diff --git a/.github/ISSUE_TEMPLATE/sweep-template.yml b/.github/ISSUE_TEMPLATE/sweep-template.yml deleted file mode 100644 index 9d687a3fb..000000000 --- a/.github/ISSUE_TEMPLATE/sweep-template.yml +++ /dev/null @@ -1,22 +0,0 @@ -name: Sweep Issue -title: 'Sweep: ' -description: For small bugs, features, refactors, and tests to be handled by Sweep, an AI-powered junior developer. -labels: sweep -body: - - type: textarea - id: description - attributes: - label: Details - description: Tell Sweep where and what to edit and provide enough context for a new developer to the codebase - placeholder: | - Unit Tests: Write unit tests for . Test each function in the file. Make sure to test edge cases. - Bugs: The bug might be in . Here are the logs: ... - Features: the new endpoint should use the ... class from because it contains ... logic. - Refactors: We are migrating this function to ... version because ... - - type: input - id: branch - attributes: - label: Branch - description: The branch to work off of (optional) - placeholder: | - main \ No newline at end of file diff --git a/.github/workflows/poetry_unit_test.yml b/.github/workflows/poetry_unit_test.yml index f0ad777d2..cb46640a4 100644 --- a/.github/workflows/poetry_unit_test.yml +++ b/.github/workflows/poetry_unit_test.yml @@ -13,7 +13,7 @@ jobs: timeout-minutes: 60 strategy: matrix: - python-version: [3.9, '3.10'] + python-version: [3.9, '3.10', '3.11'] steps: - uses: actions/checkout@v2 diff --git a/benchmark/benchmark_TSC.py b/benchmark/benchmark_TSC.py index 0f7edb5f1..4e4b8de2c 100644 --- a/benchmark/benchmark_TSC.py +++ b/benchmark/benchmark_TSC.py @@ -9,6 +9,7 @@ from benchmark.abstract_bench import AbstractBenchmark from fedot_ind.api.utils.path_lib import PROJECT_PATH +from fedot_ind.core.architecture.pipelines.abstract_pipeline import ApiTemplate from fedot_ind.core.architecture.postprocessing.results_picker import ResultsPicker from fedot_ind.core.architecture.settings.computational import backend_methods as np from fedot_ind.core.metrics.metrics_implementation import Accuracy @@ -27,6 +28,7 @@ def __init__(self, self.logger = logging.getLogger(self.__class__.__name__) self.experiment_setup = experiment_setup + self.init_assumption = deepcopy(self.experiment_setup['initial_assumption']) self.multi_TSC = MULTI_CLF_BENCH self.uni_TSC = UNI_CLF_BENCH if custom_datasets is None: @@ -38,35 +40,50 @@ def __init__(self, self.custom_datasets = custom_datasets if use_small_datasets: - self.path_to_result = '/benchmark/results/time_series_uni_clf_comparasion.csv' - self.path_to_save = '/benchmark/results/ts_uni_classification' + self.path_to_result = 'time_series_uni_clf_comparasion.csv' + self.path_to_save = 'ts_uni_classification' else: - self.path_to_result = '/benchmark/results/time_series_multi_clf_comparasion.csv' - self.path_to_save = '/benchmark/results/ts_multi_classification' - self.results_picker = ResultsPicker( - path=os.path.abspath(self.output_dir)) + self.path_to_result = 'time_series_multi_clf_comparasion.csv' + self.path_to_save = 'ts_multi_classification' + self.output_dir = os.path.join(self.experiment_setup['output_folder'], self.path_to_save) + self.results_picker = ResultsPicker(path=os.path.abspath(self.output_dir)) + + def _run_model_versus_model(self, dataset_name, comparasion_dict): + approach_dict = {} + for approach in comparasion_dict.keys(): + result_dict = ApiTemplate(api_config=self.experiment_setup, + metric_list=self.experiment_setup['metric_names']). \ + eval(dataset=dataset_name, + initial_assumption=comparasion_dict[approach], + finetune=self.experiment_setup['finetune']) + metric = result_dict['metrics'][self.experiment_setup['metric']][0] + approach_dict.update({approach: metric}) + return approach_dict + + def _run_industrial_versus_sota(self, dataset_name): + experiment_setup = deepcopy(self.experiment_setup) + prediction, target = self.evaluate_loop(dataset_name, experiment_setup) + return Accuracy(target, prediction).metric() def run(self): self.logger.info('Benchmark test started') basic_results = self.load_local_basic_results() metric_dict = {} for dataset_name in self.custom_datasets: - experiment_setup = deepcopy(self.experiment_setup) - prediction, target = self.evaluate_loop( - dataset_name, experiment_setup) - metric = Accuracy(target, prediction).metric() - metric_dict.update({dataset_name: metric}) - basic_results.loc[dataset_name, 'Fedot_Industrial'] = metric - dataset_path = os.path.join( - self.experiment_setup['output_folder'], - f'{dataset_name}', - 'metrics_report.csv') - basic_results.to_csv(dataset_path) - gc.collect() - basic_path = os.path.join( - self.experiment_setup['output_folder'], - 'comprasion_metrics_report.csv') - basic_results.to_csv(basic_path) + try: + if isinstance(self.init_assumption, dict): + model_name = list(self.init_assumption.keys()) + metric = self._run_model_versus_model(dataset_name, self.init_assumption) + else: + metric = self._run_industrial_versus_sota() + model_name = 'Fedot_Industrial' + metric_dict.update({dataset_name: metric}) + basic_results.loc[dataset_name, model_name] = metric + if not os.path.exists(self.output_dir): + os.makedirs(self.output_dir) + basic_results.to_csv(os.path.join(self.output_dir, self.path_to_result)) + except Exception: + self.logger.info(f"{dataset_name} problem with eval") self.logger.info("Benchmark test finished") def finetune(self): @@ -128,15 +145,14 @@ def finetune(self): def load_local_basic_results(self, path: str = None): if path is None: - path = PROJECT_PATH + self.path_to_result + path = os.path.join(self.output_dir, self.path_to_result) try: results = pd.read_csv(path, sep=',', index_col=0) - results = results.dropna(axis=1, how='all') - results = results.dropna(axis=0, how='all') + # results = results.fillna() + # results = results.dropna(axis=1, how='all') + # results = results.dropna(axis=0, how='all') except Exception: results = self.load_web_results() - self.experiment_setup['output_folder'] = PROJECT_PATH + \ - self.path_to_save return results else: return self.results_picker.run(get_metrics_df=True, add_info=True) diff --git a/benchmark/benchmark_TSER.py b/benchmark/benchmark_TSER.py index 61e985025..b5b7c170e 100644 --- a/benchmark/benchmark_TSER.py +++ b/benchmark/benchmark_TSER.py @@ -1,19 +1,20 @@ -from fedot_ind.core.repository.constanst_repository import MULTI_REG_BENCH -from fedot_ind.core.architecture.postprocessing.results_picker import ResultsPicker -from benchmark.abstract_bench import AbstractBenchmark -from fedot_ind.core.metrics.metrics_implementation import RMSE -from fedot_ind.api.utils.path_lib import PROJECT_PATH -from fedot_ind.api.main import FedotIndustrial -from fedot.core.pipelines.pipeline import Pipeline -from fedot.core.pipelines.node import PipelineNode -import pandas as pd import logging import os from abc import ABC from copy import deepcopy import matplotlib +import pandas as pd +from fedot.core.pipelines.node import PipelineNode +from fedot.core.pipelines.pipeline import Pipeline +from benchmark.abstract_bench import AbstractBenchmark +from fedot_ind.api.main import FedotIndustrial +from fedot_ind.api.utils.path_lib import PROJECT_PATH +from fedot_ind.core.architecture.pipelines.abstract_pipeline import ApiTemplate +from fedot_ind.core.architecture.postprocessing.results_picker import ResultsPicker +from fedot_ind.core.metrics.metrics_implementation import RMSE +from fedot_ind.core.repository.constanst_repository import MULTI_REG_BENCH from fedot_ind.tools.loader import DataLoader matplotlib.use('TkAgg') @@ -31,6 +32,7 @@ def __init__(self, self.logger = logging.getLogger(self.__class__.__name__) self.experiment_setup = experiment_setup + self.init_assumption = deepcopy(self.experiment_setup['initial_assumption']) self.monash_regression = MULTI_REG_BENCH if custom_datasets is None: self.custom_datasets = self.monash_regression @@ -40,26 +42,43 @@ def __init__(self, self.results_picker = ResultsPicker( path=os.path.abspath(self.output_dir)) + def _run_model_versus_model(self, dataset_name, comparasion_dict): + approach_dict = {} + for approach in comparasion_dict.keys(): + result_dict = ApiTemplate(api_config=self.experiment_setup, + metric_list=self.experiment_setup['metric_names']). \ + eval(dataset=dataset_name, + initial_assumption=comparasion_dict[approach], + finetune=self.experiment_setup['finetune']) + metric = result_dict['metrics'][self.experiment_setup['metric']][0] + approach_dict.update({approach: metric}) + return approach_dict + + def _run_industrial_versus_sota(self, dataset_name): + experiment_setup = deepcopy(self.experiment_setup) + prediction, target = self.evaluate_loop(dataset_name, experiment_setup) + return RMSE(target, prediction).metric() + def run(self): self.logger.info('Benchmark test started') basic_results = self.load_local_basic_results() metric_dict = {} for dataset_name in self.custom_datasets: - experiment_setup = deepcopy(self.experiment_setup) - prediction, target = self.evaluate_loop( - dataset_name, experiment_setup) - metric = RMSE(target, prediction).metric() - metric_dict.update({dataset_name: metric}) - basic_results.loc[dataset_name, 'Fedot_Industrial'] = metric - dataset_path = os.path.join( - self.experiment_setup['output_folder'], - f'{dataset_name}', - 'metrics_report.csv') - basic_results.to_csv(dataset_path) - basic_path = os.path.join( - self.experiment_setup['output_folder'], - 'comprasion_metrics_report.csv') - basic_results.to_csv(basic_path) + try: + if isinstance(self.init_assumption, dict): + model_name = list(self.init_assumption.keys()) + metric = self._run_model_versus_model(dataset_name, self.init_assumption) + else: + metric = self._run_industrial_versus_sota(dataset_name) + model_name = 'Fedot_Industrial' + metric_dict.update({dataset_name: metric}) + basic_results.loc[dataset_name, model_name] = metric + basic_path = os.path.join(self.experiment_setup['output_folder']) + if not os.path.exists(basic_path): + os.makedirs(basic_path) + basic_results.to_csv(os.path.join(basic_path, 'comprasion_metrics_report.csv')) + except Exception: + self.logger.info(f"{dataset_name} problem with eval") self.logger.info("Benchmark test finished") def load_local_basic_results(self, path: str = None): diff --git a/examples/automl_example/api_example/advanced_example/__init__.py b/examples/__init__.py similarity index 100% rename from examples/automl_example/api_example/advanced_example/__init__.py rename to examples/__init__.py diff --git a/examples/automl_example/api_example/advanced_example/explainability/__init__.py b/examples/automl_example/__init__.py similarity index 100% rename from examples/automl_example/api_example/advanced_example/explainability/__init__.py rename to examples/automl_example/__init__.py diff --git a/examples/automl_example/api_example/advanced_example/specific_strategy/random_sampling_example.py b/examples/automl_example/api_example/advanced_example/specific_strategy/random_sampling_example.py deleted file mode 100644 index 0a0ac1a4d..000000000 --- a/examples/automl_example/api_example/advanced_example/specific_strategy/random_sampling_example.py +++ /dev/null @@ -1,53 +0,0 @@ -import pickle - -import numpy as np - -from fedot_ind.core.architecture.pipelines.abstract_pipeline import ApiTemplate - -model_list = dict(logit=['logit'], rf=['rf'], xgboost=['xgboost']) -finetune = False -task = 'classification' -sampling_range = [0.01, 0.15, 0.3, 0.6] -sampling_algorithm = ['CUR', - 'Random'] - - -def create_big_dataset(): - train_X, test_X = np.load( - './examples/big_dataset/train_airlinescodrnaadult_fold0.npy'), np.load( - './examples/big_dataset/test_airlinescodrnaadult_fold0.npy') - train_y, test_y = np.load( - './examples/big_dataset/trainy_airlinescodrnaadult_fold0.npy'), np.load( - './examples/big_dataset/testy_airlinescodrnaadult_fold0.npy') - dataset_dict = dict(train_data=(train_X, train_y), - test_data=(test_X, test_y)) - return dataset_dict - - -if __name__ == "__main__": - results_of_experiments_dict = {} - dataset_dict = create_big_dataset() - # df = pd.read_pickle('./sampling_experiment.pkl') - for algo in sampling_algorithm: - api_config = dict( - problem=task, - metric='f1', - timeout=0.1, - with_tuning=False, - industrial_strategy='sampling_strategy', - industrial_strategy_params={ - 'industrial_task': task, - 'sampling_algorithm': algo, - 'sampling_range': sampling_range, - 'data_type': 'table'}, - logging_level=30) - algo_result = {} - for model_name, model in model_list.items(): - result_dict = ApiTemplate(api_config=api_config, - metric_list=('f1', 'accuracy')).eval(dataset=dataset_dict, - finetune=finetune, - initial_assumption=model) - algo_result.update({f'{algo}_{model_name}': result_dict['metrics']}) - results_of_experiments_dict.update({algo: algo_result}) - with open(f'sampling_experiment.pkl', 'wb') as f: - pickle.dump(results_of_experiments_dict, f) diff --git a/examples/automl_example/api_example/advanced_example/multimodal/__init__.py b/examples/automl_example/computer_vision/__init__.py similarity index 100% rename from examples/automl_example/api_example/advanced_example/multimodal/__init__.py rename to examples/automl_example/computer_vision/__init__.py diff --git a/examples/automl_example/api_example/advanced_example/specific_strategy/__init__.py b/examples/automl_example/computer_vision/image_classification/__init__.py similarity index 100% rename from examples/automl_example/api_example/advanced_example/specific_strategy/__init__.py rename to examples/automl_example/computer_vision/image_classification/__init__.py diff --git a/examples/automl_example/api_example/computer_vision/image_classification/image_clf_example.py b/examples/automl_example/computer_vision/image_classification/image_clf_example.py similarity index 99% rename from examples/automl_example/api_example/computer_vision/image_classification/image_clf_example.py rename to examples/automl_example/computer_vision/image_classification/image_clf_example.py index 954113e54..1b4942267 100644 --- a/examples/automl_example/api_example/computer_vision/image_classification/image_clf_example.py +++ b/examples/automl_example/computer_vision/image_classification/image_clf_example.py @@ -1,4 +1,5 @@ import random + import matplotlib.pyplot as plt from torchvision.transforms import ToTensor, Resize, Compose diff --git a/examples/automl_example/api_example/computer_vision/image_classification/mnist_lora_example.py b/examples/automl_example/computer_vision/image_classification/mnist_lora_example.py similarity index 99% rename from examples/automl_example/api_example/computer_vision/image_classification/mnist_lora_example.py rename to examples/automl_example/computer_vision/image_classification/mnist_lora_example.py index fb3d3e31d..4c58d401a 100644 --- a/examples/automl_example/api_example/computer_vision/image_classification/mnist_lora_example.py +++ b/examples/automl_example/computer_vision/image_classification/mnist_lora_example.py @@ -1,14 +1,11 @@ -from fedot_ind.core.models.nn.network_modules.layers.lora import linear_layer_parameterization - import torch import torch.nn as nn import torch.nn.utils.parametrize as parametrize - import torchvision.datasets as datasets import torchvision.transforms as transforms - from tqdm import tqdm +from fedot_ind.core.models.nn.network_modules.layers.lora import linear_layer_parameterization # Make torch deterministic _ = torch.manual_seed(228) diff --git a/examples/automl_example/api_example/time_series/ts_anomaly_detection/__init__.py b/examples/automl_example/computer_vision/object_detection/__init__.py similarity index 100% rename from examples/automl_example/api_example/time_series/ts_anomaly_detection/__init__.py rename to examples/automl_example/computer_vision/object_detection/__init__.py diff --git a/examples/automl_example/api_example/computer_vision/object_detection/obj_rec_example.py b/examples/automl_example/computer_vision/object_detection/obj_rec_example.py similarity index 99% rename from examples/automl_example/api_example/computer_vision/object_detection/obj_rec_example.py rename to examples/automl_example/computer_vision/object_detection/obj_rec_example.py index 0c555c4ec..1dfaf5169 100644 --- a/examples/automl_example/api_example/computer_vision/object_detection/obj_rec_example.py +++ b/examples/automl_example/computer_vision/object_detection/obj_rec_example.py @@ -1,9 +1,10 @@ import os import random + import yaml +from fedot_ind.core.architecture.datasets.visualization import draw_sample_with_bboxes from fedot_ind.api.main import FedotIndustrial -from fedot_ind.core.architecture.datasets.visualization import draw_sample_with_bboxes DATASETS_PATH = os.path.abspath('Warp-D') TEST_IMAGE_FOLDER = 'Land-Use_Scene_Classification/images_train_test_val/test' diff --git a/examples/automl_example/api_example/time_series/ts_forecasting/__init__.py b/examples/automl_example/custom_strategy/__init__.py similarity index 100% rename from examples/automl_example/api_example/time_series/ts_forecasting/__init__.py rename to examples/automl_example/custom_strategy/__init__.py diff --git a/examples/real_world_examples/eeg/__init__.py b/examples/automl_example/custom_strategy/big_data/__init__.py similarity index 100% rename from examples/real_world_examples/eeg/__init__.py rename to examples/automl_example/custom_strategy/big_data/__init__.py diff --git a/examples/automl_example/custom_strategy/big_data/big_dataset_utils.py b/examples/automl_example/custom_strategy/big_data/big_dataset_utils.py new file mode 100644 index 000000000..f02baf79b --- /dev/null +++ b/examples/automl_example/custom_strategy/big_data/big_dataset_utils.py @@ -0,0 +1,13 @@ +import numpy as np + + +def create_big_dataset(): + train_X, test_X = np.load( + 'big_dataset/train_airlinescodrnaadult_fold0.npy'), np.load( + 'big_dataset/test_airlinescodrnaadult_fold0.npy') + train_y, test_y = np.load( + 'big_dataset/trainy_airlinescodrnaadult_fold0.npy'), np.load( + 'big_dataset/testy_airlinescodrnaadult_fold0.npy') + dataset_dict = dict(train_data=(train_X, train_y), + test_data=(test_X, test_y)) + return dataset_dict diff --git a/examples/benchmark_example/time_series_multi_forecast_benchmark.py b/examples/automl_example/custom_strategy/big_data/dask_backend.py similarity index 100% rename from examples/benchmark_example/time_series_multi_forecast_benchmark.py rename to examples/automl_example/custom_strategy/big_data/dask_backend.py diff --git a/examples/automl_example/api_example/advanced_example/specific_strategy/federated_automl_example.py b/examples/automl_example/custom_strategy/big_data/federated_automl_example.py similarity index 100% rename from examples/automl_example/api_example/advanced_example/specific_strategy/federated_automl_example.py rename to examples/automl_example/custom_strategy/big_data/federated_automl_example.py diff --git a/examples/automl_example/custom_strategy/big_data/random_sampling_example.py b/examples/automl_example/custom_strategy/big_data/random_sampling_example.py new file mode 100644 index 000000000..1bccde650 --- /dev/null +++ b/examples/automl_example/custom_strategy/big_data/random_sampling_example.py @@ -0,0 +1,36 @@ +from examples.automl_example.custom_strategy.big_data.big_dataset_utils import create_big_dataset +from fedot_ind.core.architecture.pipelines.abstract_pipeline import ApiTemplate + +cur_params = {'rank': None} +sampling_algorithm = {'CUR': cur_params} + +if __name__ == "__main__": + dataset_dict = create_big_dataset() + finetune = False + metric_names = ('f1', 'accuracy') + api_config = dict(problem='classification', + metric='f1', + timeout=40, + pop_size=10, + early_stopping_iterations=10, + early_stopping_timeout=30, + optimizer_params={'mutation_agent': 'bandit', + 'mutation_strategy': 'growth_mutation_strategy'}, + with_tunig=False, + preset='classification_tabular', + industrial_strategy_params={'data_type': 'tensor', + 'learning_strategy': 'big_dataset', + 'sampling_strategy': sampling_algorithm + }, + n_jobs=-1, + logging_level=20) + + result_dict = ApiTemplate(api_config=api_config, + metric_list=metric_names).eval(dataset=dataset_dict, + finetune=finetune) + metrics = result_dict['metrics'] + metrics.to_csv('./metrics.csv') + hist = result_dict['industrial_model'].save_optimization_history(return_history=True) + result_dict['industrial_model'].vis_optimisation_history(hist) + result_dict['industrial_model'].save_best_model() + _ = 1 diff --git a/examples/tutorial/time_series/ts_forecasting/__init__.py b/examples/automl_example/custom_strategy/explainability/__init__.py similarity index 100% rename from examples/tutorial/time_series/ts_forecasting/__init__.py rename to examples/automl_example/custom_strategy/explainability/__init__.py diff --git a/examples/automl_example/api_example/advanced_example/explainability/explainability.ipynb b/examples/automl_example/custom_strategy/explainability/explainability.ipynb similarity index 100% rename from examples/automl_example/api_example/advanced_example/explainability/explainability.ipynb rename to examples/automl_example/custom_strategy/explainability/explainability.ipynb diff --git a/examples/automl_example/api_example/advanced_example/explainability/optimisation_history_visualisation.py b/examples/automl_example/custom_strategy/explainability/optimisation_history_visualisation.py similarity index 100% rename from examples/automl_example/api_example/advanced_example/explainability/optimisation_history_visualisation.py rename to examples/automl_example/custom_strategy/explainability/optimisation_history_visualisation.py diff --git a/fedot_ind/core/models/algebra/__init__.py b/examples/automl_example/custom_strategy/multimodal/__init__.py similarity index 100% rename from fedot_ind/core/models/algebra/__init__.py rename to examples/automl_example/custom_strategy/multimodal/__init__.py diff --git a/examples/automl_example/api_example/advanced_example/multimodal/multimodal.py b/examples/automl_example/custom_strategy/multimodal/multimodal.py similarity index 100% rename from examples/automl_example/api_example/advanced_example/multimodal/multimodal.py rename to examples/automl_example/custom_strategy/multimodal/multimodal.py diff --git a/examples/automl_example/api_example/advanced_example/specific_strategy/LoRa_example.py b/examples/automl_example/custom_strategy/specific_strategy/LoRa_example.py similarity index 99% rename from examples/automl_example/api_example/advanced_example/specific_strategy/LoRa_example.py rename to examples/automl_example/custom_strategy/specific_strategy/LoRa_example.py index 7a7a4255a..febccf1e5 100644 --- a/examples/automl_example/api_example/advanced_example/specific_strategy/LoRa_example.py +++ b/examples/automl_example/custom_strategy/specific_strategy/LoRa_example.py @@ -1,7 +1,8 @@ -from fedot_ind.api.main import FedotIndustrial import torchvision.datasets as datasets import torchvision.transforms as transforms +from fedot_ind.api.main import FedotIndustrial + transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) diff --git a/fedot_ind/core/models/manifold/__init__.py b/examples/automl_example/custom_strategy/specific_strategy/__init__.py similarity index 100% rename from fedot_ind/core/models/manifold/__init__.py rename to examples/automl_example/custom_strategy/specific_strategy/__init__.py diff --git a/examples/automl_example/api_example/advanced_example/specific_strategy/kernel_ensemble_example.py b/examples/automl_example/custom_strategy/specific_strategy/kernel_ensemble_example.py similarity index 100% rename from examples/automl_example/api_example/advanced_example/specific_strategy/kernel_ensemble_example.py rename to examples/automl_example/custom_strategy/specific_strategy/kernel_ensemble_example.py diff --git a/examples/automl_example/api_example/advanced_example/specific_strategy/probability_calibration_example.py b/examples/automl_example/custom_strategy/specific_strategy/probability_calibration_example.py similarity index 100% rename from examples/automl_example/api_example/advanced_example/specific_strategy/probability_calibration_example.py rename to examples/automl_example/custom_strategy/specific_strategy/probability_calibration_example.py diff --git a/fedot_ind/core/models/quantile/__init__.py b/examples/automl_example/time_series/__init__.py similarity index 100% rename from fedot_ind/core/models/quantile/__init__.py rename to examples/automl_example/time_series/__init__.py diff --git a/fedot_ind/core/models/recurrence/__init__.py b/examples/automl_example/time_series/ts_anomaly_detection/__init__.py similarity index 100% rename from fedot_ind/core/models/recurrence/__init__.py rename to examples/automl_example/time_series/ts_anomaly_detection/__init__.py diff --git a/examples/automl_example/api_example/time_series/ts_anomaly_detection/custom_liman_example.py b/examples/automl_example/time_series/ts_anomaly_detection/custom_liman_example.py similarity index 97% rename from examples/automl_example/api_example/time_series/ts_anomaly_detection/custom_liman_example.py rename to examples/automl_example/time_series/ts_anomaly_detection/custom_liman_example.py index 21e83d5af..caef4407f 100644 --- a/examples/automl_example/api_example/time_series/ts_anomaly_detection/custom_liman_example.py +++ b/examples/automl_example/time_series/ts_anomaly_detection/custom_liman_example.py @@ -8,7 +8,7 @@ from fedot_ind.api.utils.checkers_collections import DataCheck from fedot_ind.api.utils.path_lib import PROJECT_PATH from fedot_ind.core.architecture.pipelines.abstract_pipeline import ApiTemplate -from fedot_ind.core.models.quantile.quantile_extractor import QuantileExtractor +from fedot_ind.core.operation.transformation.representation.statistical.quantile_extractor import QuantileExtractor from fedot_ind.core.repository.constanst_repository import FEDOT_TASK from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels diff --git a/examples/automl_example/api_example/time_series/ts_anomaly_detection/ts_anomaly_detection_example.py b/examples/automl_example/time_series/ts_anomaly_detection/ts_anomaly_detection_example.py similarity index 100% rename from examples/automl_example/api_example/time_series/ts_anomaly_detection/ts_anomaly_detection_example.py rename to examples/automl_example/time_series/ts_anomaly_detection/ts_anomaly_detection_example.py diff --git a/fedot_ind/core/models/tabular/__init__.py b/examples/automl_example/time_series/ts_classification/__init__.py similarity index 100% rename from fedot_ind/core/models/tabular/__init__.py rename to examples/automl_example/time_series/ts_classification/__init__.py diff --git a/examples/automl_example/time_series/ts_classification/pdl_example.py b/examples/automl_example/time_series/ts_classification/pdl_example.py new file mode 100644 index 000000000..af91eef7a --- /dev/null +++ b/examples/automl_example/time_series/ts_classification/pdl_example.py @@ -0,0 +1,25 @@ +from fedot_ind.core.architecture.pipelines.abstract_pipeline import ApiTemplate + +if __name__ == "__main__": + dataset_name = 'Lightning7' + finetune = True + metric_names = ('f1', 'accuracy', 'precision', 'roc_auc') + api_config = dict(problem='classification', + metric='f1', + timeout=5, + pop_size=10, + with_tunig=False, + n_jobs=2, + logging_level=20) + init_assumption_pdl = ['quantile_extractor', 'pdl_clf'] + init_assumption_rf = ['quantile_extractor', 'rf'] + comparasion_dict = dict(pairwise_approach=init_assumption_pdl, + baseline=init_assumption_rf) + for approach in comparasion_dict.keys(): + result_dict = ApiTemplate(api_config=api_config, + metric_list=metric_names).eval(dataset=dataset_name, + initial_assumption=comparasion_dict[approach], + finetune=finetune) + metrics = result_dict['metrics'] + print(f'Approach - {approach}. Metrics - {metrics}') + _ = 1 diff --git a/examples/automl_example/time_series/ts_classification/tmp.py b/examples/automl_example/time_series/ts_classification/tmp.py new file mode 100644 index 000000000..e49dbd454 --- /dev/null +++ b/examples/automl_example/time_series/ts_classification/tmp.py @@ -0,0 +1,31 @@ +import numpy as np +from pdll import PairwiseDifferenceClassifier +from sklearn.datasets import make_blobs +from sklearn.ensemble import RandomForestClassifier + + +def multiclass_classification(): + # Set the random seed for reproducibility + np.random.seed(53) + + # Define the number of data points and features + n_samples = 10 + n_features = 2 + n_classes = 3 + + # Generate random data with 2 features, 10 points, and 3 classes + X, y = make_blobs(n_samples=n_samples, n_features=n_features, centers=n_classes, random_state=0) + + base = RandomForestClassifier(class_weight="balanced", random_state=0) + pdc = PairwiseDifferenceClassifier(estimator=base) + pdc.fit(X, y) + print('score:', pdc.score(X, y)) + + pdc.predict(X) + pdc.predict_proba(X) + + assert pdc.score(X, y) == 1.0 + + +if __name__ == "__main__": + multiclass_classification() diff --git a/examples/automl_example/api_example/time_series/ts_classification/ts_classification_example.py b/examples/automl_example/time_series/ts_classification/ts_classification_example.py similarity index 100% rename from examples/automl_example/api_example/time_series/ts_classification/ts_classification_example.py rename to examples/automl_example/time_series/ts_classification/ts_classification_example.py diff --git a/fedot_ind/core/models/topological/__init__.py b/examples/automl_example/time_series/ts_forecasting/__init__.py similarity index 100% rename from fedot_ind/core/models/topological/__init__.py rename to examples/automl_example/time_series/ts_forecasting/__init__.py diff --git a/examples/automl_example/api_example/advanced_example/specific_strategy/forecasting_strategy_example.py b/examples/automl_example/time_series/ts_forecasting/forecasting_strategy_example.py similarity index 100% rename from examples/automl_example/api_example/advanced_example/specific_strategy/forecasting_strategy_example.py rename to examples/automl_example/time_series/ts_forecasting/forecasting_strategy_example.py diff --git a/examples/real_world_examples/kaggle/automl/log.log b/examples/automl_example/time_series/ts_forecasting/m4_analiysis/__init__.py similarity index 100% rename from examples/real_world_examples/kaggle/automl/log.log rename to examples/automl_example/time_series/ts_forecasting/m4_analiysis/__init__.py diff --git a/examples/automl_example/api_example/time_series/ts_forecasting/m4_analiysis/forecasting_analysis.py b/examples/automl_example/time_series/ts_forecasting/m4_analiysis/forecasting_analysis.py similarity index 100% rename from examples/automl_example/api_example/time_series/ts_forecasting/m4_analiysis/forecasting_analysis.py rename to examples/automl_example/time_series/ts_forecasting/m4_analiysis/forecasting_analysis.py diff --git a/examples/automl_example/api_example/time_series/ts_forecasting/m4_analiysis/ts_forecasting_m4_benchmark.py b/examples/automl_example/time_series/ts_forecasting/m4_analiysis/ts_forecasting_m4_benchmark.py similarity index 100% rename from examples/automl_example/api_example/time_series/ts_forecasting/m4_analiysis/ts_forecasting_m4_benchmark.py rename to examples/automl_example/time_series/ts_forecasting/m4_analiysis/ts_forecasting_m4_benchmark.py diff --git a/examples/automl_example/api_example/time_series/ts_forecasting/ts_forecasting_deepar_examle.ipynb b/examples/automl_example/time_series/ts_forecasting/ts_forecasting_deepar_examle.ipynb similarity index 100% rename from examples/automl_example/api_example/time_series/ts_forecasting/ts_forecasting_deepar_examle.ipynb rename to examples/automl_example/time_series/ts_forecasting/ts_forecasting_deepar_examle.ipynb diff --git a/examples/automl_example/api_example/time_series/ts_forecasting/ts_forecasting_example.py b/examples/automl_example/time_series/ts_forecasting/ts_forecasting_example.py similarity index 100% rename from examples/automl_example/api_example/time_series/ts_forecasting/ts_forecasting_example.py rename to examples/automl_example/time_series/ts_forecasting/ts_forecasting_example.py diff --git a/examples/automl_example/api_example/time_series/ts_forecasting/ts_forecasting_exogen.py b/examples/automl_example/time_series/ts_forecasting/ts_forecasting_exogen.py similarity index 100% rename from examples/automl_example/api_example/time_series/ts_forecasting/ts_forecasting_exogen.py rename to examples/automl_example/time_series/ts_forecasting/ts_forecasting_exogen.py diff --git a/examples/automl_example/time_series/ts_regression/__init__.py b/examples/automl_example/time_series/ts_regression/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/automl_example/time_series/ts_regression/pdl_example.py b/examples/automl_example/time_series/ts_regression/pdl_example.py new file mode 100644 index 000000000..61b1eda2a --- /dev/null +++ b/examples/automl_example/time_series/ts_regression/pdl_example.py @@ -0,0 +1,22 @@ +from fedot_ind.core.architecture.pipelines.abstract_pipeline import ApiTemplate + +if __name__ == "__main__": + dataset_name = 'AppliancesEnergy' # BeijingPM10Quality + finetune = False + api_config = dict(problem='regression', + metric='rmse', + timeout=0.1, + n_jobs=2, + logging_level=20) + metric_names = ('r2', 'rmse', 'mae') + init_assumption_pdl = ['quantile_extractor', 'pdl_reg'] + init_assumption_rf = ['quantile_extractor', 'treg'] + comparasion_dict = dict(pairwise_approach=init_assumption_pdl, + baseline=init_assumption_rf) + for approach in comparasion_dict.keys(): + result_dict = ApiTemplate(api_config=api_config, + metric_list=metric_names).eval(dataset=dataset_name, + initial_assumption=comparasion_dict[approach], + finetune=finetune) + metrics = result_dict['metrics'] + print(f'Approach - {approach}. Metrics - {metrics}') diff --git a/examples/automl_example/api_example/time_series/ts_regression/ts_regression_example.py b/examples/automl_example/time_series/ts_regression/ts_regression_example.py similarity index 100% rename from examples/automl_example/api_example/time_series/ts_regression/ts_regression_example.py rename to examples/automl_example/time_series/ts_regression/ts_regression_example.py diff --git a/examples/real_world_examples/__init__.py b/examples/real_world_examples/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/__init__.py b/examples/real_world_examples/benchmark_example/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/benchmark_example/analysis of results/analysis_multi_clf.ipynb b/examples/real_world_examples/benchmark_example/analysis of results/analysis_multi_clf.ipynb similarity index 100% rename from examples/benchmark_example/analysis of results/analysis_multi_clf.ipynb rename to examples/real_world_examples/benchmark_example/analysis of results/analysis_multi_clf.ipynb diff --git a/examples/benchmark_example/analysis of results/analysis_regr.ipynb b/examples/real_world_examples/benchmark_example/analysis of results/analysis_regr.ipynb similarity index 100% rename from examples/benchmark_example/analysis of results/analysis_regr.ipynb rename to examples/real_world_examples/benchmark_example/analysis of results/analysis_regr.ipynb diff --git a/examples/benchmark_example/analysis of results/analysis_uni_clf.ipynb b/examples/real_world_examples/benchmark_example/analysis of results/analysis_uni_clf.ipynb similarity index 100% rename from examples/benchmark_example/analysis of results/analysis_uni_clf.ipynb rename to examples/real_world_examples/benchmark_example/analysis of results/analysis_uni_clf.ipynb diff --git a/examples/real_world_examples/benchmark_example/classification/PDL_multi.py b/examples/real_world_examples/benchmark_example/classification/PDL_multi.py new file mode 100644 index 000000000..ca8b628fc --- /dev/null +++ b/examples/real_world_examples/benchmark_example/classification/PDL_multi.py @@ -0,0 +1,24 @@ +from benchmark.benchmark_TSC import BenchmarkTSC + +init_assumption_pdl = ['quantile_extractor', 'pdl_clf'] +init_assumption_rf = ['quantile_extractor', 'rf'] +comparasion_dict = dict(pairwise_approach=init_assumption_pdl, + baseline=init_assumption_rf) +experiment_setup = { + 'problem': 'classification', + 'metric': 'accuracy', + 'timeout': 2.0, + 'num_of_generations': 15, + 'pop_size': 10, + 'metric_names': ('f1', 'accuracy'), + 'logging_level': 10, + 'n_jobs': -1, + 'output_folder': r'D:\\WORK\\Repo\\Industiral\\IndustrialTS/benchmark/results/', + 'initial_assumption': comparasion_dict, + 'finetune': True} + +if __name__ == "__main__": + benchmark = BenchmarkTSC(experiment_setup=experiment_setup, + use_small_datasets=False) + benchmark.run() + _ = 1 diff --git a/examples/real_world_examples/benchmark_example/classification/PDL_uni.py b/examples/real_world_examples/benchmark_example/classification/PDL_uni.py new file mode 100644 index 000000000..d9c81ddf8 --- /dev/null +++ b/examples/real_world_examples/benchmark_example/classification/PDL_uni.py @@ -0,0 +1,24 @@ +from benchmark.benchmark_TSC import BenchmarkTSC + +init_assumption_pdl = ['quantile_extractor', 'pdl_clf'] +init_assumption_rf = ['quantile_extractor', 'rf'] +comparasion_dict = dict(pairwise_approach=init_assumption_pdl, + baseline=init_assumption_rf) +experiment_setup = { + 'problem': 'classification', + 'metric': 'accuracy', + 'timeout': 2.0, + 'num_of_generations': 15, + 'pop_size': 10, + 'metric_names': ('f1', 'accuracy'), + 'logging_level': 10, + 'n_jobs': -1, + 'initial_assumption': comparasion_dict, + 'output_folder': r'D:\\WORK\\Repo\\Industiral\\IndustrialTS/benchmark/results/', + 'finetune': True} + +if __name__ == "__main__": + benchmark = BenchmarkTSC(experiment_setup=experiment_setup, + use_small_datasets=True) + benchmark.run() + _ = 1 diff --git a/examples/benchmark_example/time_series_multi_clf_benchmark.py b/examples/real_world_examples/benchmark_example/classification/SOTA_multi.py similarity index 100% rename from examples/benchmark_example/time_series_multi_clf_benchmark.py rename to examples/real_world_examples/benchmark_example/classification/SOTA_multi.py diff --git a/examples/benchmark_example/time_series_uni_clf_benchmark.py b/examples/real_world_examples/benchmark_example/classification/SOTA_uni.py similarity index 100% rename from examples/benchmark_example/time_series_uni_clf_benchmark.py rename to examples/real_world_examples/benchmark_example/classification/SOTA_uni.py diff --git a/examples/real_world_examples/benchmark_example/classification/__init__.py b/examples/real_world_examples/benchmark_example/classification/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/forecasting/__init__.py b/examples/real_world_examples/benchmark_example/forecasting/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/forecasting/time_series_multi_forecast_benchmark.py b/examples/real_world_examples/benchmark_example/forecasting/time_series_multi_forecast_benchmark.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/benchmark_example/time_series_uni_forecast_benchmark.py b/examples/real_world_examples/benchmark_example/forecasting/time_series_uni_forecast_benchmark.py similarity index 100% rename from examples/benchmark_example/time_series_uni_forecast_benchmark.py rename to examples/real_world_examples/benchmark_example/forecasting/time_series_uni_forecast_benchmark.py diff --git a/examples/benchmark_example/time_siries_nbeats_forecast_benchmark.py b/examples/real_world_examples/benchmark_example/forecasting/time_siries_nbeats_forecast_benchmark.py similarity index 99% rename from examples/benchmark_example/time_siries_nbeats_forecast_benchmark.py rename to examples/real_world_examples/benchmark_example/forecasting/time_siries_nbeats_forecast_benchmark.py index 014398615..b3d7df168 100644 --- a/examples/benchmark_example/time_siries_nbeats_forecast_benchmark.py +++ b/examples/real_world_examples/benchmark_example/forecasting/time_siries_nbeats_forecast_benchmark.py @@ -1,13 +1,12 @@ import csv -from fedot_ind.core.models.nn.network_impl.nbeats import NBeatsNet - +import matplotlib.pyplot as plt +import numpy as np import torch from torch import optim from torch.nn import functional as F -import matplotlib.pyplot as plt -import numpy as np +from fedot_ind.core.models.nn.network_impl.nbeats import NBeatsNet def get_m4_data(backcast_length, forecast_length, is_training=True): diff --git a/examples/real_world_examples/kaggle/EEG.py b/examples/real_world_examples/benchmark_example/kaggle/EEG.py similarity index 99% rename from examples/real_world_examples/kaggle/EEG.py rename to examples/real_world_examples/benchmark_example/kaggle/EEG.py index 3d40ae1f3..49a33a903 100644 --- a/examples/real_world_examples/kaggle/EEG.py +++ b/examples/real_world_examples/benchmark_example/kaggle/EEG.py @@ -1,18 +1,17 @@ import gc import matplotlib +import numpy as np +import pandas as pd from fedot.core.pipelines.pipeline_builder import PipelineBuilder +from scipy.signal import butter, lfilter from sklearn.preprocessing import LabelEncoder - from tqdm import tqdm from benchmark.feature_utils import * from fedot_ind.api.main import FedotIndustrial from fedot_ind.api.utils.path_lib import PROJECT_PATH -from scipy.signal import butter, lfilter from fedot_ind.core.optimizer.IndustrialEvoOptimizer import IndustrialEvoOptimizer -import numpy as np -import pandas as pd matplotlib.use('TkAgg') diff --git a/examples/real_world_examples/kaggle/Harmful Brain Activity Classification.ipynb b/examples/real_world_examples/benchmark_example/kaggle/Harmful Brain Activity Classification.ipynb similarity index 100% rename from examples/real_world_examples/kaggle/Harmful Brain Activity Classification.ipynb rename to examples/real_world_examples/benchmark_example/kaggle/Harmful Brain Activity Classification.ipynb diff --git a/examples/real_world_examples/benchmark_example/kaggle/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/2024-03-04_13-36-19_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/2024-03-04_13-36-19_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/2024-03-04_13-36-19_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/2024-03-04_13-36-19_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/2024-03-04_13-36-19_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/2024-03-04_13-36-19_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/0_ensemble_branch/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/0_ensemble_branch/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/0_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/0_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/0_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/0_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/0_ensemble_branch/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/0_ensemble_branch/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/1_ensemble_branch/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/1_ensemble_branch/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/1_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/1_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/1_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/1_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/1_ensemble_branch/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/1_ensemble_branch/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/2_ensemble_branch/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/2_ensemble_branch/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/2_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/2_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/2_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/2_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/2_ensemble_branch/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/2_ensemble_branch/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/3_ensemble_branch/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/3_ensemble_branch/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/3_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/3_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/3_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/3_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/3_ensemble_branch/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/3_ensemble_branch/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/ensemble_head/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/ensemble_head/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/ensemble_head/0_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/ensemble_head/0_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/ensemble_head/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/ensemble_head/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/ensemble_head/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/automl/raf_ensemble/ensemble_head/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/0_branch/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/0_branch/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/0_branch/0_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/0_branch/0_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/0_branch/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/0_branch/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/0_branch/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/0_branch/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/0_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/0_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/1_branch/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/1_branch/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/1_branch/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/1_branch/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/1_branch/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/1_branch/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/2_branch/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/2_branch/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/2_branch/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/2_branch/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/2_branch/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/2_branch/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/3_branch/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/3_branch/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/3_branch/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/3_branch/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/3_branch/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/3_branch/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/composed/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/composed/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/0_ensemble_branch/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/0_ensemble_branch/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/0_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/0_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/0_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/0_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/0_ensemble_branch/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/0_ensemble_branch/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/1_ensemble_branch/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/1_ensemble_branch/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/1_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/1_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/1_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/1_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/1_ensemble_branch/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/1_ensemble_branch/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/2_ensemble_branch/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/2_ensemble_branch/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/2_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/2_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/2_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/2_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/2_ensemble_branch/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/2_ensemble_branch/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/3_ensemble_branch/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/3_ensemble_branch/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/3_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/3_ensemble_branch/0_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/3_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/3_ensemble_branch/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/3_ensemble_branch/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/3_ensemble_branch/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_composed/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_composed/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_composed/0_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_composed/0_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_composed/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_composed/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_composed/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_composed/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_head/0_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_head/0_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_head/0_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_head/0_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_head/0_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_head/0_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_head/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/archive/raf_ensemble/ensemble_head/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-08_03-41-51_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-08_03-41-51_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-08_03-41-51_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-08_03-41-51_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-08_03-41-51_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-08_03-41-51_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-08_14-21-09_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-08_14-21-09_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-08_14-21-09_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-08_14-21-09_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-08_14-21-09_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-08_14-21-09_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-12_19-26-25_pipeline_saved/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-12_19-26-25_pipeline_saved/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-12_19-26-25_pipeline_saved/fitted_operations/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-12_19-26-25_pipeline_saved/fitted_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-12_19-26-25_pipeline_saved/preprocessing/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/automl/2024-03-12_19-26-25_pipeline_saved/preprocessing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/automl/__init__.py b/examples/real_world_examples/benchmark_example/kaggle/automl/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/benchmark_example/kaggle/automl/log.log b/examples/real_world_examples/benchmark_example/kaggle/automl/log.log new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/kaggle/kaggle_forecasting.py b/examples/real_world_examples/benchmark_example/kaggle/kaggle_forecasting.py similarity index 98% rename from examples/real_world_examples/kaggle/kaggle_forecasting.py rename to examples/real_world_examples/benchmark_example/kaggle/kaggle_forecasting.py index 4444faaca..9c372c4a1 100644 --- a/examples/real_world_examples/kaggle/kaggle_forecasting.py +++ b/examples/real_world_examples/benchmark_example/kaggle/kaggle_forecasting.py @@ -68,7 +68,7 @@ def forecasting_loop(dataset_dict): finetune=finetune) ts_dict.update({time_series_id: result_dict}) - test = pd.read_csv("./data/test.csv", parse_dates=["date"]) + test = pd.read_csv("data/test.csv", parse_dates=["date"]) test['orders'] = 0 for warehouse, time_series_id in warehouse_to_item_id.items(): diff --git a/examples/real_world_examples/benchmark_example/regression/PDL_multi.py b/examples/real_world_examples/benchmark_example/regression/PDL_multi.py new file mode 100644 index 000000000..82d3218e8 --- /dev/null +++ b/examples/real_world_examples/benchmark_example/regression/PDL_multi.py @@ -0,0 +1,45 @@ +from benchmark.benchmark_TSER import BenchmarkTSER + +init_assumption_pdl = ['quantile_extractor', 'pdl_reg'] +init_assumption_rf = ['quantile_extractor', 'treg'] +comparasion_dict = dict(pairwise_approach=init_assumption_pdl, + baseline=init_assumption_rf) +experiment_setup = { + 'problem': 'regression', + 'metric': 'rmse', + 'timeout': 2.0, + 'num_of_generations': 15, + 'pop_size': 10, + 'metric_names': ('f1', 'accuracy'), + 'logging_level': 10, + 'n_jobs': -1, + 'initial_assumption': comparasion_dict, + 'finetune': True} +custom_dataset = [ + # 'ElectricMotorTemperature', + # 'PrecipitationAndalusia', + # 'AcousticContaminationMadrid', + # 'WindTurbinePower', + # 'DailyOilGasPrices', + # 'DailyTemperatureLatitude', + # 'LPGasMonitoringHomeActivity', + # 'AluminiumConcentration', + # 'BoronConcentration', + # 'CopperConcentration', + # # 'IronConcentration', + # 'ManganeseConcentration', + # 'SodiumConcentration', + # 'PhosphorusConcentration', + # 'PotassiumConcentration', + 'MagnesiumConcentration', + 'SulphurConcentration', + 'ZincConcentration', + 'CalciumConcentration' +] +custom_dataset = None +if __name__ == "__main__": + benchmark = BenchmarkTSER(experiment_setup=experiment_setup, + custom_datasets=custom_dataset + ) + benchmark.run() + _ = 1 diff --git a/examples/benchmark_example/time_series_multi_reg_benchmark.py b/examples/real_world_examples/benchmark_example/regression/SOTA_multi.py similarity index 100% rename from examples/benchmark_example/time_series_multi_reg_benchmark.py rename to examples/real_world_examples/benchmark_example/regression/SOTA_multi.py diff --git a/examples/real_world_examples/benchmark_example/regression/__init__.py b/examples/real_world_examples/benchmark_example/regression/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/industrial_examples/__init__.py b/examples/real_world_examples/industrial_examples/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/industrial_examples/eeg/__init__.py b/examples/real_world_examples/industrial_examples/eeg/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/real_world_examples/eeg/neiry_example.py b/examples/real_world_examples/industrial_examples/eeg/neiry_example.py similarity index 96% rename from examples/real_world_examples/eeg/neiry_example.py rename to examples/real_world_examples/industrial_examples/eeg/neiry_example.py index fc1651948..5ca17bd3f 100644 --- a/examples/real_world_examples/eeg/neiry_example.py +++ b/examples/real_world_examples/industrial_examples/eeg/neiry_example.py @@ -21,8 +21,8 @@ def generate_composite_features(input_data): if __name__ == "__main__": - sig_X = np.load('./sig_data.npy').swapaxes(1, 2) - sig_y = np.load('./sig_target.npy') + sig_X = np.load('sig_data.npy').swapaxes(1, 2) + sig_y = np.load('sig_target.npy') metric_names = ('f1', 'accuracy') scaler = StandardScaler() pca = PCA(.975) diff --git a/examples/real_world_examples/eeg/neiry_industrial_demo.ipynb b/examples/real_world_examples/industrial_examples/eeg/neiry_industrial_demo.ipynb similarity index 100% rename from examples/real_world_examples/eeg/neiry_industrial_demo.ipynb rename to examples/real_world_examples/industrial_examples/eeg/neiry_industrial_demo.ipynb diff --git a/examples/real_world_examples/industrial_examples/equipment_monitoring/parma_example.py b/examples/real_world_examples/industrial_examples/equipment_monitoring/parma_example.py new file mode 100644 index 000000000..11625315f --- /dev/null +++ b/examples/real_world_examples/industrial_examples/equipment_monitoring/parma_example.py @@ -0,0 +1,76 @@ +import gc + +import matplotlib +import numpy as np +from sklearn.utils import shuffle + +from fedot_ind.core.architecture.pipelines.abstract_pipeline import ApiTemplate +from fedot_ind.core.operation.transformation.data.park_transformation import park_transform + +matplotlib.use('TkAgg') +gc.collect() +metric_names = ('f1', 'accuracy', 'precision', 'roc_auc') +stat_params = {'window_size': 0, 'stride': 1, 'add_global_features': True, 'use_sliding_window': False} +fourier_params = {'low_rank': 5, 'output_format': 'signal', 'compute_heuristic_representation': True, + 'approximation': 'smooth', 'threshold': 0.9, 'sampling_rate': 64e3} +wavelet_params = {'n_components': 3, 'wavelet': 'bior3.7', 'compute_heuristic_representation': True} +park_params = {} +rocket_params = {"num_features": 200} +sampling_dict = dict(samples=dict(start_idx=0, + end_idx=None), + channels=dict(start_idx=0, + end_idx=None), + elements=dict(start_idx=0, + end_idx=None)) + +feature_generator = { + # 'minirocket': [('minirocket_extractor', rocket_params)], + # 'stat_generator': [('quantile_extractor', stat_params)], + 'fourier': [('fourier_basis', fourier_params)], + 'wavelet': [('wavelet_basis', wavelet_params)], +} + + +def load_data(use_park_transform: bool = False): + train_features, train_target = np.load('./dataset/X_train.npy').swapaxes(1, 2), np.load('./dataset/y_train.npy') + test_features, test_target = np.load('./dataset/X_test.npy').swapaxes(1, 2), np.load('./dataset/y_test.npy') + train_features, train_target = shuffle(train_features, train_target) + if use_park_transform: + train_features, test_features = park_transform(train_features), park_transform(test_features) + input_train = (train_features, train_target) + input_test = (test_features, test_target) + + dataset = dict(test_data=input_test, train_data=input_train) + return dataset + + +if __name__ == "__main__": + finetune = False + dataset = load_data(use_park_transform=True) + api_config = dict(problem='classification', + metric='f1', + timeout=40, + pop_size=10, + early_stopping_iterations=10, + early_stopping_timeout=30, + optimizer_params={'mutation_agent': 'random', + 