/EpAy5V9KqktQQGxyTefnJmZ6QNFnTL/0efPuh74l37f6gSD7q2tt3Jr/Qnb8GYb+AyutXlz27RnVu42hrfzLI72PTucesZ57Xzu/K7zvvOdXjZTmOLcyVO36XvNBnwfhnJwCPB0gHoYTzSjVhsS5GLuk41etEMVKM69/E6N3t9/S8GoKM6bmqPZ0wC/sWluQ6HwyOkqleK0Mm50oJ55OhG1uhAV53t1YG+bjp+5T98gTHPmZQJ4R9FeDaBW2q1Dh+t1vdRPk9LC9Ye/F6sVyyNQK/bQ7rr10v/aT3/Jq/mzJtPeMc4fTj7376CN2Qs4vU8jszO1Y/MOLZk7F3AOJX2siW2kbpqGZglvWnM0e/xLc+Qkwid8HgTnPmGCDZ4+mAKDKTCYAoMp8POkwC/aLP081x48ZzAFBlPgi5wCzOvo8s0BffMHLu09OcDATpqY6a/feiZQT6I8x4aVwWMwBX4pKcCGf+0/xi7a11y6fNd2OmKXicWiQXPL5wUgd/9L+ZnBi3wkBWzA/yd/8idYObz8U1u4j5zim+izSUHbJWu7Z3Nycn6qFrN8+XKfEo4z2fHR737RXrew+/Td/W79KXVeeSVGC4zgtywN0H/8jWAWn7Aaow78oh09TOo99hu9OnoJqz8C/1Wgud//WhAqE786/b/iUo92/E6vbhcwsc09++a/C2ZHcqAif8XsaL9oee/zFt5OFltNyWvPnj26ePHiTxYoUn3PBlvZosayZcu0YcMGnxqCLYTYQokdzuKG/f3oZOaj79tnv4zDmUy1etUsLs2689y5cz4ozBZwnEVbm5g1wMrOscnZJ554QuvWrfPtcHbC4WWy88HdO/rb//7/qrmkWDkoUy1gsnX6kmWKWTALxaFEFuWxAqxokBvFneDMYewwfgjePRpPu569tjB9HsE5C5tNXFuaWLtm4bQ2yxatPm4y2j63h6WnPT56jpN+n8az/b4dTpjs/tpivhMXi5u1wc7Cm+VT57Dv2GHXcO6Pcx3nnE/72cJhhz3bw+JjE/6mQGdhs7jYQqJzb5zznLg4z6b2s2vXrn8BnOtT7j+8pNCqCqXNm8tiD8BaJJBDKJAMi9VIjbHTHmWuvHyVHTmkZlTk0lnMSFm4REFTsOBEWQkRMdQfrJyTJ1iw8va0qvvQUZUAgLmAnYau3aDYZcvliUEJisVlqCTfrH6QLcqxOOwuKlPl4UNYLRayIMHC3PLVCs4Zy7XYXBCIokoAKl6m3oElZSfqVXfe3wNEVqKsnFHK3vYEqhHIBHkBerBirT51WA+uYXfD4tcILDxD6+rVfY+FfuCg0GWL5TdtovyiUKVCNQwJKbgdbL9QlRiorFbH9/8Ga82bCs4eo9h1WxQ8cQKqGKHyogBDTuc/UELiM1DbrO7LN3Tp8BENAKeNmotl4+w5QGoom1FmPGZH6w/4gT2Yv5e6obZaD97fJw9WnMmZIxW5dI0CRxMOgDkv0IKH7BiIPahQE+nBSrXirZ1yoVQWPnKMkrZuVQhWnf6Acy7Sp+rCJTVcv6nU+GTFYefjQUKnoLxEMdlZSpk9E/U6wKiocO4fAIxlcx5eALyBMpTcDp/WjYuXlMF1RyxfqrDRWYSB+GFpJxZufUyKWf40t6vu4nUVHTutVJQykuYAq82fo4BUAEbARy833OyADDTyAwxzA6v1f/ChSq5cwtYUWGrNSixMgdUgHPyMzuA++/Hw8DumTlTN4mAJCnUdZy5rUspQJS1apqDJkzif326sV8W5M7pN+xKP4uGYuQvlX4Y1FVDeQAxqL+tXaMh4FAhDkftAIc4P+1e/UPJGMOlY16auE9dUxnV7gZeHk5/jJgPkAU/hw+QDs7xu7qVlQZdbzdeu6u7R42oBEpw5Z6GSFy5UQDp2q9ZxJY5ewupnJ9Nx9dRh4woo2QTg5s/Cbty6jQqdyLVRN/MDriND+ex03Sy+DdxAYe3dPerKL8CSDKiMc4NGAR2S5by99ao7dFCdR44oinIduXg5gES/yu5gzQnImTZrgcKmTAdYA0i1SYIQwgGEGWD5rr5dTUdPqfj8WV67NW7Dep+drUFf3gDAMsqpH/kokHJranPdwIb3396JxViDMsmfcStRQwW29UGMVHtckjQk/YjiAHaR1UcPq+rODdQ3AKYXLlDQePI/8KNfEIu7QHNGZHo6sc/DsjTv7ddVnX9PycPTNWrdaoWhEOiHUqUHK7nOK0Bkx06RTtyDceMVASjbceMyRRkoYN4shcydIT9Te7M5EH4/MJg05F4O1FGu9u1W8/H92DKjHse9jlgERBuRSrqReMTPS9nyw7ZUnS1y372mq+/vUhtw8Yip01HaW6aQVPI0dQYVpw9EtXxHYmEZh6LR1YsqR4EzAdWQ+MVLFWT3D3DQj3ApmHtIWNzck9b+Vh26dUCHrx8EtGnRstkrtGjyEuCuFGBDt3JL7+r0xYPq8TZrFiBndFiqTh0+p+aWJo2dNBJrvtGowaF2gw1fPxRUBdDzsQ+PE4wgwIvFmj9lEcpkkdjwNeha3hVduH1WLrzghgCsdrZ2onYTpfmjF2s2Nq3xQMNt1Kc3C27q/WN75QU2mEU5nzJqKuUjnvD6qby6UqcunNCdwhtKyU7SU2uf1qioUaqqq9Hu83t0vZCF+pgUbZq+GSXwmWoaaPQtvo+PHaP0yDSUHoMpokDx1u7Qvripn/tcD60Sg0lLqxf6DZjkP2t7TO0xgEc/6nvdpL0bAqiHSYg+VP4sUwWRjsH0G0JstxTXc/H5ABCXm76UWWiaJkoY7UowMGuIpT0L0raoTPbiNZCMtTm84Ku+73ssPABk/bTp9tth1CkBtOEGB/fxngW7rqlKXWpTJHVEGPVSY3W8fvRStW5dx9YwIwQFKRS6JgASDyGe1InBqCr6A0hXFHZr754SrNv6NH/GKPqv4cpARQ3nP+BnsgR1OBwPapG9qi0P0Lf+pllHL5htY7++8jxWiTOw/KOZQjMHcK5er7x2n9/P0GPAHdMnBKqMRfW/f+WS8it7AXVG6WksQ0dnWV3hp/xit/YfLNH+wwVKTpqsJ7cmad0aFM5Ixtff7NQbu8uVgLLTszuGaeniMKBylsUJk2XvQMLfgUXgoQ9b9NprefQFAMZWJGvewmBUXKkH7BweoZzXWNer9959gJJhnUZkZGMnSpkdHaQIwJ/+IO4L1uXh3KtI2sJA2qD39/ajOJeP4mWkvrQ1QY/vwPY1CUUkAJG791zavz9X+XnlqPLM51qxaml3adf+Bzp3vUOjxmYQhyRNHPdw/NiKktTuXcXavRf10vZ+/e6vryVtUP1kbHnrZrv+9JvFulMWrOe2JuuJ7VFKTcd2EpCELKdq0u74Wbfeeu+EIiKDuTcTNWtOnE+RzkcHkD/ItoDHA3r3rSrAwAbUIYdq29Z41Nyw8aZI+5OnLK0QISTv+qG45dF5wLlvf+cMfb4+rV89WTv47dT0ANSHvLp2uRtlOWzMWwew6E3S40+lqBQ1qZ07u3TleoFmTI3RjseHAa+hdMv1OrBwPXCoRz94o1lNXQP6yuPx+vKXUQMEljoDOPfKmzUqKb+JktgwvfDCBB+wSDXpK1M1NZbWRTp6ogB7zhytWZmh2TODgSIf5juzm/ancq4u95KGdcATp7VlYzhjKwPnEn4Kzjn9RZKMMmOF5rM9nP6ohcLCYw/rX9smEIPbrF9qG0EMqjXA1jaXmvWibb6y19ZPt/GEjQ2d7z86TnTec37H+cw2mBgUY+8PHTpMtXX+uofN74Rxw1BtDNPuPR06c7HMZ4+7euUwzZsFsAvcSVWHvamL8tanypqrembHUG3ZNlxJCcG6d79H/+MvbqD8XKWFU0fpyy+M1six1CNMBvmzq5A9Uzp/qVvf++FNtXVHaxHKjYtWhCst3eo46hnuIWwdamJu7Xk3T+fOPmBeJVmrVozXpOmRwsWbuHqFQKEPcA0KsjacfIjDwR/+yUkV1IRo49Ix+urz2EVTL3l7sAymDB48VqjrQF6Tp07WxnXDcAaQDh+v0aEjD1CHTMUuOAvbV+pS5qk6sRA4eKZXb+08T5m7rud3AM59dYJSUWUsq+jXX/19kY5dZ6PkmHi9+KUhbLAJUhTdNv6XXxWbOa4NqPxcDdfpVOoU7FxnoTKNGp83iL4Oc0btu2rVea9GodRrcS/kKHhcmAZop/q5f8Gkj+f+gJpPtQCc30HBOk3Dl2QBzgEEkTB+7DTwtnGN3H7lHylVeAMKgWNQ9JpDvxwlMJoc9Tf0q/xsMVAa8BjtVc6adEXMpg7lvlVTvtxnu7A1px5eBbC6CNtVLKy9vnxHc2RjE9LMCywNrQxQzzgPhcGBFrFJgH5uXhv5IRolVsYmpagVN1fR/+L1smQFptJWcF8GsNHsIvxtp+pRIe5DcS5LMVuxlY+zUQFBJBx+hcB1Z9pUc7tY0VjcJswepqBR9MM5h6D4IL/u+y42/ZSp/FoBdtBjUY0eqiCAK5gnDZDObVgI515+wMlejZszSjGAc25uQvM1FPcOdCphORt9VnaprK5ULSgX20bszMxM3zjdqQOcsmFl77M6LCwWDidMj4bD3n/0sHNsfGsP2+h1hT6ro1Jripo2L2PwnM2dO/WBff/jrm11zKPXfzQt7Hx72Bja6ojTp+/o5r05AG9ZbGYIAdqn30x7wfDHhkyKpv62kJYAdL78TiVl6y51SJwe3zhGC+dF+cBuW3fZ9WGPjpwD/A2v11PPJbHxMlI3z6M297rZMddrxbIkbdqcAhRLXhpgXrxU+sGPKvUBKomZbLz4T7+N3fOyCFUDVe7e56IeeR+gNUSPb52rVWuwi0Zko7mjVvv2v6eDB/f5Nl/s2Pikls4xxTnUXm2sSRvBlgLy4sN208Jv7ec/TWne+DmPQXDu50yowdMGU2AwBQZTYDAFPkkK/KLN0if5jcFzB1NgMAW+qCnAPIHOXn4Izx0GvLD53sVTAvQHv4F1JmpgNqc7eAymwL8mBRAYYNJ8QN962aWTV1ks4fWCqf763eeCtHIhNpkPN3D/a35i8Lv/TArYJOBv/uZv6oMPPvAt1P8zp/30bRvIOwv4NuExmUXGlStXas2aNT4VmZ+e+AX+o7HFq1d2ufTXr7pVXesVTlN6bGWQfvdrWAVlMznxBavzTBTjyd/u1cHzAwpnluxFbFr/y28yQYiF6a/C0cliyN/8g1t//jIKHUwEbZ4ToP/5R6YwwCKNzb4MHoMpQArYpKMtTOzfv19vv/22D0qaMmUKi1nLfJORBqbZpKfZUm/fvl1LlizxQcEfl3h2LWeC01n0+Ljz/jXvGThlh8FTtkhj6ga2UGMTtAaI2e+bgtelS5d84ba4PPfcc1q6dKlP9cTO86MA+KEwU3znjl764z9SBRYfOSzwrJk+U1M3b1YE9oDCMo2LytuM2gqreQEopwRgH2iHxdMOJ65OvD8P4JyFywmPE07nPed9e21hd8Jvr+2wz+1h986ebcLamdT+6LkPv/F/918HenPC7YTBCaf9+qPx+Lg8Z9dwznv02ffmZ/SPEx8Lm+XHR9PZCeNH4+LE3T53FOcs35vinKndfpzi3J3vfw9b1ZsoRwG5ZGZh2RnnU7gKMKgE9SaoBjVdv6bCowflripX1rSpSlywVH4jRqPOhZ0Q8JQfi85eyoqfn0seVOGa9+xVxaFDSkhHuWPrNgUvmC8XdpL9ABSBrGazlk1pId90dshdXKaSAwdUBRiWgi1VxpoNKDGME6vllEGWAezWEAZPR5ear1zWlfd2KgCQYsL8+YBUm7BYBBwCUPG6UGW7eEp5R4+pq7FZoydPVxwQaGvubaAKP4WxyBiIOlYIVphDgGACsP30BwjyIzyukmI1vfRNJHhqFDF9jkLXbmJRayR2tQAaAF8BgFQGwXk7AUVKqtV56pLOHjqs6JzhGr9hjWKACP0iWEwzaycWVM0+B90i+ROmgYZa3d+9V/2XrmroiDGKWbFOgWMB5yJDWFzE1pV2NpgVG79ubLBv31fZG28pvKZWCfPmK3TjRvmPyFAgqjkD1eVqPIOSGWp78ZARCZkj+B1/rADz5WaxOY3wJuRkKwT4KzgW5Y0IYDFAEW8/8Mq9alXtBioseaDRSxYrddlCAME01IaA1Yif3TuWQgH0WMRikbjw6GnVXrmpHCxUE5YtVfDcaT5lNQ/3lzN8i8b0ZokfC6vAfn2Ac/mXLyqKNB62fo2Ch2cBUtH5A3Kx+NlKjh9wjIdORt3tO8rbf1BdufmaMXEqQNeqh2kNsObtbFPTjeu6sf+IwlAxHD0TaK+3XTX3r6sFUCYFm87oEVnku2QFx6BUFh+NIiEAZAjhKGJx98Nzqrt+T4HjxyptxRJAwgx5gNu8ADr+Vme5yUyoTHnaO1Vx4LBunwTK4LMpq9cqajYgdDx5CYl4A+b8fPAc5wPZuSvr1HnspFouAYABRcVt3ITdK/cQQM8vhDRmod9wwn4GZJ5zKJv96A1FtLQqYg73cNMGBWUakEc6u9rVffqs+sgPfVXVCl28jFW6KJUApLY3NSsjZ7Rix0xSICqNwagBBsQBwQ2hHRrAxrayCYviD7HwveGzJM15YquCxo7ADgybMVZfWaZmsRvgYID8h41U26nTuvP6y0oC/Bu6Zp3CVqxA5TCR+21qMQ/7rwRbfqZOVVqqCsDYCsC5VFQWh9IOhoyfhMUveRq7ND8jVICiPK096gd0vfPjH6mtvkojUHFMX7tSwRY/Fhs92DL23ylW/p79WFF2aNiosUomjzXdvQ5c1Y7l6yhU56YoeCjKhvFYbsVgsIpFIb65GqjBpg0gr/XsEcVmZyhi6zoFzaVchSbJzw1kCgBq+ciCbip2rgundRXQzmyRx6xdpXhTqoxO4hzaX1/bxInUnbZg6QFSa8ZKtXTPbqz2sJVfBqw54SfgHHnUQ/kzkHCAMt7iatKH1z/UiRtHffDdtqXbtRCQzWzeuj29yqu+r2OX96mmoxQYawxVRIzOHb/i61+MHJetNNTmhkDtUDp85aoDy89rF67LhYrUjKkztXbhBiVGpaBI1K+S1kKdvHGEMfxJX18lkbSfPna2VkxYj20eiobYRFe3lOnsjfM6duaoUrPTtZb6Yyz2reF+LKxz5xs6G7EJPK+TF45Srw0BznpKo6NHqhIL0LdOv637Vfc0JnOMnpjzlManj1dlb4VuA/PUlVUrKQoLc+qkjIR0bKEtX7hVhZXw3bL7qq2uVXZqhoag0pNfXqQOd5cSsGCODQVusfwGFNLncfnguSYW8nGRVgb22KOGZyvWoFnyVhc2hHkl95Vf8YAF4GbFRcZqcvZ4jUkdzdiQ/O0fpo6+dl0tuKTC6gI2JMWhsLRQKTEoZ1InIY2IpV67cqvu83mhwlDOmzFyMmpxCappr9UdrHE7yOvJcanKyRwNCINCIvFoagjVrjergJcaaWs6UW/BDnZ0DPbi0YoGoklEbSkcYKW+ygV8VgyIUoOF5lDNmhHN5jbUSROCFI6NY3RcAGqNQCQozFWXBek7f9eIgl+4xgKxfP0r4RozHuU27Fn9gaju3GvVd/6hBAu3ZG1bFa952Hw+KO3XX7x0RO2eeO3YMEmPrx2ClShQAXmtHJWZYyeaUFS7C9g1Sjs2Jmr9+iAAROnHb3Tq7X01qBOG6StPA8TNtY13fIk0tV6lKVv2AIScOdmq13+cq0aU1CZOGIoVfbSSgc/iUGtKSEJ9LxpbwtZ++u3ks+PY5volYfGYSRxR4kNlJyoFu2bUzyLD2WAJlBhAO75nT5/+x3duKyo6TS8+k6iNG4HxAHC6+b38fDfWsPlAeJVatXS2Vi6NVG0jsNmBB8or9WI3CTi3Lk45WajVojpl4+d9H1Zr59tXVVHdoV9/cbm2rU1WInG5cb1V/99fF6miKU6/9WKKNq4PRlWIOFLADcysJn0uXAZCALpr72jHVjdDk6cMVSbKgZFRwYqIIn6AKe3Y6X2wu0FHjhSSn/xQvR6O+rVZi9v10PdCoc8U7AK5Rw1c89xpt777fep91CG3b52qtWtilZgECIo61a0r2OrualB5RSfgUbSe/FKK8nJ7UQBsITztwG3x2rojzqc4ZnMZfaTJiZNuLFmpF8pb9fSmaL3wYoTx8zp/ArW8d+pJn3tauTpLzz6brfTkQGyTiSL3rx6I7+ixan14+K7aOiM0Huhw0vgIlFItzNw/1NCsCW2q82rfBw2oJJ3CAjhEY0Zz75Iot+PG+aAZ6xc+2r/l6p/p4fS7nTDZa9t0ZQrQdxhL2TjSwDhzYrCNpKZobSpzNjfmbMD5uPGEXcf6wfaZPZzfcSJr1zXFOTvHwLlqwMQbd+gnotiWSH/o9Z1twDKVzLtFasvWNIC6EEWSR+n+6CKKa6+86dIdQOZtG1O0eSuK4kmhgHfd+vNvXFVDXavWLRkPOJeplGHcvmADdkNQFPXTVZTeXtt5V4XlfcoekaJpgGVD04Opo0KVjmpVHJbwHkD2Dz8sBWy7gUVwvyYAbU8iL6djvRwbHahhCeRlVOMCgckGALwuXevXf/vvp1FPTtATG0ZiH4u1LK41sPwqKwC4PVSiQ2fvAvFN1ZYtQwG1gVf35uv8xXLNmExdsimbfALgx/V6qSxOXQZAffc2yprn+Gyxvv7ieNIkAGtil77994W6fC9Si6gjv/RskOgiU8/S50DdzpUP7Ay82nC9kf57vxIWYm0+M0KB1J9u26xQiKLbrjZsSqvkP9SjePJ46AT6fKgxuqiHA1CN9NzDFh7wLO/BbSVhJZ651BTnKCCEzY+NDO4G6uErzSo8U65EN+4cs+IVNpuxNLbF1vd3N7nUeB7V7cv1igiIVcZS2tf5/AZ1SyV1vPeiS1H0JcK2RCpkAapb1GUQRQ+hOZxYPGzodQHtuuibDHTQ3+6nre8NUkdZtxof9FA3xGIJD1lbCZwd0KmQxVjRogIWSP1pFJUHOLb3RLe6DrZS17Qrdk2aYrYx7qHusXLsoQ7woDjXfrZDdXdKFTkMxcH5KEujJudPvcMQGsVulGwvdSjvRC4bGXro145X/ELmCoaTznRXPKRB59UeFZwpVn9nn0ZNHamY+WwmAEBsuNKt2v3tSl6OQuHyXpUDzjXRVzRr86ysLF+5sbJgx0fHg07Z+DSfnbDYszNmdX7fqROc13aOzdHY5kVzMbh//74Pkps9e7avfrPx6qN21M73Pu7Z+V3nM/ste8+e7bC/DZwzNbvz5+/q7NVhPrXg2VPj6ROGKob6Nj6VehcQPpIBKuKi2OJ69Mo7FTp1NlejRgzVE1tGagZAahTtIUMlH+z2/mEXCu012vZ4Eu1vhC4f79Y7r1eg8huk7dtSUC4NUzIAnD+bLhrr/Khn2vT2hyhDh7r0738tSsuXR2HhSru736V/eHkvfbUYvfDkLC1eFuWD8pvaqvX+vl36YN8+lGFztH3DUw/BORYQ6erbfhKyof33sHvMlgOnm+yL9yf9ZxCc+6Qp9it5vhWah5XKr2T0BiM1mAKDKfAZpMDDxvgz+OHBnxxMgcEU+IKkAE4dOnF2QH+NCtOp2+wwZyC0dr6//p+vBGnaVAaHg9XIF+ROfv6CyVqibqEw960fubTnBDvegGDmTfLXv38+SKsWBbAew6D98xfsTz1ENni3AblZ9pm1oFkKmi2f7XL91xxmA/f1r39de/fu/bnAuUd/y1RvbGdtZmam1q5dq6eeeso3UWAqMl/0o5ZJoFfedelbr7tlf5vlw288FqCvAZ0NxfKGue0vzGET/8/+bq/2n0E1lPL2/FNB+uPfZoKFCbFfhePmbY+e+A99TPp6hMuR/uoPQthxHYhqwq9C7Abj8MtKAatDDcTZuXMnCwAf+nYCbwTqMGtWm/g0AO3dd9/12XWZkqZ9ZvanNmlpCxmPLng4E5l2TVsoscN575cVXgPnnGs66mMWBoPm7Hftb1PPM+jZrGcXL1780zrY6mb7rj28nFeOGtBrf/zHKr1zSxkoYq0APJm8eSMWfqPkBQby7TsHADJ4wS+ayXBUU+xwJnKdcDiTuJ81OGdhszSwhx3OPfC9+Mk/zucW5kfTwz6295y42Wsnfs6zvfdpHk54Hs1ntgjnu38/CavF56OLcY/GwQnvZxUH5/c/+mxhtLBbvrX4WbzsfjjvPxone8+Jsz0XFhb6FOesfM7F2mX69On/BzjnoZyUvfeuKrAwDAMqCsOKOBLr0QiUqUJzUDPActMf+6w6bEbzz56UX3uTcmbOULJBKmmZQD3R5H/KMNAEq1VkLMCq+hrVoexUfeIE6mYsAG0CgJk1nbJiFpFM6hO2h30AvsSCqrusXGUHDqqqsIDF/CQNW7la4WPGawCIwpS8YJiAgYCO6pvVePaMLn2wC0vEIE1YtkgpADB+2IUiD8Yqdq/ar15WHkppLeX1GoN6VxKQU+eDu6oqBe7yZ2ESVciIyGjU2lIUmZmtQOAzs4LtKytW+Y++r1BsSOPnL1bY0pUKGJYuN7yQmxWUQCAqg9egB+QurFDH4fO6fOq4kiaP1ejNaxU+YaI8KJENBJhaEothAFQa6GFRttcH7eS9/4F6gK6GZY1S7PL1AHyTgXywD/UzcM6MdkjDDiA/YLVi6th4+qlJy1YpZPlK+Q8DShLntdSqjWtUHTmlsK4+JUyYpKD0YSp8UKha0jCE+zcEiCoUECs6NVHRIzMUNnI48M0QuW6Vq2bfUdUANeasXqJYgKTAockAd6hisfAyADhErafgfurHgmLd3XsA2ClPY9MylLgKsG0a6mNYeXkAi+zhb3WkfQM1E1dhCUph+3X30kUU3iYqc/1aLFtzhPcaSlpm0GmYFv0nFkm9eMF3XbulohPH1VZRpnHAnKZGGJBFXmJnmRdIriP3vm6hSBdUUa9s4hiVhl0eUE1NaTm+XR7UfLAj5B5GxScpLDNVIZOyAbHigR9r1br3FEoiADZzpilt1UJsu1KBA8OBHx/mowDyXwCAhKelU5Xck9vnTrBolqCxq5dpiMGP5AWPLRpDAfmx0EVBI3GIAeBc6wFU8i5eVgAKdrGbNyhi1jQWf62jxPmoP7i4732ocHnOXlL5P76iRMpd5JJFCt64UoHpWJ2yGGfgZf/Vuyj0HVVd4QOFLZyriIk5aix+oJpbtxWCemloUCTxi+cepih8VAZpiVpdVIL6qttUSjmpLclTMgDVyCfWyZuVofZgEC1ASu48aUy7BWTnaetW67Fjuvv6D5UBgJe6YaOCF831KcgZCOc2/2Gixm0kTftQTC0jXwHO3cb2NzFeQxejSEjamyIbkmTUK0CNLMB7mtrU96BYt199Wb1dbRpH+GOXLqKsYCXLQp4f+dKVX67Cd/eitlWnDADRzOyhaq/IVyV20H3d5NEhiYpEXSgymXomKxWAc5T8k4Zj04tKxztvquPmZQ2dPkWRa5cpYOIY6hhkebwstENgmqIeMpbAtiVynTmp20cPyIt16YgtaxQ/HbgzDOkd8rJJTXmtTBlhZG0+Y7XGSxeUj1plGpao6SuXKXjyBO63qfWZzVUQ0Jwhob1qHmjQIdTmTt88CWgZoCdXPKklOYsAfsMA5/pV1PhAhy/tVVFLHvVbBukSoqtnAR9Ri0tASS8SpUxwqof5DFDYQx7q6kQVJyxSU7ADXjF3tRIikvktj+rdlTqXf1K7PnhHzQ0tGoH98fIFa7Qoa4VSwyA0OIobC3Ts/DH6WBc0euxIbV65RWOSxgNJhqAKRnj7moBnb+vIqYMKBpZ9Yv0OjYzNVmVjlV478bru19zXxJxJemr2UxqZmKOKvnKdKjypsyjxDk0YquWzl2lSxlj6/1jsAfvebwPwuHoCi3oUtsZg0Uyddu72JdV2YdWXFKe40DgFD9DHIV6mwGP9KzeryFFDYjSV+M2ZNEtpUcmUea9augF+rp3QoctHsTKsQOFljLbMXae5WTNR6uOcgXDVddXq3Qtv6GL+eaUmpuuZJS9oVAqKoxZ5ymCTq1HH8k7o/N3zGuIJ16Z5GzUyfYQK2x7o0OnDqq9s1JQxs7Rk+mqlJVDfcf/h9ZSH7d+Jo8XKu1dMPw6QKzRCEbFpShmaDlQSovEjgcVQlsvNbdU7u/NQJO5UBPVhLJBNHBbXqVkJmjApAqUnQLRY1B7rgvTdv6/UlVvhGj8iWC8+F62xwDcI5Fl20/2CDn37pXJs1+K1HfWjJbMA50r69OcvHVN/YLKe2jJeW1eGYQ9JvKgrarCBO32+A3W5O8AB2dq0Lh7oLMiyt157owM1m3p+JwqFqFhgN2zQUWkLAE416M4gL+u+lZX068RxUyMrQYHOwzkR2CmGAQ5FE8dYjcaiFBdq5T9o0NmT5bp5o505xzDghCFY2qKMNTxWIydGoRCH2iXQWhBN+N69Xfrmd68CsWfruadS2EwCRMgY3iCxslKgnX0lADi1WL5O1poVQ1DpatcHB/NVUSegzhFaAzQ4bCj9RtSz+qlvT5xp1c53b+r+g1p95ZnF2rIWxTk2nt242ar/+Z1KwM9k/frzMVq2hDkrQD9/gEYSSJ3AJiUlA8SvnHPLsVW0usMs9LA7xVpx5MgIFLdC2OgSAAzRy3kVyrtfSW0cCAwTjVpjIlZ8MT51vYmkX0KyH/bCXp1Dce77PwTqjw/UjsemaekSYFCUxyxN71wBQAIEKilt05SpQKioCN2726k336wnfaUN6xO1ZkMU13o4j0HV4oOufvx6L5a71UANCfqyWbVCx5097gKcA9hty9embSOw/xyG8qOF7uHRRfyKint16Hixrt5AtauDMgRwGg/IMzQd69xRQzRiJPa80QF68KBbVy7lanh6m9JSvIBzidhxTvBBJU4/9vPSd3XCY7G0Pqtt3CgpKfGNt2wDk4FyZr04bNgw3yYlmwdz4Bj7rvN9i8+jcbL3rR9s45VHwTnrH9tnBs7l5+f7+svp6RmqqTVr4Q5NmjhccSjZvfx6s3IflGk+8NeWrcOUnQl8SZvsxz28cdWt195y6fqdaygQDtVG7Djj4wN0J7dL3/jmWfUCM21cPU2Pb0sHrqE/E8CmCNpbb38g9qcuHTtbr1PnsRNFFTaUyZMY+plDuUdjM+M1jjKYku6v4oo2zkH58VYR3UgDYVE+jUkEVI/WrAnR2EMC26VRkdB8XbnhBpw7g40r9caGLFQaH9pFW5ekqsStfUfLte9UrjJGTdbmLamKiunTrj03sAuvwwZ2ojauzNaIoczj0GxSBHWNtYedewq07+BxbVq/QF//2kSf5XMFinPf+lae7hVGax2KlY9tD0TVCjt02nu5QuS6j3360R613moCug9UNJBp6JRQ+hEGxtGCAc65j7qxYm+QK7lXSU8DtY1/qCbntn5+Oz2/XGyvDZwruKVEwLmsZSN84JyfdZ2ob9xssK2/WK3ii5VKCcDmfGG8Qlkb8Us06B11uEaPOi83qRw4LwTZ3vSl9GsNnKM7XvFGJdY+7F9LiVbYJpQK55A/mMjy8xCGHn4bULYv36V66p7WRlRqe/oVSN8n0ENbR33ShQ2zWaQnoSSvWuyfh7gVsiREocsZ1ycxsvdnjqKLDRHM//ViYd1U36zYlTEozkXQN3rYF/Gg/Oku9qjjPKqkubUaku2PenES/bZwFIltjM11uv3VdqZJ90+yqaMvWNmz2EAzN0oBGcAKyeMEAABAAElEQVSQViEQjp67fSq+UKyuhk7ljDdwLlquCA8bQdpUf7BN6ctiFbTIpdLqYiyOGYcBnZrqnI0JnePR8uK892k/O+XXnp1xtxOGR8u0fW4Ps2s2BwN7GEBr0JxtKrf5eSvrnzROzu/b9+xv5/v2twPOXb12X5fvRAPYYwsdGK0h9NliU6M1bEQMyovAo9lBigbsNMXYV3aW0L7magr1yBPbsekGDA0fQn7A8nfv/gGgcZf6e6u0dXOyZk6P0MUTXXrvrSLFR8fqicdTtGQ59QzdUttM1drqpz0f0C7sIfOikPs7X4sGnIsGYPVqLzDdy68d1dTRifryY+M1e264Qti31tJaD6C3S+9/uE/DMwDnNj4G5PpQcc66w6aIzmwO/wHwc1VWFW1E4iT5J34eBOc+cZL9qn3ho5nHOmSDx2AKDKbAYAr8a1Pgo3XLv/Z6g98fTIHBFPhVSwH66mpFivzQ8QF9G8DpYr5H0UyArV/or997MVhTAZ0QKBg8BlPgE6dAablBc0z4fIDtELvqctg1a0Dmk9vYSTwIzfnS0wbLppL06quv6vbt2z7Iw3brmsrb888/75t4+MQJ/5Mv2CTeN77xDb300ks+9SKbJPikh00MmEWFLWqb4pEp0NmEgTPY/6TX+zycb3WeTVD/kInIv30TeI7d8OnsuPvDrwaxY88WIZis/DwE9OcIg4FzX/q9Xu07PWCuWSwkAM79zq8GONfBxP3v/Nd+vXHYjQ2StCwnQD/4Xogyh9mE9c+ROIOn/JtJAVsAsUWJN99802d9avXnli1bfLuerY598OCBD6qzc6wu27ZtmzIzM30Tn/a51Y1WpzmgjyWcvW/v/bLrukd/z7m2vWcPi4e9Z9ayBw8e9NnOmhqCqeQZ7GeLOva5E043i8JVqHG9ieJc+b3bqIWkaMGyJZq0aaMiAeeELaufF7UrlJLQFAaGSf5CgHNOeth9cBahLG3sb3s4i1FO+tl5j/5tr51zfpHJbfv+L+tw4uI823Wd+2dhfrRdttf2cM61Z+dc+569/mg87f1P87AwOMej4Xw0rE6cnPtl5zvfc84zcG43AJtZZBk493GKcwaGdt+8pfZL17DzrJQfKll+Pd3qAeYKBLiJQ7krdOoUNZWXKv/CafW3NmoUwFDq7AVYGQHGYjloU/WkOAGg/4NC0EBjvWree0+VR48oa9RIIKO1CkaZyqw7vT5/Ru6B7RgCsvMAiLnLzKr1iGpLC5WQlqT05SsUBjjnApwbwKaVERLQOmmCilzjuXO6CjiH8JDGLZunoQAwP1mVELI0aruMPeXJS2quaNKEBSuUMm4M9pIN6iwpQuGrlnNY/UZ1YMDVpyFAsGFTZyhk2jT1NzWq8LVXFMz7aYuWaggWmv587g32f2jZZ9arBs5hmerOL1PnoTO6fuakEidhe7p5jcLHA86Fo5oVEI4lI8sWvTSoAGZ+gC0DTdXK/2Cveq5cUUZWjmJWblTghGnAUGFy+/UBQlDuUO3ztPViJ3lDpW+/pcSOdiUsQclrGbaugHPy70ZFrEbtxK/iIOBcd58SZ8xSGGHvQcWts6AI+KlGA21t6uG7aGsoeHiqEmZOUnz2WHmKm1V34LjqWlExWrmARbvJPltSL1azfQxA+7GRxCQP+M4tLyBe3r4Dqr+dqzFpw5WwdrWCJo3D4hZCxFY6Ca+p9JmklBfKY6CoWB3A1DcvXlDSpMkagQKfgXMDSDX14ItoyzpB5I1gbLJU36bey7dUcfqEmmrKNXIW4JwpoWVlorzB9Xo61InF6e39RxVYUqfsyVMUPXMMygqd6kbpr6+Ae4jCkT9kjAtopxNPsZiZExXLwx8oq+PAWdXnPVD4nCmAc/OxjMXOFtthN2CZn5cFJS/5iT6dt6VLde/v1c1zR5TAgtnYNYs1hLzgRWEPQ14WVlkwJS9zJnmWOGK71HXohNqxyh2IQs1oywZFzpmBuglpYt3+QNIQGtSF8qH73CVVff9HiqfzGLligUI3L5dfSiL1CkBbOwoul3PVv/c4EEqholYuUuKKWSiStKsrDzWSknJ5UbfxQ5Wmh9/3JscqHjgzfNwU2q5AlR49rpryAqWMSNOoLavkBpzrxGY0CFgogsXiABflkJVYDxBXy7HDynvzR7RZCUreuEHBC+fIzygaVqy9pANRI0wEHsBvoKJUNUcPq+LWLcUCBg5dvEzhkwycM5tbFmYNBiU8HqxL+8hr1195Wf1dHZq4aL5izAI5PR61RdK2m/gXlKls9z5sruo0dPwUZc2YSB5vU3tpsfpKG+Vfx+I1AGq3u0OhCUMUMXmawmYt1AB21JV73lXb7evKnD5VUWvIF4BzolxZ8fdDWsOffGpKOO6SUrlOHtfd4wc0ADg3fOsqysMUBQHy+uqjhyuKxJN6BqDUg1JME2XnHpBwWnqqMlavUPCUCajVRbM4bnAnypK04/0i3QbqdPz2cZ25eZa84q8nlxo4t0TBAGQ9+LKVNpdo34XdKmi5q8yxwwHu/HXp1DW5AW+yR47yWeWFIyPjT3XhAZwz1bhgFrejwqOVkZKpMai+hWFP62IhtbKrSKdzj+nIyQ/VhkVyRtZwFkWXa1HOSqUPGe4bJ5U0Fenk2WMsLJ/1gXPbVmzXuKSJwLYhYH49auyv1/W8azoGyBsWEarH1m3VCACyyuYq/fjUm4BzeZqQNVFPz35ao5NyVN4HGFdwEAWy09iFZWn13DWanDnuJ+Bcr3LbirTv/BHdv3NPM1E/jARUPXvzklq8hA+wLzURO3qIYl/xIP9YDR1IfooNA8JKH6kc1OTiQmJ8gG1tKzZn5w/oKCBeZWuVxo4arS2z16LgN09x4anc0yGq7azSzguv6cz9U1w7Tc8t/yqKdNQ3vtizsN7fpMP3DwPvnVP4QIQ2zwMcHD5aRa352n/8A8C5Js0cP1/LZ25QOvFG58hXTXV3ePQgrwmArlU0Xz6QprkrDDQQq8vYLi1bEIXCHIqH2E9fu90KgNyqJmw/W5r91dqFohQg7+jR0VqyOErTpvSquzNE3/uuQU5BGo8q3VeeT0SdKQxRTUJKsbub16G//YcqbNditWN1tBZOD1J+Ube++QPazYAUPbFxrLatAIBBFNHUjGsYI59Cpej1twBmQ0doC9DVWhTn+snsr77Rpt0HGjRpVKy+tC1a0ydiNW/gHGqYFAVsaqmTKLrdKF5VAO7cutkCKA+4UY/CnNUxWG8nxKOkOTVccxYYGABwUw7cda1LlRW2gB/g22jZ7W5WemYUlqvJWjAj1AcL7N/fpG9/7zKW7GP19JPpbCqhbmE+qR+7w4qyXn2wt1LHT9Vr/qxJWrc6XJXVrdp7IF9wr9qwCsvRFfHAKGDVtPEGzp251KU33kZFNLdEX38exbkNaahw+em6gXPfLfeBc19/Lo4NKYA52NF6KIW0huSfIJ+iV1EBam73O1VS3Mc99JPZv/b3dykxuU9z5iRhl4sNe4g/qtKcl9umogIX8QvC3v4hbJuc7NIqwjl91hDBzegC1no/+NGHAMLS9h1TtGTJMGBXFLQov3evubG17VZRSSuKc6F69oVEYKQOxj11dC+8gI2JWrUeKI+5tiDqe+vyXETx6pUfd6BOVuGL25dfiFc44NzpYy6U9hrV3lWgLY8BRm5MVxxW9sbJWB+zn7YTjhq4slu37hHu/C61NASppzOUtq1dYUPaNW1muubMTQB+9FNNRau6UKgM8Gv9KTj36EZLp//H5T8Xh40nDFSxfqipPJlDQwrjJ1PKy8rC7hKYzcJsaWH9WDucONh79rDXzjnO3x/trzvv/1PFueHklQAAtBZNmgwEF5egH2Gne+9BkeYtiNfGLcM1YnjIQ0tG7IlvYY36+k6XLt+8BgADOAc8l5gIkJnbob/8qxOAcz3auGq6HtuRrQTARXTc6OehujmAQiT3sAzlytv3gMPyW31Kd12dwepq61MUsO6kCRFasHSIolGarGzo1p18rLIpR031YcDWYXJhZZ6ZjFrjvATuNeBkqp9u3nHrD//kDOK4yXoGa9Ad66zeQGUPcK4CePXDE5Vs5ixUes4EbdyUoKjYHu3aTd/3XpXmz54CODdao1C9C6fptuMG4Nxbe/L0waHj2rhhkb72GxNRpQTmo0z93bdzVVhMvYoF6tYtQcrK6KW/Tb8Nn1BXAd2Dw/RLb6DciV1z1Pp4hUymbaP8WlfDW4TS7/EBLOzb1ZfcpdRnsFkdB4gebDAjLXE77ep9AGrg2YIH95Q4GtvKpZkoAdMfQ6mTJlAe1LYagOKKL6A455+t1AWJ9Ado61Oog/toZ2rYX3OpWVWXy2hHsY1ekomyHEq89OMq3qqCMsTWPTVG4YTdbzqbVVC78/egUEsd13fLzdimUw1lDSjBAxLSPhrQ60+bPdBCXVLd47NIjzVpxwYCA+gWvASlvmVs4mDO0gVVGUwfYOAsYNseFMPqWxS3AqvO7bQblElvL3Fg7t1dAtx3sV+1eS1AgVLK4jjiyHwAKn0G/Hu6AtR1pln3T9+TuztQOXPGKG5ezENwzrrSgHNdwL/FF/PVUd+hMSjZRs9nMwggX92tZtUfaVX6kgQFzWMDY2WRGhsYhzH/YOXI2STmu9H8Y+XmszysPDrHo3/be06Zdsq3beq6RZ/zyJEjvk2XNkadNWuWr55w5hWcusG55r/07Pzmo79l37H3HXDuLpsCXEHjUICLZ9NRIHBzEMq23MfgfhRzXdq8KlnjUAxsYu76zXfLdAFoeerkdMDSUdRfAG2hWNO7ArFQ9eh9lOL6uitRckzRnGmRunCqU++8U4wrSbQefyxVi7HYDQcUH2BM3dqKSt2efu18v5t6vEO//fVoLV4UxfoBqqKH3CjNnqbPlaSvPDZSU2h/QihnbS3Neg9wbs+B93F0yAac26GFc+fTb2G8xK4yP8A5L/0wttdRL5G36f/YM6n9LyXVx34+CM59bLL8W3nzn8s0/7tQ/1tJicF4DqbAYAr8slPgn6tfftm/M3i9wRQYTIEvcgrYOKKBDvg+AIXvvObW9eKH8NyGhQH6D18O0mTguYcKDF/kWA6G/dNMActP33kdae+dD1W9hrMD8Cuoer3wNLL/6Q8noT7N8Hxef6ugoEB/9md/hr3I+2pjUdEOG5Dbbr3f//3f9wEeJgf/ix6mwvS9733PB2DYzjkbnNvDJg37sQmz3fjOQP5n/YbtujWbwy996Ut65plnfLtxLZxf5KO6zqM/+lOX3gE6a+/xagRA1p//XrDWLQvQEJQ3vwgHa5966newaj0HFsMk+QvPBOm//btfDavWD2iPfu2P+lWHvW469+M/Ea+vYdUR8lAw64twewbD+CmlgNVnZoPz+uuvY3Nx3ldvGjiXhjKVTWyWlpb6oDqbBLXdwgaiZWZm+upapx4z0NjOdRY+/m9N8Dr1rcFFzgStM4lqn9nftphj6nkGz1m9++yzz/rU84agkuR8x77vog6vyb2nV/7rf1Fl7h1lJSZhk7FA4zdt0ZCJY1lAYMIdOMbbyW52rHsC4tIUgE2bHU447HrOa/v786A454TNSQ/ntYXTib8v0Pzz6GeP/m2fWxo5E9zO+Z/2s4XJHo+ms7128tlHw2znOe8533Pes3z+eYiPk4ZOOJ3wOc+Pvm/vOe8737PXRUVFPnDOFgsMnDOV3Y9atZIQgG5N8lQ3YJVYi6pbrfqrUScrxpoPlaa04ZlKWLdOXX3dKrxwVl1V5cqeNFEpi5YoKGuED4YTQJAPnDPQB1vEASDSuvd2qeyDffRF05W0DbUrFMD8ULPz+RLZ0jE+Z9wFH9Dipu4oO3xQdaUPlIoyV9pqlNZGjQOIi3kIPJnlE20vfmZqvnxJ1957i0W9Do1fNEvpwK6m8mgUgaehWd3nzir/zCU1AA6NW7ZaqfNmA9mx4NjeKg/gnZdHX2mZmlGR7Ecl0uCgxI3rSQaPCl9+TS7AoPR5CxW3eo0CUlF6ozH0oujlBziH5g4Led2AQeXqPH5KV44fU8KITI1auxzbzulYbprNJ4tlAE4+mQ8WNbxYynrqUODa974Grl5QSkamItZsVeCUGQqgrvFyXUqRD85y9fSp+9YdVf34VcXWVSuaeiZkLZAdv2EUjqepUs3nLqj04EnFopqRzOJJ2IL5xB/4p5l7WN8oN/eyp7JapdRZDX1YK6JcMQYIKhjVjOpDx1RaVqiRC+YqedkCVMIA8sKJXwBqWyyU2mKin5v+KraMxUePq/LidY1KAL7jfgRNN4U87Kx82AY3w/KcLdxA3LuLSoDKDunOhQuKnzhZWUDVITkjWISMVC/AkM24G4QWhEWqWtrVe+0OdrOHfSprI6dNVSzXDxw90gfOGUjZfvM2Vq0oztW1afSChYpdjJJYEnBTOxBieTvpibpnQ7VaivP0oBJ1ucyhyl6yCAvGodz/64DOtxU2PkdpK+cD8JF2AHwKtE6NgRQ8Y5np7egGJPxQt0/sR2VKGr96saJIbz/AHTdAIOgZoJ2BcxZjU5yrVfexM2oFinNj6xVPnomcafBjhA8Chcnje5xH+rkuXFHND15RJLBW1OJ5CsVy1B8lRVIBMBCBtzNX1b2H/E5Zi960RsnrFvuUpDyt2F0Cd3pqeK5oVA1AYmNTrVIy05Q8j/uckqxSymAt6tlJqMKN2LxJgWNy5EZ9z4CywH4CQb7wM6Cjp0vtJwDnXvuBUolg0rr1Cl4BDBqX/DANuNdW9tGu4h6CWZaXqfbYMVUBOMQCGaQvW6kwLFX94mLlAWi0PGpmtAyi1Ee4rrzyI3WT3ybPmK341avJS8QPyNTb0SvXnXyVfrBfNZw7FBhx2IKZCsL2ztvdyb0DWq0hL1eiSomKWX9LjYJGjFLK1h0KJB837NurxovnlGpKlRuxS542hTxKOQEm8jMIzvIoiT1QUYkKzHHlHtxLGKNROVyhaBZdAwD9/LjXXiA7SwuCzd/AoF0AcVexm37jdSUBx6esXaXQGZMos9jEosBl6nRsHwGHwCrYU6djt1Bku3lRAX2BenLxU1o0AmvjgFDAuX6VNpVp/4Vdym+5o5xJWdy7IF04cQl4MZx6djE2nuMVExSBepelG+2QlRX6PSGAdxGoCUYGAQLyfjsWpXfKr+vE9cMqrCig7vSiuILtLN9fNGW5RgOPhQLi1qMSee7qKR0+RX06Ol3bV+/QlISZ4F/hgKkoJ/aV6MzVMzqP0mEc9+7xjdvop6SrvLFcO0+/p/yaB5qQOUnPzHkG4G6MSnqKdCB/v06fOKfhidlaBcg2JWsClqwh1HC9ym8t0ocXjij39l1NQzEwCdjyHHVXh3+X5iyZr4kjAB292NBxP4IpK0GmTkRWCgJOjQuJJ46xvsXblt5G5Zbe1olLR1VcW4xaXxcKb5GaPmq6Fk1bohHJoxThH6fGnlrtvrRTJ28eRaUlXs+sekHj0idSBlEN4r8qFOkO3Dqka/cvk66x2rRgg0ajvFLUmK8Pj+1XY3WLZk5YADi3WmnxGYTD0DnSHIvqfgC4VuZKDOoqr/SqmM2G17FULSy+q5mTErR5Y45PWcxcgBubBtRQNaDqCg+Wja26WdAO+NWt1cvitXkD9TrWuD/8QbWu3QrWhJGh+popzo2mz4cKnCXBvbxO/d0/YoldF41Va5SWzAxWQQmKVf94Rp2eVG1fi13umhAUIMnH5IkK1JCOnuoBKrtBVTlCj21N8CnOdaPM+SrqWHsONmrS6Dg9tz1G0ycH+ZTt/AKAsX0tJ/AoedyN6o0BX7Y5t7bag3IND0C6u3ebVApolZzSD7CVrWkzohhzo3pEWtSgomMKN9W1Lp0F4m0EtJs/O5NNbdQpwwJ16GAzynnX6NuP1jNPpmoRinNRLPy7AOdKgbz27C7R8dMNWjR/ujasGaK6xnas5O6j0ubVysU52rQWkHU4GDZp0kuVceBYl3789kUVFFfot39tJVaYaYqNwOIS2O8b3ylSfXuyvv5cstYAq8QBzrnJg176lf6olQ5QhnrY4NXKGLWB+YRq4ldW7tKNW6XEgf5C2hBt2TyDzTrRwAwANqRDWbGlAfGrHtA97nVt3QMtXBQHLJVNOofo8qUBff/lfUpJDtDjT07XwoWpCgujTnF5lHfLo93v9aqgqBUrzQA9+2KSHuR36/UfN6Ek2A0omIB6XBwqpeR71Mo8lNlTp1Cwe7kBoKVBj29K1wtAgBHkiTPHmft4p0nt3UWogmWzMScZcI5WgHbClDMHuH8D5INe7ncj4a6vxhKzWqqu9OjWHeyU8+5j/QnMtHUMsFe4PO5ulT64jzVxoxISUEOcMMHnWkAl4+vPPtontPc+y8P60R0dHb5xltmztrS0+DYl2aaqdPqEYWFhPuVkJ4wWdns4fdlH37e/7X3n+Li+rn3XwLnc3FzfmGTo0OEAbAG6dIX7OBmlv5Q47FRrdOPuA8p7Mpu+RmgCZTcmDMQEcO7SJbdefdOlm7k3tX1Lmg+cSyZ/3C/o1Df/+rS66U+uXzkVECZbiSkGd/ZRCq2NgUyzcsg1ugB16+sHVFGFfTYw2OUr9SooBJ6LQq1uyzDNWhjJHg+shbs8gE8DquFe11Af3btXgxpbraZMGA4Uk4QaX4By8/v1B396gvYmXU9jD7kDxbl0wDkPoFZZsVuHTlbrwJkipWeNA5yLV3xCn3bvva6rl0s1fepErjNWY7NCfIpz/rSBF1HUe+MdACXg6q2bluurX5+seOJn4Nx3/y5PhUVDgO3SAOeClZXZz9gJYM3FoxRI9RiqnxdqaSs9il2VqPBpKKkB9FifZwBwrm8/cBpAbl96j5Kej2MTCW1pIH0sUiigPUjuXMrv6XYA0VIljU7QsIUpCslEFQ4QmJM00Ex6XK1T8alSJfajOIdlbOgis2ql3QYyG6ikXJ/tVPXNUurAIKUuHa7ghcB7QLkVO6vlveTnA+eGbOO96b0+y3g/Vyj9YQDo4wDPl1H5ZdNL7KQohQ+nHgdqNeDNVQzwd7eF/gL1Wwjj/npUvYMB5xZikQzs5JdA+QZ28kfFt/8E5+7qVkd7F/01wLkdbJCJJfgGx7fT9zNw7gJzabmAcyMDUZzDznwESsBR1gHhOl3+6rrUplxUAtsbejR+9gQlLkCd3ubqrfk0Vb3rPXqAsllnRxdKzGMVC0DoCaZeuEVf8Gi90pYlY10vlVQW+zb+jaSPZHMXj4JzH1eGnHLzaT1bGH7WYXWDlWEbn5tF6z4sSG1uKSsri7p4oS9OzjyMXefRsv+zrut85vy+U08437f3HXAuv4A+0bRF2NOnqaYMl17aw7tY9V68W6G+zmI9vmG01pLPrCex850Knbt0FxtWLNi3jQamxxI4lE1f9C8PHhjAYpVeU1eZHqPunzk1SufO9uitd8o4J0zbtqRoxSrsmCm71sdoIq+/8Vo79uO9io7o17//zRgUMCNok706gHLjK6+e0OyxSfrq42NQU2VDDPVTW1uL3jvwnt4/tNuXNts3bAeyXfxQed5KmW/x0EYptCv2n6+u5Md+wWMQnPsFE+6L/7X/3cj+n3H52YX6/zx/8J3BFBhMgcEU+GgK/Kw65qPnDr4eTIHBFPi3nALMA6mWXUXvsGvp2z90qaTZS8fZT5uXAM99JUiTxv/iHd1/y+n6bzHunViyvvqeS3/zI7eKmCBMZHD15PpAfe35QI3NGVQwfDRPvPbaa/rLv/xL34SaDdidwwbmBnf85//8nzV27Fjn7U/8bOCA2U8YQFddXe2bLDQFo5qaGt/CtS1em6WrTSKaZYWd/7MOm1Q0eO7FF1/0qTk50MnP+s7n9TMbaZ2/MqBvfAsri5so2yDCMmmMv/7mD4M0fzrLIj/ZDft5Db+Fy8C5x3+rV4cvDGCFJX35uSD9wa9/8cG5plavtv1mr87fYJ8iWXLbvED99Z8GKSONhcnBru3nOUt+JmGzutMmN0258+TJk1rNYrmpzhk4Z3VUGapRpm5l55iiwCrs/cyKxz5z6jDbXWxqmrHYo33U/vOXHSmrZ22i1B4OQPXoe5cvX9Ybb7zhg9hM5XPHjh0aM2bMP7E+sjib4lx1fp7+15/8N9UBLGRHxaIGM1ETVq7TkJlTgGuweHEDBDTXMukejSVfNnZxwHQcj07gOq9tUtMB5+xzs7qdBLxjFiX22pnstfOdCV/7+5d9OGFzftOu/+hvf/S1ExY73/nOx53/yw7nJ72eEz4n/B8No/P+x13Xvmv33PKrE9+PO+/TfM/C5BxOuj/62t5z8rfzvvNsfRIrk9YfcRTnTOXDzv/pwffdpjLXxWJ1P41dP9ap9VXYGl5R9cWrSsZjJmnzFlS2UJYBWmtmETQlbSjg3GKFTEHlKzGBqXvUKUx5DDkMD4RbIGo3LftRr9r9PmCFn9JQjopahLUrUItCUOgCbHDz8MMC0r8Xa8vKSpXs34uV420Ws6OUsWa1wkaNx+bIVKwoSwHBrE7RKHX0qB1b5Zu73lRvfZlGo8Q1bPvjKMNlYCsFtFRVrd5jR1V087q6scvKpg5KwDoULx5+0to1U+xwy/WgFMW442oEEhrIytDwJzYrGICm+tV3VJ1boNjxE5SGQlc4qmkBgB7yN4ADQAyJC8NrVIP62PkLunxwPypA4Ro3b46iAZv8k1OhR4ifgXMoMPn8+0wJoLleFft3IXVzggW+VIWu2Sb/aXNQx2IV3W4FUTPlABfwn6uwQI0v/0AheXcUOnqcwjdt9am9QYGoH6Ct5vQ5VZy5rLTIBKXMX6iQqZNREYti8YRw8VveXrfcdS2qPnpU9+/fVUhyvKZh0xkREYsC4Andxuo1Z9xEZQFRhY7KlLBf9QNQ8PYRCAAM+QPOoWxXc+GiCgHFhiHtl7xwEdZYAIjJAIqWd4AjIRtY+ARCo551l5arG8WIe+fOK27kGGUsN/Bx1EMbTO6DhzwwwHmB5C//rm715+erhvNrr19TVmYmCnzLAPMmwS9xbm2Dak6d1e1T5xWDotZooLqoGdh1xtFZJCz+LiAnNmF4GqrUcf2Cbpw4gjJUqMYtWaaEMVPUey9fxdwbv5hwZaKwFoFSnj/KiX7kO88A9xGFGFMuM4ndhnMnlXfkA7kbKjRp/hwAPSxjM0cBapHnyMsDSMmYja5fGNBeNVZ6x89hFXyBtfJQpaxZp0hU0fxjSXvyF9wh95DFYhTnXFdvqen1t1FSLFUIIGHMpg0KZYHTSBJvN2WDdO08fBwQwqVo8lnUwhmorzzsd9n9w78Xu+NOtV6/qZJLZ5QQEqjU+ajbjUdFLPeWKs9dUxR5bMz67dx/4LYkAEjAOVbZuS3UB2ZVFoSVFN8teuUfFdjWjP3wEkVuJJ+nj+D+UZ4or15TdjJZNCCAgaoa1aNAVnnnLnBtnNKWrlA4SnB+CcCgKG95UV7BjJbyygJ5FdaJr7+pxvtFGpkxUsPWrPcBin4ouniaWlmovkK6HlUfSnVZ8+YqafpEFpmhWkxlEtzL68byt66ZMvgB1soX1El/YBibhcKzh6vzyDHVHD6MakwEsN8mRS5YpACD234ydPNwX/zMqrqlkXQ+ryJAOxf5Pm3mLMXOma9As4fnu17kcODlKPOkBXWStx81ymtXVfHqK4qPilHcqpUKwa7YP86ARmuvqRtQHuv1b1W9p0qHr5/R2RuXFeQOA5x7Uguy5gKxoS7E4mQhdc8BrFoftNzWyMkjFJMQgW3zFfW2ejRj4jzNmDBLqbFJgGikM0XKTUBc3OsQb5iGBEYh4BeiboCsYoC2M9dP6EbBVUWlRAJYxKixBkU+7GGnoEg5c9JcpccOo0p26WreJb1z7HWFpgRo1YK1mjt0sWIC48D8mpVHOI6fOQH080DDh43Qk5t2KCM+WWVNpXr7xB4V1pQAzk3Ws7O/pHEpgHNdhTpwf7/Onrqg4QkjtHLOesC5iQrHYr4XEuBBSyFWtJRnwLnpE6doZGaWzlw8raqmKk2bPU3zJy1S6pB0bPMYwxE/kp84kvcpBEOAAoMDUZrsR/2rJlenbp5QflmuouKwnIyNVHlZDeO+IZo5Y75mjZ2rodjRdqOudQpFucNn95EvpU1LN2tqzkwAw1g2nqF0XFeo/ZcPqqDyvoYmpmrz4nWoKQ1XUUOhPgS4bqxu1qyJ87V01nKlx6fR5jAHgvpibxuLyP0AwVZlEU4bf9Zjj3roWJN27kLdDsvPrZuw6lyOQpi5AbMAzW3Bzk+6c79Pew9369qdIs2ZHqqnsEuNpe/38o8A6u6GaUJOkL78dKjGj8WSl6LnB0SVX9ipv/0+ykkV4dq2OlZrF4dhX9qvv/iHiyppiNCKeVifbooC+qMVIT8/KPYA/rVp78G7qFuN0lPbsWpFca6btuwVQK29B5s0eWyCnns8RtMmAaIFk6GRy/QnnB7AKw9tTA91RU8nfRbuhb1ndqPt5MMLZ3r14ZEWdXVXaPWqbC1anABkBrwJ0GVqdVRDiK969NbbxT5FuOyMOL34NOptk0J17EinvvV3NwCdsvXMU0lavCII4IzvAc6VFPWgmJOPAmI9kMMcrV/LvaZfv+eDPEAh7E3HZ2vr+lRs7diAhdVdC2pMb+1u1Lt7z/N7Hfqd31ihzWsNIMOqFSWlv/pePuBeqn7tS+lauSgQe1WgFmuDfOXG3zf+7sUG2O2CLrHGknRr55qHD9fr8NErWF52ADqh0Gh2qwlYnnMvPBAKXYBorcy3HjrUqiMnzmoUlqePPzkVC9lYXbvm0fd+8D5jl1A9/fQc+uCAOjTb/eS1gltY5u0m7xa2YNMrPfuVVJT6BvTGj9ux8azUwtlRevypYRrB/TdwrgMVug8P9egfX69QM/2T53cM1wsvYEFNOp8zcO7dJrV2lAIv5mgLgBMcMsAp/SzKipc6vKsfTJD74VPWtXYSoKetxaMTJ9q0873r5Kswbd86ASXCKLoT3YBW99XZTn0en6Dx48d/LDhHQv3TPp698X/p+Gif1PkZ63Pa5iSzXzRoLpN2fsqUKT7wwzaJWh/U+a4928MO63c7fW/nPed93wk/+ceZR3POt9cGzt1njGZ9+GHDDJwLBIjrYGN6Jnbhkdq5uxKr1Lvc9zhA2LGaC0waTz60ND90sF+v70IBtO6+nn0ccA7bxXgU2fILu/XX3zqrnvY+rV8+CTW6YT7LX28gfV36bG6gKCtzXuw6A63fRx41sWH2W+nUuW5gl16V1xSgeJgNQJOgjGGmPkpTyPdcfK+H71281KyXXilXEpsUnt0cr0WzApVX3K3/+s3j6J8O1+NbRmvHhhANxR7YgK+yogEdPgY4d6JAGZnjAUetbA/o4JECHT/xAKXITNQrx2jqRNob4DQ33zl1pg8AiD7rtdN6cvs6fe3XpikOBTmzav3u3z1AMT5E61ekARQGKyObuAVi5047FASo6j4L5HeyUl3eLsXOS1Li7FgFx1p5pLrOx+4U28m+fCrNHMC6LwOMTaAuCqD/RL4OaAmW655HzYBvxeUoDo6MUdo8rL6pAw2+o1NBnYuVa267ig+VKaEhUQkTUY9bQr2aTrq20+soZb/FmXbVYMM8JDaYjTXDFLoApXfqgtI3KzRwSUpMjdWQbcCYMwCL6esLO9SBOuqAD9kYAAyrKK8ilmGrPZrPGEu4URZtu9GshlsNWKjHog5GwQQobkf5N2QaSoTYZwYCSCLk6wtf+xGguA/buN8uDdsSi+Ic/cgY62/TpyOM7kLgvgtm1dpIPyZMyQtiFEwd4R9jfR4Oinx3LkAusGN5XqXGTxinYXPTFDiU8Fi1xm93oMyZexlXFPpl4xeMVwL1kvWZGm42qfJEuVKXpwLOMddSXQJM1e6bH86gv+PMpTjlxSkTD3/40//348qzEzZ7trJq5d/mg+7du+fbdB4REeGD5qxOc6BaG4c733PqhJ8nNo9+x/52vmt/O+BcXv4DlO2WUKaGymPQK+3+jVIU396v0vVrF7RmgfWjximFccQbb5VSb9xC2TQJhdIJyhkJpB3CdSnDR8kXew90qau9RDu2ZGra1BhdudavH+/CGpj2YBVKrus2xGjocOoFskIlUP5r/1ijA8d7lIGi5X/83QQtXAo4B5e6/7BLP/z/2XsPMLvO6mx7nT5neq+SZkYaadRl9WpbNjbYuNtgTDEkpoSQkCu54COE/CEJXEn44AuQfPy0hJKQhG4bGzfJRZZVrGb1Or33PnNm5tT/fvboEKFfxjXYxmfbR3PKLu+73rrfde9nfW8b4FyR/cG7lwD4ZvIQpp41GrZ7t/2MkLA/RZF4Fn3BrTy8eRXqs8ypSYNTgRj7nbwKotNXer3MLQXOvUzDvbEPezE1ZmaQfmPnM5X6lAVSFnjtLPBi+pnXLnWpK6cskLLA68sC+EVYVGNR7EdRbtKj1sxTlLksSN2KAtOnPuTj6VXuoFJbygK/wQKs7dkvt0ftS9+O2IFTceNBWbvucq99HPhy3WUsjHKjn9pmLCC1t7/5m7+xb3/72yY1uAs33bivIszVP/7jPzo37Mmb6wv3eSnvtVinhQBdU8pKguQEy+kleG7Xrl3OIqIAEynfaf/n2xTOQqpzf/iHf2jV1dW/tUXQ50vPK/kev7g9xBOrX/5/I3aIp1Px16EY4LHPf8r/hoA8iYJmt390yp4EAORhYfswCqGfpK/O06LfG3j74tci9nf/GuZpeNY7cVb88//227VbcM5o7Te1pSxwkQXUXyXBOYW+njdvHk/GEyIPp7c2hT5VKGw5LxQKWzByUVHRrxwkWtTTQqhUr9TvaqE0uaj5Svvei5LqnFfnTm46f7K/1aKtnDoPPvig86SzFlIFKl99NeAFKg4aF7SvjtErzvuW0yftW3/7NxYCkK4OZtuC9DxbsHCpFa9ZiQMAcGgKFZ72BvMuuswylq8xLwo52pJpSOYvmV+Bc4IP9Xnz5s2vCTiXtE0yTcnPL+bvyznmxZz31drnlaTvlRz7aqX/+c5zcdr0Ofmd6ljyffL4CxXnNm0C6ECR6WLFOSm7DJ08gzLEMMoF3H/g1I0M9Frf4eM2eOQk4EaRFdx6mxNqcpiQxR07d5qf+U0hcGwQKM1XXsravZcn7qdtGgWynHmzLcgkdGr/ERt4+FHrRPE3c16llaxdjdpDlSXS01B/cKOU5Lc0wmJmZQIejY1Yy/aHrOXwXmCJiFWvXWMZNYvNh2qSOw8oKDMXFo1n/ycJidPUaG1PPGJdz+0hDJbHqlHGcmAnplPxRoC4fXt5UKHPMhfXWvHGdUA0aTYewhmWETR/FpAr0kDxlk4L7dxnA/UNFp8LOIciWBoqVBOP7bSzTz9jIUCh0nVrrVDgWg7x3FA+mgJ8GQWcKygvtHRCm06dPmOnHnvERrs7raKs3IpxCqfNqQSiyhSTBS+TZmmAgj7UsGx6yroevc+mn3rE6ffSNl5j8WVrzIeSUxrglSdNqm9AD0wpXJ0dFrr3RzYFqDKBgzlny1ZLX7aEExLaq7HO2o+etHB7v1XXopQH4Cd4bYqQpW6UdLxZOKRRx0gMTlgnymh1TecsHdW8FYSVzigqcUCs5x7bDiaEc5S+O3dprflQYlH5xcZQA8Wh6C/AWQPPF0JV7OwjT5ivC1tWVlrOZcuBvwChAzgkCf0biYQtvQq1CymSoTw2/uRTduKpJ4iqidrQmo2WUVtrVlJk3sJ8wlWlW4Ty88aAnpgXx7o6bGT3Xut86imjRCyLehRcCThHfiPtXSgb7rN2QK65VfOt6uorLIZ/aDIyjJ0ycQSX4mdEiai/y8ZOEHpz/7MWAO5cuOUqy15OyF0U21r27nbCwJZWz7bixYssUIEqKg/KTAi4o1wygG/8mYTdbDxrbU8DeO7bZfNmlVn+SpTRahZS5/JxkOIQJoSaQnEXAHS5CAMcApprf2IHsMa0la7daPkrlpu3hNCnBYTtQtXNfX7yFK1rBiR8yroBCceAuAoZ8woX1AJhouhFuLoO4MVEUxPKNWWWfdXVFivJsxAOW7+fULlpqLmggBYZnLS+Y8et/cgBK6cOl1/BuLJqno10SJ1xj4WbCWO7ZA2KiSsJ5Uv6gEujwK9hxqtgFSGIAVCjdads6Jf3WdeRwxYvnW35m6+2gnlL8MWmUYZhHLRTlB/qKsgguSbC1v/MXmvZfxCww6wCW2YBWHoqKmiHKIilA7n6gCWpa5Ghbqt/5FFrfuaAZVJpa1YDjs6vAdijnKkLIygGttSdtbyauTZ7w2rz5RAicXwA/hTYJJvxkjCeicFxG3vqSZs6ftimmSdUvOfdlj5/roVRhet65DGghx7LWnqZla3bbFkl5XjoAWPp76Y8KD9TpwQAxZvqrIdwtD2trRagLZWi+phRA7CZk21hynqSsLHZgHFpJbk401FAOnHYOr/370RjS6AWSPtbtQzosNx8mSXUjyzaktfCXtQyY5227dAzQG37gYAB57beZZfXrLOAF3CO/rK+u8Uee/ZBqxs8bkvXLbHKuRV26vBpoJsGQv8V2fJFK6i71ZZNnVOo1knCSk5OTFqWF2ggdw7wTIH1jHXbPkKP7ju8mzCehJzcusGKCktQeUP953QdCl9ZtmHdBtTdVqLKlgt4dc5+uuO/UF9rturyGttQfbmV5VbYSILwb13H7OChQ9bbMWLz5y1ELeV2q+Jc7YNt9tMnH7D69mZbNncFinPvs4XFC6yF8LCPnnrYnt6xy6qL59u1m2605dUrLIOQv9Oo557jWo/tedROnTjppGHtqtWorO2zw8cPolqWZquod/PK5lsWEl/eOAqLuu/lPs/vyyU8aBl9XLYNADbuPr7Hdh3dTdn4cAIvs1lAw4ePHAVGaQJKKbNNq6+0FbMvo957rHn4nD268wHqTTMw5jy7bNEqK8mvsAhN8HQ7gOHxZ6x7tBOIb57dctX1hIOdbS19DfbY9m3WR6hWB5xbT3jbgmLqd4zj/HbyuWEbGwwQpjSdEH0CKD02BEz3zO4+e+zx/Ta7uNDeshWIeIHfQjZsWTlBlIeC9INuFKB42OuZkJ1uaLMt6zKBVwotJyvdvv2vPcB0UpwL2IfvzrRFCwE8GDZh9ayheZwQp808zJhmt19fbDdek2XDwGnf+VmjPX2wDzW8dLsJtaXlC1BSRa3m9LlpgMdmO3SSeWXlMoCWYrvhBkATgJN/+49eu//hPrtscZG99858Wwk4F0jDyC4GF8Z67RNBja29Y9LOnBjEkQ8MSjtW2NgxwLIDqB/tepYHOmL9tmXzLJs3PwiQEEG80cVL5I6HsHFRe/ChRjvGOtKi+SV297vybPlin217bMi++s+Hgdhr7H3vLrOrrjkPzgH6NDSEAOdOAwV1s26y2a6/LovzJuzJHR326OPdwARB27K2yDauzsDuAWsGvHn4iS7be/AwyY4Qmu4qu/XtxVaIstxRoJUv/jOQ8Vip/eEH5ttbWMfK4ybbpQ6IPA6NMfduC6EUSJhI1B5zAcQD9CFT3LPufLrT9jx7FFgwYW+5erNVz5N66yghW12E5ZxR4BzqidlTOwbs0OHnbMniPLv1tiVWXpptR3iI7+vfvo/25rO77tpoV11RbFlEbAhHooRqjdvPf4JSWtMgddZld39wDm3XYw8/OA3YcJo6ELIrr65kzl5omfSJHR1he3zHkD20q4+xJWEfuquahx4JjYuq1a6nCNX6427qXCNw0yKgK6CgAvIXR7mW8td5z9ShJkVf6E4jlGduJqKdACXUmf2oUz302GnCSmbbW99Sa8tWEPoTZcaAp53xYeJXinPPF6o1ec+RnAu+Gn81v9SWnGvqfXLOmfxOfxVpQQ9tHDx40JoY52bNmkVI3Q0ONCcoJrnvpY7VOV/MdmFatH8SnDsLlB9lrlYBWNvZRQjtZwl3u2KuLVmWY0/sHgC0PUm9H0PBsYZQw3OsJNdjoZE4Ya7DhJRGPW26wX4PMPKmmykrgXN1E4RqZcwfn7ZbrlmNgtRsyyOEp/mBOVl7nSCvvd0TwFDDMK0qQ3D/oNqw2f7DU1xz0noGW+2KK2bbwsUZlpVO+HZCF2Sj9OtxexineJDq6ID98MFeoM5ZdvfNebZpBYpz9WP2l//nMZtwVQHkLLNb3p5ms1Ct0sMNLY2Ac9tQPd12yubPX2nvuL2EdUI3wE6fPfjIaesbdNnqVVW2ZnUe7cyPWiPPbOwZAZ5jfG85bO9/9432+/esAvim/2gP29e+dsIa6rx203XVKCOmozLNPIm5TEzzQeDTxNGY9dC+O3t7zTUn0yqWFQOpMkmkD4qd4/7hqWEe1pi24KKAFdxTYGn0ITEerpgYRv0NeDFR57OhQyGAr17UcxkDgYu8swjljKKbm3anZ0ymOiLWtq3TPKcwLZx85hr6qyrCuYa4X6iLWw+Q2wCq2AVlOVZ+fbmlbUpDHdns3A+bLHIgbmWVtOEb0xnTY8wzZCfSgOrfOGvl0YOEDPZNWTpAYnAh9xFARlP0Kx2Hm7gHGCYEbrWVzOI+A7v1tEIwoQiat5I5xmxU57xxCw1M2vBBQssT9jXui1olY0Hebcyz04EDsW2sD1CwAYj50KR11/VZsDzTilYXAM5hQ24fvPS5fmC+CAqjHYd67ciOI1aRW27zUBjMmJ3utIdYR5QQ9UN2uh7wE2XQJVtZX9jM3BLb9JL3picbrRQIK32j31qYf4wxH507l/kVDyf6/TwQQttUu0puF75Pfvfb/Kv2qDRpPSXZ3nX9ZLvVGoseON/J/aSiwujeVGshWoPRpmMuzMOF750dfsM/yWskr5s8Vt8nwbljx89ye7LeygpnWWaANR8MfawF9bjHu1AYPWI3XFXDGF5judzv/PBHLSizHiN0d77dfudyW8j4ncb8M8Zi9rbtIdrcgE2MN9NOa20dY19zewyFuCF79lA988B0UyjwuYR9Hef+p+4c/cyDPXb0TNRqAbf/158W29ars3gWi4hUj6B6+YNH7bL52fb+2xcx/gCYAnQP8ODLL7b/zB7afi/j3Cy742bAufVSnNNDWjyc4aIjAqAje9hMxuNFE3i5Wwqce7mWe8Me998dxwtnYWYS8ML7pfZIWSBlgZQFLrbAS+lrLj429TllgZQF3owWYG3GzjbFCV0Qsf+6n5sHnozMZbHojmuB54AyFtS8ghnvm9Ggb7I8HzkRty9/K2IP7OCJORZRNhNO4pNAc9deTvhL7qNS239bQIt3n/3sZ50n2nTDfPEmmfsvfvGLdvPNN//qyd2L93k1PuvavSw6NTc3s+DFk/W89GTsJA7I59tKcOR+9KMftY985COOqtPz7fdG+H6Yp2r//adR++fvo7TZjdImT9H9ESGFP/5BPyGnXt/zKIFzt3xkCsdHzFjrt49+xGd/+vs+B3h+I9j+Umk8ciZm9/xJ2I63xA0/g91zp9f+9pN+ywUGfH2XxqVyk/rut2EBLYSqP73//vsd4EyLkVmE4tPTztq0EKp+Tk4TLZZK0VPKAoLl9JuOl5PnJkIrSqlOynPakouazodX8R9d88KFW8HMupbg5nYUrr773e86ym/VgMkf+tCHnPCyWoDW9msLr6S7gfA///dv/9ZCnV1WRb6KcOIX4gCaVV5hOfm5hLSZtJHhHpvzrvdY6dZrXpLinBaLFdpWinOX2pJpudRvqe9SFvhN7Uft9ec//7nTLgXOXSpUa5wbkrp7H7Dp08ctHeBAajah0DThwEbAk1xWuWChZV9DnQYyCLc1W//eXTaIklmC4wLZORakD5BaymQUZxvAf+Xb3mpZqFDGAYRCADsNu3fZaP8A9RtHOnCLCyhtykUIKCR+Shcst0IAWw+O977jz1rLM49brKWeMG4Z5iVkZjYARfbSleYjbGsY6M3HNd1jozZ57pT1PP24jZ47iVMKBx7hCXHRAOMMmZcHFdIBkgquJLQiUF/XudPWdfIEoQUBtALpqD6hYDEesom+ISADQAkg3oIrLgcgAmaoO4fK3jPOMZKrykGFIpiB4pY7aKPICo2gurdowxorBBiLjw1ZPw75LpzD08zt0oDfMgURA58QscuCACRlS5dZ5vwaB94b3rvThrc/bCFCxVpeOcDOHPMD3JWhxpUOkOZC2S6GA8Q1geLf8f02vOdxazlXbz5gsWzUZaYB5wZHh+CHXFbEsRVrNhGGK2DhhnPW3dIAfEMozXQv4SFRW0LBY2RgxCYARgpRJJuzcZOjqBft6bOWp3dY//GT8BdhyyLP6blcF9mPacovoxBn8bLFlrGgij4NkAq1mMFDR2xwoJ88BICQ8ghrGkB5CEgSUKti63WWvWgxMFXUxg8fstNALBMdqIoAR6WXl1miotgKUHzLAKCKYEuFFPJKwnds3ML1DTZE3Qidq8NBi71yAPj4fXJiDJBi1HwAf5Xr11v+grnWdeyADVM/veQtJ6MYOMMHFDVqobF+G0XZqrR2EYpj5HFutUWmxm3g7Alr2/k0sRf7cVQDylF3PNSTYY43QMiKVesAPKuoM6SbutxAuhN9QAmAQGmEC3Wz/wT5GyXkZyYO+EWo8/nk+GTfdkAtwaDplEsW9T2tstTSayste0ENgBj10Ity6OgUdamZOrrD2lAQFK5XmIcCCecbHgbmCY1bYWmBzQK2DFbV2Fhzq6Nq6kLhLujz41D3WRjluf7hQVIYs0rsVwqA5p03x2IAdoNAqd0HjloUACBImNZgNvAp0F2Ecpgmv6WbNzp2T2DHCOHFO3lop7+9F7Avg7WGAmccjsRjNk59SZ9fZaUrZgDRseOnrHXXbhtsbnTqVhAFnBwUzPKXLLXgPB7gQaHLBTyXCA/ZAOXRCUg4drzOAkACuZSvH/tNAu9N0r6ChDQvpK1kzC61kbYmGzx2BBpz3PzpmRaj3YWR/5ru6gWc9ALILSVc7zXmqyhDcbALJ/VBqz/wHCo/Y6jyA2Nl5VP2QAb0M1PATQXLFlouoYBV7yZPn7LuI4dstK0VRSaPZTKv8DA2I8IFDOWzuStWAjguNU9hDs75Nuv+/r/bdHOzebKzqGPFFi8us7ylqy1zIUBhQZZF/SgRTvehkL3TdgKLuVEse+fl7wCcIzQqTtRpgIDGrkbb/uwjKJ7V2UoU2JYsXmohnkB57rnD3OO1OI7LLMpFY7rLjfIfKp5SZZtdQnjAeautFGjsFOqaew88Y/2DfbYI5cwtW1CQCxZaIw9WHTyw19q66m32nDJbs3K9zStf6ASR3XN6B5DVszYyOGr5wSIryi1GBZNynCaENfWqmz6tsmquvfuG26w6vxTQocPu2/Gw1Xe22pKa5fbO9e+wGiCBTuC7p05tA5x7xipLa+yqzdfZYsCtNJTipKjTSGjLJ3Y9ameAg9fTd1yO6l9XfxNKa/utrbWZyR6h7egXM9MVrnISkClkXsKYlpctsqWLlzP3C9jpUydQN6LvoC6sAJpeuQzQl/Giqa3e9h3aC4DVYnMra4GsrgEYoS16I7b/9G5gmz2EVu21HOp0Fv2Im/C3Y4SobCM87sjkoFWVV9tNV90EuFdpXX1n7IknHrXetiFbt+RynMnXWWkh/QM2GR7x2/0/O2EnT4QYxTItmJZF/UhzHhTqIVReIjpha5eV2vzqQusZGrSjZ+sBEAilmJbDb24bGg6hhBZlnppm11xRZFdspN7S937ru3V28HjUFqNgds/7ylEyY47LvFLwXGPDqH3tW8DNXWl283Xl9rZrwIJRJTtwasIefLzJGus7LY82UppfAgSXhQIbClcoDDahlDq3er7deVuV3XAd6pbU3R/+sM8eeLjTFtcW2LvuLLXFsNNpQVQfATl0lxYB/pBKzsH9vfbQg3WsJeRiM4AUnxuwN0poWrQR40GrQf1o6dJsGyYEdnMT4aY5JoM25aZ/Gx4aAy4asIzsIrtycwUhaYNWVhq3Rx7ttK9+7Tkr3/J8TgAAQABJREFUK1uG+tYc23qFzzKlOAco0NwSsnvvP2VP7ey0K1BLvvEG1HSAYM7WTdpjTw6jOtdJlqdsTglhiVGMHZvOQXFrEpignvY6bH/8wQ12042ow+W7eNCmn+scQpmn0D78vsW29XLuq1FocgEUxQEBOgDfDjw3YDt3n6C/d1GvSgDoAoCaMR7Q6WefYVuwuMzmLZhD6EspnHVYGPUgjdsB+rrRwSgKg8CChNXbelUhIfEA2blfee65qP3rd+61wlwvsMMm23plCWG+6d8h3o8eitpPftwLYDTEAz5p9t4PzOW6PpSRsMu2Rjt+sh4whzJkjpGXkQPw7AIOTKDqCKCTGLP3vaPCfu8DhMVFbW/PDs5FyL6hkXN2Awpgt9xSacUodZE7yobhqVeqZM0AVk02HGL9jjlRGoq1Ufr+AdryKA/4LCQU95LaUlTmRqyx+Thq3lGUAJkDABsrVOtvE5zTbDt5P6L7qaQys+ag+l4vPTgqpbmjR49aD3NARVCQipSUsZLhF5PnSM5dk39fymz+4nPo/lLXEzgnqL9i1hwnFOqhA/226rJ5tnptiTV1Rggx3G179jbZdMjt9BUFPHCgkKTdAwEULanj4dP2/ndWsiZYzjwLsPHcmH3lnwDnRlGcu3o1oUwrjeY7A85RT4dDUQDjAdv20BlC7TL+ZJYDkjKOUH87uwcNdtsqgKNWrcmz0bEBoLdWYDnqZCCPfiOLZyoIVzkwaFPUh00bq+y6qwpsboXLTp4esb/+wkN8P9tuu2klIZEzrQTQjErvhCre9hig8bazgNLr7R23ldrSZV5CJocJxdoFFFzPPXeC/TNRUuMhgHCGDfYRoplw2S3tB1DPvNp+70PLLRvFq9a2KfvmN/ZbU6Pbrn/bUrvxpmwrRwXNg9KZi2t58E8Ya3bjJ8es43QX48AYKssZlp+eZWkut3lHeZCC38dGxixQ67fZKFQGCREb7uVhkTM9zNsmLa0rn33ob0b6UFUlnPds+tISIjhUBS1rQToQHcpi9C1jh8d48GUE1dUuIPwY89MM5utZhH/n/mIgYuP0V7lFuVZ0TYmlrQ9gC3wp99WjPByykjko4d3Agygr6Ih5gMdQnCWKuU3TnkP7pgnr3G0RQmMGCwHyALW5YbEx1HFHUPYtLUNBe0mZAzIONAxZX/8IKrtAdsw1fKjQhnn6N9aH7YGwp70TVnVjgRW+lfkWdOTgqSmbOMsMmpC7/jaiavDQSoI5TlpFlsUrUOurilv+QtRWy7Q477KJpik7tf2MRTrDls9Yk5+BQqsUralHoZFx6xjlvgk1s9pNi634csYi+pEOwLlzTzZaJf1U9jofyn31rCf3ETK01mpqapw+INketPai9y+nTb2U9vdC+yoNeik92pLp03utzUiFcu/evc5LAKCU97UWozUjbWrPysPLyUfyWjpW75Pn0PskOHfgwElra66mjhcBwdEHMFfsGWHcHJ5kDJqym66vsnVr8m162GU//UkLENwxFEgL7FYU5xbX5krEnPI32759BHAONWrmVO+8bTnK7jxIRpM5dBTA/GHuPXvGuf+Ygdq9fh5UBmrvaA8Qpj1hlcVx+9OPlQBu5zBOJThX1L7/g19YaUHErrp8ttUuBL4MhG1wpMOe2bsdwHYPfT7qk7cTqnU9Sszca1hCa1bUZ21Cmqimv3rpu5expcC5l2G0N+Yhqi0vd1NtS20pC6QskLLAS7HAK+lzXsp1UvumLJCywO+SBaRIcBr1pe//kCcSf4kMOnL/xaj+vPs6j3389/02ryrVt/wulferlZdOpOe//W8R+97Po9aOWmENCxwK8/uum1H6YGEztf26BaSM9A//8A924MABB+j49V9x9vB06Gc+8xknLGrySbeL93k1P2tRUQDdIdQBfvKTn9iOHTucxT4tQl5q00LCpz71Kbvzzjt/BZpcar83wnc9Awn70tfC9r0Hoyzim83nqc7/58/8dst1OM5mBJpel9kQOHfDB6cI2xQzHtC1P/6Yz/7obp8TZvt1meAXSNQ4YTs+8dlp+8+HWeRjUXfNbJd99+uEOpvvZpHrBQ5O/fymtYAWHeX0l1qa+tMkiKa/+k0LpElHij7rpd8uBOt8Pt+vQkYKuvuf2nRtLbxqU7q0cJr8LGW8I0eO2He+8x2nL37LW95CuKb3OI4d7ZdcZE2mLY4X8yzKK//4159DXarLFpcU2xwUBfK4RhGPAxfgjBUQMuWP2yzOk7+ekJSXAPB0PqVL53++UK3Ja174N2nLC79LvU9ZIGmBS9XZ5G9SnLv33ntfEJzruO+XNrlvJ7GGhnH0Ey7Tn0kYpSzLmFVh+bXzzY9qV0IKkSjDRVvO2TAqUZP1jebGWeYDJogRejAmSBYHSOG1b7P0ObOBayYt2t9pQ7Sd4eNnLAogoPhVCQ99RQYQEQBH3mLC8S2cbx4UJiZ7G23o4F6bPnrY3Dj5QyjSZc2qtoJ1myyweo1NAd35aIv+KKFdAYqmThy1EfadaG+xaRylCnOq/iUHh3YuIEtw+WX4FHwAeUdtmPkW3klLm46aH6WXOPBClP4noxowbxmqkXNRqUIlLzHRh3Kk8nfEQnVnCHGJ500xOH04OLl+eHYFClprLadqDiZG1Yi53OiJEzZGKOfpnm7CiDKg4r2YIsxVFucsWrkKMAlwLjPLws0NNn4A6OXMOZsaxRGHw8MDeFVxxTrLWrKIfYgViBIc8dvIXxf2PYbi2DGLtnYBAxLelP8iGaj0zcEmS1ZaxtxaM8I1hU+gDAhIGA5RNqg/SQXED/KoMLe+BbWAgZdZOs5rN2WUwNEXqj9rQ0BuE0CVXoFMXDWKAziKMzRzHgoLywFo5gElYY9IWwv5O2r9OKNDAz3mmcbxCJzkhIYCqiq8+mZLryZ/mCjc2279x7D14VOWNjCKqpjfwsV5Vr5xNUpwS2y6oIiy95mfObA3TB6pO4L+xlFVCzW1Y+spgBHUqwA/rLzEsi5bQpjVJeYHwhnEbhNAii5gQP80IQopwzCqIvEsnK5lKMWpvGsWAD5lU1wRnKzdNrJnr02ePC0pVMRhcMKRxxBQgm92lRWu30DZV2KjdIsNj9gw+42eOIYSXrv5J8cBGL04L7Mtgoc8uwan/5VbgRL9Fh/qsNEje23wKOXSDfVAjHtXRZHlLF9o+cBBvgLANuAjF5CXwgpPnjtrA0cP2mTTWfNBrCiabhgwDo89MBdAai1pZvwInTxngydOEea0z1w8TCPlIhfqZpFsFIiqUSijjWRSV93AWAnyEkGNbYSQ6MOELXYPDqCKNk15oeyShQO2GChl8xUoLqLgR9kaUFDoFPseO41aY4dT5l6cwYkg5y8CogN8zEM5T5BcBPBg+PhxnN3HLNzTS3hRt+Wi+lWwYg3qkoQlB4R0A0slXCGLjPZaiHBXY6jLTfDXPz5lXmCeKHCg5RWj3kfdX7YI2sdD+zhl47TBOA5xKAVT1MUI7duXTsiz2SiXoX7oX0wbzCZsMJBZuKeTMMonbZy25SavQcKheVAKjONkTlRQN1AvywBUdAPIxQFlQyjrjdFmRwD+fLRBP4BdGGdoFICmdA31bylhnwkzHB+bsNFtT6A8d9Jio/R1CRSGCANbuGkLYXfXma8QeBOn+sDUsD1bd8ieaz1pvkSaXbtsq60orUXtCmd5fNLa+9ts34l91j3Sg4rQEqupJMQ77asFoO5s00lr7WxDrRB4kvGeSkIozxgPpeRYzZwVVlsFTOhLt+OoJdbXnwKWCaJksprfagkxmgnYFUKR7aidPLufiHJhW0j5LJhLfulfe8c67GTzEatrqLMhnP4+rpkRBDDLoH/CGX0aGDWf8n/322+3muwyGxjstx0Aya203Xmzamzrwittdl6ZDU532+GmvY4SV3lRJVDbesKGAX56UMij/+kZbrfnDj9rDY0NAFsrCFG2jm5lglCtDdaA0765UY7iKeruJP3KKABInPzNsuo5a2wxgB4doZ0AlGw41wbMVWIrgf8qKwBKoMvGJ/vtXP1x2084X78vm/CNm1BRme9Ahj2jXXa68Tk7je2HAZMTQFLp9ItZtOuhiV4gr1YrSC+xG6+6A3BuHiBGvR3Yv9OBH5ZVrbNVS7dYQW4hcyxUkIAtHtvWSh5HCX1JhZumXroYpyiRYIYbhbUgynmZloMH/DSqQzv3K08osUVROaIepwOpFREWd/nyXFu5NBO1PA9pRxXmUcJBNoRtVkXA3np1PqEX/QCxKCdx3rbmEA+WdANeBOzyTbmEggtagHWZAR4cO4Ca0P593dbWCAoLyJKbTRjFTPoklB+PA0iXAgrfflOVXX9NEEUkI8TouO16ZgSYIM3ecm0uoSYZhlCq8gBIyzNO4GsUyAgpe2TEntrGeTtozxH6eH5VR5OZGefYbFShs62YEI91daN2jNConajhgM4BJKKGREza3LwYdbiA0LmEkJ0LLBKYtN3AK//18y6U28rt+qsLCN/qBZbhmvR3HV1TtmMn9YPrrl650K7YnAEcBWg4Ercjp8P27H5AwEYUqADB0oCLC4pzbHhCIS/bgI9a7aO/d5ldf32RZbHe2dg4RijTRjjqHFToygCc3KQb8IPWLnCudzBux06P245nWq2nK0LXkU2adZ8apd3ErKrGa2s3ovyWk2ZnTo0yrwd66SEsMiqYPsZglUouSqBLFmXauo1Bm13t5cEZ1l5R+3n4l4ctH4jnLZtRzl6agVIqfRLjT91Z1PO2jwFdhWxhrc+ue3sRNvI5yoGnzo7Zrj2dKAUC/aoM09Kdc4wTFvJsK8pAox32DkJ8fuADKM4FXUB4McLe9tj4VLtdcfUcFJVKLBc1KWqjhnngkYSjNvbsoW5HpWiaOupmbFP5Z2VEbE6V11ZfVg4snmaNKJqdRQW2umYUaC4G1FX4moFzupcS/KJ5wIVgjO73Ojs7CTP7lLOuVc4DFOvWrXNUsQT4ad/k/aKO1Xn0XfIcqrovdtN9kbbk/ZrOlQTn9PDWbNR/x8YCVgeAtmhhmdUuLrQQarx1DdNEfhiw48fHCa/I2hPznBLgWORs7XQz08T+o/bOW2bZTTdUWEmpF7Bs2n7445M2hSLvljXz7MqtBbD3KK0xzxBsM0ao5HPnJuzxR9oAY1Ghi6IwzPxLrdTnn7A5lX5btTrfKip9qO8N2LHD3bRBHo6Y5oGPBOF3aYdBVOhqFwewVQEwVDrwboJ9p+wH/4H6WDTbLt9YYRvWBgFCHVYMGGfS9u3vAprtR+2YNBEWtHYhbYZO6EzjNKGoO2jvYwDspAT54MKcTGDWAODcgD134nG745b19vsfXm459Asd3WH7xX2ngXm8tm59pW25nAfDUNqjdzBvlFeY9jiJIlc/YUwJRd13jgcygFGDcWDaNPo+5tI+wK9RxgM3a0klqGcFCmlnrSjxHUdt8cyEBQYB2kLMtSLMEajcHuYQYcKcZtQGLW85c7J5PDAiEInwqVMnJ3gwhzDs/cxNeUBCD464ud9OAPwOtvXzYEPQiq8ts+Aa7ruZ4LQ/02UTpyaZ9wOhXZmDyhvpFThHWbuBaGPtjPGnI6g1D9poH3OwmJQ76c9QICXZNo3SbjA3g3leFvchKNH1at8xJ48aLIKMD9406ij3Ae5pH+FqQyhZA8Kt4wEcoNmBg1M2ejJs3s5pyxxj/QAZwhjz3LCPOUghDyItZJ0A9c3MOdR/2n6MMNqDJ3h44sSYuboSFgwxD+JBCUSALQZkP6AHcqIE6KWulW7JsRjjR2/duHUeHLSKtTzUUR2yUzwY0kBfoAfu9BKQmmwPVLtftQm9f623C9OltKi9KhqLoheon1B/ccsttzhhnJWPZF+gfkL7ak0peZzz5kX8k7ymjtf7ZB+h9/8Nzp22xnO19BE8wInkoZfyktRyPou8y5an25r1WVZM+x9hmv8MiqKnz3UzT8mwDVtKrHIW8CWKonEeNtq3f9J27h2xyaluu/pKlEiX6YEC3XbEbM+BATvIONnaoX6PsSg7jsK6jwdisujLJxk7QvbxDxfaVsp5jOnogX1R+/kvnsZIg07fX0A7CgQipLHbjhzdS5j0U8wVeQDijnfYpg1JcI50qyHw/0yPOPPxRZjpeXdJgXPPa5rfpR+oMa94S1a5V3yi1AlSFkhZ4E1hgVej33lTGCqVyZQFUha4yALT+Djqm+P2k1/wlAlhW9sBSwTPXb/FY3/wbq+tXz1zw3DRYamPb1ILtCPl/UOAue//NGLnOhKEJHHZx97rtQ/cycLP61y167UoMi3sKQzr1772NUdhKHkzfWFadFN+zz332J//+Z87oQcv/O1/8r0WDqR69PTTT9uPfvQjnAj7nfCBF19TiwhaGPnEJz5hN954oxNm7eJ93kifmwhT/YV/AhZ+AgduKGErgbX+7KM+ZPE9LBS/PudTIZwT19w9aft54rwCef7/9UmffeAOFlzTX5/pfaH68I3vROzv/yViHSza57Bw+PXPBeyOG704sV7oyNTvb3YLCPzt6+tzgLOk00TfJR0gyUVO/VV/K3W35OKn9tF7KSUUFAB1ALkkj0subL5a9tV5k1uy39c19F797iOPPGL33Xefs+AsaE7wnEJYarswLXqvceQ0MMGX/u7vbBJw8HLAkstqCO2CelUGC67BqA/nMQdmo96j8G+zKp2QYDrXhddOftY5Lw7Vqj5eIYySW/K45OfU35QFfpMFLqyzF+53YajWjRs3XlJxTiDOFMBT5MwRi40M40PAcZ+BalN+AapvvAid4+IVwcEoMMwzPmTRjjaLEBLTgDeQKgM2wUmNupIb579AHDch1YgFSfwlYK6hEaCxXkAj1LNQU0O/gdCIKOMUziHMZaV586TwBUiFilWkrdmiTY2WQElrGjVlby5qXgsWmmf+PAsjp+yl/XqAv6TMFQMKiXagMAOkEsNDqH7Ih8ciDRU3XwUhRQVq0TDDPV0WQSUp3t2DBBXOPTwMSqurmLwBXXkJ1eMC4JHzwWWkLzyMGkSHRYBwEv2DfGZt2AcMheobsesA12abF6hQ+yfo32KAS1HCtUZ6ewBzAMZw+CRQpAmUEUKJMEqewgInBGliHPWv9lbS0m6R4QnUe+ijcGhmLpznnJN4hnA8coLwP0BDDDW1CKBRrK3L4n2o6wALuPNwgM7ivKguuTOBfFCkibFPpLvN4tg6hs3j2EdOUg8hdj1AYr7yWQ5gBDmGcxhnD8o1EUIkRTrazQQPhQkbB9Diys4xbzlhOUsrnGOl2pGYAIDox8aE344MYO9RQsrRt/pRDHOj1OVfsMI8qLMpZlYiQv4GBizc1GWJHtIClBQFWsisnYsq2yyLZGVaHJUjL84mDy9XlOsCakY62y3a1mtxHKV4lxzQygMc5QVOdAMWukm3yiOGEpfsYONjM/0qCoauXFRbikvNRyhPN4puLhysxI7EKR2yeGsr0GEH5+UYICYOsngadYj64Qea8+TmwNEA0wCDRAGqIi2tFqMuIVNEXviO8nCh2uUvm2NpVUB2hHNKRBWSjNBgqJvFeih3Jodx4LYA+UufT4jXTFQZ3dgemNRNHuPKn+p0RxOQIyomZM8IY+kpLTI/SnUe0ox0lHOuSFe3xbCx6onCgyt8LzJ1lF+5eQl97smmjmI/sFPyE7YoQGC4vg5bY+8QTljmpq5cnLOoBvrmLrYEio0elwfFNNVRta0u7NwBrYE93IzL6Qxa7Ospp45QVz3BdIuHJnCQU9bUpyjjvJzKPgDC4Kx55kURS2CiQDi8z7SLEMp6g5QNdRqVtQTtXKHTLA1VuoIKCwD6eYpQiyE8Y5Ryiza30Z4GLD7NNYzFD9qIN6eEa3N9gZ357BugXsRQBlcb7O+hTGi3qHHZCHlzce10wv4CxnpRaPQWoGBImcioseE+bNxqU60txM6bBEyhEQnABLr0A8x5qU+eItQApTZZr/6AekH8ujj1JIKd1cf4lV7VZYCO8eiU1Q91Wes4jnpAwIXAgxVp9FkAUjH6ryEg1dbudptgvyJCveYDqXrpayZxePePSZUHoBYodRJ4OIZyZTohYLNpr8VZC1GELLF+1LJGgIi8vpCVAsAoRF8WamduF3AmdWcQb2079WxiaswK6D9KCrGnQNb4lPVNdQLudRGOj/Tj2M+g3Py039Y2YCYg2mKUHt9z/Z0o45aj/hey+mEUr8Io1KFmNS97vhWm5Rn6uEQ6aACC6gIcA+oAYs7NKHQUd5wQe/Ql3eSvn76tEHW8CvocbkVtMjZiA6FBAAyUelBki8Zoi4THDAJh5mXN5p5oLqBhDmFax5xwiB7Kvrg41yoog2zKguB59AtcfbTfmpubATNdQF2EPqQeBiiHCFDlwGSHtfUDnLJPmPLyU588wAt1wHaNqDcWAejdctW7bC4hZqfD2KmjgfYbtbKceYRCrQJ4IxQk4XwnAGwbgDdaFQJvIG7hUeoEtlU7yc13o3bntQqc5F7y1d0ftZONlCvdYQw4TmNENsBCGQ8mVlf7rKgACIxQv+OAEnv3h1GIE7jkIdyjn7UXt3PvxNlRyovbmbNTqC+5rYrzz+Gl0Kk8q2R9qNk0N02jOMW9L/U5C2hO6q5NLRF7+tkTVoTC/B03Vti1VwW4FnDX6SgAmuznYW0C+6I25SFMoFQyXerLXYxtAGL9gGKtTRGgwoTBIlMmwDKAN3msJZZVkMcKHwCbWU93FDiGB/cIFxjGNnTPjppSYanLZgH3lAMG5qQDMiWm7cRZsyf3AN/RR6xfEQDcJDg2Xau8pEPko7GVkIS9UeDBLKuepbpNeE76u17UdDq7ubdsI6w5IR4FKws+OEE4x92761AI7bB77l4FCEh9AFIZHI7Y8WNSqfbb4gUBIKAoqo4UE1C52kGIsbcPAKEeu/WjPjeJSliU78Qe53H8rEqPVc1j7sG41oOaWBu27AfwGSHEq4eCDdI/FOWzz2xCr5d7nDC1IQqjmXWIffuGqfM+W7006JSVQuI57avXC6AQJxRi3IpZY6td4HdAS6oqbTpmDU1hwE/qFEpEmcDbxTkea2E97rGdI7TbfsC5CnvXu7Itk/WMdqCdhrppSixk82qzrBJ4Jk0PDuj2hPxNA/Z091EvCPfaSbrp+lGb4+EA7JbPtWdXeWx2hcZMl/UBDnZ2j3KuBuw6wn3Ubx+c033Jhfcmmncm554CXQYY/3VvoxCtxYxZK1eudFSx9JCU9tOxup/Se90j6r3+Js+BVV70lkxH8ljd810Izs2prKLeF8GNo7xWnAYQg5If151AjbetLQq0HLGhPgK307HNKvFYZ4/LntgTox2dpAxLCUFcTB68ALiGMiLzGcpqLiE4K6upD1koBnqZv9CjhZmzDdLu689NUQcTKBAq5C/9iKZ3uQmbNceDCigPPLBeNDRE22iftm7KOzSOPVDuDDDPyi9IAMZ6UXqjLWUz7+K/TvqYHU8yxgLcLCME6sKFXkf1MeGinTOPbaONdaFwlsVctpp+prCYXovxaZT+q6552lppCyOApwkg/hLawGA/Coj72+zI8Z2EDF5v77t7ESFn3aga0medGqE+e2jP6eTPC9DLVIPBxYPinGB4niRgbgAUOMTQDNw2TRt3RQg/ylzALagswIMXPAxhqEUGawiFSl6j/bTXdh6OYH/3GPcUITo19SDUAQ3P47SDMQBmH/bJme8hNDl1gmlajP3DhI+N9fAQClCilHRdtOWJ1mlrq2u1tIKAVb2d+RZwE8VpE4SOjnYxJwWCDRA22wSrqU4p3ZRZnGmloL8IsN80fVaCa3hot95s0k06Nb/X+b2MBW7SkOCYMPuFqRtR+go//beHdDr1jDyH6WDT6HcCVWRlmnDLzaj8CkYeiJlvivTquvTrmgqNAmKNcKvgpR8urPZYLg8LMz3FjsyAmpi3UH6JEZmD+SIgVmQEEYUWFHeB8+asrbSSjcyJUMKcHEBZlzqbTdkMeHtQct1Hv3nc1qPILMX6ZPtKrnsorcl28aIb1Ku0Y7Jd6nSXSoN+1xqS1r4P8+CH+olbb73VCeecXD/Ssbqf0/HJ75JAnX57oS2ZBh2v98l06H0SnDtx/Bzj9ibA1BJHSTWNeqyyFjSqtlhQRlnxMORon8dOHmNsIZRwCcqICwFcSwoZWygvjaEtlEtjKyF8mZvNrUSxtUQgPe2EPmBgiDGc31s66NuB5+EznTFp94Gw7T0ySZjYuP3RPbm2aR1zH+p6R2vCDh9rt+n4MGPOOGMYqu5cZ4L7sH17dwKG7wfyXoziHODcxsuZDzBYqjHxv15qWhqj1S701cvdUuDcy7XcG+a4V1I9LsykqltqS1kgZYGUBV6sBV6tvufFXi+1X8oCKQv8LllAi1yNLOIojOG//oSwAiw85bEIde0Gj/3J+322aT03YantTW+BSZ6SffCRmH31uxE7WMfCANXiblTmPvlhvy2o1k39m95E/z8D6Em2z33ucw6YNoLT6vm2a665xv7qr/7KUUISzPHb3JSuPXv22Je+9CXnr0CTizeFD9y6dat9/vOft9WrV/9qIeHi/d4onw8ei9knP8/CASGHdZd//VqP/c1nfLZ0AU6M1yErLHBuy7sm7SiL//N4ivxvP4dK3rVAMjgj3mjbDsLN/uVnw3agkYVEzP/ROzz2+U8FUGnQQtsbLTep9P62LSBHh/ooLWoKnNOiZnKR8sK0aJFT32t/bRful1wM1XdytuivXq/mlkyT/l6ocKD0KER2Mkzr8uXLHXB6GYo3F/b9yeOVDynOnUL55v988Qs8YRy26665loXO9bSZApQhWGwn/J9LzjA/jgYUTF1pAB1ybLIlz3Phwq3ev5DinI7TK3ncq2mb1Ll+Ny1wqbqSDNUqUP/5wDkqmsUHBZIA1ai9EuLMhcKaC3UuvP58JhQTcMY0IIobT0EANQ8X6hIJQm4qzGuMlwtww80T+y7gNheAlKArd4SBMyJPPnUZh1Ich1QiijeBUIAuFKHcABFxriG1RjexHhNAJ4KGEuOAM8BEcSASl8LrobLlRokqhnNLrcqFohrEhXMupSExxf7AKQnS5sZx6EIFwwXUmkA5jy/YFwccEIugIDII6MG/ONoTqF64UYzxuclrTIM5AyDOUBdQURzVrwRqbgkAJUfGQ+o1QfbBSRmHIPAgj4FLRw0c54kUhPDGTfMify7d1AEsubi+A2Yxp3QJQuN3hc5MCOLiUgnyI+LCk4m9gcASUkOiL3FzTr04KekQXEZaSI8L1SpXkPOiLuVCEdCRyyBUpVMOwEjQVNiM9PMdgktcn0C7gpgCeO6wg64XlzKS0jeGrSeAXriGS7STBn/K2hXAs+MjPbKdNkC8RFTe/Ajpx1msSQPOVBf9tRsIDU+ussp3zFsB5+LI88rR68IhLMUg9gT4ysDxiDIYSl5R9YsJyp7TQD9yftmN0FYAWoZj1oXz2wWgIrU7SQAlCGUlKjmB0l2cOmwAky7S4wLucPlIo9TdAI5cqJ5AITHQzPTFMfpsl9ICRJUg7HBc5UIf7RL9QTm6UPlySWGC9CkcmeRhEkBGCeqJYEiBc3hV2f/8C+hH+RQcaJN97MO+OsZRI/SzK+nIzMG+hPIi3wi6OZCnC7skgDoTAKOqo5zUYgBjsp2LCWSCCa/GlxheNvdUCEW/CczCMZxcbQpP3kwa/EhYQHbEGXOp5Y6d3PLMUY5xnRuIDRqRfcmRVNmAoKKk3UNavOQvwXidwB5xQEsjrCc0HXYmkbQvVxrhk0WN8DFBGhLTKm/abkxAPGlAlsUNWOqiPBybaZ6o+ks9cvJHG1ddIkYueaTMVSYBoESBjTiOkZDDOU27o4wThGdNqP1zfRnU5ae+EfLNLVvgfI+DdLnD/RzSx/kADadm2ksiCmDoKaFuAqGmo7qXQftWuDXODwYEQEYZT6msgeZwbquf8kRJU6iPOjkAwUBI+QrgPOC3+Djw4QS2k3JlgnSo7wrKDqr3tHPKRPV0iPwP8/LSPxFdkZCylBH9RgybTmPTUFihGQUUoZijesd/CRpejPNORkI2RZqivAQ6aK7kpZ+Jj+daw8mI7Xv2gJWUma1YWWrVVaWobAFbqs3LJswp5Jyf0jyLeqt5SYA+TeDsOODacGzIJmjj0RiqRtg7HgvZQE+zHTy031oATBeivviut77bSr3FpAXHL/+F+C/oTrOcWD7huKlHPlRM4qNcI4LiZAAluAycveSdhhmnTcZoh5rjOfM86qAPwFT5i6F2FkUqa4JQkgqXGQN0o8IAMpE/VwaCpWkoHU3byVN9qLnn8dBXus1CNSud+yYfMJuXEKqkmH4OVU76tCh0iZf7W6/uu7HxJPVuLDpok4TgDHNerG9TXKKbkLMHDu6yvt4uq61abrdsvcsqcqqpS+hwhicYawA2UOsL8J8TYhA6QXfSk4wj6lpiGn94ublugj7QR9xuqcI4Ifsw+xT7DQKc0M3MjC8aBtkvSDebhQKaoDkQCeASP2pOYatrArYBhrjycg+Ocu5bCV9My6abFFwH+BGOc39I6E1eccaKSdrRAFElRgl7GNU4KLPRf3QAVu3ZP0hYtyabO7fS7ryl0DasB5BBWY7hh3PhoKdry5T96LrANqhPJI7UUKs4CSAo55sC8NB96pTOTdv0+gWXoa6aBuTBGOOnb4SfxeYJlHGwB11RQv0e9TkABJSWHiFEKJAIdVUhDo+cStijT43xYEmmXb4m4NybB4AKSMBM3SeupEKzBwBds1C69NNGHBtOJGxoHFAWMM/DOKDQrkREtO1P9aG2d9yyAfPuvmutbdhEPaRrFyc9AqCmsSIrgM380wyJatOMheRNyptRxvqQk27yKvUrJZuyoUk49Sod+EX1iaEBsA5bYNtp6q+b/t5H/tJIN4JthG4FPKd9jY677Qxg3I5nuoA93bZpbbYtWEBISGwcYbybmqbOTdHPkjYBjAq56hHUwoX7R4GtAJ5C1CWVIbWWemWoDhGu79FG2nEm4Xbn2FuvTQdmkbIS9Y+06z+Es4DpdAwZIH1Q5JQBIdKx0TjwzgTXY2rl1FHNLjS8SeVPQ5v6tBg2HxqZtNZ21DsnBk0REn7boVp/032J1rJOnjzpwDC6z7qMMOSLFi1yHkhS/3ep+Snm+5XClN6/lO3ie6uLwbmqKh5uKp1NewAcpT14mIPBJFIeAH5DYdoC8xDUxwS4w4ETDjhs23ZR9yKd9p535tlVV2URml5gnBuIkvZPsWVSD8Tauwnn7fIxhjozUik9Eh6Zuj89ST1RWVGWmqYGqDtBytzP9TVWqc2rHTLNYB8ak/oZ2o6mUhnUF7Uxugv6fcKxtsTtwQd7GKZQoluZjRojgFke4zF9cBjV0xB1dHICMJe5WwZtQMfGaNPD1Ll++hmmxs751V5oSLbvQMS2PY5S9ESr3XH7WkJCVxHFQvMx0gNoimgtfQ3nCapPZo2ZeZomy878SOMkY2FCwO0UczuurYbo3ObSb5MI5xpx9VG0KU27Xc6Yz7jMvI6nCXixIxBfgjlunDrfxoObu88CR6OotWh1ni2Zn2EZ2CQBhAiLPfPACulWO4sAsbWeAOZGwbqgOsNqrpllGTVchPEqhsqbFPHcenCAPiUeoJ1rjkT/ofmICiJO8kUvM6RwfaWbfWlXTqHSZ5NJ5tbkjT7IsRfpdaGYqeFNMKKKWV2jVCJVhkwNUc3meLY4Y2FC507mUQZlrJZxGoAXD56N2zBj+dLlaYyHgJGMCTHAucSIBjzOp7TRR8SBEieBrhvrW83FhKNq82zLXcMDKoVcgzqVALzysE7XyX3a7j27nRCnuqe79tprncgpal9qA/qbfDkJfA3+SbbNCy+tNOl7zSm0BvP444+jyjZoV1xxhQP/JcM4J49JQoD6/FLzk7x+8pr6q03fJ8G5s6gkL1+xlTZVQRuTWjnwJEWiW161RS/3pHEeyhjq9tv+ZydR3x2yWUCzq9bwIECZz9JV59kUwWOMlypOFnUoSH2J0w4n+W6UMX+M8tU8RH0C0wQA1oT9CDXXY3U8OL44xz54d4YtW8IgwhgYCacBk2vOyDzWy3xWPQyT+tGRkG175GHbvu1RW1BTQ7joW7HZRtopbYC9NOZrbE44b2Qv56OS97K2FDj3ssz2RjlopuK+eqml8qe2lAVSFkhZ4EVZ4NXuf17URVM7pSyQssDvkAW0eFTXkrDv/Thi/3Yvii6s92azYHL1Grf9KSEBr2BxLrW9uS2w/1DcvvjtiD3CE5EhFkffutFjn/4Dn20GOkopRV26bkgGXmFan3nmGRZCuTF9nq2GG9FPf/rTdscddzgLEM+z2//Y13JqP/DAA/bVr37VWXRUmImLt/z8fPvwhz9sf/zHf+w8mXfx72+kz1LafOCxiH3qyxHCk6B6xiLUx97psz/9iM+KX4fKifLXrn3HpBNaewkLKl/5p4BdQZiGN1q7G2Zh9y//Kmz/sT1KeBGzBTxR+Z9fC9hlSwCXWBhMbSkLvJAFtJipxUe99F7AW3JRM7lYmTyHPl+8cKnfkou7M1DazELvS3maOHn+F/NXaZATJ5lGLZru3r3bvvKVrxD+Ysxuuukme+973+uEErpwcfXiNJ48eQK4+QssUCbspltutU2XX8UT8sAErKjL8eHhe9yELHKy4C7nt+NR0ELmzJrShefW+xcDzr2Y/KX2SVlAFkjWr4utkQTnVO83bdp0ScU5LbzbNEpXgoKAJ6Cc+CaIgwgHDCd0U18jfD9NPRcHE5CjEceAfL9hvInyv8OXygfvKOjMOBPpG+RRjjN4ajHfBegCouFseJ8SeKRchDOSlzCBR8GFwzqE0pXgJT+EgEcgS4YUpZgcaHDi5HJYyYHlDFW0K+ez2pc8WjNf8laf8W6RcoXRwjDyieE8kyKAwCLUy0hORFAG0hM+0hA0QkHhLJdDjcyRJA5Q5nQaXYf86nxuHCsxgL845/TgTHWhwqHfoUEIsSQ7sK/25C89gNgkzsM3pE95lJpYHEkjj8C+QgAehaUUbKg0ojATFyhA4uSo9Og7nZv3CaVF58XWCaWNfd3sK6fKzBVVgvTLZBQ3IqaQk5d+leMShI2Sq14GwmdJGE0+cVo3jiJOTtr5F+ejzkOOHGdpghBcUhiRfV2C29zAauQ7IU8j6WJX50UK8avK6ciEbgqAa6CLELSEbcxDpS2/jP1xsAJ3ecijGzBGaYvoZpfwiy4c6QmAKjyemJB+U/akfnioG25d0zE+gA8wjJCVqJyfsiuwl0cxuwxYhsS74qQpoRBwnI8zOGnTP7Kf7C77cZxyqC45Th1W+WlTfZUz3KWMUkeTdnbKXGVLmpxkcCXHhJRhYrATVcEegC9cs/koMWZSRz14vUljDCdzjPNjDXYnnTia3djGSYfTBnAYc22FgdX4IPvFSGOEPKvkfKTDxxii47Q5bVrlDFyhglKYW/napZrj5SXQyiVoFTiKb3lxHHCSoMcoaYrSKNU05FpTXZoBBLGEvMjsp3biEmkX56Xy5h4pMYzKIYqSLogRL7CZW7SHgCpRHLIDaeZUHKOXjKc3QIwAPQ4NydgXF5gl7z/7e1R3ZV8uJwe4k0x+cqHkwUF8Sd6wm2AAI+znNFBibAQFSZSpEqiZxUmnW2BOsAyHJgp6aSWEYcuD7dE1VMbASvQzM02R83A+wexe0uWfkPLhGRRhzliMtiZFQD9hSi2RT3qA5zg+4qSfpACqqU27qeczBvPZFGUEg6AcWwahaX1xVP1IM/gX1wZkQxVL4ZaVjTiZo+hn2r1AepISIVyp6raKfsZmQRvt8diTj/bbT372c0KgFhCibCXO9EqgGpWV2oPsiwNfiaD/FXgkVTKBCNM4U+t766xzjDrIF+nBPNVqYK4uO3vmMAo0R1FCyrAtW66wt627xXINIJL/pjwoegHOob1nwRgQK3kXNBmRaiC/Sx1RfZn+U8HyC+kXoMRnySap/+Wt5kSq3+qrVAUEEcgeypz+RlFM6WyN285dfbZjd7OVldXY29+ea4tXAJBlMh5IMZPzC2pwyp2TChBVJWWWRtUApuhts47+JuruhHlxQqutDI2S7/o6O3v2OHn228bVV9rWVdcRsrV8Js1c3+nTnMbhJFUFAWTKWemXBfopOKb3fJ+tKhfTGMH3XvKiI3TtKT7zLW2Q/krloEyTVA/n8DJeCJwb6Ava937QYwdPuFFhS7O73plj1XMB0+gy3S6tMUQceEXlqBbuoSJECP06CIRx+PgQilgTKKBlITSaiQM9ipKVQrk1SUCS8XmR3Xx9Lo5xYJsAipJAuXopLRQ/51eKaOeCMigzwTqaa0pdSWaU4psqoYcxQqFMBZQ6bZw66lE7VztjFw5VkTmvuGzv7DvptBlaPSBIwLY/PWD/+h+ngApm2TtumGNbNxISmDVJLuOcBmwTuwq6FmwXwLZe6x2YtlOEomxo7kGBLtdK84phyV12qqHHdu0l/PlgHwBQrd1281KrqSW8o4YTbCuhKhJuaQDcXs7pccYjBYpU2VAf+Q62mGuRB0rHGS95p2ur+uml/Gu+EsUGOp0L8EdAuOq0IHe1KFoCO3lRpPPargNx+88f77JSVN1uvWERyk1FwIOqbxqPsBN21RE+3jtjPUkZJLz6/ucarHsQJb4cYFcA9gSLGS31nXbwQKOdaxgknOZ61Mrm2PKlwFrATE73R3piqlvkIyCAizMn1GdrnKVVJhiI6DKceUmccU4glHKu5gd7qL2xC90S+RvnoYIz506jXIYS5GsAzpEs7INlSGPy3kZ/9TBVU1MTKn77HNU5KWgvWbLESaOgueS+yfs89SV66f4x+Z3O/VK25DmdcZIDdb4LFeeqqqu5r0PVGFhe/Y56ngH6kqaWPiCYXkCZfJTYGA+Yc3W0DtsTO7qtoSPT5ldn2fvelWurVhEmMQNYE9tP0w8yy0CdjhqlsqSeSNXSUXR1ShS70L84dYd647Qv0qRxnsOdblT1VXMO9TsOGE8do5Nib32pffUPZc7Bqh6nz0SIlrGHtcsCu/4tc+1tbwlaURltnuuCUPPgI3aNo+RGuvQMhs49hnrcueYQL/pP5qu5rFcpAe2dk7aDfvnM2TN22dJyu/22ywi/nEM/pLmEkyRnbFZ7dHlo1x4Ga+ZVDLYApGpRIdoq4wJ1NpEQZE+PxHF0TrznxcUVxln9AxStUz+YCbA/oK/oM2WItpSIcHwE4A/Y7bl6BAMePGahyBDw1wJ76+ZySwMUnmjmetBG2cyTBb1Gh1GyrBtBcbTTUaaeu7LEKtYVW6CCtDKPkKKe5kiyX1xjGmbV/FtdJZWLd7QvdRB8dnplpVsTP730JXA6J2COw7XoM5zD6DMJ5soJKUvGCmc0UOYE9uk8nFd5nmELAZ2Uc2ylubPUgt0eQsLywMezB8ft3vsJcd45bFddXmzXX5tjhUDbPWe6zUNY8Oz0XB7cCdpUH7A9ZTZWP4oy6aCVLkXldTPq1vOpH3lKJ9fk2i5U0bpQm921a5dt377dNmzYQNjr6512lnz40OkfncrkHPaa/qM2emE71XsBtk8++aQdPXrUiTyg9M+bh9ovfcGlNh2TbOOX+v1S3118zeTx+j4Jzp2rq7MtV2618orZ9B0z19A4Q2Fzz8Ifyl3quz3tPnvwfsIiHz5rC5fm2HXXz7f5NdmWAah5vgYB+c+UkR+7M3w5YGgfZXrkRLuNQuumM5cO8pBKiDHy5KkB2/ZkC/WtzG66rtpuvTHNKspR8uZANzSmHlqgsKlPGr8ZH2hvg32T9kt8BI889IBVo35968032+Yt6+nbNJpo9qJ1NN0DUS9nsqBsvOyN+wXN7lPb754FXkm1+E3WSFWX32Sd1G8pC6QskLTA/1QflDx/6m/KAikLvBkswPq3KWzrD1Ce+8H9hG3laaxMFl7evtltnwYoWcHi48yU+M1gjVQeL7TAAAsH3/p+xL7xw5l6sbLGbZ+4x2c3X0e4SPwaqe3SFvjmN79pX/7yl03hyrSo9nxbOioiAtI+/vGPv2ZQ2jAhAH/605/a17/+deepXT2Vd+GmxUUtRv71X/+184Sh0vxG3kZ4Eu8rgKBf/3HU+qjfC0td9sXPBuwaIOGg1sdfR5uEalbeNsliZNzW8ITu9/4tYIvms6D+BuuQ/+Xfo/b5b4StndAlWpT/1l/57O47AAd4yji1pSzwUiygBcgLgbTkwqTOoff6XS/1W9pPm96rH9bvyf5Y3+n1P7El06DrJdOXDNP6rW99C8dumb3vfe8zKY7m5QFAsJ82Hact+VnpP3XmtP3d//57Z8H85tvvsC1XbLVcQufJySWnlnIgFR0t0rPsyrEzebr4XPqs874QOKfrJ7fkOZKfU39TFrjQAsl6euF3yfcXhmoVOLdmzRpHAeTX2xwVOAY0hzqSFuqJs4TzecbZKeeBy4FLgHYEHjCNciBRqremVFM4EjWW8MdpA3IaeAjdqmNcqLch+0VS+DExA+M573HMxeVQox0IcpHjKoEzpevIERvpRakoP88KFi4xf9EsnHA4r3Ry/ne8FHqLs84BogQKcD0pRM38mNyHuRNAhKCC803ZSayg1zjHzsBTAA6oG/lw1nhJmxQx8GeSfRxvgAekHgcm91yk0Y2ylEJW6ToJOU7Juwtb6NryxynjghHE3TmObt5LjICoes4+dIBOeNQ+wJZYU6NlFxdZcMkic80uR1IGAIeMKZ1S+Eie14EksP2MI5a06Rq8hEPIXePD6Hx0bKhrk6KZvCqdfHLLA6STOj/yl7wnKJw4zkK3IBmRRjoBeYrIvuzrxQAunLGO51G25X8Mw34AB8CMcQdWmjnMqRIcI+hC6m8QJRY6fsyaz9VZdk2tlS1DGRmwWObx4IWSQl0CZQ+pBzmqZTh8YoCLsQT1jlx5AOTcKFY5YE0CBTo5lwVa8h3udeATkkMyEY1hX64XR5lNORW4hMNSTiP+ccrQ2ZGflKdkecl+Kh+lR4cJARF45vhOHerifH5lJ2cHzieDCzDUcTiDbQKFrpNHLVR/CrXCgGWtXGGByrnkh5uwCPnD9jEHosRGcpKjOubWmIBiW4I0siNJ4/zO9ZUQubuEE8546+TiVZGIEhEso/qrnQWEOFYnf7JKTO2GHVUPXJSTSyd03FyqwLwAFaKAVURxVE5moBudV5uyqfzK4c21BRQJWoQMAHzss5GzJ2wA1TI/Y1shbTBYVk6bIO3Aj7KdLqU3CWgCB+50ziPHMmVCu9HvUeCWCE532RGRREdBSclyduVbmSKG4pk+u1UwHJqIDZPtHqLINlhosM1cUwCmKETJqx/FQe1CBTMjs8iyFd45q5K85ZMMrYlQkqor5BetHBKnHJFvQDbPaJtFW49YqPG0xXLyLWvRcvMTQhaykwsCF3J9Z8TGzjN1AfuTHPVNDshKuYEKcC7VREBNwDk+OFZLSA0TkBQdKcf0UY5Rn+rFAC4BTQ7sS9nSUAR8ar6TEEDVmbBHHuizf/vhvcA1ZfauOzfYmrXFjiqRwISZqqjjMRLlHkXlUSZXaDnQYjvWdNQO1T1nff0959MqhTPCogLSqB9ZumyxrV+9wRZXrCGMfDb10A0uFKLeTFqANhJApU51VRVIymtO1eIfsk2hcCUS4PRv9Amqdw7UTFNQYTl21n7YRTsprVRFJ32YBZHCBCFDY/bwYy22fUcd4V2X2+13FNkqoiakZwJEyXIcIOD1fClRF9UenB6QeuNCCeyMHT6x29r7ztH8UCvCpqEQqi/Do4AuXlu8eLFtWrHZ5pfWouySSY6Vcs6pvloqQaqkMpgSRrqjpFt9pkAKn+q5moia9vlyIRdOe4hie1A1p2y9oNSCrbSxu3MFr+oTgGZnl8/+7zeb7MAx0rIw3e75vQKbPx+dO8g9NxVZ4BOF57Rdp35zzqmw17oG3PaLh8/akefOcr/F/ihTcstvA4QQDDPuLl421668utLWrkwj9CdJByJVqqOAGLKVOFcnp/S3Tn9MfmJR6ojGDGykvkdsihRX3UCocYHpnEHKSgIgaSIzkJLGRH5ROyXn5E7gLnlnf/U5QepdHIj8voc67cvf3E/42IX2gTuBdq6iv6ObiwKfy3YJBzARMAvuDCjrBb5taQkR3rXJdu+jfwSOyUTdcRI1pu4BFBJ5GHBeVbG9/a0rbO2aIstFUUn1TCWvMVm1Io3zkmPeO9/SX6vOcQXVNQZWQY4z9UYlw+aUNfZQg+cojZVR4BId55NiluYuTpmzA2OfS+MYW3dPwJ7cnbBv/MtDVlGUZu95xxpg0wLLIEyj4F2u6qRAJwaJdKBATm9tQI+/eHCvHTtJyOsE4Z/pk6RmO9zXY5OThFwuI7zn2zagYFdAiFfua1SFHDvLyvSvjHcKXazhli/Ik8AnKZnyV/2c7MH38Bhqbk5zFAuqbIorlmjZBIsRuhcZVAjl1wicIzm/tmmtqptQ7vv37wdwrXfuq3RPVVREGPBLwDC6n0reB2nuqn1+0xz21y52wYfkfVHyWPWxvwbOVVUTJnU2wDX9NIZVT9o7OGIHDjXa00+dtcEhH3WU0N/YdmBgmJDAYcsvW2TXbK22q69EKbOCOkdhCJyLMiYzy7AAEwaBYIIgVX+9tCf1PTMFff6vxninHqt3YUajJxY4VvNWp3+gp/F6BcWocBknBVA6HbFTLTiSakUdPnxkyj7/d48xZyq3d96y2G55e7qVzqKe0y9GeeBgJkUo4qpVcE3utFFvjNnuvX32y+1nbQLIMgPFSVW37gGgX8JHFxRm243XLbUtQGqlhJ9WuEnNVTWeq0460yjFSwUCjiEfFyd91EaSqnkgFlR/I0BMHYg2bKD6rP4/JshaFdiZe/NHlVgdskgzdUDkVywQkyQnLOxeIk988z+fo3/vI29LCb89y9IIDNJzYsCG2/rNz1xU99dMB216ZBoVyUkrqc23srVAqzXpPLjAaZHwkrVnxgHKSZdhbNMjOlIXdTLvlAdt2nlyROXAjwLHzwP/UuRV+lU+znjBDm76NLcDxVP+PPzjYXxwxnbGSOdhFKA4Fw08ztxEALpSoRCtKgvVNZebuR/zvacI3/wf/zVsba1D9vary+z2mwj/S0Nu2H/WxtrGmCOmocwJiBjivEM8NARFHCzOtpL1FcxXgNwJG2rZJE7n11wPhbHegV7buXOn3X///cCdq4Dvb3HWrRWZRFuyPTgfXqN/LmybapfJ9R5939HRYT/72c8cgE7gn0LN5qLU/2puF15f75M20ftfA+e2bsV2s51Ly8ok1akfMw+7EK6XCtXd6rOf/GeL7Xz2CCqaRXbL7Utt8aJcoEeNTYDflE2EyZDUTNVHqD8RK9rUNGY/+8V+IkrxoA0ydj4eSAux1t3fP4Fasd+WrVhiNxJyeMVSP2Hhp2airegewZl1UKMYl9F0pV4FHHDuwQd+CTh3n1VWzgKcu4kxaxPgHPWGGZ5agCBsjVmkiv9e2ZYC516Z/V6nR7/SavFisqWKmNpSFkhZIGWB57PAb6Mfer5rp75PWSBlgd8lC8g50NwWtx8Bzn3/3qg19qDGxBNj121y25+gPLd2NRNz3YOntjeNBaQiv+fZmH3hGxF7nDCLUuf6EOpcH3u/z2aX68btTWOKl5RRScB/4QtfsO9973vcqPb/xmN1U33bbbfZZz7zGSe8xKUW+37jCV6lH/v6+hxwTmlua2v71WJD8vSSsr/rrrvsL/7iL5wn9JLfv1H/niSEwae/ELYnDuA6pO+750av/dnHfDavioVALW69TrZRFjvWoDhX15qwTcVu++F/BWzOnNdRAl+EnU4RmuaPPjFtu7C5Fhfff73X/v4v/FZekupDXoT5Uruct0ByQVIfk++Ti6JJCE7fJ39T36rf9Ve/J9/LeaKXwpD9OsTz6plaIVq1JQWf61gAAEAASURBVPtzXe/gwYN277332rZt25wQIe9///sdZYS0NML3kUalW2lUmpzPLIJHeOr49Nlz9nlCtQoAueX223E8bLGinIIZVz1hljiUC0EAsJAuQOLFgnO61pYtW0whYy+EoXXt1JaywEuxQLLNXXhMEpybJCyewvpcGpzjCMFP1EXHicfiu9xtUlOIn3/y3Sf1CYAQOcZcXrwC/IZ7GiefUCc5a3jxl2iRsHKcS05rH05on5xwjDHxbBxmTF7lyPcSDtJxCvBRTg2c67GuPmt76mnraG7GeVlqc7ZeYxmVNQA7XJe2OOMJYN+kMw5HZgKFI6lNOJOF88u1TrOR50ITCIcO4Ad9eX64llPL8ZKRQ7k8FHoxAUQTV/godvMQki9O2sM4QwmW4xyWwTF+UTakXaEMMYJyzHHnr3H+Kx0fkQMc2/C/fHOOE0Uhq2L9fdayHSfosSNWXFlpGVsuN29tDYplQUfFSSojQiQcJ6xsLEcl1tW3MXoZKdLo/IIChE8FyL/0Bejc+KQsYQeBLxQN/5I2/iFdchImgDpcDuDmeC0pY30p4AKUBcgqLEchB3i52XBJ3YwT6CrO/46nk+NQHZsCFtIlyOJ54Ix+HqehJ0p5drTbxI6n7fj+g1awZIVVX3WNBebMAQwU+Mg5lXj6Sam6uVBCkURUHNWUGOEi3ZzDRYwtN6CPc10gJQJFUtWAPARycl0dL8hN3au8ry4gBCeBqhtOOeOwVJ9NvmaUss4bgXrg2EBJ4PgZfy9uVhy5UpqTCpsDkum9zu1snJM0O3ZSufN9TA6q3l4L7dhp4wf2WhzFqNzrqaMrlsMR5c44U3UuvNBqJnIYe5U3OWdxwgocJfXO2aVClqxHAumcwpJ9lFBdVw5Y1Skllu/xxznmc6qdaCV2c+BRdnKp/juV4/x5VCewbxhDqbS1SX1NuIwOlH31TpvecSXxbhCwwABdLda9d6e1nalHIaPc5my+2rIXzCcsGTYFIDjvdyZd/E89UzGqLgh0cfoPVGqEAQqRIsAo730UMxpW7CvwVMppLgiQGJ3EJHtJU8oPLeICDrNoF+dsttG+OgsThtCLXXwozalMp4FTRgjh7EVWJ6d4lmWXLoF7m434SybnUAhR1Q/+Ok5+8qL6RH+WGG22WPMRm2qtt0RuiQUXrDJvQTlpUOhi4D6urXCQCjHNSTA3/5AmF6FKpSKbdESyE+cjtp5gTRUh+Z4B56ibKB6qTailasz2nW+DqkxMAyRQo8M5hHwT5q6vI24P/WLA/v0nj9nyxXPsrjtX2br1mU5IUHUWTrvlEjPQJNcRuOmcC70s8tTY22yH65+zlvazMDuDgEKT5IPrEo5ZyimLF9ZadWmVFQRmAx5Q56hDUUBTQXkCJDyiKVWQ1LUEbUP2ldNfeRLEOFO9SDtpcDb1N+rz6D8c+IdyUW0C4+HcAgxkOuqhrkOTbGmI2kOPNti2p84Brayyd7yzxNZuIJxtEIUmjhJMAKrs7M8JZvLLOVUTpSDYhFP9+Ok91thxECWiAae/i1GH/EBKsyrm2eLaZVZTXm3ZyJXJST1Ts4UroAcFmOgCynBOqjpKnsQIcjh9x/l6TlaU15nE8JcMx7GrbBQGaprphTOdNKq/0L5Kt9zZXm5S2zrM/vkbLbbvuNeW1Hrtgx8ssNpa8CqZVO3f6YwFBipNHI7dp7F5P9DK4zv67NDBNsI00k5oE1HCgwe8QSspJ/zbpkJbdFmGlaGAliFIg5d08qRqKHs54yrnc1G3zU1oZBUUgJ9UoWQ7prbOMKkuA+Oen2eTbwpoZk47M9a5nb6UlDGOzjjonW4ZKIXDOEdQfTVj+M9/2W9f+voh6lStfeCuCrv2Sj2kqTC9QO9ObZfqFefkv4DAOVRxursIO3tg0J490AsAy/gKyDjN3BkaxIrKM2zFsiLbsKbAykuB0VRMpFXmcqoTtlaYWC/1TcrNCcoiSj8pOAikjrRRfuysflHFoq5y5g21F0hO56LHJd/YjH38ZEZjhdOHkwTlV/MR9Q1dPWmAc2bf+NZ2Ky3MsffctoyoHoTlLFT/JJibazvWl/4eanqAQgL4uvvDhHdtsKOoBPYPZQA0oWRGnfCigFpS4rXlq0pR9EUNq8gv0a2ZNCoNSi8JTwAjadySYpkDpwtAigJ1QStp3HCGZXbWOKd2pTxJHVcVUMWu+YSjOFf32oFzF88xdU81MDBgp06dQj3xGCFJ/c4amuaZurfTljxGf/XSMeork/dlev9y7neS500eq3upXwPnqqutwlGcUz/tJIUwu5OU36D9f+y9B5xcZ33u/8zM7s723nvRrla9F1tWsyXLRbLlBsYhhEBCkpv8c3MJ+ZN8bnIDJCQk3CTcAKEYG2xsY4yxLVu2ZTWr9952VXe1vfc69X5/RwwousTYxAZM9tir2Z0558w5bz/v7/s+z+6dPWq8yliPBsKg6SBQbHJmlKpnF2KVnK7yIo9jj2zlxufUNSuJ1/pSg/rtdAYBUzMpk9f6bCfDyCyDaRwVXqt79hkZa9loBSHojCOp3176EzuLLSihDFzr4K+llcFYNro5cnhUn/3cNspzoR6+r0r33hOrvAKDOn2UUQNYDZTiWDYDTm3EOoxl6eHjPuDlFmytB+lSrE8B3qVxTslM04yZ+VqyOFNFhTGO1aTTfzv1inrH9XFDnMwWu1wD8wI27ud7rLWwm7Cewdp6ksypizb8skPsMcHZrNG027L3rJuwSmjpYWewOmF5z3+9PS5UH4N69OlLANcDemBNme5anqlks728AMBfP6DgSKzGh8kfxqVm9e3NRKFtCn1lNe1NFukKuBSgshjubDliLYd9iQH9DFcYe9g4wOqaXZzdAXXNFsjwmQ2XA8BtNk4w8VurfNam2MKSkMfu19pWu2D2o9/3sADIRuIuv90c6R4VzyHA93yXnd1OYW1TpO7iHc6hbm3bMaDvPj3KfO6A1t2aqofWZiqX6+42OPASduQ9HDhiZunEdgB741PcSqhK5x6xlscC3A2szOOJU2+sDXAxBurq6dLOnTv1zDPPOFbIDz74oKqrqxlqXysLnPGXukXq+fX12uqm/fT29jquKjt27FBubq7WrFnjzGWbKuW7uV3fNtjvkTbCfv934NxywDngWiuiTn9vxZUf29DHxjo7qPb6aD37TJN27j+jWXOydd8D1YxDEpVEPppAsY16TTWav5znvWjG7NZkNDWOa9PWJp2/3ImVK/05MHlo3MtaqSjllyZr9oJMFk8kKCON8soYyx5ZnQUXTmmyMmVljXNSDrs7RrFt3qjXXntBpSX5unfdWt2y9GYAcb6LuurUT8qsjV+s3Nkt/Og2nHt5p/9MgHPvNMUm9p9IgYkUmEiBiRR4Gynwn+ma3sbpJ3aZSIGJFPgvlQIGNTS2hFjxGdBjqM+dawo7A/TbsG39ww9H6xYsOhEpmNj+C6SATTfUXQ3pXx7367uvsJoQq8X1K65ZtM6dyYP2u/us+WuVogZImE3rG2+84Two/6ybM3DhL//yL7GWuQtLEgty/eI3e6i/evWqA8Zt3LiRSVKbXPvJZg//06dPd1T0li9f/uOJyZ/s8f76DRcvbd4R0B//o091DWFW2rv0md+L1oceiGIF4q/O2OrilZDWfHyMoE5Yt+S79exTXtQUmOV4n2wDtBtf/0ZAX3jSrz68pyazavlrn/c6Ns8Tbcj7JBN/RS7T2qgbJyIjgJpNfkbgONvHNnuNQGiRW7B2zCZR7Tg75voJ1sg+/9lX+97IdUUCNGaBbSu1zRbbbIXMptVWa+fk5DjXaN8ZmeC1Y+y67DwW6LHA0Bf+9xcJ9rhYNb5WS+bNVQrBW/cQIY5BBm3MmLviAe5y84BhkpmEt6jBtfu3VzuXbXY++/3o0aMyK3H7PlMCmzFjhgPO2ee2RfZ3/pj4ZyIFfkYKRMpN5NV2tzJs4NyGDRtQRLkGzi1YsOD/VZyjzIUBU0J9vbyyeh1Yw6wWXYkEdhN4BZyJjjF5B4JohBMce00fqmDDBPD4CRFcsKCTC786Txz2o2azapFjAlNhN2MYlCjCiDWF+wkE+Dl/FHUG0sAdT0A83tQW0Pq52KimrdvVVl+vlKwMFd60THHF5XInp8qdmiJXCgFGi4YRpjILNmhWvp/rHiRsMYB1p606suA4SmauJCCaeCJOpghjwTRUcojAKTTERYwTZLCBB1Euizu6GOuZ3SZeiQ5EZmpmIRSAgsB8Y1xXeGSMuD/tlAU8YwniJxIsjyeESvDDUYohoB8eItg2AKjjM5UOrJ4Yl7sJcEXH0T54CWTwUBcACKnb9JqCJwHn8guVsHCR3GUVUkaiXCjshbGSjCZgx1HE+bg+FEXkI/1GaCdREQmOci1OGwNIgdqZhyCZm+uxCHuY9ik8xPdbW0R+hE2t2JoRa1sT2C+d70gwhSz2I9AfxjpIwI12byHABlNTcYJ+buATy78EbDDtOMgoF21meGSIY8AkRrgO0t0gLGveXDyIutPiuD/S6/x5jW/brovHTii5vFJ5S25RdHGJwlgUeVJQvoqjTbQgrxEBRvAYjGPKcmYPjI1wCIsujRDUtIgTqn/4IJGHqM3FcD1ATqa4FB61+7Ny108agYXZPRK5cqG+7E5AeSWR82MDe4044D7ZP0S5D48DK5AHTtNKGbX7c1PuXFi4OSeBYgkb4IFqUHjYygfHkt9OG25tfBJBUvI82Nigodc3a+TwIfIgUYkrVihmKsqByRlOGYpKppxiceojbTzAFx5T0/ORdkOWP5TV0WuKYi5Wv7kSSOtErpXy4RAv9ANWhkNDpPcwdQTVRoubucwmlbLh4hoMInW6EasG1IGw1aU+zt/PMTZxYG9T71yJ2CKnJitg6cJJ3EHSijQQZSiEelfYZ2Uc5SOi5u5Y9omnvpKWwbrzat+xRQ1na5WQlKPCBcuUWF5G/lFP05HBwmLS76W+chFOgJjzurFYlo96FTRLZq6H6G4IYCJMvvhRrHGHCQBj00zOUsZsH9IYtblxIDs31xw1Sj00eM7U5VwDCgx3cy7ASKtfFpUnaBjwjaq/rRYbYJTwSPfk8kWKzZxBG5VGsJG0QbHH7ik8TFsDxEll5VjSehzb1/YrGiffgsn5iq8C5sgo5HtQKjPIydKBNHSIISuPDvDAi8H8BMZdnN8PjDfGmHm4z6exYb81Ow6n6KFuxMd7sLoDriGo7yNNBodDGseCLsD+Fqt32kTagjgsLtPSgSwo+62NQb30Qo+eeHYH4FyFHlw/BZU4U50k+8kDL+rPCTRdCQRlveSPcQe2BbneIYL2Lb3DutIGuNPTzLX3WJEg0E8a8JOalqhJRanKSY4Ha8MWrN8jW/Rj9saxXtpgeOaxAXBG0jWRAH1cEukOEDoKNTVIso3x40M1zuqPWY8arJDEws1YstzInSHur5/jR9jPoFY3kV6bi4pPYIFnsil3RsC5Or2x7bLyi2bpXkCBydMs9wNYi6KfwgNHJmpj8dyfo0hEmlg2B2hH/YC1LR1jqm9pRoX8EnhIP99Be0cbEO9NVFpqpgqyM5WTEgcUQbCa+t0/bAkEWGf1fhx0hHpmY7Qk7i2WdDfudpS8GB1AlwWV8xD1mpLoBLwtX5JJa28c4AVtYw/t4tAQeTmCYiNQtv1nTsWWFynsm0CatACHffkbjdp/0qPKihh9+OFkgD4gTIAPg0lNvTsxGfgBC0Qv/ZUHxUnrbkZQnbt8JaAL58fV1kxAnmJnAfS0VI+ycilHeR6USN1UMZdS4+hvaJAHhtwaHPQAgXAubtOa9XHaYzfgeRwWkkmkg5eMsvvxjQOccI9D3D/uoeQhEBrlJ448TCB/4hNtHI4u5CjlFDeLMfY3W1NSxGnnzZIymWvGCdepDi9sHNIX/u0E6mHl+tD6TN2MbaXt67c6i2KSda9elJBisT6PhZAxq9Yh0vcq5fvCBb9aAUTH+dv6rIT0sLIKyPeMaKec5KKilAS76uL6zF62GwV0H2UjhX4BXg6VOuouqlrxlKn4pBj6RYADmjBT6xmh/I3zu7WDMewfQ7MYZ9dBV2/tNW7TznUMs5/VcVPRs/Qz29TUjBD7hbE6dGv7nqD+7dHtyk7N1oN3VGneXPoE7t2AOQ91JYH0svodRz03615TjRykXjc0YRt82a/mVsoaZc/UqlIBCnNy3coGRkpKiVIWIEQa+Wh1F+d59fUbmEXZjwOFZHwwMjjKq1vJ5F9CCuWbdGCo4Zx/mDHNON9DzQSuomSTf1ZGEyhXVr96+4Z1qb5GAwM9KLplOvNBXi87/mi7fvz3XjxD2Pntx55b7LnOIJTz9P0nUAsewKZ9ypQpjjOCqXnb99u+kdfrr80u184Rec6KXP87eY2cL3Kfdr5/B86Vlim/uJj6QhpzLdYejzK2bWmmHtYE1MzcqrUXNvaJpQ3IKvQorSBG6ekeZQNRplEfGOxoiPaxv48lIgDqCZR9a8etrWeYQLtgdqjUUcoiQytU3iinjNnGOMYPMe2hjfRSeePZj2GHM27yU+aHh8cpq0DmY0CZgMxmvW7tfiLlJobvtf7k8BG/PvvZXdT7XD1wV5nWrMYumbI2znOzm7YllvKZSP8Tw/mjAZvB5amfUbRRbh09Pq6O9gDDXsoeddbLs2lWvlc5ebEAsB7l5qA+h8iX3YuVt37aBFMM9VLe3PSfIzRa44wro+OA1VKBRymj/Kkx9hnhnNZX2JDFxJ4T+f5kyifdjg05HdvpXs43Sptox5gdtIvKYEOHdOqGWT73UOd37kUg4HsN1O2A7l+dp5XzaefpA/yt9Mv9wHL0wwbMmnqvpUlUJhAdbaU7FfVVKkMb7QsMsgOux5B/Xtoiq+8BvjeJa4KJpQ8lrRl/DZDP/oCH/sfmHlwapi/1MV5h3YrSsWhOwALVFsWNcVNDY7S7Q9Rv0sX6e1PXjI8NKAWL1TTGpjGM2fzYnfcwZrU5ebPZNjjeWuI40imZti2Z/j6a79q2c1BPAM41NPq0ZlmC7lqZpEzalPEG2rjuoGJpF+J4FkLQmTE96C/znp4c/khDaZX+ZoT766OsGXCZxHGZ9BFj44Cfe3bpySefxIq4SPezkM9A1evnrSN14p3Up3drX6uXN9ZN+9vmUWpqapw5+cHBQS1cuFBLly5VcjLPBpT3d3O7/vvt98j57fcIOHf+wkXNnrNcKamFTh0wwURrIywv4ilviakcR3/QVu/RM093avf+i5o+M0tr15aoMA+4kjwxRVeGJ/SFNl6y8kcdsraGcw3R7tdd9elCvU+tnX7qDO0AfVQm5TinMFpJqJImMVbIJk/tkdIYTxyvnTGW1bco3rM+38af/agR7tjxqvbsfhnQMA9w7i7AuUVcqz2NMfblSh3Y0wHnbOTCvfwnEhRwjjuZ2CZSYCIFJlJgIgUmUmAiBSZSYCIFJlLgVzgFbADf2hHWa1sC+ib2nEdRC0rkwXTpLLf+4JEoZOSjnIfwX+FbmLi0dyEFenjweur5gL7yVEAXgSnnVWLR+vForV3DpCkTgRPbf5wCTz/9tP7hH/7BsT21CbWftSWiIPHnf/7n+sQnPuFYTPys/d+rz+1aLdD9+c9/XidPnvwx/BH5vpSUFH3mM5+RKSWlpxPMfp9v/UxomQ3xPz8dUDuTaYso45/7VIyWGSD8k/ngX+pdvro9oE/8lU8tnWEtq3DrmcdiVYDa4/ths4nLV98I6NP/5NcFghmmWPrlv4jWB9czccPk0PvjLt4PKf1f5xojE5H2aj82IWqbBUEsmBJ5P5Ii109a2ns3/h15L/J+5Lif9zXy/fYaOadBdPa7AXM2eTvODKXZfpWXlxNcBKL40cStHWNtsN1H5LpC3N+506f1TyjOJQLEfGD1Ss0sLpC6ujV0qUnhLsABm3DNSlTKXXcrdvI0EoMIwltsZtW6ZcsWgiwjmjNnDqolk5n49zrfHTkscv2R+4m8/6v8en17cuPE6/Wf2T3Y55H3rm+JfnIc5es/uFnLL/v5WWlzfb7+B6f6tXzb7tsUPxoaGrR161YnnUzZ0IIVNoaIlG/n5invvppzGj64V71X6wgmArdg2eYBlomnnKdVTVJixUwAJSJRzO6HR3o1Vn9R/WdreG2UbwAvJQKE8WnYIVZWK2EBFjLAb2AYwCn9CnV2aPj0RfWcv6w+lNfMdywasCe1vETpnDuKoPnwgeO6unOX+rE9SwL4icnO00h8KvBcKdZMc5Q6jzqVBjyH4o2bNf4ugm2hpj6NnatX28lTGu3vojARQM/A5rWyXImVU+XJKwHAAZIZ6FXw6iUN15zWQHOjRgcHHJUAb1qy0qn/SdUzFF1a7oBakDvwP8PycV+9Z7nmq80AF4ME3wmiZ6crpbJIKdjqRWcDSwE+hAwmq+uV71yD+uqvqK23E/taQhhEuwsnVyilpJAy7lIn7ceVN3fITfuTBhAm7m8UECRclKPcm+YqfcZUxaZkEaRj0ANV4TLgqrtd/ksoFJ27qu460pmwflp+ttKAtaJQmPBg+UpGcn/cz5UrGrhYi/0TcNdgvwMReLE/SiqvUOr0GYoFYjNI0cC0UFu7grUXUPa4gAVQC23QEPuzWCE9VxnsHzulQuGSHAJ5RG76sXmsb+X8TaRFiwa6egB2fAAiUUouyFXKzfOoh2H17tmr4W3b1Q/0Aj2g6Lx8jVEexonOli1arCxU6DzcH5I0hl5SrwkqG/jV1SHf5Utcy1UNNHUQbB1WNKBWFmmXNG2aPAVctxONHVWg/qpGLpxXC/sP9w85SmlJcYlKLy1V4rTJippUDGRG1NLODwgauHhFQwTH+prIQwAzCzh7uKbUqsnKnDVLsflZ5CGBJ9I71N+r8YsX1H2RMtraDkDkwyU4Xmm5+ViWVspVkK3Bc2fVuuEVhc7XULcA3/KKNZSepSCQQML0aSomeJmYm4MTGTAVlJLLT9nvapO/9pIGzlzQAKqKPgK2SVmkW0WpYin/nrxsOC4D4giO8r1DJ2tRIqnTUA/WpewbRV21+pc2bYpiUM9xWXTd2r1x8qWtRT0HDqr3cgOQCulBYDU5FeUkFDRi585WVFkZwWA6ppFBhZpbNHa5Xn11TdSxDsCgESASwI2CIiVXT1d8RoaGDu3T1a2vq+NqA31RgtLzS+QjPcPkYe7smcqin3LnFpAf9PNcr4FwocEW7rFe4z0t8G7DTuA82jwdgUZH/WbZSh1OynMgIv9wG2pb7YApwCkE2QPU4fG+cXmB3xLTiwAJc4HWgPis7wVku6aqRTga2MTfvl++5r0E7kcVX7lM8YW3sA/lCStfyAPgwcsKtZ/WWC+QHDUlKg6YAQgg7B/VUGuX/IkF3OdNik0vJRgfpwDp5QfE8w80aWyoy1H4iiX9oihPUZRTF3awdOTAVPGojAV1/NCILtZ2q6cPGJLrSwbOLS9hISMWj3nFicJJTifOjqq2pk8drUMk+TiwhkFiqZpSnazFN2PBme0BmgoBzvXqieeOakpllVbcTNrE+nTxSgtqQb3KLUhyVIGqq1OUlwncBgBg80FjwAAdPSEdPN6vY2d7cEZuJ938qqquUAJgcWsvVnbj7Vp5S57mTUuh7Hh09uSAjhxsBdBKVE5GJipgI7pysY25Iw8qd7mauSCJNBIuB0GdOBPUxQut6qJseMjb1NRYlZVmaeaMTJWWGdgX1rmzwzpxshc4ZZA2Y5S8DgFDxWhSVYoWLchTXk6smhqC2vhKozZta0ZRpkqLF6TQ1o6rqaWeQPIw6ZGuW1eUauo0IKp01IEYJpkCkAEZPR0h7AmDOnamQU3tFwDQYlRcWklwOQUoZ4igdEDTqrM0bybAGIRVbU2nDh3tpDrQLgN6jvaP6vLFMwTCeY68qUjTZuWh5OclbYM6daxbl881api23GxwEwnWFxenaQ6B8IpyguAUucPnxnXmTK+arnZTTn2U2Sia6WRVTcrQ7OnxKs73qL0zqK9+q17HauJUWpikZYsx8R1kUVUddp30m4UFyZo7P0dz5sfzfE4g3TWCag2gx3iM6q+GdOJ4SKdPAD5i15ZB+1xUmKAUVOY6acMTgOhmTkvUbNImBOxy6GA/Kl5j9If0aUnRwJK9lMULDgQ3d3655qBulg6MZjBgN2l38GAvCzw61NFFf0CblpQcyz1madp0A6wSCbK71UBaHD44rMb6Dux9e8jVcYBLL3BjgebNz1ZhLup55MlLr47rc18+C0BUoNuXpCorGQeMy40AKEEAtAxNoYzNXBCtwqIo2VSUAW+Wh23t3N857vPYIJDoOBBOlMqrEoDFPRqBYOzva9PSm3OARRPkBSZqaRtGRacJ21oU6SqyNQ4kfvHyVaCdNk2dXYwVYaHSU6NQqArpzOlh1db2qLUNpUUg4zTGEKWlCYzjkzWpkvtjbN7YRBk6NaIzNbShQF3R5HVqYobKipNRiU5S+aQYLCvDWMoG9G/f2sGQIk+3zqefTnbp8tVO9bFYMSMzhjTL4bzxKsgzIIjCwUB0BNiwAyW9wzg/HDrOfEX7ANa2IU0tx1oxL1oDQD8d3V2aNztVS5eYGqZUe459j4wAxfUDuSTDLw9hZ95AWZZmzSzV3JsyASejUJCk3J8MAVZ2qr0V9U1UL5MAsgqKKKOzijR5UhxQjoCiBtXYhvV0qI/2JOsXDs5Z32rPR/ZjY8ympiYdPnzYsWgtKChwAJ7SUvq2H6lfRZ5h7LjIZsfZM6P9RBZbRT57J692btvsfLbZNd0IzmXmFAM5MZcEWBULBBYAmupqD1IHgzp4wM/1DzkgcUFenIpKgT85ZX9/g2ZTPmfPyGAo41VT2zhj6CvyD8eojL7eWPfTtZ1AjN1YNiZp8eICQLQ4QGVR//xY6fZgR9mh7r4eJbIAobg4n/qSBVAYqwTKmcHTx48NAWB2ogrXBfQ6yn4xlLdCzaNOl1UBqjMMPEI5+/znj/A5bebCQsqwh/pfr56BbqCeBNr+bC1YlKm8fHTvGIOZUiMD92tqbvv8On1qnPOPcXxAZaVxKiuPpz/26/zFs5pPvVqxLM8B9bpo07ZualNzQ7QmlTJ2o0+4eJV2pKdOeUWJum3VdOXRLiAWRh8w4tTBlpYejTBWi4f4KaHNmzOFvoJ+Lom62tpndrGDOl/Tqj5smkP0zwkpiSooSNey5dlcdxzteZhFbAE99WwzgFpIyxdmqrIoVi11LeptGVQKlN3UqmzNmBJP3bJ+mW6XGEiI/nCA54VaQLSt+zpU19RD/odVUpynorxUZ41MJ+OoEkDkW5dkKYe6fP4SeX1imLamV8VcAyNPXa6vU2dvqyon5WrpoiLn2ikI6uij/QKqPHuuT/W06b19qPYx3slj7D97SqYWz8QuNyeadA3r6OkAfX6P2nAK8fPcEE/DWZDFfjPztHBxrNIBhLfs7NO3nxpRQ2uMFpH/VWXRGu2m7UBtLh7YuLokUXNmc1wZ4xUgKhdlS4xbYAJV2xrUydMDOn0Sy3lo6IKMZN1xO2rcxT7q3AEHnDPozBYAGoCWZDQyWwRGjdQL581f8D+RummvVs/tWmwh+N69e2nvX3MU8szOedKkSe/JAvDI99v32u+RtLDfI+BczbmLQJCL6LPi1NzYB5BtDgge+m2vqqYka9HNqYCKLrVcidL3nurX7oNNzlhnAf2kj2emRsrqKO15fnG6ps1nvDEtXplAnTxSQFICwQFVttHW7Dnk05GT/dTLUeWn01fRZyUCRrZiDe1yD2slMOWUyngN0C/VnPVrz97LjGHSUaJLpc71AV1e5fp9am87QXt1kDbfrFpv15Jb5nK91stY98Q4wBRMaQWsNYTnc16dD3+OfybAuZ8j0SYOmUiBiRSYSIGJFJhIgYkUmEiBiRT4xacAcwDYAYT18msB/fO3sAprYwUkD9OLprr18YeidPftTAhde076xV/cxDe+5ylgk+WORevXfdpyOKR0IJePPRCt3/2NKCZsLWj8nl/C+/YL7EH9i1/8or785S+rpaXlbd2HPVh/9KMf1ac//WlVVVX9+EH7bR38Lu/U39+vP/uzP9P3v/99ZxXv9ae3gPdv//ZvO5BfRUXFL/U6r7+un/d3m/qsuRjSZwC7XtuHMgtBgN9fH6VP/iGT8qi72QTAL3v70uM+ff7rAex2wlo1x60nvhSL3czbuzAri2a7axPM9vtP2wyYMasFAxoiEzw/bb93+p7NKx9hMv4P/25cx04TmKVN+QPS9n9+Mlr5rHKcaEPeaYpO7B+ZkLSUiExKRt6zvyNlPDJ5a/vZ55F97NX2u7Gc3/i3Hfef2SyIYtdi543AfHY+U96y1c62GTBnAZ4IRGSvdt2RLXK8/X0eG6J//fvPK4kg/u0zp6iICW4/MEI8YEkCQWpCa+pncjP9t35H6StvI/Dx1tSv2RqZqugVgBdbKZ6ZmenYHEW+09IpEpyye4ikX+TaflVfr28VnWu2Bv5Hm5PH1+1g7RO3xgTvj8rD9Z+xEv/af5Gj//1rJJ8sjSLpFNnjp5WxSPpFylnk78gx7+fXyP1GynEkPSwoaWMJg0VNAeT22293wLlUAvaRdLD7DgMUdQHr973xsrzDfQ4YEwK4GYxBwQtwKHv2fGUsXo4qWpp8vV0aBE7oOHpAo0BJ6QODioV+MGWsMPsHyyuVedd6eQmIuAkIjjfWqfXQQfWdOiN3V5ezGt+sEce8KNjkZgHkzAbSyJTv+Dm179qhwY5WpaCiEw2Q5EtMVQyqcyk3oaaweIbCppwGFGMqV35Ul3r2nVb3oZMabWpQIgpWHgLnA5QbT1qGsmfMVsYiVHk5T+DSeQ0f3qu+mjNYFY7B+IHe8TOK4lg8MFLqnAVKnAkURKAiTGC/79wZtR8+DizWrGgCVjGoBBDh0ChBwlBhhkpuXYxlFKAd0W8fAcbBXSflO3kRcKMfq1bCl0RWRwiopxbkK3/GNMVkZqgHWPfqjl3yoCicCZDlScuVPwlFq5ICZa7kfLOnoa6XgYtiLE5ZgFxNdeo7flhdR04qurlLcSiYOJZUnD8IlBY/Z7bSUA+MAfQJ1tWrnfwYqjunqKHOa0ETpKAGgQbchRXKnH+zsmbO5/xp2EWi3nWAfQlg+YAJXajrxaBeEkDVLRgdjz1uqdJvnq3oOVUoEWEDexbgZf9p9dRelh9YLRbKxZpIl8kroSaXefcaeQAphvcfkg9Ac7itCZYJlTFAnXGU5nw52coG2EydMVeeTMAo2kWyCDAAZbI2rE9RqOtGfXOksUkxjpQQilagNn4CrRmzZitn8c2KBRAItjbIf2CPOmrPo8gwQowTyyMCoGihIBmSobgFgHALZyqO7zMQboTAaNubb2oMi+2ooWFUkQgqUU77oijTJeUqWbZMqQbEAQD627E1O35Mndjo+ju7Fce1RQE9BUkTUy5MnkyZXjhb41cuqeOVV+WiPMXyOfI08qVlagy4NGrBDBUsWgRcmQs4hxWhKRx2Nqvv6CH1ARaEgNHikCUxO0HwMgVTCMphq5k2b46iy/MB37rUQ5nrP3hCcQRrowAZrekcoB56CguUYfkN7BcNuBZGBmWMa+ggeNp19JiigZgSyRRHQY68CQOtpq66TcmAfLYFGXsOHDyirjPn5AP2Ji7riP5ZQRkn7eKBGjN45gmcPanO7VvU09RI8C8Ga7ccjaN058rJUPqChUqetwjQL9/5HsKCwHvdCqAEN9p4FlXETo5BJYMgM4wpFmYozuCRGYjOVmJeBQpZlK+BBnW3ASAAFUbHeQn6xQDPGRyWqvjM6YpKmw1Ymel0CpZSoBCUM8BGIJJw+w7u4w3SibQpWyZv0WqCzDncLLawwwCXrQfkaj+I7SsgFeXZgEirtiHatv5BJGGyKsnvWxSXUobCTbSGWppIi4uKHm1E8a6H/tunaJQHg8hOheLSFJc3R2PxS3WiMUXbdnfp1MkRDQ9gwYiCFq2dc22FuYNauy4TpakMnbnk05t7W9TQAuCJ/V4M5S0ahSJTH6mudGHvnqLyijiAN8C5l/r16HePEhjNUlVpNvUvGoWbYfX2N6PO06vJU3N168pJWjAnGbjKbDhRugK62r2vVbv2Nqqt2yw+Aa6QB4uGfPO5sckE3nKFG/WRD2J5dxv1nOeozZs69dKLJ6nzXmVnAF1ile1DzSs7w6MlS5M1hUB+71BIO3b1AgAAjWBjGI3dvNnSuVxDyslO0E2LSlQNQHSZQP/efW0EcS1fAJvJZ6upcd4+AvkuVFiqsC9Lx64TcG5jqzZsaqEWl6kcuCza6wOOwH6vF1gRxacFc7Poi7I0Y5YXgI2ywilbUfPevrVT+/ejlNSDNR6qhHFJAHJe+hx4kMHBbqUmBHXb8kLdvQZIChWgHTub9ezzqG+NAJall2AzikrPUB32ogEtvTVHs2YXAFl5ub9+nTsDOMpYLYZ9TCnUKkAmweuli4uAM5JZtDWmFza16ypKQG7sFb20nWaJGBUTUGVlGt8LwIQyYC9KZv/69Uvad8LKbYxKcmOBZKMc0G6A9jEYbldllUcPfbBCs2dlolY3DhDpQoktWm8CURw+BJACLBYLIJqQkAycxzgSm8gmgPJi4I+712Rr5XLgYtJpw4t92rS5FzW+eGWgvuZBqXXc16YsIJ2blmApeUuKklBwakTV5tDebqAEQNoeoFQoE7ModKHalpUVrznzsEhdnEG76dPBvb06SiDfNwaoQQvjcdOXqo0mMUmrbp8ElAjoSgPx4kaf/vYr9A/RWZpSlIDSEgDg2DjjCVS18A/NyfNp6fIU3bY6VfmAf+hiouQV1P7Dg9q+s1GtLajmhdKBQrFWBAgRlu3d3Z1A60362Efm8V1ZgIJuQKM+ffWrFwBZkrDgLUSRBzWmsS6g7U7NX5QNiEWfQYOy/2Cjjp0ANOqhfTEFS4cwCQD2jWvh3DQU47LV0+0G2BnSmfM9qIRhw8i53LTh1kvkZbn00L2pmjsnUYg2asehgL76ja0oy3o1pbgS5alUDQC79qEG5w+i5oZN4oplJTh8JKiizOog1ntAr3sPdFMHOwBhsItE5TEJdboUoKwg7XXPiBdL6TYWtuZgwUybRhk7AMD0PPl4peGi8vPigVgZwUBnpQO5LFiQqXk3A/ZHh7GAbde+QyHyD+Usxg7RAJduFDgZqmnhPPJlVjpqe6M6ceoCalbddHkhZee8e+BcZDx8/biQiv7/bJH97AM/ckimnG3gnC1MWkafavMY9lxlY1E7V2RseuOJ7H0bp0b2u/Hzt/N35Foi12zni4BzfuD+oqJSVM7y6W9ilAFjHcdCgO7ukE6dGND27R06A9QYDmGViKJrDHa/HvKhnXZ0fPSC7r2rWGtuL1VWplcXLg/rq187DvTsU14aZZQyTVNJ2R7X7Lle6mIGMHK0Th4b1r69PQBa9Df0SwH6EzftaXpGgqqnZACNJVJ/A4CrPVg2D9DemsIwSnHUQVNbNTht1epyLbw5i7YPVfIjAf3d3x2nbUpTFWPJLNat+P19qN31AnEOUQ7GtXxZkZYsK1QxClZe+ibWhAA3+/TDDa24V4wzjqH+0UfE03bGcL3Doz0Ae4d1792L9eEPTwGCddNm+/XNr9bpyHEALsbkpkg3PM7ii6hOVUyO1arbynhO9ujYcdqvw51YHXN/1HgP5VRce0rCmBYBIN8C3DfK+28e7dLBo/TJAwFn3MWQ1RkrJzKHfec69luRRR2nbuwJYGPaBFTdrbIiL0qN1j/R1qOg6xvtB04PIRIwWbcuS1MRc4E2rugDzj16bkivba/T8VrsZGkbE1mIksRzPF0+5w1qsK9dC2ek6KOPFAG3x+rA0YC+/8owc2FnlZ+TxP0BOY6hsM3z+szp+Vq5NEtzZ9A3A/PtPxak/epkvm6AcYc91QPr0YJEM7aZVhWvVUtTVAwke+AA6vX7xtTczmguGuAKi+cw8FwyynUL5tBH3cM1V3i0fXefHv/ukGrqwvT3MQDs9BeAnD4WDIwCZ6ckdGL/6dfaeytZxJdEWQCao+8+3eTXG7s6dOQAsHI3iqdxyULsWo88lAWkDrh56qiee+4HTr0zAM0sTyPPdfZsHHkWfDv16L3YJ1LvI/Xc2gdTPzfFf1Pvt2s22C9yze/2NVzfNkSuxb7Dfo+Ac8eO1armXDHlGcXxUDJjVwPPaHujhlRSEdQ99xeqivLf2RirZ8jDTTsvoxIaoq6xL/+N0p+abW7YM8JYqkK3ry7EjpxxM/U0RBVprB9njNKifUe61dKF6XdUojJRcffyXDWOGnMLfXVG6og+/tsZugVIrwM1221b/Xrm2V2MmZKBNfO4Xizkfd18L6pzfSfoV/drJiD1/fetwqmAxTFcs8v6QqC5AG2OPVvZnDnNrzMn9fOm6wQ49/Om3MRxEykwkQITKTCRAhMpMJECEykwkQK/8BRgjK/2rjCrPwP62lN+nWpEUYEx8vRyt34TAOLhe1kRy6TVxPbrlwKmOPi/ASYfe96PHLyuWbR+IprVp0x4Mu87sf3HKdDJCsDPfvazeuqpp5zA8X+857//ZMmSJfrMZz7jTADGoIzxs7ZIgDoycfez9n8nn7/44ov6X//rfzmKeZFJgMjxixcvdu7PJiojK3sjn70fX4lNa8u2gP4Ey9bLTWHlIlbzxf8/RveujnJU0X7Z9/Q//mZcj70QYNKRerjSo69+zqtcJuLfzmarLJ977jnHnsBUrm4sK1aG0tLS9Ad/8AesrrcVhLbk9N3ZWlHI++zfj+uJrQRamZBcXO3RP/5VtBbPmbB5fndS+L/eWawtikxE3liW7f0IOGfQTmS7/hj73TY79sbjI/u/G6/2PRbYse+IXIu9Z5OmHR0dDszayxJ6u16baDZ4rbCwkFXxKNP8yDokYvXq5hwXTp/Sv/3N5+Ue6NMM1KZysC7L4B6LcwuVTADcBczQ14K6y913K23FchR1WCL/FpvBtAcAWC5duuRcp7Xjdh12PZFJb2sbbHsv0+ktLvEdf+S0iJa/17LYYg529TZb7bx17T7sw5+0nZYnJK/t4rxtnxhI55zC2e1H+zr7XCt77OKklb1aGtlPJAAXKV/2WSTd7L3I+5H37PNfl+1aGl4DRO2eLD3sPi1NrM+xPqi0tFSLAHsMtr/e0sf2D2L/dunxxzVyaLeKi1DpAT4KQZ0Q95IP1beUsirUmqYQaAqpu/aUGg8AuF25SPmPQRUpVwkZ2QTGYzRGHRhGBTeD8UksoF4IYKd73wFd2LsPW8YwQat8paPI5YZdGiGwPwA1kFIBGJeK0hrKjV3bt6mv4TIqMHGAaYBkuUXYFuXJU1GsqJJclMyAUc12sq9LncfO6NKb+zTWhLpEQZ4y8jMBetzqamxxFFs8WLyWLF2mtPIyjVJ3+w7sww50UKkor3mp5yFkNczmKQRMFofaXGL5JJSwsNk8vU8Xtm5S59VmZSahJpVXrHizUiWdBkOkJYGx3DnTlYjaWhAFoI7DJ9V+AHvSgTGl5ufLC/wQRuWofxTlOvryTNI7JjNbfup749bNCp8/S7AkU7GTZ6NYVqZwXq68qEZ5UJ4z61AYGYnzjh7cread29XV0ELQP085xZP4AGUh2piWnm6sONNUdstSLCAz5T9/UZdPHQbgGFVOfiqgGUp2tD/deEiNJwBRVUxHWY/0JOHH6ht0/rUNqKudUxYgYHppHraXKYBzHg2imBNCLS4dxbnYSvKvf1Atb+zXyJFzwDc+pWJpnZKbjR0vsBn3NwYclYSanJd8DZyuke/1Term/mLI++Qp1XIRhA1TPmIqKhWdD7hjanDWL2ATGkIFcPTUWTVv365xYEIvwc/0okJUiBIIFHbpQl2dgvxeRZnNmV6t8MXTGtm/Q32oG8aVoWqYV0IbAFzGdQ1y7Z6qMsVNmYyVGRallIHWQ4fUcGgPoDPB7uxsyijlA2CxhzwfTElXztQZSi0swep3UF3Hj+r8rm2KonyY/V16PteMArWf8j9g/6Aikr9gtsKtKO69sklhlKC9lHd39Sy5KTdEmxWoKFJ8aYmi45MJXhHBRblr7NRx1e+gTANppRNNy8sHsENGuQflubamFqV6US8BhotdNFe9HY2q27tfYZRqinOBObHgCvOg14eqyzj1IbGilLJcrijK9Xh7u5p271bL4UNKBujMKShGxQ4ygOCuDw+1YYCN5PnzlDy1CtVClGEOHFYT9XB4cETJ1NUs+jizGwsG/BoGgHLzdwpwHlJbGtzxJgp2VwjcActMnspngHLZaeRhhaJIL8tDF3bG4WCfgn2N6kMBa7i7AZggCo4SJUug2NAISpAEr4fN9jY+W8klkxWH3Zt6r6i9DsA0gM1cegGqG/kEnYGggKOiEsuo35OwHcVmlm6PokUA0PQz+C5fr/xN2xRo2UNAksBm0W2Up1tpc7Dy9XeTdigttp/CUrkZi0QClFyjKQsFh7rl6+6g/QOKK5yutCm3KiallGOAgttbFeptVKy6CZSi/AHAEB4bxX5xgHH+kBKyqwGHV2vLiWw99/IpFK5iVE05Li/CJpTsHca2NT5hULPnxWNrF6tN27t1/Ewn95SsssosZadj8EjRGQfGyUwbQR0kUUXFCcA90gsv9ekr39pJGfJqelWFqipzUW6LUjvqPGdrL9IGBHXz4nLdtTofO1AvIEFYe/aMa8OrB1HLQuGkECANWDONlZT1DYM6dsGn4wB7ufGD+u8fL9N961LlRx3rxZe79Oz39gP8DWpSySRNJg+L8gneomRXQlAfN1pUrrq0eSuqluP5AEKpDiTk9vgAwXqYZwijyEOdxy7zxZdqda6mhWNLUYnJB1Sj/KAAFwh00Z8M6CYU3soKU2l7g3r19Rb94OXz6hnNJ81QF5saSxvvUn9PABWiduCGTq1YgfUg8FRRCcARC0QPH+nSc98/CtiCihhlvaICe2cUgBp5Hqw53wW0dElFOfG6/56p3B+f8d1vbGnQNx4/hmVemiaVTtGUSSmoAfmpZyFVVGOhmBKvNzYPaudu1CfpB6cDwBXmmp8g+YcNtts1hlpYFpaMXh050apXtzei0pav6dWoOGWjDIQ9dR+QbhbWeQvnAL9UUh+xG//S1+q1ac8IdsXjmlKRpsmcIwN1vKH+MZ0D1O0ZbNXau6bpjjuwdCtASa/fpTd3DOuV17Gg7exj4WMy+ZpPdfUC9vWrFpvyi42tmlw5Sw8/WKjVt5G25N+zzw7quRebKY/jKBUlaNrUFNTQ3EpD5a8ciGMS1+Mijw7s69UPv39JrQTeS0st2J/CvADtHO1ZFJ+XoSJVgJ3syRNt2r+3FXjN0itPhdle2gFbENYI3ObX/AVFmoJ6oIHULwHO/c2XgXNHk7UAhZw51SGVUHb6+92qvTSotq7LKij26pGHp6LEBFDqwlry8JBe3tSkU2ebaRKzNXVSEe0xUCfpUnPpKmqA54FLRvTJ/7YaUAS1K8DFU6f79A9fOK9jtVGaWpirWZNRAiw09doxlU3yov4Ti8VtWD986SDlpE8lJcUoBNLWAt4ODg4D0wzxdxLqhslAicMOqAAyiKVeNmWVRTH8bnXV6x7T8qX0J5MTqEPSnoM4OXxtM2WyV1OoGzPorzIzGROgIFtzsYly1wxUU6G1d+SiVEfeoh508OA4QNJxFMi6lQuQXlVRCLDjQSkRMJM0qWnE6BUVzg8/WK6PfiTNcQnZu8uv7zzVqlO1R7H3i+eck1SSk864CcXC8iilY5l5sqZDGzehpNqZhnJWAeXMrImDLPLpo50dVgXqr/mZCTpzvEt79u0CmPRybJyy6OenT5/uKFUzOHC2yDjX/ni3x7rXj6PtOcXm2fbs2eMsTjXLSJuXys3N/fFY/NoVvXf/Ru41cp827r0enCsoKMG2GCXV0QSZamYCQOipE6jHbb+qoyfrAd/SVc24IT8jkXo+Sr4P6MxlgKpAvX7rg1N07z1lys7yqKZ2RH/3j4d1EbXO/HTa5PJS2q0MlKbcwNBuLKmjgM98euG5y6i5BZkbQlWNcmyWzQHKg7BrziD/Jk2K1enTPdq184p6gTwnVUwClkVJEBvVUYBvuTochcXpM1C1QxX06GHg1b/drwtNyZpcUKiZVXGUBcaoPNdeuNKjusYzPLPG6q67Z6PiyDiWenvh4qhef6NZuw/0Ap6lq7IURcMMAzqxjLzSSl06h2phjT7yoTv1u78zU2ncXzOw8Ff+5aK27fUpC6Wr6ZMTAWmxkETpLa8girUzqO4Re3h982VUM9tR4cplcXEO0FMUICD3R/9cQdmeVJKpkxfH9ewbqBmPxtDf5GkyfV68jffxIg74BzV3QRwgbwZl2wXsG9C3n7jkjPVK8mNVPbmAus+zBP1/Pcq4p89d0OSqKbrv7nKgPNoq2u2zl0IO4Lxz33FlF6AWOomxUnKKhnpHVX+lm/vvQtlyDJXMUv3h7xaonHzfcyCgbz07ol0HdquINm/W1GKUMlOVihpdSUkcao7RKirwoO42ru+/PKqjJ7pQuYymH0Bpk7Y2wGKRAcZOZsFcXWHW8CG9suGKas+7SH/yGlW8BCC/8SGUfSH/ymjfFy6Op5+OBnYf1aNPDOrw2S76UY+mTM6i705wALuWlmHUNU/znNCthx9eouU3s8gAuLAR9dDX9/Rr6y4WPLDYaHZ1OX0faUYerrwljjKLouaZ49q0iecU6qDVu3vvvdepe5Hnvsgz4XtX+97eme06rF7avMtuxqxHjhxx5lcMnLMF6tfbTL+9M769va5vG65PC/s9As7tP3CGNh115pgKgN8c5mGjaS+wKR5rY05nSDcvozyW0F80evX0d/u04Y1TgLCDLBAopGzb+CseBdIhnTh3WW7GsEuWFGCnzDgPy+de5l73Auxv3HRC/YzhC1ggVcrzTRQwbWvzIP3BmK60S5MKvPrUf8/V8uXJPBNQx14FYv32q7QFAZSKyxkHZTKmiQVGHgfy20J/uQWVy2Ldt34N/dICxs9e5jZszpi5I8qpzRg54JxrApx7eyVlYq+JFJhIgYkUmEiBiRSYSIGJFJhIgV+LFLAVJF2sQnx9S1BfeSKgI1dCzsqqigKXHlkXpY8+hDITdgIT269PCpgylNkr/s3X/DpyPqTCFJf+7Hej9dEPYY/Eg/XE9tYpYCva/uIv/sKZWPARFH67m036fe5zn9MHP/hB5+H+rY4zKMPgBwMfLDBtExYG29n3GahhEEZlJSuZCcD9PJvBHb//+7/vyNpb8Pv6LYcA5l//9V8z2fKwA11d/9n79fdObFr/FDvUF3cFNUQAd+0Ct/7+f7IavMpUoH65d/WhPx7ThjdRISEQ9mHUPj/3P2IIhr29emjwTXOzBUw6nQmkG+/EJnIMlisrK/t/LfRu3Pkd/B0gAv/1bwX0948zUdMXVgqTjv/ns16tvxMYEXert3f17+ALJ3b9L5MC109E3njT9pltkWDGjZ/b3291/E/b/+d5zyZr7ef6iWRTmzOFN5u8NavUtrY2Z8W2tdsZqCYZWGST0Gbfam145DrNqvXSqdP6+uf+FivERhUlJmgy9nVzFsxXwcKF8uYCywxhiXO1QyorUNz0ycAlRAveYrPJ29bW1h+3CxFg1toLA+jsut9OWr7FV/zCPzLYweCeH1834JZBcLzhgEf2qeWJbdfDgdf2t+Os3FjbZP/8CAS79oZzjO1nP5F8tTSyHwviRfLZXq/fIt93/Xv2+4373fj5++nvSHpbmkZ+j9yjlScbq5iiYT5gl9n5WPm6fjNw7vSjj8qFyln50iWKo/ybh18gYKpCKCTEYztGIG+8tU4Ne7aq4fAuZQCZFZtS29R52LKiCgNEF+BhJQC040UawwPYMAysc3XDJnVdbVXh3JuVc8tKIKtMADGfQgT0/R76dmCmqBCwDfs0bX5DnZdYEIG4AABAAElEQVQvEKxMVfaKFYopnQSok4aFI6pXfB8yXMAtPo3VXdalTW+o7vQZpWdmafa6exUHMGDL7ccu16ttxw4UI+qUMqlEpbOmoByHbRt2rokoaCWx2CB62gxsGFFlClLHga+MMnDHotKDDWPvhsdVu2uLvAQdy1bcjtrYTEVhCWfeapaWQV5jEoGHxv0aO3tepzZv11h3N4GUKmXccpOiUJByxaFoBxxmAFB0nFlPelFMa1Xj6xsAzA4qq5DA/5I75Zk8T24kZVzYeYXjAbE4N1Ib8jVcQQHwBfUdOQQgB7S1iuuYNpN6hKLSxSuqO3hQnfV1Kps8BVWoXIUamnS57oLSAAxzbga+Ly8R3l+CMcIGkfSNS6c9A+jCHrBnz0Edf+NFpQMAVtw0T4lzsbTNwgKXMhEgABN0AR4BJbgBzvqwqq15eYuS2vuUi5V04pL5KH0R2ESZJYxSXYi6FiaQ7PFi51l7Rb6Nr6n+2AElTK5S7q1LSYsybEjTSQ+UNADGwhBH1kaETY0NEKZz1w41osKXAzSXvHih4hfMkycxRT4sW2u3vqmrF2uBTPJUvogyRoBx/PQRlNrSlXDX/Ypx7LDdnAx4x9oZFNzCHBsNpDF84KSu7NiJnWuzimdOUubMqVjrApahdocOksZRe4pNIE3CWJedOqdL297Q1dNHCXZXYptLew4Q4cZuFKQXtSvKKpCcNy9D4TZseDe8psDeAzBbCYq+7TZFzTelwjSAw0QHzHMBwwjoJXjhknrfeF2Nxw4plJmh0hXLlFKFCiOAma+lnTK6V25gw9SUNHlvXYlVY5cunTjOOC1epYtXKGkG0FpiLGVuDCVA4O+keNhU8odnjQ5Ufi5RxgPtbZq+ZAVqhfOxCQcsjaYeYYsWMPU0rNyiULfwnalVw4uoSV6sU/qkKcpceqtjf+wCsiMTuTdaAuAPD2o1oauNgHM71VlzQdHJKKktu02x2AK7UP9zA8AY2El8juP6ycM2VNtq1YGqXiA6BDxQjH3cNXWo8EAr93hWfVhMhWJSlFw+GRAGBa/OC+q4eFmBqExgupsUnzOF+k974KGOANCFaHN85pkJ9GbWc1HhMcAXrIFHmzVYv1u+jhqAklR5C+4kzRdxIahxjAJfNh7R8Fg3cBYADXUrJi4D91Y333dFY1dOaBAVwahcIIFpaxSdBqwKaBkEjgujOoOOJPWTimL9Bnnt66lTT8tp/k5Sb3iVNh+v0HMb96iqNEf33DUL+z7AOZLBb+qBwPPxBMNPnB7T959rA6gJaSHWi0tv4zqADjDgJZ3QGHRjK4nVZTwAZHeXCwWqHn350c1YQMZr7aoFqMWgvImVZUuHX9tQQjp8rFUFOV4gsWLdvCAVtbcQ4NqQdh3cj41rplasnqQqYKEkQIn6Kz49/eKInkTpqCjOp7/4gyI9eG8SoEJYL2zo1zM/2Cf/SIduWzZbt6+arNIylBoTeFalmF7F1nLjqzXaf+AqwPQs3YFS1iwW1ViTGKDPcNGexrhj1E5A9/98eR/Wg/1asXw2SlyAftjQudmPVpEfwBDsPhN4jmnGhvDV1+v0fQCjgKdUt91aqTV3AOux/yDPeHt2dmjrttOaVF6s+9eXoQoXQ9s1qhc3HNWbbx7V5OrFuueemapE6cVGChcuBFl01KNtO3arIDsZsGwB6YIlXxBwbnODvvb4SdQNUehbNo2fBJUBsiRhh+iNBzLuDuuxRy9j3RmliuIUPbgOVbMq2nzSbYzj3ZT/WJRfOhr9zLfU6OCpfsaA07XmVlSFitAg5HmvfzCIUpUbdtatlERgIYZ4//r1Vr385hAKeGO6f20hkGOacvh8bNjsUtv1KoBJKYD2/evzsaRF0Qm7tm9/9yLKR4O8n6n77wUqm2oAhgv4YgzQ5Tw2cFcI0i/g+TJPd62h3tKGPP3UkJ78wVXabp/W3ZGPGhXfkxMlN9cfRx4mYiFq9qGbX+vUd5+8ABiUozvvLNBNS72USxdlAI1SxkuJgM4DvX698spVlBM7gGlKtOKWHFWURonuFwU54FRUk8yeNwmIx0UA/sVXxvS5L51T50iCHlpVqIfviUW9CRVTVJmOnRxFlQlV1u4BffC+m7XyZtpR+JnX3mjBmrCZ/iNZq2/N0803JXJ+7h9rwpdePa9tW47Ij+XwJ3//Tq1dnwvE6wYk6gcOuqyj5z2655Ysym46sAAqcQnYnScCwvdIu3ajwvN9xhwIA91952QtmJ8DFIQNtD+IWl+APjbKyevnf9isg4daABAL9MCD+aT1NaUo3xhqreybiVVyAvW1k8XIe4F2/u0bW7Bhb9eKxdW6794ZpH88wI+0DxDx9dcBq2i7bke5cP29iQBZKAACom7atp+2LlF33DtLM6alK5G2uavFx/sD+sHr2KT7xvU7D1frdz6W7oBMu3f49MTT9YCDx4DmCvShD8zR1ApUsoC4cKPE+pl6+kqttu85j81lFXWlgvtDCRf/2yB1MMR1xzO2QlRTG19q1c59W1FCAnwEnMykX/llgHM23jbVbpv7MrU5g18WLFjgLMowtbkbx5dU4/dki4x1I2N5G+tfD87lA+v39CWroRnrXYNOk9x6/bVhbd5RSz/Tq2UrK7T0FqyAk6OBykOoOw7rpddHsA2u1Uc/WAmcgtoy7da5c6P6/D8eBcpq1CxAuwfurVbl1CQWa7gdq1Mc1XXsUK8e/eZxjY1kadWtgF4rserMpE9hnGtawaaaaHVkw8vAq/suYjOchu1mJYAnanC0VwEWZdC64CLjRYXMxpuxDjj3N5/bAVQbr+VzJ+uBe7BHnoaKK4tQjp8a05Y3a9Xe0ey0yevuAuKMw/50T7Ne2XSRsV+aViyt0NxZiSyuQMWMtmnrjl698upu2tsz+u0PrdXv/R6qzNxfQ51PX/2X89qx36eKglQ99GC25i32YuHMIm3avgFU7PYBgL20EWXlwLiWrpzM4mJgYdo8BhzMiwWUwBhsfNitl7Z06HuvXwAUKkc9M1fzZ0bRLtl6F8ZE1NWUFIBmoLR+yv0B6uC3vn1KV5vrUVQs1j3rJmkawKEpx52rGaTeHANkytTtK0r0AG13PO3eRubFn30FZU3sdH/ro1VAeGmcH6CZNubQwSE9+4MaFPoCumtFlf7w97JUVhYFbBrQY8+MoIa5V/Or0/TA2smaOQfFTxQv42ljEmnzzBJ965sDevwZU5FzaeVNcbRhcTwz8czB9YwwmObxgwVygMNn+vXCD1BJHknRnXdUawFAbgJgOIMC1OSC8vJckwpIZ+3Xvj0BQKghHThVhx1niu5aW6A5c+Ica+gWlP42ABnuPXQF5UD6tXW5gO0urGJH9NQPmFtuELCkVx95MI/FxKidEgfKQwkzJgbA/uwpx/bUVNymTgUmv+8+7rXsx8/VkTrxnlS8d3BSq6M2J27OLwb6mfOGKc3NRw05nUVWdp3vxbVe3zbY75HvsN8j4Nye3aew5J2vqShPr7gtFstsU4lkkXFwkKG0H6VIrOwZ07TWufX00x16bfMJQPCA7gaKXwkkl5Vm/U5QG+h/T9N/GST/0IOp9D9e1WDj+8ILKBWfO68Z85J0690lwOqMW4Dxr1zw6Xvfb9BuIN5JBWn6809mA84lqqMlpDdwmPraYy8yVvQxLpyBTXIFkCcKifhAb9n8En3oy8CghYwh1lEHb3HUZcMo5FIybdjM8x79t/PkwviM337ebUJx7udNuYnjJlJgIgUmUmAiBSZSYCIFJlJgIgV+aSnAWB+bhbC27gzqn77j04FzTCozJi7OdunhNVH6+CNRKi/lqW5i+7VIgQYeoP4eaO7Jl5i5RSFh1U0e/dl/i9bN81EH+PmfhX4t0uZn3YQ9GG/ZskV/9Vd/5UzqRR6gf9Zx9rnBEp/61Kf0R3/0R47y0FsdYzDbiRMn9J3vfAd7mf3O/jaBYRCdTRLYZKKtAly9erUTtI6AAm91zus/s0lAU837+te/7igkXf+ZXeef/umf6o//+I+dYPj1n71ffzdY9NkNAX3xq36dI6CD05j+9k9i9OEHopQGOPrLLPZ3fGRMbx4JivlH/X9/FK0//HA0q0d/mVf0s3N5666A/vhv/TrfAEDE7g/ThvzzF1DKo8+wGOHENpECv84pYO2+/diEqb2OjIw4gZ7Nmzc77bVN5mah0mMgUWSbNm0ayik3OSuhIyCbowLHKuTLp07r8c/+rVprawDZU7R0xnTNX7cWC7s5cqUno1wEYAGYEsY+LYqV8AaKvNVm7bsBTfZjm/UP9p79vF/BOeZr2YyMsVe2Gwcr9hF5YTu4UEqyjVz60f6Rg5y3+edHE+o3tFXX56vtGcnfHx91Q+N27fsin/7kNTKR/pN33r+/Re7xp91TJL1szGCA6E8bhxg4d+Ib31SosU4VgGVJQFJmt+lyE1VHAcoVxcR82Kf+M4dVt/UVLEFrVTJ5kgPCRVfOAP4CFsOm1UpyENglhqCDUCxqf+k1Nbz0uhJiWIV/90NKWLpK7hSAgRjkTwF1ghY0gdJwjWKR2NCmS6+9puYLNcpBcrb09tVKqKySUNhyA3U592iwyvCYBo6d1KkfPqeRwX5V4ltUuv5+7CkBoyhCro5ODW55Q+cP7kFNK6SqWVMV09Oj/loC00BmicBwMdNnA7Zy3lgClARGwpRTK47+hha1PfZFDV0+p7SZc5V+N4BWBWnBYMRAsbAP660Yo0UIsLRgB7fniPa89ioBxlRNX75cSSgAeHJYKBH7o7JrzuyAQCHghiAKNnUbn9f4yb3KK6tU0qr7Afjmw+cADFncg+8nWAHFgA1XzUldffopRTfWK33uQiWtW6eYysnsxPeiVNa2c6eadgGcJSUrKx9gcGBY5+svoyxXopybsG+tLMeiFo83qAgX9quQa6iAcQ11HWp5davOH9+r8pnVKlq1VNFTShREOcTsRSHLYOKisLMlP5qb1LB7v66g6lcRk6AcgjQxS+dzf6QZwWAX0FCIRBt3A1VhPeWquaLxja+r9sgBpcyepuI7b1NMGeBjfArBHKOzLIDMpQT4F2DJf/6o6je9ri7UACu5t9Q7VmMPO5P9kxUkbTv57nNvblcKAGI5UF1Md6P6zxFIRVEu7c4HFD8NmyKAQ8eWk1cBPhr0p6YeDW7er7qdu7E1RWlm1WKU6MrkSSFfoIVcWLX6PVyzn337UUjcslu1b26SD6vU6qVLlHzLzSirAaF5sUCi3Id40Dbb0XHKaxTgnH/jFo3u2qNo0j5+/d3yzpsNHEj7D/QF7kUg2eDOoMaPHlfz956Wr7VJ8SgJ59x7j7yVpZR30qGvT0NbdinAuVwA1N5Vq9SFittF4M4E7P/Kb1qhlFkAm5lAlfHUD27PjgujEOcD9r4KYNp89AiKdaiPrHtAMbOw4QWMdRGwtTpgwWwSGUvXQQ2hNnf2yaeVyDUVLl+txNvXYreaw+ds3JeLOkI0mMI/osCVBvVse1MtBPtiMvNVsPouJc+Ywf3RlzlwGfsCKrpQaQthcdqGilLPUK/iUZEsrKCeAe5YGdJoi4LtKG1cOKXRIKqS5RWAc1igtp9X96V6hRJLlFR5O8qI5KEXIDFMvlAHDcS0KuPUA77HI2yjA/Ua7cDGue6svLRB6dkVis5YxtdQH0Ioxg5QRuv2Y1cK3FRapczcKuoo1r5AoO6eWvkv7nRgd1fWNKVMvYt6UUY14j6CUAz+UdSFgDhNxdIqIPUvPHQF29dj2IqOq9O/XDtrZ+uHr+1TCSoy6+9agHUp4GYi8BLlIgh8aEX76JExlN3aUfTpIyCcqtuxby1HKS4B2NjLfsT7ma9B1Y661YrKyA9f7tVjT25BRSZTHwIEW7OGMg3A0NYd0vZt44Bnzew/oPvuytftK7N0cL9f3/vhCAH9eixRc7VmLemdx7nJP7M4ffaFcT2+cYBs7NOffSJPHwCcGwNUeP6lYT393H6Agz598P65BGGxYSbw7uGaSC7UkQL64QsntGP7GZR35gMIlaI6xnVj1RfFvSH4RtsllMKC+ud/3qG2jnaC3rN1x+oy1PMA8IB7YgCHYEIcFT4rIo31AW3YWKsXXgUCzZyuBx+s1q1r4gBEgVCx2du/q0ffeeIkaZOBXaCBK/E6AyTx7e9uQs2oBXhvte7jGFNxMhC7sd6Cyf0or+0ANPTqofsWA64Y9BzUpi1Neuyp08BQBVp/T7VWrogFQHTzHG/tbxBlJZe++bVzWBAGAHAz9IH1qZo2PU4xwFO4Nwp3Rfoeqf78OGl1QruOtuqmxbMdu9TyUi/WjQYHoo5IkY7lPjFDU3NLWF/+eps27wuqKNevj/1WrhYvSLz2PGhAy7F+ffOxqxSlLD20PgOww0X5HNaXvnJQzSiKrcJ68EMfSqR80P5QV+uuBPTa640omZ1WStpMfei+PAASAFmmfp5+ekTP/LAF9UKXPvLhPC1fCWBsC7bs/2svADFhbdrYoSe+U0P/nqN1d+dpxap4VOloN6hTMKLGxVJO/PrB8w06dOiyykqwbV9ZqmnVKLumUh6ARuAHqQKAhFie0+SSHn36u389j0JmkT72wXxsTr3KoYyOkodnz43rpZfP6vjpdt1zxxKtWZaonnY/sN0VHTs7qIULSnTXHRmqmoapL2VphDL04iutev7ZA+psHdAfffx2rVuPKing3MkT/frHf7yq843J+tgDmXrogTgHIg3HUJi4/pbWMDBpQN/57jalJvpQ8qsGFCpEodEUmimg3JuBLc1Ywz719FXt3HWZudAs/caHK1EGQmWM9tOx57X0IskNGG5H0WnnTr8efWyr4rDZu3/dDMCTcsAJlOUAXQ8BCT0PCNvd7deSRSn6zd/IwWZwBFvJFpSC6rQUJaL7PzwJgCEWa1nrskOoNvr0ze82YsnXB+hSrt/+KCqdfN/uHQb9XcLCuRY4phIluiko95ltH00Qt9jA8/mG1y5h6XhGsUklqDBWaOFCg+KigHncDkRkzVX7ZeBZrmnH/u3YQCYClto+763iXGRcSclwtshY2+a+bI7r+PHjqqurU2lpKeDHcmfxke3408aY187w7v4bub7IuNeena4H5woKisnrWFQ8AZBmZCsHuPd7zwxo56GLqqiM0n0fACwDakkCsqWT17ZtAWAtXA/qavTw+kLAuWzU4wDnzo/qH754HJtqgywrydtKFaOGGE37Z/VrEOjuwP5uPfbNY0BmmdiLlun2O+OVB2hqdcssYKMoy/20788/347i3Glg3DjduWaq5s1D4RcYluGzPDFmQY/tJx0+uqo6dhTVx89tU1tnhtbeNlkffjgRhTXUfmnbz57zA/K06MChM8B0QHW03Tnp2GQDrW3eUacSxl73U6ZnTKeMMldlCqQ7UC199rmjOnT4IH3Ebfqd353mWBI31I3r3/4Pqo8nPFo0J08f+Uiips5hQSflz0MH10Kbt+PNQUDsI6hKj+mW5dW67bZ8rIfpA+jcqGKO/XsP9erZjS16GrXRrLxSlOJydNNCYNwE+iraWlOytDph7XMfENjBQ0Hq4FF1dbdgTz0NqLQUNT9Mn2m7a84H9Oh3LqiuCYeE2Wn6zQ9k0F676TeHtRHArSA3pE/9SaamzYgGZI/C6lw6cgi1rscuqpa6uuKmYv3eJ9JVTnrt3U/bgOLciZNHdOvCfD3yQJlmzCb/gI/dUbboCoiahmT7rkH6kwFdbhjTioWJumcNqtX0cy5LB8b0dg/DBg4f7tUP6FcH+r269bbpWrISq3BsduNjgPAoD7zQx6KCzr3u2TamRx8f1okLDVjCZuoDj+RhfR7j9J1Wbp59vlVP/+AiynpVeuTBLFT3PORPj3PvgRCWnHem6zfvj6O/RPUOi+d4rnlkpAcVxHOozp3RSdSPbUHUOp4RbP7CnvNsszoRqRfvbq17+2eLPHvanLgtVNy7d6+zSHj9+vVOm2HX+l5d5/Vtg/0eSQv7PQLO7d59UsdPTAWcm0J9TQD2pCyRzx4Wgrh5xonm+kK0C1cvhOiL27Rn/3nsnxP0gQ9UaclCFoQgYoDIIND/GOqAWCHbOAtwbvq0ONQUDY7rUP/IsNben6U7H0jDHYpyRL/e0hDWd77dqo3bRujb4/TJPwJypW/vaAbefcWvbzzxsgoKPfrgI7OJHxQB4EZj4d2JyuFLev2VjZRJysX6e7QMANFs2Xnw4RmLfpByx1OeDQ/4sf944+fcGIOTUhPbRApMpMBECkykwEQKTKTARApMpMBECrwPU2CIB6dDJ0N68sWAXsZ+b4DVm9msdLp1nlsfQwlp+QqCHu/D+5q45J+kAIv39O1n/PrCo35dwa51KrYVn/rdaD10TxQr436y38RvPz0F+vv79a1vfUtf+cpXVF9f/9N3eot377zzTsci1VbERSYhftruNklnq26ffPJJfe1rXyP4sYYJ8Q85CnOmLvbqq686kwWPPPKIsxrQ7Djf6fYageRPf/rTzgTJjcfad/3lX/4lEvPVv7CJyhuv4d3+uwdLon/6ChPaAHSdrEidXuzSY1/0au5MC+S829/29s+3Yv2Y9tYGVcIk6Jf+JUarbmGlvgUafkW3I6eD+uTf+LT/NGudmXRfMs2jL3yaCch5pOO1ebVf0SufuKyJFHh3UsDaZ4PSLIhjU4AWUHnppZccuxD7BlNIsLbTwDn73EA6U50zq1Z7teOcSV1eQ5yn6WyNnvjrz6jhzGmVELBaedMizbxnrZJQM3IlI+GIAlUIuYqwG7WM9Ixr6jxvcSvXT0va99jfds22RQJP9t57NbH8Fpf2n/soMtvKtTszuM7ZbCr3uu3Hn93w/nW7/ORXO8+1/SJpFpkE/8k+TBhzzuvTK7Kv7fPT9r/+2F+336+/98i9GWhgWwTKjLxvrwbOnf/OE+ras4eJ/DSlYn0aj0KXN7cIO9ES4K4UB47oRZWrfucWBbtbUfKaoXTUrqJLsOS0z1G7gmtDZW2U70Alq7tNjc9vUOOmbdgvEdRb95C8C28GwrFBLJF5oA8DZVzWIQ2PKHilUeex+my+ckmF+TkqQWUtfkql/NRPD1IPbpQAXEgAhXqG1Lv/iM689DwBDo8mY4ucsXqVXCh4UAAQw8Kact8uXdi3Wx1YflYDLWUweOisOUfwoQ8bWqCdglIloNCVUFKKElUeMB/XT1Rs/NJFtX3rS4rq61HqLcsVe+c6uQuxGCXg5wJaCgPYGIxgEe/A5Q4NoD6zc9sW5RP0mnbXGiVMN3Uug7kIWBiZwn5h+xkFqqX9ufLKixo/vl95KHAl3X6/oqpnomwWpzCBRItxuBj8hwc6NHjyqK4895yS+weUswLVrxWr5SFYYlacob5u9e3GxhX1htRAEJW5yXKj6FaDDd4oNp2ZGWlKI/2SgJmisNz1FJjVZi7XgeLJmWY1oyDX2FanyStvUebyhYoqTte4kRUoS0UZbISihrjeACDeBVT92k6e1tTcAqUDtsXMpq1LTSSvrU5a/qGsJ4JfKG3IUZzbrJqjh5UIAFeybo1iAaZcqA/ZxxbZcZEmbuRZXMP9Gj6xX7Wvv4qS2CVNmzNbaatvV5QBm3Hx5HE/6nI1qtm4SZ7WDhXNnY9AYJxasGtt7ehWalaxMnKw1GZMHQ1k6QEE8eRipwu0HLraqf5NB9UK6ByP2lz+6ptk9rmuePIFODDsQamIe3UDgCCnhPXqZp3Zt03xKV5V3rpcCXMWyY2dqYAyQuSj/TjgHOCktx371Te2amD3HpTxUHJZdxdQ3DwgU2BCD3SNRa6sfFCfRvbvU923H1MyeZKy7FYlrL0XO14U2dyUIywUx7Ea823YjCpbneJWLsXuNUMNp1Bpw04tOzlH6cXFisPq1pufpZiiTHmwerPgmA9goZF86aSsZuflqxhwLrpq+jXFPZOc8NNnWKXGmjA82qtuAMtT33lapSnYT65ZK++ylY5Cnu3ilDkCx2GuCSkoBS83qHXLdl2FNIjLylcZeZIyY5o8qOA4sBlqFo5VcrBDweGraj5zQGNY5WaUlyutaArWmvmkAX1hEBvUzpOo3B3B/tel1BLqHFatofYL3F+Dwqi+RVdTPjJmAFyZ7SbBaS4mbNcPLMVJ+B0bxmAzYBwwQsspgtTjKMqVKSlvOmk/ieh7Jn1uC7D6PsrFUYUoZ5mV8xSXXQXFlcG4l/I8dEah+m0abryiYFq1EiuBZtNLuL4RhYbagSVb5BtpB5zj3qmnQfpw93iH3P+XvbeAr+u607VfMTMz2JYsM3PMMTME2zRp0zadwp3b6Td457ad6e1AMYUUQ03ikBNzzMy2bMsgW8zMLB2dc6TvWds9t2puJmmbTurpaPsn6+jQXnvxXv9nvW9XFc+h0uW7QJcbZmnHIew3G9uVFJekEWlJwBSRBDYDlZDqpWgUYsqLnTqyv1NnL5TSLrqwag1Salo41mlhSkjyVXK8l2DkLBCtAghh+54WvfzqIY3NiNfDW6cCjwFvAEQ1AhOcQaFmJ5BRf2+DVi2J1tplccBAWMjtROHS3g04FqLFSwMtiImwLlaTqBjtByza3oa6UpX+x5OpAGJh6gWUfWNHr97ceQlbyF6ghImaNTcWdSOyDgDB1KXaeofOna3Rvj23sMEMVkI8oEFKqOKwvUzEQiyZ4G1UBMAHsMCrr2Yr+0oh9w/RqLGlcm3YrMb7Kw1Vr8QU7ENJv1EKqip3ompUqD2HbhCInqyHHk7VjDlABUZBi/Wq7Asd+uWzORRxCOBcOqpIgbp4tU2/fHGvxXtu3LBQS1fQ32AhaKpCE0pfJ4516PXtJ6kbA6j+zdWWDVhRA6ntP1SlF17LBlpI1OaNAAzY2EUQnL5r8etUa6sbanUNKLpVqBVLtXTguZTUeODKIEUD5o1Mc8eOGytP7juPna7U63uucJ8ZiNUeyj/YLyckBAGgeGC95onCOOpE9HtV2KH+4CfVOnPVE0tND332U8B4Y30tqMLcWt2+2akf83pLG8pGKOPNmGkAxVYLnLM5krR+zWjgLx9sTo2qsFA9durUqWa98up1QBUAGKDI9Wux7ObiX34Zq9Zd1crALu7xj8do6jRAFeLkLiDONPRe1NQuXejUzu2lqNe1Ui7YwGZgFRxtrKH9uA6sJIEJ7dwIHj/RoBOobTU12lA0jKUc4wEBUNdLx46RMkTcFDd0OmvG0bf3tOnff5SPWlWanngoWqvvZ7MY8KMBMotL7Nq567bOXawABJyrlQtDVFfZq91AOyXVDqxmUwn4M5dOAjZEfdUonx461g44d1kVhTV66vGlllWrUWm6dq0dKBOb2aYofRZAbc0qQFvKcBDwZJAMam0VMCLqPTtygMyqUJ0LxuIyEQt1vj8uiPR7cJ2o/QEGHTrcCkxZwObFToCZEFT/QwFMqacJ1O1YrCfZNOaP6lgDgM8J2tlPf3EEtTF3PbR5smUbbEC+HvrPG1ds2vFWPefr0PQpgYBzyboNEPgSqkO1Tf26H4XIlZsjmKewDkCX72QsOc1GtWdfacV2sloPohRpwDkDW94F58pR58tHbSoTkDRdcbQpA84N0N81YOmXTf3ffwjl00ob7QirYmwkU5PCLJvAFGAOo3ZoYz3k9PE28vyyJk3rV3KqN2poUf8pinND54/vNXfuQIIsLy/Psmk1G0QnT56siRMn0sZ/e4HyvT5rhp0/1uFKp+s87wXO1dZ5Yr3Zi1IoarzhAXoJFceruZWaCpy44YEE2grqjD421s09dPmim371sl3XGf+2rI4HxkV1FXDuTkGPvv3dC7SbVi1fNEaPfXwk/QdtlDmQacT99GsFd7DS3lHEml8rbdQbwC0SuBiFSCxIE5O9FI8VqBcwztmzrTpy6I7Ky9oBH+NRRcQymfJOTvbEmliKjnED9jR9n5uuAmT/8z8fZl9EPMqWmUA5/ow7tCfqaGERY85RILxTeYCimUDWMczd7ZZ64fkr1Zo4dTT9SDLXB6QFDGR2LVzORq3t9Zs6fOyMNqyer898ZqwiAbDLy2z68fdvofLmo6XzU3Gy8FVaJuppzJsNLtyGUuitGwDdqJPezqtASQ0rV/rRpMQY2mGAkrm2EfQfNAXAzm69vbdE1Q199DFsAkgNQyWNnzgvxhY3AG1UH4HAmhnrzl9y6LmXrqD216DVy8bQr6fQVpnDIdV8B6D4uW1FWCD3anJWgD6xNQUFRy/95JeNgI92TRkTqv/5Ob4zlSLgeRvj9a1bjIPPV2O73aoZU+L0JOBcIopz57BqfX07tqh5V7V6YSpr6YkoNnKf7sdc1J0NMYZg5jpv5WOj/k4zqn1FQPrYcKdEcW1RjBeMh8mmHN1R+gQOr8ay+S0DrTXKNzBIadhuJ6LEnJqAKifzgSTsl01faUDBk8e7gTFtyi+t0cpljAkPR1LWKBgz53dQb7a/1a7nXi5mvE3Sg5tDNTrLQ+cvNulXr+QBY2fqkQ3hWkPf68UUrJdyNwqAbR1NgLxF1vq2UXw07W7x4sXWmoZpj6Y9vNd93h+r3X3Q97japet3E3azO3bssBT2zebyJUuWWO4a5nveb439g87zfq+7zm3ywjx29RHmsQucM1atZ8+hVK1opaWZuhwMtB1A3fazyjoERUIDxtaUDDAWN2J/XAQUB/z4QAqwqq+lGmqUYffutWv3QTa12Bv0wKYITcXW/exJm7a90QwM66VND4dqyWoUHNm7w3YcbFwHAC+79NbeLuDoPn3pqQgtXBykhqpBvbPbrl+yicDYrT/62HjmNGzUCHCnvbRo7669gHP7lJpiwLlVbGKYxb0xxK2TlkcTN3O6Afokfllt0Xry/TLpfV4bBufeJ3OGXxrOgeEcGM6B4RwYzoHhHBjOgeEcuPdzgHVq5bID5qXtdr3xjtOy4SP2ogVT3fWlx71035y7uxnv/SsZTuF75cD5qwP65x/26/B5p2Wv+MQWFK6e9GLHtLkdGj4+KAfMDlij1PbWW29Z6m8f9P53v56UlMQO6H+X2RXni4XXBx2vvfaavvWtb1nv/8xnPoOVSoxl6fX666/rX/7lXyxo7vOf/zwL2qhY/J6HkeF//PHHrQXKd3900aJFlq3sDKwCjZLMn8tx/c6A/uFrNh0D+jIB17/AnvjvP+9NoOhPU/+b2ga09iGbsksHNBrrhTde81HmSBOcuTdzvIdF/C98hUDbSSf2EtIIFun/9R+9tXLRXcuMezPVw6kazoE/bg6YIIqB4QyEZhZKc3Nz9QvsKI09q1FHWLVqldUnuxaZzftMf2+U5syiszlci66DQEfVfP6lf/zfqPHcUjp9/H2oE2WtXqmAsSjcBPuj7sMCPOeB3gAuMQpc798nD13YNecyf5s0m3OatLj+No9di77mfff0YeIPv3XwBNf1Wwu4XN9vDgO7mb/uvsd6Zejr5ol3ffw3n72bZ9an736JlU+uvDL5Z37M30Ofc33e9Zzr7z+n3y4A01yjKw9cdWtofriu2dTvtsNH1HLgsAaxf/Ryx9DSFyU2AFCf9FEKAPTySkxRe2GBys+elLO9WUmTJiti9gJ5pqZbsJMlgW0o7QGUnFCtcWAhVfrWDtUcP61R7OiPBELzmoQ1KYGmQTaHWD6mgGBuQHEDXd2ooQHOHTyILU6pkpkrJQHt+NC2urAcdXPHtg1VCE9sWp11bWrHbjTvnb0oRvgpbeFChS9aALwHEcJu+8HOZnXkXFTRuYtY8NVp7PTZiklJxGa2XDV3CoGyeuRjJ18A5bxjIxU0IhVAb7S8ExPVX1Ksxheekzf9RtDCpfJauVqDqDm4exBENDv5keIZwDrTrR87zOI6dRw+T+DylOImjNOY1csVmJkBoMXNmCFITJ2kPZufQRRaBthMUbJ7l+xXLioWy8zApesB58YCzjHHJIgGgwbcRh9iwLlr2Sp6a6dCe3oVswjAaO5ieZA+N+z0Brq4PqCsht17FEC+hUzExpTvaQTqar1zRz7NjfLDEsuDNHgQhPcaPUIBU8ZjNxsrx516Ve07oRoCb5krFipyLjajAAh9KHaA1MvLQZkTzBvstclRWqiC/XvVnH9bGamplN9KeWYBtgWjREhA0ArS0DhNkNMd4G+gsEz9e47o9uVL8gWES1wHVDbKWJMGAk7QDskKb3eUupjUDXaguAZgd+fIQXXXlmr8lMkKW7REHmnkH2kZIDDvLCxSwe6DciPgGDdppvyB9ho7UaK7ky+vqkYF27Doo/54BAfKY0SCfCeNBNSLA87qpFwuqakAIG32ZMUunSNv4IlBE2Dy5PoIBlOK8uh1yr2hW+079+jaheMKjA/T6JVLFThuJiAc6oV8t4Pr7GeiZwLkHqidedXVqwO1t0bARXf/AMWuApxm/u1uaCSgQ6ufo9sfcPSq98xJlTz3S0UBXIZQht6r1pHXMdQl4Mi+btmzb6uP/KosuoMK4kxFzxqr3upGNV1CHqe2DftixgKgUmcotn4jsVgelw7IGi07QciaI0fVWFWp6LR0Ja7dIq+0LHg1066oR4At5nADXh3saVbjiZO68attGhGdoPjV6+Q1aw6gqNnEw/uoc0Jdw1i7ug0Az5VVq/LgMdRVbykwOk4jFy9S2HiAOGBQIn4E5ChE6ihycvQBJarNxYaWeh6VPEp+cZnkbRJfi9LhYL0Gm66ppeSKOkhPKBBgaAhWrXUF6iirkDMyXYNjgNhCs9Cnw4KWuufhJDLsQAmMOb9Ry1M/6n7tt7BBvqj+7grsotMUEDlXnuGAor5Er00T6y7TQM0ldRTdogyi5T8Sq+ToUdShSGyHUWfvztFgFQqBNVjXBY2Sf/pS+YQlEIFuVl9tvtoaCtTvbEQ1zdiwMdgA5rr3tckb4NAN1R9HzAJV+izTuZtGZaZR9bVGCQxICpWPKEDGzCx/zb0vREH0lVXAc1dQiikoagR0wOYUMMaHgGc04MSEcSjUolQXj6JVNdDOm7ub9dKrezVjcrIefWAqCl0hlmVcAwHVswT5d+2rVW9Xg5YtiEDRKx6rSofe2E0/Qh18CPWZBQv9ALmYJ6AU2NvtgU0kVqBvtKDOVaAvPAGcszFaPcBuBpzb8c4l1g4ceuSh8ajJRaPORj9G2owuSVefQ7UEa8+ebtaVq70WxDXo8LfU0qMjHIBh3gTjAxWX6AUsVYsVXr0K8z0BkP25x/Vj04G30oCjJqD6M3VagOKiUNSrcWLZWUaabiktfQoQRrwmTjOWoLQj7k2uXMQO73lsXB1hwB7pQIMBwEAo/by0T8GoQm5CUW7ufPpl7ltMn9iG3erpk4Bzrx/BSdeOIt081M/iLZvTfQeqsTI9rVju2bdsnoLNqr9l5+mOYhGjivr63FGPGtDps9W6ea1RzbUob1I/3bH2CwFGysxAYWlqgAVr1DTYUIApRJW4U52tftRIyhUx4vgEhyZNCsNKMAylMC9sPp16+qdV2Lp6Ezz30mc+HaKRI70tKBJOSndud6ESX4Ntb5jWrwrW7DnYzRa16kfPZDOvxGJtQwbggC9ADfeQXF9Dg10XLrahaHMD8HS01q2M1LrV2J5Dob/0cjsWp5WagL3gxx6M1qQJABds3jL5YrVdJP4MYFdTOaBr2d1AkJVY4TlkcwRwLl8sEb0BS3w0dQpQ8GgsaZvsyrlar+zLRk0Nu+KBCNQF/VGdsisTJb4Z04MAYIAcae4732nRN5++AcgzEsU5VA6XAM5hKWmBc+UGnLuFBWK5Fs6dBzgXptqKbu3B7raac9y/dCQ/UYoDqPEgU2yk8dR5rB7fxO7yZok+/fElqKbFK5A6nHOtVd/9/h01t0bpqU+guLbUCyCa+a8H6svMX/roCupR4jHKdJcuN6De54Tr9rbWPMLoUzKA/mZS99JSfFEXcwChdehWXj3trBd41gPHcn/FRgYrY4QPik8BqJv6WPaoJ7Bv/NHPjigy3Belp8laBPwXBDhn7HlvXu8DnKviXC2aNilEjzw8Qrdudmvbq63q6MHCcUWQlqz1pT1jx2zKAnjiAqpWv3q1G/imDPgxQU88YRSy3HT2uF2vvFan1s5cbdicqTXrUtjsjJ6YAagZM3r7PFFydFIHmrHybVdlJZsI+lGb4/4BV3Klpvto2tRIpdGPtJG3eQXGCrcCi1Aba01/XHDOzBWHHu81RzbvaWhoUHZ2ts6dO6fx48dbKt1mfcvcO7nmmR/FfYsrva50mnP/RnHODviaRJ+IEhlw5sRxIxUVGoIqYY9ulzZqJlakazZFA1h6ATgCaQ946vpVDwBWB1bE+YBzKD+uibQspvMKuvSd7x8H3m7X6qUT9TBWvOGAYA5rsGLMYtxtQ03uVg52oOdraO/tKKD5MwfzVXCwE8jRnz4kSOMmBvC8TTeutqAm16rqKgPBhaBoGYB1q1MTJrhr+gzqaKaPfGiDV6/16ev/tJ89WclYImexxogaYxwQJUN1KcqeR4/UYG9dpPTksVq/IlzhIYBzuwt0Pa9B02dn0FfGW7bHPtRDM7zdQHnzDaxM9xw4C4A8B8W5MQrHIrwCYPNH37um/HxfALaR2rjJX8lpKN8ZcI5xtB8Iu6VpgHbRgWJlHWMBGw66AbRQMw7k3jeJIXUu1qZZAG7sJQaw7taVa83kfR/Xx3jJZohQLFozRnjpfsauUYwrPbTrc1cYt165gvBrA8pqY7iGJOq0D/ORQd2pBER91VxLsyZi2/2JLVlYgfvoZyh1nb8izZoQob/8lBfnZqylrfR7eNM2nNr2fIMuXG7SpImR+uRnIhRPf3aGtvnmjk4A92xtXZHBuJNogVEeviSCMXTQGi181dThqezb3Tp7pkxlt1vV38a9B/fz3v6sP8b6om7np4n0kcFcy5XrnYxbDSoiL3r6ginDUKyAHbRTNuVN8KcvpV/Fqvf06X699JpNxZUGnAvRxq1mvPHhfoY+nlulHdux4361XDFsbtmyGSjYgHPna/X6mwWAkGP0ENbVSxYwN0dpzsFc24MOp6mlCaXHEqsd3rx5kz6yz1KbM/BccHCwdb9r2qKrXQxt0x/28bvb3Lu/z7w+9D1GndKsyZt1crO+snTpUqvPMMCceZ9Lde7d3/Nh/x6aBvPYlRfmsQucy8nJV3llvKpqgqi3gaTHW0FIBiYmB2jyRD/uSwIUhb13Mw5AL78EUHm+WJMnGXAuWWOzsNOmjRpl2AMH7KgI22kTNVjJR2k6KpZnjvejJtjOfMtPGx700/zljJ8o1MHOqou+YvceG5saWtmf06YvPoVV66Jw5vRGPdbYCh/FfjdCj3wsi/4gSH7MnVpaurVv1342OewFtkXxdMMyrFpROPciEahcm+k8s3PrFoRhk+v9cDk4DM59uPwb/vRwDgznwHAODOfAcA4M58BwDgznwD2QA6wfqrgc2ffdDr2yy6l8dsGyvq4Z3HQ9usVTG1Z4KJgbreHjv1YOGCjymefs+vdf2lloG6Q83fXXf+GtFUv+tIpb/5Vy0Szm/d3f/Z0Fm5mFtN/3MAsOBrwzEJxRHvqg49VXX9W3v/1tSyr/qaeeYsdwmG7cuKEf/vCH1u8vfvGL7DLe8n932H3Q9w193ajnffzjH9f+/fstGG/oa5MmTdI3vvENa6ehnx/B2j+Tw/RtP/x5v5552aFydrqmEEB56Wkfgkx/GrW0Yyy6felv+5WHZcwEFsp3voKCBMGie/X45vftevoluxpRJ40mIP/3/9NTH9/qpVCCHh92MeVevebhdA3nwLtzwCyQuhZPKysrWdw8gGrKq5Yi6MaNGy071t5egmtAH2ax2QR+zE9AAMo+fNZ1mEeDvKcMS5SXv/Y1NWDVOhJwbuGsWRq9fo18x6NwQwDPDdu3wS5+BgKwrkRZyBsQ4Hc8hqbVLPCaH/OcGb8+igDU75jMD37b/823If3jr8vBesZ0QEM7oV+/ZvLYvG4tbg99/be+5jeL366EuMrJ/Hblm3nN9bzrfdb3/vr5d7/X9Z4/h9+u6x6qLud6bmj+/D/XSv45aCP9uQWyo2Rlb0Qxp65StSi2efuhwIO1afji+9XR0KSi0yflaGpUKkpb0XMB51JS7sJiwG2QDUQSCEahzONorFHpmztUfeKUMjPHKGLZcnlOmGipu1l2lig8Gds0IvyAWig+YUFaePCwagsKlIKSSvz998sHaKc7yEAyBpwDbjEAWi22q2cv6/b+dwhMemIte58iCMa4AdgZ6mawp00dVy6p4OxF1IraNHb2fMVNm4SgmlN9DfUoqdWorxRlrYoKtbc1KQCb5YRZMxU1b54GmlvU/MI24Jl+BS5YJJ+VK1AJIyoItOf+a3DOiaWlG8oPzjIAtyMXderYCcUBlGUtX6oACyxDkY100Xr5R0iO+mxAOycR3OJdO+S8ekGxI0YrwIBzY7C5DWTuZinOUYcHe0h/k9pzrqngtbcU3NaphPuWynfRCpThUskHADWgt54LZ9S8Z498sAEKnj1XPosWobgVIFtJqQaKS9QPVNXAT3MHcj1RqF7NmKT4CdjCAhPUYE1aUV+jUYvmKXreTMDHWNl9fUgnkIONdKNgRLRTjgoU5w7tVzNWqhmJ8Qpfdr88sYLyQNFuECjPadouV+hBnrs5AAmLKtSP9eit8+flx9w0ceMaQKZ0AEwU9QDmjJqYEWhxRymKjFc3tqT5J46ou6JQYyaOV9iSpTLgnKFJBtta5cgvUME7R7GMBdKcNU9BgMrOsADLqtSZVyT3qmq119aotaFOndS38BljlTAfxTECnb1HLqnxdpG8sdGOXTYf6BOgzgPyiz7ZDZVCcpFrBapu7VHLjj26cuqQfAGhJqxZqeDJc+UeEEteg94QcLbRBzshPX2p325A1+3v7FcLbcCNcSJq3ToFzZxhqepZ4Bx5gn8mwEaP+s6eVtkLzyu0t0dh86nLq1Cci0f5DoU39fWq/xI2pqj/VZQXK3jZbCWs5rzALv2VbdjY0hbLqwjY1aquCUU0VJtSGGfipk0hXdgSnsC+rbxC0akp2BQ/RBmOkVC9I4dpg7Ru0jyIqtpgd4taz57R9Ze2KSUMhaYVWP7OX4iiHvJnpjA4BoFknQacAwpFilCVh46p5PpNBUXGasTCRQodj6JkmC/th/cTuIUQ5/sbUT4sVsPtKwA+ToUnoAYTQ9l5pfK90B4DDRpsyVZ7SY46gOGCk9IYY6kDdXlqp905I6gXY+kPgjOo+oDqpNvLQdnYfUg353AYpbnbaq29DhiG1W2AJ4p2s1G+m8E5YmhfZmAw420R/UG2OkpvY6UWJf+0mfJAcc6Ac05sUd06r2qg+oh6aotkDxwBgLhQPiExwLPlai2+rg6UbjyDvRQUEoASmwFv+d7OOmTOytTX3ifP+HnsPHlILfZwFRbalHfHqTJsN6vKmoDMAFQDHFq5YgzwUIKigj3U1T6g8nIb8FUfylN1KqkHhAQCTYj11EMbJ2rm9Fh1ocD31r5GvfCr3ZozPV2PPTRDU6YEWWCZsZA8Y8C5d+qABZu1YlGMVi+J0mmsWl/DwsvOHONjmwO0eGEAkISBIRwAdu56B3DuxTcBFeqL9fnHM7RlfaRgM7X97R4sUy8TaHXTww+OA4KLAHTgMg2UROt1UF/7sSxuaHSqsKCfdDtUVYE9KqqN9XW1QFoDum9uCqBQHOpxUjMKdWXFA+SFUR3rAtJqVk9vE6p07oAYWViWRli2hQcPl+noiXxgpol6YGusJs3wBAwEQgXAuAIk9vwL16iioUAaWLXOCdQlFL5eRIHP198Lu9hpWGFGYePJ9dEvGnWWk1jzvfraIasfXr1iDtaayWZIANADnNt+HvW2ZCw+DRjor3AAFIPGMoPiHFhv9nqotc2m2vJ+FdxGqalwQMX0KVVYesu9XcsXjtKyxUmKRl2vljIvo/wqimhjZT08LgPoqlZaaqTWUM4LZkei3OYGOFcDOOdF8NxDn/5kCOpSXpaCkA9tMy+vSz/5aa3Kq0OB4EI0b547IE2nfvDjC5RXnDZvykTdzFeRKEuZMbmJvD97rhWlq2uoCY3WlnUxWrXSi3IZ1LZtndp3sF7jMkKxcA3V5ClAPgTRTTscpOzdAOcGoSDtfR7q4F65qrwXRSi7CosdwDDtqgEyNyqfk8b7W6BaWro/9cJp2Y4aAM3ANxVAyOVVxVi7hmg117jifgN4elh18BvfOw84OF6ffDhFKxYCtBnFOSCxwtJ+7HhzAVJqtWDObF4LBm7rAMi5ozIU9O5fTJ4ui0Jxjnt22mofff6ZC/3a8Sbj9vU7+vRjy7VmbSJWvu4AaS36znfzUDyM1uc+kaQli2iPFjjHGEsfZVTZBphW9GJJW1bSjyqWg/zsx3q2TVXVdeRpq6ZMiscaNkPp6b7qxnq3BKvF3Lx+yq8dOKlZzU29CvG3odaYpqVY39lR6Dl5zqlnnj0KVOeHdeMkzV8QbFnH2knrdcC5N9+oVklJIxBEqD72yEgUt/qwXO1USyfrcSv9tWwdtsBATHT3jJeDunjWAQTUo7zCKm1eGwM4F26Bc2cMOPd6DZa6wFgPZmkF0E5kCCC2mMsA9g2ifmp3iOsfIN24TBRR/0rtKi+tV2lllXqxkp47azz1LwXrWU9rM2hZeR6Wua0o6P3xrFpd80Suxjpcc2bX367fRsW7gDnaxYsX6esAJxcutJSuzBqU+Yy5nzL3LP9ZSlKudJjfrjS70vrb4Fw/4FyyautQbrsOODdmBH1DCGBcp3KLajUVIHj1hlhljPZSgHcfU1cP4FM3vfqGQ9dv3taWtQmoQ6I2Fu2OYmSnvv09AOzONmycp2jrljEKjUZxjsHWgGUUolVHuzsA92ptKjBjRb5dFdTRispa6tsgcK2/1m4cqaxxQYCRA7RVu4oLHEBF7rTBDlVXV9LfOqj/6bhXRKPi6aGbt+z66tffQQ01GSUroFpgzSjUzMz3laLsefBguY4cztfI1EmkN8oC597CJvV6XpOmzcxkbIqzLKGNO4I77egmyo3b3y7QO4fPYek8S594AsV12noVCmo/evqKigp9ALUzUIME7koZlA9zIGuuTv1mmicb4HNdFepp9DF5RfQjlR0oTLZRF6s0eVyA1q+boHEowdm4Bag07yvkGlGOK6tqAQ7ttOxLVy8OBSCLZ7rio+xcJ2pr17llaNWDq0dr5dI4RUQyP4Pyy0e58pfbSnX1DuBchrce35KBJawPVq2NOntlQNOzQvTlT/sytpE2L0/1Uvfu5Dn18rPNuoTq3/RJEfrUU2FKSvUAnAac20l/XnJVj6wbpbXkSzzKcO5eNDwga3KH8RDVOsqxpYcybOxVLX1NRZ4ZL9roR+pV39KK2p+flq3AKnohSsB+5FuNjf4Wte4SqbysU9W0V0dfpyaNSdRjj6ZqInaw5wF0f7WNNl1Tr2VLArRuk4HNsV718FMfa3Q73+rARrYC9cFYbd4aoszxgHOowG7bVsjUfYweWAc4twSQFmVMMxc1arFtLQDEJSWocbYyBldZsGhoaKi1Bh0XF2c1kY8CnHO1P3NCVxs0z5kf87f5MWpzRhXv+PHjQJwjZBxdjKq/6/2uzw7927qAD/mfK23me13pMV9pHrvAuby8QtrZTHV1RwGNuqkShdHqau6/8P1lvxSQcyb9RKT6sQF+BbXRsyjOTZ6MBfuWVOo5Wz78mDs73FE6dWK1DjjXWc48K47xKFgnj9j0KirAIWwGWLfVV/Pu92QNln4RiLwDldEdO3uB7dik4dWpL34OcG5BqJpQIz6MevDzrx0DQg3VQ49mMqcJtRTnWpq7tGfnPr2D4lxKMjbS65da4JynN/ePQL9Md5jzmnLgh2Zrfn+YYxic+zC5N/zZ4RwYzoHhHBjOgeEcGM6B4RwYzoF7JgdYn1B13QA3wU49y83+VRY0/bhBTkfK/eHVHvrEA15IpX/I2fM9c7X/PRJy4tKA/vVHbJLdMwAAQABJREFU/Tp+EbU5NhJ98TEvffZx7GBYRB4+PjgHzM47s7Pt3/7t3ywriQ/+xHu/w8Bqf/u3f8vNK9ZbH3AHamAMcz6zWGis/0zQusbYKRGINoDG5z73OWvBwKVi9N5nfO9nDdhhAD6jnmceDz0yMjL0zW9+01qIeLdFxtD3/Vd8XMIO+r/6+34dvMrSJP3co2s99Y2veBOA+ujbwXeeteu7P7MTVBnU3HEeeu1H2OzEffTp+F3K8dk37frmj+0qA/IjTqWHybf/9VfkmxWk+V2+Yfg9wznw55EDQxdO8/Pz9eKLL1oAsgGbp06davWnxlLb9NchWDUaC5F5wDOmD3f1pxa0Rr9uFLlKsf57/mtfV/3t20ojyL543ASN37BOfigFOaN9YVqIFrR2oGgSDFyCxaWRDPgdDpPOoWl1jTeu583frud+h6/7076FazELuHdXba0Hd9Njnh96DB1TzfXTV93tUd/Vr77rz6FfMTTPzGNXHv1H+eZ6j+v30O/6r/7YXJM5TODQ5IMLnHPt6jfPv691D58f6KLutgPbAHoMoNrWU12mhmtX1XQ7D+uhOMVs3KpOyiP/9Bl1Fpdq1BhAJZQbvUahKhbiz2I9aUDpbBDICAZLDuCo2h27VYrdZiJqCrErVmLVOt2ylLSK36TZgwV/A1UZcK62XsWo3tXm0r6wi41ZscIC5+z42wwC2HkSRPdwoA7R0qn2SznK3blDDqCkESh+xa3DUpXAkXh9ANXH9lOndRtwrpMA5fglyxU7axLKZEbNChWuri45ALO6isrVfvGymioq5Ts6QyP4Dm/gqqYXXldPVa0Cp85Q2Mb18h6VasFKbgTYQE0IsAFcEWtzlreo89hlndt3QJFEWbIWL5L/VAC9CBTZsD51mjxATc8DsMETGM1JkL9013Y5Lp8GeALiWb5BXhOmoJSHf491AHtg8zjgbFXnzVsqfPlN+VU2AEvNUeDKjfIckUEj4fx1BOFPH1Pr0UPC0UvBC+6Tz9IFcg+PpNwoP2AwBxavPUBehZcuAsGUKjIhRhNXrOb9/qo9clyFt3M1gj4wgTR7jUnHn4/yI5Q/2Au0ZGx/KBtnPeDAsaOqZiNKBqpukea9s6bLPQYbTAKqhP1RZXKTL4GgwV6H7IWAc/sOK/fiWQUBeKWQd77pKRZE6AYcCeUIqEN+mOgO6evGOrfw0AE137qmsaMzFUl5e06YAFiGqlctlqjZ13T7GJAmsrkjlixT+DzAqUhslbDLHWxvBa5rQzUMBbicmyi4XEQZMFQZ989jjpgux6kc1V+5ocGRaYpfjtXtaFTIAgAafYxVKwFgQt6e/X3y6OyzVBavHn4H2K1P01YuV/icxZRhEoAd/Tf1bgCL4EGixowCQFq16sNOuO/sGcoN2Apw2gco082ikQAE+V6ky/gf2ybgzZqXXpIHsGbQzPkK2YAyXDwQJvDjIMBj14lL6t11UM3AmyEbFwPhAc6h/uQGXDTY3IvVcZd6K+vVeitX5fxEhQYrZe4sILk41V86i53qLQWFh2v0lkflPdIAqQBrJtAN+GHBZyba19+pnssXdf2VVxRIOSUvWamg5avlEUdUkLZs+l1L/tHAcA5KtBIr36PHVXo9FzWoWKUtWqbQiSjOhXlTjuSDkdcAYHUbbKa/KKNvuKpu2lMgto/hqROA11LJK9qhHQvVxsvqKLyunkF/BadkYYVLO2/IVWtFuRSWLv/RK+UVNpLyMDmLBdkA4JyN8unvBvjLVk/jWbU0V8nTL1ShcRkKiJ0On5DK+WkvBhpyo673lfCdtOFiytozRCFYg/rEAfp5R/ON9C1tOeovA86sKQH+G6mgjAXy8A8Fpruu+qJcCeW6sPSxKN/FkHbAW/rJgWbU6couq5c+wi8BtcL0TeoF1uuxYVnKZraONjsKXz06c7oRsKEYu884bcBGb9x4H9RK3FDPGVBXm1N12GEW1/borUM1qLoVasWCESgYZXF+H+06hBUbCmtTxyfq4w/MQCkNxTksQ+tR9Tl70UFAtU427IzXLgGiWhyhs5f6tW13F3BNIwo00Vp5fxh2sQRhocdIpt7Y2adfvVXPnKYBq9ZRWISGAUgRnN3Rg41rDpaVPtoKeDEddbVg7DEHjfwQ/bWTX3YgrD7qRhfqN50dqNBhCVnL9R070oZ6V5VSsKR9EMWteYuxUKZ69xDs70DNrqPDTrC5B5WzElTo6nTfrHFaCxTk7Yvt6bFynThZiuXpWECxKE2Zido112fAi+xLrXr++WyrnqxaOgqlomDdwGLvpddPqRUl01UrZ2rNamwVsRil0ZGWAR0+iOLcWwdRXpE2rp2jjesSqYbYmh2o0itv38ISMVFbNmVoylQf4BgDzfHD63TEKIjdtRCnWqkdOKkVi8Dyml4dP9esk2evKhOLti3rJmrOAsqfzUVdtL+edkAtrIGNwtLRIxVAH3VaOi9Fm9YnMTcM0E+ea7AU58aO9tDjjwUqYxRAG0pAvpTHnbwOrFqrUJyLQHEulOC2F+CFXd/67nm1dQI3rMzQpk2+2MKhJMz11bHp9eixBqx7Lys0bBzWvYlascILFaFBbBW7tfdAi8akBrD5KVhTpnEekknon/wztuHUcaOMCPxo62bcp3maOtpK/atH7enixR5sC5uBLRoBtlK0YGGiogFTevnuTvKjtdUONNOjPXvzge3cNGVCKhBZmMZkeaIYWK1vPXMcWGSCnnhwNNfvY5VhL+BcEVatu/Zh1YkC4fw5M7AUDkBxqwOL4XzdAkxcNH+kVq+KwHryruJcJ+W+/1C33tp+UpUlRfrSZ9drNfBjMIpzd1B2+u53igEUY/UUdrSLF3n+WnHubv2kt2E+w7CEmq2NttXbNQAkiFJfnU3nzrRg85sDcGzX5s3zgd8AQ4HurOvrMOVtU3Vpj06hWHgbxehp0yJQ0xrLhkhUDi879PMXTyshyl+Pbh2HgnQQfRQ1h3pzI8euN7ezSQA79mmoDn3isVQV5Tv10rYeFZR1aNn9ADAPhVu2mQHAnU7qzPEjdtSwmmGPW/XxLXH61BMoiQENnjpmB7irIL8LtOWB8VpJG4FRtFAdMwUy3ZSTfsdGY+yyuYtpmLqpp8313Tp5pl37jxYonPnQIw9k0DZQwHLrVT7Ksu1YtkdF/fHBOdc82pqSvOs/M880mzkvXLhgbQo1G0vnzp0LFDbSml+a111z0D9kvetdp/vAP13zXlea3wucq6v3QP2sQxPHpliWoa+92aRLOcVKGxWs9ZtSUWOkTwxkZoeQ6ulTlPGrBjy9oQfWJwCnxABHetHPddF+9wPOtQJdTab9TlBYlC/9p5kX0B4pR8OdDwDVOvnpog02tzvVQf27dLkLe1bmpF3FWrthpJYsB9KNBnbl/T2sKbXT3zY19enalQYdxHraWIFv2RyH0pS38gGZ//dXD1I/ElGcQ61wjY+i2LyJ2z3gnIP3l+nIwTyNAJzbCnAbHWXXjt13dOZytcaMy9T69WnKGovFtLkVBQy8eNLUxSs6eeEi/fIiPf6psSjLYtVa3adnfojKcZEn1zdG6zZgQ57EHgDufc203p2Jgjvtwswr2DfC9aGARbqbO2w6ebmb/v6yBmy1gKmLsVpOBsI11tBcG3W5pRVXnNpeXc3pAzyqUHJ0px7YOA4lxXBdL3Tql6/cYszv1MfXZWjF4mjLptnJhoWiWod+sa1WV291agLKoJ96KAVwzlMvvNyhw2d6lZnoqb/7UpiygEkHvGk3zCuvXXHoZ8/U6DYQ+MIZkfrs55gzpbuT/1jUvt0LOHdTD65P5xpRw0Rpz5N4ielNLYt4M2owHvZwnXYKFEbdCNOqob4P+BFo6Z1CVdfWoUSWpsceycQu2p9yYYrBeNhEedfU40pxuV3nTgPz8u/hB5KAdEN147pTr2zrV01DCxvggSc3eLPxFsU5Tm5jjN61q81SnAuLiNKmrZGaMNWLMZL5wbNFKP6N0MY1ANgbsdtl/Oyk34bXk43NMxXlZaiQtVgQmFF0M8pu69evR81vlLUW/b73e+ay/wiHq62br3K1QfPY1S7NY+OaYjYtGqV/445i1ljMeotrQ6B579DPms/8MQ5XGsx3Dz2HeewC5woKCjVt9kI2TiQwLzBzKeD6vG4dPoJaenmOli4bi732GAX7+ejN12t17tIdlAwB5zaPwrLVX36+vbKz2ejQkQHtQEGukzb+yCPJmjg+XKeP9QFMoyjn5a01G9jssMbXsn32pF3UVgzo1W0t2nOkW6FBHlgOhzE2B6gRZbvD++164fVDGjM2SA8/moWldBSWsB4WgLh7514U6Q5g1ZqIjfT9bDCYzthu5q2mInOvwrWRmda8gqr8oY5hcO5DZd/wh4dzYDgHhnNgOAeGc2A4B4ZzYDgH7qUcMDduLa0s2px16ifbHDpxjYAAM+aEcDetW+ihTz7ipfFjCFaYu/vh457OAUQu9DMgnW/z08yN+NzJ7vqbL3hr8VwsZz7sXdA9feV/vMSZ3W3f+c539Nxzz1ky9n/oN8+cOdOyWb3vvvs+cOesAeeMteu4ceMsiM3s9Lt27Zr27t1rARjGpnXt2rXYphBQ+j0Psxjyl3/5l+x2e4Wd/HggDDlSUlKsNJrvDgx0BV+HvOG/8EPTrz3/OsDaT1jErEd1jgXtn3/PW/NQLvA2jlgf4fHZ/8XOQeTzDRezbpmnnv7f2DZgwXCvHQdY9P0/37bpImoQrMNrDpDf0/+EndBo+n8W24aP4Rz475QDZoHUAERmgdbYmfzgBz/QiRMnLIun1FSsByMjLeuQnp4ebKtYIecwi7rLly+3bE9cNt3m8+Z7Sm7d1M+++nVUdMqU7h+oWVjZjZ8/VyELp2qAIIA7EYWB4ko5Q5Kwt0SZ6fdQAR26yDu0jIYu+A59/p59bAKBZvGWwwAnZjHXKJ588MI4wQs+ZhbizWF6V5PvZhHYCmyY56zHv1mUd8Fh5vmhi/CuPBv6fvNe87x530cR0CO5H/lh8s517ebkrus1v83hyg/Xb+tJ13+8x97SiBIWwAHBKTdTl1Elazp/TlWAcgmoWUU88JBskREqIuhWDXCWAqiVNne2/CaMkXtkiAXLDaI4Z07n7g9EhIxK57EzKtu9B36nS0kEWMPnzUURCrUrV7QR6yU3bKrMEwMEgcqwoKxm7pRC24lZskTe41HSQjHNDcmMQVOPjOIVcFgPMF+xUf4qK1NMUqJGYLvsmRBPYA8r08Y2NZw6q4LcfHlHxSlzyf0KHQUI5dFrWZ0aNTwD+Tkr6tR18IQqcnLlQLlr1JZN8g+NUNtre1SWfVXeiUlKXo396gTsVAPY1Q8wZCwtbVBzfkCxbgQ7uy/cVM7e/dAnDmVOmKRQAzYlR6LAR2AUZYg+byzlUMnwsWNh1tyuqr1vqf/sYUWibOm/CHBo4l2QkE6JNEE9ePZiT9clW3GRql7dqf4befJPSFf0ig3yQ/WP8Cx2soVYhR5X1/VshWPdGTKPc04ZcxdIRMEGWo+GRAAXEib/8FEVZF9UMJDX1PUbFcgctOncWd2gTONiE5V233z5TaavikZZ2QCMPaQBuM3NzxfH3U7VXb6k0gOHFYd8VeTkyQpYMFvuqQlcm4f6oDRMQDXQi7knsJ2ztFr9B44o9/xpBYwZrbTVK+Q7IpnoK/UJYM3Nh0AjimomEGtkgWylZao8hlXp6RNKjQhX1KIF8p+FKh5kiKMEFQ2gubybN+SNUtrolasUBtxo1OJM+XkYmR8AOmdDm3qAKM9h+dqHolnmknlKy5igwWvFasLOt5Ebp3iAu8ipU+SOiqEbynp2wM5eFLf8mBR52fvVdemSco8cUj1Ax2TeF3ffInmkZVKG0A1c3yDKdPwCagugbjWo/8gx9Z88KSeqe74GypsxGZG1iLv12Ey0SKOxcBu4c0vN299S4y1sRNNGKglgLXAkQU1sPZ3U9brDZ2U7c0mePqgBb1kqtzkZcnBtfpShN4Ewt37aRF27eq/eBFY9rxDAw/jZM+Q9dqTaClHrA2h0olw3cekaBU6iXcUlAM4RRCYiP2DrRSGOfhGLYfutGyrcsV2d1bWKnjRVcUtXyTctHfU9FHPoo50ObH0pFHcgwUGur/40Snmc09s7VClzFikci1z3aIA+P5MJ1A3rZrRNA72VALQ5WATXAoP5K37EOHmHjCDPsIztLsdSOVttJfnq94xUcNoEoBRscptvqrmiFMh1pIJHrpJX5EjykbpKfnlz3W4ANgPt1bI1H1N3ywXyfUD+sePkGzWFc4zku7mHAhTiAekwlrE1AJQ5agDQ60PJLSQqDcvdUfIIjOUt5EULqn6lp9WFbbRPVKaCMxfSloM0UJGNsmUuj2MUlYm9K4FTedIPUaccdblc12mC+T3Yz86WPWKpmuxhnN8PuI7xg7N3suZy+HCbjh/NBYIJRqUrQxkTfRTKuosfKmBe1DGj5IawmX7+Wh0gzlXdNy1WD2wA0gv21a4jzSio7ceCLgaHgOnAY+Eo8LipARjBqMu9/nY1kEa71i+J1ar7I1VQQqB1T4duAP8smJWoVUuTgCWA0ciKikpgiO0tevNwvXzpP/6/v8gCBAuxrFp37QS8OngDhZcAYLNRmj7FDzjrbmDVLCr0EMRtgG7oNnWAPtf0VR5U9k7sUfft7tOB/eWonUgbNiZpHnAU3bU1JFt9PH14WZldB/cWYaVapqn0k5uATQJpNscNOHe8VOlJ4wDaIjVlFopzBP77CfxfvtCsZ58/B6wQiorWWC2YFwaI5dTr23N0jbYya8Y0gsKjASuMqqVUAlxx6GCb9h8BUI7w0gNb5mnLhnggFScwQ4VewdIuOSVJD2wegV2sD1atd8E5o5Q04PQGTCVQ3gvKwPVayktU4xruKfcfbdPbu85hm0nerAacWwhQCfhohClpovJmXnHnhgNFoCZdu35L86Zh0baF62Ms/NkL2J3e9tX4TJSQHgtgswVAG7FrmFndudOhH/2kDKvQSMC5CCxAvbEQHdBPfnZbtwrsmjI+nsB7OJvyaA8MZ8WFDh04XEZ6bik2fqIe2ZqkZcs9UXKUXn8N+9N32jU2LUAf2xKI2g2Wt4EofHp2MleCvAHKNqp67e1eWCnSbwDv0axppwAgQBmnTzgA1ipRKSvW/MVpmndfKiAQtn+UozvXaIF2wDuvvVKM7aC7RiZH6XFs5Yz17oHj5dx/H1J84mg9vnWqFs/xt+qCjSGiCEW0t4HkTqCMNH/uTK1facYAm3a/U6LTFzqVjJXc6hUxKORhYUtfXYtq4K59dZTjCfUCffzV5zdqJYpzgdT5W7daAOeK1NwSp794LFZLFqPMA3jjoE9iGENdCxtM4JSW5h7Adpw1UO3xkbEtlY4ebdHBI+eBlNpQsFuqWfOjrc/6cH2IvhpCWi2Aifv3dOnYqYuoTAdpE2phcQlBQKFO/ey504qL9NLHANoWzA+Rj4E7yffcGygIvd2Jsl0t9rieeuLxVDU3uOmN7XadOFeirDHeWgvgODrLVxE4e7Rht3vgnS69vL1R7YDeTz6cqCefBJxjMfbEUSz73ixHjShf6zePRTUslT6D+mhAJP45gYBbWdvpANQZ8Axi+GCNg3pvAKWTp+zYPOYxnnho07pkLVvKGOTWpsbaYtSqWrER/eOBc+TWf3i45pBGTa6wsBAryfMWtGPul8zm0iA8jc3c2gXSmDnme84z/8Mz/GEvuNLlOpc5/2+sWo3iXAp2oe7Yi7ZgsZiikelR2rO/WcdO5zKHcHCvl4XSUxywLXM12svBA5TVTjuw1FU99kAiMGacYmN8UY/r0r99a48Fzq1bORXFqUmAc36WIiK4CtB9P9AMMHErDQ/7XT/GSg8zrqEaeR5o6529faiz3dCSpbFaAAwdG0dfS1tlyLVgZIZr1O5a9cLzpdTvGPqyaM0DuC0AAPvHrx2iH0xEsRTFOVQOo7F4NuBcsel7D5bqyKF8C5zbsi5WqamDOnq8QvsOFdFPxADgZgLguVnX19PqpVPAq2/uPKWrt7P16EOr9eRTkxQU46EyFBp/9uOr9MOetFujbBeoeMA5b3fAV1M0jBP9fU6AOYB+xjUvxgnTx/C0TgJ679p5WXWVBYBv9wGVpiocVTyEhq2DqiD2t2Dr209/e5P5YrMe3DxFozIidKMIxbltudwrdOvRNaP4fKRCAcIHybuiaod+8iLgXG6PJo721pOPxikswEs7dtmwH2+j/TTqy59JZhNckDyx12wE5j57tg9LzSr6Xq5jboSe+mygUtPcdfqMgzG1F4XNW6hBJgF4RysB220vyoAmSN/FD+2x00b96bIznSX9Zn5H4tmLQ30Y1Iu/ugHgXaLMESlAzFlKRn3OgOumz7CT3hbK/+LZfu3d3aS2tl6sNMO1fGWo7tzCPvZVFDLrW7X6fn8guCDqJeb0fLcTJb8d2La/8EqRQiOjtfXBeJQCfXT7Rode+EU5yndYvM8J0COoVwYBvNcDd0UzHvs6m1RVWc66RYu1hmEUIA08t4LNJ1OmTLHWnf+z26Cr7ZlCdrU/83jo0d/fr6tXrwLw7wAWjdaiRYusjYlmbeWjSp85j0mrK43msQucM4pzY6csUGxCInNgytFA4agWvsX8K/vqKSzjUV9cO57+2hfou0LnUYmePCHGUpwcNwYLVd8e1BUZKw9i7b0Hi/fuIj30cIamT4vRLdQdX3+9g3Zfp9mMLSs2xGhEOurKnKMgz6Ftr1TpaHafUqLD9NdfDEVp0lc1VQM6sM+uX72+m40YgHOPjWMzQJwC2YjV1FKL4txdcG5EWiq2zSs0d840pvxUCCxmKQWqMB0DG8ysNRerIGh8f+AxDM79gRk3/LHhHBjOgeEcGM6B4RwYzoHhHBjOgXszB8wiZic3/cfPD+hHL/brwtUBFkSlCG7o5k1xZ7eol5Yt8BDr88PHPZwDxyi/f/txv05lOxXIusRXnvTSU094KYTF3OHjd8uBhoYGS4XtpZdeshb2frdP/b/vmoDyhYHhlhC8/SDLCQPOGavW1atZiHrySQvIaEMNwywWPPPMM5Yl4N/8zd9YKkcGqvt9DgPOffnLX9bLL7/MDn+2bw45kpKSLHDO7DL8cwPnzGU2skj0+b+2ad8FE9yTtq700L/8tY8SP2K1t02f7NN+FCADWJP4y8976QufINBJ33ovHWaX/9f+qV+HCAaw7qe0KDc9911vzZ7y0YOG91K+DKflv28OmCCKCfQYUConJ8dSBT1x4oRlxWoU5+7HCjItLc2C5rKzs61gUHx8vLVr2/Tl4eHh1oKrWWi1wLk7ufr+P30dG8smZRF8H8lieyqWrTFTJqBKEydnb5faC4sUNHuhImbPlycwwfsdQxdzh77v3c+/+++h773XHg8SfHAAbpgFcztQiikDT08vgrkm2OINmwNEY3Wd/Ee+mn/9/byfcc6Mdcb2z1hbehP99SP/zG/zfhPbMAvf5vuMVZRRljXnMHljAnY+RErNj2usdi2Sm/ea7zXvN2VoXjeL9uZ7zVhsvs/8mO95v2MocOf67vd7/5/iNde1mLS60ji07riu0bz+7mOQPGg4d0bu9Q0EAwBECAbamhpUe/uW2stKUB1KUsTWB6TEZNXfuK2yQ4cB6+oJ4qE0lZmuwMRYSxWuD1Vcp7e/QrJoE0GhqJAVqvbUceVnX0bpKEQJmRkKAnDzxNPOBh1v80a1KyFVgUA/7gQfK06dUgU2mBHIrsSh6OiTOVreSQnyQNFKKEUKAMsdHyhHbbUaLl1WxdmzcmNelErwNiCZSB+Bvl7UyqruFKqVoGXSjLmKmzBZ9u429deXUUdQtgogyACgZKtrVtvlG9jPtsh73HilYLvpG4Wy1MlzugPA19bVrngUy2L5bl/AKANxdA6iKoAaRsJIFOOCQmQrr1bZsVOqv5mraGiECIAxv5R4uQPHwACp2y9AoTHxCoxEIckNK8CDe9R5eLcCKYOAcVNRsxsvt5hoeaMI54ENqRv5YmAhZ32dWo363inU/bpsShw7GXBsLKUygEJeseqLC+QJ8JsyeYpCSUsHyie97LzxA3rxp515QkX0IilUdj3HUkuKYpPF6FUr5RcZpq6CPF1HtQDJECwOU/jeUfIiAD9A+h1QA4MERf3Jc5/IcBTdalUFoOhEsdOooQWNzVBgaiL2q76AGQCMZEpE1kRsOhMEvSDbyePKPXZEA0BGyZMmKjA9me8hsBkbhSJehAb9CLADgJGNKKq1q41+t/TgfrmjnhMMkBc2hrTQjp2V2PxeuaqGfoeiZszSCDaw2AgS9qG250+QMjAs0NpMZEflqA2frNxC0pcUqRFL5ykxcxw0UaNaT18EyrgJTBWhJCxxA2JJo4+f2ug3bNTx6LRkBaA85MCWuBoFubyjRxUN/RJHPvuPzMQWNBz7NeoJIATRbEWgJuhGhLsfYK3t8CHUmlqAGccpbCJ1ncCbJ+fxBCZ1M/2+GyBhTZW6zp5S4YljwBTdik9PVwKqgd6o07XXNKn0Zp482nuVmJWh8PumqdG7Vy2o/EVSh0M8UTDln7OlQ80FJaiLVGEZl6Ck++bLJ2s0lsPUO/K6Pve6Esn7qKxJ8gESGPT0UHdfl7oBwMJGpSskEcCIttJ86piKsdYzZZs4mjSnjbDg1j763m7oFO+IaIUlJBAo71fTlfMqPXtZTtSo4tNHK4z27Z0QKu+4aMqQdmiU+zyQq3LWy1ZzWw0VuQTGWxSFqmRAGBaa7oGoPtaqvyUPoATLVu8EhaZOwWqMEHzrDbVUl6GOl6mgdMC5iFG8TqCc+uvhwHoVpcveylvqrD0HxFuCdWKwvGMBJv1T6aMBaAXA54UlLapxeFbSjZGOrir6KGA3lP1MoDs4KloeoVhq0hYcHSXUmVz1dHbIL2ocfRV2w0HU9aZbqsnPsYLiEbHJQAahqNT58Xcf/V65bPVFcLJYtwXNUmnvVF0DUvAKiVAQEK8PY0hXsx11m0aVFaG8mRynMeOTNOBrI38d3Lt7K5jxZRDYpRK1msOnatXZVqF196djTZoGPOqptw82sRlqlyaPidNjW2dr9oxwwDJ3NswNoISG2tWOEtpvi1YvTAaciwNSddPek92ASeeBujyxxRupzJGhCiZmWlzaptMXu3QBpbNAH8C5L2CPuj4cQGlQu3d0aO/+K5bF3MYNWZoxNURhlsUrwWSGgZrGfl3OKVVlTTfgHFZ/oWHyZ6GoDau6ixfaAUawp4sL1UwsSn0DW+CD7RQ/QBDtx4O6Vokd6JXsAuxdG7WUDQTLliZQlkZxrgTFujws6SbpAWAAA87RFRKYli6fb9Ivnj1BWWKVuHIiYFmMOhBHPHy0Srv3nAEoQRF48mhljGQzAv2YsfrLvtqs2/mXlRjrg/XrTGwLYwA4BrRnX5le2Z6L4hxWrVigTp8ByBTGnIM0mAvsagPiO1mOZeIgMAGqmeH+jPseWFXbdT6nhf7htjJTQjR3RhJ1zUdNqLYFkamhWAO7oZ5agsXqpUuNWOM16/4FidjRxjDG+Oqnz1bpyh0fjUPt6InHQlErNopzgKrUyPy8dj394xKV14ajFhRDnvjQJo3iWgPKNIWku18zpyRiHYllMGksKe7RpWtlyi2uxVZxih7aEg84B1THd217pQUVt0YAyzDgrjBNGMd5qPbuXiivQnsYu9bObmwhb9kAFOxsKgsG3vRB9Q9dVPry7CsdupnbwLX3atacFGwQUeCj//Py9sU62Z/8RZUNe9qjR/JVWe2rmZNSAfSY26Z7aM9BwLlnDiouMUtPGHBurh/QIHAB897iCiwfd+UBIJUTvJ+BdW44eQtEc6YRMAnoucmmCVkxKNhFYNHrpfK6Pl24Uq47qGR6opT35b9YpVVrjV2jm65fa9H3v3+b4cgoziVj88qaF+DcAAAq7COqVm64B9h0hc97kuDE+DBAAj9U5xxYAFbpTsEd+QX5agZQcQiW0n0oifr7oVrHedlySl126NL5FmC/PN03Lw4QYhQKQ77KZp3tp784LLgFPfoA17cI+0XUf/oY0yxw7q02C5ybPMFDj30inW/yAgZFuWrHLVQMuzRxahpWvWGKxPq4oboBqII2eJNukbnkJx9K1ic/SZ8JGXXqKO357Rq1d9/WuvUj+UlXFOqbbpzHTDs7WKzNvlaoogpUBN1jgNxDFUQfYuumj7nRiqpfqdW33jc7AVvZftQESxQbjcon1rNm84/ZsGnmvq7DNc8zf7vmga7X/tDf5jvNvZSZR5/gHsrAc+bcBoQx1pDmPK65pnnsOq/r9x963g/6nOtaXecxc+DfBueSUVR004WLDSjLpWvSpDhdvNKmA4duAyNXKY0NE9OnjFBkmA9KhsLiE1vobO5dem/ryUfScYyIQyXKCxXJTn3r2wfUzTiyDqvWB7aOZdMC8xTuTbh7UA9zL2PhfZnz9PUAtzHPCqIu0oXoytV2Xb+BMtVAKxuzopgzo84LFeuJ+m+AXyD3JN7YENtRXqyiPldr9Kg0oKsEjQdezb3Vra9/4zB1KgVwLgsrVB9LidOCVxmT9h8o+jU4N01bUccbP94di+IubCOLdTO/Uxkj4gAGQxQXRd8G/HX1SrtyAOlLqq7p0QfvgnPhWLVWYE/9kx9g1Yri3JrlRqkuCKtWAHDGOkOvGqXO4mLGg4JarLmxXmeDSUBQoGx2gL/cbl2/mivfwXbNnQaUmsTmE+4DfKCPA9io4McGEjsJzrnRTH0uUtZIX23aOBF4MFBXbzpR17oBc9uuLaszGetiyW9aLXPs4jKHfvxsmXLy+jVprBfWpwn0/366mu3QG2/W6Tpzu7mzkjRxYjz9hZ9qm/t09Vqbzl9Gbdnmq7XzsH7+VDBQIVat2KW+DkReWpFDH57ABud4JSZwH8h822zkMHtWKEjAwXbGBcA3xoHgIPpSNpwM0PLrzDh+LBe12WZNQ5Vz1pR4wFXmCihJh8ea+YgPFs5OyquLtYY27qE8seyMQm00UDnXelGc68YCtBmb3zCt3xiB/bXpmwCDOe/bO+qwq81TeGS8HqHfmDPHV3VY525/FVXTS22A6H1auAKVypgA1TUPaNZ4P6XFYClaWcIaN+Mz8zYDzpk1DKPoNns2G0Vomy7FOVfb+KC29Pu87mp35jNDv9/1vOs5sx5/lnu0Y8eOadasWUCqCyyAbuh9uuu9v8/5f5f3Dk2Leew6j3nsAueu5eRBu09lM0EK60Io03LPWFLSS39RqcamYizqx2rJshHyYU7+5nZjT30NcM6A7xM1dgwWqn5sZOpz074DPXp7LyrmqIo+uHW05syNUzt2rAdRyt13JIe5k5smTkvVaGBRdyZBJYXdOnWuTbmVbspgbv3XX4oEGPdHyRgHqb39+tVr25lTAPQ/MI4+K4HxDBXZ1nrU5vahMHlYmaNG0keso6xnMA8zG0ksvJX5xN05gbUGYD1nZiR/2DEMzv1h+Tb8qeEcGM6B4RwYzoHhHBjOgeEcGM6BezwHjMXEtdwBvfaWQ7sPOVUNTMf9nMaMAJ7b6KlHN7CYxELV8HHv5UAXUbafP+fQ95/HEhK1ufnTPPSPX/TSwll3b4juvRTfmynqwjbohRde0NNPP21JxLtunn+f1JqbTmOx+g//8A8symCBZFaX3+dwgXMGYPvsZz+rGGAKc1y5ckVf+cpXLODtq1/9qgVq+P0eKkTmO8xC5Re+8AXLftbc7A890gnEGavWNWvWWDDI0Nf+XB6/vNOh//O9fhXWDWoUSgnPfM9Hc6ezs97sJv8IDgdbatdv7dOROwOKZhH8+Z/5aP5Mzv/78Y//qSk1u/m//s8szp90qpV+JMbXTT/4Bjvil7NjlmDO8DGcA//dcsD0+waUMoEUs0hrFEBNX2mCPqmpqXrooYfYvb9OBpQz1kNmV7Tpxw3wbFRGH3nkEVQzkq1sc31XMQG6f/3m1wisN2ocqiMpSESEYAUZg41lMDYrNoLuLS0NGvGJJ5S4YvkHKs65xibXgq45mes589j1vOs519/mtY/yeK/zm+dMeoa+ZvK6C+WM+rp6VVUBazQ3WkFafyRrzJiYGJ9IADCKoLsJ8gE1EIjr7etReVmFKirM7vlmxjubBbRFoQ6VxviWRln5GZ8hQBTz/tbWVgIQ1ZYVuikrA8YZCM5YRhmQ3ATxDERuyty812WZbixtzOcNLGeUX03ZmjSZMdXY9Ro1V5N+c00m4GCuy/xt6pAB7Ux6zPtd3+269j9VmbxX+bvS7JqvuNLoeq+rzMzf7063UakqeOF5IamgUK7f7FjvQXGsGYDNO8hfiZMmKXjpcrnHJsqGPWbTiVOqPXtadqwDQwhaR0SFY9/pgTUqKmTRyUpYvVX+iYA8PagZFd9W/onDai8qUiD5GRYaJB9/b3USkOoNwV5qwnTFTphI0JrgHradVah/ueXfUQgAkkdUrILSMuSPmpvn6DFSOBY/gGXq6wBaK1XtoSNqZZ7lRzAiJJjXqAvN7d3qIAASOCJTSfevlg8WlnWXL6gDhbbAARNUB5hEiavXKNe1d8kvLEohM+Yo7L6F8ozgOsrKVHPutCqzUQPraCXIDShCMLsXdZ8WDyxKw4KVtWAuwFg6Lpc2LEfzVHP4lOz5hcxLsMUijYMo1LURVXWEoWwxearCJ0yVJ8ppLedOqG3/LoCjGlQywuQdTtAZSC1s2gQFZKQCp4VR94BumMfa8m+q7sxJVd28JX+Cl+HBJg1ONdq6UPHyUkzKSKw7FxHU8bTeU5NfoEBUsuKYHPlSFqZuN/E9g4B5cbPnKBKbVQ/y3tHeoqqjx9WCaqAbgWB//A99UVNzYCnU6wZQGh6lqCmTFToJ21TaQ/dZ1GXOnFFTdYUGUegIBiILArLrx163HRArds0GhU1CEQyioj/nioopv5aKCoXwvcGxgBOoGAVPRI1sZLrcCLi6+6JI5vTBZhO4o6wKdcBTas7JRmEHeM4fpQ9TvqS7mfR7jMpS9Kq1ikgdoeKDBK6vXVUwwaHQAB95WXW0H+jGpi4/HwVPGaeY+6YoJCGJ/OtTz6083dm3H/ivTmH+BEE5t9PDT02AH26RCeTddIVmmTwfVF8eCoZ796iHAKQXQFAAkJ+nDwAY6n1gJvI0fdHW9SiOEVzLy1XTEZTysMtzANEExQJGpmA7mImKWtZY6lAMcBdlCCxnry5Vzeljqr92UT7keyzQkY93kGqwcGtkbhkGoJc8FyvexDhV3ryqqpzLCrP1KAr1Cz9uOxzkcTWKYD1BWHNOxZJ4zgJ5J6ejNNenzsuXVHfqsOwVZQoCSPQD5OwHQmsDQO1Giixl0WLFAlYKFcj+4jxVHD2iJuoIsT4UNEIpBx91Am+0+2JxCpiZNGWaAiJ5ruCGqs/QXm6XgzahmBjGd8dHKIw26jt2GrbkMfICzHF3b9dAR6k6KrPVUZsrT3a3+PNd3oCzg4PdQNBNVtkgBanQeFRQQriXbb+h1toKFN4y5Z+2GhhtFIFLnqdeu9mxTW8tBD6/jM1wnvzoIQLCgUlpE06UmJwOE2xGodI/ApA2A5A2CUjPm3pnA4qtVHc51EpzBXNexn7us2wGyBtoo541Ms4wToRNUHj6cn4Dd/VVqrXsOkp0FQpQLzAamU27sTtR6+P7PIGb+vr81WCfrotVmdpzroa8xU41CkASxTsbNp69zBsiAvq5H0iWF/l9ragOIIs66w5gCsDkAfTV1NyGXV2HMlK89PFNaZo8PkQt3Ma9vb+RYPoOzZiQoo9tmon9WyhAlJtaUa05fcmmXQARfd2tWj4nTUtQRArCXvNqESpfu+8AB9TSLqNRvQac4zPd3Z2oxnmqnIrtqTr9D1SENq+PBOTHynRnO3DZJaxaPbVh80TgkQiFE8Q1XBmMu4rKegG68nU5u1E99ghAgWiCwUBJWCE3NxG0BzKeOS0ZJTQfFMmuA1e1yT8gjvtNyoCx1FjH9vTUMu56af3asRo/LsKyQj1xosxS40tKGq/NKKhNnEafRLnYHe66eqlJL754yurnli2fwHwrnvHAA2ijDzu067qWU8/1BSouOhHgK4KuwK5G1Maa26oUEzGIGt04bUbNx1zAgSPVemPHdVTRsFVcl6UpkxmLQgcFd8EMwx1lrgEgvcsAG33MCYIZIwBu6Tubuhyq7+jDAq2b8otUQrSbCovadS2PducXoyAAWqcNKJc2awOWTU8N1coliZo1NRBwxA3FOaOE5EUgHOjq4RBljQYM5F7L/Csu6NQPflqmsppgbFmjtXyZn6W6VgycsmfvbV25VEyb5Poi0wCeAii/HpSEmKsAAY8cNRYbwyitWOlF+xnUtm312r2vGggtWh97MJZgPSpJ5uIs2oOB3B13Asrq1LFW7drRrj5bNGt8Rs3KtL9eNVD/PACVx42LxLoxDMvYZmDBJix9gwAo6eNQcOvoaMQGrg6oLkrLF6Vr+YJgRaGc+M6hWv3gF6cUG5ulj23OYC0Kq1bqG46pKql0UkcLdep8uWaiuLl2Feqzqe7koQ2L1yaU0oosoCAmPBrAPEjdyKe1dKCUBhgMxa4vfnqxVq8BtEVF6cb1Vv30p3fU1hEBcJaqhfO8LdW4QU/AOS61qk5YrXZq7zu55NUAgB5zSEAxG31gc3MT80MbmxJTUA2KAULs1J38dq4daAd1Kh8gya6uXmxku2Cf+7GnTNTsuTHMS92xVXTouReOYIE9qAc3Tdb8+wz4y5wEINPYK+58uwVosx6VPy898mga9oI+WBI7UYgqx/62nrRFAlwFKdSffr6vWfXN7qptD0fFs1cPbQKIfcxAtkBbqMa9vasJodx8rVoPCLsyGdiOvox8ZJqpWtTqdu+7AXhXq/beGPKYH+YxdubGjcASdmc3Vs5jUQMMV0UpdvbYiM6b48581+M9rVrN/M41LzePzfHu+d7dZ9/7/6Gfdc0hzW8zR65gXDcQjJlLG0hnEvNCf+ZpQ+eeQ8819PF7n+3DPetKq+s8Zs4+FJxLTEhG6YuyBpabPClN07DvrMFC+/iJSuyRS1BxcldYUIzCzD0DY0l7lzf22kYJrIAN52lYa6NIigJaAaDL0z88rW6sSVctmQi0kqRQ7FadrEMxnAJFOnTjWqveeP2maqqpv6HJqPBBVIJdVddV01UNKhP4ddKUEOvv27n13CsFMveK4/4ikPbYQxtEbS7ECUAzQktQTowFILtJv/Xv/36KNkf/tnYEdcdH0Vg8w8SqGPvnw4dLgF6LlJ48mf4vinbgSTtyosLYzFhRxuYGJ3U0kLYeSntx0Ab7ucYGFDeztXnjIj3+xHjuydjQUd2vn/8gBztiN+xEM1n/DFBSGpuYkN8cBO6tqXHozLl6HTlZABRrZyynjgZzz8uegvom5ORsTQBdQZqAgmoNQOu5a42IVzM3on8LYN3M3t/O/VgNMJ0dq+KRWConWfdo2TlO1BhzAOuatXJFFkqqCYqL8LQ2RVSUOvTsy5Vsiuin33NnHIlXeoofmwsAsw+3au+hO2oAdgznHsHcU/TbTX/XrRqUNw3cu2xGhD4H1JyZ7oXFJqDv3jaVVuZwnmQtuT+Re38U42ifnrRBT+bqTEEsi9Q391SooLyXPhSAlfFwEGiqmbG+ta0eENxT86bHyZ/5wpXL5dyL2uUXwkYZnxBslgcYL9qZ29o0PitCDz4YAfzkrQvnOy3lsarqFi1bEqk166OA0T2taY+T9YOdu+pQnMunnv3/7L0HnJ1Xdfa7Tp/euzSaGY16sboty7Ysyb1gY2NjnDhgwoVQvvAlvxTyJSSXEEJNIBAC4fuA0Ax2KMY2xt1yV+/SqM1oNJre+8yZU+//2ePjO3bupQjJxuS89tE5c8777nfvtddae797PftZVXbn2+bYxoszHPPZzhcn6ccuO9x4mk0cmYADS0mp7re73lpo61eEYZxrYr1i2KVmlV0+//zzr4BZlTpZz7qyi5Rt/GaW9uqrX2t3+lXfzfxe9z148KA9x/y+tbXVseGtXbvW+Qs9o7723Fff4Tf/a2b5+pySgz6ngHPbth2xA0cWMl7MdusSSZ4thhhPx8fZuFXlsRtuWggLdYGNk7r9RwDn9uzZDwNpJXOdFYz9BQC4p1kDH30yzHg4bGOTjfiGeoBzzJGZ1h5tmLQHHjpBumHmtlaATReTopfnztGw9Q4BhBxiI0up2f/8oIBzudbTafb4I1OkBP9PK69MwHhXTRr4Yvb7JGx0pNteeu5Z27trF2lil8F+e5tt4LlOGxg029H/iSSAeja/aPMh22H4/uXfzkKcaeDcWQgtfUlaAmkJpCWQlkBaAmkJpCWQlsCbQwJ6kG3tYCfuo3FShMTsGAuHmsDXlnvszuv89q63+1lwYe+k5tPp47dGAk+9ELdPfiVqz8MYlcPzzkf+KGAfujvgdvj+1lTyTVARLZwdIwgm4NzDDz/sguepALgW+1IP0zObood4LTKk3uvq6hzgTSlQ8wma/rJDaVSVHvbWW2+1973vfW5HnVhutGDw13/9124x46Mf/ahbaNR9fp1DaQTvvvtue+CBBxwIYea1S5YssX/8x3+0q6++2i1GzPztd+XzIKwJ7/nwlD2yk1UtFprfcaPf/vEvSFmFP3s9jl2kWfjwn7DbvTVpcwgaPPLjDJtH6gfWJX4rDnAm9n9/asq+/VCcRcSkidTjI+8P2gfe5SdIpEWzs6+mAEViORRoJJW28uxLS1+ZlsDrKwH5evn81ALuF77wBdu6datjaHj/+9/vdj/Lv4uBoxFQj4BzAjsvXLjQ+Vz515S/1hjSAPvWpz77cQL/7bYGFqm1mYVWBqgiB9asoD8L94SPYhV+1u23AsRZ7VJevr4tPj93kxz1SgGyUn/PXIiWnJUmfd/efQRmD1snwKBIBAYeghICoglwVg/4RIG2hYsATWRlc36vHTx0EMaaPdYJs1WUdJcqW/0hMNzc+np27F8GY+tCgq857hwtxKt8gex0ruog8Jzur2CBFubVb/pbYEmd39VF5JVDYDqNyzpfwLnly5e7806dOuXAeJOwJOjeAsuprWLx0D3EPLh06VICqKvc7v5sgID6Xb/pUB1SdXFfvIH//KJ6/MLfkFc/u9njBAaMQP4UaeA8IYI7JSz0w8qRqUBM7TwAYQQDARLECNSMw1wxdfqkeQZ6YeYS21KI1IewrNTQXxuugMWMiADRqASAqLHTTTZ2jLSNXBchKOGBmckL81agap7lL1oNi1kt6RUJFg912cSBfRY5tI/Usf02SvrG7Nl1VrJyLUxbFzjmL7AzlMuDzggMZwCBJg+SarXpJGC6KZdiKQZgS2lWMxeRHrIesB1wkvEjh2wS4JyprmK2MtrHXCwAQCpEu0ILllqgus68ALASAJ4iXW3umhhMZjY0SEoqr0UAi8UB54UAaZSsAkRUXQZLFX0/Msq5J0mresQiHaRqjsOIB4AuFsiAfW6+FSxfaRnzFsBCl2fRtjMW3rMDWRy1MAC/OP4jWUlwdv0qy10833zZpLEl9V4yAnPAaJ+FAV6NM58NnzptCUCMqjmIQoBadQC1aF8tyI0x0o0eJdUd/sk31Gs5BBC9pOBUZC4By59//nyAh8uRNYAuQINx2MiizS3IrcGmGk8CAhpEf/Fe9B/oK9JqwsizHCDj3BqYxQgOt3c6YNnEqeM20UcQmLStGcgjJHmUACYFUJkxdx5yFstah40B9BuiLjZInkrkY6UwPa1ZZlkLSfkJsxl0dfQfAChQEXEC0ZHmNps4ctAmzhy1BMFJX3wC24NpknS2wWUrLLAc0CFgrNE96AT27KMPRQ+jYHSCOnvyiswPe1wGKUwzYP70kRI2AVAqMThiow2HLIyORHoGzRsRxwdBSXQ4C6a9/BVLLTSvkn4Jcu4goLmTFsYfTXW2AQqc4Fyf+QOAPPPLLATDQ87lF6EfPJzheyZJFTzQcJSxoBv2oYQFywEbLl5oxSvWoFPIGV8D8paUqRPofCPt22OJkw3mQ1cSgDYiGdAcAfTNXr7UMufWA7KGlerYCRs+dIBIdLsFJ8YswPVe+nAKX+mdv8iy6JMM6Sh2kwBUk+josvDJwzala7r6SHcGkx6gmmgurIyASIovugT9X0gAjXMBgE6eoh4NRyzR0mIeUuUqIB4LwjIIQCkHYGfeosUWAMgSG+yyyWMnbexgk0WR2yiBYB/B93Lun7tyvSUAECsNrI9Uqd5Yn8WH0f3+BvquB/AbWoDOeLP44IMtb2AU5rdKK6hYRZpgKMcmTsHWRh/n1FlG5WXouxjKxEKKXkeHuHeLDTYd4GM74DPui7hjBLcT2KoY3DBwAH8wkVXMM18JTHgA1Pz4CNlsvPekJXpgiosMkqER+84C8AiDFBSVgND09zzLLr2QjKzYWBLQ9UirTfU2m2+0HSAZ/Q0YaTqlcCaaDIh0OGQjiZV2cnCePQNrUPtIAhZJdAVQbgb+v6okyxbX+WzNcqB3MY/tA1xxpGmclJlT9DuAQ+wjBLNlJanwVi4OApLLBtTlA+SWBHA0ak9u3W8r5pfbVRtrbe5c2FJp6xgYhMNNsKHtaQccOW4XLq20C2A2ySkCBAYQ7hj32LZrzE61AA5g7p9L4LYMYEc3KQL3HB4BI3nG/ug9i+2mG0gdTBfs3CbmuJMEYZN2yWU1Vl+X64K9Hvo+AVigdyDOGDkCSw6MOJ0+WMoS9CubDQIw6RQEOD/LMZ8E6e+de/oARo3DgAmoi/YaKash/oI9x0tasWzOA0SUD5iOjTz7kNfuvV2Arips0+Y8q6330j4B0f3MtWj740fwFwFSmtVxXYFl5nhJZZmw4w2jMDTRvlMw0E4GHOtPbrYX0ELUGk60w7AXthuur7ebbyhwwIO9B4Zt6wuNVlpRBJiIlN11QYB/slyFjL0upe5zL/SDB4+S6jPMEEHf8L2HVNs5sE3On+u1lUvw04E4gKtJ5E6AGvBMknmAFz8OuSKgPFKXLssEgJEJ4xcsduNGqsAJw+VaTa3HrrsmZHNYU/MjN22x6+wgXd8jAwCpsm39WlJkA7zKgKF8bCoJOxoMfTsH7NQxGKjCYjQSeEbAm6jtOaJ2VJJKsBKwHXMPnnefeW7Edu6GUbU6B2BXrtXWwkMJqFYzD/DSbhychGHt6OGEvfB0hHlM0MKM5UnrJaAegVktm2sKkXEWjJWS/aQdOjQBwxQ+IY6EGJ+9nnGXvnXJ0nxbtijbaioAnNHfO3aN238+2Aoopdiu3lgAy6HYevEP6H97XwLmrl7a02dLFtfY2tXZVgWgZ2IyYSeao9R5GBDOBKxMsJey8QD8OAyuMdj4Ttkw4Nb3332pXQt7Xzbsh6dOj9vDP2e8Hc+16zaXkf5WaX1pG30Sp62DQzDONWATMCB2dOLjkGMcUKDXF7HioiBgwwK74II8xgzad3zKDh+asi6GiCkAZ34A8nLFJbB/LVkYAliUBQg0CGiJdKxHSKH56DFAxD7bfFm1LV2WwzwV/4ngTzUl7KXnJwFdjVj9vIRthI0uPx+wP9edAuC5Zz9p0E/6XDrcrOAUIE82DcBSdYDpwshoLymLy+zOO2HGY83gOJuZn3lu2EYnu239paXofKHlwYgHLtDpyxB6f+Aw9rJ/0Fo7/RaexMcw/gfxR3mwJ9biG5YuFQud3043TVojzISzZnUAzgr//wLnkJ47UnNU/ZH6nJq3p96nz5z+V/NmnZdiq9K3Ok/rUHpG2gFrqTasLFiwADDfRW5t4LXlpP5OzTdTf8+8z7n6/No2zQTOaS4/p7oGm88FMBqzubUlNnc+6VUx0tNnJgFOjmHzEUCVHgiIA9g2cz/GmIONWE/vcbvj5tnYNgxxZQAiYea8/wHmSmNxWBnrSK2N3ZLeFRwT4wbTEZhpW5qnSAiHedkAAEAASURBVFmNbeMXJsKwzelH5qlZuVGrrQnakqVZgHEC3HucZ5gRl5ZxfJz1QO7t9XnRL5jV8BUrVuRYdQWM2wC/TwMe+/a3m7G5HNt0WbFdBHNnQZGeNAG79cZhM+vFd3cDoq4hxXWuLVwgxmK1L2bPvThqjYDORklV7WO+pXZE6dsTzZ0wiT5F+u1L7a7fvwA9wqcNxuyJhzuti3Fl7coiW38Jm5HKRcPmBwiGb2YT/gl8xw78+qlWUlIy/Y4yBiA5nuVyrA6Q0SWkAS8vCcJmh63unQCcxzMSY5Sf+YMfhtr8/ITNnZdjF64vtFnVzMMo/uQJ0nA/0QYAbwJGzAq7CJBSKamYPWwq6O2CffX5MTvdGoPoOknq2lyYFtn0Qkr2DgBrew+O2fMHpgDnk1UBI2e/Cv2XsCONMaZDo3b52gL7o9/Lhz0zwNgRs2cZczuYH6xfDwvfathVC5ljYmM+ZBIUtSXjpfrwxR2M4yfYkDHB/IjxKq7xkLGrotKHfwQsPRfgPHqwf/+wncDfDNGHkQRzBoD3eXlxNogZwKZMwPACUHphG5yyJ5+aciycF60NAarKslLAgUrzqmn6Sy+N2eNPdVP/AttyGT54vt+l8O3Dxx46HLZdh4fsZOsE89ocC+XA1ncdY1HtMD68kU1/Iw44J/sUs1tTU5NdeeWVMBte6jZTn0/bkw2n7O+19qzv9Tz92GOPIaf97rlZ2VtqYL6Wb9EhW9VztOp4PuqZqpvK1ufUPfQ5BZzbu/cYjJTr+LucjY84ZCwrKysJG6EfFuGALWROk1/oY07DnO3ZPvq7zebPU/rcKpsDY6HGyTi6unt/jHlZGNB1j23Exy9czHMDQPpxWAiPHx+3Hbvxn82o2JSX8SbpMpa04euPNDLPygvbB96bz/ieAzCXDfe7ooyHW5mnDMDeGGT9Abb+QIzH3W6YHXeS2v04IGCYfG97B2yvlzJXQ57abMTLMc4xL2N2ztiiCcK0rCXvX/dIA+d+XYmlz09LIC2BtATSEkhLIC2BtATSEnhTSYDnERaboIl+Jm7/9t2oHTzGgi/flbMofvUGn/0e7HMXriYdDIuC6eONl4DY5v7jW1H7/H/E7DT9tnmd1z72x0HbmGabO6vO0YLZ6dOn7dFHH7UDBw64B3gF9Y8cOeJ2oyowPvMoJAXY6tWr3SL/7NmzbcuWLS4Ar+9TD9szz099Vjna3frlL3/ZvvOd77i0FW9729vYpZhrzc3NbmeugvxiN7r77rtfSWmRuv5XeRcbjtiPnnrqqf+ySCHq+3/4h39wDEkzU3X8KuW+mc751n0R+5t/jbHolbQVpbCpwTq3Hv/FOtV5P774nah98atRO9OftOUEfx78RoZVs0j423Bo4fH+R+L2l5+ZslYWdrQ7/z1vganyT4LUkQDjb1jNH/7whw58+md/9mcOOJICzvw2tD1dh7QEfpkEtECaWjyV7//a177GDv0nCCIusQ996EMOyCwQlM5paWlxwDktPtfW1tq73/1uB6wSYFRjgBZ5DwMU+tRnPkkan3G7dv0G27BgmZXAhuMHvJFk4V3hTLGBZC6oI20fjCgsCv+uHCk5phahZ/6tcVCMbvv37XfybWtrdYBzMcBJviMs7mvHucA5ixYvAeh9pc2ZPccB3B9+5GGClM2OMa5uLqkFAReJUU5gNh0Kzl2+aZOVlJbY9u3b3UtjogLy9QDrxOAqVjkxBgpst27dOtevGu+3bdvmGDKUtkagOoEkBdA7dOiQCwSKTVZpbsSkoXFcbLUatwXKU78LXKexXGx2AlMqJZYY7dSm1Lwg9f6m72f0O3b6jMU7ey02ATCOKJIfkFAAdj4fgXNPTi7Ak0yChBp0eZgA1EGuNMAyfZYQKw/p4jwemHUyYLLKLzEvLBAeUv45JimCh8lYFJDUqMVIDxqeZPu+wQCEjAM5lbBPkQIym/xzsDh4YGtJDPSQBbINBrIxmwTcEYDtLZs0Rt4CAfGwKcAoIFcomt31AiL1DQGaGYQhKExqNy9gHOoBG5O3AGBbIAfbxDJHhy0Oq058bJDAW5hgYNQFugJiIAI854E9g6gUcQbY9uIEjwEHJgB+JdCFBHoR478EICYfqbVUHx9zQy8APUdtBPNRYmQC0E6/xQGuxWKAeAjYi9koVFgCAxmsFXnMJQGDgSywJLQCkd4eiHc4T1FOdCwLoFOgCEBQgDIBAiUJxoDQAcABIBGmnmg/7I3IQ2ngAqBVQrBP+JQWlPokAbskB2Co6wOoBqDMA6hAOaBcWmTO9ZXSPlKHJUmdlQCElCRa5wM4ZQMw82BLYraJK2cUzBpeQMChwnIAkyWAJGGSoau9pPNMjo7Qz30WAxQQg35BOCfVw5sLy19NrXkEqIxR7wnOBbAWRdYJ2AZjpBI0AlGZAFv8xZX0WwEgRIKpoAYIq1J3yQ79QC9ioz0WCw+hA+gGAD+/+oW6xHPpG2TiJW1porsXNjn6Okb/cL0HtJEflIWvmFSbAGE8SsNLoDcJKxTCMgNElSDgFBlE1hP8lgDcKb0uzMJH0yd5+H9tZqGNPChQPjqCPkfU/+iNj/4I5uRjA7SzgvSggjqozsMTsC0O2MRIP2DbCfQ30zJhQsqEQVDAPaenmn8hqCR9kgRU5vQaXUpAh+IDDOfj/l7GiWSmgITSUc6DISom1hJYkpQHzkf7PKSX9ZIG1VtSxGf6BFAmg40D93gkiy7KhvUlQRviAJ482YCccwFzV5AeOLeYNiMnUsMlBAYcBDg6ANgNhFYS4IzHC/CQtvkqsVfYk3xB+gvgZXIEne+hH0cnsUH6h3EtVAjzYmUVegEADjv08iKxIdiAPmTXSTthd6NtHgXxAsh9qhVwQAesfFVWUrnGctX/HgBDE2FkhH1mVnOeWHnUHgJ8TGijMECeagBcCiuOWK+KSjJpM3XCnuhs6gzIzks/w+bjzcIOYQnyyr4BL1pk1JKwtCUBXsYTgO7wE175IN4TKF3SCwtcsBSfgB9DLz0xZABTFDll0XN8B7blE5U0v4P4xA8qaM74EC92QKEh2HsmoPpJMN5lEKwsIh1zKQxVJdRTMdc+mOJ7ASCMDJFyHMCADwChUrYpeF4GW3YhqVjVdUNsBmogMH/oyIDVzcoDGJdh5YDflJ4O8dnABGmz++l/AN7lpNMsEjtVpkdqRzA/4Z6DugmqT/JFFu44hN987qWIPfZ0OxkGRu29d9fbVVcCSobpp6cnbn29pHXMgRkX4BT4dQc4cyDEpFIze2HEo96UNzis9J4R+g/QToh0fLkhwHN6p4fQzx7SxA0OIQNYvyLUTUHaLNKbFRdnAMoSUxv6Sh3HCP42Al5oaonQ9gDAKkApdL36A68I41HMOtuGOdcH0CLH8gDbaViZApA1BkCjpy8J+DAGcA5fB6Bkaspje/eH7ennGqwKFqa33jjPrtgE+JW0oX1DMRicYM3E7isqsg0cGtMu+lbzsaTfARMGhxKM6UnmIaTWo31aH/PDKJdN+0rovyLAW4jKBgEwnYHZXKxmcWwJbYMFkP4rDLD5jcSyBMJDgLUmJ/22fX8C9qKElbKBa906fi+G3w5niQckbTHAMoAdU1MBKwVoV0T/CwCKm4YRnFTb9MkAQJzoJOBjAB8TbHg6CGDw6ed2A5wrs1thublyI6xxBOW7urQhIU5d0TPAWblshkIkMmnsAJ/FuMTECjaaoHWdAeACKHMK8FOCFMY+NnForlJIGspCpqNB6q++7udZcXAwDsgScLbGEzqtGEBKGeUzHPJcDZCBcelEc9xe2D5Fv/psNX1YVwOclDIkvzFSz/YOAC7BdxcC5GZ6hq4DIsAxDTE16AE8OUCKVIjmnJ8I0Jbte2GiehFQ/GSHvecPLrXNW2A3pD0jkzE70zpJ//utjvSXxYAMPdQ94WMcpm5ijxPjTy/P/8MCr06xUUJgXnxCQQHnl2IjyFjTkhHWzwbQHwF9wkKWogtBbLUgL0Qd1T54eLCzCfqopQ2A5wHSG+POlgIYnDMHEJDah2xH0YXebu5F3XLyY1YKo2IQ3xCDMXGM/urDFrq5z+QE0HdunY2MDh+O2QMAAOMwfN5+SxXApBzmnF7mxmbtHVGLUOeySnxGCfMCHs5lK1yKzdMu6tvdD6My7Ysi20RYkHqv5eRhXxXTmULA+Dhd6YNVq7/vGGCjAbcp87WpWinylUPPLakj9Vlz1hR4Rb+l5vJ616YS/Z7aMKTvtNlE82KtpQmYo0wH2qCi+bE2o+hIAWBSZakMfda7XufrmHk/3UNtTDHOaR2wpqYO+63Ar7HBiXE/G5+RRBcn8J3S0X76cQy/Z4Cx8gHonmiM2xPPRynjhL3tplmOIay43OvS97a2MnbTN+UFgC/LAYdLV4RZAViljRhjo6Ta7gHkOygfInZNGSkboUnLK90DF+VsWplhBkl9OjIcY48KtgoAzYcDzGPX46zZAtBJ/siN9nQLPPY0c5Eo4GTYJhct8uHr8GHMcyemAPRgg4NDYcaALCsDjFUg0Bl10ppyB+3r60lYmHEpyVyiALs6ASD00a1HeL56wW697TJSTi7D/07PN3sB/IaHAfYUwWSIXwtl0S78qK5N4A/GYAjucuNEFKAg/g1dETgnm+eCEsaJKuSEW7Uh7teFj9E4F0OPlfLaDwg2NzcACCho+YyXwSxAaYANuzvIkrMvjA3EbcHikAO55TOOeLChiQlA5pQzir0xXULmZFDAx6BQsMHif+i3Fvy1wLp0ASO2x9qx6Qee6MGWonbFJcX2zrfnoAMwxmFXHT0xAI0jjLXZ2E0GdZVuqn7y29gBMg4DlpNt9+FHxmmv+lBp5IPYdwH9WEzdCwDDxql7/wD9iG/WeBZhLuUD6JiNjAsAuRcXsscHMDiPDIA244D8yAZBHRbM98FOiq4xpvrxAXHmLj34GfmHUMBvs2mj+kmdjxuH6U5+gfGCcU6bd2LUdFG9x7IDA6xZKFXrgLNHsT5qo5g2i2td+LrrrnMbuM/Hml3KrmVv+jzTBlO2Lp+hNZVHHnnEMbpv2bLFbTzL4/le56TW4OU/UteovHN5zKzXa+ucAs41NJy06tmXAZYj9S4DNN6BzQA+5jTYQAVzGuZNsuGhAb8dPcS8pXPKZjMWLFoIsJS5gx8WXdlGL/OLbuwtnowAjg1iy5qn4o+Yz42PYTfoaS96FcHvsEzBfNUDWHKYMTFhNZT3vndn4VPZHIKtDaBXLa1dPFcA/A5pE4Pmksy9eC5+9pnHbdf2F9GhhTBGApy7ZCO/Y6MeBlrm3UnNebQRBbvU2DLtRc5Oqmng3NnJLX1VWgJpCaQlkJZAWgJpCaQlkJbAm0wCrMPbczA1fesHUXtuBwtBPGySdYldzl676ya/vQ32pmIWdNPHGyuBl/bE7TOwzT2xTUEHs798T8D+9P9itzWLiunj7CSgBTTtjE2lYFPg/rOf/aw9SYolPdTPPGpra+3jH/+426WngLkYZn4VIJp21IlV7r777mNX2XEXxFeQXYuSuoeC+low0E47BehTi4wz7/3LPosB6QMf+IDt2rXrv5wqRjylgBUA4GzK/i8F/pZ+MUDg4x0fmLJnCVYowPGeW/32Nx8mUEKw5nwfH/zriH3/YS24Je2Wa/z2pb8LEuw5//f9Ze1STPhpWCo/8S8R29bAAh8LNFes89ln/zpoFyyaXrD7ZWX8st+/8pWv2D333GOf//zn3UL5zMX2X3Zt+ve0BN5oCWgM0KKsXgr8yE/ff//9Ltj0zne+0+3MVtpOsSycOHHCgZ8FdBa72Lve9S7HqiAwVmqh9/DhQ/aZz37apQi89cab7NKLNlge4JUEkUxCtS5o6qMsP+xBHofqfeP9xLnqg9QCeUqe+jslX42DGv8ef/xx2D4Os2Bf6lKI1M+rh02FhHtMRHfv3s1rJwHPhN14ww2AF5e6cx99/BG3uHzxhottPSA5jb0Cwu1m3DsD+H1W9WxY5zYSOMkmVcqPHPhdKVM3bdoEy8gFbtxra2uzZ5991oHilHpXILeTJ086YJ6Ab5dffrnrU30WKE9jdkNDg9upL2Ce7qmU6Nq1L0ZC6YAW+JWyt64OZi8+6xwB6RWgkB9U21PBQsnkd+FIEgwjAsTiPQvwRHUTRGodUM4LcINAnZucMu5oRZ6QGP0GYCgBM49AVwSUCZ0TKYCtR6AFRQ243qPosGKrBIkUlogqksCLhGiuOPjkeFckQTIUkwxAMQIGPgB0oAqog5+iAccQRvYDmlFUm7gAvwl2RVkaCAk2EIHivtRZxYBOIX5A/xB6FjiI0xRZSyapJ6A4Rf4F5PMqV5poXwC4gHyiRQQuKMAH2MkLKCRJYFDBeMEBYlBnJGiLHwSE2KgQkHuJbYRbU2+u4Twfu/0JhdMcyqccycHjCViUNngApiUBL3kmRimTQ4AtvYiMOIYygfaIegq04OrIKRK3dC2GjJO8gyegfpRJwEkppJLyb9xSDB1Jgo+ScoIAigJAKscFjJBHAvAA3YKoqCwADzFseKYoE7CSwHRiYVFL4cFw9RWILsZkCzPnG/5xMgsjF6hGYGKisS/LjT4K4u9oI5goAF/IDR3yAJJRt0q+cQ/XBQG+kW4T/nO6TH5ZQEu+142jnIj+eABKIGl+RwekO5KLj7LRKx+C9gCOShJolny5HaA87sWvSifmoa7SNfWHhyC4wS4nZUhmEJQ1ZK7AZhwQFu+quweAWFIgQv5QnXySo1CCilaiG1zMSfQo/SEb8AroQP2cTOOqr+sZzoKhErYy9a2PNLAuPZIErZ+pht4U0JIOpXRP/SMWE0Vrp+ugVvDiZAcCk0w43as2q0HSB9dG9bdqxrX0qewrif0JkeMFAEWgi7pStl+AEf73Ux/k59VFrrqyEbUB/RTQTDare2WgGzAgxZCfj9+Ai7n7KrgnkUQoF0gebURSBPS93NdVnpSlYmsxGCVtEvAcKaKkMB5kkoh12MTgMQLfPRbMn28lVWtJaTabywQyp1zpMPaZpCMTaiN94MWOR0ij99OfHrW2jtMEB0tgmVlqRWXYnI8+kc2BVHA25drPddi8mPYkbA/95PQd+SQB+/EPYDTZCdKXbKmb+lG6neD+7lx0zclD9kVxahlS4DzYF4WKwNYVVse0GLuoNydQAnWgT7F1P35RqX3VJzGArpgJbaefVFfXCe524GuxLS6U+xOz2o7do/bIY7sAChTCdlUHiwlMnfg0qZV0Qzbvo98U/nT9R93GYCXr6B0FQIfceU5VUD4J2Kz1dNh++lCr7SXl5QUw1P3e7ZWkZAUcShOm5Btl79Q14JPPpW9cK/F5SXwv6W/VohhylIwEhpMfQzzIAOYbZDV9aLznd17SCaROG5EBNiHmHIG01IcJwE9iCtqzu9P2Hem0MgDuG9aXW30tYCGHCWbkUJ/RULWN3nR9ovuNA/Y43Ub78PPZzKEU/p3iuetkU8KefKbfdu7aYxtWV9ltAMtWrswkvbT0iPIAhUonfeiEdNTVTn1HH4jJMI6NJAVCRtcTsDupX6RDGtJk9/IAfEH/A7ZC52M0UinbpC9KtyvfIpY3NdGLrQ8M+e2hx7tgnItZdU0W7HBFVgVDlPyHlzHGjx/ncqcruo67Of2BGNQ6Yc6bBEySDRtiiHLDrNEpndsTTx2yo8xZVqwiBdvNS2DBC5GOVNep/tM6lOT8BD7PRz0dQ6ObdWrmyb0SGYA58MWcm5AuY4PyixoD5UuomAm8JnBoTH7UiUk2IL8n2aEfMNQ50CuyUVrkXftG7ac/bwVsVgwIpcxWLIcFS32IHDiTMvjA/9JrJbQWYGc8DPshz+ti5cvA9rIFBqXdHejE/T9j08KB/VYLg99dd65lsyJAXIBMMfooLGA9lc8GKCzAmJtf4k/iAl7LHhnQo4BVnN+hzlgbfQbjJXrtY5yYtlfqHeU36kV3os+MbTqHPwLIQf3iHCiGLhs8QtrVJ5/cD9gljo7OgVWwFIY4zQFSeo6eUjePgJgw5Hpg7RoH7NjWNgbIArAcKepD2GwEYGd317g9u7Wb9J+tVlM9h9Svs/BdAFVz8Z3oXVj1Qs4BwDU+6RP3YIikDboH9eS3CB0tAK58vg+2MjGyyvwEulR/S9waE8aZQxwnlf1Af98vBc7p2UaH5qySaeqYOWd1snY6L5tGF9w8RFrFSMscSJtQtTFF8+yysjK3ybSurs6tb+l8laW5sD6n/tZ3Mz+n7nuu33UPHan2yG/OBM6pnrNnV+P7mEcgT+nFJIIcGgWQOjjp7DtHvoZixtDbnz/Wbc9sHwNcGbbff1s1wBTYPlkjl9uIAuby4ZsynO/QiE7b1ZfSDeYYYtNMxGGaw+fKDlU1+VQ/He3HvuTjpL9ietXmBfkl1dcVzvcaS1BlOlk2xj2pa0tL1O75LszAkyFYrmbDdJcFA5ZsRTMlTYRVFsrIuX4mxn7GU9yh9ZAKfoD0sUEfADH6U2DMUYBsT28dsse2vgRYdNBuvwPw6uZawMbywrSFquAmNFVHBzS+USw+VGOWEzO+VYx1si99oU0ceFEJn/Pi2IKeE5AL5SjNtOqhMUKzB+mvS6suPZRNIbcJwIVNx6Ok9G5C98N24cVV+HWeswDY6fkgkWA8wY9Lhqqb7EXXaXrVNxh2qa2DmQCo2ZAie+pthyVy24D95OdHeF4sJ7VtrV1/LaB0QIAx6iofo/oEMKiADEuGLn9NeRoLNC/RmB+n/+Qy1UzZpXyNWFidLToZT/sRtTPln53P4HI9mwj068dn+Bnj5IOPNozb48/BQNo3ZheuK7MNF+Y5YJ2zGSQfY77A/hhkrkcebIlyNN8YBSQvpkOB1AvY4KH6CLhYxbpnhn+ATWGkGmazTF1dndtIpmfe73//+26t+aqrrrLFixf/SuvYNPXXOmbanD5Lh1/rA7T2rtSxAvNpw9qNN97o2Oa0Tq1rBJyTn0k9Q/9aFfgVT35tPVM+Qt+ngHNa99mw4TLS9tY4dVDfy0fwSMPcEYF72VDBeNrfk2MvPNuD3+2w+poCNvRV4lcyAIDiBRiTY/jruObTXMu0CdvhA5dPAKzu6yWVd1Qp1tE7+ncE4OyJk2P2wx82WEt7rm1YM8fe+Q7YiwHF+vjdjdEol573Ehof1F6KGxkesp89+GN7+Gf329zaOnvrzXfYpZdc7piKkx6Q5dhg0qPMB3oOcZapy876SAPnzlp06QvTEkhLIC2BtATSEkhLIC2BtATebBKA4MAOHo3bD38cs588HrfT7GDSJuxlNV67AyDITYDnFtTzsPBma9jvSH3D7ED6wX/G7HNfj5JWN2lrF8MY9T8Cdv0WBWJ+Rxr5W9AMPciLOUvp+BTon3koEP+JT3zC7rjjDscwM/O3X/RZCwaDpHgSQ40WGN0i2MsX6CFdIDyluVTAXgsEZ3N897vfdaA+pcl47fGHf/iHLhWsdgCnFgVee87vwt+sc9i/fjNqn8RGutnZd2mt1/7lc0FbsYzFxvNsI3e9e8p+spNFMBayPgko7e63sRjOYv8beWghZdf+uH3qC4BtSe08jg9ZiUw++7ch23iRj/QB56Z23/jGN0gT8m37t3/7tzTj3LkRabqU11ECMxdOxUD2zDPPOH3WWLBp0yaXWlupQ5SSWIGhe++91y2oCmilsUCMaSm/rUXMIwCqPve5z7hA3C03vZXUGpsst6AElh+Cg7h31jwtAFORT06JBXO32vk6tvd83kqy1PiWWujW3yn5avx76aWXXDrxOIG6jRsvJ5h8tZUCoBMrUZxF4IYjh2GAfQTA2lHS5Kx2u89bWk7bk088yUJv0rZsucKu2LwZxoXZbixta22zru5OFrBDjuWtu6eXoMA9bqwV2O3aa6+12tpaVyeB4wWMVB+rjmKaePrpp12/iklWAHMxyWrRXqxyYrNTMFAg9/nz5ztQnNqi68SyqQV/AeWUel1sHimwXGqMTbV7ZoDxfMr+9Spb4ywxAvqYO/JSkF5BKyFK1HYF0FjHdwFyARQSgNsEFBBsRAw/ScAmhOqm0zXqXAUPFEQgqpXg+rh0geIUpHNMVdzQC+uRA6gAAElOwtrR20mKxF6CYRCVza4yT36ehQUAIKAVJABNlbAtzuX+CsCpMipNVdaorDBDkiibgGuEy6kLQUG1wbVnum36W+BAD+xBSqXldBkQUIw0jIozi6lJpfIDl1Eq9Y8Q5IxRrgJufoL4rr2K4CEgh0HiNA8OwAVNqKTjhhB6gmjXNGMHPiESs9H2VpjpOi2LYFJwVjXtgynNBZNUd17ISaE6Qo+uTfpWIB9JWaUGOUlB3uk6cxq/K9DOZfr4Sjv1WTJRG5ICC0ryVCFO4/UfYRwiwbqAfsVfRekrFRFQcFnV1hWcr+CtylFqUAcK4Q+FFQX+kt16UBZ3W/onSSA6drrXRvsHLATDXHYNLGkwQcEPRH/xPAOSzgHPuEKAM7Ek+JCllzRXSQWddD/6NkrQMSqAntoLWCEgEIGaIFnqXbIWCIR6qD0egMuqo4Kr6g+nI+ob/o+RRi8CAENgxyC6JnCWvk/AVqQWJJCNWu5Hx1yvKxqKojt5ElGV3AWjE7gmmBRwUIFq6SzlUF8xocUEnNM5Ag8CHHVgNCGg+FK4NAFKnB7pO/4nHoxs1QtIEB1RSwTuUCsEZIlRd59rE9+pTamXLqe9jsVPtqUgMv/5kL1f7XXFSyaKwnKdbFHy1UcdMnBaJNtVQNzJnL6Oo1BRJriSCMl30a+XK8m1SSovkYcRrpeXH6bGIDbjcQFw2gvQdaL3lIUHmqkOwAL0gRaAPeyzieEzgO5g0KlcabmVa2H5qkAYAOeoXMJ1qErGJ0h3BJehb7rawvbFL22zpuZjpKWrt9tv32Cz5sDOBwBTY4nkLllLL/UuXUzCPuj03AtoAYAMYqGZ0gNkC2BTXS4/5qFvBQp0clNRnOZ0Sn3J77LdOMBBFS4gjvpEPwgUoHeX2hI9dl9ztfpONRAIy/UlzlNVdCBS2o2UnZpInAIZ+blPHJsbgJHoZ4/22De//Z+2fFmlvePtF9u6Cyth7ELWEj06pbIFYnX9qttwtHeM2vbdTdYJc0kgsxhgVRbsXXEYW7vtyNF26p9tN1633LZszLdZpM2kM2kPbUFnggJSSS8oVyACpxswhMo/yt9Lnk6m9LHH9Y3OQSD0las/yABUks/6h68lGvdBlaPdAv/gMBKxkLW1xO2xxw/bk88fBli2zG65aTHtDLk0mHJeur/KDCEs2aoDgVHsEAxCDz16CLCAwbJHCm0AzROw+ZxsmbAG0rvGYwN2y3WL7ZrNlcwTAPXRPgeUwymoT+ip6fpJ7moM72p7Isk4JZ3VWIGPF7BboECnSQ7xoOvpT2wqKiAnVyYA2kkrBep29kj9HHAD8Ex7p9nXvtVge4/AkrQgz37vzlm2kBSQArRBYYh6yZMICIMRSrdo4xTAqUEC5i/tayf9YjcArRzLJa350EACkP8QKXiPwy7msc1XrbVrNtI+GAhfeY5T2yhTOixQnwMJOoC29ER1l+cAGIS9qkvEHirgnA+AiKCXYnZC/dAPrhXChL5S2/WbwIUCachmlXpZ81w5jCj9+PNHYdP/3/uZxy2yu26tsU2XwtiVR/+px6mPLJ1T8VHICfCO0np390Rs7+EBazw9yHwYli1YSSOkpm4+M2X7D3dz/ohdsXGBXXPVHObXAAnw4/I/PMUi9yn0FECo/KukzzggwLq60ke/4HokVQe4kOKojW78drWAtZP2C/igsUolBgUgpkzV0gtwXQahvleBYlh8cXvM/uN7D8JWl7Cbb1pll11aC/ugxgG6DZkIZONlTBez37S/CnGdh00ajQB7R9hQUeIY/cbGwjAqd1tTY7djC9q8cbXdcE2ZzV/AqJMhfQdKjB7QBdSd2vAB9+/Gcf0hYJH8jsb5BDJzcFLANHqecHot3USP3WyEssZgqj1xnFThzHeLYYb9RYxzAqho7vbKc4yzPdWEfnMTPbVzGsiSmt+6H/lH82GxO2tTijaeiuVZTHN1ddOguVQZus7NRyhHn2eWM/Nzqtxz+a776kjdR/P/1wLnqgEyhpSGnj6Vqig17rHGEdIrNjrdLEeGgul2tONf97bY0ESWrVo+B8a5Muw7ZEHWe+Kaw6LjjC7oKOBK+kGbFdUrYs/1uUkH9dCYKR/p6sQtOUdAKk2KHYhM8qEM6Zj7m/mHl7HU1Z/r1McCkjsfxF+HDo3bpz/1GKxq5XbrDctJwZ1r5VXyAwLrUApjuVfzG3SElR/sMAAgzWx/AylcG7qw6QzLIs19bMpvnV1hO3howPoGWthMNMuuuWGRza/PZdyRTaju6CUfVTda7L7QPEE+TFLWnHsamMxXtF+HZiAC4wtAFgjAzi4BcwhUp4I0LshO5X/ke70OWSZ9IT01rHt7SUt5770vOfbEa69faldcWWdlpMz10yb5PIHvEKADkXsYO3XXCMI5cLTLtu86AmiIeWYmbMDM7brbpki922GNZzpt9ZrldvNb6m3FBUr3rdFZoD8BqAG6yy8yMdMzAF9M26QMUqW7vuN9uhnUW9cy86Z/dW+v5k281Is6NK47ACFjh7zq9OU6l/GDDSTywS++2G/f+VGHtXUO2LVX1tuN11RaJSySAUC3lED5mgOhFfzp5id8EwO43NLOptgdZ2C+7GWzbiX6FySVs0C++fQbPudMkwPOiXld6xhdXV1uc5meccW8vnnzZsZcKETP4ZGycxUp/5H6W/qrl+xPa+Gqi9ZTtD4un7Flyxa38Uy/66VrU9errNT1+nyujpm+QZ+djVG4PqeAcwIbbtx4mUvprP5/GWcslXN9a2zYSGBrXa1B++F9J0lVfciWL6kACLjcFi/Jh6Sc/mOcUopgaZAURzouTRUQs6NjzLa/dBy2OB9A4zJsLRtQ6wjMj512pKHZiorn2vVXL7arLs9yczavnguwZemFNhipVPkLlTuELB984Ef20AP3w/5a+zJwDsY5HgqngXMaJ9LAOfVC+khLIC2BtATSEkhLIC2BtATSEkhL4NeWgIgkOqADf2lnwn7wYNye3R1nk3rSKkjdegGLO7deB4DuGuip+Tt9vL4S2HUwYf/07xH7+XOkQOF56YPv8Nv/el/QKtklx3No+jhHEtAColjl/v3f/90tBqYeqlW8WIU++MEP2p/8yZ+4BYhzdMvfuBix1v3pn/6pY/0aGSH/zYxDCyJ/9Vd/Ze9973vdLuAZP/1OflRalTs/PGU7YVfLJvD4oT+YZmVUOqfzdTS2Jux/fGjKnjmRMDbl2hP3ZtiKpdOBpvN1z1+l3G347099ERbRA3EbgfxlEwDCv/pwAAYs0ngoy9RZHtpR/qUvfckBS7XIdPToUcckJeYlAUl0CAT6d3/3dw4Uepa3SV+WlsDrIgH5+NSCqRZKxYr2ve99z4HkBJrSYm5dXZ0DYx08eNB9L2bQG2BE045t6bzsQGVosfcYLGWf++ynFDUgGPxWu/SyzaSKgnGOBXDizyxvEjyIjbEgjIMi8O4W4l+Xlp7/m8yUpe6mv3VIPvIbTz31FExBPyWdTjmLym+xTYAPNUa5RXIiAq2tZ+zxx56wF154wY1Xl1yygd+SXPeknWputloWgtesWW219EdhQSGMLFmwBmQ5fyPg3b79++3e++5191TfiIVOKVh1f91DYHgF+gSClN/68Y9/7H4T26vS1ohZUEfqXJ2vcV/gdr2rPWLn0I79rVu3urJvv/12GBBWvuLrUjLQ+2sX/l3h/KP6vFkPxEjAHNVFl5V+zAHQ0Gkt3mv5XswICq4JF6pAnQeQgCLM04xNAG8ITobRew8BMgG8mME6E0gS5FcpLoDtAm4ChQkYpQAUAW2AMwnSLSX6Rqxv/17ra2smZS+bDtatslBNtU0AEBEbR1CBQkXtFdQjiCcAjEA4CkwL4jEd61LwgIA5QAkQrZRNQF2/0GdSWYHbdKgaYozyiCYNkESCgH2MV5TAu8AvjtWHC1SuLo8RxBAfmhrkI7gtAJiPINzL8dJprBRyUfcr6CiYmwBILuUqZeo9OTpuXXt2WORYgxXNqrLsVevNCzjXpbSlOTqEH4sSTIdrCWAJ4D8HzhHQCFAiTGcCBTl8htB6dIYYOBRxEzgIYbj763fV1LWRL70EsrwudyQnAfJVfyoA6OQjr4VMRNKmvnX9RtG6PgWck+Bkq1zBazo0KlClWPD83FvsfCApSPPbb1M7D9jp4ycse9Fcq1y/2gKAZ+MwD8WJKApooRiyCk8SbE4q4Mv3HvoJfISq7wJWiaAgFABduIIEeeYXlokXFZ9+ISP1iSuMgKWYEVUOKC1eL/9G16utMe5BTV2f+QkqOwY3TtU1kkOCYKrgcYRmp+WhfuIXpycoOvCTl4FzpOcUCAOhetAppXFVHRIKrKnyXKOAmQteI7gkdRFDVJIGC/TgAHuYi2sDKu+Af1JItQndEvjFMcbxZ5Sy9bsDTqTaLOHoEIjTBeooU8FHJ6VpfZwWoAxEDC+yjaCTu1okvRTrloMCSu+l2QRiE+jwFMHtCMwa0gyykAGO41oYMpLxl8cwTo2oMuiAn0i2g9I5Bh2C4vFBG+s8ZMPdR2jjODLALqT33Is8bRbKqyBNL6C5ohW0ifxlXK2AOD9SK9g8qFFcdgowRkDOTgA2n/vn7XaqpRHGz/kA2NdaVTX2L3ACfS2RSRQKSjqwoQMBaSMUIBsANwkvaW1pCdgz+mnaPhyxHmdwKuKj7bQlAWjWiZYAtZj1VLbsnkZwNf/JxnUNL4HNBFASWEIy12/u65f7RN+7inE/bs6h6/mO/4XTcUAx7k0THPhH6ffu/1mvfeM7D9iqFZX2B3etI71aOeMdsqCIKL5VjD2CdoVkNzJGjqbTE/boE0ftQMMgDFZlyCSP9NQJxjxY/TLDtnxVud1wba3Nr8uwbLpODJm6t/rV74AVVIC6Sr/k5L20w+kl91TVVV+ax7t89fQX03rEd9JRnUAD1Qf6pHf+nT5XdibbiQWtvTVmP3vkoP38yUNWXbvG3n7bIlu9Igj7FqUiOgEfBDjIQObOv6onaGRvv8f+49s77VADACpfMQBNLI7FqwHlfgUAecGyErv2ikpbvjgD5jPu6wQjK1afCXQio1f9aDHlq3oCIwjSMI285Tf5HsnUgVX4GsciFk3pq0BLUR9zFsqQ31Uvq9URdD4AKCwoG8Wu29pj9q//u8l2HfbbogWZ9s67imHQDcHuwx0TYp7S+Ma8QmOhbJm+FENZP8C5nz153HZsa0YP8i0zWEkAPwaYP0JK2ClbfWGhXQxobnF9hpFFFuAgeoegnYSpm+sjNUr+xAFS+F167vpSknQn0z6dhEydT5m2GV0ryJtLY+t0nN9dC3FG8p2yJ8mRspMAxeKMNT/56ZB99ouHYNObb+95R5lduQkGuQJK4Zy4s2HaRgXFjOhTRVCe1jNsWnihx7btPEUa6xjEsDC5Mr73DQNeZH6wbFmRXbOlypYsygQ4yD3xkXHGkoRf6xsj1CjLAnFtNATC+zJwbnpORW9gyF58uLNMDXLUY9o48NjUB6m4vo2iYLFoGBugbwUU5P6aZyQZQ52ecJlLgflczL7yzcdhrvfbHbevBDhXOp1WV+AFfKCAegLTevF1Gi8kr84OswcfOmb7D/TyO2nmA7mAmpTZoBd7DFtNXZldfdU8W708ixS2yJXrcC9Oj5x/4bP0Sr4ghdXRzCIicDs3S0ov1Swu0hzMo3FKjosjIVAW9RqDzq+x8ZgNwjj3y4BzqTmrm4c743ZFTfun6Y8v68urv9f8+MyZM459WZs2xbasufCiRYteAeOobG0e0aHydaivpvtr+rP78jz+M63r/++9VI+ZwLnaujpSMb4MnEOu6oN+AFs7d4/Yo08eZnNOBD3E1/DfwEC/jUdGrLp+jl21pdYuXpcFMxg2xhgunZf9yMoYmrEZzTsBpdJPjimOr5xJOIPVjeSfpm0PtZR50X+A5yhjWlMBb7EhQMgwNw+FtU5mGwcAJrC6romiE7t3D9snPr6VeXmt3QET5U03ZlrVbFkqcEtMTtAasdlpPq7ZkuZ1o6QZfX7boD3y9GmybkTdHMqL7g+Qrj6CDc2tz7K33FRhy1byfJUNQJN5juxYLoNiKYuXQHICi8lhO7tSG/QdcuBcAYwEAvXg9y0BUo+7eDzkU8WXSFLApyhDtZwuVD4XuDfnMP5zvV5DpLbcw6bU7353D+mX++xGANbXXT8HexQjnHxMzCaxY425QYCvPp4/VNxE2GPP72i3hx/ZaaPjOciTdO/MIcL40lhi1EpnBezyKxfaRbC7lbM+GMDYVA/gTdxXrHzyI9QBp6jpsztUTWROBWkCglUraLubq9AxAmDrEPur5h66TC8BI8X+54VJUj6Rn3UlRQHcjYvhNWjPbB22b/yg384Atr3hqll2841lVj0L5sJMyUT2rXupAtqmAUMm10ZieXb8ZIw2nrQTDR2seZYzbmcBuorZnbeXw5IWt7ZWAef63Oavuro6tyls+/bttm3bNhjUSBX91re6TWcU/Kojxfb2qi/5I2W3r/1+5t8pf5Ky85l/p87TRrr9PK8ru0tqnWXJkiWOoVL2mfJFKiPlN1J+JFXGuXif6Rv0OdU+fRa7vJjlxTh3ORv9lHZayhVHJ/SMoHFfcwj5C713nEra9+9pAWx9EDBmid12+zJbvDTfApow48jjgPPjsCTKu/vFvE6/i3nydPOIPXj/Pms6ST8n5zBe5APcZc4c7gIoP2Zr19fY5ZfV2PzqTMsjRbGea/X0wwME2iB/oecufAKUl/39QwDnfmIPP/Qzm1tba7c4xrnLXmac0/gpbWITEnMPjXOU5l68ndWRZpw7K7GlL0pLIC2BtATSEkhLIC2BtATSEngzS4BnBRufMDt0NGE/eihmDz4Vs6YeptisNc6v8totW3x2O2xKSwHSpY/XTwI/up90i/8WsYNnEqTQJQXlBwExXs2uRq0Bp49zKgEFxT/zmc+4dG9aPEgdeqC+Goacv/mbvyGAsZ6dk78dwheY48Mf/rADG8ysr+otppy///u/d2w653pXYUouv03vU6zP/dNXIvYv34c2H9bMq5d57cv/HLL62ukFgvNR16/D0vnPpEI9iZ+sKfXYk9/PsLo5b6x/PN4Ut49+PGqPwTQ3xvrKXFgKvvipoG2+1P8bgeYkv87OTvva177m0hvLBsTCdezYMQdS0W5zLTgp7cJHPvKR85IC4nz0YbrM/74SSC3Syr9LdwU+FsDrkUcecQxlYhLTS2k6FSgSKHTTpk0uhav8awpQpesVDD186JB9jvFDGIJ3vOVmu3jDpQSRAGQR4VK6VgVofQLiBEiB5ienFdf9Lh2SoQ7JVZ+12K33lpYWe+yxx+xnDz1k9fXz7CYY3i5af5Fl52QTXCSoQOCjp6fHLaRrMT0jI8R4e41brNYC+4svvuB8Tk5OrhUUFlhRYZHNYuFfTKr19EMm4Lbde/e4HezqLwHhLr74YgeGm5n6RXUZGBhgV/gOB5zLJr3rNddcY1u2bHHpVvW7XgLI6T21kJ5atNdO+XvuuccxbIgpVoxzSoOue6bO1XU6Un/rc6rcaVDFm7fPEwScE4rGEeBzgXAFi1jIF8hJQd1EUjottjX+hGlKbC6gJYj3iB2C6xgzYrCnebzICzoggfAEvvOTzs+nKLQYI0SppM8JgDqkZQPxQZkAkQAPJFo6rfO5Z2ygpRHgYp6VbN5owfp6i5OmyAubhdvJr2kbQXrtzFdwMgkoQQwzAieIUUoACMckRzCRwjmXOsP64AJ6Aps5pIEawAt79cCyQ4U4h1RIer0cahBTxnSZ1BXmCLERKSyhkJiHFLIKkCl9qANsSa8IroizC0/jyvMAKpsOzhHso/hkmCAIAe/2rU9YZN9OK55TazmXXmG++kXmySLgiFNBtLCR4UW4RIwVgTh5/JQWVWneSMMnpjMxfTkmPxfd5DzOp0qubvzg7uWhH1VT8AL6if6iI5C52Lc8+Cp+cWUmlfpWUX2ih0lslFq6oJF0W+wiUYAbKlK3EjsHOAaox1Q3vuQaBUcV2Pcq1xXNjjW3WeSpZ61p/z7LXrrYZm2+3ALVc8yTDRgF3VC40Us/cRf+jtOssPvsIyjkUWURKrcCRBFDswTkgh2dIJCftnsE5pLfkfJJpKoToAi6gLMAuRAQDQDaScK+qzonMyRHQGaODQ4eRDGRIT+x+QhkgUgJsqLn9LN7UWwCZiSxmglYJTCHguU8MlO6QtKqi0B28vPoFHVWiCsOa14MtiQdYghTkEtB9YQYTair2MPUOwHVjzSWCrqKhS6JnQgY5cAT8ilSKHRIdYshe6XJFcsJzpYXwB1KFkhRAfgo9YoB5FK5eokDR4xM6hf1SZIgHgW6X+gh958YBiVPATKlW9NaKn4TweDEqSJQnUJuwI+ovx9wYDIKeAVBC9cGjs6VGcTe/QKfAeqMAVD18YoOt9jEwBlMmpRUEwAesbNQBqDkfEAwBbAOZs4DBVRJnWEfZB5PE5Cx6kcbaK8DoqF/Apf2tk/ZZz63B5DYGdJQLYSNbQlAAem9gtdcKZvX9e46tY36APTRT1H0WS+JRlgTV2VUXBZOM+UNkDm/odAx/INckTqDpjgdl6ilO3o5Zih95hQBHxPOt+gP/p+OjqskXvqd6zkXUyDwirugPJ03Dbrho8rhb6VrVRvbSSl3/0MD9p17nwYMVml33nEBKdxIOQeQSD0qoJ+uUY+5bGFSIY6O7pjt2j1gBw5PApYDJCjgI/qekxuxunqPLVqZbfXzsq0wB9gWFzpwheqLzQmwqrJUd9yDa7BAS5rLqK7yofzvPqsNOuRjnRycH0EGUj6dw++oqnt3f6sMdBb8FvfyWMvpKfvZzw/ZI0+dtDl16+2OW+bYmlUwrhBk1r0ld7KWWSb3nwa20aeMOX0DHtv6TK81HMeihgF9hikQG8rKi1pVXdCWX5Bn82qDsIKJkRGddRWd9gvTnoP5iKrFP6qS/KnqL9/sfMfL9X/ld2Ts0/iEY0syJ1CS4ghjhdh/1JdcSr2wKAQTxG/KXjWsdML686WvtdqOQyFbvDDH/vAPMpn3YM+SE8oksJZYB8VOylduvIww9g1hltv30X97h224P2hT4zD+oRgF+YacfLZ4BaCYubQPgGEmHROkbmJccuxNKITa5PpKjeSD7qM+5K68eEe2Yn6SHrvPL7cBzJ/T+WnGKL7UCfgc94bfdX6D+zgdxX35AkDEQXze/8A4wLkTsB1V27tvK7QrLwcSAEZF45PARLqnADdB9SN/6Z59fXE7cGzS9u4bsv5uwIgwXYmdypcRhzkyy5YtybGFsHgV5HOd6sW1AncKyBrH5wdwNgFAewKtaHNvEoH7GONcKm3qJLumo5zNCWvixjg5UdXJ9Rf9LVtlfSeA/CSWJEBm6YcAhrJtGXsPG4mfBTj3r//nWeaaBXbXOxbZ+ouyLZtxQ33i5xyd6v6hrQ4ATR9A+kMa4kE7eGCC9H1i02McYC6UkTllNbUeW7Ii1+Zhg9JRsqO7/tM9VTdXFz5r6qFhQe/O3yAH9U2M8vma+vIbbZRtSocFlnZjPpVRH40BwD8p4NwAwLniX8w4p7mp5uspsMzMeSslv+rQvFgbjMRYJfDZqVOnHHhOoDkBX+bNm+cAdKl5vy7WnDf1PJDyyak5sn4730fqXql2qS4zgXN1dXWkrgSwAvDK+Wj6dZD1oxONU+jogJ1uYa4xwW/4LaUqLp2VtHkwSUlPqyoAbGmcwBm41N7YnI/y6Q43LroU4/h06YobxyUP9Zl0TrpLhzufyvfy585HMpfWvMPDXMWHjkufo5yrlNeyU7yRmwNI52OwHO/aOWZ//7FnuFjAuUV2/VVi4GbbCXqgsjW1lS+VzspDCNg1Ppq0YyfYWLltmE1NEfwM9UP3AwH86KwQ2QMYJxaFrLiCrBjuWvpQThm9UzucnNQGGTRlqkFqi9jX1Db5T6b8bnxTClO8Oucw5wHU6trPP0pvrL7QWKg5j1JQuj6iALGgqpzRgaTtFXDue0ctHBm3G26cy3NhqZWWyNfKtsXFiKywoxCDqV9CpI4TDOiHGmDy2tltXT1++o9xn3TJIc6vrEra4pUZNm9pnpUWB2Gu1hjHPIw6a+xRXeS65BpolDNvp6Zqo2sz38sWuSABjTxfTduoPnDI3coW9afGCI2bzn9xvR4vNMSIbF7n+eITMB368DMR++a9YTYDjNkNV+aRaryAPmRskN1TDmqAsav/AAkCvpIXiMYKrLnFa7t3DdqpE+MWHs7A19BW+uSWm3NgOxtnM1ojYKoBxp56q6urw2cC6m5rc6xzepYVK/vSpUtf2fjFXdwhO9dzs/ojZT/64Ve115nX6LNeKkvXq2xtXNM6i5j9V61aRVrTi1zmlVT5Ol+6kfpb99b1Tj/0xzk6UvVMtTNVvr7Xeo+Ac8eOHbdNl21yTPfq85ieUekBzIVKIRONT/Rxa2PC7vtBl23bddxWCjh3Wz1zLZ5d0Ws9xnoZM5NsopGyOOZpBiSNT/29Udu9vceOHU6QWjsH5lnmp4BNC4qnbMEys0UXZFtVZbblMliEnAyYh+EUxHIn+3MMdAxycQbdwYFx1lh+ao8+/IDV1tbYrTffyhz5EvOzcRMOPQmRvmDs1ExFZU034aylmQbOnbXo0hemJZCWQFoCaQmkJZCWQFoCaQm82SWgB9m2jqQ9+FjMvkaK0GNnlH7IbBZscxtWee337/Db9Zv02PrqQw8b2qWjhyEtwqQeQl59VvqvX1UCSrfRP8wO63sy7HsPZVofD/63bp60/3nXJDTcepzWY89/30OLc1qwy8zk4fQcHWIc+ou/+AtSxj3q6ORnFqt0rX/+539u73rXu9xD/szf3qjPn/70p+3LX/4ydO8dr1rgUH1uvPFG+9jHPuYC+1oE+V0/cD+2F4a1O/9qyk42A2QjA8HnP5lh120iIMNi//k4/vzvpuybgIyHWJO4aZPXvvqJDKsse+Pssqsvaf/ro1P205fiNkwAu5pA1J++L2B33+W3fIItrJX8RocW25SaRf5di1rf+ta3HIvU3/7t37rd5vL50jWB6NL+/zcSdfri10ECmrOkFk91O+m3wKF79uxxO6Lb29tNrJ4aa8RIJjYFLfIKNKdxRzouO9C7yjly5LB9+tOfIeDrtXdef72tW7bYfKNjluCllXkxDCWgbAotWm7+0iqtRL8OrTz/t0jJMGXzqUCZZKPPTU1NbkzVuLoYGb4Fxrm1a9c4ZooYkV+dNzw8xGL60w60qGuuufYa23CxWOeUquyE2/l9puWMC3oNca4iCto1v+HSS1h8X21Np5occE7gRqVe3bBhwysscikJqH5KESNWu5/85CfudwUPxE6XYpzTuan2zLxO9VDQUOD6F1980fm422677ZU0WP9f16hdOlJySb2nyn1TvaPf8e4ei7ectuRADyAi2KCILiWzYH8oY2d9WRl0ozU0VqwSDDSwKdjYgMW6Oi3JdUkYzcS66AXkFqysMV9VnU0FxfAC8wLgNM/4qMVIZ5Zo5dxx7Y4nIAaA0ltSbt7y2QCeCFAePGJtW5+2wfZmK83PtKJlSyxSWWXJ6lmWO6fOgkpXROpexwZHcCGJPSdHxql3ryXauiw5waDIvNpbAOtUeYF5i8uoLixXPkBAYcB9gwMW7+0G6DNgUUA+gpYEMmESKsw3X3G1eQorAcWxY1/BQIAcCUCY8bZ2i/T0E+CkXIKvHsCY3spiyi8ybxZgPnyBUqEmmSSoHvH+XgJ5YwRbAGsFMi1UNsu8+UUQCMAq09pmZ558zJIN+wEGllv2kpUWm1VjieIiy6SNQcr15OaAyWI+p+AHcNOVAABAAElEQVRqdBiwHc9bvYO0b4i2CrVGYCcvi/uXma+ynPMBKVLfJMwcCfQ30T3Ea5BgKWlTsaEQ5QVLCpEzr8JcAj0Ea+k+MaIlBiYs0t5pE0P9BOoAKOC/gv4My6CvA5UFlsin/yjbS1sSw/i5fvqwe8wiQoCATgqy68qbn2v+anQDU4gcOGLhx56wdtLIZcDyUrR0pcUrKyxSVGj5dXWWVca8IQhIkiDhaHLYOsc7bHRyAtBBNiCDDBhDIqR5HbEpAIMZBV4AGyVWnl1uOYlCmg2rJwHakeSoDSKTLlgtxieZmMEOEgDImJ+RZ9WFc6wQVjMBLCdIq9Y90G39Q62AcTNsVnGFZXsLCGjCigiwomtkELYZWB9gm6yAWTRPKa1enj8NhQeso7fDBkapC9/5Ac4W5GUSZIapnahtVqgMcAuAMOQ1mey1jr7TAE1gQg7BfkLweGx4gvPQZ+obJ1hdQr9m0+5xGF8mR8OwaCWYs5JOs6TKyvJKCZgBkVKncIxFJ6x3tN+6SVc8OUl6SgCPAuBUFpZZJfqclZGP3vrYNAF74WC7DY31W1lhhVXmzSZwDLsTYMRxG7KhcL+1Mr4FQxlWXTbHSqlzkPtEkuM2GO61tp426x8ddiC+3Ox8K8xDbgBTpDe5WbnYX4VjixoZGbP2oS4bh02pqISUaADjJnuHCFTHLQSKppRxLgbTS09Ljw11DtnUKGBEgtu5eX6rrM0HCATjXG6RTUaC1tkNaOwMLMkwUQmM5ANwmZfvtzm1sN2zMUXAo76OMIxze63x1ICtW7vcrrmiygFnBgankPEkaSOzAaiESHGFwmlcRu7T4CefS5N4BlBaD4Cd+HiUNgEOKKVQKIJ6SQOaQ9rE6iq/Yz4bp44nT/YCMvBYEeD3EIwxvTDgjI0nSe3msWqATMUlgCm4VqCCQVh82lsnCV5GjWzs2LfPckhDXFDsAxBEO7iGWKf19SYZg+Kcl6SfI8yXSTkNRXYZ9Zhb43NAhY5OgHMPCjj3rK1YNs/ect1Cm10VsOERElYC4s8j4F9VE7LyYg/6qTAuB0HYkTDMJ+1xO3gwamdOY0OjgCYI4tdWm82uJWCbCVABfZk3F30nMC/wU3sXwfQzUSsAEJRPKlilQ+1GlkoTp1SZs6oASRLoH6C+3T0Ed3kflH3jG7Nz/YAYMqysXPMj+ofz+thE1N4WJ20YgJOwQGc++lr95wUMAHgBoELL6Yg9+nijPfFMM9+tsuuvLEXuXhsn7eoIepOR5WeOFbAqnqUyON+pPmPNONOoljME3o8nrOVUAkYi/BfPdtU1HqugfRlZAOY4r7IMe6B9YoLp6SGNWofScgoE4oP9i3b0jnBuDJlkuKB0BkHpQdLbtrez5tKH/pISXEdmJqym6r9Kw74JkOMAOtXP9E9/D4DYqbDbPJED+2lFRdDK6I8CwI19lPWFrzTbziNZtmR+gd16Y4B2GH0+ia6ELb+A/pudTR0Bmgm4QQcK4zeOzz1DXU80xOwUbWSYdbpYPctj5bOYSwGWySpE9wDGVKJ7uF7qQp8D1BMARK9RbGdgaMJt7ps1K4O0jZKL7MBM7rAXNsNOXkNDEXx6FNC/H+ZkdAl5ldE/AgSMkir4TDN20os/C+M/0TGlCRbYqwpdLipR8D5iP75/wP75X09Rn3n29uuKbNk8jw0DcB9BfBk5+E3KVJ9ni3UH3ReQhCEZ35wAlBCzZjZ5jfcjaO7J0G3lpNfNhgBWbH71cwNWXgHkF70cHiUd76lJ5uIBK8oEyEs7BoembApfVTory2Yjy9wc4L7gc7o71I+wLI7AyANgLQRLT0lpCOBDCKbiaWDZxBjMlQDjOtomXV/7UVzZaon6GjnncV4fqVqffzFmX/7as3xfbDdfP9+xB44DqprEB+h+1TB7yS9lohsOD42MtYGvF/s+dJA6H9VmHMYHbLyqgheyy0aGAjrOol+qZ02DJ0ZHkDc2MwnjYCE6FIPedbiPFLT0T3lZJqyMsDHS733YXiv23dcTszDjhB/DyETnc9GnCnSpoJC+Rn4aX06cPGojjBNiXf5FqVql55q76qUxRu+p9dvU/F1/69lIgDmlLtR8Xhtd9Hs1jLgCzeldGzRTm0xS41WqbN3nteW/HutSun/q3nrXPH4mcK62dho45wPN5AesLyRimEG9b4AU141xO3Y0DosgskFvK+jrOXNJekwfai5QQ/9XkPlDWV5H6LsTJ0eYp3rd3FTzPvlLjY9l5QFSPcIYlS+AJvrBOk0rutc/yDyAKZvmgUXMpSrQidIqlcc4M6TNREn6WuOhxjZYJ/HJJaQpnVU9Pb7F8XX79kzYx//hafxKjd189QK7ZE0Qdjb87xjr9iGflQDuq2b8zMM/Mx1xGxOYrrnxah/jxPET+BnGJI1NVQDlqqvRJ9o3NDFqNYwTc2sztd8Fv4KfaYlaeMJrBYxlOobxNWOTw5aZnbC6uSXYhFIhU2fKa8MGR0bRYf4OYQC5BawLlWMv+K0gfmzSjVX4GNo3OiZ/Cyg/M8jmNC/to97IdYJ77t4Ws29/5wTzgzhMjXNs6fIcizM/CjN/0p6akkrGTvxhYZ7XsihXPiYMuKkP1sBTp6I8x+PrALMyXWYc11iBDJGztgfkA86dyziei3/qxye14jvGABWW8D2EXjCcRkkFTv9hg3OQQzHjnICsU4DRe+nbDvkZyp6ibpoH55J2oor5gxj/GDoszHjVT/s6OgF944+mosxF6MNixsxZ+DamfaTkBgz+bNi+cW/czsBUes1lIdu4XsBg+mgY/wV6uFRlUu889uAFtQmAmW48lgVDoFnzqZg1oqOtJ+lD/E8mdb/iKuZWVYNsWGtivB50G8zq6ng+Yi6rZ9j7778fRspGt46xefNmx/Au20w9r8pm9DllrwK76e+UbbvO/wX/pGwu9a5TZXe6XuuHev5+6aWXHND2cpjna2trX9loq3vqXPmcFHhP1+v7c32k6pdqZ+oe+j4FnDt69LjNm385dalElszPwwBYse1MnnuLscXqGjEDmrU2Jeze73fZjt1nbNXScrtyc5VloQ+D6LDSJhcyv5P/F+gzqNTHNEcg5wjzkQ7NaQ7G7eRx5h8sbfA4iD15GNuQBfM3us3quU9ZEdBJzV/Qvba2EWwFZl2eT8dILa26hSfHbN/erbZ395P07WwAlDfZpZdeCHBPGwbk2+TrBaKbnlNJpL+JVGcA56ad7LnuoHR5aQmkJZCWQFoCaQm8WgK/ybD16pLSf6UlkJZAWgLnQgJ6AOvm4f65HXH7wf0x27qdRUsegLWotYQHuNtJ2/rWm/w2l4fs1NHc3Gz33XefPf/8846hJfV9+v3sJKCHmki0wE4P3WYd4RtIuXLMZmd+3+pKG3iQYrfRf+NHFT3g6qH6rrvusptuusmxxZydlF99lUASAgF9/etfZ+cwqxIzDj30K0Wf0p+uXbvWLUzP+Pl1/yimr7/8y7+0J5544r/YmxZIlML1j//4j1kInvW61+2NumEfC/4f+cSU/fBJ9qaz6/QT7w/Y++4OuKDOua5ThKDMH72Xe21n8Z9Fua//fdDecTML/woSvAHHBAuRf/HRiN1H2wdYxK0giPKhP/Tbu+8MuMVd1l/O+fHVr37V7rnnHvv85z9PGsU1v/LC2jmvSLrAtAR+AwlooVQvLdbqXYu7SkMkQLIWUOVPBdKWL62srHTMc6nF1tRis26vndSf/NRnHSvP719yka0BoBNvbQEsM0QaSQLlbCEfhSq25KZbLW/FatidiEj8Dhwp+UkmMxef9Vky1dxQjHNi8ptbNxfGuRsdI1w2gTYBGxTg7gNQ9NSTTzuAndKjXnf9dTB8rHeynyK6NDQ07Pol1TcNpMXVZ7HOXXnVlcyVovbggw+6cVnssDNTtap+CvzpUJqYFOOcmOJSjHMp4FwqSKh39a2YNTXXUBliHvzBD37g5rhinLvllltcUFLlzNSDmV2akkfqfeZvb6rP9FN4x/MWe/5pS/Z0ukh/jEFlkpx/vupSy1+y2AJ1F8OOBvMorHTR3lYbbIQpovGEZXT2WgbRuqioOAAZeOcttpyLNlly9mzAMKQ5G+m20WNHLb63wTJOd5hXyBMATzHRXXNOxrIVBJwA/ew9YJ3PPGUjXWesiOBDfkW5DRcQfaqvt+LVay1r6XJLwkboBbTlARiVIGodO3rSIgDu4qdazEvQI07QY5KAgxcwV/bCBZa5aBlAuiLs9IxFD+638cbjFlWkDWBgnMiGDyBcbtUsCyy4wPx1y82TD9AOxq5oX5eNUefY0WPIY8CC7Phn9LdoXq7F6/AR62ByqAW4BlA2QXnRw42Uf8zCADfjBKIItQGkhfGiut4y5i8yX2aWjVBW51YYF1ubrCgrxwLlsyxWWGzJqjLLXLPMQkvmA3AjAk/6N6EEkoOdFj11zCJHjluiudO8owK0EPEDDOEhha1vxUoLLpwHgA/QRV+3DTQctcjxFsvsGTYPwdUoTAUJ2ueBnSBj5TLLnl8PhpCHPGwl1tLhZDcMaHV4sI9gi9gN/BaCviSf87NXUudFs/Fh/NLZZbFjJ228ifp0TyD3CGmoYgAuSPUI6CB46VrAfLDv7Nhp0ccftwFk7QcslVsx16aKS2y8vNiKL1pnuYBqvfl5ZM8liDnebntaDlhT22n6PoOgfB6ACQBHXUNs0BqwjMKkzV9YZyvqLrA5GfMICgdsgFR+xweb7Hhzo7V3dDIHJCpNvcHFkNowx5bPX26Lq5daQYh7YssnYOU53PCSC2SuWnKBzS2ZB4CtyDpIcbf7RIM1NTZbdelsW7VwOYFI5A7TXcdAmx09dcSOnz5OcHoUMBLBq7wcgpqkPwIAOgZQs7pyvi2dtxoQQ9B6Jpptz8FtNjI8CvgOgKk/EyaVCYKqEeaNYzYeI0CNHPOJ6g4zTowOAqoEGJKRyLUFNYtsRf1qKy8oQ+4ZNhGZsJbu03akpcHa+9oYmziXCC8eymbD+rS4dpHVVy8EAFUM2GTY9jXttKMtpFIshtGr5mKbXVqLbKPWEmm0A02wBeNDq7ChDcs32Lx87Je0pO3DbXak45AdbDxgfeN9pBjzWHFeiZXlVsEYRxpAQEVVc7jXvGVWCDCuEwDhroYd1hXuBKQD+C7stYkOAJRTGQCEagGdLLH+jkxrOjJhIwAB/DEC8fj7zKxJmw045sIN5QBkQtbZNuZYqJSyapz0agnY/xSlzsuL2ZoLg3bBaoBJBB0HesL2z1/YbQ0N41ZXu9yWzi+CjQaGKECb4xPdVl6eAZC6zC68KIdAN8FghQYBtg3zbLBv36Tt3h+2rk5SpMXiDuBQXEogHmBLR++o1c5O2ubLCwmGBh1r9U8fPAy4Z5hAeh3z+iIYQQB3MbeuBFyz/mKfzVsEiAJgTz8gm8OHxngNMibJNEnrDCNfBsCF2XMSdulGmMIIfvcBvDtwYJhgt8Yz0j5jqmLkyQbQtrDeb1dcmQsAJgCwK2n3/7TXvvldAqB182zNikUA4AIAmaI2MjkC+CZqS5bl2erV+TavGqAf4CCWZ6gzIHPAHtu3jVJvAvphAAv4ggpAa5m4jD5AHBOT7XbDdZV2IWk/ozC2bd8Zt+eeG7JcgIGlgJodcA6Qby4gj3Vrs23pMrgMsZ+9B6J2mAB9T2+YMghqw94TItVlRUWAzVnUYyGAUIDJ+/f02YljsP6MiW1L0sdP5ydIURmySzYAMANMpVSmjz3RzrPraYBbC2zlkiIHWuzrE+gVMFpmgLE/z9avA9TJGpOAFloPGQIMsR+wx+59AAVaJrEh2ObYFVUMMCUjB9DDBPMsmMnWrCglDV8WY7aRMnPKXny+n3mGwFF5BL7Rlb5OAJ1hW7shz5YsJkoNc6KAToePjAKqgcMogt8CQSbQQmVF0i5cBUPRvAA+I2HPbAcQ1Ry2Ee2WAtgkMHE24OQFC7Nt3UoYzuu04dHsX75y0nYcBJBWUkD70GUAi0P9ALVA/4UywrZoSbFt3lIGoEtgSbhIGRoHRr3WcGzK9u0at6bj9N+EmNf8+CeAtIA+Ovr6rag01y6+MNfWXCDWSmMuErbtOybRjwwHaJkgbe3g0Cjz1Txbs7qAdIt+KwSsIv0/cQxA1/4pwFcAkKm++JpCoQjgrAxbviIHGws54NfxQzHbuwtQC3odgyYpwBiWEZrEPjJs3fpMW7gUdqesMfvxT3rsn74I+2vhEtuAzEtz4syvxq1vjM5inltbm7C16zJt+fKQZQngSX8IeHrydII6D1lz46RFRhnnQBUWAErKIs3rxOiQjQ/12Q3X1NhadCALIEjL6Qnu1Uv61DybXZxJ2naBSkCFBodsOW1ctbrQsklj3Ai4bv+BSWs+gy5OAlZnnA4GpwCd+m31yjJbApOdgApHj0aR87idYRoTYdz2ocvafzkHNqrLL8uyhYtCbORI2gsA577yf7bye46tW7HYygoAq2CbI9hZKDQFqMhrq9dkwc4FMBtfI8bFUXSyuTFiO7YP2fGjgLDCQXwdoERAD+CNbRzQzeh4p11ycb5t3liEHXlcvzy9NQwIchRfmMvcxWMjAMQzYB9bRrq/VcgwBFDvUEPY9h2MAA4ENMiiQwCQVwh/XlDkwxcUunrHQUiePj0EsKiReeswQOJfDJzTvDZ1aI6amsvrXXNgrYMJaKZnIb2U+lHzYW2Om83cTCkMlW5RG4k0B9Y1OrRepvJSZabe9XsKRPOrAnFS9Tub91T7dH8drwXO1dTUAbJmUwSThOxsGL2Y40yGA7B5wd65fdKONgCOGgLwy1J3cSHPgcV+fDB6zOaHjRuybO3qDAdKa++aQkdbAfMyPpaWA5zKdODMCfz1kqUh7Aa27CI/fYxtHRzET48APMMGGQ+DpCsuA7y/+P9h7y0A8zzvc+9LzMxgkSXZAltmZmYIU9usS3uW7usZ7+x0Xb92hXXdygtsWQohx7ETM8Qog8yWbRllkmRJlmSRxSx9v/+Tvv28nGwn5CxN/SSvJb3v8z5wPzf/f/d10Z6MHs9iAwDuS5c7aK+agVwN1DItS7p9Hlj9AqJOmRyhoTm+lBs32pMuffs7O4CkkzR+eIZSAbIaGnvUCGxq8m7Rcd2aMDlIWcN8gfPowgyAXZEHbwCJ7d7XpsvF7dTVLIqg/ogMB+4DQOtCLq6ishgr9ETU1bEIB5Srq+7Vjq1VtCsDAMKWb710m4UaXb2V1GGUm5kZgGNeulHeR9vcgVJfF8AfeQuA3ANb5bDwHtplHw2jjrS7uXK5V6esLUYltZN2wv7zpJ4PD/PQ1Ole2IqjKEk6n6C+ffmV6wBFnRqRF0+97U9d1qsWyqCbZzs27bRTtGW5OYCvBgdSntq5v3IA5FOn27El7QK4po9GnjVwLhjIFdEt2sJOxqJeWjCXthm48BJ14/5D3QBlDYqNCHIUiOuAkbpYnDBkSLjGTfRXRg6wOVLEN8gbp081sZjLAHjaelTC3BnHBKHsOmSI6K+g+hfrq2uXu1RU2MKirg61UA+wlID9OskH7gDxoQByttDWg+fAAtxVOPxUDCgv3Z0+XD8QLc4ZgHt9qEHHJnRo2GgvVNqjqWtRkaX/1G1wZ/0Az59rOd4J1Giqp7hKoPi5bLk37dVtFOfeAedSUlKUmppKnWUqtP2OVev+/fudRYE2p22qc/aZlZE7x7GusmPlxj5zlSH7+z/bXPWGfW7Hcn3P4DvbzCZ23759znjcoD1TnDPg1r5n+9vLrtH+dn3Xvvd+zm37fZDNdX92bNf57Pv2uwucO3PmEm3JSNI6hPJqyoT0DdwY2wC3JgE0T7M2nAUGNeWmOFfForoKpdIPzhocQ5/MyiJKsi2tgKM9ys0L1ljq8kG0G1Z2TV24hT7NxbNtOnWiW9dLgF7psxk8HclaIysPNSzE8Pdr1+JFgcrL9qc+ph97ok97914B2AsDBg91+mMtzbcpkx0syjhG3+igsrISdd+KhfQ986iv6StYNU8lYj/sZfPB3Lbz4s8Ptd0D5z5Usn3yX7IMbYXKJsFcBdEmtux1Z4H/v14Zx+mxyQsG924MzqzS+CQ2u/5uVhnZT+s02HV/ljd7Vq77tc7WZ/1+P8vP8t693Y0UoOW6t91LgXspcC8FPmUpYB1tmyS+VjqgN7f06pcAdJWseGR+VfGsfpw4wl2P3O+lhdPpxzDxfOLECQegsJWIZpNlyh/Wz7m3fbgUsEFNL5YURVeydfLKUKUmVGt8VpGC/BpI1w93zM/Ct2yQa6qGJvVuajFPP/20M4n3cd2brcb7+7//eyYkzvwf+ddW7/7Jn/yJnnrqqY/1nB/02m3y8R/+4R/0/PPPv6fanCnx/NM//ZOWL1/OZCAKMJ+yzcZvZovommR9r8uz+sMAig8yYUIsDLu/Xj39vW6VVg3ovgnu+tu/wsIn65066r3O82HfK0Dd7u++1qVDTCQyP6J9LxNUyLNJ+A97xA//vW4mYL723R69tLGXoAUrZqmPv/CIp/74D7wJBthk2Ic/9n/1zXvg3H+VOvc++11IAVcfxeoiA6NakGmx9sXUFQyktvdtbsngObMhNmDKftr8jX3X6iezqOlnLqkY8OWfUAH17O7Q3NRkDentkAeglheKFp4ElrqwPWzp6lHSY48pdtpMIJVPZt7pbj8HSwdXWrgmoV3ntPdNuS8/P98B24KxW503b65mz57jwIiWttYeXEcxbvv2t3X4yGElJyVrzuzZKIVEOUG7IGCkQQTpgkl3m1OqJppkxzMLVze+b6Cctc3bgfPs+ZnanL1ngT07vk2Cm0WNBQFt3tDgOQP57Hmb9bpZuxoQafvainwLEJoynbWdFhg0qM4+s+++/vrrzrmjUd2y9nXYsGHOxL99/l6b3b9tH6Qde6/j/He/Z1Z15WteV9vu7QpBEcabYEcnEFoj4McAgabYYTkoiE2Vmx8Bg9pbunX6sEqLCtUDDBSGileobwBKacx/opLRnRiv2FkL5J+ZLp/OZnVcOKPL+XvVCfwVShkJ9kfNEdWxdoJgXeSBqOHDUW6KVv/Fy6rYs1O1FSUKIZIZnZSo9vBIDSQNVngeCobDhsstFLAN6M2N43YBv9bs3a/ms+fkhwqFh3egY3PY3NmE5V6fQgcnK3HWTAWkpKi16IJu7d2npnJUuAL8AQP83rFEpJwHRUUrIitPfrzczVa0plzVp06o8sI59ZGn/LHlDPEOBmDyVyvAQBs2sqnTxitsSDKBwza1A5/VFBxTazE0C7SDH3m1z8ebAGwvEAyBl2F5CiAQXV98UeX0a4NuXFMUkIdX3CB1sG9/AnXO6Bz5ZaZyfzFEVFAVa0HxqLhIDUf2qfLsJWDDAYUHBGGlBdTR1qA2fx8FZmUpfvJE+UeEqA8S48L+g2qvQoUGhZMQf8qSm7eI26grLkYhY0coccQwFMgAFFGkq8g/qPrTZ9UH9Ofpi+peENFGoi19PPvAmEjFjhqhgLxsi/yqg7Ff0+GjulVRi21WIMH8IPnz7Pp7utQNVBq7cK58oyLUe+K4et7eotrSEnkFRigkMVPdKDl3xEYrdMxIBWUOBmIMUhcqbJebrmvnqX06jvqnqfNZoD7El2dLMPV2c62aemtQ4gjTlBFTND55CvBJoC41XlFB8RECldedoJQfwJ1JLXWhhN7d0QWEk6zRI8Yqe9BwVM+8CWxe0fZ9a7E2bQY8yNO4bCyjQuOxErygfdg5N9xq1KihYzU5bzKKTGFqH2jUwVP5AF6ngaAaUY7wU1BEGPU4FqMoLNbdvqluFNdGjRivqePnKADlv5Jb57Rj11ZUJW4qiGcaFRUL5OfN9aDe21ijG0CgUcmkJ8E2L+bIjXvsow/XfLOVIGmMxo2epGGAe0HBIc6+p8+eBNwpdtTqTEnPTFS7TQWDPJgcn6JR2aMB6LIcNamTF4/qUOEBBpKejB2naFTOOLmHu+lkxXHtL9irppo6jaTMTB89TSlAbu3IFB+/fFxHLhB8Q3HOGyW0AMqBQXvmXXu7DtVErj17eJ7Gjx4PTBemMuzAdhZsUXHNBQLCYYoNiJNfF4CgG7CnXxqQib+uAwPVVHpQZuMU7hMmb/JQn2oVFtuvSVPTUGMLJBhYjsXhVVRqUNwIGQSoE+JAPGDsyhsZqEnTozU4hcBhbTfgXKGOHb8FiJiipMQ4AotAHigN1gLxdSL5lJQUBtScAngT7qhwdaAiU4h94s4913T+chPp74ddqS8wD5BAj7+qURGrb27QuDx/PfEo0OOwAAd6ePb5Ah05UsyCwQSgvQTSIhAgAgWfJILYE0M1JAv1SSz8is50054UAyTdcsDIgEBgLGqDbtQBI6L7NGdeoqPYd+ZMK4Hja/Qv2jlWCNAYSpeAjwMoKKalerIILB0QPIQgrVDzqtNzL25FuTBM6cmAqgGQPUAPze3NgFl1ABGRmj09XdMmAxPRxzelq3ME/3ftuaIzp24AzfkAMkQq1IBT6r4GlP5ukme9PKr19JfyWAQWD1Dgrs1bgJLWAd7S10mgrjFVI3ljSRjjjX1qtFLT/LmeHr29o5x8B+5LnR8KJOrrjb5jTyPncGchWSqqLME6WXgNaO8iCihAXDxDbyzpLf7j4dECXOHNeD0ZJZQgRyFty7Yqbdh0VR09g5QxKIi83g6U0MS99TjXOhjVQIOnJk4Ip5+A2R8Loa5eQ+VswwWdOd+GNbNZlvozRrR+FWqAqGzVN95QPHDl4gU5WjAvjrR1I0jfqtdXF9GfY8FCeLSjCiTPepGkGgu8lJYS7Si9HMivJS/TVyOvB0CpmeK/qTkGBvRowpgEDeG6S260afWmKlTVgGhCvIASLZ7XBJfZBbsdjqVbovIAOBqa3PTT564r/xgAEVJVJrgZHkyckrFhC2q59Q3l5ItgPfzIaCx4UdsMeWeh18ViN23fWQ7cVgHUiKpUEGqTfgYjtRK87wD0auC6M7ViaZzmz/V2nusbb9Rj61uBSlkfypi9CgmgLiDgnoBM3tgxMcob5adA4LOL53mGW2sBr+qIn6OaGIbJNcH/buDdwGDA1GExGjk6AvClQwX7awFbTO2PepzpA08Ale7Om6jz+WvGjFSNHBcs36BebdjYqO/98wXuP1lZCSgEhjaR//tVR3mrvW2AYCtgUazuvz8L6I5YJ+Pzy0Blewpq6eeVqAOlqRDULAMCQxzb65audtXVVKqno0FfeWqmFpBHg3j2587e1g9/SPt1FZvT2HBFBgIueLWhrtSpUeNjgWYiUMQS9exlysAt6uIQABnqIIDE3r46hQT3ayTQTW5uvG6g0JSfX87CGNrtoAjuD8CyD+Wv7lbUBXspF0nAftHkF1FH9uqZ53dTTtqpe5IUEw6k3+cDHN1FX7KWex3QtCkZmj6HuoCxfzeKgdeu9DAHcU2FJ284AHBAQDjPl3qa9qEZmKkWCKunp0EPrEjXY4+wKMDTXYcP9Wj1mlqdu1wMhOatuCiAQdTPoihnw4fHAkkF6VZDp/bsL9WlK0C/4pnS5vjQ9+ofaAU07eVZZwJLBPP8WnT02DllpgMVxQs1qcjf2jG6+qJ39iXtPevXWjm1l/XFrV9s4x/rLxso19BAf4J+sfVhrS9sC4isb219busj3wnA2fFsP9fLzuXq+9pn1n+/870793M++Jj/cd2z6xrs/C7FuR7I5cTEFJRNY9QzgHJmNEAkCk43b/aq4FC1du0upU3xop2hDgLM9ASqawJmLbtl0Eu1Hrs/iXomgjbdnT5Hu37ys+O6fq0GwDICYDwS8IU+GKDW8Dzg2TERpO2A8vde19HjFaQximcs2PCDaLay6wukm5EOLDwliPqliXqrjDnIetI2EjgmylGp6ums4/cOzZyRprHjUZdFyfPM6W5961u7qRvClZ4QD1gKaEMdbpBvLe0M+lROfp4zL025Q/2FqBsA6oAOHEF5bGsJMHKT4KVR1IS4YuFHC2W3ETXbmppzevC+SXric1kO6FWFpevzzxQBWjdTV8cCBPtQvjpQ1bqtjKFBjLmyKCdmO1ql8xcpw0zGBXBvXh70o1mz5OPTiitBGFBZjJob3LV7Z4MuXW1ygLMQ6kYfrrmLfrt12eZgxzphSqRjr1qI4tyrr19TaXkVMBogckwYfQsftdsiCq6zB9XJyZNzeQ4xGgp0ZkhFFfe3v4D2fu8FBAA66R8AGNEWmp19M+q3dSi3tpPHJ42N0BefjKQN8NKxYz16460G7u8MbbWfEljMYWqgfn59tPPhpGGIMgGgq1H3PLivifxRxjiS5+YTgaKnH3nDlsLcVhLCAmPGhXOtodq3u5z2ENXaDtpMYEMfIO/O7gb60W3KzYrXyiVJuMf4kGY9euHVHp0qblJcSLtSYttQDjZIj4UXWK129lSh/kff5PExGpaL8i/tiAFU51Ap27W7UcWXShVA3zE4iLR269MD98VoxEhTnwU4rG90oLkUxjjGuVh5sEWBe/fuRSH/vOMUMXnyZEe93cVn2FjW9nO9rOy81/jWPneVK1extfdcZdx+t+/Zy8bgZu1s8/ZVVVXOuHnq1Km/HXe79nUdz/62zfXT3nd95jrXR/1557Htd9fx7XcXOHf02AUVFiaS11i8wxgokEVXA7T33axusUUO8xfnsmggWA1V/VrzepV27i6m7vZCUdDqRmJgwM11zDc0NtfRdieyOCJB02YAkqKI3A6Id5EFJTt3XAKeZm6iP5z6NQIorwuFRZRbWzxVhRJibES3vvxUAv2LSDXUSbt39OiXv97J/izMiE+ifvIjn/YQc+ul3B4D7DRwjvy1YhHw61gHdHaSk5iS/aRbxL3+5vUREvF9gXM2gWagVR9LA5wEtwvgP0tsyxgGBlkD4kr8j3A9H+tX7VqtYbRJQJv8s+t8r0LwsZ70LhzM7sMadJvUtIbcJr/sPWu8kwYlMbAE53+fm2tyzII3RrsOMUz4Lm9WmdxuhAKuKHeu2yZgP0k1CDt/N6MPo5Vd+eBu3bI9F+uE2bMqLy9nvgVJWyY+4mnYfx8214S71QefFJT5+5Cun717pPW6t91LgXspcC8FPqUpYDCKrWzaVdCnX63u0XGkz5nXZ3AuBuruWj7DQw+sREml/rh+/vOfYPsQoT/+4z925L+tH3Bv+ygpwErhZi9WD1oAo48Vg1i7oNrx+56sFgT/7ne/6/Tbv/KVrziD/o+Synd+18BPs2tds2aNM3i+8zMb2+Xl5enrX/+6A+39d0Bp1o/fsmULloDfd2BV62ffudk1mvy9gXOjRo361I31bCxqkycvvfSSoxj0XuNlu8cHH3zQeZnS0wfZLl/r15cB2g6fZZW+v/Tjv/HRgwtRZfiY+cEf/XuP/vnFHlUDEw9NdtObz/pqSJpNEn2Qq/3o+3YSMP/GD7r1602EIlklj8uMHkAN9K+f9lIq1gDEue7atnbtWgeGMeVDs2v5XZxXuGuJc+/AvxMpYHWNbWYzb+2K2XCamqdNnLrqJpv7s3mzZKKtU6ZMcQJRVve7+je2Xy/RgeKzRfqXH3xP/Sg05TJpms5c4WDAkHDgKo9BKPa0o2ZSXiuf8eMUPG6MA+f8TiTS+7hISwt7udLM0tVeNgdiATizv920aRM2eTVMmucCxs0lyJDkzEVZkK6wsFD79+13gLUpTKiPpu26WXUT5Y6jBDcDHItc+54B1dXVNSit7HcsX2w+a8GC+UpJTXVgOLOSSk9PB8yb7fQP7PzWph85csR5vqaOkZKS4ljxml2NzUvZynerv0w5zoJqdq2WB1I5pj1v29+OY22tKc7Zan0D7e677z5Hcc6ltuFKpjvzheu93/Wffdz7+V/+Sv3YbQ7KyZZf8iBsJuF8ulpQcEAtJprgXkwKq22wCQeYu35wF5aNtxSVCNSWMRZwirk/VOT62hrVjY1b8MhcLFBRGkD1qnnXTl09dUY+4TGKzRkhv0TsS1Fr6sGeqpVwVEB8HIHwMPWX3VD59u26ef2SIsIDlDBhvDxTMon6xcobsM4zBlUws3+FIunFcrX6cIGu7N4l7/Y2ZYydLB8AuwGAiK6yS6orOq7mvk7FjRup2KwhqjtfotoTF7FOClXYiFHyBN5EpAjoqomyj31VTLy8Ewbhv9Slzv27dX7vTjWgHhSTmaFoU9CLRH2NgHZnB+AaMExURpoCAdZ6KstUtj9fN8+fUzAqHXHDs+SXPRSPpgAC8B1yR+UrLA4b1rBQdVVVqmTzJnkWnUEpJla+Y7AqTk3BIhZAJBFAKwTJDE/mud2DgRNvq3nndlWjAHgLiC4yJ0/xlBlPTKgaLxep7ApKdMQnMsaNA/oJV3/xJeyAzioAKYOonGHyjede3P2xPWUhOs/DLSFSwZE8ww7g3vNXdWzdVlT1GpVCOkRmZ8oLpQ8SFgCgHVAM9Zy4JHkSQO2trlXt9m0ozqHqB1Toj3KbD9fsyfPra2tRF+XTP3ekfAgSDwAa9mzdoNpzPGuU9kJGTZR7cooGoghqonzmxTPtRyGrlfNcbbuhHcf2qMDUKUjTbNIsL3Mki7YSCQjf1LErB7C2KtXIoaO1csLDBA3DtOX4dh2/cgxgx0MjhuQCrMQ7Kke1t+qxJCvWLeCoZFT1Zo2do8FhmWpF3W3d/td0trRQkQkRGjdmrMIJUh8/Xkgw8ipWWvGaPW6BhiWN0IBPm8qaz2nz2+tUceUWkFEaalUjFEJA1NTrrpKnjhUWYJ/nqemT52jaxDmAGT66Vn1BW7au17VL1wh6h2kEzyglOU19nQQ+r57V4eOHyetuBGezNBwwM5YyMEA7cWTPId2knkvPGapp02cqjEDuoRNHdK7wFMFvTw0BOo0DQLWF/9XADeeAJy3vZVIeZk2cSvA+EjvXKh0pPEJ9dkaJsUkofk1FYdBNB04fIC+cV27qUM0cP1UZ8anyJch/teyqdhzao/OlxYBZsUB1IxRL2WpqbdTFqxd0/gIyWAN+KJ9M1eRxExUBtFRWeklb969XUflpVDWCNT57unJiJynYZzBB+ABt2XSSoGCd4mIyNHfqEOxy6Yh3MYeAGqAbWSoiJhDbTYACQIHG+joNQYkrLy+W54n6BupQHcStolGMSkn3R/mOIGRVF4uGCrXvcAlQWCLqWzmkKTZ3cSgtXTOFmTrApFso5QzSgoUpigrzdECBjevP6kTRZYBfUzLLJJ1CsP5FweycQQSNKqe+mIQazhefzERFKpS2oh9Ar0C79h0HYErQJMDOcWOilMicRxjwYTRB0wjgo7qb0s6327R2oykXeqJ+k4T6GCqLQI1twJoeXv3AGcHYuPZo65ZqAItGZQ4OY79wYEwvoCMUjVpaCe53o0ITBXARyLjITW+tr9e//NtmR+Fx0uhhKLDFYQHnpWrAwfz95UBwtn+ili2KBnryJtYyAHTVBIy2h/zfDUiTq5yhccA13tgN9mjfoXqdvAzUEdyhP//qCOzSUV1EdeytDW16ddVx7B3rNJpyPmZ0AmXEA5UyD5Sq/bAOdifIXsrzKQFwStPE8WHKyKJipB5tw5YYMRNAH2BX1OteeuUQ8Z4SFJaGAEylU+/4AkNgQWmxR59e1OvCsaT1ddJ2y5ZarXoLNZeeJI0bHqkpE/oZxwN7ACMcPHSL4O9VTRmfokXz04CAAnh/QHv2lWvt+ny5e8Vo/JhsFkQZmOGuYlQK9/MMT6EeORj7y8ceHg9clgSw4a6t25v0wouHiX8GatSwDNTJsN1L7SNPeaCQSKvc5683V10F5EJ1LygACCOG52V1HfFG8mk/9nqxkdhd93uq4GiN9h2rVzpqMmNG+gEXujt9gnbyslkKpyVhB42i3m3YiJ8+V6Ut+ztRgmvj/sI0ZkSwYlEXMujq5KnLAJzVmjFjpJYtTlZK2js2wuvW12jbjkrwDndNnRCDCmGQoxRznXxdcKxUZ6/VokA4Uo8/FK1Fi4F1WTD12mu3tWrtVUDcJo3FxnD8WOKKgIgREf5YRPo5Nrpme7w//zaKOGUoCgLRjosAOPOjv4PdIjBXHwpn0ZH8Dayx/8BN+ko3aAejyQsoigF0enlj/0rZNLglKysaFUVU32hbNmxo13d/fEnlrf5amBOoeVM86Cv5YgONEtLFal04dxHVPh89/uhMjR8VgNKqtCf/pjbvKAEu7CN/xlPHALlhU1kOkHKA8547ewbApUt/8pXFWrwsnvrVHUCkSd/+9jUdveCpCRlhmj4hAHU12qAQ1K/igFeBhs+c7gPkO+qA6SPyUqmbwxzor5OY5wBQSyh50RNPzfzdVTp4oBJ1vmDNp45IiEfBiQtrASz1oNxkcT2JKSgvUp72H+jVP/9kM+pzdTy/DE0BDkpOoN3m2gtP1gPM1lCXD9KiZZGatcjXUZTcvaNRmzafcAC0vOGZysnGwhL1xvKKOgfGOg48a7agX3w0R597AoVOyk/B/l69tvaWTp0vQP3KU0sWjdLQ1GBFYG8ZQ3kx+9Bd+8sAgQDZ3SM1dlSqcjIDgK2oSrsRkeFphJglOvsVACUWFORr4Tx/AGKzSQ50LBkNirF+qc2/uMY8rj67xSttwYm9LMZuYx/73eLkNvax7xowZ4tIDJizhUMWQ7bxvvWL7afrWJQoZwzgGge4/rbP7bz2vr1cf9v379zX9r9zc31m+3/Yzb5753FsUczly5d5Rt08/yQUAIOB2L1Jp2DaaE8dKmBhza5iFo03kZ9Rax0ZhzUyNs/t0l4UHvNPkKd6SvWlJzKAnWMd+8ULF1r13e/vVTGLI5JjaS8mUP8Oj0YtDCtRIOQg4NQrl1q06uUjwFcDGoEF9/iJEUCtnoDCgIu0A6amFsl5Dh+6Qnm9pN5Of+pFVHKHsnAHGLaxGbiYvlEmf6emBQLmCdi8R//vNw/pRCnqrUlxmjcV5cvRfrQ9wOJF7TpNefJGLXL+vDGaP4vyhHrcycIObdhWrgtXuqjLQpU33BvVSVRW69x14ngzip4nUcY7o88/skRfemok6lfk35Ju/fxnl5R/6LZiwkJQZoxV9jDUMKMpg6iO+rBwoeh0D3OTWATTxx82PIF2IIp2HGgUoNTYmdBQIMQQPx0paKasliosJJJF9oyTh7KwlT5QG/2eTvJieqYfr1DAWq4VcO7ll8/rAjKm8bGAu9NzGNehwsywvRhr3B27TgKpJWn50iwtmOPrWHIfPtarNW9V6vT5U5oIzGxKdeHwIXWk+4kTQInHUZJHVXTJrEH6oy8DzqV56sDBHtrBWh06egiFVj/6FUM1HEgthjo+FJXB2DigvShPALsG6tFqFAFvKzMzXKPHMr4P93IWz3WzKC6I9I2I8FFjXR99H5SBK9xJB6DdEYEKQmmu1cYPnYwPwv1Z5ECbgIXnnr09+tkvO7Sv6KpgezUdMNkA80hUCCurO7i2yzyPBj304AQU0lnEgzpnaUmP1q5p1OFjrfSDmrVkLosSwgHtUOMbkRdAWrU5inMGzg0ePNgZy7rqAJvrMKEDs0y1+mD8eNTxR492WBorY67y6Spz9ve75/bu/OzOcmnvu8qb1R32Xftpi9HMHcXG52brbHPUGRkZzuJo1/dd5333367j2ecf53bnPbjOYce3313g3IGDZ3XyWBbjfRQdJ6BuG+3JuBQHJvo+1i4OGRqNDSvPG9veVa+Va+PWk/TZUArNpR85KpFFFj7wSl20e5XE0YKUNyxM9z3IgrwUT1WU9GvndpT2848Dv3kBrmdqcHoEeZPxz/VW5hkqdQEb12z6FX/5p8nMU4SrvkbauqlHP39+HVfaRz+LRSxjYpXM8TxZcHTs+E7mTNY7fZSVK5cAdk8EDDZgkoaG9LN7c8A5vo0+I68Pn6b/JThnJ7IHbxCQgVbWsPQSTexn5ZhrYsjgGJvQsUl++93DZs8/5odsD/TDbDbZYxNHRnumMAFkKyVtJf/v2mYZ2SY2LxdfpvOHDG5xsUPFp6amaRYTXzm5Oe/rluyZGWlrlmLWgJrVw5NPPvm+vvtRdrLrP7D/ABLFR5z0t8rKKo9ParPKsqSkhAEOK++QWo8COLxbedQqY1upYBOQh/CytolFm4CcMWPGJ3W7/63nsbJmcGdoSCgrsdL/W6/l3sk/zSnw4RutT/Nd3bu2eylwLwU+OylAF5iBAqtFr/c56nOvbehTDSuezPwqlIHizAmeGjP4tI4W/JyV0zH6y7/4SwacBLHubR85BSzt7WXDiU/JkOIj39NHPYCtmPvf//t/O313U5wzhbWPa7Ox3htvvOGAeTZOePdm4ztTujO4buzYsc4E4rv3uVt/21jU+tQ/+MEPtAPLKxuP2nt3bgYC/MVf/IW+8IUvOOpId372afjdrtf6xjaRYqub32uzfWIATuzlWgX5Xvu913vNWME890KPfvgaq7sByb7+eS999ctepMVHmSL4j2ci1oxNbrf+fWcvqjEDevoBT/3dn3ozCWaTwv9x37v5VxOrg7/zo269vAULJe41iGmHBbM89c0/9yIwaJPad/Pstuq10ZkXsTxnY7x7270U+F1LAZsPss3q0qKiIibpX3bmhwyisgCR1T/WJligyAVkjRgxwllwad+zusomdPvZ5ypBime/83dqQlEpwx+VnMg4DZs0XcFMXnomY2HZ1q2+0lvqAzLyGZKGPePv3jyc3fN7bZYOlk42wW4vS1f72wJjFpizNtvm3Hbt2uX8nYUtYxawiNm1VjKvd+ZMEcBDPYtgB2kFFqhpqak8j7OOdWvlzUpn4t9gOltsavMrp1B9qq17x6rV7GYMajSgzWxgbK42le8PR6nMnqOd28A5gx/HARLZ3Jup4Nmqe/tpAUF7psGoLpSVlTn5wGA+A+pmzZrlfG7ftfswxTkLOFidt2LFCmflvJ3j457Uf680/u98rw8Y7fS//kLeZVVKmTYLe9EUuWE340hkAcSZ1ZUbaj29KLbd3LtLN08UMB8eqrgJAES5M+SOehbSVxpA7W3AFxAVRQY3rNQ63sZmav0mAI12BU2eoaAZc+QBtIOEFoUL6IJ5disnoF3qv1GhEiCtyuuAPbERSgKO9B+cjVBGJJaStD/0zfopi26ofrVeKtbZzevUcOWikhMHKXPFA9iXpnFMgreAQU27Nqj08nl5J0ZrEHPHLTdYjH2xXJHYh0bNnCuvnGzuj7LfDV2CapAH5d8NOYz+mmo1r3pJZYXH5QXgFw+0GTA0V57YabIDdpYEsns6UWkD4GPQ1E4+PbNtMwBGp9KH5yhy+iS5o3TXgYJUfzc2eO1uBDZ9CdpTZjj2FeyG+1Fmi0UdLWDOQrlncX+BKNmiPORGoNwNxZsBlCg6r1Wo+o3V6j5XJM9BqQqbt0j+BFG8SNNugKxSgucV584phfnxeFTOBsqZO79yWVFAvNHMO3sM4vmhCEVh1QBWrn2cwxPIRhUtqt5ZoGN7DqC6kaCMiZMVmGtKcNy/J7JPYiCIqgUethpAsaqtsEg3d25TSBsWkljD+k6ZJM+0BIv1o2JF4J2ATW94PPUAVm4ouXVvWuOo9fmiohYxa768BqejooeSjz9py+l73HuAJTtV0l6u3Sf26NjBAuAfd00GNh6XMxHwKlk1rTe179IO5QMNJkUN1grAOS9vX7259y3V9d1C4SVL49NHKCEwimyEKiIQwFnAvoIzR+EOfTULNbgpadOE5pgO3div/CLyKxasoRGhHAcVvtoG+XC94/MmAmpMV5wfsF53mY6W7CQAth2FwTDNGbMEu9KR2CX5Y6GKpVvJOW3dtRG1rVZNGjVDsybNQ03QV1eqzmvLtvWqLKMOS0mhTpmllITBWDg16TCgXf6hvQqJDtXEsVO4v8mKDYqmjBAr2LtHB08cBjgN1NQFM+SFet3BQwfVUImCDM9wZNYwAqZhgKBAN+0tOnTyhC6S58NQy1owfZay00hX7r2kqlTbd+/E8gnYLzURxccBlVGf+rv7ad742ZqcORarTx+19Dbq0BlstI4WqAWgdMaMuRqXMV7hXliUdtSq8PoxHeSzxppOAvOTNdPgPOSoysrOa0P+m7pUe16pGamaM/p+5UZPR3EwWrU1PfrJT7YQLK3XyBET9PDyIShGeTtWrTBJ6jbwA5jm6KFe4MJKIJAmzZwdq8nTIgk+05llH0R65AfsZy8vynZdZZe+850zKjhRpgzK80MPDNEo7K9CsLeqKe/T9m3tOnDovLJQAVmBqtOgeC+UBfuBuvZjA1qr0ROzUBhNR50PK1HGCqdO9eqN9a06demmxuT06w+/iKLWyCDdKOkDmjmu/IIiFN+G6on78zR1SiDjCKxxgcDIJqQvbcsVN21a16o1Ww4CM4ZoCfc4cjRKdzCu3bR/BjZ6U7aPH2nRmtXVKNn1AITFaM7CEA1KoYPu6YaijFnlYbkYBEDg7gU4J6xaG/Tsv7+NslmoViweo4ULgM6wLa2sxmJvdxs2kvVACQMAA9EERwOJh6F4gp1l4emjmjwhTgsWDQWyCnTqzZJrfVq7rk0b9lZjWdeov/jjIVqxPIw5HdwE1nVo1RsnqJ+atXhmtubNT1RiJgqTQFU+QGllV/q07s2LOni4TIOTR2j5MiwvTVmbup/bcxbmwG9gT9mvn/4MlcrbFeTxEZo9i7QH9LPxUDfglhugXUgo9+dhVnd9KN3VafWGK9gwp2kxqk0L53sCBro7Fpl799QCVpxUamIE955BcDgUBTQC02+eZOHmaeWNHquVS7MB6rxRdDPbvz5t2tqkjTv3AbgM6PFHRpEuSViuuqNC2ww4dxKgIkSL5mSiDuYHhImgAVWft2831pueeuZHR4FJsNnLTNYjj8YR1KauI/tZgHmAISpCqaq8AZi17YaOn23WlImDNXe2vxJYmCT+JwsDEA6gAoZaKgBIHZbEP3m2Spv3A32G9qNMFU1wOwDY2/qJwBuF1Vq99hJKTsm6f0USZcNLFVgnPv8canoX3DQiJ1YP3x+sodjfDrD/pUu92oRC366D5SjMZADOhWnRElO5HCBY36KX37jKc+3Sg8sGsWAgCpDEm3tj0QIXbgBnK/ah2zdX6+VfX+dvYEtgn2ko1gWghkWXBqANK1bAnDry5sZNZTpx8jppO0hz5g5WWiZWo6SVQSEWqA/Cmj0gAOs3yuZb6zsB567Dygfr8/ND9NByH6A9FtFi73r6fKN2bCvE2rFVDy6fo1nTAgFquoDKruoAcGvioERUohIAGbFRpGzXYGO8fmM98xoH1dlyS3/y9BLKUgKKc+46g93hP3y/SmeuB+qBGQG6b5mf0tLNLpZnQ3rXUCb27e3VqtV7sET11fKFwARjg7HQJb3tGdIWiYW+NdhorkW96jBwWUZaEmBQogYDLZIlHdtkN09sk7E99WYgbbbKBs799JkdxPubNXdWjpYtzVTKIOxsGfMfK2gHjKkn/Tw1izy18jHUN8uw61xdR1m5oiygmqVLB6MW509d4EZftVNvb2vU6rewpW/v1xcfStMfPBkKZOGGlXCPXnm9RpeuH2bBSIw+/7kxGpLqh2IXl039UFtD3l97Xrv3FwG3D9aShdnYBwdQ/rk/bg220ylnBgZt2AAYWLBLK5YEAdK0objXQ8w/5rf9ceMXjAmw/rm9rN9u8zAWn7YYscVura9rfVpbZGJza8YMGNtgY3xjB1wQjfV5XeMf1++uMQCX5Xxm/X/bXOdzjQ/se/aefc/13Tt/t+/Y366ftv//bXuvY7qu1XUuO4bxELZoZoC+YGJiMvbTfiq+NgDsEk1fyYc0rCati7HHDaD/n64J40IouyhOA4Bt3dal1Ru7iW+fQ9U/BTtEA+c8dOFim773/QMozpUAsGbo0YdHayjWjAGsZPTkGVo5Kzxar18+vw9V2gjNnzlMs+aGAMS7mYiueqk/vGgnulCP3bT+gg4dKEalOUZLF4zQKBRX/chDnZT3bvYLeiXseQAAQABJREFUoh41a+J+rv/k8T4U507o0s1gLRybADTsp5HDvRy7UrP13Z1fTP14nXsYpfuWJgKQocy4+5Y27biBomwkVqXxtJ8oYKHs2MocUv7eDq3bsJ8654gefWipvvSH74BzFTe69bMfl+rgcUCetAg98jAqetjKBoVS9/mgUoeK3EG++9ZbJ0FqOzRnfjZqV7HA5JQvHh1VgmMP2UGbs3ldA+3OVWWmpWj5CkD5cdTH/qiKUsXYArRA2jVT98J9UqeO9ukX1N9l5WXAXYNRr8wEVgp08rvZWv/616cYuwdo5pQE6sxQR21vzZudWrO5ivq7Xl/+apqGG7Tm4yX4Vx0+2KmXVpWoAhvqxTNi9ZWnQ5RKfX+Qsv7K6w06SfuZlxsDvDrUSfdwMAkP1B+tvfdlLiB/d61e+nWdSivaNWtGtBYuiVEUsKtlVZsXtD5KD/nk3JkOvbG6EMvVUPobaRo/FcWySFNs5sViEj/yRBT1TCD10u78Hv3Lr1D6LLqoGSNCUKNMALjF6h5FwQYkoddvADrbVYnKbzqLtcI0OBPb5qI2/eKXjLWqQzRljLe+8LlgVERRXkWtNtwsa1Wv0tJr5FNAbxaSGedicxlWvq2cGPtitqk2lrVFYNOmTfvteNk1freyYr/b/q5yZO+5Nlf5dP1tP62cucb9rnJti9EM1LOxvwG4tqjNlNptfsV1DNe+9l3b7gRq7fyu/ZwPP6Z/7Li2uc7tOoe97wLn9u07o/NnJlA3DNb8RdgBY+trz8+gdQ8UR4NYzOHBs6wu7dOrr5Zr+65C6k2cmBYMB9qMY5yKtTPlavPGZhbkoKYcSjv9IBbxub4sgunRm2sqAHTLWRgRg3pdEsqC9EvJSNevsUjvtRv0x1uVEhegv/qzOKzdQ1SNJew2wLl/++U25iu8WVgwWnPnRjiW722d7bQx67Tj7dU871jqpiWaOnky1+dNmaSjwsvE3uy/d6A5+mcstSIFLBk+8PafgnOWgJbR7MGfpsNmtGQTxtRdrDDotlVtfOZO59ImXBISEgmkjHEmcFxWDh/4Su7CF2rphZstwQsvvKAnn3wSYnX+xxpouguX/J6HtIZuO6sNLXBkE2XXrl1zfrdG3mwZ7N7ez2adhO9973vatnUbz7HLmRj74Y9++H6++qH3sXxkeeipP3zKId2HZg3V448/rocffvhDH/ODftGguWeffc7pNDzxxONO5fVeleEHPe577d/GpOMpysvzzz1PZXHcmcR84nNP6Iknnniv3T9z7/3kxz+B/D0OyT9cf/XXf/Wejc5n7qbv3dCHSIEP12B9iBPd+8q9FLiXAvdS4COlgI1p6gHm9h3p069f79Vh1OcaGCQG+nso1K9Cnu1rtXy29Dd//UUCiwSS7m33UuAupIAFwr/2ta85/cqPG5yzy7VJhW984xtOkNxW3r57M5VqU0Qz29acnBxnovHd+9yNvy9evOjYIW/YsMFR3LZJijs3mxix8d03v/lNBwRwTVreuc9n/XfmNFih2acvfrNL18sHNDHTXd/7urcmjmZyziI8H8N27lq/vv433dpxnsA+deK6HwNTzvJkUvljOPj7PISpgH7vp916dfs7SnN+dCUXzvB0oLmswXcfmnufl3lvt3sp8KlOAVcd2tTUxGryYzL7YWtfTFnUnAisrrf5G5tnMpcAq+8N0rIgkk20uiZ6B6iLy4BYXvjmN1SFilY6E8Rzs0coZ/EK+Y0fLY+oUA0wZ9hXgxyJLa6Ni5b7Z8Sq1R6wpZEraOaabLe5Nks3S2NTr7A5O4PbLpA+1q5aEM7S0ebhBqi4YwGmbOW72afaBHtpSakDwp0sPIkVV7OzODmQBcqdBPnMHteCegbB2bOy/Q2oM0DObNbteRrkbm2iXYe9LIBgtjSmLmfnN3VBU7ozkNuuwwX52fWmpKQ484pDAfxsUbR9ZgHHjYBNR48edYKKc+fOfQf+QxHvbs2lfVoKjynOFT33ovpOnVVyVo4CgXDcI4LlTr52BzhyIy8P9KHKce6SynZvV+u180pAJSxq+iJAqpFyD0QpjQl7wrxElwAs/Qi+I83T8OZbaty8mecXrsDFy+Q9bTqQHSqzlC3b36wo3SzyRlC3r/SGSgHnqkuuYW2Jg8WcefIbnCU3s0b0IrJFBNzyoXnpNRCoOfbWGhTiWpSFuuOgpSvlFgmQxyBm4FYVlrNbdQ11iQ5AosF5KEICtVZfuEqg3QvVu5EKHJItT9TE3MOCEHgDjATeIxOoG7Dy5gvPqqvihsLGjlP40iWo3qUBtRHFtwi9QWUGCHZj81zCefYV6ARgWRiqeRnzZihgwjj1R0aoHYUvQi7yJQDjZp2WHtTcblbo+paN6j4JOAf4FDx/idyG5cktwNRdeoA5euWG7WZ/B4pdqGfdeO01BQB+hgFd+S1aJs+MQRyrU/11Zao7mK+SnbsVDfUQlZQh945unTt/AcaQYDhQoC8woSfp7B7FPUYFyw3gwA2Fpp5LVSpfv1OXWDQyBMg0ccY0eaXFcQ0GRvJMLH2BGQ0o6ausxVp2r26RjsnYbYbOmiXvSRPkFmOLsrle7slkUXo9CXT2Eeg9X6TujatVWXhCftxXNNfsjVof0WVUC1HtMDVtoLk24L+rLaXaeTxfpwHColE4mcHC61FpKMJ5R6sem9ZDN/ZpA2BkuH+M5g1filIJAeeDAJj+bSyiH6LkMNRVPPzl3o9NFPXuTZ752eLzAJYemjp6uuYPn6+YgBiV95bp0MUCxrIovNVhtcq+kURLRw4ZhUXrDKWFo0SpQJU0FWtr4RrUWAo0NClXD858XAmAL24Qgt0oDJY1XNfG3etUThBs5JBxmjN5AYFMf12+eU6btqxHhaoJtYlhmj15LvcTp8raKuUf3aP9R/OVMTxTs6csUG7MCAWRVu5YfJ86dVRb9+1QK0DM2NkTsUVGhQ4b647bzRpJ/ZUUg7W00Ym8eigrly5fAwYoVYhXoJbOmovFLOqcWKW1dbcr/9RBHeBV19RAPdhPfRaiscPHaSb3lxmapn7yVk3rDW3bvw3g5ayCsdW9b9EjGhKdC2/ip/b+ZhXXndHew7t05UIZdmGAUZNmKD44guBssd7a86bKWksIDk/QrJylSvbD8tct0FHaeOaZ3cyFVwEXZGoyKmFDUfOKD0PBCGDAl0AyOURnCP6vX4+aZOVNZWRGEVSNBbrypb31VGSMu0IAjry8ASwY7twq79L3/7FIp85Wakxepp78QpoycmhP+byloV87tvZo3cYivivsWjOArwIJfDJfsSofWKpbi1aOAgiKBSAgPzN3cQUw7I21HdjNVigno1tPfTGNsRPPm/d//PNCHT1ZrNE8ty89iYraGNor/24nbxtU1dfrq+ob7tq1vU1vrDsMrOSh0WPSlTsMe+mYd649HADQwKDTJ4Fq3qhDta+WewvlWBEESX0chaHoaHfhcotqD4fmwLWAV+s3NOj5X+1U2qBYYIjRXLMp1hCgxbrr0AFAsS0VQL3AbgvigbgiEGToBMppdGxAH1iZBLgQo+gI6hfqxIZb/YBo3XoFsK6trVL/88sZum8lSolADAbUrXnrlEKpvh5Znq2Zc7C1iyPA6kkBR2XtZilKdluBL3dc4f4GO2BPxhAWIsWi2Ib6SlQ0z4ZsWFbST9+pUFeuXgd6T8OueDBqlYEOABnOgiKzJjP4o5/6rpQFmJtR39u8HZA3eqgeuj8S2M4TCMMdYZIBAvoN+uUvTgAVBJMPMwENwwACe/XiK/tU31oNrDAJeCgZ9TqgDPJPFWov295u0evrCrDX69T9940CHBpEXnfXjp3NeuWVE6gbRej+5ZmaOpN8hdqcFR13gO96YIpfvWBCGqY4FwwciQpZIpAbaWdqQxFACAYv3awAnNtyQ1tRTEtGKXfcqBBUXQikozIYynUEA6EZBGEseTWg00+frdCuY1i4Alo9/cUoVAGxtwXyojsElNKsZ//1um63ROr+ZVH0YbwQtejC6vEo82vhWgiwdv9KAD+gLgPnyoH2du5q5v4uA1OkAIgAzi0FnKMefu2VOr2+tkzx0f764hMpBMoDsIcEsgZUNFVAd+qG9s5+7Xq7DnAOK+TWIIAQbFnHBioOqDQUCMdAiyCejVnybttWwfj5EmPlECySM7iGIMUQ/I+zcojlqzf5yXHAJtj+1oYufe/HlxFPjdaXHgrFehBFKQCjdiiZS9c6tX4dKo9Hq7VkHvXtrEAApVat33QJS9JeQIE04JQobB6BNEm0VmC0LdvbtW7tQd0sK9VXvrRQy1YkAh8Dzp1q1g/+qVqlNyP15Yf8gSJRaSXf0VTLnWdzq25AhwBiXn55pwOtjB45QrnZqDdGofaFAmA0dY0fz6cONcP1a7HTw3bYrL2nT01VxmDKIHkzEnDI8qntZwqgtZSZfSjBPfdvewHYuqlLhmnxogTsXOlPUm8UHe/Bmq8Z4LGN/qqXHvl8DLFdFlasatKVa/WaOT1cKx9AoTIFwI9r7AGS37u7Wy/+qgFbzSY9vjJWX3wyxLGDPLCvRy8BXVTeOoVKZoYeeSRbCdGoeJG5DXe4RX7avP26tu44pY5eFMKGZSsXRa5EFCujyHuhdt1MANQDA23aSLuybyegXyD5rZF4bzVtFNDXb4AZFzjn9JM4tvVbrY9sL+sD23jH+rvGNNjvpqxt/Wjrx9u+ruPY7/ay47iAE9dPe8/60PayfrNrjGTnsM3et821vx3Tjm9/23dd12b7uY5vx3G973z5v/jHdVzXeMT+dv1ux7G+v8Xjw6l0k5JSsbd104nTHcRrKfvRvlr1eiVKrVeUkR2jZcvTae+ARVlcwNVh09mrX6/upd04qYdXJgIgD6IeQvWyuFU/+CGK2RXVmj11OO1SruKTWAzAszcupYN6rehUi1755XHKsxt1RwZ1aTjwLRbSlK1w8mgQ5bazo5cyWKI9O6+orcVPwxlvDssNBm5CBZRnHZ2AAjP22KY210tdehKQ7dvfPsoiUeqSeUlAwz4oebNYic8u04bt2AVIefCCclDgfQDIOiyEtmBLqfKPVCkzK5nrj1dWNnAs4BpUF0I3vVr9xiHUEfOpLxfSHo6iXHigmNqtn/4QC+KzXpo8OlaPPu6nLABq6/ZaH/32bQ9g0g69ufaMqm/VocZm9zcIkR5v1CwpX7T1BpAaQLh9SxPl5Dz3EIyKayzKfKhjkt9Dgeys6x9MWbQyc5tYQyHX8+KLZ1Hba2TB8hDAMdT/Eq3fgy3ytV4gthtYeEujcgP02ENmze6hFwHb3t5fDxTrr6/8ebzSMjzlR71Ft0qnTtrxbqroSivKbmF6+isRSgOcO3QQ1cfV9Vg9nwCISydd0jR0qBcLIyyfGuDJmIXW5thR2pnX6nS66CbjyUhUyBJhf95pB6K5xyDqD4ZKWHB26rVXT6E06QmQlqLhwPjRCbQV1I+RpIcJDoRQZn25lT35XXrhNfp8F0o1f1q4Hn0kDlVa6g36NKa6u2F9I3bjN1C7TNCDD4QoK9euo06/eukK/FGaViwMpw3lGZK+ZnVvSpTtrXX0za47rh7vBuesPNh42JgQW2hm42aDY20sbeNdg2RtHyvf9tNV7ux313bn76737Kfta+XWPrefBqkas2P8lI3zbbH5mDFjnLkUV33gKuOu79hx7Nx3e7vzvlzXYOe0313g3P4DZ0jrLFQXkzRqTAhwvzftoAeqfx7OczTg04DJCvo0q15lQc+BIvJdEOqA2RqFqrAvgGs7Fk07tmNRvrsXkLlKK++Poo8Z5MDXq1+/akNSmKZkzV0YrCjKucHeZgn8xmssBthmioJ9+rP/GQ6rhM39DeBdwLmXVu1UclKQHnhgpKbPCHLyVQMK2ps2vKltW96kD5ZI+V2KYJUpznlSc9nGsZ2fViXRB/rN6zdvfeAf7wnOWeK1M9lz6VKxo0BgKxNTUlOQgJ7jyJXa55b5bIWiqWoZvRnC6pAHH3xA8+bOVTi09qdhsyCQgXO//tWvHXBpPivkPkmL0I8jDSydX3zxRZ09e9YhVe+//34nY1vjZw2iKROkpqa+r1NZYbaJte9857u6Cgg5i8H+j3/y4/f13Y+yk61gfY2Jjl/+4pfkjXB97nOfo4P0yEc55Af6rknTGjxZV1unRx99lIHYnLtWOdkzsc6JqQJ+5emv0EEJ1+e/8Hnnnj/QRf+O7vzDH/4QCd2TyhuRp7/8y790Oo+/o7dy77Lvagr8/x2Ru3qaewe/lwL3UuBeCnxMKdDGAPTyNSah3u7Tmk29qqgZYJV6vzwHbmnehA59689t4pSgxb3q7WNK8XuHuTMF7jY4Z/1XU6P51re+5QTibWLv3ZsF6q3/blaxppxjE393czO1betXrl692pn0s3HMnZtNOtiqwm9/+9usdF76W0WkO/f5ffn9Gqvy/ubb3dp6mAAvMwbP/C12rcs+PrvWf321V999rpvgJ7AHMXuzaR0zHMUWi+J8AltL24C++Y/vQHOmNMecoBZP89S3/8JL2en3oLlP4BHcO8VnJAVc9aiplNkiv2eeecZxMliJ6pnNDdkiWNtcgRsLJpkag/3t2mwusI82ouLCeb38jW+q8txZpUfFaPb4qRq6dLl8huei2IRsRy9wTFOjoxLkER4BbMMM92dkszSwtLxzst3azTv/tjkhmy+1uSiD6Axus3S04JzN4ZmljL1sEt/aU3smtr8B46Wlpb9VWLXAnim+ZWZmOnCjtcW2v8F51jew41tQwFwP7Lpsf1tVbyCktZGmpGFtvB3b5qjsWgyes+u3Z+s6dm5urvP8rW21ze7HQDvb3wKLBuAZvGcAoGufz8jj/D9uo9/mul9epVtbt2AHToAoHLsuA+ZSk+XNQmCfQclM0Ieo8dhple7bJffmSiWOHqnQSbPlmYj6Fc8YUowZe1Nheoe/6q26pao1q9WwZ4cSUwcrCAjNa9xYQLVQJvgJvFjs3wlY9aBu1qLe0jKVbtuqW1dLSPcExc5dJO/MHPYHzILegJECrmFflFDrCo7o+Lo18vLzVPb0aYqfu0BugD4mfdFPvuo4XICd7EEC5AT5Ro8hoOivuuIiVVy7jrKEP8EjVFTNfnZQgvzTB8sHy1I3gKOOkuu6/q/Pya+jVdFTUdaaNwfFtyROTHCCvGZwm0FjBsL1nL+u1m37dHp/vmKH5Shl6QLqguHqD+Y4KEuZOoWPcXY9gHbYyfZVlasMdbLOU4BzGUMA55YBzo0k7VB5MRAN81W3HtKltVsthWd1A/XDsMbbCps6X96zF8gjKQbIEIPW9mq1Hjqoqk1bFUAgNiRrlNwj43UN67zW6kpUxLC5Cwvm+QXLKyVRvkOw6kzHatbNVz1FN1S5cQdjugplzp2uiOmoZcZHqBeqxw3LH88BXweaGyA62FtyRUWbN+o29V5OYoLC5wNJjh6lLsobUViUxbpRsSO4znGRR1LfhSJ1bVilcu7PH3W62GX3yTsjV73ePG86T9Z/ch9ApQ/Nucu3S/T28X3Y9GEzmhCjGZOnYWM1HLvbMDUM1Ol4zSGgnzcUxN9TMudgOdiunSd36LZbA0GtMN73lzcgo1sfAVOUCw167ehod4KCE3PHa07eLCUExavNvUMXsFPddWy3jpw6TOCxGcvUEVo8ealGpYxXiC8LwMhYl2qLtebAK6gCndaY7HF6eMajikLRzrZeYNCb7Te1pWCjis8Xa3jaKM2dNJ8AcBCKcxe1btNatTVj2Ug+mzFmNpajkSqvQ/Hy6C7AuX0aMWkENodLlBY0RH5uQGB9ncDFZ7Rl33bVdd9WzrSRauppAUA7oubaelS1wgm6+juBcwuGuXv6qul2G+1Wt5LCE7V0+jyNyMhWSIA9B+l01TltOLxNR/l+H1JveYChS2cu06jk0YpwD0eFrl2VzSV6c9ubunz1spJT0/TAwsc1KDxDHsBTvYB1N1ouKv/YDgLGZ3BRAfSbOF0JwXHAete0Yc9GVXZXafaCOZrKs4j1SCZv+wHD9CJ2cFW7d1wXRRewCGWMMKyyY4JRp8P6HIvRmEF+um02nG+jgHK0zAn8BgYEA2X5Yz8V6AT1h+b4Au+gouWBLS0B/B/96Dw2beUaNzYdAYJ0FMQASwm0t9MX3rW9B4jtDCBQF8paQ5QCpHbmBGps64Cd46QVj+Zq0qRQ0g/wA7jgBmOFTVuwrttertSkTmCWdI3MC8T21cC5kzqLhfOkscNRdBmMSghqed4UWMqiqf30UxZam9119lQXYNoVnbtYS/kPIygcyvX6YbvlraHZXsBBfix0kg7t60J15RqqMG240YTRzoWg6ugN1OCt7KE+qHB5AmgAAqF2tQ5w7t9f3qlh2YmOgtokB4gC6MFC8lBBN+pcuAe138aejoDrzCiA9E69ubkD9qEZpaBIALFA6jTABspUCwDCzh09qFbVATWU6stfyAScwxIMmGPNuhaUw05gLxiMMlqWxk0CEw1n3OROPTLgDYzoQbysHUj1Gna9ZCZ3gv+hPtS/gSi0cd053gRwURrtdUeMogK79avAe9wHMFU0UG4Czy09w105w7wUn+wDkIXiHOos27ZVA7WdU0pKth5/OE7jxrPwCJCgm6rz2PEG/dvzKMQKgBNYYjp2fkeO9emFV3fLJ6hbyx8Yh0IOFnmAAtYLuwVouGcfqn/Ai329LQBZY7QS9bXeXje9veM28dvDWBXHEGTO1tgJKPkAL7jxRWvVWwlqHz3YglIhap8AT55eQagdIYQSG6CMdA9lAwkmY7fmDnx0rLBBL63h/hq7CWa7KT7Gn1hwFHFhYAaecVKcp4KBJG/V9OlfXiQufKYHu1U/ffELMcrNAuLnnB4M1q5ebdUzz1XxLExNMAiIzIv83Kaf/ctBAtyxemDlUFSKyPNxBi0IBbsu4sut+tUrF8l/qA4tj8Ae1CzjOvTayzXkhRplpkbp848lYisJQAHsYYIuBneauoypPZ0vQu1tXY2OnaxzAvVhEUGKj0P1MNGHfhE2lUMA+/wGyO8t2rXzEs/8FsfBLpRYZWxMiIZm+mN/GuAArSGALVAk5NEu/ePPLigOS+ynHg4nL3oBulEOkYm8UtarjYB+hwrKNWfaBC0E5Cq/2YwyYLHKqtw1e0YaIAzgUgrwozeACGVxHypu61Yf0eULJfrS5+cDByYA4qGudKZZ//TPVaqpj9b/83ksYed5KDCMdhaY2ODA1k6UPa8ABK1D3bXoFtBQNJaQEVy7P/1JX8AjL+7RDwAS5cdjpvx2k/SuB/oxZcswJfDc0pOxVAQgMpAvFOUmU5zbl9+rZ/8N+Jxq/uHHcgFq4rC6pD4EnDsHgLN29W1dx85zzBg/PfpEDHV2H4stUSPDLnHRAtSAVgINAdDaPGgfl3qoANDnlQ6cO+qAmML1hc8HA/q4af9eFOdWlWJfe0bL7wfauS9bkcHkF/KnQfWImOrCpXbAuYs6da4JCM9PYYGo+VK+0lN4ftxfarovgJs7NsD1OnqsUOMBdQKByOshQy1+bn1b66PaT+v7Wl/FXtb/tv6rKWpb39deBsrZyzXGsf3se7bZd22zfr3rGHZc19/Oh+/6x7Wf63iuY7jmzOz67HfX9dlP21zntO+73nvXof/Dn7aPaz/7jp3H/rZrs2PZewbrWD/exgqRkRHU1bFA5x1YprcCqSUpNsJPL79WqXPF1zVxSpwW35cGhOIlf/KoUSeFhaZM1kP5OKr7liShQphMW2V20S365x/toS27rYWzRxFjHwJs44O1KpdIue8mu5aW9qAGdUv7DlapAdAsJMTgZn/RhUYh1ZN5RF9i5eT3Cy2AshVA+repz0OBzhmTxPhQD3mhXImNKXVKWBggIdfjgHPfO4C9I/Xb0lQ9uNIXIBblOsrT9VJA8rexgtx9CfXRobp/cQSQLOqjGwDgzldrzITBwKDxtFNeKFFSl3KtpwGU1755jHZ7Nzbg81FgHeVAQmXXu/TMz66QVv6aMzVGyx/0VRoWq6Y2p4Fe4FwPR3V059uVLCyqAHTzpf2J4ropg1gGZ2V70OZ7A+H56OI5lC3fusJ91gJd+/IcsC2PCZDZPA+mTcnJ86NceqE416+jlMFfvXwdVdY22vMkLaTcRMdY5c14pER65eVaYPQB5r289NiDKM7R1fy3X1TqUGEbi7Di9Yd/RBsLSGiPj26gLp3D+vWVWuryRo0eHqwvfxl4Nw1wDkBv1Zo64hontHjhUC0mLVP4nhftFbmHh8gDpMUovwHoBoy9Y9dVxozdqFpGwXMEoWxLW8+1D6Udj6LNa8LOecvGG5T5BuqnfsDxEGD6UMUn+ADreqEm6qP0JNoL2pG9+1v14mvtulxaq0WzwwG/aVP4zBTNOqlLN6M++8qrN7CGT9BDK8OUleMB2HhTr75+nvybrUfvi9U86l57hu1dqIxyzWa9XlLyDjhnY+o7Fedc5cTgMBvL2kIwGwvbOHnkyJHOy+Y9XGN3V7m1wuYqX/b7nZuVLdusbLnKnol22bEvXcJ2mPdNZc6ObwsQDdK1Mm/72st1Te8+vuu4737/znN/2N/vPLbrGuxY9rsLnDty7BxKyzFYOQfQpySvMraIjQtSZkYAfR/6QYChpnpaRVl77VX6sEfO078ORRRrKH0e7LOB6VlDBbjeTbvTjYVyiVbeh33wyBAV7G1lPv8y5SQcIDRBU2fjXArEjlA0cxf92kL7um5jJ9VHu776dBBiV4HvgHObaS9Wb6W9Aph8dBh9pzD6LChr08nciHr71s3bsI1PBvYGnJs8jj4NB2Q8RirTZ7V+gUFzlq8tR1sF9eG29wTnTLbUJlX+/YV/d6wbTMrflM5GjxntgEB2KquM65mcMZuBTZs2OQCdyfc/9vhj7xvk+nCX/P6/ZQ1EKZNORVzjsOHDqJxS3ndQxTJQPwFRGyS46ND3f+aPb89yJsO+//1/dAa1FqiaOHGic/B3OgRmD2EV3Ds0+7vPapSrFdA7C78Vij/70z9z5COnTp36iYBzNnA3K5Cvfe1vuVbPTxycM1sdAwY72jso8KxYS0r6TyvBd6fhh/nb8o6VIZPlDAoM+r0C50yWtKqqypkMNrraGqBParMyYdt/Vh4+qev4z85j5cDSw9Vp/s/2+/14/8M3Wr8f6XPvLu+lwL0U+DSmQC+D0HomcQ+f6GMioRdbHel2t5uGpQ7of33JQw8sY8Xke3fJPo23c++afodS4G6Dc5YUFtR//vnnUWl+1gnGv1fy2Mo8AyyeeuopR+HNJgM+7s360Tbx8NJLL2nVKoKO5eW/nVC881w20WHX8dWvfvWu9+3vPO+n8fduJpteeblXf/cCAU0UGv7XY976kz9CSYZVvW4fsctl00N/+3c9epZVf00oNnxuvru+9ddMamIz9EmAwgbN/eqVXv3jr7i3BiaduJ8lkz313b/yQjHjHjT3acyP967p05sCVr/aZpCWKc5ZfW9zZsuWLXPAOVv0Z5O2Flyyl42r3z2et88HCNKUARW89PVvqPxckYbGJWnejHnKWLBQ3tkZ2BECMaAGNVBfw+Aca8YIgAzPz04HyZkrZD7U0sLSx1LVSVvS10kfJ5mxGmL8384cVEtbK7ZEHY7SnCfzYYEBAIkB/vLzxdLRoES+Z4Evm3C3eSRbeGrzdn3MR/qh/uUCGH1pc+18Vq3bfKzNf9h+9gwN1LP37LkFAboF/CYo6Dw/rrPHuZZ2p62379j7tq+14z78tCCil03u271wHNusX2CqHa79LODoHM/59LP7zwDqNu1HT6qt4ADL68sBgerVCDzWzP37pQCvjJ2oiIzhqi+6pNKDu1Fwq1HymBEKmTQTOCtTA2ZNSpDbUW+wBAWk6nHAubWq3fO2UjIBxVagQDZ2JApkIYSpKFMsvQeHY4K/T/08y74bZbq2eYtqr14DUkhUDPakXkMA54ICAK8IbwHmoQknopKq3VegI2+ull+Qr3LnzlTs7HkAaFhbdhNAIBLdfbJQ1w8ccCw0h0yeqpjcDPU231TblWJ1VNWqvbGN9r1dXQATiUBsseMnAJhlqrOmShW/fkn+WEqFT5suv1kz5ZEAkUMwwuoANzcL6DM4ApLtuXBDLZv36lT+HsWNHKa0FYupC7LV5x+oHk9sBukweDFd52bgHDY7fVUVKtvylrqLjikmE6XDecvljrqXQYcWvAMBs0yuXiCxZhbmVr6+WlEtbQqesVBeU2fLIxmLVA440FqlVtQU6zZvlW9rp4JHT5HXqPHk2x51Ypc6cAMItaVBdW0AhEQUwzNSlDppjAISU9V39ZZubtmpqsZaZS6aqfCp4+QRG6kubyJA7n7YFmJB28Wz4f57Sy7p1Ob1qjt3QSOSkxW1aKncWSzcxv3Zc/bmej1RBkHrF09O9r94FqvW1So9dVQBI0crbvlKeaflqNcXcI6yB+uBrVeXA85dvV2qHUd3Y0lVpCTSd9aUacpJwCLbI9QB5wrrjmnt+jfk3eevsdiutrZ1Kv/0XvWF9GjwkFRF+qFiR8DSFJc8vD0BamxhlxtAQoSy4jOVnUB+A/Rsc+vW+aoL2nF4J3anB1G2bNGo4WO1fNp9GpEyFuvFMJIcoKT6ot7If1XXblzEwnW8Hpr2kKKxOuOBkDt7Vd1VpbcK1uncqXMalT5GiyYvxmYwSFerUWTb9KY67gDnwoOjVH6rXHuO7NKBE/s0ctIoLZiyTIODslC3Q0mqt01F54/p7YM7Vd93W0Mn5qi+o0GnTxUCvvUAWaWzYCNKvdxfD4FSD8DEfhT9zOs2PhjVzvRhSgZm8UHFr5cA84mKU9p4aIuOo9zZDzg3MmeUVs5aqTzAORAy7HE7cOgtwd7sDQKpV5WelqH7AecSw9NR7CPtUOaqaLus/BPbdebcSaUNTtG0cdOwwk3U1cul2rBrq2r66jV30QKsX6cDzsVRbn3USfD+Vm0n6oXcz1kC3Vfb1FzTou7WJqyMm5WaE6bZi3I1OCUEO8IuIMl2Xbvaq8qbnbTHWJr3tQAItGvqjEGaPDVFg2KCVI+d5I9+ck5Xr9/QpAnpeuSxDCxPAUsp+x3NA0B6PVr95knArW4tXZKlpPgwnT7er41bTiuGAP7Sh9M1dnwIQCTtFMXU1Lw2be3UNpR5khM79MRjKQ44V04g9Mc/P6Liy+WaPjlPjzwE1J0B7Ao45+7R+w44B+TY34spGpBNMWDL+fNc/zV37Fi5z7Z6ILomDRrkDgA0BDAiEuURN10gLc5f6MQiHHGBJtQVgd8CGTaOyAnXsiXRpL0PylgDegvbypdWv63Recko0IxE7QT1J2z2Gpv66aegfraBugUVnrmkzYI5Udqbj9rPpi7SoUtPPBKsKTOwmkfBCJ4QAZABvY0S30uralHnKtEf/cFQxq0Rv1Gca0IF7JgyksIA53I0EjDDD7hCpnAJutOLFWUT8zwXLreh8tcDXNZFULeF9hWax61OSYl+mjEjW8PyQtXe0aMLRa26CEBUWdnrxJq6sd8NCWkniByoiZOSgBECiJn2afeeKmC/C6i3AXSgepSXR47hmRhgcrKwXr/+xREA3VAtwipx4phgFZ7p16tv7sVesIN8NpoYYLRiwg2mNMAeu8sjpmZ2lP5CJwp8I7RoXjzlyE179jaiwHICOCpGi1Dzyh7urYhoC3aTYdj6AJGbUCq8dK5TZ8/x/EqkmzWtamttAFxoR+XHG/WwVBaChlNHofZX2Krzxc2qQVmqqb7BqVP9AiI0IjdVM6dGahgAzO3mfj3zi1IdR5F8KLDI54CqhmXTp6CupWejSxdaUZyrAQQLx1bUHwDUE3WoNv3kXw6Qv+L06INDNWce4EkksRLalTrK0MED9Xp11WV5++VqxdJILVwMONfXjfpRLep9tRo6OBJlwhjmIYDXABAtJE7xIw/Sd+HPZvJN2dUeFZ5qx9IZBc4q+kf0qfr6OoA5wkjPGE3C8tfbF+CGcnrsWLNuVrYCQTaTvg20PzxD6p1FCwajREctxTk2bG7Xj545p/jYJP3BQxHkRVTLsChuo02zxWtbNl5DyakS8JH6bXYA52xGVa5YFbc8UNXD1nkuEEKiqf/x3KljDx5t11urDmNzeFn/48klqH2Z4hxqjaeaiNHeVOPtGH3lCwAEc1D4A+7sA7C2+q2vP4i+HkDOxVbqmQ5dLzHr1066AY3U+5RBzjF7ZgYWlqxyg2S6UdZNuaa8FrtTHnrpi97GyneAZ+WH0lQ4UEMAltsDKkBd7NkXAOdiBgDnhqJQNwhwDvt25hfOncT29Y16znXb2f+Jz8WR7wf06mu9ug2MuWShp+Yv7weWAS4Cqrd8dvgw0NUrXSif3mQhX5T+4A9QnCNP5O9Cve4toOG2C1q6EphpaYaiAeeMwbe2h14FfWDqGJ5LYRHg3eUu3brZpramdvqm7ag2+gCSpmvMeEB43z7KZ6sSULXy9+0if7LK+Teb9cNdL3vL+tcWF78zNv7ufV1/20/rS1uf3nUMV5/fPrNj2N/WX7f+sAuEs/dcL9vPPrPz2nv2HTuW/W3v2+/2vmuzv2278/uuz97rp+u6XN+xa7H37Dx27fZyXZd9Zn14Hx8/6pZ67HUbKaOp1Cn+eu31GhWXAExPjdaCZclKScXW2NRA6Y+atfcrq3t0qvCwVixKRv0wGZDGA6CshYWtewCp27CeHgWkmw4kRl7hFqz82eihjWd4g/bm9BlrA3pQWKVc3m5hHFKHxTBQ3Nh48na4o7xZUtqJyiYW8MX9qmtopC5FGrO/EXvTEM0hL48bEw3o5AZT0Kfv/EM+dV0M6pXpWGn7sPAH5Trgzqulfdq145ajXpeWYuBcuKLCAeMMnLtQrXETKc8L45UKyOVr7SHXeIY6fs3ak6iM7gWcm8uc4ghHca70apeee+Yy0G+QFs6M1pL7fJUAWOzuaUAOZFA/9rDtHiq7jrIx7fj5i32odA5QBlm81NeA6lyn8kZFaMr0DNQd/VRe2qFz51Ahv47aLW1/S2sjCXzbAU2XrcDadGK8+rHaPg5s+vKrN7ifVuypEzVvURhQOnnWvUsIU+olFBzPnjdwzlOPPhDKGG8AxbkKHT3drjFjE/Xkl4NRCbMyyOHJWtex9X755ToVoNg2MjdU/+NLkYCR3ipAcW7tutuk2Sn6DekoPyYpaRDfA0IjV/J6Z/zX2YFdekUvLEUrfBBKw+VC1a2B9hDr9MBe1DxjqbsTUdrzQzmTviXKc8VX2a+iCYXRVp5TJ9C8nyaMAWxazAIC4Ly9qJ3+YlWLSioatXhuFPbd0XzflCr7HSv7TevqqTdK9P+xdx7gdV1V2l7qvcvqsiXbsiX33rsTO3GJHdupJEASytACA4GZh6EOMJShDKGGISEhvbr33nsvsixZvfdm9fK/31FORoQAYSDMzI9Pcn2v7j1t77PrWu/+VmRYMmFcoyw9w4vyWspzuoQ66ii7dzWht2f19Z8C5xR2u4Hxe05OtrMgTIpzqampju3CrV9u3dJcVvCcFg1qgZoWhImvEWwn24fmwrJ5uPurbr3TpvOqHmserQVrErWSkp3U5VX/tMhsCgrWWhSnuq7vVBd1XP9z63P/v926//bv3+ke/tzv+udF//vQZ6Xj0qVL1IerzCgnoVgdYcWFdVZX18KCgxYUGgVah9mi21OAPYOsukRh08tgirJs8gTUHwGg00cEohrYbe2U413bWqlTzG1aCmwFisDjx4cT2riJcL45zkK91atjbMZc+m4UCxl90dehwLq+g0UNLJYSOPfRIFga+tDCHsC4Tnv2pY0oa4fZve8TOBfjKGLW1lYAzm2zHZv2ANmm0H8usVnM63x8qdnMS51SLCqPUYiaVv53Xn9uvrn7/x44p4zTQ1+/br39569/Tceb5Dgllt+x3ClI7oHuu0JTXkANTSFAp0+bbnfdfZelpaW5P/+Pvys96jRUWN/tpmNkeGokNK0+JyQm/FnHv9vrvJv9FN5B4S9lLPvWv32LyUjynzxM6RU0p0ZBq1mjo6N/p0IKnJNKoBqJv4XinDpqUb2PfupRp7T+rRXnlGHKE21quP4Wmxrl+fPmO8/t70lxjgpDI9W39e8E3us8l1FaTmUZc1VH/pbX/lNpUxui+9NKcHXOWgH+v+n+/tT9vze/q+u6ud3MgZs5cDMH/m/mQAtS4mcv92DkrbVt23OclbVf+lw6E3tUWf42w4z/mxl3867/2znwtwDnNF7Jyclhwc537PXXX3cc5u90wwLW5syZ4yyEkRq5YLq/1qb5l+Yozz33nO3Zs8cJISvjxNs3jeeXLFliX/7yl50Vff9bF028/b7fy7/37O+2D329HWNUr00mXOsPvuZnU8d5YiD/y65aharFPz7KSkCA4RY8E099FTW7FQI/3vuxXBMG+d8832GPv9ht+eUyqJvdNs3L/vWzvoTvkrH6L0vbzaNv5sDfUw6ojdemeagWFsp4/MQTTzi2v5SUFNNLq7DVxmvFthTIpDAmNQZtOl7Hyq4maCbv3Fl75itftpLMSzY8NtFumT7P0gm36Dc2wyw6CNsloFhtpSGKZd3AQb2Ea/QLBIrBMN3fhMk6VcAbQBMMtD2sUPBiPu+Fk4c4H4RpJAQkEJO3P461AO7jTZuejKIy8Dsbb71IXHS3trHamRXLgGm+AVzfvQ73/NbmHIK9AvtUF6pXHoBnXqhLYRmGBeK+3F3fPLWO6wIowgLsQCkewG4e3JssHoLLlA8KSauXJ2CQQlz20jApSR54LnSXCkfXS1o6brQAfqBKxT7+QGpSLmjFlqrNl3hgnnpxz906r/OsuAayATqHF3/3IE/T5SyMxRCPA6wXuMeDvlD3olCu6it1+4JnPElfj+Ak5SfOAG/lJwtunX05vxyUzs763EboWO7LwxulAvbz4Hn3cg5lWzfX6yXtXfyuBtcbqMmL3/tsGX32Nd2/0tcDIKh71Hm8yCfZhTo41guIR8dACTpKacpavdysdvLHUSGjvHSSl8gGeQUSGligpfPs+vbseyTkKeft7e6y7jZUwHh5E7bUi7IhFTbt37ef7urNa2h/bHPKD6XfE1jRw8m7NzsQCqDS2KN9OMYbu6sHEE43alfdpaXWW1Vu7bWlVlNaYCXXcWrVdKDOM8riF99mjThscg/tsp6KfBs0boxFzVto3qiKdeJR78F47wN84GCVlOFuFAkqXl1rVVu3sXI/ycLvXGG+M6dZD4RIVy9OORTD0CHBcMldNgFNIveQtW2TVQK3DY2Ns5hbFplP+gjAuTCYIZRgcH4rtd44I+sOn7DTr7+I4lmXZcydbjGLbgdeBXQi73vrGq0DtblsXhV1DTZs4SKLnzaRMJ78BozSSVjNNuC7pusFVnsly2AJLHz0KAufPR3nKaHRn3/RPGgvQqZOt6Cli8wrlUXIInfYepHtwL9qnti6urKKrXn7QTu1a7tFZqTasOWLLIiwsAwW8DMCXHrwPGkLPAiR1cvz7iktsuuEMu08d9jiUfUKvmW1eY/gvtjf2WS+J/86CNF4g/BHxc8+bwOqay0UVT9f1Pe8UcbjZIRqRfHj0AGrIF9DqWuRs2ab75zZ5kk71lNZBaBXYa21VVZXWWHZgHQd7Tds8Ijhljp9hnk1A0nt3GvXigosfQGqYgrVOggnplPHUc9BWUreYA/qU2dBrl3bscPKTp2xETjEohcvNa/JE6wzHHqCeyXwq1NvvQG7PDq4r2uZgHOv2fWTRwHnJljSsjvMJzXDeoPDrJt2TE5mTk7o01bLbyiw3cd22sXTZy2VNveW2QtsVMo4C/AOscrOSjtVfgJQ6DXz6w22qcPm4+hqtz0nd1pQvCCC6ZYWO9B8uNduYM0ezu2BwygAsCzUM9DCgeoiAiJRdvCwgtZiO3z5KKHZjllJRSmgAaptcQNt2rhZNm3UNIsPFwTmY9eqrtm6g69bZvZlG58+3u5ZcA+QWjz1g3YWcK70Bo7MA6+Tn9k2ZdhUWzJtCSETg+xqaSaAyXprQ6Z96sSpNn/yfItCpaSwvMh2S3Hu1D4bNxXQZ8ZKy4gcheKcrzUDjJ2+ehhwbrvV9tTaiGkjrLnjhl0k3JUXdWIOEGHGoAygS1/aKdp2gYk4rvVbsA9hJkMigWd8yU8gp3aUvc7stIPnj1hDayNtRC/hbaNs9vjZNmfsPEsOGYjiXKuVNRfahp1rHbXO5KSBtnrJfTYokmuYv7XiuMtruITi3Da7mnnBhqelAs7NQ7FvMOktso17UMbrbrDFzEFmDJtl0Z5R1F1CGqp1ofw1NfUQeqqXsKbtVlvabiXAJpev5Vkd7c/8WyfarYBfCospQKsS9foK4Liy4hs4phu4n7MozwXYrbeOsWmT42l/u+0H/3HRsnOLbOZ0whoCziWl0NZR3toBxPbs6LCXXz1KH91uy5aNRklqgF06h+LcGyctFFZ9+d0ZNotwaKHAalQlwob22GtvtNqO/SU2fHCXffCBQYTsC8JO3e0ogF2HpJo7a5LdtWoYKiOAWr6057QpKjv0UqTP01EUaQLaqyFkZGkpaS3HDl/cjAJdHo7rUsK5pdmSxWmoawFF0m2UlPVYWSntCMBAIXly/nwZaos1difAzLy5ccAnHrZ2U7U9//Imm0S4u/vvn2jjJ0QAlhNusq7bjh+VIl0J4F0j6kYJhGuNsn0HWwEiAIJRJrxnTZTNW4jCDfAMXY8Dzm14o8OefrHGGhqL7JMfSbOVKyOATQH01gHObThmgxMDcMCPsUlTws0fWEnhsYWjCxbppn1quAEIgZJaZQWhX6s7LD+/BYgkjzTU2LixKbZ8RQqh+wjrSz6UEjq1nOfc3kKoyKoCnMCVFkho7mHDE1E5C8KR3MUYq46016HuFWMZOJ5jgCG8uWY77XPu9Wrbs4uwlDd6LCMtHrAhhJCYnXby9BUUdeoIcUvY67QBALXROOJR2SSccHFJB47/Ytq/LhSZ+H1oKOMGT/x/zZaHwlcYqqTJqBsGB3cA1iZabALH8QilqtdLO9FU1wNsxb2X82x4LiU8v8wrBSxUA5SdIGWmcaQvyOqBporLOqwWOLKmvNlyrjbZ1WzBwf4oq8XY4vmozNCs/+zpEjt1xQN7mCdhGyNRrkNBDHpD/ZnAuZ/+otzKcMYvvy0YVRhvyytoBwg9ilpbNItOh9ryO/wtLpGOh/JVSXnat7/CnnvhIqHfRtuqO+Js2VJCW9MvvvBio23YUm7pg0PtwXujbRxgoJT7nJaUplquPs0Tu7hvunMH8izh+ZSShqIioNYr9VZe3YZyXq8tJbztWELIaY5cQahSleOS0ibqbp2dOXXNOlsjgcfSbdnyCPprFAZ3ALT9DCA1doi9f02s3TqX9ofQqpoXX8tHcWl9nh0+WGrzUNRcuijIyhk/rN982fIKPW3hvOGo6kXbwFRyBNBDwMcelJdee2m/Xc/KsU98aCWO/4EW6IRqrSdviqy+YYB94oORNm+et4VFML4D+O0B0O7uDaBb9wYk7bU66qBCHVdVce/FrSho5ZHP+YAgA2zJ0nE2ckQIz6EPTCjM55mXoaYJxHMlswZYssjmL4i125YOpv30Q/kQ5cBfbgfm8QBQGGVz5yRaWDAwqRTnznTaG69U2PX8OkDCEHvw/Um0VyzYe66D6xESepG3LV3tj9+cMSRQfyd5f/AAYT6fa7ErOUV2z8p4e+RDERZA2vfs7LTX1xWwOOS6LUUlc9nygRaLopjGuAzraMf7xnBNgNIlpE1h/KqoX1XAc1evVgOzAlQNHWjLVsTapOl+tFG99CMoT2k81FcUnPKgMbR8wC64os8at+rlzoX07n52DuIf9293Px3X/6Xz9QfSXGDN/U7n0bHOPInzu5v2c+9Bv+k8GrPrO23ued393827rqPNTYf+du9b3/X/W/MThTs9fqrUDh6uttEjh1jCgGDCYlbbhazrNm5iiC0FZMxIR4UTsKyXOnT8mMC5Lrt05ZStob6suCPJUUDLpE7/4D8O0WZ1EJp4AgpSCRaOEl0vbVrfqJRxIRWxA3C9HlC3FNDGgaebOgDpKgk3Sqjo0E5bsSLNps4g5CjTk9o3+5OKSmCkKkIAZxda9rU6VOdGsF+SDR3mA1jXZV/9+g7SEW93r8pwwLmYOEKiUkalOLdzRwnlK9MGp4x2QrXGRFHW1mcCAJcDkGewQCypT3EOgLOX9uTMWYUuPw9wt59w1/PtIx8e7YSIzQWc+9XPrtKeAs4tiAec87PEITwzHwpobxutmuZ4voSa7QsZrPSVAphVcu/ZWdX0F1cshEUNy5ZNJ5x0PKAeqlq0t8X0g2rfyspasbdW0TeWYcMMstuWZ9iAiGDLBAx84cU8a22nrwNUXLQ0ygHnPFmconDqv/1tDeCc2ehhiBHdG0b7bvbrp1E+Pca4adQA++ijiYwfmEdQLHp4fpmXuu03T5fbqfO1Nn18rH30Q5HAa94OOPfqOsY5hVfIw1TUMAdYIuCc2goeYN8/nEN9c3tHjzNWKcMGV1beTV/eSijbG3b+3HXU3m7Y3HnqLwaizCrlOeordTavpI2+pdFyslCpvH7DYqND7QMorc6cFmwnCeH7FOqVeUV1tuzWGNr3GIA48pa+m2mkbVhfZ7999ppFoyZ89yr6S5T+Dh+rZHxwEbW7YYQMTuB++xRbWT9CeHYWAgJRZaO8XseCQCmzu4pzSom7uXVEfnlFNJH9QyFVBctJnV3HiZ0Rd9M/XHP/41WHVWfdBWsKzSrgLDs727GpyM8v0bGRhBzWZ9Vz1W8d49Z5t87qvG5bo8/966pbh9++b/+/dcyfs7nXcs+tY93zabGehK6ysgjZPHIu8/8EnnErfWKbFRWovyhjrFUB5DySPiOZKuCJsm0JSoZZQPbx1P+hNjwDJc/ADvLGy3ZsbbPNm9tYPFEIODfIJkwMR+m2xV55KZ/+Opj9Y22+YHnCPnfTTtWiErz21Xb6hXYWB7TYZz4RwkJO+lDq1fZNKJe+uBkwL8LueWA0ocJRnGN8VcPce8ParbZl/U7Ukwnhe+dSmzlzIosE5IjTaJw6QD/pjOwoyxrC8v9/e/s9cE6FYcf2HfYf//EfjtrcQw895IS3HDlq5B+9iMC51NRUk4pZErCdNj0cFRS3kLonUKegl/ug3P3e6jRYKiClN/fhal8VNHd/9zza3z2/+51bIPW3jlch1fvbr/n2Y3Vudx8VnMuXLjvOI1Wc+QvmO5XHvQ+d2z2ve486Xp3lu9nefm0dIzlkSQy/lUbdO+l7/vnniWX9W0dJ4bvf+y4NUvhb9/nWvv0uqvvR/QvS2cwKxVmzZlFQJzj35tw/efm5zz3mKAQqrvP3f/D933k+bhrf6dzvdN9OfqvD1usPbCpTb4Fz7PO+B95nd999t/PsdIiu5d5b//MoLf2v+dZ+lIX+25/azz2HjtF19Oq/uce77+5+71Tm+h/nftZxbnlwvxO4KGdif8U57ae86L+5+a3jdZ/alE7vN9OoMqDvdaz21bte7n79y7vz5Zv/uGl293ev0/+59t/Hzdv+6XCP0SnfaV8v5WO/565rucc7aXhbfdDvv3MezqsV1rpO//t6Mwm/89b/3Prs3q/uoYu8K8jH2LRntwOJ3nbbbW+tjnfOrft/8950Uvda7nmU18pn3bv7u1ZbK23u/epdm/L7ndLt7qdzatN13XSpc87Pz7ft27YzgR5mc+fOfas+6rj+x7htiL5381Ln0/c6n7vpGPd3Nz06Rp9/p0xoP77Xb+7m7uMep+/1u17uvei73zmPvvirbn+4vfirXubmyW7mwM0cuJkD71EOyIe4/8AZ+/kvnrRByTH2T1/4OA7mvjA279Elb5727zgH/hbgnLJX49SLLIj66U9/auvWrXNW771TtkuZZhCKG1pRt2jRIpw8t++pgrkAAEAASURBVDpy9O+077v5TsDcQdRQtmzZ4rxrHiMFnf7jl/7nuf322+2f//mfnetrFe3NjZWlxT326OfabfcFxnwMY5/7Nz9Wc2Jc+guz5+jpHnv4n9pwLPdaUpSH/eb7fjZrMsZxnCXv5VaAweSHjxOiiRW4xSw+9mP69z5UPT/5fkLRDfVkrP9eXv3muW/mwP9/OeDO8/SuUJ1S5Jeqp1Zeqx1VCFHNW/W7Fl7OmDHDUdBPxcanuajaY3d+20NfUXj5gj319S9ZGTDKwBBCCKaPs3FL7rQwjJcWEwI8csN6K8us4VKmleJE9CKca8qEyRYQxVgJ5RSk6Oh05KDBoYQyXe35Exi+AXSSB1kkhmyvJhRqCAVYBfASnpZi4WOHm7eO5V7kusXk6jg/BN31ooxWn3nF8q5ctcCQYBs8ebL5xifScChkpub1NCCaDuMsgaYgfGW1VZ4+br7AI4FJyeY7arx5KradvMBvzrll5ujFid544Zy1oV7kg1cvcOwYVLTSsdYDb3QRihagqOHSVWsGqAonHG3AGMKYD4oHEtT1ANlEFSncZFWj1V6+xkrtEosYhELU8KGQDw1WcPEMQEADYetwPKSPNC9CTAq8k3KKB45ALkL+6DoN1ohxvgrnQASG/nBCdnoNGw1MGOA4ivE3s79uHccZdqju0iJruXAax3+lRY4AgkLRy4MFfI53goT14iEhaeZB39sBLFGXSRp4pjEATGFDB7Mv4Bvn7MbB0HHmHMBYroUOSbMIlLO8BxB2V54ZnPbOxom664GbCKd5I6/QvCLCUe6aTObhMEO1zaO8yiJZ2Og/Ybx5JcTBvGC/wQHMneLgYXV/F84FVM/az56zlsJ86yLMT/iEUeYnmzJqhX0J456BgmQQ9+B591TXWO21XKu4nGMxCUkWOXKEecUDivkDBgBAyvkqy7mshh540Lvzr1nzhVPWSnqD00ZbIPnnGQ70T0b3tjRaA1ET2rKumA/QVsj0meabQNmhzOiKvR2Ak+211lGaZ+UHz1r1oUuWhPpWxJoV1oYKRt7h3dZw+bwT8jEB+5/n2MnWFUZYV7nWSKenpB+Qe+qqRbVuwxYr37iV/tPX4lcSYv7WBVBqkTxDOY4BYjD0i6jqIexjD061qzu38swvWVoEYMi8+YBzwwHiUJKh/HVRxlScfRpb7Map83YJRbK26lJLHT/CkleuMk9U6gTy9JaWW8s2ICAUJlv9A234kqUWPWEcgBo0jZ6j0gjh0pmZazd27LVm6nNPUpwNWLbIfENDrP6F16wuJ8dCRo2x6FXLgNu4B8BNR/ZKdjQlrxPbVR7X2X/cTuzYYj7RwZZ+60wbMI0IFOGUO1+VaYBMgCfneQqmRc0ue/0L1n1qt8Xh+Ai5ZQ3nnmYeb4JzKs89OMU7CMfYjnpb8W+etWDKatiYKRZ0513mkzHKKWPdxYVWtnePFe7eaXEAI/G3zDO/WZPJp1CeASUAx5+gzg7gwEzAt7yzp2xAeJiNWbactiLMig8cIKTfORs2ZoINv+12C0gbYh7y5GollGxo5CGGN+pUuZUcOGQF+w/YIGISxcxbYD7zZptHDOWIutorqIES4+Eh9RwyJZeQW5vWWdbxIxbKvSbfjmLg0OFOaN5u8gPBcsO3icJDuxXV59q+wzvtyukLNpTyvGjOQhsxeDTh9oKtoqPKTpaesFc3AM5ZsM0ZfzthEH1s294NgLpdtnDuDJs5cqKF+QRzJkcPhdChvhZCefLrRjFUbSUwcmNXix3KP2T7zu4DmqkCxojmuaMM09BMSLMBNgvfwChCSYZzHoFxu87sIWTkMUKNptiqhXfasHhCmVKv2gH9sipz7OU9r1hFaaXNzJhht0+5nQXCIXa1LMsB59rxaE6bOP1NcC7KCsuKAed22n4pzk2bYMunAs5FjDBfQvg29qL4dP2wbTmw0Rp6qm3C7HHA05526thpa2vosFnT59rUkZQlH8IJ9waB7JC//Ke2VSqNesS9KME099ba1YqLtoF8Ka0rs4TBydbNs6/Mr7QowhTeNmuJTRhCGF/KVW1Hpe08tA0n/jlA8XBbsWS1DYujXfAIs6beBrtYdsr2Hdlmpfl5NmYU9tvJhLoNTcMRXGybKGfVnQ22iFC9MzJQnvSIdHw5Cp3co7LCM+2haewA2uvEkVycT8i63eW29xhwJmqsa+6OBgzDuU1b3Uk5kUJaCxDduZMdqAqeQsms2ubPHwuMQr/k1WM/ePyc5eSVoaYx3O69d4glAqSpO2vjuL272uzlVw4zHm6x5cvH28hhcZadKZXmw7SzLbb4jjE4OxMIe8YBXOsq6mjPPNdk+44WOItPPvrwEBszNtDygA5+8lMAUiDd2TMm2z2rR3MuFGe9MXZQrjEM00ooo4EvSKjM5rDqiDUg2IDzugLIbNfeKtt/8DwKO+F258rRNnk6KnCEgRSsISEoQWblQHTPvlCJQs8Zmz19gK1B9U0qqhtQEfvti2/YlAnxRLCabJOBBgMIL1sF4HX0IOH2CLvZjNrkEhztSxeF2enzXYSorQPqykeRbBBQEqo1qN35AkRIEe+F59vsmdfK4Gpr7DMfH0Y4yDAnn9euq0ep7ailJPjYvavGoWwXheoJ6VLy1I7RoPbSXqObQmhQ/DNdKId2elppEUp9HLtrX46pObsHgPE2wuj50X62A0E1N3YDfhQzrpKDuYPoAyCYzJMFjwn46gAoV1OiaFGC+7z5vpv+UY73osISwK0qyoM3YABOYUJVtwK01tU3WB3jtDYy2Jf2Ioz6FRMLsBst9aVwFN0A7SlDCofqw+809sBi6pP7fKetrQiBNAISjEgBpkik66e+eAB0d9E/ApogguqMHZznBxCxZ1cpyn5bnJCYd66YbfMWx5p3BGAvbXsvfTS8iF0402EbN6CyRJjemRNC7e6VhFHmGf/i6So7leljY0d020OPKLQbIBPpAb1EnVChWisJhx1my24LsjkAZyUAlz/+yVkrKg8GQBsELElIxqH0Z3Qm+UCcW7fl83zPEPJvHNcYBCwJOEfSXnjlhm3cVsMcMMgeuIc2fBQh+zREoNqpL9MQTx4dhHp58UBVXjlnGwtea4BUD+9vs13764BB8u3WRQNt4a0JAIn4mcnHVp5jYxPtSANqTC9l28njQIlAi/ffH2kZY3xsB+X78Z+dInzdMHtw9UAU5wS0edoNyve1vC5b/7oU5yptAYqNy28PAr6sI/TceTuHKtykCaNR60pgMYoPCyc8UGkDYF1fba+/utvqq8rs0X9YTYS3ZCd03YVzDfb9H0mRLdo++dAA/OuEOJQyDwBrj55hj/rcPgjKi3eB6O0tvcAjKFDuqqZfOGE3gMNXr55vCxcNQAWOMQ750UV70cGrqhjIb2ON7T18xIaPCLaVd00A8gi3swC3T/x6B6EXPeyeu0fiswKcC+srK5fOogYEOJedWwU4R94/NJjQkR72AmDcxaslhBYPsTX3xwJxolJJUbyBgt2O7Z32q9+WWFlVtX3g7sH2oQ/31Zd9uzvs1dfzAeeKbDntxLIl3GMUdReyV7BVl8Y/6meokK1q40kfwlXWTEjYg3vbCK2ZRxH3Jn3JAE1BFhFOWSMfaH6dOQpvjh9L7+6cRZ/d+Y/rA3Pf9Zu7ufvoXb/rXYIoUpWqqWEhBQ2f5kYSwxA8o3mU1Jq0eFPzJy080hxJYh7ypek3nUfHyc6lY/S3fPra5LfXb9p0DrEHOse7XQzq3q97r3p3N/c392/91AHEe+xkCQpeVTZu9BBLTQyzV19Bge7UVUtCDWzFmmG0vVEWHkQbTxuxfRvKnS93WVHxBXsfIRdXrIhHTc3TLqEk+u8/OmptqMqtuF3gHMfEMPrxQo2N8U8vILk2Ly38oeHrBLRpATJTf30A6Hk97X1hyRW7dfEwBxAbwrV91B9yzTbKcgfQ5MmTVfazXzG+Z1HWfXfF2dRZhChGAfSrX9tGnzIQYBqgDsW52DjKCd3U9TxCl+/Ms317LtuQ1LG2enkSYZU7CU1OaPqDJYB36SgspjhhtAMJEc66Jdu7DzWrF06QJyfs/lUL7aMfHWmRqCYqVOsvHr8C9BWMemSi3UFIWIFzXm+Cc5q99LJAokd1kbrl3Dd9XCf3fvxQG9c8AZCcC7w6z1Hpi4mXHx+lSfWX3GsDkODxo622fVc+i7yKgUBHogAZb/nXeuzFl6+hONdAvgyxW26Lpm5SoSjc+TkKu1pP3vfaqKG9qMWinwto+9yLlbaZdEdz35/5wliUuVARF5xLf3sS1cdfPknI2dxWu2XWQPvYIxGWAiB3+DAKkmvbLbcw21bRvi5eEm6JifQbPAP1gySPckx6VB9pUTVXa2ceK0Cx9QYQIAqyzz2bQ0jla6jLDrCHHx5lw9NRmVd/qDENxaCePvHM8TYUw6pROW20e1fQdwPoXcpqtadfrbWCUtI4fwDhu6Phh/rAObpJ27CxkVCtmYDw8XbPqjjUWX2AyGsABC+hggqktSzR7r8vwAHr65uwBQJr3mioRL032+lPpR6nCIOqQ/3rg+s7V72UnUNR8gSLyf4hhXfZQATOiWWKi4tzFhC6IZxlG3dhOamw66W+u7y83FHvl01cxwmY07sWlLsLDnVdt346lYJ/XDuK6qh7X/pNdhZ3X7ftcv/Wfq5/Xsfp9Yds5O513HeXI3DPqXcd755PbZPU8oqLim36nPm0cUmM68hX1GTzs7tt3doSO3b2mM2bD6C2ciSh2wPt1VcLCfl8xcaOTiYc6zAbTihX/yDm2sxhd2zttE0bWslXwLmVAwkjHO6MHV55uQhVRiIcLE+xZStDUWAFsma8VVLca7/9TaVt3N5uMWFe9vlPRzAGDrIKoPcdmwlf/KJCtcbYPY7iXAjq+Fr8CZj+xmbGZVttyKA0W7XmDtgnuCfKPi0I6VP7z7gB44imcirP/9U6ujnz7t9/D5xT2Ibnnn3Ovv/97zuFQ+EbFHIyGAPUH9tEWapwREUx+KTT0KYCJqeHOgx9dh+UOgTt43YI+k2FTwVWDzCIlaitrazI5HsVlEAG0xGREXRAGBPe7Ax0LnViTY2soKMDUkHS+dQJiRrVfvpd51SHpIlccHBfJ6R9nWPVeVFpBJaoc9PqWhX6zCuZ9tRTTxHT+4xNxiH0wQ9+wPlev6uT1D3qfts5v66tv1XRXCnGP5ZPum/lie6pi2MF9Kgh0vE6v8+bXgg3TxQyadPGTZDRQ1FV+JKzn9Ko/HMhm/7XUyOQw7P49a+fRAVlu33gA+93QiopXTpG9/+Fz3+BONFHbOKECfaVr37FeT7Kkz+U1zq/0qi81L13dGC8YyCq60dwL5rsqNL9oU1pccE5qrgDzS1evNh5BiK99dyCMJAo/f3Po7SoEutZ6bNgpmAGEkqDW/l1TUFq2s99HvpNAw7Jbbr3rd+16Xu93E2/6/6knKhn0s51OIgyFMjEJfL3Glz3OPddz1P3pnqjxrSXUamHDHCka82aNU6apDj3vve9z9lP4Y0FemnzxmDsDI4omzpeeatNZUH3rq2Z+26gjOseFdJD19JkTPet+qb9tH///HCPu6H0yHDK9VQnVG50jJ6zjle+6ro6p77XvTjlmmOcskCZcZ+J8k/3p/vQ81Ie9i+DOp/7HFTfdT3F83Y393e37OuaXkwcdc1wJtfvVJbdY/Xu1lddQ/mscqJyoPvQPf/qiV/Zhg0bbNToUfbZz37Wub5b3pU3uielVelSXVc63HTrPvVbc7PCm/TlqzpN3ZPuV+fX9fWsta/u+e3lVGVH++r+3LzT/SmvSlmp/J+Evd68eTMw5UJ75JFHnPZEddIt20qjnqOuq/tVXXOvq7zT90qPfnPzUvesdAj80/00c31dTx219tV+umcpCug6KgfadF2VBe2rTfs4z45ypvKv/ZR27aN93162nIP+4n/+km7rL774zRPczIGbOXAzB/4qOXD8+HF7/PEfO4osjz32eaet/quc+C88ifoktf3q59QfqD1X3/f2Tf2E+hIZftTua0zRf4z09v3/nv5WvlThWFKfr77/T41T3uu8+VuBc0qHxllnz561b3zjG7Zr1y5nDPFO6dP4QOOElJQUVoBNcxbrSIlOyr/vNr8UhvXw4cMOLHcKx7XUspX37pjlna6rRVpf/OIXnYUIGm/e3PpyoBMj4Fe+1GFPYOhEXd8evcvLlhI+Bb8h4VkEymCsIzpETJSxEtfb4qKZgfYbjm071M1qcuZpy7xwKvf90Mm4+MUXuu0LP+2wClYNf2SZj33xH1F3TtSip/cu56/jxPvO9ztt3ZFuq8HQLzveR9d426MP+dqwVC0weu+uffPMN3Pg/9ccUJ+vearmfppnqr09gk1IDiGNEzQXVcgRGZO1Ilt931133WWy28jOpWOd4zlPN/1EUfZV+/m/fsnqAESSUUsbg8TN+GmzLG7GNPNBHaUXx21bQZ7lHdhjFQ1NFjtxsg2eNZdz4IRt74JpC8NhiXIaTuLukjwr3r8V4KHGosaMtxT288XJU3/kuJ09ftRCE2MtbfYkCwXs8gLSk/JNu4Ay7skfe4Cn7BrY7i7u3W+dhIgZTZ8UNnqM+WCbNCCXLpzfvUBa3sGEjYUJ66mrsOI3XjXvyxctMDHZAmcvMO+UYQBjAE/YDKW6pkZOjtzGo4etfvMGqy/Ot5AxYyxu5gzzG4CtBit/K3a3ohPAb2XVljpqtIVNHAVIEwLeJfuiwtwCchG6pKuwyooOHUH1pNCSJkyyQbNnwlK1Wt7RA5Z99owlkf+DJk21IOBDD8WoA1bowlEqCNoHNRC8TlZ7jnCbe/eaL2O8AcNQN5u12HyAX7SUupP/ev1xWkZhI8S52g2AVsM9X7100ZLGjLPEKTyTuCSAIGAi+s1eFN16AK+8CEHXceGSFe/dR3nIIRxRmg2aMsn8k2OpBjiRz5+zxkOHrbKs0hLm3mrhU2dbB84RT9SdAgj/5inbJc4whSGt2b3P6rIIr8YYIOWD9zohIws2bbP6k2csjPRFzp1jwaMyUEwLBHTBhodXptcPewlqWF7V2FsYC1QcOmC1pCWGUJ8DxgIpRqOUQxnpQLZQKmu+YcAIUNTtJYVWdOCo5e05ZEOSBloii4W9h6cAaaFsGEwoTOxmjmoenYcXduXuq+esfudGK714xcKGjLboiTzDpBSALs4F3JmzeSMhPXMsYegwC1+60joJvykVPR/G0N4K/dJL6FTAiOqTF63q2BUbQD5G33WndSVGWsGJo1a0f79FU66TJ060YMKEeuN4cSBFxuFShvONi+B5dxD69aiVbtpuFYVFljBpssWyoNI/KVEeE8AA8rHLD8d6BNckj5rqLGffLis/f9ySOUfc1CnmNzzdPAEnPMjPHmyInVzTtxmbX+Y1y9222coBMaNRd0hbusx8U4byDHHWSUkOsKyIawalp9ugeQB4EVEOLOcbCIQURPnCPdZVWGxN+wl5euUi4FyCDQKS8wd2bdiw3bIPH+OYcEueP8tCR48071DKHeBrB45RXMHUwQjzwlvWevGynd25jcgt1TZ4WKolTZlgAfEDKHN+2NwFTIJSaN/AMBn4LG/zq9a8b5NFh0VY+EzAMtTLPAEDPUJRbfMHkPUF3PBBaaco3ypees1ajp00r8gBFrdomQWPHuvYbVvzcq2ANqL4aqYTcilp+mTCycYCpfH88Ct4AQuSPOuiDSrYs8+yz59GDSTUxq1ZhWJbvFWePW2HCNcaGxxhGTPnWlg6cFs4qoPUjy5oDVnQfFFQ9EQdqv7yFcvetNVCKmmnUP8LQdnOF8jQgzLSDUnEIwQ2iaZMBwI+onS3Y5tdpmwEE0p00Oz55q9zR9PGUAe6gTzbAeB4elZYk20Hj+6xzPNXbEg8QAftUfrgESwYCHDCl54pO22vrnuNqLj+duv0ZZYEXLdt90bLLboK3DHQ5k+dBfASQ/hexnwaMPGPP8pSBGxmIXe4o+BZinPx9aOv2sX889i5o23SmBnm7xlsp06cQl2rGGWP4TZvygxLjU62G8BxFwov25bt26y7pcumjZ9iE0aMs1DG/E1tTXaWc2w8ssXa8VjPGTPblk2+Hf4z0rLLs4FF1ll7c7vNmDTD5k6chyIc4FwJinMndtqB0wdswoyJdse0lZYeNoJQuKiUEILwVMEJ27JngzV2VtvsBTNQyIq2I9SVbGDOJOrplAkzbXDMEAv3juAYnGFtlDr56BkM+gOAehBKqqyl0I5c3IeiznFHmWvypOk4WlEsO33SClGvmTR6ki2YNt8SIhJ4pih6XTxhB48csMaWBptCfzGK0MsRAHaNnY12Nvu0HTt+GLb5Boo3E8mX+RYbmoyaTCFO6m1Wg2P7FhYNzRgxw2J8mCNCebYy2M4rwLaOwzGM9seftrEbR3opi1r2ANQcPJ5nyYMGEnYyBoUw7Nm0o/6onfZ2EoaS/a5c6LJ1my5YQ1MR/reRtnxJKlCMwLmTQAEVgHMjAOeGWlIqx9AktaLIvG9XO4o5h1EsxRm5bBzhGROsjEUuTz93DFgRZa4x6Tgph9jgQf7W0dpNWFBAu3X1KAwV20xCUP7Dh4bamNGBKOH0oDi3F2WRyyjkTLD770JxbFgwfQkTCMYLAt0F32BSR+mjHRtDC/0bDnrGDJotVNd0A0TU2v5D5y2EurJwoRzp+BdCUD5EEsZPsDR1sBplr2dfrLeLV07Y7Gmhtmr1WCek+Hqc6888vxb1kiiAgImo1qG+FehN2D7AORbPrAUyasJWv3h+lC2+JRwowQgJXGUHj51ywrvedutQ6j6gqB9qNyjcvPJ6k208BHDv32qPfWoEjt1wQgearV3bYJu2HLMhqLHdvWI0YSdRtgM+EEzZi8O/FeCnVuHJaLc9SZ98JD4AD9WEW92xrcl27ytgnNSDSlYqZYb+CCetD3C32OjGhjzCr3biD0VBiXqisZLa3753PjqbKqeytG/xgpzVRYWFhJ8cYCkpKUBZAxwIs0uLARhnNWKfr6qqdsAdfZbtX/7WxMRER9EmgjZZEDYXecv2L3+U5uB1dfWoPJVQl6IsnvGYB+B5Wxvhz4rbSCfPhDY5MKgPvq6t7iGUbIVtJmRhREioLUYZcfyUSOsCzA0kbmOI+noat4vnOmzz1lbAx2JUGKPs7jsHWBDP+OdPooqZ6WdjRvTgfwyy9HT8aRBUUq/MQp3ql08QErs6HHAu2ObO9+S5EkLwuSI7AZCVkkQ5WBFuY0cxn2cscTmzzTZtzbSDJ3IIkzzVHliDmvEC6gj1/cVXmm3TjjobMzzE7l0TgtOeZ8Bhgh09vGmDKaMdPMe6WsCI0haeB6p73D/dJBAAsMqRVtu5p9Za2gpR6kzEoR8B6OlFGUW5jDTSxOMX7LXXgFdOnGokVGQMIjKx1KMA27Gnwn7809MWGzMcEAxwbi4+TNQKpTiXlQugszaHELNVNh/FuRXL6Xd8lVdXbPfeEnxqyXbrgsE2CegsACipnHL9xuYS27lzvzom+8dPrESdKskB586frUfN6zr+oGj7xCMJqN7he+M6HT0dTojvzg4v0tfNWP0G4KaPhYb40v4AJpG+/Ycabfu+00Ar1ajyz0FlK8JCUb4KpBwLUpbqZH1lt23ZXGe7Dx6xkWNCbAUAqcYMZ8/0UAcPWHRMDyDUcJs7LxFfL20tdPdlQNXXX6m0rOxywhQH2YMPD6a8e9l6wj1u23UGGCeY+89AxSwIoBPgFRW67btu2Eub8xnTtNlHHhhOFLso2gEP4DdUMl/Os8bmYurlWNSqogB7AWAEzvH826mnZdU3rLYZn6e3rwOS+kM+tCmc7MEmQlNnYRNA4W7JUJs5NwA7Z7slx/vT7vQt7lH9Up1z/WauT8yd/7h+PLde6nt3cz+7+8qOKvubWAfBc6pX8ssJnpN/Ud+7tjDZW1U35XeTLVVzKsE5sqnpJX+b5l6ybwnM0d8Cb7TpXtTW6HuFmpQP1r0/997e6d29X+2rl/527939W8e53wngPX6y2I4cq7HxgHPpw8Ipg7W2ZUcmocZbgVqHo/CagPoZ8yvgtY2b2+21bfjKm7LskXsAZO6MtRhUoi5dbrbv/UDgXCchXMcDrcRZRCy8hwf+bI8AnrkPMFOnVROqnIGOBTPf8KJ+dkrF7kSLbd9TQ59zzWbNJjT1DBRYcRv70cf44p+UoJAq4pmztfbEU7nYpqPsnrviAXoCLTun1b7+r1KcG/SW4lwsoCecPip2XQip5NmB/ZcYE40hNOhAQmX32pGjqMtuzQXmikBNdSghYrHrRnvh+++h7DYwrjrAOCrbPnzfbX3gXIwXAFGH/ezHl+x6doAtvWUgIYUD+sA5lN+0NEGgXh2gal0teCfjkcAA+grGi1qIcOQQbcHWc8DQOfh+pzv56R/Yiag5IXCZo/GYrAlQ/uSJdq6fjwp7EXVwJGqjcXb9KsJJL2YyN5LiXBpjjGgnNK4X9abgOopzzzYgbkWY5SE9gINEMosV0Ntor6y7hDprhT34wVk2aVKkRQKD11d1oizXas++ftUquN7y+UPs4x+JIpy7px07QojatW2Ear0COJfKmCQaX4aUFNVn8Qh5ycZXQ/rKK8lcyHI/2khv0ihgvo425KUX8+3y1WzyOspW3ZkOwEgnyvEBwX4AhjwT6vHpkzzrbX3g3N2EZl+6eABjjxZ7+pVaQOp6WzQvlkUL0ZZCWHlvoPdWxkFrNzTaiy9dRXEuFoCf5z7dB2Cy2f7zqSwianTZ5LER9r57BuLz9nPsknSdhGtFwS4vmzmsykuk4w93y7zquz6r7rnfkSCnHspXcu3aNYdVke1Z/nHVW9Vv2ZdV393jdazaA/XD8rPIhqI2QHVddu+UlBSnT9Y5VPdcH7qO67+596Lf9XLvS/vo7/719u3H6bf+m8719vP3/9397N6LruV+du3sSp/aLLVFjQ0tljGSsW5MktOOdzGWzcvp5pmg0MfCxnnzB6HslmER2EhefrkAJcBMGz92kK1ZlWZpGYSEJ3xvD/O8XQLnNhIe/QahWgEzJ08KB0btJqppMYtVsuCQUljsQJ4NCbA2JmeZ9PnPPVtgeynbE5Kj7IufRWl1ToCVM3betqXTnn5+I5xHgr3vQRY7TA6EP2LxSx2g3dpNhEgHnEtJc5gnKc6pjPZAxSrbu3sBMskzNSliQn8399zceXfvvwfOZWVl2TNPP4MM5G+5qA/yq886IT3dDuYPnVaZrYegwqUHqgchY5xWsmrApo5BAz19L+nEsWPHOiFdZaDTQ5KqwdkzZ2lQvZCLHusY6ypYXapOJikxibjLtzGwmegUYN2DDHxysFy6eMkZRAvsUWe1YMECnDdTuQdP59r7maiqMuj7efPmOYVZjjD3WMEujRwbxuR5/vz5juPnF7/4ha19Yy0rIUotns5rIpMlP4yBCxbMR/5vptNpSs1NFaa2rtaBXGKYqD72+cccg+MfyiNVNJGpCkFUUV7hHN8H3vUw4Al11PqGpg3lMyAVFXH37t0OxKgBdVR0lLPqV/krZQflnzrmt2/qnOXkUnhXpXMkITaGIzupwbhkI+XQ+tpXv2bKl0Ro2IXki56P8lMVKSk5yTGOTgCqc51Rem5qSA4fOkxc7j6HVgeotJ7z9BnTaZwnOQOIP1RGdLwLzrVguJJzTQOOwsIiYKNmBqoBjhKXnpEIYaVRQGQ2KxtPnzrtNE6Cj1Q2Ro8ezSBpCZN9VrpyfT0D5c/BAwed8iVjr8qMntkDDzzgNAK7du6yo8eOOo2eWw5UVtXIqNzo3mQgLikuwZjJ6lwawGTy4d5777UUGkCV0XfalC45m3XsubOskCVtykOqpnPMM88840xqBM5JYa+0pJQJwrNO2e5mVB8fH/dmeZ1m24EcVS46GcyonN+x4g6nYVaYqr0YSJXOiRMmOs9B9UXlNpQyu4SVi5OnTHYad92j6pee+wmc+QUFhc5z1SBPHciy5cscClr1RPVRdeeNN95golvtpFPlQ2mRUU2/D2RiL+ekjr144SLSyFed8q7jFbf71ltutfiEeKfeq/7rd5VZhfnSc/r4xz/uZJvyWfeg9kDPSuVN19Cmcrx02VLn+s4X7/CP0nr+3HmMF8cd2FTpV8iW2bNms9JvkePc/c63v+PU81jV14kTnA5M6dFKeSnE6ViVAwGasUicq6NTPqWmpiIb/QgDxh1MFg8iQ9vg3MtnP/dZp37puqpPciCrrDz00EN0GPOcDlXpUmcpSl1pzs/Lt4LCAifvUlNS7ZZbb3EGvnJI/Pu/f98pW7re6NGjnM5cac/Ly3Puu4WRhSRi3TbkBCuC9eyV72qfPvWpTzr1RjCerik1GLVBMiDo+QRinJNMrCRhVXakAKMyU0gZ2M8q2urqKgYirQ6MHE3duX3J7U5d06ReeSGpWpWH+gYBhM0OYCFgWqqUggb++ttf0m399e/m5hlv5sDNHLiZA/+dHBA49+Mf/9jp6x977LH/FeCcxgFq059//nmnb5IB5r777nP6w7enUWPGF154gT5wp9OXaL9ly5Y546u37/v39vemTZucvNE4QuNBTaL/J7e/JTindGocqHLxox/9yBmfauzxhzaN2zWeUEi/FMbNMiBI8l5lT99p7CKDoMbnGmNoTC9jo14ad+fn5zvfaZylsfUfMwJItfczn/mMM37Wef+et/NZ3ZaV3xcWAdsPcycMcYdZpVvQbYjV2JBIDxsIHNcB+NGAw4Hocs5K1wxU4771VR+bPlFGyv/KwY9+mmdOmIhZQzxtzb3edgvhdCqQz//SNzps/SEMp5zzV1/0s3vv9MYR+1/H/bU/XbzWbd//907bfLLHalEJCMTasXSBl3350z6ElsK4jm3u5nYzB27mwJ+XA2pX9ZL9RC/ZpdQeyzaldld2Hxl8Ne9W+BLNQzX/1ZxS8JzsPbIBaFN4UC0AzQKw+eG3/tW8W5ptamS0peG9iAUEGQDI5BdDn9nZgbpFPuHLKh2YKhlgJ5A+oQYArD2/0KKxf/kATvViF+moRZGkkJX2zDvjZsy1mGnTCS8KVHLtgp3ZusXamP8PZc4bk5hk3sBUnZBXFdhN2jnHoImTLZDfOrG1lB47bmUnT5mWIEYnx1tQDJ+8/IAjwF8ASEIz0sxvZAqO7k6rfP0N6zxwiPzwtGCAIt/kgQYVbH5Dh5hnXDxOAOAlwh125l+3mr077erhvYQd7WR1M0Z7wBMjjF59cZGVNTSbR3ySDcbWGODdYy3XM60eNS0tMPUlzzxYqd9SVWf511FSDfW1FGwJCYS9xOVujSi9Xdux1XqLCywGB1HEoCGofIWDdQApMJ7zwN4TnsG9xSdYZ2W5VR0mJCUhcrtRR0lJSbdgACg5hZtQ9+uOYXHtpJEo6CWZZ0uH1WzdaRewzQThEFZ/HJxAumMHmdcgFOV4Pr2hOHdBSHqKC60eZalMbCV+7W02EBAoNC7acdSW0T+3YisKSxiIwtZiu+EXCaB02Xxqyy0Z6Mo/WJAf8FlFlZXm5sDr9Fj0+HEWt2oF2Y7K1ekzVrFvv9XR18shEav8ZUG06Ocywk32JKcAYk2wCNLcg1pd7Z5dVpx5CbdoN3BQogXHJuBRC7KaFszrhGSMmjTBgocmWmdjhVUdPGR5m7ZYAhDCgLSh5j0k2TwTk80LVa/epEFAeQGOsdxbi37LiqyVkJll23ayMrWDhZuxFkj4UzwuhBysIIRhjkXhrEyaPN38AISqM7OsraSIMI+E1Q0BvPJsA8jE+U8Zq69vtoQhaZZw50ruP9FqCXNVsWOfNaN2GME1YymjgYCmeIIAYnCwAPZELZrHc4m1roJ8azx4jPCuhyk/Xdia4y08McE8gUSaccA3e7EwNAWFwLQMYCxW4p84aIUHd1pwVbEl8FwDUgaZD2nzJr2eQ4ZYuz9hhSAKeqnHTYQzLd+7BwiVsoTign9iCrZJzllUai0l2M+A06KxdwdxjubsXOu+lk3IG5RRwvFCQCG11VcRXrLAbnS0AaWNskHYi/xRfWg/m2lXd+6zaqDPAQBl8TyXoHDSh5pQXVePNeI0TBg33kIHJVpXPQ5RgYTYNYMamywxLsbCpQQYRFjD9k5rAgiMSh9tQRmjUF4Ls4o9W61y6xvmhQ1uQPJwC4pLM0/si94jUTdLwiMWTAhOP9osfm/F0V9OGa3EjpiQQMiwgYMALaRihaoNtmov2oDUmbMtHAWxTuy7DUUFhACjjHIdqdwJqC3KySX8YBv3MMxSFt9ifkB9baXFduWNrdaZQ77RRoTzTH1jIoDQPK2xnZC5nCMqg3tLS7UuIMWKrbut9dRZpw0NJS/CsWsrlLTa1CbaxvCZUy0wbQiwa4e1AMte2bKdCtqKEzfNQoamUjaBv4YkmndSPGUaJSwUM/IFzp3cZ1fPo/wSN9Dmzpxvw6nfPt7+Vttda5dKztuGTRuAV3xs4YzbgJpG2IWrZwi5eggbZaPFU2ejYwH2KK9kCcBXm/m1eVhqVBJKJoREDQixrIJsW38EUNGjEeBknM1MX0h6k+z42WO2/8w+xv1tNnvyNJsyaiK21DCrbm20Lbs3E4nnEk40bxsyGCXQsBB44XbLqy+yS2VXsQN7AM7NtdsnLLJQ2oJrZVxj00YUZrps1oQZNmvcHIsKjrIintHuk7vs4On9Nm4aoQyn3mEjI0dTggJRimuxMwVnbceuzags1dj8W2ZZBvl9OfuSHTt1AodwvYVHD7CE2HiLDAIqRf2qtabJvAFIVBaHUVY9/T3tTO4JO3Byr/XQNs4ZPdemZEy1Lv9uO5p7zA4fPQTI3Q0EN8mmCXYLjMM+iurSsT0APydp6j0tBYW6SNqmdvq2AupMASEGvQFZZ06bQ54vJB0RdjUrE8hgizV0NTp26JkjZlq0Tyx9mI9VMch+6aXzjsJcbGQaQE6oA16XA4tlFZTSB1USGjKV9jXUamtKWCDfblGRKJsA4TajTJeDyk1O7nl8X53Yx4fZzBnx1gp88MMfH0XRqtymTx5l99w73AYNAZQCjpYayN6dAC1vHMP30ED40DGkLwklOrNtOwps3xH8NjeCcWwPZsFJuBPmOyu32S5cu2GVKLvOnRxkj7w/1UZm+GOfRwHsp3tQnLti8+eNsXvvmohqmEQNqGK0S1LkkvJTMY7MYydqUejJZTwQi109hnmEl5P2a9nFQH/F+JwSTOFvqyoAhbmXUNRJQoPlezFCZvbY2ctVqKsV2+oVQEHzgBh5nhtRwHrmxe2oIIUCzo1i3DEQ0IA8reix4yh/bdhCaMkbtah8RaBwHo2f0tv2H24h7OwpbNldgDsjcL6HAeH1Yo8vo+z0WnZpt4UF1KE4l47zNhwxArN16xts27ZTNjgpEHBuFOBciAUAJUmNTVBRaUGXHTpUbFeB73q94uizACDhiqtr5OuoBEZrdkCBESPCra6xBMiKa4TGMSeiDYwrsKFpHoCe/M24xHW401C+Ne5yfEWMv2R/l+1eCxQ0f87IyHCgGdf/5o7X1KZo/i27vhz28j3pWPledFxaGqF71S/0W0Sma+ja8u/JdhA5IBwfAbA1c6eqMm/buD4PJ7aguQH4QH1B/FlUVd9l2agalpZk25j0RJTcUq2lu9OySxrxW0RabDiLDxjv5ObWo37YCCTSY4uBHhYvkiCFp/3kCVQxL/pTljzsg+8PwOcE0Mr8Uk7ra5eb7Sc/l/JYtN2xJMwW3danDHfgIIrimwhXWF4L/B5uo4fHct/ePL8mVMwKLbe8xkbSV9y/ClhyEaGQgYGffbbe1m+tsLHpUXbv3eGEOUTZL4ixrUPRUlgZU9VTl44fqbUdO0sY08bTx4bhaO9hvNuJ36UOEKQF4MRswnjAQACVqkqEIbwGAG+FOEBqLZDJ2XMXCVHaBLQ2GB9avCWl+AIR16CCfpbwu8Psg3fH24I5KIRRdlo7Aefyu2zTulzALgDXKeOAVQMJPetB+NMaIMBCu3qtnnCFAy09JQHfireVU3ez8vGP5V0Atim3xz5xG6pW9Ksoy509zXV+lAlYNsA++XAK4Yx9ULYTOMeo0JO6VulhZ063U05z4YsIcRsb48CBSncmoUzLq3ItMcHbJkxIJw1dVl5RS1lhHBnOAhPGulXl7cCJDdSnEvzKPMPbUANm7H/iWLf98qnd2DK97N77xtic2TH4zoARqf9XzrOQ7dUau3a9FoXKILv/A4nOAoaTQFBr15+2shLyNC6dkHmx+LI9KXtNwIStdhWlxl4WHTx4V4I9/AjjUxY9HNrXgdJ1ntUSnnzlynG2fFmf4hzdraM4V0n44D0H8uxKVqV1eoYyNoxjnBJodEf4h4u4h+v4sVKAEAcBRnRSdmsAd2KpQ8xPcDG5dUf1wIVE9J3so6oX+k5zIG36vv/m/q137aM5kuqpXqp/qnPyY8sP7/4m+5b8caqDesnGJf+ZIDj5mV0gR/YwzbF0Hu2vuqzf9Z02LXBOxVeoOde7EeTRMe596l712W1f9Fv/tLv7OuDcccK1C5wbM5RQx+H0aQBcO4oJu1jB844nzHI84zwJDfXY1ZxuO5tDXnXm28P3JPC8CBeN4tyVqzfsBz8+QSjGJlu+eCSCMSkWFguYBDjn4UGf3mz4MetQnsqn7RpA2VC5J8w3dSUzq5x2rxrboOHfHEj40zbKNPa/7iBH+TaQBRPNKD9evVbEfrWELE+mPrFwKsMbYK/VvvIVxmpdKaiREXp1uYRHgC2Z3l3P77aduwpRnDuP4hzqcssGA3J68zxabeMWINX9NRYRNsAyBgt8ZG5C233lepVlXb9iVdXX7f33zLcPPZxuEUB1hfnt9sTPzwNhB9iSham2fFWgJQ6WWAmGMA/mjlWoqZ2qx+cuv7yfE1Y0ApVummXuu4sQqDnAgC2o96HW7uMHQ1EJ8xAFkNgXOrkBCPQ6bWlJaQm+fF/CWQ62yIgAO3+6y55/DnCuqwl7fBptZbTFxkuNDSifZ/Gb39RSx71tZJoXUFmgpab4AL8RRnlzke1DCTs2Ph1IPpmFsV5AUNQ/2rvLeS3WRr4vmh1r//ARxsgosx492EU49xba8nPUncEo48UTZhmbIAt0nA1YnOpop0/X2a7d1aif+sIesLie8TjVCijwBn7963AbHYQuT2JsG22FRcDt9PcxzC0DmM80kL9Z11AWLKiyxNgQe//qBMYwwXbi/A176hXmw8xJli6Kt9V3EMI62QfYjnkkNr+165rt2eezydMYAP4YmzmLNr1RY4BqwPVcFgzcsImjRgJwRlsNbfo8FEzHj+0EJC7E73HdKfeqk649WTZqbaob7ubWG/2t/lV9qtuv6jgdI5u1bOHadD69VKfUJ7vth+qu6rLs0WJG3PZFx6i90f56uXXTHRPoN92PrqPfdT53fx2r/fW9vnM/u9fU/emzey79/qc27a+X2z64n/W3XrqWznvjhid9/EjGe4xLg+lzAOcKGPNl51QwTigitHaSzSV0sVe3L4uqiwBwr9qkcQPtrtVD4HkAuAMkSuXBIoN24Ox6B5y7c9UQ6nkk+cwiiEN1MAwnUNH1p61Ls5SUASz+gsehzz97qc2uV/vb6KQw+6dHifo5x48Fez22FcW5p55bb6NHof764ASbNDWQdhabdj3qz+s32JaN26kHacDqq2w67ILGhrREjl1EIDZdHml3ugZ1D//t7ffAOTkBn376GQjBjY5x4+mnn3YgpD/nCipggmN07A2AkKFDiZXNYFAPQyEVZYSTgtzSpUudjkEwzU9+8hM7hqErnFWCq+5c5ah9lWCYOMe+6nwyAHWkuKbVFSpAguzkzJEBRuCQCrwcgBnpNKAr7nAKupxer732OpW1AMnn+x0FMDlzlMaf/+znzrFTyFxNNqsqqxwYSMfu3bPXnnzySQZM52wQRodVq1c51LdAHF1PAI7CWixfvhwCOM6usbJSAMzjP3n8LYPi2/NLBTI3N9dxQglamzdvnhPiyIdVAgJ4FGZSFU4g1IyZMxzFMkmHS6nqFA5Q3bfgK1XO8YQ5UHikdwJaBJwJEhJIdJ3rjeeeBaoJyBOUlp6ebv/yxX+x7Tu2s4o4xlav6kubGm1BQqVl5DXP6ktf6strpUOgkuChfXv3WdqwvsG5BgB6BhowyKE4hxWk7wTy6XjnufMMHv3Uow6MOIH7F8Dnh4JgEcZGQUD1TIhHAfl9/BMfd1Y3Hzp0yIH7BJIpVK6MVMo3hsX2wQ9+0AG61NAI9NpAhZGT+AMf+IAjZX3yxElnoPK5xz7nAFt61jpWz05hYlevXu00NDIMC1AS7OXDSp2h5I+MxwItNRmR83jBwgUO5KR09N/UQKm8CYoS9Dl+/HgHCNVgSpPwMxiuVP4kEypwTs5o5ZnKjb6vq61znuPnP/95Zx9Bc6++8qqT15/81CcdYEnl/tlnn0UyewPlQSs/VlnywGQH7tLgTfkmI+ijn36UTplwBLQIavBV7zQJG8zAS3DkVQyyCn21YuUKp8xqgiVQS+kWJKp0pLDvXJ6hBmkaxJ3C4CxwVAM85YvrMBaYmJ3DiouqavvUo59yQFIpnOm627ZuIyb7i1xbq+QW2ve+9z0ny/Tb1q1bndDPCl2re76SeQVlxBwHgFy6dImzf//87f9ZkOv6deuJuZ1lDz74ICBZIysQj9AmZNiHP/whB8AUDHqaPBc4twCDoMq7AF3BaAL2XnrpJWLe72TS5M8KvzQmiqwC4z85dh9+5GHHeat27zDlTt8pRJnqttoxlfvnAQsUN/0fP/uPTvnRgFllRG2JzqtBsp61wstcYSWq6oXKjwA1ladvfOObDjSoOjgHGFF5kDo41ZnQvvzyyw54OXbcWJPCp5wSmuTu2LEDCdTXnOt+/etfd86l5yAIUUDEOvKksbHBKeuDBqU4zyCFdkHPWTKxAvqkHqo0u+2FQLpdPHcBkatp17SfgMdvffNbTv1QWGeV7fz8Age4XX7Hcmcw0P95/HU+/yXd1l/nDm6e5WYO3MyBmznwl+bA/0ZwTmMu9bnf+ta3HOe32n8psQr8frvq3L59++yb3/wmxrdDTv+v0Jcf+9jHnPHEX5o3/9eP/9WvfuWMYzTWFxSp+cf/5Pa3BueUVo1lVMYff/xxB6LQ339s0yReY3OVM40NtWJWYxr9rd80F5IhQuNvrfLT+FBzRpVZ1zjwx84v1aNHH33UUbbTCj+Ne/9etwtXe+x7P+ywzFyc1th18K2RtzhLMTrhi2e25PjVtPjT+Yy/CwxBxhizKTGe9rOf+dq4UTwv+RnYqgDkHnyozfZxXmxXlkHIhoXzPW30UE/76i86LZewqWMGediP/tXPZkx878K0Hj3XbT/6Yaft4r0eA2QU9q57VnnbI/f3GQr7g359d37z35s5cDMH3m0OaI7nGkldw6naZH2nNtptp2XX0qI+2SE055UNSnY7tes6h2vQvXTpAv3kt82vq8OWDx5qY2VkZg7bBRDl6SlnECoRqJwEA6SFT55ogePG8x1t1t6dKJmdMZ82BWPCKcCrB8WQTgCloHRUnFA98h0yGJgIw3EDYcxOnbDmk2csGIe4Bw6AzgAWAfoEWGNQiHmlDrW0+QsB8ghLycLO9tx8a0Ydq+3yeUINNaMag2EYmK/Li8gLyYSDRAHLf8JwLOE+1nrkhHXuPWQNhQU46MgbFth5p2P3mD/DgoZnAH6hyoVjshfn0A2cK2WEXGwFFgxpqCVsK4brboG8QOGJOH+YQ4dPGGuehH5tPbzfKgj7KSelF32iVFTw8KFihmNt4jCLmEpeJBMOkrBpXYRwbSJ9bWePWwt2uE4gQi9sZL2o3XgHAFExxw+TWhz2G7yg1g6UV3eSkLZZOeZb32JS4vBCma4Th0cA9x4KdOKLvQFJOWs9c9WK9u+zptxMwjYS5QHwwz8u1cKnzzY/KV8BFcqZ3tvcYJ3FBVa3azfKZZfN40YT36NI39NlbV4eFoRjNmriVAsYOQlAu9NKuX5b1mWLaGky1tYDIeoZ0gcRYs43ZaCFTRpvAdjGpAbVpQWf585bLYtMW7EHBgAe+rIg2QvbW70UfSYB6WCXCGeM5YnXpf3iBWvGptOejZIHMH0P5cgzMNhavVEyIc+i5s4zf0LP9XpKZQ31ty3brCfnOkpflKRIQMXBQyxsGuUHlT2BWRomeJIOiA7rLiohlOgh67p8lXyvdkLE9Pqi1MKxvYB7USNHW4hC9gZGWNPRY4RfvWDeeGs9RVR4SgEIMIoO0jc5zqLGj7HQabOtCwCvswYloNOXrRZ1xE7KUiCwlRPyCGDTCzWibvIk/K6VwFTAfNixOnIIjXv0uLVzH17YQHsBp7pRSuqmPHvG8LxHzbDgUWMA2gjZB0xadXCPtRNm1oN4h50oqAWnpFKGUCecNsU6cWb5srrek/hJXcWUPe6hFeXFVuxFbdiNu+j4vfknNDTKAoGlAmbNIH2ohqHc1sW+UrXrJvxUD+BBSy8vf573kIEWOXmqhY6ZiBodkGNFo9VczETx8Kz1FOSRJwo/hvqEdwDgV7j1Yn+LmzndgoHdzI9yQxtQu++Y9V64TL5D9VM+kbuhLKH4mEyYYup3AGFRPXE+t5HHtXu3WzVtiVdbtwX7MGZMSrDwuQCMY1AnjCaCCA4pjzYizADbNmG7rWZhtUdFufn3kjiAnjacPr0xwJ4TUaQEovVqJEIK6WvEXtrVxriVto0daedwLuPE9B+cYsHU14D0oQ402AMo2HT0rLUfAYbDhtrSw7gU2LRXLwYd4TgioyaPN//RwIzkaRuKCW3Hz1g9ZbSFcayfJ5AIddYDgLQbe3P0SoBDlBUFcXRST0t3szj3UpYFAzFYCOqJiSEWN2eShY2lvQACboUYyKu5ZkfPHSaUJqHJBiTYtKlzLA3QztfL3+p76iyr5DLOqO3mRRikedMW4jgdac3tjUBQ5+wybUJVYw2wLflBm+NNfvh7+tkA/wgbkTrcRrBvF06rsxfP26ncQxY7ONKmA7WNjp5sUYAlORXXbc+FXXYGoDk1fhBKUrNsaMowsszTLuZewtF5zPKKcmgLUU1BJc+fVw+R5kp6Kq2FcNrT0qbaknGLLJyykFOdh8rSVutCpW7q2Kk2Y/RMYLco4JhS20fo16MXj9iYyWPslkmLUJwDCJaTvbvDLly/iGLZHs7XYDNnTwXcGWmNbSiT5V3it6tWDtyqeYIfZYgAopRpTxsQFImtd4RlUK5rW1jkf+EAIckuW/rADFuccZulx2ZYm1+HXWy+aEcuHLJLp8/jwI+zeRPn27ikyZwn0C7gOD50cZ9lF2byuLqAcHxQ4gJ2RjWnsYpy0OhpE8dNswXTFzjhqa5evWj7Du62G91N2K8X2JRhUyzCJxpVJn/8CIRZezHXLpxGNbsTVUXqCEgOcAvPJLDNBqd52NQpOArbPQnLWopvSGlCiYc8kCIqBY55UwsL3QJs5pxIGO4AQrq1E+byGEBdmU2aONJWo+ySmIqClg/ADqDM/r3twGAncFQ34vwWOAf06eOJ/bvNdu+vJZQW8Hpdh0Xi8I4C4O1BbrWqqcNK8H1NHOFpD96fDFzgjzpXr/3yP/cD8hCqdWY6oVbHAUqiVMgkopfmT3MKgXMlOFEPHrphW7dnEdoSZ7cvyozI38HWsAGHpngjwkA4bvLjGOHgrgMDtqMoy+yE4oSiJiC6H0TVyHQjRGMw/ik/Qkz2oAJXh5LUPpsAyHHXmuEAHfFAKt5AzYBzJ4CStpdxnmpUuyKB7ajrYf6o+3Xb/n2VduhIBSp4wahqoYwGO+NH397cyu9lRILB2fuJjwyx5XdESLQVRbVGbCSnbVBcICHfRrCIH3AOKImi7iiqleQRsnRPlR0+2YSaDf4C0u/jgz5hZwvNSC9gUIhNnhgBQOBlR05mvyXbAABAAElEQVSgVHq9Bke6L/XCG8iqlXor1SjCFoYrWhbKgG+OuTS2csdc8ltqjCUfh77XYnr5BuSY11hMm+bS+qyXjtN5NF/WPFxAnHwU8ltpnq1jZeuR415jM+2rTT4DCVVEAzLExxHVhj69qtzL1r5egcokAhLNOOvZV8+4h0O8gDMSUW+aSli1AdH+lpnTZnuOKboXKkIKUYhCbldXK3BNMCp/kYTaDcG3CMDJeX75ZIWduMDipjQvypS+R/0WtUdvxlW52a32n08CzlWG222LwwmPSkQxIJqiIi9CGLfY0eNVVlvZyvOjbwPw8qaONzGGzAfGH5gUY3cuQ81siYCNXvxl1QBslZYxNIq6EI36mTf1lTGsB/JGDGbQwQec87AjhINct64StXVU74D8vL0AoHuYVJLOBBYFTBgfiq/MGx9QHbayG/ibKcueAlIJ49dJO+PbSBn1snnzYlC6IdISqoRbt1fZT36BUEHsELv/zjibO8MbgFzKx0Cahe22fWse4V2rbdqEMaQzGBCXNJZ0ARc0I/IBlFhKE9NDmDn6eX9CoLZ2d+FvywMSyrJPf2SWLSUMZjDg3MVLtfaLn1/GRhJpH3pgCGIRqKmFCQ6U+qkXMIEHvs9OJ5xtCWpQ3r7kmY+3E0ZR6Uti/j5zajBQawD+oHZ87wCf9RR+jfWoEV0Ax37+oahW+RGuNtBGUf/pAu0UgOovn9xD/+Ntd987Bp+0ABj6TeD4rIuAK2urLRdocuToQFt9X4IDGJWVE/rzaAWgIhBQcTDtGQpxwBMhtDXtjI9Lqz2BdyuAVCPtoYdD+Y0wkfs7UX5kDNRQBHw8gmcbDySjfov7o/hXA87t2FdsR08UWQn1v6cH5cse2mSUvbw8ATmjO2zGtMEsNAkBIurAn9xEdC8UC8cLXOEcb9Y5d16jOqFN36su6fWnNrcOCViVQIb8kPLlqr7Kp6yXbFeyzWmTL1C/6VraX/VX4Jx8svKfysc6b948x3+vuiv4Rj5YcQCyk+ledWxqaqrjp5Pa97u5Tzcdbtrc+9b3bz9ev3V2dtmp0yWozqHayAKi8ePCAboZG5ymv9jTgqIg8yDGuWH+N/oWs9A35Zaz8OdGod23cgBwSiS+ek+7ltNiP//VGUJUV9mi+UPhJdItjO+JrceF/WhbGDuca0OMqNyu5WpuGdi3UFNToa56GxDpDXBFCOT0IMsvbATaK0OBjn6CUOk+jNEEa/cQWn7YkBBbMC+Kvi/AgUcvXGyxb31binPJtmp5ht2+2J+2in6Fcp8HpLl7TyE27Us2GJBm6e2DgQMVaawXpdVWQLEayy9ooRqggAeIGYSqsRSTK1CSzi88ZWtWTgX6HUZ7ieJccYc9Tfryc31t0ULCci8LIqwk/YQXhjZqUW0toP4J8mwXxxYwHvYkghpjNfXjPcy9IiK6bMZ0L0sZ4u+Enj5ypAQfvmyktDGUvy7aGA/mFbEx3vh4Q23iVKBdyv75E92orV2iTWmmPx9K/SQ0N/ejhS0FeShuPl8HPGiEqhY4F4o/mcUrKGSeBkbbux+1+CuMWbl+GP1gMOA5RRS1OdS5GptZLBtgH/5QuKUN9kJxjvq8oQnY7QLKX30hqxPiUYOmraSbJomqSAC6pwg7u/GGZWYrAiB9BfNBKZd2d6N26d/B86MtHRMGyAiMB0SYA6TXy6I2TXkVtt6DuVVUhI9NHhdpt88LAqjn2qda7TmUKvNpEG+/JcZWLAuzxHiNn/3o4822bmmyF1/NA3KMtLtWxsIboNzL+S5daQecq7DzZypRNGZBdgDq4iwsuf+eAABg+rIuVCphLFSP3P5TZV6f9Z3Gj9pUL/rXE33n9rV6d/cT4KqX9lUdFXsjFkf7aNM5dS718/pe+7n1WJ/VPmgftQv6Wy9tbr2U7cX9Xd/punp3f3fPp3edQ+nQPrqG9tGx7jmdE/+Rf9xzumlz791Nr86vVwNl5cmnvCy3gHLA6FVZhnY1MLS3pWf02Mx5gSiJ+wIxe9jaV0tRc8y0MRn00SuGol4ZiM2izxa9F1XRHTuqrRkV6CXLhqDOG43QD6OxvFbGV4VE9mQcw/hKY55A+jYfX/rtRn8rLPe0+Egv+/SHfWFTfJ2+YOd2wim/tBG+I9xWoFA8cRK+hRDEoqpzYdY22M7tu2wo85yVd9xls6bPoT+kDigvaPb1TjGk1/uvl37672y/B87JeSalrO1MzARCCSATTPHnbOoANm7Y2HcsymX33XcvHe9M58HmArH82799m0H0daQrb7HPfe5zDhj3ne98x1E0EyCm8DvDhg9zYKo31r7hqL9poPibp39jY8aM5gHSmQKVfP6xzzuw1cc+/jHHqKeOSzDWrNmzHMeMlBJeevElJ+SqVKUEeGmytAmlpi9/+cvOsYKUZASU4oGUz3SsBqPf/e73AGJ2OGDfF/7pC6wGgrAFbMu9nksY1F87sJTArFtJQyXpVVjXj33sH3j476xOpkGu7ufJJ59yHEZf4fpTUcbTAFfX/ulPf+aEKlq4YIHdf//9TvqbGpvshz/8oeMAFeDyjW9+wylccha5xOvvPRcqVjZA0re//W0mNYeI473C7r7nbgaGgxn4BDjXU77t3LXT+U5O0sFDBjtplvrY2rVIq5PXT//mNzZ6zCieGYMoBvUK3VuFgUlqd6NHj3KcXQcPHnQAq9VrVjsrkQXcvdOmRkM0vcA5DRwWkMaHHn6IlRAJzrN/Y+1aB7oSiPQv//IvQGNTmNRstaNAaePGj3OcvVKh+DblRvey5q41jmNXebeV/Z5BHVHwzw9/8AM6j1RnH5ocpH/vdBxzCrsruM6P/T9EOZDxVw2DwLPXXnvNgd8eeN8DDvynQYwGRgL1BF+J+H8nRQvtd2D/AYCq553Qvl/7+tccZUY9Gw2Q1pGPT/zqCVakJDjgnIAvNUwC/b773e9y7Uv/j733gJO0qvL+b1VXdZ7YkwNMHnIQUEHQYRAFlSQiKioDiK5/8dV1zboLKCLKgrImVMBBwYARQUVFchCUJJmZYXKe6Umdu8L7/Z6aZ2x1QHTZ9+P+7QdqqrrqPjece+6597nnd3+HBcAB6Ytf+mKcXPD7HyOHjRs2pvM+dV6U6WJKXbvme9eEMf7oxz4aADnzufWWW5NO3XYcj5/8xCd4GDwm+laQ48c//u8A3dqgZ4aNDkCd7TzjjLdxUvyAABcqf/tYlryzzz6byX4pAKo9Ipys6TWogrkEqwlSO+7Y44JlbRc2vT0x7tiTlfL0M05PJ598cgAt1e/fssEomHXxksXoySvT5z//uVCHzKboKH39609OL3vpYSxwV8TJDeWo3shs9kzXjzgN/q2rrgrdueii/wwwn7rnglVwmm35xLmf4OHhBk7GzUzagz1232OHvj/66KMBNLv6qqsDBCZLnePJvvKUr+yEXjqGv/nNbwXgMPoO/VTWyu+LX/hiAA3f8973hP64+FXWn/3shVD5Lg/dOfbYYyMfWeIEyQqEO/TQQ0MHBaYJVJSh8bR5pwU4z/HoZOdYjTCze+5Ff389HkYt99577k1fufQr6abf3AQl8bnRVsvV2exY/fKXvxKgXx+2HZfaL3+3bT2sOq699lpOJcxn8Q1lLxuJ2nPt7ny+cyGv7dMG//a3vwWA+HYemvZkfLyNsNDTo/5OqNZ/Z/ofDf1v/eP0NXgNSmBQAoMS+N8tgX9U4JyhwT/B2sC1qWtcw8XL0iV4O7uc611HuA5w3e684BrsXe96146HtiztP+P7pZdeGvPzcayjP/ShD/1TAufsd9fmrot9HnTt4drUNcpfu1xDZA/mpvWhPXvA9908nks+3uv6xUMvHpBxzeMhlWwTwN//Ga816yvphDPZEAfopsNKp8dzuUx2OBtXl13WwDOTT0u168e/KaVzz+tLj+I8E2TnBvYoiATGc+Lw8XUwLLNX+LFTCumsd9ZzitaNnedS2nNPY71+eVs5fenLfen2RwDWsH86rj6X3nFaAbaBAmwSg0xzz12agykHJfCXEtDuZq/MNmuDM3ucfeed7oUZQtsDdu6Z+Lzt/om22PTaX9cQjz76SPr0Been9auWp/0APu3e0pryMkPBylLpqXB4qynNwEO+90sPTsP32huQiOFbu1L/kgWpe+XS1LdVllF8lTgs8+yhNZB/00QYwybCaN/Kzj+OQii7Uj95lgA9lZauImQQoUWpY8XT3zinG2Gnaps6hdCPGCx26yuymK1am3p45u0gXGQfG/zGYSsS/nIo7GUNsNAVxgOowi9QhgWutHBZ6mADfxt7GSXmrSKgqKEAgBpxNDfg7Mn1swnbT4is7o7U386G/bIlqReAW4m5UWBE89CRqYE9g9zkCYRoJLznts3UlTBUK6kDhrOCswFvEhvDpMWB3TyL09qjAVU04izjfmhEUqUdoMVy6rGSUJkdW5kbSwASYDobOiy1to2jzlNTfqSONNJ2UWfY7LpXrU6dqzYQXg5AQhXnaRPMr1MIOcsBQENdCvirbOhI3eS7dTmyBgxnXIT6FgBiu8+izoSXHAKTGo4NPFSEywUksoKwUisAl7VvYH+lO/q6nkOPreNGp/oJE1Mdjowy4MRu9nE6165JFfqvzD4M1Ur1tKeZ/aciDGNFHNN1o+g/SqzSH+XNWyOU67aly1Lv1i6KJPwNgBKPjTfPmpYaZ08F1IfjBO9tlT2/8jqCta5Ynzp57+H+PCC7Oub9ZvSnefquqQ4GmSqASDzkqefxxalLveBQaBUQYT11HTZzaoDb8uz9OVkxnUkSQR+WU3kVzGvs3fSsXwOQohNgHk715pbUMho5j5+U6uhPlaO0ijauWpZ6AEmWYdjCFcNYoB4421pgQitOgGVs1IRUAujlJJzfAsBgGax2K5am0iYAMbDcVXGkNQI8KyK7xt13SwXYA/GY0YewAxJZpbwKWW9YB4MCfZ7rIyzs0NQ0BqalSbBUjBoDwM2Qxl2pbwmsHQsWBUirGy+A42Q4+zUtM6YRkhZnscA56ljt7kllvGT9y1alLThnu5Fnhc5pxqk+dAx9OGlXmBHHA7rBwYcc+sizBwBNqUQ6XPJVWLsKw5rSkEnj6e8pKT8CVjnanOvBgdIJaI+9rx5k17VxPeOqNxwUDQA3GqmPLIcF9JvYcanaSXja5eQLg1HXxrU4w3A+IeccY6AFWTRPYbyORf84yFrdynhh77CdvbNeQGg5FiBF8hwBqK0BAF0Oh5Whlh1DyfZhm3qpQ/fyFdgPQHksVuqHwApFfg0w0NUBRKuiC2V0tBsZdGEvShzEriI32YxaYN1rmgzbG3YgT/mgO2IMVAQ/Ll6VelYsT1s6N6debE8O29eInRmGLWjaFd0YS7QX8qhuBYSxCluADLfQf2X2M3E7BYi2SB8P2Xc3dNTQiDj8tgAGXboybVu8Aju2CdsFvA0n1Ni9ZgGK5ZmsZVg4BTd0rUrLYQra2E4UnSaALuOnwL4i+ApgRrU7bdy2FtDCwghTOm3cjDR2JPrHENoMQ9vSjThvN61OW7Gtgpsac7ACN7fiHB4CuAA2H9gBewG4rVy5PG3qXZtGE1541zGwx+UYqxWYAPlv6ealME4toLy6NHUizNFjYIbEyduNHVjRvjItWYcd6diMrSlj1wupq6kv3b3igbR2+bp06IyD06v3Oyq1Ac7c0ItjdwEgNIz0tDHT0i5thNxkjHTQD4vWL0wL1i1I43Ydm6bThlH1IxlVAI+ZU9YSons5B3dLjJtd6J/x2BF1ckNPe1q5eQ3O7bWMceaVSjkxCtOQ+hbWqG2wOMHYSPva6bPFa59GBpvSNIAt04fOTMNhb+wvlFN7fkNasWkFjDRPx3w2hbKnjZqWWgDjbumBEaV9UVq86qkAIlaYFxpwqvdjSxY9tSRtApSx7+5EMSHsbBvAwM0cMF+ycjGg4p40HT2eRNjXIYYbB9jR2V1HKNeutHLJttRBqMhu7Q3jJw9l2bC2YppEuNRJMKv0gPFZtRSwzkoOEG2FSbOXsN7IehiMfWPGNuODKKYx49AndLMToPJ9AB02ELpwAsyNe+49Kg0FrBOhWslnyWJYwZ5aFU7sGTPGAnzChjTAwNPDQWjCmj2+CJDcynbCRJcI3zYEEN+Q9OCj/en+R1cBvsylN79hYtpzD9kgE+CFZWkT42vq5FE4xMfAEic4FpVHJo5fIyV1dRbZA4eJ6MkNOC4BxTF/OTjrAa0NHdLIof1W9rnpU9gOlywqwwbZjfOZObsPcIH2GdmOGjMUhppi2nUyAJ76MnqVJxRbLz6VJbAtNsN4JNALBlza0dEJyx3PJU8t2rwdSAHzz0RA1IA3ewB0bdhQgtWmg2dTRhbgWkGDI2CcfHJBHjZB7Gp5TXrbvF3SUUfpZE/pScBgTz2xKo0aWoR5aAw+REJYtvhMg3Oa9UMnYMQVhDJdBMBiNaxCEbIVG1oEmD5sOI7/ia34IuhTzMaSJfT1qi3sxzNX8hA2eXJPAANGwCrU1jYq/HWulVxn+Z6tm/T9uX/k4TEJDSTnyEIzuq7Knp8RbFxZHv7m5bsOff1rgnRco7m/Y14697Oy3NsxApGHH8YT+jgPeKynqxEgGyCTRUZO6iTEIfM3YIh65oghhEGfPLE5TRoP2AxZrVxfSo8AkNkCQD8HmLgIALqxqYifaUSaOb0lTYDVqKWR8K+M9XsfhDVmDcyvgGf2A1g1po09AJYWeeSydTMhgh/cCkNfAQID9Rt9aQKwDvByOSGCl8DutHoF4e/Q9ZamVtaDTWnl6kq643dL8d2MTse+emg66kgBcPT1433oSidgjCb8PZQzWv1kXeQiBFupu78f8Moa6vIU4d/WrIWtD7soaK6wvQ8njidc4UTCCMOcvn5df1q2GNu4AiC+a7E8466+ShsbYW5qSrtOge2NsIdlFhBPLuwG9LGecTEy7T8bHYZRjqmU3wAkwh751JPb8Av1pKkwDs6azpgfDqgb9sv1AEOXLelJy5dyAINDanVFgDWjG9OCpVWYG2GW27wwnUmIxSNfOTYAehs2dANCcf3YkF68bxvPv0XAGeiRcwd61IudWQtId8GCrbDnscZjrMd+CuNrOIiZSYRNnTYVcDHjds2aMuV2QHqxOXQZ882Ya0Q/RzD+mtOu0/L0O6B2QiSuAHh0/wNLYa/LpT32HMNYNiSeAKZS2rgOQBi2bSPMi6PG1afZew0L8KNgmk3tkuMAYFvKwQLmxgK2YjThyFevLqQb7+iDMbQ9nXTciPRGAJVDYKNb+nQJWW1CNlvT1Omj0qwZAuroR+RYYf7q6s4x5mE1Xb4Z4B2zE2zRFexJI4Cq4SOLadyEZggrANPR36tWlZBfCUKNAn6w2t6EAJOBzzoxaPgne3ZxfDzbZTov3/VVCnLVt+eYE0gn4MXx6v6qPmrTOJbdL9MX5z6Zfmr3VSVC8XlKv+M+++wT+1f6EB2bkpU4dj2Ya/+Zt/cY4c4x+1yvrK3ZuM/qPvD+rM1lylm5ahu+2J40Hjs8fpL+SBk1YTRbUsZe9HDWAUZJJvDRtG99exMhk8tp2YrH0+uPn5COhgFNINembSXAaKwhWUPPmjYCoCzhyhnTqY4Q16whBFK1rweX8FQ34MJunmt4lqNshleEdh4/HtDXlEbkmMO/DEMbIc5Xr97KnEky1mqSCw0f1gAgZgQ2tQBDdG0PaA0hgG+7YzFphgBsheUcPW9uZpyxBt+4pYLt3kJksw0AN0ek2dO5FxY15692fnv0id4YC12bMViAMEeMbE0bALP97qEV6dHH7ibU+YHpTW+cmdo4XNrVSajYe1emzRtykImMBiyEvRgqsCmaAftlHWNKYqRtaQ3yZGkSz7IenmhlHh8LEGzadOwjS/6NyHYRY3XtGtbQHGJR+zxYI7h0AvPJ7N0JxYyt6MVWbFideK6GBZ719azdYIebBsCPZWq10g34tQhBiyGoy2kM8th3LwF6RQCqjEHAj46FR7B5HWygFbGJMr12dNSnW+/aRojTrnTwgUPSvFOHArSuI1x3OT3+CHnyjLwbtmzajCGkZzkNMDDUHxC/B6LWwfi6cGEJfenk7JHAaews3azMR41mrqD+o9oEG7PewM6sWNnBOAFkx3qjwHOb9mjC+CHMKfVpF5gBmwRPrepPDz3Rndq3qTsthKito2yeM1jZaU8WshZ4+DFYMhlDe+4+FFtNREvmo830yfIVsAEvhjGX9hiumck7HfiCBuYCdQZAIrZKXXcO9XKudIz5nZ8dZ9nf2Zh4JrsgxsAxbXqBcdoVP5s+m9Mtw3x8Dfzuz8dkVi/T+Zv1G5gm+878ssvvvMzX9JY9MJ8s3d/6nuWb1Tv72/duDl/fSTjzlTCI9nHQRzG2shczdgx9yHpt9Lg8oYUZb8wXTzy2Na2EZXAcc9luMMC2ETqXpXnoz1IONSxaJNPhljRrdxhvmW8bAbT1k/8a1mqLFnWjr/yO7Wwawm8cEnzkkXy6575+Dqj0pHee0RKA8U4OrtwHSPyHP7kBHVudxk9mD5o1W2Oza6B1gHPvS4sWwpILRuyk10Lm9dKXB4icWtAx9H9VFkw/ug/gv46+v+/6C+CcwCSBcwKoWnlQnT9/foCH8kzEz3Yp6Ezoht758Ic+HAxTgljmzZsXE0t2v+xJsiVJ7SwYTkT1eeedF8xNM9nsmc932SUw6qtf/WoAjmRjko2tgNL85qab0sc/9nEWNvUwyZ0YYVaddER7C6hRsZzkrrnmmvS1r34tZcA5JyGZsXQoig49+Q0AeV72sghxqCPGe7cy8X3qU+dHGccQ3lJWMBehXrLjCTwR5CSo5MhXHBmToBOnYRKzQZrVP3tfwoaH4W9lgJKNS9Cdp0O8nIRvu+22qJOTrSFGBfl5CaoRwKSj6JL/uiQm4fjhWf5xYr/ooothxvt1sIIp/6z+3vZv7/u3AIvJRPe57eAmv9+ZrF0ICMA595xzARgdGCxULhT6MUrbAFW58N9/v/2DqlzWtZ1dA4Fz6qogsqx9phdY9c0rvxlsFscT8kDHmCyBMp7NmVNj5nNg22fW0cXEBZ+5IBYegriU6X04h1/zmleHHkyZMiUY0jKWNHX6IkB1sgq+7cwzAPnVgHP2x/e++714CLwGoJihT9Wb53IJcNOJKKBLVj9BjZ708ZLNTbavt7/97QHUknHONntp+D75yU8ClLouNp8/97mLQw8EVQrQEsg177R5kdZ/vgGA8YrLrwhj7anviZMmxm+GQP5Pwn8qO/XzpNefFGV5ssgxa1+4OHODe8VyqNBxOI5Gt952Bu0nbXa95c1vCYfokUceGYA95exln2sHFi1cFDpieBYBVAGQA2j1tredyWm4g0GtnxmMgFl+MoHcfffd6dCXHLpDtxzbv/rlryJ862c++5k/0cXsvmd7F1Qm6E3mNsPNGlLZsa7dkBrVxetnLvhMgM8E4NkXjuPs6mYRegOn5t8NcFOjqgPcMf/n11cAogmydMGaAedM4wOpsvjOt78T40nHgbKQofHMM88EAPtidPatO83T+10sXwjA7ue/+AWn946PsT8QjGbdf/jDH8YDbwac8z5ZBS1XHT3n3HN2AOec9Dex0H79yW9gcusK8KKAiIGX4XW+BBhXZ7c2Kk6tMPi6Oam8ZPGSWOC/5a1vCeZH2QXfw/319cVgJBSEl1G+K0cXCc//9fdPWs9/XQZzHJTAoAQGJfD3SeAfGTjn3Ou86bpGgLqgOAH12eVhkwsvvDCA8s6jrp9NY5h11wJ+52lm50DnMdfPbso67w6cF1z7u1Z0E8i1j5tHrodci7ledK3tZTrXEK5RPezgiWvXzdZNdlvzNB83mfzNudj7s3W1dXEzyvK9z/WUf/tg63pPcL5znvWUXdn8LM/1t88DtklGWevm4ZNsvWPdfDD2BKhplZcb0K7zXTv+swPnlI/reA++3MABBQ90eNjDPsqe+0zzP3GpO65lZUs86qijYm2S6dP/RHn/m/LED5zOuag3XfoDmNkI0fpcL1O+fFZduvzLDWw8/3Et9pHP9qXLrmFzfEBePvmztxfAvCoOh0ve35BOfZ1OhOda2nNLBw4jXfPzcrpsfl+6fwHnlWnbZDZmPvB/iumEV3Nqm9PEz/Hx7LkVOJhqUAL/pBJw3vTK5lWfKQf+7WftunO+z/PuwXkdffTROw6OZXl472Owk3360+cDAFiJ82tamjllFxijAPKUOOHdp0OjKc3aa1qasddsWMsAeuCwiyPlMAmkfkBLFF8BNIcbFSerjBNskBfhMGODNYcDXMZ9KbOqooPZiK/IOuXfbtcAmMnx7ArdE8AqWMlIkmOHlL8ifYV5vQJDEtBitlCxZrCYRDrS1nGqnm30VO1jI70bUBzABc7lssGKwWsmvGYTjG94HZu5r8Bv1diMVxLcg0Oj1N/Hhiwb5ThJBMcUyLOIYzOnAxe2Bhmsqjj1K4Lu+tgoF5zmxncDaXE800yACKyzcPyxrwtzHT/jfa6wnjH0VIWXe6/5gnxu1LcOryJych+tmu+jPaSBWtTQaZ4Kz8EEkyOTOsAyuUYAhKaF7QN0B+X3R30FoSAg1j6cXAfcBJ4jNrltlQ7nKogRGV36ybfcz4a56flfII1sJnq8ZEgAYUfbSEN/6eAqoS/cFs6yBuqr0yxHt+QaMeTU0zyq0AWUcaz0I+8q99XBwAQyieZg29k0zxkuTpAkzm3nnUoZwBFAjjIb9zn0LM/9hgqsAwSTh7Gk2ogDgYR58skRDqiC0yj0gurlAfUUCIWWryc/5W6O1KmKK7BE2TqulDOVCRAQLQ9ZF3IAMRUKsqBACu8nrQ4gHEkyRqnTvOfRizoc67l6lBC2gyr9bxH0PGslQvj0bEP3O0J3q+QJGg59gwmI9UzOdiMvU6PYgFj62YfknjLOGQCQob+N7DnDeFVh0hPQgPaEbpS3oB+MA4E9FfSoDjBOAbCKadQBlC36odRHv6gXAJAEc8j2lke2BYCGVYBrpQKsMADR8jrw0LkcE7ChkunQ0Id8QTnb37QNvUZhYmxFvekLgZJlxyIO0jIsGLIw1QMmk2EyJ1MITJBldD8PcNaY8RXk5xhUd73qGNt5xpdt6HdixwjUWQfGaxX5aX/U+zqcZo5XvR4yWql/tIBxihMJsIdAzwrPKamIhIowvVCHOsBHOerueCsBUKzye05HH21znKkOgufylC2bnM8BOfvbuqmnjkHGLq0kD26gbqq8Y7GOA6o5GLD4i3rypu1AhuWydSZRqNt2/SAcM0godJq+AIzjGKxiZ3IhC/KWiQmwZgEAhWieKsarPwEuqggMox25FmRg32roqLs14t4yuoJ2YxHoe3Ra3a1QmV76Ygs2wNDOBWQlcM52ysLIB1gpyUu9R8e0T0XGcwO6W6ign9S7ij2EyxIgSxc6CZAUh2crfVlfpm79ldRNP29LhCqmDnnKq+QBOa1bmH5w9/Wpc3N3Onq/V6aj9jwSlpPhqYe0W9H/+mp9aoFRs8i4EfxXQcd6sV1weDB2AH/SvqK2gvrmMYiVMuAUngFz6GwxbCrsGqA8sF6pDx3upz8r6J36kUPe9djzxroG0mrIsBek68Fo9HNPC7QXjTAf5QgZpepUsAXm0xN2m2FL2Y28lGFfuQMzCfs1bCX9tF2r1UUbFqxexBz421Ri3L1o/wMJDQY7XysoHcZwLyBV7WkjcmxGvuC7uFrQzQaeJ/lIfwu2Lat31LGiH435LI+cG9RXJrAyDuJ+WGf0qeQpV7ucp98aG2C0hPWxUEB3+a/cX0ee2Ar6ocAYaGpBb51nKJ/b+R7tQMfr0OUiNqmONBCLOV3GWrqTOjgWCtod8nriyXL64U/XEvp1a3rhvq3p9SeOJ6IQNriBtTcg5SqGtYnxZNscAnXoUI66VAlVXaowPpB1tQL4D9a8DkAOZfQbk0jddFzrYOal3UIm1k3nq2sF+6yqHiCvvPkjtAaeK5xP++ikPsBOASBknlSmMZ8W6Hv7FN2Q1asOW9AEM2c4QNHpEnLsQY49zHPqreMcfBf7EDkY7DrSL29pT6PbcmneKePSoS9tYu6FOQlwlHNcM3Vs0nZq3+gb6+ucV2E8azJ04BuCsxeDG/afNkbbtP+k1XZUSdhH27QD3rOeUHF5AFb6Bd2ryPYa1FlfXgJtfIY2WpLRag6ASME9Dfcssn2JbH0WNwz4x7VXzWbVQADmpb/AfRod+wLwzFO/n3noF1rKvsYY/EsTJ4ygblsY/8itbyh7J+g+9e5lXKJG9GGt/xqQhcsqjVwPutDB85lzJj1Hu3m5jiFNI+suAayQ8dGvubQNZewiXX0Rdh6WeQ3ofA77gRGiTMF1FcYfcwV61szaQDvdA/tgB/0lUzqCD1vjumXF8pRuuW0toXifpj2z0utOaEsvORi9bOwhH+4DtFJE3xob0Gtsp3NPrF+0lci/kqM/0JmeXveV0H/zRv+4BR0tBQitHvuXx/Zi0lIf+uM4pAvjxRAKXa6nr5lm+ey805061XlejrGhjDVGNiOUDLAvsvMKPu1Dri3MFY4hpxbHaB/KYZg610B1zOGCinqQy09v2JxuvuX32LKONO8tL4VpE4a3IfQt/bwNxk7XTcNZzzRSce2i4EABrM5vqDpl8aL4fvIMu8hcXGSNVGSPq4j9d81kmNV+xqC2RtBcnr7Q1tiPoc8A45husRGMDcZzJ+HF61kDGGrRZV+xiAKwtigRIrzEml6bJgtjHTZKgSI2ZEwdkHMZGShAwZKC+265tTN990frmIKa0htObMOPB3MejHp9AIR6nT9pp3VoRJb1tC3H4QHX/I6lsmMbOXVh96LMmBdYh2E/tQ2Yf8rCjmrjqZNg4QLt93qm8eNcumMhEin/+j/uS7pv6f6i48n9QvehxsLC3Eg/Z3uW7pM5BiUBcn9MoJw2wH1K9yKjf7QZyF+AnDbR/UvTSXrhuHZf1rz10YsJ+FuuLP/nck8vMnU8NvAMVajH6iHzHvSjBz0pIfMcNj3P7ywWYVftRk+7UjvgzlNOmprmHj48DRuF5qM/vdjROsZVszYRm+3yNs9YydZUrldD9+hr81X/XGvJllrQftCPspDah5mOCpykOqy5ttsjmDypZoxdkgar4kYOAXAj5L2sMcyL8UpVGIOuENFD2lKhMkXGaQvLA/Pqwc51oKNVxmBOnUJnS9ia2+8up+tveAhmyqeJengQoV9hcx7FXE1ZPZ0+61UA8zIWfL4LHa2tATEp6KhzK7YDWTnO7AMShSy0kQJ9A9TL1z09gLB57I3nGNLY33nq16D+swz0ZI9jucI46+qkMazZW1roI+c7Pld5JiyVG5kHGxnLzJ3c30zfyQBHd8Xar4My+pBzPNOQZQ82RDbNq65+GtAyIbLnTgQHATh6F56DaH9np+tK1ks8UzUBgC6wjmR0xjDBAvDO8wLtVF/6aKNs6CXGoZfts+5F5J/HHrkOLdF32lvtER9tbIzvesY3JiO1kL/PC93Y702AY312bEEPhmrHsUlYm+j7HmTag87I9N3seoifqsiqFz3pACTchy3NMd6U5eauXBoBGHcUrLEFCpFgyzZpA3zZJ74rb+2Nr+zv6C9K9e+dXQPTmzbLy7T6HPzb8ercbR6mz/I0jd/FuOB77Yg2IruyvWzT+711dr2gDcny8h4v87AML8uo6U6tbX5+LldWL9P72ZefB95fa2+VdaDsgnS8z178V2CAuQdQoCNCVhTJkhJcQW2voIk53XnS9WsVQCqZosvYeObVKmvVepgJC4Q4lsVZ/eYsDr+rT4xT7EcZPVm3LkeEwGXpd/eX2EdqSWeeNiIdsL9r/jpA7Q1Ec3wcwOWT6PcmnvNkEawjTPRm8GYPgG1YQKjevWAqfj0kaHP5jfUAY9WLEsImMCoZF7X/nou8dpbmL4BzGmzBGpd+5dIQqOCgOYfPCYfazjLIvtPB1k/n+mD52GOPJwE5drbMbm9+y5trQt6e2NOrMsGVENaFAGn2A+RzISAgGd5mzJyZBgLnZF2SUcxQk7JNzZkzJyYhAWyXfP6SCLWpsT8AZjtBV4YgFOxhp9oWGcUEkGTAOcM3CjqSQeqee+/Vdkeo1+MBtLzkkEM4GfBH4NzNt9wSQJf3wjIlJbGXbE8yNJ0PsG4tTryJEycEkOeEE04IR11Rpd6JAntqV5YugU6nzjs1vfmUUyKMpnk6aerUE4Tk5snJrz85ve/f3udP/23gnKEl582b9yeAu2cCzu1M1i4Gvv3tbwd458L/vDCYwXRkxuWg44MDuZ4H7DpnrZ1cGoOlS2uMcwr8z4Fzho80hKnsHgKfPvKRj0CJvBeTQh+TTG8sPnzY/vznPx/guoNggbv44osCOCe7m87NKy6/ghNbW4MNz1AiMmlNmTIl9MD+Frgkun8gcE7mw5//7GcBXLv8isvDKO2k+jv9StCUY0PA41tPPRV9fzPANB6muTQ6giGPeuVRsRgaCJzzd0O1OsYWP704QIIywcy/cj7jpz+9GvCfIWuzS52xHGU8EDgnc6PsaFddfVU4uWV+E0yWGd8MId3HRpQLNgFeLt4cB4aNza7T5p2WnlqwIJgTzzn3nOzrCG0rsFBQlYDNFzC+BHuZv6FflbGLvH+BZdHP2SWD5J133Bnsexko0zJ8oJPpTXvwt146sgWPqSM+IMpCKLubtsDFqRNNBpyT0e38888POtWsHBe4AizPetdZ6Qj04l3v+v9g3zsw+3nHu86Bb1xRA/IOBM65WFb23/rWVYDvzgrHgXKQ0UfGuVNgiFSmswl5srNrIHBOkO8HCPnWBEg3uwQu/BBWvano60DgnDbOcq8GNGjfeNrfRbTtVb9ed+LrYnI1/HAGzDRP6/b9738f9r/Pc2ITBk+YCgUBZJe/e3kSxslZ2yMI85577+FBpg+myb0jLLDy1ZY6WT//F4Zg8BqUwKAEBiXwv1wC/8jAOdlt3azxocgHPOdn1wLZw5eAfedLN3hct7ieyoBzgt+c46666qpYv2UPVq6rXFMKwJO23PnEzaSrr7463XzzzQGay+YY56uTTjqJwy2vC/C26wDzM4ys+WcPhh4i8aCBoG3nS9eCAv4ESwmU90HSyzIEt5vu9NNPj7nQ9ZGbxq5vPLTjmvUd73hH1NH2mF4guZtUXs5p1t3DG86L1kFAmOsd2dQMw6B8BJu7dhUI6Pz+z8w4F4LjH/vVNZj96HrMQw0ennG9qf48n5cgTg9+uJZXDzw85OZiPLA/nwX9L87LldyCheX0hvf2pocWuAny3BrDPkg6ilCrX7uwgZO4f1yLnf4vvemaO9j4r+3T/EVm3rfHrrn0b6cV09FzYUuBdY7h89++tmyrpi9dWUrfuJYTrSvZXGJfaSonb8/9IOUcUUgj+OwG5uA1KIFBCTx/EtCe+zyp/fZyE9d5Xhvr3Ocemkzpzrs6gd0nE0TuXKu9N63vjz3+WLrggk/joOxNc14+N73goAMI7wO43pBKOE0L7Au2Doe1AuaaOudyNr8FCeB2xzlD2TjHDB1XwoEpVqn2xMmGrrbFF4ZHp1h4M/0RRyI7rXrzI3EATtwg5U+Bc95CK+Jf/ol9Km2X5jGbpfy1AHiELfaaoxYnixv2gSRj8559XYATtfSt3FjQgWSZbMq7UW+ZhpV1sxdJBBinlqdsBGzT6rgNVzQeG1jfSFornzz5n7L5hxTWq5f88DFQOmftNeIU5UZvnjIQcewp5mwzDgMBLpEBoK0yyIgKYANSAqPBMVAFdKUDFxAJrlPKpEZsXFetuzeFvJCMMjTfCm3GSRkFKi82vKkZ4WpxMG1vLj6RiLAJ1iNkG5MMDtS4L25xc1rwHwAd8jDbRkVJudHoepxbyIJ/cKDjnKMdJeoXoCiLx3kagsHhaZjZMs4h2azC6QYwQ1CevysvwUtKJsGGIECwHI5jgRSAcgiTVwV0kV0B2qMPE+CeuCcKoXxAbPKCKRJlqyicW8LJxHfs74fDsJYP5aBXJRL1R0cAjCQ7qhj36IQXvMLmZ+gEe/sJfCR9KRsWrEfqF/1Zsh04jQvkYRvwLUWbqugJVSd/2k770LrUiGyreqnIs8SeaokyrJ33FgBTgeXhXiuO5tpdfBSQkA9nXlQ+9KEfZ6fsapDHbZebuiTwBZY0buo3LCR9rcSEHOlrduyEU1M9D3nV3riz1mD7m4/8X1tr8HeFfuipEgoZwBWwGyPZcgk4BfSH/uWtK5f6H0tE6hqOR1Gi7C2VGD/9pImhRTqdb1X00jBQgi8tzf/yjP8QBRUQNKg5yDNo/N7x6Lg0zLLDJ2tLpIvidZfUADfRLIqO9lF/h5U2UEnI/qHMzENhRL5UOgCztiOEw03Ys9ogIp3dQ30carIa5HH25VUE6lmFRatCnuqwQFGrad1svxe15TOaF+M6a4fKpU1EeQG3WBcd0VZJe0NOFEd7zBPnpOUHoFi54uPswjHVSzrBLE3Iz/v7qJfttGygaVaXugok5juBxNpd/+A+7VY/wCjtGtIF1IZcKGfTug1pA86qbpybAt4Er3XBXvkwIVHvePhu2L1Gp+NfdFw6eNqLcVI30ReANHDyEvQrANRUoiZXy6FpfQ0429DAAGRZE0WCDcnnmIPsb9vld6GL/LRdxpEJ9YzLNnubwqeu/eoCesBHsqP23I+PlvHMz3xfxV7ajXRs2Fu/tk909m7Ysgr2nDUxnvMAS4FupLXb1qWHnvpD7HlPHDY2zXnhYWnP6Xvj4IWN0jIjY62v+oMTL/oH3cfZLADF8ZxHDnFhd5wnVD1ZqwQc1+xt7edq6JYAQYApVFJTTUrydZQKptLhR31tEy/HYQn7oQxtj5bX72ot540+rcLO1N4Os8cGGKAYK42ERm3Cwb1tUyX9DsasX90EsyBsfEceNjm9Bia2SZORP/0ieFkNkmnGfqnZRAvV+LFXwNgN8BulAq9J7ThGu0jbgqNyBM/OzVaEhkb/2Re8cBeTp61XBvRVSId+5hvHpqDf0Gt+F2AZuorSBjiIcqs5ALWksHNrv9NewWzIZtNWmJBWwprWCZCA50bBXJ2ErVuwYBsEGMthMepJBx+0a3rtsSNh6gP8AHDO2qjhRccsso9x7/xH2Tn0NtYl2lj+7ibNFiaMDpzITYCZhzBmBEnaF2GCbSM18+pFX5cseAobUI49hOzgoGut7KW9cW31wAMwNbL3oD9Cv08WqjFbW7n37j1efpc9+2afXX9lazAPLerr0l/i3oXPyx4QNA/3VPTBjeLQwoRxIzFf28hXndGnU586qfPa/m4seDkNg/F3OHOPaxGlG0ANUjpnOm9gIfhP6dmbMVKZ5wB+CJxEJjWVcSXgr4Ch1V3mPyYcRWv3hx0DVkEaQWqEYwUEt3jZNhiuSmkotErN6Gknz4SPPNKVfnPbw2kNLGkHsKY84djRaa/dGV1FWYa0j6yrrAlA+1i3Yjt0sCuznCHVtYW0k+C6hFQWDAQogcVPS9ERJfiY+jEPOpGGDdX+06FqqaPWPo1+pQtsWyPjGw6tqHkfCfkq9KDoZASwMwEus11aBGdVIXV5BUJCSHkJU91PWwxtCJsroaBloVsOO9QNv15AKMgnCLs4Op38ugPSnvu0osfcpj0mJ61cQcPFHzF/8KG6fd3m2Kqw2OllvuhgEdGLD6eZecdXPTanju8F4sftjmvtDi/1NlSLPEPDYv0A2xXz94bNVfRlE36qPEDLFmw7Pt4A8GJMbY8sVNgjgTqMHGwZNoX+Wrp0C2ALgT6s9XmW6OdQxHJYxm6/bRkMQmvSHvjITjphDH5ebASH4iw36qX4qEeRqhXCaLgmpa7UqWZnbJ8Wll7XNoRMsl6KUcw36rO2JMTNv8/PVRt+tJEx2w4D84qVzBPthEMH8+Be4ujRsGfTiGWMr258jYLoBKz6jOQ9+tkk5TGCXAkdFBfhHppMzr0I2zHvGHW/a/LkSYxxWMBWrebeUhKv4J7X/9SlPB3NaoCa3Qkz6vqNXYBXOvmuGUAa+7t0xWbmkF/e+DQMpZ0QHqX0hpOmpIMOBBQKY662XPAKozEGThkgjOsjTEjol1NBjFHGRW1+oh/Rv1gLqkrqsveSD0lr13ab5x9aGGtXy4cvVBiuTTDHPbEAtmbs1ySYww3r2ASIU53UOrl2M6XqVKFOAvvKzBdrNvSn5WtYq4JZGAoYT+CcLHs33rol/fbe38HIWUe42f3Ag4yGTIf5yXGhjLQp1lFddbzzuQ4kn4dSfBa0fqbwinL511Rhi2i7z0vxrMb4cV2bR4+j3dFWNZcct68nDIm9hRCwy5dxgIL5diJMa22jAGZhz3yWYlXPOyzNjGL1XYCq9eqn8ctXczB96UZYIEehW9rHlNau3kwEsyXMhQvxIe+OHZ2ZXnZ4YxoJcbI6KjjVta81yrH2y3OwwfV8TeYwEaIL0SoyoyjShykinW3mF/7RVrk+roJAjoM+2gdqp36YxhbW0rKygDnZvze2F9KiNQB1WT9MGluXJrRhs9AlD1BY75ptUz+dK5yvmTGYS9ph7lwqSxmAv+HDDBGfS6tg3txlYj7Cz7IEifnStjln+nLOtC+yOTT6paaQ1N3SnvkyrZfp/OzL8e+8KgGAvgz3ThyrU6ZMifnX7/Ql6LvQD2K5HvzXJ6H/3v0V1wbO0eYlPsV7vLQdfi+ZgHsz+lcyFkq/029vHcwzq3tWx8jgWf7J2mCSgZ8H3lLL05HJwXh6sLZKq8mopuOOYfoXfcOcwbQoay2hswm1OmZMI+OQ+YE1klKr1YuxCJC+mjaTFzfkRsAOWoDhVaZibDoHmAoY/y42HZ54ahOYnnvTts4hhH7eHfY4GFF3hYEcm9HbM4R5lEM3AOX6AbD7TJzDXm4Gl3XTjT9Pt99+Y9pt5nSAr6+H2GkO47MGnIt6MLbUJNtS++/Z+3ygPP78818A53SuCboQyCarkQxShsbM2NH+PAP/VjA6l9atXReODulwdTSpSAJl/Jwpq+llK/g27E09sB8JXjrooAMJBXrRToFzsoV9C4DQT6+7rgacgylqJM6ULuq5EPCQjr07br8jFp+yTBx3/HHhEBPko0PnB9//AWFQv/gnwDkdiTrYvFeQjwvOCQDg3BC0rn0o6qc+dT4nAP4SOKeyO1g8JSIr1+OAiEoovSwcgltkasqcfLY1u35z428CLHXfffelM3EQvh7WLweAlxOp4CbBL91A/WW0+sAHPxC//XcZ5/4W4NzOZC2oSpCXjGH/CXDxpQATRzCoB14OslBB3nd2KbMMOOfQsZ3KObt8cDA0qEAfwYuy8U2aOCnusY/XruVhlv90fgryERxlCFsXI72EpFy2bHmEE/nJT34CdenqoAo/fM7hAXIaxQOETG6f/exnoYX8I3BOffzIhz+SfgkTmSd9Lv3qpTucyVm9nu391ltvjTCYD3NiyPCdxwBqGigXjdzcuUdAwdq6I1Rrlp8PTIZHM5zrHnvskT7+7x+P9o8dMxZg39uiXVnaZwLOKQcBVfaNrI4C5zIjqwwWLFwQTHMu0NT3bwLAMjTumW8/809k/0zAudtvuz0cyfcCLh0InLNeThSvftWroUTeI8ITDwTOvR9QmODQg198cDDOaRuOP+74mBQcm4Iia0Y5a+Fffxf4Jnjt9ttvD0etdsa+P/pVR0doUSefPwLnDiGk76dSM4vR7BoInHv5kQLn3hV9nv2evctMecU3vgHdbFv62te/tmN87gw4J9BABjoZAWVIfMMb3xA2IMtr4Lv2ImOcC+AcDIEukrPLMMg//MEP0y5MlJdfftkORr7nBJzjSJjAy4HAORcLAu4sUyaf8847L0I/Z+Vl7zFuGbPKRxa66392PQ84twfrjhP3sccdG7Y0Y27M7nt+3nduK56fvAdzGZTAoAQGJfD/RgL/6MA5N2V8CHM+mTNnTvrgBz8YTG7OE4LFBdfJwOqDpWusj3/84wFiE0Tmbx4W8UCDmzg+wLn2da3tXP/a1742HuycBw117kbSEUccEU52Q8TeddddwQQnGE/gt/OS4dxdI5vONYtrDNcML+RAxIc//OF4yBTMJxjuX//1X+M+N5+8PHTj2k8HviBA15bmJ4jL9li+9TzjjDOC4fYLX/hCHKyYMmVKgPldj7iO8CHVefOd73xntEWg3iWXXBJ5yGjsxrPlu3Y0jIIMfIPAuT+OJ2XtusbnPtfdgisELLoh4CbA33upq24+CJhTVwVc6lBw4yE7mff35v3/1/u6eqrpM1/qS1/6Lhu7hBf6a1ekYNfqdUcU0uf/vZ6QTrW1WDsb32ee0Zt+8TCOcPekn+FiT5TwO7k0d998ettbiunAfWFkik3+Z7jhr3y9gU3Z8y/qS9fcXE6r+ezG3GxCrXzmY/Xp8JegD4SJfYZHy7+S8+DPgxIYlMCzScD50P0B92C02z5XOzdra30ufPDBB4Nh1DlTR6z7NkZaMJ2Xz5AeXH3sUYBzn/k0m5h59tGOT4e+7FDCdbTFhnYAvkyrIwMPvEAZ4WputDfBWpbrw0njDnndEDY2BQjUxnuMeU0Tr7BZ7nr6we941yHiXn38jfcAKELsztf8KuQRP9Rsm8l81TZOKYrPdbS9DnamKg6qqjGoYYIKN3jTEIAhDfhScSLiDdIUNnMzWK1aXdyU9yOF61DRCaTjLKoaheA01isJAlAHs45hX7HhvL0iUT5ABmtVxfkCeQlANVpPowUf1XwQgCYwtsqNqtbaah30WlgYSDKBIP2UL9BCph8BAPGiH6ItVFT2rGqHjjBua+IAMF4FfBq8W2fyKNtu8+SeAM4BFMAR1B0dsB1URQoZZQLsF403vY4w/gC8YX3KOK3LotX4ybQiUwTUlWB2qQK00LGmg0cHtc6UPHN9DiCY7dH5VBE4R710o+lCQxw1Bj7yoml8x4vfBdb187J6gqxYBKa8zAOcXq+aHyxqOZjocrAwIBpUS+d4Blkhd+uJnolNE/hgvlE+73XIxK6zEepX9LTOIORRsi/4NQ8yTsCWbVbX3I63HwTsCE7qi/6RCUJ56YCizgAGoXIK55T52p9eVUA1gl8qyF3WQt1M8i7mAI4JCtR5b1LTF1EqgXP6HUIpLd/MKbdC/eCYoErIjnyiDPuSCuKviL6IjPwDB7iO7l7AAoK2wNGgM3ZjzSVCc/V/bQdXWlkS2Fg6QVyi2YaMSBMyAKDQW+1ExjCt0Ck5mC4COCdTRXQSxTtWzBfHiIxY+VAmQTgCIwWHUXPlbZuoSzgi6RxLjropRX4voyc6+KwLPcQX5AuYJSqL40X94duartgmPntVWK/GoKceeZw9VJUf0Q9k1893/m7dBe7YXvvRcWfZMd683zGlUuqZtQSbafuocBmATYXBIQCrzjoKnOPefnTfkH86SfPogGAob6vV0kLij2ibJD3mpYMpJ9gKx5SsX5HI7EiLPzX7MwAUoSDqm8mVH4NsCza1AlqyCfYlsLWRp4xtVeofwE1l5i0xjvhAh8ryQS35T6kKeAM4x18CQIQpGI76wT88kB5f9UjaUrcVJkdZnEqEL9uM32cN4q+m/fd6AWvCo9LMthnB8CIAL2BgKGkORikd8NEIiqySvhc9dAAWqVeAAPk+gDkO8qgJVbN6tKsGFuFr209XkEP8X3s3EfJnfPYoBISsKROIxmhF3W0Df5NJH4BEW5hH77QyFfrKAnpLPekPC+9LDz35O6aBbvaPh1JMlfXoasLmrlTd0sv2OywduuchadJIQijXeWDMXNFhxx0VjXmLPOXfEvSoU9HqhhJZjvUGHaLOlbD5YRmot/quo18AXA/6A2coc1mMMFgPcfCTvoVQz8Ab41t3jsHERJ1KtAl4kKWE7qKB5GffWRhhmLsIGftEf7rtruXp6RUdEEOMZa3enNat2ZQWLlhEyLet6aD9OIe8vgAAQABJREFU9krHvGIS7wAJYIFKMFVJVadN0l1tH2g2tA+hqA4eZOYw6ENpNzB/Pr5xU9qEgk5AbrsRxrYNfYoxxj2KCYIqGAhhj4k2Im9yEgZXA+4KrQqpJYsfQjGCHGQSU8LaRwGYYdv8hnK0IYKlZad13Dy5cEu68aYn0tLlyKZpKv3XnLbqI3yakMdr29OuE8YRInJ2OvCAljSSwz4y+SkrAXIYZ0qhr9QhbQLjxnGgDZYN1v5YD/PKkk0b0vLOzWnshIlpWvNI2gjTjrLxbuot9rWXzuygQmufXpwamGBGw4LvnoSObvdXfHnpl5FtTj+Oeyl77713PON6YC9LY318HvZynebL33zXf5X9Fgn4x/WbTnnz1UEvS7vMc67lfFZfym8jeY4eM5ZQ3QAw7OZU0vbm00rA2g9sIRwh66FpQ8ek3QlvOCRsgmU736rpQikYt9GHsCJxO5yVSAcwJv/qcRlCncHUoovIT10kraNAza1DQLaJ6mNv+Q19cL4NoFZ7Lv38l48QMnB9GjZkKuH6xqaNm7rSwiVrAIIsSrvMHJNe/qq90pxDhgCQgX0J8LdgXxYNoZ/aXc1GzskJxawAXs5pNPhuK/Vaze9LN61PvZ3daSRroMmsTYdgL2pAOO4RoMmcXsF2OIcA9U/wCNGftbHJnzEGhiP3IXzP16EXNFUIS63N9LtMaTknT9qsDZNZNuxyXSGtWJbSbXduIZTtSpgvASXhY5HdbtHTS2FqXkG423rG4Z5pzqGEpx4PkMAxSL1zzPs18BhNo2AtA9Ba8mZdx+As0/BebM4G5LuE8Khb1m9I44fCJD8SXQfAXBA97noFWbtOCA1kDNGUkJ0MkPGlEwwf22Gbf/Ax2CivvY1wqJV05JGz0gv2GZ9aWVMFmMa2MTfFizFQoU8dQob8vO5n9+O37cKHNR3A2GjAHoS/Xb4K3+ty/q4AXD0gHTHH8MuMecyk1bJYs5S1q0j9ggmPdocBwYhUkGcGHzahwDlXYrXLnnC0CuSo2VttdVyOF+702ziE4ZekMZztSsJ4rl61EV8xoV4nt6WRowyBS+7cGyBDBe36Ax21zAr60QMI8KknV6enFxvSHo1mnVlHv3poQTbNIkZywoTGNIGwtq0tMEYB2kCpArwhgKO/hMUnrc9LJZia9fv73ogvsAjIxPWG4Y8tt7urk3T9qbV5KIASNCxAg9bkLy+fMbwEMtpWTYw2e83qEn7T1dSPkOe7jCdkaDPtVVqOPeXm5TtAT3SgAup+HeGu7/v92nTPXauREzZu2Dh0gtCfq1dBsPJEzItzD5/JweOxadddKMfnD8qz/gXGnuLCbGrAQ7/MXnlacWe4SBA2gGcWbEAw6Sp009Xe4jNiirbal3aFf9dFf6iigqcSB6u70uXfuBXCmWFp7kv3SHNeMhTfLbrAPVbBDJyXA5AOmMu8OglXeidhu39102p+biXM6YhY6y5b3p4WLQG0VN2IL39SevlRk2Fja+RZ2NJqOqTktC1hX5wLGQJ1ru9iBnYWcGQ6r0Tp5EV6/uYO6sFaCHBvhX7M8Rwgu6n/aUmQPH8LXsOG8Z0HB7YSlvKRB8oQvoAvKbenQw4bn1508ETC1FoG4bKVdYDZvKNmawUW4ZZmf3tV+tkvHoC9bjasjuPovlJavWY5eJgFYFs62Xd/YXrNq2ZEaOaGBp49lC+NifmG3O1voGz0m+XQag9hAVAOoDLpStpK20T7lal9E1/wpm4pfXXZ9mu3lIbfKoe4j78KpZr9fugPvem6mzoAD29KLztkVDr8sGGpbbi2T/BcmOXQVWUs1N2R39fbQCh3ANXM96tWdRKiexZ1qufA7dZ09MtbYcUfiX9d2Vs3S6y9+9l9a+dOX/5tm2tzUu2z3znH+p1pfB94f/yxPT/3TfSH6P/xsz5904sFmjp1aszFYpD0pQuMd342vVgf0+m7EGCrv0J/hNFutAnO677EhkhYZN5GeXEOdy/cvMQ8mMY6/j2XbczWGQPlYV7WrSY3ddOVmpcgOeTCGNS4ZH2pHdy4riHd+Kv17BUtSDMIt3v44dPwNw3h/KGa7bgNrUYdnMs2kT/2pjI6LVlYBoO1hFDsgo2Hs6fUChi2EwD909ibRWm3PXYDNLdvevFBrawJQJ07sNF5TCZ5OXJcwVvffAAUr/3J9yDCgoAIX9drjzsZf89c9MhjQugAtzoca/rneiS+4t+/7/oL4JzC+R2oW1kaZKOQVWreafPSK1/5ykBGZ4qUFaci6iwxzKEdIKhp+LDh4XjT4SQISlCHqGovO+Tiiy6O0ISyuF166VeCNerTAOhkhVLp5l85P9L6z0Aw13/CzDTn8BoLksAk0ZyW7SmL6wDWPfzwI1HfzxN+VMpjlVYQ4EDGOemLDd3ovTKUuTH402t/GmAgNwY/f8nnQyFllNOJKJhO8FkW6tRyVWA3Fx0EN/76xnQrrGPW42Mf+2iaM2dOOH12NGD7B52NAgBv/M2NkaeMGFOmTIlfBSvKYKYjUqfRm970pmCl88dPfepTARaybjr2BgJu4uad/LOYh4WLL/5cyPO9//peYlmf+icMV+973/twaN4NDeyLgsUty+K+398XACtZJDJZuxC/+uqrA9gju5jMelMxCtmi3cGvzmSDPctr4PtA4JyGwc3X0884fUcSQXU6Pq+44hvpDQDAXvXqV7FAeSr9HpDhMACQBx98cLCdyVSikXoR9c6Ac/aj8lMPdQwLZpQlbRLIfZ2ubvLqAP3sZz7L5AFwDsY1gYnW9xPnfiLSy1qmQ3YUJwcyJK/59bJZIDPYzoyT/amz+G7k+Eb6661vfUs8AGVGp4MHoyOPfEX0558zzokgvuZ71wSTnGNEWWxq35T22XefCKm6QzB8mD9/frr8ssuDHvMqwW8AM70GAufsF4FzOhv9XrCdzIWzZs4KFkQN78c+/jEe4kYHMG8gaHHevHnEnl+QBJSde+65kbf/yKT3zSu/GU5ymeM01ll40R3AOUBZOp5lycuuAM4BZH0xfeY49DrlTafE+NI5bz2cSDI5OZko62fTa2XUw4qgF92xLwUcelJLUNhZ7z4r6mb/2veWe/6nzkutA05p6AAQIPnus94dzvp3nfWuuCerc/YuoPfyy68gjnwzALorwi5ZT0G29oOsd5an/lhny/uvS/4L2R0ZrHPqZcbkI3ugp5HdHBZFLqObrJmvPfG1Mc4ze2jZOv7VBx+gBUIaMthyddpb7vev+X46+5yzA5w5hjSWbZ4nve6k0AsBAG9961uzZoSNNb/PABZtxE599sLPRnsz8IE2WB20rpYjCDCzpcr35z/7eboX/Z45Ywbj7KIaWx3pnt/r+c7v+a3dYG6DEhiUwKAEnosE/pGBcxdccEHM27K26fzW5p9zzjkRyn0JJ5NlO72FAyLOIQKfBMsJnJORTUC3zwDOLa5JXZs6T9QOvnw7WF8Fr7k2FnhmPrK4yQbrZq0HBDywIKjKMgVBOdcJnjONAHbX6Zb79a9/PdYI7373u2MN/7cC55wnYz1w1lkR3sA1hm2xDYIGLcs1jJfPCh6mcT0jkN+HUA+o+CzgQSHXUtbfdY71lR3P9fogcO4vR4PrN4GVPvQLflRWHjRwfeK63Ocl1yvPdLn+EIShDvmyr9RVQZVTeD6yH3d2EOmZ8vtn/J7lXHpyQTm9+X196cEFbFbU9oqeURT+zEHh9OaTCuns/0OoEUBwXtfeXOJ5kzyW4+74K3mYnr3hNBrQ3XveWEynkddQQsv8rddj1PfK+f3pm78qp7Vs2LvM3H9GXbrgw4V0CIx4TTBD/O25/q21GEw/KIF/LgnUNkZrbXafzHnefRifw50XfVbUrguM1p47Hx5zzDGxLnAPzf0TbbeX9t1n1QuZ6+vxzBzPAblDDn4xwLlRbJRiaNw91UjxPJzDW9vPd906v9iAbcapk++HPcTNUFnZ8hiVInmzKR9bo27WklYA1nb/S5RJVjWgj3l7kV6AlIxMMjy5pWrt3OL1PbMhVsN6kxzfDo5xwsRWt25JmxYsTblNHall5KhU2HVqyg8dhq+qALiDPNhlFYAiQZaXPsMIWUZZRX63YgFw0WYqE7xHbjALYDBcGBQ++GUFa9Fm6hvsPSbFcVRx/wzAT7kgjwm/Ufeiv1FRQyq66WyeEaKKcsKPZzk6JrazD0SoVMqIpHitdITGnpXAGxx6lY0bUu+SxYTT60lDdpme6ts4/U5oo0BM0QYypw02kLIEMCLvfiqh49hNZ1zTASoSNBd4Hr1WbFzrNJNZLygbzAcHSwAEBTSQxFBIyq6PLg0n+fYwgbr3LI4bauUpP9oo21wJ4dScOMiKNDr38+RDylqZRfaL6Oce7hEgRitSeeu21L1sdepatiq1DiPE2eQpKT9yWIDnbI7dpvtGCSNJ/qAvqJjMd3W8aq4n0tGvNN3aUX/2b2iDp9n5k+8AuOEMrHJvXiCOmfJT5E9jZH/RMWCzKsg9BzBHQJp8HDVHrBXhC/sQB7aXPjjvEV5pm9lFjbIL6KVjQ3dxAJ741jsEMBVgWwhgWs3bFXWzHmUceGU7h2KEQEXmjjHaabgtMo8X1QIcUHNoVATzcanCdl+0k6Sqg8AD+7UOpY8x6YCkjyAGimaISSiwSMiHHhI6ivCNypaRSl62A9YtQBm2t4ye9iEw3GFBpBYMdA2EXcx1kV5GHJgzRGYoZ/JTpmRBEyibLwMIaptILeDPIg37qozUAdkUbYtjqIzQ7UMVDFc6dzlmzAUpb5d1gG5kGKIQRSAQhCxx2isL+pLvBfcqdUFwNcCQdSNb5UgdQp58VCaCLnsd86CZDKEnM1CdAEd+MyQptQToA4MTTiarxtf8Y0Z8MK/4mz/5qpY9kBR+F9xlXwsIsPm+yWZocuXs+NBGyORWAzLU0sk4pxwEHmlL2KD337AJIXtBK8inD53tJRPLkV9K8BLS5z90H9kLxqVV3AmAinX+Q4/fn+558o70dPsCACVIjY4SUGBI7n2mz0z7zdovzR6/H2CNkZGTwFpK0YdO/ZAsDIgon1WJ9vQCYrMsRhROd0cyo0y9R9BIPcoOedmX5BHgulpTQFzwwXy9aKeWyr7sB5DmMKuBCQjFiODqCfdsu/upTw+AG5W9BtVy/9OyCJlHeFaBc7995G6AOssBNDBPUUYfbKiNIxvTLlN2SUfsdXjafdTsNLRIFA4Yl6gW9+NYzHcx5q0Yukg7zFudcxzoOFa7DOVZEFjjnIOh7nFu0WBTti3V/b4NYMWqTe1pC2QAfeiLefYRMrWJMMwjhgxLw3EsygY2kjHdTHHerTjBc0RdlGxN04WnCWsiVHJPMS16Qkar5ene+9cR9gxZENq0j3mnCNJ46q7D09xDJ6UX7deSJowRDMJtGO8cAi+jG30oq7Nlve2hzJruIm1lzKubcbgCZ/zv165OG+mHGQCSXtDalsbR9pwUmqinQMFeJtAO/thI6N61hLDexv5zFx5QdSiHzAxHPAQ9mgjD0UT2rFvtI+qh9LBqAUhzHAV4EJmqU9F3TJoy0S5a3J1+9eun0333dwD8aVODAQZvRi4daerk4WnOiyYDrIAdaiwgUUBzqAntIuew81aSxtA4ww5WBV7zH1mELoGzSauII/rYhpVpUcfaNJFn1D0Bdk3C9hiHSjtPkgACbiTE8aqtm1L/mg1pOONqDD4lmXoz4BzJsK81tjnXTO7D6xz/c8KNbG2VvWfrNe+N9Qvrr51dPm/rA5Kt330N/YY+Q+uzXMY6bxjkDKPGj4vwqWBC6KOa/VoE2PaW9sVpff+2tNeIiemFAC5Gsk6raajwN8K2ob9bWC+th2VyKzpqFMEuxrVAimaAnSMBZI7lBNUY2OKGowuOLG24s5tzV9ieGBfU3MFjHwBUk3GmfWM1XXf9o+nO25cSdq0NkzWc7xkvdNSYifn04sPb0n6HwMIzrpBamb/qYR/Ma0+hBY452rkZnanN5mRPl6ImoaMbKW4JVv6xNYtTP8C5cUNGAu6dkEYCZuLxktGqbalVSfBjO76zdR1bAUuio9h2W+D4dA04GqDVuBb2Kmin/eC92ljBgrX5xvme8aM+qRS2XvAourJiWS7dcntXuvnOtWnF5m5sFXdjP/p6N6VxbQ3pxftPSK+YMz5Nm9SA35FRF3MEDWENUhsJgvjCdALYZP4zXDJt7Gf8bGO8raE/n9rcmdaxFtplaGvafUxbmkAo9ABP+0DPOHRaIpmqrobXXiKd/JI+cryu3VBJt91dSl+87Hp8ouX0plP2SYcfStjKVhgoAxxIUvuPNjpXRmZ8tXxVf/rFDU/gO9qAvjEGy0MY35TJ4n7cqP70wgMb08vnjE1Td2niOYERxq0l6qXM7StDewogytso5zXuyzOnunZ0HhJkq8S1ATGnU4UA6NgQbKJVYmqLq7ZsQ//CLlNP0gRYiQ8bYKa6+aZl6c67H2fPYiKRwaanvfaH5RCIAioUa7uwL56UqcO+I19DUm8l7POtt61Id/12Lfug2EYqLtNfI+xKbW3Nae89W9K++zTjoy3AuFSTpfbYw0SCC20Fw4IyKAS98qpSYRnm1Fsvn4+Y1PmZvsU2ybwl82eF8usQmGPIJtHcELvtMq2+Rlc8zrPqHaWnO27rSz/6ye3IpIM2HpQOeck4WL+1tzKJUS+VwIES9/O5rjWtbU/p3rsBIf9qDQx65MKhBhZn6OhGWP8TQNyxHLIel/bYSxY9rYKzG7VnTok1CNnx5/b8GTvIKGTKOLe/o570sR1inxpG1RtiKrQ6/GX7fBSxevGFbyEu60x7+cezEvfd3wkpyPWUMSG9/rh907FHt6Zx49ED0ygj3g0HmrAVrmW9t3NbC8A5/O8/aidioJyntcz7Qe8OhQ19rz3rIWQZnmbsDmtpE08zrn+5T70zTHecMSJv6wu+mgJk5GK9xXrAPornrhi33BNr7+3tCDtoVI4aI6Z6SrYBHJZVvCqjKnO2ZciiuWlDPv3+rlL6zvcexQ5uTK96zbT0ildPBlxNPVhry1wHByT1r+m24ECtLFNx+u1dG9KPrwVUttYx2MrL9B3gckpp371a0tzDJ6Xd9xwKuFPZeJ99oSToB74Kmfjcyj2iIF3jO0fLMlnhQIBrcC/l4mXXqNd+iN60w20dbzQzgJXbs6+l5buC63dsx823bE2Xf7czLYMR77ijRqYTjhkJqJa1qBnyUs9rrJM+W9ip5MdptoVP9QG6WpAeeJADf5Xx2BFYUwHpnv6mtnTiSaPQzWgFJXELyhOgLyrsu33ky+8dA75n82v27vcxPiIH2/rHdObh5f6Ic7h+A/3548aNC1Ccc7R+DedcfSx+fyQYAX0n7qe43z1lypTYG3eP3P2WRx99NH4XZO9eiwcZZ82aFYQF4h+MUmh+4rE82O8ezEDMxMC6bq/ys75ZR19eGQ7Az1k+NZloU2qzt+t8x6/P+7XVgONe7Abh1BcPSd+5an26856n0v57DwcjNJ3oe4wf9Yv+C9ZiZe1+QX4zOQIY7gOrspA952vXpEcf20jfMcehD6656wpdadIuufSSl05iH2l0moQ+1HsqjkmiZgupqHphhb3Qx3YYQK/76ffS9df9kPllV8ia3kBId4BzbNDYW3Rf6FJ85u/QU+/9O6+/AM6Zj/G777rzrvSlL30p2BZe+rKXBtjLUxFuqGWgKQWv8sii9tVLvxqOqxNPPBHjNS4Y5G695ZZwlr3plDdFR9spKopOMfPfE/TkJQDVvD75iU+mG2+8cUfYTJ0ldp5hUQWcCXz5xCc+EQAfFcYwlyqb4R9NZ5il73//B2kki9TLLr8smCzc8BN08vXLLktvfvMp4fyTucDvXVgeddRRgfA01JPguTFwkAoYNP9PU8cbbvhlmnvEEQBd/i2chiqYm4x33313OOEcHLZd5imdp4ZXPfroowMAE40a8I+OJcPGyhI2FYTphz70wUCUKhMHxHU/vS7asA8LXet6CMxrgszOO++8cEbts/c+AGE+E4vwgZuWA4rY8dENUB2DOghPP/108ntz3GdZyuqDH/hgtOGgFx4UjkEHqtc9v70n6iC6NZO1RkJn2Bf+6wsBwnrjm94Yg1knlzHoN2/ZzOCpcKpmGGX8KRNdVqGBwLl1sPvJzvb+D7w/Bqz5C46Uccu+lg2ldUhrMAUK4tGJZmgxv7vooovSL+mT/Qnt+2nCgvhAIoBNxkAfEOxbQVUC7DCF6SycqBqs+++7P9q5BmN06qk1J7BtFvj2XcBQ2wA96lh9yaEvSW08ZHgiQLCZyGAN1UCQU9YmAYWW8yNCbBruS4ZAQZ8+IAnUWrliZTCRCYJS/oJH/S27bKsgqZ9d/7M4bXT43MOjrrYjLqyC4DdDh1pPdW/+lfPT1KlTw7gJdpNF7nvf/W467fTTwmFtn8iEd8GnL4hxdOLrTkxz584N+bz1raeySBwS7Hc6t22/+nXG6WcEMGzuEXMDONfA94ZQFjR6FaFJH2BSOP/8TwV40Qcwx/zDf3g4xrXt1aEsM5k6aX4feP8H0p133Zle/KIXp4suviicnmeffU76DWPb8XX6aadj+A8JWfSxGeNkoryU8zNdtkm7sTf2oo0TXNcwpgURGlb5nYAG57xsToBKHV/aKEOTZtSoys17f/HzX9BHH4yy1QvBmJkdy8p1HAsK0Onr+Jm92yzSFMLO+P1NN90cDHeyRTrBqbeCDNrgu1UGOhaUkeNi/foN0U8jRo5w5k5f+MIXg/nGEHOC72S1c0FtP2h/tAuOzS9/5csRcteJXSCrfW/4O8eLYIMsNK0I9VNPnReLGh39jvOB+qXMBDM/xOmzM2ExtFwnW22A41H9VUbac0Ml//6+30ca89BmXvnNK/mtCUDD19JsJnBuzMT0PL0/3/k9T9UazGZQAoMSGJTA3yCBf3TgnIyhAuPuvPPOWK96oMADIc4r55xzTgDuncddYws2kx3OzdxPfvKTcTJaMJt/ezl3eMBFpjrnD+c/51JDdzqXC/52zvUgid85t8k8d/bZZ8f6W1Y6nytksHZddNBBB8WDpetAAdzm6WnsZwLOCdBzHTiQcc5DFD/+8Y9jTWJdnedcv7mGdT0/b968HYd3nP9cVwrU86CFh0i8vvzlL8f69qMf/egOgJ3rBvOWtc410yBwLkS1039cFypzdcBnHWXsJsGiRYti/amDQN2xn13buPbyGc/DHlPYRBD06Ol4n6lc17uZoB4NXs9NAh1dHEj7Sl/6wndhRK+x/T/jjW44jAfM8K53FtM7TymmkYSj8Drnkv70lW/3p/WbdmxJxPfP9o97aC8/IJ/mX4zTaztz3bOlz37z+ezWuyvp61f0pxsfqKQNsN0Np7tf8sK69NF318Negku99lia3TL4PiiBQQk8TxLwWVNb7HzofpF7RTK/Ouf5TKrt9bnclxu9gs4FM3sKOtuwNQ8v391AvgiQvveewLPwwRwiGwkILbwLetjdTMcRJvqsyjN1P3OAz5QFgAl1bKLrvwH5wj/skfC/jvsIzaqBwXnEhIGDzU1bAVD8zg5oDsBM7Ia6DcqXnkJ2m5df2OTVpcB3pA9HlXU1Od+6J5/TM8Nv1V5C4axdnxbfdFdqXLUhjWBd0nDQC1LduNH4y9gLYbO2zI6x99TRDu+qMtdV+gVMsMlLXau2LzxZ1NP66pTCASbTA5XkOd8T+7xsn3mEs4j6CPTTcW/mBVgleA+na9SZvqGc2nM3LgPuE4wV5eB0i2Jg+oIHgza5oYwDWWccMjLEZa0+gorY3F4Kk8zd96RVa1anCQe9MI3Yfc+UG054G9jquWF7Gd7L5/DoWB+YjMgr3HEUJuTHtuM1oR0IkrpW2ZSO0EL0H0Lgf1KEpyN22Gvy5qv+Buqh5ElXQOeQJvLgB9uhJ5h7c4AlK8izH0eX8rJEnc91/ZRV4jtlWk85BYFI9hztJQ9EkPpXrUlrWGOufujBtMu0mWnUQS9KdRPGA5xjD7megGy0S1BXkcLMR3CloSsFbdTBF1MLmYRbE6WqufPQMRxGMpvkcBRVcRoIcOu3v9CFXDgnaaMCsV62DediMBTxlcxX/mivyGrop3C80Y5QQ2SnXPElxv6NDlWdx4b5tKbByMbvsqXI/OV7zV0MJI765NEjuqZ22dWCXHDy1sIrklFcOqGRNI4NASY1hfHN9qMjUSvyQcaGVqrirHU/Sj3SRaXjWlApXcf9yJEa2If9zMniYVSxIvLTkSuLpKC0gBdQX9tblwMEC0jHsgW0luioqA/OEdW0wriAp46UAFnsA2h+qBryoPIWry6g27ZGYIoSpPOjbsquYmg+HJayThkOWuBqiZsrgOe83/4WbqAVUE+0AwTtw2WjsxQgEPXL4+iMWlsw2efUD9LlAC2FE5oSczpy1H+yLdPH/EjbGMuAJ9331hmvTHuRX1ndRIfMvwAgUNta6z9AU36D05SMkRd5ha6pN2bpeFAL4g/K0Ylq2EqBK9TF+tuHvMICUg9/t5ZFdFMGxO1e3PCb99EfNbiR5SI19R9UqHoeRdBK69wNq4g2Vd0qVpui3mpdSbAf9alZW3WyBgRbtXEljHMPp0XtTxGyErAzcmpsgLVlWFvabcKkNHHY5DSqYQKhhmEjpSnViJFaY1x0HIEQrtkIhrSmTNbIPgA7+rZlU1QO4Y5zkCh7Wu8YEeBsH9j++In2hBIpLv/jbxms7MsK470/ZGm5OL7pqwaAcyKg1IB+gBBV9FF9Mn9fok9kNFu5aUVasOZJnLjLUgfgrj5sQAEAxCjmgsm0b7cRs9LoAofr0R0nKe1+FfCfzCzmrVMPCA3y1q4wCkC1OX+QAl0xzKN9hp6gAN2k6Oc36+irh/LXAtZZumZt6uBZyXDhdezFdvXhbKfR9YSyGo4PZPJIAFvFpjSMspWbwDm7X7nY13wMuZYYh9YRBGVqX1dOj/yhKz3xJKCndkYAsiwSom40LEszpzen2TMaYLuCoRAwi4yg+QiDjTxpfwlbVUdnFSlIO2iHCFAUTMb/jKcywLmO9Ls1hC8E5DRrxOi0XxNh82gjGGcHDX3N4WycpluY61ZtJdThlnbayPhFrx1rsusBg8J+5tM40CtTQGhMhDGuhXKK9H2J/u2mzvLNNFCFWiBf2m75GCPH0FZCtz3xWBeEC92wJuUj/2qhNw2FoWf2zJFp79kAumS5IgNtjMxW9E7Y+bDGzkOUU2ZCL6OLsRwhX8vQ3q3r7EuPwTy4pHt9Gg95wZ5DxqYptBEy7BCE6dWCldvaYShclwqbtqWxDS3BOKdPwH1110nqqmsqne3uwfubEY/0Jwy8TOsrc/C7VvPyfr8Lnee7LE//9vJv91502OvHnTp1ahxG87l8Ceu84ezPjOQZW4OGdqQmKs5wSE+VutItm5ek9b3b0h7DJ6QXDhufRrFOyy66MXWUu3hO25hWAcTfCtDEkOclAPt9oLEMkZ1nPdHGmmkWxClTmhpTI7riXOt/ajmtrwnUTPlIlrXf0LkeQgo/9siG9PhD7al9NeOji4MAlD+srTHtOrshzdy3Po0ENNfIXIenCesKMys6WefYZhxpUzURvuIycy67FSxQWsH4emTVIsgwAM4NbUvTR4xLYwBrtpAedSDPWn1Mvxqf5jJ8iuu7ALdRhzxzirY5GKFI34p/eBxtnNwyLA1jnZzd73uwpVkPPjtus3crtnVzSk9zwO3Rx3vSinVdaStjO4etbW3NA+4clnaf2ZpmTAVAuh3YqcQEMLsucOhpb0NwCC9qxPc16FI1baMWG5hbFwHwXL10TdqlmfxGDU0TAZAYql17yXAD/G8XhNWLOdyxYzn+X8am+L56LcCy2/rTl6/4Db78HKQQe6a5h00ghC6sgfxOKtLV9DGk5xe8Nm9jDGJjHnm0C91mPHRj31iHt7YWADPUwYBYTFMmE6JVO6O+Uhftom2ooit51szWpUg7ZKa1MnlQf8GmS/qw2jTCAyzqbFyAAen+qDdV09zEZZVCH8jGr0zjusM/NtC+669fzH7ng7DZT0lvPHm3dOAhHN4cXrOp5h3tDOAcs6DrC3Stu5vQwY8CsH6kM61dXwbs4XqwwNitT7tMakp77F4kqpcRE5i1HVw8n7im0t6oV9bNqmJSsXW887dVsg+Un3WM9ZnPZczhak+McxoieL5gRigCahWvUFpuDfYl7vEZwZeZWvwNv+hOl1/5C+bGLvZG54B3mMh4YrZxPiUr61FRKKx7HJs55pxtTD1Ll5TSIw92E4WKNnbQfuaBBhiCp02ljXs2palT0NFhtKtomTGqYcZGI7c30naQNfnRZ67d+VzHZ+sbdVcD+dP5Hnw69bOd1Ic0isLLppq2pp98we/+7eVY6OGe+3/fkz553i8pd3I6+djd0rGvamQvudYwtUcGwzz6Ucc6JMc6w3b29jSlRUuK6d7f9xFytw8gnetSxjQhiSfDFjhrdjHN2A1wLu1zkV3Hy8Jd0zmAyoB8o++2t8c1dx6FLLMODRApqRWp9bd/43az2v6H4CDBhJlcrJ9pVegAiNI4+3DL+lx64J5SuvKqx1JP/2b8yzPSK181Hh86cjYt+lDngOZSdlTOT/jgqwB0YQ99cFtatBCbTehymQubMHSTJtWnffZqgOEMZtlhtChud91JnUMzed8+n1qnep/V0RUFHwBV5mnrv2Nc1YrcAXLUJPiKvqRO9p067yOc2XsfU3ys6eoRks8Ov7lpW7qMPcelq7ek414xBKawYWkywNM4kMNtLmpcK++ooXmy9gZ2BA5hG6CrDvAzyJ6MC/TJK17eTOSaRvYhQijmEFc2T/qHY8q/s+8Gzp211H9M4752Nt9m92VzsYA2CXzEIQl810ciPkgiL/0Y7mFLuuM+95w5c+I3/xbroJ/EvXGxBvpnxCO5D2MZpnd/3HXB7Nmz0xLmcn0V7s0Ihpd1TpyDe+e2wfr8OZYha8fO3i3De9yPt23u9ZiPbTWfrJ0hIzLw+QMTgJ4rF+dybJN9wjNcic5dsag5ffuqjQDnFqUX7NtGmNRdwE7UA0RGt+0H+swxoU1LuU4+e99Q2KrzsOP2IQfIyNoZm+huHqVsG1WXps9kzt+9IY3noEOrazZA4mRAbXypDZEt7UAfyHwz662fXQdw7vrv14Bzx5+cDj1kTgDnKJp71T8tanz0T15/qiMme67XToFz3uyi65prrkm33HwLFKW9dPYeAewZP2580CCbRgeXnX/DL26ApnVFOhog2jHHHhOdKwhLFi8dIYfPnYsSsBlFpwjSEDCkk8UQj7JfLVu2NIB2OvamTJlCyIcL0tSpUziN0ZduueWW9N3vfDcAUgKoXnPMa2LjzrQ6y2SImDhxYoQSlc1tyq5TgtVKsNODDzwYYDUZ1ARQnQIgTSW96667Agz2nve8JxRc8NJtt9+Wpk+bHmEz3Sj8HIxtPyJ/aRT/5V/eEcA571Xxrb/ANp1+xi3X4Xg3QKhzzzkn7bf/fjFglM/ASyW9444703yAMCtXrUxnvu3MYBhzkLiAto2CYWT6OIY2DufhQfDVRRddDBDprgCvCMybOXMG+ddoGgfmP/CzgK/vAqiSeeywlx4WeU6eNDlAMoKxZOfSmbn3Pnun//iP/wgwjmCfW269NV199beJhX1PgNWU9dSpU8MBprNy0cJF6dDDDo0wnIIjHXwiYx3MomPtu51dtl32kfe+91+DxUO5CUbUQeZDhA5cgWStLa3x/eNPPAGA71sRM14dOeLlR8TAlpXu7rt/G33ybsBHGhABd1JTy3Rovo8/9nj6PgAqT+rKtmedZAW0zwQo2qbTTjstjI6gOxkJDU+pDh1/wvHxkKHB2LplK7GUt4aOaqD+/NLI6EAW2CYQ0xCoGjrBa4JJNXSCpWRpOR4nteBRx0J2KTdRxDIb+lD1oQ9/KMKeKUsvNwLUE1nfBISZRuCkxtlx9IeH/hAgRwFutumkk04K3bZOMixaF0Glu+22WwAAZXR0U1tgp6xntlcjb7japxcvBtl7cPogYE6dzk4Ijukf/fhHOL8XBTjuSBjpJjM5dGC8ZRYRfGmYNZ3fJ7z2hNABHabnnnNu6JbgRkPQOn4EXtp3hgN1IhDQp1zsex/yBG8Zxu2ZLh3XTh466G2XE9N3GC+jOc0l4FEAn/lfiawEcMqOM2P6jLATbaPaoj3agP8C/LkHDlrD4c7FJv35w6rhlL8x/xvBuPkOxnxGc64D+NqfEJb5icdDfjrtnbicyBw//r7b7N0CeCaIUgChYE7LVv5OoI7H+YzHWdRV+U+dMjXqN3HiBFgobwIc/E0eOp4IBj+dE05oslBeTzjpJ8jfcNkC89R5LydsZV1mRnOMnH766QFAcNLzcrwJgJ3/jflh45y4991v39AB621fKUsf3GVNnH/lfBj5zkrTqL/MRAJ51VdBiIL1nv+rVs/nP9/BHAclMCiBQQn8v5PAPzpwThvvWtc5QSCbc5+hR3WWC1DzsItra8FjAucEiDmPycLm6WXXdnFQYvvDpnOb637nBwFxrj1kjRNIrix8kHR942at765d/v3f/z3WEa6VZU7W0e4c5wOnhwWci5zfnS+955mAc9ZRMJvMx1moVlnlPMzy9re/PdhgXVv4ECpw7nvf+16sQVzXuK5zfvTZw3W860FDwzpfz58/P8Bx5unayMv0lme4Wuf8QeDccxtTrvcFQfpyjeffAuc8DS9zhQ/qgi989nFN6su/fe5SVwavv10Cbkz84ZFyetuHe9ODC9nciN2CnefjT9PZ1Dj37GI6/pWF1OKxe653w1h35Y0lnIbPcnOkrP1jqr1gq/vI++vTcUeTTy16I6GuYCkqstnxDEu8fip33Q3ldDGguYefgmmDzdqJnLB/3avq0hlvxdE1HacOG2aD16AEBiXwPycB9zB8OU8LdHb+dk/A+dC5T/vs3od7QD7PywbqOsD9B3/Xrns5pz7G/RcRqtVheyJ7gIccdgjPvUPwcLDhqUfFXVc2zTEMRKwEYMAcwAIAAAqAn35+LwFY6QE2QX3wZhuBi1sAnxThdMkD3MFh2sd/bMPHafU65pNqN2n9KpEfIKkASlE3HTK1TVZ+hyKgSui9iAdGfQWg5EUDWQ+MTKWvJ3UvWpaevvbXafiytWnErJmp/mUHpNyuY1MOJ34OkEKlwAuHunkWdQRZtnnijIEvhb0aHU9s9sMGlCtacVz7TmMwHXAcgDaxYdtlctrMBn/4AayiXvcGPuCwMuCX7pkImwr7Ueon/+39oEOrapp6goQVmnHWcR8yLZB/rgKABQddpYt6lAAFWc2ieVIWda8i09LjS1P7Db9OCxc/nabQL2Nf+IKUZ28iZ3zt5lYcYDAJ0D8B2sPJYt5VHM0cCw9QVXg+MPY561pPOgqp6DSHZQZpUlfAXTik8z04gulLtuVpF9vSgDNy9Y1gN6gHrjwBjDrhoOqAeYw0vZRD/1Mg6egT0ldhq8nh0Ka0mpyUAWsImYHKsDtUcODl87BvEDANlEzk0fPkorT8rlvTsgdhyd99tzThsJekusmTom1p6PDUA4BBldDZniOfjgqMR/RL9BP1KuHtKdtmOqYeOTTRebjteVEOjnlZv/pwovYUemDYAXxFO6gxdSBEObqJmyY14jgzBKj5dAqkIa8ijhzzEBQUYeVsOvrd0UvZOAia0asG+jVYd+jLbvIWlCj7mjI0ZKdsbe7xWFoDetWQawZ82ADrAv2K3AU45gEL6YzorXA4QVY1HaHUsbXQQukNON5Yg9GfOco09G8B3ai3bYA5ZTEpVbsB33Qysgj2iuOuX5AVc7T8ga0FT/ujJ4Ce7JAuwGGdJQFv1D/Xwr840xlzDDrkwhqvtIWh3E89Wwnd14oMBETR77RPCKBhDHupn+uTBvIu4niu4D0tUG4Tsiwie4F9XTAMdFWFA9gOFhYA2Axl3E+7cOdhG3T60F/Uq66P8nGmCXLIIYsCryJ1DaY6xzv/2f4uOBQ7YV9RxQV3NVD/IuxSBfWZ//qA75RwBpUZz6KD6gFdWZ8iOlGlrF7Gmv2rLPIl7A19XmSdo1NYZrkuGDp6CPlp+OGC/cT3VUN5YYMKpG1FZ+soq0xePfRXAPFoe0MRBkQq1YO/w3Jy6LhARPXLnpJnS7Oos7WMbCr0QR2O2EbGYBMsIAINaus9+gCnagfhFvsCzAq4BPm2FAnRhs7kHS8symRWVFd605aQYRN1LVYA0sLeJHhVVrYu8jBMXYG06lAR3esFLLy+d11a1wdzGcBFbV49IUuHNDTBOseaHubAIdU2wDjoBSaqDEitu7QV3dVsaCsYawzlMuOpDEiiv0g7xAtrAbD/JfpXxjkdrI2U14SssHzcX1uPIvmACctyJphEx2sBe9rE+KIXsZE4+JhTDB/ab0GkaaqrtV1QlgVVAHD20/Y+fjd0q+1qMAwZ9/XSps0w66zrXI+ubEOlYGVDR1ubYGeph+2NHmTE0ofoI+C5furbiy1JMCfKFFoGXCHDUh1lNQK2qGd8l8m/x7GhziJxwVrRn7bM+tNeXOmpgzG1thOWK/xJ+qob0fmGlqZgadvQtSVtAVSnTZgxcZc0q2VUGsu4BYuGXjsFkjdloG7oCfMkznzD8NaYBJ3jYIYD1NLTwWyE3e1DDoYhL2LPm5mDmrDVDeipoD4Lb0IuddQr2JrIE5hp6E6VMSHgvQOdoJbRr+SUVsMi9+DaNWkz+U4bOjLt1TIc4Bx6h8NV1jlF34ed6kTua7dtTutpi4fzW2HvakT3O2nveuzZhs1bqEMlTeSw+e5jAAkAxGqgPQL1CBqHfUIutLfId4Iku6ljH/Ku/7/svQd85Wd55/vo6Kj33qWjNuqarun2VBcM2DgbDKmwwAY2uZvOvbn57AYSWLJLbpYlIclCTLDBJgng7ime8cx4ep/RSDPqvfd2pKN2pPv9/ccnmQ+x2UvbTW705yOORzr/9rzv+7zl+b6/B9u7oQfnpgBYSdmt5uvAk4wjgunrw0grHAEhtorPkF00PoikTYPD8MO5/DgpxOULqUuLlIt0hPDG+gs2QRFtetGaAOd65sYsIyfTKuIEzlHnaXiL+Gw/0CAYqg36pmyM8VTQyISl0u+kETOMYf1fc1z5ZMXMFCxX/FJjL8URPB7PO8579f3Aer7O06F/B36nf9//Hf1bh66rVK1ah0lJSXHiI1rz72ZNJAH1m9iMDPNSjjRvi1Fd47MN1d+LE90OOFfMu21OyLQk6r5zvbfLepH0ehOT4zbCD7gJ6W7jGSNG4XdDrB/wrnuRFM701VUoulXHJlgCcR0eF5vLd6ue6tkF/6rW6DnxcSgbYToHApmfpW5O44mpYH5kFJ3+CurLTf5eN2rmUh/z4zcFAkXxbFH0F258mgBMLu34DQHoahPyJZiPe1MvqB8j1OvGvg6bmZtGvTHBClMyLR3fH8t39Ix6Hh06Z2xi3Iapi171u4wrQ5z3AHxb8Vnv3LhNUt+zePcdafmWQ8pVqiEWwrdwsvySxrV6DtUfB+blUyMWKeT5Z4EEUVZfoB0uyaljmxAMEBEFYAYpyrCUdqvvqw1yUfzkIn2XbOjGlwhyFkCmOoqHoxzUl85zL5eNcOvmMcDR7glUG1OBWFGRjHUZWVvxRRo/UQ60m3ka0uoc9ZE+k9GcA8czpHqbvQ4iDr2CAMeSfe0b5xz1pw99uNx21yTiqygT2p7m9urymR7QD/OQ/LfsLxWwKfzM2NgKKffw5dxHdTUM/xJHGcZQhuIowvm3MpJq3DNPYc1TD9TfKd23A1gzTnaxhiD4Rf2DKDaVI0NZowrSh9DzcC8NcQXC0v04fa2aqiAKmFwNSalf98qTJo2v4/s8p0w6gbrhkcOddhRwLirSYz/7M6W2aRujOK1f0LeE0EdE4hvdjF3l1+WndS35Vi/w3Pik3g/wnEotD6G0kPFAd5Gso2jdQu8XyY9URnXOCue/PSqmkqiv4zn41JBXNlT9dzpM2RB7zLEJku6Qa/M9/uYGHgwLpy/n4kE4Q20o0pRpXn0m9+BkbEdZYk81WdVH3DsKnBMIYBzlO342M+21/fszYTeom5wvW8jvh/LjKJ5xGT/vShW1WWw8rXcERlafoeuxrwXFOUbdaou8Yxhwp370kMs8s8pajUjvova0QI5jjZE11aMJ8Q7YQ+/Hg8/zN8FPUpvT71TuGisF6pN82hJjJh8K2doQwVNS1tR8Lk6XcK8/4Xlv3lgkfv4Ga4q59rOPFdtjD4c55SClXIZb1GcB55xNysgQAHcXvmMRyeSZhXAbHQdOox2qX9QheFUZE2Qfd/iiU6ahDH70LgL7pEgnu7vxOdpsovddYD6pdNAxcdRX7E83wPW5JvbTf9PcVHWdOZnqaAg2UHnKx/jmaB+qozgK/ulsFMONOnVetpimDV0HnPvGs/VMoQXOldr+g6nciz5K5cdz6Xrc1ikPNwXOrxg76/60QcCyEQBRTTVk33Dqpt5RMJPsHMa5Eh3XeYtcz/d2e3FgTvXZPJfGmHRb9E38YEe1ZcGIy7SxeR/nsW4n+wQzznOHMH5gvi5lZT0bj+XU4Tm1RWwlnyzgUdNYlnktlvvqXqdOzdnX/46Uwv3j9ujeKHvisVji2ILM9TbcF7sF00G5eYd78KJsi/2pa9OU3cgQ2ddGaYXcUP7CkxdsaSm0Bz3Afcc79ZPv9Lv7TuEZ7vXXilPo0Pvrd4EfcRLiaBT/z8/Pd2IYipfod4ohKOYgDkT9sXgh/U2AnGIdYifErYj9UbxCMQ/xVGIhNFbQGow4ADFHEucSjxWA3SQ6ICEcj8dzr0y4vp7x/nHB/e/xTv8deAedE3gvXd+Zy719gr7DlIS2rHrNLykv2Vl13k0f4FaaX87p7wi2v31uGCXHFttQlQoP4rECj+a1+i71TWVI3x0sMFqOj6L1O3NYF/zUCmqzrAHjZ+TT1AZi6bMi+VFa6TDqSSz1JYRxm/yFUsPqeXRtFYs2Tiwu+Dl/xo4fewEW63tWQA7pDzzxQbJNPvg2OKcH1+PLX2surf+p51Md088Pf7wrOKdLyXCCi6TipUGYYLTq6mqkSLOcO3V2dlpTY5NjeKXX3LNnjxMI0x9lUEFJx4+foNLfoyfD2YVw8cJF59wH9z5oj7//cSeQIqhEgIgGdlqoe/iRhx0gR4EtQW1KIyEYSYCM1JVKSkucHRtf/epXHVhKwTfBS6p4n/jEv7NNwGtjVGqlRtT5XQQMU1NTHEUKPX9jQ6MDitTU1DgVWJVZFfVj//ZjtnHTRqfiKoAomE0LiQrsacFQcFYc0JogHAXm1BjmfEgz9/U7alq/j1pFJDZ6t0PKZrcJGEqtIxJZ42IW6QTv9HT3OAFHpcSSXdSo9O5SwXvppZecoKQGwJJolG0E//yg+2iQrIVQpSJVsErgi4CYdChXKcRp0N7b24eMdIqjuqfA5wJeULbSu91va4F3asxyDlLham/vwOmy+yNJu2hYWKAzVwouPZtAuHc65DjkIKROUl9Xz7lJSNpWO3YdZfeK0rLq3E/9+0859UfflSLI8TeO856RDiykoNrY6JgD/OidyqF2pTamsjt27A1TalQBdnJU3hmvSU1PKm+6loLFV65cdVL0qg599N9+1DYDoCm3vGwhwFBOTDZNZNIWzgKoHNZ/+PX/4CwW6/3f6ZDjFPD3V3/1P+gAl53ndECtsXEbZCKpeiaHJgBLUJ6cXeBQ29K5v/Pbv+PYUgHbvXv3On92HBaLkALrVB4KModgc8GfH/rQU06AUUCr2peC2Dm5OQ4cqVSo+vcf/Kc/cMpd7SIXJ6Jn6OzssmGcs55v/779TnpVAXlvYOMxnLK+J0UzQWdXLl9xgupq+7K1HLXALUF7Aq7UXi+cv0Cn63bK/cknn0Q9ssKpq98FZBwYGGDnRaZTtz7y0Y84zvi73/muk9pUPiMqOsqxrxy22pbgO4/HEzDNP/mUEpzUKBV03btvL22/1kndqvcVkCh7Ka3al770JQeclDSqgv1qR0qfqjb84gsvOnZUeuiHH37I3g+Etp5yuf+QQo7s/dxzzzk+QPCdOhIFf9X+6+/UO5/yc/JdZeVlzi59wWmqdxrcZWpSSvurZkIsiFgwgAIMaj9SAhRgoOdSPdSnoEf5Lfkb/SjVTSlwoRZLl+lJFLxQW1b70I5//YQxEhWwqUCH/FZlVaXTBlW/Ap28bCIQV7Y7cvSIE8RW3RZ0O8WkcN/+fQ4EqHquNiAwVqoCCo7IXjr3U5/8lOMP30lx8X67/Wj//aN1WD/avdbOWrPAmgXWLPDTscA/d3BOfYcAMU3IPv/5zzt9msbR6lO181kKdBp7KK2rNqMo3br6Y/1bi7zq79RPaEyvQ/22+n7tmpIisGB/jbHUf2mcJvhNY06NbZWmNaDMKlhPfan6WU0uNacQwNbU1OQA9NqJpXGQJowC5/R3PZvA8MAYTKlflT5VCnr6rsaWAucEkn/yk590lPT0DLquwDmlU9fYVGOw+59f/aQgLY0htPlASnZKwS7FY7174NDmH9lBCn1r4FzAKj/8p8Yjsr8+A5P1wFjlh7/a2hnvZIFZ4Iynv7lkX/ibRSflxjt9R79jGcE2kLLj6a+F2vqqeztPe4dX7bd/nTlHrZ9g57udee/3rFdo/dceLAu23/r3IbZnh8viWHT0sSD6F08v20jvij35lNs2VhByYyHk/qOpc8We/fqSfeecH3UNFj2417qUIPuPvxFKOphgS09lSYNFkbVjzQJrFvjpWUB+WH1n4CewiU1QuX6nubb6RwHN6t/1oz5fvlt+W5/6nj61K/1ObZ39tz/5L45CzIdYC9y2eQuQ2YLNso61zKfOdROICGPdyJ2EmhuLxVolXWWdY3liynysWyxNc29W+hVEC0uMsqiUNAshbVgQwJhChH6l6xDQNaNUaIPmHQVu8JLqj7+Hsabk5ieUa7sUreH9BGWtzkzZyvCgLbIm41dEjXdysxIbkki6Pd5JAfyhGyxgv37C0nqHLIo5+eqWUvPlplpIcorFpWdZZGIKQFc0Po93UFSENZFVUrKtTI7YwsQYfo8wO9PZSMZZESkZFoSiiQOCKS0gqc9WJmdsfpDnmAJxIHog5a1VIqTuzBgLTU0kMAAQB36liNAquXZWJgZsaWTIZlmrU1A+CDW6kOg4i0xKt9DkTKLdrDES4Atemub9eC92W88NjsHazfJ6BO+ISrplZ55/dQYc6FKtjb1x0tr6eiy5qtwSq8vJOUZK2sQkS8gvwBaM7YDVlIbFBdywCrS3MjFNilfsPDHJuhlJWwnIRCZEWxgqJi7sZiExcGt8EkRZ4nszrAutMFYMXQKGEZwQC8ySToq6tHRbBfSAunMCJSt0UitIoi4CFixNzsI1zhMQA/xgnS8sO9OCWZ9yOgAtDxBQXeX7furQ3BiBhfkJU/bPsLBYTJBh4TFcl8jM+I1b1nnxLZvuuGtFnixLYS1qkXJcIiVtZEGRuVKzWWgHZAHY8vlQ45lBjWd5xGJT4wmKsXg/5bU5RQu5Z3x0DIo0BKhCogjXYUck1sKAZEg6b8NzQ9Y32U+6uhkHjIsBqMmKB/CIIB2fi/YBUDVJ3ewY7QSWWSSrQKKlRic4wBulTpERjCQtcDfrY4IpskgXFxdOSlmCC7M2baPzowQRUGMa9zltJTYpysLjARSJ0nm9cxYTGWvJUSm0sXBg12mbQP3HRbWJpI0qyKix+izwFvgaJnRbbkqexYXG2+AwtpsD5iDwGItqVVJ8ssVzXzfKdQIFpf42swosMDsG9DHLepQiXX5sEW0ZCamWEpVkUQBSioaNkjaqc6CLth9sGamM8aMSCYYT4SAQIdhoYKrHhseGLZ7AfUFSscU6a6fASkBw40AlXeMjNjbrxdQuzs8iKFCCQjAAAEAASURBVBJJ8GSMCIjfUrlPUgRp+gjMjAJoDcwPUmdWLTsm02anSUEHsCHASBCtG6AiMZ564IqxGdQq5qZog/iYSFL0JiaxzkUbcAExSPGF8CCADdDXNCpCMyM2D6kfjPROHPdKT8qyBBTTBAlJMc7n96JE0kdwZt5SElMtHfUlgXuC3KZXpxhXDXKvBcANVLWSM4BHQH3wVwvAC1PUzzGe0YdKi7DLSOIdUYmkpea9g0lTmhqZQb2NJThNaiHa7sTECD5pzpLjEyhjP8GxcRTGaMNAFBI1E9YQhW/TzzyKV5Pj07zHnIUydktlM1RyeDKKSQBv/I9kagQnl216jucf7icuQR2ibKMjoi05NtmSaIPR7ljqOBk48B2D4wM2tUg9JBCbGpUKFJZKMD+SqyxY/+yA9QEJxQA2ZeBTY/BPbsgKkEzKf8R6Znqd91T6plgglETmeX5ANN/kgiUEpVpmDGmkIsMAmyatZ6iD4BZp0Ki7UiH0TrChBp/tA6RcDlu01JxUiw6PRDGM+kHdX4IqiKHepeOTUklPSiz07UMwE0AQSnA91K+5OUBdongRwI3pgFrppGiOEiCr9VqeZXhymGDalFM/E0P1N6DFUOCm5RkbnRpGMWiIdVTOTUm3BNqvoBQwP+romPVO9Nj47CjltARYEU07SKBtUyYAG6oLsaQiDcUHTRH7GcQfzLtmmOvG2eIc9h/HVxLYi47Aj6RnW3BkgnnpHGYAi5YXAcPwjeFAeO5g2j0DcTf1OVwBPSLgPur6EhFmPLED767SX04TaBwEfO2hPIZGhyyXte0q6mxBKNAn153ER07hQ4MFD1K28/SnXqBuKTaGAgZFRtCPql1wL2KXztjfx/ha4JuP+rLg562BnpX6WGntwiAkEil3gWUgAvcAVNrPCpCtj4D1NP3lJP5ohvu6FDylgCb4dzNxnAl+l0q61aJwgB3KNArwZBmQYJW6HY7tBRILHJ7j+yrWCHyUVD3hRGyE77QTh5gYH0P9J9yqcvIsJ5zNVEw0fPQx9J6Uj5LPUtt5x1kIGq+UfvDrsdginvopAEyRGhBK3l/vB+gCmOWj7S8DpqqPXuRTSmZRwaQSw17COUP5gwA6weNStpuCNJzBpnrWYIibYPrfCa/POkYG6APGLS0dKAm4LAdQIBRwxcd33dSlIMprnvq7SLTZ19sH9LfiZJbROkgAnFN8RjErxUgUn9Fah8ZWGkf9JA8JEmj9RPEbbYLUWG6AMVMsqVpDMlIByYBLmXxF4Qejqfv9QGG13gHHR+dGotCH30vE/lQZB84KxZZxlNUq/fs87yvlodBQUm5iywX8WQdlf3uB/gvfWYK/2JKYbplRMZQvXTTlPcp5Xs4Ppj8QdLdA3GKFcUYoqlUxTBKjAJTd+OBQILhwQdo8lwR6lYZ4jmvPMsby03aIivC5gM8It3j6pCjavwBagaMCdWZ5zinalBf/sAyM6YIk8keipEW97hrspY+ccPxaHuBcKm2DbSBAs4K3gJsp/yjex815CzyzlGpFpgg0J35v49jsDu/YOI5y34LLDmWUWUFYHPVJ2ycAQ/FzPuoXwTqeEWCPtuUoK3KNSOpoLHU1hmcNZc6rDSJC4Lg9YwXaBGPoJcYH8/OAg9SpMO4ZDdmilJ8u+hdG246/E+45j+3HgF3G+b4gGvWR8h/N/VNW1z5iEwMzKMonWmF8kuUkuS0HECUZFR9tfBjB9/b1IgoyAERKwwvFb0TTB8cloaKXg+0BFsZJZXqRNJF//lcXLS01GkGPYqtYF0GK0xlSQfosPpb2nUEMLwkgFfA2CB+gNQXVFakGDQHe9Q0ssqbHWE9tIBkfiiobVYBxy4JlpoWg0CaVo3tpYYfH8I30r7FxEcS88Cc8v5RyU1MjLSkVH8bawgJ+kSGuDfbiY4EDXawlCQSKTwyhPQqYwr8wNhRM09vrI8YFGM6GEoFJEZGowpEKNy0NUJJrjY8oLXArWbAa6GtK7b2P5ll+Eb7W50WBbMqiGFt4cpO4biTzH96PshcIJQhOn8OAOh1dM8BJPme9U2la9T2vlzUu6mpSQrDlZuHbqFcjo4wbR8EbV8J5XmwA6DHOvIWdJtgwjjgpPo5zNR/DraGcxPP3Cc4jlT1klRQJ4xNQ4swMBcKNcvyzD/hyeGyZ/mCasRrjY2CSUOY8SYwXM1C+E6So+nr06Lh985snuY7bHj20nVhrCja5N5YMY4NKJu+Xk8PmD2AqmqYDKGMyhtT4TaCy3r5ZmxiZ4z0iyKoVwTiJPgpwcGrGh21C2TjNhhHK3st4pwPVqBU/G61Q+NS+nb5Bn017lYEkDD8Xx3iX8ubaA0OojvZNO/VAqZgjo8QSRPMTQbZA+hwgG0Flw8Ooo44wrvPOY3et1YYxZYm3zCzqKvbQEtWNa0v2h597k/sienOo0HZvE9y/aFPUUfnjtHTqdBZtj02ZoVBsIfRvPALgaLD1U8cHqaNTzG/89H0JCbGAgWTRo7DHJ4dIURxNBoUYBzhboZ21NU/aFIBPYhKZMGjvI2PT+JJxi4mXCl8669P0HUwvx4AyRynv6WmeG9hOsKlgvKzcKOozIDiw4Tw27O5m3jLGZg7mZ4uUcyh1LgbCNTcPldKUEPMCZl4+t2zPPtfA+MlnDz9UaJu3sgmIa3q9fJ+1/IQU3pH06glkjJCfxUngB6RyvArgvMoaOBkBxxcYB6xYShLqmowrGUZj31XK00UdZfxGGxoBkuzpW0JdcIk6pnmay4ZH6GOZq6SmRVFHoi0BRVpcjAM9jo4Byw8AUU+wAYGxiTvEB/xFHWVOmZ6GzfAzi7zj0BCCST2zbMQFuGfsIPXMeMo4Ly/CclIpayrp8RNe+xvAud7hRTu0O9R2b4/AvgsIF2ljhllyGqmPUTpMoH0rfStF6PTjsFIObNXPWmNfh7bk0I/RtkvXuSm3fwrOcakf61D71HFvveMePCf+Q1CbYg/qwz0ejxM31xqKYhnq99UHK/4vDiAAs4txElQn8QHF2rXGon5acQv9XjF/8Qy6puIeGi/od1pDET+gWIyyZQq6Vz0IHD/pMYTCPQN92LePzKKsXUh0y01dikDFNBm/nJYVTv+EP+xZQXFuxM5eaLPKsjR772PMqRJCmSPIB/v4Linc8flx4EGOP2VOtoxflj+dpy72co+BAeBv6nZ0HH6MOhjKeHECXyh1/6J8tyXT5rVXcQTf3d294PiTSOruzAzjUdYqZn2jZOh7w65eO2HrinLsySc+gMjXPXBOq9UqPUHO+rzXVPB5zn8FrPfDff5AcE6XEoSlCqJdqfpvDchUyCpIFbQm7QlM4hScEzl5f0GOs+DRzwBRtKTO0SEATBUiOYnJe0I8nemiA8KIuNTinQZ4jnzxls0ODNXZ1WUD/QNO5VMFE8QjMEaFKFhOz6aKqQqmQJ4GiXouwSgiPKVwJ7UDXTfPk+eQoGoE+pvgNC3+6brpLPAUFBY456oCaoCrgJ4qrXYdSXpxXck659lGhkdwCN3OPfTdBCa+urYGwj8oGKR7+bCDbKL3lS31fZ0fw+5cvZfUqfSsDvxWV+80tnkm8aGMCtQQBS0qKCn7v9uhAJXsLfBJ4JgqSFp6mqO8IchPNtPANfRtW1cB3ixhQymSCOKTPQO2zmbnpuwp9Qg18k7AJwFsem69r9Jvyu4Ci9Sw3+mQveVMpFQmqE3XFqgn2yUmJDr1QSCeFEz0TCpbgV4C6mQngWFyKHqu3p5exzkJntSkw8+ES8psqiNeFmRkJ72rvi8HpXeVCp0+ZRc9Z2FRofPssqGeS9dUWQviUz3Sc3hwgqKGVZ/frUz1PKpnsoscmspM11TdVopOKaOpTQhG0rVU7+8/BOtJ/a96fbWjaCLYK3Bo4VrPrWvrunoGlb9UAmVnlVUXNtL3dE+9r+A3/U2gldqGnl2/V/moDSq9q95ftlNdk6KZANNlXQMYS78THNdJGaseCKCUzWQTtQ21gVnvrOPUVXeduk+79+R7HAhM7zOE1Lzam+6t+26mHQs+k/0Fm7a3tXPPMcdfSMFOz6c6JGDs3Q49u4C9wYFB5JHvpZIRdJabk8sAh8VTDtVPvbc6I/23OhfBaaoDOl/3lR31LHqu/IJ8p0zuv6cWyzShvX27znkPlaH8gtIEy85a2NH9dL58lOqx6o/KQvaWz1D9U1mrzgbOVf3XM6mDlQ10LdlSz6fv0gwo516nk5X9UvhbGjYRsKZJuOylstF99Tu1bQGoqnsqH6UDlk8QsKAyCRy6j95dvk72U7nEIzEuH5LGPdTm3Pg2LbjKbvLXWuRSfZKfXle8jkFf5LvW/8B9frTPf3zOH+38tbPWLLBmgTUL/O+3wD93cE7jBqUg1ThEgLSAffW76q81wfvMZz7j+HtBdYLf9F2lzlSqVvVdv/u7v2vbtm1z/lvW1phM52qcqgmh+jRBauqPPv7xj9sDDzzg9O/qpwS6SblayqwC5AXRaZyvSZ/GFbqWNgZ885vfdP72G7/xG85YSOlcBfVJKU/Qmvpa9aGf+9znHCVrKax+Pzj3qU99yoH/NV7VJFVqeFKiFbCn59LYR4fTfzJWEBwulWIBhILuBfFLGU/vqkP9pdTxBOppc8kaOOeYZe3//plagKZq9Y1+++0/WLQz9ezefxcATosH29iV+dyzYcyXFVg2e+GEH2VF1Op6CN5pdfFdDn1bf95TEmyf/j/c9sAuFqxYmNSC+ee/hGLdq9r9t2qf+JlQ+7/4eyI7sQPHq6f99h3AvjdusVNUi8w8yKbiIPudj4fYo/tJy8OC733D18Bpa59rFlizwE/QAurTtRalT60r6L81j9TcMbDWoblj4L91a60rBOaWOk/zaP1b52kdpOlOvf3pn3wBlSmzJ+k/1xO0m2EuPcDc1UvERMoNkQAvnrwcS6jaYCGFJQQNCTD2Dtgs/X8X62zzqKYRVyTV3CJcVohlsB6SUr3VwtNyWO1ksXp2wvwD3TZ8946NdncS5CKYiiKYC1U6F2uJsXn5lr91i0UAziCfZKvM1+frGmyU688AGikYKkWjoIgwS2X8k8L8Njg6yrquXrc+AP9kxgMhrNHMe1DdT4qziDwAhc1bLLmwmO/Fs+jK+hbBBH9ft823tlh/Y5NNALvwZKywEWRnDSXZU2SpvF8E119Fycs/BITR0mpDze22PAVUwYK/gjPBrAvFVRRacmW5xWTlEHyMQkGPYFlbs83V3bLh3m4bY61sQd9l231kTLzFZedaBht7I/M9GAmFuV4ArZYmG2jrItg4BvhCIBTQQkpKcUUFlrCxAoUeQIDTl2zyNJtlWd9iO76FZGbZKmsbkXwWbt9uUayZLgPjhRCgDSFIswrc5L10x7x3283LmsAkv1sAxIiLJR13dprFVFVYcFYhBBvAzMiYjWDfUZ5jibUQaXcpCIr4FLbIsLSNGyx0XRXBZBCFaa/5Wln/qW+yyS5gRoJ+gi6JmZufFfjsDRsB+zYC9AnIAwTq77HpFtZ4O7psmvR3Pj1bGPUQQCY5g42HVZsATIKs/9IFa73wps31tFg2KisRrA15gS5XqQ+ZW7ZbQmmlhUfHOkHN7oFOO9f4FqBHgyVms/7hCrHpUda1gRu1UTE6ks3hqPqXFZRYckQK9gNaArRq6mmyOx2MGacHgGBQVUOBLoIyy0zMtrK8ctuQu8FigmOtl/p5qeEKtu4jWJZpG8orLS0emwMN9fb22K16IM2BIdZ+1tnm0k2Wl5xNUHXRmkea7EbrTWvHPosEsMIYG8cRhAoj4Cilr3nqhjKilBVUoe4Vbd2s616vu0awHjiIiISLdjAxOmHjBPbnHCUxtxVmFgM/pNow95sGsFQgNgowsIR1osriCoAj3o/A+fj8kDUP3LW6pgbWmqhzAPBSvgtnHSgbcLQir8xKc4oJ5NNWxjrtrUtngPHGCDYX26bKLbxfBq0+yHomu+zy7UvWz5i+NKvUdlc8aCmsbXv9wHYjzdbQ2mytnaTBnAVcA9TMyMplbZnnBhRVEK0ivwxgptrCWXO63X/bLjVfsblJrxWmUT9nl60f2GliAbUqAZ6hQQT60ywtls3gHdTRPkBMfh9DcLWwON/KC4osCdUjRYSn573Whr9ob2fNf3wSxRrAD8o6CoApOzPfiouqLA9QR3DP1Myonb16zrpGe1iby7ZtZdstP8mDws+8dQFcXrxx3ib6J8yTXmg7Nm4ncBptMyuT1kc7b2q+yzrbIJAbvoBgc3QcAWECvcNATyGouW0o2mClRRUo28SQNrDJbtZesfGhfivMzyWwG0E5TdgcIF0EQeQVKKeFOVISAmolABT6pSIyOGKTwI0hiatWUlFs1QXrSRfooc2j2gWY2D7UZo340b7uPuqy4D1UyajfiXGJpAEst3Uesl9EJtv41Jw1NN2x251nLYHg94YSniuhCsgyjffutnNNF1kPbLXirCLbUraRdcdkguoAF2P9Vt9RZ3c7GgmkTbCBXsFu1u+TCOgC9M1OzllxapltLanBX0UASbbZpZvngRimSTMaT6wIyHGCFIVjBOR9wNKoHOaXF1hKdIp5R4DWhlh/JC4QhS8oYq5aif/PYJ0yjLriRRGrY5g0WO1t1k1aUJ+jZIq/Q1omKz7VqulPSmiz8VFx2GjCbjWyMav1rqUkpNqmdTWWn1HgqI/1TLfbjbrr1t7cgZpSlm1ez2bh1HyCtaypj3fa7dZb1tjS4Kzrqo+LS4izpJREAF/AbVTHPOkeqyyt5n3SrJs1//P1l6zPN2AppHMNAxyYHcKPAIgkJqdZ6cZNFpKUZkOCWgEygkhpGoHqRgjpbf2oQQYRPIyiT8qMAmYFQHTTH2p8rZXwUKBEhY699JtSy+oEdGzta7NU1rU3pORYESAgf7ZOrtkJeOr4LihawVCzyMcs0X6DAWWj4/BRzP8zgeESuJ+C39MAHf2rizbqAyJkzj5De/Cr/+ePYczpk+gT0yJiLQ1gLIF7CJpWL9cHmD1C/zcLxAnbQz/KxfiZ5fkG8O1e7h0HFJ4NFJ4IAbECODfF+rVU4/KJa2Xjg6UkqDCmQphgWE6/OUe/Kriwkw3dvZStlA5LaI95gFdR3H8UoqSd+3rxg5GUiQubeVFlnSEF3yLPHBfKs+KrswG9MqRKxARmjj5wlOfuxh9OzQCczlN+wDd+fufC5yVFgopSJ5OIO0TgIxR+nuVpBlBa7AGslBCDi/dZJY6xzHNMLgONTozy7jNAB4mWxXggEcDARbDXT4GnJwMYA3o66b0ZT00SE/Cz/hHPeELxUY2fFCtQbEWb+rQmo/V6xdD0t5/0obULxSQUuFfsQD/jQCKr+MfpaHwNfkbKQSGoYyq+OQEy2zk/DqA2axnYsyA80YG3FgBOFinfROpFFe1MgKKTjpRyUUpiqpKRDdB6KMtaUgp2dbVZVnCYbUjLsrzIeEeRb9w7Y83eCRuiTIKJXywBSft5PsH/gvWjAecygFOTgHwjqT9KGq2B4BTiAUOs+UxMszEA2/nx2cs8jz9CMY8oy6UM0vCPbCug5JTA1WyQ9tDlJV7NPYOYk0ZQB1eAeGcYY4ywyWGBTQYxqEEKzA0PQ8kK0ISrohy5BJgOuADomsU11Q41fWZ44agRIvhlE/yyIchrN/BroVM+2xdfZEXRybYMtTLinycN8aSNoWi3SgxIa2PLgpRo+37u4UaMIwV4OIefBN6R4RHvCgwK2NmBbfrpo7yk/lzykSaYdupCkCQaqCglBnAE4CqBa2icycjKJrBLB2PfIS/PPYv67GSIjQzM2Z3mPmvtZbMF40bYGxQPgywX8GTLRnxWZQ4+OsRu1E9b3a028+KnoGadsoygrWbmxtteUnnml+E/xxH1OL9sX/7z804cr2ZLniXCXI8iBjI1NYZaXJgVFeSwdpZp68qA1oB3qAbEs1ettW3RLl9CWamFfpAdePKhKclJlFe8o0Q/NjEMHJNjhw6kUu+DiGORie08GxKGRp12IkXgyZEeQGcjLlpk1ZsYbwJ3NzQu2tWbPuL5qJ1SvuCDQE0rQBaRbIotYh2TzSqord6qHSSuSSyLcYyUqWiKDhiWnRtr23cUIzpDXzNtZFTqQJCnDVvnWEU5Me6oOYApwEpinsH4qLKSfL6fijgGwBHLhoqhzwDltbUuAGeM290mwHPqZQxtPgEfHxEeS2zUy5qmzyorEthMGAd0vcKaZh/ZMPqoe9gpLhP40c+zdVL3plGeyrYtW/MA2GLZuIHtWpZYm+21tg5gI/r+IOZBruA5YpihVl6ZQV+VjUJnGOMMUj3fJkbbNwDIpAkFmx9xITm8Y80O3qcCHoP505HDcyiWnSOmF07/X8GaLGprXpRBgcqVela+Z+f2VKtaj59nwyLcKBssVvFRy3btyojdqR9gbOZD/TSCmGQ6fX08kAqA1VyH7Xkww7ZuS7EEQMvBvnn77neabITNl8kAs+FAYOMTQ2wo6iNenc732EBBPe4HsLl+fdja2vux5Sz+Rxuq/MTrSStcnkuKzTQaWxBx73GrvdljQwI1aT8r1AmaE8+bTma0Ap43wUKBDW9cW7bPfu48tkq1LRXE2FMBVWeHbIyxySptLDMbsaNdufhZ5guJzD9pD1K76+I5TpwCgG3qAOIfcMZ9sQk5xHkzNXsBLK1FFKkckRI2ngCljQ0v28svtFlL0zLQpzZLMFaZ7OH9RiwbwPKhg5uZG4UCUy5bbR1j3U6xHbQvP/NYHKRAv40bcxAhwde7wvDJC3bjOvM7YuKL0EDLALYhgL1JyXG2/5CH7ybTXwXZVeDVb36rDUhy2ipKM5h7RSJ8NIZth2ltbDTJTLLduzykx0TwB2hM6U2V7aGjfwn2Y8AuX6N+oOIZGQbolMB4lE0uM74YxgcrVl4Sag/vv5fa9m6Tn9TMY9SrbsZbSXwf6Inxxhx1pbyMMfD2AistZ8MHDrGt1W9Xrw+x+b0bX+Dl2QVT4cOB8kpLqBNbCyw7KwZb0V4vM69o72XezYYxfKdUnePiYmzD+lw78ECKk4bzxKkZe/q5RVK1rlhZPoxOmuYfk/S/wOeMS5JTw2395izbtROQNeUelLnEeKKPTbdXrnupJ5021DfKM5AanA0UT7w3w/bsEqj+j+uOGOvHPrTmoZ/AGoguGFDqv3DhgsMWKDav73g8lAkchdZVJJijT/1bf1N/rJiHuBDFSzQmEDulNRSxCuKbFBdRjEa8gjiKZcY7ZWXlzn8rVqI4iFTrlH1O19EzBZ7t/uf7cV9akOwL352gzTLnnfXiBOXPgYWjVqygKM52711HHCneRgDf/hZw7tS5dsvKSKBusRGJ0dQI8zXNp+Ip88rKUqvZlmSeYoBJ2qFSHk/Qz9TXzeMj+62bMlxmzBrDvCEOe0SEJ8EZLcNTeO19j8UhtBUDuEp65qvLdvhYC/PGCFgM+hQ20kxP9wPFzuAvbjDHvgrICjj3AQm5bccv4jickaZAbSB5/qWaoX79x6kh/1NwLmB8Fb4GYypsBb6kIifFLgWpFNR6t0MLcKoA+lHhCjhRxQgsymnhTQpIuv4/HLyRBpaqTLqXJgWBQ6p1Ol8d8hKA1QSDQd1Dv1NqiACAo9+pggk8CRyisvU9DVT190k6SQFjev7vh/70XILcJgAG9RxqFPqOztffVOED4JsCcoEKHLjXD/qUHaQ+p2uoIHW+gLn7K73sIaBF93K+xPeUZ1oNSTbX+//PDpWV7qNdtXrHOAAbLx2Wc83Aydha915lcVO7lZYY7AWOgK0DA3udp2sGylLnyS6Bsgyc906feh+BWDr0fZWNyiBgV8E697+//qb7yMZ63wB0JnBLgJzqh95JdpDqlgYtU3z3Xj2IdYBO2VZ/m+M6fhqlDtlQ39G5gefWs01j6xmuod+pLL+/PJyT3+n/KEtNOp3y5D7aBSfQSmUqJTe9VwTPr4H0/Yfq1PVr1500YL/6a7/qOEK9U+Bw6hgDYD3//YcAS9lJ7eL767beSWWldhqwtZ5DP4Hf6e96P32qfq0qevX2EewOduwqu+va99cTgXWqe7LVnKQt3j4C9pRNZUPZIlBfA9cLlKueQeU5S/mpnapMVbaBvweu+U6fWlCZwR6qf05dpu7pnpz8D19XQDzgn/SssmfAHvMMwAKH6kwEu9rCmWh//6FycdoN91GZyXa6j+qj/qbn1TXvPwLnqA7KmrrvO7VR/X2a68pvhTGR0vd0D9U5XT9w3zBsozp0r9NkWve2/9Fzq0xUJ+Q7AnYO+AWV6zsdqiuqnzovlPoYsEvA7vJHuofqjL6jOqv6r8+f3vGP5fbTu8faldcssGaBNQv8dC3wLwWc02Tr61//Oink/8zx9fL7H/vYxxwgTH1EAJyTMvDu3bsdGE7wmhTopIasPk197Le+9S0nTbwUl3/lV37FWaD9zGc+44ztBcjpPurTpBSt32uh+LOf/ayTjv3LX/6yA94LcpOCrvogjQmkMKdn+zXShUv1ViDd66+/7sB2guQ0bzh16pQDzmlDiO77W7/1W854JKA4dz84p37si1/8oqOSrHf5vd/7PWfyqpogFTwpzKnPFVSnd5KqnMB3AYAf+tCHnHGlNs3IJlL2FXi3Bs79dNvR2tV/fAtIOOeZby3aHz3NjmQWJt7p0G935ALO/XWY5XsUIjD7z3++ZF8GahuZ0PL8Ox/6pmZRguZ+99fuQXMx7FaeZxf1Z//roj37Ooog3JN1LTtY4rKvfJkFdq6vIfpffGPJvv2Cn8Arc623pzQf2Ou2T/yc23ZsQglHO9fXhoTvbPi1365Z4CdoAfX7gbm9+t/A/E//HZjbBubqWhzW2MDZ7ErQXnNvnau5aOBcrQm0AN188Y8/a2GsGxzKzrMqH0pwOKM5ApezMUTdCNTbFAvGgE8JFdUWt32vuaMTbYkd27NXLhOcGLFwVIPcpNZcItXlPAHK+Px8i9u8w0I9bCgkcL0MrDZ9+ZwNtzWifDJvYQQhgwkG8F82i7pORBaBvz07LTw9kWguQWCguclzVyR/YC6gHxfjF6XOWiTwGJORaTFllRacmGSTd29b/4kjFgv8EkNA1e8pNi/AT7AHVZKtgGp5HgdsI1RsK4N9PPNFG9Uz944QdGZ9hTUwhW3HCGooyJ5WudGSarYRlGRNrfmO9d6+iXLbuCUQIA4GIBM459fa1boii2UjZDTvucoC70Ival2nT0E/12MuIMKoWFtCuUcRrRAglTA28SVs22jhgEF+IDgfmQGm2Jw5MTxJQI3UmASeXbzbwvychRdkW8yejSjVsen17BWbPXnGBliPCs/IshDstAJsFZaRbSlbN1komwh9QD5h6KaEjKOo1XDXpl45Z5AvDszhB5ib42/LcxOWQBrJaBbxQ7buQXku1WYamqz/4lmLAGwIB7hwAuOoaaAlapEZiRa9sdqCtmxlkZp61tRqUxeu2ERjqwUToFOat2DWZFcIvExQp1IqN1gi5R2ckoB6HYp7ly/YYB2pbia8KD0R5NZ6BDZET83CUtg0uXWHuaNQX7txyTrPH7e5jgYUVkirmOuxeQJ9K3wmrt9s0evKSfUWi5Iq6fa6G+3I1ZftSssFi0pl8R0YIBwwUQv5s8AfUvXKyEy3GjIVrPdsQIEmzhrrWwDerlv/eJ+5CaT5w9lJ75ojuAVsMo96Skq+PbzlEStILaTPHbLzdy7ZtfprBHLCbONmMmsADio7x9Wr11jzu+kEZmtqdgAlbbJ0oCEpXJyuP21XAe6mp2Z4pjRsQyo1Aq+TpAYcBz6V/Wp2breaqh2kOIwFQrtrx08fQS1iiLFxsmXE56DGgBINaTT7AdhmeI/0xBxAwnwHHlhCsWeKMprmHdOyycSwucY2e1As4LoNvbftZst1ghcAXtginCCwwBgv6mVKg1cEJLarssYKADcnALfOXj5jN+vrUD6LtO27tltV/noHSqxtqbO3Lp4RZWs7eM59ZQ+i1hUL9NJg5xvO2t3bdwn00/YAHsKp20GsgU17xwgktROcDrFdm3bag+V7WfuKtdONZ+zY1WO0sXEHbEpBBXCJwM0Eql3D48Oso00BFsabJ8NjkfOkA55GeQtgag7AKj0z1bYSqCrNLaW9+1Hiabe62ru0EwJRpNkMJ0hpKCz5WMdcJdVpZnqx7dy22wpJDyklyaPXjtvF9uuOisvuit22rbAG9SEC5l1X7Oylt0gV6baaym22Hag3DKihExir9jag1o06eE82Z8cQfIwAtGXJ17s6al0drfi9GDu0fb9trd5hEaSzvIi9T50+Zj0dbVaASqLU+1wrSu1JSRMEHfOOWH/3IM8T5CjzJaICtbpAWiPAzfGlQUtIi+Na26wmfw+KcNh4tMWuNF60biDTIBRVooDUkBJE0URKZ342NmehLryR4GQ1wEQkggAtvOdzpB8esw0V621v0SFLi8yxa8037GTdKWIjU7atdJs9ULHHErnX+NKQXao/bzfuAvWilBcOfBMvRTjq9awfMYBJxA1QatpRvcv2bzpA6kEC/GONdvTkYevr7CNwHodaTDYBLjYbE4zvByYcHO23lFw2udN+olexF4G6BeZi09R3qR1u2Vhl6yuqWK9MtJaBVrty56a19PWQKRd1NdZuV3m/ZSAuFwo1HhSktvB+FfnrAHcpq8ardvbGWbE5tp0y3FSxBViZYHv3ZSdbinds3jbQ/rZt2WGZqJrOAQxdbrgA+HmBVH4TFgcUHBeBYibqGgv0R0OolrpQa9laudke2PagpQFBtHR02ksXjtntIZSLUKzJBaKNJU1tMKBBNBuPs9mUNhcZbQOoX0luJ46fKGCZRcCtCSCuBUDxJEJbZUB2+fHpKMOhKsLYWauuZD7D4wLOAcwMAy61AuK2DHY5vqkqIds82BEkx+oXJ6yOv8l3xQOCRgD9CNok1krAeoH6NA9cGm0VKJbmu1D15Ho9QI7N81M2QAyGDFmkX72Xqpvkt048QNqCGQShS+hfC2j/gs1aVmbtLnVyCnWPMHK9Oj6T5fcpAqmTlJl3TkgPgA9r5RkofBGrt3HO60P1LwZIYzNARXUsohnAfVRx9QZsnAGg4l7IZaC45bIu1p+7BgaotitW7Mm3bGzHLawP+9UD7YzMTqI0BsgCmLdEn+El1e8c8EUw6l9JqAqW4zfL6CejgSNGgcAb0NVrQTlwiWcLARoKor5AJDGU4Elpp+n4oWzAyBRUI2F4bIjK0oTqZ+/kIGquyxaLktMc7zACjDfO+8zznkEAm8lsCpAamxtobhEozwWkWp5dYAUJabwffTVjlaGuTmD9CWfNRIFxjZW0vqDNelqbKCgocDa7K77w0zg0RtPmQa11aN3G4/EAVSzYMKDyKO8r1UEcHIAl9RCVvCngjXHSKgtlzKTeZ6NOqTjFJCAhPb9lAVLuAzDNpB8Ooc6Q9ZbawsFYcp4C7QWKuwV419HTbimMbcpTMy2PNhRLWHcQYO0ycHQrJe1GrSyCOFME6rryT9rMEARglUjdzUchNpVnDSOGptrUOzsDrIxgCyC5k/qY+uUDWJ8GJgoi9WgOfX8RfUVysOJmoc47tE4PWyeqgAsof0WT5zOKtkjE04ZRiVU6tzBAkjRSU6ckJdoi/lnpg6dR+VRbSQUCrkRpryIE8ILy0jxWdTWU8iVLu03TMOuCJ+3qWIeFo8q6L77QClC9nQmh7i57gd/7rZdNB6ukCo/HXpF0AIIT54D9ZgFMYkl5XhiDEitlnsw5YdSDwYUZuz2DuqrTUwHsLWvrSTiWIu7DmDke0KoyLtKKgP+COWcSX9uNXVoAyqdXo0hry/i1KcSunx2wNpTkfKv4ZVLoZUfPWqJ7wtJQWtu0sRjQLdUa21bs1HlUU/sZ43C9OACGFebuy4CmiWlB9tATCVa6OZo2Djh3dtn+7CsXAP8M4DofdVVi67I9KnIjAPpS3dqxzWN798dYxWZGv/ibFjbtnTjRh0AEGzrmURJjw0AkccMV6tcEoN8g0Po8iqUffLLafuHDuY4y1aXzS/b8343ZjcZu4J4UxhiM38IYAyUsk8UqCUA8FmB11d44MWp3mmhbVJv4aIRyQhCNWEWoBNh9/fo8RB5iEKSZtmPHb/J8AG3MLeKA9XTvIBcgYeqq7dxVwHczgIlcduT1PnvltRZSdsZbbqYHaIVrU8heFicGRiiBiGDbtyfWHnoEdSwPaxjUhSZgoDcO99j1G7R7ykkKbOG0p0VUiSenQxlf97FRwGcH9mXbk++n7seukg2tiywezdbcjfhOcoVlAgKFh07xPBO2aXMUMFUaAFGcA+QdO9Zn5y8MM5ZCCToedTmG/isoCCvNdHFxMuBcPOrIq/bW2VHAvT7mYGyuIE6nOOvCIsq1vOPWnXGsvyZYWmKoHTuyaF97+gbjJb9lpBSj5k/bC0fZCYXRwREp2oWgJBhqjz8ZbxUb4SJoDx0dwHvnJ0nVO4rCILFi5jbxMYB5AHvT3lDAZsb2yw1k/suzh96DmAeKhu1Nc/an/+0G9WuBMgecSmPcH02WrrgxNkjHWfWGVOL9LjtF6thbtSjkoVasDAUh4cwSSHscFe1mM0eybd8eiVLhGPceQOQFLiMshfsD/9MpQlxYHKqFu3ek25YalCY5/+b1ZfuDP7pmncOJVpoNqJtO/xpBe2ZMMIgamp+x18aNqQBw6Ywj6DeA7fpQ8zt7bsEOnxhjs8y4xUV5AaHhH1zJzN/CsdUg9eiKffhnyFj3CwB/KKQN9izZV/+y185fB1ZDrTkzjfSssdOMccjmlxtu22qymQu47K1zU1Z7B+Vv0obHA9JFABCSmxk3O2ulZWTZW5/GPDfY3jg2ZZ0oscVIMZGNpcJhl/AxUdFhdujhONuyjfE3fZhStT77bKc1d5F6GWDPkwWYHSouYsaBgH0o6+7YXmKPHkiwKtbkmDJYT/+qnTw3YW++1YziF4C41KVp6y4G9JMzqG6OoDhMez+wLco+/ssR9Asush0uszY3Y+eu3ub9EIFi/BwZPkv7nALYSmYOk2JlFRHW37tqJ47P2/nLPQ5TEQMMKWVhpd+WgmJuXgjgHAA0CownjvbbNcA2mg1qj6wrQB4vkJ43CLXsMuDV9z0cw6bdYIC9WfurZ+btZuMMSt/hVshGjhhRyvQrw4I050cAIF2kU863rRuZa9MmxlHTO8czH3uT8UJ7uyVixzhU3n0rMfb4Y9F2cH+Y41sUF3dS2OLDuRw21roHD8ShDQT6uz7p8v7hUDnou+I4XAwK9GedIk7i3pqJ/kXfwCMu0Z8qZt7CuoRSmGssHEN/lJub4wjfzNPOhlnX0DVT2cil2Ps46wBjbALTXEysRATqvOKWuAOf0QgYJNOu3fiDDuc74iSi2EgkIR3999DQiDNHFAeSmgKQzDluxsM6nxD+28/Ie7E+QwjGeTf1Yc6hT95NqZUZijrrrY4NNB1SQb196Pv698iw3/6fL04BQ8+x+QbwlKGKnw06bOGxLJQqDzxabkX43VFU/57/1qi9cbINXxVuHuaXsdR/v9Sd6Q+n2KiTlVlEdqJk23uQDdJArLOU4d3bS3bkGIJO7RM8awiAHWNn5vc+UnWPA4MPoMiYlbhkv/qJaEDdKBvpX7E3jy3Z08/Xsrlb90G4i7oVyXw8MmoBRd2z1tn1JuBcMiqp+23P7k34RubRjC+lVbzKJhuVv96P7t35CbzzD/v5/xmc+2EvvPb9NQusWeCfWkCLzIsAZMNMopQS9YUXX3CCwR6Px3F2//SMtd+sWeD/jxag51o71iywZoE1C/wLt8C/FHBOANmrr77qwGxSw5Vir1K4Sk1N6sv3g3NKc680pQLdtFv54YcfdhTrJCF+9OhRJoNLJnW4j3zkI45inMA47YZSuvDq6mpHgffs2bPO7wTR6R4C4qTuplTvUlt9z3ve46io6povvfSSo0oraE/fVRp5AX4Cy5UqVgH9K1euOCq+GkMJePtB4Jyq1MWLFx3VOanj7d2714EBBYgfO3bMUX993/ve51xDO72+8IUv2DPPPOM8l95VvxOgJ1hPKrQC+tbAuX/hDfVfweNr0efWnRX79d9bsMstK++oHsdmfnugMtie+VKYeXI0Dlu1//PTi/bVYwRQWCj/Qce+8mD7baC53duDWdQkTQcAxGf+eNGeO+a3ERZ1dX+twcQQ8frGn5ByqTzInvnakn3rFKoUyOyzhmSJLGD+xkfd9vijbpRFWHwmIKGFjLVjzQJrFvhfYwHBcoGfwB3VTyvAqz5SMLugefXp2qQlNXyps0rpPQDN6Ty1d7X41oY79qd/9Aek4Ryx9SgqlRMcKc7KsRjGAkE5pD8lOOlrbrRegJsVoI584IOY7Hybu3jOFhrqLQR1qIgNwA052VyTsOE8ykoEtd05KAVl5Jl/GDWit85b8+njgGcE9ksLAM6KLDghg8VcAu8EYKVallBc6KS98bU0WdtbpO8E0iogfWpcOZAcCkMMKMzP+7jYsewGIgsiMLTcgSLcK9+xiL52i8wttOBte2w1M9dcAFyhKKy52MQmfRypoCwCwXmPvgTQBTiUmAnstd0B2oh02EgDAF5XhwN1eLYD8AG9jWKXnuYmUuyl8mw8L0AXsgNcjwV6lNVCMjOctKorqJX0X7psDW+esUTSj+YWrrOosjJSnpLGVSviBIJdvF9Ifp65ooER2cnee+KkLfb0WiTwV9pGAMOE5HsL/MAKQUBNoRW8A4DL0o1a8x0+ipJCnyVu2GYxlestiI0IwShIhLDITbTLFgASwlA9WUaJauD0SRs8X08qM1R0SqspgwwWy9l8d/eqLTTfIm1tmCXsfdRW0vOt9waByptXbR0gYmLVBtKiovRG0NcIJLvw88G832pRMTYnjeeJN23ywkXus4QaXTnltw74DqiSgKyXzZrhKVkWnreOgAZKLHeuWt+pYzY6OIq6XK6ll1SgtAUQSVBhifNdqDaFegosiMDNYmOtDZ09blN3ay29sMDiagD1UPwLSgLEJE1rcCLPRHBB6f1a+1vsyJVX7OztkxaNCkNlSbWVSokLYHN4cMSuXQWQI9Vl9fZq27/jkBG2sJOHT5ERoNtSs5OtsBJlsmSCpYBzIwNkMrjbYwtAWwe2HADQ2emkQ23sb7NTFzmnr9PScwkI7qxBIHDJbly5acN94yjabbHtm3ahNoftATTuNNXasUtHUKzpB6gotE2l252N6WNzw3br7nWCiM2Mf0PswYN7beeGPSi+RNLH37Qjp1+z4al+B7raWr4TdbgcG0Ll43LDWWurbwPuSLeNqH+tr6wmqBPsAGdXbt0g7eAk6hBl9nDNPlIl++xKLYp92CUuOcGKCktR58gAe0R5ZKDLGu82Wcicy2pKNtmODVsJDEVZ33Cvnb58FgixhfcDMsQvLLHp+tYtMl20dRIYq7AHNu+18rQSB4w53/aWnbp5nBTuI9i6AiWxaosFhuoFhrnN+zV23LYoAi37gGkfrnyEoGiMHa8/Ya9fPIKy5BjgXjGKIlsshbSe44uAC7VXUbRptjAGGVWU3/rsatKWJvJc3VZ79wZB2jlUXypt26Ya1PSmAK6uk0GjnyBvthUA06UCsrDtnkBgj9XVN7OZfAnllV22u6rGMgjA1fbesTdvnXEgkBzq5GbqNScAVV12ssVUFVUCh+21LPwK263tciug5MXLNoVqQklBMWo1GwnkofRHKt0zt9+wenxGKhDBkwcft22VO520yzfa6uz8xdME/lpQtktBUaTactJQmwAcGV+esMbOBlQ8W2wJ0LAQkLckrxhlv1SboE7c6aKODpESMb/C9le/x1HzuXj7LavvrsW1BAMfFBIoz6IEg8iiMg5Mdy87jie3wLZv3G2ZcfnOJt03rn3PGjhHikDby3eRNjjB6vHF7dTbpLgU21peY+VZ5aRRpi5MtNjJiydQS7yXKmrdumLU3YBR2cje3NtmV5tvOnDB3q177YFq6ijgnFIiHz19lMB6uwMxVFZWASlmA4wtW0tXC+rI+FDgl3UoNpZml5M+OIH3nbX62ltsOh+xguI8+pwaIMEku0YZ1tJ3yHfmlxSjkJNEBAw1wZFpa77ZiDLhrJVk5duBnQ+QMizJeqZ77ULdJZR56hxFxE0b6VtImXar4Zo1322x/Oxi27N1nxWiquembbT3tdibF45bW1czqqj5VlFQbWnRGSibTVp9ay315JYDaO2p2Wl7d+5FlS2dgB/g3NmjdqOnzqIJYG4t2WxVmaX44yRbBiKepc0Oo/42CyAkRbIs4AO05pyUpp0AKEO8YxJ9SiX9UiFgWTQAm6CkMPrUUH4WqXPjjIulKtdBeY+heFbo8ZAmNN2SgdF8wGG1S+N2i3o/S9q+NFS7MlB/TEZ1T+oaI3MouKEa46JvqqQ+buaZ3divfhY1IS/KlDxXNPCzQFn1/QoJz9KORyeGULUEqiDoXk1gV7D5takBa+I8pdDMCSOlMmptXlIbd5C+ugeVuHlggWhgldzEJJTG2AyPSlD3EnUDODKaVHvbUrJtY1waimWAV7ybhvvLAHJLqwBwNK5RIpptZFsZIy4SCVBbiuJgKgCWFM26APzqRwdRrx1FlSzCsmi/kfSBiyFLpKlF2WRUiVxR2ASArMYGqUBZ3YAkl6f6bBgwKg6IKSUcBVnsq4nGLJTPCJCwVP1ygdFzE0jjywN1zU5ZoxRFgZMysGEGqoDj+Me7wL3DlJsGO1JDywTwSqBv90+RcnBkEDhzCVVO2j1jkSTaXyj9dW9Hu7U2NTkCEcreoh/ZWIdgOcF0AtoCYy7nDz/h/9O47fTp0874LYlycQF0tVOxFlCASQJSComKtiEUWwZ5b6nKzgE/RGD7wrgMUgFTvrTFDsDDGX4v9biHUP7MYB0mhMC7wDmlGNewyMtHK8pJt2coPwLlObFxVpoMVONGaITy6wUquESf0TQ/aZEEx/OA1tJCeXeUMmeA5sZG7gHdWcC9hbTdGOwkdcHOsWHajxcgJMqySDcdixgC8is2RL0aBcQLJ/2xhxTJngTUfSn/QZTIuqgnjGIsgj4kCfgzlPo24Juy5sk+BwQHabZiqROmpAK/oyLIWKkXBa7xhSn6UTIkJeXaJjdjUd6JV3QON+Xu5h/DKIBdnO6xu/S5oCW2L62YthBnM6o7gNh32HjRATwngiWLup7N38KBYccxVj82WAJizqStlQJRpOD3FgExW3jeriWvLQHeRkcm4R8EluLXaA/D3mlbAhIvBBQtx2ZRjEMHAenbuccwMEJUfB7PlWatl81ef+E2c3fSJaKWvI5UiKVZfksJWwQMIjNYJkpIjMdfPzIB2D4J3JVojz8QjXomhUejn0RJzQ+cVLbJbRkFZCAaX7ELAuf+8hxAiNeqy9fZji3ZVpgPuIha1dVrE0AxXaRbTbNH3ptmhx6LALRetdMn5u2VV2uBoZZsY3WBVaMCJ+iqq2/BgW0u1bZTX5btIz9fYx/9pXSA/iA7e3rJvv7sqF2+c9fyMlLs8UeKUfQEhGWol6T0svirN0938uydpE4usN01ALisQQgmmwPUlh5GKmkDg2nXZ8702etHT1seEOuDu8tYy6ONUjN8AIout9dyPdE8M/V60oDKhu3vv8t4bDSSMWCZ1WyJNUFEi2yguH5j3q7f7rKS4lB74gPZgIeRpM81O3x4wF556SoAZpJt2VRu27aS6Jd3aAVIvHbTb5cYW6XELwDvVNnPfwhImKHta6/32PN/e8vutC1bWdE227klyYrXBVl2DjB7jp49xFH4On1ywL713A3r6gUYr0KdaXM85SuwlNTPtLfY+BDUU12oBc7b8ZOAS3Oz3N/DOI42RmWdnAEQpG3nFKB4lY84RkSIHQec+8uvXQVo60HpdgO2y+XeQOd8r+Hukl243Ed5ttmHP1xhBx/JcdZhTp8eZ4221do7fKwNeRhDKdMV/hm1p/OXsEsdam7+VvvFny+29z6RRb8WbC0Ns6yfXrLbjaOWk1Fke7avY205lPdbJZsYGQlQTW64O2fPfINx+mCcbazK4/0QNgGg8dL+YS6B4gAL0/x25txte+s08PRSju3YXEjqzXAnVe/8ImUN+FRYoCwJShNtdvOG3/7jZy5ZXZfLNhQU2aFdQJxl9It8r64JGIvxgtu9YA8dWm+PAJfFYKdr1+fsZcqkrdtlxQXAbGVuMoOFAuq47Py1ebvIxpaeoVr7xIf3s6m8HHDOxRzEb1/50qC9cWHB0plX7d4eC0wWbOkILceT8jQcv3q71m8vvtJGCleyqVRk2q7tcWxo4Vl4P6WgjMIGiQkArld8xPy7WTtLQZUwFiANKJG2NwXBzjYcKwKAyysAbJ5ZtZvAYX/zTLM1odqWn8s1t6GeR50WRtjQOmunLtSjdphsj+3PtPceRMQI9ddzlxbtOy932p2WLjb/lNmm6mRLBP4f4x1u3py1I5dIUw/4/sEH0uw3fxUwPt9lF1C2e+Zv5xjTX7KclFg2kZTQdslAByCYTIpNpY5NJkPF1Utee/75CWtombaSoligPQDNjBC6RTa/U//omlHnJbvc6KJ97+9Q5x6KppzZsLEJiBAR8Vkffe8cGRpRPtyyHqXgNMGGC/aVv5m1o1eabV1KDEBftlWVAjxTVj39fsqjk/lYL0py1fa+R5QK1mWt1M2/fWGYecAsUOCyPXYghzFshI3giKVEPMlmIvaOMaZmA82WaOokqWdHV+wosGRzBwAz65YVpQlWXCgVSMAr7kW3h4DXMoBuC+OJZcvLRUm8FFV4wEl6fscvyFlqg29b65LV35mC45jHx8QBP6IOSXrZFcZkfmaMyahgppJGN5xNZm2ty3b8zUbr6R1G4RjAtyQNgBXwDsXcURQae3oA1OgDSstibOv2NM6NId7gt/PnWoFIxx2lx3XFKaSfZcMi4ygp+dXVMfccG0KhMc4e2JfOu6PzTiOSr6mt66UfiQXS9DgpcWN4vygyguj9KCY2VOHfrkwAbs6g8BmL2iYb/WijGvvNsZareU4E358iTerxYx3EUEjnjCLk9hrqAM89x3x1mo0MYZHzVrEhGf8VasNdK/btbw3ZK8fq2BizYls3l9nmDen4bzYAjS2QaXKQbHYRKGFG2c9+mPLwhFhH6woQ8gTgZAvzl2hg32zKI5JNjfhTbHbyNMqhPYu2ITvGPv2b8bb7wUhH2e744WX7y2+ctXk2J2xdX2E7a5Kow2pDS3b56hnq8veA+RLtZ548RNxI4BxKc3opykVjBG22oXt1xp+Y40c+1sC5H9l0ayeuWeCHs4AWqrUgrbSYt2tvW0dnhzOJkqKKArVrx5oF/vVY4Mfptv71WGntTdcssGaBf94W+OcKzh0+fNhRtJVajBTXBKAJIlO61hdffJGdeAccJTilTdVOaKm+KVXr7//+75vU2zROkQqcFNekQivlVSkFK82rFOieeOIJJ5AuVZqnn37aUXfT+EbKp1oQrqmpcdIBnDx50gnUfPKTn3TSkzz77LMscp1xFFV1TS3w6vOpp56yT3ziEw6g19ra6qRI/d73vsfEnAkdh8fjcVKwK/W40re+GzgnVRwdUpLTvZX29caNG0zs76nl6F4C/AQHKj2rlF0FCAgSlMqdDqmtSuVOishKeSKY79Of/rSTWt35wv+m/1NaeKXSzc/Pd8pIIMPasWaB+y2AkAWpkRftaYFwWgz5vkPxm0f3uO0v/jCURSXUAoZX7P/+nQV74TopjrSK+Q6HrlLFQsgffzbMHtjpchZjpC73uS8s2t+fJgDBQt/bzfTe2Qzv9m9gFyz/usoiyRipWfX3dYB6n/tNIIAdKE+xMMxm6bVjzQJrFvhfaAH1p4LP9RkI3Go3dSCwq75+YIBADsqyUiyR4pz6cimxKo25+k99X5CdliD9QF2tQHZ/8Yf/uPGtAABAAElEQVT/yaa7Omwdqms703Ktav8Bi921zYKySaOEQtxSW5/1HX7DBgEOUtKBoSo22sqdW7aASklofqGFPbjHQjzZFsRO/aAwro9aHdEjgmeRNnut1qZfPmJ9jfWWtIE0kwcA1vI8gHBEp6TixjZi1q2dAPgqQMLYmbN2CwAsnIXTDY88apFVG82FMpexA9nZmi1nFMwJfK4Agwz//TctrK3BwktR4XrocXPllwDXhfMs7FImcMl6K2DIso0efsUmv/e88lJZwoOPWPy+91gwgIG2oC+0NVsPYJ+v8Y5l5RdYZHKGjfcDFnD9/MpKUqeShjQrw4II+oseCCK4JfCLJ7flxgarf/WItTcSsCuotPz9By2sMJ/787ykSdSOflbOSWuKSszIqA2dO2e9bAxIoyyStuy2yF0HAFzYua4ykcqGG/uh5KAV9eVGFOSOvGqdbV2WveuQJW1/0FzpBPJ5jqBwQELBhNjCTYqjRYInPUdQMhvzmmfzbq4NxJWeppe3xfrL5jv9MkBAo2XUPGDLOaXWRcBzvqudlEzrLZnrBgNvkVuR+9MJuQgoMI5aJf3ZAja49fyztgyYk0+QKxm7hRaX3rMFgWWpuwSh3reK4tlKN6lnD79gozcumh8FhJy9By2WMWoQgf4gdqZrIVyfRMWwHZge5TZ46rj1oVCXs77aUhnbBRcVmIsAflBINIp21Ff6vFn/vDX23LXDF1+ya6hzFZcW2oGtB1ELqrJIFG+GCdSfIU3nW1dPoRaVZfv3HQKK89vpw2/BBwTb7gd2EcAi3ZKqUNA8ABpqT/V3rAFwpzhznR2gDHIyPTZHXbjVUmtnr7wFTN5r2QUAONOzpJpB6SUFpY4977V1meWo2ZEiD3Wj0+ffsHO3zqKCGEuA71GryqjhbgTsFobt/M0zdhV1wwWUHHft2mW7tuwh5XGM3QQQe+3UywTYR20TwNdDm99nqahgNYzW2tGrr1gzQFhBcglA3yMoiqE8SEc7Tiq941dOOhBSIql6H9vzkM0BVl2+fgk1vlnSWu20wpwSi3LFAu2gzjEzwP3P2yBwYGESz737oBXmehxb3ASgfPPKCese6AZk8qC6Mm/D3aSuSvJgh/2AJFUoQ0VbP5DBa1ewd8slR8XrvXset+q89TyP27oGSe1ad8EuNl4wV3iQ7araaQ8XP+Sk/zze8Ka9fOE1mxnx2oENB2zvxr2kYVWq2C47ceEUgVGChhnJgFIHrSZvqyW5421gshtg8QSgYQuAFumd9uyybu5/8c5l6haqV0Cj69LKLAYgRhoEU5TfuasXCDbVkao13x7Z+ZCtz6tEhWuRtLm37K3Lp6x/rMcSM1DboZ30dY1YbobH9m/Za1tzSDdNNKp5qs2OXn8DeOsOikmZtq9mj63zACoCnoz6xu21my/YiZPHUBoLtw8fesr2rz9ISsBE8wLkeGen8a0+vktmC6lrITEjaGJpVUobc44i3soKsA7qVpGAOmG0fT/t2etDWYjz9P14YCVl/5mCBpgDFhLUHMnvpX5Bi0Jdi4AzczbBDGHMc2JRHgtHlUvXnQQI8pKuU7BdNL4jmE/NmRYBgZysFxEE5R3fCsgKWDOJ+tMSgTPNjaKAfnRfPwEsPev03Az3kxpQHOlg4xy1jgU/ihz4FGX7UCrUKIAhnSu1Cx/nCMzhEQlcoiADoBVCe1CKYKXJXEDNQ6qLUbR5N4Fpr2/WeTYXEiHBqAGNBpG6GWW5BeCJ6zdv2N07jRaDjR/dc8Cq15Wh+hRkLShDvX4KxbveXoL4WfgjAqgDQ9wr1vZuP2gbi7cCb6BItTpl526eBgxAZRN/8j78f1l2Be0zlrTMqOS0X7ELV84imDphNZs224Pb96F8lmR1DXft9TPHrHu6j1RmpXZoyz6roM2t4nPGuFL9IGnFSRkYg8pUZZYHyAZVVHz9GAZoRKmssacDNTaAFeDie2plYc54WWAZXzN4VesD32kkdegY6b3A0awMmCEL+0YSBZ1BjareP2l1pBVeHp+zQtSydJ1k6iXhQesHAGuYRalNwB2wW01sNjZwW+30kHXwtzBsnkEa4DjsTrZJ592nqCcNAECLKPCVAvttBNZcBm4719dsvQB8Kfy7KiYDKBd1HsLEDd5+a+ggzfoiqblJqV6O8l8J9Y+aaq3UmesD9O2AzluSAF4AmBOCyFxDJJqqhooj8AvfG8UeHYAg3UPDwIl+ywE8LEJ5JQbwcAn7dFB/72CDETLa5BCBL0X5NhnwPRiqbpr2cBeQqBPISPWuCpg9k3bSSR26NthhwYBaOQmkKlUqN9qWQJo57tczSIr46RlLEejHTzB/65gYtlYBWcDmRboOCn29E4CJpK0bJi2sxkdRpNMtoI9LA+ZanvNafx/KcsBdxUCpFYBz6QLnaHNdrF90dXQ446SMjAwHnFPd19hJPwFlX1ld46h7Yyn96yd3SDVYqeG0XrJMm44Awu+MA95kzaAqOZdhU7jdAhprBY6bpq0uMb6Jpv+tTgBeoM+ewcdcRxlygPTYudj2kcwC7Ah4Q91EYM4ZE8FB2DhjqMZx0vJ5hyyY9ltG6srimESLBFSE+UEhbcpuAqN34yMSgRSrqVd5AXAOUKmTNlLLukY4KaFLsgABaONTU6S/5ppLOOr0eFJyAz+GYSdGQfSvgGjT4/SnfaR5DWZOuY7xH6mWh7pRi0TZFzXdTMC9WFKQ+jmnGSju1lg7PnLGgfmq4gjSM2aT8OgYqqMN+PgOgMmk5CTbil1q3IlOCniqHsp1AC2MLVVXu3xA24OkAaUuFALV7ojPJl1vGIpypPgF0LwrcA7FKjd+rgRIu5Rnjg5C4Q9AtH2aVLWA4qH4y2KIqQTGNePU2dYB1CKBw6JQlEqiv4ylHmpbBZ7RBgHAR1A4jqf9FSWnsL8BOBwl3i7qXDB2K8wuspW5JLtwct5efOk6fifSyjbm24ZKYDAPgCvQTxy+kiEmKnN+e/7bw3b24jTp3tPsIz8T42xaU9o8tbFl3iEqgYxmgCHjYyt29syy/fevHMffz9ohUlE++f4clJ/cKGuZXbkway++jGfyh9re/XH2+M8mWGf7sr34Ha9dvNzO/WPsicdzgLTC6OuAMKZX7LVj0/aNb9fRVyzaR35ug/3yLyWxASDI3jx+T3GupafFgS0++oseK8p334NZeLbhsWX77gs3AVq6UXKtYrNdDpBbCCAZBqI9CgYJohyHelftpRf67PAbl6xm4wZ77yMAxkBUYUAy6uyDUagM53qCnMZZ7zjy+pj93d9fpf+Nt0cPlqEsF2NZufiHZbM7dT575tv1TBOm7OBBD2MyNu4suO2rX6sHqqsHllnH2mc50JNU2vCzpCo8g72e//uz9OVz9r73bLKfewpFaxSaDh8ZQFHvCqDWLKkx99v735NqRevIahbvYj1yGQUxRv/AyMeOdtlz37yGSliGPbK3jFS2pPQlBa7RjWvsSrUA+BOc6EORrR/1Kp89CuxWXR0DAHMP7tGQOIw0iuGMpVyA08deZTPk02ess7vTHntoNzBJISl570FnvZ0r9tKLvXb63DHKt9Ie/8AmVKVC7cXvDdjJM3ewdaQ9cqjEduwAZAW+8bJ+8+aJRXv19QlEX+pYqy219z6eZZmAUq1Ns/b5/3yKFOeDbAhZDxRZaYUl+Dls7+b9fCjlXj4/al/9K+YPc+l2aH+JHdwHXAQEtUpnt0rdW+X9lJLy5Vdu2vm3UCaPBMw75GF9lrEG9dL5O+0xmmsKqlLSxVvAip/9/FkU2YJs37Yy+8UPoTwHVEkXAji2yLjrDqkgu2zL5q32xCNsnqJuv3lyBBXcZoC3RHvsERQIK8muhXodwwh788yCffs7563uzg37xQ8+bJ/4eIUl8n49XUv253/aZedvoG4IlPOhD0faxq2o8bF+FYoE4ujIip087rOXXm6iPrpRHMxhkzbpYZMZq6CIqK6VR2cNHQXC16fthZc6LD8ri3oSbxu2ojxJG5EKueawEbyfQMjpyRW7ddnPunodKeG7gCRz7Gf/zTpASVTFuWBTy5L99bduA9+v2N6tifZLHyTVLOOl578zYX/3Wi/dvM9+9dcrUPwjUyLtw0/5Xb3KezzTY42DZo/vSbZf/xTgnAdg8PyyfQNw7jLzra0b0uyJ95eghqgMjqgC0mZwT44y28k3puybz7HBa8hs/wMxdvDhaBRw9Y48Ow1Ra3m4LLt+1UtbbEbZMN0e2Z9sNbtor6wdan6u9iroMQmgV9Pg02eBO5/x2smrd9hwkWw//28o80o2cXHfadYUX35thNScjQCcJShVokiZ57JrtSP21881or6cZHt3pNkvfxAIPQZVWNY6bwOEvvpqO4qMPhShc+ypxxOAB4Oxod/+5Ct9dqEeyD0/3H7xKRQcN1D2tFE9j0qprW3B/vRL52xg2IWaZpUd2Es/koM/pj0FKc04z+/iBd44MklcAWVB4O6HH8qgDeK35Y/wBbJDBACjA+Mx/a695rc/+x9vMU+8yRrKNnvqg5uthPTWwfQJw/iNS7z/S6+etcqqRPvAB0uZt8QAf83Za0dYF0GN8YH91aQNTgVeY0ZIJVL63u9+z2e1rDE8fCDJnvoFAXKotjP++q//5S51+A4bP7LtY79cxXOhWvl2G2QJgzEAfW2z3557Zsga2sYBzOLI+kOfW8qGDd5RfbWmDUHAb/8ve+8BH2d2n+e+6L333gGCBAmwdxLsvW4vklUs23IsJ7lOfkmuHSlX17Ycd1tuklbalXa5TeKSy94r2AuI3nvvvc4MkOd83LH5k617o7UiWwlmdwhg5qunf+f/nPctK57Wt/6KhXJTCYx3o5W/0VORAIhm7YEZQ4n5Bd8AdytNerA+fu/tdp29+BiOxR2bVCDdtajv0v8Ms5j65PFhVA2xYQ0a1mc+G8H9++r+3Sl9+H49cOCgNuWnA2CH0r8A8DHP0Ezb9IM3m3Xx1rhSY/z1W7+JUt0mIP+OGZ05adO3vnfMUjU8eGAV7QhgKADuJM/H589d0rkzP2J+IZpr2EMdXGYtirEW8lkthrlyMzI2/zH3Qq5zt7x/+tccOPfTp9ncHnMp8KlSwEw2m8D1G2+8YanNxcXH6Q//8A+tQLEJ3s695lLg/5wU+HQd1v856TN3p3MpMJcCvwgp8K8RnDNBcWPF3t7ebk3SmslbA7SZIHgXk/BGYc4AdfHIXZsJXaMgZ7Y10uMGxoqMZMKc1Y79/f2sAKrF2qfa+s58no0SSkoKNmrsbyaTzbimt7cXh7FSy5bVqMSZ783bTBIb2MvYhjjtScz5zYSusXA1k7vhTIRnZWVZxzULCMyksrkeE7w35zU/zXHS0tKsczqt46KiUBLhPg30ZwI+5tqMDbkTADBlx3ze0sJkQWWlGhsbre/mzZtnncts7xx3Oc/nnGg2q8HNNZnrMYEns0LcpKG5t3/J1xw49y+Z+r8Y5zaTdCcu2vXf/wSVGyYhcCX6+5f5LpAJoJcOe+jrAGzRTGh9fNWhb/z3aRWyLS4O/+hl9sllteF//r88tWubm7Xqsh55/j9gn+O3sA9jwopq+I9exJqtAOAkE5nMBWrfZlf9xhc8sc0yk8ZMXswNAf9Rms19MJcCP48UMH2282X6UPNy9uWmvzVguwHmDVBu+mOjNvf6669bqq1OMN0Z7LWj6NBcWqzv/T9fUwf9enpImLblLtP8/QfkwwT0VFiA3IlIuXaOaOj0RbXcuY8VFsotS7Az7WjWcF05lnasMs/JBhZLkhdqQt6o1LnS57qyehqORP2XsY0CnHNg0RiycYUCtq6UGxAacnAEXozd59OguDsB/5nqenWcPKWi+7cVm5mmBS+9IK/0eZoJCCIIZaACpku5ZRcCNQyI5GhrVfv7b8m3plS+C7Au3fOS3DOALwjczzD57cCyxc4OrgSXOz84ooljH1jKN/77X5Jr/na5EKh1N40swfi+cyc1dfOy/H2AV1LmAY0APjDu8EEZJYrxSwDKAe6xkfLgHt0jsXADnpvlnmx3CvTooxMawfZp/ro9itqYz/fhTyFCQxq4TGDViE0Sx5uublQrCwLaUbjISEpRyNZd8ly5wbK+NFEVF2bYrTwlED5rqciVqPvsMdXUNSt94x5FAza5RUUArTF5zGS/seiD9ZJLe4emUdg1drHDQaGK37ZTgajTuQYBPtJn2GuKNXX5qIpu3lBEVh6Q4RL1sU97yRPFIRESnpkrL9SufBgneYYBNgRyfBYr2AkMDpRXqviDHyiIwHLGqjXy37Ff7kmpmgFMtBGUnjEnMJPbJlpdUq6Od76n2cZKeaOKHL5vv7wzM4gK+nAdZAjBaGbunyrxAQtNV5erlWtuvn1Hqbl5itm9Ux5Z6Zolv0lA8tpVdtJkFHCuHHWoc7dOqLSuEGuo5dq+eKfSwzKxAPNSH6p/d8vu6Pi1Y/JL9tHKtaux3OvTw+uP5EW5WTR/ISoiqIy4cb3AfuOTYyiZtaq1EbAsNo1g0mZlp2HL6ol9DXZxd0puyCiB9QIATKLkFJeQYKlcrc7YhEJaNOXQhoJXtU5dOK7K5nKlL8zQ/k2HUZTKIRuBhWYI1Ffe1W2Ani6U95YB7axdtQGgJBDlrCKduPoRSoGjBI+2atvCvVg1opbW/UAn738EOFelpWmrtXv1YaUnpHEuxugzfbpSdkPXCwusQNjWtfnqb+nQ44cPLau6HODOyIBoOE0U9byxdkKlpaK2Un3VnUoMjNPBLfu0LGe5vLEI6wCYuVmCktztyxrBbm6WwHS4b7h2r9it1aiXhfqFYwE4o9ruWh0teF81XZUo4WTp8JoXgQoyTe5peKJHd6vv6/SjcwBKY1q3YK32ZO2x4Kvz5Rf18b