'mutation_strategy': 'params_mutation_strategy'}, + with_tunig=False, + preset='classification_tabular', + industrial_strategy_params={'feature_generator': feature_generator, + 'data_type': 'tensor', + 'learning_strategy': 'ts2tabular', + 'sampling_strategy': sampling_dict + }, + n_jobs=-1, + logging_level=20) + + result_dict = ApiTemplate(api_config=api_config, + metric_list=metric_names).eval(dataset=dataset, + finetune=finetune) + metrics = result_dict['metrics'] + metrics.to_csv('./metrics.csv') + hist = result_dict['industrial_model'].save_optimization_history(return_history=True) + result_dict['industrial_model'].vis_optimisation_history(hist) + result_dict['industrial_model'].save_best_model() + _ = 1 diff --git a/examples/benchmark_example/LoRA notebooks/1_LoRA_Implementation.ipynb b/examples/tutorial/LoRA notebooks/1_LoRA_Implementation.ipynb similarity index 100% rename from examples/benchmark_example/LoRA notebooks/1_LoRA_Implementation.ipynb rename to examples/tutorial/LoRA notebooks/1_LoRA_Implementation.ipynb diff --git a/examples/benchmark_example/LoRA notebooks/2_LoRA_NBEATS_Metrics.ipynb b/examples/tutorial/LoRA notebooks/2_LoRA_NBEATS_Metrics.ipynb similarity index 100% rename from examples/benchmark_example/LoRA notebooks/2_LoRA_NBEATS_Metrics.ipynb rename to examples/tutorial/LoRA notebooks/2_LoRA_NBEATS_Metrics.ipynb diff --git a/examples/benchmark_example/LoRA notebooks/3_LoRA_Transformer_Metrics.ipynb b/examples/tutorial/LoRA notebooks/3_LoRA_Transformer_Metrics.ipynb similarity index 100% rename from examples/benchmark_example/LoRA notebooks/3_LoRA_Transformer_Metrics.ipynb rename to examples/tutorial/LoRA notebooks/3_LoRA_Transformer_Metrics.ipynb diff --git a/examples/benchmark_example/LoRA notebooks/4_LoRA_rSVD.ipynb b/examples/tutorial/LoRA notebooks/4_LoRA_rSVD.ipynb similarity index 100% rename from examples/benchmark_example/LoRA notebooks/4_LoRA_rSVD.ipynb rename to examples/tutorial/LoRA notebooks/4_LoRA_rSVD.ipynb diff --git a/examples/benchmark_example/LoRA notebooks/lora_nbeats.py b/examples/tutorial/LoRA notebooks/lora_nbeats.py similarity index 100% rename from examples/benchmark_example/LoRA notebooks/lora_nbeats.py rename to examples/tutorial/LoRA notebooks/lora_nbeats.py index 55014c823..71ae63cc8 100644 --- a/examples/benchmark_example/LoRA notebooks/lora_nbeats.py +++ b/examples/tutorial/LoRA notebooks/lora_nbeats.py @@ -1,8 +1,8 @@ import torch +import torch.nn as nn import torchvision.datasets as datasets import torchvision.transforms as v2 -import torch.nn as nn def load_mnist_data(): diff --git a/examples/tutorial/time_series/ts_classification/classification_example.ipynb b/examples/tutorial/time_series/ts_classification/classification_example.ipynb deleted file mode 100644 index 1b2c522a0..000000000 --- a/examples/tutorial/time_series/ts_classification/classification_example.ipynb +++ /dev/null @@ -1,782 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 120, - "metadata": { - "collapsed": true, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "\n", - "from fedot_ind.core.architecture.pipelines.abstract_pipeline import AbstractPipeline, ApiTemplate" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "outputs": [], - "source": [ - "def plot_mean_sample(X,y, labels:list = [], n_channel: int = 1):\n", - " mean_sample = []\n", - " if len(labels) == 0:\n", - " labels = list(np.unique(y))\n", - " for label in labels:\n", - " mean_sample.append(np.mean(X[y == label] , axis=0)) # Данные класса 1\n", - " ax = plt.gca()\n", - " channels = [f'Channel {x}' for x in range(n_channel)]\n", - " df = pd.DataFrame(mean_sample).T\n", - " df.columns = labels\n", - " df.plot(kind ='line',ax=ax)\n", - " plt.legend(fontsize='small')\n", - " plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n", - " plt.show()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 149, - "outputs": [], - "source": [ - "def plot_mean_sample_multi(X,y, labels:list = [], n_channel: int = None):\n", - " mean_sample = {}\n", - " if len(labels) == 0:\n", - " labels = list(np.unique(y))\n", - " if n_channel is None:\n", - " n_channel = X.shape[1]\n", - " channels = [f'Channel {x}' for x in range(n_channel)]\n", - " for label in labels:\n", - " mask = y == label\n", - " for chn in range(n_channel):\n", - " mean_sample.update({f'Label_{label}_channel_{chn}':np.mean(X[mask.flatten(),chn,:] , axis=0)}) # Данные класса 1\n", - " ax = plt.gca()\n", - " df = pd.DataFrame(mean_sample)\n", - " df.plot(kind ='line', ax=ax)\n", - " plt.suptitle('Усреднённые семплы по классам')\n", - " plt.legend(fontsize='small')\n", - " plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n", - " plt.show()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 140, - "outputs": [], - "source": [ - "finetune = False\n", - "metric_names = ('f1', 'accuracy', 'precision', 'roc_auc')\n", - "api_config = dict(problem='classification',\n", - " metric='accuracy',\n", - " timeout=1,\n", - " pop_size=10,\n", - " with_tunig=False,\n", - " n_jobs=2,\n", - " logging_level=20)\n", - "pipeline_creator = AbstractPipeline(task='classification')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "# Our datasets and models for experiments" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 152, - "outputs": [], - "source": [ - "easy_to_clf_uno = 'ItalyPowerDemand'\n", - "hard_to_clf_uno = 'ElectricDevices'\n", - "easy_to_clf_multi = 'BasicMotions'\n", - "hard_to_clf_multi = 'AtrialFibrillation'\n", - "node_list_model = ['quantile_extractor','logit']" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "# Our datasets" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 153, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-10-14 17:24:08,554 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\data\\ItalyPowerDemand\n", - "2024-10-14 17:24:08,571 - Data read successfully from local folder\n", - "2024-10-14 17:24:08,573 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\data\\ElectricDevices\n", - "2024-10-14 17:24:09,029 - Data read successfully from local folder\n", - "2024-10-14 17:24:09,061 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\data\\BasicMotions\n", - "2024-10-14 17:24:09,143 - Data read successfully from local folder\n", - "2024-10-14 17:24:09,150 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\data\\AtrialFibrillation\n", - "2024-10-14 17:24:09,175 - Data read successfully from local folder\n" - ] - } - ], - "source": [ - "easy_to_clf_uno_dataset = pipeline_creator.create_input_data(easy_to_clf_uno)\n", - "hard_to_clf_uno_dataset = pipeline_creator.create_input_data(hard_to_clf_uno)\n", - "easy_to_clf_multi_dataset = pipeline_creator.create_input_data(easy_to_clf_multi)\n", - "hard_to_clf_multi_dataset = pipeline_creator.create_input_data(hard_to_clf_multi)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "# Lets Visualise our data" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## Easy to clf data" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 154, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_mean_sample(easy_to_clf_uno_dataset[0].features,easy_to_clf_uno_dataset[0].target)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## Hard to clf data" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 155, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_mean_sample(hard_to_clf_uno_dataset[0].features,hard_to_clf_uno_dataset[0].target)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 145, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAHNCAYAAAAE4JEIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd5jc1PX3v1fStJ2d7d5dl3WvNGNsU0wH03sxAUKxTTChBfLimBDAlFBCDZDwCxCKgRCKCTEQCIRimgHT3G1sY3u96/X2Pn0k3fcP6Wo0vexsw/fzPE7YGY3mSiNdnXvO95xDKKUUHA6Hw+FwOIMQob8HwOFwOBwOh5Mt3JDhcDgcDoczaOGGDIfD4XA4nEELN2Q4HA6Hw+EMWrghw+FwOBwOZ9DCDRkOh8PhcDiDFm7IcDgcDofDGbRwQ4bD4XA4HM6ghRsyHE4P6OzsREdHBwDA6/WipaUFvMYkh8Ph9B3ckOFwesAZZ5yBww47DABw//33Y8iQIWhtbe3nUXE4HM6eAzdkOFlz7733ghCCd999N+a9xsZGWK1WnHHGGf0wsr7joYcewtNPPw0AuOSSS/DBBx+gsLCwn0fF4XA4ew6E91riZEtDQwOqqqpw2mmn4Y033oh4795778Uf/vAH/Oc//8Epp5zSTyPkcDgczs8dbshwesRZZ52F//znP6itrUVlZSUAgFKK8ePHIxQKobq6GoLAHX8cDofD6R34E4bTIy6//HLIsowlS5YYr3344YfYvn075s+fH2HEdHR04Le//S1Gjx4Nm82GESNG4JJLLkFLSwsA4JNPPgEhBK+++ir+8Ic/oLKyEk6nE6effjpqa2tjvnvlypU48cQTUVhYiLy8PBx55JFYsWJF3HEeddRRIITE/Lv99tsjtiOE4Jprron5/KmnnorRo0dHvKaqKh555BHsvffesNvtqKiowBVXXIH29vaI7UaPHo1TTz01Zp/XXHMNCCFZfX91dTUIIXjwwQfjHi8A3H777TH7B4B//OMfmD59OhwOB0pKSnD++efHPb/xqKurw2WXXYZhw4bBZrNhzJgxuPLKKxEMBo1tOjo6cP3116Oqqgo2mw3jx4/HfffdB1VVY8ZPCMGyZcsivsPv96O4uDjm+NjxJPpnvgYZibatrq6O2CbeOWcsWbIk5jPxmDt3btLxffLJJxHbL1261PgdysrKcNFFF6Guri7pdyQaz4YNG1BcXIxTTz0VsixHbJ/o2jefr88//xxz5szByJEjYbPZUFVVhd/+9rfw+Xwx3//jjz/ivPPOw5AhQ+BwODBp0iTcfPPNEdukuk7a2tqwcOFC7LvvvsjPz0dBQQFOOukkrFmzJmI/bE4ghGD16tUx3yGKIggheP3111OeN87PF6m/B8AZ3Jx44omoqqrCM888gxtvvBGEEPz973+HIAi47LLLjO3cbjcOP/xwbNq0CfPnz8cBBxyAlpYWvPXWW9i1axfKysqMbe+++24QQnDjjTeiqakJjzzyCGbPno3Vq1fD4XAAAD7++GOcdNJJmD59Om677TYIgoDnnnsOxxxzDD7//HMceOCBMWMdMWIE7r33XmM8V155ZY+O/YorrsCSJUswb948/OY3v8GOHTvw17/+FatWrcKKFStgsVh6tP/e4O6778att96K8847D7/61a/Q3NyMv/zlLzjiiCOwatUqFBUVJfzs7t27ceCBB6KjowMLFizA5MmTUVdXh9dffx1erxdWqxVerxdHHnkk6urqcMUVV2DkyJH48ssvcdNNN6G+vh6PPPJIxD7tdjuee+45nHnmmcZrb7zxBvx+f8Jx/O1vf0N+fr7x944dO7B48eKE25911lk4++yzAWgP7Keeeir5SeoBNpvN0Ewxvv32Wzz22GMRr7HrZubMmbj33nvR2NiIRx99FCtWrEj5O0RTW1uLE088EZMnT8Zrr70GSYqd1idPnmwYGy0tLfjtb38b8f7SpUvh9Xpx5ZVXorS0FN988w3+8pe/YNeuXVi6dKmx3dq1a3H44YfDYrFgwYIFGD16NLZt24a3334bd999N4D0rpPt27dj2bJlmDNnDsaMGYPGxkY8+eSTOPLII7Fx40YMGzYsYnzsOnn00UeN155//nlYrdak1wpnD4FyOD3ktttuowDoxx9/TJuamqjVaqUnn3xyxDaLFy+mAOgbb7wR83lVVSmllC5fvpwCoMOHD6ddXV3G+6+99hoFQB999FFj+wkTJtATTjjB+CyllHq9XjpmzBh63HHHxXzHrFmz6D777GP83dzcTAHQ2267LWI7APTqq6+O+fwpp5xCR40aZfz9+eefUwD0pZdeitjuvffei3l91KhR9JRTTonZ59VXX02jb8F0v3/Hjh0UAH3ggQditmWw34VRXV1NRVGkd999d8R269ato5IkxbwezSWXXEIFQaDffvttzHvsd/jjH/9InU4n3bJlS8T7v//976koirSmpiZi/BdccAGVJIk2NDQY2x577LH0wgsvjDk+djzNzc0R+/72228pAPrcc89FvB4KhSgAescddxivPffccxQA3bFjh/FaonOe7DPxuPTSS6nT6Yx5fenSpRQAXb58OaWU0mAwSMvLy+k+++xDfT6fsd1//vMfCoAuXrw46feYx9PW1kb32msvOmnSJNrS0hJ3+0MPPZQeffTRxt/s3JvPl9frjfncvffeSwkhdOfOncZrRxxxBHW5XBGvUUoj7sN0rhO/308VRYl4b8eOHdRms9E777zTeI3NCRdccAEtLS2lgUDAeG/ChAnGdbJ06dK4x87ZM+ChJU6PueyyyyAIAv7+97/jueeeQzAYxIIFCyK2+de//oWpU6firLPOivl8dPjjkksugcvlMv4+99xzMXToUCM7avXq1di6dSsuvPBCtLa2oqWlBS0tLfB4PDj22GPx2WefRYQxAC1cYbfb0zoev99v7JP9C4VCEdssXboUhYWFOO644yK2mz59OvLz87F8+fKI7UOhUMw+E60k0/l+Bqtd097enrJ+zRtvvAFVVXHeeedF7LuyshITJkyIGbMZVVWxbNkynHbaaZgxY0bM++w3XLp0KQ4//HAUFxdHfMfs2bOhKAo+++yziM8dcMAB2HvvvfHiiy8CAHbu3Inly5dj7ty5SY8lHVgYw2azpdyWnfPW1taYayfXfPfdd2hqasJVV10VcU2ecsopmDx5Mt5555209uP3+3H66aejubkZ7733HkpLS+NuFwwGU54D5ukEAI/Hg5aWFsyaNQuUUqxatQoA0NzcjM8++wzz58/HyJEjIz7Pfv90rxObzWaEnRVFQWtrK/Lz8zFp0iT88MMPMZ877bTTQAjBW2+9BUDzrO3atQu/+MUvkh4XZ8+Ah5Y4Paaqqgonnngi3njjDXz55ZcYNmxYTKbStm3bcM4556S1vwkTJkT8TQjB+PHjDU3A1q1bAQCXXnppwn10dnaiuLjY+LulpSVmv4l45pln8Mwzz8S8PmrUKOO/t27dis7OTpSXl8fdR1NTU8Tf//vf/zBkyJCcfT/jtttuw2233QZAc78fc8wxeOSRR+Ie69atW0EpTXgekoXCmpub0dXVhX322Sfp2Ldu3Yq1a9cmPNbo8wIA8+bNw1NPPYWFCxdiyZIlmDVrVtq/VTJYoUJzGCoR5nNutVpx0EEH4eGHH477MO4pO3fuBABMmjQp5r3Jkyfjiy++SGs/8+bNw9dffw273R6jizHT0dER99oxU1NTg8WLF+Ott96K0Xh1dnYCALZv3w4ASa+BdK8TVVXx6KOP4v/+7/+wY8cOKIpivBfPILNYLLjooovw7LPP4txzz8Wzzz6Lc845BwUFBUm/h7NnwA0ZTk64/PLL8e6772Lnzp24+eab48bpcwVbMT/wwAPYf//9425jfngFg0HU19fjuOOOS2v/Z5xxRoz485ZbbkFDQ0PEGMrLy/HSSy/F3Uf0g/yggw7CXXfdFfHaX//6V7z55ptZfT9jwYIFmDNnDhRFwaZNm3D77bfjzDPPxIYNG2K2VVUVhBD897//hSiKMe+n88BPhaqqOO6447Bo0aK470+cODHmtYsuugiLFi3C119/jeeffx633HJLj8cBwDhfLJsuGeycU0qxY8cO3HnnnTj11FMNo3kg8sMPP+DNN9/ENddcgwULFuDjjz+Ou11DQwNOOOGEhPtRFAXHHXcc2tracOONN2Ly5MlwOp2oq6vD3Llze8VDdc899+DWW2/F/Pnz8cc//hElJSUQBAHXX399wu+bP38+pk2bhs2bN2Pp0qWGd4bD4YYMJyeceuqpKC8vR3NzM371q1/FvD9u3DisX78+rX1FPzwopfjpp5+w3377GfsCgIKCAsyePTvl/tasWYNQKJT26nrEiBEx+33kkUciDIlx48bhww8/xKGHHhrhlk9EWVlZzD6js3Uy+X7GhAkTjG1POOEEeL1e3HzzzaipqYnZdty4caCUYsyYMXENimQMGTIEBQUFKX/DcePGwe12p/W7MEpLS3H66afjiiuuQFNTkxH66ikbN24EAEyZMiXlttHnPD8/H7/85S+NsEouYd6RzZs345hjjol4b/PmzSm9J4ynn34ap59+OkRRxKmnnopnnnkmQmAPALt27UJ3d3fSc7Bu3Tps2bIFzz//PC655BLj9Q8++CBiu7FjxwJA0msg3evk9ddfx9FHHx3jeezo6IgQ/pvZd999MW3aNCNj6uijj8ann36a9Hs4ewZcI8PJCW1tbejs7MRxxx0Xk6YMAOeccw7WrFmDf//73zHvRWs7XnjhBXR3dxt/v/7666ivr8dJJ50EAJg+fTrGjRuHBx98EG63O2Z/zc3NEX8vXbrUmOxzxXnnnQdFUfDHP/4x5j1Zlo2wRl/DVrPxPC5nn302RFHEHXfcEXPOKaVJWysIgoAzzzwTb7/9Nr777ruY99n+zjvvPHz11Vd4//33Y7bp6OhIGAKZP38+1q5dizlz5uTEMwQAr776KoYOHZqWIRNNsvPYU2bMmIHy8nI88cQTCAQCxuv//e9/sWnTprQLSB5++OEANG3N+eefj9/97ndobGyM2OaVV14BgBiDyQw7RvM1QSmNyBACNCPliCOOwLPPPhtjKLPPpnudiKIYcw0uXbo0Zfo5u05YqjuHA3CPDKeHrF27Fq+//jr++9//IhAIJKzH8bvf/Q6vv/465syZg/nz52P69Oloa2vDW2+9hSeeeAJTp041ti0pKcFhhx2GefPmobGxEY888gjGjx+Pyy+/HIA2WT799NM46aSTsPfee2PevHkYPnw46urqsHz5chQUFODtt9+Gx+PB448/jsceewwTJ06MqOPBDKC1a9fiq6++wiGHHJLRcR955JG44oorcO+992L16tU4/vjjYbFYsHXrVixduhSPPvoozj333AzPZuZs3rwZ7733HlRVxcaNG/HAAw9g5syZGD58eMy248aNw1133YWbbroJ1dXVOPPMM+FyubBjxw78+9//xoIFC7Bw4cKE33XPPffgf//7H4488kgsWLAAU6ZMQX19PZYuXYovvvgCRUVF+N3vfoe33noLp556KubOnYvp06fD4/Fg3bp1eP3111FdXR13xX3iiSeiubk5J0bMd999h1tvvRXvvfcennjiibQeeDU1NXjvvfeM0NLdd9+NUaNGYdq0aTkPL1ksFtx3332YN28ejjzySFxwwQVG+vXo0aNjUqPT4dFHH8WUKVNw7bXX4rXXXkNjYyNuu+02PP300zj//PMxefLkhJ+dPHkyxo0bh4ULF6Kurg4FBQX417/+FaOVAYDHHnsMhx12GA444AAsWLAAY8aMQXV1Nd555x2jzks618mpp56KO++8E/PmzcOsWbOwbt06vPTSS4bXJxGXX3455syZw9uAcCLp+0Qpzs+J5557joqiSKuqqlKmjba2ttJrrrmGDh8+nFqtVjpixAh66aWXGmmjLNXy5ZdfpjfddBMtLy+nDoeDnnLKKTHpnpRSumrVKnr22WfT0tJSarPZ6KhRo+h5551HP/roI0ppOM001b9LL73U2CfSTH9mPPXUU3T69OnU4XBQl8tF9913X7po0SK6e/duY5veTL9m/wRBMM7nrl27KKWx6deMf/3rX/Swww6jTqeTOp1OOnnyZHr11VfTzZs3x2wbzc6dO+kll1xChwwZQm02Gx07diy9+uqrI9Jiu7u76U033UTHjx9PrVYrLSsro7NmzaIPPvggDQaDEeNPlD4e7/1006/vu+8+OnPmzJjUeEoTp1+zf4QQWllZSc8++2y6adOmhJ+JR7rp14xXX32VTps2jdpsNlpSUkJ/+ctfGr9dMhKN5/nnn6cA6FtvvUVXrFhBx48fT2+//faI34bS+OnXGzdupLNnz6b5+fm0rKyMXn755XTNmjVx09rXr19PzzrrLFpUVETtdjudNGkSvfXWWyO2SXWd+P1+esMNN9ChQ4dSh8NBDz30UPrVV1/RI488kh555JHGftickCi9OtX7nD0D3qKAM2D45JNPcPTRR2Pp0qU58WZUV1djzJgx2LFjR9xwF6BVi62uro5bFZbD4XA4Ax+ukeFwOBwOhzNo4RoZzs8WlnmSTHex3377xZRD53A4HM7ggRsynJ8tZWVl+Mc//pF0G9aDh8PhcDiDE66R4XA4HA6HM2jhGhkOh8PhcDiDFm7IcDgcDofDGbRwQ4bD4XA4HM6ghRsyHA6Hw+FwBi3ckOFwOBwOhzNo4YYMh8PhcDicQQs3ZDgcDofD4QxauCHD4XA4HA5n0MINGQ6Hw+FwOIMWbshwOBwOh8MZtHBDhsPhcDgczqCFGzIcDofD4XAGLdyQ4XA4HA6HM2jhhgyHw+FwOJxBCzdkOBwOh8PhDFq4IcPhcDgcDmfQwg0ZDofD4XA4gxZuyHA4HA6Hwxm0cEOGw+FwOBzOoIUbMhwOh8PhcAYt3JDhcDgcDoczaOGGDIfD4XA4nEELN2Q4HA6Hw+EMWrghw+FwOBwOZ9DCDRkOh8PhcDiDFm7IcDgcDofDGbRwQ4bD4XA4HM6ghRsyHA6Hw+FwBi1Sfw+gp6iqit27d8PlcoEQ0t/D4XA4HA6HkwaUUnR3d2PYsGEQhOz9KoPekNm9ezeqqqr6exgcDofD4XCyoLa2FiNGjMj684PekHG5XAC0E1FQUNDPo+FwOBwOh5MOXV1dqKqqMp7j2TLoDRkWTiooKOCGDIfD4XA4g4yeykK42JfD4XA4HM6ghRsyHA6Hw+FwBi3ckOFwOBwOhzNo4YYMh8PhcDicQQs3ZDgcDofD4QxauCHD4XA4HA5n0MINGQ6Hw+FwOIMWbshwOBwOh8MZtHBDhsPhcDgczqCFGzIcDofD4XAGLdyQ4XA4HA6HM2jhhgyHw+FwOJxBCzdkOHsclNL+HgKHw0lAsLYb3Svq+H3KSZtB3/2aw8kEqlA0/XUVxEIbyubu3d/D4XA4JoJ1bjQ/tRY0pMJS6YR9XFF/D4kzCOAeGc4ehdIZQKjeA/+PbVADSn8Ph8Ph6CjdQbS+sBE0pGp/t/n7eUScwQI3ZDh7FDQUNl6UzkA/joTD4TCorKL1xY0R9yS/Pznpwg0Zzh4FW+0BgNLOV3wcTn9DKUX7v39CsKYbxC7BMXUIAEDpCvbzyDiDBW7IcPYoaDDskZE7+IqPw+lv3F/Uwft9I0CA0gsnwzauEAD3yGSCGlDQ9eFOhBo8/T2UfoEbMpw9CtXskeGGDIfTrwTr3Oh8dwcAoPCUsbBPLIZYaAPAPTKZ0PW/anR9WIPO/+3s76H0C9yQ4exR0CA3ZDicgUKguhOggG18EfIPHQYAEAt0Q4Z7ZNJCbvfD/XU9gD33nHFDhrNHYRb7yh1cI8Ph9Cdqt+Z1sZTngRACAJAKrdp7XjlC08aJT9dHNYCi1dzZU71Y3JAZxCjuIDo/2Amle8+8eLOB8tAShzNgYA9ewWU1XiMOCcQi6O/zezQZoWavpi/SUT1BUHXPKyTIDZlBTMeyn9D9UQ26P6nt76EMGsxiX6Vzz7zpOZyBguIOAQBEsyFDCMQC7e89NVSSLl0f7AQoYJ9UDBAAKqB6Q/09rD6HGzKDlFCTF74Nrdp/76FK9Wwwa2SgUu7N4nD6EVX3yDDDhcEFv6kJ1rnhW9sCEKDgxDEQ8iwA9sxzxg2ZQUr3Z7sA3ZkQavL272AGEdExdx5e4nD6D7aQEPItEa9zj0xquv5XDQBwTB0C61Cn4dVS3dwjwxkEyJ0BeFc1GX+r3aE90p2YDWaxLwAoXPDL4fQLVFGhevTQUiKPTOee511Ih0B1J/yb2wEBKJw9CgAguHSPzB7oZeaGzCDE/XkdoFDYxhYaNzz3yqTHYPHIUEXl+h3Ozxqmj4FAjLAIg3tkktP1gVYvxjmjElKZA0BYZ8QNGc6AR/GE4PlGqxngOqoKUkUegIFryISavGh5YeOAGZ+qi31ZVoTcPvAmShpS0PDgd2h+am1/D4XD6TUMfUy+BUQgEe+xBZq8B+o9UkEpRaC6CwCQf/hw43UjtMQNGc5Ax/PVbtCgCsswJ2wTimAp1wwZuXFgGArRdH9SC//GVri/3N3fQwEQFvtK+nkbiB6ZUIMXSnsAweouUIXX0eD8PDH0MVFhJSBsyKjcIxMD9clG3Rip2G68LnCPDGcwoAYUwyBwHVUFQohhyAwUj0c0wZ3aykFu9fXzSDSYRkYaorljB6Qh0xI+V6pX7seRcDi9B3vgmlOvGWJh+KFMFR5iNaPouiJiF0Gk8COch5Y4gwLPtw1QvTKkUjsc+5QBgBFakgegIaO4g5BbNTEt+//+hmlkLEP08zYAxb5yc/i3ZGJIDufnhprEkBHyrdrTiQKqe897MCeDZSWJ+VECaV3sq3bveXMGN2QGCVSlcH++CwCQf+QII6ZsYZ6FziBU/8BavQdruo3/Vtr9oHL/h0mYISOVa+eN+pUBd95kk0dG4YYM52eKEVqKY8gQgRgGjszDSxEwkXR0yjoPLXEGPKo7qKUiCoBzWoXxupBnMS7ggRZeYmElAADVmpv1N6yyr+i0QsiTAAy88JIcEVrihgzn5wkr3BbPIwOYdDJc8BuB6tHPmzMq00s/jzSgGEkNewrckBkkMJEqsYhGxg3DMkDDS4Garoi/5baBYMjo59EqhDMjBpAhQymNNGQ8A8tbxOHkimQaGcCUucQ9MhGoCTwyxBZ+NuxpmUvckBkkqCFmyMT+ZANR8EsVFcFaNwBThlBL/wt+mdiXWASIRXrRrQGkk1G7gxFtFLhGhvNzhWk5oovhMcK1ZPash3IqwqGlyPNGCNljw0u9ash89tlnOO200zBs2DAQQrBs2bKI9ymlWLx4MYYOHQqHw4HZs2dj69atvTmkQYvxALaKMe9JAzAFO7TbA8gqhDwJ9snFAAaG4JdpZIhVNFIXB1JoKdQcaezx0BLn5wilFIou4iVRngVGuN/SwLk/BwJM/CzGOW/hzKU9a97oVUPG4/Fg6tSpePzxx+O+f//99+Oxxx7DE088gZUrV8LpdOKEE06A39//D7yBRji0NDg8MgFdH2OtchmVJ/s7BZuqNGzImDwyAym0JEd5rbhHhvNzRPWGa6Ec+pfP8ZePYhewzCMjdwTw2Edb8dW21j4d40DF8Mg44xgyunGzp2V6Sb2585NOOgknnXRS3PcopXjkkUdwyy234IwzzgAAvPDCC6ioqMCyZctw/vnn9+bQBh3mkEg0LAVbaQ9ADSoQ4nht+pqgro+xjiqAVMoMmf41UM1ZU8QqhkNLA6i6r6x7ZIQ8CapXhsLryHB+hjANR9AioMkbxCMfbcXxe1diUqXL2IZ5ZNobPXi4ehemDC3Af687vO/H6g3Bt7EVSkcAiicE1R2C4g5BzJNQcv4kEEvfzrdGf6o4HhlWXHBP64Ddq4ZMMnbs2IGGhgbMnj3beK2wsBAHHXQQvvrqq4SGTCAQQCAQfvB0dXXF3e7nBk2ikRGdFghOC1RPCHKTF9YRrpht+prgTi31WjNktBCO3O4HVSiISJJ9tNegJiU/kcwamQFkyOgeGevIAvh/bOMeGc7PEvagdUsAQoCiUtz5nw34x2UHgRBtfmAeGatPM+brO/vOo0spRbCmG56V9fCubQbk+EX5/Fs64Ni7tM/GBSTWyADh2jJcI9NHNDQ0AAAqKioiXq+oqDDei8e9996LwsJC419VVVWvjnOgEDZk4lv/lgHUc0nuCGjN3gTAOsIFscAGSARQaL82gTMaRkoCiEAgFekGVlcAxz/0Kb6rbuu3sTEMQ2aUZoxyjQzn5wh70LaRsIGw4qdWfLCx0fibCVetICgEQYc3hFAftOzwbWhB06M/oPlva+D9oQmQKSyVTjgPrITrmCoUnjYW1lEFAAC5pW/nWyqrWosCJAgt7aH9lgZd1tJNN92Ezs5O419tbW1/D6lPYKElIY5HBjAJfgeAIcPCSpZKJwSbqBkNJbrR0I86GWbICFbtHAr5FkAkIBTobPbgnXX1/TY2QMv0Yinq1pHaRMnTrzk/R5gh06Bq9+QBI4sAAHe9swkBWZvr3tvchHZo75dD89K09oH2o/1fWxFq8IJYBORNr8CQq6ai/LppKD57AgqPHw3XocNhG6+NN1qc39sYCxsBEByxARUjtOTesxZA/WbIVFZWAgAaGxsjXm9sbDTei4fNZkNBQUHEvz2BcP2TFB6ZAZC5xArhsVULAJNOph8NGaPztXYOiUAQ0oviVUBAl69/jQa5PQCoFMQiwDrUCUAbs+FJ4nB+JjCPQW1Ae+Dedea+KHfZUNPmxbNfVMMdkHHn2xvRDM1jM96hhYGbu3vXo0sV1ehvVnHDdJTMmQjbyAIj3MVgFdXlPjZkzELf6I7hQFg3w0NLfcSYMWNQWVmJjz76yHitq6sLK1euxCGHHNJfwxqwJNPIAAPLIxPQWxPY4hoy/Sf4NRfDY9QqmnFTAYIuf/+uYliPJanMAeKQjLuTh5c4PzfYg7aJqhAIML48H78/aTIA4K8fb8Vtb25AQ5cfbov2sB5l1R7QLe7eNWSMJq0EWkg8ARLr1dbHoSWjz5IzQe0dFlpyB0HVPafZZq8aMm63G6tXr8bq1asBaALf1atXo6amBoQQXH/99bjrrrvw1ltvYd26dbjkkkswbNgwnHnmmb05rEFJKkOGpWDLbX4jDNUf0JCCUJ1WCI+FRwCEBb/9achEZX7taPHgR682ngoI6PT1syGj62OkModW3CpPX11xwS/nZwYT+7ZCxdBCB6ySgDP3H479q4rgCSr41w9aX7lxY7QaVMNEzYva2x4ZVffKErsU1+PBYCUlVI/cpwsNNhdEV/VlCPkWgABQ96wFUK8aMt999x2mTZuGadOmAQD+3//7f5g2bRoWL14MAFi0aBGuvfZaLFiwADNnzoTb7cZ7770Hu93em8MalIQfwvFDS0K+ResdRPs+bmsmWOcGVArBZYFYHF7RDIjQUpRgesmKHWjUXdeVENA1gAwZICzm25MmJM6eAQsttYJiRLF+vQsEt522l7HNCXtXoGpUEQCgnGiPquZe98joHo+85Am9gk00sqpCfVixnNWHSWTIEFEIL4D2oKJ4vWrIHHXUUaCUxvxbsmQJAK2k8p133omGhgb4/X58+OGHmDhxYm8OadCSyiNDCBkQ4SWmj4mOK5s9Mv3l8jTaPFg178vS73ehQRcTVoD0vyHTHGXI6BMSF/xyfm4oJkOmqiTPeH3ayGJcc/R47DO8ALedtjfEQs1YKNFlYr0eWtLvNZIX31AwI/WDTsYILcVJvWbsiZlLgy5raU/FEKpaE/9kA6HCb8BUP8aMWGQHBALIar8J0cxi39e+rYU3qMCi15KpgIAufz+LfZlHRp8gRae2KuQeGc7PCTWgGHq1VqioKs6LeH/hCZPwn2sPx7Aih1EUzxXSFj+9H1pKzyMDmHQyfWjIKAkaRpoRXHue4JcbMoOEVB4ZoP97LlFK42YsAQARCSQ91BRdhr+vMLJ/LARLvqwGABw9cwQALbTkDsiQ+6BORTzUgGLoBizRoaUBoJEJ1HSh/V9buV6H02PYA9ZPAB+AkXrYOR7MkHEEtPuyr8S+8VKbozFarzT33Xxr9FmKU0OGIe6BjSO5ITNIUFMUxAP6vyie6g5pD10CWIfnx7wv6hOW0k+CX+aR2e0Ooq7DhxKnFccfrBVUzAOBC0B3P3llmHZIcEpGSClTsW+bJ4ib3liHTfW5r3bd/ekueL5tQPcne0bdJk7voXZFFsOL9siYYToUKaTCAaCll+vIGIZMGqElloLdlxqZVGJfgIeWOAOYZL2WGEbmUqsvoq9QXxEO3QggUpyeUKX9WxSPeWTWN2nhr18eNBIOp9XwfPRn5lJYHxOe1MNi3/SMqyc/24aXv6nB/32yLefjY7F57+rmXtc4BWQFT322DZsbunv1ezj9g5F6rWrzhVkjE41gl0Bs2uKtDELvh5b0MK6QTmjJ1Ay3r3R/6WhkBO6R4QxUjNBSkoaQQoFVe1+FUSG2L2HGU8JaN/2cucQMrZ3dfogCwUUHjwIAo+eSppPpJ0MmKmMJyDy0tOKnFgBAXXvuPXJMO6B2BxH4qSPn+zez/Mdm3PPuj7hl2bpe/R5O/2AW+lolAUPyE9drAcJemXIQdPpCRuXf3oClX6fjkRGL7VrrFZlCae/9+ZZSmrTztTEurpHhDFSMYm5JPDKEEEglug6lD26saAxjK443BjCvYPoptKSPzw+KYUV2VBRoHqKwIUP6zyMTx5BhgsN0DJk2TxAbdmshpfrO3J9fs1fIu6op5/s3w1JsV9d2wN+PNZE4vUPYkFExotgBIUm9FiCsk6nUU7B7s00Bu9fS8cgQgRiLs74IL9GgAuiLxfRCS3uOno0bMoOEVN4OhlisPZz7YoUQTXiM8b1GESnYtO9TsMOGDDCiKOzOlvRzVtWPbQpCyTwyaWQtfbWtFeyUNnb5cypappRGGDK+9S1QA71nYLA0+JBCsaqmI+F2AVmBsgdVL/25YK4hMzJJWInBDJmRfVDdNxOPDBAW5vdF5hILKxGrACGZZ56HljgDleg+QYkwmjO29X2X6ZQemWI7QLRjUfuhqZmqn0M/KIYXhw0G6whNmLw3pH7xyFBKjYmQCQgBUx2ZNDQyX+hhJQBQKdCUQy0BDSjaTqE9VGhIhW9DS4pPZY9ZcP3NjvgdyXe1ezHjrg9x7cs/9No4OL1DRA2ZJEJfBgstjZA0L0mvGjJMI5NG1hJgblXQ+4ZMOPU6sT4GCHtkaEAx5ryfO9yQGSQYnZsHskfGSG9OULRPEozVVX/oZNj4AgCGF5kMGb2VwkQIcHv63gBUPSFQvwyQsNcK0DKYAG3cqSakFT9FGhb1nbk7v4YhJQlwzqwA0LvhpW6TTunb6viGzH/W1qPbL+PddQ3Y3dF/1aI5mcPKDLRARVVJ4tRrBpszjOq+vSj4DWctpWvI9F0KdrjPUnJvEbGJhud+T8lc4obMIMHIWkpSEA8Ih0n6VeybwCMD9K9OhibwyIjFNnisBBIILE19/1Bkqzmx0BbhcSNWERA1/UCy8FJNqxc1bV5IAsFeQzWjbHdH7s6vuWx73rRyAEDgpw4oXb3zQDF7ZL7f2Y5QnDDZxz+GDal31tb3yjg4vYOapUemVLfleysFm4bU8IIxzdBSX1b3VTzJ2xMwCCHh8FI/eL77A27IDAKookKvpJ/USAAAsaT/PTJJi/b1Ywp2pEYmbMgQQtBepN34rta+98gYqddDIlenhBBT5lLi8BILK00bWYTx5VqYLKceGV94lSqVOrRih1RLxe4NzB4ZX0jB+rrOiPc7vSF8v7Pd+Pvttbt7ZRyc3ENl1fB6RLcnSIRR3Vfu3eq+LDMPAkDsyUP4DKaRUbqCvaobA8IemWQZSwyjKF4X98hwBghGyAbJ068BGFlLqleG2sfF3dLyyOgq//a6vq8Rwrxa0R4ZAPCUaQZWWUffr2DiZSwxxLzUKdgsrHTY+CEYWqQdR249Mnr/GYc2FuaV8f7QO+El1irCql9H0eGlz7Y2Q1Ephhc5IAoEa3d1orrF0ytj4eQWRfemhEDRla4ho2cVOkIUFvRe48hwVV9LRJ+4ZAh5FsOw6G2dTDo1ZBii7rVRe7mA4ECBGzKDAJZ6DQIj1JAIwSYZ8d2+Di+l45Hx6bqPbVta+7xmi+zXDJkAKIYWRhoNoaFOAMAwj9LnGVUsVV6KoxcQUvRbUlWKFdt0Q2ZCKYbpx5VbjUxk/5m8/coAkSDU4EGwPvcGBPPIHDquFECs4He5HlY6Zb+hmKVv8x/ulUkLqlLDw9YfsJTgNlAU2CUUOlJ7F4Q8CdCN2iEgaOklj4ySQeq1mb5qVaCk6HxtZk/LXOKGzCAgXNVXTGul0F/hpXQ8Mq/9pD2EhlEBde19G15iglmn02qs9hlSpRMBUOSpfdsEDtCzghA/U4Kt9hK1KdhY34UObwj5Ngn7jSjC0ELtt89lLZnosu1CngX2ySUAAO+qxpx9D4NpZI6ZogmLv61uh6pnTSkqxSdbtJDW0ZPKcdrUYQCAt9dwnUw6tC/dgvp7ViJQk/s2FunAQh3phpUAvT5WISuKJ/Ra1hLNMPWawULCoV6eN8IeGR5aioYbMoOAcFXf9H6usOC3b/UeqTwyDZ1+PLW2DgDgAunz8BIbX1FhbCXRgnwrNkIzKFjjy75CDSb+fYUUoSWmjzl4bAksooBhuvanN8S+5pWqUw8v+VY359yDxQyZQ8aWIM8qotMXwha9rcSaXR1o8wThskuYMboYJ+xdCYtIsLmxm7c0SIPAjk7QkIqOt7b1WVl9M8xD0BKn63UyjMylXmxToGSYes1gJRN6O7SUTp8lhlEUj4t9OQOFVPVZohmoHpm/Lt+KLkXFSmgPKse3iTUWHW9vQ9MTawxvVI/HRilERZu4y4piQziFDgvW64ZMoI8NmXBGWqz+KVW/JaaPOXR8GQAYHpkWdyBnpdzNYl+GfVIxIGgrvlyu+hSVwh3Qvq84z4rpo4oBhMNLLKx0xMQhsIgCCh0WHDlRM6p4eCk5lFLDkAjtcvd6heZ4mGvIjCzNxpAh6PLLvdKmINPUawbrj9bboaVMNDLCHtamgBsygwAWEsncI9PHmUtJPDK1bV688o3WOfndQi08VrbTHbdTt29jK9wrdiNY3YVgbY5W2aYmmkPiuLQLHJJhyPS1R8bcbDOaZG0K/CHFeMAfphsyJU4rbLoh2diZm5VrvI7AxCIaxcBCu905+R4AcJsE6i67BTNHayEsdpwfbdIevsdMKje2O23qUABabZn+qBg9WFC9MqCEz0/n+9W9nmkTMwZTe4Kq4tQ1ZBhM8MvaFPRGCnYmna/NsNBS12437nlnY87HBejaJm8WWUvckOEMFMIhm/RSAo3qvn3tkUliyDzy4VbIKsXhE8owfmoFPkMIBEDXBzsjtlODCjreCndvTqQNyRQWvgGAitLkHhm52Zez700HJuaOV3Y8WePIH3a2IyCrqCiwGWnXhBDDK7M7R4LfRNVOrcO07wztzp3glwnAbZIAqyTgwDFhQ6ah04+N9V0gBDhq0hDjM7OnVMBuEbCjxWP0m+LEwowIYpcgltihdgXR/Wltn46Bee/aQDEiTY0MEPbIDBf16r69EF7KpPO1GanEDkoABwi+X987Xi7VGwIoAJJmQ0sjtBTslxBiX8MNmUFAWOybZmipWLvplfa+7WmUKLT0U1M3/r1qFwDghuMnYYjLhqcRAAXgW9eCYF14Rd/9UQ2UjvAklasYLzuHAVAMjzOBOiwiPAKwk3ll+lAMmazYYbJ+S1+YwkpmETjLyGrIkeA3XmgJACzDtEyvYA49Mkwf47Jrx71/VRGsooCm7gCWfFkNAJg6ogilpo7JTpuEYydrwuC31/DwUiKYESEVWVF08hgAQPdndX264Mm0PQFD1MW+FYZHpjcMmexCS0QS4NezC12+3vFwGTVk8iSQFJmrgK6jIQDU9Hq1DXa4ITMIMDpfp6ghwwj3NFLjruSprBqpfLkkkcH15w+3QqXAcXtVYP+qIgxx2bAdKlY7tRuSeWVCDR50f66JgVlKY64qU7JzGEBkMTwGISTCKxPc2XfC0XBn8zgemTyWtRSrkQnXjymLeN2oJZNrj0zUStBieGRyZ8gwj0yB7v2xW0TsN6IQAPDcih0AgGMnl8d8zhxeUgfICjS4242Gh79D5/vV/T0UAGFDRnBZYd+7FLaxhYCsovO/O/psDLJeDbpF73ydLswjU6o7VntD8MsK4mUaWgKATod275YE1F5ZPBp9lpyp9TEAQEQhPHfsAV2wuSEzCEgnrdkMkYRwjLQ99oZveXY96u/9Jucl5qleedM8zvV1nXhnbT0IAW44fiIAYIhLm5ResSmAAPh/bENgZxfal/0EqBT2vUrhmKqFDpIVgsuEbt1w88UphscocFiw1hD8dsbdJtdQlSbNSjN7ZMwTZLc/hHV6xdtZ4yINGaOWTA4yl7TYfPyVqlWvvaO0B3K26ov2yAAwwksB/T44Oo4hc9SkcuTbJNR1+LCqtj3m/b4mWOdG89/XQW7ywfN97lPUs0HRH/5igU0z3E8dCxDAt7YFgeq+ud7ZtWTJt8CeZqgcCBsyTgWwopc9MhlmLQFAq1VblI2gBN5eaNSo6u0J0km9Zoi64Len/Za2N7uxurYDrb3YrLOncENmEECTpOcmQkwg+FW9IQR2dAIKRagxtyr7aI9MfacPV7z4PQDgtP2GYXKl1geoXDdk1vsCyDtACwm0vrARweouEIuAotPH5rwyZXObdqwhAuRZ409UBWaPTK1baw3Ry1CTCDm+R0Yfq0KNejMAsHZXJ1QKjCh2oLLQHvEZ5pHJRVE8GlC02Dy0iqeRY7MYYcxcFcZjxfAK7OHfaKZuyABARYENew8riPmc3SIaxfHW1PbNQzkRwV3daP77OqMuidod7JNrKRXMI8MWOdZh+XDOrAQAdH1Y0+vfT0MKiC42LszAGwPoIRWLqSjeABL7AkC9fuuOhIgOX+49IOHO1+mPLVdF8Z75YgfOfHwFXvhqZ+qN+wluyAwCmIEgZLCCSST4DVR3GQ+mXHk7GGaxb4s7gF8+vRJ1HT6MKXPi1lP3MrYbkq+NrdMXgv2oEYBIjLEUHDcKUpE9XAguR6GlVv08qEm8WgV2CTVQIVsFQFZjRKxUyb3LmJpWb/E0UILV1MnWlIK9urYDgKYhiYZ5ZHJRS4Z5WohFiDu+XIeXwh6ZsCEzfVQxBF0WcPSk8oRFISsKtOuqzdN/mRrB2m40P70O1C/DOtIFSASggNIZf0xrd3Xgwr9/jTvf7p1sFzNsZc5W6gDgPFAzZHK9qIn7/bphJ4OiPIPUa0AL/fZmLRlKadZiXwDYRbX7uAIEnb2gScmkzxKDpWn3dDHYqn++LI207/6CGzKDgHRK/0djCH6jPDKBHeHVas4NGd274FUoLn7mG2xv9mB4kQP/+NVBRjgJ0PQPVlE7lnaJIP8gTd9gqcxD/qFapVbDI5OjMbbrwtdk4blChwUUMBpIsnoySmcAba9vQd3iFeheUZeT8TDC+hgBRIj/gI5XFG9VjRY+mTayOGb7nnpkvEEZFz+zEk9/vj2h0JeR68wl5pFx2cITdoHdYhznCXtXJvxsia4faO0nQyZsxCiwjipA2WX7GA9fJSoV3h2QcftbG3Dm4yvw5bZWLPlyB/w5qpmUCEMjUxC+F1lnadXT+9kt7FpyZ1DV14xoqu6b635LNKQaqenZeGR2h7RjKwJBZy94ZDKpIcMIe2R6Nh4WxjML7AcamZuenD4nG0MmoUfGZMgoCYqsZQsb558+3IxNTV0oy7fhH786CMOjxLWEEAxx2VDX4UNzdwBDTxwNocCKvP2GgOgGTrK042zo1Fdwgi2xV6tAD500uiQMaQICW9uhekNwf15nHJtvfQtchw7PyZiA9DLSBKcEpTNgVB6llGJVTQcAreN1NEML9Mac3hB8QQWONEXijC+2tuDzrS34saEbF1VoBkSiyd0yXDNkcpW5xBpGFkTpFB75xf74saE7rj6GUapP8m2e/onld7y7HTSgwDq6AGXz9oZgkyAV2qC0+iMy8d7f0IDb3tyAhi7t3hQIoFLgpyY39hle2GvjYyEGZrwAuniUZbd4QkbYqTdghkx3hqnXDHNRvK059sgYGi+RZBTCZ9QFQgAkFIKgrheuv0z6LDGY562niR1sYVA2gA0Z7pEZBGSafg2ENTJmj4wakCNCAMxI6PKHsHDpGny0KTNRIqUU6+s68daa3Xh8+U9o070eG5rcKMqz4B+/OhBjypxxP1ume2iauwMQrCIKjqoyjC8AEJhb1CvnRF/g1m9mSzJDRheY1ji08+zf3I7uj2tBQyqkcr2fSoM3p1kJ6WSkRRt1u9p9aPUEYREJ9hoaqxcpcEjI0/eXjVdmU72WsdXcHUCAGYAJBJBWPQVbbvbmpAqz4ZGxR07YVSV5OG6viqSfZR6Z/gotKa3a9V90ylgINu18sUJusu6Reeqzbbjixe/R0OXHqNI8vHjZgZihF/3b0th7mXLmqr5mY4WIJBzG7eXiaSw02g1gZI8Mmdx7ZFRP2POYbudrMzVe3dAAgSfHSRRA+N4Xswkt9fB3DXtkBm5oiXtkBgGZpl8DJo9MRwBUpSAC0VKKTTYBuzle/GonXv9+F9bXdeLYKckfFmb++J9NeHZFOHXzMOQDECBIBM/PO9AQ98ZjSH7YkImH4JC0lSLVJhnzKjIbPLohY02SkcA68W63UBCLoBkwZQ4Unjga9kklqLttBahPhtoVNCbVnpKWR8YILWmT7Q96WGmvYYVxMz9YUbxtzR7Ud/oxdkh+RmPaVB+uodPW4oUFiWPzgssKId8C1R1CqMELa5Uro++KpiuORiZd+jO0RFUa7oVjMhSM0JLukXlppSaqnTtrNH5/0mTYLSL+t6ER3+xow5bG3KWxx4zPJwN6VmG010V0WaG6Q73elyeg34PdoJiaYIGTDGYUloOg2y/DH1IyynxKhpF6nUY37mhCior2gIwuUBSAIJCjitpmshP79jz9OiArhm6tzMk9MpwekJVHptCm+awVasTGWViJGUSqR0vpXfqdVt1zR4sHSppx8vV1nXjuS82ImTm6GGdPG44CUdvvk/MPxNQ4IlQzQ1zJDRkimFaKOchQCOgTlSNJ/JuFM1oDMsrm7YOS8yeh4rcHwLFPGYhFMGrbBBtyV8lWTcNIFaOK4hlhpSTnONw8MguPTEPYkOnSQ5OJPDKEEEPwm4vwUrz063Qp1Sfa/vDIqL5w+X9ziix7+CodAdS2ebGz1QtRIFh4wiTjITyxUjP+etMjw7wtxCHFzCNCH3VKrm/S7pugRIzMxUwwe2SA3KZgZ1sMDwA69PuyQ18lBnvBs5WNRsZc3Tdb2L0kCSQm3DuQ4IbMICDTFgWAZggYk6j+MGKGjH2ypntQvSF8W92O6lYtYyEgq2k9+Cil+ON/NoJS4PSpw7D017Pw8C/2B5u+K9JwGzNDpqk7cWZNrgS//pACRTcY8pNMBMwj0+ULwTa2EHn7lxuaHQCwVOphlBwaMjSNPlpCVL8llrEUTx/DYG0K6jOs7usOyNjZGs5g8epu8mQTPAsv5SJzqcsXm36dLswj0+ENQe7jdGf2sCB2KUJQbjZkWAHDaVVFyLeFj2+i3l6iN7t3G6nXcTyb7D7rjSKZZpqbtfvG6rRkFb5hhkxFL/Rb6knqdYceVurQ00FzlWnJUIOKMU9k5JExh+fl7O4HlrFUmm/N6jfrK7ghMwjIRuwLmMJLbX7QkGI0YHTsoxVQUzyy4Y1h/NSc+mH0/oYGrNzRBpsk4MaTJmtjVKmxIk1nnKk8MoDpRuzhxFDX4QNb/9mSuI6ZRiZR1oGlQn9gN+QuVTUdI9WskQnICjbq/YSmVcVmLDFYm4JMNTLRD9MgExkmmeDDHpmeG3iJNDLpUGwaY3sfl2VnDy9zajMASCaNDGspcdiEyAKGEys0j0xdh8/o/J3z8XXF6mMYRuZSL2tkOvUFlavInmLL+Eh61lIBJVpRvBwKfnuSes28FsyQgS+3v6GxkJMISBKNXzSCQwL0dgbZGldMi1Q6gMNKADdkBgVqtoYME/y2+zUjRqEQXBZYR2raFdUbwjtr6wHAyCza1pTckPGHFNz97iYAwBVHjDU+F1HYLY0KxIZGJol7OFe1ZOrafbBDu6GFJOeQeWS6/fEnIoteyTaUo+JvQNgjIyTzyLDz4A1hw+4uBBUVpU4rqkoSFxUbxtoUZFhLxqyPAdKrdmrUkqn39LjWTrw6MukiiQKKdGOmr8NLaoKsEuZFoD4ZP/zUCiC2pUSx02qEWrb2UngpntCXwRYMvS329en7L0ly3SbDHBbLteC3Jx6Z9iiPjJDjfktGWMmZmVeECKTHhUWNGjJZhAL7Em7IDALSCT/EQyzRV4NtfgR2aA8o25hCiM5wtVgSUjCmzImzpmkpxduakz+kn1tRjdo2HyoKbPj1UePCYwxlaMik4ZEJh5Z6NsHuaveBrQGTaVFY+nVCj4weWgo1e3NWqTUtj4xJ7Lta18fsX1WUdFLL1iPDDJlJupdADKgRY4iHVGLXVoqyCrmlZ94qZsgUZOGRAcyC375NwWaCymgNg2CXQOzab2vxynBaxbj6MeaV6S2djBon9Zoh5qgCbDL8IcWodFxZnrnQF9CL4pkEvwPHI6NnfxLNkLEEcmvIGCLyDMJKjJ5W92VtCcrS7PHUX3BDZhCQjUYGCHtk5Ha/oY+xjSkEsYiGUVQIgnOnj8B4PU6/LUloqanbj8eX/wQAuPHEyZGl/plHRiBpdWctNxkyidKZhfwceWQ6vIZHJpkxyDwy7oAcV2MhFtk0Q0ihkFty05AxLY2MSey7Kg19DBD2yGTab4kZMsfvrWWv2WRmyCSe4IlADG9VT8JLIUWFTxe2ZyssLO2nFGxj1RwvdGOqf3Lw2FJYxNjfmhkymxt6J3PJ3DAyZnxMFNqLzQW3NHaD5c6VZFjV14xZ8JtTsW+Kwo/JYB4Zohv7tlBu9VnMm5JJnyVGOAU7u9+WZQAO5NRrgBsyg4JsNTJGv6UWP4I7wx4ZAFB1130xBJxzwAiM01N0tycxZB7+3xa4AzKmjijEmftHFoXLdIysuFJAVtGdQBcgOnOkkTGFlpIZg+ZwRrzwEhEILJXaJJyr8FJ6WUu62NcbwuqdiSv6mmEeme6AbOhOUo5FpfhR18iwmi35rM9SigneqPBbl/2D2HzOzWLYRKhBJcYI7q9aMskKljGdTAUEHBoVVmJMqtTO39amXg4txfHIhNN0e++cra/rgouFd7MwFhhmo7BXQktZpF+za82mV0zOk3NbIVnJoj0Bw1gMZvnbDoaqvgA3ZAYF2aRfA2Gxr9odBA2pEPIkSOXag7iNag/QQ4cXorLQjrFDtBV1iztoqPDN1LR68aouDF582l4QosrpGx260xyjwyrCpT+sEtaSYTdhD7OWzGLfZOOziAKcukHRleDhb4SXciT4zaSODFSgo8MHQoD9RiSvAOu0SUbmT0OamUs1bV54gwqskoC9hhZgiNNqevgkn0QtOchcYgZXnlWEFMdrYUbpCqD+rpVo+r81Edk2JboosTWBJoAqFMF6T06LGgKm0E28lWtBuLR+tNCXEfbI9JIhkyxrSffI0IACtRc6NwPAurrO8LWUhbHAYG0KhkBASw4Nr56Eltp1Q6ZAn2/zVW1RkLOx+bLX7xhhwx5qZEp5aInTUwxvR4al5oV8S8QD0jq6EEQgUFRqVKI8qkqrKuq0SUbKbjydzNfbW0GpVjNm+qiSmPeNMaahj2Gk0skYIZUepllGeGRSnMO0dTI5SsFOp9ghkQQjW6EQAiaU56eV1WPUkknTkDHrYyRRwPgiBwTj4ZN8gjdnLmVrJGQi9A3WdIMGFYRqu9H85FrIesG5svzkHhn3F7vQ9OgP8HzTkNUYE5GsYFmDvmgYJUmYUB6/OOEE3ZBp6g7EXUj0BEqpqWFk7AOJ2EyNSXvJK7Nht9mQGcAemSyMhTb99yrRF4OFIAm9zNlA9ftCsGde/C8cNuyZR4aLfTk9giqmZmYZemQIIUbzSACwjdGylT7f2oxGRbs5ppj6ILHwUjydDKsmG8+IAbILf5WlMGRyUUcmpKho6PKHxb4pxheuJZMgc4mFlnJlyBidzZOPixl1hSBxO14DmlfMu6bJ8GAZtWTSLIrHDJkpQ7WH6ji9S7kskpQGqqU8DxAJqF+G0p7dA6Yrg9Rr2dS7SG72ofmJNQi1+FKGlvybtevY/2NbVmNMhKFjiGMobPJohuR4e+Ksk3ybZGQA5rrCLw0oxv0ZTyNDCOmxKDQZIUXFtt3d4czBnhgyRSaNTI7EvpRSo7KvmJVGRvtsUan2+xWBoCuHxqjq1722WWTyGWHDLMPzRtYST7/m9ISIbKAsynEzwS8AdJTY8PI3Nbj/vc3ojJMqOE5fUcQzZJI1KQRMoaVcemR0Q4YG1axd3g2dfqgUcLBJNJVHJlUtGd0jo3QEoCZI086EdNtPMJd3IUhCfYzn2wa0vbwZXR/sBABUFmbmkdmo91iaovdvGq1PXr40xNtEEmCp0I28LMNLzHhMpxgeK/LomDoEUpkDSkcAzU+swfCgdl3Hy1qilBrVh4M13TkLL1FKk3pkVrZo31mO5Odxkl7hd3OOM5dYWInYxYTXv5ijTsnx2NLYDRsLtRBkVAslGsnkkekOyDnpGE4DitG6pSehJZeeVi6BoLMjd96i/vLIUEqN+4iLfTk9wjBkCAAp88qKih4T94HiqBe/wU1vrMPG+i7DkDHrT8axzKWmSG9Dlz+ELboI8YAED9FsPDKpaskQqwjohlG2gt863RvhIEzsm3x8LLSUSCMj5FkMnUEuvDLhrKVUhkzYI5PImGSZaSyjaljWHhnNkBlu046zm6T3wO9pq4JMiuExj4xtVAGG/Ho/WIY6obpDmLy8HiMgxPXIKG1+UH11q3pCEQ1VewKNaE8QOeF3ekP4qlk7rw5/rDjZjJGCnWOdTDJ9DIMV8uuN0NIGk9CX2CUQIfN5jMFCS4UQYEPy8g3pwjy+xCJktVhkhkxxgQ1+/V5xt+fm2gJMHpk0BPDRiPnZG6hdfhkh/bou4RoZTk8wa08yLRGtqBSv72gGAKyBAiIQzBhVjOuOnYCLjtZqwLDYMICEmUtrajtAKVBV4jC8KDHjlBVjnOmSst8SIT0OL9W1aw/xcNZSKkNGmywSeWQAQMqh4DfdYodu/e1yUcCE8viNGYO7tN9N0ZvWDS1itWRST6qdvpBh9E3Rm31W6MZVu5peOmlPhdCZaGRYE0axyAYx34ohV+wHy/B8CEEVx8MS15CJNrBYpeuewrwxxC7G/I5fbW9FE6v4KtOk1/HECr1VQY49Msn0MYzeDC2tq+tEfg7CSoB+jvXrMlcp2IaYNouxBU1ZlyVOKzy699KXYWuQpOPrgUeGhZZoUIGaYX0bVkPGZZNy1pyztxi4XaA4AExZLRkWwwOAh/63Gc+1dMAv2DHtlPFYPWO4kdbqXdeCNtRFTKyslszONi+CsgqrbpT8sLMDQPKS+DSUfnsCRnptCixQOgJZq+536YaMlXm2U3g+zP2WEmEZ6kRgS3ufemQaFQUjAOzldECMs6JV3EHDw6B0arV5mEdmdxpF8X7UvTHDixwo1L0/pXoT0BZZBqU0pSFtGDKN2Z2XTBpGstAS00wIdgnOmRXoqHNjX4h43uuDqtKI7LpQXeS4gjXdyNu/PKuxmgnX+Yg1FFb81IIQAK+FIC9EoXQGEzb+Yx6ZrY3daZ3vdEnWnoDRm0Xx1udI6Avoi5tCK+Rmn1YULwf9lnLRZ0kgWljaJxFApgjmsAEn8yIKWWhkiFUzrmlIheoOQrClX1V5sNSQAbhHZsBjaCgytIjf39CA//tkG3wAJv5iCo45dFREbQ6jNonJkCl32ZBvk7SsprbwpL+qVhNIHpCkCFtveGS0cbLMpew8Mrs7fLAChjohlUGYSiMD5DZzKZyRlnhcLe4AltZp4tS91PjbMW8MoF0z1K+EPTId/pR6kGihLwC4qHbW2ihNK0OECaGVNn9WmiYWzktVDE8NKsbDx6wBY6039oYIqtKY35B5ZKy66D1QE9mOIVuY2z6ePob1VyJ6WEdJUqBwfHk+BKKJR3OZkcOME6EgsWCzp9ktiZAVFZvqu8Cuqp4aMkDYeB0CAQ1dPfd89Kiqr27IFOdZIQgEQV3/E8plQ0vdwCdZeGR6IuRmYuqyAV5DBuCGzIAnG+3JjhYPFr62BgAw/9AxOG3qsJhtzNViGYQQQ/D7k66TUVVqCH0PGJXaI4McamQAUx+YLENLLe6AEVYCACKl6ZFJIuQ1RK0NPa9HEu61FH9cQVnFlf/4Hsu9mlel2C0brnAzoV2R4QilK2BkLflCSlLDDAA2RQl9AUDQXdHdoIZnKxlivlV7mFNAbsw8vMQ0MqnaE7CwErGJEQ9GS6UTxCYiDwRjIRgrSkATLrJifX9q0IzC0G6P4fHsCUqCjKW6Dh92tHggCgSu8rBIPBF2i4jRpdp2W7Ko8KsGlbiey2R9lhjGwy7HnZu3NXvgD6ko0717OTFkWBdsEOxOU/+VjLBHJvuGkcW6hkTRDRmao6alVKXhOSILjwxgMlIz/G1buEeGkysyLYbnDcr49YvfozsgY+boYtx08uS42xmGjE+OaPQXnYK9vcWDTl8INknA5MqC2B2xcWbhkSkvYMXLAlASFJAS8nvmkenwhYzUa4ip2yekqiMD6KnGAgH1K4YeJVuM0FKc35dSitve2oBvq9sRsolQ9VT6wM5YT0K03kPpDMJuEY1CVjtbkxsWmxoihb6A1qQSADpBUduWnmHSE29VuhoZFlaSiiNXikQgsI7U1v77QozQyahdQaieEFQA7/i88FoIoNKcdOxWE2Qssb5Yew0tgJ11ok9xvUzQdTLZ9FxqeXY9Gh78HkpX5Hewv8WCxAaiEVrKYUgEANbXaQL0sS7NO5hLQ6YcgqGB6wlhj0wWDSP1BVZJnl5UUD8+4s1NHRkaVMAkVtloZIBwGYtMPTKtg6SqL8ANmQFPJn2WOn0hXP/Kamxu7MYQlw2PX3hA3L4ugKm6JoVRQwEwZS7phgyrH7PfiEJDM5N8nOlfUqVOGwTteZKw7ofYw6J4Hd4gbGm0J2Cko5EhkgBpiDYx90TwSylNWuzwH1/vxMvf1IAQ4LELpiF/XBEAIKhnJ5n3w0JLRqds/YG5r14B+NvqxHVTFJUaFWXNhgxbqXal6ZEBIr1VmZKuISMbQl97zHu2Udr494GINlMKdlD3xjRYgACAHfoiM5iD8JKSoKrvZt043GtogTHWZB4ZINysM1NDhqoUwZouUL8M36bI35r12RGTFDUzspY8QdAcVqVdv1u7Vqt0I4HkwJBhKdhDQAyBek/oiUeG9Vkq1u87QQ/Zi/7cVEhmGUsQiZHBmSnZhpbCNWS4R4bTQ9Q0NBSUUryzth6zH/4U/9vYCFEgePzCA1BeEDvRM4hIjEklMnOJ1ZLRHkRGWClFb59MWxQAgCgQo6R8b7Up6DR5ZNIRTLMaJskMGSBHOhlZNVZb0WP7clsLbn97IwCtQefRk8uNPlmBKENGaQ9oWieRwD5R+52YIXPw2FIAwNfbExsyO1o8CMgq8qwiRpWEG/qxlWpXNh6Z3gwttYczlqKx6obMvpAiQkusts2PVHswrJa17wrW9DxDyPDIuCLHzfpWTap0QSxiGpnkhszELGvJqN6QUQuFFf1jMI+MkCT9WnBaNSGZ2rMClNEwj0yFVX/Q58QjE275kBOPTA9aABip17pHRtKNWWswN40jzTVkshV/i1l6tcM1ZLhHhtNDwqGH+N6EXe1eXPb8d7j6nz+guTuAsUOc+OevDsKBY+JX4DUTT0hrpGA3uUEpxaoa1qSwKPk4s2hRAJgEvwl0Mj0R+1JK0eENhSuKpmFksYydRHVkGLloHqmaJjvz7+sOyLjmn6ugqBRn7j8MVxwxFkC44Wewzh0hpg3q+hhLpRNSqb7y10MEh+iGzDc7WhOG74zWBJWuiCwfNsF3gaK2vfdDS11pZi0xwWx0aAkArFUuqACGQYDX1KGceWTW6QbM1wHtesuFIaMkyFpixsjkShck5pFJEVoKZy65M9JfmeuEBH7qMBYWakA2EgaSaWSISMLevBwJflWVYsNu7doq1j3DuRT7lkNAY7cfQblnRoMRWspibIbYV/daWHRj0Z6jxpFhoW/25y17sS/XyHByRLKQzeraDhz38Gf4+McmWESC646dgP9edzgO0h9eqYgn+B1ZmgdR0Kpmbm/xGJNxSo9Mlh26U1f3zV6E6AkqkFVq8sikDi2Zs5aSPUhy4ZExhKYSiSgS9tmWZrR5ghhR7MCfztnPWImJxTatqJlCIzQxzJCxVrkM/QB7YO49rAD5NgldftkwWKKJLoTHMIeWatvSW/lKFXkA0QzPTFPmwwXxsg8tCXYJHU7td7Y0hsfMPDKboZ3zH6GAEu08RWtKMiWeRsYTkA1d0qRK0+/SFYzQpEUzutQJi0jgDshpV2QGIrONaFAxdFRGVV+bCCFFRV1miPW02zxjR6sH3qACu0WAQ7/Uc6mRKQCBjabfFDURPUm/Zh4ZppFx6GNz5syQ6ZnQF0ieWk8phS9BhmGLh2ctcXJEMgPh5ZU18IUU7DeiEP+97nD89riJsKXIyjHDYsLmsI1NEjFSDy/8+4c6UKrVFkkWpgLMLQoyE6QZmUspQkuqJ7lhEQ9W4yFf0M5dOkYW08iEFAp/KPFKjxkycrPPOPZMSZRa/+HGRgDASftURhSiIoTAyrwypvBSsFZPKx6RH2PISKKAmaM1I/Tr7a1xx7ExjiFDVWq4tTtBsbvDl9CjY0awihB1YWum+qGutMW+uiETxyMDAN1l2vcXtmnbKe4glE7tWtiqGzI+AH49RNETrwylNK5GhmlchrhsKM23adexSAAKKEnKDVglAWPLdMFvBhV+ox9SLLyUTg0ZhtGXJ0eCXxZWmjK0AGAhkhwYMoJdMtocDIGAXR09K0zZs/Rr7bPMI5OvlwNwUeSkBQbVi+2lMkKTkSxrafGbG7DP7e8bv5UZQyPDPTKcnpIsa4m1Dbj88LEYn6DaazLieWSAsE7mXz/sApA6rKSNs2cemabu+KsqFlqCSrVS8BnQoR9XiVXPJEjDI5NnFY2Cc8kyl8QimzaZqtRoCZAp4dTr8DmTFRUfb24CAMyeUhHzGdb4M1CtGR9UpQjVmT0ysdknYZ1MrCHT5Q/hq23a69NNXjfVJxv6nYBAIKsU9WkU1gMAS0Xm3ip/SDFCBMlCS1RRDQ+KFMcjAwCyHvar6Naul5CemeR2ijAfQUO+dj0EelDhl/qVcHsCk0aGiacn65oXIpCwkdkLOhkmhvfoP5p/s6aJMqr6JtHHMIyVe45qoPzUpBnYU4YW9Kh6bjzMKdg91ckoPRH7Mo+MPk8V6P2WbCCQcyD47UnDSIZhoHYHI4yrn5rc+MfKnVBUik/0OYcRlFVj/isd4A0jAW7IDHgSNRWklOInvUsuS9nMFMOQ8UQaCEwnw0rbpworAdCEq+iBRiZRmwJJMApBZSr4ZYLdIt2rkY6RRQgx1ZJJkrlEiBGrz1ZTEO+3/aGmAx3eEIryLJgep26PoZPZ2QWqqJCbvKBBFcQqQBqSB1FPaVe9sqGjOWScZsis3NEW41V5b30DArKK8eX5EcXwmHFLbCIq9ck53fBSNh3CWcYSIVpJ9EQoXUHNwBJJ3AJ0ACBWaccxLEBBQ4qhj6m3a7+/Xb8OtojauehJ5hJ76BObGOFZM4S+FeFzahiZKQyZ8awEQlP6tWQ6mzWvxMcIgRKtjo/cEQgXw0vDI5Prongs5DOs0N4Lhow21iEQepS5ZPY8ZhNaaosS+xYU2BDQjcmuFCUP0hpfD9oTMAxPoRK5GHx8+U9gds2mKO8fy8YShfB8OJDhhswAh3lkooWqDV1+dAdkiALBmDJnVvsW8+L3MWKGDCNZIbzwOHtHIwNkH7vvMAwZKaOxscylVEXkelp1OJ637cNNWljp6EnlkOKkzktD8iDkSaAhFcE6d1joO9wFIhC9F432OeaV2WtoAVw2Cd1xdDJvrq4DAJw1bXhEVkQ4k0PCiGLdkMlU8Bsnc0kNKuj+vC5G9Mr0MflWKUJwHI05YylR88HCCidaoEKCFnZj+piteloPE0B/G9T2FdrlTqpbSUY4tTnSUPhRT72ebArXpSv4HVeeuAt9Ipr1thA7oMJdot1T/i1taTWMZOS63xKruluRbzOM9pwZMvqcUNTDonhmz2M2Y2Pha9ZU0SqJRkPe7hw0jsyFRoZIgpGhyrSG25vdxr0PhFuUMFgPqxKnNen9OFDghswAJ5GBsEX3xowuzctIF2PGyFKINmTKw4aRVRKwV5QINO44s/XIpFPdl40zU0NG9yoU6GNKJ7QEpFdLBuh5argaxyPD9DHxwkqAXvRtNNPJdBmiX2uVZnxqvWji6GT0LDYWRgKAxi4/vtT/Pj2q+rNZAFmla6Z2ZZiCLTd6YmqSdH9Ug853tqPzfzsjX0+7hoyesRQn9ZpR4rJhna6FCezsNFoTrNINl6Mmaf2VVnZ4QewiaEjNWrTNPDJm7xClNCa0BITDIXIKjwzTyGxrTr9ytE9/aLZBRU2hdg79m9vTquprjC/HhkyTHgIcagufm56ESMywMFBhD2vJMIOdWMWM5y5/SIFH93oWm2qtuPWim54k7SjSHh/zVPZAIwPEFsV7fPk2qBSYqteZ2tHigd9U5Zr1sCodBDVkAG7IDHgSFcTbqsfPJ1Zkro1hsOJN0RoZNpECwL7DkxfCix1n7j0yYcFvZhNshy9zsS+QXnVfIDtPkaJS3P7WBrzxw66Yqr7bmt3Y3uKBRSQ4YmJZwn3YRjOdTKdRCM86wvTALIink9EMGbNO5q3Vu0EpMGNUsWGsMMwCyKpi7b3aNLUIUqkDkAhoUDWq8AIAVSg8P2ix+FBUJ+p0G0aGPTKJxeelTqthyHg2tUFp1cbwtVsb/xEThwAA2n0hCMO0az1Ym114if32osmQaeoOoN0bgkDCjVi1MaenkRlT5gQh2vWXqFCkGUopBP33agXFGv3ZE/ipI3y+0tLI6PdZd3aGeTSGR0YPFRKbmNCLlilscVPYQ41MT4S+bKEkCSQiHOqVtGMM5EA03ZOGkUBYcGwOG+5s9WCZ7o2584x9UJxngUrDmiYgXNV3SJIiigMJbsgMcBIZCFuZPqY8O30MYNbIRE5cxU6rYYknaxSZzjhTwW6Ubr8csSIwk21Bp059oskXM/PIFKTrkTE8RelPWF9ta8WSL6vxu9fXolGPobNxfaSHlQ4eW5r0gW4ujMfq2FirYlf+5hDGwUY9mbBOhk1mZ04bHvMdhkfGIaHK0Mik55EhIoFlCNPJhD8T+Knd0F+Emn0R3pruNBtGMiMgXg0Zht0iYquk7VvRM5JogRXtlMImCRhVkmf0oeos7lnmUtgjEzYUmD5mdJkzIusskSEjdwYiFhMOq4hhhdo5396S2lP0U5Mbhare4BMUqwJ+CE4LaEAx9D/paGRyGVrym/p7lQq567PEYHqWQhDs7vBDzbIacS76LBXlWSPCsgGL9t/BJIuztMfXg4aRt725HjPv/hD/+n5X2HvsDuHx5T9BUSmOmjQEU6uKMEn3GprDzq2DzCOTuytrgKMoCkKh3DZE6wuCRIbsIghaFIj+8Oq2tcuN4S4RU8od8Puzc2HKFhWyi0AhSsw+jhxfhJXbW3HY2MK09h+0qlBdBAHIoBmMxwqK0UUWhBQV9a1dRsfmiHG6BMguAp/fD2sG+1ZCQQx3iXA6CGQXgWxT0zqW4fkShrtEBAOBpNuH8gHZRRAIJd/OzM6mDgx3aZPSp1vqcbSLQHYS+P1+/LC9CcNdIk7eqyzp/miJBKVEC4nAorWbCNkpZP0zSomknS+P1zhf40psmFhqgycoY0NNC6ySgI5uD0YWSjhuYnHM9/n9fu2cFQgY4dLPR9Cf9nHSKgdktxeepk6QcVqoqX1dPWQXm/Ap3I2dsOjpqj6/D8NdIqoKLUm/w+fzaeMqEpJuJ5Vb4e0GrHoxRN9QC4ZTrXJxMBjA9BFOrFJD2J1PMdZF4GnpRl4W9xE7T0oBMcZT3diO4S4RB490RYxRdmrXiyqHrxfv2mZ0fVQDsciGIfP2MbadUZUPKEHUNHVg38pIb1k0329pwMEuATK0bJlAMAhh7woEN4W9byEHBVIcn2pV9d9HhbfLk7CRaTrUtXsx3CXCKgkQ5KC23xIx67kqmlAehewiKIGIISCob+vKqgKtz+OF7CIQiqWMx9bR7cFwl4jRpdbIzxaLkIkCNRTs8fEGoM3/IRvNeF/fbmuEjSh4+L31kAoLMdVF0NLeha+3NmC4S8S1R46C3+/HjBH5qG3uRG1zJ/x+zVvp1e/HkSnux54SDAYxatQoBIOx58pisUAU07sGCc1FsnsPuP3223HHHXdEvDZp0iT8+OOPaX2+q6sLhYWF6OzsREFBrJaDUoqGhgZ0dHTkYrh9jtIdBJVViPmWiPDS7g4fVApUFNgS9lNKBaU0vMItsmkpI+x7VYqQokasKJOOsyMASinEAitIhuNp6PRDVinKXba4YSw1oGgrVkmAkG+BkGap7lZ3AL6QiiGSCFFWITiktFy0nb4Quv0y8u0SipIo9mlIgeIOgUhCWhoEQMsG8OhdpfMB5INoNSLsEuo7/aAAKgttkITk51BxByPCjubQBjtf0a+z81HosEClFN1+GQ6LEPcBoHpDUAOKdr5solGcbXiRPa1S6apf69JNrCJEp0W71joDmrCSEIDSiGu62y+j0xdCnlU0hJNxj7srAKpQiPnWpN6/pm4/8mWA7Um2CGgJKcbxdniDcAcUFNgl5Onue7HAlrKpaMx43CHQkAIhz2LU+mjzBOENKihwSBHtFsz3m1hkA/Urxoob0DxpLPTS4Q3BHZDhsksps0bauwNw6QXYGkBBAAx1WiM8rWKRLa3fTe4IaL9NFufCTEBW0NwdhCQQlDssUD2Z3SepoCFVqw8EoBmJ545URF+nmeANKmjzBGGThIgQjKcrAJtCoYgEtoKehWa0Aopqyus9GlWlxj1LCOCk2lwTFIA2lcJuEYxCd56AjHZvCHZJQJl+HO2eIDxBBYUOKWW4tyeoqora2lpUVVVBiDPnFRUVobKyMuW1OyA8MnvvvTc+/PBD429Jyt2wmBFTXl6OvLy8rPtV9BehFi+oTCEV2yDocdigrCLocIOAYHxFftoP9mgopUZmiaXMkbHYzUyw0QNQwFLqyDi8JLRqQrNhRY64N42vOwjRE4IfFN02AcOLk69QGWKbB76ggpEWCWKIQnRZIKbhKm31BNDSHUCB3RLXQ8RQgzLktgAgEliHpDmmVg+cIQU2SUSeTFEEApInwSsRyE4/rJKYVhaa4g4a4mcx3xJRjE31y5A7AiAWAZbS8PgLPUE0d/vhtEkIhlQ4VBVDC+0ocMSeE7ndDzWgQHRZIORZoOgtK4aXOmFLw7hVAzLk9oARZlI8QSh5IRCJgEgiVL8cMe5mdwA2dwBFDgsqCuOfc0opQk1e7TpLcb1a2r2wBlQU6R4Zj02AEAihOM+K8gI72vRzkW+TUA4BNKAZI1IaWhIzoVYfaEiFVGQzjGTS4kG+HHs9m8dPLAJonl5IkQCgiLjH2z1BzRizSUmvd0opxCYPKikBBIBSFZRSVJU4gTa/sX9rRXqZjcFmL6BQSCU2CNbs5+EuXxCk0w+HVcRwmwVKdwiCTYRUnLywZrqoIQVyqx8qAAIl4XWcCtkdhOoOQXBIRjPKdGn3BCHF+Y062nzIC6oICYCzPL3znojw72HPyEPmDcqQnV5IgoDhRQ50dfhRpAI+UEhQUVWShzz99/UFZdS0eSEKAsboUgVLuxeegIyKAjuK8novvKQoCnw+H0aPHh3hfaGUwuv1oqlJ09QNHTo06X4GhCEjSRIqKytzvl9FUQwjprQ0vbL9Aw1RUkChQrLbjUku5A+BSFbtYehI/KBNa/82raCXZLX1yJUsiNpD1eKwZ2wQ2WwKAjQEQbLCbg9PJr6gjIauAJQAxUhJCxJ0CoDdnuZkKIZAJAUOiwUCVSHa7RDTWF3YZQLioyCSJel3qaIKWaIAIbCmMSZKKUIkACKJqBqSD2+LD3YK+CDATwEiWVHssqd1fCqRIPs1kaOU74jwNKmCAtlNASFyXEWChBafCp8CUEGAJBKUFbripleGJBVUUbRz5rDAbtc0TILFCnsa55BKKkLdmpfAYrNB7lJBJVNGlRyAIEiQ9PGJfgoiUdjstoTHTxUVoqh5MCxOR9JFic2mIqSEYNdlgJ0WAUQhyMtzwG63IZ+KaPGpUAQRzkKHVtQwRGCxJk7rjocgKICkQnI4INjEiN+4ID82o1CyqVqGHwVgAcRiO6hP8wqIghWiXXtouCCi2adCJkLS68EblCGJFtghgFhF2FQFAVmBYLPBlkdAgwqIJMCS5j0j2lTQoAJRsqV1rySiO0RAJBV2mxV2iwhFIhBsFuP37inUoiLUqYICEKAAkjX9ecGE7CdQJQLBboVkz8yQEYIAkVTY7JHfbbNT2FWtNEY2Y4r4DlEGCIXksENI0zsOAD4lCCLJcNgkFBU44bJYDdF7kUNASYEpM9VKQbpkqABEixUWUQAVNA9ansOR1v2eLYqieUPtdntMGMmhP9uamppQXl6eNMw0IMS+W7duxbBhwzB27Fj88pe/RE1NTcJtA4EAurq6Iv4lgmli8vLSWy0PRIzIn2nSZqXz7Rl6PuJhTNpZiuWAqFLcWTiHJN2F7derSdZ3+rCtyY2tTW50+0OsqS8kIK0y+Qy2LWEfSXNsrLKvkiLqarjeKY1JM46HrFIoqub6d1hE5OuGozuoGFk7qcSuxndbREAgACExHjBjXAqN+G0cFq1qMXulwGFJXCOCXXb6+1Y9XJh2gz6RaOODluJqVDHOkwxDl5paQLDfKlnNCqPWi0hSu5pFAi8oVEJArCJ8ihpxHDYpfDzEpqfeUhqTwZcMSqlx37BzHpA1j4hAiPFdkQPTXxMIpLI8iHkW4/ejJrE783oFZQo1yXXo9svh1ahAYNHHEZTVcBG1DEJExrXTg/kAAEL6+baIBJT9zOmMg1KgazfQsgVQk1TG1a8TAkAAQSjb3kb6uc3Gqc2u2egwMLtnSA9VGxHXV4YDDMjauWPXOQv32wVitKBhCAIxDG6WcCGzY+tBeDEXsGd3Kn1rvxsyBx10EJYsWYL33nsPf/vb37Bjxw4cfvjh6O6On0Vw7733orCw0PhXVVWV8jsGWzgpgjgP4YB+saXj4k+JftOl8yBOiPmjWZxrpvFpdQews9WD5u4APEHtwV6cZ8Uo3T0rIktDxhhaemMzDJlU30VMO09jXOx3s0oCBIHAwh70AFRKYREFONL8TYlAIA1xQBriiNUkCSQ8LlOhN0IInKZwQVGSSqbG9cAMGfbgV9IzZAghhsFiNC60SyCiEH5w6w99IHyuxSS/kVGrKA0NligQqABa7AKkModhgLHjsEoCCAhUShFSaERWR9qyQQrjQcjOE3sQ2C1i3OtNLLBCcFpgKc8zNDVMJ2Q27CSBQCQEFDSp8egOyJD0H5uIxDi+kKJCcFpA7FKETiol+oMr2wKBDFm/TiRRMD2MU3yIUqBzF+BuBIIe7V8CCAkbyiLChlPGsOs8i3lLZq0pooxvZgwKPVWfmj+fYdp6wLjexYgxJZqn2KLYH9LuScOQ6edieOnO2f1uyJx00kmYM2cO9ttvP5xwwgl499130dHRgddeey3u9jfddBM6OzuNf7W1tX084j6Gxlrkfv0itfdA08LIhUcGGXpkOjo60NbWZjww8nTPBIH2AChxWjGiOA+TKl2oKsmD1dw4Mc1mbKpqWslm6ZFJldJpnkzTMQTZ78ZWP9HDc9mljIxuwSLGDQdq49KNhagHklMPT1pEAflJWgEY14N+iVmlDD0yMKXi658xUlxFogt+AaqvpNlvFf1QiECJ9H4kg62SQ5RCpuFrgR0HIeGHfkDW9DEQCCCrRu2OVBjnloS7l6fylgpWTSdiDr+GPTJhw44QAquFjS/+OVdVCm9QMTwyRCQRnjMiCrCUOSBkUGLemA96aMiE9M9bRBK+X5P9tpQCXXWAt8X0WorfwWTIpGtgx/tabV+Zf1ZWdWMt6rgE/TcQEDlXUUpR0+ZN2Fcu8eCQsac7YMw1YQ+gQZy5iiV1+EMKVBr25KZKOhgoDLhRFhUVYeLEifjpp5/ivm+z2VBQUBDx7+cKpTTmIUwpNVb2iTKKlixZgqKiovS+JMmDmBCCZcuWpdxF9Y5q2KoKsGbj2pQPYibi8vv9kGXN65JvkzClsgB7DSvExAoXRhTnocSpaYDmzp2Ls846K2LSSscrw8JCBASfrvgMtqoCdHR2pvwcEPYKpPM9hN3oaUz8YU+a/hl9jIV5FlhEIWXNhk8++QSEkLQy8Iixso6c4IvzLJqIuTBx9lGESzvb0BKA8ftPxmNPP679IRCjjojZWwPdBW6ElpJ5ZNixpGHAs4eLrKjGmC2iELH/iPCSQDKvC6Q/yMwhE3+KezMupjCc2Stj1w3eQIL6St6grHny2DEJBBaTRyYRSecH/XdOxzBPNj+E1PA5h5rCkGHhJE+zvp1umiULLSF8bYoAQibvXjKqq6tBCMHq1asBAJ9+8WlGc4MZw4sYZViLUjh8a54XPEEFHd4gmroCCcdqHp/ZK5rJAofSsBePzTURi644c5XZkGGeJoGQlO0JjjrqKFx//fVpj623GHCGjNvtxrZt21KqlH/uzJ07F2edeVb4Bf1CDikUCqUgIFmlG8aQgxVY+KZMfbOZb2BmyBCiTcDJVuPswSwBSTUDDGOSEQgOmX4Qdn6/FYXFhSk/xz5z62+vwrXzL0w9OYqJDcFowp40/SGnf8TlsGDK0AI4epAlEo1ZJ2NGEgWMLnMmz0QwH0uWoaVoBEekt8nshQAAr8+He25eiDEjKpGfn49zzjkHjY2NEftg3pv0PDJhY5SNOfp+sUmRHg+WfksDitFwMxnsgWAWB/t1w8yeQdsQYtI5mQ2Z6PGZmTt3Ls4952wAgJWw0JIQNjiz/J1y4ZGhergO0Dwy0UZx1MYggoBl/3pV+7uwCrDpxR0z8MgolGYUdmYcMuNg7Px+a/oLPxOJwi+iQMBGbp4XfPo1pZpCN0kxptXI/SsqRW2bFztaPHHnwpCieSBJlE4rWXjJCC3JKkK9pI+hlGLx4sUYOnQoHA4HZs+eja1bt+Zk3/1uyCxcuBCffvopqqur8eWXX+Kss86CKIq44IIL+ntoA4BY1yITcVklIenqNV1yIu7LIHSjquEJNqMChRl6ZGSTIWO1WFFZXhG3TkHcrzJNTKkmnExCc4FQ5CrJcB33RhiarayzeCDFWwmyCVFRqaF/SInpuISoGh1mnQwA3Lv4Jnz64Xt46eVX8Omnn2L37t04++yzI/enZKCR0a9rWTWtTqM+Z40yFIgkGF6jtKpIRwl9FdN3ZSrEz9SQYd8HaPcEAC1LTT/GkEzT8lDEYBjm4e+klELpCkDRhdCUUrj1Sszxwq+KagpNiEJY7BvPkAmYtJCFIwBnGUD0I1KTX2eGt9BY5GVuvFklCyrLK7JqnaAkMmSIyZBRYg0ZIE3PpmEAhl8KKSq2t7jR7g2i2x+K2CfDeEaIQqQnJ8mcYNW9lZRSeAOyfly5NQ/uv/9+PPbYY3jiiSewcuVKOJ1OnHzyyQgEel4Bud8NmV27duGCCy7ApEmTcN5556G0tBRff/01hgwZ0t9DGziQ8APFH1LxwlOP44xjDobT6URVVRWuuuoquN2xnXKXLVuGCRMmwG6344QTTojRE7355puYedhBKBg/BBMP2At33HGH4SXJCP2+2PzTFsyaNQt2ux377LMPPv30U2MTRVFw2WWXYcKECRg3bhwOP/xwPP744xG7URQF/+///T8UFRWhtLQUixYtCmsG9Mnigjmn4rfXXYfrr78excXFqKiowN///nd4PB7MmzcPLpcL48ePx3v//S8A7d799EsWWuoAEHatv//++5gyZQry8/Nx4oknor6+HgBw5x134K3XX8by/70Lq6SJNj/55BMAwLp163DMMcfA4XCgtLQUv/5/V8PtCXdPnjt3Ls4880w8+OCDGDp0KEpLS3H11VfD5w8YMXV7lEbGvOIKBAK48cYbUVVVBZvNhvHjx+OZZ56JOE/ff/89ZsyYgby8PMyaNQubN2823tu2bRvOOOMMDJ84EiWThuLgo2dF1GgCgNGjR+Oee+7B/Pnz4XK5MHLkSDz11FPG+9XbtVDhsv++haOPPhp5eXmYNm1/rF/1HYDwav+LL77A4YcfDofDgaqqKvzmN7+BxxMp0BQcklboLTqzypS51NHRgX+98iIWLr4bxxxzLKZPn47nnnsOX375Jb7++mvjM8YELKWvkVEpNcI9sR4ZPXQjhx8GrNWA6gvFhOWiMcYTJfS1iELczuXJCAt+YzOXArISY5RQkwdCoGGDShIJXvz74zh79iHIz8/PeH4we/Jee/0NHHDAAXA4HBi/72TccevtaGj3YEuj22id0OWPNfhCpge8QAiC/gCuu+UGDB81Ana7HaNGjcK9994LABg9aW8AwFmX3QCSX47Ro0cDgoBt1bU44/xLUVFRgfz8fMycOTPmOh6//yTc95cHccMNV+OQyVWYOG5sxHUMAN988w2mTZsGu92OGTNmYNWqVRHvf8LCzl1aaCnV3ABonuRrr70Ws/YaiSP2HYub/3ATLr30Upx55pkAmEdG136ZriFvSJtbVVXFgw/cj/Hjx8Nms2HkyJG4++67I8a1fft2HHPCbBRNqMD0Yw7GV199BX9IwbYmN+obm3Hj1Zdh9oy9MKS4APvuuy9efvll47MBWcVlc07FvbcuwqJFi1BSUoLKykr88QH9O0yZUE8//TTOOussOJ1OnHr4dHzyv3fhNgwZgvXr1+Okk05Cfn4+KioqcPHFF6OlpQWZQinFI488gltuuQVnnHEG9ttvP7zwwgvYvXt3xHMiW/rdkHnllVewe/duBAIB7Nq1C6+88grGjRvXK99FKYU3KPfLv2xWR/EiNoGQAkEQcPf9D2PDhg14/vnn8fHHH2PRokURn/V6vbj77rvxwgsvYMWKFejo6MD5559vvP/555/jkksuwbXXXIvVH32Dxx94DEuWLIm5odIcKQDgprtuwQ033IBVq1bhkEMOwWmnnYbWVq1MuqqqGDFiBP75z39i+fLl+O1vf4s//vGPEaLuhx56CEuWLMGzzz6LL774Am1tbfj3v/+tvalPsATAP196EWVlZfjmm29w7bXX4sorr8ScOXMwa9Ys/PDDDzj++ONxxWXz4PN5IZlXJKb/9nq9ePDBB/Hiiy/is88+Q01NDRYuXAhA8xKedtY5OPSoY/Hdxm2or6/HrFmz4PF4cMIJJ6C4uBjffvstli5dio8//RjX37IwwiOzfPlybNu2DcuXL8fzzz+PJUuW4OlnnwMQzljSTlusR+aSSy7Byy+/jMceewybNm3Ck08+ifz8yH5aN998Mx566CF89913kCQJ8+fPN95zu904+eST8b933sPK9z7H8Ucfh9NOOy2mpMFDDz1kTOxXXXUVrrzyyrBBpI9r8X13YuHChVi9ejUmTpyIRVdfBlmWEZJVbNu2DSeeeCLOOeccrF27Fq+++iq++OILXHPNNRHfI9gkiC5rTIzf7JH59rvvIYdCOOiwo4zw4uTJkzFy5Eh89dVX2naqSbeThpGgZaZr+/IGExgyFua9UA0XPbEK8AtB+GQfPJ1d8Ia8Cf/5gl74FB/81A9vyIt2nxt+xQeQYNLPxd0X0XQTZsFvhBcsyvPB/naIQtgjqqelW0QRN955H775YU3m84N+/r9Y+SUumz8Pl1+8QJsf7n0ELyx9CX+8664Iwy9eCMucsUQpxV+f+Rve+eC/ePXlV7B582a89NJLmsEC4NuP/wMAeO7/HkZ9fT2+/fZbgIhwe3w4+bij8NFHH2HVqlU48cQTY69jQvDIU3/BAVMPwKv//RRzf7Ug4jp2u9049dRTsddee+H777/H7bffbtzjBnHm2GRzAwDcd999+Oc//4k7HnwcLyx7H91dXRFaIWLyyKi6sWvWaj36pzvwyEMP4tZbb8XGjRvxz3/+ExUVkd3ub/z9H3DVr3+Db97/AhPGTcD551+AzfUd2vmWg5g+fTr++vyreP/zb7BgwQJcfPHF+Oabb7TfRP+eN177J5xOJ1auXIn7778fdz1wDz787OMIA/2OO+7Aeeedh7Vr1+LY447HTb+5ArsbNUPF4+7EMcccg2nTpuG7777De++9h8bGRpx33nkxv3kqduzYgYaGBsyePdt4rbCwEAceeCDWrl2b8f5ioIOczs5OCoB2dnbGvOfz+ejGjRupz+ejlFLqCYToqBv/0y//PIFQRsd16aWX0tNPO50GartoYHe38frWxm66pradtnsCxmtLly6lpaWlxt/PPfccBUC//vpr47VNmzZRAHTlypWUUkqPPfZYes8991AlIBvf8eKLL9KhQ4canwFA//3vf6cc67Yft1IA9O6b7zReC4VCdMSIEfS+++6L2Nbv99O6ujpaV1dH586dS88++2zjvaFDh9L7778/Zh9nnHEGlTv9NFDbRWcdfBg9+JBZxjayLFOn00kvvvhi47X6+noKgL745v9obYuH/u+1dygA2tbWFnF+fvrpJ+Mzjz/+OK2oqDD+vvCii+nRx59MN9R1UFVVKaWUPvXUU7S4uJi63W5ju7f+9SYVBIHWbthBKdV+t1GjRlFZlo1t5syZQ888Zw5dU9tOdzSHPxvY1U0DtV1UDSqUUko3b95MAdAPPvgg7nlevnw5BUA//PBD47V33tGOjV3jDMXPflc33Xvvvelf/vIX471Ro0bRiy66yPhbVVVaXl5O//a3v1FKKf1pwxYKgD755/8zttmwYQMFQJctX0kbu3z0sssuowsWLIj4zs8//5wKgmCMZdSoUfTPf/5z3GNRVdU4/ueXvEAtVitdW9tunGtKKZ05cyZdtGiRdjxB/XjquuPuLx4bd3fSNbXtxr/oe1BVVbpuVwddU9tOfUHt9/IEPXSfJfv0y7+O6kbteggpxhg31WvH0O2PHPu55/+SHn38ybS+xRNzXrY1aXNEm1ubIzKZH776+mvqre2iRx92FL39xttooLaLemu7qK+2iz736FO0vKKStnT7aYcnQAHQvz77z5jz3uoO0DW17XR7s5uqskKvmncFPerQI6miKDHb0tZt2jzzzyXh17qbKK37gdLW7drvpCg0sKuO7j1lSuR1PHIUvfDsX9Dueu1469o8Edfxk08+SUtLSyPujb/97W8UAF21ahVVVTU8N7S0RpybZHNDRUUFvefe++ia2na6cXcnlWWZjhw5kp5xxhnGNk27urRz1+qllFLa5QvSNbXt9MtNNdRqs9H7Hnk89lxQSrf8pJ2P2x54jNbWdtJAbRf94qOVxr23tbGbhmSFdvtCxvdTSukpp5xCb7jhBkoppdub3XTGwYdGzJOUUjrjgBl04VW/paEWbUwA6C233GK8X93QSgHQ/3thKV1T204X3XwbPf744yP2UVtbSwHQzZs3U0opPfLII+l1110X91jMrFixggKgu3fvjnj93HPPpbNnz46YL81EP8MTMSAq++4JqCEFyFLMyVaWVM9Y+vrzT3Dd3x/Dls2b0dXVBVmW4ff74fV6jQJCkiRh5syZxj4mT56MoqIibNq0CQceeCDWrFmDFStWaB4YfVWiqErMftJCX0EePPMg4yVJkjBjxgxs2rTJeO3xxx/H008/jZqaGvj9foRCIUydOhUA0NnZifr6ehx0UOw+KKURBbCm7L2vsY0oiigtLcW++4ZfY6ubtpbmiCiE2SuQl5cX4fkbOnSoUQ4b0MIDhGgrX29QgdMmYdOmTZg6dSqcznBVzMNmHQpVVbFlyxaM2Gs0AK3lhrkK5dChQ/HdqjUAwl4ASqmpBon2f6tXr4YoijjyyCPjnmbGfvvtF7FvQKt+OXLkSLjdbtx+++145513UL+7HrIsw+f3xXhkzPsghKCysjJ8/Ppqf999wk0M2fe0tTQjJKtYs2YN1q5di5deesnYhlIKVVWxY8cOTJkyJekxsMwlGlKMEI2QLDsjA6EvQxIIzAk/0QXqCCGwSQJ8IQVBOf2+Yr0F8zTRkGqE3mySiKCsIhBSjHR5s4C5wCYCPiVC47Hyi0/wyEMPoGb7Vri7uzOaH1avW4+9h++FdRvX4atvv8YDf3kQrIWCoijwB/xwCAokq1axVlY0b5ZZr2cUwxMIoAKXzPklTr7wDEyePBknnngiTj31VBx//PHaxooemhJMc6MgwO3x4va7H8c7H69AfX095GAQvkAg5jreZ8o+EPXbKKTSiOt406ZN2G+//SKq6x5yyCHhD5udXGnODZ2dnWhsbMS0GTMAaGEkURQxffr0CP0fFQAoMO4lpmWp/mkrgoEADpx1BOLBvClT9toHDkkAZIoh5dp8Fuhux9gyJyhV8dD99+Af/3wFTQ31UOQQAoGA8duyLLd9Tfe4dhyVaG5pjhAgm+eB0qIC5LtcaGvVPDKbNqzD8uXLYzzCgBbCnjhxYtxj6A/2KEPGYRGx8c4T+vQ7FW8ISnsAVo8MODNsIBbl9gwpFDU1O3HtvPNx5a9/jXvvuQclJSX44osvcNlllyEYDKZtgLjdbtxxxx0468yzEGrU4t2WCidIFmW100laeuWVV7Bw4ULce++92GuvveB0OvG3v/0tbbei+QEmRvXiIoTAYrFE/A1oQsREmVDm7dlnaFT4j+ksuv2yUX8lBiaOpOFJLN6+WSnu6JL1+gYAwiW5UxH/WLXvX7hwIT744AM88MADGO0aBrvdjguvnYtgMJhwH2w/bB9sorPYrBHvA5rmJCCrcLvduOKKK/Cb3/wmZnwjR45M6ziIRTNkykvLEQoG4enqBIaFM8saGxuN1iWGOzwD7Yn5txcFEle3YpNE+EKKHi6xwCE58PX5X0Nu0O4JqTIvnGIfRajBC6gqxLI8CFYBPzZ0Q1ZVjBuSn3ZhQzOWToD6FU3w62DjE9CNSMFvpy8IUC091iYIWhhDvw6rq6sx74JzMeei+fjDrXdgyphhGc0P/pCKECjcHg9uXXgzzv3lHAgWUeu95ZchurRy/MbiCogxApkhI0laaGnavvtj88oN+GjVZ/jwww9x3nnnYfbs2Xj99ddNhozpfBERC+/8Mz74/Bs8+OdHMbqsDJbubvzyhhtir2NJMqadkKJGXMcpSVCnJZ25IZHQ19g10aw/ZqT7dOOipFBbBIUSjDGoh+3yHXYU2iWo7hBKXNp8XO6yQRAI/vSnB/CXxx7DjXfci9ETJmP8sDLc+vvfIRgMQjUZuXZbZGYiEQSoqtaCgonszcdql4SI8+f1uHHaaafhvvvuixlnplnF7D5ubGyM+GxjYyOGDRuW0b7isUcZMoQQo1FWXyF7ZKgWMct0Rqah0G6WgKxg07rVoKqKhx9+2MjCiVc8UJZlfPfddzjwwAMBAJs3b0ZHR4exUj7ggAOwefNmTJg4AUGnG6AUlkpndo0j9WGu/P5bHHP68cb3f//994ZmYsWKFZg1axYuv/xydOo1G3bu3GnsorCwEEOHDsXKlStxxBFHROzjgAMOMM4BQeT8k4pklWKTYbVaQfTmCN3+ECoL7ZgyZQqWLFkCj8djeGW+/PpLCIKAiWMmJNVBsUWQkc2ixk6i++67L1RVxaeffhoRS86EFStWYO7cuTj77LMRrHfD3dWN6urqzHZiXHbxz11IUXHAAQdg48aNGD9+fFbjBMI6mWn7TIVksWDlis9w4OSLAGjXa01NjbGCNlKd0xD6MsyGS9x2AYBWdM5nylwiBE6bE0ErAEWFBDsES+ycQSmFJKgAobDY8qAKgESCkESgyO6EmEXGh2ILQvErkYLfOJlL7XpXa4tEYooEfv/991BVFTcsvgsFDivGDsnPaH4YNno8WkGx/37746e67Zg4ZZK2fUcAqjsIId8ak/3nDykRhgyrQ6J5ZLT/LiwswC9+8Qv84he/wLnnnosTTzwRba2tKFFDsFgkKBEpbiJWfLcGc88/E2eddRZCDQ3oqKnBzrq6uO1QiH5qglFtCqZMmYIXX3wRfr/fWJyZxeMRSaFpzhOFhYWoqKjA9999h6q9pmvCXkXBDz/8gP333990DLobSx8v02ntv/cU2O0OrPjsExw7c9+YOi1Bdu4kzZsVb2wrVqzAGWecgQsu/CXaPEGUOi3YsmUL9tprr7BmicSuK4lANIOXwmg4a0a7X8KfmjZtGt5+cxlGjx7d40bOY8aMQWVlJT766CPjPHV1deGbb77B4sWLe7RvYACIfX/u0IA+KSmZp0N2dnVhzYa1WL1+DVavXo1vv1+FouJShEIh/OUvf8H27dvx4osv4oknnoj5rMViwbXXXouVK1fi+++/x9y5c3HwwQcbE9fixYvxwgsv4I477sDGrZuwaetmvPzyK7jllluyOEjtuJ549kn8+9//xo8//oirr74a7e3thhB1woQJ+O677/DBBx9g27ZtuP/++7FmzZqIc3LdddfhT3/6E5YtW4Yff/wRV111Vbj4mym0RJH+eczguRfB6NGjsWnDelRv24rdjU3w+gL45S9/CbvdjksvvRTr16/H8uXL8Zvrr8Mvzz4fFWXlSDQsc6VMW1QNGZhEqaNHj8all16K+fPnY9myZdixYwc++eSThFWu4zFhwgS88cYbWL16Ndb+uB6XXHNZ+ivU8IC1/0+w2gwqFIsWLcKXX36Ja665BqtXr8bWrVvx5ptvxoh9k8GM5qI8F876xUX40+1/wPLly/H9999j3rx5OOSQQ3DwwQdrG2fQnoBhXi0nqrmUKMU5Xjp0BBSm0CAxQgKSIGRlxCT6TnPmEqCFDVj7Dp/bjVWrV2tzxIZ1WL16NcrKyhAKhfDyc09h2/ZtGc0PBx50ECbvOw1+Atx6x2148R8v4o477sCGDRvw408/4rU3X8fiP8Y+dKLPXbjPkgCqUjzy1F/xyrKl+PHHH7FlyxYsXboUlZWVKNIbF44eMQwfffwJGhoa0N7eDhABE8ZU4Y13PsDq1auxZvVqzL3xxoTXMaFa/zI56v0LL7wQhBBcfvnl2LhxI9599108+OCDxvvZtmW59tpr8chDD2D5++9i57atuO6669De3h5pcLD5StXOBzsnZUUuzL/qOvz57tvw3JLnsW3bNnz99ddGZqLRSkMUYkLPjAkTJuCDDz7A+h++wfatm/H/fnO1UXOJXScCiR+mZdeY6kldXuDKq65GW1sbLrjgAnz77bfYtm0b3n//fcybN8/wMKcLIQTXX3897rrrLrz11ltYt24dLrnkEgwbNixlKD0duCHTi1BFNVx4ADKu1fLpZ5/iwBMPw4HHzsK0adNw7GEH4T9vvIo77rkP9913H/bZZx+89NJLRiqjmby8PNx444248MILceihhyI/Px+vvvqq8f4JJ5yA//znP/jf//6HWScfiSPOOBaPPvYIRo0alcWBav939+I/4k9/+hOmTp2KL774Am+99RbKysoAAFdccQXOPvtszJ07F6eddho6Oztx6aWXRhgyN9xwAy6++GJceumlOOSQQ+ByubSqvogsppXJaRSyLNJy+eWXY9KkSbjwlGNw1NTx+OCTT5GXl4f3338fbW1tmDlzJs4991wce+yxeOTuh7TTkMDrxtzQVjFc9M847qjJ5m9/+xvOPfdcXHXVVZg8eTIuv/zymJTmZDz88MMoLi7GrFmzcPbFc3Dckcdi2tRpGR27McHHOXVEH/uUvffFp59+ii1btuDwww/HtGnTsHjx4ozcxGxSJQrFotvuwTHHaVlQRxxxBCorK/HGG2+Ex2RqGJkumRgy0XU9UhoyUe0Jwr1tsp9SI3pQqczwDY9PVSnavFpoxSIK+PTTTzDziINw4ImHYeYRB2HatGl48cUXcf8DD+G5/3sUpx91SEbzwxPPvggAcFpFnHzSicb8MHPmTBx67BF47OnHMXJYbNjQH1V5OLoYnis/Hw/99c+YMWMGZs6cierqarz77rsQ9IJ3D93+O3zw4YeoqqrCtGnTACLi4dtuQHGhC7NmzcJZ8+dj9qxZ2H/KlITuWJadaH47Pz8fb7/9NtatW4dp06bh5ptvjgyTZNnU8cYbb8QZ58zBLb/9Nc444Rjk5+fjhBNOiAjJMw8ZodTQx9gkrWnrtQt/j0sWXI0777gdU6ZMwS9+8QtDgxNUwhl20f3OGLfccgsOOOAAzDnzNFx23mkoKh1ipH6z6zBRjTEial3SEx27+VMjRwzHihUroCgKjj/+eOy77764/vrrUVRUlHZNLjOLFi3CtddeiwULFmDmzJlwu9145513YLNlKLmIN26aqZtggNHV1YXCwkJ0dnbGtCvw+/3YsWMHxowZ0+N26tmgeENQ2sJ9NaTyvLi9cRJ+3h2E0hGA4JAglTrwU5Mb3qCMkSV5ySuzZkio2QsaUCAW243qppmgdAWgdAUhOC2QipOf587OTng8HuTl5cHr9QLQ4qepbgyqUoR2a7Uwaq3AuHJX0u1/rO9CUFExodABsTMIYhVhKc+8C3pjlx+NXX4U2C0YXeaMu02wwQPIKqQhDghxtDStngDq2n1w2S0Yo+9DDSqQm7yASGAdGiumywVyhx+qOwTBZYVUmP5kEWr0gIZUSGUOCPbI49nc0I2ArGBsWT7y7T1zN1NKEdrtASjFdqhw5llQVRL7G1FKEar3ACrN6B5i5x0ARhQ7UBJHo6aoKjbs7gIA7D2sMNwwVL93iU2EZUjsmNSADLnZB0gCrJVO4zopzrPGPYZ0iHeclFJsrO+ColJMKHehutWDkKJiVEkeCvOsce9dSinW7+4CpRSTKwvSNq62N7vhDsgYVuhAmSvyXBnXq0BgHaZdr13+EKpbPLBLIiZWavejSinW12mh4ylDCyB4ZChdAQh5FkglUXODrwNo3wFY8oAhk8KvKyGgcb3230P3h//HzaCK5oWyjR8PwTSXB3e7AZWiTgK6ZQVjypxw2dObw1S/DLnFB2IRYKmIf28nYmerB52+EIYVOVCSZ8GUKVNw3nnn4Y9//CMAoKXTj4JuzevR7rKgsTt8bVS3eNDl1z5blh8+z6pKsX53+Nyh1Q8aVCCVOoxCjWYopdiwuwsqpZhY4YLdIqK2zYt2bxAVBXZUFMSfi1VfCHKrHyAElqHOiEVihzeImjYvCIB9hhf2esNlRVGwatUqTJs2LSJBgpHuM5x7ZHoRI6zEyFQnY4QfNLFZVn1c0qCn1X0z6FBgeCJEUTSMl3hF+BRKETC7ik37pmlESoxiYXE+nwkF+sPaHZATtkZIVd3XqOhrfqDEaQaaa4wwTIbXXaKVINDzVgUqpWjzBCHrwkwjOwdI3NfFXEMmA4+HuTJpIo2MKIS9ZObKsOaCffHWetHtCYzqwT3xyMRpVaBlVmn3e6s7gJCiQhIIXKwRZFR1YfYZi/53ur+Toqrw6POVK46Bap4j2PVh9IIy1eFh+hgCAklI0TCSCX3FKMPDVMqWKrJhxGh/K1Gbavu1CZkdL2D2PGZ2D+7cuRMvPf8sqrf/hM0bN+DKK6/Ejh07cOGFF4bHJYb7LQV0j4xDN8CNDuVRXsCAPnZR0M5ddOPWaAghxj69ergxpllkvM/ZJe06oxRqVF8xJlK3SEKvGzG5hBsyvYhhyDC3Z4aTfzj8oLuWWf+MXPRYMpOig/M999yD/Pz8uP9OOukkJOoJEg8W5yaEGAKyaEOGUopt3gB+dPvRHAyB6sdt7D+FwaVSajSNNEJLWd6UdosIiyhApRSeQIKqx0masQFh1/uqb740zltBaRFKJg1F8bgK47Wck6BxZDJonIaRZqxi5IM7HV566SXjGF0uF0aUl6CwoAD5+fnY/0gtjdUKklCYbYR3JCGjUvLphJYAvbEh4hsyZiMq3phIlMamp/dm3Aq/+j7b9bBSUZ7VCB0kCrmFWxWk9zu5/TIoKGySaOhyIhBIzDxmEbXKvX//y4MocLmQn5+PkqICHDxpBA6aNBwulwunnnO6dlzxTovKDJko77LZkAlGla+P1mbov7GFGaNyBkY72zTD9gSCIOD1V17CL089BifNPgrr1q3Dhx9+iClTpqCmpgb5+fkYVVmK0klDUTJpKKaMrsDBk0ZgdGUpampqEi4GjKayejXxdAytPMOQ0T6bjkFNCIHg0s654g5FzPs2i4jRpU6MytCr+Pnnnyd8RvTK3BbFHpW11JeY9TGCXYTqkzMXl5luNCbwy7OIOemxZCZVo7hf//rXCas5OhyOsEsmA4+MIAiQJAnBYDDGkPGpKnz6Tb7bH0JApRhus2hmt4K4K2Qz5v4veu5AtnYMCCFw2SW0eYLo9stx3dZE0FdfiTwy+nVw0IEHGl13VX8IcntAC3mVppd2nfHYEzSOTApF0gk+G4/M6aefjoMOOkgzUJvdxqo93yahKk87dhv02hvxhiRHGg3pwpreaR6K5IaMP6QY2g5A+02JJGh6lZAaIzJmixRi0x4kufDIAAkEv8w7pP9dnBcOISUyOjNtHtnl1+7BeN4YQPcWiURr3KlQwKK9ZrcImHPRfMy76AK4HFZ0+4Ooa/fDYRUxqtQJC4usx/XI6N4AIdojQwAiAlSJMWSiPTKGIZNNvyVKQRCCKNcDLQAg6N9NtHBXfkXciWNI5TAseeM9EBBMGeqKyI4bNmwYVq9eDU8gBNoWgBVAPSj8ACZU5GPYsGHwhrTfLFokHeNNYdNqEkPLbMjIimoInq0pmpYKDgmqfn2rnhBEV9iYLHBkLi+YMWOGMbf1B9yQ6SVUNtFZBG1y8sEo6pX+TsIGAnP7Jqxn0hNSeGRKSkpQUlKS8OOyrgNKxxVJTSEVVsMgunlkB+uNIxAEVYrWoIyAqmK4QCAoFALVjJVEoQhzw0hjix4Yfy67BW2eILr8IQyl9tjjTNIBW1HDGQvFBU6UFWnpyqk0GLnAKLCmZ8yl5So2C33jbG48IDPwyLhcLrhcLq3JncMDkWiGn0opihxWwKfABoJgopoczJBRugHVGllzJAk2SURFgV0vbpj42FkYJvohSCxhQwam8DxVqeExEWwiZDX9B0gqzIYM+81spj5VDosY7pJuNlCjzp0lQfgiHpRSdOuGTEEy3ZMkALIa4Xm0SSIKi4tRUTAUFQV2tLgDEIt8KHRYMKrUaWh4MgotAdpvrCigUXVjoj0ybBHBqslkcl1CBQTSCUH1AFFfA38nYC8CLLG6jE5fCAIoXFbE1CaSJAnjx4+HLygj0ORFHgjyoCJoETCxQtMRWXWRc0hWI+5Lw5CxCJEFM0NuoLkaKBgOOEsjvi9PLw0QCCkRvb4S1c9iMK+M0u6H4tb0jdk0zmQ4HI4elWLoKTy01EuEV2xSuOtolh4ZQojRyMtp64Xqoxl0cI5LBnrxVKElSik69BTCYTYLRjusIARwyyqq7QQhQe+AneQ7FZMhQ1PEmdMh3yaBEC3FNl4n4mQeLX8onIoakZabZXw+I8w9ndL8beN1vjZjlTI3ZBis/klRnhUjijVPTJNPe82KxDV/jDCO3AUEYpsfJqOiwI4SZ3JhfLzQkvZG/MwlGlS0e1PU6nIYqddpPEBSQcx1hpSolH0AxaZjMX4rMfa3ysRz5gspkFUVIiHIS7JQInFClawuEguLmFOvtePQPxvvt01myBB27iMXOTEeGX1MbNSZeGQopSDQPT7OIUDRKKBwZDjUpcTvytzlDaKI+GGRvTEF+oxhCeF+SyLCnhPA1EPL1PgTiAwtRdyv3maAKkDnLkCOHJNFErQ0dwAdenfydL2CQp7+bFIoVH8WzYIHENyQ6SWYISPYRJOLP8PJX39YK5RqVSvROwX9iPmBlwXZiH3NhoyiKIaB41FUhFQKgQAuSUShRcL4PBskgSBAgHq7oEWYkjyYDUOGkIz0O4kQBQKnPhF1x7vhk3hkEorvDLdx1sNKCRFISm9bDEn0MUB4pS+rakQILxWyoqJT75Rc4rSgKM+KcpcdIVCo0C4dKcHuDI8MgmFdRQ4Je2QiB2AIfqOMNtV8b5OwIWPLsON1POIJfq2SoNenISgyu/0NwXHs92YSWmLXdL5dShq2NuYxk2eZGVl+/RywsCEL6yUU+1Jq0sjEM2SYVkjbxjjGBGJfdgaCipp0bogeg2HIOEqAvBLN42HRQ71yrCHjDykQFD8Eos/NCeqpiAIBmylEkIhKz4IQDnWy34fqFbMBfa4w5i0KEujW/1CBjtqYeZoZSZ2+zAwZQggEa3YJAQMNbsj0AmZ9DLEKKcWgCfejb84ucIdV7PGKLy49s2NM+0k/tCQIQtzMpXZ9VVIkhbVAeaKIMQ5tleSTCASQ9AwZgWSk30kG08bEE/wmy1oKJMo0S1BHJtekyqiKJpXAUDJl+WSik+nwaaJtu0U0zkVFgQ2FDgsC+qwtxrk/qKKGH9gI9ZIhE98jEx3mMcYUpY9JJ1MkE5jgV27zQW71AX4F48udmFCeHxHKMHtkojGOSY4sxNnhDaKm1YumLj+6/SGEFBVduoGZMm2ZGXbxPDJ6mCTWI5PAI0qVcPqhEMdjJrBzr91vRE+9TaSRITT8ndF1bRKihECICgoCSKYQkqinRCux3pYOtxcOEp4DEhXpE0wdsCVEemSA2BCtrFAtmQMkooaMQHwAqN6LigDBbsDXFrEvlrnEvNQZhTeTLMIGE1wj0wtE6GNEAYZ/Vc1AqwAYDzu22slPEFZS9VRlr0LhV1VYBYIyi5T+96SZDZRqnOkYC+bQEgBD8BsKhSBZLOjUDZqiqLLwDkHLP1KhiULTMWQkIeyR6WkqIZuI4k6SCQxVWVHRrRs+0Q+5TLxYPYLtP92fNkVVX0CbhH1qZo0W2z3aQ6HEaTV+C0IIRhTnoSPoBhRAimfIGKt/GYSogJJ7F3hCQ0YSYPTEUCggkQh9DLFGGjJWS24MGSHfojXTDKlQfTJUnwwIRBNkukznW2HVjuMZMsToERRSKKwSQadeIwSAptmLIpHQlxEOLYV/J4soQCDE6MFlFMNjqdeJPHwsrEREw2iJ+jLtu1ilWrsDqtebMP0aqmYkhxQV/pCSlpaQqLoSWbBGjkHSDSs50pBRVRWyzxNhkyVKPCCEQNUzDSQgJhPMKgnwBMOLAVaR1yrp55OdN+ihVEexJoru3g101gG2AsOTFe2lz8SgznShM1DhhkwvEKGPAfTURWgPE31CTG9H2v/55PhCX6+iYJc/BL+qxnhTnKKAvDgFhuIRDi2lN6xE40xlLJhveratxWIxMpe6ZUU7PQJBfpxOxQ4QeEGhiCRhTRcAkFmtGnMthh4aDGzlGVRUyIoasTKOqLGhG6q+oIydrV4EFRUCIbGr3T7yyOQ6tARok60vpKTtkfEFZfhCCgiJCo1A+42KCu1a4chg7P4iwkpAr4aWFFXTLDCPU7hDt2p0pDbrY0iUXigXoSUAEKwiSHmeZsh4Q1C9MqBSKJ1alpugL2gMgyLOb8VqyQRlzUsSVFTU6MUBC+wWCAKBL6gYD1CnTUqa2QXAVJcoLFI1dxAPyNq9AegiWPM1l8iQiRdWAgBBBKVh7w9x6B6TBB4ZqlLYbQK6/Rl4ZHRDhoqOyOnB0MhEGjIdXd0QoEKlQJ7DAb/fl7T9B9VD2xZCYkJ20VqzRBlLAvSq3vZCwJoP+NoB2afpZUrGANAE4CT8kQhxeEqyjBYMNHhoqRcw62MA/aHNXKUZpcJq24ZUGlcfUx8IwadoRoxAAKckwCYSvPnSixhaWhpvj/ExVu00xthYtmxZyo9X76yGraoAq9euTnE44X2zkJJZ8Nsuh8NKzNCZO3euUX7bob8mi8lDS6oRWhJAKfDpV59DctnCfZuyQBQEY/KJmSjNk7RK0e4JYluzB0FFhU0SML483/gspRQNgRA6wLRC6Y/hk08+ASEks+MgmRmpycIVjHQFv6NHj8YjjzyCdl2EWGCX4nagZpV6aUiJMbjCheGYIZN7j4woCIbQODa8FFnXher1OgSraHg8wqvp3AnxNf2CCKnIDstQpybMBKB0mx6ucYrhmWHhiy5/CDtbPaCUotBhwfK3X8N+Y4dhUqULew8rxPjyfIwuTSNzTjRdSyo15gfmlfMGZSO8YRHNOo84wnFmJETXkDFOgGhEnogggFi17WJDS2x/1NCh+BK1lYDWHZwQgtWrV4Oofnzy5XcQy8dG3lNGaCkQnoNDIfi9mlGhWvJg0T3GSfuY6WOT4qyiokNLzJBp2l0LQghWrV4FAh8IFM07ZXVq93KR3iLC36FVRoa2GGAeH0JIwuKP8ehpMdSjjjoK119/fVafzSXckMkxMfoYnXiK/2TMnTsX51x6PgAtnBKtj/EpKtyyChBggtOGffIdGJ9nx1CrXmMCid2eMZgnmZ4Y5ikeyuab3hxaAoCgLKNLfyAUJwhX5LGHTQpDxpx+DZXikOkHYde2GhQWFqZ3HIg0oBiJJkrNUNXG1tTpR227FyqlcNktGFeeHxF+6ZIVNAZCqBModjkEKL2tkWG7T9sjo38uRWgJSC9ziVJqFHJLmD0kEjz98nM4bs7JKCwqjDDWYjwyvRBaAsJptHISnQxgChvrixTmxQFyp5GJxlzAjPplqEFF63B+kVbbKZUh09wdgKJSOK0SqorzIowKUdAWSOk0uiSEhDMwzYJf/Ry5ddGwSEjqjEGT0DfugkkQQFXdM2axgDDvcqLQEqWwmxYa6cx9hAYwa8ZU1G39MXJuYMYVVQFV21dnZycAIEhFuPLD5zCZIcM8WEKcSTU6q4wtjqxS2FgUCPPGFISzAqx5QH659t+duwD9+1no25qi1EAMfRhaeuONN3D88cejtLTUMCZzBTdkckysPkYjO8uXGv8bnXbdEtQmgkJJRJ4Y9mC4JBFMGuIxKeKTYr7us1D8JmqAmGg7843Gasn4iOY9sQkEjgSTKns9KBIjfBSPaLGv1WrF0GFDe6yTYQZJPNc1m1C7de9DucuO0aV5EWXyAaBTDn+220KwFVpIrdfIMCMtWXsCRiapvf6QAkWlsIgC8hPoFggh8AUCOP6o2fj9DTdGjicUZcj0gkcGgKmkf+LMJUopqB7+ihb6WkQhcYuFHCBYRKPfjsq8MmyoCe4Xi8mwsltEjCrN6/EY46ZgS8zA165jKUroG9coZqGl6GJ44S8CVXRDxmoFRBZOizJSzNWbBe0hrlKa+tpUZRCEYLVaUDlidFTnakEX1wJQAggEAggGg6AU8MKqh+a0Y0xmyJQXaeEwQmNDu+E2BZoXPNz1Wp/nVQpiDiuZyR+qjU8NASFN88TurWhRcUr6MLTk8Xhw2GGHRTbuzBHckMkxMfoYRhyhXLqoAJymsNIDDz2EY2YcgIOHDsFhE8fjqquugtuticIEEjYEXnnjDYwfPx4OhwOzZ89GbW1txH7ffPNNHHDAAXA4HJh06H6468/3Qg5l8aDQD+nHzZsxa9Ys2O127LPPPvj000+NTRRFwYIFC3DwwQdj7NixmDRpEh599FEIggBBEOC3WKEoCv58800oLi5GaWkpFi1aFDFp2QSCX518Iv70uxtwy40LUVxcjIqKCvz973+Hx+PBvHnz4HK5cNTM/fDF8g90Q0YLLYkOi7HKX7JkCYqKivD+++9jypQpyM/Px4knnoj6+noAwO23347nn38eb775pqED+OSTT+CwiNi6aQPmnH4SHA4HSktLsWDBAu3c6xPC7397JRZdcTH+8fe/YtiwYSgtLcXVV1+NUCgElVLDkKmUAatKIVNguzeA3f6gFqYIBHDjjTeiqqoKNpsN48ePxzPPPBNxur///nvMmDEDeXl5mDVrFjZv3my8t23bNpxxxhmoqNBaHxx87GH46PPlEXbM6NGjcc8992D+/PlwuVwYOXIknnrqKe1NlaK6diekfCveeOMNHH300cjLy8PUqVPx1VdfAQiv9L/+cgUOP/xwOBwOVFVV4Te/+U1Mp26v/uAvzrMmNSSvv+46/O7q/4eD9p8RvqxUGha0MkOGKsYqNJdYUnlkZFW7t/U4bow+Jl1vTNCriTWzMMiYV0b1ybqRED+09PDDD2PffffF2KGlOP7AvXHvLQtRZqMxYb1ly5ZhwoQJsNvtOOGEExLOD3a7HWPHjsUdd9wBheXimIviRWkyLFGZMMFQCNdccw2GDh0Ku92OUaNG4d6HHgMAjN73IADAWWedBUIIRo8eDQDYtmMnzlpwPUYfeSRK9toLB82ahY/164/9/qNHj8a9996LBQuvQunkYRg9djSWvfw8gHD9pm+++QbTpk2D3W7HjBkzsGrVKm0fupB3+YpVEB322Lnh028w5cizkV9SgdNOOw2NjY0IQoTTbgVVFSxcuBBTpkzBxIkTceONN+LSSy+N8eBaJCFuIoWqqnj4wQdw6mEHYPq4cowcNQqP//l+AGEDZ8f2LTh2zjzkjZuFqQcfZdx7ANDa3o4LrvoDhk8/AXnF5dh3333x7rLXMbrMiaGFmvF01FFH4Te/+Q0WLVqEkpISVFZW4vbbb48YHyEEzzz3DOb86kIUjS/HhAkT8NZbb0Vss379epx00knIz89HRUUFLr74YrS0tCAbLr74YixevBizZ8/O6vPJ2LMMGUqBoKfX/tGAG2p3NxDyQiD+iPdStQFIOmxEemR8FLjxvgfxzrc/4PklS/Dxxx9j0aJFxvtOUYTf68Ujf/oT/vLXv2LZsmVob2/H+eefb2zz+eef45JLLsF1112HjRs34vH7HsULS/+Ju++5O4vzqv3fjTfdiBtuuAGrVq3CIYccgtNOOw2tra0AtJt32LBhePLJJ/HZZ59h8eLF+MMf/oDXXnsNgsWCkGjBC395FEtffAHPPvssvvjiC7S1teHf//53+HsEAgEUb7/8EgpKS/HNN9/g2muvxZVXXok5c+Zg1qxZ+OGHH3DokUfj5ut+jaDfl9Ab5fV68eCDD+LFF1/EZ599hpqaGixcuBAAsHDhQpx33nmGcVNfX49Zs2ZBCfpx5UXnIr+gECu/+QZLly7Fhx9+iGuuucZ4mBAAX33xGbZt24bly5fj+eefx5IlS7BkyRK4FU0oKAkEJTIwxqOiRDc6m4MymoMyLrnkErz88st47LHHsGnTJjz55JMxvUpuvvlmPPTQQ/juu+8gSRLmz59vvOd2u3HyySfjo48+wqpVq3DC7ONw9rxfoKZmZ8Q+HnroIWNiv+qqq3DllVdqBpFpwr355puxcOFCrF69GhMnTsQFF1wAWZZhkQTUVu/Ary86F2eedTbWrl2LV199FV988QWuueaaiO9h+pHiFF3VmcGgmsJV4fotipaxxOgFr4zFMEwUqF5v+F/AB9Xvh+r1QW7pgurzAWoQ1OeD6vXC3+0BfD5YQoHIzyX617QdaksN1Lb6mPdSeU4FqwiiZxapIdXkkYk0ZARBwGOPPYb169fjqaefxQ9ff4Fb/vD7iG28Xi/uvvtuvPDCC1ixYgU6OjqSzg9PPvkklixZgv/P3n+HWVZU+//4q2qnkzqHyYEJ5DQkBYyAigQxKyCIelEkKFxArgFFkRxUgmK6iFwTKiqKiSAIqChZMgyTZzp3n7xj1e+P2uf06Z4eGFA+1+/vup6nYfr0PjvUrlq1wnu91wXphtsakXHTyqXmWDYMpvR5rvr217jpppu44YYbeOqpp/je977H4nlzAPjb3XcAcO2117Jp0yb+9re/AVCp+Rz8mldx87e+xd9uv52DDz6Yd55yCus2bZqCk7nsssvYc/c9uPc3d/HR40/gnLNOY/XKZ/CjhEqlwmGHHcaOO+7I/fffzznnnNNc48Qp0HeGepdarcalX7uW66/4In/8zc9Yt24d5557LhpBR9bhoosu4kc/+hGXX345N910E6VSaUYsYWvKudWB/eQnP8lFF13Eif/5CX5221/46reupaevH1uKprF59hc/zxknHMODt/18ytoD0xF6zz1WcPN1V/Don37Phz/8YY499liefOTBKcbqddddRz6f59577+Xiiy/mC1/4ArfccsuUe/zCuefyzsPexn2//xNvPvjNHH300YyNmfLuiYkJDjjgAFasWMF9993Hb3/7WwYHB7fYruZ/VfT/x6VYLGpAF4vFzf5Wr9f1448/ruv1uvkgqGj9ufb/lZ94fFwH60o6HKpu1XO9//3v14e/8VAdrCvplQOl5ueJUvrRck0/VKzqsTDSWmv94x//WPf09DSP+e///m8N6Otvu0OvGhjUGzZs0H/+8581oO+9916ttdYHHnigPv/885vfCTZV9LVf+YaeM2dO8zNA/+xnP3ve+1RK6af+9HcN6AvOv0DrifVaFzfqKAz1/Pnz9UUXXdQ8tlar6Q0bNuihoSGttdYnnXSSfsc73qHXFUv6oWJV982erS+++OLm8VEU6fnz5+sjjjjCXCtK9Cv3e5Vese9++rHxitZa6ziOdT6f18ccc0zze3c8+JQG9B133a2D9WX9+xtu1oAeHx/XWmv9rS9/RQP62WefbX7n6quv1rNmzZoy/o3rNuTrX/+6bu/s1H9+ar2uBWbsb775Zi2l1OueWK2DdSX9rncepRctWqTjOG5+713vepd+z3veo9fWfP1QsarX1QMdDlR0sK6kk3qkh4JQP1Ss6l898LAG9C233DLjWP/hD3/QgL711lubn918s3m25hyfJnEp0Dtuu4P+8oWXNT9btGiRft/73tf8XSml+/v79de+9jUdbCg33+e3vvWt5jGPPfaYBvQTTzyhtdb6HUceo99x9Pt1xY+ax9x1111aStm8lwULF+ozP3e+fmawPOO9tYpKlA7Wlaa8q7gSmjWzblDrDQ9qvenvWm94wKzjf7KMVHz98Lpx/dzaYf34dtv/r/wk1RfWDYkf6WBdSR/zzqOMflj/wmM7XT9ce+21GtB/+ctfmp898cQTz6sftNb6+uuv13NmzzHvZLg2RT88PVDSD68b1w+vG9cbJ2paazP3gnUlfdLxH9UHHHCAVkpNnmzjw+ZdhrWZ9Uy9qP0nHtG1v/9dxxMTWmutd1y2TF/2yU/qpGbO35jH4WBVB+tKOqoEurevX3/6/Mv06pGK/vrXv657enqmrI2vfe1rGtAP/PHXWm94QN96ww+m6IbG2Dz74D3m/sbX6osvuUT39fXpZ9cP6ThRetasWfrCCy/UGzZs0Bs3btRxHOuFCxdupi+01s17S2pmnZRKJe15nv7mN7+pnxuu6IfXjeuVQ2X98Lpx/exgWa9atUoD+huXnqv1hgd0MrJps7Wntda6Nm7ub8h8duihh+rTTz+9+efXvva1+lWvetWUe9l77731WWed1fwd0J/5zGd0sLGsg3UlXRoze+lvfvMbrbXW5557rn7jG9845Rzr1q3TgH7qqaea1/n4xz++2XM/nzSe8cEHH9RxHOu//e1vU/Rlq2y2h29B/l1+/f9IXnREpuWwbEuaaiJOuPu227j2S5ex7pmnKZVKxHGM7/vUajVyuVyTNXenPfbET2LcJGbZsmV0dnbyxBNPsM8++/Dwww9zzz33cN555zWvlyQJfjB5nhcr++61G1SHALClxV577cUTTzzR/Ps111zDtddey8aNG/F9nzAM2X333akLSXmiyPDAAK94xSuax9u2zV577TWFHVRqWL7TzsTpcFqWRU9PD7vssot5DK3p6ukDYHR4GBZPHW8VhiTlErlslm0WLGh+PmfOHIaGhp73+Z588kl22GkXcrk89UiRdWH//fdHKcXjzzxN/4pXIgXstNNOWC2l73PmzOHvf/97M63UYVugGzwa0OPYDIUxjz38MJZl8drXvvZ572PXXXedcm6AoaEhFi5cSKVS4ZxzzuHmm29m06ZNxHFMvV5n7fr1WzyHEILZs2czODg4JSKzpetsv/32PP3EYzz5+KPM+vlPmsdorVFKsWrVKnbYYYfmqTpzL9yETshJRtvm+RpAXxEaAKa0DC7g5YjIvMgmi/9bIlyryV8DMwN9b731Vi644AKefPLJGfUDmLW19957N7+z/fbbP79+INUPvk+tXiPvTI0Seo7VxMg0xrIRhTj26GN589sPZbvttuPggw/msEMO4Y27zjJf3AJGplKr85kLL+W3f7yLgbGx5jxePzAwJSKz6667NtP2QsPs2bMYGx2hHiU88cQT7LrrrmQyk2R3++67r/lH1Ohmufn1c7kcS5dvC8V1kAR09fQxMjKCa1tUyiUGBwfZZ599zDNqjZSSPffcc2a8TLM83PztiSeeIAgCDjzwwGYaqRo2WhPIJhvwbjssNt9z25kzx1RRNdZekiScf/GXuOGH32fDwBBhlBAEwWY6u3X9wsw6btdddzX9qhJNPpujvb29eczDDz/MH/7whxm7V69cuZJtt9128+f9X5L/W4aMk4NPbXxZTp1UQpJiCJbA6c/NAHDzoFR/ERiZyeMa/DFaax56ZiUfe887Oe7DH+HLF15Ad3c3d999Nx/60IcIw3CzyRw6DjrYHPBbqVT4/Oc/z9vf/nYAotE6Okywu7wpC/9F3KZhnWS2+XdpKg7ghz/8IZ/61Kc4++yz2W+//Zg/fz6XXHIJ9957L3W9lQBEAZYG27HRQpBojZViWBqg4UTrlpLjGXhJogghwbFtkokJZL+pAGiU0r6QNELo0wG/UXotS0wCmJu3LQRRYjhyLAEFSxK1AKSlEPQ5NpmsoUZ/oftoPf/06okzzjiDW265hUsvvZRly5bhYvPu976HaFoX4ZnuUU3bxJ/vOvVqlXcefRwfP+Vj9LR5U763cOFCgpbKkY6t7KYrpgEVpwB9bZcmKj15+dh9E8dl+wfun/K3pBqSTDQ6NQuc2bnmfHlyU5lEa5ZNq06bUcIajD5j/u0WoGfplD+L7At3QRdCTOlUPL1MfvXq1Rx22GF89KMf5bzzzntB/bAlma4fGqKihIyX2UyPZVowQg2MTAPftOcee7Bq1Sp+85vfcOutt/Lu976Xg/bfi59887ItNgA9/axPc8ttt3P+GWew42teQ66tjXe85S2EUTSlcslxnCnd56WUaKUIY/W8XFMNjIyawZBxHAdsM6d1HKJSfiijh805ZQvA+nkrlxr3lo5XtuUdN7BmjXXiOZOGjGvbKO0hbAeRlqo3rnPJJZfwlSuv5svn/Ce7bLeU/PydOPXM/9qs79OMa1zNsMZbuHhaj6lUKhx++OEzgnMbjs2/ivzfMmSEMPX4/2TRSpP4Ghwbq9NDZDYvMxUN91RrM2FaDB2lNevGamgNXTmHtozTXIQKyKX4mGqiePD++1FKceXllzd5K2644YbNrhfHMU88cB877bUPoe2w9tlnmZiYYIcddgBgjz324Kmnnmp2LI06TJdaqyszZZG+8MNPKou//PlPvHbFcnByxPUS9993HyefcgoA99xzD694xSs47rjjyGazdHV1sXLlSpQ2qqGjvY1Zs2Zx77338prXvKb5DPfffz977LGHGUNhulnL9JL1RFGYxt3RWpa9mXlUHUHKACebgNAkExPYfX0zN0d03c36qOywww5c+53vUKtVqafg63vuuQcpJfO3MeMot1CD3qhW7bBTxuXGbaaH97g22+20E0opfn37H3jrwW+a8TwvJPfccw/HHXccb3vb2wAoDU+wZv3arSurf77qs5SzorEB7Lr77jz3zFPMW7QN87s33xwHS8bj9WzrBYnWGjLdkKG19NrKTxqmL0dEJl2PsQYy2anVPdJFB4ZMTmRsrLzZjKJEkWSMUZVpK2xFRVAdGsRulgUvIepp7sGapOafds37U/1w2WWXNdfxlvTDfffd14wsPPXUU8+rHxqilSbaWNms+rKVudaZwpllrPf2XDvvec97eM973sM7jziUgw9/G2OlKt1zjRMyfa396U9/5n1HHMERBx1IZsedqFarrFm/nlfvuecW2xRopY1+SOfvsuXb8b3/+R983286Z3/5y1/MuKHRWoLYwhY4hRTPPGvGtcnlcsyaNYv77ruPbbfdFq01cRzzwAMPsPvuu89wnqnA5+XLl5PNZrntttt419HvnzqGttWoUwJAkZ+x99U999zDEUccwfve+y4IK6j22Tz99NPsuOOOMz/LC8h0Y6she+yxBz/96U9ZvHhxkybjX1X+te/u/yOiqpFZ2JZEbgHUKKQwG4TW6EQhWjyRih83G36V/AgrZd0slUs8/NgjeCPG+Nroh3T29BBHEV+7+ioOP/xw7rnnHq655prNruc4DhedeTpnXnI5GTQXfuIMXvnKVzYV12c/+1kOO+wwFi5cyDvf+U6SUsgjDzzE42ue5vyLL9j6h2+Z+1/9zo9YvnQbdnjFAXz5wksYL5b44FsPAK1Yvnw53/3ud7njjjvYdttt+cUvfsHf/vY35qdVCk4cc/zxx3PhhReyfPlytt9+ey6//PLNyd/EZHPB2gsYMunqRJKeo7QR0dHW/LPEgDit/OYbyuLFi/nd737HU089RU9PDx0dHRx99NF89nOf4+zTTuSk//wv1og6p5xyCu973zHku3obt7f5EGndLBfvcCbLK2Ey0mEJwa7Ll3L4UUdz4vH/gb7iCnbffXfWrFnD0NDQVgPsli9fzo033sjhhx+OEILPfPozxsPaKkOmOTBGwha1Wh83/08b2J12+pm84XWv5pNnnsbpp3yUfD7P448/zi233MKVV17Z7MSbdbfOiBkYGGDjug2sXP0cAI88/AhZ32LB3PnM6g7B6jIVS/AykeKJJtV+lCi8lvXZmvKSLaD7yZLZrSy9jv3JfyehMRxfAiWAEALhSqMfnn4UqyXi1dvbSxRFXHnllS+oH0455RSuuOIKbNvm5JNPbuqHIAg47bTTOPLII5v6QUrJww8/zKOPPspnT/yvzQyZ1ohMA3AqkjKO3MhlX/oFc5dsx4oVK5BS8uOf/JTZ/b10dhvSzsWLF3Pbbbex//7743keXV1dLFuyhF/cdhuHHvBaMlHE2Z/9XNO5e742Beb65ve3vOPdfOGcz3L88cfzyU9+ktWrV3PppZc2v6dxtzz+lovGGDySqW1VTjnlFC644AJ6enpYsmQJ5557LuPj4zM6RNMhBZlMhrPOOssUZ0ibOdvuxvjYCM8+/SSf/PiJU6LIWudnpEFYvnw5P/nJT/jTew6ny0u4/L8vYXBw8CUbMlvqt3TSSSfxzW9+kyOPPLJZ/fTss8/ywx/+kG9961tT0udbI2NjY6xdu5aNG01W5KmnniJJkpdcBdUq/7eqll4G0Uo32Tat9ucvMZ3sgj11wtSaOVLjvSZKkySaO/98F688+FWsWLGCFStWcOi+r+DmH/6Aiy69jIsuuoidd96Z733ve1xwwVTDQ2tNNpvlxBNP4pMf+gBHH3Iw+XyeH/3oR81j3vSmN/GrX/2K3//+9+y99968+k2v44pvXc2iBQtf3PO3RGQu+NTHOP/qb7P7ij25+75Huek7X6G33YPSRj7ykY80Q94HHXQQo6OjnHjiiU3l5CYRJ554Iscccwzvf//72XfffWlra2tGFlrFajFkpkurIaM12GIAq0EsZbkktJFEqfHgKpLixIzP9c53vpMlS5aw11570dfXxz333EMul+O3v/0txYlx3nvoAbzrXe/iwAMP5KLLvtQMCc/E1xKnUadGWmlK6qhluvQ6Np/50lc48Ii3cuJJJ7H99ttz/PHHb1bS/Hxy+eWX09XVxX777cfhhx/Om97wRlbsvNvW8RymB1mkRktpU8vf0s0j7Qi8Yvfd+PaPf8Vzzz7Dq1/9alasWMFnP/tZ5s6dix9NUt9PT7eESjEQhPjT3t0111zDnvvsxUc/YSJ4r33da9nn4Ffxq1tuxjRiclu4Pf75qSVD6d/ouTR1tIQlm0RwIjP5PM0eS1tbeh21GDJoUC+dP0hYkjv/fBd77bdPUz+sWLGC66+/nssvv/x59QMYHMhZZ53FUUcdxf7770+hUGjqh0qlwn777cdPfvKTpn545StfyZe+9CUWLVo0Iy7HtSUZ20p1mEm7CWXmbZuruPiii9hrr73Ye++9Wb1mDb++/gqkY9I3l112GbfccgsLFixgxYoVAFxy7rl0tbfz+qOP5fC3HMG+r9uX3Xfe2VxsixEZ86ud/m55WX75y1/y97//nRUrVvDpT396SppE423ZjhQCneJ37LTkvBHhOuusszjyyCP52Mc+xhFHHEE+n+dNb3rTzCn5GYyEs88+m9NPP50vfuEc3nrAK/jEiR9kfHTEVM6lEU+NQOPNaGh95jOfYY899uBN73gfr3vnh5nd27VZ6feLkS3hN+fOncs999xDkiS88Y1vZJddduHUU0+ls7PzxUXtU7npppvMPnbooQC8973vZa+99uLGG298yffefAa9NcCAf2EplUp0dHRQLBZpb2+f8jff91m1ahXbbLPNi8N9vAhJSgFJKUTYEntW7nkNmWg4Td90Z7BaAJDPDVeoBDHzOrN0512qQUylEtLtK7Ak7pw8xShmdT3Ek4LtC8+fS6/X64yPjyOlZDzfTqihI6yzuHfLbQvicR9VjZDtLna7B1HdeN/5vuf1GlWYEA+VcORqYktQzxpFn80uwok1jBsPm+4lTPiaWq1GW1sbbW1thErxRMUo955KEde26E8xK883huVEsS4ncaRgx2ljMZE2xst7NttkE2TpOTQC0b0EvDbCdetQ1SKZjhitISh7eNtuj5iW8x4YGACgv79/s7DqM4Nl6lHCop4cHVmXwZLPSClgeeoXOPMKU+bBBj9kJIzpciwWZr3J8PwMx673Q0bDmDZbsiT3j89ZFSniwSoIgTtvc9BeqyTVCDU+gS3XT9pX/TsZI2LoCRNRkA7M3plYKZ7YVEZrzdzOLL2FSZzMpmKd4XJAR9ZhUc9kKrccJ6ypByTapHK2zWWam05DGmtEeJb5vwhwxDro3dYYURNrDL6kd/k/PDbTpbEOF3Tn6MpNTQ+rKIFEI1saKw4U6wyVA3ryLvO6No/qxUqzMQjJWZJe14HBxw3tfUN6tzNMrf9iMjQ0RBzHtLe3zwj0jEbqaD/G6vSwCpPjpLVGY1I7OlHogWeQIm1Sme2GrkXm38X1UB02DLX5fihvMn/3Jq8VDQ4SDw9je4pwwQJWVzbSW7NoL8VYXV248+Y1j1X1mHi0jnAsnFm5pg7IuTbL+meY8yPPQlgm1v1opwunf+Z3UN/0JFldZ5wO6mTo7e3FdSefd2RkhDAMaW9vZ8899+Td734355577pRzKD8mHqkjbIkze3NYw+MbS8RK4dkW281uM5HP8dUonSHW83Hm5KcQq06RsAYjT5kWBrN3eUnRPUgxYOMBImPj9L4wTuufKUmS8OCDD7JixYoZIzxbu4f/OyLzD4hOFEnFeIfyBaIxQEvTtdaogaaeRmRyrsFPFDIO/enG0GCmbkQf8luBN6jVjPLI5XK0N1rbWy+QRWyGZ9Pfi+sNYNcvPv/3NEhRRgBJyyKP4wnIdkDOpFzwi5t1vm4w2mZTfpitsqmFIJuOX6Q0NX8I3RKObXa+FgJRM8aIEh0pzbdARxE6EWjpIgRYVmy4f1qkNV8/E5Cv2aogJXurBjEJejLqMe39TqlWgudld+5LsTflWFGb7nm+BGnMn+l9tGaUJMYWA1PTYylz6CQ+JTLHSdkk39pU9JtzWGvdTCt1psaA1prhMOK51IgB8+7WpiSAU+630XepQSxJuvFb3mSDwZeN3bfBtrr5O5eONcWIgdaIzOYKONGaVfWA8Shhgx8x4IeTRkyjUudlaID5j4rWujn/tzRfhD0zuadoaY5o8Cotz1cfM84RTPZZkg6MrYLaqDFmWu8jZS4XUhOnEbioScY3bV1Mi3q0MnBv9gxam6aLgNbeFtuqhLGirsz7ttJ4a71kdOGaNWv45je/yXPPPccTTzzBxz72MVatWsVRRx21+Ym2kLZpSCOa1yRUbPLbpHPk+VKWTgYQJlo6rcHli5H/f+iA/W9D5h+QRlda4cgmffjzygz03n6sSLRGCtHssGwOSv+fKoZqo6fGC+Qlk8SU4oFByOcajKXi+V910wZrLPxGPj+qcf7551MoFGb8OeQthyIpma+0dPWO4hJaJ5MeZxw0lUojLFlON4JCIzS8Nf1RpMDSINNFVw5L+P6kEmz0WcpTQ8Q1tBYoORmJ0pFRitrrNPfiKZJpOJxWQ2ame8q4k4pSad1MDc7UabqWKCKlkQIe+vOfKBQKtHW2073dHLq3m0NbW1tzLAE8KelMFfFw+E/YsF9EHy3hb0KIGC0cyHSZDxs4mdY0SGrc9OQNXbvWmrVjNRKlqYYJUaKwpKAtY6O05orvXMei7i72ndPPfnP72X9uP/vO7Wfn/h6232mnKfcgpwF+BaGxxqT1sqaWgC2mlrYkm3UsTkVpzZp6QC1RzeEfDGMG3B7jPTvpmvgHNp+XS3SLwbtFQ6bhTKXPP5N+aO/qoG3bfXjz+042ETQwjhFMvr+gDFE6v1rxQ4COwvRamiRNayZi6zAyni2beKfN+oGl5fsGhrxljMxYNSRMIaSN1FLkGwNISsl3vvMdDjroIN761rfy2GOPceutt7LDDjuwdu3azcahe7s5dC+fTaFQYO3atVOu06hcarIjR0Z3m3vj+Z1jIcFOoxQNI/GlSAOc/Q8YMnfdddcW94iZonr/bPk32PcfEFU3G43MO1vVx2cmjEwt3axyrjXlHE0lIsy/tzYiU6+bCe04Do7jkEk37kQabMYW77O1S7JKJr3e2OeEE07YItjUUzFS1FFCoHQ6HtJBqYgoKuI2O8mGaGsS4Kq0ppwqpDZLUmbrDJmG6e2qBF/aBGSIojFsuw3HaW9GZDriYQAUHc0NUGuNTtkxRa4bXR/CsjVRsYyOY0SaQtrqiEyU4IfGmLGkQFppo7wWhdCIxrTbFsv23puHHnrIpONG62AJ3N4slNYbpR6UwWuj37WZiBIm4oT5aYn5S5bWr2rNFl3Q+gQyLqI1qOw8LFeBP26MFq0nMTKQerTtCCGY35XlmSGDidk0UW+ePp91GAojxqOEvd/4Zn5095/pd226HRN1HA9jBoII23GoxEkTtC2mgYNNxVKKFWhEMnRiIkQvYJy/WGmUDU/vgD2T6Nb+OC2GjNaa9X5IOTZGzNKsRzVRbAoiBt1esBxmi3RtvUwG2T8iL2TEA5u1W5lJPySVKlZ1NZlMFjoXwNCTZn77pRZDpjT5BRWbn8ZajRoRGUhSXaSana630H2+pSLUs6VZn1EypaKqueFLD5SYcQoprRmrhhS0bSgfUkMmCU0EccGCBdxzzz0Ui0Wq1Sr5fL7ZdHLu3LlTmiFqrYkGjLHm9OeYO3fulGv1FFwSrelupDIbERntPn80piFO1qzHqA7Zzhc+fgb5R1jnG7LXXnv9U5tAvlj5tyHzEkUnCp164tNDzluUGeiqa0EjrTTNE42q2GIIrXLUo160qWLEe4HJ3ZpWgkYppNnAAqXJbKFTbmMDMv1tWjzFyKd7Vjfd3d0zfk2NrIGwTmxngRBpZXDsDoJgkCiewPXmmwOTEC0mU0u1lK7fEoLsDIaM0gYhu1k1SPp7Rvj4FAgwHonvr8eylpMoTQdVHBWgkSS6q7lQG0aMKcPPILw2CMpYbkJSLGL3mMhNq/EykyHTiJxFiaLkG4Wbd23Qm0dkSi1ppaxjeiepICFuryFsjWMPQKEL6DIlzl4bWcvCk+Z9leOETuelL1MhWqrllEbMFNBLQpgwnqKiC5w8uOlmEdU2B6WGk96fbUkWdOdYNVxhrBaCZ6ELDkUJxcCMd3tbG7vM6qWtJQWjtWadHzIeJazxQ7bNeTgybbRqy6ml13aKLWjlHFHxZInsP0kmIzIvbMjESqO0KfdtNWQ2BcZ4Q8DirEfetsjbFgRlNpFh0OoAETCbkX95Q2ZLIpqEd2acurs31w/J2CiWr9DCM1GDfK/BxZQ2Tkmp6cIsqI4idGwwUK6NVqrFkGmJyEgB6M3Lr1tVhNIgBdmUoK8eKTpaj00NGS3TSMYMTkKpHhErhZJmflmoZmo2iWPslJ+lEVlu1Vu2bW9Wsh7mTbm63Z9DTktD5j2bbRqEp1o3wfTgPm8H+qY4Wajzj0VkrM0NwRcr2Wx2s+f+fyn/Ti29RFF+o8u11Wwg90IipjFewmTFUq6lKSRaIWobkKKKlQxTK5rUSU5HiHgqsVmrNDgNgCYwSgqBnW7G/vM122vtktxqyCTBlqsrtEJEJm8cp9EM28rjOJ3mq3G1Ubho/psqMCFEEx/TbssZI1FrR2s8MVDabFNpHJuVBtcSiixSZtA6wfc3kCjFLGGqbrTbA7REuhrKscHlkjXK13KnppdeyCu1pMS1JbPdQTLJc7iE5D17M88mVIogNWoKrSlBrYEYS62fWuIcVpr/bGz6jfSb1oooKk3BA221NHEyW/h7cSPoBCU8Et1t5oKdMe6wVhDVTKCu8Zri2pSvFzybvvYMOmOhPcNxItIKrc5qQu9ETHbabQshmJdx8SxBrDRr/LBZwSYbYXZhxqlpsLRGZZKXj913a1JLza7X9mSfoeEwaqYDF2Rc2ls2rf64yJzARAkHtUfRyv9Lppa2JiLTwMiQPA/uKsUDaZlGZAuzTVotbtlw3QKKAqqBrwrNvGoYMQjzk6T6pzn/ZtIJ03EyLenfKdLY8MWWDZnRinkv+ZSWwURkzHmTaPKdbU0HbGiJxL9Q6iYJAZVWLDlbB951UnBuXCeZ3hl8ayUd5626x39R+bch8xKlmVbKbn0t/eSEVsboSFSzTHVKRKY2htAhWlsomadqmcmaC4sw/FSTy2O6tCqh1vI4K1UE00tep9xbEyNDswSwKdPy103xSwidoLVNIo3ysawCUrpYlvGio7iYsrKCbKaeJKVGWsm2Nkt3hXFCyY9IlKbsT9uwpEDZdTxRBzSxFtiZ+YAgjssUGCIjIpSwUE7qJTZstIYh02C8zHSghTSOflRD+eY5Xyi1BNDmhmQyIdJWzMoOk7X0Zsq0km52WUtOrc6JAxyxHklgQuk9yybHOfXSm4ZMqpzq9Q3U62vw/YEZ7+f5pDm+MykprZshfiX6ATHJeZRiOXRYxfcklbxNZAnjNU4zbvsKHqRzOK9hx3yWWSFkQ43U4Fc3jz5YQrA44yEFVGPF2tSYaQB+hUjMvGykJ2ESJ/OytCkw4xSrF2CFBWpptCmTvqdQKTb5Zt3MTlNoUyT26Y/G6LHMnBhzOv7lIzJb3KClmFxTWzD6RAPY3DBCLRsKs6Ye1LUYVa+jE3MyNT5i0r+NdWpJhIAkNd4bqSWtNt+wp+NkGu9lS4aMFumcmrYDxkpRTY3RjnzWVDwCVhoVisMXb8jMFImfURpOahoJ2qrduWHIJCFDg5sYHx9/0cbMlOaW/zZk/u+IVhqdKjKxNSDfhjRDeIDSzd4kni0nu5ZqBZVBABK6UN5Caq4JjuYFgIKx5yCoMF0aC0rKySiHEKJpyATPt+CaGBk9tUQUZjZkorqpbAJikUcLo3zsNA3QiMpE0Tg63YgahkwC+MlkpGJ6RKZYn9ykGhtG8zYlJE419VnS59IOnmeUpOtWSQREmV7QqXFYHzXdyacbMtJCZMx9tkZlXkiZa63JW6PN3y1HEVXWTXo16bNVmsZayzJLIkT5OQOqxTFlxV7bJGgvjcrkUyUeKU0trpsqsMZ4vtiojGyZd9MlCVP8i0Bpb+rxKQt2lJSI0ihJ2CCEmzYnGs8qBWzTlsESU40XvxrPDJy2JIuyhs+jGCWs9UNEzkZmbSxpon0NQxgwGyK8LBU/DVI8eP70ktaa8bQyqz0lo9tQqqARZIM6PZXy9C80N6me1MAp2wVilczIO/RPEcMtkFYfbjRpnXrRlOxOM0KTRDU3sK2JyGitUekcDMqb6yFzULrh2y1GaL5v0hC1M2A56DBsGjIkAfHAwOQ6TddNnF4rad2tnofdFybTv2GiSBprWCWTkaLUkJnuRLVyerm2tRmXTBJNzrvpbTu2JFsdkWmsqUYUa2siMtJuGosOpqfWi+Gdat7j/8crl/5tyLwEUX4M2iy0rU0rwTTLN9FT0kpxFFKvlAlG1kESorSF0h1EWhGmkyvXNc9selrB2MrNjJlWQ6b1mpOppeeZpI2yydaITAMJF00zZIIKjDwDKkJLjzg1VCwrh0hBGLbdAUKgVIBKvaNGGWMlvY+sJZvU8A3RWlOqTyqLajB1w9ICEtuEoJvEeErhur1Yad67nrFQud7mcwgdw8TaFgXZYnxmTXVOw5BpLT+FmZVUHJeQIkRr8KtpHj3jU68b40anvVnK6WY4Ja1UHUboGKVdEnfRpKJvcGikqSZLiCawe6IVFIkiiiY2uydzr5qRSrB5pUbTRp3h/TfKq51sM3bfVGpODiUgsCa90ESmWIVoanppPI0s9jg2lpQE9RiVaKQlmn2comDmFGW7bbEo4zaNmXVhhNWdQer0uVuxMC9jasmQ4jUMmS2vlXpK+CeFoCNrUxweoSRNH57+sVGS8bGpY50EGCtSknU8MlKgESa99A+Q4m0mWpv5U1wPg4/C6LPGgKkMms/GnzO8I4OPNg2rKEgY3VClOFzfbO7PJCpJGN+0sQnADaszYzNEoymq08L9IaXhjYHJXkZBgEoNGWFp4tFR4pTpVaR6I2FyPjfSSy/M7iubqUI/7dnVTOUJC91wcqbZCg3HqREhV6JRgp1GZBqppbCGDI3B+sIRma2sCmoAfWWDemMrsSppVMZJy91LpdJmfZdeULY2avQvKv82ZF6C6PpkNGZrqpVapdU6r4WJ4W8MioysXUNpcAA7NPiOUBUAQTkNP3uWwJIWdC0Bt8WYacFYNBbUdGKhhiETPB+XSGMmtIJ9vZTOvzWvXZ8wClIn4OZR2cUoO0ivO0n4JKWFbRuCwkgaJWCRYDs+E6HxtNsaXWtbxjBKJkO7AEGip3jHia6AUKBsSNLqocRUY2W0ub6yBJaIIWiMjYLYR/vmd9HaTM1rQ0vbVPiKkKRUmgo6nqaktFYEwUA6LDaVIE+i0utmJgygOTHYmFhphGipNFMJVEfS5+hmSp+XRolqi3HawFhUUj6LMMqk/x+d8T0Oln02TtRZOVyZGlJ/vtRSA5fg5CajAw2l5uaoZyzDSpxoHJWmURwxBVwYK90ENXelEQe/nPYgyjt4OfNZUN2y8dHh2E1jZiJKWFsPTPk+TDNkXr6IDEziZOJpERmlIuK4NoUnpz1jkwwMsik13juTGC+OUEGArresmbSkFttUXzXA2xNOG6h/Ik5mfA2MPG2MFxUbTEqux0RCMh1pqjDFPaV6o1oMQGtCPyYKkinzffocS+KIsY3rifw6qsnpolDTjAqtFI3u7qLVkIHJ6jfLNWmkMGx+1ACi65Q+QqTjlOiWascXqlxqmeOt1YXApINmu5vN9YY0OlE3GvUmNJywaamlibXIisEuaq1RUUSwahXx6CjTZUuM7ptJw5ARLyK1BM0UsDPJL874+PgLG1hT7rGho/5tyPyfEK11E+i71dVKrdIA/MaKuF6lJxxD1czmVchoLKFRwoIU31FLvZG8lMQTEyT1OnQvMRufVsaoSDeVhjf1wx/+kM7OzuYl7ZQ1QWsmuy5Pk6YxoWmGX0XPUn7+2z9MRmSqozC+yhzkdUD3MjQWqzaspKNjVx599Lkp53Rscw8RAZElSHIJGa+KrzyE1uTEZKSnSZKXYmKyBGQIOfu0E6fQb8fKeOlWlG824qw3IlFRgNVYiNV1TV11x32PIObtwfiwSdlNMWSEQKRli9LRhMXilGeYrsyjaBylQtCCuGYRCxsnMw+BjZCKKDOCimOK6UaRoU618jhJ4hvWTp2ghYOmMDV03DBk4noz2tDW8CjJEkVZ7vjDQ3R07MrY6BBJMjUiEivFWApSjBLFc8OVZmm/eL7UUhpZiVWL8ZseHsYTJJZAaE3WT3C1UbCRI1EtEZmJ2LAkZyxTgZZEitCP2Wv/Xfj6f38VL+0/5tei583fN4wZBEzEigG31xgurdVKzdTSy0uKFyatG7qiVnuOWm0llcqTkAySsQJ6KqOMByGB6yHRzO1ow0rLcOPx8cmTNhyBdFNv8ARVrBxR/E96jtg35fJgoozdS2D2ztC5EDrmm9/7toNcF9/50U10zt2GKEwIW9O4pXCLqaU4DBnbuIE4DJGWhcLFW9DOr3//O4L61LlIFKSFchKmdWBu4oKkg45jU6GkBVqYdqt252QvNGEbzaUaTRvtzBYjMjNhPBpRlUoDZ5cErF63EdG/Aw/9/WEA7rzrjwghmJiYmMIJlXcb0SAzH+y04lIlCSqJIfabPZi01kSbBlDV6oyGzFbjT2Jzf3bvLB5+7JGtSy1BS0QmplAoIKUkSRKK03TZ88pLxMi87nWv49RTT31R33k55N+GzIsUHaR5bUtsxnmxNdLYVGrFEm1hEakVluPSPWcueSs1INrm8OGPncQ7P3QkfqpY3ZFhovXrCVevRoVhasykHYGrKWfKDKklMPuS9ULppSbYV6NV098yKkRFxnMvpmROuW7o3gakROsQLdKIizXV+7LtAkJYaJ2Y1gUhiBGLbdavY8nGtTjB2OTlpxkyHaJGXpjxaHDDKJUQJyaca0V5HL8GmMhHpEx1jZ22mY61T2N67/eag9j09z/Snk+V5HTlajVCuZo49aRPPfVUPvjBD07zUBOCYAiAqOagtaCzPU/ec8nlF5l7dGoEbWsppcZfRtfSiqNxqJrv6gYIufU1Wc5kminFyYh4DJsIEFSTtpaXZKIyrTJWCUm0xrMtk6pUmlXDVSpBvMXUklYKnRpclVK6GUmTCkqSWvNZvUAhNVg4zfRdJAIabL8Tqcfblabs6g1sjADLkrgZq8mx07pxNu99bIxTTjmF7bbbjtntbRy60/Zc9IkzWFvT6Okl1i9jaglaAL8t3nMcl4zxCmgdU3DKzM4PEbeXGO4yqclZnosjJVb6e1IsTm62DRBnioPypCSnQ0AwsRWl3jPJcccdN7W/TqM7udsGXYtNBGYmkpTGeGpNrWjuy0lxT0F9qqHZIMerFScY27iOJIqwHId812xUOnmlsAimYTJ01IgszMCv1Yj2pvgYgNwuu/CL398NgN3djiwUEFIisxkUk/Z3zs41cTJbTC21vLe21NGsBDFqOq1EOuz77bsvmzZtoqOjg3poQMR2WpUIEKcpKFsorHR+x74pNBAtnkGcsoPraHNjffLenuddJ/FmxvlWp5ZsY8jYxNiWpCudg/V6vUnH8UKy1VGjlyhRFHHWWWexyy67kM/nmTt3Lscee2yzgeQ/Kv82ZF6kNKuVMi8+rWS+mP4/DcVHXoHe+QtwVaXJjZEEedCg0QSpB5ppKAutiTZuRAs5Sf8fTa22mX5frYDfLVYutXwnEZLn0nYDYSOUH6TWvZOFjoXN45OWxvNimuIUQuJYHVgT4GwQ2EMCu6aQWmMlCWyqopLJkmyloZrS/rdTJU8aaUoNmSgqAhqhHKRy8eI6drqhVcMQkYRNQyaxRNMw8DIes5bvDtrcn2DaJphS3wtbotJnmAnIF4YjaB0jhENUlyCgryNvxtfK4bkGcKwxURSA9tRbiqJxdByAkCirc7MxByajMmEVpWLCcIgsRhH51lTjK46KqDS9YrAxRkn3t3ls05un4NkkWrN6pEqlCXac/H4U+BQ3rkGgMRx+6RhIgdaKen0doLGFhxObaF5ci3FcM+dCR6Ajn0Apqumc6nQstNb46b00FLEQAi/tLebPkF7auHEjGzdu5NJLL+XRRx/l2muv5Z5bb+Ezp3yc0J7WB+f/UWqpNZ0ZhiYd6Lp9VJI5VKI8aMGE1UUibRyh6XVtQyoYjiBcF5QiaXjEDRCnPWnod6Z4hnH1T1LB/oT5/wuRorUYhkEtjfx1Z/CydlruPilKKUbXr6U0MoxKFI6XoXvufOJQNPc7iUVYr07dvBupNDHNCIXJiIzlNFNIjd/BVDu5ixbhbb890nWbDpUQgqydbSnBfv42BWBaFTiWRGlt0tWt1BXpYW7GY/bs2QghmintvDdZgBCn3FAWCZZjnicOjF4StPh/raR806NsL9CmwJy0AfRtWedbOzUsB4VMHdYIz/OabLrFYpGoQYoaKYbXlSmPzlC88TJXLdVqNR544AHOPvtsHnjgAW688UaeeuqpGZsCvxT5tyHzIkRr3VJ2/eLTSlpr6lWTGhHSZtTtxm7rNJZ9xXi/yutD1VOEvIpM6kMrcr09eMuXg5R8+WtfY5eddiLft4AFe72ZE888m0q5vFkvo5///OcsX76cRYsW8eF3vJ2B9esJ0tLSWCl+8YtfsMcee5DJZFi6bClf/NIFxHHMRtuh4bvEjVB+iqXAzk4zeiaxAE8++ST77bcfmUyGnXfemTvvvBM5ppElQRImfPSzn2X7N7+Zrr33ZrfDD+er1/4P4YZ1TcZhP4GLP/8pXrXTIubt/CrO/eIFaDSJ0vi1iHp1lEMP/SCfOOMiTj/nLHbcbVdeu+1yfvqda9k0sJEPnPY5Opftz+67H8rvb72bxDIL+I677kRm2pkombG/7ttfpbOzk9/97nfssMMOFPoWcPDRJzEwMoKSgssuu4wbbriB3/3ud8ydOxchBLfffithOMJjjz3NYYf9B9vsuAs77rkP7z/mQ5SK5ryu18dHjz+Xo979X1x7xRUctO0Sls7fjtNPP58w9A1INtdDY9ltZge7BYIg5KzPfI6FCxfS27uCA1fsy8++ex1BiyHz8MPP8trXvpdCoYP99tuPvz70KLFSuJZkZNNa3v62t7LvzkvZd7v5vPeQ1/Pj399itk2t0VqxaOFCPvvpT3Hyf55O27avYvE+h/DdH/zAnFwKnn32UdratueXv/wDhx72IfJL92P3N7yHu//wZ2RFI7RAC8Edd/yW17z6NbxiVg8H77gtZ5x6KuMjRVRiSLVany+TppfCeuodt8jOO+/MT3/6Uw4//HCWLl3KGw48kNPP/gx3/vbXFHWDzVenkdD/t6mlOK6RJHVAYDs9jNUcRurdMNFOkU4AuvQgSVyE0kZEfQw7l6YlxsfTiqWGITNZwdMpTayhjk3wEqMyTYmDScxSpoPLL7+86fkuWLCAE088kUqlstk9/Pp3v2Lf1+9BoT3PO49+Kxs2rpty2t/8+te8/uBDWLzjzux74Bu48lvfNrT/ftxiEwtQk/T9zfsBdFpGHIYhJ598MnPmzCGzaAWL9jmECy75MjoM2f5NbwLgbcd8BDFvDxbvtBdCCJ5btYoj3nUUC3Y7iL0X7c17DnoPd99x92QJdpKwePFizj//fD74wQ/SOaubZa/YkW9d9+3JOxOCZx97iHcf/Br6OtvY68AjePDRJ9MTmDl42513mOOGhimHCb+44fvsunReUzcsWLYDBx99EoODgzgpZieoVfnY2RfTucNr2GmnnTjvvPP48Kc+xbvTFEujT1TzPlqiRVprlFJcfPHFLFu2DM/zWLhwIeedf376fsyYrVq7mgMPeSO5XI7ddtuNP//5z83zjY6OcuSRRzJv3jxyuRy77Lor3/v57wGQyoz9W97yFj73uc9x7rnn0t/fz+zZszn77M+ilaZejQzxnRB861vf4m1vexttfR3s+Ord+eWvfzXl3h999FHe/OY3UygUmDVrFscccwwjKRj7xUhHRwe33HIL7373u9luu+145StfyVVXXcX999/fbND7j8j/KUNGa00tqr3kn2qtSj2qUdc+dRm8qO9qramMjhCGqVITDokwaQDqY6b/h+US11LujiREpTuBE4XQ3oZwHaxZ3YiM5OJzzuS+v/2Oa752Lrff/Vc+ceYZU1JLtVqN8847j+9+97v86le/olwsctYH34+vjJf+vZ//jmOPPZaPf/zjPP7443z961/nuz/+PhdeeQl+C/1rI0IxGR6fVIRaa5SYDF2eeeaZnH766Tz44IPsu+++HH744QytXmPuKQ8LF/Vy6fXXc+O993P6f53B5664gp/ccCPR0AhJrPn617/OTT/+Pl/6yhXc/fNvUyxOcPtvzcIan6gibTN23/vRT+nt6uF3N/6cYz/wAc7/z49zwvEfZr+9duP+33yfg163Lx/+8Kep+DMA7ySIOKBWq3HppZdy/fXX88fbb2XthgHO/MKlaMvihBNO4G1HHMHrX/96HnzwQdavX8/ee29PpVLh7W8/kc72Ln7zs5/yzSu/xp13/YGTTjzZjFW5jPRD7r7njww//SQ/vPk3XHfddXz/+7/ge9/7BbEjDPCy2X4i9YLimKRcRrt5jv342fzgxpu56KJP8Ne//oKvffUqcvk8SlrNd/GFL1zBeeedwZ13/gjbtjnxhOMB6G3zqFWrHHLIIdx222088OADvO7AN3DKB97L4xvWopWmODiIUoqvf/ta9lmxCw/+7vuc8KFjOePsT/PUymdAimb67txzr+SMM87kod//gG2XLOL9Z51FVK3ikuG559Zx+NvfxwFvOYIb/nQv137/+9z9xzs45aQTAMgWnHSOKLRW2K7Esk2bjJnSS9MlLI5SaGvHb/QlKq6DgUegluJAVPyylC7bEpIwwa8b8GutMkQUalBtjBVjoiDGVjCuLOJQ4UY+blihVFxLLQqJQo2KSkQxBKUawXiRKEiIQogS2/w7SCCWZOsVkjBhuBoSBS+RzAwmozFuASwHKSVXXHEFjz32GNdddx233347n/jEJ8wxaRq1Vq/zlasv5Tvf+Q733HMPpVKRE075D3OM1tx77718/NRTOemjJ/Doo4/yjW9+k+uuu45zv/DFzdhfLWETVKuoKEJFEaipfChXXHEFN910Ezf86Ec89ccb+d5V57F4m8XoIOSu1IC+9utXs+nB3/O3390AQKVS4ZCDD+aXN3ydn9z+E15z0Gt451vfyZpNBmCr0kjsZZddxl577cX9997HR479D04+61Seeuqp5jk+eNQ7Wbp8O3782zv53Gkf4Yxzv5zetRnr8TSys8mPqDgCnbGo12pccMklXH/99dz405+ydsMAZ5z7Zew03XTZFVfxvRt/w7cvPYebfvpTyuUyN992W9OB1NE0AHcrm7rSfPKTn+TCCy/k7LPP5vHHH+f73/8+s3q7pryfz178BU4/9T956KGH2HbbbTnyyCObZKe+77Pnnnty88038+ijj/LhD3+YD3zsk/z1wUeRLdQZN9xwA/l8nl/+8pecffbZXHjx+dx51+0G3J1WEH7+85/n3e9+Nw/d9yAHH/BG3n/iBxkbMyn/iYkJDjjgAFasWMF9993Hb3/7WwYHB7fYrubFSrFYNE2S/wm9mP5PtSiox3Ve8f1X/K9c+8633EpYLGGLlN46DUrmHAmjKcZFdIHSaB1DEmKrBC8McFSAXx+HwEc7ihPOfF/zvPPmv5JPf/ZkTvvP8zn7c+eYc0tJFEVcddVVvOIVr2B4eJjLv/JlDnzNa/jbX//Kzst35Wtfuoj/OPk0jjn2WKQQLFmyhM+d+Wk+/cXPcuxZx+HJRvlho2Nz6mW0GDJK+WgUDWr+k08+mXe84x0AfO1rX+O3N9/MdTf+lDM+/nGsXMQ5Z57I3wvLAcEBO36QB/58Nz/9/e95x8EHYxU6uO5b3+BDJ5/GkW99M7naBq658FP8+s6/AmC5ZgPTWrLrjjvzyY9/gnI0ziknnsA1X72ajp4+jj/67fiJzVlnnck3r72Bh598kFfvMHeKud4ovY6iiGuuuYalS5eCVpx83Hv4wpe/Ca5N3vPIOg4116W/v5/+/n5q9VX8+Me/pu4HnHP5V2lzbbbB5rwvXMr7P/QeLjr3HLrqJnfe2d7O104/Hcu2sVas4M0HvY477/wrHzjuXXiWi1YNYxZUFBGuWoUOQ1b5Pjf88hZ+/eOvsf8b98eycuy221KWlqvUNERpNdqnP/0ZXv3q7dE65pRTP8q733EUSRjQleugd7fd2G233ZrPe+655/Lbm2/iplt+zc7z/wPfN575wQcfzMf+4yiIapx15n58+Wv/zZ1/+iPb77I9SWzShaef/p8cevhb0Bsf5jMnnsjuh76dpx95hJ322pUvXf4t3v6ut/Dej56EELBTRsI5Z3LoO47jogsvpOA5aB0TBANUqk+Tzy3DyzvUigF+NWpGaGaSkZERrrjkMt5+3AeoYpnUaS01Shs4Iy0oDlZxsg75Dm+L53qxIhL408UGCPrHqXcFrHwJZxxu+fddz3vkh7/yWhzPSr32OlJ6TTqD55UGPiZNK7WCLxcvXswXv/hFTjjhBL761a+CtNAIoijmsgsv5tWveRUA1113HTvssAMPPvggK3bdhcsvv5yTTjqJE04+BYBly5dz7rnncuaZZ3Lyf5xuUlGAb0ElWyBAkJSqCCBvt5MXFlknQ0Zr1q5dy/Lly9l/v31h6DEWzp+LmLMb/rMr6UtbG3R29zK7v9d4Glqz2267sdtO21MceZK8bXPGZ87gzt/cyS2338nOb38vSWz00SGHHMKJJ56IjhVnnHgaV3zram6//Xa22247vv/976OV4vOXXkXOs9lxW5cNmwb46CfPT1P3k814M2n7A4TRDWdc+mV22mVHxjdt5ITjjuT8L38dO+V7+Ma13+W/Tv4Ah73mDRQzHZx33nnccccdk2XW0bSITIN2Q2lKxRJf+cpXuOqqq3j/+98PwNKlS3nVDrMN70+qX0/7yMc49JBDkZ7F5z//eXbaaSceeOJJFmy7LX5XDwd/5EQcKViUy3DKKafwq1/8jBt+eQt77TO5v+26666cf/75jIyMsGTJEr71zW/zx3vu5LWvPqDpTBx33HEceeSR6Fhx7lmf4+r/voa//vWvHHzwwVx11VWsWLGC8xvRIuC///u/WbBgAU8//TTbbrvtC8/NLYjv+5x11lm8973v/bch8/8lKY0Mk5EemY42qIOF6XzqxBVIArSQJGHeRGLKJtQmlGLe8CD1ORZa+GadCckdf/gbl13yNZ5+ZjXlaoU4TvD9gFqtSjabM9wxts3ee+9tziME2y1dSltHJyufepKdt92Npx9/lIf+di/XfOXSSR63OMEPfPLlGvSY6gvdAA43SNjsTKpoQ8LQbC5SmcX3ile+kpU1H4lggQUrdtiBJ597Dqevj7g+zBXX/ZRv/ehXDKxfT+jXCcOQXXfYDoB4YCPDQwPstvueZFIMi23b7L7bboRKkXHNBqwTm1123Kn5XFkBnd3dLN1pZyJhUYsdenoMVmV4dAgtoymYIeGlLMnZDEu3WZx+KJkzexZDI2MG9KaYkoNPkhC0z9NPr2LbHXbGyrbRiEMt2WdvlFI8fvfd7L/HHuA4LN1+B+JMFisKScbHmdXWzaOrnjZpsqTaxOqgFeGqdU3Q40MPPIBlWez7mr3T5zfl63kdU8MhTo3L7bbbHtftJgiG6O01z6PqRSw5i0qlwjnnnMPNN9/Mpk2biOOYer3O+g3rqKfhZGlZrFixopmOkJk2ZvX1Mzw6TCyqTcK93Xbb01RkVCR9HYb/Y2h4mG2Hizz6wNM8+szT/PSGX4IAkVbFKaXYNPow7b1LaHi9WkUE4SCZ/GxqxcCklxKFnKEBaqlU4tBDD2WH5dtwwic/RagFYXUEF0xaM+1eDBo3GqQW9pJrd18aXm0GsV5Cn5l/toThUBNobVk5bLuAZRWwrOxmODSTVkpnY0rueOutt3LBBRfw5JNPUiqViGNDlFar1XAcD4WFbdu8ar8VzdNsv/32dHR08Mwzz7DHLjvz+OOPc99993HllVc2j0mSxJynXiPuMdca8SQTueyUWwpwGXPM3JWVOq9613v47v+8hW2235H9D3oDr3/TG/jAEbs15715UBsQRs8kERU/5JzPfpabfvlzBoZGzLXrPhs37WvuJY7QmM3aXMiA1Gf1zWJoyIzdE088wa677kpPR4EkNeD33WeP5iUr9iTWpwdJpRThxppsLseCJUsoRkYP9c8yusFCUyqXGR4ZYc+ddkFFEulpLMti9913b7ZAmZ5aghR7pjRPPP44QRBw4IEHbv4eoRmR2WX7nZoOWK63D4BH12/EW7SEJEn49mWX8Puf/ZSRTZuIwpAgCOjIvh4R+81I5a677orjOHR1dTE2Nkb/rD5GxszYNAyZ1vHL5/K0t7UzuPopqL+Chx9+mD/84Q8zGhorV658yYZMFEW8+93vRmvN1VdfzcqVL8VBmCr/pwyZrJ3l3qPu3apjk1KIqszA8WBJ7L7sViPKVZIwunEdrrLx8nny3d2EGyoIoOBY6PJGBKBUO8ovoxthYgFaSOwkJj8SE7RbuO1z2bBhlHe9+wQ+fMwxfP6Ej9Ezv5M//v0+TjrlHBBFIDcj2FfQgsmwBLValY+e/l8cdPDhzO32GKitZU7Qi6sd+lyP0RR0ploqZWJLEMWjJEGlCTQFkLEBMVYT1aTlnxhKSa0cB5nL8cPv38ynv3AJp33xQvbbd1+27+/hwgu/wF/v/Svakch083SAcs2lvRFlVwI3rebSSTtoiW2nwEAkljAsxrbj4EuPSAtIsTBKaRJ7KmGXKb0WJt+t4mZpr5C2Ce2rGDA8E40nr4clJBArScaxaY/LRMImtHI4KYhaK4XM51G5HNoP2DB7HtvUKth1Ez7VocYqQuRMYGvjhcZDw+hwMhTcqJRILIFk0pDJJQFYTjMiI6XEcboJgmGctDVER1qhccYZZ3DLLbdw6aWXsmzZMjKZDEe89e1EUUQFyazZcwzxmwRjGVsI20MIiVKKREyy0jqOQ7R+PUkoJsHPWiNcl2qtxofe+S5OPPpoE5BzQWU0KgPzFszF0Q5CWDiOCZlH4RhuvhvbtYjDBL8Wk2ubCgYtl8scfPDB5DIW1//wy4w6ihCohoExZDrmG8Ds0OMIEnJWCUf7xMG2OJktR3heSCrjo2ilKXT34HgWr/vUCqI4YUHHAGhFNjOfYuAxUPTJuTZtdsyA7ZKNI5Z1dRjs0fCT1J2E2BZIDflqTFBy0YnGySXYPb3QNmfyokrB4N9Zk5lDyS7Q69jYrkm9tRIeJkktLbUfQkqPXG4JUrao7WlppdWrV3PYYYfx0Y9+lPPOO4/u7m7uvvtuPvShDzFaq1NyIUi/b4stAKa1AWeefvrpHHvssUgp0TohiRXFIR8rl2U4Xa+21hSiBMIqucDHkRDmbKpWlpqVRWnYbrcV3PzIY9xzy+/5yx1/4D8/8AF++frX86Nzz51USkIafEgckIQ1TjvtTG6//XbO/swpdCxdTF97Lycfdwpx6mAMyzxxoqjFaTVeirxtkC+2SlvGpuZPln03pOhO6rZalIADWcvCSasaS3FCQQi0MFEygmpTJ0RBuhbTKI3SmrqWDGW7mD09tQSTzW7dzOZ/U0mzokqnGBnHmaz4KqXPY6Hpdiy+ceWX+ME1X+WMCy5it112ZVlXOyeddBJhFCF00sSPNZ4jk8ng2VlTjagjtEiaRX+NYxqkjUIIglpIODZEpVzm8MMP56KLLtrslufMmbPZZ1sjDSNmzZo13H777bS3t7+k80yX/1MYGSEEOSe3VT+Frg7aF/Rt/jOvh7yX36pzZKwM/uAInnawhaC9s9sstHQ19IU+IqqYvnylqGnEaM8og7rnoYVA+sCExXOhw81/vhelFF+6+mpesccebDt7AcPrjZXt2D6OYyIQcRxz3333NZ/72WefpTQxwTbbbY+UsGKnndi45jkWbLOETPc8Fm6ziKVLl7Jsm6UIaWGl4FKNNpVUEmpZK+VRMSXBlpXHiXqQkSGEu/NPfzL3EUd4E2M8+PjjbJ+mOf701/vZY++9ec/xH2bvPfdg2bJlrFq1HgTEvQntbe3M7uvjyfv+TBC5+LqNauTxyKMPIYUm0ZJE96b3lL5PhGkzkH5SkxkSKzOV0M6uTUXVOk4zBDwFMJqCSGUaDfJsmyTNSUeJ8ea23W5HHv37I6jyGG1xjVlZizV3/x4pJcuXb4e7cCFReul8DLESOIUEaafYpaJADRXRDeMnChCOQ6nNKMUdlixBKcXdd9+PVBorJcyzkzA19ForqSz8ZNJTarS4uOeeezjuuON429veZgCfts2GdaZsviwETjZnwuqNUlTHgLcbI6RawNvx4GCz8sYtTEao3IUL2Xn3Xfn7mjUs2mYbli1YyLJZC9m2YxHbe4uIA5ts1ZS/W1YW2zHRPd/f1CTHi6bhZEqlEm98wxuwRMwPfvRlMhmPDCYFV7azpiO3VzDvyclSS9pJtIUjQkRp3UvGy6gkoTI2RnVinDgMDbFi1qE9H+A4Gtdzqeoc40GM5Vr0dmaouTaWa9Fhg+1KnHAI14poUwrXAcsTqIJFJp/gOAKpLZxcDsezJn+yDk7GoVdWsFyLSiODq4K01FuQzy8nk5mH7XSkhmaAH2xszm+tNbqRVkqjMffffz9KKS677DL23mdv8nPzrFq3GoDVtYAYiKWV6oe/Ncfhiccfp1gssnz5cqQwYP2VK1eydOlSlixZwOzZCXPnxWy3a5ZKJ81F2F/XzKtr8rUyThTQ3ZlnbjjM0tpg0zmxBOw1p5/TjnwbV112Lhf/93X88uc/Z6xYRLoujuOYisu0qqteHufuu+/mmGOO4eBDDmLbHbdlzuzZrF69GiEtEmE1+89Vg5hVI1X8OCRu1Dil47PDDjvwyCOP4BLjCjPf/vTgowAkwkRkGtLgXPIcOenAKE0iZbPUXFdLdObz9PX2cv/jj2F7Cts2bMgPPfQwWkPRy1NJNt9WG+XN22zTTzab5bbbbpv8YyMaI6wmSWZi10EKg61MK1z7XZsFWY+H/vIX3vKWt3Doe45kwU47sWDRYp577jmarleDWE+rJomjjpwmBEDbk4aWTiLU2BrE8BOQjl+oCpTiPvbYZUcee+wxFi9ezLJly6b85POT5KdbKw0j5plnnuHWW2+lp6fnRZ9jS/J/ypB5MSKkmPlnK0PYWmsmNq4njmND/uaHRKtWUZsoEaYLzdYThlW85jU3FtWmiWdpEmFTqlT4y+AgDz/5JE8/8ndW3fc3Orp7iKKIr3z1q6wLQ/7n5zfzzet+0ryu55VRKsJxHE455RTuvfdeHnroIU477TR222tvdtlzLxwd88njj+fGH32fb3z5Yp54/AmefXyQn/z8p3zu4i8AFrIRkdEKpE2URgssK0c2u4i2th3I55dgxx00tsH/+cbXuf2XNxE8/CCnnnce4+Uyr33f+wkSxZIly3jk4Yf50623sGHlM5x99tncd9/9gAAHKjmPk44+mquvuYpf/+6XPPjUOCd+6gJKJbORjvldhI3p2mT6lCAtZPp7xcoi27qnvCNlpziexnt1nEmCtVZDRjYYPBVCCBbNncsTTzzB088+S3l8E1EU8f5jP4DnunzszLN4+tnnuP/XP+MT553Hew9/C4VtdkKHYZPgLBdriBXEAdLSCDfFRpU1SWkToMGykAvnU3GMAlk0ew5HH/0OTj75s/zmpttY9fRj3HHHHfzkxptwppV0hlHMSK2tadTFKa5l+fLl3HjjjTz00EM8/PDDfOD4/0BpE1dL0EyUA1SiSfxUeboNhSTQMgahmriMpFIBIZCORtrpmEtNHEYcecZ/cd8DD3DapRfw93VP8fjGMW6644+cdt55tFWMAm5Qtma82SAESVJF2uY+42jynZRKJd5w0IGUy2NcefU5lEtVRp4cobJ+LUmSULFzBLkWA9VyCHWeUjILrcGOi1B78ZUUQNNYBQhTYjfHErR7JjI17ufZVPSRUUhfVCE/MUzX6DBLNqyla2AjtWeeRJcN0aIEMumwhq6EXOqtxwItZogYSYf2uIolNFHKjhzHJppo0kkZXLebXHYhudw2gCCOikTROHEUMjo4wMMPPshDjz7FQ0+t5qGHHqK3t5coirjyyit5+MmH+e71P+Ab35is5LGUJhYWjmNzyplnc++993L//ffz/ve/nz322IMVK1aQ72jntFNP4yc/+Qmf++w5PPDAHTz55DP85Ce/4ezzv0qIg2xsel4RIYxTEUtJYzMMpNMEp3/nyiv4/g9+wMonH2foqb9zy89/Ru+sWXS0tSM8j0WLF3PrrbcyMFpifKKESEK22WYbfvazn/HIY0/x5KNPctIHT0IphZQWRS+fzntTGVcJYp4brhGkhlOchhuOOuoohBCc9NETWPPsE/z6tru57IpvAFCzjPvjNCKNkTED3NTgyDWq12ynGZUWlon8fvDYY7jsm9/k13ffxrPPPsNnzv4sxeJE02EadPKT/Z1S0ZYizA0gvHFOPfU4PvGJM/nud7/LypUr+cuf7uLbP/g52slMsoZ748RJBV/pZvrLSx2w5cuXc/utt/L43+5l5ZNP8uGPfpSRkZHJ6HlKxxHHRWq1lVSrz6FVgGjoz5Yy+/FShcdq7QzrDoRsFCGAwuajx76DsbExjjzySP72t7+xcuVKfve73/GBD3zgBVtZTJcoinjnO9/Jfffdx/e+9z2SJGFgYICBgYFmefg/Iv82ZF4mKQ1sIgwCBNDmZrAzGXSSINavox7VCOwQKcokgUSl3ClJhybpAoRACYf77vojBx54IPu+613s+6538dYDX8+vf/h9Tj//Qi666CJ223dffnTbHzj/v0zVjFam30sYDJHLZTnzzP/kqKOO4pBDDiGXz3PhNdeam5Pwhv3358arr+a+e/7A0YcdyPsOO4KrvnkNC+cvBOkgUyWktUYLQeSYReK6fThOe3Oza3WEP3bOF/ju5Rfzyje9kT8/+CBf/5/vs6QA7uDDnPjegznskDdy1geO5cD992d0dJQTTzwRUgpw3aE56QMf5MjDD+fjp5/AIW9/A+2dDocddgAai2qUw1dqyjUlAoFCpAqsLrNksx75TpPOECkWRanJKIOwnUmisBkMGYnCsiw+8vGPs2zJEg475BCWLnkNf/7zI3QmFX5w7fVMFIu86YjDOeqUU3jtK/fltAu+TDnW1AcGSVJlk481tkhJs4RAOC7MavDEhKjaGM7cOdRlTJwqT6kSLrv8vzjiiDfw8U+cz/a77c3xxx9PtVbFndYpuRpExNrCT/LpKU3n4Msvv5yuri72228/Dj/8cF73qlex60474aZKbjwlqxM0euFM8rSoNE0lK41onKA4aw6RZaPilE/DUwwlCct23pXrf3UTa599hgPe9X5e87ZD+PxXr2Z2fz92AkkkTPpEa6R08VyT54/VEKBJ4slGhff+8Vb++rf7eOzxZ1ix4lC23e4Alr7iAHbdaV8G1q8jwqWWVKj76wx+R9ok2ibSWarKeHW6uGHGRqovJA3QKEwaMhkrwJURGkEpzNMT11hUHqKzOoGemCAX+E3+IhkmxDWr6bU7dhtOaqQFWQuRGoBJfQZlbTlINN1pFHA0ioljY0DZdtvUQ60cnmdwSn6wiTj0uefev7LiTUean732YcWKFVx//fVcdtllXHjRRey/16v41U9/x8c+93kAZiWC9poiFja5bIazTv4gRx11FPvvvz9Zz+Waa64BINfm8brXv57rrruOW275La95zVs56KD3cdVVP6JrgcFFtDUYtq2AML8Rp+EbpG1AyraLRFFQJdoKWS679BL2etUBvPbN72bj2jVc9eMbCbJZytkcp5x7Pr+95RYW7Lg3K950JDYxn/vc5+jo6OCIw47h5PedzAEHvZ499tgDS1oUU+PbktCb93AsSaIEG1Ku3ShN7RQKBX75y1/y97//nde88Qg+fdHVnH/6x4DJaIztm3fVoQQZx2qSiTbagwSWQ9Lgn7IFTj7DyR8+nne85VCO/fhnOegt78HNt7Pfaw+kkMviqJhYWgxM1FMsYUIcl/HlGlRadfmJT3yEk056H2ef/Wl22GEH3nPshxgcGaPuJERxsfnO/WAD1Zb52Who+pnPfIY99tiD49/6Fv7j0IPp6O/nTW96E83tPPbROm62+FCqhtexEWml+q7FFy8nLhrBJt3NQLMQxcjs7k7uufMPJEnCG9/4RnbZZRdOPfVUOjs7NyNdfSHZsGEDN910E+vXr2f33Xdnzpw5zJkzh/nz5/PII4+8qHPNJEK/5Jq/fw0plUp0dHRQLBY3y7f5vs+qVavYZpttyGRmyE2+TFIZGaZSnACgYNnkFy0GrfHXrYdyiaRXo3IgQrAHBWhQeU2SRtoy2UU86dtoDdsXMlhKU125EjcKSTIZVvXPRQH9ns0cz4XhpyCqMSbasbL1ScsakFaGJPYYqXhUZQadd7CSmKUbTLrB7utjtXSoBxJXaJZoG8upUO3Ms668jqydZWESUnMihIZC+05TAIfhpgoq0TzTYaE0LBtajfQVMpfF7vGwqq1VGy3idUD3YgLfJ4hWooHaWAedFaMgg64Css0odeku5bmREFtK+pXAUZCVECqfRE6QlzUeKywHYLuchysFE+s34OUUiVsyOI1VJVAKb/lyZG3AlLy3z4WCAQZTHYHiOnxcqt5senp6GB8bp6bGaXOrqKqFrBWoCUUuqOEkCiEltdnzWV9TOFrQlVQY7O7CjWOW1gRQwpVDxljo2444rlEfXYk9bDSJt2xbBuIRJurjbDOoUTlN0mvYUvPlAOHmoXdbxjauoiKyjOfbAU1vpYh2C4z5iq6spMNdC1qTy22DbU+mm+IoYmTtaqS0yDh9rCZBAP2JoN9+DiFA9e6IdD3CjWWC/DoIEzMngYGePkr5NkDTVx1ltholkg5P5bdBI1hSX4cbQTGZg0YjuyPikQE6qyZCnmmPDMtstgutFZXq02gVEfudxPUOOmZBHA2R6JRgLJHk7B6s8iaCkoOTj3imbRG+zNDPIHkqOE43mdhmeCxDQyO3WwNkZNXgH/q2a5KrbY1UixOUR8wcFULQt3gJlepzoOvU4zYKZQtZNpuLzOcpOy4ly8ZOImYFY6h6SqDnSPKFEKtnG9TYc1TzNlqAXdeIYYnV3o67cOHUi0+sg9oIQWEOT2J013zWpFTz2zejog3RWlOrrSJJqqhYkKspXJmg2+chCv1ESjMaRYxGCXGTq0cjVJk5rgOjLhFQbE/YoboKjUDM2Y2JoUH8ahWVMTiKOXPmMDAwgFIx+XwRIRRS9LI2aieyBcQKUYvxrJD+3AiWSEALgpJNl6XACpnwcjgybKZ9Eyw8bOygxkZ3PkWRoyOcoOBWESTUydLjdJAdX0WCZJA+HMehIoaoAfMzPXQUZlOs+qwZ97G0YptZGXJujqHqCCNjmlg4SGCWqtE9vx+rBUukBh5Fqgi/5OB7fazpMYZiXzHB0hqhQbmS2XPM+vETxVNV38z98jhzhZkjvtvLRNHHsQU9TpkJXWB10sNbX/8KjnzPuznzAx9ifTatxMpqfBnTaw1jkyCUi5fMRhf8Jpjbcbrw6iF1UTEkngiceg9xpogWEYFsZ6PqY5bnMNubOh+CRPFk1RhHPZUiHbJGmyqh3Ty1rCRJ6jhOl3GopTHSlZZEYR4R9DCuFXWhcYkJU7hsHkGPEoQaOqxNeJ2dkzryZZAkSUyl3IoVm/UIhK3fw/8dkfknikoSqqOjTSMmKyT5hYsM2FZKhvI9FDtyqBwmszBijBidoWnEZLMLCEUercGRAlcISlqzobcfLQSW77OobjzPoSA2yPo0t2zrhKTUhjUKljaKXiU+QhTpzI8hUws9sWx0xuSYk9FRCl4IaEItqKDRwm5GZJRWRDKtIoo1otWcxzxHyTGsvLmojkw9HNsLmkbMRreP1Zm5DDudhI1GiUERamNEkUWoHASQ7ayg0gIIN31GL4KcZxRsrBQ40uB2MOFsT/vYKJy0SV2EAcPmO7qwYnOyMCoRp96MSS2l99BKc58ynlppRAagqiyyjlEUTk3jyzayYR0nUSAEzsKFtHUYxRcJTTlnPMVc6tnLNC/feD+WlQVPkLYsQlXKhmNImK7SOg2O2Fa7GeWwCmENhcDSCksngCCWFn4KqvZcFzdteRAEg1PxQWnaRFiSDOCm24qWphdOoi0qJZXyAZkmjdZICjDMF3C7uihIM9LD+V6eyS1ig9ePRtCWVCkIRVmnUQKnQmeugygvzTViSGLRbJ8hhDQpJsDOFHHbNxKEa5pGjBU55Du2w6obDgsrayEtyCdpmNw2EZ0oGiPEYEjA0OuXk36U9ExF0/jqyQo7DJ5oCu2+UlOYV1VLWFtrTVAfBV1HJIKOsbhpxITtDvV8DZWJyTgRXaKMl01w8ilIPVJUqh6JlUVKm2zajy3OClTWpOn09CZ+qcHlJQGFNHVbph3Lym5mxJgxFLj2bLQGaWtUJk1dOQWU1jxb8xkMYmKlsaVAqgmsaB1WPEq9XkcrjQMk0kFjKs3CWpk4nkC6UxvOCiHwvBpCKHTiMFxrM0aM1oh6Qk/BA5FhY2UWOvFAaLyOiHo+oZa1cGWAQCMS0+nbIiEmwPcsPGGclaqbx8XHIaKdEmG0Ht+VJsqKIo7j1PRuxG1hNGUjbg+qRFEdpRXl6hjd4QQFGSBlQlF4jI0MTg6cUoi0OKGamU0pXaeZRNM3K0ctjTbL0PQIA/CkaK63wHKb+BNbR6zbsIHvfO+HPLVyLX96fD1f/NR/snHdGo495n3kpaY9rCFcwbjjUbfyDNOPI7twK3MQiYPnzSKTmWfmXH2UKCmjpNFnGTEfKyrgJmZdeapEliqFGSr8PEuSTT8PHKc5vxLlGxJHIXDdWYSlPsJKH0LYSKHwvDJO2wYs1xSdLBRDLOwwMdsqmvU5SdmTJNiTvE3/4vJ/qmrp5ZAo8AmqVYJ6jcj3iWxF7GkKkaR94WJEGoKrhwklP2Rem49UGmfE5M6xJNbsbqStDHjW6WA0MIsub5mKnGKUEDkuxXyezkoFZ2SYWfOzDAqLtX7I9nYGB9M0TNttyJLA8vJkZ29DHJeo+wM4MmZObogBNY9YWiR9fbhDg6ggwCtVEXaMjguMomkXNlaaOlI64cIvf4PLLv+WAfhNK//cf+/9uOLGnwEwa8LgFKStsdI+SbpzIeMqR6w0RbsNAfSKBMIyVAapyCwBOVyviJQJqgukLxA+uCWNa+kUpG1RDWMiOanUTNVSuqEnIZHtUE8UbbaF42WIysp4nSRsnDOXnvEKWSlbaO5bDZlGWNUYMlpr6nFAu5uYripuO269jJsC75x587AKBSwgKzR1LainHlOhXoVMDpHyIyvLQwJ33XUXh7z5YHM9jUkhpkaZBDZuNBV1ttsNXmiMveJ6FJ4x9HRERVhElk2jcjXrWLhuH2E0lla4VJppiQZpmEyBzO1aMIJGSvNuYu0ZTpecTWJXsUYFIoHIdhjs7mV718aOaowOjzLc1YNvN+C3mjnBEEKFZChRER1Y+QRnYi0L44CKZ2MFgsSXWHaV7113LR856RQmH3zSsFgwby73//bXOAuWIoOKAT4KC8tpYI0SRh2oaYt+bxZBMEigiwgrg1QWjuca4jprHgW92vSpqo1Cvg8Vx5SfW4mtTIkscdzs0SM8D6u9HZWmIYQwuJ4oHkEEYI9KdOIjLAvZIemghskAjTUmnxnbrMRyCiQTFdwwobxmDR097dj1MRztEomQpM8AQnV1DU6mG8sqIKU12S4gCel1bCpxSJl2ZttbjihVxyeIYgevPTI4nMhESIrEhKkBM9dzyIiY5ybGzXMBOjDr1svZZCxBKBxcHVGvr8dtS4CIOE6ALpTvk/HL2ASgQIUWHXKAbN1lwskhbY857Rk2as1YVXH+hf/D1VdfNuW9mnUneNU++/DV7/+QMGfRS5lsUiJvGccpxmGQWXhSklHjZPAJXUnkCLywhh/lUVqA0FhAECWmdxjQEVYJIotSWMLxNZan6C0MobRgU3U2tVpCFAQ4nkfk13EApQWJcKmm1UrtsaI6sYkOp0RieYiojfKooGtOASkFmTCk6mUJbQcduwgVYOkQKSQ/+unP+MKFF5FowbLttucHP7qBHXbYgWf/8hd2O/BA9PTKUQw05aE772NZzxLcIMCpGYoNACsRiO5F6HoGRYglclhOD0k0Si/DZGXXjPOhw7aoJ4rAdlGRuWaYqjfH7iIJBVqDUHlyuV7Gx1fjOD5CRPRmx+jyithRCEowW0kGcpLElVRcGPPbycXrobTB4G5y3aYh6Qxy11138eY3v3mL87bJLP0yyb8NmX9AxsaGGSvHOCrCUwGBq6hkzUKrZRIqlbW0ux3U6i5BDB1eCVsmeGOa2DfbsVtIsCIfrA6wjHdfTZVt3pIkWlNOf9eOTWxJ7ETROTRIZc48qkozhs0sTNOwxDPKUddqSGnjut1smFB0eEM4MsLRATE5Qtcl199PuG4dmZKP1RuSUKCOpqokmdRgyRLzgQ++m7e/7Y3kagmiYwFkTBhca01S1viWwI1CnBQHYOdSIFi+D5HrocMPGU2rAnKJD27GGBNJiEzGKak2XCGwVIRrZ8lkhojrFpQEdJgmnXnPGDKh1jToz4QQyBS4ZiUJ2JOdsLXSlBxJTI48VfKywoaePnw/ZK60TShyCkbGbB4WCksKivUIz6ql5/KIAptMypsT2BZ22idGRRGFepl6vgMtBBkVkg3q4MUIYTbJWAlcYM9dd+XPP/8pSZfCHjJKZ32PhXK78XRiyke1qfShYx4MlSCq4iKJccgmIRWZIbJs4jTKkE3z+q7TTRiOEgRDWFYhbfzYaOZpgRK0a0Pr5qTmCE4OEqiOBzhUsFIo0cbeflzLwpWSJAiw44iuiRGGe+egEUilUL4mTiQZMUFZhsyqTzK65jIJQWCThBKVJLzlgFfwioceAgwnT702SBTYiJpFRgSs7p1PUg/YNhgx79ZrR/jjpqN2PYYs+IlGZnuxkzpxXMItDKMrXTiZdiiFBKFFpr0HVR/BqY5Cpptg9WrclNNjev8bHQTEw8O4gC0EKpshyMTIUoxVFIBCei5OPkLio4ExK4uSHtkkoKDqKECqCOnUwFMkgcTzA/ySJGNDph5CR6cpp7Yg1hXiegWEJJddhN1IgSURbbbAIibBpkobMwXRw3oNv1olY2mcSBE5kjAjUEHAUMrdP9dz6HJsJlK8UCblffJqJjrpZW2yUhNKB2UnCGcStOkQISeGCTaacWlYa5IAG8gGPh2UIJtFeQl5y2UMePv7Psj73/IOVKGG5Q8jEkHRWUhPVx43ScjUqmzKzWIYh+3r4+hMO9nYp57JUqPAnIzHeNTGRFRithpESxPRzbsRSSyJNFhaM1pNm0wmAY6KqUYBlaROXgncDvM3KTR9uRGGVB/jQ4N4mV50vUynDUrbKDVBaJnqx6zvQ34C29bY1CBTQ2tBpZwjk+vCDetUvSyB4xL5Fp4072rRvFncdMMPqXgd1HDJiJiCCNFak1mygB/e/Rdzn1QIQo/ENViohdWEBX0aBh9rjm6CcWJspSEMiFNOLmEJ6lYvIirhEBEGm8hmF2w2Jzpti4EgIrJsIm2RWILYbuAZe6kX01LsjE2EpJK0Y0V5lB3T7paxZILvWWi9ijDXi3InU9OjmTxt9QIdfslUQnlbJq7ba6+9eChd4/8b8m9D5iVKFASMlmPqVhasLJ72iTMmDOdJHMO2fgABAABJREFUl0CFzfYEIJA6R1+mgldVxGmtpZ3VWFYM9XHzY2dQPcuppbwBecuiHCdoDa4UtOdzVOs12lQEgc+80gRPFzoY1Q59GmIs6tJF2h4530cnCTGCSmjjx/3MzQ/iiIg64AuQ7aZqgCCgo5YQOSEl7TKSwMKGISMVbncHs9vayQSJ4cJoMykCrTVrR2sEwOwJk0KQjmq2w2mUAeYtiTEBNBkVmDxKYRaUNtCpxxmkgNTtxHEVW1rYGUUSWGglSAKJrRU5z4ZyQDlKcKUgb0YV2fACU2h/PVHNexvOCCw6yFOljRITdDEaQlVkWChdsq2NB1PGU4HGEqYJY4eTNq2sW2R944mHtoUSgnh4GOG6qFqNtrBC0ibpqZXIqgDdCUoNNiMyQZjgAp7WLJu7GN3XAe1jiBD6urMMFObQyxBQhjjlj7A9KPRDZZACNWpkcSeq0Nee8snEuJZsll23RmXipIJjtzVTS9K2IRK4aFwtyKbRMqetDRkJRFLBKpvxmygUCFyPbiFRSqPCkNiSBG4mDa9r5g0PIgOI0thYf1sVS5nmk75yyFkRlqNIIknsW7TlI9qWbNcEVPtjCyiVNYlIGOt0ibShWR0SWRZQbpbHJ6FE+j6eFARKU0sU7dn5lEvPIGSEyE1g2Z3Y2Qkst0JNJ5Cx0H4Azz0HQWAaeDo2hf5Z2Nks2rKphjG5yEeVSiSlElJrZK2OlYAI0lRGIYPjlBFACKx3bMreXMBmcX09GlibmcMCfwA7CYjyFlq6iHoI5TqhbePkY7KyE4d2wqF16LxAt9koFVKvryOfWYTQEJcTlDNAm62ZoJvxWNAzjaxYa015dARbKDrcAB1oYluiLaiqOkpnyVqSzhSk6qdrL2NlsBMXtIUWGjdjk41jEleh07kzVukiXw0oBC2cS55GOQKcLkaERSIEhUqVtqAG9TrR+vVkpKTHKVDqKLCoq4ewI6SznKEuPcKgi8LcXqRlUXvyKYTWhMLGlx5uoptNbIUw+sGRDk/GecIoT4cuUvdshFB0OIqsBq1DxmpmPXWk6cYgrGEpC7c9RgiQiUBLiSsjOrMTFCs52itFclYjAqmoZI2Jlo81llNB24bIMQksnIwAEQNV/HqVjrwgQVIW7UTCwQOCxMOSCTWdoZYma7NEgGawNsZopouFS7voUCXCcj++8CBvoS3JktoGPJU22XULhJl2BmpDLGwY2aWN4LiABZagmmjq9DOXDUTRBLbdgZMSDVbGx6iXS6g4xu7qI7YcSlaOnJOCxUWGirIYEAFRh0UiNVR9yBZMerCWEIRt9MgSdrZELCWjbi4dmzo15aJdizXZOSytrycPUxqOTpdsNsuyZcu2+PeXW/6NkXkJopRifHAA35r0mwKRIQlnkRM9LO1axrZd25KzutHaVBJ1e2WsWKHHzZBLT2JvsxP0LEPn+1HCNmjzsVVorbGEoc0upqmMDtsiW2hHS0E9bV7G2CiFUpW4qnlMb8Mzeh6bqgkbCn2sL/RRq9QYr4Vmq1eSJOjEUUYZ1KIaUTSG1ZcC06rQSc3kSZUmiDUScNMZ4qj0mlGtiUFIlKboCpwoIlNPN8esgrYUHBaUIQlbeF+0MTySEHI9KGHjipguymScRpWUQgiw82kJcCDRWhGkjH5Ka4ZykqfbLNbn7WaJZ5x6pIHSJFozphSREMQ6QxIZbogF8TC2FPhasDK7kLAV5i6M0gLQKiGIAjwrAgWZCZPvV5YZeydnFnw8sAEZjJBvD5ifDBsjxpwKy/IRBlKAFRZBKXTd5K2rup1a1njIbbU6c8JNFLTxoGUpJli9mmDlSqIqaCQ2CQWqZANDWqWFBCmaYwYgpYPrGKBV4G9E6wSVRmSkZTVTIQU0XlqxpN0chU4PtzZusFqeZKyji/a4TM/4aqL1z5CUyuSCiCgl8uopTpANfMNv5Gi89hjLMZtBVLahJPBDGyujJt+fUmYuYCJYamgToSUY7nCJFchKjChHjMcFqpk+gw0CVCTQSUI+JROrJKY0XEVz0FqCHVOrP4udKSLkZGQhtCQqCNBCUPVcQtsicWyE57G2GLB63Gdj5FCSnZSyLipj3llq3+HkE1y3jBBQkhYrXQc7a2KeAkVbUmPY6SKb72EoP8+U8ZJgd3rUXBuNSRuHJYdgeAhyBUQgkaOarLUAKV3TviEaIfElsS+IgnHaKAGaaqKaXeq1UiTVKv6G9diVKm0qIK4LVJzFik0KMSdLSBRzHasZeQrSnjsZO4ObElaGVh2Nxg43NStY7KJk1thE04iRjsJrj8nmY/JuhCfH6LAreHZE3UmjBVIiLAuUojsosbg0gKoMkhRLJo2hHRIdUxweAiGwc1lyvoluFu0CKlYErpeud4gqFey6T4+EQLo4sSbjOwRBzjhxAgJK9GaGyDsxBZHO6wQKmQRpG7Butm7h1AyWquDUsLMJIQESs7YSLfE9s3Y74xidNfMsqrqEZQfPXQzxAuJ6B1pJhNT0MMoC1pLkFYkwKTONR80y5+mzJshlKsiCz2Bi1nRnOEH3aJl8lBLo1RXz/UHyqmq+37kUepdTsizcNLJqMHwaGW0ENEhBJVEEZBDpuvb99SgVoZSiMj5KEkUm2pamR2PXJknxPpHKsboe4lvCNKtN9ZLQpopS523crEVUV9THMgyq2WgkGer02RtZ4KzFUQEayerMXALhkIQvruT6/6X8OyKzBZmohZRmaG6Xcy2c+gQVZaOlwNYRyhtHR11o7VCuZ3h2Y5m+Do9yNQtkyblFsrKEPSTQSoDUuAsWIaQksfIUaxIdeXRZG7CiCvP0AKWCUZClFkPGsi28XJ6gWiXJZrHqdfpLI6xpm4USEguFZwnqiaBuezxXTpAyxZCIGBWBW02gUxFg4eu1xpSdDVYN3CSmXQqKaEbKEdm0+kkqjWVlIArAL8Kmh0FYjLm9aKeT/gkTibIchcxmoZD2DQmrUBujnoJRm7tpmtKpOt20hUP0iwkiK829pgvbyrtElToqEahKhWErg3YlIkgQoULlJIkO0AFEiUW3qhDkcsTSorRmLUpIctks7dIlrttYHSHCrrM8a7O6HlFXFmudXpamnbeVUiSp0RCEITknDVWPSmSSgBDUXbNcMgUbS5oS5kYqPBAOI24XVT/H4nAES9axnAQhICd8VHE9qhZSzmYRlSEyQYrN8CFj18zQKJBVhUoVr6rXUY6Dmw8oiCohNpkowHcyYIkm50VDPK+fOC6iVIjvb0AlKTeObaN9c4lOKzJRBm0zWk3ojavo2KS1rJzFdvU12CRmXkjQBYh9i8A13lhbFFLN5/DbPOaEwxjotSARXWhdQ+gEXRGoNoG0NCoRxIHECUqQ7SQZG6NuW4y1SQNarcXNMmwixdrIYwfpp0gaD4jIq4QxBBNxQr3qU3UdcvQziwE0EJGDat70scpsQDuAqynaOUadTgDiaoyd+JRThteJICKnanQJn6Ru4nrC0riFBCE1vnAZcruZsCyyuoxjd0IY0xZXiYVFLdfPItdGOb2MhFX6onGcYIxybhZDGZd51RGcJEZP1BkLNtDW1oZVKqLKFTLdC6jVVhLFRbQwRrbKamwS2iwoJzBcKtNfHEdVJx0HF1CxKTGmHiNKFcQcsJyEuckQ9hozd93586knxjDJWBnqKQDft2pUausgLZ3OjiniSpo+sjV2NkG6AiyPOEmwdIStYzrjMp2U0a4gynkktaTJmRJaDm4SYcU+uQnDGmy32Wg0Ub1GrVTEKxRoK1eoZvMUrQK5apn2qEyp0EZkO0wMj9JWq9ADKCkIhA12BI5HOW7DypTJWZC1fbL2JpTtIALIOgLbM/oxW0+Iyw4yn8H2u4gz43RnxhmOe7HSJqhKWySWjUCTsUZRQmPFkoIA34qJ6nUKnV2Mb7IJ6llEZgInG2GJBEvXqOVMqxiZ2HQojRSKrO2jkGzCpH06ZUz/wBi+62BZ0G4rvKRET1w0UTxvDnOsHC5Qjaq0p/quJG26tUaqAEuMEcn5xJE2+sObja+qJIlPvb4OW842xSK2Tffc+VAsUgVyKUeTSmzWWYaE0okUhRh6u7NkbcnKgTGqrgeOpGhbBIUCAk0kXaRWzA0icG2EjJkrNrCJeYTC47ncfLbx66Sn/ZeTfxsyW5CkNoEX1Db73KpHZHSdwJ4LgHKrCBnhuqNEcRsqyVPXio3jZufI2Iput4YzDjpMCZM6BZE/TF9sEY1HqSJ3KSWz6LA30ROX8MIsFXcWSoMtRZOgKdvWTr1WY53TwbwgwlEx3UmZKB9h6TpVy8LRAhG6BLqDRNkIoK5sqsJFO3lENUYBa8U8cnadvFMj0+kjVJ2e2ijlpJOSHzGnYLwEJ9KQbTNVPmkn77LyoBiw1F/TDBPbWUXkuzAwgJA5ZFJD1MaoFjoB483EWGajTEKGkzYyetSwbqbKplFhImwHy6uT+FAfmyDqmYXlWmg/plCr0F+sISO/md7IUscLfOJsnroQdJWLdJSLIASRFJAT4GhUPMqiTC9P13yqVo7hMKLfc0mSpMkFEoUBOddHlsGqp8ZcXuGKhE7Lx65XmjZZEkuqnfNYRRYScBKf0C7gBXWkBZoMUvrI+ig6sWnz/c3mlKyBKkAYe9QKGQp2htiP8YIJVKTwSw5eW4STS8gGPr6TQVsCp6XMHkAIi0xmodkkoyJaGg9RpuBlAbjSADjruIyX6nSmZG5ONmlS1isFKpQIByxL4eQStq2vYcLpoC1bR6gA0crCnu/F7piPNVtRG96EGBknrljYmQRVt4h9C1Up43VoSlHMhr608qYaG49SKjpliUG6aUs34Lr2KGXa6QhHcaPQbK5KE6fxPV/lGNX9VKwsQgmyMqSjWsK2bKQTU+90GQ07m+90LMIY4kCWiFlilGwUGCwWIDIa3SXwhx1ErIkti2p/Fi1dQpGnmEY6OuIKA04v87Kmks5SEV1RiXGrja6kzCIxiLIEul2g68YI7KyXGHD76JUWolxGdnbiOL1E0QhxN1gaYzhqaB8YoNw3hwlp0V2tIrVGWBZKJDi2KXPWXjsgicMQa9wn7tc4sopKBEJDuG4dc2xQEuT4RmxloW2bNi8AFZtigzFBXEsB/Z7FpmwXNTy6PJd53XnK4+OMxTHaFuTjOt1BEU9GOBkf6eSIimmVYL6djTh0hDW6whJaCeKKT7Z/NvGEjz8+iNMzl8LQENmgzuzREZw4ogfQUjLW3kmlUKA9CtBRhFSasbYOItuhd2KMfAXqrk3Ra8MpBOSdGokTwVzwUvI9W3RBfRQVG1CwHXbgywDbrdGdH0fVNSgoeyaC1RWXUU4VoTXZWoSUIRkXVH0d0vLp6uumsrZIEoGXaDyRIFNazcCTRE5M3pp0ckd0H4mwcQTMz+QJadACwiy7SkYZR2+T08+E04YOQxa5FrWoRl9ayOYDumMBYnwVknGCqBNwyUmJJWW6rp9NS+9NGt/xMohE4ZUrLEjGsft8lJBsEnPMvE8UcT1mApgYLCOFQGsLUY9pd1yKgJ+Z5JHqDet4YR7CPINiDOEp2sU4Y7qHUFms8zIs30x7/WvIv4Qhc/XVV3PJJZcwMDDAbrvtxpVXXsk+++zzv3pPYVimam9OsWNrmyTOE2EbFlSrRm+SkHV7KYYCkUjGLUUsNK6M6M8OYvsaymbn0/mEqmuTrUuCMDSAL1tAh0s0Idjo9jMvHKJQG2REOkCeDksSxAHjlSJhElLNSxQlxtoEs4rQXiuzpiBodI4FDa6PhY9IXBJVQGkLY0qoVGlKVOJRifJUojwShWeHuHZEjzWODiRuGs2xAo3yJOTnk5TLROPj2ElCxiJli0ywHEWkPAIfBDWEVgidATR+1pQx5us1AlyEDtFhQDUSjIgO2sQ4w9EYoW1jI0ksC4cELyeQvsaqVsnlavRHPnap1KQ+B+NJho7DWKYDlYarx9s7kVrTVqsx6rVRcvNkqz65fA2tRsnjMs8fZ5Pby0gSk48Vyo+Jkw4iNFrZ5Kwi1rh5Z0GmQN6u0J5W+2gk5HtQuV6U5TIWRBAltNfLzC4ZJRMjiX0zNk5OYmcUXj6mWskQegWsrE3VH6WrAlbdJSnEDLu9RF0ZxESMcCCWHnl/AJQmKDnI9phs7DMOYEtTbh8rklihYk2SKFQikXYvihG0G1F32ojrGqkUJj4YGUyAnaO3OA5KIy1T9eErF1FVhNomyOaJdZZsNELODnGJ6U/BzgiItEdgSQqqTqxNWa+QkqSjm41xTH85IOcHCEujE4EuamrqScZyfVOMGFcKlrABW8VMtPdTqPqQQJksQzKL9gqmA3YqPY6FHAmQRNiyjidaUkqei0pMVdZA1IdKI2wZHVITHm34dIgK7VRJahZx2i9HtRkiypHEo94/i/lDm3DjiIUDm1g3exaBkyHW0EgsWGGE0+i1FgdYOmEk7kCS0CFqWI3KrBwmohhJ5taHqHbkqOChR0YATS5nIWVC0peWUYcWTpzgJDGRZVObO4+ebIYg9MlUViPRDHr9+NlOUFWqkQLVyeywiO0qVKduls+76T6r8XEBR4DvCZKaS6YSkPhpOXEmYbh7MbJeQWnBeD1mVkovUHWzaCGphS5twsLLxYjaKLZVQ/bkCEvQ3tXGxmLMSKadnkwRXQSdJFgjG+kvhAgBYXEVUtgsGNwEQCIl1UyOnGfAwrVsDrevh/FKhYFYE1uN1hwxXaUiuTAhF06gqxb2rF4iWSK2QrQWEFtk8+3/P/b+O9zSokr7xz9V9cSdTuycG5ooSZKIOiAoJlAYEyqvgI6i4giKDkExYUIlmVFUdFRUzDpiRlFRGMcwOgKC0N10ODns9KSq+v1Rz97nHGgdx3F+zvt+reva14Hdez/7CVWr7rXWve5Flk33laQBdGcUvJ14sqCoCLqFpCMdp6WhZpzaetsipcWiMNa4CsjWGLI1Rt117sAJVAuK3EUQvbZAGouxAisESSWkXUowrFQC5SmEUijPEKmCMCulMuwAU3kNQpizMJF0iLqOt+YauUmMXwPbQIl5as1tjPrDqNoydLuNnp7G80Lyahcr5qkg8aZnSSenCGLQI24OjZsVZDLA0wWrxsdJVUgSRHSk11cI9jCs8AWV5gy7wwZWSEalZNWyZXR2NLnfGhI7AH2fyyLQBGYPPaT+l4y/OpD59Kc/zctf/nLe//73c/TRR3PVVVdx0kknceedd7J8+fK/2nnlQpOqB6txpkCHOhQgVJtYK5brlLwzSbdYh1QZo2GLqWyAzPhMN4dYNVemXkJDHgZUOsuR1nmXha+ZrUVg3IaXe0MENmMwn8Xv7CT0a7Tygjm7uFTYBQTasQtkKAP1rsL4viMCC8OcEnSlwaoMqab3fJG+QOoQrasYE9EtIrq9egkJ080BRr1ZaEtEaw5hZ9AqoAhG0CpE6pxqxxknEQvm7GpsvDTdkSmnIyGspZEEtFlNG2ASlmNpBzDda1wmCgogQUHRgQBWBxBlMDo7jsVDWEshFa2gyrKwi1RdJhlkvj6AMRnCNNHCMDMQMB96ONZKm46wdHIfUSh8O0VQWAbTSSQVmsIgrQTq5EBQHUOVOn7GD2jEc0hhMFbQ0g0SU4FMoefaGNFmvuFIFtW0SxK6UmmlNdIYpDFkXYUOBV3PMjts6cim4yrEgqGWhW7ObLOBjlOQBUlVEhWSblHQjgNyCjLPYIWPsAmyGMfKmG7TJ59bWtaYA6mokHqryI3njOTiFGkxhGCIwaTLaOY0UryqZkbXSAmxNbWoN1VOmwHGwogqXQaLDoEX0ZrzyFUV7c84IFNk+MB0O+P+mRQY4P4aDGZtRpNZpDRYIxDzms3mflp+lbatoK2ijoe0YLwqy32fmnZRUOFXIIOJeBBrFmru06SN8ucpRIGWGmlzAu0TZQJlDDOqTtoZwFiJLzQjtKjKLrFNkKLk8rQVOiujbwMCBlz5fFM5HszE4CArp6dRRrN+bDe7V62iqVy1UquIgTbGuLC/KBKmqdO1PtvFSiZqktw6telVRQsvbiFzjckEg/E8WnnMUiO3AUlSo1KZW1grNiavB64ySygmMZBk5HnCIIKmDJm0XejMLnnmu4FqIajFFm94CJKcoNMiU5JCKbT0aFUjRlot4k6KzktuVew4bXVPM4/BE4bCSiZaKUYox8Wylnq7xfSyAVqBz3IZIFq7kLaDqnnoufvYr/yuFBZTF2RND5Nb8pbCr2kCqSkalvm8RqoCuoFbIysqMco4nv7Wbsa8VeVzdio3041BmjaimnfxrCGTiqxpyb0BCilAGCKl6XanKWKJroL0JwlsgC488iIklpLIy0g9ybAdo4EPFMRdg8IBkqauUeSWmJTAy5HKYHIHQE0JYPY0BBDPpWzQ92NDSc1X0MkJGwnRIjNo42FanQqD6QyJiunEdcZSw2jm0VU+vtUoHZFnBdKOksgELQoG80nk9BRZR/TL5wFMFRgy2AmLHrYgwJtykhVrxDhJEBAWOUpr4jxxdABARj6FAi0FIh0kSlus7LQBSSPLKVauZAyNczt7LVs0NRIkmop4sGDd/5bxV1f2PfrooznyyCN597vfDTgi7bp163jpS1/KhRde+J9+/39K2Xdi5y4yuZQjo0VBsyzJNfkwiIKgiNkox8h0nVRG+LVxhLCkOmBmfog1rUmkNUhlma9XGWN4UecfNzwFeClapgiRleJNSz8lEAgTUJgAgaCCW4i1VkolyRE+RHUHvD766S9z3uvewfhvf8CMUjSlxAVHSzKrUGihXafUJcf3wHpgFdoGHLR6NVd+8J958mNPIra21JApvU1hqXVmUHnO9vHt7HvCE/j+t3/IQQcegjUWYyzWGFqBYr6iCHPLcMvJW0lAy4LZcIpcOpQ/pDWxhZapkAuBloZCFrzmxReSTjX51LuvYdsyQa9WSVr3Eoj+ptYTynvguO1Ht3H2U87mx3f/mMbAnrutCiuIMo+6hYpJkfMCJEQDjgeTGcl8XnU9c6TjR2kBqecxF9cQ1jDSnl8iF2ixFKIgVSm53HM/kTWTlrCAiQFoPgAE/inXIa1Cospnpko5ddGX9PBlgcRSAFZorNAIDOumNH4BKjB0GjEds6gJnKXfv8oImK420FLRSNqERe4evwFUzrCZR3s+0bJ9uHus5TZ4maFkwglHP4KXnnMuL3j6s/CyFqJss9CtW3RkCax7+dZirU+HKl1CCmncMxUGg8EKQyHBCLNkzi4e0oLSIampYU1EpFI2iDFCs/D5wkqyto/InL7QWGUYWzUMhnPM6hUYE+IXBj/TCCShdX23JmoeXV8ymBgGE7vkGQth0bjV6nsS35fMFwm5zfvnWms28fMcE1rGBqFb8mKUBiUFvjIubVEotHygddjz8IyPr0MylaIXza1QCOK8SlRUKaxiRggEORuyMei6CMPHv/hFLrj87dz6H1tZZ3MiLyDTBi0EiXUcnOm6ohCCMMkZbs3QDn2CPEMZTeAZBlUHtfYwvnDdO3nK445fuMdIstxHNN115KFPpZKghGFOKsaUT1E6DgLXikVL3006NNJolC0cULH0JBwXJuafOfwCKpll/J4dHP7ox3HLlz/DUQfuy023/Zonn/5s7v7FLxipVPDyop8qXzwK5WFcUylCleNJjS7BDrgu2H5V93uSGSsprIcIB2mngiLPSuFJwcTQMFp6xGmXWtIBKdBCk8mce3fcx2OOeAw3fvdG9n/Ifgx0LIMtN7/BFXx2A+iGIA0MdMD7Ezm4QrjojyhFbYQoCcBlRHax9I2xgtRWKITTryr8QdTQCvxgaezjuOOO49BDD+Wqq676rz4Sdz1/IWXfv2pEJssyfvazn3HRRRf135NScuKJJ3Lrrbfu8TtpmpKWGh7ggMz/yLABEfUHvAcdPYVWLaQ/QyVrEIiMthrGWElQGwdhsUVE3KpTa024XjPKktYDduFIrxIX3iuQGGEwahohUjdZywl7ybmX0Jpr8vGPXknNGDzjc58dBDzqIqEqukSkRGFKnihsDsYIchGTlsXJwgYsK3JWlOH3xMa0GUILxa5ajLAplWSGTKZYYbEyB3q9eNwQ/gztsEVmPKpW0OsN6xcZqlRF1ZGbRoluk+qmU/8VFqsg82NA4ZsUvBwjDInMaXqtkiYqGMwbLDPz+CKlbjtMMozREqstQgRO0dhANbG0YtBotHjw6nXF0wHWqD5p2Beaow4/ih//6kcsq44i0RTWIzcKKzSIAiE0Vli6YU6uoToheMEllzDfnedLH72Cjq3QFA1MYHsNZN11o0mUBAxhUeBbhURgsaQyo606FIvAsGcFQ6agbgyzdohcCPIgISwyGh1JEjqSpBUWi0EgCLRHoBWBVox0yx4z1LEiRNgUIzQGvSDQ9oB7olnI1/eea6Nj8QsHUnbVquhikAGhCVH4VqFQ/edvgImybLpaCLQV7vwUgM+UGMHXinz3DOush0KCicBEDuykGi+q0qyAas9RTQxxUzBjBeO1hZv5+le8nlt/cCsTuyeoVCsceuShnH/p+Wzesnnh3Mu1ERifio0ITUAqMpqqgxYa46VIUkAQWk1bO+gubR1jG+RJiiijUPMDFVoixuZgiwprrFeW8gsW9APcO2lZHTakIXpQnWeZMga6JqWdZhhRgh0LPooiruLns4hUkBsByn2+UFBgSU3v4ZmFeSyc+q40xs0JACtRJqJiFK8873zm5+f58HUfBmlIVEJXpqTWknot8FpY61HJJSubCbYUSxPKw0YDINy82CZ8VmlBHc9lpDHM+JZCuOjnSJEhwwp1oPAlRlgSYLxUc2oTMcUAGs85S9ZFz4JKRrXTwk9zssyjUJZCwYDSZD60Q4NrEq05eGRvrr7+ak54wgluzoqFZ72nIa1EIZEWcuuhUVgr8awiEhYtMrTMUUXBQNsSZy5qDdAqsyNeKtAdyd/tuz+//973WCElYtG+opUi931y36fwvDLP5Pq7pQik0hiV4ycF0XyK0ZDOe6AUVnoY6WGlxOSWIvAwQYxb1YZamjAX1+gGHsIWFCLBlMKevb/1LqyfsP3zzpW7BmWglrhXbxgBzQrMVxxHKsosYe4i2YtkglwqTve2mKVOk84kfr3ovyuFJRbt/hQXxSzZpMZf/YA2G/+F8brXvY4bbriB7du3EwQBhx9+OG94wxsIgj9c1v2njr8qkJmcnERrzYoVS3s5rFixgjvuuGOP33nLW97C61//+v/xc2vKHMsDNksLMo0pogKhErrBPHE2iDCCoDYBwiKLGL/ZwLTG3ec9S1ILGWc5Q4ASObasdslFTttrY4UzoLaoYgmoCEtkQnKbERd1KsyhRMYWdpASEJMu6KcoKJTEakEzrZJHFTKcJsoEdTxtqfmG2MwSiS5CQFOsIDCQyYgBu5xGColM6YqUTOZkoqAo5fWFMAjVplAwB/jGp2IC6t0mFoEMIJELVHYjFtCYxZIpgzDz5LSYCvL+QgVQVlHNq4BkUjRYxjSe0Awxy4wdRuHhofr6I8taimHlYURGIQqyUrVUWp9ZPYK2fl/SXAgYpIsSFmSVoZEhMOCZhChqY62gk9RJ8jo5gkxo8NqMzCcI3CbTVYJtqgImRllTAiVLLnNSlfbvkZdNY/FpKUVQSBIvJfUWAEyc+1SKEN8oGt4cvtBEdto1PizTamFuaGQxRu4hfCtAKzDlBhvpOqYYglwjZAFopChQMkeKYomJklIjZYEnHHExzixe0+3GM3XoBgnWTDNRDDCihRN5K++iAJKy/4tnLFXjIa2PEZZMFGS4qEmOJkfTFinKSrwSoGsMc6rFveEOdzKDMNwUDLYtQy0IC8HEgEALwwGHHMDJf/9ENqxZQXtmjqve/j5e9NQXcNtt30TICEzIZLWGkSFrmhmRsQhhkQQ0ixpG5gjVRckuRhhaQtLyJLsAZQuq6TSjpfx8q+6T1dpUTUonr9BBcJ9VrNFVwv7Kshhr6XhgUChr8ZMWhRAkvsUTHrnUJCZHC4MWRTn3KQG6u38dYUjDnJUJVDJY0fSI4hApZsiEIKVGJiIEgtD4BNZHWI/7lSUx7hzKKeDKunHrsLCqZ5KwRuGZKjUZksiCXGiETBns5Ay3XLTRCpirOrDcUq6HkO/NooVhl9BMWIFvXbSzE9QBRZQnJCIpgbVFS11GynR/HU97mraS1HVEw0RIBBpDN/Joy4JKK0FYB5z9RQHuZVisD3lYNmnUHssL5cqDsWAls9ToqJiaTgkwyNKRqtKlStOlsqygTYUxO0CnFBaQ1jKczDNUlvz314JnUZGz6e1I0A4h8HxWBKMY5SG9CKVirBc6XRqh8TBQzu8e+Gv5Pu2wDghikVAXHSqdDkGWgdYIrVEsgCKZerRqNYwUGAzGJvjZHJaMMsOJQBDrkOUdR75tdEoQIz2IGkRhhmQGtMBkHpn2yIVirlqhGyti3aFmuiihsSHoEOaFpNWzkQaUEagixNMevjV4xqCsJUxTTAGzzQqTAwFCOZsiMK5FRTmn60WX+MHW6U8e++yzD+9+97vZvHkz3W6XK6+8ksc//vHceOON/42juvF/nY7MRRddxNzcXP+1ffv2/5HfGUybrJ4cX/qaGmddc5z1Mzn1xG1qs/4cRTxJjoUswJvz+iDGepbZRsS0ivC8GQpvhlx20LIglV1afgsrLJ6VDKYNjBnEFjF5HmKwaAwtQu6xq+nYEE8YqiLhqms/zkEnPJ3q3g9n9ZGP5wVvfhOtTgeZZ4RygsLLsFhu/M4XOfK4RzG0aT/+7jnnct+O3YS2S81uRZgZvve1r/Kok46lsfcoDznmMN565eVkOiOyEWubbsqOFD6Bjks9HMhlTpd238ObqkV0POfm3Ln1Tk5+ysls2ryJ4044jm/d/i1ksQtVTJGZDpecdwknHX4Sh687nCc97El84v2fIFc5iUzoyozteJz3uneyYv9jOPCgA3nV295AR+YUZYWA0JqT//50Xn3JG3njq9/GAfs+ksMecjxf+fgnGezczyUvfwnH7LeOJz3yodzy4+8iVY4Qmp/+6EesWbOG5twMNg/4549/hQ0bHs6tP/o6Jx3/cA7bZxPnPft0alu3UU3hsve+lxu+8GW+ddP32LByPzas3sA3b/8mM8EMt919G8942jM4ZP0hHLvPsbz25a+n02qjyeh6Xc694DxeePaL+ch7PsJxBx7HI7Y8gje86jLyrqCwIXPFcgobOK0cURCrLlK5zWo0m6aR7uIdb3otRx1xBJs2beLYYx/Opz71Saygbxp/+m+/4PTH/B1Hb1nD/znliYzffTeDRjKcBXT+Y5zznvUSjj3gGA7bdChPffTT+M1Nt7Kmm7MsLQjmBfs99iQuv+5azr/oNRy18Sge89BH8dkb3stUvIud8Q5+OnEbA+uHuO47H+WJT3sMD1u5jNMecQRf/Pev8fv4fu6LdrIrGOeb//ZtTj7tZDbvtZnDjzycCy+9kF3ZbsbCScaDKYww5CXYU1ZRKSqYcJBOqcNTSQxrpgRD6QDPf+bzOeGIx7Dfqv155AGH8NZXvpgdO3fT3Xovq2yLlWKKLd2d7N/+PQ22EqmthHIbw3Ir+8mtHMRODihm2S9P2JznjGpDWAILYTTDpcJpM4bxakFTC7pWI7wm0ptH+zNsi3Zwr7+bMbubmXSMuWyc6ZLg7add5kyTOTPHnJ1lhzfObm+S2WCOpt+k43VJVEKiElKVkqiUrkpJy7RPJ3brJ0xy2nlIRoOatYzYJjXTxlhLIrp05TRdMcZKs4th5pHSEqmUhjdP3WvilalYjSCziilbYdrGzNqYOV0nzYf453d9iqc//FT23f8o9jnhRM5982XcEXeZrlk6fkpSrtdvffPLPPGY4zl8/UN43jOfy71j99LxOqDH8LJtfOPrN3DSSSexZdMWHnHMI3jnFe+ka7pLnBGLpana7Awm2BruZGu4i99H97MrmGCslnLfCsF99YJz3345mx79aIYOP5x9H/tY3v6hDyFyOPjYxwBw1tkvYfmq/TniyBMYMSlz993FK856Dic+ZF8O2rKFJz3hSfzolu8zJKapi3mksGw8+om89V0f4mWveCWH7beZJx69P9/+2LvZ2NzVBzE/+O1dHP2MpzN0xOEc+4xn8Mu77gTAI2am4fP5O/+V6kMO4t/9Ge4dbPPOr36I5Q9Zz+du+QIPPf4o1u2zkdPOeCq/m/4dc8Ecs2GHJh3e9srzeeS6VRy9z1687q1v5MxX/xNPvuA8JgcUMzXJfEXSjhyA9IqC+vwsiZhlPpinLdtc96738/gjH89haw7jxEMey0ff9hFGZjKitouC/P7++3nMPzyf4SMO4/BTTuDLv7idqaCGUNBMJnj+P53HAX/3SA7Ye2+efuwjufWzn2a1zlihNQ0teeqpz+c1F72dq157DcfscyzHHnQcV1z5bjpxwnytxVS9w7K99+aar3yCp1zwj4wceSRHnfRY/vWLX6UQGZnUpNLy69/ezfOefg6HbjyKgw4+ljPOOIPJyck/a0991rOexYknnsjmzZs58MADueKKK5ifn+d3v/vdn3W8xeOvCmRGR0dRSjE2Nrbk/bGxMVauXLnH74RhSKPRWPL6U4e1FtPp/EkvL2lB0X3QyyQJqtViZHeH1fd3iWbbJGMJbE2R9zXRU5OYbkLHJNxXyZjU83SKCTqqTcfr0PJbpeFzscFGx7JuXONlBbY0dhmKXODSHapCQsB2scqF9WWArK/gmvd8gFt/8AOufMfb+PGtt3PJFVfgFZZqDnFh6HYTrnvndbzjqjfx8a99nNn5Jn//kovJgchaxr7/DV77wudx3vNO57bvf56rLr+EL9xwI9dd+S4GbZtK4ELwjuRpEWY5pMsZLmC0VIFtRTATpLTLzqpvet2bOONFZ/DZ736Wg484mHOfcy4z0/MgQoI8YM2qdbzzg+/iyz/8EudccA7vfOs7+cqXvoaWOX5W8KH3foLrP/tVrnvna/nhFz5EMTXB9772DSIsqmxqZ4zko5/9GmZoIx/7ynd57lln8aKL3sLZ5/wjJxy1hRu/81kedsKjueScFzCX5gzJCJm4kt6gElI0BLPW0ul0ede7rufaa9/EN7/yYXbu2MaFr38nAOe+4GxOOvnJHHv8cXzv33/Mzb++mcOOPIxOu8MLn/5CGgMD3PCtz3LVR6/lJz+4ncsufieRF+FLp8p72w9vY+r32/j0Zz/FZVe9h89+7nPc+MXPUHhN8pplhxhkO8PsElUmpU9RNv8rEsnzXnYRN37xq7zrja/gtzffyIfeeiFrKwXL7QSDuGfynsvfwCtedxmf+N4PCDzJha84Dz8wZMB4lvF3J5/I9Td+mO9+61M84dEP42nPeyn379hF1lWuvxdw9fUf4+ADD+Q7P7iJZ531HN74yjdy7933LVkv17z5Gs588Znc+L0b2bh5I6964asoigKwbL1vKy985gt4zJMew+dv/jxv/+Db+dfb/pXXX/z6JVwl3/g08gaNvEFoQgSCNIpo1mpYBF5RMDA3Dyaj7bWZ8jv8Nm9y5Y1fZd36NcTrVpO6ZCWRzQls0W9LYSxLiJiyTOl0Ucz7Ect9yxrPsHrGujSED/kQNJQllk5EzdMCX/tQVvXlMmVGthgP20wFHdp6BtPZhU6nmPUSxsM286JLlqfoNIeuRnQ1dAro5NDJsd0cnWQUqXvRyWhMdsizjDzLSLq7uMfOc3+hydOMKJ1lMNnBSjPFqJ1nRDRpiA5rxST7sZ1hOQ1inlRlGG8O6U/DA1OrwiBEwXA6w0jW4p0XXsi/ffELvPeKN/G922/jnW+8Ct/4KKsQCJJuwgev/CDveNc7+NSXPkdzrsMr/+FCrAmxVvKzW3/GxS+5mP/zD8/haz/4Eq+//FK+9Kkvcd07riXMXeoCYKhlqXfdvSyEJitL+KWQeNIj9CM++c+f4xvf/Q7XfeByfnLTV7j6qrczsnktSUXyg8/cAMAH3vhGfv+97/GDT95Ad8ZnYiLjpEc9ku/c8D5+8Y1P8rjjj+L/nHU2Yzu2uXSniCiQvP3af+aggx7C7V+5gXNOfzqvfM1rueeeexDSktomZ7zo+Ry070Z+9vVP8PKXn8/5l10DwEQMmSoexKuzQLfb5X0fuJY3v/fNfPTLH2XXjl2843XvcJ+1KR+58h38y2c/wxuveQMf+9rHmG3P8u2bvo2WlvnYMFOzTDYcH+r+EUHuuZTQqinLQNty1Ruu4bqrr+Pcc1/Bd77+XT50+TvZWKvhF5pelvE1772GZ730/3Dj925k3eb1vOxF57Fbd/hdVGGyUBxx8P58/fqr+PV3P8M/PPs0zvjH13DTL/+D3/k+9waCTFhu/OznUTWff/7Kv3D+xW/k/e94Pz/63k+hHy+E973jfTzm1Mfx5e98nsc+8pG88IILie+ZYzjxibe2eP6pz+OovffnR5+6gU9/9HrGxsZ4+tOfzn93ZFnGtddey8DAAPvss89/+3h/1dRSL0/2ne98h6c85SmAI/t+5zvf4dxzz/2L/57tdrnzoYf/xY8LsKeWWAvTBdRNN0AcIGwvNyyodGNUJun6mk61CkoQVxTdjsYYj9x4dAt3jNVRhkosRA3Ou+BVAKTdDsuHBnnVK1/FRRdeyNWvfjU6lfjkFHnOVW96Mw/buIE88Bi56t2c+Ki/43O/nebv9x3ljVdcy4UvOZMXPtU1MTxk3Wq6r3wRr3rT1bzl/LOXQNxlYp7RYh4rFGSGNHXTZjqugSkIynz5mS84k9P+/jSU8Fj/5kP54Xdv5cYbvsPLXvICQlnw+otfy4zVrEp3c/wpy/ntT+/kW1/8Nqc84TH4heK6D13Huee+lNOecCICy0fe8ko2fe8H7v4ND6N3z4Kx7LP/Q3j+y17FKjHNMec+n/e+532MDg3y0mc9ha6Q1F95Pp+97jru/u1W9tqyuf8MfKtYU1/Dyuoq8rzg/W+9mA17byBKNS96zjO57JprQcDKUcVA4JEFMcOD+yOwDAY5n/zcx0nTnDd+8OPUg1nCaD0XvsPjZc94GtdeeTUrVqxgIBxgZKDBhy57FfeLtYxujjju0Y/lBz/4Pv/nmU8lbtS4d+5elFTsPbg3U2MzTPs5I2Ka3/1+K5/5yrf4zA0f4oRHPozAGjasX+fSYxjCMrrxzn/6Bx51zDrmvDr5+efyzGeeQSxClJEcdeDBPPbIzdjuNAI46lUv5os3fY/PfP0nvOS00gBJOOH44zjreS+jqNU56+WH8dH3X89Pvncn69cei07dJvnSs57Hox7/HLRSvOr8ZZz46KOZ/4+ETfus41OXX8rTnnwyF7zwTDydMrV+Py59+9t49hOewhXvuoKW8gBF4fkEtRAxO4dMc6RvSYxPAXRCSTV11V0jsx0+8OkbePUVV9DudNlrr03c+JEPUmtFTHs15uIaATlaSLRQxL4izGZoiA7xyv0Q1mK0Js8M+fhOtDdE7o0TTRfIwl0zyy11BULV8RhmbvcYWlnUsgbL4uVsn9jNbNrkO6/8z4sM/ifGS9/yGnRYo0tEEAf46RyhzRnVmhEL00TssqMMJk0aeQqiy6ruFLvjEYQRrG1PExYZ555xBtI3FNWYLWseQ0trXvqyf+KKt/wTHSOxVpDnOZe89RIOPPwALPCm97yBUx5+Cr/82XYOOugw3nv5h3n+i17GOY85lbDQHHvAWnjRS7jkyit525kvopc+DnIITMzmfJ5cCLazgr2qFcLGQrp5bmyOfffazOOPPJhsPmTL8jW0j3wYqTFU1+8FgBhdSXXVOipFhrCaQ/baj0P22s9RlgLDm85/MV/9l+/y+W/+mOeeeRammyGt5aRHPJJznnw6AOc/9/lc85F/5paf/5SDH7qWj33iaxhjufbyS2nGDQ7Zf5Dn7pznTRdfgCnq6HQl1rrzNKKK1RWsjinygkvf+jbWb1qHLwrOOuccrnrrOyj81fi6zac+9ElecN4LOPGJJyItXPrmS/nht3+ItJLBcJBuu+n0sIQPQjA5IBlsJcSpJtjd5hMf/BhXXHwxZ53kOEEsH8QechCzYZX7hxzN4kXnnM8pj3oaKrK89MKXcvKxJ7Pr3glWHXAUbF7D6f+4nqiYoyMUj9lrHx7x/Z/y+a98k9cefhADBnwrOGD//TjnH9+IRbBh4wF85mMf4Ge3/IRHHffwPhfz1Kc/nSc++Tmg5nnhG17G+z7xCX75i1/x2NojeP9H/5lD99uP157/MtoRLKsO8OEPf5h169Zx1113/VkA5Ktf/SrPfOYz6XQ6rFq1iptuugnP++/DkL96+fXLX/5ynvvc53LEEUdw1FFHcdVVV9FutznrrLP+2qf2Fx31tqSaCvzcohoDqFWrmRUpO1SpIBs4rsRIHDG2uOcJbs7d1wlpsIJhHfDDf/kGl7/9bdx1553Mzc+hi4IkTel0u1RFjMTieR4Pe/gxsHMnqjCs3/8h1AcGue8/7mBunyfxyzt+z4/+9Vdc9q4P96sDtDEkScodZoB6uMAQT6xPJHIEmiwpib1+BcsQOrOkpXDgE45/Ausb69k9lyBFwoGHPZR777qzbNwnuO66D/GxT3ycXdt3kCQJWV7wkAMOQlrB/Pw8Y2NjHHbYYbS9Kh3hU5VdjjjkAEeeG1pNMe7g4gH77ssaO8uInGNMDdIYGmbzPgdjjesNdVTFbfj3T09i0zV94qDRGmstWWGpVCrss3YtpBprYNnACiampzGxREjXPFKIDF/k5NZnJgn4zX/cyz4HHcxgmFEYQ1sbDj36GIwx3HnnnY7rZS0H7rMZpRRh4YOEwRUruPe3v0IbQyt311D1qyihkFlASIgMZvjVHXeglGLzUadwn1nogOxZzaBOyEpexEMO2AffakbzWfYZcu/N3Hs769esotXucMEbP8DXvnMLu8anyAtNkiTsuO9+N5diSS491h56JFNxFbQBJRldsYLpqUnUIo2ejQc8FK1ci421I87ATk9Os+9DNvGb397Jr++6i8996av0WCEW54jsuHc39X0PwgpBI6yyvDJCsnUCsIRhjqk3KNpzJCKgJUKqiWuN8cwnPJETHnYMuycmuOr663nhua/gK5/6LDP1QSzQwDBRd5ode9civN33lQvEgvKRykfaBGstjW6LXEdUOi5UXyyzGCmJ4/WEfp3OvItuaWmpexVUOsdGPcZk8dcrMf2tXU9gKgwZwbjnk3rDDBQtlmXTVE3CCE2GaJFlqgxHQS3tstJO4euCUDvu3ff/7Ue8+f0f4Xf3bqPZnKcoCpIkpU4VPxDYYgDP83j8QcdRz5x8wuCGQ2gMDvL7rb/jYccczl3/8Wt+cftP+cC7ryodMtfBO0lTprWhFpaaTfUBguFhouY80igKGzOeWNbWnWI2wJlnnsljTvxn9nvUqZx0wvE84Umn8ujHPIaZXTsoynYUbS9md3WEFRXBsmrE3O9+xevf+W5u+v4t7J6cpCgKumnKtnt2Es04kQhh4eAtbjM10qfwYlasXMn4vGWXv45b753k4P33ploJiQfWMRRUOORwp08mBa7/VF422e0OYIIBV/EWV1iz9mC061BCdXgN0xMTBBpWjN/H5MQUT/y7J7JBraQ1PQ0q4uCHHIIGGv4y6CRIa1FqFC0l87Lg/niIQdnivn//NWmWcfzRRyOkRShoRXXGZY1ceOgZV7yy6fBD0SrCb1tWjjrx1fGpglXCQ+QZH3zP9Xzy059gbOcEeZ6TZymjcUSUV5izVXIbsNd++zkeo7CsHYrZuG497V0zNPKGEzcFHr3/eg5YsQprV1IMFTTqdcZnZjBK8vN7fsf3b7+NFUceBUKUcVE37rnnnj8LyBx//PH84he/YHJykg9+8IOcfvrpfOADH/gvH+eB468OZJ7xjGcwMTHBpZdeyu7duzn00EO56aabHkQA/ksMEcfs+28/+5M+O97ezVQys+S9WFr8vMZ0vAxpDet3j5HGIxhSaq0phLXowUHEwADp+C60xZXsAb4f4Gc5slrFX7sGISUjyyrMjxuaue73w5ia6ji+TTlCJcFCagxzVPjtXds49dQn89znPI9X/uMlDDYCfvLL23nFK17hWpdZ+umDcT+kp8STeW5j9HSO8n1arRaves2lHPH4J+EVObVknFW1Gt20Qbe2HFk2PCyQ3BVswEszRmyHRln1of0BVmrBpFwoFM+1oeh2mWqWwnHlvPfTnM99/etceulrueiNF/GIQ/dnfRzwhg98mp//26/x7WJqHDS9GuPeAANlBMJa2DrVoeI73k5NGBolo15YDyEEnaDOTruGVXYnQUn1j4s57LJRvJrb/Fo53DXWYqKZLngBFrpt35EprSWtBFRN6volCVjLFLvEKIn1sIVFaMNAmjAZVgBBKAVLhtX4vgdCUfcFrUIghKCwAqM13bIrcc2vobOiBACGXdFov7ww1DnG98GXkBsKFIaIotR2mIxWQDyKl7dJpPtOYSTGSl7xhiv59g9v5/UXX8jKdRvJlq/iwmefTp7nSN8wX68hrKUqBaOBh1cYVLsgRIAxaCFplJtLHtUQ3YIgsP1qNWMMtUaNdqfDWU9/OmedczYjosmM12B3MEo97TC0YTNFWb8VSYnpdACL8BRS5chkEk+BHwRMJaOkqkvUnmCgXmd49Wr2CwIeduzDWXnIoXzyllt53FOeykDWYWTVMqbTDC0FHWNpSM+1u9A5lJ2k81I5OfY8glnHk9ANiy8NQcviVd0cKsr+NEZaYiFh9l4ARoaHOee6zzA5OcGMDVxLkUUj8hU10WFgwokDSs+iahXE8CqmJ13vG2E0FZUikyaU3bf9mqbwFPexktHWDNXCzfiuFzBeGaYQkljCoLI0A0WKS9d0VcTdlQ2sm9/FsGo54cpGAXKheXtdd7Fl14D7Z7dxyj+8jNOf81xe/4bLWB20+OHtP+d5r3gDWThMrtO+dfEmJ8iX+YQ2R3uB65MErGq36LZbvPgVF/Hox52MkIJNwxUC382/YvVa7ivcGutGMXXbK6Fx+jAzGVTaGSM1B3Ye+tCHcu8vf8TXv/olvv2Tf+f05z6X4457NO+8+gN05MIWVBE59aiKDEMuescVfOvmH3LJqy9m85pNjGB59vnnkxQFSRBSKIURks7AIPeuXkecKSqZAaWYVpaJqNLvw2YBG/kEUuJFbp7oioeNHgBalcAXmsBTDBbzNP062gpEbktagmWijOBMd1JaNmDeq1NI3xVYGMO9kx0IXG8kJ1Zh+rIXM40B0lWulUG+yhIOuGaXxeAgjd3TzATD/RLDQnnMVhWiYmHCdevOBAhr+Nw1V/Ke97yHC173evba52AqcZXLX38R06lihx1dNFclDa/LXGWArYWhow2R7an0lPtSWTEkhMBXvuujFQ7hD66nlWac/JhH8brXXEg8umnJrVq1ahV/zqhWq+y9997svffePOxhD2PLli186Utf4oQTTvizjtcbf3UgA3Duuef+j6SSHjiEEIhK5T//IFDPPDyz9PYIIRCBYdYPMFKR1avU8zmMMcgwRFYqBKtXMT85QRE5g6msJcoLB2KiiGD9eoRcyNuMNkLm550hUMbSLc2Mj3s4g5kzLjmSeWn5j3//BcYaLnv9W5BSovOUL37jKwBkURUwmEJQFAW3/OvPOHn9WkSec/c999Ccm2X/jevx/ICHPvShbLv7d5y2116AZXS6waqRBulcwC4tUKUGfS49KgYSPGTZfVYNDBBWKth2wRoEW8vN/KZv38z+azeh4wGszvjtL37B6c9/AcoYbr/9do468giecfYzqOcpq7Vm2333YQUoK2nU66xYsYKf//znPOSERwMQ65yf/eq37HfQoXSyAhtV+/e0p9ExhEKWYHFKBCRmHWuk22SGiiaeHqOn/9XVhqGiRUN0ECX8arciVK7pqYNkKnCNIsOAsGnxhKFGRhYEbN6yD1/+7CfZ2qxQsVUIDb