1Tso849Jmtr2ktinoIFKm2q1qnr19QWX2FErNSdHDzYc0PmSc/rHsGprt05d5FlO+KUPQI1kKsPuv6G3Sv6j4BH5TYktIVCfDgRcDfzUaQCxC1qrFKVYCX0TGxOrT5oFZlrSHY4oPVV7dl3Xnx8WmUjFpQgvPmnmK1YQkWQFmoBXrFosrn0IOOIp28dxb76FZtWrwKJToU3UKSqYMBQD1jutx4QcdO/0izQ3a9mP8ciwm2KQRYyQ6AZqP9eVa907Snpt463872lcL+E1/ObUx7bvYzbbjzM/P3j39uPjMv87nZzmxv3v/Uts7tfryvcJ7HOhD/mOM4z2l+Os9pfjrf5hzPwkLOfZ3X8+y5nNfkPK/527wM7G2ev4ZtA7pZd0nd2AYbEKy2tkGNDahpAsTtyd+mZfPz5IPi26BjVDcqqIOPb1nPnzTfCjdKenlrsBLeiCpUIjUQ9xdHq85fP62K4nLLUuvwjheUEZGJ9pMvkNWIqvtLUGO5pPqKGs3n2XfDqnzAlxiVVWN3exXIeKKXfnGttixcpxSfeE2Ax7ViC/2ouRZ1PUCw8BTNQ6Usjn7UKHAN0hZV8tz7pLmBfmMcRcVIpQC8hbgSMLbulPxhu16gnRosx+tHeuQ6MaW0wHBlACWF0C8Za9EBgJpi4I6y3lZ5Ylebg1pWKuBiKBFs05q2zk7zPVZi7Y2Kmg3UiqAkC5YqAr6po1/wpHOLQakxkNroCbnsznVN0NnWoHw5Rp3OQB11KYqdNuC0W81VAEAOpURhw46VZwDtcDsAWvl4h8obaoAjhDpKvBZhd5xDO40xuuqJ5t5BjRBiQEuA5haaa6evRhzENPXYBM+qH+CoCQCtpn+ART6TWEwGAV6hoMqchDtlCT5I9VMTKhzoBOSaUjZpkIM6bRhRbJNW01zzI+rqo+Ee+hB3bG+B2oBM27BCK+W+IecViyVrBJadxjrdDlQ+TV8+AMhkFggEYCWdgq2oAUWbB7FpHeolEBulTMCqYCT/6jq7VTM1qH7sL80CAURklJGQpCTKmgv9sQGHjYVsEjBUNoBXjAHngKOaG+oA84ZRtAmy5jfMmMnUdfM25dlZR5x1x1l/uKV/9stZp0x9cc61dDFH4kXf1Z0Qpkj6g1X+zJ2Qh9eBLqsdgKp0q0bzyAvlwDz6gJX0/5Psf6unC+vTAUWTpjtikwDPKMNcoSlfZk1WN2WjjfxrIg8GsckOA4JdSPoleTEug/YZpA1oABCu6O1CSRN7VcYJedhLpgJrWopzBPxrUPy8BVg/FuyN4lGYEjjHcF+Xuvo6ZEONNpQ8D2KBhIfdneA+AD/vftJ8iHLsgy1wVtp8wH8XNTXXyTY4AjAeryQU5bwBScbYthx7b2OvOgRwHOXqj2VwnBYEoWDHecYgZkr621SGQmVgWLCWAJ+ucA22IFWqlyYoKyNAEx3Uh9q+drWM9MobpeCsYI4BHG1siMeoi90Gykd5sQWVUR+OkxOaoHmMKfwBKfpIrKdgKApf40M4OABVofjXx9yUsSaGK5Iv5EUgMGGANQ6iHqLMNgA4Mci8moFkU6lXocyTtRmItr1NUfRR2dEJmhnz0527Nh09XqyWvl7sywHiUgO1FLWk3PQgJccG058CIgLDHcOO8TyqS7A+2rA8QKnJnqgNofQVjUoSEBMCoqgSAqt1zeoGinN//a3zgDx2HTq0XHv3RCsUKMLGBEHhPaCHDwbU0TmBSpKnXvmlWOzsJwC/xtXaZgMQ89XBwwGoqrkBqlGHJ2exlcfu8s1W6kuvXgHg+txnUXNm7Hnjqk3vfdiLzWkdAFcy6mexLOZjEQdpRpYCzmH/erlGp89UYaUZg+JWDIpeXkqIx/4w1p9+2xflRqMKNqvTJ3t1/MR9VO6iUFICwMtgzjCL++N45toNUMYp2XZGJz/u0qlTRcz/xen5g2natBXbU0A1iqsFgv3ttx9hrdylDeszdGj3PE0MueuP/uQB47Umbd6SC2iWpoQUDyxMAQNRY7t/x66/+851AI5h7dy5WC8/Hw/k5YYKVCd58wDFq0ngww3aswcIBvjHi2txw0bbvGaxr7x7p18fvlek4tJRlP1QClsSaUE7UXHM0cS5A+ZgGw2EUnBlCseMFtRHGwBeQpSJqlZqKkpcsd5WHvr4ObDU5di0hedOTOuNN69iD9mlF59fzzUnWdvQrAKQUx5+1IpC4EktWQYIuH81faM3cGWXHjypQrE3UAefywK8QtmZa6UpAH6zAbsMq6zyHuDgPO07mMRCEBTAKkf1B394Se2dA4AsS/X5XwIAigeQZ5jOqYDkgdweDuqdHxQDJ81aYM/ivBAUAL0VEePOIirsG4FsbFjKn79Qq4vn6ij32D1nz0elCkWsOBf6FjfFJ3owhw1YZgoGlE/hwyl97XfPq28kVAd3L9QrLwYqnrQ1fUZ147jOna3Q9Wt1WsCY7rk99D1AUh+fbFXBw3rl5iVwz0nMKaOGBrhmLvQuYNm7793TlasFem7/dv3aryxSMOdtqgfu+ot6bFh9tWFFuF553UMZCw30yFMIIHhvn4vukzYfH68FuupQcgqqktk8PyX6Y0/PO9obENGNZwDgw7Pjev/DBhpbTwtsmo/zQ1SsmyKifLhH7JOD6NspU4OoPj4oAGhDpW9orFU7AEEPHs5ETRE7S661ps6mv/tepaprpwDEfPS5l1KAnj31rTd7df7WlJJSfPTlfxeEgp07SrbAbRS1YkDDv/perx5UTWnz8iB95VdpK5JdUX2z650PxlVS+VA7tiXp0OEk6g5jAjo7uisKKAuVUGd9eN/Ue8YtpR3EDWaUh9VwQjKKfbGo0kX50U6zgI32vLxkCseYNtXVu6DcRnu7GLvjVHeFk89hlJeQYKx6aRdgenXl+pS+fQS76qpm7dpI2r4Yg/IYQBZpYNQfj380qCPv1SiVPvfVwyHKynTTrftteuO9Cnn4pOv5fbF68YCnPA1saHNVRb1DP3y/EaW0CS1Mj9HrhwK1fAljiKZZfeObrXpU66F1XM+Xv0QecSxv7N4NxIYMPkJGM/qbv3miwqI2FPYAkBcnKDWN9iPOjbl/T2IBpCNt581rEzp9qsWKJ6SlY2M7P446TVsU70F7ik23KaPcH8MbPbhnQ7nzEscu0hbmLF56bSV5Y8ZvrqgAuujWpSm9+fYlVNMCdfjFhaiDBgLlTeji1Sra41Gg5EU8p4agNmlQL/IQe+L3jkzqyq272rI5Qp/5YjZtIZbggJG//41y0qZKuQtT9Ctfmqd52OsaqNG8zHXTVauzyaEP3xnW7UfNjAVH2SaUMoL9MOUqjjobBuzp4T2tmnKH3vpWFdA9yoCxvsrLCyavfSwFyUjyMYTtTBtm2suORgeKcy2UoyIUIf310quLaL+YwwGOtFHmz36MxTUWqzZ7u156OUo5uf66XTCBemEVYyF3PfdCKrbP/qhO0s4wzzFAffrg7WEdPzWBOqBD/+7fhGnDlgDg71mdOz2l73zvXdrIYL3ymXXauB47cNrZQZRRz5w4Qx/wsZITk+mz9mHV+hScY9RH9WYuxAUDeMa3LvSxlD7ehrl5mj5WIv0U/8yBcz9FYs1tOpcC/5wUMA8yxj7s3SPvWgos+ZvyrcloE7z9WT44/XOucW7fuRT4+aTAp+uwfj7XNneWuRSYS4G5FPifS4F/jeCcuXIzgWrezslZ5938NJ+bbc24xQBuBqQzgQsD2pkJ4GdfZjsDn00TRDfnM2MaZ6DE7G/eZh/zmZk0NtuZY5rfncc03z07DjLHNOc0xzWTzWY7ZxDFnNu5rdnO+XJ+5vzb/HSez5zTvAyEZ47149ua45htzNucx3mf5nOz7Y9vbx3s5/zPHDj3c07wX9DTjU1gofr1Kb1xzsHE2z/chKkpzJXrN37dQ7/+ugerjF30x9+y6Y/fsqGOwbf/UJX+/te8KBf91//kqS0bWb3OJGNxhUN/+mc2nXtIcJ2JkWeq3z+c6JPfzOEiWfX477/ooRf3sQo+jnpMkGbuNZcCcynwL5sCpl9zvk3fZvo8E0w2Nu4ffvihKioqLIV8038uWLCAoNYrluqV6RfNtmYf852DPrq9vFTf/9rX1FL8ROlYXu1Yu1FZe/fJayEKPMFMUrLN7NCUxi7fwEK1wLKpyli/kbg27VN1KSuPO1AJ8kHxAnstlFFCU1IUCCzlmZxkKc/1XinQyJlL8ieIGbAZtbmNudieRhLpA6ybBZxjYniW71xRMrKXVaj9+Mfcx2Olr8hT5nMH5Z6YipVrEMoaRuHNqHoQ9GBMMkPQdwaAofH9Hyiwvlz+C/LkuftluaUvAEQDnGOYg0MKgAUtGQo6He++Jdup4wDH4fLd/6K0Oh+wLBgFkCnNYiU3fuWypi5dwMoMZaalq+RA5c4sPBjoJnDNcXxR+fFl++D4eAXlom6HwsosANb07Wt6cOIktxOheSgeBS8HKgQcdPFhTGT8+1xYyo8a0MzopCbLqlV76ZJ6WHWeM2+BQrbvkQegIjPZ1op+ssUM/gxpolnuz15ZrP5TP1IVlo2pW/cqZu0mLFUB5/yYdzJRVCbxZ/DQdjS1yHbhovpu39YUQF7Mth0Aa1kAdiynB8ibaa7ALvekii+TD4BicSs2WzPy9U/ua5x7d6DkJezGAkOwYiOAG5wUKa+kZM2gWtULgFly7EeKIMCcsWWbfDfu0GxcAsE1c34zLuOiCdAwv63pwidq/f535duF7czSZfI7sE8eyclcq1EWZBsXYEPzE/DJxShXoajXdO0yCnx3lbkoTwmAc+4ZGRY458K2LsgF2jnNMCpL5R3lOnfzBLAUgYz8Tdqas12J/ikEaj1RkBtCaeyx3r90RK4xs1q4PE8NFc0qf1AJ+O2D2lgSQUnAB6yCTXzbARgyitqQUdQydq+rl61WOlagXgT5baR/YfM9IL2PsUhCDYYA/Io1K7V7/UFlheShxoNpI5bEdc0l+hh1vbaBVuUszdHetQeU5D+P8ukB0jOm4ubHKrhXYNltLs7Lw5pmg/zdg7B3LQHwO6qZwCltW79Lm9J3oIYUpKLeh4BcR1VVWqH1C7fqwJrnsU+MJ22n1Y2C3U183a4U3QTcmuJ6V6qnvpWA2hPswIBWyI8gD1T6Jl2BWmya9J8Gih/WTO+UUoCMdqBWuHTBcpR6vDQyi5Vz22MdO39UpeUlqIC5KHderl7Pfw0FrRy28UYBDTWotnIdvfkB6nVNysPCdP+K5wCAUrk7nhdmRnWv4YGO3vkYy7x+rQE82jN/nwWznC+7qFP3TgLduOjLB35VK1KXkaZTAHiAc1fPqbypShk583R44wtKC0jB2tJAGv26iXLYQ9LbA9AxOSMF6756Pa55SDmdITAchQpRMOCOO+2ECXqi1DY9gP1pv2KBPXev3qNlGSusax+zTwFClOudC2/qSfUDAAJfbL2Wafeq/cqLRZ1lJhhrwwndbLyvMw8uaLSrT3tW5WtdzkpFBMTSNgZgGTil23039eHx9zTVParn1x3SplzAuYAwtbYC67JQyfkcZdpR5zOG+d35zEHF+P98OZ9JzD7m5WyXze/mGOa5y7zM5+ZtPjMv509nu+/c1jxvOV9W287+zmcws80/dR7ndTv3e/ZYZl+zjzm3Exoy25ltzNv5ch7bbGu2M89o5nezv/nO7G/Uv43auYu3Q2ce/kgdqIO5oy7WNzCqkbFp1A5jsPpaT8A0i6CdP6lvU3F3ERZr57GneiQbENtCoLq9Ww9qUcpSAsrBQG6japto0MmLH6u5tpGgW4r2A1CmhmZYdX5ydkxNozW6Sh4bl5i0dKzoVuZjjxWPBR5qg5fPArgADGzZrPyMVdixRlFrPdREfSpsrKON8lJqVKZSkB+Koo3zJptGaTtqHDYVtrXIAVSUDESdBmwUCjhnhsYmB0Yg51qAvxpQOxsl4hmF+t98VNtiSRcf01dzHFpcPUFFshRwzh/YdWl4kpJ9UZihreN/dRiwDvi2rKVOQXYv5QUnoxLij0Jdu+qpb54EUiPd/LFS5XiAnK70nwYU70fXy8BySVzTPCxkJ4EA77RWa8Qb+/WoZM33DrCAoDZAq+KhNpVzn3YoiXAUFxeERWgRi84C6FOaOP+dtgY5hkeVCyiUg2pWEMF9c+2TNPf9tIlNkwNqQdWtf3gKKz0/ZQLdZ5J3vqa/4R4m+LcGO9bH5PU45SEH4GsBcB1L9ixwzvT7RagaPhro4TrtyopMUCRqcK1D2NF2NsInuCgMxcYQ8sSAc0ZVbdYAdKYvtTtYBIRFY1QcR3PHGrRHbajqRQHSpWBF64m1XG1Hp9pmxzVGcNaO3NbMyARtfYpSsXI14H1rR5vaR/oJeKO0h+1tFP2SG4BdZ3OzNeYJC0W9DGjOzDc4y7+pL8565pwj4VZ/Zi9Tb0ydMecxiwzN4oeGuno5UIIdTotVfEqWVnmFApi46ByKgPWeU4wF0AikrruP2ZXrHaE1qJuZcni3F2tQLFYjUf/dGBNnAYsmFs//GiQIXY9iZWNPpwWGB/lgw8YigmTSJgwwDjZZA1xLLWqYVQBgRpMylbHhItrSJJolA4cbpad67D+vd7QKNzfFslggnnHMEGqknQNYAKNa6+NtygOKlHYeXBkjcEj63AnyY4KFkuRfahrKhTbVNdbIZZi+OJJ6AIDnibrYMP1M0diA7gNmDaOKasr7IuDphYzLQgnaTyBPVYLqa+kI6nVhQVoSGKMlLqjbca3m/kcpX0ZpsRZIr3sQ6Ax10DggtkQU+aKBZhBGsgDCTsYh9T0dWKkOyx/L85yQOCVT5ny5zxEuu5nrrcNysAer7bCoSNSqohhvTKmtrZP7wUqP9PWhzvgYtWDK5zTj4jHGRXYWSgS6MfZAvdgHYK8F9chhFJWTAO3SUe5znfbEEcGh67fbdbsQez8smz2hapPDg1B9BVIwoFm2v0JpFIqLbbp2vU+NTe30j1wnykj+lPPY+BisNQHNc4CYAOj6emYtmOavv3MOgITn9peXKT+fcV0QCxCMDSbwxg8/HFddQx82jNLnvpSIStuY3nkL5dFhb1TVfLR7H0ANKknuUD72KWw+Acu+/y423FVNOrwvTp//QihjfxcVXAdW+pAFJ4MN2ncgG6WgCCzVqY2kq+keRmizq2sHuW5s2Ln+sbFJ2gkHqoLGKjNEaRlYKmYHWPBcZeUYkF0lCqOM6e2efAYYDDiYkEi7kuWH0ps/NvTu2CujZHWiWZcuoFLqn6EXnovXmvWovmK7ac5ZVzOuv/12If1zm9asTtVze1Gh7XPXN/7oNv1Mh3bvXcI7WTEAbRQhYrkALQ8B5759W0O0BZs25ejl55JQ+nPHYrZPJ88+RLENcO7gWm3bHgYI50rZ5AYpYWQ1wDJtT6NN16+2YFVaz/EoCyhqevtilRmBlXlmlOYvCMbiEeWsRrsKbnRgqV4FpDdO2+IH0IPyW2w49+iv9GwPQC0UZFHtvnAacO6tq8yzjuCYsVo798QoOMwkLBbuwIMff9SFStp5VNqSdWDfMgucO3KkGyvOeq1cFab9z6UCUJl5XLMP61me2PXBu1N6UHhLB/Znatc+IGXSrL56VH/0x9c0SN3buWMZqloJAHokDAtMnrZ11KHmaV2/0q57dztRIJxGVQ2oNIDFCGF+Skmlz1gUQHoaG84R3bjSqKLCbuoFzxCo4AWw0jM01AsVtHjlzPdVDPNRRgWt8NGEfue/naXvSdSLhxZo/wHgwWjKKClb3zIJOFeFElyDMlKW64W9kcx1UW6P12Dh2q41a7n+XfFKTPCwwDmqHrbuAKhHC3Xy1BVAvK365S/lAQiR5g1TqG9Vq7LKX9s2oGb1Ek4NGWY+m3ENbdjEJNvUA5veaKOOVaOUNgz4RR6gPBlFe5eWFk3+BbKw21111diBXuyiznZzldRBf/oggEVj5Z2aGqulK1CBxQViBEjyLvDq20dKNGnr4d6ytGt3PFal9GPsWVtn1xs/aEBZbUrL5vnos8/HyhcQ+u/e7FFBkUO5i0P02V/1VHIydZzy5UYdLC9xAKkNsZhnTKuAl37jl4NIm6fg3Ls/HGc8U4hSZLr27UdtFVjRKLoa4MrkIUMDxqyzukl9vUkZ7WFc4k3j5gcQGBbqDUiHklgW90pds7GI7MoVznMHC2z65iAUEUPCvHhj+5kQjJ1uiBYApAWjAHjlxoS++/6Y6po6URgM1QuHIwFuuV6KD90rqogDevvtOsUw3nr1cKjmA7tdu92ht35UiyJjFp+FafdW2gueP8Z5WG9gQe+xoy0oZg6xYChWrx4I0vLFjLfInz/4KxbQtwZq0ypf/fLnvZSSbBYLGHDO9Gc+WMy66OyFTu6PxfqdNoBRA3N5oujItklhtDPBFmjZ0w1Qfqtd9x9UUFeneDYIpR8Cbovi+1RUb+fz7ME9UMQZd2It+t1rqDTWUvdXa//h+SygQuGXMWBfB+3iZcC5H5znnoGNn1uoROrxhYvTunm7DaB5TIdfSQfg4zpIK5MHdcy9Hvshyo8XbgH1hup1VBET4j0tRcHf/b0yFZbUaSVt1mc+k0W/yAIh6i5DKV7sTF80MUSffnMK5dFWNbY1ojSO5Tfj6gj6l+RklH5pJ1PTmSOxu+j2zSHdpV/pG+izFPQCghhHsQAwMSESNU9foGRTN6X2hhm9f6RDd+5XKieHPHwJ2/r5/oCNnJj+4tIZG0CbjbazUc8BRi5eEkRbN8IcE8qPpNuLL8dpI+AyQ1cucQrFaVedPuZAWY+FRS4D+sq/MZa0QaxL5FinsfZ++0NstFF+fG21Vq8xrkC0Z32DOv3xKRTpTpFXqTp0cD+qd8tpu4wNLY0sY02jPDcLJEmKWOM98/PpSMakz0/3mgPnfrr0mtt6LgU+dQqYhxmjjmKUVszvSUkM8JOTP/Xx5nacS4Ff3BSwevNf3Mufu/K5FJhLgbkUIAX+tYJzc5nzv18KzIFz//vl6f+qOzrPxJuxUn3ApJFTdY7pE9QAsFz9hof2b2Vyh1WDX/ttLByYZB2EzXj2ZbZdHO2ir/4XT21eb+y8XJi0cugv/5pJXibhiHWZ6Zif+LK+Y7bnb/9vTx3eRWANSX3in3OvuRSYS4F/4RQw8w/Pvs3lmMCusSW/BJR19uxZy6rcBGEbGxsttVgDzq1bt86CGsy+BmYwLwPOtRQXAc59Ve2lJcqIidWW9RuUtXu3vOZlYAnqgzoY5xvH7u9mgcpv3MSGcEyLd2J1mo5yE4H8/opajTb3ytY3oUm+8yLYHAEUFozVq2dSogZu39Pg+YvyYyV60I518slfBFgWxtlRNmH1vhXJZbKdA8tWigLZqZNYMD5R5uolyjy4V+7xKbJ7As7hs+kKMGOu3I1AsYOA52xzvWoB50IaKhVowLntLzwF57BJhcex4LlpWrpZAvgd730fcO6EorCS8933otxRenNF5dbFjiIcijUTV69r/MIV2VgUELR1uzyW5WkM5YoelPhGWptl6x6SCwFTd9RFQpYQrFq5Egs1X9kf39bDsyflYcCBLSi9LcOuk2AKS9eJMJsA1IQVSZgZGdc44FwVedRXU0VQdIHCtu2SR84SgJKn4By3Zk2SW+AcaWkrL9LoyaNYGjUqYfteRW/IfwrOcWwnOGfmlu1NrZo+f169t2/J9gk45wWE4gLoZ14zLZWavnlapZcvyhvL0eSN++QN2DgBoNDfWK3udmOfNq4Z/HqCUYpJJJgSvHSJPFJSNQg8WMxq8FBgt4ytO+SzabscsQncG8ELADIXJratiJdjVpNPStX+9lvywdrMl3TwO3AAdTq2BZIhIkWeoDxDpMvYNbkBdfRXlqjm4iW1cd2LFi9V4q7d8shIw+YXuIFA+yyBaQOEDM6MA1OU6zx2s1W1xdpDum2Zv11x3gmUCQ+NAhk+qn+kdy79QLMxdi1YsVCNFS2qKqxRKEDgAmBKX6AxCrMVUJp1w2IWZRhXIi9pcWmWZVwoaicuLr5ACXY9qLujcwXHVV5RYgWfllOWd60+oMzQRfIGnJvhvuubS3Xi7EcExJsB5xZq75oDivPHJhiQA3RNJS1PdPPuDZTtmrU4b4nWLF9PICxAJcCmJ69/JJcQu7av3611ycCIwAWW4tzdo6qpqEYBbacFziUAA7ij7NQz3qUb9Q8A5wo0gdLVysXL1FHXogrKplG5mJ+VjV0cKo/4702Qf1P+dtmx0vJzeCnRJ1Y5KQuUFJ0KuwBw5hjSo7b7On7hmMrKy+Th66bFQEmvbHwFcG4ByAAWdtOoLbZVAs59qPZRA84tAZxDAS8one8BJFHzutfwUMcA53pQ1lqRg1Vr9m4sfUJ1qfKSTt0+IRfAua8c+A0tS17CGGZMNSj0nbl2VuXN1crMmQ849xyQSzJlyFUDGGQWFN/Uw8ePKP9uSspIVltPo8rrHhHc8iYolWQp4Xmg6ujK2MWNfWa8ALsIxEeGhCsvaQnqg+m0a54aAmYtay3VkYtvqrj2CQFCP+VkLtbuNfu1OGEx5TgMYGJK12vv6OzDixrr7dfulRu1NmcFqn3xluLc6OyE7g7c1gfHjmi8Y1gvrH9eW5dsV5BfiLWA2riPGGVr0+6adtYEKM372deP//3sd+Z30w4793O25z9pn2c/N7+bc5pFSQbiMYuFDGQzOgru98kiKQPSOL8zP51Ajrle59t5TCcYZz53fubc3lyX8+XsM5zX6vzc7GM+M/uYYzhVTc01ORddGYXzZcuWKSUzXk8abqI002OBc+MmUk7bEIcl53xsGxMiolAY9EA9bVJFbYXUwbMqfFxEuwR4lZ0LOHdAualLUW4MAcwaV9tYg46dPUqAvAm1pnSUFQ8pJTgNPQpvE8ZT82idrj66pEKUDNMy0rUOQDIMZaryqmpduHZBI45hrPW2Kj9zlWLdIv4BnGuow3oWcC46XalB/oqkHTLioaO0RRY414rFKgpUybTz6aisWeAc2T9MG9kEFNFE+zeCnWMI/Usa0Fyyt6+CSCejZGKAiz4XwDnUtEqwmvQHoFkRkagkoo/+BGXNq5M2qMQ+qIqWRvnTR2YHJ8jXP0Bl3U1qB1gLBPyJw6I6yLSP5K/DqHGwqsUGcGQauHDUXqOBgIcBxu60VFvwWFZMCopyAfS6Ugt5VdjbrJL6Grl6BKCsFqtsYO9c1P4CGBs0cpF325s0CziXExiKklw4sI6X6WqArrBgpc+sBkgaGh7hWvwthbsU+rBIaDjDaszQN4wAD1XaRvUYEG6Ma8z2j9ZC6k8QfThXaR2rAqXQYhT0jV3XPCC4MGxZm4Chyvua5BHkqwjSJIjeAgyewCyFhet25xze9CNBWJBGBoRoHPCyGri9BRgrMgaggj7PfQqbT8CmTgdgeyAamVM2OYZGNT85hTYHNVOupwW4pp1ri4+LUwaqgWGfAN2dDfUo0tlQMMRyFqV/s5DQlH1n3XAuBnz2M27nZ/Zy1jlTd6qqUNWsrAISntBYegJA8Xyt8AScY9B0tq1Ojb5TAPu+qMNMy4s6kucTqdX+IZQHLBaxPm1CqS0C+HBVbDS2vk/VDKcoW50AOTWAQh2AXGHAivNCUQsjXYLZhpGFBc4Nkc91wKHl2PASs7fGhosY28UzHjRAO/8CpqEq1lirXjxg44G4Yn281Qdw2TnSLRsSjZa99CzW0LTpJhfd+W+WB1oP9g+hXQ4BVOt2TKgGgNN9zGapDiZju+rNSoVh6sqj4V6U8xhfGgCV8rzQgHOMs4xq4fiEAec6VDbSDjiFdSJ95WK3AEuVbZTr7UJVsBrFLgN4mvJm6mEq9tiBjENA8vnPwJ1ArCgd16M41w18H0CeLwAiTXRBzYrzG8vlFsalFV2NKNd1AhWhOAfEPTjEQgzKlyeQebBfAHbe2HJzHybUb6OST/F2Z1FIMDbkYX7BmqJe1gMgDgHPJQJkprB4wxtwZ5JFch3d0yhpYT3+qJN2rI8+3I066INCpJc2ronV2hUhLAQFZgVgKq/oVVV1Ey4RY+ofJC1dsVVNCcUG1Rdlado8oPLr2Df+1XfOAqd56aVXULpdi2VnIApCgBAVKFcd/eEkiw/6AXRm9NkvxamqbEJvv0X5GvPU3l0+WO/NKAZlIrMAgPUFunsXcO69KRVVAU+gXvf5LwKUMuYw4Nx7HzYDXTWiqMX4Z2+EIlFXMzCLg7Jjp66Oszivvgmlq2LgnjLqY3OfxlAptM+gQAbguWF9Isp3wLyB2Py1TKiitF8NtQOAb2NAH8Y2kXxLdwdoS9TixRGWCtLJk7W6crlKQQE5ev5wvFatR5mL88IrW+Dct779WC1tbVq1MkWH9i4EnPPQn/xZAQqFAD57l2rn7lSUwtiYNniEBr308Yze+N4d1N16tH49i4yeT5EvkOUZLGrPXnigsfExYI61WL+GsR/tmylMVuk3TAfjLeQb29qmWISAOmNlLy4dg8A2pOe4DbeLIGwl07Upn3IQjt04eV1ROYAbR5daW8jDPm/gMS+gb2+tA3BctToAANVdl87Z9Ob3L1GaxrH5XKMtO6OxKDVgpgfW37M68VGPPvr4JmBeovbvWwA454UKFGUDyHbVmnDtRXnMKLIhcGr1N+XFDn3wvo3x1T3t35+qHbviAOdc1FAzpj/+01ukw7R271qq55+PUUiEGcvwTER/RWdiwWUdqBGWlQyrqrybcjiqvv5JwEgX4Jk41JRDlb8Ze1Ogv7YW8vrxkOrrhoGZRqgnLBaibUrmOXD7ljhsNL2BBV0AKSf01f92kXJiwLl52rMXOCsSUJNTNrTaLOW6yxeblJaUq+f3AsoGTemHx0pVUtWtNesWaOf2BCUlA84B+JjLfIJq4kdHH+nMqQLt27ldn/viItLbDejPgHMlqq3xR00xUQee81YcwJmrh4Fw6BN4uBinvnWQfxXl2GCWd6ipZYhrdwDm+lNvfLR8RQwKWNgtA252dGCHXjHI8VpRBB3UyLBZEwVszAKXffuDtXqtecag3aLOHHm3HHCuX3v2zdPOXVhWRz217DTg3JvvtKu02oblPODcgTDKm4u+83afbhVPafHSMH3uV1HwRPXRQOgeJEpFmV3f4vt7xaNakRusX/8c8C33cZO6/t6PxllYVYidbzrpGEu9p4zSbpl0eYoizjAuRGUWZcfK8jGsR7HebunHuQaofNQoAaOUjvrh5i1AuNmBfI7K4JNRAM8W9fQOWVDl6Pi0/BlTrFyKwuGuUFTD3FVwd0Lf+3BEDS1d2gdUevhQGApnqM3SAPD4gOJcH+BcPTBtgl479BScu3qrD3CujrKShrVrMJal3B/zmKOAc03tRlmzAbi0ByvrRL1yIILnHOyEG2b0jb9sUm1nsDat8dcvve6utGTgR9pc04ozMsOe3kUtHaZ9HFZxiQGMUTUdsAFC2mlDvJQ9LwFr1gi4DQ/1j0yrrBQ4n3d7u4N6P4MNtSt22qgSro5R/vpAju9GGgDOvXFDvQMtgHMrtXd/OpAcfQf1vw949c61aX33rTOAil4oQi5SYlwU6mx2Xb/TQx6M68VXE2ljfS07YTN0aahy6OQxm06cvwU0FqbXPof7j7FqpQ/43d8rpQxXad0G1PleQ70u0ccaRz2dpmHUReNmbKH7umaAkSdYfNWLemQLZZA5l0nmUVjokJLiq435tJOUDxuKfxUVI5RnlFSbeqmHM7RTHNMjFOU/X23dyZgux1uDtCXvH+nRnQe1tK/k4QvJwLje2Ek/haav0A4Zi9Wh0Xr6klgtWhKsq5eGcTSoVgSOPodfitP6zbS/qMsZ9fvhETedPm6zwDnzVPebvx5I2xBoKc5dOGMA7OOoBWJT++pyLV8ZaIFzxkHJAudOnUZZPBUA1IBzK5lnMc//VnVi2MccDmXagISuFGx+fOrXHDj3qZNubse5FJhLgbkUmEuBT5cC/5xu69OdcW6vuRSYS4G5FPhZp8AcOPezTtG54/2kFJgD535Sysx9/uMpYCacv/p1m7571o7qHDNxvMy/WajGHfkBChTYRIwzOfIf/u203mXyjNjR37/MdnlMQn7tt4HmNjxVmrvPhPn/iz3rrTImidj26RH/fpd/8hejtPPX/9FTr73kYU3+zI36/slkmvtwLgV+bingBBSckIL524ARxpLcWLSeB54yFueLFy/GrqhHt1EgM5bur776KkGh9RbUYPYxQWDzniE421D0RG/8zm+rp6qSAHiU8pcu1XwAJp8FCwjKEmYHBpgZG1X35Usqu3sf5Tc3Ldu/V6HzU2hIgLu6R2TrQi2uC4W05jaNlJdrwhvrkLVrFbJmjSaqK9V77hzqFaOK3LFWQVuWWBai+IGyvy9pR8tCoH0WaMsOpNV69rQePbqntIXZWsDqY4+UNM0CUs1iyWaQK6ItvLmmGWajO9tU9c73FFRdppBFWLXufE5uKdmW4pwLE/gz3OM0U7qzqKW1f4hV6/GjigJA8N/zvDzXbuY6zDWgjQM4N3jxqgYuXCXwii3N3t3y3bCS+WqUeDvaZUe9xYbylKOuXZ2ATZ1AfMnbtilxUY5msUF9cPo44IKnstZjBbhqjdxQwTLgnIsB54C0IBSBDyc1VdOgmivX1PzooeanoAy3Y488UQ9zwR6Wy+TFvZE/ZqJ8lny0oQI4gUpeDfBV3PYdityUb1m1Ik3CsUkL8yZ452jv1uQVLJeuXtFUaISS2dZ7AYAiq8yNwpq9HsW5qydVcuWqQlBYS9hyWJ5Z6ZyHcwA+TfcDGPSMyA4gOAis54qdWvCC+Sji7dB4d5eKP/5YAaMjSltDGdp5QG6paZzfXCvEtskHrsHMbE+WlKvryLtyaW2RX26eAvbvA3wERkKJxgUbQGbDgSq4IIKqrkz2D9SUq+bSBbXcKlDuolwl7tghj7R0VHQA/ggSWvdGUGmIclbaWaZLN05hdVSu7flbLHAuHsU5Nxem6IcG9RB1sveuHZFbPND4uiXqaOxRyd1SxaF4sGPTDmw4o59eqynPQHM216d2m2HYpAZ4BsKbeFlWrifmHQAAQABJREFUUZ3D3br24LIeAHJNYkM6NTGNpVGM1izL19rsTaggYYlKJKS1owb1gOOqaqtCLS9D+zYeUmIAKo3k4iTpUtL4GHDupjpbO7UMu+T1yzZY0F1xRZHOFpwCHp3RtnXbtSZps/V5UQfWp3c/QtmiTvl5O7RvzXNKDnoKzg1M9Ol6zQNdRJVtHIXE9atWq7upQ5UVKC2G+Gj71i1KDo6X+wR2VwDv00BlDqIMvgA2IbP+CsHW0cvDjzAsVj49NbpRdl13H90isAf4SCAoHHvW7au2a+X81Qr2Cped8Ucz8MpHBT9EbadK2SnzdWjdC1g5ZwAhoG7pGNS9WnO9pzQwOaRVi9dr6/ydQG4BuoTi3MWH5+Qx7aFf2/UlLUnIIziMQh/pdebqGWCgWuyy5unghsNKBpwzSEW/o1fXn1xHce4xRdpdWfPnAXU0EgguVlR0uFYsW6OE0GTgJeALO+AF29hR9pmmfgVwX1GekUAfT9UC67ubURm7optPrmLxOwhwhDIXanxrlm3U+px1AEpJpA2KEU2PdPL2afW1dmjbqnXakLdakVhiuoMWjZBS1zuu6KPj78tBAO7lTS8qHxVAY9V68+ZNAt/VlpJndDRl4RMI2dTeT/sy7bl5m5cTyHGCOs5jGpDGQHIG3DFt/DB2kgMDA1gdDf79T/OZ6QvMsZzXZY5jgDajkGUgIKOgZd7md/O5USE1KnEGeDM/DRBoPnNua45l+hjzcvY/zj7E7O+8Tqs/+eQ+zD7mb3MN5vempibUWABbVuTI7juAWhWBedoMO6CjC0CrL8pMwdgk+9LGT7vY1DvdreuPrmLBBnQ9OADvCmgTEqqluSu0fvFGxWOpCxqq7sl2nTyPXTIKq/EJ8Xp+FyqGKM5xp+TgkBoHa3T9/lWV0i7Nw058PYpzwX6RqqSOXSq4ApA7iGXURm3KXK0EytAk0GgbinKPamtQpJxVfHSyMlHAiqbNgg3SIE1XNfWvqKUJ6mYEq9YoFO6iFOyK5RZRvlbSvwbYZ5j+MhBFoXiU2pIBvCKoiz7sb5zF4O8sy9dS7GiLAOdcB21aAsychg1mICqexAexUrXryXifqlubsHfFQjM80QLSS7vq1QVwF8kxs2jPwoCGjRqbCZzO0tBP0RbaSXd/8iUAKKB3bFy3mis1QtObEZeqRSi3+gGeGUW7EpQyywAEHdhohkfEaT7QYg4QllHPqnNM6357ExDPpBYFEGwFTvWmT5/kmaANW8+qwR51YRnrjTpkUgCWg9SxWPaD10WVjrx355kEVZ8K25juD3dpEDAuyw9wzj8cy1fTgiBowrsaVZsKAqcOrP4WYIMZ4e6rBoC88sFWAO8AJQQD47igMm/6cPLblTKDcas8TX6Q5j6UnxEA80oA90bg+IAoFMUA/dynXbG5bVMH4N4EgflZ/Gi9gLzmJ6Wg+AWETz2qb25Uz8iAUhISgapMHrrJRt731NfJCxXXSOCmQPLE1AnzMmX+2Z/Oemp9+DP8x1m3TL0xiyFKikvUNdSvkbR4pWbnaDnKfXau9SrtXK0rCl2e2LVDKvgizbokIFarsMO1UV/vdPaiuIjinL+v1sTHYdHrZo2F2lDpbB7oVD9gqydqaSmo7aX7APa4YTfMLVLEGeMBgXK/9WMjKurCqpU+M436m+sboCSgN5N/dvKiBcXWW801GvacBSCIwabXqP+g3kaeOwI8FIPtaQjKwjDc5B/tEDlpzmGUF/1oGxyoynRQnmqaUTdEETCZMm2U7QJ9gGc5R+Fwn2521T5VnAOcWxRolBEBGtlvdNKu8v4OVQ93yA/1ujz6ysX049QE7JZRsQMK7ESxjkZFMcEoQ1JnoqgvBvw2La1p0eAt1Ee5qW5rttQHA6nrC6hrKR7YiHKdI3zfBDxYAzjXBwQTHZVImQCcGxlTO/2GB2UjJhpg0N1LwRzRIPPTAFk27s+dtsT0v6as9hnrZsaQPX3dCsd2NyUKlUrSimQTxZJ6btf9ignVVoBEYt3X1wR42lOn5GiHnj+wAPA+0Brq9Q3a1QWU1YmqUkPDrB6XuGugv1tb1nrowN5obM69dO+BQ2+8fZk67QrQtgBwDugqxJ16Pquyx6gBAc7VNg4A6MzqtS/GAbSN6p23BoFkZrRrW7AFF8VjM+rJOHiaNvDGTeYh3p4kjzr10sFIfe5zgRY4d7vAhnVlMwBfDapaOdqHGp1RMTPlhyLCixpLGTHtaFe/Q73tU7yn1dvhUFGpXYVAHampkVxjpFZx/SQTVn82rD5tjJlQ9UQdr7ZhCGijUitWJKCIlomKmYcuXqzX5UuVCg7M1ksvJGn5GqxcA40lKaCnUZz72/uMzTq0fnWGnj+Ug02qB1atBVjHtjBOWopyVIbik0Ec6WeGgPOePJjV994sADwfVP6mRZbiXCCLCU6dGdGZc/eBasZ16MAaC5yLAEpyA4o05d/c4gz90gzpiuinBgYdqHlNq6drGpDODiBn3m0scPTCmjFJmzcBT6PINwqs1N8zTfmZBkTDrrpoFLW7Hi1chOLfvlgWQWD5eHlab797njSZwLpwnTZviyU/KSycuQNlrtPH+3TizB2AoETt3Z2l6DBPQK1OPSquQRUqQodeYDFINgAMbZ+51kJsJ985YsdO9gHgXDIKgXEogaE4VzWmP/3z+4wlJrAUzUNxLkZw4PQlk7R3gCvUlxnG9VOMB0cGHCgaon4MPNbdTf/EAsyKGhbAuHcDJMZrQ34gMKMrimt29XSzTbtNXZ2kQfWYygEKF+fEYK2L+tcCN5UCa379968CWCXphYMope0DugZKnCAt6xqndPFSk65ebAQKzUU1EFA2BAvRY08Yk3dqzZpF2o9qYHKKB20zucD/d+9P6IMP7unq1TuoJm7XF7+Qa9n8tgBt/t03iygXftq1hfs+CPibCsBmKp7VAtAGcZ9TlNHBAcYTHZPqNdfePQPsiUIji8KM7eZhwKiNGyMYF7kAWtnZZpJ8xuq6aYaxBzB9k015C6dQ+otWTKSHylCOO/JemaZQtN25L4O0pW2MQI2Na61vsOutI30cG4XQdA+9fhDbaFQGv/duv648HEb9zV+/9pvhFjCKkzEKy7Oo4Tn0F2+g5oe17saVcfoyqo/JCa6WLfMHxybU3FZEHUzmPKiSYh9r4E6eIMl7nn55hp1hIcG0DWvqYdoWVKi7uPYO6lhLowMVQtrqmS6Ay1DSNRO1ST/Kw4y6sCjuMmW53U5+0UbW9FiA5uvPk9frgnXv8aje/HAI8HdAu3dE0P74A6EzvuQZj0cmnTg+oCNHGL8wPnjtULiWLAS2uz+ob7/bIO+AJL18MBCwlfE83esYDUZlA1Dv0Urdv92hBRmZenl/DIpzHkBwdv3+nzeovitEm9YG6LOvugOazvIMYMZKJv8YRwIFGwXUsTGum/zr6pyiHaF8VjPOKe1m3DylTRsStHVbiGISSQfUWbtoizoA59qpq+WVAK1V2NjGeGjPjjjawSBAWBYa/y2Kc7QbO3eu0oGDtBuAcwxzNET5eFBg7JTPAUQCHB9cBPQVqauXbTp/rR374gnU2FKAw/yAS81SKqkMEO/D96Z1ueAe6Rem1z+fBciH4hzg7e//XpEelxYDhSboM59dzucBnygGml6THo0xnuusacOpXyMz2EcDv39SBhmqAsR2AOq2a8XKZNoDA5V6WWliQN2uNlNWHaqpRuG3bErTjlbg4Wht3hrJKNnTAucK7tVp6ZJwPfdcsrKyGQcApE4x73zprI2FmKiFTjTqeaM4tzxYBVfH9P779Yjke+nQ83HavNOo2THGImH6egHx3hmlbXIwzrbrK182MJ+vujpmdP404NyRY1hJR+vl15dq6VJ/6zzDQ306xfzCKVTnUhifHTx4UOvXrrbAOYqw9bJRPsyvZLkFz5m299O+5sC5T5tyc/vNpcBcCsylwFwKfMoU+Od0W5/ylHO7zaXAXArMpcDPOAXmwLmfcYLOHe4npsAcOPcTk2bui38iBa6wavW/fn1aD+uNNZqZCJNymJg8+r6XMjNcUWOZ1Vf+w5QKsAAw35uX2WYRK4Z/5z97AgsAzX2iNPdNLF2vAs8hmPQ//TLH2rDAVd/8hhcTWUwYmxnxuddcCsylwL9oCjjhBSewYGAKE2A9ceKEZdW6evVqbDdydP/+fV2+fFlxKKq89tprllWrgSKcgVlzE7MEZ+tKivQ3X/2qBuoAWgiAb05O1YLtu+S9bKlcI4I1a8OWneB27+lTls0QEQ4tPbQfO09AJPAEOZjUxEJxhtXdjtpGDWIZ2j00rKClKxS1datmujrUdeEcE7j1il2dq5hNy+QWGycXlMCE2oIVZSNAM0vga6ajXV3XL6uw4AbqFUFatBcr04Xz5RYGSMVS71njIUawyppBpT1yAHXVvv2mgipLFUhQ2XMXoFb6PEvBzXjGuNBo2YAWZgikdp88rtFjRxVIegVu2C7vdVvlGhZC4AUre6xF2y9d18j9Qmx5UGfZulEeeRyHQIkLYNisAfVG0LypaVfHzdvYTTYqce16ZWxcJ9eeVj0+c1IDHd3Y+ixWTP5muaWloNZHRACIycBzZuLbXLejA0u5GwVqvHqNIHGwojZvkecmjoGyzixA0CzXadZTuwCuzGJJZ6xrJ0n32kYsbzasUzhvtxgiW16o4ACj8Y8V4JohKDt5h+NePK8hOoP01esVTP65RYeSBmgrlD6R7dxp1aC8FJ67UpHbDsktMZ40NSoWmNlifTeLgoujrVX9ly+qq7xEHnEJSv/cF5BUIPj10Udyb2xS/Pwc+e7eL/d5mdjwkgEAYrME1V0IHJsIiK2mXgNHP9JwWZHcAFmidpDOWRlyIVjsAhTDjXHNXLeh+QgwjzbWYP97SQ03LqPAN0+JW7fJPZXtUc1xAaKYJQhjXN6GUQ4qby8FdjlDMLUcVcR8bVq4BUiCdIb86h/u18Oqh/rg8nvywoZrxebVGkMV487l+wpAZWnb5q1aAPzlCzBkIBOjXmTsIA284481p7+xX6U0904DhBU90ONiLGxtQwTZYjSKUlFvV78iCOpv2bhbmbHZBP49UQtp19Xblwh431NYYqh2bt6lnIhF5B5gjL1P90vvotZyl+DsiNauW6sNSzfKzz1ApajsnblxGih1VlvXb9OqZAPU+aq445HOY3FaU1mrjXlbtG/dYcWjgocpnganh1VQ9VgX7t8kGGfT1k1bNNyPzWHhPfhCm/I3r9eiBFQeUbgxkR0TvJ8BfPBGrSoAq1p3rtdOPRhAYetWSYFuYvk6MjVE4DpZU8Axnc1dKHFFafPGbVoYlyu/WT/1ALCcunNcxY2FqEZEa9faPcrg3j0ocwMTg7rNca7dO08qTmkdim1bcnYCXfnrSvllXbx/HjtJD/3Kri8Azi0iuIU1YGct4Nw51bTWKytnnvatP4DKEYqBhNr7ZwADuSajOOdGHuUtX4IFXCcB9kcEK721ZtUGzU/MVSDqSB5ARSYAicEySnYGwEB5C4Ue9N/UMYHdVcV93bp/CzhmSgmx0eTrDKoYzYqMitb6pRssBTxvL19V9FfpzO0zqiuvwqo2W2uXA09FZgIMBGnIPqxzpR/ryqVz8nJ46qXNL2nzgu0KRzXLgHOmvV27dq1SUlLIbarBJ1CN+d3ZLpvff5qXAWXMywm8Of82P00bb6y4e1EtakNNp6WlxQLnDERnzmdAOCfwZgA5A7Q5wTjztzmm+cy8nd87/zb7ms/M2+zjfJu/zXfO/sZcm4H3zPU4+xCzjTm/eZtzGMDOvM335vjmM3Pdt27dsq5v8apceUQACZKH3Ck5yP4AXh60/16AO6SehmZ6VdFZYvVdw30jSkStZtpBkLoLGBe77I2rtmhx5nLqsrdGyKcrNy/p3qO78goggLl7n+bHLLQUHMewJK6kzTDft2JrvBQgfDOWxaE+EVi31er0dfqp2QHlb96kTRlrlOiOopGp1zQEhU0Nahsekx9QnFFhjeE+vbnHAdrxKgDwqpZGeVO+soBmklAr86Q9GwawaOrFFhKgzBNoOo59YwDVIiBCgjkmrR73+1Tlqg+orApwrqSrSfbBMWVHYq2HklaIyR+2aaHPKRrpUnNPp+J9QrQUSz8v+r+i/hY1T/YryAv7wlCC8W7Yh3Nd4IcWDIRLHPVRtGlSCHWjb3JCt9qqAfHGgfhiLbgohHTuIv2rhtsJ+DeiEjsDmBClTMC1dEB5U7cqbSMqxHLTF5BhWWCksgLDgA3YD1Wt+n5jfTlMc++t6KAIJQBTRZI+tDwWHOhO+2rspW3AqZVYIt8aRmkK5bp0bETzggEzAIYcEIQjlJEaoJ9GYENv4LhFWJBHA1A2IuNTNNAmB/BKMhRHEjCUHwFTEzo1gXIDPoFr8pus+x7hHitRFqsc65N7aKCSgmIVwvHaUPeqG+1T3wx2ppOoP7F1dkKyIoHwx9mnsbnWUgXMTExVKuCcgbkGUR+baGpWKLCIAeeMZZ+BSJ11wJRn83bWTfO7KfvO+sAl/f++nt322d/NjuZv58ucw8wZFNFnN7TTLyehmAOMvjgy0eprH5OudZP0+yi3OlDI8yWwnovy0WJgwwlAxQft7WoH7ozHk20zCj6eZGwroHw11qb9WOz6UiKTjdIcKmzG+tSPNKV3tlQRzTDLWKU2obb1uLtdraRXbGi4FodEKgMZK9NXjqAC1zYypIrWRgocCknR8cCvvhqgL27p60B1zUXxXGs8Ftv+ZJZZ72BGN/T8lNlZMSLgOC7qsQGMtTUBYA0Ax4YpJTpOEbTPpjUsB2y8A3Bt8iUCZcQFQXGArJFW29LHfjU9bVxDD2pQYZQtLHepD33Uy0pg7hYUxFwpvwmAgSmovIWxsAF+1Do/t/cUnONnN5Z35SjvNoz2yissUPOCE5TpDsTKuKKPstw00a92xpk2QMOkqCQggTANsvqsgbHqOJaWYbHYHHoBjdDvMmIBVeF5/JOyamzIaeHQ3sGicRBgsauN8QqOVbHxikV90Y86StOudkCVyrZZtbfMKJKGorEUcOz2Y8ptr146tEab8gFOUQgzUOwECTkK1NCF+tC5C8Z6sESLs2wopWVhRRsEfI6C2vcvKAxw7vDzC7RhA/Uh2ANloqdWrcc/wnq2sVd5y1z08mfjgZpRCHqvH5XndhSXIvXii7HYFtImU17GRmZ14ZJN335nUF2MYz7/QoQ+/7kA2iIX3b1l04c/bFPfUDVqTPOwAE1gjET9MAsTGFdBUmic+YYp8sC09hQXLLcpNyg83biB9ebpBoboHihlxWjXHh/5mEaLMmLWYUxx3spirE3vDenWvZtKSYvScwdyLWW2GwWdKGuXYdWaADiXCjjnBdxkrM1dVIca07e//VjNrS1atSJVLx1eRH/kqW/+5QMVo/a8JHeRXnwpW9mLaDEhODtI8zs3sdV85wZXiF3p9iWAc9EoJbroxOl+7FofMd6a0cH9y+knQoCxaGs9AXgpnS4GZOG5x9jZ2rhHMwdjQMFpFjQO9gMpAgeeOl9nQTwrlmOpuh/oNI1+mHGYAe2mACRbGxw6fb4PeK4Wa1DS4lAqysShKrht1zvvXyA5BgFJNgK5xFsgpGkeOtvY53gPNrLXNS87Xnv3sOAk2gfoqFc37tShVBYEyJasvKXeLKqgj6W8XL0A5PgBi6RQ4n3hULwOHKZvonzUoED256TNCBNDO7bmkvdRqIGZrGNhiYNnOsonzKemKTvmEcbkzQRyjgaku33HoYtXpqgbNaRbvLZsDwXa4v6o93bSYIIyat4PHw3p++9UKA5Y9NWXsNZd7YVq4pS+9vULjN+eKs7t3+dlqfnxCKL6RhTnLjYz9gIcTTDgXCgK2DOo/9Xq4rVG7DKBa/amauFCxrYBT8vMlSvTeuudWyosuquXn9+pX/nSIoVjj9tYDzj3lyWqq/fT7i0ozh32UVwa4xI6WTM+NiA7wxXGwOQf5dN0XTbq4zh/mzw4cb4QK8ka0nOd9uxOxJb1KcBj9kEEVP0AVA/vO3TuUg/Kxt0odqWiqBUAAOsAfCyW3TGgfYCaW7bG0sdi+c3lNjc79NbbA0Cv08rLcNMXXkaF1c9d76Icd+JKp/yDbPrKb6UBUnJ/XKdjEOvXB3b9xXdRzsPKeOfGDP36FyOVHO+m6zewqD05jsIiKtx7U7RtB+35J3bC5jnHhWdb03NOo9Q8yaKaWRZkMYSzlClHqWO9AE0fHm1TZU0NanNBeu3VbJ4J8N3kOo0m3wRlu7ePe3xAnnCPY8MDehUIbveuaD0BuH3zgxF19gwCm4UD3flZinNuPLNNs9/Hx8awq23guSJcrx0O1QoguIfFQ/qb77fw7BGtfVuA4F72kgdzlKOAb0WApkePlqr4SY+W5GTplYOxWp7ngQIg7ht/UqNGwLmtG0L0S695KDGRdpW2kKbUup9pA85xTrNQztRD8/skZa+6ijbyPMrclV1avCiJ645gAQXQPcClKdOsMdHIIOq7T8Z17FQ5+T+i7ZuT9Qr5WFU9o2/+3WV193dqF+Dc/n1piok3Y2n6Riyj716f0nfeOseCEB/q6BKU3EJxMJrSx2cbaAdGgBgzaXux06ZOmLpz+zoKne9NqLCsVHtQ6HvtCymo1LGQjOv4xjceojhXrrXrk5mrWcZiR/KA8xj1PldTSMmNWWh2O9nJ458myUOmCzTN2M3c49lzbYCspcwLJ+nwgTTlAOH6Ar8ZWJqum3Yc23W2O32mF2D5sdYDgR6grQlH9fPD97qwf61SXl6kDh+m/M5jEQ3tnwXOoTh3DnBufLJRh57DCn1pkAof0pa836newQnt2ROrnftRZI5kHMT1drQCbX+7V2ev25UY6af/+BU/5ed7qBM1wbNYtb75zlEg8AS9+tmlAL6+KNvxrDzYpdMnPkbZ7oxSk1N04MBB4NhVTPeQbta9GzVR5n4oj9ZzhlWi+edTvubAuU+ZcHO7zaXAXArMpcBcCnzaFGBUNfeaS4G5FJhLgV/wFJgD537BM/AX6PLnwLlfoMz6V3Cpk0yS/KevTuv75+0aZiKPOSItZILiIxTn0lNddf3ujL7421OqZwUyMSor0DAfG4z/8lueTMy4sfrXRaevOPRn37bpAasnxzieOcZP8/LymNVbv+etfTvcLduUn2bfuW3nUmAuBf7XpIATYDATicai7/Tp0wQbLlrQg4HkjLqPAenOofSWmIjtCVatBqh7Fpx7GgBmJTIrnb/59a/LTnAwG0hlCRPfaTlAKitXyDUpCmBsRBO12HkW3FI3EFUw6k9p69Yw4YvS3HAPag+BBBzDmdhlIreyXn3nLmgA9ZCQ5asA53ZgrTihoYJrKr5/Xd7hAUpcOl+hWZnyjk4kWIHKwAiWfgB0AQTu3YnMDBcXquIKlqmdnUoj0BW4DHtMIL1ZgvMT/U9BPV+C7O4RoXjnjajunbflhYKbD6CW35YtQF0oDvkHAs/5WkpuswAWZup5jOsf+PiYBmuq5ZeUocgVAEsJcUxQo/5QW6HWB0/k3T+qlCXL5Z+ZpCkvFJ5cbNxbsDwCCPNOktZ1HWq+dZeJYNQyVq5SyqaNuLFOqvn2HZVfL1C0p4+SVqxSYG4OsF8QAVQsfwjk2ghYBIQDWKCQM1ZcpvYz5+Te3ytPrjUQSM8rNBjGblbjqEfNAPz5xsfI0y8QK9pOrFpPq6z4scKyMxW/DnvYlETNAhW6BQcziQ9wZyaVSYfpsmK1XL2kSibgo+JTlLZiqfywd7MBDow+fKCZ2/c0OTqh4I3Y0C5aoXFgFMIHlpWNFxZrZqZ+HCin99o19aGo5U2AKvNLv8bKby91nj6rsfsP5cl2Pijk+QE+eYf4sw/HHh3TjH+QvKIS5AWENXW7QB3XL6mXGfswYLjwRQvlHx9l5d/oGICeO6pHUXHyDvLRRFezGgtQMjx3UtmJ0Upbny+PDGMxGwlYFoEgYYDgr1AkGEeprFQXCk6rur4McGyT8hflKw7lAgPODWKb+KSmUEcv/ZBy4aYNW/PlC6R288Jt1Es6CCbFo2iXw89YFEA8NTUzrQHUdCaQRoj1jQV+SSbdUT3oqNLpS6cJCnQrc16qlizOI0g9pgd3HgJ+9rDifhXKZWsVGxAt+9iEnlQ80rnbZ7E/HVBebq7WLlgJWOahdgCXB0X3sNmrJo1dUTLJt6AtPzd/4NMynblySp7Yp23fvEPLk9dYyk3l3aU6f+c0+Vel/GWAc+sPAnlQxsmjUcDBOxXFukja2giE7tm2h0DFDAoOV9UEkBZHecnNXIgqULoCSLMJYNeh/iG5T7opyi8C26oITeE9V9VerSu3r6ipp0HJWclaiYLXBBbCD6k/1XUNWrJ8ubYt3a7MoEzsE8d19ckF3Sy+RgB0QksWLVd2JuCkf7BasO979Pg29/JAgUHeKEFs0sacrQSzg3S17Cppcto695cPfEnLkhbJBkhT31Gvc1cuqKalTtkozh3YiLVtQDxBEdRaUAe7UXJL9x88oBi6EEBah60sqg6F99VU36ykhHTlLliGZWuMfFAgtBG1HBrConTKDQUfgogoYoWgmPag6YnOPLhAcLJFeWm5Wj1/BXaVw7rx5CbWr+3KSZuvzcs2ARgkaNDRz+fXdPf2bcO3EsjJRcFlsXx8Q9SBktG1B2dVVlikUO73xS0vo3C4C2vIWBXcLCAQW0dQbIOSk5PJH8ZeQC6mLTYv89P5u/XBT/jHQDJOUMYJo5lNncCZgeIMMGdU5BobATsaGiwVUQOimbbbKMSZt7GTDAsLw24zHHWLYKuNN/Dbs1CbOa7zmp79+eO/O/82x392H+sP/nFer/Onczvn9+bns/dljjc0NGRZiBsAKZu2fyxghLwFgiIoP0p7ax+fQSkJa9GIGLl5uatpuMZSCywrLLOsUtesWIeyIABW8Q2CfzXKTp2v/JVblU5Zn0WCoqjiiS7eOKs2lEHyluViK7wMW98w6veAyupK9PgxKqmjdq2mXd68ajOKo7GqravXR5fPqt3Rp807tqI4t0axrrQ3AHyjKNBU0xdW9vVqFMjMqArGmLQGRh2E/mgYH1FbR4cCyb9sYKE4YKIpgOdWlLZ6Ub6cxWo2gvoW7R+mYNqmAMgDnOYIgtNOU3ZHSKNeeqQaLJNLOxo13j8MwMRxgsIVCVRmVNXagfAqgUDHgIPSAiK0LCAckVE3lYx2A7x1WiB4DOptMb7BwHZAo4B7dvrOLpTiMOZFPcxLCbzRHtSD/mZgqS75uPtofkgCipkBgKpYTQPOtXZ0ahIgPDgAYIkxQwJ2rabPq54cQMWtFZtYN60GSJpPezxB8a4e61ZzZwfMs5tiw2IUB0QUxj360CZ5EqiHdZMfeWv6cRt/V6M0eX2oA5CqTyluoVqInWYw5XUStcgelL6aAEEHUXeLxv4yB/gplmO1Ttn1qA8gh/FFJKp9qYHhXLMX5ZcySQR6GoDJNBo+lPFAAHIbwdUa4KmSIQMy2a22OdYb+BU1zQrAvE5ARjcizSFYZ6bHJADFBAJFj6OI1Ep/OEq/gx1veAyHREV0oFMeqLfGk1ZR1Kcg6pMTHjXl2pR383bWV2f5N9+Zsm7ezt/Nz2dfzu+c35vvnL8/+53zMzPGMzZmhY8LKeOl6k8IVeTCBUoG3vIOCFYr7VozlqjDwyjFkib+5Hc2tnhplLvBkVFgwh6UWqexpg3RZjPWQmH18Xib6ia6sEl3Uao3EBzWp4FA755ci4Ev/bl+MxIwI4Mx8rsJYO0hsFcdyoBB9CsLQrAm9qFPJs17p4aBq/sB3voVir1qEqqJfsDzw4z36oHt+hkLhKAcm8T5I8m/WQ/gUsrENH2TH/UoDMtUf0DYEcpsA9s3ohpop/5HR8YoGgU8lgYAUQ5gYd6E/faoQj38sUqnjLKwYZIH3s7xQbXQpg+NDSsOgDU3DEgPuKBuEDWnqT76TJtiKAc5PlGKY8EITQXpbcA51IroY1z5wMDXxiq2pLtZVaMdcvh7Ktk/AnAuRKHUqy7Kb9NQN+MyxjIommawaCQ4wFeDwA51qMc1o97nEearJK43nQUBoRzTBiQ+YUexi/zzpi3x9fDBthYLYiDG2s4W1MWwz6WtiwAktXehHNQ8rD7ghgEAYrvNW7H0p43Fg3p8q5Cx85T2bltj2et5uE/IL+R/sPceYHId153v6TA555wwwAAYZCISAAEQALNIkaBIypIpU17bsvx5vf68svTJa+/bXT/ZT2kfZT3JsqS1gq1o0iRFEiQYARCJyGkQZjCYnHMOnd7vX4OmQVoUk0hR8lygp7tv31u36tSpU+H863/iLY4X0HEX9vPpXTA6Hjphaxcn2v0fWky/lmJHjoUIMfgEQHEvIfGW25ZNxbDkMXZmDeHkkWlCWg4ALOuzZSsBhN9f6kJV7to5Zj97/AghMWMAUM0n3F+WC6PX3Nxvz704AUMu7ZlB9O8DnPvdB5Jpe2b7OffQwy02OArj3J3V9oHbAXel0c+EmAjAajk25rWLl0Zgrhq3GMaNGWlJAJ8J5zc4bXv3DAFKgo0tIdtuuiHHlq4Iwn47QmjMePqzJMCbMdZQC0P+gU47CiN21bwSu/uORYzfYm3Pvl7AfMcBtebah+9ZYGs2El46FasH0Ku+btL+8XsATRuaYVAqAUy4zAHvfwhD0s6nj1kibME3bqu21Rto10kwOgK027d3AkAWZU/12R23rQI4x1gCIurHn+q2J544AWgkTKjWdbZlG3qcz2aEuCmkD3IlLFanGMLrTlndpT5QLl5LzwJUiK5PwGB2/tyI7QLs1dvrsWsI17pmLfYqfYzI5IQ4zUwG+OO3nraw7XzqMuElm2BszLMdd8yDTTjZDhyatu//cCfAqz7bcfd2mLLKYLGiQaK/CtX6+GMd9vBjTwKcK4FBbq1VzUm3vaqn5xoJnz3EuDUf8Akg5QIfz4c9Fwa7F/cr7GozYKVC+8AHs7FvMIvCYPa//98DjOGDduuNK+2ee3IdcE6htoPY/vHRsHV0jhGatJ/2E2PZbDSKAezIcAc281Hb8xK8roRK3nRdMSEeYQBNnKZOAVET3jyOfnCcRacjh1pgi7sMs2clgKQy2ANjAa5P2X//n0+iU8WAG5cAKkyA1Zn2SLqXm4LMZRthFayzsqKldtdtuYQS9TIWHLB/ebQOBjD6pDXFtnZ1Kv1svA3AjLZn75Q9t+cEwMCj9vGP3mx/+AeLsUk+a7xEqNavnWOjQ4Ldsr0EsFCCFc5hA4GAc6ip2Ls6WiesFUBaCLRwRibhozPiAEsaYKIxgIFHCD9J+TatBViUTxVPWHwS9ZiS4Mo3ClL9yEHGyXvaqfsR6qrKquYCnCMs5/f/+QRj9R5YuapgDCwlFCis2xigxkZAdf/UC3BuirGKx37v/lwAp7H2zIsAkh4hVGznZdtxb6WtWpnNRo0464cxbP+hQXvs+VrrpZ4+sGWh/fEnCqwC5rT9+2mDj/XDYnec9kdI2JtK0SNCmQNIimCHvMwRPIwNWmFXq7s4zHjZw3g9kbYNMJnyi/3xoUdgc2tstoXzCwhnWmrxidpQFLLMXDGCxcLmzebdQ5Rx9xDMpgH7nXuz7Jabssg/wLkfD1tb9yC6k2V33g7DblEC41eAc9jHRxxwrt5yWRv4CG1q/ZoYu9Q4ad/+YRfgrWljim0fA0iZkRUHo+oEoDqYvneft3bAfJvWLCOUawEMwzDONQbtbx88h1zSbMuGTLv/I4kwkwnYS0eFvgQBh/Wz2+EEgLtpyqw2mIzcItjTOhgoX3jusl3CDi1bPBfwXKpl5U8xRvZgMwBuEZJ5DB0/dqwHkOxxAMFByrbI7t1RaTWsmX71G7uslzHEzTdvsA8CnMtnU5YfYOggoVr3756yf/z+LuSdBCvkaoCcKVZfDwj5UVjlLzdaeUUeLP/lNrcqAVs4ZYf2TdrzLxAeuaPZ7iZU6v2/CzNcscJhwzj3N/tgg7wAoK2KUK2r2ewoCDZTIeakfuZBwo8NEW619hz97cCkJcIyns76ggdgtuzkc8/Xk99LhESei70H3J3JvF5lTAFAhzIwjbC689OACJvt/OUztv1GsU5WwuyaZj/9SasdeLmG+TH2Z8cCW7gQ1mGm9gyfYJybtqd2TtjoeCO/ldjK1RnYu5A99uiwHTjebPPnJqDb2TZvfjo2b9JqCJX7xONs8roUaysK0u0zf5Jk27bFMtYCwPiUmBYfIhpCsf3W/athPGVMjS3sBzj3MyIBPPmznYQAroDd8U7WqtYBbtWaNxv7XI8dz+eZ7RKaZen1do9Z4NzbldzsfbMSmJXArARmJfA2JfBOuq23+cjZ22YlMCuBWQn8kiUwC5z7JQt0NrnXlcAscO51RTP7w+tI4FJzxD75pxF76QIQBRZP1lV77IcPelg0M3vycbNPfoFdy+x+1HHrMrM/+YTH1q0mRFtSxP7uWyH75iMBa2QXu3Ypvp1DKW9d7rW//3y8zS3D2TDjT307Sc3eMyuBWQn8kiQgUIUcrHoX+9E//MM/OBaizZs3s2P3gw408dBDD+H4ecIB5xSqdf369Q5oofv+zQEMm9iZ0/bg5/7aPAAcqmD6KIQtLjMh0XKrqmANg7FpfNSGL9VZhN8zF1ZbGWx0sZkZVnvooA1erre0tDRLJbxWCG/KZHOrjXMujnBhBeu3WNa6DeaFKWf64ik7+dxOFsWbLYmF+1yATGkAraZChPeAvcMLC8yS9RssOU/OxA5rPbDf6l8+DKZtylJLCgGS5eGY8tsIQIt4QBalgLHSly10zs9WGM5Gd78IA0rEEhfONz9AwZRCdviXEdoxJx9mO8LN4hwONl62wReftQt7drMbfRJwYTlgvTwHFLgM48no0LhVFJZb5dbthIQct3YAGS29nZYAM1wSDnQ5m4c6+nB0tuIYSrUq8psjVj4WpqdaWu3kY4/aSF0dzjGcrhXl5kVGCnc2BKjCh7N+3tIVllteaaGuHht8jrCqhKPtDgUsYe4cS87KtCTk1AMTjJfnFV+7yvLK55h/eNIGn4aB76XdFmARvaBqjiWVl1kMIMEs6icJ0IbKJrqEcGerDRw7YgeeeBoGhQkrLiVMWkkBgOlxxyaY1t9vJXMqLf36m6wXJ3Tt6VMWwPmblxhP2CtAejjNB8nbEGwdioBUuHyFFd6xg/IBcDt2wnpe3E1oogYL4IROL8aBTPkUrlVsQjEl5VawYrVl5ZEf8tENgK8Gh+cYjt/8oiJICtEPPB2dsBL6YASat26j5QDqCxKOsP3gbjvyrz+yLJyRpXPnW3xppcXml1nqvEUWC1NOEEfcBIxJF1rO2dO7AZYBhtl+w1bCmV5v+SkF4Kx8NjQ1YqfrTtqjTz9qMZkxMC/cRDi0Sjt14gzAqIM4mbtweubg2CGcGY7vILraPwCnDv7PVVWrbCkhoUZgkhIT276j+3G6ERZo2yZbDEgnzEWH0MXDLx8jGi2gvE1bbM3ClbSRNGuBqeZJwq4eu3jMhQ9dPLeaNhaPMx92GRib+pG52M02bd5kG1ZsBHyT5ELvPU1bSEiPw2l8i62sXAXbVgJgnfO2Cya6mtPn7fo1W3GQ3WlZACRCMKGMCTgHM+QLgD8VQuv2m263vIJcO3r+oL18Yh/sd72WA7ilrKgCB10WTo8J6+sccIxzYtpbuHiR9Xtw5O59xmou1Fh2UaZtWL/RllQsceGaj9Ucx+kKuxpg0y0Ay7ZUb7d02JbOtZ6wFw8/Q9iiGovHm1JYSr0QMlOh6pobL1l3TxMO+kzbsm6LbVq81ZJhN9p9arft3PM4IaY89if3fdKuQbaTkSmrb62HmeMZq22ss0VLF9rdN+ywotQi2MQAJEVGAUYdJOTWy5Q3ZFu2bOFZBXau9ry9BCB1HEaU3LxCK8zPteQE2hv6PTIEvCOSYPNgKFyyoApnvs927nvKDl844WzS1tXbbc28VTYWHLH95/fZi/teQP4Jdt3yjYSNuoY2nQiQ8Jztful5qzlzxrLzsq20fB7O3xRYkwasBSaV1sYmwADZdu/We23rYoBzAH8UElsgto0bN1oFjh6BXnToXYCa6Gf34Rf8kR3WofcoS5u+C2Cm7wrB2gFAS/a9Druiz7q2ENu2atUqwjmVOdCcgHYC9+glux4F3kXzFX2O0tZx9Xn9pu/RczNXvPpv9P7Xuyb6+9VpKf86dI/K8Rz2Lg6gU+mCcsKTnoX5eBQ2KK8NdQ2aj7FpCfZj8ZLl5k3w2omLh2mDu2Fxi7FNAEhXAZj0sIHjcO1+nOnP2gQ2cd2qawl9vJnQgAXWSzjHZw88bQeO7cMWwuZUNgf7jCMPAE8n9rsbcFgqoRdXLltpG1dtJGxjiV2ub7THAMh2TvfZtpu226bqDZbvzya/AtX6rAtwYg22sAEQghfncVYK4atIe0x2A1s7DAtlKmVbWIAdBvDVC1jnUmMDgKxpSwJckAM7VzJpxUELEg8QLJW+IwfAWGIyQGfs/ABUKbXBIatpBzjXO2J5CRkA62AIAqAndtIBbPYQbSaJMJuVAIgXAnaLoW4bCB1dO9RpfdgVL2C1dPrCNOwLnIGAOKYAIY3j3PdaMWDPeYBuvbBsXQKQcra3GcfrqKUBJk6hXU/ikOwDHDcIq974cICo4vTJsHLlZaYA0yWMG8C6ppFBS4bFaQ2sZIsyCqx7HCa47kbrHhiwFMBT+Zn5AKJgg5sCTIbTVGEqk6AWy8G+KiyojlpA5S8B9GuBEbU4kgJ4Lg1nNeHG/NMA5sZsiHYcS9kqAV3NJYRqDi7RXnTyPOFuG3o6AA4BsKJ8KQD6/QA2xJA7zvgkDhBdDnYojz4oFvBcOyCyGkJ2tgNyEnNhBox8jCqsFXB0P30Q8a2ph4gVAPxKpe8OY2NGhwdtmucvoL8phmZpeGKITUiNljEwalWwsOXn5gKiAChyhXXxav2WXl8NllVZdU7XRK/TuauPq3/X+eh3vUc/R++PgvMEkj0KmPglxlphwEMp8yqww3DFpWXYOEDOPuifxgGuB9hllQCwpQTQXC6hq4eg0ukCNDcBM1gZurAextBBWNgOdF+wtiBh2GF0nZdYQBjcZMY14nIRIyLAEdpoJv2XPwYbix61TI7ZMYBzDehCAuCvQphR8wC8BQC7DU2zcQLvuh+wWGkBtjktHwA4zGYwjjUPwozZRwj4iaClwFCazfjHRzsajUzAgDNiaehuKfVdyGaCAP13ByDXOsBzXYDgFOovE72SVg8A+m8GFD8EgE8Be/PIdzYg4SmQBMOA6Xpht5ugfkuxiYsBDaaMBQC/Aw6AldEDuKUYwGkZjLIpgFEFulTbEmguhRC1KWyGwFhaP4xzNT3cM9huk7D05MBQW0g/nYhGDwBc7QPolgiD4fzsMqtA55IAsI6T/2ZsQy2ssyOwOqVCYZUP1C+DTQYhgGMTtF+Fok+lP82iP06CsbEfMF0D4NpmNoR4sUee8URrr+m1M4cvwuBKHRASOT4edj7GZkPt/TZFCLvK4iLAHtU2Knm2NTCvj7fUnAwL+eKxbVOEIhyiL+y3D26bZ3feVAk4NsaFav3aN39CCMGw/dZH1tiWLZVsosOuAWY5eWTSfvLjVlic+tgEkGofvn8uIIkYO3MiwPljdu58neUyZq5eAKAFoFZHexPPoKy9pehojP3O3Xn28Y+lOGDOSwBHHnrkHKGSLxHedTmMXJWG+QAQJjkTJrrT6O/P2/FTjBnRs2zaXnpKHP32gF28CNMkgKXFi1YCSMrDbjZZLf222mY61/k8abDBBa2luQP0SIDQffNt2/Wl9PFee+rZy/YsTMLpqfl2330rbd3GNFi6GDVME3q0doLwiUesgdDgq1dV2EfuXUk5EpHJMCC/kzBPDQMyz7R5C2H7Y2Nhd7dCGBIStqnRSvLiYCxdYfcAKstI8zgWp0d/dgg2tqDd96FtMKrlWA7AudgYECVhAB2w+YaD8TDQdtrjTxxig0wAVjiA/YBFp+n+mpu7AWT1kxb99coF2Llha207C4hlkvDzRaheEiFbYda7AOsoVFKrVy+zW28ot0ULY+2FF4YA5fwrujQAQ95tdsON8xhbwepEOx3sEyipzX7y8EOA1Qh/umOjLV1SYPUwZD39DCCYQ4S7Bxg2b245YVdhgByYtsaGGKtvSUKuLTB2Vdgtt+cSqhWgIWCqL33xKcC3ADRvWU+o1grGQYS/VB0CVOknPOvxYy2AZ14GGOO3AsbUCYC1Q/SRtbVdsB5OwmyYZGtW5wOC60ZHm2G5CjtgaFISdmhwBMBWE2MAxl3XLke+bFIp9wMSGrX/9blHkFOe3XvXGkBJaVYAcA7iMKtvGrVnnqmx53adt8ryVQ5MuGxJDIyzMMA91cbmi0voX8TmlSdbDragpwewXbNCdMJs2nPGfue+LfaJP6i2DIBzTZcn7ZtfO8p4zWs3b18AYJC5VQWsrmzGwUyRX8DERzvt5UO1hGpls1dmLkD5TJgSfbCwEeq5s4FNKam2bEUV1wfdOEwAwkzsgNiVx0EX19cNM86fsGs35NitMOFlAoJTe/re9w9Svi7AeovtxpsraVds4OKZly4FYR6rRwZDjFvj7YGPVsKol2h19SH715912a7dhwnpC7C5AvvMhhDNeesuDVh9uwemsVi7YUOZ/dEf5DhWyEMALP/l4QbCqB6B9Wyl3XxLFaxvjAepf6HK1A176SuPHGu2J584Y53UVxr9dkY6G5lCANa6mFehf2InW7VyDnPiZLt0+QJhh/uxH/mWyJhlkDC+ly+PYQsiNgd79PGPFtnaVUl24HCf/Z8f9Vor84ybt+fbXbcDki4DoCzAPiDdRx/pgXHurNtE8JG7K23zJgBkgFCffGbEHnnygvUCel65pNJyctOxscyX2IxW39ADM6bmEyvst+/Js5Ur/NbQGLAvf+WINbXH2oZ1ufZb97HBqhKwN32bgIgsEVC/AGa/u5dQyb3ME3IBvMO0zvirtbWHdZFu2FbTuHe5lhAIcdoAgHAEO0D4ecZbg0MhygzAvr0N1r1Cu/22JdibXDt5Ysr+7uuPAfztAJC42XbcuYg5dayTad8V4Nx3v/844/FEgMOrAITmAJCL2JNPw4i5+ySAuxHkUWLlFYkAVmH1bk3GLuVaB33GndvTHHCusgL2Vuzy5z73DIyfZ5lTLLCP/vZ1tJE01jZUhxonwKSH3rS2TNpjDx+BCa/ZErGTeQVl/JpAvscA7DUx9o/YouoqKynOB0h5gfY6BsAzw72C2KqWphHOd9GHjNld9y239RsAoANu/umP6+zgwWNs/Mq3e++BOa863dl3DeGff2ocvekFHNsAKHQuIbXZMMZ47/ChUUCsZyljO+FrGb8xlx9nLFJPaO3mlnTCnsNgjO35sz8UcC7eusWO+jQhwX/0Y8qWZDfeusgBDTERyL/bnn9mF/O8l2BrnG8fuvset1bFMIMDW3sFOCfWOWpc+6qcHdSvb+dAZ9Aad1x5exOpaBfDtAaSDNY0QdMAbfaYlcC7KQGpaYAJZZBwEJrUS+/ey0OTeO3cUz60Q+/N6LwmRbpH77+KPL8l+VAuLVpP0a41AdNET7sNf9Hh7IBgyJgO7ZB4MzL5Rem90W9Reeq52g2pPL5nh/QPHZCTQc+XjIQ0j2FRQZ9nj7cqgVmZvVWJzV4/K4FZCbz/JDALnHv/1clvao5mgXO/qTX7zsqleckZHLcCukTDZEWn9pqe/+vPQnaynlAELGRUFJvdd6vPkthNeJSd5c8dIWwLu9UzYZOpntNr/9dffYjFl1X24Lc89o1/CeH8xZHDoss7OfBL2Y+/hJP/esJVvbdTt3eS7V/JvQpJdgRHl+YaWjguLy9nQW2NY4P5lWRo9qG/kRLQPFY2orW1FSfDM7Zr1y4WunFC3HuvLV261IEVZE927tzpABZinFNoQelk1LZo7isnXy2gmC/+zV9bH2HBKnHEl+PIxP/Frm0ctDj6/axUhlmdLq8osxJ2AicvX2QBHNrndx+y3rMXLQanbYwck6QVxFGelpNiRdVLLGXJKotlcVegh/Bwt/WdP23tZ8/aUFurBdiB7Yehw0c4zhAgh9S5c20+YUiTAAOFJ8ZtornROrGJTScJdwMwK5a8ah0mAqNbTsUcK1p9jaUsrYJVzg8T2jEb2PsSTpJGWEZw9yQTOg5QWQV5TQJU40mEfU5O9/Fhm2q8YF0nT1g3u7vDPUPOmRrwEloKI5dSWmFFS3G0LVpsERb1u0+8bE0XLpLXCRxOhA3BER9iNTsBoGDJ4mrL4rrYomLSZjf0+JgN1BDW5+Rh66FOpiZhwBBYDwYhDw7igvkLrGLFSkspKZthh6utsWGAUO0NOJVgHPIg4zgxobFYngwLXeGW9ZY9p8z8OPunAFK1HnrZ2usus4bGOhogt/g55QD31lk2C82+1HQcCBh5HMwBnA8tLx2yrvMXbZJwelphFntADE6G/PwswG3LLWHRcutUqLGXj9jE+XOWAKMgK0BcC+MDcvanE3oFB33+8mXmr17kZOft6rIJ8tt5+oz1N7dRR5M4oAXYgfEH53A6csuBjS2ZRXsDqDTeQHjAk2esA0es6k/dhgc2mQAMMGkVsOZs3GRpLLZ7fJM2XksIop2P2SjgoAhgmhjYj5JhGCtad52lLF4CADHZplhAv9Reb7sPP2e1zedZ9L/W1sLulpNUwBoOoXOmCRlXf86een4XDIjxtnXbdkIszreB/kEXGvVi3TmYOAZxmEwikhDMArjhqbe8zDzbsnKzleB0rwPQtfv4XusDzLeI+r121WorJLSgjkb08cix43b05EmrXrLYtq3bbHMKK2B2G7NTjafs5ZqXcYjV46tS35wAEBJwnifg2D+CIN7XkNa1yzcAKEnDwXkJhgqAQTisN2/cTL+9CNBdLKGIKN/BFwnVWmtrl2ywbetvAJQj1sJxxzh3/NxpQjYdp0L9tn3LjVZRXmFtg4125vJxHGcXbLBnjOfTR5MWJAXuvQjAy9L5ywgRVm4Xe+ttF4xvUzhxVpOfVUtWWy5gB1wR1gQgRmFnmwCKzSuG0euam6wcUNBEoMcuNp22I+h2LTKYBHiQCOAgB1szhWOmo70RFplYWw8ocOOSLdiMVDt09iBgu+fND/vPA7ffDzBlPuEEp6yxowWGkj04zC/DlDLPbtl8i+WnApyAymYQ8MLRCydxJp5CXwO27tq12JtyGLwG7FzdeRgxLgMq7SbU27Rjo4kDyBAfi54SflJhqYsr8nGathLO62nCfwVs5fLVthr9KEjOQ3Ng1+qvtT37X4S9Y4CwjHMAXq0l5Fc+QKNBO3fpDKw2L1tnXw/gI62fAqqgDcTBCHjxLIDBSLzdvfUeu375jTAoZTngXBOOeTF4lpcDkGWQppfs6dW21SnOL/gTvVaX6LNeWqvWOm4n4AqB8wSa66Ltad26uLjYvcQslwuoR0xz0bV03avn6xU9omlq7VVH9Pfou36PnncfXvMn+nv0PXqfLouei37Wd72icog+U7+LMU/AOY1tCwH/7Dz5rLWgC/Gwb8bh0CvIyLP5CxdYZdUCGxgdssMnDxJK8CIMKNUwKSHjvDm0Va819zfY4WOH7OTx4/R1uXbd2k22gLbjY637QuN5O3TqoNVcApjAv3gAGslpCimYYEN92EH6m6ULqm0D7HU56cU4ohscYHIEW7V5yyZbWQUQ1g/QDEsVBiw2EY5YA0Czun5AO4FRHO2slcNGYti7ICDXUQBncYAn5sCOlQbQuRt2r3raxwT1F0f7SARk5GXAHhMIoj+EE6ZPzQZUKvaVNPqjccztRcJCnyf8rBdgRklctvnou8SsORHBRuEETeRZhWlZLpRmHkAZhfgbwBneBZinE1BN10Af8DcO0mI0QJuH2bmpXQ8AAEAASURBVA4QUw4sbaUAq/IJa+yDWqcflagHXNjUCygaFrAwXvXYBHwEcYTWpawDA+P4OiIAA30wkBACjT51nDbdQ7+eSJ6WAiaoTMm11sEeqwFIMEBf5yNviQC40gCipkzBwjIJTM0fxKFLfWKzlAcB2OoBWB0Z67VuWC2LDLsB482kAHP+cUJwU0auyU3MsXLAgTmAtYANGvyl1oO9aB3pdbozTV8zzXhagCoP7cwP0DIHZstCZJkJeCGOccEQNqN1asAa+zsdO5ljTMLhGgLohdeJLonnwWybTkj01HRAXJR1BDDYFADjOYCNBcYaGOmDSbDFsgGRVBFGMx/WwAzAhFFfhXT6ah2X6KPHa9vDz2srOvfzDt0b/U2fo8/Qu+zB0aNHAQo9b1nLAYNgN8cB4sMlxKYDfBnovoAR0/RrIdhT0wEtZMDiFobtqVvgQMB1efTPK6jDQQBWxzvrrAudT4xLtkzATElB6g4gtgcdFWNgNnamCDBiGvojkHgLmyZOA2gTwD8dMELCNH0nYEONB2Wnk3hOFmHYcxnDZaBvYq3TdHOIem8fHYYdapDQjvJLET6aNhyhzSjQazb6U0Z6uVk5dJmxM2FfRwnxCgB1FLY8geQEwgvG+m2U+1wIXcCAYk9KZkwap/aFbo8DqBuDnVEh5+Zm5FscoNp6gPtNUzA/wjqUhC1PxKb78D268Qr5S0Jm2SlpAD9zAF/EOn252Ms9gN3DaTAfAwr1w1AYRv+DgKFiyV8BAISKtBzLBRgHZgct1YaIacBzfQBKh2BkRb8IB0xtuBCCGjMnxiMbNlxkanxLuOEQ8u0bVzjnPutHJqi3tV8YtNNH661zEMY12ofrv7k3OzEO5qosNsGhmzD7XjzbYqdPXAbINMU6AMyV1HM4hj4e1t6qyiy7cWM+oehTCa9HmOmTQdjKHrPMrGm7864l9FOwGicxFqWNXzwbtMd/1kO/0m/VS5MJI1li6QCohgYiMHMOwCpFSMSGYXQqAEvdFHoAe1SgELBfNmOZYbv39iK7//40AGweO3gAhrOnAUHDRrT9xmrbDngvC8CZQgtGyNxgj8d2720h1GoDGwzIN+OgeOwTVgddCAL8SLH1G5cBrEm1C7UtDrwxDiNxGCa3CP2Cn7lBCgyACxbm2dp1+Ta3EgAKhNMvvHiRkIjnAAMWEQ6xmpCkACPTCf8dEFBq3P75n49aSyugnJWE/btjKcCueAAeIcBR/Yw/umA06wX4CdgRvU1lfBAMplojwLnUlAnAeZUAgPIAlvrsxRfbCE95wsbGQ4Rp3AR7FCGNc9AlmP/CtAcPZVRw3pPH+wHOnWSMAlAUIF2I9iT2T/E2FhVmAbqaByAnizz10JZhhW7qY8wDeBeW7RjYq+LiwoCkMu3a9XMBwKUDCPbByNdvP/rpTmQxYrffsc02XlfJ3E0hCwkP2huxZ3d12VO7nrJ5C3LslltXcl8e4Rg9gG9GbN/+WsBQ9EVgTuIBZOdkF6H12dYAuGxk5JR96EMlAOcIFZyXRKjYUfv7r+2EBXDctl6/inIuAHAGCBvgdJhyjEMzevHCAGEWCXdaNwoTWzJzRs3b1J4nYIlLsmXL5tj8BckAZtpg7qplDNnDNfhX6SdDbIxygJ6FVYCRShmrJQCu9ti5cxP24Fef4jlZdsfNy+2GLamADhWKGEY2gO4vPn/W9qKL8yrW2W03L7YF8wk1iv6eq52yR56uB7TXaBH6wUTmE+lpJbTGDOumXV26fNTuvXO1/e7Hqxxwrr1tyn7wnYOMpYK2+boldhOhOgtLZoBzasV9yPL0yUHAj7W0iR7ARrGcFSclYxnsTi7hc9etr7CyCpi20JtDB2oIvUsPFRYvJy/Gw/Fs2CgtzbYbbgb8vyxN02A7dTQIs+PL+J0J/XlTNRtnitBDGiz119gUsMceq3Hg0arKJEJrLmKOngpAk/o7O0Fo+UY7fqKNdhe25CRY/tKZQ3pheW5PAaDvsWtXJtnvfTzF5s7zAqqeIPzmJQCm52BFg11yyzwA38yh0CsdPI66NxgNu2EbOwNwtAcGbdoX+uCXDQGYnJQYISysNmPk04+MwyR4FoBgF3WtUJnMm3lprpQDy/s1S8rshuuY3xf7WecatB892gdwrt+2bsqHdY6wpEXIBNRmgHnrrmc6WdO84ECGd8GkuG4dbHTAEuoaWbd8EbDiwUZjyM1mAtp5GkyOxKXt6hyx3u4EAIWlMD8Son51mDWOgH3rW0cAvPkIv0yY4TsJR1sszIJ2AwOYDcQBKgvaj396CiZDwM+TsLjRn4GKoI+aAiAXS/sjRPC6SuuBrfHw4UaAZo20VcbF9Cd0seSLTR2lmbZ2bZFdc0027TaWzTRE+/gnQrX2tsMYvhY2vvlWAOMlww0bUIjeg9OEP34SucQCWFxmq9ewiYt2UXMeFsv9bdQhYxHGEnGJjDtgiI/1lgNyS7fLLZft5utT7KMfL7Y5ZegExfja/7ebNnHBVq2eY7feto68pLIhQGWgS2AcoDDU3e0B27WzFqZv+oFBGE1ZP9EmKj44drkF1Tm2bHkBa7SpdnD/JQDB9KWw6UWwpzFgbvyw/KYwp6lenGHXbYXhuATQICDcn8HieIQ1lEXVmbT/a2zuXHQD/QmAYt2/Z8xeeLYPcHSD3XBLqa2FxTIJxsXOjqDt3deOHW4AfDqE/GDRzKKvTcxlg0ahNbTEwwQ+bf/5D5Js65Yk5jCEi36hF6D1v9Jv99scWBnzi+K4nkgodIRnTp+1RupkGZtn7v3Qh2He2+TArQLOSQYR5mBBWI41hKILceA5Tr+t4y0B5zQI02KbJmSNjY1uwrVwwQJQzFoimz1mJfDuSUC7LltbWjHYoyDRQUpXVroJ9i/7iQK6BQQGo3VpgViHdux0dXaBtO6k8UVA1S6n45757ec+X+2ECZIosltaWuhIJ5hUZTN4W/DK5XKKREF1es67DTp75cGv82GShbCebgbDDZcZDKaye6/kdR010cmY7EBLcwsGz0dM6wWWxn3OKr3OM97JaclKuw7lOB5mt9j8BfOhIi16J0m+6Xtl8yYZQXax47iXrQlDPF8jiXzQ9MqHFnNmj7cqgZlB2Vu9a/b6WQnMSmBWAu8nCcwC595PtfGbnZdfJXBO41gBD+QEiC7S/2ZL+9endBqTP/bYY/bZz37WOUrkFFUd6bwWCkbZxXixHjYnFliy2AU+r4ydubAE1BOiFWIUHKg4GWL6WYDosC99+W+svuMe+9GzCdbOoiDrem/50ELFvx18Iw/f/b/j7J4PxMyGa/03wbzq06FDh+w73/kOC0kvuzameYXmPZp/iiFmx44d9olPfMK1wVfdOPtlVgJvQwKyDdIt6d33vvc9F8JPQI6tW7e60HJiJxLb3L59+9x6wF133eWAcwrpp35A9kXvWlo/V3PWPv//fM7G+glbB7hrTdVCS8PZ6mdhWYA4hQvBI2hpgKKSKkrMl4kTkPBqI02EeWvrM88goQNhcNNu4DCMM7E4xJKwYd70PPMCEnBxEFnADo3BjtHRZZMdAGAG8PCxMK/F+whhfWLys13afhg1WCq2CI7ZadaNBgm5FOoiHBZANC/XelhviaUM8cXZ5s1PgbkDZ1LfgE03ttloJ6FaRkARk494mExSKisI25lHDDK4VFRWDyvkIUKaUs6Jy7A8dLBqTxnDOEj8gABi83G8lpc74F0Ep/p0O+s/bZ0WGqJsupUVWw+OYoVuTSgjrCwAAQ/gHeILkmUW8GHJCXQ12lhPN2whLPjCMiQ2OAH5kguLLAGQhRcwBx4+wHM4l6DkmOrAed9B6EI2lkrO4US/xRGy1F8FW146DlwWr0O9AzYF88ZEG3IbHbUAtsWHEyMDYF1cDiFyCbvjQvLgDIwAjJhGDpOtOD56+nGmT+HcIzRZRpLFFxDuD2CiDzDGBICOcZyL4dY288EgBNqI1WjqIo4AaXnsOC/NdWlPwC7nxZEbB0sG3lWbpu6mmrssBCjPQ/k8OJ49gBdiy8gvZfQRGsYDwCRIGL4g9TbRQp309bP+jQyRkQf2m7gCQs0VFRPiDAd/LLIAdDIFQGiqrR0GOtzugB1i03MsobTUYmC2E3OMnM5DMOY09TZa71iX5RNWMD+9kLCAMLdQ41OATcRa0NTaZD6c7eWA2tISYB3A4TA4PmDdhAvsxzE/gec1QujEOBxBiX5CfMF2VJZdYskAMvsAozT2tcAIFSZsZB7hA7MsAce79HoSUEKnA8c0wZiYQvolgPYyqHvWCicHAZV0ALzqppiABNDJePrkdpgFLgBgHO4bsY3rN9r6pRssLSaDEKrDAM46wOD4cdAUwdJCOjgDRiYIe9QFayBrj4VZpVaWD3AShhWghQ6ENzA8BDisiybr47cKHL1psPuM8XxYtchb/8gUTDeE9WHNK4H2nQZoM5Owo2KiS4RlqmW0DQd2K8xE8VaeW2E5hDj1Ao7QEGECB18X+e3q7UIWGVaeXQWjC+3LO0m+eqxtqMuaBrpgGYJJh/IlpsbBgNaGg/A4YAm/bVyxydZXb7IMQI+dwzAUwepgjFEWl1UDVoEhh2eMoJvNhKobpZ6yaEMVBRWWAjtShJCdEwxUOmg/ff20SZy2+bC/pQMIBe9AvQ/DVAh4A0ap6TGASRHAvDiD/OhrKqDUPGxNTLIf5soea8d5Gw9YqphQrDmEeIyFdQKqABsmLGY37W2MkHuJABSKCRWclIpeeWBOgnmrY7DVOmDymlB7wVEYCztSH+Opfbtf4vpE2PHus7ULYez0JhAea7+1A1AVg6fAbFr/FagmChr7eePrmbGcNPXfDp2LvnSPxugCyZ0/f94xm4wAaI2C78uxSwoZGgXLvfZZ+q60rj6iY8joeX2P5u3qc9HPr703+j36+9X36txr07v6nD7rpfVXhRJ/HuCPxkQLVyy2y+Mt1kdbTKT+kmnrmbCNZsLQlgCwbBCGsPbuFnBuY1ZKSMp8WIWSAeR4yftkcALHMYDCpjoclB5YrsoBvhbiSCMEJOxXXbTBpr5GGw4OAyoBCAZz1gQx1y6evmhD2KFrFi+1jWs2A2YpspHhcULQNeOUHLVi2LIyAYbFASSKIU8KIas20Uff0AN1kAAyAu5OE9JbIJ8p2l8H7TAej2t5Tq5rvy40MiCjgMBVAhVgMz0obywJxZF3sVwJmJYBe1oaNoXAcFYzMWDnAXumhhNgdKN/oA6HaM8E9gZQ5iMEKsxb2KUU+pA4nKMCTUDiyCviwlv2A/oaB6AxxXeFZ8VdjDvc60BJYr4CWuTmD5P04cPUQy8goD76mXGu95IXMb8BIQBgEQKME3L9hA/AnnwCg3SXTYCY/NyzMCPHKmBREpteN2xh05QxJH3DZiUA4hUrXSxtPeSFhYnl9BQYSjMBwQnQVQ/r33HsKv5bqwLEUeyDrQx5jvroq7CdqXzPBiCTDogykXr0ALhCyrDA+rgnCFtkAF2gnADpfFzjV9sk3RRkmI5sZJ1FERBCvgT9tQHs0wBhWMXGJsBcDP2DBi8ai4eoS4XnjMeJPE3ddQKOn+Taivwyy4MhN8KzxwEDG31cCtem0m+rvUVDHr8RcC7aPtRuom1BnwV+i7ZXfdcRvTbaRqJjMn2P/h79fP7cOVikDtk8Qq9XAGbXCEegOYXsnsmbD9bOoBuTgY9Hz2B9Qx6XAAIOI7ciNkOsgJk2EgbMRh8zQkhageR9hNNMoBJjkDUZpn8mZDLIhnTaTSJ9hxz2LaMAl9H1QdLLz4MlGDBbQkDjIQBlZDUFAoI05JnIfYJj+AHT+dANekEbpv4GAFSOIMtpnuMXOI9rEkhLDG1p3BsraiSeL9a5MfLVS12PAt7w0Qa90nnKIhDgKM8TKHWafkK660dvwqQzSF8yODjgwGmVlDET4PowffwgZQXPaWHZPBqOj7I4UBt5Bi9KvhNcWxSYbog6r+1osTb68dhsQl6n058gYy9jJJ/CLAOqyILxMAP9iyePHl7ivgmjS2NcM4I+j+ATHGVMTJUgT8rJb7HYqETGU/HYgxhXfvSU92Ha2jDj2UlY5sb70cMWGJ1p2BMAscKM9+LZyJFD/1pMuL4igCgCwfR14MNqA3DK9ePjAHuoP28iwBqAPXkF9HcZMZZOGFSBdrpg96k53wrAPYyPixDYeYAwKLNCx/Z1weBUFwBARehiQtqXzsVOJAFYon0qFGBbC6DcjinmFlovmnahU5takuz5vf2wy3baDhi1fvsjsDbCftbZHgRsNMS9YwCLYIEuJoQrIDAPoDIB5wKTfuvoAtBK3rv7aMf03WLJjGGcm5IesTzCHhYWp9OmCTvbBQixaRDWZ4XypU2T31iAh8mEji0sJoxnAWsciQKIRqy9lfFT2zDyTbbSMsKU52LLEsUAB8hyIEz/OUB4zXGYp5JtTmWqxSUJCAazEeC5piYAue0w+I6jv+ifz8M4pt1rTz97CjDmKOCYSvvgbUWwzMbgs56wpuZBB+yfW5ljeYBpYuOwKl7GCNhJrDqvOBvoQQ71sMm1Y7kB7zGjwPZgj2kgBYRxLC5Nxr+KbR8IAjIZtG5AhKPDAJKwTwKOpKZ76dsTAZLE099DLINed7ZNAw5rJ52AzZlDqPBC+iaANDRRm4TNq/ESANHGDsKqxgNCAdCXTeujfQ0hv652+rb2IQDR9AphGA8Z+3e0AcY7MMb48qjduaPUtt+czxg3xaZGIzActzrbUQILcWmZGEEFgGe+RpsJ0J7Ghr2AKdELGADp4pjqUDnkMSElCMM0TItFyQCD/DC2TVpb6whgHOw14deN0LkCzCYg/zlzUigDm5OQA7e60JovH6F8AGIWEGa2HCBWAjTXAjyPMt5rJ/8tzaMwFOYRljMDcNxMnzwMO9fFBpWPfgh2yUgwlt+S0TM/AM0WO3v+OCF9V9hHfmsuMqFNwXBWd77bmM4xVsuwkrJYMC9q3DRUlY+dSn29MGQ2j8P+xxYLgIKT3OP107ZSaVOZsVZSDtgvBTsBaLW1CXa5bpEAYVwoX4SJIXtF0M9EK5+TbExvnI6CMWeDzCCyClkxoLiCQvpw2qdM7TDhYetqsVuw1GVm+m3ePNhmae8CsQ/TUXZ1EFaVEMLD1KUDOwFeHRyIYfMBbaTDbMPqOPvt+xnDz4ERkLzX14/A7DeEjOlfC5lr0p4l4+jaH+YIVq8AujdKGx7HD675DHYF8GtSUoRne9Bt9I82Nom9biV0dE+XbIAIZ7Cf2PYkNvlk58UTEjuBjQ5sL6CuujoCdvYStgRbVlESCwiMsQDgTgyks3OXYfu7CKNhKnP6eXNhnISMHBWlfWM7sAuN/N4O4C0G45SIPRmFyezI4V7GvhNs9skFxAUL/XKRHnnsQs0A65sAGfOTraKScWMS0HG/1hwYZwYVCjWW9jIODgJAMuFzp5jP+mIBeceHCQXrA+hHvgGKjg17YJEWXgKm3QmIdFBT5SmOjQS5+bTVkjgAxz6nHp2tIZi2WStgLls+J8uBvVKv6O80bbCnPczaSavTjXJ0OCsHYCAgyjF0vxMmw2ZszcAgTPDY0bS0eGxvHOGtPXb2YqPduDXZPvzbBYRqJWwwqnj6ZDd1PAzwMJn1GoXJZo6vgQ15C1ORqBHt3gDdwlzXNgEgL8AmMG5E3HHUYyrtr6AEcGM+CgbouL1Z19EvYdMZwrkNB0mpYcLv+qwAWWTkyJbRPmj/tTXYrtYBGBH9tnBBpmVnMmZC/5kcEcYYm1lPXxoctYr5bDRDjzVvnaaf6u0JWANA5Q7AqUFsfmqmwHlJrDlN26GjzOGyzD7x8TTbuCGJdkbem6ft3IUmbMowc68wzJWaM02yua3b9r20304cPWWLFiyxez50H5u/r2XuKwDrGPXDxkvshELvSq8ZXkgsb/t4S8A5TWCGCLWw6+ld9vLhwxjqPPvwhz9s5eXlbzsDszfOSuCNJKDJxu4Xd4NiPuAWBlavWW233XbbK7t43uj+t/K7AKHaga3JxLq1a2ldGCnoLw8ePAi95UWQ1Un2iT/8hHNkvF66yq8WpHWPXhPsrlnGZEU7tqOH2w14uYFOeQKE8lq3uBH97b1+1+RKDtnnn38BtP9Rmz9/vls81y7Mn3doEtfb22svcP1RrhfN/H0fvs+BGaOTuZ933zs518Ei/nF2CR7Yf8AB6CTLjddtfCdJvql7JRvZvLraOuheGwhn0c/ggQV2FnIELvydB37HUbFrUjt7vBUJvJNu6608Z/baWQnMSmBWAu+eBGaBc++ebGdTfrUE3mvgXHd3NzspX7Ldu3fDhnLJbrjhBjfnK8Dp/G6N9V5d4tlvb1YCGpd++ctfJoTDj+zjH/+4XX/99Y5lZOb+iH3voYA98UyQRd0YaP5Z/Cb86l9/fcqWFCWwuLjTdr/wHULIXceayWdsf20VIWxYwGXt440Oll5efbDuksci5eJyj61Y6LVrqnFS8X35EhZc8gDbzA6VXy0vvh07dsw++clPOuf5nXfeyc7adW5jopx1mms9+eSTjqXm1ltvtT/7sz97V+da/y5zsyd+IyWgdQrp1rPPPmvf/OY33YZYzWkF0oyHjUOgCzEWCeAh5+/KlYSp27iRcECrWWAn3OeVDX9aFzx3vsa+8KXP40Ty2I6bb7b1y1e6UF64rtgNLec7q7aAoXyw0XhgwhC4iuVqQk3h/AZQFMHxjtePFU3cjYk4v1l31W79CM5RD05/GQ05GLw4MCNiYnP3BAGLsUTjVq25LhH2hkQxyrCQLaeGUAKAHkIs5Ct9Dwu1JEZaOKsAYHhxooUScNLiWsJrbBEY1MK8u1VwLaviWPcC/tO1eMt5yfFCOjjrPThVxRAXwcGKx51bSFNhQADLqHwhOUdx33sACoDWsLDogWDPirBO4GGR34Oz14ODwMOCtUdl5v4ITlO8T8gL5hfuiwjxo/xQIA8gBpcXQGZ4tTmna9nAR9i9CAvX4VHyJ8+eyoJX15vCQjNgMQ9yi3Er5lPIjLSnKCeO0QjnPTACKUSsR57yGGTu7DIyAsAQQW5hGAEjOKxd7CN0Rcx8HkKyergWjyi1R7lhqZEMPIC9wKVRz+QPRzVoMa5lkRqHdUB1AYOIX2kAfFNdhNlFr3rkB6qE9Hh5cI64Z7DIrX/kkrzwlzyHeQ7xXXghTz/lik/mPmQtDzbODahIqD9YHEYBteEkQdGo3yTSRNcIyyn9ocTUC5sgAchNhifcGl8s18GXw5PCFBunJQoVwMEuwEYCTlV/RIwg7JxH9yZDo6gojhb0zQvwxYsuCYShMK/khM8Cn5A+dagwVbF4LeLRGxXfJ28XxxTlH6NuxXoTK7AlWQ1zLa4RF55uCuBFEFAk22ptwkuIuzOH7ejh4+hGxLZtvcHWVq0jZCQgOZxRcmqROkWjbCQfEdCD8gXxfMvZluAD6AjATekHAXfJeSC2HoXIQ+r8lqSH859c45iZ4L5JnNVTOOZVmbHcAyktoJ04x5gjBRnBwR3Cca0QmHE4IsSkE6I9hEg3DMNMSHmnjtQW4gGniaUg6JkA1DZK2siXfAWVR8+U9UQ67MQZGPj2n8DJnGfbVt9ga8rXAbrIQN4AxgVeopBJOFJ8PC+MvIPkbZr0w7S/WLxF8YAXY3mWEDhT1MskaYvFyO+Z5HdUBfsRwXEbROkncKCo/rx8F5CNX50zKUI9ONAJYIOQ2jXlU0g5AStiud8POESyFftfEN0ICZCBAyYW4IREFSacoYCJ4wBqx5GP6gV+DUAc47bn2HO294V9OAoL7b5bPmIryq9x8hQYOQqck83VWFr2+LXriNHz0h2tRb729ygwRjZYYwWx2Gmd9Ny5c86Gyzczb94803s0ZKTSioJwovfr3Hs9ntez3+iZuiYKnFM/JeDc6mvXwGiJXUHuwBZ4URe0NVoTVRqDyQQgA2upwiMnAKiKJZyYgJk0Zkw1znz0f0rAHdIWm1wcTGwB9HYSnZFtmABcN0HaYq6ZRg8vweJ46KWDFuydtA0r19vaZesJb5rv9AhUMc+VrskmY0sBRcqeqM3LnBPI2UbpNwI8ixbi2vkoba8LdsTGtmZCS/tsPmDoXIBJXvVjUh7VBS9uj1pB5Z5yqgsCNMQ9ArUOoNMnJ/rsTE+bZUYIIwuINIf+Qc9xjJi0V+CCjt0K6zqjY6SBBTVIOWkTOId5qVsMk66eKShGAu/x3CvbpCOiflT/eeY45wRgw8oiHznXBQCC/4t0aDbukCMaS20NsOxdAFiGb9MWAWKeC8NbPHZP16qUeoFvcbKS/RQwKSAbxT8BsVRGTtkl+peThH0bo19aAovZfNj4lM8J7BouXRcSXFLX9dICCT4gm0e/ipl00lBYbcnF9Vl6Fufp/RwAy0/5KIYDndNFO2CVwFXqneCE4jm8yIdAYCSg/67sfYT5bCH0pWxlFQDMYkJip1OHCgXb19YCoGkA+ZAnfElqb1cD26T3V7e9q9t19Ler24d8Lvo+s1EB3aC9Rzew6fPVtkP366VzOvS5nbmhmCerYYudt3iJA2bKZ04PrxGNK+s06esOlVu610s7uAAwVNfMg21uNUyZiaQFVxZSn7lPqkHvSn2o1iWZGT31X8mD5NjOzqzzkA4M0QeXFsFSBYg1nWtpjjN6zbUC+jgdJ0GFJxWoCDNLXqhnMjVBfyuIA7XrrpO+gLvAlkp/pO/Kswe9A4jGPXqu8qQsSU8DvBgBUYczZWMk5NpBH31Se3cbYOghKwKVMTcr14oAvkrx6EmcHksWOvQMvXQor2IwjqGd6BnDhKOt7QRUDjA/kQ0JZYBYU6nzJPQhlrTiuTaBa2PRby/tNsI4wA246LjDdCIqp+SvOpH8VU6NGLiNPkiy4Qfq0411aRuSjdqtOw2L1iRCUjkjUmzO+2kriTTgWADqzjQpQQFTAQ2FuF6MmDI3IckZQcYwjtJ2Ey53ZWT4B9iU8Tp9bEICYxXGiG6cTN4DU2zoYthLUdzY0ccgQf1giAxpuEj10U/yHAxCGFQJrjJ7fk/YfvLoGdIxWKAq7Y4PpAMEYYSMoZjWmJT746hMzR9iBSrTwJ5+XWAHDaHd0JXKC5Om9EbF9HM92EzyyBcEoqFhgLRoKhKVk53qW2AxAeZ8AKFdu+BcECaugBSLB8dRdp8QnIyBNG4RY/EUwBXlX5sOJBvUz8lLjHsaik5TPtWBhNXdEYGdidCZDx2EvTbCBrcFtv36HEsDwCYQo8CpYTISFwfABTlG6GsYxdBVMbLUuEbgKSpeQJJx3p0lpl5UP1Q14BTywDxBOqlxm/I+TT0GZDw5vNSRgC8qp8qHurg1Fh6DbGesnp8xuxig9HzpR8DpDGURMJs5meQjxmqBgPS7jPk08pkiPwK5aQr0/LNBe/QJQF/DFwjHWmbXE3Y2M5Pel/FRELmrz4uRvmluwXjJB62nxpwkhSwZJwJqmiTfDBVd/VEMVyd+9M9H/lWPQWQQQrYa787oj8rEdfwWx3xJGFm3V4ssqn8dHCYxLkhhsAdG0pVdRizkpT1QmGkB2NBcJ3vulXTJKgBuPqn6yUQQBtMI7WHvvqA99LMa6+mrI9TjSrvjDpgacxhbo5uBCXQUecYCXFOgMw+6ojmN6kMhhUN0NGJXUxugibgy6D7NrbRpSFNYv+oQPZgCMKWQx2rf6h9k1zV+j+X3WKI+GEyqYTFjhRJgf1SfhVzRQ+l7EFuvftgBi5lHhci/a7voSLT+BHyVHAUQFeBJ/RYky3bqRMi+/0/1RJeIs1tuICzqhwCxFaPbyGOKzUa6J8GVj2eRH+mR+jJud7qozSmSuXRZgF2BU6lqJw+xhakOpSeqN7HFKT1NSTWNde0QWcQRzjWe9xjZbOpHoLphXgIwJ3I+Xs+VTWIc7iHNqWkYQZFVDA9KlH7zGzhj177VxsEaO/AXWXW6e/JUyJ7Y2cg4uM82XVtmt9ySDQsxz0U/1N5Vh1I+L5utYmMn0SX1GipnHGnFM4am7fM8tXGVPwZqUOVDNhTcM3KhwJKvyk8bcn0415EVZyNkL5wdo01SBGusC9nel9rcxqvFi2HuXk4IZ8L/ghtzAGXZY9k/yU/svdJlphxOXgK3TgI8lPzD2Ce135f3E4r3YQCDLf12200J9sEPpdGnausCsoJpU/UlfZBtkW65Q2+0B1JCbmyYpmyqH1TJjcXcz/xROaWnLHvQZgCcIyvJQjqtOnS2AfnHAqqMw2ZIgVUH4wDrDh8ctUu1PVZemmQrlmeBDWNUGKeNfDxvijE5eqzxZqzwdK6cM/cqbdkE2XjVrVZN2tsj9t3vX7Zjp8ZhDcy1//SxTMLtwlBJHtV2xrFpEdqeT3VDHtST9wHIfvLxpwjN/ALhhxfaXXfusA3XrkIneY53hGs0VqSzgKHT2UjOqNxv93hLwDntZrp48aJ9+tOfce+rV62yT3/m0y6MyNvNwOx9sxJ4IwloYvBHf/RHtnv3HkBamfaRj37Efv/3fx8DpSH7L/f4wT//wJ566ilQ/In2jX/4hjM+f/+Nb9iPfvgjB5gSqOwrX/mKzaua97oP1gJGB0AvXffCCy9itGOhyr3d/vIv//KVe5595ln7IQ6uPhauv/rVr4LSL3UN+pUL3sMPmmApTMtf/eVfucXzm2+52R544AG3SP7zsjEOVbRCCf3t3/wtdL4XoNldZp/9i886O3D1BPDn3ft2z+3du9e+/e1vOwBlEjtf/+f/+B927333vt3k3vR9Cnlw+vRp+8EPfuh2mYnpUHV74sQJB+JU3ZVXlLtJ7JtOdPZCJPBOuq1ZAc5KYFYCsxJ4f0hgFjj3/qiH/wi5eDeBc1oE0Ni1trbWsQwdOHCAnYj1jolIAAuN+z72sY/Zpz71KauqqvqVjVf/I9Tz2ymjxvFyjnz+8593m39UT9qUE50ntUN1/+zuMAtKcmCw0x7cxcHj3HN+n/3LT/+aHYcpLNj8VzvRtYyFHoAtWrF7nYNlE63xWDJrEQW5Hnb8egmJ5bUVgOQSCaOh3aREMWGXNKw12r3KAocWPbWYOHu8WgJqW3/xF3/h5pef/vSnbcmSJYQSAhiCgNUmNf8VgFVhM//xH//ROcRLYVIS83o0BJRS1NxLgDttKnq35mGvzvnst19XCUiv9BqAKUyMcw8//LCzHXLyCpwgW+JY6GFYF9u/gHTSy+uuu842b97sNtcJTBfVz3MXL9jn//cXWAD22Z0f+IBdRxjHbLFRASaIOO849kLUOX6AbHg+gldWh2MAsvgALMmTHwnKI4HdYbE1ogVPnC5a9Nd7hBVShTrVOilrprw4xwK9h13htBK3GC78jBZ/uYOr+A3Pk4BwWgR2K6xavZc3Sl46rYrzjKAPDyBewEAvoUJgH/ASZszLGlNYHjktR7vFZ7VDPZg8uQ98dAvRM0AhH0Acv3YysygtmgsB6wIh0mUhWAvWHhxjXsLyiF1NjA6ufFwaIuyJyqaQXKyzkxkcA3pXPlUmua/lBdGDBa7TKYAhYcoYAsQTjsDSx6KxD2+/d5rVbj1fXhBWmMN4KKcEzuOmWIAH4b5e1isAG8FMFgO9gcLVubIpj+4enkLdu2eRR4EbBDbgwVdkrmV38oE8tCDvxCI5iwGOd+cI1pWAmzxyBktWyra7het4hsK24Z/iI+VlwVzMIXKYhCgPvjLyTVlhHKJa8GFyoZy7eCxC6FSA53gjEzjDceaqoh10gsfxSAHBpokCEO4bwiHBjnVYpzywwIUBzIEDA/yCjLkwlgypuAKShYSUIOdylCIJVw8CRglso/KpLvUcD7pCTpwjbhoAGK4qSsk9AggipxnnhOpVru8rIWH4XXoiuch5LwYmXBnICAcBeQjL84cuKwQQrkmAdmJTGbABwtGGkYGXsEcTACK7+jsJj3SS3fidVl5Sbls2Xm/zC6txyKfjgI9XLpzOCCwnRgIBS1UmAXC8tC+fQH/SdfIi8Uq55JrAS+vy7sGJ4BgNVY9y9FDuAGAxx7comVEe4EkOxKK01N5CnBcDiKtI9NVLWSI4SENXQh5F5BDT03C2emBOEXqtPzxgTUOtbHjtgxFK9sVnE74Ru9BXQ1jVc9bbOmhLFq6yG1feDDPVYsAGMAPA2BeCnUtOQrVfSVAQHbXmmQMgJt+k4X7KGKG+hDGVzkm8fu714CUTW2BEjmmujdDeAuTTR1slF1zMefQvSHuVzfDh0XV5p4YjKiPFIClkrD/ITvKTLLlHOq5BzRTepWGx2Q23WvdoB34ZQk3iYfNQ/m5YI3cf2QO7TYctXbjc7th8ly3MWQBIJM6B4BWNREDkKHBOT4n2+focPa4GwOicvqt/j14rUI028tbV1VlNTY0D5GnMpyggeikyh2y6bPuv46HyKlSrwomrXNcyxsnMTXZ15voC2jY1MqOT9B3S4yBtQQBNWS/ftGCMlB09nfIC8pXeAFgQ+FV9hNr3JAC4lqFmQkN2oNroAgA0MTL1Enrx9KVTdvlSvZVlltv1a7ZadRnjM0KyyqaofaAFTqzAY9AiID1qf5yZwKndNREkZCj9Cs5CAT1ld7oBAF8mNOMI0XPmJKQTxjTfcgCAOiCR9FeNlbYm3Q+QbfycLj2G1OjolRef+1HME4QWPdXTYmnBOFtdWE64VoFldQ1l17WUQe/qd1xBSU82WCpNc3GH7JJaiM/JgrzzfB7tukmZYdTesd4JaIhaYx9w0nJe9wtsLCDHyLggw9hY+h2B6fpgWKkdGSD06RhMbGm2OCXb5pI3+CddGUiK9GVzaHs8LEw+dVBsWSNXXkdYwvcmQNknAaGNAhRYBkPronRgr1wfEtAVgKMg3AJqKf8MLkgAe4AeBOkHMVGuPGrHSnem3LQfrpedVGhasZg5PVCelB/alvKnflhlbIMptQf7LoY8jS+Uzigone6hfpg9eyyDsXp1fpmVEro0g35NNqJvgBCxCuOK/0DRhXQI9BYFvkmno6/ob9H27PplTiodtfNou1U7j96vdNWm9bvSiY7DlFY0HZ3X77pHYzn5b8tKxDpZCKADhjmuFftgH2UZhqUtwLUh9FTgxeEg7GTkvwsmtiTYHJfAyniNL9lSJCOEOVP3fCYNROLqizenY6oGyUi/idWuQ6GyuzpsiPMVJaVWQRjyHOmBOm90SfrmXnxTG5DcZ0Ap9Kkor3RjivY6LjvPz7pLlkyOdNV9DNdrQ4Vj1aHex6UbpAuOw+ULvzzMdTClsRFEz1No1QDjNHjArHVsAMalHsB/PpuTV2gVyRlWoD6Ke9QOVU6VJ/qKlkvaqnzoCPJshR6uAzjXSLtOKSBsNPLKI8+Q4rgyqQ3pHsfqSx8k2xLkpX5tBjB7RTevPEvP4aOTC9gOZDyTIfVQ7h9fdUoHInYii+hCr4C86AkNVb2cQkEKLKHa0EjG3YMsxJirMWCYPkwgWv0QQyUImKsnC6CuvpRL+cZoi2sEaGGXhJJy4z0BHWQoIvRnIcYHCtV6AYasERibE9iwEB/H/ICxaX3DlL34UqedPNdoawkbe89dhDJcrjDNtCWlpZcewsuDAYhhHEaNc5KHMs4SM5cArG4awTUx5NGna3i2xiJqu0rDKR1v7uA7WQfwgW1AqXy8GH25sjitQYk1ZogIAMe1DjgHc5FsnMZ8zl5SJizEKzowAVDm4sVeoo/RxwMCTkrzo99TdunCOMC5STtTc9nWrcyzu+8pt2WLCQNPw/BhozyqZ16Sm4e8i5UxzLjBAf6Ud8YnTgCMKyQHAb40zhIjqsBQ+qfyCXBGkV0ZdJ3GWqo32Tu1eU65eZE+awyk77ILukZtQ1cIyKKHRMFMmsegLFyDTtB3KSxjZ+coLFFjXA2vbhLjaC4Zgv161zPdsGfFWFFBBKKUDLtmdRLrFOgZD1JbmFGHGfvHQ8j7TP5kU0Pk0Ys8QyqvZKw8OYHwaLJ05Y8rpyuSVFnnyb4KonKrHE5+nNTd+hdUHXIB3FoUgxLySB3uCu5V8ZWEAKA+OpQpQEqXW8atoVX2M8GFCtcYsQeGxed3j9iJ02dgGIyxu3YsxJ9NuGnWr5QPpat8zei8NEswXj1ZuqLvrjScEasVuAQ1Hv675+s+CUj513ldLZHrFEITWFRKpjz7YugrPEN80qiXULyMXfhAvZEB5KnQq2HmJapGH98lDyd9pYFsFRqzuRFbVN8Jw1s2OIZkRwTe3Dpqx4502oGDjVZZVm533VVu6zcRkjtD93OvxvK8uZfOKF2VS3MK9FFXuX6RgqjUah8Kw+t+mEnBycmB3jgpkJ3SVXWoTyeJGZ0kXdmyGDY3uI1hVP6k5rQUUmBohugSJeKDb5S5UIR5r8YVkofG+qoHgT57YJ9sbu4lxPOkpRCqG/QvdtxYt+6xU6frKLff7vrAPLtuE+yfedxLZ+FFNqoGjX8iHkLFsxbhdEyPlG4SGjgkxk4MovIroJp004PuzNgaPlMW6bnLo/LK4eqW69RXacSgqnA2ZNoHm3bAvv2do7Acd9jW7UvtxhthQIYZMQYQL7eQGe5DGQSEVMIK0axIIJ3do7BVMkaE3S6FzQI+UKFDhInf+2IvESniCW2eaDvuiLNN2+MsJ0ttayZfkrrTM5WROpLw3RwKwUbc/FbjBc1f1W6uHHxQVTtbIXuDkJytpx9xbZXLFLZWdiDAu+yZ29RI8rJJPe0he/ShHsjU6m15dbbddmuZza2Ks4RkmHexf4QQ4F7Z25lnaP7ER9aepgmLS8hpdDwZeypwcA+MjOc599Su8+h6ut20tcru/iAhhSt4JoIN0wnoXqme8ilZqzJ6e7sJF/uEPf3kLtgl5xFe+05bf+1KEyg3ovbE+C3MnBk4OXLXdjPu1a1v83hLwDkNxDRg+8Lnv+B2QGvxdhY49zYlP3vbm5cAvc/jhAz59rf/D/SY3e8qcO6HP/yhm6AnJyXbg195EGPvc2xsclgIPDd37tw3BM7J2GiyJIDVZwCZatf2vwPOsXvupz/5qWNP++KXvkh88jKMwDtpym9enD/vSjlIf/LjnxAH/Fu2bfs2e+CB1wfOyQ5oEvaVB79ijz/+ODHcc9914NwQYVr37Nlr/w3woeT73//qr94T4JwWoxSuZufOndi9z1sJNk8TWDkTBpjMX7PyGudU+FXW3c+rz/f/uV+drr//ZTObw1kJzErg10UC7xVwTv2eXm/2cIspv8IxxZvN5+x1b14Cv0zgnAA5AsqJUffs2bOOWU4MyXLo6ZycVZrvRRfmpXsaF37mM59xwDktys8e7y8JqE5Vl6ojga/0vnz5cgew0sIH0VMM0iC3iKXv9ZdbmKP8GeE3cMOF/4vVj24kZBUOdcwMG0DZJe/wJW7hKZZwEmWlHls032vXLvfa/AqFCWEnJgseCaSZwmcthruFRu6T6Ym+3l9S+tXmRvNCsTdG25/Y5DTH+MIXvmAfAHQUBTpenUu1PbVHgVq1iUisMmIEizrldK2cY2Iy37p1qwvrumLFCnaEZ16dzOznWQk4Ccim65B9V5+izWECcEbHDPpdfYD0TGCM7Oxs27ZtG4yUmxxoTuxFcujqel1bc+Gc/c2Xv8BCqYfwKLfb9es3Wn4mYVbFsiaHj7oKvJEewAtixhBXEOaBU7BLwPqhEEkRoeVwCmhhO4QzR4ujWnTH7cKVwJVwkskXI3egknN+IyGuWDWWA0JOeP0gm6NVYedQ45S7zkdiOjjPSurMNSzqEiTLIsP91nP8nPm7BgiHWmhxixcZMXq4TE65mfuVhg7drhwJHBcG5CVmOYFwYsi3UAXKR4Tz4mTRdfrnYdHYx6LtTBmvZIF8BliQ1z85YJS7GUe+nqcCcCAb/SbhuXM4TSLQHYRwegcBBUUAcnlZDI9h0d9HqD4xa814ZCQkHOA4ReWk88L2MnL6FCE0Byxr/iJLq6h0YWWd59kVkMV+eYm1rf6K01AACYW7kyNRzCoCkrmFbypEC9jKsMd5qSTzaH6VaX1Wrl3O3TdW3dERgbsEnOM8twgcp8V+XSCsg2oYqAtPDDgWmRiBKHF0CKwQhKlDjE0+WOLikLfCKiqf6qRkF8cJm9t3qcFG65sIOVZqafOrzIe+hmE1nKaedK8YjMBiOWe4wIohHIUOYKZ0OC+9cTrncs7COnUgUJmKphIFyWwARioEih7ipkPmqhr5JmbcJTBZOQCNrhakbcaZKfCHvskBK4CKnOwRvNACYQUBfQg4Nzw1YmdrCK15qZ5wgoAhCQM1RtjIfsCOQwODlgZAde2aNbZ84QpCuxbInUl5ElzoNjnixFYXIm+OkZFy6p9XoD/0VyJ2wlU59NIX9EptEkXijypSF5EvdEYaN4ks+AiYAFYUXmJoiyisFHkXM6FjTVTCaqi6U4n5cPjROMPIVWm551OHcpq2jrXbsYZjVnupFkHiMOaxE/4xax5qJEz8qGUn5wG03Wrr522wfH+RxQS4AMcSH0hPD1KuKScyB9LkyqQztALkgLNN8iWPzrlDlsg6L/IE5YNAmgicM1I6QjbBICZApBegkUeKR0Ejaofk092jsgB4kB3ipIpCnvmDDvEgZ8fkiJIMXBgyAWgITXum6ZSdbjllwZhpS4AdMgSDR39PH46gLkxJtl27aqOtXbDBChLzsXwxbwicu3qep89Ru6zPGjfou8bfGr/LbotpTpt5+wjRm5eXZ9dcc40LAStwcwJgYK1j655fx0P9y6uBc9cSEiqV6sGO8+9KpbgqpiYpJzUp4By6K0YfH/okV1kYwMoULI7SnRjahtgkXf1Sz2KZO3b5qB2pO0rYsBEcbrCNYmf66RtaCQ0cAwh508pttq56gxWmlsBURhhyUkJ7EKn4t3AyckZaKe3TLxPozWXAvI20YZ0WI47Asv2AkroB+yYlp9jCtGyrIoRp6hWbhooBwqZIYjPB+BBFzgHnBBKSzZSZdk2MzwQ4tDMwzp0GOJeELq8orLBiwponUX4fRtYL3YuYLWWvpTMKYadw49IDtVKZPclLIASxvbmc08Y9ol1Tn0NmIAnid54tO8YHugEHmsM0uzSmKc8ALJ9tXX2wFwEbgG0Vw22DMHB1E00nBJNeWW6eVQHWFiApngf7uM7Vm7zk1InamxzGMi8Curk2T37UP6hNtwNoP0P6oyCgluSmAUpKgE2T/KkP5OVC2qrfcqZHEqSMyDqIPeIKvmH/qQ8VRA5xpzfI1vVnIEhkqwVEUB8lYKPaimye63+4p45Qo42EJx3ndzF6qV6moEiZYCOZ6qOIkNxV2XmWh5M/ASYXDVrErjkxDRgNexdli5PclbYO1YeO6Dgs2paj4DhtiNFv+h5ttxrrR1nmosA5zRWuBtZFbYVL/Mqf6DxdMk+jPuKwB/qscL2TlKcRAGA7YbonZDNRUlX/CGDBERhc/YTLLaBs85NyrRIpJkprkJVgNiFVmMYY/FNduZKp+HouaQvAOkH9tIJouACobIRzc0rKrBJgezZ6qT7RJaZ7eWk48YqFwg6j/ZygXVE5YpJji4W7x49u6nl66RBj4Ew75JlUv4CmAug4MCV3QFIDWHXSapubcMbDUEio4wj92DDpDxBiVeGQC9KzrRxWvTxsdzr3RCk5XH6UNz1LuqkH6uCzA1LzYYp66hsdIVRru7WPjFpGYb5VwyKZg74nz4hrphHpNjKtflpAKgHB1D8JMCLwjtqWxl161+P0El6Gy11/LJWUbBErktDYbAaQpBsQJ3pJG48MkRrge/eP/hfWVwFtpOuujlzCjFgos9iIBaBUm3M2wPWBXMj16g3V/bkmpXzw2S/DAyOnS8xV1oxQwuKqA4Tf3hKynU+dtwuXmgAKE1YyLo28JhKqlChNHb2ERE+3u+9aapvWp1veFdYlkiaPPIf0NezVeEv9umpcqTMI4PcZwLzmAsqTz+WTcYn7pzH6jMBURh2Sj+pH6umG0qo8xskCznElMpUhVrqMKbBFDiQTgx0QkEZyVQIuLW0AwXpI8Fw7RPjGJ548a0eOt8OmlGbJhLadIExzGwAXcKGE+8ywW2+eb9dvy2QzISBikhGIWLaUVN1L7IjSVdWdlEqANuVFIG7lWRmchlLLwZCpG/Vlal96vrNvr4y/VWPUh4ou28lj1IZkC1VGlxbPkY66cZk7pXbjfuUmisijBZAJEvI6THvzMqbs652CFKWBsIcXWE9ItAzahPbudHRgA5sBhaYssK1bSu2GGwkBXEIZESU5d6+ZZ/KFCnXAK+mIvpJTWfyIbDRXoqXkT5qlbJIR/moU58rJNwfiUR+hsihxsizRKJ2ZilF6+qb510xfpvmXn3p1IFQuc+Mk6pHT7i7XB9CYxoaCtvdgmz2zuw7wIOFDU1PIawwhjAHON0+yNhbEVz/frttcaAWwZsVTiXq2DqmFnqtWERGwi8/61fVDjAH0JPiReRfPK/XGpbrX3U8ZgtSrZO6nUGKho4hK8JW1OKmCn/mxD4YsPccTZoyjMQoJqC9z5yivFyPhNuYwR/YImMSzNC7WM4MYuwN7Wuzxp/ZDgJvFZtgcB5xra++x9o4uNhal2vbNy4kcUmilFYQuhrXLaZhEq/zyLOU3TLoR5ieOKZ6MqY3O9COSpJ6n0bLTZKdT0irZF72r4K6+SUi5Q33UJbq2LdZxB1YnhRmjxFoAxiVEG/Ojx35drENlQy9DtD89R+Gpxe4pkLvYFusujdpzzx+x+qY2S8kpJq1kwh372ODQhfymbNWquXb77RWsVSS6TbySrQyCyqou2MM8wCObgI5iAciKngErInMit1lKBk+36B7qSpt+nTpfJSPJhCGBK7T0VOsPbt7vysoN7Ho4sj9gf/e1/dY/1Gm33r6SSInlLry0iOP1CKUZgKZTIFq9NA4aZxPC6ZomNjTWWyMgyGRCDcfADN/N5r7L9UMIE0DgunIAavFWtVBsmiTk5qrohtIkS6pH1acY3VRCrwZw6GgI3Ylgqx1ol8pSGa6qCnczYtYpZAJ4mQz66MckOaUnOzZTt3onfdmGxpD9+Afttv/QeVu+OBu2xiqrXswGjiTaBPcLSBpRx8Z/3UQVOpvbQIjux3521LoI15qUzLwIqrsu5kztnb3WRwji6oXVREepslUrAHhmaoME/RLjXOVZz5bR0VqzPvd299rjjz1qO598Ata7OXbnHXcT3nUd7Ha6SPzImiVLd5n3kc4Vq6pU3tbxusA5Ddo0ONPALDrwij7he9/7nn3/+9+3jPSMdw04p0VgDRKjA83os2ff/2NKQMxmX/ziF+38ufPvKnBOjA1yHEr3tHtfuq8Jy3e/+137u7/7qpUUF78hcC5aQ2pDv/d7v2c1Z2v+HXBONP0Cq03Dibx23VqQ/XS4ztpF735v3wUE27Nnj33qv37Krt96vT3wwOsD56I5E5BQtkA24t1mnJMsT506ZX/6X/6UWOtD9t9gaHgvGOfEPvjwQw/bKLsDxSCYB0hQ9aQJreyjmAmjE9uoXGbf34wE1LPPHrMSmJXArAR+vSXwXgHnNC5RHyh2gTc6UlNTbdGiRQ6Q/0bX/qb+rjGDXhrDvXYO8+ta5ncCnNNiksYsYyyIS5cEilDIqJMnTzpGK50Xq1x0oV/Xv/ZQCNAocG523PNa6bw/vqv+HnnkERe2tbq62v78z//cAR1fO5eWw/XBBx+0I4f3mTf1D6257Uao8HEI4rROzPHAgk14VRZm1q/wWgUhHbQjkGkRi4kzADktmmqRJTqS+xVOX94fgn+TufjjP/7jV9hToo6uHTt2OCZHObp/0SGHm9qpNi7p89VtVLbupz/9qZuTyaGukJpf//rXXZjNX5Tm7G//8SQQ1Ru9S5ekU1rnUD8pPdKhNQptjHvhhRdMm2Tvuece27BhgwNj6rpoGrr2TM0Z+9yXvgAxmp/dvrfbdWvXW35WHgvdLLoCApDDUKviCnETZiE2ICY6LWGUEc1WAABAAElEQVTCvOBn9TXinFlaBWVBFsTuNNc5BwvXyHnkwEAsbocBjznAgoyN+id5x4TwxbEgpiAt2kedsFqpFVhJi9+KPSMnvPLsmOt0P2CjYABQQ3e7XXzkaYurb7U8FmsTtm83X2mZvBgz+VEZVEj90aP0HLnkAchp0V25c6A4LQ7zfDlvFbZSy7TuJi1q65u8OPJwOovJda64ONR0D9fKDeGVI5/F+BnnnRaeKT+r8g4AJC8gBlgMbTPOJ4BzAg4IFuCAcyQoN4W8BKSplfkIsXpCtRethwgH9S0dVrHxestfvda8WZmwsmHAuUXP12K2dqEjRicvAcUUxktlEODMeT6c51Zy43ryoHKqKHIe8caN2COYBMQGqHgzYiPTA+RbEMhI9c0fPdKBMHibOTgnOYpJT2FdlVoMGVJe5FCdxhugXfmxAKH8eNXElOLyIG8M8hppbLKmg0et7dhpW7BwmRVu3mj+clh1YLYRY5LqXc4PPVfPVzYEmfRgP6Wfeo5jPxKQQx0apVYu5duTGFUAMT+pTuX0FsNZjApP3cj5JZ/nJOx/AuoIpKJFefEjxKDjfhb6PQpVTDI6JMowzipdK/Y6lXsMdqYLMDYeOXrCLjfX23BkhHRZe8dxWlxYYIsWLrBlC5ZYfhqgToWChJHNh5NTjDLKOzWMnGaAcwKXOglLF0GoSZqOeYAMuJIhR4/AYeBVwqKNoo68MMCJHUSu6klkFcShDvEQgW2AAeGRFWuMqwjSCvODGGpUHj0bCj2nqz6ckmJcCJIPsfbFABT1Ep5LIWN7xtvteP1xO3L6mLX2wDwHCGXaR3jLdL+VFhTbyrkr7BpCmJYkV1h8GEemQHpiiOEaObaUc+lBAEFL23VIq/RCg3HoI1/d4xw85I1bnDsVcBKZ4oUcaFQhQG3TODzFPuafBjziFNgl5pikQjBchOQs4jk+nGivOJmVBUmSPIVkXNyTkR+OULXVMRiVTgCce/Es7HLD7TY9St3i/FL4zso5JbZ82XJbWLnM8lIKYdxLpgTeNwTORW3w1XZWNkEv9fvRMXpXV5cbvws0p/NighZgvry83AHmdJ3SEjvVa8d/KtWvw6H8vxo4tw4gdxa1TzvDxumf9Fz6rf5AbTYEkBN+QT5Tbgf65BwqEsCJJuWn5czYO9496IeAUCcbjtue07vt4uWLNgoDl0y1n5haKVAnL4S577rF260krRK2uWQA33JTSyvUNGTbsA20K4EZZDXVS01zTSMbvuupo2H6V58cpFzrIU1fWpIVAnAsAcSUTRtU2FA5iAUmUguOwbYJVCGbqF5GDlGBv1ybU2PgMzM1qx3rswtdzbAb+WxBYbFlw7QZj3NQbcJPGlJX3SuQQRBlFZDOh37LOSzAjNjFxMw30wPJORm2xCCsq2pZlF/AOayFPrp8uSaPLZcDFdXHZggkN2qN7R3WMzyEI5W0kIOfMNpx5CU3t9AK0wBKwSoDEfVMqDTqSI5br8C2AiCIYRIDq745qH4FOxtQGso4+eweDll9+7BNEkatojDFSrMIz6wywdyn0JcCPrjOlHMzB/qguiYdgZOVdwGnBeYVGNojg81/HoeMkS15UXhsglBiByUjagH7qZfq5DKsZHUAy3pGhmCYouTYx3iemUrcsdzUjP+fvfeA8vOqzn739N5775rRVGk06jOymmVjjI0BU28WBJJAypdcSINkJWtlXUhySRbkSyBfMCGVHsAFV/U+KiON6jRN7733en/Pkf/OREAggMl10Ks1mvJ/3/Oess/e+5z9nGdbQnSUxTGOoehOZfCTDIoNTzZlvY/kbPqrNVz/zbWbeSr2Xx1W0JwXw698eM/hGM89AsFqLmte63tMTIxFRkY68Jzu0dd6naHfdend8oW8ddhB9tNJLHYLOemaBnhLutKRSTjYVAbtE7ArlHclRsdYUni0xZAuO4Kn/FCumm13U3OrFPlG9BX3+zFWTiiUn49xWWWQZgBadYwOWjvlLzCeWSnplhESa9GAzx14nB6WbyM77KPnGUOKoguRDb68sLcyZbJNgqqQkBFgPbMFnxIp5nO1TS2k3nondkhpWlWI/kkrKM3uAHq9rrsFJsAR3keN8aNW6QdvgIGJoZEONBcJAxXJ3Y2MkK4ekm9dkh+R4XrJ2OtdeqHqyBjLT5pj/owQH+rsG7LJ6WXGJBH2umiLAJSNqQd4wCOuTjxDve4C7pBP9NoauQ8DnA9CmbxoBWdLLLwCptAk6nr3/ZoK7uK7e736BbbmVXxCb3wCN+Y8pV7SXJcNVmVX0QWyko4VWWWp7SoLWXAFMUaOcZX6Se/QHF6qFjPK9KdSXi5RlkpyTG+SJ+aSq5+K4Z86Q2zRg92r2LV2O3Eehs7eUdJhLrsUiTqPkZoSA5ChzHZvS7W0BACY+Oxu3KiP2un8POly7lXpbsR5l2OU4y9Oxlwt7r6TmvFXyQPzWn6xilHV9CPfdembXFZ97EBWzHE/fvHFHuhJ3SAdqP7GUWaJcHf9I5nRuHgB3lmlTmKE88aOzE97kY2sxV443GTdHLKZX512diY4yM/SUtJg4i6y8s1JlpTEgUIOEAp8ScmuHvJW1Cov+tJH/SndwD/58Xi7TjfResaGv8m+y0dDF6qe7kuNYPzk27jpK1nBQb17sIAew9HV2DpZ4wmJj+ag/B6BrtTN3nSuQFcaY7E9q0LSUct+2Ab5tfirU8OLVnOpw146UmN32oaJT0s3MigMUGpmum3dUc4aMJ4DlD4WCshMqSs1b3VISWNIqe7SOEhm+MYlueQ3BsYdHKER8uv0mdO1tP9uO12VXRuV4laFSTdLMNVXq7TjbnkqTd2Bhaef9E++cOCafDrJouwTzyBjznXlvXqDWCTnYNSrqRm1b3znkjV39gCGZj2KT+xD/2Ylp5HecSOHwzIsNR3GRJ7X6z3ypCEQ87WcPq0BNefUxhUd0HC2RnMO39w7BH0me8OtVMTJOeOxgvxJ9/jgl6vdqqOGQWOln1W05hg8huhqPSioPGAhxlpgYPWiSw8tXURd3BqNcdN6T8Ww4oAK1cuunR2wbz172m51DpD6GS8ZeRN7YVJyLHOw1LZuz7SMZOwVio7sp64vpZ5UCalg1UdraSLcfEb5fKktvvjXbm4gk9II0rPSA2LvlW/CdOFWblTlXitHNoZ+417Z7ECEREBlfuSiYZpjWkdThg7ZUajT/072UbpaDy649vEOytUYLuGit7ZOAw68bBdrbtngFAA7AG/BPlEWDwC6tCzddlalWUFhmIWHIe9qixNOKsUrECdkVhoFOaH/VgHRqf1izVSfO+ZdKqghUMcKFKdn9LNkQepC81e6V8A0d48ax9hp3aY/eEvp4dNcIoXz3/xtNQcw+u1Nj2y2Rx7NBHCKPuZjinV9vkAaYD/JGmO0Qpmz88vW2NRnp07egV2u0yYmqQhlay0VHZ1iWzaVIaPxlr/Rj709dBbPaW9B2kJdT3Pv6giKFwOhDvf5sXaR3hDwTOxvuItOLy5jq9V2x0zM3/S8ZNKVQ0F3GQ3pKea3u/jGx04WNcRKyd3fvgapVKedq75hpSVx9rZ3FLEOD4eVkxtUHkpW/aYSECH3AyIAUHXanv/uZQ789BocRMQm0FVMgrDwEIB3WbZrd45tKgFkHYNt8tGBF+YDviKuj6ugxsWVT1ljI+OU9U0OIH/HsjOy7YnH32m7d1Wxl43tUIeoxhoz98hdz0Cy/ONe3xc4J8UtB05gJQXfFITTRPdc3/nOd+yLT30RCt+QnzpwToskbeTV19e7zTqlI1n/bk8d7n//+eoBBfqUGlRBvtczVat6df1iw9PL//ov/2qfJciUyGL3h6Vq9Tyj7x/64IccA8S9jHP67Pu9R3//77i0QBMA4CMf/ojt3bfXPvCBHw6c++5z33UMdZqfrzdwTn2ik++/DbBvAFaFT3ziEz8T4JwY+L4FcG7jxo32x3/8R/cZHH5qwvnv9uSnVuT9gu73wP0euN8DP+Me+FkB55577jn71Kc+5dgFPE30+BD6fb2fLGbcj370o/b2t7/dc+vP3febN286n0HpirSO0WGIN/r1XwXOaS0noKWYKMR8fOXKFZeaT76UDksIKCcwne77Ua73v//991O1/igd9d98j8ZUh9u+8IUvOKaoX/u1X3MpwTTOYiiQvy8Wbd3zv/7Xr9uuyrdbR1+YJcZ6WXQkm8ts5OhUZAA7VUHa1Hl1k+K/uVlv+Ndrs/Wd73ynY4b7wz/8Q6eTkpKSXDo1BcDW6/Afp7EKMCsAJwDd5z73OfvN3/xN+9jHPvYTl/vj1OX+M///7wHtt+nLI3f6Lp9CekLAOe31HYEdPzs72/kSAmOuPyzmuf9W3S3700//ObrFx94OcG73tu0WExzJrimbk2zWKghqa6TJC2RjPJgUTgSdtXXtxcatF4C9NZhaHDhMdohT96vB+s7nPpwQZjPbbQqzqazUq474YoHy0HHsXIPiZdNdTDdi8mJ+eRPcdhu9igKwU7oGKGJFu+1sPiuFpWOoAODnpfQlSzBttt6xlm88ayGwb0YDUPI/dAjgXDp15b1BHIwjoKpwhosM0Vc6ie1Oq8MmI1YrtxurqAqBKa8gAsKBBJMVjLm7p87uMs8QiGNX3G1SKzrhmH9I6SjKBjEBLRFhERDIV0Ak2ram3WFt+GqZrE1rZ55VH+oSoN1nFQ596BIAD/p4DValNQKVLtJKeV70hRcBPeNg5uKVqzb+7AvW2tNn6XsPWdzOXebNAUDvUNpGSq1Vgl8qnicAMlIEdb3L1cOmPEEXm6c+pMQFYUUT6WdAEF6Aq5VFSOxvLnDJQWeDfYsKUVeBJxgxjR0AgBUC/BprBZH0Io35GqnN3P1uX5n2EaHyJu+32OW0ca8ApwCTotJYVbn0nbfGWkfc1eFuAx65oqyZ+kZrO3HWeq7etIKiTZa0b4/55qXCOhdN8AyApgBVvEJDpCoovdYSqRkDkFU/B8YkQMN7VgB2KkWj2Ny0Sa/+cOn/eMjFdODkWVqh3gQj/AmM6V7JkxiI4B+i5cgtQQq2/13QJXBN3HDhgGAUKOPFXALZTMNy6NjraAO9R6VI7Tc5xkn7HusdGbTRxXFKWCIw6W/J8bEwGiVZFHMm2CeE4JNALYDmAKZJRBQkFMjMYGdYAqSg4VIjlZ7Nj9SIApXME6VQvYivML6cuae+AQolal5S/1XGUmx5CzwMnI22MBaLC6RVXIFhiMAtgVOxrggwMhdAWkZSywpkGeQD653uZcAU1BJYaWYVUBwB/QCCmMErYvMQwGfS+qa6rGWozXqmB22SPiRBrQWEBAKCSbbciBRLDUyy8LVo4mhKUU6dCGa51LhMdgXsFqn/HBEegZEU/lD6W38C+IGaS7xfbVkGfOuDbAbg3/jSg14uKKqgD3ODAN2MzzQp+yYBFPlZxBrpbgUQon5eyJ4AAoswVSg97Ao6Zo3ygnyCYeighRIahmmZQN48faxUahImfxim/BaQZfqqf2HQbow2Wt/0AMA5+JEIOEeRtSQtJcFiIqMtKizewvziGDlSBdFXYo3VnvYPStXqWdd59Kt0tC4PKEagmu7ubscypziN7tMeZWlpqWOa85AOqByBbPScgHO6T19vpEttXw+c28FB8/CYSOaQpj+yoOA9shCA3EjnrqKX57E1K8iQPyC3AIGKCd4t8+ECAbglJFL3+KG7xBxH0kB0zhrMZj3WyBh2DrbDuDXtwAT+Pv6WEBULa1qGJYVmw+YWQZpQwEuSUXTPPOCtJeafMNarlLu4zHPUYYV5t4iMTDEPp1Bbc3OkNCWtmFJCBkH5HBjsb9HBgJEYFz8AngrurSBPzgqg77wBPPth/wTckxFwoT5EwJcx9EbOZU5nCMSPMpPGlgDlEcyPDBQTm4Ltd4HWAjKFUQ+lKVumLxZ4yDELad5jDhd55xKgWVVeDKYKlAfQj9Er2AVUrgLz8wSE1c8OpMvM032L9J1kyaXe5m3T2MPRuSkbXyDlObK2hn4M8Am0qMBwiw+OsnD0pIC4vvIr0D8KtwsctwrwZAn75b1Mf3hFARRGzwjIgy6FC50xARpBXy7MB9rsHHOUeoeGwhIUzHhTxhrp0GVZfGFQEdBPTHLLvEMB0iXm8oJkgEC2L30cwJ1iZuV12B9ag03yRv9r3gs0In0kvS02K42k7I0Xc3gW3TmJ3IxRF6UznWa8BRIJ8w60WP9QiwYcGI4dD+Qdap/Y9PS4Y7+lPpp3+lo/5zQn7/1dRCFa20snyPcScE6HGeTLay2nZ/SlmKuYxHUJOKdMRfoSoE6X7rn3u2fOL9MWgeEFoBRAeB5bOsvv04zpNOlwZ5WyHr9lmfETuCM6KNQSiH5HIlMOaIoc4h0iQ4voQNkbgR5oK3IWAOgmBJvkh7wJOC9Gs0X05RTAyAls7STsg+rZGFITRwHCCybQv8JnkkuNpg8yIMYoBJmRB8zC38V4tLw071KZ+8ivQjYdW86iP3qUf4yfDO0SY6x2ycAJRLNE3dyMoV6yb3PcNkIbh+dnbYavRebXKm339wU8QnrdOH8AnoxlMOMlUyonTXNjAVstv0AMSYGA3wMAX0ivunSX2CXJzRQyOEd/ztAXszP4pnMAKgNpY6g/5aso5By2O5bQTDNk1M1x9SKMf9iSlTlYrqCV9KPsVfxRph5/Zw5hc/2QMT/qL791Bd9nSf4deodpQjkCzSHfGi/kWUAN2Spv+kjzS6BRASbkt8p/FrTFf5V5KE3CQCwxsdS2NVhd5YtILwQyZ/3x8bxBxvNKBxiZZg5Nw/SrgwdhjK8/4+Ygdvh2cjVWaIOe9VnBH5n0tc6OOWvsGLWu0RmbBigsTzIITpDM9DArzI+15GgdJmDMeX8Qvpn0mi7J0Sygx3nGXGBJMV0JaLVC36+hZ/3RUQHIloZnhbosMUeopQOwOumk/c6v5/MlfGgKkBp089qBq2i0bH2o+kCnIISY453zjN08/orGSqlqBcQTEEhpUJdJ5whvJHrG14LwF9bmAwDszFpj86T1Dk/bqOYlAxse4WcJif6kRY+GWIMxY/2idUmI5g39KZ0kNkf1Ba9BzgF8ObAV7eM9fvS7QG0CcMmGofadjp0T8B8bpc8FrRbgXz0gFmitGTRP0JL8BYAvfogXProAvYGwJiIG3MdsQKDxWLAVc+i2ACxdCO+g1+VGMwYC+E6vYhORPfmWPjP+pC2dtfq2EesmXeTMNHUVKA/ZSkqPsqyCCMhjfC3YX+DzWXSqakY6YMrTIRWZFskfKo/x4yfWdvzgfEd/wNTUinmvYyM6tKQxpL3UOQB58mVuYMGptwpCxvlagAkWTe6G0zED01p/7g0gtblA6ncPrCB//PPjoAafvNqXyAh6ex7ZZlYh89hNXHc/2K/GRsjKUD9s7UMTNqyDRQg7ZsUyE0JtQ3acpWRweBQEtA/9Jx9X+lvjtIROWOZL/xzwj7+taG0pvx9dJ0CyNwyLAlmiZN061Jux0prYX7pKtoXf5atIF4rRfBE7M48sO7Avf/NnTIKRbz+VpU03xkrWcFyLXnonkIkSSl/7ICQLzBfJtnz3ZXSBH+lKgxdCbap72eqbBq1xcMIm0OUCK/oF+lhcYqDl5UdYCqC5kAD6Gh3ji97x19pLrUBnq406FCX2belSredW0afOz2fyyfcNYA24ynwTRBepw/emDMYQr8jJA8rJgd8Evpvn+Tnqvoj+FegtCHHww6EQuxiNQC5kQzQzkGGhA1loye+RAIjV/e4YaqS1FgNgpnUl7ZycWLU7raPW0g1b6ijtB3AXzjo9ISrCMnLCLS3LC6Y2rWTcypP/WbdSR6WgXtMaEh8/kD5Yw0/Aq2cwBCiTPmKNq2HiFoYXvSu7y7jQRzowLLUiJkQKRu+iQ/Gj9F1LAs25AMywnxrKPV7IwKXzS/a/P3/eRsYHHHDu0UezLC6OOvC8GFG1JsPk0RWMAXOVJxkHpTGdt5Y7U9bcpP08fDPaFwQYNyU5kgPNAASTWMPEYpMC0aXIiFLNLrg9FEca6+Yfou3+tsqaJgBGzUD2DXyYY2vUS4cJ9fkidVc71eNqm/Z9ZXJQO4wD9pTPF+ieRd7v7uPv+lyHqANAXa7SP72tq/a1r8BSeeGmlZWn2pMA5zKyGV/Kc31HmTpk7Q65SAHSSh1mm55apn2j1tw4bYMD6DL0dmCQt0XHB1lWdpQlJgcDDPSxiGB0jVgYWVOurQThSyPPlIn4oJvu9v8YzOUvvfw1O/zKtyw7i1TEj7/bKnc9wNzDp9QrVRd0prxPfuRL1kM//XjX9wDntEgZGR4hT/B1l57wgx/8oNs0UwWkQPT92WeedRvyctZ+53d/x9GCr1/YyanWffdeukdOob7LwVN5utfl92XU9DctkLQQfOH5F+zggwdtyxby1FKWu4971zuc3698zzs8DqTK9sXZ8Tynv3vu0XcJiyi5Vb7q47n0mRacnkCD5zl9V1meNnrK9Tz3g7572q1yPWV42uV5RvfoS5+vv0f1Wv8e1Utfnnq4NAhMBE8b9Kw+1+W5R+/S33Wq+N6ydd/6PvF8/qP0uZ7Vde/zcrR8kGyVsf5S2Z575Zzwq7vHU/f1967/WQsKD3DuPe99j33gAx9wTqzukfjLyKiNaq+nfE9fejYRdJ8cMfWBJq87yfzqc/zBPed5VnLhjwPiuf71X//V/uqzf+Vo8L8fcM7znL7fve6W/2GAaHUEKdcD59QHd+VBcwGVDmObG1/9/VW5U99ILnWpTD2zvk/d/Xdf9No9HvnRZ54x1M+e3z19fO+zKuBe4Nwv/MIvOMY9lSPNI2Ph5FVzRJ3D9fx3n3fAOf38/YBznj7xjLPu07tVzvq5pr9LENT2u/1CH/K7HBG3UNH7+FKw93d+53etv6/vNeCc3uEZT1X1NZ3CuHouTz08/ag6qC9Ujx90qd1asH72s5+1Z55+xvYf2G+/9Vu/RU74uylqPHKt+zx1dn31aoGeNq7va913d17KmeLk+Ku60FOX9ff+oHr9z/n7XRn6n9Oe+y253wP3e+DnsQd+VsA5gZ6+/e1vO3Yw2QqBnurq6hyQTpunYpdSUFt2SGCMt5AybRspnn5er8985jP2d3/3d6Y1zK/+6q9aRITOTL+xrx8FOCe/RaA4AR8aGxs5YUkKosuXnZwIRKeDSQq8yR/6r15iYd6/f79L3ffz5a/8V3vqv/d++cGSg2effdal91QK0JKSEufnC6DV2trqQDFiOHvHO97hDoQsswki9177Qvde8q3vXz95D2itJzCS9k6+8pWvuLWfZ63wk5f+7yVo7n/kIx9xwbfDhw9/zzr83++8/9PPWw/IP/CsVaXD9bNHl3v+rrWpJ1Wr0rgq/Z9Ac/IzXtuveLXjdDr+9u1b9pef/jOACGv2zoMP2pbsXPOanLGpURJisSEqoEsgG+GhqaQdycw0b8oTI9bqOOxa3b023tlr8xOwdrFvtUb6tsDoQAtLSrCg1Czz5RS5lzbTCSStTkzaUhesTtw/DZvPAvUkkm6+MKgFJydZRGqK+QQqXQ6bw7Pc3z/EvR02MdJH6jSAewRvvAEURaWmWUhSNEGFCRuqvWL9Lx+1qK5+CwGktFxYZHMJSRaQlmJxBTnmT6oyLwKsAvSQm8nWZiZsdWTIZru7bHp4iA3/u8H5QNLkBaUmWkBGinmFEdxmc3htijZx0G+WrzkOIy+xWy02F+2wR2ZmWyh94RMVRtAUW6w9u7FJW2nrtvnebpucm3CBMaL86ItwCyX9V5DuJ/WjIlWrEyO2OtRn86TZHiNIskQQlmQztN+Hvou1oIwk7fzbzKlqmzhyHLavEdKYFtP/2bYCS05gYqIlArYJSIwH7EfAQPs8jOUaYLvV/l6b66F9Q6Rwg01FgJTQsAgLSUg2X9j4vCNJIyTGOjHUtnfaXFc3AH3GmqCEggL+gAnCYuMtOCPNvNI01pRPcG91dMoWehjvjjZbmJp0G/Z+yIFPWIiF5GRaQHoKIEgCeHTH6iwho/4Bm2e85xn3eVAPCuz7koI1PCHWQlNS2NEHPFJzxdrOVtv4nRbLTkyx8I35tpyWaKCWLCovzwKTuC8ogGdXbXhm1Nr6mmEPGrTImAiCXD42Mz5tE9P0H0HuEMCEORl5lhRFXwOqEXuTArgDEwPWAfBraKzfZmiz9sRCGe940vQlxSdaNKw11BgA3LC1DiBvANMTIhMsNyHPYgJjCIT5wRxA2sa+TmsZbLGopEjLQAajA5XOzMvGFkatEza2jr5uG0W+FFGJigi12MgwwmUEeCfnLD6KoF5cBoHUEJsYheWps9vVLTwyhFFftLGJYbcmUDo6BZGTUzIBWkTa0OCQTdF/xH5gYAgCWAVQG8BakA8yTcBRwceRmUGCwYM2RIqcKeaNF6n6ogi0pMXFW0psskWHx7v52jM7aLeaG/Ah5y0tKc0yEjMsLDDYBdYm58ftTm+j9Q/0WgLpVwtSS0gzG0E6wBkbmCfDxmCbdYx0AySaob6r7KVFWgz9RtZWC1zws5SoNEuNzgLwF2DdI13W2nfH5onapDLOswBL+kg1PAU4RykAtTUbRxryaNo3PThuYwMC7CnokkC/JlssfRdCtEZpWReZA0OwRnUMt1v3WDdyt2gRMNtF+cMUFQtDTHIqsh1KufMASEatsa0FhogpQIuA+hIZv6AogqtLNrI8Yne6Gm2c9JsRwTGWh5xEUFel6hpeHrPbQ00wK3UgT3MAQ/wtPiYOvRlJGqFBgosE1jNggovJod8D7Ozps9bZ1fkDgXOvqtbXdLPHV5dfp3WfUrKLZe7OnTsOQKPDQVr/ad0nQI1Hj0u21ut4j573lP9G+K62rwfOFZZutOaRFhuYGiaA52fx6OdkdGNSRDxAVX+bgjmrpb/F+pirifGplhGTZ5HB0QBjmf/L/QDj2kgr2G3RpIzMTclzTIB+ABwGVoasaaTJmrobAUlNIg+wXwg8FBGHHgAADJtLbESCpSekWahfsE2ITa6r1SYA2UXHaW0JiA12rSnAOWIqm8efDo9LtvDYRGRmyiaHoNUgYBsSGmjxKbGWw/yJ9QbAia5bAPwzjn3qGexz82ee1I9+xE+CSNEVxbxPRsfEhMY4cMw8fn0fOrSzp8f8sR1xzOe4iBgAET4ADWatrbvdhjgoFREYCgtdLiwsUQ5IM74yZv1D/TbQPWAz6A/ip9jZMAuLj3a2cXp2yuJCo6w8oQDQR4j1TBB8H+2wcf6eil0UKGwEezcHgCwwRBFi1hgCTwEKCIQlZJY15fjYBKw+CzDg+aM3kqwgPcci/EMccEiQ1zn0yuBwP6lde9C5o+h0bBZ6NiES8Cz9ERMDgI59+UXAuR39HdY72O8C5akpOTCPpBLMJfgN0KUf/dDb28Kz3uggWFwi04jz+ZOWc956Rumb/nbSow0SGJ63EHztBOZiBLpipHcM1RoIE2S+hTD+M+ii5q4mG0GWgsJgGaK/x2Alm59Cx8OAMkcUOwy/wh9dNUW7+2Xv0a+RpPtMw75lJ6SjwyMBrAjCx/2waY6NjNGP8RYbFofffTcmde880/z0zFHNaa3JdbBN81nzW3EPAef0pfWBwHKK0Wj+ishEMQP9rAOS+fn53wOc0/s0b3SPyte1wPh0TrZb20Cra+MM9nQGEEtUCqDeqGjeNevGb3qKMcEmpzOnipNzLAn9h4eIhSHYjYz2DbQjoz3oShi3+KfYW2xonAOXJkfjN1HfWXyGruE+ax7sgllU7IqpFkd/BAHe0WGDwYFu6+5tJwi/YpmpmaRKTUV+w2Apm7Nu2Hk0flPjo+7Agy/B9DjYkgScmRsHuLEWbjkpGwBJRNs0PlJDc52NYXuiEojN0NZRZH9+Zo654s8YhwLvWbFBUmpG4E8K+DEiezg+wdxC7tAdObEplsm8DgFYoWsKBlilru0cHbAR2ETX5knvCjAhBgRYFv5MIv6XgDsCifbgBzZjV6aRuwRkISUiy8J9I+nzFWw6THutjRwSWERWYF5ENwcgN3hXNjA5BHCnw4YHBskkB8ujd4CFxZBmOT6UdHXDzL8wy0jItljiTYJI9cAq2dTWSgrpYFJ/RyIjgGLGh2FhBBiE4xam9Lf4ybJ346SGnuBzOQ5RzKf01AyLDIqmfWF4MwHI75K193Qiy/3I9KjNAboR42xYCIAT7HlqTAqA3nBq6W1tQ+12Z6jVPbMhKRv7LPsCSI5aDU7QR71tuGD4AvE5pLDOwN8MgoFy1brGl6yjpx891Es9ZslOxxjGw5AYIVazUQd235CRbfER+ODMsW70Zutwj8seH4pumZ+btvHxMeYfAE380cTYONgdo9m3Aqw4N4vuIE0y/xKiYy09Jd1iwxPRBUHUmfFjrEfGhm1guBfASr8DN4u5M5S9jkzGIQugfhhgSQFHu/B5Ons6mE+rAMISAfYlwTQWii8BGH6yE3+sxabxvTJis5HvQuZTGP6RoX8W8XX6bJhxNK9p+m7JEpIAJAUDKFpQ+t9VbEuOxaG7pwCOtg13Wy9+USiOSySA+gkAsdOzM9gY+dWhSCjxRmQ2DV9VoKnBgSEbRUYFTo5Afycmp1haTJKFCmTHvQL7yZftRp8PI6Pyx5TeO8ov3FJlCxMS0Xch4JCweYsTdru90eZg5pK/lon9CudQgEBdY8hJfVeDjc6Ooa9IMZxUih4NsTHs1MA484C1xSDserMzUxYYumKxaf4WF+NvMyP9gN1IU40fmR6b6UA1XYx1W3+bA+DGJcTRD4vsQwL8ZX2kTSWBfuPiIULi51HGZxKfdBWbEh0V41iiEiIYQ3SzrmWAh6Osb7r7evBJ0eMcTvAOBNaE7CXpnbQjLIh1E37Z2OSIdfXAZIztyaTP0+KzsM+hMFwuWv9Sh7UPtNjcyILF+iVZbnIeB1IibIYqdeHrNrG+GaR8Oh+drrqQmjojnL7vtdW5RVjo0E1xiQ4w1zc2gKwAbOZfCP6lGIc1fxewMTpwEYKei2Hd4E965PGhSXxu0iajB6LYb8tOz2R+RgOwBiDICArw1s/87WCd1T86gh1aAAjkZ7Gs81JIr5qC3x0ZytqAvurivY3o0VnA9+noqhzkIJg1rnBmTgYG2tDnvZYUjb8avQE2z1jsrFnT6Ly1Yj8HsFlLACeDyRsdm+CDjQXMiU5bnFzGV8Z2YscE8B0YHsCfaXfg9eDIYLeWm+JAjw4ZhCOzy4zBAuuteNbuOkQzjHzOzACNY00cylikJyZbZmI6cyvUgbkEgh2aGXayPwhwbHkauwK4K8SXNL/J6aTCTaWNrCOZA1PzUzActsLYOW1x9FNWYhqHhcIZw2UbXZt0+mqUdO0RQRGWk0mcI1A+C7oesPKdzkEbJBX0EvYrkH2G2GQ/i0ymfei2VWx4PLo9Cd3sHxDGOg6dhX80Q92Tkmg34zDKXFyYZZ6FprGmyLepsVX8wyn0K8BZfC1Gi3VMKCyE2ApAlWHh3mS9W2UPdQoSH/y6yXnkkz0O/KFYgb4A7qWnkeYYNGbNRYBzn7tgA/gElTtLYYNOZk3DOoa1oB92M5qxzs/DPqZ5WTDscV74C2KFm6D89tYVa6hbJk0yQG7mdgKHmhPj+JwOG5uesdx8f8sv5tgHBwlWOTxXe2XY+nsZK9ZUASCBR0YnWJOPW3jUim0siuJwbhjL4CAYstdge1u2tvZx5ueMltCMv8DXS5aWDriygLVRVDDj6oVPsmLdXQD4JzjkQOp5HdAIDWetlg7IFKBwRISv9betkqq1z86cb6ZOCbDhZQLS80KeYOXEF46LDcHfwg5lAfaLBssksCRrNdwHa29bdm3s7oIwbR79FeWFfHnjI5NKnbTDfn7zLlVrWoo3suZl3R3edvN6L9snpMrGTsxyGGUC3215cRTW9hN26/ZxK9iQBnCOVK1V+xzOgqU6Otot2dGc8mT0608Cm+N5nDdX7t1idTJmzs6fO2//9E//ZMdPnACs9Ke2pWKLc9zCMTxRLFY9wDk5d7/267/mnDcJmIrSppoCVLE4F+sXadqI06mKcRYb+q7ftcDTJr427+UIqoy623X21FNP2dGjR+1Dv/QhcgK/2b1b9wi04gGsqC/WX3IS9bycSzmb8xgS9VBYKAsLlLXepfooYKTPtRDT/bqk0FUPffdccl7F0qD6SvEtU1/dr409nRgLw3lVG3Uy5Idd6hfVy/NO1UF9JzY9TzBP9+hz3af+8Ti9sbExtD/M3a/36O86haI0O+oL1VvPaNyiGRsFTj39rDJ1j+ofDaXzEg5/X2+/64MgNqfUn2qz7ptBYU1SjtqtsZGzrbLlpGtM/7PLPf9qn+rd6if1i55X+zyOu8pQ2xW8U1sFeFJ7tCi4t//vfZ8HOFd7rRZE65P21ife6sZC4+GLsgkJDXHjoXH2tF/9pLZoM8FTj1nq2cemg96tdqkPRHetS89ps1j1U52zsrJeq8Z/BpxT+907cVhncer0u75U/sc//glraW7+D8A5j5x6+kDvUb1VV8nmEMZc94RwIk0TXWWrnf4sDKPZINaY6P71l57TQl6LHxlb9avaIDnTKTU5+eoD9bX+du8lGfIwzu15YI8pdZBYWjSP1JbAIE6Q0Veahx7A2X8GnFNb1AbNH425fteld6scyalnLqt8fa7+kBzOIstqbwBOZkJiwmv33guce/KdTzq57+/rd0Ze5QchdxGvyq1+p/KuT5XeVfVRXfRezRXPuLv77vlP8qETnX/9v//ajh075kC873vf+1zd9azkVfXWfZIZzT/V2dPv6mv1lWec9Hfdp3mr5/Ruz7zVvfr9h82ze6r4Bv9V5uv+db8H7vfA/R54Y/fAzwo4J9vl8c/UYwJHKVW6gBiPP/64Swsvu6RLNk7gDNkU+Q96Tj6Z7O76S3ZJtle+gp7RO2TLZLfWn2LW8/pMz99rp2TPPL6+5126R/d6/r7+nfpZ9+k9qpPnPevvUZ1UrsdHkH+jstaXqfro77pHfqznXpWj5//sz/7MPv/5z9sv//IvO+Ylj73XZypr/f16l/6uS3/X557fPf6r512qs6ddsv+euqkOHt/IFfQ6/LceOCcWMfm2ulQHBdNaWlocmFJBNaViVSon9bW+5J+onT/JFQ9TjUAU98rAT1Lm/Wdfnx6QjEo+Nc8GCd7K99aaVetayYvGUusBj4/6+tTifqnre8CjSxIBrXzjG9/4kfYP1j//o/6s+f4Hf/AHdvLkSZeO+V69/6OWc/++/3k9IBsgW6BLts4jk7Kn0hf63OMXSHdo3Sx9r30HjxxJt+g+9x0fop6Dtn/z6T8FkLJkj5eV2UZOpc8T3B9j/b/A4UDiVgCRYLNMS7OgLeXmX16mxbqtNBGYqrlq3bcbiF3Mc59Oq3O6mlR20Xm5Flax0/xy8mAu432wVU011ttYzTWzZgJ23L/MyfUFvhZZk4flbbTMXbssiA18zh3aKuC6xctXbObmNdbegwTQYFYgILwCE05y4SaLKdlAOrFF67h80QbPnLUYgiAKTM5Hxds0LENhJRstY89OC8oG6AegAMSd+bCZvtLRYbOyrbduEQQchr1hBTAMAXL6KCgj1aLLN1kgIBZ615Za2mz02lWb7GyzFVK9gTNwwbAF9jbiysotfvsOC8hOhdWOPRvAOkvXG2zl0hXub7ZBgsGzBIu9CZIER8ZYTBYgnu3bzR8w2BrjsnKHPrt1w4Zb2gkGAwggbBgA2EOpfaLyMi16e4kDCk6dOG+TZy7YEGPhQ5BqOSzK5tmbCMvItNwH9lko4DlvAkHaA14jcL1K4Hby/EWbuH4T4BzpfhzzBVg/9gwTABmFbdtufhsKYKsLs+WWVhs9ffJun7BXNEvHKxVvIOXHpqVb+OYS89teRN8AkhS471aDDdfetJH2NoJVBIsIS3gDKlsmMBFdscmid2xh/BJditll9s3mrtyyyfpmgjzssdB580J/BVKP7AyL41C1F8HPkYvV1nnpks339Voie2Z+MQSOCBh7AfJMfaDKwjdtMq8ITqoT4GrobbJTtaftdkcj4EmAKARQZwlYTtI389hEP1K7lCO/20vLLT08lWBzEAGIMbt255rdaLltQ9MjjnlomXYGEsRLBhxSsrHItudWWCRltRHwP32jmmBOi6USGNpdtsM2EKxTEL+xqcHOXKq25r52K91WZpWbd/COBPp33m711tvF26THam0hkMHeF0C/yJgw/FoY4KjfDHIn4PuW4m2wB4RYfUOTnT9/HuDJIgHbeObhKsEc6gZ4cRI5WyLAnJubD6goyUYGhm2aoKLmdgDl5ZduIAXOJksNS3XzbWC6z+6011t94x0bGASgSLDJa5XgIJM2DbBBSUGxFW4oI+gSZu1jnXb0xHGYZXoIEGVZ5bZKy0rIYhS9SNfYbKerj1sPQcyi7BI7tOPNAAsTbXgFsF3XNbtaVwvYr51AI/qEOZuA/xEA68zE8DiMIN5WXrzFthcy32AevNR4yU5dOOH2ugvzN0I0uAZgAzY+ABkLgAUYeEuCzS2dQPN0P4x2HYMAekiTBwh0Y2G+FW7MtjSC4H6AXYcB6Ny4c8sa2psJVhKEnYaJjByUobCJJCfowFG5FQI8iwBwOjrVay+eOWKNPZ2WArBxf9leK0ncCEBo3hqH6+zE2aM2BttMfkah7dm+h0Ap4I2VKWvsa7QLNy9aO4HHpWkAmICBYti7j0oMs2bAMD4w3Txc9ZjtKdgHE1f0DwXOefSrx1/X77o0hj0ApuTb69CU1hybkG+laNX6xrM28Tzn0dHuYf7zlOP5/Y3wXbZIe8M6ABAIIDclN8mevvotgJjtTn4U2C7MLbDNuTBdhCURQB+1MzfOW00DgG+AazuLH7DCjCKYSb2sbqTOLlw9Z823miwX/ffA1j2Wl7iBeeBrt/sb7WxdNbJywwXDA2ExjQqPgMktDLAlayjkpqAg37Zv3gooIZxgfaedPnPapblMAhwcBPPpJDGHcXTVNGCSaQLdCSkZBEWzATYT2xnCFhLowwTRhkSr2rXH8iMLLQIWu9EpAn/dDVbbcA3myS4HRsHiMS+IEwG8Kywoss1FZQ4sMAkI9Ob1a3b50mXADGG2mfjc9tIdgA4AnA512rFzr1gXAIYMgLP7y/cB4MiEEWsWAEyT1V6vtZbGZgcs8gGAHBIb7tj7xgCpTI6OW0Fmrr1j11sshZRglzpq7bnLR2hnhxVm5sFAGQAIFCA6OjckKgTADUC5WdKzMveTUgEnM4eH+0dsGgA80V1LBtC3p2oXQI0Mx4SkNGa99Fk9ctuCDZ3EhkHkRTCWoDX9mZeZY2UbyywT4INYwWoartiV6zUAtxfQfWW2Y3MlgfMogCJDdvlatd1qrLXI6AjbtXmnbUovQ5cHWgepki/cuEAA9QbAlEl6ENvMej0+HnsOEKGnrY85G2MHDzwCsDWJ9LJDdvjUy9bc0WRh+ByxcayPsH9zUwSKAXxMAdCJx45EEoeYw14JODg/P0Nfe1sKunFL2WbLT91oMX4Az5YnraWn2drxTXZk7bLNpJ9WyloqATjg7rrbsSm9uvWtual/CuFqXvf39xGMb3NxAvlliteJGSWcVLfyvbS+FwglAHCfnlUMLxMZTsOnkn+mstz16jfP3HYhYt45C+j4WMNhu1R3DjA1zJ+0YwyQdiKB5pSsDJsFADAyOGozQgYRnBdIY39ZpW1i7ihQPQxorqWv3W7VXQF8gG0mKO4Q7tQlOiTaCrOKbFPhZvRQLDZ11q433bIzNdXmh5+3DTu4dSNxZIBjowsDdurcYWu4dRMgVRjzYLeVZG1CVwcDrO6xy3eu2rVbtQ7cJhbCsHDAYvERrGNhwBpdBECeZlU7DzgwyuBYvz338ncAw7dYcmYioIEImwRIPUsbwsOCYTsLBBQOQxjzMi0/283zkZ4h7OEkytAAksTapoJC7E6ZxQVEOWak5t4Ou9l8BwBth40RFxOTrw9AqDBscn5uFqm30TMxlAVITODVY1dPWudEl23YWGw7ivfBTJkOwxdA6q6rdvHsKQteCuHv25HtYjdmI7Ojdkmy3XzdhkeGYVxGNQEujYyPstCoUOtowvaQln1P+QO2ISsTr47Y3M1qe/nESfOFITglKwtwKvHpIdbxADUXYL0LJZ10emKaY6UdHhxmjT/MFFwGoBtv5VtKrSC70GL849xBgaHRYavGX+rqJQ3gLH4PnFZe2HqBPHOz8xinHVYYXwxLZLBdbb1uZ26es0Fkr2LDJttRsgW7HYN+m7Ar9ZetBt9WgJFtm3fZpqxtsC/GWC8A/isN9Xb99i3iXQDLACyHhBEzB2QRDJhkhMMsslP7K6usIC0XmZ7D96m1c9cui9qJ8uNsiUMT6pspZHRSIBd8wkz8KfnP45MC1RE/xk+Jw+Ztwx8tyi8DCB8PoxQgvMluu954HRBqAzFRGKVZk4iRMAi/MTct07ZvZM4mbQBM7G23sc2Xr1zmviEr3VwESGMHAMlEmLrGacMlu4pNX0SWK/BNqvAFQoJiAd9M2o2GO1aLjhkAHOoN21hEGKDAuHAHMJyanrJgwEEHdu2HwayAeTNm525Vu/cE4++kAmqbn5xFtxBnRr5Do0NtfAYQC8CgjQLBAo7r7x20cQ6ZLLJOCwPcnF2UZ9tKt1gyBxJw722Q9Mr1zY3W0HIHn6bPAbHAbsEnB4tvXKqVM04bWS/I7+6fH7JXTh+xzvYuAGqZVrWjytIBD4pJsbm71U5cPGlD+I4F6UX2yO4n8CkibGBhhDlQZ3UNsOj2sodIXJvzRvhXYrwMsK7GFoDnYbZr0y7bWbSbQxnedvb2GTt+8SiAqmHL2ZCNCvHGdx7HJ51BV+DrA7ZKzwJYBchTtmZiZNzmGOcYYtpl+DCl6P5oDpnItx6ZG+Ld9dba3IIcjLD3DPs1YBw/1pUpcelWlk/7MgoAV4UCruuwMxfPWl1Lk+VtKLTKMuZNXA5rhSmr6Ttr566fsLnBBSvP3Wa7SisBZsVibyfsRmuz1TTVMw963XojFFBROMDOhMxwQEWNDvy4a8suKy/a7EC7tY3X7PTZsy7Gn5SW7EDsQ31Kw07cehFgIc+mY+8jAxM4yEGsl3k2Dyg1gvZuYT2xmXKSQgBTo5+Hp4fsav01DqHU2QD+yjyC7cOee5Q/jGIAV4vzCmxj+gYO5wRZfXeTnb1xGZBRuxXl5dvuTTsAkqYC0/dlXXLbaq+dZ0702ebiEtuZz5oC8O7c0oodw9bX1N90QHyBjkPCwdvA+Kg5OIHuWADItm/ffivNK2O9Dwtfwy07duYkMF10LMBHX/p6grppTR4VHY5fRSpxfJtU1rh++GEj+DNTApfh0/hh5/IAce/Yut1ysPfQ0uAb4NN0Nlpt0zXrAaS7AljPF8iN2C/T0rOspHST5WcWwEgbChBs1I5dPGaN3fUAZOPs4a0P2kbWTQI/3hlrtuPo0d72QcvJyrd9ew5yUCAJcOuKtff1wXSGLmM9sjDLgTvmU3QiMYpoQHv4Uf6wRZblb7Zt5VuRu0jG/Lqdu3AKme62zFwOAQK2HRuYxrcLwh8oQTa2WmsT74QFbXaG/XIAtCCTwE4EWn5Brh3cGw+extdu3FqycwDF+vrRnzCgKZ2xF+vYaIBfu3bG4ffE4c8EWW3Nsv3VX9cAMB6zbHyouLhI+nGIL4B+jJGYqLdVpNjeg8GWWwCzJ4DsuRk/MCMrdunCIqloJ61/ELwQjOLxMeBkkKXxcW/r6G23g/uC7fF3JpMFEVIs1kdPfeGGXbo8irxxUIQU8pMAyhYWB0mrvGaH3sQ6pyTCsXw23F6y6vPDduNGH4A49gsAhSt9q7f3mBUW+dkDBxMtIy3JWht8WWMCsu7gkCDAxhUOZXjDEhsS6kVfhNlDb85lDQjItmfVvvm1STtystci8Quzs+IALXK4A/DvFHIexv2by3Jt74EoKyxjbwDawaVFX+vrMjt9apYsOGPW08/f2JKKZJ0eBWgT9AyHPKbxv6fsg++Pth3bw2HCNDt7atm+9s2zgEzF7plBvRmn2U5iSMJOXcOvqQG3kmJPvPURq9pTxX060KBLbLVAjvlFSyn35f7+4/33PcC5ZjYrlObjK1/+ilu0irEiiRMXcs62s2mzf/9+e+7Z5xzjnBT/A4BstAnXB/pawbXIqEir2FJhYgXzBLP0uQAjOrlaX1fvgjkCkch47tq9yx48eBCFFeGAKi+99JL99V//jZukoh7Pys5ygZqKigr3bm3033vJiVQQUUGj69evg5Dk5B6OuZxRLSjf/Z53O9Y8LcB0quMWG15iYtAz0yj1jIx0UJJ73OkvT9li91AqyosXLth2WBb0DhlysfGJGSufct/5rne60x4/LIiktp4+ddqUOkob2r1MdoHvBPx58MEHXYBOIBrd09DY4N7hAfnsrtxtO3fudGlrtejVfWIRUPoMAau2bd9mt27echTPD7/pYStBebaxKfXyyy/jaM6zcRHnQD8PP/ywDbMg+NI/fMndm8fm25ve9CYrLi52ZV66eMlu4sgq0CbnXJuomzZvsl1sROpEyw8KCKpfFIi5fu26a5+AUl3dXQ6wKMGVvAgopUv36kSN2C8kB3qHFmyqy4EDBxy7oKf/7/3uAc4pSF3FWKWkJFtPd497XosMyadYFQryCzAU/Xb8+HHAl8fcGH/oQx/EQOxziw+lAFaKos7OLpd2VX0mxgVdAm0eZ1NI6WDDUVCf+exnXqvGDwLOSaa0uayNjKtX2EwdHHCTU6c6BK48wmJf7VzPOCc5PYsBvlB9wW0IfPovPu1S9UhOJHdf/cpXQUGPsvlT6IJd2iiR7GnTQP2ptuTk5rxWN807sYpUn692iyt/FsRTbMKqv3WaQcAs1XH37t2OHVJBm3uv9cA5bcRsZONXbI3dbILJudG8lhxW7q6EKjTdbbb/IOCc+kTzXX1dc5mFL46x+kB6QEFylS8mSQFHJVeaEwKpKdjb1trmKMv1vOb6+/6v94HS3uxk6F7g3Nve/jbHpvHUF55yukoLSp28rKqqcqfj1cZp5ssl5O0C81invfuYe6qf2CyVMun7XfpcoMsvf/nLjm2uo7PTpT8oKiqkzvGMwT703gOu3k1NTW7c3aIWRL50jgLuOv1ZXl5uOTk5blGreryIbjty+IjrB8m79IEC4RqXx9/6uDvN//3q8z/zb1iv+9f9HrjfA/d74A3eAz8r4Ny93SRfTYxq8teVkvU3fuM3HBjGc58A+PK19CXgjPww+dGy4wI/6ZIfrACcPhPoTv6zbJLA4fKJ09PTnZ1SGxW8SE1Ndf6HbLgurRfk18sHFjBHP+t5lSd2Mr1LYB3PJTup+shfUv30HqUX0tpCPpwugXousbEnHyA5Odn5NKqXfIZDhw65z2U75S+IOUvAH/n5srdaM8gGy79Smspz5865v8vXlX8oX1G+QAosFlrbCNyuS3U5c+aMK1v+g4JQuk9+kcBpapP8D6VWku2XLy4/UX2nNYX8GNl8fa60dt/vcIJ70U/4n+ohQExmZqa9+93vdn6VxkC+lvpe/aJ+0/pBXx7w30/42tceF4uh2IglF2/EINxrDfk5+UGgGK0r/vZv/9bJh3xpzSXJtFgYs9iEv3/97HpAuuqP//iPnR76+te/7vTm6/F2zf0/+qM/cvsE0mvfb9/k9Xjv/TLfGD2gvYH14DnVWvs4sh1aj0t+dAko5zkEpp9l12RTpFc8X9rwaAKc9rlP/okFz05bBYHpJGx41MoqLB+wCMTECfEBy1gXG+STFsIaPe3RN8F2E2Hjp87axKUaTsDPW3hqugXIL9HSEDYGf3yDAMBlvgUb2TxaBDQH+OalF2ymtd1ymEdxSWnmk5zGpjX2G5DCEuCybIBgIei2VfbphthQ7z11xsLZ0t7F4QAAQABJREFU7A9PjDHfBA5I4qfMAGwJDCeQXsS+VhKsM3carZdyw9lnCYCtwQug0Gp0rPmzeRxckm9+Ki+Qk/Fs+Ht1dNsMAczmo0cI/Izin6QCjkk2H9b+/exHjQAmCM9Ot5z9B9mvYVOe/Y+2KzUWRtAoloCZTzR18CYFygq7xAQnQjYSKCMwL7DgQkurtb9w1JbqGwBhkZoqBxvLRrsY/RYWYasHJBG/udyC8wD88e6pk8ett/o8wQxO6ROIiKKePj5esJH1WxDPRe3e4tjp5i9cs4VXjnEqv9+CM7MtCCDSKgwefoDLwrJz6INEGPI4AEwgYmV4kDpfsRvPvmT+BJfTklMsOD0V1oFZGyDQtIIPmMweW+TeA7YEW0BfdbUDrqURlAxKSbdVGEQ4Ug7wigOt7Mf4ZgOUrNqsLDk2e+2W9R45YQMNBN0IeMWkZppvBHuDyNOwxpt+i9my2ULYb14FrDbAHmbv5VoLJICRAOOR6rlG0G6CQJUf4KqYUtLqRsJodvOGDeLnTeALJiATwQSXVuNI+5mWbCFFG80/I81WAjixvgqrAWxzL1UftrPXLpgvbU5ISgUskcwhTT8AUTBAsDcXgozs2b3Nqkp3wp4Q7kBOF65edilGE0kBG5oA84mCewSAR7oGYb6Jt7fveQyAQTEsM9N2rr7Gjp45Rn+a7dhG0KakgsOr3qTnPIvvW2vx9Gfl3r1WnJZvEbBM9MFG8vz5l+xyfS0BbB/2MIvcXp8YN3q622CrGnVB3wcfOggAYRchKT9rYB/18JFXCBp2Wzjsf6mpsNfFRtnsyrR1EAwd7BmEcU3sI+mWBYAxgKD4EPvd9a2N5h8eaOXbtlhl4S4LYYxuNl61K9cusUYYsUTmYCxMa4vsF/Z1dcAYOQ5bQ5Jtr6i0ghzAoERtr9y8yl7jeQeg2b57u20FFKC9tSu1F+3c6dOsQeJs3/YDVl6wgwCbv9UP19uxS4ftBnvEwchJpoAoYcx9WBi62Kvvx/eOTYyzvTAE7CzeCZNAsF1sAihw5mXrae2GMSsONph0AszsY64okEPKV/aYA2DEy0zLcoHiEECLPch3N+CTsPAw1hNlVk4gVmm0bgCgOo8uWODZROQxCZYhL/RF150ugCJTBLxT7AB7ekWZWQQ1lwAKVNvZWzUwC87b7pLttgc5WGReV98+bxcB2UbD/FVVsdfKABL4ME8bAD2eIviswGNYZKhjUIqEhUoHmdsGW62zrYM+TbB3Pfw+O1j0sMUExP5IwDnpX+1F6vL42lo/ad2jdYfWHFpjlQH01BpLv+uSbtf9nmdUhr70u3S25+/u5jfAf6q7BzindWZGUbpdn60l7W+PDQAwGBAYKjTaKjdttT1lO8Fe+NrNrjpk7jQyPAjogTHcug9UgZedazjHvns1+trLKrfuZHx3wFYUZ0MAPF6qPkKA+gIsPbBgASSKZQ24gJwMsAc+1DfofLWKbeW2C6BGNPLb3kmcBTtwu6HRggD3ZGamEtyMAVA2D4i0A1aPXg67MwdTsgF43GWlFHtnSwdg8eVx28FacW/RAYu2WBf3qQbs1z3UBXNWGKBQUmmzVh3oHQG0AtNKeKTtqXzACnJh0CRA29PTbieJF0nm87Ahb3rwMQDcPnbj9jUCmM/DArdgldt3296SfZYYmQRortlOAiC4cKEaVi4fmLMyLTI2GmDKNDGLQRczQxHb7q3bAM49DpNQip1vvWJfP/+0NQJ2jqEdBYlZAFgSYPuMhhXU17qGuu12Y531wWAn/ZOenOnYTpSms6Oj0zEBFW8rokz2AJgzbbS7phqQOgyzIVHhFpMCOw9zdQy91AoQ2If0iZXbq2wvukNsnmKHvFALCK7uFvGHcOKEhywFtqQ6UnOfP4++nx6z8orNgCF2AphMhUFqwY7WnGAunnYA4g0ApeIARs0Aghugn4ZgpREoLHtDjj3x6JMAWLIAnfTb88efsytXLzI3vAhuw7CGnfEPgkllsAdwVxOBZUAxiUmWyN+l2xZhXmlnzT2Ov5SbT0B4536YTnNc0P/ilWoC8aP25oq32K6NlYw/TKeAAGeJoawBJgwk/iHmO6UAFtPWNClup2aJ1TB1V2HmFUhooJ90zzBtqj5R+DQpCSn47yHoth7qJMZTMfOuwNqTjH0GyE6cRGxyszBtEQaH/ScEhiAO7UO0sACT0dQcwBViREqJeqbpBPOmg9iOYXcGrBbbuYptTMImycaGAaoXAE++wkjbsJWmF9qBrftJf5dntbBtXqg5T/yy04Ew4pIA6pJyWMDsgfYBx/JUXrbFtlZsA9ASYy2wpB0/f8pa8WtyAQ3s3wmoDLBh69Ade+a5b9oKddpVscV2lG6DcTDdJrA5569dtOMApsW2m47dTsR3nIcNqneox3rIOgVxkhWh1w/uexhgwgaAj8P2ree/jg2rBYTkBRCaPoGVNZFxiiAAPwiw6YYDavZYTEI8MprugBlKvdfZ28W8H4BlKMr27thlG3NybZx48GH8u1bskg92KCUT5iPiZ+P4kx0tzfhWw1a+tcwe3H3IssPzASfM27HaYwCjzjq2oL17DlkhQJSxuQG7WHvGmjkQkg/wWgDWLEBbOrxx5fZVO1lDavnJQYuBGTiVvRRlRRwaGSKO3mvD3UOWB2jkkcpHbHtZKfoEIDDgvGdffgXA2gLse8nsX6UDMoiCEQ9WyOZbNirbje+al54PGDEGWYKljpjaOIxRialxsO/stvy0AvTotNVcukoMvA6GRcgwUjg8EObrZKStpQOA8AJ2sMzevOsxywFQ3AWT1VmAc7WXLzlW3UrizQXFOaRmHrKTZ49ZB7YtD390J8C57Lg85Mrfzt+4aCeqTzmmq3iYMpNgAfYiVeAgTJDd3fgUAM9SYGV++BDgmLQNLoV19a2rduz8SRf3TMA3zWZ/KxQ2zcGZEbt9/bYtTS5aclQiYNUkiwFwDy+yKS7X09VjGwrz7UDlg1aQUgxz2qiduHLarjdcB0yyBBAQ0C6Ax3HWHV3tPaxD1qwoZ4Md3LYfFs906jRqF69fwj84Y9FJUeAR9gAe3mTDUyN24sxha25tAAiSbvu2VcHGlg/QdxXgaa0d50BaD4x6ibDUpRKLDkBXiyGvHRbZSdZAyfEp9ti+xwHtbLIBwCvHrx1jT/K0zY/Noo/jAADloEsAW8PUOQP441bTTWuuvwMzYIQ7cBEbzWccVBjCJ+1GzwbFBGLrt1lJdhE4RC+7WV9n1dqTRX/EZ8RbHAymi4xdX3OvTQDS2VS8yfbjTyXBuErk1c5cx+5dusDabs4e2FcFiLMEAu9F9N5VFx8Nw4/eXbEPn/CASwNb33vTXjn7kgNribhHMd9gwENjABF7wFb0tHWhfxLtEPPgYPlD+BRrduLmYTt87iWYdLsBe8cBqsklnSQAHvRVR3sH66M+xwAp1vJEdI0OkwyizwZhzQsgzWblriorLihBj8/bVQ6R3Ki9BpMULGmwOsfgz88BRu/r7LOJgUnYfgvsIACqglQOEaFHLwPmPYremJiYsX07H7TK4t0w/Y3Z8Rsv4PNes5iQGNtfccCKs0p5Lyk0b1yyk/gBnWAioojppuJf+pPye5w5OTDZATC4FYa4KHvTAeYg/oOAZ1caL9srxG+7kbkY1pKKwYtVWIx4Da112JwJGNRg9ooELI+d8g31s86+dvy/Tux/gu3fd8A25ZTCojlrFy+dt5vYNRKosw5KgNEUHU58vLexzxbGFmA2y7IHd+wHzJhHiukhAHBX7PTJ046Fdid75ZvwS6HIsaOnTloTANX4hAjGu5I1RTFrxyhrZ03xT899DT+kmwMc6JiUVPwJLxscwZdBJ05B8hMcEmSHHn7QKjZuw/h427W663bk+Cs2MDqAziMtLzo7JYk1FKnll1l/37nTZA2sTf1ZK8XJHrJOCAsRE+q0tTZqH97PtmzdYrs373QpPxua6uwioNQhgLSRsLtrnvgB0Gpv7SAOP2wpaRm2Y8tOK2PdpPSvl1sYk9rjNgEYbW9Zle1Bp6wGMraNNXbs+CnsWrTt3LYXIONWANhBgA8H7Ez1OQ4vnAVrFA5ALBdgoHyaUesYaLQObHRMWKw9wNzdsW0rrNkRVgso++jpw8Q98GmQKTFsJkdmwg7H4Zy5NLt0dsxu3Rhh/ZUJkDMLGfdl64I03FD4hXO4bPf2MA4PmB0+3g+ws82SWftnZ8LEHuoNSEyHl8YBbvkTO4GROjPMai4s2Wf+6gIA3Q7wJ3Ec9EnjEEQ0ttmb2MU0sjTNu0bsve8Fj/MYbLusx1h22dEj6J+TrbCAr2I3ctAXMHVOkM69axnQHHZtvN0ePxBp7/+lND4jBT3sbJ/800t29GQbGAuYwJMzGL9QdO8qa1yzkvIgwIqkLZ/2tWe+1WWnTjUDpA+m7qmMcQj7DwLWzwOQH7b8EkizglPsO98awLeFeIsYSX4epE3RMN/hdwg8GRI2ZfsOZYK3iIDx28u+/uVpe+aVFnylBcqTnYWsC5bRSQ6b3Ga9Lzk5cCjd3vRoKGlmYewbNdLYzpBBqc4BAxOQpXRSQc8vrGGXpwCkLzIPvdHpfva7vxlDXCUE/23VDr+8bH/3j0dIuW6k/95gOZkx+LKw4IUtcDDtOPL5CqC+eHsbwLnKqu2OxfHugQKxcJOmlvWOl774T99/3Ot7gHNa7L3w4ov2hb/7gguKKeCkYJsogTOzOKEIEOTZZ+8C5wRQErBKYB4tbASeUgDHj02tD//KrzjEnzbdlFrxLBsb3/q3b7mUh1KcDSw2zrDI1kLuz//8z13ZAgApWPXp//fTDhCjwJZAJQqKZWRmUAfSJnBS895rksDAsePHXZAsMiLSikuKXaBL4CwtIgWOUsBNQafnn3+e03KdLiio0yUC/yjwpcXnH/zhH7y2+HzmmWfsn//pnx3ITuCyvfv2IjgxlNHhTvoIgPOud7/LHnvLY5abl3tvlf7D70ePHHXBTVH+P/nkO1xw7rnnnnPPPvG2Jxy460X6/AQOW1Zmpgv+ibnsLApBAb03P/pmF/RTYLO9vd3+4tN/4YKPWkQqUKhNcaUPeOihQy4AOTgwaE998Sm3wFb/vec973EBTAUkv/SlL7lgqdr75JNPOjDZCy+8YMePHUeRBrvgpMZE7xZoSuk53vve974W1PwPDeMXbb5+9atfdWMfhQO6b+8+B2B8+umnXZBVAErJiC5txn7lK19xJ8fS2cw5RH21CaAFhYKeAjP9oMsDnBMgTgHKR9/yqAvOqn8UCKpjrNMI6ooBMZM+FGBLYDeBpv7v3/otB0xS0FfBZpXxfwgkBQUFO/CiUtroUj984+vfcGOemZUFk8u3X6vO9wPOSbYUlFIgWEEQtalsU5kDKwq0qKCtwFLa+FwPnDuN3GsuaE74sxH91a991QVbJVMCjv4NwFGBzQ4eOGgVWytckEWyfBJnTTIgIKjYzxQE1qVNFLHOiHntsccfc0GxmzduuvsFulMgN5/N6l27dgKsLIaBMf61dnl+WA+cE6BNoEeNiTZjBPQ7d/YcytXLBY1/8Rd/0dVD6ZS/+MUvuiLWp2odZkxOUFeBN8VqoUCzAukCqt6+dZvPTthb3/pWO3jwoAnEJ/DZP/7jPzqwpfSMNJpOUzY1NjkZeeihh1wb7gXOCWymtM7/8KV/QFZ7rLSkBGVZ6U7m6r26NAZKbyeQ5S/90i/ZNBsXmo+lyP+HP/wr7p57/9O4inVQwfd//ud/cfJVwQJPOkQbVNJHnnp/8akvOgCwgAQFGwucjLc0t7CpcQp5AMyJnG7ms3acTbHXqUxd0imat3MEDKrYQJGOUjD/5+dikO9f93vgfg/c74E3eA/8dwHnFEQRcO7f/u3fHEOsgHMeQJz8cNnm7373u85PEABNYHAB6AW8ly2UDyg/9FOf+pTzY3SQRbZPX/KrBK6R3RaoXZf8DZUh8M3v/d7vOb9OfvLnPvc5x4KgwLqHkVapAlWefEf5CwLFyXf1AP3ku6quCtTL9xDATvUX8EzPqkz5Y6qLDmPIJ1Wq9Mcee8z5kAK1C4SvcuXXyrfXu+XLyd9Qu+VTyLdX2wXM+/CHP+x8kL/8y790v3/sYx97za8V6OyTn/ykA9YLcCIAvtK0y1dU4EbtVp0//vGP2xNPPOH8vb//+793/Sa7LTCB/DetbX7913/dHUrxgPJ+muKtdn7iE5+4e0CCdZZ+l1+lcZV/rQMKqu/rdX3gAx+w3//933frwdfrHffL/en1gBc7BJJN6YqPfvSjbn2dxdrm05/+tDvAcp9t7qfX1z9KSRoPHdSSjtPhNzFJvB6XdMF94Nzr0bP/c8qUXpA8CiinfQLtI8hvkD0W+Fy2VzZPa2mxz2uNK1vn0RmyydrTWmUPqK3upv3t//MnNtfdZTnY5SI2SguLSiwWH0PAs7XFZVvo6LUOAa1g/koq32wpG/Js/twZW6i7TdpQmOV3bTdfNnKJgLoT1F7YN2/W295shi/jg/Sxpr52/izBiijLKy200Owc84kkhZcXZ8IBOChlXDABAq+lZZu8cc3qCDTMst9TBDA/uqTIfAiQiO5nZZYoJYECX4AD3mGwnXW0WdfT37aw7k4L5pCo/54HzJtNfi/a4A1gYY1DlKscWlSKlJnTpARl/3MCsF0wwa/EnTssMDnFtW8G4FYL753hRHlBaZlFAKKYJNigw4CpMCLFFG82nyRAhEpxSgoaA6vmTSpOHwJTK4Cw2k9V2+3qy5ZIWtjc8goLLczjc2geYCNYWSQLBe30pz+8qPsEe629ZATxw/YLDBe9Z4/5EyzWtTQxCkudn/mnc0ASEMkSB0IXnn+FQGOXxW7fBWvdDvOKjXGpj7wJ3HkBRvMi6LwKs8by7UYbOHGGYMwdS0zJsrTNFTDiZdJfyzZTW2OzF8/D5jJh0ez5+gAk6OTA5kxTo+UAVgsnCOKdRBBYe7QrS67eXgTbfJOjbZU+aX3xJRu+eJnUfaRIg8khtKDMvABOKE3YAswi3vhvfoyJ1/SszVNuO+M9C4tA2oYSi4StxQdAnVcoYIC5CfaISJ9GUM6bAMpyY731nTxnnbW3LBPGm7hdMGAkAzwh8OqNrHjBarPst0qAkrR8A3fsxbMv27nacwAvE20zQft82FjEgjFEAO8SAa3m5kYr4IT8gcq9MPgF2dEXjzq2s+zCXOSuwLG7rLB9odRGt67etDGCQDs3boWRZDesKlHWNzVuJy+cs6Y7N2EL8bMymEwE3qi7AUMgAY4qgo2bCXzFwWS1NDduNbcu2ksXDtsEAdRS9g+35m8j1WGADU4OAEa7ZHXsmYmw6MChA1ZZzjh7BcC2dNteOPIibBX4nNjz7YBT5K/3T3UTsD9vTTcaLS4k3iqKtsFUUm5BBCaHCaK/cvEUYC6YRwCPPLL9QdLQLdilmnPWNdBhmTlZ7BMWEzRT6ibSFQ102ZVLNTDCTVlBHntr2ystNRZGL4LZpy+esdpb1wCgBjjGq8UVssXUEjzsm7AqAlVVWx+wuHDWHAvjduLWCTsPsGCBlGcVBRVWmst4ht9lCrl49QrMRPUWCvhvD4HTPSW7YUwIsbONZ+zF0y/YQOcArDR5tr18Bym0UgjYj9tZQDN1BAtDI0IA9BFEzC61KAKjXaRCu3Dl7oGbgo051GM3gXTYdWDkGBwYJgCeS9CqlNRNMbCtEIDq6rTLV68ztiOM0SZ7gEBdWmSydcFadfbmBbvOXI5gj654I0BVxlsMOkoVt6Vgs1WVVAL2SLRBmFlOXjvDfvMF2CKM/d9i24g8RcAcImafo1cOu/3DaAClTz74HnuwEOBcYNyPBJyTHy8drcujp7XXrMPVsu/aQ9RaTOsofS59rC9d0t2ev+l3laXf9fVGu1T39cC5oq2FNhI5ynwhnRYgjCvVV2wE8FY6oJl9OyoBU6YBIOUwO2CaoydOAToNdmCeZcDJ1+uv2/jgGHOsnPGudOlEl0lXWQOY5Vg1TCRTQ04WijKLGCdYxEjZeqP5mtUAnF2CMWX7zm0wDVa6IGxTS6O9fOyoNXUQZCdYuHPbToKluTa5OG6Xb1+0KzWAfkntV16yk/m+FZaUKPaVJ6y2HkYtGCwTYEw8tPUhC1+NsGu1V62pswm2lCgOrecS0EV/+QF2Ze17oeaqdbYyZ7F3e6v2Wm5sGtkuYakkMH0a8MQYa79N7E+Lca3xNqyYzNv8gmzAglW2IR7QOcrqQsMFO3H9OLLe65h9NsG4qljZxMwUAfwbdrnmAuxVgPmI97yn6klAKql2rq3GvlH9DAx1pMAmFWpVIeyZuRtIZw6A1WcRsMIlO4seGOwehC2yApDPTsBoqcjsmtUzpy/CvOkb7WVVD1ShN1LtwknYNusaSTnGYTnmSSzrcjHXzM5PWhP7+FcuXLHEKECsVYcA6RbD8rhkjV1NgOfOkzIR8FVxLoHgZOrTboNdQy7N3O6tlTAGclANoe4EqPX0i087MJzYMDcBOkqAjXAR4FUjMYbqc/gegJBz0H1vffOTMNvlWB/AueeOP40euOiIKHbRZ2XFZQBk5uxG4xW7fOEqoDPANgBRtpVvJ11jGqDiWbtRfw3QSQ0GfA0gyn6Yojba2StnAGnVujjhns2wCSYXAFIElDQ/Ya3trYAjW7jfB7DALitOBRy9EmjNMKCevUkfoutjASTGEEPraSZV+/icbQA0saWwwrJiMm3Zd9luDt6ywwCQBVTLz9oI++gWl1J8bQFCkL42yjnvUopnAKaTbKfzvZ2UtmevXLAuAMiRAPbSswiqZ5EOkiCL5OeVMycoe9WKSkqRUwBspLJcwd9oxL+5eOy8hQAm2b9rr20oKbSTAKjqGmCJA2RTXFhA6lHS0uMfKovTNWS9pakF4EgkoLaDjn2O7HLW2NlOvOeU2yfJySPdZW46qbDriUHeBqCUZQcBd2YCYPQlNWhjr4B2x7Ft1y07P8u2btqMPMTD5jQDQ2yd1Vy/YuO9406/CjSTk5XnmLL+7flv2i1YIsNjwq2ibJuVZZW7dH8+AUsOUHfu/AVArn2WmQ0j0iaAX4DSvAHCNHTUAxi/7Jhmt+GPFkBUoVTJZy5cBsgTaBthecwkphcWBqiFOdba1mTHTh4mJWMQIEDAOXkHLcQvwur7AYbXHAF4UmupOQAZsD1KkddU1wBglAOopVW2BfbBAEDjnSPt9sLRl6wVPzma+V8E+D8D0A44fGuGnek8zFkdLV3YvAJ7dPejgNLL0GUjduryMfv2Cy/aWoCPFW8m5R/6JAZWZKUOP1tzyno6ey03c4PtLt1DGtsc0n16IfMQV9C+SVJ6Vh7YZSVFpdbd2mNnmYdKh15UVmgpsB2LeXKWgxGdABNrL10HVOxveyv22/ainTB6+gCCbbHqy/hFsCDHZwL+yE+1UZiEGm7Vw54aDsDvEOydeYB6/GGDHLUXTh2Grey2xePTb4dpMAMGqmVkSul3T5897eLKSRnJ9jCkMcUAMxem5+wsc/woY78Gc+6mwlLH0OUHi1t9zx07dQw/fmjGirJgBsTepxPHVZrouvbbduYyYDRirXu377Xdxbtg2uyzF84dJQXxPCCsAtsCC3WgH+3Dz6lD9zSwvvEi3ly1s9K2wIAY7Btq7dTrZVg6O5kriZRdULAJQF6fNQP4EAh1D3uSZVn4fcRC2ydhaAOcWwcgMgnAfTnylsp37yVv0un2wrB4xlr7Wy0+OtneWvk2q8jdasOrA3b01st2vvqMrU6v0I6N+CaVgMdYYwBabRtpBUh5HNbuekB1CTBr7mB+s77B/vQPDdrp2lPWO94Jmxo+DQcYFidg6MM/lr+TtSETfVWIPYqEEQoGro52O8/4rpHGcmvFDttdvhsgZRzpQPvxeautBuayUFInbtwMCBAmvhsXr7Ng8LItHO7YAVNbfEim9Y52wZ57gnurkVl/1n2bHXOeH+u6rp5eWEuvcgii1umrg7sO2YOlh5i/ZkduvsjYP+PA6kUcKtiPXCSkxlo3qZ0vgetorMO+ofsFrC2GwTiE9VUn6biv3rwCGK8XVqpy27qlAjDsAKDF4w5wnJ+bbxtgJYtmH1rpWnt7e+wC83mOtUrF5i22b9M+bFWKdQKyPnXjDPHVapiokq2E8lex67W3L2FHwRcUbeJQBoB3ANwTS6P23aPPc8j4joVHx1sZc0nAXj+ANT0jHVZ94yTAxKsA6mPtzfsfYx5ssxVYBS+i/145fNQdMJP/vH0HspicaXdgpzt3lTHq64GBOtm25m23kvxSwOFh1tjBnAaw2Mv6rqJqBzakwuYmAPS9cJg0ogvY+jwH/PQXhmN5CaBmh92ubYABbY5DMrAcbtqBr0yKUcb64kXWh831FpEYbhvQC+PT88hhiwWZv1VtQe8BhgwLBsjEWupS3WX77pnnAS5GYCe22oYEdB6LiM4xxoJDQ63NTTDPBdgBMBcVG7ZiDwKd/nrxyHOAMccBJOUhD4BhAW35BfgB4OpBB/D+29cBSwdZ+eatVkobI5E77b/LL21u5kBUTKQ98tAjrAGXkFF0RkMre+ywnRWXAjLj8BPgvc5+xop11vj4lG3MLbS9mx+AHTqNcZnkwMo50puifwF5bwUU7+W/Ztdu1gJWG7eKTZVWWfGgJZBSeB7Gy9v4ZefYDxijP0vKNsGKvRldHe3kt6YR/wtionC/MFiFq0iTytiHA5yDRfJlgHNNrXdY72TYTph6i9LxhfzR/S1B9qWnLoMVmLHy0mI78ACHATJYP6MvBOjy8fUi9bK3nTuzbEePQ7oFS+S+B5KJ7wezFvECpE52O9JyR8WsAQwmGySAw5pqGOc+B3nBtduWy7x/6CA6bBsM8qRXbWla5gDSLPOpGgxVgr3jPRnY3yiwNCv29NPtHLDoAnyaBNtaFr4fAPz5FYi8Zu3w0QVr62m1tx6Ks/d/SAA5gHPTa/Ynn6y1V05cg40w0d7ySAUEZlEWHefNOAPAZ2sggL7sbveyf/mHNrt5u4dYRSI4qFRXtrF2Uf3XvBc4iLVm/Z2+9n8+f5P1d4Lt3Bpru6s4qJjAHgHrikkYgI37MrLJAgr73mDHmn3ty6P2/JE63rFseytz8YUB88bj+0yu2UuvDFpzyyrvgSzsPTEw6wXiw63Ys0/3AliuJ40rh6b25xJTCAYPsYbvsQAOZtCuNs3jQ0Xa73802nbvCgTMD3DupSX7/D+8ADDTz/ZVVdgDVTG0wxsXdwFg6Ut28uS3ICtKADj3KLGknYwZbHxCpdNAsdjB4Yzn4yAm7vuPuxb6HuCcFlw6ofy5z33egU4+/vHfdwCXZJCnStcoR8mTqlUgFIGv3vWud7mFmVivvvmNbzo2iSdI9firv/oRF7QSI4JotsW29Nu/89suYCdgk5i/9IxAXgLniVK5HgdawDnVQQAhpeUU2jnIvfvfUyStb3A1m3Ff+9rXrKO9wwXp/j/2zgM8q+y62ku9F4QaoqigghBVdIGEkOi9TPO4Oy6JEzuOa4qTuCROHMc1cYkTx+MZz3gKvQlRhCR6ByEEqKACqIEEQr3/777834SMPWMnGeexHe6YYvGVe88995x9zn73WnPZzLLNP1M3s6SbgUO2wLTvMkDNquM+9vGPORsN//av/0Yn3eYoX3z7O992wBhbfNqm9nPPPeeoghngY8pXBvXYBmMeG0CWlDNFjg984ANamL3w0dP5ub//K9/xMudnibTPfu6zTsXdvvx8QJ8pADxTnM1Kq0K35NwcqpnSZ6Q7C2ODCM+dPafs7GxHwc8WygZ+felLX2KT84QDL9m5TeUzbEFsQE9cHLLcDGSW0Pze976vGcBP737Pu5VNMtHsOg3eGaSy1CrVDGizxOWf//lfsLl3w3m/JR0NljpBZZLdI/tMS3wakPTGw/qKLeC/8Y1vOIomlvQ0IMpkoP/ojz7mnIfBhfZ+O2zx+/V//Lp2ktSciXrIn/7ZnzoJWlNHs81Zg87e7HCBc8X0i/mc9x989A8c1QsDnAqBtH72s5cdwOuj/NxsyzrYqHruJz+hP76iP/7EHzuKLJYwsg1hSwq/89l3OtCfnZ8lbO0wxThTKbENZGvHXbt3vX46vwics4ohUwj81re+5fQ1g9ksqWpwlbWLJW/t8+z6HgXnbHP6xZ++SDvsdJLRmzfbwz7eUVncT7/7m7/5WycB+qEPfVCm8GHqdxWVFQ4waRCbfcdnPvsZpxLcTtC+w1QgLcn9jW98HUI/wulTBuBZe1nff//73+co+xkUZwnuNx6PgnNx8XHOc23QbEgwkvS3bjlJ+JMnTwFDjnYS2AZk2nPwi8A5e3YNprxIxcDvMwasXLnSUZczyNDAsc9+5rNKTkl2kvf2TBlgZ8/T+977Pi2hCsVgW1OMNHjO7oNZVBio5gLnrCrnTz75J06y3cBL20gyKNLGEANJrR1ch9khWVvbd3/5b77sJLZtvIiKjNKinEWul/3cnwaEuqxaTYXQ1Gp+7/d+z/lsG4saGhr12quvOap0q7lHTz/9lKOcY++zc7fktqlTmtKjwZz3Ify/9o9f0+ECNru9vR3I1cAFq+BPiE9wqhf+b6lCuP1cmz/+weMWeNwCj1vgt60FftPAOYv/LLb+2te+9nr8Zqq+Np9ZnGyL3Pe+97362Mc+RrHGYQewMADL5mkD3Qy+MlVjsyg3+M3AdVNGtsIWi4MMMrN41eZDA/bteywutvebxbuptdnnWgxgsbS9dtWqVc53GzhnsZ4Vc5iSscFmBsFZrGuf9/GPf9yJnb7zne8412Cq1TaHZmZmOvO7naeBfhYrG/xnMbhdrxVkWBxl87QBcQbVWVxqanwWD77nPe9x4h87r69//etO7PCZz3zGiSGtv5l6nJ2nnY/F11acYYCdXYMVK9j5W7xsxTwG+n/lK19xYnGL+XJIIluCy9rW2s3UZO2zLW5xJbLerj7tAufsXljb2Hmb6p6Bc/8bh613LLZxChz+N77w8Xe8LS1gz77dO1N6tFjaoE+Ll10qJW/Llzz+kF+pBcxe+/Of/7yjeG3jiO2pvN3HY3Du7W7R373Pc8EVti9jexI239vcZqCCS1nO9gWsLxmcYcC9rfsNVncV7dlnGDhXA/z2gy8yf2KvE88G9UL2jaYtX6kRjDHuEcBtvWihsPldv2efqq5eRk0OQCB9urwuX8SutVRuY1B5ycFaI3mcPPyDAaRGAnOx32cZcTauO89cUmMeCms3azUOtaxRSzLkhUoKsmYoffCiQTQohvAqYh4eJBZp5lrOHDuu4HAcMChWDUxjbykohH0yNk9RGxruHXCqrE3qYxBgrvqVnyn45g0FsWHus3KJPAwWw0JmkOT2ECDfkDdWLOhcNG7ZrvbN2xQyiD3T0sXyz81ywD4jawbqG1S7b7/ukRQYGxmhIMCAHmCscpQWIoG/orCd9YmP1TCJKzcq1D29ejh3NnW7qeQuqdC5HbRN8z2lAAml5pCUHQsE6MuONmAZryDhB/xCNrqfJPBtimzr2QOMHRGmESTufRYvQo2NzwXuY4PTuTYPD8BGFFQG2Dt6sGe3rtyoVtziZYoBvvDABtQN+MHNm/YFmnPaoblVfcdQAj5YpMZBXBzm5yp81lxgOBTpSMT2l1wCwNuu2+fZewQ08k+JVz17rK1VlUrAWih0bpY8SA54AIhAfj28d4AF7Pirr6pclza/KjdUmmJTgQoXr5MX1opugagZkNjiqtjaBvxBdaePz2xhP+te6RV5okoXs3St/CZPwn82WF3etnmPNRD2Yr60izvJpv7r5aohWXjtRIlSUZAaRzLIMx6FBRJDbsAnQ2T1+lGR6kKupryxXHlH9qCadtoptMyebUolafLz9CMJ30wCF7up4wcUnoD6SNZ8EhKDOnbgOCSSu2bMmUnV/lh5Yelpme57HSRvSrEdxEo2GXgjBzWEeGAHpCZUXY+N4zksP7FrCwj1cpxN3PiMCVjS5c5chx1TIgAczgz3qrHR2qOTJLrCSEqvyFmlqeGTHeurB4MU5ZaelO1XtWG1OpfikkxAEX8Pf8CNUu0+tFMd/Jc+Y5ayZyxGxS1MVQ9Q2jqxz4HYJo6ZoGWzV2BhiV2uu49a+h5o9/liHS876+wprpyfg83pXdYDJ1By69Dc+XM0ZlQc7QowSkL3fvcD9r9PqaqsliTxWJI+y4DUUGDkPpmtZME5VNYqL2P3GIA9MM9d+5AmRE1W7gwUmMZgf4miSN2Dm9p+bJvKbl5GiWO01sxaoykxWP+R+G7sBFBD4avgzDFURgYdBbClU7NQjPNTYVmh9hzdra6WLq2avwzLW5ToeH5vAbXlH85H3e2yYmJJvqM8lBqR6ljU3QGSKzx9hGRimcIZX+ZmzNKtljpHXcTm2Kkk1sZHJqIo6Ockwe6jWHX84llgqivYSo4DAFiF5eN0GxFUjvXqiQtHVV5TJnc/rPpIvneifDhhfBqqZpmaEjEB1SgfXW2p0J6TB1RxrYyEe5KyZs9VQhjfAQhyv7dN+8ryVFB0EDUTb23KfsIB58L8AOeOHHWKXmzP3MZTF9Bmf9qYaoeNw/bLDhuDLfa3Qh4rlLHCHitqt6IgG6tt39HGZNd6w/UZ9nmuz3Y+6LfwtzeCc5NmpemOf4vuD2K9TbLvEtbdlVfLgVh8AcvmawogQYCXn5rbGx0g4np1hWP93dszgOJGD+pgCVo5a6mmolDk6e2upv4mXncA4PSsfAGZ1y3bpEkRwI9DqAG5d+hi43ntR/3wFsp26eylL5oHFMoe87Wqa8CrqD62NKHoMY1nc5HiwhKAKW+r4Pw+QK1jqJmgsgFAMTVxmgKBcbr723Wh9hLPToFj0Zw9e6F8etwBVs4Do7QBMKUoJQ41OG9iQpLMHe7Yal3CJvrCFQfsWslafPb4aQpx91UTVlyF9N+TpSXMDgOopAGItLUrHrg9E+W4SVxfqBfWoySW953O06mKEwCqKH8A/UwePY1nEItz5oqrdZd16OheVd+q0NS0iXpn9rMAWWN1vOacXj25U3WAPFkTF2j1jKWKwQZwiLG9bfiBDqOiVHjyEN/ZreXzV6ASk61Iktqmpnqt7howMODrYKNmzJupcP9IFe46jLX5PU2dkqq0iakUjQQ5NnD9wyglNdfrUP4h5mUvVF2yUABDsdU/SC1dLTpfg8JlSTHQ7F32zn2wfBtSBN8zb3KmpifMdOCdNv7tIipFeftRjg0PpHguy7HyM4DV7FptHDhUDJQF4G8qUZtWPa1YFLIMKNly4FVdLi/R2NFjtWrhOsfW995As04wBhUXo+wof2DnxVqI5e8oP+wgB3p1ubpEB7AbvdlwW3MWANyiZHkY8OcmCpMT0iZoZtJcRXhiq8jj24td3+0mChPIp5mi2YKMhVpEW43xB3TGJvsEFoenr5zQA6xePfyYix8MKTY4ltcs1LTEdCDcSN0bvKsD1/do79k9uFS7azW2z+lce6hXKHGBm2613tL24ztVWlXqKFqty1yJKhMqeCgN7j2M3S7g3HhsMRctWASwhj0mKlBnSs5o28G9AOj+ygZEm508XyOwouwbwpYay7wCBAZaG1qwkpuhuLRY5Z/chzhDPVAIQBLKUMFAGkgxWXimK2WXHHvVfsCBnPkoh06bx3wUhdoU6v+AeyfOo4wLGDEiOlStwFXhgPsLZ2DfGsvYjZphB59zpPQCUHQhQEILeyiZmg1kPRIwrZ247uqDCh0ALqorqXasAJdmLsF2MknN91r0yq5XVX77OmBlMiDeck0cOUW+Hp48ER06X3FcheSj6oA75wJS58zKVtKIOM4ZSOXONR0+zT2rRTUtMUnxieOxrqzhOsqwOR4HXIsIBha0zrhKLHWno1E79m/H3rYVqGSBNma8U6NQl7rXd1dnK05r/8m9auhCCRWlHZzE5d7j5nzfXPpCTCCW4n0dulRbou3EYsNc70xA22lpMxQN2GQR2I22Sh0+c1AXz13SuLBYLZu3AtXUKSi7tjGvFWh73l4FRoQqZ/ESzUoGriePdKYCpa2zh8hp3tUMAJ/c6cuVMCJBaP6q+iYWh/TREqwSZ2ZNZ/8r0SmIuEgsnQzoOWka4EtkKPERIdrwAGpJbUBXJ9VS3wqMla5lGUs0njl78F6nLmNxmIeyXn1vg4b9gUSGUG0mLp7FWLR+5nIHynzQ16ZLNcQlBfm619/BWiFDmSmZivaLIX4dUlNHs6Mqdw57V38s/SyemBo3WQMPeoHRCnUQJeCRwKNLMxZrenK6eoAzTlVf0D7yjOpwU9Y0wHqstaMpgBkidiprASQ9n0dhQZnmTURtNCVDZcA7BcRMgWOZ/6fNUkoganDIORrwcQMw8Dz2lU2tdxjHpylrxgKNp53bUQE8Xn1axYBXLcRZ/qw92luxv2XsnJE6RxnE46OwAB4cBmKuLQZe3YtiUhv3dgmqktkOCO8BdHW38672nNql0yhnBXmGanX6es1NmKsWNepA6R4sIot5bSgKjtnYiWYxH4SqjwKQy02XtIdnt6KsAsArXUsYn2PDE+WjYLUBFeefydPZypMUFASibpmlB8CjBs71UXwzDfXDNJ7rAOI7d8CeBqzoC4uALwEkUyg2WZWzRmnRExiHUCOtvaIDp/J1hWfbJ5J1kO+Quu52MoZSNDBlkRJGTUZ02ktldSWose1WfXOVkrGnzJqRTbw6nkfdh2egHaCFe3JwF3OqH6B6rhanLjaOkWvcpd2Ac21YLK5Y8IRWMe8FBXoAqJ7lnA4CLZYrBXXAHJ7dZGJub+bo28SHhecPq+R8CfBlChDbZFTgqlV47BDgZbhmTmbsiRrPfI59IzGiQaCFJwoca+1E8qYbs55EdS6NMaJT15quKp/vuQ0j4clca/HZQBegIvatGTNmE/uNg5hBHb2lXK/s3qx7d7opnMnAbjYbgH407e2BcnmDjpcX6kBRHupovlqVtQ574jmo0PYBcB/Fxe4gEFWPZmXM1oK52YCxUbpUdUEHTuwBcryNPS5wVPoKTRydSlzmg/JbNYqUR1QI9JZE3DJtxhTdb2zSsT0FGkW8ngkHYYI7Q6i19TBP3WY8O3OpBMD1phLHJWjZghw+K1Fe3UCRN6t0kLYqZ83AphH78UMotQU7c8Qy4v9RIwHKeC4r7vC6k/m6fOs8ytLTlT0xW+N9zT7VTXcGEbUBoD8FCNs30A04lw1cOke+A36A4liz70csyHsQBUnUaqfmKjqI5582u1Z/jRiBe4iyXgzzZ+78JZo6dirrJl9i9y6dvnwWiO04kHOXVq9biaLuPeyggdrqH/AcZjgqnI5yHeuE+xTTFF60wqJKB7S3cSaVNYEv13Kj+ZYDIZZcAfCM8HNgv/stbShHpih71gpUb9PlzbjT3FGnY6cKYDZOKyoG8CorV+NHpQHvotLY26rzdWdUyL0aZGyZA9Sfzd59OPv45wHn9hbtU03DTdY7c3kWFyklNFU+Q9G6Ueau73wLx51bjYDJzJVZsRQYebPucIN9cnPqwZg2lL+nT/vyu1FTuwtXE4bqbRAQ6UNAzTdwmL7XLU+fQWKbIPYgBvXtfz6NMm8l/WW6nn2G+A47VPu85ltDKgKCe/m1gwCUAdr0zASsfiN0/Migdu6tYYxrxg41iXVoFDkTbIqZI86e6NNLLzB3X7qs5bnRevcH4oG7Ua9tH9bf/l0ZMcg5ipzC9Qe/P4/ihFBs0on/eTa9AMUHBoZVUyHAuQbWT3VwVSOUlT2GZ9KPuNCdeIvrJOxkBaIrwHvf+WYZIOBIILhQLVjkCQSLYyDAn7cvbcF+iC9rRD5dDTeGYSwadOBwCWpzfjBbPG8ZI9lu8QBKH9b2rT24aQ6gZtemJ59G2GxqAAUJ3eRLqtV6/wH5nbFasW6UYkZ72DYOEPiQXvhJE3EL6pzME5/+Q9SWsx6Cc/l5/fqXn+xhDzuYAuSZWrYkWKEjUfvr7NKe3du0d9dmxceN0aYN63FHpICOtb+zd+JcFbEK0ZwbT8LDM7dZ9793/Bw4Zx9j4ND3v/8DZ5H2IzbYDQyzRJTrcIFzBuAYCGbJJDsMSLINedsYNgDsC1/8ggxWsk17S7aZnatLKcwgJUt27di5Q9/65reUvcgU3cJUUV7hKD0YuGbJp3e+81lkEH/entV1LvanJdlMOSN8ZLjz/aac5lpAPvo6OwfbGLTztkSgLcjsWiwZaH//6j981VmQWjLBlKxefPEl4JhXnUp9A59skdoJqHWGSevDH/4IyaPxjoqFJebe6jDrTUvAmYyjJdVsA9KSFzFUCNqCt4Bk3p984k+chbBV9lri0eA2SwhaQjKRoC6be2ALZTtMcc4SlgYEfv4vP+/AQo9+f19fL21+W3/40T/kc4a0iWTm+wCnDBr7y7/8K6C5h5a7dk/N2umjf/BRZ2PUwLVRUMG26DY5aFMwsyRnFjCdff8bD3tdL5PIzp27nASMWWnZdZmCnCU1zTJ21cpVjpKfvdfgpe9///sopGx21LbeA9Bn8JBtyhos9VbV9y5wzhKFz9InPvShD72ecLh+7brTvgZfWftagsjshU2J5Xvf/d5/Audc17B82XLHq9zsOl3gnP2bJVtNoSyMDclfBs7Z9RQUHNanP/UppxrbYMzs7GznK6xtLKFpSUYDOh8F5+wFplBnSeQHVMy9tvk1B5yzn1sS3tRczB71i1/6oqNcYj+3Deyv/O1XlAdkZgpln/nsp0n6xto/6ctf/rIKC4vouyn6p3/6J6fvWyLVAFRL5M7LmOckmy0B/GbHo+CcPYvve9/7HPUX1+utv5mym9m5PMFGtCXd9+fv/4XgnLW5Pdt2/PTFn/6nKnWzhrP+ZmPChz78IWd8eBno0Z5Nu3+zkFS1jaBfdDjg3Kc+7bx30xObUPaLdOA628x817ve5fQ914a+6/02LtjzZ/Bu7uJc5/6YzZhV59p481aHXaslz/P35ctsYa2fuNrQNjO/9MUvOeqNdp9sDHi0/37qk59yEhEp3BNTrrHn4vuArNYnzUrCEmaWnH/j+b7V+fxu/dt/f9L63WqHx1fzuAUet8Bvcwv8poFzFiP93d/9naOcaypTBne5kisGd5mymtmjfuELX3DiYfvTEksWbxuUb7GDxUAGmJmCmkEeBtkYUGfx0Xe/+11nLrT5MJ8CkK9+9asOUG4Wogay2ZxmBR52DvZ9n/jEJxzwzN5ncbWBbQbZmXKNxb8WW9h3Waz5F3/xF05M8O1vf9sB6uzzPkV8ZUUjdg0WJ5gqr8XiBvRZ7Gixu8UO9n22FjGlJQPo7bzsOz/84Q878YrFu1YkY4UOVjjy6U9bDMVmCoepFtv7TJnLwDl7v839BuMZuGdqd5assu+yudvARCsIMbtLOwc7ioqKHCUvs8V1teUvKlJwXvzf/M2SZ9bOVihg98pieoszLTYyINIKCaxY6Nd1WKGUqfrZuu63PSH362qj36TPtXWwxbG2LrDCMSuisb5uyVqDVA2AfHwf//fumI1hNiYY/GuFVVZQaGPQ230PTEXMxtJCisqskOntHof+91rs8Tf9ulrA5jI7bL63ecUK1WxPwwq4bJ1rc7Ot18023eYYGzsMjDd4zqBtl2Idk6JqrpTo3z7/F7pdVqrEiCiAnXmauGad/FFeG0ZNyo15arjlge4fOKSrp09gS9OvyfPnyZ+E8j2UOu6jSBGQOlG+rOV9UYTyp5LfiyI/txAU1/j8e4WoxewtQGkHq6pcVKmWzJYn8QIZBHZ62S8gkWbWTMPdD0Gx+t17HJWg2GlpmvLUk/KOQzXKNwRLNBJMJh/VBzhHImZ4EIXWhnpV/fQ5BVVdVwjgnPeqZfJMTiSBQFJxkOQLMNowcJn7YK9q2Mfr3raD5PQIhaxbI2+S5u4h7E+y4zt0r1XN+YfVcbBQIV6o36UkACaQdKy4oT7vYI1KSFX4uDj5RWGPFY66WhRgVwCV0J196j9doqNbd6vHJ0CTFy/VqDlzUF9DXY5qf/wj2UQHcqNQc7hzUL1VNaref0iN2OBMJHkUtmql3Gano0bC57FLbtvCcG5cG0Da/XYN3KjSA4pASwDSEpas0Gj2Hz0jULokieEGtGaJ02EUuPBDU1/BMTUcKFB7WJRil69V0GRU4YBEbHwauFGhvvxdqjq0V6F8rz8KhG1APNUkQsPpMyNR2vKKScCaN1x+kVjFRRkAGcg96dZ9+tYFHBzCgLHGL8hWQOYyeQDFQW8AmnFdw0CB/DcMCNNTWqpbLzwv99vYkE6ZqZBV6+WVQOIOpYMOQLxhlCR83XuAkWgT1BX6SMDUFJ/RtSOXiNtmKnbFUnmlJgjJDvoHfZz+MwDBgGGdrhs4V7wTJZpLjpJIzvRlig9FudANdSdAlLMkVnaS/POJ8tS02dPV2ohCFJ/rwYZ7cvIEp5DUHWDPMuPdwEwWLzbRbskoM+TOy0a9DqUJ75EUKQ+p+Op+1E9eU3WdwajDSkUlcdmCtZoZk61gN1T4UEW5iaXQ1vzXdI3Ef8rUCVqXtVFJweMdhbkOzvhizQWdJOnWWN9IzD6VBGAG9q7Bji3orqKd6vfv5ToAWlNySaQH6Nr9CyTwd6uq9LrmTZivtXPXKDGSWI2WaBkkCXsNG9kLxQ7suhRbxNYbKIgA3/ShsJScmqQQf0ABFGeGvVGiHKK/191UY+0dQKFRWrl0leZORCnEwxsIAqivhrZCoaKy7oYDfCTFpWhT5rOaMnYaCbUAnpshVbVUakvhKygfVWoyNlmbZj+h5FBiDncsfwaxSas8r93HADSw/8lAaWX5tFz2+310qPSQ8k7lya1L+tDq92t6PLarJIqrW1CHKUIxoqZcCZzvKmC3uKA4RgFv3R9qQ53liM6dOcdawVupgBR1d2t1CStaXzJCcZFJCvcCZuH58ACYMmCnqv4GwFCNIlFpWLtwo+ZMyEChxtdJ7pUCPW4/tBmFpFISXX5KGAeICJAyM3amokiq97NHf+bmBe0+kYeF2G1UbOaiiDFbo/1iSXL5OwqHJ5qOatvureq/36snAOdyJy1zbAx/GThn+8f2y3nuGIdtPWXFRbb+sTWZrV9s///RvAy3+D/FEfb+N/7M+cFv2W82Tz2qOJc4ebzON59DhaeRcQsAs67BeQZD/IKUw9g2jbVtsG8g97dHZ2uxVwOEtSRtbwcqGaNjgaNWKTMVAMMbNTLgjFvdtdq2fyvwT5lGjRmrZ1ZRZDWSZCrARY9bt6ragIhO7cMy8CpJyVRlz1+oKHI014GBd+Xv052ue1q0BLefiVmK8InS7U5g2LPbUAc5rkmx0/R07nsVO3IsoCzKGCBuJQ2Xtec0lmgPGjWHnIkldy+cPO/YiJr9W/QI1ED7AFEZuwZ4DmuxVrtVW+/YmS3D9SQLZbBwgKJu5qPK5jrtP16k4hNHGf/NlitKK+YvUwbqcOFYAnsAfjfeadKWoi26UHcaW9dEILc1ShkxkacAuIhn9Pb9Wiz2tukiY8MEvv8dC591lN9O3big107uwi75tjYu3KC1M1cp2A9rbJCk1qH7JPHzdQQA0ECuDdkbtSB5IbBJMHHBgCobK7Tj1DZV0xYTJgOZDvrp+N5jWIr1KjkpHnAKG3aK8uiwKCX1AUs90PXL15jqfEi0z9PK3NUaGcz9YYyv7qhU8eVDWGjmYWXaoTEkRufPRGEqGQvREOYbEun1qEUVAbAeOVmkuAnAikuXK3Gk2dr60Yr9gDz3SDafBJ47CMgcqKfWvFPxYclMeQ16dT/5AZTfJiZP0tqMJ5UQlQBgcJvx+4AK9h9WJBDh4gUrNDt+hkagYDPAuHgVSCPv2D6U9cqJi2ay9x+C8lex7nQ2KC4pDpU4YIcuFPz7Iag8+5kruhw1oR7mtznAz4uBbhL4XOujNfeuKx/Y+dSVkyiIYnsLnLhy5lLlpjFfD1gAAEAASURBVOVodFgcbYI1G0DHq6d+yjkdxlo0Qk8vfBfKacRL/OfJf3cAB/Mu5qEUBWAD+Lw+c5WSmJOutVZpz+F9agIcm47C1pJ5i1EAG6N2vNJOAi1v2U/edHykVi1Zr8kxMwBwUFfjDtcAXxQfKVRVRbmSUECMTgzHOjsPy9JWZ66Lj44lHqKgAKWtQe5hcwswP31xmHkve1YGSlcZxEgoI7r5YM16E1h9t06eP6YubG5NiXfVklVakDZHMahqmQpMM3nE/CPFKP+dZPz30JoVy5QeO0VBnE87bVQNjLrvPODG2VJg1GitXLRUSYDhzQh/vIJV6w0gn1mA30vpo7G+zC8OLtmuszeOcc+LyBk1see0VjmTsxXtFcF5ASJ33tBBFPzKsB80IYVRY2N07WY1ineoiaGsmhSF9bCHH9c0CJTkoe7hTp3HMvEBMczsifP07twPKZb700t/MADo6LXD2nV8C4qH9SiVBmvi+MmAPU/QD1NQnvRTQxsKO9eOKu/APlTAIlFWXKXpKSjkUijizljdMIhabDV99FAB1417F3BvOkqH/UDtxwDed5P7iwBaWU0hQXosilo8Z8evntD+C/mOPegCAKfFaSs1Cps9Jg+seBsAaPJUDHA4aW4qVsPky0quofoIEMU4FjM6Bmt44jpGmmHgGIOzq67dUNeDLuziU7Vm0WpNjk6Sd68HSpzcw5L9OnSpSDfrcZMIDgT8m6vFAFdzoqdzfdYHGx0ALr/4kHwiAgGQNyh9zAza0PKOuHMNosR57Qw29gfVSwy3bMlyQHWuo32AIoNC3ntAo8eN0gqAwan8/IFbp04wfu/atUdBw0FaMgNgEAgmAvXWQTfUKNuvEycAATEvTqcgIZ2x9izFHWdrLipgdDD9P04RA4EoiRFjsw9yjxijFniuE3vqaShULUxfiCJnClSJu2p7GEcvHMDO9ISjTuvPXJKBve4SQP/xEQkK5P3dwy3ae2GH9p7eac3LvX2auCvbicd8iEDaUUDdd3m3iioK5d7toeVpqzU3FnEaNdF2u1ETPKZE+tR64NzpjCU+7v7qYXw7c+sUxQVbVEeeeMXCZVo0fTFjdzTgpR9icEM6eGWfCq8cdGL0+YyNrTeBq9jn60fVKzYxTtH+4fIZoOyEmKa9t11VNTW604iyZlKyVueu0bQxQNsegdxDgC36366CHajr1cor2MMR7Vgxb5Vzn4KIaTr7mJ+wszxUvF0dqOnOZt83i2cmImQsd9BXHczFl29d1s+2/1jDfsMUiyzS4gmLGYOxl7wC2HtsF2qZQ3rX8o9o8fTVxGzdgOJHVXj0gGPZPC9jLhakyzUqgAIpxoa7/Q3Y2B7UiSMnKDYax/mMV3VTJcUOxxknghXPemIksDL62U7UYmNveXWZmhi347m/Ty5+h9K470MA5i39jTpbdU4Hig+r4kY1Sws/iiXSOcdcTYlLVQjqph2D7ToDyPfyzs2sA/20ZAFgXNpCrFyxz2bUeMA9vnLvol5jTOm+289cuhZIdh7rxj6dYvw6VHAQCHMIJgb4MT0HO13UYrE0zzu+gxi2EYXnWVo1dZXGhaAiSdBtxSFHUJ/dvG8n6t2J2BynoGx2QxeLTwPOjdSU+CT5+1msTOkQbdgJwFrDuufenXuKi8GVLzOHvoJCsxsQP9D9IfrB3nN5qrheRxwbAACaoSVzlmoW9sL+zE0dgyjt3ijV3sKdamY8yVmYo+yk+YpxIxZgnO0g3jmDkqwBtQ13arDbzNbMlHny6gugGOecdhLvBoT5YGW8VItSligUdWRTfi25Waq8oj2qvF3uKGg/nL8M7vQmounRBWLsQ8wXTSiLL16bo8Z7qD8jvtR9r1tTk6ahxGf9h+iHmLuHX5UNtaq/1YBKMAUrQPJTEhnr6cddxNQlty9p24GfEZeXO1D4uOhYrV26SdPj5vCsRfAp/bp5v1L5hXuxVb2A2hy2vajUjg1Lop+g3Md8cfVeGbazxGZ1FDVgb7wIiDeKfL+N33sL96Gg16zV69coa1KWIj0pPhnEGfOmm577EUqrZ67I3zcIdepEFN9CsZj1wVbWGztcTyzk3XTqRD+unPXMGxVwJiNYF1LcxWsigOfGjPMB/h3GypTiQ2DWM8f79Z3vFjuKg0tzZ2jDRuxfY1GOpzt3tg7rWEGf/uVHeRoT660nnpoIODdKB/OHVXCkCSDsvjY+E68Zs4NRAXfqKHT10qC2/KyXa7+kzIwQvft9iQDk3o7i3Fe/Rg7gQhmukWP0wd+bqsRk9iVsbU9/9WBDgO0RNd8e1s6tjcRJ19mDwRI4cSx7wDb3+WJL64VCr6f8KOq7WT2k55+vBHa9DyfhptS0QGJLP0SZQrjWAJgOWpotBFNuvVMrvfhCI4D6FQoiQuFDkhEEw74W4LC/e1h7t/ezz9lPH7+r9RtHAPQFqLiwUy+/QqEi1vDr10coK9fXUccbYh5qbR3Stle6tHVHFzFvrz72kRCYKSxyG4eVv7df//5SHuc8Uk+/g2KVzCDOww0L6Xso2G3Xnh27aI84bSQ3smBBBlySzTwo5Nl+CP3/oeKcYXMPf/13l0K/FJx7/vmfOGoPLnsG+6I3A+dsoWZWoN/8xjcdKOTvv/r3rwNBtslmm7mmEGZ/ll4udaAeU5v6h6/9A7aUVDeyUfYoOGfQkUFSpnrxVocl1k6dPOUk97717W+96Uvt/CzRZBuFdh59vX0OQWyJOzv+9it/66hE2Oa2bRgacGOKVZZ8dIFz9jrbRDRQxmAYs38y65W3OqqqKvWjf/t3KNV8LAmQkJ08ydmANAULU46w5JyBil/+8pccoM/AuUcPS0S6ftnPLcm4ZfMW5KMnofbxD6/DPI++xzY9LcFWVFTs2FJ95jOfdjZCLZloyS8DzGzTx8AqU6EzRT1TAbFrevSwxIuBhG8F+LjurUGFXbStgXNmaWXSv5Z0/Ou//qvXP9KSntauF6micmchaQpjpmxmlVwj3gJkeitwzlTzzGrWgDNLsFoyM5WKpq1bturbQJV//Mcff11xznUilrg1MNHa4lFwziDMzbStKa39MnDOLDis7xgYZeDWu979Luf7Xd9hfc2StlYB+UZwzgAqU4prbWn9OXDuTz/3p4717he/+IXXwblu+skLL/zUgVJNyeSTn/okA3ay81WWZDYlxZiY0ShF/hPKjX7s4w4652XJGbNBsySKwYlvdvwycM6uwZRlLDG7ePFiEj9/gyrjwZ8D52yjx5RbTI3NknPWhr5s6NoC2Y4agrovfuGLDpy7HirY1OF27NjpJO6/973vOuPGm52jC5wz1T2zjLEAxvqmJcwt0W2b+W8EZs1a+WXsd+18rKJm0qQ0R5Umm8AnLo5FKc/Wmx1vBs7ZOGIJr08Auw5wj02p0vrwo8kvgxa3ofoYiqz5F7/4RSfpYFCxKXKa6t8P/uUHzrU/+p43O4/fzZ8/7A+/m9f2+Koet8DjFvi/0gK/aeCcxVhf+MIXHGDG7MUN+LI5y+bGUpKSFhOYwu1f//VfO+oFBtIZQGPxogFyFp8bcG6QnBVyWOxgSRpLYBh0ZnGAS7HOYLG///u/d+Ztm+dMRc4OiwkNWrPPsBjQADhTlrP539TbLPljr7HDpUhtc7mdt8XFFlNZ8Ywpxb0PiN8VE1vsbnGbFelY0YfF6RYbWlxiiX/7bAPgLEayuMAsXy1GN9DfkkwWr/2q4JzZWVoRkRWBmBKfxSoNDQ1OMYQBeKZuZ8UsNofbL/s3U+0xCMGgPCvueKuYy7n4/+JvLnAuPj7eAdgMNrRY3+I3A/ZM7dbiJOuTBvdbws0FSPwXv+oXvtxiF/v161DJ+oVf+PiH/6MWsPjWnjOLmQ18tf5i6yXrE9aHDKiyscF+PT5+/S1gY/D3vvc9p/jH1oY2Ftm6+63WIf+ds7JEs+1b2JhoKt9v9+f/d87p8Xt+s1rA9idccYGNDbYXZT+zvuIa322+sSp7K/y0YjObB23vxNwT7LVu9GdYJt0qLdGLf/15/rxEZX8MiWbsvVavlw/7MYMhWGaySQvRrXaKRK+wwd7Vib3c8sUUkXmp5fpFVZRWqr/XTf4AZkFsWkdh7xLEmt47NVlufNfdw0fURhI7kL3A0BWZcl84BdW6CCf5MYyagtmZOopzvd0aAM6/TfxykXNJmj9TKRvWoVw2XoMkOQbdPUhWcx8Y/9iVkEdPt4awd6p84d8VjOVnyNTJ8lmzWp5JSSRLuMZBdl5JaAxzkcO9D1RNkW0/hXSjAfcD16yXB/O/e4jBfexKk4xu239E3fnF8gXw854zTW4oX9TX3lRt5W0NoCznx9Z+kG+AwqMjFE4ixZuNa7xw1H/uik6yD+IxIlRpy5YqFKcIs6gVe5FmZWqgFhseGkJBoftqla4d2K+7XN9UCvPCSDJ7TkW9zYfdePbUyLU5QImd+DCwSD8WVW27dqiMfcuExcsVsygbK1OgPCrHzb6wn30UmyO8SS4PFBar6cgR9Ywap7HLVshvAoBFEBvw7CcN1HCPigFIDuZjTxur6IW5VOH7qfb8WXWQ7BlEDc+NVvUPYBM8OlBhE8bKP4377xeqxkulurBjm6K5lrTcZfKfj1Jf1GhU7+xcUQpkw3wYAKKPbFM/toU3nvuRgltbFIaVW8CKtXKPtnZC/Y8K8mFPYlmPXpqkS24ACX2VFaouOKXK4otY5GZo3JqV8koZo36UkYaBDXk5ST6KT4e7dbUBJaSiXSgnlGoJyi2L0hZrHOCVQST3sN65jCLMKwde0nBYv1JnTNKtinpdOVUGtOiuGM7Xm3tl1i/D9DeD4WwetalzMtDFXBR0EqLM0gvolDY9W3MUQGYzdnCnOF93zc2ciz3qJqWFzpDfQBDJ4y5V3C7VlrxXVEv2YfKMyVqXuVHjAxJ5pqjWd+/TxVsXdORUsWpuVJPsnY6iWZZC+fwr10u1vWCLBoJ7SY4t07zEHJIjfiptPoMiyg7dKC3XUqDAJ+ZtchRthmjjuySuDwLD5J8vxG6wl0R0hu6U1+rshdNAXh2KHB0N+BIozz4UzMBNugBzBukfpFI0OjJWmYB2M7AQ8+WZ70Rx6lL9Je08uMOxbPUM9MJqa7aeXvAeTQifRD/wVBcWieUAJluLXlFT9y3NQb1l3bQNivWPc/p0u9sDkpioXBiQ0dmOpexcLU1dpCDAlvxL+Y5ilVefpz6y9oOaETudftqnCpTg9hTsVTnqG8mTU7UOoCc+KJ70nYdaAfGKsU29QP/xoq/EJcVhw1aFksZZ+vewRqLwFOiOPTPg7DD90Ky8+ugTpvpoFpM505aRnJsJgOiPCg3WuvUleunAj7F3OoVyShBWcJkk+dYqNTqNlGUQbYaSwvVjKM7tUzuOEitzcjUbxZiRvqhnDgcAXfXrbOtx/WzLT9Xd3KWncp5UztTFwEcjHJjdiocfVZx7dC/QFZPZzwxmtvWNgXM2LpsrjO39Wuz2f2Fet2fMBc75kvwenRSj/aW7VN1YzUITRQnmDS/GndFRY2XWnYnxiSSjGVMBZUvuoJQEHHMG5cRBxs5Z3J+NuU9oSuws+Q4Hqhs4o6m3Tlt2vwIkhHImKl1PrHiXYsJSeK7dNQDc2tRegQUf497Fa9hKJTs2f5GMzWXXy7SLfeD7KEktX0WCfQLqQ55Y03XhsHRxJ45NhZo8Zrrek/t+xWG36caY0YtK3sXbJdp1juRt521N55nvfdCpC8ewoWziGRwZibAmFtT81w8Y08tg3k+qGLkjchIjgG0WojQ2VSNQtbREdBO2dgeO7tfmvds15I+6DvPA2gXrNWXcDAW6Md91oZTVelc7jjIO3TypxJRkrcpYrwkjsb52p3CNcep2Wy3Aw2v085NKwYLuHQsA50LHALVe1FYg3AbmhY1Z61FQWsH8G+yo291FAe0gwM6xi4BzkNdPLXpGC8ZnyQ/QqH8Q1Rvgh+0ntwLyYp+dnADYEaBLxSXqRCggPCwE9RGUYpmvBhhDbS72ZEwxNdWRWC5PSZ7iKEtFoOZj887trhqAsUPADK/qLkVAEwGQV2av1exYgA7vGGd+rmuowepxv44Br8WnAc5hF5cUDrDEf2iSco9Qris7h2rOAUcp6akVgHOhgHPNjdpc8DMUy65qIsqcmzLeoURgoZu9N3ToKipRh4+hTJcInIDaZvwkBTH+9w13kZgvRwkqH7Wrcs6XtgYYPH7+iKM4FhYVpjB/YpNu5mPO38OLO8g1DvV7EDdFacqEdFRw5mhs8CjmvX41o1J2kP51AAjxZtMtJScmaeOcdcpKxBLYN4rQxgcA47Z+euRHOlnF+QB/PpPFOIsNryf93oDz5r47Onh5v04C3wWiHrQ2YxXKhckqu3ud+W6fmutbUImbyXXkOnazD5hbT1w6rm0FOxWVHKM1SzdoUiQKwEyUfQZ0AygXHT/k2KaOxi4uPD4MpadCQPK7qApGaqQPsckAo647Krfs23Sj5trr1qGQgCBlpeOahcJumG849wYl1x7OjbbKR8WpCQB8zLh4vXPDezULNb0wFILA0tSMe9negkO6VAqEHx2gFeSYJqMkFeIWKKJS1fe3ACVhhwoUHUoBxNLsXKXy7/dQyX1118sA1RWan7VAK2av12h3A/awf3dv14mKQiCgQnKdrXpiwzPKTl2oUCAsS6ZXd1ZoP8/hlStXmXNCsFuOYj6u07WKcopHghVO3GJKXs5YTGHEAPNPDzC7L0D+jPHMZelYGgciFMIz1I6a3rm6k3o+/9/oS9ewsItAyWqh1s1/UmOCsRJ289bNtjoVXT+kfFT+RoXEoHC6AZtE8p4+rMGJj9s8WlR6r1Qvbn4Z0DQI5bU1KJDNAJDFwhDYfV9ePnblo7RuCUDaOFzG+gZ07OoR7bmwC6imS9nALosnrGL+ieQZHFRjS6MKzu7HBnS/kmcmauSokbp2GZvcS+VY2UY6NosWl9B9iMHAUciHm0V7SFAo41wCKrYLgf5QoB0gRkdV78iNYsaRPQDGlSgUjdBSxrwlU5Yp2W88/XCYe3hL+48CeR09orAxkdqw4ilNAqIP4B6aonH7UIsuVl4A5MxTO+PgCoo4ZsTNUP+9HlTHDqGceUwJ2Ccum7lME7m3rcD5x2pOaW/ePoUCEi6fuVJzsZ0MRVnZiicq2m9gD1rgOONNHDcJkDQN1dBTqN5dkCeWitHhkQoG2LVSkmHm0V7WK5avCwzwQ1FqumZNXKBYiiaQJVLTQJOKLh0E/sknv18OJBIJxLZBS6etBvIaBURtUNpdbT39Gspq+1Bn8tfG+c9oXkIWYCBFMCg0d6EMeKB8tw6W71P/gwGtmbxeGXFZanK7o3285wzqWImRifSJNSjtpaNo5sd7gLsbTjJWblYde3cbcjagNIl6sEc4wKIftt89yivdrcPX9nOTBjUDi/GW2hYUvs6ri/gxHLvfYNRtPfq4RruHgGUDrEW8UQJLZt/XLHcTUK8LtOdoqEtlDaV6fsuPdfXGZfkDSE2l+GU155MMKG52nW3d9wGoDILazWd1av7sDEeNLiII8MnNl5hHKm0s1Qu7fqgezw6e9UytmLCccd9TB67t1u4jgHN3pfev+CPabqW6vHp14ibPIDHeLYowMjMXALeyBvExBTQvAOxGFQFFHys8qqgQlLUoYKgG9r2E0qAVN4QBUwW4hxAPez18Dm290t+LqpeHEkcnAiCvVMpo9rfpD21DzapouQqktk3nSlB69glW7txlykkHUiZ296Ovd9Knjt84rdew9Pb3pE8teJK4NAOlTQp96KX3gHtK2y7wGa+ps7GHc12t+dMXEGZ0A/4e0eHCArqLh7Kzc1DVRC2W9eq58jNA1NvUgQXsfNTVlqeuQGUxGphvEJj0jk4Qf760e4tGT4nVONZ7lYw311nvhTGXjqVAyZ2+Nci6Z5BxxKx7bb73Yi00fnScMmZi/858FEAf62fmPXbjsLadeE2l566hWBiuXIDuJXNzmOuseMRX96CjTrEvvrdwjzr9WpxxclES0BjPjxeLrW4WK2XYn+cf2804dUk5y7NR9syiei5A5wH89hZvUVCkn5bOXqnsxKWoneKeR78rvUXegLmiqumaFjDOZk/L1igvnguK1gaAFs9ja37wCIqUrU1asDwLy9t6Cu5Oqau1w1HzC6QgzBTbhwGtrCSgb4C1E0vmlNFJWjJ5PiqoFCr4AZIyFl1tKdNLh37sgO4GpM6ZOF8bFz+plGheA0DYw/xbeec6BTR7VE4MNnPWFOWgfDgu2FS8/Rij+3QdhdI81gm3q24rLWkCKqiZigoN0zmU7PahStiMY82GJ57UPAooQt1G0hdRzqNI58zJDhUevgGYeJ88COsEzmdEiC82yREAgyMoygAsx9r4zJkGVKMr1dDI2nko2FFgCw7mdUkjKN4PBjTzYXx10/mzffr+Dw6qEfe5pUvmwg0Bzo31IiZgu4D3nijq10+e36/o0W5as36CxsJw7N/nhjptJ2PQA214OlJT0v0VHECMy7lUXsfi9NV+7c4vxcI1UO96b7wDzvWhOPf3/4AycWmF5s1P0HvePQlbdsY+voe3MXaI66Ht2Ya5XtaloqP1ulzaiOJ5L2tTLziFANgfYO+0cEDEYAUHujEvduv0KYr0qurhUfpR+w3gdaMo9I0GjPPR5OmeMFuM+zfd9bPnW3Ts9HUcLhkXnxyP7XkI3Jab+rC5Pwjstg+luI6eW1q/IYqYN0jFhzv08qs3FcHzvn5tsOYu9FQotrLDgJ0dHQBye6TNr3USPt3TH/3+CITVRqjxloFzA/rxT7cC6IXr2ffMRmgpmP0Gg+2atHMb4O6O/ai+JzqiAlkL0lH1Z1ykP9geyn+AcxYxMe89bBlrnf/y8baCc/btpsRgSa8JyPm6wDmDxZqb8US/fk03GTxNatwWjWYPYb/+J+CcBTUf+uCHnIVlNjCM2SG+EZ5xtYrZd1piqRKQ7TaKbKbMUHXjhi5TxWWJuV8VnKuurtaG9RucJMSHP/LhXwrOWSLSEhcms25JzZs3b7EQD9R73kvwy+RmSUoDakw5bMXKFa8rWLjO2/58dFH9q4Bzttg0NRBT/7LF9dp1a/GXZnMIMtPAJ0uIGlT03HM/cUA2s3M1+MulwPGfvvvhCTz6o9f/bkk7U6az5KcBhc13mp3v2Q6QZ5uxlsh8FJyzTQBL3hroVVRY5CyI04CZDGCzauY3O1zg3AXUVN7xjmecZKhrc9c+05IDprA3jUDgI7//EaddDZz7FlDZx0mamkKZJX9dx1uBcwYlmnrYLwPn7DpsQ/n551/QH/7hR51reNTCyvrmB3/vg871vhGcM1jyBz8wcK7lVwPnSBjbd/3433/swHmPgnPFxcWcw/NY5N5x1F3sHGwT/LnnnnO+2/qZJXnfWJ3oagv781FwzhQmTU3FNnVchyW8TfHO1BaXL1vmqEma9fIbrVqt3/35n/25o+ySlJzkKNcY+Orqv/b82edYwt3axO7h1q3bnI1oA9AMgHO91vXdrj9d4Jw9f1NQKrQFS2VlFTLbifo4cKSp6ASzyc4HuN7iVNFY37EKz63btjpJdh9vH61duxao8UMOrPv6i9/wlzcD52yT2frvH//xJ5zz/+Y3vyEDFB4ddwweNtUeq97/K8BRS7A/BucebeD/uEeP/vTx3x+3wOMWeNwCv00t8JsGzpnKm8Fu9fX1ToLb5liLRWx+sljUkhCmHmOAmRWQmIKaxU32HkvI2GsMrjMg3wA0K4Iw0M3mdvuZgXPvfve7HRitsLDQgT9sfjPwzmB5O+z7TMnZVNss1jQ1YotzTfXKYisrTrHPs8MS8HYeVrRh8F1OTo6zhrBEkUFvFhcaFG+HzeWm1mvnZ2sIg+isqMdiXJuvDWB/IzhngIqpa70VOGdqbQYSGlTkUpwzINDAEyuscMVPFvPZ63bv3u3M7faZdq0Ws1gsbNdhf7eiHyvgeNQ23rmA/+Fvj4Jz9vmmvGeHnYO1p8UmFvtZTGxwoSn8GUBnqlMWo9u9db3e+ct/8TcrwjC1boPn3ixO+y9+5OOX/xpawO6N/TLA054ji3ftmbe+Yc+fKa+bjbGt90x1237meh5/Dafz+CP/fwvYuLdp0yZnzDOw1sYzA3RtTf7o+uF/2mC2Frcx2kBJg4UfP6v/0xb93Xq/a66wPmd9w+YNO+zvNo/ZfGI/s7W2FYnZWtZigKefftopujRwzv7dVnGWbLt9GXDuL1Gcu3IZhYZo5WTkKHndJnmztzMYggME2/5uBpYd2q8rxUcoIO3V9PWrFTJhjAaw5uq80aCBqntSU5cG2NTuZRfbm03kICrF/VnXd5y7rLa9+agUDCp0dbY8qd72CkWZB0UdN5JWdhgM4wY418+cV79ju86XnFXszCma/NQT8hoLCEGiot+DBJrl6nm9DyfvibL/cF2NKl96XqGVKM6xr+DNXolnAgpnVPCT9n4IdxnYhbJQ7Usvqpd9tWj2CwPXbJRnRqbcQv35QOZVqu3b9x1X974j8mNvwnvxfHnNTFB/Z4+6aoDSbrVoqKlNg3faGIc75ZU0WiPnAVHFjNHglXKdz9uLvai/UhaTjJs5Sx7MsfiicFMsvjAwkGvsHlDnlUqVHdqn+9hDTgZ0CF+cK6/pU3gtmgncP7NPG7Ydf/uTDe9+lHzad+1EqQGb1NzFikSVyYPEJtkYkuSAarSHgRm+9c3qO1ig28UFgHOjFb98pfxRAXQnUWktNliL4hwJmXMGzsWmKmH5BgWNGadebPb6ASwHGu5q+G6betrucn3N8h3hpZGzZsoLS6ymmps6RyHhKBKBaSS9AxYx3kUDzhmEhr2Pmwcb2b4kEukpg+fPq/anP5Z3/W2FouA1gn7kGRPHOdAWgI9u3sxtwAhQIc57e5jLag+dUO3Ri0rCqnWMKQZOpE0DiHtRpPIY4gJR1ntAIvBq4zWUbXZjs3hZSziH3EkoFQQlUMTriY3efV2svqiX9r8gjRrU5NmTVHP1lq6dua7IsGjNnD5bgSS5h1Dng0/kvnA6JKM83D1RVIhBKWgs6hhRYGb+aut6gLpHgQ6UoNJQc915VianT9byeczDUXMVppG0PaopABtb9r6iuuZq7B4NnNugxMBk68yoQ6DWUXdOxaeOoABQR1JithbOXajgoSAU5y5peyG2SmEDWrwIFbS4bBRMAnXlLnaFJ7boxpXrWj51uZ6a+xTqNgAMAGJ3SELkV57mnI4BGw0oZ8osNV9DYaf8stxHeGjazOkK8wuTdz+KczwnXQM9KM+5ox4XopFALLHAc2MDI0gwDmGH1KiTFSdIQpntUQ23xl3j6RNPZr9LU8YAn7mRBOO+ljde0fYjr6jhQY1jv7Yh/QkU4sYz1+FI6A44V3deu47lqbX9HonsuVqWukyhYSOUf3mf9p7aLW8gvo+s/ohmk2juJ8ls4NzewjzsJcuVPClVGxbxeYGx8gaGMxWsoguFOosCooFzsUkJqm1ENYF7GkzR8MSkdIUHjHH6uikS8Rf6BRaXJJfCseRLDkvVaJRQ3Nknb+5o1Wmub/uxV1CjKsOWMFDTxk5HSXOlk/QO9hoBmDGoI+UntAubrnvsvS5buEgZU+dg7zeGNjJwDpWSpiN6ZcdL6mnu1KbsTcpBTSYkcISOFh/Vrdu3nKJm15rJnl3XL9eawuJ1i+GtgMfWJhZ/WxxnbhlvZ7xAY/zGHjZXucA5W8tOmJak8s4S3cWKFcMoR6XMh/4WSpJ9bGQcbjHhJMyGATUAL6qw1jt5SDXVN3gEhpU8CvuzzBWaZQlTH9RUgA8aUUXbsec1VbE+MnBuw4pnUSdJccb+IeC7Ow/KsQUDsClBfSshRVkZ81H9ClN5JYpz+/OBSjq0DLg3Y0KORnqEqaatQnsvbtVZLIinj0vXexa+X2NQpjLwoR87zkuNl5UHLFB1v4rkY4p6sZkqO3eV5DXKHhPSNCYmFpsvgC/m10HGGAYZR1UtyC9QiWMTUBiKkL8lrBn/Km5ex1ZuP4pNR6TgYaCkscAAazQnKRN1tCi5AW81AoftOr4VYO8kYEoiik7rNCECWzNPg8CGVHev1hkzLpQf5/ri9UzGsxoXHq+TWPJtPrlDTbeb9OSip7QCEDfQJ4hxa4Dnvwlwbp+OlRQwB3voqex3KiuJMcgDYJH4oYLnbsfZrahCYYGaQFK/PxCVnUu0gUjOJpMoxkqbeznAeE8unfsINE+SPBS4fUxoDOqQcagkoWrL3kBNaznqdru172ieOpnfx8WPQ+1nkTInLNM4wBfLa9U13NKB4/lAQocUN2mcVi9HadMB5wD5+O8+NIeT3Acy9Any1lPL36nEEZPU2NSg1wpfYlwBnEPZ54l5zygpJgVwrlb5V3fqyMFC4plkrZiLhTY2zQEAG5a4v/oAgBdFzCsl14Gf5ioEVa1jF4vV5t6i1OmpGhtNEh+ADSIK0ILELfGUJ9a/IcwPo+gLY8LHAFibKmK7aprLtP/EXp0uP6/mBy0aNwrrsvSlypm4EOh5LO3jp8auRr1U/JyOXi3S6OgYvSvn/Q7Aa/AJ5I/uADbtv5ynE8BHQUCKG1FSmhCXostACHtQ5mlpuM/4OhsFLRTnsCV8QCLfAecO71J0yiitAcaaFJkuP8CEvuEe1bbcUMFJbD4BnaLGRygyIQJ7SECIti5NGj8JuBC4e5C5cJi5gjlw0IvgxHuAwgeAneixGhOIShTQWR/PrvWv/cf2YnWIAhHxVjiKcWuWoFA4aZ5GGRjINbQQD+0pOEjMdpoEuq+WL0UlalwakJvZWQ7oJnbKe8/v1yVigzBUanIX5GgSsFQbsdRrjK8Gzi3Inq/ls9YrZngsj8ywur3aVQRwdOhIke62tuuJde/QwhTmT+4LCKtquwDnLmJdebVMgcwPI4GQDJyro5AiLi4WMCdewSinejDZGyTfD3zuxTzn5++n2MAETQkzy2QUCFGHbEJh6WgZ9uJntuoWgFwQ88mkpJlav+hZFJiTgHZ8Vdder4LyA+Rldyo6eLRWZ2zUPECoUJ5rA+da3RodJc2Xt25mOh2hJfPXKX3iDICnDkc17gDFEuPGj9O6Rfx89GR59mIjDzi3++J2PaCoI2f2cqzA1zKHRwCmDKEo9fAZLbpwUAnpcSghhepa2VVVX63Bknk61piJKGb5PBz3ga5gkphT3BTAnlY44F98eKzCfEIRTHRTPRbp+y7uQ/nxGIpzd+TtH4p1fLZy0wFKI6ZwB92wKr3pWDcXHTuioIgRWrN8k6YxbwcCVw4O96E4d0el1Zccq8QHgOnLljxUbx1o7VbReQC4kiJU0uK0ChgqLWYyffqeiquPaw9xcbhXpNbMAZadiD0xe4GmwFzZxtrk8mEdPcG4NWaikkalUixzjpgDu+KUcMcOOmQY23kU54aYWw2es3EmwMeL548+OmKCRgAG9TEG1bXVOHabp87TV+7fouAVxbnp2VqavkrJUSnM5n7EdK3acW4bsUmePH29ecaeUQZj3gjU3Dz7WFcIxblr/DsAWX/7oDakb9Lc+IWqB8rLA2o9f/ECCnfJWksMOBVg0JOiHFP1PdNwWtsA8m7drNXGnCe0aHIuYHQ49pyAZd0oMV7bp6KqA4yFPUpPmqGWuruoWJfJI9wHe+IUjQ4ZIy+gXGdRQ8+2e+jthUJWeJQSAPXCvCMZWT3V0nlX566e1fZ9W1DorMNS2Iv5Ll4rsldq8rjJ8jN1R9Y4lwCMD6Om2Y+F7bxZGVh2smbAspNKGxTncOeov6AX9v1QvV6Ac9MytSp1Fe/1Ag7cgeXsbg098NX7F/8h17FUHb49Ona7CAtJbK1rajWfuXNZ+kqN84tzYNL6bmDS0gNYUJ5WZEi0wkdE6EZrhUqBy6PjopQSNxFIdxRwNuCcFQVxbXb4oiYXCbSaDDwcFWD3sBvb62qdrzqJwuxBVdVVKYACrOnYkObMYKxISEYF3BsL0TadwoL85Z1bsXMO0YrMJzQ7dR7PTDDrQWLI4WaVPDivrfs3A871KXcqypjpFPl4A6RRlHHo4AEAIi+KF1BanJLFeYTq7PVT2gWY3ok9aVY6Y9CEldhwx1Dggz0xMcpxVKxf2rNVMZPHKjYlVjcqKlVx4aoSRo+j8AkYm+ffCC2LSK2QyNYVvpxrNHDr6IhxgML0Bfrf/f772BDvYd7drbpKFGiHQ7BUzdTiuVlKGz3eUXnuGvDQGWL/nQd2qMObmDQ3VzmoKEe6A4cxVncDrF2+ge3qqT2qvluq+YsygLiziT2CdQaL4wMntys42ldLZ6xSdgLgHHChwXYlN8uIu1lDEs9bAdCSdAqOvMc5cQmrW6B7oPTjh1V/r0lzlixA0a0BcaQLThHXjKnpisLBzxTeTeESGSbGU2+KS3xYT0QqhfkwPJj9ZCCnVhQTz7HmeaX4RdSgy7FQZT6JmugUyEwbz1hJfNBP0dTVuyi+so67fvWK0tPTaAOsWkNN2dOfs+nTldbr2rHvYewyOXWSFmUsoH+FoUh5mvmgUC1Ypq9jXTkrdQHgqYFzBmayhL87pNqqHu4RSsR1QyjFDaJq2gUs16jomCH26ZIAy0YAp3ZTzDTIaxlnb7FtcUeMt4BeQ3eVPiWEPfIozZjur0uXewHnsDhvataSZQu0fl2SA87BmqOgNqSThX167if7FDXaHR4iDehrDNbUwzpyugNwrgP71khNTfdzQDamNKy2B7TzNUC0Q1exHg/Ss+9lnorn3IHivvKVcyq5UgGcOgHBMdTr4tgb4Xucg/fCVDpjfAcPcXU142clc2Btv5qagd9ahtXSWq+wkf04CsTxnIbKLwDludsoLpfTFjVYed93Zw3iqbb2XmyUW7Vm3UisolnntPrplRfv6/jpq0qf4aGNTyUTx46gqM7dAecK9vVr7+4+bJartOnJMVjbhqqooAvFuVrG2UhtXB+szGxvBUfwDHiSu2iX9mwb1Lat7cTBbfroRyO0cNFIVHMRLDJw7oXXcKqM1DPvnsn3Abf7DXHuDYBz+dqzvQAlSpTPKZbMzJwCYGsLddZ5DjjnBNP8fwPnHo4jDxvnv/77LwTnLNHyAxTQDh46pOd+/GOsOrMcQMT18duRxPvhD3/oBBCm8jUHWwHX4YBzQCOmPGHWp6Zs8VDlAqnS48cc606DxUztzMAuU2j4T+AcdgJm3WqbdZ/70885NlNvBfzY937yTz7pgDipWB9+nUTdyPCRjhKVLT5tE9AkaC3xZpaL+Sx0OnlozObJFqx2rXv27HESb4+Cc7ZYNWU0Sy6Y1ZSBZ64klQuci2BT+iMfBpx78q0V5xoaGhgYhx01tssllx2FOasgy8nNcdT8Krjmf/mXH7LR/S4niWGqaa5KMjt/26C0RbJtutthycgtW7ZqEglJa+NfZDlp124KFKYmYt9pCl0hVFVYEtLU3cLY+HMpzv3zP/2zFmRmOra4JgFvyh922He7kimPKg46/8hvdl6WnP3pCz91gMRxseMcZZEAaPwvf/lvHAWO5SQyXeCcgXTtJG3bO9oBtTpklmJmsWWLYlMlMXDQ2fhzfcEjf7rAOQO3zKLqk5/8k9eVCO06TZ3k83/xea1Zs0YGAVrCwNT0vva1f3SsW5955mkHyrJ2sf6wHvDRkkhPcu9MjcR1uBTnLJG0G7U012FJ4G/SL6OiIp3ksSWYDYa0DeV//ufv0q4b9F7UUabRtq5rsITwmynOPQTnfqAWFjCbt2x+XZnRkvCf++znnITwl778pf9QnDNwjv7477SXqaaZmoA9R3ZY/7LPKy4qdhK1Bp/Zvemgiic6CgsC+q7Zibn6lOuaHv3zUXBuFnZnH/zg7zkb5K7XmL2TqcfUVNc47WuKM6YW+a8//FdnordEsWscsESQ2a9aG778ysvOvXD6FG1fTl//9Kc+rQo2KT7wgfc7if0f/duPnESSAbf2GWa1bM/LEL9MYcb6nvV9FzhnCfP3ve99jl2ZjRPnWUyZRZs9h3P4076Xm+CcuiWPrd9ZX7bnzJTnzHrGUSb86B846jaua3zjn7ZRZc+aWdJu4P5aPzELG+tDhQADf/anf+aAon/3lb9zIECXHZL9u/VFOzeDNb/+9X9E0jXGURi08SQiPILJ9Pv/pza/3ti2Tqf5+R8+/snjFnjcAo9b4LeqBX7TwDmLSQzusjnT5kmD2lxxnMUFNh/aHGmAvcXfZhuYjsKJgXP2s0fBOYPRP/e5z70OzplCks2JLnDOAHgr5LD3m6VrXFycc+/s+wwUt38zazeb10292WIuU3Azm3LXOdmfFisZYG/rBTtHiwVsnjZwzuI9m1ttDje1JotD7Pwt1jP1BYttba41hTs7j18GztlnWwLqUatWW4sYMGdtY3/a+Rk4Z4U9Nu9boYWtQww+s+u019u5WWGIK96z67BztP8fHx/vxCeuWPrt6tBvBs698fMtBrHzsesxuNBi7JqaGkeJzu6ZWT4ZXGf/bu1tr/9VDiuusdgzCUUe13X/Ku97/Jr/3Rawe2rFNPZMWFGHQZZm72t9wtYBBnja2usnP/mJA4aaaroVeTw+fr0tYM+Maz/D1lS2PjR48bnnsIq0op+34bB7bwVNBkTbWGZrtcfP6tvQsL9DH2HjgB3WL2zst3nLDpsnbE/H9lRsT8BV4Go/szncwHTbd7A1ub3XeT/9reZyif4VEL8BtdPk0HAtnTpPE9ZukE/6JLlHAF4N92LV2qp7AFylp05rwCdQ05/cqNBJzCOAUMNdzEP1HRq81ar+6ko1nTuJgtQAYNl8jV68VKq6yXt3qx0oayQb86Gr58udamU3rFnI2j5c79vmKFZgg0CjDXuA9E8eUUxivKY984y8UUBx88eqlbEPxsY5LLk60AscVFOtqpd/9hCcm5yGVesKeTJ/CzWBYTcSi5asQcFkuK9d9ezV9HANwezHBa1aJx/OzT3YzqFLg80NupdH8ryQ5C97bYErF8p7XgpUAABbO5awd7FNrWfOrbylBhw36vs7NDZrvsZOm8E51OlS3h71kBybgOVYWCZw26hYuflRqc91ieQx/ppskOPcgdVPxaGDugsglDpmtMJysEoinnEjPuGF/M82iw34I+FLu/aXXFXPzh2qrK5STA7OHtkL5EGS2C3AC3sgL+AMXsfesk/jHfUWHlYtYFwXyjHjc5cpiP0sd3xiLKEzUFWGVet2lIOwok2epthVz8jPFIY9iB/aWjWEusDQvXb11dWq5cxRdTXXYlebovBnn6VfteviK69pJPtdKXMWKGApKoDWxpbtI0FnO/puqEkNAEH0XynTrZ+9qO4KlGAmpGrU+o3yjh/PZbEv5O1HfyGB5s1bh2mP7i51A7DUoUh4s/gUtnLTNWr5Eqxax2LVSmIfRbx+g/F4eQ/gnEFQ+0m4XDVwLhtwjuTemIB49lc96e9tOluJOsShF2mfYaUvmKkmkpSXjl7SmOhYrVy2WqNHxNCyAJr29fQfdIT4G8l0kqcBgBDe9JcBvvFq7RVglgLAuCs0LDax7DkHAl9NnzhLC6fnAuvF8zkeqmuq1fa8bbp+6wrQX4LWLlqvlOA05zPvDd/TKRKBlhi+23SXva05ypyRpYDBQNSuLms3dlruIweVu3ClZozL5hxCVNpyFtu/zQ8V57i2J+Y8ic1qJM8pinMk3g6Un9bBkuNUqwDcAXndr7mNIlsJtsfYM7EnbspYfsNW6OoJQIEiJdfozjUZlBeIAmOQNTxgxZVbl1SA4tQlLG+9UOAZZAxxG/DR7KkZWJxla0wQiiKogVVjjbsL+OxGU5kSUQJYn7lJ40ekACN4Y5/Y4cB3e07vVgfP1tzJKAalrubZGYm60D7lndktH2xjf3/lRzTLFOeAUyv5vL0oFxk4l5I2EduzTYBz2H4BGlp7HT53GLu/c44S4gT2xxtaarjXF9nvHqGs+blADBNgKL24Y0P8ouvxXA/YnExSeASAoD9/dpPMvl5fqaJTqGMCjvSjwOSH1avBTFNTpmnBlExHocWN5/oc4N+O4xTxVNWgdjYLpayFQFLjScIGq2MY4OHGPiz2tmmgrQ/Vro2Oql1YULijkmOxvK1DLA/hmp9dYyqn5qyJbG/XisJtr9n2cG2f3JQ/bW3nGsN/1wE6u85Hwbn0edOkkB7Asl6S7/RVS3ryLHkylnm5Y13JGIJ+quqxYM07shcFnTJHJWoA0KS/ux9lsVRUWpYqBXVIG9ebUPPKK9iDffMV1K4itW7Fk4rDxtPUS/oAm6rvX0Gh45Cqr9UqLTlNWcxJkSPDdKP2hnayDrzT/UBLAbUykhlbPUfqxn2gqvObEXM4gVLiTMC5DzBuxDrjVV9fm0qaS7Ufy83Ku5VAkJMZmFB0uVTGM+SmuYAFk7F7DnYL4rpMK8mujVQzA7APCnF+KAmRe+ZnPM8dd1BYPKrjF06idtMmT3/GuZ4BpQI2Zc9apNRIbKMHwrDtu4N643Yg3BNYco3S0vmrNWH0VJ5rL6Ac1OFQbzmA1Vw5UEtqUoqenvcOoJnxOgFk+xoQblN9M2qJz2CjvFIBKFKi06gWVMQOAc4dQfHJHQDkyZxnlZm0CHDOF2iHvfb669p9YZsqHpQqIWU81pNhOnP4HLasQ5qTOUfTsVwMAMIzpU9v53cUT/m7L5CNP2OND2Opuebc67qLDdwpHT1/wIE9DFixySgmIh5oYLGmJsxCsStMrcQrR3he9/EsRiaFU9y/QqmjsJXms/r4rwlo6ei5YuAtwDJs0p9Y9iwJ+zRH0OO1ghdVXn9Vk7CL2zTXFOdSdGsA2OvqDhXuP+yAc6vnrNb0xKmoFAEGEstcuX9VOwFmS0vKsPqb69iHFp8v1H13xumFczUjmRjFDTUhxhk34Ex3AEV38BUfQCtf1IN8PZibeMabOlH8vFqsE9xHU90cItYYBsJJHpuo3NmLlAYcFuoxUq1ARNtPb9fBU/mODeg7VjyltDGT6As253urqadZu/j3s6gGhWN/ugnVQQPnrqDAtBvQuLXxvnOeuTOYe8JiZIpzJ0tOOIpzo1JisMLboLSI6ShToSgIFHCjtVKHDJyruqCYlGiNS45FPQfFwPsd2LDOQ6VpttO2Qwbn///+6cHz58lYGuCJ/iP3qBtYqgmg7+TlExRMntSD9vsAzMHq5BlMGDNeS6Yv1PQxU3AkCmAuGNBenrHjZ4/IO2CYcS7HsSMdiRpQD7HPVex693LtFbT3mJFRWpq5WBNiJ5Ewb9Mr215CIa+K3GOGVgDORQnYEMC+0+OeCq8/BOdaWjtJ0D8N3AkwC1hoSpLV3ddVcOmAAyYHh47QKCyAqxpvqba+QZNSJ2HnPEejATvMxn1wmLGDnm9/d+c6A91DFc59MTvn1p5WXWBeLThBH22tNZE942AYo32IMVZp9viFivEbTU1Is4oqD2snEE2gN2BcxkpUTOcApfOsM9fe6qtVcVmRDhw8rHDsVpdkYMk6KZ1n9IEKz+Vr/0HAucQ4rc0GnIuZJk9A2+NXjmAfitoVIFrOLAPn1vA8hDtzw6075EXNThlINz59nKLHRKucvHYVVq3ZzIVzp81FhXUE/ec/tHgsovHk3vkCDPoy3rhzP7uB8q7euILFfb4a7jdpEHCol9eMoJhgDvDj4kk5AGhYSWIXfBar4PziA+rHxjM3dzlKuAsYE8OJv3oA4VChQhGx4FSR+j0HiaeAUbmOobZ+HS8p1pELh4E9AAqx250yZqruAjkVVh/RTqxao1DwWj9vI4prwIrk8m38q6RPHLhSoCNHjysFcG5afLquVlzT5boLipsyTovmLVQs7e7JGEoGneu0IiF7DvuJlfypGwnnGoN1D1jnRNkxFTPGNKOU5RcI0NYJ4AnUMYd4JpP5fpTfKHUDOBdcOaDdZ1HzYp2xehGwdFomqokh8mMMbAec2nV+uw6XHwKK89aaWRs1Ky5DjQCDBy7t17lz55hzUrQeIHJa7AwKOrwcq9ZTt3gOT7+qhnoUmXI2KRuL5hEeUfLqAVKhwGh/eZ4OV+xnjdajeZMz9OA24jMoDvtihzt7wRxNRXHNnzHAjRjIxnRDk2yN44n6ZyC/fIi1BomdrtWUq+hYsUorSuUZyL0FAh0gFz4jfarmpc8CQo6noELMg+dVcNxUgFuUDvS0aOZiR4HUDSjpPgrPZ7AT3owqs/z7sZXN1Zq0dcRP3tpTsg2YeCewto/eu/QPlDVxmToBYc7ePIZa5WHV3ajRbNaQVnwQ5x/vzNd1HXXY0NI2p8+hODdKY4HJatsqsT8940BmC4glkxmnvRk7XSpRVq41xHXaXBHCPOFPfNqGmuFVFNTMgrQWANEUJ71RNPXmvKZS/D1/+lxi7hhUIxm7icl/tu1VR+F8YeYy+nCmoikI8aPdWoaxs73FM1W0WwP3pdxpgHPTs5z6pbOXjqG+vh8lPC9lZy3SbGw+fb1DdJZnf8/xLcSwLZqP4vXy1OWoWI+jAGNYTffrdaz0tF7J/3/svQdwndl5pvkh55xzDgRBkABBEgQTmJudg9xthZJaY0sreZxDbY1nZ1f2jteWa6t2bNWMLI+tlWwrdlInstlNghEgQSIRmUgEkXPOcZ/3b18Xq5eyXZJlWxKvhCZ5ce8fzn/Cd873nPd9xxK2pxHjo3I60GeNVbWWnZFmxw4eQtE0ifGBCRZt7cOZBU+Q65Z6qy/jqeYUK2sbPL8WrKXPWtd4C4AR1sELawg+xdj+7buwHN1Dn0G7J3ZvuXcPdThiUkDCI8z1Tuw4aoleidQPb+5vBUC1CtvgizYzPwh/st+KC46gXhloVU2V9PlvWmh8AODc43Yk7UNwbgG1zYbeFmKptwHisQJFSf3UbqzQAzKIt+hniXPrsEAtv4my5/SolaI4NzU3abV1tQSx9OWnTzJPxTadduhJ/6mYBpSce/ZErRF1cmI2L95fp4m2Dndj2XwNSPEm02/GespwBdi6KB+b8cKTDvAPgW7tM63Mc4jZgPNyc5LZDIGzToSUoINtBoiyZqjBXn/7TZtjTlpUUGSH4FpiQyKslmchkHpydh71s+dtb07Jh+AccLriaKbYjr3o3DQg2RixIVBZ690lu3GrxcYmRu3A/p32+NMJAL6M0+xqmMFudYLPDAxsWn3DrN2ouoNip/q2bBROk4Cycd75Wjlg3aCdPoXi3HMfgnMUm00OoTh3acX+6v+V4lwAYlwFWBWHoXi3ZucvDQNzL6CKl2p7SwKYv1A4m1t2p3rdXv/uul250cx8Jtg+9XKqZaWx/sFuQAecQw300MFc+/gn2ASUTGzMxiC1G6ftKOBgmV9LPyuAdrOo1I1z/eMjlGf7BvPMLuvtb0NEIdte/Fim5eUzr+MLM1NbAIXcJz8d3M+la/02OFJrTzydao89jo21RxjCX+PUqRYr3AVQ/OI2y9uG9TH9yxqKcxfPoZAHOLew1IEdbSIbtCKssgLFuW+3U9/97Plnku3kY6jZRWvQ3ADOM/v2N4GU35kHkl633/r1SMC6UMdm9oNz6/bNb72JoEO0vfSp3VZYHAh4yrVNjNgP3mCz1ZsXAOcysWrFhvdwoaNMCfLuxPkc3BnpOAvl8eO9HgrOSX3hr/7nXzlAmZJkZShQBZGo0oK7JiWCP77BQq8WgKX0JBsmJYjW1laxav26Y/2pBJwSTzm5OXb23bP2FSyTNFEUyLV/f4mjTibliHdYDNPi7nEgMsEl+sxX/8dX7U3UKQSlvfjSiw5g4sEikA8LK64J5oO3/RXArzfZzSg55M+SKHRd78b6BvaM044ShRQ1vvwnX3YU32JiY+1LJBYFep07i3Xod77jXLssJney41S7mgTpSOHrLewTtLj9/AvPOwvcsiG9237XXnrxJUel7pc/98vOLtyHgWWuaxTUJKn3PCZJttXpAABAAElEQVT6KkOBh0paFLMbU5avSlT+wR/8oSWR2FCS8Oixo/+gVCF1Pi1qKlEoGEyJLoFJUtzYxqLSl/7gS05S82GTZ004BTb9gN23goAEeympqGej56Xkieyu/sv/9l+cibiUJJ559hmLjGSHIi8l1gSZ6Tkr6fLRl66loqLCSXJqB/EZkotSg/OgA5KNpSb7x44ds//0+//JSXzKNkrJAh1T72syrARvC7sgDpJcFQT4sOer87rAOSX9ZOUlq1DtItSir+y6VKY/oA588Ve+6CQ59QwF0/3n3//Pzrlk+buXhY0VrlmLwbo+KSE+++yzJEZ/1QJIGum6pKjyBgpoOvbrr7/mJHNV/kpC/fmffwWljUjnfqWiooXly5cv2x/y7ARVvkzdUz328/d3kpFa9FAyWpauUhKUIouuS8dTElnw6dTkFPXv2/8Awak8pdimBLOUyqR0omelc33zG6gDUle3bct1krmqq6pPqqtSlmi402Bp6ekkNdnt5O1N/YwkEZ3uPPeH1Y8Hn+eD4JyetZLbgsU8WRxapx1JYUUJeS3YSL1F7UmWzeoH1Aspmaoktn6v8vvWt77lLMCrvRfsZCEgmJ1qq3ilc42//du/7YCselahIaH2lyR56oDfZMumclLyXPVAZTAwMODUbylCqu783u/+HoPZqP3e7/0ez/WokwhWuQyPDDuLUEqmCxRQEkplowS3nqvKSoCpru0bJAtjY2McqzNd88NeSj6pzqk+lJdfok6dQWHuNxylFafMm5rtf3z1q+xAuOBYsqndOG2E+jhHu5JKT3t7hx0GSP3f/4//wincHNu4N6inkZERDnygHfxePFsFTD9/r5/He/75e8qP7vhRCfysl8C/N3DOiUmIqXsApbSxRWO5XoqVFNtrbFbyRfCb4DSN0T8qOCe12y9/+csOKPalL33JiQt0rilUYxT/Kz6QYltZWZkD0UlxViCexmnFQnppA4ViN8UsUmMSnP4wcE7JfEF+snz9nd/5HVR+/6MTmypWUCyl61AiSjGuwHhZtX7lK19xxnnFLILvpHqn9xUPKB5TLKw4WRt+FFfLWvUfA+ekUq2YTnMXlauuwbWhRvGu7kNxrhS+dC2ue3Ru9F/gP5of6brT0tIcGMp17n/q0C44QrGQNiMoBpb636VLl5yNB1Lr0zxEv1Ps88NeinH1/DSX+Kdiyh92jEfv/2RLQPVZ8wEpQwpyESCqjWyKzV0v1Qe1J4Gw2jym+F3zP9fmD9fnHv35kysBzZ/Vb6k/EpirubDa84PtSs9S83R9VmDxPwdu1HxYlpoC8tRXutQ6f3J38ujIP60loPqlvkBjqPp9KVEK1NCagjZWahzXZ7QZT2Oa4DnNrfUd1VPne0DvXcAJ//0P/k9sVzuwbgqy4/Hptv3ICfM/WGgeKaEQKowrQDr9b73NutA9C0zLsW1PPW2+MZGOUpwXSiVSFdgkYbUhcO7cD4CKRiy0cI+lPvakeY1O2TzKEwPslg8qyLUoFqH90rNQQ+P7JASk+rFJot3Lh/n0+JiNXUKd5Eq5Ex/sPPO4BQKnuYWjsqa59gpAEA/M3R9QCztPLBOsi7W+INRkQ7KxlXv8lHlmpiGwwHWjemNAVW4C10g6TLGesMw6yOrEOPd21EIAWTxYm7B1dqV3d9jgxes2R2IynvEx9Nge28qJk/Ab66ZSPkOZBRuSje4xGyLZ14DNTPKBUsspO24eAGVt58/aYDeqR1kZllx20jyzdnB/SmyCZwHmbW5ho0vSZpMYZOT6NQC3ixbr7WMRpYct6MRJc2ONUOsMLNfYpjfXHAjss8Z32YW+yrpDe3urRZbusxiUWTyT6GeC/BywbMNda1Jb5j0OtFh7y/rfP2eDY9OWUrjPYolhPBNRfCDRuYTl6OoF1A7a2i28EHjk2LO2FQ+86L3J2EKJ8hyk8LbRN2jz77xlM3du2zr9Weyvf56laze7++3vmw/fTczYboEnsfHdDszoS+Id2651EpEeWPW487PeP2ATZ9+xURRu8C20xDIU+HJRvgtHlQDYYo2s4IYXG4l9SXVRD1d6uqyPdbh7F8tRTMqy+NPHzDM3DQtdII/AUJsFdtDz3iR5eB9A7fzVt4GpGuz00VMozmEbFQC45I7VFv3s7Y7b9m1ACs9EIJZjB6i2JBRQEfRY87SD+w5bQU4Bl8TaEqDfygbWbdTrNepdKMBMqE8EzwgFgqU+u3iD+tdS56gbbS/YZtNTM9bZ1gk44cs4e9oK0nYBk4SyxjprlwAGbrZct8BYPzt++DhJcex/SeINrwGD3LlCzF7t1G85QJTuxLoWm9f2ToCIq2+aW9iGnTj8uO1NPu5AGQ44d1OKc+12esdpe37vxyw6jGdE3ZkkEX2R+/ug7rpBt9jTJWUE6vN2/c4NG0OtbS9KErsydgFeRFFfqavc1yoyBZtktLyBFsP8BAq42+TUsF0iIV/ZcgkL3A02Cxdjc0RiDyVEhAvtIGocJVkHLNI7CiugQZJ9b6D2VOmMXSfLHrNtqJv4osw1MjaJPdpluwwkB3doR/actNO5T6EcFmHnW7Bqvf0uqiue9r888Xnbk1zoJD07UKGQ4lxbX4fl7Miz5w4DzgnS03VtTnJd5VjPfgjO7SnZh8rBOAmtm85a7+6iYitAsSUmIAzAh0QUEM886/kLtEs/QIEwLzSvAFf65wbsOlDJbeZG/sxRcpkjSPWovqMRJTNvO7TrsB3NLbNw2mbHdCdQ0llrZl1TSlEHgUJzUgCWvMMdhZdzta+wRk4/RNrwY6h2HS14zCKxOpQCtOZCeqZaB1Q/+uBLfa7mGlrzVH+suEzr3lp3VRyg/lc/eum7D8YMDx7nZ+HvKosHwTklbn1RSHTum851hQ5vjc+oDfths6vE6vT6mNX1YFtMDsAdK9EiFDwXNpawI6t31BZP7DlkR4A+Qv1CAB4WgVxv2FWgqmXPNTt29JjtAlgKA7qaoc3c6W2065UVNgP0XEwSVuBcDH1+F/3OW++dw2pzzk4//rQdyDlhEaiX9My227m6V2i3FVacvMc+e+KXAeeSgavpL1Y43kijvddSbr3TfU5CX6pWt4Gg+yZGUStDnR0VqhQUa4J4322Dvov2p37VE1jAn2fvwf3NrE4CFzTYe7dJVi+MWt4urPaou11Nd21letH2MG4e23XK0gNRtJtdAyg9a5XYnW4AOZcdxAYzuxjVJD8UXhbsDnDo1cr3bGy834oZJz++n9gxPM2qum+gOPcKw+OovVAGOLfzKSxJgVboS6ZWsd/EXvRa42Uhi/Y8Vq0lgHO+UEOyau0aaUd96zW7O9Fo+YXYhqFgV3WJe+wdsPT8DCtFhTRJqmuM++pTDGActpd+JhAYAQUYFIWmKPvm/kaUTC4CQ2CTm5NkYRGR1t3VS95kGdvZfAfSzUFZzoMkcFNLrb16kVxe0Ao2ZiWOmkwYCppSdeoYAA68/J51D3Qw/iTYLwDOZYZtJxk8Zt+/8C1+32o7snbZc0CDmbG5KM6hZtX2pl2/cBXYNhuL7cetML3I/IkHVrm/lslWe/sm4BwbBkp3lvDdPOrQRbs72mypOZl2uOAMNpf5AIAka93mefYY8NHfuAEL+AHk+wIgzq9hJXz/Fip5Z214cgCANsXiIpKslWc4v7jAmsxO+pqDlh6cBsC5YTe6qu2d8ncAJubsGMD97txdKABG0Y15oR7UY+9U/MA6KadkjvE84FxOClatk230l+854Nw+AL/jRR+Cc3Oo/968U2mvo0wUn5tgz5553vIiOB7g3IrAORTcLt4CnOuus+S8JNu2Y6c1VOMY1t5r2+gPD9DnxzGu+DC2egOvuq96YE3rQ84FENKP2IPNEOOAzE3U9XPlqOMSv21LzyCZn2jNXR021Dtkh3KK7UnAm5gooCT61YtNVShTXbBpVJoOHtprpYBrsahNzdNub2ELfvH2RRvq6MXGM82eOHoatax8QK5p+z7g3OBYtx09dBhw7hmLcUukjqLY6jVp1zouYiF4BdWgWXtB4Fz2YQvfCAFQXMLqs8UuYc8pkRTll9Lzsm1oZsLq7jQ5IOTx7QdtR0I2tpwBqBwhwEJ8vLYKdQB4HeATZhFe4Zxn09pG72BFexZQ5JalJqZabkaWjcwPW0MHCrgZ2XaqAIWxhL3AZitW219tr5993eaml7H9K7YSgJ54KQsTW3aMtVPm5dZU12xp1LkzB58DMN1li5Tjpdr37L0L5y0+PQm4/jnbHbcXtWQPu9EI5FP7Gs9s2U7sfcKOA+2EAuUxOlj/6IR9UH3OrgCXZe4mD8gmlV42aNRVVlt26jYrKSphzEoxfwA/hntiQNXRDVoz+lbaBODlaSsAb/doyzdu3bAWNjREx6I+mJWKutcc1qID9GsJ9hTxR05MKszDqrX3tNobl9+2vvkhK9hVaMd2nLDU4BT65yX6O9kpX7S6lnpinRB76olnUDnMZ7fOOjat1wHnLqOYGW9PAu0UJO5yrOWvAM69+eY7Fg249iwKfbtzdn6oOAeU2bmMEmXzBbt69brlM+aWFRxDIYnYC3jeN9LXDhcfsBIAtSCUo6WKuMazWqWvF0TmiyJtEPGJ+7o/fVU31/yq3RvrQFUKBSzyp/e7+xife/h3LJatZ2xnTAGqtr5WD5T3NvbV3f33sAncA3BWgpVrNOCclw2M9gGOnbV6NhWEAcc/UfIslr77iUsm7EI9duGIomTxXJ8/8CyQH4rZmz6UrqxMK+yNKsC5oUEUCp93wLlQwDnvFS+AyHU71/a2XWw5x/xsDWD0lK1PrjqxyRxKp7v3Fdoh4MpwX1Tv3IHhUIFcQyEQXgmbRrSP6V+9UcYdne61itu3cB+oNn/WODPz87AtVT72trGfw0pLigCwsIZ2j7Gu/g4rvwWker/VgfoP7y1D6TQHEMzXBgCAKhqvO31DUBSWnrvP2NPbP4binC/Kh6/Ym1Wvs5nFzT7zxBesDHBuxY0x995tu1Zx2Yl39uzFsaLkjKUILKOm9a8MWXnjBbtZWQnYlGA7mBOMrQ3atdoL5sPGnv1Ai3vTADxRlXNng4/iy0XmNCvqR7m/UGI2pgIoBnbb9aYrdr0Cm+DgcMtJywXOWgYwrLaACF/6K5TVMkooD39U0e7bq2+/AsA2apnb821f0UHLiAYAIsYdXOQZ3nnbKmoArFERfaz4aQecU5q2hvv+gDmXB+2ijBh3/44y8rcotQF2v0+MK6vW/Tv2oTiHKBN13oNNJ2PEvxUNNfZ95q7J+dlWWLKbsWvMKi5fAQD1tn27C21HTh4KlcHw4BgeU48219wdqMyHeVkAULHw+aExyol5Qn0bji1YqabsSLMBoLxeIME4oL8zWObuSMlhDT3IhibHeT6XrKL5um3LzwX+O2nZYWxUB/jtBWT+AMC84XYV4+6GnT5xnJiGec6qD4DzdXu/8g2LSAy2x/Y+aUdSTqK4GY5V9IY19jXbWTYcdQxhjVoqiI3YAvjRi2eyzEaa2vZq4PuLDjh3/KmTWNtu2G3WFUf7h6ykZI8V5xdabECsM366cT+zsEIbxN6CrMNZ99fGACn3XiJWr7hWhaWoO5aee2zJZ86q267SOQHgoYJ3ChvcwCB/613pcqD9qhvXUGPzspMHj9uOpCKeGfMKlCErOlDeZjO9Yqa9u0vtwL49FhsEQ4Di4oVrlwCup3Ah/Jjtz0Yt1i2MKwKw55zTxBU8Nvo+chNaE2Haf697DYamy27Vjlhudhab5aMtdztzIh/WG7h+D+KuZTyM6+vn4TNqAdJm7GRZrn3205nYWm/Zf/+fV4mt+uyxM3sdcC4+SXArUy8U56our9jXvl5u8QkhsDc7LS/Xn/X4ZXvjnR5sk8dhKjKANGMtKRX1NmC3G9e5lteXrR6lvSdPRthn/gNWranMu/ndH//RbbvDXP9AaQ4iWPkWn8Kaiscy16d1X2Ba1gXW6Bhm2cSzsrJunj68rzpH3NV3b8PefW/QrlbcZt1QHNJ2rgV4M4h5tg9bvCiLTb7b07Fh33ul167euMIYnQwjUkDMEmGvfG/YKm40shEkCFW5PMsBnJNV6xrX9cG7a3BWK7aw3IGNa7wV7o205sZl+96325iPT1rZIca6J2MtkfvYJLbtubdi3/wrbL8r1iwtJsB+/3fC7cCRIAecu/Ae4Ny3zyJsEGkvfWKXFe9lwxoh/8TEmL39g/N29m2sWpNS4XqehEsjxiUOobdgfNX/lW/SmpX+++O9HgrOtaGy8F3AHllQfuqTn6TiQ2vGxToTX4EuUt8SuKWkkKwbHjvzmAOUCBT6BokyKWPFxcc58IgW2s7hUS4AZZmkjBboU9NSAeT67AqLHeUkbj772ZedxJoW57RA/OorrzqJrIOHoGWPn3A6by0YpyEj/TBATRNLwXw11TVOQvCJJ59wdmcpCBB8I2lNKcwpoaZEX2pKqgNY6Vj6jtQqlDD6zMufcZSrQliAqq+/41icVNKha1Fb97ktbxsTcIhdJlGCmwJZQHwJm5uXfvElR/Xihz0KAXm6FoFyUqC6w7G/Q+Lt8OFD9ovshtUEWICilPlkcVsCWCgIh/28NLgxJ5GoRJV2pmnn73/7b39mFdxzamqqY0uqCbXgtodNnpXUFVgku55TlMFv/uZvOJZYulZNSBdYLPoyCcOqm1XOM5S6oMvSVBN3X2DF9Ix0JxH50ftTEk5gklQNVDGPnzjB5H4/jXPNUdfqvd9rOwoKnOcr5ZL7PfftNWA0JWy0C16LAX/xF38B1DdhR4AvZXv7w15K9P3pl/8Uv/tzlkJCQGBSYmKCA0uqPD+4gIQqndjnPv85x15MZSrC/9d+9degdcMd1YXi4mLn2pTg/M63v+MAVSo7PXcljrWQIaVFAXcCFWX7m52VzULRpH3r775l32JxNSQk2ElcCvQLIcErFTMpJDby7A5RX48dO+aUr+qTEhjfoi2Mo3om1ZVf+/Vfc5IZghiVeP3ed7/nqIFI6XA/bWydAeoiA/P/QzJrlbL9whe/4IB9CuoHBwYcKPUCoFYKMtKfpF3qXGoXOtarr76GDPaiowYnBZbgIKRnaZ++fr5OMliL3UqcfXSBxlXe84B5t0iqy+5W9UhAocA5JZLn2C0s+FMJ4Z27djpJGQGkDlhKIlrH/EWASS2sC06TKp36h/JL5baHBQtdZ3RMtHMcqcNJ5eI4A7aS5AH0em+xmK7yFbB5CNBM9nBqszqvFu+kJqfjXrt6zUkyqV7qmant6KXrUFmqPR84eMC5DtU32aR9BdhRdWd38W5n0Ungnu4lLS3NqW9KnH/0pbqjZJXqzze/+TdWy5+C8b7whS9Ydk62U+ZKKNyovOEkvtJo03r2O/J30Ay2HFBSfaSejdT+tEt0cHDAvkrdugSEp+tSXVDdcwF+H72Gn/1/K3R49HpUAo9K4FEJ/HSXwL83cE7jnZTeBPtrY4aAb4FyGr8VnytW1vtSiVV8++OAc0oACZBTYuNloCopVyl2Ki8vd2J+bb4QEHL8+HE2Hvy5M/bKlvCXfumXnPhVwJbUkTQmC6YTEKe45WHgnOImqRLrvgT9SUVL8YmS/IpBpYAr+EdgmcZrHUPnlCqcxm7BQwIFdT2KfQXTKe6TipwAIl2zYo9/DJzT9772ta85UJIUILQJQDGL3hekIpU9xeoCFjW+a9PDv+TrRwXnPnoNgiQ011KcqlhHynralKL5kTYMuFTqFMs/+NIzfgTOPVgi//7+LnthVztSG5e118PmzYpzVZ/UP6gOqN1onqL29+j1r1MC6jf/+q//2ulTBLqpbWkjouZq+p3me4J9Nd9Qgl1zJq2pKHn+sJfU8NX3a6OToGFtvPphc76Hff/Rez8/JaD273rp7/rRmoXGAG2o1HqeNm5qnNDanOqSxjStR2jzmOqVvqMxop0+58/++E9sfXTcdgKUFJJ8S2V8DCrMNY/MaPKCi7bYBliG0tqqX5Cl7D9ioSQMxocGbWl22sJR/PAJQHmDc22QlBpBsWxybd2i9qIoWwbIxrrb6o2r1olt5Zz7qoVlJ1pUVq75YLm0TrJ+ZoHvAbglMPb6+rjZAsm5zktXbIrEQkJcokXmYU2XQELVg13Qc7IGRTUkBWUS1lLcllEi+853bPNGpfkBagXt32fegHMe7Ob3jowxzwAU17wBvEgsrtRW2+rZdxxlvY3oeAsDUPBjI64b6llTnR020nHP/IAcEvcK7ItBbWLGZudnURVRos7H3JexdGKxuKe+yUZWVi3zcJmllqKet7JsE4BidyuvoTSxiFJejgWjnuYWEuWsgSyvcc3AcOFcr3dQoC20ttnQ+XO22N9nHjEJFrkPwI0ydOe5TGPvtBkSYIGZqRYSzM7vgXFbfeucNd6uNO+4KIvfXWB+rH94xsWYD/ew5R9C2YE/oKix2dFsE1fLrfFmrYUGhVtSfoH5k6xdJ3k9js3N+t06YCIgqiNPmHtSAQodoyy8z3IeXxLMUk5BTaCv32arKm2LZ+ubm2Xhn/8sgBwgxPkLNg8wuEFizzurwIKA0Dyxud3A+m56ed78uJYg4AEf1nxWm+5Y/xUS5RwjJCrCEql/vvHJtkEiaXx+2Zb9Ai2OMgqLjbS1oX4buHLJuoBYkoMDLXb3LvPKYfMla05bick2j9KAAz9yD/dRgnvv6lvW0t1op46fxK70DKoyKD5Qd+ZJBtcInHsfcC7O7Ojpo9jCRZLcqHbszQJISm0jhkxKTcKqzBPVkQU2Sc46G0tzY7ZbZky2zS7PoMhxw67cLidJu4IrSL6jGqO1vRsVlSTp71oacGZpSallReea77qvdXQDgwFh9E31sDadYnuy9po39bMfRYg77fXY6vSYDwm/Y8exJ0QdIhCr1ubWBnv74hvAhG525vjTKM4dQaEi2FrG6+xsxSvW2dCGteIZ+1gpVq1hssNDJQrFuYstVSTTr6KIYvbSsTMW4emHalWF1fY2OcpxeRl5WLKmYXMFVMr1L7AGhzwMqnVJlp6UjqLVhjU0VaMAdclm3IYtf3eeFZMsXEFZoLquDqWIGktKS7EjWDkVJhaZj5ebVbV+gNUcdkWsZaemZ7CGhgWff6yN9M0CF9Ra20itBWDPV7bnuJ3MftpCgWEvtpXb2cq3zABivvjcF7FqLaQdrKE412nvXnzX2noB5wryAHZk1ZoqYQqUJmbtCnZvN0kKurl5frihn2Gynnrb0dHOXCHUMtIyLSUugYQrgA/KR1ML6yT+PFmfjLUUfgKJ1yspoyu1121hbsEOoMqzD3WscZJx5Xcu2d3OdqCGDDsFlLQtLQc4A9W8tuvOs53GpligRG42yX2UisZR57neeB47qUaLog6+dPITdpz6Fk6iXX1sT0+Pc40fBefUp2ptXevU+pzmPdoIpI04mnPopfmG+l/Xj6sf/1n8U/f6IDi3jQT0BGDcGgDX1oY7v5tnDNpkE3Yw6+v0j/5eKEe1AjJdsn7gDqkE7t19ADhn1a43V1pTbb3F+Ydj4QaISD/kDax7d7DH3q04by2AR/HJcVZIHY0Fup1eRG0JkLu1td2xUj1YcgAlo6NYiwVbM3Z575wHSlqZt1OAc4dyTlg46ko9M3ftvZq/B+dI+v/S6c9ZPFZ3WxuYhtLX1QMBvVV7zoYAhE5gI5eG4kxtWyMJ+HqDcqCdpQFrJWHRBjS9RMIU2BqJLAtk3MwgBxZAXysQ7DzqOm0oVkYDPh0/cgzX4S0SkvVWXXnTEWo4ugdHI64pEuC1obvKLla/Yy3tLfQxUhMpIgEfaRNL0/SFd4FiqsnPzQDcFdunDn/GUsJS7WZnpb128/s2imLd81hOnt75FBAKMB/KW1NLqCjdPgdEcdlIrWIf+aLtyT7igHPrPJfeifu031etZajeirEjz0srsI7Ge1ZdIwXHDUvLTLcMnlV4cAh2zIyJ07O2MrNCu0EvLIGxj1xA5yQwBPanba31qFv52PFjh4g9orA/a8MGrpkezY01/T1WmlNqaT7JNjU9Zt8DgmsdaCCXEGX5OYWOatQq4Nz9oV6rBRIZmRoGGkm3Tz7xacsIxvlKVq0Xv2vdALn5gHPPlwLOxeXa/aVOe7/pHau4fI1+Otse2/cY6naFgBck2IkDWrCCeweApvlOox0qKrUj+0qBB1DGa7wCHLluOYC+Oaj6hTH+emAtt0b8Mzs1i5IX+TSgjtDQaOsfGrZrDe9bfedNi0wKA6IqxVoxG0vHBuz2bgHputt+VEH35+CiQ0w1SB/0HmpezQ21zni7s4DkOMDS8rI7lr1d2BTehoMGBonPsOcPPYt9ZZa1TLTZOxfexap1CsDvkJ1EPSo5IgElnjH68Ov22oU3LSGXRPkTL9gOFOf8sWpdAmTvGeuyckC1hu56S9+ebnuLD1t7632Ai3r62nWS9YmWQv4tmLySL9DMKhZ3Nu1p/tTR+LR4C4j0s+65HrtYX24tWMonRsbZMY4RA4jUSBsrJ88VtRVkZ/JKbft2bCKjY1ARGrILNy5Yde01+uMIK87bxXiejAXisjVisV7bVmeLAHC7s/PsTNlJ2vp2QI1pe+3N77HJ454dO1LmgHNRWwlwFpu2DHRxveMyCrOXbAhw72PPveRYtUZwXn5r9xY7HLCsubHZoohpi/fvtWWPTfvg0lWstectB9XFbMa8CK7FLRCFpxUsfyfmGZsDLTk2w9Ji02x+ad4uN5zlOV60dWLUU4ce4/p2oZp1zy6gEjc1gUVuykE7sv2URcVE2ejKAIprl1FKaiFWw2ozk+NkJAPGYg840oNtegM2jP0oQhZgT/gCscNOwMExVJjet/PlFywmPRGlM1m17jOPJQ9Uoq7buWqUlVC9FJCkNhqB4qAUYPuoX++jUHXlTrltQ/m4qKTQ5ukrqyuqbQ5bwfjoOMRjUrGdDqOueQBmaLxfBGpHKIW4Oi4m3oaXhh3733rydqHYWe4DZkncnmN9WApfvVZhs5Rr6S4gR8CYeOLI6dlxlAE/sCpgXG+greKc3ZYekQY4t2q9Y30oU9Vb30i/RafGsi73jBXGSXFu3S7f+sAuUd9ygRefAtrZgRXt2CrWlB1X7c2337IY4oVnS5/DqnU3fQSKgcA6d+faHHBO+f+d2DieKX2SdatFe+/meaDBPhTMIoF5i9k4gLqyFypyq9o0Og+AtWbxsQkWF5mCZfEWyouo+jZcsoBYH9uzuxjgNJd8K/DW7WsArcO2K3cnysTHLIP6MLYyYZfqALRuEaez6UZjUUpcsvkT9/YN9KMAWGX9swO0S6ASYOLC1D3cx4ijzllTdcuyse19DhvlnekC52g37liA3q+w1258jzxln71w4mNWxrnCPLE6Bmaaxe7+PNCcwLlNQKcnjz1hIehq3SRX28zGm8jocCsgnkyIigFqxjYYmHJmagUVaQ+Ljk+wxIRYNkGs2S2gqMqbt2x+ZtlKDx0DbNlpU8R2Vah7ddNfxtH/HCo8ikLeAexaF+xm81W7fvsSGyKWiWlybPs2XPBYk+gj1m7qbMQqljE0LcYeL3nOnsh9gXvxBpz7Ptb2KLktrttnn/llNgmcRvlt3eqAmC5euYA1/aCVHjhop/Y/bgn+iYQq7ja0OmzlDReApa5bfFgi9pPEkv4LQJTlANsDFhMYb3kJ2y0hOtGBkNeJBcan2Wjj4W3xrGlnxqI2zHhym5itHIVnOdntB4QrzkVNGVXUC6gD9o12WzIx7Ynik5YRlwFoTJu9hj0s3MAKwGg6c8ac5BwLdQ+xATZNVNy9and72iwmNMGeOfQLdgCgcMN9BXDqupVfLIc18rSjZccA6o6bN+BcRSN28LffpN+cIV7cDzh3xpJCk4i512xsZpA2Cu/xxluWAcRWdvIo77MZh/nKYH8P44CfMxZGIeTiT73eZKPI4vQaKoG+lhjF84tNZPPHPEBUPWBSJdOYLXLwOyyzEOU6yu5qZbnN9I3ZrkQplJZSr1NoG+702432nfPfo4jdLZ+xNzcGQBXV5O65QRRO69mg1Q1s6WfPPfUkFt6lAE6ewKvX7Pz1Ny1cinOoPh5OP4GSZDTY0aY1MT94F6vWjqE2RzH25G7GER/mz4zBa4zE1QCKH1y/YCMzY3YGaCkkPJi8+W1roowDAv0sMyONeQW2s6jDutNWxrmndZTs4hCRyWD+7EPuvpq+rxxgcXJ4zvah5FhYsNdmfVAy67hgNTeBfUPy7PSup9gMnMUawoK1DTegfvaB9Xa1Wx7gen7qLgvm+IOo0tcD+onh0bhZVsq8CWAznHm8Y0N8DatWBLWeefY5YocSNkiEsWnP38ZGNuzalVZbYo4TEQHkB5/iQT3r6pwiT4KKPaDbzh059FH0l4wtq8x7Q0NRPQzxtRXitKaGCdggzhnsb2dO7LSnH48CnNuwP//qBcDtLtTZ9gPOMWYnoZ5I+nt2FKvWK2ye/auz8FVB9twLRag/hrL+voZaXrc1oB4nl8hdOzMsMyvAFhfcGTeWEQtatfsob//CmVh7+fPpjMU+KMu72X/9wytWT2x06OA2++Snii0pHeiS/sUNuHGLDSbrq1Jm22DDfC/3MmFBxMsR0YxttN3eewYj1I0Ndy/8Typ5kTTmGLO0+XULDcdGNRid9XVPbFu3YI8G2fR0F54oB6W4LFSIvez73+mza5V1xGPhuDjmU0bhAHesDwHbvf/uCjzIjC0sttuLn8i0PfujiSk3EVTrYX5TZ8GsARQWZVp2bjTx8RxrUDN25dKiNfcGoqobbP/rb4db2dFAlI837PzZdfv637wGYBtjH//MHriOIABG5n4oHb77Fput3n7fUlhDeOaZJ+zQgT08J65B4Bw1la2BlIPU9xwm0vnzR50vPRSc02KZoBApMAj+kDJbWmqaA24Jovrbv/07h9KXspgsjZ4Bstm9u8jZGSUVKtkASSZTC22CacbHxh2VC0k3pmigjop0JrECfZS4EwikxeBjx47RwDKobC3IDv5fBPLLDpAmkEuTy6eefspJlH30ZqXgJeDoFZJvgv58gcgE2vhwDbruz/6HzzrXLjjnHOCVYDQdTwCLknydeE4PskAjiEaL1rE05Pq6eud+xpi06t8vPI8fPVCYFMJkP6nr1kswlu7x6NGjH72sf/j3X37tLx2lDzXE3G25AHLNzo6+Z5971srKypyFSU2iv8bnWlqaebhmkQRy3iQwlPA4ffqUo+alxcrLwIavvCI/9H4HqNNC5ic/9UlnQv2gooDr5EqICCwSDPYrv/IrDoT04A50TUp1bqmQCJ6T6oSU7XRudWb72LWnxKNrwu46rv5U8k3flYVpEwutauQ5gH9+wFpadB0cHHI6CCUnpcyhZJyStnW1dQB1OxxQ6saNm3SEOY7SnhKbP+yl3c9f/epXAfXehzD1d+rd9vztzjUIyNN9/gZQoJ6rK6mgpJCgRSm+CVDSfQkw9KSh37/fw3cWLAz4TWDob/32b9m777zrKIZo8VhlpLogIExAo1QTG7hH7cQ8eeqkk8CQ6tzyMrtzOL6ubYj7VYI6g2B4dmbWxtmVrLq1QD1P5boEUSmxK3jsTZQMpf6iRqzEq+rC0uKSo/KoQFCLJ9rRoAS3krn6joBUJTV1L3ouUhUQTPlngJQC1aQap/aohW1dv5//h+0gIz3DgT71DH9YUkzPRlCZ1AHXUIYTuKn7S01ldwllrzIRyKo2rfM3NTbZ31FnlLSWnLPUFGXjJYULnVs712WBLNhNbVttSO1Oi0FhYeGotfyS86z0vAUsSmVSuyvXaMuCE9UuN4HzPv/5zznAm+5b1rt6DlKWLKBMnmTQ1w7Miorr9jff/FsnuaRr0yLTpz/zaedapABz5cpVp57s27fXaXsbnOPosaMOHPiwpJLqteqOVDsqrlc4oKfKVACtgD8luNTWBOXq+GqTut68bXn0yOxMAMZVW1c9UZ+mxIM+89qrrzn3r/IRDCj4MSmJgPCHJMF+WFv42XhfNf/R61EJPCqBRyXw010C/1bgnGIsAWOKqQSsCQTTOKvxSxasit81piomEiiuzytek9rab/7mbzqx5+uvv+6AZLt377YvfelLjgWnYkCNa7JE1WelsKY4QLGioAwpu70MQKVjKLEjZSvBZ4qnFY8oxuhmYq7jaIOErE4VFwla0wYRxTc6nsZRjbO6JsV+2lQjeE6An46pchUcp3vTeKrzC7KTQpPiPcUnOpc2k+hcgvQULwuol0p0HYk8zSMEEipOlvKWNhUIFNT9RbHTV/ME2bkLINN4rrmGVO10bP0pCElgka5LsaNeilcECyoO1/cVc+n82rms2FMKex//+MedOOxfumarvH4Uxbl/7Dpc8IPuQT+Cb6RCJihSz1WJOwGOSubpeQru0fN62Eadf+w8j373ky8B1XXVTT1DPSfFma750MPOrjal5yyYVDGubIg13/iXBj4fdu5H731YApp7CVSSirb6NSlZqj+Wwrf6Kc3b1K+qL1Lfrt+rX/zoXE5zfPWvevZqp4J6XX3Wo7J+VAIfLQH1+66X1tD0EqCp8U2xguvvGte0Jqi1Llm1ulSPXNCG6mQb61Z/8uU/Mh8guuOpabZtEeB6chzlMxZcg3xZegeGWpxn7QVIZXuBxZYcchQRuipu2CRrJAHupN+BhdAUQBGORXWUefwApSL3YxcDuOJGsmujp8kGSDr2dbWy+3mRJG0IO4rDbIPkxTzWr14kGXYcPmLB8RG2BrQyzXrGSCW711mX8fD1Ni826a2zfrMGVBMYHWtRhahR7NxFUs3Lhj4AgLj8gc2wZuMjAC081AJYi4zZU2jByRmo0yHLIKWzkUFbr62yAZIYIyw+e6AKpPUueRwtAVl4YA0ak5ZjESTStZX7Lpaag4AXYVx/MFNeT9Y01iibSSxUfTNIupUesUigMDat29pgnw1UXraRpjqSmMvmH8x1+KJ24o5qDccNjI231D1YpBHLbbCWMkfybhSrmsmpae7P39lB7uWJDTcJNm8S25EHSiyatR8foKbVS1XWjA3rzNSYBQEHegIP+aSmW2IRxwPU2AhgXQwgwEg4LnPN9y5cs2UUGXxZlXYj8UHqn/iI73qvWuKOfAvcfRKLsiDrrK0B3uuyYO41hI2roIHcHxAEQEZQiL9Fsi4ccPIJM0ClddaEZq9ftaHWuySht7g/gDYglyUW96e47qiCPRa3ez/jEADe9BB2rxXWX1Nvy4AbwZSDF0n5DZ7zDOXhCQyZDuQdnknSeWHKpmtuW89751EbHDUf1mJ8Y7FYY906sHiveeSgxsT3t1Dt6+5txc7vrLX0YHVz/KgdzD+B4lyKo0wwuzRldzpq7I3zKONFudmJkyRLYzOtv2fQ6msbraf7PnWWeyQx4uEjEz7yUtSJUN8QO5h30DKjM6x3vBc7r0skmO46DiKl3E9WPMqIAJvNTY1WgXJL//ggu+4L7ciuI5Yekko7m0SR5CLgzC3HDiw2lKSNn49t+GFbQ5lPjE1Sqije7T9IcvMwCgmhzjrx2xffAjx0B5x70orSDmKXFWjNo/VYKr1mnU0ddhKliedLXyA5EuNY042giHGFxO7l6gqek5u9ePQJy6LN3Olttuvtt0maDILA+FiID+XHs3SsgdnwHYoFVE5GPmvz2azlkzC/CpAEqJCyDZsoYK7MuBw+ivNFdysqAeWAogso1xU5ajPJ8TE2MNtqVajT1ZBAnAZGCwwJA8wLJ2ETYHMkoofnu82PxMzBnYfsRM7T5BxCKcPLdr7qXfNYdLcvPv8FK0rdSaINmGywy1FQagcUySUR+WzZsw5cQfMDnJu2ysZKEpy3aXvurJkfJkkVZz2DnXarrtqGR0ecBLrqEkZ0qCRxiyTx/ALDLTszC6WgFJQu5u3tS+cB9HotKRULIWC+3IQsFDJQf7tfZ9duVGC7umUFQDQlwLFBkf5ABPetrqnG6oCC1gG4QoDm/H1CyNVs2tzmEApZdy3MN9h+4eTHKZMzFuYf5WxS0bxHa8yaC7n6YvWp+rvibc1/5ECi3zvlTH5C8yD93vX5n4cYXDGqC5zTRv6krAS72nUF5ZghgBPGl5VNC/YPtczUTOCnPGr2llVhq1bTWMW8MwF74zLLQmFJ9oBNQ43Mba7bQHuv5VOfj5SWWVp8Kio2K8Av2PU1XLcJoJAwchnBIUGo+KCowhr/aP8Yqh4e2CUDzpUetzDWcJuA3d6/TLIesKfs2GkrzT2CClWE3ZsSCPsKc282XGfvt08e+wyqMMDVGgUFznENbwG0jMwM2+k9J6w4q5DkPqBG4y3Ufu451x8InOwNWOMGaO2+6m7B9H1ZaVnOXHNToADqVpWo5PlFBqM6tNtKSDJ7bqyjzNZtF66X05b7LT05C+DrKdsGCDKGJeit1nK7WXUT2HcNsA71yDBACxQ/FzcXWePvYX43B6izyz5x8FOWHJJiVe037AdY0I0xzj1/4pOozDyOoksgcDROTgtjVl59HqjmGmOXlz1R9oJjT+qHlbYg4d6x+/ZeBRbUQILFXF9R3l4AQJK2d+qxhO0EMlg1vwDyWyjEegLOeTBGoT1H0hOoD6A6KDLUUey6jd3t8iJKf0XbbX/xbgsA3ukZHrAb5BJbO7ssOioee7xDgESg+kCHFa1XrBLlobGRcQCgQBLnxAicYxPFsKm5KRtCVS8xLdk+8finLC0ow/qYL7156VUS0d2WD/D6bOlLlhGfY/fnu+xC41mA2BvAbBl2ct8Jy0lnvQGAeUXjOnDk+zffs7uNd4GZD9jjh07YGCpbN+9W4EjVSQjj7thx+gIreKHIKujekz4yJSrFigHkvLmPuoYmoJ3LtoilaHFpIQpy+yzaN96pAxVYV7a0AjkmpAKclVlqcjpjji/W2M08wyvEaO3kHAItJAzVLDYOLKB8OrY4bHPL06i0pNkLh56xjIR0ax0DNC4/a2ODE1hhl9KfAZxFYtU6gepRzXV7+wo2mNmohp142gqwzUSj6kNwbuSeXam+4gAyWduzUYU5TX5phcR9A/OLu+Q05s2HsdsTdTkPnr/7gpcFWQQKZskoDG6nn3WzWoCZqzVXsGzzBtoDrsorQWwkwO4Dp7x/CXAUEC8Dlan9wDLp23bYgjdiF+01XNcl1oCGUE4LskhsR919sH31XbE+wKulmSXbnp7lwKtpSbkO/PODt35gA4M9iG8cwgbyaYtyg3qnj1zwnqc+XLaLFVdtdHIWq9YX7RAWumEb1FEimv7Fe8Ba71trQzMKkpFOfisISK6qoc7uNrfZ4swC+T5PNnSgyEY7cVR7UCVKik4ByiywZOCoe/e7HBvVwdkeoPpcO0pcJLXGEZS0qjqv280b1RayHo0C4XHbWbAdAeNVuzfYAcR02zqJJ2QpGR4RQh0h7EUdaG59zvq7yUVH5NozB1CWA5xbWJ8A4iq388A2McR1pxm3C1BX4hYALCvtvfqztMcpO7b3lJ0ueNoiUFlcow0OYjtbXvOBVaBam1OcZXsBA6W41UW8XX+72RZmeIbUT/24e9LPAFhAXFlcaLwVMX4nEn+3DDYDBgKQDQxYCbDtwaISC6dPHSWWr6y/ZQ2ssQX7kSslztqdzvhEW6u/12qXsGyVgIY/Kl7hxOlSSl4lhpwFIB2eGLWgqBB7/NSTKHIWoaCMImZVOYqfl+nfttnjWM5uT8rnHMQrHdftPYRtonxRcNv7OP1IIRs6UBJGva9zqtMuYLVbUXXDduQW2WNHnmX9zwvAssqxG50enQSER8GSvlTWlpqn+BHXJAHr7AL0Cw6OJT4fsxvVlWw2GbbtlFEJ6pFJIUnYWALhU243sBn23PCwIzvKsCUtY9OKjzVhmXmt6qq1E+94AGaEs+4pZbdNVLb6Z4cBqacsIhiFvIMvoVy6G5toRILqzztltS0+254+8IRtT9kFQEQ95D6q79+0NyrJ6fcDzp3CqrUAJWsUjN3X/Bxwu1wbD9ouUqU37KljT1pqZKq13u+gnlbbOJCQBxtNAoj//bEudUelbpM6Gobdfe72XEvOSmSjySCAUbkN9g1ZGjDt8cOnLRGQbGlxBdvWWjZOvG8zWGLnpe+ypw9+HFXTILs3cdcqai9jzXiXmIgYkE0HXmwWcUNpeX6DjbbdqA8mxzK+PG+PZeOKhurlhaZz9s6NV22ZjUmffurTVrbrKLO5NYBebDyvXaSNDtoBNowc33fK4n2TFD2jLgf8RR9481oloFqcHSxFkCYxwFoZIxvZcDA9QB1dl3JegLNZywPAc4kND2HhEeSAt6Msl2aTrN9eZ1NQU3cb4koZdnjvScuK246QIeImd68R+xGzzW5acfZexkksTal791DVu1FXha1phxPHh/rBgjCP3PBetxmvcesa6HTU1p7EHvjgzsPch8A5VGnJJWttrAxIt7QIa3L3QEeBr7zmXfrNWTZZ7LETKM4lhgPvuq87Cna3m+tRw3oHJfEs8v9H2cgSYneB7usa61DKJIbxwk2ROZYH7dBzlf501ctiw6KtMG+nJdGf9gz0WhUx7f37/ZaTmQsYuB/lziSbdad/q7/GhqhbjJ+LdmgPIBGbWCJRgRyYGbXXrryOamAXVvAId7MZxYd2s+y1YZMA8rPMk3zZLHWGTex7t5UC4fpYddNNwOV3HEW740Wn7EAGG4d8Y3mCa9Z4D6tmxoqukQ7bB5B0vPiYJfolOkpkq4yHNS23scm9YhNYtD72zOMwPWnWh3BSDXX0/lA3iuWYcso2eEO2rH7k8Nm4BsiWl5PFBhQUKRm/Lt64Yi297WzSibcztJGMpCxsumesbbzBLnxw0eYBynakFlspaqvh8UHYe49aHYB5ZcU1FLSZh/lFOxbTm/5uWKbPEnd30f4DKBcUovccQSE62GqIyXSdU6y5PfHks1ayDTVDD8A5NmD09ayw+b0KFb8JFOcAvwBR3QHdp7ADX2G8i46NYANGOtGln3V0jvO5e6xZcF9wFhtslptGvXsdFdminSlsZEizHXm+CCoBxv31uzYMiH7ysRJ78okii0tEWZ0J//QIVq1Xl+2bf/OqxcS529PPomB5IA3w3g376WHUEzsZX+a4lkDmxH6Mo2yYmw21+z0+qFu22rOnw+0zn4u3tFT6w3kv+7+/fJlNTc2wIVitvrifTUooozONc6Ol4SngKNYNDKBgebaV6+qkzLFVJw7cAiRf4PszbNSNjvaBzUm0iPAQGA+U/wGj2bUBBCeDZBQw57zhsjxR4nS3U48l247CcIMftO9/955V3qil/w5DyW+X5e0gvsSqdYN5/oX35nAWZb1isQurVjYAHYzlmAaLMWtnz9YST9CHeYYAWxKrecn1xpfxPwzAmlifefhv/ccweA3AuaFNe+9dyutb34XxCLOPfXyP7SqKYH0bBWbq+7m3z9l771wAJEyHtXkKW929TkxowLuyk5czgUzJVR5u/Ed//qivh4JzGmA0UREApwU0b0b1+IR4FgLYecgCm5I3WuzVJE+VRsk6JcEEt+hHwJsmdEqkCSIS6DTIcQQg6fuCcmRJor/LpknBiYCbJDoDJb5kodnQ0EhQ28fNbjkLx1l0Okp0PWxBX59R0mtkeMRJ9IwwQRacosVmFyAnS8RpEmsCd3SNunYdU4kv2UHqPX92HhWx0COgb2Rk1FksVFnonAnsrlDyTxBMG7s9dT69BBbqdy6Vtoc9CClkKbknAElwU2DAh+p5sSyI+3Odun7t2pUyWS/XMcxOAZWNylRKf7pGLYDr3EpkjY2OOUksgXSytZIKloAh/fujL0062++2Ww+gmJKHut6PTrZ1bi2MKumislhg4UPnlmqgkin6zsOOrXPpu6oPAsRUZxIBgVJTU50EgAKnhcUFJ8nmst/UgoDKQqpzukepjOk56dk+TBXBdT9KbKr+qGyCAatUjvq+FEcExKmMdK0CmvRs9VJ56X608KtrkwqbIFCdU+WoxIO+o2Po+0pM6LlLGVFlpEUKAX5K5A70DzhJRR07kAEoJzubXTHs5Pz7ZzfG/QwMDDpw2CYkta5J5ackxvraunOPqvP+LN5qwURlrXLWS+fJzMp0gLUeFq0XaVt66ZmnpqU6xxKUpzqq5KWrbQli1fMX3PXNv/mmk+wM8A/4sI1wnVs8+3Um02q/Uo978RdeZEBLcY790f+oLPVsKisqP4TWtjadZLLuV+1b5RRBGUllUXVK/YIUBZXo0Wd0X0rO6771HFX2KnPVeSWmdd2qRyoD1VVdtwv0VB2aZvFXsJ3qhlTmlCzKYqEonPMq8afkbVdnl2NfoDLXe2p3qjcqT8G5ugftuA7i+biuRe1U16Djqh0G0PaS6Wdc1/HRctC/dX+TlEUPQKaSBzqu6r+uX89JZaF/6zqk1CfrWNVn3a/eV/+mQFMAoGx79f1OlAlVt1SP9fxUBrKxVt17WJ/2sOv62Xrvxxm2frZK4tHdPCqBRyXw01sC/1bgnGI2weSXgbJlOf7cc885MbdKUuOtNndI3VUxtsZjjbcC4aTWJosgxRdXrlyxb3zjG04s/PLLLzvjoj6r7woMU9wmIF7jusZFvaeNGILIdT6pNUvVTTGM4jiNxRpn9W9B47Jfd0FWGudlDSrFOAH3ukbFCtpYoWtSckjjuuIQWdlLBU3Kt7pWFySicVzf10YBjbd6X5CbNiRonJcCss4nuERjrO5fEKFiPgFt2uAiFScdQ5sBdE+KBcrYwKJjK16QGp7mDdpMotjWpfLkik/1HcWPOoZU6nQdemUTEwra030rnvxonO186Mf8z08CnPvoJSle0bMRROdK4Om5KT7TJho9K8U4j17/vkpA9V/K7lI+1KaMU6dOOe3wn7pKxbECtFSfpQYpNW+1oZ/PuPSfKq2fzO/V5gTPCUpWf61+UG1QSveCgF39okBggceC7NQOXWCc5qh63uoPBUzq7+r/Hr0elcDDSkBtXvN2vTSeaZ1LL72ncVp9v+qkxhvFCGfPnnXm7Nokqh9tFNNLx9F373bctf/6p3+ELdykHWTtKJ9jrrP2MjuB/ecq83ISWqGJ0ZYNiJa4H4cEEgDrKBOM3rhlcw0ttsUYugmk48YSljtKDgHxzJ+zt5t/bol5Ave4seC/udhvi/eabIyEw1J3l23MrNrmqh+/C7H1iETzRSUojcVS7zgSh/yPRUdbud1gCzXVtjSBMtomtpPEG5v+QRaUmmFhu/agiJbtqKEtdbbbVPUNG2eM22BOz0Te/NglH3Nwj4VkYksu21aUqVgJto2xXpsFghq/QwJ5eBq1OQgcFpc9pCqXkmahgIE+QBkbxEFDzU022tZkvgvTJICWsIyivLA3cyNRG1a410LzsK0kNtliTUDA4ArJn/H62zbZ3eFchxvlSFbFvMJQwwKEiEERyIfYBDoNS9MuW2hrsAlikVXs9NxQ9fCk/Nb9vCiLTAs9sM8CU7HTkbJFW5+N3qq2yfY2c2PdaQ11P88UNiQD7gVyrZshWAByLrelSduYHKXMGm0R+GqZtcZVoJJNEgSGPWJoeoSFFaHohoLB1Dh2oo3NttjZZD5TJGKIGwVkbfm4m3tcoAXmJGGxug3b0x16qA7gtgKUN0H/ttQ9hPoeOJ67lGFQCAwKsYhiFIVQ7wmLZi1zY9pWBrqoG60218SzHpvGNnUd1T3sZVHx8WetKYqNpX4k79xIPq7199r0rRqbJmZcJiHsSWzrw1pfGGuevvl55inwESWkAZLv11sqsUXrtILdxSRoi7EKimU/urstkrTuGe4AbrtoPiFbtp/6kRKegvvDmnWzLtfa0WWD1KMZlObIs6CAwfP2CLKkCOANEjAxJHe7ADBu9NxC+2weS7sc25Wej+KTbKZg+ljDukNZ3QCySkjlO1jn5Uam0/7WrRlFnbqeRtZ/e2xtYdlRzQrFvmcVq6vh/hFUKBZJgc92XwAAQABJREFU2ux1kniOqhVxtqz23AOBeQo5TuxOyt7P7k23Y/t6zQbu9WFxxee38XxDAm0FxYxJdvPf6SNhd/cOFrdmp3YA+8UlYS07ag2jbdbR223jgzO0SyyInFTDJlaAfqh9JFt2Wh5zgDjrw9b4VtVN6vGGFQFI5GVsAwoDgKRtDM2MAP/VAQR0W0JQItaVhwBhYrC/HLMerA3vdDY7ZbhIfyBLqUiUj1ZQ/7iP/ZMH9aAUsONYDrbKAaFW01tL0rPSvNe8UM17zoESBc4NYn11C+ur3pFeB4A5tPOgxfqxWYgmOM/9NQJESlULLTaUGlAsSkgGepslIddid3u7bHSKdULAVe812jeJ6yCS+tGAJNnpaah7RJAkGyWJj9oc17ONeUVhGhu8SapK4WZscQRArtb6742iIBmHg8xui4gKtSXOK9vNhtYGVGdQ0AQQUnIwBDDJJ2zN6ul/vFDt+9iJl+xoPlZ6KIC5FOe0aTo1NdXpQ9WXKuZSv6u5ieZ1Wj+WWrc2H2ut0LUOr376wT7c6Yx/Rv+j8UlzLM01Bc6lbEuy853vY613j3aFnZSHP4o7iZYHIJYBEDA/PWO1qCLKOm/nziLbmYWVXRCWliTQRpeHsGlusoaaOxaMPdruAtTQkrMdQLhrotuq79Vb2/02QIo5R2U0jHEuBGWWiX7yQYNLHK/YjpYwv+P93n5gtDu1Nke9LKKvyE/aZaGoPg4v9FF3gQ7oy7fFF6BqeRqgDuUx4JStLQEf3VaBytgk6kz7MnejSrTTluhjW4FCm4AxhidGsLVaAPRVO0TFC4u8mNBI2854mER9nsJms5Y+dJjxLRP1lYL8fEsKZ1P8FslYxpnbwBB3gMt9sbHbl3/IslMy6L/nbWCqGUWrFubow5QnIDqQiy8QgU8IidjBbpvChq8gNx975xcdGPUu9qXXAF6nJrAGLTrj2A4GuAENgxosrkwDOVWhllZDz+mJ9epxy0GBVNadsmAbmRxGBaiCc3ZYVnYmij7bsZ0L5pkMc493rXOg21HnlGqgN31HEMnbiIBwBAbSLTMtE9AGEBcFvu57KEWSWyhhzElmni5Ia47+t/U+8A+w/ibgZD7AR0EyNqNAWSOL9+1OTw1z1Q4bQ2XNg9xiYHCABQFjrwl4w648IhL1x8c+joVkBveGSgwqlQOTA4BmWXZo23GsM3F2Whywmns3rbWl1eJC4oDaiiwhnnGXAXaJZ9i/2udY9PVhHVqcVoSi2AG6QJRgsAxs5Zq7uvpwzIFsoh/x9NqkzNxQzYrCZpQ+M6vAluivahk7uwa5ngR/7K4LLDU8kx48GKh+CmCpCfveW46aW1FusWWiEhgSEGaT89PWhs14A7a0k8RaLDJgpxaMHWGwjc+NoyTTb7Gc54WyZ1BLywYAHAKAuWHTKLVJeWhPTjGWiBGMJ9NAcVgQY0kdnhLOWIddYXgW5/fF2nbZhqeGsLWrB+LrdBREi1C+86Q+DbNxoK3zjvVSX+ZXZh3FrE1qgL97sEUHArOj0JaLS9PKEoBoax1trRnHqlQryd1jaREIDtC/TWALXgdM0waM7oP6bEF+AXVkB1CZnw2iiNQGrNVyt8HmRmeImWjfAb4WmBBkg5ODjE8ob8eiaFpy1DITs20RhTTZM46RON+5A+u27IOOXbIHENiS5wJKSfXAZQ1Ak4tAOcewTN1uwWtKoK9hDThs9QDj97vuoU4W5lxHeCygN+NYS0+HdfZ3U8ZTWEETZ9I3a7wP9ibfSj+Tm55rIf4ovBLbtXTXEfshdLFrL+qEeU5ZaCzqmeu2m9UoOaIslJe8E0cilAZD3Hj2M9bS1UFbbLchAEECSNSmcIdCVXHVi36grs2SgtLs6b3PWiFA4RqgUiNleQugLyQ2mjigBJgzC+Ve7In7Gqyqp9LmUewVeLkv6aCFeKMgTEwhxdN64MXGrlpLzEkAUmGsZuxRTrYVIEo5yEly4isoy4J2OoIzAX6Blh6ZZrtTUKRkHJZFY11vA7H5mh3EWjafOuUJeD5Ljq6XutbQcofc8SgwY4btId6JYtPJGBsn6lAVbEZpcJR8rTwXfFkP9CUv5w4c1I4ys9rRk6eesuIUVJVR1KwnbqjrrGddKQEAGRg/JpONGdQT1NCqqm9j+x7O+yWWRQzsQ7/lhuX00NIINqA1drupDrA0x/YBagUQr4wsdlt3Xyv5SW0AGnOUrTYJwDR2RhFn5iekYI9ZwDGAQzoGrQkL2gA2IBQU5FhOPHEzFp1rW8RkwCmVLTeBTkdtRzJWsLvK6EsCAdEmHTvTO7TBkYlBoCBytf4+wIDYCS5O0OcNW6CF2TOlLzqquzMon9V3V5IDbEV9LwVr1QOoPqYDIPsSR65bx0SLXcZGW7Do0eKjxBz0Ze6hqG/70cYWrGag2ur6q51ncKRQtqIZKBdiYw2A2UmMPEhuen15zVF98wMyCyK2SoTXyMrOsrCYYGtHnaq2mToKuLsbgDkvdTtKYhwf2GdoCigLOLGTY0XRfo/vfh7oLpJYaor3Wq3tbpMN9QywoWOBzQ3exObULSCaGsa9kPAwxoWn7VjmafopH555LaDqRVtdmLfH2fBUlL6TVraOKqrOXwO3MGL5efn0QXssiphbMdosmzkae+pQ62qmboYBNO6yyARs7lGUvYuqam8nedvheaAf1L5RbPVjfcPPx9uSGAu2M3cT8HoftqGJfnpuZYlnWIy6KaqExIRL7KboQfmxrvW2jRGzJQQmYBlKP54UTR+3YO3Ac3c6G2yAmHZzmY08qG36hNLfJHrR99WZLXjY6d1PslnlCMAglsztxA2NDXS5nsSWRdido6hMnNd0D7XYjgrONwMQidV6IgB0cAwx96oNbgxYR89dq715CxA1gXiDeJI2Mk8bucv8rRWwbXBizBa5t00PeBo3H4DhYNTZsK3NzEMxNcLa79/DorWFuNBQwsXaFVvikKBQgGuEi0a6WTeqJCZtJUZP5f6oW/QNC9TfW301bASqh13op+4AeAUDVzJOKA4a6utHJXrOTpRhOZt1wALW2QTV02ZVKA36h3kD8++zHTFsLKPdbfC/buC3KuYb/dSXbfk5QPJ7mO/EEH8BZDGuthMz1dEXzDA+7WPOlcRmhCUU1e8xH2rE3nhkcgigcsW2AOT9PNi4AhAdSdydm5FpSTE8b8ULd6pthjmyNrEUpu2kDKPoGwHesYWvZ9PN3aYBNlBFseloNwqJUUDHKPoxvlWjJNuvOjrPWIuCdmgi6wEhjPW0azfeK2GedXTPUYtGvb2tqx3gtN5m2Axx8NAx7F23WwhzU01vh1E0e/ONbmLvSVTnAFBRYtPI7e2FQmWSNyq9wKg5jLNjHyq/dVA3xXBQBM6Pj88WisAhQP5x9LW+qOijBNu1Zm+9U4ES54jtLckjjs+2MGyk4aWx69601jurduHiVSzo16zkUKrlF6QzzvrAPjF+1k7hejeF+iV2q1xgLDbZPp6Rdq/Lg00DLSimB9onXw6DacF6fMHPXvteM/OF+wCzsUDkuXzen75JMSfTM/pguQROjG3a9WujbOroQ9hpGXjOm994Mx/zZ37hTfsMtZz8DzcJ3ro5QhwzDeu1RB+qNQ53Zx0yPj7cduwKtR072RgXg+LzhJtd+oA5ZNN9NlcBUR9JtpQ0HA693ZiXbVntrWWE2KapD+N2sIy1oR1szMJedRql0draYTYcjXO/GKoSLgVgDRsSHEmMG2JtHWtsQlqyX/1cMPcTxPx5C8XHefsuOajwyGXqWZylZhC/egNkz43ZrcpbVlfdYPnbttvzz79gpfsVszBRZ666xXPc2PShLJjp85bm4/z/R349FJzT0TRZERynH720QKtkmCZ1qzSITcAavTSBU0JOv9dimxZ7XS8ljvS+Ek5ajNPkR9/XcfSjc0glQhNALfJqcdh58W+pdOncm0wepMik32vgcy32uc7x4J86tkAeJb80ARU8p/P8w3c47iLgin6vc2vxT9em69Z7+pyAHl2rkoeuxUSdQ5/Tj67VVSZ6X9/R+zrPP/ZSuQiIWuZPLW7rflzgkOt7uiaX8oXOLVBHx3Ul4PSerlN/6jr08oAaFryo+/2H+3Qd8O//1Hd0j7o317E+8hGnPJxr5LP603Xuf07yROWlDmQFKVw9b31X16j31gi4dK+6Z12f7lFg5Dxl4bpHPfd/6jz6nspdamiy8dT96Lw6rspIx/j/3T9lJHUy1Tvdk86ha9NnBTxJclXfd5WJ6o7qkKtsVUa6dn1X9cH1UoMT8Kjn7np99NkJwgwIDHCuWcfTNbqet56HjvfgefR7HUP39eBL16rzuK7B9R21BV277uP1199g8gBVT3lIqUCfV7nrvqX80sIAkgOY+ru/+zvObvEHj+/6u46r8lR79GGXseA1nVPvu9qR6xnpPV2n7kHXrJd2q2jnis7tKk99Tp9xlat+p/J0Hcd1btefrrarY+q+tVDkeqY6jtqG63z6jo6nH5Xng+/r/K5y0/f1XX1GPyozXYO+90Nfum7KQveoOvrgSxCwF991XZd+p3LTsfV5va/r1rN2lYOuTb9/sG6pvAQbqiwePNaD5/rZ/vuPM2z9bJfMo7t7VAKPSuCnpwT+rcA5jSsaczROa1zTmOcac1R6Gve0wUBAjZJ2in0EPGlDi2v803c1NmkcUgyiuMI1bmu8db3vOq4rPtW59JIqkiwBpWwlZTZ9TrC5Yk3B74oddEzXS+dzXZPAP12LNqS4YnF9Tvel82hcdc0fXGOkaywVuK/z6PuC4vQ5jdU6ps4nuF3XrphRcYWuS9ek69Y16H19VmWk9zVma3zW8XXN+q7KRcd8MJZw3Yfe1+8FzKt8dU6Vra5Fz8J1va7P/0v9+a8Bzj14rSoPlZfqmcpMZaNn9eAzffDzj/7+b1MCarOy9BRQpbj/QeXtf84VqT5rY47alFTLpJioOPnR61+vBPQMBB0rQSywWJCzADj1TXqpLaqvkRqnQCZBugIk1a8LqJPCt6BmWfO6+ud/vat/dKafphJQXdJL45R+9G/1Ia5xS2Ov+niN1TU1Nfb1r3/dgagFnr8MYK/NdPq8vrcKONbZ3Wl/9Kd/DORz33YyDhZHRVgoi/WLs8BgJD18GTfSUC7JZtFboJZ7cDhgwKatDaPgNjhgW3PzHI8VTc7p7gsuEBFqnsAAboFRgG2srZEkcAOm2kTNYY1E3QZJmM3pZVgo4hB2/RsKC55RsebNeTcDWfQm0eGBgr8bVllr7NTfIAG6tUbmgbUjI7nmGR4J/JUADAYAqPNij7qO6s/a+Ch2Oaxhsr7pEco6QSJWR2GRnIPzkGhyPCFJZG7Oz9gqSdyNsTmOyxoBK7HuzOe9wgEkgGncATw2V1ijmgH4Gp8AGmN9kXVTLddu8TsPxlAvgQB8Xooq66hJuLHJ0Y2k5jpwz9oEan0zumbWFEhYu5M09wiJBaCLNjfWlpRQgBy0zblRrhnYbXwGYk7mdVyLFFKiub/4uL9XyiOmmyeeGqPsdC2ofm3xzCF7AM9kSRvGNaF0oDXd9UWE8lhPwZJsfWyWU8wBJ3IuVvrdgljrjPbm/rB3YZf++jJ2lzzfTZIiW9MThmwTnACxHuCjRyzrPPFcM+phmyQi3cguupGs2lrg3nS9gHBbJL23OO4Wz3sLVRCvhAzziqZMUHdxc+OzJOY3SPivjXCf09SPZcqP9U43Yj2PMDZexgCkBbMY7sVaKHDbxui0rQKZbQLOaWe5B3GdF4CaZyTXi9IhXwaOW3HsmMZXR9mUGU0CMQZYABUl1mPWSQAtrs+SyLoPuMHmTcpbYJyKZYnnMIvN1xQJrzmS1LKM8hS0Q+Iswh8r2RCBK54oGUxb3wKJOD+StKFs5iWJ7evOmhhPZpU4anxxBovGcZLPPhZF4j0KJQ3ZpE1j9Tu6OoUaxATqH9wvKnw+QV4oYwDT1LLJZHbZDpcetv15h7jmSGdtdWJ+DAgTWyGgoEgf4D93oBJUNkaBPpaJTWNRL4rxjXPa9DrtZ5Uk7cT6NAooQJbrW5aOKkoo518AzptYnyFBNUvsQJ1FJcCT9T8v6mQgsWx4ALAk9qIent42Qb0fGR9xHEniI6P5PfG9aFfucIXkygTKN5MTUyi6BVlcEBuFUSVa9Zm2+c1xgEPuEdUngXNuKL9sosLXDVxUi1pIAEBkGUnjkuwyEvZhNrA8YIP8+KASkBOchQKL7PVYe0fdaxzgaJbEsS+gRVwQdtBuKENQ9mvc39jSpKMYRmO1cMbNAJL8gh7m1hdsmr5jaol2O8cmIlQ9fLHD8gOK0mbgUNQovEkyLWB5NwpM485m41CSllLTUXJwiwSwrNImuf5ZlCVYObYoNhP5orjESqGjwjLJNSnZv7IMREtS3IvjdQPAXLlw0QJQvpDi3L7sQ9hJhTjwew/wozYFaZO6+lL9aJ6izSqXL192NnJrM5LgOn1GcxJXf/3g5119Ng/hZ/KlMUYxreIiza0K9hTYkPsI4Mu00wb1HMNRnIulngbRHhen52wC285Vt1XAxjgLRDFRyW4l0NbcFgFXmPuN0b+uMycEIA4NluKTF8nmBaz4xm18ZZJ2ChREG/BGxWxlDkihpsEmB+Yc+7CDxQfoG6KYOy4DHoyi2LaGSkqMRWATFoD61+IGm6lXBlFfGXeszhIDU6nH1AeGjy0S2tOAQ8NrI1gHYslNIjjKmzGO/meWRP8EbX8KMGMecG+d/tybvtQLiMyfMSAyCCcZ/lyeW7bJkSlyY1sWHh1F/WWDNm3JizFIfZhshYfnJuij3LCUxcKPsdfNfcFWsXycA8SYBMxlaLQNxtpNjj8MKFR9C0tgVBYP7Cmxx4EhogEP5mmvI/PD9FvrgLDJFh3AWEjiUa1hk7Idx4ZuGPUw9bUJkakobzJek6DUGLeEstT4LBApAEYQ8WkwCpNeAIAbjPlTq6xJYMspe8l1nomA3wBUNH1oIw7kRv+u9jY+CTC8MOuoKUVHRJkfeQm1awFwc0DmkyjBCB4Kof+TNbKSpLREm1oZo5+mH5qbdvI/XoAWSyjBtLYCz6IklpKUbC+d+bglBtLuiLl1DzOAgCHA8QmBKZR1sM3wDEeXBlHnQ+iCOhXJOfxQfKFI0R5ap67MYTcH4IgqWQJlHI+i4AZlsggIrDKWDeICoMAGfYY7inOg5Kj3hGD/y5qIHwq5K5s2Rj+5vD6PUxMqn8FBFsBYQ6+Eoh1CAtTtEVRX11HSieA7YajzeHN/m0B7C/TT4yj7TC7MOJCbG+Ol+r5agICOu+2WyPV87DhWrXGZNr21ZIOziFSghBlNO5CNZwDPwY34TupAAzwjrxDATODAIOA3H8DETf63BJg/ye8XSZpLbCCYsdSNvmmTPn5unr4cS755PrPkybjLmOzjSU6XsS4UZU0pjEFjAUMwlvH8JG4QE8S6CAl6xRFLHqs2yVg3RaylMT2ENZyg4GhiK/J6XO/C2hR1eMgWUf3yYCxSSDFDPFDNhomh7lHAtW2oVh1HWS+deIXnh2rTCv8LB/iJpg76bSL0QR3ccF+2qWVEDagH89S7mPB4xskwAH/6UeLDddrE6CznAbQNZRyTlbcX60PLxFbT3NvEIt8DxtH6x+oq4yEQphTbwmn7Yf7ENrTZUcbCGcYcnwAvi6EPEBSC6T01ZIMxb5F6QIy4QP4PGCYEcMWHTQJkJZ36O04bn2G83CDmUhufQ12p5X6L1dyosayobSiTPW/bUNXzIgBRXm5kasI8iTEjaAuBjNsajyao68PLCK3QR8YGxFucVzzj2oe5rRXq1hjtd2Jx1Px5xiE8F18gYzfiXeWhpyhfjWeLjKkCLLwYg6UwrH4shn7JDfW5McbaacY09YMxxMwhOi+xHNI4WBaTb1+YtIVp1hbpl6NR6fQnjkMawkYBgSdQMZuZHqTOLBI+a5w06wLsqK2tY70uwF568kXbHrvNvFF0G6PPGyGe8fHzoV2yRklbXKY/HAY6E0gcjJpacjDWwChvSr2am7Al+vIJ+ushfh8IvB8bhnIwdXFpa9ZpI/PLc6xnTNKPgv7QbqV6F8ZzjkFRLYS2aIBSY6hvjqOM7YccdBi2i8HUY1+AGSlCTW7O2SBtfJ54OcInAoAWpSSU61Z5HoINp4Hk9LO2yTwDG3ApBta0AvgAXYZ6Aq8e+IQVxe/m3BvEdgM2zlgRjopVEvcRAGCt3ReKXac3J4kZe+kz55wNB9po4QXU77YBuEN7GWGsGGLT0Cbzg2QUGkNpZ6AsNkPMOAmEOcd4t0F7c19zB0JGyYtnEBzKxhXyz6IWxxYmnLYuC9740ET6N3LXxGyeeo4bKFUusZbKZ7y2AlAbzOBZ+zBO0s+scWxippnJadY62aQc4Em8ieovsN5lXArCgdaO7nnSSlOPAlgFAPMhMDN7j9hy1TLZcBJJXwMORltiPJ6fsP+PvfeMsTO98jvPrVs5J7ICQ1Ux5ya7ye5ms5NaPZIgaaRJ0u7ODrCYNbCA14AXBvzFhuebsR7b8M4nz1cb8Jf1YDzBI409ktptJXZgN3MsFisHVrFyznd/v+eyhIZ2PV61ZtSS+t5isarufd8nnOek9zn/55xlwEoNZNHeha6uIjsyRg8bq4yir2dn0A7lgMpIoIM+I+9ULHDgYZF94LX59cSva/BUMYDLWvy+euSghv3qUvzI+Vn8Viq8FeOfNnOIpQL+zfBMqK5eBtQ+hb+7NL0EOLsUO80zD88Mm/jX8/j602sA4vFpN5HvIkBr7IbHyOIAGb++C6i3PH7j9a/HcwD9SuCrWXh0GtC6zzKNjLMRoHcWes+sYM9X+smouIJPvztaS6kihk3bypJYJbMY89iiubkpEjOVAuAHf5DlGQqQ+xLPzTOA156gIxcAum6Qza8cP6WS+HINz4iN6NIqMjbOYIOeoKO0n631LdGIDisGZLeC/K9hD+bMckgm1QoAV7sbOKyEr76JrRhZn+SgAPzL2m5DBx4PeDbbiofw561rN1Np5i//ylcoz/si2dAbAOuT7XK2n+cvZB2QYXMJzzSsCRoYPcgYAH/NwxNm4m1inlXIpODHTXyaBX3fuWkew9ZidyPPBQBwEVHoz4ET+GKabN7rZDjM4nMVA7LMwi9V6KN6Di+USSfWYHpmngxvPE81qEeI4aNvi9NzGXqG5/MpngXN7NYAYLOqGryE/hx9T+PvTJAleHWFJFfQIFe5EQMzD8jmRjnlFTJZn3qZsrovJ19ogefwx9ByGZ3Q3rI3dmkPYBSBc+vYmqEh+OXJJvvdmhYOEaJqaqqxwbux37vEj5ChF6Dv9BPkdnad5zB0C9f4wm3hYEMJcoEVr8iQDT/D2mxz6Io9BQ7etLTXk3xI3A9+l4/vgMoWZ3Oc7wNzUUkSqZZibBA0hY9X8Q9mKas6PoZ+mqQPaNncWEK50iKy721xSIcDNa9XxW/9Tn3s38vz8CZllPvF1YDzAfjY1ipuBMP59LnCg28Z6LVNpr/Jx7T7GFzGMvEj/MAc8y+vIKtxVVEaQ20jIDP8qvExdMwMh+jxHbbYy5EeZcxrV1MpGfKgBSBwXEiwWoAOR5AnDjI2NWXJ6IgkI8MsH/1RxWCCzOnjWsSt2N0G/gJAYQb+tZz48iKHWR7jKwAuX1yhmCqmK4dMvfU2ZaQ/3KAkekn8b/9rNZmey9KaPLhP9b7vfQigvS8qa/DNatBj8g82/g6J3voe9ZKI4Wx87Te/HpcuXWQMDBCAJf+hp0tZK/iPcdE973z8138TOPfxmyzcWaBAgQI/CwoYzLQ8z7//v/99Cmi++StvxoXz1HVGM5hpbmfT+z9SFtYA89//P/5+Csb8LMZW6KNAgb+eAj+N2frrWy58WqBAgQIFCvysKPBJAef+/87PwIOgDIMyfv9NBVoEjv3xH/9xAs6ZEcFMVWZvs7+dvv5bY3Q8O9d9HBCW9+7cb187LwNKvj46R9/76N8713r/zrX/X5/vXPfX/dwZh9c4j4/bzl/Xx0c/+1kD5z7a93+Ljh+9pvD7J0cBs5OYyUQ+3AHG/iSjkZfdoL906VLKOLdz0OcnaaNw7U9PAZ/rBC65hgaKP/pSBg0im0nz3/27f5f0uuvkuv3e7/0eJRq+/qMDWh+9r/B7gQIfpcCO3dJeaT/lqx397nuCyNUjgs4FzlkiXZC4mW1/93d/N5U837neQ3Zm9f8/f/+fsXk6G6+TCezSqZOUeCSIBkBnlWC7h1/b2wAXAGgvAaxA/TSGg/0lOJIjaC2oLUNgB0QRODY2btkALyIoAvyJDU7tO7vTBMzdBLVU3DbBGdJVselLNGCbnVCALkVsSLsjmoBUvJUhwJVh53ub9C45Nn9tIm2XaqcJTCBcBBgAlfF+MTvZRQCiLKmWI6jkpRnACFmuyVAyT2CQrgUxPgAZHq4EWLjKRv0agDeCIERquAf/ioBFlpPnOa4RcWXWf/b2GQdzgBY2nEBkjoHSRzlKmuVI/yWAIMfueI5rBDWVEOjNENCkJi1dMxnowqD5m/kaBSTwmykl4FkEyI0Nf0njK2O/bhZDu5yl/vT36JRt49S+YBH7YHA0y2eA1iRfwizSfwY6OOZt3lynn2020KEQ33TJBnURpYwypitjHLktbmSfKUfgMMd65AAjGeyDYJws5bNU2ok50qd95QiSZQhMpXtcO2lXxFzYYXezvIigdOILtwYIbEFp+MO5SUd+ugCQEVLzzVgJDGxZxs3L4bES6JAlKx0RXdaba6CD88sQhEk0pPdteHuV7GsbgMU8dFxC6aAsc5XnBfFtAzBbI8ixRdSnlACP2ZBkUzf5BZ958j4Hvwv6hMCwWwnBO/Q0g8owRg/qLtO5pcEM6pVBWKdjgGkLXtzg3k3GnpO34I8y150g6BRBK4P1ayUcTC8TgLHMKfrpuH7tw+i500OQrp7yP78SZ7vOE9xtSH0bsCLclkBjlciOc1hn7Os5AInwZCXZocrINsi0eEFDBrIKXdf5KiZYYnYLppSynmwAZlgzEEVgagukgvCJEto31lbidQCCnMU6/LhKdox0YBv+sh/XQn708xRgIYgrTUsJMBq4XSkmKLZJ8BIgqRCSDPuT6/Q3SMawm9duxKNb96Oz7UC8efHzcarjGYI4NQSTCdrGPGMsj/oMWQUckXoJYbXM45Z6gPkQHkqgpGLaNfC4ypgF1lhIN8saCm6Q9kbxtnjfz5TFIrIOmSkrZ8RMYIsyxnWC7IyzCBDKArb08xXoCfUYAfNFJnIE3Tz4Kh8qE2t8Pr1KuWDANBsALs2CVoIcLBAM/v673447lDfs3NMVv/7m1+LknmcSCOr7BJ77+/t/BIpL/EffHuQx07YZnj089IUvfCH5Yjt+wM7zm89Pvj4NPpq26qPAuefJJFHeSAnrpEyRI0pOlaFz5JEsgbFtbIlZHLdZmyw+1BafEaNkvbaRSQOISAO6GyXAAsKzgI62yGg6T8bJGUBz6whMETy6BW/PAwzpJivNrWvXAdHWxyWyYp47cgYgFRnFud0sOOZrKmXNCYcjK+g5pJL0DJgn5ARwRmWGg0boEVktx7g2APSsZldofzOqAEiUooNEB21jOzbpexMbtEUAGnhG5OCndTLXMHzKwlFWka9igqQ5AsI5ZFUwiPdl1M3qHTmVgQlmV4fl0K0GmNcBsC2TxWcF39KiqBl0wxa2YXpliZJxZKi7dpmxrsUXPvu5ePXEG1HNXLkZCkMnxl2Ofi5BRpIssR4hzYA3rKKnoGgqkVbMXCydq2zoE2wAmNkC3GI8ohg59l51uPReV4a1ba4LIIPstoknkDeAJDnKqmIssSV8KJujRzLaS/2UtOqAAJJt530MchFyuo0ORoMBzp0lI9100vEsBrcjU9im3v5Hcfm7lxPQ7fzZF+I33vh1sgCaCRR7ANhtHQBCCWtYWQTgGz7CAuRBafRTAp3LQB1kGYdTlybaii3oLW+WowcF7GzQhtyQ/BXuAfeT9IlzUr69ppTWswDEMGvJFjiPEj6DldCpaLHke2AjoI2ggG3aKWLdLRmsTVxemgBwSAIJ7Mo2tm8dsM4sYOlBQI/vv3sFoNJcPHPwNCUXPx976tqBEaq/9JlIsIEeL+Y+jv2n7zUGuMyYNwVNYceYId95W2hiFOdWtJX3wVSP8iMcCgGwO+gprc+WPMvQtC3brLsyWKkth0e5DF7GjmIHszyjZKCZ5Sa36c8yhptkFxKIJ48G9msV3ljCH1wFVLeZW2RMFljLAKBZjpsD9+MyWa7WCeS/QuavS5TH3UMWXiwTMgiwDRlxfSo44JDkCfrmAAWZKXQLu7CGvbWkcAXfggwci/Rc3SAxDPpCW59sgLaMuWwxl3XGI29vMYccwAEaYB6sFbZdkDr/J1ndRKazfsY8tMfeRW/w6A5hbBOuoe1N5iRQTSCIgNsM5T1L9P8Adneb7fbGD2KgnyyGZF371Zd+MzobusiMCn3wHdbQ+foNlosvQpdts67aM3JDMxLmTkadcjIIIRFprMq987ccqPohi89WBHiIIbBeAgnQUwBbBNTr6zsnAbyl8oK2Wz2K3ywv0C1zhw7MQeDcNrxU5KKTLCXWraKkLUI+uXAOWo3js62hG4qL8e8BoK2gCx5TVvSHl9+NkYFRMjMdiq9hD7sA+hQzP7RBLG1jt+H/KoCq8iOjT/y5wfvyZjU6RBliRHwJP0SPoGm3sMekT8FXA4zFPOQtAVtY83TwJMf48Qg5jAAYBzrJo5Cdz5FD/jdjFilhGTtJWZBDr1EeXX/1wKYyynvFjGkTus3DtwscIt1mXBmIqR++vDUdgxPoGLJEjs2Mx8E2yshf+FocbzyDWitGDlfwndaTbFQrh9IXH0yeWBVwC78LstXfqZAX6U+bIAspo0v4GOoKaaOt22Tt9YHUbywgzwp864sxcLpjTKwV8/e1RT/b+KZFrG8J3zougi69QgWsHeICfoWjkVUklOxpi2TlmoMFyHalLlPXAZIdme5P5S6v3rgRR6iY9flLX4lTrZQABkS1iv+zzDddkzeTAyFm7OJ39YN2zfnJYyXoP/UR5E19uVoJYKxu4jPZyvmlVWbOiqrPRrSQ5Ag2Q9aRMRrIcihFHzSHQjWDXjH3C+TdtA/WO8PcN0m24zME7+R5tGwTkDreJaDAFWz+JrKU41lBqzNB+fQfXvkemUZvRlfrgfgf3/yf4jClu5OsSzPGgqTTL3pcSdNuw29LgDXV/eXYVSC46Bifg6SuvGf+c/Qc+kZdX8b4fF7ZZt1049Zhxk39SOYlX5vN2fmVMq9yeC6DfGziwDgGfeoE+mHMGw5GeWT9NrH36hu52+fVJQ7UDKxTQQXOK+Pactra4HDMOADMdz/8YfQ+HABgtTu+8pnfwic9ix/CASfGu8LhJv0GDyNwLIjx+uTHuJj7Bs9vzkP7nk08rLSwhowj2UR4yAzpZcneYwW5Z5tG5dI1fBrbEAin7Vxjvj5ne+hGOfVfluzSJeov7MN2KaDzjM+L6lFXDh7E18KTwefmPe5bRZ4W0D1z2AefnzMAEh3u49kBMm3+IK6Z5Q8baHnyZ48+D3ifQ3VQCLXGmOAF5EzdmoW26kRpuYEu2eT5YFs/P/En8kTGWAFdjiuL7teuCXrb4BnQTH7eCgNpHgAeew1/u/bJ/wDSbYZyrior1fYLXPZ5lwtYew8/rPK86iGbUu5Vkep3zs5R9W4GOgEmS3sKzGuDPq+8u5KAc3OA8r/0ubL40lcrqWyIbuK+TZ5/PZiQxcctpy8FSblR93OKkD+tksmex3olzxnKNLLBGGQjtwxKYCztG03xpuof3kqqQR3Be7xQr+gXfBmuU884T5MMuV+pXBS7D8NvsHi6VhpukM0RnHHSTSwRz/mMi1sFB05OA75fhAfgc32xFa4dGKKy0R8/Ivt6lrLEbfG//M8VcfwE/AJ/L8xmYmScA1gcnlyHn7U5yoXPB9//7lvxAYewzpw8Eb/1m7/JPvaLzIeBIJ++NrF/ziPZMsbNv4/9KgDnPjbpCjcWKPDJUkBlYXYIsw+8fOnlBIwze8vOJoyBbTe9/+RP/iROHD8RX/21r6ZMFJ/sqAu9FyggBX4as1WgYIECBQoUKPDzQYGfd+Dc3xaVzED2R3/0R/EHf/AH8eKLLybghhkSCq+/PQp8ksC5v71ZFVr+m6CAwVQ3MHz5DPBxXgZLDMYa7Cq8fn4p4LPd+++/nzLX+LvAZbPTmXmz8CpQ4L9HAQPYgjR2wG+CNZV9wZrKfgIp0YhgOcG4/+bf/Bs2QrPx1a9+Nb72ta+lLCIpsMouqz9v37kd//z3/zmbkpn4CuW8L71wniwiVG8ggLHhZj+7lVW0Xcr1glnyz3/s2LI5TmgobSKnrG6e1Ed3uWXuBiexOnZe0Wn04Ya5AXTLLrkRzT43ATD0HJu/afeXAEEKiBppEaVAX25QJzAMQJ4UjLI9bhFcwMUEC3iDtjNkFqEeEfexSc8p+HXHyaXuObPfn/SpwB0hDBsE9g06Gcwrpn03iuktH1Sh2WzaqAX0wFc+QOVmMtmAuc6AXbrYqRN32CBwbODGaEiG3eYc5ZQMgLnfy82Mn4AA/QomdL7E2BIozJ3+XDGb4WS6KCJYXETQ1PlYCja9/J2gBRu8tMOuNdlH3EjPUMZpy91rxiawSOIJ8ssRlHBX2bcMOG1x8n+VIEPaaOYqrUEiGddbEtYhJ7obsCCwwF2MlaCNgDr+zDEfQYa+LziLJnkRiBWVxMusJ347Zsdl1Ibe8216v427582aG3wzAGN5VAMjhriyzomr1ghgLcFPBkFLuKaMe7PcYDeOg4gi9zgA55wGkYCCAuRc2CzAADOrpHGkYA505TL5S8BfAjXQjnyyaYCLYLdh1RRYZuwGmtO90k8+YjxAMPnJ234xCGdmgIQfvMn6MgfnJLEz8P88GWvu9T2KocnHZKojENBYTAYHsj/09RHg6jGCG+co//vqC69GR/ORBMQxqCpNGSVtG/TgDxhri/mmACNjSDAIJpPWitiBAeR15rzFG6XwE+EWJkrgh/Fk4GmDuIS2+E6Swm/QLkVOHLD0ky6CcJghkyhmrtsCCFnrIgJ1hsAQToCq8ghzh2G87QnlMe+P34vhEbIbEXCrJmPFKkGc271XY4AAXma1KC6REeLV82/E3gYyqwGaNUS3xjUGrMsAtBQbRIbeYKQI4EE8+E4YjYE9P1PkDdCKT5UmhtcFecjPhla4jZf8x33waJ5PWRn0hPIsOMA5SbVi5sA0WRvGz7zWyILk8hUjZwLqkhzSoPRM2WQIlN8ffRADC4NkWKTSTRlZCsnmMDY8FDcAJW0h4+efwT5f/BJAgQ6Cw2Xx48A5R6ceNePv5cuX4ybZE3aT0c6y2Gb39DO/famHd14fPTS0894v20/n/VHg3MUXL5IBZBe0z8808bdsqMy7UPCeDCFQzoigmQ0NBrvGGXSzwdsMUWrlXj3lGlvO98FYdzwc7ZGFyb5Rn/Tn2MQgZbFuxSxZiV449WK8dv716GzpSFm7iuhHIIlgCsVPYIPBfJlOPlPyCWHyP7qZvrQl6oMcIJ8tB8unxehMouYMHK1CQJJLeKl74N10HdlPUuhZEJGBc2RBcPIassvFjt8ArSAYpBveygf1pYHZitYZi+VQ59bGYmjiQfQPDJAJhQpAZFxaIYI88mQiHvV0Ayh7El2Uvf7ca29Q9vQ5RlzLWPEDkGFlSJ2qQlWvqTcTFIU5yv+OOQEDhSZwT17PMgeuV/+qqGzDCy0haeYTQen5N7hOgAXfgm4w00ipuh0wk3OlvRTgda2VOQQzw+dZ6JzF5uW8Ab2svV4GXNIz8pB59gFkWYqSGirn0NcscZKebsoa3n9E2em98fpLn43PnPtM1GYpHUlrrCB9EPhlckLbWCnFnzkKVoCFGLfdCxRyefP/+ZNvWY0fXrgFve1PT6MI45dDb/uh6+PnBtoFpZthVEJ6O1clfbKjbySUX5s8y8k/+gQ2ou0V6NVL2cyefuZHkLyikbJ75RsxRpnobtZwZHCMbDAt8caFV+OV0y+krF1rjFlqppmh38x0ZIvFrKfgFCpWwzX5NSR0nQA9aT5co4UoQhiSHwGfA9tDrABdc50A4vSMKZAAORPQrG+kbSwTzJhKwOV9gXSwQKUpa9g48qLJdz5m0NPf2wTQNQ8Ia/hJfwyOPQRMMEcWMf3FbMoeeOU+Zcwp87uPzIZffuXXKbF+itKOZCtiLBuuv+An+jdjXrFAC1gvB5hEEIpytQVvbYB4yCIXfp78CmZkBkjtQxL6NKidMbo+edqzEDTG+NUVAsyRSC2kwJI8/6QpJbUjREZApcCoEmU6+TjKQYKWxBJjsMTpw6FHAHcAhsOjtZXMnQyZt/vuxJ2Bu1FaUR5vPP+FePPsFylD3oZPwwjhpU3WTN8Xo8dY5A91iF4Tlly75Jr4lrRlrMqq89/Wv1M3bTIebCW3QHvWgM/lt+TPsgbyiPPxJQ/qOwqaYmlTm/pRm3QANIO/9X65RxSKuioJOJ/TZh8ZU++N5TPxVVdZln2FzHZT0TsyGA/ud6Nb6+LihZfis5RW3U3p2GIOYGxw/zpjwuNC7hkHJFOWhOemAePkFNNVsXpDPcEw8761ICNWYws9SiZBB6rPoUqV5bL6J4xYnS7w0AMDgsdoOjUr9XzI2Mqyqk/BaWbsczpKfw69kkBn/CUwlASQ0Ut51vs9D1JW31oyX5VVbcUcJYYfPLoVPUN9UVJfES8/+1q8ceKL0V7WhXwADNQe2RX9cTaCMSs/unb6Y87VZwp1DQAa5q8ucmybrMEaa2d5Te2HWY3VUBJI/a72ylAGngaYev591ynnOtG2+qOIbKdJ4PhcugmUkxulSxF8WuyzEW3rXmzij5q9cWJxIh4OPsAfmU1ZBC1FK6i1G/68f+9uyjb48quvxGfPfz72VR9mzOXMRQjnKuTMYvVYBO0U80k2hDn6HJYfo7TlfftkPOpZXwwLH4v3Ib7lvn2WlK8ST3IJKwA9BFzn/boS1qYYvz754Np9UWjMQYDktocemK/zst0sIKD0gmYbHJwZnB+IgbF+Mlc9SfJkJscVANaDlGe+8sE7PAPn4uXnX44vvfBlSlCbXZ2xwPH2JwDXv+SRZBNYCw+rJFAZ8y4XNMhzRZoW48mxcIL4XUNGBn2YEQy5BShKPxklx3MZNpxflQOBcALn9GHz/ibrm4QV8kk09Ivrr34SfMZQ+Mf7snpy5skUSQa471MeeJ5skbUc/qrhuoXlJ2RApoTyvVvcUBLPnHs+PkvGwI6GgwDIzACIjlTmtPPOlb+VcZpXEhLfCBLWTxYZ5TV2CSema9T9fMK1NGUbrKG2Q3n14JMvbX+OsQOXgleU96R9mQ58KGhOECiNbJcLNwScyryzgBEF2BVBv23A0On5hrEtkkW27/FQ3KPkLgtDhQxKlnJX39DduMezxczEZJw/cT4+d+GLcWTPcUrXW0EDnnS00FG9ielILkpixvRM6J6IM5CuzB0eTMrCiSIr6W9ood1ID14ygQPGfiWb+HSesiPCDuhNID1ygSzKjxxn4jbsj+vEywN4RTCpPn2SP5SBYPP7D+YofToCeG41mne3Up2ujJKtU/HuO8NkxaulVHhX/MavURb2RbPUJQlJ/CgQuwi7mtYImqYxJQeGDO7QM8e6B5klc+iRpyvCCrjCvNQPzHOb/h2rYDp+cK2Sp65gvfhOQFzo9qPPk03IgxyFHed5kLnDw7SQxsDt/kq7/IS84MBjdHQh3nnnHnMka3Bte9SQIfzx4+m4fW8E3MpwNDYcil//4vH40ufJtNiqDUNm8C3EsK9hd7SzCTSL7puZWYq//Oafx3e+9Y04fOgApVp/PV4iJpXlcIE+v3PdhuZyqkuaeJT3Pu6rAJz7uJQr3FegwCdMATPK/et//Ycphf0BFKmbLAcOHEib31s4s5OTU2kD5ganA/7u//5308lFy7EVXgUKfPIU0IIWXgUKFChQoMAvNgU+rcA5A+9Xr16Nt956K7q6uuKLX/xiKnv6i72aP9+jLwDnfr7XpzC6AgV+VhQQJGkZIQPLZqTZyUrzs+q/0M8vJgXSRig8s5O1yNKA7hGMjo6msr8CNizJbbY5sx+9++67yc6fPXs2lXC2dOAOQDMPstumrOCd+BcA54rZwP5VwHUXX7lESRcyugDCc5PSjUuDcUYxDGYYYCe2xy8ErAEImDkmlwItbG7ywRqbvY7TADlHpGmDIAnZELiYzA0GIQgiublrw2zCcjE7uf7ChioZBgwkFAHUM/BlwDplIyCA5Zape9tuCGeNnpktDdDg1vho5ChLmqFMZFHL/tiizJGBG5rjWjbZ+R1YcvpKG7HsAGcJFhlAdTfYfWGnR3f8JLMVWSmM8VmSxCBFBoBEylLlhY45/SBoKerJPXSv5T8zJhFzMC6RYmHOyJfzcfQZJ+6bgH0MLGySBaKYjfDYAjALDQwSOwauyr/coaYk1/bkEGSEjpT6yTbuSrRxICng6aZ+CjDm57lN58ZfhQAIonO8BkLSKCBchgBVqTRJwTpBEHZFgFYAE0FHN8cNBkK6tJlfAsElpfGIFLCTds6WN2zV4CBsw990xL6VIDk3yc3sYsksu/Y6gV35zCmsr2BKxrLJ7v0K9E3BXKbKtr3xCoLEtMVNOaKtZjgSkWFwJu3lE3g2w1TKGsh7WTbU85lU8tTOpMgN62B8ijEZIzHesAlA0CxJaayuhkzkvPyCR2WsIrMeJLoxNmVMoAFrkrKBpFUxmME90tHgJxkDpwhSvnvtSlwly8UEJZ1WCeBs0M8aB1Oq0OtHDxyOl559IY7uO0oQzJJ37OEhL6m5p7yEWEghB8zPp0E1+IlejLGljyRvCt8DFhM056dpWXk3IyITXt1kooRdEksmCJ2ABEFrgM3sMJdACvYjzIJAhsLkuhDUKoYniowYuTQpgIcsc1v/wkC8//D9tBc5PzUH3Q00mtFkmnJb1XH84Kl4iSxCx/adjDrKBQo2SIA2M07QXB7A4XwBtMAPZkkw407qz1nIh/Sr3ki4AP7MsmjmusgDNB27/EfwD5m3TQEhBngM0prbSz6TjemZuTgvmqQjwVGusAwsaNfAke/4XtKjMNTwxHC8330lrvR8kErIEukGp8r6La9ECaWdjp44Gi+cvRQn2k6nMnhZ5icYeXh4+EelWrXhPk9Ziv1P//RP0wEIwfDPPvssAUEDfiwD1ySdyDg+7sGI1NAv2H//b+DciwDndif9KGu7Hq4TiTYQUv5juQQPbCNDrCxri0wmsAXgDIHa8F8RerQY5rSUk/pqlnJrVx5cict33otRSj9uPs1yuUlGphz6o729JX7l4hfiTNczlPuj7KiMpo6B51hO1gO+SbKCsoBX0qiQJXWoGe8ck+OT91LJLO1eugfeVFaYRQquk5nFu1EZtIv+gN/EX3Fx0pkJNCG4WgWNQAuf8XZBZ0KbBB7ApUzRQeXBKGuMa3blcTzovR7vfvBejFMidB1eWoNWq2S2swRlV3t7PE+W2NOHTlHq8wDTIiMd7aRmGEvCoUO3vJ4UwKCOoV/69/8UVIWQSRYZkBkp/dyXV2xjM7xmB6zNr7zyLeS43llrd6Sls4Kq6HFBH7yPvG8i3/oW0k9/wM9TRiHWT50mOZYBbty8dy2u3vkghsYHCayTmQ29toatsAJPXXVDPH/2Ylw4+XwcaCSTF5pUO+Q62Gf+xQB8V6bwRX+OVdCHsucffpZ0qhPzM77US0+XWe3P+vChBpAxY2L4nD/VlfIea8UVfDGH9M21gkGYY5H23zl5PfR3LQXMaIa3yBx28+G78e7V96IfkNwytsWShktkT9pkLVsoU/jsyQvx4onnKOnYmsoGb6VMfPIDvpQy4hrQnrbKOZsZdYPJOBVtnWCJNGd+17dxlP69je60N42hwA/bSnwueJpfE2yTuUlPwR6YA+jFPJ/ONaXpob+sB7LU44k8gBTIvqi+zZBJawng3F3AY+9c/34MjDxCH3KgArqYjWoRf6qB0uwXTl2KV09/NnZRlrQCflFOCJ9jt9SNYmCE0DNqaf9UFzCoBI5J2oC1c4zaX3nYV5qly6/tUp65Pj9veM82+Jf4gVRaW9jrNXRICf5cyTZl9fTH+OdAlBUz/sikyZ4kXkFCeE997TzXyLLbM9Qb79x9J7rH7pN5jmzLpONZ49CGgKXypoo4cepMXDz+ahxrOpPKkOf1Gu0wPNky9cMEs4BvzZJnmfI8wPHpHNQ/COyWOhCay7u6BVkQZUUi23zxt3MFC8L6r6MrWRvaEfAnVTagufKVMrb6hi9ZA72h5DMdpsk9yLVk9G/X26yvtwfJvHbnevQAvFpenWSNoRllrdcAMlZV13AI4Jm4cOb5OLLrUCrBmgX0loM2m9IeYpoVUYCQX0n/0HWG8eXwZaWrfuGGjpyypHy4boBucqxFykxMOdxNgEhFrENWueL+BKpXB6G3zdqVt+t8IFGhgdnmpL9ADLPdpczPfIwm40MHwNrjO6wiPPce98c7H74XvX0PWbcFPifrJiUs18gaXFNfG0fPnIwXnvlMHGo+RZa8BujF3GgDUUg2oAjdkLI9yy/MQdlShhynzzMpk5/0hKj6xro3LoE62FKX6ZnItXNRvRciCQo3mymSkHRsoqS0Yi5mjxZ8LF87D7Q3v6jdXVK0bHr2skP64blilaxjI3P4NLevxN3ubh4fyJe3QWZCwMgCxCoBdh49fDQunscnbTsR1dtNZN2syM8jte0aSmM7pFna3XklHckY0to+fTOBS72Gb2ngS52PeD99Iz9meUEdLHBHeUoHHaCJOk3/U/Cz62/eSwFl+nY8idGsgEn8fJSqBw3IMxkPHt+Nq/euAwTsTiW9fUZcx99bI4OZHuHp00fi1QsvU0r4dNQUcxCC+aRnVkGXUg56psMhLgx9b5D5U/4w23AxgG4Bqs7Fb9iZz/Lr6HOUtkO9nvSN7Ty1Dzs8wILxqRAfvxJZuJ/faCMPztfBBowJENbSpflnPonnPJWIiHHKc//H978Z3YPdsUrZcCbGM6lyOB8lZcWAi+DR59+IQ3tORV0xJZyhmTSVxupJ5dCeZTGGl9eZ+p+MgQuT2HhdAiLRYxa65nmXj72Ty7woHZ6hTcmkh5LRL2ec5AhN7/nsZUa5dHAgyT2/2gCHTVx1hSaHDUvTp/8Mdjz/DLIJoHo2bvffjf/64fdianY20XUTf0bQdaZsI9r3tMels6/Fmc6LgMgpAcyzmc/gyQ/DN8z70HkepUf60+/QECg9/mDgdJxo4eIwA9+Sd0mXnuZXxEE735dwAmzzK8afNCOvFym0zHcTW51A3GS522afQxlXTaeslciMPoGUVlbNxPbh1cn49ls3AM/1Q0uyifPxIs+DG+tZysGeis9/9kS88mpV7GoTzOn60BftFaHzM2RTT4cWsFHuO6SXBpFx8F9+YK5Gfil5Ly1W+mnGYH1H38myn7L1dJ5pHyDNzvliv23q6fqa4W6bbISbyc6ZnZxnQxkn6YLUUuIXn+vzdtCMyBE9PbMkdfoBIMFpeJNS8IAa5yjXvUqGvrr6pnjhxWfj86/vi2OHSijrK9VdA9pgvik7PGslsHIb/TU/uxx/8Rd/Gn/5jT+Jzo59YGF+PS5dvEQCe/WAOsBZ8nvi5LRiqTXe/livAnDuY5GtcFOBAp88BSynYiD1G9/4RjzsxokjiCJyubm5mZSXK2nz++DBg2nzxZ/llEvJG4tPfuyFEXzaKaARLLwKFChQoECBX2wKfFqBc25imKnGzHNmqTLA82nIgPBJcmsBOPdJUr/Qd4ECBQoUKPCLTwHBCL7cDxgaGkqH7wTIac89XGfmOX+fnp4OD+hp29988/DMNtcAAEAASURBVM303dHRkey8tj5/UnobgN2d+JeUarW9L5LZ/sKrr0ZT4+4o9xq6cuPSEpg5AtlmOchYMoprPZVswDZjKgfTRBAlItYTGxWcHWeTPW2Xcr9B3CIC/G72bhkoY9uznMtT2+5Ns1G7xR8peELpEzM3kNqF/WxAMPxqNg4BK3Sa4qnGb4sAFOQozbc98yRmPrgaReOTUb6H7AmnXopMSxP95Z9RbdtMaW73CzAyzJ+ACSkrjxvT+UACXfE517Ipv0H5MEEMBsiYdAqk8AfX+qJd3nMv3kxmDCm/Cc37lmndCeBuci8H8N2WJ7wlcI7PmKtBTsv+rHlq32AJGQ6KNvnmYzfrU3tPu8msMdbRoVi7/l5MTE1H44nTUX3keBRV19KwA+ZCaGPASWBQjqDjmu8R4CmG2CnITL/0yFjZpKYDAz+EKdmH50JRYL7clOdzs2sYfDE0x2Wp+TLW0QA3Ib30vnM3eCE1XEqzhGUZPGFlmoFH3PAnlCOLpjnZCvPaZn3NjmcwlPQhqfUc2XtWKPu75drSlvkEUk4BLjFgvAGwcA2wV9aAKQEas32YpWmz2MANga7EE26uuwlvkNWMH4AcDSaCWIEMDJJ5wJPb5U8hGvItA0pUMWBJS/rCBgAFjBiMNHxlvhQDJm70J9Ak7xm2z5FaQtkR0LfBGi5uzEb38IO403M3Bh6PxSJRBXmvgqDM3l2748SBI3Fk78ForGhkbGRJovxqTmRFEgbGx6+ZlNVI+AgveZ+epJBZEh2RayaBUsCBP/LBSgPUXEuQSrBnBlpuATISOOe1FlAza1vKjmBwizbNjmjGuQRUzEMU0vwNR+bLZwnkTFSkc0B4BJpHN8bjLtmuunvuxySZaFaXFhjbSpRXZaKtbW8c7zobh1tORHNZSyo3l8AIKbolKBy+kE+hbQ69kQ8pwjsM28C942c4ThQeQabgE4bJnKEFHxjwJOUTb/KDX82owVWAHqRBvj0z2Bm4KrY9RrZFO6v8LmuXkcXGYLVRKbOCpDLF9sn9Ao9zlAqaWZohw879uD54jXJ0Y7FBGSXwIATqiqP1QFs6sNzVcjSaincTSM+XvxI4NzAwkIBz6lNfswT/uglUf/Ob34QubalM6969e9Oz1c6+beKzpwL+0d9TA7+k//04cM7s5o27m5Iuka0N3yqPFilMbMPCbaMfLc1rANvMZAaat/lwhaCiJSbNPCRsqgR+l7dWyF5yd+xBXBu8gQwOxsLiAryzHRXYwYam2ujq6IxnO18gQ1I7GR8pLaruU3m5FspfCiib20ugqfKDtha0gL7UtHlZ0nnc4su20zLyn3ou2QL02waZNGVhbcfTnDNwJDf7HnxncFrMSLITyH8CxnCfZan5ON2rzkyKi7bN9mq2o0XK0o087olb3Tej/8kIJU0XEyijCJ3f0tgcJ/ceitN7DkVrtTa72d6YBvc7GKaTpJ9+LTXpNyLDuOzfPhkX76VgPxNR96nvVNNputy/k0EIcWH86KH0AePmb22gWc/UV/abB+GpT1lX5iBAYRtdbJA9NSg4iUma6VRQY8omiN61eGj/aC8ZAu9E/+NeyidTspUv7VpdXW107jsYJ/cDDKzvjIZsLeAnQdOpU/7jF+eVADk2b7Y0BsfLdcpn42TG/OE9gjASA/J70ivoDJeJf/k1cozwR4YJq08E50ozG7NVbZ8rbNYU7YSNlcJTxYAhtrGhHhzYYu1SiUgII+Bwo2iRbF734m7vvXg0MBTT6J0V5rcNHepqGuJI+4lU6rqrfi+l95AGBpYDLJBAF05PstmvxHfhaDMB+5iQvONqJrgHn6VMSIzQ9XFSrrllsLV5Hhcwc13SSa4zFzgD+cH5a+Vs33K0mtfEH2RkFcxsmdqUJZBLPIZgGVFJR6FRAuC56CXD49W+D8n8+JBymDPwENnduKCuviYOdHXEsf3nAAUeozRk9VPrk18G8WvSV7sgr4IfALzIvBhY8onM6PSUfbjE1U5j9h5NZzpzwZhlMZfWayRPKnXHujjGHL7ERgbwkLYrVxnF62RpwnlhmfCbuEGZRfwTyWhHfyUhKOCjBCJSXvl8cmYqbo/djNtTN2J8ephSdmYDzkVVXVXs7doPkPx0dNYei+aiFuwUQB0Fw4E6KPvgp1DRLJNU7+WRmXzmy4kJ4hMAJW+4EFyV148ANiBCUWqLRhhoDqXkGjjOlKkPvvEOLXxOf9A+efkj8Q8fCqtIQHsokSAWCogczb1m3+xjTjcGbkfPcHdMzo/i9y8xbDJOA5LvaNsfpw6ciM7GjmgsaYhykfWb6mcPzOQ72pHJpCMkpnPyD79dFN7TJPurfCijmR04LSh/mnFuHT8WUkEdfSKoxb2S0XxtZSxyAh7bNvPJZyV0KeVgB8EHXFyEDAvi80+VtBpqjeH2LU0C2LmDDN6PqakxOpvHJmxEXXkZoJaOOHT4VOzfDY+WtMI3Qp0ZC/04L2mYvvOGKk1C8Ae/5L/SpJ2jF6oh1JWMSXnzLZ8P+JnGlJ8g4xYyzdjRJZbr9jkhAdf8TRtEy66LQOyUKYtr1ddmE/TAUvKPaDZPX+iAjXyy/iTuDN+P7r4eyiJOUbJ8HvKuRVV9aezDZztz8GR0kf2xoRifdFOfhoM7CElqBrqrCfiRhum4UeVJlwi8kt4ytdcIIEpZELlyZ+qJ123IBuQtATzOkIbyPqAzhKe5BjZPK5YOEgkY4qsU2ZTPJdsKOlOwPEWaydxYwbzhM57hhucG4i7Azgejj5BBS5gDmkTwKypKoq2lIU4ePRRH2g5Hc7aJ9tB4aR3h0yQQaYLwCANwLRiXmShdYA8OeUDnR/OVp7hcSU00cV7IX9KRaX74Kt7P277M1peeq1xnCaBYYeNWEvjHcrNmWYZ+KJy1zfIEyNS+0CD/1pklwE869FDK5YfvkdnyIQl0JmOT0tDq/Zpy5tfaHocOoGP2nOEgDjJIJz/ic5pK+iX9lKfITs1IVGXOKSk7+TI9BwidQzdir7xflk5r6HX+4rhYg5RJL/3JgSspAY/6HJJmaGZSSrSmuaoKzJbGeDJkbJZwPtdsk5FPUwwZkEnu4neLoi8AVn00RYbAh1dinGykG0voO8C1ZWTtbm6rw16whu3PRnN5By2W50uR0lNpYj8z3eNZ8YztM5JAywQuhT/lYp8D8h6NsqkF42caMH/y030M9U/+WY95+jbGJq8b0wW8AQ8q24nn9S18vrQd9z942//cg/AZkeePjL4o+nCZTIS9fatkXZuIW7dnyZiL3gHAXYwctOyuipMnW+O55+pj7z60WRmjhFS0mHjRbHMBz+tRZTjYRwpz+oBrEt8yL6ej3vAFHbwNpcLY9F31o72eReP3bfR2DlsnQNXDZALUwjLSPB/LC2xzpPv5iGvz4Gv3dorwRd3XEYAqkPcpdbjGdc5npAP/GI/H1+OH3x+NO3c4PDaNfXY8gLir67bi8LG6OHuhJY50VERDpZlypZef0zHjVePZZz6DXmXMTC0AnPvj+OY3/jQ69u+Lr37ltyjV+hrZlfVPuC59OS+fhtVN+S/e+FivAnDuY5GtcFOBAp88Bdw48ZTi2NhYSvG/pvHnS4CcmzoGsxsaGlLKf7MR7Gy+fPIjL4ygQIGnxrtAiAIFChQoUOAXmAKfVuDczpLph/gq+Bc7FPnb+1kAzv3t0bbQcoECBQoUKPBpocAO8MJyrJcvX44f/vCHKQuSQPidks8C5iy/buYjv/fv35+yze3Yen8KbLhDqdZ/mUq1FsWv/dpXyDj3SjTUNybAWsq+YqCE4EKOrB0ZAG1uobuZm8osGmEFrWXGCHdPM6VsjFL+KEdgULCWATyBdmkD20Co7gbBh2wCt7jbmt8AN4DNp+nbQGG6LH80Oh+MIIuQH+cAQxQZwd+m1Cnla7ZGh2P4m/85yoceR83RZ6P05S9Gdt9+Tq2zuWok1igsm8ApWMnufYbT1e7k57aq+Waj2UaZn9nQBKNZmsgAkVmx3OQ12EWsgM1mO3cj3Q1cfnDyn6gDdCCoR3DWQFARAeaUvYzPbUfghaf202avO8duSPvNBrOZPNxAzmxWEggECEfAJm2M242zp+0iMkVsPLgVc9/+yxgYHI69lz4Tzc9fjKKmJqI0XA+4hwbTlvk2QYoNMrS5i2T4tIQ5GGwxhGAowXcNnAhWcjVS/Maj9UzH4P9OwEVqGXTwA2bHpn4+oGDQy/cZHsGddJvLSLDE/nc26vNBrmJKB3nY3fka2RYwZ5YRw5ds4SfgE40REIRlOOHuhrzB2fzWOH8CQDFrm8AtQzFmLypj1CWsm2UU1ykDCnfxiWPicsfPO2z7k8mwnCVnhiBeEq+lAdNXMXkSAH8KRXNFpEyeco7T0EU+ACRgT2pxR2pZSqRyUrzLbxDHPgGpMTJBPAY8F9ZnYhpenF0juwdTNtRdDhiktrScwEEt5bIqopzgpAEgSzWaSUC+SAEM+Qy+NJijK55or1zyhyPJjzU/pvx6SFbGqsCk61xxvgl0bTAHoaGuKAXU+DZwBlW43uDSGuuwwvtSrkToETTPhyCgBYEMISLeY3AlA2DJgO0cpU4nN6djngwRK5Sn29ikBBOtyP5V5TVRW9EatQlUVp2AcykTIRFRgapOCDZ2UvQq8NIZGChyPgJk4SMDPSlobyYaZguJS6BFkagJovE5wQAspIFxA26uWQVz8T6DOVvws+DYjELKFYJEvCrLfWXQ2pgofyaASoZMhcqwJN8w0oMMmTVman0qRleHY45gngG+MuhVxPqVVlVEtetXXAu8lS/bBDwicK63tzfM3tnV1UUHkQ5BW8K1p6cnzpw5E6+//no0Njb+6LlqR1+ni/nv0/Lc9ePAuRcuvhBVu2qUHKgg98kPZp8CGAt/mH3HEpDKOYX/iKnD6+jXHLp8HcDsBp/ltRO8wz1C7gR2zWzMxPjqBFkDZ2NVHhXoQ1C4rKw86ivqo6WsjbBiJbqLNtXhKjx4RVC1+iPDT3lS/jXjokE9ddNaCkQKLsvrPflPRI+yY1BywzKMtoOdERxgqC+vP7zSvI/5OVqWGMuVD1CbCct2kFkzFqlzBUh5n3xs4NdsQ8qtRmGNbEHLmzNkaZmOaTJ5LZghhPu1U5XomNby+mgtq41q5le0SYZD5AIMF6BkqazGk9bq37xOsXf1584KqPny16gPDJwqI3wjC5Aw6WmGl+aWsniyRuo/10hdaH45x6mucv7qxERT3nENU0Za5NAZIlZ0huYEELGBzimCxlkDvrS/CCBwcnkiptcmY4U5Cy53DatKq9AzDejTOkq01iL/AJ+Yo1mpnIXQDEsbasccg7wkQJkmGSNjTeNk7k91u4C2lHUNAqA+UrYsbZn6VOsgqFflrK1WK+Z51fn7rY6SY9Eh8KIQ+KST0MfFW8RuuG+FcoKkfKEXbBbvW4J+MxYpQTsXM6szzHEuZWHTguhSlJdUAILYBfi4FYBuZVTCm2bZ3eDeTXwSwcfOxX6kr5mLkk7kPUQjfaZmzWcE5DfuN+i/kx3PVZJf5C+tuvOQE/Igau83U5fcaS9+FgDtnLtaGxA43+XSTp0LH0szrDCfEpjnaks9FiFXC2SbGV17EpOrT1KGJB0ntXEV9q8ePVpTugtAeTPXs360KbhSH4Akn2l8zk1QvyVcLd1OJ9g14N/Mf6cMojRwTDvW2bLr5TA0rMB1rAm3pQxe0pYv3zfzWMpqBRjNO0vknXVKtUJfkDWsv3yDnQR8k7Ik0s46zG9wv5RSpIIYE6HpdA2faXr7SQxvAdDFbmQFrbkmnOaoqqqFT5ujoWhXylZWLIA/iToT0bAxF+VO8I/Uld4Cj1JpPokK/zltC8qa1SkvT3o1+D/QtxhAER/TTl7fyHt5/lPO8muQ51A0CDynFArKklcF7mrXpN82/lWxRpB5K8l5v7CSdaUkJNkBJ1enKNE+mQ4FrFGYUV1Xnq0k81MTJXYboiZbhaaBu0UpCjRifdYYu75+KsMs5eGXpKPoIYGGfY/Bb6IANvlFfZpgadBUXzGVFBaoiuu2ytjlGz0Gx+dKyr3KbTljzALWMYtSAi3zqY3ly2s700TVpGe93/m6dqlUK8s4w6GYKYBJc9iKpbUp6E+SEvUodr0GPVpT2RRlgAKLoo57kQnmpdyo97IspivITWlc/MbLNdGmyM9KDtKmTeFade+Ob6k+xf3iVt5jmAJvNrhuET5znRy5GZflfYFzKXtU4mc55anc06KPKYLmtFOp1CE8lZpUBhCmbbLvLQO+meRQ0fTKfMoEuc7hCsv0MkX0TFU0V+1iDWtYwwrKfJKdm/628a8sR6oedOTy3E4GSx6U6MM10MeSwvn/mXKSrwRA0mYmmeVNbJrzN2O3z4HainUW2acJ4WN5einjtANCewvAUN6WkH0fXVKCzRfctQLQ1dLK8nYZh06801Esbzq/qXiMnlkiy1wCdjMqS0NXAp6rBeTZQGlP7aFz08bIPSpbl4gRcrX/O1fo69j0IRkfHgNzyvNTvuw492NPPKyiyKhP5G1Bq3mP1CKvTiToDf9Q31FacD1iwzPMeiyiR5R717ZcoiHLyXelR/WaIGT5qpKxlnOvMj22OoqdmGR+S8xPrz4X1ax1LXa+snI3h+NaoQo90p50lF6p5DvzcGrSSf3nffJu8muQHWeu2G7iw+RHnacHHIzMQCPX0Hlq97lOiJpaxudArBs8oSRKOz5EdyV+pl0PgfmMpz4utuwy9p7pgKtVF8GzDotvhVbe34Tmc1uzMbI8EgtrCwl4bd8lyHJFOSVMK7H3gFeriuoZu5l/uReeMqd2sb6+9l4+y8+QD+UNv7R2edlTapJed031abhKyqhwBGHKD+6BeI/j8TevycsyB5xYC+cqbZVChyDvpoNLvFOknHtwyTWF9zw0tr7KwZ0l/NG5rZicJtsqQDr9wyy4zfq6bDQ1FFPWFF9QlwmaKWNlKkZAhTlKTvtspZwlP4zZJt8qj+xmuM5L+jsH/RCXyqyuK4wMPZueMQWI4l/hP6Wyr4xLcG9ecp/KAS1ktIFptvQLXV0cQbH6VNIg8Tg01nfJZ4T0mdcnN+SB8Wyu8tw0uRVj41uxMM+ouKmMZOollRkOq2SjoZHnQ4BvZejpJDepfcePLuI5XNlxbpa/nZmZj7/4sz+O//zNb0Tn/o74ja9+jYxzAufkK7W/0FX3LvK+KcP7qV4F4NxPRb7CzQUKFChQoECBAj85BXQhCq8CBQoUKFDgF5sCn3bg3C/26v1ijf7TBpwzaOYhEL/doDDDkd87wI2d1fM6Xyk7yc6bv4Q/85sRbNBBix+nwced7k6b/pS2f91r51qv+WWn9V9Hhx//TP6TNilA5SZY4VWgwM8xBeTVHf0hSM5T6ZZsnZ+fT5nmdvjZQ3hNgKzMYl9XV5cO4zmtHf2zow/u3b1PqdZ/wanubHz9y78aL7xwIaoBjXBUmj1mEVDo7yo2TCvZ6CQzwwblVwzOZ1cJRPBN5IEYKRvIjCVTwgZzOb+TJSS4NlPCCWc2m1NmDUF2XrvGdjzZyVK2LKNJJWxGU0LIa1N0h81W9oG5lo1kSiZSJ4p7aJNXpoYSW2ysB+CBHECCrcHBePIf/izKAM6Vn7gQxZe+AHDuQBTVsjFcjZ4tVbbdsDUwwvYxgUF2btlbZlPaDX5omYBz/JqpIERcXkXAgLGzKQ5UIzJmR1sC1LQCLVKUmgv5pKgOwFulm9QEdtn0TvEKU5JQ8ilf+pPLDEIYgZBeRK0ybCRLiowlzormoZmlZtmMXqJcqx9wgt5dazMQZdhZzwGS2LxzIxYoYzLaNxi7L74RDS++GkWUHiyqpX/XAlptEKTfJABH/jPIRskwggJuufsycGpGt6yZlgiUOQc/L2XD3mw/wBf4ZiOb4I7r7F1Zs7bRgq986MAZ53939gmUlyhquMdSVkK3lllasrkQgK/aqiG4R3CIoNEaIEHgTPRisCTfjvfbrqFVCMiWeHUKphso3mLTfz1muBbaGLAiXOMGv5vmQixcK7PGbAowSZkkCIUzdu+iAFsCVQiOyRFcTSABeHqLAPEatPE+CSwnOHJBZekqaGMgS1ql0dK3CRcIu/E5vUMrZUWOyEP/tLP4MtCRsAqZ6Wx7gaC6/bCAfEt/g0wGXg1FWEKM0BQZPgAIZQlBsW5bBJ3NGLNF2d4cczDQ47UGeYRm+CXt1gjqOJcKeKmK4JYZfuQXEyNtEmzZAEgoBVahkf0bLCN/DGvBHMi4UUw5tQwAlAXWggLBfC4Ygrkl+hpYUdzMUECGIGYp4KOY+2BCAuKUOIPWjmGdMmCWGN4ymO518Ek2U8v1BNIzfJMtxCC7gBfLEDpzc6YYOjXstEawx3Cd4ZcEkODaUsZspgbDo5tEl5x3JcACA3WuuUFGA3IL9LlIqTEpX2twlXu2GaPgUYO6Bo8EpxjistxXKTMs5d5iooQCYNacHwFay9llWWsz1mHwaS0TK9w7z9cqOkVeK2FevgxoKyMCHqvgkWr6LEeOBcj19fUl4FxnZ2cCKl+7do2sCX+R9O1rr70WJ06cSIegbWdHz37U5/I9Xzt6PP3xS/iftki79K1vfSuVo7/w0gWAv6Xwg2G4PBDCVU8cD78lkA+8ZkBQ0FWpcizoQGCw4EnWKg+EMmTK+ia9pg6STxf5nkcPyeVmsGGtAekI1KmSZ1hr1z6K8tleKHZKf8gf/SkByrL6yJ+C8rbRAJvwkaE7JTOvU4VLbPAuPEbfOTKtytMbpHURSuT/SCVt2FK+tTyESq5ULyB9CTiH/KPvN5EBe8bCMge1MeNGF5TRdsVWFXyOtAiqzVKqjU8Xmedy0pFKlX6AWcS2mV82zbFstQ5bVBGrpOec5h6vUWYMACezxjuWz1QDCtgRYAh1n+opxoGMqteqGG0FwJ8U5GZuJotRkp9aFNZhmdEY+OXKXB36gplpvtBnAh/9sm+sAXMmoootFAytDIIIh0rYb76QrChnnpZxNKPkAuu3uD3PvQaQ1QNAGZKe4TdkV8AvI+PdCviAK1JfAAlTkDcvz+pNdZjz2lnPxFe06BdwtrQOyrr62WC+NsySk+mb3vPAaH/mW5F31FkG4jOMx8D2Brzoemk3BaGUAkLZplzvMroEgkBHQNxei4LV9xBAvsp456HdYgoey8PyqC3Ly5VRzxzrGIcgLkPLy/IHOkhdql5nxVJb8pr6VG1n8N0W5FFXyDHLbQl4yh2p7GW67+nn+ENCL5eUJX5WMsbGqKEdIfdyL+AUxrmSeDn4hFV8CpIQyLaBr6EcKmfySrVrLM8xP+e2wty2+daWGOjX3kvnotRHAyPCJjGGYvkYx2nRUcBfSkIJbSQfg3GYZXgbOVxnJs5OCIfgbn8Tmo0nAt21O/qX2kItmJAzabPOT2VRzcKK038l9DJjWQl6P2/zWHd4SAC8klkC/bU5gvkE2Ws1SmDXMnhbQI3ogC2AHcvZpZjJTTPmZd4XzJG6Z32VHcaUqQM4h0wou5avx35ainwT2inD8ray7l2uvyAO+4dp0qfLHArRdSxXd/E+WjBKAPCViGRBmLc8BEAbWln5yTmWZmq4Eq3EGpYyznV80VUOZ2iHlX8zbVZp03EaNilr6VZBjsnpMwr0KSvCZ2Nd9MiWIc4y9nJle45+9AsEujkWvgEhKQtayArWR0AYFQjheLWDs9C+C/2Q111/roMmVeldV0/PwZUDWMH16sQtwCYZaJUhDbX+oppOvaxW9ACBY0pWHdnQJzP7cA7Zknp5iVaf5bWNvpNSn4EuGbwG5abYconMX/ddmVoDLLcalGelTLLvKDHSWH9GGfBaIE7pW3lQF9iO1skVsk/tlzpDH1S/WonTr3EceoEJiCM/MEN9WDMhe9BIntZ+baHT5YUpZEjPj96TX1PD9YJYzcK3Rha2NUC4ypPA62o/sz1TWYt+4tlmCR2v/1JO+9X43XSGTOXfU37XPICSxpzX63qH+rNyVZU+W8qCzD30pebSU3euXqEezc9I/Y8NghZZ/Sc+9XfprH61rXQQQzl0vbDTKXs4F8gVavuUsZw7ipId1vt0JN6tPKjT0sigP/1KI+/hSxlWftX5yV9FhgVNyZ/TuVl0sKCzPDzK1tSVXgLFoSdA+bQq6j01UX7U6g/7c/w7fKqNd76VjM+1pLgtdEX+aaMKymfJTukZLG2cA98AxL+SW0CHAoiGatrNeq6thXfsO6+RAVHRDpwGPwgKLKV8s4Bu6SpNpbYwOXk18C8rsYWWqgVEnl2k7YW09tJC2FIl9wiUw9qjipqYWw3rjgxB83XAsKusobzl7PzSvtqPM3VuzktdIRVcF3Woz05eW8XYy8lwllUO/eLSNeYp6E+dKt/L/6wwLfl/3tpoIf1dCVTysYIJPMiQk65c1y9Cx/pl72ZCL6HcscDZbfTPPPbesuU+lchJSnSWOZR56IF54nUnverzh2ul5nf0zm7HW3Pdi7Dhro2zslR1/gpnrl7X91Ou0Dqsg7ZYm+l49IV8/rVUvPPO+xjKMvektdIvs2/1ta35zKO/6tXoUsa+JYCOMeghFJFJUKCf+nsVpwl8GfLIiLFrpdi6MlBkjlPNKOXUDjXIdjE6dx2doDaQpuoSKWKr+rHq1vxfec51lXxJV0enH1fBeC3bm4UWtg5mLulPtalSlqeLqwXPoC+U2pTZjr/lh230iPIo7wpW9TlRMJ1P98qZsmjL+nQCbjfxfVcw3Et8W92+rJx78Q2yrHnWeTI+R5mXdH1JDzppcxZoj0yt9FIEV/t88M0/+4/xHQ5AHu44GL/5q78RL124RII89zVomEbyZYFdMV42mp9++vMn/a8AnPtJKVa4vkCBAgUKFChQ4KekwE9htX7Kngu3FyhQoECBAn9TFCgA5/6mKFlo579HgU8LcE6gnCUEr169Grdu3Yr+/v4UROzs7EzBxlOnTqXygQYNV1ZWUrYkywo+99xzYemrYmvx/RK+zEZy8+bNsITXyZMnU1nFnzZw+uGHlMZ5+DDWALhIvyNHjqRSjT9Ovrm5ubh8+XIC2OzevTsuXLgQ9fX1bGYUfLkrV65QrvJeXLx4MQ4ePJgAdD9Ov8LfBQr8PFJAAIb6VoCCILpNAnU7AFpl28z1vnxP4MYOiMPP/N6k7saDe93xL//Zv2AztSj+h899Pi4cPBCbUxMx2dMdm5TCKaIMZElLc+w+cTyqDnVFrplT4GSd2J4iRPToSTzpeRTTE0OxND/Fhv56NLXWx65D+6L+2LEo2dXCJicAgw22gueWYvHRo5ih7OPcxChB5hUwc2zjNu6K+q5D0XT8dJQ2NbLJzCarmfNGR+PJ9Wsx1d8XmQVKmAAE3AAE2HT4QLS0N0cJGRWm3n8vJr/zX6L6yZPI1rXF6p4jsdDYFiWH90Tb+ePReHAfG7BVBKLYACY7QU5g28R4TD8EcDc4Hiv0IxirdldDNB86GjVHTkUWsKEh2OUFMhr0D8XCg75YHn4c65STyRI4LG8k88aZU1F39HD63ewymXXWYGE2tvruxvj9uzEx+pj5EVQtJ6xctysa9h/g+oNR3N4E2Ieg7PJYbI8Nxlz3SDx+MB5LAPPWaKe0tjb2027TwS5oMBeT/+XbMf+9/0o5k+moaumMotZ9sdDM+Dr2xf4Lz0VNZ0esAaAbmx+O7sGrlOadiP0t7als3MDQcIw8oR/WPZW6O3wm2hr2cZK/mgAB2UUAQvVPDlJq9GGMPR6I5fnZFFTb1dgah7uOxv69BwG7QDt4a2J0KAYf9ZEMbpVSQfS9f0+UkwkIHGRMMc5rD27E9OJMdDTvjef2QhtKk26WrsbI2uO4M9AdPb0DMT27lOx+W3trtEDjJ8NDRBDW4vC+E3Fwz2nWqCy6h27Hw/GrUVKRi8aG3bHKafaJ0QkCoaVRXV1N9iUAGwSs2/a3sdGfjccjj2N2bhqacn0dZZoOnYrO1gNRRZYwQy9rgC8nFkajl3KcfYOPKONoKRxOxFP+79C+zuja0xEt9V3ITyklAofixsMPY4nsVR172+Po3o5oJvNBCWs+M0cpzoH+eNA/ELtb98YRyl21N+4B61gUC6tj0TdKaSzs+8TMXJSQ5aqlmcwsNRWxTrm6OcCfeylBc6j9QFRslMXE4/G409tL4KgkmpGV1fU5Kk8MxSxjg03JRlAah/cfgoY1lEx7xPifJFBrG6UYz+w7Hgf3UkqsrJ6gRhGBrfkYXexn7XuiF3mZWgDMQBBmV31dHOs4EMcpWdVS3sL8MtFN6bUP7t+MNXj+eEdXHNl/hOwctEOQanZpIrr7b0T/wMPYXbc7zh0+H7uaWhOIYBqZ6acs1r2eWzGz+AQc6Fa07mkku0dNzM0SeNuqpKwSJWlbjyRwVM/Yw7jVfzM211fjzLEjMTM7FQ8Hh8igAe2JppSTSaBjb2u01e6K6YGJeDIyG0sEo6p3t8YBx9XWSmlbgnjwqLjVScr+3R2+G92j3bEwuQANS6Klpi06LRt34HhUVlfCh7MxtTwYd2/djbWFzTi4/2gc7DgS1aVkNyOouLA+Efe6b8fM+HQ01eyOU8fPpKyaW+hPM3v0jD+Kh/3dMftkjn7Lo7lldzTv2xWPJycI7G/Fqc5TcbKVNUGeze5pqdZLly4lX07f9Qc/+EG8/fbbyb99/fXXE1hZ/bqjb9XBn3bgXDmVUw6ePhzvDVyJ0bnHKftNV8u+OGaZUfSGIITJWUrn9tyOofHB6KIE6cmOU7G7uhkbFDG3MgMfdcdt1rES4PKJ488ggwfIAlhGacjZGB7tgYdvxfjMUALtNqMHd7d0xDppIFeezMfe3S1xsOtYlFXVxyjreq/7RiyvT0fr/t3I6XqMjI3GHKUYiwDAynt79x2j7O4hZPNJjIwMxtrKYtTWVEdn5544c+h0NCFXZshZJdPoOCDu3sd9MdCPLZxG9xOQrQB8vrdzbxzuPBxtVc0pm8z81Gz0P+il1NWTaGhrj71HDkYzcmYwdoGMZPeHb0f/4MOoLaqN8/teiH1N+5IuXIR/+9DRt3rQ1diCDTLeVDXXRMseyt6SCXJpZjr2IBPP730Z/m6N/tmheAd7MDb3JD0TqKhH+vpjZZHS7VVkGSJzLPFssoSURVNzYywvL8fo2OOYW1omi10F5Zc74gXmWFtbQ1/mCbFk7GKMYgce9HXHKCUd1wHA1ZXvpt+jcbLrTOxppfSfgILNuXiAzn8ILTKAaU8duRDte7BdBFRXmeMgsvag9y5R60ycwFYcaz5Jlhl0O6D7nonuuPXoegyO9aDf1qO5tjE6d3VEfQ1rhk0tAYR++ujZaKxuJVPNcrx/7/0YxfbVNdaRzaUpRgHQz07O4G8I8C6KPXv2kOmlNpYpXT86NkbZwiUyg9VEV9eBFKStLwdQBP9kYLA5aNDH/B4NoMcnx1MGn/LKCnigFZ27N/ZReru6uIZybBEPewaid6Qbe70dXYe7KH14MOqzu2JleT4ejd2N2/euRzFZuo4dOR372zuiioMBK6vY2yfoqJF7MTgxzDMb9KtuwJfZE0Xou5mZhagFoHEeX6i9vjmerK3E7YG+mMK+1dYSrK4oi2n8shl8HUG+K4AAWrHD7bt3xerUXEyMTMDPgGzIuLQXu3ak42S01JmZCP+N4P/cwnwMwOM9Q30xPD0GoGMuyqtL49Durnim5WR0sObF8MX04lwqhXij7w7ysyuepTTgYfwGgZaLm8v4C92UgbwPIH0DO3gYvX806kqbyFC2HP0zw3Hr4c1kzzKAdurrq6N5d0MKdK8sASMqaUIvH4t2/Lx1+vngzrUYRb+3IlOl+JlPhodjnvKS5WWl6bDHOgCiucWNOHDgMHZmDR7ujmkOilRUl8ceMigfpcTt/joyKZcQyC/diOmtyRicGk52cxB7vroM7I6Mfu3Q4VzniTi8qzMqy0v17uIRdLjfey8BvI4fOx37Wg9HBXInfHVyZTwe3LkZS49n43BrRxw9eDyqa/BpALv2z/TFzYHrMcz9G4vL2OnKaNnXGs3tu6Kvp5+MgTVxuuscvkUnvvlq9DzqjpsPHkR5Q03Ut8KjU33YlWH8dTLawXe7m1qiqREdRBbUMTJIT89OA0KoiA745gR+xp6GdoAtQFIAtaxtraAjbsa9wXvI4CggU4BxJeWxmxK/xw8+w1rKh9Vkh12MB+O348HY7ViHYZ9BPo/tOUr2P+wh/YxMjKFnb8YcmbuOdB2Ow3uP4gs0wfOZGEa/3B+4T6nIW6zpTFRV10YLerqpvi2mxyfBrmzFmSP4h6QV2qDtuyP9cXd8KM1vF3w6/fhxjI5T1h0FswGiYt/utjjQ3MbvOXQY8okMVnNIppN9iJMHjjL2NnR4dWyRynhuayaG4aGBwd4Y6MPvxt80G2cL/thxyt4fbGqPOnyf5VVKIvaPRw86Jksm2UOHO2Nve1eUF9ehezeQ30dxv/sDdME0+vd0nDxyLsor4BH08hNsz72+a/Fw4C76YCEqa6vjwMED8D52ZAagIDJ4oouy0K37Yxn/tHuoB3vfC31Kowm785jnh9nFeQ64lEY9PqplENeW1+LYoUOxRZboPnyxqdkn6BT8sCbm3nksDuIf1ZbUpgM2q4B0pvDTH0Dje+jCaUqplvB8tK96T5zuPBkHKUtdjW8FvDaG5vrj9sMblJDfiBPYomPIdBWZ/5gGmfOexJXBd2Ngqj9aq1rjlcOvRgP6ZANg5ejiKLbyYfQO9+G7zqSxWuazGRs4hh40k+fRPcfQv2cSUO3hyK24C09vkKlKH2weXTQ0NAadAarir5VVlcf+DkrPU5JyfBAehUeyIIB2tzXHiUMn0dEH0aO1CRgzvzUfT6bHUxbe4dERaDUd22VbZKKqZy2ORQf+eQ18KCh6BD34sLuHUvfzcfTwMfRyJ5kpK/HxqcY2i67q72FNlqO5uiUO7ceXbSBrJSVLR+dH4ubQTWT4EbZvjkyWpZT43B0dJw7FwCi6dXYtTuMrd7XvTwCiwVH07oN7CazWgj4HXxfDA4xtGfAbdqa2meeu9r1RDyBoZGA0RmfQ/2RLa9+1N851XYDXO5mfMDzAn/iTwzP9PKfcjt4nfejpuQTw2VfdFsfbj+G/Hor6KrKCAwDsGRtAV9xFhy3GoY6j0Fw5M1/jJnp9PO73Ywuw620texnv2dgDD2Sh9wDz0xfoHniAPp7E185ifxoYz65YWGbtZ3OUMz0FX3VEKdjP4fG+uDt0h0ftHLaiDrnEJxmbQBZy0cw9q6zjJvamE/oKYh4cGIzxyccJRLqrbVecosT0cWxATYmZFgXQIycrs4z9djwa6Y0l/LLidfR5VTU6/ziZwVhDfCEzAi6sL5C1Xt95mTFCL3SWzy6WLJ8FIHUPnd3T0xt1PNu+eOZctJC1shx04MrmQtweREfxXPWYNRS+3NrWEi3Mc3V6joNxOXRER3TyLFxECdeHj7vxPXo5z7cZHZ1d6ZDg45GxWIR3MhysqwBo1Xm8M0qxaYM8C02MTzFnZLChFR3Huuw5HNUcjBMQpo6YwZYO8AzeN94fI3MTZEllH4D5dezZj347F7sqOaCGDzE2Mxi3H/HsA8937cFv4NnYDKoC2VY43GZ5+UHaqS6vjdMHz0UbOqqIfY2FzfnkE17n2XiCfRTBr3vQ9R3I1/gs2WeXVqBXa5zvOByNZGV9iA690ns/Zlfm4sBe9jAAs/X39uMnrEcVtDOr6TrP4nVkNm2oqosl9kTGxtGn+EGV+CknDx2LY+iORp7pfGZlkmQpnolH8NcDnm0mFyaxH6vo2tLYwx7J8a7j9HOCOVYmHnvUj9893BsN+I8nDzwfrfBNBp28QAbgwbFHcefhtSjn8OSx/cexM0d51qvBZ91gD2E07j+8E2PDD1jBTeS8OdqRu1J8iZGhEcZWHYfhieaGvbS1GDd7r/DMNYJ9J6tg+a4Y6Xkc77x9Ofq7H8XZIyfjN774a/Hi+Utk6QOuKXCO9RJM66+wcwJ10tHHfhWAcx+bdIUbCxQoUKBAgQIFPh4FCsHWj0e3wl0FChQo8PNEgQJw7udpNX65x/JpAM4JhHvrrbfiD/7gD+L6dYIHPOzvgDcMzgjW+p3f+Z34O3/n76TSgY/ZSP2H//Afxu3bt+Mf/+N/HF/+8pcJbJiB55fv9Yd/+IeJLm+88Ub8o3/0jxJI0ODqT/P6B//gH8Qf/dEfxSIbc1//+tfjn/yTf5LKM340SGv7BnX/3t/7ewQAR+LcuXPxr/7Vv4pnnnnmlxak+JPQ9Pd+7/fi3/7bfxv/9J/+0/jt3/7tH/HrT9JG4doCBX6WFBAo5+uj4IwdAJ3vC9hQBwiY83dfXvvjemGLTegHd+7E//X7vx/VBGI/z8b2ATZWl8fZkCZguwWIoNiSO0QadrW1R/tzz0TVuTNsZBNQv90fU1duEeDqTeXjVsk0YHan6tqK2EUweS/ALgFxZmjZHpuPmdsPYpgManNjwwD8OMHN5rJlTEC+RHPXwTj2ymtR1bEngfkWyLA2eeVaTN68HUuMI+sYCHwtEiRsJUB1+KSgmO0Yfe8H8fi734vaSYBzbMivtRyOybqWKDnaGftfZ/ObgFo5QfkMQfptADxbvQ9jguxQj+4/ivlFApfQpIRN9CKOwNe27on9p56NunPPhnXq5gmq9l6/ETOAfopXzBJnNhJoWkPZMYDP+589F02dBwjMkAFjaiambl2PyetX4gmg8RXAjFFKPg2yi5WzwdzAZnkn2Zaqjx8ie95MrN27FjPXr8ZQz1DMzXFivKiMACGZmFiv9qNH4sDzZ6OGwNv029+Oue+9HeOAc8oIPuaaW2K5jjK6XYCiXn8tGo4eiCWy+90fvRnfee8vYnCkJ/Y376es1i4yEc7FOGDGhdVFNrMb49TJM3HhzPnYV78/JdroBQz17q13Y+jJUKwDKtwG/LcFkNJgbmtrWwIXnT72bColNzw8EFcAXo+ODBHE3hsvXHwuWlvaYp6ySTfu34t3370KKKosLp6+EK8dP0/JsqoYXBiMd9gk//DOdcq6AMoj01MFAAIBcFUANoYJptRUlMarF96I50++SSamivjujW/F927/ZaxszMee3fuBvrHms2TUAMBXUVUZE7OPY3J+goDdbkrBVMYqa7i0RKYpQJjbBApOHj0ZL154IboI5JUVVcQIgfibPTcIztyO6XkAJ2QEEjBSDEixqaE+jhw+HBdPvRm7CFj1j/bH2x98i4DxPTb+d8fz588CdjsEWKMkHnQ/iPeuXI0RQHxnnz0fF599PfY37gcMsRjXHr0b79/5IYFCgICUsaksr6Qcj1lONgGikDWCAPmFixfihdOUqSSjxIP7D+LbAK3mKMMkALSUAN0CwLw5gnhzAFs2Vghw7e2KtvrdMTs/A2CIoBKgWLPOHD94Il587uU40H6Y4GeWoEVv3Hh0Je48uAMwgkw1hIVyRlWRq5YmQA8E05/vOgvwpCEezT+Ob1/+rxxk6It9BKJffvES4KSDXJ+JRwCSvvvOf46JJ48JXpyNN89/HvBXa0yuz8YNwKBXrwJQGe4nyEImO4In9U1VBNcyAIBmCPITEHvmYrx84hKgnMp4//4H8OK3Y/rJRJw+fATZ2yb4NEWgd5GgH+BAMi22kzWxCwDi6vhSLE2vUR6OFBWABQ4fORyvPvssdN9LBqocAJOxuNt9Jx4B1JhbnAL8SlgvZSErj8ZdbXH01An4+hQlcQEeATB8+/vfI0jUD+CjLV55/vU4BiBgbWsxHo0+iO9+/61YnFokuHUqXr74WtQ374rxham423srrt27CrBmBNkkC1ZxddTUA7ysLyGA30+gqSK++MqX4vXjgJIqG+OHP2CthwbJzAmwiVLYDwBFXEY2PCzyhS98Ic6fP58AhEnp8t8OeO6juvejOnnnul/Gnx/NOFcOoHTPkfb4T7f+E8HKvpRVq6mijiDd8Th38tlo272XNZ5HJ70T7914F55titef+3ycPHiGbETYqbEb8c57l6PnQQ+BuCPx6stvxr4W9ClBtXv91+L9698DuPIwZf2qpK8awFSldVUAkuZiY249njl6Kl58/mV4twXQRG+8/d1vxTBgt5a9yGBlOfxDEB3w8xKAnqX5JQBTB6ODIOHi/AoA0akEnDM/1e7W5njj5dfi1H4OABVVxzAgq2v9d+Neb3fMECDdJJBp5pt1ZLYOkPmJY8fjAnalpZoANgHhex/eiPc/uBZFNYDKyO763HPnATJnADkPxluXvxX96NpDgAY/f+5LcZgg/FIOQN3IjXjv2nvR1zuEDsO2V5VGaT3F2hoqY25qPBYBzj1z+HT81ktfi7bGffEhINg/e/+bzPNRHAIYI+BrbmIKsNo2IJ06ZAJwEHZ1Hbnbg103A+k0Aej5RTJKAvhuA6DwKvx99NBhQBNkBaGM6kNoew9dPzI+ik4DcM75JgHNJbkaAtMn4/nTZwAGtMPGW3G9+3r88IN3Cc7PxfHTZ+Oll15PweaxyYH48Oq78RB5bm5ojovPXIoze58F/FIWPU964927P4w73bewWVNRgZ6pBpxtILgMkMrQ8Ag6aVd86XO/GnubOwhGT8U3vvsNQES38TmqAR+0oUsWWS+yAAEg9qcgiV3cgzLmPXLB8d465TUbAcaeP3sungGQ1QawfYZn01t9D+Mmz6GTPJNuARIgmVb6LgXEdWh/Z5xHz6hP1qmDe/X63Xjn1vdjNibi1LMn4sKxF6Oj+gA6ZCgu3/w+7dyMPYAIPvPiZwCKoasJAgs4u3Lz/biPPl0ERFdaTD4dSu+V40+skLVmDuB6O8CXr7zwGvpvXzyaehJvvX8ZgFBvCozX1wN64oDUMjIyv7IQE6xf057dAD72RAY7uOzBBACCa/iG9XVN8fy5i3Hu2LloBHQ4C2i6mwNW97u7Y+wJQH38rzXK5YrkqSsjsL3nYJw/dQHQQFdsrW3HBw+uxncufwfAaiYunn0hXjl9MWqq6+Ix+vKvvvtX0d19FzloiJdfeilO7nsmZdszQP/u/ffjxt2bHDJYxD6TfQ0+La8pA4CzDOAww1p0xkvQ5Dig9QXAq3/+V38eD0Z7oxkZrK2piVV4cGt9HR8BoBhgwomJmRgaAdx1/Dhgm9KYQUfPYxMRefR3I8H/k3Hh6IsAStookbsSN/uuAlK7HaOPhwGPkfUU4F2OlD+WZz3UwRqePQtg/BClKsviEXR96/23o3cKkC5AsIvPfzbaG7piDsDDbYBVH/zgnagC0HQRn+nMqbPMozpGZ8fjvVvMETDTPD5ZJb5yBf5SRQMZIhvK0U290VrbHp996XOAxk/DK0tx+f0fxLf+y3cBdFRGa0c7NnAWwNZ0AlIuLy/hEwG8290OYLUa/bMSS7y3glzWwxdnz5yN86fRjYBicwDehoYG4vK1HwAs5kAHwJM1wKuW4jPrWefew/Hc8ecBX3EAhazFtwavxDs33wYINIxNfzZePPsyft0eALDLcQVdcu36e8kPexmdePrAM/gt9TEI6Pe9Ox+yhhyEABCBCwtgsA4XvZbn4rIYHx0HuFURX/jMZ+IMPvva3AL+2tV4++YVMkzlAIYAGELvzS7MxSwgzsmJ6dgF+PUQwLsS+H0a/TK7Qilt+K8G3riADD534oXYg0+6Cqjj7shdwME3YqgfoPIsuQjx1Zbx2QSwHuBQ5dmjx+MEAMgMvt29BwPxzofvJaD0sTOH4sJz8IHtML8Pb7wT71/5IWufi9cufiZePPcyh1hKY2R6JK7dvRrXkMMZwG1F8Gg1+011TQ2A9NYZ9zyl52viiy99Ad1wElDUbHzv+jvxwbUrqTx9SyPPGMjYBjqkhHWrxhefBAz9mIM1Rw8fjIrSYuzNVCwCoF1iT6YMHj4kb51/JTqbD6gaE1jsTs91QEAPAG7NMEf8bvwOfV1BN2dPnI2ThzmkVFMeE4tj+DTfib6HAwBk98RnX30TsMsh9MAmNu92/NWVb+FXTcWprmfiqy/8GjJaE2OAUq4AKL5191ZMPuFQCYDULGXpq+HfcsDc/b19gJjq49Wzn4k3zvwKvnD2/2HvPaCzTO877Z96Awl1VJGEUO+9F0B0hql2bCfO5EuyJbvOtrObzbdJnMS72W/37NmW7GbjJBsnju0ZjweGLkCo9y4E6gJJSEhCAgFCvX3X/fjgM+u1s17bx3HRy8GM4dX7Ps/93P1/3ddflV3XdKvtGmuRBQ7G0F9h11149oz7wO1GfTS2rwPhIfQl3lqcw/9LX7pCObu4Oyv2UJzyk/IUQp9sQKoxxva7tMEpwLRF6oeBddaB+eywivoBUSbGJ3O9SfIlZew0B18aOujzR+8pEAi4kLoYw9zWWPQ6h9vVxnpqA0gsITyddsCaA/D0+fKc2vs61NbboTnGPmPQcgOOdQFq9Ajx0TDt2pF5+PHsMqVyGGuLtVwP663q6mrGtxcc+vAnJe9eoMpFyv+FZjkM4gBsFUTb8HPy1grQ4MIL5gBAUS6AVinUz8xkc38Blhls6tG4OgfaNAQA/Zz6YdqgsXmZ7wz2Dga6SlIS825P6v3Iw/tq6m5VP31vaOgB1hQ534T5mEffBRpu725n3r6gjPQsZcXkWRDwC9YFN7tvq3ugk+c3w90B4ZOCdo8bNYTfz54zBqzaq7D4sDJjGTvRmvVyCKCqrQKweZHvBUzj1+oK61vq/D5fbyAyDqg8nme+GgTE7abnT+mDXj4DDF2ybL5RIdHKTylSdGgMUL2tZp/M0Af208/1AprSTlgb2gOjI++WF+NpbHysYmMAyNjPNf1sXX2z+voGtY9+oiz/CIBkBADnlkY4mHWb9ev01CxrrWidKj6igD0AuqwDhjmwVd1WzRzsMQec8M2Rdn6vF/2Mm43mWWc5c5gpOzZdmdkZPFs3tY90qLapAfBzVqHh4UDYjCsLrP2ooy95XivU01DWpr4c8ntB21t4tsjfL1trxzBMZtkpmYxzHF6iz56dn1ZXb6c111qgP14GSF5n7btNW9xH28hPK2LtlCUvdw54LU2rrr1SvXd7AQI9ODBDHY2IYW3P+ufJNPWKPpaDgIdColScYeakoXqx8Yxx7R5gOPWEedMmY7IL1+vhAvTm7QOgD5TKvkwm+y2nUgoBu/ephTHlUsNtrmmCvi6Qw0NuegIkb0ufstcToJt+9RHQrjkoEgD4Zk+/v7CwoKekft8C+gsDJM9g7yKB+bQXcLE5LN1Lu+oavEMfCRgIPG+McjvrHLpwcAUAPER7O6oAX0BZ4PUe1m0tzQ0cBnNRenKhUpPSAdSd9GBuhLGiiUPfHQrnIEhBRoHig+PZ63HU3dn7aunr0tDAXW0zLhuo1YV5ghtQnAG0Zzi8F069L8g9wvgUwxp8QeWNl+i77nCACqDd+4CWn6xppHdYz2ae0F6T9cap11jj5lpzJpMC2Fj3LOPctjmIxFTOQHPm9/f52gXnvs+C2/2x3RLYLYHdEtgtge+3BH6wYO/3+627P7dbArslsFsCP8wS2AXnfpiluftZf1MJ/LSDcyYY+MEHH+j/A8Iwqayio6N17Ngxy65mjGgGqLt165YVoPpX/+pfWfCcMST9+q//umVi+/znP6/XXnvNCqz/TeX4k/pv3/jGN/Snf/qnKigosO49ICDAgll+kPv5h//wH+qv/uqvrDKNBCj58pe/bAVuP27tMyaqP/7jP9YXvvAFK62jCfj+0R/9kVIIInz8fT/Idfwk/6yBGP/8z//cqre/8Au/sAvO/SQ/zJ+Razf9pnmZPtdAsybdhfnTgMvmtwEWDIBs0rSadK0GXjbghmnvBqB7Bd4Zm9jgnW794R/8nlwJbqYCFQQsrhFYxK4QfkB2ADZ2WBeWRjnZD0xnDGeBZYflzOe+rGnQfEeXlczJJQZQCejNvFa6+C3TAABAAElEQVS4DnvPffKMxbIAMGa7RDKszn4N3K4mkDZrAQ3eoQQK2dB/wWb14vKqXAiahgGiuRJAfU5A60EDp5IbuuVL3ix3Ah3OAEZbJvC2sEqQ3FP7ow7IyQ/Ab7RP49evy50T5nsxGdgm5WvVN0TbmN1cYjm9z4azCbo7sem6OTyq5epKDTY2EWwieB4WScByPynpVjX3YBTb0HM23QMVUFKGNcxBywBf9/htC5gWdCBUbgAT4D16QZltcA3+QEE+bAoTHdUiQYOe8nKtYqLyorx9oqJk74WRgx/Y5PQ/g5oC0rBFYGtbJ7j+5MZ1zXV2cWJb8o1J0l4CZsaQNkNAY8eT96bEEhh212Z3h5avXrQ2030IVrjHxWnHm0A5v/cwvtr7eZDOjVPck+26UPt1y6Di7YQtLTCRAMd+bbnYYPmYwCj3kHJ3VXFhkZLDUvVy5qVaOpoIIrbJzdOJgEoYJ819CQquaQJA6gnGED9PP7117BNseEcQvFxRM+/v6CRAip0puyBVkfHhlvWiurJZj8afY/lK1lGClPGY2jawAdYNN+k6gb1pzAYBBKWiCbAZ898s9WiW5zUJHBkc7KdTpWeUH3/SSnNzq/uaLrd+YAEIYcFhBLlj5YONZQ/wxCoB1H5j6SAYYE6zh+0/QBAK4wLBoUcEQkZ4viaYklOQrfyEPFLXuKizrV2dBHafkXbMPxSTmQfprLCgzBK0n8KG4U4K43MF7xAkycH8RrButAW4jOeIPSeBZ5BKYMMAeW2t7Rq4MyQ/wMXigsNKPJAmN+w0DzDCfdT4DaCLToLLewgiUocJSixi2ZggaDaDXc6OulR8vFR5KTmkvMOqRyDl6u1bGp+d0j4CRREEJH18vbC7LGkE0OXRfeoyQEV4YLgiaW8OBKSfEKQfAPZ0IJiSlZ0LyFAA88B13W1SZ18rQdpFYLhQCz5aI23PCBDgDAGmYOwBR5ILlExAwpijOofuqL6tCRjvhZKAYDOAArZWN9TZ265WAph+WHMOZxxRdlge0ISjumb7dKv1tgYxB/thsYsMDcGG4ooh5LnGZx5h6ZsiqOOpktwSHUksteCHBuCX8trrmro/AZTiRYAGO4W/F2mZVgEQATcwDRp7UmTIAYV4YITCdDNBqumRiSnsNq4qyslQJsHjF0AHLYPd6mlvJ+XqFuXhT/kHEMJ1xow3g2GM9+9z03GC+GnYPMCk1DTYpfI79Zp/ukCwPAfwKo9A+gqwQYM6Ozqp0z4qzSgCaqA8eC5dWHUamuuAU4YtO9KBwDB5Ywt5AUTVj0FhFJumr6evPnXqUypLPCYvZx/V19VbxrmsrCwLnDO2OWP+NX1tSUnJ/2JNfnUw4ttBOfP/X/2b1Wn+lP7Px8E5JycnRcSH6c6LLk1hk1mYW8BeMwO4s1cZyenK51m5YhUdmOtXVUcVsMCkkiNzCGIWk593XY0jlepoaSXl4l6VZJYqPSYbuMoDO9RTVbZeI0BdZwFgMcGxWE32E7ykjmKGmZiapP+nX6A+5OcU0z/7WSabGxXlgER3gQmcFH4wTIHBQVZg0dSF0WGC76RCC8Vgup8xwcsTiwv9wxjWyvmFOWVkpqkstUgemJI6B3pVcwe7ETCTMXQFYeqwJ9g5NvlID6cfEajeo6L8fKVHJ8mPlIqPgS4rCLYaU1MQ4PPxUwYa3iLwfFdV9VUA4PZArSUqjC4FVAAEoA3W3Lml9vY27SPwGhESIVcvAqOMW9P0e+P0J8YEmpeVrXdKPiM/IJvWsTZ91HTessl6uHhboG0Q44E35hIXgIwJjFfdg316NDalMGw3USHhwL77ENNtAyrxbADxIqPClZuTo/2YoR7SZhsAiabpu338vRUUGGgBxXOYaEYwxmyubCkH8OoUzyWA/npqYUr1XY1quQMg6O6oIyeOWzaawR4Cue0dmJ82lcrBnfT4TKCgYL3AankdUKuVQLNJGxhyKJBy99Y6c5H5h7MWmPKc4Hh4dLjeOf1JC9KY4t5vNpQDZreZiRDQVwjWMKxIlP0M1zlwb5hWhRXJf79CAoOs+1gDJhgYf6DH9DcHCb6fzC22LC1dI6bOtTBOPgae8rWgXtNvPwZquH9/lCC+vVJi41TCc/FyD7AA6qq+KoLd9fIOBSCjP47H2NN7t08NvS0EqbcBrrJUFJ3H+730eOupKtqreYbNjLHPrT5iP5DFzipWFkC2UWyJK9hUY7GEfqLohCKx3vQClF0H9ujv67eMhEEhAd80vTpgBeP+7mJQ2gb4D8EkFQak582cZJX51APq+8zkLJB1LH1pPqaoA8DEd9Xe0a7Zx5gOqctm7ekEqLBAP/ngISZZetUk+tzi9FIFYXCbeHyfNlWhu8A3IQEhKsk/rBCguhHguEvXL2uDuWJ2RpKyk7Kx2h3AarWohp4W1dLPrtBvRxDED8IAuAYkNbMwrbGpCcvUF4ER9siRU4oH1HvBvODDK4ydwBl2pOkMAroM41589nlj2HGhnc2rp3dQQ4MTCgwJBSYIwjxF+kWMTA8Zd2YpNy8srMUA0tGARk8x45TXXNcE4IwbIGw4P+Pn6qOluRXucZxDBHOKSYtSWfphxXtHaxPYsOJejSru1jD2rquk9CSHBTI0g32ooeW2HjE/ScG6WJyarwDGnRek9my+06KmtmZr/AwGOD3IXGcT29jk3JRlyZzFJHkoPErHi08BsiYAmD4HuqjX9fIKbF47CuQeAhjH3AHr55nnDo5glOJQgT/zbmOk8gMANFD/feyQM5izfP2AhzkgEQMs9oL+srWpFXvvCKCXO9Ckt2yBS5eY7088eIQlbIk5WJyOF5xRWMABzS2PARjVqLOVuQmmv7TUPIUnHOLvH6u+pgrT6yNgyRT610LANUAX5jnVwMdVnbdp/7QDriWcZ28MWI+ZLxngythVA7EhnT1+XKkYX7eAVqoA8K40VHFI4Dl9SZBiI8PluNcNE9qMersBKpjkhmIxDmW896LeWX3MzCjj9336kf3MGY/xWZmWWbGCch/ARmlSlyaGRWNx2kNZL2OsGgUEWgGYPqDi/GLqfARrAQBAIL+ajttyJf13dkYWMF+SBQnVNQKyc39RANal2SW0p0NWWsjGvibV1tYAJD2mDQYrjPmVHakVJwFZjInxOUakUNrSzxW/rvSQeE2tzOnWnVo1tjdoDejPGL8iD2IUY27v6OoGeLSu3v4+1lADtPN9GNNCrX7GhQMhUzy/CQ5uOAKBZmflKiMuRzsrO2oHvOwwoCEQS2QwVlTMfUtAe+Mzc9TdGcuCXJJdCDAeBfRjxzy9WTUA40+Y4+WVFCotIQkD3bKaO1uAq3rkFQTkzEGO/EOFXM+K2h40A73etsrB1ycQGx+HF+gz5oB8zEGSidH7lin3RN5pHU8+bdnnrnR8CLTzIXPGGa4fCyzt1Iu6twhcNcyczRjKjDEuMuKg9mMINOauh8CpkwCcLnYuOky/mBLDnG39hVrp/+713+PgioMCqQ/7vPbqxdoi4zDrEAAq0x8dKzquxOBU2azYqgNArvLuLebks8qnrRXFF2GBXFZl503LXLp/T6gOp5yhb4zlwNaqGvsqVddQh4F0HniXAxj04y6Ac49p30PY0UyZB3EPb5SdA7SkrBhP2u+2sf9Ywbgwy/jlybOPoNz9sWUvYH/uBxp/gv2TsRX7ZjimQWdHR4ySD/UA47ibAamOHFZadDIHjtbU1txIv4i52WZNASH+gPis0zhgdv8BBruni9Y9HwUOjgNgWuHA1T3aeAXrTpPuMj4jQUmJwHyAjw0c+DLlGky/XphfpBj/GIAmN+ad9/WX5V/BSjmtUD9/jJdApw62gNKzGuOa5uaeyZWDMCdOnqZOpQO1bVuQ3bWqK4BcMxawF4l5LJgxZA/G2LXtdQt4HxkatvYBQgKxp7IecwXcX+R53b3XZ9XL9MQMHSk8ArgGGM56oLm9RS8BynwA0YJov2btYoC2ofERefruwwqWo7SodGt+c3e0X9UtNfRZ08BgacriMMAGJsh2INWGxmaAaT/mNEVKj0yRI8DZ1ONx1iENagXG2sucJIx5txdrhQXGxpEZnuED1tHAXyVp+crLzdEeDrJ0jt9RZc1trNIDmB69dDDsoAWT2thuW2PYYN+AXLFGRlIfglinOO5xswzGU5PTQFzbSo5PVEFaFmm0XWizd1UDJLZht4W1lz0S6voWMOP0zDSHw6ax6FJHM09zqCNB9tjueifaVd1cZR24S05LBp7Lsw7H3AWma2/r4L85AJbDAbCoXADoveqfuqvbneUAk/dY+ntQRzEy0mYWOTjxCDC/n7WqAwbSYg4Lnks7qkDg9saRTn2j5irjfZ/2s4cQFRYOaOrFtflwSMEGO94kY3gna2v6U+roQdblrtyjgd4MXG8OCkSzR3A4r1jhGGIH+u4x9jRpnLHJL8RPgdghHalHi+xzTDM/2MTamZdeBFCYb4H5D+fGVF1bxWGhScx/AHJFxXJnf+EOc+Se1lZM3i8BgDN4P2M+hssZvvd6Rw1jYgew+xqmwYOYzvfR1tf1EMB6mvnvCt+VmZ6ispLTjO1RmqOdXb59Hvi8HjvnjqIxaPqyRn0IBP1gYEyJUbEY515TbmYe4w5eRgucM3w/8K/RlQPOWdDcD4Ag/F+Dc2YRswotbRZtZiPNkc7hx+FlrmuDUwZr/HaB0H61uffjcG2717BbAj/OJWA20NdpN+ZPs1H+Kk3L93rNpu2Zn11lgHfmNLDpF37YGzrmJLz5/b/1N/RDW3y/uX4T3DP9kmn7pg/4+OnM7/Vedt/3oyqBH2DU+lFd4u737JbAbgnslsD/oQR2wbn/QwHt/vMPrQR+2sE5c3+f+9znLLvZyZMn9c//+T9XLCfFzZzSzO0MPPe7v/u7+su//EsryGhgpYiICP2jf/SPLHDOGL9Onz79rfmsB0Ft87Pf6WU+7xknYs189zvNe82/m3ntq7Wk+W4DlpjP+07vf/UdS6QRMfPUVz/36u+//U/z+QZWMS9j0Ple5t3mGgzUYq7h1RzXXKP5rFffZ77frNHd2Uz6bvf+8Wt5Bc6ZObSZY3/xi1/UO++887/Ah+a5/MZv/IauA5gYq4YJ+Bpw7tuNc+Z7zfebAKcr6ST+pjm4Oelo1g6eBM2/2/vMZ5kyMusJczLVfO6rtcWr52Pu5dvv03yuuZdXZWLe86qcXr33CYEv8xw//pmmfM33mb835fvqu8zPf/xlPtvUBfM+8x0G4vyzP/sz/dt/+2+1C859vKR2//vHsQRM2zG/Tf02p4p7enpk0g0PDAwAJWETod2Z/sjf319JSUkqLsZgAlS7FzjNtNWP/7xZyY0Azv3R7/2/WsXmEe26V+mkxovJyNU+UhfbhQVYwYINIJiHVy9jV3gsn8RYBcXGaLOLTVw2/u0xeTgX5ss+AoiM08HbBG9tgAXsgdZsMf9sEsBeKK/UUEcbQeZQBQI2OXM9dkAs21ynsWlxVZz6R28BPDTZA8Rzs0ou80sE7rHQpafKjo16o7jZXLXlvtmncLe3gihbs+Maff9rcrv/QJ4EbhxK35Atp+aFjWdrjwOptWwIwtHHbGzqGUHo+YsX9ILgw960HPmXHJOztxcGqxWt3evRTFOz1gl8+kUnynWfp9ZNcPbprDwACDyS4+UQtB+LHNe7De1Gujd7jCAmpd8WdozRymoNAhGGESAIZcPZ1QSrSBG3Q1DOWC/My26fC7agZc21tmmq/Jq8SdXihY3HrfQ4divSomDv2SCwv4OBzMFnD6lJ6NMH+rR++bwmgTm8C3kmBbmy9QnBwMR1E7izcbYhJRYnyx916VLLefVhCQj3i8cmcZz0rLG8x4FATL/qmysxB0wqj7LPjMnSeO9D1TfWYUmxUWpuEsGiKAJgpO0EMJx6Mk6wo05Tw1M6XXQWU0m+POi/J0jH29zdoI6uFtIMuis40k/PSeXV2zGsEM8YHU57jeAZEKCjLWns+nSju0IdWAJ8AB+NbS3lQIq15/2AoGl9U426u7oUEOyrk0dOKy+qTHZbzrredVWXmj+wAll5GXl85hEsJtQrytnAGLVdtWoHUvHhuRWx6Z+MjcoNU8skIE5Te6NluY1OiNSx4mOYGGxUeaOStFhPFZF80LIkGHuRHaa0WexrLQSHph4+Um5EIYG5I6Th8tGT9RlVNJcT/GuTw14HpeYBGQIFDBDAt8U+c6LkuFIi0rTX3hN7GakC7wLa3bmhNbclZWG6y4oostI3zi9OY6irp212EyDdUcFRApvJgHMYCC1wrhKgkEBYxKEIFecRmCcwNbH4ULWtNRpo75OPh5+KMkpUhDUIWRHmkXldA7YZxIAWdjBCJzGirGH1aW6t1dzCI1KvpyqaQJ03xow1qtsD0hc2Amk8H3tCiq9DAB9FBNJIlYPNorqzXnVdzVY6ocycdOocKQG7CAQCmBUUFio3ppD0roE821WVd99Uw90GwJ5NriVHKcAYHo7umsaEUUswuYnAtSugZyn2r6OxxVaKsdu9t3W97qoWZuaVAix5OLeIAGeQplfnVd1YrQECza4mmIwBIic6Qz4ufppcekrgrZng/H1FBAUBahTqMUHrOoyIBsTNSUomlVWkZeXZtHHVHEBLPcHkoeFBgJZ4nSJwHIs5YYrUc7cGGrBdtcgZ+DcR2MVEWwx0hAZMBQSwi+OwjvGsH/Leyi7AAvpON4we2RnppNgjzbDjPs1jt7vSel3N9Akm3evPHf85nUg+iQXCV7Um4MmcyqRq9SWQ/tFHH2FgGtLRo0etwwimnzV986vfpu2b16t+1/z3t/+b+bufxpeZy5k52c2bN625WlJ2IsbMF3qGBXAJqMbAPBMj49qP9ausqFRh1NFl0nvdm+lT+a1K4BY7paRm0I9tqWekFbvaAsHUUpUmHMYy6I2NbR0zTRegALZE2lw+Y1bOIdqTnbElzgE2NakRUHT12RqB2wKVZB+RBwale6TXunkbuxvw2v5gfxVR76NIXbxIf9bUXU9wvAOTlIcyMRVlY1P0wuSzhAXzDmm3Wjqb5OHpphM5R+TK6NLQxdg7OU6auIPYdBIJugLOYeNYWHmphs5WDfcOkfYvUGXFpUrAYudAmQyMDeoqBsQpILyUrGTAYEf19vQSeMccl5IEZFuiMM9Yaq6d6u5WqLKn3EojW4BVKSM6DWAQi9jqilrutlOGTZYlLz05RW8XfQbrUohaHzTqQssHuj+MXZJ2UZxhDFEJtE+Ct1vPAFI7VNfWqOlx+g5sf0eBSUOxsawCz9whLVkz97jjAPxVnIuJJ0CNlY0axvQXHB6o7NRsHfAO4x7t9WT7qfpIg11TUSN/Rx+9lnkSGC4FDGtDQ9MP6GtqCTrfVQR98v59+zXVj4WItLkHGf+z0jJJ4xjKGEoatokHulB+UU9XXyg2JUopAD3eBJN3tjZoa/dVDXhgQJhQgPm3T37Csn1OkaqyvOG6OujHnQEuc/i87PgsUpXaqHu4W/VVjVpd2lBCYhKBWCykjCNrgC5d9w08zFj/clUnAQWSgK5uNtaqh7R4wQdD6f8yCDwH00ix8AFc3xvuV397rxyxlxlAOC0ukz55r7qmOxlzL2BBfQRQHKmDpMnsA66eBkaL41kcTqVPcj+I8WhbPY/v6FLNJVKLTyoxIUY5iaRC2xsKQGGHMW8QaPAa5sIJC2D8ueKTigaOvAtAcKO+BiB8BPuYl3Kys5QQHcMYvQ4s2KUblbd5RjbU0XQdAS4NIAC9Ssn3PcQeWl2rbdK/Z2dkKjIxUs0A1kNDw4AG/krDYBYKtOdqS4pryrelvxn4s1sO/DqSc1zpBzOpd5sane0H+ABooR8/iJn1EGv5rjvYP7metOQkHc3Kw4oaaNly75FusQaz2jAp3eIyYhnv0xTmCojP3Mqk7b7NeD81Oa8ILGwnj72uBMrqBUDdBxffo77dkbuvB1aaPGUcSmbqxrwRO1XXSJcqsXveBwqLSoxXgTEg+oYyFdi20t22dmFrmlm0gD8zz52cm1R9a508gdHTeeYGgnEnZbs57TA8NaoPb13AbizG+iKdZL7hScrLwYUBXekoV2NnB8+etLNASc8w0/YxbgcD5ZSlUz8AcbfstzX8ZFjXK24CRD1SSESYstOydZDUr7AX2DAHVNFUwdg2BOCHLRJwzkB3azvAdvfqdemj61jy7JSOYTInKQe4xZ00vAC3bfQBE9SfMAyaaYd5/jEWODdICseWDton0ENeaY6S45ItO3F9RaNlUExlrAoBjEEPRJtdA/abVW1VnV7O0c8BP+anY3/l0MTD2QHmfs2sDcblFxquwNhg5j7Tuj8ygBnPV2cLTmGDiwE43qsZQL4LlR+qf/qODkSGqiAeO5ZXGHVhS2Okly2/Wa5RxmeTfvq1E4CBzC9XXyxiKqvV9aZK0me7YSfMxIKVjBHTQd2YeytvVWvp0QurLEoLDwMNBlOzxHcACbdgS6NuHcZGVMr868HDMV2uvapt121lpWQoKzTFgrKMIbKrv0ddjBWbzElyAWlMG3R3wliGIa+86TLAFPfD80qIS9D9/jE9GJoEDg3RMebWh/YbOxXWYNJAX+c7h4cGFIP9Kot2E+hJimD66nFA1JuAu/1YIQM9A/TpgjeVeyBZ0xtzusZ8prqhWttYANOBno6WHCVdohcAh4Pu02Yrm6rV140xjUM4eRxqiD1o0nG6YXnCVEUf+2DKGH4PMJ8r1Sp26Wae68yzaSVlJSoPGMsTWAquRyPM4ypuV2HNXLIsg0WkKwxmXmhS3df1tKqWn3MCuIzhkNI2oEpv112MgE7KyslVNrYyX7dA5u5TutV1XZ3AxB4Au/nUhXj6ffRUejA/hDkPE19Ds5US81TuOR2JO8n+i6Nu9l/WxZYPOdCCJRjL17H8M6Tw9tTY9DBzzAbg1SEOFflhbM0m5TEGY+zeQzNDjD8tmuEQQwZjSBrg7TRpcKtbq2n3a0Co8RiHjQ2YVODbQN7PpnS74jYHo56zpsjT0ZQTQLohpJWdVPVoBYdqb8iHsTOF8Y1Julq6Gy2jXVH6UeUeLLXmljOrD/TBrS8DhLPmCApTPia/CKzJ5hk+Wp7Wlbrr6mI97Edq5tNFpy2YmwGXPq5BVwB+DRAenxLHcyogzXso6Wn7VdtZxyGXMZ5RiA4Dfidi09pr7wakhhEN42cv9SX7GAAXY+8CYGEVEKwD+0apAFTGPLbH2dVKATz4eEzNHNSZn36iLOCxfN7vt9ef/uSlqhpq1MOhDkcfB0UAlm4ubpOachzz3R4VFuQoKoJDZqQZXl5aVR1j77XWGwoIC7Sg3Tj6MVubbWtOaoxrpg+247DQ0ePHLHAOPk0dGPgu3vyIOcqSEmISVJheyJqOvok58MSzSUxttbrbe8fa1yvKL1AGJnFXTKOL20uqba5Vf08fkL6nzpw6rS3svg1tdZiUMfsmRCuRuXcIdcvdBlMdRsKrwF1TlEN8ZJyOZRxTKCm+zSGX+v46gMZa1hNrSs9IIZX7ljo4+LXJfptZH+THFVJvPPTSWKwZn+ua6q3079mMa8kYLz2dPbElkrZztBWwt04Oq3YqBKQsASr0YJ+x+0G3btVUYOwe1qEY+ksAsdigWECtF8wzqoCKm+W9h3WhMWgCTLoCrC0ALjcDnt65cwcQfz/2yQK5bDqwhu3CjPhQkcncX0wc7Xmf2bnAfD2PubhFw81jyorMZzwsY43qzyGFecaxbl0rv8r9rCsuKY5xxkOt9W3sSWwrnTlqCba5AHdjTX6ppv5a1XTfom9eYe111DJ6utmzbgHKbsFAWMVzZOHKgYoivZ58VPv37FPdUKu+XnWR/mkYw2kCQDEAHEChEwD/s81nWCQ7VFFZhXHOFTjRzHWygT999RzjZNvdDrVw/14cMDx65IjCOFhXce2W7j8cVxjpixMZD72Zw9pZJmQMbxzQ6mju0j7WiyV5h4G2MTfb7rB/cI+5Ef3T08eKiMNUzDg1BsT9fBorIqbI3OQsa626tcOcZvSurrVUszZdUDxzgyMcnPBmXrqEJrQdy11jXQ0HY4yZO41++DQm/EhSkc/oYsWHHDTrwnzsq6N5x/n7MLXcblH1jSrSyx/Um2deV352HodPSFZLvTcpcXHOWfCc2V+y+QHxg+8ZnDMLNrOpPDc3p9HRUWsBZxTjZjPtb/tlNsTNJrhZiD5iYpzOhMDPz5fgBZtoP4KX+X5DspvGbQIgZiN997VbAj8JJWACXSZf/TinnM3mRCGLfxNw+16CeOb+TADJBB9NioH7nEJITkkmhUKgFXT6Yd2/6XdM+zYBw4MQya9er/qkJ/PzpIeY42TujBUwNIG4+Lh4TgV6fc/38eozd//8UZXADzhy/aguc/d7dktgtwR2S+BvKIFdcO5vKJzdf/qhloBZ45h0pOHh4fr7f//vW3OtH+oX/C1/mLGpGducAc/+63/9ryotLf3f1lOtnFwzaUIN5PHv//2/VxwGnX/6T/+ptbHxq7/6q9YazKRtNXPbAwcO6NSpU0pgE8EAUuZlgKd2DCQmDey9e5xq5e8Psalv3pOZyWYq818ztzSBTGO4M/NZL04Hm1Raw6RLMP9uUpWalFom6GkCmOZlDHnVgBXmMw10FR8fb/3s2NiYNW81djYDWplXb2+v6urqrPea/2/mtQZOMT9jAjjf7WU2b4ydxKy7czA5mPXmlStXrPl7HilvRkZGLHOJgdKCg4MtW5/5XnM93+31CpwLIthsoJlz587p93//962ff3VvNTU1+nt/7+9Zh1PMOsFc7x/+4R9+yzhnUuhWVFRY5WpSuRpg0ZTlETZgjEXlFRhngqC1tbXWszLPyKydozArmfI0Fjvzc+Y7TfmbsjR2QVOu5u/CwtjkJNB8+PBh637mmfcbA595r0nda4LN5mUO0DQ0NMj0y+ZezLM1a4evfOUrFmBnoD9zDaas3n33XavMzTrGQIGmXpg2Zq7ZPAsDb+7fv/9bz9h8V2VlpUwdNO/zw6SRlpZm3fv777+/a5z7bpVs9+9/rErA1ONXL1OPTd037cX0jab/Mf3Fq30104+a/sb0o6aNmr7Q/Py3ftOmx+/d0Rd//7f0eGhQh7CWHD4Ur4SyM3IFqrEL8iR9IemgZrADXbqiB6Q3cXJ31cFUICj602VOOrv4B8g5N192QNA29Gm2e0mNyp82mKkEOLbc1KKnly6yifpAAQQJfE+UyCEwlH8iqGkDTEtgTjYAY0A9W6Q8mSIg3FLXQP+8X9mvv4U5jtRMgGpoEwgaG5DajuApP0NQewvj1+B7X9HesVF5ETR3Ov5J2YVHEyC11zpBZbPpagc0Z88+xOw1UhFd/lDOW5vad4yU4EePyw64jaPU2sI2sVCBNaO9k9Ps/nIJCtEKhptHABH7Q4Pkzv3aESy1wYRiixHIFqOeyWO1s7Sm9c476jpPAP0xadvon4LLjsvhQLh4ENybLdSvgWaIjFHuK6S5u191W5PVtxXt7aP9BO6dcouB7DzhekizyXAE2ygb4DN0D6TDJe0Z1zw+NiKf40flc7hQdhiQRGDHBugQug4b3KLuTHdharugYdKv5iaV6kjG66QfDdEGm+HTzx+SGvSq+kY72PA/RBqoRN0nzW43weIDMUFKyIzDqoXpZGsPMLStnmGZaAVi6u8aAEbI11E2/sMAPjYot/6JHiCV6xqbGyWYvAGRuInNyU2l8adVGnNa/r4+gC8v1PWgjYBmuR4QTMokWFySeBSIIJqNe9J2vphQw506lVeVA0DaqLSoTAUxRzFU7NGNrhu60n4Bid8GQd1zOpl4AiOV2Yx/QVrSMVU23gIaaFdScpKOZB7TId8oWEZnLCpzaiI9V0X1TfmF+Si/sIDNfuwXdS2kjnFVemEGKRn3WynMTK14tvwc08I9TGrUeY9DpHU6wel30u847mj08bBuEWDtedBDANiWdIZr/JyTEiOTdDL9tMKxs5gT8JPsV1+vJ1A32Yk10FtH8o8pYV8ypjvSJHG97fewm9TXYc55SmrbLBVmFpLa0U13sR1cxoyzaLPEOJul0lTMUm6euv9yiDQ2N9Tb2qMYTHunC04riYCSA/XiKbDL9Y5q1RHUMfdzlJRdz2cx8gCIEdFlXMV0FhBBEN+BZy4tkEK3tbNFI53D8gP2OnnkmNJik4izO2iAYGhle626+7tJgeRAvVuTzfIOBq80gj+lBGsjCdS56uHzKX1Uf5739/LZzMWyTgCnHZIrbfY5toG6OwQjsRKR5EiF2YUqiz+CMc5N1X3VukZ9W32+rDcImOQBTTo4uegB4OatOuCCgX4MjoE6UXxcyb5xAEpumiGYVkmw7h5zK1/mEekZqRpbmFDbAACTmwupYDMVQQpKZ1sn5pROpCp8ARzXyjywB8NXgN6irPIiU7VGwPXuHAHPHgKMd7toetRP5ogmDXE6qYdKUop1aF8kARl79c1S3hhuHg4MEaiOVXEm6Ra9MFkSIFzEsHil/xZwSrmgUfRm8Zs6TqDX280HKKXGmj+YOZyZe5h5jul733jjDWu+YvZfX829XvXT5s+P99vf6d8//t6flv/+38C5rATN7nksksBZKQa7e+7oHgCAM1D0CdJ4pUbHAUc6WvaoWwTlOnrv0s/aW6moDCxwkEDiqdwTwCwJ9JU2FoxZ0VJFyrM22oWzXj/1hpL2Az/aYCoixeld6u7tep4xNpjklFTSjxYSIPZQP6kmbzHOPCaVdUpmMumnChXsEaC5lWndJuVZI5aUIL8DOl3yBnZJ0hjaO5Dic0V9E3doozdJdbkIiJdH3cCq0tllWZFSctN1KBJLkB0puhn3tjBMdbR2W+3ZFSPOiaNlQGVJpE11Jr3WUwKrjdg62jB0LWOlBIAGpE6KjAdyy8cgFktd97XSp95ouaSO0VrMIx46kX8U+C6RvtINOGGLAOQ93QLYGWUcjI2K02dKfwEbW4haRhv0QcNfa/z+GPW6lDI7a0EiZjxc2FrAElWjuo56wJdl+pljgIgEtff6kbKMPh4I5XZLheaXZpSYkczcnKDujXor/WlKaiKB5njtc/RkvLDTst2KHi5PWkCE7fyOjiQV6UTBcfqBvTxD+kDMd7fvVGpqfgrwDFnHugMgXxhwTxFGnm+CfE8BZDuYV9y4UWGl3yw9TCpP0iSaMtsBjJx+OkG6wkrMob1A2FhQT7+jCGDAaQxKH1Z9hLGsx4KCTxRQLw4k6jkwX9O9OsC5emLTe3lOxcplbuCDeW+dvrIfyOkmKdEMsFlK8N0Y2240VgLvzgKzJAEHJ8oTmyiDt17aLGsKs2Z3I+ZXQP6irEIAp1IrHefM+iM1DNYy7jQCRzy3xvrtFRv6StJNZx+zzK5eANbPCGxX3C1nTLlG/2unc8fOYchMx1ZIsH4Lu9gCFre2y9hO27UfIOfnCs8onkD2XVLXXq66QeCa1G1hB1UGfHAIeHh57bmVLvTqrety8d7DWHBYJQeBgYHpl0nH1j89pFu3b2kO60sizyskPhSb3m0ssk+UxBhgzEnulIv9Fik+iXd2ATG0AOYsv1gFDDxCyj4+i383gEPXEMYg5gLG0OeGkXCZtMXhoWE6DHiSyrW42LlpBSC5pqmNtHuAEPZL2FUpb4D2AO5vk+sZnB+hfCswKt6XbyBwSqlpv1FafjyvDy69p4FHAwAMkTqaX6oYH1ID0zdvkPSxdRATVUOjJqfnlVNcqMK0XIW4+ll2vJFnw0DmAC+jpILDlhhxKJK0xCMYJPsUDoyeAuDk7e4DCoiIBbjVAFaXbn1ECsY5ZUZl6OfzP6UDwJHPgKe7xrt1o6la9+ceaRtKnSFEbvaOOpZXouxDaQp0A06gvbfzvg8vXqJO2ik3rwgoJxdTEPMm2tT4i3Eg72rGhlpghgAg4BPWIYKXmAabe2pZ598A/vAGGjyj7GjAS9I6tg426UbHFeC4WWUl5OhY1hkd2HeA2TDWQAyEt5tvkPK4U5klKQqPCMdqNqx77QNKxQyVAODnDiC440BsnzJe2lhUXWWdpofnmDOk6kzJKfoQP9IYkjJwfJj23AzkRYyNdLbG6Ojl66kc4LNjCYDEjt6k5AQoHewFzr+kZYfnOnykWPnYPn0x1Zk0nbNrc6qnv2hobcHA5GDNJ1JJCbtJfbjdUauKNsDZkP06DkycEpZA776ipvEu3b5xW7YvxbiLSYyDAF5emJ14gqOLowB3t9TT1qVc0gDnxhVZaSHr+uq0L9KLtLmArnuwMG9zMIV+3lhDu5mbz0w9VBr9eClAY6RnNBbiVZ5fB/OZCuxnmEX3Ouvl02X5uAIdkYo0n+/1xkq6trWkGmCW663lpMNd1HGMZ3mAJ57cu6kfT5gPXmi6qsbBFutgwNvZWI9oo482p3UNaLmJuu0DWHQk95vP3YW1ywbz+e5HjAf15RrHkJWbnq2izAIAVn9rnrW4/lzVXdXqBHhxcXLD7FcC6Mn+FQc/Nl03lZbPczxwSPabBtnZxrj3RPW1DRqnTkdFk9ay6JSi/KNYQdgwr6e+AyH3ALjuOG6ZZZM1L0mPS1MhRuFwv1jm7hySGb+na40X6BsnlACEejz9jMLpb7ftNhgrJ9Q+0KJrrBfd9rioLPOsjie+xnzKQVfuXtD5hq9qgdTarx95R6dyXmcfz059k8Cr9JVDg8OKS4zT0UL6IO9DjE3OGlscVxX3Z6DBKPqCOGxR43OkqmZc89zvpTTmWweBse2BtzaZ9y8BRddW1ujB4Ci2rEi9VfIpJYYmWcbsgfke7JpXLNBnB7jH3hZLOOu9tPh0DsgU64BHOBa6Nd2b79NXLn+ZtNQvKetC5QGh7mfcdqKdz289Ue1AI0D8LTkwzpzIPqU84NYtx3XSatfqxq1yDJAryi3MYYw9TL3wA6Lu0c3Wm9jdpkipnqATaceYZ0ZYfdDsAodDmEdeqS5XTG68UhgLJxkz2qtb6ONDlJ9J+8fWaochkqGedc2c2jrbAeLGsMJiycsvIbV3HPULw+vMGMbRG7o3dQdbJwfLluwxbfljXU2h3gAckQLX5AUfn36oG7WY9ujDszjkVJRYrCAX08dgcqc+NXM4qBpT2lPS5JacJFVrfKoFdHcD5Z2/fgEDpS1jT5EOJ5bIx82POorJlnnuTe5hZHRQkdGRKss7olgDy2IKXNpZpWyaOSBSz3Wt6fRrJzhw8BTgv0EvSJudisUxFkBuH5CdI+muV7E/1jbUW9a3YOYZp1g3JccAymJYHF4YtQ4dmAND9s4kmGVtusT7M9IydJRyjd7H+oq+fWrpkapIkd3OPlwwh9GOlpQpOiCWtNeuPMN5dTxssw5arDxdY16exZhUCAjpq27sdeWkCR+bnlBRWZEKeP6BrkHMpeZ1E/i5vrFesWFxOll0Rgf9I7/ZBrXI/TVY5WZg7DTAXvKi6w77xyv0bAnp8RgZseXTBmw5ZLeM9bWrp0udN3poe3E6c+R10tVHsd7e1CTr1vruStKtt7A+XbP2Wl7MvSQdOqY+gPOowGjuD1CRw3wVpD1uH2xWcESQXit8UzG+Mda68AXjTfdojy5UXGdMWWJ9na0zHAI5wJjRONKu9+ouWPa4IwVH9Fo+e9R7fOkvN/R4bVaNwO8VtdWY4b0Zm04o61CGdWhsaRtr4sN7unoN6NjRRtl5pJjlPVXlVZY5O5VUtwejD3K91CLaoZEVTU1Mq62hXRvPORDFHCibQ137gAefrMwDQDYB/rVqceW51cc4rNuxLgrR6WIMfBxKNEbUed5Xw8GpGmBeY9Q8WnpEheGp2mPrYjh16jlrMMZ8kxI7JvEQB9nKGI+iNTf/RN+4+b4GSXMcHhZBX/Mm8zJ/3Tp/Uzcu3bDAudfPvgFEn8/2CmtTfpk5Kxsd/GZd9a1f/N/v8/U9g3NmM9ps6pjNfhNAcGVDq6ioSKdOn/o+v/qH92NmA91sZNfV1rERMam/83f+jpI5PWAU+j+KlykXEywxJ3dN8MJspu++dkvgJ6EE7nP6w7Rnc9p8kU3W3/zNf4nKO/RbQcb/0z0YaM5sPFWygTDQP6DP/uJnLTW7N4GnH8bLbN6bAJfZ3DemCBNIffUywToD7Jlgl9lEMu8x8JwJTh4rO6bMrEyLjn/1/t0/f5xKgB2F3dduCeyWwG4J/ISXwC449xP+AH+CLt/McX6awTmzdrtw4YI+85nP6J/8k39igW/f/njMnNDM+4z5zMBeBuz4Z//sn1mglQlwGnOagd3M/NCAYGFhYRaEl5ubawUnjSntS1/6kmU7M1CVOZhhIDzzswbA+/SnP23ZzYwF5Pd+7/esnzFzTwOpGajEzEdNsPMf/IN/YMGLxsrUzInsf/fv/p01lzZgnYG4zNzYHKIyENsv/dIvWZ9tUh6+9957+pM/+RPrOw00Zl5mHm7Wjcac99Zbb1kWtu8UGP3v//2/6z/8h/9gAWnGcmbu38CCV69etVLnmLWwmb+btag5EGPKwUCGZt783YC8V+CcgcjM+t4cjjNAmgHfTADX2Kf+43/8j5ZRzQR6DfRnALdX4JwB6YyN7vLly9Z82xgAzZrYAHQGUjMQnoH3TBmasjc/Z56ZOeFvnqWBEc0z+7t/9+9aa3cDrRl723/6T//JKnsDsJmy6CP9gAkcm2s5c+aM9XOmnpi9iRs3bnwL9DPX+2/+zb+xUtqa8jKpe025mJ8x12WegTkoZK7TXI+Bgsz7/sf/+B/WfZn6YuqNeY+5fvNZBo4z12rSCP/Wb/2W9WzNszNB3THAyFfP25T1rnHu21vs7v//cSsB045M3TV/mn6iqYl0HNR3A8yZFFzG7mj6RQPqmr7NtBtjQzJ9kwGVTVt+9RnG9vaIlG1/8fu/rfE7PTro468TBH8SOPXrjPll09NZtkQFbJ4s68ntOo0217CZSkoQ+mOHp0/0gna9jInBLTBYTgSgbX1JqxoVARgXDGQGmAzoslhbqQWsaZsETnzY7Hc/ni9bTCU7W8BwAAbsh/InmTCACTaHRjVDYKe7u0v+aUmKf+dtuRFEtrEH6gL62WEDlbAR/0vwYX1Z2xMj6n/vy3K/P4QJD3Du5M/JPjwS05wtqSltTIYT2a1ty4agwOT5r2uZ4II3KSf3nHtLtgVFssWSYUdQVJj0nmHFe3G7BtuAi5wBHExg7OG9u7KnjPbt95MjqcTs9nvLyQBYkUB0wAE7pExar2tTD2DgGt8ZXQZszAFGO8piE1jIBL3sNnGOAe/tEDBZHnuggQqC2p2tSsQc5luK8SEZo5K7m1YB/VYoCjuu2eDXdoBHW+zPrF/8hkYfMsacLJP/0VLZGhiY09h0rNSDHQLpyxjnunW57bwePn2ow1nnVJJ0BlOWD6W0hUWJNHENVwG5ajH2BCpwf5BG790nJcw9Alwe2k8aF1fgNPttzAkE8VbWVrHDPCLQvmD1nYdzyxQbCOBEiptHyxOqG6ggEFvOex7K1d2Z4EaOzia+pWSPNDmRgnNeGDIACm4CuS0sPdOJ46d1JLJUoTtcN4/7OUHYToCKr13/mladgb7YdM+JLJEj33+j9xYGt+tWsO3N/E/qREQZwXlH0i4+BWgbBLy6rl5OsxeXFhMwPEqqz3BKmHF6iQD4cKcuV3xESl1nJeekYcyb1p3WXjkx7ptguhOWBusEOwGdjc11NvHn9RTjSTQw2DFAh8QIbHkYmVaBK2oGanS5mdP/Y0N8vhSdGKvjADs5AfnysycF7vYG9rchfXjjQwJGw4pMjyTt3xlFu0UTwnNku39Vvfc7VYMxaJx97XTGoAIMPW7ao7sDvfoI+9CWx44OFwEqRBWRoslFfU/u6GrjZQvmy00o0Nn8c5idDtBWSbNlYJehZgxpddzDpgryC/R4bFJ3mzGp8bwS44FQACe212xJ3weA7rBBWslxzU3MK8A7QGdOnFFebIqcibgubL5Q82CnrlZewgw4RLBtSzEEcD+Z9zbpybIJmGLT4zv7H/XpfO3X9GjxgVKjUrBJvQ1YS0CKdrEBGNhCiq0P6i9rbvUZBpFcHUsCnNvrpirK7mrDZW0tbulXX39X6aQGXt1Z18jTBxY4Z9LTRcYe0utFryucNI1OXNOTbawRmL56CGjtdXLFKBGh0fn7wCSdwB0OCvY5IB8HP9qFLcYFuiAg2wePHpGid9ZKo/cmsEkxJkV7UvU+XltQ21iXPqSMRzDImOdv0ued5PllhGTK186f9FXbar3fjempXAsPJ3USG9nhlCz57QkgtuSsZds1Vc026hvlF7T6dFVvFL5BytvDBDt9LHDf9LfG2GsOBgwODlqHMI6Tvs7M2V69TB/73V7faX743d77k/z33w7ORSSEq2GqWRMvZ+gLHPSIudpj0rftc96r46Tgy47DjOFqDr9sqnm8U1dabmhgeAhA2UaRAEknsstUaEwbwCyr/BoD2vroFm1mYIRUneH6BDayg76kb6aerDKmjD+nzjXd1h3SkUXFRmOtwpIFLD00PKByDlHMLwNDlRYpDwjGj/YztfSQIOx1ay0QAwDyzvHPKMIzgs/joDmB+95J4BKuaQYzSmpmCkPhsjpburXMnwfoz728SCm+RY+BncxuE+B16qmeTD222veJ42XY80jx7OhqQQDD84NYl2rU2FLPeLVlpao8R/q84qg8uVPXNzbcCaRjVGo+D8zWAhwUojIg5jifOLkBd9oALD14NKELjVdpJ3cUg+HnU7mfVrDnfup2nT5s/ms9nHiot468pdNAEq4OHpTYBm1tgTR4FVgn6xhDd/QOwcvCqAI5EvRcJtA+OAvUQurbR4tjCsdWIrCElsoW+PJloKkQ+WM5cdlykiO/V53WtcDnjTwYkeu6s/VszpScla+7P5+FtWxlWBV99OkV5VojzfqB/Qew6RWpOLEMaxxjKGD3xPMR7JF1aqhqIhVglE6dOM2zJNDL3IF8NHr6cpaUbq0A23Vy3rdHb515h2f8TXDuG1UX1I/Z6mDEIb2GQenQ/kOaXX+omnsVfF49KeaCAQWAhoGJ3EmhvU4KwNH5UcC5W+q7h0mIwLWb2x5Vt5NmbmVGQRHBCgTEcNkE6CdWu0k/+mLphR6NTVugYB4GnMOYwcI8w0kByGctDPJ8bhCcb8E890QHSJ92PPW0iuKwyOwLYkx10CyB/UstX1N9z81vpn0u+yXFBWFK3XEjPTn207UZXe67plrAHg9Gj09ie0oIPaS7T0Z1EeBhcvwRFiFMV4xR4ftD9Aw7Z/OdZtJ9X5dfOKnOD59Shn+69trt0UvgzmGg9ppGDhdhhovk+fnG+gHtlevZ46cKw/R2wJ3Uf9RNW/pAAwtMPZ3VQ6AxW6C+IiDDHFJ6+rn4YMAFcn/5SDdpP3UtTaSCW5Svt5/OHT+n4vgsBVGfbPicJer7dSycJk2rvZe9jp04pixsZN5YzBhpNbkypRvtgI8ANq4YgYqLD2OfjdPyzFOMc1/TMCaszBLgimzSxGI6tWfE3aCtNRO8rwDSmMIyefysGb8AAqj3Jq3s+Mo4Bq56jdDuzZowgLX0/XEOUYxgxsKqF8DhAkcHZ+ZHDBbADCukXr8/0a8V5qBJB1P080U/ryjvCOBdhDIvptTQ36aLTTcBfya0l/SgWTGp+kTJaUX6kO4QoGP25WPVAFV949JleXr4kx78HKmXMwG0PC3QzjzDdg4LXLl+lXmXh8ryy6zUgC+3gXnab+vGtQoFhh7Qmyc+wRhLesU1GzUN1Oty13kgfiyapEc/nvSa1a9tYpCbmL2vm8AXDXerFZsbJV/mnwM9Qxq5O06K8xj5+5i5FPM/DpAYKGqN5z4xMaaNZ1tKAkZ6o/Q1RWOCsqV9P6G+3Oyt0w3gtvukR3X2cFQGkO/RFFIL+mfStzljoXuGXYs0rYD7e/wcdIY5W+aBVHlg3VyjvOe3n+nO+F0AnEorpfyxw9iu+PetxXXgsDrrd3BYEKmajyspJAEA5rkaJ9p14/oNsaPC3x9VVlyWtdYm/5Qe8PxuAaG01DeTpjWN+p5MG2rD4ghkFurKPkiIPDZdOQhgx/OWlpZX9Jjxfom2mJ6YqmPpJ5TgmShbJsuP1qlfQKm3sfUaQHKPm7uKmIucTT0L/IKl1oD2G890pfMyhw+u8zN2+kTxW/R5+TzbPQBLjla7uXTnpir7q1gr2OoN5tDZoemaXHtI6vcr6gAsDQ8I1Yk8rLJhzHNRF65wHx2THboGcDc5PqkT2O2Kkoq1j3YBfknq5SVV3qEfutfAWmcHQLCQ+RhzEwyZa/SbgZEh9JN+jBNgIbS1jeWXmmTONv+Uw41xMTpZ8poS/JM4WOAM4DmnOxivLt46r1GgFDsPe+Y0h3Q266QyA9Ox7/lj3tpS51C7rtazzgF6LUwu1DEOswRiwbLh+57azugOB1/ev/h1w2hhxDypU0nnLHDuYu+HOt/0npafreizp35Fp9LPMoStYeFsUyWHHYZHRpWVn6vj9KUBpGm059cskFMVaWybG5oV4u2P4ToU+HQMqBeDL+lZDwSEYRHjgBGgKV/CnJu+4AH2KrPP5R+qd8o+DaScTj+7hen5kVqHG3WdvnQQY6YzNszMyGyVpQHZAm7twdb1bBNgeKpTX7v6dQviOlNQpmwgWG/6KjA7LeiF+ucHSP98Xkuz2EQzTgPE52rN8aUagVorOay0g62ysKhQBTynvQ5eah/uoP++pifAzelJGToeX6Yg9yB6IGyjWIebsYC9f+MbCkoOJs18BFAc67fmu8A+PhYotceZuQplsc1pp5cYaR9NT+ole5uhwcEqLSwBusfOZ0cqXoCs6r4bzC2uanxgQs6be5SbWmiB2zGhkcD2HLah3XePML+4xQEvzLdlh8toMwDt9n7cHdAQoFX/oIF8K7ETTir/WLEy41LltGEDnNmJyeuyXLwAIrGkHY4pkQf1cInx8x7271u1N4Hrh5WWlaIjWAjD94TziY6M0RsYe7s54FNB6uM5lZ4t4sAMtmostsvPVzCRxWi/F+teysNm02SC2GD/1qROn2MsCdNZAK4UTINOHOZ5srWIKfiOLt74OvDXoMU6BdInnD36mrVuCrQLYjTY0vDzcV2tva6+rh4shRxwLcZa53vQWse95BkOzLEOAix7MvsCw2oqlmAAOcC5Ox20tdobmlqc0Zm3z1I2OXI3h26WsEXSrls7WgEJ83Qm/00MbgFc8Q5g4HO1D7WQNr5Wi8uLimUcXX+5jb2ym/q4pqAwDlabQ3C8bCCmN2mMM7MzmhmYo++P1mtlb3FQIEnO9CELW3Pqe4rZ/TZWznt91FsbBdPHnuKAV2E0+xpOXhaYP4Th/WrDRQzJ7RZs+QnmZIc8WTdRisuA1CPzD/TeNdbqWEIzE9J1LqFMIfuC1cKa+L1G2vfYfZ0rPas38s4B8ZJql1nb7PIj1ZDetKq5jgMdwToD2JrKgQ57ruHFDunWSa1+4doF1vqLikuhX+RwRjOHxl48eaEwDqB7A0rbOJhdk29m/3v5ghTqDx8DcDNny81XAXXR9EUmnXDX016V91eQnhVA8PEac6pIHU4uxr56TP57AwAi1zX2dAzIuhyzZTeHoEL01snXleQNjMl+zhblOPYUi2RntW7WX1E44P6xkmM66A04B4D/DYxz5nBaTGSszrGu8rD11PUPr+vm5XIdDIvUOWOcyysglbxB0tk3YQQw+z7GvmzapdlTMb+/39f3DM6ZIIQJLhgjQFVVFZtrgQQ3Pq1f+ZVf+X6/+4f2cyaf75e+9JeW7twENL7wr79gBRZ+VACbgY7+9It/Sl7fCf1rvjuDHL4/K4vaH9pD3P2gv5USqLhVYQWDGhobyFNvpz/78z+zTAsmzdL38jL2yQ++/oGVOus5ATQD3r3++usyNsof9GU2jUwQ7/z583r6dMHSK38LnOPf7rPhZh3KLQAAQABJREFUZOwP5eXlllnCBONMUM/8TH5evj5N/2ROx+++fhxL4PsftH4c72b3mnZLYLcEfjZLYBec+9l87n8bd/3TDM6ZNea7775rWd5++7d/W5/97Ge/6/ztFfRhTGbmsIQB3gy4ZeZ7BgQz5mQDfpjUmcbQ9p//83/Wpz71Kcsi92u/9mt6xAbqb/7mb6qkpMSCtv7iL/5CX/va1yyj2R/8wR9YMJUB58x7zHvN+8xa10Bp5u+//OUvW+CVseKZQKf5fmN+M2CZuW4DtBlLm0kpa6A4A9n9i3/xL5jHPtWv//qvW4FS83nGWmfgOnPtf/VXf2UBc2aNbQxs38n6/N/+239jjfmvLROaKSPzPeZzzBzZgHEGvDOAi7l3Y+4zINznP/95C64zJubv9HoFzv3O7/yOlbLx0qVLMmVg7sMAYQYM++Vf/mULJjNGFHP/BiJ8Bc4ZCNBck7H7mXIwqXUNNGj+zhzK+cf/+B9b12Xu59ixY9Y+wn/5L//FKmuzXjflZOC6mJgYmb83oJwpFwPsfOELX1BZGSn4+FmTqut3f/d3LQDOmAYNDGeeqQHnzL+ZNYdZdxtzi4HdDAxnytJY58x7TQpfc8DNmPhMORnDYHp6uvWzBvwzAJ0pC/OnuX7zbA2QaGChP/7jP7bK4Rd/8Ret9YWB/F6tRb761a9aoJ+BiUw92wXnvlMt2/27H7cSMO3G9KOmDb5Km2wOnRnbp2lvZv1toI5r165ZIK1pF6a9mXpv3md+zuqH+YxpwI8vff63NAa0Erk/gPRlgFIEKp3Y8FyDbTNyN7t5oK/aFg3Uk3qJQFzCyeMEqFy1MDik8ZFhLXI63Xyvo+te+R84SLCWgE90AoFUXy3UVOo5G8VujjbywDbndJjAIcHHrW2zYe1oBc9tCBrskCJr416/Zq5e0d2+uwrNz1L0m2/KgcDCDrDBJuatbfqIDa7dgUCT/QYRJ8CBwa/+hdxH+uRJuiCnU5+UbXiUNth0JabLxjJhDY5A22LUGP3ql7QGWLWfPn/vuXdkC8hku9dFdqSr2SZN1bOb1Vq8VSs3okquBQVSgB8WvgFNs3e6+vwFG8Z2GLo85BkSLP+MVO09ALS1AUTU1ql7t2/KwctDEWWHSS1LqiHGsi3AOTs2tm3XDTS3DtS0qefDoxq4fUtLQ/1KwirhdfiY7EmBsuPqolXKB/GXtSXswp+O2Oy2CS4YcG6EoKhf2VH5HS2RfaA/LIMpCyBCimBlY1X3MCtdaf0Q68EUKdReV2HcCWAN7IJsPC8T0Klsua42wLmgSGx6PLeBe6OktBoBtnQkdeFeUlkBEO6QKpay2uB5k21FrrzPZCHISExXhE+E9hC0nt+eVcdMkz4ieDDYNyB3n33YR84SoDupSLuDjIeOlrHgNmBGBda2FZtVvX7uTRWFFGr/BiAlKc9eEoAw5oW/vPzXWnZc5aDkKeWGF3EjtioHLqgaJO0X3/1G1ls6HFxipdxcxBwwOEV6RcC5gfv3dPgowFn8UQXvDaMO2WiBdKwD2Da+ceM92XnvKAk7xPjQlHrb+ij3LdLCYBaxY/MdqGybtFXfPMe+xX07ke41BftKlg4FmFSgACn8aicgerHlvNoIIBmzX15xnk4TTI1xJ62P9lmB/XsP+vT161/X2LMHisekdTb3dUXuOSTcBQSIXpIGqhcopo52OA6AmIEhIU8uwIF9w/d0ofoj7XA+9dSR08oIzSN07aTu6TZdbvgI48gYxsAynct5A7MRqc2o54+WHqsRs0oNJqVVbGg5OVmaHLqvO02kClpeVRCggD3B3K11sAPq/Y7dOllUNuRA0NWkjisGSkoIjaK12RLwXcdQSFnd/ICAcDPBNjtgiSx9Jucz2PewOwJIvqBi9Ux063z9e5pdmVAG0MKbGe8o0g0bHbVqw2ZRHYBNHwDIzdEfZCfkqowA0B76lor+Sl1pvCJTmT937tcA55IJYC1rmODOjVpSLQEjRpOu8PWC13l++zEWkorI5iUGu3rSTnYA8NiTjjaU1GIPgCFJ9USF9HDyIfzuRWCGxw0Ewf8QoCGIQrsJIaB9FJNcWli8ZQZcBCK493hA71dxf31tcuY9maRmLkunX/OOkzcFv84WVh0BtSsETV9gZjpNcK4Uk4+/Zwjgqivg4LKanrXpqxff08rjJb1Z9CY2LdL5AgMbI67ZOzXwvZmfGijfHLo3IJ2Zc5l5jPlt+uCPw3OvzL1c/M/M6+PgnDNpuwMP7ic14jUCfQ/pmQwgs42N0AkbRaAKSFudiFlrLyCToZZ65/t1vuWymhqxKtI35CTl6k1ScaeEEEjnGa3wa4y6+f7lDzQ6MoEZJFZvHnsbqCmY1G2YIoE1TRq4CqyI7d292FaiCVTn06b2apR+/WoF4NzqkkqOHAUEAoh18tQ4Fp3rnZiHAIUSDybrU8d+XqGk1DTg3Praknqm7upGzy1N8rnxyXFaerakruZuPX/6HJu1h1yBbnZegXOoH42Zx0CxJg1aIbBrXFw0QI+TZYl6tDqrqrYqXbh8gbq8qURSv70JfJthoCpsc+ubboBx06QXJAU3fcPBiEAA3zLFA865cv82UMiTWNfOA692jHQrNi5Bb6S9o6B9/moHnLvcTtudntXbJe9gSzwrF4c9WqFMnu480+3OW6rtrsLGaqN3St9W0aEi2o4rKfW2NERQ83rLRT3EChUSFY5x00Yd9V2kgXxGOmsP2uIeOWG2dNrmoBNp8F5izty23QRSC1RWdAqWpzzt2+NHL8PcYuuR6keA+M6/z88/J3AdC8h2UtmRxfLHpLMNXHcfgLCmvlLNLa2UD4H046+RDgy7JDABA6xebmJpGeohUFtBe3fWW6feViTQ3aP5R7pQR5rGyUEdPBCl17LPKCYgSo+3plR176ZqsBsZ4PYoEFPKAaBoLK2bwJQP5oeBum9ymGeIlGhpgDSOWMFIi/lySns99sjT3gsrratlft0GnFsHsrajrH29AkgjmExaxAwF7wswvSAA5RRB+5sqb7+pSWy5iUA9b+a8DTgGlGPvA2RsB8iLXbvxSxhjbmm/d6A+feT/Uex+LIYAunb02nOkg7zcf011fbVy33bRO9mnFQs41/9slNSV5VaAOzE2lTSCR3XANwRr2jxWp3rAuWsKisZoU3ZOqb4pcgXEIwGy7hPUbmgnPV0vQe3IUHkf2qfbAFELpBD0cfCVj7x57szpuANORXAXZrJhKx8MZempmYpn3maAFxva0PzKUyud9U3gmcfUpQNhYfrU2U8oJzxJPjvEmQD/ntM/XwaoauptlVuAs44dAVQkhZ431keT5vPRxmPd7Lytti4Mp9i/SoqwkoXSdgDn3rvwNT0AqM4/TFq8rBIFOALl7DDC2KwDHDWpvK5Gj54+01nmgbmAau6MTWZOM7UCHAmw1d/XTzq5vfLxI/X21EMA2kG5UI+9XAy85IBpzGB2jIlOdPhOW8wrHBRP/3E6/RR2N+JcgKKLqwvqZm7xVQL6/eODHKzw0WEAwrOZAA1YqVwYUA1MUtFToyvYRYNI/XmilHSz2GA9nTwoRwG2z6prAtD3+hXapjvp+Y4oLQGbFvbDagyWFRVVOhAeoXNArGmBqbJdtcfsU6NL3R/yxF7ybMt0LPq0fJx9aBPrevgY4BcYpLb3tg4kh8qL+xu6O6yhrgek5MSATEo8Y+LZwSC0wwR9g75wB6Deh/SciZjgDhtLl2cQgCRjK9BYHekPLzZcs4x87n57VXy4CAtwqaJdjRHLkXR7POfG22poruIwhQvgnEnFmoJByJ12vEnK4me6O3FP5bTBl8w/Thw9ybwlXVukHq3BOmbMaiHhITqTcVLJwUl6gnW47iHjK5ZqT8Ch17JPAroAd7rSBrna4aVh+lHAueYWCy6K8olRU1eLeh/3Sh7bpE70kPuaOYLAIYRtWhrrFFsOPOz1dCGNM6mXSccduSfGSKjpz+ZVce+WrtRew6o1zEGQQGxNb2ErPq0AJ1/mQIB3zPEvtF/UtfZrcnRx1tsF72DUK7AMSaa+LYGn3B6p1a27GHQBfN/IeUuZBpyjnlX0AogAzkUACp3OPa6U8HSMn6avBHTiwMpFjKDjI/d1mnlwCZY7bztPyyi4BEh0E9NkDc9wg8M76TE5ejq2gHGugxSZL7THyx3I0h2jMCZOnqXN1jrrsnU5OGP0ZpzI4Rke9MY8aoPVkfTlo4yHH1z+mrqw8Dp42CkL49nrOWcVvzeO8W6PFkjd3dbfrGsNH/LMXgLOFetYFOCcRzB1f5U51qz6H/fpax99lRTNmxyuOa4TiW/IwQ7jXP95xmV+7uWmPln6ro6Rln7dblXdgHO1DbXMdx4opyBfZdkAwdRR0x8+AUqq68dCWFdPnfQAVvUlvfOYOrDi2TrZWgZM101csjbMOhnzNs1ihf7ezc1J4RjnDlNXIgNjWCNtMVV8opEn3F/Vh2olzayrnTtGvOPM6wBu/MNIK+1IPX6uhvFWDtx8IDdsaWcwm+ZEZVkpPm1p6y9YI4ytjXN/72lx4qWOp50hlWuu1h2xjgGgVtbcpr7YqLgEW1l8KZ+5jxTWrcwBL5Jq9JlyMVoeSyB1JKZu83qyiNRpqA1w7n35J/jTlwZaaaqHOziMseokf9f9AFWmPrOoYM5qxhb07MwD9rIPFa705HTW01EAzZ7WXKXxASbm1o800D2kPQ6k1MSSXILB9ABjCQ5z4Dqe3wCHf25e5hAWbewIbSaqQF6MI46mD+OzRxjrbmKDHZgZVXZhNgcNMuS4asfarp2+8obcfNxUxtrhiAVycQCGufDADAeOGm5oaGIAcC4Z+9sR0miH0W85iKNZWEXvqAaQfBZoOOd4psYxvLa0t+nlkyUFeocAnCOxYs29w9yApsiclqsFNo0Li2FOXQRcF87z5rAJJXH/+bC+fuOr1rrCgL3Jqcl6reQcJu4UZu/eFrg/wPh7rQ4DHuvHTNaXZbnHMJqFUeLAh7TEkaU+0s5e0uOJp0qKApxjXPB399addiDH+pt6jKH2tU+8wVwD6J150BQmuJvtV9XFeJfHHP107luA3/6U1yblTorTgQYM1FUA58/IthHLnG1TXa2d7B2vaB9rdlf2Xne4sc1t/NRAYWjj6MN5hhjSjuaQNh140JFDey/5rLFNDkvd+qpl+Ddz/DTWDGdy31RyABZR6uQaz/Ae676r9R/p3oNupWem69P5n1WERwT3Z0C+Bcu495WrzFmxsWfQJ76Veo5xJkgN9xv0fssHQIdjeh3T8dvchzvzyfWdFcA5UgR3V7OmrsGwGqEz2W8oNSiZafK2ngGwjj8FnLv+EXbbZwqPjQTgI9U5tuMXM8zZ9uxjTu0KYM18m+dt1pNIHa17CvYNVgbAaArWwH28j6eswTXmf/2sw6/d1Mb8mrKA5M8WnVX6gQzSSnsx5K8C/w3pfPkl9d8f0EEyArxT9pai3UM5KGCD9XuLe5whvXQtc5SPFMY+0inM4+HAg7OP56yU9WMLY6Q5jmXN/Jo8bQDnLpSr/NJ11qoH9dqZc8rNz2dtbhD6b4JzHH3k2gw09+rX988gfM/gnFm8mVPhZoPtnXc+YW32/7iAc2YD0MAzX/qLL1kb/z9qcM4Yr/78z/7cCrD89u/8tnXC8mdxgUur3n39hJWAgc2uX7uuv/7KVzhdNPx/Dc6ZDZ+mxib9yRe/qGqA2t/4l7/xQwHnTH9jNubff+99yyZpgnHv/tK737JmmDZvgoUffXTRAureJrhlDJPGQmKME2Zj36TR2k2b/ONaIb//QevH9Y52r2u3BHZL4GevBHbBuZ+9Z/63dcc/zeCcsXwZCKyxsdGyjb399tvfkzF4enragrOqq6v1JpvjBt4KDw+3HpGxsv3P//k/LZPZ5z73OQuqMqCTgcgMBGagNzPXNCYxA1uZw1bGGmbgrYsXL1p2PwNzGPuZMawbK11PT4/1HZOTkxbsZtZ6BqQy805jMDPvM/CJAbiMCc989i//8i9bsJax6Rk4rACowgBt5jrN5on52XfffddK+WmANHPvZg777S8DzpnrNClEjfnsFThn1r8GDDOfacCw/5+9947OMsvzO7/KEiiijLIEEighBEoIkBAiVqKo0NXBc3qCj2e8nrH/sL327NrTs2v/4WPPen2O9xzv7syedsep6gpUkYMCCAWUQQmEEighCYGEJJTRfn5PzTuDy9WzDjO9bTdvt0pCet/nee597nPv797f536/9jJwzY71jW98wwEA7fd2rq++XgbnTGnqX/7Lf+lAbHYu+4xZsNr1W5LXwBm7flOmsrJaXP7GG284G1b+5E/+xNmwZgCf1akp6xkQZ3G4HcugQ/usAWZm++ra1GJqK++//75TZwaouWxbe3p6ZPfPQEMD9aw+DWSzTT1mAWuKLXY9ti7xVXDOwD8751fBObOHtXozcM4S1NPsXrT2YmW0cxuwZ/fYrt+ses0+1xQEzcrSAD+7nwbGmRKh1ZXVp0F5dj8MMnqlOPfV1vXq37+sNWDza2vn1n8ZnGAva8/2O/uyfs/GGwOCDeo1MNXUOA1+tWfH5uDOZ3jvwzud+sEf/KHGuruw/IlWWdEBZb6NVevuBC37c6w1zvMYK7nLtequrSJxtqCs06cUnI5qI8mQpclxPRkc0PzouOYfTGh1YlZB0QmKLj2sYICh+bZmzZ77jITokoKP7demIwVyD6PvVgiAlpFMHJ9FbjyUtEYidOyLM+rELiUxL1ep9H9eyWl6gUvGKmW1ZKEbyU5PK/MC8CDKmEMf/UAB/Z0KBNbzPnFaHtvTtQ4QZjal66i9+UDJeAKtDf7sh1q8/KkiSfAEvPauvEhauKP05obd3IunE5q+WqPZKzUAAVJI+SH5FOyifCtaGnmo58Ojmh2Z1JOHjxz7vNCk7UpmV3Qw9bpOvbVd+lzrm/2089hhbSnIk3skCVDGEXJ2EBcckOvdWFnXTM99dV69pPmuTuWgmBpafhTFud164QuEAOy3YtQTi8TkHOQ1vyC3O91aOfOZellMj+LeRZSVcGygPCC7DfriDWC3RRL7HSRtz9z8UBNzozpJ0vBgBveZhMcG553DhqoSxZnm3puK3hahzSR27t4b0MTQI+3ZmYUtHvaCAGNwVnZmeXn70358jV2RP4nKyJBQhbJg7k2CcQj1scr7JIJJBo+NjGkTbWlPToFO7CahGL4L4MNfz0jgXW+5jjLCBc0BabwOhFmSUqoYN+BodxR8VkdRvmjXT85/onkSFWWAmge3lwFCeutS+xVd6SEBhP3Lu/tOqyyx1IGhZkxtYfyergHO9WJPeKi0DKutoyjOJTqJx/n5Z+oEHvno8o/kEbaunMIc1C0m1dnURdIlHCvR/QoPCyUxQpKZhf11MgdmA2PWpZH+Ueykj0QpJJhUktXnIhBXtc43nEWl5r7zfGVg/XbyIFBLbKGTyFpGle4uCjZnrn6mwalBpeemYy17iqQbdqncQUvZdKPuYco/A339KMAW6FBRqaMydLvnts7UfC6FviABf1L58fuptwBsqLDbrftEdzt6SG4d12mSuzEosm0AZz4muV8/dEsVtyto9s84Xq7GBx5qoPseFosBgHTFCkEpY32FFOzqMtewJD9f4BbU2/x8gxQaAjABTGB79ieWJ3UTm7gLVWc1SoLeM8BNaYAa39j3beXE7MW2KAD73zV1ADd+1vixRgBospNy9N7eD7TND3DOEp0e82p60KwzKORNz01jW7dPx9JPAkZsApyrwoLusryXvfQ7r/22cmOzSOCh+MO5LpLc633Yp7SMnXob1ZcEIAB3+penbiT3b9dge9lMX+CubTtSUEJ66CShtmwJUTZqcjFBgJn0e+tYVnmhauBJss6ddroJ6DMB4C1yE2AmT9zjeZLTAHefAM6ZJdAmnvGdKZk6WmxJ/RyFvcD+iIR7zUCjzgMnzD2aUjkJ1v079yia4/igQjNHX3Xz8U19CNixNLWob5S8iyLdMeyMQp0NDQbOWXxVUVHhxEi24dg2H1gf/PKXPf6/Si8be6z8rpcLnDM7W4urs3LSNYH93dOlObpFnkD6dAPnwgCj42mfYWbzTT/4jCRx3UCTvgBsvAecTf5XabFpeo0EXCEJ00gfkrBugHPzD/TZ5TMoSQ/J7CzfOvK2ErckgaFiDwcM8YD+ymyXW1u7lLYzE6u5g4oG3hjElvzc1UpNkTw9ihpjSfo+hQKJ3J+6p7OtWKNiHbprW46+eeRb2MQloq7moTVT9pxAEabjsvpJVGYzz1iZwxrzzl3aoaeySQInbMXxhT7ZHcW5F0sbznUYAOaPVV9UWATr3ECB9Ktm/dgzShK8vkp1jXXy8HdXPGPJ64AZhdv3AqdFwfQEaZD50Rd1P1PnWCP2fVjH5qO0hHKHF1aPL4AXhmfG9dnNL7Dga9a2lFS9g7pJMqBBM7Damfofagq1u2+WfcdRs7Ik/yL97QzAQwXWZjdIJhu0+t7B97A5Zt4D4PcckNEB5+pRBXnahUKpqXL5qeVGGxCgNwpt2EZujQU6ALReRRGE5+gFUNIq43cgoHg0Ce5IYFhPyrzCsQaBiq8B6V2ruKxFXHFM/eTAXtRCM1GZQfHLk/v/cALr6IYKNTQ2aHsm6p/YWcaEoDgHJMXgqaer4yis3VJlPWMz9ffe8XdRC0vRo6lxfXSNZ/zhPWVsy9T7xe8pFfh5dHlIV7suOOBc3NYkle07oqy4TJR7AlANXFXfZA+KcwDY3X3Kys4HVPFXA9Z0i1hU5tKvZsTukv9qEOPhCkpe8/KAvPFhfDRV1nDApAj/EMrviUIQMAuQ4eWGy7rV16jJuSnFEvec2P26Du08jnpYvHypoMnVCf209v9WDeBc9JYYfffI31JWdC73kN6Y0GBy7YnO372sG6g3Ba776f1ClKaAnLue9upM5XlNjD0GMAAsyzvswImzs0916049CkOfa+v2rcCip5UdjvIQKnmL7oCBgGjXiNM6AbRjd8QrLCkIxbhKZDrXtQ94JwcVK0/GlHUsgjcBsYj+dhUAztfXj76MNhqMihrqg4uAQT3Yel+pvaaWzjbgpWVtCQ3RibKjKqU/jgdKMsW5x4vP9UV1hRpR0vJ3wDksl2MzFI7WmCm8Plwd0yXihbb2OwoO2aLS/WXKjN2BKtQz1KGwoaefLz5A+bBPjPYEeKBO1jwWVHu7HrvSm9gpz+u1U29jm1oAjMcGCtrEQ5Qhq1Cx6+rpUjDXFB4drsGBQerqkTJT0gDzdioQleIN20Dh7cOYj3Yh1+/n68mYGoXSaYKjNmawxMiTccCKmyiHXUJlcUIhwaHKIJZ888BJpYcmAusBqM4B6gEJnbt8RZGhKI8BNuxCxTDAF9VA4ogJNhQ0DjSQj7vAs4ttO89x5o50gCfGbQCCqooqxaA490bpKe2KBNacd0e594a+QOlrQXOAbuU6tvMNRTCGGVz0YBxVRCCvWuC6bXuSFMI8vPv2fT3sGsMu+Ag2x1kyEHmd6zfQzt0XsAz41gcINIwxKjIgnPYeRD9kfQQW6cQNVW3XNTKGot6WzY51/MGcw9oRmuXA9U8XgJJQbLt+/YpCQn311mtvKBNlNT/iOc6gZ4BzbX1tulxTATC2omPlgHOAFO7zAkyuQNXzIv0r8Grh68oC/J0GRKqgDzp77rzCvcPZBPCGCrHdDCRGXuf+3X2OTbqpQtYBKPO8ZQOStna2q2eqR1sztgLq79bWFwBoa7axxpSSiJ/Z8OAT4I6Veyj1FKugDeBiAMvBpT6dbzyrOhQPze508+YAoLHDeh3FuZRQU9ZClRns5Qtscc83XcJG0hto/336vP1ATcCrzD8W+Xt1L/epq0pLT1f0FjBMQUohsdKEKjuuOLaSKRFJeqv4NSyYcxldsMqm3usfNupMAzHS8IjeKQdANsU5LIrtuudRf7rSc1FVKG4us3Fn9/Z8zQ3P6979HgAnP+3ai50xYJI7gLVtBPICazEQxoO43h8VrDBUqDZ7R3L9KB6ihtnc1QBU9akeoTbmG+IFQMN4R8xpKrqbaeuzz5+p8W69LtZ/jrLVvPbv2q/jmW8pFgjZ1DGnN8Z1l/HrT9kMYEqTFs8c2fEm47WHznd/pgstqCDTR3zr6G+oZOcx6mRJtwebnRh2aHBY+/ajYAe4HeMb4czDxqib6501aqyrA85D9ZE5zyCKc10P7ioxLQllxyzFYJnrQ1+yivIhj7Vz782WPDQgApWtNKA35jKc6fEcNrIoKp+vu6h7qARvQsltb2ohcdlRYk9AXmDtuZVnbB5pxqr1Tzmmt14/cBwVyiIF+bGWw1P4bGNO/cuDxGxA2qMLOpH7FkrI+VrfhG1wJzEp1tbuAMJlZYdUwiYPP4CdujsNQM/nNIdCYhHzmGOZJxQfmODEyU9mHquhuxZQ70famhmluB1x6hvo02DngFJjdrDZZh+2mSjfmsoWCs3edFzezG98PNwVSBwTRswd5MuGHQBzex6udp7X1bZzGryPbTl9495sVI4LDzoQ8WYUwkzpuhUbzy+untOs2zOVlx3TAcblSI9QSgeQxP/uPMAhrqFSw4+HVcDGsoKdefJb8mWDCEp0wOCbQ3Fs23NMh1NRQEM52tpo+zAxy42z6hu7pz2Fu3WcOk3yM3COOSexWivgXMXNC6g1Dqno2F6NP32k9o7bKEH7an8uFvaotho0t0HZHMlw4gEPoO8I1DaTUGcN8Q8ESMTumJiuBbvxn10Bhh7vYx7pzniPKt3hN4Fsbd60lfGUTV4TALg3L6qvp9dZLzxWzPUw3nvSz85yDzsWmB9dRHAHdcYCVOWO0OYiUENrbWt0wLnxhXG9fhoVuzTqH/hxap7NBS2fOzbSB3OO82y/p3Bs2W21YJHnoL4baPLOZc2uzCuJtYTZ6TXmWF2MdVuUzeaH6KhoNo3Rx9DvrhB3UjEKAp4PA+I2RfNAFER5NPWEcbxrpp0x+WfMn7spn6eSYlJ0ouCU9m8/5MDMC2vzWLG26Apxaz/zVVsP/UbRt9l8kUTdsckA+G54AbjzMuDco0HWAHL1/u5TSvRPVA3g7p82fqRxHALeLjmtU3vfUQBx8TLQ79RzNlkAzl1quIqqbRIg8ru0mxxnM9gzoNOh6T59euET1EWnlMDcaZW67GrvRt8cRbld+fQzqMlRrnXWRNxpn2ZHblB/MP1GVHC4A4vb7xcItFsftesKG9ia65u0jjrfjqQMINdDX9oGM082+/UhbHk/rziv9ns9PBfbdbr8bWUxP/PiOVh2W2edYJh45roqAR3jtsfoGHP87VvYlA0E/3EFdrSoiaezTvPGvlMKdw/TpTOXdf7T80pOStYbbJYuKnKBc1Q899E22XzpNMC1c2d/IYpzL09iDpcd5ga6/dIoztm1WfLyhz/4oQPQ/aLBOdudPsSilCkmmI2F2di8PPFz1d2r7///1YBNvl84C8UsJppFxavXn9eALUiYYmMnam3/uYpzdpBuVBx+9KMfOyoc//B//Id/JeCcKWZYYvJ7f/A9pWxLcZJzpg7hetnfLCF64/oN/cH3/sCxsdhEotHusyUE7Pmzhf9Xz6Grxn7Zvv/Fwtgv25W9up5XNfCqBl7VwH9qDbjAOUssm6qU2T68er2qgb+OGjD1s3/0j/6Rk2D7nd/5nf/A1umv43y/yGPaJg6DkkzR3NTSDPgyQOP/62XgnMFVtoHJICiz73RBWQa8mXKY2XT+g3/wDxwbQrMWtfmaWX8aHGWKcAZUWSLT1MLtM6ZGZspqZotrz7UBaAbT2csAP7NHtefeIDwDp1zvM5UzmwPayxKApkBm1qLvvfeeY+tqNquffPKJoyxiUJZBYPY+g8kMNGtHscnur8FsZln61dfXgXMGtVmdmTqe1Z+BXvYyaMwU2wzkM6U2syb9ug1dLnDOPm8gminNmX2qQWpmZWvXbLG22c1mkfw3IM/AObsWuz+W8LV2ae3RVe8Wd1u9GnRjgI3Vkymq2D02wMzq274b/GaAnMGIdi6D1+wemGWv1YfBafZ7+6ypCNrfbH5tCVRTjzOQ5z8HnLNz3rhxwymH1YW1AQMW7Vo/+OAD57ly1bldtynbGQhoqnr2Obu/9mVtzK7BXnb/DPg00NLe/0pxzlWDr77/staAtVn7enl+bJvg7OXqI+xv1j+aAqX1WaYIac+bPYfWb9lcm7VTcnTrGmYR+f/5n/4X1NV62PkdiZXabmW/eUxB+dv0IoyUxCJJj1GUy85dV087CleodqSz4zs4c6fc/IC3sG5dw6ZsbXJaK90P9KSiHnWADW0pZtH/cLleDA2gOPc5qjxjCistVMjJffKIjmcdEkU0kgqsiQLDcT2LS1ob6NfUpQvqvlWHDVmC0t55T16ZWdoICCSZCpTC4raZtXqY/c8Cyd+RYT34+McKGLgj/x0oTr32tjy3pWmd5Bjsgq25ygtKxo2E0CgJo7mLH5KE8VJg+VvyYae7O6oPyNHpxdSoxoDmntxoZud1oCJOoHS2L1tuvii7Af2uo6i3RqJ36e6IRlvuaAJYOm5fieILC+RGMvLWFx87iavs/fsUg5qHZxxWWihbcJvIApuEG6Ab3xeAGfsqqzWBImdGdLTCqR/vwkKJhOIaC9er3DdWtp376G7gHIDb8pmz6gZQjj1UpojDpfKIIXHli6KSkW2W4Oa4dx62oJzwEUlmwLmitx1wLsQW/Nc3NDP3VFcbzuvWvRuKSo1SVEIcbWNQw9yrInbMF2VjXxlKgo7FdlZe5MViuhtpPFZjWF1fBZhAaY9Uz3PgtJbBVpRCLushgJ687Tq9UDUBqMjI08EdxQBMiazVeaoVm+CL9ainzfSp5PBBlWUdRa1su6UygPv6AT1QV6u8xn3ywq70hMpSDwNxbNIF1J4udZwH7kNZiN3opUm2gI51+rol8Xt15fp59Q50osx6HFvAo4olyWUKb/OzC7oDLPXh5R/KI3JNe/fn6cnUvNpqb2trcATAzQnAmiQncbDKMjzr+1Y1vNxJDvvxtYkSAlsCS/QN3McG7rrujfegMOiluWfPnbrZhZrSSRLAsVvi+ey6HkwMUoZz6ui7o+SdJFWPvoOyQBplfEFy+Zna7zfqZu0NkvmTKiwoUkmBgXP+uoPV7FnAuY0tazp68DXtSziE8gm/n+AeAqrdRwnJkpqnC9/DZmsrSZsNPSPpc+Neja61k4Ql/bJ/X74eowB0v/u+AgL9VXqoXPGo/HhthJCoQ7UEmzBvYAFTU3IjAWbKc9wpYJs1tQ62oWZzFUssEivhfiSL5lFclPJSzIbwGBZMyTxnbsBBffr41ke6N9mFcsZ2vZ//Te0ISeeeb6BA8wTFHFSP6s8B2S046iYndrxOTBGkyz0Vuth0UV6Ac3/7tb+lvNjd1Mei+h6hZFDDeQHnUjPT9U7pO0rcDEy5jmrCxhMSLTVqam3ijngoc1cGSkMPgSFbFcFzUgxUkbI1i3ZIm0MNzpNyuKHUQrqSn0k0U392D81muKe/V9ebqtQ9fBslRyBCxvoXK+7alYbqX+YBYIE0FDK8dHOkGZWVi9injagoI1sHdxdhH5lC4jMI+8lnutp/XucvnZHHgrs+KHlPZdnHUcwJdWIJs8G2WM2cfGxTgm18iIyMdPpla1Uv983271+Vl41L9nKNT/bdYnbbHGEx195CoINQkmsAH/QujmqmO32eP+3Nn3bu4+WBitkGdl7D+uzGefUM3gV8JPG+CDD5bF2piTt1FBAqncQ1DUGTK48AiM6w+aNLEVFROv0aVq1hqbR0X6CVOd2f7lIlaip9vWNKR9mzpKCYJKa/Bu7d1RdXrunx6hobJ7CeJAEfilLUAG3+89bPdav9FuBcrr5z7DtKDEpANRTti5VldZJUPHvnAv0aaiHYrrqRZ+1q63L6kuID+1ERzVQAwJe1YY910ntYRXryP0v0+3h60Y9gr0j/MzbzSBVN2Nx1tmjFc03e/oBYc89RN0nWwT37sIFDkcw9WqNTT3T21qdqG65lo00Y1nzHlBuBkit24qYO10OC+kLtOXUPdaPmlqH3DvwafXC8ozj3Re2PNTn8SN8s/TUd2/UWcEUAKkLA28DMlwFNqtsrgAd8gUI/UGHyAXIrWK/Se/VN9utCw89QAEKlLwuFPO8tulXRRKLVDVuvAqwSc7DTDUN1BmTIxm7KhhYNZUbRi7HCbPYMJHiMCmgTFpgGtDx7Ps01M2Zj9xaNElbRLuw6UQva4h+kx0/HVNN6Q5evX1R4UriOlqMcF4OtNHDeOs/6I7PlbLiheuzNg6LC9QF2vNtItE9gL/oJyqe9D+4DS2Xpmwc+QHFuG0lboJzOc7pZV6e4rckqQf0rKy4LJTlAKsC5/qm7ugwo0NV1n8Q2wDFqhzVN2LSjXFV88ABwmcEK2M5Tv6ucf8OSzdhJewJteVNWP+4ho69GZ4ZRqqtX670WYcirVT/iF4CoHSEZOowNYiZWlqZeZyqtZ9p+qmtt54HZA/Wtsu9qd3y+o6i3YeMIY+lZLPxa7rcpyjsUgPmU0ilH92yvPr9+QVMos+Vl0Tdj52mqfjMon9e2AV1htR0PzPHu8feUFYZd3jqqP6jTmMV1JaDc7d4OJWQm8p6taqA/fP5kgT7skErpA4Owrd0gAe4FQOPO5oUNACtTmLN+0R1bulXA8kmsCqubb6rpTrOTUPdHzejZ7IziIqNQGmI+F5vp9LnPqKerDbWq4b0bm1ZRGT+kfOCicKCGJUarvrkhgJhL6u7odubwh0vs+d2pZygQfnLmUz0EAiktLdXRvKOKco9ykvqrgNl1HXVYvd3gWTHFuXdVvMOeUVMNBIhdeKhKwLnOrg4FoXqbmBLHfHRYI0Mj2sszWIadc0LIVr0A6Jhnk4GpH77g3nsCCQV6bEa9FPCTcj9lU0HLvducpwJo7pE8sTHdIA4ze9eSwgPYJu8Bhg/DSvKR6voa9Pn5C6hMBetQ4XEVZBUBjQRzZEA+lC8b7t9UBTBuKEpUR1GO3M2Yssg4Wdt+HcC6UltRKn6z7LT2RKO4vODB+Anc2vkJkBPgHDDL8Z2vAXwYOEf5Hj3QNZQ5b3QAzu1NduzweruG1H/ngQ7ve10Fu/YBVvEMMgJauTyAWgxIc+O++TAeGvRi9pWzxEVtvW2oSxJzApxseNHvYjMfHBjMeJin/TlHHLhnjhj+NqpV166exxp9WceO089Q35vZTGCqkDOoItr9rWmslVuAl44feU05sQCAz9xpI5UoIV1WWuo2vWHgHCDc1MpTVQ/c1Bnm1ZE+kXq7GPBxZy528qbOBTi3hOrU3SvER3VAlDkqSChSD/OPVuzt47PidLwYS+pNwLNGuNOnGIT7wgM1KK7/y2cQpTbsLqeXscLtrVJ1R6WePJ+SX7Cf5qYXFO4TQ3x4iH6mCDtdf9rvnCq6qgFsLuv58gpjCHaAO3kOPAIYK1C1XH+qy62XVAso5bnqpVNs2sjfVsT4gJpd2xU1NTQqDfWwtw+8SflyaDs8I2wguAU49zGwy9jIqN4FnCtBxS8YhTHvZeDvpVld7QWcu3cFtVJUaTMPanEM68yeO9oU4qOS8gPaiTqgD+pdjoK20TnEbja18aDduhPTbIC+LAJtdw50qbr+qu4/6MYGFeNOb3AzlPHy0okrdx/CRjYR+HVRtweAGxvPA9dhJY4y5uu5bysO1c1VIPPx9YdqQ3nwiwtn5Ovvgw3qUR3Z+TqxoY/Od37OOMPmn8UX+hsn/qZK00/w5C6rbahZlTeqsPwexQkMBTuswmN9oyxs1qMVoELgmIab2Eei6JySEMu4OOwoziWkJqo096DSt2agCubPnIBCoeRlFswvUBH1ceK1MKf/WFh+op4hgzLPAwA91MZmADT6J48VH2UBrx+kracRfxrY1DZ6Wz9AqXqVTVOH8kuBA7GY5DmwecvU2pRuT7TrHIpt69NuOr7nLSwo93G8JdWi+nft2iW4L3f6GsC5rGPEklvUiOLcZeYcz1A1zc8sRIEPcA6o1pM+/ingXH1HrX5y7vuKy9qqtD3bNDw+qvaG2yiCpuvY/hOML8yRGRfAHXneAFe5fx6AQ17MVb2pVzf6kbn5ZQcGvNx+Xv2zPfSxjLMz9EOAx3tQ+irNLcA+PhwweV13R4mRgY36ng7g6nBQR9PLFO9FffN6CkBVe7cekLGSOd1jldHPFqUVyue5r5qII67UX1NgRKCO7j2msm0on6Pat8L4cmcUBbTqz9X7qAe7XexoDZzzSmI85zoYvw26q2Zj1aPZIe0/WUifOAOk1u7YuB8vAdRnbPVFlXCDctkov4HSKCg5qr9eaG8za6JPfcEz2s3c3TZUtfQ2yjeU55y+aPnZonbttDIeAzbMZiwFpseavQLY/DZq6knJWJtzjh2R6URsfgDIj1T/uEHXKtlcBvxYtAO7XubpkUFRzCnbUNW7iDXrI73Fxr19qWx2cAtlPjmmz1Fbr2u6xfzlDZ0GoLdNQhZrmM1p/b1q1ELNjndG6SjMriwBlbWx0S5gs/YBH+5M2UHcYkqmCIzxP5vb+dI+A1mfCHBjvZL4bY3nt/fRXSDLs+p8QN14v5CvH6D69DIAdb7K2TiWnMj8lvLdZ0PEVRTe23tbWP9E6f/grxGTprFB7wXzpgl1M5/6CDBwAsitiLZ9OuNtJQHZ1/R/Cc5NTE3q1IH39Nbu00DYm2kXWLXOjfOsVTEmVNLmDJwDVNuaTTtj7uv2lJjtrj69yBoE6qKpu9PlhfJ/c22LfJn3HMlHbZwyegcw5vOk2P8d01NitE10Nv6U3USKluh/Hjx9wKaxWux7m5hLLRJiE/dwv+NQcTaAMQP1PR9kvycWUPW9dQO10RZtjgrTyfLXlB9BO2ENYYXn9O70fcD2CjW3NShlR7KOY6ucinrmzNSMfkbMNjTzABvebL1R8JZCAKQNmrv0+UUlJqWw0e5NIN1iNvOZyqJp4IFNE5sYOGeR1y8cnLOH79ChMvv2SwXOtbW16Sc//olsR/8vGpyzunBN9n5VJ7tWB7+sL1vgNWWBmaczDilrCaRX9+kv7papLfx7wDlbyPkvAefu997Xhx9+6CSX/qrAOUtOfvyzj1m0/9xJaL2DAocrIWhX3tjYKFO4MLWK73//+47yhiUfX73+W6kBRt5Xr1c18KoGXtXAf+M1YGPRv/gX/4Id631OctmgklevVzXw11EDpkxmcJWBWKagFk1C8L+Xl82hzEbVgDWzNjV4yhTgvu71f6FwbOCaqZ0Z0GbKZAZfmX2pgVymUGYvA94MhDt58qQDtRroZSCdQU6T7MqzzU72vBocZf+2zRlfBecMhDMVNktu2svmEr/xG7/hKOP9+Mc/dhKfBseZipyBaklJSc777D9//Md/7ECAZhdqsJ1ZgloC0BYZ7JyueYjBKqaWbGDZ3//7f98BAC2J+tXXzwPnTA3NYEM7j0upzuxnTRnNoDP7npKS4sAUXz2mC5yza7P6N9U6i+dNmc1sUk1Nz+xw/92/+3eO6pvVtYEzBpDNzs4698mANFPxc5XHzmEgjqnE2XtNdc9AQVOcszoz2NDKauU2EM7q3uZlLnDOQDyL7+1zBrXZMeyeGpRs99iSzAb3fR04Z9dk5bXPf1Vxzu5dfX29M1+wazU7WIPgTFnLjv/VOYRZ6xrAaJa/Nk8yENDq1drdy++1urJz2ftegXNfbWGv/v3LVgOu9aqXr8sAVHtZu7a/2/Npz4etqbW2tpIsKHVgZgPoTJXRXi943zrgwEMsU/7Pf/KHmh96qG0k1HYDIGQA5gQXZcojAdWy5yTl7k3q6aXrWOyhMsYO4+QTx+WHctkGn3fzYZGcRc4Nzrl+f0BPzl7QoydPFYiVUHQZVs0kPp8DSQ10tSkkPUlbDxfIKymVz4WzKEq8hRSBJfygJlB+m9ZsfY3uXa+S79qG0krL5bM3T27hYU4C2w2VFEgbPguU7RmoFxOTGvnkR/K516zN8UnyNXAOWNfND2AIqI/OGvCI71jSPam4qJkLf6q16SkF56AWt++YPCirSEgsD/Towc0mzQyMKikVJYT9e+WZYPAzeAeWIUZakUPX+sPHwHU3UQa4r5ji/dpBH+uO0sPtc6h3dd5Wemys4ukrvTJQ5tpC/fA5sejukBV+JFgW5oHmbmmMBGsEycCQ3Xu16dBhuQVTHktMrK3CwvnIPRigD6BofXBUz6jPrju3FZubq9gywLnERIA+FsFRvdkAdlhgYfnOcAtqGh9rAnWRk1h8lqUfY6c8dl7scjfLocv1WB4CXkWlRCojJ1fDg1iLNbSi/BCk/Pw87UhNJ5Fju/RZlqZvXVsGL1t6rmDaij9KQgZL9T/o0/XWOoCwLoUlRCo2OU5zxDTdrd0KCwrTwdx9WM/sY5d8qAYfDgNffEpSus5ZOD+AokpuEuoPVGL/SJvqWurU2ArsGBGuI6UoPqUekS82dZdazuoKqgwBQb4oB5zUwaQiB6BYIqlkShQXgWnuDnWhxnBUh7BVimMnv+0/n52Z454068NrKM5FrauoFBteknUNAB/rC+uo4uUBSeSi+BRImwNdYUF+DTVBBBMdC9ogIEMDB8ZIHFSh1nMPK8cAoMqsrEzWHrGJ7brnLNuXl5Urm0RQIAnl2blZXcfarKqRthrqg5LqUe0mieLBuDj+bJgyXld7c5vWsYo9gFrHfpSWNgPndNy9o3PYK7oFr2HXdFIHkspRnAsk6YcCWgNKTz33VbrrKDZc76HKEeM8H7Or86rsJnHSdhkGc4lkU4lWiH9aiCdnF58rM2cPCaoC1JBQTKPJeVLPqyh0rKJ26A3IEmDWbkCOU7SFqlsVJDdvOcnbXTnZgIEz6m67R9v00KH95SreVexYxj1ZnNTnTZ+pgXtoiibH80+i/pGrzSSiR7BJvNFWhWrVNXluQr0EsO0kijnBKE5d6WasbTgnj+eAc28CziXswcJoSffGAFYA5+7SjrYD3Z4+BDjnHy8v6JtZ0JPq9io1NjeRuPdEEYcEHoklA1NMFSgXNZDM1L0KB2Lwpq9wp10vr5HmAho1O8wtXqEkvwJQLxrB/vI6SbZb2DD5asdO7KbpGzuAZEB6UCncR1L4ACpFoeokiXmBpPh9oKvooC1Y6RZpx3aAJdrvo+eTOtv4oeprrisYO7VvH/62DmecVBDAUE1NjWPRan2u9be2IaAQ+NVioV/1l409ri9XPGlxpAuc242Kpzsw9jL30FDvZfo7U8X045kMJhHrCzg3jRpbPTDSFWI263vysLtaRg6zvaNd8zOL3KcSFe8uwTY3BBWpOWCOSt24hWWu+5oOo8qZSXvbDOz1DIvMtr561TKezM2soi6Sh9pZoRKwVB1A9efclWqAmFUdPfaGDu4sVASgz+DsAIpzZ3Wz5aZ2Je3Wd47/mpKC40nik/RHSfPOxG2d77iAjekIxypG1SpQbc3tGhkfU+qONOVi0x0XHuPAHXToWmZ8XMeq2xvwKtAXS2xA1uc8l7eBJ6rqUDhFpSUrPxsrO6mHDSnPiHF380yWkNyOD8bOEmCwsu0S6lRXOc4yQO1+7UsrUACbUh4vL+hWZ5NjBTmJ8lwO/fo3D/w69sdxauqv1Rf1WLWOTGC99x2dzHkTq1KsuIBsZoBELrWeU2U7z+6Gtz4o/ZaKU1Dk9jLY8AVgWT/9EODckw5l78lCcTRet6paNf5wjI3wSdpHW98es533ox5l/SjPryVZEYRizEGZDZWUOcbpjv5O3ahH5WpiWNm5GYA+wervHtD0xFMS5mkqKSrXtphtWgOYv3P/Nsnkn2rFZ1EFBfuAkgBQNm0BCp9R32i3qmqq1Ds0qFjO/82T31QKcMMYxz3HeNA/PIBtJopzRe8rDZBhaA4VOMC52toGxUdtQ+nzmLLiswBaUAGn/P1P7ukitpUdgFx5AGCpqItVNmMhPdEJgJXowC8Z4Rkkbr1Joy+gBLQMUA0aseHL2Ia9ogcqMMQ6bb2ARvWXgW7mUFXZqpBIwMvuIc1NLCt7224dyD2k1LDteu6G6s1Ijb6o/VRzk/MO6FKAQmd4SNSXfdPDTgDk8xqeeqC06BTK8ZZSoxLV8xSIog6489FT5ezMBz44pDgUqp4+fQzEdF0XgMgT6N9OA2xnhaE4x/UZODf4uF9VjEudAGHbMrcpAzXUW+116r87oGTqe/+eYtSqEr8E96wfZWxdY4xytyQ6Sk+bUdycR4Gps79dFTdQOGWMT2H+HBEXCcDfpYfEiXvTdulEHtaqobFaRS2psaddVfUVqBY9QAlpr4ozixSL/dz86oJaUZc1NaTRoVHgqlTG+3LKuZNyPNPHn6HWhW1zaUmpjucdU5QH4Bwj9DrKefWdtVj6VaE8+kRvvfu+DqIwtAUo1ZTvhgHnTHHuDsq9IeHBgNbpmno8rdZGQGuLRXYzHm4HLNgUBFgAEEEfs8y1GBAexFgWzvjtzs8d2I9WMH53onScQFwVn5yiqWdPdW/orqLDI3UCK8I9wLprgEe3aR+fnT+nmUlEVrbvVmH+PiXGxDGurmCl3K1K7Gg72joUH75NJ/e9TSyUCxQHYN5apYrqSkUnxOj10jexsd0jD6yn67tQfW2/oPmVBWxqj6LYekyhm20zyQrj67CuojhX1XJNaflp2pmTqYeDY2quaadNb1cBCqnJQLb+qARajGqabIvECIQ0jIP+KOEF8xts94b7dKMGhaqRB1ipblVMUoQeTY9raGBQW4BRjpa8pW0oebqxy+Th6KDOXfpEo9Ooae7ZpX15h6jLrYShSxpDSayKttDR26WgraHAxm8oJypXG8DMBr3eaKnAnjIFRczXAeGyAM6eqmawDnDuDBCbgXOnlAc4Fwg494L6ur9ALNcNOFfXQEyxWyW7yvUAQY1rqIN6BXrqYOEB7ncxcYdZYZpoMxDr2jxjC0q6gJ3+nsSqyz66jyLiFzc/0cizIcUlRGk76yQDvcOUb5K5f4KOHCjX7rhtxEQvdBvlrYtNwGe038zMPSpCRSk2JJJNBisanx4iXr2EHW2HtmwOw8r1XeUlFQAAPiYevazmphbtiNiG4tzryo7LpS8wBew1NY7ccjYXjLGB5/Thd7F7P6Qgd8C5JTZcoIx79f4lVfVeBbom1ss/oY1ZD+LCej1ZmVZOPrbWqMJFcj4f4KN12vVzlP8sZrdxz9sUMmnvw9OPdbO5nlivERvdYKXvTNHKwozuNHU49tvFKGfn72a+timQ+dmArjVf1Z0eNoyEYWG/96R2pKSz0WZdA9P3VMu9ullfrZCIICC44w44Z2PSFTarXKg9q7XnG/q1136L2Po49Y4VLaq8ldz3Bw/GtN9ArqIj2uoXxXwA5WJAtQrAubqb1xUTGk6d7sCWehK1aeBKIOm8rDxsrVGHDUTRC7hsnWdwcXnOaU9+3Ncgr2j5AS4OodpZyQaGOuK2rWw42pmepiU2sLR1dDpqtPuyD2ArXK4AVOdGZwb00fmfaGjwgZJS0lRccEAp8V8qlz5AMe0GlqwNzcSrQHGv5aO8Tf++4ful4lxl5VXgJankQKmjeLoZ6Kqpu8WByGdWUU3OKkSZFRiOsYWpgKZmJgHr6vXhxZ8oLiNaew/sZr6DWuvFavpOf57/YuXwmUA/A/eYixHjvmAc9GBM9GfOuNmbzVC03SHy4ZW11cS9txUY56OUtDRNT67oQS/qlt7+zJv2ErtnapPfZvoe4uBm2yBTpe0ouh/OKdHOYDbNUp/DC5MAZ8BILY2OMuFrr590FETdsWptRiH3KmBlIPe1fA9zo2QU0Jj/LQLJ3hlhfLnxhe4/6tY+7F3Ld2P57Z3IBhvUtXmu2gGsq3juJniGyt8sEzyx6huaNDIAhLw7R/m5eSilRgNYGhooNsqYwA0bHYjXIpQdQm8AAEAASURBVIDb/YgbTOW5ghisnnbq5uemrL0ZRHSr6mB+sor9737mBwdzy1HzDNSTpTGU/GpUc7OWcnmp9MBhZ5OCP8rYDxa4hwNVwJg35fXcE/i1GEiZDWOBgHPEf5fZBDG1MKG33wGcSzZwbotGAc/PNH2CamyzDme/pncK3wVA3uKILS2xKa6+7wb1eYFNZVMqoF/ZxFyhtalNTxhHM3IyeHawXqcvNLvcRcBO2whkfQvaso4iojdrGxPPxrFHr2Hj1iX50j/tyEmTD2vA7Y13AG09sY/eo+K8A/SX0ZphPlXJvKKavmbT5k2oqp1WzrY9jEXuKJcOqP5ug64Caq8B3+3fe/BLcG5THOAcCtdYtT5iTcPAuVN73lUQ7WONTUfjnL8KcK6SsSQBcO5U3mllhGUw/0BN050YCXDu40ufomf3TNlsTomOTtDNqjpNMmbsjNuuQhTPTSHbCyDX1DtXiafXmDv5sfkhxMtfnrTXp4wfzV0tzMsbUKmfZu60U5uDgnEzHNXjUWxzd2TpAGNePGuqq8TdbWwAO3/jOvDyIkq9+U5sGkIftEwfbbHRdebRg0Ooledmo9Z+XKnBO/R08ok+rjqjodkHygKce6vgTQWuBurcJxd04YsLSkoEnHvzTRUd3OeAc9xEYrYvwTlTpzZw7kt8job4X/j6S61aDTiy3XMvXvzF7lRb6D527JizQPBVq1ab7Jjaky2Y20SGGNhZGLckgX0xYmkJJSk7rr2s4/S0HTQedPh/9jn7m/3eBwlZ16TJjmvHXGeFwq7ly88iTcqE0z5rL0sm/fhHP/654NyXn2dR6c/O/fIxXDts7XdflplFPlOt4t923Wv87Pqcne/l875cZnuPLSi6rsl13atMjk0q1spl1+86lp3XjuXUjZ3c9bJ65Fi2491Vj/Ynqw/7jB3Xfrbz/EefdR3jz77buawsri/79deVwVU/dmy7T5zAOba998/rh9+vch9cFh1WVtdx7Tx2TbYjyB4s+/fLx7Tf2722Y7nuq12LvccpJ8e2c9n57bMvl9F2mdlnXMd0zs/7vDm/q/z2N1Mps8/ZOez3Vrf2MnthsxMdHBxUFIucthPC3mN/d73HPmfHtfLYz/Zy1ZOr/K732Pvs+HZN9n47t+u+v1w25yB/9h97j33OvrxRvLMFNlfduc5lx7RzfllvtHUWPa3d2O/sb67rePm4rjq247terrJ93fvtnPYZU99zXpTB1CosmdVJkPFVcM513fbdym/lc9WLq6yWwProw4+cxNp/AM7xfmvHzvle+rzVuR3D9XnXdbu+2zWamsQ//se/7yQB/xYJ1MPlh11/durQ1DB+9MMfafrJNGp3P3KgOrsHrnqy6335vPZhLp2//8fnfrmMdk1Wb/ZZ+9mu1Y75l72sXv68XjkvFQWg+Rf9hP19hQWLl/tRDw93dkQwqf6zNvflZ0zO3XbPW8/zZV9k12HHdtW9XYuXXc9X3mPvs5dd7wYLFzag2WdsAduL58fK5KoTO56VzZ5JD48v25zz4V/of74s4y/0lK9O9qoGXtXAqxr4K64Bgy4++ugjVWMVaeP7zxvX/opP++pwv4I1YGO6xU7f/e53HXjIBUn991IVZrFp8JRZgBqMajL1X32ZQpkpOxqwaiCWJRxNTc42fhj09SaT5q8D50yNzuaJBlWZgoUpjJnFpinKGRhi5zOwzhTWXlacs59NScyUz+z1Mjj305/+1IGuDJjLz893wCkDwFwvsyo12M0AL1Nk+973vudYc5kCngFWdj5XbOeaH5hdlwGRrrmJ61j2/S8D5+warewGmdnrvxScq6urcyxJ7ToMKvun//SfOtCdqc5ZXG6KeHYOuxaLKb/zne84dWIwo33GymMv+25lsvZqcKOpzBk8aJCcgYfl5eUOZGj1acCexbB273OBO+y4BjXal6nCWaL58uXLzs85OTkO3GjXYAp5Nu8z1Wy7j9b3GnRnlqoGuRk8ZzChgXoG29lc0MpnFr32XttwZ/fGVOPNAteObed2vezYdh8M4DQw8fvf/76jIGgKcy7FOXuvXb9BftaGXoFzrtp79f2XuQZc7dwVr9i80PWyn02B054hsxI0yNfslM2m1VQlXWsL9ozb10Bnl/6P7/0zvZicViqJuCRAlcStWLBkp8kzicQSSZ+1uySEO3v0HLAunPl8IH3C4sCQVicfk+gKlKc/UBjrjRtjDzTXfAtdMexOCoHTCg8CMLF+0tKmu9hArQD9RKQlKDCZZFZQDPAe8+gV1mE2+yuEpKoHC8HL/di1Xr+p5yQzI8Oi5ZO5S24xMSgxsB72/Km8USnzjU2WB2oyG4vLmvziI602VZMg8dXmfai90Yd7RYTID4Ued2ybkEFxFFVWWBSev/aZRhlHPDdHKBy7Mb9oA5MWNQsMNv5wVJ7eAYrfh51sXIQWgWcWgOx8gwx48BG5U61Pzurp3W71Lc4rFnWa5EMlzobO8aYmDZ29qOiZJwoleeeWzngTHerslH6xuEoCiGtLTpI7cM3S3V49uXhVL/oHEN3bos17AQlDsNEkWbO6RHIX4M4PuxUP4J6NqWeauXRVHQDD3uEhigfM2JTIfWGhfxOJXQ8SEYsocdweRT3r1scaB5x7sxCL04wTJPmDnOTeFPZjlxvPqe5eraISI7Qv/5AQGFFdba0GBnsVBwC3Iz0TayEW1lEWoFPWczaqumEvlRmTqK3hEYj7zQOT1Ku5vYP69FYRyoWW2Hn8BAuZikrg7Elt37ZNJahBbY/KBPhYwhrsEqpK59mBvqrUbdlO8p9VZA2NdKC6YaqlY4qIT1Q54Nz+7UdQa/BBseALbAQvALj56K0Sfg9sZ6CCrePeH+rVeVMKeoBVa8lhHUEBLHYzSSWok2cktJr7mvTTqz+WO+Bc6ZESbUXZqKH6lu529qMOEKiM7CxFk/z39kIviLa6BMRIBSlpSzLJgHgAiedq7q/HXuYqN9pdhbnFyttVoGXaWO3NOnX2dCg8IVTFJYXKQBHJD92anvvdOl/zhR6QWI5NjNNeVGgsWTL2FHWPOySfHjx0rERLDgL75JZQlmAnqXmh+gu5hazrCIpzBxKPsOs/SHcmm3Su7mP1dt5zwLl39n9DsYAJ5P2xCZpTJRaoV5svsn60qrdLsQ0N8ANea1Tb/bskcSgfyfzkyFQsBUlOk2Sefzbt2EWGoyKZSJL9OV1EM8oUjS11gEtzJDt2Y4VUpGXUOxp4ZuvrbykBlcejhUeAHFGTYXm+rq9aV7E3G0O5Kik+VelpWdi+RmtifFpt3bewQm1RAKp15ag6nUx/A7uiEGwSK3Th1jlUdbz0u2/9D9rLsZbWn6sXJYZL1VewuL2v7VlYtQLOJQXEY78K+Ihi03VAvAbAORuzDwIGmn1lQ3uDOogNQ8MisKrM0NaIWJKtxEhAD/MLKECuLtJHRCmFpHYASi83USmqbK3WAv3EQYCr4uxdmuV91ahVdXf0KtFs9nKPoM61U9PeT3UTwOgW68uz2CFuo0/alpaJ2mEElkePUTy6ql7WVc3K6Dvl31F5+msK2RypmzXY+l0kEUcslJeX52wqSEpKcmIKV/zk6mNdffKvynfXmGLldY1NBs5ZjGdrvCkZKRpzm9AcSflVErALPIMbjA9m0ZocuZU6dNfdR/2qbqxxrKQPpOfrUN5+LXuvqMqS4vUogmK1uL8AxTLgnSDGggdmeWqKVoN3FUVsmA5gGRGSAJAyDfzTom6eW/cXmwAciwHSUBVkvBpg0/a5iirgsxUdKT+pQxn7gXZCdB9Y6QvAuXqekUzsib9z7G8oZQuqloDXBs7dxuru7B0gp2cjQAmAX5HJutPZgbJGqwMBJqclK2U7bZFzmILMs2fYjy+tKQQwM5kxazNJygcjA6pm3X6MDS3JqUkqP3KY/g0wkPGp9kY1VpmbVFp0WHnbzf4rQrdRE70G6NZLLJ0cngC0lKuQ6HCgjlm1A0h0oihlymi5tMVvFX9Xcf4JakT96nMsXidp1++UfQvbulPaAvRFx4cqybQutl/Utc5riFu56VuHv6OD28qAFgGvGPv7xnsZSz5xFOfyScJmJmWr785DNZJMX1xbUnJKMjDGTp4FQDwyhUuAJ7PAxZY0TY5M0pawED1AMfBG8w3dbgeWZ9x6g81PUVhm32m/rUaS1sv0YfnYZxdkFCrSN0wG/n1c+VNUIu8oZEsEYwUQMMn6xbVngNb3dPtOOzaMs0rJTNMHxz5QEmUcezSi88BjgwA/WclZen/fN1DqQxVooV+XgRtvXK9VQkSajqDQlZO8C/UY7G3JQ/Y/uYst5zndBpgtBKzYf7BMTUNNqNJ8aSebEZMJbJPDfTBr2wUnkb7E5oFNwFbb4tIU6hOuRyOTAA6o69xrVHxaDABHEUqd8RzzNpazzbRpd+BO2lv6fvkHbdbIOpa7Nz9XRytgDlavBl9v3ZrA7Vin3XajzFevhbWnykhENbDoDW0LT9K9Jyjn3byiSQecy1Pp3iNKCI/TDHaUda3YhmLVGovinAPOReQyfqIeSnw1MI3yz40rwN93lI5iYBGbwjoHe3SL65pfWFQi8VFy6nYF+uOuQB5hjmfQQEB/n0DtjEmnfKF6+OieqlGp7WNs2Z6Q4gDVYcyjbj+g/Z+7AOjtq0OA4DmZexmrw/QQNeHrgC8tbTVAyUHanb5bSaHbqDtgUyzLW7qa9HzuOaBJlg4DSqREpTmW9x9/auDcA5UeBJxDTTEGOz+ypA6MVosN5EWgnVHmfafefU/7DZwDDjTFueH5YVU2AXIxL41A8SZ/315+v6GKK1WAbU+wPd6qzPRsBYRgpYgKzio5lVlUkf18fZQcnahk+ofnC3Mor14G0KsjL+Or8n1ARiiAjcwaFF6pcRTVC4DvyrmP4eGhGlsZ535cU2d7L4pZAUrdnsqznkAdojJEfTV2NWp8eEJpQJpvFL2DGtheyv9UNxqv6er1SsUQZ504dFK7o7NQqnTHbreJsbVC8/Qr5QWAc6i/mYKdKcKNTj7UFVRbq1CGzSgE7thfBKj/XNcrajQ9+oxxOgmINZkNb/6OiqXtAFigr/HB6jc2PBX10xj6B9TugA8bb9QrIiAUy+KDQDsJGn4CWFlzQ4+JZXOyClSQXQygT1tcWdSF6jN8pgrIw0fZmXmKjUxkbr9IfDegOx1tGn08qvDEaJ08/qZ2x+ShxLzqKM7dZMxNJf57LR+L1/hdgHMoyAKCnDt/FsvDcGLSN3jW9yhosy/YxhIqhL3ArVd0vaZO2dgVvk4sME/M+HnV5xoaG1AM11+UXeTEEwZMLLEWsbL4zLSf6EfjFUWf/BSAsYHnoLYdlb/4ABQCC5WNRXA/yoMXGNOHp2e0izZwgo0AWVGx9PXTQGXXadf17KPx1M7s3Yz5WGuzdjHMOW/1NGoM28e4CFQcsXvMS8jTxNKUrrRfUVsL6lHhWKPue03ZWNSaCpOjODfcoJ/Vozg3Oqz3jrwH4FUKOBcsryUfxp9ZXbtPP3v3ilbZiPI2YF2QZ5gDlrXcb5U/GzP20Jcn0W9sou9doh08fTzlqEDGxUYqilhnFRvQOvrQW5x/DqvwkkMHlJu2Q8tPUEMjJr3b26et8XGMhwe0OzXHUSau66nl/l7HRnxV6Sk52s6mGC+Ukx8CRN/ubsZmslORieG0d7NqfQ3I20vVd67qSs0FgLx1/drrv6XDWN2SlVc7FtSV1dcA50Z1oPggSoCof/lHk5vEvWHlS3DuBueKDg9ns8MerW1a1/X2WvqNftpclNMvx0UlYckNAAlLYZs03FELjmJelxrNxiz6ZHvOrzA/mV+c0ZH9pdqXXYj95mNdBADsA4qOQQX4eP5bQNHMe7Ccra7FwripgTnJC6Wk71BqyjZANg+NTD9UE/3xwCDnDozRm/veVzGg+Qsv2kkHG1wqrxm4Ql9TppLsIwpwDwc8b3asbWeJFYpy9ul49kkgqnjmbShuPpvQre4GfXLpE8Wlx6AQWMz8SKqurGZ+Pawg7C+zsnOxbiZucfPGjhdz2/kFVLewXY/CipZ50/zaMlaoWINfBQiFcc0rzFb6DmLS52uqq2nWo/4Rxo1IlRUBACalAdB7qW2gQz+69GP6gXXG3h1KB5T1Is4Zpm02ESM8HBpy1NJOnjjmgIkeKx70ryiB11whFg/Skb1HVbK9lA0fW4j5V+h/72DVauDcXe0rMXU0oHwHnPMCcnqhDua+V6nTMaDEN06/rjDm/rea2tXEfMDXD0gMUDOJMcAfEG+NmO0xsL/FNlsZz9NCsbxGHbDlbjsqpzdkm1X3FhRo7548gK8lNdIWbt24RZ+UrNJ95cBSOykjqqjjwHo3a9R7f1DJ21KdDSvB9NWjCyNqHaon7u5iM0ywDueWqgSAN8Q/Qi2321DVu4o1+JTePvWm9qfsVzh2u8PPRvVp6xnmDo0qB/h8L/8bCgdGxX0V7P2Z6nsB51AJnng6ATR5WInMkW7f6ULduhkIzk8ZrDtsYyz2Y46+CPT17Pkzuc/6oAKXCmBO2w10YyNCnapvVqIsOebE/IWFBfT7bsQytzgWCpKo1x1A2a0wFeAL4LV18JauMAcapl9IJ57I2bFHm0L9UHTroz20qau3W75hvihpH9A7Gaa8DehGzPZh3UeaYC59quR9nd7zDTauBQC5PdfE/IQq2rDERtEvKTZF7wCFZqLgZuDcqvtTVGb7sX9l4xygdi6bHnOSc+mvO9Vyq9lRZ0xnvpOckiR/nP1MffUZCwC2qSOcMT+ZvtGP9dg+NiLUsMnk0fCIEqKjVLb/sAIiw9VI+RpoCy/WN1A6L1QRfWqEvx/PGzFNTTWKu/cA7IJUsKdAUYyTZm/fw8aQzo47bM6eoT3k6STqjNsDU+knR/UZc99BNqjtpG0baG3g3NWzV3TtSgVWram0wVPELCgpevOwsZ5rqrzANNxPOh2+G33wX0MgfD04R/Bgiww2WZmamnIWpm2RzSYx5on+3V//dWeC91VwznZP2W5vWwi3BfM1KtWHIMN2iduXfd52j9tx7Xi2GGcJAttlZZ+zxW3bmWC/t+SDTZJsEmWL13ZMS3bYOex39h7bkW8qVLYw//PAOXuvLQLaw2g7+leY5FhnYi8/FvjC6aw3+yO5yzHsvXb+8bFx53qMoAwLDSPInNHioi3jsbPQn50GfMZ219tnbFfuLIvuj7k+W+RPgwR2KWNZIsAmxqPcaFtkN2UDK4sdy8pvSS87li3KvzxJts/ZtVoCwK7dtcBp77Fy23Xaz2ZjY5//eS9Xeaze7MuOaS9L1tjn7J7Ye6wMT9ndO0/wa/fNda+tjCGcwx4Iu3d2LVYeUx+wn5MIvOyY9rW8tOxcU1BwkLOb3+rCkhF2Xjum65z23ZVwsPNMTU451kf2nuAQ61TZ9UD92L89gXoCWXA0/2hf6s5+b+3RrsH+nkiSJiw8zIGh7BqGGAysLFZHVqeWHLGXQXOmBnC/r0/5NjH89rc4toei+bvLDsnui5XLrtuuwerF7rW1T7tvVn47timhmTWU1Z21T2u3iwx2SSxoWn19XbLLyrnIccf4nH3ersuO6aq7dQJyWyize2L1Y/VmX3Zca4POfYbQdd0HK5Nd339qu7b328vKNTvDvkyehUUCLCuTvcziyhbI++73/QfgnB3f6sPKa8+dfd6DlTBr39EMjFZWO8bPA+fs83b/7dm1unM+T70bvGh19fOANOsfzBbp7/yd39W3SIx981vf/HObLCu31aEpYnx+5nPnuv7VH/0r515Y/dmivt0Xaysvn9c+Z8+M3W+X1ZNdu/3ezmf33j5jZbLPW4LNfrZr/XmqJ07l8R+D4p7w/NhnrF1aOS2pZ+eyctr9f/jwAedhXzd/s3LbNcTHxzt2tHav7T3W3qzfs+u0f1tfZGWw63KuDehvS+gWp99z1Z3dH+s37bmwz9n1LmM1McN9tjZtz5q1YetrXu5H7fN2H60dB3Mt3EhXcX5B33/R5/sFFevVaV7VwKsa+JWqAeurXeOjjSevXq9q4K+7BixGccVff93n+kUe35TTTPlsiFjerEMNarI4yhWrWhxkinEGbVlMY4puNg/5u3/37/45OPey6trLinOmnGYqabbpwqA5g51csJXNDwyYs3+bvaqBey6r1r8MnDM7UYv5/t7f+3tOzGfHMdjPYjGL3+ycdhwDxkwh0OAyg7kMsrPzmAqc9Rl2DIPGagERDLArLkZmnnnMV1+/CHDO2pUBhQYoWlnMSuy3f/u3HRVAuz67Ly5wLgYYxdT8zHLVyvP22287cxu7bptX/OAHP3D+bffn3/7bf6vvA5698847jmqcxes2hzaQ7Td/8zed91n9WbntPlncbfVqcbHVj7UJaxsGTJp1qt2Xd1Gitvm1qchZ7GztxJQH7XoN8rO6Npjw54FztpnK6rsa6Nnakh3Pym8vi70NErR7afCgbS4yQNKgOQM87brsZXMbs8O1edAf/dEfvQLnnFp59Z9f5hpwxSmu767+1WIZ62Ot7V+8eFGVlVi6MD81YM42zFp/ZWOPvd/eay97Pu53detf/+H/ijUKqjl+mxQPVBTImqUfCVAvkt4B9HGeUwBhKMkE5bIAXFqqddZ3BmsaNN/bj0IJGzRJwL5AKUOLs1gozSuY+XEg4JwXu5TdUEZbGxzWNDvSH9zvQIXkGbvrsULxR10DS6k5D195xsQrvfSQNkcCm6GKtcSi7+zNBj0bGNSaf4DcWMtbxWZnBdWQ0K0RiszJ16a0HOa+HlqoBZSpI8E6Oa5VwD6xrhS4LVGx+SS/4xJRsjPbFtTsSPqtdNXpEX3W2NCkMHgkkUfikaXkxUXW1wABwrdlKgiQTZaw72nR8H0WhUnkhgCU+bC/7cX8EqAAOnmo7kUUkdhDZciNsWx1CleEi5e1catO88+BlgDaVrcArjnrFB7aQn3EFwDqkeRZf/xEK/WNWmxt09T0E6AQH/luAqzzQaWCJKX71kiFYyETCOztseGl57da1F7Fov3EKCpaW+RHoi0AK7fkvH3yT4zB2pCd/ePtutCE/Qs7098owKo1/SiKB4GkKFl/XcRuqvGymtjpHgk4Vgw4t8U/Sr1Ac/Xson88MwVMZioloSQmWRdCyWuDdYgtlOtQbp62kgTqHx9C5QaljMdzJON2c4yDJKwjNUOC6zZKgs1tLTSmDeVm5atwV4kCNgdhI9iuGuCM+5xnBQUOf9S6vH1YCPecRylsiQTQBHBCpA4dPInl0CHsqnxU03JF9W1XWeD31hsHjqswZS+qI6hVABj0oZ5yEUCmaxhw7kCZDqUfVrRvPIAXdmyo47UPtqI49xO5RbwARikDPtiGkvSgkyQZG5tyFBZ9NmFg5kN7JdS3tbsAVKAO7ipVaux2VF4GUSa4rIGxeySKtqsEK6ttJOvc2JV/r+eubrZdR+GkW9l5GSrZQ/LfdtCzhnj9diUKCg0oGDzRFhLUZi1JnlAzz59odpq1qBdeqDQVoy5yAKu+UGC7Lp2vPCvPEOnogZPKiztIsm2zurGZutx4huexF6u/o3qT5HtEMIqIXOs0SdgbWLtd76xgHe+F3iwmARQbow5U+Gq7b1OXj5ykebBPBOAc5mOodq2tPHcgjUyewdTUnSTMH+tqZQUWhyPalpGAQgKqNeEkVUkC9zzo0cWqc6yFT2kvSZ4TqMtFMsaPzPerrvO6mtpbncS9f0AIqnLh8nLz09TsKMfsVtAWwLmich1NO4EdpZ+u3wOUB0gwi9PfefNva3dCNlDRsvrHsNqtQdlpuB/FuR2AkaeUEBCnDVTxZjemHXWP5vYW+gsvFRWjVpkQpQdjgySnABImppyk52YSPZbA21hBaWGV9eIA7Lwys5SenKm1uXXsC89pcHpQ8ajrHEVZKRN1hueoX7WjUlhdU0+if135JJUKSQB5h/mob75Xbbdbdfd2F4Dkmnxpt16mzudLPgHAa3iwX6G+QSTS31fZjuMK5bkxGNmUPG0t22KOw4cPO2uF1pe+3K+6+mXrZ39VXq4xyVVe+7etIxs4Z7Hh1uStqu6tpZ+aduLHRdZVN23y0w7iv90Z6bTbFUCWJqzHzGI5SsdzDqLimKkXfhu6PUlylr67v3cU4CGdRCzwblQcY8Iclqc3eT5vck+maZ/klgIisNMiD0YicfoJzz79T+7uQpWhzhNDzmigDwi3olpTxH9HDp8AcCrGnjNEA7P9unj7gm613SLpn6MPgM5iA2OBO8mhrC7zjHZgu3lOY7PjKLsc1u6UXE1MPgKYuuP0cwu0SU/6Lh+stz0YE1cAJnwZc1NTSYDvSAdo3VB7S7ua65sAPiNUVJin7IzdjIMbGqb/qbhxkWTrkJJQdCvf+yZqUBkoejxSU1+tWpobNTs+AywYKF/g6lUf1vgB5sYfPyRZuaDdJPG/UfhtyhejFtQ3z9V9ocnxKZ0q+wDlmpMK9tosb2LxuZUZwDmg5u4K+npPvV/6AUnoEhSOfIG3VjQAuHi1+XP1T3dqb+4uQL18QB9PnpMObKa7ndyHqcptpu/0gCRYI/luSdUUEro5qcCMYUFYBrar7jYw2PwzB9Atzi3UFhTvxlGsrWtpUtfAXfqNUO3P3Y+y016UDlBvGrrpAEtjDye1CYu0ACAidx/GXC+Ai+V5pw/YmhCn94+9B/iUjBX3Y50FHusduo+lX5beK/4AcC9ZI4uDqu65ynysEYW2FJUVHNOuFCAqLMlWUTi8j3LYVdSlukj45uUU6fChYxpbG9fNgRr19aC8SVcTRkLeF3hohST/yotl1E+wK4tO0W5AMX867u7bd9UIpPvMbUZ7inYpH8AzbtNWDWGxeQOYx+YxBtKdBIhKASha8SX5PtBMcr8B5bUh5inesr7UgPol90VNPh/XwtJTJYYAwmF3uRMVsAEAp7NVJPzHn2g3gFMJoGYswInBJy1AV59f+VRxabE6dewdpYdliwiCxP+ihqZQGQPw6kUdLB2Av+TQYc0ARTXTRnv772vBVJFok57EHR5sCFghr+FNrjchJgXLasDvNV/ac51q6cP9/b1UvKdI+Sh1+m8K0EOu89y1yxrs7udaAAb3lQDMZxAPSh12z1FEGh156LSNEN9IlIjJAfsuA5IPa2l20bEiLj94BAARhThi048/+0RjQGIlbEIwxbmtXluJxxibiVsaerBqJbYbJ+f1+tunVbQ9T8HrNAgsMofnhgH1qtUFTBoRGabCYhQ/yUHac3jv7j3q6CmxLaID0CruqFu5ESeukFOJjY3GajgXcC/RgQIrmi9rYnFUGTuxE9x1QgnBKYy10w4oUXujSgGrPjqEdfKu7GytAwV1D99TU2M74+0kABB5RsZ6Ny8ELTataN7jOfHtMMfYDij2ngoy87WCZfqNW1d1ueqqYlJjdbIUOC8aS79lOQpKFcCGS8CshwvKga5KFIx18Spj5ejEA2Bv4oqOWm3fvV15RQW0wQDd6+/XnVaUKelTvXn2fFBVdkfQZYPNJ2adnBCZAry/H3vzGLVhKVjTeUMzE09UCKB1ABXASNTiplHirOfZbAXi9GK83gdcVJBWpCDi7NahZl0lBurr72Ms3+yM9cYvrBAPLxPXT9CXBkZt0cmjr2tXzF6tz6I4hw3wzZYq+rhtAE6vAYpkUYczjoLSpSsXFQZoerLgBJBYNiAcOXA3rG1RlqruqFb1jRrtROX1jbJ3KMtmNWHzeLu7FVXKJygo+gBPYNlJ26SIxGxrAFHEawC3UTxbnR0A1nVVwElT2lWQhu1sgRJROZyeReG0s1U3O9tQjtrQaznFKG4Wy2OzBwqDPdhSAuNTvg0KZgpY/mykWMdze5y2/QSlxfCAcL2z713twYp2EnCuEuiqFXBtRyRWtMCVGVjRugMoGjjXzIaVzxo/0TjrGKcPo3CWuR/7VzbBoFg1BwhWcQ/4sfca/b70zpH3lQS00zlMLImS2Tjjht+KF6pjQYBf2F0T4xp8FUr/mcWmpfiURGIsFM6u1xL7TzpK2AeK9iuRmNmd5/n+AHBrLbDS7FM2NGTo+CEgONSqhyaHAApvAYcPoSTpJj/ieO8ALIiJvZ9hodvHBpTwrSGOhXVpajkKhZtQPwQuYuxZmVnRt1//rg7llKIMuKzbjCPVqH+PDI9ha7nf2SARhp2wu4c76nKPdb3jBhtwbmoLuVKzvQyO3aJ7KMgZ0zE7Ns94g2Uwx/em7+UhpDNfUyjzoh3bdzBWMHY+mnIUm3se91C+BM5brB0AinPYrN8abtbN9nrNP1pVVlIh8XiJEkKDuZYexpZGgLA+LawzVyX/vomc6KoHMD7P4YMRwDL3LTpR8A5Wrvvpm1BmRRnvelWVIzhSeuAQGyqOUO4QtXa16yrWtrNLWLXSx5azGSfKn42ZHhuOOnPj3SadufKForfH6FA56oAA8EMj/fS/dxinx4nNPRTgF+hsrHqx5sYmlw3Ut0MApdKUwkaa4SlTKmvmXt2nnW8HcMxXDFD0czZ+3O7q0Z2GVj1/DLS3Jx9ltyJF0L+PYcv5ac1n6gau8uCYYahHevh6aNV7FQh4GQXspyZOTtxymHEnTx6rnpQDG1PuX5CBc4x3B1JKHYXZZTqarpFuLE6BoKfuOVatZVllivNNlDf/IxxRO8qk12ovYQf6UCdOnVBi/A7qeELtd1oo630AuGUHqg6knTJUsOlhSZuA+7PS0pUTtxNFuUXi+kosSlGEBWotA65MZFPbKmNRP/awVcRZ02Nz2gYoVQIQFRsdiOruY7Uyb6m6UafnqOpuAuL2pf9Z96N8GxOa4nkK2ABGY0NDWcFh6jSaOQgqxA3X9IT57Jun3qKM2DAzTo/Mj+hzYjIbW8szjut0/vvaglqtDSSLpvZ67zrzukvMx56ysauMuC4XBc0p4pAGB2zzoBICmWN62xoGIJpZAm9xj1RuXAHqjumacZ/S1VY2bLHZKyYEgLK4TDuS0rj1qLCPASFjiz46NQyQvZ0x7A0lhm3T+PKwGvtuqhk47/nEojMv9MEGfNl7nra9oIGRQTYcejnqyW/vxE7ZN051wHmf1n+qR+MTeuvgu3pr7/vAjwCnzJUfA3FW3MFyljEhmTjrrb1vKSMqA8aEvV/rjzU41a+PiIUmiXd3A7DuSclzlO86O7p0v2cIdfJFR0F3kx/r0vR38xzTnRgkdztQ33biUiaZDfS7BicG+foDnBYqNwNlTTZE9g4PAUASs93tUkJ8DCqOucpJTHE4szog3JvEOuOPJmBc/FgHRtzIE3EnoMk5Yr/HxGgZzM9OFJ9Qkl+yugDdLzCHH5kfZaxKYC6WJ695HzXWNLJBqksZ6Zmoy76DMiLzMweco4HaBNjAOcZbe/3Xzqu+FpwzuMPgmiZ2QNoitQE+zmSOcxrYYxO+cBa1XwbnDJIZGR5hgtrEgnmTA6jZwGyLy3vz9jq7yw0i+df/27+GYrzDItOSk6R4//33tCc3V210kpcuXSJQ63UWwG3B3MA4W5QeIkisrqrW0IMhzc9BORK8GExUeqjUSTAYAGJqA1+nOGcL7lNMTivp8IaHh53PW5LTrtc+d7DkoLIJaAwgsXJbksAsXzs6Oti1soVg7CB09V1ncd7gsK0xW1XKop/tlre6GBkZQXK9lgVG/O0B3f75P/vnjtS23RxbcPz000/16SefMikOVhwLXwtM8kZ4oBeAdQxoKSsr06m3TznHss+QRXEm080MshcuXPhzQGqJc5tKWERkhKNUZYuXe/bucZIWNtn+updBNbbLv5tFTds93MvCvu0KMVWA4yeOO4uhpuLX093DvW52yrIMBLRCEGa/tx11du8siWSglwFGZz4740BWdg++++vf1YOhB+of6AeueuRcu4GD73FPLZlhCmaWTLBFWIOEDpUdckhbB2ijnKZeZ3VjiSxrK5ZMsknJGIuvBiFZUsV2Jp0+fdoBEg3gMrn2ixcuOsf8rb/5W85irrUrS/JY4mlocMhJuJx87aSTwLB6+Sf/8z/RRdqWtekIFnIyMuksGCTtuHYvTRGwj/qxY9tCxvMFLApoN5awe4cH0KxJLJli7fCTjz/R1WvXlLd3r/N3U0Ow+/67v/e7jiWStaOvvqyurB5++pOfOupux44fV1JioqOANzCILQkLn5EsIFryxZJB1va6Oruc9mqqepkZGUjBH3Xugws6/M9p165OYpzrvHWr0QlInPqlk7fnwDomA1qtvbgU52yBwmBKK5+R7lYnBlaaUp4Bi3aPY1l0M2VIK9vXKc4ZVGhKEa0trc6CoPUj1lZN+cFsm+y+fd3L2qo9N//mf/83+t4ffs9p4y4I0vqhs2fP6k//9EO1tbY6HaBZUVlbsWfZEo7WN1h7MJsZewaXgWXXaFf2MjULa2fWHu0ZsmfeEnzWpxkwa+Cd3XdTorCfLQn7+huvf91lOr+zz9szYMm/DnYNWh9hbdnKVry/2Lmn1nZMrcPuq7VB63cK8gv0m7/1m/rBv/+BGgjC7ThZWVn6vd/7Pee67P5YG7hlZaAere8yiNJ2p1q7taSj3Vere3sezEbKnx2XR48e49l54pR/kkDZ6sXqO2VbivNMtTS3sIvsHu3fS9tIgth9sHpzJQt/bkH/yv9AAPzq9aoGXtXAqxp4VQOvauBVDbyqAWrA5mYGH7nUzAyosvjFYjL7m8VpBkPZnMLsMX+dTVwWC1vc5FKc+3ngnAFSNkcwZTCL+22OaaCcxYpmxXoTaf9du3Y5ymIWI9pmkt///d93YvKfpzj38ccfO7GkgXGW6DDQz1TabGPED3/4Q33/+993Np7Y9ZkKmsWApo5nUJ2p0dl1WExsampm92q//3/Zew84Pa/qzv/MO70XzUgaNY8ky7LkJrnIluTeITSTmF4MJkCWsJsGS9h/wAYCAZIACQkhIcFkgX8CbBJTghMIGBt3W73XUZum6b29M//v975+vYowbMiy+ST/nccezcw7z3Ofe88999xz7/nd3/nCF76Q1hL5wxFnKsa/BXBO31XAmOA013yuFwXAKRPrmQfOCYRTXoLiZH22LTL+6VsLvjGlq766MhF4JojRdK+mJZOZzUMerulskwdhfE7wne975StfmQ6zKAtZ4PRjla9pWvWhfUZf34CzexTqjAA+1zCCE5W7chUM95OAc+4v+E7Z5Ezxax9ZL8uxvpYlo6E68wxr8je+8Y2pb22P7zeoax193veZ5neOce5MjZ37+d+jBNRb149nghT8WRvrmHQ9676MdtZ1qWtO9yYEK6vnXvln/W6KuI9/+Lej5+SJWMP4WY39q2O9WgJzVUFJUVSQiq0enN2y5SujmjV48So2TcenoosA4tDeQ8HmAimiyLBQCP8M6Ueqm6ujkSBauafLG2WHZEN6kINnp45Hx6HdbIofjjHW2cRl2BytjBlSlpUuXx3nXgEYDqBeAcGQbHd7jLO31bmV9HwEwmYALsxk+KoojnnLl8Z8gp+lLavZpGUfgP2PCTZ2Tx09FF1s1k5wOr9uRUssv+KyaGgBkFsBcG4SUMs4aZAGj8cwoImTuzgE3DFgRJAg2BiHPKuiYeX5UQNrQ9HiJaSt7In2Q7viBGwBE70wlUyTEpJ2lAqgI0Vg02WkWF3F+0llkqgEYFWbhuFljID2aQLH3SO0ASDCDADDAvbfFqw8LxavXxelAAQz9NM0h/Em2a/sOXyMIOFpZORhZICBJQS6WpbFvMsvjepzWjgkCsPK0ZNxAtaJtr27Sfs5FlkAcRWwOKyBAaL6nGX8PkNQZF88zsn6gYn+2Hg++6KwZZQi23FAgUMAFXfu3U7QfB+HVevjojXrYQ2bH720cX/77thHAG6gezQKxglqEqgpJiBQVVXKfmlTXLx2NYG3EkB2h2PvgYOArMriUljK1pyzNh2IHSfNWCeARNNI9Q50x7JF58QVsAg11jdG73RHHDi9K3YBkmg/CTvYGJkmAMBAuBNFgNd2bdnP4ey6uPG6F7CBvwkwVknsOwgz4aFnAMsRsCfwvHrR+QQ06GMipycAfD1OitGjpBJbfyGb982XxvxS2C/ok1GAc4dI2/UgqUMzjTMAIi6PpTBHjAyPxvG2U+w/n+DwaxtjZAJZE1NhTirh8PW8WhjkaE8zOngU5qpHD/4AwNQQQa6L45IW2kFaKnJGRA9gx+0w2j259yGYY+YTFN8U5zaQjohxcaDnYGxv3R5Hjx2GvY0DnGUcbFxYFWMEzU+f6IgJmD+uvGRDbFp/DUGJpsRy+/j2RwJigtgA28v589cluZ4YPBhP7n8ojh05HhtWXkkaphsTAJGoBuCv4djetiO2thIM5gT+lRdcEssXLiHlF2lkAR4cOozOnexDb8nYQcDIlLgCBJcuXBYXEhhpgp3wUPupeIL9fZCAccn6tbGmZQ2shAtgTykn1VJ3PL3/8dh/aA/sM0vi6guuhfVlAcxQfcid9qEj+07BJon8ykqqYoFsUBN9ceD4VoLvs6QRvC5uOv+2KCfQva1tezxx5IkoHM/ES694Wayavyqx6LSRRlWWneP04xKYXjZdtDkWlnNAGuDc6MxA7Dm2Kw4QYDRV65q1sMuxrzqBfh3HbuxrPRQnujojO5wl3S2H5cknNYttqls4jz3hiwlGLoq+zp54GNaLCQzLhResof/Oj6ayWkAYU9EBW8fW7Tui83hPzK9ojotJh9mw2DR+A4ndZx+AoJP01TCAvAwpwcobsJX17MFveSZKCW7//LV3xvVrbk5p3PSL/sf/+B8plqFP4d5v/oB2Hjinfc7v3f57nEP+T9XJ+eTMducPYLvHKXFDy+oWgJ6PwDLVDsNZKbpcQCrQ+jgfcNFKfEf3P5/ev5OUu12kQ7wgLgd0ubh2PjYWhhuADTvQz207dsPi1QBb0iWknlwGw0dxtMPwuPvIDmwU9hqbXlhCqrtqQCXECU9L6tA3GRedvw7w0aZoJq7VhS14lH3iHgDCl62/MtYvuzjqC6qjDcaTx48+ErsP74G9bC2pv24FOMkBf4KrM9kpgHWH4tFjj0UfdvZy0jKv5Z7s+CQAgJMA5w4COjsJGK8PXiTASAzEMoKTjQ0ws5y3OpZQ1wHmSlOEnsKmX7D8vLjsgovBeS8kFSTZdWBE2nrwKYC1O2EgqsWO3xjnLSOoWTQJ69bh2If9PnTwCAHJYdrFXFQPKIG0Ya0Eb0/3dMYl518cd15+J8Cdc+Jg1+H4wZ4fAoTtjWtgD1pPILWyoBQgAwn4AME8eeypeObEFti9igEN3hQXLrgYViDYX4nId/R3woryMIwiewl2wy5HHerKYdw63RV7jxyI48TNhgdGYFXDjtLGAgKxVZU1sZrxZkC5oCgDA80zcQCAQU1tBYC+CxNTWgX2Xfu77/gRWEp2ETCfTEC7K85lLq0ojI7x49jRp+PA3sMx2AXxAKCuUphHy+tLAJWNAYg6GE3YpFe+4JWxkhSoI4PDgLkfgh3tWApaX3vB9YAtF0XXJPUH0LJr907s1KK4FCBwy/yW1H718UTfEer3JOkrSWMmEypg7/HiiTgycpC4zpE4va87prrxZ4iZzhbBul3Oof26+bECxs1VtG8atrC9e/bH0fbDUdVUEhdcdD6MlyujDtbQAcDSe0n/+RTAlZKCsrhszWWksFwNILcieka7Y0/rvtgGYKB3qDcdNigHxFlGP/bCotPWfjwWlJImctNLYu3S8wgut5Hm87Ho73U+Wkf6V5gGAROOjQzGQcp5bOuj0bRsXmy69BpYBlcAnINxDqBE51AbLIvbYTs9HMuI81y8DmAm40EQx2Hm8ZOniKWyRpqZZWLBp3Gs1NU1wjKzPDFEzQxMkE78SVLk7ojl5y5Cr0g/3CBrVGkMAtDcfnhnbAd0VThTCKBsfaxeeUFKW9cF4OMQbLL7AHz0nx5imuFAQE1ZVDVX8e7W6DnRBfBlSdxM+sxz8R8GWL899OjD0dXXHpdgwzfAFNmYAbyP3KdgltpzYk88vWcHAJZh1tTXwX67OmoBoYsaaaeN2w9tJ7ZyMuo5LHHB2rUQE8yL7qGeOE6g/zg6dvp0D/qGjgLcKS0FGAE4aQU2xrFYD0nIjh1b6A/Yh2tmYt0FV8CAdSmgJ/YqYCA+MXiItOGPxBA+4vnnnAf5w9oory3j0AdgEHyJA4ePklIPf4I5vrQygz8GkKp8Op55ZDspalfGyze9Ii6n7wUQbIcN6vEdT8KK1hQb1gGcqcNnmZ4BkHCIwwxbAJhkYc8ktew560hHCVACoErvwOnYeQh7dgSwxsplsD5dQJpT0k/DkreXOPtx4ukSaczMTuK/4rNFCXNTfaxYuopxSJrhmsrYcmwLaRp3RHkG4P55l+HbrwLcUJrAtkc6YZDbvTv6ewZp35oEAG6omxddU93xTPvWxIrbexrw4WyG1OxAMgFQOo8eRH9KKsvi50hvfcGii2Elxo87uD12HNjC2n8ZLJmkwobNr5/0xdvbYSp66jFSAaKj+FirSRNcid2ahW2yawiw8dHtsZ21xwpYHDeuvxYbUAmz0in8sENx9BD9196FH4PdxVcz81Idh1yWNy1MgI6CworYsecozE278QFgjbqY+QO2S8fgJLZ6T88JfMUt0XWsLTYsPh8G5quicl5V9E8MxH4YP7cx358AxAtMBuBVCX8rx250x3EA0WUwN770SlLLwizXD5Buy1Fs2cF90VKLX7QGZklsj7qd5QDD/r798dDBh4g9dwL6QkcBOlczjxQB8BybHI6nTgJQbCO1JvPcDRzEaGk4N3qYH/a370ePDkXfMVC6I6SBpg85TgshEOByDsOsWA0r9/y6ON4JLmPLNvo5g09LimxkVSF1GTrTN9QVz+x6goOPR2Ie9um6zTdHbXUD7HwAOE+14ssdjFPtAnLHWaLQxnrYpkiTu3X301HdCAARluDNAKYrkeU+UtRuw4efHZyOWzeQ2how43TheBzohO2TMejhxbXnXwjb3fqoAzgscK4PRsOd9OFeGLhrq2A8dgwuqMfv74tWQFRtpMsdOAU5DfbEucV4eAlg3QWLm0mpe24sB9zZyuHHLZTfD5joovUXp7SSDcXVHmUCAH08sXO1HSP2X7OcdcUVMAY3s44bin0cwtgC++XJnjbs8Rg6TmrU+tKoXIz/uW17xEBR3H7VHbDXbQYgRGrgVnR0x/bAK8PWrI8LWy6LMnTlMOuwJ/f9EL+vN9a2wDTacmU0lMrEWcCaoj/2ARz7Hge+GpbMj/WXkra8jvThAPUPHjsIduII8fce3i8xFZkZSV1aWVENMKw51qxYwZwBsy0AqW2seWWUvfi8i5nz15BeuQGmN1Ij93bGHup0El0/d9ly9PryWIxPPwlo2PTWO/Zviw4wDVnWPkWlBTAhl2CrZ+PU8bYY7B+N2269DZlsiBIOhR0+eQgb80hUMN9evvpKGBZhJpflr4AYN4yej3Ng7BTpMc+/YDV+8fqYX9zM/AQImesQbdkKQLAPIOTlV14ey5jLpkcL4kRHK373TvzjE/xOtj10fpZ6F1SVALycD/jxvFgG22gfGITH8ZNHaeMFa5hPOVhVzWG1KdrRP3WaNNh74sC+Y1FRVsPhhQthccPGo4cdtH/L7h1xrL07BgDPTTHOKxo5uFU+GifBERQOZmDUuypuADg3v24BNpc27rGevfjc19HGNVHLoZ+u8S5A74/EHvyjK5duis3ncnALpjYWbsgZH/7UVsbwkzFMmuBLsMn6dRMs3I62t8YhfLberm4OxE2CJWFDgmfEFqxcsJr07usT8dPh/r3xyL4fgD8aJKXsFXEpgM96U2oD5HIMPHMQ5u22fVEBA/mVq2ChBzg3VTYep4aPU6dd0br3aIwwh+EOROV80kwvqIot9PsIc8nVl14bL177kljMOnZn205syQ8TpuS6C2+Oq2lHBcDmWeQ6NNwXTx/dCjPuFljdFsWmc6/mPS2MQw5LAlJuG4ThFzB0fwwDll8TqxasSetc7VLroeNxApzDCHsuRYVkHSwjqyR7KZVgDky5vgqQ4whM90/CGnikjbVZ8zLmritiMYyXmUxhYuw+dOpgOkxSCvh4Pfs66zhYVor/fhLQ7k78jP2H9nOIrJt19WzS0wqAumIp9uAnnQ/48AWbXxiLS5bC+g0uAhbL0xwGqcbHXYgezvTOktJ3dxw+dBR/4tL4+Vf8fGy+5soEck1OmAt2/tcf858C6u7v/9rrR4BzLlpO4dR+nxPYpqG59ZZb45J1lyQwiuxg//S9f4pvfuObALgW/DPgnAAygUGCXgQ7XXbZZQmo8Q3uHQYwc/MtN6dT90888UT8yWf+hEnkAAPgUoIH9yaAmkARN98feuhhTum/Na6GGthT7W6Wu4En4OOlIEQF08mM5Wa5G+jveve7EhhG4M/ZwDmDLZ4oN9giNbrgI4EzLlAFBf2/sAUIJLvlllvS5rrseAZRPvzbH05BGDfwXdTKJtZP2oEfPvJIQo0r7DwrgO01ePIAAYWZmWwKDLih7uUGpCfTf/DgDxKAcOPGjXH1NVenzUlPuAtYW7R4cdx11xsTEM8NSRcF//DAP6ZARS0AoDsA1TXUN6SNTJkLBlCk22+7DRDe0nSCwQV30u70xv/5j0AcU3A+8O0H0iDeAErZTdGnn3o6gdqUr0xeAnIMgJg2UlCOYDwDUZ64+PYD34aNijq8/I7UH058BhIE6giCuhKqSQFAgr6OHzsejz4G7SvPXQgwzdP/BqU0IK0Azr7/ve+n3wVcGeBQZ2U1NJBl/9gf1tEgh0GnPBhpG/1/Psjrd7zjHalM628KIhkG/tM7/lNiLzDg0ssG70OAtD75iU+mDa1fuPMXYCz75SQQ5fDpP/x06tPzAPa98Y1vSBvApg8ySGTATZ2UPcFNYgFTCeiG/l8JZehrX/falKbJd/7e7/0+bT2WWONaWlhIsZGsvN5AmQaRrPvZl8Engy7WQUCWsrHcCy+6MOmF4DL12UCZIEXLXbxocdJvnzt56mRifjDVVCOO/Qxj9KfRaxkV7S/BWwJTzVPtuC4pLUlAwb/9m79NJ8vtqzxwzsCX7B+yui1ZuiS9X3CV49D6Gkz6xbf+YmrL8wHnBIUKQn33u96d5CrI1XFn4HBR86I0ln8ck5tpswzUffWrX4s/J/DlmMmzO2if1M0vfelLCeio7r/nPf81MastXw6NKG3dDVD083/x+ViGE349wTzlqQF2vN1///1Jb02V5QmgMca/LBd/Rzs7YZ2TwXAtzptjxT7QbqUxdnan8ntaQDN+/oJ39fR0Jx1Rp9RlA21FyOvnAHCa4uYQbIf//S//ewrOKmdPmb7q1a9KbTRI4WkL9Uwd8hKgqvxFYBtEdJz+Df0kFfNdd92VQK9u3ApK/PKXvwz1+Tc5gV+ewMSyhAjqPXwoB0DUjioLacEdk16ewLV/LmBz0XF0LptO/7aXFmDumpPAnATmJDAngTkJzElgTgI5CXjo5p577knANf1WD4t4IEUfzi8PRugTyeCmL+06y/WBJ1RlBBMAlfcXBTIJSBN89xu/8RvpkIRrMtdtHojxgIjv0zf1kIV+r+sT36+vLvhLn1xg1nnnnZcqKJjkDW94Q/KDXZO5lhJkJ2OdB5Xyh7D0IV3TubaV3U7AnL6YoDHXur5Lf8+1muxoBguto2BAD0Y83yXb3kc+8pHk/wlS05d3jeoaxzradtckXoLG8gAv05Dq4+VBL2eWLSuePqT1U6auiSxPP9M01B4Sc+2g7/j1r389pcm1Ha7dXOcry7e+9a1JtvraHp7SX3VdoMyUi+sF2eoEEOoLC8JxneW63fZ7uEi5epBDwJrAOOtk3ytffV3717Wk9VVOHqaRgU4gpc/bT+4J5A/o+LPgRVnwZGR2nW8fe1DGZ/PB2f2wRtjPggJdQ1iwsK+JAABAAElEQVRnGfQ8NKR8XR94OEdQo2tlQZbqiwfuBN65pvLyfdbd/vu3P4hyZo/O/TwngZ8sAdfR2ifHgPsOfnccuq/g2HfPzTHlPsXtHPRzTGtX1Ov8vdoSnxXosXv3LuzaRwA2DcUNjJVNl1zMCW+yQbhROQWwjEBUOeCEigZSn84j1StsOlQhprsGY7qzL2YBC2XZRylgE3e2kiDefIKU89hLgcEtSwqiAjfbeUCWhOwwwcz+rhhnLsiOGFCBEaQOBgVO2hdTPpWCyWMGli42scdggYeVYbIHYB5MCGnjqaosShthaiGANl3NSXwOAxYOkXyEzfuJ3tMxOtwb09CJlWCfqgABllRTDwNNxIizBJVnC0iFChhnohuAXTflsiYPWN4qSD1S6kZxvWwDAsgA+o30xng/7HdsDhfQvkJkpgyLAQkUwbZSSPug3IGtgvYR7c2wiT7D6feJXuaiHt41WcQuPO3H3pbPA3zBVxbbU4QsimADm+kHYEHgcpy6zGQBdJFyNVMJ5xvzZWY+gK1yUutRbyI1tJ9DhGyCTw0NcCIeQAhB0pqFyIxDvSDOCPz0RdcYgVyCFU0VS6IOgFYRdZuEFWOKvbr+rr7oPUGQZ3QCNhIyVswni0gFm+6AxPqG+mMU5pCZEYKa9HWGoFEZwZHyurIoNLsfIZA+3jsyMg6grTaaa8g0ArgIyASBR0KPMxxwJlXbMECqcjb6F8D4VFFGED8zHAOchu8eJQsDgCfwJfSvNewGJMBG/3eeSCl4XvDCOwDirU+B7THuHQfEVQJYcwEpMytgKSpA7rPMIQOAxDpH2mMQ4EcjAcLG4gWkOK1GXzIpQ0of7+8cIRhaNhn1sPnIpFEEWMM0bR4cHaCd4wBlsoAcaSRdU0kglwwMMOGVI9O+MTKMjLcm8GcDbWwoXkI4ukrxst85E32k7j0JI1Vx2UwsrF0MA+MC9Kko+rJkZqDv+9GX0bFBygZAWjFNsG4/zER7AJbOxPVXXRcbLtwcNcVNpLEdAzjRiSw44Fu9iL5aCDSvCLbBXoCfgBoIji8sW0wKw6UAUwAKoAMzmenon+6GHacnBU3ngz40yE6vUTzlDdM/g/TUuGyS0+jjFPu3RSmY3kCQxxP7/WPDsLH0A+ghfbnjA/a2zAzRHFgkxpBJHwHPnsEu4BflANqaEztMYRHQy5khAEaDcWykn0AReivcpiTDPHsQ9p0fJMbJGy43CAvLBePt9BSZPcbbCB4XxzkEPOuLG7A7OfDf6QHSdMIaWQQzQWP1AtKawfAGGGIaFpoe+r1fQAlAnNqaWgLEsCIRNLSN3aMDyIbxQtrL8umS9MxsKeAosprUApwoKcxwQJh7ujsZo0Uxf14TTDJlABKBPTIMTWXp/vswMmIQwnYFS0i1LAboI8BZ2QF7BpEhqpEl9RTqEIe6YDj7hwcIhFfFa297XVy1ajMyq0r7l9pY965lP3bPUbugXfZ6Pj8t/eH/gn/yMrCpzjX+7iFsD7Y736y/fB04gUEARjAmMTYriwBEwHBhf5eWkCp0FLApejYCKEFwb0MGdjUABcAn6MPxGEH/utAR7Zq6VosNLuQ9AkjGAAh3AnwYIkCaJWCcZeCe5t5tgHy0x5eSKvCqiy+LRYDuZrDnndj1IQAI9dWNjDWYImE4GgYo0TYGOyrsJPVl80nrupyAKDacMTLLGBkE4NlOysZRIMkLyxbFvOL5ZmqESMdsRANxeowxPEXGkKlJWHaYAhlrVRW2r55AYwX6NcUYhMxiPBsLsOEN5dS/BDZU9Fe2sL5x2Fa0DQTD55O+y5RjBQDSJ7Exw8+2vc9UaYBPCgmmdwJAefRx0mu1tcW1sOm9/NI7YlF1M4BaxivAqzHm74WkrWvCxsHrypg0C9I4bEGd0UGqPWBTsZD5opF2FAKimiZwPE5beodOxqjBTepeU04mmEJAF8TIZH4bJCYiq+okAd0CbIE2qhxbNA+mplrmrAk+P3G6Dfa6UdoNiJ797DLaVwBQi3+TDDodaxNTtI8sLJXMVbCUjERfDALcGRoiPSqB7Gn9Bto4ANjF2MO+PfsAPi2PV73oddFcCWCctp2GYXUUIENNSX3MB+RcDkBqjBmmY7wj+vA1ytCpebCA1oBSLgbYoOMyRv90I7dhCAXqSHleVTkPEAxsPtRgAhDGaN9UTA3gy9DeDLIvKoZVCPB9LWmoywH3jMEM1et6k/mokgBxLe2rZB4qZS6YnSXjFACcrn4yO6EzNVWw32Kry7QP6PAA/XISG9gPKAOaC+Zu6gNIbMuOrbAgHoZNalm8+vqXkT6thfmA+W6wG99uFmDKQmTbgE7BTDUNGH60J/pIJ10ESLuJ+VYWvOJZ/oaiTiD3YcbQOIcIqlgjVxDXmsYXmGIOG2N8jQAAmCRoP4UeTPJZMXpZCTi0FhB7fXk1fg/rOUBwzCpRTbraWnSwFDBQBgBWFr0bZC7ogr10AjnUAyRpqJgHeNryRwFPMIcMn2a+N0NPJjIlpIKdHQCw/Ui0HzwVl5BS9wYAO0sA6ak97X2QXfBcHWOhuXJ+VALOQIr0B5m0ABh1Uddh6jivvom0cvVRhT2H+xHGHOYkbLYMXcArYZisSkD+aXzHUeaXfmQzNAzrKqCWQkBMsimWA/iuhdGtsprDGoX4LAD2humHsvICAPSL6EOYVJn7ZgHmjWfxWRiHY8MTzKOVAEDrGL/4y9i1UeTfzzzfO8j7p/oBtZD5qHAIEBGAyYe3xZqmS2AofTUgIPYc0KEB5q1u6pqpyGWgqiiC6Q89VI7tIwBjaO+8KnwyQJPFtB60GrJkDI4gy4Eu0uDVwBxLH1A3DxCM4r8OKhf8BO8rZJyUASCpBaxSW12TxoD20THQwxivZB5dSnrNWsZAAWCPKezYGH1v/Uc5WFAPw+o8bF0xtniyeDo66S+BbUP4FNO0tRj9n8XICZDatnU7zH618fMvfEWcO/88/LQS9LCX1JodaX9kYeUiABE1lD8Z3by7m/4txw9bUEGmKhgqZckTrDZmHyGTAfaIamCHaqD9GQARs4zD0SlsDHa7H1ZSU0KSQwx/pjBq6b+m0gZY+ebFOOx6pwdHSTk4RJpE5FpTlQBgJVi5LNPw4MxonMZfG+0bjCbmkEX4GkUcupmmHdqlbmxDD8zWk8z9RYzBKfylJ/dtJaXxfuaj+rhj88tj/ZJ1yfftpJxhfIt6bGBzhX4F6xQdBHz+fvTkOGDrcezG4pqF9CF2noMIBej+DGOxa7KN+eIkOsshBuaSWljOpgANjXAQYpD2j/YPxSxpO8H6k2qZ2pcDv4Fxrgh2qklsoodb+pinKouqSFl6Dn5OBeU6DgtSSsQh5D7CPRm0Y0H9EtiG9YdhedSG83kP7RyZhF0K+elHHe08Ht/+7t8DNqyPm6+9PS4756ooZ71javsxWBqLSfvZDONaLeNxGpn0zPTjW/al9gmOa8QWlHNwoYi5YhzQaD9g4JHRIWQIQJY1ZhG+2wzpoiemxwH4YlNZm03wHUvAeqSIOauctUUtNpF1EOuGEXzyAdZgk/gGdXXItoy1I9Kaxhp7mGKA+XR0FBuFfZ2HnauFGRsuV3zV/jg5ehwbcZoxTnYu/NuZEtbAMG/94MGH8drr4s5bX59SVmZYV41iL4fRZ1QJJlNYk8shMYoa0sOSpXD4WPIPG+nbhhLWVLApUwXs/1ia79r6HbuAi9F7me1YfSfbPzLCGMSnHoPcJQswUnCgNqYGgGdtObJAn9Vx+0HfZwHzci2+cAZ5T1L+MBPEAGCjQVJvV7JObCItew1jJFPCnIdedYx0MK6AQjHGMWKAKmfjCOCwbU9tJ/VvxMtedCfA83VRjg8/whzYRcpSnZHGyoW0YxE2G4AUtR3CznQDshzENlcxV9Xh61Rhz4sBzjkaB4ewT9jsCWzuvDrmq2Lmkix1xC53A0rrQ3ZTY7xwahYfgT6shOEPe1vHXFiCnzaCne0bGE4A7KZa2H5ZsxUxPmfYD5imzEFAWT19zAd81sA4rcfvLnGeYo3fy2G2HvYPeiey+D+M//IpGNAByH3v4ShiPXrNhdfG1fg182oa0XMy/w2dZv03DKhrEYemYF+nF/UnT46eiEH2LxaWLgF8Tupc/hP8OMN81TdBpsOx9vS+BuaYOoCLs+ovOjrE+0fZG5yEDGzCvQA63gMeTdhL+6OYw0cdrKtPDbdiO0nBW9MCAyDrNeZCfYlhAMLdUx2kge5kj6AYP3IJsidNLADHccCgsvsKKBukH7UHs5VZgLO94HO+m/ynW6+5PW4+75ZYUNIEeL4HsOgp5pSpWFJ9TjSXNifGeRw21rewzuFXtE/1JBbOhRz+qcfOzjLfyTY8wlg0tfAYtkXWUv0h2eNn0TEzJg7ib40iH9OAC3qV8baCsbigakGym6MTo7TzNGtJGBzxM+orF9C3zMXocCH2ZBhf52TfCdowiX7VcPgINmDGM90GOFJGPECyPO/ILQOk24O+bWGu2LptV2y4alO88JoXRXNRM4z1rLM4UOeBpULYjotHymKqbyYe/v4P4+EfPgbw8sJ4+Z0wzm2+ItksdxgcizSUf569cmc/87/91N9/BDjnZpkbZ26UnzrVxsb7b6VNYzfNBE79wwP/kE5VC+o6k3FO8Jgb3Z7U3rhxE9S75yZQyndY7LiJvo6Tkp7s9u9+/+53vpvSPf7xH/0xwLn6tKHtCSyBcvfeC5iOzXsDC3/911+BAe5LaXPuxS9+cQooGOgQXOMG/FsB2RkUcdP9bOCcm9sC1D4BmKoEp/O//Mp/iesB0hgscQPdDfG//9bfpwDKGwGveaK+p7snBQYELAmiet/73xctLS0JHLV1y9b46te+Gt/5x+/EjTfdRBDgnWmiN3jy+c/fR31HU5nXXHNN6gjlpRwFFAokMkXNz73o59KiWPDRfTxjvQV5eWrf4IBpKN3s95k777wzXve61yWWOandTV8jcOnd//Xdqa4C+wR7nX0J6LF97/ut90EB3kXgYXMqy4W5MhPkaEDA4IFMYX/Faf9bCSoIpDOQ40aoMjYwc/DgIYIZ1ybmBTdWBVN+DUCTbXspQZoXw8ZlQEWmtr8F7PNnAOtkt3r1q1+dYwPEyMqwJ2OD6TAFEQk6tA98j30u65bt9lTz617/utTHsrB995++m8BGPvcb7/qNlD6omwDIFwBVKZ9f+k+/lNpl+hDrJnjuda97PZPwSHqPMvWSae39778HtrUn4rrrr0vBFnVDnTZw89WvfDX++it/nQI0BnRcgKuz1k3WwXcCwLvp5ptiB6cNZRYwoCI46cYbb0ypS9zkkMVOoGaeEe7MPnFMCcg0wPMA42cdMhYgKCDMIKDgJ4NLylww4l0ErJSpTHj3E6gyWOXG3mf/9LMpsOO4+Gn0WsaGB7//YGKQENgni57BQSdiNyQ++9k/pV+/jH8+8xxwznEoC4QsHI4Zg0m2TRZBAV0GvX77w7ArAkQ1cHY245yBP0GOb7777ljO+HF8GWSzL7Vjl17GaWh04/ku3ylA83v/9L0EShXQ6am0/GUAKwHr6DeKYhPqi0mf7FMXx3/9V3+d2BoFTr74JS9ic+ocNvTH0P3DKXBnuwVwvuENr08TmWPLoOOJ4yfiYjb8ZQwR3CZo1DEmy+TzXU6WX0NHZKhUnwV+nsO7TIP79fu/ngKmyk4b4gaQAFoBf3sFrAGw036aCsf0t/a793paZIJ+F2RnH1y18aqk40Ns2N37gQ88ByD+1V/9VSau4qQz2r0/+9yfsfCrSfZQoJ/BW/vl4x/7eNJX6/bSl7w02eEKHOvHHn8sPvdnn0N+synoaXDRy00qx2X+a5aNihkWWi5IZKlTdj+b64xJ7GdT4FwpcxKYk8CcBOYkMCeBOQn8B5aAPohgs6985SsJkKZfLMhDH0sfW9Cc/qsHL/S9BSwJKNOX13cW5OS9Xh6U0I8VuGWA0s/vu+++lD7VNYu/C4B6y1vekg7QeIhHP/JDH/pQqsNnP/vZ5OMLDMuvtTwII3hNkNw999yT1kyuuWRot2yBWPrK+mHWzXWE6zjXI9bZtZn+q6wj+sP6WrZLH8x1k4fD9Bef7/IZGc5sj2X6nL6rYDDBebZd39XLQKNrDtd0ysU16POVK9hLtnfLc40t6FDwmSA6ZaS8XZvpr3vAx3Wo9wiCc+1lmQL/XMP4Xf9c39S6yMTnmso6uUaz7oIGLdfPDBwL/LO/XVt6CEmgo3V1n8B1jmsUdUKwnn6zYEDf63rZvwmWlF3aOlsvAXLKUDDju9/97lQP7/Nn6yYoT7nlL+UvYMh9B/vUPQrrLyjTteoNN9yQ6modBG7a565R1APXkPrt1st9APtAFryfnZ+cr+Xc9zkJ/OwkoC471rzUde2XzPnu/bke1ua67nZvTfuRt6eOOce7Y9mf/fLZXbt3scfzEQJnmbiDdeY1m0hBCiihgLGVaGWyhrtZwwM2KuDEMlQjbGZmCO4DPGEzfZY1ZspFBHihgA3jAoK5RHj4M2nACOq58+ljRfy9iCDALBvyswS9Z4lwyZVBbruYAZAyzVavRblRW0wAIAOQgB17QAZs/wMIcsOgQDQC9jlDgHycjVuWuLDoUA4B0tksKUYJuhUQCAuYrwoImrvZPQsIy/9mi2cAXAAqInBYPMWDPkNgTlQEW+wE5wAtE/iwvhh2EDTUlQDjrO0yvOn7/asn9Qsq+GJznjksywY9Vae+IwCRsHcElBMIbZIQEP1TADCCBrGZXkyqHWRBE+g1YiGw2liHSQoG7BSZ0RQgKZCpTia+QtIrGaxAhhn6bAbQFzvo3MPL+Ep9UcTPBggIYACvSHsCxdStwNxptJs4cwp4TPUQbN4HK9veA1G/pCaWXkjGgToYBCh/epCMGAPUZYS+JChXwF5BpgpARCOAtPmUSiqyKQVNecUEh4oInsgUxSvZWM+JaxpQS5YNfMEXpthSfIMzPQQOCcASqITeDbBVFfowDBvFntj65DNxeMdxUjBdEi+8/WWxbPF5lEuaF4ScZEO/CCZT74AZInNSFaEhkwVjACoIFlOHUttJmiKDck65BsMJ13kXe262H6aSQlitKEVAwzQynuKLXREqjf7TFlkXSgVooIuEbkibB5gSfSiEqsrAWiGgLtQxlT9NEGbM/uW77HhFMjDw4g4CxwOkUlW/MgS3JtDvjr62eGrrk9G6/2gsrJsft10LiwcpgipIIZXahA7S8agPOkT4R70ixAFDAsBOZF0BWKskS/uovVcBwp4S3EGwKY2aVHc7l3Yh+0nLI4A2LXiC9hcQ/FdypeiQACW7j/BekkERe1YGGsEMJTnKIkGSqxR0yfIhcMkUjCuiDNmp+gk8DRAYnuZwera8KIGGTOe4GzbIIzAInLe0JW4lFduFzZfBkFeHjgOsAYRShPJVZGgHQV3HjkEs0yD5laVsgSwG/GmY3ZHAgVPIRE0WEGvLBe3SmQT1DKzxDHU13Fc4i61ABlnGn30wy99mkIMH4O2bkgxjDlYfTVWhFEY8z0hDDkjFoLfgBMB0w/Sbwa0h7EbAejWLDZiEza67tzN++MQDsZc2nrf0/HjVba9OKehKCXrqA7mPKjDZFNj6lvqy2lK/P5+fZh/+33jl/a48cM690qqmqjQO7eEy5pQSbUkyVPQ4A2GavsLqpvFZjB0DP8Bv2TQute1T6M8k/V2AHmfQoXGAAcOAq8bR1WkQCA4pmVZ6APvuPLgXdiwOxJSQFhTGx0vPvyiBbgWoTFDOBIqZwZaUM1f5rklATAlAxXgqZQxWor+FzHO8notamDYU7QbWlRhGCD+qWtgujQTlkUptlOCjYzhNC7RHYJ8KnqGtWfR2wjkHhiEtjClgMasJYDTF3CTblTZSw11WUJPsk+CYYRju+gjCawMgRIpJUpv2ASbdsW9HbH/86TROfu6mF8bNMJXUkCpwnAAnkGxqTNuwk2XaC+SN2lMHE3iPae0cSYzBSiyQ8xmzHHXJ0pZZgCdAUZGN1lM7J/CL/WXaZfDVPvA/bVkRQINiZOhcm8B3yHQU4KDgjjImiGLK03oLPExl8NskIBFGJzLmizdN8r7uKVIlYmscp5Xl+gOsJQE7HGg9HE899kSM9Q/HleuviJfddGcCNc4yyczoI1C2AOniGeZvykv6w9jOUkfrbwCYWiZbqj4WcL/gQZl8QPByHzaEzzLYJK+sQF5sADMr5WpH6T3HNX2njKZozyS+hrY3F1hGrgSBU5pzAA5TgPOz2Gn7MMO7C5CNLGkDABT6AFdqyzIVBTEOKLmfFIGtJ4/FE49tjbGh8bgCNpsXk2pvCYC+actCV5ReQQJzAFrg/Rn7gDlQuy/rTTFUNmnO530G0gUmgYDH9jl74O8xd3Bkwd5nbmWOxSYymuhDAANJRhxGQM8FGftoAXKZZdDNAHqwvAI0J7Wd8aasnV+tlz6B8Q/7Xn9ghDHYM9YBc5CgTmfxDKCxwdh3dA+xjydiGnD+TRtujI2kBm2sX0A/F9HvzAXKF/2qVEcZax6yECw6xViaoA1Am6gUrG6UV8Y8rf807Vhx/qTOhVOC6fgP0E5WsAafTz47F+g7FdNvtrwAOWmf+YX6OzZycpqhPwr5m3osk6k6PgsAQWFkBYA4BviS6XUYoFbfGKBExmERgLsCxtko4IsDpw7ED59+NDpPdsfVa8lEtunOWAqQSR96BhlmaY82g+okfdKfdB50HNlLshMKjOMVNFWbIFAYaCV6VIivV4I/YlvV3xn6PIvcphlPMzyAluV0gLqrp3QasgNAiB6OOQ+jM9W0TXtDo3kWnbKrebfr52LskuB1ik6A29OACcfoe1Ndz2JnzMRkVrKnHn8qOk91JBKHO0gPLKNvKf6T/TDO+OW1vEFdxH/l/RN8Zv0cGaV8Zj0F9QvsmtVf5TkfKqIM/uVn+h19EriojcnyvN5azqvFS2KMl0/jKzF/z6IEExgz/z6LnggIdIzrF/JS3q9sGNP0ZRFy8ZlJ9FImvH76b5p2OU847gWTtnWdikeefJy0wB2xZuWF8aKNL4qV9edSuqxV6skMvYM/ox5leZAydX4nAJcNUU/rXslYV8YFrJcwEOgZ445Uz/aD+ldJ+uki/NUs755Gf+y/DH2hdhaiZwVZbRS6DOBbgJ9ydZwLvrT/SzlkNIOvJIu1fk+yMdjwDPeoQzJCutYZBqg0yNwwRgroKQ6RzFLeFIzg7W2dsZM9nme2PR3nrj03bgU4t3ahKZ71z7AX9FUJckozhf4VTRxHPsm3VM+wraZc9Q71yPlulnfnmCu1566iGJPP9tisSpZ01topSccR76KPZdieBQheyHfXldOMf+cDD8k4W+rjZ5nBXHsVAkQslGWY/reMsXH6kXSdQwVdpJ9lfAgKdA3c3RY/eOSf4uCBw7F6+cXxqlvfCBPoeTyBfLAHzn0MBaqOzWBO1JOcBFg0zuEhepn5lsNTzIUIn9egi+oQD7j6m/JB46jc6dhllqEgbDD6rA/halz906d1zuFW3sGdyNUx7AwjwLcIQFoBazZ1d4L6aKumsUXOzXCPMg5Ledc4KbFPclCmP0CcM6gysAgOk73sdDz5xGNxbH9rNDeS0vvWX4jVS9amuduRMA6ATGCzc0EJuiZbpLL0b8pzHBkogEL6gJ5UCtzLWGQum3JOp06C/9SBQvTM6W4irTkY2/S/Y8lnUv70ZBPxndAQbYjjK4Nu4NFEBnvsGFSGrolk051inGO6eTdzJj7OLPPz4DgHHQBGyjSXqQAExlqtvfdEbNv3aGx75BmYc5fEbZffAisv6dGrSOPK8+PUcwyZOacrL23LDHUcA+DpeNJn0p8pVO/4zzqkeYT3TdMGwYLF1NNeRPqp/1zzSGg0Q18xipABX44D9Y3Tg+Osa8dcl/FfJYDDUtYssxo7VHcCmU4WjyZ/KoPAStFT15QT05MwtXEwgzbOlGLnOGzlurKjryO2bN/GfvOWaGpeGC+9+aVxxdIN0UB/TWLHhrWj1KyM96CNaQ53gpyl3ZPYmmHaqj8BTBzQsrYS2WLXtKXOCa6rXPum/kUXnUsZHNznfOJYdv3IwULKKGWN5Lyjvs/gs07hjzgbuy7OsPZ0rZRbO+l9o+uAjJVZMX1fgowmKbeT9vVMwD5MHcqYL9hQSIdbdsAo/PRTZCToG42bb7yddOc3AWCej9yYFwoHkSdgW7SllDXreO9UfPP+b0Mg9C32hleRxfOO2LhpAyBc6qU00NGkywrGy67z6195/QhwzvR+ArcEfG1gUfff3vveOHfVual4AUCexH7TXW9KlJlnAufSaX6AMs2LmtOpcRXEyw1yAwV+buDADWU3/7/4378Y7QQN7r3nHhaPG0gfeCBtyjupu1ntZSDETXrBejfelAMplZZgqHCo3QgXXCUg5Nprro2u010/ApxzA1zmJ8F5t956a7zjl9+RgiSWbVseffTR+NAHP0RHzMYrX/XKdJLdv/3ar/5a2oi/YsMV8alPfcqP0uVGuYAYwVMGVz7xyU+kTXkBPh/+yIdTilHBTXngnPcLkPvgBz4I1eCauPvuNycwmYV1tLfHPWzUP/XkU4CyboaZ7reTbNzAv++++xJYSnCMASI3+QXyff7z98Xf/s3fxO9/4vdTcEDw0vNdtluglnKUVeGNb3hjAn6dfa8n+N/zX9+TwHQy9xm0MY1k/nr/+96fmKwWNTfHH3/mjxPQzk1V2bxkeTBVkuAvZSGY61sorcEJT895gn8jm6de9r+BJhe+nl5+12+8C0rkXN0NeNg/J06ejP9CIOYFL3zBc4Ef2/CF+75AWs6/SsEVGbrcwBW4Z/Dj7b/09ueAc/k6v/hFL07vy4MR/dzg1X/7b/9PAlveAdhPEF4+sGGdZBE4dfJUvOSlL8kFrdBd9UOAoZvLptKV9UAwnamGZSYQXPWmN90FG9vS/Kt/4nc3pa23qYpMJfV6QFvKSd1Tl3/5Hb+cAm7K54Mf+uBzZZmu988/9+cJ/PSFv/xCXAyYswOQ4U+j1wIwP/LhjyQgouwRBrwE5uUv5SuI0z6Vcc56GbQUDGe/vvznXw5COReM8wROJ7molcvb3v62dBL9+YBzBggFfjmWPKErmPZmwIfqo3orMDbfB/l65L8LJnPcP/74EwQ+/y6B887eSJJ1UKCuNv3rX78/6YULGkGQ9qfy/vx9n09gPfXTy6DZ2976tpQW+pZbbk7pqAywGbR7+9veHkdhsxAIamDuX3IJ1Hw3jBcyt3ki30BD3u4py0ECeefz+ZvenGOgcFyaglYwcCvj3MCqCxwDbbfQLyL5vWSdfOSRRxM41bRW1tHr3nvuTUENdfFjH//YcyBN+08WlcZ5jfHJT33yORtn8ENZaLPUYYPDy5cvT2UJVHXs7yPIK6ul49ZLGRmAtK6OAb8MWhu0tE/9+tlc/xuz1s+mAnOlzElgTgJzEpiTwJwE5iTw70wC+sX6mH7pq+gjuw7yoImAK8EeeZ/Qe/VT3HwRsHamX5kvQ4BH/lCLn8lA5qElfSvXPP7NjTPXKvqRvsdL38ff/XvaMOez/Pu83/WIvpJrSe8RQKWvZN2sh76k4DXXQ3fddVfye/Nt089yjWE5AsgEpllGvl2pAmf9c3Z7vNe2W4bPntl2wS3W37Yrr3z9zyoyPe+9+ect0zrq+/vdZ/Py9j2+z3vsh3yZyl72NfvKLw/oeDhLYFn+Ht9rffS39c893OE6QP/c5wWlWQ8/tx2+x7K818896GV/Kd8zy/S91tW9BnUk/05lZR0tK98e6+D7zpbxmfUXzGhfeGjGe8+Uqc/7Pu9Rh1xH2VbvsW3593nf3DUngX+vEnA8+OXl2PIgpHtV7m24bvSgnABUD905Rr0cc3lb4LP5MahN2Llje/zux34nHVD9+TteHps2XguDAzaUwA8RYYymY9BNZPYrOaCHJbBAgpsEo1g1+0mGjV0/N2ggAMpw4DQBEu7kAnDFkjHDxnQRG7iFbDbP8l633We4x3Ab4TviQ2zq0yz2umGdYrOWTV4DYIJnchun3ktQio3hWYJHvtG4i6xU7Bdz+SA1SrdRX+rCPq279laBIAvBajfz+a+YZwphIiv0d4p005ldfu4joGBZfOaGc2IPI4hsoJn922R7CgzEwQhE5dOz0zAEpIAom9eFgA3YHycoxIa0kQcLsiz2CrKgB8kYa7X4lTorOXe++UrBYgImRMa4mdS2BMp8PsmDT9j1t5J8Rn3ZzpkxuGiFKCUFm6hAoWVxGSC3OSnS4C0UOd2GjX9qKp55bEvMO7cmVl63PIpJx5o9zPxzcjhme0cwjtxYyH4LjCgFNRVRuIxg53qCWeKU6cBUVdpgIMTqWDdDWUwn9DcBFNpuOKZ4mlPk3HBy5FjsJFXrSdjooJsgXR2pY2ELOH7qYBwnfU11tj5uuuTm2HTFdVFT30Q5zFUUBvYyV3f0bxb9yxDUTcEsPrZzZpUT9xXMAhQjrshPbPArBwOmBg8ogL+rX7Id5q5c8GAahRCQkQIkyLeYRqkuqXPp4xS0RU9n6eMUQODvSYesE0VNC4ohOO+cM01A08DbwWNHorXjOMGA8Siqk1FpKNphXHIPXBDlZWtIEXnF9bG0cSXBAoBktJMRxSuF8AEMICI0y3iCCpHfYWHkRQY0iqiftadV/C13L1qYYlIzBEiVFZpCOcgD/ZpGdycIGNlCBEg5gncIjqQAJsE/5UmQyHCaqcFmCO5kAJWppgIo5JKzPvQ87FuO+XEYtDriYNt++qyDe/HdYJvoywyS/mhXdB1vizLSFl578aa4lnSQiytWgJfFH4C1xlRbaqMBnIxgPsYboSP8GdXdcLdBOAJUAGimx+kPPi8kEG2gRNYgWSfVYxs7jaytu6AeQX+GxAzC24eaQXURbzPZCtub5IZ8HFPKTwkiKmRkJ/oDY5E6mEq3E4DjwY5DcazvJFhYZAUL4QRA3S58hEO7SUNLjTZfujluuey2aK5rIfBXnA4J6DOancbDx/obeZ8kb1d5yf+1l7Lwy7nJuUYfW8Zi5bQJIoCqRlh3kKv9kAMN0E/0l31aCCDC4J/9aYBQ8KWpCNFSbL7jgyfRhyTvDOAcxqesQ63th6K18zBrjhHSQdNr3NNBoFnQ1QBpRU0Het0l18Uq0gVWAEpOoXzshPVQKxwjgheyzB/Cb32fAWhZE3PDibtUHZlEACQ4wnIBaAaPw9PWUJ6AD/UbM8kXb1FB+R9TgX1yTkIugpVR2uJ0oJxxyHMZdH8W/TTA7Ui0RAP5M1DAyRhzgnTMB4/vTSw/xaTLmiiFIIH2tR5tjfHTI6SZbInbrr4lLl5KylXG3CTjZ5x6aLRKsZNlzmuUqQ0XrCN4znoKgi5iQhEgrBwcYzamMM3zPE99Z5xvNU+0JYGqmJOdEQUnOgaLBTYgP8sWyOXM5r8zfOCQo2V8OZfmYDAz2NYs9s0+SDMk8hslsH2w+2Dsad9FwHmAdR3AcarSxXrjKHHLzmOnYmlTc9y44eq4+pIbYDaF8SRVdYzvgjUoiYC2KQ0FQilHfZFC7Euar2mH9WPKp71aUVtL25C7uEgB2xl0ho8AC8jAqT7OIjfnOO7lf21jKoN53jR0vl9LngPr8DcPGBTx7qQ/SUN4F09zo0H9Ayf3p1SXY8QIympKY7wE+0P64RO0revEUCyZv4J0jtfEFSvWRSPsYc6ZylF2HKEa6rr6pG7p8GSz9iG1YhwAKUv1s11aUYHgyX7yjGAyuPQooQhQgeB02yHTHSxA/AeXcGK7UuDqpYB83CtK0kej4Tosys/+B8Q5Azh1mucFYnsJ99E3OXWa1HSkfJQpr7SSw1Z83tPXw8Gmg2Ty6Y0ljcvilqtuIyXiRYkZSwkrwTwwvpR6McR5MRVJ8xLAMkp3TrO3jGIVw1LoPK8/Ijir0PEJfRNdmvy+GcbnFMAkwT7KooR6FzOunYOmBBqkyZ5XABbJ0M7UN5SLpvAf/1IOzUzv0I9wbskyt9rXM8Su27tPpn7sBvBYBNa0mLrIjnbkxGHG52H87Xlx6+UviuvX3kLKx3rGzLNlMX9NUEnlbf9oVJStbwXyie7hcwkg90KcgsGz9q1y4ANBkHxoN6TnlIxzmH6gDyRgIHM2jyS9150QHutBAjVXHr8yxroNFuBBSTzLX3jAeTkdkqHw1g7S0LaSQhRWJFlzZ8oAhpEWsuMEqYQPt8PMVxdXXrYhrrkMFigYmbSPyWeifVpSx4DjQVsm/Flh+rtzMqJI+wLaeP2OBGyj5hn013qlwx7Uyxamb3SGUNsEXOMDVvNRRPr3WYwU03BMYU6dKwSJ5nweHrU8/i1GNgLBvLRfY/gRQ8iideQU6YT3wxQG42mp/iGMvQOwdQEqM1tfZXV5bL7iatJkb44mGYV5Z85jEsyS06VEy4lOOT5NbT2OPbSNAoBl2sJhoUr0rzIATCL7n2D+YgB33JoenwAYZx+qC/aGDG4JPI4cnA/oamvO39UXNMB+U38p35HNr+iCcqKNFFqQbBoQMXS0s7c9Dp46FB2jnTFRzn4PqYTHYU5sO9oep7A1gtY2bLwCO7o5llaQ6lzb4TvVA2qjBAW9JaZnbQV2ETPJpyofbUBnNZX6aHiV1NM64W36Z+os4My6Z5EJvcA9lE0fWa5Ad9PWai8EKhc6MeLTJTn7OU1ShNpw1xNqsWDMTBZAG39wiLRCkHT01F5A1a0YZ9oOsGyU/aKO052xfcczrGNLY+MVNzAOXxLzYeospp6uSK1lKj/9RBuwa1PoRZbDOHqQ2nKOciQ9zGhjUh2sG+s2qumoVQ7OWUUKABsyi20dQ/c53kVLi5lrkzVMskuv4925tmOL7Cr7j0LG+JzRkXTAuaoU3apmnhZ6OUDK9u8deZR100nYu2RzJUU0AKUTpEw9euQoIPjSuOLCy+IG1k5LaluiQsA4L9NvyLKGgKaVPsA40U7XaNZXv0lQneCzWXRRy+NcXko9nPuUBQ9SV3rXPuc+Hgd0lrPzAiIFC2onZ9Fjv2tsshz2sl9Z3dGX6A5t5NfcGOAbBpJ7EBU/OlcF6ycPt03ACnysh1S8x3YBLJxNLIDuJ7S2HSOV8j4Y0Adj4+or4rZ1N8YKfLZC9ked/5yR1Ax1UECsB2iK6YMs/oP2JAeOzvkWucbSe+iZdsJ1BBYk2RuHjjLz4NwsdsbZegajqZ1iaFI+MrDe2BFr71sdnxnHsH+gzioEt/OVA3n7kXbAvw+SRv5Y6zHS3ZIitQTwXKU9AAMlWKf9ew/CUjgV6y69LG6+krTldaSwRn7acg8weWFpkk0Ad5jmJDtkigNsQPJpAdKm7UVOTq77aL9A5AwVsE/tAIHefrcfC3B6ZlGESdo5pn7QGkYUuoYvwOhy4Dpug9TMnFDk7/QtuqjmyMTrmllPOberIzBP6RRy6GgitnUeir2drYltdp77ILD4deC/HTzeSgrfoVg6f3ncQgrpS0h7Wwfbps+OB4dcZmFqRU+FPw6RUeD++78FBunbsXzFinjJi14KNmsj2xDaD9uDdaDvBeeny2/P/pj74Kf790eAc4JdBCsJKDLlyKtf8+q0YW2xOj1uRt96620IteA5xjkXGHfe+YqUklUmrbvfcveP1MLFoBvzXoLvDCYItLrxxhvjV37lV+L7D34/BS1MZyozlVeO+e7zAJaOp1P1pppxY/rMy6CAm9bW+0tf/FJK+ynwSDCK7xGg58lzU2kKdsqnJHRB5t//8zv/MwC+9sQAds8996Sif/3Xfz0ee/SxuPyKyxOTwZnvs04C4QRRyYp1w403xIMPPpjAdMePHU+sYGcC51zs3XvvBxJg6O67/ydwTlm+5z3vie9+97tx9ear4/d+//dSO0wX+UXaIRhGVqzXvOY1sXDhQk7C706fCWj6JIC960lpU8OG/fNdboIKuPq93/29uAXA4F2c6Dct6JmX73/00UfjLXe/hYVANn77tz+UQGhpMfnsjX/4B38YXwF4qIwF9rnA96T9X/z5XySQ1b0fuDcx3+VPI6sz70Seq9l0/c33/iZgqRxwzgDR5z73ucQS50bBr/36rz0H3JLBQBCcsnvnf35nSm1ZipH3Mmgiu9/73v9+2OZuT+mRDJyYWlRA4/MB52TV6iN9hex+eca5PHBuG6mXZFsTkGiblIFsFILhWlpa4rfe91vP6cezIkjf3Cz2fvXlk5/8VHz729+Od8AsICAqnxLqzPuf72eBVKbfFMhkHV7/+ten1J75e9/6i29NbGluVn/ggx/If5w2sx2PDz30UEqNJCvivv37fiq9fvvb35762frL7vArv/or/ywY9A+kEriPd+zZvScB50x5JAuFbIxunNvPBqvOvhzPjuvnS9VqQMngkilwH/7hw4AX+2BjWxa304+20aDa2WM5X75j7DM8p35+DYbHxDiH/M+8ng84p+7KEGK6XtOk+qyAtrxOG1yThfE73/lOYl4z9a2skgbslL/65pgz7df/8kJ3BDC++lWvTrJxnNqvZ1/qjfqTv44fPwYoMQfmNTAoq4fvlN0iX880wWGfrO/Q4FCiC1dX/+jTf5RSFDuuBM/m7ak24Xc+8jsJ1Cb7iOyHXgYSZfFwvMok8pZfvBs9X57+Znm288knnkxMjnmwpmwpB/YfgKmyM21WWYdxKGi1QQKJtQE/m+t/Y9b62VRgrpQ5CcxJYE4CcxKYk8CcBP4dS0BfxS/9o+d8pP/N+v64Mv3c66d5j2tB2do89OThI9nZBHAJQnFto5/ngRnTkJ7pC/oe185e/38Jliq/fFDyx7Xpp5F9vry8jH5Sv/jeH/fOJOR/wT/59/men1RW/j7v+Ul1+he8cu6WOQn8m0ogr7t5HRcA+thjj6W9ENn0PRwl05xrY/c38jruPo+g1jyw1XIsw3G3e9eOlKrVNDsvfvFLOTR5bTSSNjVT7Aa6oSv3Wwi8G0Bk573APDEEHWdgFRlhk9gNYQM8QnY8yeyG7rRgB9bPplhKm+bJNhtAIoDAJ25cGxA2COTTboWz98vmLD+x+ZtOSHOfaVc9Ce79xQTY3Cj3Z4OZMp64ZW/YWHyfO9kFnHye4RkZcrIEmryMgbjlbho6Q1VWRVY7gx8GYd1/1pIbODXmU0jgKyc368+mOBvOE8jBy0Cn4BnZiXzIfWqDOQY4DSwAhaYdfAB4zECPlyEyUyW5yW0AZooNct9r4MsWGHJxRa0khRraJje0U7Asv9TmXcaTvWZ5l2XIGGLpbnvLBiFIIGPwnjrxEXVADtxi1aePEfp4ZDq2PLI1GtfURMsty5NcWh86GNMA58qoVyngswxgjWkaBHYoZueVxfxrm6N6DSHLRuoPw4rNTuAKylfmhbzDKpqWZ7aY4Lz9A3NGls361t7WeHT/47Ft/44csBqQmSw504UTpDKqis3nbY5rzrsuFi1cRjyOgmlDEfpkman+bPYnYICRFzfNlbOAykL4cxC8rHAGaq2BwUDlabNlp3Nr31/EPgp6NKJjWW7EG2ZNoR6DgIBYZORHbXPv4jlLsQ8N8DlGktwpy1iEf0vdi2AFyQyRNu2ZLc/E01ufhvECdl0YPsZhVPB0fxkpjc9tWQ6wbHOsWbKGNE6knSX86xhxN90gkyDEYe5Vnx0pjgblmfTMqAw3yVrhlYLjPCtDnB/xVypEu2FdKmSsTgHMMbwlA4hhGjVLkF6poBjG7SSAB69iWOgKlRv1R9i0ByAIzwh1U5sS4xzPEbImCHIynt77JIxB26Pn9AB9T/op0tL1FZDKsqYWhqRL4tq1m2NV01rAdqShAphRUML7ATfK4mBgUqYQ+ySZAAJRBqF8k6CdxGaEUtlm+04gr0EuA5cyP6jL1kk2jAnqiJYYzsGOOFoZ+fxd6UxRrgFINIiCeB9jWGYEwUv8n8rMenN6i29DF2ACEezxxL6n46m9TxGwBEBKn2cnCEYBpK8BoHTJBWtJ73llnNu4hvRWHsgoTPvqxgY8SCxAP+9rGMswNvHTXknHknD++ZM/7vP8XWf/Pf97/vvz3ffj/nb25z77fJ/ly/xx351XlIPPGm/K+8hmy5GZ77obrouKpno0S123Bw3UE65Hp9VnA2vT9L/Mg45R2aJMe5dlHMwQ2BNKUUAAs5hgu08zhAjgDccW+u+RbT+MdoLL0wYKqccYQTxZ3JbRRy/ccHtctORiUrQBZlG3DRaiD4ZMk31O78+PNxl86GlZbhg7Kd7ri7jflIUGoB0vMrHJ7pG6Ltkj9IubtTAil50v1WyBKjm1A1yAfgl61Y6om16adW1KYoJiBAsW9y+mA3OJ0UV6sn2HYO3a+VS095xIrCGTzEuDsLFqM1c1rwQUuJG0dBeRXgwAMm911h7HSGgJihmTMkMJJPO9Audss5VyBObmDmScgEXUmJdTPH/mbpokOERgr6CtDONT0IsygNeLp7FlsIY47m2vptosJwJ/vAzcO69riAukDrQfeQbuWT6kh/k70yvAlrHYemxrPLzr0Thy7ACZWwBNcMskdmuK984jve7mSy6NK89fF0vqlvNcjo2zGGYs50zlyvEjvrQQtkyvwvAzIALsi8xBM/gspros4SsFYQmaO3fPFqtn2AnriMEZ574R+teegQMsga8slaryheSw17Ye4ScdNRgvIEJWNvVjkiC+epXTX4L49i0y3354GylLn4hW4mWyiY0XcdCdtJhTsN82A5rbcMnVsWHVZbGUtJKV6Kh6bv9a92kKsUZqTm7G8zf6MfUlL6AO+lNe+mECGROYkdtmEDK8V6m2pnMttJ+p5ygsOjngHHANfAduTOPWrtOHmmC+Vl+KtKPYYsRCO3kntl32NIP89rKgrCyMVvuO7Y2Htj0UhwHtjMOma7unAEpMg25fsrQ5NlxEfHjlJtLBoqPIO417GDyFtTiHSxSs3qWWoieyK07RDo40pbqX0DfF2Ows9RL0loAOpGEtGKUWDjnamZXZh4LUTT5BI4QUIku+e37CmUc206SXPDaVHlWm+p6q57MAMO7RDcnyfVb/gL9NQSJxFND/U7ueiF0ndgOY66EcxjsAGpl8SmDwvZwY36bzr4uVVasBKQJ+p+BkC6n7JHos4JTepDTneWwAbxYIUUD68UIOYxToM/OMupZsgjZHcBHjQFBWyt5EWQI6cnbIsaz0tKN+6QfaRudMIJzUS531HYmBELkXI3PlIUBdPyV32cbZxA741NOPxcETB0g/OwzIAQZM8vEVwEpUVcJ8zxjcuO7KOGfeYgA8AHZwYKxrAb5jYqqkXHtQSdtGGYa8tDF+bryaFzFn0s4kBz/lb9yW1UHHDuuPUWH0mTYCCBPcrFaXYmMgcPNx9BdfGoC+MvBtFqCu2opcDQTO4efxXtcCstWO0Jq9ffvise2Pxv5D+yAFgd2J6syM8g9zxryGurhg7Xlx5cVXRUv9Kub7miQ5e0p7KXqhjM5J4Cc+y1K2dVPT9DuEBmpHHJ86aNZInZzWblKHEg/b6JPjq03w+RQv109TVj5lGbKf+nPOOtpW/6aE+UIMFJVA2Gqs6zPHqvf4d8fUBHNCW++peHLXU9ib7dE13oG/jZxgts6OYc+Iz1900Zq4fN2lsWLeyqiaIf0oKWiZgNFP7Dwl5fpPzbSPKD91h9LlP22HAHD6KbdWY05mfnYMewjKuiQ/FqM/BfpWK5il0lhVfkJLkYOHoAQXpjHM4Q3fUQTbGAvGnHz4XTzsFKCsdBiH+aVoSqZB5M2je9tPxTO7Hon9h58kPWcvh3EEMlNr1liCONdAHnPVpdfH6oXrYAGUAQwbZR9QmiBA6zODPUt1YyxZNyFLylnPXBuZWB8ZG0nO1FnmSWWPyGkfMuFATAZbiGsbo9gqLb4SYxQnnfPnKVjVs+ogRqBYDI3C1QixVpJ5XBJ17a8gbPu7mvfK59dF+t2vPvnN2Hl4Z4yPjCb7O0E6zAHAcwI7L7nokth4wcZYyUGcmkJSzGerkR+1xy7LWi0UWt5orADjDp2kbHVMb8e5VC86x3MoMBzwPH9J1o/5S/eVR8QyJvuT2OL8O7JQ2+2rGecJntLvdYzar5yWSc/l3sWvMKOjjgDTeerZdbYyTWMeeznKYYAdrTvigce+C7szqXBZM05hZ4ZhJy+sL46W5S1x3cVXx+XNF5G+tvY52+LY0PNypMt0TwuTbAsBhLkeS39jDGlA0poQWyiIzDe7JnWMaf+TY+Xn9J22dEIfx/6jQ9PaCNsoi7trBNtsWxOwFQXUplheuigqy32s+hjaHkRQDgDGe/pi7859sZO175GBg7BSkh6d+ul3FuLXrVq9NjZcvjHWLCbbYXEd/a7uoh72E32kf+krynkXZyS4mI/pGPXMd3OkAz+GscVnU7RB3SxEzkWupXgmPWy3+uVw4x7ZAx0p1hwNp630IYcmEnse5cieijFMf8/gL+p7TDB/jyMLS/R4o9JHfVMbh5nXH2ndHg/vfwYysRORGR/mINsoBz1YZ5YUxdIlLbFh3VWxbvk6UnUvjsosrLaApidgkpwAPGffydjc1zscf3f/N+Nb3wI4d47AOTI6br4a1loPR6aeTnY1dzCQ2tsAv/6V148A5zx1KlDnSdLO/Nqv/WraQDPt4nMX0r3hxpvSrzLO3X333Wmh94LbX5BOXJvWxRSaTvRnXum3Zz9zYSvYw2DCOBSufwAIStCVp/zf8otvSae8fda0Oaa08YS6aWA8zZ8HaeXLzr9H9rSzgXMHYLEzDagsaZZr3QSoeFkHwUy/9PZfSqf+XwTTlClfvATOyfh0OQC+T//Rp9Nn+X+Uj+xpgksE6dx4042Jcv2jH/1oQoeezTj344Bzlvfe33xvAvGY0iYPnHORKOhN2QgsElTY0tIS27dtj69/4xvR2no0pbKRFSy/2M7XLf/dE/pf/OIXSYvzl/EiNkBNk+n9Z16+RzDWm+56E4jkstR2Az5nlinoSWCZmxcfBMwlgMn2/DjgnCC3d8Jmdt6qVT8CnBMoKUPY5k3PD5wTWSsjoACffB/LCPHggw/Gu9/17rgVOvs3An40hY/AOdP4vO1tb0ugozwY0vb9NMA5dcDUQR/76MeSbpl6VKDV2Vdex/5PAudkQttBmlGZx84EzglUdDxK6/+Zz3wmARW3c7r7p9FrdVwgoUwJpk46e3wKJFMOO3fsTMA5ZfDR3/koRuhbiVFRdjNZHM6+8nL5ccA55StTiAyTgg337NnLCbmixOwn+1k+9dXZ5Zri9b77cum0/vwv/jyx/Z39/ucDzjlZCC771B98KrFACBrz5Hy+noLAZJj8e+oic58AQoGB/xrgnONHtpCXANScB4Pem7GDr3nNq89uSs42n2ELZaH8x3/4x/hdAG7tjNNbbr0lXvua15KPe9NzQVU3zgS9HT50OIEk29va07hUF7RZAnP/pcA5AbgyFp4NnLOiAueeEDh3BsuhduMfGcc9gG+VuYGTahhZBMNaR+X1s7n++fzwsylzrpQ5CcxJYE4CcxKYk8CcBOYk8G8jAf1qGZJNMSpjnYdGZCprbW1Na7h3vetd6aCMYLq8L/pvU7O5t8xJYE4CcxL4UQm4fs3v9bh+d/3vfpsgOhnmli9fnsC/2ivBC97rWlrwr4f38utxy3Gdv2//7vjwxz4URWXF8bKXvyyuuJJUrTBLuHFq6NRtYlkxytloNgg+O2lQiMBIMWkbKTt3Atz9WUFxflE/NogNI8wQIJkkSDThbmv6W0r8xuYru7kEwnxKYJobxul5NrAN/LLDnE6iTxiM5/25LW+363PBC9OuCL7wsm5++VwmBdsMbJPyhk/dSucN/NU6sy/MXSlQx0+GTAyDeIc7sQYQSik3BwbjM9owQ0ApBbl4V+4JA0ZwILDpnDZyqWc6le7GOz9bZgGb7xmZbijV3w3j9DB/YgAAQABJREFUyt5gWBORUywBFerkVr1BjukU5HDLHwBUqnWOhYR9eONTxJMsiV5gZz3thbPJ7d2mZyEEQTnjqU5uQhcarDVVDoFWY6Kz5AOynGwr8nwkG7uf3hUNF9TH0lsXA0IriGPbT0Z2YDZqK0gUVm6YGWn1zETP8d7o6D8di84/J5ZsXhilawmMQapPswkcUh8Bc+yzp/SuBltIPZcpp3b+L7KOoEb3KCnMOmFjaz+R0uxNjcKQA7CxsC4TixoXxEULL4xF1UtI4YrWpH16AhnW23LtWtqetl/8rqoQATMtzRRAMfUvcXxQIYEMhp/sWR/KaR7hfIRnQEGQoel6BDMoPwMP3luCfstmNUMZBsftPVOL+XfistzDl9EMv7jsC4Ok6l5O8wQXjMbJ9tY4eGRfdPS00xum7SHUD+NO06L50bJgSZxDSrN5xfUE3dyHqwTEIksT5dm/yHOEupsSKwHCqEOul32nP1nrXPhArZDZIQUfDcBYBg2UT8hohiEspWIbDcihmSkQlwNF8FkRbA8INMlNhAt3plRRpKeVocpnbbV/N4Wi7x6Y6owjHfviIPtZfaRKFMAxYmqi8knAHgvjokVrYkV1S9QUzSMwCEMtz88C0JtNAXvlpVQN28t/hx7SFj9VtrlQM/8+yz5lywXdyLonm0kCOHCnPTJGH5tu0FFVSd3U1CQpxpzsRym9Hk86vh3PykZWhQQktd+wMQIXklJxh23XGphesLXnSOwBCNE91B0TY3QKnVyGTi5a3Bgrz2kBqHNO1Bc20b4qACLTsZOU8LIJur+mLdW+amv9rj090z7zkv/lZR+e6Vv6s2V45e28v/u5oLQUWMOu59/pPXl2ZT/Ll5f/2d+9x+/5v3ko2Mv9ynx9Ldv6+5U/NJwHv+XrkR76Cf9YvuV4nSkLWecEba88d2UU1grw1PqpLTlrLFxAvhdtpGkGBc75c6lBbOaKGUAgI6RRNfCHdjKWTApGCYCO1KnDpw/FdvrwFKmwRgmkGxwvImBdU10WLYsWx0WLL4ymYhiSAAanWYogvEFfbbMlIJlUE7Xe8KUXmoYO5jKe+LEjzISL6o5aajlJixmqjrx8KlL11bvVUEFH/uclYM6AvXdrpWZhB3JuNeBvAFTtzVkvn3b0GWefBpzbB9PVqTjSfgRQxIkYzgK24o6C4hLibo2xonFFrGlaHQvKYWyGjSYL+Mdgqq2wNfa0s6OXljKv/bbe2VtJCgiZwRjNGPhN9aOWPGzg33A8E1gqgemQP5iebpSytFkl/AdjHV8C85Sac5Pvtg+YYnJMO/gBoG9wKZAiVRmiXMeqoKYyAREEaI+ePhYHuw7Gqe5j0TvYgW1EziX0diWpxefTh80rYllFI2nCaqkn/ciYKAEsACcZNRT+AONmapMhWW287c7ZesEv9kvqNerp3OAkk3oalEC6T93njnHGiuXYi/ozgv1T/wpg4kN7375RFPaa8DxTwMlWk1JkAkizdXxK/QB3c59AuVMwCB1uPRCn2k+go8B4MthTUg1WVcPw3bwqFjetiMWAyupJm1rOHEEXImu0HHsnjBTTmFpgS9RFx4wWld5JtUj2kJ/VYdPF2SeCC7KC17DxglLlmfFz9W2CMaYH4v2WJejdVH3maMwBlZUHEAveazrP3D1qNaOAyX8UELztr+Jd2UkZHttjb9f+ONR+MAaH+6ksMigtjKqKMoLoi2Il8/3CspXoGgc6eL/vtUbaZfvF8SyETtuf2kX/CFsYoxfUPhLAc48gSUEZ6praB9RknJ52bOEnTZBKWSCDEAHHtXpfkZwH5219Lv0ENdZUjY470+2pBaZmRnZ2FpcA8RykRP+MfvZzgA9dtPEILDuH+47E4EAPKk156HJReWHUNdfHymWr45zK5bDroKf8J3BFf0L/VUkLrrBVySdMJQv8ErgpoF4mTCRAPbRI6pDzc+prxojjVGaydCEje9662yq1UA+zlLaWAEJROhPQ942meVhLxeEH/ib4VdC5mqMlc161aTlACX7fwCkYHnfF0Y6j0QfYYxBAq2DTyvIaGBGXxnmLV+LPLI6aDL5mKi835vUrUq9ReUGVamTOkjhackL1745TR6TalAOg5OyT9zi80oijSFuXhSPOUvP6LTNa6TR2Bt+lACqmqcRAmmu5I0OJmU7SeS8VlWsVfrNzH1KHYfHE2NHYeXJPHO06CTMwrPxQQBdPFkUl9IHLmhfEiiWLY0Hd4qgGkGRyYEvPzQ45T10d9PJzISg5K69Xos3JeR+20h7MWQotrH0jhFdQDvWkfY4vfTQBl2qEl7OCpTgnJuYrxysdpSxyZdpGZImPnAxRmsOVv7MG/zEGBEIOknr+EKmD95/YA6NlG8OZOgPYKuOgyQL87paWlmhuWBQNtLF8GgZkQGseKtAeKHVrYZlqle9Lto9edeSnuZN+sAflGy/BDsjOPIWsbGcaw9oVbP0k9VfnlZjQ3dxqh1GB3tBNDlnsPnfwvZR5q5D14GxCatGTpbOkpTQl+iglk0p8piq1z2dOk+76AMyk+9t2cNiBVJ/4ME4vZYACm5oasTXLYjHg6voiCF6cYRiDFei9epEsNi+f5DOIjqmTtbMnYdLkv9wosj+tu3OlPYvO0VpTHheBtDU9t0yFLoYF9Js6NLfOVJctwbUFc6lrGexUlvllhrmljLFh6miNSRbfRB0Ywx5M8m57mdwZtLUEQHxfPHns6TjUeTT6iTlPM58L6FYTm5vmxaqWVbGkvgUdraN1sIXyJZO7MpZJVij5BPW1NkrfGci6+98k9ZYbXL5UW1mReggblHxwU0XT96yXUtu4T+CyUlAH4IbmPkFd1lYNcd7ENieblBtzvs/DPzMA/RKInvnE8eP71VKZ3WU7HWXddLznaOwCPNc/iD8zwiqHOSRDatqq5tpYtuScWNGwIpqLFySbZs/ZffaGM4f94lrNVqutrhLUr5zdsFfVVXvDY3U5L1NpyCycphfkUICxl6lNdtIc01tube+BHkuwfP/iutu1iO0UjK6dtTXqyyS+phNkbqbM7VJYyzEIayTnOXbqaBwdOoAW048YuEL0vKG2Kc0TS+a3RH1xrdqdY7ljINhOW2KaZm2l/gfn1ag59hq5C2tz9OcsTs6eOkOoc7bUNLvOeQL0KMgtHOwk9aQlaloOOJcDlLtnkmMmzM0HqRXYI2VpymHrIuDY8nO954ybYSzyF0ycc+yBweOx4/QBDo2dZA1CqmAOHLFxE/XV9bFi2Qp871UxD5+mglS05QBgtZhTmSEeH+Q9rgvLSUE8HH9DZr1vfP3vY/nyFXEHhzev3rgZlsFibJJWhV7klcUANpXts4rAz/+660eAczt27EignAceeCClaLz77rtTGpn8AsrF1W2AmERg5lO1umgS/OGJIJneTPvpAiddDPBJFmgurs5MkfIUwDxBGt/8xjcT+MnyL4N2UNamnHGKBOwSICMTlvW44+V3pHJdgPnO/ILNU/yCfr70pRzj3Ac+8IG4+ZabU/oJgSNf/tKX47bbbwuZtzxF6yULgGlS7733XmzQbJgGVDCPl8C5Hzz4gwQS+ZPP/sk/W8QpF8t3Yfe7H//dlI5UcJfpW49xCuRP//THpGrlZNmb735zAgGll/CPwLl/BLS0aWOOQSq/oBXwJ9jI1J5XbbwqpWC0rbZ7/aXrE3Amn0ooX9aZ32Wcs57W78ILL4zXvu61pMm8+Tlgjn3ognTb1m1hUEcwznt+8z2JLSHPoKZMBBJ+E1bA5oULU6pW2d4e/P6DKY1vW3tbkt2VV24A6JZbGCbgHAxaqwDOvfe9730uVauMcwk4Rz9sgnHu12H6WrpsaapynnFOkNCb3/zmxHConngJVBMk94ewtL0BhjbT6brJ8I2vf4NUlR9H516T7reN1tcA1ivufEUKXgkUExjlZTDL+mzdspW0lHckxru8rL/2ta/Fxz728fT871KmqX/z7GqWKdhK/RLwdez48fjEJz6RZGsa3De/6U0JVJRe8r/4RxnbFhkoTH0q45wb0fnrrW99a+xAhwXOffBDH8x/nBj+BJH94AcwzhGcs34Ctn4avRY09TZSkR5mfL7ila9IKXHPZCtUVwTnedrcVK0yzgnSM1Xr/AXzQ+bBJUuXPMdwpu4oFwGOyvHggYMJYCmQ1LH/spe9LIHi3OgQ/Oh4Noh4//33JwCdevTpP/x0YkbLj/XnGswP6n9KRfvHn0lMh6av9ZkzL1NJO669/o50rvn0WNqUP6C+9vlf/uUXYv369c8BMYfRQxkRt2zZkuyDjI8GMwX35RnnTPErC9y/5DJl7evf8IZEIe5zAjmfG5fozgTjTDulnGyn486Uy9qWnYAkDVIIpJOBT/sjYNExbqocgcVbntmSmPlMIWw6KIOzMmH+OMa5RgDOvw9zpePBKzHOfRnGOWQlMPjMVK3qtumBn3jiicQC+KEPfSg9o275nHUtLsLRI7Ch/msXDKiYKuFnczmlzl1zEpiTwJwE5iQwJ4E5CcxJ4D+uBGS1fvppWEdY126F2dr1pQd6POQg2KSBwxXP5+v+x23xXM3nJDAngf/IEnANqE06zr6GoDlZ5zxE5ppeNvM8o7nrfe/18OX111+fgHOut/3MrykCf/sO74uPfuIjMUT6optecHNsvn5TZEpL4zTlDY5NsrVZGIsJXi+vb4q6SlK/QgcgW8IE6J++mZHo6O1hr6A/JtnDKWHNvKiuGrDJQljFGthoLolhUuEcnzpNQLwtqgm4tdQsjMZyUjpnDH7DYJXtjxODJ2MABo8FsHQtm9cY5aRMNOzZzf5j20BfnOrvSYwf1dRlAcwQGXaTB2RrgX1kAQwtC8oWsvkMC09vFyfYuwBi1UR5Q3P0jsGO1X+StfwATHiFUQOAqXlBcwpYtPedjpFRNsYJGsyra4qlDfWUY7iTyyA4QZL+yaFoG+yG9aePU/KkMWGNX1tRFysWtEQj3yVKm50eiH7+fryzi4BIcTQS5K+vbCLYLIQQZhmADt2dbTHVPw7jUSX7IY2k+TLYNBNDbG6fIE1qd8/pmB7rjwp2iJtJ99VQWA3Tf78H+aNq4byoqapjm7ksunq6056FDB7182uid7ydfmpjIzsTTdULY1HtOVEzPT/GOqZitHskCkYmo7q0KooGCJHsmImDu/dFzUW10Xx7c5Q0FXOw2TTp8KAR+C1FprL/ZNup1y5Squ4+EZXUY/Gm5qjYSMhwAUEuaBMy4wBs+tgCbydc0E7AYxRgYAPb9QvYzidINzxIO6qALdWVxyhAt8GhsRg8PhijXX1RUs1+XCOBB/qqcqQ4Gorqo7KpNjLUZYbA9HgP5Z0m9DLCHgMRgooy9jDmU6/FszFQNkxfnIreib6orSc1OgG7od7B6EP2AurqYCVqbloSDWXoD7pVRgDZMgayg6SNPZHSp04S0BA0UVVaEQsbmqKRfi9BV+TDGBgdAvzWxv7JWCwAONpMeVUlsinBDsE46OjvJeDaHhXz6hKLf0VRFcUj50l0m3Q1raeOxGnStoHpQQeaooF+FhiSGZyJhZXzo6l+CfpTBYhwLIbbe9EzAt0NZdHL6fvOkc6YYl+uiAhgCWN4Hs+XsPckYEamiQxB0rqqJtKFLkHv6A9KFos6DePK4HRv9PR3RO/pPvRZRj8AoPXlMa9xHulTG6OO/wy2nh7rJN1TB3GxsmiuaYl59Y6jXEB5jBS6pwf6o7dnAGa8+lg8f1nUsJc7UTAQA2On4kTnqTjW1sHPMNDA9FKzuDbm1yPnibIonyJgie7NY1yXUM/+gU72708SHMlGw6KF2JBh9LuPPV6AQtisUrplXl1NVFZUU+8BxqzpnkphsGuMJmIP1VVIBmYCgzSGSoanx0ml2hFtw10xzRiqnCrCTtQg44aobQCEAYOejFPD2J/WzpMERCfp20ZsFgEbwG7GWgZgpjre0xmDIyPsiZXGfGxMbRkBR/SjP3uaIOV+0kQBhughoIOdkHBgSUszQDoCSbBHLheYVNmcdMF9P/dH3R8UYOa+n7Y4H9vwc3/+l15n3+/vXn7XRudBbn6W/9mYgu/8/9h76yBL0+tO8yQzM99kZobKYm5QC9ZjmWTPaGPDNB5bliZWHv9lz4yl2AjPRqx3LUesbckeQZO6u7q4srKSmZmZmbFyn/O2b2+vLHSMPR5vfq1UVeX97gcvnBfOc37H+m8F3Kz/1u/94DX1M/3R8UCfzWr/9U899Hd6Lf2eXlvHEf23/l3P0c/1+z/p0O/r9/Rc63NYv2eylFDgA/MDsnKwjr1xQdk0QAKAv7zscOADyGk/m1tflLW9dfpggITRrlyBGk5RzVlF6XBlZ1WW51bN7yJ9A8XPWVUsRFZPV2VwY1wG6YeLpPk64f5+Hu4S6e8N1Akoc+QoPk5+7PEGo2ThIXu0xeXlWcDXHfZGPchSfcK+M+PJAQov6ijEJvrQ1zw9Q2i7a7KLs/6ENJUOTrbYDNp+QBD3daGFaiulfPhsdWVZ5tdXZI1UZAqvuZHaOIRnDPQPpE9hfynq7aMDWWK83FnfFD8nNwnz9xU3Lw8DMh3R97dWtmWLFFfahz1CUCLxUtDvWHaOtyiXaaAkUvBtz+GEfYm985MoYDL3l4wN245mTI3kXs6O9pTTusysL8kBtiwQO2TLeL3EnvX2ziYgGoCOs5MB2lWVMZAxyW7/WFbXNulDYNz0LT+fQAkiNao9qkHqnlZDqsDS3vYmY9CcrOxybdRJXBxcJMA9yNgLL1JTaj/eQ4lkFnu0wrjpiqM4xjsElbG/y7CEvVraW8HhCjwGNBAGhGNBjcQZQG4T5cC5jWmZXBzmZ9Q4jx2xE76BoYyJ2JatY/FB+co/IEocsWtHlOUacMH+7qo4eKJcRn3PY8P2d7DmACoKJwdiqz2Awnex+0v4FY6oF3cnF+AYf+YR/uKE+pY69LUWX9IGVnHiz2wsySrzBlWoceW5Atm/D0AYxIty0n5wQF9bXlmXNcYdezdHgu+1HQaIC/DTHrZnfm9Jpkh5qcpF4b6RtCPsLCT2AXOlJdSgRifJCrMxS6roHXHytpcQ/DOe7oHYf2w/ijYW5i1+qOwc7hyh8qnPTH/0YqxhbrBN296lbs+Yt6kyr7+fr7gy39o43DV+k2P2/l0dAGR8QvkJ5+8e9N8D+iNtCvWctS3mGmu0sd09lJqwU/RHf183CfZ3Fy8n5jMAKgeMvfNba8beuni4Sih23t/Zw4A3Z5T/KuPM7PqsHABQ+/r5SDDzL7AvIKtdmT2cI11rr8wtzVJ+h/QfVwljDHAFarE98EAhSZ8rQryZ373EXut5G4cbpGpnPOdZtknture3YxSPHEidrfDaHuO1r38wnvpd2VyZl/0D0vPiVPeh/YeRPtvfIYCzAOj5/haw1QYqVIsb87K2viYv9wkKYJz28nIT/yA35hMKtXiTTt6XVHJ72Aqek/cIRAUzwAN0BTUgtXdbh/sys8Sz7ByLhxfjUgD9Cl/Kwem+zDMejq4N43sFFtjcpoydxD+UOVuor+xvHYqHEJigsLwbwXZca53+Pr+0Jg4uHuKCYtT69hJtZ5F30/c4Ei/8Mpr21PbITTaYC25TN/YEMXj7eYkvcyMnwCYFapyYWGgK5c2NLepw1fSvbdKNQx2JB2N5IGWk8Kwr4zGECvU3K1O72ArG/ii/eN4viDrEWgFS7lDGM8x/V/b3sH/+zCuwUfiLzs6YT6GQNLwwJGMrC7J+zNjMPMQfexceEiF2O/jESJca6R8m3sx/FQha1zkY7dKB8+xcHZkjbPGM67wPk0nslC82zsOHeQ+qhIvstRwc0kZtPSTAi2sGhIo7PmhN76k82CFzuh32X1YAhBdWBkybtWMO6OHlw7oiQoIdw5jbudEn6Eub2CFss4PaIL9g5kduzLkZdximlnYPZIY5x+H2gYSyZokIxAY6o4ZksygzpEfuYz4ztbYixzunAEteEsX7+HlR//Q1WybdgX7RAK1+AD6kq9/QecOaaTvugD3b+Bw39zRYAfCXfvqS9I1n0EShgHcvGQeWWedsM14o7KRlG0Bf9GG9o8+t4NQZ99hjjFim/CfWJwClgFqBpz3dPeiHIeLvxhyIIAQFfLZ3sPvMOY+5rn8QMzl9RoVbscc6Zi1r2dOnnWnjEcEWgj1ZVxAksnqwJGNzAzI+R1palHQdAV+D1KYHhmA3wdwc3CTEI0qCXcIYG+jTzFtnsUkKAgX4sX6gjy1vLtH3COYBLrJnPAoGSrNn3ry0viM7G9uAMKz1PKlHys6FMeAMgNYeIMmePnLAPRc3uSY/2wTQOCCb5ebmLGEAe0GeQZQNa0jeYolxfn15RWwOjunLoYwljM98dup0KguyJJM7U3KyxhrPLog2ivq1mytzfcaXvRnpWeuRqZUp2eX9FfTx9fGVmIhoOVIFwUMHCfaJ5l7BBlg7WN+QFXy+qkLmwfh9ABw2ubZoysYeJVRPxg8vT+aKjAMb9GmdJ6rN9vTwNSmmPUmdjccc9S/mNowteyhNL22sMN7PmVTxmrLVhz4YgE/a191L3OmxNkCAq2Qdm1vfkv29fQmj/EOwJQ6MKWfMb7fpezMA4ovMaz1ou5GMcYHOqByiAD25N07Q0Tipk5dIm4y/nnVlCPYpgnmpqscek0/cj7mMvxepaOkzWzzL8soEfZBmyVixwdiwyb21XH0Z71QpXodxT+bXR6wpFwCbd1lX26MQ5sd8INgvivm8hXomPbwB8PZRgUUEhvF0ibHugNSj9ow5bqzPfcJ8KS+ALztP5lWucrR/ytp+1qiqeZNiPYR1tJOCgbT/A+zQOuW5vbJh1ju+rGucEXPR9MW6bplaASSfncau86xAaZ7YYV9sqb2ClEeMvc5B9HtsL/1jl3Xlso6HEGS+gX7M8w9kkbniLrZMAWVHJoKBwMv21NsSfXtzR0Nt7MXTlTW/bzhpewOZC3JdmodiX3u000XS3c+tYecYUzVFuQflH8B4HkQfUMVrBeJ2D0jlvLzA3PRQQvxCmf8Fsc6hEJljHrxkDKO/r24tU3aOzINCmW+w3mI9so9q7jTtc2CugyCyJdO2fD0DJCoshoAP+jDAt7+7H+Mr4yd7NtvMPZYXl2lbB+Ie6MF4xpyU7HHkdRUP2sxL+vY+7cbB1YX9ERfa1A5lS9pT5inO1HF4QJgEMl/2IKhAAUGFUA/pvwv7q0D7rCN3l82Y7MS9/L39eM8g7FIwLd6ZvY1DWWQPYp1x34vrh7N2dOMeR9jTHcpqHbhteXGNschForGLfszndO6wYb8uQ7uk1yVAbpa+8JLAogDmHLE+ugfiJTu7x4xvfqzHItn78QSO1DobkZ2XS8wJWMUCDs7NrsijB0+loakZkZ9E+fRrb0hpURF9BACUOaJCe07MUxWcM2ihrkP05x94/D1wTmGOt996W/4SWMefDvKl3/+SZGZmmsh5BZMU6vhfgHA0FeL/RIpBhTEUDFFFuAcPHmKUvYyy2+Url83C6ZRF0zIg1y5GRK+jiyldBC0DZzwklY2mPdXUEDdIKfrqa68a0MX6LpoC51vf/JZ8cO8D0jxGyRf/5y8aaXPdtFPHhDor9NWDUSEb6B8w4NyDhw/ky1/+skk/6MJzPXn61IA0wcFBBogpLCw0izKFud55+x3SOb4lsbGxRpVNld/0UHDu6ZOnEp8Qz3t9nedTI80GGMp3H9y7J3/+f/25lF8sF4WnoqOjjWrc14GuZqZnTCpO67urUp6mYlUlgrjYOJMm0/rZPovmP/iD/2DU8DSCVxXnFE7R91KYTCErjQRWRbvgoGBxoSH6+fohIZpsnDBajj/q2KOs+/r75Stf+QrXO5KrV66YOgkJDTGL3iU253RBur2zbdKHqhqYgmaf+9znJDom2lxWU0QqVKiATxEN8I/+WGEuG3lw/wFA0jcNXPPlr3zZOIUUZtNF90Pq/0tf+pIpE4WSLpRfMPWt9aQKevpO+q6//duo0iUmmHqwgnM93T1y95W78pu/+ZsYVAw/kykFyb71rW8ZZ9Tvfen3DGCkEULPgff0+vpcn/+FzxuQSCf7E4B2//4r/56OtiuvvvKK/Nvf+bcMWh4YRcr6q39g1PKuAHZ++cu/b55LN4YVZFIFvbq6OlNGt+/cNg4vrW99J40K1LSiCm7p37UuFXzSlKdf+NUvmM0vXQj9uEM3GnRzWkG0v6Jf6XtqaicFnPQ+CqGp+lc3ZXD58mWj7qfwoH7veUWF/DXl3djQSCqUPzFlqvdSlTgFxH6adq1KjX/2f/yZabuaGuAXASlVPVCfW9/xfUhdVWdTaErV2jQdpwKS+rsJgDd91qu0wyAASj20fY2NjxuVPgW6tO/puVpXv/4bv25SVWmfVhiy8kWlvP766wYo05TJmrJXgSxVcrQCXuain/g/BbcU5vsPpFVVOFGBSX1u66F2SK+jKU/teIdvfuubBoDTTRVVRFQIUJUhv/rVr8rFSxeNfdGy1NSxmppZ+5iCbmq79FCb9u/+3e/y+UcpmzWVr15L6+bHHQrcaWrUiornEoMd+OxnP2tgUe2bulmk99MFlD77CeU8Ojomjx4/ou8s0w/yDVT4bcA2vdft27cN1KibbdonFELUXPQKKatipLZVtTGaxjYnJ9cAhQrp6Xt95zvfBZL9utnk1HMUFtS61frUOvkun6uynapLqv3QTSm1f7/3u78nzTh7tW4VnPvkO/+kd/9x5fLTffbjy/anu8b5WeclcF4C5yVwXgLnJXBeAucl8N+vBHSep2sqnZvqvFDnZVY1EQ08+MefT/33e/fzO5+XwHkJ/I9VAlZ7pXZJ9+ZGR0fNelXXxnro+lFtmB5W+EGDYTWtoCrF6+/0GnqcsBneP94v/9v/+XUZmhqU1CzS3JQVsynqKLNzK7KKk8QeJ0MQztW89BRJjU4Rb8cAlALOZAklnGEc3L3sISzMLMshMII6TkJwpqZEJ0hyZDLKYqGyj/pA18KwVNVXs40tkpmULBmx6Tj52Jtjc3dAP+uvkQVAhuzoRClLzcYJEshG/LH0zU+Rhq9PRqamgRoOjcM1MiRQztxfyjJqWJryKTc2T9KDsnAWHkjXIGm0BrqBl9wlNCoRJyWOp4VR9pVWSVd0Ir44pRKBCNWBPjk9JWs4IU+JsleVguz4RMmMiDRwm6qUrbCRPTI7Kf2jIzK/uML+tZavDRvBXpKdniUpMYkSjPOWF5epuUmpammSTSCfhORUrpUrXrpxfAYUuD4srQS5nSwfSUpEvEmT5OnDZzgtxtkYr+/pZo9pEsf9nnjgMI1iT9SbfdLJ8WlxxVmSzN5rdFQ8oIYb6fpapae3DefnocQmWHDgrcvc3LzY42SID6Zcw3LEY91XlgbAsRY3UfxDGYzNdH+bILGbdWCfbVQ80jwk6C7OiFg0BLQZ4DDAR8A4R9tRX+c6AdNDxzLbvCD7wHHh2WzGl6G6YAHWVP/LAhvyg6eyOgowyT2OaEMKeHj6oeKCg20Nh2tgGDBBBveMspcDnK3z9Wuy3LOEkiEuYEC4zT2gRZwpwZ44DuJjxSUSUJC9o/kxfr8IqrHHvhzOITfANc8wF/FKxhnktibNC00AMqQkjgwSd5yLa0A0iyuLnLoPoOArWal5khqbKb6uQJtsuO8DY4yj1NQz3sm+16gcAvnZ4/hycXEmhWOEJCWnSExoIkXgKtMLc9LW10p7mRZLRCip5TJQdsChDaSoZdzW2y9dQ4MSl5Iq+bn5PHuwUXiY3hiVvrFOk2FgEweyi4eb+OH8cUHt6mgHRZH1fUmNp+/EsYeO2s4U9drZ0kpZ4mQICzIpF+dWF3Aw4qAial/7b0JsCo7cAFLIjwKXAYJSN76Aq4lcJz0hW0JdQ+lr9jjVcJCuDsjgUL8sTlF267vG2eAW6CZRsZGSGZkqSQHxwAKOKJAMS01rPYDqgcTHpUt6agZ+ApURPJR5QIOmzjba4bxEBsdJceYFIMQAXGg4BzdHZGB4kPebMA6UM1SEguMA69xdZQNnpGZgTKPMM2JyKFdX6R1tl5aeBvZG9wjkTceJvk1fQykLR4+my3VwtJGwcJyEOPqXJoGNeG44Spy6wfSdBEmMixEfnI+a+nHvYEMmF2akd2xUJufnZA97ZEdKpxBvFA3iLOzzJ6B8F26UHBZXlqSysRpH5KLEWKKkODEPdZxQo6Y3DahQ3d6E83SN9wqT7KxsHIW+NH6AyPUp6Rhok1HKendzHw4BkDYYqDLa19hWWxRpLuVcl1xLLiClB8/6EdCmNlRtsPWHi5nDalut//5Jf6qt1jrXH/2uBr5a/66fWSE2K4j2Sf+BnqeHfs/6PHqe7mFaATb9vZ5nncfqv3XvWP/Uc/R6+nf9nvV6ek3rc1m/Z/1TP/tRh/Ve1s/1mtZDFckWNhfk7Yrvodo0Li74HiIioqjvFIkLswC4uNJWNqSlr0P6xgdQq6AO4/NwCOL7QA5q4mheuof7pK+lm/oPktKsAkkIieL5+WyHlMLTndRjHwCR9iEAKwDr4ECcmIAChwDCEcFRkpqYQcC8H/Z8SZo7GmUJx3WYJUzOgEFW51fZW13HcbxtAqejwhMkPChJZqdxXgOhHWHb7UhDHB4TRP8vABqIBERxZVzYleXVWelHuXV8ZkpWd7coy1NxYu1gYbxNYzyIiYgFDHJlTNmSXuzIQF+/hOCczMaPoPvPGBnGqk3pbuEaw1PijhM/g73icJ7t1AF4fG+KMuk1KQaXgfPUWPvi/IyKsMjRFqp0i9sSHRQll0hvFgwU2keqxefdDQYWTqY/qaLc1CROfnxDHji2z0hDvI3qC75PicDG2QGQzM0sUHak7QZAC4+ySGZ+LuM+wJotTlvAu+W1BRke78OXMmSgsX0UmwyQwTmZjNmJEfi3SBW7g3O0Y7Bdekd6jcM+FzuTHpcMrAFYhhO6k983D3eiJmfDOJkj2eHZqJK5yfLJqnSRXrB3pAvQCNideYMbALM/46EnkOHGxIL4ObpIYe5lQCYLzu4tqW99DNA7JO6+XuINND2Pc3cdn+Uh4NNLFP0slkjsjB/zpD3qccH41JycHCUyAhuQmSURfhEo9wBjvMShy/v1Dw/JyMSUrGDHjxk7nQC0gnHSJ2Nn4iyx4uPqh107Jr39ADapm3J8KclpqZIamgHUE4pqpaoFNUsjtsYTkLAktUwSouOBhB1lHcB6eKpfOnraZG4ZsBjIzt0b0CA8BCjOibLfBrhwlsLkbLEERzKuLUtHJ053ADB3bJUzfsS11VXZxM97jKLPNnBfeHSYhIdFoNqzI0vzzFGwsU7AAVFhUZKXmi9RwdH4H+xQKVwH9FhgnBhhXjFFW9vG4Y7hxnyEhgRIclK0JFuSANkpq/Vj6RkdlKaBdnH19eA6GZLGXMqF9rvLHKgFUYA++plCYNnZmWKJjMYB7iirh6ukUeylD3bI8vwyc7sz8eT99BkVItheAspxjZDcNFJhBkViy7ew18+Bh8ZRZwXGwPm/hRN9Gxtgj4Kts5czcCHgGfY+KjZBbIEfF0cnAAeBGp2AIiOCJDsulxTMmQQHeMkucPTE1qSMTY2gmsacGND7JQqibrQ7fx9PiUkKl9j4SECqaHE68CfzGGm6mXduvFyRtNQ42mKKBAI97NAWhlFca2rpAoyzZdzO4nMypnk6yO7LHRmcHZTukR6ZwS94RDl6MQ4GRwOABXnI2PCYUSctTCqRxMgE7ABtZaBHapobAR5cJSgsBFuyIouAW7sEXJza7wOl+WHjgEEP6AMzzKkImLB3cpBQ5jhJGXESGRou3vYewEHANkDb/T200XEA4dVF2aYP2rnYig+AaEJiPPWUKhGAqJqCvn2iV1pG2ghc2KEOiyQjLoMx2410ntsyMj0mnT19QO3HkpaUJumxaYy5rkDqi8zp26WLOhyZRCmYfu8CJOOHDzUAeG5lUueaL6U4h/TlwbFASkfSO9gnPYNkpKKNe9NO9+hrOhbvAEzt7u7Q16KwU1Hc90SmZmdM29OgkrDgcCnMzpM47Lyn9kHWDvPAPGPMucew/1PTA9hc3o8+qIEECZRnWlQac7IIk0p9cHhAOrq6TJrF5LQUySD9oS9w4j7z0U7aQF13p+wAlhXG5kp5Rp44eb6UjeN5GQEwahjoIqUwUBayYz6O/tjpSM0uCLyyBljjyNyvRKIjkwDQ1qR1sJn00YOAJDwHoNjm6gbgM7YPRsEVkG2Tec7Ozp6kpJJJjnXRNP7VjeVVMwYEhgdIfEqiJDGf83EMAlLD7u5uyMwc6prjqMLNfAS22TBeeXv6SIKFd4ynDnlHDTJQOLOprYl1yqpEMedKz0xnDeNjrj0P6NKB33l+dpmxJEKuFt8Sd+bX26w5ZjcmpHeoGz9uv2EFVI0tBI7Cm3WgrntcCVrIiS6QnNA8AF8HaZ1ul8bBOtrKhsRFxhG4cmQCnbZQ5D2hDTs62CKqE0tAjLdMTxFIxD0dsM/hwaz1aD+xzP88UJYSQJ1dgkBm56awo9MyPj0HiEbAjBtwJ1BVfHScpCQmS4AJsnCXiflZsrW1AOVM8VmC5MUXsKaKlAPHA+nd7Ja6gTrZndmXNN80yUsskMBgX5PeemhpSNqm2mVoekT2tlClc3CnfVKP9O+ZKWzrtq3kJBVJakwGwOKZjOmYwxrs6Bj4CdsvrG/HZ+YIOloG0N4Wb+bL4YzVrsCNC/MEX1F/VBZAbbDkZOVKLOs2X4AgZ8BHhRnHZiekbwQwcWaC8Q6FUdqOv1+AxEXHShJr2CgAQU0zP8pY0s76dYG1TRKsSg5zAT+grGPmY2Nz09LR24li9YwkEaibRxryCMbSY1RUexe6aXe0UcpwGxvmip85hHcP8vXkfSnjrSP6bYYkx6WZFM9DYz3S3FVlAmF8gCMPCS5ZXgReBY4OAMxXuGyXVJrh0ZHiBKsyy1xneQ6olrmKP3C6zovSUvLNmuOU9cAOwWyLjDP9gyPUN7aKsYlFM3M2+m1yOOsm3jEgWjwPvWRvc0+q2+oMBBoU4s/aIdcEJR2xvllkPOzu6mddNwfUFSA5+M79UeLWZN/rrN87e1nP9ZPKdJW5CGyBX0QAAQPutMNtoVpIQ58suYyHGuQyNjos7T1djIcbYomLNnPc2Zlp7MmagZBVzTY5MQmA1kdmZuewj2sGCFOgUoGsLN5RAzGcqaxd5mxjtLkB7MzYzDhKm2vMqc6YhwCgYm9z03NZJ0QwrrnQRhakkT44DaMSw1iXnZENeB8IaEew1cGm1NGutB0ocFeYUwQ8F87chf0J1hz9o73S2d9O/102vEMQPJAlOoaMdUvUyaEkMuYWabt282WPY0jqWmuZG8xLRLQF++IiC9hgAbr0xjafONvICkq/Om/2A/rfYx2nnMIe46IT0F5KAmvDRLWPrNft4XrYj5lem5Nu9nWGxwdlDUjwFADXRdsSwYNxMQn0jyz6rR/z0nXp6uthvTOMWpybZGGzLMwtbbEb86zVBwZHZbB7TAI9A3neNEkKjyYAxUZmjuekdb6P8RC1Z9bPKlYYjNpcNGWgenKjE5Nmjp+fUirB7uHsw2xIQ99TGV/vA2gEumU+sDRB4HpTu6kzFUf79CufktKCEtQAreAcwVaswVX9Xf/TuS//Z11W/Mx//j1wThc6qkKkcJRGzmvqy7z8PBPxsIFDYGR4BKjuLw38ojCGKoHF0Jk12l7hmX4m8woZKfClAJQu4HQQ9GCweAWYyepE0PtoetWv/q9fNRCWQmgKs6iqkfXQhVsF4JBed3Bg0MBqCqEoMKKLNoW7VLY7PCxcOjs7DQSnalKq6KWqV3quqpZpGorBwUG5dfOW5OXliRsD/wqEskJzummoEJw+mzUlrYJzmk5Ro+0UrlHgTxd8Ck4pwDTExFhTXip8pI6SD+99aBTVVgHErOlt9T303qqy9f13v29SjCpApNCUdsLe3l7Ap//dQGEKsygIps+rkWj6XH/zN3/LZCFC7ty983HqHwUU9UefU8tWy/KHHVq26rz52te+ZhSqFG5S5QMFAQ/ZONRnDgaCimJjQFWzvvEXf2GAR4UKs7KzzEJY61mfXYGd1157zahSLSIb+c477xiVwE0Wb5+jvvSddENVP9PUnn+GSphCU7/2a78qqhSm5aCbst/4828YOC2Rd1SlOC1zdSypQoOqsDVQrpkZGQa80u8rTKbtr+JZhQG2vvjFf2PKR9uTqiL+1m/+llEZ0+vkklJXNyIUnHvnnbdNJJ/WswJfCkNqeWhZPHr0yEBwv4xKmL6XwpiaM1vb+1/95V+JExFUCo1pZLVCRNo2NCpQyyU0NNS0V1X90jauKmEKdGnqSqtC3Q+rC/2dQkoKIH7n298x7Tkfw69pcfXZdMNBld5UHXCEcsrJyTZgoaq+afS3wohvvf2WUXVTRT5tD9EWi4zSFn/adq1tRVP9qvKYQmna3hRU1fJXRbjqqipAsxqiqjdQXvuiuYeWsyoIKiiqAKpCkBY2zTVq1VouWr+qQKapVb9DSt9KoC5t36rqFxUZacDHb/zFN4y6nqoQat+8D3ipShy//Vu/xaZj5A8tsgPo+KamZvn9L/0+g2syapO/Ztqv9kHdVFGgVO2TpmB2YLH1FUDZVCa9OqAorKpgo6pPahmWl5cbkFPb/MDAgFFyU8OqbVrtmtZNXW2dAV4V8C0rK5Vf//VfFwtl/KP6l/Whj45YAPLu3wTuVcg3kncuu1Bm+qa2X11oK9hYisqi9gGFEbXt6nsrnKd18cd/9MfS09NjHBKqOnfp0iVzjiptTgAtqlqJ/uh7K+CofUJBzl+lf2m71zbyJgChvq87mxW/+7u/a+BLbb96L02PrDBhTnaOsYva5jyxxW1t7fJf/vS/GMBW2/zvf+n3jB1X+/JPc/zDB61/muc7v8t5CZyXwHkJnJfAeQmcl8B5Cfz0JaBzNT10vnp+nJfAeQmcl8A/pxJQ+6Qwgton65pa1/u6p6Z7JXroOZ/8XP+uexW6b6LrYquN0+uo4twwTvav/9//WbrZqPcEpAljjepDlLCbM8FdqK6tbaHAsjMtabkJcq3kmqQCqSkP0TjYBIhTI5M4BzyI6vZBoeuQ9f8S6moBAGVF2UU42IrY5A2RWcCgD558KP0j3RIWHyy3yq9LdlAaztAzedJeKe/VPRA71EBexflzNeOCUa8YWhuTdyoe4KQeAlCyBxRAnYe18UsUM5Z2Ub86Q6EulGvl3JYLkRfZit+Rp50fyovmShxbBzgEwomQR6MO6ZkDFB6m2WQ/xpEQwt6cqwvqAgBzRjEDpaGzk1Ngv3i5nFMiSTGxsmWzLi2DLexltOFgQEGNCGtXnPsKB6oykTcKGhfKy6QwvQCHii/OznX5fsV9AwVokOsbVz8tsWHRMn00JrWDz6X26QsJcgyRGzk3pTS71CjsjEyOyYu2WqkG4LBB1cEP5yc+bmLcD1F82MI5tYLzLl6ulN+W9OQslNFc5WHNfamufYTC3QIBh6i1uaBKhIJKiFOEpPoBekmkrDQDUI3voKxDehSAAlvUGJw3XMRjFXBjZ05881BGeN1PHGKAwh3xnHNPPTRNprYV2aW8Ro9lqpYo9YkTic4OE98y1EQiUeXZALppO5WZFyuokAGTuJ8ZZRQHyg+5IjnCUbp/sMvebrQEXAwV53yUjQDnpt7bkLnqRTbDUXzz2hNHL6Ad1DuCQoLFBxDFwctJBjq7cTrOiSYs8nXld/sOsrGC6iGApk+0t9hFiNTvVMuLhQpx8nOQACeUjFAQPMIZsIQS3Sb1EhuRIJdKbkpqQhpKLCLDMwPSAEzV399LmaNKQRtVqYsVnHlHqJzEpSbIa6WfkvjABMCJFeqjSl7UPWf/2FNuXS2X9JgUsbO3Y4+8lX22ZtnY3ZfSi9flQo46AvwA11blUet9qWx+boLMQwMi2aN2J1IfCH8XlUKc1m72zlJWUirFWWUoZblL/0C/PHp+H3W7WeBKMgE4o+SBVMSe/QZKEDMosG2gloUyB2lsXVRdBOWeNRy6ayguRMZb5NKFS5IDxOUCbNEz2SEvuitkqH8INQXUGhwBOwAwVRFKYcichBy5XnwDZatwnEMr8qTqqbT2dYsnTrgLly9JOk4+BxyE3UO98i77hZps7FLBdbmcdRUbAEy4MwBw9kQa65pR9wNcRaXAGZjl2B4H4u4KAekzqFO5y8X8i3Ip5bq4sT/1ou+FPKI/L04tUEah4ukIBOeCegOqgLMrKAFtrQKMuJOaMBz1GpQ59gWnzopR84mKjpLSCyWSgqP5zPZYBkkbV1NTD9SDogPghqebKy31BFVD4ChsQjzOomuXb0i4H04ZHJoP6RvNA030VWe5W3pdihLySW90IK0j7XL/yWMAKTspyrogF/LLgHIcZWpzjADsBt6vHht6gg1DEYO+fkAf3LJdwZkzJf4o4X3uyr+Sy8nXccyTwg2n53+rQ22xAs96Tatt1v1HtdfWf1vhMz1H/279U59B/64/eljHBR0H9EehOL2G/l79MdZxQM/V+1rHAT1Pz7GOH/pvPaz3so4l5pc/4f/0msaG/IjzpkjD+Wb1dw3Mur21izDSGQAwsHRBqaSExqLGgw9roFme1T5HQdRebhdcxXGaQ4ZjR2lc6JRHTx/JCk7Y0swCuVF2XUL9gmQP0KWiq0YeNjyVReDKIE9flDxQQ8KmqorZNr4AV8DYrLRs7lNi6nN0YkzuVz+WfuyDM3bdF2UTTV2nKS2X9ydwdM+g8uEvob5x2CNn7DJKSAA7qu7pHGQvV25dlsKYC+JvS5D59LjUMIZ1DXSaVGTe2FxNe720uICj/gCoIEZuXr2FamEsMJc9weoj8ojg+T1UHrMKMuTahSsAaHZAq9Ny/94Do+yRlZolN7H7QagYTe5PSs1opVTXVNMHFdTxNvvFp0BFCrRtkjLyFEgsOyldPl/+r3Box0jtaKt8t+YD/BIDqIO4SxAgqivgtQsOWgd3F9l8CZC6NimrKNoE0qZDAN5UyegIhRoNDocgkJLbZZKTUShejsAKgCEt7XVAX00AMTwDfjyFE7ewudsAUGHBYdiAK1KYWYI6jAugcpc8r60g08+4JCTFyq1rN1COCZVpgIoXddUyMAKADIx2qeCKJAcloxB4yjhZK0+an8gU8KE7zmFvwMcT2sMh/eGU5zoAZosNj5DPvPJ5iQpMlBlUbt6s+o60DTTyLABuXrFGlfMM8GAVZ/cCbcHZ2U18fFG8QlVUlXr0WqogZc/75eJnupBRKtGo7SygdFfb/kKaW9sxt4BIOJ81ldsa6pmq0hcVbZGiwhLJictHXc1LBvFjPGl8BGTUJ5mFmXIj64Zk+GYCcozKW3XvAP/1Sxqg9CtFr0l0KKCWwykgWo08q3kiwxPDAPHYMoAFkHWjZLuFzT6mD8YER2G3bpHKLUZGloflaV2FAQtsUXpTsMYL/6c9Dn5VuB1D4dTBwx6VqjDUf7wZZ1D1Ao5YWV0yTuYLzL3K8kqAgINkAAWvju526evskgPGJHdsti2KbmvU38HuIX6JMLlSfFFyYzJN6tWu4X55//ljxsk1YIB8uVpyEd+Dt8yjdvg2fsS50XnJis+Si+WXJSQyVPYw4h1TLfK0+oFMjI6LJ3PHINSjeFimEyik7amqlJ3EhycD+FwnACNd1lH3/N7D70gHaQnPHAnOQC0tyAa1LeapTqRzP3Y4kLGlFSCvRdRFUQxyZr6zR0I+lIbmd4DiSOeYnpAhNwpuAl2Hy9bZjlRQh+pP3gP00Dbkjk9kn3azztjuiqrUhculwKXXJdjOgjjBkrzX8L60TjThf/KRT1+5JZmAS3N7i1LRWSMtjd1i8WD+xZwtEf/rgeu29Ez1MP+qxZ88jYU7M8qp6so9ANzfBqpbWVyVWP8EuV30uuSn5TGX25Pa1ir53ofvomK3AwQaZJRaNf3fKvPqJX0PgDFfgMwAB0RWAHFPj1FpQi3tDGWtmNQIuXCxCBAhEph3X3r6BqWqohFQco8xFD8y77Szu4naJVApcGAJ89cr2YzdBIj0zgxJZUuN9PX0MZ4mycWSCxIDELu6v06QB5mT2gclGBW3svxSyYwBDGQ+1jLRIE/aPpTBsUE4EpRvfRjvULzbQvUI8sOorfkC77xy7bZkRmXJCQp7Ne3V8qTuqezs76KchzohIK4tMN/KIXNEIC37l06oKqGqSMDJS+YNe7T1dbVZgMj5eTnMuS9JUmA8vMGh1ALc17fXyzxCGB74PlUdaQdf3DaKR76ozuXizy/PuYAtCZHp4Rl5WltJv0KpKDdRbl26wXVizTzpQWuF1HY3Ab64yd2CO3I5/SKqZ2fSy3zpEXOEtr4usXFB8QmICf0mA5ms764x6u8S+BMur114TbKis2XxcFGedT6XhpY62tQmqpME5KBC6o1NdKV9Hbu8pJ8C9QNR+vmgnIpCmSM2XtO/bgG87qH0GJwYIOWIgMSFMCelTIewDQ2N9bS/KYAyMkIBoO7TB7dWgfFQwCzMpd8yPwlCbOeIdcuTikfS2t5G+3WVu596xcxxdym7nv5uqXlRhfrYsRRkFMsr5Z9BwfBMhtb75XlLBfXei0jjEesK1PaY558C6u4DA0/PzUoI3MXN3FtyO+GmScP8YOC+3Gt9X2ZYywWilBiKmpkqPu4AGC2tLAMy6ZrHm/aOUjX2j8kZil6oJKPxGBoSLlfL6CMWApLog/0TrYZdWJjeAOJF5REYaf9kDwXIVZRCnRnzsqQsG9+uD+ssVE6rmh5LXdcLVCHd5U7xp6QwsZz117Y86P0+7aFKXPfc5WbSq1KcUiLuBOd0TXdJRVOFtI92c38U1QgocUZh+pAy0dTXiyj0+aAAerPkNSlKL2QefCTt3bXy9NkTxuRlMz/3YU3qhj1Zpy/MbY6jHAY8h30LdAWsQrFLA8o2gBUP9g4kKSWV+fsFAsHigOHOgIZ7pLK2CoXpSdYxtB76g6qQrrFO9GTumJuRK5dzL6AOF4pq5azUtTVKTW01HEKgXELNPS42VfaZs1W1Mp42tGAXz+TGJcZOvudPwM/C1qJ85+l3GWs7zPo7gDJ3QoVZU7TrOvaIOnUjgONSKQryGUUErjiaMfDD2ndNIIA3fTDAjXWIvSdlj/K2uy1rtUkCT8b4O8p62FlnG9VuV4VfFNnXluFSAvF/X2KOlENbsTHptpvxj4/0jWAHgPSxA/bMC5cAYndJRZ6emyZXiy5LkncscTc2zL2eyYuOWtbatnKbsT4tLhV4jLIaH5b77z8EQAa2zSyWK4gLuXg7yzJBEF39bfKMedD25i4QFWO0B+sKJ4C6M11XrDLv8JKSzDK5BCjv7+4PNNfKePhchiYA5Qks8Kb+PAAmyd/MuDQJNM++g1swqn4WnhUFWpZOKwBru/sb7BP4yKWLzGdic8QNFeoxeBO1jePUoSZkdWZOZMM6aBO7e4RgVQHPeq3klkQQjHDA+quq44VUVVYY1fsbNy9T7vlmbOsHNKt4Vsla70DygHPLCy8BlwUwhyQQqa9OauvqUALfYO9CVfRJNA4I/BLJuxmgZztPJ/p6qdzJuCtRnqHSOdYk7794V7pgCjzpg0HMSxQm9wb0V/B+jdTZPfNDjPGH2MJAgDfWxNSNCnmtLazwOy94lwIpygHEY206SyBiPcBfKwDxPn3DA4jdnrXsDgqAGtCl6pN3rt9hbEgn2OpYukf7yI5YCSS5IRlJiXKxqEy8UTDummPMq6yT2ZFlyU3JlevMoRPDLUYx9xmw7aO2SmPnvbF93qgVn1J+p0ckcuVnfZNrZWXKlaKbEuOVKNsoTb9d+zcETrxAdc9eor0SxGn/o2AyZSpSUtLkM3ffoBwvGl5FVfl1J96scBgnDTpnoxrB//A12N8D53StshphUDEAAEAASURBVM2CspuC/yvUsRR6s0RbDKy1w2RpHfnU/v4B8yApEPR3bt+WSyyedYGkKk8K+nR2dbLBhlQpSm26kRIGdKSAk6ppWRdoeh9VevouwI0CLQo5KeTyg4eCMFYoTyEvhX0UwlOZQR0UP//5n9e5h0nrWvnihQFnIkkDqiCcpjRUaGwceltTOOrGYFgoGzh+vkRdrEEpdxmYTmEXBaOsh4Jzz54+M5uExSXFBrbTxZwqaO1BhiuUpWCewimtbIZ8eP9Dqa2pNQvLPGC614FyoqMt0g4YqLDLFFCNM+cqoKJpMxUi+t6b3zPqUXNQrfo8d+7cMT8KaikoqOCcFyS6hUmKfq5wlqqQKaikKR0V2NJn/mR5Wp9f/9Tn1cmfquopbKObm6EhRM6xQarfV7BRy0fr7f6H90356UI7kcauC2VNfaubpGVlZXL12lVzab3ee99/z0BgmhNclfpUdVAjkBUEVNBKATH9vsJhWgcaCdUAmKYqhgrXeQPSFRcXGYU7hfdU8U3BuRrALYvFYgA9fS8F/6anob6ps3/9b/61+cwK9czPz8t/+o//yQBUWi4BRBK4uyG3zn0npyYN2OXNxoyCRaqApu+lEKCmv52fmwfGSjLnlwJJ5QNPnbBhp/X09BkTNxa2HkToartRaVRtG9Z2q8pi97iGwo2RQGT6+5sAaHr/H3fM0c5rgbP0GVTRUYFMLVczAPD8H6VJaTCRQAqdXr5y2ZSPqtQp7Kdgl8Jq2m40XbG2P217P0u71nrWaz16+IhJwaJpA3ovVSXTvqD9TGHIBO7xS7/8S0ZNbnZm1pRZxfMKNjKEhSARS/Q7HfwUpNPnP8AAV1ZWGoW4RYyWLp40LbCqnqkRU9AwISGRthpiID1tA1/4lS+YNK2qVPijDoVO//RPAbtoVwqJaX/T+re2V30XTQ2t5aD3UhukQJymxNJ7qGKd9kmNkNc0swcQ1f2oMCrI+fqnXjfn6ibPAm3pv6L6pjZGy1hhUu2jWrc/CYjUZ9fnUcj24aOHpj1qPw+kXLWfaf9Qm6YgpgKnzwHntI1eAoRTuFfbsaoQKkCsYF9MTIxJJ63vpLBkFRNa7YMahaJ2b52N9Rnakj5nKr/7PACqthFrv9PNKoXsNLWrwpLPaa+PHj827UrhVgVAFXZW8E6BuuqqatMetR3oO+tnWj7/NIcOZefHeQmcl8B5CZyXwHkJnJfAeQn88yoBXUPpz09z/CyOwZ/meufnnJfAeQmcl8A/Vgn8oF1TKELXrLofoEGmujbVSGRdI+v+ju6laMCbrit1z8n6fV3/HuFwH5wekK/99X8EnukCJnHj3HgU1XIkJiQehIlNTQC25x0P5NT1UErz2AtMuoHDHCdl01OZXCYKOSgQVZUU0pkFkaJnT7p6O2SRiGp/NpALcopRo0oXB3GSobEhuVfHPsrJguSgYnIt9aKsjC7J0+YXRjEoryhfrmRelgTPBNkGKqgeqZP3n91jI9ZWkhLTieaPNylJZucnzab0+PKQufenCj8r16NuoRO0Kh90v8mGeIXsozCkSmKZaVk4zr1Q7VqW6vpa0j8tS1BEmKSnZJK+KRKozMYojA30AlCw015E1L06ike2+qWm57ksAq9FhcVJHGo63kRSHx+dyARR1F19zThz/aQcWCDLUoh/2EnaUDV7TpnMo3SSm5KNok0GKULHpaqnAkBhRUqSy+VGxiti8bfgDNyWBlRIqnl3EtlIUnqiRMcQ8IdzchKFvE6Uz0b6xgAFEuVu+ackIzUX0MxJHtbdk8rq90mpsgB4EC0ZKDRYfDMk9CRS3Bdx4Azuy1rHAul5cJ7EhohDEGmBTgAsRmiNfaToIUWeT6GP+L9BurJYBef4PdHxeA30L4Ar7IsAx210ooxUM0QKP3dJLLGIdx6p1hAnOxk/laNK0nP1HMuJO6nsMkmjEgyMSTrSk9EjWRkGaEQRIIqgaO/rpOwptpPjbZRE3tyT+UqAKRecMPEAHDGkdvUDFvJ3AKTjOQiC3ZvbRcHkWGwdUMFS6mvHVg5nj3BIkYp0HzUdX+ANh3qp2X8ujqGOqKnloeCXTmpBFOHWRqQL8G5r+VByUAspLiPdMP69J1UPZXJ0lPRTPpIUn4BDGbUlVA5niOrvQT3pEEfi1axrUoZCjKbwHCblzH0AyIUlFDBQrygpzMeRcixNdS1cZwEIJIPfXZbYkGjSHKljqFc+aHpfJjYnJAy1wgKce06OTqRwmqGNtKLuM0GKL2ccZpelJKPEwCvdqLl8WMl+JrCGgnZ58RdQKYiSudMpaRqtktHeEdJKhkpmbKFkJ2ahmIMTGrWAxo56ymFNUlCtuJl/06gDVgPNdU53kLqW94tIJTVrJI74Q5leJdXT4ADqbOyxobxRkFlIKlQ3VFbG5GnjMxleGgfCi5LyvGIUDU6ksQklw8EhSUrLlGv51yQjGCcvgGrdyFN51vJIFqYWJT4qRVLiM1EXDJBJAlW7+hpRFWkhFZur3Ci+JjfT7gKqesqjgWfyfv37sjKxgtpdsmRGp5k0dppauQEn4tDYsLj5e6BqkCup/qniT2rYmWX61FAb9uNAMtKzpSS/xACTjT1ACqj4aBrYBO6vfe4EKHGCwNTBvnHAVzspK74g+aqWgVpQz0yXVLY+N0p9CdiLYvqygpU1TTUyO8P+MYo8V3NvSAzpkrZt14G06qSpvk7WF1bZr49mvzOdtIHBsry3igrmE+nr7kCFw09+8cYvys0UAuddUJrgP+thnWfqnz/rPFLP1++pnda/q21WW64B0br/qvuHur+ndlr3rfVc/bva9w2cW2rPdc9U9w71u7pPqNfRc/VQkQLdm9TA4Lm5ObOXHoJ6l/oh9Pt6Hx03dC9f9xX1fL3nJ59Lr63//oceP/jdlf1l6dpql3Wgy0XKvKennzHoVFKTUuRKQRlqOl4yA5xT21knva29QCgWuYAD7wi7UdXXIAOcb0HF9GZhualLe3xVIwuT8qAO/8VYLz4i1OBw2kbiaN3AWd8JPNLV16tkC/22RK7yPX8USfoBg75feU9654dJhxggGVEFkhKYISeOm9I6+QxfSIc44WBNi8mXDOBTf3fgMdR4OsfbZJgUgjEp0XIr41XxPvKXZhRzWoEFHLzt8JmwZ41KjQN9dg5Fkx4g1f2tA8aCHNpiKXBQOGnL9o1Ds667ThxIcf3KTfoNKodtja3YsE7j67lCf8qx5LOP7Sb18/XyoPt9fG39tNt41FqzgDRQdmKs7QEA6RpoBTrdZtzKlV8p+mXUiixSzXN+u/EdGWE+EEZ/zQaciQmJpa9GAJifSQcKQy2D9Sg+LkhydJIUJOQBsoYCqJ3I8MgQ0M2AeKO4WFZ60aR17uvoRx2mVY4dd40KXlhIBApk7gZWHh0akpkxVHOiCOovvS3R9K016rm1p0UagAN2Tral5Eox1w8jVfiwDPKjabouFJdJNmXrDfw8p0qElW+hFNWFapKnpGOHAoFaTrHPs5MT0t3aQYpQVHsSYuUXXv+CxPglywQw2N+qE3agHuDERTIiSyQzDsjS4VAGZtqlo70DR7egTEmZJWaSjhElI3x0naNtMjjRBwSC7670LjBclgE3qjpwjKOSkxKP4ltoEuASKnukkB9EWWh1CXXUkEi5VnaHNGSJwFhAPv2VQMuPAExspSyxTDICMlGmaUZxrlW8w5kb5FyW/LAiIDIXQP8pbP7b0km78gCQT81MJ/VhKOlZT1GhG0ehswUoe1OSKcPPXHlDolHo6l3rlftVD2SgexAFGU9JS0g1yneMjNI/1if1nfWoEZ4yL0pCDbMQFcYw2SW1/MBojwx190k4acgvU8YRqclSRcBCD2o9dqfA4pHhqJkBBgLtrG/t0KdGUI7dIv18pNwuvQxEDmS9swGQ1ClV+GE0LXxxQbHEJ8ZLl4LaAAShqMfczr1tFFDPPGxlaHNIajqe4z9uBj72NIq7Fu8Ek7a3c7ZDOofaZW8V4AJA6/alO4xVmfgvNuVbH/y1tE81Mh44M05kSpZPBsA4QIAnWahWhqW2qxc1m2lU+eKAoHOAE1HBcXgp3fPd0g2c6XTmJFfyr+L7SpKprXnA6Wf4WPZogxbUgZgTARzsb6s/coo+3Svh/O5G/g3JDS0ygRNVY3XyuOcxQM0s43O25CYkyxxBGc87q+WQAIJryfjIk68CqaEAfDAkjxuwM619tDdP49uJiqLP0KNGF0ektR+12ok5SQxLlzfKPid5abmkA1yV581P5Xv33wE2P5XENPy/SVkABn4yOjMizYyhS8CrEToPSCiUhLAEo9QzhJ++b7gD+ONQrt4pM/U+j6JPfU0rggsnEmmJlxD8XQ6kId5SiJL6nRodBhoOlOuFBFrEZqP0fMozAVfVVAOGUfYZCWLBbi0BiDU2NsspysKXc65JPsqEoQTKbB+uy/uNH8iLwSoh15+ko3ym0OdLVNKm8d+30Z8UTgmxBMnrdxCvCMuWl9un8rTtidwD0j2h76RbMpizAN8AdPat9ElTY5PsLx9JPMqdOq4HhNOnabOj2JdewBkHWzu5UXhTLiZdMMCTAvfzwMmBjFOJQJGuqiBHsILOzxdY3/gAfF4uK8deZ4ntOr5ulI7ut9yXXftdycFmFKBQqn61551Vsg5okpNVIGUJlwE0olGoXJUaII9a5gIvgfITUhONWqKmVJ6dR9mttxGlthkDy/z85Z+TPEuuzOzPyeOux6hlV6HOdcg6wCKlaaUSAZhkj/DH5B5K0wNVBEMMYpMDGccyJSkkSfxsUHGivhqGq2XDFiAkO405WBn1YINiY7MMDXF+kK+kotzlxxh+SErDKWDUvo4Bk4K4lDEvMzEVOMYe0LbPqHUPMI9NykBZLzfPqEU2AuUs0q5TAJCvFBLQFJrNvGVFqgYrmAO9MCmrE8KTuEe6AYWnUXntGeozCuFBESFyN/cV+XTKp2jL9vJu/5vyVvObMj85K4mhMXIhs1T8gwMJ7lig3juNMIwPyoiZtJWk8CzmHj4yAlDTNdqE4ti+XMwDJsrMJ/XvLO39iQEfQ7wjsWcZvF8gqnzbqA2iHg634YKS5BUCp8pjECo5c5W+mTZ52nUfVatBSbPkSUnKVTmw35MnXe+hWDYraSGZ8mr6GxIXkAgcuyn3Gj6Uxq4WUo2fSiRrJktkLPbRg3MX6E+1MjI2SJp34MeSNwCvUK5yYLztq8DP+8CAc6EwJjnpqF6GpADwj0t9/3OZB+jXFLXZqCYnhiWyznQk0Io5Zv8gc6UtuXDpghSxjt7FRqsg1PwSayxgrCjGIxdSKu8dnMpE35QsjsG5MC4W5xai8oiQkYMNwR9D8qK6khS3CxKTECbJeZmoTOucu0N25zdpy8lyOQ87HRwOKHdK8FW3fO/Jmx/1J1QiE7EJL+0JPiJtbftgm8yMz6CK7iWv3nxNClCStDmyw1bXybs135HlnWUDzhfEAGoGJxnV66Vd7Ab2rHeUsgecS7GkSxIKqb4EGGwBlbV3tOBnJkgrNgZf/GWxYTyrJ5iks6mTVLC6bkoEtAxl1gCANz8rHUMdRgE7j7Z+KbEYYD0Q1etpedTyHDGXPhTbQqUEwRlb2lUT8N1A5zBzjHTgqeuonMUQlLIhQws9UldfIyMDo/j5YyQlLhtQ3g/bO8OcrZF5dy9zIjcClMrlKsET/ti19l6CXwCzByb6JTQ8BFtdIMms8Y/sdqV+qFJaO9oJYuP3BDTExySLG5DlAqm4uwmkWVyfZA8hjn5yRVyOvDi3S1q4nqs/+x2JsWYObMM6eG5hlnv3GNtYjm0sYM3mA8MzBhxbC2zcDPAaGRMiRSX5RsSrqb4NheAZxLUspk0ns08AmCUd481S0fhY5pirx4ezbkLh28PDCbW8aelCbbeFsdXW30lKii7Kp9I/LVFu4dI0XCVvPv+vJgtgJO+Vm0YdhlpIpetMcM22dMx2EyBSzRrhpcQDqaYnMEeib+0yvvV1dAO9TkocgVVX4Ck05XM96uJtXR0EJZxJPErewXAcmm10c31TxgcnZHl20ag/ljK2+wL9rh9uSS17JH3ME1w5r6AwVzyCvKQL0HeobUT8Set6ueAS9h31TPYiNA3tm3X3pW2ih0AKHylIyiOtfYBR0h0aHWKugzrg6rLks4a+CYgc75NqUpR/q+ob0jpeDdjpIYVRFyTSKVb6uvqks70dqDQexbnPykXuY0/7MTv4uvzgHTRtrvkr7VD/+4cePxSc08WVQmaqxqQDiIIodgxOCjRFYDAUoFJQKZSOoIsuhUH00O+oTPYsC6wJYDWVXNfzdVGl4If1POvDapSULuB0gRdtAc5DaewHD30WXZgpSKWgkEayuAEbKRATTKShwjSaClSfcYPNlpecr+kbg4iQ0AWj3lcXigqk6aag3k8XliHBIUTFEmUBvKPP9UkATcG5+rp6o3Cm6T4VINLn0PtZF4dKsbMaNItJBQAVzNFz9FqqKKUpQhd4pmWe17qoVaU7LQtVjFNYUKMb9TNdWLrzmYJRNjy7wk2aqlEhMAWVzLNxbT1Xy0Lf+bdId6rQ2o+De/Q9tdy0DnXhewogpgp9uhGqC2Zrfeg1tQy1bFbYzNBDN0kVotHr60JYy1DfU+tKy1Jbo35fy9AK9ehGq95TF7r2DvZGKU+voSlsdYFtLQe9t6pzeTI5VmBJwTlNY/lLwJOawlXBIH1HBf20LBUY1DKwLqD1Ovq8usGrf2odRkdHG6U7VfZS8Mjf39/8Xr+rh15TYSxVK1NQTmHOmNgYUxfWNqbPr+coIOrl7fXxRrFeT5W9tAy1PKkK08YUgNP2oO//4w79jpbt6opK734U6a3losCdlu0Y765lqm3Xls0/fea0NAwE5LK2a02pq/dUYE3rTr+n5/ys7dpaz7oBohCqLRusWg8KFupGifZfbYP6Pvo7vb7Cgxq5McEm0wkDtX8AUrb0VVUtU7U3fTe9lrYbPV/bspa9boRr29F61++qLdHr6v104+Unbabo86himipSXrt+zUBiWsdaV1rv2tf1edUKaupXrQu9rt5Dn0PfVSFFbbNad/osCqZ5AkU6u5BpnvfTtqr3USBWIzRN/2UTRetGz7X2jx9Xt/qZ3muddqhQm9oKrRtLtMW0R72v3qevt8/cQ9uw2kz9XMtEz9e2qffWdqbvqP1Cn1nLTW2W2l19Hn0vbYMKfyrgqu+r9sXa79QeqYS81o9ea3rK2l4/2pDTfqf1on8ODyFty/21zWm70nJTRUprf9H3+sc9qLjz47wEzkvgvATOS+C8BM5L4LwE/hmVgM7p6uvrzdpJ52/WQ+dv+qPzNeuh/9YgCQ1m0nnU/18PLSctF51ba5mcH+clcF4C/3xLQPdRtL/q3or+XUGISoLgNChN1826t6J9WteeP/dzP2dU33Vtb7V/po9jBo9wTPdN98p//ksF5zpQxAgwyipXS69JXBD7CbbuMrkxIu/UfJcUVX2kwUThI+9VmZ4goBB1LjuUw0oJJIwPJBWbjRtbm6RXnSDNTGsb6YpwCgCoFWeVS5R3jEl99qD1A6llk/uMLAOppIdaAq5ZYZ8kLCpErl+5ynVSUVDzlGkAr4dt96S5p1niI1JwzFxHZQZYCds0uzYpT0nR1jrUSNoRV3k9/w25GX0bEGZD3u8BnGsjWJBo5mKUpUpQd1OVlqn5CRS13pe5FQIP07keG7NxgXHsQbA3BuRUjWNjeXoFUC0BxatiwIJmaRluBILCwZ/PuUGJ4kaaTd20mEMp7L3Hb5OSdM1Ept/Kew1oMJBUbGukh6pCpU7TVB5IREyE7B5vorI1jTOBVErZtyUrtIA9D3vSxIxIRc1TGRjqZ1Nb1WyKJRJIxMHGgTQmI4CKT6TuRT1KWhbUL14njU6OaIrMR033CVR9jHLOpgngLM68SbQ6G9lr7nLSdiJztUA1ROuHJYeJf1aA2Pnb4SBmT7gTpZbqExkEFvMt9JHQzwSLUyzpV4HUyOfKniAb0exfvdzi3P5Tma8npdXYuAQnRIqlNFRcY9gnJuJ7v5s0XI9WxG6FFKpJqNxcchTbQMbUHfZ6B0jN0zAjiyMrYsGx63PdUxwL7FAleSmL7+zKTOWi+Ia4SshFP3FOYd/Wh/EYIAa/vBDwLmeAaGekGENQ7KN/s1V5Os/+7wgpbdlnW5YlaXNrlDbXZgnOCJTL2TdJTZvNfqMd6WTGpK6lXlrrUYmJSpTSa+WyZ7cjjx8/MGqC5SUoCUbgSCIlmNbh8vaSNHQ1mP2jJBwbpajBhaKAdkILbjfARy1peFYlIpY0c9uoKJLayM8tQG6Uv0KK3kwULlBNWF1CuaUSp1yNuOAEKSu7KHkRClHayfLBvFShQtfW3GraugbxlmSjcmbnjiOHoOzn78rayQqR/dmi7SfMJ0oGNrvkYfv70tvWL1F+qDvm3sX5nsceD6qPqLc8RdWho6+dlED+cqf8Dg7LXQAjHMR2a5IPdJoaCuBjQxpjlEWWdlFQaqqW8d5ZCfONkeuXbkoCe0vHQGSNpDOuou+u7awZJZ0jHNPTo3PAd4Go0lyRXJRWAmxQUURR5F7Lu1I/3ICTw03ulNyVDD6zc1SbsEB6pgoAoCdCNiOApCtyM/UOziRPeQhw9kHjPVLu7sil1EtSnl5KGkVvmd2blfvPHwDoAa1E4eAvA2AIzkXpy5v0qrNSgWKY7vGGhwIsYlPmAGMauuq0uiQ3C4ctkKS3g5dulckiSlDV9DMVAoiORtGr8LpkhKbIFsBt01AroFw1TpolCYpEAYZ9wWlUyoJ8gki5ilpfNEASe9Tj26PAv0+kp70HR5O/FOUVkWYLxSXnQNRyVuW99jdRmXiKE8lVPn/1F+QWYKCvpiP7xKE2+Afnk5/4+Cf+Vb9rtcn6dwXddM9fM6noHmkqAgcKxKk918/U76L7iLq/GYFyptp0/b3uOer8Va+h5+heoO4V6h6v7jGaPWr2IBWO0/153dPWPUS9t+6ZWmgb+qP7m5/0p1j37s148RPf5u+f8IPf01SHcy+nAb12ZYu0nbVNzWQkAhQFuLx96bpR2lKlkpGVUXmCGsna0roZw46xUROzU6gsusklnMz5QJduzqT04jr13Si6UN+aOvT6FUAQ+qcvYMv66RYp6Dqws89lbXoNx3chKiKXUQTxQ/m0T959fk+GSWkXl5Akl9JRDAvKArZbkCedbwI11+KcJhA+75aUpF9AkU6D8belcbRBnnU8Fjt3O7mGkpHjjps0tzfJwt68pBYCiTKmBaH6CG4oG0eo6aBq0ttOe/fyl5tXAFoU9OW/cWC/+40PAbwGgHmwr44uMj00geIUztT8QikEwA1yQ+2ENvCw96E8G3xkFKeu5V2VkpgiVCp5HlLEKezxuPEj2CSXIPRfKvwlA87VjTTLd+vfxpc3Jlk4ba9ie2IBwF1Rlts43ZPnPU/lRcsTk3bwEuPbVRQmw1GEO8VPMEDq9qctT2XDZlMKiotIsRYp9c8agIlnJSYjQjLTgZt8UBMVNwCOPRklxVjlo0r+7cK4elmKUGtxQF1llnSk9Z21jNMt4uarwhHUCenj7FBjygKuKUFZNtQv1LxXN2lP337yjuzb70tOXi5p6/JJV+5lbNnM3DiwwjMZAdCzkGru86//CmpFicC70/Lt+m9J22gz0EoQaq7APPGFsofCWeNAJWpMNdh+OykoKJJSVKoiPYEGUXxpAvZ4hq1R9b7bgHDpllSjkDkw3y1x6fFSkHZJQtwt+PPOUPlZBo7uJpWYQng2crHwqhQnl4oP6p5jAJQVXQ8BgYFdUL/xQzVsenJGbJ1tKbcS0rRelAhHlMJO96V5vFLer3qTNKQ7Uk4bzAPu9nH2RWXuRIZmCSgACBifmwDIjpXPXES8IyJa+oD3P6CNjgHQh5E27jLfy4xLRRVwD3AEpRcgMVtPG9JdFsvlVFTXUNvZP92RXoCyKjIIHW7touiVJxEZifKEPjIzOQXAEC0lqAoFeQUx5NujNHNKCvJOBDpQ4ALiU3AuD2DHztFZZgGNHlY/w2czgF/DQyJJuTo1NwbItCdlpF67mnrdtOtN2x3mdHVSQ9rVlY15KSoGfgGECnOJJh3pvrTMNsuThseyMAqUAwB35/Id0m6mkzlsS755729QtGmVyGSgxNwrkueTDfTvBjC2I830tQcITwyQQjg1q0BuAudkM5bhIZKe5W6A7mekDl2VrIQsUs2myOD8qDQwdofi18tDCTgWGN2ZudYp7X0aEP7eA/rQwZGUppfJ7aw7OPkDZZzUri9I19rUUcf050QCSRl7gErkHGBMSkya3Ey/g8JyOinLd6RzoUXef0TQwvI2KRIzgR4UeAdsZrwdXhuUp00At23dEuOfKK+XfdqAMzukH9Yx5t1H91DJdUWttkyKgfS9nL2lDVD8YQN9FwGW7NQcuZ1/SxJQvsVbin1CNaz+KXDSoJTcLJDE5HhA+CFpqm7Hz5eG8loZ/iAEY/DtH7xEFWxjjLHqGaluN6QwtZB+CGyC4vLyCqmDOxtQWGuQU+dDAEUXADnSNgLzxgckyatFn0bl0MKc60RGSEH7bsXbMoYCV2oOcyLKKcSLVNGMKXOM9xVVldILqOsFTPHqzbuSG57D3O5YnqJI+whb5uLpIlfyrtE/ilGOQjl0ukkeP3wsx6snkh9fJOVAuOpDf0lKxf7lHgD+x0BA02ZMvkhfGUQc40Xbc3EPcSGjVJHEh2UQQKGCG2ekPOyRVsZ7TaVaQHrXUmxHmLMFBWVUdLsfS/NQM6qOKBgB+i1Oz8sWCnUxsXFyCVVSi08c6rQ2wMAd9PsHpKyewIYB52MTgnzDTMr7WQCb+zXvSTvqg/5ugfJz5Z8HnCuW2aM5edT1AMjnBSKcLlJCO7yWex2FTgUmSYc7324CJwaom3jSLpYDPKcDJfnZIvCDsuW9RmDZmWbaVTCfXTWBHc0tqHaSLje3MEdyExnHUAJV5dVZFEpfVDAvI8gmLSVVyotKJdIvGBu7CxzcjgoriqMowIXEAFjZsA4aHpcw5gaX80olF7vnZu8LkDQiD5s/lF5glpBARI8YLxTmsSMn9zT1WtddT+BNNcri3vJKHuBc6msE6zjKW33fk7eb3pSNpTUpy0IFq+i2mSsMzw1/pBAKQGbB536FdWF6ONAQamDDK92sUx7JaN8ocHmWZDMmT6+NyovWZ0a5vCC1WJLDmE+h9nnEBH9xc0k+fPLQqLDlEJj1GkrhCUDea4dL0jjZII9ePGHuQgpm5uH2rHXm6M/+zBvLAYhKostNOtaRzVH524ffBmqbMT7PfFIGW4KiGTddZBGVrydtD8zY7Wnnj3o54BzvcuJAZjKCoB5x703mPFm52XIZuDTcNx6Imjl347s805jERcWauk0PJ9CLMpleniJ1Zr00NDVK7oVcAKBcmWPuVfm4AtjeVwqKCrHT8SjaATaxNlrAZnQ2tJNec9GolpdjH0Kpgw3mOZr6uLG9FpAY9bGQQNkjAGtjbZ+2iVpfLgItEYmo8wFwrtMGWqqlGkAqnj5/JRuQ2RelWtapM4fT8pygkFbSWmrK31duv2ICdez27YD/quT92rfkAEgwN5E2mk6AlrZ7B1sZXRmUxzUPALZaWd/70yawo5EoyTKHUKi9rrkGeA6I1dFebt29gaXbxZbXkjaaLG4EM+Sn5aOkh5IYllfBzBfN1UbhOtIvTF4hyCaZ9dYpNHcTadYrGytpx9MSlRRlnnGCdNj+HoGsD5jrJ6LW6uwoiwfT0tiDza1vIj2yE3bhEoqXuczJvWTucErqxyrNeM9ADviIyA/9xoBzfR2A5PeAbocksyCbAJ8bkkBgztoBYjvt7xnoK9IrnnHiU0C/WaScJ83qAUEYHc9Q2W5gfuws5cxPnEiJ3dpCOvHtVUnKTgTSZ11BQJUN885NUjM3tAANVnWbNKbXL9wSS3gkQP0RarL98uDFfSC8eTNOeCDQNNE/YYJpStjjyEOd193VExXRJVS6K2g71ahSusqtkleZY2Silo/CK8r9db218kHVE9l3OZGSgnIDzlkA5+oHK+Wtyr9FDW8C+35BrqFWGx8cLe7Al+tbi/K8t0ru1T9CNdgd21VKIB5jgHsAsPwmwYqUPfs/brxzKaqqgbSxJ48fAwTDGwDb5qKQ50PwB5Ma+tgBa/thaalrYr/Bkb2hUvZiUrGfLpTtMMEOtUCuE+Lm4yE2rK3X4DXsj1jf0g9LUwpRKfUx6b6bCdh4Dxu+RnspBL67GF9GwJcHreSEIJZOeVb1VAaBuTPzMuTOxdclzjtVlgmQ+NvKP2fMb5Jg5n53cz8jcW7JUoPdU3YqOjIaRWHAuSLAOcZuc1AvZ/y85D+WfvzoCPnfGJz76E4fqSnpgkp/9NCUjc4sphQ2UfBF4Qz985OHbsCp00EXWbpg0sWXwkGfXEhZz9cFmp6rizu91o8DVRQa0WuqBKLeU+EX/Y4e+n39UTDMejiw0NXrffx8uqCkshVwU1hE83W7IEH5g4sy/b6Ccw31DUYh7k++9icGHqLYzWJR3+fja3KuPr/+nDFoWQ9N+ann6O+N0+XvPlJQSZ9JfxRI0rIyBzWpUatOlK8CdfeQu6+pqUWpKtk4ZLT8tA5WUQYbYVOgqalJfv7nf16+8KtfMAtf631/2J9abqYOzXu/NDCZXu+T76Df0+fUxbL+6KELaH3Oj8uHMjv8u7r6+F15buu19PvHR+xcWY+/+0wb7hHXVLnsj9BPnacRYUrd6Wf6LgrOTTEh/43f+A0Us8oNPKjPp4vzHwXyaNmZ9sAz6bkqJ+pIlKb+Tv+tz/7JNmfdDFBISctEr60/ep710DakAJW+iz6bAnkKYVIIH5ePpoS1HgoHWt/f+rsf9qcpG66t4Jn10NQN1vLV+vm4TDlBI2etwJU+0yfvqfcz70YZmuNnaNd6vpabNV2vWhAtXy0n/b2Wkd734zrnfP2dPoOWq5ab3v+TfeCjetd2/v/2PS0XfUYFsk75jpa5tUz1u58sc/MOP+T/9Hl2sTNf+9rXzb110/DVV15lYHYw/9bPre1J30Pr6wfrQp9by1b/1Dah9anPpPWph76bXueT5a8fmbbHc36yHH7II/5/fqXXOeRe+/xoeWo56vPoNYztYqGo99PDnrrXvq7fURthrV+td2u/+GS/dQSG0+vpoc+qfVT/reWo7/aD/U7btd5Xz7NeW79rtT9aVtrOP/6Md7ZlMFR7+Mk+o9/5xzs+qoN/vOufX/m8BM5L4LwEzkvgvATOS+C8BH62EtCAj9/5nd+RDz/88OOAB52/WedwOr/SH/23zrfeeOMN+cM//EOxWCw/243+hZyt81JVVdZ5661btz5WLvkX8nrnr3FeAv/iSsDsW2G/rOtxDdJTe6c/usen60eFJhSK+OIXvyjXr183jhCr3dN1pK4bj1Eg6Z3skf/4l38kHf3tgEKR8uqdV4CSrkgYjmYXcZKZvQl52PWB1HfUGoWsi6QlG0QVq7WvRTzD3VD2LyUdHEoSexp8Z0M6lznpaGvHITgvyagdGAWMoEwgNGfpW+2SD9vfkqZ20nOdkUYQxajooAh5FadwNko6zkAHR2yK9i/1ynu13yF95pxcQhHsatoto6LGqls2j9dQH6qUSlIone6fyK2su3Ij5rbsOezKB72ow+P09yYl3atX35DUqHRStNmR3qVXvv3293A87cnFK8A82eUS7IwTD9Bv+WhJXtSS7hIHkLdnkOSW5UrLeBMO6E7Kz09yUVkIdAsR2xNboCgH2drbkqrGCpmdmpFM0gh+hvR4lsBI9iaOZXJ1GOWVaqlsqAPkIIiUvc4Qf5z6FwB7LKR1dSStGY701ql60kw9Is3dhly7BMyTmIfyFkpFgmPhdFGqRivk/gcfmnd+tfRVSUMx5xRn0gPUNRprUbM/tZE32GDOTigXrzM/cZxkP/MFzqmmBdn335fQC6HineqB6hprdba9jvvY43iCAklrs3jmEPz6WRSmYj1MBDree3POGdc8Gifl0AtSf7UuyYEj4OWnYsUrk70FL5QKt1HDal6U8fs4tGwCJLA4Slwu8Jk/9zjif+MAZqRjnWlApS8kRnyvAs7lA85tnsnKBzsyUz0r3vFuEv5GiDgnsccHcGWDU0B3Vs5IV6Tw3fEKNbwMxL2Hm0a3rlfZC5lBNWtkSuaOZqTFpUGGQ3sl406GXE2/RRR7EntuL2XpeFKa+3HUPnoh/kEhknOxwETEN9c3kgbIk/Q1hUTnAxLiaNZ9kl3qcKCvVyZwPgYDahXklkh8ZALl7UWw6zoO3BdS01WF0tsKqftOOCeMNoMzg3sGu4fIAY7AoaVR+fDZezKOIyceJZe7ZbcljnQ0dvz3EbxRL1XU1eoyYFs+zl6cJB72XqRE7pTvP31LDp33SCt7Ra6n3hUvJ19pnSOzBWn+xobGUYAqkbukrbKEx+OUO5E1lGuqSTfV0Ia6Em3wRsl1VOAWpBUI7qXvieQV5kskaSWd9l2pbpIWH61KR1ebjHVNo5YVJnduviKZwAWOwBLz2zhvcFzVNAAb7bO/BzzhTdqfaxdvo7hYCvAQiHPJVkaWhuXNhrdRjgHuJAXuZ0o+K4m+ibrJCfSHWhtg6cN67A1phUtQKLmResukan3Y+wTn7X053X0pnyN9WGlSMcob9jK6PsR7fyDDk6Mo38TLq2WvSTLl5WrjDOS0YtQa29s6CZD1kay8LBlHIaWll/Ya4GmUI0P+H/beBCrLNM/P/oHsOy6IIpuAyKrghiIIqLivZZVV1evsmWS+JGeSSTIn62SSL9OTnvTkm5nzJZOeXqqqu3Z3cMEFZBWUHRRBFBFBRGURkf27/k8159TXSZ8zk9M9Z2bCW8dyAd73fe73ee7l+V/39cME5zFBcKYLG1qBQW9h37jZZIajYB0BNsmOzQRK9dHDF31YHq8DFJ2hqNaHwcqPCKxl2pqaTbEpQyt8lztWhKbeZuATCrnEk21ct0FZxGutCFxJ+/prBIPX1a6LOld4UrNj0zqGgWan9UHEXDF7/LmMJzb/tDmXnY/2u91TtHt7c3UEg+UsicM2dtgczfr1OVOc/Zulz9hGfgPgbEOybaq257E+3zbs2v1F2xRt9yUNoLY/2/PbPV2D8+buddrBGNCQnJzs1CTsHqq9N/vdxgv7/ed1b3GIwnHD81qNYE6cxoR5t+0+lpI2uU27ElmaQ4EvndhvK+iO6gp2oLKaCmIJkRSMTSqY63gzIHQOhqkwoJAx1wn1jvfqIlau2y2NGGMW663d71BMXcVn6EmU97Buv2jWlcoi3a9v11rGoh1b85x+4M69Np0uLlA3NpYNGZu5Bvcoxj+Wc4d/r/lIN+tvOLbPQ3nHlRSaRHylN++XyN/uWp0D6H5GgXXTuq3s6ndjrKvVKOdLcjo1HwqdAVPUHujJp7DE1DVjxqptwSLlqUN7DgN8YH70AGik2Hmjqwbg5LxaAUnt3niQjz8RtMRiUuwPp1juPrMQAxIR4NUnVdNdxWcaooPr92gjZjNPCs7jgIJ3ABvO1JxT46MmINh4fW3L17ErRTmw66flnzqbu3cTjbmLIvNi/1CAC3cNENN6vvos8OxVuc646GDuIWWvysHWEuhEzd7tuQPUR9uMYTNKTQGeX6jKyzcA9V4qIS3O2aDvS4HUfcbT+f5+rEQ3rldq6uUUJp/1RCbuxRC5RBMYK+88buVzvIQt5iZ1BMBN0oSSge9zUnYqfhmQPPfBe4ceY4Iq0SXAidDoUO1hDRKPXciLoi5VRj0zyLm6wgEYFi4J1tsHvqbI4Djd73+gzyo/xDJZTwRljI5mfEWJy1LVi92tBItY+fUKjDmAApvzsKFs1GL62HEK4I39jUAy53WPcy93Qy7GnGXYYYr0jHE3mejcaABcH+LPPTxcifMeUXdPF7GWtzU6NMFcYCNRZZhzgqP1cobkq4c3gQUAfu93Egk3RvR2IMAUJh5sc/Gh2EJnA7HvDehs/Ycqqi9gLArSOzt+CYAyzSlaU2FQ98gjFdRcUH1HvZYQ230IC14cFqU7L9sdcO5hZw8x8knanbFLcYBBg2bPrb+OfeeCFgIG79y2SxvDKVoDzhDyrntP24Bjrjm209ikOC1eHaGrgGGDjEPJMfFKA3TwI+rP1dqX8fAuxW1LKXv14iXxe1uJUdwgf8wxI4wLTT23db3qGkDzHQbpSeofLoyl67Q9NU+JC5OdWN7nM0Ocx5ex7WGnI3LVovrSMEcFE2M8zufX9rKN8wlItb5N4dgiLWI5HgPRM8DFHxV+BETVqtSMVO1KJ2Ldm/GA+d+ky7CKmzCZMX72EEuat+egtgMKRLgFOXGQHaPtgHNEAbd2KWpJtANPt3S36c7DdgyDMUBvSZjrFnOdYMNlc0jvsyfM8YDMsUelr1qnd7a9Rfwp5l+gunbGueJ6TGRNZZiOh7CceQOyY+QCkk8P28CcbQmm1z7sYcApBWcAIAKBH/MxnK3XIiKQXUBKeieIZLxbrmtFxc48bt/mAxiD0oiRfu7MR88B8AeHhxDZvAcgayPxiu4Yjctol7P0hcPKY/67O+UL+NFq3F19pBFht6tsrlRKZqIiAPhbG28D1bcpldddHZ/KWAtYapsdmHeOTw+ppqoM41WfVkckACrsxyqVDPDgTZRoB/PGkxiRa5xIdBfiUldh381bk69tsTsxZPlqkP7YIPWCK2c068ucevcerYsFxgJqt4jPockXqsWoV0YbTmH62p270zGyTQxOABJf4fwqVkjYEu3euAcwfR0OZWDKBxVETxfKl/4wf/0u5gikyBH3Ocl5dIcI+EtsDmnGErweI+66iHSMQ7WqA3pfErNYaRs5f7gubU6KfxjQ+zHXYB2bbHqxniVrx6Zd9BFEDOKvanjaoKJqUqPo06cmqFfTD6VEriH285DWxKUT1+qtQfq86y0Xdan6HODpOEbAY8rGJOgDAMZIp6HpFzoHKF/WVuRAIoc3vquNK3PUN4XFkLVP9Y0y4mbpm+hLN6/aLH/XYM7sSZU9KNWJyhPq6e5RFpsHcjmHI4Fu/dlENDz+XAW3Tqq8A6gYriCTPmHgwRAWqFqxH0mJ65Mw2AFeARLbcQ4zjjfealAXht2VUVHal0/0bBhjGXO2+1j4SpqqWHeQYEYcs8VpBmPjy9+cjS15g5Z4hzEeuAKsNKmwHNvvq6dKI/pzO5+xwY+2Nhma7seMWqfPz52UGwDTXqJaDwHOmdnp86bPdfLmCWKcX+tgzmHtZI3nylquoatOxeX0JV3dSt+QDiy+WysDVtF3ean7NVBd02XV1zQocmmsYiLpk1n/3OKcXRYVCkyXplDvFZiR+QSBsl4xXpXXsFkFw2b0sgiuQeDS8FRNumJro88uYhNKRfMNDYwOyBuQPJz3vSV5E6Apc7aASKDgcdX21er9c+9h6pxUzuZcbU0CEgLMgkjQsxk21gDhnrt0TjNDRIZuOKTMdZl6vWAYYLBEV9gEYSyDbcrI5nMKdA/RDYyPBs49IzJ5HcbJvWl7FRUUxbwH8Burd1lDhc5dKVDipkQlpK7GkHZHdRW1SGTCgZI3In0BQOa1Z9h4NDqCpbuhmQjhLkUsI/qaBLFVEUm2yNET4p/LaotUSxTxM6Q6M0RvR0fFKQegPRtr2yLm31S71QKcVXj9gh4MdikLs2Lu6hyFe4YyQyM2nL6ksqNCJfAlxpPkbt+B/XCr3F65A2MxHnAcXovdACnzlRu7RwGspXHRq+1RCwB0Ib83Y1mMw+CWzzyedbGrD+MhFm/i169XXGfdM6z8gzuIwX6saszQM2MC0NoGzI0Fk9jymQUTbAQbJfqzGcPZbUy0oTq8dbfSiYj3ZuNDH8BYRVsVa4JCYMHnmp2gls5mMzOMbmPuvdwnjL6KDRDDHSpi7dvEhpXV8auxke3GJrga+yRztpkB1Q3U6EzhKQ0/GdVGonZzM/KwZS9RXWutLnIc3dgBt+/O07Y1O7XEYxmA90Od5dy9desGfc9mHd5ifXsMMIC47gdUAVRY1lTsMETreT6XEQ/VA84xReMzjVcYGxeMFVnAJrIp/mtlXVh7vRHT/EodyDvggIG+rh5sjHuhSmDuixUYuIEkbUOOJ3dIDPjemb6TCG7W9nyGd7kGrlZcUsudRvpZkgazjit2USybrZgucg229rXqgwJSHDF8bkzbogOYi1f6hbPhp1SfXwcKxeqXs3W/dmYcAtANYS47Tqw3AG/tZZ1nnF3GWnRPxm6tp5+1DU2jNoYBvJ1iY+D4NMZu5jDexDqXFF935vtrN6Zjs1/JZ2hjIZ8jvEd/71PHKPia2F/jMzKB7y3e9TWfb11HHbG7pZgu21krMKfBBJgOjGmgbfziGOYAs5j8HjntcJXz2TccSHX7YW1cxHyH64bRCAMhlsUa5u/V5YpJiNXurP0AoEl69uKJflz2Pd19Xu/0fTZni/CI0XnifM+cOqXI8AjAuTfZwLXNEXjZ2sRsc1/89wW/8wsF55wX/D/wf7/92wbOVXKjYoP+6//zX//aWsAWr9///vedCEXbBfbLv/LLzmLUblDaAtYsW3fZMfPtP/q2jh45qm988xuOVe+v7Q3+Al7IwLk//dM/w471EIve/6V9RNYaVDT/mG8Bu/FiuybPF553YpnfOHqUzjt6vmH+TrTAz+fG4d+Jppg/iPkWmG+B+RaYb4H5Fphvgb8RLWCFwc8+I/anocFZe1nh7za7mW29YqDJVm6cWWHRioH2tfXsyt+zZ49j2/gbcQB/zW/C7M5ZWVlOW3366adas2aN0y5/zW9j/uXmW2C+Bf4SLWBr67lfc99uIJ1ZhAyesD6ttbVVly9fdmCJb37zm8qnCGO2IXt8GYSYmppUE+Dct977T9zUv+Xc5Hz7zbcpVnAj1WOxPGbc9fgVoE3XdQds8fdcRDxMtpoa7uhW4w2NLxjDCrBE7lNshAV+WsDN4QkKJKOjbNCl8LyOWKH8TfuVsphiv6cfHBQ7p7vO6fz1S7p396H8sWflrs/SMWK/wn3DiBXxpOQ2DQhwUydLPiBK5oX2bDyg/ATgIo+FFCisNPxSZXcBY25d1OuX48pP3aOdMXtI9hzR2eaTKq67otCFK/TmnncxNoQ7RrWmxlv64OMP5e7roUP7DmsTRpJA9wBuyM7omYFz3OS93drBDnh/rUpfrRoKAE3Ei7lMuijIK0g+xPYtmMHGyb3bielJxy43PQ2wtGaj3sx9W7HLYtkVzXEDODU9bNbHRHPd7riLHWwJETXblJ+xR8v8o4kW89fzSSCojiu0Z5GmMKAc2LlXayLTtcSNKBXAuWez3KDvLtXZM6cd499OCh1m7pvxWKBCjHM15RXyW+Clrx77hhIiNkovgDrqKW5ighu9NyjPFNJC9iyXb7ynXPxZq7NP0QHnLk2p+WaTPJM9texwqBbHEdfKeAiX5YBrk/3T6qt+oYGbffIf89KiZAz3+SQYLOdDpQgx+XxcTyq61VnYRjEuVmHbouSVwcbqYL7ODfmpx2N6VvFCXVefYM+LVFAu4Nx6NmACxL0oeK3eMmKMErwA5zhf4gDmPDAmco6QteNAcyPtk+ppJlGB2L9JCrLCpLCAne6u4wB9I6/0Yva5ajzL9Sj6vrK/mkXBMM8pUroSSfZ0qoeIozqKBwXYjYKVunkNN/6Ji6GoN0MhdZHPYop9FLj4xN3cgQApoI0ODQMrTSkpbY0ysrYpYUWyFgOPuGMnufnwFrFlZxwTg22U3ZCZoX0b9ys5KJmiqS9gHEUujEOfn/0Eo9cDrSV672j2Qd7PShrThcL/awwnjSq5cZ1Uii6lYbTN4jzwIe+2joiyM1dPyXURoHrOPm3FpuEx663Stiv67CpxS739XDO7dTjrLSCfMApqUxw7Mad3qhyz1vTEDLGWuVjiulR2C7iP8zcIg4gfsIfbGDAL722aONNxjFSzr92JHVyl/J0UwhNTHQPPOETinf4mnSk6q/qGOrn7u3Meb9C+LYcUtzhOgRSD3Gnzu0/b9V7ph7r3skvpAKKH0w9plf9K55oZnX2p2u4GIlmBiAafYWnahHEOcM7fT5daL+virQsUoN30dSJO0yLW0jWMqfFBoz4rOKHe531ag23lMMXrGIrMnAEUzJ5jsCt14hU9MXjEJq4iIrpbN4lEHiW1wxcgju32cgecc5tmUzg/9RxY4CWAhSWHvJVzRLkYDzzd/bGQTTsxcT+++n2irSjssOl2fdIWTBR7lbg4XoFUrcd5P1VYuS5gcHjeP6Dt23K1CagpxHMFr+LjFMVrnpbr09MfaXTgpQ7nHnX6mWCvRU4fakCZPeZ+tz/P9cn2b1/+d/va/+rx5e+3r9vfbZOz9d1mW7MNIHPJO3ad2i8D5yyZJYSEH0uWSUxMdPp1g+0sQszGgTTMYxs3blRnZ6czHli6hZnrLPXH4Duz09XXExPGPNnGAoPvbM5nNRN7Pfs3g/jsl9Uv7O9/2YeBe/aYO/653+3Y7M8dfe36buF/Z7P9Pfodrmug3WkgyJBgDJLMQTesxUAI8DJNsazpSTOFwiLm7TfoQ7AWJazVfgqZazEGBrgHAfa8Utd4lz4Fvrl/tx1jTLTezH9HscCdtBYejFHdBdC43nBFd2/eBqbBLLd5mxYGLSLOsg0oqVD9E0+Vk5OnHfE7Feq6jGu2Th9d/4FaiH3dRMzb8e1fI+o0TN70/ZBfmK5aVUg85/2BB0CNazUJHHqzsoZ0mR75+fsqcIE/0Cl9IgoWhg4AVsrhr2ecuN/DjDtZa4FHsJiOzozo/kSXrvDeCs5T6Af+TUa2cCDDYiRTON8X0S6+6uzr1any02oeqCNWKx5LD5bNQANgSRKiX7Br5ETVSUx4Nc799Xe2vKuVwSsdcO5k2afqe/xERzh39zK++gH8vOJnekaf6HTZCVU0FAOnLNSbO49j9Mn8Ag7EgNaO5epc1Vndf9XhXIdukz66cbWG6MEn8g3yxvLqjaWJqjMHODMD4Ez/O855G+gXrPWYIQ9SRF0aAPTCedA70YuN7Jo+PfOJXgAMxFG435m1VxnhRMB6Lec6nlUntq4LFNJvVFUqJilWB3bvIw6VCDwHnCM9a5LzFfDuIvMZb38fvX3o64rg6w96O/VZ+Y8BfO8QV5ikIxspUAPc3SZC8EJ1gWpv1hK7muSYLZMAGPzd/YCuiHEF4C3gum9rvuNEsflhfDJz6GPsYwHEui+g3d0mSbZhMjOJzWsaYOM1EeJBFJYNft6evp149Cji8zB1jnbqyq0i4DkALz6rxMTVADs7lRGToaUYsdxe+zgmpA+qvks7XMVQFq6v5/yaEkPXOAC09SL9GHOKmosAo8vkM+2tQ1v2YoaLV+vgXZ29yjn6mLizlA3agc1rxaJlevL4oS6UXdRFjLUr01fq2P63lLgIkApY5zW0w8PnxMPzflrvtGo5dqrAmBCiG8s10P1EAYxdQbyG+xQSB/pZG9hfYrEaJkrRy82Ducgu4tg2A2Ut1iuu14HxQZW2lOhayUU9IWFn+YqlgItvaGvUVi1dsIzPH7Z9clAFwB63Gm4oKMRXe/I4R8OJcwSuH5/FoMl5fqn+PNBXMxsQFmKT2ab4mGRi3Ib1YcFH6hwEFM9k/ADWWelGmzFhmXR9ocKKszp3HaCD93jwjXeAvDIUCjSN2kWdY+2Yzq7ofnMncxHS1MJXYE5ux+p0i+hJIFTPYMZU5CtA3lwoWLuQlwDEezD/25ycoa/t/IqiAe6mGCtf8P4NiP3w8kdA3UQU+wdiGc7WwYwDivDFZsXI0wfcWNZ6TWcLC4gQXgYAd4jI5DTMj4gOeIEh5ib1vbU6dfIMxx3MOYCRLXkt88HnQPhXVYD9JyQqjGvjkNaGr9cEEKZZHc/mp4FXAABAAElEQVTdPCuT4O7O3qUdQDKLPBcDlmFBZvNJEWBgaUOpYjfEEbEcqLstbbrXdJ+49wCALz/6U/okumbrB8Yxrb7k+Hy9fJSWkKYDuUDpdo5N+Ogx1qXSB5d1qbaAucg9+fhhUUZyYptRUvzW8RQLsCQ95hrgcy67Kj+sdPt3HSRCMo35SgDPP+lAos1YhIquYUpifr8nfxdwZDrztCmsYxii2MSygnNtN2Bv0ookoCMiO5uu6dL5Iq7VcB3LOgK4DFhBHznhgqFv9J6utJaouqpa8eFJSgGibazHfHibDTVeMwokHnEWa/QM88q5MWQcMJXhCGBpHeAc8M+SZOY5s/ThrCnaLzsG5y7MxEH07wc3HdLhdUe1PCjcGUuezTzV+doTQLwFzvnx5tZ3lRm7HQOxrQFc+fxe6GobMPGdC5oYnNGe9Le0Hqjw6fRjx2ZVV3cD0DeW+E/mgGEAiczTxzgTzzcU6GT5CWdcfWPn28pNyddC+m433vsQ1tqrdwtV3llM6v2s0uM26wXg3E1iRm3zj4efB3MNUvOmkeEAV82yuBgbeakJxDypKYk6ShxubJiBc57EzhLL/aJDnzJ/ugOM7Uns40YA1gMZO5QIxOvF5zSMsflWW73OFp/kuhvV1oxsjL95bPQItTOEOStJb0C1H5z5GOByRjvYkLQvYS/AElGtzad1+uYZXntCb+/6qrYlbOPoiLjHxnj5yiXskE+VnZutPZv2aYVPNC3GBqmZR4BzV9nIU6OlQWGKWLGSjUP3MG1dd+ziQVg4AwAMPbAGMuNm3QQEzTk6CXgZDzT2jR3vEDFNtDbn8NPZQaC3RsChE2rpaHDMslkp27QLOCpxWQJ9dwAG10GVd5Xpowsf0Ff5aP/WfdoUtxHLVSDt6KpBPsO24Ts6eeGUXvaOK2/tHm3FTjvt/RoArliXr17BfII1dFseQN525vk+bHDiHK067cR9bsPwtxuDd6gPMBzXVv+rZyoH5Dtx6RTWzkhFx0eq8067Wmqa5MLcJRDYbYGrSX5MUmRrw0lMjiNO2ySsAkzNyVEcsageRLeO0Q82EEl9BSC9te4ORjtfTGu52sXnl8BY5sO6d5jPrIY1zMmiM8T7DmpXDn1xXK6WL1jixH8OubxQA3Gd1ypL1PX4EddwLrG2W+T+2hPzbBlj2in5hHpiYiP6ODLfmSO95nUr6oDqrp3Vk9FeZW7fojwgrzCvSOecM5NaUwfrlIoS+uN+bd2dqUcvHqgC4OnZw0EHyAwEwCMPlnUFcxn6y5HxSYRKMwC6cTq4OZ+5fxpzbKyCnGWtQ3f0yfWPVdNQTRstoC9K11E2sCUuTmR+wbzboKrn7YB8F3S76Q5Rymnalblb0djxbLPDKOdB61AD8ONZ9WEJXktflg04FwgkfKvpJtf4RcDKJ9p/aB/R3dmMeQvZkNOuE6UfO/b2nRiCj2YcJ1aUOaRzzmMk7qxw5lmDw4NK5f1MDcyqqriSa3YYKMyf+Fp2cNn8lL7F4kBfMi9+PTxBrCzHt4M43FgiS+mkhxmJuiYf6rNrJ+i3mJNOEuuOnfUgG9s2R24E0vblOn2p5r4m7IkXiTe+68yp38g8rgg/0gGddzSoR2xcer/wU9179gj4eoMOpR7k62GqaithXvYJttNexpejjPcHgR+RIWFV7sd+eKbyPP1XKXP5BB3dcliprFfd6BtGsMy2P+7UKaDuV4z/8WkJAHTjxMreZM7W76x/HGkTGwhtnT7NZ0cH4MCnC7n/krklE8B0s5YHmilZjnH0omNovKRBxuiENanAtEeVGYEFGTh/ihXpwxf3dbb0HBG5DQqJi9TR3GNaj1HOh6NEYaVHo5xDjWU6X3xJK2IjtJPNAKssqhUL56cV7+necIsSmAMcTj+u4KkQFZwq1PmCAkUzhzfjXPaW7J+Ac8wiDZzj1xfGOdu29As0znH8/0c9bGFmu6l+53d+x8lVT1+3Tt/5zn+BCv3C0DY3+P6iGsV2/P773/v3UK+3lJuXq3/2z/6Zs9A1G52ZuywGt7GpSe/98D0dOXpEhw8fdnaC/aLezy/0eWlrI7dLS0v153/+586upl/91V/VwUNc6Ojhf167036hxzD/5L/wFjBg1HbA9/b2KZkbN1nZWb/w15x/gb+OFvjipuFfxyvNv8Z8C8y3wHwLzLfAfAvMt8B8C/xlW8AsyV8uTH7++ef6j//xPzq23+985zuOkXyuIGjrlbk1i1ma7OHv7/8/FQznntOKibaetCKkPcwgPPfz9j1W3LQip5m/517Dvs/ejz3m1qIvXrxwipMGtNhz/q8e9jP2fPZ7INEAX34++/45U4m9vn3NCqdWGP3p57Qiqa2PzUby5eew5+3p6XGMVDZf/+CDD5y2sXXzTx/v3Pv76dec+7s9r72Puffw06819x7sOKx95h/zLTDfAn/1FrBr1q45+2V/tuvO+hS7fu2X/Vt5ebkDD5tZ6N1339WuXbsck+TcdWffYw8rfDc9aNZ/ev8PVMeN6fjkBL37FuAcu+AXc5PUiwJR7yuKhp0UGCqKKMxRHE/KUmvzXd1+cFteixY4NiNfYBQPgBZXin7TRF1NUwRyJ+YjOmolhaZ1WrFgJVYdH71wfaJLnaccaOch0V0BRANabOobGw8TpRbJTVGs9Nx6rSe651Txe0QaDWrfpiMUFfcpyCPIicV6zXeUYf+4WHORQhPgHBGo22N3cVt7RAXNZ4mzuYrlbbne2vsVCtzRABrjKq+4pvff/5GClwbrraNvYfbZTGHB34GMBiiqFZVfViNGDw8KkvFr41XfflMdj24DAwRpJVFAFs22AHMCjIBj2Rn3woTv6cZu/Dh2ua+nOBWC+p0d+dODvPd6vX/mIyx3mNkWhihrfTZQElBVUKzcZnyAvPpU0VmC5eY8kavj2rdzn9ZGruemdBit7UMBCPsHBaIzgHM+mI62YSNZk7CGiIgFKrpxUTUVlfKlGPW1499UIrFJU/3u6i9+qgcn2rXg2ayisiMVvmu5POIAOIhyo2alyWbKCBeBIm7dkVeyl5YeAoqLM+MctgsKblNdAIS3RnW/4QEFpQmFx4YoOG2p3OL4TP05UShATz4d05OyboxzFKs5luW54YBzfH0hxWkD5/qmNVD+DHCOqB5iZ4PyAolqpVwwwnOfHNPzqlH5JXpo6WF/ecTQlh72vJSdn1P6vTuj+5WP1df7kOMcJ4ISi4QVNccw7A+Mq/9+P1FTXap0KdWTuIfa/eu7lJVIvCBGCzIlgKgeq/l+vU6ePSvvhb5KxmDTfq9DLbeaASQDlRydjGUHwBAwZJZiqysGBS4UfnZWoRGRWhmd4BTgzRTC6K6KjiqdrDjpRJMtYGzesDWD6KeDSgpMotTp4xRNW57c1menP1b/k4fakJ6K9eyIovxjLNHWKQ63Pm5xwLmOjntaQ4Eoa9M2eVNEq2upU0HJGVFfBZzD8ha5leKpuy7XF+qTCz8mzm8Ye90+TAXHtHRxuFOQGpod0c2OWxS2izWOFWQbhf2+7scOWPbaewzAhWhcd0BXgMdZiltjmHNcsfn5YLgLpXicQLxbJNFuFkdk0FgjcMnnhZ+oqbWZIqwHYF+ajm59S/Ehq+VHGZJMKbX0UmQt/xjwoQfT0nodWXtAcT4GupEgQ5GvrqdBJ6sAO14Qn4aJaTfgnBW4LrcUYb8o1ILxBfr1fb+mNcTyjQC5VbVU6kcf/Rjb1oi25GXrWPYxp6BkxZph7GDlDcQ31dTw/K5auSqG4swjNRE7aBFSkcR3LeYaNAOmQTuT9H2v3Mc17TNFbGSItkSt0+oA+hhgkBcUaBv7WvSjq8QAttc6cUnrEjZoL0Xf5KWJwE0AJgA0ZR3VgHMUhzAx7cjLdY5hqQeQFJ+wmSduPa3SJ6c+1PDACIWjI1hYzDhH3NFPHnPzuLm/W58616/OzQfnvvazfp/7Gfvdns/AOdvkUUM72HzJbHE2P7P5qG32sH+z6NVVFGZXr16tsLAwZ75Vi/XsypUrzusbNGebQwymM5DaIl3NTmd/NxjPNvkbVGcJImZctvmrPZ8Bdwbkzc1F58C5nz7On3Us9u82Jtn3/y9/hkvu8UiPLgAbDDx7jJGES3DGbDSeCgkKUWJMvGNqscgoK9rXPq7XxaqLTuy3Wf/SKaIezgPA4LO2gjyoiDonOvVRwY8B5+5pdWyc3tz+NmaLeIxOnnC3gHOvWrFJYROl30uIWuVEQAUvXKK79zucMWjgdb92YsjasXqHlros062OSv2/p/8U2+EDxwb51fxvYruhn6OkOEuhtBmY7xzRfR3P7islNZW45DGMO3WYOrgGY1cSlxki71HgzhkPjsHYMlcK555E0S5SyupkjGHYiNy9uH5GdHsEExfg6aXLl+Tq6ap4oLJDWzA2rVgLMB1M2/g74Nzp66fU+rRRKYlJOgCYkhyw2gHnpjlHHw4+0ulKwDnMk1Ero/QW13A04FwV/deJys/0tGdAx/Pe1t61+4nXw05ihWv60Q/Pv68qoJzwFeH66r5vUPjd4sTdTXPtdBD3dxbLXQemsKj4lYDLnrp1vZao0gleI4IxjfhjjEOuMxQwbRrxk740AGNe9IpoJUWtAVwC/ONLPcSylbZc08nzn1OEHXL6qe2Zu5QBLLzMbER8TyeA2OXyS6qsqlJs0iodyMe4xjF8Ac5NYQUbJk60VoW0kycmleMHv0qUdSzxm136vPgDdfYCzsUl6sgmwLkQxu6uWp0sPaG66lonomz/jgOMkwmOnYWMKSKx76qQ86qlsYVzCrsaQF0VBennU/3EEUYSYwo8O0Mdi7XcNOZDD3dMs8xLAohiXYXNa1VYkhOz+poIwIevAIDqLgNgAM4RuZlAUfvQtt3aANy5dEGYPKeCMOD16IeV32NMKdGKpRH6RvavOvGfnrThAgrY/a/7dLH1IuBcsXw4bw9lEgceFa+W5x2O7ejZE+yGzFdy1hH9unC5Ht6/R9/9GZF+5UrOTNKbh48DJgBF03NN0Lc/HOhUSRUbAe5iBYoNU0DUIjYGlGt0cBQbY5Qi/cPkhS3QBbhzGuPgLDDJNHZEL4CzdQlJABExjM3enCuAj2NPdRkbXnFZEZ/fcy2PXKo39zF/iqaI7oBzwHXYE89dPodJq0YLQ/21b8dewLl0ALKFACO8H2ywRQ0X1FzfqMVsQsjeDDi3MlEDzwf14amP1T3Y7Vig9qQTi+cewZWGedNlQB+f/1DnrmEVJpruzXe/oazYjUSIe3PWv9a913cd43BnSydG1UXO5o32R/fV3n3f6cPCFkY4MApUp2MTmsXQNYbp19PXS4lhq5UZtRmTXDDXJ3MT7Kk3ie/9vOhTdWLuCQgI0qZ0zEVZyB4Au30Yc3pGHqoUcO7c+UKFLA7T7h1HlLoyDbDhC3BuGOCjoR/o5dRJxzK4Z/NepSemOOBc6a1rulB0BQtXBCDGUa1hPjxKTGNheYFOV5+Q7xJv7cvbq+1xQL1eC5mTuqj7BeAc89jSulLFpMUAxvurvRmraMdj+pA4B8B0BYSwPtoVEMlsUGae8/fyVSTRq8nRa5mTRgFIAm6OY9sD4LqEmbKDft83CGhne47yiPdN9knnzCZaHqNRWc11XS0n7i/EXwd3H3Y2c/gxHzJwbgRzU8u9Wp2/ypzbwLldX5jlpkamda3uGta5YuaMkdoNoJsEWNZJ9GVBJXPxomu831h9BWPr+pR0wDnAR+Yn9wDnigAyKyorie9N0Oqlqx1zZ9vD21q4IsiJBV0ASDMzbaCE/WdTRVK1vN0VFwkwDmy31JsIRc6Vgcl+XWq6oMuVl/WopxsL2BLtXX9Ah1MPA86FAV24YCN7Qp/2GdfqeaAsL0C+d7U5lvkYwJkrxMwIn1/xnUJdv32RPn0ai9SbWr8qDztnH/Oys7p1oxIQMU5HMw8pmbhtA92GmAN9VPoRsMvnDHyiH/0lLMhsUFpgawV3Ntdw7dw9r9J7VzU1ho0T0+DQ/ZdqvFuvGd9Jx8pm0e/enGE25zEQxzYeebCeiyDaNYk+bSnWPTKueP+jqu8DSjrzKfHbHfIJ8FZ6UgqA4HalhMTxM4FArlNEutaq4NoZACfmiFxnuUnb2bBj4BybegDL7hLb+aMzHzrWwJzkbDY77OG699KZZgDAilMafzmpb+z/ZeUk72BONoS19wKRj4XOvaPde3cpHygrzCvc6bef0jYlwJGV5cTb8j5DQ8PU9bxT9UQ4BgT7Yu6MBbxaKldMeFNY/jgQTbtPa4H3AiJmI5QVlaEozlEzsw4Q+FjbV68TQH/NwDhe7sCda3O0A3AuAagyGNPkMMavsq5i/Zixyw9w7kDWQeCpDC3yCMAqjl3MwLmhDgecG2ITTw62va2AzjPeEw44V3TtslxoWwPntqZs1wKujQvA8xeAyF18JrUrewcRzvkK9f8C+u4nqrriTo0+u3BSy5PDOb9XqLOtXfdaOohBXszxRQFLcf1jInfhWnRjDmdrigBArLCQUMVFA7wGhbJ08nHmm5WsWS9her3b1A6wGaht2G93bshTwmJsZJxPw2xGqbpbg5X5jF66vdSu3N3Ki8nTMrelQHmzGgaca+5p1jX69s7H3crMzsYQvRlQ30u1vM9zHIdPiKe2Y5zbFrNDQSwwxgCpCovP6Czw3yjr450Y5bav30mc/Ao+Dqy4XEENgIrFpVf1ZOCJMndtpu/B9Ez8/ATXdnJ0Ov0rgDQmMxfXSeR53N9jNJwBioskqnVTNIZWYHAz5poZu/XFbb135X3HDrdg2lXJcal6axdg9xID55h3M/+4N3hPF65fdMC5dcCf+Zt3KSYohs/Qi3N0CLC9AfMl3MCjZ0oiAnjrZqzqrCtqgeevlTJff/lUh984pI0xWwHnFqn1cZO+d+bPHTPswezDOr7lbS3xXcKRkVrHUVfexxLXeFnPhl5oVXQi681Ztd5qYq3goohIwPJFgU6S5TSLe4Oz7P8e/BfOcaXFpCmSscSbcWdIg2ofaddnFz9zNlTZHNfmdLvp6zeEE0PLOWum6ibWVpcYL9s727WeufjRzLcU7kMb8bzjfAaPxx5hnGOsIWo+jXXVwZRDWsE5V32nVKcAAB/19Whv/huAc4e550H/S5xuF2bhTy6fwIJe6WyKeTP7qFKYh5gBfxBYr+3xPWLEzwI4E8GdusoBxWursfxiql6JsCjIjw0dfC9DhDGQziZAt9kFGPYXKZZIa9t0EuxFUh9Hf2eQeQdz5qJrV9hcNqpErNf7uNY2RWRoGfbVac6DBy86iZst0A36smVxUTqWc0zrgjD1cm4wegPOPcTgisG0+IKWx0RgFdyvVY5xrl8fl/wQg+YdJcem6Mj64wqYXKyCk4UqRLK00sC5g4D5QKx2b2IOmjNwztC5L8aCeXCO4fDn87AbhrYw/NYffAtArVGJCYn6F7/7L1BqRjgw19xi8Ofzav/zsxg4Z/a1G0z+TYtucayWb2yvawWI/n40lhUVzs3L/J35Sk5J/p+f5G/Jv9ji2DTyhYWFzs3Zp/1PtW/fPh08eFAxaJJth9/8Y74FrAWsuHi/875zwyYnN2e+Uf5OtIAtY+Yf8y0w3wLzLTDfAvMtMN8C8y3wN7sFPvzwQ/2H//AfnLXJf/7P/1lbtrBT80vwVmNjo4qLi9XE5iYrYloUVl5enlIpjlmB0WCw06dP69Ejdgmmp6u7u9vZJGVrIbN+7MW2bWtAs360tLQ40JwVIg8dOgQEsNiZ/5pFxJ7Hip1WuLTiqEUqWlHT1k/2PAaszT3qiCYpKSlRc3Ozs4a077P3bYVRe0/2MKveJXazW6SXFUEruRFt7+Of//N/7hjP7WftdcwwYkVTW5uaqSSbm262NraIr/fee8/ZAGUAjkW1Wqyj/X7+/HnHYvXWW28pMpIdqnzdjtee6+LFi060144dO5xCr/3d3pcVjMvKymRQ4L/+1//aAfUsIuzChQuy4q6tg+11rQ0N5rGC8PxjvgXmW+Cv1gJ2HVo/Zb/PAatzf7d/u0bB0WBhA+fs+rVrzcxCdj/Kvj73cwYSGTj3f//gD9TYWqfVgHNfwe6xJWGLE93miQ2hf+yxrrUVsVv+CtfrYm1I3YYVqYMiZZtC4wDDsrK1hEgRr1niX3nu6clxCgmUzSmI+HkGaJF7uAInFnOTXsACxMa0nlBN3Q2sBBQaKDrEYMTaR7F4TSTAAMYeytVq7L6hUyUY514/I3LnoPIwIxgANcNd31czwyq9XYqR47Jej04Czu1VXtwOjWK/K2wqIBrrqpZSYH5r/7taRVFiEutPEfE9P3rvRwqLDtM7GPWy07jpDkAzQYFpYHZA57kh39jUQmEuQKnrktV4r04P+u4S1xKhrUTnhfkuB24CDKaQZ7vSJ70AsCgiBLoHa4nnUoq0PhToxvWwvwMDB1Bf9VUnBsyH4w/HTrMPA8haADs/l0AnlrL2UTlRJ4XEM73EBkbcTdQGhbqtoPjt68RGVXIT/fTZk/Lx8lNuznatiU91QKhr1ZdVXXEDAMtdXzn2Newe6RrvddH98w/V/HETpjg/peYA1ORRtI+nMB9MYZyingPOFU7rXu1t+a8JUMgR3nMs8AP99VT/jF7ceKX+qudAWSNakuijxZmL5BXjKxc/gB5jnPnfzABRrFjjHhR2Y9MJV0gWkatZlF2JajVr3cQjImCuP9WjsodE165U8M4geWawA30II9ynrzRaIwUku2vhAWw3sdz85r443KamHxAuWjmlR9XdGnN/qUVr/LRoVSg2OqxKcOzDbeO6W/1At7tbVTp9Tc/iH+vQbx1QNuBcqNcybqZPaphIrSbAOYuh8gr2VtrWNM79bjVjh4jEfrc/ey+RfMQJ29lFXBA8C9cCnyW2KzeKzT5mrcDGYzGoQ1gxrtZeU0ljiQaxTrwmFipyZSTmwB2YdrI4ny0Sz6JasVsUfo7VCSPbGswDGOei/FdRPHPVa+yDtfdvqbiihDnDY6WxkXsLRTwfYM3G200UFE4LLhWrxG4KElsAfYjhrTqlT4CApicmdZDo0f1bjmh5yDJeaZrje0lh5aauV5ZisJoEnMtS/8M+NWKBcV9KPNNOzBSBkfLCMGPehNeTgw4c6EWBzoe4tQDvQMAZT4oP03r0sodj4/q5fpnYYPAIoIIlGIb2AutlxFkhMoio3CmsdPf04Q2iY0e7KHIk6Vj6Ya3GqMcsgHLUsKrvY9ipukCU4hCRShnamwhYhsHqMvakAmxBLmMu+s1Df49zew0WOKLSbl7X9977AXYPYrZ2b9eR7COAc+E83wKnqFfaQBTtjSo+T1fMVGace6jbD1uJyQWMw2gXB1hrZg5X5iETxAi+AhaY8JhRADaICGIBg6f8+JqLuoaeYrvBdnATS9JYL4YdXy0jatksfRnEmplRxD6/m91ENgIDP3nUpyzmVVnpWxwThw/XoJmpKu+X6ASf79jwuA4CbG1fs0MLgWfmHtbf2mMOErO/26+fCY7N/eBPfp/7/i8/j4FxHR0dzrzOnsegNpu/WX9t8ya7Xm1+Z3NBA+dsHmjGYEuzsd/tZ6yftzmeQXg237J5rpnkbCywTRhzMd72fPa9do/WNryblc4gO3vNucdf9ljmvn/u+L/897k/2+8jZlobayM2awRzoI1FfPoQtH5e/lroG0QR34Mz1CxQz3W1EXDkFiaUoefEhs1w/YZq+4btFL2xlQFdvQKMezBxXycuf66O23dpn0i9s/NdxQcnYDv0AKwcUetoE+BPEf1eB2BeIjBBrhZT1O54ANiB1WSAeLH87Tu1A8BgiUuoKltL9Z0f/5H6iGnbybX57q6vK4roUy+u6QnAscbeRp1rOK8urCEpa1P0Coj1NvFsCxgLMrduUUJYvPyxjHmAQ0wB7Rg454Ydzpuxxp9jDMTW5kU03sAY0W33ylUE/N3e1SF3Pzf5s9Fm67oMJ35uGbYyD8xVPdRWTl/HONdd58QrHszYp1RMS2bUGycKtbP/Pna4M6rHOBcbF+OAc+HBkVhWAH+Jbht4/EzvbntXe5L2En+H+WbBa7X139UPT39P1Y3lTrzr1/d/E3g3k/fohRABcI4i7VmiHdtf3lZkfBTDuBfgXL1jatq0eQMQLgYg7LGufHZf3Ak2RE6AzviTPH0ZwwMxIPnpFbGJLUTIllCENcvmBPOChayFUuLTtTUhV7HAMt7uHkSV3lcpfUMxa52oOOLRdu1T/KI4B5yz2OkXxL5V1dfoStk1+VDkPrYfIB4zZt9zbHtF7+se44K9p0ObAX95ztoHtVhhPtWtqptEoWXoUP5RJQK8+bgZkDmpNorfhdhbWhpalZaYTtzmQtXUV+qV6xBm0fUAUesUCPTlNsUcC9jKg6KtRah7MT4EAASadRccUv2zT9TwhKhWAKc2YJYRrqPFxJptXrteW5O3ENsdp6DZUCem78e3PlRJ61UtCVqmb+Z8QymhrCsBCVzp921jQkHDWYrQ5UA3QEvYWeKx+dzF/FNw7QKf4YA2JG7StvWs2YC2Ou7c1kcnP1Z16y2lZqXo2OG3lBRi4JwnxzeurqedKq68BjjXrPDV4VocjXGuhijd11PE3mcqYyUAE689/ZpxG0hrFqPqDPZXG+ZDiJoN9PTB+OZKdO1r5mGNKqq4zPXS6ozXBkhkYYPJSszRSu8YzglvZ2w6f+WSbtyqkG+gpw7uPaBUoloDmeu8oh99MNGhC5jVWutaAGSXKBfTUmxUvAMyfH7mJABkN7GEOdqzbpdWGNDBZOIlkXY/PPE9TEnF8li8UMe//svKjCEaFYDBjDb3XzPPAtgymMzAueVhy9X5pFs9xAYnAJimxaUDoQU78wsbE1zZdPAaqMCFMT/EbbFWuC2nyO8NMDWOsfAe7XNV1c0VzkYNL+YDoUAT1uevCUvRIgD+J68Ay+6UECF4DktjqHbkHgCqWEfEORvsGFUHpp7o5qMbjijCbJi7Nu/GtkT/gHGurI6xtqhYy4mhO7jjDaDJtRp5MqrTpad16sZnxO0G6hCGt9zYLIDTAAece8wYVgRwd50I45VpAEihi7DN3VZve4/yM7FSYm/z5ny22D1XovNmPYyGmOJcZesFMbwBTC68XYlqHPdQ59M7tP/narhfzVj9Qm5+nlrF2JGdgnkrbBuQLnNOYJQqPr/zGOXcmT8d2v+G1q3Eysx1PAP8ODRNLPztal2+fgW73YR2UcNeF7eBud0Uc6ViXQPCjVoVrXw+w1Ta7F5vh06UnFTJ5WIlc+19defbbLhZB/DFecccvgPAqajlqsoqKzD9JgAzJquFCMh7vXcVkxKtTZlsbPFZCDfxRR9j8zSj07wAjANpI4OC/BXM+TWmlidNusA8pPF+M8DlOCCcOyAL0YJrMMzy3F7ebNiZeoZx7hSWv/OMNwv0RvY7yozfxhrAnzHTDQMokBjXYAnQ/+TrBcA8bzLn3gZU2Q+gWaBbFfSV9ElH2TyRHA04BzBlmxl+UPQ9+ucTjLGe+uVDv6Ydyfm0WbATTzo481wX2gpU0nZZdNaAels1AjjXBKjkG+aldVvTFbMkjmsRcM7m6Mz7PHhvXmzI8eY682WjkZezHmJ+zyaC69jPrpWXYm4bk5ePG+AnMZjpOdqayLUWsIwNOcD+dxt1oaRAY+PD2pKWqR1p+RhTAZ/oF4ZZ5zRzPX9U8IkDyuasAZxLBpwDQitovgBsfJr5/6S+efDXlZeaz/E959hOqhCT1SwT9cOHMH9v3KVlnsvo/5iHMOe+zhhdATi3ZNFyIiRX6AFAefO9eszlMUQ6bwHiBsJ9zSfHfS6GPQYJpsfuMxi8ghSxIFIBfIajmGM7X2HuZJyoaqlyxl4D56KJf92SslUb4jYpgvH+NXPVG72leu/sdxnfiWLdvF9ZSZgvsVhaHz04/Uwt/a06AUA0OjAJILZfWzDOTXoSEdsAFMqamMFY27LzlJ26A6DPW2eB4gqLP5dHgKv279ilPbSXGSwN03kyBLOBAdHAuWVJKxgLI9X1ADi3qc0BtbM2bNHiAGxwAGIujPU4AwGZXOTFZ2fjmi/Xobu7bdIAIB95rCsAyNW3K/RiYIiYcx+MWynaxjp0UzzjDePpCCBmbVctltFzWBQHtGNbvravAnzEhOvOPGIYcOvW/Zu6wiaw7qePtXVbjjZjUrY5fx3gXAHXm2+IF3OkXcqOyZU/sOHY1CuduXACcI7P1n1Me48A423Erutp8243zopx1QMqXrt+zQHntu3byuaGR9jnb2Ds9NSubfsVE5LA8bFmon8x+/bELOsy1u3BbAIKJ1rcNgJYZHTvYD9WVTbs0Z4GmblzHQYCNe7O3q0tq7ewVltO380adLjTMaa2NLRwfzVNe7YQQR68irm9F23wQk1PazHOFepp/xAW3jTWTcSQByxUQ1st75P1xos+vXHsqDZxPfm7LCYuvUF/8tF/1ZP+Hr2187iObz3ubGCwfsbM2KX3qlTUdEUvRocUE7Vas4Mu6my5q0XBAdgr12pFxAo2vQGucpWwF8Cxm3lyjEFeAY490u4T2PjfzyaLUmJfi8ouqPdpn7x9vOn/gxiXc5WVvE0hxJuPEZt65wn254qrau0A6Oe+rJm1VwayqYfZ0mva5QHj+o/OfqpHzDHXr8FknrpfEcDsN2m7U9j6utkosyefjTpEDQdxjU+z9utgjvMhG52qWqqZZ27V28QcJy9Pou9aQN/G158+AF4v5M8jWpkY45jcb1ZUOedNTk4W91hYO3EMHnxu7tiTzYjszr0MN8YQP1/mpcz5PRbMYPcdJA6YSFzWvneY00y+ntBS1qDriWrNS9mhmIVcz4w33YDkhaXnsaATrR21XMcYKzcEYr3lnJqiD+4a7NQ1NiMUMQ+JWBXFeXTAAecGnj/VJ1ff0/3BO0Q5r9XhDccx4C5yjHMFZ804t1JHDh5xwDmLznV2ZrAxw/4zqHEOnLP+53/34cIi5YuVG0/6f/rDbtR/+9vfVu2tWpk1wBaCVkx44w0mKslomn/BN+htYWu2OSsa2I6xV6OvnOKJRZdafrItRtPS05ydYFZIsZudf1sftvCuqa6RFaNuAytOAS0uWkyEB4WTt99+W9HR0X9bD23+ff+cW8C6KLs27PdfNLz6c37r80/3M1tgHpz7mU0z/4X5FphvgfkWmG+B+RaYb4G/MS0wB87ZOmwOnDNIzdYyP/7xj/Vnf/ZnzsYrK0ZacdIKkYsWLdI//sf/2NkEZeaPX/u1X3NgMjOm2WaohZgXDKAzk5sBdla4NNubRWMZYGe7wX/lV35Fv/u7v+sUMP/pP/2nDphnjWKvbYVLg8rsOQya+7f/9t9q+3biQyiG/rf/9t+c92TFTiuS2tzZgDxbx/7SL/2S/tE/+kcOkPcXf/EX+nf/7t85z2evbb9s3fvxxx/rwYMH+pf/8l864J1BfLYmtrWpGUvWEh33P/7H/3CKsH/0R3/kAH9WYDWwziA+s1T9w3/4D50Crj2XgXb2ddugZpbx3/u933M2Sv3hH/6hPvroI+fv9vwG51kh1uJvT5486fz9n/yTf+LYyUNDQ53CrMF69j4N0PvjP/5jpx2sQDv/mG+B+Rb4y7WA9S1zUIP9Pncr0H63rxUDAZ84ccLph958803nWjN4wr7X1uNzP2tRlQ33GvWtH/4h4Fw94Fwi4Bw2BnayBxNb6s4N1qeve3WNgtK1mmsKXhiKOWsXkU8PVIOhbmEYsVR5+7USUAoXFLczeW6gFsPLTM9mEIwPAIDXZCBFu2Gijc6qFHvdJEXZeCxHvfefaPTZuJKwCW3fskNRwRHODeb2vmbgm8/U1depzNQcbtruokiB1Q3oyaCDaxQVSzFUsf1ZuzbsV178DkCKcZ4fa8TNawplN/yxfe8QPQk4N/aSfvuMfvzBh5is2NR6nKJZKnFk3Kgfo3DweLafG7zc6G7BMMeN8E0Uz1vuN+h2ZxPPs5hC5S4i4KzARUQd/xk4R3gbRzoLbIWxifKP3cgdnnwB6AEAgBHgxavnCotZrldjExroGVR66kZuhO92CmWvgUdaeut1qfyCHj/tpVi/ibibDC33iOA1/DXwqpfYyhIVni/QIuxI24nFWhNH1C397zXMPFU3yuXt5aa3j72jeOK4Jvqk9kv3devTegWM+dO3r9aKjeEKSMV8sdgqAUBtDdOauDCtDgrNi9KCFHqUWNgwbHPEqI41j6u7ukdDvSNaEb1cizYGyWM1JjkiWF29+WGKIpwwmhye1GDNsB6d61XQ2EItXoeRMAdwbgnn1BiFv3t8NuXP1dvYQ2wTxrmdRLUCzk3zc88+f6lXtQvkT0xs4D7KEIBzVLso7GKqu8Me9eJxDd5+Ib8Ib/nn+sk9nPdGLXi6b1YjjcRUVdxVy8MWVUyWaSh+QId/6xBFs21a6gNgxzb64YknQGT1RAJ+jgXRR5tyM2TRPDeu31AIUOchCt0JFE3cKDTMcH7Ocm7arXh7LOA8+In+jqLwuOqaa3W9BrPdqz4ti6Eg+WQAQOaV4sJXaQ9QQ1zIKsA7Fz0C7CoqLlRbR7OiADJ35+5R/OI0ClIUM6Y5F+pLVFpRxnj4Shs3ZyhjXRbFtEC1tGGtunxKHrTvnu17lUaRd8GECwaaE/r07I846Fm9kf8W4NxBzBYhFHOmnZiqiqYbqrxZSZHVVTsy8/S8d0A1rdWaCSR2Ky9fCZFJxKwu5EycdSIGzRTjCmjmDiThzu8GvE1w3ZVTnLyEaa1noFdxKbHMgV6r70Gf4lbEcQ1u12rgH89ZL/UM9enz6lPEJjdpRchyvUHcWtKi1VznQBWvn2KSqtR54obHMMhkrSXGi+i3YAC8q41F2C/OiLRY/b0jv6G06FSNYJkrvVkKOPd9DCPTyt2TR/zpIYX5YJ2i6DjqOs61U6YbNVXMZTyVgsHv8VAPQFCtApjz7AB6S7cIS+CeGSxBVkfHYQKYJIARisi8X+8pCuCTw6pobwBKvUrk1GMFLMPmy3Uz2PuMzy9KOdgtU8OTgVAX6DaxYuex1HS0ttH/JCp701bi3xKwmQURpTaoi3XndLHoPBZNNx3ZeUy5qdv/f8a5uT7X+lJ7zP3u/OUv8T/7eeuLbb5pD/v7awwRFrltEgCbIxrYZnUFm7PZfNM2R8wZ6Gwzg83JrN5hwF1cXJzzPAbW2XNahK19zZ7P3puZ5uxhf7bXjeY+vc1VbQ5q82D7e1RUlLO5xL5n7njmfnd++K/wPzueuV9zrzuBldPsPxa57cknhw+Ks5JiHvCCN9ehFciGZl5QCGzHtnVWD3u7FEZhbmIC+BYILJzo7b2ZezF0rnHsNb0U7y8DdNexmcMnADNV/jGl0Sf6zXjrucug6gZuEWl5UY/b+jBAfRH7tWTJMt1j48qFywV6/qpfO3J2YF/KVQjwZVlTqf7o/W8TKfcMq9QeHQecCw8MlyfQtNk76nubVNh4UT3Ap+vS0mFVXNXc2IygcUybN23R+ri1mEMXOfYUkHZGCtqR343UtdHRk+OcYczteHSXawfr2cMmBTGGenq76XFXjxYSd5qVmc01QwSdx3LmySM6D2Bd3VaphcFBmCgPOJC1jTuviJ80SOFixTkMNG3M+5N0fNtxCv9hxANW6+TNU4w9z/RO1le0N2EX0K63xt0m1NbXph+e+r6qmyqVmJSor+75pjZGbwKcA/YDiunoIaqV2L72kTsUYVcqAAvWzdJbmhid0IaM9dpChO1Sf+YSXIM2Btr/CfSlb+UIOU7KscxBZvSY/qWirgIr5k25+jAPwMQz9HzEAXs2r6WtkjY5xzsEGFfH3OPcxQIFhAQ6IOPaiFQAPN4Pg8CjZ91EtJVQzG/QkvBQvcG4HoFxru95j04XYRt81KrVMQnExh1VbCjGOcaBk1c+xxIFOLdmkw7vosgMNOkNwGhxcm1D974A5zDOWUR1VFgU0XQlesa8Yj1gYGZijpZ6YmxlrHADrLe5jM1jOEP5/JBhMA+w8/HO8zuY1Aow9gAVAucE+AfpWe9TAEgvTEf096syAMEimRe8VAHx1Rew3JnJ5tj2N7QBOM/6GQMQOp9hSar6RK0AAGELl1FoZz4VFgMs0w04dkl9D59ixlun7RuJJl+ynKjGFn148iNet1Frsik+7z+qhKXJXE+e9PgT6nl+n3kCG7huN/D5YZhLiqHvrcacOaxttHs2n18w8B8UiXNUFotsR+jBLy+ue4ZlQOYJrDC9Ol9ygQi8FvkGe2lJ2GIilh/JD1hsa2q21mGMWuyH2QfjmY2XZeUlwGmvlJ9PfN9qjG5uIcyvXqnpeZ3Ol5/RPTZbRIRGamfuTsUylj4bGSRa/TQRt4+IZiPOft1OLXdfTn87RQH/qb7/6Xd1kXmsz7IQvQ04txlQbRF9xATP+nD8AYV5jrGu2QEfU1JT1I3Fsrn9jlbRl29NycLwG8VZaaMhZ6lzjFP8adoB5hYyJ3UDjugmvvA6AGnx9SK+NgGkvQjofFIDLwa1CTBme2oescFRzC+GVP0AC3LhGSN/lLEhRxnOdRDCa8zoAQa14oYrKi8rd2KKdwGDpCWuAaR4oUrAuctF1xUWEaX92/msQlKYaw3rFHatc8Cti6MW6o29h5SzKkt+gDZ4ndT7AvgbaLiY+WTchnjW5RHERN5VG+bMnZnAw0BRIUA7ZkK0qFYziplKyGAYbwOx7Fxg3Hry/AWmpmrmpBc0xaYE/2UBGqGvGgeiTI4m0jXjkJb7hgJRYqFtaSSW8oxGGIt35u8GyMpiUwgGPJ67f+whccSATdU35Ib1LX874Fzsek0SoX2lphg4tlTRzK13bshXyopU3SdG+STHVwIwmLoySV/Nf0dp8WuIAMagxHyt3cC55qu6Tr08MSqJ8yWDKHFsZbdvKSwuFLgpFzh2lXM8NnbYuGAbAmxDjs2vDDr1Aj7qHXrCdUX/aPMg31ktigxVb9dj+Uz6KiN+g7KZc4WxFhgHNivDcHcJy/NLNqvsytqnzJRsxxxpPdbQ+HOdYf5V3nwdy2sgG13e0ZrYLXo63qurWOoablRjBlulw5v2M88CzMbIOOLySt+98Bc6XfwZsJu3fv3I32Mc2c56B5gSqHiEq+jCnXO61nrJsTztXLtT4z2vVdNco9mFLtqat0VptlnIcBP6GoAO59hcOVctqc76HBsbByfYpNCKUbaiBMCR+0vhyzTFBswn97q1ZuVq7dy8jVjJZKBod7V0tQE3AvY8eQSgnKLdG4nBXBrF8MOmFkxXFS3lOs1n7B3kRaRnvhM/7+XqzbrpAsd/lujxWX19368oJzXXiaY8XXxCZ06fYByf1pEjh4gWJcrUK9R5n320TUnDVVWykWcJG5XiuO4evXioG0Q0hhPPuG1DLobfFPkDZprF2UzIk6zfJvn8PelLFwk7K3GpfS/7VPmwTKcZ718BgwcvXcx3u2j42SAGqpXAvrlKW7Ee5s1Vt+lLfnDyv2uwf1CZaVnK4TqMWLSM51tAZPRTld8px1ZWpAWTnszVj2COzNS4K9YxwM7i0hIgU4xz2XnKTM6Ty7gX8Zaf08exQSnITQfz92g3UejLApZyps3oyUi/qjgfPy04pWWrwpSUnsRcqV81ZVWKCo8EbMvT6uWJGLo9nDPUxgvyDTlHQXymJ4nA5c+M0/2vhlXZVKUyAOshgLjgYPoYIt7Npp0QuVq7ttCvLwsHOMII/qQds/tFNT8iKn7TZu0AYIz2iuYskLPhqqSBa62iVMOvXmo79882Y3934zhutdzQxetnuL79nBjjrdFZxCz7OnbIM+dPqoC5ziTg3O6Du7U9A3AOA5rNS8wc3dyOGZvxte/pE4x025l/PVN1XSVxpWPaxdp+XcwGNqf4c07aOEHkLv2f/aw3430Qx+7JmG+AdA1w/Hki11sAWCNTIzC1uuvRvUcKXxrOMbI5DAurzcW7sJFdYWNZXQ3XemSEdm/do1Tm9n6Y+Z4ZgNxVqcKL51lzTWpt6iZlMC8PYdNTI+PsVYsuBxp88603tGlVFuDcQjV1NepPPvhj9Q/06d197+rYlmMONA2VrmHbZNdWrivNJfThr7D2psllxEV3GDe8WANksvZOJm7VE7MbC12nn7Gexp0zFm+ws24ys+cEn2dbD6bay6d1v+eeghYHOLHTZs2Pi4hXDn2ybcxw53sfDVpfWcxnUgtMGqYDxJzGh9r8A6vg9ICz2enTgpMawmabsSlbe9fsUTjgXB1zPINwH5txbudh5a07AHiI/RoYruPhHX1c+KlutNTAEGUCzr0BOJforENfTLzUA+ZCZ68Ucu/guVYmx2nJskUqL2Y8Zl2wLWsr9sw0QFs2NHBUtunC+tAFQMl2bC58fvbZTgACd2Kuu3qrVHX3W+Ue6IWVz02vXo4CugYCXOYrHYDOj818L1j/lTCuFRPv7bnQW3tzgTWXEWnL3MhG2Zae25zHl1VDLHX8mtXAk/uBIxO4H/xcn2ECf4BlOIXx4PDGNxU49RNw7swXUa0GzmWykcnWaDbJnP0pcM7utdh//7uPeXDuSy1nhQwrFNgNefuzFRosKsduFv50ZM6Xfuzn+kcrGNiOLlugWoHB3oMtYG3haaBcEAWXoGAmbPz73+aHLbqtnft6+8iDfuksjm0BbwUlK47M2RD+Nh/j/Hufb4H5FvhZLfDFjcKf9dX5f59vgfkWmG+B+RaYb4H5Fphvgb8JLfCzwDmD0f7BP/gHDij3m7/5m445ztYy586dk0FpVqj87ne/69g4/v7f//s6deqUA6AZkLZhwwbHvPb+++87a0+DxQxqM3DNYrP+9E//1InX+tGPfuSsB80CZ0CLAXO//du/rc2bNztmtj/5kz9xrHEGp9nPGCz3O7/zO+rq6pK9JwPZDNyzTVm///u/72zCMujN7HP23N/61rd07949bgJl6itf+YoD4RkAaNG03/ve9xzr3W/91m85RVgryP6bf/NviCVs1CeffOI8R3t7u7761a86wM0cVGgF3N/4jd9woDt7jS+Dc9Ye/+pf/StnU9of/MEfOM9jAJ29b7P0GXRnr2/An73WD37wA+IxtjkQohlNbJ1uNjp7DwYs2vfPrxn/Jlwl8+/hb0ML2P0Xu8dljznb3JfhBrNamnHus88+c67J48ePO8VMgyTs+w1msIf9zMQkFh3An//ywbdV31RL1BhFrje/pgyK2sHsFrdd52acK265jHmkSEuCl3FT96hGX2Jxu35JA6PPHGjYzC1LiKOzIvoMxQ8X4D0zcXgSZRXouZDYRl9ggYc6UfaZuse6APSwZ2Kg6Lv3WFU36wFeppTHjv8MTCFLucH7FDDBAJySyhKFBYcTd4qlJDwOO5irHjzpBJa4SuRRA/fUFml/1mFtW7Wd3eWjWKYKKVSWaVnICh3be1zR7JCeJVrqEjfiP6CfDo+O0rvH31VGynpuBntjqJhSN+DcJSx1rfUt7DIPUl7ONqCoTooHZdimXgG9rdfa1esUSkHFdtFPTBO4MjHo3NQNwmoS7AXcRJSOAYjXqouBXHqUnLRaa9ZQSAKMq6iuwuRA/BMROblpedyP9FHXk3u6TrG+tg3YLSyIG+ibFbc0npvrvup61KGKqlLdrL6FpSRc24nFWr8aSwwGiqKr51V54xo37d0AAI8TcbVG409ddLfkgWoLG+Xz0k/RgUsVwXEuS8MqsIybzzCME7VgVGXYxnoeKngj0Nv+YOKJANdaMHdVD+rpsyfyWuqjJeuxG4QT8epH6QoZ6Kw35Tu+z9WT5yDmZax5UkPnRzTTRZw5kJvf5p+Ac8PAWm3EnjU81zMMOWErAfd2+GKcAx6huDp0iiDdW9NEtfooaB8AYhz3RT0N4OHt3QNEvEbcat1zecBGBmQBzq3AjoaxbOL+pB439ut2Y5vuE0HXoHoNx77Q/l/dh4WMomgQ8bYc4ovXvWoglvMEgKRnsI8y8zPlQSybGU8GH2EuiErGXIBNDSMQOVqOWWmCSGHuyxO3tlCBXthZKA529j7QpZIiPex7SMRMqNYDiphJqK66TsMYEDambcaksZmiNVDA+CvHVFRciUkWk9NGbFEZSXkUjDwpnhBRSEG5iTFugZuHtuZQaF+bhUHEl3hUwC4KQJ7BbhSm9mhtxHrHBHCh4gxAxA8BdCaJw3tHR3PeBBbF1EjBcZgCfHlDlUo5L1yxXx3Ysc+Jxy2/WYZhrUsxq+O0CetbRMBKirC0G3CmQWSTWIV8PYOJ0qLAj3rw4eP7ugxUdvsxhi6Km5s2bdTLFyMUsWqJRurVWmCgbeu3UbSOoZj8UpfqLwITFWv29TSF0G1KXwWUhPHlUT8GFEA4i1/1CvKmGIetI3kvkZQLdaWhCNinQO6TC/Qbh39FKRHJGnoN0AZE84P3f+jY3HJ3Y5zbflgr/IjapXAzDHBhz1d9q8aZ62zO2owhZUTlFOT7unlfsSmYuDYRk7cYOxLoKscyga1pBljOBztJKHGLQRSOm7oxWgLO3u1+oJWxXINricUbHQHIu67h58+4jgBQ1uURgbtMPeNPiW6jwFVTjU3KTevS05W2ah2gJbAktpEzZZ9j8C1ToD/xznve0XZsJAu9rY/7xT2sX7eNGAbC2cPmoiPAU3af3fpr+2WSAKsvWLKLbQSx/tzqDAbZ2det/mDPY5ssrKsfHsb/QV3CfsbmWTante+3uog38Uw2XszQnj7AP37+RG57WCH4Sw8bLn5Ot/zMcNg70c37AuoE7Bh79ZoI0CkHprLIXauHdWIkKccYeKv+JvUbbIMbNwNJTqu89oZ6KMRuTNwAoAHctChUL10xO2IHvVZRov6hAW3ekKHMuPVawfU8APxY8QBLY1WZpgYnsc9kKRtrVzDXwt2ONgeKHAbayt+xXdkJOZiolqgS69R3fvgdogv7sSrt09u7vwokhlmTgiONSgRwqwprC4B0upyifSjg0K36OrU9bNeyFcuVlpKqlcTQ+WFeM7PRxCTAMmObm9V/MJsZvDYIzFJZV0UxvZpO1EXZABRgwqq7Wat7HQ+4llcpN2M7RrZEJ5a4qqUMaP0SBeOn2rIuG+MrZir/hRp9/UpVdeUqBYIdHn2qTMQFxyioGsRQhXXtRM1ZPel6Cjj3jg6k7HYscRPANe0Y594/9R4WtzKtjl+tr2Cc2xizib7J84s+0ArE1efU9rxViWmJQNDRmDtb1dZ0R0uAGjI3ZSpmhdnGPHjXPBj7ZieB51AK+QL++Hj7YfIbUMPtetXU3sQaO6KNXM9LllD8rm3Ug84HrIuWacvmTMWuWMUcg2J670MnfrSfdk9ISMC8ZzafxQD2GMG6iHW8ekVdPd2KxmT01sGva8XCKKwvmNquMMcB9EtYlagDmw4RRx2j+o46naXAbVGtmyi8H80/rJTYZHkDt4xjHbsDqFaAca65CThh/WbgivUqdQrdmJNCMehiNVwVlgBICAYBuOcG2DsGAO8CJLAEkNmP+ckzPsPrd4p1teGShJV1LZFrUSExunmzxol4j4hepuz0bK3BLOfC4NmM1ewM8EJvd4/Sk1OUmQ4oTwSwgeNND9kkUHmKc6pTq4ihPLb9CIaYaLX3dXF8l/S0ewDIcCMWrJ1OZPXd29hnTvwIUKFOadnrdPTwG1oVQmGes9SQ/q5nQGVll1nTNikeMDp1/TpgnWaAuzaF0JetW7NGK8Nj5I1dbtZMipyjZDc6Rf8l2Gf8uP6fENFaebtGJeXXnOL5hk3rAHaWs7miUY3En8cuX+1E/kUDW3vTn7R2tRLzeUUdD7AxsfliS1o2wGk0IMyIqu4DpmF9HOh5qvjoBK43NhCErdJT4LDTRJv2PnmsnKwc7dq0G3AOgywWtQnsf9//+LsquHpZPqFL9c43vqEMQC28sBzhmPrGe9g4gY2d88mMgRsyNgANDAGyW23Z9QAAQABJREFUX6OP8+Ua2aQkPvNALI/MMhgvsJNiHrb+ZSHQYKgnMBX9TkXnLV2uuwy0+gC7FfM84jF7AAavE4nnyniyi/jYLfGb5eq1AAPgbZ0rOkdU5mOtoP0yATqiQ6M4/6d0G6D2SvUltbXeVgxQ4N7sA1q3Og1w7rnKsSleLiIWGQjoINBkavhaDfUP6/RloKTyz7QYcPaNfYBzCVkYDQMADxaoG+j0EnDJ9dpSJWYka93GtZhRe1RxqVyLiAvflI6BdWUcECNx9hzfS6LQJwA0fZjThhDZ7scGkL5XQHPtTchkaoh/H+C+wEpFJEURjduH/R1TLXG9u4mGTotZqyBgu77+Pp3AdtXE9R8RGaUc5gErl0RiiHvN3KtD5zn2du4zhAKn7d+NITo6XTOj07rKnPdq1TUgzThArV1OVGsH4NwZrLrXLl0B0EvWV3a/TdsmOfdOZjFRd768rytYoEpKS7U2Pk07MvLV87jPgZknsPWaoXcD8cRerB/Yo4C9Ew8pwB/qK8fCvNiDedGIWAM0sQkAyAbbtPVVsasSOO/b1VxX74CyeZk5So9NZ44eoPaedgeybuu4ywa+SK79DcTaAvhgXXz45KFjwboNoLKEtcORXe86MZW9gJXXbl1UK+2VuiwB4xwACKCMC33l8P/H3nsAW3pd9Z7r5JtzzrFzuH07J3W3pLaCLUs2NsbP2LgsoGrwkIoqoIguyuCZAkOZYgaYYQaYh/2M/QAjWZbUltTqljrndG/fvjnnHE+e339fNaXHMzPIfn42j3Pa162+55zv22Httfe312//F0pp//epvwLS/Rq+MGD/00c+a0+0cPDRi81xuGGONc0rpEB9nTSysGIoYj5jmeF0Uiy/Bbzcbc0tG+w4NlSWWeRwFol0a96OAeWGsGEpQSW8SesYvY/S8Dnr7OyyzYytrVu32TJrtmuXrtja/IK1tuy0QweOWWFeDZDxkJ25fNpu3rpq+Rk5dqT1qO0AUAkBLkvNTwd7zl89D4Bc5A6BnNz+FOM+aKdunOIQwSuWYL34H54mdTfQ5Aqp0L95+hv2j6g4C6xRRrnHga5K0wWLojhHium3br9Jlr31VK2tzD9LgEqvX+QwBWvj7Ru2WysKv5WkKw0CkUVRM13GRnXwoTBUTNpHgHRU324O3qSdUE29f5MDDC22d9th1p7zdvrSKeDPVWD0A/Y481c515lk3H/9lb9xmRLKS8uBW/fzXNiA3aehVAfoC3R27fYNKwQGffqR5+wAgGkiEHVpyV97/TXAraCdAJQXOGfc+xsoCr8C4JmW43Nr6ieAd8tZH+hZYJJUu1eZw76Gj6oAXD10/BDrq4S9+tLLtuwOqG7h+gfd+lDKX0Hgem+Yg2P0dYg1f25ONs8FCYCrHgDgVwHTBq1hY6Vt27HTZqZRl7t0lweqJADuPju4i+dRUijPrkzZRXzPt86/YsUVAKStj7q03lp/DS32k4L7dZ4p7pgEmd7/zDMAp4fNuxoEULrMAQHAueJM10dHSfucF8qzMM84X3/xa/bSKy8AzoYB556yxw6fRFlU6XZRxsab3uu5B8B61o3/k889YT4A1AuX37JODpQ01TfYAebIxlLmaZ5dIgDxa0BkUdY0uf6QVeFL04ARuyc5RHQVf9xxj2fWcmvdv8ut8y8DbA90DfJsctDetx/Ve9R+pSZ8peuanTv3FoeKllmzcXAMyLyQZ4dhwMsLHW+z7j7H+jBkewEfDwGGlqGaKMW510+/TNtJce5H7CD+MhsA+u7Abfvf/+Of2Nj4qP3Y0z9GatTnnNKdFxVVgYFvo/L3bYDDmaU5O3LoOL6g0G5duGr9gLobNzTanr172C+oQOCKwxHMoIL+k2GPm0/zUTP1s2buQcX1/KW37DoHl6prKmw39fMH/XburYvAYHPWuJGDN5SzvqjO1iLLdv42Coc8Z+EG7BHWcjsaWiwzI9Om5obsAuq2p8+dtUSGj/I8ah/Y9QHUwss4YEBqUyDOyalJe+rx5+zEHqnsoziHH+lkfv06tnrp1mXa45B94rGPkKp1CyAoaaYZU92sFb6BquD02pxt27sdP7iBPYSz1okSc1lJEb5un5urBED6eEaIhUEgWXsEGTfyM3q+n2I8n79+noNjdxgzHtYNLZZdkGEdD9psoGOQNmqww8CwzTUcHGPZ94DDGi+dedEdYNi+ZTOQ52EyDxTQhh7GzbqK4eBov+3a32pPHn3amvI3obY4A1D9deuf7XRpfD+4/8OWFc1Hce4le/mbL1t9TT3Kls+xl/0OOIcUp3zgwz/rOHEKnNOyO/VKtUCqBVItkGqBfzMt8N9oF+3fTH1TBU21QKoFUi2QaoFUC6Ra4N9iC3wncE5BRoFbUj07evSoU02rr6931ZMChyC4F1980f7wD//QbRpKMU7pVqViLhU5KXooVaogt7fYAP7Sl77kFLcVsBSMps8pWKlUqApY6jtK5SpoTFCaFEQU9Dx//rx9+tOfdlDLX//1X9sLL7zgVN0EvAjQE/CilwKcAuAE9T3//PP2i7/4i658v/d7v+fU7gTgPffccy6YqvsqVaPK8eEPf9iBbDqwpftJgepb3/qWAwP1eQVpT5w44VRH9B2l7BIEJ4U9pXr914BzAugUsBWoJ8UBgX5S7dNmq15SrRNQ+BDcUbt/4QtfcPChFOxKSkpc0Nd9OPV/qRZItcC/2AIaw4Ln9HoIUzz8sN5T8EXg3MNUrcoCoFSt7x5j+p4+G41Frb3vvn3xL//ArhFs2Yry5Sd+5JO2d+teUr1kEbj32STKW291vGmnL75hJSiifPCJj1qAoPIZNpLvdNx2t25gw7OkqMSdMl/Fn0SWSSdEkGJT40arJ7CxNBPm8+fZML9iWQ3ZduzIUdtWTDrByUU7c+0iAA5qKzXVdpLUQ611WyzhRwlv4AbqCN8EWFqwqvJaAo+o0aUFbGJ2zO733LcxFLFKSCPy5EHUIxofIT1m2F6/dsrOXztn5cWV9uGnP0qazjqCQWt2+vVvA8592cqqK+3HPkYq2hbqRyBgGYBj0Cb43puk3GsjqEpqmcdOWsS/bJdJHdlBSpcgigxVqNKUFZc7eC2COsc8gcj0jDTbUL/JakrqCRRF7Nu0z53uuygDKT0Nyl21jTY4P2BvcTL79tW7Vk+qp/e1nrQtKDNEARYFFpwhrWvfLKpKtRXUsYp7BW1uappN/T4b6BmyGhRAT6DusUvgHCmV3iSQfBkgKy07aR/7KCfZy3fa0jjBtQu91n6h0wqTJVa0REq5WIaVVFdZoDwDiArQsg9erIsUgSh0FLXmWclJ1OgAmxZem7OVjhnSGq5Y5mbSeTbmmYcgsbOjAPZRjEZOacAySoDp0oHdBgDw3orZ2OUp1GVillkbsKwilKNQrkuOkMJzbMXmUfSoaq60nEdJfbqbFDjLYZt8acqmr6F4soU0sUB7wUaUl0KkiWWT3TvERv75uA2/PWiLpFvK3uSx7JIcgkohrhe1saFxp7g1wEn+O/G7tly/ZO//1NP0IUFHlI8CbPLPoxZ0+8FNe4EUP8H8TDv61DECT6V27eo1a796z2Ko3tWQKk1BnRBprgSGrywsOWUQQRLN1RtRmlmzS6Q8Og+MlpaVbnsP7bbtm7a7NESXzl+wa4AYmQQZHiE18U6CkRl+QY4ExVCE6iDgmp2VYxubdgImkRpsdQrgoxNFV1L6kPrtEN/Zt/OwZZL6rO3+PXvjTVQxgBNPHH0cRYd9wBk+wNQX7T+/8J9I1UparCc+4oDQQlKfxghuLYRJ0wQocY6gpyfmsfejhlVaWIzq4xW7jKLMysqq1QFbVBPcyEgLOsW5eRT3klGvlZXW2uaNW5U52QVTrgLIBuizg8cOoaqyDbBxza4TbDyLYlAmql1HUTY6QODUF/LZnaHrqN6csu72LlNZGuoaCQgW2OzcAioInUCsXZaL+sKJ/Sft5OYnSa+Wj02fttdQ0wpQp+c/+BMuXdoiqo8Xrl+w/+cr/9Epjjz6xAkggmetHCjJA7ShFEPnb14EQrrOGsFrhx+hrYozCPzfsJuXrlsQW60trrHS8iJLzw/ZKhDEwgzaPAQ/6/ANW+tI+0Ug6zVUPd4mpZwvPWjHgPlaUEhYWCZQh5LMtesXUV0oQE3lOEoWrRbJSNr1gZt2EThuoKvbSgHPGmuaLC+jyGYXZu1u/03r6kF5OLfIPvbkf0ARiVS0BLkVxHnof93BeGz43a84QX4GkPPNev/d1Nm7v6tA3cOXviNVF77mUisLZNMY1C/4f3eowQWNoAjW1sIoWa44UCktnYw2Pq7Dh4KhdeAthk/X9CDFD635VlnLLqA8l+AeWpeF0tAMlMqEK5u4J+7FPQKAT1L7k9rNu8upuUKf1+thWVQ2pUAUHPbu13/xPa6PxwDr4LcqEN8RUHWu6wz2ythDbWsG5ZgENlpVWmXbNu1wYPS1/muoWZyxMH28n6DybuaiGH11seOKXTx7zoJhvx0lYN26rdUyOKw/ujJOGvEzdu3uVQC8kG2paLAqIO4FAJsHUwPWN9gPHJHuYJPDgGdFgMWd9x/Ya2detcW1WdIukrJw82ErChTaVRQ9/jeCsOOzqK+ceNI++vQnSGPMvJYgPRnt1wbE8Or1b9kogMLJvY+TKnuDtXd1ArVeJig6hQ0Voh5ThfIiQAt1np+btziAjtJdbd28DVgxxzraO+x11EDmCXhuQsXmsUcYa4Bn7ffv2qk3TzuwYP+eQ6h6HaesRdY13mFnb3wb5cHrlptJGl7mxxzUsFcjq9Y/0MtBlDbYnWXb17rbPnrsIwRKqwCCbtjfX37JQVcfOfpR+8D295FSLYO0cWHrHeuyr37zKwTbL6Jk1WQf/8CPu1RcIdIPRlk79KCocurSN+0BKR537t1pWzZts0mU664CSExOTlkR966urALMzAUm81gY+HFlbsUKgPk2oYAkgLBrDEjj0llS2w1aTV09UPxJB84J3rpw+bxNk6ZsK+mtpTxXk1VPP6AKe46UiLS/RlNT4wYAwGJgoLCNorbY09drc6SErt/YZD/ygY+jzFZrI6NDTmVnkPJubthMuvdnrK60we6SCvEl1IuuXbxmB3bsI+XmB21rM6lagVHCtJPAuVOXXrN7bW22d9d+e5QDS52DnXYWhS+pi5YWVFpNeaNlE+j20+cxoLnwSoR/F7i2KAHY7O1F1e36t0lnfc+adjaRzv2E1eU1M6dfB+h6nSD9hO3c1GLHUT4rKaxinZEALDtLpqZL2MUaClH1+G9SAiKZOTA1aB0jd20OoEAQyLOkx2uqaLRuUpi+8vobqNjNkJpzj51AdbOmlLZtQ33m777qFH4Ezn3ouY/YBpT2AgCIUeaKocl+fPkbPPvdBbRptqMoI/UNj9tF+m9ieMTy83JYa9F/JaTCRDVombVZbMmDreXblg3NVpKf69L1njoH7DY7wbgEZASWlFJS70Sv/eML37ToTNwOALjsBRYvAWybQp3pbZRqLgAF4S2sCbioIq/aIqzJulDm6xppt/BSxDaisPTosZO2oa7ZwYff+Lu/B9gYteNHTthJQTn+Mugh4AXfkv3115Sq9XXmAfzvJz9l+4Gec5SqFZhmmFTJZ1DlvU+6v5L8Ijt05CCQf5B0vmeAE8cYJ/msp6o56JELEAFIDmQ6NzdjWRlZthmIflNli00DE3/r+it2c+A6a9dCewroaUf5JhvioMhrba+jzt6JOtxWe3TXSauuq7EZDli8ffMccORN/FfMrdeqKiqApFi7LAw6pcaxkVHmEMA5lCGVTnU1Om8XgLZPnz7vYLtngMW3Vu+w6bEpe/Hb37BXL7xgpTVF9iyQ7uHNByh3HpCfH5BrFBDvNCDvJdu8Z4vtP7iHSSBmV9++Yj13eiyPOViHKnILWMeRfnJldR4ltTmUBHNIe7qFebrJ+uaHSGf7lt3ncGBjeY09wsGBiuZa1DJRCjuvOW+Afthox3YfseaCekvGkoDkb9np26SsJc13Q0UdqYErLEGKxuHZYevu77KZ2WkrqyqzJ1Dn2lnXwtohCvD+NocEzlkD8P779j3uUq92DnXbi6dftLNvnEFVayMpFD+KaiLwSCagNzBRH6pTZ+68YWfOvgXY1WLvf/Q5Mpdx4Af48kH3fVJf+5xvysvLZ32HjZLBbY2fNNY2zQ0brKG82ebHUJO6cBZV2htWs7kKJaZHGP+NNjozZm+iJPugs53xUsWBlUdR8GyxZcC7Czeu2LnL50jbOMuau9yqq8odODc+NW4dfd02vTJr5WXV9oETH7JtwC4TgExvXQdE4xDL1rJm+xDPGpsaeD5BPWo+sWxffvUr9sKZbzjFuOc//Gl7HBvKDeSiOIYCGiqFr91FhbDjjMSvSMH8NAq7ADIAYm/jeyP8sqmxhmeVEpeCPr6Y5DlpGSgv6CDL2g08u6TFUQs8jaop6V1ZR2qfZiN9trJAGuVbgMnXLqHkmQYYc5i58qitoUJ6C5XecxdQ0ByftLK8cmvEx/vTPIDl49Y/0mOdvR1WTt1PogL22Jb3oTCZTppUDkYBziHMx1rnE3YQv7XEmuxl0r5+6+UXnU9+/zNP26MHHwe6AtBnjI9xuOk88LVSycuX7TlwwEI5Qep3mfLeJhV7wK1Jq4oB51ibrIVXbW5+DuAoYM0VG1CNbbXkSsDO3H3N3nzwbVII+7HF99me+kOkBp5B8fdFa++9jXJlqT22FdXtpha3Zj5383UOI12wmflpK68s49msEugbtbLFaesc7QSw7ge6LrcnmA/2kwqTnNvMdcBKb5x24JwU5w7tfJTnQr+9wEGbb7/1AuveAOrPT7r+K+YwFl6f1Jyon929af/44stW29xkJx4/DviZQWrat+we6qceYMKa+morLC5lzY2S4lzCVqdWUUXOsI2NTVZXX8dzwKxdRHH1PDZXUJBthw/u5kDpVtTPovYWynXd93qsCF917NBh29TURFphAN3BDvvqqa8BFs9YVVEtwNMG2ozn8OUR6xnqtCnSt2dnZNtJyruXVK3etZBb+7x27mXLKcq0R/c/ZgebD3EILN/Bwn//Iqp6PEdHfRF76oNP2nFUpYuypD7vZ76IOWBRytjTHCx58kNPWwmqm3c53HOVA1/LPENUVlZYbUUT4H0xY9Rjk3PTDmBtxr+2AEBnsJ5UGuGzbZdYpybtxKFjtoO1zhLQ2vUHV+3cmfOkVc61o6QG3gNciVikdU11sw44Zzdu3OB5ucDZg9YPc4y3zon7bn7N4hDbPsDHwwcB54Amr6Nwf/bs6xxkmrVnPvBBDsHsY48CBW8O/v1fX/0/mUdG7EdQhn72yLMo2eaRgpzxFOfQzH1SHPOcPTk7BaD9KHPTButiDXb5/EXWTVHWK+VWVl5mafimeIJDSjy/elADbCzZYBtYN/hy/XaeAxWXL5y3CIeojqAevru1xUHYd4DY30aNMowt7N21DyX7QzwDZVs7SsJnL73O2nPACgAxa2t4LiO16zwpuAfGmO+7uyyQk24HDxxF5fApnoPK6IcrduosaXo5IPH48fcz3z/hDvLFo4BzwI3/GeXAq6jY6SDJxx4l/XwFc74/wDhdsr7RPvuHUy/aHOBcy8EW27ZtB/6s3a4C105xvSqU70rZv8gDplU679UFNPMXw4zbUtvYvMnS8oP2YLQDZd/XOWizZBsAAXWoKqswze6janzpwg2bnlziINIB27/rEDBsheurU6QfvoESupdnl+0NG62QeWk1EuMww5gNDPXTlqjtA30/jgJrQ94Gm52csRdRsRzgYOLmxm32zMHnLCueZ9/6x5cBUl+1Rp4vP/Tss3bw8EH3/JmAJk46cG79KUPPQPI9eq74bl8pxbnvtuVS30u1QKoFUi2QaoHvsgW++0nru7xh6mupFki1QKoFUi2QaoFUC6Ra4D23wHcC5wSYCIb7+te/7mCxI0eO/JOCh1Q3lOpQGzv6zGc+8xmnsibVNwFwUkmTwnZXV5dLm6rPSQXu8ccfd8FJpV/9wAc+4KA0Kdfl5eXZr/7qr3LS+7qD9aQi91BlbWRkxF1P31EK1FOnTjllO8F8+9hokuqIApVKwaj7nzt3zinjCfgTyKfvCPgT4Ldt2zYXLNXnBf8JoJOy24ULF9wpXaVJbSNgJNUSAX2C6pTOSyliBeYJcNuJIsF7BeekhCcw7o//+I+tiQ1ABVtVj09+8pNO4UTtpzZVuRToVcpYwXtKh6vPSYlO30m9Ui2QaoF/fQtoPOlHr4fjR2P+zTffdOCcVCulQimY9aEikT6r7yQ4WR0Dfurqf2C//3/8r3btxlU2eOvtqRNP27amHZyaJ4UlDMhMeMqu91222/evkzqy1I5zgjobQKZnsJs0Xm2omI0QDE7id1Af4/S5G+ME1IvzCm3nlm2oO5XY8OAIAcRLpC9asaa9G1Bx22X53hxDzIIN3gecwr6Mr2Rjtnmn7SGdSg4wzhib6BduXrIuIIc1gvPpmSiVAfP40hUgjqBmsuzUJ1ob9truqt1kbU3Y5faLdpNUm4V5xfbokcetgoBOHEjqyoVLnLp/hTRAxfbkU0/bbtJoKTXgEulbJkgNeBOIbaC7z7KDWbb/wG7LAtoZmOiztrv3bWRwgg120pgFlLoEgILALjiYlZeW2GY21guBt8YJ7l+8eYUT7qtAyhs4Yb/HilF3mUe1oJ20O1cJ4ieWfbaFNJG7gHbyCRZoI/92zy27DuizTLoVBZf0k0Y9BV1Mjc+wsc3p/R17gSWkRgdIxCb9PdI2pQGSPHHycasu3mQLYzHrvt2Het+Y1eXWWMFKni0NowqnPkbRKEmfZISzLGsx35bW5i2nIc0Kd2ZZ9lq6DZ8dsMXRJYukx8xbTrK2LCVxAZIB5AijruArJSVNRa7l15HmpYT+RQgr2gPMcHvIJsdmgPKSlp3GtQnaZ6PI449kWGLFZxVllZa+I2C2Cd0KNvNHLo7bBIBeTmOOFR3NN28lfQhYkwRISV9A5aonZCPXqcNkLymFZl1wDSQPcbhMi6NuML80g5JOl90L37dwediOPnMMFUDSz2CHfgIkyygAdvV2EcS4gH2kWwvQW1VztUvf03G73QY6ey28uka7oR1F4C4eUzqlBIHqXGyuBeCsFkhgym6iajA6NWy1KEy0tO50ilc+7Lq7s9uuA86Nj07Yxs2brGV7K0BEheu326jW3EH9cHJiGoUUVPaAdwJZQIEEnCPMwUqt2kxwYgcgRbY313q7e0l5dt78KA4o2LKlYTvjzOOUEt9AxTFGUO04wa8ju06gjpVLV6wREJkF7ui2u4w3BbiPANbUV9VY/8SA3Xhww/r7BiyBSkIGyjF++tvjRfYCZCmL1I/VwBg6yb9KQOQC6luzBMMatzUSBNnJ+CDtViRJQHXQzpEqdYpgXUPVRgJyB6wUIGIuOkZg6prdunOHNcMc9km6Qto3iNJZFAhncXWGNVMaQc1Wa60+aOnBbGC3a6SQvQDI4bMPojTQUMq9V1dJx3jLXnz5Jaf4t+8wadQOkGo3vZj6AM5h/zdRx+l40OE4s9bdlK2uHIBpwu7duQs8OeKU+AKsHQKMkQRtEOd7GcAszQSGG2sI8IeXSUl4joDuFKBDnVOsqSmss5XwEsDHfSBKAMOZNQJizaQz3mnZKD1OrE5YR2ebtd+5ZcsryygwhCwIoOoJMG5yIk79IYeU1Y/tfhyY8DB+L88WlhZtdgbVN9ZLZQS9CwAKlfZIKPP0wjQpKidsDVsrYoyXKihOm9EjKORFXGBrnPe9BC2Vhjc7G8CKvlIZlxYXbAElmYhUSxi7ChH5vKTeQ6EyC9glm+BsLBwFWpyzeWDAXAKxhYWkO6N8njjqIYABsyvT9NMMacICVkh2m6ws4CbWVcu0/8LyPFDrHCokRMe5tgC6UDoqYwTw4xHGAoHKIlQ1g8B2K/TH+OQEfRNHKS2PuaMUOMCPusasU4iSvyoCaFHgTSpjAuu0Pl1A1WMGQEWB57zCPMvJzXFg3srqsstSM0Hd37px1uaxJR+AiBS3clCFagL02EDgLYqayU2Cre0D7VZLat3WzXutqqyWYKvHegm2XUUdcLhjwKrxLy3bd1kVdk3VrX2wHT96zYZRjgqhopWjNs8IWAwIeDUapj0DBOWabefmnQBeRTbYM+jGoPzGnt17UFJstZxAPmmU79jfvvAV106C1x478iRtUkJyK9oJQKZ7sov0bW/ZLEH7Q9v2247abQ7AuQWkcR8gYY16KgVYAPhTy1lBjFI6a2xotOamDfgcs3u32+ze/Q4rQN2sZd9uUnw1oVQTt5mZSXv74nkbHB12/nPPDlJ9AvsuJmbs/shtu3Xzls0CizBDWIAx6AmQPpSU3WEADakxNeAPTuxgziuutluk2nqNVNHz0wt2khRbB6Uoh00oqePAWJ+99vYpa+u+54LiTxwDBqnbjPIQ6R2Bc0ZQk7x054INzvST9nKDbRF0y5/OzgfAKB2Ucxbo0u+ec5RiML6GvaLKVlNR4+bDHOyyHYWUO/dvMUaT1oqazvZtpCQkoDxJ0P/G7esuDXpOXhY+vNW2kM4vlBlEteuuXbt/kfTyw8xAfpf+0KN2FIAJHLQEOFNYVMz644QVAdyOj43apZvnbXx62OorG+zYruNWXVSFKlYH4Mxpa7vTRhq27XYUP9NYW48KF+Ag8333TJ9Lndfb10e6MOC9ffsBClc4QNDOYZ8um5teob+ZJ6ijl/lNFGoomAFQU8PcjhIdiqJd+Km23juWDMVQZtkOzLLF8lD6GgfaudHJWglltgLUanajkllf12DZjIO+gX67jopiV08XQfekg4gCzEXJEMo4gTVA9GUrQcHwMN+pLqm2wclxQLRrlGfRpfTbu22vVbCWGnzQa6++9gppfjtQrQJ8OnYChbpGygwwHWduJpXdrVvXbLC/2+qbUNZCmTwKpNpGkP/Bg3bglWmXstCXFQLSQWVnxVx6ydpK+g9wLgho1oEtSwEqtyjPWrfusC31G5k7QyhBLdjZt4D924ZQYqxAbXin1W6sY43BGgSo4yZrk6GhIaCZIH4BJRtAkFjmKsJKi6iWJgE2ym0P65lGxvviHIqmr77KHD1he1H/OrD9kBV4SdOIz1lLLpAW7wUOQVwBhM+zp5/5oG0nFV5mIh0/y2EHIPobAClDvYAJOXm2Y9dO1naF9oA27uwAgAKAlIJXiLmQYeL6MEoads1pmxt3AKQ0ENhnzdZG6nfPjO3atct21e2yomDh+ngbvweoesO8CyHbiiLb5i2k58w1G0Ot7Xb7Pfqwn7G+gi2Q+M5PKD8bO+Uww+TElJVlVdj+rYdtc30zKVEXeP6+aTeu3wX6rrJD+49ZM7DJzNi0nQbuukBay+LKAtKEowwG0JeVxnzgI20zvlegSFtPu1U3Vtn2lm3UM9tGBkhfziGM2dkF0s/iYPA1Api9rH3lXMpyi53CWyUw0QPS7l3nMMHS/CKKha3O1rPozwVAi7vUof3OPfOy7tjB4YAt1ZtYX2ehBDRg11mTClSNANSno/gmH8PSDlUiD0DdsrPbfa37SFO5yeIcSLh294ZLtVzbWAdAegh4rc666Yc3UaK7c/MO8FoNaUEftS1N+BjA7aQ3bqOro6wTrpBim3TClU12bP+jbj+lG7Xle+13eF4YZNglnHqvlCzjWnPjC4qZc7Zs2upSBI/1TdqtNpSAY3O2Zc9G28kzRSHrgQjrp2s8q1xhPb62uGa7N7TYQanXAT31oyR8u+2Odfd1AnissJ5hDcF6IkAdV3mmkAJXDmp9mus3Ur9Z1stKNf0AWLUedeNj2GgzytdJ1gnzAC0vcWjiDIdupCj67PEnUcECNgGci6OEO7XKd3uv2z2gWJaodmTbHhSpqrEhfCCwzwDK1FHg5xD1U2pXb5R2Zg2XCwjatHGjlTdUAJpPAsddoA/n8K2sy3aRrpgUvbHVhA3y/XPXz7NmmwZsqrfju4+hRJ1hU2HWbJ23rON+J4cLmC9ocz03BTLxZwB0UpfOzs2yXU377EADKsg8V1xC1fT8rbOWZC34vkPvtxbmw+Uwir+kYD7PnOTBxg4fPWx7WvZyeKCQZzyeC1E5lQpY2902ANVS9tC2W0FFvk0ujqF4d9NB2x5AqyDrZ6Zk9504/jQPmLW5bhPKhJtRXpx3a8XexR5r3rYBf3nIKrJqbZU+vT+OvjQpeJfGtWZDPXgjquZAXBNAurfbrtiD3nusm1izUT8fylkeDuJklKazbhmzQIyDL5sOMb/sZJxEgNJ5zmI8y5Zad++3VtSZ46s+O/XaKbsM/JeeG0Cx9RDpP0lLyhjk1IdNo6bejr98+/wVd6hr337qXpjLYZhh9tDu2iCAng4lBNLSAN6gwVao5IrHylmT7aQtSkuKAWAHARnvODW0zUDfu1EbzeXA7Bo+o727k5ShbbaIf9/C2mA7z5IVJaXYM2p7d990cNXS7KoDG9OyOLiUSaridJSWV3imiAVs9/a9QJw7XarWB/gJqc9mkoJ33/Z9rEtQhkZdLMqa8/RZ4M3zZwHnonb0+GH3PJnLONEaEL7T2nim0IGVRQ68HT1x1OqAAaexqfb2u9YDxBoBeAxAuwU9WaxpACATYewsZJtZT20pr7Uwh+TO37+G8vUoc2SlHW45DHBbhZr7KilEUUK/csNmBmastrDW9h2kDSvyOCA1ByDYiSLtZZtlTennOTAtxLML6ubRNEB8raOSmYDWW1Hx3U1bljhfIvB8jbXsI2Sw2MyaJd2TjhpbJwemvuHWnVLqPbb7hGXzXCeV0TVG9X3WItco3zzg/S7WjJuYR5aWF+02zzRD+Jkw+wPak9T+hVxqFP+hAwpbGnfSFg0o5s7jiy+zz9Hv+mfPnlaA20raD5U2IMLzrEn72QMpod/3tOznQHUNY4f69d1x66UJ0rfrGVIAMEtrCwG6Ts7PoOrmtc1NW+3AZtR4UcF70HvfLgGfa321b88RDlodAXRDtZB1T99AD+D9abfG3LGjxd534FFUwWvw+z6XhnoIGO/NC2e477Jtatlkm3awL4AC633WmT1dPW7/JImvl2J+EKVA8gKzwgpYEwq3TU3NrCWTKA+2AUzeQUEx1/YDyDU3s6bITDAX9aKS3mF3gDwLtR9BpoBt7EckUW6/P4yiaQfzMP4e7+LWuso14GevRE5vfnHWahtrUZHczwGJWvp6nme0N2wE5b2m6kbWbCh0xrLszVNn2KO5Yju37bIPf+g5Zyc6uBV/B5zTs5B2ueAT6aMUOOeML/V/qRZItUCqBVIt8G+lBVhdpF6pFki1QKoFUi2QaoFUC6Ra4Ie8Bb4TOCdQ7Cd+4iccwCXYQ2ocD18KNkqVRp/5hV/4BZcy9bd/+7c5tX/RpUCVkppSZfX09NjP/dzPOWW2P/uzP7NHHyVVG4CYwLNnSKUgNbeH4JxStQpkEfB2/Pjxf7rfJCcClZJVUJ3+Fhj32muvuY1jQXPvfgmKUbmeeuop0/2UElblUtrW3/md37HGxkanJKJNZ13jj/7oj+wOG0Qqh0DBuro6m5qacgp1Urf75+DcV7/6VYLKLf8iOCcYT2pxn//85+0jH/mIPUzV+rnPfc6VSWWQEp/u/1ekaJUan5TolFLs4UttqyDrCqfaKzixLxhQ6Wv1+9Qr1QKpFvj/b4GHsJw+qf/W2NGP/ls+68yZM/8Ezn384x934JwU5x6qDD38nlR0BMB98U//F7sEdCTwp6Gy2crY+PaRfkgBkhWCngOzPQSFRzh1nmNNBLoU4IugbjALKCEVmilU0pbY0HbBLgIJOaT8K80nHQ+byQKflL50nEBwOmpg5fUVqDgVs/lPwAjqYXltkc3ZQYKOEy5oVVFWBTiXZzGCdTMEHkeBeiYnxl0QwMfJ+7zSfMvgOjHSrMaWY1ZMfs+qjCqnkjQw0Q98NUIwjJSlAEN5mYAbwCbDQ0PW0dVtIQJLDRs2EJiudGp6kfAa6YVWCCZNEMglyARcUFpeYjklWbZGoGEawHiCIOf8LHAZqjNJYCsvwZfcnEzaqBh1GtILRjw2wsb5GEHzdNKE1lTXosqg9Euc6Pet2EJ0gmDyCAGSVVTKUHtB0aKA7ykQuBCZtd6R+wT/RwFfwqQXDbj6pXH9ZYImGZZppagDZKejvEGQYJR6TBC4CRFsbwa4yEkvt7UFD2nQpmwZoKaKVLqVvhqCTYAy+N01D8pUqPRlE8xLT+SRamfVkplh8xeSNjUcskg/aRpXkg5oXAjQf76kpSVQqkLhK0kAXcH1QC5BDaA6YtpET1GzWqPdF0kpOQ7wM0nANmKkxgmhzJRHCrQqy4zRtwRY5nNot4JxlAdmbWmE1KHTnOQvAr6rD1giR2laCTZzr8xYtmUuA8hNxfjMBGoa0ygbxLDFXGCLYgvQ54urUzY832f9kSFby41a7ZZ6K0cNJQO40wvgFyX6PzM7Y0MjYyg2BaykstQKq0jr6Y+jNDNtM+PjQD9TtrpEHVFF8BIoyUAZLh+4s4ZAr0tJTMBtbGaIIGECFYIyB835GQN6hQlWSQlgaHAIgCDDKoFEClHSSPDZRdKojs2NOHhyZWEFtRSvZRcBJhZkujEZXSGtJhBBCaBUZjLbnbofQInAF/RaDbCCAKok4MnAeD/wX6eru1LZ1VU0E0wKgrqQnC6+QhCV+zCGaDinslZcUOQUfZQueWpmxubH0diaW2NujTqYMCs7k0AfYBc/UoicY95XqkCBZ7V11Q7eBMFxQd1llJiGUXAcmxoHekgnPTJKKIA9/vSILaDmN0K/jNJ+S6gtKmCv8ZmZCyQJ1EcTAzCWWnGgltCJn+sM29BUvzLiEpDfbEXAjUpXOUL6ufYH92nfJIHICmCXetRZCFISvVsivfMwKl/TwENU0CpQEikqLOC/kqilzdnE3IRN8p6UrRIAGEF/OsG7PMsrKLZ8ypJOkGWOMg6NAG2QorcSSKEsv8wyUIuMcu2FyIyNT43ZwtSyhWJpVoQvzCnPBewgzR3w1MTkMH4MH7CwSp/5LLMwy3KrSU2FCo4fhbMyfExhehEBIVJXE+ibHJsgxWjU6ui/MtpX6SYFcA0RlO4Z6UepY5n2K+P9assjdaUC52vY6CRgW+/wgGunyuI6yoHqMEogK6i7zM/M0z/jjOc1/AwHDAiKBbhfTlGBFZaXAv7gE4BNJkmpJyWjYoLUFShrZnhJH4u6yzJAhFRg5GsFjShwW+T6PgTIlnQqWIOkpJMSoV7p2emWXZhDe5FGDT+a40WJMrMSSIixSzkHhwaAFeMoOqGYgfpQ0JNmoyhX9Q73EEycs9Jq1F4InOaSEkzgYIwg7dzCDMofAAG0eWl5IeUuYKwJ3Ft2NjoBWDJLPaXC5sPXpgPdFeUVubEsha/5+QUbGuulfkvAkKhf5dQAFhCUBlBaNux/inE2PAp3BJiN+mlZSQ3jCAVCPNgUSkqDw722gB+UGkwGoEkeCnBegpZR2lMpHAtyCoGEsmwRoGBwqNfBShVAJhWogoUACMbGR4DabjqAr6ailnqjPAZIIX8WZzKcArQcnO52ClZKy1WdW8X49AJYLNg4inPTpBFbBExSWkg/UEsGELQOzZTQF1KeXJxbtDHg2/BqzIrKy62iqtqlDPfgU+l0m2AOGaEMCepXVFzhUocmMyPAPFOo182SBnuGsi+jjEJ9cjOBhSgbwViBc+n4wpqMeqDBAhtlLHWRDjCC+scGFFYqgJxAM2gXL/0/i1rkA8YUAAXzdLOUQlBFUThSKccVVFYq5nn8TQFKXBVlFU7tSJDCxMy4jTNPzzFXKqWZMGs8iEtXV0EatjzmgCXU40ZQUZtfnbMcwM1a5mEpgAnm1POL4LJBoPQ10vmVAmrWktIzM5c0qqFlG1kaQEluFBsBtGJqCTGWBRpk05dhVDW9ACaV9FUGNjEPPDQIOLwSWaRfgU1QhMrH10yg8tlDMH1qehr7L+PZogGQvoC5nfqhMDsF7NvD/eeANysBQqWe50dNVeNnknXM5Ngc/bQKwIweKoWQcm4u80Qh9SsoKGR+mreJ0WGnmphbkON8lbORuJ/0dFEHlAiSi6IcWkTa3BLukQNooERwUuvpYVxp3aQocDCLubkY/8AcK7A2EA1YFeoz2fjFGVQZB2mLNZTapHpYC4CUz3pmfnId6JgEJs+tKLDaqjreL3I+XukQF5coH4plC/OzzG+5qFcxdkkdvLiywHoG9VaUeeaAaKPyCthZAOikIKvEKkl9mK/+Y/yM8v2l6DJ1LqK9yyyfAwUSyIwy/08wR4wNTFmcdUMBvqkMPxBALWoluUTdmWM4RLAwxVyPkmSI+mWVomIJwG+rXvOhjlRKWjr1V2Q17FTJF1YXSX9XbTX4w8xElpuflWr8QU+Hg4u8AL6bAVGkOhRizSgIR4quo1NDtrSw6EBGrW1zCvOZKTlwMD2DcuA49jELmMtah3EbBFpIB/wroj6FXMeLIu8QfmRqaQQwz2u19XWk4WaM4EfjeJqVOOsNAM7FiSggDYAw/jczHziH9H/TqJWNTI7aNPNphDWvn/k+ryLH8qpyAJ+XLBROs4oMDjCgHhfFxieZs8fHpwGgc1k3VONPi7GhZese7LIB1AEzstPoQ9YztEtIABBrkwVSGA6PczhhZoI5LtsqWJNmZwKrM49OkhpwGtud5sDbsuZjbFTpcrMYK8UoVCrlteYOpZufANLKBIhrqGqyXPwYRri+ZsG2xoaHHcCcz9gop4/TM9JR9gTgRflwhDlkYmTSrd2DmSFUhVmLAW9HgZATuKoS7lNEmkYPNjDMenSY9shnvm4A4CrUGGQt1oUS5ASgbB731TNDSQEqV6wppSK0SGrZUVTsJpjL8gCxasobGCNcHyhnmpTb6r8p5grtN+i5RsCTfEAJYFEx664oY2JqZNrm8FEhFKFKa5jrUDsKAWyySHXzbB+KjXNTjAGUt5pqGywdcC6MotQSkLqeETQOwrRfWkbQzVPBXNIAM295Vj1Wmcm4RWFrObpGH424vi4M5QJwVXMfFNe8abxHmsv+dusZ62EMeYFLG62GwxRpKBHHgOBWgKaH5rnP0jhDPU7a23L8XT7LHaAs2l/wzDjjbIW+5hSCm+eUejevEBsFkPVzQGgIPzXJnJcBwNjA4ULZjjfhd6rDS4z1PsbAxOykhQCPmlHbzMcfxTOYh9WH+MEJ1ikrgDf+EO1Xmmu5zPcuZTEHY/IDpcAsTYDmadY38YB02Z2U08N8sQkAs8I9bw2ODACYDjq7kUplZTnrDUB3HTrRmnSCZ8KJ8QlSkAMWo6qaw7opDog/Czw/M0WKeMDtZRS8ohhNgHVFJlBOHj60hMMGWSizzeIrtC6MoqxXhapjBTBQkKeeKPP/cmyS+X7AZlAl9yfSOIRA27Pe8QNAzy6P2eg89sVz0wqK1l5AxsyibCttKmGtwf1meC5kPJfllaLqydqdvp5g3vIx/5WzNqwqa2CNFLK2jvvAlKh/AebXar7Pr0a9Lg0fk7AVyjCFCtsAh75yc/NcevEs5nOfgCB86xjryQk997Ifx6BlHmQM4ifLikqtirYQyDrEXD4B/CjFuDqe+8qy9EzCMzpc0TyHDyZGxnk2QQmTFO7VhZWsWYESebYbS467OXxsaJL2A55jjZZTmsFzKYqNYbinBWOs40czS1GrDbp1cz+grJ+DAjUoTFfmosLnDVEEDsb1dFpvf6+DAWvqaWOer2Wj2itIAqSPsW4eZt0cBU6tra9lzsS+WS8tcjhjcnYEkGvKFmfX8GWMQ6CrdNJ35uRlWzkHQ/KA7SfowyH8TJJDV6WsWSqzUcLkMIdAvbnEvHvumgacC+C75ccKWHd7QnGeC5dsdG7YRlkPLc6uuLWJVKYzS6WizGpkmQMQpGItZm0iFTcBaAMDgG7hiNU3NvDsjZKhB0CVA0/3SNm8iqqoDpjVokYpUC3OWNbz3/T8BH1F/SKA0/gO2akPdeoZ5nEdYlH60KX5JQfnys+khWRrlczbpBJmzTYxOcScNkQfx50NaC2nA264Jdosylw55g6ZSI2vmDm0BFXYEIDqKvDc7ALPjDyz61kc1wcUm8WBiSLUzUkdTt/kkp62DJg8iwNPU8yrA6P9bl6srWzkcEg9tsjzH3sUOujcM9hr46wty9gzbQY6y2QPhqNn+Dv86eIciqDdHO6IWmkVbVyMmix2toxtTrNuHB8bZ13KuhEw1outhrA3rdWqy6t5rmA9rDUf880Sh4JKgWCby9lb4Dk35l2xcJK5ZnHZegfHeB4CDEWxuaEYG8I/LQcXSes+YkOjQ25fWanf07leAXsjIeaN+eU5d7CqjLV6TrLAVhaBAEe72ROZ48BhAcq6Gy3AYOjt6HWHPE4ce8yeY998eyvZSbDlOHs8CdZJUpmTb9KGlA7pfC97xSnFOUwm9Uq1QKoFUi2QaoH/ni3ABJZ6pVog1QKpFki1QKoFUi2QaoEf8hb4TuCcgjg/8zM/Y69y6l0gnFKzSkVO8IcezAWa6Udwl9Th9FmBc0qNKijuvYJzv/zLv+wCBVJlO3nypFNiU7ONEhwRwNfd3W2/9mu/5lTkXnrpJfvpn/5pE/Siez8EZVQ2QWcKxglQ+/KXv2y/9Vu/9U+pZhsaGhwcI3BPqWKliHccSE/XkXqd6qc0sUr3KoW89wrOLZGqQPfTd3XNd4NzUtETQKdyqbz/8A//4O4lpauH8KDbrNOGHT9q2zQ2/KVQ988BwR9yc0oVL9UCP7AW0NiRH9BLfurhJqJ+r5f82kNwTiqWP/qjP+rAuaKiIney+uF31r9HIJCA/z986z/h284TaFokeJhBkBllD+0M+1FSCkSdCkcERRQBIpkogUlNSgBNMomPJCikTWCwMjY6GdsEqJ1iDJvaQTZSlaxPAes4nxPQoDRMAhGMa3n1PifDEwTMVlHw8BKk9qPuovuu8p01j7TlCNYRsF1P2cGJewIhETbBFUDNJCBchNJLTnQ9qKhgjj4X8AJ6EWQPQBX4KWecNlEgi1iuAymCbEgr4K/AuJcfqXzFqYM2aL1AOEk23KOoqBA/YDOaGgCixAluORUoNtdhQfguAVnqGBBUgS9bQ8kpycntdCnTJVHWiFBvoBhPUHUH7gJCIXrhgg9e7h+n7eIgH2FS2uhsfJKgbIxgg9JQqj09KDIVEJzM8wFHoDDgITAvpS3BSkTOHIgA4oNaBe1FICsKsJBOfbMS9DNQHM3t1L1IfMZ/gCCx4Rznbx9Bi5g3jNKY1zIj6QQ0AggloPIC6SSwIhgjyB+BxuACfvJ7JglqraWhGhMgwAf05MCziM+CqygtrGZzYhwFMm7h40R7GkBDLoHmOIGPqeCYTWWgvhVQEJ02JuAR4YPhLMrKdRQA9FMnPwGsIH8rHWeAjf0A85sHqC5JcFI/CW+EPliymH/VlkNhW/ITTKWPBDkSkXMBKrbYKS//ibIOveJsLB7iPqhNJEgXJ/tLElRI0IeC1BQ8wNj4DsF97iE1pjjgguAmtVUohEIA/azAk64ri5YtrFEHBxQSBPQBtUfphwhtiV4QfbbmbB1uj4AKWgeAJiqWAhP56bkEdQkIkR5Kp/1lUILCpDzm596CtcJExFaUK4sypZHSNUR6SY0vZGGoL+sR2myJa8YBOrIZI1JfY/RZhH7T2Je6kxRLPHzH2bVsHTtT6i/ZrYd2TWCngpyCjDOBLEn9jj9SxwvTVmsoFgUoZwb39xPpSXqWKJcATMYhRh8moKh7omVLOYjgMW7ySF8rtaW0aD7lWAcolgmKER+2PEDREHCkoKOofgj20isEtlAioaG8a8AUlC9JIIqEgQRjqT9B1hA+IkibUHV8CuqHjP8w/UhjY5v8nmtKmQe5H6cO5+WDccZNFCVBqUIGgYgYitgdSjX4HAGO8lNSjmRg8126mfGWDMbow1X6fdHVSj4vjv8SALPM7wKCIH0Es6PYOf3h49phgJgwoJPsQ7CSTz6MbvLgsyiaA3EFOIRo3zQCeH4Mx0e5BEpGaOf5lUV6g5EbymZ88Z0EkA7+TylSE9g/1eUl5U6uCQCREMDMj5S3ZDdJyubHlv34OSnSgRfxO2BUAtYx3pPKiPpZZQ+ofNhuGABgBVWtiOBYxrQUr2LY9pr8KHNJBvaU6wcqA+ykg+ghtHdoT/nQdOYCKa7JbnAclBd7B8iVwkuA77l74+v8tHMQG1lDRSeKGk+AYL0/6BylC34J3lvDDwsKE4UjXyuFG0Y/ykaojdBeAlEEgXkoY9CbifALcCNyRwnGWNy7yHACDFU/yp7xu16C/hpDUe+yrSbmsLE15iv5XayMvo8C6Gm8p5OiMZ3gKxp7gJDAu/iqGH407gOsAbyTv0kIygE6jTGOFbgNERQmjEr7otRGH8eAymLeVRjFZXkDAGPUJVHAUl9FqXtE8xj3j9M+6iPZEl9zc64Csgr4CYhM4rMS3N9HsN9PADOkcYB9eLh3QvMpY5Bm4baUi+BhlPuFvaTa5Zpe7DeJIiHDgn5EAQogUf0dRF0vH1AnM5KHP1+H/JQmU+sEP/fBm7n+89IWScZXBF8jxUj5ez++wCenpfLxPr3jlMti9JVS/aYBqQq4pzWZp/DFzGVrlFX956PtlTpaY0CAmXzGGvOzmwvpw5DmQvpJaid++lzKLrrvSgQ/xnycgbpjBuMqjn8JB5dsxb+Iggr3YT5zRCBtKPhP6WBDgAC5gGP5KCMqzbhUQ1foKyze3UNqm8GE5j18ExC67C3O+PNKkQibS5By1R9gfGMfq7SNAOM0/FWIMmk6iMnH4Q8pFv2nUcMf+lzjUPbqBRqQoi4LAWyFDzEWNVbVtyHmAsHYMeADXUOatO6islE+FMB+k3FslHKt4ENxX/jciGuLKGM5wvcckI9iZwFKvD5sMcwYDXN/D20nu9WKRRbpxa6X15ZRLcKXE7wWzCsogW7hnvg5xrUOK2h9ElL98ZHraw1sDP8jVbowtirQV2s4L3X1M8a0lpHiZpLvhqmfbExqTiHaKMA/vPIZmicI+ru1EL7Bh30oDaTqwgqBdl2hTtgGF9d6hYrgR+epR9j5kaK0csBVlIYJwCe4T0xrIHy31H4CwDF+bFfrFk+AcjI+VY4ENqU6hgB3fVoHUeYY/af5Vrbs49/yxXyIMmtca91G+Zlr1AZYKDXBFgAptPYMAev6GLerHIRIePFxrG81Vj1+4ED8pRd7SgLNJLB/MADaB/APfyAnr/kpTF9GKF+SNZPmWc3Rq/hwvSdgLc9XaPlWxHxP26tT+Ilgq7IJtVcAG5Lta66NMK958dNKnxegDzyyO3x6DD8hNbIEYzudPk7jOz4aJoo9hKlTWNfUvE3dPPS5QGIhY6qpYFYdAtRaRYBGiLZLB+hJUAY9c3tpAx/jWcrJWvd68SNSQ9K8IIWmZY/Wo7QJf7B+2hNfyveia4wzntczAZDT8Y1pYa3X+J4+I0retVYQG9W6KGELaivaUPXKAqyS2pGuldR87OcOWpvRdlorg1Cri9yQka1KqVNtpMnI+R7KKv8UELiDMpXWcbFVxpnaj3UeEyI+nb80DrgOf9G21IB/JAHzldJQn9FTiuwmzI9UxWgqZ4NSIxSkRyNzCCSH9KC5lgFgH6Wd8ej0BwAtNp1BHRjttDPqnPTFCv5e6z+GPu3P/Icf1NokAcCnOXsNm1nR3M54y6aNQZneeUbCr+E/NB8KgtZ6wMs482meYB6UkpR8ElbAcxHzDeXIZR3ppw1irD80BpKsQZe47ir+KIA/zGb8COrSenuJNVuYdbPqR8NTVu7FgSqBjzQ/EGaO5XqLLWONVMY0VsQzD0Q+79YgGcy7furwUEU2zPcEsPilPiiFTC6q5x21Z0LrLg4Q+GmbIPX1cgxCSJ0AAEAASURBVC96xs3RFNnVTb4txk1l+1rrMTljKaxa8CdJ7DuGP0toHmIe1nzi/C62EcdfYunOFyewIRYc1EeKkz76BGVeLwdsmL+1FojRWPIlgoZ9rEvyUFQr9HPohnaJsqZI0r8B7s+NqJfaWuMcUJS+RRObdUDUzcOai3woGbsC8l4YHxLGT2l/KkTdKBSdQgPKtjQuqNcqFZXf0UEgH880UhDMYMz5+JzWQmtaq/E7qaMJ7IzLB6czT/jx+4zRKGunIH2fQ7vLx4fxe7PBefd+HF9GM1Jv/C4wsWBzKfVmB3nmRR2TUWgeFp064BHDFmk+rsEf5sEAn1NvrAFe6SCD1lQeyiFf6actpeaq59II78k+NL6knKjnMo0lwYPyMjHma1U7obUDn2G4OrXbEP+dxlomgrqynv09pMrWvoEHn6DnOg9QnhtzGstSpcXn63nSBxio5+JVoKxlzyJtyMXxeRoFjELai8Mx+KocFPMK+BNS32u9xMiTX5Q9aTyH8JlB+gXPDvg1Rznj+IZsbAzgS+sHxrJmYo498X3qQBsFWY+kYceYIONScwwWwL0053k0dvUH+9DazIev0ZpUYyYGYOvBXoPA11Jf1ByjgeTjs1LDlA2r/B6eY/gy459207OMe25hTer8I3bC3BzBj0Zp0BAHr5WaWwd5vDy3aUy5ZyPaFS/n/IzUyr3YkQ44au0gf+LnGURlc1XEJIP0I9/GlpjLaAMfdkpXacmCD6JNGReCDTXX+9gX4T9wemoe1vaoIfu5h9p0jc/pMISAy2ye4/2ab3g2Mh9rcr67TB+ymWEZQLoZmt9ZY6+kA5kHSKPt+o058Z32U2p72Z2eQfILci0TkNK3jCIuazmt9WK+9WeLDG82bli2yoFFQMhDpITfjaJfVV0VNkYxeaaRFQqa0zrFQXMaD1Thu339NwPnNJnqFLucsTaytbhNvVItoBbQ5CDbULBGSgrvVmX4995C2qxSCgK1kZtY36VY8f1um4f9or/dwy3j9r2+1Kc6na6NdilCaNyvb6a/1yulPv/9bgH1s/pJKh1ZWSygWQT+4Prqe5i1vt8Nlbp+qgVSLZBqgVQLpFog1QKpFninBb4TOKf1++/+7u865bbW1lb70pe+ZBs2bHDf0FrrT/7kT+zNN990KnB79+51ynLfCzgnKO706dP2xS9+0X78x38ctQeClLyURlXgnJ6v/uZv/sb+8i//0qQGp1SvD9Owaq2ntbp+rzI98cQT9qEPfcilk/3n4Jw+K3DmU5/6lIP7XnzxRRNQp9/r1K1+r2voPrrGd0rVOsYp0p/8yZ905f3a177m7qdnP/3++eefd9dXutp/CZzTva5eveqARH3vz//8z+348ePuOUl1VopZpYptbm52in4C+lKvVAukWuBf1wLyXQ9/NNb0o308vfSsqPH9d3/3dyZw7mMf+9h/Ac4pEKXP66VrrBHEHkchakwnjkll6AkLBtAGPwEXNrVjgEph/zKqBQQQgE4CUg5hE1x/BHsoMJMAComzUR7TRi33V+q2dHanE2ts2LJZLGgkiPKZ4KkoG6pEXAiU6D5ci32IAIE2SkMwRNcBhGBTeho1iRFSY0UIAPkzCajoHgRaBvp6bRS1EO2eNtY2WkvtdqtOK+M+BEHYXJYCiAIwCq4FXJBEW69sWrPBHCZIqCCNgsTaOPYQhIIjc4COqsJ+MEEYbRizwRtkX4cgAyUVSeKgAp1kp2gEPakHAQHdRxCej+BunACJh41ebfHr5HVMgAQb3j7aROlBCQG6QEXcBcRRhkH1ZQ51tTWUXQIozSXTCOgTNOgb7reB4UECz6R2JQXclqrtKOmgmEMAQRv7VIl9bzafSe/nI3CiVEsB2s1HsMkFdVaDlJd9XD4fZE8nSXBN8GGMDfCkAhcEG1xIlIBNgN11wYtxgrxSavOxge3nvsRp6EMCA2yeJwlOhf0KENKXkIfxWQCXKVROwgRECTanCdqZIlw2FrZJ0jcls+OW3UBAoAkVnVKASD8wIwEawQtx7hcV1EKwwk+ARkFVrwIq3EuAIywTf3NvKhnhh5CjC/pglJxAJ4wIQLZK36wSBOEv6o+l6nv8TY9yHYIQbOwn1dd8Zy3J/hyb9EjoEUjgHi4QoSANqRe5vpSo9K802olucUGUGIFIrITfU3cFlCi3lCo8CgwSWA9jZwIBBGyscv3Z1QVS2BF8o5XTUIbwEpAdGR2y7gcPCCpFrKGmzlpIGViCskAa0I7S2MmunL1xeQWDFOiQEoViRBSeIBNBdMaag+BkUzQM3eKCuF4+n47dYUwAMASZoRq8lE3lVKDFQ5m9jEGuSPCL/udHY5Vmdz8JFBK9AFCy5RjwRZigoQIUCmjrc2BMrl/iwFZLaxOAXpO2CBQaAyaMK20baX2HJgesv7PHoigzbarbaNsbWqwUtSsFZiL4i2iQQCX384cJdhNEI65Ea9HWKG+oH2TBHkFigHB+AjUxgvmr+ADBOXgdygfkQP8KbFGAL0b9o4yPAH0rhQIv9io7WqX8Sb7nYexpbPgI7PCG8zleAn58E6ugVti4IDQvaiIJ2s4FROm7hfCsUy+ZX5uyAGmigig9Ruhrp47XdY+AWdiaUF/YX7vbyknJrGiOLEZ+S7EzLJgAE3emL4QaxNNUVgFIVB4oIUDHKpiu2L7Sv0Yw2jBQhwaxYCWBFwKlFKAM0IchAmL8ivFIG+FTaDCC94wDoEgFA2kKoEAfChWEqPieUmtGsV3Bfg5oVQCbIKRABwEd/Np9RwpiM6uTTlHIm8EvCXhLZWJ4EIW6gXFUOCpIkX0AxZw6ZwMKiHNZagcMJZgFlb8kdqP6Cip2fay2J8BIWBifrroSkGcOcOFKQSsEXlUZD9eSfUfoJ/0NbbceYKReSWDi9fg+fccfBe68BBkVpIwSEPYA8vmlAAOYYraAzfJ5ArRKEb2Gf00KpKGd5pdRK0TpUPiOLwd/RFvPz81bfw8Ke5MocVbU2MbmTVZJur18y6Gs2DEBvCjQQ0L3oi/Vt7hqxg51xwZwEFwfm8E+Ewqg0iVJ4F0f8K6bEAQXoxCmNLAUWv+jfKqv+hcfpzLSGpqP3XzLdTWbZNCnmiPCBCJjjEPBk/I3SSBG588cxAKAyu9wA7acmEblCvUkAHepmCpVqOD0BVKotbW3O1Wc0opilz66mjGYYUCeqKao72P0wxqVE5Ch4GSCe8imBeuEaEcFtyPYigucc3eVS8Fg1Z9uo8LUCb8iZyUwUD41QSPpLS/ztEArzRcJBY6xN9fX9DnTBnZHAJ37ssRg71qYD/aLc2ckOj+FN3Xva55ZAq4aW8ZGUWd1KcJJIexhLCyhMjfUP2R9D/qYA3Ns17adtqNxO/fUt7E1rT1oNBfHJvgs6CFExRkNzOHYHG2bSGOO0Xii3rJHwV8JqFrX18yhtJSzIQdbuarSS8zpDvTDhBXEd9Av5eYqNAUeQPOF5nwFpOlf3Br1pY2AGgXNBElfnsB3RbAhD/no/UCB0/MDKM5NAajzWVTGosADc6iFjqMKJaXJbNSmtjdttq0ojWahOBdlsDOjO3AuQH19WjNRD40zBe8j/McqnaSgsvqWEtHW6+tC+TsHNTLGXL0EiDEe6VrsHpyAduBydCJtxo/mkjh9KCiDFqSmrJ8YDwKykjwLJ1nryG9r/SDQzceY8GhMaB0E6BHRPBiZtomVCdYJMWyUgDzfX0ZNrrO3jfXlqOWlFZM28wDKgJtRDOT79LsgrpiAbezSS7spkC5PINidSYCGUvnBZwTn4Pv9SeJU2KJSw2JEfJYXdicl4PX1Fb/h937Nl9irbE/tQIWwWflZwAPmCMH6CQzExzjVXLQMSBVjfYp0G+3LvIStBBibUvwUDJDEoLWWm1mZRbEQJV1gm4RgG8qfYL0gtZ4HPZ2sp4LW2rzHWqr3AIRqDYadUaSEwBB++IADWARWCM7WOldQHmgX9ZchY7ncn1upB+hX3qftBYGLQRaeIfuJMY7W19saq1yW/hPkqTWowMUAEFLS0WPyabJn7AUgWWCn5kGNS/lAAY46lJIIAHpwwGOMNJvjKGTF8cFB+Qe+twy0OjE2ZT0dPS6d4qZNpJ0n3WEeqRSDjDmsirJQPtaVTP1qTtYT9Bl9kKT/PMxdPtpSzl9rEXUaKDwfE8yk9+VHsTv1E+/xxOHaVPYapH3XwTkBLlxbdVHLcE0PAx8XATjHfMT8oPsE+B3LM27F9fCBCeq5TF+CYVGWCKkM6b8F1GKxV597puB9nkdGhlCV6hvCd/htK/PE9sZNloMqV0Tjgt4J8qzixw8IWvUCengYjxHup8MuWrM5mJ25XWuvhNbI1CmJnWne0FzkZ64JYOc+xgodjE3QDlp/8r6ur4qrHWXFsmW1kMZzOnOFAE5RMiH8rUBDB8ZjNxgncNy6XWkODTJHCzCfQ+F5ZHGIw05LPDdh6zxbLKmOoyhFMt+voDy1acNmUrUesBJ/lTtAk/Qt0Y4om8mRMe9qiaKDP+5gE/Yp8A2HyLhjVUzxlFKdpqXY/B6bEjjnB26Sb9QiU3OyVpP8irZm1PE5D991Bx7wiZog/IxhrxbxGE+AAzyaL9be8SU+rfe5USbKYF58SZTrr/E9WhAl02Xmw1HG7BQcHe3E/lKEcTKJyuGd27fYJ1sjHfMWO7bzGIpoKKsx9wTweel8TnBinHVDZI22Bg7UM26E+TwJCOvnvbQI45B1jsoi36c5T0tKrYc8MkDqDTVFz2i1gu+iEaJa17mFFu1G/XVARSC85k0dbknSz3oWlcJ6MArwxL3CIWAjIHyBTbJ74aZBnqek9D3PgY3u5V7GOZAc/eHXHA3IOz6vNNQdKKj7rLl2BykzD5CyGBlwDp8IEg4C2Gp+izOnJcKMBcaJXLvmKSkOCiKT7Qhi1xyvgxiCzdyHKCeVol1oYfyQAD5B9TqEhuPlPeyS76sfIwLS+KwPOw7pGZhxIBAPC8Tn6uAEbcV3g/hCHbATmOagRa4Qwwdp3RXBf06iZD26NsxakDqiIihbGUVBsG+ox+ZRK2worrfW2l2kX0f5TesM2inB+NYzmw5LeGlnZ5YaSho92KnmrDB2woyA3cgGNbo4xIJfFDhHzZ0/ifBFgekUzI1FHSjQngUdwr/l77Fd+lr2L5+iuVftp4MvUX70R1CuyhTCj+pZlg53wO4aNqM4/cLCAqqF85bMYO5CNFmKslOo8/d3oziL4nFZZaW1bN1llUWV9Dvfx97lD9OYc5SKmUdMrs8KXBMIRVWbO3ulHEk+E9c8yMLHo7mZMZjO5xxGiJ0KPBYQTXWYY/RcRfHwJS4dLXWSojsLd/oHS6ZuUk1OsEehPRKGCm3Lj1vPaR7GZvBXCaBSD84uTX6WPl5BUW8wQiYB1JCVHSCQxVjjOkuo6w1299sU/kYqqUpb3FTVbNmoB0Y5cOQBnBS/p8MqHuYFr5RpAS6LSFNfisp5bgGHP3jWZEDiv+ULeVF/zRNaRzvXSVd9t6/vGZzTIkub8Uof09XZ5eCZnTt3QAgq/3Hq9W+9BdS3D8EuBSa0AHkvLxH4CrQ80AYQ15IygAIdqRc+h8XMDOkR2tra3EmFuro604Ly+/16OGbHkTJWv8gBVlVX2bZt297TrVV+BcGUamoGaeuDhw6Sv7rwnwJb7+liqQ9/X1tAgOMsec97e3vcRsmRI0eskklXkPMP5vU9zFo/mAKn7ppqgVQLpFog1QKpFki1wL/DFvhO4JwOHwhk+6Vf+iW3llfK08cee4zNBb+9/PLLLs2p0owK8BJYpzSq3ys498Ybb9iOHTvs13/9123nzp1uc+ULX/iCSxereyu1qlKXfu5zn3MHlh6WSfDZpUuXHGSmZwCle33yySftb//2b/8rxTltLpw/f94EtumzAgIF/ulZUGlWv/zlL5M+YdqkfKfP6Fng6aefds96AgmlhqfUNwLspBr3Uz/1U/b888+79ebXv/51B/7pMNUnP/lJU9kF1qm871acUxl0j4eQnlSv1M5Sn9Mz5c/+7M86YFCpXaXk9xAi/Hdomqkqp1rgPbWAxvTDl/5bY00/GpParNXf8lNSlRweHrbnnnvOjh8/bmVlZW4MPzwkp2sQ9mEzWkgLYXr+1s5vkiCGtqKTbLy64LMAA1QovOyocg4bdTA2nxUIY9OVXXC3YSy1Cik8sC3KDxvhbKDqzLSXjV3BHdqUV9Dcwya6QDp3ghiAgpCa+x03d3XQhrcO/Etda4y0cFfuXLHBqWFOWhMZYqNbKhwTI6PupL5Slhzec8h2NWy3chSvtPmtQIACBa5clM2p/CgKqUCxAhAEKthu565cjs/72WSXqpIgCbES2mj2AGcI9BIgok9KBUSBJO0+C4hTwCHJzrJSiGgXV4Fc9+LeAp30ElyQZGOYm7qNaPWPAhQ6xR+nrVYIag2TPqad/aO+vl5gEG5MEG6RtHPDo4OkJiV9WVWNPbL3uG0jeFBIuikpcKnddF8FoxRgdWCLakM7+wGoEvydJDijNERSqnmnZC5YGwtwup+QhaAAL4EO2U5CgRNeSdRctDGtYA9Nr6JSXwWBVGd+QbBAQZTEEoGivoQN3wH4IzUooSg077CKOQCfCdKi2pTlbUexY1+JhTaAJBQmUBVYcveQ6pECHq5NFSXhXqoPncZ/0+66p9pQO/r8Q8HPuALT/BH8JwBAgdA4u+4x2T39GuBzQTpOAWYPAS8+sH5N9SU/CkwLnEsSUFRQT32uk+tJ6h8jKKFiKECtsJIAiCR2zScop4KG6ku1DxdyzcS1USugWQj5CpxDGYJ0hO3dndbRR7BnaRpFFoVqCDSTumyB9DqFpKXa37rHdm/ZbWWZjD/6xkfglUics3cBMOuV53sC52jjOIXyST1E9kOZ18NABG3oDCk6KpCmYI1sWgCEgllSwVM5Xen5nYLoAhM0hhPYDReiXxljRF8EQAkIUlA8SXlXCYwoKKSSKIinfhJgKOhmfGbA7nXetPaeLiBB7pEBDAIIOTzTRwB20cqyS+zAjoO2a8Ne0kHWEpAXSAVMASyi60lpUEHBJG0tsMz5KsaOwlASYRAAJwBDIa21dyI2PvyNFx8Bt8pniLBwISk2RBk3CrwGRBPRhzBTBL/eaRPqJjUWgR9KR4TBEAAjqIfd0pWuX4k2cUHagjaPY+QRFDrGSNHa1nvTuvrv0qNAwxmAkXx3lJSr00CtxcX5dnjbQTvasJ80psXOUtSesmOp1ynIKMCGLuB6BBa5hYKoCuQJNtH4SJPN0xeiIdwYgoKQOcktyXfII0lRRvav62pPV7CIwCQoV95V23FFriFz0QhSwNKjsU7geY06xvmevK7UYwQFa1g7eA/4JEwa3uv3rti9vnabTwCfAfdJ9Uvp1KZIIag0a3t37bODOw9bVUENJdaYw4vTnoJM1EdJRe3kAxXHolhEzrBHgEv6lZ5yPiJI+YKUSXbpxZZdEJ0xpcGjeUbB9xh2Gue6UrBQH3J1B4VQUWqlgOXDzxOYBJQhmu9shnfoe+AI6irwmbgwbSTYADgHFZaOrpvsh9+w2ZUZ8wIKSNFylv35KaCkjLR0O7L3qO3ajJJFHmnAE9n4CoqloD3KR4KopCinALfqLtvDTXMD6ow9yT4pqusz+X1jLkwK7JH0CjVQMNGBSpTJ9TvllEpiHF8lOJeS8zm98NG0jwAYZg36H6hC/ooxGldgmnW4X/6J4KnUQRb4vKsHweUH/bfsftsd0oWvucCq/MAEKTUnSDWmdKbbd26zA7sOWU12s1OqDAn8ojQaw7JLKRgpyKv7K3W5go9+oAQpyAia47fvlFO+T5Adn2e8qzs0LDEG2lugIUFZzWGuPuA++GKBo0wkXIEAq9pUH6dfpEQn9Rb+5+bl9fcZl9TZz0CRt9Kk6xHQEp+z2z037FbXDZsi7ZiCsLrSLOlYJ8ennBLjdmCPQ7sP2Nb6LcBuGlnrfSYP6eYoyiEYHotxduRUlvCVUdWd3wUZU6qQlOVUW4pFexD85zoUY9326BvNQFiwm0fEDMhnKUgdk9/gO3zEDV4FzuV2pWgpAEvWmCQAntTcq36XDemW2GISUKKt64rdar9tkzxTJhiDUgScJd3qDGPQCzyyGWjukdaDtqWyyTJRH4oy/gVrSulHKe3Vo86vawKiXho4Uomh0YEUaE/ZsNZXlEvKQg4EUFlVN+B3QY06XKBCqSm0jtBnpdbpHBjfV+jdQ98IKtEcJX+jlwBypdhWMNup7QBdq5elZCQISj6mZ7LHbnTdIm3nyLq9ca0lUtKOTw9R1rhtrNlqx3c9SRo49R8jn/YJABnHmNulesSsRgAd1TeuLDAHD+3KrvaWQqOAeNmw2lSdoL/k3H0YaBL7S+o92koX8ADlJLims1MXpddaR1QXcwLt6BPoQcB+fZyznpLSL2q48DSunXUYACE67kH76h4ABWtAJX2kP73df8f6mBcF/MkvrS6TcnGGlH+oHjU2Ntlje95nrTX7wFcz+Krsgntjg1rPODieekn5zMGL2NW6dWrFrI6iPfiOfKEAUPlUAdgeoAq1gxZnSfpAoJrcMdya+32IvpJfURpUqQnK/tATcsrDFN/ZSlxOj8tJIVS2ovlX7yVohKgPlbnAmt0fvG83e4BxlXqRNzU7rbBvoLSby/MrVldTb/tRrt/T3Gp5wA5+wHeVQMCQFBNpFLeeE5wZFUDOfKtP+NQvrm+Yt6lTXH3B/OwXqKEFP/O8gBWMSaOIMlI7fA3sC1/m99Rfil7QMCqw62+tHWWTHhYKCdYb8kFa32ndnZRPpr0FJq3wXfh9tw4cHOmy2+3XAOOHAd64Gb52nlSpE6SL9AKKba5ptMP79ltzXR3zokARzb/4Q+xFs6L6QZhTkjVcUuAcBRR4nZSqHPUR7CflUs1dUq0UcEIxqQLlREHNr3UoVXJtwftSUItrDpV/cc5Wba6nKNb2vO/Uz9RuvL/uo7AF+tGpgQF3gQTxHm2MmfpwFlIdG5zvw4/edClkV/AxXg47RLCpcVI8L0zOkDK32A4fOGx7Nh4iHW0l38MmaD8fB2uYiii3/o+y8fDnDmKoDqoj0Of6yk3PH0JtXM+7stLa2JPKqco5j0wb0M98SI+Keq7T/KrDPLII2WkCeDGBL5biYFokS1+kP4RT8ryADat73POCDhNgI3quXMPOekYHWXPfIl1yN+UgfXcwaMuAt5PsN42TPrWsosQO7z5sR7YcRo08F/BJ6ys9m6lemhuwC/k3yiKIOsoaQOllBZqF6G+lmNZzW1K2jB16M2lXzffMnQJh6TD+Gxtza1fAMGzcgawam7SQfvQcGOBemh8FIbs1N9eVT5abigZRgUNxzSld8r77E6FtaPdZUmq/fPNV6x3vUddaCHhuheeMCdbcSptaUVbHPPiE7d562PJDeXLJ1Ae7xJYpGOXiF9iD/Inmp3VBHPrMrc3oG2dIms/5HGVRm9Ab7t80L9atOV3X0lhSdeW/ZHOotmHfHE1zdqDnppD6iU/6sHcPStUJxrDAd6mEhriGn7W41qJa49B99DkX5DsLAOP3Bu/YxQcXncq2D9BX31Ha4UlSq2YAqh5uOWKPbDpiFXkVfFW2Q7/Ih8p/UQeBc+oTeoK/ZSzcgL6I8hnXdZRbM7lbQfO5pKBhiqGXujiCTWstrz/8U01HG/AvxhVug7rjU7iFFNEonLNtd8gIG1V76aXvBrFNKV0mWPvo8JsOxigNbE9Pv3X0dthMeAQ1Pp7fWItNMwanxqbJGFJoe/buA37cbaV55bSV7IR7URKp5Ls+xM403znIltupCPITmnG1rqRo2Bk+j//ys2ZNw0/qAJrmah3W4E1+6FfaVd/VJ2VQ6/0vX0n3cw+tXdVvET1bUwguwYe1PsKm8GnKDax/eQShYr9Sl9eSYXVt2m4M3bAbg7dsgrnPQ5/FsKVl6j47PonP9FoLBx32t+y22pJ60u6SKlzLFQxWzxcaLbIzzUVSvNXaQxA5JcLd80E+x1B1/3Zqc+/0gevDd/qRkr7n1/cMzgmMGhkZsW984xt26+Ytq6uvs0984hMpOOo9d8UP5xd6e3vt7t27SBKv2lNPP/WegxPKzXzq1VMuYKPAjYIrTz715A9nZf87l2pyctLOvHnGBd4EJJ5830mnrvD9LoZOlU9NTdnrr79u58+dZ7PUbwKpPvrRj76nWys9lIKG594+5wJqn/2fP+vAP6WfSr1+uFpAwZArV67YKy+/Yu2cMPzsZz9r+w/st4IfGOD8PcxaP1xNmypNqgVSLZBqgVQLpFog1QL/A7eAlNykzCZAS3Ca1sxSeFteXrY/+IM/sL/6q79yoNf27dvdpoMOxOj9X/mVX7FPf/rTTmn7M5/5jAPSfv/3f9+effZZp+amVKhaj925c8f+4i/+woF3elbSYTSlf50leC7wLj8/36VhvXz5sjtoo3V2XV2d6QCM4BbBdILhlE5VByU+//nPuzIJdtu6davb/Lp+/bq7p1KkqlyC23TP3/iN37BHHnnEqec1Njayees1PZ8o9ayeE3Jycmzz5s0AGn3u2gJn9N/19fVO0e7YsWMO5BOEpzprD0Ag3Fe+8hUHyal8UqxTWfTsoTSxur7AObWdoMTf/M3fdAp5Au+UqtWBItjT2bNn7ed//uft3r177uBVbW2t3bhxw8E9Bw4csD/90z81/e69Hur6H9hUU1VLtcD/Zwu4wCi7hy5Y/M4ntS/Q2dlpt27dMj3b61Dd7du3nf9paWmxLVu2uHGmw48bN250/ujhGNXucEKp97S1yuZ3UmCK9oXZPWY/nI1oBUo4KU6oTtu7Oo0u2CzJz/rntAWqgDrBMjZLFVoSdBFik1wgloJqgibibOYKdOKb2sV958WzpAtkrD9Tug1TLisFrEmCd5duXrT7A902vTKP7wLIAZyTIlBRUalt2LjJWoEhanLLXLo3F6VRMIB7u010bS0T6HIvLs8e9DvBb36n3/M/l85Gu7MKTim4xQa/gkR6Uwo67KSzscz71MPtVOstXV/KIQom6dfaoKZC2sCXMonAQEUspALmoBMX/eYjfE2RKam2rJJ2Z4iActv9drvb1g78sUSwGBUO4EUBCjkFOSgIoa6zsdWKs6pIEUVQ2V2CbXLuF9Z93OWk9KBQI/9WIIC//SiBSL2FXez1/tF+ueJEqAYqJaQLMOsXLmqiUiu8oH6jMrQJze8CBwKt1NYuKKGgOZvuiQWCcP1RG7g+YtN9k+yeA4OgkpBOutggqfL8xV4r3lNoaZv47yI2wNPoMxcsFErBJrna2W2Aq/3Y/Mce1HzaxF9/KUDDjyuVyijoSu8AOwALSbEuDpylMsteXVlVTuxfCn3rpecvrqsggH4UjFOwUX3kLuWCQih5qB95uaAaQXbe5h/awFcgRW2noIHsgM/JrvQ9wUz804ExpLGZXZmzW/fv2532+05VJ0baXaV6jPKTnZuFgm2z7dqy0+qK6ixL6VdJg0v+RVXHtbXsRGpyDqik7xwQyP0VnF1XelIJ1RoKiAsaooa0gZ++1d/qc6yN99UargJ8d71dVHbVX2pJernYlWxZkUzZpN4TfAG4ou8qaCP4jP+nTbge359CgeZ22zXAqzs2s7RC0EzJhecIYC5YIWuabSjNtTTtstrCesv1kfYLG4kKqCLYohLJBlUXl5qYuqokKrHCPlI7gFriPoSKVRb+KWPgCpRBAVV+KQfEXwriu5RO9LHgOAV7MDvgE9k8wBafIeTprqWhTffzb+yCAK3GsIBI5xN4g2rR5hSNMowDyLUB7NztuEbAbhLICFyDyNEq6hC5Zdm2ecsm29PYYo3pUvIiuEdgR8Er2ZkQQy+RYbWVXqqOgnHCM/QbBbScGiL2vh4Iww6BCdTnApXkD7XuoUeIb6ll/l/2zgPerqrK//vl1SQvvfdC6DWEGnpViojSixJ0rOjY24zzUf8zqMxf7IoVRQSsIAgoRQUEQgslCYSS3nvykrwkr/+/33XuDo/YkgwE5u89yXn33lP2XmvttdfeZ63fWRvC+O9xbqa70b4RIIuQfcjObkgV1FvIqB1wryAyQRn+q0SQBvNCUcIWAtJtWZemAX584rmn0nzAus0EBF3izmVdvWfPMXunA/edkEYPHstyciwTSeDOzDbauei19iuZy3YMRaqIJW4pg/sNTcovqkQQXsXmVrsugjawVwQ1LcKAOTKCE5catAe7TKljiuwHWBnZeEURbSUQy1ninQTwHDvUKcBbyggBCcYU1LChaV2aMXMqL7E/lhaTWW8TdjSCi8ioGnDY6DGj0pHjj0pjB45LfVg6uLqFJZBpJDPiVQAARJi0qzImOEi5Ebqjzi0AAhmiTsWqtpEDDl6wR4A47DWOiAZ1tWEGZw0My2cH4CIBM7GkOXep/eTRxFYBfuD6CpaItqFVH8fObAc9p46ZgdFxs6F5WZo9f2qa+tTjgMiWsVSfEDT6ITartlv3NGaXUWmfvfdKuwzelWU+hxDELrKPwRI8YDmkhU+D/rXam5C9do0At3InYIomQp27FMmLoAm+x73QzieHqRWgAVfHMrQcEO4jwA5SQ6fM6mW7moHGJd+oIsrQ5AuScHyy/woU0zZ4oyCnTSwD/8zC6emxp6ekOYsXkF2I+QT9oBkQaSVyHjpkWJqwz3iyW+6a+ncl+6p9D/3UuhSbBNr/ncOUOEHfWmBA+1e0EjKnSkES2gz/h62ERm4t/vCpvAT3yLOgBAPNgj3pvSEftUSdhsJoZ1iDXzPbWK+gGoAwqIEgt7gWElsArk2f9Xh6jBcB5i5eGBlrBfA1oavOpUYNHZn232P/tP/Yvcjc2Q8etO2OdTQi9UWA3C6pskCTJFRAnGZNQgPowDmmBTAg0ciGW9XDou0cy+h52hzOCWhUVgbYBTuaTU7dFzYhsKSN9rHtmOYU91stbeU4JGjAOUShM+gt35paN6SZS15Ij9AHZy6YTTZF5hnYIrOc1dZ3pGFjBqV9dpmQ9ht+SOpXOyQAos4vxFoJxSjag/4kGEX6MHIBnoLG0BHq6yJgQsY5JG2KwZ8B/ONrzEc9xDH7NIKA/0JHitkSAHpkYBkCsAXqhV2zEyBLl+91WU5tYh36W4PwzHDsGCLIfFNza5pPZrnHnp+SZix+jmWS1wH6Qg/IOtiKPegxoFcaP/7ANH7MwWl411FkS8WyMRgxZIQdcJyOFx4AKTFTphYljrWmLbTaAXzkm81n+9mv7COVobTwI522E4ZWcxxLtnKpm1lNHVrkWSCircli4MwNEbCiiM5sZ7RBkYDy26IrlrcJQPymNHPZnPTos9PTrPlzAgARcy54EMDUr3//tNfu+7DzLNFrGHNuAKIxrlKm7QQ3NCQ7f6CzHeVxXJd8lyoXzCcwwqxU9h6BnLE5nvsswXW2K+QhM2jiGjMqeU+2xXKVN8Xhs4UAvAr6kltkMULxO5wc0H4C5wRpC2jX9ixYNis9Pu3hNAMQeWMTy7MjF0xMgO1H9RuSDqYP7rfX3qkHmWcdRwVzaGN8ntGuVdA4zjN9JnD+yiHqhlZBM855lDO6I3CPg7IUW3yzr5uST/2MPiovZtpyHlw0k+OL0tGWRH3wETbEe+IcxFKHAG4B9Y69FmhiJsXpM8TStQvTVACsT5PpcckaMkAyzpvtrQ0j3K22Nu3HWLH/nvsDIh/LnLRn6BlaBx+NVEEhjP+CvXw5KgDzjhnszpeiH8VYCc80lhnH5cJxYIttCl2QcwFU/FX+8CNHMUdBV9voU00sHa8tcQnmGubFZgwUzdlBdmuETH/gN9dqE3iDhnNkCkZx5y2fl55mvJ899xkASmS7Qt/MqirYtlvvboDImc/swZy0flSqB5hYqaIInFLmGkT1VNA744ric5lp+4z0C5xzTqri2j/iGY+sdi3OPTjgcvSBDfcad/VCnWZO5JzDZzdOwKnjgXKx9Zgf+Lwcp6DBiThZ51p5fhB8DWPIHtie2Wuhb/3m9el3T9yVps2cRuYyMgEiuZaWjdC4LvXu0z3tufv+ad/dj+Ilh11iWc1qaaIUNzgrNnjxmH8FFZst0xaoFHAd/PsLOp2f8E+fY2RQQ8aC90JOksamyHz297nMZ0+hcsLVHAFYEDrYqaR9Okg7VgF4z65newuWFTgXtKEDgigxIyHHDTw3TeNlgHue+CMZINdQf5g6auHFgnrmbPgED9v78LT3gL3JTsp8zXFLbkpARMiAqGLMdT4jiFW+1POcEVcgKtU6oqFLdJBoUDniK8eFZjuPtJeH3NRLOyI2spI+VmF/ptgKgXMSz/82OppzNWVn08XLJdhYTBBlcg+ppdWlVYDjn5s5J019blqat/J5AO6NyJf7IKOa59e99tonjd//wDSkz1D4I2O8OgJ/waOyc7yTXyuVNRhxjAxVVpacsb188QaRsvPd68IoSp93q8C0Ln3XPlhQbWsU9fAFufiXY6UybS3rEdSNNKDJ5wyfaByXAc4hI6ZryIRZOi87TF80NU1+/uE0e+EC5jK2IfcC9K6jHUaNGJkOGX9w2mXoWLJ3svR1EhSvNiMjnq2Vhy85+iKiS0OLBpc6N8cH1dDnlKAPYKJZLX1ewlxGW3jdjmz/Y+Bc44YNaSoO/8v/6/IAZBw4YUL6+Mc/Fm+n7whB5XteWxK444470s9/9vNwnn79G19/SUBjWyid+tTUyFYgwMq3ld/5znemSZdO2pZb/7+/xoDZd676TrqFZZD64kCaNGlSevd73v2K8y3Y1aCcSzbdeuttqSeBwLPPOTuCU9tTuYGs733ve+kOgJF1dbXpi2SOOOzwwyMotj3llK995SVgIOQXP/9FBEnN4vGZz34mgpRmnXt1tjy8vTq1l2stS6AsgbIEyhIoS6AsgbIEtkUCLl3qnFnQmM8xAknMLOcmeE5g3Y033hhLGxpQEJR29tlnxzKHfjeLk4A7X0TyfkFfAuTM2uySrr6k9KEPfSiyyFWzhIbAMgFtgs0+/vGPh2PKT+dygtx8KUnwmnUJHDMDm2C+rl3N9sTqC9QnKO7mm28OYJ3XDRo0KJZ4lS7Ba2533313XDd+/PgAsg0ZMgQHRzE/E0RzxRVXRFZss8r5DPf2t789lqO1bAFs73vf+yIjlaA8M9P5It0RRxwRID/Bfmalu/322yNLnHxJ48SJEwMsJ+BOMOGf//zn9MMf/jAdddRRQYMvdGQapPvBBx8MUKDAHuUheNGlZs3gN3To0HCGBDPlP2UJlCWwTRKwX+U+ZvDTfmU/+/3vf5/0TQhwXUtGGvu9/Vh7oZ2x7x5//PGRsVwnZLyZbJAKJ77BnS4AoFwLKnzM+DJFpOgzjnA5DuHw+YdHU++4Hkxsja9n4001i4AOdcM+um2rqFtHqQAQQTGGXTyu69QgfoXRZaNkBA2iHM0Wx8ORD9DJwOTMRS+Q5WNRWrmxgQAXDtwWAC096ll6bwT2bGQaAmiuB0v5Cd7qEOBBkQGQMeJLWXhxi08DHMYQBQNSTwSicTIbENRDW8RUuB7vs77kCHThPK5kaaYIDQX6wBvZ9bpTvMIwc1UsY6TsgnKCRTiFDXa6zEoBnON6N++VXZzv5NhJq3lje9GK5Wnh0mVpI1lLNm5maR5k1Ysxqj82dMSQEak3Gb26ALAwHof7ntulvwg1Q0g4zA2fSXXAAeCzCkd/hYAj7uFwsSGaYglBw7e6xD1l8Fg5wQe0GfjxFkMpLi3lMq0CnIrNtuZME4GJNe1p/XyWoVkOjIpYWyvLLVaytEr3Pt1S3QDyiw0GuNGL63k1PUBz6FdkilHwopqMHuAwdznVFnRQZ3yEDWwvolLtJZCbgfIKAIAhvQhMwiVec/cimMP1Ot/5MFOTgdloe29gp+WLYA3fDRvwC86KYIR8hy5aBEElA6+RSVCJQo+ZYkj9RXEGniiAY5atuvozhItTfyPL7M5lSZo5gMtXAfTcDIinDXBidV1lGjCkP1kDR6CjQwAk9YBHgvEuDEiQQBEQ0SSwCEUsXRNAJqJH6mG1+iwviorLzGgUWQY5YNAZ0RHUJIgn6gCCbFGLC81Fhi55GzMbbm5VVwRBcVY4jUsXx7JpZoKgHJdT21jKgqKkPe/dEQTl/GYAEQtXzieQPp+MF2vSBoCrTSxUV9uzPQ0ZPCTtMnCPNKTnqAiMdAUog3jQJYOCAhSi2hIIBC1VeJRpaKaAgiln5BvBqpI54ZYK7IzIpiKohx5Tku1W4pBv9s7IS6gI40wdp6vgPbKaxRUR6kHWXMlx+TEIGlcjLIEQVmuQcjH8zV30HNkulpJFAaAnbV1dW5OGjhmSRg0bmYazxG7Ptl7oGHJGZ5sJ7ppNpZp2rKaPG0yPgBXIBTPbmD3PNrINYkf1zATlQQPQEuxyzfZXraHgHPu3wcEIRnsTbWh3CIAS3+NK256yIphnmhgAGGYMM+Nb2FzOVwoAxWAXQATrEsjXmJauW5ZmLZiT5iyZy9K7a8lwyfLE9bWpb4++adehu6ehfQFCqKMgCgM4R4lKS3hgsfwvRRmchM6wkfRtg9+FBSraXfmIaosMatTbBoBYO2+2sxirIEfeBWILnFMUZvsywGqgTlCJSlMJsKnQ6iKbHRgq7CkQAcBHakIbshe4k0GFgo+Ws0zrfLIJLVrM0qy8aGxvr62vSb3790pDhgxl2a9xqV913wjeVbiulCBAN22NfZ/Mlq20re3hcm6hl/Lj5qeN4Ua3rHCJNYLIAqmEv7g8sNmBzOZYbPY3mfVitdY+Ec1P6cjHOtV7bTQX2EcF0jiORvtSZthGzsYSoR3rWdKb5wUA5D4/NPC8YibYSjLN9Rs0gHFiSBrWZyB9sDf89Y42C/3hfjeoAA8BFdhKVkWDBqRjIB1b3K5O8bJMyBNapFXKaVrOM057swdsG/UQuluxJx0G4GlHg+wB8OV0LMGGTtrW1YAhlKOyU3R2fbPVyKnZQwQaOscIZUaXzf64jGW+5yydCzhpUVpBtkADw7Vd61PvXn1iKeERg4alvnW9ADXZ7yiDf4662gM37Z/Z0LxP614lcN2zEgAvxfLVEILiRbY1ZCCILPqvTed3zrVE31QSardbMXqo/todwf+CeoFx09e9TvioUyXpUGcd07RHyF7+OWrAfSGAlhcWzGBZz/lpY/OGAHx0YXk0++CYoWNYkm54Ggh/XQXeCFrxPsbHyMTFj5gvcSxOIHuHJDoFf9RZDtM2saFsnjMpqXriUUd34SvF9wIqIEDTNpV+aqJY7YxwiFKjW2fpfivVYsXS5QJrqdPdo/Le1rYpLW5Ykl5YMistWrU4rQdk7dKvFdjKvkPq04gRA9PwXrumATUjaL/6uMfWiwy92Cz1w6VtW5xT0rbV9gWNIrWE7ilQZGv/dJNqbi9t0K88BF+VznuftAvWte+pExXOIcgGKSi0wrGKbJZbrrfNOd+mUsKfltu/jh+xnCRlCKZcu7khzYDHmcteSA3rGwBMAO3FFnXt1S31G8p4P3h0GlQ3LPXsYqYr20XLTf3YFrlR0oLKBaLITQ19IeixLfgu0ZE9Vpq5wWUOq+Ah2sEDcRm2F0PRgg7aqkrD/iDEy3asoC8Fv2QtDJCqFUN7nKNDC+R1GqZ4MT3xpULwIqCulU0N6TmysM5dQNZjVqJqww/hssT1vHw3jGf1YYNHpH69sDWAWWqwX2bSKoiifG2CNLLFGAA72vVYutL+J63MWQRGBvBLu6cdCjCMjHEjO99oG0EitJf2gtvM3uY83PaH7disSRCGUrWHxn1eoyzFetjw8koBbWSGQ0TYztUAc59Ps5bOTCsYE4Xy1tV2S91re6VRZCPdrf+Y1J+Vt0SBa6uiTm7UngixcR4BPgu58Rt+tZgVGkv0TzoE8gnotldoW5WvWmn7ResDwCqEXvQcag/K5cJ9ixAEu1mvtpJavL2d+WH0YZ9RqMfZucA5+612phIZe349SyguWkUbLpnH3G1JWg/4yoxe9T16p8HwNnbkCLJc9acfMqdhxKAgmo7SsN32NUFcMa47j9DoKUbsTBMgUi14JclatPHOVbStGeTDTdzv8RK/yoQ9eAtFU9UoU/sEDy77bX8wm1+NdcXLHJTJ2KpymmXYIlML2bDJ5GmdZnZraGYp2pUL0oKFs9LyVUsjE6R2uwbf2aDh2JlRw5mTDiEjYm/KpReXdL8AX8KMCqStEPiIXrRSj31SwHUsAe645TVunG/i2bYZXSyARAXPAfcHjanNFZxp5kGBWIKmbMloc75FyzE/cIbEOw6hAvxFnmge847I5ha6xZgGrU7VzRg5feWzaSb8NaxaE8Bc50Pde6Q0bMQAMs6NS33ryYhYyZKW9KUAICObAONbOFvoIfTIRat2GEH6bIkQOaoe8Q+ZRVtxTda9eGHGH9LqfMgC2AM0Db3OCgXO2dscBap5jcZxzhdczLbKw0eI1hdautDO1bSp7WZXFKRqHxTsJcBz4br5ZLecwhLmq1MTS5O2YutqyPjcZ3Bv7OiwNKLXqDSwelA8C9lbtAc+r1uz/HTwnC1oWpq1czRo0ec0JdRnRj3pL/pPEOlNnLO/Fk8gjmHeD6fcX8jBYdSXqcxxGALiOTTsD8fj5TR02X/88gDUUEPYGn4zt3OcbARMvXjlmjR78fy0YM3stL5lbci+vns9eJGBacywsWlAn0Gx1L3aEfNsqYA+i66hrbR7RS3S7ths20lbyRJyXjl4UXzy1XnOlg5IAfnlPm2+cx6btSjTi5Gq8uCbz4wUBwXBVcw76XjQxTLHjjLocDu2wqdIs9TRWIyXm9OSDfPTc0ufTfOYszWyDGt0NQrsh/9i7PDRaeTAkalHDXM2s0pyv7Xou/LZwDHAjKH6M7ow7gu+pEELvbT/007xcoKF0tdipQPp9C0pCd3BDZul2ruVPoof2/VXx9mVV15J0OCmAFaVgXPbJb7X9MV33nlncmmd9evWp//+v/8d7bs9b/brjDXzwqWTLo1gh0v2TLp00mua551FnIGt2bNnp0veekkE4C655JKdApyTv3CUs4b2+edfEM7xHQHOWcZN9HmzYcybP68MnNtZirMD9WjmzQz43e9+NzKIlIFzOyDE8i1lCZQlUJZAWQJlCZQl8E8nAedQ+XFZR4575835sHN6AW1mbxo5cmSA7Do/L3mNZcRbb53u/3vHrcO6zEj30Y9+NIBpLpcq8E6QmptgN0F4f4umDLAbPXp0AOs60yQ91u+9nY9bbj6X7zcTnPXkcwJrBLEJiMvXKgOvyaDCzJu0em19fX044DrXme/9azTkurzeMnzxQzoE84QjL6gp/ylLoCyB7ZFA7n/5nubm5jRlypR01113BfjXN8jtX+7xNjn9TwDwQQcdFOBXQbhu4TjVEYxT3M1lJCs263nmh85JAgLG8wRJAG2IQ2Y30UnfYVQ2vMx8VhNmwhGqq94llXREEwYozuOwNTFFgEDCMVwKPuAkLRzW1leyx35g0/BE4wNm0Z6WTSxf2shyLgR14MHwYy1LvHat6U42IXJAEfwEZxHLDXXBuYshLAgvYgR8x2bjeHWJsjYc72YGMGDgcigGDiO4ZXAVegUwxFvjfJd1aavUtkJbB29R6yTX4S6gIMSDbAxcGxAoHOm6pLmE0iIojAQEfxXBxSjQk+HMVt6b2zeyRN1msurg/GfJq8Z1LFEE/TXYY99kr6sFfACdbQIcoKMGoLcZHgxGWr+brnR3N4OlbiW3enx/6R/O0xZKKFMaACl+K37v9rjhcFEN2vNctqXGzoXtBrgIGLf5iRNd+cJwZDOoALDYBcd2kSWErBItBImhK3SRDA8RDICXqA25K1JVIrLgWEfoYhFkjXZDl6q4qVi6TQrQAO53yZ8W2xRwpmDzXo5jAM5cqtYgtUHcUi1FW0qyGeu4T9qqocVgo6wapBWxwl92wHy0R6FrhAmQuUGTEJAy4qKQP/dUUoDO/U1NLJdJ2c0sF6XOthv05JZqwIR1BCyr0VHpNhMURwg0QZ8BSaqJpVRLIKRiGSPuKwWSDZpLh0uzGkx3uS0//ecSg4ZzFajBx2YDJtJH+wpWEgSn/pq5qgWZcoa61We+RvtRisBP2mqzgAJKgXM1liK5m0CU9djvm1s3EozdnDYha5ddBqZLW5Ptqo4FE6v7wFE9YA+yhlA2FEEDtoLgpxRIg0BEs6oY+HFTv4r+wQ/0h/hXBNzUIdugIoLGhCG5xwW0Cg337hdLsJTos7JNsWAP4dsAKPQqI+51qVua1dXYuJxgjUAZSguwDP3L0pRpcxv9sGkNfDbS16CBbDGVBPhrupHlkb5Y34VsioKtqMN4q8ufqQhAB6iDgJ3txG+XMhNH4HJiXlsHHZFh09OmSfN+zgU/XOsSu0rEzEKCzkK3pFEAgrZKYgyMQouBrwjOxSEKAkSWADzaqh0dZC5DxpblcnFmV4EYf/FZBGINQTUCOt64uZFsV5sABxHgIyBcV12X+rLMda3L7tEnBLja7mbraAUhIFjArJyV8GJZCC10zGX/DHx14R641KyEPRUgqF6Zec/+aRa6GgBM9nE320qwqEOHumLQNdSTAgQ9qr+CONRqtQQpshv61PYDyuJoSI2GaGPsMROYGUZaQPBubgbMKyCpWc3gOPbZIaGmGtBAVXfCdugoWTPMdmaA2nYz+GoGp4patJ9+4NKplZF1EgK9BtvreAGVwTtKxu8i76JgY4FGZiUyY0YErr1U2sOwaRuL22JuD8MCibrEWGt5gEkBDEQwFvBYUUsBlnBYFfuhXXHJ4SrAMC3Nm+mHZH0ENNtKO5mNLGwMQAaX6arjXy38aRccC8ycWIVsMrCNr7QXtMmzQg+jj/4ClLBh1D/+B6tBiwdsE3paB7pgJrICVAnf3N9m4FpghzpDWWZbalcn1dX4Z4em7WSGTZFwRamNo2goZbPt+ddIP9zEeLiJpWQ3waszDpd+rmXJxDp2l6JzufJa6pNHtSL+OQb5j/qN6dJF2IrzarOAuQ50Ijp3gCXkuaRJfGK0445oZ34KnGu24VAS9VMQcyzVGrpJefBaDU8uLdeMHJxDKVu11flEq32WejNwzgyS7fSJzcirER3dzHLsrQAHtFUuZdm1qmvqUdsjdSXzTB33drFzqHvaCOqPLLz+pI1DFU2RJd0utcsYalsLUhOARWugY0iV/76sYMYvbZ65GdVWfgaNzilCRtgW20BpuAlBiqu40CPu8hX9EXl4rYObS7KR+DC2OmRqHZtbN6eGlg2pkRccWp0jOe8iuF5VB2ATUHFXgB51HTz3MccU5lCjPLEjUTeMCZxrV0ngLfiEr6AmDIR0M4Zo071EMqhf2fgagf0r9BCbHCeD8oIrzbM6VulgQN+rUCfZ5cwx1/bRXii26LZ8V/wU7PQXWpCCO9duAtTSwHx0bWtDLKldg/2vCZ2iTMbSGtqyK5l3uzE6aBlDyyhfedlWls+sBZ0BOAfN1Q4o9g9POC5Hpbahtq1QTXlVXpJgi7QBNHRcVk0EVjrTNmudY4jZ8bQv8tTBmOwYGjZWlbHNHVs6lcsviuQk+ugy2E3o/lrGoEb8AKZiq+K7Wxd0sZb5aA1zb+cztRhWlymHDTYKVB8VadgMftKA6p/90sxhZv1SGu2+LMOnfbWDyUi0jQBX9N7jVB98RtbVAGxYJJYA+QRAi/HG5rSUWM4cJTA7VKnFOIrs/AcQA1FIiGoeIrZw+9xGdHRD81rALLQhpdXATx3t1pNspPXaT2RqRmGXsC1svyOROk5fCR0saES0bOqFDUNliMBd0Jka5VHbLHjie6FDWvXi2li612vhTSB+zLeVIzcJN5N8wV4eVzrOoGLeFPXJkzM2dq7R/vAOBLWiF9hQgUmb6IcbmZMKSBJgX0nbdce3042+WMvvjgpe0mSu08LbBzWRSlF2AABAAElEQVSMCzWmkgomkDF6aTXBonxBgPNe/xX9T4okNRiO7/H8gdIGvZ5mD9sU9jFaBVWDK9nnuakNhXWUD6tFH0Dc6AH30cece/gyiS+zVAMAdalWT4lNN/Nds+M9IM8Wng9b8NcJzjfDd1Udc2xeerAPOh52wJt2QjUIwlUeS6Lx7HLuvsTgs63zsJjP2R9Lm/VthB7n0D6HYHEDZCRQ3g6qbCNrtnaUTmpJtrzV2f7u5ldWjs6FxMxrR2LpU+Rd9HHqRQcD5EcTuJzz6va1aV0T2at9FlFe0kcmvu5dAenyAkA1oECXiO+AdzPh1cTYIAHyRh3szqWCAo75VUm3IX8zJMYcM0jBFil35GcLCc5UNqGLIR/1kAuwA/Fih/oG0NHyNSXV2F7BxQoylr/1BMwqwqjec97PtZju0KlQX86bZXZ106rURCZP7bEZNx27KtHPOp7t68jQ3c2Xp5C7cm3mhSSvqS3Nz4rlfjFAkFztM4u0OTayVWKvBOjKtXxTXdBgluUA4Pn8D6ExZ1Mn3L1IOr2HsZqhmTIogfJjLqqOhSCL/snVpf7JTV6IjMyK73DgM+NmxsBGXjRqaF0dfdJrqlh2uK66OzvtzdwmAHP2RYQS2eMQlxY9/lGX/6g+aCqOcpZrBdGbNdZ2iudv7H1UHARTv2OaY4MikC/5YLesWKI3Gsdy+Ud59jufqJwL6XNw3MAqsfuyihlhebYRHMmYEkvbMzFxDNtcgS1t9YWqzTHmF9qPTeW5t0cd4z3P5DXMLwU2VpI1LuwFdRX9EXk556aHuFRrBfNhN8dkj7X7fM0/l9y2X7pcvXwU98alO/TnZQHOWfMN19+Qrr76atJA9ilnnNuhpnj1bvLh2t2gSQ52ZGpcJsjMBwaCXMInB03y+X/0GcGORYvSueeeF8EVl/6ZdOmkf3Tb/1fnw0nHhFMZ1zAga6jcNHSreSvq7LPOjoH74osu2mnAuVz/mW88s6BhBzLOWYZLzZqF4/EnHi8D5xTIa2Azo6Db1n3VZXeuvfbadN1PrytnnHsNtFOZhLIEyhIoS6AsgbIEyhIoS+AfScD5WwbOmbnOrHNbz/H+URnl82UJlCVQloAS0P8QTlM+/e7mss/z588PALBApXzec/ox6gC69Ge5JTNPCpZ1qxRVgksD2A8uWh3hLKu4GWcs/uhwUBLwNa5uEElnphfr2I1lpXRk6onVUU5QUieoTk6DSzlYZEA3XO94ZrsIQsJ/omMaonHW6tXFYYxHN64v2OAYG4EKAThAdfDluoCLIV0vKIJYBp2ktgvEVVsWyDwDMAYBotigSWrDFx4O4eCAAwYUIkgSQQZKlSaOExrgaj3oyBR/j0Erg6mRiawU+JNvL9kSlFN8BJ0EDRa16eqmJq737Xgd42anKDUR8ikuQ1rUqdQFH3RJa5avTVMefTyta1iXauoITrLMn23oVgQs4AMfn20dYMctstryJa4t/ljJjm2WVtz9d8qNi+STTRkW30Lf4muJRx360lzoIvIt+dE8XYX+6VPTN9kFPiOrjiUFf/JY1N/Q0BBZlgR5Cw61PHe3DPjebbfd0pETj0wjySxV2ZWAHPqqlujEV7YuCWpQrp36o8kRa+he0dTBcARh1G8UUQ01EGSgtIIgQsQYsjigSwCWkUqDkQItA/AGLyqfICfbWJ23dV1GShCXgKRi+VX1AeJFRPBf0E+TICYANQaG6bUv9i+V0sCYOogiBkiIQ9FXCHAUwRXKiPqKT/kzUG22B6+z37i8lTpZh9yqCGBEtNCubBei7Mh6EWAwgUnURUDVjIXtEQTlEoQnqNR8EwaegfNAJ4ElUUkEKAXZmcUuMp7AvECkdsrT5ihnAzFC8mTZ8jEpReugHx1G0wzCGIWjXWz2dpb3q6SDVRBkakMmcmqGHgOqUR79XkCOQFxFaSAplurlvNG8FiNX1OcyqmABjANyuTQJmsBXTtvaJgJcDDy3mwHIsiktgvMRGiqC52FVpJGibeI2eGuupgwoqrO9qEObIyH8jCScevDkUwuriNWfAqjFF4OA0GMiFPO3uLk4sKAH7/EI5PHHmwS78M/6/fQCNu1XexeXfqKmyC4JGIyGV09SJQEsQFaxccBMEQKeIhsT57XGLdxXWJ4m6KtMPViuTRCEgDPlWwMgJ0w7jEU2DhQs+kBRasizFZ1VI7q4pBlBdkF3hTJBH1/NvGdmOLAklM0pu5bRfXlgN2Aut/QGPigfmQjGarXdKVcAQIVBU0CBgoW1eZGFMDqP7StgkPbPO/d1Yck1xyi6Bp+CsbRRBiRtB/sguujyggqfrYtAFfsygeqKWmu111q2vTC4IWAHuZJqf2FzecwOQG68ZkPJbnJACyLjaGf/oGfBU8idkvzPri3oqOKc/IVS0n9CdrAGf4XdkVeEh54EMIbyqtADek/U5pJrwida0XEBE5GtwyOCmuivVfBXRX9DpYI++YgvBbFcqa4TDOWEdlfgRIfgT2rP+mU/DZppONsh+h53CgJPLKmn3TZTiD9dUrRCQlVcQYWCAeAvshFyuMq+1FzSDaqzDrOvmWHO68IqYmsMcke2SEG8ipDiXuQA2XK+HdBuACmQna1kv4+lBG1wCBCUGH2LSgqIV6FfseSl6lMgqKCxAMK1hQ2FbBREW2m9ViyWyexBZn8JMBJyFtzo7gUGf+0XDiayHbWjl9rRACVwTPtQ6IN6LONCXuARHTA7G4Ir+rFyRZfN8uReS5kxt5BW6Ax+uFP7DRXwzfUxjlE37WcLWW6R2Uw7UkhOAQmrVVuq6YdVAgeRoYCVsB/QaIn2d/tnLHVn46g3fPiiQ4tGC74MlJvNM3RUphhHzL7WgqAM3gtZoKKQTwtt2wR4AO5jLGzHrraT8bKdjMZAkLnWzJ0G5ZUkZdmfo09Ld8mmc8hgvv3WTJNF4c6Y6C9h24PMEHyMSugzbFAmgA5BsZIoL4qd75oON3XZMURQQACOtcWUX9h+dBTUc2SalBS7BPcHiZZFQcpYUFITbYeVpb6K1A0QCa1GyS5SrNYAGggQOXNT6raAjjD+lE8xkiJ4Wp4FSuT5ZNAYeswp0HrKX9Cj10sOXavY/UF59nvbyZHIea9ZO4NWi45ztD0EaG28oooCwbQWPCFY5WUfVQ/Vc18+cXPZZpc3pnkplT9QoE0VbOL1aAI00x8Zk6qxG9oxxRO82ajcogzsC9btCcd7Wo5vEuwFfCoc5hVRA/YuAJMeKu1qfNhJ2iFsP79ZSBNa0T2bkbbRTMpr9ALaVbvlHMlnAi1EpaikTluALOE9nmWwqY6DjhJFL5ZO24x+CIBDvVb/YBgZO6ZwHRPLSs77TBBNhaI5N+oCDYGucQKAfGyzECfXqUJRjOyomOofVzifs9/KFX/Y0UVsKbdEnfJm+5uZVQtsW3RFptHODs5W4pyQCgTqO18W5Kl9KOajAlLYONYFoJGF8hVa/SZY1X7ZlbIF7jmC0bYAMgOEY8ZL+M/3x40I12m39s25srw4V4pGkC1JomxFIT+IV5NnZbFFX/B48MoflEZApJlV/S6vnvNy5SnIswOUmfQKyHKZ2QACqWyWwcSug+Uv85yQ1pMDuLIPoptmsQM4F6RKE7cVG1rod+nlw3ptK/W7SjvpnBEbE9VQj9f4EoBtYJ/RZtlqtrTjj0xrwQV3+RxLMdQpgWwoUAcDiGBUN59DQie9D/pdMlydVYN9PjBDJQNjAOcaKwHNcZ0vJZiP2xGtIm3kOujEvnTp6B59AVI4Th+WOL6FHfEbBJo9s5Wx2tmMc3CnOtrFeHwKmpQlBfDfe/30Z8iHnwGOB/js83c8mzDXtKEjc7R3cG0XAI4Bwncssmx5sN9pQOj47cyV9AsoM5erjTrUOeh1qXLnAdZtewmuErinL6ELdqgSWx7zL26zWL0AWJwYP8yW26YMkZ7jGNJjjOIi6lUGXTQ6brIFKdpTzY7Xy4/9W9iacyYOxXVxeRSh3LjGMvgfZfAHywI9cBJjrCc4Zd/DBhQvxFAU9MfzLGXbembJN/t2AWJUChJCOey+kOKzrP0EEgt5QkvRhuobP6K/oRMctI3RFK8sLpIxv3O8jefEdgoScG2fkSfb0a9RuJ/xI0QU+u/YZ87cFmTpeBsv+Sh32inmLBYftoIMy5S7KawEoGkKqmqCJjsOmRNTjRlOtTdFW5iH0OcOxwaBsgEQ91mxNEZZamzw14J+6eepov3NxF4A6+hvHFNvzG6sza9hXmtWOhvT8RFB7PD2sgHnzDxlNqMepGMtZ5zb4fbYqTc6UPoAs2DBglhqyOVxXPZm6y07oDLga+vz/+i3zthzzjk3DO6kSZP+qYBzOn4CfLh8RSx5stvuu70k2LWedazPevNZIN+b0oUXXLBTgXO22xsFzpHBYUcyznn/5AcnB3DuoYcfKgPnFMirvJnh8bnnnmPAqUj77rtvDKqZpBkzZsTyWD/4/g/KwLkslPJnWQJlCZQlUJZAWQJlCZQl8BqWwJNPPpk++MEPRkY7M84de+yxL3mWeA2TXiatLIGyBF5jEtA34ZbBcfp5XJZVn1AGXPndLf/2WnedtfouBCWtW78urVm3Jq1v25C61vdIoweOTUN7DGJJN4MilI+fl/QEFFIETMKXiuM03Mh4oiNmxOnI0MCPcO12cpBKgu5ZY/lmaDHQG8FeCirKwvfK/bWSWjqgw1eHsc5mg1a+pi+sQR7lOpzPOGIJURaOZJzlERzgHD7g1NTUntY1bkzL161Iq1esTE0sVdSObOq6dU0DhwxKg4cMTj3ruocTXlmU/O1RZwRXqUFHMv/hi/MQZCxErg2mWVdVRFwkmJ0AiEF7DvO9CK2YscmAiNcaIIUVLlWYnOe7zmpO4pAWutOR5s9ZmO78/V0BNjLbqVnUfBF263b2d/bpUdhrelP/lG8NSwG6CZLL9At687y8eI2759zVY5dAnzt3bmQpVVc97zLrvXr1iqynlql89E/q9zzpxJPSHuN2SdXdWVKK9lBHDPJGQEVwh05+dUyHu7tKFjpHYxAgiVikQeQAhRkmQ/YE3owwlNQx2pAQBY0GHyi0wCu3SiJSHWQ6iOYPNAkVUJbZDczSYV2VACGlJvoLtLSio8uWrktzF7Pc1MalBN1ZpjeCKdTHLbUAkYYMHJx2H7drquvKclrwGpt0u6lPXBdfPCYp7AaHjIlRNecN6gD0CvQYgRECIgYFg/fQby62HDcCGAkwVrGMkRk+AM+h46XaCAASYhYUQ8GCKGCPjUCHQVntgL8NlqH/AS5C4PZeCo6MikuWr07zlyxN69Y2cF0rWU+qUr+evdLQgUNT3779UyVg0eBB2smWYuYc1v+NYFBhTgjedLAsFBHBLgTwOtqKrBdSETwAaJCHAmAoeEJZE3SUBGhqJ8DTQQYTtKyQD+fbCJS3EtzRPhmANU5WDYgnkDNGNLkvaOK7AVFF1QISrInsmgHN2dScls5dmJYtWpoa17N8poHKGkBNGAmDXAb3tKODevdhOc1haeCAgQC0oI12MhuPQXHlU0v7kXQwyjdAaBtSI3RiQfxRoNhCb2OJO9qzHVkY8q1sBzTnUmYGAIOBjQSjGtEnA+PCQ8jEWNGDIKQFwxqXGW5sdpk0ApcuE9WN5bntH23wGaAgMR6UJb9uTZta06oVq9JS2m99w9rU1L45lnrtO4D26zs49e81APVER6WV/x3IaBPVaTMFztkGgtS0gyFPPrwuKtAOcp3BMAOQBXDOvmTYEUAaYAWD8MXGOBDjgh24GJ+iEougPvXO8otlcIuxworswYIrtLPqRRuywJwEXRX0W5dWXsySdvNWLE5rN5DBhmt6snzmiKEsgTqwP0uiCcKkSuoJgK1Z1WiDAOJBRwd93gwjoadh6AX+0VzaAttFQUoja7W1htztGwIzzD5JKJTzRdYQlaAkHIEV0OgW/dmB1IHDSuQEeUWT8tOl2jCcqZ1MbauWkUmpoTHG9I2b11FHTRoyiOUjsSVdexIUrSNbFcFqM6cYPBZl3LCyKS1bsioto403kyG0umst4yTLpY0eSFZJgp/oiaQYaq5oou9JI7agAlCAfayDbKm2UZgmkWbVTejfJnglX2RHt1Tdqn5RgqSjV60AHDdUrCdo3gT4tGvq3kYW6wi2KibaCMWJZUkF09JQBqoFKQhYXsvqSStWsPxgw5rUxHKgddQ/vP/g1G/wgFTTF5vFms1mHCWuG0sLblyzLq0lRrJ+7brYJWLw0BFp2JixLAPbjblR0W0EJpW6YwTpFbPHinEDntDNCGRrE6DPOUEgtLV5foWvZlSS3g/PgnAZEzDkAWJBjyva7KPww+WOcTaqGb5cv89lcgkvA0aDTwLNLes3pDUr1qRla1enNRvJggnvvfv1SKNGDE39eveMZUyLmtrTBmJPa9euT2tWrk4Nq8nAtNFMZmQgrO/Kkne9Uv9BvVM/stlVbHIMpieYlgqFK3SHvkFbaudDYHzEkAYvbVzTHODQ1tQVo9SFsUP1s/+FQvK7nQxPCsvlHLWPyiFAXygDpYYtlsfWCpZtRecqaevK5m6pcW1zWrt6eVqzehnzzzWUWZH69O+XBg9nWbc+fSlfQJ51kZWocV1awTJwZipvaGTR6SbH5pR6du/OcvZ90oBBfVKvPpSLngiYCiCo2urNXLgF+OpNIkckFLrb0S3ZFjiVpA35VzWj22ao4nZTmFbQNsJOtBxmcTX7cAfgohZ4EozueF4rcLkFJYJg+7eZcRvXbEzrVjSkBhKXNDauQadbeWEF+z9oeOpB+1WRUSkmE8GjfyABlaAidL8tLVm5nCWLsbVkC+s1cEDMV3sz9+nOuOIswOxtxQ18xM0WwG4Z0UgUSRs2MR66y1tdR9d4IYYrKABanc+TwUqQtWCMKrOWisJS8ZGdtsc2cGtnPiUQPkAMXCtYdfOGFl72WJk22L82biKzUmvq1b9PGjZscOrdtyfzfXqCbcC2cf3atIoELM7FNzJOtgeA1RmaBDNGdO0OjyPSmNFjUlde7rF/RfsJQLYDwlOMp4IvthhFbrSdEJoZajVArRBczKaxc9g2AbMVtjnyEuBmt2MiBj/YFATuCGHGww7Gsw7qcbS0H5KGN7WuY1nX5Q1pBcsPr960lAxntEXXAWn4oDFpyNABkRXLNhTkFUv5Wh5Anw3rWLVg+bK0aOnytJEsvS6z253lJ9XV4YMGpvrutINGRxrifidq8MuEoCOW/qQ/KTYucXnZRuZBm+iJaGbqwQmzNjGh41ou4hqbbRODE9ypCRxCR1DgCjLcOpYrN8GxnTf5bVy/iqXp1/OMtClt4FkpkYm2V/dalgEdFrpaw7OS4MRm7NEKlgt9npXnGkgUU4VeVTgY0ibOrwPIAz+18NWf5yqXRe/FcpRd7A+w5fOS/Din9feWze8C16nDSQLvavHTMn1eKg5Xa0eQa2RN9kbsmsOLe8wD0jpoYd7HeB2CoKJmaGuD740bmtPqZevQzw0sG72eZ4rm1L2+WxqKjekzoB/jGnpMPXBCvdRBhrH1qxrS2sUr0lrGwaZNm3kxh2cO5qiDh48K3a4lg3SB88Pecb3LhKpnzjB8AaQC4G3YwZItVD+1ty716pykhnao1vBSr+Awng7oU9IRTwaMk6iFthTbu7mSzLnQ1hHLW3I+ZNKMuJh/Cw5uYbzAVq1ctSKtWON4uCG1sFRmt2712MQBacDw/vQrQPGKDRp9tmijj65roM1XLk0r19AXaXczmw/o0z8NGTwk9cUG+0JTZFmkPgFfzWk9VDYxgnUHvMRasU5Q3SzY/uT8TZkzgXV5VnNqV21SPzmHDW2udh7AnAKma5gP+TKEzw3aLZdu98U49dR5t8mSaQrkoe6Sgb1hM/POdWllw4q0atNyrm8iM2vXNLhX6eW/Pt2wYfAXGejQRdUSHlcvW5GWYG/WrF4Tsu4JX30ZO3v274vtte4CUE9PLADD8pG7CDxoRZy2+XJCmEPGczVFWVW5HC3yp1KOsTk542WIyFasHsCbPhDHnWK8gClsrbrvCxG+WKh9w5ykJmzFGsb4ZWuw9Y0bEGNH6k42un6Oa0P6pfq+9QVIDJqc+zRtbkqrVq6hHzLHW9eAXq9L3Xr0TENHjGKcH8S8jTk4nQfSY5dC5zCIOeQabRadkCs8iG1R1i08j2mLKhkThcFHRmZ0y7YINhm/7aMbsb9NXCG4v85xEtbiPM6bCl9aQV/MCFsBINc+2Y7w2unDSqXoJ1LGRrmx0ZFd7lWAnMA5szbbjyJjOQL0uSwAy5RQ7Qs0PjSxOcxzaIe3vwqc0xnhm3ktLC1QTQpnrVY4JFRwNt8w1elQvAVYMPKbm36TviNwjnT3WwPnsiNDp0d22Oiw0PGRHXI7woHlRmp76Opcrm/hO5HyvHXKS97iLUzfwqDxPe6n6ZurSUGqYyXzbcf3t+fDeajyYPGkW94tf8vmNdSls8Y6My1e01lOHvd8Eylsvb5wQNAHkLfnrE+ZuPu98+Z9lv/3ZJh59dN6vWdL2dCiQcuysWx59UH25ptvRjk70v777xfLcMhZHU436besVvnivE6nl9DFseYSTdblluXT+TodU+cCnLO8SZMmbRNwTnlkfrI8Ld9yQ/ac58sWHZLPnGkr1y2C1u/y4meW/9btJIK6upM+y0uu2zrdsr56LuuNxy03t7HluluPNOq41LH86KOPpiefeBLnTt90yqmnhOPONva+DRs2pHPOPicJeDrnnHPS297+tr9oM6/LPFnnjmzKR7qlP8vTvmcmwLVMzrcGznlNvse3lW1/r8/6mWl4aPJDAZyb/NDkvwDOWYZ1Wk5n/cg6+FflTB3qqfy65JPX+L1zvVEmbRuppKWL87mNM13b/CmNnXRYmuXTtrMNt6axxkET1cs61Ln9vdfj6o73ZnmpOy/ZuG5b7EW+RxosVzlaRxBA+VnvpMHj1vv444/HUjs9ATBffPHFcU3W3ReefyH97Gc/S9/73vfSpz/96fS6178OZ8jAaFvpzX1XujtvmSfpyO1hG/4FX51v+offO9nPf3ht+YKyBMoSKEugLIGyBMoSKEvgn1MCS5YsiWc1M+ecddZZaezYsf+j5+d/TimWuS5LoCwBJZCfy33ey8+6Hve7z5Sed/O7x+LZs3ReX4svaD311FNp5qyZafHSxalh8/o0bPiwdPwRJ6TDxh+W+vViKRjdr/4PUAOOVJyr/vNg/MOByqG4xiBEcY469TaXjnu/W4Dg8MQKrYslWz3G7mn9oFsCnHyPMxbBjjcH+g0KmUmgqMEyvAvPDzsOeAJxsIhzVp9Xe1q0eGV6+tkZafqMaWnRgnkEXwk+Ezipwwc2dtexacKBE9Jeu+0Oj33wHeETwF+oj8R6YykWSaBcd6VoqNZPjxRhEmRMYCCWvoIUs+8U3HCVnmR2j0TGAL7ocPdeTsQez/sGB6lXwJ1lzps3P9191x+SS+gefPDBqTuB4dyufm79PYp7jf9RB931Nchz5sPv+iL8nf0fsqKvYt26dQGW80XBp59+Om3cuDH169cvwHEC5Fzm29/KR1/SAw88EL6mgw46OO06bizBAzOb4TNG5nie2M2QgtwN5Chzt8ItDk0c5xBxpYjHql3qWdHi0GyQmCPRdjaX14bC44eDdvXQEiPbD7rtlZHZS15V0Rwwsf3858XsxNwIqjemyZMfTXffx9LKy+cSuCPISl+uKQVWutZ1TQfst38668w3p4GDBxOIhhZk5nJv+jyDJosrlRn1UacsWQ/xiqhL3ooMN+i2QWev92ZuFKxjGyiHCgPHAZzDF0yQXWBarsc67AEG0C2/yvp1GRuwtGLL9Kef/Clo0e/I8osbN6e56PaUJ55KU59+BsDEWvhvSvUEZscAEJ1wwIR04IQJAAp6cZ+0SL+dyy8ErqA5mskoDSFkQztmDRPcVtBtzW72PVPq4Mcj6lUsK0n7yxskGpwxCB88UbT9vYMITJF9yPuLrBhdjMRpv5SfrjR5Cl75An0BKEER2loB/xCYe+AP96Ypj0xJy/GTN+nXFOzjEorItgnQi5CtPceNSyced3w6/LDDUl2P7lFckV1LnVFDuYr65NO2CX7VJ/pOZDCEHuWifroMsMaywmXM+F1BW1VAcw6ku2a12Z2auc5wOrA4gDT40EttH+wg28jkCc+xjBIBewO28heAAgQmvtJrNwDiWTBvQXqCFz+ee+bZtHzFcgJtm1NN92rADgPS/nsRd9h3QgAfwqcI/7GscEktFGHYdmQY7JV4jB/yJAiEfhljDHQ5TijwgjfaArrFPqByqIO6yWl3rwhB+Z1f1FdUYBn2TUvhO4LT+mrXLdOSWwleG5wXsLaBDJ9z581Jkx97KE1//pm0hsCyWQ579eyb9tt3nzQRW7wbc3WXm5U0A4HNhg8hrUqAD/8CNKd8M29IPsjxmG0n2YIACMS3o4P+82KiD2pzxDC5gGNuFlJ8hD5w2HrlI/O95TzFe42gMrNMrluzNt13zwNpxtMzAG4sjKBsdU3XdOjBR6YjJh6bRo8ZCnjO9qGfEHczA6d26Kknnk5PTHk6zZk3N5Zo7NGzPu2y66h01PET05hdRqSuLFnsGGbfqEBXKlQOga0EzIXyFFnfCpoLW2rgmYA5PJsdp6qdJfPMSufGrWZn3cQSX61ooflL6tBRs9TGxmVmqW3d3Bxjs9nhZNIl5gT5P8Gc5YEH7ksrlgNWpa26VtakQ/afkI48+sg0YrfRqaZHV2TMEmn0T8GeU598Kj07fXpaOH9hWkkCBGmfeOTR6ZQ3vAGdHUpsgiA3FSvjkDP12/1thrD3tp066hykGADoa9hB2w1ws20bG4WIZalALl3oewLnLLDQQfsnfUz5WZn3qC5bgtfOMwAaUc167OOc554n5vREmvHCC2kJwAj533Pv3cLnv/deuwM6Nkbq0mlt6dmZM9NT8Dhn1uy0fMlybA5ZJAF11/fsnkaPG5n23X/vtPe4PVK/7v25B6V1LgeNAeiuVHttVwgXxJK36BzyI58Aijkf9kOh2BGDaRjhfvtngCQcWx0XqMLL8hZzRuyymQ7NCNe4tjVNf/IZaJ6S5sx5Pi0HZFRJnHqvvfZKxxx3Qtp9r31SneAiyhAssACdfPShB9NzL3At4MB1jYCcqKpHffc0YtiwtOc+u5FkYM80CFvkEniRdQ465KbUbGho3rQHtCOy8yUO2Q7QNDya7ciMNyEHzhUgXGd+tiQWhEqd77msu+OgS/mahclgP7ezUSNfN/CSxlNTnkpPPPwEL8nNpw+uCJux374HpiOOOC7tRvt17wnAges74E8QlWOwt3eg9w3Yo3seuD/dO/kBAGmNac/99k1Hoq/aoF4AYUK8MuYNeUM/whbm33xqD1uQexPgR5cxrBFhpYKWdA9mGUIZrwDOCVIxM6QoFDMPSk6F4xifxQ6t9FVn72ah3QRgfBGZrR+478E0+wX0jhjkuk0b0+577pmOPfaotA92s0fPHgCvVPaO9Pxzz6bJ99yDnXk8wKtbsrnJA5X17T8wHXLIoel1r3t96tuvT9AY9s6JUhAMTeqaAqYtMl0xVqiLAD8yoFqQlfOymKdrHB08Qs4yxZwBwVi2y4hXRpnouEtAOqHBNgic27SxNc2aMY82fJI+ODUtW70ogFWjR45LRx56bDryqCNTfS+AHJIU9GGveL5Yu7IhzZgxMz05bXp6fs4sgC2NEd/sw9xm913HpSMOPzTtMmpUqgVIF8BbaI16LcM5S2RUolCVnw9N4mbocnlgIVDdod8Mn9pSx3v7GglDwdxlO6NOapHh37HHDREzbQzbVhzgepYznz7tyfQoduP5OQsA46wCdLUxjRw2KB1/7Alp/IGHpr4DsBfUv2FTU8zDb77pxjRn5qyCNMFzFKbIWtEXs2j26tc3HTbx8HT44UeksaNHsqRvEWuN6xQ913be7MPSFgVRj6ppa3u9WqMtFqxqayors5n5SxPsuG0bdgDoqSDzrfqbfOEBu+cLXBuZXz7/zMw0+b6H0/x5C0kuw8tTHBsyZEg69vjj0wEHjieez5LR2H5t7gYAjsuXLeaeGenZp55O82fPRQfIkIld6k3cf/e998GO7s/4OQZAfTeeZ5iXklUzrHb0X+tHJpF5FQ4yrzYBKuuStI4DzvMKO2p7oTMwqX0hah36Cu4WQcA7DLZE5mDmmdgl57KxTCy8+ToPt6bN69vAf6xIDz3yIM+5T6WVrCzYuG5jGjxoSDrsiMPSxKMnBoCu0vg1PLYAKFsJoGw6uvnk9CdJuDQf/AIvJPFv6NBh6YB99qPdD0wjR42MpW4hFVkSD+8A9EzLOIesAgQfy67bSNEGfvKFK5xfg59nPsmcRpC84wlgq+ADK6ROkkeda90LDI/zU8Fz0lAlaNdpEZvg4XWA/6fTFtOnz0hzF80DPLcEu9uSutd1SyMHDUsTDhqfxu29R+o9sF+qq8Xuo6ybGwu79Pijj6Snn5oac1XLGzhsaNp1H9pwwgFpxMjhgHNZRpvOq/eAqUjBghfGhpY5LnPe2Y00yh+zRtoOxmljM2xXhG3zBvikkFYdAnDprkgCaO0XZeU8ABslkKyKiaPzJV+2eQFb8ewLz6aZs19IawEXK9v62m4BYtzvgL3TPozdA4YMBFNRmTaBpzBJ1uQHH0qzXpjJC2OAH1euTEMZA4878aQ08aij6LMDUEPHglIfomr7i2pof4qm4nvQ7AMiB83i2O5F8sHm80pkyAcYF1sA8Zgv8izbBECxib5GTwMASrl2nhgrkRG6yRWFrWHZYWUUm3rEfCleyMG+Ql5sIRoIs0318cRTkONPDNqMu9AVc4eQvfYMmTtghzwpolROUdr2/f0L4JzGRLCPyzUsXrQ4DRo8KJwWglh0QDhRru9RH04Il2wQQOH2t4BzOjd0ZLjcpw5/wR3W4Vt/GqE+LO0ag/720R3lNOLMWrhwYYCOBPkpCMv1bUtBXjr3rNdrdKgIVBsIUKQ/Bt1zc+fMjYmVThRp8Z75PKAvBGlaC1jJgdv7BDc56ZZX7x2GomVgjWTrfPGNxuXLmHRSbivpoOVRsI9OLHn0Xh1AyuL5558PWQoydL137920mSSG1Ckd7p7LmzKM8pcvf4kMBcd4rYAsZSivtpsZzgYPGgwgqDnqkSY7Wz9QswZbMu1ef/ddd6dvfPObPOT1TMcdd1wM6D687r333ihwexjTZdQrjzrjvNfNc5ZrMEf52BbyJ03W0RlktyPAOfXNTm35lmt9ylTaXC7EPqrD1zd/lZVvk8ydOxdb1SVo9HplrXNsjz32AKWPk6zkVFsOP9JuuZap3HM7+d1zDiJzKM9yrEddH4LzqQk5zJuHIxWgm1v3bjxcjAZN3rt30Opbrp6zvffB0D49/el03XXXpQcefDDtg0zPOfecaC+XOlFP7VMC55Th6173uvSGM94QbS0N6s8ADJn6pl7vyCZ/8mnfy7TJk5vG/yMf/WjaDL0vAc5xj3y6jKz32AfcQ/+Rg85H5eT2t4Bz1iFvtt9GdKOR77ZZ7z69Qz8sS52yfI15K/3LNwwKOQ+hrpr0LI559co2tE7bSPuhjisv5Wy/d/nb/fbdl4dzHya2fVM20mB51qPOyady2ZeHDWmUB/XXa7QfQ3iDUN7UN/kS5Kcj1vaRNm3AmrVrQg/tC9ob6e5s46xD3cx6mG2iuj1Ye0F/tm7vse2kzWv99FpT7WvsLFvbZV+TJvvLt775rXTHHXeEzlz2vsviXMgUO7Fo4aItwLn3/+v7t/Rn+XazrBEjRoS9sh+5Wb9tKPJ+M5ND5aFe7rrrrqGbcdEO/fkfjFo7VF/5prIEyhIoS6AsgbIEyhIoS+B/nwSc4/lc4LzV+eHWLzj87+OoTHFZAmUJvJYloK1xy8+v/nbXR/jb3/423XjjjeHPam4l0E30c8zosenNAHXOOO2MeC4OuBePevqLdXgWoIaC4yJ0wner8FwcLn7opN2ycS5vHt5693R27Ha6NMos7tPf4V06ZnFAQ3/Bj45ZqSju8q/sNpKV5E/33JNuv/329PQz0wm+V5KhrEcEHvStteDf2wUf15vOfFM6+sij4tk/wFulyg3PvLj5yyCHtb9Ym5du8TUHs9xTur+498VSXnK480XcolwtGfd9BDLuve/PaQT+iAMOOCDGCM86Trh3bsP83fOv1U0as/5lGh0D3ZV3Pq9PRD+Fx/WrPPbYYwGG07+pHPRb6kPTb6jfyrFTX4sy0Yd03333hc9t3332TWNGjyYoSZYynO1Faxk+5Z9NmneJKdwjxbHSb097oGi5IqC1VaNyrrimuNEri6s9Ht+iDn4VB6K8rcuQDvV0ycJl6UZeWP/1Tb9IqxtWRcC4Xz988viU9VfXwed4+D/77LPxQQ0OX1VU//f+FAS+yJf0lGiJr/7Jm8fz7ziJhgdQQq4Mthm+LG1xnkMRHCkVGcqbKyxdRxH6z31JXReUfsOZADu0M488NiV8lT0Aq3I6Asr98ZUdeOCEdOrrT0lDaGMzHiibNuyR11QYZAoGLN8eSAAmfkvfVnUHMwaDZMs/gh1K1/gzlMC+9uJWBC3zkaI1o3zv6yybF28J+IBZ/HwhfSV+vYfvn5weeejh8IUaTDMMpG7rl52Lr3ntmoa0z557pIsuujCdesppsaqP+ic3hZYiC75LXib3xeqkqURLqQ288EWLJLvcbeB4y/kC5uvfQouFZhF0yoWXijMgaDkB3Op8f6kcP+y/UwlA3n7779K9995DFpeN4c8VSNXEElTrATINHDAonXTc8el0xoyhBCi7EGyOF4sBNKgHETCjrMg29yJj0U5Bs8e20C7F+YC8y1duH46HkDzf6Vhxw4uHSvx5lVsuWr9rjGEcaCHgFzaVIN0z06elX//6V+l+gCpmW6uvN1tSdVoBmKAvcYHjiatccN75adigoegjeq24aTllZzsb9LXy0Lmosai1oHArYuJnIfeiZZGP/zIPW+4vvsh6lON5O0bxqzjpiSjPc9hVbMbMF2alK790ZXp0ymPoHwkeXG4MwN8Jx52Yzjv/QgLj+wOo6oofmkAo5a1dszo98siU9Jsbb0mzZs+hz5JYAn98+JQh6rgTj0lnnHl6GjduHHbJMLMZhkp2IeqHJomMvURe6bjB0JAR10egOfMYlxfATeVoxqIIilJ6ETSVLeAS0V7Kp9haGCcEU19/w8+Yu9wcvn4lX0XA/OgjjoS/c9OBhx6UeuDLl5zVxEx8MeDnP/t5mvbUk/C6lrgDQBb66BvecAarA70njRozNkAQ1RwTUBub9BXfirr94Z4bycu2tMVLLzau68WEovl0L92qjmhPX1IwJ6PM4qB3CUCZO3t2+u1Nt6Tf/f53acGihWkjdsQ2GT9hfLr00kvTxMMPSz2Zz6jM9rPrbrgh3YR9XbNqNWMj2fvIOqP81hGItzftuc+e6ZQTX5eOOeJoYr89AxBlvw4oB/TG0tcKrCA3aA5ygzVp02airdqIvAWf/omLCr5CRsWhfJn8Rsa9AAhSAQJavHB5uvYn16a7774T4MfitJmsaoJsDmGcP5dVmw6feFTqDdDI8cM40VMAdn//u9vSjGefZWwBOML42E6/bWCu0Ny0ibFxYDrxpBPScSccl0aNHAMggvPUW2lA3126XrIVhGaVLYgXGKGewo9yKLHmzblfK7MK5B3gbo5rASCkpLvKBp1m3FoKEPDaa65Nv73lt6kBHRQwIVjvsMMnpvPOuyAdeujh8IeNEZBQitPkedJmwDq+0HLNtdemP917L/rQlg4/8kjijuelCeMPSL2J3RcWozNTElvatjrseM3sKu4JKFlmunRLMZ7Lu3JSPzlRiKeQgcXGtcrBE/BNP1xCNrV7mWNff90NaQ52QxCaurgH4MdzmK+cfPJJYA8GolyOLsQZH56croOnB/58PzFmMngRJzNGb5ezK/UnTnoIcjn1lFOIMRIrpQ1aW5AbsjNznfOCghA/S1uJh4Lggjavsb2KJpSrTL7n/eH5YvOzOK89pjUlJPpUB6DcVemWm29Lt/7m1jRr7gvwVyw7P3bMuPTGM96Uzj//fOLAvYuxAL5dAnY1/e/P991P4ou702xkIg31xLbVlxrmq6MBJBkjPmC//eKZw8xXxeaVbKF7Jf5KhHuosKJFH/Q1kEJHtZWQy+V2S5fFzZslRHvmAy+eClEV8+w16frrr0s38wy4YOFixgOgUfTD0YCKzr/govR65mMjR4xkvKsI8N80gMe/u+022np26LgNJKC/HaDncubpxhh7gpN445lvTGfyXLXLmF2YqxcAIit1nLSvxKff8/ZiI4S88nyo4KFonyAajrx0y+X5C9IJZYmTAtAAatEWq1avSrfdenu67qc3EDddTNWUjDCHDB2ULrhQ/l4fzxTV0LiJGPSSpUvSH//wh3TPH/8IIHQBGT2rYp7TSn8W/Kj0jzv+uHT66aenPXbfHd5oCYiUFfXGF14qAVEHgaXmDBZllevixZWgzn7GwbAruUd5UUlX4SOaUvtiNjC583rOx0hIo6NOwfIqMjg//PAj2P/r0rTpU6NfbuIlFTPHnf6G09JFb31LGjFqRNh3b1lBtsc///nP6dabf5tmzpkZceC6OuL4ZKlthMehxMZPPunkdNrppwUYK+wcnJP2hT1D+4qXByiu2CTQTX6wNbafPT6Wu+aHwEsz6znXLJ7WnTsU/MicrBVzhJJclB3HzN48bdrTMa49NXV6gFa7C6YmU2cjGWcbGReGgSk47YzTmaOcSLsyN6MjzJ+7IN3IXO4PjC0buE5sjd167fqGVM+YcuLJJ9MHTyaTLsBO5zq0c8iVeh0zwhwHebZNthUy6a4WlOjceiyM9lRKJd7iyoLTIMDzEgLDtmRzU3N68IEHCx/F9KfBtjDv1FagULbFesY2AYyvP+V16NxpwceKlSvSvTzr/hQ8ylywLWZE9Hl51OjR6dzzzktnYXeHE/9XKa3K7aX9iAOl40FmXMEBSWYrtNGfHLBhsh57nra1jR1JipGSsr211F5RRvxQA7yhNJb4NW+57vzbT85bryUXGu4B9i3Hi7MhNWVuGZyL3ft3cPsL4JwOiCcefyKW9Lv77rvTUUcfnXozkdRhpHFzIiJY66STTkrHHHtMAC2s+28B57x+2tRp6f777weINCcm4zr/bawTTjgeJPyR4cwIg7iNTGi4ddzNeGZGuv13vwvaGklTKKhPB8n5F5zPm4PjALCsTfcz0JpRTaXRaXLsscfGLvDFJQt9I+HQQw9Np2AIBcD8+MfXpN9Rph1GwJWOFYE9KpiAwfHjx2PYz0y77bZbTKClRQeNE+wHH5yM021DgHEy0EinzURQ1AJ/mlB2r/v6174eSNZxu4wLkIqgHGWrQ+eYY44J43oAk5y8CZgR5XsfRmvOnNlbZCjw6nhkeDRtJG9/wHA7yX/m6afTEcjV5T+XUq68miVQx9A73/XONGbMmLjedjHz1LRp04IXZTdi5IgA23zqU5+kA25Mf/zTnxjMC8fS97//fTrWcJSvIybjAsjuuoPJK8CixerG+vVx/h3veMcWgJ48bC9wTgMxb95cjPT96U9/uicGFydBG3kjwcF2AOBFAYc6wsya5UOZBv1nPBCZeUxQmln9ZjNIj0bPPvLRjwQ9go+inTA4AhUFTQVQinuKdpoY96rr6s01P7kmgEICt4444oh06qmnhiyvZQKnTtgZx4wdky655JK0H5MZHa3uixcvTsOHDU//9u//ln79q1/zUP3rOCa4avc9eNsHoNPxxx+fLmCSLw0C52wjAYfqoPqwkt+i1Q866KB0xhlnpMN403BHNvuzoLmHJk/m7dSH0moedH249LjArrvRmR719S8BznlOkNUjjz4S/db+qv5344FK/T/jjWdEP7PP/jXgnHza72fyttNdd95FnWvSIsColrMr/eZd6KBttI43Y+688870KwYqZa7+aw9OPeXUAHl+5StfSWZJ22WXXaJPOPFYyeD905/+NIBk8qXcvO+KK66IgWJ7ZKTs7XfX/fS6qF+goHQolyv++4oAvDmp//3vf59++atfcbxHtJuOWvuMMvIh+iQm+S6LKqjsYRxgM3hT3W0Ub4Ycc/QxMZHwwU5FzvZi6tSpMfBt6GQv7MNZD7UXAtSi7zPpdEncZTxQKUN1VhoOBN1/+OGHB4jNh5GHHn44XX755WnWzFkxsRbc1r2+e+iO9lr7mDPOnczg34OHZ+Vnv1AWe+29V2SGPIwHa3XUfr4Kmdz621u3vLm9CN02w997L3tvyGJ75P3Sax29yltZAmUJlCVQlkBZAmUJlCVQlkBZAmUJlCVQlsBrVQI+e+qf8TnU7OYP88zpC1s+pwpY6oav5Mw3Gvw4M14M3NqvF85fmNv6+M7kN9OQ65SWzsf00z322KNpypQp4TvxBTX9gYK15uJwngZQwuxuvgj55je/OR004aDUsxcBZcp5NTd9g/fdB3AO5/cEMnDV49dxyy/B+f3VplEatmezXfKu78RN0FvePKcvRL+NACt1UH+GL9PuToDKXV+TslAO+raUgb4VN/VW37T+KjPWjMZfGL6PXMFr9FO+XS3kBlYQuOWWm4Pfo8gYkP3Skq0/yZc2x+LvNajSWQ9eWbZydGL7asnt7F22kT6tWfhwf/GLX8SqHb0BIQmEHER7GjS33eXJ9t2HjB69AixhqEfXVaE3lrPz+I6q/+KPOuemr9At/1ZKguN8sV//qLqYdVv7OmvWLPzffwpAnfxdcOEF6eijjsbGbt8LwlHpDv3Jkaods2uOFfpojafoF89A1v323S/s5apVKwG4PphewE88hn73tre9Hf/yAQFSNl4RWkT70ZhB/attu7JO+aktsq301d8GIOA3v/lNxCO0u2Pw49vWJkhwjDTLoz78E8nqYYylCt7kJe871DQvw02Zn0yHv41d3PybmwFD8DI7MZdZs2eFP/ngQw5OFwIYOPjgg8KWyn/hm56WbgK8+wzL8LmMsD5pQSzziQ1NeXxKyOlEAtPGO0aRYCCypXCvWWy2rT1DC/4mt4ZFjcPkTR4stzNvfm+nDwqY199vXOnJJ58In7d9bzaZ1g44YP900cUXk7XqkPCdW57Z6TznmKJc9JU/B/jKsf+oo49Kl132PmKEu8dYkuvNssz07MzPzLPxuMeZuxjfM94zEzuyBPqdF7wFMMRRRx4VMQ7tojb0jjvviDhODXEUAUmOnW3ECswo5ByogSXdxh8wPl323ssivre1PbVet62Pvxy857Jzm1qmL/H/6Z4/RQxGkMBc4somuXCsv/DCCyNW51jhZlxD2zp16lMB/DBmaDxPvm174zALFy0gXjciklYcMfGISEjgvdumn165bVtun6wj+bd357qkyziUYH5jldpQk3iYRMLYYObPZCz2QXVfW+lmecY8jdubEOXZ554NG2qffMtb3hLAQmNbr0Q7BQHb8Eca5dH5mvbRuK3zNeP5zq3VPZ8dBCaZ8AHBRKnGL437+bzhPMe4l9dannM5Y4DGzIy52cYeVzaRHRA9z/LdBhK36xLrcbN8v+fdeKm0Pv74E9iNlfFihqBdaRNUdumlkwA/9g78gQk5nH+qiz/+8Y9DHmPGjAl9NnGK4EjnQt5rTE89yPPZbSX271vRbS2luE4e8wsn2gfbUn6nE6e0HQQF2n7iE5SL7S047ln4t50dG02SUlVVHXMbdd1y5MtV104CfDWEhDjqaZZnlq+f7i/3lp8tcj0mKpKmewB3CpYW4O98TL7PAwT/RmLg0qvuOS48TnbPGwAgz+IZRB089JBDI+mU1xuPvfOuO0NfnQfIo/03P4s4j5DXl5uvsA+dZNVZlsrPNnuW8Uw91UbKs+O4RsNYuyuWyYv0SdtDDz0U8WOzk4oTcS7uc7E2yjFSUORgsEFve9vbAregnS14KvoIveTlbra/WV7oI5iZJ7Gh69A5k2Apc3VPf4V9bTox/F1325UXi86JpFGCP2+77VbwGjdGe0/Abu5BBkw3M5c/zzxVrMRpp51GgqmjYrW2LBtl6/Zyt6FlWnbWT+n3t88Hjg9itQR5irkw8Y0raop1ct7p87B98AL6o/Ma9U1Q9SOsgOhc3AzXU7jOZFjq5bnnnpu0O3l8yDy9UnxZ7v/W7a8C50xd73J+f8YJY9YsFeUgJswaQIEht956axhxj1/6tktD8H8NOGdj33XXXfHgpLJedNFFMWkRbPPII4+E0RFc5GDgoLCt2xomFvdD449/9ONQfJXCCcETGK9f/fJX0Wnf8c53hCPEyZEPnz/8wQ+jg7/5rDeHYRfM9bnPfu4l/Al8ufLKKwHz3AVIqGsBYnvD6TFIOQDeBcjH8gQPSbeKKl+Cg35946/DaaECOoAL8HGJzusxpiI+Pe5EQEP8+cs/Hw8dgttOPvmkeODQYN10002R6es0AFqf+OQnwmApwz+CYvbBU0eJE18dO48+8mgYPDvqp//j0yFDM5sJHhPUJQjG5Xz69O4Tb9k4QXFSpVET1S1Iywnavffcmz7zmc/Y49OxgApPPfWU4FceBev8/Oc/B3A1OQA4fh89enR0Yg3PVd++ioeRdhylZ/K2Y9+Q868AGL397W8Pfp2ou20vcM7B5oc//CGo3z+ksbuMDb3RYAhCu4M28EHUyZV1Tpw4Mej50dU/CoCT13mPD6gOamPHjOVNovPCYP6Ot97U3d12342B743hVHLSpk7f8LMbQl5vIP22xsNB8tvf+nZMYAV7nXfeudEPHCR+8IMfxNsg9oeTQSB/+CMfjjbRAfCTn/wkyrvsssviYVGw6Pe/9/1oP43Y297+thhwHZBsAx8+Bc7N5UHzAFK5ngFdw4cPA/z4TNDqoHw4PF5xxRe3DGDb2k80fD4oqv+C/U44/oQ08YiJ8WBg2/vA71stTsI6Z5xTxwVYCQ7bk4FDGTuxE4A6b+686Bfy7CTprwHnnNDqjLz+uuujbS5+y8VhjG+/7Xb67f1JYKUgxN4s5TL96enRrtdff32gvt/z7neHfipbdfZrX/saoNZTkP95vClUBRr8xpigCN7TiWQ7qZdeN3rM6G0VTVy3cMHCADXK64W8zSlozYmEdH79G18Ph5yTwV/8/Be8XXE9Ge26AkI7PICw6pdy+um1P42HM/u8jlflpd7Z97VHOt3tyz7UOcg6WN3Fg412Sifn6dgXJ2DakccefSx4czlf7cVw6nBC9rWvfi1AgSeccEIANLVTZjKUpl3G7RLOe8G/Tnw+97nPpT/98U+8ddMnvflNb4rrzZInfWYQzcC5A+jfp512atgNJ03xoAXvxx53bNhHZeFAa5/zHhH5l1zy1ljTXv3XDvn2y45vO28SteM0lu8sS6AsgbIEyhIoS6AsgbIEyhIoS6AsgbIE/nkksLXz1GdCd4/rQ/C5NQcnr7766ggs6ENx1/+TnbBZYrm8V8LBnOvYlk/pcJcO986/9bkJiNC57LO8Dve8SoP86sPTV+emH0pfkv6AV5unOXPmhM9EWvSJ6teRF/ccmMn8bouMXu1rcpvkdtInJP22iZt86Y+zrfRV6X/TTyVYzBcZ3XNwVb+g925dhj4TfVX6VFxlQx9dbutXm/+/V78y0XdpMFnfsL5EA3IGszKv+pvcBdD5+Vrf5Mk2dbOtDLLeS9YcX9iWB1/+1EdtwFl+vN721A+prntN1m/Pueffrybv2ks322VrHj1uEEwbmulVBgaj9bv70rW66Yvb2hr9iPL5v2GTL/3PBpRtR0EAAo31Ldpejh/aUTOXytP73//+eHHac7avbZfl5fetx5KdLYOsm53rNVZm3EVfr7rpuOeL3tKqf9hz+rG1LZMmTYokDfJqWerDq8mTss06p3zdjEfMBcBixjh3mjVEzgAAQABJREFUYwe+OK5vW0CEL/Iba/M++ROko3/YPmgsxD6qz9ukCsZa7mGsNAFFbndjV/Ke27ezLF/O75m3XGaeszheuMqNgXXHctvPl/dNviC4SN3M4CL11/FBPTUm5X3yqy5rZ99NrML4aG7PV9vWyLObtMqb9lPbYRvKp0AGgVcm2nBczP3LuKAxOo8JhLAtbSPBgrah8UfnQJ/85CcjnmCsNutLlvPO4D3XZbtIm/zZruqY7WJCF+NExqtsw7wpD9vQa22rPH9w/JQ371VO9l1j2qOJcbq93Dzl9tladrkuP71G/ozJqKP+FrRh7M0+Y7INxwLbKd+X6VQegltuueWW4Ff+7M/GmdRtk29oW/N98WUn/5Efdct+aN+yT0mjcTzbQZ5tg1PIHKcdcfN68QLOdQSEOM8xc5fxd3m3PbWj2tP83eO5rnzulWBV2txyG1inu/w59gkctz2dozqWy7MgYjM/Oo9x/iLN9kHbzcy6jh+O9Y4Z6rF8WY/XOjd1V49zna8EX3+vzMyf+mV82nFC/oz9O28Rd2BsMAPnpDO3d+6DHpOvJ8kGmTEGPrPYttpgbZVbZ/n62/teiS23W5apspY/7Yw02z6Ogz5jmMHZ9jFuazvYziYtUj+1NYIGjd/6QofzOrEd32RVP8sSRP6e97wnnhc9Z315LHy5eVN2uUw/5bGzPG0T28vd5yZtjnMxxwJfANDW+FwhfdpH9fOXv/xltKVjvUmetEOWbf/05QHnDo712tLR2FHb+NXYpDeP89Kg3XPuIa3aGOn9MSBV29m2MvmRdugb3/gGCbAejLi4QDLH96ynzhO0PwJ31XHnNY6Vbq/0PK5zu1lfti+Oa26Oz/YZ6dDmOB913u13+6Jtoj21neXfXRCdfdZxw3mb/NremZesM+qN393LWyGBvwDOKSQHakEvgoAcoMzgpjFToAvmL0hf/epXA9CxL0plNi8V6K8B59bSIb/0pSsDfOTyg6Y4rK2pjbdYTN+t4fiXf/mXyBomYnJbN0EpTtYFuB1zzNFkl9s1BhMdRxqwHgw2//apTwUIRAVzEv/d73w3TWZSsRsgPbNaNbL2u4O1Rk7QSX8G4XUMapZ79Q+vjkHZBwVBZiqkRu+Xv/hlgI0cCD//hc8HgM4O6MORb3d87OMfi0FRgJwdVzToBz7wgVBqAYZOBjSiH/nwR6KTapgE7DiArmKi+/kvfCHAM77d8wW++6Cp4n/1K1+NCZ7XSa9pl+VV8Jr1CESyczhpuuaaawJU86Y3vwmk6QV0lgEBytIg/gDw4Ou57l3vfle0pwOe5Vx80cWBWD0XcNi5DFzVDAbybFs7GAgKrKjowsD/qzQaY6hMH5/yePr2t79NetxDAgRlp7zt1tvSv//7v4cMBI7tt/9+0aTbC5yzvb585ZeDv4suvigMuAOUbSMAbMniJemzn/1sPOyYjlIZCAK7/L8uB0zXD0P4hsj0VUsaUfVKY+DDqzrtWw4f/8TH07FkS+zTp2+0g8C5D33oQ+FwMyOfRt8B8L5770uXf/7yKMO20rhqPDQ2AvXky+VtzU5mFq6pOO9uA3Tl2z2f+rdPbWk/B00BjU5cP/qxj8aA68OHuxMpgXM+6Kgf9geX1NVp+81vfDPdw0OaS29857vfifK2ZyBST03nKQjMt3Q+/OEPB5LcQcR6nagqZ3nqDJxzgmfmQmmayCR9NCA/U0g/+ODkQNSP23VcTAR80FJ26tzkhyanL6Kzh/EmiAOUDySWY99Snh77yTU/SVd95zvpRABgH/rwhwLNbipqMyl+8pOfIiVsRfpX+suFvFnZTNrfPwIAU6fOfNOZod/LmGRfddV30m94iDidybU0S4NvBliH/WN7NidB2gUnBKYqPZO+7kAoWM7ylJMysO8IdnUS64TXDHP2Tdvoi1+8gj77aDgWfGibcNCEWOpVNPjVP7o6Cc6zf7pErxNeQW3Z0aK9EAxnWeqwk8gPfOCD6FtRj3bVSeVveAPwne98R3oTQDgn1fY/de9f3/+vMXF9/SmvTx9EbmZb/MLnvxD8GLRQB71enpycm73PPiQoWhDzWWefFY44J072DzP8Cej0IcvJoRPGb37zW9i9X8QSCk74BDnr0BNYq7Nhx7fyILzjsivfWZZAWQJlCZQlUJZAWQJlCZQlUJZAWQJlCbz8EsgBBUvOzlOPuenMzef1a33xi18Mv4Ivxbnnlxfj4tKffH0uq/O5nfm9syNaWqTLY35303eiU12/U75W34vfdax/97vfjUCDAQTfzB/Dy5b53p3JR+e6fGHW7A3KXYCDPjx9BdKs79YASHaKd77vtfpdum0Xafa7/ogcZJJm20ieBQXkVTP0N8m74Dn9Hp3bxLLc8jF/6wcTCGFZrhphMMz7tsfPFoXu5D/Srv9J35v+K+k2IKtPRl+bviuDKRn0uZPJ2+Hq5Mt+p/z11epr1Wclb8Yi9PGp0/qz9MfJn8eyXqgrluFuO+e23mGC/oc3/jVaPKY+u+X+6DE3P+VfvdanLbDMAKyBL32F8pvviRtew3/sr8ZNfMFX/+JoYgcGnAUdaZsM2MmfAVfb74Mf/GCAQux/bplP29vNNn812zPTIa25XfVxy58+VP2mgj4MJkurtsX4iefV53e9611b/MpZhzOPweCr9CfrnrJVL9U/6TL2ZKDc/qcv2hiIbZdjdTnwKtjD44KyzASl/TFAa3xB0Ku/9V0bo1J/3Sz/lWzL3I+Uu3Xl9sq8+inwT2CYAWb93pMANhrnlL9Mm9flsUdwiPKwXxpQ1x8uoPDV1sut1UZ6M92CAWwfaXYuY0zTmJX9L7eD/dRdOXUe9wyu33HHHSEf4xPGj4xhZflYh7v37Ywt66Z1+d161VXbxH4okMoYTOdsZLajNGbd7nyvwBgTu6gD8uoLEPZfAXhen3XnleIty8+63POW+fSYMVrjLY7zzlHsYwI35LWz3L3H/mh2TzNIOQ8Q1GSM2/ngW9/61mg7Y1BunevL9e6sz8x3/lT3jOnbD+2Txt5sB+Po9i23LAPj7YI8BA867mtb1EdjaOq0Zdpu8qeNzb/z/ODl5lG5W4dtYZ1+z1uu39/GFI2XO2c7FoC17eE4Ie/yeA/gTxNSOOcxxi+PHrd848TazTyn9Zhblk382Il/5NF+l3VIPsWMXHXVVYG1EFQkD9rUfE1n2Xi/u3wYJ7bd1V37n/Mcn6Uyb5lX789lvVKsWpd05bb0t+0jf2ZUcxwXkCTAKAPnnKsI0JIPz9tHffYVjGW/816fTUzsYszWeawxZOOzmbfOdb6cvFluZ7lbtseyrZdPf7uLxTFD2Y9+9KOwGT4LGSMWO2EZ0m4sWntp+zie+5yVxwL7rfc6lzM2LFbCsSLP5V5Ovv5RWZlHr8syllf51jZ4Xn398pe/HPgX59WO5c53PguuxDmrz/WOJaOZs3qvOBlfUBJ/Ygxc2WiDtD+ed3+l9VN+cnv5me2LfSXXnY/bHuK0xCEISLU9nMN5rdc4P/Ocfgz1V710bmDbel655Xmu9Vp+rsPf/+zbXwDnFMgCJoi3ABb54he+CGDjnYBKzg2ARRbWtWRZuuaaH6e62rp02fsuC8DP1sA5O5UG4z//z3+ylGfRMII43DQuPmi4ZOSBE4rlBn3Q39ZNAJudVKDbuQBSzFyVmHcIdlq2dFmAfAR+ZLSoRkGAz1XfuSqWV3TA7T+gfwzOgugyst37fcATJKMjwqx1omrzJpBKMOEDOM4+/OEPxYO8fF/DMbOgXX/D9WEQnaC6CQB7//veH4PhBUx43gNgbQMT2Xe9690xUbvssvfGAJrL/9L//VK68aYb06iRo9J//td/pnHIy7XBpcdJ+wmglceNe1GGdnQHsAkHTgCwdFh0bjPr6dj7yEc+Eu3Su0/vLQ8xH/jXD4RREyTmMrvKRZSxwC35fSsZpRy4Om8OanauDes3xJKaGhLr9MFeWYlMHsKDfS2DuxniPvaxj0XWrU9+6pNhOC1Lo3vuOedGx/PhZNKlkzpX8Rffn53xbPryV74cKU7Vr9NPP41Jf208iCl/9cpJiG+UqjfqkyBFH+z22HOP9N73vjcmX50LduASOCco8Gdkl3MSkgdnJyvKJmfkMyucExV19L3veW/wejIPfR8D9OYkxknsH/7wxzQXY+rg+K1vfyuu9+FEcJKDjaAuN50DAuBcUsFsfraLdectA+fUPel/93venU/FUsI+tJqq9+tf/1pMhjPNWy76O198MDBDm4CowyceHg8+DohutqGgLt+a8uGqM3Du0//+6fTg5AejXwjmywZTdLM616tnr1jy10HjrwHnbBMnQraT+uGk1hSi6tJXMOY+bF5++X/FmzAaaQFs//Ef/5FMAav+XTLpkpgEq8sC41xW1L6sgdeRJgDPSbU6r6PUh4Ts8Pw74viLU+qwmd8cDG1Hy3OQ0U45scmyFowroM06P8fAat9x82FLUKSpXQ+Djg988APBk+dW89Dy+c9/ISZUrvn+iU98InRVoN6PS0j3666/LgbmbC+kR3thWu7zyZIoIPj7TKbVM8GPvq2gzPPmG5o6yg/Y/4D0X8hTGVz5pSvjQdklgW13bV3e7FcZOPeZz34m7Jd8awd8a8LJgEsgC+YVwOlEI8unAV4PAdDrQ5vy9m2SnrQNypGL387PHb1vO6spX16WQFkCZQmUJVCWQFkCZQmUJVCWQFkCZQmUJbDNEsiOVG/QQezv7IT2t9991r/iiisCLGCA3MCXz6M+Q2f/gfd7r1vnY3FgJ//JDnXpcM90Zdo8n/n0e3aM62syQ5kvn+kPEcxjoMjn4c5B553MTlSnj9DsMj7T6xfRD5P5yvLOv+Xnf8umfhkkkObsKzGgrH/VLBAGCQzSGajxU0BVvi63beZbnrMs/K7fzaClgQT9iaPxb+bgsudfq5v86N/Tp2ZASz+iQSz9kwY87Hv6tgUR6K/x2Gt927qN9LsZTPaFcH1f9jGXO9MPqB9sDEEe/XX6pNT53Ef9tCz7rW2d91eDf+nIu/VLS6bP355Tv9087v7/2LsPcE2KMm/4LUkQEJScz6gIBgRXRQUDgkpwxVdRVEyIq5+7Ct+6+plWEfU1fOvKGjArRkyrgMi6rqCiYEDFrKu8IkNQBARBcnLf+tWZ/1A8npk5MzwnzFB1Xf10P93VVXequ6rr/nc1HyOoZa7OHLtYirlhoJ52/q/eNI9/yJ9dAlABdAhI8ksCjoAf/JVArKAkGxW4M8+Lx1ZO5EEubNh+LhNdpT/g781HiwfwI77mAzBgzhX95spdB54zV2x+VTCWrSbNpW2GBvywQ7JFj2MJyAMgCTBJ0BVgYOfyVRz8ycNG8WclFoFmMTd65HvNKQMZmB/Hu/EAYAS/pI7I0PE4U2jPvpWvc9nwyu7EbPDHtwA0iFfoPyIHe7ES9C4sq+ngV58jjiLOZDUviS3MtW22PIdP8Sc0a3/0QoeAc3jEE7rDK9v2P/5I26Vb4Fb9L3ABXxT7dj/ZzFS7DA/oa5PzOQfQpw8EDuRT+BD9nr5QQmOSvPp5ezEvXxfiX8WfxHzYr8+aAmApf6b0GT2hK3yExuzDO/15MQAoh17wZ0U9sajQJy9+ADu8BIBnfsjLLMaq4tt8j7FAAJNLqjf1j3sfftRrQyN+8MB3As4Zx/A5gHMB2Xp+YJP0JPZrT7figuwOPxNlzLZHAaPhT9xWuepwnxQ5jZsn5cW+ppJnrmkjFrkRswZmRCsAJHsNjfws4Jnkq1V4Ew/kO/HLLukUaAfPeJqqzlrALPxEn6ri6y1ekpeJPA/x93zqKI25j0zwxvfyMRKZwFvQYfjL/fbulXKu/hnjD33xc/FnqVPbMgZFqzbFTwDO+YoX+8SHcSkdAmQ5b7wqzm0MwLb5UP2+e4Ei6XQq4NMY2VlclPaAN3xJrRzx6DpaAOd86c9eP+hFADp03TMWH3RKeVYCks9YzbOSsj2LGAdZFAafXhLQhudqvBoe2ZljfMYPsFeL1ACWGZPSh77fc4UvttEpPWmnfKf7YFjE2r0IoO+kfwBJfjh1xS6zX6yAGTpIvfZ0hE6bZ2OYFvF/fZz+HnDOWMV1+dml1fPeWxYy4nON3+gU7sF1fil5/Xc8W3zNkLjGWuyUwLnzi8P+YkHhA2xZLQlwzsA5Cer7LW95SwGHXFpBLtDTUwHnDGbe9773F+PbpC7zu/32d0sRi/aTndsaOrhi2NNJlPjesuoUsNo9Ck1HvPaICkQavX3NAgCh7CSN+x3veEcFEmnkJkneeuRbayNP58XgDDSsXLblVltW3vcojSeJQwG+4hx8qtBgBIjQf+Cx479wfHWIMTCN8rVHvLY2OJ2eld4MXv/++X9fAVX/MAKcO+YTx1TnY2B6+GsOr0u1QvC+//0fKGClOw7//Kp/Lsa/fchZtJ+UIR5MXgIbGWi1wDkZgek4wh132LF+WhTKlgMJcM6EE+Cch5s2Gcyo38pgn1u04lxpWcONpaFee821w58um/y++Q3X3zCcXuzCp26t6udTtgZ40vIC59DEqQHDWTkP3SaETjvtW3Vw9YsCJgSC9JDGcccRPvnAJ1fwE5uFoE2i+8Nfffjw75/79wpKPOGLJxTQZ3mrbZHReCB63WtfV+VmdTOfms0DEVvQFtT10gIK3Gzzzeqb1b5L78H/vNKhvP71rxt2uvdOwyeO+UTplYb6aVgPiFKAc58ucty/DAitQLjpZpNLELu+NOCc5Zk/WkBiyvjXf31L7YiXB8Htc69s87NlxTAdw0EHPXWYKJNNknakUzHIsRpdgHNk9eyDnz35NnXp+A477NCIqd7nx+qDmZic6lOtBgXKvubqaxbbx0033lQfOIHUyPbN//+bF4NxrermrVkP2QsmFtQBFADkEaXtPKeAGE2OeTvDANsbNNqxFStXW321KhNtK8vGpi0vJnYpBwZEAH7vfMc764AHT/fe6d61w1cePpTnbYJ/etE/VQCgdmmiTrqqPIQdUwYTVh8EAPbGpIGtRL5HvOaI4bjSfvcsHdfrio2YyLYi3MeKDHTIxx9/3OKlbt0DvBZ/YVU7b5p+9CMfHW4qOjn66A/VB8L4FvlfV0DJJxaAM1vT3gyurSDI7xp8A9vlQUn+JQHnXKMvn8S9vgyggPY8JONB2zAgMiF/xRVX1slKK4fqjBcUW8pgTBnLl6bn75evzJ67S6BLoEugS6BLoEugS6BLoEugS6BLoEvg1kjAc6B5AZs5tUyoKtOx81ZU8JUEcxVetpvPwDn82EK/ff63x56127z4FEAQhPaM7Xne/IkgiZch2/lG5cx2CnDOfACQDfrwYEN7O7k+27StSH2h29yEZK7BOUAawW5BHDybuwTIEfwW1JCv1Zt7JOfaY/oKcM48HNCOl17NNc73hBfzRVYEAj7Chzky81XmtwVoBSCBlAREEuRbGfjCm2T+U6AKQEcwThCPbuhNsBIIwjyawBef4wVd+s2WcqLzueBdu1N/aEFD6HPcns81L2ybcwR4oWP+xbw8+zbfPZf8oHF5krl5AFeAB3aKN3OS5lrpkE8yH4zHzPMKXJKLLbKzn2v/im/00ilatDXAq4+UF6H1C9qZgKoVq1wTiAX2MAevfQpeAgfwV5k3nUtdRsb4Qkdoidz5kRY4h7+sFsgnW4VHfEq8SX+vL5yYmKiBZ/wCEfj0mfiPIHquqyd9UepEwzgS2pNSNn05H/sJ3+wS2CjAOXFM/QhQjtSWhV4LK5BHPl2bFefmgy5H6cU7+gPeRDPf6VOIiW24J/nkpdP0sYAU5AP0CTQioG7BDfGNxKByD1uPbJU5rqR8W2hUbnSac+wMwJivBGJgo+KOdIgm90v27NnYzepl+nsb21hQ4hj6EGMm/Qn/k3rqzWP+CU2jdYye918fF+AcnYQ/oCvX0U8GbNOYlM6s+CRGZXwEfE4uAc7FtkfrHjOLf1UcWkf5Qzs6xOEAr7Qt/R1fwl/oz9O2+BpjHTFu9qYPIQ+gDzaubxQHBAAx7lGuTZ05/iuibsWJUX5SV1sf/iR9IOAcYJyXbcTOAOHphY2yQ+BPL+N4fhL3Y4eJo/Kf8hmfAhUCLxvnzkSbWx6RhD/jcSAcYzWYB31ggHPKSz4yCs3Aq2K5FkDy7CheqW+Ey+Bf3BNfFLtRVitf/8eVlKvOqWwFf3wgLIQ+DX82/Th+4EYCRBInBVqSgMZcY98wHWxzj4InMY7Fozpnip9WLqNjlvA6msczFRuEcwGQ1h9OlP5c0s7wr780vgEsA1Rlh+TjHuMc4wG+CajcqnTxN7WQWfrBn0SXdOp/niGd1w8YX+vb9IkW6+E76PZf/uVfFvcj+PNMRccwCvwwG1euF+aMybVVSR3kzDdNZUM10638CV/2NvWgrT2vCrri9/kU/Ovz+VM+Je3PODxtljyMv/UtE0XfyaP8lO24p5slsEzg3CEFuPKU8pbMPQp6Nomje8Mb3lAHxM985rOGg8sKUaPAOYhVRnZMAZptWwZdPhvom923NmkAkJQf+fBHKioWAMekwLISo/bgpnH/4Ps/qI3bvbuUTmi9RQPlFji3eXEOf/d3z6nLo6dskzSM8UMfOrp8nmHyE6IemnzWcsFdFtQBuM4uRmYQAHz41dJIH1UM02dAPfguCTj36VLOh48+unaYAc5BAAPUmZh71atfVTvP0DO61zkvCTgH4MQRbl+Ad5Zcni5wDuiKM73sTzcD58jSQ+/Zvz27DmQg48nOw6M3Hu55r3vWT7auKHAOX/g2ccJxvfSlL60DJZ3SKV8/pXjEoT6QZWJwWcA5NvPyl7+iApV22GGHOsjmMKMng02AJgNNAxM2ryOXoIx9KlfHuF8Z0N2rtIP//PJ/VmCejp9uHl46w8cWR8qxbrElu/m7eq+fWwOcs4T0RwpwipNbEeCcJZs9uJ900snloecfFy8fu5i4cvC4x/2vujoa4Nxhhx1WOxgrEF5YnOnjy+DA50Qjp/a+HE8FnNNZGiCe9Zuz6qDo/AKEvPHGG4Yzf31mRXrzAy1wzgCY8z74WQdXXiG52ScfA/xFZ2jgxOmSvnTiVjhkc9rU297+tsWTxaFtWXvl3XCDiZ1zaydK/xdc8IeyguUGFZznU8MGP0sEzpUB0ac+WfzB0R+qoLUWOKfuCpwrbxoYKAPOKQtAE3J/u4ntKqCRfUe+Jlre+MbiL8pqho8oNuWTvR//2MeHde6wTm2DOvJ0asq3IqjP1uq8X/GKl1f+VxQ4p00/Zr/H1EEQoLRJAcl5fuwr//WVuhrmr4sOAZ2truftNfSvWOod8YrJrd/VJdAl0CXQJdAl0CXQJdAl0CXQJdAlMDMS8IycydPUkOfV/Pf87k18K84FOGceRbDVRHLuzzO8+0bLSFkzvQ8/qb+lKXWbbJbyrC0PHgUIzEFZPUCQwQpnnoHNZ8wHQIv5SXMYgHMCH5kfQz+ePMtnYj+8zud9dIN2uvAfDwJTgDhsThDqYQ97WH2x25xe+Iue8ee+lBV+XVemoDOZCVALTAqGzQddhs4l7dGvrbFDKydJ5p4lwUurXFhVwTlflbAKj4DWfE/4om/6Mvcs2Gw1D7Sb/9qjzIsJqHuhkw2Yg7Wyh/kqAWWArLb9KmcuE36k2J//oSl8xs/Ix77pE4hAvALfAnt5MTjB5JThnvmawqv4gEAenrJaIHAKwIAE6CHICrSU+EnswHVtmlwkfLfyqidn8YfOwhc6ADoEYNkiUJmAutXI0CweI16Ed/yKvwhOAucGfDSXvOAjvLT2hEd0mZMHSNIOzcEDNng52zy2vhAASaCcnxFEBozT97BRfpWf9pK7fgkwSQB3YmJicVtQZ1vvONQ4FU+xHTqRkkc7C3AO3RZoELOKH5UviUz4WjYMfCU+ZFWdAD0ix+Sfq314U79jfQRAVQuc01+27UxeMsIj3TnWTsXy6F/fKB7j62P8Uctrjsetx9Cf8tEmpR5759gZ4CO96BeMx4zLjH3w4n6bvABIYmrGcGJqZKMci8Jot+5TRuykVjgDP+GpLdq5JDTFZrUzvoWPMQYFuAKKy6qPiZ0BlVkcxXltEcBRu7WaF8DE05/+9MVyUU/kmDpnYx89qNsWOQAWAXHQo0VT8uJNXkaRz5gGOIdP4ksB5+hemyQfYBFt8eDyRTP7jOHCZ/bj4hNNNillj/53zTnx6YBU2J0xDF8KOCeJr9GvBSq0O+MZefhavOpjALfIiZ0CpbV9SC1kln/CP97ZqD4ATkA81mdKAYs8/7VJ3vR35GCRIgupiBUD9gBjGc/Lo73au6ety7FztnEm5Uopt/2vjWlbsCOwL/o545UA5+iXbiwopW/Q7zsXGzXOATbT/+kHxWszjlNPy+tM8KTM8EOmOVavhGdxdf11gHNsT3+4oICK2SAd851i11k1UTv1Qot2CCfhuZgd8KG+hsYXzxVwLjLFH375U3s4laxuSU+eKYCK8cke3/Wud1We+FkvctAhXekr2IDnEfagDeIvXw9UHxnqO2ai/0B7NjypTz2xV3vX4XLYqHbluYkvNO70bBubs5fPisD0zafCKOAHADk2EhmmDvX2NCmBZQLngCie8fRnDLvcd5fFMvviF08c3l8+37n6GqsPhx162LDnXnsOxx17XF2qU0N5yf/3kmp0nMi73/XuujrZM0sjBM7RCCmCITM0ygnCmIOxitM6ZWBuADCVATKOoz909PC+ovC1SlkAM/e+970KcnTy2+3Kg2ZW5prFQEpltR6D96MLKE3HZKU0jeZ+ZXLpuc/9u9pBmXRpgXN3uvOdhmcf/Ozhsfs/djHfZ/zgjNrBnVSWHn/Zy15aEcQAPD4d6XOanyqfauX4PbyjE8CHfDzcM14rzukknl8+1XrZ5ZcNL3jBC+pbSKkACOcjBYh3xzLZcfjhr67GbsBrhT3OSRkHPvlJRYZrTSnDnxWePlRko4EDx+27374Vfa98DzlAjtuXldNe/JIX14EwB/D73/2+dgQ6OSvO6RDahL8A5z5/7OeHifLggweOEkjPoOxvH/u3lVYO6bUFGKSO0RXnnvQkn2od6sDG6mdLS3RIP184/gt1mfddH7hrHQj95aa/1E/scnbeDuEAJE6dE3jqU4qzK3kN8A30k+jCSnifLPJdZ5216xsqOi+26JoO4x/LpzjpC7gRfXl4ApjzuWGDM28yeMDy4PHQhz20vglJNpf88ZLqgO++w92r3aMt6YrioN7pU63lAZqzfdE/vWixs5WHzT/xgCdW/T71oKcuBiy5dmJpZ+zBqn6Q0Jwfu2bf7tN+0OSTyVZfG01WU1OvVRINZp5TgKAeEuJkde5AcgYxWXGOPEy0/fCMHw73f8D969KlOsLqPMu1G0qd5K1Dce473/5OHfAZ2L3xTW+sD57K/c8vlbeyS0e6WbEPAwbAUucs52tQ3wLn1ImXV77ilcMpZRLTZ5T3KANHD2wmxAKM1QZMwnh4u7oMVL556mllBcnPD2eUQfdhhx1abZdedQo3FtvekGwWTaaOysZ/ZfyuDMiVd+WVVxSA3Cm18//vMiELGEZm2gXg3IteZMW5jYfXHP6aqnv31xXnimy9NWGg0X6qFU9HHHFEBRTjxaeX+Ub+8MMf+XCxu6HY4zG1w46/4KN8MtjDwNOf8fR6zWp4BqMAhMC3dyhyl7QRqxdahfH+BaT8piJ7A/G3vnVyxTkTHXyjNwKSdKSf/tSnK6D58MMPLyDRvx22KvYs8cf77btf1S2f740BPGgTZOj4v3/539X/+VT1fmXQZNVNHfNlxX6sVOdhdd07lIHGmpPtMvVOvR/vwHfqOvrZLoEugS6BLoEugS6BLoEugS6BLoEugS6B6UrAc59nTZOpUyXXPe/7JEiAc158C3Auk7Dy1TmERYW0x1OVO1Pn0DFKi/9SaGqv55o5F4F28zHmOswzCCzsWb5s4Lk+984U3dMp17O6eTnBjHbFuVF+5gOt0+EneWJ/5ogEVs3HmB80L2UVB8BFcxTT4Sv6TF5zSuaczKeZXzN/CxiyJHsPTfNhj5fMoaMn/83laI/mJgVstUWrXQm2hu/5QP8oDdGNPfkLJgtkCZgDV2lveKEfK0AA7QB2SC984QtrWxQ3SGzB/PBUMYTRemf6f8uXuujAuZz3P+fo0zy6wJc5bYBHYCtgLPOEub8ezPMfetA3aF90JaDnyy1ZMQdgQFzCfDLAEj7NDScgmL6DbOaLTqO36AsYR4wGSEWsgA+x4QG/Vks0f2ve3jy44CQZZF46up9rVcYW0RF/K6AKkCQG1ALn9Hd0BlgFlGVeGegBwBNAQKJ3/aVFLuwBBvDOv4Zn+5lI6MdP6glv+a9O59CVT7XqM8XXApxr75EfP/xQQGjALeJM5r/pdj4kNIdue21GLETbA6BCJz0BXrXAuehBfnab1XUAmYwp2LP7rF4KkES+UvrI3D9uGYQf5bd8qR+d6BWzRSe9GA9oY+KDYhHyuC/36+ONH4CwtFv65GMBQfQvADHiODM5ngsfozLL+dCrH3DM7sQegarwrQ/UJwQYKEZmFTbX6RrAGrhKH2nxEYA68TO2DQhCf9Jo/ePW3VTl4Qdf7Ka1HWNr4DB+hq8UJ/UMEeBc+gH65neCISAPgDm2DbRjLMdO3c8PJT48k/xGb7Gx8B092rNRduaz82Kt2h+wDnuV8GysA5il7fGjYtABYOtD9C/6Uef4UTY+k3YaPqba4yl80w2dGI/77KMYJuAm4Byf6jrZRN/Ko0dxXH4JcJkt6/uteiWeLdFt7k19yplpu1WXOtQvOYaTAKoCGtOn+9w6HvHHp7I7MVtjUYvVOGeMYyVdgCu2bUVF7Q9gEkBX/Dx82YfXWukYflJ2ZOZ/UmTonGN7OvTMIJbNL/KDwLYTExOL2xsbJgP9vv7Dy1ni4+7FHzuFDdFuxY/plI3OdsJP/Ex4tadHYxV9BWySeDke+Rp9hRcc2DC9ot0zNH9JN3yU534vAiib/vmatGH2wq75nNbWx8m78tWDVim6w6868edlMm3KolNo0xegFcbB/fKiUV8RsKu+EF6GXS8oAEJlqUd54SdyHCc/K3NZywTOGSwd/OyDq7OP8b3vve+rEzSAQgBawFMAGTpvTvCFh75w2KOARXQIBs4//tGPhx0KYOf5BTiWJeMhdDkVSvIg4Zjhfv973x/utv3d6gAoS5mOCtgbA1Zi4pAOeOIBFaG+oDTwYkkFFHfN8JsCVOO00GXg4QEAgMggw5siaxXgmeV9F5ZB4eOgowuAxOQL/vKpVmU9sQyknlVW02OoGsuX//PL1SB1bv/yln+pD3kGNccU8MwvyzKPr3jFK4YHPuiB1fmrlxH7xKPO4JBDDqn1AGkd+sJDh8uL4T6/vLFCthwtIwXA+2Sh02dZgQ+9gWWgp7GSC57+/h8mgUQeoiNDdEM/67x82pFcDOp9dpQM0G5C7XnPfd4wURrGi170j3VpSnwZeBzwhAOq3nTI5Kk8A6+bbrqxyO3TFSCnoR1TgD6AV4CAOgk6eOSjHlkbnAdesgAsQguAGICZhmfw/fSnPd1XTMvA7+n1U6gZwI3q1v8rywD3P0rj98bd1VddPeyz7z7VAaOJM1iwYKIAKyeX49XAgdO+U5wawJcHGSu+QUNnokGZnAkAGdpf+cpXVlAY509PPyr2+ZKXvKSCvIAbdfpxTso3WWrgct6551VbBQo0gOGEDXaO/fyxFal7SFmpbt9C653udPMqXDpVYCltY9eiz5cU0GJAe2Rj0H7QQU+rjvnAA59UOxwP1er9/Oc+X8u//M+XFyDl4bXj1eFCtmsD2gw+d77PzgUktiE2b5E8yOsg3v62t1f5mVx68G4PrrrV2Rm8vOTFL6l1P+5/Pa6ixOnlgx/4YP3sMKCizuHRez+6diAcL3ptOpXVS9v9xje+WQd9Juhe/oqX18krEyI6WKuU+eSowYKBLHDtuwtie6LYx2te85php/tMPnSyN0mH9oFS9znnLKzy9cldg410vujVsbMrbQEI1j30Sn/aMXtmN9ooINjOu+xcO41bCGbRH7ZgtUggSPZr0EfmOh3tj2z5IHy89GUvK+Xcua6kqJNhHwYJ9GrAf5+d7jP51mupzzU2/IY3vLGi9D2MA86x3a9/7etVpz8tDzuvePnL66eFrdrmU8fsiT9ll88+5NkFgLr98PEyoPY5Vu1eB2+gwjYuv+zyunQ6XQDIAtF5oACy5dt8UpjNbF0GAasXO2NrPt1LLzbLrutQ73LXyclU7fsJxQ9MPnwcUEG92g9f77PVBoL0xAcaXLAJgw6+jS/wMLrrA3at/KBx2WlmJk2WXW/P0SXQJdAl0CXQJdAl0CXQJdAl0CXQJdAlMJUETLRKnv0c28wDmLdzzuZ51KS7T7UKDPnsCYBLVg/Ifck/VT2zda6lRZ3+oyvJcXvOscDXwrLClTmkE044oc6peXYWpPRM73nf83VbTsqbzf2qCJxLsIC9sa0vlrkQ8yTm0MxNm480P+T6dBJ9StGV+Y5TSkAywDmBg6XNTU6njtnKgxdbAiJ4Srs0N2ZuylyWuUorQABAhO/ZonF56wlP+DCvdtRRR9UXm9tgsvlR+vKFD8Aedi/QZb4uK0CQiXY5XbtYXjqnm59vHNUPuvAZXYRn+cxHm7MFBjGXaS4aX+xSnCD35d7p0jEX+fhNuqFHAWU8mMMM0NX8NJ96clmIAG8Hl9WCtOm8lG0+M/qLjPA9V7yjoU10Rb8AWObjAXrpLG2QHzGHym+ZKxbEFJw014zf+ZAiV/vIGl1kjBfz62xRjE58yJekvIyNbzElc78CsABLFl0wz09vZOPFfQsWmFdnw+7P3LDy2/rGJYvoCH02daSe2E3ysM2AHgXLgZKAGvQnuV+/LvkPHJGguziTGBsAXYA84+JhRcrBExolfNr85yeBivDJFoHDxE8CLEte9/M/+kNtVT8LwMtWLSCgXQq+811tHe5LGfXCGH+UnfLbYnPOno/Jp1rRxw7x1wIDcy95iJm6x7GYBxvmg/gigCv+Fs+xmdw7U/vwkvL9b1PGJ4DUrrFRcWFtkNwXlnGpeBf9irsad4vN4c9CMa4ZK1lBSRwqCym4dzYT2rPFXvx3jEcxazFWsUO+Qj+RsTU65RPHxpc2Gf3wM8aD7373uytwZ4+COwBKE+eXT/7UZz/OhP42hR/ncqx+/AFcATKKS2pLYqLhTwxPTE5sU19Bx895znOqPtFMx547bPSnD2GriYu2NMzGMd7CO/q0J6ApL2qI0eoH0MfXy0cG7VhMG/RZTONTPtW4FE/GqfrM3BO9RZ7R+UzxGL7Ui2ZJnfgD8NcP0qOxKD+BP2NRY20AVWNtOA7+BwhQn+JeOBBtkY+xshf9upb61KHOcafW9lOXvbpGZakdeYankwDn2CifQnfuYZvAc3yKPp8e9X2uiwMDl9E/mViNlS+Ovxk3b0srL7zqzyJbeAf9vf4bxkcevpKezFPQk+eJt7/97fX5Hu/AqfoRie70iz49LxnLsfMA0lIPOc2ULqO7tnw6trlmnKXfZot412+bi6HDYGHkk994NMA5PskLaAHOyWPjb/E1H+Y2qtDn0c8ygXPbFKN6QjGQhz/8YcOaZeB10YUX1YEYYT5iz0dUcArBf6SAi75SGtQ6pdEAbDEqRkeRlnjkXIBg7rLgLsPa66xdUaoMwEMUx6nRfqwAsU4uZfjc5ete97rqSIGlRhMwmQcVoJeh+Ju/KW8L3KUsMcg4rrryquEv//OXYY/SgW5aJrXOLWCn0793em0wj3jEnuWTmo+pQBKfeuXott1u2+HRj3