/6yS1IKdl3333dj/Q0KPyYKAqoT7h7ZhGkKiTP2qBcRKaYawGKOWVJCNm4/8EYY7jjlm9z7FOfSqWzgzExRGI95qR0atKAJwxNr0Zb1dkWOX7ZHOuYLNbxo3+7gzPPfj6PfezjmI2qBLt3s23HDjjyCIKqZjDz8bRmJApZEwXo+RSdO+85DsqNanHlVG7A6P7mZ1RBHMYceuCB/PrerazdtDdrxBQdv0YQrsHLc4peU9MyumNKAqOs1bAkZL7ALyyyMoBIBZ6X4lc0eUehWy3YsIa7zQTGQppl+CZntDuDTocICks3EHS0pqF8t5sv6mKelUAmyAtkUaCVYrIyyIZkt6sU7M6i6qNkmQMSQin82e3uXIMaYmAN1aJLHMwwV9sbawWNpsYYS6Igt5YZBmgtD1k9P4syBXQ0tnM/A9LDCoXSaaliICAISHyP+UqdGapYYGK4Qpa1Ge3M0sBSS2eYrgzQHKizy1uIxA2lCUp2mQiGmYmGGNYJ6MxFDH3b1wCxWjgNkJrlGz+8H2MM513q7ENAhzu/6qQstk53CGK3hoqi4Ge/+TVHHHcokPPrrffTnJvl4I0bETPTHHLAAWy973es32svMBahJGtHq0S+IjOGuba7z6EU+KV6rbABo1gmEOycS4h8RTV0c6FRq/D0U07ipGc8j4c/4Wmc/cxTecUb38nA0BCe7yNNRkXkfXD2o9t+zplPO5lTnvQodlY20mq1uHf3bg6q1dm5fCVh2nYK055fvmC+4orvCymQGA7Zsoqv3XA3nTQlMAmTheLf/u328lyhGnSQgSOuNgZDbNUni5zcg/UkRT2A3FLiITQSXV/HyLLl/PDHt7HXQUeBitBac8evf8m+Bx7kQj1l77Cegxd5Kd2oBlJw0Np9iOOIH3z/pxz81Cc7nlgQI9GsR3F/+R3Z1fgCchZ6vIFlcH6aW396G4868fE84SnPdr9jDPf9/h722rIvFZEzQJNAFgzSYpmdpJuFZEFEAXTDkLm4ykC7rOj6AyJ3bQEHHXwA3/jK19i8fiPV1f8zna2NMeT5njW2/ivjfwWQ+d84qsqn+gBvzGJpRpoo6dCp1Jmr16mNt5GAkRJ/zRqmd+/EFAVCStpBRiXV+BpEEBBs2OC6xi4atdBDhK7kU2cGAYxGPr4QtOeb/PbOX+MHEj/dTSEsg0Mj5HnO9Z+5jtOe8mS+961v8vGPfxyAcRuwTCRYLfB9n8sveDnRW95GPUu49A1v4JAjj+Twgw5C+T6XXnopT3rSk6isXMWjn3wqE50m//K1X/O7O+7j4otfT+L3IjIetSRjwHSp5S7l1awO0miEJF0XI+j1C/nE9dexZuNebNqyL5/+yPuYn53l2WedDTZn06ZNfO7GG/n+t7/PXutW8b5Pf5mf/+LfWbthE7bs0vy8s8/m3e9+D8P7H8imffblmvdezcx8i8JKhBCsXja4oDpbWKcCKgKUFAxWfASCtlDcZZeX5y7xTUqjcMBmL7mbQdlmwGkL020FfRAzHTvSeFF6iBvXrebb3/8Bd9/9e/TIJoqVVZ5wxrP4wBVv4dLzX8QLz7+QmekpXv+qV3Hq005HVgYd2OoBGS9CxjFRPoNfooCWqhLqDkEQEKiAZjJNR8QkJYhq7L0fzz7lFM6/+J+4OhActNcIv9zeYevkPI89+TR0GZHx0bQBzy4YAJ27h7Blny18/vOf5+iHHobxQ971ljdjjEH5xnWwMDHC8/rz0OqF6F9cetxdz0V5PM8dM9OKrKysSpVk23SH01/6Kp755JO46JLX8vJnnYSpLuOWu27n1u9/j4vf+k4nYlEqoJhWD8jUSe08mTLkvqUaD6JaBff//i6+8NUv8eiHPZrhSo3tv/oll33kesIo4rhHn8BAMY+WEpEW+FbQDaCtDUgf6LqITDny1IX1ZcttrkprVuyeIh2Kiegi2xNQG0EXbn57SkKROIM+tNEdZHY7LVXBIvFMQS0vsNJj1WDETKaZbmfkRGxtrKRqulSNIeq2CXUGuJ1YexFdr0pH5bS8av/ZNWRKMDRI20RsTWusnJogThNG27NIBVODQ0RC4nc0K/2cLGsyEQzTDkJMK0cKhQ2qWAtzzRa/+M2dCAGmFtDOLKvqVYo853PXX8sxj34cP//Xn/DRj/2zW7dJQSMsp6fv84q3voW31V6NHyouevXlHHzkURy/YT0YwyUXX8xpp5/O8vUbeMLjTkEb+Opvf8Oue+/iHZe/hZESrNaUhNIudExAQ8yS+XXmCo9t0x32Xl7jmquuZLAasnq/w8jlVr7yxc8zunwF9YEBlBCsW7OWn/3oZh511GF0Zwuq+69j77028/mvf5fjTjqOGW+KKy5/m+uijgPaRdxAS4lZ5BT2hrCw2u7ijNNO5C2XXcELXnUZ51x4Ib+8d5zr33W1+1CnoKEcwRtgXSWkXQIGKwSzjSFAECnNusGFNPuQaHH2Wc/lg++5kn332cJea1Zx3T9/jPn5OfAktuaDNSyfm6XSamGXB8wEEV0EUWGJg4jzX/pCLn3tlTSs4pgjHsr4fW1+8sMf8NJnncvyni6RsdAsGIwgK9d2pdMmsQEr1m3m2//yJX75s59SbwzyyQ++m5nJMer7bWLvcBayFqaEUaEtGOhMYVsKXxcIa8mVT7MEtOxB7sECY5HgBWc9i099/JOc8cLzufA1lzIyMsLdd9/NDTfcwIc+9KE9Ctj9odFut3nTm97EKaecwqpVq5icnOQ973kPO3bs+G9HY+BvQOYPj/oq91o0hDWo+Tuo5m061GlHMVoqpDVOJ6DTdiBGwcia1bTaO5kKE+KRZTRqIw8CMQAdY7Dl5ixyw/J6xMqBiLDi8+Of3MLxJx275PPPePZzueDSN/H2yy/n0ldfwiOOPZaLLrqIl73sZSTKpx26Y1WiiAvPOINXvOB57N69m8OOeThvu/JKEAJrJY8+7gS++tWvcvFrX8uHr7oSz/PYsnkTZ59+JhVtkb32DMYQJTOEufPAZsI6kx3N7qTJgBTUDcyXpSMvu/C1XPfeq7nzP/6d9Rs3c/VHPkklqDNR5Jz67LP59S//nQv+4QKEEDz7ySfxouc+jS99/1/7xLMXvvAF7Jyc4tIXvRApJc961rM5/nFPojU/z/qhmGroRKYAdCbwQnBdfqASeGxeVmX7dIdcu0W8I1zBtFfvRxO0FeA5nQjKkDHAeL3RBzC5cH9f8Oy/55Yf38rDn/AMWu0OH/zq1znh+OP51mev48UXXcazTz6BKIo58QmncMGll7Frzhnz2dwjxcd4EVIpfKnxyxybFgpjKtR9H4qMpJA0FzV+lL7kqktfw+uvuppzX34BU7MzrF69hhef+zL3/TJE7escYwXRks6NIJXgyiuv5KyzzuLJT30ao4ODvPzss2mlCUJYrFWAv6gFwQKQEcJt6rXA69+vIgiwsYfsFv22K10tmOvmbDrgIK777Fd5/9vfwCNOez7GwroNGznp5FOhMIhOec26wJRVY6ISkJWdmI0UJPk4nj9CPTL88Lafc/WHP83M9CzLR4Y56sij+dgXb+KAtaPkc9PkSqIKTVDyhDraYJVLCfaAjC4KdJ5TyYul5UxAJqqEtosyiTP0hdPKCXv9Y2rLXHqquQvyDs3Qpbar3Q6+0WSyjk4Ma0ZiRmoB90xOoXVAW8a0JVCvIi1ECgohyXVPxs5FZn0Ja+wYDToQjoD0KOKQZq1CZ/dOKvNNhudnGW43ifbdF1GXmOkJKiZBmQItPZqqQl1FFB2ByQQ/uO12jnna05Zc57Oe9lRed/FFfOB91/DOt7yeo486ile/5jX840texJrBmJGGJPDmiOOI815+Ps879wJ2jo1z2DEP5x1vfwe+kgSbNnLclr255tM38oHL38r1V1+J8nw27rWF004/g7vHW6wedNdV91S/x1FhA6TQrPWbJIySFpq7djdpap8Pvvtatt37e5SSHHjIQ3n39Z+hEQesrvu87pILee2b3sInP/lJVq1YzV133s3b3vYGzvmHF3HSyc9laHiYM897Oe3mPBJnh6UwTv7SZtQSjdIgGmWKWMCEGGZDtctXPnoV/3DRWznh4Sewed/9eM0bL+MFz3bkYKOXKsWuCX1q/VSxIChyhk2O7IWJgBEv4c0veRoT0xO88twXIKXg1DOfxzGPPoEIi5/n5L5Ps1ZjoDVL7he0WObuVVCQiYKLzn8Fvldw6Tvex86xCVatWs2zT3dE/Ooi37kwhrkOqHYZHbc+iYx44T++komd2zjn2U8limPOePazOPmkE2h3OiD9UgTTzT4PSIOCuA2qyAmzFLCkfoloxYOBYNcXpFKw14phfvTFj3DBW97HSSc9hjTN2bBhA4973OOQewCQf2wopbjjjju4/vrrmZycZGRkhCOPPJKbb775L0L2/au3KPjvjv+pFgV/aCTJLlpTc4zFKym8gBXtGSIkHWvxdY7OMvxqQVCFRDYY787SCBusq6/b4/G2dzOm8wLfwLCUrKiHfZJcf3RnYeZe8Cukg3tz15hTxd08WkXqjKnZeWZtjAVGTMYqdpK3FUa746RBwK6R5VTzhKDTQspREJKhVVV2ZdPMmCrSaJZNjzFUW4nILJ7ahjAZrXaIl7nNRw4M0BpazmQrIy0erKzrNG7/+BA4kLTctKjRZWt1NQPTjl9kwphmXCVRAVUDnY7boFapOZat2gDA/K4d+FOOuxQOGgqxiWDNUgVmk2vysQ6/q0u0EAw3c/xkHIAVm/Yi37ULPeOOEdQKtkeDBCbAhiFtJdinsxUrStVNfH5b3YgRkg1xwGBrB1ttyKzXoCpzks44igqxHKSdFegyrFz1BetH68iky8xUQcvmzCsPiWHdqEc973LnbEje4xGZnJV+ipybdQ3/hIABmJFDCK1pGh9fWTbJ3aT43FHbTIU2HaqEJIw2CzyvxuCKCmm3S7J1G0GRU1QrhKZNEBcYW6Wwq/BXVRFlN/V8rI3NDd5IjIw95roZW6c6SGnRtRBhDTSXtuoQCHxhCbIuHT8qNYhBKUse+shu4XCE0KwNDfHuMUQQYNdVyPMZJMqpEQOeHqbSHadrQ3YF6zHtNmtartHV7gGf5SOraO4aQ2Dx/GUUVjE25MDWPrSIWzugMgKD60naLVo7d1BNHxyqLoaHUMUMFS/HBjXGHNWLRtyhIoDlB4JOYfIuLHBHbQsZktUTu6n7OfN2FVJJRtZUEUKwa347U2mXuAjRZpDUPrhBhhACZTWBTlgz0iDubHfRn6GNEA8tfHB+J/nERL8Tt4xjgg0bsJN3IHTOdrGcQviMNmfws4VrE0rixxmqViWtrGFmlxMeFIHHinWbYOw3oDMY2QKh44mRd9k2czdNKVlZXYmeT9gVDwEWzxj2qcX4nseubsp4rgmKnP2HB7DWMtPJ2T2XUJSRkaFKQEPlhK3tBBRMFptRokPua6Zsrb8W9jSWVQJWDsVYaxnbdh82iFBSIdIY5UkqQynx7DYKBDNilNxKhiqKoDbCxLZ7qYwuSNumc2sQeIysrdM1hnvaCQZB1XRYk4xzT2UdGkXDk6xHMTbdZRJDI/LpiK1Ya9kytIVABVhruW9sjEJ6VIqMKAwZqSjumCrI8Ni30iRMJhn3h7CNNYwnKYWFvz/iUE550pO44BWvZGpgEBCs7OxGVwQTrMAzmpAxTK5ZX4wgvB2EuaFIJHLlFsZnpxg2g2g03qoGd+xqPqg0HEBaw7pYkAYxu+cTfFEwIDLqzFGv1EAImp1Jtvk+W/KcwFru8zyqSR2d5yAE7fogrdBRLFbbJirwGYzcOc8Wmp3dDA0c2P49ns1phSHG1wThcqLwL8td/X+iRcH/jUOpGOnPECcdmrWAuTgmzjqQgc4cocWLnCEPzSyhtLSztiMBPgCgaGuZLdwmsb4WUvP+QKhOl1RYFRD6iuFqwFQ7ZddcwsoKtG2ABQKTU/EtwgqCRsF8dwQvaxFmGWvHd9Oq1wAB0hGIs06Bp1LAKcrGK1ahUoXN2giTkXdUH8RY4UirI7WQQXJmdk4zGw2SSEnVU0QIZOq2JxNLpsregxvikPnZGbpGUhhBIRRaL7RrEC2YCQaJipSMkCyRCJvTKS99VMwzykIbigLwpEAYi84UBDCXzDEQLZR7UpIWaxrmPEh9hZ+497Jt2zEtlx8OBhRC5YgyYhIUObNeqV1ZNunsBjWMcMTVupIY6TEvXFh2RVRhR6bRZpbBWpUNQZ35Xfdwvx2lnUvuHm+xfrhCWFXoZkHbarRQdFOfTrtJLpxXpKxhoJgnKyzKLzsXW+saxcXughpFE6yEALzy3Ipy+aaEeNUxPNxmlYyPExS5k1kvusRlisj0GoIuso/91JIq01uRjy8gK70kW3Y/ENJJ/wsbYqwls732E1AhZY2YpCkGyFUDTZemCNFWsTORrPIiavWQLHfgMapsQOs2aTqG0FPstkNM2EFsWiD8iNk4ZLCbsmIuR1e7SCkxxqBNgRCKWEo6xtCRgbs9ZUQm73aJs0WgSwhUo4Gem0NqQ7vwiVWOyFrEKqJjFJE1UF3mJuvMVnc/41EyJMJa6lFIIFqI3GK0odvMqYQp1bTFtDBYqRjMBAhJZVnkOoErSeBJPCmYHdtFmraxpgphwwGZZH4pkJEeXmT6QMZ0u6R33+3mgPZZxuKiA4EaGMAbHUFKDVO/A53hLZJ5DyoVF5HqpdxUsOS3gtJ3TXXOdDwIwGg+SyID7kt9NitFswQrgc77tmu4GtCIPHbPJ0y3M2Y6WXlmax2QlRYtojJAY/FKr93xaAwSx+UZHI6oVhdIpkI5YOp5CpMLdGFIM0UMeFh8ack1FIXB0wVSLXWXZNDBV8MIIagoxepimh3eEG1Z4a7KBkAQkLA2iCBV1BBMAq20IK5GJEWXTtEhUIGzcfUBZlsJifXQWqGbCRkRAvCDkK137+SLt3yNTcefTJamfOYD7+P+bds49dRT8UxOtdOiXakzURnFK1ONDZ0xb1MH1kxQKhg7x8+22yipwICxBb6SDFV9ptsZcZGihSJTHp4tWNGZJpjLGB9xjnFQ6gnlwjq7Ljza5X0vVEhQJFSsJfA9uiUXJc4ztJR0/YidoobqjtG2Pk3jkZfgMzQWVaaurVcghEfgLxCJ/7eNvwGZ/+KQMkYFhqjdpVkbIJERxswgCjd5VKjxwwGsydG6wzIPpoqCbtGl4i8lGs/mGmMhkIKq+iOhuqLXIMRtfMsbIbOdjG6umegIsjL/XitaFBaoRIi8Q21FlRk9QrD7fvwip9pqk1ZjakMRzamEtFtgKwXCdrGySuIFVJMMm82QZV6/+ulN113HVdeWJXJigcyGkBx71DH8y1e+hqz6tOcyOnMpXe1i7L6B0EAojZOJ7zahgOnBAYyRJGV/lgKPVr+KqIxohB6NQDDamuoDDQBTaKwPIgWTCUQAu5o7aeZNVlZX4kmvf3414+Tc00BQt4JqlmJMCkIQrF2LKqYo0qTP4Ld5jvDCktXvxpzvoj1BnoONaMkIYxUehpryGI1HGWuPMd4dZ0D6DIg2odRsFWtJC83vJ9ssrwRY4fGbW7/FmWed+aDHK0pkcc+vfoGRHj2eBamFyPbPR5b8GyVcrVOG0yWyQpJJD0+MU8wNETQdUGvW6wzMziKjHhiNnOK0sf0qrd69EiWQ6W1Yu8v0nNSGNWGLXcKFMPYf3p/cWLpJTnv3OL4uGGl0UDan8ByonROWAZUwoxXGhFz7re9y2YXnu6Z1S9SVLKvWrOVz3/kp4EDUaF2xtZnhW6gm4O2cIKpV6WCwNkeIkKon6WSGDh4jADrHGoOdmXHNLoUAa1EDg8hqxQGZokBbSdf4VFTOQJAQGekags7kyOlf44UGY2CyJWEA4jQhWL4c2cyp6GnaZoTWTIJQ49RlQuT7hLmbH3HNpxYvEHV7Q5XzWhcFVBvQHod03s3znmMjlSPuSuGeje9j83zRfXJR1XYUI4aGWV0v+Q1l5RM6p0BTKIsyglp90EViejNHLTqvRUBm1gRohIsGphOkMuR3qsp93XIN48D9YifMU5K1QxWGKwFT7Yy02+Zd11zNB9991aL5XP4VcMzRD+cTH74REASiQ335ICp8QG+dcuOVUhA1AtqzKVlLUki3QfmuQym5NvhFgVBLIxUqaBMELlJgrSVQTVaSMMYqDBIPzUp2Y81yMA0iwBeC3Fo8WwO6dPIOg+EgWaHZNpthrAACXPGmcwBiXyBViBSSGz79eX79hrdjrWW/LVu44ZOfZMuWLWy/9/cc99iT+nSB/j2xLtX4g5/9ADGoUEYAFiuFAzJlEYoxGmMMo7WQ6XZG1wsZzcYoTIRvcqrVmJluQFe7NRwI45xSgeN5KY9OLz0WVqFIqBiDLXKktX3BvWqa4ImCpldDeyuZLsCBT8GwhqF22msvhRWCwB9F7ql9SjluueUWHv/4x//Bf2+19qS09pcbfwMy/8UhZYBUAiU0YZqQhjFNr0Kg3YPyI43vDeB5dbrd7RTFPKOepZtOUvGXNtyazt2GNex7D04nLR6LIjIAvpIsq4fsnk9olTyPBh0CodGFxQgfBSibMjo4gg7Xk97ze/wiR2QeQdnkscg0JrQIutS7hmCiTd7txULc+XTqkue99Kn8/f85sX86/i6BrFQQ0TCRChGeI+PWBkOCSNHqOuDlZ4b5uS6F50IkRWCoZJrhbhcvkFTkNGPeMJNigChJKHyfNAoJ0ew1VHNaB21wu68z/EYX4FtIwRQGaTQSxVw6Rztvs6a2hop1hqdWrlqpc2qpE9BCKoING1DVCszOYaAPlExREOQZufAIbIEBZoU7VlRkGGOYEz5YGDBdhKgxFA4x2Z0k1zmd7jRVIPI99h6ucf9Mh7luzlg7RSjYcujR3HjTzZhFaoMN3cbXKUHFRTcsih6QEcYSJQmp78i5TgfHbRCx7dIRFZTRFEqSEBMVM2S7nCbLTL2BFDlS2AU+n4xKF7DcCHrRGEEprOHGSD1irOXq59aaMeLABw1KKqSUhBLCmiL2DCbpUFiBAkJPQNk7ShoI5SRhd5DjHvN4Djr0CHyVs3owJg4qJLlmx0wHKxUSy6g3z0h9LYlw829sULB81lJLwG+18QOPXKQoVaeqJBNAG+kCD92c4ne/wy89TtcvCrxlo9gyBSqyDHxJMwswFUXVJIRlk0bFHCq0WAtJJ2BmhYvuDXoG6ftQGabSvQeLomMGaerliKDGymqVdqf0rKOM3oa3eMiyP5YpcgiGHCfBFNAag8qwW9MlP8uRscFbsQK6s4hs1ql8Jz5qwDIZj4CFRqFd9LYPUCztrMl8NaeiKvhBCL0+QypYwolCCAIhMbJWRugs9aSDRFAxCQ3dYr6M7CmjUdaVHj9wVEKPSiAhuZNLzziRFz798Ux1KggDi+FcVFZvVuUUFTmL8Pbg1Zc8DWEhrgd05zOMthRS4aHxy+eUG7dGex2mlYjRtotUGV5QAD7GZFihia1mc3c70/4AAxUPco3WXVTXADENpZgqCrQum1/mLmq+czbBWIsvQZoCTwk8nTi5jMFlIDPWrVnJLV/6KL+p7s0KkeJNjpOVkdyVK1fw3X/5Kt7oIDsz91wraZdMToGAAzcdiGhCj96mfRBzHbxKo5y7Bp1neO0OtTyl5cfMhnWGkzZSKYrRlUxOOvL8aDKHrbi7nUhAKBfxLe+nHw1Be4rYWrpJSljYfgWUAFalYzSVDyJEolkdxQz5CtvOMTYH4RpSWiuRLIog7mEcccQR/OIXv/ijn/mfHH8DMv/FIYRwURk/I046pGHMrF9nmWijlEb6Fs+rIYQT35pv34swbdDzZDojKMFIV7u6fgQM+/8J+/sBERmA0VrIVDsj18ZtBGIe43t0UsgK60LuZTWBimO6tSqVVguv24VOCy9QFJnG78D61rxLZ5RDKtcSoDug8APNCA2GRxoO2ggIRquEazaS7+44j14tGMog8ih0AdpQCz3kopx+EnjEaEQBhe8jBNRsl7F4lEYxx1QQgZKEaZn/XkwosxqL28xVYBCecBGjrM260TXcn+4k0xlb57ey1ltFjI8SMNptMzg5gbQGIwTF4AhxtYyMSQ8jBNKWv2O0AzK+AzKJDNEIpLX4ukAbw1xJNh0o2sAylFT9qEyezLrj+BWUFKwfrjDZyphqpWTaoCp11m5a4PNUjaGaTyE9j5E165jcdh/WWKc9JCzSQNztkssKlYEG7ZkZNAIPS9W06aiFCF9aVPEmZhHGkAYhie8TFt2+0U+FQAoFmH5qyZZRF5RcCqSF6HuUdd0hEW5j9+RSc+GNjJB0OhgEIeBJC4v0eQY7gnp3hmh5jXp9I6asPqtUA5J2xqq6JaBgg9hJEWuSbpUkcCcnhWR80CDnFZVOQSUr6FqQqkucKcIsRRlN2vSwhRMoMEKQ+IpKViAHBpBhiC37dNk8R8U1dJ4zl3l0dYVG4MCMKn+TyigzQ8swhSEgIfR2k6SGMFyG8EOq+RRWRXTziPluBU9LQJOqLjPJLLWo6lIEi8aSiIyQEA5AMuNIxc1dEFTBdxEW0SN+a+02ZmvJc4VSkqpuM2w7TIsK9ycZ+1QjpJAOBJmCdt7CCqhED4jWeHvoLCxDjOe0TipZgjIWWxlBdCZYl0/xG1UFBGFRphb+EI2y1BsaGR5gZHgvClklH9+KsQJbWY5FYbHEFYk/M1v+9oNtXT9RZI2LytR8OvMZxvogNF7Z20lbQZHnCxGZwscYkH6XwszjEaO1OyeV+1RMl2phyP31dHPQRRubOwJ2XWqmgG4GBIJMZ0x3uswnuUsvhYYizWiIhJqcg8ZqCL3+yXpWszpUjBSaptTkJUfK83y2bNlCZVgymEla1Fkxs5upapWhaIg4jCk6XWR5IBMIlDH0eswaa8iTBDk2xhCKlh9T2ApGdPHCmG3TzsFo2IyBpMlsxaXoE2GxUtIp086BtfhlWlxZ4/TDtMAbXMZc6vaTFPDyXVgRUPUUI4HTijFKIMpWE1YKiu4A5j9BCnEcs/fe/zMl2n/K+L+uaeT/huF4MoYwS/CMxgpJJ67ixRopY0RZmy+EoBo7VUZPWLY1t6HL9EAvGtNQCv+PMcCtLcPELMl1SylYPRgjgarIXEdlzx27W3b5pVggxGWBT1qqceY7dhDKjLg7ycBsm6Bw5eOTA0PMrlzphJqqFhtYCgvdjiKZiqBMn8kVgzi43ktLLJy/tpZumV8frgeMrKnRk9ZVxQit2MMID136bZHJAEFWqZGV3ryflectJP1AtTEYvZBa6fdeKlqEMmTzwGaGIuc1NNMWtkjQ0zsYnhhDWkMShLRCnzQzmN4GLj00SxUr/VyTlxt2qwQKsXEdpdvaoK0jcNaKhVDpcDSMJz2CRRoy4J7/snrI+lGPWM3R0BmRSVBoYgvVwhnduFpDeR5hxW1CmacQFnTJmaokKVGtjhCyLzJYKZ+tlq6EdHh6GpGDVopdo8uIkw5CG0QJZLpCYEoEY8vn1q9YWgREAVrlfQ5x3X/znniWXJo6kbUaRRxjytSmNAVSyj5pLyoUMjbUvTlW13ZTDSTGWiZbKcZaaj7sLXYQKFe5Z8QEmXbXVQ8c4BsfsKhhZ6zjvCBsjqPv/T0bdu9g7fjuEsRAVo9oRUFfgLLVKD3PRaXmvfNSBWgryUSFQi1DZwJjQ7LGaqbKuTHMtDtuOk6n83v00AbE8v2prVpGVHX3ochKfkLYpTAFY52lPeNgAciYHjl+cC001vTBC1nbpZsAFbjP2CzHZi4ylRuFKqNlq2yCJwWpsYxlC/wXC7QL57TU/JLU27cZC84POFCyO1gGSIRNqPT0dOorQEi8ImGzSKgoSUX/J0AmWeCu4Ud4gSL2E6pqllrNUh+JaIzE+D39ASGXRodwZM+F/3F2q3d/rXERLmlNn2uSFwscmSL30Fmpi5PPYq1F617Zvepfv5JuPRqb9/WiKkUTTwq0sYSyAQh2z7l7tqwWokqgIUr7RDxcXoPqR5CWSYuUqkx9lUMIPD+g0G2WMcHmqa3kwj2bkXikvA3lcwZMadJVWWJtrCabnMRqTeyJkpsGbVVhipjCGGJfsW71KHJggRdY6VoyLO2yCq/S4+CUtkgpi1CKyvBIv+IoFZKKNCz3ukizsFegJFY522SERKd1TPGflXH8dcffgMyfMZSKUX5Z2992i7kT11ChWZiZvc/KEMqtstAJ97fuR1vDTAlkRv6zaIzOWch1Lz32QOwzorqEQiMx+DbFCwKKXiJUZwvyn9bSqVSwYYjVGjmxA69wxnK+Ap3Vw0wPDDEVuCqUwhMIoZjQPi0jnGKwdQbGmGTBmxcCsSgt0dHO4/elIJByiREUVqH9ZRgVoMtjeWiU1TSVu0/SGlSROw8WFsoDrXFpJUAJ0wcyFDnZjvswY+MszyJqmaQx18G0xrB5CiVA275iNVoprNW0S4PVAzLgFE+wFomCsgS7Z/SqpbBbswQAA0ULYRZKfKWQjMajROX/6wd4wa28Re51iC008iYr8ikGjADrjFRUc5t2VHd/MyURFjpR7ASztEa0O0hPUgaECNOiX2q6cmqCStLFCMn9y1ZS8VoopVHG9iMyHSEoetyb3iP5A0Bmvtx066VxK8ovPDAiI4SgiOO+RootN0+/BKRGSYJYU0kMFRWzeVmdVQMxXpka3RS18YRBhg2s8RCiwCs35B6QMdYgViwjDyKMEFghEb6P9jxy5ZHHAeFgTtN3HrEyllYEk3oeUz43UQJ49YBN1KsMYFJD1vIw4XJ2Z46kH9MhJiGK1iCEQusu7eQ+MttBCEF9xMkjAHiBYvmAS5fMJDN08s6S35D9iIzjmiA9qC2HZfu4SqnGGih1e1TgVHuFbiKs488VRlBmp/CkYk3ofnc8K+hqA8onFQJtDVJIovJYC1HcpXNxLCtoixAwBNlMWW2mEMqHqtNfqnXG2OC1UOV8/oNAJi2BjJClpg/0T3aRSGFfJFI+2K3vAxlr+mveCxRG6b6zg9EEPaVxrfvd4vMsROdOYduYDGO6FKVzoMqKTbwQKT2kdPfBqDLVaAvqXi+FW8EUDbSBwJMsr4f9a5YYiBoLaTyxyA7rDISTWOjZAotA+QpTAvJMauYrgkbYICxBpSg7ohspQIAYrCzYHGv698SsGCboaV6pmFSDkoINIxWUkqiysbIwhmXzkM3M0ynXT9UYMAUicMBW+gYRx45cXa6DTEiGPEskYEgVdDrbMCYHabHSHccKd3918Ycr0P43jL8BmT9jSBkjlEVIS5x2kUZjpKIlauj0AaWqQvQXUSAErazFtuYEutzs63+oUqk3FvNjHmCI7aL8tcQgrCau1TBWoHvpkjxxRMgeuWLFCkTPuKqQdmUVc9WIqi/xpUAjmfNqFJ7A94cQQpKVVS9FWoZ4dfcPboKdEuD0yMvt2YWKCyM0yrqeKBaJKRdvYDOy8nz9ogAERekp9kPRVmO0dpwPUfIJ4tJ4dBOKqSnynTtZPq3xy8oVWR0g2mcfkuERrBAkYQzW0G1lTkBOen1tlv7mLry+UrBvCwIpCEvSW6vETgNF033BLniTQyouJcmhme5cYvxbeQsrTP9eaSOxxuk5KN/HKzfasOLKoq0QFFKiPUUSly0Idu/G8w29x4rxWIlm1dQEjXYLi6CzajWrK22GmMGLC3xt+0a/KyW57cevy1u6kFrqDWttH8gMGGfM+kBGLN2IrLVk1i7cw9w9s161ihcX/VhXYPx+hOqAVQ1WDcSIvGwzEdYQ2mltxCrFE5bIi/pp2FSnZNEAzSigFQVE++5Ld/Ne3LtmPZMjo2gJtvRolbG0q4rCFDQzxxMRgbu/8gFOpYrrmK7bcJIoZjbvqUVPoVSFIBimWt0b5VXBGpJkB1onCCFojEY0RmMGlsXUglpZvgo7Wzv7AApAqd4GZfuCbv3hBSWo2a/8jGt54EkXVSisQhq7kHJSHgOeouEpsLA9ybAqoN1LA/ohuii5MX27EfZ/f1eaMZb2KlcW+mr1y15ry0AohE7RrR34fqf/3QcNXSxEfL1oEXG53PD1HoDMHlRki2JhTppiwXYWQdJ3nFxj2jI6JAy9HLfRrvrHKyMuaTqOtc5+iN4zKO9/PyqjemtWM1C4qFuSKWzhNvzVAxFSCkz5rATWlfcvHouBjPTwSk2b8guIMjIujGV31a2F0WiBGyRKh6LXukAuH0SUds5gML6Hv3YteeBRy4p+laIA1g9XCMo9o/9cyoi3N9MhnHPPpAdkZOi4N9Kz2LBXKda7NXaJZEZRzNFu/44k2+H4hICQzqHQ+m8Rmf/nhpQBQkikb8BaKl1neOYYpMgtlGFhrIW8S88OjZaTfb5coENC/3GSL/xBzwroLzZ3TsrJn9sZR6ztRWWKLrpYpD3h+wSbNxNs2oRZthajfAIdoVTAcFnKN+UPoKUDMkoojHRNLU2ZWtKLIzLe0inULt+vlJtj2u3278VcNIldpDRjS4AX9cLguHJPgDwtDfGiiIwuCmS5aA1QjEKx3KKHLXK44STwfY88CJCN1aih5QjPY7CMeiVhxW0KFtpzqePIlEugZ4ik5+OXGie+LRjyXQuEQio0jhNb64Vh9YLhFWV0S0uBsCn3N7eR6xxtNN2SqxTLcmPAYm2ZVqrV+3NACNGPzqSecuArijC+h9WaoJu4FIwGoaE6O0u97ebe2MgonTimErgQuBdpIlGq+QpJIgR5TwnYaMgTyHsGsujPM5c+c7Snqnbn3ev99cDUUpZlrgdNGZHpbV6y0wEMgbfwXGXSXCpSZ83COvGrhPEAJnebzZAHgQz6HmyaZJiyiabBopOEWrn5tmTInPLxSg9cYak23MYz1Z1yFTelATf5ovA5oITElj2Xxsp5VhddQjI8r9wAZEAl3uTADFCUQEEIQVT1UeX8X1FZgZKKVKe0soW0o5ASWZ6rLpY6OQsfEiA9rIYiXVhPuZFIaxE9BCZdUcCayEcKx7ObkBXaUiKFI/x3u9vQZSsDd5FOG+X+JGO8dLJW0KVazPe5YX1Bsl60CAgyg+clwJ7JvpQg0X14UTVmPyKz6Fp7gH9P/Ji+DbPoouj/VuZ1+/IC1hT9MmPRK722pRMkOvieS7H0no0ScR8s9MCTKq+1D2SUT806VXbdT5E74rD7Tfc5KZUrm188lgAZV3Gm+nZNgCwF7Mq3asYQL45GldGqnpinLhaieNYatDHIRoO000ZpQVV3UEKwajCmHi2swT7YEpbpkno32LKEPcK1zhF+xRUIyIVkQS8G3ZNlaBqP3bkAGWCtpijmEb170otk/S219P/e6BN+y/RGw6YIDDkBbVWBue0wfS/s/neYuANZdrP2raZGBUQEGGp6/o/8Sjke4Fkt+SfdW2wS4YXkQNukpL4hXxSRKfJ8geBpLTIIUNUqQdk0LdAxUoUM5XOApa2qaFNFqZJQCKg4KMuxRZnm6fWqWQBi1lo65TlVlcQaQ5EvtIPUMqcTTZU3EWwZro31wmeConBA7IFAxhhMUaDKfLSVAoFEqhqmBnrQEG7ciNhrA0m9hpB+n14zWHowuR9gPPdm0s7JctlPLfU2Wc8PCEtP07cFg56HlJJO6dU3lFwoQ1xkrHXZYFQr97N50eTu2bvZ1XbdhQOhiGn2Q8g9nZoecOmNSr00yqrkBwkBK120QnYsdrcinfOx7RxZdnBuVerMV+u0tUGKEFM2l1KV0vgEdXzpY3r3rjUJE7/FlvNStHbA+G8g7/SjMQ1PIUxPsbUUr3tAaiAp+xr5pbiWEBbdnEfMzRGGHbzFEYgiWcLZckR06zYaLySIPfJkGBBEwlIU8/2ITNFyIThRmqusOU8kBbGSrr2EN9CvPPOjmKHYaYp0iy7doossI14mW5hnQggogUWnVqelXe+iQevE+DyvvuSzPWDT42A8cHjSoxG4z7TL9EZvLPBk/gCQAQdSJORtRTrvkYoq7SJAGYsQPSDg1ksgJavLFNNuG9JSAXVp6S1yXTT7c1OrgHu7GdNltGltFLBSagLoA5nFQmS2OoIRoKxzKpQqlgIZa6EzTdHcTtFLKfe0lxad49LUUnndewAyPRvW11UpehpJBYnMy0aWFlVfhrTaaecApujdiw5eMNjnJQIoWUHQ+/2ycrF05kypWC4qQ0hhqZeKVUKA8GZp5g4M9UFC1CA3Bdub2xcAah/I5H2g5PWekRDYsolmt5yTQ8YsAEtA9P7buuPoMvKphUH3iM1Zhmm598MYDlwzwGht6R7QO0eFYbYqyBVIC0PdHq/QPbuejIYpS6WEAM/LUMK5lVZWyK2gUKOE4Qpcir/8kRLI/C219P/oUCrGizTVkZColtIoRdtaQQWTdyCZdZ6IkEhVpgeCWl/6XOpZmvmfUFv/J0RkpJSgAjplSD8JNIUpw495xyk69oiei4yS7zv6p2d8rPXwuzPUtFvYLeuIsz0gI6KeV9tLj5Sb4CIg0ym9eSkgkpI8SxdSNtZ18xUqZWhllcHlFWy5qcfGbTDK5MiSD5Sniwm/gNXoNMWTPYl7CHWMly54Y1onRCpClRGCHifEl5JKeZiOFxDX3LXMzxQL0QQLIPAD18AOILAFkZIkwpB5AWCpm52YB3idxuRQpklsaaxrSmGsYS51YKOmczyRL9Fi8IJwiZAZgB+GLjokRD8GHNYGEIPxks8JCYUX0alUyEvFy9xY0qIg7/TIraVXFdWpBtU+2ZdSy8X2ihZ7ZMW0uQTILEjPu39/IJBJS7AZVqrY0mgXO7YjfE2kEqR1zXzznn5SdyHN2Kt4Iag6npUQWE9QdB0YSNJdhCrA1xEid+jQLwFp1nVclV61X1KCDGUsslbDkx4DoZsXU8kUpuwLFCwyxsrzsGVaaWLAzfUhpfHIkTJAPcBx8JRLPRS6jbV79k6rJYH3D/Jk9B8HMg63umq8Vh5irINu/ejCovs/7HvUPAfkCm8ZtUXaKkVJRC9kwO+TgmahEQI2xgEjgdfXktkTkDG2IC+jTKqw+H6yYDN0DtP3Yme3kgT00w89cvuSc1x8rX8ktbTEhrEQtdJW0/U66N4c9at40i4AGe3jiZRIdRHS6wNNACUjhDClb+K4byopo6HCYGUBQQxhg1Exhy8MI3WBEIZW1sIWCx3CZWWIsc4Y8+k8O1tlyrjHlykjMrAAZIQsU+9Au1wTobVLQXzPcSvJzMYkWGHRaHT5HDvzs5AbrAC/tudGx4uBjMQyW/LH446TEsDkmG7SBzKy5O1ILEHgzjEjwi+BWWZywnA59cqWUmcKRJ+7YxeKJP4Xjr8BmT9zKFUuXtXGktHARTNy5dEMR12fppEtsPIgZH0tAJPUHDdGaKSeY97mS/Lpexx/JCLTn8jKebWdcuO78TNfYMMhR7sP5V2KPHvQdwGsTSlKVrzpdJA2Zzh3G++8dCqX3/rat9xnlcCPInQP3ZdeB0py3333IYTglp/9GwADnkvH5EnSJ8wCKCPwrcEPFUHkoUvmamRzYqGp5gvhaq0LtM4586UX8pSzX+5SS3mO75eGDPAIEMZHFm6hZ9mka0dfLr6sBCQ333wz+9SrzM/O0g1CqoMBypMYbbCmBlikdKqxnlzwJKU1ZOk0k6VVazCHZ9okftkNugQyaTqOKjlDXun5xmhWxiN9IFjXGrwYL1h4jlGttsfnssTeW4unPPSQ4Ht330bloINI5DTBQEGnMkwaRUhhictU3nyeoTOFLdzGDkBYp+pV+43ksBJbHaWnviCqbiNP8ozUuMhE3VP0ul4X5Ua0OLWkte7zG8IwXDDuUqOHLV5h2Xj0E3nHhz5FGiwCMr3NbzGQKUfuJxTJANZ4WJPjmzbVzAGSuBYQlICtKPVihnwPgcUKn9wPUMagqu54I5FLL82n89yf7i6f5wIdSHo+Juky3RgkVR5SwJBwzsjiTbE3pAxdlM/afonvA0fFc9eZFEm/OhEWeDL/aURGgPB6aai8/K56EN8DHOBZFwXl9Uc0GUCIXuq3S47inngNHW1QAvaKQwZKQPfRT3yaDfs+Ys9AxqQUZdTS0xbPy7BWu2c3/ltI58h9Vz2nVj+UL970vT5Z2R1sDxGZPyG1tABknE001pB5XUwv/ZJ0XZfoXsWP9mioMUTp4Pn+YO/G4PWcEzysKaBIETpDGcvWrTuI1gzzi1/9EhpruP3WH3LA2hHCmW1O+VtnZPO76EVyMkHfGclNznw2/wCyrwChCGVZiNDjx1gWqv2sXXBIe98DsCGiR5SXKUZatu68n1V778PtP3UikWmgCfwH235YcEolhthYWrFASxDaojMBusB2O/3KPq8syZcyQ0q3toXXICijLnn5zESf3+TERWUZedOLGsz2xnHHHcd55523x/P7/+f4G5D5M4csyWO2TBGEXkilNF6T4QjUV7r+JkIiZUhKyLx1G9c6X6GwaJwQ057GmWeeyVOe8pQ9ll73xuLUEiqg+4Ay7l5Y1i7iBiyOyBidkXnu32zXLdYqbSSGogRFspwixhrieqOftjDCfW9xRKZZGuqh0mDmSZcFHGPxtMA3pr+Z9Xg8yhbsY+aJS8Y9QNjIaLfvoBDOK7FGY0lRZUTG07b/272oTJ7PYkzeJ6UmZaTn4Q9/ONt27KA+0KDwAlKjqY+Uxtf4+H5Kpd4iHmmhxW4yX3Dm+a/lKWe/nMluk5wAiaaWW4SQFEKThLJUb07Q6bTzYFgAD1ZYBpu72VtV2FAUVK2FxirCyoL3GteXppV6Q4Ye/RsnbJk379DjPgoBslT3pfzdHrm69wxEVyIoBa1UQNWvYkXv3CSIRcYxdOc0Xz6PqpI4xpWbo72eL4s1UnogRiknkofv7qetWqznvHmAHEXH80tXNYNetKL3dxGQSWSHgoLTTnsJAwMH8+UvfgHf+FgsUcMjKO9XAWWaUeDbsvNyVHXN0GN3LZEXLURIii55rxq33PiU75EUmqkyGrMm9LGFWwOL00q9IYRYiMoUe46k+srvp8M6i3gPcrGWzB8aPVE8zz1V00t19hvqiSUI98wzz+Tpp53m0pXADMMIfxUgKCzcU1lHIgI8KdirElFdXFRQAp7e2l5cdWhMilYCW6YXpLEYM+faN1iN9WPSKOgTpw30+2a5g/aiFXuIyPwBILNmzRpu+uY3+/doMQjsaWcVSQJW9IFMZNt4Iu/bRaWqhOFK4mjdUg5KUfSrq9RiqT4B+BEPf9Sj2fXzbzLs5cQlqGot4v+Md11pfM8hmU6mlwKZUmSzx92TPUeop4wsyrvc0/Uxuu8AWXxkLyqjUoywaLkULHQD/SBu2uJ75y7F6YZZAe1qCYQThTU5ptvtcxsDaxxvRrm5mRQekVfpHz/r7TXlX4uPLSyyjND9JXky55xzDkIIrr766r/I8f4GZP7M0SP89oan6gyUBr9lYEeSkffJWIoplgGCAQ/qYcRAGaab64mo7WlYu5Bf9v4wR0YphVYByeKuxkDR48lk3X4O+oFAJlVuI/CMW7xawUC+MGF7RFRtNVG1hi31GYwsJdAXARljXSVWTbmy6yxJ6G/I1pVEeot6wOTa9sEWOis3T5CLyqutMGglaNspVC3r526VxTH9pUDqECUrgCXLpvqppUS7sHgQBKxbvbov8DWbFwSRR1wPQFhUSfBDgBYpSagwAgyCKenIs5U0xRQeUeR6nOS+JDOuX5DqP2cHHrzSYBYK/M40NWMQgeu1E9erKM8nqtVR3p4NlPAD4rxHVhRo3XGpuT6oLO9BGWGx1vaBTNLvkF2CRSXI8zl85ffTWhqJFb1yWYEoUwPz0hnVgTIaA/QBrZKqb8zhAdFAcOW7OHwkrO0DugKP3GonBAfOs9fZAkAv007WWlKd8r6PXIOwvc3booI2Xb9JYXP8uNIHjHnTlVcb7cBHEsXIKF5Cnu/pdrjzLAnLZVTDC0J21hpYIagrQUPmWKsRQqLUnkP5PYDTI5XuafTakCx2UNSfBGR691G4slxc6kaWTgFqaddyd3cskZ4ipoNFsquIsKrGLlaTyhAPw15x2I/W9UcvXezcHPQiIKJL8G/KKIBXWKwtGxiqkKwxgrUFypRzUYp+yfNcOsdcmb7A5AvRt35q6Q+XX/ciMqYo0GUER0mFCkuHI0/JO2nZ5gJ8SqdHLfRsCsNl+P4AooxiW3ys1n0gIxc91948CUY3snLvQxG1FdRKCkC73NhFWWUqEKxvrEcg6OQdumVllLsAx5PpORWq5OD0sjBBH9gttJOA0plAIkplYaNSrIRiEZBJA4ORDybZ90Y/moVhwEqkkHgjIzgFdIHpZphuF2shLc+3SKb75d/d3CdSC9WBDwYyHmiLKiN0fymezBe+8AV+8pOfsHr16r/I8eD/Y0DGWkue6r/Iq8gMOg/JM0ueWayu4Gvw2x10phlrpfx6ps32uS67mimJDRAYlnsZCMlA6V018+YfTi/13pfeEm/miiuu4KCDDmLNmjUcccQRXHDBBUy23GJdbOq++M1b2HLsk1m57yGcfsYZ7NixYwmQ+fJXv8ZpJz6F9fsuZ9+Hn8TrrvgACYqhdPFRFiIyUimCqN7PPRuZLxHDu++uuzjjxOOI45iDHvIQfvjjW/vGV+uCCy94NQ896gnE9QH23Xdf3vuBDy6UiesMURS87nWvY5+HHMLGjY/kNa++EmElWOf1Sc9ywmnP56WvfhvnXfp2htbvx7pDNnPdJz9K1g158Ytfw7Jle3HAww7mpu99E40m0Qk333wzQgjyKUfk/OjHPsbg4CA//OnNPOr4Y1m//qGcdto53H9fRpgL3vqm9/LxT3+Fr3zjexw80ODQgSr/9v2bsdZyxx1bOeXkF7JixZGs2nQEL37xK+jM9arUDGee91qe+ezzuOaaj7LhoMcy8pDjecnFbyWPl4MQSKVYtmEjgytW7vGRp2nKGy57GwcedxybNm3i2Ic/gg996IPu2Upn9G7/1R0c8fhns27f9ZxyyincfffvqJQbwX33beO557yIjYf/HbUtx3LMSc/mG9/4kgN0ZTRi34cdwVvecRUveMWLGdmyivWb9uJ9n/wy7TJdOn3/doQX8Pl/+Q6Pfeo/cMT6Izj1Uady66239s9Ta81tt93GySefTBzHrHvIMfzjay6n0+wQUinnoQDrSqH7TRK7swtpJS/uz+tMZ/z2V7/lg9d+gKsuf0//d1TUpOPPk+rURUXK68zabVpZC6kTvML1JZqv1ZmeniYvU081v8aoX2VlURCU0ywUkpG165n3AtIgRBrNujikKEqvXdWwucVk+kEvoSNs5qqmiiTZ42cqJobM0um0+u9JI7C5QSfZHr9jMo0pHPgX0omQgQMyolcVuAcNFmM0dWUYZQKJoWNgm1nGh9/9fp52zJEctWoFWzZu4MUvfvHSXjdCAYKbbrqJRxz7CIbqQ5x00kls377d9SIDCGt86Rs3c8yjn8ny5Yey1zEn87orP0Cn49J0fgnWrQCt22ij2dHcwf3tXfThWglgsqTLuZe8lVV7H0QURWzYsIG3vOUtWGs58sgjAXjOc89k1d778JCHHo62mm33buPc55zL6n0PobblWB7xxFP5/g+/637TSnJCNh79RN585Xs5++yzqdfrrF+/nmuvvbYf/bAE3Hb7zzjs755EtPlhPPy4p/CrX91R3gNnB2/+wS2I+nJmbZXawFq++Kkv8pAtR3HzzTfzqEc9iiM3HMlLTn8J85PzNMrqpfHWBP946dsZ3P9RjKxYyT9ddgVnvezVnH322ahSSTrtAZm+rk9KT9zUGMPl7/s4+z/iEIZWb+bAAx/L5VddDQpMCWS2bt/O0595BkesP4KHHvbQJWtvamqK008/nQMOOIC99tqLh51wMl/4wk3sP7I/I7XleI0qJ511Fv/4T6/n4re9jTXHHsv6Q0/kde98PyZxxRZ5HrBp5Rau/8j1PPOpz+SI9UfwuCMfxxe++IUlEZlf/+bXPPX0U9l0wGo27r2OM844g8nJyQfNxT917Nixg5e+9KV84hOf6OtN/SXG/6daFBSZ4dqXff9/6Oh//OEec9mRjAQzKOs2o1hF+KZLDjSzZp+cuGT0c+NL00pSSq655hpqtRr33nsvr3nNa7jwoku44M0XUCtRerfb5W3v+iAfu/oNWBVz7qvfyotf/GK+853vAK7J1z+c8wr+6c0X8YTDj2Fi6x284FWXoaMGr3nRa4h1LzVQ5vfLc4nrDbrdaYSvsV6GkIKs/M0rL72ECy/7J/ba99V8/v038twXvJDbf3IrjeERjDasXrGST3zgclZvOOD/x955h0lRZX//c6uq4+QAMwOSGXIWUMA1YEAFVtFdFCMGVBAFFcGIoICKgoo5r4E1rnndXVcE17hiQFQkB1HJMMOEDpXePyp0dU9PgkH9vet5nn5guivcunXvud97wvfw8VcrGDduHG2aZ3LWyUeDofHgw0/y0ksvsWDBLLp1a8OCu5/ljbfe5ajB/ZGqZDTbSvPUS28xdfy5fPb+O/z1ub9z2XVX8Maiv3PiiMO46qoLefDelzh/0kUs+XIJVVmJnXGGoVOJiWpCdXU18+fPY8G9d5ERruKii65jxszbefHea7nuwrNYvX4HW8v2MvOBh2itmPiETGVlJcOGDWPQwIP54N8L2bJnD5ddNoMro7fx1PwZ1k2E4P3/fEKz5tm89ffH2PKzyemnn0GfIS8xbty4ekfROeecw8effMwtM26kS6++/Lz1Zyr3/mQNA9tiMn3ufcybfiXZOUWMv2E2V111FZ9++imyMKmuquLIoUOZd+3FBPx+Hnv9bUaPvoTvvjuU5s1K3PvMv/debrrqeq6ZMo3XFr3FxGnX88pRJ9GlU0f8Nli4/vb7mTHjajI7tuGBWx9gzJgxrF27FkVRWLt2LWeeeSY33ngjTz/9NDs2r2XipCuZfO1tPHn/ndbwxdr1a7oGWVnWAmqoUGmZ671upT0Ve5h6yVRm3DmDkpaJdkpyHJ8hiNk7Wl8giBqpRo3FqI7vRdEEslpFRWYuZYoPqipQVZXCwkJkISiKVIJhoGXkQrQCQ68mohts13MBQXFVJb7cLGK6ZWVRzCx+nv5xve+pgqW1/ubM5J/ZkvS9DvzMj7We12J8BkI20R2LjGG7Tw0wJQVMI8kKrBtxa5tharSIbefHYDGGKVAkkztvu5penQ5m/U87mDBhAlOnTuWBBx6wO1WiOhJlwYIFzFswj5z8HK678jpOP/10/vFPqzjsh5+v4JxJ07nn5qvpe9QANq/dxMQrZxOXVK6/9nI3E1NXBLpeiWZqrguyWlbI1jXrXcsKCx75C2+88x9efPZJWpd2Y/PmzWzevBnTNHn77bfp1asXjzz0EIf07ok/GEQ3dKqrqjny2CO5e/atBKp+5i8v/52zLxjH55+/QUmL1hh23ap59z7ELbfcwnXXXcfLL7/M+PHjOaLnW3RuU0xFZZw/nnMxxx5+CM/efzvrdsOkKyda7TZrxg0G7SzNaCTKQw89xF333kXMH+OGiTcwZcoUHv3Lo5THypl/53wWvvI2T86fQdc+h3LPfQ/w+r+WMHjIIAAEMjHbyhVwMrpM3QJ2eoxrb72XR//6GnfcdDuD+h/C5qrlrF69HiHrLnP33HsWMOmmyZSWlvLI3EeS5l40GuXggw/moosuIhgM8smitzh74jQ69PsDAwcOdJmwn33tDS475xw+ePllFq/+kssvu5ZDB/Zl8AlDUG2qg5kzZzJ37lzG3zCeZx55hnPOPodNXy4mP0OmrLyKYaOHc8G4C7n3vnuIRCJMmzaN0aNH895779U6jmsTwzA4++yzufrqq+nevXujz69L/qcsMr+mBIRBNuXujkf4guTYAMAJJqshLpBJditNnjyZI444gpYtW3LYYYdxyy238PrfXgcsIiRFSGiqxrz5tzGof28G9u7Cgrvv4vPPP+fzzz9H0yq56abrmDz5Ai46eyQ92uVx7OGHcuO1l/L4E8+CEOTYxShjZsIiA+APhRC6w9xqPUu5HeB61kUXc+yIobQvbc+c2TPIzsriuRdeBMDnU5g6aRIDe3WnXasWjBlzOqefegovv2X5xjF0HnjsaSZOvJSTRx1F587tuf3mGeRkZWGoArHbRCu37tu7Wyk3TL6Q0tJOXDNlKsFAkIK8Ai6+eAIdOrRh6rQL2LVnN99//31SBokiSfjtFFxVVXnooYfo17cbffp048ILz+D9Dy2Qm5GdhxEI4QsE6FAQpmNBGL/fz6uvvko0GuXpJx7l4PbtOfqwQ5k37yaefekttu2w08olhby8PObPv4lOpe04/vijGD58uAsg65LVq1fz4osv8vCjD3P4ycNo06YNRx81lFGnDLWGgR1MetOUyzhi0MF0K23HxIkT+fzzz4lEIshmjM49ezHmgvPo0aUjHdq0YcbNN9Cu3UG8/vrfCHqCRY8/dhiXnDuOjqUdmTZtGnkFBSz9z3/INhJxSlMuOYehxx1J2w5tmXLDFDZt2sTatWsBmDdvHqNGjWLixImUlpYyaMhhLLjlap596S1iFWXW8LXtMoZmWO6MkL3Ep4mPmTplKn0G9GHEyBEJinrb7J4pm67Z228H82qmQWW8goAqEYparlNNyBbfj66ze/dujHiVtZhKCiKrAEMSRDLibNGsWkIZaiU5kmmxwjoZHXKiTb+GCGGgO+480yLDM4EqX5SKiu+orFxFtc0V42YzmX7ytXIKiJEhC6ZOOJkjjziY1u3bM3ToUGbNmsWLL76YdB9VVZk1axZ9B/Sla++uPPXUU3z88cd88flyEIJb5tzBtInnM3b0SEpbteTwY4dw/Q0TePLJlwmYIdeVYgiBYahonqycasd6bLtRfvjxJ0rbteKwww6jTZs2HHbYYYwZMwZd1ykosNx/+fn5NG/WjLycHHRTp0uPLpx9wdn06N2X0vatmT11PG3btOIf/1iCacjEdRMkhRNPPJEJEybQsaM1jgsLC1n8n48AeP611zEMk8fvnE733n05cejxTJ5obSbcZIWkvhcElSCqqnLbbbfRs3dP/nDoH7hs4mUsWrSIsC9MyBdi4aMLuXLyJYw6YShd2rfkvjtnkZud6bq9JFMh5riu5IAnbihGxZ7d3PP4c8y9+XrOGXMWHdt2YMiAQZx77qmJ+Brg0ismcsRxR9CpcydmzpyZNPdatmzJlClT6N69O23atGHC+Wdw/NDD3XdsuVihR6dOXD9+PKXduvGns8fQv3c3Fn/wX3RdwrCLJ40dO5YxY8ZQ2rGUSddPorKyks8+/wKA+//yNL2792LOLbPp0qULffv25YknnmDx4sWsXr26wWPakdtvvx1FUbj88ssbfW598j9lkVH8Ehfdc0STXtNx1QghME2Tbdu2YZomhYWF+Hw+m1vFIOCLoEZJmG4VC8jslGUq1Uo0Q6uR3uoCmZTU63fffZfZs2ezcuVKKioq0HWdaDRKpDpCyKegIKEoCj0G9IW9W5CFQdcuXcjJyWHVqu8ZMKCI5cu/45NPljJv/iNuirRuGESjMaoj1WTbVWs1U8IU/gTduxAocgY6UQzJikEpt1N2DzmkX6KRuk6vnj1YY08+U5g8+cyzvPS3l9i8ZRuRaIx4PE7PbharaXl5OVu37eDgg3tacRCGQMJPj+7d0UyTqoDPogIHenXtZN1DklH8Pgry8unZpTs+Xw7R6E80K84FYOfOXW5VW+twmaC9kwyFw7Rv356dO632FTdvzo4dluupyp+DZlrLcIvYDoTuB7JYs2YNvXr1IiMrB6JbCMdMjjnyJAzjIlat20hRswKQFLp3747fn4uq7kbXKykpKeGbb76pdywtW7YMWZY55qhj2FZm1e1xeDyESNCsd+1q9ZlkGhQVWen8W7ZuQS8IUB3RmH/rrfz3n2+xdftONN0gEomwadMGJFPHsMFFty5drfcpWwnvBUVF7N65g5z4XrbbMUa9upaiCisqpUWJ5c/evn07Xbp04dtvv2XFihW89tprbvtNQ8cwDDZu+oGunTomYjEMgW7oyME8qN6deGA7m+mNN97ggyUf8OKiFwnIAUIZdiaK3wI+YQl22kDDn5kFO3dgCIEvZuDXLEtFRrSKylAmEV8AX6yaiK6zqVolFm5LXPJjGgIOaufeWkKnwLeTuKJgqE66ehg54KfFzYNrfUeaVkWkeiNCyGRkdk5LaPlzxU+Ux/ZSGC6kWdjiANqxaQOGrlPQspXL5Jwk8SpE+VpModv1s0A2DIRkosvCzTizqPgtOn4BaCZkSJkIqjlI3wsZmbz55gfcedfjrF27mb17K9A0jWg0SnV1NeGw1eeKotCnTx9iIoZqqHTp0oXc3BxWrVrPgAED+Prrr/noow+Zc4/l1kRY7K7RaIzY7l0ofttqZLs7NQ+/TrXTJXZ839g//5FjT7+Yzj37cfwJJzBixAiOO+64JELPvXYAtWkY6LpGdWU1C+Yv4D///g9bfv4JTdOIRGNs3rwFw5Dd2JpevXq51xBCUFxcxPaduzCB79eso2fXLgSDAQhkY+7VOKTfoQDoWk0gA1jjLxSibdu2qEKlIFhASUkJ27dbVkQlprBrxy469+lqv7dq5GAOB/fqSsymMRCGsOcNVvyJEoC4ClqM77//nlgsztFHHYlQJEzVQNIDGErUJdID6Ny9M2DFx5SUWBZKZ+7pus6cOXP461//ytatW1HjMWJxlXCuzR4sKSCb9Ghfav0ZCiErOsXNC9mxc3cS6bLTf37ZTzgjTFZ2lvusy79fyfuffEBWbs0svnXr1tGpU6e0fZhOvvjiC+655x6+/PLL+klg90H+p4CMEAJfoJ6SAPspwbAfVVWRFNx7+bGUgIqliEzTRChBgqZJwIQYJpWRH8nw5+HzeVxMaVxLGzduZMSIEVx44YVceeWVNG/enGXfLOOScZegqSpBRXZfatTQ7UA0nWDA/RbTNKiqquaaqeMZccJQcnQD05+JyGsDWJVMvXXQDDkT3UxYNnxSJjq7MGWVinicuMMCadcOEiYIJzDMHrSvvfE6N992O3NumMKRQw4hUNiGW+fMYdny5c7DAiA5zJtR4fJUmFg7P9kJ9HMyOYRsBasKgSIrCCEjy0GL2RSLJdMwDTcNW1IsICMA2edjr6YjO7VXSIDS7XZQp09YXDKoGorkyWRxAKehQdyxYAj3N5/Ph6Jkoaq70bQKixsklZ4+jYRCiWKTGUoGUS2KVaPWyspwFIDsBNYK0w22jcQjyHqcu66/iU+WvM8NM29gYMsWZLZux6mnnkQ8HsU0IoBVO8UJekUWVOqG9Z50jZC6F4xst5+deAcnRdN5jsrKSs466yyuuuoq/H4/mlqBsvsHAFq3LIFAgrFYNmWq1CqyA1k41ZotZWstgu+99x6bNmxiUMdBSf0x5oyzGDy4P3//+2P4iFlkjrKMLAS6aRKKC4RpAbG8eDmVoUxiPh/l/lzitcYlmsimQUF0D3JQxxA68Zi9SCnZVi0af+06wufLJKbLFpeMEk8KIHUknJFJuVlBtYgg2deSQ37MWAxTNt3vkkQKwF6rZpIprNpsspAQpoZqB1v6fLn4fLkWT4lQMEyoMH3kOTwuepyNa1fz59Mv44ILRnPzzTdQUtKZDz/8kAsuuIB4PO4CGUcMjESQp9MUOUBlZSU3XHM1px3THx1Bddhqc0gpIKSYOMZ8B8iYHkte1M52k+2A3349O7Hh0zf5x5c/8O57Sxg9ejTHHHMMTz71pHtOhVphEVAZFsPvHTPu4LP/fMZd8+6iY75MSDE55eJpqKqKENZYNE2zRpyFwB6jTjC7w5mkZGCqUSTHmmxE0jIWB+QAih1gHVACyJLsblIhUQMs4lAZ6DFXxzkWGUMzMW1OIJ/ks4FMJWgxQk6wlux3WdElPQiUu/oTEtmgPsnnziNn7t1xxx3cc889zJgxg86dO9MqHGPKzXcRjztJC5ZO9AcUKy4vM5OAvgckgWGYLmsvJOqi+TwBzs59qqqrGX7MCdx2+23IoeR+dsBVQ+WDDz5g+/bttG7d2v1O13WuvvpqioqK2Lx5c6Oulyr/U0DmlxBZllFVNVFDxBZnN22aBqapIWwlnmPo7PVJSHoFkWgVipKV8IO7FpnEDu6LL77AMAxmzZrl7rDe/MebAAQlHxKgmAaapvH5F5/Tsmc3iFeybtUKysvL6dLF2ln37dOb1Ws20vPig/AD5LV1AzLV7dWY8UQmgyllYni4MyTTD6YEwmBnNKHAln72X7r274yiCzRN45vvvuOCCy1T7tLPv6B/v76cf9aZ5IQF1cESNv3wAw4AyMnOoqSoGcu++oIjj+gJUoAM4LsV39OzRycqwxqh1KBoSXaDIR1FI0sZLpBxiJ6i9u5LkmSEphG0tyRbYypFNpBx+MQikp8KU8Hn86ObppVVo1YTJEZpaSkvv/wyVZEYjgPiow/fR5IkOndoY7fJzopRLPeFBVw96aR1SM+ePTEMg/fff58+ffrY30Y917PEsao4KZ8AES1ClpnJN598zB/PPJNDTz6dNloUxe/nhx9+BkAzKxF2IK5zqpCFS4IXNG3mC0+6vmanCXlTr03TpGfPnqxevZrOnTsjyzKx2E58uSQYQT207rIpUxGvsIIlg7lQvdPqV1tBT5s2jaP+dBSmadImuw0+2UfPnj256667GDbsMAAyJMu9FFACSIqMrmr4NAmnNmAGUWTDsmbETTu7VlMJaHF8hk7QX4FfiWFoMpFoNkrcQCmTMFr6MQynj9Onw3vFymrKRNP2ommVaTOcMux3FdEiVpC8kJBlBY1Y7ZlL9rhRDSc+xkDy+zENDc2XADKKkoWiZKGhEDMFipzlZoyhq3zxxVIMw2D27CkovjCZGaU13Epgpc9//fXXdBzQEd3QWblyJWVl5XTu3B5JCtCvXz9Wr9tIh3GnIICqkIwIZBGKy4jqXRDKtd6tnekjjMQibAJRIcjQNVeHZWdlctppp3PamDP505/+xPHHH8+Nc26kWWYzfD4fhm6gCQMFgaFpLPtsGaefdTqjRo2C3Ruo3PUzP2z+CeiPooSIx2PpSyc430l+upR25q+vPEfUUPDFrO8/X/aNfZiOaao1TpclGWHPr5CX6M+WvNw8mjVvxvJl3xEf0Be/aaBrcb78diU9enXBNAWqboBiZSwJIRKhAXqM0tYlhIJBFv3nI847q5vVd7pTD0tzN2ua7pB51gyI/eijj/jjH//IKaecAkAzYyur122gW4+EdQohkHwGwXYtEX4/UlW1G38TNHViKdd0Mpfc/lOC9O3dl1feeJW2LdsQyE+fyddQOfvssznmmGOSvhs2bBhnnnkmAwcO3K9rw+9ApslFcVk8kxcvISQkyW+bhWNISiZICjm6hhpwUpQNNK0Sn89aBMrL97Ls21WQr4GdqltYWIiqqjz44IMceeSRfPvttzz+6OOAFUAMKoppoPgUpk+ZTvHcWSjqXi69cR79+/fh4IN7IMsZTL/+WkaO+jOdWhRxyvBjUZr7+fqbf/Htt99y06Tr3HZbcYcyqoeDwdQNJALoSpRKT0rlM48+Q4s2LejdriuPP/gEZeV7Of300wFo3b4Nr7z8Kv96/yP6dGjO4689xrLl39C2TRvLBWEaTLzwTO68+3E6dmzNQZ178+zDf6Vs7150ATGfgc9wQrrcVTjBY2NYNWGsNGwrXsXJ0nGBjGwleAfUOAKIGiYRESKDKmRbcezwWYFy7du15cXFi1i1eQcF/jjBbIVTTjmF+fPnc+7YscyYeAY7duzksqtncfapwy23kqd4nmUdCqPrVRhGekLCVGnbti3nnnsu559/PjfffDNdunRm166V7Nixi7PPnugeZzgaSUrQDcbUGDlmFh3btub9N17l8ONPZJ0QPHn7HAwnFdqMuW/RofTXJcGeqM2F5OwyPWb3dJWvdV1nwoQJjBw5kssvv5xx48YhyxWs/eJzFr3/KffNvgYC2UkWmYp4hWWJzCq2FreMZu718pvl09HXEUlIdMnv4p7XunVrOnXqzd6KFfgExNRy/HIzIrKGooLh1tExQRJkRauJBYIUZmSQLZlEtm+mgixkWSVg8yVJZQKCAlMIZDlAKKM9sdh2u+xIeuKxVFGUBJAJBJrX+N0v+1EkBc3QiGgRMnwZ9ZcpEBaHr2ozciuGiQj60dlrWWiEhOyJ39FNnYq9FaxdsZYdcgh2rwcEhXnZqKrGww//leOPP5Jlyz7moYceSrqVY8m48cYbmTZnKsInMf/6+Qwc2IeDD+6JLAWYPn06I0aMoHXzbE4ffhSS7OPrH6v49rPFzJo6wS2kaPWZQGCgCIFmj6sqSSLDUMHQmf/ws5QUFdL3qCCSLPPiiy/SvKg54awwmNZ7Xv7pcg7r2Z9MKYg/ZNC6fWvefv1tTj/ldETVDm6cOcsaxyb4/SGqq1OXYkdsSgLZz+kn/5mb5s5k3NUzmTLxWjauW89dDy5I9KFeXcs1sJ8tfQjpZZddxvy75tO3dUsObt+GBU/PY0+5ZXmNx4MJbjEHwDgbUTVKMKAw7dJzmXrtDShKiEM692Pn7p0s3/gJZ489CRuzErPZgX2SD5VkwOVsqE466SRyc3OZ/sg9bNuxk25JRwnbFqVjGBqGXoVpz6uwaVBB8vrkWmScL0J5jL/oEh5/+gnOPO8spt1wLfn5+axdu5bnn3+exx57LIlIsT4pKChw46Hce/p8FBcX07Zt2wZfpzb5Pdi3icV5uakWGUiYYb1xMj5w6fOt8+z6S4bOkk8+p++wMfQdMJC+ffvSt29fnnnmGebPn88999zD0KFDeemll7jqxqsACNkEYIquEwqFOO+y8zhj3OUMOfl8skJ+nn7iNgACgSKOH3o4Lz99D++8/ymHDD+LQwcP5q677qJNmzZJfBVO23TJg8h1E0n3U0UGJpI7+a648QoeX/A4w44dyWeff8HDf7mfvDzLyjP63D9z4rDjGHfZlRwy4ix27drJuWeeYd3L3lFOmnAup58+gksuuYGRR59EVlYWo04a6U65GpSAkuQgrUS7PO0MSonqyYBbvA/drbDEHvLRDdnlsNljg8iJF19E586d6X/ECTTreTRLP/svoVCQ1157jd27dzPghDP400VTOfqwgdw3e5rdwORAUWeHbxg1d361yYMPPsif/vQnrrnmGo444kguv3wGkUgsaZF1UtaFSCgewzCQTcGd06+iWVaYsccdzaWn/5lBQ4+mXz8rdkkXOqZIBlU7dR3DtBSB3+lLm5jQ4iKy3XkprL7dunXj1VdfZc2aNfzhD39g0KDjuemOB2lR1MwCdJ64LoFlrq5Wq613ndcmqdCg8378sr+G/1wIGRV73mjlVKqVVCoO4ZklsiShCxmfoZOvq+T7FRStmkyqyZWqCIdtriQ97IYhGJKECAYRQiIYLCYQKGqw7162ifF0vTqttU0I4fLJOMHm9ZLi2YUjVRusy4aB8PvRhFPnKsO11JqmiW7oLP1oKUceeiR9Bxxi6Ylhp/PMwheYf9OV3H33kwwadAp//esz3HrrrUm3Mk2TUCjEhAkTuHr8VM4efjbhjDBPPnmH1VYpwLBhw/jb3/7Gv9//hAEnns2hI87irrvm0aZlsWVh8GfazZZclvOASFgxqm1XJaZGVmaYuQ8+Tf8BAxgwYABrN6zl/r/e7xJXzp49mw/e+4BBg47i2D+ejDBMpt48ldy8XAYPHszI089j2JGD6NuzCyYSfn/CtVTDKuP8LQfIzMjk1adf5Jvv1zBw6CBumnsLt956m3uoVgtDs/c9ppNrr7mWk/50EpdcfiODThpLOORj6NDB+P1BVDXkci/5U4GMPc5vvOISrrrqKmbcPIPeQwdw1oTz2LnD0vsBp7ScYxiRa9oabrjhBvr06cOZZ1rWrZJmBZw8/ITUxtv31FDVMrDL9Wq2zsggknS4X/ZbDMRO7lkon5YHtWTxq++g6TrHHXccPXv2ZPLkyeTm5tYK8n4tEWZa+9z/Hdm7dy85OTmUl5eTnZ0clBSNRtmwYQPt2rUjGAzWcoWmlWg0yu7du1EUhebNm6f8toV4fCd+fwHBYAso+wEtutv1P4OluDMzuyLilbBrraU0ipKxtmmabNlipXYWNitkTfkaADrldcK37TtMTFYGghimQYfc9gTLfoR4JZokqAyGyc3tBBVbMSq2IAFlmc3IzT7Ivb62K4IR0ZBzA1QHYH1EA0y6Z4aQhUD9uYqYUs2mUBAdhcxIJc2yAmyu/pGA7Cdrj+VTLstSydZyASj3lZNX6QPDpCBQTYXIJx6NktO8iFBsK8SriPtkogFBNBamXDEozStFi1exau9GALppBsIt1ChBi94AxLdWgWagNAuBEFRVr8GUVIKh1qzb+7PdDx3wIbNj00YQUNi2AysqLSKxfH0XB0V28nOgGTt9+WQoEh3DQaezYdt3YKjsIhd/ViFZWVmwc43l9/ZKTivIKHT/1PUoVVVrQAhCwdaAoyhMFDnDdTemivN+fb5qAoEIipJNOGy5rvbu3EF1eRlFwUqEgD1yEREdInKEgJBooZYjCZNdai4/5hWBgE7hIEZ0A7pejS+aixy33okmwbosGcOENiE/uVol7NlgARFfGC2ym1X2otG1oKtLiFddXU1ZWRl+v5/CwkJM06SycgX+qEZANSCzCLItF+bWrVsxDIO9vr3khHMoyajpW99evZ0d1TvIDeTSMqtljd93Vv2MX7esbLuNEFVajMJyvwtkMoIBhIiwlyxCoZAFnvdugcqtxMIZxKQYQiiEjGJiP22hPDcXTJPmgQBKYWGN+zVEKitXYRhxQqE2rgXVK7siu9hatZVMfyZtstsQqdhL+fZt+MNh8kuSn1HTKhFCRtq1ie0VFqTODWXgLyykOrISQwhCwYPw+a1NQUyPsXbPWoQQdMnvYr2Xrd9YsUfCqpEVyc5GNarx+5sRDCZzFsXjcXbu3ImETsxfSSUGLTKKkFVLp2RldUMImWg0SsXubTRjNyCsmkpqtVV+JStxzWhsG/HYdqoM8AVK2Fa1DQmTLoaMyDkoSY9VxavYaM/nArMAQzXIyckhIyODqrI9VOzaScxnUBnWaJ3d2opJiVXArrXoAqpCmWTldGTbtm0YhkFBQYFVIsORHStBjWBktbGyHGUJJdePtisKioSvKIym7SUS+QFJDpKZUVrz3e3aRSwWIzc3t0ZMkSM7IzupqtxCG1VDMw26HXEqfxw5kiuvvhZNqqJCidMysyW5wVwr7Xrr8sTJ/kwoLMXcuwV1bxiQMMJR4spWVARb4wkA5Z136d6hjEEROyC3tWslA6D8J6jajpnRjCq5GsOIUUUQLRqllaahI7GNZi4pnWma7Nj+Dc11HcOfiVRYihHR0HZFED4JX9GByebTdZ2vvvqKvn37prXwNHQN/23Bqv8PxHEtaZpWY7cguQGTTiHIoFvXJGIKqxKpqVu1XBxK6zTFIr1uq7jNh+CTfPhkH8g+BBCwXQExPY6e3dwuDmniizlsv5VIQBzQU33BDpo3TDJkBcw4INijalYwHiY/BjLQUfARJ1y9l8jOXQgTgqbfem5JJNFtG8JAtgPLNFPCcGrJKArOMHRqyuiaD9VQ7Ywvqx+CpukBMVjWGKe5tnvJ1C3Ts6TZtN96NSGbx6Faq0Zydjemda9su/p4uZSDjsxuu15Lc7+XylxA0Fqognj88mkIylItMt76PJHIJiKRH4hENhON/Eh19Yb0Pn68rLnW83rjYxyrkuNekpz6KKYgE4EkTAwTMjXDYug14cdo3LUO6UpiJ7YrIDBMCMqSdaxT3FGLgqHWyurrZZQGy7dvmgbxgISZ0woyE4ucs3MTpqAiVpH2mR2LTCANezWAT8kg5uxQzahdjTrR//6MDHQ3+NRup1qNJgnitgkmEChCCoRchmvrve775kZWHKtM+nIF3gKSpmm6FplU15Kux6iu3kB19XqckqmSJAiUlGASxbADf731nxxXadBTnd5N8XVYcW03VLq6UO77w3CtnJqTfi75kipJqyh2TJaZSJsPJ7sIFPteQckiIZSEhIEgamhJ5QkM0+DHSotHJyeQ4zJwO+/Mcb8FhZ+cQI7bh/hCVvkPEySsuBMnSDXJ8u2paSR8TgkSEyNiswcHrcBdJ67J0KNpLWou9X8tFplNmzbxwlPPse2HjSz/fg2XTpvDxh9+4s8nj7T615uxZD873lgXe56J2F63SrcsLD3l81SnkxFpQUxyGxNu9iSxdZ1uxCwPgBAghamQJDSsdx8kURBUYFfpBtSAHSvm1au/cfkdyDSxJBdgSw5OTedacoBMwJdHxClCrO7x1FiqqdydyasoilvTxTFlOxlOAZFgTI1qu4jaNsuwXmkFc6rWglYuy8ipk8CJNTatyfz4nbcyqEVz2hbkk5WTTWHnEg4+qIhBLZpz+akj8fnAVHWyqhV8dl0POeB3i9I5RFlOpWfNkNBtDm9J4FaOlnUTw5AwTQXTNNEMjSrbV5zl9qXdOE+bXXZhzarjJNnBc7pe5SoTzdCsOAj7/ajRKDmiDAkdXSisD7fEQCIomWSlUroHrUyyALHEO00FMkJKLp6HzUsRKEaWQ8hyiE8//Y4WLQ6lRYtDKC7uS1ZWFpmZme7HEeseJrLi1DNKA2Qc7lwHyCAI2YuRpkmIQIAWQR+SsKqSb9OtaxhKzCr7IGCP7RMs9it2UKIv8VzxatcznxpwmFqewAmWFVIAkVGYDDKF4JVXXqFfu370adWnxjN3797dXZgDaca68/1eO6o3UzbJD+bhDyV2yr7MbHR7UZQlCUwTTa8mEpIwMZGVDHy+PITfbwEZJxtuP5hF66u7FJADyMJavKNa1C0cqadscBxXsmkaxOz3rfisBVe1az/JOlY5DlsituvPAenWQcnPIjvAVY/UqNadADK6VfuMxDv0ujCthVwQ99YoCuTUuNftt99NixaH0KrFIRTmFtK/TX8GtBlAsw6HcMJJp1oHSTLlsXKXZqJFZosaBSMlOw5QMuCgrIM8IE3BsMGBz9lHSMlB/tZDaC6Qwym0aJoYdgyYFFTsc31I9ph2KlV7xaVrqMV9IoTg2af+wrFDz+Cwk8/nm5Vreff5B+lS2o6ffvqJHu17M6DNAJrnNU+M9Q6HkFk6hB9+2mJZtnQV1GoEdkal7HM3ugF7A+gzE3XpUiVRnsDpkFQdbj2fKmwiSSUHvxzEBMrsY8N4MrfilfhMK9ssYm+ek+IPjbrBzAcffJA0r1M/B1p+D/ZtYrEQv+xWB/YCmwSQiVupwbLk1lXJDjanSo0AEVS1HDMetJaqNLtUL5CpsOu+uErNVjJ+J/ZeL0M3o+g+GSkmE0CFPRtdjocySaIoFfU7OxHTRAjBmLGncuwpZwKCDElQZZhIQLFcRoY/RkZ+FuVb9+LTJMxKa+KEQhnIThq0Pdn8gSCxikrihpzIMqrcAqZh7URNMLTEkFQN1e4TXMZiZNkqZOKduEpi5yBM005ntJSUs+A4hegkWcbQdWLVFcgBnRzK2EMBEbsIaHO/r+ZOzJ+FiUDBQNKiNt14yk7Ok4WT9LWdMgvwhz+0ZNmy/sRi24nH96AoGYRCB9U4R9d1qxo3puVykBIAyQEyuinwYQMZGcKYBOwCimpMwZ8XwCdJtA762RiJs0cTGGYB+WIXhhJhl5yJiSAkS2Q7BQWF7T6IVYCpo9mKPJXfyFsw0upbp6puzbEqSRLHHXccvQ/pTaVZSV4wj8Jwwp1TrVcT1+NIQkpemL3dL/uJGgLVNPEJyJEl5Kxsqir2ovh8SIovAWSEiRYvozoIIJDlDMKhNtY7FQLh8yEZBobsVKraN3HiZAwjhmGo7sLoiBMnUxGvoEqrIj9gBZGbhmHFM7mxdIm6TaaiI/slfLJl1XS4WXxG8iKVHsh4LLdCQlLCCKFgmpo1DzxWPS+QUUwDkKwMHpEOyEAMP0F7wSUj2RoDcMFFF3LMiX3xA4FAcyp0g92R3WSaBu2zLZ4jUyjssunx84P5ltUmBch4a1KZtu4BOyZIFsia6alrlpySbJ1ot1HyWWDaNiRZQWAC4aHekOUMDKMMXa9CUZIX2vosMkVFAf71r79YjuKIQa69KduLSVFREW+++yaqEqdDXsfESeU/QbTMiiHzha05Bki+CCh5SGEfshrGMOIEhJWr6DNNq5xHoCYQcMGWA1JTdbikWDFuNveWz5dH0M6Iq5YEGBAgjqFGIRB2+Z3KJQnNDlZGsuYMpmnpVil9fwD079+fZcuW1fr7gZbfgcwBEAfI1MxcUhBCskCMEUezuRdk3UQydAozDiJavQYJkzKjijxw3Rpe8QKZiL3QhxXHIuOzMnNQKfEbyDaDpSznsQeD5uxCshVhVAjioqb50p3A9hwpyMslozAfU1j3KABaa4JQuJx4fAcIjcqwTmaV7O4g/MEwYTs12xAGsiTj81sLspOZIckSIr4XE4EhWZWjZbvkvEBQrVW7boeg69LxuYXa3PbaFhRTNzENEIaCMBVMoaHYocJOITrXIhOvxB+ALKOSMpGHKSR8pkZumpRLJAldCaNoVWTHt8D2LTWP8dfvQw6FQnTs2BFdb0VVlcWMmZnZukasjLXQJawxXoXqFH50XUumVZ8637CsWtWaD1MDyY4byPEptDJhczROuci1CswpUXbbcR1FjjXGEY+SdVxLqUCmhkXGZcWt6aqRZZnMzEzymuWxXd9OQAnQMddS8JqhsbZsLbqh0yzcrCYhpPPMQsIv+6nQY+QrJpq6m2BmM3KKilF8trsWZzxUUR2zFkzFEISy2ibR+otAwHUvNYTbpzaRJBlZDqHrETStEr8dv+IVB8hUq9UUhgqRZBldktm2bRsFBQX4fLLr+lF8OWhqOf5MDZ9qWgDJXlAUT5i7aZr1W2TsoGlZzkDTyu3FOj2QCeoWFYQwtZpAxp4zUQLkiGpr7gVq6qPs3GxaKG3IkU0UXw6mUsDG8o0opkkLKQixCqqESUyLIQmJ/GA+hmEkNjOyow8SnC26pqHYFjPTVNFlQAPZBitekOOK644PWL9Lwq1CLQXkpHEuy2FUtSxt5lJdFhlNqyJm8w5pcg5RUUau01+YKIpCu3btMAM67ZOATBCqdgDC2pxWWjWrpFAIKdt6j7KRgaqWWRYZXdhApiItkPEWjLRunupa8qHaldQlyW8BehlaSEHCagUx/ASIQ2S3Zb2KlAFQJkv4PX0sZIGpmdbm0Ve7A8fRbb+W/O5aOgBSewq2SHIvObsxRTNAixFQgpj27rvaJ6H6wjXcFeDxC0vWAi2EcOMLNAmqwjJIMWQs5s9QqDU+XzMMZCpEjnudvY5Jtwaat//1TGjhiQUojhpkScLlkNCNCDFFpyqku8+pBAKE7GwGExOf5HNdS47ItqI2sgrR7HmoOLshU1BmVwYPmmZioDoWKskLZJwI/YQpVjJt5WBTumm2BcoBMkKy/jYNiZBWAZiUmNW17sL0pFpYooZ53Y0vaYDIcsDdBcbju2reS9fdKrqykhJ341pk7F0sJplU40NDNwWVmt+q0eMJgMz3K7QQFtjYQz4/+6w67bKhEhApWTSe56jNtVSjYrFtkZHTWGSc/vRJPgSCmBZzwem2qm3ohk5ACZAfzK9xrlcKQ4UIJcu1MqhqGaHMLHyBgLXwIZDlOKqxEzBRNIMQ2UkgBuyFw3Wn7I9Nxpu9lN69lOmzfq+IVxDVogjFh2m7TyxGbus8SQoQ9JdgGgIhm5i+KJrtVlI0w4qzsiWm28SANrhLNKbm/xV7c6OqZUnuJef9KRgoQKYvw808TAIy9oKmI0OzrlDYKa3VUTM0t0iippZjRDdxkN+gJGBS4YuwN1NBk6so8hkU+yXisS3E4zuQ5TiSZLhjRAiRNpYoHtuN7sxxLQqGkd615AEyQJIFQYSSQbITJ2Nlnnli+TwAKxXImKZONGrF+Ph8uQT9BVQ7mWTglm0QiFrdpG7/2ZsFnHgUEi5kmzTZAjLR9JXWXasRiRikJJEUVBt4+Hy5FigRgjwEAdOinQCQInssMIOBLvuICAnVm2X5fyRO5ncgcwCkISnYul7t7sZ8mulyd4T9ltk9KMEWRUY3dAwzMbk0Q3Or+5ZpZYC1M5NsS0/EKMOQLLdSmS7Yokogh11lEREhCFq7x/LagIy728H9XTKqyJINSgxBXtxEyJLr8nD860ZIJreomNziFkiS5LLBmsICMpIsI3viT2RhgC+E6pNdReX3cCY4C16Wdw45St07cZ3JppluTq6MzZJrB0MnLDKWQhOKHVisKwTNOL1iP5EXrp0QzQzksJ0CdinFUNIbctsmH9AAi4xXfD7LRK+qe9LEMGiJQF85PZBxLDI+SScTa0GsVP2WRUoIRApobKZXURS3CpvG7XRmv1bh9nGiYYldfjqLjGG7RsAa56ZpeuIraoJud8ExTDeOqyJeQZVaRVmsDIAWGS1qDWp0JC+YR5vsNvjt+RGP73TnhGXBUgmFLKXvMyRCUQOR5p0ohYUoGU4gbMOICmsTB4xqWmXaIOagEiTbb1kwtlVtQ/OM2Xg8TjxugxUlGy2uEq+0SfFklbi6x/pNM5PisbzWmCTQ7QXW9kKuKLkIoWAYMaKxhBXRdQ3ap+f6wo53FuGxDgqX/0hgyj43gDRVVEMlZgo3bss0E/QGjkiY+AXIZhxV3UM8vp1QqIJweA+VlSuIxazSLgn3kmq3tZK4ugNDEugICyyoiQ1HEhhNATJeagYphdFdkoIe63iCN8l7vdRNTTT6M4YRR5J8BIMtCCkhVCGhY3mxHGelMEWi6rV7YaculmnFJxqa5Q7yjFFJ8oOQEIBfmFZkklqVONcjLtgivWvJFJKrUxXJY9GxrxUXIXQs5mj2WoSZejDX+s3D9OxYuyujFW6Zmt+i/A5kDoC4sQNpFKUDZFRbUUnIFhuqDWQU05rIPgERI8rK3Sv5ftf3rNi1ghW7VrB692p3EFfagYZOdL+uV2FiIEyTjIggapuk43o82RRrpwg79UBqAhn7X3eyWL/nyxp5qoMUhO0SsXyosjAJykGCmVkE7JRFJ0DMwHAXQ69VRhImZk4rqyaRQ8mNhpXzkFAi2S5hiD/RtnTBvqaJ6QQR20AG21rgjZEBkBVnEVQQkoxo1qlOMCKEwHJUyS7nhyuyv6aFph5RlCwkyYdp6qhqufu99W532dkIcg1wIGzfv2uRkSxrVcz0EdF9VqFBf00+FvQ4RfFd5JoWeA6aKphVNYGM7Hf71vbyuXwfkKzoJUlyM5asv2tm2CWsNoa7qJfHyvm50lKeecG8RKB6A8Tvz0cIKcmiqaoVBG0Qo+AjGNGsYZLmukKWUYIOAN9fi4wVF2WaWiKAP0WaZzS3xk7UphY0TSsgGdO1yChKFmosih6TMVV7ntq7YkUzkwCEA2SCqQtlimsJQJIUNwZLje9GVfcmu3TshT5sz38DqNYSzyE8C1pdLB2OtTMi5ZGR0ZGMjFIimp+fVInKqEllzGSbKqgig2CwJYFAcyQpE8Nwst4MYrHtRKM/J8XJGEacSMQqe6FFZXTHxRavTO9acsaynGyREQE5oSOcZ/NkL3ndS964He8cUtUym48FgsFWVuyakAj5QkSc7D6ccici2VoGbjYVmBCxdD+BrCQAIoRA2LF6AQl8jo6J1bT41Qj2TdHhuh22IEwTyRuoaxN/CklQ7ehIZ/7a2WiaoSVAi62X4/GYO2d/i/I7kDkA4k3BThW3NonpEF3Zyla1AyYjZZbyIpkozzrHTGQCCZOCUAEHZR1EYajQvp/jqjKRdNU1b3oXK9M0QY9bqd5Om+qJkZHduAzDNTEK2WIblex7+ESCSdcRZ7IpskJewLICKf6EApaDmcRNK6tCyEFMWzn78ewIhGUKtU4OetI5PQpAEu7uy1SdbKiAnUZq4hdmUoyMECbCrkmg63KDyJ1q7AC9u9NGLMTe6/lsFmFVTbiX4vHtSJJN4KYUpyGIE0iSnGD3xXqPZVggQTJMpECatGLdYjMuNgyK+Zkicxu60GsCGSHc57ELFtQgwwMLrFt1WRLZLqluHOv7BJBx6tREtShxPY4iKRSFi2rvpDQihOz2Wzy+A02rIh7/0aqxoymWJcY00maRpbZpfy0yFhmcsximJ1cLyAHy/fkE7QB0ocbxCZBkDTBcBmg1arvnPEk0imlvITz9nzY+BsALIj0JAoqS5VqxotEf0Wyg4s3gM20Qppq4VjIAkao3ahEnOFSR/HaGXpCwFLRqQQmJMiETNwU5oWL8/nybfLA51dW5qGqJxakFqOpuRKACBBiaSnVkE6apY2gCtcqPnGHHIcWrarqWTLOma8muaSWF028y6gMyie/iRKM/AVYwszfeKOwLE3Es257w8STXkmm6JJMAVNvzPU28kWOBDQgTn0066LqhPJJwLRnWxiNFT7ixV7qJKNuUCIS2QacQEtWE3DWAQDaykkjnV+0yLhE7gUAxFZqFEmzcvzX5HcgcAEmkpRo1dn2pu1bFKRJp+36J7EGxrQr5/iBdC7rSJb8LnfI7UZpXykFha4cV8AUoyigiJ5Bju5XMBJCx9bPf3kl7LTIApq4mZWzUGyNjf6GbuhWHQsIKItsWA7+weC284jx7QbjAZfz0Vv6VwrmoquXu8PsLEfbE9aO6FPohJYRwwIsStPrIakDSvdyAX83JOEj4nAOSBcIM00CSFSSfY42RAKnOaHz3+qk7QCHjmoca6VZyxOfLAwS6HkHXq1HVMjeQMBrNwOdL7+qSZBmv8b6SDHT7XSuBAEqzFJI3G7wCyFKIEBEkKYaJ6aY+J4k/bGU8pHEtpXLI1JWxZH2fADI+2ZdUv6Y4oziphlNDxVqYBbpeTSSyETDRdB96NIBwFrNassi8bd9fiwzUn4at61GUmIRAoKMhdItbJeC3+tE0g1aqdcyu7m0aBGMGIPCpTuCKHTdiGsTs56sJZKQE4EnJdAwEipDkIKapE4//DNjFRp3r2u9QMy23n2NhEVocZ7vTEIuMd5yEbBCpCStyJCwHkixvCcAg4/cXEAq1BgSIKIHsOKZcZvO8CGJ7/WTk5iOH7IU/XlVzY+FNvXYsUpk+fMUZyBn7B2ScGCNZDuH3J5OcZigZrkXGsY4IUyTHlWkx1+oBJNoZqDm/fXbafEAC2fm9DiAjYdaMj/E8k2zK1tzftc5KkrDvLSQJHRndjuMio9DaXNmWvbgRJ67H2R23spmCIlArz9NvQX4HMgdAvGbJ1F1fcnqjguywgpo6RHaBqaMYTrBwBEzdyviRfPhlP4YNJLyEYIBdw8kirnv2ubfI7Xo4ARugpAIZxyJjtSEN6ZJD6FSSyWuvvZb43UjEoDgmR0kKsmnTT7TO78nKb1amtKmmUlD8nskgRzEM1d5l57rR+X7irmvpugnTOPn8K+zjfQklkDp5HSe/E5QmhBso+8VHS+nRrAe7du+yLAmKE+xp9WFDFtOaQMYT8LuPQEaSfG6182h0CxE7kDAeD6JpwRqWoiVLliCEoKKy0ipOZwoMX5gKp4SlEPibNUMKpSxypu4qUitDxXpen7AKMdbwfftCeG2JdQGZRMZSTSXXtm1b7rvvPus4eyzk+K3nzfRnuq6mxorVb7nWo5kGECAaySIpodpfu5WsqSwy4A34raqx2GtaFVVVa1CUnSBMopK1uBiahqJYwDIWk9FU1S1doEg6ftUgK7MLPs1ZmK3+j2pRixfH1gc1JL+dVfy1Bp+RRCjYCoTAMKr5298WUlpa6gFIdlya8GGaJuWxchv8xtwA1nRARgjBa6+95gaHeseJIvsTllSgIClYvmbmm8+XY7NXC2S/gXD6p9yHLAfIyM2147ckMHWXCNJtl511gxJ0dYMQwi0q68jGjRsRQrBs2TJkOcwHHywlK6sLu3fvSNsuSIBUny+vhoU0pISIOqSPmPZHJOsUh0jQ645WgmmpNTZv3kZOTi++Xb6SqLnX6n09lrA22ZKIkTFrxseYBpoDZLJaWwBXi1oMy7Y4Oj0aLIJmXVyuLCeuMa7H+anyJ+J2MoBspocKRx55JJMnT0772y8pvwOZAyAOlwzUVjzSUkKKkm0RXTn+1AonJS+fCRNmcMYZkxK1l2zxpl4nfa9bqF1Wwi55lt8GA6nuA1NXXYKptEGWKRtZ9xgHJMgisbCLhEL1pcSJpAUyvgRPi25YsSF+f4HllrABgQ8Nv/0MSVc0jYRrqYZFJqXRImGmHXJob5Z8u4SM7AwkRUFSEm6l1PY5MnbsWE4++eREH3iOcZVnbhurLME+uJYccdwkul5txU/ImcTj4VrbBYk4n7iuIGW39Lh0hJuOnCQuv4aC8MkI3TomaFuivMF9AASy0BwLmpScnl0DyLgZS+ndOF4AaJom+cF8WmW14qDMg5Kue+SRR7qZFc7nkksuSXtNAL+/mevaMQzLQpMEZHy1g0uvlWh/K7TIcsgOGtUxjIT7wDRNYjF7Pks6mRkxJCVhxXIqL6uqTFWlbUn1+5GcDB5DTwR52vrC61Y677zzksYnYM2fUM00cKudQYIBqzyEBaJMTMdlbMPWgG0BLIuV2RYOo04g44hjkUkCV5KPsD3//abpuhUdSY1Fee2111CULIL+Vm6SQbxSwVAlspsVWWNcSC5AlW0iUNMhOKyyLJneEiH1iRAygwYfwurV75GZ6avRLuv6esK6IddMg5YlGUUJoWKpTR9qUnwf4JKPJmWWpbHGWG1yoq4FmlZBPGSDHY9VxnoXMWRZtdxZKbpQN6LWuxMyki8LCjpYxzjWVyFbsXZg8Qh7rKRObM/OyE6q1Wp04Vi86ifFq0/Gjh1bY46feOKJ+3VNR34HMgdIakvBhsSEcHaV7g7KUVyhfIRwzk/mOagNyOiOW0nOAvtcZ0cU1+NJ4MTrWkoHZJLcUHaqp3UT+3dP4JzmGduphvx0QEZIEjlFxWQ3z7HiK4RwM3iQ/egoCKCZErbYPQ1P/5lGrRaZ1GA+JOFmJgT9Ptq1KEDXKzGJoQQcs7SCidmoGBmnTwDLgpRRWKsLoyEiy2GXf0WSgyhKMSDcGJS0bZGcIElh7U49yk/2pckssTNAkH0Iv4wStMZf0O6zGnEykoKWY9UDSuV28QIZK2OpbteS9xkMw0q1zQ5kp7WCjRs3ji1btrifuXPnpr2mdf8AmZldCYfboztcId6KvnWAy7rYtxsr1qbFcS8l4mR0vTJl7lbRLGyBK9nnTKQgIBGx42N8gWBi1+51+dnf1Rof00Dx+fIxbVoCIaCSMuI+gWH3W6bfsjhEtSjRuKVPEnH/6RcxwzTcQPqksSIp5BsGQdOkWNMQtYyj1Lmn+LOI7g4QLfOjRRTCObn4vaUk7M2OsPvCMAyIlltgXcgQqjuNP1VCoVyKigoxjOrE9Tztsiwbps0GnL42mjdOxmdHliX1l2OR8XJUpYmP8UrAb8WjxGQdVRYukDFNg0jkJ0KhckKhvSAZNXShbo9Dl4PKF7LBTIIpOW3WFwkw6oDT5pnNXf3mJFLsjxx//PFJc3zhwoX7fU34HwMypmmiRqO/yMfQVLRYjEhVJWo0mjSwg8EWZGZ2TgSNeU2MvgzwBT1AJnmXp2kaDz/8MP379ycjI4NWrVoxfvx4ysos06iiZLkD+603/s6JA0+k30H9GDZsGD/9ZAWsOUDmvX+8x6gjRxEMBmnfvj0zZ860gFJKzIhTwkCymSG91o+Yobvepu+//5bBgwcTDAbp0aMHH330kXWeJKHrOhdccAHt2rUjr7AZfQ4+lAcffBafkockJUDfFTPnk9v1cFq378bsG2Zheq0FpsGRJ5/DZTfczuQp15CXl0dRURGPPvooVdFqxl05noIuLeh6WG/++c6/XFKwDz5YSpv8nuzZvobq6vUsXPg3WrcewnvvfciRRxxJ68LW7gQDmDFjBk899RSvv/66u3N4//337Wf8nqOPPppQKERBQQEXXXQRlZWJ+AjHknPnnXdSUlJCQUEBl156qZsynypCCELBlvj8+YRDbamujjJ79mz69etHIBCgY8eOPP7440nnLP/mW4adfArNu/Rn8JHHsnbdOqufFYX16zdw0kknUVRURGZmJgMGDODdd/9tv0grm6lLt0O4885HueLS6xnYdiDdO3bnkUceca+/ceNGsgPZ/Putf3PWH88iHA7Tu3dvPvnkkyQg88EH7zNs2FkUFQ2gbdtOXH755VRVJQe9Ov0H9YOGcDhMcXGx+0ktAluz76zdvNsmBzRJSp1ZZHW1yTRNOzW64R/D8KOqGpHIHuLxOLFYjIqKn1BVjaoqhWg0hKpqqNHtaKgYIo6qaggy0DSNWDxO3DCs+DFnwVejiWdxqBMaCGTmz59Pz549Xf0wYcIEKiutTJ94PAdVtfTNm2+9S9dDT6JZ8/6MGnUJW3/e4XLflMX28vq/lnDssBNp3749Xbp0SegHjzhuSYFILnUiKwRNkw6qSiAWZ+JlkygpKSEYDNKmTRvuuusuALp27QrAqFGjEELQvn17hFBYv/ZHzrtkAqXde3jG8bsJIKNWc8ghh7BgwQLOu+ACsjodRuuBJ/DIY8lz5bPPPqNv374Eg0H69+/PV199lfT7Rx8uIyenFzt2/ohpmjz77LN07dqV9957j65du5KXW8wpp1zC9u2JuBxN07j88svJzc2loKCA2266jQsvv5GTz7/Spo8QyUHIjkXGtsIYJsy950E6duxIIBCgdevWzJ49O6ldmzfvYuTIiykuHki/o0/jw08+RNeqqapay/btGzj//Kl06XIM2e0H0fMPJ/Lcc8+55+p6NcOHn8/VV89i6tSp5OfnU9y6AzPuf94CM4EshBC0bNmSp556ilGjRhEOhyktLeXdt991r5Ppz+SnNT/xx7NPIb9zCSUHteDss89m586ddY6/uiQQCCTN8by89BbExsr/FLOvFoux4Nw//Sr3vvypl/HZOwtLAXuzDDw7jnC+fUzCdG+aOkLISdTwCxYsoH379qxfv57x4y9BVXdz9903WztjSaY6EmXOnQu486E7MWWTudfOZcKECbz++utgGnz42Vdcd+l1TL99OqcOO5V169Zx0UUXATB9+vREezwWGcfKiMf6EdNjOBnZ06Zdy913L6Bbt27MmzePsWPH8sknH1NQoKDrEi1blvDSSy+Rm5vJkiWvMWnSTNq06cXpp58JwLx583j2xVd5Yt5NdOrchbufeIlX/7GYoUMG2G2xWGyfeuktpl49hc8++4wXXniB8ePH88rfXuGPQ09k6mVXseDR+zn3wrH8cNwPBAJFaPYwN4W1qxJCUF0d5cEHH+Gue+8iJy+Hi8+7mClTprBw4UKmTJnC999/z969e3nyyScByM/PZ/PmzZx55pkMGjSIpUuXsn37di688EImTpzIX/7yF7dPFi9eTElJCYsXL2bt2rWcdtpp9OnTh3HjxqUdG7IcJmQHHl544YV8+umn3HbbbQwZMoQNGzbUUBw3z5nDTddeQ4tm2UydMYcrrrjCimVSFCp37+HEE09k9uzZBAIBnn76aUaOPotV/3mF1p2crAPBffc9zfXXX8qZl43j/bffZ/z48RxxxBF07tzZvc+COQuYMWcGh/Y6lOuvv54xY8bwwQcfIMsyGzdu5MQTR3DDDRN44IFbqa7OYuLEiUycONHtM0ccIFsfkFm4cCHPPvssxcXFjBw5khtvvLHW6sNecYGMJFlWQ19GvVYyWbbmU2qbVFVlzpw59d7zQMjVV11Z0yLjgHxDd12A9QEZSZJYsGAB7dq1Y/369UyYMIGpU6eyYMECVFVF1xWqq6PMn/ckDz80C5/fz5Qpcxgz5gz+8d4/qIhX8O6HH3DppOncMutmBgw4lC07tnDVpKsAuOmmm9x7OdYYWUqxILrB8CYLnniBN958kxdffJHWrVuzefNmvvnmGwA++eQTWrZsyZNPPsnxxx+PLMsomkpVdRXDR47kjrvuSozjkSNZ9f13tPYnMqoeefhhZl19Cddfei4vv/9N0jiurKxkxIgRHHvssTz77LNs2LCBSZMmJfWV7JB2GqpLjheJRFiwYAHPPPMM0eiPXHjhVVx//W0899xLANx+++0sXLiQJ598kq5du3LX3Xfxj38u5ujB/QkQR0JPABk9bustYcWhZB/EtTfN5tEnn+auu+7isMMOY8uWLaxcmRxfeP3113PHHXM56KAgM2bcwZnjr+GrZT1QFIVoVKNnz95Mnnw+2VkZLPnXUs4++2w6dOjAgAED3IylZ599kSuvvJL//ve/fPLJJ4wdO5Yhhx/Jscf1RrI3X7fffjt33nknd9xxB/feey8Xjr2Qf335L/Ly8wirYQYcPYDzzjiXudNvJe7XuW7GDYwePZr33nuvzjFYmyxZsoTmzZuTl5fH0KFDmTlz5j5dJ1X+pywyv1lxgYyAUG7i/zgBwxaid4DM+PHjGTp0KG3btmXo0KHcdNNVvPrqOyiKhbSRFFRV475bb2DQoEF0792dex+5l88//5yvvvoKE8Ft8x/hgssvYPSZo2nfvj3HHnsst9xyCw8//LB1DdemnHA/SYYNaLwWGT3mpulefPFYTj31VLp27cp9991HVlYWL730DLHYNjRtC1OmnEaXLlkUF8uMHj2cs88ezcsvv+pe6+677+aKSZM45cSj6d6hJQ/dOZOcrMyED9hWmr27lXLD9ddTWlrKtddeSzAYpFmzQi44Yyyl7Tpy/eRr2LVrF8uXL0eWQ6h2intcziMzszPBYEtUVeW2226jZ++e9OvXj4kTJ7Jo0SIAMjMzCYVCSbsHv9/Pq6++SiwW44knnqBHjx4MHTqU++67j2eeeYZt27a5z5GXl8d9991Hly5dGDFiBMOHD3evXZesXr2aV155hXnz5nHSSSfRvn17jj76aE477bSk42bcNJ3BhwykU8dSrrlyIkuXLiUajSJkmd69e3PxxRfTo0cPSktLueWWW+jQtg1vvPN+ko/+uOMOZ9y40+nYoRXnX34+hYWFLF68OOk+YyeM5fgTj6dTp07MnDmTTZs2sWHDBgDuuOMOTj/9VCZMOJtOnbowePBgFixYwNNPP000mpwJ5Y1JqU3OOOMMnn32WRYvXsy1117LM888w1lnnVVvn6Wjuq8r0De1TU0R8NtUIvv8CTdBCpBxrDE+yVdrKQdHJk+ezFFHHeXqh1mzZvHiiy8SiThp/QqqqnL/PfdwdPceDOrejb/85Sk+/vhjVixbgSzJ3H/HA5x3+QWc9OdTaNOmDT0O7cGl11zKww8/nHQv3UzjVoKkYPgfft5KaWkphx12GG3atGHIkCGcdNJJABQVWen3ubm5FBcX06xZM3KaF3HEcccz8fLLk8dxhw688dbbrr4UmBw/9A9MGDuajt17M+3a65LG8V//+lcMw+Dxxx+ne/fujBgxgquvvjqlmR43uVaOYRioqsq9995Lv3696dWrA+PGnc7ixR+4x917771ce+21jBo1ii5duvDA/Q+QnZONjhV8m4Un8NuxxviCICQqzCD33P8Qc+fO5dxzz6VDhw4cdthhXHjhhUntmjJlCiNGjKRXryO5/rpL+WHzz6xfvxlFyaZ5856MG3cZvXp1pW27Voy/5ByOP/54XnzxRXfjC9CrVy9uuukmSktLOeecc+jfvz+L3ltsP7els8eMGcOYMWPo2LEjc+bMobKyku2rttM+tz0PP/gwffv2ZfZNt9ClYyf69ujNE088weLFi1m9enWdYzCdHH/88Tz99NMsWrSI22+/nffff5/hw4c3yRz8n7LIKIEAlz/18i9yL1VV2blzJ0IIioqKktKOa4g/AzKaW75MWyGoqophOKbMahQl0wUyH374Iffddx8rV65k7969aJpKNBojFpMIBgFJRlEUBvTqwk578WrdsTU5OTmsWbOGY/u25tsVq/h06Vc8evejbnCarutEo1Gqq6tRPOy+DpBxI9cdJl3TSt+1M54ZOLCX+0iSJNG7dy/Wrl1j/+3n4Yef5plnXuXHH7cSjUaJxzX69OkDQHl5OVu2bKHfgIFoSBZ9eqyM/r27uUGJDplTr66lrsKXZZmCggJ69OzpbAApamalSG7fbgUAOu3XPfE24XCYtm3bEhdWwcKSkhL3+Npk7dq1dO3aNclKMGTIEAzDYNWqVa5S7t69e1IcRklJibsDrUusTAqZQYMG1Rm306tXL1DjGKagpLllZdm1axe52VlUVlYyY8YM/v73v7NlyxY0TSMSifDDT1uTgEzPnt0B8EtQqasUFxfXeP5O3Tu5C1RJiRUounPnTkpLS1m+fDnLly/n+edfwgHdVsyMwYYNG1yXATQMyDjWQKttPSkpKeHoo49m3bp1dOjQodbzkphYMwuh0mxQnERtKdg+n4/rrruu3vNTJRL5EU0rR4ggphlFCAlVbU40GncrfmtalZ0uDrG4gt+wzOpVdnaSoihpLDIpgb7paoGlyLvvvsutt97q0Q8a0WiUnTt3EggE8Pv9ln4YeAhi5/f44zrdu/UmNzeXVStXMaLXCFZ9t4qvPvuKR+961L2ubujEojGqq6vdOeAAmbRZVJICepyxp5/CsaddROfOnTn++OM54YQT6N27t3VImnEuyTLVkQgzrruu5jj+4QdLX2pRBCZ9urW3TrKJB73j+Pvvv6dXr14EPTE2gwYNqrXfVLUc0zQIhUKUlpa6RTtbtGjhXrO8vJxt27YxcOBA9zxZlundtzcx2x0YJoKmRkHJ9MTHhN02xWIxjj766FrbAfYcx8rQa1ti1TAq37aX0MGtKSvbwl133c3bf3+Dn7dsRVU1YrE44XDYtcYIIdOzZ6+ka3p1nANkvPM0IyOD7Oxs9u7eS0AO8PXXX7N48WJyW9UMoF63bh2dOnWq8xlS5fTTT3f/37NnT3r16kWHDh344osv6N+/f6OulSr/U0BGCOG6dw60yH4/SkWF+//aAjfthoEdXOmIrutWMCcQiZShKPlomsbmzZsZM2YM48ePZ/bs2eTkZPDee68yceJNGIa9UDnK0FAJeNLpHDERVFVVc+nUSxk1ahTFmcVJ9w4Gg2gi4hycADKuRcYOhLMZIB2LjGEk7mEFdpr2R+bNN7/ghhvmM3fuLAYM6EV2dg533/0Yn332WY3uiONHIYqb6+0AGTvl0qf4SGXE9Pv9CFnC1BK1W9xUSoep1kz4931OQTos15lTrK4hUt9xzrW97WtIUGnIkzbtBUKpEggE0dW4zX+coOkXksSUKVP497//zZ133knHjh0JhUL86eQRxONqEpAJBCzFGhBQidUPNRZ1xeey+nr7VJZlKisrOf/807nootEEgyX4fIl4ltatWyddpyFAJlUOOeQQwAKPdQGZJIK+YI6bRlqf1GaRccZS4yWXaLQK0ACFQKCIigoJn88kGAzi9/vx+/2YUoxIbAfVwkeLfIsTSt+2LeF6c+auy6yaSL2G+t1KGzduZMSIEa5+yM/P58MPP+SCCy4gGo0SDAYT49O1oph4q7kXBPOJVFVz81UXM/SPZxBTNVRFJSIilGSWJAGDWi0yYFlkVOjXpxcbNmzgH//4B++++y5jxoxhyJAhPPbYY7XqxbTj+E9/Ih6PW0CmehcCE7+iYPgykXxOEHPD5lqqCCFjmhpC6Ph8PmRZJmYz6spyuN45L0syMSGI4CdEHKlqK4Q61gj0DaVSI9QiXh3is2kK5JiKaZrcd999PP7448yfcz2lPdsQDoe44YYHiMfjHmJGqU495PR7atKI95jKykpGjhzJ7bffXqN9zsZmf6R9+/YUFhby448/7ve1fnctHSCRJGmfzdcWiDFx/DtCWNadeDzO8uXLMQyDefPmceihh9K+fTFbt+6wj7Nfp5DQNI3Pv15BwF7EV65cSXl5ucUfgaBn7x5sWLuBDh070LFjx6SPlRJpN8Y0XSAg2/wjQklO6xZ2CvZnn32J4dTyiMdZvvwbOnVqh6Jk8/HHnzB48GAuu+wqDj30WLp1G8j69evdZ87JyaGkpIQvv/ySuJ10rWkaX3yzEneY6g7fRS2gMDUF2/265u7bUUymMNNmbvn9/hrvrXPnznz//fdJwb0fffQRkiQlxZbUJaZpUlZWRnV1zaq7PXv2xDAMPvnkkzqBjLfekuHN6hISH330EWPHjmXUqFH07NmT4ubN2bjZCvL2AhnnnfkdRmTSK+p0O21JkujXrx8rV66mQ4fWdOrUNWn8pAKBfQEyy5YtA+pXmKnp4A2VpiTFg0TdJbAWRb+/IG3bAv5CtqkSMbuGmvd3XdeTuUag0RlLX3zxRZJ+6NSpEz//nKCWD4VCSJKtH7780t0QrPp+BWVlZdYO3dDo16MLq9ZtokPHUtq1a0dpx1Jat29Ni7YtUlhvbV6rdEDG+U6Syc7O5rTTTuPRRx/l2Wef5e2332bvXotawufz1ZhrNcZxcTEbN260fkzhbdJqKTjatWtXli9fnuTq/PTTT9Meq9hEdE6NMyGEyx8je4g+c3JyKCoqYunSpe53uq7zzbJv8Mk+qmS7IGu8wiot4LqWrI1DaWkpoVCoQa7mROOccisamhpn6dKlHH/88Yz988n07t6Jdu0OYvVqK8bGyZwTou75kLbEQ4r069eP7777jrZt29ZYIzIy9o07yys//vgju3btoqCgYL+v9TuQOYBSV6mCusTJcNm7t4rly1fy7bcrWL78a77++mvy8/NdH+769et55pmneeKJF5MvIAQ+n8JlN87lq8+W8t3X33HtxGs5+OCD6du3LyZwxdRJvPnim8y/dT7fffcd33//Pc8//zw33HCDdQ17gdMr4oidKgfFixLp1VIykPHZVp/HHnuBV155kZUrVzJ58mTKy8s5++xRKL4cSktL+fzzz/nXv/7F6tWrufHGG5OUAcCkSZO46667eO2fi1m5dgMTrruVsr0VnrxuDxldGqmRgu021waU1ASUBobLXOyVtm3bsnz5clatWsXOnTtRVZU//elPBAIBxo0bx7fffsvixYu57LLLOPvss123Un0Sj8eprq52lbhX2rRpw5///Geuuuoq3nrrLTZs2MCSJUt48cXk9yt5FsbUBaC0tJRXXnmFZcuW8fXXX3PGmWdgGKbVZ540TZfLyC7151Vo3v+nW6AURWHKlMn897/LmDJlDsuXf8+aNWt4/fXXmThxYo3j6wMy69at45ZbbuGLL75g48aNvPHGG5xzzjkcfvjhrom9NkmX4t8QaeoYGUnyu+m5FseNnLZtsl2jB3CJ5JKBTMoCJPuIalFUw+In8bJnl5eXs2zZsqRPYWFhin54hoceesg9x3EJ+Xw+LrvsMv67bAVfLF/B2AvGceihh1ouEy3G9CvG8fTLf2fuHXewatUq1q9ez9uvvs0tN92S1Ly6LTJWf8x/8Emee+45Vq5cyerVq3n55ZfdgE+w5tqiRYvYunUre/ZYdYhqjOMzzvCUB0lkd+lI6Er6mKgzzjgDIQTjxo1jxYoVvP3229x5551pj3UsipJk6Wor1kQDIWpQC1x22WXceuutvP7666xatYpJkyaxZ88eC2TKwUQNo/LNCUoNG4wEg0GmTZvG1KlTefrpp1m3bh2ffvppjczE5H5M9K0aq6Zdu3b85z//4eNPl7Luu7VMmnQz27dvxzQNq82IeoFMQ+bLpZdeyu7duxkzZgxLly5l3bp1/Otf/+K8885r9LyprKzk6quv5tNPP2Xjxo0sWrSIk046iY4dO9bp7muo/A5kDqDUVTyyLonHLRfNxx9/zB/+MJo//GE0w4Ydz7Bhw/jb3/7GvHnzuP322+nRowfPP/8qN900qcY1wqEQ0yacy1nnXsDZw88mlBHi4UesQD0TweHHHsX9C+/n/ffeZ8CAARx66KHcddddtGnTBgARtCeCCWgmIcOezEqCtdgBMg6J0owZk5g7d56dqvsxzz23gIKCAhQ5g4svvphTTjmF0047jUMOOYRdu3YxYcKEpDZfddVVnH766Vw++SoG/XEsWRkZjPrjSGow1NRWKVmp2yKjpasiW4tFZty4cXTu3Jn+/fvTrFkzPvroIzIyMli4cCG7d+9mwIAB/OlPf+Loo4922WsbIo4yNgyjxrgwDINbb72V4cOHM3HiRLp06cK4cePSpjRLNphUNSPp/Pnz55OXl8fgwYMZOXIkw44+in49u1CzHovkpvj7hJlkkdFsF2GWbKZdoCwLVDP+/vcnWLfuJw4//Aj69u3L9OnTadGiRdrjvc+eKn6/n3fffZfjjjuOLl26cNVVV3Hqqafy5ptvpj3eK/tqkdkXK1F9Egy2IhAoxu8vSB+EjO3etkGkU8+mPovM7qhFE58VyEri31myZAl9+/ZN+jzzzDPMnz/f1Q8LFy5kxowZgAVAHXdDOBxm2rRpnDF+GkNOPp/MjDAvvPCCdWE9xrAjB/PWwodZvHgxJ554IsOHDeeZh56hectkiv46Y2TCBZDRjKyCEubOnUv//v0ZMGAAmzZt4plnnnE3evPmzePf//43rVq1om/fvgA1x/GwYfTr18/pRNeFGCNQ6zvMzMzkzTff5JtvvqFv375cf/31ad0k1jvIsIjiHIJwp6CnnEFqDbFp06YxZswYzjnnHAYNGkRmZibDhg0jGLTKTVSQYRHNOXFOShBvbbgbb7yRq666iunTp9O1a1dOO+20euPzHNHicSZNmkTv3r0ZdtoFHHfyOIqKChk+/CjXte9kYtUlDbHItGjRgo8++ghd1znuuOPo2bMnkydPJjc3t9EbB1mWWb58OX/84x/p1KkTF1xwAQcffDBLlizZR1dusghzf6ktf2XZu3cvOTk5lJeX1+CdiEajbNiwgXbt2iX5dX8pqaiooKKignA4TG5uboPPc9xIOTk5SNJuVLUMf6A5ajyMLMvurkpVy4lEfkCS/GRmprg2dm+AaBlkt2STEaEyXkmBnoehQ66oYFdmmL2xvRRnFFMQSm/aM3XDYnPUTX4s34yMTPPcYnwBa+Ct2bOGuB6nVVYr/FQTj+3A58sjFDqI8vINCFEJZJCd3b7Bz15ZWcnevXvJCgiygj5LGepx2L4icZA/EwpLa5yrV6noe6IgCfwtEqb+uB5nzZ41CCHomt8VIQTbd2xHUzWqfFWUNqt5rXSyZ88eIpEIWVlZZGWlZ+asT6qqqigvtxiN8/Pzk8ZlPB5n586dSJJEcXFxbZcAYOem9WiaTkAxiZoKpt+KfWjWLKWwW9VOa2cYyIKCjkk/VVdvRNMq2KMLYvjplNcJVVcpr96E3y4WFwq1cokbd+3aRSwWIzvbh2FsBQQZGaXIacoTeCUSibBnz5707dtP2b17N9FolOzsbDIzazKv1iaqqrJjxw6EEE3i708VTdPcxamkpCQpFmTT3k1UxitpkdmCvGCeOyYCgQAFWSHYuco9Vi8oZXXlZgzToE12GzL9DX9GR3bs2IGqqun7aNc6iO2F3NbWXAPYuwUqt0K4gL1SLpWVlYTDYX7SLBdlp/xOLnBZtXsVmqHRPqd9gwKRwdLZlZWVZGRkkJPTsJimGmLolO/ZSVVMb/S7r00qKzdhGHsxjDB+v4ymVRAIFBMI1D1mDcOga9eujB49miuuuIJoNEphQMUfswAooXzIa7N/jdv7M1RuY6fUnLghyM3NJVyxAfQ41dnZaEY1TraD39+MYLBu/eEdn+k2H7+U6LrOV199Rd++fdNuRhq6hv9ukTmA4ryYxriWTNN0XUt+vx/JRteGbi2g3owZVbUWRMe/m3xzG+V6qmAbDp+v5Hf982lLFNgiZAnJJyMFFaqUKGVyBaZTWV6PuTWcMnwZyHb5ecOwyf/sYGFJatyC7yh8VQQSrLk1ilqm3327aeEphhnHImOapvvcqUFvDZEaFXf3Qby7x9Rx0RjrgrCPUXXhetzStsuJK5Jr7nqcseUXJqqu8nPlz6wtW4NiJkgYo9GfMWwXiNU+E9O0FLTfX1AviIEDY/1wZH8tMk7phKYWb82e1DHmWmTSupaSLTLlegTDNPDLfjLqKLtQm6iq6uqTtIGmzv3sjEAMI0GHrwSS2u7oESfw2DTNtAUj65N9dQcmt1vGtNvTVO/PNJ3SINFE9WilJkDatGkTjz76KKtXr+abbyzumg0bNriuLIC4LzdBG7EfJUxc8VmVqlV7CimK4tJR+BRnA+9YAOu/X1qm8v/D8juQOYCyL64lTdMwTRMhBIqiuABB1yNJA84wVLcOk1VFOfXmtqlXj7uuH2cRN2VfItCwHl8qwJw5c+jfpj8D2gwgPyefzMxMCnIKGNBmABNOn4Asya4fWTdi6Ho1AivryqlA3VhJmlw1ilrWAmT8MsIvI6VUu3WykiBhCnfqhjQGyDTEHFuXfPDBB5SUlFBaWkppaSnFxcVkZma6n8YsypJTJ8ckKXOphjjZammAjDO2/PZz7YnuISwZllIQilsxORr9CdM00XUdny+GacbtgNaGWVeef/55SktLad++fdLzZmZm0r179wZdozbZ3xgZODBcMrVR8AP1uJaS3/2euDXH84I1CxY2RJyg8kAgkH5cuRmOmvXZvQ7UKkBAIDtpzAdt/hYHyDhZgI/c9Qh5OXk13m1mZiYnnHBCjVvW1TeNkYYyRjdUTNNvZ4oamE6tojQ1xCRJ4i9/+QsDBgxgyJAhfPPNN7z77rt07dqVn376yZrbLVqSWTqYzE6HkVnczu2PH374Yd8ap4QwkCyXFTaQceKTlOykmBiXMb4OSVs7bj/kgw8+SPv+nc+Blv+p9OtfWrz1lhxwUp848TE+u7iiyzxpapim6jICq+oewESWw+l9oi6QSbbISIApKRimXSOnDouMI5dccgkDhw1E1VVaZrUk7AvzU8VPVKvVHGSnkEpSwLKemAbxuMVEq+k+gg2oLO2VtGChoRYZSeBrXnM3IoRFn66ZGrqpJ+3ChfTLAZn+/fvz/vvvu1kUiqIkRew3Csh4j3EysNJaZOoAMnbgqE9YFXvDvgzypAiYGsFAc2Q5g6rqtVbxuvhuTFPH73cWxiK3tER94gT1ATRv3jxpHqSmiDZGHHBlPUvjx5kkSRiGcUAsRemqKDviFFeNuzENHoshdjV60yAiyUS1KEIIcgO5jW6Dw1IL1M6Q7CXg27kWtIi1UchvB74QIl7lXiuoBCmPlbsZVI41Zsz5Y7jsvMvSXj6dFahJLDLs/3xMFcMw0XU/Pp9dCFXJTKuzW7Vq5ZZfSZUWLVrwzjvvEA6H07qf99mNowRQ7WxORZatqte2CElB8eWgxncjycF6A31TpSn6r3///m6m4a8hvwOZAyipu77UnP104gAZJwBKCAlJDmLoUXQ9giT5bfeTFd2f1hoDiYXLSAAZHRMFC8g4lomGAJn8/HzaSe2IalFaZ7cmw5dBfHcc0zTpkNvBbqdAlgLoetS1FGlqoNHKKj2QEZZydbgu6pioqqoiSVKNBUSWZDRDQzd0TClx7ca0b393gKFQiLZt2xKLJYo0euMnnIyNBgGZNGPJAWhJyrcOICOEDyEUTFOjQ04rJAwikQqEkPH58hBCIuAvIhbbSiy+lWBQQQgTSQq4VbsbIjk5ObRr1w6wmFwbCzpqE+972JdryrJ8wIBMXVYHv53d5LiWnArQDjCTbCK5PfYzZfuzG+W6cSQWi2EYBpIk1R5f4Fw3tjfxd34Hlx3ZOx+d1G/XImMDmWYFzVw90BCpC+Q1RprC1esVwzDQtIALZJQ01a7rE5/PR7t27RodF1mvCIEqhcAARSLBMwQgJAL+Zhh6FL+/YanMTr0xh8Ryf99FKBRyNyu/hvzuWjqA4riHoOFxMqlABkhyL1n/VmIYcYSQEhW0U8XjWpKFnOxCEkqDYmSSLudwsZgGVapFwe2TfC5IApLMsCYCXfftM5Cp+YPnOrVYZDRNY8eOHezevbvGbw6xm2Ym6us4ZHiNbVtTxchAItUeGmdylzwpmZIn7Tzp+qaZiH1IU0TRa/ETpkosbvER+f2FbqaG319ouQdNA0WxxmYwWFL7e0oj3iKNTemP9/bXvrhdDmSZgjotMk6FYV2rkdmk6zoIGR0otx8pL7hvhfUcneOvi5DTC5BkvxVEn6bEg2mabuq3aqhohrZP8TFeK9pvzbWk6zq6rtgAX0ofe/gLt8krms395BO6Gx/jZCNKkp+MjA61rwdp5EDMyV9LfgcyB1gaEydjTSQ7gMtjcncWG90uNR+P77aPyauRGuiKJx1SGCp+WXFTbE0hNRrIuFwspk5l3EpNzPQnm169QEbXfIBoGosMJAOZWtrs9J1VGC+5v520Vd1IFC80MH41IOOtpOvIvrqWvJa+pLaZBrgB3ukXG8lenOLxnRh6FISUZG0RQhAKHYQTQW2YgX1S8Aci4Hd/XRRNTYrnlboWa0VSbE7mRLBsasBvuSRhYAXYhmvhSWloG+ocT74QICyek8LS5AK2JI95WZLdeDurNMm+ARlHfnuuJQMQ+HytycgodbmWfs02eUW1S8T4zLjHOr3vfdjUFq1fU34HMgdYvHEy9YmzO5dlOUn5OFHohh7BMOKeIN86zPtCJMCMrhJAdrN5vDEijQUyhmlQoVpZDZm+ZNOrlwFT0/xJO/GGSq2KwGuFqcUi412QHMuW2zbboqSbCSBTG4dMbdKUWUuOxc15542N90gCMrKcHigYDhOyVGufJbLNbEugL69G7ItF9NYMTfOB2XCXUvI1mh7I7Gt8TGqbDqRFJt1iLYRAsS1q6TKXTElht/33vgb5OtfyXjutKAEo6gHNOtfifkyej07Ab0SLuG13rJ0NES+Q318gcyBcSwCy7HPJDX/tNjliZYhZ/1f06oRrqZExiF753SLzuzRYGpOCnc6tBIlAWtM0iMW22dcNJwGH9Df3pGCToKE3PP7VxgKZqBZF1VU37Tq5nSE3XVrT/PulqOq2yKSfvN5zUoGMs2vUjF/PteQlSQvYRUQdINPYeA8pCeiK9ArUcSvVsWNODRT3+2sWiAPQNB/RaHaD0q3Ttvc3CGQOpEWmvjgQx72UGvCr6zoR2arbI4CcwD7yrNCI/pGV2tmyU8a8Gyej75tFpqkCfb1ta4r3l7S524+2HShw4KwfAgPZiIFmx9k1MrDXK78Dmd+lwdKYGBkvf4xXhJDcnbOqlgH1WGMc8cTJBDw1edyMHSEaDWQq4pY1JqyEk1hGASRJISPcHkW2XBH7ohAa5Fr6lSwyTQFkHHHesZNu71XwDdmBS54+UCQzvVJ3mIzrMJFb8QDWtXy+3LQ70crKSjfTal+zjA6ka+m3ZpFpSByIG/CbJgW7zP5/TiBnn4J8HdlfoAdpLDJywiLjpF+nZfWtp01NCWSaYiH2jsumADJNDY6dtcGHbhnW7WwyfoNt/TXkdyBzgMVR/Jqm1TlgTNNMSr1OFe/O2coqqX2n9pe//MWKmPekYPs1tYZFJl2NIa8IIXjttdes+3uCfYFaGUatSrGW8m1aICOn/z9WxV8hBF9//bX7naqqyVaONDEy+2qRacjE9/adI16w4gBcJ2umsQpeeC0ywkxvkXFcS3Lti6EQggkTZnLGGZPT8sI4TMtgUb47lqTGyoG0yOzrwnOgiPoaEgfipGCncy056c2Z/n1jj3baUBvQc/VDA6Q215Kqq3TI7cCitxf94hYZZ74vX74csApMCiEoKyur8/i60oMbu5GoTRoKrtLph7rEBTIO6acdp/hLWGTGjh3LySefvM/3+SXkVwUybdu2deMonM9tt932azapycUb75JqJfCKlwjP5/PVGDySB8jUGeSbdHPHtRTHryVSfnWj4anX7v1Tjk2Nj/HK/iirWhknpdotMq1atWLLli106dIl6Xtvf3uzlpzrGsJoECFgatv21yLjVEZ3wIw3OLmhu2dJksgI+wnJKrLQ04OsBriWrHuGUJSsGq7KioqKJBCTlZWFEIL777+ftm3bEgwGOeSQQ/jss88a1N4a7dtPaSrXUqLafNO2q644kLrYfZ305rqCfOtbXLxWpqZ0lSiS4oIwR/YFyDRFCn5D56OjH3r06FFvu35rcTuOOBZ9N7DfoVXYjxiZA9XWfdEP+yu/ukXm5ptvZsuWLe7nssvSEyv9XxbHjVAXkEklwksVWUootVq5Y2qcZCuceBUCE8W2yLhAphGT1gtkUtOuU8UwDJfPpbFSK5CpwyIjyzLFxcU1lKO3v70WGUfJ76tFpkbbGiipynJ/gAxAVk4mOf4YwlDrtsjsQ/aFUycMICsri+xsi+X1hRde4Morr+Smm27iyy+/tIrXDRtWb9G73yKQaWp2U0casljXxu4LIEyrsGQqYGiMePumKSwMUDNOxpFfO0amPnH0Q108Xk2dEt7UpS9ci4w/Re/+xmJk9lU/7K/86kAmKyuL4uJi95ORsW+U9g0R0zQx4vov/vEhJ7mO0klt8TGOyHKAQKCIYLCEe+55gJ49e5KRkUGrVq2YMGEClZWVNc557a1/UTrkJIJtD2bYGRPY/tMWIOEeWvT2Ivr160cwGKR9+/bMnDmz1lge74Kf6c/EMAwqKyspKytj3bp17iJ3xBFHUFRUxCuvvMJtt91Gnz59kq5z991307ZtW/dvZ2d55513UlJSQrNmzbjuuuuS+FXatm3LnHn3cf6VM8jqdBit27blkUcecX9PdS3997//pWXLlrz77rv079+fcDjM0D8MZcPaDS6zL8D9d91Px1YdycrK4sILL+Saa66p0d6kPkhZ+J544gm6d+9OIBCgpKSEiRMnJh2/c+dORo0aRTgcprS01K3mLEkSuq4zefJkDj30UJo3b87BBx/MY489lrSgpfZNQUEBl156aaJvJB9tDxnOnHn3M3HiRDp16kS3bt0SfWPHyGzeso3Ro0eTm5tLfn4+J510Ehs3bqz1OePxeBKI8TKUzp8/n3HjxnHeeefRrVs3HnroIcLhME888USt1/P2XVMBGW/g9P64ltzSFQ64NU10vbrGp6pqN+Xl29G0qrS/ez/xeAWGEcEkVusxkqliGFFiagWaVoVhRNy2SEgNLsCYTubPn0+/fv3o2LEj/fr1q10/vPYapaWlBINBhg0bxubNm5N+f/311xkwYADt27dn0KBBrn4Ieix3kpDq3Qz86U9/cueGrutMnz6dnJwcVq5cCVjjLSMjg3fffReAf/7znxx22GHk5uZSUFDAiBEjWLduXY3r1gZkqqurOeGEExgyZAhlZWU1XEtLlixBCMGiRYtc/XDUUUexdu3apLE0a9Ysmjdv3mD9kNqmxx9/vFH64Y033nB/03WdCy64gHbt2hEKhRgyZAiPPfYYSjCxPo6dfBMnn3lB7foBW3fOmcP5559PVlYWrVu3dvWD09YffvihUfqhLtlX/bC/8qsz+952223ccssttG7dmjPOOIMrrriiTuQci8WSmFEd03dDxFQNfp7+8X61d19FjG+HKqm1liqoKz7GkUCgOWAp4AULFtCuXTvWr1/PhAkTmDp1Kg888IB7bHV1NbNvv5On77kZv9/HhOtuY+IlV/Daa29iGAZffPYFV4+/mnsX3Msf/vAH1q1bx0UXXQTATTfdVOPekpAQCHyGDzkis23vtqR7AVxzzTXMmzfPdRm+9NJLDeqbxYsXU1JSwuLFi1mzZg2nnXYa3bt358orr3SPmXffw9wy5WKum3QRL79vFWo74ogj6Nw5UfU7NbV51qxZzJs3j6KiIi6+5GJuvPxGnn37WXRT55VXXuHBex7k7nvvZujhQ3n++eeZN2+ey0Bbnzz44INMmTKF2267jRNOOIHy8vIatOUzZ85k7ty53HHHHdx7771ceOGFfPrpp7Rs2RLDMGjVqhUPP/wwhYWFfPHFF1x55ZW0a9eOs88+O23frF27ltNOO40+ffowbtw412U076Enuf6G6YwfP5533nkn0Tf5AlVVGXbyOQwaPIQPPvgARVGYNWsWxx9/PMuXL08LnJ35FQgEkkBMPB7niy++4Nprr3W/kySJY445hk8++aTO/mrqwMKmCs50QKXLLWREWPJ+z/1uX2Nkq/1vj+6L0XUFyZT2mTsGrGeaO3cuhYWFbN26lWnTpqXXD7Nn8/TTT+P3+5kwYQKnn366O4Y/+OADzjnnHO655x46d+7Mpk2buPbaaxFCcNW1V7nXSQ34TydHHHEEDz/8MGC9t08//ZTCwkKWLFlCly5dWLp0KaqqMnjwYMCqEH/llVfSq1cvKisrmT59OqNGjWLZsmVJ7zodvUNZWRnDhw8nMzOTf//734TD4VpjZ66//nrmzZtHs2bNGDduHFdddRX//ve/AVi4cCGzZ8/mgQceYMiQIQ3WD057nnrqKW6++eZG6YczzzyTTZs2kZ+fj2EYHHTQQbz00ktkZmby7rvvMnXqVLp07szow7u46deLP/iYklbt0usHW+bNm8ctt9zCddddx8svv+zqhxYtWqCqKqeeeiqHHXZYg/VDbbI/+mF/5Ve1yFx++eU8//zzLF68mIsvvpg5c+YwderUOs+59dZbycnJcT+tWrX6hVq7f+LQQaezeFjU2AkWzvpk8uTJHHXUUbRt25ahQ4cya9YsXnzxxaRjVFXlvvvuY1D/3hzcqxtP3T2TpUu/5KuvvgLgwTsfZPzk8Zx77rm0b9+eY489lltuucVVODXabwqy49lkaBnoarJJ39kBTJ48mVNOOYVWrVpRVFTUYNNvXl4e9913H126dGHkyJEcc8wxfPjhh0kmzxOHHc2EsaPp2L4t06ZNo7CwkMWLFyddx/Xj20B42rRpDB48mG7dunHNtGtYtnQZsahF2/7EE09w6hmncu7Yc+nUqRPTp0+nZ8+6FzCv4pwzZw5XXXUVkyZNolOnTgwYMIDJkycnHT927FjGjBlDx44dmTNnDpWVla4y9vl8zJw5k969e9OyZUtGjRrFaaedxiuvvFJr34wYMYLhw4ezaNEi60c7iPfEo4Zw8UUX0a5dOy6//PJE3xgqL7zxDoZh8Nhjj9GzZ0+6du3Kk08+yQ8//MCSJUvSPqcDqlMDe3fu3Imu6xQVFSV9X1RUxNatW6lLmtoi01SukwOZgt1YcTLRJFOq4b5pjEyePJnDDjuMVq1acdRRR9WtHwYN4uCDD+app57i448/duMZZs6cyTXXXMPYsWNp06YNhx9+ODNmzODhhx92A34BZOoHMkceeSQrVqxwWbfXrFnDpZde6o6/JUuWMGDAALce1Kmnnsopp5xCx44d6dOnD0888QTffPMNK1asqHFtL7DZunUrRxxxBCUlJbz55pu115eyZfbs2RxxxBF069aNSZMm8fnnn7tj/9577+WCCy7gvPPOa7B+gISOWLBgAVdccUWj9YPT/45+6N+/PwcddBCnnHIKZ5xxBi++9JJFXmhLXm5u7frBlhNPPJEJEybQsWPHJN0pSRJvvPFGo/VDbbI/+mF/pcktMtdccw233357ncd8//33dOnSJWnH3atXL/x+PxdffDG33nprrdkR1157bdJ5e/fubTCYET6JFjcPbtCxTS27ynejqirxeLyG1cXZAacS4dUm7777LrfeeisrV65k7969aJpGNBqlurranbyKojBg4EDYvgIMlS4d25GTk8OaNWvo2a8nq75bxVeffcX98+53r6vreo3rOCKbspXlJCArM8stBrd9+3YXyPTv3x+oyV5bn3Tv3j3puZs3b87333+fBGR6OYF6krVwFRcX1/C7etPKAbp160Y8HicQCLjF2nbv3E1JUQnr169n9Pmjk8ziAwcO5L333quzrUIIduzYwc8//8zRRx9d57G9evVy/5+RkUFWVhY7d+50n/Whhx7ikUce4aeffiIajaKqKr17966zb0pKSvjmm2/svrCmb69upQiRKBzp9o2h8fWK1axdt75GAbtoNJrWXO91gTZmN1afeIFMQwuo1iVNkVrsbVciRiLEkUd8k3RMRUUllZUV7t+hUJjc3NqzBvfsKSMajZCZmUVWVu1B8RvKNhDRoxyUeRDZgSzK90ZR1SgychJYaKy8++673HzzzaxZs4bKysra9cOAAe45Xbp0ITc3l++//56BAwfy9ddf89FHHzF79uxEcLxhEI1GiUfjboxPQ6xhPXr0ID8/nyVLllBVVUX37t0ZPny4u2l6//33OfLII93j16xZw/Tp0/nvf//Lzp07XX3yww8/1AjY9Y6jY489loEDB/LCCy80aFx452fz5pa1e9euXTRv3pxVq1YxYcKEpOMboh+ca2zdupWjjjqqwffPyMggOzs7Safdf//9PPHEE2zatIlIJIKqqpZryxeyK5RD965datcPae7j1Z1CCFasWMGGDRsarB9+q9LkQOaqq65i7NixdR7Tvn37tN8fcsghaJrGxo0bk1wGXgkEAvucAiqEQPibpmBdYyUQCLhAJjUOyKlQW2thN49s3LiRESNGMH78eGbPnk1+fj4ffvghF1xwAfF4vOYuRPa5QZ/OnBcIqquqmXLdFMaOGVvjHunaYeiWMgn4E+4G0zSTFJnzXN5gx9RAMq//1pFUYOdYr7zn+gL2LsTejQghauyiU4GMoijuoux1bTjHNZZHxrlOQ94TpH8up4jf888/z9VXX83MmTPp06cPGRkZPPjgg3z77bcNuob1hwQIfIqC5KmALYTA0DUwDSqrIhx8cD8WLvxrjfY1a1Yz3VpVE+7P1HsXFhYiyzLbtm1L+n7btm0UFxfX2Rep8UX7C2RqZHHso6RaZKz6U8lzSNOqkaQQwWCQaDRKLGai60odQK8aSQK/P7PGtbwS8GcTi4EhFGQ5jC5Zi5OC0uhx6YijH8aOHcvVV19N27ZtWbp0ae36oRaprKxk5syZnHLKKezYsQPDMMjLy8Pv9xMMBglq1hxoSNafEILDDz+c9957D8MwGDx4MP369SMWi/Htt9/y8ccfM2XKFPf4kSNH0qZNGx599FFatGiBYRj06NEjbYyhdxwNHz6cv/3tb6xYsaJB1hPv+E7VHfsjziavPitfXXP7+eefZ8qUKcybN4/u3bujKAqPP/44X375pV1WwrlGKudYTb1Y232EEFRVVdG7d29eeOGFGu1Lpx/qkv3RD/srTQ5kmjVr1ugOcMQxuzvo+P8nqS1zydnlQPqS96nyxRdfYBgG8+bNcxeHVLMxWIr+888/Z2DHQlBh1dqNlJWVU1paCiZ07dmV9WvXN7hiabodsBCiBqj0ApDmzZuzdevWpIWrIaXe00bTK35o3r1OTpR0wZ/xeDzpOrJptb99+/Z8+9W3SYp46dKl9bZNkiQyMzNp06YNixYtqnfXVds1PvroIwYPHszFF19MVZW1gG3atKnR18IFp9b7cZWY7UPv17MrL7y1iObNm5OdnV3v5bzWmFSl7vf7Ofjgg1m0aJGb+msYBosWLaoRyJgq3nfigLn9kaYCMvWR4nktVE76eSQSoaysjGbNmqVd+BqamZPK7quadgaTue+bLUc/TJ8+HbAWl9dff73Gca5+GDgQgFWrVlFWVkbXrl0B6NevH6tWraJjRysYXtd1CgoK3PnuFLJsaCyPEyejKAo33ngjsixz+OGHc8cddxCLxRgyZAhgWTNWrVrFo48+yh/+8AcAPvzww1qv6+3j2267jczMTI4++miWLFlCt27dGtQ2qAlkOnfuzNKlSznnnHPcYxqiH8AaJ61atWLx4sUMGzaswW3wiqMfJkyYwK5du4jFYmzYsMH60RsIvp8ZaT179uTNN99ssH6oS/ZHP+yv/GoxMp988gl33303X3/9NevXr2fhwoVcccUVnHXWWeTl7Vu119+yOEDGWxgScEGMLMs1kHN5eTnLli1L+hQWFqKqKvfeey/r16/nmWee4aGHHqpxP5/Px2WXXcZ/v/qOL5avYOyVMznkkEPo27cvAsH4KeN56a8vMXPmTL777ju+//57nn/+eW644Ya07a/NlJ8KZLy7gaOOOoodO3Ywd+5c1q1bx/33388//vGPhnZZzbRAxV9nkbTU4x3LjtcKFNItJXDuBefyyl9f4emnnmbNmjXMmjWL5cuX17sjc353AgUXLFjAmjVr+PLLL7n33nsb9FySJFFaWsrnn3/OkiVLWLduHXPnzk0i9Gu42JkuZiLrxvqP9R7O/NMfKSws5KSTTuKDDz5gw4YNLFmyhMsvv5wff/yxxtW8gb7p5Morr+TRRx/lqaee4vvvv2f8+PFUVVVx3nnn1dvSpoyTaSrXUn0xMg6IcXh/nDR0TdNcAJoqDeVKSU3BjhqWLqCB2bB16YfHHnuMTZs28dxzz9WtH/77X7744gvGjh3LoYce6gKb6dOn8/TTTzNz5kxWrVrFmjVrePHFF139kGWT9QWUhlnHjzzySFauXMnq1as54ogj3O8WLlxI//79XWtuXl4eBQUFPPLII6xdu5b33nsvKZQgVVLn65133smZZ57J0KFD3ayohkgq+Lzssst4/PHHeeqppxqlH5w2XXnlldxzzz37pB8AVz/861//YvXq1cydO9eyxkCNwp77KkIITjnlFDdTqSH6oT7ZH/2wP/KrAZlAIMDzzz/PEUccQffu3Zk9ezZXXHFFUlrt/0/iJUDzWmUct1IoFKoxSZYsWULfvn2TPs888wzz58/n9ttvp0ePHixcuJBbb721xv3C4TDTpk3jjHGXMeTk88nMzGThwoXu70OGDuGvf/sr77zzDgMGDODQQw/lrrvuok2bNmnbX9vC4TWvOyy1zvN269aNBx54gPvvv5/evXvz2WefJZmQa5P9Ne86yshrBXMWeNmUMYXJsaOPZdykcVx99dX069ePDRs2MHbs2HrdRk7bzjzzTO6++24eeOABunfvzogRI1izZk2t56WyvV588cWccsopnHvuuYwcOZI9e/bs12QXZopFxv43nJnFf/7zH1q3bs0pp5xC165dueCCC4hGozV2YA2JjznttNO48847mT59On369GHZsmX885//rBHgl06aEsg4FpmmjpFJlVQLlSzLbr9VVFTUOC+13ERd4iXF0w09Ccg0pI9q0w933HEHDzzwAEOHDuW5556rWz+ccQZDhgwhMzMzyb0wbNgw3nrrLd555x2OP/54Ro4cyYIFC2rVD/VJt27dyMnJoVu3bhQUFAAWkNF1PSk+xnG7fvHFF/To0YMrrriCO+64o9brptMVd911F6NHj2bo0KGsXr263rale2dnnnkm1157LVOmTGmUfnCuMXr0aObOndtg/ZAqjn447bTTOOGEE9izZw8XX3yxfQMZwgVWjNx+ZuyFQiFeffXVBumHhsj+6If9EWH+H68YtXfvXnJycigvL6/R8dFolA0bNtCuXbsGxzUcSCkrK6O6uprMzEyys7MxDMON5m7WrNk+17GpU3QVKrZAuABNCrB9+3ZMTMr8ZbTIbOGaiOuTbdu21TAvp/6Wn5+PEIJdu3ahKMo+uwh37txJPB4nLy+vQe42sBY2J4CtpKQESJC6BYNBAoEA5eXlmJioAZUqswpFUuicn4jFOvbYYykuLuaZZ56p9T67d+8mGo2Sk5PTKM4jXddd33FJSUlSzI4zBkKhUOOtkeU/QtUOjIzmbK2yrllcXIwU2Q3lmyGQDQUdGnSpeDzOzp073YDApogX8IrzXnNzcxscq5FOvH1WXFy8X24q55klSUrrx3fa7H3fpmmyc+dOVFUlHA4n0f3X9p7TSVSLsq5sHbIk0yqzFRv3biQ3notA7Jc+iMVi7Nq1C1mWm2QB2Zf5mCrV1dWUlZXtl15IJ+Xl5VRVVbk6dV+koe+sIfoB9hJxz74AAGsASURBVF1HpBPTNNmyxeL/at68+X67Ur2STmf+0qLrOl999RV9+/ZNuylp6Br+q/PI/C+Jo5icXZ5jjVEU5cCAGLCCfXNbAyBSdo8NDSj0FsFLN9j8fj+RSCQpI+uXriCbLlPKsSrEYjHXhReRI2hCI1IV4W9P/42zRp2FLMs899xzvPvuuy6PRH1ta6xVwds+bxsdS52maftmXbB39cLQAOv/pml66iw1fFzVFR/TFNJUFhnHGuOUetgf8bqWUoOQDcNIa6ESQpCVleUuWN7zvAyx9fWhY5HRDZ1K1SaskwDDus6+6oSmcrs50hQMsI7Lsqk3lE3RtnRzs7q6moceeohhw4Y1Sj9429RUVbkdaQo2ZK+kshAfiDn/S8mvzuz7vySprg6vW+mXFOHEVTQQyMyePZvS0lJKS0vJzc0lMzPT/ZxwwgmuhSYWizUpBXljlFO6rAOnv123kl8mJsfQDas20X/e/Q+HH344Bx98MG+++SZ/+9vfOOaYYwCSntH7adWqFf/9738brTjr6hennfu0cNnBz8JQkxVoA+sseWX9+vWUlpbSpk2btM/+ww8/NL59HmkqINOUC7UXcDhg1xEntsrrFnbEGfNeDijn74a2TZZkdw7uje91v4P9q8j9SwOZOXPm1DpfTjjhBEzTrDf2an/btj9jKt3cFELw9ttvN1o/ZGZmuuRvTeHsaEjdrn2VxpZc+eGHH+p87v3VD/sjv1tkfkFRFMUNQI3FYu5u75cCMqmIu6FA5sILL+Soo45CkqQaGWmhUMhdiFVVTdot728798Uik6qM/H4/8XgcWZYJZ4XBpgMJhoIsfH0hbXPapr1ebdlVFRUVZGdnNymQyc7Odt1fjRanjpKuuSy1SRaZBtZZMk2TvLw83nnnHTfNNlUcLp59laa2yDSFmV0IQUZGBpWVla4b0hl/3sU3de54x1YsFnNBaGNr9vhkHzEtRtwuAqgoCnEt/n8KyFxyySWMHj067W+hUMitRO/0WVNKUxQ+TAc+Q6GQWzIhndSVfelkeTUFkGnKjWGqNBbItGjRos7n3l/9sD/yO5D5BcWZyLFYzC2t4PP5mtTvWd/9HclQMhrMHpqTk0O7du3w+Xy1ptbLsuwS6sEvD2Rq44HIysqisrKS7OxsNJHMqlwXkKstLd2Ju2lKICNJ0r6b3B2Li6EiJM/u1K6zVFe6ulcc11b79u0PSHwM/DYtMmDxH1VVVbnEcc7Gor7A50AgQDweTwreb2x1Z7/kJ4YFmIQQBHwB4tH/W0AmPz+f/Pz8Ws936nalA4RN1bamtsjUJ3XRVuyrjmiqtjVUHFdaQwtcKorSYLqOX1p+dy39wuIoRWdX+Uu6lbxKpFVWqwZbZBqiGB1rQlNUkd0XZVfbhA8EAhQUFODz+WqQd+0L6di++uQPmEJyYmAMLXl32kjXUn3V15tCfosWGbDGtROU6SxA3viY2ixlXpeql/0WGv6evZWjg0rQnWP/l4BMfXKg3ErQtDEyTTU3mzJG5kACGWj6Gmi/lvwOZH5hSd3d/dLZVPsy8RuiGFOf69eyyNR1XyVlUd8fILOvwb5NrpA8z+TgjySLTANdSwdysXHkt2qRAcsq4/DDRKNRF8TUVTbEAX1erqLGAnm/nJg3YSX8/x2QaQgg3B9pCtdSU2y+vNIU4MqRxlr4GitN2dZfU34HMr+weAM6f0m3Uqo0NZBJVVK/haylVJFEcibJ/xcWGSFcMONc2TQMsHllGuJaOlD1lVKlKYCMN4OuKedOqlWmIRlc3pgP5/jGLjw+D9BMBTL7Chzc4PbfAJBxALKiKAdE1/1arqWGtOm37lryXvd3IPO7NEqcysfwy2crwYGzyKTuXH+LFhlIrg3z/wWQAQ+Qsd0bjlsJAQ2ohaNpmtu+3zqQOZBZHF6rjMPaW58Vwete8ravwcG+HiATUkJJc2hf+ulA9E9TAJkDZen7LbqWmhIcNLW1KFV+t8j8LvssOTk5ZGZm7jdZ0r7IvsQ/NNRU7VVWv0WLDCS7lxpS8C5V9lVJ/RJARmDY99IT3zfgfR9o/hhHUust7Yt4GX2buq1eq4zzfusDds6YdygVGmuRCcgBZEkm5Avhk31JAGRf3EsHwu3mSGPHvGmabvD/gQIy3vm4r4vx/7JF5vcYmd9ln8Xv97s1Ww6E/OUvf0liG/VKYyaZEIJXX321wcq5qYCMIw1VBBs3bqSgoIBvv/32N2OREULw2muvuX8fUIVkB/xKqUCmgRlLF154Ieeff/4BtcZAMsjcV8V5IBdqsPhBnHbKslyvOySVUqGhlkFHZEmmU14n2mW3S3y3H3EyDZmrdemHdFLbmE8d46lyoCx9GzduRAjBsmXLEELw8ccf07JlS/bs2VPv8enk1wj2ra/vUtt2oMZ7fbXGAMaOHesWgfytyu9A5jcqB3rwNBQkeAd4fRPdUVYNYTWtSxq7o2nVqhXffPMNXbp0aZRF5v8f11IKkNGNpO/rk4ZaHwD+85//MHLkSFq0aNFgZeyI19qwvxaZAxVb5lQ3h4YF4guRqADvEFw21q2TGrvVECBTm344EEBvX8e81610oC0KUHv7WrVqxZYtW+jRo0eN3/bFitbQNv1fsMg0Zf0z2D/9sD/yO5D5H5PGTjLvJK8PJMiyTLNmzSgoKEhiOz3QbZRl2a1D8ktZZBoz8b1m7wOys7LBmWwH+OqGmfR9Q9oHDVOWVVVV9O7dm/vvv38fGrr/ivNAAxmwrDIFBQVkZWU16HgHyDQFhxIkEgJSmYYbIr8lIPNLBJB7ywrU1j5ZlikuLk47ZhqzUWuoNIW7yzn/lwIy+5Ml55X91Q/7Kv9TQMbJzvg1Pk0ZTDV//nx69uxJRkYGrVq1YsKECVRWVtY47rXXXqO0tJRgMMiwYcPYvHlzEhh5/fXX6devH8FgkPbt2zNz5swaAKQhuxXHdPvCCy9wzDHHkJWVxcKFC5kxYwZ9+vRJOvbuu++mbdu27t/OzvLOO++kpKSEgoICrrjiCjedFaBt27bMmTOH888/n6ysLFq3bp1UJX3jxo0UFRXx7bffIoRgyZIlCCFYtGgR/fv3JxwOM3jwYFatWuVSwAPccesdNG/enKysLC688EKuueaaGu1NFW//Pf7443Tv3p1AIEBJSQkTJ05MOnbnzp2MGjWKjIwMhgwZwjvvvJNUk+eCCy6gXbt2hEIhOnfuzD333JN0frq+ufTSS2v2zfx7Of/KGRS27caAAQN46plnrLBf2+W0efNmRo8eTW5uLvn5+Zx00kls3LixxrM1RFmecMIJzJo1i1GjRtV7bDrZXyBzoF1LpmlSbRhoikLENKnS9Xo/mqwQMUyqdYOIYRKFBp2X+nF0hJeQr7EbAl3Xefjhhzn00EP3ST94xdEPeXl5DBo0iDvuuKNR7XHG6TnnnJM0NyZPnowQgpUrV7rPmZGR4TLp/vOf/+Swww4jNzeXgoICRowYwbp162q9T+rmorq6mhNOOIEhQ4ZQVlZWw7Xk1Q8DBw6kQ4cO/PGPf6xRKXvWrFmN1g/e9jz//PP06NGjXv0QDocpLS3ljTfecH9z9MOhhx5Khw4d6Nat277rhzp0pyRJ/PTTT5x33nn16oeGyP7qh32V/ylmX1VVmTNnzq9y7+uuu67JdiaSJLFgwQLatWvH+vXrmTBhAlOnTuWBBx5wj6murmb27Nk8/fTT+P1+JkyYwOmnn+5Olg8//JBzzjmHBQsW8Ic//IF169Zx0UUXAXDTTTe512mM2fWaa65h3rx59O3bl2AwyMMPP9yg51m8eDElJSUsXryYtWvXctppp9GxY0fGjh3rHjNv3jxuueUWrrvuOl5++WXGjx/PEUccQefOnZOu5V2Mr7/+eubNm0ezZs245JJLOP/883njXev533r5Le647Q4eeOABhgwZwvPPP8+8efNo164ddYmjpJ566iluvvlmbrvtNk444QTKy8v56KOPko6dOXMmc+fOZc6cOcybN4+JEyfyxz/+kfz8fAzD4KCDDuKll16ioKCAjz/+mIsuuoiSkpIkuvd0fdOnTx/GjRuX6JsFD3DLVRdzzRUT+Mvr73Pttdcx8tDOdO3XElVVGTZsGIMGDeKDDz5AURRmzZrF8ccfz/Lly1EUpdFxHfsj+wNkDlTqtVeqDYMO//mmCa60pdFnrDu8Jxl2XI5T/iASiTTYMgTWAihJEvPnz6dz586N1g/OGP7ggw9c/TBw4EC+/vprpk6dSkZGRpJ+qE0Mw3Df1ZFHHsmjjz7q/vb+++9TWFjIkiVL6NKlC0uXLkVVVQYPHgxYu/orr7ySXr16UVlZyfTp0xk1ahTLli1LO0a9FpmysjKGDx9OZmYm//73vwmHw5SVlaVt4/XXX89tt92Gz+fj2muv5fzzz3eff+HChcyePbvR+sFpj6Mf5syZw/Dhw+vUD3fccQf33nsvZ555Jps2bXL1Q4sWLXj44YfJz89n7dq1+64f6tCduq5z5pln0r9//7T64UDHzTWV/E9ZZP5/kcmTJ3PUUUfRtm1bhg4dyqxZs3jxxReTjlFVlfvuu49BgwZx8MEH89RTT/Hxxx/z5ZdfAlaht2uuuYZzzz2X9u3bc+yxx3LLLbfUAB+NATKTJ0/mlFNOoV27do0qC5+Xl8d9991Hly5dGDFiBCeccAIffvhhkhXrxBNPZMKECXTs2JFp06ZRWFjI4sWLgWSTstdiMnv2bI444gi6devGNddcw8cff4wWt3aUf33sr4w9fyznnXcenTp1Yvr06fTs2bPetjrXX7BgAVdccQWTJk2iU6dODBgwgMmTJycdO3bsWMaMGUP79u255pprqKqq4rPPPgMs98HMmTPp378/7dq148wzz+S8886r8R5T+2b48OEsWrQo6ZgTjx/GhLGj6dSmBRMnTiQ/P5/3Pv4CJIUXXngBwzB47LHH6NmzJ127duXJJ5/khx9+YMmSJbX23YGS/UlNPRAcKb9VcawykUik0VQJ48aNY+jQofukH5zxOXPmTFc/dOjQgcMPP5ypU6c2eHPiLbg5dOhQVqxYwY4dO9izZw8rVqxg0qRJLFmyBP5fe+ceFlW1///3Hob7DIzc5CYIJ0IDUW6pYEqSkYnHRNOMEpOsFE3ty1HLMm94iVDTn5fknMI4lOSxzMxLokJIRwMO4gUiNUxDEUS5CQwws35/cGafGWaAPcMMMyPr9TzzPOy9197rsxd7v/dnfdYNHRGS0NBQWFlZAQCmTZuGmJgYPPbYYxgxYgQ+++wzXLp0CSUlJSrzkj23lZWVGDduHFxcXPD999+z1+uKpKQkjBkzBo8//jjefvtt/Pzzz2xz3o4dOxAfH6+2Psjs2b59O9544w0sWrSoR3147LHHsGHDBjQ2NirowwcffIDhw4f3Xh+60c6DBw9CKpUiJSVFpT4YC/0qImNqaor33ntPb3lri6ysLGzcuBG//vor6uvr2dlIm5qa2JeXz+cjNDSUPWfIkCEQiUT47bff4Ofnh0uXLuHcuXNISkpi08jWSpK/jjqOTEhIiEb34+fnp3B9Z2dnFBUVKQh4QEAA+zfDMHB2dkZVVRWArh0Z+XNkjtX96vuACLhx7QaWLFyiYMeTTz6J06dPd2srwzCoqalBZWUlxo8f321aWf5SqRRWVlYQCoWszQCwc+dOfPbZZ7h58yaam5vR2tqqFLruXDYuLi64dEkxYhAQMLzjD2k7+Hw+HB0dcbfmAWBiiuLiYly7dk2pVt/S0oLr16/j6aefVrg3XdObiIwuh17LsOLxcH0stw+WPM1NzairqwPw35WRhQKN8pZhaWmJuro6tLe3o62tjVPNWObo/fTTT0hNTdVIH0pLS9kITF5eHqsPsv4ana/TFTJHxtTUFP7+/rCzs0NOTg7MzMwQGBiI6Ohoth9FTk4OIiIi2HOvXr2KVatW4fz587h37x77rNy8eVNlh13ZszBp0iSMHDkSmZmZnPQqICCAvbazszMAoKqqCh4eHigrK8OCBQsU0nPRB9k1KisrMWbMmG6dUHl9sra2ho2NjYI+7Nq1C59//jlu377NzjatmT50rZ2XLl3CjRs38Nhjjym8UzJ9MBb6lSOji9VX+5obN24gOjoa8+fPR1JSEuzs7HD27FnEx8ejtbW1R4GRPawPHz7EmjVrEBMTo5RGfrSGOn0SOs+Lw+PxlF5k+fZbGZ2dPFULmalKIxOhrjrsyZ8ju28GjNI+dZHVlnv6GMvyl5/jRvb3/v37kZiYiJSUFIwePRpCoRDJyck4f/68ymvI29w5X1Oz/w17N+F1dH6USAnA46OxsRHBwcHIyMhQss/R0ZHz/DvawlDnSJHBMAysNbi+uaUFWhs6FoIVmPI1uoY8soVEW1pa0NzczEm3JBIJbt26hTlz5misDzIaGxtZfZBIJLh37x4AwMnJidNoLtl7LpubaOzYscjOzoa5uTkiIiIQEBAAsViMy5cv4+eff0ZiYiJ77uTJk+Hp6YnU1FS4urpCKpXC399fYXFOeWTPblRUFA4fPoySkhJO0RNTU1P2+Zc1VWpj9I78RKfdOTLdvdv79+/He++9hw8++ABhYWFwd3fXXB+6SfPw4UMEBARgx44dcHBwUHi3ulog2BDpV47Mo0BhYSEbCpR9FDqHG4GO2mtBQQGefPJJAEBZWRlqa2vZPiXDhw9HWVlZj6uZ9mZooqOjIyorK0EIYcWmu2XgZag7SkKdPh4mPBNIIMHgxwajIL8Ac+LmsMfy8/M55ScUCjFo0CCcPn0azz77bI/pVX208/LyEBYWplDr07gGJFumQNoOE1nHR/AAnimCgoKQmZkJJycn2NjYKJ0qGzLcV2gjIqOvZT26g8/nsyvAa8vRsrKyYh0ZLvNOSSQSXLx4sVf6MHToUABAUFAQqw+EEDaiN3DgQE7vmXxEBgDGjRuH1NRUmJubIykpCTweD2PHjkVycjLEYjHCw8MBADU1NSgrK0NqaiqeeuopAB39+bpDVi5r1qzBgAEDEBkZiezsbDzxxBM92il7pjrfk6+vL/Lz8zF79mx2n7r6cPbsWUyePJnTOZ3Jy8vDk08+iTlz5sDKygoikUgnEZLg4GBkZmbCwcEB3t7eWm056EsMTxEoLHV1dUoffgcHB7S1tWHHjh2YPHky8vLysGfPHqVzTU1NsWjRImzfvh18Ph8LFy7EqFGjEBwcjKamJixbtgwzZ86Eh4cHpk+fDh6Ph+LiYly+fBnr169Xup4m4hwREYHq6mp89NFHmD59Oo4fP45jx46p/KD2BnWiCqY8UzgKHLFo0SK8Pf9thIaGIiwsDJmZmbh48SK8vb17vAbDMHjnnXfw7rvvwtXVFRMnTkRDQwPy8vKwaNEiTvb5+Pjgiy++wIkTJ+Dl5YX09HTk5+dz6kyokv86Mjx0OE0EDMAzQWxsLJKTkzFlyhSsXbsW7u7u+OOPP/DNN99g2bJlGDBggJJt3dHY2Ihr166x2+Xl5bhw4QLs7Ozg4eHRs5m9cGT6IiLTG0QiEVpbW7U2i61s/hWpVAqxWKwUCemsD83NzbCzs+uVPsgcm1WrViE6OprVh5qaGly5cgU3b97Exo0bu7VbKpWyDoLswxgREYGlS5fCzMwMY8aMYfclJiYiNDSUjeYOGDAA9vb22Lt3L1xcXHDz5k2sWLGi2/zkRy19/PHHkEgkGD9+PNuZuDvkmyvlWbRoEebNm4eQkBC19QEA/va3v2HZsmXw8PDAX//61271QRU+Pj7Yt28few/ffvtt7/ShC2JjY7Fp0ya89tprWLduHby9vRX0wd3dXa3r9VYfNIV29jVgsrOzERgYqPBLT0/Hli1bsHnzZvj7+yMjI0OlsFhZWWH58uV4+eWXER4eDoFAgMzMTPalf+aZZ3DkyBH8+OOPCA0NxahRo7B161Z4enoqXUvTdVuGDh2KXbt2YefOnRg+fDh++eUXhRByV8hHZNTp6Mj1YyyyEOH1uNfx7rvvIjExEUFBQSgvL8ecOXM4hc15PB5mzJiBjz76CLt27YKfnx+io6Nx9epVlelVfbTffPNNxMTEYObMmRg5ciRqamqU2uTV4r+T35mQDmGWggcwDKysrPDTTz/Bw8MDMTExGDp0KOLj49HS0gIbGxu1m5YKCgrYZxEA3nnnHQQGBmLVqlWczucyk2hXGHJEBuhwPIRCodaa6RiGYZspmpqalI531oewsDAcPHgQSUlJGuuDjKioKAV9iI6ORmpqKgYNGtSj3fIdfWX/72HDhkEkEmHEiBHshIMRERGQSCQK/WN4PB7279+PwsJC+Pv7Y+nSpUhOTu6xnID/RWa3bt2KGTNmYPz48UpDquUhhHTpyMTGxmqsDwAwa9YsrF69Gnv37u1RH1Tx5ptvsl0IIiMje68PXWBlZYXDhw/Dzc0NL730kpI+qEtv9UFTGGLkq0XV19fD1tYWdXV1SgXf0tKC8vJyeHl5cX4AH3Xq6+vR2NgIa2tr2Nradpu2paUF9+/fB5/Ph5OTUx9Z2PGRq6ysBNDRea2nD0NTUxNqa2thZmYGBwcHjfOdMGECnJ2dkZ6e3m26mpoaiMViiEQiTn0O7t27h9bWVs7pNeLBDaD5AVr4trjfbgFTSODo2vNHp66uDg8fPuT0PGgDiUSCu3fvAuD2v5WnsrISUqkUDg4ORt/XjSutra1s/xRnZ+duKxQPHjxgh2urM2SbC+o8w42Njaivr4e5uTns7e21aocqGhoa0NDQwDbBcEX+WeypbAHu+gAA9+/fR0tLC2xtbTVeU6+6uhptbW0YMGCAThcYltlqY2PDOpl9hUQiQVFREQIDA1VGWrl+ww2zakPRGer0PzGEUL58/5ru0gDqzYPS1NSEPXv2ICoqCiYmJvjqq6+QlZWFkydP9niuprMj63SeFtkyBZIWABaQcAy29uUcMp3zkUqlnJ8tqVSq1DGzP2Bqago+n4/29nY0Nzd3+1HU5fuqTiStc/8YXaPpwofy0ZjOz39v9EHeJkNalbsrtL1MgT6gTUv9FHUcmU8++aRjSKmK38SJE7VuG5f1U+TRZOQNwzA4evQoxo4di+DgYHz//fc4ePAgnnnmGQDo8n4FAgHOnTunkC9X+3TryMiWKej4iEih3v+Yx+Ph5s2b3d73zZs3e22mpustyXfK7CunyxCQb15qbGxEXV0d6urqUFtbi9raWjx48AA1NTW4d+8e60DowpHpbrTZhg0bFJ4Td3d3+Pj4sP3HdI2mcxN111TZG33Izc3VyqrShubI9IU+aEr/qdpQAGgWkXn99dcVeu/Lo4uQp7qOjCZRBUtLS3ZKdFV0N7rK2tqac/+dvlgvBQC70nXHwpEEAAOJRNJj9EK+7FxdXbu9b1dXV62YKuvAKpFIVNbaJRIJ21QoW43aEKKD+sLKygoNDQ2QSCR4+PBhj+l1EbHqLiLz1ltvsbPNSqVSVFdXA4Ba61X1ht5GZFSVV2/0wc3NjR0qrmlERl5fdO3IcI229ZU+aAJ1ZPoZ6kQtZB8PBwcH3fXt6AJVc8l0hS7mQuluWLqsn5E6ThbQN01LDAATSCGBCSdhl3ey+Hx+j8PxtUFPNUCxWMz+mpqaIBKJdL40gSFjYmICOzs7pXlUZAsmyqJcDMOww8C1TXcRGTs7O9jZ2QHo6NMjEAjA4/EwcODAPpmfSNNmnN50Hu/pPZFdW1NHRheLWXYF14hMX+mDJvQ/VejnGEsfGXUcmb7u56FOGco7WX3RtAQAPEgggQmnSef6ekI8oGfhlLdbIpGgpqaGPac/RmSAjkkq9TlggevHTr5/TF9PstiXjkxP9LaPjHyTr67LUdsrYOuD/tPYTFGgpxdMfoE+fTkyMjt6oq8/xprYpnMny+R/TTQm6MizJ2Hqs2avTvQUypbZbW1trbRURn+MyBgCXJsf+rqjL6BZ05KuFyDtbR+ZvnwvaWdfitHB9SMs/1Drw5FRp5ZlDBEZndsmF5Ex+e+keD0JU581e3Wipxqg/AdGJBLBwcGBreH3l2HXhob8x667516fjow60Q9ZNEZXkdLeRmT04cioO2+XIUGrN/0MrlEL+WhMXzY7yFCnRqOviIy6fVB0ivwyBRwjMvL2G1LTUudlMczMzODo6MhpKD5FN3AZNk8I0YsjI28b12dEvllJF8+UthyZvqhEajolgiFBIzL9DK4vmL5HidCIjAbw5EcucXdk+qIdXh6uTUudy4w6MfpDPnLR1XMlc2IYhulT3ZB/Lrg2j+h6lmhN++3I6Evd4PK/NXSoI/MIkpaW1uMMl1wcGTc3Nxw7dkyLlnFHnajHH3/8ATc3NxQXF+vaLADcHBmGYXDo0KE+dmT+u0zBf3WdqyOzZMkSvPDCC7q0TIHuRFO+344x1gyNAS76oIrOkTTZMy6jrzr63rhxAwzDsEOBc3Jy4Obmhrq6OpXvZOf0gO4dmZ40onPZdaYrZ15XdBclnTNnTp/qgyZQR8ZA0dXD86hFZAghcHFxQVFREQICAvrCNLVqW33qyPx3LhkTjv0ZNKktbty4EaGhoRAKhXBycsILL7yAsrIyta7RXX8LeeemP018py76+Lj0FEnTR7NSZ1Q904MGDcKdO3fg7+/P7usrR8YYOvvK59PbDr/a0AdNoErRz1C3j4ymL5JM1DSF64tFCIGJiQmcnJz6fEp0dWfO1TmyZQr++8HpqfOeJn2LcnJykJCQgHPnzuHkyZNoa2vDs88+y2miNtbMTn0aVNmkr75ZlK7h2rSkzw7ZqvTCxMQEzs7OCk5LV4tFagtj6uwrn09vHRlt6IMmUEfGCNmyZQuGDRsGa2trDBo0CAsWLEBjY6NSukOHDsHHxwcWFhaIiorCrVu3FF6w7777DkFBQbCwsIC3tzfWrFnDvuDyU8L3hCx0m5mZiXHjxsHCwgIZGRlYvXo1RowYoZB227ZtGDx4MLstq1l+/PHHcHFxgb29PRISEtj8pVIpBg8ejA0bNmDu3LkQCoXw8PDA3r172fu4deuWQtNSdnY2GIbBqVOnEBISAisrK4SFhSnVDNavXw8nJycIhUK8/vrrWLFihZK9qpCV4Zdffgk/Pz+Ym5vDxcUFCxcuVEh37949vPrqq/jLX/6C4cOH4/Dhw+wxiUSC+Ph4eHl5wdLSEr6+vvjkk08Uzu+qbOSdRIWycfeFR+jz+Ps//8XaKJFIcOvWLcyYMQMikQh2dnaYMmUKbty4oZEjc/z4ccyZMwd+fn4YPnw40tLScPPmTRQWFnK+hnyfnM4fxb4OqT+K9EYf5OmsD8nJyWhvb1f5seuuo+/06dMV3o0lS5aAYRj8+uuvADom0bO2tmZn0j1+/DjGjBkDkUgEe3t7REdH4/r165zunRCCpqYmTJw4EeHh4aitrVVqWjp16hRcXV2Rm5uLsLAwresD8L9VvJ9++ulu9WHq1KmwsrKCj4+Pgj60tbXh//7v/zB06FDt6UMn7ZRx69YtxMfHY+jQoRg0aBCrD5qgDX3QhH6lFh1zBzTp5afNYW08Hg/bt2/HlStXsG/fPpw+fRrLli1TSNPU1ISkpCR88cUXyMvLQ21tLV566SX2+Llz5zB79mwsXrwYJSUl+PTTT5GWloakpKRul7fvjhUrVmDx4sUoLS1FVFQU5/POnDmD69ev48yZM9i3bx/S0tLw5ZdfAvhfjSYlJQUhISEoKirCggULMH/+fJSVlXU78mblypVISUlBQUEB+Hw+5s6dyx7LyMhAUlISNm/ejMLCQnh4eGD37t2c7GUYBvv27cPKlSsxb948XLp0CYcPH1aa9XLNmjWYPHkysrKy8NxzzyE2Nhb3798H0OGgubu748CBAygpKcGqVavw3nvv4euvv+6xbNLS0hTSsGVTWIgFb8Rj/v+tRHl5OYCO1WOjoqIgFAqRm5uLvLw8CAQCPPfcc2hpaVFZbupQV1cHAOzMrlzpqgao7yZNQgiaWtv18tOWRmhDH3Jzc5X04csvv8T27dtVRmTk93X+340bNw7Z2dnsdk5ODhwcHNh9+fn5aGtrQ1hYGADg4cOHeOedd1BQUIBTp06Bx+Nh6tSpnKIFDx48wIQJEyCVSnHy5EmVfYFktn700Uc60QcA2LNnD1auXInY2FgUFxd3qQ8zZszAxYsX8fzzzyvoQ3t7O1xcXLB//37t6UMn7QQ6HCaZPnzzzTc4fvw4qw+dZ5LWBE31QW2IkVNXV0cAkLq6OqVjzc3NpKSkhDQ3NxNCCGlvf0iyTnnr5dfe/lCt+4qLiyNTpkzhlPbAgQPE3t6e3f78888JAHLu3Dl2X2lpKQFAfv75Z1JRUUHGjBlDkpKSFK6Tnp5OXFxcSFtbG6moqCAAyDfffNNj/uXl5QQA2bZtm8L+Dz/8kAwfPlxh39atW4mnp6fCfXp6epL29nZ234svvkimT59OKioqSFVVFfH09CSvvPIKe1wqlRInJyeye/duIhaLyblz5wgAUlRURAgh5MyZMwQAycrKYs/54YcfCAD2WRg5ciRJSEhQsC08PFzJXlVIJBLi7OxM3n77bSKRSFSmAUBWrlxJKioqSEVFBamvrycAyLFjx7q8bkJCApk2bVqPZTNz5kx2u6uySU5OJhUVFeTvf/878fX1JVKplE0jFouJpaUl+de//kUqKirIyy+/zPlZ61wOkyZNIuHh4WqfW11dTSoqKkhTU5PC/rq6OlJRUUFqa2vVvqY2eChuI57Lj+jl91DcxtlOXenD+fPnCSGEREZGkg0bNihc5+9//zsZOHAgqa6uJoR0POPffvstIaRDaysqKsjdu3eV8r948SJhGIZUVVWR+/fvEzMzM7Ju3Tr2OV6/fj0JCwvr0v7q6moCgFy6dIkQ8j+96fy+5+TkEH9/fzJt2jQiFovZ8zunP3r0KAFADh48yKbRpj4QQoirqyt5++23SUVFBWlrU/6/AiDvv/8+u93Y2Mjqg0QiYXVDXl+0qQ+7d+8mhHRovq+vL2loaCAVFRXk3r17rD6cOHGCzUdX+tDe3k7y8/MV7kGezt/wruhXEZlHhaysLERGRsLNzQ1CoRCvvvoqampq0NTUxKbh8/kIDQ1lt4cMGQKRSMSGc0tKSrBu3TqF1UvnzZuHO3fusF40oF5tPSQkRKP78fPzU6jFubi4sAvPkf/WUuU78jIMA2dnZ1RVVXVbS5M/x8XFBQBQVVUFACgrK8OTTz6pkL7zdldUV1ejsrISY8aM6bYW7efnx9orFAphY2PD5g8AO3fuRHBwMBwdHSEQCLB3716lFWRVlY38NTrfp6xsampqAAAXL17EtWvXIBQK2f+znZ0dWlpa8Pvvv7PnaEJCQgIuX76M/fv3q32uoUZkHgV6ow+lpaUAgOLiYqxdu1ZBHxYuXIi7d++qbKbqrvOsv78/7OzskJOTg9zcXAQGBiI6Oho5OTkAOiI0ERERbPqrV69i1qxZ8Pb2ho2NDdsU3dPqyi+99BK8vb2RmZnZbT8d2TM3fPhwdp829aGqqgq3b9/GU089BaDrfjLy7621tTWrDzL70tLSEBoaqhN9kKUpLi7GtWvX4OzsDB8fH3h4eLD6wLU5ryt6ow/q0q8mxOPxLBEx7pLe8tYGN27cQHR0NObPn4+kpCTY2dnh7NmziI+PR2tra4+LO8o+Wk1NTVi9ejWmTZumlIbP50MsFqttm7W1tcI2j8dTeolVdQLu3KYuW2cJ+J/oqErT06gc+XN6O4pAHvky7i5/mcDIxF1mMwDs378fiYmJSElJwejRoyEUCpGcnIzz5893eQ+dr9FdGhkNDQ0IDg5GRkaGkn2ydJo4MgsXLsSRI0fw008/wd3dXe3zu+o4qu8+MpamJihZy71ZVNt595be6oOMxsZGrFmzBjExMey+trY23L9/v8sVywHVDijDMBg7diyys7Nhbm6OiIgIBAQEQCwW4/Lly/j555+RmJjIpp88eTI8PT2RmpoKV1dXSKVS+Pv799jUERkZiWPHjqGkpATDhg3rMp3M6TI3N1ewEdCOPlhaWipcsyuN6Ordlkql+O6777Bu3Tqd6YMsTWNjI4KDg/H555/jwYMHMDExgYODAwDA0dFRndtWoLf6oC79ypHpmKipb1dx1jaFhYWQSqVISUlhxb5zuynQ8bIWFBSwtYiysjLU1tZi6NChADpqSWVlZSpXM33w4IFWbHV0dERlZaXCbJvdLQOviu4cBUBz4fH19UV+fj5mz57N7svPz+d0rlAoxKBBg3D27Nluh8B2J+55eXkICwvDggUL2H29rQHJIyvvYcOG4dChQ3BycoKNjY1Cmrt376o9ARYhBIsWLcK3336L7OxseHl5aWSfoUZkGIaBlZnxyqK29CEoKEhJHyQSCe7evQtA+b3saTjzuHHjkJqaCnNzcyQlJYHH42Hs2LFITk6GWCxGeHg4AKCmpgZlZWVITU1lIxpnz57ldO/vvfceRCIRIiMjkZ2djSeeeEJlOi5rLPVWHwYPHoyzZ89i9OjRavd9kkqlyM/PR2hoqM70QUZQUBAyMzPh7OwMkUgEhmHY6JQmaEsf1MV439h+QF1dndKH38HBAW1tbdixYwcmT56MvLw87NmzR+lcU1NTLFq0CNu3bwefz8fChQsxatQojBw5Erdv38bSpUsRFxcHT09PTJ8+HTweD8XFxbh8+TIWL16sFfsjIiJQXV2Njz76CNOnT8fx48dx7NgxpQ9qd/QkAuqKhIxFixZh3rx5CAkJQVhYGDIzM3Hx4kV4e3tzOj8xMRHLly+Hp6cnJk+ejIaGBuTl5WHRokVsmu4WOvTx8cEXX3yBEydOwMvLC+np6cjPz9faiy/7iMXExGD37t2YMmUK1q5dC3d3d/zxxx/45ptvEBcXBxcXF7UiMgkJCfjyyy/x3XffQSgUorKyEgBga2vL1kS5oGpOEkInw1MLXeiDzLFZtWoVoqOj4eHhwerDhQsXcO7cOSxfvlzJAe7JkYmIiMDSpUthZmaGMWPGsPsSExMRGhrKRnMHDBgAe3t77N27Fy4uLrh58yZWrFjBuUzWrVsHPp+P8ePHIzs7G0OGDFE4Lv+MdefI9FYfVq9ejbfeegt2dnaYOnUqWltblfShK6RSKby8vPCvf/1LZ/ogIzY2FsnJyZg2bRrefvttuLi4oLS0FIcOHcKyZcvUjqZoSx/UhfaRMWCys7MRGBio8EtPT8eWLVuwefNm+Pv7IyMjAxs3blQ618rKCsuXL8fLL7+M8PBwCAQCZGZmAuiodUZEROC7777Djz/+iNDQUIwaNQpbt26Fh4cHK0q9ZejQodi1axd27tyJ4cOH45dfflEIIXcH14+rphGZ2NhYvPvuu0hMTERQUBDKy8sxZ84cWFhYcDr/pZdewurVq/Hpp5/Cz88P0dHRuHr1qkrbVH2U33zzTcTExGDmzJkYOXIkampqFGpfvUVWfubm5vjpp5/g4eGBmJgYDB06FPHx8WhuboZAIFBIy4Xdu3ejrq4OERERcHFxYX+yZ4srqpqWiNy8N3T4dc/oSh8AICoqCkeOHFHQh23btmHQoEEAlB3QniJpw4YNg0gkwogRI9jnLiIiAhKJRKF/jGzYcmFhIfz9/bF06VIkJydzLhOpVIqtW7dixowZGD9+PH777TeF4/La1p2z3Ft9iIuLw/r167Fv3z4EBQWp1IeukEgkeOWVVxAdHa0zfZBhZWXF6sPrr7+OiIgIzJs3Dy0tLWyFUyqVQiKRcIreaksf1IUhmlZpDYT6+nrY2tqirq5Oqabf0tKC8vJyeHl5cX4A+wOVlZWQSqVwdHRUaj9tb29nO4KpW1vXNrdv3wYAODk5dVl7qqurw8OHDyEQCNSK9KhiwoQJcHZ2Rnp6eo9pq6ur0dbWhgEDBnRZ06iqqkJ7ezvs7Oz6/PmTbwZQ9X/s6biuEYvFqKmpgYmJCQYOHAigow9GdXV1r8PbFN0he+7ln2lD0IyHDx+irq4O5ubmsLe37zJdS0sL7t+/Dz6fDycnJ7XyUEcfgI4melmFQR1tkmmatbU1bG1t1bKxN8iamh0cHBQ6S9fW1qKpqUkn9kgkEhQVFSEwMFClY8n1G06blvoh3XVCk58/Rt8zq/J4PLbzW1douvJ1U1MT9uzZg6ioKJiYmOCrr75CVlYWTp48ydk2oOumLSI3F4+upkHvDvmIhkQiUbJBZjfDMHr5P6tqWtJ3/xhKz6iKpOl6JWl17OqpXs71neytPgAdzXfNzc1qz3Kur+ZVHo8HiUSipLeyTtb6nLG5J2j8th8ie0FUvWCyF10+UrNhwwaFYZjyv4kTJ+rMTi7ipOlU3gzD4OjRoxg7diyCg4Px/fff4+DBg3jmmWcAoMv7FQgEyM3N7XFEgrwY6OPDLL+irSpHUFW53bx5s9v77mn4qzrI/29ltvT1tOwU9VH1TG3cuBE+Pj7w9vbuU32Qh+uoI66OTG/1AfifhqrbVK+v90CVkyqVSln7Kysr+0wf1IVGZPoh5ubmaG1thVgsVhoyrepFf+uttzBjxgyV19JlBy4u4iQfWVAHS0tLdkp0VXQ3usrNzY2dFbcrR8YQIlsmJiZs+3ZnVEWyXF1du71vV1dXrdkmn69UKmVrgwCNyBgyqiJpc+bMwbPPPgsrKysIhUKF9LrUB3m4rm3E1ZHprT4A/3NkZFEOro6JvqYgUOWkyqIxJiYmGDRoUJ/pg7pQR6YfIgsRtra2KgyNBlS/6HZ2drqfYloFuozI9ISqYenyyObZ6crJMoSPsomJCdra2rp1ZOTLjc/n93jf2qJjKgQThVC2IZQZpXtU1dptbGxgbm4OW1tbpYpRX9slm1uqq8qDtpp7ubwnPB6PbR5va2tTmLemO/QdkVHlyJiZmfWpPqgLjeH2Q2Q1BVW1dX326+iMLiMyvaWnGiCXuSp0jaraswxDaMbp/FGkQ68NH1UfO0PQDFnkk/x30UhViMViTkOvtYlMa7n2k5Fvau3rd1OVXhhD/xiAOjL9Eh6Px75g8jP4yteODcGR4RKR0ddw3Z5s02TRTW3T1ey5gGE5Mp0jMrSPjOEie55l/yv5odf61Awej8c2azU0NCg574QQ1NfXA+gYctxXz5i6/WTk7dZ3RIbIrWhOHRmKQSILc8pP+y3/8TWEj0lPERn52ouhRmT03bQkb4s8huDIdK4BGkKZUbqn88dOphnyncv1hcxBkUqlSlEZ2egh2bpnfYW6ERlZOj6fr/fOvm1tbWwznSFUbLtD/18ril6Q7ycjwxBCxPJwGeLcOW1f0ZMjYwhlaeiOTOd+DbRpyfCR/W9k/y9559MQpmtQFZWRSqVsNEYgEPTp8yV7/2VOQU/IHBlV61npms5Oqnyzkr7/tz1BHZl+isyRkUgk7EfXED6+8nCJyHRO21d0Z5v83DeGEJFRtbimIcygK18D1GdIncKdzqPNDE0zrKyswOfzQQhhV+lubGyEVCqFiYlJn3dGlp9bh0vzksx50LcjQwgxmv4xAHVkHknS0tIgEom6TSPfT0b2wHYWJYZhcOjQIZ3Z2RPdzYMiv//PP/9k14LpK7qLyEgkEri5ueH48eMG4SjIT/0vQz4iM2fOnG4Xv9QV8v9f+f4xhl77M3a46ENXyDchmZqa4rvvvgPQt47MjRs3wDAM+75nZ2eDYRjU1tYqNB09fPgQra2tKC0thZubG8rLy/USuZWPysjvV6WthhCRAaDgyCxYsEAv+qAO1JExUPri49K5ecnQalc9Nd/I9ru5ueHOnTvw9/c3CNsM5aMsn3/n5iVNm5Z2796NgIAA2NjYwMbGBqNHj8axY8c0sk8+YkT7x6iHvpxPQPF/ZAgdfTtjYWEBU1NTEEJQU1MDV1dXXL58GcHBwXqxh2uHX/nIpL4cGZletLa2aqQR2tQHdaCOTD9G5sjIhiVqU5TUnZZbFVwjMqampnB2du5TMe2u/44hjFiSoao2CGi+tIO7uzs2bdqEwsJCFBQUYPz48ZgyZQquXLmitm2qmpYMocwo3SP/YTPE/xvDMOzaRoQQmJiYwMfHRy/OAcC9w6+sQqmPjr4yZPnKJvw0NTVVSyO0qQ/qQB0ZI2TLli0YNmwYrK2tMWjQICxYsIBtD5bn0KFD8PHxgYWFBaKionDr1i2F48ePH0dUVBQ8PT3xl7/8BVu2bGHbktVBFurNzMzEuHHjYGFhgYyMDKxevRojRoxQSLtt2zYMHjyY3ZbVLD/++GO4uLjA3t4eCQkJ7AgDAAgJCcGGDRswd+5cCIVCeHh4YO/evawTcevWLZWh5lOnTiEkJARWVlYICwtDWVmZgi3r16+Hk5MThEIhXn/9daxYsULJ3q5gGAb79+/H2LFjYW5uDhcXFyxcuBDA/2qptbW1mDp1KqysrODj44PDhw+z50skEsTHx8PLywuWlpbw9fXFJ598opBHd2UjY/DgwSrLRoaZmRkqKioQGxsLkUgEOzs7TJkyhZ1OXF3BnDx5Mp5//nn4+Pjg8ccfR1JSEgQCAc6dO6fWdeTzll+XypA+iMaKtvThu+++Q1BQECwsLODt7Y01a9agvb1d4X/EZbqG6dOns+8GACxZsgQMw+DXX38F0PEBt7a2ZmfSPX78OMaMGQORSAR7e3tER0fj+vXrnO+/qakJL7zwAqZOnYq6ujrcu3cP5ubmfaoPAPDZZ5/Bz88PdnZ2CAwMxN/+9jeF4/fu3VPQB1kznampaZ/pA9ChnzNmzIBIJMKQIUPw2muvsSt1q9s/Rpv6oA79ypEhhOChRKKXnzYXGefxeNi+fTuuXLmCffv24fTp01i2bJlCmqamJiQlJeGLL75AXl4eamtr8dJLL7HHc3NzMWfOHLzxxhs4c+YMPv74Y3z99dfYsWOHxnatWLECixcvRmlpKaKiojifd+bMGVy/fh1nzpzBvn37kJaWhrS0NIWPbEpKCkJCQlBUVIQFCxZg/vz5rBB29TFeuXIlUlJSUFBQAD6fj7lz57LHMjIykJSUhM2bN6OwsBAeHh7YvXs3Z5s//fRTrFy5Eq+88gouXbqEw4cPs7Neyj7KmzdvxowZM3Dx4kU8//zziI2Nxf379wF0fADc3d1x4MABlJSUYNWqVXjvvffw9ddfcyobeVSVjUyUGYZBbGwsLC0tkZubi7y8PFhbWyM2Nhatra29qvlJJBLs378fDx8+xOjRo9U+Xz5vmfjqvaMvIUDrQ/38tKQR2tKH2bNnY/HixSgpKcGnn36KtLQ0JCUlKf2Pehp6PW7cOGRnZ7PbOTk5cHBwYPfl5+ejra0NYWFhADr6trzzzjsoKCjAqVOnwOPxMHXq1B7XUQI6Kg8TJkyAVCpFVlYWPDw8IBAIVKbVpT7s3r0bCQkJeOONN1BcXIzPP/8cnp6eCk28a9asUdCH119/HQ8ePICZmVmf6UNbWxuioqIgFAqRm5uLo0ePwtraGrNmzUJra2uvOvr2Vh/UguiI9evXk9GjRxNLS0tia2urMs0ff/xBnn/+eWJpaUkcHR1JYmIiaWtrUyufuro6AoDU1dUpHWtubiYlJSWkubmZEEJIY3s7GXi6SC+/xvZ2te4rLi6OTJkyhVPaAwcOEHt7e3b7888/JwDIuXPn2H2lpaUEADl//jwhhJDIyEiyYcMGUltbSyoqKkhFRQXZvn07cXZ2Zs8BQL799tse8y8vLycAyLZt2xT2f/jhh2T48OEK+7Zu3Uo8PT0V7tPT05O0y5XPiy++SGbOnEkkEgmpqKgg7u7u5JVXXmGPS6VS4uTkRLZt20YqKipIUVERAUCKiooIIYScOXOGACBZWVnsOT/88AMBwD4LI0eOJAkJCQq2hYeHK9nbFa6uruTtt98mFRUVRCqVKhy7e/cuAUBWrFjB7mtsbCQAyLFjx7q8ZkJCApk2bRqnspHh6empsmx2795NCCFk37595C9/+Qv5888/iUQiYW2xsLAgX375JZsP12eNEEIuXrxIrK2tiYmJCbG1tSU//PAD53M7c+fOHVJRUUFu375NKioqyMOHDzW+llYQNxLyoY1+fuJGzmb2lT7Ik56eTlxcXEhDQwOpqKggAMg//vEPcvfu3W7zv3jxImEYhlRVVZH79+8TMzMzsm7dOvY5Xr9+PQkLC+vy/OrqagKAXLp0iRDyP73p/L6XlpaSgIAAMm3aNCIWi9nzu0qva31YuXIlu11ZWUkqKirY6wMg77//Pnu8oaGBACD//Oc/FWyXRxf6kJ6eTnx9fVkNe/DgASkvL2f1ob29Xaf60N7eTvLz8xXuQZ7O3/Cu0Fn1p7W1FS+++CLmz5+v8rhEIsGkSZPQ2tqKn3/+mfUmV61apSuTHhmysrIQGRkJNzc3CIVCvPrqq6ipqVGYBIrP5yM0NJTdHjJkCEQiEUpLSwEAxcXFWLt2Ldzc3ODj4wMfHx8sW7YMlZWVXU7x3RMhISEanefn56cQrnZxcUFVVZVC26x8R16GYeDs7IyqqioAXdfiAwICFK4JgD2nrKwMTz75pEL6zttdUVVVhdu3b2PMmDEAFPvJELmZTuXzt7a2ho2NDZs/AOzcuRPBwcFwdHSEQCDA3r17lVaQ7apsurrPzmVz6dIl3LhxA48//jhsbGwgEAgwcOBAiMVijVer9fX1xYULF3D+/HnMnz8fcXFxKCkp0ehanfsa0aal3qNNfZBf3XjevHm4c+eOwmzgsmt1h7+/P+zs7JCTk4Pc3FwEBgYiOjoaOTk5ADoiNBEREWz6q1evYtasWfD29oaNjQ3bFN3T8zphwgQ89thjyMzM5BRJ0LU+REZGsvtUdfiVz9/CwgJCoRD37t1jy7Mv9KG4uBjXrl2DUCiEQCCAu7s7/Pz8WH3Q5H3Upj5wRWe9I9esWQMASmEuGT/++CNKSkqQlZWFgQMHYsSIEVi3bh2WL1+O1atX62TsuhWPh+tjh2n9ulzz1gY3btxAdHQ05s+fj6SkJNjZ2eHs2bOIj49Ha2srrKysOF2nsbERa9aswZQpU3Dv3j12v0gkgoWFhUa2dZ6jgcfjKTWpqerw1rkTHsMwkEqlYBiGdWY6iyXDMKzD0FVnNPnrclm3iSudV/SVv0f5OVs6l6PsvgBg//79SExMREpKCkaPHg2hUIjk5GScP3++y3vofA0uaRobGzF8+HBs376dXZ24paUFdXV1cHZ2VvfWAXS0m8ua0YKDg5Gfn49PPvkEn376qdrX6uyE6r1pydQKeO+2/vLuJdrWh5iYGKVjVlZWCs5MT44MwzAYO3YssrOzYW5ujoiICAQEBEAsFuPy5cv4+eefkZiYyKafPHkyPD09kZqaCldXV0ilUvj7+ytM3qmKSZMm4eDBgygpKcGwYT3rfF/pgyyvlpYWBf2Tz1/WJ1DWTNeX+hAcHIyMjAwAHc16sv5U7u7u6t46AO3qA1f0Nmbu3//+N4YNG4aBAwey+6KiojB//nxcuXIFgYGBKs8Ti8UKL5FsxkYuMAwDayOv8RUWFkIqlSIlJYUV/c7tpkCH519QUMDWIsrKylBbW4uhQ4cCAIKCglBWVgZfX18MGDCArSk4OTlp7WPi6OiIyspKhdVo1Z3rpbthzrJ9mgxx9vX1RX5+PmbPns3uy8/P53SuUCjE4MGDkZeXh/DwcAXb5NvAu7MrLy8PYWFhWLBgAbtPnQ6NXAkKCkJmZiYcHBxgb28PBwcHNDU1oba2lvNqvD0hlUqVaulc6Vzj03tEhmEAM/2s4KwNtK0PqlY77lwZ4TJacNy4cUhNTYW5uTnbz2bs2LFITk6GWCxGeHg4AKCmpgZlZWVITU3FU089BQA4e/Ysp3vftGkTBAIBIiMjkZ2djSeeeILTearQhj6cOnUKTz/9NICuRw/KkF+aAOh7fXBycoKNjQ2rDQBga2urlTx6ow9c0ZsjU1lZqeDEAGC3Kysruzxv48aNbLTnUaeurk7pw+/g4IC2tjbs2LEDkydPRl5eHvbs2aN0rqmpKRYtWoTt27eDz+dj4cKFGDVqFCtcq1atQnR0NDw8PPDss8+itbUVJSUl+PPPP5GUlKQV+yMiIlBdXY2PPvoI06dPx/Hjx3Hs2DF2aCQXuDhVmjheixYtwrx58xASEoKwsDBkZmbi4sWL8Pb25nT+6tWr8dZbb8He3h4xMTEQi8XIy8tDfHw8p/N9fHzwxRdf4MSJE/Dy8kJ6ejry8/Ph5eWl9r10R2xsLD766CO89tprWLZsGQICAlBWVoZ//etfWLp0Kezt7dW63rvvvouJEyfCw8MDDQ0N+PLLL5GdnY0TJ05oZJ/8/84Q1usxJvpKH6ZPnw4ej4fi4mJcvnwZa9euVbgWF+czIiICS5cuhZmZGdskGxERgcTERISGhrLR3AEDBsDe3h579+6Fi4sLbt68iRUrVnAuk48//hgSiQTjx49HdnY2hgwZwvlcebSlD05OTpg4cSIePHiAH3/8EXPnzlVZKZNFm2Rl2Zf6kJycjClTpmDt2rVwdHTE5cuXcezYMbz//vtq56dtfeCKWqqxYsUKNvzV1U82kkRXvPvuu6irq2N/nYcMPkpkZ2cjMDBQ4Zeeno4tW7Zg8+bN8Pf3R0ZGBjZu3Kh0rpWVFZYvX46XX34Z4eHhEAgEyMzMZI9HRUXhyJEj+PHHHxEZGYnJkyfj73//u8LQ6N4ydOhQ7Nq1Czt37sTw4cPxyy+/KISQuaCriExsbCzeffddJCYmIigoCOXl5ZgzZw7nZrW4uDgkJSVh3759bJv/1atXVa5rpIo333wTMTExmDlzJkaOHImamhqF2pe2sLKywk8//QR3d3fEx8fjiSeewIIFCyAWizWqcVVVVWH27Nnw9fVFZGQk8vPzceLECUyYMEEj++QdF71HY4yMvtKH0NBQjBo1Clu3boWnp6fS+8YlIjNs2DCIRCKMGDGCHUUUEREBiUSi0D9G1qxSWFgIf39/LF26FMnJyWqVy9atWzFjxgyMHz8ev/32m1rnytCGPmzbtg27du2Cn58fXnjhBZSXlwNQjsoQuVWmZe9AX+uDh4cHYmJiMGLECCQmJkIsFsPOzk7t62lbH7jCEFVfiC6orq5GTU1Nt2m8vb0V+rekpaVhyZIlbLhKxqpVq3D48GGFGkV5eTm8vb3xn//8p8umpc7U19fD1tYWdXV1SjX9lpYWlJeXw8vLS+N+H/0BQgiamppgZmamt0mjuqKmpob96Hbug1NVVYX29nbY2dlp5f87YcIEODs7Iz09nVP69vZ2VFdXgxDC2vfgwQM0NzdDKBT26Sq7PSErRxsbG7S3t6OpqckgbJQPZZuZmcHBwUGv9lC4cffuXUgkErbzaH9YVkJdfejMvXv30NraCpFIpNBXqb29ne186+LioveyfPjwIfh8vtaanrtDIpGgqKgIgYGBKisyXL/hajUtOTo6wtHRUX1rVTB69GgkJSWhqqoKTk5OAICTJ0/CxsamV22bFPVhGKbPF1PjCpelADSpyTc1NWHPnj2IioqCiYkJvvrqK2RlZeHkyZOcr8Hn8yEUClFfX4/6+nqYm5sb7FT7ZmZmEIvFCh0m9S2YAI3IGCs8Hg8SiURhUcRHCW3oQ2dMTU3R2tqqFJGRX1/JEMrSUL8F3aGzBumbN2/iwoULuHnzJiQSCS5cuIALFy6wPaKfffZZPPHEE3j11VdRXFyMEydO4P3330dCQkKfeIIU7mzYsEFhGKb8b+LEiTrNu6tlCuRHB2nyAWQYBkePHsXYsWMRHByM77//HgcPHsQzzzwDAF3er0AgQG5uLnsda2trdl2Xuro6g1uvSoYsStrW1tblGio3b97s9r41Ha7dHdSRMU5k/zcTExO96oOu0JY+yNNVh199rnitDvrQB67oTG1XrVqFffv2sduypqIzZ84gIiICJiYmOHLkCObPn4/Ro0fD2toacXFxSh3JKPrnrbfewowZM1QeUzXUUJt0taaR/NBrTWoxlpaW7JToquhudJWbmxv7N8MwEIlEqK6uVuiZb2gfZZlISuRmme7syLi6unZ7366urlq3S94G2tHXeODz+RCLxTA1NdWrPugKbemDPLJ3sLW1Fc3NzWzZ6HPFa3XQhz5wRWeOjKqpkjvj6emJo0eP6soEipaws7PTqOOXNuhqfgf5xep0EY5VNey0K0xNTSEUCtHQ0ADAMEff8Hg8mJqadhuR4fP5at23NpB3+AzN+aN0jUAggImJCaysrMDj8fSmD/pCk/fE1NQUlpaWaG5uxoMHDyCVSmFlZcU6MrqYO02b6EMfuGJYakuhdKKniIyhfPwEAgEbOtaVc9VbOgulIThb8hE1Q/lfUnrGxMQEAoHAIJ4hY0EWvZV19JWNvJVpm6E1RxsT9CmkGDRdRWQMzZGRiZSJiYnBhtM7h64N5SMkFAphaWlp8KF1CqW3MAwDW1tbdgi6bNkIQ+noa6xQF5Bi0PQUkTGUjzHQEfHoPMmjIdE5ImMowtnV6sQUyqMIwzCwsbEBwzBsc7ShNysZOtSRoRg0xhKRMQb4fD54PB6kUil4PJ7BODIUSn9EKBSCx+OhqanJYKO4xgJ1ZCgGjbH0kTEWzMzM0NLSYlCRLAqlv2JtbW2U87YYGlTNHkHS0tIgEol6fR2GYXDo0KFeX6e3NgCK88YAio7MjRs3wDCM2gtS6hJDKDtVyELY8tGYOXPm4IUXXtCTRZS+xtj1ofP7np2dDYZhlGaP7yq9IWCo+qAKY9AH6sgYKMbw8PQF8pEDmSPTeTK8QYMG4c6dO/D399eLjcaEpaUlzM3NtdIvZdOmTWAYBkuWLOm9YRS1oPrAHaoP+qEv9YE2LVF0Qltbm1ZGochHDmTOi/xkeDJHx9nZudd59QdMTEzUXvFaFfn5+fj0008REBCgBasoFN1hYmJC9aGP6Wt9oBEZI2TLli0YNmwYrK2tMWjQICxYsIBd+kGeQ4cOwcfHBxYWFoiKilJaKfy7775DUFAQLCws4O3tjTVr1rBT7KuDLHSbmZmJcePGwcLCAhkZGVi9ejVGjBihkHbbtm0KK2zLapYff/wxXFxcYG9vj4SEBHaSKIZhMHLkSGzfvh3x8fEQCoXw9vbGP//5T7Z/TFeh5lOnTiEkJARWVlYICwtDWVmZgi3r16+Hk5MThEIhXn/9daxYsULJ3u747LPP4OfnB3Nzc7i4uGDhwoUKx+/du4epU6fCysoKPj4+OHz4MHtMIpEgPj4eXl5esLS0hK+vLz755BOF83sqGwAYPHgwNmzYgLlz50IoFMLDwwN79+5VuM6tW7cwY8YMiEQi2NnZYcqUKbhx4wbn++xMY2MjYmNjkZqaigEDBmh8HYpuMDR9mD59usK7sWTJEjAMg19//RVAx0y31tbW7Ey6x48fx5gxYyASiWBvb4/o6Ghcv36dc35NTU2YOHEiwsPDUVtbS/WhH+hDv3JkCCFoam3Xy0+NRcZ7hMfjYfv27bhy5Qr27duH06dPY9myZQppmpqakJSUhC+++AJ5eXmora3FSy+9xB7Pzc3F7NmzsXjxYpSUlODTTz9FWloakpKSNLZrxYoVWLx4MUpLSxEVFcX5vDNnzuD69es4c+YM9u3bp3JW6E8//RRBQUEoKirCvHnz8O677+L333/v9rorV65ESkoKCgoKwOfzMXfuXPZYRkYGkpKSsHnzZhQWFsLDwwO7d+/mbPPu3buRkJCAN954A5cuXcLhw4eVZr1cs2YNZsyYgYsXL+L5559HbGws7t+/D6Cjeczd3R0HDhxASUkJVq1ahffeew9ff/212mWTkpKCkJAQFBUVYcGCBZg/fz4rym1tbYiKioJQKERubi7y8vIgEAjw3HPPKSwgqQ4JCQmYNGkSu+7MowQhBE1tTXr5aUsjDE0fxo0bh+zsbHY7JycHDg4O7L78/Hy0tbUhLCwMQMfqy++88w4KCgpw6tQp8Hg8TJ06VWnkoipqa2sxYcIESKVSnDx5stu+QFQfHiF9IEZOXV0dAUDq6uqUjjU3N5OSkhLS3NxMCCHkobiNeC4/opffQ3GbWvcVFxdHpkyZwintgQMHiL29Pbv9+eefEwDk3Llz7L7S0lICgJw/f54QQkhkZCTZsGGDwnXS09OJi4sLuw2AfPvttz3mX15eTgCQbdu2Kez/8MMPyfDhwxX2bd26lXh6erLbcXFxxNPTk7S3t7P7XnzxRTJz5kx2e9CgQSQmJob9P9bW1hIHBweSkpKikH9RUREhhJAzZ84QACQrK4u9xg8//EAAsNcYOXIkSUhIULAtPDxcyd6ucHV1JStXruzyOADy/vvvs9uNjY0EADl27FiX5yQkJJBp06ax21zKxtPTk7zyyivstlQqJU5OTmT37t2EkI7/qa+vL5FKpWwasVhMLC0tyYkTJ9h8uD5rX331FfH392fLcdy4cWTx4sWczjUGHrY+JP5p/nr5PWx9yNlOY9KHixcvEoZhSFVVFbl//z4xMzMj69atY5/j9evXk7CwsC7Pr66uJgDIpUuXCCFdv++lpaUkICCATJs2jYjFYvZ8qg+Gqw/t7e0kPz9f4R7k6fwN74p+FZF5VMjKykJkZCTc3NwgFArx6quvoqamhp0lEuiYMyQ0NJTdHjJkCEQiEUpLSwEAxcXFWLt2rcLqpfPmzcOdO3cUrqMOISEhGp3n5+enMIzaxcUFVVVVCmmeeOIJhc6+jo6OqKmp6fa68u2zLi4uAMBet6ysDE8++aRC+s7bXVFVVYXbt28jMjKSc/7W1tawsbFRuK+dO3ciODgYjo6OEAgE2Lt3r9IKslzKRj4fhmHg7OzMpikuLsa1a9cgFArZ/7OdnR1aWlrUCtcDHSHoxYsXIyMjAxYWFmqdS+k7DE0f/P39YWdnh5ycHOTm5iIwMBDR0dHIyckB0BGhiYiIYNNfvXoVs2bNgre3N2xsbNim6J5WV54wYQIee+wxZGZmcppgjurDo6MP/aqzr6WpCUrWcm/y0Hbe2uDGjRuIjo7G/PnzkZSUBDs7O5w9exbx8fFobW1l1/HoicbGRqxZswYxMTFKxzR9CDvPh8Dj8ZTC5Z2XsAeUp85nGEYpjMzn89l9EomE02Ru8tftamI9TeA6eVV397V//34kJiYiJSUFo0ePhlAoRHJyMs6fP8/5GlzSNDY2Ijg4GBkZGUr2OTo6croPGYWFhaiqqkJQUBC7TyKR4KeffsL/+3//D2Kx2Ojn9bHkW+L8y+d7TqijvHuLIeoDwzAYO3YssrOzYW5ujoiICAQEBEAsFuPy5cv4+eefkZiYyKafPHkyPD09kZqaCldXV0ilUvj7+/fY1DFp0iQcPHgQJSUlGDZsWI92UX14dPShXzkyDMPAysy4b7mwsBBSqRQpKSnsiJ3O7aYA0N7ejoKCArYWUVZWhtraWgwdOhQAEBQUhLKyMp2uZuro6IjKykoQQlih6M1cDkTFqCVN8fX1RX5+PmbPns3uy8/P53SuUCjE4MGDcerUKTz99NMa5Z+Xl4ewsDAsWLCA3aduDYgLQUFByMzMhJOTE2xsbHp1rcjISFy6dElh32uvvYYhQ4Zg+fLlRu/EAP/VCFNuH3tDxFD1Ydy4cUhNTYW5uTmSkpLA4/EwduxYJCcnQywWIzw8HABQU1ODsrIypKam4qmnngIAnD17llMemzZtgkAgQGRkJLKzs/HEE09obC/VB/XRpz4Y91f9Eaeurk7pw+/g4IC2tjbs2LEDkydPRl5eHvbs2aN0rqmpKRYtWoTt27eDz+dj4cKFGDVqFCtcq1atQnR0NDw8PDB9+nTweDwUFxfj8uXLWL9+vVbsj4iIQHV1NT766CNMnz4dx48fx7FjxzR+YWTzx8hqE72ZnXbRokWYN28eQkJCEBYWhszMTFy8eBHe3t6czl+9ejXeeustODk5YeLEiWhoaEBeXh4WLVrE6XwfHx988cUXOHHiBLy8vJCeno78/Hx4eXlpfE+qiI2NRXJyMqZMmYK1a9fC3d0df/zxB7755hssW7YM7u7unK8lFAqV5uKwtraGvb09naNDDxiTPkRERGDp0qUwMzPDmDFj2H2JiYkIDQ1lo7kDBgyAvb099u7dCxcXF9y8eRMrVqzgnM/HH38MiUSC8ePHIzs7G0OGDFHbVoDqg7HpA+0jY8BkZ2cjMDBQ4Zeeno4tW7Zg8+bN8Pf3R0ZGBjZu3Kh0rpWVFZYvX46XX34Z4eHhEAgEyMzMZI9HRUXhyJEj+PHHHxEaGopRo0Zh69at8PT01Jr9Q4cOxa5du7Bz504MHz4cv/zyi0IImSuyyIvMiZFFZnoTkYmNjcW7776LxMREBAUFoby8HHPmzOEcNo+Li8O2bduwa9cu+Pn5ITo6GlevXuWc/5tvvomYmBjMnDkTI0eORE1NjULtS1tYWVnhp59+goeHB2JiYjB06FDEx8ejpaWl1zUwin4xJn0YNmwYRCIRRowYwU7GGBERAYlEotA/hsfjYf/+/SgsLIS/vz+WLl2K5ORktfLaunUrZsyYgfHjx+O3337TyF6qD8alDwzp3InByKivr4etrS3q6uqUCr6lpQXl5eXw8vKinRONmMbGRtTX18PS0hICgQDV1dXg8Xhan+RqwoQJcHZ2Rnp6ulavS6FQjB+qD9pHIpGgqKgIgYGBKpueuH7DadMSxeCR74inrcUim5qasGfPHkRFRcHExARfffUVsrKycPLkyV7bS6FQjBuqD8YFbVqi9MiGDRsUhmHK/yZOnKjz/OVXwJY5Mr1dvZlhGBw9ehRjx45FcHAwvv/+exw8eJCdxKmr+xUIBMjNze3dDRkoN2/e7Pa+exr+Sumf6FsfdAHVB2UMWR9o0xKlR+7fv8/OONkZS0tLuLm56TT/lpYW3L9/H3w+HxYWFmhsbIS1tTVsbW11lue1a9e6PObm5sZ5iKUx0d7e3u3U5IMHDwafT4O4FEX0rQ/6gOqDMproA21aovQZdnZ2sLOz01v+uojI9IQuh6UbKnw+v1/eN6V36Fsf9EF/fE8MWR9o0xLF4NFFHxkKhUKhPBpQR4Zi8KiKyFBHhkKhUCgAdWQoRoD8fDHUkaFQKBSKPNSRoRg8qia+o44MhUKhUADqyFCMAIZhFJwZHo/Xq1l9KRQKhfLoQB2ZR5C0tDSIRKJeX4dhGBw6dKjX19EG8qOUOkdjbty4AYZherUgpbYxpLLriTlz5uCFF17QtxmUPsLY9aHz+56dnQ2GYVBbW8spvSFA9UG7UEfGQDGGh6cv6c6RGTRoEO7cuUMXLuwDVq9ezUbIZD9NF+ajaA7VB+5Qfeg79KUPdB4Zik5oa2uDqamp1q4n35TU2ZExMTHR+rpLlK7x8/NDVlYWu00nyaMYMlQf+hZ96AONyBghW7ZswbBhw2BtbY1BgwZhwYIFaGxsVEp36NAh+Pj4wMLCAlFRUbh165bC8e+++w5BQUGwsLCAt7c31qxZg/b2drXtkYVuMzMzMW7cOFhYWCAjIwOrV6/GiBEjFNJu27YNgwcPZrdlNcuPP/4YLi4usLe3R0JCAtra2tg0gwcPxrZt2/DOO+/g8ccfh7+/P/bu3auUf+dQ86lTpxASEgIrKyuEhYWhrKxMwZb169fDyckJQqEQr7/+OlasWKFkb3d89tln8PPzg7m5OVxcXLBw4UKF4/fu3cPUqVNhZWUFHx8fHD58mD0mkUgQHx8PLy8vWFpawtfXF5988onC+VzLZsOGDZg7dy6EQiE8PDwUygYAbt26hRkzZkAkEsHOzg5TpkzpdobOnuDz+XB2dmZ/Dg4OGl+Lon0MTR+mT5+u8G4sWbIEDMPg119/BQC0trbC2tqa/fgdP34cY8aMgUgkgr29PaKjo3H9+nXO+TU1NWHixIkIDw9HbW0t1Yd+oA/9y5EhBGh9qJ+fFleC4PF42L59O65cuYJ9+/bh9OnTWLZsmUKapqYmJCUl4YsvvkBeXh5qa2vx0ksvscdzc3Mxe/ZsLF68GCUlJfj000+RlpaGpKQkje1asWIFFi9ejNLSUkRFRXE+78yZM7h+/TrOnDmDffv2IS0tDWlpaQppdu3ahYCAAJw4cQJvvPEG5s+fryQ8nVm5ciVSUlJQUFAAPp+PuXPnsscyMjKQlJSEzZs3o7CwEB4eHti9ezdnm3fv3o2EhAS88cYbuHTpEg4fPqw06+WaNWswY8YMXLx4Ec8//zxiY2PZqdylUinc3d1x4MABlJSUYNWqVXjvvffw9ddfq102KSkpCAkJQVFRERYsWKBQNm1tbYiKioJQKERubi7y8vIgEAjw3HPPobW1lfP9ynP16lW4urrC29sbsbGxj9QaTIQQSJua9PLT1moxhqYP48aNQ3Z2Nrudk5MDBwcHdl9+fj7a2toQFhYGAHj48CHeeecdFBQU4NSpU+DxeJg6dSqkUmmPedXW1mLChAmQSqU4efJkt32BqD48QvpAjJy6ujoCgNTV1Skda25uJiUlJaS5ubljh7iRkA9t9PMTN6p1X3FxcWTKlCmc0h44cIDY29uz259//jkBQM6dO8fuKy0tJQDI+fPnCSGEREZGkg0bNihcJz09nbi4uLDbAMi3337bY/7l5eUEANm2bZvC/g8//JAMHz5cYd/WrVuJp6cnux0XF0c8PT1Je3s7u+/FF18kM2fOZLc9PT3JjBkzSEVFBamoqCAtLS3EycmJ7N69WyH/oqIiQgghZ86cIQBIVlYWe40ffviBAGCfhZEjR5KEhAQF28LDw5Xs7QpXV1eycuXKLo8DIO+//z673djYSACQY8eOdXlOQkICmTZtGrvNtWxeeeUVdlsqlSqUTXp6OvH19SVSqZRNIxaLiaWlJTlx4gSbD9dn7ejRo+Trr78mxcXF5Pjx42T06NHEw8OD1NfXczrf0JE8fEhKfIfo5Sd5+JCzncakDxcvXiQMw5Cqqipy//59YmZmRtatW8c+x+vXrydhYWFdnl9dXU0AkEuXLhFCun7fS0tLSUBAAJk2bRoRi8Xs+VQfDFcf2tvbSX5+vsI9yKP0De+C/hWReUTIyspCZGQk3NzcIBQK8eqrr6KmpgZNTU1sGj6fj9DQUHZ7yJAhEIlEKC0tBQAUFxdj7dq1CquXzps3D3fu3FG4jjqEhIRodJ6fn59CvxcXFxdUVVUppJHvqCcLXXZO05mAgACFawJgzykrK8OTTz6pkL7zdldUVVXh9u3biIyM5Jy/tbU1bGxsFGzeuXMngoOD4ejoCIFAgL179yrVXriUjXw+DMMolE1xcTGuXbsGoVDI/p/t7OzQ0tKiVrhexsSJE/Hiiy8iICAAUVFROHr0KGpra5VqihT9YWj64O/vDzs7O+Tk5CA3NxeBgYGIjo5GTk4OgI4ITUREBJv+6tWrmDVrFry9vWFjY8M2RfdUs58wYQIee+wxZGZmwszMrEe7qD48OvrQv3rpmVoB793WX95a4MaNG4iOjsb8+fORlJQEOzs7nD17FvHx8WhtbYWVFbd8GhsbsWbNGsTExCgd03SlcGtra4VtHo+nFC6Xb7+V0blTMMMwSmFkeWGSzSPTU6hZ/rry6zX1Fq4r23Z3X/v370diYiJSUlIwevRoCIVCJCcn4/z585yvwSVNY2MjgoODkZGRoWSfo6Mjp/voDpFIhMcff7zb1YCNCcbSEr7/KdRb3r3FEPWBYRiMHTsW2dnZMDc3R0REBAICAiAWi3H58mX8/PPPSExMZNNPnjwZnp6eSE1NhaurK6RSKfz9/Xts6pg0aRIOHjyIkpISDBs2rEe7qD48OvrQvxwZhgHMrHtOZ8AUFhZCKpUiJSWFHZKsytttb29HQUEBW4soKytDbW0thg4dCgAICgpCWVmZTlczdXR0RGVlJQghrFBoOpeD7HwTExOtTIbn6+uL/Px8zJ49m92Xn5/P6VyhUIjBgwfj1KlTePrppzXKPy8vD2FhYViwYAG7T5MaUE8EBQUhMzMTTk5OsLGx0fr1Gxsbcf36dbz66qtav7Y+YBgGDMePvSFiqPowbtw4pKamwtzcHElJSeDxeBg7diySk5MhFosRHh4OAKipqUFZWRlSU1Px1FNPAQDOnj3LKY9NmzZBIBAgMjIS2dnZeOKJJzS2l+qDdugrfaBNSwZMXV0dLly4oPBzcHBAW1sbduzYgd9//x3p6enYs2eP0rmmpqZYtGgRzp8/j8LCQsyZMwejRo1ihWvVqlX44osvsGbNGly5cgWlpaXYv38/3n//fa3ZHxERgerqanz00Ue4fv06du7ciWPHjml0LXlHRhssWrQI//jHP7Bv3z5cvXoV69evx8WLFzk7SatXr0ZKSgq2b9+Oq1ev4j//+Q927NjBOX8fHx8UFBTgxIkT+O233/DBBx9wFkp1iI2NhYODA6ZMmYLc3FyUl5cjOzsbb7/9Nv7880+1r5eYmIicnBzcuHEDP//8M6ZOnQoTExPMmjVL67ZTuseY9CEiIgIlJSW4cuUKxowZw+7LyMhASEgIG80dMGAA7O3tsXfvXly7dg2nT5/GO++8wzmfjz/+GLGxsRg/fjw7KkoTqD4Ylz5QR8aAyc7ORmBgoMIvPT0dW7ZswebNm+Hv74+MjAxs3LhR6VwrKyssX74cL7/8MsLDwyEQCJCZmckej4qKwpEjR/Djjz8iNDQUo0aNwtatW+Hp6ak1+4cOHYpdu3Zh586dGD58OH755ReFELI68Pl82NrawtbWViu2xcbG4t1330ViYiKCgoJQXl6OOXPmcA6bx8XFYdu2bdi1axf8/PwQHR2Nq1evcs7/zTffRExMDGbOnImRI0eipqZGofalLaysrPDTTz/Bw8MDMTExGDp0KOLj49HS0qJRDezPP//ErFmz4OvrixkzZsDe3h7nzp3TShiaoh7GpA/Dhg2DSCTCiBEjIBAIAHQ4MhKJRKF/DI/Hw/79+1FYWAh/f38sXboUycnJauW1detWzJgxA+PHj8dvv/2mkb1UH4xLHxjSuRODkVFfXw9bW1vU1dUpFXxLSwvKy8vh5eWlcb8PSv9hwoQJcHZ2Rnp6ur5NoVAoBgbVB+0jkUhQVFSEwMBAldF2rt/w/tVHhkL5L01NTdizZw+ioqJgYmKCr776CllZWTh58qS+TaNQKHqG6oNxQZuWKD2yYcMGhWGY8r+JEyfq2zyNYBgGR48exdixYxEcHIzvv/8eBw8exDPPPAMAXd6vQCBAbm6unq3XDTdv3uz2vh+lie8o2oPqA9UHfesDbVqi9Mj9+/fZGSc7Y2lpCTc3tz62SPd0N1zQzc2N8xBLY6K9vb3bqckHDx5M11WiKEH1QRGqD9yhTUuUPsPOzg52dnb6NqNP0eWwdEOFz+f3y/um9A6qD/0DQ9YH2rREoVAoFArFaOkXjow2ZmukUCgUCoXSd3D9dj/STUtmZmbg8Xi4ffs2HB0dYWZmppVZYSkUCoVCofQOiUQCoKMvjHwfGUIIWltbUV1dDR6P1+PaWY+0I8Pj8eDl5YU7d+7g9m09rbFEoVAoFApFCalUinv37uHGjRvskhryWFlZwcPDQ+UxeR5pRwboiMp4eHigvb2d9f4oFAqFQqHol8bGRkyaNAkFBQXsjM8yTExMwOfzObWiPPKODNAxJ4CpqanSKqAUCoVCoVD0Q2trK/744w+YmZn1aoqUftHZl0KhUCgUyqMJdWQoFAqFQqEYLdSRoVAoFAqFYrQYfR8Z2QoL9fX1eraEQqFQKBQKV2Tf7d6ulGT0jkxDQwMAYNCgQXq2hEKhUCgUiro0NDTA1tZW4/ONftFIqVSK27dvQygUan2yu/r6egwaNAi3bt1SWpCSol1oWfcdtKz7DlrWfQct675DW2VNCEFDQwNcXV17nCumO4w+IsPj8eDu7q7TPGxsbOiL0UfQsu47aFn3HbSs+w5a1n2HNsq6N5EYGbSzL4VCoVAoFKOFOjIUCoVCoVCMFurIdIO5uTk+/PBDmJub69uURx5a1n0HLeu+g5Z130HLuu8wtLI2+s6+FAqFQqFQ+i80IkOhUCgUCsVooY4MhUKhUCgUo4U6MhQKhUKhUIwW6shQKBQKhUIxWqgj0wU7d+7E4MGDYWFhgZEjR+KXX37Rt0lGz8aNGxEaGgqhUAgnJye88MILKCsrU0jT0tKChIQE2NvbQyAQYNq0abh7966eLH502LRpExiGwZIlS9h9tKy1R0VFBV555RXY29vD0tISw4YNQ0FBAXucEIJVq1bBxcUFlpaWeOaZZ3D16lU9WmycSCQSfPDBB/Dy8oKlpSX+8pe/YN26dQpr9dCy1oyffvoJkydPhqurKxiGwaFDhxSOcynX+/fvIzY2FjY2NhCJRIiPj0djY6PujScUJfbv30/MzMzIZ599Rq5cuULmzZtHRCIRuXv3rr5NM2qioqLI559/Ti5fvkwuXLhAnn/+eeLh4UEaGxvZNG+99RYZNGgQOXXqFCkoKCCjRo0iYWFherTa+Pnll1/I4MGDSUBAAFm8eDG7n5a1drh//z7x9PQkc+bMIefPnye///47OXHiBLl27RqbZtOmTcTW1pYcOnSIFBcXk7/+9a/Ey8uLNDc369Fy4yMpKYnY29uTI0eOkPLycnLgwAEiEAjIJ598wqahZa0ZR48eJStXriTffPMNAUC+/fZbheNcyvW5554jw4cPJ+fOnSO5ubnkscceI7NmzdK57dSRUcGTTz5JEhIS2G2JREJcXV3Jxo0b9WjVo0dVVRUBQHJycgghhNTW1hJTU1Ny4MABNk1paSkBQP7973/ry0yjpqGhgfj4+JCTJ0+ScePGsY4MLWvtsXz5cjJmzJguj0ulUuLs7EySk5PZfbW1tcTc3Jx89dVXfWHiI8OkSZPI3LlzFfbFxMSQ2NhYQggta23R2ZHhUq4lJSUEAMnPz2fTHDt2jDAMQyoqKnRqL21a6kRraysKCwvxzDPPsPt4PB6eeeYZ/Pvf/9ajZY8edXV1AAA7OzsAQGFhIdra2hTKfsiQIfDw8KBlryEJCQmYNGmSQpkCtKy1yeHDhxESEoIXX3wRTk5OCAwMRGpqKnu8vLwclZWVCmVta2uLkSNH0rJWk7CwMJw6dQq//fYbAKC4uBhnz57FxIkTAdCy1hVcyvXf//43RCIRQkJC2DTPPPMMeDwezp8/r1P7jH7RSG1z7949SCQSDBw4UGH/wIED8euvv+rJqkcPqVSKJUuWIDw8HP7+/gCAyspKmJmZQSQSKaQdOHAgKisr9WClcbN//3785z//QX5+vtIxWtba4/fff8fu3bvxzjvv4L333kN+fj7efvttmJmZIS4uji1PVZpCy1o9VqxYgfr6egwZMgQmJiaQSCRISkpCbGwsANCy1hFcyrWyshJOTk4Kx/l8Puzs7HRe9tSRoeiFhIQEXL58GWfPntW3KY8kt27dwuLFi3Hy5ElYWFjo25xHGqlUipCQEGzYsAEAEBgYiMuXL2PPnj2Ii4vTs3WPFl9//TUyMjLw5Zdfws/PDxcuXMCSJUvg6upKy7ofQ5uWOuHg4AATExOl0Rt3796Fs7Oznqx6tFi4cCGOHDmCM2fOwN3dnd3v7OyM1tZW1NbWKqSnZa8+hYWFqKqqQlBQEPh8Pvh8PnJycrB9+3bw+XwMHDiQlrWWcHFxwRNPPKGwb+jQobh58yYAsOVJNaX3/O1vf8OKFSvw0ksvYdiwYXj11VexdOlSbNy4EQAta13BpVydnZ1RVVWlcLy9vR3379/XedlTR6YTZmZmCA4OxqlTp9h9UqkUp06dwujRo/VomfFDCMHChQvx7bff4vTp0/Dy8lI4HhwcDFNTU4WyLysrw82bN2nZq0lkZCQuXbqECxcusL+QkBDExsayf9Oy1g7h4eFK0wj89ttv8PT0BAB4eXnB2dlZoazr6+tx/vx5WtZq0tTUBB5P8bNlYmICqVQKgJa1ruBSrqNHj0ZtbS0KCwvZNKdPn4ZUKsXIkSN1a6BOuxIbKfv37yfm5uYkLS2NlJSUkDfeeIOIRCJSWVmpb9OMmvnz5xNbW1uSnZ1N7ty5w/6amprYNG+99Rbx8PAgp0+fJgUFBWT06NFk9OjRerT60UF+1BIhtKy1xS+//EL4fD5JSkoiV69eJRkZGcTKyor885//ZNNs2rSJiEQi8t1335GLFy+SKVOm0CHBGhAXF0fc3NzY4dfffPMNcXBwIMuWLWPT0LLWjIaGBlJUVESKiooIALJlyxZSVFRE/vjjD0IIt3J97rnnSGBgIDl//jw5e/Ys8fHxocOv9cmOHTuIh4cHMTMzI08++SQ5d+6cvk0yegCo/H3++edsmubmZrJgwQIyYMAAYmVlRaZOnUru3LmjP6MfITo7MrSstcf3339P/P39ibm5ORkyZAjZu3evwnGpVEo++OADMnDgQGJubk4iIyNJWVmZnqw1Xurr68nixYuJh4cHsbCwIN7e3mTlypVELBazaWhZa8aZM2dU6nNcXBwhhFu51tTUkFmzZhGBQEBsbGzIa6+9RhoaGnRuO0OI3JSIFAqFQqFQKEYE7SNDoVAoFArFaKGODIVCoVAoFKOFOjIUCoVCoVCMFurIUCgUCoVCMVqoI0OhUCgUCsVooY4MhUKhUCgUo4U6MhQKhUKhUIwW6shQKBQKhUIxWqgjQ6FQKBQKxWihjgyFQqFQKBSjhToyFAqFQqFQjBbqyFAoFAqFQjFa/j8bdnyJURUtUQAAAABJRU5ErkJggg==" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_mean_sample_multi(easy_to_clf_multi_dataset[0].features,easy_to_clf_multi_dataset[0].target)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "# Transform initial row in feature vector. Easy dataset" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 131, - "outputs": [], - "source": [ - "stat_pipeline = pipeline_creator.create_pipeline(node_list_model)\n", - "feature_extractor = pipeline_creator.create_pipeline(['quantile_extractor'])\n", - "feature_matrix = feature_extractor.fit(easy_to_clf_uno_dataset[0])\n", - "initial_ts, transformed_ts = pd.DataFrame(feature_matrix.features.squeeze()),pd.DataFrame(feature_matrix.predict.squeeze())\n", - "transformed_ts['target'] = feature_matrix.target" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "node_dict = {'quantile_extractor':{'window_size':10,\n", - " 'stride':50}}" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 132, - "outputs": [ - { - "data": { - "text/plain": " 0 1 2 3 4 5 6 \\\ntarget \n1.0 0.199062 -1.086262 3.0 0.074049 0.882922 1.441785 0.958333 \n2.0 -0.306763 -1.338495 4.0 0.077907 0.776345 1.698066 0.958333 \n\n 7 8 9 ... 18 19 20 \\\ntarget ... \n1.0 0.166667 0.810975 4.220176 ... 0.743115 -1.850372e-17 -0.203329 \n2.0 0.166667 0.915196 4.251629 ... 0.704191 -4.166667e-10 0.154370 \n\n 21 22 23 24 25 26 27 \ntarget \n1.0 0.978945 1.732495 -1.493878 -1.435400 -0.606625 0.835160 1.409857 \n2.0 0.978945 1.330520 -1.521644 -1.508412 -0.882112 0.815954 1.292295 \n\n[2 rows x 28 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
0123456789...18192021222324252627
target
1.00.199062-1.0862623.00.0740490.8829221.4417850.9583330.1666670.8109754.220176...0.743115-1.850372e-17-0.2033290.9789451.732495-1.493878-1.435400-0.6066250.8351601.409857
2.0-0.306763-1.3384954.00.0779070.7763451.6980660.9583330.1666670.9151964.251629...0.704191-4.166667e-100.1543700.9789451.330520-1.521644-1.508412-0.8821120.8159541.292295
\n

2 rows × 28 columns

\n
" - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transformed_ts.groupby(by='target').first()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 146, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-10-14 17:12:42,894 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\data\\ItalyPowerDemand\n", - "2024-10-14 17:12:42,904 - Data read successfully from local folder\n", - "2024-10-14 17:12:42,909 - Initialising experiment setup\n", - "2024-10-14 17:12:42,910 - -------------------------------------------------\n", - "2024-10-14 17:12:42,911 - Initialising Industrial Repository\n", - "2024-10-14 17:12:42,912 - -------------------------------------------------\n", - "2024-10-14 17:12:42,912 - Initialising Dask Server\n", - "Creating Dask Server\n", - "2024-10-14 17:12:42,922 - State start\n", - "2024-10-14 17:12:42,934 - Scheduler at: inproc://10.64.4.172/17848/75\n", - "2024-10-14 17:12:42,935 - dashboard at: http://10.64.4.172:60191/status\n", - "2024-10-14 17:12:42,936 - Registering Worker plugin shuffle\n", - "2024-10-14 17:12:42,953 - Start worker at: inproc://10.64.4.172/17848/78\n", - "2024-10-14 17:12:42,957 - Listening to: inproc10.64.4.172\n", - "2024-10-14 17:12:42,958 - Worker name: 0\n", - "2024-10-14 17:12:42,959 - dashboard at: 10.64.4.172:60192\n", - "2024-10-14 17:12:42,959 - Waiting to connect to: inproc://10.64.4.172/17848/75\n", - "2024-10-14 17:12:42,960 - -------------------------------------------------\n", - "2024-10-14 17:12:42,961 - Threads: 8\n", - "2024-10-14 17:12:42,962 - Memory: 31.95 GiB\n", - "2024-10-14 17:12:42,963 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-in7kt88h\n", - "2024-10-14 17:12:42,964 - -------------------------------------------------\n", - "2024-10-14 17:12:42,968 - Register worker \n", - "2024-10-14 17:12:42,973 - Starting worker compute stream, inproc://10.64.4.172/17848/78\n", - "2024-10-14 17:12:42,974 - Starting established connection to inproc://10.64.4.172/17848/79\n", - "2024-10-14 17:12:42,976 - Starting Worker plugin shuffle\n", - "2024-10-14 17:12:42,977 - Registered to: inproc://10.64.4.172/17848/75\n", - "2024-10-14 17:12:42,978 - -------------------------------------------------\n", - "2024-10-14 17:12:42,980 - Starting established connection to inproc://10.64.4.172/17848/75\n", - "2024-10-14 17:12:42,985 - Receive client connection: Client-60de385f-8a36-11ef-85b8-b42e99a00ea1\n", - "2024-10-14 17:12:42,990 - Starting established connection to inproc://10.64.4.172/17848/80\n", - "2024-10-14 17:12:42,992 - LinK Dask Server - http://10.64.4.172:60191/status\n", - "2024-10-14 17:12:42,994 - -------------------------------------------------\n", - "2024-10-14 17:12:42,994 - Initialising solver\n", - "AssumptionsHandler - Initial pipeline fitting started\n", - "AssumptionsHandler - Initial pipeline was fitted successfully\n", - "AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 0.3 MiB, max: 0.4 MiB\n", - "ApiComposer - Initial pipeline was fitted in 0.5 sec.\n", - "AssumptionsHandler - Preset was changed to best_quality due to fit time estimation for initial model.\n", - "ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 1 min. Set of candidate models: ['xgboost', 'catboost', 'logit', 'dt', 'rf', 'mlp', 'lgbm', 'one_class_svm', 'inception_model', 'nbeats_model', 'tcn_model', 'deepar_model', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'topological_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca', 'topological_extractor'].\n", - "ApiComposer - Pipeline composition started.\n", - "DataSourceSplitter - K-folds cross validation is applied.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generations: 0%| | 0/10000 [00:00 on (/n_quantile_extractor_{'window_size': 45, 'stride': 1, 'add_global_features': False};)/n_logit\n", - "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception <'list' object has no attribute 'supplementary_data'> on (/n_quantile_extractor_{'window_size': 35, 'stride': 9, 'add_global_features': True};)/n_logit_{'C': 0.08018172582949566, 'penalty': 'l1', 'solver': 'liblinear'}\n", - "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception on (/n_scaling;)/n_logit\n", - "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception <'list' object has no attribute 'supplementary_data'> on (/n_quantile_extractor_{'window_size': 25, 'stride': 3, 'add_global_features': False};)/n_logit_{'C': 0.4647070387880487, 'penalty': 'l2', 'solver': 'liblinear'}\n", - "IndustrialDispatcher - 2 individuals out of 13 in previous population were evaluated successfully. 0.15384615384615385% is a fairly small percentage of successful evaluation.\n", - "IndustrialEvoOptimizer - Generation num: 1 size: 2\n", - "IndustrialEvoOptimizer - Best individuals: HallOfFame archive fitness (1): ['']\n", - "GroupedCondition - Optimisation stopped: Time limit is reached\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generations: 0%| | 0/10000 [00:43']\n", - "IndustrialEvoOptimizer - no improvements for 1 iterations\n", - "IndustrialEvoOptimizer - spent time: 0.7 min\n", - "GPComposer - GP composition finished\n", - "DataSourceSplitter - K-folds cross validation is applied.\n", - "ApiComposer - Hyperparameters tuning started with 0 min. timeout\n", - "SimultaneousTuner - Hyperparameters optimization start: estimation of metric for initial graph\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SimultaneousTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [logit, quantile_extractor]}\n", - "logit - {'C': 0.9004533434023936, 'penalty': 'l2', 'solver': 'liblinear'}\n", - "quantile_extractor - {} \n", - "Initial metric: [0.792]\n", - " 0%| | 0/10 [00:00 on (/n_quantile_extractor_{'add_global_features': False, 'stride': 8, 'window_size': 5};)/n_logit_{'C': 0.9004533434023936, 'penalty': 'l2', 'solver': 'liblinear'}\n", - " 10%|█ | 1/10 [00:00<00:03, 2.43trial/s, best loss: inf]2024-10-14 17:13:28,918 - build_posterior_wrapper took 0.009996 seconds\n", - "2024-10-14 17:13:28,924 - TPE using 1/1 trials with best loss inf\n", - "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception <'list' object has no attribute 'supplementary_data'> on (/n_quantile_extractor_{'add_global_features': True, 'stride': 5, 'window_size': 35};)/n_logit_{'C': 0.9004533434023936, 'penalty': 'l2', 'solver': 'liblinear'}\n", - " 20%|██ | 2/10 [00:08<00:40, 5.06s/trial, best loss: inf]2024-10-14 17:13:37,233 - build_posterior_wrapper took 0.012998 seconds\n", - "2024-10-14 17:13:37,241 - TPE using 2/2 trials with best loss inf\n", - "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception <'list' object has no attribute 'supplementary_data'> on (/n_quantile_extractor_{'add_global_features': False, 'stride': 8, 'window_size': 35};)/n_logit_{'C': 0.9004533434023936, 'penalty': 'l2', 'solver': 'liblinear'}\n", - " 30%|███ | 3/10 [00:17<00:39, 5.67s/trial, best loss: inf]\n", - "SimultaneousTuner - Tunner stopped after initial search due to the lack of time\n", - "SimultaneousTuner - Hyperparameters optimization finished\n", - "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception <'list' object has no attribute 'supplementary_data'> on (/n_quantile_extractor_{'add_global_features': False, 'stride': 8, 'window_size': 5};)/n_logit_{'C': 0.9004533434023936, 'penalty': 'l2', 'solver': 'liblinear'}\n", - "SimultaneousTuner - Return init graph due to the fact that obtained metric is None. Initial metric is 0.793\n", - "SimultaneousTuner - Final graph: {'depth': 2, 'length': 2, 'nodes': [logit, quantile_extractor]}\n", - "logit - {'C': 0.9004533434023936, 'penalty': 'l2', 'solver': 'liblinear'}\n", - "quantile_extractor - {}\n", - "SimultaneousTuner - Final metric: 0.792\n", - "ApiComposer - Hyperparameters tuning finished\n", - "ApiComposer - Model generation finished\n", - "FEDOT logger - Final pipeline was fitted\n", - "FEDOT logger - Final pipeline: {'depth': 2, 'length': 2, 'nodes': [logit, quantile_extractor]}\n", - "logit - {'C': 0.9004533434023936, 'penalty': 'l2', 'solver': 'liblinear'}\n", - "quantile_extractor - {}\n", - "MemoryAnalytics - Memory consumption for finish in main session: current 18.7 MiB, max: 19.3 MiB\n", - "FEDOT logger - Predictions was saved in current directory.\n", - "FEDOT logger - Predictions was saved in current directory.\n" - ] - } - ], - "source": [ - "result_dict = ApiTemplate(api_config=api_config,\n", - " metric_list=metric_names).eval(dataset='ItalyPowerDemand',\n", - " finetune=finetune,\n", - " initial_assumption = node_list_model)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 147, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " accuracy f1 precision\n", - "0 0.723 0.742 0.728\n" - ] - } - ], - "source": [ - "print(result_dict['metrics'])" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "# Transform initial row in feature vector. Hard dataset" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 156, - "outputs": [], - "source": [ - "stat_pipeline = pipeline_creator.create_pipeline(node_list_model)\n", - "feature_extractor = pipeline_creator.create_pipeline(['quantile_extractor'])\n", - "feature_matrix = feature_extractor.fit(hard_to_clf_uno_dataset[0])\n", - "initial_ts, transformed_ts = pd.DataFrame(feature_matrix.features.squeeze()),pd.DataFrame(feature_matrix.predict.squeeze())\n", - "transformed_ts['target'] = feature_matrix.target" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 160, - "outputs": [ - { - "data": { - "text/plain": " 0 1 2 3 4 5 6 \\\n0 4.781661 21.857035 2.0 0.004143 0.303948 0.000000 0.989583 \n1 6.982644 51.614110 1.0 0.004286 0.862304 0.000000 0.989583 \n2 0.631072 -1.492669 10.0 -0.000874 0.217731 2.014866 0.989583 \n3 2.141406 3.358323 6.0 0.018356 0.034031 0.000000 0.989583 \n4 8.295791 72.098259 1.0 -0.002390 0.864106 0.000000 0.989583 \n... ... ... ... ... ... ... ... \n8921 5.898298 34.676828 2.0 0.007734 0.866926 0.000000 0.989583 \n8922 0.135169 -0.445727 33.0 0.009239 0.517216 1.597863 0.989583 \n8923 -1.173111 -0.637535 23.0 -0.000499 0.140819 0.574335 0.989583 \n8924 6.817212 49.579918 2.0 0.005960 0.859749 0.000000 0.989583 \n8925 5.778912 34.677022 19.0 0.011177 0.631451 0.157468 0.989583 \n\n 7 8 9 ... 19 20 21 \\\n0 0.041667 0.503727 0.498850 ... -1.770833e-09 -0.213938 0.994778 \n1 0.020833 0.619377 0.333216 ... 1.145833e-09 -0.166598 0.994778 \n2 0.218750 0.544344 2.323477 ... 3.333333e-09 -0.783021 0.994778 \n3 0.125000 0.768398 1.151543 ... -5.000000e-09 -0.435438 0.994778 \n4 0.020833 0.333090 0.166928 ... -3.125000e-09 -0.133151 0.994778 \n... ... ... ... ... ... ... ... \n8921 0.020833 0.670338 0.416115 ... -1.135417e-09 -0.180066 0.994778 \n8922 0.458333 -0.037192 4.296933 ... -8.322917e-09 0.094318 0.994778 \n8923 0.489583 -0.333333 0.811278 ... -5.000000e-09 0.574335 0.994778 \n8924 0.020833 0.561447 0.498850 ... 7.291667e-10 -0.185983 0.994778 \n8925 0.020833 0.551156 3.885481 ... 9.166668e-11 -0.245496 0.994778 \n\n 22 23 24 25 26 27 target \n0 5.585047 -0.213938 -0.213938 -2.139382e-01 -0.213938 0.154251 1.0 \n1 8.131040 -0.166598 -0.166598 -1.665976e-01 -0.166598 -0.166598 4.0 \n2 1.696814 -0.783021 -0.783021 -7.830209e-01 1.231845 1.580572 2.0 \n3 3.662803 -0.435438 -0.435438 -4.354381e-01 -0.435438 2.125963 5.0 \n4 8.997227 -0.133151 -0.133151 -1.331514e-01 -0.133151 -0.133151 3.0 \n... ... ... ... ... ... ... ... \n8921 6.532028 -0.180066 -0.180066 -1.800663e-01 -0.180066 -0.117773 4.0 \n8922 2.757423 -1.858625 -1.503544 -7.933832e-01 0.804479 1.559026 4.0 \n8923 0.574335 -1.723006 -1.723006 -5.000000e-09 0.574335 0.574335 5.0 \n8924 8.019215 -0.185983 -0.185983 -1.859831e-01 -0.185983 0.676157 4.0 \n8925 6.436382 -0.303235 -0.274366 -2.717411e-01 -0.114273 0.452610 4.0 \n\n[8926 rows x 29 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
0123456789...192021222324252627target
04.78166121.8570352.00.0041430.3039480.0000000.9895830.0416670.5037270.498850...-1.770833e-09-0.2139380.9947785.585047-0.213938-0.213938-2.139382e-01-0.2139380.1542511.0
16.98264451.6141101.00.0042860.8623040.0000000.9895830.0208330.6193770.333216...1.145833e-09-0.1665980.9947788.131040-0.166598-0.166598-1.665976e-01-0.166598-0.1665984.0
20.631072-1.49266910.0-0.0008740.2177312.0148660.9895830.2187500.5443442.323477...3.333333e-09-0.7830210.9947781.696814-0.783021-0.783021-7.830209e-011.2318451.5805722.0
32.1414063.3583236.00.0183560.0340310.0000000.9895830.1250000.7683981.151543...-5.000000e-09-0.4354380.9947783.662803-0.435438-0.435438-4.354381e-01-0.4354382.1259635.0
48.29579172.0982591.0-0.0023900.8641060.0000000.9895830.0208330.3330900.166928...-3.125000e-09-0.1331510.9947788.997227-0.133151-0.133151-1.331514e-01-0.133151-0.1331513.0
..................................................................