p0BWctKA92Ac4xLMj+pz39abXx69y+WUBCHpLQa2UyD3ho8clIgy9l7fHwPSo40KAMkM0bLcBl++5TVn8rq9id+s1ThyOPPHK4unSmj3vc/sPTSgOdKPVCfX746A/Xhy/lesjYf//Hls/IrlfK+NLwxRO+WOuyopo3B3Uw1193fQGj/U+VIfCNlebkO6t8736v8iaswZIHnksuvaTS55OjGxeZPL3wpFFZPQ3o7LnPfd5wcdEhOZLbWrdfq8qeswAKgwzneACjXL/c+TIYB5wziNv9IbvXCQsPSoBHaANi0mg5Fp3OG9/4puEvpTEDwQHcGNgsKXG+710Ezrxb0eve++xdHbNy2de6BZxpMMgpcxC/Lfz+15f/qy6jqUMC/stqYamDU/FmwsknnVwdiQ4MuAiPP/7xT6p8HlMGa/s9Zr8Kysp99nkD8NRTTxv2Lg7m0MMOrR0fQCZQ1ete//oir53qW48mU1vHxvkA1+k471BW4/LZYwMmNk42OmKf89XpaE/eDDEZCBTnvi8V3XPqB5b79i+OHs06cO3tT+XzuWjWEQOxjiYO0gCFnr57+nfrgE0bVP8Vf76i2EkBjn3mswW0dX0FxgJrebNMWzQhBax5h3XvUK49pHySsyCWbyxv+Fx3bR2wka+HMqvIaUdnl8Hd/yrgOxPK6GUzXy46eVABkmpHfIEVG+mAHp/8lCfXTmtiYmLxw5iOzSdzTysd2I477lABWt4g5WskoNM3/O83DFavu//97l9Webzd8L3Tv1cR8K9+9avLCpL3q526Ve0uuujiKhvtV3udKmmfRx75b6U9LagPGzfecGNFlX+/DOIPL+Xde6d7l8/YXjqc8MUTygqb76929+xnH1wHTQYKBkRAe9qdzsmAas+iww0KzQaS3jA5/bunV5n6jCvwGb8AIHzif5w4bLftdnXyfasCTPtzkeXPfvqz6i+s4Gnguflmm1fb8ylYx9rajvfYsdq8z97yLXyWtuahSHKOHwESfkzxdXff/u5lBcs7DhsV/ZGfhxPyZTfuMxFiEoeNH1FWyANU9RZ9Be6VB8hX/fOrarkGtVttvdXwk9JWTOx5AHe/B51XvepVw+/O/12VYfRab1rqz/gHikutrl/sEugS6BLoEugS6BLoEugS6BLoEugS6BKYlgRMpCbl2DyHzRyMVS+sim/ewmd3BBYy6Z6J2ORPOXOxb2lRv/+Zr8lx/rsuoGDOT7DAZj5HYA+PJtzboHl7n3tnO5k/WZVWnKOPTN7Tg7kywUPzQYJT5iSW9GWSJclemVJ0lcD0ygicwwd+soUnwQ7z1+YJzSGy0xe/+MX1U5LJ4975lkb5MIcoGMu3tMFkbc58tJd1A9wRbxAI82I++1AWOcx1MgcroYfsbc4l0OYa/ymxdWAlQXSrepjfNodv3s/8fgKY81mHlZFFP9oWHbFBwVhz7vTkc1B0ZM47K7mYn/eSNsCEuER0h2eyG5VZW89sHaOjTdGt+W4vDptr54P5ErEo8/viXuIfrgvGCk6yUXzNh4SH8NXalWNxPXEAi060K84BXUnmjM0nWzBBfy9mZT7afLL2qd0Czomdmc82Py4+k3qyH6cctKW2XLylrYVP9TmmK4Aj/LE5sRfBcu0uunWvY+UaB5CHeKaYglV15gtwLjy1ssSjeAMfKT5JL2xQjEqMUSKr8ChmwIbFc+hWzIZPFQ+Izbaydb86nBs979qtTcrO1urQOfrQhrQ9wDmACDEZYI18yhRNuV9+Pid6dY2Napv0L17DP7nfakszxVMrk9A2WpfzSXwJH8mH4oH+xNOMe/hP9Osn+VcrQO1R4rPie4nBAYNY/EH7JBdj8rkA7YZXfOFXcs6xfsJiIPSo/+MrxI6B4OP/5Y3O3cseJOfFiwFe+CGgwYMLAHt0JcjUWW8aw4962VKrO+ck5xxnz87Y1wc+8IHqU+lIDFz/IB8AEnC56/RttTnjHfzjmUz4KfIx9tGH6EfnCjiHR3SHX7aWeCdd8Bk2404pss9e3JzdWoAD0MzzlL6/7ffdFxlGjs5JKWfy33h+W36UbyzmHPuLn+BH9Wlin+Lc6KUPbQ84WVwXLgEvaaNsRL8hJg14pd847LDDFmMMwkv24+FmspToJ2X7H5vVftrreGyBc2LVbBTWhN+U3As8h1+AR5gV92VBI/Fwz8h8k8WL2MBU+J1J6mbuF1/hTS1sjF2KW3u5QZsCXjXWNBblD/kgNvmv//qvtd/zIg4dZvEubZAfZrPGdvQPPKcNsxUpYzryjszrhTH8kL2UctWZ+rS/gDfhovT5+jLtCn/opcPcSzbmaQDnvJhknGc+g18BBk1fm/pyXyWg/1QJLBM4Z8C02+671UkKCgCQIGirNDEsRver//5VHZxZwYmCgDsMkilNR6BDA5i58A8XVvDYeuuuVxvUNttuUzsAq6J9//s/qJMiXy+GzQh89gGAxCBgNFG8BxFgqVO+cUodJBjsTg7ob1cGvw+qtK1WDBgI5qSvnFTL3L8A1QwC3Q/wgyZGB3QEcWlAGeAcINl97rPToJPjCP5QaL/ooguHLTbfYnjCAU+onVo6bw99DNZSjjvcfYf6qckrr7iyrrhnMCf/lltsWYErGi+gjcZrFb5HFVCdt1yg5o877vjh7AICA1C8dwEU+mQi55zBk86TDH2Ccv3y8OKTtkA3HDkndkp5cxKw5YoyAN68dLpWETO5dO4551agjpX/fEYReEpDMUA0IaXD5tRXu91qteHgFyiGswEWBDDSeNxHhjp0A2sAHU4JkEmHoXGff975VTcTCybq4GebQv+JBczz3e98tzoYK4VZhYy8l5Q4Zitn6YjWL3rVAd+xvAUEcOmTvBtuuEF9K8RDDsf805/8tD7MkCF7BKCyNDj+2rRwYXkzrHyOk57ude97VTCR1dwu+P0FdcU2D34e7KLX3MvJeJj42le/Njz/7ydXtfPgQYcm8454zREVpKROA9PRxCEb2OuYrCLGJqy2hi86Q4/PBy8oD517PGKPCihj30CLZE+u3oJ5UlmRTh7gqI+W1Ql/X0Cbymg/iztat8E1eZqMAQKjJ+A79V1x5RWVJoC47e++fQWQAkFqw5Ze1Qma+CAPHQhHrT0CaZm4BFgEHDToverqq6qtAtSh6Uc//FH1CRy89uW+6669rrYJvAFOsk8D3XTM2uWb3vTmIo/vVN0dUYBcbXKfzxl74FUHW774oovrAPKQ5xxSOzF6+uAHP1RsfjKPleO0+anSOQvPGT5bJhfPKaA/vqp0t/WzuyaplOezo+xcu9PJrFb4v3+xLR2SDoZfA5o1EOR7DDaAPPlMD6qnFaAlmj2Aam97PXKvKv/zS7v6Wll5jt63L+3BJ2A9tGo7Vg7kOycKoJDctX1vZPokrnLwzR6AVP0HtgR+SydKNscUMJ83AzYogDlAZX52m623qbL/avGvfyyDgPh1PhztJkC+Xeq45pqrK9hW+7SyniWk2Th75/sMArU5AwfAQgPJ1xfg6G/P+m3x7Xcun0t+yvDc5z13KnGPnOvAuRGB9L9dAl0CXQJdAl0CXQJdAl0CXQJdAl0Ccy4Bz+U2c0C29j/iPOMLuALOmbQV9BL8SmChZSDltOdm8zi0p85MCjsvtTziy4thnuXNhZgHwpvnfy+kmg/NPfVgjn88+69qwLnYS31JtczXmvfwWTVzUOZpzB1l7mM64m/1LP8ocM68ixc7YxfTKXM+5Alf5obQDqwi0AcUYM7IFxny1YD5QO9UNKC9Tea9PlJeEBb8F9SxCot2R9/0Zo7LvJU5UoAHPicrXsVu2vLm4niUDv9zLjYWnQmgCz5amUYsJZ+nBVoyF+g+9j46Pz0XfE2nTnPPdAf0wY+aG6Yjc+z4EHswv0zH+AbuMK+YFZ8iJ3spvnk6dc9EnthnSwfasukvzNeyTX0DPQFZmXs3v2sVlgAeovvsZ4LeZZWJbjzZtCm0OBf+BIv5EIANNmglJzEVc8Hy5QV7oAGgHKCeXXbZZXEgOiu0CK7jm37FjmaK5+gB3+Gh1Znr7bWFZd5fPI6PZJMAK/mMnnxpZ/TqXvmBkemULwWcE8+i69RXK5jDn/CIBMfGY8B+YkboBMgR5zSHL3YH+IFPNlsXvih5tVkxAW1RDECsg320ssSv8slGW55pnUa+oSH8oVsfgEcgBv0AUIQYG/paeeAzdLuf/xHX4Z/EZMVCxADFpMhlplNLm7oiQ+fDJx+Kto997GP1HDCLsY94t0U/rC7Eftmmft7mfroFbrGwC11bjMPqSraMy+WLTNSvzsjZ/3Emddmk1JH/dNgC5wBujLPFDKeyO2WkPHsxNsA5YHMxau0YcC7tUv5xJ/VGXmQYelKP/+GTnQGWvad8bcu4TDzy4ALuE2dXhjYE/Gg1K9gK9APx8pWu88N8jlgcXQLTA+3MJXAOn5EB/24MZqEQmAF+AyiJLbY2HXmIIXqm0u+LQ+o3xBu12TZFhqkr11Jm/o9jL5ZP1m0/6JxkH/ukR3gLMdr4RbgDPojfNHYxVgVipStlei7DK9CW674OBnAdv9nyOQ5eUoZypcgr/9vrucZG9efohBcIcE5cvPWFymCvdM43uQ8fxq7xw3hj3/yU1edmO4XP8Aaf41kCBoNfRJexqDEN+rRffFj18o1vfGPVGR2L2XueYBMwSeYAYGVgIfQz5gHShtWpvuwdp/5x8M8GlRdfQwfx3fwfjIJFyuCZ4GLQT3cBSbf0ODYe9fzka5aerflbdqtvcL21TfQ719PNElgmcO5Zz3pW/byjyQQdHPAEg1t3vck3FhjiRQW0dUl5w82KTZIOa5NNN6kDGQqgdA8UHgYpadNNNh22m9iuGmBIYbi//tWv60pmHy2N901vflPtYKYCzuUeTklHA8BjpaQ7b3Tn6pg4LIo2aNTp+KyhZPU2ID0G5x6fopQ0DE570802XQyc27R0Wk94wuOrIaljk43LJ0IXAcSmMiI8XlbQuFZ7U6/yOEmdm/yuAy7pWNqEZp0Hufzp0j/Vhu06GTpvtajI0OcflW/Vv40L2AZIK/LxYOotQ+AkSZ0mljbaeKO6Cp8BVBLAETkAUUkam0GzMtZYfY1h6222rkjjC4uzuLTwBFiVNFEmlqycR+/k8vOf/bwO4uhTfc75JKQHEStU0dHZvz07t1e6NijAN0jmJaVrikM+poB/PvPZz1SgZhq/stRrI5+3HvnW6iQ47T9c8IfFxVkxzyCa0xtNHI4Bp86B85DPZMyyBiH05tPD3v5jP0l0bfBtYErnbSeTPOqssioAJIA1bYPutKmzfnNWstW9h1IrF2ov7pFfYqPsgezUaeVCyzfTM3AYZ7mkxJmTmQ4ED+jZdptth4kFE7WzXGxrZTDeJu0dIJOs1lhjzTIQ2qbodcvijCc7Q23rkj9eUjvQ3Jd2T0Y6m1/8/Be1XS0ogD+8Kc85D5w+Aepcmwy+Fp69cHjIQx9SnXl7zTEbwIPBFz622nKrCsyLTrQBIDOTT2Tjs9HQ10tKykh5BTdX7danopWnzeIBWDXJKnfaDXtx3zVXX1M7S9d1whuV9rxeuQZgzL4kdLAL8p4E904Oxtghn0ifOmAyib+oNzY/8tIfmdOXlTo9PPENo4mMPUjJq7xMvP358j/XupLfaoIGAXcodvir0v7xL7G19e+4fm2jbIdvUp63Dfhutr7OHSYnlw2ePKAZ9F9a/Nd+++1b3zaYLGlpv7db2sV+rUugS6BLoEugS6BLoEugS6BLoEugS6BLYA4k4BmwnRB27Dk8z9yeoQHn3vzmNy9ecU7wS2DBs6989jYp+zlgpVaJfik8OcZPEnrNl5hjEJg0YW5+UmBVkMc8THjyrJzj3D9X+1UNOBc5mgsDsAkoUDA/AY3YV/Iuax/dxwbNcZ1SXl5Vh9UgzFOZl5vvCR9s0tytZM6bLWqLzpuTMT/oRXBAAqAd80Dhez7zhzd8+EICUA7gEd0A7uQzfOa4vPgu4Gz+1woXQAHm0ZLwujLwi97MNQr+A3KQgeC4AJ8AOnnwUeb75ou/iZyXtNe2tFugD0FZ4CoAKnaojZnTpEPALIAC+hXAE+gDs80AAEAASURBVLwLj/RHFvNBl6HDXkqfQXe26MZxjWeVOVW268V5c6ZiaeIHCSannLm00fAQGtDE1vCijQGKAc+hH6gIcC6xEjElAAj6Y6OuCzab4w7g3EpK+ssAmrTPmdIp2kdlmrroyzF+6cfenLb+HegKjVlxLr6UDUrk4R79K1ng2Yv/z3/+86tfSnyqZi4/kWX+z+Y+vKUv0MYsJiCwLgYhOL7HHnssBj/iUd8HEAJECDwgNgjgCswiPhaZho+MeZwnx7TVXB/nPjqNHv1Xnz29AHAAbNALWtlgFgMIbfbyoZsM3G/jcyx+4F4xLrYLEAN0xf7nQo/4kvAm6dv04fpy5wDn+FGxH9fozQIjFjege/zJZ/yqDYoh1tjTxETtH4E9xKXZuPyS65FndFsvjPlHHeFP0eSLVmMYC1TwFXwO26MLPiV60JfIi774WWWInQEsAZWJ1ekvs/CEvKP1uWecKTzhpbXR1EHGAZYB3YihAa7q6wIUI392mFXJgM4OOeSQGg9UZsZB/I5YtWcQ4Lu5GqfiWZtK+8Kffl7sVp/g2Y8OAefoSnJP9G3BEG3O3ngOEBDYKv1iZDebe7xI/IKEXvbmvL6cffIz7YpzMA50l36B/wyoao/iY/V1GYtbqUxbtcjJC17wgtqfkkebRv+311bkODJ3r+N2X/8059EJAPiRRcA5APh8sVD/hjayiM9Iec7Rv3ErnYpR62PYgD5VG5zthLZs7FHfp5/XF2p7APw1hl3GoOEDjcYDFuyiYwB6IHP9PB4AQ/MZVKBI/QxdTtUGx61HtEXu5M1GUwea+X+yZ6/ak+cGfiI4qMhCOe7z30uAgHPsks+16BO9waXIo92qK/ekXdQT/WdYJnDuuc99bv306oIyoUCQHEVQmuRHCRRmixESvHwMriq45LlpUQOTj1KAP+JUlaOjB0QxSWU1rXe/613DPe55j5rP9SUl5Wm4aFOeRh4lMzbXY3ToSZ0cRUuva8rIinNAPYc8+5DhgeVTkzdVmtesnzDF15SJHBZ1kspRHlrsa1p0Xb1tQo8y0WkLTVbLW3PN8tbS6osayTJkSH74VHeSsm0AgtffcHO9aRjhxT1kqIzS/ZcV79astKfM0KTc269VeFpjckCCXgMaPEafOed/yld2myKb9lyO8XB66VCPPfa42iHtct9d6gACDcCRBoMGid8qb2H88ytfWVfG01mhNYnsVl8k15zLXjnqiM2ElsV6SsaRPb2pYxTopSwy0CaUUe195F51kosBIlnTCfmw01HZOOeafO6JPts2ZcCsI/9kWX3trgWA6NO0BsNLS2ioPLD7knGtYnNrFftEU1t2W0ZoQGPsAm3hEX0tje6NzTl2jWzYQfjNOfJyPm1VfnmPfOuRww033lAfSgPsdK1N+LDhSRntgItsABmthgic6bPRBmVLSsqg1+gBnYsfhF0rPLS2pRx10nWloehpckgyOehy3ubzt3xeEpsk78X8lrL5i+uLbOVzzy38RW5ctGdn6iM/8tdhKyu6aLPLgx/7lCufMmxJ7qcv+0zAuiavc5FDynOv/K3/R5M2edQ7j6orQhrQLw2omLpLLTcf9qMugS6BLoEugS6BLoEugS6BLoEugS6BLoF5IQHPyDbJc2Gemb00ZTJaMMiLlx/+8IdrMM+kswlcAAjBTC9Uepb07CjlmTX7enIWf9CvbvMbOfbfsWdce29xm4sE3BGQ86KayWWrQOHDM7G94JetnReZRVZuUdWqBpyjB3IGYKQLL0XuUQJSwBttUGB57EiZUu4x53TKIuCcgLnAQTu/cQsBz6M/bJe+yQYP5kDRzX61SUERqyy4JkgrWDTVi8TziKXa7tBD5/gT3DEfL6BsXs9KEcBxXhbHu4CRYKQALRALuzAv1uo4ep5PfE5Fi/m6rPiEZ3YocMmXAiLhg0wkxysDX+ZNvXBuRSRtjG6AXgUc8cSvCmiay9ZHWGkH6MVxy2f41vfMNd9sy5Z+Am38UhZE0Aad0yd+u8QorLqGZsFMc6OCyfoN5+Szx9dcpPCCBsfoYId40bd78RqwkR8xl67tCSRrf+bm+Rb8AWbRpfanj+SLAJEErPknQB+BdMHcdo5bvbZxJTxEpuGpLd+x+WpgIgF18UbgDX4EzcAebBNwTJ8OIKjMuqhF0efCRUFqIBft06ouYgvykYm9+fa5SmjlO7U7+gMMM46x0hFbZHfAcHhELz7Fs/hQOiQHbdT8PZ25Hn7oLTIR/0iaSs65No59q9OUp+2xTe0O6I+N4pH82R/Qh6/xAF45RwaAu8ZoeEhcgz75WvEi7dZKWe7PSlmt7aTumd6HX7rDI1sFStKW6BXwLZ/uBp6LfcrHtqMP7dhX0AAhyYDfBRoRJzQmZwvaipQxPduh75nwR+iU7G3qQqPYEV7FdI3x8AzobyEQetAu9Qf8CH3hD9/sNv2LPsSKpZ43Dj744LpioP4Fj6nTfTOhz5avyN4ef0B9eLOxPyB/5wBvAMTxZtPO8OZZA3iQb3Xdql/sUvvUf/LL9Ah8zg+37bAyOks/eGZrxmfGmvaARSeccEL1lXyH5z8+kq3hEa3u0y9a/dLqX3wUkKOxqX5xJuxuuiJBW3QZ/eFLu+Jn+Aj+kR3SwR7lOQRwjj+RH3gTSN492qfr9KqPNK6j17RfAG38pj57ZdjGmVKuMh3b+E5t3DEbFfdln/jUZ9MNO9NXA68uWLCg+lHPDnjRXumQrvhRbTjjBHxqs15iie2Ok5/ploW39PPGLlZrBOizMI2+zVcY+ZD4O76CjbJHL60A0nu+BMLVlyiPH6Vj/Q258MMTExOLZSkPmYxbhy3P7Ec96uC76Y3PxB/QI77QZkyCp/hyPoSd6v/om76Co9EO2bdVVgF2tVmyaeds5rJdtvzPp+MpgXMaDnTlm974pgFwzidDITRnMhmceoDigDe44wYF/XhgWUFuoxk1xFF+rr32moL8Pm14wxveUJzi1oX3v6sOcDRf/z8zEuAMdDxvfMMb60pc3jI86GkH1c5XjdUZXvzH4Xvf/175FvVbh9e+9og6wGjfMpwZyuZPqTo+gzBvsfnU6GP3f2wdRBkcr4yJzunV6mwGvwYnBhXeuFjegSHZeNOWbKxe6bPCJrw9RPU0fgnQHb/tTfCf/fRnwwN2fUBdKntJgMdbUjDeQeIty+7/ugS6BLoEugS6BLoEugS6BLoEugS6BLoEVlQCJmw970kmUk0ym4cQODdh7jlQ8MvkroCC1eY8dwuC2QQR3Gey2j7ggRWl59beF37MGZhnMBmdcybHBWKtMIA/E89AVVkpSV6biWkBLJ+HEoh2bi7TqgacoxuBOfPC5oWANwTaBOPYUCvv9nhpOqBjKfkFgwQl6dx8I9sVNFneuael1TkT17QzqzywU3pHszlAfAmoCMTiTUDk0EMPHSZKkCeAgZmgZxxl0g3/QO/0a+NbBLOAVfxnA/jkb6yUIEAEDCKYnBVqQkt0nP/zeS+gbt7SqjT0JzhrZRlgAL4S7xIZrSx8oZUNslOgAJ8703bp0Jy9uX76xZtgOx0KbvK3eHReGWzd/7nuM8hfH8g+JfRk1SfAFgFJtqlfYJ+C7IKVQCBPecpTahsMT61O50qfeCFbNCXIyt+K/QnECqbjwaYvF0wXQLcBGPkUmAAs32y1HbIQOKdjbVOwOj4bADQgA7JLfY7HmfBEnuyGntST5Dyb087YJB3xkwAD/AjbQz9wEfCD/2ITAubAK/L6UhXAncCymKiAu/luQee5BLOER/qkQ3EQ/aYxmhWrxHT1aXRoLDZR+gNtTpAdaO7oo4+uvOFZAD7jGTJjw/gEjKRPsiJfie3MpP2qx6aO6JZPoRNgBrrAK1vTv+FLP6Afz2qd8T9sga74F/0MmQDVAQuSBR0a4wWUP5N8RV9T7YEb8GfTvrQldke3gCzhUZuiE/yQjY3Nkxe7BVgGKmSr/A8/pA3yPa1c0ZD/M6XP0KZ8iQ7pjO+gP3zi0Xm642P4FyBP4BVtFghGnJBt4tmYjf7oEY9AI/ik/9jKTPYZsUv8OM6zDR49H1mF2/MD/4JO8U1AI89G2hlbZHeAdO7xsoP+3318KD8EIOirVPoWerfiIL3zx3Nln3hlX/yLPt34E3/0SH/44yf4GX6U3dEP25QXiEx/KZ+XOoDl5zpGy17oD29sxnHi0fET7NPYTB/BNu35T/qgH6uzslNlhX/juvQZGQd4AYSPkWaqvSkbLxI7cZxNnWjUX+sDa/y2rLLGz+jrtSv6YG90CAQHyEm38rJTiW06hz/AefoEkDced59658pGyZ1tWu2YnaLD8x29BSvBz/IjzgPU0Yn2anVP/T2bJQP36mfIC54Aj/xu2iBZki25SjPBs/LVY1MPuXtuNUeBP21Lv21Mgj//bfjTZ7BRfThb9szIth0bH/BVnjOMX/jOzGmw7/n+zFgFPgc/fwWcI2yOun5z+x3vrA9vTzrwSVUpBlAzlQzUdRg6Fsh4nQoHNluJQXIYOue3/dvbav2QwY981COr8c1EY5gt3laWenRWHPChLzy0Dv4NZKGX11hjciU3K3QZYHlL6LiyIt3LX/HyOjE03ye5xin/q4vz/lFBv3sY0kYg+zPQH2c9s1WWzsinWT0kcOYGxQ992OQbC8tLgw4A8FY5Pin6sIc/rHYive0urySnl5+vNhD2ZtPGG21cgXMGT9OT9+2mV0nP1SXQJdAl0CXQJdAl0CXQJdAl0CXQJdAlMCsSyGS7Z7ocC0B61raKiTfUrYghoGUyVx6TrTbzMiaZfabNc2ECdgjPJPOsMNFUgj6p5Qct4c1584BWTzAHal7SOZPR5lnwngCL/ybcn/jEJ9Y3teWby+RZXGBD4BFgSoA7fIY2fOZ4LmmdTt2Ccz6bY06WTgQZ9ygrObAj89T2eFkeflr9oyEBCOULoEyUQIlA+vKUOR1exp0H/4AD5l6shiFwF97YqFUD8GMuXaBOAGW+J/S3wSF85IVaKyvgU+AS7/yL4Do7NwcqEN3GJ2Lnc+VnllfW9AfgIdBHd1ZhEYDkd/AQPua7XbZ8R5+C6mwVSEKQVd/Bh+ofBPOs4qldC/oJxmrX4Tf2kLbelj8Xx+EJfXTBRwmyAgaKX+ENzXgThAT8ELQEguCP3ec62w6Pc8FH6kSLFFqsxOaTewLI2po5XhtetTk8WJXEZ2cBHtxPp9onHYvf0K28+nwxHMCQgF1iv6kvdIx7Hz2pj7+QyJwt6tuBxQTXBc/5GPaFZr6f3zRuAYjHO91qm4Ln+gk6l4+t8qv4FKfiZ5UxlwmviZEBM7JJ/NEJHvlIMgEgAFQFbAGUBPrgg/AE1Mp+AbXoF48TpV+kS/42oAh8Kiu+NrodF//KTdkpUx30Rn9AYWwvOgl/9sZAbJQOtU+rCNE9OdjwRYcWaQDM8nIHHvlb94+bl9A/nT1+2Bwe0cwG2WloRiPfMgpSUTZ5ScY1AErGB/Ja8RIAhn6n0lnumym+W106Bu70oo3PPMdG0cx+2ai2ys9Y5ZLd0TN7Nh7X/lIeO6VDQBY69IKLc1PxM07elE8fyuTL2v/kb1UuYH+fVwVOwZtYJ9vTnmx8ojYI8KdNseszzjij2nUALco1HgBmMf7VlwDVqXec/KB5eRId4M0zklg0+8Qj/tJP4MkqnlntGC/is1a5MpbTLwC36vfdM5c8tfpDB993SgEm+QQw4CP+bM7zG2zU2IXP90lPOmLP+gn+hv6Vw0b4E4BcYHP+033su9XfTPAenthnWxc9owuIHHiK77fypjaZtuUedAOHaVcHl5Uc2ax8eDROkFfCCx0CBuLPuDz6rBlm+QffQP/mJwDL+Bf80hEeJHkkujSO0Veg25iH/vhN+tQm2bQ+EY9eaAFQCwC5letM6LASWX7ia9BtS/vTluAd9BF0Ff+evhvP2hlAsT6RXwEM9FxN/2w6z1NkIb8+U98yumpgy2vouq3u/wo4R5gmwjjEE794Yn0o3Xe/fauDgEqcqaRDZKAGoXOxghgDOu/c84bjCt/Hl0ELGvYuKNu99350fUuDE+lpZiWgAUNAW+nQJODdS2fz6NLZrL3O2sMaq69RPzdrIKkj22zTzSaXeN1qy5klap6Vrn3qtLQVg8SV3Zl50DZAed9731c7h2c+65l1MAntvbxJJ8c+tGVOf2WXzfLyP9v5TRJprwZfHlx02tNPcxtkmD6dPWeXQJdAl0CXQJdAl0CXQJdAl0CXQJfAbUMCmajNRHMmh03UmocRAPGWuoncqZ63TdpOtSJbypltKYaf0XpDuz3eTJqbQDefYBLa3JRr5gEzKe151xxMgskCCHOZViXgHD2ZW/B2PEAGOQtSeXteUEcgIcGn6G46sm/tWH7BPvNPgtXqEKwVQFieMqdT77jz4MMKEV5ytwFLJJAF0ClIItiKn7mYT18RfvGUABH5+6/daW9WMxEgAl7BJ/CAWAHAnH1Aoup1n00ZbGVlSOZ18WiVDPoSnGPrCfThJTaZ/XznK/qkPwFJ/YWN3Zo71M7waBUPK7Qk2MrHRodtO8fvXPKOliR0mIOXBGn1gwvLimQC5mxWEFPbE2zVFvkqfNncG/tMeXOxj4xDDxrQz+fSEx1JuY4v9NeX2wuIAxgAgEB/aQygfeqDALUEYIEF9Y3sWRm28J9yawUz8BPba4vmC3yWTHwTMBDdoUu+9PH8CcAjm2S78otPutd/Kfp0P3nsWT7DyxfN5Rgg+hQHsZIjusWKQi9a0c9ugY2MyyYKWIze2C+9uU5ONvnIRLu0SIOxHJlEn8pVpnpzXA/G9JNyFZe2hy4AJKseGaMB+6GzzYsm7Q/QiA2yaeAJcQp+NmPVrCjEjskj/meudcgurSJk3MPmksKjNoQ/+gDydIzn6IKs6JFO9Smu87Hi93hMOfKPpqVdG827PP/RpGz6k+jQinNW6LJ3HT3sLXkBjIzJ9IVkAqCFHzrEX+SgD6FnAEHjgshCPTPFj3JDs3pG6zSeRC87NWZp8zq28ZGAq/hzrAw+l61qk/pJtso2yYGd0iX7VL/8c5XQD+THTsVw/Zfs0RbdsFH8GZ+hWxsEnsMbnRmjAiEBO84lP2hHdzZ2CGQEKGZs7bzEftHJ5xhn69/3KKB/PpHOATz5Xjr3bIEvfob+2OmSeFXmuPlHM30oF93tf8fi5MYuxpwBd47yyF/QkRdwjNfoT19orw3TMyCnlfeAscnBPWnnVWiz/IM3ulhYxmNszZiELtBki406Nr6mQ/zRE73qP/kZMvH8zxb0f/KRhTboXvXYJ5Gzc9K4dZn2lTr4Pz4Cf/p4umj1jGbJHIVVZoED8Scvm6a/9IPuS/l0p10a/1hxjn4leWw9TUrgr4BzPnEI2e274ZSxdmksOxaHvfc+e1f08KoquIsvurgalO9U/7E85K222u2GLbfYctht990qGpWT7Gl2JAApbNMBXXXlVbXT1aA13E023aROoHF0adSzQ1WvZSYkwIF/9eSvDseUdrfnno8YHve4x9VOeCbq6mXOJwn0Tng+aaPT0iXQJdAl0CXQJdAl0CXQJdAl0CXQJWAi2GYi1iRxJopNMAtm2drAZSZX3WMy1sStTdBEPsnxXE3Ehh/1hy/0hK/Qja+soIDmnHcsud895qBMvtuUM5cpwLkE/gMmQnv4zbH/kv9JOZfz7f/kma09+Qv8WkFPIMOqI4IzeCL30I3G5aGzvQ8vKytwDu3aJPrN0wukpF2Rj/lSoBbbXNslWqeT2oCW/P7btDFBPhs+6bDl0XFWmRm1hdH/06FjLvLQJZunS7zQm32ru/CS/VzQOd0623ZGh/RmE6yzOYc3dhpbpUe8udc22ueoey55R0/oRgd92dvwxD7l8R8v+NMv0KVzeEr+9v90ZTrufJFzys3/AFNaenMteQXR0+fhE//kYYve8A3IQhbuj2y057bslDmOvXKllt7I3Dk2iMaswhbd5B577Q5/rsVmw5cy8CMplz34D/jinrlMaJHQyBb5EmOu8O86HUh0h1686kPkT3K/FH3Rr00MNDpPnpm049RhH5tCF52g2b7Nk/El+6KTgHXc65p7oi/lSPixkYMUW3WMt9lO+LEBUNmir8g5/EYfdIjmVsfRedojefCxeJRXCm/Ky7HzsaGU59w4knLpCy1o9z826rz6IvvQgF50yy85n3boP9rdo52Sg7KlyCOyavmrGcb0o/xskZe9c/SGLzq0pxN8oA0frofulnb34jH3IRVfsVEySR0zxdd0xINOPhSdrQwchz78evZDM56jd30FedBt7ME9ZDOXKXoJD63+wlNkTn/44RNtzjtna8cCeMK/PLHL2Cle23JT9rhk0Ool9aStod11tsYvolkig9Dpv/vQn37c/enr6VAZ9Mg+c5/8yp+rhKZWD/wMutPX41FCr6T9tX0F/mwpQx73ZsMbubhfXa3e/Jfac/XErfxBc1sX2eMJb47RKoWn0OF/9INueek7ftT18JH7Y6/4TcLPuHlK2Svj/q+AcwT658v/PFz+58urMgiVYRkY2lbVxKAMNC8toLkbynExk2HNtdasDQpSkxx6mh0J0IONU/hLcQirLXJUdLLGmmvUzjiTgrNDUa9lpiTA+UOHeyORf4Hm1gn3tKpLYPYfSld1iXb+ugS6BLoEugS6BLoEugS6BLoEugS6BG6NBBJMUIaJ18yDZWI2ZWdi1/XRCdb8zz35n3vnao8edLc0+2/SPLzK41gyOW2eMBPwmazGT/LMFS/qBZzzeSgri3hjfHSODC/mW0KvPX7CY3iIzskl58bJl/ps6re1yXnJSiu+emIlI6ul+AyQlTcSdJrq3racJR2n/NQriHDKohXnrN5iRZ2VZf6plWPk2fId3bYBkPb6ynDc8hV+8cUu6dC5+CTnpdgIO5Yn1+czv+EtdonW0O9ce34+8zEVbfGbeEygPPn42uhQwA6f8avxy64nzbUc0EIvEjrZYeyLb5Xw2OpOPnns0e+6fcrJ/fXmOfrBV+Q8lYxDa67Z555WT/KRg3PpO+T1X/7cPxNsKj8+Tz2Rs7pCF9qcF0wOPeGt1YNr0W/KVWarb+fdG/5nkrfpygtNtiQ04QOd8ZktndpfdJXz8iojfKUs5yKTVla5Pu599BK67NHgvOP8T72u5Rw66Rmdzk+Vcl7ejPn0lZEXfzQbfLa0hSZ7W+rP//Dd3uNcm5LXPkk5kU2bP3lTrv/t9dw/jn1bdlvvVGW7LoVm/+kFH+w41+2N4ZzTpnPPTPFQKyg/6s3mXOTrGJ38EBpaH+SaxH7DS2w8tup6eHMsyZN8+ExS/kzzmbraPfpCT+zTdedCU/JEVxmztflDe/L6315v65zpYzSEHnX5nzTVtYw1+QjX4WbQnrF2fAj+nU/Z8rayc2ybyk5S/4ruU1fkmv/Ka885n3P8YGuL9UL5kcfW6kdefOJ51C5z31ztw2vaWfj1Xwq9/ueavRR51D+LfuiIzvkafsYmv/ORif+5N2W1ZdyaY+XCwyiXvNWZuqYqN9dG6fA/vLQ255x7lEsm8V+pa6o6bsvn/go4d1sWRue9S6BLoEugS2A2JHDLh73ZqLHX0SXQJdAl0CXQJdAl0CXQJdAl0CXQJdAlsGQJmEy1mXDNsdyZkE0AYTRAYCK2DcqajA0wYsm1zc6VUT7wglbJZHL4DTX5Lx9+/cdvJs1NNkceuWcu9j55ZYU2ADOfyLPag4S20Id2aarJc+dzfaZ4SvnqSnIu59UrQOAzV758gu599tmnfm6G/ZC7c/KHr/CW8pa2Tz25Z2UHzoXX8OV/e+z/isjJfXOR0K6NxZ/4z1Yle1sCe/gKr6OAUPlcZ08rQwrf4T1+iN9sj/ES/lcGvqI7/IUXOnF+tO/AD53JJ8nnPtt80SValpTwg2a02pLXseR/eGPfjmPnSypzNs7TRehiW+h1Dr328bktD85J7pPPPWlro/e0siAj+d2fMmpBY/oJzaEh9IUH50Or41YH/qPP9eRvaQ+J4df/3G+P/5ngKfUua49+Cc0SOpPCx6g8Qrfr6FdG+Mv9sen8T1nt/9SZ+saxT/nKan2FutAZfqMr+UKb/NGFc6FPmTb/5QlvkUP4nysf2/KMH/9jk+kH8t919IeH8Jb7cj3/w3crC9da+fg/Uym82NNN+FC/sZ0U+duHX/npRz734dc5m3Pp+52Xr9WdcmaKP/VLoS/0oENSr+R8+Mk5/5Ock4c8yMHe/zwv4SnX3JMysk85s7kPr+ENLVPR45w8Uq7jHT9S9Ok41x3PRQov6IsOyb6lseUlx2jFD/rda3OPzbmcb3lyTtkSe/V/3Al9oTF05H+utbzmOHljh2lP7pEn1x3jwR6vbXuVJzIcN1/LKq+lM3aWthS+0YduqdWD6+Ev90aP8juHT+fkzbwBXp2X3D/uFFkrV12hPbShpU1ocA5v9mgLXf7nmvvDi3NJ7st559SZ+5PntrzvwLnbsvY7710CXQJdAnMigfEPLuaEjV5pl0CXQJdAl0CXQJdAl0CXQJdAl0CXwCoigUymZiI2k66ZRM3ErEnWdqJcPgEtKRPK7fXcPxdiGuUhk9JoCq2hyznX7XPsfsdJuZb/c7XPp1oDnPN5HbRKoTf78ExvzuW/40ySO07+cfHU0qPOpJz3/6KLLhq+/vWvD2edddZw17vedXjkIx85bLzxxjUI0NIb+paHxtSTe1Y14By+Rnkk0/DreD4ntAtOxZ/4n/aXY9ckPNn4IHliG/Il8NP6nPnON7oF4togH77CQ47TPuczP2jDjy3H9uElOmp5ic7qDYt+cr+/0fdc2TJaRut2bpTG0N/mb204vOR6ZJL7ZnMf+kML/hxL2Ufuybuktha6Wxml3JTXjhfSjnPfOPbqi6zR4dgWOws94XP0f0A4xgHucT15wkP7Hz/RX+TU8j8OnqZbBrqkJfHmeugNrbnHfTkO/f7n2PXw2eZ1LLX5Js+M9xfdUvTof/QautDgXOjO//Y+13Jd3qmuuZ56aoZZ/FG3lP3ocXiumcrPVHIP38kb+YTv3Luk/1OVmXtWdK8uW2t//odG5bqm3YUudOQ+10OXc+1/523KUkY7Dki+3FtvHONPeFC3FLtp6wsP8qBNnpxzj7z571r4cD7ycD1l2ufY/XOR0JPU0o6uXMt551qencdjeLW3zSVPobWlDX+hE31S8oVW/8OzvNnome7afLWARWXkHudmivfQqo7QkX2upT3KI6El1ybPTN7bnnc9clFexutt2TPFU2ha2j70odFx2px7/G/pdBydoXmqJI8t9+bYfeQnORe/43jcSV1S6g4tzjmWXMv17F1zb2jLffaj9ylj9Jx7nZtLfaJrvqUOnJtvGun0dAl0CXQJrPISGP/gYpUXWWewS6BLoEugS6BLoEugS6BLoEugS6BLYIYlYOLURKzkOFs7mZpz8mQCN/lzLv9TXsp0frZTaLAXLM/keiac0RP6kncqGl3L9eSfKt9snFu4cOFwSvnsKJCZFec23HDDyoMATtIof/6bHBf8wEeCPQmokMtMJHWpkw3FjpwDHPrVr341fPaznx022GCD4eEPf/iw0047Deuss06ls6UF7dna80s7VofkPmllBs5VBsoPnmytLMKnPOE1+efzfmm8hI/kCR8JlrMj10btKvnm8z48RYd4SsArdOda/s/3PZ7QPBp8c9656Ct8JADL5yRPeI584ityz0zt1SepPwnN7X/Hocve//CU/865D2/xraPlpvy52Id+dIaf6CG8jOoqeZdGb8qNHFJW7sl5ZY0rKTM6Co3OSf67FmBcwKmuJc+SaHKflDJy7H/uTX0146KflNeem+p4uvmmujfn2jIc5z+6JP8jG7rIueRr78k1+cKz6/mfe2oh5Wf0f86Pe68eKXJPve15tuu/tobe+NG0Pddy32g5oTcyy//RfVvf6LVb+z9lx+Yy/vIfL663vKS+lqfw5R5b8ifvVHv3t2VMlefWnFtW+XjTNn0aEP3RQXuf+v2XLzyF5tamk8+12OytoX1p90ZPS6K55cPYFj0t7e6XJ/SHdufJRN5cw09Sys3/2dyjo6W7/d/KQR7t0Tl8yIcnNp18/kszradlySeyjVzz3305zrXw7lp05Jpj1/AXPqM7eSVlZQvPo+VP5rz1v8oNzVPVketodhx6ptJJ8trbkgev7nNOUl/y3noOVrwENMRPhD7nQhs6I5vU4ppEHrY11vDsu1oph14nfW9k5PpNZVvtdpPPr/XGgW+2gps+d3zjGmWH7sl6bv51Hi3hJzyFF/9znH3yTnWvspxPntSUcvN/vu5vuonfpEdb8avFPqsax0xwB86NWaBTFqdBFgPuaQoJdNncUiiT7f2W5/q/LoFVTgLdH65yKu0MdQl0CXQJdAl0CXQJdAl0CXQJdAl0CcxzCWSieJ6TuUzyrNB24okn1iDVzjvvPGyyySb1c63rrbfecPvb335xYMTkuAQ8IBAguCUl4OOc45lO7YS/uujh8ssvH771rW8NJ5xwwvDgBz+4fqYV/QKqgm8CNvKhb0VodK+UQMCqAJyrDPWfVU4Cq4pfmq5i5gO/aLDxM/ygTXJOit9ofZdzuU+e3OM4yfXcm3Or+l7gWuKnySQysCc/52ZbJuqWVrRedAcMov9Mcl5Sri08OjfKZ+TgmvvYGhm1NOW4zSv/KP3+ZxutR/7lSSkn94SX/M9+lIacnw/70IaW6KGVZWjMteRveW3PJb+98/IFNLIkecuXMuTJvfahpZ5cyk/KCJ3xRxnzpG0FhLWUolaaS5Hvsgiebr5llTPb15eX7uXNP9v8TKe+VYGH6fApT+d1upKanXwrqo/2vosvvnr47vfOGS644PJJn96GrY0lSn9Qfob11r39sNlm6w9bbbn+sNGd1x3ueMe1hrXXXmt2GO21VAlcdfX1w49+9Lvh7IWXljEa0PHqw8732Wq4x46blrmOqVcTXFHRzR/gXDHC68tA+7rrrqu8rLvuulM+gKwoo3NxnwaIH5Mza5eJq7XLW5PTHTjNBb2zWmeRzY3lgeHqq6+uE2Jrr732Sq/vZcmPPeDXINhEIJ6TPIyxFdfucIc7FJRsmbisTjk5+n4+SMBbI9dee20lxYRuHozmA20zRkOx2xuKfXrINwE/ntSOQMZTYi+lS6BLoEugS6BLoEugS6BLoEugS6BLoEugS+C2IIFf//rXw6c+9anhkksuKRP3d1zM8mabbTZsu+22w3bbbTf4jKu5VUH/AOc815ubkhwH6LC4gDEepJ4EhLN33tzXb37zm+Gb3/zmcN555w177LFHBc+ZKwuwQL72HsfLMwfT1o+tDpwbo3J7UV0CK5kE+INsSOdP4l/4I9dacEquhU3X23va/8lzW9qPyhLv5CO51qacb8/Nx+PQHXrZhX4yKQCqNl/yyuM4cnHc/k8Z7bmUl345/+VVTupOWW0ZOZ7uXnnKSQoP+W/fXm/Pr4zH4S88tf9zjC/HNvkyvvC/fcnANVubty039ydPrk1HbinTXmrLyDE7lFr/VE/0ny6BLoEugS6BWy2B07937vDO95w6/OJXF5Z+N+OX7G8uXhdaeoN6YtON1xv2ftSOw4EH7DJsuqmXvmb+JbSbKbntHl144RXD29912vDVU84sGI3rh+tu+Mvw7KftOrzg+bsN6603LtzCpHyXCJzTYRskAInY66TTUZtwWaeAftYqIIog4G+tuv785z8Pv/3tb4dzzjmnlmnSBDBlZU3eCPjTn/5UPzuAr3vc4x7Dfe5zn/rJgZWVp3HRbeB/bQET/vbss4ef//zndTLPG7I+zbAqJO3lqiuvrGCj9df3tu/aw/UFFHfhhRcNv/zvX9bJurve9a71ExT4BZgzUXjmr8+sx7vtvlv93Ma42lZketVVV9XyDbTbidVc7/slS4A/vLLo1MSuzWTunnvuWX3U8jwQLbmG+XtFe/3d7343XHzxxcM973nPCp679Tzf/LA+fznvlHUJdAl0CXQJdAl0CXQJdAl0CXQJdAl0CXQJzD8JAM4df/zxdY7H51rN3ZpX9azuJU2fOzWnutVWWw13uctdhq233voWz/Ke8xOUnynwXBsIjgSds3kp8bTTThtOPfXUYZttthl23333YcGCBZXG3CeInfzm2dC5PPNkKSfzFx04Fy30fZfAbU8C8SXxCyTAB/IpOcdX2JK3BTLFX7Z5nGvz3JakGhlFHnjPOfFDclkefz0fZBf60RK+ovfw1PZLyWOflDJyzf+UEVvxX3kBQjlO/rYcx+53LfWuqM0pJ+WlzNCda6M01BtW0p/wFJ7tw29Y8l8+4wvytUk5x35zTt7ozflWVm1d7h+tx7klJfdmS5n57x62IbkWe6kn+k+XQJdAl0CXwFgk8N3TzxnedhTg3AXF5zaAueawVnRzV1//+lzrrvebGJ550N8MD3rgtuUZ9ubVacdCWC/kryTwhz9cMfzbO785nPS1AOduGp7zjAcNh/7D7sP6688CcM5AwIQCsMT5559fASNWyrrqyqvKqnDXl2UJ1xt22HGHYfvttx/udKc7/RUDK3LiO9/5znDySScPZ5555rD55psPL3nJi4fNyn5lTUAmZ/zgjOG/vvJfg08oPOYxjxme9KQnDXe+851XVpbGRve1114zLFx4zvDJYz45/OIXvxge+KAHDgceeOAwMTExtjrmsiBv/Hpr1v6hD33osMPd7z5c8Ic/DCd84YTh9NNPH9ZYc41h70fvPRzwxAPqoPvcc8+tn9j43unfq28D//0//P2www471AnDcfLx7W9/e/jtWb8dNt5k42HvvfderoH8OOlYGcsChP3JT34ynHTSScNPf/LT4ps2G1760peW5Vk3W+kmApZX/voDtnnGD3847Lbbg6ttmoRfngfBv65zZKTx1xn6mS6BLoEugS6BLoEugS6BLoEugS6BLoEugS6BLoERCQiqnl1eRA3o7P73v39dUe6CCy4YzC952c9185IbbrjhsOOOOw73ve99K4DOFw4CZhAslgRjExweqWosf9Fr/sBecmy+7Etf+tLw4x//eNh3332H+93vfnU+DC0BKKAzgWr0uW956GzrU28HzpFCT10Ct10J8CeZy4xvCRjF+QCY7OO3Ap6RX5LPNnr/bVeqk5yTV7bIcRRgtDLICO10G7tAc/iK7WSfa/4nT9tH5ZwynU8/pnzX8t9xW0Z7TR3y57qypitX5Uih1//0+86nnNF8yS9Pm+Rb0rU230wdj9K5tHrkzUZ+UuQdHrKXL3Kh99znunvyXxnkL0V2bRnySc7lfD2xlJ/WFmRTRupEk/qUlVWDl1JUv9Ql0CXQJdAlsAISmAo4d/uyglz6DEXeVPqRG8rqZtUnO1H8suRzobs9cMHwwuc/ZLjvLluVvqHHvKtgZuhnzoFzVqYyyXLyyScv/pQm4MgfL/5j+dbvBfXcQx/20GG//fYb7nWve41FDMcee+zw8Y9/fPj5z35eVzV65zvfMWxbPi2wsqbzzzu/gmyO+eQxZSW9s4enHXTQ8A8v+Idhiy22WFlZGhvdVu766U9/Ohz+6sPrKoOPevSjhxcU2ViRb1VIJin/7ch/qxOWQHD77LNPbU9HHfWu4Uv/8R8VbPrMZz6z2gNn+8tf/nJ4xzveMZx26mnDpgWI9frXv254wAMeMPbVCY9651HDad/61rD93e42HPHaI+ogf1WQ92zwYFXAb5zyjeGT5VMoP/jB94fttt1ueEfxUT5/4qFqlU7loY3dfPnLX642+eQnP7l++sWqeyue+iBixWXX7+wS6BLoEugS6BLoEugS6BLoEugS6BLoErgtSwBA7hvf+EadYzR/5KsC5m1t5tx8AeOPf/zj8LOf/ay+zAsw94hHPKLO4corCJvA7vIEeZdX5gk4t3UJFHsx0Qun5p8f+9jH1hf0zK2YI7MJSieA7X+CF8tDaxvERncHzi2v9nr+LoFVRwL8Dv/IlwT0gjt+gl+xB1RxLcCVXMt1+eODRv2La7fV1MoiMltZZYF+tsIGJP/1QfZsw5bzbCHH7pFPv9XaT/qvlCdfbCh25ZyU+3I9tCgjdilf6nW8tOR+qc3vXOrN3rnQt6zy2rKWlnc2r4XP8Kbu8DZKR863PDu2Cq5ro/pLWQHW0UPKyDX71O3a8ib3JuW4LYd92NDWns89fd8l0CXQJdAlsOISmAo498D7bTvc7a6blgWQJsHTV15x/XDxH68s2xV1f+ll1xa/X0DwpVpe/0mPv+/wvOfsOmy5xQbFT684Lf3OpUtgzoFzQHNfOP4Lw/ve//7hOc85ZNh///3rkvneVvzKf31leH85b0Wsgw566gD0NI50+WWXDe9693uGj330o7XslR04Rybk9e53v3s49vPHVhneZoFzBoAjHuOyou+3/dvbhi984QvDrg984CoFnPNp3qOOOmq44PcXDIeU9rPXXnvVh28rOB701INqc3nGM55RgXNpOx/76MeGT37yk8M15fMaMwWce9/73ldXDvOZ2Je+7KV1wJ36+356EgDuPeYTx9QH1tsMcK6IxidfvM3+ute9fvinf3rRsEf5lLbV9lY89RHEisuu39kl0CXQJdAl0CXQJdAl0CXQJdAl0CXQJXBblsDChQsr8MynWAHn1l9//cUBVUF2ABEvAJ533nkVPPerX/2qBl4fWObf5Pc1DAHgBHunEzRfXnknANze5xzaTjzxxPrFkYmJicXzC661AIEEqtEWQIGypktr6k+guQPnWk304y6B25YE+IMAlPgE//mS+JkAouIvWunI255vfUt7vr3ntnAcOeB1VA6u5bpro9fnq3xCd0tvgEstaEo+edI3tfeljwr/eHXM1tyTcmKPrqe+AO38t6WMXM//1OHeJaXkzT5lJr/zsf+VFZQV3sKTPT7JNvIe5c09tlYe8jsXP+A4ene//3TjnPuiw5SR8lo6pnPsvmwpSx2S//ScOnOuXuw/XQJdAl0CXQJjkcBUwLnX/vO+w/5/e4/iiBdVUeAtfyn++uqrrh/+4z9/NXzu2J8MZ593aekXgOb/Mtx1YuPyudCHDHs9YvthrbJaXU8zI4E5B875ZOrb3/H24Xfn/254+9vfXoBNuxaFr1UHCN5cfP3r//ew9u1vP+z1yL2GPffccyxSMPgA7Hnfe983TJSJk1UBOOftTiDDz37ms/UNytsicM4g1WSdt1sN+JKuvOKK4VOf/vTwoQ9+aNh5l11WKeCct1QuKbq/vkwGbrTRRsN6661XB8FXFJ4POOCA4dprrh2e+tSn3gI49+X//PLw4Y98ZPhD+aTrigLnMogn87XWXGtYbfXJt6Mi80svvbS+zXv70nY32WSTW+gjefp+6RIAbvzEJz5R+szb3XZWnCsi8ZBmov3Itx5Z38J61sHPGh7+8IffCvDlzb5g6RLvV7sEugS6BLoEugS6BLoEugS6BLoEugS6BLoEugRaCQQ4t/XWW1cgnHknc27mhaQcmx8yz/SDH/ygvgy3wQYbDA996EOHBz3oQXWerl2ByX3ub+funGuDuAngLk/QXhnKdK/5MqvMfexjH6sr4+29997DTjvtNKy99tqLg8fKlt88sZT/LW/1wjJ+RvN34NwyBNYvdwmswhLgf/hDgBfHNitvxk/EzxCBa3yQzXX3SQHL5J7kqRdvgz/kYIsc2v/EsbLJKfSG9hbE5jj6H7UP/Euxl9zf9pPKdl/sLP9zX/I6ny3ljl6LvN27rKSsNoXGnM8+dCWv86k/5+bbPrSHrpZess719nz4t2/PK4OO3RegXf675pxrNim2MFpGrkdnNfNSfqaiMfVGJ9HFdMtcSnX9UpdAl0CXQJfAiASmAs695hV7D49/3L0KJuqWX5rTpV5//Y3DcV/4+fDeD357uPDiKyq27va3X3N44f/zkOGgp/xN+WLbLe8Zqa7/vRUSmHPg3L//+78X8M7rh+uvu74C6Hbfffdh3XXXXTyg+NrXvjYAAU0UgNvOO+98K1i95a0f+MAHhve+572DiZ9VBTiHp898+jO3SeAcwNzvf//74dcFcPPQhz3sFuC5qwoA87jjjh/e/Z53l0my+6xSwLlYdQa2+X/11VcPBzzhgOGqsn9K+dwlIGXSV0/+6nD00UcP55dV6VYEOKcuq4L5RDC5W1Vugw03SPGL95Um/xY91C2+0A+mJYFPlU+1fuLjn6h5b0srzmHYp7q/8pWvDO9973uHxz/h8cOBBx44eLt9xVIHzq2Y3PpdXQJdAl0CXQJdAl0CXQJdAl0CXQJdAl0Ct3UJnHPOObdYca4FziWQa/5HEsi96KKL6qddzzjjjGHTTTcd9tlnn+Eud7lLfUk6QeLkb2WrLEFcKceCtwnotnmXdqxsdJhL9pWG448/vq6S95SnPGXYZpttlnbrCl8LP5FHB86tsCj7jV0CK70E+B/z5RaGiC8DnFtW4kcAjO35yuX1fcsqf2W/Tq7kaXNMTvlvvzIl9Et0jI8WMKkfBJZyrU0BObXXIoc2L1mwI59Rt7CBa1tuueVie9RHX3755bVftAiDRQ+UI1/kqd7lkSkeRu9xLpuyQuOS8o7eXwucBz+hdzr0hd/oMPLM+ZRB32TOL0QuruU+x6NplI5W99PRlfsvueSSGj/lmyx0seGGGy7Ws+vqV1bGaqM09P9dAl0CXQJdAisugeUBzqWWX5958fD6N508/PAn59VT1153w/C8g3cbnv/cBxUfvk6y9f2YJTDnwLljjz12+N9lVTkd90FPO2jYd999h3vc4x518Gbg5jOb3hD0RuDGG288LfYNHDyg6OxtkoGIlciSPviBD1ZQhoHjW/71LcMWW2xRByyuG4Cqb6pBgjcWDW5s/1PqWaOUi841yj1lZFGLV6f6DVINNgxGHIcW5bpnzbLPPWUkOdyE7vL5TPs6YC33rrmW8teu5YR21wANb7hx8mHKeQNhb1F+/vOfH/Z/7P4VKIWnZSU04Ql9NRU60JS3GdRFdpGFvNdec02ha62SZ7Vy342VL7KVx2DPQEt5NvfjTZnhOwNCdV9TgF2WnpRWk2eRPAG/Ii/X0LPOOuvUslt6yXa11W43/OY3Zw2nfP2U4YwfnjG87GUvq4M/19COjuMB58qnbO95r3sOz3ve8+onepWfwaC8tluTql6KfG4y+C08kYPyq30skk3KV3dsiR2RwNprF5uwetuiB6NJOdLNInsr5a2+2qQcVl9j8jMXtZxiazeW8sgnPFTgXFlx7uqrrh6ePAKc+9pXv1aBc+edf/5fAefw0LYdtJAhHvAiebj6yU9+Mpz0lZPqKnePetQjh2223bbqFw2Rgzayern/DgUI2ybX6eRG9rFITqvdbrWi+8l2kXoio9ib2tM2lBe62FWbYnvut6Fh7UKX/CuSQq9ylYc+ZZE1u0xynezw7Zr75I8dOMevhL/cZ19t+vpS/l9uniTWlgFh6WA6wDn1qFtZqRd91dcs4j15wktoS76Wn5a+qY6VQYd0EhteY/U1hrVuv1a1hdyDnmuLX1MXuaHNJuEtckl+e3I879zz6ueHt99+++FpBz1t2HOvFV1xdOWaPGnl0I+7BLoEugS6BLoEugS6BLoEugS6BLoEugS6BOZSAssDnDNP4PlfYP673/1umav7Tf1U68PKC653v/vdbzEvG57MnZjLMF9g3kBq503a49yztH3mGy644ILhS1/60mB/73vfe9hrr71qkHhp967otVG6O3BuRSXZ7+sSWPklwKeZ17z44ovrapd8WOb6XZNav+ac+VigZCt1Jn7FlzmfvNmv/BJafg74WHIig8jB3nnzzjbXxSX0Jcmz/DXN/B1o1ldKodU5GzthO/mSjzlzC4ywDXndh7fch+fc59gmTiJ2881vfnP4/ve/X+MCvkoEOC7v5z73ueFHP/rRsOOOOw6PetSjhm1LTEd5rrlfQodtunLMffLblNWmxCvs2TS+EkeUT35b6su+LWMujls+RmlyLXxpq3gix7T10Os+8pFXCt8WpeAjcj7lKSMyWn/99RfbdGTsmjpC2yhdqXd0z65OO+204bOf/WwdCz360Y8ejM3Q05Y13fJGy+//uwS6BLoEugSWLoEVAc6dc85lw5Hv+Mbw1W+cOfzlpv8ZritYkac/6X7D85+3W8E0rb/0CvvVFZbAnAPnTj311OFd73rXcPp3T69vIj54twcPu++2ewU2TSyYWAxgGx10LIljgwgDj7POOqsOmoHuDF42uvNGw/0fcP/FtwHOvec976kAq8P+38PqQ8lVV07mXXudtQdgjc0337wOTtxkAGGAcUFZ1eySRZ+hBEpab/31hrvd7W4V1Gdgo/7LL79s+D9n/qZOFAGRGJj6lKq3HV2/4x3vWAelQHvuAevwqU2D2rPPPruA564r/68vQLLVhjvd6U7D3ba/W6WPDAymfML23HPOrXzWCaFSAJ5PPvnksn11eMLjHz8t4Jx7vX1y3nnn1QG5gVcdrJWxLbo9bHjw8IaozcDJ25o2oDwPclaGuuLKK4ZdyidQycsgkcyt/oYm96sHLwb6201sV/nPIP7nP/95zUcuBoNWADRgB8wCpvRAQEZ3vvOd6ycVlLdw4cLhd2W1NPRut9121UZOKp/8/exnPlPl94pXvqK+SeOtGTL20Bng3MTERF3BauONNq50V32sf8dh6222HhYsWFAHi4uNZDkOyA2/+GZHQI3KlshqsyK/9Qp/seMLL7xwuPAPF1ad0zW+1I9/D0NkBvzmMxd0dPlllw/XXHvNsM7a61Twn7dC5DEZeU6Rx5+LbVmRMW/PrghwDg9s8HcFUGeluiuvuLJ+gpXutYeA5376k58OVoo87rjj6sTnY/72MQO5kvc973nPKgcTq5f88ZJhwzttOOy6666LJYlm7cDEJXmp0zmAQO2CfbATunWdrZ356zOHzbfYvNqByUf25T5A2rve5a7DZptvVvOrxPnzC/3koh1dedWVVafAuGS7vAltZOJBhj2q3zkyUV4L5v19scnf/Oasqi92R6fqN2nMht2DDu0mDyH8ius+Vc1HXHvdtYuvnXbqacNJpU2vW/JPBziHNnb1hwv+UMopwN3rrxvWKfeyKzaoTnaBD/JJ20QDmcvDrrTN0DeVvOQnZ7buQT4y+Uv5xvo6d1intjnlsBdp4cKzhzPP/D+TOis2ch25FNti93wMWXlYp/O2XuU+65nPGi4t9h+fNhU9yz7Hw/bUJdAl0CXQJdAl0CXQJdAl0CXQJdAl0CXQJdAlsLwSWB7gnLLNhZk3Offcc+vKc+b9gOZ8ttW+nXMwByD//2XvPuD2nM4/gB8iYsRObPqGmCFWzQhBS4sQe9TeW1F7U9SetVNBaW1BlSpi1x4RK9SuFXtEFv/re14nefp6ZbTpXzXnfPLked77Pvc51/mddZ1z/e7r2Puzn1j+tu9g/6xxj2Bs5JaWZ6X3zDPP5JdGebvzkra922IgLntzY5Pm2MSRp1DktZ/Rr1+/vP+FtEcGL1LWUBGoCPzvI8B+xG5x4YUXpsceeyyPScYG46KxyXhhD7SMR/62p77YYotlD53zzTdf3lN1vXGsKuNLQdD9xmst//6ueOX66L6lJTSmP6b43xW3yFW+R5dO473G+H4b3+VRMHHN3jY7APsBe0PXrl2zrcg+/PgOjfKMKe0xxXW/hPK74MdOx+Zy++2359NXkNuWXnrpbFdQdjhoP357tjGvghFHJLyt/vnPf87tbPfdd09dunTJWfbu3Ts98MADaYkllkjrhA3R/KQttgwl3SJXuV+ul799u1aC3+XvIi/71nNxShR7BBuA04vYBBr1gcbn5OlT0pH22MrRMl6Ra1y/S96+S5rl2zV92Vw/YMCAbI9hV1EuXtzUj1CeLc8VGfz94IMPpt///vfp+eekYh04AABAAElEQVSfz/GKXUTbZSOdaaaZcnp0COmyQdGTGtNsmW5Jv7Vvsvbt2zedd9552f7DC++aa645Ms3GdFt7vtxv7V7jtbGN1/hM/V0RqAhUBCYEBP4V4txzz4XHud/8NT3O41yYuXmc23bzZdIuOy4b3KFmR2GffDI4bPAfptde/yi9/ubH4YxscMwj7YKTMlXqPHeH1GXBGWOs//Y8XzB/I555+eUPIu1mb6hzzD5t6tQ0fczTbYMLNTjdd3/Y9we+FxyHidO8nTsGT2fWNNusU8fcWFLgbClkeBXX6KP02hsfxd9DgnMwaZp5xvZp3vlmTPPP2zHWwq3LMGzYV+n1N0L+1z4K7sDw4MBMGvPeDDkP6Q58cVB6esC76e13Pok02mbZlvzxHGnWFsTBz78Ylp586h/pqfi8897nwatoG3rUtGnxRWYNXWeGLP8oiUf/63snzllIIHydd+55aRCiVCi8iBQ9VuqR1lh99TRnEKMsHr7LU1PL4iGVUVj++Ic/ZrIKMgoCDiXwhBNPGBkdce6ss87KJJZlllkmk3EocZRs5I9evXql1VZbLZO4PIQogtR2z113RwN4NZNjHHXZvv2U2aOXI2aR3PLmTOR/0UV98uLINccSvDDwhazIfxakqg4dZkgrrrhiWnONNVKHIJgIiDlPBiHpjjtuz0oXUt7wEcPTLDPPkjbeZONMXEK4ofgqH+X5g/c/CI9dPLqlTIh5LRTrv//9pbTpJpuOFXFOWR955JF06623pnffeTd75fo0sKJIIb3MONOMeQGy9FJLpyWXWjJjcNnvL0u33XZbWnLJJbPCTulFoNnnV/uk7ssvn9PwRin5PvowGjoCTyiSFHfyr7raqpkAp07FcxRj//79M3nIBtoagQncTz/9jPTQQw9mkg/i27LLLJt23mXn6HAfhVe9awOnO7Jit+6662Zl76ab/pRu/tOfsue1hUOhnG766TL50psTP/7xj0cS5yjlXRbqEl7zgqwU9ae9IPmIx+Mhgt64KJ3qzsJXG3s8FsMPP/xIriN1NzQ8iA0N8tKCC3ZJyyyzdFogFgbIc7C45ZZb0iMR9+133s4YITytHu1d2TsF0YlcL7zwQnK0xatBknwt2pw4MNxu++3SsssumzG75+57Ut8b+gah861cB2uttRaRcnvwNtHYepzTv/QVdXFfELa+CJKeo1iRm5Dhtt5669wvLWgcIar/PBeK/dRRHvU2/QzNxMZtt902Pd3/6bw46x+bo126LJhOPvnkLJP/PgkS2oDYvPSGi/ahbVjkCrDXPpDLkOi0rcsvvzxdF/W9eCzm3Ndmke6Q2fRpZeSJTHzBAuxP0RZszMJZHeu75ELwG9egPzwRJM6nnnoqvfpKc79HPFsgMPn5z3+Wlo82X8Kfov398Y9/TC8OfDHqe5m8kFG/yKUWinDcaaedUqe5Oo0klSn/wIED01397spkXyS6idsEQXbY8Cy7MQcZckzEOYsSm9LGUsRG6X4YY8W0cYSuRZBFtnatzz326GN5DDF2wF47FQ/xcoXYyJ4++uboNhikDWfjAAIvMl6pR+MGgrLyI8Aqt0Ugz3nGlYUXXjjXi00NhLj2QdhDrNxxpx1z/TYu1KV56KGH5rayysqrpMMOP2zkwrNgPnbfDTP42D1QY1UEKgIVgYpARaAiUBGoCFQEKgIVgYpARaAiEAiMLXGuGEt924expn8lXvT0wrSXm+0H2POzT1pC2X/zjP0DwR6bj/2Bcq3EH913yZfx2f4Zj3f23uy/2pe1pySOUPIdXXrjcq9luvY/KnFuXBCscSsC/zsIGPvYmOwR22c3nhlz7J8aE+2p2se2/288tH9tfEJs6tGjR97vNvYVUpH7ZYxsHLtcb/y7xGmJZHle3MYxVfzG58tzRV73SpzG3+KV6+W3v4WSfvm73G98vuRZ4pS/5Sv4WzrkFkqa+Y/4Tzz3XPebZ7W//OUv2Vaw/vrr5736RqJyY97SKPn6XfL2uzGUPEodeMZHnmPzvP12aTfuc7eWV0m33FOuJ554Ip1xxhnZXsd+xtaz6aabZsJZwaJ8N8osjSIbG8+NN96Yva66vuuuu2anF3DRJtnyEL3YdIpNRbwiR5FL+n6Tq5TF341xxXHf9SKXv8sz+gM7DQ+wvhHZ2b+Kkw7PN+brOem45jd9otRDY1z35NlIUvN3aUeNz3juu4JnhEYZSlx5C9ISz6fIpo71cyRHtlqEQLY9JLcSv+BUZPVsSYN+cuKJJ2Z7M+cM6kNZyM8+xAbMrtrU1JR6xLiw0kor5d8tyXNF1sbvgkGRVZ7SRJwrjmQ23HDDtPbaa2f7anm2YOBvzzQGf5fyNF73u9zzXZ4rcRv/bvlc/bsiUBGoCExICIwrcW7w4GHp6mv7pwv7/C29O+jTGIPNt1+nXbbvnrbbesngikya+j/9Vroq4jz33Dvpo4+/SB9/OiQIcEODf9A2TTl5HMs9Q/u09FJzprXW7BJOiDrEHPZtxPveOCBdc/1Twc/iWOyrtEqPedNmmy6eCXiXXv5oeuTR19KgDwenSeLkx+mmmzx1nmvG1GuthdIKy3N81jbuv5763jQgZHgvffzJF+Hk6cs0OEh47dpOEuS5tqnD9GRoSuv26pJJb41zDWk++eTLdM11T6c/3/pM+ihIgDNE/C1+sUSabtop0s1xbcAz4YTqvU/DSdGQ1DbmyWmnmTw1/Wj6tOlGS6TluzVlQtxT/d+KMvTPxLm33/0kHEMNyXPx1O3bpTmCPLd2zy5p1Z/MG062xu542++dOEcBQUThOtjkPWBAgBOEChP7dLFo+FkoUj/5yU+yly1s+zGFJ594MhN7+t5wQzrh+OODeDdnfsPRJs3pp58+8nHEn1NPOy0rI9sEKQgRjDJyz71BROp7QyYEHXzwwelnQY4hC6LOkUccmZWW7it0z29KItMce+xxobj0SJtsskl+U4My+Nyzz6Wjf310EKkez8S/HXfYIS262KKZnIfYgnDCg5i3PX666k9z+o69vPwPlzeTV4LENeUUU2bCyNVxXOPi8cbRnr/cMytHFgOXXnJpJhJutvlmWcG1IcPF8nEhCyy33HLLsSLO2by6Iry0kffgQw7OCzaK3rXXXpceuP/+7OkOAcZbLQh83oA4PjB9OzagZgyyGeISJZcClBX4BeZPzz77bHgjuzoIX4+kffbZJ2+MIdM8/9zz6fzzz8+LRCSsn8TxngLPcgcfdHB6KxTNdYKsuP8B+2cPcRaSRx11VMjxQH4r9JRTT8mKp2cc73vTjTelqaaeKu21115ZBm/RXNzn4ryht9lmm2XiTlOnpnzPoqB4nENoo2hadFAYb73l1ryZ5jjRbbfdJvXs2XMkqUleYxN4hHvooYfSSSeelOabf75MVFp88cXD89xn0VGfCnLgeWmeeLN1gw03SAiW6guR6+sY5LhFXmrppfJiZtpppk0rrbxSfjNKfZ5yyikxkHRK2hvCnGsPPvhQJigigZH/yiuuzC62tdEjjzoybbDBBlnkcfU4R+mG4Q3R9vWzXXbdJX36yafp0t9fGnX6XNpuu20zZjyTqRvt5qwzz0qzhWK/eeC9yKKL5DdheB28NohuyFJvvPlGJiR6k0XQ1xEe5YMMuO+++0aba4pjjifJ/R8hj9x77713bnOPPf5YOu3U09JDMTZ0is0ERFqkLHirfwvJJYN0tdlmv8j5yANBSzoWlnvtvVfeqD0z5Fw76nvtXmuLMtbBoub+6AeXXhr9LY5LVn9IbOoZqU8b1sYthgRE3KuuvCovmmx2/CLksgGCyKbcL8Xbb8aadddbN5fHWIHYd/xvjk9fDP4iSGsr5PHAIgkJ7tRTTo1J59FMuBwTcc5izVtkJxx/Qn67Tt68wfHWh4i83vrr5UXxxRdfHAvkp4OstmQcf7ppPnL5lZdfyYvXO+Nt6F8EeVTfsFhrLcAEcVE6xo+VV1o52ntsfMdYzcOfhf3fX/57Hk932nGnTKg85JBD8ma146WRWDffYvM8ZnieVz39Yfvtt0891+qZN4pKvtqLNsaFvLfgjjr6qDw2lPtj/x0aRQ0VgYpARaAiUBGoCFQEKgIVgYpARaAiUBGoCIwzAmNLnJOw/cFCOLBPZV3P8xtSgz1XxnInVtgnLMZU8Wyg+9u3fQdpjK3RuxTI8/ZZ7G/x8nTnnXfml1Id0WovouQpv/EdSlmKIaAS58Y3wjW9isAPBwFjmNNSvCjNUUIJPIkh1CIu2atF6OVNSnwvMPMoZT8WkamMU/aIjS+NY4zfxkj3SrySh+vlWnmujEv+Lr/FL7/l73d5rvwtjt9CkSP/8c1/7vkUkpDL0mmU1bXGfP0u+ZR75omSZ5FDOYTyd0nXt3vSIZMPO9Fll12WX7bfZpttMq68fhU5fJf40ix5uVbwko5QnvG7xCvylnTKM66XOOTy8bd4voViM2hMV7wSXC/Pmr/s5Wsjffr0yfYX7QLpfMcdd8wvnpvfyvONz5b0yrd0ELqQszzjRX7psPmwISgD2aRf0vOs67Ao1+ThozzKW7Dwt4+4jdeUoeXcLT+OIThH4GWNEwzezpqamnI+Je2SZ/m7yOPvgqNr4pFJun4rn1Bk9S0UufwWTzru+e2e3675u4Tyu3yXNEsavksaysrTIc+SbHbZ5rH55pkA63lYllDSa/ymF5100knZztUjiHHsr+x/nlN/nB8YK9hhjAvs44h5bFON7VWaPsoiNJabrKVO2dyQKTkx4USkkTjnWWkWTEo6RV5/N2JR7vsu+YorjlC+S5quNabl7xoqAhWBisCEhkBrxLmjDlk9rb8uj7Cj5iK4fBBEtRuDjIbQ9vJr78dYbjz/KnX60Qxp9527p1VW6pzu7PdiuuLqJ9JTA94KfkGzc6KcyjdJGZJjaE5TBIFumSWb0m47d0vzz9fxW+Nxn0seSRdd8mB69/3PwjHV16nnz7qkNX42f7rx5mdTv3sHBg9leE6HXKaaSSaZKC043yxBXFs01t5fpetv6J+efeGdODGxmWzeKEPMCDEnpDTNVJOlZZbqlPbYtXsQ+KaX1MjAq93v+jyUrrz28SDOfRlcjEnSAvPOlL6O8r706gdRtiE5bplHcrniSpf5Z0m77tQty3zZHx5NT/R/I3vNU2YyfDMjRf4TpRk7tE87brtc6rV2l+BeNesNIwVo5cf3TpwjkwkcMY2ygQzz8EMPZ8UAs563MZsqvCat9rPV8iQuvkm55Yc3MQS83r1/lzdlfhVe0CgeFPBPPv4kk5IKBohzjoiV/q+P+XVWakzqCFCnBGHl5ZBl3/32zd7kHJ1JYTn66KOzFyWbLogxNo822XiTfG3LrbbMLH0y8T524AEHZkIW72bHHXdcJhWRG/nsmiDDIfjwtPSrfX+Vy47s421Ib0LOOcecQWhpm/H42wN/S0hdvw4iHmUDKQ3hZJttts5valpolXT332//7AVs0yDxIT59F/mlYEAJR3LS4C7qc1HGwluZF/3uonTxJRenhRdaOO2+x+7ZU5YNJp60jj/hhPBY9Wjqtny3IDKtkebuPHd+vik8AzpuFpHryiuvzHkjcjnik3ILKwry1SG/Mm666SbZixhcDzvssHTfvfdlUtn++++fj+ukzCEu/uXWv8QxnbPk8iOjSevyyy5P9913XyZC8RRHqaT4/S7qHb6nnX5abjPq1gLA5lwhzs0626xpiy22yDKob4r7BedfkBez3rLgOU964xIoxzwM8ni35557prXWXiuT37Q7x5XusssumWz4s2i/e+6xRwwgXwbZbPM4KvaztHq8ZYMAqa1rOz+a80dBInwrE9OuCwLjgQcemFbssWI+SlNa3gjq0LFDVpLV+93hAfG43xwXbe7TdMQRh2ell+zjSpyjVCO0IYo5PsPiHT6nnHxKuuHGG9KqP1017bf/fpkIpq+S7YgjjshteNfdds3u5C1U1M8rQaw79thjs2czngovuOCCDKe2de4556bbw6vivPPMm8ml5e0ihEv1arGL/LfhRhs213W0mT6BrbeRNtpoo7TY4ovlsukrSGLeyPvFL36RMZTJ2b89O10R7W/qIFV6m4qLdsS1eeadZyTxMgszFv+pDyRXGPOettHGG+V6QOay+Oy6SNfcRks/uyWIa32CvPl8eArcKN7aUQbywaPv9X3zESE8v5FLOZDmXL/ooovSyiuvnIl53PKrCx4HEUcRO8fG45z4ZPr10b9OTZ2acjtEbNUOJm07aXjIWyATbvVN9aSd81qovnjxg/vpp52e5UVQs9grC89GqORjjD3owINy3K223iqTTSdr1y6NCLwQJo0BnrVZgQxnbNM39Qd1uNHGG8aYMXF+G0t8+KpfRMfZZpttZHbi//7S3wemfXLdOYLZ0SrjHvJUPe6P1ScqAhWBikBFoCJQEagIVAQqAhWBikBFoCIwgSMwrsS5YjAtxlsvP9pHYDB39Jg9px/FHqKXXO1H+giNz9mPabw3pirwbHneXlney4kXjxEFnBRg75Q8PiW/MaU5LvdL3iXtSpwbF/Rq3IrA/x4CxjD7rb7tvRqXvITNJuSYzE7xgjgnDOwaAhKOPVwOEZDu2DI8g+hk77ycxsQ2hDCEkMfblb+LowljrTzZsbzYXrzZSdNH+uIjlbnvm2zS86x9e/nKSzw2CM+wrRi7fbxQ7xljKZnF982LHhuPsU+ZySQ+MiB7XZkPpEluY6V8pO9Zwf66stoPFkdZyCZ98sjbfbKTyXXl4tmPTUNa9t3Z26TNLsSTl/SUzbe/ySQ9crJxuEduhEXpywce5LIPLh1BXDLZ6zenuM6pgXjii6vOPK/sZGBjImvjHFXmtnKtzEvSYh+xR6+NyIO9SLCfjngub8+XZ2FAJniJq4zqXx31CwLeXXfdlf/ebbfdskc08qgDuKkjmLP5sWWyU6hP1wrGMHJNGeEkbXYK87e8lFm+nveBo/S0PeQsbQxu5PBiPCck5mQkMOWDN6xgJA9tn4zkL2n6rc5KW5Kn8mpv5JOHvKQlHXgWfLQJZRWXnOpH+nCUHgwKqZ6c0lUG8isfGVyXZqn34qHPdScqIW0iuTlBiYOMpqam3JbIJB3pa2+lD6hPdci5ymnh1EV+yIRsI/IQV72SmZ38pptuymMHvUk74BCkyOBZ9a+Myudvz8MEHr79TQbY3hCOZji4cE9anCeQC6bS8PEbfmQkf2nLbD3w09adJOS+Mmrn0i/tEm7aCsKfPqI9tTyWFwY1VAQqAhWBCQ2B1ohz3ZbulBacf6Y4hS4I/MH0GhJ6zzvvxjz3zqcxL3+U3vvg83DAhBg9URo6fERab61F0h67LBfzcbt07Al3pL/c/lwmi7lv7OaRTkLG5DYxvmOQIdy1bz9Z2mjdRdN22yyVpv/miNeC/8WXPpr6/D6Ic4M+y893X27ucJIzPD397Fvh5e3LSNd6HTG7ed0+bNiI1DTn9GnbLZdODz78arrz7hdDhmFp4rD5f/WNDGSR/cQTI+MT6esgrLVLW2+xdNpsk0VjHhvl+Q1x7qKLH05XX/d4eMz7MkRGcA+SXjwzNIh5iHTtgi8lD/O++8LEbSZKc87Gc/PX6Y23PgqdEWG97AlE+eP+xDKPz5C4t1wQ9/bafYW06CKz5LLkRL7jv++VOKeQPgIlCHg8j/GA9sSTT6QnHn8iHyVK6Vl9jdXTL3/5yzzhmnhN4hTK8jEpI7NRFnh9QtJCFllm6WXSEj9eIh/bSbHTYATEOQx7RI0zzzwje4Zz3fGYZ5ze7A55p513yp7UKAhfhHJxR7ytSKmTDiULkWmjeEOCkrVVEOd4Oithv/32y8rTYosuln579m/L5azA3PLnWzIJjywnn3JydslLydeYkICkrfIpd4PeH5SPbeSlClnIUamUmAsuvCB7m6N4CDA55phjwrPVnWm9ULbGhjin/Ahljg+9/PLLRi46eJPipWuRIAYdEeQoLoYFR1Xy/IbMs8WWW+TjOyk/JVB+kaT63dkvk8d41KNoC5Q3Sv8B+x+QMUc0pKBZAF111VX57YxpwuPaNts0EwK9hcVDnbQoeMrPU536toBwjCjSIQWQgud4zt/97nfp2eeezYo4gk0h/lAMC3Fuodg02zVIhQhVAiwRG/9621/TCiuukAlfFgHjEria5u2PzOece07q0aNHVghLGnvsvke6KzbsFo2jMA8PchuFklc13v1mnmnmrPAuHUe5alfueSsWAUx6vXv3zt7ccv8oCTZ8e/NEW6OwOtbS2yKCtjMuR7VK3+JMOvpRxjUGOh7E1M8SQaQ6/vjf5EUFT3rXxYL00EMOzYs2i6+FFl6oQaqUDjrooHTH7XekhbsuPJI493CQUrUrGwXqc48gEWrrAoXaxqY0kcd22HGHvMHp+NNjfn1MrnvENYsQfd0GAQIkL2bKjLwnWABccvElebGH2NZ9+e75aN5OnTrlsaP0/xx5DP/pj4iYFpQWLtyXy4+3PYsipNEDDjggjy2SQqA8/7zz86KGJ0TeKi1kjFW8wamnRWM82Dfa7XLLLZcJfTw42kDWlrmTl08JZ55xZsbe+DImj3P6heORecP7+JOPgxS3XCxEu2UMZ7OYj36yz977xBgWdbJw13TmGaenaWPxU/CA/XHH/Sb9PTxzImIiTloctQzahwWbOrHI3W677TKJtsRz9DNC4ytBFrT4OuTQQzLRFwFTvSENwkUwJujjSKdLRvvipdMisASLMSS8C+LNrTliYwOR2ZuY4x6ax/xxf64+URGoCFQEKgIVgYpARaAiUBGoCFQEKgIVgQkbgbElztlDscfg23reXl75be+Ngdm+pv06+wlNTXECQexFCeL52N8r+xT+Fsrf+Y/v+K8877Yj6uxb8HTHGO0l3GLwld/YpPcd2Xzn5ZayMl4jLdijto/hxUZEgxoqAhWB/30EGscj442PMdEesxeajQ3Gv83DOxUSjHEPaci+MmcJ9vqLpzo2KUQjL1xzTmAf3biCiORlZHm5Lg12GWOtZ6Tr1BvjsP1iDgjsvbMjGRMRzJy+hNjChiA99hZ74E4fYosjJ/KO/WEvWBu3ObfwjPyUSX7sG/JwqpDxV2Bvkz+5lZWthSyIg/KBif3/8ry/xfWMPW4ntxjD4UA+42dJk3MNxB+yeYlcemwNxl32DMQ1e+lsP2wG8Lr11ltzXuxuHGLAgU1SnXDGIC9HbXKqYP8fHuw9bDP+tqctD6QhnsHIwEYBL/Hshzs9Rn2QUxx1IE4hDcGstA3l9Vvw2wdBzUlT7FtkXmeddbKjB+1CnbAbkMV8Vp7XbtgVeC9T/7CTv3mHrMrH1sRGRxYysGuw0fnb3j1bJ8cSiG0IW2wC6hqG6lgZkbx8EA8RPhdccMFMxFLn2gkvr2SBqboiZ4+wj6lPtlYv1YunfZBHm4KLtu0Id2ViA+JUhfMC7ZzHRphqN9oK4mBTtCXtLZ/sEzYMNj/kLTIrCzKcMioH2V9++eWMqfpUl65pv+oaUU1fII/4jpHVlug88NPu5MUWoj94plecllX6h/bg2FNpI9Epj3Jp62ypvMN5UaDYTtW3spCBLsJWxO6mbfHAp+0pS6lb8bVxbVedIfWpF/ZRGJFJ3XgpgSMMdQ1fz9E59Cf4sompE20DwZQjGWOEvkFGdaaO2Ia0A30SHtqjepQnJyjaibiczrAV6m/yQBYsuMubvYd9TvngqH7hARd1U0NFoCJQEZhQEWiNONdu0jbh+CZIc6EHxBAan69ivB6RhgVJLi7lOQFeQ4cNT4t3nSN7WFt+uaY4lvXLdOSvb0t38Qg3ZHiabpop0nzzdEwLdZkljjqdMk6/G5T+csfz4bxpSIy9zV5JF15w1nTogaumrgs3c3ZKPTQS58gwVZDyBn85LOaCEZHn7DnNzyKdRx57Pb3+5odBlp40rbtW1zj+dcF0fu+/pXsf+Hv6PLzCzTDtlCHDjBF/5tSxw1RZhrvufSm9/+HnJavUtcus6ejDfxYnNHYYee1bxLko+PAo/6wzT5N+svJ8aekl54zjWSfLZb7iqsfTo0++EXNUs3c7805MTaFHTBwe6GZNK/eYJ9KeIZ7/Kl0Z3vgeeeL1TAKU2cRBpNtjx+7hDG3R0HFGvy7/XolzFBCKIAXB5EsxEBQWo5/nuTPPPDMrr47qQ1KZZZaZQxm8LSvOQ4c1v3ViMhcoSDZgTPK9L+ydBr44MLWfsn0m71CKLQooCibp7yLOUch4ObrkkksSb0oUaoQmigF5KSwfffhR/k1GHsGmDoVr6622ymSyLEj8t38QxO4JZdcRlpSoxmARdOSRR6bX4rhKhBgesRCREKt4eypvDTQ+Qxk6LTxCXR6EHQre1Vdf9U/EFwsJ3ryuvubqtFbPtcaKOHfN1dekK6+6MrD+NN42ODeTdhwf+scr/piVdModxVr5BYojGSg+u4YXta232TorPUVOx832Ce9QCFjbbLtNVqQpnwJlihK29VZbp3aTtcvHyXITDVdK7BFHHJm9/FG4eMPiSvqlF18Kpe+5OPLzzSzbCeHtTtz+T/UPzKfOimLJuxDnKK3XXX9dVhDLBtzoiHPaGs9eFMell1o6u0huP9XYE+c8j9xz2OGHZ+Zvn4v7ZE9epS2TT13fFMS+OQPHgw4+KCvlFgza8cCBL0R7bBNK99JZ0VwsFqCuO0ZYOzg7lFnHv35XoNRrg6++8momKf2rxDn9AonR8azvf/B+Vvi/CiayBb3FrwXKiSedmBX4kcS5Qw/Lx6fuvMvOmWzXKCMS3+1/vT2T1pApBeRF3tX0odI+5CsYB2yg7rjDjmn2wGm3IMJZXFl88qJmUVCIc+LDfa211g5l/MMgiq6XfrnXL13OiyDEWcr+u9EnLE56hHLvSGQLwkKmzJHH4r/hMbbo5x+G0m8R9PlnzQTaP4dc88WCaf/998uLRUll4tz5FwTh9/F8hHAhzrlnQcet+nzzzR/k0f3T8t2Xz5sWyGzKfuxxx2bPbY0LCBsgvw+iLGb4mIhz8rCgVPa777k7L3Isory5qE/NN998uc899uhjqUcsXBE8G/OygeJY3AeiDg6I45ItBC2GWwYbCZeFZ8CLo59vvMnGmSxs06QE4+dh0S4cMSufs846M2+On3zSyXmcbyTO0QTOj7ZhkdhlwS7pN0HMtJgrwbyAUMxbnevV41xBpn5XBCoCFYGKQEWgIlARqAhUBCoCFYGKQEXg/weBsSXOkcZemH0+hmF7Dn772MNhfLbX6uVCRlgvFDISu1dC2Utzzcff5VqJ09p3ScO+FqMuQ7L87W0wILtPDtfGJr3W8hjdtZJ/SbsS50aHVr1XEfjfR6CMX2VsYLti07DP6YV5+5wcMCC2sOkgFdnL5vULaYhNwDjqb3vZ9sgRluz1IrQgSrGT2FNmUzOWsh0hrXCugECG6OMFbYQbNi3EGUQbgY2MQwNx7L8iGxuf2W6QYBBrEIE8Yx/f9yJht0K0QQqTH7kRdpBk7AcjrJFbWZH0jIeISWwVnvX3pZdemvORJtmK1y+EI+l6odrL2OxCxlFYkM+evI94CGU9Ys8ZsQtZjO2CrY39x/434hg52Ajhy06FFIZUZF+a/ZA9gpw8erHRsK+wfbAD8lTK9mVfHjFMXHjA1Id9QXlgWryBIURxHqFu5ONb2RHs2DSRhpRfPN+lXZSeUMoIS/I0BUFsq7A12odHKFMXTnYxd/LuZT5T9/BH/GNTgRU5tRfzrPohhzpWbo4S5H3WWWdlvMilDWpTyF88kcFEfcGY/dGcqj6UE3EOtuwbMEAqQ4RkhzW3sgHCCi6+lR8ZDIFPPGloF8hYbI3qiAxwJy8nBcrhWfYc9asNaLPkID9ZtWflgqXfdAvzPIcr6lv5YaO/IVZKE17ajvIjhrlf2rMTebRd5D92K6RC7Vv7dF3Z9CltVtvQb5RNOtqV+iEHmRHEPMemIl04FdtoqWvf5NCHTz/99Ny+pQkrccnqvnz1O/giU/om87777psxJFMhNJY+r53pB+peedmFEC7hBkv2T3Y6sjrxCalNv9KOYKWuiryIkMYT7dp4oe9rX/ochzXsYPQ4bUhfY4fVjhF/9XPyyptjD88Zn5SrhopARaAiMKEi0BpxLhSCb44UHYVKrH5jooi/EemyB7WUFgsC29abL5mW79Yp5vu2weH4Ih11zG3ptjueS4svMkf6xSaLZ89100w7eSbiffxJjPl9B6Tf//HR9MFHn8fc8nVqmmP6tPeeK6WfrtI55phR43FL4hyZCNBrrUXSLzZaJOb+adOwocODCPdBuvyKx2I+/CLt88sV4/o06Yij48S++19Miy4cxPoNF00LLThzOPGZPDzETRJz+5fpqmueSldc80R6L46Ble70002ZTj1hrXCkM+fIArckzpF1vs4zpi02/XFwOzrHfDZ5zB/58fRU/7fSUcf+JT038N18DUbtwiNdj+6d0xa/WCJOOuwYOhNPvyk92f8f6dzzH0h/C6945ice+zbotWjaeftlQw+hE4wU4Vs/vlfiHOX/+eeeD6bgR1lpLUSnRimP/83xUcHXp1lnmTUreTN0mGHk+e+UgammmjoUuamz4tqzZ8/mNwJCQXg9lCYkLkedUhYoLqefcXpWzChQ30Wc8/bBHy6PowVDUeJFjsJMKaT0kRUxi8Lmb8okBYkCQXnlha0ExLm77uoX7M2uQQ45v1zO30gqvMMhhvHgZlPnuiAVedPnxBNPGKnIlYfKYguByBsGTZ2asvJOwSwKB6Wc9yYkLjiMjcc5yrcF21397kpIbN4WGfHViPwmg+MTEaIoWBYFQjNx7rRYPNzSKnEOeY23upfijY/ttt8uH1c5/TfEOYqe65tvvkW4VmyXsZJnCchrN//p5qxIOeKVEr/ySitnJfjWv9yaN73ODIINYg/F2VsmFkMljD1xbqHcjorHOc87ghepC/4nnXRSeOBrJs7BvSxkKKutBYtYC5ojDj8iTRpM2wvDO5Y3TxrjqzeLmNnnmD2T3CjXw4L0+UC0TWX2NpH2umqQu7bffvu8oOM5jJKtzVrgNKZX2gN5LOwcm2kRw7vX2BLneofCbWFxdODuOFWLDe1aG0fAshjQthCgXoy/c9ssxLnoX5TpQw89LCvNu3wXcS4IdxY1hTjneGKLcIuTbbfbNt6m2SgGtWbiHKVeP9h22+2ydzFe7Jbrttx3EueU3QboBx98mNaNxVwhzumTg6K/3xNvMluUIVlacGiPPNR5I01wTVDG0ofyhYb/4PzO2++kAc8MCFLny3lBhETn7Se4Lx6Lu28T58LjXHjKbPQ4J0mbIjvttHMM3PPEM83EOdd23WXXvPh1XLQ23VjP2pWFholybIhz2qJ6s7lw04035QWsRfPavdYOIuJuadttto2JpX+8KbRKJqs1jrcWSDxtGpvI1yueQWBsGSyOePRDrt10003z+GjRVoI25IjlRx5+JPWITYwzWxDnjEve6CpBf5GvhWRrxDke/P4QYxRSsXHB4rP0y9HVXUm/+Xs0M+A/R6x/VQQqAhWBikBFoCJQEagIVAQqAhWBikBFoCLQgMDYEues0a3Xy36L/Q2/7VXYf2C09VIhYypjtD0D3kgYyYXyfEPW37lf0xin/JaXPRueUOxN2Htj1C3G8u/a+ynP/zvfyi2UPJS1XxBh7H8pY/U49++gW5+tCPzwEDAmlDHN2OTjOMdCnEMeQoRzrChiT9++fbPdpVOnTtmrG2ILI599WPft2yPXsNMgTiHMOEHHmMfGgVjX1NSUSTDsDPatEVXYw4xBvsmA7OK+uPZ1vWxtfPbiOpuO55Bc2DY4VBAQz+RHHvF5x/K8sZy9wzfijLKwoRW57TH7jajG+6exGDkKCQjRBwaIToVchABIZuM3OZDFkHjgKD5iE2Ife5D07EfD2ZjPBmC8RVQjO5uZD5uW/XfkOnvmCGTsZtI2NxXinDKaM5SNPLx8IVEpGzzMJUheymf+QihC1jPOI4HJk/2GvVCd2WeXfiNxSR36NM4XpY14js2RnZF9hJcvhCTkL+VT7uKVDI6IY+wTysWbK9zkRQY2OFiRgb2QbeZXv/rVPxHnEOXYerRBZEVxEec8i+wEX9ghrNEBkN5ggoDFdsVmxf6AiKU8SI9IZWy16gERDvmMp0JtwNzPpqmM2pD5XxtnU1UXyglvcqg3BEF4w0db0P5gJF9lVEdsBNqFOkBcK3LLl81Z25SvNJRVP9HWeWYr2CC3sS9piwhfyJLSYz9S59qEfNzTjtiYkQr1RTYXOBfCKccCCGZN0TfYLsmhLOpc36MTFbsPmeDpqFb4lLotpLXSRtzTNhHV5K+tIc7xMqne1YH+Qscgqzzhzwuhti09tm3jg7KrRw5etGl5Is7RV9gCjQ1k1cZ96y9wck/f1FbkYcwhuzFLHbJFqn/9QvuRh/6ovnbYYYfsKEQdK3MNFYGKQEVgQkagdeJcMzmuJS7mgbbhiW7O2adL3ZaZK62/btdwzDRdrJvDO12EQe9/HqS1v8YcOnEQ1hbPXt7ath31glg8Ho7FBqW99u2bXnz5vfzM7LNOl/bYuVvquWaXmI9GjcnfJs6ltPyyc8WRsMsHZyiOkY240iPTu+99lj24zTrr1EG2/jKdcModIcOkab1eXdOCCzgy/Z9leOml99KhR92anhrwj3A89XWaeqrJ0wnHrpm6d5sr5oUsVpAARx3V+uHHgyPOZGnbLZZKG2+4WHC/mklzzTFTzJlfpUOOuDXddvuz4RVveHiRmzgttMAs6YBfrZwWXmjmmL9GlUvc3/V5KF182cPhGGlw9uL305XmTbvt3D3NP1/Hf8KgpF++v1finLdRsPIphL/5zW/yZG4SL0FFcB+L1GThcOhhh+aJHaO/KBxtJo4z4gMcE7oJGgHHxE4BcLzqtUHwQUr7OBTaPffcMx/5SvG7wFGtoSjMNrujWs8ceUTg6yHT5UGcuyhId1sGEY5yQcmgpBx7zLFZEeW9btXVVs3K45ZbbJkmD0Vpqy23/BZx7vZQ6pDRev+ud2qrXNESKM1/ufUvmeQ0VfupEsIMxYSXO/IjyFhslI0j8ZWJgoHsh0gTPJq8mPnRj+aMtyjaZbjee/e9jBVCkwXJ2BDnKDoUSEo2L3o8aCEVygvBB+HG76LUUZRPPfXUrJTtvPPO2QsYBbCE/GbG7y7KCzAevvbZZ5+sQLmvThD0Dj/iiLRA5IPEtMaaa5RHRy7QXnj+hbTSyivlN1AoVxTyvn1vyES6nrH4+TA8jFGe1QtZS0BAs/B6JurpmvDG1znkt2gQeAnj0e/ss8/Oi8Vdg7zTSPY5+qijMxGMEn3yyYhzza6oPwtFk6cxGFiUNOZX8vVtcXnsscelV4O85lhe7UNdCtopchTC3zKhRB5z7DG5bina6phyarFyTLStqSPf/cKDGUXy1FNOzYoovJddbtlwj9keeTanx+PbZCETeSweeJxDnHPM6YYbNRzVGp7YPou2g6BmYVgCT3CwQpxDRqLc6lN/+MMf43jV27OyjQg662yzZiLgNddcm4lLxeOcfnX99X3TIUGQWi1cYTsm1SK8MfA499fIZyHEuThWWKBs84r4XCxQ1oijlyn6FhkCJf/PN/85+sMx2UMcb4YW9QiRSKZbxVtWPM7pTyX0WrtX9o637jrrpr323itfttCCq00EmPBW5ojl7t2Xz2lYyCLXeWsGztqvxUxrQd8779zz0p9u/lOaZeZZsgt93v+uu/a6TJbsFIuSRuKc9n9BeJzTnxH5vIlT+ofFzU477hTtsnP2nGnzwRvQBx90cHrt9dcy2cyitFEWBLXLLr8sTdJmkjES5yxk9WdvCc0QC7qXo+wIi9qWxdMlsflxZJAkb7/9jrxQPiMImRZTMDDOWvA4blg9nHb6aXkRq923DMZsb7/xINdt+W55saw/lmBTRNt9J/DdOBaR2oYNmVPimsXeLjFuOHa7BGPa6UGIteg94YTj8zhf7qmnww47LJ6/J0i0K+Vjjo3zH37wYfpicPPba8byxjmjPPvP36Mmy3++Xv+qCFQEKgIVgYpARaAiUBGoCFQEKgIVgYpARWB0CNgHsK5nOLVvVvYt7CWUPQXP+20PzF6Kdbq/hRLPdd5MGMAZoe0DMPQznNu/axm/ZdrSERrjNf7NYGvfEjHDfhnjtv3VknZJrzyfE/sP/VeIc8gDyARNTU0j93n/Q1nWZCsCFYH/IgTKeOXb2CcgPyHAIFwZ/xBOEHbYcthnEHQQvxBwjFvGU/YUYxoiDJIPspux2D4uj3P2To11jn1FKhKf5zoEPbYHe/4IWEhD4hp7EWeMUY635HWKDYP3q969e2f7F8JQIemRmyMEcrPfdQr73HbbbZeJO2x6PLPZ7+VVCjnHfrxgfxl5xj4xmwHnCchO5JK/uYBtB9mJ3Mbssr9rjxvppmAHP/vd8meD8bfn7Efb20bgIZ998S3DPoe8ZD8ehtIhJ9IWWwD7CM9eCF7w4DXNs5xLwB2ZiO0BIQke8ELygpOxnBc2c415jD1NmuoRGaykac70vDpDdHKKjrnTPnuZI81DpbzwUj51ijiHFAcb9j0EPfkgI0lDvSBNkZ2dBWkLScxL6uKzo8GP3GyIMEOEOuSQQ7L88uWsol+Qw5DayG6eRM4s7QK22pQ9d3XLyQiCHBylzyboHty1NcQ7NhD4lX16MgjqQRqw0I7JI235ek69myd59uMQBOlQe9HGkLS0AYRTtlt2FDYO9kLtmk1JGycDfLQv9aeN6mu8uSHUIWeSTzvTJtjDnK6k7dI/YKotFYIaGZH2XGc/1D8Q/uShr2rnMICFvoHwR271I3/ETuUS2DGUX72zr/pb8Fv+7H7qUr9ueVRraSv0GuRJbQqenDWQmyxk9ZtNSbvVpgoRjkMGZdXPYc5WLk/1z9ZNVm2myKjdlXqTt9/Iedqldq6Pl76lvH3Cds5mre0bt6TPvshJAp1RPWwV9jz1CI9iX84A1P8qAhWBisAEiEBrxLluS3dKnefuGOP3xMEhahNco0nSlFO0i/k0jqgPclqHDuHNN4hk7ds3H9NeYBs69OtwxvRhxG+bZuo4ZYzlE5dbI7/feefTdMAhN6eHHnst5poRafZZp0277tgtPMktFPFH2cz/iTgX/oYmm6xtOmS/n6Q111gwxu/meask2rwcb94DGDJkeHo78phyikmDPzN5qzJ88cXQtMfefdMDD7/8DXFusnTcUWuklXp0Lkl+izjXYfop0m47dU/rrL1wyDKKL+YB+Z9z3v3p8ivDk96HX6Q2gdlSi8+ZDj9k1SAWNjsAG5lw/Lj5lufC69z96e+vvR/z2oi0xKKzp712XzH9eInZY14ahUHjM35/r8S5Qe8NSpfHJP6HUFAphMhWmOomUkoF5faUk09Jb/7jzayUUJoKoaxlQcrfFPJ777k3k68Qb556qn8+3pSCc8CBB+S3ZSgRlDETOeWCkkKpoSj8/aW/pz4X90lXX3V1Wn+D9bPiQumjhDnKcMEuC2ZlxCKCMrDD9jvkyZ8ytdnmm+UNJEooj3O3hDJCiTrxxBOCoKciJo5nBoWieXMm2Pw8lDZe7ShAl192eXoilMSePdeMBrlmmmPOOfJmEOXRmxDcFg94ekD29ISYY0FE8YEXZYYSe3rId1+4E14z3hbYcacdM/FodEqJNw8ovpTXVVZeJc0191xZfoS+jjN2zLJ7XnnUB6Xz5JNPzgs8+VuMUDpLHvCwEKHQTzfd9Onwww/LCq37Fg2OkaSgeZthgw03yIvEUm/kP++88wP3qxIvdeuFwrfOuuvkxSDSEVIYOZYN3Ndff728uVae9U3hpbA53vfU007NiwGbeer6y8Ff5oWZxYR61o6WjqNR24XS9nm0sWN+fUwmA1HwkcO8VfJBlKVfkI4Q8pCd1C+Fryi4jXlbjCA+IuftFO6qf776z/OxrCOiXhASkag++/SzrEQ6ovSVl19JN//55qyEy9PizAKm7SRt89G4vCpanN1z9z1ZsafEanfKL52/v/z3vBhQ9958OvjgQ9KboQgj3VF6LQK8UbTpJptm4hy8HLlrQTFs6LC80LF4snhwLC5S0tOxUIQfxVf7Q1Kz4EO+Qly1+ECys6gRENocnUx+JE0LcPnqn0ND0T40CE/9+t0VSvsCmbAoLZ7gLrywd15czhmkT0fY2hQwPD0b/VOfo4xvHaS9NaJv8H5oEXbWWb9NG8SiTdkWXWzRNHHg8Gn0i4032jgG1Q/S2oHP3vvsnct31plnRfuZPmNrYXNHEMXOCvLt8rGo2TL6mvpHqOPJzKBqYcTTG9lbBguE7bbbPmPSrdtymXhrgcxznjY+R/TtPfbYPZddu7DIvOiiPumZwHLbbbfN44S6hLkNCC7Dm2KzdN/99g1y5cqxeHkzFriXxkLn2tQjFuHrBAFw0TjaWX+xCL8wsHfEr3aMcGnhaANCO2gZeMZ75NFH8ttEFuTavfrU33jrdDRr3igI2bl33W3X3bKnQeW2yOGh7txzz831mxfTXRce2a8b80LafDQIgo5jnXyKyfM4ZPEpnRExRlxxxZX5jS5E0x123CEvwtWptIcPH5G2jgXTuuutm9v64C8H5/Z1TpApO8dYzeOcjSNpWRjnN5NifPWmFBLvttEuhkUe+tqTTz2ZN5fW6bVOmmXWWRpFbOX3t/FqJVK9VBGoCFQEKgIVgYpARaAiUBGoCFQEKgIVgYpACwTs13kpz/4pwzDDbdmXsHYXfLtWrrdIYuSf9i8Zp5FEGGLtrTKAl/07933saUjL7xKQJeyX+LgnT3uV/rYnw4COkGLPlHG97JkWGaUjvfJ8SXd8f8vPPgtZfNsvszc8pv3s8S1HTa8iUBH4fhEwFhhzynjFvsNuYmxAmOPpCpnq2muvzUQne+7IcewSZayyD2w/1/GRTbGnjLCDzMNWcuKJJ2abFNKNj71YBCy2MS/MI1R5uZ+nKGQraSK0Idyx0yAU2Wv3nDGZLQIRSFocHrCDCMhb5gDEJ3YqNhLjmj3nfkHAMn4jzyCJIQEZj5FwPEMW9ov99tsve2Wzn0428ZDp2DzY3YSCk/14e8JsG+YJhDjpIVDJz30knlVXXTVjwrZFNvHYq9idpCk9srMZIfHBY5dddsn3EXnMKRwSFOIcshQ8lI0dECHKfKWeil1ImohJvPMhJrG1sCvYzy5lgJGX6xG92BHgSVa/hTK/5T+++c/8xVaDHAV3xDIELXUGZ57QhFJucmgX2o6yOGkGQck8Y1+dzaekp344L0D8U2bEKYQozgW0DfZTRCfkNNgiCaqX4hSCDuAl+kKSQ1xDkrLfD3t4mMfN5+ZepKxCXCSneRoe6ojtBDEPzgh9AhIlexS7HS96yq0eEfr0Idho64iC2jEbFzspHMtcq03RUTynPPQATlq0I2QvJEHxpUdedmLlgY3yIn6p6z5hS9FP4a/etWnP8WwnPozYyLQj39oU+7L85KGuEdjoGaUP6w/+bhmkxyYNc+2rEOfE8yzcfOsHSHJsZOoXcU56SKhsik0xLrDNOPaYDQXmAqKf+9oFOxVctCU2GmOMOlDP2ov+pf/ob8i3cHUNTmyP6oCnRvnIz99s64h72r70jV/qkedC7U8/1F71f6G1dp9v1P8qAhWBisAEgkBrxLkD9wmC2uoLZIKa4Xsi69qYMiaayPq2eW39zbD+LZTMaREz4jbfatY7Uz4i9bHH/xFe2V5Id94zMHTDwcG1+DrNOVt4nNule3AvFoh55JuH4tFG4lw5JvXQA3+allh89m/l2fJCSxliVyDm2hRzypCw4b+Z/nrnS8GzGZgGffh5Zr1NM/Xk6aTj1krdlmsamVRLj3OIc7vu2D2t26t14txlf3g89b74gXDe8+ko4tzBQZyb89vEuXvveyWdHcS5/s849n14mnfuDmnfvVdJ3ZZtyviOFKLFj++VOIfQRMH7bXgCmzHeEOFpzGRLMTNZv/bqa6n/0/3zxL5Sj5UyaaaF/N/6E0HlmquvyZ7kKGwmase1IkgdGkdZeiuHAoR8kZn600ydPYJ1X6F7VpZM8LxUOaJy6XAxjZE//wLzZwWPlyWK4VJLL5UJOJQIhLcvQ5FYYonFs4JDueNdbv8DDgwPWjdn8hGlaYkfL5GVGguQJ594MlwavpuPd7XYodTcecedWQlCmFkkyDM2VSgUlDmLJAo2z1MWCI6u1SGQbyiklF7p9ruzX7hfHBgLqEXzMamr/GSVvAD7FkjfXLBooGS9/trrmcgGm2mmniY/gxRDiaJ0qY+PQonlNQ2RyhGz8KLocsNNyRTIQZniCvnJJ5/K5CceuijwjrqkUCPFrbVWz7zZVp7zLCXR4pGXLb+PPe7Y7OVsyiBcIabBHlkG6U2dkLUxiOOYWtggfiGaUZgRsyZtO2le0FiYzhzKGlIWkiaMKba8hN3/zQL0l7/cM9imS+SjUy3mro62NO2006QDoz5XXHGFNPU3i5zGvCn4FgB9gjRlEURRVm4K7nPPPpcXs/OHMs3LGqUaAdIRlkhcFhae53mL0k8xn2nmmfKCVXoUYWWh3E4+2eRZ4UeqtFmpffAed3a8qcWj4npBkKPsq7cH//ZgXlRooyuE3EiO2tpb/3grv71EUdbHeP/yVgql+I9BqnooFnWLR1vutly8JTZZu/TA/Q9kYqW6QiTlKhz2Fr6O141hOS8QyYdsqf++EsS0s397dn5biDK9+x6758UYRfy+e+/LSvorr76SFei5Os2VFXqKvDQtrnjNo1S7ZmGmjDDttU6vtEL3FXJ7euBvD+Q2YQHsSNftQkHvGuU76sijcrngjAiqr1moIzM60rVttEWEvtNiYYJ8t2q0Awuulu1J/VrEI5hpd8q32qqrZUwQSO+I/jrllFPkMpRNXpsW3nx7881/pBXjrWn9fu7Oc+fjXrXL3kEaVD4EW4sHbdNRshZYn8QY0CXqWb3qb2++8WYsYu+JBUf/yHOytEksZhBJPWPB1TJ4g+jWW25NN/3pppyvNkAeshoHLWDejkUeT4FP9X8qzT7b7HnzZIoog02TRx55JI7kfTGT2mx26PetBQu2d955J48bFke5DS+1dEIQNI7deMONaWjgttRSS+Yxy/hljL8ljndGJP1JjElI0jPNOFN4Hnwuj5/aojcEd9t9t0yIJbvFmfHfhpA8LOaMOcZHHhaRffUX9y1IRx9GKQGjj1fvVgQqAhWBikBFoCJQEagIVAQqAhWBikBFoCLQiIA9Hvtp9mkKca55k9ymfvOGvr+Lsbbx2dZ+2zuwd2iP0B6JPQgkEi9cNm/4N3utk579D3uNxQBd0nPdffHdt99kXxSRwT2EECQBe1lFriJjawbsku6/+i3tkr787IX0i/1bBApyNMXeWCXO/avo1ucqAj8sBMp4UKQ25hinkFCQtApxDqlEYH+wz2kcs+9rLCxjn7TYDdgYvFCNGGSf1F43ApQ9ZKQbthI2CXHt1/YJAhCyT3ZeELYCJCUy5JNG4nhI46XxnL3Anix7Wjmdxh4yQhVZBOMYj2DIYohCyDNksZ+L/MOOY34w3srDGGjMZncwFpLX/q19XLYfBCo2DcQ5pCbkM+VURnkhTyFJsdGxV0hPumwACH/KYs+dlzUOHRo9zm0TL12zXcAQnvAgIxsYUtCO4fAAAch9abInkacQjAopkM3QdfOfemI7KQQgRCakIfONsiNXIR+WoBwIjO4pu7oxJyGTyVPZyxzmGfHgixynHSCMIfEV5wVkgwf5EZ14EGRr44XM3r89fPYy+LIham+ITe4jT5l7vMxvLnIP+QtRTv0rr3yanQFclOsLgUs+6p9sMJcOTNghEefIqD2bx5E6EdSQFbUl6WkfPrBRt8oFT2RCe/zyhac6ohNw7oGoKK8e4VzAfXmRV7lPOeWU3wdUbgAAPNlJREFUnAYbAK+B7E/aGPtB8ZYnf+22lMcpZto7mw9bgwB/8rD5wEgebGnajPLoN04z0i+0TbZFMrAv8zLoNCvPOK6WnU36hTgHTzYPMqpj+RT9w7e/S/C3dskrHowLiY1OJIhb5Hz11VfzGFHsJ3vssUfuK6W9wFA/UM/KJ23YaKfuaXvK2KlTp0y2VAbYw0o9s7WwH2lj+oM+Bx9Bn+MoRR+VhvjKr771KXY7bc6YoU3Qe1xHCObYgVdhfU0oWOQ/6n8VgYpARWACRKA14tzhB64WntW6xLz+z57VxgUeZDeksP5Px1x1R5Dl7n4xvLENznPF0GHNL6GNGPFVmm2WadPO2y+bCWlt2owidDcS50aM+Dqt2K1z2ueXK8SplB3HWgxT3JAhw8JB09vp9jtfTPfe/3J6571PgyswPOYUc28kFf/xnnfMEWuEM6fOMS80Jz+uxLnrbxiQfnvevekfb300RuLcQw+/ls465770RP8305DAaJ65molzywdxr9HrXsuCfq/EOUA99tjj6aqrr8rkLcoIT1QUSYSffwQBhVen5eNIQAoRpWNMwQT/19v+Gscfvp69XX3y8SdBbPsyIehsvMnGWXHwFg3Fknc5aS4Z5C+EDkccPhXP9w1yCcWasueYTG9gUCJ4qaKsIYlQwixOEFbeCJLLNCGzuJRhSskBBxyYFaDJQ2mhfM0bijK2JUVraHig6hoenbz1Q6mhCEkHee6eeONj2LAhsdiYOqffbtJ22TMUcg7lyVseZPdmiOMjEfkmCnYoZeizzz5P7wapRTwEvtV+ttpI5aQ13BDNEOcoR5QbCyyKzaTtJk1TTD5FViqXXGrJrPA5npNsPHghIc4WSifvbz3CYxc8SlBv0vPWy8TBiiWfMlK0lNHipEsc3zlNkNFaBkq09C2sHL1ZyDtFOVVmbzzBwgKjMVDq7r/v/rwAs3igmGtLc8w+R1bubggy31uhzCqftrRijxXzgoGCf9ddd6c3Ateppp4qytMjIRxSnv8cZB9K6cAoD7IO4lZrBCtyUPItOixOLRQpoJRUC6xpp5k2k34sUEaMGJ7bi7doBPgI3nCyECturcl6zTXXZpKTRaa2ZgGAPMdjl/rSP7w1ZcFNMbbRuFJ4j/tR04/ysafIUNptU2zQKS+F31G4FjpckVvIIeF5q0ZbR2r7a6THcxjin3bkmFv1pn0h8GnfFn2vvPJyKO1XZeKXfut55LJlll0mkwgdy6veLBzVF+zgYgGsj2rDFh7wRAilhH/8yce5fSiHvy2myPR2tOnpIx1v3SHJaZvXhpc2pMcvPv8il1e59TPe8fThSaJ98ET22muvp/aBHaLkorGg+eijj9OdocRfG0q859XJgQcdmN8myxXR8B9MeUlE6Pv8i8+jrmaOPtExxqdp0lOxoP30s09zmRHTtKsHH3woe5uzeIAJUmKXBbtkD4GOZx4QC9083iz547wx7A029ZPr8O578qLE8bj6oXFGm3r//Q/yhNJloS6ZVAnn1sZB7R9e2quxSVuBv8XLWj3XygQ+fSYvtqOfDIy39OaPOia3uoWjRdY6UU+OAR7dYsbiyTM39L0hzlIflMm2+qqy2NywcFRX+rh2dttfbksvxfWvoz90mqtTbm8WWRa22snL0Zb0Wd7/kIGbfhRvLkX6cEFq1XbWX2/9XJ9Dhw4Jr5enRLrPZKKhRZu+MPpQiXOjx6ferQhUBCoCFYGKQEWgIlARqAhUBCoCFYGKQOsI2Ney72T/xsuzjKDFAFz2Dsp36yl8+yrjuDW//UN7TPYDpG8/VZB+SdPvYgwuKdlPEkoce5FeaEWgkJ6Xj+3L2WtolFX88kxJa3x82/8rMpOtkTiHPMBgbW+0hopAReB/HwFjQRkPSmntpRrvEMeQ3uxl2tMUkOkQp+zl2i+1l24csW9fxiy2MvfstyI0GT8RgzzjJXz2lkKcsz/M25i9ZcQ5e/n2fI1TiD7IOrxreUkd0cyeLuIc4hKyHWKUNKUt2MtHbEKc8zzCGtuKl8f7xR67sc24W4h2xmvltw9tvmCXsG/PVoPsg3DGzoHE1iNIUsU2hkDEqxxCEZISzMgmXWmx1XCGwb4Au+L9ijcupCxkNeVh/xDH/jkbEzmReexlyxP5x33pKxf8pVs8znkZn72IzcxchcTlGTYc9UIO2LtvjhHf/OUejNWZb2VUZ9JlMynlFK/YtcQlIwKSY1qV396/ctuzd1+57PHLly1RGRHBlJstym/EpUKc84y6R8hkJ5BvS+KcNqj+Ebaaon60GQQwcylPa4iChTiHuEU+mLA5IDyyp7BbwM2z2jZbjrkPruwi2mpxHMBGpY7Y/hqJc/BTfvXQJ0hrrRHnlP20IHsi3LMpqkPtTUCcQ9xDBtPWECrJiATG8xkZ2ZnUDxtJ0SWKbcXczG6lftjmyIg4x/4Ia9iqK04E2JORDj1T8CZbIc7Rj+SvPZT0S1sgq9+CvqENwPOMM87IeCGx6cONxDlxjQH6KjsiW5pxA3GQ7ck1MqsnnviMEdqdtEs53aODqGvjAwIkj3Pasjz1ZSRa/Z+ep/7IIC14Kp/76pQDFaRKWMmneBGEF6+L2h97sDpm80Ly1U7odaXsylRDRaAiUBGYUBH4TxDnEOaefe7ddPkVT6T7//Zy+vjTwTHPpHAmNXGei4YMGRrkNS+ljT1xbo3VFky779wt9IPmFyjGVF+ObH16wFvpD1c+mR546NXgTTTL4OjZdpO2SV8MHhonyQWBLwT7d4lzZOkbxLkzz/1fJs5FISkLlBxvrDhukLI0cZuJ8yRNoTbBF2VjTBVU7iNwSI9ih2gkHYSUosAinyHQNIZZY0OFhy3K6AcffDjy1tRBpqKwUnZ5XXth4MAgFo3I6UlXfJ6a2k7aNitm5BUc1XpvKBwISAcffHB69ZVX89GTFDUKHOWjZaDUUDJtSg35ckgmpJXNlUYFY2S8OPKTQjPtdNPmt20oTRYzHUO57hAyNz7TWl68cV0RSjSlBrkIPupjeMgxJMiGHwfp0DGYjrikmFESB4eXwBJgQ9Hy1sE/hUiDdymKkwUVBZOHK0S20cnUTBSKs4ajM3fq1JS9g0mXcobIKC3YNb7F434JlHt1S8mmDHsDKZO/4vqHUadfhVxC27aTZGXRJhqsHX/qDv2VMscTHcVdnVlcnRFK8X7h0rrnWj3zxltO5Dv+0x4QzdSjNCwkLcbKgqg8pt7IaSHjHiXegvSf2nrI+3LI98brb8TAMiwr65TvNlFPhEWW4kVsWLTHEmaIY0opty9H2yiBWt5+qvZZsbfIggUviYJjTzt0bF6Ealdvv9WsEM8404wxMEYdBB4WzbDo3HnuNGPUtwWXuPCmoCPXIR3qDxYU6l37LXhPGmnwvOab3NoYAhQZpY0IZwGvj5b2Ud5oQQYtoX37KfN4MFnU7YsvvjQyfXhMGffIK8BeO/DdYYYO+ahd/VfaFHvjDcKWhQK5HTlr4dVaEN84YqGQ6ykWIlMFvtoNLNtP2T4tsOACuR4/jjFlSCzyBbjql9qfdv12jG0l8EjJO59xpQTl5WXuvUHvZcz1N4sjGKsD7V7/HF0Q/9NPP0kDYzwyFqgLuGoPjUH5tR0kN33LmCTet/px40Ot/IYNXLR3ZXQkbFOnprywF92GhzEDedmYIij7dNNPl+vRvQ/jKGNBGzWGkkVdecvrzDPOzO1lyy23yF7zxNN2PCdPCz44lTbjfutB6jVUBCoCFYGKQEWgIlARqAhUBCoCFYGKQEWgIjCuCNgTYZy3x8ajij0uwfrcR7AuH/PaPEfN/9kTs6/CI449ih5BnigvkjbuizWmbw+i5ON3IZXYe2O8RwSwz8HDCQ8r5BTf/pX4Jd1xkXOUxKP/JX2yStvejfL1C0KJvZJCnEOCqKEiUBGY8BAwNhjnEFCQo4xVCFWIKPbYLwuyTiFuISzZXy/B+FaCMcy4Zr8VIQrpxr4zsg4SmX1SdgkvgEsTUQyhBXGOrcP4xA5x0kkn5f18RzjyVMaWhayGGMdWgfDCqxm7iWDP2hyAYGZsIyM7BjIV0pKyIOGwWRgLy0d+ZFYGNg+2Gl7tEOeUg9cuY38hlLHnISfBiLc53kjLcazGVTYI5Gj2IYQsHyQecpCdnDyFwUKZ5G9fnQMBhEXPOdIUQcneMxy91A0rtgwEKqQuHsM8Q072RSQ9TgeQwZTHuM6mgJgGI1ghTbWcY9S7MrMBsim4rxzkagz21hG/lN1+PqzYAgRxpQMbtg6yIfKpOw4mtAP7+ciOyFHF/qGsyoUQxS6w995757lImvLSBs25ZLe3rs0oEwzVrzxgqN3Cxmky5WV9Hs+QpJSn2RbxacYJ6Q/Ji1MJ7UgbgTfylLrTfuAtbcRM7RwubMGcG/QJ4pz9fm3Cfe1K+eGD7ClddjbEveLVTXtE+EKSU3Ye37RDcR2Dat6VFqcH7InS0z7pBeR330c5yKDO6RH6AAcNCIxk1JdgdvLJJ2f7GvIexwFkK8Q5jle0H6Q+acur5Af3YtdRn36XPixv/Qc5U3/2jDjkVD5ENNgg1yIjIu2xeSHHwlm7ham2qE2Tt+TttzYIGzYgxDlYKZdnkAOVS93Q9aSlz+lX4shbf1R/5OP0o1MQB7VPmJQ2Jm8EUYRXbVgf81HHpdxkqqEiUBGoCEzICIxv4tzgwcPSRZc8mq657on0zqBPYt4IHsiU7dLcnWZIa/58wdR1oVnSWefeH2S2l4MPMjzNPuvYeZwbF+Lc+x98ni75/aOp741Pp0EfNPNsJmvXNnWeq2Na7Sfzh6OqTunXx92eHnz0lXCsU4lzrbX/iWLSb95RyTSlUVFcpiSUzQwTqUmVklFIQqNij/lXSU+a0pKOTwmuUz4aQ1EqinJf7pGjfNyjZEi/pFmumfsnmaTtSGWgEOcoUaefcXounzTl49miNJR8yjcMyCcPcSgirSkWo+J5k6BNTtcz5JGHz+jCe7FQui68c913/32BcZu8ALBQGT5ieFaSBwZB8JZbbg0yzCxpl112yR675CmPEshXcCvXGr9h5Rmh4NV4v+Xvgn3Bt5S7lEtao8NOPNgNj3x54WvTptn1dUm35EdNc1a0tFq2hdJe5IWgw4sd4tzRcSxpt/B8WBYuJa2W3/IqaWoTbeOY2Nbqusha8JFua209l+eb9gDrxgVzyatRBnn5eK4xuKa88vOc/EuQbpGx5Oea+PAo17KMcb3Ui3Qo5NLyfH4m7g/7Rt6SPrwnadGOS/mlPfLZyK8EaZO1UU75ysO3ttUYSvlc8xzPfljUpRylfNIjcz6SORbrHTt0TAcceMBoSWOl/PKFgbTkT0bX1ElruJZylbI0yksunxLEkc9XIXskmvMhq4881MUYQ8RFViyytSx7eV6a8mvG0BuAzeOjfMY1KHeWO9IjY2kz0pFPwaUxXXLBpuU9+buH1GfRdvbZ56S99vplXhRaOJZQ8pTGmPpj8zPjXq6SV/2uCFQEKgIVgYpARaAiUBGoCFQEKgIVgYrAhIwAgps1OsMocgByhmANb91fwtjuKZQ9EkQA6TJWM/wz1jLg22MRR7Du9yl5+S57HmX/wR4PcgCvNwy63bt3zy8fMhaLbw+hpJV/xH9jK2uJP7bfZb+F4ZgxHyEAcQ4BYFxfVhzbPGu8ikBF4L8LAeOAj3HGx2/jFOIcslW/INUa6xCyEJJ5LkNAa2pqysQbBBT7nca4sn9qHCt/GzsR3RDnjMfSQVJBDOJ4wFiIVOOF7+Jxrpx4g3Bz4oknZoKT8Zz3MgQp3q94nEN+MhYj4yFnCUhYSM7ytD+LuIMUhUzlGlIOD13IScY5ZbZXTGa/7fUWewYvWdLyDLIXorOx2phPdsQeTgyUEakLeUmayGrIe4hN9o0ReJDZjPnXX399Lq/r5CjEOfmaL+DtOaQueSISIu4Zp80dvOAhQPUIwpZys+WRASmIbQjBEY6Ic9LktELZkY0Qy0qe8C91rzx+i68uy2/1KZR2oZxeHkcMQ9JT906RKvOsuJ4tHuTIiRhOJkQ6bQduyvSzn/0sy+gZdr3evXs3n1gVc/f++++f5yL5Is7BRF7Ka37iza1PkLM8p6yIY9oM4qGTg+DH4xlS2q9+9avsUUxa8FUmMiozMpW5Dz7uwZu8yHfwUofqDpmtOK4wT8pfm4UBD7TuI2/Jg9MFJLjicQ7hkhz6QyHOOTq3eJxTf4heCKKIpAhfvOg1Rf8iKznJRmZ146OtFhIlApnyI5Uhj7kPf5ipJ+mUo3ELcY5s+gs8OWcgm3zk57fgb8E15Soe54wNCHfas3oveGobiIT6hHaqT/H45oPgqE7Yt4wl8qVryEubk7505YkYqAzaLZIpop9ywVhfR+DTBzynHfEOqS37W/9WbzwKqjdkO31Of9YnlQGxzhhBR4QdUqM+gZhZXmAgj08NFYGKQEVgQkZgfBLnvgoS2lVXP5F6X/JwejOOLI0pLc3Uceq01WY/DmdYC6Wp2reL41q/SIcecUu6/8H/DHGODBdd/HC69PKH07uDPsvchFlnnjZt8YslUs/VF4wXASaP+fWrtNsvr0v3hQyVONd66/9O4lzr0X/YV/ffb/+sKFISzj7n7P+6wnjT5uqrrs5v9Wy9zdb57Q9HZn719Vfpy3ib4YUXBqYLLrggK0qbbLpJVnL/6wrxHxSIos/lsIUrxc+iy2K2Knn/QdD/H5LmSfG1115PN8bRverS21gWcLVe/x/AH4ssLFotVB05wBPhBhtukPtfWWSORRKtRKkLs1ZAqZcqAhWBikBFoCJQEagIVAQqAhWBikBFoCIwRgR4NGHstjdmD4X3Gmt3+yiNeymujU0o8XwzRiPPDRgwIHvCR7xgLPdir/Bd+ZQ0GIYRPRj07eOtHB6KeMJBtGD0LUF8BuRiUC7Xx9d3waHIxZgPM4ZqRnSG5lKm8ZVnTaciUBH470WgjDkkND4gvSCfILsgFjlaEVnGkYbGQOQYYxjyC69SSEXILsYthDAELXYJJ3VIG5HGsZFIb0gzhXSDhFQ8VCGzIEEhvBTPT4hgxx57bCZr8S619dZbZzIcT1G8tjkyk2wINUhtArmR0zzrGV69EOcch2l//Zlnnsl2JYQvJB6kOKQ05UH+QcDihQuJBjkPBn7z2oXojNgjGC/JIU0EHKS5HkFmU0bEKx7ZjPXGdwQeZTO2Ivcg5CEmwgHhSZ7SRcpDTEPukTZs2XjgC1cEOdfh7Fll49UMmQgpDYbqyZGWTnZRJ+rC0aPShAGikLmHDRChCNkMsck3oqGyF29n2kKZJ3xLX106StbcihAHY7/F9UHqEk8cXua0A/WmfEhN6ttJMohz6gUpTD0qG+9p8j/ooIPyXGTORJzjYY4HWZ7OzLnq2NGvZFH/CFTq0hyNiOn6K0GiN0fvueeeOT/31DHsyIv0pQ7VUyFU8g6HoKk+4enUK3WAzEYudQsHbUjbQBSDpfpVRwJymqNa2en0D6Q1dVTqQn/iLY+nPvWHOEdvkR9s1IG25Do54YP0pY48ow2Jg/ymD2hHSJvaEb1HnSKh9gviHNKp+Nouohwiap8gHJrv1YG+ph1oo+rHvI8IJxT9Q50K6gcRT1/R9rR1GKpvhD/lRlqEP50C0RHZjU6hbRkzeEYUV13yeEcGbV4bRTg07mgT+r968oz6dwqStBxrrE9pR+oSTvBHQtTm2Ge0cfWqH/BGqPzy0LcRFJGBtQ/lYr/RDhEPnZ4kT9fFL+XOha//VQQqAhWBCRCB8Umce+edT9PRx/013X3/i+FIKhwbtZko7b3bSmmTjbrG/DNpRvfddz9Phx31nyPOkeHQI29Nf3v4lZGnIu6x0wppqy1+HDK0zTIMHTq8EufG0NYnCOIcZY8ycdCBB2XFYuFYAJ1++mlZWaLQ/bcoCaecckrqe33ffDzmfvvtl5ribQkKXJHfWzgX97k4Ld99+aw0UaInpGDjjdKuLhGrLLgoeTX8sBGwMLJoshCzcPCGUuNbXD/s0v3wpbd4tSDkftxizbj07/e7Spz74beMWoKKQEWgIlARqAhUBCoCFYGKQEWgIlAR+D4QeCWM5cUozDjLoGrv0P5myz1O18cUyrPi+W3/Ufo83TCkM/7ygIN8UYJ8StqN3lgYlpEekAMYae0jMOo2xi9ylnwb75X0x9e3tAWGcGVq9DhXiXPjC+WaTkXgvx8B4w3SivHKx28EYWQeRBRjHSIKIhJSEeJJGTPYIJC0EHYE4wgiDiIb8jL7DcIM0g3PUUg+iEjIaIU4h0iGOIPEgoyFhEQO14477rhMJjOe8ziHNITsxOsUD6Dyt2fOwxpyjGcQkYyxyGqINQh8riHWIEMh8iDjFMKa8tqDN+6R2TPyQTJC0jG+77zzztm7VSHO2RNGXkLuQSZUHvao4klPfsZ6+SBWKZty2UMunvzIjPDl2z1EauOy8skXKakp9prJT0bzG2KdvWfkOEQypCwyIPghDyEmwle+iEHqtnggQ2ZDwEIAg4/ywgwe8Eb0clyueyWUuUz+iFGOr0XEg5P6QMSTTplPxEda5NxB+4ErL4PmStfgz86hvEh18iUf2xZyoLpEnCOLPBEukc3UP4ITvBDDXNNutCG4I1shYEkbsc517XXXXXfNBDTkPB/l1z7pBupQXNcQvJQHnnB0zK560q7JpB6Q2eTlfjkqmMczpK5yVKv2p61rm9Lk8ZA9h50VERCZEBkMCU79wVEdkE09koftBzZkFBDRlI0c8qM3aEfKr03oU657Rn+DvzbJ8x2cedIr+SBQqgNlhjG5Pee3dGEjDfUilHpFzmSf5WXPeOCjLyCbqWNENvmqfxhqR2yU+oVya7fGAW3A39q0dqiNSkN96TsIgPoBGRBgHdWKEFj6kDboOkcGiIUwITMZkAh5NvSsOtEP1Jf+Ig/tDMa82GlDSJfqwDihv2sPgroq5c8X6n8VgYpARWACRGB8EucefuT1dOqZd6f+z7yVRgz/KsbttunEY9ZKK/UYxeN5b9AX6ZDDb073P/RKzHmjjmpdb52uMSaPsplffGl4nf39g9lr3IgRX6exPap14IuD0n4H3ZheeGlQ6BcjUqc5Z0h77LpC+ukq88S43zznIc7tuud1IcMoj3PHHhm65EqdR7aA9z8YnD3XXX3d4+nDjwenDtNPkXbdsXtat9fCoQ99+yS+vjcMSGeee2/6R3jaazNJm7TU4nOmww9eNebnaUemWX489PBr6axz7ktP9H8zDQlZ5pmrQ9p371XS8svhQ43CoMQv32+//WnG97Y7XgidYmgaMmxE2nbzZdLuu3SLeXj88oQmCOIcReWhhx5K55xzTl4QzdVprrTb7rtlBcdipix6SgV8X99XXnFl+lO82eBoSG/3UOimmHKK+Pur9PkXn6dB7w3KCthGG2+UZW/csPq+ZP7/zJeSTEH0pgbFuyi1/58y1LzGPwKIkNxYW+Toj3Xjcvxj/O+kaPy0ILfYLEcC/DvpNT/73RPgv592TaEiUBGoCFQEKgIVgYpARaAiUBGoCFQEKgL/mwgw2CIqMKjbR+E5phiex1eJGa+ff/75bFRn5Gc85lmFwZkRt/ElZPLYn0N+4IEIyYLRnIGW5xRGdc8w0IrrI/ynDbYMyI35MNojajBcL7zwwiON5zlS/a8iUBGYIBBoHBeMWYguSCY8ZyEr8TJl71M8Y6AxA6kOGUV8wfiHpGI8dEQjshNiDC9RTgpis+AdjIc4hBq2DCQsXtsQb3qEFyv32DeMh0hIjvBEquLtDnkGqagQ58iIlGTPnG3E/qzgeWOs9BCe2LeMc0hJyMtISshP9t0FBCAf5CukOR/58GrmGE/3HOeICKR8ZaxGhuYNDckKmRo2SDzGdaQg6XtWmciCIEdGpEMEJuQ6AS7mA2QfZB5pyZvNTnz2APMGMhyPXfKBLYzNQTymkRNxyTWkJWTGMpeIb/9avuZH82Qhy6kz+IiPaFY83Jm7lNO3jz1wL4+rD9jBCOkI1mUOy4WJ/5CxxekTxENEJ6Q53r8Q4dSdOtBuEJyQpZTL3KqsCFWON0W+0nYQ5xAeEb8QxJRXOkhSiG1k8hycYQ5LRDNHwmobyHb0APXUL7ywkUtegvyVDflKebQZz5sLtUsELfIrj/YqjqNZ5e8eEhmPdIhXTdHOyKvN8kbHuxlZEU71H/e0QekifWonnlOP5FMf6gYp1bP+9oz68dEe5aVu6TfkcpSpNohsCmNtREAKgwv81Y9+g/SnvBw06G90EeVQXm0OkZ/OhKQmnrov7Vz9iv/b3/42y+Y+2yu5SlxxYKQ8vDkiVJJZGtqfb+OGtqpd6+/qofQZeMBL++UFz3VEOwRSMhk3yEjvMu4g/+n/xh7PFqKoMUVe6rK8nKDPCohxCIXalD6GpKd96HvSUAb5CqXd5z/qfxWBikBFYAJEYHwS5+657+V0xm/vSc++8E6Ms+aXlPbds0daf91FQ2eaNHSMEenRx95MJ556R3pu4Ht5bJ915mnSTtstl7oFaWySII1NN90UoYNNkv5V4tyzz76b9jnghvTK6x/keWKqKdulbbdcNm28YdeYrybPMvR/+u103Im3h5xvx1GtKU0dpLOjD/t5Wnyx2UKnGxp63FShYwxLF170cKrEuTTmNxB/qP2GMsF9cP/+T+fFypRBRqPM9erVKys4FP3/hvBRKLt3h/JIuXz3nXfT8BHDU4cZOmQFb7LJJ8uKFWWHkkRpq6EiUBGoCPwwEajEuR9mvVWpKwIVgYpARaAiUBGoCFQEKgIVgYpAReD7RgB5gpEdkQLJgyGXEZVBlCHU73EJxeDr2RIYvxEeGJJ9MzzzkMJwzgDOSMvgzSgtP3uvDOI80DDsI18w6NtzFa+EIp9nitG2XCtxxsd3yzSRDJSFERsJUDnqi5vjA+maRkXgh4NAy7EO8YiHTR7m2FuQWgpJCiEMSQuhjYcnZC6kFGMfL248lhXva8YzxGFEHuMKQg2iDvsN0gvyERKQccgz7hkbyYM8xBaEZMOJAvKcsRz5hscvJCWkIaQgcoqPQIVYxL6FqGZMLoQYeSBsiWuukC4ZkIDkiaDjOeQtYzO5fBDLELeMjcZPZS1pIvIgBCFyIXApl3zhQB7XPIcIZq6AB+x49CMDnKUpb8Q15YQvzOBPRnkhtqkHZUDkkj7Cj2uIcOLKz9wiP0QgQdoCmcmCPGjegoNr5IWR/Hkfg11xSKEOPO/jt7pGeiKD8iBay6fEyxnFf/42TyJpwZqsyFDmY3XGSYCymyPdawp5EfMKAU59wkrg7APx0hyLhKYdqpsyDzuBSXnkCbvinQ/G8vOculVeZCn5y0d+0kE2UxZl97vM29o0rHw8o960P5jLS53DAUlUm4WZelI32iyCnjqjh5j3PQ9v7R0ZVd68vCFr6jfSRNjTjpRJuyIj7MkPJ/mU+IiQZCCndPS5Qv4iF9zVFfIaDLRJdS0+HJBHHd2K+KbulUs9aAtkK4Hc4qgv5EPpwohcrpPdM/KBnz6sTbruvnjS0M9gg+inrcKHnDAr/c/4Qg5piW980Ybcd13flJY+gzSn/OpSudW9NiNN5VIvCJGImcpDDnjyeIfEq80YO3zISw4f6Qvi+9RQEagIVAQmVATGJ3HuxfDydtwJd6aHHns1xvdmgvJ8c3dMq6w8f5qn8wwxN36cbrr5mfTC34NYF+9iBLUuTTPV5Kl7t7ljzhmRZp9t2rThel1jDpz2XybOvRF5HHjIzenx/m9EHjEnR7pd5p8lPM7Nm+bqNH3oCZ+nvjc+nQa+jLgXMsZ0MHl4kOu5+kIxn02UBr3/Rdp7jxVirpk8XfC7hypxLiP0P9o7ilJGcfCbgkCJonD49vd/QyAbhZicI6JB6ziUL8oMJWbSkHWyWBiRtyo1/w01VmWoCFQE/jUE6qLsX8OtPlURqAhUBCoCFYGKQEWgIlARqAhUBCoCEzoCjLu8lNjTbAojMIMpI6+/7RfaRyx7iWODVYnLiCvYd/RBMpMX4y2DPMMxsp7vxv1Ue5mMvIz4rjN8MywzhCNjkMmep/TL3+RlYC9/k6FlKNf+1T3QgoV05c3DDUM3rzWMzZU41xLx+ndF4H8bAWOKsUgwPhgXkFKQXZDRkJCMEWXMYqNxD1kLWcWYZbwVD9GFbamMT+5Ly/1yr9hw3EMulrd8jKFsPvInUyMpz7hpTC3EOWOvozB5dJO/NDxLBukYQ+UjHen5bXwlN5JSsYeRS9nI7CMNHwQ28TyHzEO+Uv4in7TJJK5v96VhDJWXPEq5zAHiF+yU2zOuiYN45NmSprmjeEeTnjjylS6ZySRNdeAj3YI/+Usgk49n1QNZ4eVvQdlLvRTMyjPuk8eHLGTyG76eIQfcxS9xG/NSv9InK5nIKH8fz5V7ylTwQ4CSBlkR55AukQp5TkMQUx/KrQzk0UYEMsGoyCo/bUYeZIe3uvdbHHm4Jz+4SbeUBTbiShu2rpd48lIucdRLabOui0dueZARaa5g5J7yS1Ne8iSv3wKZ4KLNFXzIqE5gLb78xCttDm7y126EUu9kho1nYU+GIod7iHm+pS9tOJGFzI3yyMvf8kPihIlylI/48iAXGUoanhOk77dvQfnhUzB13UcaykFWoRF/95XdPb/dgyGcyKXM8vYhl7SVyUd8+YujvRg7HDdMbt7mOGIp2IlbQuPvcq1+VwQqAhWBCQmB8Umc46XtjN/el6694amYi7+MyaF5vhwex7a2aTNxGhFjt4uTxO+JjcXxz9g9PAhsXw4dltZbc5G01+4rhA4w3b9MnPv8i2Hp1NPvSn3/FE7EPhsSc5s1uDxG5LlD3eIZCflkWHKEDENDRvPOgvPOnM44Zd3UscMU6fze/z/Euc6dOqT99hn3o1q/DA9+221Rj2rNlVn/qwhUBCoCFYEfOgIxIddQEagIVAQqAhWBikBFoCJQEagIVAQqAhWBisA4IWCD3RFlvNkwkDKaumazm8G4GFBdE8bFMCqudHwzuEpDHgzJPKcwcrvOOFsIAOLI1zejMI8vPNAwMEtL/JIuecQRpCuNct/zPozXvoVyzfONRu58cxz+K88qB+Mx0hzvM8gENVQEKgITBgJlXGlZWiQUoZBxyrhT4hl/WgvluXKvjDMlfrlfxrRyvcRvvF+u+ZY/UozjLB2HiZC8ww475CNOEWtKKOUp6UrPR37KUu6X+L5LXL+Nz+K61lhmv13zkZ5x2jVjulDSdb8xDffEb0yvYOJeCeIIjXmXe54tQT6N6bnnGdd9/F3il3nL3+4VuVrm7740S9rut5ZmSVe8Evwuf7vvuSJfieMbXq6bX0p83yUf9wQyIkXyKKaeeVfr2bNn2mCDDbLnNXmYWz0nlLRKGXz7lOC+tH1KWy733fNp+Ux5tvFbnBK3XDdfN7YpeZi7Bb+VpfF+yd99acFEKKQ2v8XxbJGx/C5/iyO0/Fu8Rvnc93GdnI2Yeb4xXc8JJU1/N14r13Okb/7zfKmDcr08V/J2vbV0yjX3SzolD3+735hGiVPSg6vylNAYX9ySFqKestPRtCPH2yLPrbDCCvmoYR4axYV5Y92UdOt3RaAiUBGYUBFAnDvptH7p6WffirmseY44+tCfp3V7LRRr1lHj79ji8+zz76Y+lzyS7rjrhfTxJ0Ge4x4rkkWcm6lj+9R5rg6Rz1fpqQFvB7HNffrQRHFv6rTx+oulDdbvGqS1KSONR1Pvix9I77z3aRoWcvVavUvac9fuqalp+vzM6P577PE3U59LH0kPPPRKyDA4ojbLMMkkE6eO07dPCy80S+iZQ1L/AW+lT+Jo1okmoqNMlO+tuMI8ac/dlg9yXZt03oUPpiuueTR98NHg1HGGKdMeO68Qx852DX3w27hc3/fpdOpZd6U33/44iIFt0lKL/ygdffhqmQTYUtaHHn4tnXZWeKl/6o305ZDhaf7OM6UD9h074txJp92Vbr39ufTF4KGhW4xIO2y1bNojcJkqjpsdn2GimHC/0QC/+Rqfqde0KgIVgYpARaAi8C0ERi3sv3WrXqgIVAQqAhWBikBFoCJQEagIVAQqAhWBikBFoFUEGEsZoX0K+UxERtFG427Z7i2G1VYT++aiNAUG2mLgZhRntG00iPstT15mEDuKVx7HhPE400hEKwbgxvyL4VY65CtEAteLvOUaeVwrxuHGsrk3rkEe0vBRTuX7d9McVxlq/IpAReD7QcBY4mMcaBxvXCvjkXGh3PMtND5XxgtjUnlGHESU8py/xSvp+l3ulTFRfPeFko97rvnbh4epm2++OR+36NjLnXfeOW222WbZY1Z5tozV5HbNp8jYmFaRt1HOkg8Zyn3fRR7XxfdRVnnJpzF9cUo+rnu+xJOOe41xStrmEEF6gniebfxbXGnJmwwFx5J/ea58N2JR4pT8xSl5S8+nYFbulbglH9fJ9H/tnVuQVtWVx5fcmhYNAVRuyv1SWkZNlJkBVGgEqQlGiRCnykvxrm8++JBUyUtSmRcrpkpTOsZYVpIqgkYEmUJgEi81KkgMqOFic2tQ44XhqjTQQDPrt07/YfNNNw0jRkrXrjrf2Zd12//Tfb79rbPO2hqPin+ITvOTrlIuY/BqjPnCp+8cyWBr1Ndff91effXV2G6UTHxz5syxKVOmxPcp/NByIFNzULt2DBuEg3ixG/wokoFtmptshJfxkhYe0Qkz6OljTmAID2P0KZAOuyRXMqCHFhrG4UEXB234pQte2VrOsVYmc5XNjCETPcIZ3aV90KADnZIl29Uu9cJPwYaynzb09Mk+0SGfsVIefdiKXeIRPe2yLnmiE07ShyyN6VpzHQicY3vYt956K/6W2L6Wvvvuuy+yzbFGo5Rzl+4YyI9EIBFIBL6hCDQ17bYlS9fb1u27YmtTYPi32d+z7313kN8zz/wZtn9F+Hbfu23h4nW2afOnnsG0xQPwuns21/Ptu1cPsimTR9p+75s3f401btzh3w+tnmm03m6aPMqzzg6LoDlsWLFyu730yiYPWvNssJ4495/HXeZbvo6xfn2rzKvQdFSwYeOmHbZs+Ubb0PhJ2FBX5xl83YZrvjPQpk4Z7YHWh3wb1nds/YZP/fuTbLP1Nu7aIXZTw2h/Ce4Czzh72P77Nd++fMVWz3x6MManTx3ruFzq32dVQH+pf83bH9nS5ettx//sty7+PTtmZD/74czvtGvvtm17bNl/bbBNW327dg+cG+YZ9mb86xU2ckTf+I4r5ZZ17Fi6vNED7v5uhw5Wa0nsvalhlPsg/m8wX8l7pvUMnDtTxJI+EUgEEoFE4AsicOaLji+oMNkTgUQgEUgEEoFEIBFIBBKBRCARSAQSga8NAnrAypmiB7XUNcaD0dN9OCo5PIzl4aoezpb8PMSloAt6Hsxyhp5DY+LRmX7o1KauNmdKOdZeWw/J9bA+mE7jQ/JFih7pUl+eE4FE4OuNgO4D/O8TfMK9TMEotfcD2tBzaExn+ggcpq1AIdGDoOjQwQG97lmlPAXAiL4MgkEOgXOvvfaaLViwIAJi2L5z+vTpETgn2bpP06YgiznR1n28/F6ABhso0huNtg+N0aSOHN3XaQuvWjpkqU9yS5vUB41ohX9pH2PwEfxEv+Yne8QrWzhLDmP6foJPY/DSlh5wYUzXhHHJVXCX5lyOwUORHNrCWPqwHVkUzhzCAT7JCAL/IGvs4sWLI9CJTKhkB+MaDx48OOyDDn54pZc+5Mhm2SD50gEW0GAb9VIOdegYk52SpzPj8EkOeJXzLOWJh7N0QatCP0V2U4cfWv6HVIeHeaita1Tyw6sieeUZmaWdyKBAU1skl37q6IVONtBfi6t4OIuHa0BBr/ppU5fe0kb1l7qwG1tL3dCVBXrooJFdrMGg436xdu3aCLZdt25djF999dWx7e+QIUMCZ/GUMmp1lPqynggkAonANwUBtjItC/fGmltwOXxadck8ePBIBHUhrzqqezvjLS28CNfq2dB7nDSGAr629B1Cu+Sl3Vkp+U9lwyEPXCNwrn0bWAuf0HQqXEp9weHTjO1oT7CfVGsNhhNdp5J9gqrC5GSbqu/bkuZs1DNw7mygmDISgUQgEUgEzgCBk3/8nQFjkiYCiUAikAgkAolAIpAIJAKJQCKQCCQC31gESic6IHT04FN0HY3XAgi9HgDrYTYPY3mYrAINY2y3ilzR88CXMQIPeChLPw9poeVc9tGGlzMHD34ZhxY51BnXA289RFcf5yyJQCKQCJwJAtyfOHT/4L6iOmcOxinqV1tn0emeqLbGOXP/oiiIhrrGS3rdI7kHUmSP2txL2R57y5YtEajH9tIEVGmra3iQBx/yqaNb91XG1U+dUtsWD2PUGS/vvyU94xSNq655QkuBrsSHfmxivtR1j4cWXs2XdjkX6aNfdshGYSVdnDlKTOHhoGhcfbTVH5U2GtFzRpbmJhr6xas+YQ6t5ItOeqHVGHXJ0DatbCFORrChQ4faoEGDjm/xWsqhLl7xq09n6aOtQ/iqDXaaF/WyDb9khTL/YFxFsmhDB0bwcE3FK3rRqs04WEm3ZIoPeRpnDNmsCRinlHapjg7qaotW9LQ5kIsd0MsueBhrj7fkV52z5EuW+JEh20UjesmnXf590qZAjzzosE38tMWrvoqjuibwgLtoWEfx99TY2Gh79uyJLen5exo+fLjV19cHXSlH+iQzz4lAIpAIJAKJQCJwAoEicO5E51dRO3iwOdRWy8DKghNf6CwWWFCzIPJFui+eevbsGQ6Zp556yubNmxeLyptvvtkmTJxovfwtjdZjrfHDYuCAAda//wA7cpQtDCrHDXvsnpDd/my1QIFOi18WOFocwdXU1GSPPvqoveeLkq6+uGlpOWyzZt9ut902Mxa8LJq6+yJGP2oks1rUVHNivr5Mc3uP2cLnF9q8P/zB9u3dZ909fSO6R/gC56677orFM4voup51vtByJ5LzsYBElt4uYIHVrRtvkvjcWw55RGcX3yP4kL3z9tv+Bst/Rrrens5POexzGes/uhoaGuyaa66xESNGhA2xWPPFVyxLXb+K8Kpsr3rBha0ZeANq/vz59oGnDu/hNt166602ddpUG+yLfdIyRvRomyDknHSN6afPdR5xvCjgddgXfAsXLrJFixYZPyDq/Xof9WvYcqjFevXqZXPnzrXRY0ZbXY+6iFxFhxapIUQfbXPQTM5jkYw+p+d6Srd4z/O9nLc1bbOnn37aVq36S1yDCufWeKvrxkmT7G6/HrwJBO5goIUtMsGvvr6XtOc5EUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBE4KwiUfjn56joSXNJ2REN/+OrcpwU9B76uzz//3DZt2mTvvfee7d27N3xio0ePNrKXXHDBBfbZZ5/ZJ598Ej66Ae57Rcb69euDZ9euXXbRRRfFQ9sLL7zQtm7damw3yIPeSy65xMaOHWsDBw4Mv+727dt9C5h9QY9sfGyyobSNOmNnq3SG3dnSk3ISgUTg3ECA+0r5f0+bUva1Zym+fmg6o0NeKVP0ZZ/kn45MnjsQvMz9mGclClCSDM7Syb2xM5nQyibJEE8pC5qO7rW19NBylHMsaZDLOH3Q6P5Of62Oci6Mq9TaLfnIpWi8PX76KLKxlicGaz4kR7Qlf0nKeHu2yJ6Sv5SpfoIjub6M8eyJa8y1ptBHEW1tOwaLD8ZFW3SfVMVWinCHR3yd8YquFFgrjzHJlA7hozH0dKSrllYySp2Sw1ly0Fm2o1F8yE66JFPz6YhX4/CUddrIkxzaFNkum6rekz9Ph6bk6Mg2zae0gT7+nsiGCR/3Cv6e+LvKkggkAolAIpAIJAJnhsA5FzhXml8uNmKR0vYjoNUDrAicI/Dpsccft/kebHbxxZfY3XffZVOnTrWeHklPafXNfwloI3iLYDP4kMnCQouPUl9H9aMsiNr4qB9pe4OSN39+/esnbc2aNXZ+r/MjcGvW7bfb92d8351IFwYdQV6185AeX5766qtagO37bJ/9+U9/jgA0HEbduvmblt272bjrrrMHHnggFjzYrEUWc0AufTrUJvjrsAfxgQ8LbuQtefFF+/3vfu+qj1mPurrg6dG9h40ZO8ZumTHDJjdMtgPNB3zVWS0ifRl7kt2ymbOww4YDBw7YypUr7ZlnnnFHWBOjdu+999qkyZPDeUabYDWK+GRvdLb1s8YlMFLz2v/5fntx6VJbtHCh7fh0h19TD5zz+RDg1s0XfT/76U/Ddn4idelSbQkBNsgW3nGGoFo/hzrprt7KcHrmKRv8jI7NmzfbQw89ZB988KHLsuPYf+tbvW3SpBvtnnvuibnxA1b2BvZtcup7dr7PdBiTH4lAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJwGkiIN+afF+nyXZKstKfJrkExfGi7Pvvvx++NvyLHLx8S+Ac/TyohZcguL59+8YLu/gJocPvhk+UAz8bL96S+QQf5bhx4+zaa6+N4LuXXnopAvDIqHT99deHnPCxtfn3ZI/8fWqfckI+KP8f9CWP5HTGn+OJQCKQCHzVCOT96qu+Av8Y/Xmd/zE4p5ZEIBFIBBKBRCARSAQ6Q+CcCZw7dMqMc5XDgwAsFpIEwOGEwTnz6K9+ZQuee85Gjhxpc+bMsfHjxx936EBLMNZ5HljlndGvrHFd3YFzqlIFZFUBXwqQIkofnbwNgnPow79/aI8//h+29m9/M96gJOsZgXMzbplhvT3ICr7uHvzmBriqtuitIogL/djI2wAr33zTXnnlFVv919WeDe5wZG4jqxnOpPvvvz8yxBEEGIFjbjtOn5iLZ9br1tXbLgt8GJdDiDoF+bz1uWTJEtu2bVuk+obGVdtll11mP7rjRzbds/VBd9SDDQmo66hgL4Uz8nkz9OWXX46DzHAEFv7kxz+2f/HrQLY9irCGJw7HQnKwm2g9zmQJ5CAQjgC2P3kg4XN//GMEsBFE2OqZ9Ahkq/PAvwcffNCuuOKK43Pl2iCDrHUEDpJtTzhIV6XbidwOxnDiMQdwhJnrtWf3bn+bttGeeOIJT228N+w4j0yHPq+LLr44cLrzzjuPZ/sLmWDi8tCP3J4ZOOdIZEkEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBH4shGQ30t65A+jXTsmmvKMrxP/GH4yeGkTGLd8+fLwfw0bNuz4jhP44vr162cff/yx9e7dO7LJ4acbPny4++8+iGxyBNHxsvHOnTtDJv1kP8F3unr16vDd4r9la7EX/UVfZF1++eU2bdq02HEDWuzAds6qlzafSR3+LIlAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIdITAORM4d3yrVoKQvCgISYbTG84eH4gsX06BU+fhXz5szy943i53xw2Bc//kgWbQEUiljGtysMAnZxCBd52V2NbTg7DIMIdFOJDgJ8AK3R999JE99thj9s4773qwlG+h6n0Ezt3mW5X2dScS26EScAWfh4pV6nRqmyfjbHnwiAcArlq1ynUdCfq+fft4RrWxNmHCBGvw7G1sucqbmdjCPDQX7KEOYATQYVeocPlVP9nnWqy5uTm2RcDptXjx4rAFjHgr9PZZsyIgjDZvhnIO3nYAAtu4Dj7W7LTLly2zF154wZ1dO8IGxv795z+PoLaYd9s8EYVdXNc2COiKEn1tctlSt7X1mPV0p1vjxsZw0v3Ft0zduXNX0FaOvK6xfe11no1v4KCBEegXjjTHATzbC5wL/NHhUghxY37YesCDILGILSi2bN5i69autY2+FcU6DzRs9rdhNQeCJcGKbXhnz54Verq7Iw/8OSjYgNwMnAs48iMRSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUTgS0Kg9NHJV6cgMflCT0e1eKCVj4vAN17A5aVessNB87K/OEu2ObLDsRUr2eN4CRif2qWXXho+tCuvvDL8nI2NjaGaYLl33303fKgExOHXm+E7X7BdK1u4NjU1ReY5gvGuuuoqGzVqVPg/lbUOIbKvnG8IP8UHPOI7BVkOJQKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCJg51TgnAK+dF1KBwchT2Qcc6+HZzLr6qcuEcD2yCOP2LOelWzkiBERODdx4sQIXiJD2VHfnpXgNzK1kQ1O260eI7OZB2d1VtDZzQPsjrpenDMERVWBUh6c5uxsW/Dkk0/aG2+8YQRRQTPbg9Bu/cEP7Nt9+liLB7kxBzKvyYGFziqIq9KOvF27dtncuXMjAK9Pn28Hz7Bhw+wWl3PjDTfEtrRkuCOgDQcTGGAXTiTkVsFyzK/KkIdOAtAIEOvqWIGb6Aia+81vnvLscgdDHm+H3uA6bvItbse646vFA/TY2pa5lviH3UzaS8zF680HD9hvf/u7yAqHbV09cI+McQ8//Asb7Y4u2tgWTje3iQC5kOn1shDshkzGOMh8p4C1DRs22LPPPhtBhZovAYpDhwy18RPG2zS3e9DgQR7IVgXFcY2xMjQUeuhlW1ZXFLrIgnfIg+HkEHzTgxaX+daw6GP7CLaDZbyurtoSuLl5vw0YMMBmzpxpd9xxR8wrtrxtsz3m4+K5Bhk4V17drCcCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAh8WQiEn64QXuvPK4Y6rCKDAz8e/jcC53hRlsA3XlxF5jJ/ebaP+zsJcKPwAjD0/fv3j505eDGYrVzJQLdixYrYpYKAut2+uwP8vBBMEF1DQ0ME3q1cudJ27NgRfkB0EjRHUN3w4cPDLwjPF51bLb8A+P9gJN48JwKJQCKQCCQCiUAikAgkAolAIpAIJAKJwNcLgf8FtMfUj9vdYsYAAAAASUVORK5CYII=" + } + }, + "cell_type": "markdown", + "id": "c6c216c5-4f22-49a4-8cdc-e04fa3a0b098", + "metadata": {}, + "source": [ + "User-Program Workflow \n", + "- model parameter \"prior calibration\" (specification for desired data)\n", + "- prior-predictive, SBC, posterior-predictive\n", + "- purpose: analyze, statistics\n", + "\n", + "\n", + "Six software Workflow \n", + "- policy parameter \"prior calibration\" (specification for desired behavior)\n", + "- behavior-parameter classification/mapping\n", + "- purpose: prescribe, science\n", + "\n", + "Details in https://github.com/hyunjimoon/DataInDM#supply-of-silkroad-project\n", + "![image.png](attachment:49465262-0d0e-4530-924a-86d154fd9c04.png)\n", + "\n", + "i) User-Program workflow (August)\n", + "\n", + "ii) structural dominance analysis, pattern recognition (September)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6866c023-2d95-40e4-a7bf-2b65254f9d85", + "metadata": { + "tags": [] + }, + "source": [ + "# 1. User-Program Workflow (Analyze)\n", + "\n", + "| Step | Goal | Program's work (P-rows have `.function(input)`) | User's work |\n", + "| ---- | ------------------ | ------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------- |\n", + "| U1 | Draft | `Vensim` assists U1.a() | a. Translate mental model to SD model |\n", + "| U2 | Classify | `PySD` assists U2.a() | a. Classify parameters `est_param`, `ass_param`, b. Select `obs_state` among stocks |\n", + "| P1 | relate | `PySD`, `.build_function_block`(U1.a) | |\n", + "| U3 | Specify_project | | a. Supply value or series of `assmed_param`, b. Choose `family`(:= dist. of `msr_err_scale`) |\n", + "| U4 | Specify_regularize | | a. Choose `prior_family`(`est_param`'s prior dist. type) , b. Set `prior_param` (`est_param`'s prior param) |\n", + "| P2 | predict | `draws2data.stan`, `fit_prior_data.sample()`, `fit_prior_data = (U2.ab, U3.ab, U4.ab)`: Prior predictive check (opt-out prior) | |\n", + "| P3 | infer to verify | `data2draws.stan`,`.create_stan_program`(U2.ab, U3.ab): Infer parameter from (synthetic) data: SBC | |\n", + "| U5 | Specify_tolerance | | a. Set precision with `iter_sampling` (:= # of samples), b. Select posterior approximator |\n", + "| P4 | infer to validate | `Stan`, `fit_post_draws.sample()`, ` fit_post_draws = (P1, U3.ab, U4.ab, U5.ab)`: Posterior predictive check (opt-in prior) | |\n", + "\n", + "\n", + "##### Q. family and prior dist change\n", + "How often does the measurement model (family) change? Can all (or some) changes be covered with prior change (e.g. adding hierarchy; family poisson to neg_binom is the same with gamma prior for rate)?\n", + "\n", + "What scenarios does user decide to change prior distribution (not prior parameter)?" + ] + }, + { + "attachments": { + "db58966d-7db3-43ac-9114-da7b079d88c4.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "07c14386-a246-4f4d-a4e5-ad85fa338c19", + "metadata": { + "tags": [] + }, + "source": [ + "## U1. Draft\n", + "From mental model to SD model.\n", + "![image.png](attachment:db58966d-7db3-43ac-9114-da7b079d88c4.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9dcead94-0f1f-4396-8b41-65c41d68df57", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "original name stan variable name is stock\n", + "----------------------------------------------------------------------------------\n", + "Adjustment for WIP adjustment_for_wip \n", + "Change in Exp Orders change_in_exp_orders \n", + "Customer Order Rate customer_order_rate \n", + "Desired Inventory desired_inventory \n", + "Desired Inventory Coverage desired_inventory_coverage \n", + "Desired Production desired_production \n", + "Desired Production Start Rate desired_production_start_rate \n", + "Desired Shipment Rate desired_shipment_rate \n", + "Desired WIP desired_wip \n", + "Expected Order Rate expected_order_rate V\n", + "Inventory inventory V\n", + "Inventory Adjustment Time inventory_adjustment_time \n", + "Inventory Coverage inventory_coverage \n", + "Manufacturing Cycle Time manufacturing_cycle_time \n", + "Maximum Shipment Rate maximum_shipment_rate \n", + "Minimum Order Processing Time minimum_order_processing_time \n", + "Order Fulfillment Ratio order_fulfillment_ratio \n", + "Production Adjustment from Inventory production_adjustment_from_inventory \n", + "Production Rate production_rate \n", + "Production Start Rate production_start_rate \n", + "Safety Stock Coverage safety_stock_coverage \n", + "Shipment Rate shipment_rate \n", + "Table for Order Fulfillment table_for_order_fulfillment \n", + "Time to Average Order Rate time_to_average_order_rate \n", + "WIP Adjustment Time wip_adjustment_time \n", + "Work in Process Inventory work_in_process_inventory V\n", + "FINAL TIME final_time \n", + "INITIAL TIME initial_time \n", + "SAVEPER saveper \n", + "TIME STEP time_step \n" + ] + } + ], + "source": [ + "vf = VensimFile(\"vensim_models/ds_white_sterman.mdl\")\n", + "vf.parse()\n", + "am = vf.get_abstract_model()\n", + "stan_builder = StanModelBuilder(am)\n", + "stan_builder.print_variable_info()" + ] + }, + { + "cell_type": "markdown", + "id": "477f3043-0f99-4bcd-9c78-da7e53081fd0", + "metadata": { + "tags": [] + }, + "source": [ + "## U2. Classify\n", + "\n", + "| variable name | `est_param` | `ass_param` | `obs_stock` |\n", + "| ------------------------------------ | ----------- | ----------- | ----------- |\n", + "| adjustment_for_wip | | | |\n", + "| change_in_exp_orders | | | |\n", + "| customer_order_rate | | V (series) | |\n", + "| desired_inventory | | | |\n", + "| desired_inventory_coverage | | | |\n", + "| desired_production | | | |\n", + "| desired_production_start_rate | | | |\n", + "| desired_shipment_rate | | | |\n", + "| desired_wip | | | |\n", + "| expected_order_rate | | | V |\n", + "| inventory | | | V |\n", + "| inventory_adjustment_time | V | | |\n", + "| inventory_coverage | | V | |\n", + "| manufacturing_cycle_time | | V | |\n", + "| maximum_shipment_rate | | | |\n", + "| minimum_order_processing_time | V | | |\n", + "| order_fulfillment_ratio | | | |\n", + "| production_adjustment_from_inventory | | | |\n", + "| production_rate | | | |\n", + "| production_start_rate | | | |\n", + "| safety_stock_coverage | | | |\n", + "| shipment_rate | | | |\n", + "| table_for_order_fulfillment | | V (lookup) | |\n", + "| time_to_average_order_rate | | V | |\n", + "| wip_adjustment_time | | V | |\n", + "| work_in_process_inventory | | | V |\n", + "| initial_time | | V | |\n", + "| final_time | | V | |\n", + "| time_step | | V | |\n", + "\n", + "The rest is `aux_var` which are derived from the defined." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a3d2b4a9-532f-4f3f-858e-64b4877c1997", + "metadata": {}, + "outputs": [], + "source": [ + "est_param_lst = [\"inventory_adjustment_time\", \"minimum_order_processing_time\"]\n", + "ass_param_lst = [\"customer_order_rate\", \"inventory_coverage\", \"manufacturing_cycle_time\", \"time_to_average_order_rate\", \"wip_adjustment_time\"]\n", + "obs_stock_lst = [\"work_in_process_inventory\", \"inventory\"]" + ] + }, + { + "cell_type": "markdown", + "id": "33ac5c16-f572-46ba-8d79-062f601a5e24", + "metadata": {}, + "source": [ + "## P1. relate\n", + "From SD model (`.mdl`) to Stan ODE function block (`.stan`). No new information is added." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d1a14086-45cd-4f99-9e69-51aa57dea790", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "functions {\n", " real lookupFunc_0(real x){\n", " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", @@ -798,6 +280,9 @@ "\n", " # Begin ODE declaration\n", " vector vensim_func(real time, vector outcome, real customer_order_rate, real inventory_coverage, real manufacturing_cycle_time, real time_to_average_order_rate, real wip_adjustment_time ){\n", + " vector[2] dydt; # Return vector of the ODE function\n", + "\n", + " # State variables\n", " real work_in_process_inventory = outcome[1];\n", " real inventory = outcome[2];\n", "\n", @@ -807,22 +292,25 @@ " real minimum_order_processing_time = 2;\n", " real desired_inventory_coverage = minimum_order_processing_time + safety_stock_coverage;\n", " real desired_inventory = desired_inventory_coverage * expected_order_rate;\n", + " real production_rate = work_in_process_inventory / manufacturing_cycle_time;\n", " real inventory_adjustment_time = 8;\n", " real production_adjustment_from_inventory = desired_inventory - inventory / inventory_adjustment_time;\n", - " real desired_production = fmax(0,expected_order_rate + production_adjustment_from_inventory);\n", + " real desired_production = max(0,expected_order_rate + production_adjustment_from_inventory);\n", " real desired_wip = manufacturing_cycle_time * desired_production;\n", - " real adjustment_for_wip = desired_wip - work_in_process_inventory / wip_adjustment_time;\n", - " real desired_production_start_rate = desired_production + adjustment_for_wip;\n", - " real maximum_shipment_rate = inventory / minimum_order_processing_time;\n", " real desired_shipment_rate = customer_order_rate;\n", + " real maximum_shipment_rate = inventory / minimum_order_processing_time;\n", " real order_fulfillment_ratio = table_for_order_fulfillment(maximum_shipment_rate / desired_shipment_rate);\n", - " real production_rate = work_in_process_inventory / manufacturing_cycle_time;\n", - " real production_start_rate = fmax(0,desired_production_start_rate);\n", - " real work_in_process_inventory_dydt = production_start_rate - production_rate;\n", " real shipment_rate = desired_shipment_rate * order_fulfillment_ratio;\n", " real inventory_dydt = production_rate - shipment_rate;\n", + " real adjustment_for_wip = desired_wip - work_in_process_inventory / wip_adjustment_time;\n", + " real desired_production_start_rate = desired_production + adjustment_for_wip;\n", + " real production_start_rate = max(0,desired_production_start_rate);\n", + " real work_in_process_inventory_dydt = production_start_rate - production_rate;\n", "\n", - " return {work_in_process_inventory_dydt, inventory_dydt};\n", + " dydt[1] = work_in_process_inventory_dydt;\n", + " dydt[2] = inventory_dydt;\n", + "\n", + " return dydt;\n", " }\n", "}\n", "\n" @@ -844,20 +332,7 @@ "id": "75a0b7de-7c90-4823-9b38-7370f3cea02f", "metadata": {}, "source": [ - "## U3. Specify\n", - "\n", - "#### Estimated parameter $\\theta$ \n", - "\n", - "- declared in generated quantities block for `_draws2data.stan` and model block for `_data2draws.stan`.\n", - "\n", - "| `ess_param` | (min, mode, max) | distribuiton type| \n", - "| ------------------------------- | ---------------- | ------------ |\n", - "| `inventory_adjustment_time` | (6,8,12) | N(8, $1^2$) |\n", - "| `minimum_order_processing_time` | (1,2,4) | N(2, $.5^2$) |\n", - "\n", - "\n", - "Q1. Can `msr_err` (min, mode, max) be helpful info?\n", - "Q2. Shouldn't `msr_err` distribution determine `family`? Then, `Poisson`, `Neg_Binom`, `\n", + "## U3. Specify_project\n", "\n", "#### Assumed parameter $X$ \n", "\n", @@ -874,6 +349,20 @@ "|`initial_time`, `final_time`, `time_step` | 0, 10, .125|\n", "|`table_for_order_fulfillment`| lookup function|\n", "\n", + "## U4. Specify_regularize\n", + "\n", + "#### Estimated parameter $\\theta$ \n", + "\n", + "- declared in generated quantities block for `_draws2data.stan` and model block for `_data2draws.stan`.\n", + "\n", + "| `ess_param` | (min, mode, max) | distribuiton type| \n", + "| ------------------------------- | ---------------- | ------------ |\n", + "| `inventory_adjustment_time` | (6,8,12) | N(8, $1^2$) |\n", + "| `minimum_order_processing_time` | (1,2,4) | N(2, $.5^2$) |\n", + "\n", + "\n", + "##### Q. Can `msr_err` (min, mode, max) be helpful info?\n", + "##### Q. Shouldn't `msr_err` distribution determine `family`? Then, `Poisson`, `Neg_Binom`, `\n", "\n", "\n", "#### Latent stock $Z$\n", @@ -897,7 +386,7 @@ "id": "6671aae2-5375-4056-b4f0-83cda80ba708", "metadata": {}, "source": [ - "## P2. Variational_prior\n", + "## P2. predict\n", "\n", "\n", "- based on `est_param` specification (a = lower_bound, b= most likely, c = upper_bound) in U3, its prior is automatically set to $\\theta \\sim N(\\frac{a+4b+c}{6}, \\frac{c-a}{6})$ using [PERT dist](https://en.wikipedia.org/wiki/PERT_distribution)\n", @@ -908,14 +397,14 @@ "| `minimum_order_processing_time` | Normal |loc = 2, scale = $.5^2$ |\n", "| `msr_err` |lognormal, inverse_gamma|\n", "\n", - "Q3. feedback on PERT?\n", + "##### Q. feedback on PERT?\n", "\n", - "Q4. how do we usually determine `msr_err`'s prior parameter?" + "##### Q. how do we usually determine `msr_err`'s prior parameter?" ] }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 6, "id": "edd2203d-618e-464b-b3f2-15ed26b1e7cf", "metadata": {}, "outputs": [], @@ -932,7 +421,8 @@ " \"time_to_average_order_rate\" : 8, \n", " \"wip_adjustment_time\" :2,\n", " 'manufacturing_cycle_time' : 8,\n", - " 'safety_stock_coverage' : 2\n", + " 'safety_stock_coverage' : 2,\n", + " 'real inventory_coverage' : 2\n", "}" ] }, @@ -943,24 +433,32 @@ "tags": [] }, "source": [ - "## P3. Draws2Data " + "## P3. infer to verify" + ] + }, + { + "cell_type": "markdown", + "id": "bb81280a-e5a7-4b6c-aebb-48a18dc44f61", + "metadata": {}, + "source": [ + "The first argument is `ass_param` and the second is `observed stock`. Design for `est_param` is under-development including \n", + "##### Q. how to express multi-levle prior? Auto scale?" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 7, "id": "b393e0fd-470f-43f5-a535-866f689aed24", "metadata": {}, "outputs": [], "source": [ - "# first argument is `ass_param` and the second is `observed stock`. Design for `est_param` is under-development including how to express multi-levle prior\n", "ds_draws2data = stan_builder.create_stan_program(ass_param_lst, obs_stock_lst)\n", "#print(ds_draws2data)" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 7, "id": "a5d2525b-e441-4e79-a0f3-8d5c2c953c63", "metadata": {}, "outputs": [], @@ -972,13 +470,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "16713c97-3371-4ecb-aa04-310ad0480034", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'sf_path_draws2data' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m sm_draws2data \u001b[38;5;241m=\u001b[39m CmdStanModel(stan_file \u001b[38;5;241m=\u001b[39m \u001b[43msf_path_draws2data\u001b[49m)\n\u001b[1;32m 2\u001b[0m fit_prior_data \u001b[38;5;241m=\u001b[39m sm_draws2data\u001b[38;5;241m.\u001b[39msample(data\u001b[38;5;241m=\u001b[39mdata_draws2data, iter_sampling\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m30\u001b[39m, chains\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, fixed_param\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, iter_warmup\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'sf_path_draws2data' is not defined" + ] + } + ], "source": [ "sm_draws2data = CmdStanModel(stan_file = sf_path_draws2data)\n", - "fit_prior_pred = sm_draws2data.sample(data=data_draws2data, iter_sampling=30, chains=1, fixed_param=True, iter_warmup=0)" + "fit_prior_data = sm_draws2data.sample(data=data_draws2data, iter_sampling=30, chains=1, fixed_param=True, iter_warmup=0)" ] }, { @@ -986,607 +496,9 @@ "execution_count": null, "id": "3939e7a3-259b-47a3-9360-0b17a54726b3", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:                  (draw: 30, chain: 1, y_init_tilde_dim_0: 2,\n",
-       "                              y_tilde_dim_0: 50, y_tilde_dim_1: 2,\n",
-       "                              sigma_tilde_dim_0: 2, z_init_tilde_dim_0: 2,\n",
-       "                              integrated_result_tilde_dim_0: 50,\n",
-       "                              integrated_result_tilde_dim_1: 2)\n",
-       "Coordinates:\n",
-       "  * chain                    (chain) int64 1\n",
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-       "Dimensions without coordinates: y_init_tilde_dim_0, y_tilde_dim_0,\n",
-       "                                y_tilde_dim_1, sigma_tilde_dim_0,\n",
-       "                                z_init_tilde_dim_0,\n",
-       "                                integrated_result_tilde_dim_0,\n",
-       "                                integrated_result_tilde_dim_1\n",
-       "Data variables:\n",
-       "    y_init_tilde             (chain, draw, y_init_tilde_dim_0) float64 30.28 ...\n",
-       "    y_tilde                  (chain, draw, y_tilde_dim_0, y_tilde_dim_1) float64 ...\n",
-       "    sigma_tilde              (chain, draw, sigma_tilde_dim_0) float64 0.01 .....\n",
-       "    z_init_tilde             (chain, draw, z_init_tilde_dim_0) float64 30.0 ....\n",
-       "    alpha_tilde              (chain, draw) float64 0.55 0.55 0.55 ... 0.55 0.55\n",
-       "    beta_tilde               (chain, draw) float64 0.028 0.028 ... 0.028 0.028\n",
-       "    gamma_tilde              (chain, draw) float64 0.8 0.8 0.8 ... 0.8 0.8 0.8\n",
-       "    delta_tilde              (chain, draw) float64 0.024 0.024 ... 0.024 0.024\n",
-       "    integrated_result_tilde  (chain, draw, integrated_result_tilde_dim_0, integrated_result_tilde_dim_1) float64 ...\n",
-       "Attributes:\n",
-       "    stan_version:        2.30.0\n",
-       "    model:               pp_draws2data_model\n",
-       "    num_draws_sampling:  30
" - ], - "text/plain": [ - "\n", - "Dimensions: (draw: 30, chain: 1, y_init_tilde_dim_0: 2,\n", - " y_tilde_dim_0: 50, y_tilde_dim_1: 2,\n", - " sigma_tilde_dim_0: 2, z_init_tilde_dim_0: 2,\n", - " integrated_result_tilde_dim_0: 50,\n", - " integrated_result_tilde_dim_1: 2)\n", - "Coordinates:\n", - " * chain (chain) int64 1\n", - " * draw (draw) int64 0 1 2 3 4 5 6 ... 23 24 25 26 27 28 29\n", - "Dimensions without coordinates: y_init_tilde_dim_0, y_tilde_dim_0,\n", - " y_tilde_dim_1, sigma_tilde_dim_0,\n", - " z_init_tilde_dim_0,\n", - " integrated_result_tilde_dim_0,\n", - " integrated_result_tilde_dim_1\n", - "Data variables:\n", - " y_init_tilde (chain, draw, y_init_tilde_dim_0) float64 30.28 ...\n", - " y_tilde (chain, draw, y_tilde_dim_0, y_tilde_dim_1) float64 ...\n", - " sigma_tilde (chain, draw, sigma_tilde_dim_0) float64 0.01 .....\n", - " z_init_tilde (chain, draw, z_init_tilde_dim_0) float64 30.0 ....\n", - " alpha_tilde (chain, draw) float64 0.55 0.55 0.55 ... 0.55 0.55\n", - " beta_tilde (chain, draw) float64 0.028 0.028 ... 0.028 0.028\n", - " gamma_tilde (chain, draw) float64 0.8 0.8 0.8 ... 0.8 0.8 0.8\n", - " delta_tilde (chain, draw) float64 0.024 0.024 ... 0.024 0.024\n", - " integrated_result_tilde (chain, draw, integrated_result_tilde_dim_0, integrated_result_tilde_dim_1) float64 ...\n", - "Attributes:\n", - " stan_version: 2.30.0\n", - " model: pp_draws2data_model\n", - " num_draws_sampling: 30" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "fit_prior_pred.draws_xr()" + "fit_prior_data.draws_xr()" ] }, { @@ -1598,24 +510,36 @@ "source": [ "fig, ax = plt.subplots()\n", "#compare with real \n", - "ax.plot(fit_prior_pred.loc[:, ['y_tilde']], label = \"\")\n", + "ax.plot(fit_prior_data.loc[:, ['y_tilde']], label = \"\")\n", "ax.plot(state_dt.loc[:, ['Predator']], label = \"\")\n", "for i in range(len(obs_stock_lst)):\n", - " ax.plot(pd.DataFrame(fit_prior_pred.y_tilde[:,:,i]).T.loc[:, :5])\n", + " ax.plot(pd.DataFrame(fit_prior_data.y_tilde[:,:,i]).T.loc[:, :5])\n", "ax.legend()" ] }, { "cell_type": "markdown", - "id": "960d1e72-a5e4-4dcb-995f-e9d689c26149", + "id": "12626a31-08bc-4a5d-ad74-39916446e4ff", "metadata": {}, "source": [ - "## P4. Data2Draws" + "## U5. Specify_tolerance\n", + "\n", + "#### Q. how to set 10^-2, 3,7~? ode_rk45 precison" + ] + }, + { + "cell_type": "markdown", + "id": "960d1e72-a5e4-4dcb-995f-e9d689c26149", + "metadata": { + "tags": [] + }, + "source": [ + "## P4. infer to validate" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "f91d6908-3ff1-4787-b80a-f43b85e0a6ff", "metadata": {}, "outputs": [], @@ -1654,10 +578,19 @@ }, { "cell_type": "markdown", - "id": "472ae07f-9550-47c0-be46-791ff7f3c234", + "id": "f5191cd0-da48-4846-bc30-6d11304479a7", "metadata": {}, "source": [ - "## P5. SBC" + "###### Q.brms state\n", + "```\n", + "Confused about brms family quas\n", + "quasi(link = \"identity\", variance = \"constant\")\n", + "quasibinomial(link = \"logit\")\n", + "quasipoisson(link = \"log\")\n", + "```\n", + "\n", + "###### Q.hierarchical auto-scaling, formula (+others)\n", + "https://github.com/hyunjimoon/DataInDM/issues/9" ] } ], diff --git a/test_scripts/stan_file/demand_supply.ipynb b/test_scripts/stan_file/demand_supply.ipynb deleted file mode 100644 index 731b943c..00000000 --- a/test_scripts/stan_file/demand_supply.ipynb +++ /dev/null @@ -1,598 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 8, - "id": "c1026b9e-9c63-4bbf-afea-4d3b8c4a4898", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from IPython.display import Image\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "import pysd\n", - "from pysd.translators.vensim.vensim_file import VensimFile\n", - "from pysd.translators.xmile.xmile_file import XmileFile\n", - "from pysd.builders.stan.stan_model_builder import *\n", - "\n", - "import cmdstanpy # 2.30 is fastest (as of 08.12.2022) `cmdstanpy.install_cmdstan()` \n", - "from cmdstanpy import CmdStanModel, cmdstan_path\n", - "import arviz as az #!pip install git+https://github.com/arviz-devs/arviz\n", - "az.style.use(\"arviz-darkgrid\")\n", - "\n", - "# set your working directiory\n", - "os.chdir(\"/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts\")" - ] - }, - { - "attachments": { - "49465262-0d0e-4530-924a-86d154fd9c04.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "c6c216c5-4f22-49a4-8cdc-e04fa3a0b098", - "metadata": {}, - "source": [ - "User-Program Workflow \n", - "- model parameter \"prior calibration\" (specification for desired data)\n", - "- prior-predictive, SBC, posterior-predictive\n", - "- purpose: analyze, statistics\n", - "\n", - "\n", - "Six software Workflow \n", - "- policy parameter \"prior calibration\" (specification for desired behavior)\n", - "- behavior-parameter classification/mapping\n", - "- purpose: prescribe, science\n", - "\n", - "Details in https://github.com/hyunjimoon/DataInDM#supply-of-silkroad-project\n", - "![image.png](attachment:49465262-0d0e-4530-924a-86d154fd9c04.png)\n", - "\n", - "i) User-Program workflow (August)\n", - "\n", - "ii) structural dominance analysis, pattern recognition (September)\n" - ] - }, - { - "cell_type": "markdown", - "id": "6866c023-2d95-40e4-a7bf-2b65254f9d85", - "metadata": { - "tags": [] - }, - "source": [ - "# 1. User-Program Workflow (Analyze)\n", - "\n", - "| Step | Goal | Program's work (P-rows have `.function(input)`) | User's work |\n", - "| ---- | ------------------ | ------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------- |\n", - "| U1 | Draft | `Vensim` assists U1.a() | a. Translate mental model to SD model |\n", - "| U2 | Classify | `PySD` assists U2.a() | a. Classify parameters `est_param`, `ass_param`, b. Select `obs_state` among stocks |\n", - "| P1 | relate | `PySD`, `.build_function_block`(U1.a) | |\n", - "| U3 | Specify_project | | a. Supply value or series of `assmed_param`, b. Choose `family`(:= dist. of `msr_err_scale`) |\n", - "| U4 | Specify_regularize | | a. Choose `prior_family`(`est_param`'s prior dist. type) , b. Set `prior_param` (`est_param`'s prior param) |\n", - "| P2 | predict | `draws2data.stan`, `fit_prior_data.sample()`, `fit_prior_data = (U2.ab, U3.ab, U4.ab)`: Prior predictive check (opt-out prior) | |\n", - "| P3 | infer to verify | `data2draws.stan`,`.create_stan_program`(U2.ab, U3.ab): Infer parameter from (synthetic) data: SBC | |\n", - "| U5 | Specify_tolerance | | a. Set precision with `iter_sampling` (:= # of samples), b. Select posterior approximator |\n", - "| P4 | infer to validate | `Stan`, `fit_post_draws.sample()`, ` fit_post_draws = (P1, U3.ab, U4.ab, U5.ab)`: Posterior predictive check (opt-in prior) | |\n", - "\n", - "\n", - "##### Q. family and prior dist change\n", - "How often does the measurement model (family) change? Can all (or some) changes be covered with prior change (e.g. adding hierarchy; family poisson to neg_binom is the same with gamma prior for rate)?\n", - "\n", - "What scenarios does user decide to change prior distribution (not prior parameter)?" - ] - }, - { - "attachments": { - "db58966d-7db3-43ac-9114-da7b079d88c4.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "07c14386-a246-4f4d-a4e5-ad85fa338c19", - "metadata": { - "tags": [] - }, - "source": [ - "## U1. Draft\n", - "From mental model to SD model.\n", - "![image.png](attachment:db58966d-7db3-43ac-9114-da7b079d88c4.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9dcead94-0f1f-4396-8b41-65c41d68df57", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "original name stan variable name is stock\n", - "----------------------------------------------------------------------------------\n", - "Adjustment for WIP adjustment_for_wip \n", - "Change in Exp Orders change_in_exp_orders \n", - "Customer Order Rate customer_order_rate \n", - "Desired Inventory desired_inventory \n", - "Desired Inventory Coverage desired_inventory_coverage \n", - "Desired Production desired_production \n", - "Desired Production Start Rate desired_production_start_rate \n", - "Desired Shipment Rate desired_shipment_rate \n", - "Desired WIP desired_wip \n", - "Expected Order Rate expected_order_rate V\n", - "Inventory inventory V\n", - "Inventory Adjustment Time inventory_adjustment_time \n", - "Inventory Coverage inventory_coverage \n", - "Manufacturing Cycle Time manufacturing_cycle_time \n", - "Maximum Shipment Rate maximum_shipment_rate \n", - "Minimum Order Processing Time minimum_order_processing_time \n", - "Order Fulfillment Ratio order_fulfillment_ratio \n", - "Production Adjustment from Inventory production_adjustment_from_inventory \n", - "Production Rate production_rate \n", - "Production Start Rate production_start_rate \n", - "Safety Stock Coverage safety_stock_coverage \n", - "Shipment Rate shipment_rate \n", - "Table for Order Fulfillment table_for_order_fulfillment \n", - "Time to Average Order Rate time_to_average_order_rate \n", - "WIP Adjustment Time wip_adjustment_time \n", - "Work in Process Inventory work_in_process_inventory V\n", - "FINAL TIME final_time \n", - "INITIAL TIME initial_time \n", - "SAVEPER saveper \n", - "TIME STEP time_step \n" - ] - } - ], - "source": [ - "vf = VensimFile(\"vensim_models/ds_white_sterman.mdl\")\n", - "vf.parse()\n", - "am = vf.get_abstract_model()\n", - "stan_builder = StanModelBuilder(am)\n", - "stan_builder.print_variable_info()" - ] - }, - { - "cell_type": "markdown", - "id": "477f3043-0f99-4bcd-9c78-da7e53081fd0", - "metadata": { - "tags": [] - }, - "source": [ - "## U2. Classify\n", - "\n", - "| variable name | `est_param` | `ass_param` | `obs_stock` |\n", - "| ------------------------------------ | ----------- | ----------- | ----------- |\n", - "| adjustment_for_wip | | | |\n", - "| change_in_exp_orders | | | |\n", - "| customer_order_rate | | V | |\n", - "| desired_inventory | | | |\n", - "| desired_inventory_coverage | | | |\n", - "| desired_production | | | |\n", - "| desired_production_start_rate | | | |\n", - "| desired_shipment_rate | | | |\n", - "| desired_wip | | | |\n", - "| expected_order_rate | | | V |\n", - "| inventory | | | V |\n", - "| inventory_adjustment_time | V | | |\n", - "| inventory_coverage | | V | |\n", - "| manufacturing_cycle_time | | V | |\n", - "| maximum_shipment_rate | | | |\n", - "| minimum_order_processing_time | V | | |\n", - "| order_fulfillment_ratio | | | |\n", - "| production_adjustment_from_inventory | | | |\n", - "| production_rate | | | |\n", - "| production_start_rate | | | |\n", - "| safety_stock_coverage | | | |\n", - "| shipment_rate | | | |\n", - "| table_for_order_fulfillment | | V (lookup) | |\n", - "| time_to_average_order_rate | | V | |\n", - "| wip_adjustment_time | | V | |\n", - "| work_in_process_inventory | | | V |\n", - "| initial_time | | V | |\n", - "| final_time | | V | |\n", - "| time_step | | V | |\n", - "\n", - "The rest is `aux_var` which are derived from the defined." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a3d2b4a9-532f-4f3f-858e-64b4877c1997", - "metadata": {}, - "outputs": [], - "source": [ - "ass_param_lst = [\"customer_order_rate\", \"inventory_coverage\", \"manufacturing_cycle_time\", \"time_to_average_order_rate\", \"wip_adjustment_time\"]\n", - "obs_stock_lst = [\"work_in_process_inventory\", \"inventory\"]" - ] - }, - { - "cell_type": "markdown", - "id": "33ac5c16-f572-46ba-8d79-062f601a5e24", - "metadata": {}, - "source": [ - "## P1. relate\n", - "From SD model (`.mdl`) to Stan ODE function block (`.stan`). No new information is added." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d1a14086-45cd-4f99-9e69-51aa57dea790", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "functions {\n", - " real lookupFunc_0(real x){\n", - " # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)\n", - " # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0)\n", - " real slope;\n", - " real intercept;\n", - "\n", - " if(x <= 0.2)\n", - " intercept = 0.0;\n", - " slope = (0.2 - 0.0) / (0.2 - 0.0);\n", - " return intercept + slope * (x - 0.0);\n", - " else if(x <= 0.4)\n", - " intercept = 0.2;\n", - " slope = (0.4 - 0.2) / (0.4 - 0.2);\n", - " return intercept + slope * (x - 0.2);\n", - " else if(x <= 0.6)\n", - " intercept = 0.4;\n", - " slope = (0.58 - 0.4) / (0.6 - 0.4);\n", - " return intercept + slope * (x - 0.4);\n", - " else if(x <= 0.8)\n", - " intercept = 0.58;\n", - " slope = (0.73 - 0.58) / (0.8 - 0.6);\n", - " return intercept + slope * (x - 0.6);\n", - " else if(x <= 1.0)\n", - " intercept = 0.73;\n", - " slope = (0.85 - 0.73) / (1.0 - 0.8);\n", - " return intercept + slope * (x - 0.8);\n", - " else if(x <= 1.2)\n", - " intercept = 0.85;\n", - " slope = (0.93 - 0.85) / (1.2 - 1.0);\n", - " return intercept + slope * (x - 1.0);\n", - " else if(x <= 1.4)\n", - " intercept = 0.93;\n", - " slope = (0.97 - 0.93) / (1.4 - 1.2);\n", - " return intercept + slope * (x - 1.2);\n", - " else if(x <= 1.6)\n", - " intercept = 0.97;\n", - " slope = (0.99 - 0.97) / (1.6 - 1.4);\n", - " return intercept + slope * (x - 1.4);\n", - " else if(x <= 1.8)\n", - " intercept = 0.99;\n", - " slope = (1.0 - 0.99) / (1.8 - 1.6);\n", - " return intercept + slope * (x - 1.6);\n", - " else if(x <= 2.0)\n", - " intercept = 1.0;\n", - " slope = (1.0 - 1.0) / (2.0 - 1.8);\n", - " return intercept + slope * (x - 1.8);\n", - " }\n", - "\n", - " # Begin ODE declaration\n", - " vector vensim_func(real time, vector outcome, real customer_order_rate, real inventory_coverage, real manufacturing_cycle_time, real time_to_average_order_rate, real wip_adjustment_time ){\n", - " real work_in_process_inventory = outcome[1];\n", - " real inventory = outcome[2];\n", - "\n", - " real inventory_adjustment_time = 8;\n", - " real safety_stock_coverage = 2;\n", - " real minimum_order_processing_time = 2;\n", - " real desired_inventory_coverage = minimum_order_processing_time + safety_stock_coverage;\n", - " real change_in_exp_orders = customer_order_rate - expected_order_rate / time_to_average_order_rate;\n", - " real expected_order_rate = change_in_exp_orders;\n", - " real desired_inventory = desired_inventory_coverage * expected_order_rate;\n", - " real production_adjustment_from_inventory = desired_inventory - inventory / inventory_adjustment_time;\n", - " real desired_production = fmax(0,expected_order_rate + production_adjustment_from_inventory);\n", - " real desired_wip = manufacturing_cycle_time * desired_production;\n", - " real maximum_shipment_rate = inventory / minimum_order_processing_time;\n", - " real desired_shipment_rate = customer_order_rate;\n", - " real order_fulfillment_ratio = table_for_order_fulfillment(maximum_shipment_rate / desired_shipment_rate);\n", - " real shipment_rate = desired_shipment_rate * order_fulfillment_ratio;\n", - " real adjustment_for_wip = desired_wip - work_in_process_inventory / wip_adjustment_time;\n", - " real desired_production_start_rate = desired_production + adjustment_for_wip;\n", - " real production_start_rate = fmax(0,desired_production_start_rate);\n", - " real production_rate = work_in_process_inventory / manufacturing_cycle_time;\n", - " real inventory_dydt = production_rate - shipment_rate;\n", - " real work_in_process_inventory_dydt = production_start_rate - production_rate;\n", - "\n", - " return {work_in_process_inventory_dydt, inventory_dydt};\n", - " }\n", - "}\n", - "\n" - ] - } - ], - "source": [ - "am = vf.get_abstract_model()\n", - "stan_function_builder = StanFunctionBuilder(am) \n", - "ds_relational = stan_function_builder.build_function_block(ass_param_lst, obs_stock_lst)\n", - "print(ds_relational)\n", - "stan_file_path = os.path.join(os.getcwd(), \"stan_file\", \"ds_relational.stan\")\n", - "with open(stan_file_path, \"w\") as f:\n", - " print(ds_relational, file=f)" - ] - }, - { - "cell_type": "markdown", - "id": "75a0b7de-7c90-4823-9b38-7370f3cea02f", - "metadata": {}, - "source": [ - "## U3. Specify_project\n", - "\n", - "#### Assumed parameter $X$ \n", - "\n", - "- declared in generated quantities block for `_draws2data.stan` and data block for `_data2draws.stan`.\n", - "- specified with its actual value or series or lookup function (aggregation)\n", - "\n", - "| `ass_param` | value/series |\n", - "| ---------------------------- | ----------------- |\n", - "| `customer_order_rate` | N(10000, $100^2$) |\n", - "| `time_to_average_order_rate` | 8 |\n", - "| `wip_adjustment_time` | 8 |\n", - "| `manufacturing_cycle_time` | 8 |\n", - "| `safety_stock_coverage` | 2 |\n", - "|`initial_time`, `final_time`, `time_step` | 0, 10, .125|\n", - "|`table_for_order_fulfillment`| lookup function|\n", - "\n", - "## U4. Specify_regularize\n", - "\n", - "#### Estimated parameter $\\theta$ \n", - "\n", - "- declared in generated quantities block for `_draws2data.stan` and model block for `_data2draws.stan`.\n", - "\n", - "| `ess_param` | (min, mode, max) | distribuiton type| \n", - "| ------------------------------- | ---------------- | ------------ |\n", - "| `inventory_adjustment_time` | (6,8,12) | N(8, $1^2$) |\n", - "| `minimum_order_processing_time` | (1,2,4) | N(2, $.5^2$) |\n", - "\n", - "\n", - "##### Q. Can `msr_err` (min, mode, max) be helpful info?\n", - "##### Q. Shouldn't `msr_err` distribution determine `family`? Then, `Poisson`, `Neg_Binom`, `\n", - "\n", - "\n", - "#### Latent stock $Z$\n", - "\n", - "- declared in generated quantities block for `_draws2data.stan` and data block for `_data2draws.stan`.\n", - "\n", - "#### Measurement error\n", - "\n", - "- `msr_err` is specified with `family` and its parameter\n", - "| `msr_err` |??|lognormal, inverse_gamma|\n", - "\n", - "\n", - "#### Observed stock $Y$\n", - "\n", - "- declared in generated quantities block for `_draws2data.stan` and data block for `_data2draws.stan`.\n", - "- $Y \\sim$ `family`(Z, `msr_err` )" - ] - }, - { - "cell_type": "markdown", - "id": "6671aae2-5375-4056-b4f0-83cda80ba708", - "metadata": {}, - "source": [ - "## P2. predict\n", - "\n", - "\n", - "- based on `est_param` specification (a = lower_bound, b= most likely, c = upper_bound) in U3, its prior is automatically set to $\\theta \\sim N(\\frac{a+4b+c}{6}, \\frac{c-a}{6})$ using [PERT dist](https://en.wikipedia.org/wiki/PERT_distribution)\n", - "\n", - "| `ess_param` | Prior distribution | Prior parameter| \n", - "| ------------------------------- | ---------------- | ------------ |\n", - "| `inventory_adjustment_time` | Normal | loc = 8, scale = $1^2$ |\n", - "| `minimum_order_processing_time` | Normal |loc = 2, scale = $.5^2$ |\n", - "| `msr_err` |lognormal, inverse_gamma|\n", - "\n", - "##### Q. feedback on PERT?\n", - "\n", - "##### Q. how do we usually determine `msr_err`'s prior parameter?" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "edd2203d-618e-464b-b3f2-15ed26b1e7cf", - "metadata": {}, - "outputs": [], - "source": [ - "initial_time = 0\n", - "final_time = 10\n", - "time_step = .125\n", - "\n", - "N = int((final_time - initial_time)/time_step)\n", - "data_draws2data = {\n", - " \"N\": N,\n", - " \"times\": np.arange(1, N + 1),\n", - " \"customer_order_rate\": np.random.normal(loc = 10000, scale = 100, size = N),\n", - " \"time_to_average_order_rate\" : 8, \n", - " \"wip_adjustment_time\" :2,\n", - " 'manufacturing_cycle_time' : 8,\n", - " 'safety_stock_coverage' : 2\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "217fa4b0-cd0f-4fb5-a3ac-647da6531ed3", - "metadata": { - "tags": [] - }, - "source": [ - "## P3. infer to verify" - ] - }, - { - "cell_type": "markdown", - "id": "bb81280a-e5a7-4b6c-aebb-48a18dc44f61", - "metadata": {}, - "source": [ - "The first argument is `ass_param` and the second is `observed stock`. Design for `est_param` is under-development including \n", - "##### Q. how to express multi-levle prior? Auto scale?" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b393e0fd-470f-43f5-a535-866f689aed24", - "metadata": {}, - "outputs": [], - "source": [ - "ds_draws2data = stan_builder.create_stan_program(ass_param_lst, obs_stock_lst)\n", - "#print(ds_draws2data)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a5d2525b-e441-4e79-a0f3-8d5c2c953c63", - "metadata": {}, - "outputs": [], - "source": [ - "sf_path_draws2data = os.path.join(os.getcwd(), \"stan_file\", \"ds_draws2data.stan\")\n", - "# with open(sf_path_draws2data, \"w\") as f:\n", - "# print(ds_draws2data, file=f)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "16713c97-3371-4ecb-aa04-310ad0480034", - "metadata": {}, - "outputs": [], - "source": [ - "sm_draws2data = CmdStanModel(stan_file = sf_path_draws2data)\n", - "fit_prior_data = sm_draws2data.sample(data=data_draws2data, iter_sampling=30, chains=1, fixed_param=True, iter_warmup=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3939e7a3-259b-47a3-9360-0b17a54726b3", - "metadata": {}, - "outputs": [], - "source": [ - "fit_prior_data.draws_xr()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "515002a4-35be-4752-a705-d09f70a9c43f", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "#compare with real \n", - "ax.plot(fit_prior_data.loc[:, ['y_tilde']], label = \"\")\n", - "ax.plot(state_dt.loc[:, ['Predator']], label = \"\")\n", - "for i in range(len(obs_stock_lst)):\n", - " ax.plot(pd.DataFrame(fit_prior_data.y_tilde[:,:,i]).T.loc[:, :5])\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "12626a31-08bc-4a5d-ad74-39916446e4ff", - "metadata": {}, - "source": [ - "## U5. Specify_tolerance\n", - "\n", - "#### Q. how to set 10^-2, 3,7~? ode_rk45 precison" - ] - }, - { - "cell_type": "markdown", - "id": "960d1e72-a5e4-4dcb-995f-e9d689c26149", - "metadata": { - "tags": [] - }, - "source": [ - "## P4. infer to validate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f91d6908-3ff1-4787-b80a-f43b85e0a6ff", - "metadata": {}, - "outputs": [], - "source": [ - "sf_path_data2draws = os.path.join(os.getcwd(), \"stan_file\", \"ds_data2draws.stan\")\n", - "with open(sf_path_data2draws, \"w\") as f:\n", - " print(ds_draws2data, file=f)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b22c5ede-6e25-41da-9a97-cc2d641d2c7a", - "metadata": {}, - "outputs": [], - "source": [ - "idata = az.from_cmdstanpy(\n", - " posterior=fit_posterior_draws, \n", - " posterior_predictive=[\"y_hat\"], \n", - " log_likelihood= [\"log_lik\"],\n", - " observed_data = {\"y_hat\": lynx_hare_df.loc[:, (\"Hare\", \"Lynx\")]}\n", - "# dtypes={\"y_rep\": int} if Poisson family\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b44779b8-93ce-4539-a892-695e121c684a", - "metadata": {}, - "outputs": [], - "source": [ - "az.loo(idata)\n", - "az.plot_ppc(idata, alpha=0.03, figsize=(12, 6))" - ] - }, - { - "cell_type": "markdown", - "id": "f5191cd0-da48-4846-bc30-6d11304479a7", - "metadata": {}, - "source": [ - "###### Q.brms state\n", - "```\n", - "Confused about brms family quas\n", - "quasi(link = \"identity\", variance = \"constant\")\n", - "quasibinomial(link = \"logit\")\n", - "quasipoisson(link = \"log\")\n", - "```\n", - "\n", - "###### Q.hierarchical auto-scaling, formula (+others)\n", - "https://github.com/hyunjimoon/DataInDM/issues/9" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "local-venv", - "language": "python", - "name": "local-venv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/test_scripts/stan_file/ds_data2draws.stan b/test_scripts/stan_file/ds_data2draws.stan index 44395179..c15b8303 100644 --- a/test_scripts/stan_file/ds_data2draws.stan +++ b/test_scripts/stan_file/ds_data2draws.stan @@ -1,103 +1,57 @@ functions { - real table_for_order_fulfillment(real x){ - # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) - # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) - real slope; - real intercept; - - if(x <= 0.2) - intercept = 0.0; - slope = (0.2 - 0.0) / (0.2 - 0.0); - return intercept + slope * (x - 0.0); - else if(x <= 0.4) - intercept = 0.2; - slope = (0.4 - 0.2) / (0.4 - 0.2); - return intercept + slope * (x - 0.2); - else if(x <= 0.6) - intercept = 0.4; - slope = (0.58 - 0.4) / (0.6 - 0.4); - return intercept + slope * (x - 0.4); - else if(x <= 0.8) - intercept = 0.58; - slope = (0.73 - 0.58) / (0.8 - 0.6); - return intercept + slope * (x - 0.6); - else if(x <= 1.0) - intercept = 0.73; - slope = (0.85 - 0.73) / (1.0 - 0.8); - return intercept + slope * (x - 0.8); - else if(x <= 1.2) - intercept = 0.85; - slope = (0.93 - 0.85) / (1.2 - 1.0); - return intercept + slope * (x - 1.0); - else if(x <= 1.4) - intercept = 0.93; - slope = (0.97 - 0.93) / (1.4 - 1.2); - return intercept + slope * (x - 1.2); - else if(x <= 1.6) - intercept = 0.97; - slope = (0.99 - 0.97) / (1.6 - 1.4); - return intercept + slope * (x - 1.4); - else if(x <= 1.8) - intercept = 0.99; - slope = (1.0 - 0.99) / (1.8 - 1.6); - return intercept + slope * (x - 1.6); - else if(x <= 2.0) - intercept = 1.0; - slope = (1.0 - 1.0) / (2.0 - 1.8); - return intercept + slope * (x - 1.8); - } - - # Begin ODE declaration - vector vensim_func(real time, vector outcome, real customer_order_rate ){ - real inventory = outcome[1]; - real work_in_process_inventory = outcome[2]; - - vector [2] dydt; - real time_to_average_order_rate = 8; - real change_in_exp_orders = customer_order_rate - expected_order_rate / time_to_average_order_rate; - real expected_order_rate = change_in_exp_orders; - real safety_stock_coverage = 2; - real minimum_order_processing_time = 2; - real desired_inventory_coverage = minimum_order_processing_time + safety_stock_coverage; - real desired_inventory = desired_inventory_coverage * expected_order_rate; - real inventory_adjustment_time = 8; - real production_adjustment_from_inventory = desired_inventory - inventory / inventory_adjustment_time; - real desired_production = fmax(0,expected_order_rate + production_adjustment_from_inventory); - real manufacturing_cycle_time = 8; - real desired_wip = manufacturing_cycle_time * desired_production; - real wip_adjustment_time = 2; - real adjustment_for_wip = desired_wip - work_in_process_inventory / wip_adjustment_time; - real desired_production_start_rate = desired_production + adjustment_for_wip; - real maximum_shipment_rate = inventory / minimum_order_processing_time; - real desired_shipment_rate = customer_order_rate; - real order_fulfillment_ratio = table_for_order_fulfillment(maximum_shipment_rate / desired_shipment_rate); - real production_rate = work_in_process_inventory / manufacturing_cycle_time; - real production_start_rate = fmax(0,desired_production_start_rate); - real work_in_process_inventory_dydt = production_start_rate - production_rate; - real shipment_rate = desired_shipment_rate * order_fulfillment_ratio; - real inventory_dydt = production_rate - shipment_rate; - dydt[1] = work_in_process_inventory_dydt; - dydt[2] = inventory_dydt; - - return dydt; - } +#include ds_relational.stan } + data{ int N; // number of measurement times array[N] real times; // measurement times - real customer_order_rate[N]; -} -transformed data{ + real customer_order_rate[N]; + + vector[2] y_init; //init measured stock + vector[2] y[N]; //measured stock } + parameters{ + real inventory_adjustment_time; + real minimum_order_processing_time; + + vector[2] z_init; // init state value + vector[2] sigma; // msr error scale } + transformed parameters { real inventory_initial = 2 + 2 * 10000; real work_in_process_inventory_initial = 8 * fmax(0,10000 + 2 + 2 * 10000 - 2 + 2 * 10000 / 8); vector[2] initial_outcome = {inventory_initial, work_in_process_inventory_initial}; - vector[2] integrated_result = ode_rk45(vensim_func, initial_outcome, initial_time, times, customer_order_rate); + vector[2] integrated_result + = ode_rk45(vensim_func, initial_outcome, initial_time, times, customer_order_rate); } + model{ + // auto_prior from U4 + alpha ~ normal(.8, 0.1); // 1,1 + gamma ~ normal(.8, 0.1); // 1,1 + beta ~ normal(0.05, 0.01); // 0.05, 0.1 + gamma ~ normal(0.05, 0.01); // 0.05, 0.1 + + // real alpha_tilde = 0.55; + // real beta_tilde = 0.028; + // real gamma_tilde = 0.80; + // real delta_tilde = 0.024; + + // U4 parameter uc + sigma ~ lognormal(log(0.01), 1); //-1,1 + + // U4 parameter uc + z_init ~ lognormal(log(100), 1); // E[log(z_init)] is `loc` of lognormal + + y_init ~ lognormal(log(z_init), sigma); + + for (n in 1:N) { + y[n] ~ lognormal(log(integrated_result[n]), sigma); + target += partial_sum_lpdf(log(integrated_result[n]), 1, N, + } } + generated quantities{ } diff --git a/test_scripts/stan_file/ds_draws2data.stan b/test_scripts/stan_file/ds_draws2data.stan index 665d94de..ab387717 100644 --- a/test_scripts/stan_file/ds_draws2data.stan +++ b/test_scripts/stan_file/ds_draws2data.stan @@ -6,6 +6,11 @@ data{ int N; // number of measurement times array[N] real times; // measurement times real customer_order_rate[N]; + real time_to_average_order_rate; + real wip_adjustment_time; + real manufacturing_cycle_time; + real safety_stock_coverage; + real inventory_coverage; } @@ -15,7 +20,37 @@ transformed parameters { vector[2] initial_outcome = {inventory_initial, work_in_process_inventory_initial}; vector[2] integrated_result_tilde[N] = ode_rk45(vensim_func, initial_outcome, initial_time, times, customer_order_rate); } -model{ -} -generated quantities{ -} + +generated quantities { + vector[2] y_init_tilde; // simulated initial stock + vector[2] y_tilde[N]; // simulated stock + + vector[2] sigma_tilde; + vector[2] z_init_tilde; + + // U4 parameter uc + real inventory_adjustment_time_tilde = 2; // abs(normal_rng(1, 0.5)); + real minimum_order_processing_time_tilde = 0.02; //abs(normal_rng(0.05, 0.05)); + + // U4 measurement uc + z_init_tilde[1] = 30; //lognormal_rng(log(30), 1); + z_init_tilde[2] = 30; // lognormal_rng(log(30), 1); + + // U4 different msr_err + sigma_tilde[1] = 0.01; //lognormal_rng(-1, 1); + sigma_tilde[2] = 0.01; //lognormal_rng(-1, 1); + + // calculate prior predictive + vector[2] integrated_result_tilde[N] + = ode_rk45(vensim_func, z_init_tilde, 0, times, + alpha_tilde, beta_tilde, gamma_tilde, delta_tilde); + + y_init_tilde = to_vector(lognormal_rng(log(z_init_tilde), + sigma_tilde)); + + for (n in 1:N) { + //posterior predictive + y_tilde[n] = to_vector(lognormal_rng(log(integrated_result_tilde[n]), + sigma_tilde)); + } +} \ No newline at end of file diff --git a/test_scripts/stan_file/ds_relational.stan b/test_scripts/stan_file/ds_relational.stan index e11c9bfe..b0739133 100644 --- a/test_scripts/stan_file/ds_relational.stan +++ b/test_scripts/stan_file/ds_relational.stan @@ -48,32 +48,44 @@ functions { } # Begin ODE declaration - vector vensim_func(real time, vector outcome, real customer_order_rate, real inventory_coverage, real manufacturing_cycle_time, real time_to_average_order_rate, real wip_adjustment_time ){ + vector vensim_func(real time, vector outcome, real[] customer_order_rate, real inventory_coverage, real manufacturing_cycle_time, real time_to_average_order_rate, real wip_adjustment_time ){ + vector[2] dydt; # Return vector of the ODE function + + // State variables real work_in_process_inventory = outcome[1]; real inventory = outcome[2]; - + + + // est param + real minimum_order_processing_time = 2; // issue 16 real inventory_adjustment_time = 8; - real safety_stock_coverage = 2; - real minimum_order_processing_time = 2; - real desired_inventory_coverage = minimum_order_processing_time + safety_stock_coverage; + + // relations real change_in_exp_orders = customer_order_rate - expected_order_rate / time_to_average_order_rate; real expected_order_rate = change_in_exp_orders; + real safety_stock_coverage = 2; + + real desired_inventory_coverage = minimum_order_processing_time + safety_stock_coverage; real desired_inventory = desired_inventory_coverage * expected_order_rate; + real production_rate = work_in_process_inventory / manufacturing_cycle_time; + real production_adjustment_from_inventory = desired_inventory - inventory / inventory_adjustment_time; - real desired_production = fmax(0,expected_order_rate + production_adjustment_from_inventory); + real desired_production = max(0,expected_order_rate + production_adjustment_from_inventory); real desired_wip = manufacturing_cycle_time * desired_production; - real maximum_shipment_rate = inventory / minimum_order_processing_time; real desired_shipment_rate = customer_order_rate; + real maximum_shipment_rate = inventory / minimum_order_processing_time; real order_fulfillment_ratio = table_for_order_fulfillment(maximum_shipment_rate / desired_shipment_rate); real shipment_rate = desired_shipment_rate * order_fulfillment_ratio; + real inventory_dydt = production_rate - shipment_rate; real adjustment_for_wip = desired_wip - work_in_process_inventory / wip_adjustment_time; real desired_production_start_rate = desired_production + adjustment_for_wip; - real production_start_rate = fmax(0,desired_production_start_rate); - real production_rate = work_in_process_inventory / manufacturing_cycle_time; - real inventory_dydt = production_rate - shipment_rate; + real production_start_rate = max(0,desired_production_start_rate); real work_in_process_inventory_dydt = production_start_rate - production_rate; - return {work_in_process_inventory_dydt, inventory_dydt}; + dydt[1] = work_in_process_inventory_dydt; + dydt[2] = inventory_dydt; + + return dydt; } } From ad76f2acf4dd84cac345f0b94bf6a080d277b620 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Mon, 22 Aug 2022 23:55:01 +0900 Subject: [PATCH 26/45] WIP stan model interface --- pysd/builders/stan/ast_walker.py | 2 +- pysd/builders/stan/stan_model.py | 76 ++++++++++++++++++++++++ pysd/builders/stan/stan_model_builder.py | 41 +------------ 3 files changed, 79 insertions(+), 40 deletions(-) create mode 100644 pysd/builders/stan/stan_model.py diff --git a/pysd/builders/stan/ast_walker.py b/pysd/builders/stan/ast_walker.py index 8e42652f..089cc46f 100644 --- a/pysd/builders/stan/ast_walker.py +++ b/pysd/builders/stan/ast_walker.py @@ -51,7 +51,7 @@ class LookupCodegenWalker(BaseNodeWaler): # This dict holds the generated function names of each individual lookup function. # Key is x + y + x_limits + y_limits, value is function name n_lookups = 0 - code = IndentedString(indent_level=1) + code = IndentedString() @staticmethod def get_lookup_keyname(lookup_node: LookupsStructure): diff --git a/pysd/builders/stan/stan_model.py b/pysd/builders/stan/stan_model.py new file mode 100644 index 00000000..61d3cb3a --- /dev/null +++ b/pysd/builders/stan/stan_model.py @@ -0,0 +1,76 @@ +from typing import List, Set, Type +from dataclasses import dataclass, field +import ast, os, pathlib, warnings, glob +from .stan_model_builder import StanFunctionBuilder +from .utilities import vensim_name_to_identifier + +@dataclass +class SamplingStatement: + lhs_name: str + distribution_type: str + distribution_return_type: Type = field(init=False) + distribution_args: List[str] + + def __post_init__(self): + if self.distribution_type in ("bernoulli", "binomial", "beta_binomial", "neg_binomial", "poisson"): + self.distribution_return_type = int + else: + # TODO: Check if it's a valid stan distribution + self.distribution_return_type = float + + +@dataclass +class StanModelContext: + sample_statements: List[SamplingStatement] = field(default_factory=list) + exposed_parameters: Set[str] = field(default_factory=list) + + +class VensimModelContext: + def __init__(self, abstract_model): + + self.variable_names = set() + for element in abstract_model.sections[0].elements: + self.variable_names.add(vensim_name_to_identifier(element.name)) + + +class StanVensimModel: + def __init__(self, model_name: str, abstract_model): + self.abstract_model = abstract_model + self.model_name = model_name + self.stan_model_context = StanModelContext() + self.vensim_model_context = VensimModelContext(self.abstract_model) + + def set_prior(self, variable_name: str, distribution_type: str, *args): + for arg in args: + if isinstance(arg, str): + # If the distribution argument is an expression, parse the dependant variables + # We're using the python parser here, which might be problematic + used_variable_names = [node.id for node in ast.walk(ast.parse(arg)) if isinstance(node, ast.Name)] + self.stan_model_context.exposed_parameters.update(used_variable_names) + + self.stan_model_context.sample_statements.append(SamplingStatement(variable_name, distribution_type, *args)) + + + def build_stan_functions(self): + """ + We build the stan file that holds the ODE function. From the sample statements that the user have provided, + we identify which variables within the ODE model should be treated as stan parameters instead if variables within + the function block. This means that instead of the variable being defined locally within the function body, + it instead gets defined within the transformed parameters/model block. + Returns + ------- + + """ + if glob.glob(os.path.join(os.getcwd(), f"{self.model_name}_functions.stan")): + if input(f"{self.model_name}_functions.stan already exists in the current working directory. Overwrite? (Y/N):").lower() != "y": + raise Exception("Code generation aborted by user") + + + + with open(os.path.join(os.getcwd(), f"{self.model_name}_functions.stan"), "w") as f: + function_builder = StanFunctionBuilder(self.abstract_model) + f.write(function_builder.build_functions()) + + def data2draws(self): + pass + diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_model_builder.py index 925c79cf..9d35a9f5 100644 --- a/pysd/builders/stan/stan_model_builder.py +++ b/pysd/builders/stan/stan_model_builder.py @@ -156,29 +156,6 @@ def get_stock_variable_stan_names(self) -> List[str]: return stock_varible_names - -# class StanDataBuilder: -# def __init__(self, abstract_model: AbstractModel): -# self.abstract_model = abstract_model -# -# def build_block(self, predictor_variable_names, outcome_variable_names): -# self.code = IndentedString() -# self.code += "data {\n" -# self.code.indent_level += 1 -# -# self.code += f"predictor= {{{', '.join(predictor_variable_names)}}};\n" -# self.code += f"initial_outcome = {{{', '.join(outcome_variable_names)}}};\n" -# self.code += f"observed_outcome = {{{', '.join(outcome_variable_names)}}};\n" -# self.code += f"times = {{{', '.join(outcome_variable_names)}}};\n" -# self.code.indent_level -= 1 -# self.code += "}\n" - - -class StanTransformedDataBuilder: - def __init__(self, abstract_model: AbstractModel): - self.abstract_model = abstract_model - - class StanTransformedParametersBuilder: def __init__(self, abstract_model: AbstractModel): self.abstract_model = abstract_model @@ -222,7 +199,7 @@ def build_block( self.code += f"vector[{len(outcome_variable_names)}] initial_outcome = {{{', '.join([x + '_initial' for x in outcome_variable_names])}}};\n" - self.code += f"vector[{len(outcome_variable_names)}] integrated_result[T] = integrate_ode_rk45({function_name}, initial_outcome, initial_time, times, {','.join(argument_variables)});\n" + self.code += f"vector[{len(outcome_variable_names)}] integrated_result[T] = ode_rk45({function_name}, initial_outcome, initial_time, times, {','.join(argument_variables)});\n" self.code.indent_level -= 1 self.code += "}\n" @@ -263,14 +240,13 @@ def _create_dependency_graph(self): return self.variable_dependency_graph - def build_function_block( + def build_functions( self, predictor_variable_names: List[Tuple[str, str]], outcome_variable_names: List[str], function_name: str = "vensim_func", ): self.code = IndentedString() - self.code += "functions {\n" # Build the lookup functions self.build_lookups() @@ -279,9 +255,7 @@ def build_function_block( self.code += lookup_functions_code self.code += "\n\n" - self.code.indent_level += 1 self.code += "# Begin ODE declaration\n" - # Enter function block self._create_dependency_graph() # Identify the minimum number of variables needed for calculating outcomes @@ -393,9 +367,6 @@ def recursive_order_search(current, visited): # Exit function body self.code += "}\n" - self.code.indent_level -= 1 - # Exit function block - self.code += "}\n" return str(self.code) def build_lookups(self): @@ -404,11 +375,3 @@ def build_lookups(self): self.lookup_builder_walker.walk(component.ast) -class StanTransformedDataBuilder: - def __init__( - self, abstract_model: AbstractModel, function_name: str = "vensim_ode" - ): - - self.abstract_model = abstract_model - self.elements = self.abstract_model.sections[0].elements - self.function_name = function_name From b3c5a1f2e81fd914648c680b1c10a97c9ff2b267 Mon Sep 17 00:00:00 2001 From: amoon Date: Sat, 27 Aug 2022 07:42:05 -0400 Subject: [PATCH 27/45] Two not compile tested stan files --- test_scripts/demand_supply.ipynb | 32 ++++++++++++----------- test_scripts/stan_file/ds_data2draws.stan | 4 +-- test_scripts/stan_file/ds_draws2data.stan | 6 ++++- test_scripts/stan_file/ds_relational.stan | 26 ++++++++++++------ 4 files changed, 42 insertions(+), 26 deletions(-) diff --git a/test_scripts/demand_supply.ipynb b/test_scripts/demand_supply.ipynb index e6d68605..098ceddf 100644 --- a/test_scripts/demand_supply.ipynb +++ b/test_scripts/demand_supply.ipynb @@ -65,17 +65,18 @@ "source": [ "# 1. User-Program Workflow (Analyze)\n", "\n", - "| Step | Goal | Program's work (P-rows have `.function(input)`) | User's work |\n", - "| ---- | ------------------ | ------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------- |\n", - "| U1 | Draft | `Vensim` assists U1.a() | a. Translate mental model to SD model |\n", - "| U2 | Classify | `PySD` assists U2.a() | a. Classify parameters `est_param`, `ass_param`, b. Select `obs_state` among stocks |\n", - "| P1 | relate | `PySD`, `.build_function_block`(U1.a) | |\n", - "| U3 | Specify_project | | a. Supply value or series of `assmed_param`, b. Choose `family`(:= dist. of `msr_err_scale`) |\n", - "| U4 | Specify_regularize | | a. Choose `prior_family`(`est_param`'s prior dist. type) , b. Set `prior_param` (`est_param`'s prior param) |\n", - "| P2 | predict | `draws2data.stan`, `fit_prior_data.sample()`, `fit_prior_data = (U2.ab, U3.ab, U4.ab)`: Prior predictive check (opt-out prior) | |\n", - "| P3 | infer to verify | `data2draws.stan`,`.create_stan_program`(U2.ab, U3.ab): Infer parameter from (synthetic) data: SBC | |\n", - "| U5 | Specify_tolerance | | a. Set precision with `iter_sampling` (:= # of samples), b. Select posterior approximator |\n", - "| P4 | infer to validate | `Stan`, `fit_post_draws.sample()`, ` fit_post_draws = (P1, U3.ab, U4.ab, U5.ab)`: Posterior predictive check (opt-in prior) | |\n", + "| Step | Goal | Program, work, `command` (P-rows have `.function(input)`) | User's work | output format |\n", + "| ---- | ------------------ | ------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------- |\n", + "| U1 | Draft | `Vensim` assists U1.a() | a. Translate mental model to SD model | `.mdl` |\n", + "| U2 | Classify | `PySD` assists U2.a() | a. Classify parameters `est_param`, `ass_param`, b. Select `obs_state` among stocks | `.json` |\n", + "| P1 | relate | `PySD`, `.build_function_block`(U1.a) | | `relation.stan` |\n", + "| U3 | Specify_project | | a. Supply value or series of `assmed_param`, b. Choose `family`(:= dist. of `msr_err_scale`) | `draws2data.stan` gq block, `data2draws.stan` model, gq block |\n", + "| U4 | Specify_regularize | | a. Set {min, mode, max} of `est_param`'s prior param (optional) b. Choose `prior_family`(default: PERT Normal) | `draws2data.stan` gq block, `data2draws.stan` model, gq block |\n", + "| P2 | predict | `draws2data.stan`, `fit_prior_data.sample()`, `fit_prior_data = (U2.ab, U3.ab, U4.ab)`: Prior predictive check (opt-out prior) | | |\n", + "| P3 | infer to verify | `Stan`, `data2draws.stan`,`.create_stan_program`(U2.ab, U3.ab): Infer parameter from (synthetic) data: SBC | | Prior predictive check plot (summary stats.) |\n", + "| U5 | Specify_tolerance | | a. Set precision with `iter_sampling` (:= # of samples), b. Select posterior approximator | $\\gamma$ from SBC-graphics |\n", + "| P4 | infer to validate | `Stan`, `fit_post_draws.sample()`, ` fit_post_draws = (P1, U3.ab, U4.ab, U5.ab)`: Posterior predictive check (opt-in prior) | | Posterior predictive check plot |\n", + "\n", "\n", "\n", "##### Q. family and prior dist change\n", @@ -185,7 +186,7 @@ "| production_adjustment_from_inventory | | | |\n", "| production_rate | | | |\n", "| production_start_rate | | | |\n", - "| safety_stock_coverage | | | |\n", + "| safety_stock_coverage | | V | |\n", "| shipment_rate | | | |\n", "| table_for_order_fulfillment | | V (lookup) | |\n", "| time_to_average_order_rate | | V | |\n", @@ -206,7 +207,7 @@ "outputs": [], "source": [ "est_param_lst = [\"inventory_adjustment_time\", \"minimum_order_processing_time\"]\n", - "ass_param_lst = [\"customer_order_rate\", \"inventory_coverage\", \"manufacturing_cycle_time\", \"time_to_average_order_rate\", \"wip_adjustment_time\"]\n", + "ass_param_lst = [\"customer_order_rate\", \"inventory_coverage\", \"manufacturing_cycle_time\", \"safety_stock_coverage\", \"time_to_average_order_rate\", \"wip_adjustment_time\"]\n", "obs_stock_lst = [\"work_in_process_inventory\", \"inventory\"]" ] }, @@ -342,10 +343,11 @@ "| `ass_param` | value/series |\n", "| ---------------------------- | ----------------- |\n", "| `customer_order_rate` | N(10000, $100^2$) |\n", - "| `time_to_average_order_rate` | 8 |\n", - "| `wip_adjustment_time` | 8 |\n", + "| `inventory_coverage` | 2 | \n", "| `manufacturing_cycle_time` | 8 |\n", "| `safety_stock_coverage` | 2 |\n", + "| `time_to_average_order_rate` | 8 |\n", + "| `wip_adjustment_time` | 8 |\n", "|`initial_time`, `final_time`, `time_step` | 0, 10, .125|\n", "|`table_for_order_fulfillment`| lookup function|\n", "\n", diff --git a/test_scripts/stan_file/ds_data2draws.stan b/test_scripts/stan_file/ds_data2draws.stan index c15b8303..fb7a9f43 100644 --- a/test_scripts/stan_file/ds_data2draws.stan +++ b/test_scripts/stan_file/ds_data2draws.stan @@ -22,8 +22,8 @@ parameters{ transformed parameters { real inventory_initial = 2 + 2 * 10000; real work_in_process_inventory_initial = 8 * fmax(0,10000 + 2 + 2 * 10000 - 2 + 2 * 10000 / 8); - vector[2] initial_outcome = {inventory_initial, work_in_process_inventory_initial}; - vector[2] integrated_result + vector[3] initial_outcome = {inventory_initial, work_in_process_inventory_initial, exp_order_rate_initial}; + vector[3] integrated_result = ode_rk45(vensim_func, initial_outcome, initial_time, times, customer_order_rate); } diff --git a/test_scripts/stan_file/ds_draws2data.stan b/test_scripts/stan_file/ds_draws2data.stan index ab387717..1ae85edd 100644 --- a/test_scripts/stan_file/ds_draws2data.stan +++ b/test_scripts/stan_file/ds_draws2data.stan @@ -18,7 +18,11 @@ transformed parameters { real inventory_initial = 2 + 2 * 10000; real work_in_process_inventory_initial = 8 * fmax(0,10000 + 2 + 2 * 10000 - 2 + 2 * 10000 / 8); vector[2] initial_outcome = {inventory_initial, work_in_process_inventory_initial}; - vector[2] integrated_result_tilde[N] = ode_rk45(vensim_func, initial_outcome, initial_time, times, customer_order_rate); + + vector[2] integrated_result_tilde[N] + = ode_rk45(vensim_func, initial_outcome, initial_time, times, + inventory_adjustment_time, minimum_order_processing_time, + customer_order_rate); } generated quantities { diff --git a/test_scripts/stan_file/ds_relational.stan b/test_scripts/stan_file/ds_relational.stan index b0739133..11cd1d42 100644 --- a/test_scripts/stan_file/ds_relational.stan +++ b/test_scripts/stan_file/ds_relational.stan @@ -1,4 +1,5 @@ functions { + real lookupFunc_0(real x){ # x (0, 2) = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0) # y (0, 1) = (0.0, 0.2, 0.4, 0.58, 0.73, 0.85, 0.93, 0.97, 0.99, 1.0, 1.0) @@ -47,23 +48,32 @@ functions { return intercept + slope * (x - 1.8); } + theta, x originally real -> real[]; inside vensim_func + - cycle o,x + - integration # Begin ODE declaration - vector vensim_func(real time, vector outcome, real[] customer_order_rate, real inventory_coverage, real manufacturing_cycle_time, real time_to_average_order_rate, real wip_adjustment_time ){ + vector vensim_func(real time, vector outcome, + real[] minimum_order_processing_time, real inventory_adjustment_time, + real[] customer_order_rate){ + vector[2] dydt; # Return vector of the ODE function // State variables real work_in_process_inventory = outcome[1]; real inventory = outcome[2]; - - - // est param - real minimum_order_processing_time = 2; // issue 16 - real inventory_adjustment_time = 8; + // `ass_param` (users should supply `ass_param` with vector (not scalar) from data block + //real minimum_order_processing_time = 2; // SHOULD NOT BE DECLARED issue 16 + //real inventory_adjustment_time = 8; // SHOULD NOT BE DECLARED issue 16 + real inventory_coverage = 2; + real manufacturing_cycle_time = 8; + real safety_stock_coverage = 2; + real time_to_average_order_rate = 8; + real wip_adjustment_time = 8; + // relations - real change_in_exp_orders = customer_order_rate - expected_order_rate / time_to_average_order_rate; + real change_in_exp_orders = customer_order_rate[t] - expected_order_rate / time_to_average_order_rate; real expected_order_rate = change_in_exp_orders; - real safety_stock_coverage = 2; real desired_inventory_coverage = minimum_order_processing_time + safety_stock_coverage; real desired_inventory = desired_inventory_coverage * expected_order_rate; From 1d9545e321e3da16d30bdb935a22095645da2207 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Mon, 29 Aug 2022 19:29:10 +0900 Subject: [PATCH 28/45] data2draws implementation --- pysd/builders/stan/stan_model.py | 65 +++++++++++++---- pysd/builders/stan/stan_model_builder.py | 89 +++++++++++++++++++++--- test_scripts/stan_vensim_integration.py | 17 +++++ 3 files changed, 151 insertions(+), 20 deletions(-) create mode 100644 test_scripts/stan_vensim_integration.py diff --git a/pysd/builders/stan/stan_model.py b/pysd/builders/stan/stan_model.py index 61d3cb3a..26f3be24 100644 --- a/pysd/builders/stan/stan_model.py +++ b/pysd/builders/stan/stan_model.py @@ -1,15 +1,22 @@ -from typing import List, Set, Type +from typing import List, Set, Type, Tuple +from numbers import Number from dataclasses import dataclass, field import ast, os, pathlib, warnings, glob -from .stan_model_builder import StanFunctionBuilder +from .stan_model_builder import * from .utilities import vensim_name_to_identifier +from pysd.translators.structures.abstract_expressions import * + -@dataclass class SamplingStatement: lhs_name: str distribution_type: str - distribution_return_type: Type = field(init=False) - distribution_args: List[str] + distribution_return_type: Type + distribution_args: Tuple[str] + + def __init__(self, lhs_name, distribution_type, *distribution_args): + self.lhs_name = lhs_name + self.distribution_type = distribution_type + self.distribution_args = distribution_args def __post_init__(self): if self.distribution_type in ("bernoulli", "binomial", "beta_binomial", "neg_binomial", "poisson"): @@ -22,21 +29,29 @@ def __post_init__(self): @dataclass class StanModelContext: sample_statements: List[SamplingStatement] = field(default_factory=list) - exposed_parameters: Set[str] = field(default_factory=list) + exposed_parameters: Set[str] = field(default_factory=set) class VensimModelContext: def __init__(self, abstract_model): - self.variable_names = set() + self.stock_variable_names = set() + for element in abstract_model.sections[0].elements: self.variable_names.add(vensim_name_to_identifier(element.name)) + for element in abstract_model.sections[0].elements: + for component in element.components: + if isinstance(component.ast, IntegStructure): + self.stock_variable_names.add(vensim_name_to_identifier(element.name)) + break + class StanVensimModel: - def __init__(self, model_name: str, abstract_model): + def __init__(self, model_name: str, abstract_model, integration_times: Iterable[Number]): self.abstract_model = abstract_model self.model_name = model_name + self.integration_times = integration_times self.stan_model_context = StanModelContext() self.vensim_model_context = VensimModelContext(self.abstract_model) @@ -48,6 +63,7 @@ def set_prior(self, variable_name: str, distribution_type: str, *args): used_variable_names = [node.id for node in ast.walk(ast.parse(arg)) if isinstance(node, ast.Name)] self.stan_model_context.exposed_parameters.update(used_variable_names) + self.stan_model_context.exposed_parameters.add(variable_name) self.stan_model_context.sample_statements.append(SamplingStatement(variable_name, distribution_type, *args)) @@ -68,9 +84,34 @@ def build_stan_functions(self): with open(os.path.join(os.getcwd(), f"{self.model_name}_functions.stan"), "w") as f: - function_builder = StanFunctionBuilder(self.abstract_model) - f.write(function_builder.build_functions()) + self.function_builder = StanFunctionBuilder(self.abstract_model) + f.write(self.function_builder.build_functions(self.stan_model_context.exposed_parameters, self.vensim_model_context.stock_variable_names)) + + def data2draws(self, data_file_path: str): + with open(os.path.join(os.getcwd(), f"{self.model_name}_data2draws.stan"), "w") as f: + # Include the function + f.write(f"#include {self.model_name}_functions.stan\n\n") + + f.write(StanDataBuilder().build_block()) + f.write("\n") + + f.write(StanTransformedDataBuilder(self.integration_times).build_block()) + f.write("\n") + + f.write(StanParametersBuilder(self.stan_model_context.sample_statements).build_block()) + f.write("\n") + + transformed_params_builder = StanTransformedParametersBuilder(self.abstract_model) + f.write("\n") + + + f.write(transformed_params_builder.build_block(self.stan_model_context.exposed_parameters, + self.vensim_model_context.stock_variable_names, + self.function_builder.get_generated_lookups_dict(), + self.function_builder.ode_function_name)) + f.write("\n") + + f.write(StanModelBuilder(self.stan_model_context.sample_statements).build_block()) + f.write("\n") - def data2draws(self): - pass diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_model_builder.py index 9d35a9f5..c610786e 100644 --- a/pysd/builders/stan/stan_model_builder.py +++ b/pysd/builders/stan/stan_model_builder.py @@ -1,7 +1,7 @@ import os from pathlib import Path -from typing import Union, List, Dict, Set, Sequence - +from typing import Union, List, Dict, Set, Sequence, Iterable +from numbers import Number from .ast_walker import * from .utilities import * from pysd.translators.structures.abstract_model import ( @@ -10,6 +10,9 @@ AbstractModel, AbstractSection, ) +from typing import TYPE_CHECKING +if TYPE_CHECKING: + from .stan_model import SamplingStatement class StanModelBuilder: @@ -96,7 +99,7 @@ def create_stan_program( ).build_block( predictor_variable_names, outcome_variable_names, - function_block_builder.lookup_builder_walker.generated_lookup_function_names, + function_block_builder.get_generated_lookups_dict(), function_name, ) self.code += "model{\n}\n" @@ -184,6 +187,7 @@ def build_block( stan_varname = vensim_name_to_identifier(element.name) variable_ast_dict[stan_varname] = element.components[0].ast + self.code += "# Initial ODE values\n" for outcome_variable_name in outcome_variable_names: for element in self.abstract_model.sections[0].elements: if ( @@ -197,15 +201,81 @@ def build_block( self.code += f"real {outcome_variable_name}_initial = {InitialValueCodegenWalker(lookup_function_dict, variable_ast_dict).walk(component.ast)};\n" break - self.code += f"vector[{len(outcome_variable_names)}] initial_outcome = {{{', '.join([x + '_initial' for x in outcome_variable_names])}}};\n" + self.code += "\n" + self.code += f"vector[{len(outcome_variable_names)}] initial_outcome; # Initial ODE state vector\n" + for index, name in enumerate(outcome_variable_names, 1): + self.code += f"initial_outcome[{index}] = {name};\n" + + self.code += "\n" - self.code += f"vector[{len(outcome_variable_names)}] integrated_result[T] = ode_rk45({function_name}, initial_outcome, initial_time, times, {','.join(argument_variables)});\n" + self.code += f"vector[{len(outcome_variable_names)}] integrated_result[T] = ode_rk45({function_name}, initial_outcome, initial_time, times, {', '.join(argument_variables)});\n" self.code.indent_level -= 1 self.code += "}\n" return str(self.code) +class StanParametersBuilder: + def __init__(self, sampling_statements: Iterable["SamplingStatement"]): + self.sampling_statements = sampling_statements + + def build_block(self): + code = IndentedString() + code += "parameters{\n" + code.indent_level += 1 # Enter parameters block + + for statement in self.sampling_statements: + code += f"real {statement.lhs_name};\n" + + code.indent_level -= 1 # Exit parameters block + code += "}\n" + return code.string + + +class StanDataBuilder: + def __init__(self): + pass + + def build_block(self): + code = IndentedString() + code += "data{\n" + code.indent_level += 1 + code.indent_level -= 1 + code += "}\n" + return code.string + +class StanTransformedDataBuilder: + def __init__(self, integration_times: Iterable[Number]): + self.integration_times = integration_times + + def build_block(self) -> str: + T = len(self.integration_times) + code = IndentedString() + code += "transformed data{\n" + code.indent_level += 1 + code += f"int T = {T};\n" + code += f"array[T] real times = {{{', '.join([str(x) for x in self.integration_times])}}}" + code.indent_level -= 1 + code += "}\n" + return code.string + + +class StanModelBuilder: + def __init__(self, sampling_statements: Iterable["SamplingStatement"]): + self.sampling_statements = sampling_statements + + def build_block(self): + code = IndentedString() + code += "model{\n" + code.indent_level += 1 + for statement in self.sampling_statements: + code += f"{statement.lhs_name} ~ {statement.distribution_type}({', '.join([str(arg) for arg in statement.distribution_args])});\n" + + code.indent_level -= 1 + code += "}\n" + return str(code) + + class StanFunctionBuilder: def __init__( self, abstract_model: AbstractModel, function_name: str = "vensim_ode" @@ -222,6 +292,9 @@ def __init__( ) # in order to evaluate 'key' variable, we need 'element' variables self.code = IndentedString() + def get_generated_lookups_dict(self): + return self.lookup_builder_walker.generated_lookup_function_names + def _create_dependency_graph(self): self.variable_dependency_graph = {} walker = AuxNameWalker() @@ -242,8 +315,8 @@ def _create_dependency_graph(self): def build_functions( self, - predictor_variable_names: List[Tuple[str, str]], - outcome_variable_names: List[str], + predictor_variable_names: Iterable[Tuple[str, str]], + outcome_variable_names: Iterable[str], function_name: str = "vensim_func", ): self.code = IndentedString() @@ -312,7 +385,7 @@ def recursive_order_search(current, visited): if isinstance(var, str): argument_variables.append(var) argument_strings.append("real " + var) - elif isinstance(var, type): + elif isinstance(var, tuple): var_type, var_name = var argument_variables.append(var_name) argument_strings.append(f"{var_type} {var_name}") diff --git a/test_scripts/stan_vensim_integration.py b/test_scripts/stan_vensim_integration.py new file mode 100644 index 00000000..b77b7452 --- /dev/null +++ b/test_scripts/stan_vensim_integration.py @@ -0,0 +1,17 @@ +from pysd.builders.stan.stan_model import StanVensimModel +from pysd.translators.vensim.vensim_file import VensimFile +from pysd.translators.xmile.xmile_file import XmileFile + +vf = VensimFile("vensim_models/ds_white_sterman.mdl") +vf.parse() +am = vf.get_abstract_model() + +model = StanVensimModel("ds_white_sterman", am, list(range(0, 10))) +model.set_prior("inventory_adjustment_time", "normal", 0, 1) +model.set_prior("minimum_order_processing_time", "normal", 0, 1) + +print(model.vensim_model_context.variable_names) + +model.build_stan_functions() + +model.data2draws("") \ No newline at end of file From a009a47cc7e9fb4fce98c510667d0e0f0c33d186 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Mon, 29 Aug 2022 19:48:47 +0900 Subject: [PATCH 29/45] Updates to data2draws --- ...model_builder.py => stan_block_builder.py} | 151 +----------------- pysd/builders/stan/stan_model.py | 39 +++-- test_scripts/stan_vensim_integration.py | 2 +- 3 files changed, 36 insertions(+), 156 deletions(-) rename pysd/builders/stan/{stan_model_builder.py => stan_block_builder.py} (65%) diff --git a/pysd/builders/stan/stan_model_builder.py b/pysd/builders/stan/stan_block_builder.py similarity index 65% rename from pysd/builders/stan/stan_model_builder.py rename to pysd/builders/stan/stan_block_builder.py index c610786e..8f8f816e 100644 --- a/pysd/builders/stan/stan_model_builder.py +++ b/pysd/builders/stan/stan_block_builder.py @@ -15,150 +15,6 @@ from .stan_model import SamplingStatement -class StanModelBuilder: - def __init__(self, abstract_model: AbstractModel): - self.abstract_model = abstract_model - - self.variable_ast_dict: Dict[str, AbstractSyntax] = {} - assert ( - len(self.abstract_model.sections) == 1 - ), "Number of sections in AbstractModel must be 1." - for element in self.abstract_model.sections[0].elements: - stan_varname = vensim_name_to_identifier(element.name) - assert ( - len(element.components) == 1 - ), f"Number of components in AbstractElement must be 1, but {element.name} has {len(element.components)}" - self.variable_ast_dict[stan_varname] = element.components[0].ast - - def create_stan_program( - self, - predictor_variable_names: List[Union[str, Tuple[str, str]]], - outcome_variable_names: Sequence[str] = (), - function_name="vensim_func", - ): - """ - - Parameters - ---------- - predictor_variable_names: List of name of variables within the SD model that are handled by stan. The code for - these variables will not be generated, but instead taken from the argument of the ODE system function. - outcome_variable_names: Sequence of name of the variables which are the measured as system state. - Normally this will be the observed outcomes among the stock variables. If it is not specified, it will automatically - identify stock variable names and use them. - function_name: Name of the stan function to be generated. default is "vensim_func" - - Returns - ------- - - """ - # Santize vensim names to stan-compliant identifiers - sanitized_predictor_variable_names = [] - for var in predictor_variable_names: - if isinstance(var, str): - sanitized_predictor_variable_names.append( - vensim_name_to_identifier(var) - ) - elif isinstance(var, tuple): - var_name = var[1] - sanitized_predictor_variable_names.append( - (type, vensim_name_to_identifier(var_name)) - ) - else: - raise Exception( - "predictor_variable_names must be a list consisting of: strings and/or a tuple of the form(T, Name), where T is a string denoting the variable's stan type and Name a string denoting the variable name" - ) - - predictor_variable_names = sanitized_predictor_variable_names - outcome_variable_names = ( - self.get_stock_variable_stan_names() - if not outcome_variable_names - else [ - vensim_name_to_identifier(name) - for name in outcome_variable_names - ] - ) - if not outcome_variable_names: - raise Exception( - "There are no stock variables defined in the model, hence nothing to integrate." - ) - - self.code = IndentedString() - - function_block_builder = StanFunctionBuilder(self.abstract_model) - - self.code += function_block_builder.build_function_block( - predictor_variable_names, outcome_variable_names, function_name - ) - - self.code += "data{\n}\n" - # self.code += StanDataBuilder(self.abstract_model).build_block(predictor_variable_names, outcome_variable_names) - self.code += "transformed data{\n}\n" - self.code += "parameters{\n}\n" - self.code += StanTransformedParametersBuilder( - self.abstract_model - ).build_block( - predictor_variable_names, - outcome_variable_names, - function_block_builder.get_generated_lookups_dict(), - function_name, - ) - self.code += "model{\n}\n" - - self.code += "generated quantities{\n}" - - return self.code - - def print_variable_info(self): - var_names = [] - max_length = len("original name") + 1 - for element in self.abstract_model.sections[0].elements: - is_stock = False - for component in element.components: - if isinstance(component.ast, IntegStructure): - is_stock = True - break - - var_names.append( - ( - element.name, - vensim_name_to_identifier(element.name), - is_stock, - ) - ) - max_length = max(max_length, len(element.name) + 1) - - header = ( - "original name".ljust(max_length) - + "stan variable name".ljust(max_length) - + "is stock" - ) - print(header) - print("-" * len(header)) - for x in var_names: - print( - x[0].ljust(max_length) - + x[1].ljust(max_length) - + ("V" if x[2] else "") - ) - - def get_stock_variable_stan_names(self) -> List[str]: - """ - Iterate through the AST and find stock variables - Returns - ------- - - """ - stock_varible_names = [] - for element in self.abstract_model.sections[0].elements: - for component in element.components: - if isinstance(component.ast, IntegStructure): - stock_varible_names.append( - vensim_name_to_identifier(element.name) - ) - break - - return stock_varible_names - class StanTransformedParametersBuilder: def __init__(self, abstract_model: AbstractModel): self.abstract_model = abstract_model @@ -244,8 +100,10 @@ def build_block(self): code += "}\n" return code.string + class StanTransformedDataBuilder: - def __init__(self, integration_times: Iterable[Number]): + def __init__(self, initial_time, integration_times: Iterable[Number]): + self.initial_time = initial_time self.integration_times = integration_times def build_block(self) -> str: @@ -253,8 +111,9 @@ def build_block(self) -> str: code = IndentedString() code += "transformed data{\n" code.indent_level += 1 + code += f"real initial_time = {self.initial_time};\n" code += f"int T = {T};\n" - code += f"array[T] real times = {{{', '.join([str(x) for x in self.integration_times])}}}" + code += f"array[T] real times = {{{', '.join([str(x) for x in self.integration_times])}}}\n" code.indent_level -= 1 code += "}\n" return code.string diff --git a/pysd/builders/stan/stan_model.py b/pysd/builders/stan/stan_model.py index 26f3be24..e547a139 100644 --- a/pysd/builders/stan/stan_model.py +++ b/pysd/builders/stan/stan_model.py @@ -2,7 +2,7 @@ from numbers import Number from dataclasses import dataclass, field import ast, os, pathlib, warnings, glob -from .stan_model_builder import * +from .stan_block_builder import * from .utilities import vensim_name_to_identifier from pysd.translators.structures.abstract_expressions import * @@ -37,7 +37,11 @@ def __init__(self, abstract_model): self.variable_names = set() self.stock_variable_names = set() + # Some basic checks to make sure the AM is compatible + assert len(abstract_model.sections) == 1, "Number of sections in AbstractModel must be 1." + for element in abstract_model.sections[0].elements: + assert len(element.components) == 1, f"Number of components in AbstractElement must be 1, but {element.name} has {len(element.components)}" self.variable_names.add(vensim_name_to_identifier(element.name)) for element in abstract_model.sections[0].elements: @@ -46,11 +50,31 @@ def __init__(self, abstract_model): self.stock_variable_names.add(vensim_name_to_identifier(element.name)) break + def print_variable_info(self, abstract_model): + var_names = [] + max_length = len("original name") + 1 + for element in abstract_model.sections[0].elements: + is_stock = False + for component in element.components: + if isinstance(component.ast, IntegStructure): + is_stock = True + break + + var_names.append((element.name, vensim_name_to_identifier(element.name), is_stock,)) + max_length = max(max_length, len(element.name) + 1) + + header = ("original name".ljust(max_length) + "stan variable name".ljust(max_length) + "is stock") + print(header) + print("-" * len(header)) + for x in var_names: + print(x[0].ljust(max_length) + x[1].ljust(max_length) + ("V" if x[2] else "")) + class StanVensimModel: - def __init__(self, model_name: str, abstract_model, integration_times: Iterable[Number]): + def __init__(self, model_name: str, abstract_model, initial_time: float, integration_times: Iterable[Number]): self.abstract_model = abstract_model self.model_name = model_name + self.initial_time = float(initial_time) self.integration_times = integration_times self.stan_model_context = StanModelContext() self.vensim_model_context = VensimModelContext(self.abstract_model) @@ -95,22 +119,19 @@ def data2draws(self, data_file_path: str): f.write(StanDataBuilder().build_block()) f.write("\n") - f.write(StanTransformedDataBuilder(self.integration_times).build_block()) - f.write("\n") - - f.write(StanParametersBuilder(self.stan_model_context.sample_statements).build_block()) + f.write(StanTransformedDataBuilder(self.initial_time, self.integration_times).build_block()) f.write("\n") transformed_params_builder = StanTransformedParametersBuilder(self.abstract_model) - f.write("\n") - - f.write(transformed_params_builder.build_block(self.stan_model_context.exposed_parameters, self.vensim_model_context.stock_variable_names, self.function_builder.get_generated_lookups_dict(), self.function_builder.ode_function_name)) f.write("\n") + f.write(StanParametersBuilder(self.stan_model_context.sample_statements).build_block()) + f.write("\n") + f.write(StanModelBuilder(self.stan_model_context.sample_statements).build_block()) f.write("\n") diff --git a/test_scripts/stan_vensim_integration.py b/test_scripts/stan_vensim_integration.py index b77b7452..3947761c 100644 --- a/test_scripts/stan_vensim_integration.py +++ b/test_scripts/stan_vensim_integration.py @@ -6,7 +6,7 @@ vf.parse() am = vf.get_abstract_model() -model = StanVensimModel("ds_white_sterman", am, list(range(0, 10))) +model = StanVensimModel("ds_white_sterman", am, 0.0, list(range(0, 10))) model.set_prior("inventory_adjustment_time", "normal", 0, 1) model.set_prior("minimum_order_processing_time", "normal", 0, 1) From 161e55ec8c9d4fc80bd702d344cdbe74e8aab6e0 Mon Sep 17 00:00:00 2001 From: Shinyoung Kim Date: Thu, 1 Sep 2022 04:47:54 +0900 Subject: [PATCH 30/45] Check if variables with priors exist in the ODE function declaration --- pysd/builders/stan/stan_model.py | 7 +++++-- test_scripts/stan_vensim_integration.py | 1 + 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/pysd/builders/stan/stan_model.py b/pysd/builders/stan/stan_model.py index e547a139..47e697aa 100644 --- a/pysd/builders/stan/stan_model.py +++ b/pysd/builders/stan/stan_model.py @@ -85,9 +85,12 @@ def set_prior(self, variable_name: str, distribution_type: str, *args): # If the distribution argument is an expression, parse the dependant variables # We're using the python parser here, which might be problematic used_variable_names = [node.id for node in ast.walk(ast.parse(arg)) if isinstance(node, ast.Name)] - self.stan_model_context.exposed_parameters.update(used_variable_names) + for name in used_variable_names: + if name in self.vensim_model_context.variable_names: + self.stan_model_context.exposed_parameters.update(used_variable_names) - self.stan_model_context.exposed_parameters.add(variable_name) + if variable_name in self.vensim_model_context.variable_names: + self.stan_model_context.exposed_parameters.add(variable_name) self.stan_model_context.sample_statements.append(SamplingStatement(variable_name, distribution_type, *args)) diff --git a/test_scripts/stan_vensim_integration.py b/test_scripts/stan_vensim_integration.py index 3947761c..5d342dbf 100644 --- a/test_scripts/stan_vensim_integration.py +++ b/test_scripts/stan_vensim_integration.py @@ -9,6 +9,7 @@ model = StanVensimModel("ds_white_sterman", am, 0.0, list(range(0, 10))) model.set_prior("inventory_adjustment_time", "normal", 0, 1) model.set_prior("minimum_order_processing_time", "normal", 0, 1) +model.set_prior("alpha", "normal", 0, 1) print(model.vensim_model_context.variable_names) From 9d8e1f2a46dca1ab1f7407f6e1ed1ce2d364da03 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Thu, 1 Sep 2022 06:05:13 +0900 Subject: [PATCH 31/45] Update codegen to match stan 2.30 --- pysd/builders/stan/ast_walker.py | 29 +++++++--- pysd/builders/stan/stan_block_builder.py | 34 +++++++----- pysd/builders/stan/stan_model.py | 71 +++++++++++++++++------- test_scripts/stan_vensim_integration.py | 5 +- test_scripts/stanc_test.sh | 1 + 5 files changed, 96 insertions(+), 44 deletions(-) create mode 100644 test_scripts/stanc_test.sh diff --git a/pysd/builders/stan/ast_walker.py b/pysd/builders/stan/ast_walker.py index 089cc46f..b9131f8b 100644 --- a/pysd/builders/stan/ast_walker.py +++ b/pysd/builders/stan/ast_walker.py @@ -78,8 +78,8 @@ def walk(self, ast_node) -> None: self.code += f"real {function_name}(real x){{\n" self.code.indent_level += 1 # Enter function body - self.code += f"# x {ast_node.x_limits} = {ast_node.x}\n" - self.code += f"# y {ast_node.y_limits} = {ast_node.y}\n" + self.code += f"// x {ast_node.x_limits} = {ast_node.x}\n" + self.code += f"// y {ast_node.y_limits} = {ast_node.y}\n" self.code += "real slope;\n" self.code += "real intercept;\n\n" n_intervals = len(ast_node.x) @@ -87,9 +87,9 @@ def walk(self, ast_node) -> None: if lookup_index == 0: continue if lookup_index == 1: - self.code += f"if(x <= {ast_node.x[lookup_index]})\n" + self.code += f"if(x <= {ast_node.x[lookup_index]}){{\n" else: - self.code += f"else if(x <= {ast_node.x[lookup_index]})\n" + self.code += f"else if(x <= {ast_node.x[lookup_index]}){{\n" self.code.indent_level += 1 # enter conditional body @@ -98,6 +98,17 @@ def walk(self, ast_node) -> None: self.code += f"return intercept + slope * (x - {ast_node.x[lookup_index - 1]});\n" self.code.indent_level -= 1 # exit conditional body + self.code += "}\n" + + # Handle out-of-bounds input + self.code += "else{\n" + self.code.indent_level += 1 + self.code += f'reject("{function_name}: input value ", x, " is out of bounds!");\n' + self.code.indent_level -= 1 + self.code += "}\n" + + # Return nan just to make it return a value. + self.code += "return not_a_number();\n" self.code.indent_level -= 1 # exit function body @@ -140,9 +151,9 @@ def walk(self, ast_node) -> str: output_string = "" function_name = self.walk(ast_node.function) if function_name == "min": - function_name = "min" + function_name = "fmin" elif function_name == "max": - function_name = "max" + function_name = "fmax" elif function_name == "xidz": assert ( len(ast_node.arguments) == 3 @@ -170,7 +181,7 @@ def walk(self, ast_node) -> str: output_string += function_name output_string += "(" - output_string += ",".join( + output_string += ", ".join( [self.walk(argument) for argument in ast_node.arguments] ) output_string += ")" @@ -284,12 +295,12 @@ def walk(self, ast_node) -> str: def rng_codegen(self, rng_type: str, arguments: List[Any]): if rng_type == "random_normal": lower, upper, mean, std, _ = arguments - return f"min(max(normal_rng({mean}, {std}), {lower}), {upper})" + return f"fmin(fmax(normal_rng({mean}, {std}), {lower}), {upper})" elif rng_type == "random_uniform": lower, upper, _ = arguments return f"uniform_rng({lower}, {upper})" elif rng_type == "random_poisson": lower, upper, _lambda, offset, multiply, _ = arguments - return f"min(max(fma(poisson_rng({_lambda}), {multiply}, {offset}), {lower}), {upper})" + return f"fmin(fmax(fma(poisson_rng({_lambda}), {multiply}, {offset}), {lower}), {upper})" else: raise Exception(f"RNG function {rng_type} not implemented") diff --git a/pysd/builders/stan/stan_block_builder.py b/pysd/builders/stan/stan_block_builder.py index 8f8f816e..9c952d66 100644 --- a/pysd/builders/stan/stan_block_builder.py +++ b/pysd/builders/stan/stan_block_builder.py @@ -25,6 +25,7 @@ def build_block( outcome_variable_names, lookup_function_dict, function_name, + stock_initial_values: Dict[str, str] ): self.code = IndentedString() self.code += "transformed parameters {\n" @@ -43,8 +44,10 @@ def build_block( stan_varname = vensim_name_to_identifier(element.name) variable_ast_dict[stan_varname] = element.components[0].ast - self.code += "# Initial ODE values\n" + self.code += "// Initial ODE values\n" for outcome_variable_name in outcome_variable_names: + if outcome_variable_name in stock_initial_values: + continue for element in self.abstract_model.sections[0].elements: if ( vensim_name_to_identifier(element.name) @@ -54,17 +57,24 @@ def build_block( assert isinstance( component.ast, IntegStructure ), "Output variable component must be an INTEG." - self.code += f"real {outcome_variable_name}_initial = {InitialValueCodegenWalker(lookup_function_dict, variable_ast_dict).walk(component.ast)};\n" + self.code += f"real {outcome_variable_name}_init = {InitialValueCodegenWalker(lookup_function_dict, variable_ast_dict).walk(component.ast)};\n" break self.code += "\n" - self.code += f"vector[{len(outcome_variable_names)}] initial_outcome; # Initial ODE state vector\n" + self.code += f"vector[{len(outcome_variable_names)}] initial_outcome; // Initial ODE state vector\n" for index, name in enumerate(outcome_variable_names, 1): - self.code += f"initial_outcome[{index}] = {name};\n" + if name in stock_initial_values: + self.code += f"initial_outcome[{index}] = {stock_initial_values[name]}; // Defined within stan\n" + else: + self.code += f"initial_outcome[{index}] = {name}_init;\n" self.code += "\n" self.code += f"vector[{len(outcome_variable_names)}] integrated_result[T] = ode_rk45({function_name}, initial_outcome, initial_time, times, {', '.join(argument_variables)});\n" + + for index, name in enumerate(outcome_variable_names, 1): + self.code += f"array[T] real {name} = integrated_result[:, {index}];\n" + self.code.indent_level -= 1 self.code += "}\n" @@ -113,7 +123,7 @@ def build_block(self) -> str: code.indent_level += 1 code += f"real initial_time = {self.initial_time};\n" code += f"int T = {T};\n" - code += f"array[T] real times = {{{', '.join([str(x) for x in self.integration_times])}}}\n" + code += f"array[T] real times = {{{', '.join([str(x) for x in self.integration_times])}}};\n" code.indent_level -= 1 code += "}\n" return code.string @@ -137,7 +147,7 @@ def build_block(self): class StanFunctionBuilder: def __init__( - self, abstract_model: AbstractModel, function_name: str = "vensim_ode" + self, abstract_model: AbstractModel, function_name: str = "vensim_ode_func" ): self.abstract_model = abstract_model @@ -176,7 +186,7 @@ def build_functions( self, predictor_variable_names: Iterable[Tuple[str, str]], outcome_variable_names: Iterable[str], - function_name: str = "vensim_func", + function_name: str = "vensim_ode_func", ): self.code = IndentedString() @@ -187,7 +197,7 @@ def build_functions( self.code += lookup_functions_code self.code += "\n\n" - self.code += "# Begin ODE declaration\n" + self.code += "// Begin ODE declaration\n" self._create_dependency_graph() # Identify the minimum number of variables needed for calculating outcomes @@ -206,11 +216,7 @@ def build_functions( eval_order = [] def recursive_order_search(current, visited): - # if current in visited: - # return visited.add(current) - # if current in eval_order: - # return for child in self.variable_dependency_graph[current]: if child == current: continue @@ -256,10 +262,10 @@ def recursive_order_search(current, visited): self.code.indent_level += 1 # Enter function body - self.code += f"vector[{len(outcome_variable_names)}] dydt; # Return vector of the ODE function\n" + self.code += f"vector[{len(outcome_variable_names)}] dydt; // Return vector of the ODE function\n" self.code += "\n" - self.code += "# State variables\n" + self.code += "// State variables\n" for index, outcome_variable_name in enumerate( outcome_variable_names, 1 ): diff --git a/pysd/builders/stan/stan_model.py b/pysd/builders/stan/stan_model.py index 47e697aa..13c11840 100644 --- a/pysd/builders/stan/stan_model.py +++ b/pysd/builders/stan/stan_model.py @@ -1,7 +1,7 @@ from typing import List, Set, Type, Tuple from numbers import Number from dataclasses import dataclass, field -import ast, os, pathlib, warnings, glob +import ast, os, pathlib, warnings, glob, re from .stan_block_builder import * from .utilities import vensim_name_to_identifier from pysd.translators.structures.abstract_expressions import * @@ -12,11 +12,13 @@ class SamplingStatement: distribution_type: str distribution_return_type: Type distribution_args: Tuple[str] + init_state: bool - def __init__(self, lhs_name, distribution_type, *distribution_args): + def __init__(self, lhs_name, distribution_type, *distribution_args, init_state=False): self.lhs_name = lhs_name self.distribution_type = distribution_type self.distribution_args = distribution_args + self.init_state = init_state def __post_init__(self): if self.distribution_type in ("bernoulli", "binomial", "beta_binomial", "neg_binomial", "poisson"): @@ -78,20 +80,39 @@ def __init__(self, model_name: str, abstract_model, initial_time: float, integra self.integration_times = integration_times self.stan_model_context = StanModelContext() self.vensim_model_context = VensimModelContext(self.abstract_model) + if initial_time in integration_times: + raise Exception("initial_time shouldn't be present in integration_times") + + self.init_variable_regex = re.compile(".+?(?=_init$)") + # This regex is to match all preceding characters that come before '_init' at the end of the string. + # So something like stock_var_init_init would match into stock_var_init. + # This is used to parse out the corresponding stock names for init parameters. + + def print_info(self): + print("- Vensim model information:") + self.vensim_model_context.print_variable_info(self.abstract_model) + print("*" * 10) + print("- Stan model information:") + + def set_prior(self, variable_name: str, distribution_type: str, *args, init_state=False): + if init_state: + # This means the initial value of the ODE state variable. + if variable_name not in self.vensim_model_context.stock_variable_names: + raise Exception("init_state may be set to True only for stock variables.") + self.stan_model_context.sample_statements.append(SamplingStatement(f"{variable_name}_init", distribution_type, *args, init_state=init_state)) + else: + for arg in args: + if isinstance(arg, str): + # If the distribution argument is an expression, parse the dependant variables + # We're using the python parser here, which might be problematic + used_variable_names = [node.id for node in ast.walk(ast.parse(arg)) if isinstance(node, ast.Name)] + for name in used_variable_names: + if name in self.vensim_model_context.variable_names: + self.stan_model_context.exposed_parameters.update(used_variable_names) - def set_prior(self, variable_name: str, distribution_type: str, *args): - for arg in args: - if isinstance(arg, str): - # If the distribution argument is an expression, parse the dependant variables - # We're using the python parser here, which might be problematic - used_variable_names = [node.id for node in ast.walk(ast.parse(arg)) if isinstance(node, ast.Name)] - for name in used_variable_names: - if name in self.vensim_model_context.variable_names: - self.stan_model_context.exposed_parameters.update(used_variable_names) - - if variable_name in self.vensim_model_context.variable_names: - self.stan_model_context.exposed_parameters.add(variable_name) - self.stan_model_context.sample_statements.append(SamplingStatement(variable_name, distribution_type, *args)) + if variable_name in self.vensim_model_context.variable_names: + self.stan_model_context.exposed_parameters.add(variable_name) + self.stan_model_context.sample_statements.append(SamplingStatement(variable_name, distribution_type, *args, init_state=init_state)) def build_stan_functions(self): @@ -117,7 +138,9 @@ def build_stan_functions(self): def data2draws(self, data_file_path: str): with open(os.path.join(os.getcwd(), f"{self.model_name}_data2draws.stan"), "w") as f: # Include the function - f.write(f"#include {self.model_name}_functions.stan\n\n") + f.write("functions{\n") + f.write(f" #include {self.model_name}_functions.stan\n") + f.write("}\n\n") f.write(StanDataBuilder().build_block()) f.write("\n") @@ -125,14 +148,22 @@ def data2draws(self, data_file_path: str): f.write(StanTransformedDataBuilder(self.initial_time, self.integration_times).build_block()) f.write("\n") + f.write(StanParametersBuilder(self.stan_model_context.sample_statements).build_block()) + f.write("\n") + transformed_params_builder = StanTransformedParametersBuilder(self.abstract_model) + # Find sampling statements for init + stock_initials = {} + for statement in self.stan_model_context.sample_statements: + if statement.init_state: + stock_variable_name = self.init_variable_regex.findall(statement.lhs_name)[0] + stock_initials[stock_variable_name] = statement.lhs_name + f.write(transformed_params_builder.build_block(self.stan_model_context.exposed_parameters, self.vensim_model_context.stock_variable_names, self.function_builder.get_generated_lookups_dict(), - self.function_builder.ode_function_name)) - f.write("\n") - - f.write(StanParametersBuilder(self.stan_model_context.sample_statements).build_block()) + self.function_builder.ode_function_name, + stock_initials)) f.write("\n") f.write(StanModelBuilder(self.stan_model_context.sample_statements).build_block()) diff --git a/test_scripts/stan_vensim_integration.py b/test_scripts/stan_vensim_integration.py index 5d342dbf..e29d2785 100644 --- a/test_scripts/stan_vensim_integration.py +++ b/test_scripts/stan_vensim_integration.py @@ -6,10 +6,13 @@ vf.parse() am = vf.get_abstract_model() -model = StanVensimModel("ds_white_sterman", am, 0.0, list(range(0, 10))) +model = StanVensimModel("ds_white_sterman", am, 0.0, list(range(1, 10))) +model.print_info() + model.set_prior("inventory_adjustment_time", "normal", 0, 1) model.set_prior("minimum_order_processing_time", "normal", 0, 1) model.set_prior("alpha", "normal", 0, 1) +model.set_prior("inventory", "normal", 0, 1, init_state=True) print(model.vensim_model_context.variable_names) diff --git a/test_scripts/stanc_test.sh b/test_scripts/stanc_test.sh new file mode 100644 index 00000000..d0311581 --- /dev/null +++ b/test_scripts/stanc_test.sh @@ -0,0 +1 @@ +~/.cmdstan/cmdstan-2.30.1/bin/stanc ds_white_sterman_data2draws.stan --include-paths ./ \ No newline at end of file From 5584248c56c457d7a8717cdea1f03137b6feaea2 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Thu, 1 Sep 2022 06:38:16 +0900 Subject: [PATCH 32/45] Update lookup codegen logic and create cmdstan model --- pysd/builders/stan/ast_walker.py | 28 ++++++++------------- pysd/builders/stan/stan_block_builder.py | 2 +- pysd/builders/stan/stan_model.py | 14 +++++------ test_scripts/stan_vensim_integration.py | 4 ++- test_scripts/testing.py | 32 ++++++++---------------- 5 files changed, 32 insertions(+), 48 deletions(-) diff --git a/pysd/builders/stan/ast_walker.py b/pysd/builders/stan/ast_walker.py index b9131f8b..827ea8c3 100644 --- a/pysd/builders/stan/ast_walker.py +++ b/pysd/builders/stan/ast_walker.py @@ -45,7 +45,7 @@ def walk(self, ast_node) -> List[str]: @dataclass class LookupCodegenWalker(BaseNodeWaler): - generated_lookup_function_names: Dict[Tuple, str] = field( + generated_lookup_function_names: Dict[str, str] = field( default_factory=dict ) # This dict holds the generated function names of each individual lookup function. @@ -62,17 +62,16 @@ def get_lookup_keyname(lookup_node: LookupsStructure): + lookup_node.y_limits ) - def walk(self, ast_node) -> None: + def walk(self, ast_node, node_name: str) -> None: if isinstance(ast_node, InlineLookupsStructure): - self.walk(ast_node.lookups) + self.walk(ast_node.lookups, node_name) elif isinstance(ast_node, LookupsStructure): assert ( ast_node.type == "interpolate" ), "Type of Lookup must be 'interpolate'" - identifier_key = LookupCodegenWalker.get_lookup_keyname(ast_node) - function_name = f"lookupFunc_{self.n_lookups}" + function_name = f"lookupFunc__{node_name}" self.generated_lookup_function_names[ - identifier_key + node_name ] = function_name self.n_lookups += 1 self.code += f"real {function_name}(real x){{\n" @@ -101,14 +100,7 @@ def walk(self, ast_node) -> None: self.code += "}\n" # Handle out-of-bounds input - self.code += "else{\n" - self.code.indent_level += 1 - self.code += f'reject("{function_name}: input value ", x, " is out of bounds!");\n' - self.code.indent_level -= 1 - self.code += "}\n" - - # Return nan just to make it return a value. - self.code += "return not_a_number();\n" + self.code += f"return {ast_node.y[-1]};\n" self.code.indent_level -= 1 # exit function body @@ -119,7 +111,7 @@ def walk(self, ast_node) -> None: @dataclass class BlockCodegenWalker(BaseNodeWaler): - lookup_function_names: Dict[Tuple, str] + lookup_function_names: Dict[str, str] def walk(self, ast_node) -> str: @@ -143,8 +135,10 @@ def walk(self, ast_node) -> str: return output_string elif isinstance(ast_node, ReferenceStructure): - # ReferenceSTructure denotes invoking the value of another variable + # ReferenceStructure denotes invoking the value of another variable # Subscripts are ignored for now + if ast_node.reference in self.lookup_function_names: + return self.lookup_function_names[ast_node.reference] return ast_node.reference elif isinstance(ast_node, CallStructure): @@ -204,7 +198,7 @@ def walk(self, ast_node) -> str: @dataclass class InitialValueCodegenWalker(BlockCodegenWalker): variable_ast_dict: Dict[str, AbstractSyntax] - lookup_function_names: Dict[Tuple, str] + lookup_function_names: Dict[Union[str, Tuple], str] def walk(self, ast_node): if isinstance(ast_node, IntegStructure): diff --git a/pysd/builders/stan/stan_block_builder.py b/pysd/builders/stan/stan_block_builder.py index 9c952d66..0f4b44a1 100644 --- a/pysd/builders/stan/stan_block_builder.py +++ b/pysd/builders/stan/stan_block_builder.py @@ -310,6 +310,6 @@ def recursive_order_search(current, visited): def build_lookups(self): for element in self.elements: for component in element.components: - self.lookup_builder_walker.walk(component.ast) + self.lookup_builder_walker.walk(component.ast, vensim_name_to_identifier(element.name)) diff --git a/pysd/builders/stan/stan_model.py b/pysd/builders/stan/stan_model.py index 13c11840..d765fca0 100644 --- a/pysd/builders/stan/stan_model.py +++ b/pysd/builders/stan/stan_model.py @@ -1,11 +1,9 @@ from typing import List, Set, Type, Tuple -from numbers import Number -from dataclasses import dataclass, field -import ast, os, pathlib, warnings, glob, re +import ast, glob, re from .stan_block_builder import * from .utilities import vensim_name_to_identifier from pysd.translators.structures.abstract_expressions import * - +import cmdstanpy class SamplingStatement: lhs_name: str @@ -129,14 +127,14 @@ def build_stan_functions(self): if input(f"{self.model_name}_functions.stan already exists in the current working directory. Overwrite? (Y/N):").lower() != "y": raise Exception("Code generation aborted by user") - - with open(os.path.join(os.getcwd(), f"{self.model_name}_functions.stan"), "w") as f: self.function_builder = StanFunctionBuilder(self.abstract_model) f.write(self.function_builder.build_functions(self.stan_model_context.exposed_parameters, self.vensim_model_context.stock_variable_names)) + print(self.function_builder.get_generated_lookups_dict()) def data2draws(self, data_file_path: str): - with open(os.path.join(os.getcwd(), f"{self.model_name}_data2draws.stan"), "w") as f: + stan_model_path= os.path.join(os.getcwd(), f"{self.model_name}_data2draws.stan") + with open(stan_model_path, "w") as f: # Include the function f.write("functions{\n") f.write(f" #include {self.model_name}_functions.stan\n") @@ -169,4 +167,6 @@ def data2draws(self, data_file_path: str): f.write(StanModelBuilder(self.stan_model_context.sample_statements).build_block()) f.write("\n") + stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_path) + return stan_model diff --git a/test_scripts/stan_vensim_integration.py b/test_scripts/stan_vensim_integration.py index e29d2785..e4008a99 100644 --- a/test_scripts/stan_vensim_integration.py +++ b/test_scripts/stan_vensim_integration.py @@ -18,4 +18,6 @@ model.build_stan_functions() -model.data2draws("") \ No newline at end of file +cmdstan_model = model.data2draws("") +result = cmdstan_model.sample() +result.summary() \ No newline at end of file diff --git a/test_scripts/testing.py b/test_scripts/testing.py index b95c57ab..494c18dc 100644 --- a/test_scripts/testing.py +++ b/test_scripts/testing.py @@ -1,6 +1,5 @@ from pysd.translators.vensim.vensim_file import VensimFile from pysd.translators.xmile.xmile_file import XmileFile -from pysd.builders.stan.stan_model_builder import * vf = VensimFile("vensim_models/ds_white_sterman.mdl") #vf = VensimFile("vensim_models/arithmetic.mdl") @@ -8,24 +7,13 @@ vf.parse() am = vf.get_abstract_model() - -stan_builder = StanModelBuilder(am) -stan_builder.print_variable_info() - -ass_param_lst = ["customer_order_rate", "inventory_coverage", "manufacturing_cycle_time", "time_to_average_order_rate", "wip_adjustment_time"] -obs_stock_lst = ["work_in_process_inventory", "inventory"] - -#print(stan_builder.create_stan_program(ass_param_lst, obs_stock_lst)) - -f_builder = StanFunctionBuilder(am) -print(f_builder.build_function_block(ass_param_lst, obs_stock_lst)) -# for section in am.sections: -# for element in section.elements: -# print("*" * 10) -# print(f"name: {element.name}") -# print(f"length: {len(element.components)}") -# for component in element.components: -# print(f"type: {component.type}") -# print(f"subtype: {component.subtype}") -# print(f"subscript: {component.subscripts}") -# print(component.ast) \ No newline at end of file +for section in am.sections: + for element in section.elements: + print("*" * 10) + print(f"name: {element.name}") + print(f"length: {len(element.components)}") + for component in element.components: + print(f"type: {component.type}") + print(f"subtype: {component.subtype}") + print(f"subscript: {component.subscripts}") + print(component.ast) \ No newline at end of file From 09c6086568b01b2d3cb6cf17bfe4c0aef452dd43 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Thu, 1 Sep 2022 06:38:51 +0900 Subject: [PATCH 33/45] Change filename --- test_scripts/{testing.py => print_vensim_components.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename test_scripts/{testing.py => print_vensim_components.py} (100%) diff --git a/test_scripts/testing.py b/test_scripts/print_vensim_components.py similarity index 100% rename from test_scripts/testing.py rename to test_scripts/print_vensim_components.py From e2d4df85bd734ab6ed2fcdc7f391898a8e896b1c Mon Sep 17 00:00:00 2001 From: amoon Date: Wed, 31 Aug 2022 17:55:33 -0400 Subject: [PATCH 34/45] Six manual comments each for draws2data and data2draws --- test_scripts/BayesWF_PreyPred.ipynb | 1360 +++++++++++++++++ test_scripts/data/hudson-bay-lynx-hare.csv | 2 +- .../stan_file/prey-predator_data2draws.stan | 67 + .../stan_file/prey-predator_draws2data.stan | 45 + .../stan_file/prey-predator_functions.stan | 20 + 5 files changed, 1493 insertions(+), 1 deletion(-) create mode 100644 test_scripts/BayesWF_PreyPred.ipynb create mode 100644 test_scripts/stan_file/prey-predator_data2draws.stan create mode 100644 test_scripts/stan_file/prey-predator_draws2data.stan create mode 100644 test_scripts/stan_file/prey-predator_functions.stan diff --git a/test_scripts/BayesWF_PreyPred.ipynb b/test_scripts/BayesWF_PreyPred.ipynb new file mode 100644 index 00000000..0ae6f2f4 --- /dev/null +++ b/test_scripts/BayesWF_PreyPred.ipynb @@ -0,0 +1,1360 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "cea8444a-acd1-429b-8c64-511c6400e7d2", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from IPython.display import Image\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "import pysd\n", + "from pysd.builders.stan.stan_model import StanVensimModel\n", + "from pysd.translators.vensim.vensim_file import VensimFile\n", + "from pysd.translators.xmile.xmile_file import XmileFile\n", + "\n", + "\n", + "import cmdstanpy # 2.30 is fastest (as of 08.12.2022) `cmdstanpy.install_cmdstan()` \n", + "from cmdstanpy import CmdStanModel, cmdstan_path\n", + "import arviz as az #!pip install git+https://github.com/arviz-devs/arviz\n", + "az.style.use(\"arviz-darkgrid\")\n", + "\n", + "# set your working directiory\n", + "#os.chdir(\"/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts\")" + ] + }, + { + "cell_type": "markdown", + "id": "90dd73ce-2613-4b04-a210-5c1858273ce4", + "metadata": {}, + "source": [ + "# Structuring Uncertainties in Dynamic Models: \n", + "## Bayesian workflow of Predator-Prey Population Dynamics\n", + "\n", + "Angie.H Moon, 07.2022" + ] + }, + { + "cell_type": "markdown", + "id": "8dbac1ad-23e0-4423-a8b5-a0a9a11b85ad", + "metadata": {}, + "source": [ + "## Data: Predator and Prey Pelts in Canada\n", + "\n", + "The species of interest in this case study are\n", + "\n", + "- hares: prey, an hervivorous cousin of rabbits, and\n", + "- lynxes: predator, a feline predator whose diet consists largely of hares.\n", + "\n", + "Spikes in the predator population lag those in the prey population. When populations are plotted against one another over time, the population dynamics orbit in an apparently stable pattern. Population oscillations can be modeled with a pair of differential equations similar to that used to describe springs. The first plot is the number of lynx and hare pelts (in thousands) collected for twenty years. The second plot is the phase plot of number of pelts collected for lynx versus hares similar to that of the dynamics of a spring in phase space (i.e., position vs. momentum)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e1b00493-ec54-4eba-92c3-b1e465855427", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0.5, 0, 'year'), Text(0, 0.5, 'pelt (thousands)')]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "obs_stock_df = pd.read_csv('data/hudson-bay-lynx-hare.csv')\n", + "# data viz\n", + "pd.melt(obs_stock_df, id_vars = 'Year').iloc[[0,20,21,41]]\n", + "pd.melt(obs_stock_df, id_vars = 'Year').iloc[[0,1,20,21,40,41]].rename(columns = {'variable':'species', 'value':'pelts in thousands'})\n", + "ax = obs_stock_df.loc[:, ['Predator', 'Prey']].plot()\n", + "ax.set(xlabel='year', ylabel='pelt (thousands)') " + ] + }, + { + "cell_type": "markdown", + "id": "dd624d4d-178f-41f5-8586-8c15bd59763f", + "metadata": {}, + "source": [ + "Phase plots are as below:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "77abf5f6-b8a8-499d-bcb2-c705afb42c48", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Prey pelts (thousands)')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(obs_stock_df.loc[:, 'Predator'], obs_stock_df.loc[:, 'Prey'])\n", + "plt.plot(obs_stock_df.loc[:, 'Predator'], obs_stock_df.loc[:, 'Prey'])\n", + "plt.xlabel('Predator pelts (thousands)')\n", + "plt.ylabel('Prey pelts (thousands)')" + ] + }, + { + "cell_type": "markdown", + "id": "5763cb30-424f-4716-aab2-ae96fa680338", + "metadata": {}, + "source": [ + "## Mechanistic Model: The Lotka-Volterra Equations\n", + "\n", + "In Lotka-Volterra equations (Lotka 1925; Volterra 1926, 1927), Volterra modeled the temporal dynamics of the two species (i.e., population sizes over times) in terms of four parameters, $\\alpha, \\beta, \\gamma, \\delta \\geq 0$, as\n", + "\n", + "$$\n", + "\\begin{eqnarray}\n", + "\\frac{\\mathrm{d}}{\\mathrm{d}t} u\n", + "& = & (\\alpha - \\beta v) u\n", + "& = & \\alpha u - \\beta u v\n", + "\\\\[6pt]\n", + "\\frac{\\mathrm{d}}{\\mathrm{d}t} v\n", + "& = & (-\\gamma + \\delta \\, u) \\, v\n", + "& = & -\\gamma v + \\delta uv\n", + "\\end{eqnarray}\n", + "$$\n", + "\n", + "$u(t)$ and $v(t)$ are rendered as $u$ and $v$. The factor $\\alpha$, $\\beta$ are the rate of birth and shrinkage relative to the product of the population sizes where as $\\gamma$, $\\delta$ are the shrinkage and growth rate as a factor of the product of the population sizes. Both u and v have positivitity constraints. as long as the initial populations are non-negative, i.e., $u(0) \\geq 0$ and $v(0) \\geq 0$, because the rate of change in each population is a factor of the population size itself.\n", + "\n", + "The dynamic system has four limiting behavior:\n", + "\n", + "1. If both population sizes are initially positive, the populations will oscillate in a fixed pattern indefinitely, remaining positive.\n", + "2. If both population sizes are initially zero, the population sizes will remain zero.\n", + "3. If the predator population size is zero and the prey population size positive, the predator population size remains zero and the prey population grows without bound.\n", + "4. If the predator population size is positive and the prey population size zero, the prey population size remains zero while the predator population shrinks toward zero size." + ] + }, + { + "cell_type": "markdown", + "id": "10c2e11e-1038-41a3-9eb8-a75592ef9aa7", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Statistical Model: regreasion framing and uncertainty embedding\n", + "\n", + "### Solving the inverse problem\n", + "Bayesian statistics is somewhat counterintuitive, as it involves formulating the generating model (from parameter to observed data) then using general principles to solve the inverse problem. Specifically, a Bayesian model requires a mathematical model of what we know about the parameters (i.e., a prior) and a model of what we know about the data generating process given the parameters (i.e., a sampling distribution.\n", + "\n", + "Mathematically, a prior density $p(\\theta)$ over the sequence of parameters $\\theta$ encapsulates our knowledge of the parameters before seeing the data. A sampling distribution (or likelihood), which may have a continuous, discrete or mixed probability function, $p(y | \\theta)$ characterizes the distribution of observable data $y$ given parameters $\\theta$. We limit the observation as stock variables as every SD model can be reformulated into the combination of stock and parameters.\n", + "\n", + "Bayes's rule gives us a general solution to the inverse problem, expressing the posterior $p(\\theta | y)$ in terms of the prior $p(\\theta)$ and likelihood $p(y | \\theta)$. Stan provides a form of Markov chain Monte Carlo (MCMC) sampling that draws a sample $\\theta^{(1)}, \\ldots, \\theta^{(M)}$ from the posterior to use for computational inference. Posterior quantities of interest may be expressed as derived random variables using functions $f(\\theta)$ of parameters. This feature is used for decision analysis; for instance, imagine a optimization problem of conservation cost of the park where prey and predator ecology places at. The cost can be computed based on the posterior distribution inferred from the observed time series.\n", + "\n", + "\n", + "### Uncertainty embedding for forward-backward symmetry required for calibration\n", + "\n", + "The Lotka-Volterra model is deterministic in that given the value of the system parameter and initial outcome state, equation solutions (simulated outcome value) are fully determined. However, for empirical research which use posterior inference from the real data as it final forecast, forward model should be re-designed. This is because symmetry of forward and backward model (i.e. data generation and its inference) is the theoretical justification of calibration. To pass this internal consistency test (or with enough resource, SBC which is rank-statistics based), we need the two process to be the mirror image of other. This is why we purposefully embed uncertainty components, waiting to be captured in the inference step. The purpose is to test resilience and identifiability of our models evidenced by the perfect retrival of prior distribution for every uncertainty we embedded. \n", + "\n", + "### Linear regression analogy\n", + "\n", + "Like in a simple linear regression, we will proceed by treating the underlying determinstic model as providing an expected population value around which there will be variation due to both measurement error and simplifications in the scientific model. Consider the typical formulation of a linear regression, where $y_n$ is an observable scalar outcome, $x_n$ is a row vector of unmodeled predictors (aka covariates, features), $\\beta$ is a coefficient vector parameter, and $\\sigma > 0$ is the error scale,\n", + "\n", + "$$\n", + "\\begin{eqnarray}\n", + "y_n & = & x_n \\beta + \\epsilon_n\n", + "\\\\[6pt]\n", + "\\epsilon_n & \\sim & \\mathsf{Normal}(0, \\sigma)\n", + "\\end{eqnarray}\n", + "$$\n", + "\n", + "### Adding measurement uncertainty (epistemic)\n", + "Before embedding parameteric uncertainty, linear predictor $x_n \\beta$ with predictor $x_n$ (row $n$ of the data matrix $x$) and coefficient (column) vector $\\beta$ are deterministic. The only source of uncertainty is from the measurement. This is expressed by assigning a normal distribution to error term $\\epsilon_n$. Equal expression is with latent error variable $\\epsilon_n$ as follows[17](#fn17), \n", + "\n", + "$$\n", + "y_n \\sim \\mathsf{Normal}(x_n \\beta, \\sigma).\n", + "$$\n", + "\n", + "### Adding parameter uncertainty (epistemic)\n", + "Next, we add parameter uncertainty by coding estimated parameter as a distribution rather than a fixed value. This distribution is called prior distribution and from our example, Normal distirbution is chosen to endow the uncertainty to the four estimated parameters $\\alpha, \\beta, \\gamma, \\delta$. Considering their role difference, $\\alpha, \\gamma$ as multipliers of $u, -v$ and $\\beta, \\delta$ as multipliers of $uv$, prior parameter are chosen as N(1, 0.5) and N(0.05, 0.05) for each. For this selection, refer to the original case study [Carpenter18](https://mc-stan.org/users/documentation/case-studies/lotka-volterra-predator-prey.html).\n", + "\n", + "\n", + "### Adding aleatoric uncertainty\n", + "This applies only when we decide to add measurement uncertainty i.e. having initial value of stock variable as `est_param` instead of the default `ass_param`. For this, detailed explanation is added at the end of this file at Appendix A. " + ] + }, + { + "cell_type": "markdown", + "id": "a87666b9-f6c5-4723-a2be-373f2dd29f0c", + "metadata": {}, + "source": [ + "## Prior predictive check" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "2444ecf8-c272-4dad-9487-37fcb88ddbbf", + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "prey-predator_functions.stan already exists in the current working directory. Overwrite? (Y/N): y\n" + ] + } + ], + "source": [ + "vf = VensimFile(\"vensim_models/prey-predator.mdl\")\n", + "vf.parse()\n", + "am = vf.get_abstract_model()\n", + "\n", + "# set time\n", + "n_t = obs_stock_df.shape[0] - 1 \n", + "premodel = StanVensimModel(\"prey-predator\", am, 0.0, list(range(1, n_t + 1)))\n", + "\n", + "# ode parameter \n", + "premodel.set_prior(\"alpha\", \"normal\", 0.55, 0.1)\n", + "premodel.set_prior(\"gamma\", \"normal\", 0.8, 0.1)\n", + "premodel.set_prior(\"beta\", \"normal\", 0.028, 0.01)\n", + "premodel.set_prior(\"delta\", \"normal\", 0.024, 0.01)\n", + "\n", + "# sampling distribution parameter\n", + "# model.set_prior(\"sigma\", \"lognormal\", np.log(0.01), 0.1) \n", + "\n", + "premodel.build_stan_functions()\n", + "premodel.draws2data(\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "c4bd395d-bc67-4175-b765-6e2232018faf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:50:20 - cmdstanpy - INFO - compiling stan file /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan to exe file /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data\n", + "17:50:28 - cmdstanpy - INFO - compiled model executable: /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data\n", + "17:50:28 - cmdstanpy - WARNING - Stan compiler has produced 6 warnings:\n", + "17:50:28 - cmdstanpy - WARNING - \n", + "--- Translating Stan model to C++ code ---\n", + "bin/stanc --include-paths=/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file --o=/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.hpp /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan\n", + "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_functions.stan', line 1, column 0, included from\n", + "'/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 2, column 0: Comments\n", + " beginning with # are deprecated and this syntax will be removed in Stan\n", + " 2.32.0. Use // to begin line comments; this can be done automatically\n", + " using the auto-format flag to stanc\n", + "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_functions.stan', line 3, column 21, included from\n", + "'/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 2, column 0: Comments\n", + " beginning with # are deprecated and this syntax will be removed in Stan\n", + " 2.32.0. Use // to begin line comments; this can be done automatically\n", + " using the auto-format flag to stanc\n", + "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_functions.stan', line 5, column 4, included from\n", + "'/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 2, column 0: Comments\n", + " beginning with # are deprecated and this syntax will be removed in Stan\n", + " 2.32.0. Use // to begin line comments; this can be done automatically\n", + " using the auto-format flag to stanc\n", + "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 28, column 32: Comments\n", + " beginning with # are deprecated and this syntax will be removed in Stan\n", + " 2.32.0. Use // to begin line comments; this can be done automatically\n", + " using the auto-format flag to stanc\n", + "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 33, column 4: Declaration\n", + " of arrays by placing brackets after a variable name is deprecated and\n", + " will be removed in Stan 2.32.0. Instead use the array keyword before the\n", + " type. This can be changed automatically using the auto-format flag to\n", + " stanc\n", + "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 36, column 4: Declaration\n", + " of arrays by placing brackets after a variable name is deprecated and\n", + " will be removed in Stan 2.32.0. Instead use the array keyword before the\n", + " type. This can be changed automatically using the auto-format flag to\n", + " stanc\n", + "\n", + "--- Compiling, linking C++ code ---\n", + "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -c -include-pch stan/src/stan/model/model_header.hpp.gch -x c++ -o /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.o /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.hpp\n", + "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -Wl,-L,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.o src/cmdstan/main.o -Wl,-L,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data\n", + "rm -f /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.o\n", + "\n", + "17:50:29 - cmdstanpy - INFO - CmdStan start processing\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "856fd81897f842338dcaa2187cc3ba1d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 1 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:50:29 - cmdstanpy - INFO - CmdStan done processing.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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MeanMCSEStdDev5%50%95%N_EffN_Eff/sR_hat
name
lp__0.000000NaN0.0000000.0000000.0000000.000000NaNNaNNaN
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gamma_tilde0.772295NaN0.0462250.7198070.7901430.806936NaNNaNNaN
beta_tilde0.025194NaN0.0056540.0208340.0231660.031582NaNNaNNaN
delta_tilde0.013350NaN0.0013470.0123040.0128750.014870NaNNaNNaN
..............................
y_tilde[19,1]44.503100NaN47.74210015.64220018.25680099.610200NaNNaNNaN
y_tilde[19,2]20.489500NaN25.3186003.2563608.65386049.558400NaNNaNNaN
y_tilde[20,1]52.825400NaN60.81840012.12870023.608500122.739000NaNNaNNaN
y_tilde[20,2]13.521200NaN13.8803004.8816006.14999029.532100NaNNaNNaN
sigma_tilde0.010077NaN0.0000610.0100070.0101100.010116NaNNaNNaN
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" + ], + "text/plain": [ + " Mean MCSE StdDev 5% 50% 95% \\\n", + "name \n", + "lp__ 0.000000 NaN 0.000000 0.000000 0.000000 0.000000 \n", + "alpha_tilde 0.483423 NaN 0.168838 0.311738 0.489268 0.649262 \n", + "gamma_tilde 0.772295 NaN 0.046225 0.719807 0.790143 0.806936 \n", + "beta_tilde 0.025194 NaN 0.005654 0.020834 0.023166 0.031582 \n", + "delta_tilde 0.013350 NaN 0.001347 0.012304 0.012875 0.014870 \n", + "... ... ... ... ... ... ... \n", + "y_tilde[19,1] 44.503100 NaN 47.742100 15.642200 18.256800 99.610200 \n", + "y_tilde[19,2] 20.489500 NaN 25.318600 3.256360 8.653860 49.558400 \n", + "y_tilde[20,1] 52.825400 NaN 60.818400 12.128700 23.608500 122.739000 \n", + "y_tilde[20,2] 13.521200 NaN 13.880300 4.881600 6.149990 29.532100 \n", + "sigma_tilde 0.010077 NaN 0.000061 0.010007 0.010110 0.010116 \n", + "\n", + " N_Eff N_Eff/s R_hat \n", + "name \n", + "lp__ NaN NaN NaN \n", + "alpha_tilde NaN NaN NaN \n", + "gamma_tilde NaN NaN NaN \n", + "beta_tilde NaN NaN NaN \n", + "delta_tilde NaN NaN NaN \n", + "... ... ... ... \n", + "y_tilde[19,1] NaN NaN NaN \n", + "y_tilde[19,2] NaN NaN NaN \n", + "y_tilde[20,1] NaN NaN NaN \n", + "y_tilde[20,2] NaN NaN NaN \n", + "sigma_tilde NaN NaN NaN \n", + "\n", + "[90 rows x 9 columns]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_draws2data = {\n", + " \"n_obs_state\" : 2\n", + "}\n", + "\n", + "sf_path_draws2data = os.path.join(os.getcwd(), \"stan_file\", \"prey-predator_draws2data.stan\")\n", + "sm_draws2data = CmdStanModel(stan_file = sf_path_draws2data)\n", + "\n", + "prior_pred = sm_draws2data.sample(data=data_draws2data, iter_sampling=3, chains=1, fixed_param=True, iter_warmup=0)\n", + "prior_pred.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "595077fd-df8f-442c-a806-30c7e168f423", + "metadata": {}, + "source": [ + "For prior predictive checks, if the real observed data is indiscriminable from the simulated, we usually view it as a sign of pass. Real data is an external reference so as long as the predicted ranges are not too off, we give a pass to prior predictive check. Summary statistics such as N^th moments can be used for comparison. Few comments:\n", + "\n", + "a. we use real data below as a representation of our knowledge, so prior predictive check is not double dipping (using data twice)\n", + "\n", + "b. Bayesian prior corresponds to frequentist's regularization so having a tighter prior than posterior is not unnatrual; simply our determination to find a model concentrated around certain model configuration\n", + "\n", + "c. if tight prior is well-placed, it prevents diveregence from frustrating geometry and boosts sampling efficiency" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "82f4938e-f90c-4043-bcf5-c869c39676be", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(15, 8))\n", + "#compare with real \n", + "ax.plot(obs_stock_df.loc[:, ['Prey']], label = \"Real_Pey\")\n", + "ax.plot(obs_stock_df.loc[:, ['Predator']], label = \"Real_Predator\")\n", + "ax.plot(pd.DataFrame(prior_pred.y_tilde[:,:,0]).T.loc[:, :5])\n", + "ax.plot(pd.DataFrame(prior_pred.y_tilde[:,:,1]).T.loc[:, :5])\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "27ddb4c9-9f11-4324-84f4-d426fb01bc6f", + "metadata": { + "tags": [] + }, + "source": [ + "## Posterior predictive check" + ] + }, + { + "cell_type": "markdown", + "id": "8c54a353-1b50-4e1b-ac5d-bd9933ce6124", + "metadata": {}, + "source": [ + "Now we estimate parameter values based on the real observed stock values. Here we use more diffuse prior." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "7e4236f8-d6c8-45dd-852d-c1b759a54248", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'predator_birth_rate', 'predator_death_rate', 'prey_death_rate', 'predator', 'gamma', 'final_time', 'delta', 'beta', 'time_step', 'prey_birth_rate', 'initial_time', 'saveper', 'prey', 'alpha'}\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "prey-predator_functions.stan already exists in the current working directory. Overwrite? (Y/N): y\n" + ] + } + ], + "source": [ + "vf = VensimFile(\"vensim_models/prey-predator.mdl\")\n", + "vf.parse()\n", + "am = vf.get_abstract_model()\n", + "\n", + "# set time\n", + "model = StanVensimModel(\"prey-predator\", am, 0.0, list(range(1, n_t + 1)))\n", + "\n", + "# ode parameter \n", + "model.set_prior(\"alpha\", \"normal\", 0.8, 0.1)\n", + "model.set_prior(\"gamma\", \"normal\", 0.8, 0.1)\n", + "model.set_prior(\"beta\", \"normal\", 0.05, 0.001)\n", + "model.set_prior(\"delta\", \"normal\", 0.05, 0.001)\n", + "\n", + "# sampling distribution parameter\n", + "# model.set_prior(\"sigma\", \"lognormal\", np.log(0.01), 0.1) \n", + "\n", + "print(model.vensim_model_context.variable_names)\n", + "\n", + "model.build_stan_functions()\n", + "model.draws2data(\"\")\n", + "model.data2draws(\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "3f4934f2-b300-498c-9e30-09d2d613701b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:30:01 - cmdstanpy - INFO - CmdStan start processing\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "970776a8be0c41f696fbebf6a37489e7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 1 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:33:38 - cmdstanpy - INFO - CmdStan done processing.\n", + "17:33:38 - cmdstanpy - WARNING - Non-fatal error during sampling:\n", + "Exception: lognormal_lpdf: Random variable is -1.92663, but must be nonnegative! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 47, column 4 to column 45)\n", + "\tException: ode_rk45: Failed to integrate to next output time (11) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 37, column 4 to column 126)\n", + "\tException: ode_rk45: Failed to integrate to next output time (10) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 37, column 4 to column 126)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: ode_rk45: Failed to integrate to next output time (4) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 37, column 4 to column 126)\n", + "\tException: ode_rk45: Failed to integrate to next output time (12) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 37, column 4 to column 126)\n", + "\tException: lognormal_lpdf: Location parameter[9] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: ode_rk45: Failed to integrate to next output time (19) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 37, column 4 to column 126)\n", + "\tException: lognormal_lpdf: Location parameter[9] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[17] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[16] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[16] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "Consider re-running with show_console=True if the above output is unclear!\n", + "17:33:38 - cmdstanpy - WARNING - Some chains may have failed to converge.\n", + "\tChain 1 had 99 iterations at max treedepth (99.0%)\n", + "\tUse function \"diagnose()\" to see further information.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "sf_path_data2draws = os.path.join(os.getcwd(), \"stan_file\", \"prey-predator_data2draws.stan\")\n", + "sm_data2draws = CmdStanModel(stan_file = sf_path_data2draws)\n", + "\n", + "n_t = obs_stock_df.shape[0] - 1\n", + "data_data2draws = {\n", + " \"n_obs_state\" : 2,\n", + " \"initial_time\" : 0, \n", + " \"times\": [i+1 for i in np.arange(n_t)],\n", + " \"n_t\": n_t,\n", + " \"y\": obs_stock_df.loc[1:, ('Predator', 'Prey')].values.tolist(),\n", + "}\n", + "\n", + "fit = sm_data2draws.sample(data = data_data2draws, iter_sampling = 100, chains = 1, show_console = False, seed = 1234)" + ] + }, + { + "cell_type": "markdown", + "id": "398d77aa-8074-4d7c-a16b-4442b4b1c4f1", + "metadata": {}, + "source": [ + "The following is the summary of posterior draws. It includes loglikelihood for each vector of parmaeter values $(\\alpha, \\beta, \\gamma, \\delta, \\sigma)$. " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "d2b09acd-9180-4285-be7d-86a8db135a4f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MeanMCSEStdDev5%50%95%N_EffN_Eff/sR_hat
name
lp__-28706.50000038.11650065.862000-28807.200000-28708.900000-28596.4000002.985680.1635272.54392
alpha-0.9540920.0000340.000061-0.954163-0.954111-0.9539763.203370.1754502.16126
gamma-0.1764300.0000140.000029-0.176466-0.176439-0.1763684.281430.2344961.51663
beta0.1819820.0002050.0003500.1814010.1819990.1824852.906640.1591982.69473
delta-0.0905450.0000610.000112-0.090727-0.090552-0.0903353.390070.1856762.07456
..............................
log_lik[16]NaNNaNNaNNaNNaNNaNNaNNaNNaN
log_lik[17]NaNNaNNaNNaNNaNNaNNaNNaNNaN
log_lik[18]NaNNaNNaNNaNNaNNaNNaNNaNNaN
log_lik[19]NaNNaNNaNNaNNaNNaNNaNNaNNaN
log_lik[20]NaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", + "

110 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Mean MCSE StdDev 5% 50% \\\n", + "name \n", + "lp__ -28706.500000 38.116500 65.862000 -28807.200000 -28708.900000 \n", + "alpha -0.954092 0.000034 0.000061 -0.954163 -0.954111 \n", + "gamma -0.176430 0.000014 0.000029 -0.176466 -0.176439 \n", + "beta 0.181982 0.000205 0.000350 0.181401 0.181999 \n", + "delta -0.090545 0.000061 0.000112 -0.090727 -0.090552 \n", + "... ... ... ... ... ... \n", + "log_lik[16] NaN NaN NaN NaN NaN \n", + "log_lik[17] NaN NaN NaN NaN NaN \n", + "log_lik[18] NaN NaN NaN NaN NaN \n", + "log_lik[19] NaN NaN NaN NaN NaN \n", + "log_lik[20] NaN NaN NaN NaN NaN \n", + "\n", + " 95% N_Eff N_Eff/s R_hat \n", + "name \n", + "lp__ -28596.400000 2.98568 0.163527 2.54392 \n", + "alpha -0.953976 3.20337 0.175450 2.16126 \n", + "gamma -0.176368 4.28143 0.234496 1.51663 \n", + "beta 0.182485 2.90664 0.159198 2.69473 \n", + "delta -0.090335 3.39007 0.185676 2.07456 \n", + "... ... ... ... ... \n", + "log_lik[16] NaN NaN NaN NaN \n", + "log_lik[17] NaN NaN NaN NaN \n", + "log_lik[18] NaN NaN NaN NaN \n", + "log_lik[19] NaN NaN NaN NaN \n", + "log_lik[20] NaN NaN NaN NaN \n", + "\n", + "[110 rows x 9 columns]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "e756c478-5d27-4f52-8bec-a03eeed82ef6", + "metadata": {}, + "outputs": [], + "source": [ + "idata = az.from_cmdstanpy(\n", + " posterior=fit, \n", + " posterior_predictive=[\"y_hat\"], \n", + " log_likelihood= [\"log_lik\"],\n", + " observed_data = {\"y\": obs_stock_df.loc[:, (\"Predator\", \"Prey\")]}\n", + "# dtypes={\"y_rep\": int} if Poisson family\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "c605e71f-7fa9-4a10-93b3-76ba792dec58", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hyunjimoon/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/stats/stats.py:802: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", + " warnings.warn(\n" + ] + }, + { + "ename": "KeyError", + "evalue": "'var names: \"[\\'y\\'] are not present\" in dataset'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:69\u001b[0m, in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 69\u001b[0m var_names \u001b[38;5;241m=\u001b[39m \u001b[43m_subset_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvar_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilter_items\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilter_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwarn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:146\u001b[0m, in \u001b[0;36m_subset_list\u001b[0;34m(subset, whole_list, filter_items, warn)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(existing_items):\n\u001b[0;32m--> 146\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39marray(subset)[\u001b[38;5;241m~\u001b[39mexisting_items]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m are not present\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m subset\n", + "\u001b[0;31mKeyError\u001b[0m: \"['y'] are not present\"", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [51]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m az\u001b[38;5;241m.\u001b[39mloo(idata)\n\u001b[0;32m----> 2\u001b[0m \u001b[43maz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_ppc\u001b[49m\u001b[43m(\u001b[49m\u001b[43midata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.03\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfigsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/plots/ppcplot.py:260\u001b[0m, in \u001b[0;36mplot_ppc\u001b[0;34m(data, kind, alpha, mean, observed, color, colors, grid, figsize, textsize, data_pairs, var_names, filter_vars, coords, flatten, flatten_pp, num_pp_samples, random_seed, jitter, animated, animation_kwargs, legend, labeller, ax, backend, backend_kwargs, group, show)\u001b[0m\n\u001b[1;32m 258\u001b[0m var_names \u001b[38;5;241m=\u001b[39m _var_names(var_names, observed_data, filter_vars)\n\u001b[1;32m 259\u001b[0m pp_var_names \u001b[38;5;241m=\u001b[39m [data_pairs\u001b[38;5;241m.\u001b[39mget(var, var) \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m var_names]\n\u001b[0;32m--> 260\u001b[0m pp_var_names \u001b[38;5;241m=\u001b[39m \u001b[43m_var_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpp_var_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredictive_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilter_vars\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flatten_pp \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 263\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flatten \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:72\u001b[0m, in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 71\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin((\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvar names:\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00merr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124min dataset\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m var_names\n", + "\u001b[0;31mKeyError\u001b[0m: 'var names: \"[\\'y\\'] are not present\" in dataset'" + ] + } + ], + "source": [ + "az.loo(idata)\n", + "az.plot_ppc(idata, alpha=0.03, figsize=(12, 6))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90a2b21e-180b-496b-9767-2f4751510bbe", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "6f549da1-2157-4520-a58d-49706e9590ff", + "metadata": {}, + "source": [ + "## Appendix A. Measurement error, when stock initial is estimated parameter\n", + "\n", + "Lastly, initial population is added to the list of estimated parameter. Instant reason for this modeling decision is measurement noise; as population cannot be directly measured pelts (our data) are used as its noisy proxy). However, more fundamental reason is aletoric uncertainty, namely unmodeled uncertainty. There are factors that impact predator and prey population size other than the current population size. There are variable environmental effects, such as weather, which will vary from season to season and year to year and affect population sizes. Infectious diseases occasionally spread through a population, reducing its size (Hewitt 1921). There are also more long-term environmental factors such as carrying capacity (Carrying capacity is roughly the maximum population that an environment can sustain. It is often modeled in the system dynamics as an asymptote on population size.). However, our forward model is restricted to two differential equations involving two stock variables (`simulated outcome`) and four `estimated coefficient parameter`s (four flow variables can be expressed with the other two). Hence, after restricting the architecture, we are reaching out to the best version of ourselve by declaring the initial population as estimated parameter. In Stat/Machine learning terms, Stan optimization algorithm returns `estimated parameter` value that maximize log posterior among the feasible (restricted basis function) space defined by the modeler in the form of stock-parameter relationship.\n", + "\n", + "Continuing on `simulated outcome` and `observed outcome` coflow, `observed outcome` can replace `simulated outcome` also known as state-resetting but we maintain the error term to compensate for measurement error and unexplained variation in the data (Challenge: check whether this is equivalent to the original text \"Solutions to the Lotka-Volterra equations replace the linear predictor xnβ, but we maintain the error term to compensate for measurement error and unexplained variation in the data.\"). In the case of population dynamics, the data $y_n$ consists of measurements of the prey $y_{n, 1}$ and predator $y_{n, 2}$ populations at times $t_n$[18](#fn18).\n", + "\n", + "The true population sizes at time $t = 0$ are unknown---we only have measurements $y^{\\rm init}_1$ and $y^{\\rm init}_2$ of them. The true initial population sizes at time $t = 0$ will be represented by a parameter $z^{\\mathrm init}$, so that\n", + "\n", + "$$\n", + "\\begin{array}{rcl}\n", + "z^{\\mathrm init}_1 & = & u(t = 0)\n", + "\\\\[4pt]\n", + "z^{\\mathrm init}_2 & = & v(t = 0).\n", + "\\end{array}\n", + "$$\n", + "\n", + "Next, let $z_1, \\ldots, z_N$ be the solutions to the Lotka-Volterra differential equations at times $t_1, \\ldots, t_N$ given initial conditions $z(t = 0) = z^{\\mathrm init}$ and parameters $\\theta = (\\alpha, \\beta, \\gamma, \\delta)$. Each $z_n$ is a pair of prey and predator population sizes at the specified times,\n", + "\n", + "$$\n", + "\\begin{array}{rcl}\n", + "z_{n, 1} & = & u(t_n)\n", + "\\\\[4pt]\n", + "z_{n, 2} & = & v(t_n).\n", + "\\end{array}\n", + "$$\n", + "\n", + "The $z_n$ are random variables, but they are deterministic functions of the random variables for the initial state $z^{\\mathrm init}$ and system parameters $\\alpha, \\beta, \\gamma, \\delta$.\n", + "\n", + "The observed data is in the form of measurements $y^{\\rm init}$ of the initial population of prey and predators, and subsequent measurements $y_n$ of the populations at times $t_n$, where $y^{\\mathrm init}$ and the $y_n$ consist of a pair of measured population sizes, for the prey and predator species.\n", + "\n", + "In summary, the measurements, $y^{\\rm init}$ and $y_n$, are drawn indepently from a normal distribution centered at the underlying population sizes, $z^{\\rm init}$ and $z_n$, with noise scales $\\sigma$.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "38fd5398-b605-44b2-ad09-84ec57a257a1", + "metadata": {}, + "source": [ + "## Appendix B. Stan code" + ] + }, + { + "cell_type": "markdown", + "id": "3b48c946-921f-4ee1-8eb4-0bfe06c9f37a", + "metadata": {}, + "source": [ + "The following is the auto-generated stanfile with modularized funtion block in the cell below.\n", + "```\n", + "functions{\n", + "#include prey-predator_functions.stan\n", + "}\n", + "data{\n", + " int n_obs_state;\n", + " real initial_time;\n", + " int n_t; \n", + " array[n_t] real times;\n", + " array[n_t] vector[n_obs_state] y; \n", + "}\n", + "\n", + "\n", + "parameters{\n", + " real alpha;\n", + " real gamma;\n", + " real beta;\n", + " real delta;\n", + " real sigma;\n", + "}\n", + "\n", + "transformed parameters {\n", + " # Initial ODE values\n", + " real prey_initial = 30;\n", + " real predator_initial = 4;\n", + "\n", + " vector[2] initial_outcome; # Initial ODE state vector\n", + " initial_outcome[1] = prey_initial;\n", + " initial_outcome[2] = predator_initial;\n", + "\n", + " vector[2] integrated_result[n_t] = ode_rk45(vensim_func, initial_outcome, initial_time, times, alpha, beta, gamma, delta, sigma);\n", + "}\n", + "\n", + "model{\n", + " alpha ~ normal(0.8, 0.1);\n", + " gamma ~ normal(0.8, 0.1);\n", + " beta ~ normal(0.05, 0.001);\n", + " delta ~ normal(0.05, 0.001);\n", + " sigma ~ lognormal(-4.605170185988091, 1);\n", + " for (s in 1: n_obs_state){\n", + " y[:, s] ~ lognormal(log(integrated_result[:, s]), sigma);\n", + " }\n", + "}\n", + "\n", + "generated quantities{\n", + " array[n_t] vector[n_obs_state] y_ppc;\n", + " vector[n_t] log_lik;\n", + " for (s in 1: n_obs_state){\n", + " y_ppc[:, s] = lognormal_rng(log(integrated_result[:, s]), sigma);\n", + " } \n", + " for (s in 1: n_obs_state){\n", + " //elementwise log likliehood\n", + " log_lik[s] = lognormal_lpdf(y[s]|log(integrated_result[:, s]), sigma);\n", + " }\n", + "}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "8e742a91-7265-4876-83af-81dea8d8f14d", + "metadata": {}, + "source": [ + "Function block:\n", + "```\n", + "# Begin ODE declaration\n", + "vector vensim_func(real time, vector outcome, real alpha, real beta, real gamma, real delta){\n", + " vector[2] dydt; # Return vector of the ODE function\n", + "\n", + " # State variables\n", + " real prey = outcome[1];\n", + " real predator = outcome[2];\n", + "\n", + " real prey_birth_rate = alpha * prey;\n", + " real predator_birth_rate = delta * prey * predator;\n", + " real prey_death_rate = beta * predator * prey;\n", + " real prey_dydt = prey_birth_rate - prey_death_rate;\n", + " real predator_death_rate = gamma * predator;\n", + " real predator_dydt = predator_birth_rate - predator_death_rate;\n", + "\n", + " dydt[1] = prey_dydt;\n", + " dydt[2] = predator_dydt;\n", + "\n", + " return dydt;\n", + "}\n", + "\n", + "\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "local-venv", + "language": "python", + "name": "local-venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/test_scripts/data/hudson-bay-lynx-hare.csv b/test_scripts/data/hudson-bay-lynx-hare.csv index 471f33dc..e83dada0 100644 --- a/test_scripts/data/hudson-bay-lynx-hare.csv +++ b/test_scripts/data/hudson-bay-lynx-hare.csv @@ -1,4 +1,4 @@ -Year,Lynx,Hare +Year,Predator,Prey 1900, 4.0, 30.0 1901, 6.1, 47.2 1902, 9.8, 70.2 diff --git a/test_scripts/stan_file/prey-predator_data2draws.stan b/test_scripts/stan_file/prey-predator_data2draws.stan new file mode 100644 index 00000000..84000cb8 --- /dev/null +++ b/test_scripts/stan_file/prey-predator_data2draws.stan @@ -0,0 +1,67 @@ +functions{ +#include prey-predator_functions.stan + +} +data{ + int n_obs_state; + //1. add data, 2 to n_obs_state, we need n_t (unlike draws2data confusingly..) + int n_t; + vector[2] y[n_t]; //measured stock +} + +transformed data{ + real initial_time = 0.0; + + array[n_t] real times = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20}; +} + +parameters{ + real alpha; + real gamma; + real beta; + real delta; + + // 2. add sigma + real sigma; +} + +transformed parameters { + # Initial ODE values + real prey_initial = 30; + real predator_initial = 4; + + // 3. change 2 to n_obs_state + vector[2] initial_outcome; # Initial ODE state vector + initial_outcome[1] = prey_initial; + initial_outcome[2] = predator_initial; + + vector[2] integrated_result[n_t] = ode_rk45(vensim_func, initial_outcome, initial_time, times, gamma, beta, delta, alpha); +} + +model{ + alpha ~ normal(0.8, 0.1); + gamma ~ normal(0.8, 0.1); + beta ~ normal(0.05, 0.001); + delta ~ normal(0.05, 0.001); + + // 4. added sampling dist error + sigma ~ lognormal(-4.605170185988091, 1); + + // 5. likelihood statement from family + for (s in 1: n_obs_state){ + y[:, s] ~ lognormal(log(integrated_result[:, s]), sigma); + } +} +//6. all new +generated quantities{ + array[n_t] vector[n_obs_state] y_hat; + vector[n_t] log_lik; + + for (s in 1: n_obs_state){ + y_hat[:, s] = lognormal_rng(log(integrated_result[:, s]), sigma); + } + for (s in 1: n_obs_state){ + //elementwise log likliehood + log_lik[s] = lognormal_lpdf(y[s]|log(integrated_result[:, s]), sigma); + } +} \ No newline at end of file diff --git a/test_scripts/stan_file/prey-predator_draws2data.stan b/test_scripts/stan_file/prey-predator_draws2data.stan new file mode 100644 index 00000000..9f9208af --- /dev/null +++ b/test_scripts/stan_file/prey-predator_draws2data.stan @@ -0,0 +1,45 @@ +functions{ +#include prey-predator_functions.stan +} + +data{ + int n_obs_state; +} + +transformed data{ + real initial_time = 0.0; + int n_t = 20; + array[n_t] real times = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20}; +} + +generated quantities{ + // 1.add real infront of all params + change from ~ to = , dist to dist_rng() + real alpha_tilde = normal_rng(0.55, 0.1); + real gamma_tilde = normal_rng(0.8, 0.1); + real beta_tilde = normal_rng(0.028, 0.01); + real delta_tilde = normal_rng(0.024, 0.01); + + // 2. manually moved from tp to gp 2. add _tilde to ode params in param ode_rk45() + // Initial ODE values + real prey_initial = 30; + real predator_initial = 4; + + // 3. 2 to n_obs_state + vector[2] initial_outcome; # Initial ODE state vector + initial_outcome[1] = prey_initial; + initial_outcome[2] = predator_initial; + + // 4. add tilde + vector[2] integrated_result_tilde[n_t] = ode_rk45(vensim_func, initial_outcome, initial_time, times, gamma_tilde, beta_tilde, delta_tilde, alpha_tilde); + + //5. add sampling; sigma, y_tilde + vector[2] y_tilde[n_t]; //measured stock + real sigma_tilde = lognormal_rng(log(0.01), 0.01); + + //6. prior predictive + + for (s in 1: n_obs_state){ + y_tilde[:, s] = lognormal_rng(log(integrated_result_tilde[:, s]), sigma_tilde); + } + +} \ No newline at end of file diff --git a/test_scripts/stan_file/prey-predator_functions.stan b/test_scripts/stan_file/prey-predator_functions.stan new file mode 100644 index 00000000..e3c5b54e --- /dev/null +++ b/test_scripts/stan_file/prey-predator_functions.stan @@ -0,0 +1,20 @@ +# Begin ODE declaration +vector vensim_func(real time, vector outcome, real gamma, real beta, real delta, real alpha){ + vector[2] dydt; # Return vector of the ODE function + + # State variables + real prey = outcome[1]; + real predator = outcome[2]; + + real predator_birth_rate = delta * prey * predator; + real predator_death_rate = gamma * predator; + real prey_death_rate = beta * predator * prey; + real predator_dydt = predator_birth_rate - predator_death_rate; + real prey_birth_rate = alpha * prey; + real prey_dydt = prey_birth_rate - prey_death_rate; + + dydt[1] = prey_dydt; + dydt[2] = predator_dydt; + + return dydt; +} From 3227876f71d7832a7108e886010748dd76996ecf Mon Sep 17 00:00:00 2001 From: "Angie.H Moon" <30194633+hyunjimoon@users.noreply.github.com> Date: Wed, 31 Aug 2022 17:58:29 -0400 Subject: [PATCH 35/45] Add files via upload --- test_scripts/BayesWF_PreyPred.ipynb | 1360 +++++++++++++++++++++++++++ 1 file changed, 1360 insertions(+) create mode 100644 test_scripts/BayesWF_PreyPred.ipynb diff --git a/test_scripts/BayesWF_PreyPred.ipynb b/test_scripts/BayesWF_PreyPred.ipynb new file mode 100644 index 00000000..0ae6f2f4 --- /dev/null +++ b/test_scripts/BayesWF_PreyPred.ipynb @@ -0,0 +1,1360 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "cea8444a-acd1-429b-8c64-511c6400e7d2", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from IPython.display import Image\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "import pysd\n", + "from pysd.builders.stan.stan_model import StanVensimModel\n", + "from pysd.translators.vensim.vensim_file import VensimFile\n", + "from pysd.translators.xmile.xmile_file import XmileFile\n", + "\n", + "\n", + "import cmdstanpy # 2.30 is fastest (as of 08.12.2022) `cmdstanpy.install_cmdstan()` \n", + "from cmdstanpy import CmdStanModel, cmdstan_path\n", + "import arviz as az #!pip install git+https://github.com/arviz-devs/arviz\n", + "az.style.use(\"arviz-darkgrid\")\n", + "\n", + "# set your working directiory\n", + "#os.chdir(\"/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts\")" + ] + }, + { + "cell_type": "markdown", + "id": "90dd73ce-2613-4b04-a210-5c1858273ce4", + "metadata": {}, + "source": [ + "# Structuring Uncertainties in Dynamic Models: \n", + "## Bayesian workflow of Predator-Prey Population Dynamics\n", + "\n", + "Angie.H Moon, 07.2022" + ] + }, + { + "cell_type": "markdown", + "id": "8dbac1ad-23e0-4423-a8b5-a0a9a11b85ad", + "metadata": {}, + "source": [ + "## Data: Predator and Prey Pelts in Canada\n", + "\n", + "The species of interest in this case study are\n", + "\n", + "- hares: prey, an hervivorous cousin of rabbits, and\n", + "- lynxes: predator, a feline predator whose diet consists largely of hares.\n", + "\n", + "Spikes in the predator population lag those in the prey population. When populations are plotted against one another over time, the population dynamics orbit in an apparently stable pattern. Population oscillations can be modeled with a pair of differential equations similar to that used to describe springs. The first plot is the number of lynx and hare pelts (in thousands) collected for twenty years. The second plot is the phase plot of number of pelts collected for lynx versus hares similar to that of the dynamics of a spring in phase space (i.e., position vs. momentum)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e1b00493-ec54-4eba-92c3-b1e465855427", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0.5, 0, 'year'), Text(0, 0.5, 'pelt (thousands)')]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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F17RtyOvXaGrF2u9QCU3xtR0Y/DWGYfvDhcnQxLnmM1w5n1R6TpkmSb7Q++B5+kzE22tCna9fvXo106dPZ8gQe3br8Xh44IEH6NixI/n5+SxevJh169Yxbdo0EhPtIO033HADc+bMYdq0aVx++eVceOGFXHvttezZs4eWLVty5MgRZs+ezWOPPRaKrxgTaAUXA5jbFmC16gPu0Kdyt9IDrgJbIYQWmhpNrVEWrJuBt+NIMF21utSXNRjnuukY+TtrHXi8oTB//nzGjh2Lz+fD6/UyatQobr/9dj7++GNatmxZbtaVm5tLcXExZ599drk6SktLycvLA6Bnz5506dKFqVOnctVVVzF9+nSaNm3KiBEjIvq9wolWcNGmtADHrhw8Q68JS/WqSSuU6dYxKTVRx7F7NRTsrdXyZICAdbG5fQnenueFVC5vrwlVzqZSU1MpipEEogMGDODuu+/G6XSSmZmJ03l8+K5oSWlZFs2bN+f1118/oZ6UlOMP0hdccAH/+c9/uOqqq/jyyy8555xzMM0qQqfFIXoPLsqYeUsxlC8sBiaA7SqQ1l5HM9FEHefGWWA47BlcLbEye6DcKY3a0CQxMZH27dvTpk2bcsqtMoQQHDhwAIfDQfv27cu9mjdvfqzc+PHj2bNnD//3f/+HlJJzzz033F8jomgFF2XMbQtQDhe+tqHffwug0jrovHCaqGNunAXth0BSeu0vdpj42g7CoR2+g2LYsGH069ePO++8k7lz57Jjxw5WrVrFW2+9xfLly4+VS01N5bTTTuPll19m4MCBZGdnR0/oMKAVXJQxty3EatMPXOFz1rTSOuA4rF0FNNHDyN+NuWcNdDutznX4sgZj7l8PRQdDKFnDxDAMnn/+eQYPHswTTzzBJZdcwqRJk9iyZQsZGRnlyp5//vl4PB7OP//8KEkbPvQeXDQpycexZzWeYTeEtRkrLRvDW4xxdA8qtXVY29JoKsP0Ry+h++l1rsPXLuAPtxRf17orynjk/vvvr/Lcddddx3XXXXfC8ZSUFG677TZuu+22auvet28fTZo0Ydy4cfWWM9bQM7goYuYtwVBW+Pbf/JR1FdBoooFz0w9YTbMgs3ud67Ba9UWZbh2XMkQUFxeTl5fH5MmTufDCC4+5EzQktIKLIua2hSjTha/NgLC2Y+msAppo4inG3DIPb+cxGPWJJel0Y7Xupx2+Q8S7777LJZdcQtOmTfnd734XbXHCgl6ijCLmtgVYbQaAK7xPTiq1Ncp0aVcBTVQwty/E8BbVyT2gIr52g3EtfBtKC8LiN9qYqGppsyGhZ3DRovgwjj0/4w1DeK4TcJgonVVAEyXMjbNQrmR87YbWuy5f1hAM5cPcuSIEkmkaOlrBRQlz+2IMVNj33wJYadnaVUATeZTCuXEWvuwR4Eyod3W+tgNRhkMvU2qCQiu4KGFuW4AyE7Ba949Ie1ZaB3sGp9NoaCKIY986HPk7a5U9oFrcKVgte2p/OE1QaAUXJczti+yAs053RNqzXQWKMAr2RqQ9jQbA3DgTAF+nU0NWpy9rsL1E6S0NWZ2aholWcNGg6CDm3tyILU+CHc0EtCWlJrI4N87C16oPqknLkNXpyxqC4SvBsWd1yOrUNEy0gosC5vZFAGHJ/1YVlvaF00SawgM4dq4I3fKkn+OBl/U+nKZ6tIKLAua2hShnElbrPhFrUzVtg3K4tILTRAzn5tm2IVWIFRzJzbGad27UgZc1waEVXBQwty3AlzUQzMjsvwHgcKKaZWlfOE3EMDf+gJWSidWyZ8jr9mUNwdyxDKzQJyPVNBy0goswRuF+zP3rI7o8GUC7Cmgihq8U5+Y59uzNCP0w48sajFGSj2P/upDXrWk4aAUXYRyB/bd2kTMwCWC7CmzRrgKasGPmLcUoPRry/bcAvnZ6H05TMxEP1SWEaAM8CZwDpAIbgZuklD/4zxvAA8D1QDqwALhFStkgTKac2xagXMlYrXpHvG2V1gHDU4hRuB+VklHzBRpNHTE3zkKZbnzZw8NSv2qahZXaxvaHG/jbsLShiX8iOoMTQqQBPwEGcC7QE7gV2FOm2J3A7f7jQ/3nvhVCpEZS1nBhbltoW4GZroi3HbCk1K4CmnDj3DjLXoZ3JYetDV/WENvQRK9IaKog0jO4O4GdUsoryhzbFPjDP3v7E/CklPJj/7ErsZXcpcAbkRM19BhH9+A4sBFP74lRab+sq4DlN7XWaEKNcXATjkNbKB10Rc2F64Gv3WBcuV9iHNqCSu8Y1rY08UnQCk4I0RcYBrQGEoEDwFpgrpQy2BS7E4CvhRD/AcYCO4C3gVellAro5K9/euACKWWREGI2cDJxruCO+79Ffv8NQDVtizJMex9OowkTzo12clNf59FhbceXFUiAugSvVnCaSqhWwQkhOgM3AZcBrQALOASUAGlAMmAJIX7AVlT/kVJa1VTZGbgZeAF7H24A8Ir/3N+wlRvA7grX7Qaygvg+MY25bSHK3SQsZtPBCeBCNcvC0K4CmjBibpyFL6M7qml4f7KqeWdUUrqt4Pr8IqxtaeKTKhWcEOJtbMU2B3gYmAusllL6ypTJwN4nGw88DTwohLhGSjmnimodwGIp5T3+z8uEEN2AW7AVXK1ITk7GNM3aXnYCpmmSmhr+LT6Vtwg6Die1WXq96qmPvCqjM478PNw1XL91mw+fFzp1ip/7GwriSVaIPXlV8WHIWwIn31ipXKGWV3UYhmvH0hr7c12JtftbE1re8lQ3gysCekgpq3zcl1LuA6YB04QQtwEXU/1MayewpsKxn4E/+v/e5X9vBZR12GpV5twxCgsLq2kqeFJTU8nPzw9JXVVh5O8m5cAmSvpegqeebdVHXneTLFxbF3P0yBGoJrvy3fdaHD4MH75n1C8LM5G5v6EinmSF2JPXKb8h0fJS2O5krErkCrW8rlYDSMj9hqM7N4Q03mWAWLu/NdEY5U1Pr3rCUKWCk1LeWptG/EuT/6mh2E+AqHCsOxBQopuwFdkZwCIAIUQiMAr4S23kiTXMbQuA6O2/BVBpHTBKj0LRQUhuXmmZwkJFbi74LFi/Hrp1i7CQmrjF3DgLlZiG1bpfRNo7vg+3GK84JyJtauKHOltRCiHSgQ7Az1LKkiAvewGYK4SYhK0MBwJ/AO4FkFIqIcSLwL1CiFxsI5b7gKPAB3WVNRYwty1AJTTFyqio3yPLcUvKLVhVKLjVa2zlBjDzB0W3bvWbwWkaCZYP56bZeDudCo76L20H1WTLHihXMo7ti0ErOE0FgvKDE0I8JIR4sszncdhLiEuADUKIoLyWpZSLsC0pLwFygMeAvwKvlSn2NLYifBVYDLQBzpRSxs+8uxLM7YvwtRsasR9+VVjpxxVcVaxYqXA4oGdPmPkDKO1npAkCx66VGMWHQh9cudpGnfjaDsTUCVA1lRDsDO4y4NEyn5/DNj55CFtJPQFcEExFUsopwJRqzivgQf+rQWAc2YHj8DY8MRBxQTXNQhkOHAerjkm5fAV06wrnnmXw7AuKTZugc+cICqmJS5wbZ6EcTrwdTolou76swbjnvgJFhyApLaJta2KbYCOZtMUOqYUQoj3QH3hASjkfeB4ITzyeBoK5bSEQ2fxvVWK6UU2zqgy6XFqqWPMz9O8Hp44Ch8NeptRoasLcOMuO0pPYNKLt+rIGY6Ds7AIaTRmCVXD5QDP/3+OAg1LKhf7Pxdj+cJoqMLctsDfeM2LDWsNKy65yiTJXQmkp9O9v0Ly5Qf9+9jKlRlMdxpE8zH1rI7s86cdq3Q9lunR+OM0JBKvgfgDuFkKcC9wBfF7mXHdgW6gFazAoZed/azc0LGlD6sIxBVfJ3tqKlfZ7v772+5jRBps3w6bNehanqRrnhpkAeMMcvaRSXIlYrfrqfTjNCQQ74v4ZO3rJR9iRTCaVOXcFMDu0YjUcjCN5OPJ3xsbypB+Vlo1Rkg/Fh044t2KlomMHSE+zLSdHn2q7y/2g/8OaanDmTsGXIVDpnaLSvi9rMI7dq8ETGt9YTcMgKCMTKWUe9tJkZYzHXqbUVEKs+L+VxUrvAPiDLicdd5L0+RSrcuD0Mv/pjBYG/foqZs5SXHWFdhfQnIhxeDvmzuWUjLwtajL42g3BvegtzJ0rw5aiRxN/1HvNTEp5REpZGgphGiLmtgVYyS2wWnSNtijHsNL8Cq5CTMoNG6GgAPr3K6/Ixow22LARtm7Vy5SaE3HKqQBRdbT2tR2IwrDzw2k0fqqLRTmjNhVJKaua4TVelLLzv7UbWm1YrEijmrazB4MKlpTLV9jv/fuXLz96FLz0CsyaDVdE39NBE2M4c6fYCqZZFOOhJ6RiZfbA3L4YT/Sk0MQY1c3g9ld4dccOmZWMHVkkGRgJdAP2hVfM+MQ4tAXH0d0xtTwJgNONatrmBFeBlSsVrVtBq5bllXHLlgZ9esMs7S6gqYBj3zrMfWvxinOjLQq+dkMwdy4Hn15Q0thUF4vy4sDfQohrsGNIniyl3FrmeDbwFfBtOIWMV2LK/60CVlqHcq4CSimWr4STqtDFY0Yb/O01RV6eIisrdmajmujilFNQhgNv9/HRFsV2+F72Lo7da7DaDoi2OJoYINg9uEnA/WWVG4D/84P4Y0lqymNuW4iVkhE1y7LqUBV84bZuhUOHYEC/ypXXGL/1t/aJ0xxDKZy5U/Flj0ClZERbmmNZ6rW7gCZAsAquNZBQxTk3EPo8FfGOUpjbF+Brd1JM7b8FsNI6YBQftsMbAStW2cf7VxEEvnUrg5499TKl5jiOXStxHN6Gt0f0lycBVEoGVnpHreA0xwhWwc0CnhJCDCl7UAgxFHgK2xFcUwbj4CYcBftib//Nz7GsAodtH/0VKxTp6dC+fdXXjB1tkCth506t5DS2cYky3Xi7nh5tUY7hyxqMmbcUlBVtUTQxQLAK7nrgALBACLFDCLFcCLEDmO8/fn24BIxXjvu/xd7+G5TxhfO7CqxYCf37Um1y09Gn2u+ztNO3xvLhXDsNX6dTISF2Mkj7soZglBzGsW99tEXRxABBKTgp5XYp5SDgfOANYJ7//Xwp5UAp5fYwyhiXmNsWYjVphfLPlGIN1aw9CgPj0BZ27VLs2m3Hn6yOrLYG3bvDzFl6BtfYMbcvxFGwD0+P86ItSjl87exFJoeOS6mhlglPpZRTgalhkqXhEPB/63hKTO6/AeBMQKW2xnFo67H4kwOCSMI8drTBG28pdu1WtG4Vo99NE3acuVNQ7hR8naIQe7IaVNMsrCat7AzfAy6NtjiaKFMrBSeESACygMSK56SUa0IlVLzj2L8eR9EBSmN0eTKA7SqwlRU7FCkpweV8GzMa3ngLZs+GSy6uubymAeItxbnuW7xdTgPXCUNBdDEMex9u+2I7mHisPmBqIkKwGb3bCiG+AgqBdcCqMq8c/7vGTyz7v5Ul4CqwYoWdPcA0ax4M2rcz6NpF54hrzJhb5mCUHIkZ68mK+NoNwVGwB+NIXrRF0USZYGdwbwODgNuANYAOFVAN5rYFWE3bopq1i7Yo1WKlZWMUHeTAjiOcNb5ZzRf4GTPa4O13FHv3KjIz9RNyY8OZ+xUqKR1f9ohoi1IpVqs+ADj2/Iwvxn+DmvASrII7BbhOSvnfcArTIFAW5vaFeDuPjbYkNRJwFWiXso0B/YNXcGNHw9vvwA8/wi8nhks6TUxSWoBzw0y8vSeA6Yq2NJVitehqx1rdtxZftzOiLY4migTrJrAHKAqnIA0Fx761GMWHY355EkD5XQU6NdtKDxH8dR06GHTupJ2+GyPODTMxvMV4YiD2ZJW4klBp7TH3ra1XNZs2K/J2aH+6eCZYBXc/cJcQomk4hWkIHN9/i00H77JYzWyv7iEdt+By1W6pccxogxUrYf9+reQaE045BatJa6ysQdEWpVqsDIGjHgpu5izF1dcq7r1PJ1CNZ4JdopwIZANbhBCLsLN6l0VJKX8VSsHiFXPbQqxm7VFN20ZblBopKE2koKgVPVpurblwBcaMhnf+BbPnwEUXhl42TQxSdBBz8xw8g64Ao96pJMOKldEdc/134CkCV1Ktrv3yK8UzzytcLli5ykd+vkFqqt5rjkeC7aUZwAZgOeACMiu8dCxKAMuHuX1RXMzeAFblwLaCbNombqv1tZ06Qods7fTdmHCu+xbD8sas9WRZfJndMVA49m+o1XXvf6h46lnFsKHwxKMGlgVLl4dHRk34CWoGJ6WMfYuJGMCxNxej5Ai+dvGh4FasVHQuzGaQZxa1XYgxDIOxYxT/fg8OHlSkp+sn3IaOM3cKVnonrMye0RalRqyM7gA49kms1n1qLK+U4vU3Fe9/CKeNg/vuMTAMSEmBRYsUo0fp/h2PxPY6Q5wRL/5vAVashOLkbBxF+6HkaK2vHzPafsKdPScMwmliCiN/N+b2RXh6nBsXztOqWXuUMymofTifT/H0c7Zym3Ah3D/JwOUycDoNhg5xslBH/Ypbgo5kIoRIBS7EzuxdWSSTO0MoV1xibl+IldYBldoq2qLUSEmJ4udcuOjCbCgFx+GtWC171aqOLp2hXTvbmvLC82N/0NPUHefaaRgovD3OibYoweEwsVp0xbG3egVXWqp45HHFzFlw5eVw7e+McgHHRwx3MusHr070G6cEpeCEEF2AuUASkALsBZr7rz8IHAYat4KzvJjbF+MV8TEArPkZPB5oKTrAKjAObYVaKjjDMBg7WvHBh3DokCItTQ8ADRVn7hR8rXrHZPLeqrAyu+PcMKPKkF2FhYpJ9ysWLYZbbzH41cUnlhl+kj1ELloMWVlhF1kTYoJdonwBWAS0AgzgHGxl91vgKNDoLSgde37GKD0aNwYmgQDLnQf588Id3FJN6aoZM9rAZ8Gcn0IlmSbWMA5uxtydgzeWfd8qwcrojlF0EKNw3wnnjhxR/PkOxZKlcO9dlSs3gA7ZDlq3goWLtTFVPBKsghsGvA6U+D+7pZQ+KeUHwHPAS+EQLp44nv8tXhScoktnSG2RgpWSieNQ7V0FALp3g7ZtdWzKhoxTTkVh4BVnR1uUWnHc0KT8MuW+fYrf/1Gxdh08+pDBOWdXvfJgGAZDh8DSpeD16j4ebwSr4BKBI1JKCzvBaVknrxygf6gFizfMbQuxmndGpWRGW5Qa8XoVOTnQz58eJxB0uS4YhsGY0bB4CRzJ1wNAg0MpXLlTsNoNQaW2jrY0tcJ3TMGtO3YsL09x062Knbvg2acMTg3COnLoUIOjBZArwyaqJkwEq+DWAh38fy8DbhRCJAohXMA1wI5wCBc3+DyYeUviZva2bj0UFcOAfvaP20rrYO/B1ZGxow18PvhJL1M2OBx7c3Ec2GhbT8Ybyc2xUjJw7LU10/oNiptvVRQWwMsvGAweFNye8ZBB9hbewkXhFFYTDoJVcB8BA/x//xU4CTgC5AOXAA+GWrB4wrE7B8NTiDdO3AOWr7Df+/tncFZ6BxwFe6G0oE719RDQqpV2+m6IOHOnoBxOvN3OjLYodSIQsmtVjr0s6XDAq68Y9OwRvEFU06YGPQQs0vtwcUdQCk5K+byU8nb/3/OBPsDvsS0nB0op3w+fiLGPc+MslGHGjf/bypWKrLaQkRGYwfkNTQ7XPqIJHF+mXLgYjh7Vg0CDQVk45VR8HU6GpPRoS1MnrIzusHc9t9/hIS0N/v43g44dam/tO3QIrFmj+3e8USdHbynlNinlm1LKl6WUOaEWKq5QCqechi/7pLgYBCxLsWIV9C+za6r8Cs44WL9lSq8XfppbXwk1sYJjxzIc+Tvx9jgv2qLUmZy93TFVKUM7b+Xvrxi0bl03V5ZhQ21r4aXLQiygJqwEm9F7lBDiwjKfM4QQHwghlgshnvPvxTVKHLtX4zi8DW/3+LAw27wFjhyB/v2O/9CtNHt7ta6GJgC9ekLLTJ1CpyHhzJ2Ccibi7TIu2qLUic++UDz9bjcAHrphbb3CyfXuBUmJsGiJ7t/xRLAzuKexlyUDvAScBswHrgIeCq1Y8YNz7TR7j6Lr6dEWJSgC/m8D+pU56E7BSs6ol4JzOAxGnwoLFkJBgR4E4h7Li3PtN3g7jwF3SrSlqRVKKd59X/Hs84qWvTqjDJPko+tqvrAaXC6DgQNhkTY0iSuCVXACWAIghEgGLgL+KKW8EXsfrnE6eiuFc+3X+LJHQFJatKUJihUrFRkZtu9aWWxXgbovUYLt9F3qgbnz61WNJgYwt87HUXQgLjIHVGTBQnjjLcWZp8PDjySi0jvWKzdcgGFDDbbnwY6d+gEuXghWwbmBYv/fp2CH6Jri/7wWaBNiueICx84VOI7siJvwXEopVqyA/n0pF28PbEOT+rgKAPTtAy1a6GXKhoAz9ytUQiq+jqdGW5Ras3iJncvt7jvtgMm+jG4hUXBDB9vvi3Tw5bghWAWXC5zl//syYJ6UMt//uS2283ejw7n2a5Tpips9ih07Ye8+6N//xL0IK70DjqO77QSRdcThMBhzKsybb8f508QpnmKc67/D2/UMcLqjLU2tWb0GRHdwu/1WwpkCx+HtdXaDCZCdDS1b2ulzNPFBsAruYeDPQoi9wKXAk2XOnYXt/N24UJa9PNlhJCQ2jbY0QbHSv//Wv9+J5wKWlI5DdXMVCDBmtEFpKcxbUK9qNFHE3DQbo7QgLpcnPR6FlNC79/FjVYXsqi2GYTBsCCxeaqfY0cQ+wfrBfQH0BG4E+kgpp5U5PQ94LAyyxTSOHctxHN0dV/H5lq9UpKba2bgrErCkNOphaALQry+kp+tlynjGJadgJWfEjV9nWdath1IP9OlVxko4QwDlQ3bVlaFDDI4e1WG74oWg88FJKTcCGys5/mZIJYoTnHIaynTHzfIk2BaU/fvZS4kVOebsfWgrvnq0YZoGo0cpvp4OxcWKxESdQieuKMnH3DgLT79fgcOMtjS1Jme1/d6nzAxONW2LcqccC9lVHwb7w3YtWmy7Dmhim2Dzwd1cQxElpfx7COSJDywfznVf4+t0atyYUO/br9i+HS44rwqFk5CKldS83paUAGPHGHz2hWLBQhgdfzYKjRrn+u8xfKVxYzhVkdWrFS1bQmZmmX5uGFgZ3TFDYGiSlmYguisWLVZcdYV+eIt1gp3B/a2ac4G1qEaj4Bx5S3AU7KM0jpYnj/m/VZP3QaVl13uJEuxZYlozO4XO6FP1IBBPOHOnYDVrh9UmPhOE5KwpP3sLYGV0xymnVZn8tDYMHQIffGT7e6ak6P4dywS7B+eo+MLO6P0bYAXQqCbrrrXT7AgPncdEW5SgWblSkZho52+rCisEvnAATqfBqFF22K6SEr0XFy8Yhfsxt86zZ2/1VALRYN8+xe7d5fffAlgZ3TBKjmAc3V3vdoYOsbNnLFte76o0YaZOsSgBpJSHpJT/wU6E+kboRIpxLC/m2um2cnMlR1uaoFm+0n6ydTqrHris9A448neCp7jKMsEydrRBUZFOMRJPONd+jaF8cWk9Ccf333pXMoPzBQxNQrAP16e3P2yXzi4Q89RZwZVhEzAkBPXEBeb2RXaEhziJPQl2ItKNG2FAJf5vZVGBmJR1zCpQlkEDoWlTbU0ZTzhzp+Jr0e2YWX28kbNa4XZVvkphZdgHQ+Hw7XYbDBhgZ8/QxDb1UnBCiDbA7dhKrlHglNNQrmTbwCROWLXK3nro17f6cgFLyvpGNAH/MuVImDMXSku1kot1jCM7MHcsjdvZG9gO3t2723EjTyCxGVZqm5AoOLCXKbdtg127dN+OZYK1otzLcWOSAG4gFTuE18QQyxWb+Dw4103H23ksuBKjLU3QrFilcDprNmsOlatAgDGjDaZMVSxY6K3WuEUTfZxyKkDcWk8GHLwvuqjqMlaIQnaBbWgCtrvA+fGbTajBE6wV5aucqOCKge3A11LK/SGVKkYxty3AKD4cV87dACtWQM8ekJBQg+FAYjNUYlq9sgqUZdAAcLvQCi4OcOZOwdemPyqtfbRFqROVOXhXxMoQuLbMA18pmPULQdaxA2RmwMLFivOrcr3RRJ2gFJyU8sFwNC6EuAd4HHhVSvl7/zEDeAC4HkgHFgC3SClXh0OG2uCU01DuJvg6joy2KEFTVKTIlfCbIPM9WCFyFQBbofburVi02MsN14WkSk0YMPZvwNybS8mYe6MtSp2pzMG7IlZGdwzLg+PAZqzM+u0zGobB0KGKH+fYYbtMUyu5WKRWe3BCiLZCiF8IIa7zv7et+aoq6xqOrcRWVjh1J/a+3q3AUGAP8K0QIrWubYUEX6kdgLbLOHAmRFWU2rDmZ/D5Kg+wXBlWWoeQuAoEGDzIQK61OHxY71XEKq7cKSjDgVecVXPhGKVSB+8KBJRaKPfh8vNhbf0jgGnCRLAZvU0hxGvAFuD/sN0C/g/YIoR4VQhRW0XZDHgf+B1wsMxxA/gT8KSU8mMpZQ5wJfZe36W1aSPUmFvmYpQcibvlyeUrFIYBfat5si2LSsvGOLITvKUhaX/QQNvAZfmKkFSnCTXKspcn25+ESsmMtjR1pioH77JY6Z1QDheOfaEJJDnEnz5Hu8LELsEqpoewldG9QEcgyf9+r//4g7Vs903gf1LKmRWOdwJaA9MDB6SURcBs4ORathFSnHIaKqEpvg5RFaPWrFwF3bpCkyZBzuDSO2CgMI5sD0n7PXtAYiIsXaZncLGIuelHHIe34u1djXVGjFOdg3c5TBdW804hm8Glpxl076794WKZYI1MrgDuk1I+W+bYVuAZIYQC/gDcH0xFQojrgK7Abys53dr/XjHcwG4gq2Lh5ORkTLP+AWFN0yQ1teoVUOUthg0zoNe5pKa1qHd79aUmeQN4PIrVq48w8SI3qalJQdWt2vYAIKV4D0ZqaCxDBg8qZNkKX1AyR5tg722sUF951cr3IbUViYN/iVFPw4tgCMf9XbDQAxQydGgyqanVD2mqTW/YMj9oGWqS95QRxbz7fgkOR5OYCNvV2PpvTQSr4Fpy4l5ZgJX+8zUihBDYRiUjpZSeINuuksLCwvpWAUBqair5+flVnjfXf09S6VGKOp+Or5pykaImeQPkrFYUl0Cvnh7y873BVe7OoAlQsjMXT9vh9RPUz9Ahbl582cPmzUdo0SL6g0B1BHtvY4X6yOvYt5bkjT9SMvLPeApLgJLQClcJ4bi/ixZbuF3QLquQ/Pzq+5erWScSjnxK/t7tkNisxrprkndAf8U/J8OPc/I55eTo9+3G1H8DpKenV3ku2CXKtcCvqzj3ayDYRe0RQAawWgjhFUJ4gdHAzf6/A+4GrSpc1wrYFWQbIccpp6ES0+IuP1YgwHJlCU6rJDENldA0JM7eAYYOsWfZOnZfbOFa+m+UMxFP30uiLUq9qNbBuwJWZiA3XGiWKfv2gYQEvUwZqwQ7g3sU+EgIkQ38D3vJsCVwMTCWqpVfRT4DKga4+SewDntmtxZbkZ0BLAIQQiQCo4C/BNlGaPEU49w4E2+P88B0RUWEurJihSK7PaSn1+LJ0jBCbknZQ5g0SYElyxSnnxb9p1yNHVjZ+fOX9t5bUlq0xakzwTh4lyUQhszctxar3dB6t+92Gwzor1ikw3bFJMH6wf1XCHEI29jkJcAFeIAlwFlSym+DrOcQcKjsMSFEAXDAbzGJEOJF4F4hRC62wrsPOAp8EEwbocbcNBvDUxh3JtQ+n2LlKhg7tvbXWmnZmLtCZ/bodBr07w9Ll4asSk09ca74CMNXSumgK6MtSr0IxsG7LKpJK1RCs5Bk9w4wbKjBK68qdu1WtG6lH+BiiaDN+6WU06WUI7AtKFsDSVLKk4NVbrXgaeAF7Ogpi4E2wJlSyqgsLDvXTsNKboEvBE97kWTjJjhaAP371f4HZ7sK7LAjPoSIwQMN8nbArt16KSfqeEtxrfgIb6dTUc07RVuaehGMg3c5DMMO2RWCrAIBAmG7FutZXMwR7BLlMaSUFrbzdUiQUo6p8Flhux08GKo26oynEOfGWXh6XwSOWt+qqHIswWlt9t/8WOnZGMrCOJKHSg/NADhokP2+bBmcHV+T4QaHU07BUbiPkjifvUFwDt4V8WUKXGs+A2WBUf+EKp06QkaGvQ933rl6BhdLBD1qCyGGYAdVbgdUjDSspJRBBoOKH5wbZ2F4i/F2j78RecVK+4ffunXtf3BWIG3Owa34QqTgOneCZk3tfbizz9KDQNRQCteSyfgyuuPLHhFtaepNMA7eFbEyumOUFmAc2YFq1q7eMhiGwdDBip/m6bBdsUawkUxuwo4JeS3QBcis8ArKTSDecMppWCmZWFmDoy1KrVBKsWJF3WZvUEbBhSgmJYDDYTBwICxdZsuniQ7mtgWY+ySeQVfEZdbusgTt4F2BUOaGCzB0qMGRI7BOh+2KKYKdwd2Bbe14o5QySIeqOKe0AHPTbDz9fgWO+juTR5LteXDgYPDxJ08gKR3lbhJSVwGw41LO+kGRlwft6v/grKkDrqWTsZJb2FbBcU51GbyrI2BJ6dgr8XUZFxJZhvqfgRctgR49QlKlJgQEuwDdEviw0Sg3wLlhBoavND6XJ/0GkLXyfyuLYWCld8BxMLR5bAcNsN+XLAtptZogMQ5uwrlxFt5+v46rgOFVUV0G72pxp2A1axfSGVx6ukG3rrBwkV6diCWCVXDTgPjycq4nTjkNq0lrrLYDoi1KrVmxUpHWDDpk170OX5uBmNsXQ/GRkMmVnQ0tWui4lNHCtfRdlOnC0z9Yt9XYpjYO3hWxMgRmCBUcwNChsCoHCgt1/44VqlyiFEKUzf/8KvCmEMIFfEsFXzYAKeWakEsXLYqPYG6Zg2fAZSGxsoo0y1dCv3725ndd8fa6EPfy93Cu/Rpvv9BEujAMg8EDFYuW2Ptw9ZFPU0uKDuFa/RneHuehUjKiLU29qa2Dd0WsjG6YG2eBtyRks9mhgw0++FCxYiWMCE2UO009qW70zgFW+V8zgG7YiUh/LHN8VZlyDQZ7edITl8uTe/Yodu6EAXXwfyuL1ao3vhZdbHPqEDJooMHBg7Bpc0ir1dSAK+d/GN4i27ikAVBbB++K+DIFhvLhOLAhZDL16wtut16mjCWqMzKpQwyMhoFTTsVqmoXVuq6bWNFjpf9Ro199RTcMvL0mkPDjcxgHN6PSO9ZXNKC8P1zn+PYxjh98HlzL3sfbfjhWZsOwgKi1g3cFjhuarMVq2auG0sGRkOAP27UkJNVpQkCVCk5K+UMkBYkZig5hbp2HZ9CVcWlGvSpHkZQIXbvUvy5vzwtwz3kB15rPKT3lj/WvEGjbxqBNa8WSZYpfTIy/+xuPONdNx3F0FyWnB5XRKi6oi4N3WVRaNspMCKmhCdhZvl/9u2LPHkXLlrp/R5tg/eB8QohhVZwbLITwhVas6OFc/x2G5Y27zN0BVuVAr152/Mf6opq0xJd9Ms41n9tRH0LEwIF2ZgHL0ks5YUcpXEv/jZXeEV+n0dGWJmTUxcG7HA4nVosuIcvuHWCYP6KfnsXFBsFaUFQ3WrqABuM+4Fz7NVaz7JAtW0SSwkLF+g31/OFXwNt7Ao78nZjbFoWszsEDDfLzYX3otj80VeDYuRxz10pKB14elwZTlVFXB++KWBndQz6D69wJWjTX6XNiheqsKLOBjmUODfSnrilLInAlEFqHqWhReABz63w8Q6+Ny+XJNT+DZUHfPqGT3dvlNJS7Cc41n+HLDo2nyKCB9vuSpXXwYdLUCveSyaiEZnh7T4i2KCGjrg7eFbECMSkLD0By83rLBbal8JAhigUL7BUKhyP+xpGGRHWPdFcDs4CZgAL+7v9c9vU1MAE7jU7c41z/LYbyxV1qnACrcmy93DuUk09XIt7uZ+FcNx1KC0JSZWamQfv2sEz7w4UV43Ae5vpv8fS7GFzJ0RYnZNTZwbsCgZBdofaHGzbE4NBh29JTE12qU3CvAX2B/thLlJf5P5d9CaC5lPLDMMsZEZzya6z0TlgZItqi1IlVOYpOHSE1NbRPjZ7eEzA8hTjXhy4z0uCBsGwFeL1ayYUL1/L3wHDY/pwNiPo4eJcl8DsP9T7ckEDYLp0+J+pUZ0W5F9gLIIToBOyUUoYuQViMYRTsw9y+EM9JN8bl8qTPp1i9Bk4PTWi9clhtB2E1a49z9ed4e00ISZ2DBhl89oUiV4Z2z1Djp7QA16r/4e02HpXaOtrShIz6OniXRaVkYCU1x7E3tDO4Fi0MunRRLFqs+O2l8TeWNCSqnMH5lRoAUsotwSg3IYRDCNE+VMJFEue6bzCUFZfO3WA7ThcUQN++YfhBGQaeXhdibltgJ0INAQMH2O/LloekOk0FXDkfY5QebTCO3QHq6+BdESuze0izewcYNsT2SS0u1isU0aS6JcoVQoj3hRDjhBDV9iYhRDshxO3AeiAuA9055df4WnQ9ti4fb+Tk2O99wzQb8va6EAOF8+cvQlJfeppBl86wZKkeAEKO5cO17F18bQditYm/YAXVUV8H74pYGd1x7F8HVmg9nYYOMfB4YPmKkFarqSXVRTLpCdwHfAkUCiEWYofl2geUAGlAJ2Aw0As7XNedUsr/hVPgcKDyd+HIW0Lpyb+Ptih1ZlWOonk6tG0bnvpVs3b4sobgWv0ZnmE3hGQZd9BA+PxLKC1VuN16KSdUmBtm4Di8naJRd0RblJBTXwfvilgZAsNbjHF4W8ii9YCdycPtst0Fhp+k+3a0qHIGJ6XMk1LeBLQG7gEOY1tM3gc8A9yEreC+BE6SUg6MR+UGwOqvMFB4u8enczfAyhzo27d+AZZrwtN7Ao5DW3DsXB6S+gYNNCgttd0bNKHDvXQyVtO2+LqeFm1RQk69HbwrUDZkVyhJSDDo3x8Whs59VFMHakx4KqXMB972vxoma77Cl9kD1Tw+gyPu228HWJ44IbxPit5u41EzHsW15nNK2g6sd30D+oPDYS9TDqhrclZNORy7V2PmLaFk9F3gCDafcXwQcPD+1S9D11esFl1QGJj71uLrfmbI6gV7mfK11xX79ikyMnT/jgYNI7RBPTDyd8K2xXEbmgvK7L/1CXNDCU3wdj0Dp5xmpxmpJ6mpdpLIpToBashwLZ2MciXj6fOLaIsSckLl4F0OVxIqvUPIXQUAhg6x37W7QPRo9ArOKb8GiFvrSbD339xuEN3D35a394UYJUdwbpgRkvoGD7L9mrS1Wf0xju7BKafZyi0hNdrihJxQOXhXJBwhuwC6dIbm6bBQh+2KGlrBbfge2vRDpdUj/XWUWZUDPXvU3/E1GHzth2M1aYUzRHniBg008Hrt76CpH67lH4DlwzPw8miLEhZC5eBdEV9Gd4xD28BTGNJ6HQ6DIYNh8RIdWDxaNHoF5+l1IYyP3zQiJSWKtesi6CztMPH2vABz808YBXvrXV2/vmCa2l2g3niKcK38CF/X01BpcemKWi0BB++QLk/6sTK6Y6Bw7At9bK2hQ+0Ev2tDP0HUBEGjV3DevhdjdIjf/PI/54LXGyYH7yrw9J6AoXw4f/6q3nUlJxv06qn34eqL8+cvMIoPUzroymiLEhZC7eBdFiszELIr9FrolBF2lu8vp+gHuGgQbD64d8pGNqlwroMQ4p3QiqUJlsDSXp8IZvdRzTvja93PzhMXAgYNhFwJR4/qQaBOKAv30n/ja9UbK2twtKUJC6F28C6LatYO5UoOi4Jr2tTgjNPhm28hP1/370gT7AzuKiCzinMZ2ClzNFFgVY4iuz2kpUXWDNnT60LMfRLHnvo7sQ0aaGBZsGJlCARrhJib5+A4sBHPwCviMo5qMITawbschgOrRdewKDiw3XeKi2HaN2GpXlMNtVmirOrxow/+oMyayGJZipzVtoN3pPGKc1CmKyTGJn1621Efli7XT7h1wbV0MlZKZtymeQqGUDt4V8TK6I65V4IKfR8U3Q1694JPP1fa2CTCVJfw9I/AH/0fFfCZEKKi81Mi0Ar4V1ik01TL1m1w5EhoE5wGTVIavs5jcf78FaWj7gDTVeeqEhIM+vRRLF0aQvkaCWpPLs4tcyk55U9guqMtTlgIh4N3RayM7hg5/8Mo2Itq0jLk9U+cYPDI44olS4/7x2nCT3UzuDXAx8An2PngZvo/l339E3v58uawSqmplHAHWK4JT68JOIoOYG6eU++6Bg00WLceDh/WT7i1Yv7bKGcinn6XRFuSsBEWB+8KhNPQBGDsGEhLg48/1f07klSXD+5b4FsAIUQ+8LaUMi9SgmlqZmWOollTyI6SC5+v40ispOa41nyGr8vYetU1yB/5a/kKGH1qCIRrBBiF+2Hlp3h7T4Ck9GiLEzbC5eBdFp8/i4hj31p8HUeGvH632+D88xTvfwC7dilat26Ye6WxRlB7cFLKh7Ryiz1W5UCfPuENsFwtpgtvj3MxN86EokP1qqpnD0hK1P5wNVJyFHPddBK+mUTSuxPAV0JpA3XsDhAuB+9yJKVjpbTEsTf0IbsCXHi+Lf9nX+g+Himq24P7by3qUVLKX4VAHk2QHDqk2LYNzjkruk+C3t4TcC97F6ecinfApXWux+Uy6NtXaX+4iiiFcXAzzk0/YG78ATNvCYblQSWk4u1wCo7Bv0G16BJtKcNGKDN414Qdsiv0yU8DtG5lcMrJiq+mwNVXKhIS9Cwu3FQXbrwqtwBNDBDYl+gXBQvKsliZPfFldMe15vN6KTiAwYMM/v6GYv9+RYsWjfjH7y3B3L4Ic+MPODfNxnF4KwC+Fl3xDLocb6fRWG0HgunCnZoK+flRFjh8hNPBuyJWZndcy94Fyxu2TAy/uMjgxzmKGbPg7PFhaUJThur24Oq3qaIJKytzFE4n9BBRFsQw8PaaQMLspzEObEQ171znqgb79+GWLoczGl4qs2ox8ndibpqNc9NszC3zMLxFKDMBX/ZwSgdfia/TaFSzrGiLGXHC6eBdESujO4bPg3FwM6pF17C0MXgQdMiGTz5TnD2+ET/ERYhaP6YIIQygDbBHSukNvUiaYMjJsTfdY2GZw9vzPNw/Podr9WeUjrqtzvV06wZNUmDpUsUZp0X/e4UVy4tj5wqcG3/A3DQb05+uxWqahaf3Rfg6nYqv/UngSoyyoNElrA7eFQgkPzX3rcUbJgVnGAYXTYAXX1bk5ip69Gjg/TzKBO3oLYQ4RwixACgGtgH9/MffEkL8NkzyaSqhtFSRmxsdB+/KUCmZ+DqegvPnL8Dy1bke0zQYMKCBx6VUFs6cT0h+axzJ//ktriX/RCU2o2TUHRRc+SWF13xL6Wl/xdd5dKNXbhB+B++yWM27oAwz5Nm9K3LWmbZB1SefaWOTcBNsLMorgC+AXOB6bL+4AGuBa0IvmqYqfs71UeqBftFw8K4Cb68JOI7uxtw2v171DBpokLfDNqVuaDh2riDpg1+TOH0SqmkWRee9QMFNcym+ZDKeodfYy2INNNRWXQg4eEdi/w0Apxurecew+cIFaNLEYPyZ8N332u8z3AQ7g5sEPCOlvBJ4r8K51UAEQ/1qlq+wZ0kRS5ETBN4u41AJTXGurl8A5oA/XEOaxRlH95Dw9T0kf/hrjKO7KD77aYp+/QG+7mc1yMSkoSISDt4VsTJEWLJ7V2TiBINSD3w1NexNxSZFB0n85HrU7jVhbSZYBdcBv9N3JRQDTUMjjiYYlq/w0rYtsWVp6EzAK87Guf5bKDla52o6d4K0Zg0kLqWvFNeif5D8z7NxyimUDr2Owqun4e15vp6pBUEkHLwrYmV0x3FkB5SE1zK1c2eDAf3hs88VPl8D6Ou1QSkSv3vQXu1xhncZPlgFtw0YWMW5IUDoMwVqKkUpxfIVPvr2ibYkJ+LpdSGGtxjnurqHTXc4DAYOhKVL7e8ar5gbfyB58gUk/PgsvvYnUXjFl7YBjjsl2qLFDRFx8K6AlWkbmoTTHy7AxIsMdu6C+QvC3lRM4cz9Cue66ZSOuBWjRd2troMhWAX3D+ABvzFJkv+YIYQ4DbgTeCscwmlOZMcOOHBARSfAcg1YbQZgpXXAtfqzetUzaKDBnr2wPQ5j5xgHN5H46Q0kfXYjGA6KLnqT4gmvodI7RFu0uCKcGbyrI2BJGe59OIBTR0JGRuMyNjHyd5Ew41F8bQfiGfK7sLcXrJvAU0B7YDIQMJObC5jAG1LKl8Mgm6YSVgYCLMfgDA7DwNN7Agk/vYRxeDuqWbs6VTO4zD5c+7pVEXlKjuJe8HdcS98Fp5uS0XfhGXBpg43wH24i6eBdFpXaFuVuEhEF53QaXHg+/OOfim3bFe3bxd5Da0hRioTp94HPQ/H4J8Bhhr3JYGNRKinlLYAAbgXuw06l08t/XBMhVuUoUptAp47RlqRyvD0vQGHUK9t3+/b2k+3SeIhLqSycqz8j+V9n4178Dt6e51N49dd4Bl+llVs9iKSDdzkMw84NFwEFB3D+eWCa9l5cQ8e58iOcW36iZPRfIraiUStHbynlevR+W1RZtQr69XPicFjRFqVSVNO2+NqfhGvN53iG31wnYwrDMBg0QLFoib0PF7Vg0jXg2LmShJmPYe5aia9NfwovfA2rdYw4J8Y5kXTwroiV0Q2nnBqW5KcVyWhhMOZUxZRpcN01isTE2Ozr9cU4uIWEH57B22Ek3n6/jli7QSs4IYQbO/fbMOxIJjuBBcBkKWVpWKTTlCM/X7FpM5x9lgnEpoID8Pa6kMRv7sGRtwSrXd2yOw4aZDD9O/v7du4UWvnqi1GwF/ecF3Gt/gQrJYPis57A2/MCMIKOm6CpgUg6eFfElylwrfwPRv5OaBp+A/GJFxl8P1Px7Xf2jK7BYflI/OYeMF2UnPloRC2Ig1JwQoiewNdAW2AJsAfoA1wB/FUIcZaUMrwODRpy/Hd4QH8n4ImqLNXh7XYGasYjuNZ8TkldFVyZfbioKLjSAoyCPTjy92AU7ME4ugfj6G6Mgr04N/8I3lJKh1xD6fCbtGVkiIlEBu/qKGdokhX+YK/9+kKXLnYy1PPOjWL6qzDhWvwO5o5lFJ/9DCq1VUTbDnYG9yZwGBglpdwaOCiEyAa+Al4HdJrKMLNqlcJ0QN8+Jr66R8QKP+4UvN3OwLn2a0rGTgJq78zcto1Bm9aKJUsVv5wYwh+8rxSjYK9fYe3FcXR3GQW2B1W0j5QjOzFKC064VLlTUCkt8XY6ldKT/4BK7xg6uTTHiIaDd1kiaUkJtkKbOAGeeU6xKif6GUJCiWOvxD3vFbzdxuPtcW7E2w9WwQ0BflNWuQFIKbcKIR4APgi5ZJoTyFkNXbpCcrIR8xlSvL0m4FrzOc4N30Pzuq25DxoEs38En09hmvVQcpYP5+pPcC94A8eRE30PlMOFapKJatIKWgo87UegmrREpbRENWmF1aQlqklLPVOLENFw8C5HQipWapuwx6Qsy5mnw99ft10G+vVtIDM4XykJX9+NSmhG8ekPRCW4QbAKbjNQlct5IrC1inPlEELcA0zEtsYsAeYD90gpc8qUMYAHsGNepmPv890ipVwdpKwNEq9XseZnOO+caEsSHL72w7BS2+Bc/RkMraOCG2AwZapi/QYQ3esmh7l1Pu4fnsLcm4uvzQBKel9kK68mrVBNWmKltISktGP7Z6mpqZTG+tNDAycaDt4VsTIjE7IrQFKSwdlnKT79HPbf3DDyIbrnvYa5N5eiC1+DpPSoyBDsrvjdwKNCiJPKHhRCDAceAe4Ksp4xwGvAycA4wAt8J4RoXqbMncDt2O4IQ7H3+74VQjTqoH3r10NxMTHp4F0phgNvrwswt85F5S2vUxWBfbglS+vQ/MHNJH5+C0n/uxqj5AjF5z5P0a8/wDPiFrx9L8bX6VSszB6Q3Fwbh8QQ0XLwroiV0R3Hwc0oX+Ts5y6aYOD1wpdTItZk2HDsWIZr0Vt4ek/E1yV6qUWDncHdhx1vcq4QYg+20mnpf+0H7hVC3BsoLKUcVlklUspyOWyFEJdj7+2dAnzpn739CXhSSvmxv8yV/vYuBd4I+ps1MFYF/IJi0cG7Cjx9folr+Qfw9nkktRtK6cDf4usyLuhsyZmZBtntFcuWKy79dZCKvfgw7vmv2e2abkpG3oZn0BXgTKjHN9FEimg5eFfEyuiOYXlh33pIbh+RNrPbGwwbqvj8C8VvL7UdweMSTyGJX9+DSm1NyZh7oipKsAoux/8KNanYs8iD/s+dgNbA9EABKWWREGI29qyv8Sq4HNsvqFXL+On0qlk7Cn43nSbrp2AseIekL/+IldoGz4BL8fT5pb00WAODBsI339pLtNX+4H0eXCv/i3veK1B8BG+fX1B6yh9QKZmh+0KasBM1B+8KBAxN2P0zdIqMggM7y8DdkxRzfoIxoyPWbEhx//g8jkNbKLp4MiQ0iaosQSk4KeXVYWr/JWA5MM//ubX/fXeFcruBrIoXJycnY5r1D/dimiapqbG7AqqUImd1PoMGOklNTY55ecuRmorZ5ha8w6+Dtd/iWPAOCT8+R8K8V6HfRBh2NUarnlVefsrJHj77opCt25Lp3+/E7qqUgvUzYPoj9tN2p1PgzAdwt+5FXeKIxNW9peHJK9cW0rqVl86do5ugRCX3BYcLY+9aUvv9ImLtnnmG4uW/5fP5lw7OP6/2yiHa/UFt/BGWvw8nXUtyr9NqLB9ueWsVySSUCCGeB0YCI6WUdTJ6LywsDIksqamp5MewYcGu3Yo9exQ9hJf8/PyYl7ciqampHC0ohKxTYOIpOPauxbX8PZwrP8FY+gGe9ifhGfhbfJ3HnhCfrkcPO5rEnJ8K6dyp/AzOsW8t7h+exrnlJ6y0DpRc+Kpdh2FQVzPTeLy3DUXenTsVP81VnDSMmPhOSRndMHOnc3TQ9eCMXNi188+DN97ysWLlkRP6fE1EtT8UHyH50z9D884UDrslqN9gKORNT6/agCUqu+tCiBeA3wDjpJQby5za5X+v6A3Yqsy5RseqWA6wXAeszO6UnPEwBdfNoGTU7TgObSXpi1tJfudMXIvfgeLDx8qmpxl06Qzz5issyx86qfAACd89SNK7F2HuWkXJmLspvPILe3+vgTnJNhaKihT33Gf/f6/9XWz8D0tP/j3sW4f7p5ci2u5554LbBZ/GWXzKhFmPYxTspfisJ8EV3jxvwRJxBSeEeInjyi23wulN2IrsjDLlE4FR2NkLGiWrVimSEqFLeFMnRZ6kdDxDr6XwmukUnf8SVtN2JMx+hpQ3x5Dw7QPHHG3PPdtgVQ787eUSnIveJuWd8ThX/Q9P/0sp+N3XeAZdqQMbxzFKKZ58WrFxEzz4VyNmour7Oo+FwZfjWvJPzK2RS9qWnmYwbhx8/Q0UFMSHkjPXfWvHnz3phpiKxxrRJUohxKvA5cAE4KAQIrDndlRKeVRKqYQQL2JbZeYCa7EtOI/SiJ3Jc1ZDr15xbFVVEw4nvm5n4ut2Jo69Etey93Cu+RzXqv/ibT+c3wy8jLRf+Bh66DkSf9yGt9NoSkbfiWre0DR+4+S9D+D7mXDTDQbDT4qxPn7mX1EbZpPwzT0UXv4ZJEZmb3DiBIOvv1F8PR1+cVFEmqwzRuF+Er57EF/LXpSedGO0xSlHpGdwN2NbTn6PHaw58LqjTJmngReAV4HF2IGdz5RSRn9RPgoUFtqOzg1lebImrExByZmPUHD9TEpG3obj0BaSvriViZ4/kZCSwE3z3uTl/a9p5dZAmDdf8ebbitNPg0sjF2Q+aAx3MsXnPINxdA8JMx6JWLu9ehr0EPDpZyq2M9srRcK3D2CUHqXkrCfBdEVbonJEdAYnpazx8UxKqYAH/a9Gz+o1YFlx5OAdKpLS8Qy7Ds+QqzE3zMTwlpDYbTwZlsm/3wO3W3HVFY3snjQwtm5TPPSIoltXuPsvRswGGbZa96V0xM0kzH0FX6fReHtGJuT/xAkGjz+lWLoMBg+KSJO1xukPx1dy6p1YGdGKrVY1OoRDjLMqx7ab6N0r2pJECYcTX7cz8PY8D4fTxV9uMzh7PLz9juL9D2P4yVZTLQUFinsmKZxOePwRI+bzoHmGXY+vzQASZjyMcWRHRNo8bRw0a2rHp4xFjCM7SJj5GL6swXYwhRhEK7gYJ2e1onMnaNIktgeASOFwGNx9p8Fp4+Dvbyj+7+PY/PFrqsayFA8/ptieB488ZNC6dRz0bYeT4rOfAstHwtf3gAp/PsaEBINzz4U5c2D3nhjr58oi4ZtJoCyKxz9xgntPrKAVXAzj8ylyVjee/bdgMU2Dv95rcOooeOkVxWdfxNiPX1Mt//in4qe58IffGwwcEAfKzY9Ky6Zk7L04ty/EteRfEWlzwgUGloLPY6yPu5Z/gHPbfEpG34VKi1ykl9qiFVwMs3ETFBY2wv23IHA6DR663+Dk4fDs84op02JrANBUznffe5j8rp0VY+KEaEtTe7y9J+Ltejrun17Esbeil1PoadvGYMRwOwBzaWls9HHHnp9xz34Wb6dT8fa9ONriVItWcDFMThwGWI4kLpfBIw8ZDBsKTz6tmP5dbAwAmsrZsFFx3wOF9OkNt/0pdo1KqsUwKD7jYVRiMxKm/gW8JWFv8hcXGRw8CLNmh72pyiktwNw4C/fMx0n+17kkvzcRXImUnPFIzAdWiFqoLk3NrFqlaNEc2raJtiSxS0KCweOPwF/uVjz2uMLlhLFjYvtH1xg5fFhx9yRFkyYGjz4Mbncc/4+S0ik583GSPr0e95wXKB1zd1ibGzoE2rWDyf9WDB8GTZuG+d4pC8eenzG3zMXc8hNm3lIMy4NyJuJrNxRPv0vwdj3DTgIc42gFF8OsyrH33+LySTeCJCYaPPU43H6n4sFHFC43jDxZ37NYwetV3P+QYt8+eOetZDJaFEVbpHrj6zSK0gGX4V46GV+nU/F1ODlsbTkcBn+5De64S3H7nYoXn4OUlND2b+PoHsytczE3/4S5ZS6OogMA+DJ74Bl0Ob6OI/G1HRR3aae0gotR9u1T7NwFv5ioB+pgSE42ePYp+NMdir8+oHjiUWIvKkYj5e9vKJYshXvvMujX11nXONgxR+mo23FunUfC1/dQeMXnQaV/qiuDBxk88hBM+qviznsUzz5lZwGvM55izB1LMDfPxdwyB9MfFs9KboGv4ymUdjgFX4eT4z7dlN6Di1EaWoDlSJCSYvDc0wYdO8C9f1UsWar35KLNtG8U//k/+OUv4JyzG9gDhyuJ4rOfxig6QML3D0GYI46MPNng/kl2XNZ7/6pqb3Ri+XAtf5/Ej68j5bXhJH18La7l76KSmlMy6nYKf/sJhTfMpuTsp/H2ujDulRtoBRezrMpRuN3QPfaCA8Q0TVMNXnjWoF0W3HWvYsVKreSixc+5imeeVQwaCL+/qYEpNz9Wq96UnnwrrrVf4/z5i7C3d9o4g7v/YrBoMdz/kMLrDb5/u2c/Q8KMR3Hk78DT7xKKLnqDgpvnU3zxP/EMvRarZU8wGpZKaFjfpgGxajX07GFbCmpqR1qawYvPGbTMtPctclZrJRdp9u9X3HufokULePgBo+EGCgc8Q67BlzWYhBmPYhzOC3t755xt8Oc/Gsz5CR55XOHz1dy/nav+h3vpZEoH/JbCq6ZQOvZefJ1OBVdy2OWNJlrBxSDFxYq1a/XyZH1o3tzgpecNmqfDHXcq5Fqt5CJFaanivgcU+Ufh8UcN0tIarnIDwGFSfNZTgCLx67vAqlP+5lrxi4sMbrrB4PsZ8PRzZXIlVibe9kUkfP8w3g6nUDrmrrDLFktoBReD/JwLPp928K4vmZkGL71g0KQJ/PkOxfoNWsmFG6UUL7ysWJUD99xl0K1r4+jDqlkWJePuw8xbYiftjQCX/cbgqitgylR4+W+VZx0wDm0j6Ys/oJq1o/jc58HRuOwKtYKLQY45ePeOrhwNgdatbCWX4IY/3a5Yt04ruXDy2Rfw5Vdw+WVw2tjGodwCeHteiKf7WbjnvoJj9+qItHnN1Qa/ugT+9wm8+XaFvl1ylMTPbwKlKJrwWsRy2cUSWsHFIKtWKTpkQ7NmjWuACBdZbW0l53LCTbcqvp+plVw4WL5C8eLLihHD4drfNcK+axiUnPYAKimdxGl3gac4Ak0a/P4mgwvPh3ffh7ff8bdp+UicejuOA5spPv9FVHrHsMsSi2gFF2NYlmKVDrAccrLbG7z9hkG3rvDAQ4o33rKC2pzXBMf27fa+W9u28MB9BqbZCBUcQFIaJWc9juPABtw/PheRJg3D4PY/G4w/E155tYT//p/C/eNzODfNpmTcJHzZwyMiRyyiFVyMsWUr5OdDH73/FnJatDB4+QWDC/xPu3fdq8jP10quvnw/U3HNDQqfD5581Gj0qZ18HU6hdNAVuJe/h7l5TkTadDgM7rnT4LRxTrZ88QnuJf+ktP+lePv/JiLtxypawcUYOX4H7356BhcWXC6DO293cMdtti/RdTcpNm/RSq4ulJQonn7O4oGHFB07wDtvGnTo0LiVW4DSkbfha9GVhG/ugaKDEWnT6TR46sZV/HXAg8zfO4IpVnhjZMYDWsHFGKtyFGnNoH3splhqEEy4wJ7NFRTA9Tcp5vyklVxt2LRZcd2Nii++hMt+A6++bNCmjVZux3AmUHLOMxjFh0n89oGwRzkBMA5vx/nJ9Rjp7Xi3+DkefcLkhx8bd7/WCi7GWJljp8fRAZbDT/9+9r5c+3Zw9yTFv/5dvT+RxnYD+GqqrdwOHITnnja46QZHg3bkritWZg9KT/4jzvXfkvjpDRiHtoWvsdICEj+7GSwfxRe9xgOPpdGjh73fPH9B4+3TWsHFEAcPKbZvhz699WARKVq1NHjtFXuD/u13FHfcVUhhYeMdEKqjsFDx8GOKJ59W9O4F//qHwUnDdF+tDs+QqykZcw9m3hKSJ5+Pa+Gb4CsNbSOWj8Spd+A4sBEufh2V3skffNygU0c7buWy5Y2zT2sFF0Pk6ADLUSEhweC+ewz+cIvBrB+83HCzYvv2xjkgVIVcq/jddYrvZ9guAM8/Y5DRQiu3GjEceAZdQeFVU/B1Hk3CnBdIfnciju2LQtaEe87zODfOonTsPRidRx07nppq8PyzBm3bwp33KFavaXx9unG5tcc4q3IUTif0ENGWpPFhGAaXXAx9+iRzx10FXHuj4uEHYNjQxj2IK6X43yfw2uv23vArLxr079e470ldUKmtKT7/JcyNs0iY8SjJ/70CT++JlJx6BySl17le5+pPcS9+B0//X+MZcBmJFc6npxm8+Czc8gc7l9wrL3JCdBmvV5GfD0eOwJF824r72PsRO+RaftlzRyAxEQYOgEEDDQb0D31+ulBhVBbeJV44ePBgSIRPTU0lP8pJqlavUfz1QUVmBrzxWvUT61iQtzbEk7ypqankyiPce59i02a48XqD3/wqdvdEw3lvjxxRPPGU4sef4OQRMOluo97BB+KpL0CY5PUU4Z7/d1xL/gnuFEpO/Qve3hfVOpK/I28pSf+7Cl/WYIovehNMV5Xy7tqluPkPipJi6Ny5vBIrqiH/bJMUSG0KqanQNBWaNoVDh+wVp1IPmA4QAgYPshVe3z52EuJgCMX9TU9Pr7IxreCI7o+usFDx5tuKjz+FjBbw4P01PyHrQSJ8BGQtKlI8/pRi5iw443S46w4j6B9tJAnXvV25ys6OfuAA3HyDwcW/DI2Sj6e+AOGV17FvHQnfP4SZtwRf1mBKTnsAKyO4/FjG4TySPrgEEppQ+Jv/HEu2Wp2827Yrnn1e4fUeV1apqfZSZqpfcaU2sd8D55o0oUqn/ZISxeo1sGSpYukyWPOzHUPX5YLevewkrYMGQq+eVWdF0QquGuJdwf00V/Hci4q9e+GiC+GG64ygpvp6kAgfZWVVSvHu+/DWPxTdutqR8Vu3qt0gX1Ki2LET8nbAjjzI26HI2wG7dkF2Npw83GD4cOq8nxXqe2tZivc/hLf/oWjVGh6+36BHj9Ap9njqCxABeZWFc/WnJMx+BkoL8Ay+mtLhN4ErqeprSgtI+uhSHPk7KfzNR6jmnSMnbzUUFipWrIKlSxVLlsG6dbZ3RGIi9OtrK7zBA6Fbt+NKUyu4aohXBbd/v+KlvylmzIROHeGuvxi1spzUg0T4qEzWufMUDz2qcLngkQcNBg4o/7/Kz7eVVl6ercjydijy8mDHDtizt3z9KSmQ1RZatoS1a4+f794dTh4Ow08y6Nmj6qfmYOStKwcOKB55XLFoMZw2Fv5ye+ijksRTX4AIylt0kITZz+Ba/SlW0yxKxv0VX+fRJ5ZTFolf3Iq5cRbFF72Jr+Mp0ZE3CI4cUSxbcVzhbd5sH2+SYu/fDRlscNmlqRQXH61XO1rB1UCkOoVSiq+mwKuvK0pK4MrLDS77Te2TmsZSJw6GeJK3Klm3blXcPclWZOefC/lHbQWWt8PedC9L83TIyvK/2hpktQ38Dc2aHV/qU0qxYSPMm28r0dVrwLIgrRmcdJI9uxs21F5Cqq28tWXRYsUjjykKCuFPtxqcd2549h3jqS9A5OV1bFtI4vcP4ziwAW+3MykZcw8qtfWx8+4fn8O96G1KxtyLZ9DlUZe3Nuzfr1i6/LjC27EDXn81mT696xeUWiu4GohEp9i6VfH0c4rlK2BAf7jzdoPs7NhYlgo38SRvdbIePap47EnF3LnQspWtsGzlZRxTYG3bQHJy3f6vhw8rFi6GefMU8xfaitN02I7/I4YbnDwcOnUqr3hqe289HsX+/bBvP+zbB3v3wcaNiq+mQocO9pJk587h22uMp74AUZLXV4pryb9wz3sNHCalp/wRz4BLceZOIfHru/H0vYSS0x+ESh5A4un+FhYqWrVqqpcoqyIeFJzHY+9p/PtdhTsBbrnR4Nxz7OCodSWeOjHEl7zByGpZql7/v2Dw+RRrfoZ58xXz5sO69fbxVq1gxEm2whs8CDIz7QHCshSHD9sKK6C49u1T7NtXXpkdOnRiW04nnH0W/PH34Tekiae+ANGV1zi0jYQZj+Dc/CO+DIHj4EZ8bQZS/Iu3wXRVek1jvL9awdVAuDpFzmrFU8/Y5ubjxtoDSIsQOMc2xk4cKWJV1r17FfMW2LO7xUugqBjcbujS2cH+Axb799sWbGUxDEhPg4yMMq8WBpkZ5Y81a1q/B67aEKv3tyqiLq9SmOu+IWHm4+BKovA3H1XrNxd1eWtJuBWcdvQOAwUFijfeUnz6OWRmwJOPG4w8OfZMzDXxQ2amwQXnwQXnGZSWKlastPfttuc56NDBIqMFZGYY5RRXi+boGJHxjmHg634WhZ3HgfJVb12pOQGt4ELMnJ8Uz72g2LcffjERrr/GqPOejEZTGW63wdAhMHSIQWpqSlw9sWvqiNMdbQniEq3gQsS+/YoXX1LMmg1dOsOjDxv07qUVm0aj0UQLreDqgVKKtetg+reKKVOhtNR21v7Nr/TSkEaj0UQbreDqwK5dim+/h2++VWzebFuhjTwZbrjeoH07rdg0Go0mFtAKLkjy8xWzfrCV2vIV9rF+feGO2wzGjYGmTbVi02g0mlhCK7hq8HgU8xfAN9MVc+fZkbPbt7fzYZ15BrRto5WaRqPRxCpawVVAKUXOalupzZhlR5NIS4MLLoCzzjAQInZTp2g0Go3mOFrB+dm6TTH9W8X07+wYaQkJMGokjD/TYOhgbTSi0Wg08UajV3Bz5iree/8oOasVDoedtO/qKw1Gj6p7TEGNRqPRRJ9Gr+C+/U7h8SpuucngjNMgI0MrNY1Go2kINHoF99D9jriL36bRaDSamnFEWwCNRqPRaMKBVnAajUajaZBoBafRaDSaBolWcBqNRqNpkGgFp9FoNJoGiVZwGo1Go2mQaAWn0Wg0mgaJVnAajUajaZBoBafRaDSaBomhlIq2DBqNRqPRhBw9g9NoNBpNg0QrOI1Go9E0SLSC02g0Gk2DRCs4jUaj0TRIGkW6HCHEzcBfgDbAauBPUsofqyk/Gnge6A3sAJ6WUr4eATnvASYCAigB5gP3SClzqrmmI7CpklNnSym/DoecZdp+EHigwuHdUsrW1VzTF/gbMAw4ALwBPCKlDKu1kxBiM9ChklNTpZTnVnFNZTLdFI6+IIQ4FbgDGAy0Ba6WUv6rzHkD+15fD6QDC4BbpJSra6j3F8AjQBdgAzBJSvlpOOUVQriAR4Gz/e0eAWYCd0spt1ZT5xh/uYr0lFLmhkte//l/AVdWuGyBlHJ4DfWGZawIQt6qfi+vSSlvqaLOjoRhrAhm3IpW/23wMzghxK+Al4DHgYHAXGCaECK7ivKdgKn+cgOBJ4BX/Dc63IwBXgNOBsYBXuA7IUTzIK49C1uBB14zwiRjRWSFdvtWVVAI0RT4FtgNDAX+iP3gcVv4xWQo5eUcBCjgvzVcd12F6yaHSb4mQA72PSmq5PydwO3ArdjfZQ/wrRAitaoKhRAjgP8A7wMD/O//J4Q4KczyJmPf38f87xcC7YGvhRDBPFT3pvw9XxdmeQN8V6Hdc6qrMMxjRU3ytqnwOt9/vKb+DKEfK8ZQ87gVlf7bGGZwtwH/klK+5f98qxDiLOAm4J5Kyt8I7JBS3ur//LP/ht4BfBxOQaWU48t+FkJcDhwGTgG+rOHy/VLKXeGSrRq8tWj3MuzB70opZRGQI4ToAdwmhHg+nLM4KeXesp+FENdgzyxqGhAOReK+SimnYg+WgdnEMfxPv38CnpRSfuw/diX2IHEp9iy4Mv4EzJRSPub//JgQYqz/+G/CJa+U8jBwRoXvcAP26klPYFUN1e+RUu6rj3wVqU7eMpTU8n8dtrGiJnkryimEuBBYK6X8IYjqQzpW1DRuRbP/NugZnBDCjT3Fn17h1HTsp43KGFFJ+W+AIf6ll0iSiv0/OhhE2U+EEHuEED8JIX4ZZrnK0lkIsUMIsUkI8ZEQonM1ZUcAP/qVW4BvsJdgOoZTyLL4f3DXAO9VkKUyXhJC7BNCLBJC3CiEiMZvphPQmjL90i/3bKrux1B1X67umnDR1P8eTF9eLITYKYT43j+gRYqR/t/QWiHEW0KIljWUj4mxQgjRBPg18FZNZf2Ee6yoOG5Frf82aAUHZAAm9pJYWXZj3/DKaF1Feae/vkjyErAcmFdNmaPYT4yXYC+pfA/8Rwjx27BLZ6+jX4W95HEd9r2bK4RoUUX5qu5t4FykOAP7R1fTgHA/8CvgdOAj4Dng3vCKVimBe1Obfhy4rrbXhBz/g+ZzwJdSyu3VFN2JvbLyC+w9HQl8L4QYFX4p+Rq4AjgNeyltGDBDCJFQzTWxMlZcCripefk8UmNFxXErav23MSxRxiVCiOeBkcBIKaWvqnL+pZznyhxaLITIwF7zfi+cMkopp5X9LISYD2zE3qx/Ppxt15PrgEVSyhXVFZJSPlLm43IhhAlMwjag0ASBf8/tPSANuKC6slJKia3UAszzG0b8BajSKCwUSCk/KvNxlRBiCbAFOBf4JJxth4DrgM8rLsNXJBJjRbDjVqRo6DO4fYAPaFXheCugqjXoXVWU9/rrCztCiBew15jHSSk31qGKBUC30EpVM1LKo9j7LFW1XdW9DZwLO/5lpwsJfjmnLAuApkKIit8h3ATuTW36ceC62l4TMvzK7UOgH3CalHJ/HaqJVl/eAWyvoe1YGCsGAEOoW3+GEN7fasatqPXfBq3gpJSlwBIqbHj7P8+t4rJ5VZRfLKX0hFbCExFCvMTxTlJX0+gB2Ms9EUUIkQj0qKbtecAof7kAZ2CbV28Or3THuArblPnDOlw7ACgGDoVOnKDYhP2jPtYv/fdwFFX3Y6i6L1d3TUjw70H9B1u5ja2HUcMAotOXM4CsGtqO6ljh53rs/vFdHa8fQAjubw3jVtT6b2NYonweeFcIsRD4CdvyqS3wOoAQ4t8AUsor/OVfB34vhHgR27rnFOxBsV5WZ8EghHgVuByYABwUQgTWmo/6Z0cIIZ4AhkkpT/N/vhLwAMsAC9tc+BbgrgjI+yy2dedWoCXwVyAF/15ARVmBD7B9Yf4lhHgU6A7cDTwUbj84vzwGcC3wUeB+ljn3e+D3Usoe/s/nY6/1z8M20x4LPAy8KaUsCYNsTYCu/o8OINv/dH5ASrnV3x/vFULkAmuB+7D3VD4oU8f3wEIpZcA6+CVgthDibuAz4CL/9xgZTnmxH1j+D9sc/HxAlenLhwOGPRV/e0KIP2E/6KzG3lP6LfZvod5m9zXIewB4ENvycSe2wdMT2FZ+n5apI2JjRU39wV8mGdsy+enKfj+RGitqGreklCpa/bdBz+AApJT/wTYrvQ9743MkcI6Ucou/SLb/FSi/CXsD9lR/+UnAHwLmrWHmZmwLpO+xf2iB1x1lyrTBdnosy33AYmARtjXV76SUL4RdWmiHPROS2PsUJcDwMve2nKxlzMfb+uV9FXtPIFL7dWOwl2MqW87JwHZUDeDB/n/MA1Zi+yPdj22AEA6GYA88y4Ak4CH/3w/7zz8NvIB9zxZj39szpZT5Zero4j8OgJRyLnZ/uMr/Ha4AfiWlXBBmedthLwO3xV5BKduXf1WmjnK/PWyl9oxf1h+xf6vnSilDsQdWnbw+bP/Nz7EH38nYfXpEhfsbybGipv4A9r1MAf5ZRR2RGiuCGbei0n91uhyNRqPRNEga/AxOo9FoNI0TreA0Go1G0yDRCk6j0Wg0DRKt4DQajUbTINEKTqPRaDQNEq3gNBqNRtMg0QpOo9FoNA0SreA0Go1G0yDRCk6jacQIIZKiLYNGEy4aQyxKjSZmEUKcA3wFdPGHfgoc7wRsAC6SUn7uz9j8V6APdrDnfwOTAkF9/ZnRH8SOh9gCO8DtW8DLUkrLX2YMMBM7f98twDjsgMjXhPt7ajTRQM/gNJro8g12cOIrKxy/CjvY7xQhxCXYsT4XYudUewg7ivwTZcpnYcdPvBk7PuJb/nKVBdL9B7DCX9c/QvQ9NJqYQ8ei1GiijD+zwmVAZ3/kdQN7BvY/7GSfm4EZUsqry1zzO+zAte0q5lnzX29iJ7K8VkrZ2X98DPYM7kUp5Z/D/b00mmijlyg1mujzDnAvdraDmdgpQTpgR4nvjh3B/r/+BKIBZgCJ2EuWP/jza92DrSizAVegoBDCKaX0lrl2Sti+iUYTQ+glSo0myvizH88CAjO0q7HzYq3GTuMDMBU7hU/gFdiva+9/fwo7Pcmb2EuUQ4FH/efKJpgF2B3ab6DRxCZ6BqfRxAZvA28JIe4BJnI879wB//v12PnAKhJQdBcDr0gpnw6cEEKcW0Vbel9C0yjQCk6jiQ0+wd5T+wh7ZeUj/3EJ5AEdpZSVJWoNkISdcBYAIYSJnSxSo2m0aAWn0cQAUspiIcT72Ob7H0opD/mPW0KI24F3hRBNgWlAKdAZmAD8UkpZCHwL3CKEWI8967sFSIj4F9FoYgi9B6fRxA6f+d/fKXtQSvkf4EJgAPB/2LO9m4Gl2MoO4FbgR+xZ4DtADuXdCDSaRod2E9BoYgQhxNPAJdjuAla05dFo4h29RKnRRBkhhAB6ATcBD2nlptGEBq3gNJro8wZwEvAF8HKUZdFoGgx6iVKj0Wg0DRJtZKLRaDSaBolWcBqNRqNpkGgFp9FoNJoGiVZwGo1Go2mQaAWn0Wg0mgaJVnAajUajaZD8P7TPsT+pWRWiAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "obs_stock_df = pd.read_csv('data/hudson-bay-lynx-hare.csv')\n", + "# data viz\n", + "pd.melt(obs_stock_df, id_vars = 'Year').iloc[[0,20,21,41]]\n", + "pd.melt(obs_stock_df, id_vars = 'Year').iloc[[0,1,20,21,40,41]].rename(columns = {'variable':'species', 'value':'pelts in thousands'})\n", + "ax = obs_stock_df.loc[:, ['Predator', 'Prey']].plot()\n", + "ax.set(xlabel='year', ylabel='pelt (thousands)') " + ] + }, + { + "cell_type": "markdown", + "id": "dd624d4d-178f-41f5-8586-8c15bd59763f", + "metadata": {}, + "source": [ + "Phase plots are as below:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "77abf5f6-b8a8-499d-bcb2-c705afb42c48", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Prey pelts (thousands)')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(obs_stock_df.loc[:, 'Predator'], obs_stock_df.loc[:, 'Prey'])\n", + "plt.plot(obs_stock_df.loc[:, 'Predator'], obs_stock_df.loc[:, 'Prey'])\n", + "plt.xlabel('Predator pelts (thousands)')\n", + "plt.ylabel('Prey pelts (thousands)')" + ] + }, + { + "cell_type": "markdown", + "id": "5763cb30-424f-4716-aab2-ae96fa680338", + "metadata": {}, + "source": [ + "## Mechanistic Model: The Lotka-Volterra Equations\n", + "\n", + "In Lotka-Volterra equations (Lotka 1925; Volterra 1926, 1927), Volterra modeled the temporal dynamics of the two species (i.e., population sizes over times) in terms of four parameters, $\\alpha, \\beta, \\gamma, \\delta \\geq 0$, as\n", + "\n", + "$$\n", + "\\begin{eqnarray}\n", + "\\frac{\\mathrm{d}}{\\mathrm{d}t} u\n", + "& = & (\\alpha - \\beta v) u\n", + "& = & \\alpha u - \\beta u v\n", + "\\\\[6pt]\n", + "\\frac{\\mathrm{d}}{\\mathrm{d}t} v\n", + "& = & (-\\gamma + \\delta \\, u) \\, v\n", + "& = & -\\gamma v + \\delta uv\n", + "\\end{eqnarray}\n", + "$$\n", + "\n", + "$u(t)$ and $v(t)$ are rendered as $u$ and $v$. The factor $\\alpha$, $\\beta$ are the rate of birth and shrinkage relative to the product of the population sizes where as $\\gamma$, $\\delta$ are the shrinkage and growth rate as a factor of the product of the population sizes. Both u and v have positivitity constraints. as long as the initial populations are non-negative, i.e., $u(0) \\geq 0$ and $v(0) \\geq 0$, because the rate of change in each population is a factor of the population size itself.\n", + "\n", + "The dynamic system has four limiting behavior:\n", + "\n", + "1. If both population sizes are initially positive, the populations will oscillate in a fixed pattern indefinitely, remaining positive.\n", + "2. If both population sizes are initially zero, the population sizes will remain zero.\n", + "3. If the predator population size is zero and the prey population size positive, the predator population size remains zero and the prey population grows without bound.\n", + "4. If the predator population size is positive and the prey population size zero, the prey population size remains zero while the predator population shrinks toward zero size." + ] + }, + { + "cell_type": "markdown", + "id": "10c2e11e-1038-41a3-9eb8-a75592ef9aa7", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Statistical Model: regreasion framing and uncertainty embedding\n", + "\n", + "### Solving the inverse problem\n", + "Bayesian statistics is somewhat counterintuitive, as it involves formulating the generating model (from parameter to observed data) then using general principles to solve the inverse problem. Specifically, a Bayesian model requires a mathematical model of what we know about the parameters (i.e., a prior) and a model of what we know about the data generating process given the parameters (i.e., a sampling distribution.\n", + "\n", + "Mathematically, a prior density $p(\\theta)$ over the sequence of parameters $\\theta$ encapsulates our knowledge of the parameters before seeing the data. A sampling distribution (or likelihood), which may have a continuous, discrete or mixed probability function, $p(y | \\theta)$ characterizes the distribution of observable data $y$ given parameters $\\theta$. We limit the observation as stock variables as every SD model can be reformulated into the combination of stock and parameters.\n", + "\n", + "Bayes's rule gives us a general solution to the inverse problem, expressing the posterior $p(\\theta | y)$ in terms of the prior $p(\\theta)$ and likelihood $p(y | \\theta)$. Stan provides a form of Markov chain Monte Carlo (MCMC) sampling that draws a sample $\\theta^{(1)}, \\ldots, \\theta^{(M)}$ from the posterior to use for computational inference. Posterior quantities of interest may be expressed as derived random variables using functions $f(\\theta)$ of parameters. This feature is used for decision analysis; for instance, imagine a optimization problem of conservation cost of the park where prey and predator ecology places at. The cost can be computed based on the posterior distribution inferred from the observed time series.\n", + "\n", + "\n", + "### Uncertainty embedding for forward-backward symmetry required for calibration\n", + "\n", + "The Lotka-Volterra model is deterministic in that given the value of the system parameter and initial outcome state, equation solutions (simulated outcome value) are fully determined. However, for empirical research which use posterior inference from the real data as it final forecast, forward model should be re-designed. This is because symmetry of forward and backward model (i.e. data generation and its inference) is the theoretical justification of calibration. To pass this internal consistency test (or with enough resource, SBC which is rank-statistics based), we need the two process to be the mirror image of other. This is why we purposefully embed uncertainty components, waiting to be captured in the inference step. The purpose is to test resilience and identifiability of our models evidenced by the perfect retrival of prior distribution for every uncertainty we embedded. \n", + "\n", + "### Linear regression analogy\n", + "\n", + "Like in a simple linear regression, we will proceed by treating the underlying determinstic model as providing an expected population value around which there will be variation due to both measurement error and simplifications in the scientific model. Consider the typical formulation of a linear regression, where $y_n$ is an observable scalar outcome, $x_n$ is a row vector of unmodeled predictors (aka covariates, features), $\\beta$ is a coefficient vector parameter, and $\\sigma > 0$ is the error scale,\n", + "\n", + "$$\n", + "\\begin{eqnarray}\n", + "y_n & = & x_n \\beta + \\epsilon_n\n", + "\\\\[6pt]\n", + "\\epsilon_n & \\sim & \\mathsf{Normal}(0, \\sigma)\n", + "\\end{eqnarray}\n", + "$$\n", + "\n", + "### Adding measurement uncertainty (epistemic)\n", + "Before embedding parameteric uncertainty, linear predictor $x_n \\beta$ with predictor $x_n$ (row $n$ of the data matrix $x$) and coefficient (column) vector $\\beta$ are deterministic. The only source of uncertainty is from the measurement. This is expressed by assigning a normal distribution to error term $\\epsilon_n$. Equal expression is with latent error variable $\\epsilon_n$ as follows[17](#fn17), \n", + "\n", + "$$\n", + "y_n \\sim \\mathsf{Normal}(x_n \\beta, \\sigma).\n", + "$$\n", + "\n", + "### Adding parameter uncertainty (epistemic)\n", + "Next, we add parameter uncertainty by coding estimated parameter as a distribution rather than a fixed value. This distribution is called prior distribution and from our example, Normal distirbution is chosen to endow the uncertainty to the four estimated parameters $\\alpha, \\beta, \\gamma, \\delta$. Considering their role difference, $\\alpha, \\gamma$ as multipliers of $u, -v$ and $\\beta, \\delta$ as multipliers of $uv$, prior parameter are chosen as N(1, 0.5) and N(0.05, 0.05) for each. For this selection, refer to the original case study [Carpenter18](https://mc-stan.org/users/documentation/case-studies/lotka-volterra-predator-prey.html).\n", + "\n", + "\n", + "### Adding aleatoric uncertainty\n", + "This applies only when we decide to add measurement uncertainty i.e. having initial value of stock variable as `est_param` instead of the default `ass_param`. For this, detailed explanation is added at the end of this file at Appendix A. " + ] + }, + { + "cell_type": "markdown", + "id": "a87666b9-f6c5-4723-a2be-373f2dd29f0c", + "metadata": {}, + "source": [ + "## Prior predictive check" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "2444ecf8-c272-4dad-9487-37fcb88ddbbf", + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "prey-predator_functions.stan already exists in the current working directory. Overwrite? (Y/N): y\n" + ] + } + ], + "source": [ + "vf = VensimFile(\"vensim_models/prey-predator.mdl\")\n", + "vf.parse()\n", + "am = vf.get_abstract_model()\n", + "\n", + "# set time\n", + "n_t = obs_stock_df.shape[0] - 1 \n", + "premodel = StanVensimModel(\"prey-predator\", am, 0.0, list(range(1, n_t + 1)))\n", + "\n", + "# ode parameter \n", + "premodel.set_prior(\"alpha\", \"normal\", 0.55, 0.1)\n", + "premodel.set_prior(\"gamma\", \"normal\", 0.8, 0.1)\n", + "premodel.set_prior(\"beta\", \"normal\", 0.028, 0.01)\n", + "premodel.set_prior(\"delta\", \"normal\", 0.024, 0.01)\n", + "\n", + "# sampling distribution parameter\n", + "# model.set_prior(\"sigma\", \"lognormal\", np.log(0.01), 0.1) \n", + "\n", + "premodel.build_stan_functions()\n", + "premodel.draws2data(\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "c4bd395d-bc67-4175-b765-6e2232018faf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:50:20 - cmdstanpy - INFO - compiling stan file /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan to exe file /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data\n", + "17:50:28 - cmdstanpy - INFO - compiled model executable: /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data\n", + "17:50:28 - cmdstanpy - WARNING - Stan compiler has produced 6 warnings:\n", + "17:50:28 - cmdstanpy - WARNING - \n", + "--- Translating Stan model to C++ code ---\n", + "bin/stanc --include-paths=/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file --o=/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.hpp /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan\n", + "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_functions.stan', line 1, column 0, included from\n", + "'/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 2, column 0: Comments\n", + " beginning with # are deprecated and this syntax will be removed in Stan\n", + " 2.32.0. Use // to begin line comments; this can be done automatically\n", + " using the auto-format flag to stanc\n", + "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_functions.stan', line 3, column 21, included from\n", + "'/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 2, column 0: Comments\n", + " beginning with # are deprecated and this syntax will be removed in Stan\n", + " 2.32.0. Use // to begin line comments; this can be done automatically\n", + " using the auto-format flag to stanc\n", + "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_functions.stan', line 5, column 4, included from\n", + "'/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 2, column 0: Comments\n", + " beginning with # are deprecated and this syntax will be removed in Stan\n", + " 2.32.0. Use // to begin line comments; this can be done automatically\n", + " using the auto-format flag to stanc\n", + "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 28, column 32: Comments\n", + " beginning with # are deprecated and this syntax will be removed in Stan\n", + " 2.32.0. Use // to begin line comments; this can be done automatically\n", + " using the auto-format flag to stanc\n", + "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 33, column 4: Declaration\n", + " of arrays by placing brackets after a variable name is deprecated and\n", + " will be removed in Stan 2.32.0. Instead use the array keyword before the\n", + " type. This can be changed automatically using the auto-format flag to\n", + " stanc\n", + "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 36, column 4: Declaration\n", + " of arrays by placing brackets after a variable name is deprecated and\n", + " will be removed in Stan 2.32.0. Instead use the array keyword before the\n", + " type. This can be changed automatically using the auto-format flag to\n", + " stanc\n", + "\n", + "--- Compiling, linking C++ code ---\n", + "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -c -include-pch stan/src/stan/model/model_header.hpp.gch -x c++ -o /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.o /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.hpp\n", + "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -Wl,-L,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.o src/cmdstan/main.o -Wl,-L,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data\n", + "rm -f /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.o\n", + "\n", + "17:50:29 - cmdstanpy - INFO - CmdStan start processing\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "856fd81897f842338dcaa2187cc3ba1d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 1 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:50:29 - cmdstanpy - INFO - CmdStan done processing.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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MeanMCSEStdDev5%50%95%N_EffN_Eff/sR_hat
name
lp__0.000000NaN0.0000000.0000000.0000000.000000NaNNaNNaN
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gamma_tilde0.772295NaN0.0462250.7198070.7901430.806936NaNNaNNaN
beta_tilde0.025194NaN0.0056540.0208340.0231660.031582NaNNaNNaN
delta_tilde0.013350NaN0.0013470.0123040.0128750.014870NaNNaNNaN
..............................
y_tilde[19,1]44.503100NaN47.74210015.64220018.25680099.610200NaNNaNNaN
y_tilde[19,2]20.489500NaN25.3186003.2563608.65386049.558400NaNNaNNaN
y_tilde[20,1]52.825400NaN60.81840012.12870023.608500122.739000NaNNaNNaN
y_tilde[20,2]13.521200NaN13.8803004.8816006.14999029.532100NaNNaNNaN
sigma_tilde0.010077NaN0.0000610.0100070.0101100.010116NaNNaNNaN
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" + ], + "text/plain": [ + " Mean MCSE StdDev 5% 50% 95% \\\n", + "name \n", + "lp__ 0.000000 NaN 0.000000 0.000000 0.000000 0.000000 \n", + "alpha_tilde 0.483423 NaN 0.168838 0.311738 0.489268 0.649262 \n", + "gamma_tilde 0.772295 NaN 0.046225 0.719807 0.790143 0.806936 \n", + "beta_tilde 0.025194 NaN 0.005654 0.020834 0.023166 0.031582 \n", + "delta_tilde 0.013350 NaN 0.001347 0.012304 0.012875 0.014870 \n", + "... ... ... ... ... ... ... \n", + "y_tilde[19,1] 44.503100 NaN 47.742100 15.642200 18.256800 99.610200 \n", + "y_tilde[19,2] 20.489500 NaN 25.318600 3.256360 8.653860 49.558400 \n", + "y_tilde[20,1] 52.825400 NaN 60.818400 12.128700 23.608500 122.739000 \n", + "y_tilde[20,2] 13.521200 NaN 13.880300 4.881600 6.149990 29.532100 \n", + "sigma_tilde 0.010077 NaN 0.000061 0.010007 0.010110 0.010116 \n", + "\n", + " N_Eff N_Eff/s R_hat \n", + "name \n", + "lp__ NaN NaN NaN \n", + "alpha_tilde NaN NaN NaN \n", + "gamma_tilde NaN NaN NaN \n", + "beta_tilde NaN NaN NaN \n", + "delta_tilde NaN NaN NaN \n", + "... ... ... ... \n", + "y_tilde[19,1] NaN NaN NaN \n", + "y_tilde[19,2] NaN NaN NaN \n", + "y_tilde[20,1] NaN NaN NaN \n", + "y_tilde[20,2] NaN NaN NaN \n", + "sigma_tilde NaN NaN NaN \n", + "\n", + "[90 rows x 9 columns]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_draws2data = {\n", + " \"n_obs_state\" : 2\n", + "}\n", + "\n", + "sf_path_draws2data = os.path.join(os.getcwd(), \"stan_file\", \"prey-predator_draws2data.stan\")\n", + "sm_draws2data = CmdStanModel(stan_file = sf_path_draws2data)\n", + "\n", + "prior_pred = sm_draws2data.sample(data=data_draws2data, iter_sampling=3, chains=1, fixed_param=True, iter_warmup=0)\n", + "prior_pred.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "595077fd-df8f-442c-a806-30c7e168f423", + "metadata": {}, + "source": [ + "For prior predictive checks, if the real observed data is indiscriminable from the simulated, we usually view it as a sign of pass. Real data is an external reference so as long as the predicted ranges are not too off, we give a pass to prior predictive check. Summary statistics such as N^th moments can be used for comparison. Few comments:\n", + "\n", + "a. we use real data below as a representation of our knowledge, so prior predictive check is not double dipping (using data twice)\n", + "\n", + "b. Bayesian prior corresponds to frequentist's regularization so having a tighter prior than posterior is not unnatrual; simply our determination to find a model concentrated around certain model configuration\n", + "\n", + "c. if tight prior is well-placed, it prevents diveregence from frustrating geometry and boosts sampling efficiency" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "82f4938e-f90c-4043-bcf5-c869c39676be", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(15, 8))\n", + "#compare with real \n", + "ax.plot(obs_stock_df.loc[:, ['Prey']], label = \"Real_Pey\")\n", + "ax.plot(obs_stock_df.loc[:, ['Predator']], label = \"Real_Predator\")\n", + "ax.plot(pd.DataFrame(prior_pred.y_tilde[:,:,0]).T.loc[:, :5])\n", + "ax.plot(pd.DataFrame(prior_pred.y_tilde[:,:,1]).T.loc[:, :5])\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "27ddb4c9-9f11-4324-84f4-d426fb01bc6f", + "metadata": { + "tags": [] + }, + "source": [ + "## Posterior predictive check" + ] + }, + { + "cell_type": "markdown", + "id": "8c54a353-1b50-4e1b-ac5d-bd9933ce6124", + "metadata": {}, + "source": [ + "Now we estimate parameter values based on the real observed stock values. Here we use more diffuse prior." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "7e4236f8-d6c8-45dd-852d-c1b759a54248", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'predator_birth_rate', 'predator_death_rate', 'prey_death_rate', 'predator', 'gamma', 'final_time', 'delta', 'beta', 'time_step', 'prey_birth_rate', 'initial_time', 'saveper', 'prey', 'alpha'}\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "prey-predator_functions.stan already exists in the current working directory. Overwrite? (Y/N): y\n" + ] + } + ], + "source": [ + "vf = VensimFile(\"vensim_models/prey-predator.mdl\")\n", + "vf.parse()\n", + "am = vf.get_abstract_model()\n", + "\n", + "# set time\n", + "model = StanVensimModel(\"prey-predator\", am, 0.0, list(range(1, n_t + 1)))\n", + "\n", + "# ode parameter \n", + "model.set_prior(\"alpha\", \"normal\", 0.8, 0.1)\n", + "model.set_prior(\"gamma\", \"normal\", 0.8, 0.1)\n", + "model.set_prior(\"beta\", \"normal\", 0.05, 0.001)\n", + "model.set_prior(\"delta\", \"normal\", 0.05, 0.001)\n", + "\n", + "# sampling distribution parameter\n", + "# model.set_prior(\"sigma\", \"lognormal\", np.log(0.01), 0.1) \n", + "\n", + "print(model.vensim_model_context.variable_names)\n", + "\n", + "model.build_stan_functions()\n", + "model.draws2data(\"\")\n", + "model.data2draws(\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "3f4934f2-b300-498c-9e30-09d2d613701b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:30:01 - cmdstanpy - INFO - CmdStan start processing\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "970776a8be0c41f696fbebf6a37489e7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 1 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:33:38 - cmdstanpy - INFO - CmdStan done processing.\n", + "17:33:38 - cmdstanpy - WARNING - Non-fatal error during sampling:\n", + "Exception: lognormal_lpdf: Random variable is -1.92663, but must be nonnegative! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 47, column 4 to column 45)\n", + "\tException: ode_rk45: Failed to integrate to next output time (11) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 37, column 4 to column 126)\n", + "\tException: ode_rk45: Failed to integrate to next output time (10) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 37, column 4 to column 126)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: ode_rk45: Failed to integrate to next output time (4) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 37, column 4 to column 126)\n", + "\tException: ode_rk45: Failed to integrate to next output time (12) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 37, column 4 to column 126)\n", + "\tException: lognormal_lpdf: Location parameter[9] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: ode_rk45: Failed to integrate to next output time (19) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 37, column 4 to column 126)\n", + "\tException: lognormal_lpdf: Location parameter[9] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[17] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[16] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_lpdf: Location parameter[16] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", + "Consider re-running with show_console=True if the above output is unclear!\n", + "17:33:38 - cmdstanpy - WARNING - Some chains may have failed to converge.\n", + "\tChain 1 had 99 iterations at max treedepth (99.0%)\n", + "\tUse function \"diagnose()\" to see further information.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "sf_path_data2draws = os.path.join(os.getcwd(), \"stan_file\", \"prey-predator_data2draws.stan\")\n", + "sm_data2draws = CmdStanModel(stan_file = sf_path_data2draws)\n", + "\n", + "n_t = obs_stock_df.shape[0] - 1\n", + "data_data2draws = {\n", + " \"n_obs_state\" : 2,\n", + " \"initial_time\" : 0, \n", + " \"times\": [i+1 for i in np.arange(n_t)],\n", + " \"n_t\": n_t,\n", + " \"y\": obs_stock_df.loc[1:, ('Predator', 'Prey')].values.tolist(),\n", + "}\n", + "\n", + "fit = sm_data2draws.sample(data = data_data2draws, iter_sampling = 100, chains = 1, show_console = False, seed = 1234)" + ] + }, + { + "cell_type": "markdown", + "id": "398d77aa-8074-4d7c-a16b-4442b4b1c4f1", + "metadata": {}, + "source": [ + "The following is the summary of posterior draws. It includes loglikelihood for each vector of parmaeter values $(\\alpha, \\beta, \\gamma, \\delta, \\sigma)$. " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "d2b09acd-9180-4285-be7d-86a8db135a4f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MeanMCSEStdDev5%50%95%N_EffN_Eff/sR_hat
name
lp__-28706.50000038.11650065.862000-28807.200000-28708.900000-28596.4000002.985680.1635272.54392
alpha-0.9540920.0000340.000061-0.954163-0.954111-0.9539763.203370.1754502.16126
gamma-0.1764300.0000140.000029-0.176466-0.176439-0.1763684.281430.2344961.51663
beta0.1819820.0002050.0003500.1814010.1819990.1824852.906640.1591982.69473
delta-0.0905450.0000610.000112-0.090727-0.090552-0.0903353.390070.1856762.07456
..............................