89215.89829834.6768282.00.0077340.8669260.0000000.9895830.0208330.6703380.416115...-1.135417e-09-0.1800660.9947786.532028-0.180066-0.180066-1.800663e-01-0.180066-0.1177734.0
89220.135169-0.44572733.00.0092390.5172161.5978630.9895830.458333-0.0371924.296933...-8.322917e-090.0943180.9947782.757423-1.858625-1.503544-7.933832e-010.8044791.5590264.0
8923-1.173111-0.63753523.0-0.0004990.1408190.5743350.9895830.489583-0.3333330.811278...-5.000000e-090.5743350.9947780.574335-1.723006-1.723006-5.000000e-090.5743350.5743355.0
89246.81721249.5799182.00.0059600.8597490.0000000.9895830.0208330.5614470.498850...7.291667e-10-0.1859830.9947788.019215-0.185983-0.185983-1.859831e-01-0.1859830.6761574.0
89255.77891234.67702219.00.0111770.6314510.1574680.9895830.0208330.5511563.885481...9.166668e-11-0.2454960.9947786.436382-0.303235-0.274366-2.717411e-01-0.1142730.4526104.0
\n

8926 rows × 29 columns

\n
" - }, - "execution_count": 160, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transformed_ts" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 157, - "outputs": [ - { - "data": { - "text/plain": " 0 1 2 3 4 5 6 \\\ntarget \n1.0 4.781661 21.857035 2.0 0.004143 0.303948 0.000000 0.989583 \n2.0 0.631072 -1.492669 10.0 -0.000874 0.217731 2.014866 0.989583 \n3.0 8.295791 72.098259 1.0 -0.002390 0.864106 0.000000 0.989583 \n4.0 6.982644 51.614110 1.0 0.004286 0.862304 0.000000 0.989583 \n5.0 2.141406 3.358323 6.0 0.018356 0.034031 0.000000 0.989583 \n6.0 6.900998 51.500275 2.0 0.000755 0.874662 0.000000 0.989583 \n7.0 0.113258 -1.039021 18.0 0.011574 0.772800 1.886971 0.989583 \n\n 7 8 9 ... 18 19 20 \\\ntarget ... \n1.0 0.041667 0.503727 0.498850 ... 0.885914 -1.770833e-09 -0.213938 \n2.0 0.218750 0.544344 2.323477 ... 0.749433 3.333333e-09 -0.783021 \n3.0 0.020833 0.333090 0.166928 ... 0.931344 -3.125000e-09 -0.133151 \n4.0 0.020833 0.619377 0.333216 ... 0.931344 1.145833e-09 -0.166598 \n5.0 0.125000 0.768398 1.151543 ... 0.730208 -5.000000e-09 -0.435438 \n6.0 0.041667 0.272347 0.725212 ... 0.885914 -1.510417e-09 -0.189545 \n7.0 0.229167 0.777667 3.279687 ... 0.634906 7.458333e-09 0.493963 \n\n 21 22 23 24 25 26 27 \ntarget \n1.0 0.994778 5.585047 -0.213938 -0.213938 -0.213938 -0.213938 0.154251 \n2.0 0.994778 1.696814 -0.783021 -0.783021 -0.783021 1.231845 1.580572 \n3.0 0.994778 8.997227 -0.133151 -0.133151 -0.133151 -0.133151 -0.133151 \n4.0 0.994778 8.131040 -0.166598 -0.166598 -0.166598 -0.166598 -0.166598 \n5.0 0.994778 3.662803 -0.435438 -0.435438 -0.435438 -0.435438 2.125963 \n6.0 0.994778 8.182152 -0.189545 -0.189545 -0.189545 -0.189545 0.277471 \n7.0 0.994778 2.627060 -1.146882 -1.146882 -1.146882 0.740089 1.416937 \n\n[7 rows x 28 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
0123456789...18192021222324252627
target
1.04.78166121.8570352.00.0041430.3039480.0000000.9895830.0416670.5037270.498850...0.885914-1.770833e-09-0.2139380.9947785.585047-0.213938-0.213938-0.213938-0.2139380.154251
2.00.631072-1.49266910.0-0.0008740.2177312.0148660.9895830.2187500.5443442.323477...0.7494333.333333e-09-0.7830210.9947781.696814-0.783021-0.783021-0.7830211.2318451.580572
3.08.29579172.0982591.0-0.0023900.8641060.0000000.9895830.0208330.3330900.166928...0.931344-3.125000e-09-0.1331510.9947788.997227-0.133151-0.133151-0.133151-0.133151-0.133151
4.06.98264451.6141101.00.0042860.8623040.0000000.9895830.0208330.6193770.333216...0.9313441.145833e-09-0.1665980.9947788.131040-0.166598-0.166598-0.166598-0.166598-0.166598
5.02.1414063.3583236.00.0183560.0340310.0000000.9895830.1250000.7683981.151543...0.730208-5.000000e-09-0.4354380.9947783.662803-0.435438-0.435438-0.435438-0.4354382.125963
6.06.90099851.5002752.00.0007550.8746620.0000000.9895830.0416670.2723470.725212...0.885914-1.510417e-09-0.1895450.9947788.182152-0.189545-0.189545-0.189545-0.1895450.277471
7.00.113258-1.03902118.00.0115740.7728001.8869710.9895830.2291670.7776673.279687...0.6349067.458333e-090.4939630.9947782.627060-1.146882-1.146882-1.1468820.7400891.416937
\n

7 rows × 28 columns

\n
" - }, - "execution_count": 157, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transformed_ts.groupby(by='target').first()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 158, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-10-14 17:24:46,814 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\data\\Phoneme\n", - "2024-10-14 17:24:47,323 - Data read successfully from local folder\n", - "2024-10-14 17:24:47,332 - Initialising experiment setup\n", - "2024-10-14 17:24:47,337 - -------------------------------------------------\n", - "2024-10-14 17:24:47,338 - Initialising Industrial Repository\n", - "2024-10-14 17:24:47,339 - -------------------------------------------------\n", - "2024-10-14 17:24:47,339 - Initialising Dask Server\n", - "Creating Dask Server\n", - "2024-10-14 17:24:47,351 - State start\n", - "2024-10-14 17:24:47,372 - Scheduler at: inproc://10.64.4.172/17848/87\n", - "2024-10-14 17:24:47,374 - dashboard at: http://10.64.4.172:61112/status\n", - "2024-10-14 17:24:47,376 - Registering Worker plugin shuffle\n", - "2024-10-14 17:24:47,396 - Start worker at: inproc://10.64.4.172/17848/90\n", - "2024-10-14 17:24:47,397 - Listening to: inproc10.64.4.172\n", - "2024-10-14 17:24:47,398 - Worker name: 0\n", - "2024-10-14 17:24:47,399 - dashboard at: 10.64.4.172:61113\n", - "2024-10-14 17:24:47,400 - Waiting to connect to: inproc://10.64.4.172/17848/87\n", - "2024-10-14 17:24:47,401 - -------------------------------------------------\n", - "2024-10-14 17:24:47,402 - Threads: 8\n", - "2024-10-14 17:24:47,403 - Memory: 31.95 GiB\n", - "2024-10-14 17:24:47,405 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-70jkow7u\n", - "2024-10-14 17:24:47,406 - -------------------------------------------------\n", - "2024-10-14 17:24:47,413 - Register worker \n", - "2024-10-14 17:24:47,416 - Starting worker compute stream, inproc://10.64.4.172/17848/90\n", - "2024-10-14 17:24:47,417 - Starting established connection to inproc://10.64.4.172/17848/91\n", - "2024-10-14 17:24:47,424 - Starting Worker plugin shuffle\n", - "2024-10-14 17:24:47,428 - Registered to: inproc://10.64.4.172/17848/87\n", - "2024-10-14 17:24:47,429 - -------------------------------------------------\n", - "2024-10-14 17:24:47,431 - Starting established connection to inproc://10.64.4.172/17848/87\n", - "2024-10-14 17:24:47,439 - Receive client connection: Client-10acbaa4-8a38-11ef-85b8-b42e99a00ea1\n", - "2024-10-14 17:24:47,443 - Starting established connection to inproc://10.64.4.172/17848/92\n", - "2024-10-14 17:24:47,445 - LinK Dask Server - http://10.64.4.172:61112/status\n", - "2024-10-14 17:24:47,447 - -------------------------------------------------\n", - "2024-10-14 17:24:47,448 - Initialising solver\n", - "AssumptionsHandler - Initial pipeline fitting started\n", - "AssumptionsHandler - Initial pipeline was fitted successfully\n", - "AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 4.6 MiB, max: 6.8 MiB\n", - "ApiComposer - Initial pipeline was fitted in 2.5 sec.\n", - "AssumptionsHandler - Preset was changed to fast_train due to fit time estimation for initial model.\n", - "ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 1 min. Set of candidate models: ['xgboost', 'catboost', 'logit', 'dt', 'rf', 'mlp', 'lgbm', 'one_class_svm', 'inception_model', 'nbeats_model', 'tcn_model', 'deepar_model', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'topological_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca', 'topological_extractor'].\n", - "ApiComposer - Timeout is too small for composing and is skipped because fit_time is 2.485536 sec.\n", - "DataSourceSplitter - Hold out validation is applied.\n", - "ApiComposer - Hyperparameters tuning started with 1 min. timeout\n", - "SimultaneousTuner - Hyperparameters optimization start: estimation of metric for initial graph\n", - "SimultaneousTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [logit, quantile_extractor]}\n", - "logit - {}\n", - "quantile_extractor - {} \n", - "Initial metric: [0.083]\n", - " 0%| | 0/100000 [00:00 on (/n_quantile_extractor_{'add_global_features': True, 'stride': 8, 'window_size': 5};)/n_logit_{'C': 7.013310954134132, 'penalty': 'l1', 'solver': 'liblinear'}\n", - " 0%| | 1/100000 [00:00<13:58:19, 1.99trial/s, best loss: inf]2024-10-14 17:24:53,635 - build_posterior_wrapper took 0.010000 seconds\n", - "2024-10-14 17:24:53,647 - TPE using 1/1 trials with best loss inf\n", - "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception <'list' object has no attribute 'supplementary_data'> on (/n_quantile_extractor_{'add_global_features': True, 'stride': 8, 'window_size': 30};)/n_logit_{'C': 4.629539905077404, 'penalty': 'l1', 'solver': 'liblinear'}\n", - " 0%| | 2/100000 [00:09<150:53:16, 5.43s/trial, best loss: inf]2024-10-14 17:25:02,519 - build_posterior_wrapper took 0.010000 seconds\n", - "2024-10-14 17:25:02,523 - TPE using 2/2 trials with best loss inf\n", - "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception <'list' object has no attribute 'supplementary_data'> on (/n_quantile_extractor_{'add_global_features': False, 'stride': 3, 'window_size': 25};)/n_logit_{'C': 1.4623270887022872, 'penalty': 'l2', 'solver': 'liblinear'}\n", - " 0%| | 3/100000 [00:18<191:34:27, 6.90s/trial, best loss: inf]2024-10-14 17:25:11,161 - build_posterior_wrapper took 0.011999 seconds\n", - "2024-10-14 17:25:11,171 - TPE using 3/3 trials with best loss inf\n", - "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception <'list' object has no attribute 'supplementary_data'> on (/n_quantile_extractor_{'add_global_features': False, 'stride': 5, 'window_size': 45};)/n_logit_{'C': 0.9861086567698546, 'penalty': 'l1', 'solver': 'liblinear'}\n", - " 0%| | 4/100000 [00:27<215:40:41, 7.76s/trial, best loss: inf]2024-10-14 17:25:20,255 - build_posterior_wrapper took 0.010998 seconds\n", - "2024-10-14 17:25:20,272 - TPE using 4/4 trials with best loss inf\n", - "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception <'list' object has no attribute 'supplementary_data'> on (/n_quantile_extractor_{'add_global_features': False, 'stride': 7, 'window_size': 15};)/n_logit_{'C': 7.162224422667747, 'penalty': 'l2', 'solver': 'liblinear'}\n", - " 0%| | 5/100000 [00:36<232:48:32, 8.38s/trial, best loss: inf]2024-10-14 17:25:29,728 - build_posterior_wrapper took 0.010000 seconds\n", - "2024-10-14 17:25:29,734 - TPE using 5/5 trials with best loss inf\n", - "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception <'list' object has no attribute 'supplementary_data'> on (/n_quantile_extractor_{'add_global_features': False, 'stride': 3, 'window_size': 35};)/n_logit_{'C': 5.358362329054688, 'penalty': 'l1', 'solver': 'liblinear'}\n", - " 0%| | 6/100000 [00:46<245:06:36, 8.82s/trial, best loss: inf]2024-10-14 17:25:39,416 - build_posterior_wrapper took 0.010999 seconds\n", - "2024-10-14 17:25:39,446 - TPE using 6/6 trials with best loss inf\n", - "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception <'list' object has no attribute 'supplementary_data'> on (/n_quantile_extractor_{'add_global_features': False, 'stride': 4, 'window_size': 10};)/n_logit_{'C': 5.481147127254816, 'penalty': 'l2', 'solver': 'liblinear'}\n", - " 0%| | 7/100000 [00:57<228:34:40, 8.23s/trial, best loss: inf]\n", - "SimultaneousTuner - Hyperparameters optimization finished\n", - "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception <'list' object has no attribute 'supplementary_data'> on (/n_quantile_extractor_{'add_global_features': True, 'stride': 8, 'window_size': 5};)/n_logit_{'C': 7.013310954134132, 'penalty': 'l1', 'solver': 'liblinear'}\n", - "SimultaneousTuner - Return init graph due to the fact that obtained metric is None. Initial metric is 0.083\n", - "SimultaneousTuner - Final graph: {'depth': 2, 'length': 2, 'nodes': [logit, quantile_extractor]}\n", - "logit - {}\n", - "quantile_extractor - {}\n", - "SimultaneousTuner - Final metric: 0.083\n", - "ApiComposer - Hyperparameters tuning finished\n", - "ApiComposer - Model generation finished\n", - "FEDOT logger - Final pipeline was fitted\n", - "FEDOT logger - Final pipeline: {'depth': 2, 'length': 2, 'nodes': [logit, quantile_extractor]}\n", - "logit - {}\n", - "quantile_extractor - {}\n", - "MemoryAnalytics - Memory consumption for finish in main session: current 9.2 MiB, max: 9.8 MiB\n", - "FEDOT logger - Predictions was saved in current directory.\n", - "FEDOT logger - Predictions was saved in current directory.\n" - ] - } - ], - "source": [ - "result_dict = ApiTemplate(api_config=api_config,\n", - " metric_list=metric_names).eval(dataset='Phoneme',\n", - " finetune=finetune,\n", - " initial_assumption = node_list_model)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 159, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " accuracy f1 precision\n", - "0 0.262 0.21 0.148\n" - ] - } - ], - "source": [ - "print(result_dict['metrics'])" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/examples/tutorial/time_series/ts_classification/classification_example_advanced.ipynb b/examples/tutorial/time_series/ts_classification/classification_example_advanced.ipynb new file mode 100644 index 000000000..4d1dd3ec0 --- /dev/null +++ b/examples/tutorial/time_series/ts_classification/classification_example_advanced.ipynb @@ -0,0 +1,1542 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from hyperopt import hp\n", + "from fedot_ind.core.architecture.pipelines.abstract_pipeline import AbstractPipeline, ApiTemplate\n", + "from pathlib import Path\n", + "from fedot.core.data.data import InputData\n", + "from fedot.core.data.data_split import train_test_data_setup\n", + "from fedot.core.pipelines.pipeline_builder import PipelineBuilder\n", + "from fedot.core.repository.tasks import TsForecastingParams, Task, TaskTypesEnum\n", + "from matplotlib import pyplot as plt\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error\n", + "from fedot_ind.api.utils.path_lib import PROJECT_PATH\n", + "from fedot_ind.core.architecture.settings.computational import backend_methods as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "def plot_mean_sample(X,y, labels:list = [], n_channel: int = 1):\n", + " mean_sample = []\n", + " if len(labels) == 0:\n", + " labels = list(np.unique(y))\n", + " for label in labels:\n", + " mean_sample.append(np.mean(X[y == label] , axis=0)) # Данные класса 1\n", + " #ax = plt.gca()\n", + " channels = [f'Channel {x}' for x in range(n_channel)]\n", + " df = pd.DataFrame(mean_sample).T\n", + " df.columns = labels\n", + " df.plot(kind ='line',subplots=True, layout=(1,len(labels)),figsize=(20,10))\n", + " plt.legend(fontsize='small')\n", + " plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n", + " plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "def plot_mean_sample_multi(X,y, labels:list = [], n_channel: int = None):\n", + " mean_sample = {}\n", + " if len(labels) == 0:\n", + " labels = list(np.unique(y))\n", + " if n_channel is None:\n", + " n_channel = X.shape[1]\n", + " channels = [f'Channel {x}' for x in range(n_channel)]\n", + " for label in labels:\n", + " mask = y == label\n", + " for chn in range(n_channel):\n", + " mean_sample.update({f'Label_{label}_channel_{chn}':np.mean(X[mask.flatten(),chn,:] , axis=0)}) # Данные класса 1\n", + " #ax = plt.gca()\n", + " df = pd.DataFrame(mean_sample)\n", + " df.plot(kind ='line')\n", + " plt.suptitle('Усреднённые семплы по классам')\n", + " plt.legend(fontsize='small')\n", + " plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n", + " plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "finetune = False\n", + "metric_names = ('f1', 'accuracy', 'precision', 'roc_auc')\n", + "api_config = dict(problem='classification',\n", + " metric='accuracy',\n", + " timeout=1,\n", + " pop_size=10,\n", + " with_tunig=False,\n", + " n_jobs=2,\n", + " logging_level=20)\n", + "pipeline_creator = AbstractPipeline(task='classification')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Классификация с помощью геометрических преобразований" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "ECG = 'ECG200'\n", + "topological_model = ['topological_extractor', 'rf']\n", + "recurrence_model = ['recurrence_extractor', 'quantile_extractor', 'rf']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "ecg_dataset = pipeline_creator.create_input_data(ECG)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Topo Hyperparams" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "topological_params ={'window_size': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(5, 50, 5)]]},\n", + " 'stride': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(1, 10, 1)]]}}," + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "stat_params = {'window_size': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(5, 50, 5)]]},\n", + " 'stride': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(1, 10, 1)]]},\n", + " 'add_global_features': {'hyperopt-dist': hp.choice, 'sampling-scope': [[True, False]]}}" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "recurrence_params = {'window_size': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(5, 50, 5)]]},\n", + " 'stride': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(1, 10, 1)]]},\n", + " 'rec_metric': (hp.choice, [['cosine', 'euclidean']]),\n", + " 'image_mode': {'hyperopt-dist': hp.choice, 'sampling-scope': [[True, False]]}}," + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "rec_metric = 'cosine'\n", + "image_mode = True\n", + "window_size = 10\n", + "stride = 1" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "topological_node_dict = {'topological_extractor':{'window_size':window_size,\n", + " 'stride':stride}}" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "recurrence_node_dict = {'recurrence_extractor':{'window_size':window_size,\n", + " 'stride':stride,\n", + " 'rec_metric':rec_metric,\n", + " 'image_mode':image_mode}}" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [ + "feature_extractor = pipeline_creator.create_pipeline(topological_node_dict)\n", + "feature_matrix = feature_extractor.fit(ecg_dataset[0])" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_mean_sample(ecg_dataset[0].features,ecg_dataset[0].target)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [], + "source": [ + "feature_extractor = pipeline_creator.create_pipeline(recurrence_node_dict)\n", + "feature_matrix = feature_extractor.fit(ecg_dataset[0])" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ + { + "data": { + "text/plain": "(100, 3, 87, 87)" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature_matrix.predict.shape" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "explain_config = {'method': 'recurrence',\n", + " 'samples': 1,\n", + " 'metric': 'mean'}" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [], + "source": [ + "from fedot_ind.tools.explain.explain import RecurrenceExplainer\n", + "\n", + "rec_explain = RecurrenceExplainer(model=feature_extractor,\n", + " features=feature_matrix.predict,\n", + " target=ecg_dataset[0].target)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "rec_explain.explain(**explain_config)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "rec_explain.visual()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from fedot_ind.core.operation.transformation.data.kernel_matrix import colorise\n", + "metric= 'mean'\n", + "name = 'test'\n", + "for classes, rec_matrix in rec_explain.rec_matrix_by_cls.items():\n", + " aggregated_rec_matrix = rec_explain.aggregate_func[metric](rec_matrix, axis=0)\n", + " aggregated_rec_matrix = colorise(aggregated_rec_matrix)\n", + " plt.imshow(aggregated_rec_matrix.T)\n", + " plt.colorbar()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [], + "source": [ + "topological_list_model = {'topological_extractor':{'window_size':window_size,\n", + " 'stride':stride},\n", + " 'logit':{}}" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Dask Server\n", + "2024-11-04 12:17:22,942 - To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy\n", + "2024-11-04 12:17:22,978 - State start\n", + "2024-11-04 12:17:22,988 - Scheduler at: inproc://10.64.4.172/5996/1\n", + "2024-11-04 12:17:22,989 - dashboard at: http://10.64.4.172:8787/status\n", + "2024-11-04 12:17:22,990 - Registering Worker plugin shuffle\n", + "2024-11-04 12:17:23,009 - Start worker at: inproc://10.64.4.172/5996/4\n", + "2024-11-04 12:17:23,010 - Listening to: inproc10.64.4.172\n", + "2024-11-04 12:17:23,011 - Worker name: 0\n", + "2024-11-04 12:17:23,012 - dashboard at: 10.64.4.172:61698\n", + "2024-11-04 12:17:23,012 - Waiting to connect to: inproc://10.64.4.172/5996/1\n", + "2024-11-04 12:17:23,013 - -------------------------------------------------\n", + "2024-11-04 12:17:23,014 - Threads: 8\n", + "2024-11-04 12:17:23,014 - Memory: 31.95 GiB\n", + "2024-11-04 12:17:23,015 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-ofvxoj7v\n", + "2024-11-04 12:17:23,016 - -------------------------------------------------\n", + "2024-11-04 12:17:23,018 - Register worker \n", + "2024-11-04 12:17:23,020 - Starting worker compute stream, inproc://10.64.4.172/5996/4\n", + "2024-11-04 12:17:23,021 - Starting established connection to inproc://10.64.4.172/5996/5\n", + "2024-11-04 12:17:23,022 - Starting Worker plugin shuffle\n", + "2024-11-04 12:17:23,023 - Registered to: inproc://10.64.4.172/5996/1\n", + "2024-11-04 12:17:23,023 - -------------------------------------------------\n", + "2024-11-04 12:17:23,024 - Starting established connection to inproc://10.64.4.172/5996/1\n", + "2024-11-04 12:17:23,028 - Receive client connection: Client-99a03bfb-9a8d-11ef-976c-b42e99a00ea1\n", + "2024-11-04 12:17:23,030 - Starting established connection to inproc://10.64.4.172/5996/6\n", + "AssumptionsHandler - Initial pipeline fitting started\n", + "AssumptionsHandler - Initial pipeline was fitted successfully\n", + "AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 0.6 MiB, max: 0.7 MiB\n", + "ApiComposer - Initial pipeline was fitted in 7.2 sec.\n", + "AssumptionsHandler - Preset was changed to fast_train due to fit time estimation for initial model.\n", + "ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 1 min. Set of candidate models: ['xgboost', 'catboost', 'logit', 'dt', 'rf', 'mlp', 'lgbm', 'one_class_svm', 'inception_model', 'nbeats_model', 'tcn_model', 'deepar_model', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'topological_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca'].\n", + "ApiComposer - Timeout is too small for composing and is skipped because fit_time is 7.248042 sec.\n", + "DataSourceSplitter - K-folds cross validation is applied.\n", + "ApiComposer - Hyperparameters tuning started with 1 min. timeout\n", + "DaskOptunaTuner - Hyperparameters optimization start: estimation of metric for initial graph\n", + "DaskOptunaTuner - Initial graph: {'depth': 2, 'length': 2, 'nodes': [logit, topological_extractor]}\n", + "logit - {}\n", + "topological_extractor - {'window_size': 10, 'stride': 1} \n", + "Initial metric: [0.759]\n", + "2024-11-04 12:17:32,500 - Run out-of-band function '_register_with_scheduler'\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-11-04 12:17:32,502] A new study created in memory with name: no-name-3fe59ef6-c84c-4c3b-88a4-1ddc5fdd9233\n" + ] + }, + { + "data": { + "text/plain": " 0%| | 0/100 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
accuracyf1precision
00.560.00.249
\n" + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_dict_stat['metrics']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": "(100, 1, 96)" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecg_dataset[0].features.shape" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "multimodal_pipeline = {'recurrence_extractor': {\n", + " 'window_size': 30,\n", + " 'stride': 5,\n", + " 'image_mode': True},\n", + " 'resnet_model': {\n", + " 'epochs': 50,\n", + " 'batch_size': 8,\n", + " 'model_name': 'ResNet50'}}" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Dask Server\n", + "2024-11-04 12:44:17,000 - To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy\n", + "2024-11-04 12:44:17,032 - State start\n", + "2024-11-04 12:44:17,048 - Scheduler at: inproc://10.64.4.172/21776/1\n", + "2024-11-04 12:44:17,049 - dashboard at: http://10.64.4.172:8787/status\n", + "2024-11-04 12:44:17,050 - Registering Worker plugin shuffle\n", + "2024-11-04 12:44:17,061 - Start worker at: inproc://10.64.4.172/21776/4\n", + "2024-11-04 12:44:17,062 - Listening to: inproc10.64.4.172\n", + "2024-11-04 12:44:17,063 - Worker name: 0\n", + "2024-11-04 12:44:17,063 - dashboard at: 10.64.4.172:63240\n", + "2024-11-04 12:44:17,064 - Waiting to connect to: inproc://10.64.4.172/21776/1\n", + "2024-11-04 12:44:17,064 - -------------------------------------------------\n", + "2024-11-04 12:44:17,065 - Threads: 8\n", + "2024-11-04 12:44:17,066 - Memory: 31.95 GiB\n", + "2024-11-04 12:44:17,067 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-woqorv18\n", + "2024-11-04 12:44:17,067 - -------------------------------------------------\n", + "2024-11-04 12:44:17,070 - Register worker \n", + "2024-11-04 12:44:17,072 - Starting worker compute stream, inproc://10.64.4.172/21776/4\n", + "2024-11-04 12:44:17,072 - Starting established connection to inproc://10.64.4.172/21776/5\n", + "2024-11-04 12:44:17,073 - Starting Worker plugin shuffle\n", + "2024-11-04 12:44:17,074 - Registered to: inproc://10.64.4.172/21776/1\n", + "2024-11-04 12:44:17,075 - -------------------------------------------------\n", + "2024-11-04 12:44:17,076 - Starting established connection to inproc://10.64.4.172/21776/1\n", + "2024-11-04 12:44:17,080 - Receive client connection: Client-5bad0a97-9a91-11ef-9510-b42e99a00ea1\n", + "2024-11-04 12:44:17,082 - Starting established connection to inproc://10.64.4.172/21776/6\n", + "AssumptionsHandler - Initial pipeline fitting started\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:02<00:00, 3.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Accuracy= 0.45454545454545453, Training Loss: 0.80\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:02<00:00, 3.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Accuracy= 0.6545454545454545, Training Loss: 0.64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:02<00:00, 3.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Accuracy= 0.7090909090909091, Training Loss: 0.69\n", + "EarlyStopping counter: 1 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:02<00:00, 3.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Accuracy= 0.7090909090909091, Training Loss: 0.60\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Accuracy= 0.7272727272727273, Training Loss: 0.60\n", + "EarlyStopping counter: 1 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Accuracy= 0.6363636363636364, Training Loss: 0.64\n", + "EarlyStopping counter: 2 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Accuracy= 0.7636363636363637, Training Loss: 0.52\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Accuracy= 0.6909090909090909, Training Loss: 0.67\n", + "EarlyStopping counter: 1 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Accuracy= 0.7272727272727273, Training Loss: 0.54\n", + "EarlyStopping counter: 2 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:02<00:00, 3.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 10, Accuracy= 0.7818181818181819, Training Loss: 0.47\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 11, Accuracy= 0.7090909090909091, Training Loss: 0.58\n", + "EarlyStopping counter: 1 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 12, Accuracy= 0.7454545454545455, Training Loss: 0.50\n", + "EarlyStopping counter: 2 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 13, Accuracy= 0.7636363636363637, Training Loss: 0.52\n", + "EarlyStopping counter: 3 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 14, Accuracy= 0.8, Training Loss: 0.47\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 15, Accuracy= 0.6727272727272727, Training Loss: 0.58\n", + "EarlyStopping counter: 1 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:02<00:00, 3.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 16, Accuracy= 0.7090909090909091, Training Loss: 0.58\n", + "EarlyStopping counter: 2 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 17, Accuracy= 0.8, Training Loss: 0.49\n", + "EarlyStopping counter: 3 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 18, Accuracy= 0.7636363636363637, Training Loss: 0.45\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:02<00:00, 2.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 19, Accuracy= 0.8363636363636363, Training Loss: 0.44\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:03<00:00, 2.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 20, Accuracy= 0.8545454545454545, Training Loss: 0.37\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:02<00:00, 2.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 21, Accuracy= 0.6545454545454545, Training Loss: 0.71\n", + "EarlyStopping counter: 1 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:02<00:00, 3.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 22, Accuracy= 0.7272727272727273, Training Loss: 0.49\n", + "EarlyStopping counter: 2 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 23, Accuracy= 0.7818181818181819, Training Loss: 0.51\n", + "EarlyStopping counter: 3 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 24, Accuracy= 0.7272727272727273, Training Loss: 0.58\n", + "EarlyStopping counter: 4 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:02<00:00, 3.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 25, Accuracy= 0.7090909090909091, Training Loss: 0.72\n", + "EarlyStopping counter: 5 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 26, Accuracy= 0.7454545454545455, Training Loss: 0.52\n", + "EarlyStopping counter: 6 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 27, Accuracy= 0.7090909090909091, Training Loss: 0.63\n", + "EarlyStopping counter: 7 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 28, Accuracy= 0.7090909090909091, Training Loss: 0.50\n", + "EarlyStopping counter: 8 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 29, Accuracy= 0.7090909090909091, Training Loss: 0.66\n", + "EarlyStopping counter: 9 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 30, Accuracy= 0.6545454545454545, Training Loss: 0.76\n", + "EarlyStopping counter: 10 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:02<00:00, 3.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 31, Accuracy= 0.7272727272727273, Training Loss: 0.64\n", + "EarlyStopping counter: 11 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 32, Accuracy= 0.6727272727272727, Training Loss: 0.72\n", + "EarlyStopping counter: 12 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 33, Accuracy= 0.6727272727272727, Training Loss: 0.70\n", + "EarlyStopping counter: 13 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 34, Accuracy= 0.6727272727272727, Training Loss: 0.87\n", + "EarlyStopping counter: 14 out of 15\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:01<00:00, 3.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 35, Accuracy= 0.8181818181818182, Training Loss: 0.48\n", + "EarlyStopping counter: 15 out of 15\n", + "Early stopping\n", + "AssumptionsHandler - Initial pipeline was fitted successfully\n", + "AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 2.3 MiB, max: 196.5 MiB\n", + "ApiComposer - Initial pipeline was fitted in 257.7 sec.\n", + "AssumptionsHandler - Preset was changed to fast_train due to fit time estimation for initial model.\n", + "ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 1 min. Set of candidate models: ['xgboost', 'catboost', 'logit', 'dt', 'rf', 'mlp', 'lgbm', 'one_class_svm', 'inception_model', 'nbeats_model', 'tcn_model', 'deepar_model', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'topological_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca'].\n", + "ApiComposer - Timeout is too small for composing and is skipped because fit_time is 257.743017 sec.\n", + "DataSourceSplitter - K-folds cross validation is applied.\n", + "ApiComposer - Time for pipeline composing was 0:00:00.\n", + "The remaining 0.4 seconds are not enough to tune the hyperparameters.\n", + "ApiComposer - Composed pipeline returned without tuning.\n", + "ApiComposer - Model generation finished\n", + "FEDOT logger - Already fitted initial pipeline is used\n", + "FEDOT logger - Final pipeline: {'depth': 2, 'length': 2, 'nodes': [resnet_model, recurrence_extractor]}\n", + "resnet_model - {'epochs': 50, 'batch_size': 8, 'model_name': 'ResNet50'}\n", + "recurrence_extractor - {'window_size': 30, 'stride': 5, 'image_mode': True}\n", + "MemoryAnalytics - Memory consumption for finish in main session: current 2.3 MiB, max: 196.5 MiB\n", + "FEDOT logger - Predictions was saved in current directory.\n", + "FEDOT logger - Predictions was saved in current directory.\n" + ] + } + ], + "source": [ + "result_dict = ApiTemplate(api_config=api_config,\n", + " metric_list=('f1', 'accuracy')).eval(dataset=ECG,\n", + " finetune=finetune,\n", + " initial_assumption=multimodal_pipeline)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [ + { + "data": { + "text/plain": " accuracy f1\n0 0.63 0.0", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
accuracyf1
00.630.0
\n
" + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_dict['metrics']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Прогнозирование с помощью топологических признаков" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [], + "source": [ + "horizon = 365\n", + "PATH = Path(PROJECT_PATH, 'examples', 'data', 'ices_areas_ts.csv')\n", + "time_series_df = pd.read_csv(PATH).iloc[:, 1:]\n", + "target_series = time_series_df['Карское'].values\n", + "input_data = InputData.from_numpy_time_series(target_series,task=Task(TaskTypesEnum.ts_forecasting,task_params=TsForecastingParams(forecast_length=horizon)))\n", + "train_data, test_data = train_test_data_setup(input_data)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Построение бейзлайна и топологической модели" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels\n", + "repo = IndustrialModels().setup_repository()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LaggedTransformationImplementation - Window size of lagged transformation was changed by WindowSizeSelector from 0 to 1095\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[28], line 2\u001B[0m\n\u001B[0;32m 1\u001B[0m pipeline_based \u001B[38;5;241m=\u001B[39m PipelineBuilder()\u001B[38;5;241m.\u001B[39madd_node(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlagged\u001B[39m\u001B[38;5;124m'\u001B[39m)\u001B[38;5;241m.\u001B[39madd_node(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtreg\u001B[39m\u001B[38;5;124m'\u001B[39m)\u001B[38;5;241m.\u001B[39mbuild()\n\u001B[1;32m----> 2\u001B[0m \u001B[43mpipeline_based\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrain_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 4\u001B[0m topological_pipeline \u001B[38;5;241m=\u001B[39m PipelineBuilder()\u001B[38;5;241m.\u001B[39madd_node(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlagged\u001B[39m\u001B[38;5;124m'\u001B[39m)\u001B[38;5;241m.\u001B[39madd_node(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtopological_features\u001B[39m\u001B[38;5;124m'\u001B[39m)\u001B[38;5;241m.\u001B[39madd_node(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlagged\u001B[39m\u001B[38;5;124m'\u001B[39m, branch_idx\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m2\u001B[39m)\u001B[38;5;241m.\u001B[39mjoin_branches(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtreg\u001B[39m\u001B[38;5;124m'\u001B[39m)\u001B[38;5;241m.\u001B[39mbuild()\n\u001B[0;32m 5\u001B[0m topological_pipeline\u001B[38;5;241m.\u001B[39mfit(train_data)\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\fedot\\core\\pipelines\\pipeline.py:197\u001B[0m, in \u001B[0;36mPipeline.fit\u001B[1;34m(self, input_data, time_constraint, n_jobs)\u001B[0m\n\u001B[0;32m 194\u001B[0m copied_input_data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_assign_data_to_nodes(copied_input_data)\n\u001B[0;32m 196\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m time_constraint \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m--> 197\u001B[0m train_predicted \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_fit\u001B[49m\u001B[43m(\u001B[49m\u001B[43minput_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopied_input_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 198\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 199\u001B[0m train_predicted \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_fit_with_time_limit(input_data\u001B[38;5;241m=\u001B[39mcopied_input_data, time\u001B[38;5;241m=\u001B[39mtime_constraint)\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\fedot\\core\\pipelines\\pipeline.py:112\u001B[0m, in \u001B[0;36mPipeline._fit\u001B[1;34m(self, input_data, process_state_dict, fitted_operations)\u001B[0m\n\u001B[0;32m 110\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m Timer() \u001B[38;5;28;01mas\u001B[39;00m t:\n\u001B[0;32m 111\u001B[0m computation_time_update \u001B[38;5;241m=\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mroot_node\u001B[38;5;241m.\u001B[39mfitted_operation \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcomputation_time \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m--> 112\u001B[0m train_predicted \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mroot_node\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43minput_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minput_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 113\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m computation_time_update:\n\u001B[0;32m 114\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcomputation_time \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mround\u001B[39m(t\u001B[38;5;241m.\u001B[39mminutes_from_start, \u001B[38;5;241m3\u001B[39m)\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\fedot\\core\\pipelines\\node.py:200\u001B[0m, in \u001B[0;36mPipelineNode.fit\u001B[1;34m(self, input_data)\u001B[0m\n\u001B[0;32m 198\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfitted_operation \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 199\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m Timer() \u001B[38;5;28;01mas\u001B[39;00m t:\n\u001B[1;32m--> 200\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfitted_operation, operation_predict \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moperation\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mparams\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[43m_parameters\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 201\u001B[0m \u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minput_data\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 202\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfit_time_in_seconds \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mround\u001B[39m(t\u001B[38;5;241m.\u001B[39mseconds_from_start, \u001B[38;5;241m3\u001B[39m)\n\u001B[0;32m 203\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\fedot\\core\\operations\\operation.py:87\u001B[0m, in \u001B[0;36mOperation.fit\u001B[1;34m(self, params, data)\u001B[0m\n\u001B[0;32m 75\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"This method is used for defining and running of the evaluation strategy\u001B[39;00m\n\u001B[0;32m 76\u001B[0m \u001B[38;5;124;03mto train the operation with the data provided\u001B[39;00m\n\u001B[0;32m 77\u001B[0m \n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 83\u001B[0m \u001B[38;5;124;03m tuple: trained operation and prediction on train data\u001B[39;00m\n\u001B[0;32m 84\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 85\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_init(data\u001B[38;5;241m.\u001B[39mtask, params\u001B[38;5;241m=\u001B[39mparams, n_samples_data\u001B[38;5;241m=\u001B[39mdata\u001B[38;5;241m.\u001B[39mfeatures\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m0\u001B[39m])\n\u001B[1;32m---> 87\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfitted_operation \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_eval_strategy\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrain_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdata\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 89\u001B[0m predict_train \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpredict_for_fit(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfitted_operation, data, params)\n\u001B[0;32m 91\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfitted_operation, predict_train\n", + "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\core\\operation\\interfaces\\industrial_model_strategy.py:150\u001B[0m, in \u001B[0;36mIndustrialSkLearnEvaluationStrategy.fit\u001B[1;34m(self, train_data)\u001B[0m\n\u001B[0;32m 148\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfit\u001B[39m(\u001B[38;5;28mself\u001B[39m, train_data: InputData):\n\u001B[0;32m 149\u001B[0m train_data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmulti_dim_dispatcher\u001B[38;5;241m.\u001B[39m_convert_input_data(train_data)\n\u001B[1;32m--> 150\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[43mmulti_dim_dispatcher\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrain_data\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\core\\operation\\interfaces\\industrial_preprocessing_strategy.py:209\u001B[0m, in \u001B[0;36mMultiDimPreprocessingStrategy.fit\u001B[1;34m(self, train_data)\u001B[0m\n\u001B[0;32m 204\u001B[0m fit_for_every_dim \u001B[38;5;241m=\u001B[39m curry(\u001B[38;5;241m2\u001B[39m)(\u001B[38;5;28;01mlambda\u001B[39;00m data, prev_state: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_list_of_fitted_model(data, prev_state)\n\u001B[0;32m 205\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m operation_for_every_dim \u001B[38;5;28;01melse\u001B[39;00m prev_state)\n\u001B[0;32m 207\u001B[0m fit_multidim \u001B[38;5;241m=\u001B[39m curry(\u001B[38;5;241m2\u001B[39m)(\u001B[38;5;28;01mlambda\u001B[39;00m data, prev_state: prev_state\u001B[38;5;241m.\u001B[39mfit(data) \u001B[38;5;28;01mif\u001B[39;00m operation_for_multidim \u001B[38;5;28;01melse\u001B[39;00m prev_state)\n\u001B[1;32m--> 209\u001B[0m trained_operation \u001B[38;5;241m=\u001B[39m \u001B[43mEither\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43minsert\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrain_data\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m \u001B[49m\u001B[43m\\\u001B[49m\n\u001B[0;32m 210\u001B[0m \u001B[43m \u001B[49m\u001B[43mthen\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfit_one_dim\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moperation_condition\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moperation_implementation\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39m \\\n\u001B[0;32m 211\u001B[0m then(channel_independent_branch(train_data))\u001B[38;5;241m.\u001B[39m \\\n\u001B[0;32m 212\u001B[0m then(fit_for_every_dim(train_data))\u001B[38;5;241m.\u001B[39mthen(fit_multidim(train_data))\u001B[38;5;241m.\u001B[39mvalue\n\u001B[0;32m 214\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m trained_operation\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\pymonad\\monad.py:152\u001B[0m, in \u001B[0;36mMonad.then\u001B[1;34m(self, function)\u001B[0m\n\u001B[0;32m 132\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mthen\u001B[39m(\n\u001B[0;32m 133\u001B[0m \u001B[38;5;28mself\u001B[39m: \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mMonad[S]\u001B[39m\u001B[38;5;124m'\u001B[39m, function: Union[Callable[[S], T], Callable[[S], \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mMonad[T]\u001B[39m\u001B[38;5;124m'\u001B[39m]]\n\u001B[0;32m 134\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mMonad[T]\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[0;32m 135\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\" Combines the functionality of bind and fmap.\u001B[39;00m\n\u001B[0;32m 136\u001B[0m \n\u001B[0;32m 137\u001B[0m \u001B[38;5;124;03m Instead of worrying about whether to use bind or fmap,\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 150\u001B[0m \u001B[38;5;124;03m A monad value of the same type as 'self'\u001B[39;00m\n\u001B[0;32m 151\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 152\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmap\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfunction\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 153\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 154\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\u001B[38;5;241m.\u001B[39mjoin()\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\pymonad\\either.py:106\u001B[0m, in \u001B[0;36mEither.map\u001B[1;34m(self, function)\u001B[0m\n\u001B[0;32m 104\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\n\u001B[0;32m 105\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 106\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m(\u001B[43mfunction\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m, (\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mTrue\u001B[39;00m))\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\pymonad\\tools.py:50\u001B[0m, in \u001B[0;36m_curry_helper.._curry_internal\u001B[1;34m(*arguments)\u001B[0m\n\u001B[0;32m 48\u001B[0m all_arguments\u001B[38;5;241m.\u001B[39mextend(arguments)\n\u001B[0;32m 49\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(all_arguments) \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m number_of_arguments: \u001B[38;5;66;03m# pylint: disable=no-else-return\u001B[39;00m\n\u001B[1;32m---> 50\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunction_to_curry\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mall_arguments\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 51\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 52\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m _curry_helper(number_of_arguments, function_to_curry, all_arguments)\n", + "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\core\\operation\\interfaces\\industrial_preprocessing_strategy.py:195\u001B[0m, in \u001B[0;36mMultiDimPreprocessingStrategy.fit..\u001B[1;34m(operation, init_state)\u001B[0m\n\u001B[0;32m 192\u001B[0m operation_for_multidim \u001B[38;5;241m=\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28many\u001B[39m([operation_for_one_dim, operation_for_every_dim])\n\u001B[0;32m 194\u001B[0m \u001B[38;5;66;03m# If model is classical sklearn model we use one_dimensional mode\u001B[39;00m\n\u001B[1;32m--> 195\u001B[0m fit_one_dim \u001B[38;5;241m=\u001B[39m curry(\u001B[38;5;241m2\u001B[39m)(\u001B[38;5;28;01mlambda\u001B[39;00m operation, init_state: \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit_one_sample\u001B[49m\u001B[43m(\u001B[49m\u001B[43minit_state\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 196\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m operation_for_one_dim \u001B[38;5;28;01melse\u001B[39;00m operation)\n\u001B[0;32m 198\u001B[0m \u001B[38;5;66;03m# Elif model could be use for each dimension(channel) independently we use channel_independent mode\u001B[39;00m\n\u001B[0;32m 199\u001B[0m channel_independent_branch \u001B[38;5;241m=\u001B[39m curry(\u001B[38;5;241m2\u001B[39m)(\u001B[38;5;28;01mlambda\u001B[39;00m data, prev_state: \u001B[38;5;28mlist\u001B[39m(deepcopy(prev_state) \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m\n\u001B[0;32m 200\u001B[0m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mlen\u001B[39m(data)))\n\u001B[0;32m 201\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m operation_for_every_dim \u001B[38;5;28;01melse\u001B[39;00m prev_state)\n", + "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\core\\operation\\interfaces\\industrial_preprocessing_strategy.py:157\u001B[0m, in \u001B[0;36mMultiDimPreprocessingStrategy.fit_one_sample\u001B[1;34m(self, train_data)\u001B[0m\n\u001B[0;32m 154\u001B[0m is_multi_target \u001B[38;5;241m=\u001B[39m is_multi_output_task(train_data)\n\u001B[0;32m 155\u001B[0m model_multi_adaptation \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mall\u001B[39m([is_model_not_support_multi, is_multi_target])\n\u001B[1;32m--> 157\u001B[0m operation_implementation \u001B[38;5;241m=\u001B[39m \u001B[43mEither\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalue\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtrain_data\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 158\u001B[0m \u001B[43m \u001B[49m\u001B[43mmonoid\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43mtrain_data\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmodel_multi_adaptation\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m \u001B[49m\u001B[43m\\\u001B[49m\n\u001B[0;32m 159\u001B[0m \u001B[43m \u001B[49m\u001B[43meither\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 160\u001B[0m \u001B[43m \u001B[49m\u001B[43mleft_function\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdata\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[43moperation_condition\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moperation_implementation\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 161\u001B[0m \u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfeatures\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtarget\u001B[49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mnot_fedot_input_data\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\n\u001B[0;32m 162\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moperation_condition\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moperation_implementation\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 163\u001B[0m \u001B[43m \u001B[49m\u001B[43mright_function\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m:\u001B[49m\n\u001B[0;32m 164\u001B[0m \u001B[43m \u001B[49m\u001B[43mconvert_to_multivariate_model\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moperation_condition\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moperation_implementation\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 165\u001B[0m operation_implementation \u001B[38;5;241m=\u001B[39m operation_implementation \u001B[38;5;28;01mif\u001B[39;00m model_multi_adaptation \\\n\u001B[0;32m 166\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moperation_condition\u001B[38;5;241m.\u001B[39moperation_implementation\n\u001B[0;32m 167\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m operation_implementation\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\pymonad\\either.py:91\u001B[0m, in \u001B[0;36mEither.either\u001B[1;34m(self, left_function, right_function)\u001B[0m\n\u001B[0;32m 89\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m right_function(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mvalue)\n\u001B[0;32m 90\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m---> 91\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mleft_function\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmonoid\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32mD:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\core\\operation\\interfaces\\industrial_preprocessing_strategy.py:160\u001B[0m, in \u001B[0;36mMultiDimPreprocessingStrategy.fit_one_sample..\u001B[1;34m(data)\u001B[0m\n\u001B[0;32m 154\u001B[0m is_multi_target \u001B[38;5;241m=\u001B[39m is_multi_output_task(train_data)\n\u001B[0;32m 155\u001B[0m model_multi_adaptation \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mall\u001B[39m([is_model_not_support_multi, is_multi_target])\n\u001B[0;32m 157\u001B[0m operation_implementation \u001B[38;5;241m=\u001B[39m Either(value\u001B[38;5;241m=\u001B[39mtrain_data,\n\u001B[0;32m 158\u001B[0m monoid\u001B[38;5;241m=\u001B[39m[train_data, model_multi_adaptation])\u001B[38;5;241m.\u001B[39m \\\n\u001B[0;32m 159\u001B[0m either(\n\u001B[1;32m--> 160\u001B[0m left_function\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mlambda\u001B[39;00m data: \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moperation_condition\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moperation_implementation\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 161\u001B[0m \u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfeatures\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtarget\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mif\u001B[39;00m not_fedot_input_data \u001B[38;5;28;01melse\u001B[39;00m\n\u001B[0;32m 162\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moperation_condition\u001B[38;5;241m.\u001B[39moperation_implementation\u001B[38;5;241m.\u001B[39mfit(data),\n\u001B[0;32m 163\u001B[0m right_function\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mlambda\u001B[39;00m data:\n\u001B[0;32m 164\u001B[0m convert_to_multivariate_model(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moperation_condition\u001B[38;5;241m.\u001B[39moperation_implementation, data))\n\u001B[0;32m 165\u001B[0m operation_implementation \u001B[38;5;241m=\u001B[39m operation_implementation \u001B[38;5;28;01mif\u001B[39;00m model_multi_adaptation \\\n\u001B[0;32m 166\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moperation_condition\u001B[38;5;241m.\u001B[39moperation_implementation\n\u001B[0;32m 167\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m operation_implementation\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:473\u001B[0m, in \u001B[0;36mBaseForest.fit\u001B[1;34m(self, X, y, sample_weight)\u001B[0m\n\u001B[0;32m 462\u001B[0m trees \u001B[38;5;241m=\u001B[39m [\n\u001B[0;32m 463\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_make_estimator(append\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m, random_state\u001B[38;5;241m=\u001B[39mrandom_state)\n\u001B[0;32m 464\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(n_more_estimators)\n\u001B[0;32m 465\u001B[0m ]\n\u001B[0;32m 467\u001B[0m \u001B[38;5;66;03m# Parallel loop: we prefer the threading backend as the Cython code\u001B[39;00m\n\u001B[0;32m 468\u001B[0m \u001B[38;5;66;03m# for fitting the trees is internally releasing the Python GIL\u001B[39;00m\n\u001B[0;32m 469\u001B[0m \u001B[38;5;66;03m# making threading more efficient than multiprocessing in\u001B[39;00m\n\u001B[0;32m 470\u001B[0m \u001B[38;5;66;03m# that case. However, for joblib 0.12+ we respect any\u001B[39;00m\n\u001B[0;32m 471\u001B[0m \u001B[38;5;66;03m# parallel_backend contexts set at a higher level,\u001B[39;00m\n\u001B[0;32m 472\u001B[0m \u001B[38;5;66;03m# since correctness does not rely on using threads.\u001B[39;00m\n\u001B[1;32m--> 473\u001B[0m trees \u001B[38;5;241m=\u001B[39m \u001B[43mParallel\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 474\u001B[0m \u001B[43m \u001B[49m\u001B[43mn_jobs\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[43mn_jobs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 475\u001B[0m \u001B[43m \u001B[49m\u001B[43mverbose\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[43mverbose\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 476\u001B[0m \u001B[43m \u001B[49m\u001B[43mprefer\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mthreads\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m 477\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 478\u001B[0m \u001B[43m \u001B[49m\u001B[43mdelayed\u001B[49m\u001B[43m(\u001B[49m\u001B[43m_parallel_build_trees\u001B[49m\u001B[43m)\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 479\u001B[0m \u001B[43m \u001B[49m\u001B[43mt\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 480\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbootstrap\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 481\u001B[0m \u001B[43m \u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 482\u001B[0m \u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 483\u001B[0m \u001B[43m \u001B[49m\u001B[43msample_weight\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 484\u001B[0m \u001B[43m \u001B[49m\u001B[43mi\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 485\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mlen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mtrees\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 486\u001B[0m \u001B[43m \u001B[49m\u001B[43mverbose\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[43mverbose\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 487\u001B[0m \u001B[43m \u001B[49m\u001B[43mclass_weight\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[43mclass_weight\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 488\u001B[0m \u001B[43m \u001B[49m\u001B[43mn_samples_bootstrap\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mn_samples_bootstrap\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 489\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 490\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mi\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mt\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43menumerate\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mtrees\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 491\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 493\u001B[0m \u001B[38;5;66;03m# Collect newly grown trees\u001B[39;00m\n\u001B[0;32m 494\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mestimators_\u001B[38;5;241m.\u001B[39mextend(trees)\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\sklearn\\utils\\parallel.py:63\u001B[0m, in \u001B[0;36mParallel.__call__\u001B[1;34m(self, iterable)\u001B[0m\n\u001B[0;32m 58\u001B[0m config \u001B[38;5;241m=\u001B[39m get_config()\n\u001B[0;32m 59\u001B[0m iterable_with_config \u001B[38;5;241m=\u001B[39m (\n\u001B[0;32m 60\u001B[0m (_with_config(delayed_func, config), args, kwargs)\n\u001B[0;32m 61\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m delayed_func, args, kwargs \u001B[38;5;129;01min\u001B[39;00m iterable\n\u001B[0;32m 62\u001B[0m )\n\u001B[1;32m---> 63\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[38;5;21;43m__call__\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43miterable_with_config\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\joblib\\parallel.py:1918\u001B[0m, in \u001B[0;36mParallel.__call__\u001B[1;34m(self, iterable)\u001B[0m\n\u001B[0;32m 1916\u001B[0m output \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_sequential_output(iterable)\n\u001B[0;32m 1917\u001B[0m \u001B[38;5;28mnext\u001B[39m(output)\n\u001B[1;32m-> 1918\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m output \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mreturn_generator \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;43mlist\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43moutput\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1920\u001B[0m \u001B[38;5;66;03m# Let's create an ID that uniquely identifies the current call. If the\u001B[39;00m\n\u001B[0;32m 1921\u001B[0m \u001B[38;5;66;03m# call is interrupted early and that the same instance is immediately\u001B[39;00m\n\u001B[0;32m 1922\u001B[0m \u001B[38;5;66;03m# re-used, this id will be used to prevent workers that were\u001B[39;00m\n\u001B[0;32m 1923\u001B[0m \u001B[38;5;66;03m# concurrently finalizing a task from the previous call to run the\u001B[39;00m\n\u001B[0;32m 1924\u001B[0m \u001B[38;5;66;03m# callback.\u001B[39;00m\n\u001B[0;32m 1925\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lock:\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\joblib\\parallel.py:1847\u001B[0m, in \u001B[0;36mParallel._get_sequential_output\u001B[1;34m(self, iterable)\u001B[0m\n\u001B[0;32m 1845\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mn_dispatched_batches \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[0;32m 1846\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mn_dispatched_tasks \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[1;32m-> 1847\u001B[0m res \u001B[38;5;241m=\u001B[39m func(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1848\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mn_completed_tasks \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[0;32m 1849\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprint_progress()\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\sklearn\\utils\\parallel.py:123\u001B[0m, in \u001B[0;36m_FuncWrapper.__call__\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m 121\u001B[0m config \u001B[38;5;241m=\u001B[39m {}\n\u001B[0;32m 122\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m config_context(\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mconfig):\n\u001B[1;32m--> 123\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunction(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:186\u001B[0m, in \u001B[0;36m_parallel_build_trees\u001B[1;34m(tree, bootstrap, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)\u001B[0m\n\u001B[0;32m 184\u001B[0m tree\u001B[38;5;241m.\u001B[39mfit(X, y, sample_weight\u001B[38;5;241m=\u001B[39mcurr_sample_weight, check_input\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[0;32m 185\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 186\u001B[0m \u001B[43mtree\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msample_weight\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msample_weight\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcheck_input\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[0;32m 188\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m tree\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\sklearn\\tree\\_classes.py:1247\u001B[0m, in \u001B[0;36mDecisionTreeRegressor.fit\u001B[1;34m(self, X, y, sample_weight, check_input)\u001B[0m\n\u001B[0;32m 1218\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfit\u001B[39m(\u001B[38;5;28mself\u001B[39m, X, y, sample_weight\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, check_input\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m):\n\u001B[0;32m 1219\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"Build a decision tree regressor from the training set (X, y).\u001B[39;00m\n\u001B[0;32m 1220\u001B[0m \n\u001B[0;32m 1221\u001B[0m \u001B[38;5;124;03m Parameters\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 1244\u001B[0m \u001B[38;5;124;03m Fitted estimator.\u001B[39;00m\n\u001B[0;32m 1245\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m-> 1247\u001B[0m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 1248\u001B[0m \u001B[43m \u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1249\u001B[0m \u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1250\u001B[0m \u001B[43m \u001B[49m\u001B[43msample_weight\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msample_weight\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1251\u001B[0m \u001B[43m \u001B[49m\u001B[43mcheck_input\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcheck_input\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1252\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1253\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\n", + "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\sklearn\\tree\\_classes.py:379\u001B[0m, in \u001B[0;36mBaseDecisionTree.fit\u001B[1;34m(self, X, y, sample_weight, check_input)\u001B[0m\n\u001B[0;32m 368\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 369\u001B[0m builder \u001B[38;5;241m=\u001B[39m BestFirstTreeBuilder(\n\u001B[0;32m 370\u001B[0m splitter,\n\u001B[0;32m 371\u001B[0m min_samples_split,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 376\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmin_impurity_decrease,\n\u001B[0;32m 377\u001B[0m )\n\u001B[1;32m--> 379\u001B[0m \u001B[43mbuilder\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbuild\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtree_\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msample_weight\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 381\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mn_outputs_ \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m1\u001B[39m \u001B[38;5;129;01mand\u001B[39;00m is_classifier(\u001B[38;5;28mself\u001B[39m):\n\u001B[0;32m 382\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mn_classes_ \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mn_classes_[\u001B[38;5;241m0\u001B[39m]\n", + "\u001B[1;31mKeyboardInterrupt\u001B[0m: " + ] + } + ], + "source": [ + "pipeline_based = PipelineBuilder().add_node('lagged').add_node('treg').build()\n", + "pipeline_based.fit(train_data)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "topological_pipeline = PipelineBuilder().add_node('lagged').add_node('topological_extractor').add_node('lagged', branch_idx=2).join_branches('treg').build()\n", + "topological_pipeline.fit(train_data)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "### Прогноз" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "forecast_base = np.ravel(pipeline_based.predict(test_data).predict)\n", + "forecast_topo = np.ravel(topological_pipeline.predict(test_data).predict)\n", + "\n", + "forecast_base[forecast_base < 0] = 0\n", + "forecast_topo[forecast_topo < 0] = 0" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Визуализация прогнозов" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "execution_count": 1 + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "plt.plot(input_data.features, label='real data')\n", + "plt.plot(np.arange(len(target_series) - horizon, len(target_series)),\n", + " forecast_base, label='forecast base')\n", + "plt.plot(np.arange(len(target_series) - horizon, len(target_series)),\n", + " forecast_topo, label='forecast topo')\n", + "\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "print('base')\n", + "print(mean_squared_error(test_data.target, forecast_base, squared=False))\n", + "print(\n", + " mean_absolute_percentage_error(\n", + " test_data.target +\n", + " 1000,\n", + " forecast_base +\n", + " 1000))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "print('topo')\n", + "print(mean_squared_error(test_data.target, forecast_topo, squared=False))\n", + "print(\n", + " mean_absolute_percentage_error(\n", + " test_data.target +\n", + " 1000,\n", + " forecast_topo +\n", + " 1000))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/tutorial/time_series/ts_classification/classification_example_basic.ipynb b/examples/tutorial/time_series/ts_classification/classification_example_basic.ipynb new file mode 100644 index 000000000..5eac38f81 --- /dev/null +++ b/examples/tutorial/time_series/ts_classification/classification_example_basic.ipynb @@ -0,0 +1,1634 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from hyperopt import hp\n", + "from fedot_ind.core.architecture.pipelines.abstract_pipeline import AbstractPipeline, ApiTemplate\n", + "from fedot_ind.core.repository.constanst_repository import SPECTRUM_ESTIMATORS\n", + "from fedot_ind.core.repository.constanst_repository import DISCRETE_WAVELETS, CONTINUOUS_WAVELETS" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "def plot_mean_sample(X,y, labels:list = [], n_channel: int = 1):\n", + " mean_sample = []\n", + " if len(labels) == 0:\n", + " labels = list(np.unique(y))\n", + " for label in labels:\n", + " mean_sample.append(np.mean(X[y == label] , axis=0)) # Данные класса 1\n", + " #ax = plt.gca()\n", + " channels = [f'Channel {x}' for x in range(n_channel)]\n", + " df = pd.DataFrame(mean_sample).T\n", + " df.columns = labels\n", + " df.plot(kind ='line',subplots=True, layout=(1,len(labels)),figsize=(20,10))\n", + " plt.legend(fontsize='small')\n", + " plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n", + " plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "def plot_mean_sample_multi(X,y, labels:list = [], n_channel: int = None):\n", + " mean_sample = {}\n", + " if len(labels) == 0:\n", + " labels = list(np.unique(y))\n", + " if n_channel is None:\n", + " n_channel = X.shape[1]\n", + " channels = [f'Channel {x}' for x in range(n_channel)]\n", + " for label in labels:\n", + " mask = y == label\n", + " for chn in range(n_channel):\n", + " mean_sample.update({f'Label_{label}_channel_{chn}':np.mean(X[mask.flatten(),chn,:] , axis=0)}) # Данные класса 1\n", + " #ax = plt.gca()\n", + " df = pd.DataFrame(mean_sample)\n", + " df.plot(kind ='line')\n", + " plt.suptitle('Усреднённые семплы по классам')\n", + " plt.legend(fontsize='small')\n", + " plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n", + " plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "finetune = False\n", + "metric_names = ('f1', 'accuracy', 'precision', 'roc_auc')\n", + "api_config = dict(problem='classification',\n", + " metric='accuracy',\n", + " timeout=1,\n", + " pop_size=10,\n", + " with_tunig=False,\n", + " n_jobs=2,\n", + " logging_level=20)\n", + "pipeline_creator = AbstractPipeline(task='classification')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "# Our datasets and models for experiments" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "easy_to_clf_uno = 'ItalyPowerDemand'\n", + "hard_to_clf_uno = 'ElectricDevices'\n", + "easy_to_clf_multi = 'BasicMotions'\n", + "hard_to_clf_multi = 'AtrialFibrillation'\n", + "node_list_model = ['quantile_extractor','logit']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "# Our datasets" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "easy_to_clf_uno_dataset = pipeline_creator.create_input_data(easy_to_clf_uno)\n", + "hard_to_clf_uno_dataset = pipeline_creator.create_input_data(hard_to_clf_uno)\n", + "easy_to_clf_multi_dataset = pipeline_creator.create_input_data(easy_to_clf_multi)\n", + "hard_to_clf_multi_dataset = pipeline_creator.create_input_data(hard_to_clf_multi)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "# Lets Visualise our data" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Easy to clf data" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_mean_sample(easy_to_clf_uno_dataset[0].features,easy_to_clf_uno_dataset[0].target)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Hard to clf data" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_mean_sample(hard_to_clf_uno_dataset[0].features,hard_to_clf_uno_dataset[0].target)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_mean_sample_multi(easy_to_clf_multi_dataset[0].features,easy_to_clf_multi_dataset[0].target)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "# Transform initial row in feature vector. Easy dataset" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "stat_pipeline = pipeline_creator.create_pipeline(node_list_model)\n", + "feature_extractor = pipeline_creator.create_pipeline(['quantile_extractor'])\n", + "feature_matrix = feature_extractor.fit(easy_to_clf_uno_dataset[0])\n", + "initial_ts, transformed_ts = pd.DataFrame(feature_matrix.features.squeeze()),pd.DataFrame(feature_matrix.predict.squeeze())\n", + "transformed_ts['target'] = feature_matrix.target" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "node_dict = {'quantile_extractor':{'window_size':10,\n", + " 'stride':50}}" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "data": { + "text/plain": " 0 1 2 3 4 5 6 \\\ntarget \n1.0 -0.532565 -1.170139 3.0 0.097182 0.785926 1.667704 0.958333 \n2.0 0.056140 -0.909639 3.0 0.075741 0.892806 1.435281 0.958333 \n\n 7 8 9 ... 18 19 20 \\\ntarget ... \n1.0 0.083333 0.898154 4.501629 ... 0.743115 1.125000e-09 0.416926 \n2.0 0.125000 0.841321 4.168296 ... 0.768841 -6.666667e-10 -0.030488 \n\n 21 22 23 24 25 26 27 \ntarget \n1.0 0.978945 1.417548 -1.641826 -1.608472 -0.997747 0.669957 1.205923 \n2.0 0.978945 1.714364 -1.550198 -1.541755 -0.762200 0.673081 1.489222 \n\n[2 rows x 28 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
0123456789...18192021222324252627
target
1.0-0.532565-1.1701393.00.0971820.7859261.6677040.9583330.0833330.8981544.501629...0.7431151.125000e-090.4169260.9789451.417548-1.641826-1.608472-0.9977470.6699571.205923
2.00.056140-0.9096393.00.0757410.8928061.4352810.9583330.1250000.8413214.168296...0.768841-6.666667e-10-0.0304880.9789451.714364-1.550198-1.541755-0.7622000.6730811.489222
\n

2 rows × 28 columns

\n
" + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transformed_ts.groupby(by='target').first()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Dask Server\n", + "2024-10-21 14:55:37,211 - To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy\n", + "2024-10-21 14:55:37,217 - State start\n", + "2024-10-21 14:55:37,220 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\scheduler-ub6_f_nr', purging\n", + "2024-10-21 14:55:37,221 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\scheduler-wq5_42et', purging\n", + "2024-10-21 14:55:37,223 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\worker-5pg8n1u_', purging\n", + "2024-10-21 14:55:37,224 - Found stale lock file and directory 'C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\dask-scratch-space\\\\worker-a8c6mb2y', purging\n", + "2024-10-21 14:55:37,232 - Scheduler at: inproc://10.64.4.172/21832/1\n", + "2024-10-21 14:55:37,233 - dashboard at: http://10.64.4.172:8787/status\n", + "2024-10-21 14:55:37,233 - Registering Worker plugin shuffle\n", + "2024-10-21 14:55:37,246 - Start worker at: inproc://10.64.4.172/21832/4\n", + "2024-10-21 14:55:37,247 - Listening to: inproc10.64.4.172\n", + "2024-10-21 14:55:37,248 - Worker name: 0\n", + "2024-10-21 14:55:37,249 - dashboard at: 10.64.4.172:58597\n", + "2024-10-21 14:55:37,249 - Waiting to connect to: inproc://10.64.4.172/21832/1\n", + "2024-10-21 14:55:37,250 - -------------------------------------------------\n", + "2024-10-21 14:55:37,250 - Threads: 8\n", + "2024-10-21 14:55:37,251 - Memory: 31.95 GiB\n", + "2024-10-21 14:55:37,252 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-p1tlllrf\n", + "2024-10-21 14:55:37,252 - -------------------------------------------------\n", + "2024-10-21 14:55:37,256 - Register worker \n", + "2024-10-21 14:55:37,259 - Starting worker compute stream, inproc://10.64.4.172/21832/4\n", + "2024-10-21 14:55:37,260 - Starting established connection to inproc://10.64.4.172/21832/5\n", + "2024-10-21 14:55:37,261 - Starting Worker plugin shuffle\n", + "2024-10-21 14:55:37,262 - Registered to: inproc://10.64.4.172/21832/1\n", + "2024-10-21 14:55:37,263 - -------------------------------------------------\n", + "2024-10-21 14:55:37,264 - Starting established connection to inproc://10.64.4.172/21832/1\n", + "2024-10-21 14:55:37,267 - Receive client connection: Client-62d9d225-8fa3-11ef-9548-b42e99a00ea1\n", + "2024-10-21 14:55:37,269 - Starting established connection to inproc://10.64.4.172/21832/6\n", + "AssumptionsHandler - Initial pipeline fitting started\n", + "AssumptionsHandler - Initial pipeline was fitted successfully\n", + "AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 0.4 MiB, max: 0.5 MiB\n", + "ApiComposer - Initial pipeline was fitted in 0.2 sec.\n", + "AssumptionsHandler - Preset was changed to best_quality due to fit time estimation for initial model.\n", + "ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 1 min. Set of candidate models: ['xgboost', 'catboost', 'logit', 'dt', 'rf', 'mlp', 'lgbm', 'one_class_svm', 'inception_model', 'nbeats_model', 'tcn_model', 'deepar_model', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'topological_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca', 'topological_extractor'].\n", + "ApiComposer - Pipeline composition started.\n", + "DataSourceSplitter - K-folds cross validation is applied.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generations: 0%| | 0/10000 [00:00 on ((/n_quantile_extractor;)/n_topological_extractor;)/n_logit\n", + "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception on ((/n_fourier_basis;)/n_topological_extractor;;/n_quantile_extractor;)/n_logit\n", + "IndustrialDispatcher - 5 individuals out of 13 in previous population were evaluated successfully. 0.38461538461538464% is a fairly small percentage of successful evaluation.\n", + "IndustrialEvoOptimizer - Generation num: 1 size: 5\n", + "IndustrialEvoOptimizer - Best individuals: HallOfFame archive fitness (1): ['']\n", + "GroupedCondition - Optimisation stopped: Time limit is reached\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generations: 0%| | 0/10000 [00:36']\n", + "IndustrialEvoOptimizer - no improvements for 1 iterations\n", + "IndustrialEvoOptimizer - spent time: 0.6 min\n", + "GPComposer - GP composition finished\n", + "DataSourceSplitter - K-folds cross validation is applied.\n", + "ApiComposer - Hyperparameters tuning started with 0 min. timeout\n", + "SimultaneousTuner - Hyperparameters optimization start: estimation of metric for initial graph\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SimultaneousTuner - Initial graph: {'depth': 3, 'length': 4, 'nodes': [logit, quantile_extractor, channel_filtration, eigen_basis]}\n", + "logit - {}\n", + "quantile_extractor - {}\n", + "channel_filtration - {}\n", + "eigen_basis - {} \n", + "Initial metric: [0.956]\n", + " 0%| | 0/100000 [00:00 on ((/n_eigen_basis_{'low_rank_approximation': False, 'rank_regularization': 'hard_thresholding', 'window_size': 25};)/n_channel_filtration_{'centroid_metric': 'euclidean', 'distance': 'chebyshev', 'sample_metric': 'cosine', 'selection_strategy': 'sum'};;/n_quantile_extractor_{'add_global_features': True, 'stride': 2, 'window_size': 5};)/n_logit_{'C': 6.9870997389038, 'penalty': 'l2', 'solver': 'liblinear'}\n", + " 0%| | 1/100000 [00:00<5:40:02, 4.90trial/s, best loss: inf]2024-10-21 14:56:15,677 - build_posterior_wrapper took 0.020967 seconds\n", + "2024-10-21 14:56:15,680 - TPE using 1/1 trials with best loss inf\n", + "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception on ((/n_eigen_basis_{'low_rank_approximation': False, 'rank_regularization': 'explained_dispersion', 'window_size': 10};)/n_channel_filtration_{'centroid_metric': 'euclidean', 'distance': 'chebyshev', 'sample_metric': 'euclidean', 'selection_strategy': 'sum'};;/n_quantile_extractor_{'add_global_features': False, 'stride': 9, 'window_size': 20};)/n_logit_{'C': 9.584642642281313, 'penalty': 'l2', 'solver': 'liblinear'}\n", + " 0%| | 2/100000 [00:00<8:43:20, 3.18trial/s, best loss: inf]2024-10-21 14:56:16,068 - build_posterior_wrapper took 0.021000 seconds\n", + "2024-10-21 14:56:16,070 - TPE using 2/2 trials with best loss inf\n", + "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception on ((/n_eigen_basis_{'low_rank_approximation': False, 'rank_regularization': 'hard_thresholding', 'window_size': 35};)/n_channel_filtration_{'centroid_metric': 'chebyshev', 'distance': 'manhattan', 'sample_metric': 'cityblock', 'selection_strategy': 'sum'};;/n_quantile_extractor_{'add_global_features': True, 'stride': 6, 'window_size': 15};)/n_logit_{'C': 0.0829525514507987, 'penalty': 'l1', 'solver': 'liblinear'}\n", + " 0%| | 3/100000 [00:00<7:26:29, 3.73trial/s, best loss: inf]2024-10-21 14:56:16,284 - build_posterior_wrapper took 0.023040 seconds\n", + "2024-10-21 14:56:16,285 - TPE using 3/3 trials with best loss inf\n", + " 0%| | 4/100000 [00:02<20:47:01, 1.34trial/s, best loss: -0.9407114624505929]2024-10-21 14:56:17,770 - build_posterior_wrapper took 0.024544 seconds\n", + "2024-10-21 14:56:17,772 - TPE using 4/4 trials with best loss -0.940711\n", + " 0%| | 5/100000 [00:03<29:26:25, 1.06s/trial, best loss: -0.9703557312252965]2024-10-21 14:56:19,378 - build_posterior_wrapper took 0.021000 seconds\n", + "2024-10-21 14:56:19,380 - TPE using 5/5 trials with best loss -0.970356\n", + "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception on ((/n_eigen_basis_{'low_rank_approximation': False, 'rank_regularization': 'explained_dispersion', 'window_size': 30};)/n_channel_filtration_{'centroid_metric': 'chebyshev', 'distance': 'manhattan', 'sample_metric': 'cityblock', 'selection_strategy': 'sum'};;/n_quantile_extractor_{'add_global_features': False, 'stride': 8, 'window_size': 5};)/n_logit_{'C': 0.14323116482051435, 'penalty': 'l2', 'solver': 'liblinear'}\n", + " 0%| | 6/100000 [00:04<23:34:19, 1.18trial/s, best loss: -0.9703557312252965]2024-10-21 14:56:19,818 - build_posterior_wrapper took 0.022002 seconds\n", + "2024-10-21 14:56:19,820 - TPE using 6/6 trials with best loss -0.970356\n", + " 0%| | 7/100000 [00:05<30:12:27, 1.09s/trial, best loss: -0.9703557312252965]2024-10-21 14:56:21,398 - build_posterior_wrapper took 0.021999 seconds\n", + "2024-10-21 14:56:21,399 - TPE using 7/7 trials with best loss -0.970356\n", + "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception on ((/n_eigen_basis_{'low_rank_approximation': False, 'rank_regularization': 'hard_thresholding', 'window_size': 20};)/n_channel_filtration_{'centroid_metric': 'chebyshev', 'distance': 'euclidean', 'sample_metric': 'cosine', 'selection_strategy': 'sum'};;/n_quantile_extractor_{'add_global_features': True, 'stride': 5, 'window_size': 15};)/n_logit_{'C': 3.8955607183989067, 'penalty': 'l1', 'solver': 'liblinear'}\n", + " 0%| | 8/100000 [00:06<22:28:26, 1.24trial/s, best loss: -0.9703557312252965]2024-10-21 14:56:21,608 - build_posterior_wrapper took 0.019989 seconds\n", + "2024-10-21 14:56:21,611 - TPE using 8/8 trials with best loss -0.970356\n", + " 0%| | 9/100000 [00:08<35:31:26, 1.28s/trial, best loss: -0.9703557312252965]2024-10-21 14:56:23,922 - build_posterior_wrapper took 0.021008 seconds\n", + "2024-10-21 14:56:23,924 - TPE using 9/9 trials with best loss -0.970356\n", + " 0%| | 10/100000 [00:09<37:10:19, 1.34s/trial, best loss: -0.9703557312252965]2024-10-21 14:56:25,393 - build_posterior_wrapper took 0.021008 seconds\n", + "2024-10-21 14:56:25,394 - TPE using 10/10 trials with best loss -0.970356\n", + " 0%| | 11/100000 [00:11<37:17:59, 1.34s/trial, best loss: -0.9703557312252965]2024-10-21 14:56:26,750 - build_posterior_wrapper took 0.024029 seconds\n", + "2024-10-21 14:56:26,752 - TPE using 11/11 trials with best loss -0.970356\n", + " 0%| | 12/100000 [00:13<41:09:11, 1.48s/trial, best loss: -0.9703557312252965]2024-10-21 14:56:28,551 - build_posterior_wrapper took 0.024999 seconds\n", + "2024-10-21 14:56:28,553 - TPE using 12/12 trials with best loss -0.970356\n", + " 0%| | 13/100000 [00:14<42:03:20, 1.51s/trial, best loss: -0.9703557312252965]2024-10-21 14:56:30,134 - build_posterior_wrapper took 0.020999 seconds\n", + "2024-10-21 14:56:30,137 - TPE using 13/13 trials with best loss -0.970356\n", + " 0%| | 14/100000 [00:16<42:22:52, 1.53s/trial, best loss: -0.9703557312252965]2024-10-21 14:56:31,687 - build_posterior_wrapper took 0.020999 seconds\n", + "2024-10-21 14:56:31,689 - TPE using 14/14 trials with best loss -0.970356\n", + "PipelineObjectiveEvaluate - Unsuccessful pipeline fit during fitness evaluation. Skipping the pipeline. Exception on ((/n_eigen_basis_{'low_rank_approximation': False, 'rank_regularization': 'hard_thresholding', 'window_size': 20};)/n_channel_filtration_{'centroid_metric': 'chebyshev', 'distance': 'euclidean', 'sample_metric': 'minkowski', 'selection_strategy': 'sum'};;/n_quantile_extractor_{'add_global_features': True, 'stride': 5, 'window_size': 20};)/n_logit_{'C': 1.319640495646339, 'penalty': 'l1', 'solver': 'liblinear'}\n", + " 0%| | 15/100000 [00:16<31:23:21, 1.13s/trial, best loss: -0.9703557312252965]2024-10-21 14:56:31,900 - build_posterior_wrapper took 0.020999 seconds\n", + "2024-10-21 14:56:31,901 - TPE using 15/15 trials with best loss -0.970356\n", + " 0%| | 16/100000 [00:17<34:56:30, 1.26s/trial, best loss: -0.9703557312252965]2024-10-21 14:56:33,457 - build_posterior_wrapper took 0.023038 seconds\n", + "2024-10-21 14:56:33,459 - TPE using 16/16 trials with best loss -0.970356\n", + " 0%| | 17/100000 [00:19<37:51:41, 1.36s/trial, best loss: -0.9703557312252965]2024-10-21 14:56:35,068 - build_posterior_wrapper took 0.024730 seconds\n", + "2024-10-21 14:56:35,070 - TPE using 17/17 trials with best loss -0.970356\n", + " 0%| | 18/100000 [00:21<32:49:18, 1.18s/trial, best loss: -0.9703557312252965]\n", + "SimultaneousTuner - Hyperparameters optimization finished\n", + "SimultaneousTuner - Return tuned graph due to the fact that obtained metric 0.970 equal or better than initial (+ 0.05% deviation) 0.956\n", + "SimultaneousTuner - Final graph: {'depth': 3, 'length': 4, 'nodes': [logit, quantile_extractor, channel_filtration, eigen_basis]}\n", + "logit - {'C': 9.823134236772418, 'penalty': 'l2', 'solver': 'liblinear'}\n", + "quantile_extractor - {'add_global_features': False, 'stride': 2, 'window_size': 5}\n", + "channel_filtration - {'centroid_metric': 'chebyshev', 'distance': 'euclidean', 'sample_metric': 'euclidean', 'selection_strategy': 'sum'}\n", + "eigen_basis - {'low_rank_approximation': True, 'rank_regularization': 'hard_thresholding', 'window_size': 10}\n", + "SimultaneousTuner - Final metric: 0.970\n", + "ApiComposer - Hyperparameters tuning finished\n", + "ApiComposer - Model generation finished\n", + "FEDOT logger - Final pipeline was fitted\n", + "FEDOT logger - Final pipeline: {'depth': 3, 'length': 4, 'nodes': [logit, quantile_extractor, channel_filtration, eigen_basis]}\n", + "logit - {'C': 9.823134236772418, 'penalty': 'l2', 'solver': 'liblinear'}\n", + "quantile_extractor - {'add_global_features': False, 'stride': 2, 'window_size': 5}\n", + "channel_filtration - {'centroid_metric': 'chebyshev', 'distance': 'euclidean', 'sample_metric': 'euclidean', 'selection_strategy': 'sum'}\n", + "eigen_basis - {'low_rank_approximation': True, 'rank_regularization': 'hard_thresholding', 'window_size': 10}\n", + "MemoryAnalytics - Memory consumption for finish in main session: current 34.3 MiB, max: 44.1 MiB\n", + "FEDOT logger - Predictions was saved in current directory.\n", + "FEDOT logger - Predictions was saved in current directory.\n" + ] + } + ], + "source": [ + "result_dict = ApiTemplate(api_config=api_config,\n", + " metric_list=metric_names).eval(dataset='ItalyPowerDemand',\n", + " finetune=finetune,\n", + " initial_assumption = node_list_model)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " accuracy f1 precision\n", + "0 0.963 0.963 0.963\n" + ] + } + ], + "source": [ + "print(result_dict['metrics'])" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "# Transform initial row in feature vector. Hard dataset" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [], + "source": [ + "stat_pipeline = pipeline_creator.create_pipeline(node_list_model)\n", + "feature_extractor = pipeline_creator.create_pipeline(['quantile_extractor'])\n", + "feature_matrix = feature_extractor.fit(hard_to_clf_uno_dataset[0])\n", + "initial_ts, transformed_ts = pd.DataFrame(feature_matrix.features.squeeze()),pd.DataFrame(feature_matrix.predict.squeeze())\n", + "transformed_ts['target'] = feature_matrix.target" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ + { + "data": { + "text/plain": " 0 1 2 3 4 5 6 \\\n0 5.966985 38.522001 3.0 -0.001364 0.300924 0.000000 0.989583 \n1 2.726445 5.845252 30.0 0.010189 0.098683 0.022019 0.989583 \n2 0.873085 -0.957327 16.0 0.000105 0.094746 1.850185 0.989583 \n3 0.963595 -0.921340 7.0 0.003412 0.220262 1.687961 0.989583 \n4 0.710418 -0.989141 28.0 0.000204 0.061140 1.739863 0.989583 \n... ... ... ... ... ... ... ... \n8921 2.164995 2.743942 12.0 -0.003806 0.110030 0.000000 0.989583 \n8922 -1.239693 -0.473465 22.0 -0.000775 0.125696 0.000000 0.989583 \n8923 5.932263 36.119172 43.0 0.010063 0.060494 0.294614 0.989583 \n8924 6.315938 43.871301 3.0 0.002808 0.873119 0.000000 0.989583 \n8925 0.645619 -0.675459 16.0 0.010057 -0.061551 1.836893 0.989583 \n\n 7 8 9 ... 19 20 21 \\\n0 0.041667 0.375589 0.828100 ... -4.437500e-09 -0.213951 0.994778 \n1 0.041667 0.801460 4.898707 ... 7.291667e-10 -0.342460 0.994778 \n2 0.343750 0.174343 2.144298 ... 1.041667e-09 -0.713092 0.994778 \n3 0.156250 0.688291 1.923668 ... 3.437500e-09 -0.681677 0.994778 \n4 0.583333 -0.366485 3.064565 ... 1.979166e-10 -0.461327 0.994778 \n... ... ... ... ... ... ... ... \n8921 0.260417 -0.156627 0.572108 ... 4.166668e-10 -0.393694 0.994778 \n8922 0.479167 -0.315068 0.794347 ... 9.791667e-09 0.558378 0.994778 \n8923 0.020833 0.661946 1.267773 ... 1.625000e-09 -0.036827 0.994778 \n8924 0.041667 0.048379 0.333216 ... -4.625929e-18 -0.186165 0.994778 \n8925 0.187500 0.902991 3.320024 ... 3.802083e-09 -0.433040 0.994778 \n\n 22 23 24 25 26 27 target \n0 7.513700 -0.213951 -0.213951 -0.213951 -0.213951 0.328340 6.0 \n1 3.750432 -0.364030 -0.360435 -0.353245 -0.331226 2.910553 4.0 \n2 2.164974 -0.713092 -0.713092 -0.713092 1.137093 1.753821 2.0 \n3 1.915187 -0.681677 -0.681677 -0.681677 1.006285 1.742063 2.0 \n4 2.148468 -0.856751 -0.856751 -0.856751 0.883112 1.832129 2.0 \n... ... ... ... ... ... ... ... \n8921 2.513585 -0.393694 -0.393694 -0.393694 -0.393694 2.513585 5.0 \n8922 0.558378 -1.772245 -1.772245 0.558378 0.558378 0.558378 5.0 \n8923 6.444689 -0.331441 -0.331441 -0.331441 -0.036827 -0.036827 5.0 \n8924 7.870049 -0.186165 -0.186165 -0.186165 -0.186165 -0.186165 3.0 \n8925 2.854033 -0.916433 -0.916433 -0.916433 0.920461 1.524702 7.0 \n\n[8926 rows x 29 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
0123456789...192021222324252627target
05.96698538.5220013.0-0.0013640.3009240.0000000.9895830.0416670.3755890.828100...-4.437500e-09-0.2139510.9947787.513700-0.213951-0.213951-0.213951-0.2139510.3283406.0
12.7264455.84525230.00.0101890.0986830.0220190.9895830.0416670.8014604.898707...7.291667e-10-0.3424600.9947783.750432-0.364030-0.360435-0.353245-0.3312262.9105534.0
20.873085-0.95732716.00.0001050.0947461.8501850.9895830.3437500.1743432.144298...1.041667e-09-0.7130920.9947782.164974-0.713092-0.713092-0.7130921.1370931.7538212.0
30.963595-0.9213407.00.0034120.2202621.6879610.9895830.1562500.6882911.923668...3.437500e-09-0.6816770.9947781.915187-0.681677-0.681677-0.6816771.0062851.7420632.0
40.710418-0.98914128.00.0002040.0611401.7398630.9895830.583333-0.3664853.064565...1.979166e-10-0.4613270.9947782.148468-0.856751-0.856751-0.8567510.8831121.8321292.0
..................................................................
89212.1649952.74394212.0-0.0038060.1100300.0000000.9895830.260417-0.1566270.572108...4.166668e-10-0.3936940.9947782.513585-0.393694-0.393694-0.393694-0.3936942.5135855.0
8922-1.239693-0.47346522.0-0.0007750.1256960.0000000.9895830.479167-0.3150680.794347...9.791667e-090.5583780.9947780.558378-1.772245-1.7722450.5583780.5583780.5583785.0
89235.93226336.11917243.00.0100630.0604940.2946140.9895830.0208330.6619461.267773...1.625000e-09-0.0368270.9947786.444689-0.331441-0.331441-0.331441-0.036827-0.0368275.0
89246.31593843.8713013.00.0028080.8731190.0000000.9895830.0416670.0483790.333216...-4.625929e-18-0.1861650.9947787.870049-0.186165-0.186165-0.186165-0.186165-0.1861653.0
89250.645619-0.67545916.00.010057-0.0615511.8368930.9895830.1875000.9029913.320024...3.802083e-09-0.4330400.9947782.854033-0.916433-0.916433-0.9164330.9204611.5247027.0
\n

8926 rows × 29 columns

\n
" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transformed_ts" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "data": { + "text/plain": " 0 1 2 3 4 5 6 \\\ntarget \n1.0 5.860285 35.044228 2.0 0.010183 0.859622 0.000000 0.989583 \n2.0 0.873085 -0.957327 16.0 0.000105 0.094746 1.850185 0.989583 \n3.0 4.781473 22.104585 5.0 0.002226 0.289339 0.000000 0.989583 \n4.0 2.726445 5.845252 30.0 0.010189 0.098683 0.022019 0.989583 \n5.0 1.309320 -0.292210 21.0 0.001156 0.110845 0.000000 0.989583 \n6.0 5.966985 38.522001 3.0 -0.001364 0.300924 0.000000 0.989583 \n7.0 5.442467 34.217175 5.0 0.002869 0.310618 0.000000 0.989583 \n\n 7 8 9 ... 18 19 20 \\\ntarget ... \n1.0 0.041667 0.321436 0.614287 ... 0.885914 2.479167e-09 -0.196559 \n2.0 0.343750 0.174343 2.144298 ... 0.695167 1.041667e-09 -0.713092 \n3.0 0.083333 -0.038430 0.498850 ... 0.788571 -2.166667e-09 -0.215854 \n4.0 0.041667 0.801460 4.898707 ... 0.591460 7.291667e-10 -0.342460 \n5.0 0.447917 -0.297297 0.776556 ... 0.670729 -1.250000e-09 -0.542402 \n6.0 0.041667 0.375589 0.828100 ... 0.806000 -4.437500e-09 -0.213951 \n7.0 0.020833 0.060477 0.663817 ... 0.806000 1.145833e-09 -0.241564 \n\n 21 22 23 24 25 26 27 \ntarget \n1.0 0.994778 6.413422 -0.196559 -0.196559 -0.196559 -0.196559 0.075523 \n2.0 0.994778 2.164974 -0.713092 -0.713092 -0.713092 1.137093 1.753821 \n3.0 0.994778 5.694767 -0.215854 -0.215854 -0.215854 -0.215854 0.131830 \n4.0 0.994778 3.750432 -0.364030 -0.360435 -0.353245 -0.331226 2.910553 \n5.0 0.994778 1.824445 -0.542402 -0.542402 -0.542402 -0.542402 1.824445 \n6.0 0.994778 7.513700 -0.213951 -0.213951 -0.213951 -0.213951 0.328340 \n7.0 0.994778 7.416788 -0.241564 -0.241564 -0.241564 -0.241564 1.253535 \n\n[7 rows x 28 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
0123456789...18192021222324252627
target
1.05.86028535.0442282.00.0101830.8596220.0000000.9895830.0416670.3214360.614287...0.8859142.479167e-09-0.1965590.9947786.413422-0.196559-0.196559-0.196559-0.1965590.075523
2.00.873085-0.95732716.00.0001050.0947461.8501850.9895830.3437500.1743432.144298...0.6951671.041667e-09-0.7130920.9947782.164974-0.713092-0.713092-0.7130921.1370931.753821
3.04.78147322.1045855.00.0022260.2893390.0000000.9895830.083333-0.0384300.498850...0.788571-2.166667e-09-0.2158540.9947785.694767-0.215854-0.215854-0.215854-0.2158540.131830
4.02.7264455.84525230.00.0101890.0986830.0220190.9895830.0416670.8014604.898707...0.5914607.291667e-10-0.3424600.9947783.750432-0.364030-0.360435-0.353245-0.3312262.910553
5.01.309320-0.29221021.00.0011560.1108450.0000000.9895830.447917-0.2972970.776556...0.670729-1.250000e-09-0.5424020.9947781.824445-0.542402-0.542402-0.542402-0.5424021.824445
6.05.96698538.5220013.0-0.0013640.3009240.0000000.9895830.0416670.3755890.828100...0.806000-4.437500e-09-0.2139510.9947787.513700-0.213951-0.213951-0.213951-0.2139510.328340
7.05.44246734.2171755.00.0028690.3106180.0000000.9895830.0208330.0604770.663817...0.8060001.145833e-09-0.2415640.9947787.416788-0.241564-0.241564-0.241564-0.2415641.253535
\n

7 rows × 28 columns

\n
" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transformed_ts.groupby(by='target').first()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-10-21 14:56:55,596 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\data\\Phoneme\n", + "2024-10-21 14:56:56,087 - Data read successfully from local folder\n", + "2024-10-21 14:56:56,088 - Initialising experiment setup\n", + "2024-10-21 14:56:56,095 - -------------------------------------------------\n", + "2024-10-21 14:56:56,096 - Initialising Industrial Repository\n", + "2024-10-21 14:56:56,097 - -------------------------------------------------\n", + "2024-10-21 14:56:56,097 - Initialising Dask Server\n", + "Creating Dask Server\n", + "2024-10-21 14:56:56,104 - State start\n", + "2024-10-21 14:56:56,114 - Scheduler at: inproc://10.64.4.172/21832/14\n", + "2024-10-21 14:56:56,115 - dashboard at: http://10.64.4.172:58755/status\n", + "2024-10-21 14:56:56,116 - Registering Worker plugin shuffle\n", + "2024-10-21 14:56:56,128 - Start worker at: inproc://10.64.4.172/21832/17\n", + "2024-10-21 14:56:56,129 - Listening to: inproc10.64.4.172\n", + "2024-10-21 14:56:56,130 - Worker name: 0\n", + "2024-10-21 14:56:56,130 - dashboard at: 10.64.4.172:58756\n", + "2024-10-21 14:56:56,131 - Waiting to connect to: inproc://10.64.4.172/21832/14\n", + "2024-10-21 14:56:56,132 - -------------------------------------------------\n", + "2024-10-21 14:56:56,132 - Threads: 8\n", + "2024-10-21 14:56:56,133 - Memory: 31.95 GiB\n", + "2024-10-21 14:56:56,133 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-w9ppu209\n", + "2024-10-21 14:56:56,134 - -------------------------------------------------\n", + "2024-10-21 14:56:56,137 - Register worker \n", + "2024-10-21 14:56:56,139 - Starting worker compute stream, inproc://10.64.4.172/21832/17\n", + "2024-10-21 14:56:56,139 - Starting established connection to inproc://10.64.4.172/21832/18\n", + "2024-10-21 14:56:56,140 - Starting Worker plugin shuffle\n", + "2024-10-21 14:56:56,142 - Registered to: inproc://10.64.4.172/21832/14\n", + "2024-10-21 14:56:56,143 - -------------------------------------------------\n", + "2024-10-21 14:56:56,145 - Starting established connection to inproc://10.64.4.172/21832/14\n", + "2024-10-21 14:56:56,148 - Receive client connection: Client-91de19b7-8fa3-11ef-9548-b42e99a00ea1\n", + "2024-10-21 14:56:56,150 - Starting established connection to inproc://10.64.4.172/21832/19\n", + "2024-10-21 14:56:56,152 - LinK Dask Server - http://10.64.4.172:58755/status\n", + "2024-10-21 14:56:56,152 - -------------------------------------------------\n", + "2024-10-21 14:56:56,153 - Initialising solver\n", + "AssumptionsHandler - Initial pipeline fitting started\n", + "AssumptionsHandler - Initial pipeline was fitted successfully\n", + "AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 5.2 MiB, max: 6.8 MiB\n", + "ApiComposer - Initial pipeline was fitted in 2.2 sec.\n", + "AssumptionsHandler - Preset was changed to fast_train due to fit time estimation for initial model.\n", + "ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 1 min. Set of candidate models: ['xgboost', 'catboost', 'logit', 'dt', 'rf', 'mlp', 'lgbm', 'one_class_svm', 'inception_model', 'nbeats_model', 'tcn_model', 'deepar_model', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'topological_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca', 'topological_extractor'].\n", + "ApiComposer - Pipeline composition started.\n", + "DataSourceSplitter - Hold out validation is applied.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generations: 0%| | 0/10000 [00:00']\n", + "GroupedCondition - Optimisation stopped: Time limit is reached\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generations: 0%| | 0/10000 [03:39']\n", + "IndustrialEvoOptimizer - no improvements for 1 iterations\n", + "IndustrialEvoOptimizer - spent time: 3.7 min\n", + "GPComposer - GP composition finished\n", + "DataSourceSplitter - Hold out validation is applied.\n", + "ApiComposer - Time for pipeline composing was 0:03:39.954146.\n", + "The remaining 2.7 seconds are not enough to tune the hyperparameters.\n", + "ApiComposer - Composed pipeline returned without tuning.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ApiComposer - Model generation finished\n", + "FEDOT logger - Final pipeline was fitted\n", + "FEDOT logger - Final pipeline: {'depth': 2, 'length': 2, 'nodes': [logit, quantile_extractor]}\n", + "logit - {'C': 2.5395989642679324, 'penalty': 'l1', 'solver': 'liblinear'}\n", + "quantile_extractor - {'window_size': 5, 'stride': 3, 'add_global_features': False}\n", + "MemoryAnalytics - Memory consumption for finish in main session: current 68.0 MiB, max: 97.6 MiB\n", + "FEDOT logger - Predictions was saved in current directory.\n", + "FEDOT logger - Predictions was saved in current directory.\n" + ] + } + ], + "source": [ + "result_dict = ApiTemplate(api_config=api_config,\n", + " metric_list=metric_names).eval(dataset='Phoneme',\n", + " finetune=finetune,\n", + " initial_assumption = node_list_model)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " accuracy f1 precision\n", + "0 0.126 0.119 0.069\n" + ] + } + ], + "source": [ + "print(result_dict['metrics'])" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Классификация с помощью частотных преобразований" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "EMG = 'NerveDamage'\n", + "EEG = 'MotorImagery'\n", + "fourier_model = ['fourier_basis', 'quantile_extractor', 'rf']\n", + "wavelet_model = ['wavelet_basis', 'quantile_extractor', 'rf']\n", + "stat_model = ['quantile_extractor', 'rf']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "emg_dataset = pipeline_creator.create_input_data(EMG)\n", + "eeg_dataset = pipeline_creator.create_input_data(EEG)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Fourier Hyperparams" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "fourier_params = {'threshold': {'hyperopt-dist': hp.choice, 'sampling-scope': [list(np.arange(0.75, 0.99, 0.05))]},\n", + " 'low_rank': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(1, 30, 3)]]},\n", + " 'approximation': {'hyperopt-dist': hp.choice, 'sampling-scope': [['smooth', 'exact']]},\n", + " 'output_format': {'hyperopt-dist': hp.choice, 'sampling-scope': [['signal', 'spectrum']]}\n", + " }" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "stat_params = {'window_size': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(5, 50, 5)]]},\n", + " 'stride': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(1, 10, 1)]]},\n", + " 'add_global_features': {'hyperopt-dist': hp.choice, 'sampling-scope': [[True, False]]}}" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "wavelet_params = {'n_components': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [2, 10]},\n", + " 'wavelet': {'hyperopt-dist': hp.choice,\n", + " 'sampling-scope': [['mexh', 'morl', 'db5', 'sym5']]}}\n", + "discrete_wav = DISCRETE_WAVELETS\n", + "cont_wat = CONTINUOUS_WAVELETS" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_mean_sample(emg_dataset[0].features,emg_dataset[0].target)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 61, + "outputs": [], + "source": [ + "threshold = 0.9\n", + "output_format = 'signal'\n", + "approximation = 'smooth'\n", + "low_rank = 10" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 62, + "outputs": [], + "source": [ + "estimator = SPECTRUM_ESTIMATORS['eigen']\n", + "wavelet = 'gaus8'\n", + "n_components = 5\n", + "low_freq = True" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 63, + "outputs": [], + "source": [ + "fourier_node_dict = {'fourier_basis':{'threshold':threshold,\n", + " 'approximation':approximation,\n", + " 'low_rank':low_rank}}" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 64, + "outputs": [], + "source": [ + "wavelet_node_dict = {'wavelet_basis':{'wavelet':wavelet,\n", + " 'n_components':n_components,\n", + " 'low_freq':low_freq}}" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 65, + "outputs": [], + "source": [ + "feature_extractor = pipeline_creator.create_pipeline(fourier_node_dict)\n", + "feature_matrix = feature_extractor.fit(emg_dataset[0])" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 66, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABqAAAAMtCAYAAADwvLD1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzde5wcZZn//W93zyEBknDQJGQJmF1dAeUMP4woi5KHgMGVFV3ZRVEXQdkEN7CCi4vIIisrchKNIIgQBRRQQTkFYpBwCgEC4UwACSRAZgKEzOQ4M314/pipnqrqquq7qqurqiefN6+86Omurr67u7q6+rrquu5cpVKpCAAAAAAAAAAAAIhJPu0BAAAAAAAAAAAAYGQhAQUAAAAAAAAAAIBYkYACAAAAAAAAAABArEhAAQAAAAAAAAAAIFYkoAAAAAAAAAAAABArElAAAAAAAAAAAACIFQkoAAAAAAAAAAAAxKot7QEkoVwu680339SYMWOUy+XSHg4AAKmrVCpat26dJk2apHye81GajWMRAABqcTySHI5FAACoxbFI820RCag333xTkydPTnsYAABkzsqVK7XTTjulPYwRj2MRAAD8cTzSfByLAADgj2OR5tkiElBjxoyRNLghjR07NuXRAACQvt7eXk2ePLn6HYnm4lgEAIBaHI8kh2MRAABqcSzSfFtEAsoqLx87diwHWgAA2NCCJRkciwAA4I/jkebjWAQAAH8cizQPjQ0BAAAAAAAAAAAQKxJQAAAAAAAAAAAAiBUJKAAAAAAAAAAAAMRqi5gDCgAw8pXLZfX396c9jExpb29XoVBIexgAAGwROBapxbEIAABodRzj1ero6FA+b1bbRAIKANDy+vv7tXz5cpXL5bSHkjnbbrutJk6cyISaAAA0Ecci/jgWAQAArYpjPG/5fF5TpkxRR0dH3WVJQAEAWlqlUtGqVatUKBQ0efJk4zMwRrpKpaKNGzdq9erVkqQdd9wx5REBADAycSzijWMRAADQyjjG81Yul/Xmm29q1apV2nnnneueZEQCCgDQ0orFojZu3KhJkyZpq622Sns4mTJ69GhJ0urVqzV+/Hha4AAA0AQci/jjWAQAALQqjvH8vfe979Wbb76pYrGo9vb2wGVJ2wEAWlqpVJIko7LfLZF1kDQwMJDySAAAGJk4FgnGsQgAAGhFHOP5s14T6zUKQgIKADAiMK+AN14XAACSwXeuN14XAADQyjiWqRXmNSEBBQAAAAAAAAAAgFiRgAIAAAAAAAAAAECsSEABAAAAAAAAAAAgViSgAABI0Zw5c/S+971Po0aN0oEHHqhHHnkkcPmbbrpJu+66q0aNGqU99thDd9xxR0IjBQAAI819992nT3/605o0aZJyuZxuueWWuve59957te+++6qzs1Pvf//7dc011zR9nAAAADBz3nnn6YADDtCYMWM0fvx4HXXUUVq2bFnd+zUr3kQCCgCAlNxwww069dRT9b3vfU+PP/649tprL02fPl2rV6/2XP6hhx7Sv/zLv+j444/XE088oaOOOkpHHXWUnnnmmYRHDgAARoINGzZor7320pw5c4yWX758uWbMmKFPfOITWrp0qWbPnq2vfe1ruuuuu5o8UgAAAJhYuHChZs6cqYcffljz58/XwMCADjvsMG3YsMH3Ps2MN+UqlUql4bVkXG9vr8aNG6eenh6NHTs27eEAAGK0efNmLV++XFOmTNGoUaNUqVS0aaCUylhGtxeUy+WMlz/wwAN1wAEH6Kc//akkqVwua/LkyTr55JP1X//1XzXLf+ELX9CGDRt02223Va/7yEc+or333luXX36552O4Xx8L343J4vUGgJGr5ru2UpEGNqYzmPatpBDHIna5XE4333yzjjrqKN9lvv3tb+v22293BCOOOeYYrV27VvPmzfO8j9+xiMT3Y5J4rQEAqBX0/egZbypuSmWco9tGh4o32b311lsaP368Fi5cqIMPPthzmbDxpqDjO7e2SKMGACCjNg2UtPtZ6ZyF+9w507VVh9lXa39/v5YsWaIzzjijel0+n9e0adO0aNEiz/ssWrRIp556quO66dOnG7XLAQAACRnYKP1gUjqP/Z03pY6tm7b6RYsWadq0aY7rpk+frtmzZzftMbPujTfe0Le//W3deeed2rhxo97//vfr6quv1v777y9JqlQq+t73vqcrr7xSa9eu1UEHHaTLLrtMH/jAB6rrWLNmjU4++WTdeuutyufzOvroo/XjH/9Y22yzTXWZp556SjNnztSjjz6q9773vTr55JN1+umnJ/58AQDYUm0qbtKB1x+YymMv/tfF2qp9q0j37enpkSRtv/32vss0M95ECz4AAFLw9ttvq1QqacKECY7rJ0yYoK6uLs/7dHV1hVoeAAAgTn7HIr29vdq0KZ0zgtP07rvv6qCDDlJ7e7vuvPNOPffcc7rwwgu13XbbVZc5//zzdemll+ryyy/X4sWLtfXWW2v69OnavHlzdZljjz1Wzz77rObPn6/bbrtN9913n0488cTq7b29vTrssMO0yy67aMmSJfrRj36ks88+W1dccUWizxcAALSWcrms2bNn66CDDtKHP/xh3+WaGW+iAgoAMKKMbi/ouXOmp/bYAABgC9e+1WAlUlqPjcT88Ic/1OTJk3X11VdXr5syZUr1cqVS0SWXXKIzzzxTn/nMZyRJv/rVrzRhwgTdcsstOuaYY/T8889r3rx5evTRR6tVUz/5yU/0qU99ShdccIEmTZqk6667Tv39/frlL3+pjo4OfehDH9LSpUt10UUXORJVAACgeUa3jdbif12c2mNHMXPmTD3zzDN64IEHYh6RORJQAIARJZfLGbfBS9N73vMeFQoFdXd3O67v7u7WxIkTPe8zceLEUMsDAIAU5HJNbYOXJr9jkbFjx2r06GiBkVb2pz/9SdOnT9fnP/95LVy4UH/zN3+jf//3f9cJJ5wgSVq+fLm6urocbQvHjRunAw88UIsWLdIxxxyjRYsWadttt60mnyRp2rRpyufzWrx4sf7pn/5JixYt0sEHH6yOjo7qMtOnT9cPf/hDvfvuu46KK0tfX5/6+vqqf/f29jbjJQAAYIuRy+Uit8FLw6xZs6qV1TvttFPgss2MN9GCDwCAFHR0dGi//fbTggULqteVy2UtWLBAU6dO9bzP1KlTHctL0vz5832XBwAAiBPHIk6vvPJKdT6nu+66SyeddJK++c1vau7cuZJUbVsT1NKmq6tL48ePd9ze1tam7bff3rGM1zrsj+F23nnnady4cdV/kydPbvDZAgCAVlCpVDRr1izdfPPNuueeexzV2X6aeYxHAgoAgJSceuqpuvLKKzV37lw9//zzOumkk7RhwwZ99atflSQdd9xxOuOMM6rL/8d//IfmzZunCy+8UC+88ILOPvtsPfbYY5o1a1ZaTwEAALSw9evXa+nSpVq6dKmkwYqdpUuXasWKFZKkM844Q8cdd1x1+W984xt65ZVXdPrpp+uFF17Qz372M91444065ZRT0hh+6srlsvbdd1/94Ac/0D777KMTTzxRJ5xwgi6//PK0h6YzzjhDPT091X8rV65Me0gAACABM2fO1LXXXqvrr79eY8aMUVdXl7q6uhzzdSYZbyIBBQBASr7whS/oggsu0FlnnaW9995bS5cu1bx586pntK5YsUKrVq2qLv/Rj35U119/va644grttdde+t3vfqdbbrklcCJJAAAAP4899pj22Wcf7bPPPpIGT47ZZ599dNZZZ0mSVq1aVU1GSYPzG91+++2aP3++9tprL1144YX6xS9+oenT05l/M2077rijdt99d8d1u+22W/U1s9rWBLW0mThxolavXu24vVgsas2aNY5lvNZhfwy3zs5OjR071vEPAACMfJdddpl6enp0yCGHaMcdd6z+u+GGG6rLJBlvyv4kGQAAjGCzZs3yPaPk3nvvrbnu85//vD7/+c83eVQAAGBLcMghh6hSqfjefs0113je54knnmjiqFrHQQcdpGXLljmue/HFF7XLLrtIGkzYTZw4UQsWLNDee+8taXAupsWLF+ukk06SNNjyZu3atVqyZIn2228/SdI999yjcrmsAw88sLrMf//3f2tgYEDt7e2SBtvifPCDH/Sc/wkAAGy5go7tLEnGm6iAAgAAAAAACOmUU07Rww8/rB/84Ad6+eWXq2cOz5w5U9LgZOWzZ8/Wueeeqz/96U96+umnddxxx2nSpEk66qijJA1WTB1++OE64YQT9Mgjj+jBBx/UrFmzdMwxx2jSpEmSpH/9139VR0eHjj/+eD377LO64YYb9OMf/1innnpqWk8dAADACBVQAAAAAAAAIR1wwAG6+eabdcYZZ+icc87RlClTdMkll+jYY4+tLnP66adrw4YNOvHEE7V27Vp97GMf07x58zRq1KjqMtddd51mzZqlQw89VPl8XkcffbQuvfTS6u3jxo3T3XffrZkzZ2q//fbTe97zHp111lk68cQTE32+AAAAYZGAAgAAAAAAiODII4/UkUce6Xt7LpfTOeeco3POOcd3me23317XX3994OPsueeeuv/++yOPEwAAIA204AMAAAAAAAAAAECsSEABAEYEk0kWt0TlcjntIQAAsEXgWMQbxyIAAKCVcYxXK8xr0tQWfKVSSWeffbauvfZadXV1adKkSfrKV76iM888U7lcTtLgYL/3ve/pyiuv1Nq1a3XQQQfpsssu0wc+8IHqetasWaOTTz5Zt956a7Uf8o9//GNts802zRw+AKAFtLe3K5fL6a233tJ73/ve6vfLlq5Sqai/v19vvfWW8vm8Ojo60h4SAAAjEsci3jgWAQAArYxjPG+VSkVvvfWWcrmc2tvb6y7f1ATUD3/4Q1122WWaO3euPvShD+mxxx7TV7/6VY0bN07f/OY3JUnnn3++Lr30Us2dO1dTpkzRd7/7XU2fPl3PPfdcdVLOY489VqtWrdL8+fM1MDCgr371qzrxxBPr9kgGAIx8hUJBO+20k15//XW9+uqraQ8nc7baaivtvPPOyucpegYAoBk4FgnGsQgAAGhFHOP5y+Vy2mmnnVQoFOovW2liDdmRRx6pCRMm6Kqrrqped/TRR2v06NG69tprValUNGnSJP3nf/6nvvWtb0mSenp6NGHCBF1zzTU65phj9Pzzz2v33XfXo48+qv3331+SNG/ePH3qU5/S66+/rkmTJtUdR29vr8aNG6eenh6NHTu2OU8WAJCqUqmkgYGBtIeRKYVCQW1tbZ5n6fDdmCxebwAY+TgWqRV0LCLx/ZgkXmsAAGqZfD9yjFervb3dKPkkNbkC6qMf/aiuuOIKvfjii/r7v/97Pfnkk3rggQd00UUXSZKWL1+urq4uTZs2rXqfcePG6cADD9SiRYt0zDHHaNGiRdp2222rySdJmjZtmvL5vBYvXqx/+qd/qnncvr4+9fX1Vf/u7e2N9Xl9/7bntHTlWv3HoR/QwX//3ljXDQCIplAoGH/5AS3vnnOl5fdJU2dJu/9j2qMBAIhjEWxZrnr6Ki18faE+9/ef0z/+HcciAICRi2O8xjS1Bv6//uu/dMwxx2jXXXdVe3u79tlnH82ePVvHHnusJKmrq0uSNGHCBMf9JkyYUL2tq6tL48ePd9ze1tam7bffvrqM23nnnadx48ZV/02ePDnW5/Vi9zotee1dvb2+r/7CAAAAcXv7RWnlYml9d9ojAQAAW6CV61bqidVPqHsDxyIAAMBfUxNQN954o6677jpdf/31evzxxzV37lxdcMEFmjt3bjMfVmeccYZ6enqq/1auXBnr+gv5wfYB5aY1LwQAAAgy1MqoeZ2UAQAA6qqIYxEAAOCvqS34TjvttGoVlCTtscceeu2113Teeefpy1/+siZOnChJ6u7u1o477li9X3d3t/bee29J0sSJE7V69WrHeovFotasWVO9v1tnZ6c6Ozub8IwG5Yf6V5fJQAEAgDRU59LgWAQAAKSnidOKAwCAEaCpFVAbN25UPu98iEKhoHK5LEmaMmWKJk6cqAULFlRv7+3t1eLFizV16lRJ0tSpU7V27VotWbKkusw999yjcrmsAw88sJnD91VNQHGgBQAAUkEFFAAASE+uejIMAACAv6ZWQH3605/W//7v/2rnnXfWhz70IT3xxBO66KKL9G//9m+SBg9YZs+erXPPPVcf+MAHNGXKFH33u9/VpEmTdNRRR0mSdtttNx1++OE64YQTdPnll2tgYECzZs3SMccco0mTJjVz+L6GOvDRgg8AAKSDCigAAJABtOADAABBmpqA+slPfqLvfve7+vd//3etXr1akyZN0te//nWdddZZ1WVOP/10bdiwQSeeeKLWrl2rj33sY5o3b55GjRpVXea6667TrFmzdOihhyqfz+voo4/WpZde2syhB7IqoEqcdQwAAFJBBRQAAEhPTlRAAQCA+pqagBozZowuueQSXXLJJb7L5HI5nXPOOTrnnHN8l9l+++11/fXXN2GE0RSGSqDodQwAAFJBBRQAAMgAKqAAAECQps4BNVJZMZ8SPfgAAEAqqIACAADpoQIKAACYIAEVgVUBRf4JAACkggooAACQBRyKAACAACSgIrDmgKIFHwAASAcVUAAAID25HBVQAACgPhJQEdCCDwAApIoKKAAAkAHMAQUAAIKQgIqgkKMFHwAASBNnHQMAgPSRgAIAAEFIQEWQryagONACAAAp4lgEAACkIMfJMAAAwAAJqAjy+aEEFCVQAAAgDbTgAwAAGcDc2AAAIAgJqAiG8k+04AMAIEXnnXeeDjjgAI0ZM0bjx4/XUUcdpWXLljmW2bx5s2bOnKkddthB22yzjY4++mh1d3c7llmxYoVmzJihrbbaSuPHj9dpp52mYrHoWObee+/Vvvvuq87OTr3//e/XNddc0+ynV8fQwQhBHwAAkIJcjgooAABQHwmoCKwWfCWCPgAApGbhwoWaOXOmHn74Yc2fP18DAwM67LDDtGHDhuoyp5xyim699VbddNNNWrhwod5880199rOfrd5eKpU0Y8YM9ff366GHHtLcuXN1zTXX6Kyzzqous3z5cs2YMUOf+MQntHTpUs2ePVtf+9rXdNdddyX6fB2qMR+ORQAAQHqYAwoAAARpS3sAragwVAJFqTkAAOmZN2+e4+9rrrlG48eP15IlS3TwwQerp6dHV111la6//np98pOflCRdffXV2m233fTwww/rIx/5iO6++24999xz+vOf/6wJEyZo77331ve//319+9vf1tlnn62Ojg5dfvnlmjJlii688EJJ0m677aYHHnhAF198saZPn+45tr6+PvX19VX/7u3tjfnZUwEFAADSwxxQAADABBVQEViV5iV68AEAkBk9PT2SpO23316StGTJEg0MDGjatGnVZXbddVftvPPOWrRokSRp0aJF2mOPPTRhwoTqMtOnT1dvb6+effbZ6jL2dVjLWOvwct5552ncuHHVf5MnT47nSVqYAwoAAGQAJ+YCAIAgJKAiKAwFfcg/AQCQDeVyWbNnz9ZBBx2kD3/4w5Kkrq4udXR0aNttt3UsO2HCBHV1dVWXsSefrNut24KW6e3t1aZNmzzHc8YZZ6inp6f6b+XKlQ0/RyerAirm1QIAABhgDigAAGCCFnwR5GnBBwBApsycOVPPPPOMHnjggbSHIknq7OxUZ2dn8x6ACigAAAAAAJBxVEBFQAs+AACyY9asWbrtttv0l7/8RTvttFP1+okTJ6q/v19r1651LN/d3a2JEydWl+nu7q653botaJmxY8dq9OjRcT8dQ8wBBQAA0mPNAVXhZBgAABCABFQEtOADACB9lUpFs2bN0s0336x77rlHU6ZMcdy+3377qb29XQsWLKhet2zZMq1YsUJTp06VJE2dOlVPP/20Vq9eXV1m/vz5Gjt2rHbffffqMvZ1WMtY60gFFVAAAAAAACDjaMEXQb6agCLoAwBAWmbOnKnrr79ef/zjHzVmzJjqnE3jxo3T6NGjNW7cOB1//PE69dRTtf3222vs2LE6+eSTNXXqVH3kIx+RJB122GHafffd9aUvfUnnn3++urq6dOaZZ2rmzJnVFnrf+MY39NOf/lSnn366/u3f/k333HOPbrzxRt1+++2pPXcqoAAAQBYwNQEAAAhCBVQE1hxQJKAAAEjPZZddpp6eHh1yyCHacccdq/9uuOGG6jIXX3yxjjzySB199NE6+OCDNXHiRP3hD3+o3l4oFHTbbbepUCho6tSp+uIXv6jjjjtO55xzTnWZKVOm6Pbbb9f8+fO111576cILL9QvfvELTZ8+PdHn60AFFAAASFGueiwCAADgjwqoCIbyTySgAABIkckZt6NGjdKcOXM0Z84c32V22WUX3XHHHYHrOeSQQ/TEE0+EHmPzUAEFAADSxxxQAAAgCBVQEVgt+ErllAcCAAC2TFRAAQCAFOVEBRQAAKiPBFQEhaESKHodAwCAdFABBQAA0kcFFAAACEICKgLrpONSmQMtAACQAuZdAAAAKaICCgAAmGAOqAgKQ0Ef8k8AgCQUS2VtLpbVls9pVHsh7eEgUzgYAQAAKeJQBAAABKACKgJrDiha8AEAkvDAy2/rw9+7S5+7/KG0h4LMoAUfAABIT86Ki5CBAgAAAUhARVBtwUfQBwCQgPLQ902etmuwVLcFjkUAAEDyaMEHAABMkICKoJCnBR8AIDnl8uD/SUBhGBVQAAAgfXSGAQAAQUhARWAFAMtkoAAACShVK6BSHgiygwooAACQJo5LAQCAARJQEeSrFVAEfQAAzWedWVogAwU3jkUAAECKmAMKAAAEIQEVgRX/IwEFAEhCaagFX44WfLBQAQUAAFLEHFAAAMAECagIrBZ8VkAQAIBmsk54KJCAQhVzQAEAgPRRAQUAAIKQgIrACgAy2SYAIAlWAirPtzYsJCMBAECKqIACAAAmCGVFYMV8SiSgAAAJKJWHElAkHVBFBRQAAEgfJ+YCAIAgJKAisCaBL3OcBQBIgPV9Y33/AMwBBQAA0sTcpAAAwAQJqAjytOADACSoTAUUalABBQAAAAAAso0EVATVFnyUQAEAElCdA4oEFCxUQAEAgBRZc0BVOBYBAAABSEBFMNyCjwMtAEDzlaoJqJQHggyhAgoAAAAAAGQbCagIrDPQy+WUBwIA2CIwBxRqUAEFAAAygKkJAABAEBJQEVQTUBxoAQASwBxQqMW2AAAA0pPjuBQAABggARWBdQI6CSgAQBKqc0BRAQU3jkUAAECKmAMKAAAEIQEVgXUGeonjLABAAkpl5oCCCy34AABAinJUYwMAAAMkoCKw5uCg1zEAIAlWBVSBVieoGtoWOBYBAAApIi4CAACCkICKwIr/WWekAwDQTNbXDb32UUUFFAAASBHHpQAAwAQJqAisCijyTwCAJFgnPBT41kYVFVAAACB9zAEFAACCEMqKwJoDilJzAEASrO+bApNAwUIFFAAASBFzQAEAABMkoCKgBR8AIEml8uD/aXWCYVRAAQCA9JCAAgAAJkhARWBNAl8m6AMASID1fVMgAQVLdVPgWAQAAKSHzjAAACAICagI8swBBQBIkJWAogMfhlEBBQAAUsRxKQAAMEACKoI8FVAAgARVE1BkoGBhDigAAJABFY5FAABAABJQEVjxPxJQAIAkWHNA5WnBhyqrAirdUQAAgC0Tc0ABAAATJKAiqFZAlVMeCABgi1CdA4oKKFiogAIAABlABRQAAAhCAiqCQp4WfACA5JSHJh2kAArDmAMKAACkhwooAABgggRUBFYAsFQm6AMAaD7r64YWfKiiAgoAAGRAhZNhAABAABJQEQxXQKU8EADAFqHago8EFKrYFgAAQHpyHJcCAAADJKAisM5A50wfAEASrO8bpoBCDY5FAABACmjBBwAATJCAisAKAJYI+gAAEmBV3HKmKapowQcAADKgwrEIAAAIQAIqAqsCqkwPPgBAAsrVCigSULAMbQucDAMAAFLAiVEAAMAECagIqgkoYj4AgARY3ze04EMVFVAAACADmJoAAAAEIQEVQSFvJaA40AIANF91DigyUKiiAgoAAAAAAGQbCagIrJOOSUABAJJgfd/Q6QRVVEABAIAMYA4oAAAQhARUBMNzQKU8EADAFmG4BR8ZKFiogAIAAOnJieNSAABQHwmoCGjBBwBIkvV9Qwc+VFEBBQAAMoA5oAAAQBASUBFYMZ8SB1oAgARUqIBCDSqgAABAenJDx6W04AMAAEFIQEVQsA60KpztAwBovuE5oEhAYQgVUAAAIEW04AMAACZIQEVgPwOd/BMAoNmG54BKdxzIIA5EAAAAAABARpGAisCegKINHwCg2YbngCIDhSFUQAEAgBRRAQUAAEyQgIogb3vVyiSgAABNVqkmoFIeCDKEOaAAAED6mJYAAAAEIQEVgf0M9HI5xYEAALYI1ncNc0Chim0BAACkiONSAABgggRUBAXbKehUQAEAmo0WfKhFBRQAAEhfhXbAAAAgAAmoCOzxPxJQAIBmKw991dCCD1UkIwEAAAAAQMaRgIqAFnwAgCRVqICCL06EAQAkYOWj0rkTpJ9NTXskyBgqoAAAQBASUBEUcrTgAwAkx/quIf+EYbTgAwAkqFKSipsH/wGScuLAFAAA1EcCKgJ7ALBE4AcA0GTDLfj4oY8h1W2B4xAAQJI4FoFThZgIAAAIQAIqglwuV52HgwooAECzWd81eb61UUUAEACQIH73wiU3dDIMLfgAAEAQQlkRWWehcxwOAGi2ChVQ8MOBCAAgEUPfNxyLYAgt+AAAgAkSUBFZQcBSmcAPAKC5qLZFDVrwAQBSQdIBLhyKAACAACSgIrLaIBEUBAA0W7UFH2cdo4ptAQCQIH73wiXHcSkAADBAAioiKwhYLqc8EADAiEcLPvgiIAgASAQt+OCNOaAAAEAQElARFawEFIEfAECTDSeg0h0HMoQWfACAVHAwAgAAAHMkoCKy4j4koAAAzWZ919DqBMPYFgAACeJ3L3xQAQUAAIKQgIoon6cCCgCQjOE5oFIeCLKH4xAAQCJowQenHCfDAAAAAySgIhpuwZfyQAAAI16ZOaDgRgs+AEAqOBaBU4WTYQAAQAASUBFZbZBKZKAAAE1m/bDP862NKgKAAIAEkWSAixUToQUfAAAIQigrosLQK0cLPgBAs5WrXW9IOsCF4xAAQCJowQcnWvABAAATJKAistogEfcBADTb8BxQ/NDHEFrwAQBSwbEIAAAAzJGAiihPCz4AQEKG54BKdxzIEjYGAECCOPMSLlRAAQAAEySgIsrTgg8AkJAKFVDww3EIACARtOCDtwrHIgAAIAAJqIisICAJKABAs1nfNcR8UEULPgBAKjgYwSDmJgUAACZIQEVUqCagUh4IAGDEG27Bxw99AACQAk68hI8KJ8MAAIAAJKAismKAZTJQAIAmK9OCD34ICAIAEmG14Et3FAAAAGgtJKAisoKAJQI/AIAmq1QroNIdBzKEZCQAIBV8/8CJOaAAAEAQElARFYaigBxrAQCabXgOKII+sLAtAAASxO9euFjHpbTgAwAAQUhARWQdbJVowQcAaLLhFnwpDwTZw5kwAIBEWC34OBjBoBwnwwAAAAMkoCIqDL1yZQI/AIAmK5cH/88cUKiqbgschwAAksSxCJyogAIAAEFIQEVkBQHJPwEAmq1SrYAi6AML2wIAIEH88IULFVAAAMAECaiIaMEHAEhKma438ENAEACQCA5G4INDEQAAEIAEVESFoeNuWvABAJqtTAUU3GjBBwBIBcciGJTjuBQAABggARWRFQQkAQUAaDarAirPtzaqCPoAABLE7174YA4oAAAQhFBWRPm8lYBKeSAAgBGPOaDgi4AgACARtOCDE3NAAQAAEySgIsrTgg8AkJDhFnwpDwTZQQs+AEAqOBiBExVQAAAgCAmoiKyz0EuUQAEAmqxcPemYoA8sbAsAgARx4iV8VNg2AABAABJQERWGTkPnWAsA0GxlWvDBDwciAIBE0IIPTpwYBQAATJCAiihHBRQAICFWjoGf+aiiBR8AIBUcjcCJFnwAACAICaiICswBBQBICBVQqMW2AABIEL974ZLjWAQAABggARWRFQTkOBwA0GwVut7ADwciAIBEcDACb1RAAQCAICSgIqq24CPwAwBosmoFVJ6gD4bQgg8AkAqORTCIOaAAAIAJElARFYZeOVrwAQCazfqqIf+EYWwMAIAE8bsXftg0AABAABJQEVkt+MpljrYAAM3FHFDwRUAQAJAIWvDBiTmgAACACRJQEVltkMg/AQCazUpAEfNBFS34AACp4GAETswBBQAAgpCAiqhaAcWZxwCAJitXW/AR9IGFbQEAkCB+9wIAACACElARWfNwlCiBAgA0UcUW8CEBhRoEBAEAiaAFH7xVOBYBAAABSEBFVBg68OZYCwDQTPbzHPLEfGChBR8AIBUcjGBQzoqJcCwCAAACkICKyDrYKpGBAgA0kb3Va46zjlHFtgAASBC/e+GS41gEAAAYaHoC6o033tAXv/hF7bDDDho9erT22GMPPfbYY9XbK5WKzjrrLO24444aPXq0pk2bppdeesmxjjVr1ujYY4/V2LFjte222+r444/X+vXrmz30QIWhV445oAAAzVR2tOBLcSDIJo5DAACJoAUfvFEBBQAAgjQ1AfXuu+/qoIMOUnt7u+68804999xzuvDCC7XddttVlzn//PN16aWX6vLLL9fixYu19dZba/r06dq8eXN1mWOPPVbPPvus5s+fr9tuu0333XefTjzxxGYOva48LfgAAAmoOFrwEfTBkOqmwIEIAABIHhVQAADARFszV/7DH/5QkydP1tVXX129bsqUKdXLlUpFl1xyic4880x95jOfkST96le/0oQJE3TLLbfomGOO0fPPP6958+bp0Ucf1f777y9J+slPfqJPfepTuuCCCzRp0qRmPgVf1RZ8ZQI/AIDmcVZA8UMfFrYFAECCOPMSftg0AABAgKZWQP3pT3/S/vvvr89//vMaP3689tlnH1155ZXV25cvX66uri5Nmzatet24ceN04IEHatGiRZKkRYsWadttt60mnyRp2rRpyufzWrx4sefj9vX1qbe31/EvbrTgAwAkwX6eA/knp/vuu0+f/vSnNWnSJOVyOd1yyy2O27/yla8ol8s5/h1++OGOZUza/D711FP6+Mc/rlGjRmny5Mk6//zzm/3UzHEYAgBIEgcjGMLcpAAAwERTE1CvvPKKLrvsMn3gAx/QXXfdpZNOOknf/OY3NXfuXElSV1eXJGnChAmO+02YMKF6W1dXl8aPH++4va2tTdtvv311GbfzzjtP48aNq/6bPHly3E+tehZ6mQooAEATUQHlb8OGDdprr700Z84c32UOP/xwrVq1qvrvN7/5jeP2em1+e3t7ddhhh2mXXXbRkiVL9KMf/Uhnn322rrjiiqY9LyPVbYHjEABAkjgWgRNzQAEAgCBNbcFXLpe1//776wc/+IEkaZ999tEzzzyjyy+/XF/+8peb9rhnnHGGTj311Orfvb29sSehqgkojrUAAE1UKQ9fzhPzcTjiiCN0xBFHBC7T2dmpiRMnet5m0ub3uuuuU39/v375y1+qo6NDH/rQh7R06VJddNFFKc9HycYAAEgQnT/gwhxQAADARFMroHbccUftvvvujut22203rVixQpKqAaHu7m7HMt3d3dXbJk6cqNWrVztuLxaLWrNmjW9AqbOzU2PHjnX8i9twAooDcQBA81AB1Zh7771X48eP1wc/+EGddNJJeuedd6q3mbT5XbRokQ4++GB1dHRUl5k+fbqWLVumd9991/dxk2gHLImAIAAgIUPfNxyLwIUKKAAAEKSpCaiDDjpIy5Ytc1z34osvapdddpEkTZkyRRMnTtSCBQuqt/f29mrx4sWaOnWqJGnq1Klau3atlixZUl3mnnvuUblc1oEHHtjM4QeyzkIvEfgBADSRPQFFzCecww8/XL/61a+0YMEC/fCHP9TChQt1xBFHqFQqSTJr89vV1eXZKti6zU/T2wHTgg8AkAoORjBkaFOoEBMBAAABmtqC75RTTtFHP/pR/eAHP9A///M/65FHHtEVV1xRnTchl8tp9uzZOvfcc/WBD3xAU6ZM0Xe/+11NmjRJRx11lKTBiqnDDz9cJ5xwgi6//HINDAxo1qxZOuaYYzRp0qRmDj9QYSgDxbEWAKCZyrYTjpnsOZxjjjmmenmPPfbQnnvuqb/7u7/Tvffeq0MPPbSpj938dsBsCwCABPHDFy604AMAJKm3v1cLVy5UR6FD0983Pe3hIISmJqAOOOAA3XzzzTrjjDN0zjnnaMqUKbrkkkt07LHHVpc5/fTTtWHDBp144olau3atPvaxj2nevHkaNWpUdZnrrrtOs2bN0qGHHqp8Pq+jjz5al156aTOHXpcVBCwxCRQAoImss0ppv9e4v/3bv9V73vMevfzyyzr00EON2vxOnDjRs1WwdZufzs5OdXZ2xvwMPBAQBAAkghZ88EYLPgBAElZvWK3vPPAdbT9qexJQLaapCShJOvLII3XkkUf63p7L5XTOOefonHPO8V1m++231/XXX9+M4UVWGGpeyBxQAIBmss5zyBPvadjrr7+ud955RzvuuKMkZ5vf/fbbT1Jtm9+pU6fqv//7vzUwMKD29nZJ0vz58/XBD35Q2223XTpPRKIFHwAgJRyQYBAVUAAAwERT54Aayawz0ck/AQCayTrRgR/5tdavX6+lS5dq6dKlkqTly5dr6dKlWrFihdavX6/TTjtNDz/8sF599VUtWLBAn/nMZ/T+979f06cPni1lb/P7yCOP6MEHH6xp8/uv//qv6ujo0PHHH69nn31WN9xwg3784x872uulg+0BAJAgfvjCB3NAAQCSQMVt6yIBFREt+AAASbC+ZwqUQNV47LHHtM8++2ifffaRJJ166qnaZ599dNZZZ6lQKOipp57SP/7jP+rv//7vdfzxx2u//fbT/fff72iNd91112nXXXfVoYceqk996lP62Mc+Vp2rUpLGjRunu+++W8uXL9d+++2n//zP/9RZZ52lE088MfHn64mgDwAgEbTggxNzkwIAABNNb8E3UhWGDrZowQcAaCYrAdVGAqrGIYccEnjW7V133VV3HSZtfvfcc0/df//9ocfXVLTgAwCkguMRAAAAmKMCKiIrDkgCCgDQTEWrAqpAwAd2bA8AgATxuxcAAKSIFnytiwRURPmhDFS5nPJAAAAjGhVQCERAEACQCFrwwYn5SQEAaeD7p/WQgIooTws+AEACikNnOjAHFBxowQcASAXHI3DijHQAQBKC2u8j20hARWTFAUts/ACAJhqugOIrG3YEAAEACeJ3L1w4Ax0AkIYc1dgth2hWRNaZ6ByHAwCaqToHFBVQ8MKBCAAgEbTggzfOSAcAAEFIQEVkZVutM9MBAGgG5oCCJ1rwAQBSwfEIhrApAAAAAySgIioMHWwxBxQAoJmKJSqg4IXtAQCQIH73wgdzQAEAkkQL2NZDAiqiPC34AAAJKNGCD0E4EAEAJIIWfHAiAAgAAEyQgIqIFnwAgCQUy2VJUluBH/mwoQUfACAVHI8AAIDkUXHbukhARVQYCvzQgg8A0EzDFVB8ZcOOACAAIEH87oUPAoIAgCRRgdt6iGZFlGcOKABAAopDCag2WvDBC8chAIBE0ILPxP/93/8pl8tp9uzZ1es2b96smTNnaocddtA222yjo48+Wt3d3Y77rVixQjNmzNBWW22l8ePH67TTTlOxWHQsc++992rfffdVZ2en3v/+9+uaa65J4Bn5IwAIAABMkICKyJoDig58AIBmYg4oeKIFHwAAmfLoo4/q5z//ufbcc0/H9aeccopuvfVW3XTTTVq4cKHefPNNffazn63eXiqVNGPGDPX39+uhhx7S3Llzdc011+iss86qLrN8+XLNmDFDn/jEJ7R06VLNnj1bX/va13TXXXcl9vx8cSgCAEhAhZMvWxYJqIjytOADACSACigAAJA6fvcGWr9+vY499lhdeeWV2m677arX9/T06KqrrtJFF12kT37yk9pvv/109dVX66GHHtLDDz8sSbr77rv13HPP6dprr9Xee++tI444Qt///vc1Z84c9ff3S5Iuv/xyTZkyRRdeeKF22203zZo1S5/73Od08cUXp/J8peF5sQEASBRfPy2HBFREVhywRAkUAKCJSuWyJCqg4Da0PXAYAgBIBC34gsycOVMzZszQtGnTHNcvWbJEAwMDjut33XVX7bzzzlq0aJEkadGiRdpjjz00YcKE6jLTp09Xb2+vnn322eoy7nVPnz69ug4vfX196u3tdfxrBuaAAgAkge+b1tWW9gBalRUI5EQwAEAzFUtUQMFDdXPgQAQAkCSOR9x++9vf6vHHH9ejjz5ac1tXV5c6Ojq07bbbOq6fMGGCurq6qsvYk0/W7dZtQcv09vZq06ZNGj16dM1jn3feefqf//mfyM+rHuaAAgCkge+f1kMFVERWuTkVUACAZmIOKAAAkDrOvPS0cuVK/cd//Ieuu+46jRo1Ku3hOJxxxhnq6emp/lu5cmVTHoc5OQAAQBASUBG1DwUCi0OtkQAAaIbhOaD4yoad1YKPoA8AIEG04HNYsmSJVq9erX333VdtbW1qa2vTwoULdemll6qtrU0TJkxQf3+/1q5d67hfd3e3Jk6cKEmaOHGiuru7a263bgtaZuzYsZ7VT5LU2dmpsWPHOv7FiTmgAABJogVf6yKaFVFbYfClGyix8QMAmqdaAVXgRz5sqkEfjkMAAEnieMTu0EMP1dNPP62lS5dW/+2///469thjq5fb29u1YMGC6n2WLVumFStWaOrUqZKkqVOn6umnn9bq1aury8yfP19jx47V7rvvXl3Gvg5rGWsdaSIgCABIEidAtB7mgIqorUAFFACg+awKqAIHWQAAIC1U3HoaM2aMPvzhDzuu23rrrbXDDjtUrz/++ON16qmnavvtt9fYsWN18skna+rUqfrIRz4iSTrssMO0++6760tf+pLOP/98dXV16cwzz9TMmTPV2dkpSfrGN76hn/70pzr99NP1b//2b7rnnnt044036vbbb0/2CQMAAIREAiqi9qFWSEUqoAAATVQaOtGhjTmg4EALPgBAkoa+bzghJrSLL75Y+XxeRx99tPr6+jR9+nT97Gc/q95eKBR022236aSTTtLUqVO19dZb68tf/rLOOeec6jJTpkzR7bffrlNOOUU//vGPtdNOO+kXv/iFpk+fnsZTAgAgefz0bVkkoCKyKqAGSlRAAQCax/qaKZCAgh0t+AAAqeB4pJ57773X8feoUaM0Z84czZkzx/c+u+yyi+64447A9R5yyCF64okn4hhirGjBBwBIUo5jkZbDHFARtVdb8HGwBQBonmoFFHNAAQCAtFBxCxcCgAAAwAQJqIjaaMEHAEhAdQ4oKqDgQAs+AECSaMEHbxWORQAACaDitnWRgIqIFnwAgCSUrAQUAR/Y0YIPAJAKjkcwKMexKQAgBVTgth4SUBG1F4YqoGjBBwBoomoCKs9XNgAASAlVLgAAIEVU3LYuolkRteWpgAIANJ+VgGIOKDjRgg8AkCRa8MGJM9ABAGmgArf1kICKqFoBxRxQAIAmYg4oeKIFHwAgFRyPwIk5OQAAQBASUBFZZ6IXy1RAAQCahzmgAABA6qi4BQAAKeKEh9ZFAiqitqG5OAZKFXpQAgCapkQFFDzRgg8AkCRa8MGJFnwAAMAECaiI2m1zcVjBQQAA4ma14GsjAQU7WvABAFLB8QicOCEXAAAEIQEVUVth+KUrkoACADRJaajVa6FAwAcAAKSEJAPcODQFACSIFnytiwRURPYz0QdKzAMFAGiOInNAwRMt+AAASaIFH7wREAQAJIkWsK2HBFRE9gRUscQBFwCgOZgDCp5owQcASAXHIxhEABAAkCRavrYuElAR2QOBA2UqoAAAzVFiDigAAJA2gj7wQQUUAAAIQgIqolwup/ah+TiogAIANEu1AqrAVzbsaMEHAEgSLfjglGNbAACkgO+f1kM0qwFt+cGXjwQUAKBZmAMKnmjBBwBIBccjcKIlEgAACEICqgFtQxVQtOADADQLLfgAAEDqSDIAAIAMYA7C1kMCqgHtBSqgAADNVW3BRwIKDlYLvnRHAQDYUlgt+NIdBbKDACAAADBBAqoB1tnoAyUqoAAAzVGtgCrwIx82tOADAKSC4xEAAJC8Cr99WxYJqAZUK6DKfAAAAM1RHGrzmmcOKAAAkBZa8MGFCigAQBpyxEZaDgmoBlhnoxepgAIANAlzQCEQAUEAQCKsFnwcj8CpwrEIAAAIQAKqAVYw8J0N/Xqxe13KowEAjCRrNvTr5dXrmQMK3mjBBwBIBccjGMQZ6ACAJHHCQ+tqS3sArcxqwff1Xy+RJP1x5kHaa/K2KY4IADBSfOQHC9RfKmv7rTskMQcUAABIETEf+GBODgBAkmgB23qogGqAOxh442MrUxoJAGAk2TxQUv9Qe9c1G/olMQcU3Ia2B84CAwAkghZ8AAAgPZzw0LpIQDWgLe98+d5YuymlkQAARpLX3639PnF/52ALRws+AEAqSEDBiYAgAAAIQjSrAe2uCqiBobPVAQBoxOaBUs11zAEFAABSQ8UtAAAAIiAB1QD32egDJQ7KAQCN6yvWJqCYAwpOtOADACSJFnxwsubgYFJ4AEAS+L5pXSSgGuAOBhapgAIAxGDzQO33CRVQcKAFHwAgFRyPAACA9OQ4GablkIBqQHvB+fIVywSBAACN82zBx0EWAABIC2cdw4UAIAAAMEECqgFtefccUByUAwAa11ekAgr10IIPAJAkWvABAID0VOj+0bJIQDWgpgKKFnwAgBh4VUAxBxQcaMEHAEgFxyMYlGNbAACkgO+f1kMCqgE1c0DRgg8AEAOvOaDcVbcAAACJoeIWPjgjHQAABCEB1YC2vPPl6/domQQAQFheFVB5Wt7AgRZ8AIAk0YIPTswBBQAATJCAakC7qwJqY38xpZEAAEaSnk0DNdd1thdSGAkyixZ8AAAgAyqcDAMASBAt+FoPCagGuFvwrdtMAgoA0DivBNSYUW0pjAQAAEC2iluCPgAAIHmc8NC6SEA1wN2Cr1iuqMw8UACABr27sb/muq07SEDBjhZ8AIAU0HYNQ6wz0JkDCgCQJFrAth4SUA1wt+CTpA204QMANKjXVQG1dUdBhTwHWbChBR8AIFF83wAAACA8ElANaCvUvnwlKqAAAA0qur5LtqH9HgAASBMt+AAAQIqouG1dJKAa0OGRgHIHDQEACMt9MsNWtN9DDVrwAQBSQNsbuDAnBwAACEICqgGd7bUvH3NAAQAa5U5AdbbxdQ0XWvABABLF9w2cmIMDAACYIKLVgFFthZrrSpz9AwBoUNn1XdLZXvt9AwAAkBha8MFHRRX1lfr0g8U/0ANvPJD2cAAAI0i5UtYFj16gu169ixZ8LYwEVAO8KqCYAwoA0Cj3d8koKqBQgwAgACAFVL1gSM52LPLbF36r37zwG53055NSHBEAYKT582t/1tzn5upbC79VvY4K3NZDRKsBnV4VUCSgAAANqmnBRwUU3OwH3VRfAwCaju8a+Ove2J32EAAAI1DXhq60h4AYkIBqwCgqoAAATeBu58r8gghEAgoA0Gx81yBAW74t7SEAAEagYqVYvVzhWKRlkYBqgFcFlHveDgAAwiqVnX/n85SYw41tAgCQAtreYIjVgq9SqagtRwIKABC/YrlYc12O38IthwRUA7wroFIYCABgRHFXPBU4voKbIwDIyS8AgGbjuwb+2vPtaQ8BADAClcql6uUKxyItiwRUA5gDCgDQDO4WfHnONkYQqq8BAM1W/a7hmASD7JPA04IPANAMA+WBmuuogGo9JKAawBxQAIBmcH+X0IIPAABkAifFwKWiCgkoAEBT2OeAQusiAdUAzwoozkIGADSoJgFFrAdutOADAAApsp+BTgs+AEAzOOaA4mdvyyIB1QAqoAAAzeD+LimQgUIQTn4BADQbLfjggwooAECz2OeAsuSoxm45JKAa4FUBVSYIBABokPu7hAMs1GKbAACkgGMSWGybgj0BVSEmAgCIiaMCCi2LBFQDOttqX75iiYMtAEBjijUt+Aj2wIUWfACARPFdA2+VSsXRgo9gIQAgLvY5oCoci7QsElANGNVOBRQAIH5ldws+8k8IEnTssa5LevfVxIYCAGhBxX7pzSekctl/GVrwIYC9AmqgPJDiSAAAI4nXSQ05jkVaDgmoBnhVQDEHFACgUaUKFVCox3CbuPCD0o/3kjatbepoAAAt7PfHS1ccIj10af1lOSbBECsA6J4DigQUACAuVNWODCSgGpD3mBTeHTQEACAs98kMY0YxsTNcwrbgowoKAODn+T8N/n/RTwMW4ncu/OVzw6GlciWgkg4AgBDs8wrSgq91EdFq0Fc++j69+s4GrdnQr6de71GJOaAAAA2yElAnfHyKFi9fo28e+oGUR4RM8zv5xXE9xycAgAbQgg8u1RZIFWc7JBJQAADAjgRUg87+xw9Jkj77swclUQEFAGiclYA6bur79N8zdk95NMgmgwCg/ZiE4xMAQBxowYc6OEMdABAX+3dKhd+0LYsWfDEpDLXjc08cn7T+Ylnv+6/b9b7/ul1LXluT6lgAoFVs6i/pgP/989C+8920h6Py0IFVW4EgD3yYtOBznIHMwXrmbe6VVjxMshAjx7ouadVTaY8CsWHfBKecTzKSACEAIC5eJzX4ff8gu0hAxcSaID7tCqg/P99dvfzPP384xZEAQOu4+Yk39Na6PknSv1yZ/r7TqoAqcGAFE77HHhXPi8ioXx4u/XK6tPT6tEcCxOPCD0o//7j09ktpjwTGAo47aMEHH5Wh/yy04AMAxIWTGkYGElAxsc5Sd08cn7SB0vDBXtpjAYBWsXmgVL3cX0z3R3OlUpG1+87nCfLAj0kLPiqgWsrqZwf//+Rv0h0HELc3lqQ9AsSJk2MwJOdzLEILPgBAXBwt+Ph+aVkkoGJSrYBKOenTUeAtBYCwstTqzv49QgUUfBm14GMOqJbEewUgLYHHHeyb4M0dEORsdQBAM/mdAIHsIlsRE2sOqLQTUG0koAAgtGIpOz+U7a1cqYCCEb9ADxVQrYnWRRhx+C4bEWjBhwD2pFNZfI8BAOLBSQ0jA9mKmFhnqZdT/mAQqwSA8Irl7PxQtg+ljZ06fJlsG1RAtSQSUACyjOpsuLiDgwQLAQCAHQmomAxXQKU7joEMncUPAK0iS/tOewVUgQQU/Bi14KMCqiVVSvWXAVoJCYsRgu8R+HPM0UECCgAQE695n2jB13pIQMVkOAGVbgbKfhb/e8d0pjgSAGgd9hZ8H/6bsSmORCrZxpInaAcTvi34qIBqSVRAAcgiWvDBJTd0nOoODtKCDwAQF05qGBlIQMUkn5E5oAZsJVij2wspjgQAWoc9ef+ebdJN3lMBBTMG2wYVUK2JBBSA1Bh8t3ByDDxQAQUAaDa+X1oXCaiYWHNApd3FaaA4PIBi2v0AAaBF2FvwDaS877SfyED+Cb5MWvAxB1RrKtdpwVfsG/zXSvo3pj2CbCv21X/fs6ZvXYiF+TIbGfgegZNfCyQqoAAAcfFswcfJMC2HBFRMrLPUyylXQPXbAqcDKY8FAFqFPWFvT+SnoTyUKCjkcxxYwYxJCz4Ch60jKFlYLkkX7Sb96ANSqZjcmBqx9DfSD3aUnrgu7ZFkU2lAumRP6af7pz0Sc4vmSOftJD3527RHgiTRgg9BHOe8cMwBAIiHo8KW37QtiwRUTKwEVDFDLfiogAIAM/Z9d3/K+05rLAWSTwgUsgUfwaDWEdSCr69X2viO1NcjbVid3Jgaccs3Bv//x39PdxxZ9e5r0vouac0r0sDmtEdj5q7vDP7/5q+bLc/32cjC24khfhVQJKAAALHx+Erx+/5BdpGAiokVKCynfLDlTEBx4AcAJuz7zrRb8FmVtHm+odEoKqBaUyWgFVvONr9nqb/5Y0HzdWw1fHnz2tSGEUm+Le0RIFF8j8BbpVJxnJVOCz4AAGBHeCsm+aEKqFLqFVC2eUzKHPgBgIliBueAogIKgezbh28LvrL35Szrelr6+T9ILy9IeyTpCXyvbO91q7TgsxQ60x5B9m3uSXsE4RQ60h5BNpVL0nX/LN19ZtojiRct+ODi1yqaCigAQFwcLfj4fmlZJKBiUhh6JdNOQPVsGqhe3jxQ1up1LdLKAwBSdMNjK6uXV61Nd79Zss0BBfgz2T5sxyTlgKqaLPnNv0irlkrXfjbtkaQnKAFl/9HVahVQo8amPYJssr/fm9amNoxICu3+t23JAYLXHpJeukt66CdpjyQ8k5NfOEEGLu45OcqtctILACDzPOd94lCk5ZCAiknbUK+kNBNQ5XJFV9z3iuO6+198O6XRAEBrWtdX1IMvp7fvrFZAkYCCMZMKqBYJBm/guCU4AWW7rdUSUO1b1V9mS2T/bPavT28cUQRVQNm31S0tYVFusepEh6D3qkW+R5AK+1npTBIPAADsSEDFJD/0w6qUYoBn00Dt2c1F2vABQKDNHvvOOX95OYWRDCIBBSNGLfhs1wfNK5QlJmdND2warDBolaqusEzPHC8N1F8mS7a0JISpVmyVackHVUDZn8sIe+/ffU16a5n/7bkR+hO7+pUywt5PRGZNAu9OONEiCQAQF05wGBlG6NFx8qwWfOUUK6D6irU/Wu0t+QAAtdZurN1Ppvm72UpA5QnWIojJ9tGKgW2Tcd54nHT1EdL9FzZ/PGkwrYAqc4w3IrTi59QS2IKvxZ5LGD/eU5rz/6SNa7xvzxeSHU/SOD6BB3tQkBZ8AIC4eCWdcpwM03JIQMUkP3Smepot+LzO4vcKrAIAhq3dVNvGqpxiBsp67DYqoGDMb3ttwTmgTIJWL909+P/FP2/uWNISVL3eynNA8UPRW0snoLbAFnz2z2DPSu9lcrYE1IjqRsFZx3AZ+mi7K544Qx0A0Ax8v7QuElAxsQKFxRQTUF4t+Db2t0jACQBSsqFvcD9pb3mX5mGN9T2SJwFV13333adPf/rTmjRpknK5nG655RbH7ZVKRWeddZZ23HFHjR49WtOmTdNLL73kWGbNmjU69thjNXbsWG277bY6/vjjtX69cx6Wp556Sh//+Mc1atQoTZ48Weeff36zn1o4vi347IHtFjkeCBWA3wJ/gNhfn2KLJaBGUhKiWUZqAmokcSTzfbZpewVUS88H5VL9ruGzjFpUQAEAmsGRdNoCf/6NFCSgYlIY+lGd5lnzXhVQXm35AADD+of2k1t3DAeM0uxdX2YOKGMbNmzQXnvtpTlz5njefv755+vSSy/V5ZdfrsWLF2vrrbfW9OnTtXnz5uoyxx57rJ599lnNnz9ft912m+677z6deOKJ1dt7e3t12GGHaZdddtGSJUv0ox/9SGeffbauuOKKpj+/+upsI445oFrleCDEZ6/e5/StZdJrixobTubYK6D60hsG4uNoq9giiWJLoc3/tpbZ59hsXCO9cHvw/Gr21pd+cz3Zrx9JCSgLyWQM8WuBxBnqAIDY2Jt6aPD4khZ8rSfgVwPCyEYLvtofen0eSSkAwLD+0uC+c5vONvVuHgwUDZTS25db3yMFAjx1HXHEETriiCM8b6tUKrrkkkt05pln6jOf+Ywk6Ve/+pUmTJigW265Rcccc4yef/55zZs3T48++qj2339/SdJPfvITfepTn9IFF1ygSZMm6brrrlN/f79++ctfqqOjQx/60Ie0dOlSXXTRRY5EVboMKqBaLbBton9D8O1z/t/g/2c/LW27c/PHE5egz35Lv6fs0zy1cgs+vwSM5HouLfLeXzNDWv2c9In/lv7hdO9l7Aklv8+q/foRlYAiqQBvFVWck8SnOZkqAGBEsZ/UwPdL66ICKiZZqIDySjb94Yk3qmf3AwBq/fDOFyRJb68fbmeVZvVoqUILvjgsX75cXV1dmjZtWvW6cePG6cADD9SiRYNVMYsWLdK2225bTT5J0rRp05TP57V48eLqMgcffLA6OoZbTU2fPl3Lli3Tu+++6/v4fX196u3tdfyLXa46+UL9ZUfiwXqpz+x5vfNy88cSp6Dn1JJVbUNIqntr5fc0SCs+l9XPDf7/md/7L/PAxfXXk2vhFnyBCXBa8MHJ7wx0WvABAJqhWgHF74qWQwIqJoXC0BxQKZ41/84G77kAnnp9bbIDAYAW8tyqwcSAVQklSe/67E+TYM1XTgVUY7q6uiRJEyZMcFw/YcKE6m1dXV0aP3684/a2tjZtv/32jmW81mF/DC/nnXeexo0bV/03efLkxp6Qp3ot+ELMAdWqCari5vrLlEdQIMwR1Ev5PVv8c+mn/0/qeSPdcbS6MBVQC86Rfn6w1Lc+eLkssO9TWu37LKiy6/4L7Qt6L+OYAyoDlYpx799b7f1E81Xcf7boMQUAIHMcVU98vbQsElAxac8PvpTFFFvwLXzxLc/rf3H/8oRHAgCt4Zk3ejyv7+rdrL5iOkGj4lCwvK1AgKeVnXHGGerp6an+W7lyZRMfrcEWfD2vSxd+ULr3/+IdVhIGNtVfpl7yLWsCg7v2H2Ap/wK783Tp7WXSHacZ3oF9mqcwCaj7L5RWPSktu6O5Y4qD43m1WLTAXsEUxG/f4giUpLz/6XljcP/+lx/EsLIWex+RGlokAQDi4mjBx7FIyyIBFRMrUDhQSu8s26Ur11Yvn3TI31Uvz3vW/wxtANiSHX3ZQ46/T/j4lOrll7rTOcPcmgOqjRZ8DZk4caIkqbu723F9d3d39baJEydq9erVjtuLxaLWrFnjWMZrHfbH8NLZ2amxY8c6/sWuXgs+x/UBB+sLz5fWd0v3nhfb0BJjlIAaoRVQWQnwWW3L6qFqwodhC76Na4Yvj96uecOJS5aq9cIy3Vb9EvuO5H/KLfjuPW9w/77wh42vixZ8cLFaIFWG/rNYLZIAAGiU1xxQfi1gkV0koGJiBQpLKVZAWXM9/eHfP6ptR7enNg4AaBXuuZ7+e8bu2mm70ZLSO6HAqqRlDqjGTJkyRRMnTtSCBQuq1/X29mrx4sWaOnWqJGnq1Klau3atlixZUl3mnnvuUblc1oEHHlhd5r777tPAwEB1mfnz5+uDH/ygttsu7SBwmBZ8AccnSSQGiv3Ss7dIG96Jeb0mLfharAIqSBbnC+qLaX6zrqelpb/JTmItDm+/LC2ZG7wNmlZAbV47fLnQ4btYZmQxWWoqb1gB5fu+2p5vvQTUywukt140e7woQifADL4PSCajDuaAAgDExtGBr8WOKVFFAiombYXBl3IgxTmg7GfNp9kKEABaWdonFJSpgDK2fv16LV26VEuXLpUkLV++XEuXLtWKFSuUy+U0e/ZsnXvuufrTn/6kp59+Wscdd5wmTZqko446SpK022676fDDD9cJJ5ygRx55RA8++KBmzZqlY445RpMmTZIk/eu//qs6Ojp0/PHH69lnn9UNN9ygH//4xzr11FNTetZe/LZVw2SFabupRtx3vnTTl6W5R8a73oGN3teXQ7Q1y5yAz75pVUm5LL2y0Fk1k3WXf0y65RvSy39OeyTx+el+0q3flJZc7b+MaavMSoikRiNeWySti6F7QpjWglkTZwu+oPd01VPStZ+V5hxgPrawYt1W+H0JJ+sM9JqAIJsKACAmtOAbGUhAxcQKFBZTnOjaeuxCPqeiKxFGH2YAMFOo7s/T2W9aj1sgAVXXY489pn322Uf77LOPJOnUU0/VPvvso7POOkuSdPrpp+vkk0/WiSeeqAMOOEDr16/XvHnzNGrUqOo6rrvuOu2666469NBD9alPfUof+9jHdMUVV1RvHzdunO6++24tX75c++23n/7zP/9TZ511lk488cRkn6yXui34DAPA+bb4xuTnmd8P/t+0XZupAZ8KKMdz3wIroJ75nfSrf5Su/ETzx2TMcJ/W/Wxzh5GGFQ/732b6niZR/bb8Punqw6Wf7Nf4ulot6WSXM/yJ3GgFVNfTxkOKLM4EFC34EMAeb6AFHwCgGYhtt64EIg5bhvahCih34idJVreoQj6nQ3cbr4v//KLttgoT2gNAgM/tt5Ok4cRPOaUE1HA1K+eI1HPIIYcEHoTmcjmdc845Ouecc3yX2X777XX99dcHPs6ee+6p+++/P/I4m6deCz7DgHUSCag1rzRnvX4VUKZVJS3H8D1ddufg/999tamjCcW0bddIbO8VR2IpiXmFnvvj4P/7Y5gDsZUroExb8PlWQNkuB75XtgXffVXa7n1mjxtG2G3F5PM3Ej+jiMRvDg5a8AEA4kIF1MhAdCsmVnInrTlDJKk0VAHVls/pw38zznEbLfkAINh5n91DklQYSvxQAYXWYVABFXSwbhpszaJSv/f19sDwpneTGUtcgj76pvPqbDM+tuEkbwTu+0wTS4FBW8O2bo3Y8FZ862rlOaCMK6D8kjuGFVD21+Xmk8weM6xYt5UWex+RmEqlMiKDgr985pe68qkr0x4GAITStaFL37n/O3r2nZHRVcBRYTt0fJnjZJiWk1gC6v/+7/+q8zFYNm/erJkzZ2qHHXbQNttso6OPPlrd3d2O+61YsUIzZszQVlttpfHjx+u0005TsdjEvucRtaUcsLQ/thU8/eHRe1RvSzMxBgBZNW50e/WyVcma9hxQ9pMJgED1WvA5qmVGaALKpP3gHd9KZixJMK2W2eo9zR9LaKYVUCPw/Lg4ElBJVEBteCe+dbVaBdSbTwxfbrQFX5QKzJ7XzZYLixZ8aCafTWEkVECt71+vi5dcrEufuFQ9fT1pDwcAjH3nge/o1ldu1TG3HZP2UGLhqIBqtZOaUJXIL7xHH31UP//5z7Xnnns6rj/llFN066236qabbtLChQv15ptv6rOf/Wz19lKppBkzZqi/v18PPfSQ5s6dq2uuuaY6t0OWtA9VQBVTrYByTlx/9L47VW9LszUgAGTVnjsNVovaE/ZpzwFlfY3kSUChrnot+DIyB1Qzfyj4Pa8R1XbPxrSqrWB7T7PyQ40WfH43mi3nSD42afuOc71JzFkVp5WPDF82TUD5Pa+KYQWU/b1v1mbfjGTlSPyMoiEVVTzPUG9lm4qb0h4CAETyas+raQ8BqNH0BNT69et17LHH6sorr9R2221Xvb6np0dXXXWVLrroIn3yk5/Ufvvtp6uvvloPPfSQHn54cKLeu+++W88995yuvfZa7b333jriiCP0/e9/X3PmzFF/v0/LFUl9fX3q7e11/Gu2tqEz5wdSTPS42zbZ2zcNlFv/IBAA4mYl50d3DAdrhyug0tlvUgGF8PyqgOyXR2gCyqj9YBOVS1Ls+4rAHny2i4aP23KBwBG47wv6DESqgGqBBKtpsjSM0kA86/FiTzo1WgFlWn3qc5dYxZqAykgyG5k3Etrx9ZeH402FXAtXigPY4nQUOtIeQqw8W/CNxN8LI1zTE1AzZ87UjBkzNG3aNMf1S5Ys0cDAgOP6XXfdVTvvvLMWLVokSVq0aJH22GMPTZgwobrM9OnT1dvbq2ef9e9led5552ncuHHVf5MnT475WdVKu2WT/bGtseRyuWplVprjAoCsKnoke9KugGIOKBir14LPdA4Wewu+pgS2m1kBlWICqlyS5hwoXXFwclVGUebVyUoFlKmRWF0RS2IpgTmg4vwxH3cLvqd/J507Xnr25sbX5cWeiC9uNruPX3LHtPorqf1UXGjBBxe/AOBIaJHUb5tjciQk1ABsOdrz7fUXalHsj1tXUxNQv/3tb/X444/rvPPOq7mtq6tLHR0d2nbbbR3XT5gwQV1dXdVl7Mkn63brNj9nnHGGenp6qv9WrlzZ4DOpzwpeplVpVKlUqkkme9CyOjcVLfgAoIZVtWpPQLWlnLh3n0wA+GtCC76Sf4V5ZHEHWbf/W/vKk3lMLz0rpXdekrqelgYSatUTqa1ZVo4BTfdpI3DfZ9xaL+U5oOIUJVka5PfHD67zpq80vi4v9vH2rfdfbocP2O5jUgEV8J46Krqa9DmlBR9SMBJa8A2Uhz+fBDwBtJL2AgkoZE/Teq6sXLlS//Ef/6H58+dr1KhRzXoYT52dners7Ez0Ma0WfGkleuyB0oI7kDogDaQ4NxUAZJVVAdVeGD4fI5+z5vRLuwIqkWkaMSL4bauGQVB7a5nSgNQ+OpZRGT12FPYz+tOsgKpJ3G3V/MeM8ryyEghkDqj6t6U9B1Sc4q6AaraBjcOXJ+0dsKBBJZpp+8Fin22xZiWgwm4rhi1AAQ12XJEGT4Z1TBI/AraVgWa2/ASAJurIj7AWfPbvl6HjpdxI/L0wwjUturVkyRKtXr1a++67r9ra2tTW1qaFCxfq0ksvVVtbmyZMmKD+/n6tXbvWcb/u7m5NnDhRkjRx4kR1d3fX3G7dliVWq7tiSomeUsUnAZVyKykAyDIryWRVPUm2lqoptQ8ZrmZN5eHRSsK04DMNBtkD26UBqRTD2fNNTUD5rNt+fXuTEkOOxF0TKsc8RZkDKsVjwChtAk3n32klcSSgHImPJlVAhf4xH7B8qyWg+m0JqI6t/ZermCSgDD+n5SYEuAcM2wf6Ccw/8XsSZkZCCz5HBdQIeD4AthwjeQ4o9setq2m/8A499FA9/fTTWrp0afXf/vvvr2OPPbZ6ub29XQsWLKjeZ9myZVqxYoWmTp0qSZo6daqefvpprV69urrM/PnzNXbsWO2+++7NGnokac8ZYq+AarOdNW9VZlEBBQC1itV2d8P7TavyKO0WfFRAob56LfhMK6Bs67FaCZdL0o/3kn6y7/B1UcUdfHZUf/h8Tk2SVHGOw3TOmIYfM0JQP83gv+OxA7bXps1plBGmiaWgyiZHC74WOK6PkgBPU5S5uBptwWffR//99KDRmXn819L/TpCeuqnxdQXhrGMM8ZsDaiS04OtP7MQSAIgXc0Ahi5rWgm/MmDH68Ic/7Lhu66231g477FC9/vjjj9epp56q7bffXmPHjtXJJ5+sqVOn6iMf+Ygk6bDDDtPuu++uL33pSzr//PPV1dWlM888UzNnzky8xV49VvCynFI2tujTgq89n+5cJgCQZVbVqlcFVFonFBSZAwpxidTaa2i5dauk3jcGL/f1SqO3bWActvXnYzj0DNuCL4nWVvZWWlE4KoCCqkp87hO88igjiof9fQjapdkrekZicNu4AirgvbLftCXOAdV0EZJGRhVQQe+pvVIzhtanf5o1+P8/fE3a8/ONr69GK7yPSENFFecZ6iNgW+kvj7wEVKVSoW0V4GMkfT5G8hxQ1gkOfidAILtSPb364osv1pFHHqmjjz5aBx98sCZOnKg//OEP1dsLhYJuu+02FQoFTZ06VV/84hd13HHH6Zxzzklx1N6sVkmpVUDZ5ippc8wBZVVAtf5BIADEzdo32vebhaFkVCmtlqpDZ7YXSEChnnot+IwDqvZA8VBA1X7mXKPBbtMqGPMV+ly2X91iFVDGreparALKtLLJsY2NwH1f2ARwvXW03BxQMf4OadZZvcavr8H+x7hVpkEyK0uq7+MI/IwiEr8A4EhIQNnngBoJLZ9Wrlupf7jhH/TzJ3+e9lCAzNlc3Kwjbz5Sp993etpDicWIngNqBHy/bKmaVgHl5d5773X8PWrUKM2ZM0dz5szxvc8uu+yiO+64o8kja5w1aX1alUZW4iuXk/Jec0DRgg8Aalj77HbbhEtUQKF11GvBZw+oGi5nBUHtlUqNTsTtqIKJYbs2CWwn0f7L3gat2OCZ0sZJIpPgt8wrMGJhOA9QYAu+kV4BZfhembZ+a1oFVIyvvWliLaxmndVrnAg0WC5KVVuqrTJD7iNG4mcUDalUKiMuKGhvIzgSntvFSy7Wu33v6qdLf6qv7/X1tIcDZMrC1xdqxboVWrFuhc4/+Py0h9Owgn2e2hHGOiGACqjWwwQTMWnLypwhrh8EVluptAKpAJBlxXJtC75CyicUlK39eYGDKpjyS8KYBlRLHpdjDHbHXQFVMUjCJJGAiXUOKPsYDRM6mamAMmwxFsSReIlhGyn2N5YU7N/Q+BjsjCubDJdriWqZJiWBmzavQYSqJd8EuGGi2HT+r2YbAfP1IFtGQsXQSEg6ATDD5z3bqIAaGUhAxcSaKz69Cqjy0DhcCai81YKPHxYA4Dbcgm/467CQkQoo9wkFQA1rEzGaBynknCb25RudiNt0fiPj9RlUFzgC9EnMAdVoCz7TZFKUxFqac0DZXqOg9z7Oip5SUbp0H+mn+zur1EwtmSv9YJL01I3xjcl0HqBG5x7KkijJUhP5Jp3V63h9g94H+2W/5WKcTyqKXMif+KYnCdCCDy7WfCkVjbwKKLuR8NyoFgC2HCNlLqsqx8+f1t8fb6lIQMXECl6WU/ow9BUHfziManO+pe2FdM/kB4Ass9qT2tvddbYP7ket/WrSSrTgg7F624hhELTsMV+S/XgmzhZ8sRwnmVQgNCn47XgM2+vWcJIuQjVTlKRV0owroGwJqEYrQdZ3Sb2vS2tfkzavDX//W785+P8/nNDYOBziSEBlZA6oZs5XZqJpCSjT8ZpUYPqsN2jBWF+jkF32wz72SAtsIXYjIWHjOOOegGci3t70ts64/ww9sfqJtIeCmP3kiZ9o7rNz0x4GWpRnBRSHIi2HBFRMrJPn0zpjflP/4A/R0R3OH2VtBasCioMmAHAbsJI9tnZ3o9sH96N9A+kE+KoVUHm+omGqwTZQnq297AmoBpMrpvMWGa/OJACcQALZnrhLbJ6sCAHrVBNQhq0F7QmoKFVLdgO2arSGWyMmwbQNWwJtJU0SC5ESUHGOt1kRhxjngDJ9ryqm731ITUtA8XsS3iqqOJI0JGwQxf8s+h/d9sptOu7O49IeCmL0xvo3dMVTV+iCxy7I7r4ho8PCIL5fRgaiWzGxKqAqleH5O5LUVxxKQLU7E1DDraRowQcAbla1UXth+OvQ2o9uSikBVbLaAjIHFOqxgsUm85CEnYMm1hZ8MQeiTdrQuYO5zfixYn+McowJKOPlstKCz3DOqiAVjyq8qPp6hi/3b2xsXXExbsFnmKxINVJi+NhelZVxaFb1jXEyKM4KTMO2f2GFnXw8dDKb4xPU9/amtzXv1XkaaPT7sUEbBzbqmbefCR20dAQ8R0B0uhVacr3W+1raQ0AT9BX7qpfLzDmYeb39vXr+nefTHoZeX/e6Fry2oGbfPRL2x1sqElAxsc/VUUohI7upf6gFnysBZbXgK1IBBQAOlUrFs91dp5WA6k8pAVWxKqCy/0MRaauzjRi39irVXna04IuzvVwcCSiD9bmfbzOOzewB41LAHEalAemZP0jruv2XiX0OqCa19qr3WG6mc3GVPbbBqDb3Dl8e2NDYupIQqQVfC7RVDPO5XzZPevtlwwFkqQKq0eR/s1rwNSkBZWmBQDaSUZ1XyPVRqKiiL9z6BZ228DRd88w1iY/L7ht//ob+5fZ/0bxX54W6H0HO5FHZMDLZk58koLLvK/O+on++7Z+1eNXiVMdxxB+O0Ox7Z2vBigWO/bG1DTGvXeshARUTe6ekNOZbss7UdyegrMqsgRI7egCws7cmbfOogNqc8hxQBQI8MGZyFn7IFnxZroAyqUAouyugmvB5dlRABSSgHvqJ9LuvSlf8Q8C6oswBFUNVTbOZjsPRgq/BBFTfuuHLWamACmyBaZqESDKpGDSMmOeAevUB6TdfkH66n9l6k6iACtwGTSrRorynCbTgM6nYMm0BCgSoVCpavWm1JOmeFfekOhZrPqE7l98Z6n4koJLXSHLi7U1v66E3HiKJ1URrNq/RA288EPp9yueGf2eX0pzDMgCf92EvvfuSJOm3L/w25ZEMemL1E95zQKHlkICKSZstA1VOowJqwLsFn1UBlUZSDACyzN6a1F4BZc2ll1YF1PAcUCSgUEe9FnymQVDP6hPbfYsZroDye141r0kzKqAMW/AtGwp6rVvlv4zxsaNpZYX9crOTFYYt+EwTUI2O1x7cyEoFVNgEcL3l0vzxHalaL+A+rz8WcgBJtOBrsGqpofn3YuBXAWWasPdDCz64mLR1KysbJ8JOHjM53B0c57q0fiylFaoFGklAffrmT+vrf/665r82P8YRwe7Y24/VSX8+STe/dHOo++Vlj5VmY38w0sXxeX9j/RsxjKRx+Vx+ROyDQQIqNvYKqGIKyZ7NVgKqw3sOqAESUADgYN9X2+dbqlZApTUH1FBijDmgUF9cLfg85iFxBEYDqntMGM+HYro+g+BuEi347ImOUkACyqQdVtxtzTKTrDCcB8iRgGpw32t/rwc2NbauuMZhulwa7dpCi1IBFbBco/uX2ERpb9lg8t943qmYGCfs66BCGy4VVXznTMpKwLmj0BH5vpxxn4xGXuf1A+slSQ+vejiu4cDl9fWvS5JufeXWcHe0fWVkZX+A+rLyXrlPdLC+a1ohqQ4nElAxcVRApZDs6RtqFdVRcL6l1rheX5OVFiQAkA2bbRVO7bZ9eGfb4OW+YkoVUCUqoBCWyTwkQcmKOnNANRp4iTv5Y1JVUxPMbXYFVEAQPRdnAsr0PhlJVpgmwhyvZaPjtVfv9fkv1mym81/FOfdQEiK1izR8XiaSaMEXlAwyqoAybYHZpM+p8X4x7GMThEd4WQlihj2DnqRT8uKochjbMTaGkSBIb39v/YVsWqIFHxU2mVVw/Y5i39y6SEDFxB4nTKMCqljyPmP+9qcH2738/L5XEh8TAGTZFbb9Yt62E28vWHPnpXNwY7VxbSMBhXrqteCLUi3jNQdUoz/KYq9GMqmAcs+I3uQ5oOKsgAoM1kdZrtn7MtO5qEwroBptwWcbT9D7UleD++BI83U1OvdQg0ySO7FX64UNRjXruzFCBVTDyX+P6tNYGFaGel1vlCzk+ASDrDPQK0P/WezB3KwkoMJyVHSNgOB0K1QLxNGucVznuBhGgiC9fSETULTgS5xJe9RWkVPOs6p2JD3HLQUJqJjkcrlqEiqNCihrjicClgBg5q7nujyvb2+zElDpHCBbJzHkOahCXfW2kShzQHktV+e4Zl2XtOndgGG4k0ExJrRMWmDF8Zhe7AHjoDmgjBJQplUwpkmNrFRAmW6DMbbgsys1UAFVaG/ssU2fRyaTigGMWwtGSD6aaFoFlGnCzGSbNv38hai6eutF8ySV0UkJPtebvL8cn6COLLbgC4tJ75MXNdHXXxqeq3Sbjm3iGg58rOtfF/m+Wa2AQq2sJHjcc0CxP25dJKBiZLW7S6MCyjpTv63QGm/pn5/r1i1PZGNSOwDN90JXr36+8K+ptbXz8jfbjva8vn2okjStBFT1hALmgIIxg2CjcWA7ZAXU5h7pwg9KP3yf2foHr/Bf1oTRHFDu65s9B1SMLfgChxohUdX0H2oB+ypHcjOoBZ/t9SvHmYBqoAIq32gCKkILOuOkYgK/NcImMYKWM02Ap8n49TVYLu55ve6/QJpzgHT3mQHjsq/XZ11+r7XxPH0EfeBkBSgrlYpvwmYkJKCQjKgJqKLtGKIt1xbXcOAj7GfarzoyS/i8Z1c+l3e+P7xVLas1shUtwppCpJRiC752V8By9rQPJD6WeiqVir72q8c0+4alWtWT4uTQABJz+CX367w7X9BVDyxPeyhVU//2PZ7Xd6Tcgm94Dii+olFH3RZ8EZIVZY85oILu+/bL3o/nt/6g5YyFrECI5TE9OOYtSqEFn/G8QhmZL8g4CRFjgrKROaAKDQaxIlW1ZWheL9PPlsn9TeegM5JAC744K6BMP6dBibh7zh38/8NzAtZlX61BpZPf9bTgQwzsQeasVDxsqQHmrAb8vWyp79FIZ39fs7I/MNFKnx23Vmi5aco+h5g03KpzJD3HLQXRrRhZFVDlFHZUA9UWfM639GPvHwywTnnP1omPyc/mgeEfNu+s7w9YEsBI89TKnrSHUFUa2lcfN3UXx/XWHFD9xZQroGipirrqbCPGlRWl2sumiQ77cYdvsN+dDGrgs2Xazi/xCqigBJRBIiNStUwcSY0mi5KAijNBWQp5nGl/7EJHfOMImwCut1zTgnS2fYpvsiLuFnwGwSh767mm5Z8iVEA1XH1q2IIvrNAt+Oq1YXXJSFseZEdFFd9gbctWQI2AOaB+/+Lv9fEbPq6n33q6JfLGrbqtIFirzQlXqVR03uLz9P/97v9TT192YhdbKncFVKvuj0ECKlZWrDCNFnyloR9mBVfAsi3lQKqXTQPDP3LSqBYDkJ4sFfVYLfbcifv2QrpzQFmJMff+HPBnMA+SaRC0Gug1DMba28sVN/usP8YWfMbrijHp5cdRARXUgs9gx2c8/0yEOaDSZBrYdswB1eB7VWkgAWVfvtEEVNzJwsRb8MWYgDLdpn2XsSdnWmAOqChzsMX5nvo9l0YroKhOQAStVPEw0py96Gz19PXo9PtOT3soRqJWQFE5lW2tVgFVUUXXv3C9ujd266YXb0p7OFs85oAaOTIUCmx9VrKnnEoLvsHHdLfgS3suEy/2BNTG/ux/AQGITz5DZ81WW5e2OcfU0ZbufrNIBRRM1W3BF6X6JGQFlJ1fsD/OFnxRK6CaEbC3P6/ACqgYW/CZJgazWAEVtB05EngpVkDZlzepXDMdRxyt9ZJ+TxueA8rweRlVQAUkeGNj+tkKOQ+BaVIx1oCKz3NpNGFGCz642Fsg+Z2hnpWKh7BBS785rVpRq4w/6rZCRUSyQm9P9vPcMrI/CML2lC3uFnzV94dDkZZDAipGVmA1jQooa64SKwlmseYyWb2urxpsTdsmW9JpQ18SPygBZEWWElAvdK2TNLyftLSnPAeUVdGaJwGFuuq14ItSVTN02bTawh4YTqUCynfB+B7T9yHsr1tAEN2oBV/cSYiEq2V8h2FYsdWsCqiwc0AV7RVQ7fGNw/g9DUrGNCtZYZMzacEXYb6yRueAsiepTCoKozCuWgqb0DFs59esCiiTFo9hk5sZOpZDNtkD1K0QcLa8tfEtLVuzrOb6Vkng+KlUKi0xX0rkBFSLvz8jXSvsDxxj1PAYW+FzY6K/1K/Hux9XMcQJPVl57nnlSQqOECSgYmTFMNNoK2cFLN1nzLfbAqtn3vJMomPyY09ArScBBYx49n1iVtrKPfdmr+5/6W1Jzv2k/e/+tCqgrBMKMvJaoRUYVAEFBortreQ8KqCME1A+wf44K6BMW+slUgFlP6UzIIieC1sBZdpar8E2YUkoGwa2mzYHVEBlmhdHxVSj+2DD7SNSoirFFnymiTDjCkyD3wL2ZUwqCiOJ8NlquP1pk6ra/JKaRklFw4QZEKAVAs5ePnnTJ/W5Wz+nV3teHVEBz1ZJ0NCCb+Rrif2BbXPKjZATLr7zwHf05Xlf1pylc9IeSmg1FVB83lsWCagYWfOIpJGAGqi2bHK+pW22lny/fXRlomPyY2/Bt6rH52xpACNGd+/w53xUeza+dv6ybHX1cltN69LhOaDS+PFpfYdkJVmHDAvVgi9soNgwGGsP8JsmoOKsgPJ9Xgm34AusgLIFzBttlxhlvqA0f6hFqoBqcLyNzAFVtiesYhxHHHM7JZ1UTKoCyiT5WE5iDqgoVUuNvkZJVECFHa/JNsjxCQaZBGdbIuDs8uRbT46oSe9bJWAb9XVu9fdnpGvFOaAsWakCCss97rtevUuS9Ktnf5XGcEKzf6bzubzn/rhV35stWTYigSOElfsppfAFaLXXcwdS3a2lsmCzLQG18t2NKY4EQBJWrhn+nPcVs/EjtLNteN/o3k9af1cq6bRUtb5D3CcUALXqHXgbBqzLHmfIGwePTRJQ7r8b2A/UjMWg+itouUY4WlsFPCf7mXt+1R5RKiHSnAPK9Fg3UhVMjBVQoecpSKAiJepjRpmXrSEmlZVBd4/SAtQvAZVAxwTjbdogaWT8W7BJSUW/6ky/wB8t+BADxyTxGZwDKsxus1gujqhJ7yuVSktUcmRmW0GsMrk/cPEb40hLcrQ1PL9pMhzvQS43oubk25IR3YqRFSwspxCw9Ju03t1aKgsGbG2tejeFbI0CoOX02D7nxZTmVXLrsCWg3PvNgi2Rn05LVSqgEFYTKqBMK2hKEeaAauREnWZUQD18mXThrtJbtfM+GI/FdA4ov4qcRubr8l7QdrHJ7QcDl4uQgIpzDqiwzz3OipQorfWCWvX53SdWzZoDyjQB7pcgsV8f4X354yzpp/9P6t8QsFCMFVDGLfiaVKno99o3+p4S9IGLPTjrFyBMM+ActTJmoJzdGMWDbzyoea/OC3WfVgnYRm7BRwVUpmVlf2DKUQFlkLjt6evR3Gfn6u1NbzdzWLFoxQRUPpf3PCFgpCUHtwTZy060MCtWmMYZ89U5Q9xzmbRl7y0esAWgB1KaYwVAcuz7xGJQlUCC7FVP7j22PSGV5v6cBBTqqrbg87m9kbllTO9rD9KYVPcMXuG/vrpMK6DqXjFs3n9J61ZJ130++liCEgf2Fny+LeEitGGLJVEVlWkCyjBxYNqCr9gnXfEJ6Y7TzR4/9LZm+Pquekr68d7SM783W1dgBU+EZEUic0CZJHcbrGySnK+N3+eokQTl+rekJ34tvb1s8H3zE2V+tUbn32taBZTP59/vGMyrCtZzvQR54c8vCZBmy62oCY2ie5+doU3/G3/+hk5beJq6NnSZ3ylD4w8SJpE0UBrQjctu1IreFU0cEYLc9epderz78brLtVwCKuR33bfv/7YueOwCnfTnk5o0ogh8wgitkoAqVob3wQWTuXTRErKXnWhh6VZADe7I210t+LZqz96H1Z50GshINQSA5rF/5vuL2fjM2yugtul0HojZEz9pVkC5K7OAWnW2kYYqFQyD3fY5oEzOrq+3vnoiV0AZ/ODtfTP6WIICbPYWfCWfs6pbbQ4o4zaBhuO1t28MWu75W6U3H5ce+XnQg5qty/Ouhtv9zV+X3l0u/e7fzNYVNBdVVlvwJTZfmUHyo5EE1IbhOR8VGHhpVgWUYTKrWckdk9eXFnyIyLcCyqcdX9IaqYDKesunNZvXGC9bUaUlqgXCJCd+9dyv9