log_lik[16]NaNNaNNaNNaNNaNNaNNaNNaNNaN
log_lik[17]NaNNaNNaNNaNNaNNaNNaNNaNNaN
log_lik[18]NaNNaNNaNNaNNaNNaNNaNNaNNaN
log_lik[19]NaNNaNNaNNaNNaNNaNNaNNaNNaN
log_lik[20]NaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", + "

110 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Mean MCSE StdDev 5% 50% \\\n", + "name \n", + "lp__ -28706.500000 38.116500 65.862000 -28807.200000 -28708.900000 \n", + "alpha -0.954092 0.000034 0.000061 -0.954163 -0.954111 \n", + "gamma -0.176430 0.000014 0.000029 -0.176466 -0.176439 \n", + "beta 0.181982 0.000205 0.000350 0.181401 0.181999 \n", + "delta -0.090545 0.000061 0.000112 -0.090727 -0.090552 \n", + "... ... ... ... ... ... \n", + "log_lik[16] NaN NaN NaN NaN NaN \n", + "log_lik[17] NaN NaN NaN NaN NaN \n", + "log_lik[18] NaN NaN NaN NaN NaN \n", + "log_lik[19] NaN NaN NaN NaN NaN \n", + "log_lik[20] NaN NaN NaN NaN NaN \n", + "\n", + " 95% N_Eff N_Eff/s R_hat \n", + "name \n", + "lp__ -28596.400000 2.98568 0.163527 2.54392 \n", + "alpha -0.953976 3.20337 0.175450 2.16126 \n", + "gamma -0.176368 4.28143 0.234496 1.51663 \n", + "beta 0.182485 2.90664 0.159198 2.69473 \n", + "delta -0.090335 3.39007 0.185676 2.07456 \n", + "... ... ... ... ... \n", + "log_lik[16] NaN NaN NaN NaN \n", + "log_lik[17] NaN NaN NaN NaN \n", + "log_lik[18] NaN NaN NaN NaN \n", + "log_lik[19] NaN NaN NaN NaN \n", + "log_lik[20] NaN NaN NaN NaN \n", + "\n", + "[110 rows x 9 columns]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "e756c478-5d27-4f52-8bec-a03eeed82ef6", + "metadata": {}, + "outputs": [], + "source": [ + "idata = az.from_cmdstanpy(\n", + " posterior=fit, \n", + " posterior_predictive=[\"y_hat\"], \n", + " log_likelihood= [\"log_lik\"],\n", + " observed_data = {\"y\": obs_stock_df.loc[:, (\"Predator\", \"Prey\")]}\n", + "# dtypes={\"y_rep\": int} if Poisson family\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "c605e71f-7fa9-4a10-93b3-76ba792dec58", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hyunjimoon/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/stats/stats.py:802: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", + " warnings.warn(\n" + ] + }, + { + "ename": "KeyError", + "evalue": "'var names: \"[\\'y\\'] are not present\" in dataset'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:69\u001b[0m, in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 69\u001b[0m var_names \u001b[38;5;241m=\u001b[39m \u001b[43m_subset_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvar_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilter_items\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilter_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwarn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:146\u001b[0m, in \u001b[0;36m_subset_list\u001b[0;34m(subset, whole_list, filter_items, warn)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(existing_items):\n\u001b[0;32m--> 146\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39marray(subset)[\u001b[38;5;241m~\u001b[39mexisting_items]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m are not present\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m subset\n", + "\u001b[0;31mKeyError\u001b[0m: \"['y'] are not present\"", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [51]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m az\u001b[38;5;241m.\u001b[39mloo(idata)\n\u001b[0;32m----> 2\u001b[0m \u001b[43maz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_ppc\u001b[49m\u001b[43m(\u001b[49m\u001b[43midata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.03\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfigsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/plots/ppcplot.py:260\u001b[0m, in \u001b[0;36mplot_ppc\u001b[0;34m(data, kind, alpha, mean, observed, color, colors, grid, figsize, textsize, data_pairs, var_names, filter_vars, coords, flatten, flatten_pp, num_pp_samples, random_seed, jitter, animated, animation_kwargs, legend, labeller, ax, backend, backend_kwargs, group, show)\u001b[0m\n\u001b[1;32m 258\u001b[0m var_names \u001b[38;5;241m=\u001b[39m _var_names(var_names, observed_data, filter_vars)\n\u001b[1;32m 259\u001b[0m pp_var_names \u001b[38;5;241m=\u001b[39m [data_pairs\u001b[38;5;241m.\u001b[39mget(var, var) \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m var_names]\n\u001b[0;32m--> 260\u001b[0m pp_var_names \u001b[38;5;241m=\u001b[39m \u001b[43m_var_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpp_var_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredictive_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilter_vars\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flatten_pp \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 263\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flatten \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:72\u001b[0m, in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 71\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin((\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvar names:\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00merr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124min dataset\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m var_names\n", + "\u001b[0;31mKeyError\u001b[0m: 'var names: \"[\\'y\\'] are not present\" in dataset'" + ] + } + ], + "source": [ + "az.loo(idata)\n", + "az.plot_ppc(idata, alpha=0.03, figsize=(12, 6))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90a2b21e-180b-496b-9767-2f4751510bbe", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "6f549da1-2157-4520-a58d-49706e9590ff", + "metadata": {}, + "source": [ + "## Appendix A. Measurement error, when stock initial is estimated parameter\n", + "\n", + "Lastly, initial population is added to the list of estimated parameter. Instant reason for this modeling decision is measurement noise; as population cannot be directly measured pelts (our data) are used as its noisy proxy). However, more fundamental reason is aletoric uncertainty, namely unmodeled uncertainty. There are factors that impact predator and prey population size other than the current population size. There are variable environmental effects, such as weather, which will vary from season to season and year to year and affect population sizes. Infectious diseases occasionally spread through a population, reducing its size (Hewitt 1921). There are also more long-term environmental factors such as carrying capacity (Carrying capacity is roughly the maximum population that an environment can sustain. It is often modeled in the system dynamics as an asymptote on population size.). However, our forward model is restricted to two differential equations involving two stock variables (`simulated outcome`) and four `estimated coefficient parameter`s (four flow variables can be expressed with the other two). Hence, after restricting the architecture, we are reaching out to the best version of ourselve by declaring the initial population as estimated parameter. In Stat/Machine learning terms, Stan optimization algorithm returns `estimated parameter` value that maximize log posterior among the feasible (restricted basis function) space defined by the modeler in the form of stock-parameter relationship.\n", + "\n", + "Continuing on `simulated outcome` and `observed outcome` coflow, `observed outcome` can replace `simulated outcome` also known as state-resetting but we maintain the error term to compensate for measurement error and unexplained variation in the data (Challenge: check whether this is equivalent to the original text \"Solutions to the Lotka-Volterra equations replace the linear predictor xnβ, but we maintain the error term to compensate for measurement error and unexplained variation in the data.\"). In the case of population dynamics, the data $y_n$ consists of measurements of the prey $y_{n, 1}$ and predator $y_{n, 2}$ populations at times $t_n$[18](#fn18).\n", + "\n", + "The true population sizes at time $t = 0$ are unknown---we only have measurements $y^{\\rm init}_1$ and $y^{\\rm init}_2$ of them. The true initial population sizes at time $t = 0$ will be represented by a parameter $z^{\\mathrm init}$, so that\n", + "\n", + "$$\n", + "\\begin{array}{rcl}\n", + "z^{\\mathrm init}_1 & = & u(t = 0)\n", + "\\\\[4pt]\n", + "z^{\\mathrm init}_2 & = & v(t = 0).\n", + "\\end{array}\n", + "$$\n", + "\n", + "Next, let $z_1, \\ldots, z_N$ be the solutions to the Lotka-Volterra differential equations at times $t_1, \\ldots, t_N$ given initial conditions $z(t = 0) = z^{\\mathrm init}$ and parameters $\\theta = (\\alpha, \\beta, \\gamma, \\delta)$. Each $z_n$ is a pair of prey and predator population sizes at the specified times,\n", + "\n", + "$$\n", + "\\begin{array}{rcl}\n", + "z_{n, 1} & = & u(t_n)\n", + "\\\\[4pt]\n", + "z_{n, 2} & = & v(t_n).\n", + "\\end{array}\n", + "$$\n", + "\n", + "The $z_n$ are random variables, but they are deterministic functions of the random variables for the initial state $z^{\\mathrm init}$ and system parameters $\\alpha, \\beta, \\gamma, \\delta$.\n", + "\n", + "The observed data is in the form of measurements $y^{\\rm init}$ of the initial population of prey and predators, and subsequent measurements $y_n$ of the populations at times $t_n$, where $y^{\\mathrm init}$ and the $y_n$ consist of a pair of measured population sizes, for the prey and predator species.\n", + "\n", + "In summary, the measurements, $y^{\\rm init}$ and $y_n$, are drawn indepently from a normal distribution centered at the underlying population sizes, $z^{\\rm init}$ and $z_n$, with noise scales $\\sigma$.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "38fd5398-b605-44b2-ad09-84ec57a257a1", + "metadata": {}, + "source": [ + "## Appendix B. Stan code" + ] + }, + { + "cell_type": "markdown", + "id": "3b48c946-921f-4ee1-8eb4-0bfe06c9f37a", + "metadata": {}, + "source": [ + "The following is the auto-generated stanfile with modularized funtion block in the cell below.\n", + "```\n", + "functions{\n", + "#include prey-predator_functions.stan\n", + "}\n", + "data{\n", + " int n_obs_state;\n", + " real initial_time;\n", + " int n_t; \n", + " array[n_t] real times;\n", + " array[n_t] vector[n_obs_state] y; \n", + "}\n", + "\n", + "\n", + "parameters{\n", + " real alpha;\n", + " real gamma;\n", + " real beta;\n", + " real delta;\n", + " real sigma;\n", + "}\n", + "\n", + "transformed parameters {\n", + " # Initial ODE values\n", + " real prey_initial = 30;\n", + " real predator_initial = 4;\n", + "\n", + " vector[2] initial_outcome; # Initial ODE state vector\n", + " initial_outcome[1] = prey_initial;\n", + " initial_outcome[2] = predator_initial;\n", + "\n", + " vector[2] integrated_result[n_t] = ode_rk45(vensim_func, initial_outcome, initial_time, times, alpha, beta, gamma, delta, sigma);\n", + "}\n", + "\n", + "model{\n", + " alpha ~ normal(0.8, 0.1);\n", + " gamma ~ normal(0.8, 0.1);\n", + " beta ~ normal(0.05, 0.001);\n", + " delta ~ normal(0.05, 0.001);\n", + " sigma ~ lognormal(-4.605170185988091, 1);\n", + " for (s in 1: n_obs_state){\n", + " y[:, s] ~ lognormal(log(integrated_result[:, s]), sigma);\n", + " }\n", + "}\n", + "\n", + "generated quantities{\n", + " array[n_t] vector[n_obs_state] y_ppc;\n", + " vector[n_t] log_lik;\n", + " for (s in 1: n_obs_state){\n", + " y_ppc[:, s] = lognormal_rng(log(integrated_result[:, s]), sigma);\n", + " } \n", + " for (s in 1: n_obs_state){\n", + " //elementwise log likliehood\n", + " log_lik[s] = lognormal_lpdf(y[s]|log(integrated_result[:, s]), sigma);\n", + " }\n", + "}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "8e742a91-7265-4876-83af-81dea8d8f14d", + "metadata": {}, + "source": [ + "Function block:\n", + "```\n", + "# Begin ODE declaration\n", + "vector vensim_func(real time, vector outcome, real alpha, real beta, real gamma, real delta){\n", + " vector[2] dydt; # Return vector of the ODE function\n", + "\n", + " # State variables\n", + " real prey = outcome[1];\n", + " real predator = outcome[2];\n", + "\n", + " real prey_birth_rate = alpha * prey;\n", + " real predator_birth_rate = delta * prey * predator;\n", + " real prey_death_rate = beta * predator * prey;\n", + " real prey_dydt = prey_birth_rate - prey_death_rate;\n", + " real predator_death_rate = gamma * predator;\n", + " real predator_dydt = predator_birth_rate - predator_death_rate;\n", + "\n", + " dydt[1] = prey_dydt;\n", + " dydt[2] = predator_dydt;\n", + "\n", + " return dydt;\n", + "}\n", + "\n", + "\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "local-venv", + "language": "python", + "name": "local-venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From fd5e4c1bc91c2935e4c4abc120c159d6c3439719 Mon Sep 17 00:00:00 2001 From: "Angie.H Moon" <30194633+hyunjimoon@users.noreply.github.com> Date: Wed, 31 Aug 2022 17:59:34 -0400 Subject: [PATCH 36/45] Six comments each for interface upgrade --- .../stan_file/prey-predator_data2draws.stan | 67 +++++++++++++++++++ .../stan_file/prey-predator_draws2data.stan | 45 +++++++++++++ 2 files changed, 112 insertions(+) create mode 100644 test_scripts/stan_file/prey-predator_data2draws.stan create mode 100644 test_scripts/stan_file/prey-predator_draws2data.stan diff --git a/test_scripts/stan_file/prey-predator_data2draws.stan b/test_scripts/stan_file/prey-predator_data2draws.stan new file mode 100644 index 00000000..84000cb8 --- /dev/null +++ b/test_scripts/stan_file/prey-predator_data2draws.stan @@ -0,0 +1,67 @@ +functions{ +#include prey-predator_functions.stan + +} +data{ + int n_obs_state; + //1. add data, 2 to n_obs_state, we need n_t (unlike draws2data confusingly..) + int n_t; + vector[2] y[n_t]; //measured stock +} + +transformed data{ + real initial_time = 0.0; + + array[n_t] real times = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20}; +} + +parameters{ + real alpha; + real gamma; + real beta; + real delta; + + // 2. add sigma + real sigma; +} + +transformed parameters { + # Initial ODE values + real prey_initial = 30; + real predator_initial = 4; + + // 3. change 2 to n_obs_state + vector[2] initial_outcome; # Initial ODE state vector + initial_outcome[1] = prey_initial; + initial_outcome[2] = predator_initial; + + vector[2] integrated_result[n_t] = ode_rk45(vensim_func, initial_outcome, initial_time, times, gamma, beta, delta, alpha); +} + +model{ + alpha ~ normal(0.8, 0.1); + gamma ~ normal(0.8, 0.1); + beta ~ normal(0.05, 0.001); + delta ~ normal(0.05, 0.001); + + // 4. added sampling dist error + sigma ~ lognormal(-4.605170185988091, 1); + + // 5. likelihood statement from family + for (s in 1: n_obs_state){ + y[:, s] ~ lognormal(log(integrated_result[:, s]), sigma); + } +} +//6. all new +generated quantities{ + array[n_t] vector[n_obs_state] y_hat; + vector[n_t] log_lik; + + for (s in 1: n_obs_state){ + y_hat[:, s] = lognormal_rng(log(integrated_result[:, s]), sigma); + } + for (s in 1: n_obs_state){ + //elementwise log likliehood + log_lik[s] = lognormal_lpdf(y[s]|log(integrated_result[:, s]), sigma); + } +} \ No newline at end of file diff --git a/test_scripts/stan_file/prey-predator_draws2data.stan b/test_scripts/stan_file/prey-predator_draws2data.stan new file mode 100644 index 00000000..9f9208af --- /dev/null +++ b/test_scripts/stan_file/prey-predator_draws2data.stan @@ -0,0 +1,45 @@ +functions{ +#include prey-predator_functions.stan +} + +data{ + int n_obs_state; +} + +transformed data{ + real initial_time = 0.0; + int n_t = 20; + array[n_t] real times = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20}; +} + +generated quantities{ + // 1.add real infront of all params + change from ~ to = , dist to dist_rng() + real alpha_tilde = normal_rng(0.55, 0.1); + real gamma_tilde = normal_rng(0.8, 0.1); + real beta_tilde = normal_rng(0.028, 0.01); + real delta_tilde = normal_rng(0.024, 0.01); + + // 2. manually moved from tp to gp 2. add _tilde to ode params in param ode_rk45() + // Initial ODE values + real prey_initial = 30; + real predator_initial = 4; + + // 3. 2 to n_obs_state + vector[2] initial_outcome; # Initial ODE state vector + initial_outcome[1] = prey_initial; + initial_outcome[2] = predator_initial; + + // 4. add tilde + vector[2] integrated_result_tilde[n_t] = ode_rk45(vensim_func, initial_outcome, initial_time, times, gamma_tilde, beta_tilde, delta_tilde, alpha_tilde); + + //5. add sampling; sigma, y_tilde + vector[2] y_tilde[n_t]; //measured stock + real sigma_tilde = lognormal_rng(log(0.01), 0.01); + + //6. prior predictive + + for (s in 1: n_obs_state){ + y_tilde[:, s] = lognormal_rng(log(integrated_result_tilde[:, s]), sigma_tilde); + } + +} \ No newline at end of file From 2ff05deff10864a274f8755e82492b94099d4114 Mon Sep 17 00:00:00 2001 From: amoon Date: Wed, 31 Aug 2022 23:00:49 -0400 Subject: [PATCH 37/45] implement draws2data --- pysd/builders/stan/stan_block_builder.py | 29 ++++++++++++++---- pysd/builders/stan/stan_model.py | 39 +++++++++++++++++++----- 2 files changed, 54 insertions(+), 14 deletions(-) diff --git a/pysd/builders/stan/stan_block_builder.py b/pysd/builders/stan/stan_block_builder.py index 8f8f816e..dee2afdb 100644 --- a/pysd/builders/stan/stan_block_builder.py +++ b/pysd/builders/stan/stan_block_builder.py @@ -60,11 +60,11 @@ def build_block( self.code += "\n" self.code += f"vector[{len(outcome_variable_names)}] initial_outcome; # Initial ODE state vector\n" for index, name in enumerate(outcome_variable_names, 1): - self.code += f"initial_outcome[{index}] = {name};\n" + self.code += f"initial_outcome[{index}] = {name}_initial;\n" self.code += "\n" - self.code += f"vector[{len(outcome_variable_names)}] integrated_result[T] = ode_rk45({function_name}, initial_outcome, initial_time, times, {', '.join(argument_variables)});\n" + self.code += f"vector[{len(outcome_variable_names)}] integrated_result[n_t] = ode_rk45({function_name}, initial_outcome, initial_time, times, {', '.join(argument_variables)});\n" self.code.indent_level -= 1 self.code += "}\n" @@ -96,6 +96,7 @@ def build_block(self): code = IndentedString() code += "data{\n" code.indent_level += 1 + code += "int n_obs_state;\n" code.indent_level -= 1 code += "}\n" return code.string @@ -107,13 +108,13 @@ def __init__(self, initial_time, integration_times: Iterable[Number]): self.integration_times = integration_times def build_block(self) -> str: - T = len(self.integration_times) + n_t = len(self.integration_times) code = IndentedString() code += "transformed data{\n" code.indent_level += 1 code += f"real initial_time = {self.initial_time};\n" - code += f"int T = {T};\n" - code += f"array[T] real times = {{{', '.join([str(x) for x in self.integration_times])}}}\n" + code += f"int n_t = {n_t};\n" + code += f"array[n_t] real times = {{{', '.join([str(x) for x in self.integration_times])}}};\n" code.indent_level -= 1 code += "}\n" return code.string @@ -134,10 +135,26 @@ def build_block(self): code += "}\n" return str(code) + +class StanGeneratedQuantitiesBuilder: + def __init__(self, sampling_statements: Iterable["SamplingStatement"]): + self.sampling_statements = sampling_statements + + def build_block(self): + code = IndentedString() + code += "generated quantities{\n" + code.indent_level += 1 + for statement in self.sampling_statements: + code += f"{statement.lhs_name}_tilde ~ {statement.distribution_type}({', '.join([str(arg) for arg in statement.distribution_args])});\n" + + code.indent_level -= 1 + code += "}\n" + return str(code) + class StanFunctionBuilder: def __init__( - self, abstract_model: AbstractModel, function_name: str = "vensim_ode" + self, abstract_model: AbstractModel, function_name: str = "vensim_func" ): self.abstract_model = abstract_model diff --git a/pysd/builders/stan/stan_model.py b/pysd/builders/stan/stan_model.py index e547a139..26917161 100644 --- a/pysd/builders/stan/stan_model.py +++ b/pysd/builders/stan/stan_model.py @@ -101,27 +101,30 @@ def build_stan_functions(self): ------- """ - if glob.glob(os.path.join(os.getcwd(), f"{self.model_name}_functions.stan")): + if glob.glob(os.path.join(os.getcwd(), "stan_file", f"{self.model_name}_functions.stan")): if input(f"{self.model_name}_functions.stan already exists in the current working directory. Overwrite? (Y/N):").lower() != "y": raise Exception("Code generation aborted by user") - with open(os.path.join(os.getcwd(), f"{self.model_name}_functions.stan"), "w") as f: + with open(os.path.join(os.getcwd(), "stan_file", f"{self.model_name}_functions.stan"), "w") as f: self.function_builder = StanFunctionBuilder(self.abstract_model) f.write(self.function_builder.build_functions(self.stan_model_context.exposed_parameters, self.vensim_model_context.stock_variable_names)) def data2draws(self, data_file_path: str): - with open(os.path.join(os.getcwd(), f"{self.model_name}_data2draws.stan"), "w") as f: + with open(os.path.join(os.getcwd(), "stan_file", f"{self.model_name}_data2draws.stan"), "w") as f: # Include the function + f.write("functions{") + f.write("\n") f.write(f"#include {self.model_name}_functions.stan\n\n") - + f.write("}") + f.write("\n") f.write(StanDataBuilder().build_block()) f.write("\n") - f.write(StanTransformedDataBuilder(self.initial_time, self.integration_times).build_block()) f.write("\n") - + f.write(StanParametersBuilder(self.stan_model_context.sample_statements).build_block()) + f.write("\n") transformed_params_builder = StanTransformedParametersBuilder(self.abstract_model) f.write(transformed_params_builder.build_block(self.stan_model_context.exposed_parameters, self.vensim_model_context.stock_variable_names, @@ -129,10 +132,30 @@ def data2draws(self, data_file_path: str): self.function_builder.ode_function_name)) f.write("\n") - f.write(StanParametersBuilder(self.stan_model_context.sample_statements).build_block()) + f.write(StanModelBuilder(self.stan_model_context.sample_statements).build_block()) f.write("\n") - f.write(StanModelBuilder(self.stan_model_context.sample_statements).build_block()) + + def draws2data(self, data_file_path: str): + with open(os.path.join(os.getcwd(), "stan_file", f"{self.model_name}_draws2data.stan"), "w") as f: + # Include the function + f.write("functions{") + f.write("\n") + f.write(f"#include {self.model_name}_functions.stan\n") + f.write("}") + f.write("\n") + f.write(StanDataBuilder().build_block()) + f.write("\n") + f.write(StanTransformedDataBuilder(self.initial_time, self.integration_times).build_block()) + f.write("\n") + + + transformed_params_builder = StanTransformedParametersBuilder(self.abstract_model) + f.write(transformed_params_builder.build_block(self.stan_model_context.exposed_parameters, + self.vensim_model_context.stock_variable_names, + self.function_builder.get_generated_lookups_dict(), + self.function_builder.ode_function_name)) f.write("\n") + f.write(StanGeneratedQuantitiesBuilder(self.stan_model_context.sample_statements).build_block()) From 5f1b79ae245dc2943a257789fd145f1e01cffaee Mon Sep 17 00:00:00 2001 From: Dashadower Date: Thu, 1 Sep 2022 13:15:39 +0900 Subject: [PATCH 38/45] Add constraints to parameters and better input data integration --- pysd/builders/stan/stan_block_builder.py | 37 ++++++++++++++++++++++-- pysd/builders/stan/stan_model.py | 24 ++++++++++----- 2 files changed, 52 insertions(+), 9 deletions(-) diff --git a/pysd/builders/stan/stan_block_builder.py b/pysd/builders/stan/stan_block_builder.py index 7a7ae5d1..55985cb8 100644 --- a/pysd/builders/stan/stan_block_builder.py +++ b/pysd/builders/stan/stan_block_builder.py @@ -86,12 +86,14 @@ class StanParametersBuilder: def __init__(self, sampling_statements: Iterable["SamplingStatement"]): self.sampling_statements = sampling_statements - def build_block(self): + def build_block(self, data_variable_names: Tuple[str]): code = IndentedString() code += "parameters{\n" code.indent_level += 1 # Enter parameters block for statement in self.sampling_statements: + if statement.lhs_name in data_variable_names: + continue code += f"real {statement.lhs_name};\n" code.indent_level -= 1 # Exit parameters block @@ -103,11 +105,42 @@ class StanDataBuilder: def __init__(self): pass - def build_block(self): + def get_dims(self, obj): + try: + iter(obj) + except: + return None + else: + dim = len(obj) + inner_dim = self.get_dims(obj[0]) + if inner_dim: + return [dim] + inner_dim + else: + return [dim] + + def build_block(self, data_dict: Dict): code = IndentedString() code += "data{\n" code.indent_level += 1 + code += "int n_obs_state;\n" + + for key, val in data_dict.items(): + if isinstance(val, int): + code += f"int {key};\n" + elif isinstance(val, float): + code += f"real {key};\n" + else: + # Multidimensional data + dims = self.get_dims(val) + if not dims: + raise Exception(f"Can't automatically process data variable {key}.") + elif len(dims) == 1: + code += f"vector[{dims[0]}] {key};\n" + else: + raise Exception("Multidimensional data not implemented") + + code.indent_level -= 1 code += "}\n" return code.string diff --git a/pysd/builders/stan/stan_model.py b/pysd/builders/stan/stan_model.py index 780829a8..50c71f47 100644 --- a/pysd/builders/stan/stan_model.py +++ b/pysd/builders/stan/stan_model.py @@ -10,12 +10,16 @@ class SamplingStatement: distribution_type: str distribution_return_type: Type distribution_args: Tuple[str] + lower: float + upper: float init_state: bool - def __init__(self, lhs_name, distribution_type, *distribution_args, init_state=False): + def __init__(self, lhs_name, distribution_type, *distribution_args, lower=float("-inf"), upper=float("inf"), init_state=False): self.lhs_name = lhs_name self.distribution_type = distribution_type self.distribution_args = distribution_args + self.lower = lower + self.upper = upper self.init_state = init_state def __post_init__(self): @@ -92,12 +96,12 @@ def print_info(self): print("*" * 10) print("- Stan model information:") - def set_prior(self, variable_name: str, distribution_type: str, *args, init_state=False): + def set_prior(self, variable_name: str, distribution_type: str, *args, lower=float("-inf"), upper=float("inf"), init_state=False): if init_state: # This means the initial value of the ODE state variable. if variable_name not in self.vensim_model_context.stock_variable_names: raise Exception("init_state may be set to True only for stock variables.") - self.stan_model_context.sample_statements.append(SamplingStatement(f"{variable_name}_init", distribution_type, *args, init_state=init_state)) + self.stan_model_context.sample_statements.append(SamplingStatement(f"{variable_name}_init", distribution_type, *args, lower=lower, upper=upper, init_state=init_state)) else: for arg in args: if isinstance(arg, str): @@ -110,7 +114,7 @@ def set_prior(self, variable_name: str, distribution_type: str, *args, init_stat if variable_name in self.vensim_model_context.variable_names: self.stan_model_context.exposed_parameters.add(variable_name) - self.stan_model_context.sample_statements.append(SamplingStatement(variable_name, distribution_type, *args, init_state=init_state)) + self.stan_model_context.sample_statements.append(SamplingStatement(variable_name, distribution_type, *args, lower=lower, upper=upper, init_state=init_state)) def build_stan_functions(self): @@ -130,21 +134,24 @@ def build_stan_functions(self): with open(os.path.join(os.getcwd(), f"{self.model_name}_functions.stan"), "w") as f: self.function_builder = StanFunctionBuilder(self.abstract_model) f.write(self.function_builder.build_functions(self.stan_model_context.exposed_parameters, self.vensim_model_context.stock_variable_names)) - print(self.function_builder.get_generated_lookups_dict()) - def data2draws(self, data_file_path: str): + def data2draws(self, data_dir: Dict): stan_model_path= os.path.join(os.getcwd(), f"{self.model_name}_data2draws.stan") with open(stan_model_path, "w") as f: # Include the function f.write("functions{\n") f.write(f" #include {self.model_name}_functions.stan\n") f.write("}\n\n") + f.write(StanDataBuilder().build_block()) f.write("\n") + f.write(StanTransformedDataBuilder(self.initial_time, self.integration_times).build_block()) f.write("\n") - f.write(StanParametersBuilder(self.stan_model_context.sample_statements).build_block()) + + f.write(StanParametersBuilder(self.stan_model_context.sample_statements).build_block(tuple(data_dir.keys()))) f.write("\n") + transformed_params_builder = StanTransformedParametersBuilder(self.abstract_model) # Find sampling statements for init stock_initials = {} @@ -158,8 +165,11 @@ def data2draws(self, data_file_path: str): self.function_builder.get_generated_lookups_dict(), self.function_builder.ode_function_name, stock_initials)) + f.write("\n") + f.write(StanModelBuilder(self.stan_model_context.sample_statements).build_block()) f.write("\n") + stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_path) return stan_model From d57897cbf68c2fc83eb0adc178dafa90447e9c37 Mon Sep 17 00:00:00 2001 From: Dashadower Date: Thu, 1 Sep 2022 13:26:26 +0900 Subject: [PATCH 39/45] Change stan model directory --- pysd/builders/stan/stan_block_builder.py | 10 +++++++++- pysd/builders/stan/stan_model.py | 21 ++++++++++++++------- test_scripts/stan_vensim_integration.py | 4 ++-- 3 files changed, 25 insertions(+), 10 deletions(-) diff --git a/pysd/builders/stan/stan_block_builder.py b/pysd/builders/stan/stan_block_builder.py index 55985cb8..8e90c8b0 100644 --- a/pysd/builders/stan/stan_block_builder.py +++ b/pysd/builders/stan/stan_block_builder.py @@ -94,7 +94,15 @@ def build_block(self, data_variable_names: Tuple[str]): for statement in self.sampling_statements: if statement.lhs_name in data_variable_names: continue - code += f"real {statement.lhs_name};\n" + + if statement.lower > float("-inf") and statement.upper < float("inf"): + code += f"real {statement.lhs_name};\n" + elif statement.lower > float("-inf"): + code += f"real {statement.lhs_name};\n" + elif statement.upper < float("inf"): + code += f"real {statement.lhs_name};\n" + else: + code += f"real {statement.lhs_name};\n" code.indent_level -= 1 # Exit parameters block code += "}\n" diff --git a/pysd/builders/stan/stan_model.py b/pysd/builders/stan/stan_model.py index 50c71f47..c84380d2 100644 --- a/pysd/builders/stan/stan_model.py +++ b/pysd/builders/stan/stan_model.py @@ -1,3 +1,4 @@ +import os from typing import List, Set, Type, Tuple import ast, glob, re from .stan_block_builder import * @@ -85,6 +86,10 @@ def __init__(self, model_name: str, abstract_model, initial_time: float, integra if initial_time in integration_times: raise Exception("initial_time shouldn't be present in integration_times") + self.stan_model_dir = os.path.join(os.getcwd(), "stan_files") + if not os.path.exists(self.stan_model_dir): + os.mkdir(self.stan_model_dir) + self.init_variable_regex = re.compile(".+?(?=_init$)") # This regex is to match all preceding characters that come before '_init' at the end of the string. # So something like stock_var_init_init would match into stock_var_init. @@ -127,16 +132,16 @@ def build_stan_functions(self): ------- """ - if glob.glob(os.path.join(os.getcwd(), "stan_file", f"{self.model_name}_functions.stan")): + if glob.glob(os.path.join(self.stan_model_dir, f"{self.model_name}_functions.stan")): if input(f"{self.model_name}_functions.stan already exists in the current working directory. Overwrite? (Y/N):").lower() != "y": raise Exception("Code generation aborted by user") - with open(os.path.join(os.getcwd(), f"{self.model_name}_functions.stan"), "w") as f: + with open(os.path.join(self.stan_model_dir, f"{self.model_name}_functions.stan"), "w") as f: self.function_builder = StanFunctionBuilder(self.abstract_model) f.write(self.function_builder.build_functions(self.stan_model_context.exposed_parameters, self.vensim_model_context.stock_variable_names)) def data2draws(self, data_dir: Dict): - stan_model_path= os.path.join(os.getcwd(), f"{self.model_name}_data2draws.stan") + stan_model_path= os.path.join(self.stan_model_dir, f"{self.model_name}_data2draws.stan") with open(stan_model_path, "w") as f: # Include the function f.write("functions{\n") @@ -173,8 +178,9 @@ def data2draws(self, data_dir: Dict): stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_path) return stan_model - def draws2data(self, data_file_path: str): - with open(os.path.join(os.getcwd(), "stan_file", f"{self.model_name}_draws2data.stan"), "w") as f: + def draws2data(self): + stan_model_path = os.path.join(self.stan_model_dir, f"{self.model_name}_draws2data.stan") + with open(stan_model_path, "w") as f: # Include the function f.write("functions{") f.write("\n") @@ -197,8 +203,9 @@ def draws2data(self, data_file_path: str): f.write(StanGeneratedQuantitiesBuilder(self.stan_model_context.sample_statements).build_block()) - stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_path) - return stan_model + stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_path) + + return stan_model diff --git a/test_scripts/stan_vensim_integration.py b/test_scripts/stan_vensim_integration.py index e4008a99..44b0ad19 100644 --- a/test_scripts/stan_vensim_integration.py +++ b/test_scripts/stan_vensim_integration.py @@ -11,13 +11,13 @@ model.set_prior("inventory_adjustment_time", "normal", 0, 1) model.set_prior("minimum_order_processing_time", "normal", 0, 1) -model.set_prior("alpha", "normal", 0, 1) +model.set_prior("alpha", "normal", 0, 1, lower=0.0) model.set_prior("inventory", "normal", 0, 1, init_state=True) print(model.vensim_model_context.variable_names) model.build_stan_functions() -cmdstan_model = model.data2draws("") +cmdstan_model = model.data2draws({}) result = cmdstan_model.sample() result.summary() \ No newline at end of file From c0f88e1a1828f750c138849f945eab49c0a18f4b Mon Sep 17 00:00:00 2001 From: Dashadower Date: Thu, 1 Sep 2022 15:09:57 +0900 Subject: [PATCH 40/45] Update data block --- pysd/builders/stan/stan_block_builder.py | 2 -- pysd/builders/stan/stan_model.py | 6 +++--- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/pysd/builders/stan/stan_block_builder.py b/pysd/builders/stan/stan_block_builder.py index 8e90c8b0..89632bc4 100644 --- a/pysd/builders/stan/stan_block_builder.py +++ b/pysd/builders/stan/stan_block_builder.py @@ -131,8 +131,6 @@ def build_block(self, data_dict: Dict): code += "data{\n" code.indent_level += 1 - code += "int n_obs_state;\n" - for key, val in data_dict.items(): if isinstance(val, int): code += f"int {key};\n" diff --git a/pysd/builders/stan/stan_model.py b/pysd/builders/stan/stan_model.py index c84380d2..f2785d02 100644 --- a/pysd/builders/stan/stan_model.py +++ b/pysd/builders/stan/stan_model.py @@ -140,7 +140,7 @@ def build_stan_functions(self): self.function_builder = StanFunctionBuilder(self.abstract_model) f.write(self.function_builder.build_functions(self.stan_model_context.exposed_parameters, self.vensim_model_context.stock_variable_names)) - def data2draws(self, data_dir: Dict): + def data2draws(self, data_dict: Dict): stan_model_path= os.path.join(self.stan_model_dir, f"{self.model_name}_data2draws.stan") with open(stan_model_path, "w") as f: # Include the function @@ -148,13 +148,13 @@ def data2draws(self, data_dir: Dict): f.write(f" #include {self.model_name}_functions.stan\n") f.write("}\n\n") - f.write(StanDataBuilder().build_block()) + f.write(StanDataBuilder().build_block(data_dict)) f.write("\n") f.write(StanTransformedDataBuilder(self.initial_time, self.integration_times).build_block()) f.write("\n") - f.write(StanParametersBuilder(self.stan_model_context.sample_statements).build_block(tuple(data_dir.keys()))) + f.write(StanParametersBuilder(self.stan_model_context.sample_statements).build_block(tuple(data_dict.keys()))) f.write("\n") transformed_params_builder = StanTransformedParametersBuilder(self.abstract_model) From 53febea98a58cfdf7354944e5a17a7f469ae038d Mon Sep 17 00:00:00 2001 From: Dashadower Date: Tue, 6 Sep 2022 23:16:06 +0900 Subject: [PATCH 41/45] Codegen cleanup and draws2data implementation --- pysd/builders/stan/stan_block_builder.py | 120 +++++++++++++++++------ pysd/builders/stan/stan_model.py | 81 +++++++-------- pysd/builders/stan/utilities.py | 42 ++++++++ test_scripts/stan_vensim_integration.py | 4 +- 4 files changed, 168 insertions(+), 79 deletions(-) diff --git a/pysd/builders/stan/stan_block_builder.py b/pysd/builders/stan/stan_block_builder.py index 89632bc4..2f55fa47 100644 --- a/pysd/builders/stan/stan_block_builder.py +++ b/pysd/builders/stan/stan_block_builder.py @@ -12,7 +12,7 @@ ) from typing import TYPE_CHECKING if TYPE_CHECKING: - from .stan_model import SamplingStatement + from .stan_model import StanModelContext, VensimModelContext class StanTransformedParametersBuilder: @@ -57,7 +57,7 @@ def build_block( assert isinstance( component.ast, IntegStructure ), "Output variable component must be an INTEG." - self.code += f"real {outcome_variable_name}_init = {InitialValueCodegenWalker(lookup_function_dict, variable_ast_dict).walk(component.ast)};\n" + self.code += f"real {outcome_variable_name}__init = {InitialValueCodegenWalker(lookup_function_dict, variable_ast_dict).walk(component.ast)};\n" break self.code += "\n" @@ -67,11 +67,15 @@ def build_block( if name in stock_initial_values: self.code += f"initial_outcome[{index}] = {stock_initial_values[name]}; // Defined within stan\n" else: - self.code += f"initial_outcome[{index}] = {name}_init;\n" + self.code += f"initial_outcome[{index}] = {name}__init;\n" self.code += "\n" - self.code += f"vector[{len(outcome_variable_names)}] integrated_result[n_t] = ode_rk45({function_name}, initial_outcome, initial_time, times, {', '.join(argument_variables)});\n" + ode_solver_code = f"vector[{len(outcome_variable_names)}] integrated_result[n_t] = ode_rk45({function_name}, initial_outcome, initial_time, times" + if len(argument_variables) > 0: + ode_solver_code += ", " + f"{', '.join(argument_variables)}" + + self.code += ode_solver_code + ");\n" for index, name in enumerate(outcome_variable_names, 1): self.code += f"array[n_t] real {name} = integrated_result[:, {index}];\n" @@ -83,26 +87,32 @@ def build_block( class StanParametersBuilder: - def __init__(self, sampling_statements: Iterable["SamplingStatement"]): - self.sampling_statements = sampling_statements + def __init__(self, stan_model_context: "StanModelContext"): + self.stan_model_context = stan_model_context def build_block(self, data_variable_names: Tuple[str]): code = IndentedString() code += "parameters{\n" code.indent_level += 1 # Enter parameters block - for statement in self.sampling_statements: - if statement.lhs_name in data_variable_names: - continue + added_parameters = set() - if statement.lower > float("-inf") and statement.upper < float("inf"): - code += f"real {statement.lhs_name};\n" - elif statement.lower > float("-inf"): - code += f"real {statement.lhs_name};\n" - elif statement.upper < float("inf"): - code += f"real {statement.lhs_name};\n" - else: - code += f"real {statement.lhs_name};\n" + for statement in self.stan_model_context.sample_statements: + for lhs_variable in statement.lhs_variables: + if lhs_variable in data_variable_names: + continue + if lhs_variable in added_parameters: + continue + if statement.lower > float("-inf") and statement.upper < float("inf"): + code += f"real {lhs_variable};\n" + elif statement.lower > float("-inf"): + code += f"real {lhs_variable};\n" + elif statement.upper < float("inf"): + code += f"real {lhs_variable};\n" + else: + code += f"real {lhs_variable};\n" + + added_parameters.add(lhs_variable) code.indent_level -= 1 # Exit parameters block code += "}\n" @@ -143,8 +153,10 @@ def build_block(self, data_dict: Dict): raise Exception(f"Can't automatically process data variable {key}.") elif len(dims) == 1: code += f"vector[{dims[0]}] {key};\n" + elif len(dims) == 2: + code += f"array[{dims[0]}] vector[{dims[1]}] {key};\n" else: - raise Exception("Multidimensional data not implemented") + raise Exception("Multidimensional data with dimensions higher than 3 not implemented") code.indent_level -= 1 @@ -153,18 +165,17 @@ def build_block(self, data_dict: Dict): class StanTransformedDataBuilder: - def __init__(self, initial_time, integration_times: Iterable[Number]): - self.initial_time = initial_time - self.integration_times = integration_times + def __init__(self, stan_model_context: "StanModelContext"): + self.stan_model_context = stan_model_context def build_block(self) -> str: - n_t = len(self.integration_times) + n_t = len(self.stan_model_context.integration_times) code = IndentedString() code += "transformed data{\n" code.indent_level += 1 - code += f"real initial_time = {self.initial_time};\n" + code += f"real initial_time = {self.stan_model_context.initial_time};\n" code += f"int n_t = {n_t};\n" - code += f"array[n_t] real times = {{{', '.join([str(x) for x in self.integration_times])}}};\n" + code += f"array[n_t] real times = {{{', '.join([str(x) for x in self.stan_model_context.integration_times])}}};\n" code.indent_level -= 1 code += "}\n" @@ -180,7 +191,7 @@ def build_block(self): code += "model{\n" code.indent_level += 1 for statement in self.sampling_statements: - code += f"{statement.lhs_name} ~ {statement.distribution_type}({', '.join([str(arg) for arg in statement.distribution_args])});\n" + code += f"{statement.lhs_expr} ~ {statement.distribution_type}({', '.join([str(arg) for arg in statement.distribution_args])});\n" code.indent_level -= 1 code += "}\n" @@ -196,12 +207,62 @@ def build_block(self): code += "generated quantities{\n" code.indent_level += 1 for statement in self.sampling_statements: - code += f"{statement.lhs_name}_tilde ~ {statement.distribution_type}({', '.join([str(arg) for arg in statement.distribution_args])});\n" + code += f"{statement.lhs_expr}_tilde ~ {statement.distribution_type}({', '.join([str(arg) for arg in statement.distribution_args])});\n" code.indent_level -= 1 code += "}\n" return str(code) + +class Draws2DataStanGQBuilder: + def __init__(self, stan_model_context: "StanModelContext", vensim_model_context: "VensimModelContext", function_name: str, data_dict: Dict): + self.stan_model_context = stan_model_context + self.vensim_model_context = vensim_model_context + self.function_name = function_name + self.data_dict = data_dict + + + def build_block(self): + self.code = IndentedString() + self.code += "generated quantities{\n" + self.code.indent_level += 1 + + self.code.indent_level -= 1 + self.code += "}\n" + + return str(self.code) + + + def build_rng_functions(self): + ignored_variables = set(self.data_dict.keys()).union(set(self.vensim_model_context.stock_variable_names)) + stmt_sorter = StatementTopoSort(self.stan_model_context, ignored_variables) + for sampling_statement in self.stan_model_context.sample_statements: + for lhs_var in sampling_statement.lhs_variables: + stmt_sorter.add_stmt(lhs_var, sampling_statement.rhs_variables) + + param_draw_order = stmt_sorter.sort() + statements = self.stan_model_context.sample_statements.copy() + + processed_statements = set() + + for param_name in param_draw_order: + if param_name in ignored_variables: + continue + for statement in statements: + if statement in processed_statements: + continue + if param_name in statement.lhs_variables: + if statement.init_state: + param_name = param_name + "__init" + self.code += f"real {param_name}_tilde = {statement.distribution_type}_rng({', '.join(statement.distribution_args)})" + + processed_statements.add(statement) + + + + + + class StanFunctionBuilder: def __init__( @@ -242,7 +303,7 @@ def _create_dependency_graph(self): def build_functions( self, - predictor_variable_names: Iterable[Tuple[str, str]], + predictor_variable_names: Set[str], outcome_variable_names: Iterable[str], function_name: str = "vensim_ode_func", ): @@ -299,11 +360,13 @@ def recursive_order_search(current, visited): ################# # Create function declaration - self.code += f"vector {function_name}(real time, vector outcome, " + self.code += f"vector {function_name}(real time, vector outcome" argument_strings = [] argument_variables = ( [] ) # this list holds the names of the argument variables + if(len(predictor_variable_names) > 0): + self.code += ", " for var in predictor_variable_names: if isinstance(var, str): argument_variables.append(var) @@ -370,4 +433,3 @@ def build_lookups(self): for component in element.components: self.lookup_builder_walker.walk(component.ast, vensim_name_to_identifier(element.name)) - diff --git a/pysd/builders/stan/stan_model.py b/pysd/builders/stan/stan_model.py index f2785d02..ed361ed6 100644 --- a/pysd/builders/stan/stan_model.py +++ b/pysd/builders/stan/stan_model.py @@ -7,7 +7,9 @@ import cmdstanpy class SamplingStatement: - lhs_name: str + lhs_expr: str + lhs_variables: Set[str] + rhs_variables: Set[str] distribution_type: str distribution_return_type: Type distribution_args: Tuple[str] @@ -15,10 +17,14 @@ class SamplingStatement: upper: float init_state: bool - def __init__(self, lhs_name, distribution_type, *distribution_args, lower=float("-inf"), upper=float("inf"), init_state=False): - self.lhs_name = lhs_name + def __init__(self, lhs_expr, distribution_type, *distribution_args, lower=float("-inf"), upper=float("inf"), init_state=False): + self.lhs_expr = lhs_expr + self.lhs_variables = set() + self.lhs_variables.update([node.id for node in ast.walk(ast.parse(lhs_expr)) if isinstance(node, ast.Name)]) self.distribution_type = distribution_type self.distribution_args = distribution_args + self.rhs_variables = set() + self.rhs_variables.update([node.id for arg in self.distribution_args for node in ast.walk(ast.parse(str(arg))) if isinstance(node, ast.Name)]) self.lower = lower self.upper = upper self.init_state = init_state @@ -33,6 +39,8 @@ def __post_init__(self): @dataclass class StanModelContext: + initial_time: float + integration_times: Iterable[float] sample_statements: List[SamplingStatement] = field(default_factory=list) exposed_parameters: Set[str] = field(default_factory=set) @@ -81,7 +89,7 @@ def __init__(self, model_name: str, abstract_model, initial_time: float, integra self.model_name = model_name self.initial_time = float(initial_time) self.integration_times = integration_times - self.stan_model_context = StanModelContext() + self.stan_model_context = StanModelContext(initial_time, integration_times) self.vensim_model_context = VensimModelContext(self.abstract_model) if initial_time in integration_times: raise Exception("initial_time shouldn't be present in integration_times") @@ -90,9 +98,9 @@ def __init__(self, model_name: str, abstract_model, initial_time: float, integra if not os.path.exists(self.stan_model_dir): os.mkdir(self.stan_model_dir) - self.init_variable_regex = re.compile(".+?(?=_init$)") - # This regex is to match all preceding characters that come before '_init' at the end of the string. - # So something like stock_var_init_init would match into stock_var_init. + self.init_variable_regex = re.compile(".+?(?=__init$)") + # This regex is to match all preceding characters that come before '__init' at the end of the string. + # So something like stock_var_init__init would match into stock_var__init. # This is used to parse out the corresponding stock names for init parameters. def print_info(self): @@ -112,12 +120,12 @@ def set_prior(self, variable_name: str, distribution_type: str, *args, lower=flo if isinstance(arg, str): # If the distribution argument is an expression, parse the dependant variables # We're using the python parser here, which might be problematic - used_variable_names = [node.id for node in ast.walk(ast.parse(arg)) if isinstance(node, ast.Name)] + used_variable_names = [node.id.strip() for node in ast.walk(ast.parse(arg)) if isinstance(node, ast.Name)] for name in used_variable_names: - if name in self.vensim_model_context.variable_names: + if name in self.vensim_model_context.variable_names and name not in self.vensim_model_context.stock_variable_names: self.stan_model_context.exposed_parameters.update(used_variable_names) - if variable_name in self.vensim_model_context.variable_names: + if variable_name in self.vensim_model_context.variable_names and variable_name not in self.vensim_model_context.stock_variable_names: self.stan_model_context.exposed_parameters.add(variable_name) self.stan_model_context.sample_statements.append(SamplingStatement(variable_name, distribution_type, *args, lower=lower, upper=upper, init_state=init_state)) @@ -141,44 +149,16 @@ def build_stan_functions(self): f.write(self.function_builder.build_functions(self.stan_model_context.exposed_parameters, self.vensim_model_context.stock_variable_names)) def data2draws(self, data_dict: Dict): - stan_model_path= os.path.join(self.stan_model_dir, f"{self.model_name}_data2draws.stan") + stan_model_path = os.path.join(self.stan_model_dir, f"{self.model_name}_data2draws.stan") with open(stan_model_path, "w") as f: - # Include the function - f.write("functions{\n") - f.write(f" #include {self.model_name}_functions.stan\n") - f.write("}\n\n") - - f.write(StanDataBuilder().build_block(data_dict)) - f.write("\n") - - f.write(StanTransformedDataBuilder(self.initial_time, self.integration_times).build_block()) - f.write("\n") - - f.write(StanParametersBuilder(self.stan_model_context.sample_statements).build_block(tuple(data_dict.keys()))) - f.write("\n") - - transformed_params_builder = StanTransformedParametersBuilder(self.abstract_model) - # Find sampling statements for init - stock_initials = {} - for statement in self.stan_model_context.sample_statements: - if statement.init_state: - stock_variable_name = self.init_variable_regex.findall(statement.lhs_name)[0] - stock_initials[stock_variable_name] = statement.lhs_name - - f.write(transformed_params_builder.build_block(self.stan_model_context.exposed_parameters, - self.vensim_model_context.stock_variable_names, - self.function_builder.get_generated_lookups_dict(), - self.function_builder.ode_function_name, - stock_initials)) - f.write("\n") - - f.write(StanModelBuilder(self.stan_model_context.sample_statements).build_block()) - f.write("\n") + builder = Draws2DataStanGQBuilder(self.stan_model_context, self.vensim_model_context, + self.function_builder.ode_function_name, data_dict) + f.write(builder.build_block()) stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_path) return stan_model - def draws2data(self): + def draws2data(self, data_dict: Dict): stan_model_path = os.path.join(self.stan_model_dir, f"{self.model_name}_draws2data.stan") with open(stan_model_path, "w") as f: # Include the function @@ -187,20 +167,25 @@ def draws2data(self): f.write(f"#include {self.model_name}_functions.stan\n") f.write("}") f.write("\n") - f.write(StanDataBuilder().build_block()) + + f.write(StanDataBuilder().build_block(data_dict)) f.write("\n") - f.write(StanTransformedDataBuilder(self.initial_time, self.integration_times).build_block()) + f.write(StanTransformedDataBuilder(self.stan_model_context).build_block()) f.write("\n") - + stock_initials = {} + for statement in self.stan_model_context.sample_statements: + if statement.init_state: + stock_variable_name = self.init_variable_regex.findall(statement.lhs_expr)[0] + stock_initials[stock_variable_name] = statement.lhs_expr transformed_params_builder = StanTransformedParametersBuilder(self.abstract_model) f.write(transformed_params_builder.build_block(self.stan_model_context.exposed_parameters, self.vensim_model_context.stock_variable_names, self.function_builder.get_generated_lookups_dict(), - self.function_builder.ode_function_name)) + self.function_builder.ode_function_name, + stock_initials)) f.write("\n") - f.write(StanGeneratedQuantitiesBuilder(self.stan_model_context.sample_statements).build_block()) stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_path) diff --git a/pysd/builders/stan/utilities.py b/pysd/builders/stan/utilities.py index 10c83f44..1a0df209 100644 --- a/pysd/builders/stan/utilities.py +++ b/pysd/builders/stan/utilities.py @@ -1,3 +1,5 @@ +from collections import defaultdict + class IndentedString: def __init__(self, indent_level=0): self.indent_level = indent_level @@ -20,5 +22,45 @@ def __str__(self): return self.string +class StatementTopoSort: + def __init__(self, ignored_variables=tuple()): + self.dependency_graph = dict() + self.sorted_order = [] + self.ignored_variables = ignored_variables + + def add_stmt(self, lhs_var, rhs_vars): + if lhs_var not in self.dependency_graph: + self.dependency_graph[lhs_var] = set() + self.dependency_graph[lhs_var].update(rhs_vars) + for var in rhs_vars: + if var not in self.dependency_graph: + self.dependency_graph[var] = set() + + + def recursive_order_search(self, current, visited): + if current in self.ignored_variables: + return + visited.add(current) + for child in self.dependency_graph[current]: + if child == current: + continue + if child in self.ignored_variables: + continue + if child not in visited: + self.recursive_order_search(child, visited) + if current not in self.sorted_order: + self.sorted_order.append(current) + + def sort(self, reversed=False): + """ + reversed=False(default) means it will sort according to LHS given RHS + so a = b + c would mean b, c will come before a + """ + for key in self.dependency_graph.keys(): + self.recursive_order_search(key, set()) + + return self.sorted_order if not reversed else self.sorted_order[::-1] + + def vensim_name_to_identifier(name: str): return name.lower().replace(" ", "_") diff --git a/test_scripts/stan_vensim_integration.py b/test_scripts/stan_vensim_integration.py index 44b0ad19..c122e9ca 100644 --- a/test_scripts/stan_vensim_integration.py +++ b/test_scripts/stan_vensim_integration.py @@ -19,5 +19,5 @@ model.build_stan_functions() cmdstan_model = model.data2draws({}) -result = cmdstan_model.sample() -result.summary() \ No newline at end of file +#result = cmdstan_model.sample() +#result.summary() \ No newline at end of file From bd75d7280a1ae4c5910940168b46bb6dc8d55c85 Mon Sep 17 00:00:00 2001 From: amoon Date: Sun, 11 Sep 2022 08:24:25 -0400 Subject: [PATCH 42/45] Rearrange vensim file structure --- test_scripts/vensim_models/Current.vdfx | Bin 0 -> 27401 bytes test_scripts/vensim_models/Inventory.mdl | 342 ------------------ test_scripts/vensim_models/Inventory.xmile | 242 ------------- .../{ => demand-supply}/Inventory_GBM.stmx | 0 .../{ => demand-supply}/Inventory_PN.stmx | 0 .../{ => demand-supply}/demand-supply.mdl | 0 .../{ => demand-supply}/demand-supply.xmile | 0 .../demand-supply_wolookup.mdl | 0 .../demand-supply_wolookup.xmile | 0 .../demand_supply_pink_sterman.mdl | 0 .../demand_supply_white_sterman.mdl | 0 .../{ => demand-supply}/ds_white_sterman.mdl | 0 .../vensim_models/prey-predator/.DS_Store | Bin 0 -> 6148 bytes .../vensim_models/prey-predator/output.csv | 15 + .../prey-predator-hier1.mdl | 0 .../prey-predator-hier2.mdl | 0 .../prey-predator-hier3.mdl | 0 .../{ => prey-predator}/prey-predator.mdl | 80 ++-- 18 files changed, 55 insertions(+), 624 deletions(-) create mode 100755 test_scripts/vensim_models/Current.vdfx delete mode 100644 test_scripts/vensim_models/Inventory.mdl delete mode 100644 test_scripts/vensim_models/Inventory.xmile rename test_scripts/vensim_models/{ => demand-supply}/Inventory_GBM.stmx (100%) rename test_scripts/vensim_models/{ => demand-supply}/Inventory_PN.stmx (100%) rename test_scripts/vensim_models/{ => demand-supply}/demand-supply.mdl (100%) rename test_scripts/vensim_models/{ => demand-supply}/demand-supply.xmile (100%) rename test_scripts/vensim_models/{ => demand-supply}/demand-supply_wolookup.mdl (100%) rename test_scripts/vensim_models/{ => demand-supply}/demand-supply_wolookup.xmile (100%) rename test_scripts/vensim_models/{ => demand-supply}/demand_supply_pink_sterman.mdl (100%) rename test_scripts/vensim_models/{ => demand-supply}/demand_supply_white_sterman.mdl (100%) rename test_scripts/vensim_models/{ => demand-supply}/ds_white_sterman.mdl (100%) create mode 100644 test_scripts/vensim_models/prey-predator/.DS_Store create mode 100644 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- Vensim - Ventana Systems, Inc. - - - - - - - - -
- - 0 - 100 -
1
- - - - - - - - 100 - - - BL In - - - BL Out - - - - Widget - - - Desired_Inventory - - - Production Completion - - - Shipment Rate - - - - - - - Desired_Supply_Line - - - Production Start - - - Production Completion - - - - - - - (Desired_Inventory-Inventory)/Inventory_Adjustment_Time - - - - - - (Desired_Supply_Line-Supply_Line)/Supply_Line_Adjustment_Time - - - - - - Demand - - - - - - Shipment_Rate - - - - - - Underage_Cost+Overage_Cost - - - Widget - - - MAX(0, Back_Log-Shipment_Rate) - - - - - - RANDOM_NORMAL( 0, 200, Mean_of_Demand, Sd_of_Demand,0) - - - - - - Demand - - - - - - Demand_Forecast*Inventory_Period - - - - - - Adjustment_for_Inventory+Adjustment_for_Supply_Line+Demand_Forecast - - - Widget/Month - - - Back_Log/Desired_Delivery_Delay - - - - - - Demand_Forecast*Lead_Time - - - Widget/Month - - - Inventory/Minimum_Processing_Time - - - - - - (Inventory+Supply_Line)* Unit_Overage_Cost - - - - - - Supply_Line/Lead_Time - - - - - - MAX(0,Desired_Production_Start) - - - Widget/Month - Desired Shipment*Fulfilment Ratio - - Desired_Shipment*Fulfilment_Ratio - - - - - - Deficient_Amount*Unit_Underage_Cost - - - Month - - - 3 - - - - - - 3 - - - - - 1 - - - Month - - - 3 - - - - - - 5 - - - - - - 5 - - - - - - 100 - - - - - - 3 - - - - - - 10 - - - - - - 3 - - - - - - 1 - - - - - - 9 - - - - diff --git a/test_scripts/vensim_models/Inventory_GBM.stmx b/test_scripts/vensim_models/demand-supply/Inventory_GBM.stmx similarity index 100% rename from test_scripts/vensim_models/Inventory_GBM.stmx rename to test_scripts/vensim_models/demand-supply/Inventory_GBM.stmx diff --git a/test_scripts/vensim_models/Inventory_PN.stmx b/test_scripts/vensim_models/demand-supply/Inventory_PN.stmx similarity index 100% rename from test_scripts/vensim_models/Inventory_PN.stmx rename to test_scripts/vensim_models/demand-supply/Inventory_PN.stmx diff --git a/test_scripts/vensim_models/demand-supply.mdl b/test_scripts/vensim_models/demand-supply/demand-supply.mdl similarity index 100% rename from test_scripts/vensim_models/demand-supply.mdl rename to test_scripts/vensim_models/demand-supply/demand-supply.mdl diff --git a/test_scripts/vensim_models/demand-supply.xmile b/test_scripts/vensim_models/demand-supply/demand-supply.xmile similarity index 100% rename from test_scripts/vensim_models/demand-supply.xmile rename to test_scripts/vensim_models/demand-supply/demand-supply.xmile diff --git a/test_scripts/vensim_models/demand-supply_wolookup.mdl b/test_scripts/vensim_models/demand-supply/demand-supply_wolookup.mdl similarity index 100% rename from test_scripts/vensim_models/demand-supply_wolookup.mdl rename to test_scripts/vensim_models/demand-supply/demand-supply_wolookup.mdl diff --git a/test_scripts/vensim_models/demand-supply_wolookup.xmile b/test_scripts/vensim_models/demand-supply/demand-supply_wolookup.xmile similarity index 100% rename from test_scripts/vensim_models/demand-supply_wolookup.xmile rename to test_scripts/vensim_models/demand-supply/demand-supply_wolookup.xmile diff --git a/test_scripts/vensim_models/demand_supply_pink_sterman.mdl b/test_scripts/vensim_models/demand-supply/demand_supply_pink_sterman.mdl similarity index 100% rename from test_scripts/vensim_models/demand_supply_pink_sterman.mdl rename to test_scripts/vensim_models/demand-supply/demand_supply_pink_sterman.mdl diff --git a/test_scripts/vensim_models/demand_supply_white_sterman.mdl b/test_scripts/vensim_models/demand-supply/demand_supply_white_sterman.mdl similarity index 100% rename from test_scripts/vensim_models/demand_supply_white_sterman.mdl rename to test_scripts/vensim_models/demand-supply/demand_supply_white_sterman.mdl diff --git a/test_scripts/vensim_models/ds_white_sterman.mdl b/test_scripts/vensim_models/demand-supply/ds_white_sterman.mdl similarity index 100% rename from test_scripts/vensim_models/ds_white_sterman.mdl rename to test_scripts/vensim_models/demand-supply/ds_white_sterman.mdl diff --git a/test_scripts/vensim_models/prey-predator/.DS_Store b/test_scripts/vensim_models/prey-predator/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0 Date: Sun, 11 Sep 2022 21:47:46 +0900 Subject: [PATCH 43/45] Update draws2data --- pysd/builders/stan/stan_block_builder.py | 148 +- pysd/builders/stan/stan_model.py | 112 +- test_scripts/BayesWF_PreyPred.ipynb | 5619 ++++++++++++++++++---- test_scripts/stan_vensim_integration.py | 43 +- 4 files changed, 4922 insertions(+), 1000 deletions(-) diff --git a/pysd/builders/stan/stan_block_builder.py b/pysd/builders/stan/stan_block_builder.py index 2f55fa47..3514c33d 100644 --- a/pysd/builders/stan/stan_block_builder.py +++ b/pysd/builders/stan/stan_block_builder.py @@ -1,4 +1,5 @@ import os +import itertools from pathlib import Path from typing import Union, List, Dict, Set, Sequence, Iterable from numbers import Number @@ -16,10 +17,23 @@ class StanTransformedParametersBuilder: - def __init__(self, abstract_model: AbstractModel): - self.abstract_model = abstract_model + def __init__(self, stan_model_context, vensim_model_context): + self.stan_model_context = stan_model_context + self.vensim_model_context = vensim_model_context + self.abstract_model = self.vensim_model_context.abstract_model - def build_block( + def build_block(self, predictor_variable_names, outcome_variable_names, lookup_function_dict, function_name, stock_initial_values: Dict[str, str]): + self.code = IndentedString() + self.code += "transformed parameters {\n" + self.code.indent_level += 1 + self.write_block(predictor_variable_names, outcome_variable_names, lookup_function_dict, function_name, stock_initial_values) + self.code.indent_level -= 1 + self.code += "}\n" + + return str(self.code) + + + def write_block( self, predictor_variable_names, outcome_variable_names, @@ -27,10 +41,6 @@ def build_block( function_name, stock_initial_values: Dict[str, str] ): - self.code = IndentedString() - self.code += "transformed parameters {\n" - self.code.indent_level += 1 - argument_variables = [] for var in predictor_variable_names: if isinstance(var, str): @@ -44,6 +54,11 @@ def build_block( stan_varname = vensim_name_to_identifier(element.name) variable_ast_dict[stan_varname] = element.components[0].ast + # Create variables defined through assignment + for statement in self.stan_model_context.sample_statements: + if statement.distribution_type == statement.assignment_dist: + self. code += f"real {statement.lhs_expr} = {''.join(statement.distribution_args)};\n" + self.code += "// Initial ODE values\n" for outcome_variable_name in outcome_variable_names: if outcome_variable_name in stock_initial_values: @@ -80,17 +95,14 @@ def build_block( for index, name in enumerate(outcome_variable_names, 1): self.code += f"array[n_t] real {name} = integrated_result[:, {index}];\n" - self.code.indent_level -= 1 - self.code += "}\n" - - return str(self.code) - class StanParametersBuilder: - def __init__(self, stan_model_context: "StanModelContext"): + def __init__(self, stan_model_context: "StanModelContext", vensim_model_context: "VensimModelContext"): self.stan_model_context = stan_model_context + self.vensim_model_context = vensim_model_context - def build_block(self, data_variable_names: Tuple[str]): + def build_block(self): + data_variable_names = tuple(self.stan_model_context.stan_data.keys()) code = IndentedString() code += "parameters{\n" code.indent_level += 1 # Enter parameters block @@ -98,11 +110,13 @@ def build_block(self, data_variable_names: Tuple[str]): added_parameters = set() for statement in self.stan_model_context.sample_statements: - for lhs_variable in statement.lhs_variables: + for lhs_variable in statement.lhs_variable: if lhs_variable in data_variable_names: continue if lhs_variable in added_parameters: continue + if statement.distribution_type == statement.assignment_dist: + continue if statement.lower > float("-inf") and statement.upper < float("inf"): code += f"real {lhs_variable};\n" elif statement.lower > float("-inf"): @@ -120,44 +134,16 @@ def build_block(self, data_variable_names: Tuple[str]): class StanDataBuilder: - def __init__(self): - pass - - def get_dims(self, obj): - try: - iter(obj) - except: - return None - else: - dim = len(obj) - inner_dim = self.get_dims(obj[0]) - if inner_dim: - return [dim] + inner_dim - else: - return [dim] + def __init__(self, stan_model_context: "StanModelContext"): + self.stan_model_context = stan_model_context - def build_block(self, data_dict: Dict): + def build_block(self): code = IndentedString() code += "data{\n" code.indent_level += 1 - for key, val in data_dict.items(): - if isinstance(val, int): - code += f"int {key};\n" - elif isinstance(val, float): - code += f"real {key};\n" - else: - # Multidimensional data - dims = self.get_dims(val) - if not dims: - raise Exception(f"Can't automatically process data variable {key}.") - elif len(dims) == 1: - code += f"vector[{dims[0]}] {key};\n" - elif len(dims) == 2: - code += f"array[{dims[0]}] vector[{dims[1]}] {key};\n" - else: - raise Exception("Multidimensional data with dimensions higher than 3 not implemented") - + for _, entry in self.stan_model_context.stan_data.items(): + code += f"{entry.stan_type} {entry.data_name};\n" code.indent_level -= 1 code += "}\n" @@ -174,7 +160,6 @@ def build_block(self) -> str: code += "transformed data{\n" code.indent_level += 1 code += f"real initial_time = {self.stan_model_context.initial_time};\n" - code += f"int n_t = {n_t};\n" code += f"array[n_t] real times = {{{', '.join([str(x) for x in self.stan_model_context.integration_times])}}};\n" code.indent_level -= 1 @@ -183,15 +168,16 @@ def build_block(self) -> str: class StanModelBuilder: - def __init__(self, sampling_statements: Iterable["SamplingStatement"]): - self.sampling_statements = sampling_statements + def __init__(self, stan_model_context: "StanModelContext"): + self.stan_model_context = stan_model_context def build_block(self): code = IndentedString() code += "model{\n" code.indent_level += 1 - for statement in self.sampling_statements: - code += f"{statement.lhs_expr} ~ {statement.distribution_type}({', '.join([str(arg) for arg in statement.distribution_args])});\n" + for statement in self.stan_model_context.sampling_statements: + if statement.distribution_type != statement.assignment_dist: + code += f"{statement.lhs_expr} ~ {statement.distribution_type}({', '.join([str(arg) for arg in statement.distribution_args])});\n" code.indent_level -= 1 code += "}\n" @@ -207,7 +193,7 @@ def build_block(self): code += "generated quantities{\n" code.indent_level += 1 for statement in self.sampling_statements: - code += f"{statement.lhs_expr}_tilde ~ {statement.distribution_type}({', '.join([str(arg) for arg in statement.distribution_args])});\n" + code += f"real {statement.lhs_expr} ~ {statement.distribution_type}({', '.join([str(arg) for arg in statement.distribution_args])});\n" code.indent_level -= 1 code += "}\n" @@ -215,30 +201,43 @@ def build_block(self): class Draws2DataStanGQBuilder: - def __init__(self, stan_model_context: "StanModelContext", vensim_model_context: "VensimModelContext", function_name: str, data_dict: Dict): + def __init__(self, stan_model_context: "StanModelContext", vensim_model_context: "VensimModelContext", function_name: str): self.stan_model_context = stan_model_context self.vensim_model_context = vensim_model_context self.function_name = function_name - self.data_dict = data_dict - def build_block(self): + def build_block(self, transformed_parameters_code: str = ""): self.code = IndentedString() self.code += "generated quantities{\n" self.code.indent_level += 1 - + self.build_rng_functions() + self.code += "\n" + self.code.add_raw(transformed_parameters_code, ignore_indent=True) + self.code += "\n" + self.build_data_rng_functions() self.code.indent_level -= 1 self.code += "}\n" return str(self.code) + def build_data_rng_functions(self): + for statement in self.stan_model_context.sample_statements: + if statement.lhs_variable in self.stan_model_context.stan_data: + param_name = statement.lhs_expr + stan_type = self.stan_model_context.stan_data[param_name].stan_type + if stan_type.startswith("vector"): + self.code += f"{stan_type} {param_name} = to_vector({statement.distribution_type}_rng({', '.join(statement.distribution_args)}));\n" + else: + self.code += f"{stan_type} {param_name} = {statement.distribution_type}_rng({', '.join(statement.distribution_args)});\n" + + def build_rng_functions(self): - ignored_variables = set(self.data_dict.keys()).union(set(self.vensim_model_context.stock_variable_names)) - stmt_sorter = StatementTopoSort(self.stan_model_context, ignored_variables) + ignored_variables = set(self.stan_model_context.stan_data.keys()).union(set(self.vensim_model_context.stock_variable_names)) + stmt_sorter = StatementTopoSort(ignored_variables) for sampling_statement in self.stan_model_context.sample_statements: - for lhs_var in sampling_statement.lhs_variables: - stmt_sorter.add_stmt(lhs_var, sampling_statement.rhs_variables) + stmt_sorter.add_stmt(sampling_statement.lhs_variable, sampling_statement.rhs_variables) param_draw_order = stmt_sorter.sort() statements = self.stan_model_context.sample_statements.copy() @@ -251,19 +250,38 @@ def build_rng_functions(self): for statement in statements: if statement in processed_statements: continue - if param_name in statement.lhs_variables: + if param_name == statement.lhs_variable: if statement.init_state: param_name = param_name + "__init" - self.code += f"real {param_name}_tilde = {statement.distribution_type}_rng({', '.join(statement.distribution_args)})" + + self.code += f"real {param_name} = {statement.distribution_type}_rng({', '.join(statement.distribution_args)});\n" processed_statements.add(statement) +class Draws2DataStanDataBuilder(StanDataBuilder): + def __init__(self, stan_model_context, vensim_model_context): + self.stan_model_context = stan_model_context + self.vensim_model_context = vensim_model_context + super(Draws2DataStanDataBuilder, self).__init__(self.stan_model_context) + def build_block(self): + stan_params = [stmt.lhs_variable for stmt in self.stan_model_context.sample_statements] + print(stan_params) + code = IndentedString() + code += "data{\n" + code.indent_level += 1 + for _, entry in self.stan_model_context.stan_data.items(): + if entry.data_name in stan_params: + continue + code += f"{entry.stan_type} {entry.data_name};\n" + + code.indent_level -= 1 + code += "}\n" + return code.string - class StanFunctionBuilder: def __init__( self, abstract_model: AbstractModel, function_name: str = "vensim_ode_func" diff --git a/pysd/builders/stan/stan_model.py b/pysd/builders/stan/stan_model.py index ed361ed6..4e762638 100644 --- a/pysd/builders/stan/stan_model.py +++ b/pysd/builders/stan/stan_model.py @@ -8,7 +8,7 @@ class SamplingStatement: lhs_expr: str - lhs_variables: Set[str] + lhs_variable: str rhs_variables: Set[str] distribution_type: str distribution_return_type: Type @@ -19,15 +19,17 @@ class SamplingStatement: def __init__(self, lhs_expr, distribution_type, *distribution_args, lower=float("-inf"), upper=float("inf"), init_state=False): self.lhs_expr = lhs_expr - self.lhs_variables = set() - self.lhs_variables.update([node.id for node in ast.walk(ast.parse(lhs_expr)) if isinstance(node, ast.Name)]) + lhs_variables = [node.id for node in ast.walk(ast.parse(lhs_expr)) if isinstance(node, ast.Name)] + assert len(lhs_variables) == 1, "The LHS expression for prior specification must contain just 1 parameter name" + self.lhs_variable = lhs_variables[0] self.distribution_type = distribution_type - self.distribution_args = distribution_args + self.distribution_args = tuple(str(x) for x in distribution_args) self.rhs_variables = set() self.rhs_variables.update([node.id for arg in self.distribution_args for node in ast.walk(ast.parse(str(arg))) if isinstance(node, ast.Name)]) self.lower = lower self.upper = upper self.init_state = init_state + self.assignment_dist = "assignment" def __post_init__(self): if self.distribution_type in ("bernoulli", "binomial", "beta_binomial", "neg_binomial", "poisson"): @@ -37,18 +39,54 @@ def __post_init__(self): self.distribution_return_type = float +@dataclass +class StanDataEntry: + data_name: str + stan_type: str + @dataclass class StanModelContext: initial_time: float integration_times: Iterable[float] + stan_data: Dict[str, StanDataEntry] = field(default_factory=dict) sample_statements: List[SamplingStatement] = field(default_factory=list) exposed_parameters: Set[str] = field(default_factory=set) + def identify_stan_data_types(self, data_dict): + def get_dims(obj): + try: + iter(obj) + except: + return None + else: + dim = len(obj) + inner_dim = get_dims(obj[0]) + if inner_dim: + return [dim] + inner_dim + else: + return [dim] + + for key, val in data_dict.items(): + if isinstance(val, int): + self.stan_data[key] = StanDataEntry(key, "int") + elif isinstance(val, float): + self.stan_data[key] = StanDataEntry(key, "real") + else: + # Multidimensional data + dims = get_dims(val) + if not dims: + raise Exception(f"Can't process data entry {key}.") + elif len(dims) == 1: + self.stan_data[key] = StanDataEntry(key, f"vector[{dims[0]}]") + elif len(dims) == 2: + self.stan_data[key] = StanDataEntry(key, f"array[{dims[0]}] vector[{dims[1]}]") + class VensimModelContext: def __init__(self, abstract_model): self.variable_names = set() self.stock_variable_names = set() + self.abstract_model = abstract_model # Some basic checks to make sure the AM is compatible assert len(abstract_model.sections) == 1, "Number of sections in AbstractModel must be 1." @@ -84,12 +122,14 @@ def print_variable_info(self, abstract_model): class StanVensimModel: - def __init__(self, model_name: str, abstract_model, initial_time: float, integration_times: Iterable[Number]): + def __init__(self, model_name: str, abstract_model, initial_time: float, integration_times: Iterable[Number], data_dict={}): self.abstract_model = abstract_model self.model_name = model_name self.initial_time = float(initial_time) self.integration_times = integration_times self.stan_model_context = StanModelContext(initial_time, integration_times) + self.stan_model_context.identify_stan_data_types(data_dict) + self.data_dict = data_dict self.vensim_model_context = VensimModelContext(self.abstract_model) if initial_time in integration_times: raise Exception("initial_time shouldn't be present in integration_times") @@ -148,17 +188,44 @@ def build_stan_functions(self): self.function_builder = StanFunctionBuilder(self.abstract_model) f.write(self.function_builder.build_functions(self.stan_model_context.exposed_parameters, self.vensim_model_context.stock_variable_names)) - def data2draws(self, data_dict: Dict): + def data2draws(self): stan_model_path = os.path.join(self.stan_model_dir, f"{self.model_name}_data2draws.stan") with open(stan_model_path, "w") as f: - builder = Draws2DataStanGQBuilder(self.stan_model_context, self.vensim_model_context, - self.function_builder.ode_function_name, data_dict) - f.write(builder.build_block()) + # Include the function + f.write("functions{\n") + f.write(f" #include {self.model_name}_functions.stan\n") + f.write("}\n\n") + + f.write(StanDataBuilder(self.stan_model_context).build_block()) + f.write("\n") + + f.write(StanTransformedDataBuilder(self.stan_model_context).build_block()) + f.write("\n") + + f.write(StanParametersBuilder(self.stan_model_context, self.vensim_model_context).build_block()) + f.write("\n") + + transformed_params_builder = StanTransformedParametersBuilder(self.stan_model_context, self.vensim_model_context) + # Find sampling statements for init + stock_initials = {} + for statement in self.stan_model_context.sample_statements: + if statement.init_state: + stock_initials[statement.lhs_variable] = statement.lhs_variable + "__init" + + f.write(transformed_params_builder.build_block(self.stan_model_context.exposed_parameters, + self.vensim_model_context.stock_variable_names, + self.function_builder.get_generated_lookups_dict(), + self.function_builder.ode_function_name, + stock_initials)) + f.write("\n") + + f.write(StanModelBuilder(self.stan_model_context).build_block()) + f.write("\n") stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_path) return stan_model - def draws2data(self, data_dict: Dict): + def draws2data(self): stan_model_path = os.path.join(self.stan_model_dir, f"{self.model_name}_draws2data.stan") with open(stan_model_path, "w") as f: # Include the function @@ -168,7 +235,7 @@ def draws2data(self, data_dict: Dict): f.write("}") f.write("\n") - f.write(StanDataBuilder().build_block(data_dict)) + f.write(Draws2DataStanDataBuilder(self.stan_model_context, self.vensim_model_context).build_block()) f.write("\n") f.write(StanTransformedDataBuilder(self.stan_model_context).build_block()) f.write("\n") @@ -176,17 +243,22 @@ def draws2data(self, data_dict: Dict): stock_initials = {} for statement in self.stan_model_context.sample_statements: if statement.init_state: - stock_variable_name = self.init_variable_regex.findall(statement.lhs_expr)[0] - stock_initials[stock_variable_name] = statement.lhs_expr - transformed_params_builder = StanTransformedParametersBuilder(self.abstract_model) - f.write(transformed_params_builder.build_block(self.stan_model_context.exposed_parameters, - self.vensim_model_context.stock_variable_names, - self.function_builder.get_generated_lookups_dict(), - self.function_builder.ode_function_name, - stock_initials)) + stock_variable_name = statement.lhs_variable + stock_initials[stock_variable_name] = stock_variable_name + + #f.write(f"real {stock_initials[stock_variable_name]} = {statement.distribution_type}_rng({', '.join([str(arg) for arg in statement.distribution_args])});\n") + + transformed_params_builder = StanTransformedParametersBuilder(self.stan_model_context, self.vensim_model_context) + transformed_params_builder.code = IndentedString(indent_level=1) + transformed_params_builder.write_block(self.stan_model_context.exposed_parameters, + self.vensim_model_context.stock_variable_names, + self.function_builder.get_generated_lookups_dict(), + self.function_builder.ode_function_name, + stock_initials) f.write("\n") - f.write(StanGeneratedQuantitiesBuilder(self.stan_model_context.sample_statements).build_block()) + f.write(Draws2DataStanGQBuilder(self.stan_model_context, self.vensim_model_context, + self.function_builder.ode_function_name).build_block(transformed_parameters_code=str(transformed_params_builder.code))) stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_path) diff --git a/test_scripts/BayesWF_PreyPred.ipynb b/test_scripts/BayesWF_PreyPred.ipynb index 0ae6f2f4..48f0207c 100644 --- a/test_scripts/BayesWF_PreyPred.ipynb +++ b/test_scripts/BayesWF_PreyPred.ipynb @@ -2,11 +2,17 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 11, "id": "cea8444a-acd1-429b-8c64-511c6400e7d2", - "metadata": {}, + "metadata": { + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, "outputs": [], "source": [ + "\n", "import os\n", "from IPython.display import Image\n", "import matplotlib.pyplot as plt\n", @@ -17,236 +23,131 @@ "from pysd.builders.stan.stan_model import StanVensimModel\n", "from pysd.translators.vensim.vensim_file import VensimFile\n", "from pysd.translators.xmile.xmile_file import XmileFile\n", - "\n", + "import matplotlib.pyplot as plt\n", "\n", "import cmdstanpy # 2.30 is fastest (as of 08.12.2022) `cmdstanpy.install_cmdstan()` \n", "from cmdstanpy import CmdStanModel, cmdstan_path\n", "import arviz as az #!pip install git+https://github.com/arviz-devs/arviz\n", "az.style.use(\"arviz-darkgrid\")\n", "\n", + "\n", "# set your working directiory\n", "#os.chdir(\"/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts\")" ] }, - { - "cell_type": "markdown", - "id": "90dd73ce-2613-4b04-a210-5c1858273ce4", - "metadata": {}, - "source": [ - "# Structuring Uncertainties in Dynamic Models: \n", - "## Bayesian workflow of Predator-Prey Population Dynamics\n", - "\n", - "Angie.H Moon, 07.2022" - ] - }, - { - "cell_type": "markdown", - "id": "8dbac1ad-23e0-4423-a8b5-a0a9a11b85ad", - "metadata": {}, - "source": [ - "## Data: Predator and Prey Pelts in Canada\n", - "\n", - "The species of interest in this case study are\n", - "\n", - "- hares: prey, an hervivorous cousin of rabbits, and\n", - "- lynxes: predator, a feline predator whose diet consists largely of hares.\n", - "\n", - "Spikes in the predator population lag those in the prey population. When populations are plotted against one another over time, the population dynamics orbit in an apparently stable pattern. Population oscillations can be modeled with a pair of differential equations similar to that used to describe springs. The first plot is the number of lynx and hare pelts (in thousands) collected for twenty years. The second plot is the phase plot of number of pelts collected for lynx versus hares similar to that of the dynamics of a spring in phase space (i.e., position vs. momentum)." - ] - }, { "cell_type": "code", "execution_count": 2, - "id": "e1b00493-ec54-4eba-92c3-b1e465855427", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Text(0.5, 0, 'year'), Text(0, 0.5, 'pelt (thousands)')]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" + "id": "a5635cb8", + "metadata": { + "pycharm": { + "name": "#%%\n" }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "obs_stock_df = pd.read_csv('data/hudson-bay-lynx-hare.csv')\n", - "# data viz\n", - "pd.melt(obs_stock_df, id_vars = 'Year').iloc[[0,20,21,41]]\n", - "pd.melt(obs_stock_df, id_vars = 'Year').iloc[[0,1,20,21,40,41]].rename(columns = {'variable':'species', 'value':'pelts in thousands'})\n", - "ax = obs_stock_df.loc[:, ['Predator', 'Prey']].plot()\n", - "ax.set(xlabel='year', ylabel='pelt (thousands)') " - ] - }, - { - "cell_type": "markdown", - "id": "dd624d4d-178f-41f5-8586-8c15bd59763f", - "metadata": {}, - "source": [ - "Phase plots are as below:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "77abf5f6-b8a8-499d-bcb2-c705afb42c48", - "metadata": {}, + "scrolled": true + }, "outputs": [ { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Prey pelts (thousands)')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "- Vensim model information:\n", + "original name stan variable name is stock\n", + "------------------------------------------------\n", + "delta delta \n", + "predator birth rate predator_birth_rate \n", + "gamma gamma \n", + "predator death rate predator_death_rate \n", + "Predator predator V\n", + "alpha alpha \n", + "prey birth rate prey_birth_rate \n", + "beta beta \n", + "prey death rate prey_death_rate \n", + "Prey prey V\n", + "FINAL TIME final_time \n", + "INITIAL TIME initial_time \n", + "SAVEPER saveper \n", + "TIME STEP time_step \n", + "**********\n", + "- Stan model information:\n" + ] } ], "source": [ - "plt.scatter(obs_stock_df.loc[:, 'Predator'], obs_stock_df.loc[:, 'Prey'])\n", - "plt.plot(obs_stock_df.loc[:, 'Predator'], obs_stock_df.loc[:, 'Prey'])\n", - "plt.xlabel('Predator pelts (thousands)')\n", - "plt.ylabel('Prey pelts (thousands)')" - ] - }, - { - "cell_type": "markdown", - "id": "5763cb30-424f-4716-aab2-ae96fa680338", - "metadata": {}, - "source": [ - "## Mechanistic Model: The Lotka-Volterra Equations\n", - "\n", - "In Lotka-Volterra equations (Lotka 1925; Volterra 1926, 1927), Volterra modeled the temporal dynamics of the two species (i.e., population sizes over times) in terms of four parameters, $\\alpha, \\beta, \\gamma, \\delta \\geq 0$, as\n", - "\n", - "$$\n", - "\\begin{eqnarray}\n", - "\\frac{\\mathrm{d}}{\\mathrm{d}t} u\n", - "& = & (\\alpha - \\beta v) u\n", - "& = & \\alpha u - \\beta u v\n", - "\\\\[6pt]\n", - "\\frac{\\mathrm{d}}{\\mathrm{d}t} v\n", - "& = & (-\\gamma + \\delta \\, u) \\, v\n", - "& = & -\\gamma v + \\delta uv\n", - "\\end{eqnarray}\n", - "$$\n", - "\n", - "$u(t)$ and $v(t)$ are rendered as $u$ and $v$. The factor $\\alpha$, $\\beta$ are the rate of birth and shrinkage relative to the product of the population sizes where as $\\gamma$, $\\delta$ are the shrinkage and growth rate as a factor of the product of the population sizes. Both u and v have positivitity constraints. as long as the initial populations are non-negative, i.e., $u(0) \\geq 0$ and $v(0) \\geq 0$, because the rate of change in each population is a factor of the population size itself.\n", + "obs_stock_df = pd.read_csv('data/hudson-bay-lynx-hare.csv')\n", + "vf = VensimFile(\"vensim_models/prey-predator.mdl\")\n", + "vf.parse()\n", + "am = vf.get_abstract_model()\n", "\n", - "The dynamic system has four limiting behavior:\n", + "# set time\n", + "n_t = obs_stock_df.shape[0] - 1\n", + "premodel = StanVensimModel(\"prey-predator\", am, 0.0, list(range(1, n_t + 1)))\n", + "premodel.print_info()\n", "\n", - "1. If both population sizes are initially positive, the populations will oscillate in a fixed pattern indefinitely, remaining positive.\n", - "2. If both population sizes are initially zero, the population sizes will remain zero.\n", - "3. If the predator population size is zero and the prey population size positive, the predator population size remains zero and the prey population grows without bound.\n", - "4. If the predator population size is positive and the prey population size zero, the prey population size remains zero while the predator population shrinks toward zero size." + "n_t = obs_stock_df.shape[0] - 1\n", + "data_data2draws = {\n", + " \"n_t\": n_t,\n", + " \"predator_obs\": obs_stock_df.loc[1:, 'Predator'].values.tolist(),\n", + " \"prey_obs\": obs_stock_df.loc[1:, 'Prey'].values.tolist(),\n", + "}" ] }, { - "cell_type": "markdown", - "id": "10c2e11e-1038-41a3-9eb8-a75592ef9aa7", + "cell_type": "code", + "execution_count": 4, + "id": "22c05fb4", "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] + "pycharm": { + "name": "#%%\n" + } }, - "source": [ - "## Statistical Model: regreasion framing and uncertainty embedding\n", - "\n", - "### Solving the inverse problem\n", - "Bayesian statistics is somewhat counterintuitive, as it involves formulating the generating model (from parameter to observed data) then using general principles to solve the inverse problem. Specifically, a Bayesian model requires a mathematical model of what we know about the parameters (i.e., a prior) and a model of what we know about the data generating process given the parameters (i.e., a sampling distribution.\n", - "\n", - "Mathematically, a prior density $p(\\theta)$ over the sequence of parameters $\\theta$ encapsulates our knowledge of the parameters before seeing the data. A sampling distribution (or likelihood), which may have a continuous, discrete or mixed probability function, $p(y | \\theta)$ characterizes the distribution of observable data $y$ given parameters $\\theta$. We limit the observation as stock variables as every SD model can be reformulated into the combination of stock and parameters.\n", - "\n", - "Bayes's rule gives us a general solution to the inverse problem, expressing the posterior $p(\\theta | y)$ in terms of the prior $p(\\theta)$ and likelihood $p(y | \\theta)$. Stan provides a form of Markov chain Monte Carlo (MCMC) sampling that draws a sample $\\theta^{(1)}, \\ldots, \\theta^{(M)}$ from the posterior to use for computational inference. Posterior quantities of interest may be expressed as derived random variables using functions $f(\\theta)$ of parameters. This feature is used for decision analysis; for instance, imagine a optimization problem of conservation cost of the park where prey and predator ecology places at. The cost can be computed based on the posterior distribution inferred from the observed time series.\n", - "\n", - "\n", - "### Uncertainty embedding for forward-backward symmetry required for calibration\n", - "\n", - "The Lotka-Volterra model is deterministic in that given the value of the system parameter and initial outcome state, equation solutions (simulated outcome value) are fully determined. However, for empirical research which use posterior inference from the real data as it final forecast, forward model should be re-designed. This is because symmetry of forward and backward model (i.e. data generation and its inference) is the theoretical justification of calibration. To pass this internal consistency test (or with enough resource, SBC which is rank-statistics based), we need the two process to be the mirror image of other. This is why we purposefully embed uncertainty components, waiting to be captured in the inference step. The purpose is to test resilience and identifiability of our models evidenced by the perfect retrival of prior distribution for every uncertainty we embedded. \n", - "\n", - "### Linear regression analogy\n", - "\n", - "Like in a simple linear regression, we will proceed by treating the underlying determinstic model as providing an expected population value around which there will be variation due to both measurement error and simplifications in the scientific model. Consider the typical formulation of a linear regression, where $y_n$ is an observable scalar outcome, $x_n$ is a row vector of unmodeled predictors (aka covariates, features), $\\beta$ is a coefficient vector parameter, and $\\sigma > 0$ is the error scale,\n", - "\n", - "$$\n", - "\\begin{eqnarray}\n", - "y_n & = & x_n \\beta + \\epsilon_n\n", - "\\\\[6pt]\n", - "\\epsilon_n & \\sim & \\mathsf{Normal}(0, \\sigma)\n", - "\\end{eqnarray}\n", - "$$\n", - "\n", - "### Adding measurement uncertainty (epistemic)\n", - "Before embedding parameteric uncertainty, linear predictor $x_n \\beta$ with predictor $x_n$ (row $n$ of the data matrix $x$) and coefficient (column) vector $\\beta$ are deterministic. The only source of uncertainty is from the measurement. This is expressed by assigning a normal distribution to error term $\\epsilon_n$. Equal expression is with latent error variable $\\epsilon_n$ as follows[17](#fn17), \n", - "\n", - "$$\n", - "y_n \\sim \\mathsf{Normal}(x_n \\beta, \\sigma).\n", - "$$\n", - "\n", - "### Adding parameter uncertainty (epistemic)\n", - "Next, we add parameter uncertainty by coding estimated parameter as a distribution rather than a fixed value. This distribution is called prior distribution and from our example, Normal distirbution is chosen to endow the uncertainty to the four estimated parameters $\\alpha, \\beta, \\gamma, \\delta$. Considering their role difference, $\\alpha, \\gamma$ as multipliers of $u, -v$ and $\\beta, \\delta$ as multipliers of $uv$, prior parameter are chosen as N(1, 0.5) and N(0.05, 0.05) for each. For this selection, refer to the original case study [Carpenter18](https://mc-stan.org/users/documentation/case-studies/lotka-volterra-predator-prey.html).\n", - "\n", - "\n", - "### Adding aleatoric uncertainty\n", - "This applies only when we decide to add measurement uncertainty i.e. having initial value of stock variable as `est_param` instead of the default `ass_param`. For this, detailed explanation is added at the end of this file at Appendix A. " - ] - }, - { - "cell_type": "markdown", - "id": "a87666b9-f6c5-4723-a2be-373f2dd29f0c", - "metadata": {}, - "source": [ - "## Prior predictive check" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "2444ecf8-c272-4dad-9487-37fcb88ddbbf", - "metadata": {}, "outputs": [ { - "name": "stdin", + "name": "stdout", + "output_type": "stream", + "text": [ + "prey-predator_functions.stan already exists in the current working directory. Overwrite? (Y/N):y\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21:35:13 - cmdstanpy - INFO - compiling stan file /Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan to exe file /Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['alpha', 'gamma', 'beta', 'delta', 'sigma', 'predator_obs', 'prey_obs']\n" + ] + }, + { + "name": "stderr", "output_type": "stream", "text": [ - "prey-predator_functions.stan already exists in the current working directory. Overwrite? (Y/N): y\n" + "21:35:16 - cmdstanpy - INFO - compiled model executable: /Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data\n", + "21:35:16 - cmdstanpy - WARNING - Stan compiler has produced 1 warnings:\n", + "21:35:16 - cmdstanpy - WARNING - \n", + "--- Translating Stan model to C++ code ---\n", + "bin/stanc --include-paths=/Users/dashadower/git_repos/pysd/test_scripts/stan_files --o=/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.hpp /Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan\n", + "Warning in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 29, column 4: Declaration\n", + " of arrays by placing brackets after a variable name is deprecated and\n", + " will be removed in Stan 2.32.0. Instead use the array keyword before the\n", + " type. This can be changed automatically using the auto-format flag to\n", + " stanc\n", + "\n", + "--- Compiling, linking C++ code ---\n", + "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -c -include-pch stan/src/stan/model/model_header.hpp.gch -x c++ -o /Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.o /Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.hpp\n", + "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -Wl,-L,\"/Users/dashadower/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/dashadower/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" /Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.o src/cmdstan/main.o -Wl,-L,\"/Users/dashadower/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/dashadower/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data\n", + "rm -f /Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.o\n", + "\n" ] } ], "source": [ - "vf = VensimFile(\"vensim_models/prey-predator.mdl\")\n", - "vf.parse()\n", - "am = vf.get_abstract_model()\n", - "\n", - "# set time\n", - "n_t = obs_stock_df.shape[0] - 1 \n", - "premodel = StanVensimModel(\"prey-predator\", am, 0.0, list(range(1, n_t + 1)))\n", "\n", + "premodel = StanVensimModel(\"prey-predator\", am, 0.0, list(range(1, n_t + 1)), data_dict = data_data2draws)\n", "# ode parameter \n", "premodel.set_prior(\"alpha\", \"normal\", 0.55, 0.1)\n", "premodel.set_prior(\"gamma\", \"normal\", 0.8, 0.1)\n", @@ -254,70 +155,60 @@ "premodel.set_prior(\"delta\", \"normal\", 0.024, 0.01)\n", "\n", "# sampling distribution parameter\n", - "# model.set_prior(\"sigma\", \"lognormal\", np.log(0.01), 0.1) \n", + "premodel.set_prior(\"sigma\", \"normal\", 0, 1)\n", "\n", + "premodel.set_prior(\"predator_obs\", \"lognormal\", \"predator\", \"sigma\")\n", + "premodel.set_prior(\"prey_obs\", \"lognormal\", \"prey\", \"sigma\")\n", "premodel.build_stan_functions()\n", - "premodel.draws2data(\"\")" + "draws2data_model = premodel.draws2data()\n", + "#print(premodel.vensim_model_context.variable_names)\n", + "\n", + "\n", + "#cmdstan_model = premodel.data2draws(data_data2draws)\n", + "#result = cmdstan_model.sample(data=data_data2draws)\n", + "#result.summary()" ] }, { "cell_type": "code", - "execution_count": 63, - "id": "c4bd395d-bc67-4175-b765-6e2232018faf", - "metadata": {}, + "execution_count": 10, + "id": "03a7f6b8", + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "17:50:20 - cmdstanpy - INFO - compiling stan file /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan to exe file /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data\n", - "17:50:28 - cmdstanpy - INFO - compiled model executable: /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data\n", - "17:50:28 - cmdstanpy - WARNING - Stan compiler has produced 6 warnings:\n", - "17:50:28 - cmdstanpy - WARNING - \n", - "--- Translating Stan model to C++ code ---\n", - "bin/stanc --include-paths=/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file --o=/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.hpp /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan\n", - "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_functions.stan', line 1, column 0, included from\n", - "'/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 2, column 0: Comments\n", - " beginning with # are deprecated and this syntax will be removed in Stan\n", - " 2.32.0. Use // to begin line comments; this can be done automatically\n", - " using the auto-format flag to stanc\n", - "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_functions.stan', line 3, column 21, included from\n", - "'/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 2, column 0: Comments\n", - " beginning with # are deprecated and this syntax will be removed in Stan\n", - " 2.32.0. Use // to begin line comments; this can be done automatically\n", - " using the auto-format flag to stanc\n", - "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_functions.stan', line 5, column 4, included from\n", - "'/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 2, column 0: Comments\n", - " beginning with # are deprecated and this syntax will be removed in Stan\n", - " 2.32.0. Use // to begin line comments; this can be done automatically\n", - " using the auto-format flag to stanc\n", - "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 28, column 32: Comments\n", - " beginning with # are deprecated and this syntax will be removed in Stan\n", - " 2.32.0. Use // to begin line comments; this can be done automatically\n", - " using the auto-format flag to stanc\n", - "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 33, column 4: Declaration\n", - " of arrays by placing brackets after a variable name is deprecated and\n", - " will be removed in Stan 2.32.0. Instead use the array keyword before the\n", - " type. This can be changed automatically using the auto-format flag to\n", - " stanc\n", - "Warning in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.stan', line 36, column 4: Declaration\n", - " of arrays by placing brackets after a variable name is deprecated and\n", - " will be removed in Stan 2.32.0. Instead use the array keyword before the\n", - " type. This can be changed automatically using the auto-format flag to\n", - " stanc\n", - "\n", - "--- Compiling, linking C++ code ---\n", - "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -c -include-pch stan/src/stan/model/model_header.hpp.gch -x c++ -o /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.o /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.hpp\n", - "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -Wl,-L,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.o src/cmdstan/main.o -Wl,-L,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data\n", - "rm -f /Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_draws2data.o\n", - "\n", - "17:50:29 - cmdstanpy - INFO - CmdStan start processing\n" + "21:41:27 - cmdstanpy - INFO - CmdStan start processing\n" ] }, { "data": { + "application/json": { + "ascii": false, + "bar_format": "{desc} |{bar}| {elapsed} {postfix[0][value]}", + "colour": null, + "elapsed": 0.007932186126708984, + "initial": 0, + "n": 0, + "ncols": null, + "nrows": 2, + "postfix": [ + { + "value": "Status" + } + ], + "prefix": "chain 1", + "rate": null, + "total": 22, + "unit": "it", + "unit_divisor": 1000, + "unit_scale": false + }, "application/vnd.jupyter.widget-view+json": { - "model_id": "856fd81897f842338dcaa2187cc3ba1d", + "model_id": "cf5c2e06a2c843318dd06e9fd052021d", "version_major": 2, "version_minor": 0 }, @@ -339,7 +230,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "17:50:29 - cmdstanpy - INFO - CmdStan done processing.\n" + "21:41:27 - cmdstanpy - INFO - CmdStan done processing.\n" ] }, { @@ -380,298 +271,4592 @@ " N_Eff/s\n", " R_hat\n", " \n", + " \n", + " \n", " \n", - 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" beta_tilde\n", - " 0.025194\n", + " sigma\n", + " 1.000000e-08\n", " NaN\n", - " 0.005654\n", - " 0.020834\n", - " 0.023166\n", - " 0.031582\n", + " 2.648300e-23\n", + " 1.000000e-08\n", + " 1.000000e-08\n", + " 1.000000e-08\n", " NaN\n", " NaN\n", " NaN\n", " \n", " \n", - " delta_tilde\n", - " 0.013350\n", + " predator__init\n", + " 4.000000e+00\n", " NaN\n", - " 0.001347\n", - " 0.012304\n", - " 0.012875\n", - " 0.014870\n", + " 2.665870e-15\n", + " 4.000000e+00\n", + " 4.000000e+00\n", + " 4.000000e+00\n", " NaN\n", " NaN\n", " NaN\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " prey__init\n", + " 3.000000e+01\n", + " NaN\n", + " 7.108980e-14\n", + " 3.000000e+01\n", + " 3.000000e+01\n", + " 3.000000e+01\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", - " y_tilde[19,1]\n", - " 44.503100\n", + " initial_outcome[1]\n", + " 4.000000e+00\n", " NaN\n", - " 47.742100\n", - " 15.642200\n", - " 18.256800\n", - " 99.610200\n", + " 2.600000e-15\n", + " 4.000000e+00\n", + " 4.000000e+00\n", + " 4.000000e+00\n", " NaN\n", " NaN\n", " NaN\n", " \n", " \n", - " y_tilde[19,2]\n", - " 20.489500\n", + " initial_outcome[2]\n", + " 3.000000e+01\n", " NaN\n", - " 25.318600\n", - " 3.256360\n", - " 8.653860\n", - " 49.558400\n", + " 7.100000e-14\n", + " 3.000000e+01\n", + " 3.000000e+01\n", + " 3.000000e+01\n", " NaN\n", " NaN\n", " NaN\n", " \n", " \n", - " y_tilde[20,1]\n", - " 52.825400\n", + " integrated_result[1,1]\n", + " 4.435270e+00\n", " NaN\n", - " 60.818400\n", - " 12.128700\n", - " 23.608500\n", - " 122.739000\n", + " 3.500000e-15\n", + " 4.435270e+00\n", + " 4.435270e+00\n", + " 4.435270e+00\n", " NaN\n", " NaN\n", " NaN\n", " \n", " \n", - " y_tilde[20,2]\n", - " 13.521200\n", + " integrated_result[1,2]\n", + " 4.638610e+01\n", " NaN\n", - " 13.880300\n", - " 4.881600\n", - " 6.149990\n", - " 29.532100\n", + " 7.810000e-14\n", + " 4.638610e+01\n", + " 4.638610e+01\n", + " 4.638610e+01\n", " NaN\n", " NaN\n", " NaN\n", " \n", " \n", - " sigma_tilde\n", - " 0.010077\n", + " integrated_result[2,1]\n", + " 7.858080e+00\n", " NaN\n", - " 0.000061\n", - " 0.010007\n", - " 0.010110\n", - " 0.010116\n", + " 4.440000e-14\n", + " 7.858080e+00\n", + " 7.858080e+00\n", + " 7.858080e+00\n", " NaN\n", " NaN\n", " NaN\n", " \n", - " \n", - "\n", - "

90 rows × 9 columns

\n", - "" - ], - "text/plain": [ - " Mean MCSE StdDev 5% 50% 95% \\\n", - "name \n", - "lp__ 0.000000 NaN 0.000000 0.000000 0.000000 0.000000 \n", - "alpha_tilde 0.483423 NaN 0.168838 0.311738 0.489268 0.649262 \n", - "gamma_tilde 0.772295 NaN 0.046225 0.719807 0.790143 0.806936 \n", - "beta_tilde 0.025194 NaN 0.005654 0.020834 0.023166 0.031582 \n", - "delta_tilde 0.013350 NaN 0.001347 0.012304 0.012875 0.014870 \n", - "... ... ... ... ... ... ... \n", - "y_tilde[19,1] 44.503100 NaN 47.742100 15.642200 18.256800 99.610200 \n", - "y_tilde[19,2] 20.489500 NaN 25.318600 3.256360 8.653860 49.558400 \n", - "y_tilde[20,1] 52.825400 NaN 60.818400 12.128700 23.608500 122.739000 \n", - "y_tilde[20,2] 13.521200 NaN 13.880300 4.881600 6.149990 29.532100 \n", - "sigma_tilde 0.010077 NaN 0.000061 0.010007 0.010110 0.010116 \n", - "\n", - " N_Eff N_Eff/s R_hat \n", - "name \n", - "lp__ NaN NaN NaN \n", - "alpha_tilde NaN NaN NaN \n", - "gamma_tilde NaN NaN NaN \n", - "beta_tilde NaN NaN NaN \n", - "delta_tilde NaN NaN NaN \n", - "... ... ... ... \n", - "y_tilde[19,1] NaN NaN NaN \n", - "y_tilde[19,2] NaN NaN NaN \n", - "y_tilde[20,1] NaN NaN NaN \n", - "y_tilde[20,2] NaN NaN NaN \n", - "sigma_tilde NaN NaN NaN \n", - "\n", - "[90 rows x 9 columns]" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_draws2data = {\n", - " \"n_obs_state\" : 2\n", - "}\n", - "\n", - "sf_path_draws2data = os.path.join(os.getcwd(), \"stan_file\", \"prey-predator_draws2data.stan\")\n", - "sm_draws2data = CmdStanModel(stan_file = sf_path_draws2data)\n", - "\n", - "prior_pred = sm_draws2data.sample(data=data_draws2data, iter_sampling=3, chains=1, fixed_param=True, iter_warmup=0)\n", - "prior_pred.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "595077fd-df8f-442c-a806-30c7e168f423", - "metadata": {}, - "source": [ - "For prior predictive checks, if the real observed data is indiscriminable from the simulated, we usually view it as a sign of pass. Real data is an external reference so as long as the predicted ranges are not too off, we give a pass to prior predictive check. Summary statistics such as N^th moments can be used for comparison. Few comments:\n", - "\n", - "a. we use real data below as a representation of our knowledge, so prior predictive check is not double dipping (using data twice)\n", - "\n", - "b. Bayesian prior corresponds to frequentist's regularization so having a tighter prior than posterior is not unnatrual; simply our determination to find a model concentrated around certain model configuration\n", - "\n", - "c. if tight prior is well-placed, it prevents diveregence from frustrating geometry and boosts sampling efficiency" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "82f4938e-f90c-4043-bcf5-c869c39676be", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(15, 8))\n", - "#compare with real \n", - "ax.plot(obs_stock_df.loc[:, ['Prey']], label = \"Real_Pey\")\n", - "ax.plot(obs_stock_df.loc[:, ['Predator']], label = \"Real_Predator\")\n", - "ax.plot(pd.DataFrame(prior_pred.y_tilde[:,:,0]).T.loc[:, :5])\n", - "ax.plot(pd.DataFrame(prior_pred.y_tilde[:,:,1]).T.loc[:, :5])\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "27ddb4c9-9f11-4324-84f4-d426fb01bc6f", - "metadata": { - "tags": [] - }, - "source": [ - "## Posterior predictive check" - ] - }, - { - "cell_type": "markdown", - "id": "8c54a353-1b50-4e1b-ac5d-bd9933ce6124", - "metadata": {}, + " \n", + " integrated_result[2,2]\n", + " 6.848940e+01\n", + " NaN\n", + " 2.985000e-13\n", + " 6.848940e+01\n", + " 6.848940e+01\n", + " 6.848940e+01\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 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8.437470e+01\n", + " 8.437470e+01\n", + " 8.437470e+01\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[2]\n", + " 2.586540e+03\n", + " NaN\n", + " 1.091940e-11\n", + " 2.586540e+03\n", + " 2.586540e+03\n", + " 2.586540e+03\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[3]\n", + " 6.051890e+09\n", + " NaN\n", + " 0.000000e+00\n", + " 6.051890e+09\n", + " 6.051890e+09\n", + " 6.051890e+09\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[4]\n", + " 5.138520e+22\n", + " NaN\n", + " 2.685698e+08\n", + " 5.138520e+22\n", + " 5.138520e+22\n", + " 5.138520e+22\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[5]\n", + " 3.240000e+21\n", + " NaN\n", + " 0.000000e+00\n", + " 3.240000e+21\n", + " 3.240000e+21\n", + " 3.240000e+21\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[6]\n", + " 2.391610e+13\n", + " NaN\n", + " 0.000000e+00\n", + " 2.391610e+13\n", + " 2.391610e+13\n", + " 2.391610e+13\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[7]\n", + " 3.763690e+07\n", + " NaN\n", + " 1.789030e-07\n", + " 3.763690e+07\n", + " 3.763690e+07\n", + " 3.763690e+07\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[8]\n", + " 2.183730e+04\n", + " NaN\n", + " 7.643580e-11\n", + " 2.183730e+04\n", + " 2.183730e+04\n", + " 2.183730e+04\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[9]\n", + " 4.737180e+02\n", + " NaN\n", + " 2.047300e-12\n", + " 4.737180e+02\n", + " 4.737180e+02\n", + " 4.737180e+02\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[10]\n", + " 8.040510e+01\n", + " NaN\n", + " 9.950000e-14\n", + " 8.040510e+01\n", + " 8.040510e+01\n", + " 8.040510e+01\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[11]\n", + " 5.390210e+01\n", + " NaN\n", + " 1.421000e-13\n", + " 5.390210e+01\n", + " 5.390210e+01\n", + " 5.390210e+01\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[12]\n", + " 2.033830e+02\n", + " NaN\n", + " 5.118000e-13\n", + " 2.033830e+02\n", + " 2.033830e+02\n", + " 2.033830e+02\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[13]\n", + " 1.404200e+05\n", + " NaN\n", + " 9.026700e-10\n", + " 1.404200e+05\n", + " 1.404200e+05\n", + " 1.404200e+05\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[14]\n", + " 3.189040e+15\n", + " NaN\n", + " 0.000000e+00\n", + " 3.189040e+15\n", + " 3.189040e+15\n", + " 3.189040e+15\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[15]\n", + " 1.976130e+24\n", + " NaN\n", + " 6.982814e+09\n", + " 1.976130e+24\n", + " 1.976130e+24\n", + " 1.976130e+24\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[16]\n", + " 8.919290e+17\n", + " NaN\n", + " 0.000000e+00\n", + " 8.919290e+17\n", + " 8.919290e+17\n", + " 8.919290e+17\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[17]\n", + " 3.202990e+10\n", + " NaN\n", + " 0.000000e+00\n", + " 3.202990e+10\n", + " 3.202990e+10\n", + " 3.202990e+10\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[18]\n", + " 8.469270e+05\n", + " NaN\n", + " 2.213000e-09\n", + " 8.469270e+05\n", + " 8.469270e+05\n", + " 8.469270e+05\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[19]\n", + " 3.011780e+03\n", + " NaN\n", + " 8.189500e-12\n", + " 3.011780e+03\n", + " 3.011780e+03\n", + " 3.011780e+03\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " predator_obs[20]\n", + " 1.818460e+02\n", + " NaN\n", + " 1.421000e-13\n", + " 1.818460e+02\n", + " 1.818460e+02\n", + " 1.818460e+02\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[1]\n", + " 1.397060e+20\n", + " NaN\n", + " 0.000000e+00\n", + " 1.397060e+20\n", + " 1.397060e+20\n", + " 1.397060e+20\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[2]\n", + " 5.553470e+29\n", + " NaN\n", + " 1.619291e+15\n", + " 5.553470e+29\n", + " 5.553470e+29\n", + " 5.553470e+29\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[3]\n", + " 1.795760e+35\n", + " NaN\n", + " 4.798553e+20\n", + " 1.795760e+35\n", + " 1.795760e+35\n", + " 1.795760e+35\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[4]\n", + " 1.485850e+21\n", + " NaN\n", + " 2.622750e+05\n", + " 1.485850e+21\n", + " 1.485850e+21\n", + " 1.485850e+21\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[5]\n", + " 1.178420e+08\n", + " NaN\n", + " 0.000000e+00\n", + " 1.178420e+08\n", + " 1.178420e+08\n", + " 1.178420e+08\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[6]\n", + " 3.606810e+04\n", + " NaN\n", + " 2.111080e-10\n", + " 3.606810e+04\n", + " 3.606810e+04\n", + " 3.606810e+04\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[7]\n", + " 1.217540e+04\n", + " NaN\n", + " 5.095720e-11\n", + " 1.217540e+04\n", + " 1.217540e+04\n", + " 1.217540e+04\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[8]\n", + " 7.516690e+04\n", + " NaN\n", + " 1.455920e-10\n", + " 7.516690e+04\n", + " 7.516690e+04\n", + " 7.516690e+04\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[9]\n", + " 6.067630e+06\n", + " NaN\n", + " 3.168080e-08\n", + " 6.067630e+06\n", + " 6.067630e+06\n", + " 6.067630e+06\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[10]\n", + " 1.512850e+10\n", + " NaN\n", + " 0.000000e+00\n", + " 1.512850e+10\n", + " 1.512850e+10\n", + " 1.512850e+10\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[11]\n", + " 5.494240e+15\n", + " NaN\n", + " 0.000000e+00\n", + " 5.494240e+15\n", + " 5.494240e+15\n", + " 5.494240e+15\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[12]\n", + " 1.225440e+24\n", + " NaN\n", + " 4.297116e+09\n", + " 1.225440e+24\n", + " 1.225440e+24\n", + " 1.225440e+24\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[13]\n", + " 3.605190e+33\n", + " NaN\n", + " 3.460495e+18\n", + " 3.605190e+33\n", + " 3.605190e+33\n", + " 3.605190e+33\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[14]\n", + " 3.630470e+31\n", + " NaN\n", + " 9.462292e+16\n", + " 3.630470e+31\n", + " 3.630470e+31\n", + " 3.630470e+31\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[15]\n", + " 6.891270e+13\n", + " NaN\n", + " 0.000000e+00\n", + " 6.891270e+13\n", + " 6.891270e+13\n", + " 6.891270e+13\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[16]\n", + " 8.099040e+05\n", + " NaN\n", + " 1.980050e-09\n", + " 8.099040e+05\n", + " 8.099040e+05\n", + " 8.099040e+05\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[17]\n", + " 1.414850e+04\n", + " NaN\n", + " 8.371540e-11\n", + " 1.414850e+04\n", + " 1.414850e+04\n", + " 1.414850e+04\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[18]\n", + " 1.980560e+04\n", + " NaN\n", + " 1.091940e-11\n", + " 1.980560e+04\n", + " 1.980560e+04\n", + " 1.980560e+04\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[19]\n", + " 3.525110e+05\n", + " NaN\n", + " 1.048260e-09\n", + " 3.525110e+05\n", + " 3.525110e+05\n", + " 3.525110e+05\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " prey_obs[20]\n", + " 1.071110e+08\n", + " NaN\n", + " 0.000000e+00\n", + " 1.071110e+08\n", + " 1.071110e+08\n", + " 1.071110e+08\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " Mean MCSE StdDev 5% \\\n", + "alpha 5.500000e-01 NaN 2.554790e-15 5.500000e-01 \n", + "gamma 8.000000e-01 NaN 3.110180e-15 8.000000e-01 \n", + "beta 2.800000e-02 NaN 6.248130e-17 2.800000e-02 \n", + "delta 2.400000e-02 NaN 5.901010e-17 2.400000e-02 \n", + "sigma 1.000000e-08 NaN 2.648300e-23 1.000000e-08 \n", + "predator__init 4.000000e+00 NaN 2.665870e-15 4.000000e+00 \n", + "prey__init 3.000000e+01 NaN 7.108980e-14 3.000000e+01 \n", + "initial_outcome[1] 4.000000e+00 NaN 2.600000e-15 4.000000e+00 \n", + "initial_outcome[2] 3.000000e+01 NaN 7.100000e-14 3.000000e+01 \n", + "integrated_result[1,1] 4.435270e+00 NaN 3.500000e-15 4.435270e+00 \n", + "integrated_result[1,2] 4.638610e+01 NaN 7.810000e-14 4.638610e+01 \n", + "integrated_result[2,1] 7.858080e+00 NaN 4.440000e-14 7.858080e+00 \n", + "integrated_result[2,2] 6.848940e+01 NaN 2.985000e-13 6.848940e+01 \n", + "integrated_result[3,1] 2.252360e+01 NaN 1.101000e-13 2.252360e+01 \n", + "integrated_result[3,2] 8.117590e+01 NaN 2.559000e-13 8.117590e+01 \n", + "integrated_result[4,1] 5.229360e+01 NaN 8.530000e-14 5.229360e+01 \n", + "integrated_result[4,2] 4.875030e+01 NaN 1.420000e-14 4.875030e+01 \n", + "integrated_result[5,1] 4.952990e+01 NaN 2.843000e-13 4.952990e+01 \n", + "integrated_result[5,2] 1.858490e+01 NaN 4.970000e-14 1.858490e+01 \n", + "integrated_result[6,1] 3.080560e+01 NaN 7.810000e-14 3.080560e+01 \n", + "integrated_result[6,2] 1.049320e+01 NaN 1.590000e-14 1.049320e+01 \n", + "integrated_result[7,1] 1.744350e+01 NaN 3.550000e-14 1.744350e+01 \n", + "integrated_result[7,2] 9.407170e+00 NaN 1.590000e-14 9.407170e+00 \n", + "integrated_result[8,1] 9.991370e+00 NaN 0.000000e+00 9.991370e+00 \n", + "integrated_result[8,2] 1.122750e+01 NaN 4.260000e-14 1.122750e+01 \n", + "integrated_result[9,1] 6.160610e+00 NaN 8.800000e-15 6.160610e+00 \n", + "integrated_result[9,2] 1.561850e+01 NaN 1.770000e-14 1.561850e+01 \n", + "integrated_result[10,1] 4.387080e+00 NaN 8.000000e-16 4.387080e+00 \n", + "integrated_result[10,2] 2.343980e+01 NaN 3.500000e-15 2.343980e+01 \n", + "integrated_result[11,1] 3.987170e+00 NaN 1.730000e-14 3.987170e+00 \n", + "integrated_result[11,2] 3.624250e+01 NaN 1.279000e-13 3.624250e+01 \n", + "integrated_result[12,1] 5.315090e+00 NaN 1.060000e-14 5.315090e+00 \n", + "integrated_result[12,2] 5.546530e+01 NaN 1.350000e-13 5.546530e+01 \n", + "integrated_result[13,1] 1.185240e+01 NaN 1.240000e-14 1.185240e+01 \n", + "integrated_result[13,2] 7.726770e+01 NaN 1.279000e-13 7.726770e+01 \n", + "integrated_result[14,1] 3.569850e+01 NaN 9.240000e-14 3.569850e+01 \n", + "integrated_result[14,2] 7.266950e+01 NaN 4.260000e-14 7.266950e+01 \n", + "integrated_result[15,1] 5.594320e+01 NaN 1.492000e-13 5.594320e+01 \n", + "integrated_result[15,2] 3.186390e+01 NaN 1.066000e-13 3.186390e+01 \n", + "integrated_result[16,1] 4.133220e+01 NaN 7.100000e-14 4.133220e+01 \n", + "integrated_result[16,2] 1.360470e+01 NaN 3.550000e-14 1.360470e+01 \n", + "integrated_result[17,1] 2.418990e+01 NaN 7.100000e-14 2.418990e+01 \n", + "integrated_result[17,2] 9.557360e+00 NaN 3.370000e-14 9.557360e+00 \n", + "integrated_result[18,1] 1.364940e+01 NaN 6.390000e-14 1.364940e+01 \n", + "integrated_result[18,2] 9.893720e+00 NaN 3.500000e-15 9.893720e+00 \n", + "integrated_result[19,1] 8.010290e+00 NaN 3.550000e-14 8.010290e+00 \n", + "integrated_result[19,2] 1.277280e+01 NaN 2.480000e-14 1.277280e+01 \n", + "integrated_result[20,1] 5.203160e+00 NaN 1.060000e-14 5.203160e+00 \n", + "integrated_result[20,2] 1.848940e+01 NaN 7.100000e-15 1.848940e+01 \n", + "predator[1] 4.435270e+00 NaN 3.500000e-15 4.435270e+00 \n", + "predator[2] 7.858080e+00 NaN 4.440000e-14 7.858080e+00 \n", + "predator[3] 2.252360e+01 NaN 1.101000e-13 2.252360e+01 \n", + "predator[4] 5.229360e+01 NaN 8.530000e-14 5.229360e+01 \n", + "predator[5] 4.952990e+01 NaN 2.843000e-13 4.952990e+01 \n", + "predator[6] 3.080560e+01 NaN 7.810000e-14 3.080560e+01 \n", + "predator[7] 1.744350e+01 NaN 3.550000e-14 1.744350e+01 \n", + "predator[8] 9.991370e+00 NaN 0.000000e+00 9.991370e+00 \n", + "predator[9] 6.160610e+00 NaN 8.800000e-15 6.160610e+00 \n", + "predator[10] 4.387080e+00 NaN 8.000000e-16 4.387080e+00 \n", + "predator[11] 3.987170e+00 NaN 1.730000e-14 3.987170e+00 \n", + "predator[12] 5.315090e+00 NaN 1.060000e-14 5.315090e+00 \n", + "predator[13] 1.185240e+01 NaN 1.240000e-14 1.185240e+01 \n", + "predator[14] 3.569850e+01 NaN 9.240000e-14 3.569850e+01 \n", + "predator[15] 5.594320e+01 NaN 1.492000e-13 5.594320e+01 \n", + "predator[16] 4.133220e+01 NaN 7.100000e-14 4.133220e+01 \n", + "predator[17] 2.418990e+01 NaN 7.100000e-14 2.418990e+01 \n", + "predator[18] 1.364940e+01 NaN 6.390000e-14 1.364940e+01 \n", + "predator[19] 8.010290e+00 NaN 3.550000e-14 8.010290e+00 \n", + "predator[20] 5.203160e+00 NaN 1.060000e-14 5.203160e+00 \n", + "prey[1] 4.638610e+01 NaN 7.810000e-14 4.638610e+01 \n", + "prey[2] 6.848940e+01 NaN 2.985000e-13 6.848940e+01 \n", + "prey[3] 8.117590e+01 NaN 2.559000e-13 8.117590e+01 \n", + "prey[4] 4.875030e+01 NaN 1.420000e-14 4.875030e+01 \n", + "prey[5] 1.858490e+01 NaN 4.970000e-14 1.858490e+01 \n", + "prey[6] 1.049320e+01 NaN 1.590000e-14 1.049320e+01 \n", + "prey[7] 9.407170e+00 NaN 1.590000e-14 9.407170e+00 \n", + "prey[8] 1.122750e+01 NaN 4.260000e-14 1.122750e+01 \n", + "prey[9] 1.561850e+01 NaN 1.770000e-14 1.561850e+01 \n", + "prey[10] 2.343980e+01 NaN 3.500000e-15 2.343980e+01 \n", + "prey[11] 3.624250e+01 NaN 1.279000e-13 3.624250e+01 \n", + "prey[12] 5.546530e+01 NaN 1.350000e-13 5.546530e+01 \n", + "prey[13] 7.726770e+01 NaN 1.279000e-13 7.726770e+01 \n", + "prey[14] 7.266950e+01 NaN 4.260000e-14 7.266950e+01 \n", + "prey[15] 3.186390e+01 NaN 1.066000e-13 3.186390e+01 \n", + "prey[16] 1.360470e+01 NaN 3.550000e-14 1.360470e+01 \n", + "prey[17] 9.557360e+00 NaN 3.370000e-14 9.557360e+00 \n", + "prey[18] 9.893720e+00 NaN 3.500000e-15 9.893720e+00 \n", + "prey[19] 1.277280e+01 NaN 2.480000e-14 1.277280e+01 \n", + "prey[20] 1.848940e+01 NaN 7.100000e-15 1.848940e+01 \n", + "predator_obs[1] 8.437470e+01 NaN 1.848000e-13 8.437470e+01 \n", + "predator_obs[2] 2.586540e+03 NaN 1.091940e-11 2.586540e+03 \n", + "predator_obs[3] 6.051890e+09 NaN 0.000000e+00 6.051890e+09 \n", + "predator_obs[4] 5.138520e+22 NaN 2.685698e+08 5.138520e+22 \n", + "predator_obs[5] 3.240000e+21 NaN 0.000000e+00 3.240000e+21 \n", + "predator_obs[6] 2.391610e+13 NaN 0.000000e+00 2.391610e+13 \n", + "predator_obs[7] 3.763690e+07 NaN 1.789030e-07 3.763690e+07 \n", + "predator_obs[8] 2.183730e+04 NaN 7.643580e-11 2.183730e+04 \n", + "predator_obs[9] 4.737180e+02 NaN 2.047300e-12 4.737180e+02 \n", + "predator_obs[10] 8.040510e+01 NaN 9.950000e-14 8.040510e+01 \n", + "predator_obs[11] 5.390210e+01 NaN 1.421000e-13 5.390210e+01 \n", + "predator_obs[12] 2.033830e+02 NaN 5.118000e-13 2.033830e+02 \n", + "predator_obs[13] 1.404200e+05 NaN 9.026700e-10 1.404200e+05 \n", + "predator_obs[14] 3.189040e+15 NaN 0.000000e+00 3.189040e+15 \n", + "predator_obs[15] 1.976130e+24 NaN 6.982814e+09 1.976130e+24 \n", + "predator_obs[16] 8.919290e+17 NaN 0.000000e+00 8.919290e+17 \n", + "predator_obs[17] 3.202990e+10 NaN 0.000000e+00 3.202990e+10 \n", + "predator_obs[18] 8.469270e+05 NaN 2.213000e-09 8.469270e+05 \n", + "predator_obs[19] 3.011780e+03 NaN 8.189500e-12 3.011780e+03 \n", + "predator_obs[20] 1.818460e+02 NaN 1.421000e-13 1.818460e+02 \n", + "prey_obs[1] 1.397060e+20 NaN 0.000000e+00 1.397060e+20 \n", + "prey_obs[2] 5.553470e+29 NaN 1.619291e+15 5.553470e+29 \n", + "prey_obs[3] 1.795760e+35 NaN 4.798553e+20 1.795760e+35 \n", + "prey_obs[4] 1.485850e+21 NaN 2.622750e+05 1.485850e+21 \n", + "prey_obs[5] 1.178420e+08 NaN 0.000000e+00 1.178420e+08 \n", + "prey_obs[6] 3.606810e+04 NaN 2.111080e-10 3.606810e+04 \n", + "prey_obs[7] 1.217540e+04 NaN 5.095720e-11 1.217540e+04 \n", + "prey_obs[8] 7.516690e+04 NaN 1.455920e-10 7.516690e+04 \n", + "prey_obs[9] 6.067630e+06 NaN 3.168080e-08 6.067630e+06 \n", + "prey_obs[10] 1.512850e+10 NaN 0.000000e+00 1.512850e+10 \n", + "prey_obs[11] 5.494240e+15 NaN 0.000000e+00 5.494240e+15 \n", + "prey_obs[12] 1.225440e+24 NaN 4.297116e+09 1.225440e+24 \n", + "prey_obs[13] 3.605190e+33 NaN 3.460495e+18 3.605190e+33 \n", + "prey_obs[14] 3.630470e+31 NaN 9.462292e+16 3.630470e+31 \n", + "prey_obs[15] 6.891270e+13 NaN 0.000000e+00 6.891270e+13 \n", + "prey_obs[16] 8.099040e+05 NaN 1.980050e-09 8.099040e+05 \n", + "prey_obs[17] 1.414850e+04 NaN 8.371540e-11 1.414850e+04 \n", + "prey_obs[18] 1.980560e+04 NaN 1.091940e-11 1.980560e+04 \n", + "prey_obs[19] 3.525110e+05 NaN 1.048260e-09 3.525110e+05 \n", + "prey_obs[20] 1.071110e+08 NaN 0.000000e+00 1.071110e+08 \n", + "\n", + " 50% 95% N_Eff N_Eff/s R_hat \n", + "alpha 5.500000e-01 5.500000e-01 NaN NaN NaN \n", + "gamma 8.000000e-01 8.000000e-01 NaN NaN NaN \n", + "beta 2.800000e-02 2.800000e-02 NaN NaN NaN \n", + "delta 2.400000e-02 2.400000e-02 NaN NaN NaN \n", + "sigma 1.000000e-08 1.000000e-08 NaN NaN NaN \n", + "predator__init 4.000000e+00 4.000000e+00 NaN NaN NaN \n", + "prey__init 3.000000e+01 3.000000e+01 NaN NaN NaN \n", + "initial_outcome[1] 4.000000e+00 4.000000e+00 NaN NaN NaN \n", + "initial_outcome[2] 3.000000e+01 3.000000e+01 NaN NaN NaN \n", + "integrated_result[1,1] 4.435270e+00 4.435270e+00 NaN NaN NaN \n", + "integrated_result[1,2] 4.638610e+01 4.638610e+01 NaN NaN NaN \n", + "integrated_result[2,1] 7.858080e+00 7.858080e+00 NaN NaN NaN \n", + "integrated_result[2,2] 6.848940e+01 6.848940e+01 NaN NaN NaN \n", + "integrated_result[3,1] 2.252360e+01 2.252360e+01 NaN NaN NaN \n", + "integrated_result[3,2] 8.117590e+01 8.117590e+01 NaN NaN NaN \n", + "integrated_result[4,1] 5.229360e+01 5.229360e+01 NaN NaN NaN \n", + "integrated_result[4,2] 4.875030e+01 4.875030e+01 NaN NaN NaN \n", + "integrated_result[5,1] 4.952990e+01 4.952990e+01 NaN NaN NaN \n", + "integrated_result[5,2] 1.858490e+01 1.858490e+01 NaN NaN NaN \n", + "integrated_result[6,1] 3.080560e+01 3.080560e+01 NaN NaN NaN \n", + "integrated_result[6,2] 1.049320e+01 1.049320e+01 NaN NaN NaN \n", + "integrated_result[7,1] 1.744350e+01 1.744350e+01 NaN NaN NaN \n", + "integrated_result[7,2] 9.407170e+00 9.407170e+00 NaN NaN NaN \n", + "integrated_result[8,1] 9.991370e+00 9.991370e+00 NaN NaN NaN \n", + "integrated_result[8,2] 1.122750e+01 1.122750e+01 NaN NaN NaN \n", + "integrated_result[9,1] 6.160610e+00 6.160610e+00 NaN NaN NaN \n", + "integrated_result[9,2] 1.561850e+01 1.561850e+01 NaN NaN NaN \n", + "integrated_result[10,1] 4.387080e+00 4.387080e+00 NaN NaN NaN \n", + "integrated_result[10,2] 2.343980e+01 2.343980e+01 NaN NaN NaN \n", + "integrated_result[11,1] 3.987170e+00 3.987170e+00 NaN NaN NaN \n", + "integrated_result[11,2] 3.624250e+01 3.624250e+01 NaN NaN NaN \n", + "integrated_result[12,1] 5.315090e+00 5.315090e+00 NaN NaN NaN \n", + "integrated_result[12,2] 5.546530e+01 5.546530e+01 NaN NaN NaN \n", + "integrated_result[13,1] 1.185240e+01 1.185240e+01 NaN NaN NaN \n", + "integrated_result[13,2] 7.726770e+01 7.726770e+01 NaN NaN NaN \n", + "integrated_result[14,1] 3.569850e+01 3.569850e+01 NaN NaN NaN \n", + "integrated_result[14,2] 7.266950e+01 7.266950e+01 NaN NaN NaN \n", + "integrated_result[15,1] 5.594320e+01 5.594320e+01 NaN NaN NaN \n", + "integrated_result[15,2] 3.186390e+01 3.186390e+01 NaN NaN NaN \n", + "integrated_result[16,1] 4.133220e+01 4.133220e+01 NaN NaN NaN \n", + "integrated_result[16,2] 1.360470e+01 1.360470e+01 NaN NaN NaN \n", + "integrated_result[17,1] 2.418990e+01 2.418990e+01 NaN NaN NaN \n", + "integrated_result[17,2] 9.557360e+00 9.557360e+00 NaN NaN NaN \n", + "integrated_result[18,1] 1.364940e+01 1.364940e+01 NaN NaN NaN \n", + "integrated_result[18,2] 9.893720e+00 9.893720e+00 NaN NaN NaN \n", + "integrated_result[19,1] 8.010290e+00 8.010290e+00 NaN NaN NaN \n", + "integrated_result[19,2] 1.277280e+01 1.277280e+01 NaN NaN NaN \n", + "integrated_result[20,1] 5.203160e+00 5.203160e+00 NaN NaN NaN \n", + "integrated_result[20,2] 1.848940e+01 1.848940e+01 NaN NaN NaN \n", + "predator[1] 4.435270e+00 4.435270e+00 NaN NaN NaN \n", + "predator[2] 7.858080e+00 7.858080e+00 NaN NaN NaN \n", + "predator[3] 2.252360e+01 2.252360e+01 NaN NaN NaN \n", + "predator[4] 5.229360e+01 5.229360e+01 NaN NaN NaN \n", + "predator[5] 4.952990e+01 4.952990e+01 NaN NaN NaN \n", + "predator[6] 3.080560e+01 3.080560e+01 NaN NaN NaN \n", + "predator[7] 1.744350e+01 1.744350e+01 NaN NaN NaN \n", + "predator[8] 9.991370e+00 9.991370e+00 NaN NaN NaN \n", + "predator[9] 6.160610e+00 6.160610e+00 NaN NaN NaN \n", + "predator[10] 4.387080e+00 4.387080e+00 NaN NaN NaN \n", + "predator[11] 3.987170e+00 3.987170e+00 NaN NaN NaN \n", + "predator[12] 5.315090e+00 5.315090e+00 NaN NaN NaN \n", + "predator[13] 1.185240e+01 1.185240e+01 NaN NaN NaN \n", + "predator[14] 3.569850e+01 3.569850e+01 NaN NaN NaN \n", + "predator[15] 5.594320e+01 5.594320e+01 NaN NaN NaN \n", + "predator[16] 4.133220e+01 4.133220e+01 NaN NaN NaN \n", + "predator[17] 2.418990e+01 2.418990e+01 NaN NaN NaN \n", + "predator[18] 1.364940e+01 1.364940e+01 NaN NaN NaN \n", + "predator[19] 8.010290e+00 8.010290e+00 NaN NaN NaN \n", + "predator[20] 5.203160e+00 5.203160e+00 NaN NaN NaN \n", + "prey[1] 4.638610e+01 4.638610e+01 NaN NaN NaN \n", + "prey[2] 6.848940e+01 6.848940e+01 NaN NaN NaN \n", + "prey[3] 8.117590e+01 8.117590e+01 NaN NaN NaN \n", + "prey[4] 4.875030e+01 4.875030e+01 NaN NaN NaN \n", + "prey[5] 1.858490e+01 1.858490e+01 NaN NaN NaN \n", + "prey[6] 1.049320e+01 1.049320e+01 NaN NaN NaN \n", + "prey[7] 9.407170e+00 9.407170e+00 NaN NaN NaN \n", + "prey[8] 1.122750e+01 1.122750e+01 NaN NaN NaN \n", + "prey[9] 1.561850e+01 1.561850e+01 NaN NaN NaN \n", + "prey[10] 2.343980e+01 2.343980e+01 NaN NaN NaN \n", + "prey[11] 3.624250e+01 3.624250e+01 NaN NaN NaN \n", + "prey[12] 5.546530e+01 5.546530e+01 NaN NaN NaN \n", + "prey[13] 7.726770e+01 7.726770e+01 NaN NaN NaN \n", + "prey[14] 7.266950e+01 7.266950e+01 NaN NaN NaN \n", + "prey[15] 3.186390e+01 3.186390e+01 NaN NaN NaN \n", + "prey[16] 1.360470e+01 1.360470e+01 NaN NaN NaN \n", + "prey[17] 9.557360e+00 9.557360e+00 NaN NaN NaN \n", + "prey[18] 9.893720e+00 9.893720e+00 NaN NaN NaN \n", + "prey[19] 1.277280e+01 1.277280e+01 NaN NaN NaN \n", + "prey[20] 1.848940e+01 1.848940e+01 NaN NaN NaN \n", + "predator_obs[1] 8.437470e+01 8.437470e+01 NaN NaN NaN \n", + "predator_obs[2] 2.586540e+03 2.586540e+03 NaN NaN NaN \n", + "predator_obs[3] 6.051890e+09 6.051890e+09 NaN NaN NaN \n", + "predator_obs[4] 5.138520e+22 5.138520e+22 NaN NaN NaN \n", + "predator_obs[5] 3.240000e+21 3.240000e+21 NaN NaN NaN \n", + "predator_obs[6] 2.391610e+13 2.391610e+13 NaN NaN NaN \n", + "predator_obs[7] 3.763690e+07 3.763690e+07 NaN NaN NaN \n", + "predator_obs[8] 2.183730e+04 2.183730e+04 NaN NaN NaN \n", + "predator_obs[9] 4.737180e+02 4.737180e+02 NaN NaN NaN \n", + "predator_obs[10] 8.040510e+01 8.040510e+01 NaN NaN NaN \n", + "predator_obs[11] 5.390210e+01 5.390210e+01 NaN NaN NaN \n", + "predator_obs[12] 2.033830e+02 2.033830e+02 NaN NaN NaN \n", + "predator_obs[13] 1.404200e+05 1.404200e+05 NaN NaN NaN \n", + "predator_obs[14] 3.189040e+15 3.189040e+15 NaN NaN NaN \n", + "predator_obs[15] 1.976130e+24 1.976130e+24 NaN NaN NaN \n", + "predator_obs[16] 8.919290e+17 8.919290e+17 NaN NaN NaN \n", + "predator_obs[17] 3.202990e+10 3.202990e+10 NaN NaN NaN \n", + "predator_obs[18] 8.469270e+05 8.469270e+05 NaN NaN NaN \n", + "predator_obs[19] 3.011780e+03 3.011780e+03 NaN NaN NaN \n", + "predator_obs[20] 1.818460e+02 1.818460e+02 NaN NaN NaN \n", + "prey_obs[1] 1.397060e+20 1.397060e+20 NaN NaN NaN \n", + "prey_obs[2] 5.553470e+29 5.553470e+29 NaN NaN NaN \n", + "prey_obs[3] 1.795760e+35 1.795760e+35 NaN NaN NaN \n", + "prey_obs[4] 1.485850e+21 1.485850e+21 NaN NaN NaN \n", + "prey_obs[5] 1.178420e+08 1.178420e+08 NaN NaN NaN \n", + "prey_obs[6] 3.606810e+04 3.606810e+04 NaN NaN NaN \n", + "prey_obs[7] 1.217540e+04 1.217540e+04 NaN NaN NaN \n", + "prey_obs[8] 7.516690e+04 7.516690e+04 NaN NaN NaN \n", + "prey_obs[9] 6.067630e+06 6.067630e+06 NaN NaN NaN \n", + "prey_obs[10] 1.512850e+10 1.512850e+10 NaN NaN NaN \n", + "prey_obs[11] 5.494240e+15 5.494240e+15 NaN NaN NaN \n", + "prey_obs[12] 1.225440e+24 1.225440e+24 NaN NaN NaN \n", + "prey_obs[13] 3.605190e+33 3.605190e+33 NaN NaN NaN \n", + "prey_obs[14] 3.630470e+31 3.630470e+31 NaN NaN NaN \n", + "prey_obs[15] 6.891270e+13 6.891270e+13 NaN NaN NaN \n", + "prey_obs[16] 8.099040e+05 8.099040e+05 NaN NaN NaN \n", + "prey_obs[17] 1.414850e+04 1.414850e+04 NaN NaN NaN \n", + "prey_obs[18] 1.980560e+04 1.980560e+04 NaN NaN NaN \n", + "prey_obs[19] 3.525110e+05 3.525110e+05 NaN NaN NaN \n", + "prey_obs[20] 1.071110e+08 1.071110e+08 NaN NaN NaN " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.set_option('display.max_rows', 500)\n", + "draws2data_model = CmdStanModel(stan_file=\"stan_files/prey-predator_draws2data.stan\")\n", + "draws2data_model.sample(data=data_data2draws, fixed_param=True).summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2df7336e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "vensim_df = pd.read_csv(\"vensim_models/prey-predator/output.csv\")\n", + "predator_obs = vensim_df[vensim_df['Time']==\"Predator\"]\n", + "prey_obs = vensim_df[vensim_df['Time']==\"Prey\"]\n", + "print(predator_obs)\n", + "print(prey_obs)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "175723e8", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21:35:47 - cmdstanpy - INFO - CmdStan start processing\n" + ] + }, + { + "data": { + "application/json": { + "ascii": false, + "bar_format": "{desc} |{bar}| {elapsed} {postfix[0][value]}", + "colour": null, + "elapsed": 0.02982783317565918, + "initial": 0, + "n": 0, + "ncols": null, + "nrows": 2, + "postfix": [ + { + "value": "Status" + } + ], + "prefix": "chain 1", + "rate": null, + "total": 22, + "unit": "it", + "unit_divisor": 1000, + "unit_scale": false + }, + "application/vnd.jupyter.widget-view+json": { + "model_id": "59e61f6b616a4da999fdcea533eff501", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 1 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/json": { + "ascii": false, + "bar_format": "{desc} |{bar}| {elapsed} {postfix[0][value]}", + "colour": null, + "elapsed": 0.011063098907470703, + "initial": 0, + "n": 0, + "ncols": null, + "nrows": 2, + "postfix": [ + { + "value": "Status" + } + ], + "prefix": "chain 2", + "rate": null, + "total": 22, + "unit": "it", + "unit_divisor": 1000, + "unit_scale": false + }, + "application/vnd.jupyter.widget-view+json": { + "model_id": "791b58947f644a90900be3c5c23d1362", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 2 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/json": { + "ascii": false, + "bar_format": "{desc} |{bar}| {elapsed} {postfix[0][value]}", + "colour": null, + "elapsed": 0.008853912353515625, + "initial": 0, + "n": 0, + "ncols": null, + "nrows": 2, + "postfix": [ + { + "value": "Status" + } + ], + "prefix": "chain 3", + "rate": null, + "total": 22, + "unit": "it", + "unit_divisor": 1000, + "unit_scale": false + }, + "application/vnd.jupyter.widget-view+json": { + "model_id": "f7561158c44949719db91ffa25878cc2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 3 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/json": { + "ascii": false, + "bar_format": "{desc} |{bar}| {elapsed} {postfix[0][value]}", + "colour": null, + "elapsed": 0.008424043655395508, + "initial": 0, + "n": 0, + "ncols": null, + "nrows": 2, + "postfix": [ + { + "value": "Status" + } + ], + "prefix": "chain 4", + "rate": null, + "total": 22, + "unit": "it", + "unit_divisor": 1000, + "unit_scale": false + }, + "application/vnd.jupyter.widget-view+json": { + "model_id": "7f9ea84bf6464b97952ed2e48400c280", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 4 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21:35:48 - cmdstanpy - INFO - CmdStan done processing.\n", + "21:35:48 - cmdstanpy - WARNING - Non-fatal error during sampling:\n", + "Exception: lognormal_rng: Scale parameter is -0.35927, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0993442, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.660059, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.281241, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.370239, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.461061, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.06516, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.29728, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.543055, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.89186, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.407345, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.133491, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.320497, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0575835, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.776685, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.481048, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.033315, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.809869, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.92462, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.258852, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.50955, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.942951, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.32302, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.45268, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.55317, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.40387, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.909495, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.180182, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.506396, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0856983, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.14035, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.264944, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.208248, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.41345, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.65845, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.443364, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.304627, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.603235, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.725652, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.705265, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.3365, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.31374, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.81671, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.142696, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.285047, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.651361, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.736793, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.89878, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0576807, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.65057, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.810977, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.588169, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.5184, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.49928, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.272202, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.153855, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.93218, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.37493, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.71005, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.381298, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.20178, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.672085, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.723975, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.176555, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.156593, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.999396, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.244627, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.66332, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.377372, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.416673, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.676613, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.35462, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.770583, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.617932, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.861086, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.72747, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.320969, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.75121, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.56646, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.360464, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.491906, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.312387, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.39292, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0030473, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0374209, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.46099, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0340156, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.37009, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.128983, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.82666, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.536448, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.935713, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.585806, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.320439, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.153323, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.363018, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0633754, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.86716, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0729951, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.712894, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.239762, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.754347, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.337862, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.118582, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0377957, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.496742, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.41993, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.927733, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.664246, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0606163, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.414403, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.611026, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -3.0017, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.53478, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.329186, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.892092, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.609596, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.414647, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.968789, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.46523, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.140659, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.750219, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.893711, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.822057, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.527799, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.15895, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.12452, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.37493, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.31121, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.957128, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.40359, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.34293, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0219288, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0635329, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.85278, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.39879, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.171495, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.297811, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.606641, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.54392, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.597124, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0181431, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.281291, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.88712, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.08893, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.759493, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.05882, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.9906, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.753432, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.380215, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.727311, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.693967, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.694499, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.906346, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.333378, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.132804, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.879842, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.60988, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.783468, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0123155, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.568175, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.198205, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.957773, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.120075, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.66145, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0559885, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.23058, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.484613, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.202723, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.682424, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.370654, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0317011, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.36481, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.605476, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.445622, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.133932, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.45793, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.214253, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.88378, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.11161, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0583002, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.527803, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.935408, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.52982, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.72023, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.634493, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.554356, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.742121, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.708876, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.30032, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.65031, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.670152, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.00948, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.55566, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.124594, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.59017, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.64146, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.152171, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.64517, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.65356, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.622368, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.280646, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.25198, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.347419, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.3094, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.367862, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.74321, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.16678, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.40237, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.131857, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.351974, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.473331, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0181901, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.17162, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.361497, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.11496, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.879243, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.666677, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.488432, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.785002, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.628846, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0592864, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.157297, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.308373, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.535939, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.478475, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.863638, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.299253, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.400924, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.292352, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.373107, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.567744, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.837864, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.561889, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.864482, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.345946, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.97852, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.730909, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.36102, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.902606, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.903555, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.204649, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.471874, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.108939, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.20566, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.00531, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.111763, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.00817381, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.324807, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.89694, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.68592, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.250985, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.05474, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.39176, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.79127, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0948379, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0364849, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.835351, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0215847, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.161725, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.912573, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.646892, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.62594, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.799666, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.327922, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0195257, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.806177, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.21171, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.589463, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.150943, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.728764, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.232215, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.231424, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.8392, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.704811, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.515764, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.77922, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.501661, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.18257, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.747069, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.185354, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.169828, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.238099, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.476668, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.272794, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.46597, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.548483, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.18012, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.721149, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.339771, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.646572, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.518573, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.408684, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.24157, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.62918, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.216509, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.13811, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.550478, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.46374, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.22481, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.963143, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.32564, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.430683, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.303658, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.41753, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.99976, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0315227, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.70441, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.816263, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.906941, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.5081, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.401578, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.484559, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.40865, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.791225, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.97156, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.187545, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.700332, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.12789, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.875139, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.906447, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.35538, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.843937, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.743275, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.10514, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.487319, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.15799, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.49682, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.852248, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.48632, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.41054, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.00876, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.73179, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.87524, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0261832, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.955303, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.792209, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0434208, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.474958, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.529296, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.972597, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.12932, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.28489, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.213303, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.549845, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.960324, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.983658, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.07618, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0239392, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.898212, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.264877, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.750579, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.855571, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.38399, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.309332, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.28746, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.633202, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.471537, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.414635, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.269273, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.596826, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0780305, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.354556, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.33238, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.171412, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.85984, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.77508, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.44685, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.363044, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0044119, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.948895, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.257678, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.06943, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.531964, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.351931, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.79136, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.389175, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.814451, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.133499, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.76029, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0334785, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.60996, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.353229, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.1322, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.35186, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.20157, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.514985, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.08699, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.642138, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.413166, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.280029, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.170022, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.4522, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.04413, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.517626, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.471539, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.535026, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.276228, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.295497, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.387831, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.71092, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.988063, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.81109, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0866864, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.861023, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.79088, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.31282, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.60401, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.8869, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0884652, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0882868, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.489689, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0992156, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.85724, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.769873, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.359664, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0858813, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.14264, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.61375, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.287743, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.84064, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.623182, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.310513, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.377424, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.78918, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.934279, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.44912, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0982832, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.634226, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.0805, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0508027, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0827432, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.49409, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.39017, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.05291, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.668027, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.582894, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.533942, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.54064, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.547616, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.51137, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.291613, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.898067, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.01042, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0794351, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.508025, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.972977, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.15814, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.541509, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.29634, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.20703, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.212492, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.285595, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.64402, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.205054, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.25539, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.10544, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.0289, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.26967, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.48983, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.411471, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.338604, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.306226, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.17721, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.943131, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.324429, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.634386, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.109656, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.373612, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.515017, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.743055, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.160749, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.21131, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.884407, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.681126, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.27187, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.80807, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.701518, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.708683, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.00716355, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.475349, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.415758, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.840596, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.196817, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.80301, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.92707, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "Exception: lognormal_rng: Scale parameter is -0.00321963, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.62312, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.131417, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.530299, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.938076, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.198343, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.40892, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.685947, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.05652, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.372359, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.126025, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.81656, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.84304, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.254241, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.50201, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.261232, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.822175, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.96627, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.20151, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.92345, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -3.08777, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.633982, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.764821, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.379198, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.459407, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0631257, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.44987, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.55443, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.440763, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.088458, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.642684, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.30335, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.11109, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.244681, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0730597, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.105518, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.37316, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.08311, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.861652, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.142153, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.797811, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.960168, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.11015, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.19728, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.706689, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.654392, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.887765, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.54851, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.675341, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.29002, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0740122, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.696549, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.436638, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.819401, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.192657, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0592685, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.176185, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.696874, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.17741, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.93004, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.138966, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.36582, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.776103, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0306238, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.578017, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.742152, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.704862, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.651253, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.495487, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.43697, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.00172, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.199965, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.624426, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.182993, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.21214, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.524175, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.29142, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.548158, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.635855, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.64523, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.788924, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.488593, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.892148, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.62669, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.35816, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.175369, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.472594, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.156724, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.39194, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.517203, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.57922, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.472604, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.19855, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.15584, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.07744, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.653832, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.03234, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.439487, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.779927, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.14201, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.417388, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.458184, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.37978, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.27884, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.07678, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.696833, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.05866, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.0491, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.857837, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.08112, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.256546, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0634006, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.837488, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.1176, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.019658, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.683643, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.04269, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.249937, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.06216, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.32536, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.327659, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.534853, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.21823, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.6168, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.07796, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.235678, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0442793, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.404701, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.770245, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.265461, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.276643, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.23973, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0594214, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.307447, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.7343, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.01697, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.02332, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.726361, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Location parameter[3] is nan, but must be finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.07818, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.485182, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.17362, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.230683, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.26535, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.200436, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.571407, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.0117, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.35187, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.662452, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.9533, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.12572, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.76939, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.08108, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.765278, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0540348, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.86647, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.673867, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0778396, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.397904, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.00300863, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.345403, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.112682, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.60693, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.629108, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.895067, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.181402, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.503, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.08787, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.977495, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.972455, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.02277, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.743843, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.920748, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.781162, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.74024, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.488391, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.29146, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.179721, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.641726, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0349566, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.383639, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.316697, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.4893, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.780332, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0410572, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.360385, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.814337, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.279754, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.658604, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.000328574, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.29115, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.990018, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.14256, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0802451, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.70804, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.433089, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.107371, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.698405, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.12211, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.131542, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.21542, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.517623, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.29085, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.01912, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.12112, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.03937, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.570315, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.61091, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.471526, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.294212, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Location parameter[5] is nan, but must be finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.120774, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.441999, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.141249, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.219342, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.09485, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.79241, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.171886, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.31569, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.341149, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.401621, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Location parameter[5] is nan, but must be finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.24107, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.782565, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.444456, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.386475, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.935047, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.27979, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.950703, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.71389, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.518992, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.45014, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.199993, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.97085, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.385578, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.417401, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.79913, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.437985, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.247791, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.902339, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.791479, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.963298, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.257003, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.5065, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.273538, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.03471, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.583826, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.42922, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.790319, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0738462, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.869229, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.850616, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.00734, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.06189, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.507203, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.14688, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.36029, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.311637, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0939374, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.156784, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.40506, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0877099, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.276628, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.375037, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.584881, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0698794, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0218818, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.998898, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.167184, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.64965, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.33808, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0769161, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.01613, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.00941, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.12108, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Location parameter[5] is nan, but must be finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.28307, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.458725, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.97553, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.884916, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.246142, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.390068, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.222067, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.910253, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.10381, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.24232, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.436453, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.64977, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.37415, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.20175, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.25587, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0991202, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.36999, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.696498, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.728123, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.204079, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.18118, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.29707, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.807969, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.4072, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.731523, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.175537, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.40194, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.3333, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.63348, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.306504, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.104546, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.18359, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.527797, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.849717, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.258674, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.178718, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.343571, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.724647, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.39084, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.450829, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.558848, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.723249, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.0825, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.713594, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0739939, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.143036, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.440982, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.34146, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.607743, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.85145, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.51658, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.275284, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.3261, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.758709, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.79439, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0669442, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.1291, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.28887, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.938787, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.07963, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.124667, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.2252, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.348617, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.54722, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.324515, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.163704, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.746485, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.223662, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0471154, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.820066, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.681463, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.55886, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.87665, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.107486, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.8343, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.850975, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0764203, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.802267, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.423953, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0929828, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.834943, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.82871, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.462979, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.637511, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.95665, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.15616, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.03164, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.30056, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.456111, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.520454, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.44975, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.245285, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.27673, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.158791, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.802378, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.310518, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.904544, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.101759, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.25797, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.594245, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.195562, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.182098, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.033773, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.258464, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.64751, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.882485, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0551209, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.511593, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.894398, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.140448, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.625736, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.600543, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.209798, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.84568, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0822397, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.352342, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.03022, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.114703, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.481909, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.683931, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.374759, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.193165, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.87378, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.952885, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.37361, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.62904, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.06183, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.238927, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.990406, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.243415, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.71182, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.885526, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.3603, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.1434, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.863038, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.417451, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.991601, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.999824, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.318252, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.57855, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.44934, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.37356, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.80505, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.455078, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.432023, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.947744, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0419287, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.19514, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.130407, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0348842, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.776256, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.252832, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.353314, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.612475, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.46987, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.89932, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.604844, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.883552, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.513641, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.501657, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.10431, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.886401, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.685288, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.48275, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.243604, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.713731, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.54944, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.18616, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.372016, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.130198, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.51397, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.23251, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.18624, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.8564, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0689138, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.14797, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.670667, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.414445, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.661901, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.256316, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.192545, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.335243, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.52102, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0449694, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.150029, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.28102, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.25933, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.27689, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.630084, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.810618, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.459353, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.73256, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.37333, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0681667, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.466419, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.93062, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.396506, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.88057, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0444305, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.56712, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.906657, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.54585, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.32401, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.300273, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.759926, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.197847, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.15135, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.198292, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.55505, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.114629, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.210294, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.129401, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.297182, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.325757, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.243349, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.72101, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.195517, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.804757, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.439618, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.739885, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.277284, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.24808, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.267164, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.774089, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.324489, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.727104, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.674432, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "Exception: lognormal_rng: Scale parameter is -1.37765, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.83335, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.41958, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.735718, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.70228, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.593544, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.590788, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.902173, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.38379, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.68813, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0945314, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.497482, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.93805, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.81429, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.960887, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.386351, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.609543, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.28126, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.54951, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0794058, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.26656, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.371112, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.292244, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.501799, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.890293, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.90055, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.548003, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.859247, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.04073, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.128014, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.38528, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.21794, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.39961, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.566553, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.554092, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.153486, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.831204, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.675743, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.538589, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.64154, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.72777, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0762611, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0145862, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.46288, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.24248, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.182242, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.119434, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.24899, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.447753, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.71145, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.697504, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.139665, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.04385, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0509544, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.670976, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.31709, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.846789, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.10406, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0846179, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.11815, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.14536, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.511375, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.74634, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.283795, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.93069, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0181476, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.711278, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.378115, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.263449, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0778498, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.982092, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.39302, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.514232, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.536658, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.27242, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.95124, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.22041, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.60119, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.68242, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.60141, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.207985, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.238989, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.77512, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.46796, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.344704, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0642928, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0837346, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.762559, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.121772, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.3129, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.211393, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.453202, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.41806, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.15914, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.42507, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.57339, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.399578, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.753428, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.48213, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.448013, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.00546562, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.948914, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.4628, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.477883, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.0346, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.83118, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.57733, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.38023, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.61975, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0655335, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.234174, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.762111, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.125435, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.45984, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.98519, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.81401, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.82936, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.05504, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.841637, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.158962, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.95447, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.319845, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.595255, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.51395, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.381628, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.224619, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.907179, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.675091, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.240177, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.09188, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0250849, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.765614, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.474265, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.00321, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.351651, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.148501, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0158709, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.2214, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.2953, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.156978, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.44425, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0602595, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.818148, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.03998, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0745862, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.83803, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.13276, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.2728, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.5779, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.313341, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.315819, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.188507, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.15262, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.683384, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.25921, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.57868, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.245911, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.880154, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.14713, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.336542, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.04628, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.926622, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.48351, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.15916, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.226916, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.57378, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.03291, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.87542, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.878236, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0102152, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.94613, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.710451, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.887021, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.29525, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.166195, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.64062, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.268962, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.784377, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Location parameter[4] is nan, but must be finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.823464, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.19864, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.781776, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.73098, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.18888, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.844403, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.246037, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.763024, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.362801, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.902355, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0190017, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Location parameter[4] is nan, but must be finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.283911, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.520606, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.81052, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.16618, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.583885, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.102439, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.387157, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.404827, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.389471, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0128427, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.00522, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.258905, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.94279, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.131846, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.809636, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.34261, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.447861, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.460475, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.235666, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.412861, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.67044, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.3675, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.44099, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.736077, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.844796, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.750826, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.206682, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.1601, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.21188, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.502186, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.568883, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.822966, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.697023, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.150391, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.446798, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.344353, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.476646, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.10671, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.789653, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.280265, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.086716, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.143689, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.610067, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.243085, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.02584, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.505685, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.741844, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.447902, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.658478, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.403068, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.01247, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0631646, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.74944, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.659422, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.73375, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.038112, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.12301, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.589011, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0559371, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.443563, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.335911, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.93711, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.251679, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0751479, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.782932, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.191729, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Location parameter[5] is nan, but must be finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.33133, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.14002, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.572245, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.39115, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.28627, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.62231, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.50815, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.296664, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.08576, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.57378, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -3.76167, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.935332, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.28589, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.517282, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.701994, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.77748, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.320279, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.309417, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.45547, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0941346, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.864681, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.952359, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0456044, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.3784, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.14975, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.442506, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.329233, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0645598, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.198206, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.35681, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.62447, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.503991, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.416306, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.267934, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.2902, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.64661, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.03462, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.146809, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.273585, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.145559, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.36711, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.566987, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.158149, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.535083, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.673506, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.1603, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.539956, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.312572, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.760882, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.511345, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.208699, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.60328, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.674591, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.891245, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.74847, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.172457, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.2676, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.84245, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.298487, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.785075, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.964671, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.480822, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.642092, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.00038, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.33144, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.613096, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.015328, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.798836, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.23215, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.754033, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.68751, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.228653, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.229689, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.304055, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.624199, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.91553, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0559392, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.35079, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.56569, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0411685, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.699515, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.227997, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.467288, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.22848, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.05915, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.38556, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.72145, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.710568, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.2531, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.14504, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.76645, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.319293, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.654206, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.265158, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.17425, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.229781, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.343448, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.365754, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.990764, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.435122, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.224375, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.632772, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.431208, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.689706, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.06643, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.278703, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.300878, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.462347, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.19411, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.512005, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.665198, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.094509, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.840848, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.52618, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.048687, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.242846, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.20761, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.31049, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.15039, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.141504, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.120721, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.54138, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.82192, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.4248, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.362115, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.10255, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.66044, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.498215, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0904366, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.01102, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.372291, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.06216, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.466885, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.44035, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.61997, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.787257, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.2241, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.2715, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.375788, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.198751, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.68909, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.363608, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0670367, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.756466, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.96378, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.585092, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.141696, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.21238, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.3319, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.103999, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.260146, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.107307, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.223139, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.667788, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.536579, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.349068, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.121301, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.781455, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.236068, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.505583, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.69907, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.50084, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.53027, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.38942, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.28002, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.25408, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.03162, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.414345, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.555315, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.36924, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.31548, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.125568, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.11574, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.76929, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.053233, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.199075, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.646986, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.2328, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.905941, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0429356, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.51868, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.623293, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.600057, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.698435, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.775345, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.895809, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0993979, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.124429, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.277206, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.56516, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.41052, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.994298, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.226897, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.6515, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0630148, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.633604, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.972307, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.627, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0642562, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.363718, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.1399, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.05294, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.47862, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.593208, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.536484, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0409189, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.028912, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.570017, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.68241, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.626988, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.623145, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.03059, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.62618, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.633552, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "Exception: lognormal_rng: Scale parameter is -0.440071, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.315423, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0132192, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.588095, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.484249, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.586364, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.599173, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.952137, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.36739, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.562241, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.674153, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.3827, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.29818, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.151768, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.9964, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.90434, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.244481, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.39757, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.325619, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.111555, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.536244, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.166942, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.445043, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.411467, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.741884, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.39221, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.99131, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.526444, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.42305, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.27304, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.770155, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.583318, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.830509, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.854422, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.368033, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.29968, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.25878, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.492596, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.552128, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.35404, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0957394, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.70919, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.69826, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0888248, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.23422, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0455371, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.784468, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0151496, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.365978, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.71807, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.72484, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.935854, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.22532, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.187015, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.158266, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.41546, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.89151, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0236936, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.391584, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.31456, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.50059, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.234852, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0555086, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.145383, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0747779, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.373264, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.90569, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.576395, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.07545, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.911213, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.626641, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.750003, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.868075, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.75875, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.85538, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.5837, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.40191, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.891975, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0450508, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.855162, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.19311, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.276893, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.951657, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.17144, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.802381, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.20231, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.318408, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.50495, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.94997, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.00422, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.44262, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.370787, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.202671, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.597546, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.76873, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.60776, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.124358, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.387855, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.539524, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.326423, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.990985, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.589106, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.214734, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.128278, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.012602, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.480948, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.770023, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.59293, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.80314, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.701292, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.16695, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.362991, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.470218, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0825137, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.936581, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.200699, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.367175, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Location parameter[4] is nan, but must be finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.72395, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.123787, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.07547, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.094636, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.858272, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.59008, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.70897, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.521134, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.362027, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.54016, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.47161, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.89328, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.86376, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.318147, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.723109, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.919442, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.754393, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.169082, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.325694, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.332474, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.592443, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.434053, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.475104, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.676696, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.688635, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.407115, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.59908, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.668649, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.229533, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.360795, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.522101, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.305621, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.179128, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.18441, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.24497, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.0003, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.25128, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.174648, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.181886, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0301048, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.45532, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.661129, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.234133, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.774171, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.267047, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.70957, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.698546, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.908144, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.31645, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.171376, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.124801, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.19395, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.3526, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.318526, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.895373, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.683213, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.126551, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.32268, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0211079, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.2372, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.704483, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.364999, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.11913, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.65384, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.45844, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.730882, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.109158, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.86476, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.450587, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.10941, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.4527, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -3.23039, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.755606, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.389955, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.287703, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.354535, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.469414, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.209907, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.63685, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.172051, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.621538, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.50185, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.518676, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.105568, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.35187, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.577933, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0762424, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.800215, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.54172, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.753818, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.20021, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.494405, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.20485, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.560843, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.34702, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.80449, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.124235, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.8379, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.478435, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.187303, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.3229, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.238051, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.477798, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.788413, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.29224, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.183501, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.227897, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.13056, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.385502, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.657969, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.886651, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.51727, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -3.67067, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.720349, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.527344, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.973592, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.46771, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0123831, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.45976, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.65775, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.46149, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.378111, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.25498, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.464432, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.940018, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.25825, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.08403, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.352044, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.759456, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.601402, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.85512, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.829153, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.734552, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.20835, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.32276, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.962772, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.50481, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.664571, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.888273, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.62812, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.701729, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.76157, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.754988, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.326773, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.80099, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.485783, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.639059, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.32526, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.169097, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.07738, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.125891, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.801106, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.720481, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.7274, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.738689, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.262515, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0748652, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.79398, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.09643, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0561194, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.300459, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.811397, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.01296, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.324413, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.385125, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.550712, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.07021, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.347501, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.958754, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.570721, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.508815, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.568234, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.31516, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.07601, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.29974, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0702503, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.830493, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.00405947, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.638325, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.124831, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.440968, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.38272, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.170184, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.106972, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.414386, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.35396, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.14149, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.364635, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.197084, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.320921, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.30041, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.03102, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.601217, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.451277, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.278178, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0799095, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.866758, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.952763, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.651413, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.274247, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.586604, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.82188, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0959599, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.29242, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.30656, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.221695, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.826979, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.79433, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.16043, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.565137, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.02059, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.915738, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.170947, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.302241, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.56734, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.439392, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.43223, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.171285, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.00441, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.31155, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.442248, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.72511, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.336687, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0294175, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.337269, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.6586, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.956136, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.880759, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.510155, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.860745, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.109266, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0947511, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.710451, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.04869, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.422725, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.205305, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.597353, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.02635, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.51907, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.20852, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.201193, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.118161, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.45907, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.471122, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.586794, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.471047, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.30717, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.499892, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.628218, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.875803, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.47369, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.14651, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.787553, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.3377, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.589692, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.497966, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.975413, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.241135, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.38325, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.759132, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.62975, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.39035, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.10999, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.23026, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0677992, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.25724, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.643761, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.340019, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0105718, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.102548, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.20088, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.44603, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.393608, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.48613, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.07092, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.19931, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.6763, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.2528, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.00573183, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.250204, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.772242, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.629882, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.268164, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.914009, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.488841, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.355706, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.04067, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.11433, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.20057, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.884925, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.46519, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.450864, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.168359, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.66209, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.102395, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.472171, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.630303, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.100848, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.25414, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.82374, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.31855, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.06055, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.08377, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.908327, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.25221, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.912152, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.155028, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.40031, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.104013, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.402968, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.620169, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.14437, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.59664, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.858326, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.16168, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.91464, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.372827, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.623646, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.35173, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.252727, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.72874, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.38167, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.87978, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.343359, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.60072, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0754577, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.997955, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.236101, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.933102, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.14704, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.372269, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.73063, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.96666, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.19334, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.02789, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.13515, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0231491, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.38444, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.476806, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.740654, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.05443, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.85715, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.21494, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.851177, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.202046, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.426683, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.923435, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.412586, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.965117, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.61275, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.810657, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.32771, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.2716, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.59417, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0833182, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.809049, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.18495, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.60527, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0767928, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.51054, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.836247, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.503607, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.48942, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.0986731, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.118313, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.00381909, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.294689, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.02685, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.269739, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.352231, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.451854, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.08448, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.02785, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.14863, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.69887, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.180796, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.65828, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.99797, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.2649, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.328115, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.37471, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.12041, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.873639, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.135602, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.2583, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.839577, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.38958, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.699801, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -2.37641, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.417285, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.271144, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.399997, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.593914, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -1.59087, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "\tException: lognormal_rng: Scale parameter is -0.955824, but must be positive finite! (in '/Users/dashadower/git_repos/pysd/test_scripts/stan_files/prey-predator_draws2data.stan', line 33, column 4 to column 72)\n", + "Consider re-running with show_console=True if the above output is unclear!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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MeanMCSEStdDev5%50%95%N_EffN_Eff/sR_hat
alphaNaNNaNNaNNaNNaN6.234340e-01NaNNaNNaN
gammaNaNNaNNaNNaN0.757316NaNNaNNaNNaN
betaNaNNaNNaNNaN0.038457NaNNaNNaNNaN
deltaNaNNaNNaNNaN0.023717NaNNaNNaNNaN
sigmaNaNNaNNaNNaNNaNNaNNaNNaNNaN
..............................
prey_obs[16]NaNNaNNaNNaNNaNNaNNaNNaNNaN
prey_obs[17]NaNNaNNaNNaNNaNNaNNaNNaNNaN
prey_obs[18]NaNNaNNaN6.598890e+1123699.000000NaNNaNNaNNaN
prey_obs[19]NaNNaNNaNNaNNaNNaNNaNNaNNaN
prey_obs[20]NaNNaNNaNNaN466123.0000007.171750e+14NaNNaNNaN
\n", + "

129 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Mean MCSE StdDev 5% 50% 95% \\\n", + "alpha NaN NaN NaN NaN NaN 6.234340e-01 \n", + "gamma NaN NaN NaN NaN 0.757316 NaN \n", + "beta NaN NaN NaN NaN 0.038457 NaN \n", + "delta NaN NaN NaN NaN 0.023717 NaN \n", + "sigma NaN NaN NaN NaN NaN NaN \n", + "... ... ... ... ... ... ... \n", + "prey_obs[16] NaN NaN NaN NaN NaN NaN \n", + "prey_obs[17] NaN NaN NaN NaN NaN NaN \n", + "prey_obs[18] NaN NaN NaN 6.598890e+11 23699.000000 NaN \n", + "prey_obs[19] NaN NaN NaN NaN NaN NaN \n", + "prey_obs[20] NaN NaN NaN NaN 466123.000000 7.171750e+14 \n", + "\n", + " N_Eff N_Eff/s R_hat \n", + "alpha NaN NaN NaN \n", + "gamma NaN NaN NaN \n", + "beta NaN NaN NaN \n", + "delta NaN NaN NaN \n", + "sigma NaN NaN NaN \n", + "... ... ... ... \n", + "prey_obs[16] NaN NaN NaN \n", + "prey_obs[17] NaN NaN NaN \n", + "prey_obs[18] NaN NaN NaN \n", + "prey_obs[19] NaN NaN NaN \n", + "prey_obs[20] NaN NaN NaN \n", + "\n", + "[129 rows x 9 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draws2data_model.sample(data=data_data2draws).summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4f21609", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "premodel.draws2data(data_data2draws)" + ] + }, + { + "cell_type": "markdown", + "id": "90dd73ce-2613-4b04-a210-5c1858273ce4", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Structuring Uncertainties in Dynamic Models: \n", + "## Bayesian workflow of Predator-Prey Population Dynamics\n", + "\n", + "Angie.H Moon, 07.2022" + ] + }, + { + "cell_type": "markdown", + "id": "8dbac1ad-23e0-4423-a8b5-a0a9a11b85ad", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Data: Predator and Prey Pelts in Canada\n", + "\n", + "The species of interest in this case study are\n", + "\n", + "- hares: prey, an hervivorous cousin of rabbits, and\n", + "- lynxes: predator, a feline predator whose diet consists largely of hares.\n", + "\n", + "Spikes in the predator population lag those in the prey population. When populations are plotted against one another over time, the population dynamics orbit in an apparently stable pattern. Population oscillations can be modeled with a pair of differential equations similar to that used to describe springs. The first plot is the number of lynx and hare pelts (in thousands) collected for twenty years. The second plot is the phase plot of number of pelts collected for lynx versus hares similar to that of the dynamics of a spring in phase space (i.e., position vs. momentum)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1b00493-ec54-4eba-92c3-b1e465855427", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "obs_stock_df = pd.read_csv('data/hudson-bay-lynx-hare.csv')\n", + "# data viz\n", + "pd.melt(obs_stock_df, id_vars = 'Year').iloc[[0,20,21,41]]\n", + "pd.melt(obs_stock_df, id_vars = 'Year').iloc[[0,1,20,21,40,41]].rename(columns = {'variable':'species', 'value':'pelts in thousands'})\n", + "ax = obs_stock_df.loc[:, ['Predator', 'Prey']].plot()\n", + "ax.set(xlabel='year', ylabel='pelt (thousands)') " + ] + }, + { + "cell_type": "markdown", + "id": "dd624d4d-178f-41f5-8586-8c15bd59763f", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Phase plots are as below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77abf5f6-b8a8-499d-bcb2-c705afb42c48", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "plt.scatter(obs_stock_df.loc[:, 'Predator'], obs_stock_df.loc[:, 'Prey'])\n", + "plt.plot(obs_stock_df.loc[:, 'Predator'], obs_stock_df.loc[:, 'Prey'])\n", + "plt.xlabel('Predator pelts (thousands)')\n", + "plt.ylabel('Prey pelts (thousands)')" + ] + }, + { + "cell_type": "markdown", + "id": "5763cb30-424f-4716-aab2-ae96fa680338", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Mechanistic Model: The Lotka-Volterra Equations\n", + "\n", + "In Lotka-Volterra equations (Lotka 1925; Volterra 1926, 1927), Volterra modeled the temporal dynamics of the two species (i.e., population sizes over times) in terms of four parameters, $\\alpha, \\beta, \\gamma, \\delta \\geq 0$, as\n", + "\n", + "$$\n", + "\\begin{eqnarray}\n", + "\\frac{\\mathrm{d}}{\\mathrm{d}t} u\n", + "& = & (\\alpha - \\beta v) u\n", + "& = & \\alpha u - \\beta u v\n", + "\\\\[6pt]\n", + "\\frac{\\mathrm{d}}{\\mathrm{d}t} v\n", + "& = & (-\\gamma + \\delta \\, u) \\, v\n", + "& = & -\\gamma v + \\delta uv\n", + "\\end{eqnarray}\n", + "$$\n", + "\n", + "$u(t)$ and $v(t)$ are rendered as $u$ and $v$. The factor $\\alpha$, $\\beta$ are the rate of birth and shrinkage relative to the product of the population sizes where as $\\gamma$, $\\delta$ are the shrinkage and growth rate as a factor of the product of the population sizes. Both u and v have positivitity constraints. as long as the initial populations are non-negative, i.e., $u(0) \\geq 0$ and $v(0) \\geq 0$, because the rate of change in each population is a factor of the population size itself.\n", + "\n", + "The dynamic system has four limiting behavior:\n", + "\n", + "1. If both population sizes are initially positive, the populations will oscillate in a fixed pattern indefinitely, remaining positive.\n", + "2. If both population sizes are initially zero, the population sizes will remain zero.\n", + "3. If the predator population size is zero and the prey population size positive, the predator population size remains zero and the prey population grows without bound.\n", + "4. If the predator population size is positive and the prey population size zero, the prey population size remains zero while the predator population shrinks toward zero size." + ] + }, + { + "cell_type": "markdown", + "id": "10c2e11e-1038-41a3-9eb8-a75592ef9aa7", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "pycharm": { + "name": "#%% md\n" + }, + "tags": [] + }, + "source": [ + "## Statistical Model: regreasion framing and uncertainty embedding\n", + "\n", + "### Solving the inverse problem\n", + "Bayesian statistics is somewhat counterintuitive, as it involves formulating the generating model (from parameter to observed data) then using general principles to solve the inverse problem. Specifically, a Bayesian model requires a mathematical model of what we know about the parameters (i.e., a prior) and a model of what we know about the data generating process given the parameters (i.e., a sampling distribution.\n", + "\n", + "Mathematically, a prior density $p(\\theta)$ over the sequence of parameters $\\theta$ encapsulates our knowledge of the parameters before seeing the data. A sampling distribution (or likelihood), which may have a continuous, discrete or mixed probability function, $p(y | \\theta)$ characterizes the distribution of observable data $y$ given parameters $\\theta$. We limit the observation as stock variables as every SD model can be reformulated into the combination of stock and parameters.\n", + "\n", + "Bayes's rule gives us a general solution to the inverse problem, expressing the posterior $p(\\theta | y)$ in terms of the prior $p(\\theta)$ and likelihood $p(y | \\theta)$. Stan provides a form of Markov chain Monte Carlo (MCMC) sampling that draws a sample $\\theta^{(1)}, \\ldots, \\theta^{(M)}$ from the posterior to use for computational inference. Posterior quantities of interest may be expressed as derived random variables using functions $f(\\theta)$ of parameters. This feature is used for decision analysis; for instance, imagine a optimization problem of conservation cost of the park where prey and predator ecology places at. The cost can be computed based on the posterior distribution inferred from the observed time series.\n", + "\n", + "\n", + "### Uncertainty embedding for forward-backward symmetry required for calibration\n", + "\n", + "The Lotka-Volterra model is deterministic in that given the value of the system parameter and initial outcome state, equation solutions (simulated outcome value) are fully determined. However, for empirical research which use posterior inference from the real data as it final forecast, forward model should be re-designed. This is because symmetry of forward and backward model (i.e. data generation and its inference) is the theoretical justification of calibration. To pass this internal consistency test (or with enough resource, SBC which is rank-statistics based), we need the two process to be the mirror image of other. This is why we purposefully embed uncertainty components, waiting to be captured in the inference step. The purpose is to test resilience and identifiability of our models evidenced by the perfect retrival of prior distribution for every uncertainty we embedded. \n", + "\n", + "### Linear regression analogy\n", + "\n", + "Like in a simple linear regression, we will proceed by treating the underlying determinstic model as providing an expected population value around which there will be variation due to both measurement error and simplifications in the scientific model. Consider the typical formulation of a linear regression, where $y_n$ is an observable scalar outcome, $x_n$ is a row vector of unmodeled predictors (aka covariates, features), $\\beta$ is a coefficient vector parameter, and $\\sigma > 0$ is the error scale,\n", + "\n", + "$$\n", + "\\begin{eqnarray}\n", + "y_n & = & x_n \\beta + \\epsilon_n\n", + "\\\\[6pt]\n", + "\\epsilon_n & \\sim & \\mathsf{Normal}(0, \\sigma)\n", + "\\end{eqnarray}\n", + "$$\n", + "\n", + "### Adding measurement uncertainty (epistemic)\n", + "Before embedding parameteric uncertainty, linear predictor $x_n \\beta$ with predictor $x_n$ (row $n$ of the data matrix $x$) and coefficient (column) vector $\\beta$ are deterministic. The only source of uncertainty is from the measurement. This is expressed by assigning a normal distribution to error term $\\epsilon_n$. Equal expression is with latent error variable $\\epsilon_n$ as follows[17](#fn17), \n", + "\n", + "$$\n", + "y_n \\sim \\mathsf{Normal}(x_n \\beta, \\sigma).\n", + "$$\n", + "\n", + "### Adding parameter uncertainty (epistemic)\n", + "Next, we add parameter uncertainty by coding estimated parameter as a distribution rather than a fixed value. This distribution is called prior distribution and from our example, Normal distirbution is chosen to endow the uncertainty to the four estimated parameters $\\alpha, \\beta, \\gamma, \\delta$. Considering their role difference, $\\alpha, \\gamma$ as multipliers of $u, -v$ and $\\beta, \\delta$ as multipliers of $uv$, prior parameter are chosen as N(1, 0.5) and N(0.05, 0.05) for each. For this selection, refer to the original case study [Carpenter18](https://mc-stan.org/users/documentation/case-studies/lotka-volterra-predator-prey.html).\n", + "\n", + "\n", + "### Adding aleatoric uncertainty\n", + "This applies only when we decide to add measurement uncertainty i.e. having initial value of stock variable as `est_param` instead of the default `ass_param`. For this, detailed explanation is added at the end of this file at Appendix A. " + ] + }, + { + "cell_type": "markdown", + "id": "a87666b9-f6c5-4723-a2be-373f2dd29f0c", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Prior predictive check" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04b91769", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "vf = VensimFile(\"vensim_models/prey-predator.mdl\")\n", + "vf.parse()\n", + "am = vf.get_abstract_model()\n", + "\n", + "# set time\n", + "n_t = obs_stock_df.shape[0] - 1\n", + "premodel = StanVensimModel(\"prey-predator\", am, 0.0, list(range(1, n_t + 1)))\n", + "premodel.print_info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7831655", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "n_t = obs_stock_df.shape[0] - 1\n", + "data_data2draws = {\n", + " \"n_obs_state\" : 2,\n", + " \"initial_time\" : 0, \n", + " \"times\": [i+1 for i in np.arange(n_t)],\n", + " \"n_t\": n_t,\n", + " \"y\": obs_stock_df.loc[1:, ('Predator', 'Prey')].values.tolist(), # (predator, prey)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2444ecf8-c272-4dad-9487-37fcb88ddbbf", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "\n", + "\n", + "# ode parameter \n", + "premodel.set_prior(\"alpha\", \"normal\", 0.55, 0.1)\n", + "premodel.set_prior(\"gamma\", \"normal\", 0.8, 0.1)\n", + "premodel.set_prior(\"beta\", \"normal\", 0.028, 0.01)\n", + "premodel.set_prior(\"delta\", \"normal\", 0.024, 0.01)\n", + "\n", + "# sampling distribution parameter\n", + "premodel.set_prior(\"sigma\", \"normal\", 0, 1)\n", + "premodel.set_prior(\"y[:,1]\", \"lognormal\", \"predator\", \"sigma\")\n", + "premodel.set_prior(\"y[:,2]\", \"lognormal\", \"prey\", \"sigma\")\n", + "\n", + "premodel.build_stan_functions()\n", + "#premodel.draws2data()\n", + "#print(premodel.vensim_model_context.variable_names)\n", + "model = StanVensimModel(\"prey-predator\", am, 0.0, list(range(1, n_t + 1)))\n", + "model.set_prior(\"sigma\", \"normal\", 0, 1)\n", + "model.set_prior(\"y[:,1]\", \"lognormal\", \"predator\", \"sigma\")\n", + "model.set_prior(\"y[:,2]\", \"lognormal\", \"prey\", \"sigma\")\n", + "\n", + "cmdstan_model = model.data2draws({})\n", + "result = cmdstan_model.sample()\n", + "result.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3bbaa2f5", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4bd395d-bc67-4175-b765-6e2232018faf", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "data_draws2data = {\n", + " \"n_obs_state\" : 2\n", + "}\n", + "\n", + "sf_path_draws2data = os.path.join(os.getcwd(), \"stan_file\", \"prey-predator_draws2data.stan\")\n", + "sm_draws2data = CmdStanModel(stan_file = sf_path_draws2data)\n", + "\n", + "prior_pred = sm_draws2data.sample(data=data_draws2data, iter_sampling=3, chains=1, fixed_param=True, iter_warmup=0)\n", + "prior_pred.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "595077fd-df8f-442c-a806-30c7e168f423", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "For prior predictive checks, if the real observed data is indiscriminable from the simulated, we usually view it as a sign of pass. Real data is an external reference so as long as the predicted ranges are not too off, we give a pass to prior predictive check. Summary statistics such as N^th moments can be used for comparison. Few comments:\n", + "\n", + "a. we use real data below as a representation of our knowledge, so prior predictive check is not double dipping (using data twice)\n", + "\n", + "b. Bayesian prior corresponds to frequentist's regularization so having a tighter prior than posterior is not unnatrual; simply our determination to find a model concentrated around certain model configuration\n", + "\n", + "c. if tight prior is well-placed, it prevents diveregence from frustrating geometry and boosts sampling efficiency" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82f4938e-f90c-4043-bcf5-c869c39676be", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(15, 8))\n", + "#compare with real \n", + "ax.plot(obs_stock_df.loc[:, ['Prey']], label = \"Real_Pey\")\n", + "ax.plot(obs_stock_df.loc[:, ['Predator']], label = \"Real_Predator\")\n", + "ax.plot(pd.DataFrame(prior_pred.y_tilde[:,:,0]).T.loc[:, :5])\n", + "ax.plot(pd.DataFrame(prior_pred.y_tilde[:,:,1]).T.loc[:, :5])\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "27ddb4c9-9f11-4324-84f4-d426fb01bc6f", + "metadata": { + "pycharm": { + "name": "#%% md\n" + }, + "tags": [] + }, + "source": [ + "## Posterior predictive check" + ] + }, + { + "cell_type": "markdown", + "id": "8c54a353-1b50-4e1b-ac5d-bd9933ce6124", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Now we estimate parameter values based on the real observed stock values. Here we use more diffuse prior." ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "7e4236f8-d6c8-45dd-852d-c1b759a54248", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'predator_birth_rate', 'predator_death_rate', 'prey_death_rate', 'predator', 'gamma', 'final_time', 'delta', 'beta', 'time_step', 'prey_birth_rate', 'initial_time', 'saveper', 'prey', 'alpha'}\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "prey-predator_functions.stan already exists in the current working directory. Overwrite? (Y/N): y\n" - ] + "metadata": { + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "vf = VensimFile(\"vensim_models/prey-predator.mdl\")\n", "vf.parse()\n", @@ -698,184 +4883,14 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "3f4934f2-b300-498c-9e30-09d2d613701b", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "17:30:01 - cmdstanpy - INFO - CmdStan start processing\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "970776a8be0c41f696fbebf6a37489e7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "chain 1 | | 00:00 Status" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "17:33:38 - cmdstanpy - INFO - CmdStan done processing.\n", - "17:33:38 - cmdstanpy - WARNING - Non-fatal error during sampling:\n", - "Exception: lognormal_lpdf: Random variable is -1.92663, but must be nonnegative! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 47, column 4 to column 45)\n", - "\tException: ode_rk45: Failed to integrate to next output time (11) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 37, column 4 to column 126)\n", - "\tException: ode_rk45: Failed to integrate to next output time (10) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 37, column 4 to column 126)\n", - "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: ode_rk45: Failed to integrate to next output time (4) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 37, column 4 to column 126)\n", - "\tException: ode_rk45: Failed to integrate to next output time (12) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 37, column 4 to column 126)\n", - "\tException: lognormal_lpdf: Location parameter[9] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: ode_rk45: Failed to integrate to next output time (19) in less than max_num_steps steps (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 37, column 4 to column 126)\n", - "\tException: lognormal_lpdf: Location parameter[9] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[17] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[16] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_lpdf: Location parameter[16] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 51, column 8 to column 65)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "\tException: lognormal_rng: Location parameter[14] is nan, but must be finite! (in '/Users/hyunjimoon/GoogleDrive_hmb/pysd/test_scripts/stan_file/prey-predator_data2draws.stan', line 60, column 8 to column 73)\n", - "Consider re-running with show_console=True if the above output is unclear!\n", - "17:33:38 - cmdstanpy - WARNING - Some chains may have failed to converge.\n", - "\tChain 1 had 99 iterations at max treedepth (99.0%)\n", - "\tUse function \"diagnose()\" to see further information.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "metadata": { + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "sf_path_data2draws = os.path.join(os.getcwd(), \"stan_file\", \"prey-predator_data2draws.stan\")\n", "sm_data2draws = CmdStanModel(stan_file = sf_path_data2draws)\n", @@ -895,245 +4910,38 @@ { "cell_type": "markdown", "id": "398d77aa-8074-4d7c-a16b-4442b4b1c4f1", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "The following is the summary of posterior draws. It includes loglikelihood for each vector of parmaeter values $(\\alpha, \\beta, \\gamma, \\delta, \\sigma)$. " ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "d2b09acd-9180-4285-be7d-86a8db135a4f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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MeanMCSEStdDev5%50%95%N_EffN_Eff/sR_hat
name
lp__-28706.50000038.11650065.862000-28807.200000-28708.900000-28596.4000002.985680.1635272.54392
alpha-0.9540920.0000340.000061-0.954163-0.954111-0.9539763.203370.1754502.16126
gamma-0.1764300.0000140.000029-0.176466-0.176439-0.1763684.281430.2344961.51663
beta0.1819820.0002050.0003500.1814010.1819990.1824852.906640.1591982.69473
delta-0.0905450.0000610.000112-0.090727-0.090552-0.0903353.390070.1856762.07456
..............................
log_lik[16]NaNNaNNaNNaNNaNNaNNaNNaNNaN
log_lik[17]NaNNaNNaNNaNNaNNaNNaNNaNNaN
log_lik[18]NaNNaNNaNNaNNaNNaNNaNNaNNaN
log_lik[19]NaNNaNNaNNaNNaNNaNNaNNaNNaN
log_lik[20]NaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", - "

110 rows × 9 columns

\n", - "
" - ], - "text/plain": [ - " Mean MCSE StdDev 5% 50% \\\n", - "name \n", - "lp__ -28706.500000 38.116500 65.862000 -28807.200000 -28708.900000 \n", - "alpha -0.954092 0.000034 0.000061 -0.954163 -0.954111 \n", - "gamma -0.176430 0.000014 0.000029 -0.176466 -0.176439 \n", - "beta 0.181982 0.000205 0.000350 0.181401 0.181999 \n", - "delta -0.090545 0.000061 0.000112 -0.090727 -0.090552 \n", - "... ... ... ... ... ... \n", - "log_lik[16] NaN NaN NaN NaN NaN \n", - "log_lik[17] NaN NaN NaN NaN NaN \n", - "log_lik[18] NaN NaN NaN NaN NaN \n", - "log_lik[19] NaN NaN NaN NaN NaN \n", - "log_lik[20] NaN NaN NaN NaN NaN \n", - "\n", - " 95% N_Eff N_Eff/s R_hat \n", - "name \n", - "lp__ -28596.400000 2.98568 0.163527 2.54392 \n", - "alpha -0.953976 3.20337 0.175450 2.16126 \n", - "gamma -0.176368 4.28143 0.234496 1.51663 \n", - "beta 0.182485 2.90664 0.159198 2.69473 \n", - "delta -0.090335 3.39007 0.185676 2.07456 \n", - "... ... ... ... ... \n", - "log_lik[16] NaN NaN NaN NaN \n", - "log_lik[17] NaN NaN NaN NaN \n", - "log_lik[18] NaN NaN NaN NaN \n", - "log_lik[19] NaN NaN NaN NaN \n", - "log_lik[20] NaN NaN NaN NaN \n", - "\n", - "[110 rows x 9 columns]" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "fit.summary()" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "e756c478-5d27-4f52-8bec-a03eeed82ef6", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "idata = az.from_cmdstanpy(\n", @@ -1147,37 +4955,14 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "c605e71f-7fa9-4a10-93b3-76ba792dec58", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/hyunjimoon/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/stats/stats.py:802: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", - " warnings.warn(\n" - ] - }, - { - "ename": "KeyError", - "evalue": "'var names: \"[\\'y\\'] are not present\" in dataset'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:69\u001b[0m, in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 69\u001b[0m var_names \u001b[38;5;241m=\u001b[39m \u001b[43m_subset_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvar_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilter_items\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilter_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwarn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:146\u001b[0m, in \u001b[0;36m_subset_list\u001b[0;34m(subset, whole_list, filter_items, warn)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(existing_items):\n\u001b[0;32m--> 146\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39marray(subset)[\u001b[38;5;241m~\u001b[39mexisting_items]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m are not present\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m subset\n", - "\u001b[0;31mKeyError\u001b[0m: \"['y'] are not present\"", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [51]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m az\u001b[38;5;241m.\u001b[39mloo(idata)\n\u001b[0;32m----> 2\u001b[0m \u001b[43maz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_ppc\u001b[49m\u001b[43m(\u001b[49m\u001b[43midata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.03\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfigsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/plots/ppcplot.py:260\u001b[0m, in \u001b[0;36mplot_ppc\u001b[0;34m(data, kind, alpha, mean, observed, color, colors, grid, figsize, textsize, data_pairs, var_names, filter_vars, coords, flatten, flatten_pp, num_pp_samples, random_seed, jitter, animated, animation_kwargs, legend, labeller, ax, backend, backend_kwargs, group, show)\u001b[0m\n\u001b[1;32m 258\u001b[0m var_names \u001b[38;5;241m=\u001b[39m _var_names(var_names, observed_data, filter_vars)\n\u001b[1;32m 259\u001b[0m pp_var_names \u001b[38;5;241m=\u001b[39m [data_pairs\u001b[38;5;241m.\u001b[39mget(var, var) \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m var_names]\n\u001b[0;32m--> 260\u001b[0m pp_var_names \u001b[38;5;241m=\u001b[39m \u001b[43m_var_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpp_var_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredictive_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilter_vars\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flatten_pp \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 263\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flatten \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:72\u001b[0m, in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 71\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin((\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvar names:\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00merr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124min dataset\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m var_names\n", - "\u001b[0;31mKeyError\u001b[0m: 'var names: \"[\\'y\\'] are not present\" in dataset'" - ] + "metadata": { + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "az.loo(idata)\n", "az.plot_ppc(idata, alpha=0.03, figsize=(12, 6))" @@ -1187,14 +4972,22 @@ "cell_type": "code", "execution_count": null, "id": "90a2b21e-180b-496b-9767-2f4751510bbe", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "6f549da1-2157-4520-a58d-49706e9590ff", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Appendix A. Measurement error, when stock initial is estimated parameter\n", "\n", @@ -1234,7 +5027,11 @@ { "cell_type": "markdown", "id": "38fd5398-b605-44b2-ad09-84ec57a257a1", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Appendix B. Stan code" ] @@ -1242,7 +5039,11 @@ { "cell_type": "markdown", "id": "3b48c946-921f-4ee1-8eb4-0bfe06c9f37a", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "The following is the auto-generated stanfile with modularized funtion block in the cell below.\n", "```\n", @@ -1306,7 +5107,11 @@ { "cell_type": "markdown", "id": "8e742a91-7265-4876-83af-81dea8d8f14d", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Function block:\n", "```\n", @@ -1334,13 +5139,21 @@ "\n", "```" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a66fe301", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "local-venv", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "local-venv" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1352,7 +5165,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.10.6" } }, "nbformat": 4, diff --git a/test_scripts/stan_vensim_integration.py b/test_scripts/stan_vensim_integration.py index c122e9ca..36b70d77 100644 --- a/test_scripts/stan_vensim_integration.py +++ b/test_scripts/stan_vensim_integration.py @@ -1,23 +1,42 @@ from pysd.builders.stan.stan_model import StanVensimModel from pysd.translators.vensim.vensim_file import VensimFile from pysd.translators.xmile.xmile_file import XmileFile - -vf = VensimFile("vensim_models/ds_white_sterman.mdl") +import pandas as pd +obs_stock_df = pd.read_csv('data/hudson-bay-lynx-hare.csv') +vf = VensimFile("vensim_models/prey-predator.mdl") vf.parse() am = vf.get_abstract_model() -model = StanVensimModel("ds_white_sterman", am, 0.0, list(range(1, 10))) -model.print_info() +# set time +n_t = obs_stock_df.shape[0] - 1 +premodel = StanVensimModel("prey-predator", am, 0.0, list(range(1, n_t + 1))) +premodel.print_info() + +n_t = obs_stock_df.shape[0] - 1 +data_data2draws = { + "n_t": n_t, + "predator_obs": obs_stock_df.loc[1:, 'Predator'].values.tolist(), + "prey_obs": obs_stock_df.loc[1:, 'Prey'].values.tolist(), +} + + +premodel = StanVensimModel("prey-predator", am, 0.0, list(range(1, n_t + 1)), data_dict = data_data2draws) +# ode parameter +premodel.set_prior("alpha", "normal", 0.55, 0.1) +premodel.set_prior("gamma", "normal", 0.8, 0.1) +premodel.set_prior("beta", "normal", 0.028, 0.01) +premodel.set_prior("delta", "normal", 0.024, 0.01) -model.set_prior("inventory_adjustment_time", "normal", 0, 1) -model.set_prior("minimum_order_processing_time", "normal", 0, 1) -model.set_prior("alpha", "normal", 0, 1, lower=0.0) -model.set_prior("inventory", "normal", 0, 1, init_state=True) +# sampling distribution parameter +premodel.set_prior("sigma", "normal", 0, 1) -print(model.vensim_model_context.variable_names) +premodel.set_prior("predator_obs", "lognormal", "predator", "sigma") +premodel.set_prior("prey_obs", "lognormal", "prey", "sigma") +premodel.build_stan_functions() +premodel.draws2data() +#print(premodel.vensim_model_context.variable_names) -model.build_stan_functions() -cmdstan_model = model.data2draws({}) -#result = cmdstan_model.sample() +#cmdstan_model = premodel.data2draws(data_data2draws) +#result = cmdstan_model.sample(data=data_data2draws) #result.summary() \ No newline at end of file From f638ac581971b8ee798ce0d8ebd5f7a39653c719 Mon Sep 17 00:00:00 2001 From: "Angie.H Moon" <30194633+hyunjimoon@users.noreply.github.com> Date: Fri, 16 Sep 2022 09:06:59 -0400 Subject: [PATCH 44/45] Three checks with draws2data, data2draws function --- test_scripts/bayes_checks.py | 103 +++++++++++++++++++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 test_scripts/bayes_checks.py diff --git a/test_scripts/bayes_checks.py b/test_scripts/bayes_checks.py new file mode 100644 index 00000000..d897a129 --- /dev/null +++ b/test_scripts/bayes_checks.py @@ -0,0 +1,103 @@ +from pysd.builders.stan.stan_model import StanVensimModel +from pysd.translators.vensim.vensim_file import VensimFile +from pysd.translators.xmile.xmile_file import XmileFile +import pandas as pd +import cmdstanpy +import numpy as np +from cmdstanpy import install_cxx_toolchain +config = install_cxx_toolchain.get_config('C:\\RTools', True) +print(install_cxx_toolchain.get_toolchain_name()) +import cmdstanpy; cmdstanpy.install_cmdstan(overwrite=True) +# def data2draws(): +## 1. D +obs_stock_df = pd.read_csv('data/hudson-bay-lynx-hare.csv') +n_t = obs_stock_df.shape[0] - 1 +data_draws2data = { + "n_t": n_t +} +## 2. P +### a. set_prior_struc +vf = VensimFile("vensim_models/prey-predator.mdl") +vf.parse() +am = vf.get_abstract_model() +model = StanVensimModel("prey-predator", am, 0.0, list(range(1, n_t + 1)), data_dict = data_draws2data) + +### b. set_prior_var +##### 1) ode parameter prior +model.set_prior("alpha", "normal", 0.55, 0.1) +model.set_prior("gamma", "normal", 0.8, 0.1) +model.set_prior("beta", "normal", 0.028, 0.01) +model.set_prior("delta", "normal", 0.024, 0.01) + +##### 2) sampling distribution parameter (measruement error) prior +model.set_prior("sigma", "lognormal", np.log(0.01), 0.1) + +##### 3) measurement \tilde{y}_{1..t} ~ f(\theta, t)_{1..t} +model.set_prior("predator_obs", "lognormal", "predator", "sigma") +model.set_prior("prey_obs", "lognormal", "prey", "sigma") + +##### 1+2+3) +model.build_stan_functions() + +### c. set_prior_demand ?? + +#model.print_info() + +## 1+2. P(D) +model.draws2data() # write stanfile +draws2data_model = cmdstanpy.CmdStanModel(stan_file="stan_files/prey-predator_draws2data.stan") + +## 3. A(P(D)) +print("DRAWS2DATA========================================================") +print(draws2data_model.sample(data=data_draws2data, fixed_param=True).summary()) + +# def data2draws(): +## 1. D +data_data2draws = { + "n_obs_state" : 2, + "initial_time" : 0, + "times": [i+1 for i in np.arange(n_t)], + "n_t": n_t, + "predator_obs": obs_stock_df.loc[1:, 'Predator'].values.tolist(), + "prey_obs": obs_stock_df.loc[1:, 'Prey'].values.tolist(), +} + +## 2. P +### a. set_prior_struc +model.build_stan_functions() # TODO check cache and build if not exist + +### b. set_prior_var +##### 1) ode parameter prior +model.set_prior("alpha", "normal", 0.8, 0.1) +model.set_prior("gamma", "normal", 0.8, 0.1) +model.set_prior("beta", "normal", 0.05, 0.001) +model.set_prior("delta", "normal", 0.05, 0.001) + +##### 2) sampling distribution parameter (measruement error) prior +model.set_prior("sigma", "lognormal", np.log(0.01), 0.1) + +model.draws2data("") +model.data2draws("") + +##### 3) measurement \tilde{y}_{1..t} ~ f(\theta, t)_{1..t} +model.set_prior("predator_obs", "lognormal", "predator", "sigma") +model.set_prior("prey_obs", "lognormal", "prey", "sigma") + +#model.print_info() + +## 1+2. P(D) +model.data2draws() # write stanfile +data2draws_model = cmdstanpy.CmdStanModel(stan_file="stan_files/prey-predator_data2draws.stan") + +## 3. A(P(D)) +print("DATA2DRAWS=========================================================") +print(data2draws_model.sample(data=data_data2draws).summary()) + +# def draws2data2draws(): + +## compare with vensim output +# vensim_df = pd.read_csv("vensim_models/prey-predator/output.csv") +# predator_obs = vensim_df[vensim_df['Time']=="Predator"] +# prey_obs = vensim_df[vensim_df['Time']=="Prey"] +# print(predator_obs) +# print(prey_obs) From 53a25763579c4b7179b04d7f7a0917e87724a19d Mon Sep 17 00:00:00 2001 From: amoon Date: Sat, 17 Sep 2022 20:38:20 -0400 Subject: [PATCH 45/45] Bug fix and analysis notebook for preypredator --- pysd/builders/stan/stan_block_builder.py | 37 +- pysd/builders/stan/stan_model.py | 4 +- .../Prey-Predator-Demand-Supply.ipynb | 569 ++++-- test_scripts/bayes_checks.py | 173 +- test_scripts/prey-predator.ipynb | 1521 +++++++++++++++++ .../stan_files/prey-predator_data2draws.stan | 48 + .../stan_files/prey-predator_draws2data.stan | 35 + .../stan_files/prey-predator_functions.stan | 20 + test_scripts/stan_vensim_integration.py | 4 +- 9 files changed, 2153 insertions(+), 258 deletions(-) mode change 100755 => 100644 test_scripts/Prey-Predator-Demand-Supply.ipynb create mode 100644 test_scripts/prey-predator.ipynb create mode 100644 test_scripts/stan_files/prey-predator_data2draws.stan create mode 100644 test_scripts/stan_files/prey-predator_draws2data.stan create mode 100644 test_scripts/stan_files/prey-predator_functions.stan diff --git a/pysd/builders/stan/stan_block_builder.py b/pysd/builders/stan/stan_block_builder.py index 3514c33d..405df940 100644 --- a/pysd/builders/stan/stan_block_builder.py +++ b/pysd/builders/stan/stan_block_builder.py @@ -110,23 +110,22 @@ def build_block(self): added_parameters = set() for statement in self.stan_model_context.sample_statements: - for lhs_variable in statement.lhs_variable: - if lhs_variable in data_variable_names: - continue - if lhs_variable in added_parameters: - continue - if statement.distribution_type == statement.assignment_dist: - continue - if statement.lower > float("-inf") and statement.upper < float("inf"): - code += f"real {lhs_variable};\n" - elif statement.lower > float("-inf"): - code += f"real {lhs_variable};\n" - elif statement.upper < float("inf"): - code += f"real {lhs_variable};\n" - else: - code += f"real {lhs_variable};\n" + if statement.lhs_variable in data_variable_names: + continue + if statement.lhs_variable in added_parameters: + continue + if statement.distribution_type == statement.assignment_dist: + continue + if statement.lower > float("-inf") and statement.upper < float("inf"): + code += f"real {statement.lhs_variable};\n" + elif statement.lower > float("-inf"): + code += f"real {statement.lhs_variable};\n" + elif statement.upper < float("inf"): + code += f"real {statement.lhs_variable};\n" + else: + code += f"real {statement.lhs_variable};\n" - added_parameters.add(lhs_variable) + added_parameters.add(statement.lhs_variable) code.indent_level -= 1 # Exit parameters block code += "}\n" @@ -175,7 +174,7 @@ def build_block(self): code = IndentedString() code += "model{\n" code.indent_level += 1 - for statement in self.stan_model_context.sampling_statements: + for statement in self.stan_model_context.sample_statements: if statement.distribution_type != statement.assignment_dist: code += f"{statement.lhs_expr} ~ {statement.distribution_type}({', '.join([str(arg) for arg in statement.distribution_args])});\n" @@ -236,8 +235,8 @@ def build_data_rng_functions(self): def build_rng_functions(self): ignored_variables = set(self.stan_model_context.stan_data.keys()).union(set(self.vensim_model_context.stock_variable_names)) stmt_sorter = StatementTopoSort(ignored_variables) - for sampling_statement in self.stan_model_context.sample_statements: - stmt_sorter.add_stmt(sampling_statement.lhs_variable, sampling_statement.rhs_variables) + for sample_statements in self.stan_model_context.sample_statements: + stmt_sorter.add_stmt(sample_statements.lhs_variable, sample_statements.rhs_variables) param_draw_order = stmt_sorter.sort() statements = self.stan_model_context.sample_statements.copy() diff --git a/pysd/builders/stan/stan_model.py b/pysd/builders/stan/stan_model.py index 4e762638..633a6872 100644 --- a/pysd/builders/stan/stan_model.py +++ b/pysd/builders/stan/stan_model.py @@ -188,7 +188,7 @@ def build_stan_functions(self): self.function_builder = StanFunctionBuilder(self.abstract_model) f.write(self.function_builder.build_functions(self.stan_model_context.exposed_parameters, self.vensim_model_context.stock_variable_names)) - def data2draws(self): + def stanify_data2draws(self): stan_model_path = os.path.join(self.stan_model_dir, f"{self.model_name}_data2draws.stan") with open(stan_model_path, "w") as f: # Include the function @@ -225,7 +225,7 @@ def data2draws(self): stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_path) return stan_model - def draws2data(self): + def stanify_draws2data(self): stan_model_path = os.path.join(self.stan_model_dir, f"{self.model_name}_draws2data.stan") with open(stan_model_path, "w") as f: # Include the function diff --git a/test_scripts/Prey-Predator-Demand-Supply.ipynb b/test_scripts/Prey-Predator-Demand-Supply.ipynb old mode 100755 new mode 100644 index f83a7daf..69b1d7ff --- a/test_scripts/Prey-Predator-Demand-Supply.ipynb +++ b/test_scripts/Prey-Predator-Demand-Supply.ipynb @@ -6,7 +6,10 @@ "metadata": { "colab": {}, "colab_type": "code", - "id": "hBkiivD5LW7e" + "id": "hBkiivD5LW7e", + "pycharm": { + "name": "#%%\n" + } }, "outputs": [], "source": [ @@ -15,7 +18,7 @@ "import pysd\n", "import cmdstanpy # 2.30 is fastest (as of 08.12.2022) `cmdstanpy.install_cmdstan()` \n", "from cmdstanpy import CmdStanModel, cmdstan_path\n", - "import arviz as az #!pip install git+https://github.com/arviz-devs/arviz\n", + "#import arviz as az #!pip install git+https://github.com/arviz-devs/arviz\n", "az.style.use(\"arviz-darkgrid\")\n", "import os\n", "from IPython.display import Image\n", @@ -26,7 +29,11 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "# set your working directiory\n", @@ -37,7 +44,10 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "lxLXpPsoj6a2" + "id": "lxLXpPsoj6a2", + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "# Structuring Uncertainties in Dynamic Models: \n", @@ -50,7 +60,10 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "RlXBUmm_j6a3" + "id": "RlXBUmm_j6a3", + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "\n", @@ -81,7 +94,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Three sources of uncertainty in dynamic model\n", "\n", @@ -94,7 +111,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Predator-Prey: parameter and measurement uncertainty\n", "\n", @@ -109,7 +130,10 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "bhoL22XVj6a4" + "id": "bhoL22XVj6a4", + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "## Data: Lynx and Hare Pelts in Canada\n", @@ -132,7 +156,10 @@ }, "colab_type": "code", "id": "dX9-7-Qbj6a5", - "outputId": "e6827254-f9ef-4906-df7e-46577e7ffc67" + "outputId": "e6827254-f9ef-4906-df7e-46577e7ffc67", + "pycharm": { + "name": "#%%\n" + } }, "outputs": [ { @@ -179,7 +206,10 @@ "colab_type": "code", "id": "zxIexRJoj6bA", "outputId": "68b09259-ba06-48b9-f3b0-cb2e38192899", - "scrolled": true + "scrolled": true, + "pycharm": { + "name": "#%%\n" + } }, "outputs": [ { @@ -216,7 +246,10 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "REdoJXx2j6bK" + "id": "REdoJXx2j6bK", + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "## Mechanistic Model: The Lotka-Volterra Equations\n", @@ -269,7 +302,10 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "Rkg3Z5Zbj6bK" + "id": "Rkg3Z5Zbj6bK", + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "## Statistical Model: regreasion framing and uncertainty embedding\n", @@ -347,7 +383,10 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "vTYEL9m4GIFi" + "id": "vTYEL9m4GIFi", + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "10 Bayes's rule for parameters $\\theta$ and observed data $y$ is $$ \\begin{array}{rcl} p(\\theta\\,|\\, y) & = & \\displaystyle \\frac{p(\\theta, y)}{p(y)} \\\\[4pt] & = & \\displaystyle \\frac{p(y | \\theta) \\, p(\\theta)}{p(y)} \\\\[4pt] & = & \\displaystyle \\frac{p(y | \\theta) \\, p(\\theta)}{\\int_{\\Theta} p(y | \\theta) \\, p(\\theta) \\, \\mathrm{d}\\theta} \\\\[4pt] & \\propto & p(y | \\theta) \\, p(\\theta). \\end{array} $$\n", @@ -370,7 +409,10 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "KbkjXoUBGIFk" + "id": "KbkjXoUBGIFk", + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "### Multiplicative error and the lognormal distribution\n", @@ -401,7 +443,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Translating the Model for Generation: pysd Program\n", "\n", @@ -413,7 +459,11 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "mod = pysd.read_vensim('vensim_models/prey-predator.mdl')\n", @@ -426,7 +476,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Default time_step is supplied from the vensim model which is .03125 here but can be changed if different precision is needed. We aim our model to be on continuous time (as opposed to discrete time). Whether the time step is small enough can be heuristically checked by comparing the value of state variables for the given time_step and its halved version." ] @@ -434,7 +488,11 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -454,7 +512,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "From comparison below, we judge time_step as .03125 is small enough to be considered as continuous time." ] @@ -462,7 +524,11 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -505,7 +571,11 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -540,7 +610,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Translating the Model for Inference: Stan Program\n", "\n", @@ -551,7 +625,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "To compose one stanfile which consists of six blocks (data, transformed data, parameter, transformed parameter, model, generated quantities), users should input three priors: relational, variational, demand. The table below expresses each prior's mathematical identity and location within the program.\n", "\n", @@ -564,7 +642,11 @@ { "cell_type": "code", "execution_count": 165, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "from pysd.translators.vensim.vensim_file import VensimFile\n", @@ -588,7 +670,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Draws2Data" ] @@ -596,7 +682,11 @@ { "cell_type": "code", "execution_count": 127, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stderr", @@ -657,7 +747,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "We first plot first ten sampled (out of 4,000) trajactories and briefly check the range are not too extreme. As can be seen from the figure, blue and orange real prey and predator are comparable to its prior predictive corrspondance (green and plum).\n", "\n", @@ -673,7 +767,11 @@ { "cell_type": "code", "execution_count": 128, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -709,7 +807,10 @@ { "cell_type": "markdown", "metadata": { - "tags": [] + "tags": [], + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "## Data2Draws" @@ -718,7 +819,11 @@ { "cell_type": "code", "execution_count": 33, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stderr", @@ -1142,7 +1247,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "\"Exception: ode_rk45: initial state[1] is inf\" may be because ode integrator is stiff; which may be resolved by using ode_bdf from [this](https://discourse.mc-stan.org/t/exception-integrate-ode-rk45-parameter-vector-1-is-inf-but-must-be-finite/13953/2?u=hyunji.moon) post." ] @@ -1150,7 +1259,10 @@ { "cell_type": "markdown", "metadata": { - "tags": [] + "tags": [], + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "## Inference Data" @@ -1159,7 +1271,11 @@ { "cell_type": "code", "execution_count": 152, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -1200,26 +1316,30 @@ { "cell_type": "code", "execution_count": 150, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "ename": "InvalidIndexError", "evalue": "(slice(None, None, None), 1)", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3621\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/_libs/index.pyx:136\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/_libs/index.pyx:142\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: '(slice(None, None, None), 1)' is an invalid key", + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3621\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[0;34m(self, key, method, tolerance)\u001B[0m\n\u001B[1;32m 3620\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m-> 3621\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 3622\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/_libs/index.pyx:136\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[0;34m()\u001B[0m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/_libs/index.pyx:142\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[0;34m()\u001B[0m\n", + "\u001B[0;31mTypeError\u001B[0m: '(slice(None, None, None), 1)' is an invalid key", "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mInvalidIndexError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [150]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mlynx_hare_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mLynx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHare\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/core/frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 3504\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3505\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3507\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3628\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3623\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3624\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3625\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3626\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3627\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m-> 3628\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_indexing_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3629\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[1;32m 3631\u001b[0m \u001b[38;5;66;03m# GH#42269\u001b[39;00m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:5637\u001b[0m, in \u001b[0;36mIndex._check_indexing_error\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 5633\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_check_indexing_error\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[1;32m 5634\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(key):\n\u001b[1;32m 5635\u001b[0m \u001b[38;5;66;03m# if key is not a scalar, directly raise an error (the code below\u001b[39;00m\n\u001b[1;32m 5636\u001b[0m \u001b[38;5;66;03m# would convert to numpy arrays and raise later any way) - GH29926\u001b[39;00m\n\u001b[0;32m-> 5637\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n", - "\u001b[0;31mInvalidIndexError\u001b[0m: (slice(None, None, None), 1)" + "\u001B[0;31mInvalidIndexError\u001B[0m Traceback (most recent call last)", + "Input \u001B[0;32mIn [150]\u001B[0m, in \u001B[0;36m\u001B[0;34m()\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43mlynx_hare_df\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mloc\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mLynx\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mHare\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m]\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m]\u001B[49m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/core/frame.py:3505\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m 3503\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[1;32m 3504\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[0;32m-> 3505\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 3506\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[1;32m 3507\u001B[0m indexer \u001B[38;5;241m=\u001B[39m [indexer]\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3628\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[0;34m(self, key, method, tolerance)\u001B[0m\n\u001B[1;32m 3623\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[1;32m 3624\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[1;32m 3625\u001B[0m \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[1;32m 3626\u001B[0m \u001B[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[1;32m 3627\u001B[0m \u001B[38;5;66;03m# the TypeError.\u001B[39;00m\n\u001B[0;32m-> 3628\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_check_indexing_error\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 3629\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m\n\u001B[1;32m 3631\u001B[0m \u001B[38;5;66;03m# GH#42269\u001B[39;00m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:5637\u001B[0m, in \u001B[0;36mIndex._check_indexing_error\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m 5633\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_check_indexing_error\u001B[39m(\u001B[38;5;28mself\u001B[39m, key):\n\u001B[1;32m 5634\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m is_scalar(key):\n\u001B[1;32m 5635\u001B[0m \u001B[38;5;66;03m# if key is not a scalar, directly raise an error (the code below\u001B[39;00m\n\u001B[1;32m 5636\u001B[0m \u001B[38;5;66;03m# would convert to numpy arrays and raise later any way) - GH29926\u001B[39;00m\n\u001B[0;32m-> 5637\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m InvalidIndexError(key)\n", + "\u001B[0;31mInvalidIndexError\u001B[0m: (slice(None, None, None), 1)" ] } ], @@ -1230,7 +1350,11 @@ { "cell_type": "code", "execution_count": 160, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "# # stan_data = az.from_pystan(\n", @@ -1262,7 +1386,11 @@ { "cell_type": "code", "execution_count": 161, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -1294,7 +1422,11 @@ { "cell_type": "code", "execution_count": 163, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -1324,7 +1456,11 @@ { "cell_type": "code", "execution_count": 129, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "fit_posterior_draws = posterior_draws" @@ -1333,14 +1469,18 @@ { "cell_type": "code", "execution_count": 125, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "ename": "SyntaxError", "evalue": "positional argument follows keyword argument (3705576184.py, line 14)", "output_type": "error", "traceback": [ - "\u001b[0;36m Input \u001b[0;32mIn [125]\u001b[0;36m\u001b[0m\n\u001b[0;31m log_likelihood: \"log_lik\",\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n" + "\u001B[0;36m Input \u001B[0;32mIn [125]\u001B[0;36m\u001B[0m\n\u001B[0;31m log_likelihood: \"log_lik\",\u001B[0m\n\u001B[0m ^\u001B[0m\n\u001B[0;31mSyntaxError\u001B[0m\u001B[0;31m:\u001B[0m positional argument follows keyword argument\n" ] } ], @@ -1370,22 +1510,26 @@ { "cell_type": "code", "execution_count": 116, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "ename": "AttributeError", "evalue": "'list' object has no attribute 'items'", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [116]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m idata_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_hat\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_likelihood\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_lik\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobserved_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdims\u001b[39m\u001b[38;5;124m\"\u001b[39m: dims,\n\u001b[1;32m 7\u001b[0m }\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m#idata = az.from_pystan(posterior=fit, posterior_model=sm, **idata_kwargs)\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m idata_stan \u001b[38;5;241m=\u001b[39m \u001b[43maz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_cmdstanpy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;43;03m# prior=prior_pred,\u001b[39;49;00m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior_draws\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;43;03m# prior_predictive= \"y_tilde\",\u001b[39;49;00m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#posterior_predictive= \"y_hat\",\u001b[39;49;00m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;66;43;03m# observed_data= \"\u001b[39;49;00m\n\u001b[1;32m 15\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43midata_kwargs\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:846\u001b[0m, in \u001b[0;36mfrom_cmdstanpy\u001b[0;34m(posterior, posterior_predictive, predictions, prior, prior_predictive, observed_data, constant_data, predictions_constant_data, log_likelihood, index_origin, coords, dims, save_warmup, dtypes)\u001b[0m\n\u001b[1;32m 765\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_cmdstanpy\u001b[39m(\n\u001b[1;32m 766\u001b[0m posterior\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 767\u001b[0m \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 780\u001b[0m dtypes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 781\u001b[0m ):\n\u001b[1;32m 782\u001b[0m \u001b[38;5;124;03m\"\"\"Convert CmdStanPy data into an InferenceData object.\u001b[39;00m\n\u001b[1;32m 783\u001b[0m \n\u001b[1;32m 784\u001b[0m \u001b[38;5;124;03m For a usage example read the\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;124;03m InferenceData object\u001b[39;00m\n\u001b[1;32m 830\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 831\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mCmdStanPyConverter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 832\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 833\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior_predictive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 834\u001b[0m \u001b[43m \u001b[49m\u001b[43mpredictions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 835\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 836\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior_predictive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 837\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 838\u001b[0m \u001b[43m \u001b[49m\u001b[43mconstant_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconstant_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 839\u001b[0m \u001b[43m \u001b[49m\u001b[43mpredictions_constant_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpredictions_constant_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 840\u001b[0m \u001b[43m \u001b[49m\u001b[43mlog_likelihood\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_likelihood\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 841\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex_origin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_origin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 842\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 843\u001b[0m \u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 844\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_warmup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_warmup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 845\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtypes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtypes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m--> 846\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_inference_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:463\u001b[0m, in \u001b[0;36mCmdStanPyConverter.to_inference_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 446\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_inference_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 447\u001b[0m \u001b[38;5;124;03m\"\"\"Convert all available data to an InferenceData object.\u001b[39;00m\n\u001b[1;32m 448\u001b[0m \n\u001b[1;32m 449\u001b[0m \u001b[38;5;124;03m Note that if groups can not be created (i.e., there is no `output`, so\u001b[39;00m\n\u001b[1;32m 450\u001b[0m \u001b[38;5;124;03m the `posterior` and `sample_stats` can not be extracted), then the InferenceData\u001b[39;00m\n\u001b[1;32m 451\u001b[0m \u001b[38;5;124;03m will not have those groups.\u001b[39;00m\n\u001b[1;32m 452\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m InferenceData(\n\u001b[1;32m 454\u001b[0m save_warmup\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_warmup,\n\u001b[1;32m 455\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 456\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior_to_xarray(),\n\u001b[1;32m 457\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_stats\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample_stats_to_xarray(),\n\u001b[1;32m 458\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior_predictive_to_xarray(),\n\u001b[1;32m 459\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictions_to_xarray(),\n\u001b[1;32m 460\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprior_to_xarray(),\n\u001b[1;32m 461\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_stats_prior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample_stats_prior_to_xarray(),\n\u001b[1;32m 462\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprior_predictive_to_xarray(),\n\u001b[0;32m--> 463\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobserved_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobserved_data_to_xarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 464\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconstant_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconstant_data_to_xarray(),\n\u001b[1;32m 465\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions_constant_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictions_constant_data_to_xarray(),\n\u001b[1;32m 466\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_likelihood\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog_likelihood_to_xarray(),\n\u001b[1;32m 467\u001b[0m },\n\u001b[1;32m 468\u001b[0m )\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:65\u001b[0m, in \u001b[0;36mrequires.__call__..wrapped\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m((\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mcls\u001b[39m, prop_i) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m prop_i \u001b[38;5;129;01min\u001b[39;00m prop)):\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:412\u001b[0m, in \u001b[0;36mCmdStanPyConverter.observed_data_to_xarray\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 409\u001b[0m \u001b[38;5;129m@requires\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobserved_data\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 410\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mobserved_data_to_xarray\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 411\u001b[0m \u001b[38;5;124;03m\"\"\"Convert observed data to xarray.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 412\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdict_to_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 413\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobserved_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 414\u001b[0m \u001b[43m \u001b[49m\u001b[43mlibrary\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcmdstanpy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 416\u001b[0m \u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 417\u001b[0m \u001b[43m \u001b[49m\u001b[43mdefault_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 418\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex_origin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex_origin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 419\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:306\u001b[0m, in \u001b[0;36mdict_to_dataset\u001b[0;34m(data, attrs, library, coords, dims, default_dims, index_origin, skip_event_dims)\u001b[0m\n\u001b[1;32m 303\u001b[0m dims \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 305\u001b[0m data_vars \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m--> 306\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, values \u001b[38;5;129;01min\u001b[39;00m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m():\n\u001b[1;32m 307\u001b[0m data_vars[key] \u001b[38;5;241m=\u001b[39m numpy_to_data_array(\n\u001b[1;32m 308\u001b[0m values,\n\u001b[1;32m 309\u001b[0m var_name\u001b[38;5;241m=\u001b[39mkey,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 314\u001b[0m skip_event_dims\u001b[38;5;241m=\u001b[39mskip_event_dims,\n\u001b[1;32m 315\u001b[0m )\n\u001b[1;32m 316\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m xr\u001b[38;5;241m.\u001b[39mDataset(data_vars\u001b[38;5;241m=\u001b[39mdata_vars, attrs\u001b[38;5;241m=\u001b[39mmake_attrs(attrs\u001b[38;5;241m=\u001b[39mattrs, library\u001b[38;5;241m=\u001b[39mlibrary))\n", - "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'items'" + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mAttributeError\u001B[0m Traceback (most recent call last)", + "Input \u001B[0;32mIn [116]\u001B[0m, in \u001B[0;36m\u001B[0;34m()\u001B[0m\n\u001B[1;32m 2\u001B[0m idata_kwargs \u001B[38;5;241m=\u001B[39m {\n\u001B[1;32m 3\u001B[0m 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\u001B[43maz\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfrom_cmdstanpy\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 10\u001B[0m \u001B[38;5;66;43;03m# prior=prior_pred,\u001B[39;49;00m\n\u001B[1;32m 11\u001B[0m \u001B[43m \u001B[49m\u001B[43mposterior\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mposterior_draws\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 12\u001B[0m \u001B[38;5;66;43;03m# prior_predictive= \"y_tilde\",\u001B[39;49;00m\n\u001B[1;32m 13\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;66;43;03m#posterior_predictive= \"y_hat\",\u001B[39;49;00m\n\u001B[1;32m 14\u001B[0m \u001B[38;5;66;43;03m# observed_data= \"\u001B[39;49;00m\n\u001B[1;32m 15\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43midata_kwargs\u001B[49m\n\u001B[1;32m 16\u001B[0m \u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:846\u001B[0m, in \u001B[0;36mfrom_cmdstanpy\u001B[0;34m(posterior, posterior_predictive, predictions, prior, prior_predictive, observed_data, constant_data, predictions_constant_data, log_likelihood, index_origin, coords, dims, save_warmup, dtypes)\u001B[0m\n\u001B[1;32m 765\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfrom_cmdstanpy\u001B[39m(\n\u001B[1;32m 766\u001B[0m posterior\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m 767\u001B[0m \u001B[38;5;241m*\u001B[39m,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 780\u001B[0m dtypes\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m 781\u001B[0m ):\n\u001B[1;32m 782\u001B[0m \u001B[38;5;124;03m\"\"\"Convert CmdStanPy data into an InferenceData object.\u001B[39;00m\n\u001B[1;32m 783\u001B[0m \n\u001B[1;32m 784\u001B[0m \u001B[38;5;124;03m For a usage example read the\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 829\u001B[0m \u001B[38;5;124;03m InferenceData object\u001B[39;00m\n\u001B[1;32m 830\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m 831\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mCmdStanPyConverter\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 832\u001B[0m \u001B[43m \u001B[49m\u001B[43mposterior\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mposterior\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 833\u001B[0m \u001B[43m \u001B[49m\u001B[43mposterior_predictive\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mposterior_predictive\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 834\u001B[0m \u001B[43m \u001B[49m\u001B[43mpredictions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpredictions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 835\u001B[0m \u001B[43m \u001B[49m\u001B[43mprior\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mprior\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 836\u001B[0m \u001B[43m 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\u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:463\u001B[0m, in \u001B[0;36mCmdStanPyConverter.to_inference_data\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 446\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mto_inference_data\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m 447\u001B[0m \u001B[38;5;124;03m\"\"\"Convert all available data to an InferenceData object.\u001B[39;00m\n\u001B[1;32m 448\u001B[0m \n\u001B[1;32m 449\u001B[0m \u001B[38;5;124;03m Note that if groups can not be created (i.e., there is no `output`, so\u001B[39;00m\n\u001B[1;32m 450\u001B[0m \u001B[38;5;124;03m the `posterior` and `sample_stats` can not be extracted), then the InferenceData\u001B[39;00m\n\u001B[1;32m 451\u001B[0m \u001B[38;5;124;03m will not have those groups.\u001B[39;00m\n\u001B[1;32m 452\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m 453\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m InferenceData(\n\u001B[1;32m 454\u001B[0m save_warmup\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msave_warmup,\n\u001B[1;32m 455\u001B[0m \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m{\n\u001B[1;32m 456\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mposterior\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mposterior_to_xarray(),\n\u001B[1;32m 457\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msample_stats\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msample_stats_to_xarray(),\n\u001B[1;32m 458\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mposterior_predictive\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mposterior_predictive_to_xarray(),\n\u001B[1;32m 459\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpredictions\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpredictions_to_xarray(),\n\u001B[1;32m 460\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mprior\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprior_to_xarray(),\n\u001B[1;32m 461\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msample_stats_prior\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msample_stats_prior_to_xarray(),\n\u001B[1;32m 462\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mprior_predictive\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprior_predictive_to_xarray(),\n\u001B[0;32m--> 463\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mobserved_data\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mobserved_data_to_xarray\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m,\n\u001B[1;32m 464\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mconstant_data\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mconstant_data_to_xarray(),\n\u001B[1;32m 465\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpredictions_constant_data\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpredictions_constant_data_to_xarray(),\n\u001B[1;32m 466\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mlog_likelihood\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlog_likelihood_to_xarray(),\n\u001B[1;32m 467\u001B[0m },\n\u001B[1;32m 468\u001B[0m )\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:65\u001B[0m, in \u001B[0;36mrequires.__call__..wrapped\u001B[0;34m(cls)\u001B[0m\n\u001B[1;32m 63\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mall\u001B[39m((\u001B[38;5;28mgetattr\u001B[39m(\u001B[38;5;28mcls\u001B[39m, prop_i) \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01mfor\u001B[39;00m prop_i \u001B[38;5;129;01min\u001B[39;00m prop)):\n\u001B[1;32m 64\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m---> 65\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mcls\u001B[39;49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:412\u001B[0m, in \u001B[0;36mCmdStanPyConverter.observed_data_to_xarray\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 409\u001B[0m \u001B[38;5;129m@requires\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mobserved_data\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 410\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mobserved_data_to_xarray\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m 411\u001B[0m \u001B[38;5;124;03m\"\"\"Convert observed data to xarray.\"\"\"\u001B[39;00m\n\u001B[0;32m--> 412\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mdict_to_dataset\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 413\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mobserved_data\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 414\u001B[0m \u001B[43m \u001B[49m\u001B[43mlibrary\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcmdstanpy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 415\u001B[0m \u001B[43m \u001B[49m\u001B[43mcoords\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcoords\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 416\u001B[0m \u001B[43m \u001B[49m\u001B[43mdims\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdims\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 417\u001B[0m \u001B[43m \u001B[49m\u001B[43mdefault_dims\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 418\u001B[0m \u001B[43m \u001B[49m\u001B[43mindex_origin\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mindex_origin\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 419\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:306\u001B[0m, in \u001B[0;36mdict_to_dataset\u001B[0;34m(data, attrs, library, coords, dims, default_dims, index_origin, skip_event_dims)\u001B[0m\n\u001B[1;32m 303\u001B[0m dims \u001B[38;5;241m=\u001B[39m {}\n\u001B[1;32m 305\u001B[0m data_vars \u001B[38;5;241m=\u001B[39m {}\n\u001B[0;32m--> 306\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m key, values \u001B[38;5;129;01min\u001B[39;00m \u001B[43mdata\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mitems\u001B[49m():\n\u001B[1;32m 307\u001B[0m data_vars[key] \u001B[38;5;241m=\u001B[39m numpy_to_data_array(\n\u001B[1;32m 308\u001B[0m values,\n\u001B[1;32m 309\u001B[0m var_name\u001B[38;5;241m=\u001B[39mkey,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 314\u001B[0m skip_event_dims\u001B[38;5;241m=\u001B[39mskip_event_dims,\n\u001B[1;32m 315\u001B[0m )\n\u001B[1;32m 316\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m xr\u001B[38;5;241m.\u001B[39mDataset(data_vars\u001B[38;5;241m=\u001B[39mdata_vars, attrs\u001B[38;5;241m=\u001B[39mmake_attrs(attrs\u001B[38;5;241m=\u001B[39mattrs, library\u001B[38;5;241m=\u001B[39mlibrary))\n", + "\u001B[0;31mAttributeError\u001B[0m: 'list' object has no attribute 'items'" ] } ], @@ -1414,7 +1558,11 @@ { "cell_type": "code", "execution_count": 117, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -1446,24 +1594,28 @@ { "cell_type": "code", "execution_count": 112, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "ename": "KeyError", "evalue": "'var names: \"[\\'y\\'] are not present\" in dataset'", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:71\u001b[0m, in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 71\u001b[0m var_names \u001b[38;5;241m=\u001b[39m \u001b[43m_subset_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvar_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilter_items\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilter_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwarn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:149\u001b[0m, in \u001b[0;36m_subset_list\u001b[0;34m(subset, whole_list, filter_items, warn)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(existing_items):\n\u001b[0;32m--> 149\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39marray(subset)[\u001b[38;5;241m~\u001b[39mexisting_items]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m are not present\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m subset\n", - "\u001b[0;31mKeyError\u001b[0m: \"['y'] are not present\"", + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mKeyError\u001B[0m Traceback (most recent call last)", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:71\u001B[0m, in \u001B[0;36m_var_names\u001B[0;34m(var_names, data, filter_vars)\u001B[0m\n\u001B[1;32m 70\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m---> 71\u001B[0m var_names \u001B[38;5;241m=\u001B[39m \u001B[43m_subset_list\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvar_names\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mall_vars\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfilter_items\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfilter_vars\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mwarn\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m 72\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:149\u001B[0m, in \u001B[0;36m_subset_list\u001B[0;34m(subset, whole_list, filter_items, warn)\u001B[0m\n\u001B[1;32m 148\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m np\u001B[38;5;241m.\u001B[39mall(existing_items):\n\u001B[0;32m--> 149\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnp\u001B[38;5;241m.\u001B[39marray(subset)[\u001B[38;5;241m~\u001B[39mexisting_items]\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m are not present\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 151\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m subset\n", + "\u001B[0;31mKeyError\u001B[0m: \"['y'] are not present\"", "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [112]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43marviz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_ppc\u001b[49m\u001b[43m(\u001b[49m\u001b[43midata_stan\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/plots/ppcplot.py:261\u001b[0m, in \u001b[0;36mplot_ppc\u001b[0;34m(data, kind, alpha, mean, observed, color, colors, grid, figsize, textsize, data_pairs, var_names, filter_vars, coords, flatten, flatten_pp, num_pp_samples, random_seed, jitter, animated, animation_kwargs, legend, labeller, ax, backend, backend_kwargs, group, show)\u001b[0m\n\u001b[1;32m 259\u001b[0m var_names \u001b[38;5;241m=\u001b[39m _var_names(var_names, observed_data, filter_vars)\n\u001b[1;32m 260\u001b[0m pp_var_names \u001b[38;5;241m=\u001b[39m [data_pairs\u001b[38;5;241m.\u001b[39mget(var, var) \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m var_names]\n\u001b[0;32m--> 261\u001b[0m pp_var_names \u001b[38;5;241m=\u001b[39m \u001b[43m_var_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpp_var_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredictive_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilter_vars\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 263\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flatten_pp \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m flatten \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 264\u001b[0m flatten_pp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(predictive_dataset\u001b[38;5;241m.\u001b[39mdims\u001b[38;5;241m.\u001b[39mkeys())\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:74\u001b[0m, in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 73\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin((\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvar names:\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00merr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124min dataset\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m---> 74\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m var_names\n", - "\u001b[0;31mKeyError\u001b[0m: 'var names: \"[\\'y\\'] are not present\" in dataset'" + "\u001B[0;31mKeyError\u001B[0m Traceback (most recent call last)", + "Input \u001B[0;32mIn [112]\u001B[0m, in \u001B[0;36m\u001B[0;34m()\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43marviz\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mplot_ppc\u001B[49m\u001B[43m(\u001B[49m\u001B[43midata_stan\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/plots/ppcplot.py:261\u001B[0m, in \u001B[0;36mplot_ppc\u001B[0;34m(data, kind, alpha, mean, observed, color, colors, grid, figsize, textsize, data_pairs, var_names, filter_vars, coords, flatten, flatten_pp, num_pp_samples, random_seed, jitter, animated, animation_kwargs, legend, labeller, ax, backend, backend_kwargs, group, show)\u001B[0m\n\u001B[1;32m 259\u001B[0m var_names \u001B[38;5;241m=\u001B[39m _var_names(var_names, observed_data, filter_vars)\n\u001B[1;32m 260\u001B[0m pp_var_names \u001B[38;5;241m=\u001B[39m [data_pairs\u001B[38;5;241m.\u001B[39mget(var, var) \u001B[38;5;28;01mfor\u001B[39;00m var \u001B[38;5;129;01min\u001B[39;00m var_names]\n\u001B[0;32m--> 261\u001B[0m pp_var_names \u001B[38;5;241m=\u001B[39m \u001B[43m_var_names\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpp_var_names\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpredictive_dataset\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfilter_vars\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 263\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m flatten_pp \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m flatten \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m 264\u001B[0m flatten_pp \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(predictive_dataset\u001B[38;5;241m.\u001B[39mdims\u001B[38;5;241m.\u001B[39mkeys())\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:74\u001B[0m, in \u001B[0;36m_var_names\u001B[0;34m(var_names, data, filter_vars)\u001B[0m\n\u001B[1;32m 72\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m 73\u001B[0m msg \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m \u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin((\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mvar names:\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00merr\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124min dataset\u001B[39m\u001B[38;5;124m\"\u001B[39m))\n\u001B[0;32m---> 74\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(msg) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[1;32m 75\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m var_names\n", + "\u001B[0;31mKeyError\u001B[0m: 'var names: \"[\\'y\\'] are not present\" in dataset'" ] } ], @@ -1474,7 +1626,11 @@ { "cell_type": "code", "execution_count": 83, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "a = {\"Prey\": prior_pred.y_tilde[:,:,0], \"Predator\": prior_pred.y_tilde[:,:,1]}," @@ -1483,23 +1639,27 @@ { "cell_type": "code", "execution_count": 93, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "ename": "TypeError", "evalue": "unhashable type: 'numpy.ndarray'", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [93]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m dims \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_likelihood\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_hat\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m]}\n\u001b[1;32m 3\u001b[0m idata_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_hat\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobserved_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdims\u001b[39m\u001b[38;5;124m\"\u001b[39m: dims,\n\u001b[1;32m 9\u001b[0m }\n\u001b[0;32m---> 10\u001b[0m idata_stan \u001b[38;5;241m=\u001b[39m \u001b[43maz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_cmdstanpy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior_pred\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior_draws\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43my_tilde\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#posterior_predictive= {'Prey': posterior_draws.y_hat[:,:,0], 'Predator': posterior_draws.y_hat[:,:,1]},\u001b[39;49;00m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;43;03m# observed_data= \"\u001b[39;49;00m\n\u001b[1;32m 16\u001b[0m \u001b[43m \u001b[49m\u001b[43mlog_likelihood\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\n\u001b[1;32m 17\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlog_lik\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mposterior_draws\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_lik\u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#predictions=[\"slack_comments_pred\", \"github_commits_pred\"],\u001b[39;49;00m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#predictions_constant_data=[\"time_since_joined_pred\"],\u001b[39;49;00m\n\u001b[1;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#coords={\"developer\": names, \"candidate developer\" : candidate_devs},\u001b[39;49;00m\n\u001b[1;32m 22\u001b[0m \u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:846\u001b[0m, in \u001b[0;36mfrom_cmdstanpy\u001b[0;34m(posterior, posterior_predictive, predictions, prior, prior_predictive, observed_data, constant_data, predictions_constant_data, log_likelihood, index_origin, coords, dims, save_warmup, dtypes)\u001b[0m\n\u001b[1;32m 765\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_cmdstanpy\u001b[39m(\n\u001b[1;32m 766\u001b[0m posterior\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 767\u001b[0m \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 780\u001b[0m dtypes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 781\u001b[0m ):\n\u001b[1;32m 782\u001b[0m \u001b[38;5;124;03m\"\"\"Convert CmdStanPy data into an InferenceData object.\u001b[39;00m\n\u001b[1;32m 783\u001b[0m \n\u001b[1;32m 784\u001b[0m \u001b[38;5;124;03m For a usage example read the\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;124;03m InferenceData object\u001b[39;00m\n\u001b[1;32m 830\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 831\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mCmdStanPyConverter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 832\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 833\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior_predictive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 834\u001b[0m \u001b[43m \u001b[49m\u001b[43mpredictions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 835\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 836\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior_predictive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 837\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 838\u001b[0m \u001b[43m \u001b[49m\u001b[43mconstant_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconstant_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 839\u001b[0m \u001b[43m \u001b[49m\u001b[43mpredictions_constant_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpredictions_constant_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 840\u001b[0m \u001b[43m \u001b[49m\u001b[43mlog_likelihood\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_likelihood\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 841\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex_origin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_origin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 842\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 843\u001b[0m \u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 844\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_warmup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_warmup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 845\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtypes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtypes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m--> 846\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_inference_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:466\u001b[0m, in \u001b[0;36mCmdStanPyConverter.to_inference_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 446\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_inference_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 447\u001b[0m \u001b[38;5;124;03m\"\"\"Convert all available data to an InferenceData object.\u001b[39;00m\n\u001b[1;32m 448\u001b[0m \n\u001b[1;32m 449\u001b[0m \u001b[38;5;124;03m Note that if groups can not be created (i.e., there is no `output`, so\u001b[39;00m\n\u001b[1;32m 450\u001b[0m \u001b[38;5;124;03m the `posterior` and `sample_stats` can not be extracted), then the InferenceData\u001b[39;00m\n\u001b[1;32m 451\u001b[0m \u001b[38;5;124;03m will not have those groups.\u001b[39;00m\n\u001b[1;32m 452\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m InferenceData(\n\u001b[1;32m 454\u001b[0m save_warmup\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_warmup,\n\u001b[1;32m 455\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 456\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior_to_xarray(),\n\u001b[1;32m 457\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_stats\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample_stats_to_xarray(),\n\u001b[1;32m 458\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior_predictive_to_xarray(),\n\u001b[1;32m 459\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictions_to_xarray(),\n\u001b[1;32m 460\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprior_to_xarray(),\n\u001b[1;32m 461\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_stats_prior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample_stats_prior_to_xarray(),\n\u001b[1;32m 462\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprior_predictive_to_xarray(),\n\u001b[1;32m 463\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobserved_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobserved_data_to_xarray(),\n\u001b[1;32m 464\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconstant_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconstant_data_to_xarray(),\n\u001b[1;32m 465\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions_constant_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictions_constant_data_to_xarray(),\n\u001b[0;32m--> 466\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_likelihood\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_likelihood_to_xarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 467\u001b[0m },\n\u001b[1;32m 468\u001b[0m )\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:65\u001b[0m, in \u001b[0;36mrequires.__call__..wrapped\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m((\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mcls\u001b[39m, prop_i) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m prop_i \u001b[38;5;129;01min\u001b[39;00m prop)):\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:65\u001b[0m, in \u001b[0;36mrequires.__call__..wrapped\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m((\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mcls\u001b[39m, prop_i) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m prop_i \u001b[38;5;129;01min\u001b[39;00m prop)):\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:312\u001b[0m, in \u001b[0;36mCmdStanPyConverter.log_likelihood_to_xarray\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;129m@requires\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 309\u001b[0m \u001b[38;5;129m@requires\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_likelihood\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlog_likelihood_to_xarray\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 311\u001b[0m \u001b[38;5;124;03m\"\"\"Convert elementwise log likelihood samples to xarray.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 312\u001b[0m log_likelihood \u001b[38;5;241m=\u001b[39m \u001b[43m_as_set\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_likelihood\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 314\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstan_vars_cols\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 315\u001b[0m data, data_warmup \u001b[38;5;241m=\u001b[39m _unpack_fit(\n\u001b[1;32m 316\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior,\n\u001b[1;32m 317\u001b[0m log_likelihood,\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_warmup,\n\u001b[1;32m 319\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtypes,\n\u001b[1;32m 320\u001b[0m )\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:592\u001b[0m, in \u001b[0;36m_as_set\u001b[0;34m(spec)\u001b[0m\n\u001b[1;32m 590\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [spec]\n\u001b[1;32m 591\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 592\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mspec\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[1;32m 594\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mset\u001b[39m(spec)\n", - "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'" + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)", + "Input \u001B[0;32mIn [93]\u001B[0m, in \u001B[0;36m\u001B[0;34m()\u001B[0m\n\u001B[1;32m 1\u001B[0m dims \u001B[38;5;241m=\u001B[39m {\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124my\u001B[39m\u001B[38;5;124m\"\u001B[39m: [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtime\u001B[39m\u001B[38;5;124m\"\u001B[39m], \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mx\u001B[39m\u001B[38;5;124m\"\u001B[39m: [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtime\u001B[39m\u001B[38;5;124m\"\u001B[39m], \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mlog_likelihood\u001B[39m\u001B[38;5;124m\"\u001B[39m: [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtime\u001B[39m\u001B[38;5;124m\"\u001B[39m], \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124my_hat\u001B[39m\u001B[38;5;124m\"\u001B[39m: [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtime\u001B[39m\u001B[38;5;124m\"\u001B[39m]}\n\u001B[1;32m 3\u001B[0m idata_kwargs \u001B[38;5;241m=\u001B[39m {\n\u001B[1;32m 4\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mposterior_predictive\u001B[39m\u001B[38;5;124m\"\u001B[39m: [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124my_hat\u001B[39m\u001B[38;5;124m\"\u001B[39m],\n\u001B[1;32m 5\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mobserved_data\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124my\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 8\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdims\u001B[39m\u001B[38;5;124m\"\u001B[39m: dims,\n\u001B[1;32m 9\u001B[0m }\n\u001B[0;32m---> 10\u001B[0m idata_stan \u001B[38;5;241m=\u001B[39m \u001B[43maz\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfrom_cmdstanpy\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 11\u001B[0m \u001B[43m \u001B[49m\u001B[43mprior\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mprior_pred\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 12\u001B[0m \u001B[43m \u001B[49m\u001B[43mposterior\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mposterior_draws\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 13\u001B[0m \u001B[43m \u001B[49m\u001B[43mprior_predictive\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43my_tilde\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 14\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;66;43;03m#posterior_predictive= {'Prey': posterior_draws.y_hat[:,:,0], 'Predator': posterior_draws.y_hat[:,:,1]},\u001B[39;49;00m\n\u001B[1;32m 15\u001B[0m \u001B[38;5;66;43;03m# observed_data= \"\u001B[39;49;00m\n\u001B[1;32m 16\u001B[0m \u001B[43m \u001B[49m\u001B[43mlog_likelihood\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\n\u001B[1;32m 17\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mlog_lik\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mposterior_draws\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlog_lik\u001B[49m\n\u001B[1;32m 18\u001B[0m \u001B[43m \u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 19\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;66;43;03m#predictions=[\"slack_comments_pred\", \"github_commits_pred\"],\u001B[39;49;00m\n\u001B[1;32m 20\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;66;43;03m#predictions_constant_data=[\"time_since_joined_pred\"],\u001B[39;49;00m\n\u001B[1;32m 21\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;66;43;03m#coords={\"developer\": names, \"candidate developer\" : candidate_devs},\u001B[39;49;00m\n\u001B[1;32m 22\u001B[0m \u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:846\u001B[0m, in \u001B[0;36mfrom_cmdstanpy\u001B[0;34m(posterior, posterior_predictive, predictions, prior, prior_predictive, observed_data, constant_data, predictions_constant_data, log_likelihood, index_origin, coords, dims, save_warmup, dtypes)\u001B[0m\n\u001B[1;32m 765\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfrom_cmdstanpy\u001B[39m(\n\u001B[1;32m 766\u001B[0m posterior\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m 767\u001B[0m \u001B[38;5;241m*\u001B[39m,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 780\u001B[0m dtypes\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m 781\u001B[0m ):\n\u001B[1;32m 782\u001B[0m \u001B[38;5;124;03m\"\"\"Convert CmdStanPy data into an InferenceData object.\u001B[39;00m\n\u001B[1;32m 783\u001B[0m \n\u001B[1;32m 784\u001B[0m \u001B[38;5;124;03m For a usage example read the\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 829\u001B[0m \u001B[38;5;124;03m InferenceData object\u001B[39;00m\n\u001B[1;32m 830\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m 831\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mCmdStanPyConverter\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 832\u001B[0m \u001B[43m \u001B[49m\u001B[43mposterior\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mposterior\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 833\u001B[0m \u001B[43m \u001B[49m\u001B[43mposterior_predictive\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mposterior_predictive\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 834\u001B[0m \u001B[43m \u001B[49m\u001B[43mpredictions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpredictions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 835\u001B[0m \u001B[43m \u001B[49m\u001B[43mprior\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mprior\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 836\u001B[0m \u001B[43m \u001B[49m\u001B[43mprior_predictive\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mprior_predictive\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 837\u001B[0m \u001B[43m \u001B[49m\u001B[43mobserved_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mobserved_data\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 838\u001B[0m \u001B[43m \u001B[49m\u001B[43mconstant_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mconstant_data\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 839\u001B[0m \u001B[43m \u001B[49m\u001B[43mpredictions_constant_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpredictions_constant_data\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 840\u001B[0m \u001B[43m \u001B[49m\u001B[43mlog_likelihood\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlog_likelihood\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 841\u001B[0m \u001B[43m \u001B[49m\u001B[43mindex_origin\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mindex_origin\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 842\u001B[0m \u001B[43m \u001B[49m\u001B[43mcoords\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcoords\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 843\u001B[0m \u001B[43m \u001B[49m\u001B[43mdims\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdims\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 844\u001B[0m \u001B[43m \u001B[49m\u001B[43msave_warmup\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msave_warmup\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 845\u001B[0m \u001B[43m \u001B[49m\u001B[43mdtypes\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdtypes\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m--> 846\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mto_inference_data\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:466\u001B[0m, in \u001B[0;36mCmdStanPyConverter.to_inference_data\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 446\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mto_inference_data\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m 447\u001B[0m \u001B[38;5;124;03m\"\"\"Convert all available data to an InferenceData object.\u001B[39;00m\n\u001B[1;32m 448\u001B[0m \n\u001B[1;32m 449\u001B[0m \u001B[38;5;124;03m Note that if groups can not be created (i.e., there is no `output`, so\u001B[39;00m\n\u001B[1;32m 450\u001B[0m \u001B[38;5;124;03m the `posterior` and `sample_stats` can not be extracted), then the InferenceData\u001B[39;00m\n\u001B[1;32m 451\u001B[0m \u001B[38;5;124;03m will not have those groups.\u001B[39;00m\n\u001B[1;32m 452\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m 453\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m InferenceData(\n\u001B[1;32m 454\u001B[0m save_warmup\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msave_warmup,\n\u001B[1;32m 455\u001B[0m \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m{\n\u001B[1;32m 456\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mposterior\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mposterior_to_xarray(),\n\u001B[1;32m 457\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msample_stats\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msample_stats_to_xarray(),\n\u001B[1;32m 458\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mposterior_predictive\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mposterior_predictive_to_xarray(),\n\u001B[1;32m 459\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpredictions\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpredictions_to_xarray(),\n\u001B[1;32m 460\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mprior\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprior_to_xarray(),\n\u001B[1;32m 461\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msample_stats_prior\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msample_stats_prior_to_xarray(),\n\u001B[1;32m 462\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mprior_predictive\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprior_predictive_to_xarray(),\n\u001B[1;32m 463\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mobserved_data\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobserved_data_to_xarray(),\n\u001B[1;32m 464\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mconstant_data\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mconstant_data_to_xarray(),\n\u001B[1;32m 465\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpredictions_constant_data\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpredictions_constant_data_to_xarray(),\n\u001B[0;32m--> 466\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mlog_likelihood\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlog_likelihood_to_xarray\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m,\n\u001B[1;32m 467\u001B[0m },\n\u001B[1;32m 468\u001B[0m )\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:65\u001B[0m, in \u001B[0;36mrequires.__call__..wrapped\u001B[0;34m(cls)\u001B[0m\n\u001B[1;32m 63\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mall\u001B[39m((\u001B[38;5;28mgetattr\u001B[39m(\u001B[38;5;28mcls\u001B[39m, prop_i) \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01mfor\u001B[39;00m prop_i \u001B[38;5;129;01min\u001B[39;00m prop)):\n\u001B[1;32m 64\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m---> 65\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mcls\u001B[39;49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:65\u001B[0m, in \u001B[0;36mrequires.__call__..wrapped\u001B[0;34m(cls)\u001B[0m\n\u001B[1;32m 63\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mall\u001B[39m((\u001B[38;5;28mgetattr\u001B[39m(\u001B[38;5;28mcls\u001B[39m, prop_i) \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01mfor\u001B[39;00m prop_i \u001B[38;5;129;01min\u001B[39;00m prop)):\n\u001B[1;32m 64\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m---> 65\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mcls\u001B[39;49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:312\u001B[0m, in \u001B[0;36mCmdStanPyConverter.log_likelihood_to_xarray\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 308\u001B[0m \u001B[38;5;129m@requires\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mposterior\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 309\u001B[0m \u001B[38;5;129m@requires\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mlog_likelihood\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 310\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mlog_likelihood_to_xarray\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m 311\u001B[0m \u001B[38;5;124;03m\"\"\"Convert elementwise log likelihood samples to xarray.\"\"\"\u001B[39;00m\n\u001B[0;32m--> 312\u001B[0m log_likelihood \u001B[38;5;241m=\u001B[39m \u001B[43m_as_set\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlog_likelihood\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 314\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mposterior, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmetadata\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mposterior, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mstan_vars_cols\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[1;32m 315\u001B[0m data, data_warmup \u001B[38;5;241m=\u001B[39m _unpack_fit(\n\u001B[1;32m 316\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mposterior,\n\u001B[1;32m 317\u001B[0m log_likelihood,\n\u001B[1;32m 318\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msave_warmup,\n\u001B[1;32m 319\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdtypes,\n\u001B[1;32m 320\u001B[0m )\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:592\u001B[0m, in \u001B[0;36m_as_set\u001B[0;34m(spec)\u001B[0m\n\u001B[1;32m 590\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m [spec]\n\u001B[1;32m 591\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 592\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mset\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mspec\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvalues\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 593\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mAttributeError\u001B[39;00m:\n\u001B[1;32m 594\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mset\u001B[39m(spec)\n", + "\u001B[0;31mTypeError\u001B[0m: unhashable type: 'numpy.ndarray'" ] } ], @@ -1531,24 +1691,28 @@ { "cell_type": "code", "execution_count": 81, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "ename": "TypeError", "evalue": "unhashable type: 'numpy.ndarray'", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [81]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m idata_stan \u001b[38;5;241m=\u001b[39m \u001b[43maz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_cmdstanpy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior_pred\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior_draws\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPrey\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprior_pred\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43my_tilde\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPredator\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprior_pred\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43my_tilde\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#posterior_predictive= {'Prey': posterior_draws.y_hat[:,:,0], 'Predator': posterior_draws.y_hat[:,:,1]},\u001b[39;49;00m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mPrey\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_data2draws\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43my\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mPredator\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_data2draws\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43my\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mlog_likelihood\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlog_lik\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mposterior_draws\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_lik\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#predictions=[\"slack_comments_pred\", \"github_commits_pred\"],\u001b[39;49;00m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#predictions_constant_data=[\"time_since_joined_pred\"],\u001b[39;49;00m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#coords={\"developer\": names, \"candidate developer\" : candidate_devs},\u001b[39;49;00m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# dims={\u001b[39;49;00m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"slack_comments\": [\"developer\"],\u001b[39;49;00m\n\u001b[1;32m 15\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"github_commits\" : [\"developer\"],\u001b[39;49;00m\n\u001b[1;32m 16\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"slack_comments_hat\": [\"developer\"],\u001b[39;49;00m\n\u001b[1;32m 17\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"github_commits_hat\": [\"developer\"],\u001b[39;49;00m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"time_since_joined\": [\"developer\"],\u001b[39;49;00m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"slack_comments_pred\" : [\"candidate developer\"],\u001b[39;49;00m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"github_commits_pred\" : [\"candidate developer\"],\u001b[39;49;00m\n\u001b[1;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"time_since_joined_pred\" : [\"candidate developer\"],\u001b[39;49;00m\n\u001b[1;32m 22\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# }\u001b[39;49;00m\n\u001b[1;32m 23\u001b[0m \u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:846\u001b[0m, in \u001b[0;36mfrom_cmdstanpy\u001b[0;34m(posterior, posterior_predictive, predictions, prior, prior_predictive, observed_data, constant_data, predictions_constant_data, log_likelihood, index_origin, coords, dims, save_warmup, dtypes)\u001b[0m\n\u001b[1;32m 765\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_cmdstanpy\u001b[39m(\n\u001b[1;32m 766\u001b[0m posterior\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 767\u001b[0m \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 780\u001b[0m dtypes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 781\u001b[0m ):\n\u001b[1;32m 782\u001b[0m \u001b[38;5;124;03m\"\"\"Convert CmdStanPy data into an InferenceData object.\u001b[39;00m\n\u001b[1;32m 783\u001b[0m \n\u001b[1;32m 784\u001b[0m \u001b[38;5;124;03m For a usage example read the\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;124;03m InferenceData object\u001b[39;00m\n\u001b[1;32m 830\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 831\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mCmdStanPyConverter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 832\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 833\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposterior_predictive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 834\u001b[0m \u001b[43m \u001b[49m\u001b[43mpredictions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 835\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 836\u001b[0m \u001b[43m \u001b[49m\u001b[43mprior_predictive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior_predictive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 837\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 838\u001b[0m \u001b[43m \u001b[49m\u001b[43mconstant_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconstant_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 839\u001b[0m \u001b[43m \u001b[49m\u001b[43mpredictions_constant_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpredictions_constant_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 840\u001b[0m \u001b[43m \u001b[49m\u001b[43mlog_likelihood\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_likelihood\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 841\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex_origin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_origin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 842\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 843\u001b[0m \u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 844\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_warmup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_warmup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 845\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtypes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtypes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m--> 846\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_inference_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:462\u001b[0m, in \u001b[0;36mCmdStanPyConverter.to_inference_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 446\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_inference_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 447\u001b[0m \u001b[38;5;124;03m\"\"\"Convert all available data to an InferenceData object.\u001b[39;00m\n\u001b[1;32m 448\u001b[0m \n\u001b[1;32m 449\u001b[0m \u001b[38;5;124;03m Note that if groups can not be created (i.e., there is no `output`, so\u001b[39;00m\n\u001b[1;32m 450\u001b[0m \u001b[38;5;124;03m the `posterior` and `sample_stats` can not be extracted), then the InferenceData\u001b[39;00m\n\u001b[1;32m 451\u001b[0m \u001b[38;5;124;03m will not have those groups.\u001b[39;00m\n\u001b[1;32m 452\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m InferenceData(\n\u001b[1;32m 454\u001b[0m save_warmup\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_warmup,\n\u001b[1;32m 455\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 456\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior_to_xarray(),\n\u001b[1;32m 457\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_stats\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample_stats_to_xarray(),\n\u001b[1;32m 458\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposterior_predictive_to_xarray(),\n\u001b[1;32m 459\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictions_to_xarray(),\n\u001b[1;32m 460\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprior_to_xarray(),\n\u001b[1;32m 461\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_stats_prior\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample_stats_prior_to_xarray(),\n\u001b[0;32m--> 462\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprior_predictive_to_xarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 463\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobserved_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobserved_data_to_xarray(),\n\u001b[1;32m 464\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconstant_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconstant_data_to_xarray(),\n\u001b[1;32m 465\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions_constant_data\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictions_constant_data_to_xarray(),\n\u001b[1;32m 466\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_likelihood\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog_likelihood_to_xarray(),\n\u001b[1;32m 467\u001b[0m },\n\u001b[1;32m 468\u001b[0m )\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:65\u001b[0m, in \u001b[0;36mrequires.__call__..wrapped\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m((\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mcls\u001b[39m, prop_i) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m prop_i \u001b[38;5;129;01min\u001b[39;00m prop)):\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:65\u001b[0m, in \u001b[0;36mrequires.__call__..wrapped\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m((\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mcls\u001b[39m, prop_i) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m prop_i \u001b[38;5;129;01min\u001b[39;00m prop)):\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:227\u001b[0m, in \u001b[0;36mCmdStanPyConverter.prior_predictive_to_xarray\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;129m@requires\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 224\u001b[0m \u001b[38;5;129m@requires\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 225\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprior_predictive_to_xarray\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 226\u001b[0m \u001b[38;5;124;03m\"\"\"Convert prior_predictive samples to xarray.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 227\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredictive_to_xarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprior_predictive\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprior\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:231\u001b[0m, in \u001b[0;36mCmdStanPyConverter.predictive_to_xarray\u001b[0;34m(self, names, fit)\u001b[0m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredictive_to_xarray\u001b[39m(\u001b[38;5;28mself\u001b[39m, names, fit):\n\u001b[1;32m 230\u001b[0m \u001b[38;5;124;03m\"\"\"Convert predictive samples to xarray.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 231\u001b[0m predictive \u001b[38;5;241m=\u001b[39m \u001b[43m_as_set\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnames\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 233\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(fit, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(fit, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstan_vars_cols\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 234\u001b[0m data, data_warmup \u001b[38;5;241m=\u001b[39m _unpack_fit(\n\u001b[1;32m 235\u001b[0m fit,\n\u001b[1;32m 236\u001b[0m predictive,\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_warmup,\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtypes,\n\u001b[1;32m 239\u001b[0m )\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:592\u001b[0m, in \u001b[0;36m_as_set\u001b[0;34m(spec)\u001b[0m\n\u001b[1;32m 590\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [spec]\n\u001b[1;32m 591\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 592\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mspec\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[1;32m 594\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mset\u001b[39m(spec)\n", - "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'" + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)", + "Input \u001B[0;32mIn [81]\u001B[0m, in \u001B[0;36m\u001B[0;34m()\u001B[0m\n\u001B[0;32m----> 1\u001B[0m idata_stan \u001B[38;5;241m=\u001B[39m \u001B[43maz\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfrom_cmdstanpy\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 2\u001B[0m \u001B[43m \u001B[49m\u001B[43mprior\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mprior_pred\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 3\u001B[0m \u001B[43m \u001B[49m\u001B[43mposterior\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mposterior_draws\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 4\u001B[0m \u001B[43m \u001B[49m\u001B[43mprior_predictive\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mPrey\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mtuple\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mprior_pred\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43my_tilde\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mPredator\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mtuple\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mprior_pred\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43my_tilde\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 5\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;66;43;03m#posterior_predictive= {'Prey': posterior_draws.y_hat[:,:,0], 'Predator': posterior_draws.y_hat[:,:,1]},\u001B[39;49;00m\n\u001B[1;32m 6\u001B[0m \u001B[43m \u001B[49m\u001B[43mobserved_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mPrey\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43marray\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata_data2draws\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43my\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mPredator\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m 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\u001B[43m \u001B[49m\u001B[38;5;66;43;03m# \"github_commits_hat\": [\"developer\"],\u001B[39;49;00m\n\u001B[1;32m 18\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;66;43;03m# \"time_since_joined\": [\"developer\"],\u001B[39;49;00m\n\u001B[1;32m 19\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;66;43;03m# \"slack_comments_pred\" : [\"candidate developer\"],\u001B[39;49;00m\n\u001B[1;32m 20\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;66;43;03m# \"github_commits_pred\" : [\"candidate developer\"],\u001B[39;49;00m\n\u001B[1;32m 21\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;66;43;03m# \"time_since_joined_pred\" : [\"candidate developer\"],\u001B[39;49;00m\n\u001B[1;32m 22\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;66;43;03m# }\u001B[39;49;00m\n\u001B[1;32m 23\u001B[0m \u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:846\u001B[0m, in \u001B[0;36mfrom_cmdstanpy\u001B[0;34m(posterior, posterior_predictive, predictions, prior, prior_predictive, observed_data, constant_data, predictions_constant_data, log_likelihood, index_origin, coords, dims, save_warmup, dtypes)\u001B[0m\n\u001B[1;32m 765\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfrom_cmdstanpy\u001B[39m(\n\u001B[1;32m 766\u001B[0m posterior\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m 767\u001B[0m \u001B[38;5;241m*\u001B[39m,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 780\u001B[0m dtypes\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m 781\u001B[0m ):\n\u001B[1;32m 782\u001B[0m \u001B[38;5;124;03m\"\"\"Convert CmdStanPy data into an InferenceData object.\u001B[39;00m\n\u001B[1;32m 783\u001B[0m \n\u001B[1;32m 784\u001B[0m \u001B[38;5;124;03m For a usage example read the\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 829\u001B[0m \u001B[38;5;124;03m InferenceData object\u001B[39;00m\n\u001B[1;32m 830\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m 831\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mCmdStanPyConverter\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 832\u001B[0m \u001B[43m \u001B[49m\u001B[43mposterior\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mposterior\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 833\u001B[0m \u001B[43m \u001B[49m\u001B[43mposterior_predictive\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mposterior_predictive\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 834\u001B[0m \u001B[43m \u001B[49m\u001B[43mpredictions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpredictions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 835\u001B[0m \u001B[43m \u001B[49m\u001B[43mprior\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mprior\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 836\u001B[0m \u001B[43m \u001B[49m\u001B[43mprior_predictive\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mprior_predictive\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 837\u001B[0m \u001B[43m \u001B[49m\u001B[43mobserved_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mobserved_data\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 838\u001B[0m \u001B[43m \u001B[49m\u001B[43mconstant_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mconstant_data\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 839\u001B[0m \u001B[43m \u001B[49m\u001B[43mpredictions_constant_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpredictions_constant_data\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 840\u001B[0m \u001B[43m \u001B[49m\u001B[43mlog_likelihood\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlog_likelihood\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 841\u001B[0m \u001B[43m \u001B[49m\u001B[43mindex_origin\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mindex_origin\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 842\u001B[0m \u001B[43m \u001B[49m\u001B[43mcoords\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcoords\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 843\u001B[0m \u001B[43m \u001B[49m\u001B[43mdims\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdims\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 844\u001B[0m \u001B[43m \u001B[49m\u001B[43msave_warmup\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msave_warmup\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 845\u001B[0m \u001B[43m \u001B[49m\u001B[43mdtypes\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdtypes\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m--> 846\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mto_inference_data\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:462\u001B[0m, in \u001B[0;36mCmdStanPyConverter.to_inference_data\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 446\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mto_inference_data\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m 447\u001B[0m \u001B[38;5;124;03m\"\"\"Convert all available data to an InferenceData object.\u001B[39;00m\n\u001B[1;32m 448\u001B[0m \n\u001B[1;32m 449\u001B[0m \u001B[38;5;124;03m Note that if groups can not be created (i.e., there is no `output`, so\u001B[39;00m\n\u001B[1;32m 450\u001B[0m \u001B[38;5;124;03m the `posterior` and `sample_stats` can not be extracted), then the InferenceData\u001B[39;00m\n\u001B[1;32m 451\u001B[0m \u001B[38;5;124;03m will not have those groups.\u001B[39;00m\n\u001B[1;32m 452\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m 453\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m InferenceData(\n\u001B[1;32m 454\u001B[0m save_warmup\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msave_warmup,\n\u001B[1;32m 455\u001B[0m \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m{\n\u001B[1;32m 456\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mposterior\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mposterior_to_xarray(),\n\u001B[1;32m 457\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msample_stats\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msample_stats_to_xarray(),\n\u001B[1;32m 458\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mposterior_predictive\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mposterior_predictive_to_xarray(),\n\u001B[1;32m 459\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpredictions\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpredictions_to_xarray(),\n\u001B[1;32m 460\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mprior\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprior_to_xarray(),\n\u001B[1;32m 461\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msample_stats_prior\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msample_stats_prior_to_xarray(),\n\u001B[0;32m--> 462\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mprior_predictive\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mprior_predictive_to_xarray\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m,\n\u001B[1;32m 463\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mobserved_data\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobserved_data_to_xarray(),\n\u001B[1;32m 464\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mconstant_data\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mconstant_data_to_xarray(),\n\u001B[1;32m 465\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpredictions_constant_data\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpredictions_constant_data_to_xarray(),\n\u001B[1;32m 466\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mlog_likelihood\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlog_likelihood_to_xarray(),\n\u001B[1;32m 467\u001B[0m },\n\u001B[1;32m 468\u001B[0m )\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:65\u001B[0m, in \u001B[0;36mrequires.__call__..wrapped\u001B[0;34m(cls)\u001B[0m\n\u001B[1;32m 63\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mall\u001B[39m((\u001B[38;5;28mgetattr\u001B[39m(\u001B[38;5;28mcls\u001B[39m, prop_i) \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01mfor\u001B[39;00m prop_i \u001B[38;5;129;01min\u001B[39;00m prop)):\n\u001B[1;32m 64\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m---> 65\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mcls\u001B[39;49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/base.py:65\u001B[0m, in \u001B[0;36mrequires.__call__..wrapped\u001B[0;34m(cls)\u001B[0m\n\u001B[1;32m 63\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mall\u001B[39m((\u001B[38;5;28mgetattr\u001B[39m(\u001B[38;5;28mcls\u001B[39m, prop_i) \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01mfor\u001B[39;00m prop_i \u001B[38;5;129;01min\u001B[39;00m prop)):\n\u001B[1;32m 64\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m---> 65\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mcls\u001B[39;49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:227\u001B[0m, in \u001B[0;36mCmdStanPyConverter.prior_predictive_to_xarray\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 223\u001B[0m \u001B[38;5;129m@requires\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mprior\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 224\u001B[0m \u001B[38;5;129m@requires\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mprior_predictive\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 225\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mprior_predictive_to_xarray\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m 226\u001B[0m \u001B[38;5;124;03m\"\"\"Convert prior_predictive samples to xarray.\"\"\"\u001B[39;00m\n\u001B[0;32m--> 227\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredictive_to_xarray\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mprior_predictive\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mprior\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:231\u001B[0m, in \u001B[0;36mCmdStanPyConverter.predictive_to_xarray\u001B[0;34m(self, names, fit)\u001B[0m\n\u001B[1;32m 229\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mpredictive_to_xarray\u001B[39m(\u001B[38;5;28mself\u001B[39m, names, fit):\n\u001B[1;32m 230\u001B[0m \u001B[38;5;124;03m\"\"\"Convert predictive samples to xarray.\"\"\"\u001B[39;00m\n\u001B[0;32m--> 231\u001B[0m predictive \u001B[38;5;241m=\u001B[39m \u001B[43m_as_set\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnames\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 233\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(fit, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmetadata\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(fit, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mstan_vars_cols\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[1;32m 234\u001B[0m data, data_warmup \u001B[38;5;241m=\u001B[39m _unpack_fit(\n\u001B[1;32m 235\u001B[0m fit,\n\u001B[1;32m 236\u001B[0m predictive,\n\u001B[1;32m 237\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msave_warmup,\n\u001B[1;32m 238\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdtypes,\n\u001B[1;32m 239\u001B[0m )\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/data/io_cmdstanpy.py:592\u001B[0m, in \u001B[0;36m_as_set\u001B[0;34m(spec)\u001B[0m\n\u001B[1;32m 590\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m [spec]\n\u001B[1;32m 591\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 592\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mset\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mspec\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvalues\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 593\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mAttributeError\u001B[39;00m:\n\u001B[1;32m 594\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mset\u001B[39m(spec)\n", + "\u001B[0;31mTypeError\u001B[0m: unhashable type: 'numpy.ndarray'" ] } ], @@ -1581,24 +1745,28 @@ { "cell_type": "code", "execution_count": 66, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "ename": "KeyError", "evalue": "'var names: \"[\\'Prey\\' \\'Predator\\'] are not present\" in dataset'", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:71\u001b[0m, in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 71\u001b[0m var_names \u001b[38;5;241m=\u001b[39m \u001b[43m_subset_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvar_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilter_items\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilter_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwarn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:149\u001b[0m, in \u001b[0;36m_subset_list\u001b[0;34m(subset, whole_list, filter_items, warn)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(existing_items):\n\u001b[0;32m--> 149\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39marray(subset)[\u001b[38;5;241m~\u001b[39mexisting_items]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m are not present\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m subset\n", - "\u001b[0;31mKeyError\u001b[0m: \"['Prey' 'Predator'] are not present\"", + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mKeyError\u001B[0m Traceback (most recent call last)", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:71\u001B[0m, in \u001B[0;36m_var_names\u001B[0;34m(var_names, data, filter_vars)\u001B[0m\n\u001B[1;32m 70\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m---> 71\u001B[0m var_names \u001B[38;5;241m=\u001B[39m \u001B[43m_subset_list\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvar_names\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mall_vars\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfilter_items\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfilter_vars\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mwarn\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m 72\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:149\u001B[0m, in \u001B[0;36m_subset_list\u001B[0;34m(subset, whole_list, filter_items, warn)\u001B[0m\n\u001B[1;32m 148\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m np\u001B[38;5;241m.\u001B[39mall(existing_items):\n\u001B[0;32m--> 149\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnp\u001B[38;5;241m.\u001B[39marray(subset)[\u001B[38;5;241m~\u001B[39mexisting_items]\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m are not present\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 151\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m subset\n", + "\u001B[0;31mKeyError\u001B[0m: \"['Prey' 'Predator'] are not present\"", "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [66]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01marviz\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43marviz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_ppc\u001b[49m\u001b[43m(\u001b[49m\u001b[43midata_stan\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/plots/ppcplot.py:261\u001b[0m, in \u001b[0;36mplot_ppc\u001b[0;34m(data, kind, alpha, mean, observed, color, colors, grid, figsize, textsize, data_pairs, var_names, filter_vars, coords, flatten, flatten_pp, num_pp_samples, random_seed, jitter, animated, animation_kwargs, legend, labeller, ax, backend, backend_kwargs, group, show)\u001b[0m\n\u001b[1;32m 259\u001b[0m var_names \u001b[38;5;241m=\u001b[39m _var_names(var_names, observed_data, filter_vars)\n\u001b[1;32m 260\u001b[0m pp_var_names \u001b[38;5;241m=\u001b[39m [data_pairs\u001b[38;5;241m.\u001b[39mget(var, var) \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m var_names]\n\u001b[0;32m--> 261\u001b[0m pp_var_names \u001b[38;5;241m=\u001b[39m \u001b[43m_var_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpp_var_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredictive_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilter_vars\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 263\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flatten_pp \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m flatten \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 264\u001b[0m flatten_pp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(predictive_dataset\u001b[38;5;241m.\u001b[39mdims\u001b[38;5;241m.\u001b[39mkeys())\n", - "File \u001b[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:74\u001b[0m, in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 73\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin((\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvar names:\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00merr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124min dataset\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m---> 74\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m var_names\n", - "\u001b[0;31mKeyError\u001b[0m: 'var names: \"[\\'Prey\\' \\'Predator\\'] are not present\" in dataset'" + "\u001B[0;31mKeyError\u001B[0m Traceback (most recent call last)", + "Input \u001B[0;32mIn [66]\u001B[0m, in \u001B[0;36m\u001B[0;34m()\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01marviz\u001B[39;00m\n\u001B[0;32m----> 2\u001B[0m \u001B[43marviz\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mplot_ppc\u001B[49m\u001B[43m(\u001B[49m\u001B[43midata_stan\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/plots/ppcplot.py:261\u001B[0m, in \u001B[0;36mplot_ppc\u001B[0;34m(data, kind, alpha, mean, observed, color, colors, grid, figsize, textsize, data_pairs, var_names, filter_vars, coords, flatten, flatten_pp, num_pp_samples, random_seed, jitter, animated, animation_kwargs, legend, labeller, ax, backend, backend_kwargs, group, show)\u001B[0m\n\u001B[1;32m 259\u001B[0m var_names \u001B[38;5;241m=\u001B[39m _var_names(var_names, observed_data, filter_vars)\n\u001B[1;32m 260\u001B[0m pp_var_names \u001B[38;5;241m=\u001B[39m [data_pairs\u001B[38;5;241m.\u001B[39mget(var, var) \u001B[38;5;28;01mfor\u001B[39;00m var \u001B[38;5;129;01min\u001B[39;00m var_names]\n\u001B[0;32m--> 261\u001B[0m pp_var_names \u001B[38;5;241m=\u001B[39m \u001B[43m_var_names\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpp_var_names\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpredictive_dataset\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfilter_vars\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 263\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m flatten_pp \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m flatten \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m 264\u001B[0m flatten_pp \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(predictive_dataset\u001B[38;5;241m.\u001B[39mdims\u001B[38;5;241m.\u001B[39mkeys())\n", + "File \u001B[0;32m~/GoogleDrive_hmb/pysd/venv/lib/python3.10/site-packages/arviz/utils.py:74\u001B[0m, in \u001B[0;36m_var_names\u001B[0;34m(var_names, data, filter_vars)\u001B[0m\n\u001B[1;32m 72\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m 73\u001B[0m msg \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m \u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin((\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mvar names:\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00merr\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124min dataset\u001B[39m\u001B[38;5;124m\"\u001B[39m))\n\u001B[0;32m---> 74\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(msg) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[1;32m 75\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m var_names\n", + "\u001B[0;31mKeyError\u001B[0m: 'var names: \"[\\'Prey\\' \\'Predator\\'] are not present\" in dataset'" ] } ], @@ -1609,7 +1777,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "### Posterior Predictive checks based on estimation" ] @@ -1617,7 +1789,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "fit_draws2data = stan_model_draws2data.generate_quantities(data=data_draws2data, mcmc_sample=fit)" @@ -1627,7 +1803,10 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "HfhfnzdooHKL" + "id": "HfhfnzdooHKL", + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "The output displayed in `xarray` format shows full information of how posterior is approximated conditional on user's input specified in U3 and U4. draws posterior approximation such as `method_variables()` which are parameters for the optimization algorithm. Although users unknowingly (sometimes intentionally) use default settings of default value of `method_variables()` (a.k.a hyperparameters in machine learning, and sometimes looked down upon as \"nuts and bolts\"), they affect the sample more than we know. Just as sensitivity checks w.r.t. different parameter values are recommended for SD models, variability of `method_varibles()` can also compared with outcome. However, estimation being computationally heavier than data generation, not much literature as far as the author know address this problem seriously with the exception of recent paper on deciding good enough posterior approximator after comparing the output from different precisions. [An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models](https://arxiv.org/abs/2205.09059).\n", @@ -1638,7 +1817,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "fit.draws_xr()" @@ -1647,7 +1830,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "fit.method_variables()" @@ -1659,7 +1846,10 @@ "metadata": { "colab": {}, "colab_type": "code", - "id": "godbsO8CoN_V" + "id": "godbsO8CoN_V", + "pycharm": { + "name": "#%%\n" + } }, "outputs": [], "source": [ @@ -1668,7 +1858,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Code\n" ] @@ -1676,7 +1870,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "print(stan_model_draws2data.code())" @@ -1685,7 +1883,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "print(stan_model_data2draws.code())" @@ -1693,7 +1895,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "We recommend users to use positive instances of `divergences` and `iterations at max_treedepth` as potential signs of sampler's failure to explore the entire posterior. `divergences` counts when hamiltonian at each iteration is not preserved; `iterations at max_treedepth` means the number of post-warmup iterations which hit the maximum allowed treedepth before the trajectory hits “U-turn” condition of HMC-NUTS algorithm. Both can result in biased sample.\n", "\n", @@ -1703,7 +1909,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "print(f'divergences:\\n{fit.divergences}\\niterations at max_treedepth:\\n{fit.max_treedepths}')" @@ -1711,7 +1921,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "for \n", "alpha = np.random.normal(1, .5, 10)\n", @@ -1726,14 +1940,22 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "fit.method_variables()\n" @@ -1743,7 +1965,10 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "O6db3n9kTDve" + "id": "O6db3n9kTDve", + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "There were no divergent transitions[27](#fn27) reported. The $\\hat{R}$ values are all near 1, which is consistent with convergence. The effective sample size estimates for each parameter are sufficient for inference.[28](#fn28) Thus we have reason to trust that Stan has produced an adequate approximation of the posterior.\n" @@ -1753,7 +1978,10 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "NyLuBnr7_e8Y" + "id": "NyLuBnr7_e8Y", + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "27 Divergences occur when Stan’s Hamiltonian solver diverges from the true Hamiltonian, which must be conserved, because of numerical problems in the stepwise gradient-based approximation of the curvature of the log density.\n", @@ -1763,7 +1991,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "# Angie 0731 ends here" ] @@ -1772,7 +2004,10 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "RcyvwyW1oPZ0" + "id": "RcyvwyW1oPZ0", + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "\n", @@ -1870,7 +2105,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "drawset_pd = fit.get_drawset()" @@ -1879,7 +2118,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "y_init_rep_draws = drawset_pd.filter(like='y_init_rep', axis=1)\n", @@ -1890,7 +2133,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "predicted_pelts = pd.DataFrame(index = lynx_hare_df['Year'], columns = {'Hare','Lynx'})\n", @@ -1903,7 +2150,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "min_pelts = pd.DataFrame(index = lynx_hare_df['Year'], columns = {'Hare', 'Lynx'})\n", @@ -1922,7 +2173,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "plt.figure(figsize=(21, 5))\n", @@ -1945,7 +2200,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Posterior predictive checks, including posterior means and 50% intervals along with the measured data. If the model is well calibrated, as this one appears to be, 50% of the points are expected to fall in their 50% intervals.\n", "\n", @@ -1957,7 +2216,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "scrolled": true + "scrolled": true, + "pycharm": { + "name": "#%%\n" + } }, "outputs": [], "source": [ @@ -1972,7 +2234,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Plot of expected population orbit for one hundred draws from the posterior. Each draw represents a different orbit determined by the differential equation system parameters. Together they provide a sketch of posterior uncertainty for the expected population dynamics. If the ODE solutions were extracted per month rather than per year, the resulting plots would appear fairly smooth." ] @@ -1981,7 +2247,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "scrolled": true + "scrolled": true, + "pycharm": { + "name": "#%%\n" + } }, "outputs": [], "source": [ @@ -1995,7 +2264,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Plot of expected pelt collection orbits for one hundred draws of system parameters from the posterior. Even if plotted at more fine-grained time intervals, error would remove any apparent smoothness. Extreme draws as seen here are typical when large values have high error on the multiplicative scale." ] @@ -2004,7 +2277,10 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "RlcVAAh1CGxv" + "id": "RlcVAAh1CGxv", + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "29 The posterior mean minimizes expected squared error, whereas posterior medians minimize expected absolute error. Here, the mean and median are the same to within MCMC standard error.\n", @@ -2026,7 +2302,10 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "bqAvgOUjj6bP" + "id": "bqAvgOUjj6bP", + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "# Conclusion: What are the Population Dynamics?\n", @@ -2081,7 +2360,10 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "RtBQ8nGYE5kM" + "id": "RtBQ8nGYE5kM", + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "35 If you complete a few of these exercises and write them up, please [let me know](mailto:carp@alias-i.com) I'd be happy to extend this case study and add a co-author or publish a follow-on case study.\n", @@ -2107,7 +2389,10 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "id": "_aj_OKChEyF5" + "id": "_aj_OKChEyF5", + "pycharm": { + "name": "#%% md\n" + } }, "source": [ "## References\n", @@ -2241,4 +2526,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/test_scripts/bayes_checks.py b/test_scripts/bayes_checks.py index d897a129..40e0653c 100644 --- a/test_scripts/bayes_checks.py +++ b/test_scripts/bayes_checks.py @@ -1,100 +1,87 @@ from pysd.builders.stan.stan_model import StanVensimModel from pysd.translators.vensim.vensim_file import VensimFile -from pysd.translators.xmile.xmile_file import XmileFile import pandas as pd -import cmdstanpy import numpy as np -from cmdstanpy import install_cxx_toolchain -config = install_cxx_toolchain.get_config('C:\\RTools', True) -print(install_cxx_toolchain.get_toolchain_name()) -import cmdstanpy; cmdstanpy.install_cmdstan(overwrite=True) -# def data2draws(): -## 1. D -obs_stock_df = pd.read_csv('data/hudson-bay-lynx-hare.csv') -n_t = obs_stock_df.shape[0] - 1 -data_draws2data = { - "n_t": n_t -} -## 2. P -### a. set_prior_struc -vf = VensimFile("vensim_models/prey-predator.mdl") -vf.parse() -am = vf.get_abstract_model() -model = StanVensimModel("prey-predator", am, 0.0, list(range(1, n_t + 1)), data_dict = data_draws2data) - -### b. set_prior_var -##### 1) ode parameter prior -model.set_prior("alpha", "normal", 0.55, 0.1) -model.set_prior("gamma", "normal", 0.8, 0.1) -model.set_prior("beta", "normal", 0.028, 0.01) -model.set_prior("delta", "normal", 0.024, 0.01) - -##### 2) sampling distribution parameter (measruement error) prior -model.set_prior("sigma", "lognormal", np.log(0.01), 0.1) - -##### 3) measurement \tilde{y}_{1..t} ~ f(\theta, t)_{1..t} -model.set_prior("predator_obs", "lognormal", "predator", "sigma") -model.set_prior("prey_obs", "lognormal", "prey", "sigma") - -##### 1+2+3) -model.build_stan_functions() - -### c. set_prior_demand ?? - -#model.print_info() - -## 1+2. P(D) -model.draws2data() # write stanfile -draws2data_model = cmdstanpy.CmdStanModel(stan_file="stan_files/prey-predator_draws2data.stan") - -## 3. A(P(D)) -print("DRAWS2DATA========================================================") -print(draws2data_model.sample(data=data_draws2data, fixed_param=True).summary()) - -# def data2draws(): -## 1. D -data_data2draws = { - "n_obs_state" : 2, - "initial_time" : 0, - "times": [i+1 for i in np.arange(n_t)], - "n_t": n_t, - "predator_obs": obs_stock_df.loc[1:, 'Predator'].values.tolist(), - "prey_obs": obs_stock_df.loc[1:, 'Prey'].values.tolist(), -} - -## 2. P -### a. set_prior_struc -model.build_stan_functions() # TODO check cache and build if not exist - -### b. set_prior_var -##### 1) ode parameter prior -model.set_prior("alpha", "normal", 0.8, 0.1) -model.set_prior("gamma", "normal", 0.8, 0.1) -model.set_prior("beta", "normal", 0.05, 0.001) -model.set_prior("delta", "normal", 0.05, 0.001) - -##### 2) sampling distribution parameter (measruement error) prior -model.set_prior("sigma", "lognormal", np.log(0.01), 0.1) - -model.draws2data("") -model.data2draws("") - -##### 3) measurement \tilde{y}_{1..t} ~ f(\theta, t)_{1..t} -model.set_prior("predator_obs", "lognormal", "predator", "sigma") -model.set_prior("prey_obs", "lognormal", "prey", "sigma") - -#model.print_info() - -## 1+2. P(D) -model.data2draws() # write stanfile -data2draws_model = cmdstanpy.CmdStanModel(stan_file="stan_files/prey-predator_data2draws.stan") - -## 3. A(P(D)) -print("DATA2DRAWS=========================================================") -print(data2draws_model.sample(data=data_data2draws).summary()) - -# def draws2data2draws(): - +import cmdstanpy #; cmdstanpy.install_cmdstan(overwrite=True) +def draws2data(am, data_draws2data): + ## 1. D + n_t = data_draws2data.get('n_t') + ## 2. P + ### a. set_prior_struc + model = StanVensimModel("prey-predator", am, 0.0, list(range(1, n_t + 1)), data_dict = data_draws2data) + + ### b. set_prior_var + ##### 1) ode parameter prior + model.set_prior("alpha", "normal", 0.55, 0.1) + model.set_prior("gamma", "normal", 0.8, 0.1) + model.set_prior("beta", "normal", 0.028, 0.01) + model.set_prior("delta", "normal", 0.024, 0.01) + + ##### 2) sampling distribution parameter (measruement error) prior + model.set_prior("sigma", "lognormal", np.log(0.01), 0.1) + + ##### 3) measurement \tilde{y}_{1..t} ~ f(\theta, t)_{1..t} + model.set_prior("predator_obs", "lognormal", "predator", "sigma") + model.set_prior("prey_obs", "lognormal", "prey", "sigma") + + ##### 1) + 2) + 3) + model.build_stan_functions() + + ### c. set_prior_demand #TODO + + ## 1+2. P(D) + model.stanify_draws2data() + draws2data_model = cmdstanpy.CmdStanModel(stan_file="stan_files/prey-predator_draws2data.stan") + + ## 3. A(P(D)) + model.print_info() + print(draws2data_model.sample(data=data_draws2data, fixed_param=True).summary()) + +def data2draws(am, obs_stock_df): + # ORDER is important + ## 1. D + n_t = obs_stock_df.shape[0] - 1 + data_data2draws = { + "n_obs_state" : 2, + "n_t": n_t, + "predator_obs": obs_stock_df.loc[1:, 'Predator'].values.tolist(), + "prey_obs": obs_stock_df.loc[1:, 'Prey'].values.tolist(), + } + + ## 2. P + ### a. set_prior_struc + model = StanVensimModel("prey-predator", am, 0.0, list(range(1, n_t + 1)), data_dict=data_data2draws) + + ### b. set_prior_var + ##### 1) ode parameter prior + model.set_prior("alpha", "normal", 0.8, 0.1) + model.set_prior("gamma", "normal", 0.8, 0.1) + model.set_prior("beta", "normal", 0.05, 0.001) + model.set_prior("delta", "normal", 0.05, 0.001) + + ##### 2) sampling distribution parameter (measruement error) prior + model.set_prior("sigma", "lognormal", np.log(0.01), 0.1) + + ##### 3) measurement \tilde{y}_{1..t} ~ f(\theta, t)_{1..t} + model.set_prior("predator_obs", "lognormal", "predator", "sigma") + model.set_prior("prey_obs", "lognormal", "prey", "sigma") + + ##### 1) + 2) + 3) + model.build_stan_functions() # TODO check cache and build if not exist + + ### c. set_prior_demand #TODO + + ## 1+2. P(D) + model.stanify_data2draws() + data2draws_model = cmdstanpy.CmdStanModel(stan_file="stan_files/prey-predator_data2draws.stan") + + ## 3. A(P(D)) + print("DATA2DRAWS=========================================================") + print(data2draws_model.sample(data=data_data2draws).summary()) + +def draws2data2draws(): + + return ## compare with vensim output # vensim_df = pd.read_csv("vensim_models/prey-predator/output.csv") # predator_obs = vensim_df[vensim_df['Time']=="Predator"] diff --git a/test_scripts/prey-predator.ipynb b/test_scripts/prey-predator.ipynb new file mode 100644 index 00000000..4cee2631 --- /dev/null +++ b/test_scripts/prey-predator.ipynb @@ -0,0 +1,1521 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pysd.translators.vensim.vensim_file import VensimFile\n", + "from stanify.builders.stan.stan_model import StanVensimModel\n", + "import numpy as np\n", + "import cmdstanpy\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline \n", + "import random\n", + "import arviz as az" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 1. data2draws: generate and prior predictive check\n", + "\n", + "1. Define Data template\n", + "2. Define Joint distribution p(theta, y)\n", + "3. Define Approximator" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "prey-predator_functions.stan already exists in the current working directory. Overwrite? (Y/N):y\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "19:36:59 - cmdstanpy - INFO - compiling stan file /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_draws2data.stan to exe file /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_draws2data\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['alpha', 'beta', 'delta', 'gamma', 'sigma', 'predator_obs', 'prey_obs']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "19:37:04 - cmdstanpy - INFO - compiled model executable: /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_draws2data\n", + "19:37:04 - cmdstanpy - WARNING - Stan compiler has produced 1 warnings:\n", + "19:37:04 - cmdstanpy - WARNING - \n", + "--- Translating Stan model to C++ code ---\n", + "bin/stanc --include-paths=/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files --o=/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_draws2data.hpp /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_draws2data.stan\n", + "Warning in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_draws2data.stan', line 29, column 4: Declaration\n", + " of arrays by placing brackets after a variable name is deprecated and\n", + " will be removed in Stan 2.32.0. Instead use the array keyword before the\n", + " type. This can be changed automatically using the auto-format flag to\n", + " stanc\n", + "\n", + "--- Compiling, linking C++ code ---\n", + "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -c -include-pch stan/src/stan/model/model_header.hpp.gch -x c++ -o /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_draws2data.o /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_draws2data.hpp\n", + "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -Wl,-L,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_draws2data.o src/cmdstan/main.o -Wl,-L,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_draws2data\n", + "rm -f /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_draws2data.o\n", + "\n", + "19:37:05 - cmdstanpy - INFO - CmdStan start processing\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "347d4438467042c1acb00f5c246eaf3f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 1 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "19:37:05 - cmdstanpy - INFO - CmdStan done processing.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "random.seed(100)\n", + "\n", + "obs_stock_df = pd.read_csv('data/hudson-bay-lynx-hare.csv')\n", + "vf = VensimFile(\"vensim_models/prey-predator/prey-predator.mdl\")\n", + "vf.parse()\n", + "am = vf.get_abstract_model()\n", + "\n", + "# 1. D\n", + "n_t = obs_stock_df.shape[0] - 1\n", + "data_draws2data = {\n", + " \"n_t\": n_t,\n", + " \"predator_obs\": obs_stock_df.loc[1:, 'Predator'].values.tolist(),\n", + " \"prey_obs\": obs_stock_df.loc[1:, 'Prey'].values.tolist(),\n", + "}\n", + "\n", + "# 2. P\n", + "model = StanVensimModel(\"prey-predator\", am, 0.0, list(range(1, n_t + 1)), data_dict = data_draws2data)\n", + "\n", + "## a. set_prior_var\n", + "### 1) ode parameter prior\n", + "model.set_prior(\"alpha\", \"normal\", 0.8, 0.1)\n", + "model.set_prior(\"beta\", \"normal\", 0.05, 0.001)\n", + "model.set_prior(\"delta\", \"normal\", 0.05, 0.001)\n", + "model.set_prior(\"gamma\", \"normal\", 0.8, 0.1)\n", + "\n", + "# experiment with different values\n", + "# model.set_prior(\"alpha\", \"normal\", 0.55, 0.1)\n", + "# model.set_prior(\"beta\", \"normal\", 0.028, 0.001)\n", + "# model.set_prior(\"delta\", \"normal\", 0.024, 0.001)\n", + "# model.set_prior(\"gamma\", \"normal\", 0.8, 0.1)\n", + "\n", + "### 2) sampling distribution parameter (measruement error) prior\n", + "model.set_prior(\"sigma\", \"lognormal\", np.log(0.001), 0.001)\n", + "\n", + "### 3) measurement \\tilde{y}_{1..t} ~ f(\\theta, t)_{1..t}\n", + "model.set_prior(\"predator_obs\", \"lognormal\", \"predator\", \"sigma\")\n", + "model.set_prior(\"prey_obs\", \"lognormal\", \"prey\", \"sigma\")\n", + "\n", + "## b. set_prior_struc\n", + "model.build_stan_functions()\n", + "\n", + "## c. set_prior_demand #TODO\n", + "\n", + "## a + b + c\n", + "model.stanify_draws2data()\n", + "\n", + "# 1+2. P(D)\n", + "draws2data_model = cmdstanpy.CmdStanModel(stan_file=\"stan_files/prey-predator_draws2data.stan\")\n", + "\n", + "# 3. A(P(D))\n", + "prior_pred = draws2data_model.sample(data=data_draws2data, fixed_param=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare prior predictions with real data" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Table shows the sampled prior values and the generated stock variables (target simulated measured)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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MeanMCSEStdDev5%50%95%N_EffN_Eff/sR_hat
alpha8.007130e-013.015580e-031.015970e-010.6343818.013080e-019.701210e-011135.0506037.520.999037
beta5.003440e-023.600690e-051.005720e-030.0483605.005870e-025.168280e-02780.1594149.780.999358
delta5.003150e-023.309850e-059.968640e-040.0483625.006130e-025.166600e-02907.1004825.000.999081
gamma8.007640e-013.235840e-031.033300e-010.6376728.008460e-019.680380e-011019.7105423.961.002980
sigma9.999980e-043.103050e-089.826520e-070.0009989.999960e-041.001660e-031002.8105334.120.999089
..............................
prey_obs[16]2.523556e+231.427397e+234.532478e+245.8901502.391560e+034.237560e+151008.2805363.200.999747
prey_obs[17]7.584205e+223.809929e+221.210792e+249.5883001.336750e+064.774900e+201009.9605372.141.000200
prey_obs[18]2.511688e+228.106517e+212.589852e+2330.0667007.341840e+096.552710e+211020.6605429.050.999684
prey_obs[19]4.809385e+212.680713e+218.512878e+2256.7743001.479390e+119.269910e+201008.4505364.071.001120
prey_obs[20]1.344627e+217.791799e+202.473762e+2225.6109006.158320e+076.613120e+191007.9505361.451.001420
\n", + "

129 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Mean MCSE StdDev 5% \\\n", + "alpha 8.007130e-01 3.015580e-03 1.015970e-01 0.634381 \n", + "beta 5.003440e-02 3.600690e-05 1.005720e-03 0.048360 \n", + "delta 5.003150e-02 3.309850e-05 9.968640e-04 0.048362 \n", + "gamma 8.007640e-01 3.235840e-03 1.033300e-01 0.637672 \n", + "sigma 9.999980e-04 3.103050e-08 9.826520e-07 0.000998 \n", + "... ... ... ... ... \n", + "prey_obs[16] 2.523556e+23 1.427397e+23 4.532478e+24 5.890150 \n", + "prey_obs[17] 7.584205e+22 3.809929e+22 1.210792e+24 9.588300 \n", + "prey_obs[18] 2.511688e+22 8.106517e+21 2.589852e+23 30.066700 \n", + "prey_obs[19] 4.809385e+21 2.680713e+21 8.512878e+22 56.774300 \n", + "prey_obs[20] 1.344627e+21 7.791799e+20 2.473762e+22 25.610900 \n", + "\n", + " 50% 95% N_Eff N_Eff/s R_hat \n", + "alpha 8.013080e-01 9.701210e-01 1135.050 6037.52 0.999037 \n", + "beta 5.005870e-02 5.168280e-02 780.159 4149.78 0.999358 \n", + "delta 5.006130e-02 5.166600e-02 907.100 4825.00 0.999081 \n", + "gamma 8.008460e-01 9.680380e-01 1019.710 5423.96 1.002980 \n", + "sigma 9.999960e-04 1.001660e-03 1002.810 5334.12 0.999089 \n", + "... ... ... ... ... ... \n", + "prey_obs[16] 2.391560e+03 4.237560e+15 1008.280 5363.20 0.999747 \n", + "prey_obs[17] 1.336750e+06 4.774900e+20 1009.960 5372.14 1.000200 \n", + "prey_obs[18] 7.341840e+09 6.552710e+21 1020.660 5429.05 0.999684 \n", + "prey_obs[19] 1.479390e+11 9.269910e+20 1008.450 5364.07 1.001120 \n", + "prey_obs[20] 6.158320e+07 6.613120e+19 1007.950 5361.45 1.001420 \n", + "\n", + "[129 rows x 9 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prior_pred.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We select the five prior draws and compare the prior predictive values with real data." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(15, 8))\n", + "ax.plot(obs_stock_df.loc[:, ['Prey']], label = \"Real_Prey\")\n", + "ax.plot(obs_stock_df.loc[:, ['Predator']], label = \"Real_Predator\")\n", + "ax.plot(pd.DataFrame(prior_pred.draws_xr('prey_obs').squeeze(\"chain\").prey_obs[:5,:]))\n", + "ax.plot(pd.DataFrame(prior_pred.draws_xr('predator_obs').squeeze(\"chain\").predator_obs[:5,:]))\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even though we have used the parameter value from the literature, it is hard to get prior value that generate observed data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 2. data2draws: estimation and posterior predictive check" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "prey-predator_functions.stan already exists in the current working directory. Overwrite? (Y/N):y\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "19:37:35 - cmdstanpy - INFO - compiling stan file /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan to exe file /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws\n", + "19:37:42 - cmdstanpy - INFO - compiled model executable: /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws\n", + "19:37:42 - cmdstanpy - WARNING - Stan compiler has produced 1 warnings:\n", + "19:37:42 - cmdstanpy - WARNING - \n", + "--- Translating Stan model to C++ code ---\n", + "bin/stanc --include-paths=/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files --o=/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.hpp /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan\n", + "Warning in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4: Declaration\n", + " of arrays by placing brackets after a variable name is deprecated and\n", + " will be removed in Stan 2.32.0. Instead use the array keyword before the\n", + " type. This can be changed automatically using the auto-format flag to\n", + " stanc\n", + "\n", + "--- Compiling, linking C++ code ---\n", + "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -c -include-pch stan/src/stan/model/model_header.hpp.gch -x c++ -o /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.o /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.hpp\n", + "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -Wl,-L,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.o src/cmdstan/main.o -Wl,-L,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/hyunjimoon/.cmdstan/cmdstan-2.30.1/stan/lib/stan_math/lib/tbb\" stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws\n", + "rm -f /Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.o\n", + "\n", + "19:37:42 - cmdstanpy - INFO - CmdStan start processing\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "863c2bbfc7e7479295f97f1016380562", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 1 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f8cc139b45e347a587064067b1cd83f9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 2 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "44790171a43d4ab19aedf91002dd45ab", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 3 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fe6fdb21a63c43bc861cae07a04a9666", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 4 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "19:40:31 - cmdstanpy - INFO - CmdStan done processing.\n", + "19:40:31 - cmdstanpy - WARNING - Non-fatal error during sampling:\n", + "Exception: lognormal_lpdf: Random variable is -0.0189927, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 45, column 4 to column 46)\n", + "\tException: ode_rk45: Failed to integrate to next output time (10) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (13) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Random variable is -0.162859, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Random variable is -23.1761, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Random variable is -23.6648, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (3) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (5) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (7) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (8) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (10) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (10) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Random variable is -3559.73, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Random variable is -70.0241, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: ode_rk45: Failed to integrate to next output time (8) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (14) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "Exception: ode_rk45: Failed to integrate to next output time (11) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Random variable is -0.694429, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: ode_rk45: Failed to integrate to next output time (16) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Random variable is -119.269, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Random variable is -121.445, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (8) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Random variable is -0.505307, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Random variable is -24874.1, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Random variable is -708.793, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Location parameter[2] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 45, column 4 to column 46)\n", + "Exception: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Random variable is -1.52834, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Random variable is -1.63778, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 45, column 4 to column 46)\n", + "Exception: ode_rk45: Failed to integrate to next output time (7) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Random variable is -1.1263, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: ode_rk45: Failed to integrate to next output time (11) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Random variable is -0.538217, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Random variable is -87.7603, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Random variable is -86.7737, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 44, column 4 to column 47)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (6) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (15) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws.stan', line 45, column 4 to column 46)\n", + "Consider re-running with show_console=True if the above output is unclear!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "random.seed(100)\n", + "\n", + "# ORDER is important\n", + "# 1. D\n", + "n_t = obs_stock_df.shape[0] - 1\n", + "data_data2draws = {\n", + " \"n_obs_state\" : 2,\n", + " \"n_t\": n_t,\n", + " \"predator_obs\": obs_stock_df.loc[1:, 'Predator'].values.tolist(),\n", + " \"prey_obs\": obs_stock_df.loc[1:, 'Prey'].values.tolist(),\n", + "}\n", + "\n", + "# 2. P\n", + "model = StanVensimModel(\"prey-predator\", am, 0.0, list(range(1, n_t + 1)), data_dict=data_data2draws)\n", + "\n", + "## a. set_prior_var\n", + "\n", + "### 1) ode parameter prior\n", + "model.set_prior(\"alpha\", \"normal\", 0.8, 0.1)\n", + "model.set_prior(\"beta\", \"normal\", 0.05, 0.001)\n", + "model.set_prior(\"delta\", \"normal\", 0.05, 0.001)\n", + "model.set_prior(\"gamma\", \"normal\", 0.8, 0.1)\n", + "\n", + "### 2) sampling distribution parameter (measruement error) prior\n", + "model.set_prior(\"sigma\", \"lognormal\", np.log(0.01), 0.1)\n", + "\n", + "### 3) measurement \\tilde{y}_{1..t} ~ f(\\theta, t)_{1..t}\n", + "model.set_prior(\"predator_obs\", \"lognormal\", \"predator\", \"sigma\")\n", + "model.set_prior(\"prey_obs\", \"lognormal\", \"prey\", \"sigma\")\n", + "\n", + "## b. set_prior_struc\n", + "model.build_stan_functions() \n", + "\n", + "## c. set_prior_demand #TODO\n", + "\n", + "## a + b + c\n", + "model.stanify_data2draws()\n", + "\n", + "# 1+2. P(D)\n", + "data2draws_model = cmdstanpy.CmdStanModel(stan_file=\"stan_files/prey-predator_data2draws.stan\")\n", + "\n", + "# 3. A(P(D))\n", + "posterior = data2draws_model.sample(data=data_data2draws)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Without lower bound of parameters (as zero), estimated result can be negative, e.g. alpha from the following table." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MeanMCSEStdDev5%50%95%N_EffN_Eff/sR_hat
alpha-0.5015160.0011170.045904-0.577483-0.501387-0.4258631689.09150.3690.999997
beta0.0508070.0000150.0010360.0491010.0508220.0525004566.26406.5041.000920
delta0.0498340.0000140.0010060.0481860.0498340.0514805092.48453.3500.999600
gamma0.6906320.0012790.0668150.5802500.6898150.8008412729.97243.0311.000430
\n", + "
" + ], + "text/plain": [ + " Mean MCSE StdDev 5% 50% 95% N_Eff \\\n", + "alpha -0.501516 0.001117 0.045904 -0.577483 -0.501387 -0.425863 1689.09 \n", + "beta 0.050807 0.000015 0.001036 0.049101 0.050822 0.052500 4566.26 \n", + "delta 0.049834 0.000014 0.001006 0.048186 0.049834 0.051480 5092.48 \n", + "gamma 0.690632 0.001279 0.066815 0.580250 0.689815 0.800841 2729.97 \n", + "\n", + " N_Eff/s R_hat \n", + "alpha 150.369 0.999997 \n", + "beta 406.504 1.000920 \n", + "delta 453.350 0.999600 \n", + "gamma 243.031 1.000430 " + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "posterior.summary().loc[('alpha', 'beta', 'delta', 'gamma'),]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## loglikelihood and plot posterior predictive" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Remedy: edit stanfile. We will add three components manually: bounds, log likelihood, posterior predictive values. \n", + "\n", + "0. Go to stanfile folder, find file ending with '_data2draws.stan' \n", + "1. add to parameters in `parameters` block\n", + "2. add `generated quantities` block in the end, declare `loglik` and `stock_tilde` you wish to generate with estimated parameter values" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "19:41:52 - cmdstanpy - INFO - CmdStan start processing\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "85e282c4fb824bc9bf1f07138602c173", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 1 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9ba8a5d779ab4130bcdad724fd13728d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 2 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "32d3bc05a05f4cecb8a2dba5a770a458", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 3 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "519324c57a204063877539de8385b000", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "chain 4 | | 00:00 Status" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "19:44:23 - cmdstanpy - INFO - CmdStan done processing.\n", + "19:44:23 - cmdstanpy - WARNING - Non-fatal error during sampling:\n", + "Exception: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[2] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[3] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "Exception: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Random variable is -1.02187, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Random variable is -0.668657, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Random variable is -1.76731, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Random variable is -0.126457, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Random variable is -0.17157, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 44, column 4 to column 47)\n", + "\tException: ode_rk45: Failed to integrate to next output time (16) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: ode_rk45: Failed to integrate to next output time (17) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Random variable is -1.5884, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (2) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Location parameter[6] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[3] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "Exception: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: ode_rk45: Failed to integrate to next output time (1) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (12) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (6) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (6) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (6) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (7) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (8) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: ode_rk45: Failed to integrate to next output time (5) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (9) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (10) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (12) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (10) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (13) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (18) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (16) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (17) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (18) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (20) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "Exception: ode_rk45: Failed to integrate to next output time (15) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: ode_rk45: Failed to integrate to next output time (8) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Location parameter[1] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "\tException: lognormal_lpdf: Random variable is -3898.94, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 44, column 4 to column 47)\n", + "\tException: lognormal_lpdf: Random variable is -113.318, but must be nonnegative! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 44, column 4 to column 47)\n", + "\tException: ode_rk45: Failed to integrate to next output time (2) in less than max_num_steps steps (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 34, column 4 to column 130)\n", + "\tException: lognormal_lpdf: Location parameter[2] is nan, but must be finite! (in '/Users/hyunjimoon/Dropbox/BayesSD/ContinuousCode/explore/stan_files/prey-predator_data2draws_full.stan', line 45, column 4 to column 46)\n", + "Consider re-running with show_console=True if the above output is unclear!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "19:44:23 - cmdstanpy - WARNING - Some chains may have failed to converge.\n", + "\tChain 3 had 4 divergent transitions (0.4%)\n", + "\tChain 3 had 1 iterations at max treedepth (0.1%)\n", + "\tUse function \"diagnose()\" to see further information.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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beta0.2952670.2990040.4236310.0493570.0512581.0431602.007350.13979754.00640
delta0.0276690.0271040.038407-0.0399730.0493910.0513892.007880.13983436.81500
gamma0.4832990.2523550.361095-0.1340670.6582940.7917572.047480.1425926.65533
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" + ], + "text/plain": [ + " Mean MCSE StdDev 5% 50% 95% N_Eff \\\n", + "alpha 0.101465 0.736660 1.043940 -0.570700 -0.480258 1.908170 2.00824 \n", + "beta 0.295267 0.299004 0.423631 0.049357 0.051258 1.043160 2.00735 \n", + "delta 0.027669 0.027104 0.038407 -0.039973 0.049391 0.051389 2.00788 \n", + "gamma 0.483299 0.252355 0.361095 -0.134067 0.658294 0.791757 2.04748 \n", + "\n", + " N_Eff/s R_hat \n", + "alpha 0.139859 28.35380 \n", + "beta 0.139797 54.00640 \n", + "delta 0.139834 36.81500 \n", + "gamma 0.142592 6.65533 " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random.seed(100)\n", + "# 1+2. P(D)\n", + "data2draws_model_full = cmdstanpy.CmdStanModel(stan_file=\"stan_files/prey-predator_data2draws_full.stan\")\n", + "\n", + "# 3. A(P(D))\n", + "posterior_full = data2draws_model_full.sample(data=data_data2draws)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MeanMCSEStdDev5%50%95%N_EffN_Eff/sR_hat
alpha0.1014650.7366601.043940-0.570700-0.4802581.9081702.008240.13985928.35380
beta0.2952670.2990040.4236310.0493570.0512581.0431602.007350.13979754.00640
delta0.0276690.0271040.038407-0.0399730.0493910.0513892.007880.13983436.81500
gamma0.4832990.2523550.361095-0.1340670.6582940.7917572.047480.1425926.65533
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" + ], + "text/plain": [ + " Mean MCSE StdDev 5% 50% 95% N_Eff \\\n", + "alpha 0.101465 0.736660 1.043940 -0.570700 -0.480258 1.908170 2.00824 \n", + "beta 0.295267 0.299004 0.423631 0.049357 0.051258 1.043160 2.00735 \n", + "delta 0.027669 0.027104 0.038407 -0.039973 0.049391 0.051389 2.00788 \n", + "gamma 0.483299 0.252355 0.361095 -0.134067 0.658294 0.791757 2.04748 \n", + "\n", + " N_Eff/s R_hat \n", + "alpha 0.139859 28.35380 \n", + "beta 0.139797 54.00640 \n", + "delta 0.139834 36.81500 \n", + "gamma 0.142592 6.65533 " + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "posterior_full.summary().loc[('alpha', 'beta', 'delta', 'gamma'),]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Giving bounds not only makes assures reasonable estimates but also shortens computation time in most cases. Total sampling time for `data2draws_model_full` is 2:30 min while `data2draws_model` (without bound) is 2:48 min; the gap may increase with larger number of estimated parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([,\n", + " ], dtype=object)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "idata = az.from_cmdstanpy(\n", + " posterior=posterior_full, \n", + " posterior_predictive=[\"predator\",\"prey\"], \n", + " log_likelihood= [\"log_lik\"],\n", + " observed_data = {\"prey\": obs_stock_df.loc[:, (\"Prey\")], \"predator\": obs_stock_df.loc[:, (\"Predator\")]}\n", + ")\n", + "az.plot_ppc(idata, alpha=0.03, figsize=(12, 6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For some reason, Stan gives a warning (based on loglikelihood) that the fit is very bad." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/arviz/stats/stats.py:812: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "Computed from 4000 posterior samples and 1.0 observations log-likelihood matrix.\n", + "\n", + " Estimate SE\n", + "elpd_loo nan nan\n", + "p_loo nan -\n", + "\n", + "There has been a warning during the calculation. Please check the results.\n", + "------\n", + "\n", + "Pareto k diagnostic values:\n", + " Count Pct.\n", + "(-Inf, 0.5] (good) 0 0.0%\n", + " (0.5, 0.7] (ok) 0 0.0%\n", + " (0.7, 1] (bad) 0 0.0%\n", + " (1, Inf) (very bad) 1 100.0%" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "az.loo(idata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "source": [ + "## TODO 3. draws2data2draws: simulation-based calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stan code" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "functions{\n", + "#include prey-predator_functions.stan\n", + "}\n", + "data{\n", + " int n_t;\n", + "}\n", + "\n", + "transformed data{\n", + " real initial_time = 0.0;\n", + " array[n_t] real times = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20};\n", + "}\n", + "\n", + "\n", + "generated quantities{\n", + " real alpha = normal_rng(0.8, 0.1);\n", + " real beta = normal_rng(0.05, 0.001);\n", + " real delta = normal_rng(0.05, 0.001);\n", + " real gamma = normal_rng(0.8, 0.1);\n", + " real sigma = lognormal_rng(-6.907755278982137, 0.001);\n", + "\n", + " // Initial ODE values\n", + " real prey__init = 30;\n", + " real predator__init = 4;\n", + "\n", + " vector[2] initial_outcome; // Initial ODE state vector\n", + " initial_outcome[1] = prey__init;\n", + " initial_outcome[2] = predator__init;\n", + "\n", + " vector[2] integrated_result[n_t] = ode_rk45(vensim_ode_func, initial_outcome, initial_time, times, delta, beta, alpha, gamma);\n", + " array[n_t] real prey = integrated_result[:, 1];\n", + " array[n_t] real predator = integrated_result[:, 2];\n", + "\n", + " vector[20] predator_obs = to_vector(lognormal_rng(predator, sigma));\n", + " vector[20] prey_obs = to_vector(lognormal_rng(prey, sigma));\n", + "}\n", + "\n", + "functions{\n", + " #include prey-predator_functions.stan\n", + "}\n", + "\n", + "data{\n", + " int n_obs_state;\n", + " int n_t;\n", + " vector[20] predator_obs;\n", + " vector[20] prey_obs;\n", + "}\n", + "\n", + "transformed data{\n", + " real initial_time = 0.0;\n", + " array[n_t] real times = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20};\n", + "}\n", + "\n", + "parameters{\n", + " real alpha;\n", + " real gamma;\n", + " real beta;\n", + " real delta;\n", + " real sigma;\n", + "}\n", + "\n", + "transformed parameters {\n", + " // Initial ODE values\n", + " real predator__init = 4;\n", + " real prey__init = 30;\n", + "\n", + " vector[2] initial_outcome; // Initial ODE state vector\n", + " initial_outcome[1] = predator__init;\n", + " initial_outcome[2] = prey__init;\n", + "\n", + " vector[2] integrated_result[n_t] = ode_rk45(vensim_ode_func, initial_outcome, initial_time, times, gamma, beta, delta, alpha);\n", + " array[n_t] real predator = integrated_result[:, 1];\n", + " array[n_t] real prey = integrated_result[:, 2];\n", + "}\n", + "\n", + "model{\n", + " alpha ~ normal(0.8, 0.1);\n", + " gamma ~ normal(0.8, 0.1);\n", + " beta ~ normal(0.05, 0.001);\n", + " delta ~ normal(0.05, 0.001);\n", + " sigma ~ lognormal(-4.605170185988091, 0.1);\n", + " predator_obs ~ lognormal(predator, sigma);\n", + " prey_obs ~ lognormal(prey, sigma);\n", + "}\n", + "\n", + "generated quantities{\n", + " real log_lik;\n", + " vector[n_t] predator_tilde = to_vector(lognormal_rng(predator, sigma));\n", + " vector[n_t] prey_tilde = to_vector(lognormal_rng(prey, sigma));\n", + "\n", + " log_lik += lognormal_lpdf(predator_obs|predator, sigma);\n", + " log_lik += lognormal_lpdf(predator_obs|predator, sigma);\n", + "}\n" + ] + } + ], + "source": [ + "print(draws2data_model.code())\n", + "print(data2draws_model_full.code())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/test_scripts/stan_files/prey-predator_data2draws.stan b/test_scripts/stan_files/prey-predator_data2draws.stan new file mode 100644 index 00000000..0895151e --- /dev/null +++ b/test_scripts/stan_files/prey-predator_data2draws.stan @@ -0,0 +1,48 @@ +functions{ + #include prey-predator_functions.stan +} + +data{ + int n_obs_state; + int n_t; + vector[20] predator_obs; + vector[20] prey_obs; +} + +transformed data{ + real initial_time = 0.0; + array[n_t] real times = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20}; +} + +parameters{ + real alpha; + real gamma; + real beta; + real delta; + real sigma; +} + +transformed parameters { + // Initial ODE values + real prey__init = 30; + real predator__init = 4; + + vector[2] initial_outcome; // Initial ODE state vector + initial_outcome[1] = prey__init; + initial_outcome[2] = predator__init; + + vector[2] integrated_result[n_t] = ode_rk45(vensim_ode_func, initial_outcome, initial_time, times, gamma, delta, alpha, beta); + array[n_t] real prey = integrated_result[:, 1]; + array[n_t] real predator = integrated_result[:, 2]; +} + +model{ + alpha ~ normal(0.8, 0.1); + gamma ~ normal(0.8, 0.1); + beta ~ normal(0.05, 0.001); + delta ~ normal(0.05, 0.001); + sigma ~ lognormal(-4.605170185988091, 0.1); + predator_obs ~ lognormal(predator, sigma); + prey_obs ~ lognormal(prey, sigma); +} + diff --git a/test_scripts/stan_files/prey-predator_draws2data.stan b/test_scripts/stan_files/prey-predator_draws2data.stan new file mode 100644 index 00000000..3807455d --- /dev/null +++ b/test_scripts/stan_files/prey-predator_draws2data.stan @@ -0,0 +1,35 @@ +functions{ +#include prey-predator_functions.stan +} +data{ + int n_t; +} + +transformed data{ + real initial_time = 0.0; + array[n_t] real times = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20}; +} + + +generated quantities{ + real alpha = normal_rng(0.55, 0.1); + real gamma = normal_rng(0.8, 0.1); + real beta = normal_rng(0.028, 0.01); + real delta = normal_rng(0.024, 0.01); + real sigma = lognormal_rng(-4.605170185988091, 0.1); + + // Initial ODE values + real predator__init = 4; + real prey__init = 30; + + vector[2] initial_outcome; // Initial ODE state vector + initial_outcome[1] = predator__init; + initial_outcome[2] = prey__init; + + vector[2] integrated_result[n_t] = ode_rk45(vensim_ode_func, initial_outcome, initial_time, times, gamma, alpha, beta, delta); + array[n_t] real predator = integrated_result[:, 1]; + array[n_t] real prey = integrated_result[:, 2]; + + vector[20] predator_obs = to_vector(lognormal_rng(predator, sigma)); + vector[20] prey_obs = to_vector(lognormal_rng(prey, sigma)); +} diff --git a/test_scripts/stan_files/prey-predator_functions.stan b/test_scripts/stan_files/prey-predator_functions.stan new file mode 100644 index 00000000..dd78fe40 --- /dev/null +++ b/test_scripts/stan_files/prey-predator_functions.stan @@ -0,0 +1,20 @@ +// Begin ODE declaration +vector vensim_ode_func(real time, vector outcome, real gamma, real delta, real alpha, real beta){ + vector[2] dydt; // Return vector of the ODE function + + // State variables + real prey = outcome[1]; + real predator = outcome[2]; + + real prey_death_rate = beta * predator * prey; + real prey_birth_rate = alpha * prey; + real prey_dydt = prey_birth_rate - prey_death_rate; + real predator_death_rate = gamma * predator; + real predator_birth_rate = delta * prey * predator; + real predator_dydt = predator_birth_rate - predator_death_rate; + + dydt[1] = prey_dydt; + dydt[2] = predator_dydt; + + return dydt; +} diff --git a/test_scripts/stan_vensim_integration.py b/test_scripts/stan_vensim_integration.py index 36b70d77..a2b02521 100644 --- a/test_scripts/stan_vensim_integration.py +++ b/test_scripts/stan_vensim_integration.py @@ -3,7 +3,7 @@ from pysd.translators.xmile.xmile_file import XmileFile import pandas as pd obs_stock_df = pd.read_csv('data/hudson-bay-lynx-hare.csv') -vf = VensimFile("vensim_models/prey-predator.mdl") +vf = VensimFile("vensim_models/prey-predator/prey-predator.mdl") vf.parse() am = vf.get_abstract_model() @@ -33,7 +33,7 @@ premodel.set_prior("predator_obs", "lognormal", "predator", "sigma") premodel.set_prior("prey_obs", "lognormal", "prey", "sigma") premodel.build_stan_functions() -premodel.draws2data() +premodel.stanify_draws2data() #print(premodel.vensim_model_context.variable_names)