P2Hv68ZN8/I5Psz0v117V/1rYXf0pfnfbn+wvbDoRabAyqfqx8yf/CNByVJL6x5oWljikurJKBKthNjaqrQ+Li3LBJQMcoPBQvTOGN+wOeM+bzt73Gj2xMdkx9nAir7Z0AAaEwxg1WP9sTSJ3cd77itkEs3AVWkBR9CM0jChA0UewW7e1dJj17lbGNlr4AymV8kcLwG3MEk34BqiBZ8lnzIk3ZMW6fZlzNpwddw8DvM+iKKOwFVMkxABbZQs+5vf43qLx6wIv+bghJKXvcvxpCAanZbxZrHMElWxFCF50gu+VVAGVboeI7DXokW0FbLuA2mwfsQ6T2NcHz06oPSC3cEL2PUgq9JlViAshNwDpOkcM8BlUVhnk+rJGjKMt//PL56uPKmVZ7fSFGpVPT6utfNl2/hCqhWSNyG0Z7PRky4Hvv3RiFX8DwhoBXmtYMTCagYWR2dSikcrFhB0naPSesv/PxekqRdJ45JdEx+7AHovmL2v4AANMbedi8rCSgrab/X5G21wzadjtvy+ZytpWry4y1XqICCIWsT8Q2CGlbBeAV3vYKsv5wu3X6qdPeZw7eVDFqnNbMCyiT5FricTdizAh2vUUCAzf76GiWg4kgsNTmgbPoemiYOHJVhMSZ+Qicr7K9vwHKFzoAbrfvbT/ntC1jOsJKu2UlF93obTVZE2f/4tuCzb0f+q/Iehz0BHJSAirECKsprFOVzes2npN/+i7R2pXvFwxfLBttX6MpKjk8wyB4A9Jqjw3190hppwZfFCijHaxliSFlPplnCjNNemZLV5/f2prf1vYe+p2fffjb0fa96+ir9+rlfN2FU8QiTWLa/Py2RgAo5B1QW+SXOWqUCyr6d5JTL7Gcc4ZCAilEhxRZ8VlC3rVC7o9lmVJtjmbQxBxSwZem3feaLGWm7ae17dtputOftVkvVVCqghsZGBRTqq9eCL6YKKOvy2tcG///iXcO3GbXGilCNZMoo+Wb4mGF7jJvMrSI5g76+LfiiBKwNq09SbcFnGCwzbcFXDEjkeD1m2Oduet+2DpOVDV8s9pltq3HM/9WQkMmVOOYhM0lSNtKCz/E5DUhAGc9tZbKc6XsV03u6blXAQ5hUQNGCD43zbcGXYvIm6mPXtODLSPDTHpQdiRVQYcZpnxcmi8lCSfrvB/5bf3jpDzrm9mNC3W/t5rW65PFLdP6j52vjwMYmja4xYT4TrVYB1QpjjKpV5lNyvwdZ3B8jPBJQMSpUz5hPI2A5+JheAcuOodKsrMy3RAs+YMtStM8BlZHPvLXvsfaPbta+NI2E2fAcUHxFw1SDbaA85wgJCIzaf7xUDKoXYm3BF7ECyuTHZOgWfKYVUCFb8JkG9RttE9YQw3Uat+CzvS6Bc44ZJKAaalVneN+wFVCqmH0+4phTqRFhkytxVOs1ew4ox+QTxYDFolRAmSQVm1QBZd+vlIOeV8gElCoBzz8bvyWRTX5JgFQTAhEfuhj0mUrRSA/Chgn8+wXSs/S6PPXWUw2vo7e/N4aRxC9UBZRtu81KS05TI6EFn/0z0SoVUO7vEK+TGkbCe7OlIboVo7YUK6CqLfg8gqnt1QRUtgK/kjRQzM4BAoDmyOIcUFYirN2jalQabn+X5hxQ5J9Ql3UWutE8JKbJCq8KKHcCKud9m1FwM2AsJkED08qmmusN1p2ZOaBimDOm1eaAMq2Aev5Wg8dsIKhvel97BdSmtX4rc/7plzxzzFEUFJhpdlJR4T/PccwBZa8KNGnBF7qqzbACKtbkruk2aFgh6HlXg+S/6XKh99EEfVArS4F/L2HGN1AeyEwVl13UMVUqlREXrHVUQGXwvZKkDQMG81Z6sLd9y2IFVEWVUMlCRwu+FNrbm/BL7rZsCz57e1Tbc2uVBJSj2jPj3y0wR3grRlawMI0KqIGhHbnXnCFWgLU/I/Mt0YIP2LIMZHAOqL4BKwHlUwE1tN9MY39OBRTM1ftRZBiwrjcHlPvHvD1RE/rseo/1WTa85T/GsOuK0vYv9I8ywwoooxZ8rTYHlGkCyjBx4EjMBQS/33zC5EHNHrOR+7aNGr58z7k+q3Ld37d9YJSkYrMqoEw+zzEnQe2fCd85igznEvO8r70CyrAFX8NzQNmuf/SXAatq4D11zJ1lWgHl99lyv+51PjctGpBDOpIOHpYrZd3wwg1atmZZQ3NA2WUlqRG1siwr449TIZ/9FnxRx2L/zERNYjVb1EqmLL0/fspyzj/U6qJWQOVyg3Mv/eGlP8RSzReGI2mpcmY/4winNdKfLcJq2VROIUNrVRh4zQHV3jYYxHzl7Q3q3TygsaPaEx2b20AG23EBaJ5iBueAeuDltyX5J6DSrIAqDX2HMAcUzJlUQIUNbAcEY3N+CSi/4LFhMmjx5f5jHL6z60+TeWo87uclbALKtALKfptfEsL4vYrQ2qsZP9RMj3XjbMFn/JgNVAqZJk06thm+vOJhv5U5/2y0+i0zc0DF3C7S8d43Yw4oe4WZYQs+0wqooGSppff1gHX5/lGfcQWUyT7aXQFVluRVEZqNYzhkh99Z9mlWpNyx/A6du3jwxIDHvvhYpHWUKqVMnnUftQXfSAzY5nPev+Gy+L6FZX+/NhazVwElhWuX2ArJA8c+y3bZbztrJY4KqFy43zoPvvmgvvfQ9yRJT3/56VjHFSRoX2f9PRKSg1ua1v80ZUhh6Gz1NAKsxYAz5j84YYzGjBrc0SxcZnB2cZP97C9/rV7OSjUEgOaxzwH1ytsbUv/c9xVLeuaNHknS1L/bwXOZfC6dBFS5XKnGrbwqWgGHui34TJMVXhVQAUFxx48xk4C1aTLIYJuvCZT6Lhh8P8+HD3lYHCXBZ1IBJQW8p1HahDW5AiqoGiJSCz6T526okWRF0Ou73fuGL+/8EYN1KYbkY5PbKho/hmGCzzj5aHtdfFvwNZKAMvj8DS5o+BhxfrYaWJejctUwAW7cJrXevpzjEwRLM8j88rsvD48j4r6yMvSf7YpMCBP0t6tUKi3bSsyPXwu+kWZTcVPaQ/AUtQVfVt8r++c96ucsS+zJGftzC7sfeGHNC7GNKQz3e5DV7QbhkICKkVV8VEr4w/HaOxu0qX/wh5lXBdTWnW365K7jJUmr15lM3txc9qqngYxUQwBongFXEueFVetSGsmg3k1F9Q21JD1s9wmey1jJn5dWr3Mk0JrN3vKv4DM/FTCszjYSpVLBswWfi6MFn0Ew2jS4OXZH/8f0fYwIVTF2/bYzS5tVAeVowWdQBSOZJRiSTlY8cqX017+EW6cj+N1gFYxx279GKoUiJFe2fm/9dUkByUf7Y5rOATX0+Jt7pIU/kt75q/ddwgo9B1RCLfgcCagG3tPn/xSwWITKuVir9cIm1kxb8IWtajMYywgLZCM6R5AzI/PwdLZ1el7fyJiyUrUReQ6ojIw/TvbKlJH2/KJWuiXFPQdUvTG2WgWUvb3giEjc2l7ysFVDaSVA3Z8Bz21oBLw1WxoSUDGyKqDKCZ4xv+iv7+gffnSvXn1nMIDiN2eI1WYq7coDt6zMSwWgeQZcn/ON/QGBkgRY+8GOQt73oNJK/vzHb5fqlBufTGxs9oqrwkg44EVCTCoVAr5vPeeACgge5/wSUKbBTZ/xtm/l/RhR1mX6g/2JXw9fzvs8ph/TxEE5SgKqweB/Q/MgeXj1QemOb0m/PircOmNtL+euamswSRd5HDJ7XjWtJxtN6HiMbd4Z0l/OlS47yP9+YRjNF2SYWDJNrpi0Xyybbvde47At/+I8s+VMWusFLWe/fqv3BA0uYN112F+TwASUQas+0201o4FDZENWguajC6OHx2Gwzbrne5JqA55ZEfk1zt5TaZjfXDZZTNhY7lx+px5e5d22t6/Up2ufu1av9b6WmWRuEHuSpt4Ys/oc7OwJNfs+YSS0efN6/b32e142FzfHPRwj9vfDPicXWhsJqBhZU4kkOWn9TY+tdPztVQElDSegspDw2aZz+GCBOaCAkc+9T9yQkQRUe0CFkT2Zf+uTbzZ9TJaiLaDEHFCoq24LvpjmgHL/cLGf7GJSBVTz2D7jdVSIGCYhGk1W2NuiNa0CyqQFn+HzMq3SME5U+TzGPedKT94wfN2aVwLWH0MSIvTcQwp4zRsI6sfaXs70PW3gtVy5ePD/9jNU13VJt86Wup7xX5f/g3iPq944PJczrMC0fyaMWvCFfE8jtW40TECZbKujxpo9TiMVUEGtBY2SpbTgQzR+FVBpGtU2qnq53ph6+nr0iRs/oe/c/52a27LYNowKqGGOCqgMJWwqlYrn9rKyd6VOv+90nXD3CZ73+/mTP9cPH/2hjrz5SM9qj6xsgxZHgqDe91e2hu7J/po7KqBG4Pdd94Zufey3H9MPFv8gcLmccqkloNz736yc4IDGkICKkRWwLCf4gdhmlDNY0u5TAdUxFGhNuwKqUqk4gs8DpTI7EGCE63Mlvtf3BbUYar5qAqrN/yswreSP/YRm5oBCffVa8JkGij2WC7pv6DmgDKuWygP1lwkdKK1e4b3c1rbqhKAkUr2xBFVAORJQPhVQpnNWJTFf0IpF0n0/km4+cfg697ijVAoZVxTF+d43kqxo8HlFqSoJ3AY9kmOFjtrFbv66tORq6fIIVVFGz6uB1p5eHBVQBgmo0O+pabVejFV49utLQSfdNLCtllNIQFmo0IaHrLTZ6iwMt+DbWNwYsKT0x5f/qLV9a3XrK7c6rnfPAZV2UsNirwQIE0PJyvjj5JgDKiPvVaVS0dfu/pq+Mu8rNe/PW5uC52Jf0r3EZ6XS492P6+AbDtZtr9wW11AbFrkFX0Zjf/bnUyqPrBZ87s/ENc9eow0DG/SbF35T976bSykloFyfaa8k80hMDo50JKBilB8KFhYTnNdojCsBVbcCKuUE1Mb+Us3Ju6UEK8YAJM9debmhL90KqP7i4D7H2i96SSv5QwUUomm0WsarBV/AfXM+FVDGc/f4VUAZBJlrgvqmgWKfsY0aN3x59HY+6/J7CMPEgVELPvffJq9lwHvq1VbR1AaPIIk7wG1ctWPaMjHGlnaNVEBFqe4xrYBq9D31un+hvfa6SJVP1mOYPP84XyOZzf/lqIBqoAVf8IKeF52LREkqBiWH4qqACpjj17Ev8Evwua6nBR8iyEqSw96arc/22fAaX8Gn9W5Wg+SRK6A8no9fpU4arHZgYQLKfnNApfmc1g2s0yNdj+jx1Y+re2O347Z8Ljj06pfQqaiib/7lm1rbt1Zn3H9GvAOOqhKyBZ/HdmvaAq7ZrLH5zgE1ApIc9ueWy+Ucz6+eutVtTeJ+3Kx8v6AxJKBiZAUsk0yojG53HjT5BU2tM/0Hiul+cL1ab63qSSerDiAZv3/8dcffaSeg7HNA+Ukr+WN9f+RzI+OMKzRZvRZ8jQSKg4LivnNAGbREc9/HLihQ6x6f37r9HsMk8dGxlfcy/oMJGJd9MZNKhQiJNePETwzHfu73JtJcVAHLhq68kf/2Zpp49b6z2X1NEnxRtsGwLfi8KqAaYrBNR0psByxXtCWgfNt4NpBQtS8/aR+z5RptAeqogArarzWwrdpfq6JfZaXMti/jRLF1PccnGOQ4VnVszunFHMJUZtRLClTXk5HgZ9TXtaJKTbvEf1/w7/rKvK+kFmC2XPr4pTroNwfpr2v/Gup+9uRhllrw+bF/VrzeR0d1m6v6oxS2Qj8BYVrwud+TS5Zcoo/+5qN6tefVZgzN2JLuJfrEjZ/QXa/e5duCr1U5Pu+255ZTLtRn3nQfGTf7Z6QmGZWRxDnCIwEVIytgWUrwA+EOULb5BFStQGvaLfj6BgYf3x7c/fj5f9H6lAPSAJrjWzc9WXOduyVf0qoJqIAWfGlVQFnfH1Q/wUy9FnyGgW3PYHpAYNR+xnCsFVDuJIfB8ZRpBZRJUqORwLbxHFB+FVAREjXNnAPKrWbcFZ/LddbVSGtB4zZhhmOrd9c42sv53cdxvelr6fHeeyWgGjl5IWwlWuwVUAYVOo0kFQMXM0gGRdkGjSugwm6rBpWV7uVM5+mjBR8iSLMN2g8f+aGOuuUobRzYGCoBZW/jZlfT8ikjAc9GKn3scaP+cr8eeOMBPb76cb3W+1ps44viyqev1MbiRv1s6c9C3c/vvUtTXt7zUrlv80xw+Ob9K4l/nkxETUBVKhVd9cxV2lTcpEufuLRp4zPx9flf1zub39G3Fn5rZLfgc22LYRJs7gqwea/O07Sbpumpt56KZWx+avZ1ldrbRkJ12paGBFSM0qiAKrsea9vRHq04NBxoTTsBZbUA3MpVufWL+19JYzgAmux3S4arn9qHWoS6W/IlzdoPtfu0LJWkcVvFfVa5meEKKA6oEIZJFVDIwHZQNYC97VecrdPc7Ti8gitxV0A1Etg2nQPKpAWfcaVQlOC//2JGY5HqtOALsa6GqoBifo3qPUbQ84pzuzdOQnh8nr1a8DXyg9zxECZJmAar8CoVZ4KmbLBvaCRRbFytZ7pfMXjMoDmgGkkU218rvxZ8URLAgWPJXhAU6fI7yz5p1z5/rf7a81fd9sptjuBqvTmTfBNQLRD0b6QFn309m4vZ6EYzpmNMqGC/u6Ira+zbnuSsJPFK2AS14Mvi8wtTdea3bE9fT/wDC8GvRWexMnJPjs8pF6qizt2m9LSFp6l7Y7dO+cspcQ/Nwb2vy+L+GOGRgIpRIYU5oOwJpY5CXttt7R00rQZ+U05AWePtbHduer2bRu5OHsAgK6mSdiJ8YGgfHTQH1N47jUtqOA5WAooKKBip14LPNLhZdw6osvPvvD3obT/Tf+i+5ZL013ukzT3ej+2bDHIfC3gloAwTGjX3a6DyxnedEZJBfu24ap5Ho+9pkyugjJMQMSaN3Ovy/QHdSFWJ6fMySVxGCf6HrLpKpQIqyjZoWvXol4BqpAWfxz6q3nJxJkED57nwSXqtWCz1vF6ztPOuBi34TBNLtOBDDLJQMTRQHghXAeU3B1RWg/62/USYNlru4K0jAVXKRgJqbMfYyPfNyhxQQQlPe3LNa/6joBZ8WQu+u8cT5jW337e3vze2MTXKMQeU7Zgji/sBE46Wj673y7QCKjf0n5dmJ+mYA2pkIgEVo+EKqOSCq/22ZNe4rbyrn6ThQOtAgskxL1blg3vuldEdbIrASFN74D34/7QroAaKVgWU/37nvWNHJTUcBxJQCMfaThpMVpQ9lnMHj+0JCHvQ2+sxHr5M+vU/SdccWf+x7UyC0cYBVdOETgPHRVFaHPpVKpgGto1b3xkmNQY2D75f79ZpwRPUHtG4qiRgWZNtxHhOpQYqoIznNzJ47yMlFQ2TeUEt+LyCBZWK9MR10utL/NfvHqPJNhhYsGWwrprEpklSMYFEsfH+wiBRZdyCb+jxVz0p/fIw6eIP+d9PMtuvuJO0JvOm2cdSu+Dg/6jShoc0W/BZypWyI7habxymbdyyEvwMmhfF9H6SMwC9qbip8YFFZB/XmI4xkdcTtTIsbkFVQfZAvtd755voqGQzCWJPcLirvUz19mUnAeVowRdiH9IKHNtPLlwLPnvlnn09zZ4byp1s9zrBYSS0R9zSEPWPUSE/+HIWE2zBZw/kbtPZ5rtcNQGVdusrK/DrmntldHv2evgCaIw74W1VQGWlEtOdCLfbwaeatNnKzAGFMOpVQJkGbT3bW7kSHe++OvxnwXa84RVAffI3g//vesr7sX0roEzmgIoQAA56TK/qL1ONVK7ULGM6XtOkouHzuvtMad5/Sb8+yv4gtvUM3TewBV+ICiiTwL5xlYZBsiJ04MDwvl5J28B1yexzGpzRqX1MrxZ8Xj/I31gi/fHfpV98MmD9Mtum46yAcr+HvnMUNfCeRmnBF3cbSN+TEz0ec+UjAWO0LWd/7Z641mfZqBVQ9Z4XxygIZhK03TiwUWs2r4n1ccuVcuQ5oNxtqbIYeI6rAsr+uqTZgs8eCO8odESe06Veq8WkBFUB2gP2XgmAwBZ8GdsW3ZUx9V5zx/htF/t8T8pKnn2MjgRUBpN/YdVUQIVowefXOjKOBFSlUtEb69/wfI2DkrlZ+zzAHAmoGLUVkp8Dyt7KKigBZQVa+4rmO5tm8KuAGkUCChhxiq6ASzUBlXIivK+aCPf/kbNdanNADf6/wBk9MBKmAiooSeDVgs9131tOsj1swXlb0H09H9tnLDVzpXgsF7pVVJ3HNH2NGrmvUZLLNAFlsEyYsT3+q8H/r3nFe3lrPUFVHMaVWB5/e10fW5BcEd7TKBVQpknFGFvaWa+5aQXURluQt299wGMYPH/TZFCc72lDFVD2cQT8DjJ5XsYtQF3X+31+vD5r9YI61n3sJw5seMtnWXcFlN/7YLgcFVBwcbR5cmzP9e/70d98VP9wwz/EOg9MuVJ2ts+qM5B8fvjzFtRSKitB6MgVUK6WgllJQNnfn6jJp6EVea4zaUGVQPbPimcCyq8FXyWb7SAdFVB1tsUw80WlwV0Jad+HRK3uyhL35yxqBVTcCajLnrxMh//+cF3+5OU1t7mTyl7bTUP7DKSCBFSMqnNApVQBtXWnfxJn66Hk1Ia+dBNQfVblgasCyv03gNbnroD67L5/Iyn9BNT6vsEfmFt3+Cftd9gmrQTU4GuWpwIKJurOAdVABZT7vt3P2v62H0tUaq+vF3hvpAIqSgssrzF4XR82sG1cLWOQgEoiWeGl3tmn1nta08orQlVJ0LJGCTPDdYVdxu8xjKt7Gk0qNtCCz6t9lFdyoNPW2qj3zYDHCJk0anQbdFcGGbeIC/NbyzR5ZbJchO1Z8p/7zWsfUi+5U/1MRtj+/e5DBRRSYAVBn1/zfKzrDFMB1ZYb/i0wYPucuoP+WQmaR62Acgdq7cHdVOeAauBljfpaNFNQgrBe8tBve8tiBVRFFWcFVJ3xZaE9Z5COQofv+5PF5F9Y7ucQ5vPiqBK1HaOZti8NctmTl0mSfvbkz2puowJqZCLqH6PhOaCS+0Bs6B8OGm3T6T8H1JhRgwdX6/qaO1lcPfa5V7556AeGr095bioA8Rtwtdqb8p6tJQ0notNiJaDGjPLfZ27vasGX1H692oKPs4thpF6g0rRdm1cCyhU87tjatrzPen0roAyDmzUtIWKsgPJNevkk00wYJw5sy5nOE2oUAG+w+sT3vrb1Wq9P0HsYpgVfI60Fa4LpTWjBZ9xW0WC7MU4qmr6nXtUyXvsArzmgTFtNhkzCNFwlFuU9rfO4UcbhXmfDlZXuvw0Sa8YVUNZ+1uCkwrhb8Fk4RIGHqEHmfIwhqXKl7Khkqle9YD+Lv2g7FvIK+r+y9hU9985zMY00mqgVUDUJKNt97c/bbt6r8/S7F38XcoThOCozcrlYWvClyTQp5tUCzW95d/VaVoRJAKY1/rc3va1LH79Ur697PXC5tnxb5pNkJvpL/brv9fu0cWBj4HJhWvD5Ve41fQ4oV8VmUHtLtA4SUDEaroBK7gtwfZ89AeWfhd5mKAG1vi+ohUrzXbt4hSTpuVW9+trHp1SvL6YckAYQv6ItsfzbEz9SbcF3+1OrUj1wWL/ZSkD5V0BtO9qZnLIn+5vJSnQxBxTCMagYCUxA1QlOVyTtuLftb/vyHo9RN5hpkIRwr7vefesu10Diw/ch4kwGxTzeRloLOtZjvddBCYCg9ZtWLRmsr9Va8EWq/gocXO1l05MVTOcEM9luIm2DpomPBt/7evcNCroYJR+jbM9By3ksUy+oU/apSgxatvoYfokw09eXoA+c/OaBCRPAjXMi+bAVUPbx2xNXXmfcf+aPn9EXbvuC3t70dkyjDc+eaAmTdMnlcr4JA6/kQalc0mkLT9P/LPqfpj7fRgL9fgHpNH9j2l9Ld5uzoNvct2fl+ZgKMwdUkpWFZz54pq58+kodd+dxgcu15dpCfT6y6rxHztPMBTP1/Ye/HzhHV1C7UbtcLtfUCqgg7paUni34OGG35ZCAilEaFVBWIFWSvnHI3/kuN2aoBd+6zelWQN334mCP8v5iWWNHtesL+0+WlGzbQgDJsCqgRrcX9JG/3UFPvr62eluan/l1mwcT8UHz5rUV8vq79w5XfKxPaN9ZrLbgS+Th0OrqTAFlnCRwVEANLecOxu78EdvyPsFs6wScuhVQhkFmowook6oS71XV3L+hOaAClou1BV+cCRLfO9feN7ACyrCqxOvvMOszfY2MW67VuW+jScUobRWNH9NazuPHt1cCw3GGe8gqq6BxhB5v0LoUkCCJkNDxuq/pZ7zRysooy3m9p5WK/3M3qoCKsg163K96vXWBoA9q+QWZk1SulB1B0npVDfbrHBVQ7jlHbBeXrVkW02jDS6oCal3/uurlMNUSYTVjO0mzeiWwBZ9tXPXm4GmFlmONzEeWlKdWPyVJemuTzzyJQwr5gn8Lvgy+9n6sisXbXrnNcb17Dqgw75djDihboUXSFVAYGQhxxagwFDEsJthOzqqAuuzYfbXrxLG+y1kVUBtSbsHXOTTX03ZbDVYXtBUGD4bSnhMGQPysBJT1OT/mgJ2rt20eSG8+uvVDc+FtHZCAkqR5sw/WqPb80H2S2XfSgg/h1MlAlX0qlQKXs4KbrkC042+fhIpfsiJKdY97DH7XNTpfUFwVUIFJGJPqkyjjNXxPG2lDV23BV7OQ2ThqV17/MRutgmmo4svweRlVFBlu98Yt7TyW8woAeH19GG/nJgkon/VGeUzjFnx17me6rHGyNOYKKJPWgl7vabnkv93b19k22mf1pp+ZkBVQHKNgiP0M9DAtrOwBxTgnki9XyqEqoOzjHHDN1eZ33+6N3Q2MsDGRE1ABn1mvZEhPf0/1ciHfvEqHmu0k4qaQlYRBUJKpXss6x3WO3Gc254AK+rtm+ZTmUxvb6R8ftSvkCr77r1aqgLJ05J1TCbhbXdZLgNrZ98+JtuBzvR8joUUiSEDFqjD0aiZZAWVVNE0cNypwubah5NhAKd0esvvtsp0k6ex//JCkwbmgpGTbFgJIhjW3W8fQ5/yA921XvW3zQHqf+dLQ/qa9EPwrp72Q13vHdEpKrnqUFnwIxQooNNoqq94cUJWKHL+G/ZIb1fvUSRI1VAFlmsyKkvRqIFETe7WMSbKiweC/CZMKKNPnHjSWSO3a0qyAMknUmCYVI7yn1USA4U85r894vcdoNAlqso1ETipGTHoGBl0MEoFR9z9Gz8srATXg8RqVnP8PHIfr+folwkKPl2MUeAjxsbQHdeMMZFYqFUfFjqONk8fn2pHQCWj5ZL9srw5KWiOBcb/klVcFVE/fcAKqmbGjRtbteC3qvM9JCWrdZh9X3RZ8rmrCLFaAhKl4TCthMLbDLAHVlneelNqKSSe7bTu3dfxd8/5EfDuatd+u91jMATVykICKUbUCKsEEVN9Q5VBnW/CZKfZAa5rd7qyqB2u81riSrBoDkAx3BVQul6tWQaZZAVUMkeQZNbSv6ismM94yCSiEUqcCyiSYXC477++ZRKr4B8kdl0u113k+tmkw2mu5CImaoOVM58bxfIgI1UhxVkAZJ70aSKxVxx4wPtOqHff9fK9vsPrEsek28NyNWxz6LuR/H9/rg8br8Rn0DAB4fH8YzwHl+0fwODwXM0no+CRXggcW7rPaSIIvzLp91xXwuF77Uvt7Wur33y+WXff1rBg1HEfNXFFUQIVx3nnn6YADDtCYMWM0fvx4HXXUUVq2zNmmbfPmzZo5c6Z22GEHbbPNNjr66KPV3e2spFmxYoVmzJihrbbaSuPHj9dpp52mYtGZHLj33nu17777qrOzU+9///t1zTXXNPvpBXLMMxIiIG1PGCQ1B5RXENx0rpcwVVXNFHVumrAt+Nb3r69ezmq1QVrzCgUJShA6ElAeJwP4JTyzWAHlVm8+Mr92ls3+LG3TsY3Rcm35Nt8EbSsmo9xzvrlb8Jnuc92tSJOsgHInZKmAGhlIQMUojTmgTM/ktwczraBwGqyqB6utVdtQZUR/imMC0BzVBJRtQqNR7VZCJ73PfLXNnUGSx9pHJZUkLw2NLU9wByZCVUD5JWBcgQe/CijPpIRrvdX71gkWmwZBPSugTJNZppUKDSRqjKtlmjUHVFAw3bD9ohfPirighJ5h0sS+vprFIlSfmLQ1C13VlsZ7GiGZ59WurXqdx/eH6fYQuhKtwSRo3BU6dcdhmjAzWMbrb78VmCTWvN6/UrF2zGWPCiivuaLsy1aXa7C6kAooTwsXLtTMmTP18MMPa/78+RoYGNBhhx2mDRs2VJc55ZRTdOutt+qmm27SwoUL9eabb+qzn/1s9fZSqaQZM2aov79fDz30kObOnatrrrlGZ511VnWZ5cuXa8aMGfrEJz6hpUuXavbs2fra176mu+66K9Hn6ydMMNkeXMxKCz6/ILn7tnrB9mYKM4+QnTvgXLcax1VR1NPXo/tev88zWdWIKIHkgfJAzXVZqYgISlzYX1OvpIZfhUdWnlsQ6/l4vTeSf4KwGYmE7g3denjVw5KkvGG4u6YFX4u9/m71EkxR97lJVkDZVVTxPC6L87sDySABFSMrmJlkOzkrKGoFSf3YA8BJJsjcrCoCKwi9dcfg/9du9P6yAtC6rLZ1W3UMV2hayec0K6CsfaBJkqdapZnQfp0WfAinznZiVAHll4ByB+LtgZl6c0DVS0CZVg14JaBMA9ExB/8buW/F57VzLlTn77CPGaGaw+JIMBpWtZkmjRpKQIUNkgcs4ytCtYzxNui3mGnSy+M9tX+PVW/3qoCyJxUNE4bGiTWTbTXGJF3QY9Z7jMA5pkzehwjbs+lyXsl/zxZ8Q3/XtEI1SNibvr71nj8nyTjMmzdPX/nKV/ShD31Ie+21l6655hqtWLFCS5YskST19PToqquu0kUXXaRPfvKT2m+//XT11VfroYce0sMPDwZK7777bj333HO69tprtffee+uII47Q97//fc2ZM0f9/f2SpMsvv1xTpkzRhRdeqN12202zZs3S5z73OV188cWpPXffCqg63zvuAHyxXNSL777YcLC3XCk7Eir1qrIc1zku+rd8SjMgHXUcNRVQtmSIV1LJnTD48p1f1swFMzX32blhhluX+znUCyhf9fRV2v/a/bV09VLH9VlpwWffhmrmgKqT9AuTUExbTVvAinT2Q2fro9d/VN0baudIC1Md2ahpv5umE+4+QQ+98ZDxuRL5XD7RJFmzuRNvjtc8xNe3u5LKvo0WctHmhlvfv14relfUXS7oRIJWTApiEAmoGKVRATVQtioMgvckbbYKqSRbBLoNV0AN7rB2nzRWkvT0Gz2+9wHQml7sHuyR/nfvHS5/70y4pZ0Xax9db79pX2YgoQqoMNVZwHAQsJEAsE8Fgvu+vkFcj+vjasFnFFA1TXyYVGA0kIAKuq9fxZhjXREqK4yrOUImYezBKN8WfFErQeJMVoSoKjFlunizkithk0OO+YKGXg+v5IC7XZvvQ5hULUVIKDdaedNQC74oFVCGn1PTRJVJtV41sWT7/JUM54BqtAWf8fsAEz09g79rt99+e0nSkiVLNDAwoGnTplWX2XXXXbXzzjtr0aJFkqRFixZpjz320IQJE6rLTJ8+Xb29vXr22Wery9jXYS1jrcNLX1+fent7Hf+ywB5AzOVy+vZ939bRfzpa1z1/XUPrdVdA+c2r43V7UMsnx9gz0uItDHdixx5I9kp8uB/nrz1/lSTdufzOSI/vJ+zzueTxS1SulHXuw+e6vuqzEZAOqnJyBPK9WvC1cAKkrLJ+/9Lvtbm0Wde/cH3tAik8hUe6HjGukGnLt424Cii/loLW7cbsP/PslasRT0Q57HeHacbNM/Tyuy8HLufeN7faZwLeSEDFqJBCAsqqgGqvUwFVsO0giim1u3trXZ/eWLtJ0nAVxJ47bStJenn1ep34q8f02jsb/O4OoEVUKhWde9tzOvf25yVJe+w0rnqbVVH05+dXpzI2yVYBZZDksfatSbUutfbptOBDKI0kV2rOfK24/m+t3/4LxKeawusMfq/xmbbj8lwuYgA4TJKuUpFe/rPU+6bPur3uG3DsZ9KuLWjdjusNE0smSS+j+/okFaNWgvgxSeZFSj6GPSaPMWFmnDQxHK/n9halAqrRJEyUpIbhuuJowVepSC/eLa3rrl3WNMGXRgWUtW77a1Dq92+jV/MZN0hA+b6+YasLOUbxUy6XNXv2bB100EH68Ic/LEnq6upSR0eHtt12W8eyEyZMUFdXV3UZe/LJut26LWiZ3t5ebdq0yXM85513nsaNG1f9N3ny5Iafo58wVQ72pEdOOd392t2SpKufvbqhMZQqpVCByqDWen4B3KzMARU2CGsPGNufQ72Wds2cXynq+vK5vO/7XFFFz73znA773WG645U7Gh5jGPb3J2wFlO/zSbni7uvzv65ZC2bVVqHI+/PhWVEX8F5lQVuuLfNjDCvoOQQl5oK2N/ttUSug1g0MnqD84JsPBi7nHrPX+xDn/IFIBgmoGA234EtmJ1WpVKqP1VZnDqh8Picr1ppWC75Z1z9evWxVQbxnm06N6WyTJN39XLdO+NVjqYwNQHwWL1+jXzywvPr3rhPHVC//9a3BJPNl9/418XFZrGImkwqo9oTngKICCuHUq4DySVa8eJd001eljWs8gpuV2vuq4lqX3xxQQ5f71rnWaVoBZfA5i70CyiM4/er90rVHSxd/qM5YDBMHZYMElPF4IyQrGqnsqm4PruvirGqrWX+MVRphKzniTHLVXB1h+673mI45oHyq1Ry31XuMCIm1WN9T0xaVAZ74tXT956XrPld736AWR0bVXxH2K0HL1auA2rzWf7t3n2AQZv+5ZK70x5mDc0y5x2Ffzm+8BH18zZw5U88884x++9vfpj0USdIZZ5yhnp6e6r+VK1fG+wD2HHiIRIXfXCKNzulRrpRDtc7zSyxVKv6T3melAipUYsL1smYlYRC2BZ+lLd/mu55KpaJvLfyWVm1YpW/f/+3GBxmCIynjat8elJxy3+74akhxe3tr01t66M2HtPD1hdXEgcVvu/BMaKaQXMvlcsbbUyFfcLzmQXN5taKa7S3gZQlK/NgvNzoHVN35+VzfJ61YiYZaJKBiZAUMywl9OOyJrvZ8/bfSmicqrRZ8i5evqV62KqAkqbN9OHv+Yvf6RMdUz+rezTr5N0/oEdvYgaxY31fUKTcs1Z+fq+21nKbeTc4Dz9Ht3mfIpNWGrzT0g8AkyWMl95OqgLIepkBwByas7SRslcb1/yw9+wdp8c/9K6CCEg5+7bysywOuauaaAKr3cI0SDFFbYPkt5/VcVj8//HdfwHGJSbWIfb3uy0HjM2rX1mgiwffOwxerbRVdgfKmtuBr8D2V4fviedcoj9Hoe9rAdmT/rvB6r9y3eY3L+SDej+c3DtPlGq6mMk2QSFr4o8H/dz3lMY6QCb7ahQzH4V7OoLLLumx/r9Z5HN9Zt9fMTRkiAXXrN6UnrpWe+FXtOLz+rrmeYxQvs2bN0m233aa//OUv2mmnnarXT5w4Uf39/Vq7dq1j+e7ubk2cOLG6THd3d83t1m1By4wdO1ajR4/2HFNnZ6fGjh3r+JeEMAkoe5C40aBmUAWU15j8qjhqlstIS66o1Ug1c0DVS0A1seopiGlFg3s7cVeG9ZX6Yh2XqaAqp3rbWhYTnn5qkhS25113e8pIIsHeBrGQK/i+zll8/evJKee7veUUnJgLmm8pjgqo6rpCfEfUnBCQkW0I4ZGAipHVMimhueodZ+QX6lRASenMUeVnVNvwDmtDX+2XVFZ85+ZndOuTb+qff+7fWxtIy2X3vqybn3hDX8tY5aC7IrOz3fur5rV3NiYxnBrVFnwGP3La8skm7ktUQCGUMHNAVZz/lwYnuHf/UKzUXBhaj08VgeMxGmzv5FeNFbgu00qFEG3Cxk4avmrlYu/7ue8bGNiOUgFlENg2bvtnBbbL0m+Ple60nRHsFezzfE9djxs1cRBrtYzBe99I8q3Rx4jynpoGOqx127/Hquv3SkB5tOB743Hp5wdLy+/zGWOMny3T99RkrqSg9UlSj2tya+NkrMFny3h7jvJ59qhsWt/lcd9K7XKS92tXMw7XMisfrR2HyXg5ScahUqlo1qxZuvnmm3XPPfdoypQpjtv3228/tbe3a8GCBdXrli1bphUrVmjq1KmSpKlTp+rpp5/W6tXDLarnz5+vsWPHavfdd68uY1+HtYy1jjTYA5mOgGKdXZlfsscvMNpX6tPj3Y97zp3jXq9v6zyvBJR9WTmXzVJCYOPAxsCqrHrciZ1QFStNTB5EXZ87+F3zuqQU7gra3sI816wkbByf74DEhP1519uekpJTLjChWawMHxe554BqtOXmxoGNqVZO5XN53/dHqtOCL+C9st/m99q+veltvbDmhbpjrPf6BLZHHbqt0YpZJI8EVIysYGYpoS+JAVumy6SVlBXQTOpMfruyK3g7ylYRsWkgnSoIE8vfzlZFFmC3qmdz2kPw5E7sdLZ5nyHzylvpfL6sBJTJfrOjLdn9prWvJAEFI6EqoKwkhC0QPWqc4ZwrFVfiyqeaIs4khO9yIQLRgWOwrvdIDtnXueGtoJX6j8vOr2LMd10B6zOqFHIvN3S5+xnphdukxZfb7uuxr3FUu3lV1XgkoIwrRgyWa7gFX4SETnXxCAmGVCug8rXXeVYOetz3V0dJq56U5n7aZywJJAJrks6mSWyT99Vj/xgUvI7zPY3y3lv3se+ji33+z91vbqgw47CqVY2TigH7jS3YzJkzde211+r666/XmDFj1NXVpa6uruq8TOPGjdPxxx+vU089VX/5y1+0ZMkSffWrX9XUqVP1kY98RJJ02GGHaffdd9eXvvQlPfnkk7rrrrt05plnaubMmers7JQkfeMb39Arr7yi008/XS+88IJ+9rOf6cYbb9Qpp5yS2nO3CxOo9QvU+wU1v3Xvt/TleV/Wz5/6ef1x1Eme2JNYgYFm311bssH0l959SQdef6BOv+/0yIkJd6A2KDgtJZdwCzM3jV0hX/B/DpV0Eh6DDz38uO5kab0KDr9kbqoJKJ95w9zqVdT5afb7FLQ92d+fXC7nW/UXdoxrNq/RgdcfqC/d+aVQ94uTez/qnnPPS6lcqklyuyup7K9R3ieV8IkbP6HP3/p5vfTuS4FjrDs/n+OYLHBRtBASUDEa6nCX2JdEyVYBZc1TEsRaJo0KqFffcbbj6WxrjU2PfR2yLKtt2vqKzh8yfp/351et87y+2aoVUCYt+IYqoAYSmgMqzNiAukFAr6CzIxhbDGjB5w6K2//2q4BqNGAdoQLKtAIhVJWY7bpiQKI/zhZ8oQPAUuBRSr1KFut5eX2PeAXia14nw8RSpOSj4bqMEh9h991REmYNjtc0Yeb13nsloLx4zUPW1xM8FuPkbojqwnr3bXQ7sit01D5GYKVik7ZBKURlV6W2Ws339TVIQNUdr89JDFRAhXLZZZepp6dHhxxyiHbcccfqvxtuuKG6zMUXX6wjjzxSRx99tA4++GBNnDhRf/jDH6q3FwoF3XbbbSoUCpo6daq++MUv6rjjjtM555xTXWbKlCm6/fbbNX/+fO2111668MIL9Ytf/ELTp09P9Pna+QbNXdv26o2rHX/7JX78gqP3vn6vJOna56+VJP36uV/rl8/8su743ON46d2XdNBvD9IVT11RO2ZXcse3IiLhCMGvnhtslTnv1Xk1lQCmcnIG2Ouux+drKfYKqIivpbtlWtoVatXHDkjshdmGsvJ87ILmBrLzrIDya+mYjadWk7RspALt3pX3SpKeeuupOEYWmd/zyeVqK8P6S/369C2f1swFM4MPQz22y64NXfreQ9/TsjXLHMs+sfqJ4PHVeV0ve/Iyx2Ol1RYU8WqrvwhMWR/kpBI8VgVULmd2try1TBpzQP31LWcCqlWCq7QXRZaZtJBLw2ZXVaNfBdRf06qAGvpcmyTwrHaCxaTmgLJa8GXzrUXWVLdhk+SKRyKhXPIPUAYFhsse7d3c1zvvXOdvj/H6LWd4hrJ5gsQj6Gy/bsC00tTwgKHRFmOmwXSvqqtC+/B1/Ruk9tHmLfiaXQFlcsAVpQIq7IFclNaCRokl03UFDq72otfnz2sdjqRGo8lS0yRwlMRrg9uRnbW9x9mCL1JlU8jl7PsIz310efg2x/UGHSXiaHGIGiYBylGjRmnOnDmaM2eO7zK77LKL7rjjjsD1HHLIIXriieDAXlqCgrbzX5uvY3c7tvp32Aqo6u3KaXNxs85/9HxJ0j/+3T/qPaPf47jdMSZXwPSCxy7QhoEN+skTP9GJe57YEi34/CoBQlVAuVvw1QmwJ/Xcowb63S343MmeTFRAhZwDym/bz0qwPahlWt05xXySIWkKapEY1P6tniy0hcsp59hXuLdF9xgf635MK9et1Mp1KwMTjV6XT1t4mpa+tVS3vHyLnjzuSeMx1tuun33n2eFlXScEWO9VFl5rhNMaZSgtolBtwZfM41lzQLXnzd5Gq91UMakB2pi22Zv6tzs0eSThZOULEvCS1URu34CrAspnDqjNA+kEOEpDgVmTufPak54DihZ8iMLvu8qr6sGRgCoGBJNdwVi/9lSxVkAZLBelRVrQY3q+RrZlgyqgjM/iNAhsG1dWRKjm8GrZ1T90AkC9BFQ1keXeHiIkaoIYtRaMEtQPm4Ay3L7k83kIWlejLQM933uvcXh9bgzmIasZiunn1CRZ6vd4MVc02uW9zrOsBDwvk3GYHre433uTVqeq/Wx5VUBVTxIwqBhtWsKMYxQMcrToCkhUtOWcn0d7UNcevM57fSe52Jff7PEd7TsHVKWi9ny7Y1lH0ing+6pey7qkNFKJ5Zcc8ZwbK6GEm/tx6iUgLe7tJCvVEaZzQIVpP5aVeFTQmO2fo1AVUM0WsDmZft4zkv9riLvK1J246S/1Dy8btN16vGbL3h2sfAq7XwyzfFnlzHzG0RgSUDGyAoZJfUlYiaQ2w1Plhyugkj9o6i+aPWZ/CvNTBWHXhizLao5ic9FdAeX9VZPW5916WJMKqPahOaBM92GNqrbgy2h1G7ImRAWUV2KpXAoIbrqD3a771axXtYkqz3EEjLfm+MQkoGqYrDCq0vBI1Ji24AtMHBgkK4wrEAySWe77VysmbEGBfqsy3Wtf4/G8aiqgTBNmERJrDSchDF+jevcNur9RIjDCaxSYHKrzvnhWqw2JtQLKNFkRY3I6ymvpVQEVNJZIieK4K7vKtduD3/7Tvb80asHnSlr5VdHSgg8NcgcIC/ngihVL2DPa685f5ApadlitOT1udyer/AKvSScEgioQlvcs14w/zNDNL90cbj11Emp+LRKzEvgt5ANa8LneuyQFJjXqnLTkt+1nJaFWM6eVz2ciK3NA5XK1iRbTx65XAVWulHX8XcfrO/d/x/Nx05bP5X0TzpJqDv3tSUPjbdhad9DcmgHCfEZ9l03/pUZIJKBiZO1rkm7B12YYhU5zDih78Ha3HccaLZcFGTnhBPCU1SoZ9+e4w5aAOvvTu9uWi3bA0qjy0D7QZN/ZVq2ASmbfVB7a6WT1vUXGWAceYQLbNe3VfIL6QQkH3zmgPBJa7mW8/vZdroFkVpQEiVfybWCT9/3cjx14wGCQYIi9Aspj/1qyBQWsBJRnBZTXeA2D4rUr8x+jYzGDCqhI7fFC7rujVNgZV38ZvBahk0N+iWEXr5aM3g9SfyyRKhoN19Vo8tF2Fq/yIRNQJuONmgAPtY3U+axV9+Wu99vz/Y85YUYFFFwcc0AF7L+CWqbZg5r1KqDcwd26CShXYsJdARWUXMlKCz47d2LonEXnaMW6FTrrobMC7+cOxNerpAp6DePUSAs+3wRhiu9P0HZif83rtXXLSqLPtOLO/vp7VkAFJIXT4th35FxJJ58ErOX5d57XI12P6NZXbq25LQtt4XLKBX5ugyqggiqTvF6LqPPSNVLBmZGPByIgARUjK2CYVIKn2oKvYPY2WuMbSKEFnxVo/tj736M/zTrIcdsP/mmP6uU0kmNByjEfZAFxslfJlDP02bF/jj/2/vdodPvwj86vHDSl+plPK+FsJZNMWhi2V+eASub1rVZAkYCCkShzQNkD1h4t+DwroFxBUb85oDwTE/J/jHrLeT2vSG3NfNZVc73Hcy/2+dzPPRbb5f4N/ss1Ol7jqiuvyi5bUMBKrHmdqel136DtwX2773hVez/P5UyTiiavZdh9d4QEQ8NVbQH38X1Mj0SxaULSuHVjo5VdDVZA9W/0fp5Bj2n/vFot+BpJRgctI5lv92HmXqq3T/Wq1PQai1ECnwooNIc7UNnmaonpF+A1Cd7WC+QHJSbcFVB+rQCDJr1PvALK5/Eqqni2IPSSy+X8q568Vu+I9Tbv+UZN7LkTmjUVUClFqOu+rtZNdbahLLbgC5zTyvY58qqISeP98Go1F8TvdfZKyNRUFNkfN4Pfj0FzQFUqFUcCyrGs66l4JYGibp9hW/Zl5SQANIYEVIysYHBS3xEDQ32kjFvwDY0vjaSK1WrrvWM6axJm/3rgzrruawdKyl4CKiPf94Ane5VMX4aqB635kv55/5107dcOrDkQ23HcKEnpteCzdjMmVUZtQ/urpBL3pRDVWUD9Cqg6iYRyUCWLK4jvWwEVIQHlm2AwmNPEOJkVIVDsVSVWDFkBtfIR6QeTpAXn+CxnWoHQYHWPV/C/ZD8r1QokG1ZAuatDIrcYM9lW/VYV4b0PXQEVpSInzm3QMKnomZwNeK6OljiBUbH6y0WpKPJdxh3EGXrMdV3SD3eRfvMvPmMxSUAVwo3XKHFpmlCNUK1X/du9/fo8Zs3+0uPzUTcR5vG94DUu1+JUQMFLUFAwqALKftkkeGs6b5PXmDryrgSUz2czqHol6eCnX2VGGO5AfL1grl+1QdzPPWo1TND2lKagcYTZhrISbHd8TgO6gdSdIy2hhGYY7n1HmCSz/fPkfr6ZqIAKSDh77QscCSh3q8U622LUz15FFaM5/6qP6/GWZOG1RjgkoGJkJaBKCWUtitVApdnbmE+4QsvOqnTo8KnWqlaPkfEBjNlzFH0ptbPzYu1jCj77JqslX9oVUCYJqPZqAiqZsVZb8GXw7Cm0oLqVLCX/RII7eOxOXHk9htecUvZ1+v3tta7BK7wWMljG43qjShaP5x5UAeVYzdB97jht8P/3X+j92A1XyxgmKxyBgqHl7BVQQa20vBId7iSJ6Q9O4wqoOsmVmmU8/jZ6/JDLN1LdE2UbNE0OeVU0BiagYqyAijOx5pf4ePqmwXZ6L97p85g+6yt5fV5Nt0HvgGvg48b5ebaWq0nq+6yrpgLKK7lkuj2HHC+HKBhiTxiFCZr7BePrJaByCqjk8RqHq5LEXQHlN6bAXXGKAfSolT7ullx+CUD7ur0eM271gv6PrHpEz7z9TM31eddvzDQThH5qWjqGqGpKs+LOLqjasF7F0EB5QHe8cofe3vR2au9J0P7EnWQN03LTvt6gaqg0+bYUdCV+ypWy+svDCahixTmHV6XOsZHfe2tS5WeaQApKFqK1kICKkRXMTKrCqBi2Amro3U6lAspKQLXVSUBlrgIqW+MB7Owfl80D2TjzS7InoLxvTzsBZcVlTZI87UP7pqTmgLLyXLTgg5F623C9YLJXCz6/hIPjh1qpdnnrMbxaTRlXlURoIWUaUDUKOltBfdtzKNX2svd+jKHLG972WrD2MQLX5fG31/1DV0AVa6+zb0PWfs7rvklWQPmKOaFjxCRxmUCixn1/r+UC54DyeO+9H6T++mKtgPJZ19bvtY0joArIzZ4w9krSef3tuU7D7cY4oeP3WnptI+4ElM8+L0oFVM37ELYFH+AvKMDuDmr6nTWfrxOeyilXkzypCU76tc7zCLwGJWJ8kwAJhweCAsnGcnLuWuoE2IOSeI3aOLBRZz90th5646Gax7QHpNdsXqPj7z5e/3L7v9Q8biFXCHwOacVwguYOclTueSU1fILxWUl4Brbgsz1va7krn7pS377/2/rynV8O3J4qlYouePQC3fLyLbGOPZcLbsFX87raD3/qJGiDKqCywF1Z5N5vOE4cqFQctxfLzn21XZzbYiNzQBGjbV0koGJkxQuTmoulGLJVUz7FFnx9pdZMQA1kbDyAnb0qJ4sVUH7VmVYlZFoJqOEEWRZb8A1VZ1EBhTB8g6AeVQ/1Kpa8qoDcCQe/agqvYKnX+PzG69ceKvC+DSY+vILO9udga0sReF9r/Rve8ljMIFlhGgA2aZHmvr91n3KdFnzV7cVrvK7nGiUJETTmKNUyYZKKpmJNGpkG9U3H6/Xe+yWG3XeNkLg0/myZvPch39Ot3jN81aY15vsQ++fVc19mOJa4KoXCLue1r/Ubv/vEGM/9ZyOJsKDlOEZBfWM7xlYvu4OaDbXgcwVTgwKZNYFX27ZbKpd8EwZBc0AlHXD2nQMqZAWUXb3nEKYaJKwrn75Sv3/p9/r6n78eGEhe17+uenmTqx1yIVfwf+9SDE4HvlaOw4Y6Y8xICMr0dfX6fNz+yu2SpBXrVgQmhRetWqS5z83Vdx/8bmzjNhHYgq/eNm/7OLlb1mVhDqigz3u9aiLTRGOjypVy4Gs1aetJjjF4vSdZeK0RDgmoGOUTbiNXHAqIuudU8lNtEZhCzLduBVQumwmopJKJQBR9tqqnTFVAVYITPNUKqJTmgCqGSEC1D1WYFhMaKxVQCMfn7HVLvVZq5aLHfb0C265AZqNzQDVSBRR5XSaJBI+KibAVUPVagDU8XoMguSTP5KP9uVTjyPYKqJL/Y9RNNJoG7ENsq4HLePxt8vj172B4/zjfU9OkYp1tNej1KPt8ZoMew7QCyiihE/Izn7fN79G/3vwxHRVQHglVr8f0vN4w+Wa83YfZRtz7VJ911Zszz2RfnAtbAWUlrjlGQa2goG1QAsrRgs8guemuyjCdy6VScZ75X1OV5apQiaXyKAZB4zBNtuRy/i34rHV0b+iuJn18EwYxxLlWbVhltO7OQmf1sj0ZJQ1WePi1YqyXlGymwDmgbNuXu52dJEdSIyvVHl6VTRbfz9nQ9esH1nsv63pv1mxeE89gXXLyroBa0btCA6WBmjH5Jdu8Xv+szwElOcdtf++85rcLqoAKSh42ND4Ft+DbumNrxxioehoZSEDFqFphlFBMdWDogcxb8KWX5OkznQMqYwkfZ2eabI0N2NA/fICQxQoovwRP51ACqi+lpFl1nqVQc0Al8/kfTt4l8nBoddaXlElA1bMCqugfAHYHYxuqgIoQsPZdLkKCIOgxaxJtrnGErYDyXMwkSRBlvCErWTzbsNkPdIZur5fo8BpfM96HmkViTuh43tfweylKBVQjz933MQ0SRlK0FnyNJtZM5p3y+8zbry/2y3h7c1RAGW67Xtc3vA26q5NMK6Vc+1qvfWq1ojFCCz7fpGLYz1Y2AmzIBiuIWNNay7Z9ByagbPerOwdUzjkHVKlcciawXLe7A6b2gGexXPStxDKtjkiEfZcQ8bGD5oCqqKK3N72tab+bpk/c+Inah/d5PaMq5IZPMKh5f1ytwSzuBFRQC75UW9YFvFZh2jgmNQdXPaaJMK9WfRsGNnje7tbUikLX7uTBNx7UjJtn1FTf+VXYSPUTUO7EnP22tJIm7v2o+7m6E2j252Cv6HIniMJsl3VvrwQnoGoSYSl+DhAfQlwxKiTc4s6qgPJrc+VmjS+NHeH6zYM7kDGj2jxvL1TnWcnOjqVSqejt9cM/ZDcNZCfAD0jSpv7hbXLlmk0BSyarXgJqm6H9wPr+YiqJXWt8eYMzeK0E/0BCFVDW60ELPpgJUQHllYCqVMyCzO4gaKXkHQD3S0DVBFm9H7LuGf3WY5iszLjypk6SLjABZTAO923GCY4QFVCVivS7f5NumemznEcFlDUmrxZ89aqRvLYb02SFn0gVUA0m6Tzv2sB2U3ddjSbMvJKldcZRXc4gGeS+zbr86oPS5R+TViz2GG/A+hzrMng8x7ptdyj1m29HXnNA1ex/GnkfIiZeTRNVNdWmAdWGNftLkxZ8puOgAgrh1QRwbZtfTQLKZz6cenNASbVVGTWBfr9kREU1rbP8gt81VTSO3W+yv13CzMfU29+rT9/8aV2y5BLH9UGB5HKlrMe7H5ck9Q1VcTczKWCfnybo+di3kXUD6xy3u+e4cb9XaQWrgxIX9ZJkjsRFBhNq7kSL37DKQydc9Nk7AgR8ftwt7Jrp9y/9XpL0aNejNe+VX4K2rLJ6+nr06Zs/rUsfv7RmnTUVULmc721JqUk4y/94rKKKY/88UB6oub0Z3BWpbvbtwi+Zm5VqM5gjARUjKw+UXAJqcEfSblgBZX2+k2oRaLdu8+CObOyods/b26oVUOnspL089Nd3HH/bq02ALLBvkzOvfzzFkTiV6iRRrP1ApZLO56oUYv689qEde1LJcWv/TAs+GKlbAeXVAssVZPWrDqhJdPgEXptZAeWZgIoYADYKKA9dtv8YNm3BFynR4rMu0+WsZdavlp75vbT0Wmlzz+B1Xi3XvJIQ9iCOZws+j6Rc0HZTO2D/8Tuu90is1SwT8BotvkL60zcHK01CnGXs8SC1f//1HumGLw6+zl7LmVa1mWwjgRVQXkk6wwSUowLK8PW1todrPiV1PS1d+1nvxzFKQBlUSQ0u6PifpKEksOFrWfJIQBknFQ3GGyVBKXkkh/zu7/pseVZADf0/SgVUTUu/kC34qICCh5zPSa6OCih3u7uIFVA16y0XA/fzNa3z7EmxStE36Dx0B8/HjDsgO1Aa0C+f+aVeWPNC3WXrjePml27Wq72v6qpnrqq9b8X7uVZU0cbixlCP04igCig7+xg3FTc5/m7Lt/kmr9JM2AQlHPxef89lAxJZSTKuCgxKVMn/PalUKk1L0nglJ0a3jXY8dvWy/Fu8VSoVXfv8tXq191Vd+fSVNbd7PV+L33O7cdmNenjVw773a5S7EtT9eXcnge0JqKDnE+azVS85VFY5VAWU1zhIQLUeElAxSnqOpYFqENWwAirFNnfr6lRA5TPYgu+tdc55HDb2UQGFbNnYn81tsjiUSA5qwWclzq19Q5LqVWjZpVUBZZIcA+oGAT2rZepUQHlVIJgmqsolswSUSQC4Zgx+942QrPBbLnQFlEfyqp4oLd587z+0TMF2ck/fenlWUbjXaV32mgPKaxzu+xq/p6bvl0EFlF+QvNgn3Xma9Phc6Y0l8nzuprwSDL/+J+n5W6U/ney9nO+2VWfdngsGJaA83r+6FVBD19lP8IqaBO235nOIkNAJnXS2Le9ZAeWzPpMKKJPtK9bEtvxfc8/l3O+p37rqPUbFe5mgRKXpeKmAgoegSqRiuajVG1fr8icv11sb3/JNGJgkoOpWQAUkJuzB1WK5GLisX3Ik7oD5b174jS5ecrE+f+vnPW/3Dd67xigNJma8uF9Xd0WEvV2aFPAaxpAMcYzFleTza1/mbhOWz+Wdcyq5EiVpJW1M29SZtCezL+uu+EqKV2u9est5fT5MK93i5k5QbNW21fA4ApJ87r/X96933h7wfB3t7Tye2+Pdj+v7D39fJ9x9Qr3hR+Z+3u7Ph3uM7v2inzCfqzDbuOW+1+/T7178Xc046r0/aB0koGJkBTOTroAKOwdUUuOzG05A1auAys7OxJ0sowIKWWNPQO2507gUR+Jk5Wr8Ejy5XK5aBdW7OaC6oEmG51kKMwdUMgmoantAElAw4TeBvMWzksUV3PRt7+Ru1+AToKy7Pv3/7P153GxXVSeMr6pnvENuQhKSMIMiQxQcUCEO2PoivDbardLabYsDjQMabZVf+1Hfxvn1Q7dt4+sQ0W4RtBEBEVQQmSFKBoYQEELIQObh3iR3vveZquqc3x9V+9Ta373W2nufqnPqecj58gn3qVP77L32WFXre75r6W0ggif6pXKJzu865SrfNyeg2Bm1eXz8n1TnXMOwCa8DI0meg52zurM+uh4kBVTpv+fqq6OCwfZS7jfLTOp66JbppeEWtJH7vdIgGG7/J/l6FrkgFatB6EgkhBjGRpk/uQF4CfWtHgztkF5X11P2h3b+AAGl7eeyJDp+x/S1REClEoGl+kK/N5l8zDgfks9ouDemiFLry0Dn9OmQCCQBRsWIfvoDP01XfPIK+ukP/LSugIo8WNOjnk9ACWH0rFwu6GjFUFua/ZYSxOGqe6+if7jtH8R7LNx8/GbzfS13kGTH+tK6+r4ayo9K2hzqodznrYbSFFBYNebn4g7ppf6SqShqUgX1D7f9A/3E+36CTu2cCt5LVXTl2hfbF++/8/30sve+jB7afCir3hi0HGlEel9FAsrob9FUBKSeQECt7BeLxghn3B+mmg3CfCLuPHVn1HSOoiyq9o5vHafXfua1SfNshRT0ykUUUDnKvRxgzjciosvffzn9+jW/Tp8/8XlPNas93JDywEKH3YWOgJojnL+w/RxQqSH4JgRUSwotDheCT1VAOfXYLvphgw7n3ao26fDwxcb29IP5MeftM0q2i1FEAUU0PQt2uwLKKbXceds0hl0OqA5ZiITgKwSnc6BKMBy71bVCd6iiU1wikeqSFdIP1lRCIxWpCqjhDtF/f+L4v1HGuZWrLIiVk8ghfu/OmZA4KIM/SFwP7geoSFRJ7YqNRK6nkCuJJFWlgNqaXts+5beRuz6s9bW8ppSbkayoRapK+3QUlqsUUMPwWlA9XMe9vHpAti9prWYSlPz6cEc/Gz70SqLf+3Kiq/6/8WuuWKx+8KSugZR1U4MsJFLIQa2+CAGVvD9Kea54OS2Ma9TJ1H1H6TCF5ByXHLqfPfpZIiK64egNqrM3RemB4b7MHFCogGJ7Ee+1VBopJMzL3vcy+sV//kW65/Q9dPPxm+kb3vgN9Oc3/Hm0P6tLq+b7pgIK7OQhxnaK6XloKSKKsvDy9WA+nHkTUKm5jrijfFSMPIc0J7EQTYfg+8V//kX68L0fpj/51J8E79UJWeegOdNRsSLhZz/0s3TVfVfRqz7+KrNcLlKVfzGSwlpPlrIqFzFlDCdoLdUckoVIQKEKk0Nb3w6YY8nCoBjQi/7+RfTTHxgr8H/+yp+nV133KvrJ9/2keV+QAwofKuQiRCCg+N9B7rg57i1rro5tHfNet0kwd2gWHQE1R0xD8LXrqFxeSgzBt8AcUKciIficims3KaB2wOHcEVAddhvOsjXZlkInBTEFFNFUDXm6ZQVUWZZTlVECyeNCnA5aOpvcAwwp5FiHDlOURLd9iOjXzh3/J+XyIQrJIIkcqt4DB3uKE1Rzls7iQA3KpBIasfukgoLz2zm0T98/vTbwcyXYbcA17Smg5H5JhBknoM7qRE2MbKvWjTAmXk6pHKd4qsNeaDNa18QGT6V2Qi6TCssRv3JALjdvBVQWSRexo1TmL8UOLFcRUDUI5ew9z8pLIfjc6yv/+/jf9/3a+F9RAVWHNJo3qZihlOJtFCO9zWi/gMyq6lOUUdLrT/7V+DPld5/hG9w9JNNBQE4eHo3sScnpwYkJDKOH9eHT/tyRifeiSsBS5xCNQ2m9+J0vphuO3uBdP7Z1jH7lql+h0zun6Xc+/jvR/qz05QgxEmLjusYelDi9c7r62wrBJyl1clRXuVjqMwUUrBkrBB93jPd7fdXBjrm+msKJ7RPBNWt+ckIZBoRN4pF7bPtYvFAGrNxO2n4Xc0ApZeedA8oL50k9UyHD7YwpoDhBS+TvH1Rw8TUsjUUOAXXzsZvp1hO30pX3XElFWdBHDn+EiIhuPHYjERFd8ckr6D9/4D8HSqtgv8P+QBs1AkoK81m9N+NZEJBKrD6LYJ4Y1mGPoiOg5gjnMGyL33F5VlZyQ/C1TPKMipLOTJQah/bJX7CWWibvUrAzBAXUdheCr8PuwgYLC4mE6SLhFFCWOnNRCih+xKSoR5crBVQ7BF8OOdahg/f0+tt/Znr9xCS8g+hUBId1igMYn8rndaPKQVIqBMdTojNaKpesgJqF0BEIqDv+OawrxbE9d4e1RDhECCjS5hTaSVVASYRk8q9BbYwE24JyytzzH/Pbp+B+qOvK/zHO5VSWRLddSfT6f0d0/E69PH+9sk++PiuxFMx9xlrlEHN4zaCAQseJC11Ti4DKHCN+HRw/XjkEV0BJ+yPVlnmeK7PUp52pYllJfYq3Yn1KGFdX5m9fNv735F3TOr37OnSYOlzRsWwRUhoBEgup1Ov1TKc4Ap3+GILvgc0HRJvGX10UgmNS5w+964foUw9+in7sPT8WtIs5lSzEFFAcwRgbn72bg6lqo0e9YCx4nX3mFixKn3ybt9qAq9wsVRCqTNDJnhsikePI2SP09s+/PYsMSIG15mvngEpQQDmkluO4+9Td9K7b3yWOG4al9GxUCNoYoWTN86yIqbQs0tIiCHGdmAqonkyiOuyYuWV98JCB0pnyx5/6Y/rg3R+ka++/NrSBz4lBOD+0+ZDXB5OYy3B0R9c4vM8Vjv1eP2kv1VnvHRYLWY7SoRbaVkANqhB8aTziosLcnWHEjaaAmuanmnzI7gLnKypKznYKqA67CDvDojoDxq93z/p0ZlkqnioH1Ga7Cih+PqfkWVptOQdUp4DqkAfmPHzcs8e5UIgYuSA5M8E5rzqAkXDAYoqqpmkFVG0HcALxI5Iyk7P17y4Pr1m2VA6WGs7vVHslZ30x1HPBiMRMhICrrnEFjbBukvuVQNKlEpQVAcXIlWJE4pwSjdVnH/x/x38/5yeJ/uLfjP9+y0uIfvQD6fbi9VRyYZ4KKGnuxfqFNZ1KgqJar3pqPbVfKYqiFAXUIG1eNo7JRFstInCO5woRBeSQVV9wpip9j9pSynZYSjmsa/XgOKxnVWZSbhf8Tuuw+5AaCg9fz6KAGhV+GD0MPWWF+xqWQz8fi+dP1wk0VFWd2jkVOL7PDM5E++EQU0BpTl/JwavlerEUEVSSn7fGCk04BzJKywGFSh8rB1RMOROz88X/+GI6fPYw3XvmXnrZl78sx3wTFhEWywFlhSZs0tH+r9/2r4mIaLm/TM97wvO891LJ3qgCypiPuSqgYMxxTFXiqBTWIrOvD7oNKzcWx6wKKH42cEUjAkmt4ByE85a/d8epO1QFVPAVIoM4jSEg+Vg0g+W+7zPWQvDtBp9xhzx0Cqg5wjkz2yJ4Nifqh/2rEYniBI6Aaltk5EJsrS73aW1ZtpU7W3eLCipQQO10CqgOuwebQIgOdqECKiUH1KnWFVDTcUoheVyI07ZyQOXkp+rQwVNAHbx4el0iDdx1dJxrDuDAwa44KJMUUIlqmUDBESGH0E6rDZVI4H9LfTJUJZZT3P1orO2wTnDYS876Ypg4p0JfJWKtInkiCqhkhYdUpO6cTu7zcnKVcj+JiLZPTv/mT5w/eJNuizcWShi7uZKKwn2iLYK6RyQWnAIKCESx+oiyyZGqc1VAKXuelx9uU7iOFCJTDDXYJKmo3DPT3MfOaKVfUv69oDnpjJbsmJS55BnsGt9b3XeUDgzVVxGdZEJoYfJyc0BJeZD8LeQ7Xr0cUMWIzuxMiSKLIIipALijuUe9LAXUylKEgEp0JKMdaJOl/ggUUBrpNIefQ94cK3OF7Y5KPwcUvo9/xxzlh88eJiKif7z9H5PtTsHcQvDB+LfhaP/csc/ZdlgksrK2YmWRFJ45rBsnXSEEH5KWpsImMv6WAkor55CjgOJ2IAHlEfeR9WGFqDw7OOufG+yMtIh4aa3nENRYlhNzS70lc03NIxRoh8WgI6DmiL7y5aspOEXO/tU0IduiQvBtT4ic9WV9uXFn63AXEFCv/Mcb6Vf/3o/nfHZ79yhMOnQ4C4TodXcep3/7hx+mUy3nVJKQQqKsr4zJ6O2h5pRpBvx8yQnBN9DytswZTmjVheDrkAaeRZYTCZPzQXJIBoSR5tgt88sl5xdJdYBPyl3520SvupTo5L2GHXg5sZzPQIV2SGHNFqmAkpzcqIBKcVhr92LbIikHTnILKeRj6tOvWl38adLANvb3xrHp3/zpyh3+pLpBMBRDudy8FVB1594koFJC8KEdsPbdU+vJ9kpkqVGGl/P6JeVBktbRiESSsEkFlCv37v9K9HvPnK4x7Jd0jkj1BQ8JSGeqMEZSmx5hxK+xcvwhBqmu4Cwo/fs6dGCwSACL0PE+OiPkZo96gRPZUkCh4xVD8HFnsBluLOLfQQfp5nBTKRlitW+H4MtSQLHfK5ibxiIMOCk0KkdZTudcaCH4iPz5R5UJOsY5gjWR6I+T8jjNApN0jYyjpoCiMj3U2CxKqfPWzguueWpDSwFVynuuet9YN6lkTgpyVHG4vjRSvCzLICeR1d+YOipHAcVtRlUlVyrhvJtKUDgzt0fbnk2DUrcvN7yiBazLyvEWtDN5rwvBt/fQEVBzRNt5jM5OQtsdXEtUQPXbtc/Bha5aWUojoIqMg6sJDEcF/cmVtwXXNwcdAdVh90Baj5+65yT97ntvXoA1PioCynBQVMROS6HtHLwQfAkOlJV+2wqo8XikkGMdOkydgOBodD/SRQVUhBCQHMBUhvelKqDMJ/jRFMW2D/4W0en7iP75d9LrsogE7bqlCvrS72bl3PhabSQ6iVV7c1QlSBYmOKwlRZnYr9J/z11LnlPsl1QmgaQS63IKKP5jGfYCr3vjqNGm0gYSrFK55L6nfo6k9L+Ef0kmOUph/lLXINZXKaBS91YNAkokwIX15t7/shf5dYlEWw17VShr8Jo/JDpxF9FH/5ddLqm+yH4uhTES2yjDMoVA5ll14TrvnjruIKByAnrb1s4doqmNojmggIAqyM9XhPVZpNioHNE2yzEXKKuUeiQn7CxhxHiYLcmBazqSoTx34GOoQA4cfz7uQQ4oQylSB0h2aXVj+DcMwRcjPVIwCwEVzZlk9Cc3l05TCqit4Vb197lr54ZtJ859lgIK6szN32XBUgWhAirIAaWQ4iWVgTLTIuZi6zKHgLLIrJ1iSp5jXzFXnqUu3Bnt+OSutU7hjA/sNc6cGPi4WGQhf78Lwbf30BFQcwTPY9SGysgpcvavJSqgJvuzbYLHOW5TCahFKqCKoqSv+28fEN9r21HeoYMFbT2+9qo76O5jGy1b42OYoIBarULbtbuvikwF1Mpyu0RZyth16DAFe3o9SQGFzk3DsRuQFRpRBYSIFEYvmWCIhJDqLel2BHXVcABbpEyfPeyT6kyWrtUhPtTrgr2pCijtXrymKaBSVW1BP1LGKJEgENU9sFZ5XVunInZIbXKCR1NAzUiCWm1q9YkEgREuMhgjqfoIqaiq+hL2YCrprCq7lH3EnULFyA8VqRHFyQSnUC5Wxjnz6qjEpDqtvscUa6ICSiKoBTtUBZRD9x2lQ4iAKIHlp+X+ySUPYiSM6kCFj4dBMfAVUIaKJuaIn4WAWmUhYSXntOb4j+aAYn/3enpOGCp9JQHmgJo3+DrgeV8skrIoi4Cs0hRrKSH4pDbmAct5z+3NaTdnLnId8pyA2re8z2x7BN/vNbtyVUxa3rI6wHENckApxFFAmMNeCQgogxiKnW2DUT0FVEBAGaH8UBlk2bs92vaJa4OYi67FjGMD7eBnnxkGNLOdDrsLHQE1R6ytTD9M2wgr5XISHcjOAdXujnWOW6d4kLDS71fOYKfsWgT+8iN30gOnt8X32naUd+hgYTDU9/F3/OGHW7QkxMaEHN9nnE1TBVTLhDhXQKWE4JsooNqyc2swPmfWV9LO9Q4dxgBCoExUQFlP13vXFUcmL+/aE+urSRrhfctrgvM00Zme4nSunPVs3Co1WRFeSyGXYk5izb4cBRSSJClzKqq9pDmd/I25debq1K8xV55tRg4oXhePn1+W05Bypn18bBvKAZU8loVQJIUIIyBmUu3VFFARokq8nkt6wRyqc4/rUiKxE+ch5ZyKrefKsZS478U1AnOaohKr7pXKQRlJmab2SyHDuqeOOwgww+wREFCKczElpFIsH0hybqCSaLvYFsta9UhOZS/fUub+4M7tYSH4QbTjM4eAioQmxDrUsH9z9iOZSiwgCzwFFBm5acpyYaG5LAIlS/VUttOf2N6z1olXT0T1o5IYUOWshCDmgEIbUnNa4T7ywkaCkgoJt1gIvizy0RhXjTwnCgln68zcHm2byjDvvgjzk0Mg4jnpKaCMueE2diH49h46AmqO4DmOtloI17ax45y8aQqoaQi+xkwS4Ry+lgKq3+/RhQfXiIjogVMyAdQGPvC5B9T32naUd+hgwcpJdGJjsXmgjpweP0110TnrahlH7Axbyq3k4Aj4VIXRyoQoa4uA3h6Oz/U1I2dehzz82q/9GvV6Pe+/pz3tadX7W1tbdPnll9MFF1xABw8epBe96EV05MgRr4677rqLXvjCF9L+/fvpoosuop//+Z+n4XBxD0tU6GkKqMl3kGB/Cc551fmtOB6raxKpAa+rNmqSRmjb0koaoSG1kVRO6pMby1F4zbJFcuBadqT2S1SVAAElqdA0+/iTmKVArFXzHAvBlzgPmlM8UkR8w903MnJAcbt2ICn8+qEEW/i+4ioi4x7NXrVjM8w9LyqG4HPlOKmqfEeIzalTAdYhoJIVQNIeFEgkEtZlOZJVn3X2Fm/DvAavh9tyXTnnlEcoa6R+QhviuT1S5kXZW8EDC+5F5/TpMIVzAqJzF53OS0xJrDk5o0/Y98LyqfllsGxBhefEtZy90RxQ7PvWvJ2iajg8Qf2lKRkCRQSocVDpYpF4s0JTvFgh64qioGHpfwZbhNrCQnN53IpB2AjvWWPeVH+ihIJFRij7JaaAwjXL+zbPEHyIoixUO2PkMyegsB4rB9TM/VH2PpEfgm9QDGoT8TujHZNwzgkdOUvfOcFshQHtsLfRebjmiOWlqYqnDQWUc9yuGMoijqVFKaAmYxELd3XxoTEBdeTUllmuSRw9G0pZ909UHDudAqrDLoLbV+cfsBPXLgKORL7onDW1zJTYafc8GmWGuFueEOeDlkKDbncKqEbwpV/6pXT//fdX/334w1OV4M/93M/R29/+dvrrv/5ruvLKK+m+++6j7/7uab6f0WhEL3zhC2lnZ4euvvpq+vM//3N63eteR7/yK7+yiK4oUAioOgoo/l71t0BUSY5LTVGVSlaIIQMZltYSncRSGxnEDzqA0ZZC6LvWRqoKJpmEiKgXRIe1MlfFCBz4kvNfUUUlkxAJ/apNUE5eWwoo/vf2Gf/6+rlSI7q9xUgpl7q25qiAUolikvvPrxlhWzwk54BKPEPEMsp4I9lWWwElkDB1CXDRXrjHjW3t/TGDAio4P5VzW1pHqfPgyncKqA4RSGqD5d6y9z57kVe3QXRJ70t/E42dnF4OKHTSKgobUdVgECk5yLk3poAKVFneR4Pe11zyLRdWKEAO7IuXp2bMQIl1ErWjjIiNfyoxGqu7UQWUssYVo4y3bLJAVauR37eZFVCCEojboJKWSJjDuYF5y7z8ZNLDP6wsIodM9PYlqIt4KL/BaODZH6i/jHneHm2b4SFzCOhZzgovBB8V3nrDvjcZIrRDs+gIqDnDOQ3bUEA5R2o/8RCbKqBaJqAm7S0bCigionPWxwk4N1sYOw0SOeeUZm/4yF0d+95h1+AfP3OYiNonlGMoirLaw4f2rajlKmJnUQRU4rm50m83B9TWRAG1vtJ9PM8Ty8vLdMkll1T/XXjhhUREdPLkSXrNa15Dr3rVq+hbvuVb6FnPeha99rWvpauvvpquvfZaIiJ6z3veQ5/97Gfp9a9/PX3FV3wFfdu3fRv95m/+Jl1xxRW0s5PoyG0KmgJKUu2I5QxyyDtbDMIhVi4oYyBGRCythNeSCZ0M4kcaS/FaCmlUk4TIUpXECChlToM49AIB5a4ZP7ADG8zrCQRUCvnm2WbkgOJ/8xB8VBKtSQooi4BS8ig1sQZj5SwiL7ZGtPwDwTzMMQSfVkZT63mXcE5ZOSSGZ1FAJc1XpC4tBJ+6f4R+BeQbFtFII2l/CHbMpIDaXd85O+wOOIdqLFxTv++HsXJIddpL5aVwceaT/6zsYDQwQ0+pDukI8TCLE1103mv9Ecg3TQGFsJzv6GCftxIhlYzAvqQqJMpycQooLS8Vvs515KcSULlEVYxcsNZQTohKq79a2MU6sHJAEdnnjrUukYBKVUDN3B+DGOIKqGE5DHK+mQQO6+vOaCf5/Irt/xxiCOfGygennfELUzp2qI3OwzVnOKehcyI2CedIjSmLHFyxth3WLnTVakSptVQ5ehf340Yamq96/HnV3zfcdyos0KHDAvC6q+8gIj3c3qLIUp5jyVIZuZCcbYfgy1VAOTvLsh3y3j280Cmg5otbbrmFHv3oR9MXfdEX0fd///fTXXfdRURE1113HQ0GA3re855XlX3a055Gj3/84+maa64hIqJrrrmGnvGMZ9DFF19clXnBC15Ap06dohtuuEFtc3t7m06dOuX9N3+wdew5Yic/0qP5UBIdu5IDWFRbKE/r5zqsXZ4INE3KAZVKVqQqMCR70bGd5ExOJVGU8kkOLMERbeWAQgIDQ7Fpc4r2WMRlYGINVYkGzeFuJXTm7aMCavXg9PXQ/ZA35sEjoFLUPbOoYCL1WXMlrcMUBVTMjsoBM4O9VhluQ+xccW9jaMyZFFDRC/ExqhRQqWMklYN9mrKfpTZEBRTUFyWzcH1NXndOnw4RSA5D7nCsG9atR72sJ/St0FNbIz/qilcvbomIg52rIGYioISxsH7TBaH1FEdyLCdMQOplkG+5sFQoHJyAGpUjLwRfSWVAIPC/F6WASv39nePIL6lsJeppLF+RSTCXynXjmrvunQsz+i9i6ipt7VlrvigLL3/dqBj5xJChzilm9HNYCk5OyKICqk99nTgufRu3R9sm+TYLcZoDcz7CA7nDHkVHQM0Za8tOAdW8U7VSQCU6Up3DtWhbATUhlGIKqOVKodWuQ5pDGpqv++ILq7//7MO3t2hNhw4y7jux6b12+dM42ggDKoGTNBY57kLwtaUschhmh+CblmvD1q0qBF/38TwvPPvZz6bXve519K53vYte/epX0+23307f+I3fSKdPn6bDhw/T6uoqnXfeed49F198MR0+PFYZHj582COf3PvuPQ2vfOUr6dxzz63+e9zjHjffjhH5TkDPUa4poMCRKzp2FbJCImqwXc1ZmqpAcM6GKk9E6fdhaXV6by+yRzR7w4LwUhoTVA0IuZLENoVrqY7oFAJLVEANQye8OFdFSNyUypwSUZgDqm6/UspkkllBCD6l7p0zfrnV/dOXg7NxW7AdzS6pbbFuoS7xtXS/sE+lcJGiAkojoJB4HfltzqKASiZkhTUorTcS1mVZUJD3biYFlFQuMlfq2CYqoNBePHv4PUG/IiFMq/ojRKZ2Xfwc6NBBzgGFQAdiruqpaguIFMkproaTg2aGhZ/L0wpLF1MB1VFAYfvYjoOWI6ekUOljEWGWYgUVUNr8pBA7G4MNeuVHXknXHblOfN9SnXnlCr8cHy8rnBoReYqVNmGt5WiYOo3QKcvk/mQroCLkQurezCEmrBxQUsi6sizpjz75R/SO294Rt8NoxyItsTyG1EQbzbMuk7Aty1I8C/B+izQOckDBMjD3WanntIopkRCzKL6sM0lVQHX5KPccOg/XnLHmFFAthuBLV0BNCJ62FVCTLw8xO51DeNgyQcYhHaoD9uXnrdff26Y5HTqI+E+v+5j3+kkX7g/KbLdAgkvgiiaL5FnuLyYEn1OA5iqgiNo5m7Yn6ln3MEOH2fFt3/Zt9D3f8z30zGc+k17wghfQO9/5Tjpx4gS9+c1vbrTdX/qlX6KTJ09W/919990NtMJD8EUc0e41OudTHcAI7Qn5FAVUzBndX57aN5zmZ6CllWmZSiU1izNZuq0M78VcSdX4phAHiURNajlR0YAEVIIzmUrBWa4QVdK11DlNIVdmVVNxJVcJbVgKqP40HwntnA3LB6/BGa/ZpWGeRJWoFJIIKKGcqhgTCA1eV1/Zc00ooII5VMYIlYlR1WeGLeJajdQ1swIK7BUJceHsFduQ+j5jCL4KndOnQ4iYqslzMGqhyhI+qoPwceB0ttQWWj2mTfA6lgMqhYB6w41voK/5y6+hjx32f9NlKWMEYiKVCAsc0vwjIhpy18af/Muf0Bs+9wb64Xf9sPh+qhoGFVCWXQExt6AzynTeR/aHVbaxHFAK6VVdssgU79b038gWsSCt2esfuJ5e/alX0y/98y9l1S0pk7QcacG5AV+XPQUUErRGH1LG5RVXvYKe+6bn0vGt42F/FFIM2xkUg6Ata+7wPe38CkKRxs74GfzMpr3K646A2nvoCKg5Y325xRxQk02aq4Bqm+BxqoGViALKvd92jioOPDN/+OueSIPh4uzp0EHC5w6f9l7/4rc9PSizqFxqqQoopywatq2AmhBeqbnzeB8GLajKOgVU8zjvvPPoKU95Ct166610ySWX0M7ODp04ccIrc+TIEbrkkkuIiOiSSy6hI0eOBO+79zSsra3RoUOHvP+aA5A8ag4odGaW4Qef6NiWygkOSirjeaewbulajymgOEnSX6bqVyFXSUlIVl0J/ZKUBfNWQI2GREc/z67jfRkOeyQfU536SECpzn+CUGeJcypdF8eoJpmlrUFRKUS+Agrn2RFQdQjDnLU1C2LEgahaEcqlKqDKwld9VaSvRdLx68oYbZ8mOnVf3r3JCihB/ZdFPhuvebtamSr8aeIYSesN50/ba0Hb0tmbcL5JdYn7nhGBXQi+DgxVDiiLpFG28PjPvN/amGPFDLuH5JRhY6CA0uoR7EW1Tgyv/OgraVgM6f/58P8TLatBIiaQtKnKGn1HB3SQAyrTqXz36cgDV/wrS6IjvyiLIASfqlgpF5cDKlX9EnPe1w1RmYuc3Gapqp867Vj1PLj5oFm32k5Z+pHKS5+A4nvWOkMK8vPMjQpdIYivU/bO33/+7+n0zmn6u1v/LnjPJMXJ389eyE3qqXZIJL22361yEqKKOmsNQX9Sz+oOewudh2vOqHJAtaA+cI7UVAXUvklOkbaVEU7hsLLLc0CVZUk3HZk69v/u8q+nX/2OS1sPEdahQy6e9YRH0Gd+/QXetZuPnFZKNwu+fy2V0WqVA6rd/e6Iuf2raQqjpX6v8rMMWggP6h5e6BRQzeHMmTP0+c9/nh71qEfRs571LFpZWaH3v//91fs33XQT3XXXXXTZZZcREdFll11Gn/70p+mBBx6oyrz3ve+lQ4cO0aWXXtq6/R56XAHF1qeWA4pKChzWqmMXnfqKQzVFGZNCMHBHJ1da8Ht7/enrmAIqSZ0kXS/Da8UQlBaCo59IcTJjXZMyb/4Boj/4KqLP/I1cLkspxb05Q8ERLRETpaCEUeZevFbTXmkeaiugXL/wXoP44Hbw+hw5FXPqS7bM0nfpetKaFkgIKwRfigIq6DsSUO68qTNfjAz57S8ietXTiU4f1uc0INaUMeKfy+UIwiQqtqmOi5S1Gln3SOI6dYL2/SFoQjijVbti60bZpzGCkl9Hgq+6tyOgOoRICZnkkOrYRiDhEiigqPQ/Eo2n+THcV5D7RCEBxBxQrK6cMFR9cMdJY2HlzrIUUMmEQem/tvKvpBA7y1xdLLUdGUsHSwGFTvQgrJtwRg0w7+WMiIbRM8rHSEoMHTnPPEkaXL08nFtySMEISWap3mLjooWnS7EJx00laA07yjIkaK3QnnXDi4rnhnKW4WskjYNz0jjbqAxJ/eotJIUjZ1vddYCwwofy14simjvUR0dAzRnOaejCKDWJKpRU4sbbvza27ex2+iE+Dwz3SA6oeyGvzhMu2E+9Xs8LKdahw27FwbVluuDAavX61R/6vFG6OfDQoNaXguUF5YDa2Bmff6kEVK/Xo5VJuMBhw+R4WZZV7q71lY6Amhf+y3/5L3TllVfSHXfcQVdffTV913d9Fy0tLdH3fd/30bnnnksvfelL6eUvfzl98IMfpOuuu45e8pKX0GWXXUbPec5ziIjo+c9/Pl166aX0Az/wA/SpT32K3v3ud9MrXvEKuvzyy2ltLcy/1i7cHkMCKjUHlEQsCc5NdOrzcoFSQVLopCigWBktBxT1pvdqzvBphfE2pesSKYeqIjWfi1Cv5rC+6Z3jf6/+A6VcIgnDHfvONo3wQ8dzoIAyFDSxdaP9qAx84gkElFqX1i+F6MD3KpWTs6MU3osQDN79GWWk1+r1hHKpCiiJrMgJwcf3n5aDIketRzRdd/dep5cJ5lSxtQRiWCLd64yv9Fq8VyNu3DmlqMas+5FUTCFepTYkBVRwlmnn1OQ1d/x1CqgOEVjhr4h8Z2ZOWCdEqnMyVrcZOstQDIkh+BRlRQz4W0nqvxpmqgzv1xzsWDfmuMH+zaLAWemvmO9rJBnm2okpoDRHP9ZDRHTP6Xvo697wdfTfPvrfMnpiI0fFpJVX6zbGxawn82jGNXFq5xR985u/mf7Llf9FtMOyMbWdnFxxRPWJQ+mc8EhMsMk6NzwFVDkyiZi66kHpXLHOVGvP9nt9/15DkRrsJXx4oC5xKiB1PMyQiBn1dNh96AioOcMpoNpQGTnlQGoukwOr46dRNnbaDc3lyLjV5QgB5UJyLSgEH1etvf6lz6bz9o+d+V/+2PO8cosMEShhazCircGIbrjvJL35Y3d3B/IccP1dx+lt199DZVnSqa35Pi01D/Dv1JccWq/+/sef+cbq76c/6lCbJlVwhG3sXHIKqO0WwtpxnN3OU0AR8XCBze4tPhZdCL754Z577qHv+77vo6c+9an0vd/7vXTBBRfQtddeS4985COJiOh3f/d36du//dvpRS96ET33uc+lSy65hN761rdW9y8tLdE73vEOWlpaossuu4xe/OIX0w/+4A/Sb/zGbyyqS1PkKqA0hZIEdHZrDsqoAorCa2IZroBiOaC8PjDSoJcZgk/9cSz1S3IKCwRfKsmFdXFUDt4axEdVjjush8Z4w71aCD7JQV3APBg/ZuW2jYLJihptjEooIxEwBP0F57wjZZJUbdhmBrkwr3IiAZxIStUOwddX7NPmK7Lve0t6XcH5o6wRDBGHjl+LPA8Njrzm9/bgNd4z+bc6y1JJOmjX6rtEGFp1S/XFFFAFlO0UUB0s8I8idP5GyJ4c5OSAMpU+mCMm0TEuldMUBDGggilKUoDjGMkpiyjTHNJYblgOazvRifIUUNa4emRaMfKUMDjPuCZwXP70039KW6Mt+ssb/zKxF/VgqUc4Yuopqz8pIR7roCxLevcd76aT2yfpPXe+J7TDWpuxZZtIzM2qgNLadHV7SkVjf1jrf1SMkvuTpYCKENsxYsgimWIhA02iPnFcrLJa3d57xtmcc6Z22N2wPx06ZMM9tb7VhgIqk4ByDtezO+0qoI6fHf/QfcR++2mYpZZUBhocUXbROWv0DV9yYXX9e776cXTfyS36/fffUpXbv7o7ts5gVNDX/tb7aP/qMh0+tUVEROftX6Hnf6mel6RDHN/1R1cTEdGbPnY3XXvbMfo/L/1a+sYveeSCrZriW556Eb3/c+NwYG/9ya+rrl90aJ1+5BueRH/64dujIS+bQmpo0EdM1FrufGgLTgF1YC19D7u+7DSs1uIPLnQKqPnhjW98o/n++vo6XXHFFXTFFVeoZZ7whCfQO9/5znmbNgfw4OaCo1ByAKc6dgMVRYLzXyImpHtjJETlvCjDJ/ADBVQqsZRB6EiqgSAXitS25bBW7MgK5ydcR5VDMRIc0YpTf4TfBw1SI1BAJfRdup6kKtGqUsbIVEDB2HjXhffqkGFZZGFKuRRCRyAhxBB8QjmVgIqs+74S9jJ1rSL6AgEl2SuRSNVa5X0V1r5kbzJhZqzVXn/SlrYmkYDSQjlK5w+8r5J0CcR+jEwX9xB73SmgOswJuflwNPSoFzh/LbWCFXrKdEAbNknOUMuxbaHf6+cpDIDgQALLygHl1QMOad7fjcFGbSc6EdFyL0JAKf21wjQWZRGqV7yPCd/eFGXZzBC/8qW1k6UYQkLAaENVy2ntQF1W6Lbg3ow1MktowqwQfBFiaGOwUb3GPavdi+qiYTlMXrezElDWeWWFBQzWf4Sk1whp69yQkHOWBfdafVVe5673DotH94j1nFERUIPmCahsBdTE4bqx3a4C6ujEwXz+ATtUkXPyLkoB5dQHa6A8WOr36D9/y5On5VrOoWXhvhObdGprWJFPRESfue/UAi36wsK1tx0jIqJff/tnF2yJD0eE/O6//3J69Hn7vPfc+m1bWeTg9m8s5Ob5EwLq6Jl2CaizO/kKKKfebDocp3twYanfo5XI+HXoQETMCQhOSkkFQxQ6M1OdmxLhoBITEYLEXQtsY/fx/E4aaVA5XWZxJkv3S2RbYgi+VAcw1i3aoUGqj5MLA31O0ak/2laqFggcVKIlOb9JGPfI3GtlpHISEWgpoII+SO/VIVdmId+k+xNIL/daJAul+WPXhjDveD9/7REQOcSHdB3s5TndeBm01zpDMEdRkLNL2s+J42sRypUaTHnf9auvqcaUNnA/S4S4RoAnnb340IEw3rwu7QGADh0YnBMwpmTgqJ0DqtfzHK9BuDhLGQNEywjOi0BZBfVa9sZC9Kn9oV5tJzUqoNCRnJwbC+7jTvrJzVloQgFVlEVARFj1SLnC2kBqyLocR35WCL5M4BgGijzvs9ioJ5OoUG0Q5mkWBRSO1dnhWe99boO63wlC8BUj8/xKDTco2Wtds0gZJNAwBF9MpWWRwlYYQESMfEsmNEv9QQKOLgfU3kPn4ZozXNikrRZIilwF1L4FKaAeOjP+ocvz00hYWnAOKEcsuTxeHMtL/YogW5RjX8LprXAuFzV+X8g4I4zzImGt1Tbz0EngOaAsXDghpE9vD1sh7B02qxxQOQqodtSZbhzWIuFKO3SYgofg47lQRoIjlkLnJjqEXV3uPa9cqqNYIr4SCAZPacHIJXQwO1QO4BmcyVI5TQGF46vdi+1FFVDK50tdp36yw7ok2sIHViSCYXLNUg8FdQt1WtDIwqIgeuuPE/3zq+S6RELEWNMBmcK9cS5sZcQ23g7W/9CtRP/nu4juuMq/HqurlgJKuk9ScQn7efu0XL9EhIn7L7Vfgr18DlIVUBKJJK1LTf2XOg9JaxoIKHUsJtejIfikMedra5R4RlMi+aYooLR+BPPvynVOnw4hYuoiNQRcZnilwEEaUT1Uf0ec5KlKKsneugRU4CiWzh223QICCnImcWc95oDisMbpzOBMbRUHUZyAUtUWpM/PqBz5OaAMso0odEw3FbYOMa9QYYHSjblt59mXGHFn5joy9gSSu6khHaU1m6WAQpv417yyUBVQOQpBDHdnEY1SX5E09uw1+mO1MypGXn+Q2K4bEjVHfRfYKz5saNxrEbT4FSXzTOqwe9B5ueYM5/zdjQoo59TcaZlAufPo+KB/3Pn7zHIrC84B5Rz2mvPXXV+UY1/Cqc0wP9Gixu8LBdKH5dntXUZAGWu1WqcLUuql5oA6tG+ZzpmoMu86tmGWnSfc+beaoTByOaAGDYfgcw8udOH3OiTDU0CBM53/iONkDTofkxRQSFyx9wPyQ3oKP+I4x/Z4DijvoQrWz5gCKpkgwdskpy0qoBSnrUT6xZz1FfGhlDt9hOimd0VC9UWc9RJRUxZE20BAiaq2QhgTwamfTPBJc6/UdedVRP/yRqL3/3pol9cmkkHKWAZ5rPieyVVACWXe9GKiz3+A6HX/Oq8ujVxJKgf7D9uprrHbcN6JSFUZIWmJ9fPXx24juu1D4XV+Pw//Z+aAktYg2CaVS9mDqWtVKhcooDS7XLnJ57lol9CGqIBKIP+rewVbOArlLKvKss8UtJvX1z113IHBOfstx2tqPpw6jsXknCVUetsLnd0xp7NUp/T+LCH4pP5r+X8wBB8SUKkKj6L0HfVnB2dnmpOVvp12IZmMYOePFIJPI0BK8gkbd60NzKSM8b7KwPsKCYmYJQSfGLowkYyw1Gt4r7VfJRKGE48xWGrIsizpzODMtC0kkfj4Yz2sf8NyaBJzOeRtTDXqkbCGYhOVoFY7uHeI/HFH8rOuci9GqFnvFVSYY+Pa6ULw7T10BNScMSUpmnf+FpONl01ANexERTgC6kkXHjTLLToHlHNMawSUcwrvJgXUSYGAGi1o/L5QIBF4basGY3BrUCIqqjOo5X3ukJoDqtfr0ZMeeYCIiO546KxZdp4YOPsycmS5cHhNk7uxM6hDBxUByVOCoojlVPKcj3gf1OmV05yd4KCWnsJPUUB5zs2laTntCfzKAZyhLEgpV5ZhUVSUac5kUf0VcRIX0jiy+674WqK/+vdE1/+FXl+qsx6Jmq2Tcrlg7lNIxRnIFc1xHoQIVMqJih+hnJkDKkIEYl0S8XP8DqWsUpdaTlurwpjHSDTJXpx3Vy66tqTxZu3+/lcS/cW/Jbr3unB83f0j9r3VUkAhsa2tI29OUxVQqeMrlYPzRyUZJ9ervFkZc59C6ptt83sT51SzN7DFve6cPh1CREM18eNqBhVH4ORHx73lSLYc4eBQ18gRyfa6OaB6vV5gowWs2yOnqKBBMT1jUeFhhdnir88Ozqo2pTh8c0LwobNbI9uKsvAVUAZBSNROaK5cUivmnNfqDkItzpFMsxRQsVBzHDj+AQFlkY64fwF8TcdgkuBU0tnB1OdgKSDxOn9vVIxsQt04YxCx8U1Vd0qqLIs4sgg2q2xOiL1YTivhTdWmLgTfFw46L9ec4ZyaoxZUKMPEUFcOq0vjHxVtKqCKoqTNiRrs0Lr9ZWS5397YSahyQAlhzcbXF6sskbApKO06BdRskAjG3Tak2wZRseaI0oUpoCbnUoLC6ND6+Am5jZ32VIVOxZSTY8mpMwcNn51OPZZDjnV4uEPJAVUCceOeRkWHpKhs0pz6irMzpqjiZEXPcMZqii0Mm1YKZSTMmyDx+qmF4JPUAkL9HmkQIT62Toz/veldcn2Bw1pwuLt7cO4DIkKYe8mJjX2Q7Of3x8ppBCU+RZ3UL6v/I72ctJ7xHt6W5MQPwsQkrC0JWUQVv6bMH94bhF6c3Cu2K41RpF/3fkJfD3yMJAWURGxrJCiRPz9FAYpJV0/qPCTMj2tXPX9gTcZC8InnauoZrZwZXt1CGXHda/YqtnROnw4MUg6omUM1GbCe/I8RExyWoiCw13KgCzalIkWpo5EPqFgZFSNPAWXZYam9BsUgy4mO4ARUdB0YTmZOYozKka+AkohH9veickCZodkia15V7iGRYvQl2yHPx5B8Rd2gGCSHysS1ZuXrCk2w92vtHFCwrsuyVPeHlTsO8ysFZI8xPznnYIywsXJAjcqQLExVcFrqSMseaU5zFFJW3bGQgV0Ivr2LjoCaM5Z67ZEoro1+4gfN6gJC8HFn/lokrNRyFYJvMY7zyqm/Im+LwWS8P3rHsdZsikEiSxZF4H2hYLvFfER1cHZ7SLdPFENyDqjFhopMzQFFNLW1zRxQjiBbySB5XA6oQcN7y43dUufY6ZAKt1YCB23pKwOWmFPR+xLP74PQS4EDWHOCIvElkUvOubnkv/bKMJKq6lehq1bmFYJPdOLCtWLo90sL16aG2cI2BTJLI/imF+TrSQ5rwZlMJdFgE8ppBIYwlpp9iBhZIZaZgD9FjaHDeFWpZKnnxBDmWLquzoPgxEcnSZQgUGxJXtNIJkjtCPt5IKiO1Xk29iovF9ynkFkjeJJZzFtE4fiqRA+SpTMooAJzDUIupoBy/+YqoAKCWlj30pxKbYCDdnxNqo/diw8JePPA81F131M6COBLF5zVQcgnxVGfQhQgaWHmhIKypkOXh6ECYksLUeVQVwGFIfhisELwlVQGpA2H5jzGfEqjcqR+DKUQHJyAksKnaWOFhFqggDJIDYuYI4orjupAVIAkkg9ZyhgghubZF2vvDYqBmSeJA9f8Dg+3a7QZq4eofg6o4CyA/ZGaSw7PjWExtHOXkf5eYG9EFWcpk3B/mAQ67hXvJ154hmplowRTBqHW6/VUch1D8GmfH10Ivr2HjoCaM1wYuVELT1lMHb1p0+gcrm0SUNyxvB4JK7V4BZSdA+rB0+NQML/5js+2ZlMMElmyKAIvhu3hiN772SN0emtA/3zLg3Tfic34TQvAbgqxKOEPPnBr9bdEljpSalH9SM0BRbSYsJbu/KujgBo2HNYwN69fhw6eE9AigrgCSiOWkByKKYhEB2UhO0vRuSmhKtMnX9kFeXscOPkm1lfXqa84rDF/kNSG6KyPOPA1BZRGoEXbEJz/0r3aXAVtK2RTbH0EdcovxXtRjUFENNwW6pJUGgbhgGSmRCrGSCNVGQLXpNcqCYGvtTUtzZdgh6Vw0eoXVUawRrR+iiSKQkTzUDoSsSSuc+inVw7I3BQVkLQIk/fu5F/t/MH2LdUnr9ALKYrrOWE/EylknnS+GXNqfQ5w27oHZToIsJzZ+ES+dl8KLHLLCrOHYZ3MMGEU2l+1LzlWjXotzBKCD0OzjcqRmgPKlZfqkdqYRaEGjYaXNEd/GfaH28RDsSHxaI0Lvt8kUtdyTAFlhSZsSs2FxF1MCafZ6+71ylp7y2gjG0CsYN1Foa8ZS4HmheArR9GzTnsPYe1XZ1dKO4ECinQiPqjX2ktwX47CKRZSkMgntFPt7bC3YcdE65CNKgRfC3l4KgVUoh91dQG5YbYmpM5yvxcNyeXIu53hYg4YF7JMC8G3G3HP8ZDEWVQOrRj++z/eRH921e20ttyvCIc7/tsLF2xViN1OQN36wDR5phiCr8U8dBLc+kshURyB1q4CajwuKSECHVzZQcN7q8h8qKBDh6kTEJ2KJeSAYk7FQDEADlCJXCkL9j7576PaQnwKH9UAwl5y9vb6vnO30OxFxRYi5iRXrktKkGB8NUe8oZjQymlqqtoKqFJ3iuMaSXLqCySBaG/i+MbGg5dZYj9TRjvGmETICvcaFVCe0iRRAWWRPIhgHuRiqr3R+nCuS/iX2wblpDWSTIJqxCW3S/pcL30FVCoJmqWAEr2d8FoixxL3LhJG2npBElXNG+fqW5qOR4woTj1/tHNWOsvwc0DMJ7a7vx93WByqEHwRkoZDez/F0WiFmgocycZ7lqLAykGC7/Wop7YZQ5/6ajtV/YoqqCzLwIHLHdqeeiVCqKGDfZm5CWdx/kqqjjp5hUblyFNTSaEXeZ2eMqwsjc/fPOQShNq9OcoYSxk2K3AMeZjDwWgQhj3UbIT3tiGHp9ff4CtD+rmBaz4HRVkkKxVN4hpyY5n3RubZUiyijRbxHlNAxdReqapRMEC4VO8cxPKBArXU+95hb6Hzcs0ZzunaRh6eXAWUI6AGoyL64TkvbE1InfVI+D0i375FwMqrs1vxpx++Pbi2W0Pw/dlVY1t3O8GjkSGfvU/KmdA++PoUQ/CtuFxliwnBt5Oxj9y5sNVivqrBhOBezQjBV+WA6hRQHXYdlBB8ZTFVGiyt+k/Xa4RRz3ry3SA1AvJDcCgHCiiDhOgvkaqA4qQJ2qvV590rFhTsjTjU1TaFc1d0iNdR3giO+co2mAOV0Ig49VW1jECkxEgI7XpsPPgtXDE3GuhjgvbWIeDUvF4pdUXULbFysTZjtvD3tbqibUjzjPu59P4x69IUUF4IPl4OFEXRNSiUK0ZKuZQ1mHlNywGF+zQagk8i53GfphCvQhseYc/O3mAfMCytjv+tlJkSWU7UheDrEEMsz0gqCRGrG0NE4VGWmpMn5TWvk9fb6/V8siQ1HK/QTkklfeT+j9AvX/XLdHrn9PiaRoRR6eWQQgVUMMb8aIF6PEd34fuKZvEb5eTL0sJsub8H7PMjIJUME4uymDls3dX3Xk3XP3B9Vggys9xkXK6+72q678x9WfXE8uPsjHbob2/9W3pg44FkO9zfnATJyQUWC8Fn3ZuqzIvVg+8HZcuQ1KzeQsLZIGEktZHXjFDPh+/9MP3q1b9KG4MNryzfr5gPDuvOygFl7KVAjVrqIUYl9ZcFywbpmhZCrygL/7xSyM+6ZGSHxWHveNr3CFzujqIFgseF+Ut9WH5tafzjoyzbIciIiK68afzBd2Y7Hrt1ETmqOKoQfEoOqL2Ctub2CxUbO/IH63V3HmvZEhl9Rk6sGyH4FrWPdiYkzWoCAeVC2v3u+25u1CaOQQ0FlAvX13R4y1FHQHXIRbVUBEfxcPL0YUBAKY5dM6eSQWoECgTDKW6FzeOOaC5u4qoVz6GqOIDBvODCO15O9JffO1VWiU5c4ZoXvs26Fw2w+kqG8kbrl6SGAC9MimrHnFO+RkaCLaXQd81epQ3JvqAMKzva0efU8j5yG0wFlCOgNLUJ1JXi+YoRBOr92ve42DwYcx8jwzSljbcWBPJNfC3Y4RCE4AMCRprTVBImda26e298B9Grv57oyGdJHnPjmqbADBRFjljSnDZYrgzHPEZyVdeNEHz8vLQUUMtr43/d0+vB58UEndOnA4f7aDecv7EQUDmwnvy3QmlR6d+bE4IPlQjWE/k5/QlUDWVJP/KeH6G/vfVv6fc+8XtB+ZhiSCOgpLK8TsxFozmSe9SjhzYfoh97z4/R++58n1q/ZYMVak67d1SOghB8KqFJoBii2R6+fnDjQfrx9/04/eA//qBnr+Q8t4hV7PfHDn+Mfvy9P04vfFsYESYnfxS+/t+f/t/0y1f9Mn3v279XrUO6FxV0O8VOen9gngMFlLG3OGLrRQvr9gv/9Av06k+9Oihr5YtL3cNS6M4Y0YLv/cT7foLeestb6fU3vt57n59BEpliEcGYs847S0q9P6KdpT6XXps5BJP0dZ+106OeGloy9WGALgfU3sPe9rTvQrSlgBoVZfU7YCVTAUXUnnP6196eni9pGjpsMcqNqQJq74Tgk7BbFVASFkWSWNjYkcnSc9ZXWrYkDlEBteAQfDsZ++jNH7+naXMCuBCBOTmgXH66pkPwdQqoDrUhqWDck6JLKzT1DoEjlzsjg9wfUJ+qBIk4wLkT1FIDVCopyAEV5F4Ch2qqs97Z+fHXEN3ybqL7P6mXS3JiS/cqqo/gGp+DROWN5HB3dZX4OsFhXQr2VnPPrj90C8ljFCEh0G4LKXaMttPKWWsVc0Bx21JDIarkY831kVyXZgvs04fwgQ5t3cB3bXXdC2oZlTBU7uPXA0LZ7fulqkhorzSnQtvJCqjJv2/6fqIjnyF6y39S9qlxDUOSYuXVuZpIlHvnGZ9TIa9VEgFO/pxaKlj+b0VADeB9gjXTfU/pECKmZEhR1cSIgrIsAyeoR2IYjktUSxXwYFngdDaIrICI8I7i9N8LVqi/m4/fLNql2ViUBQ3Kgfea12sShMbcYdn/+fH/Sdfcfw393Id+TuyT51QWvgNopIZpU+GTa8H74HznahIM25Ybwu6+s1OFknTvfWfuoz/99J/SqZ1T6cRRSXTt/dcSEQX9wnYkdc4njnyC/upzfxXuh7Kkq+69ioiIjm4djfYN14RHYBY6EWn2jUICyiqbFbpTULJd/8D19M7b30l/9Mk/CtaTRVan5kwKFFAYgi/4iqGfcw9uPOitRT7eIgFl7UsYt+A8M85bSwGZGp4vdsZLyFHCdSH4vjDR5YCaM6ocUA0/Kc9DQa0khoxDAurA2tzNCnBofZlObcXVT0RTx/nOokLwTcKApSg3djN2e4g7jpObA3rkOS0sxAxoCqh9q7uDmFxlxIm0VlcXTuSOPDssPOsJj6Dr7jzetEke3Nm5khGCz6mlms6vNg2r2jl2OqRCywFVjBUjRERLa7pzkzt28Yl+6Sl5DkkNUZZ6rhOiBNJoYgdXSpWC8ojbm6O8ERUvkhM3QrhoDmCx75WHmcSxDcqRXEZTn4gkgdB3tFciqtAZTUR0/I54m5L9nj3Ga/HeMrwsheDTyBWt/hLITG88UkMhuteRcqJdmp0pYyTZJthw/A6lTGStigooJEMS+y4RlNJ1VECNWN0S8eVXpreN5WLzsHUyvCaV4+2lhuCzwo565bhCqcY+lWzh5z0/e8WzbPLv8vr4X/f5wcvyNdMpoDow9KYSqAo54e2yCBtwlhJFckIZTlhUQMXypPByPMzeLDmg0LnN/8RwXVi3pNKyFFDauBSlrxAaFSPVJiKi41vpv92kudVyU0mEGr/HU0AJxAuvEwknTopJhI8FHk4O531jsEEv+JsXEBHRTcduoi95xJeIfUPgexuDDXW9SUTKD73rh4iI6P6z99NPf+VPe2VXXSjVFMCexRxipgLKICN4uMR+r5+836N7R3h7cxjmRJfaKam086IpeyvYH+XInluDVN23so+2hlvV69haTFZlggIqKItKQ5j3VGWYVS5mr3iNfZWwyHWt750Cau9hb3vadyEqBVTDjkpOQKU6K5f6PXJFBw0TZA4vfOajiIjoW552UbSsc6xfdWv8aY0mUIXgUxznr3jh09s0pzbuPrahqnh2G7R8S4tCWZZ04/1yrqfdoix79Hnr1d+SUqZSQLWYV4nj2tuOEVHaufQr335p0+YEGNRQQLmzqekcUF0Ivg7Z6DGnDz7VXhFQq0DoKGRF4FDlZ55AakjOaFEZwxyqsbB5Vb+YsxRVK4ECQXPWC8QB/6GnhdBSHdYpjngjB1SfPfMlEVUxJ7lGJAQqGIlIkAgzybkuOPWH24aT3a5OLmu0G1znCqid8F7JXknJ41576huYUy0UokTUYJvSawnJT10nrulgrZbT0JvVpdL/16pLWluSWqYW6SNcL4vpnnH7Qz1XEudeWiNJ6zeFQGR2acQS7rXqzBOq8soJbbj6tPGOEeD8/dwQfEMpBN/u+s7eYfchGipLcWTmPs2O7ZjKAHDKmiqgRAdoEIKvR2a9Zl+QdGH2bo22gvKxcIOcIMEcN6oNAsFhOpIzfqZIihWNuLDGrSiLMASfQdjgvZrqJAUeYVDy75FE/+Pj/6N6edV9VyXnBsL+nNg+oe4fiyh57Wde6xEwZVnS+tI6pSJYP6W/fvjrIFyksp+J/LUbIwliykkkS6z7A+LO2NNerrZSP78CBVRmCD6uBtu3tM97n6/FnDMTX4/Kka1yirynnUHWfRIsEkyqT1NLBusAv2pNLnQ5oPYeOgJqzmgrBxQPBZUXSqqdJ/kdnBrn2U86P1qWKyY+cdfxxmzSEAsd9k1PeSQRET1i/+4Ixfb6a+8Ur9905DRd+ivvpruPhU9N7TYsSqUjoSxLetIvvZP+v/fdIr7/k3/5iawn9JqCUwj+yDc8SXx/bWW8frcXpCR82/X3EhHRez57JFr24kPjL8htKn4ciZTTplO2Nk1AuRxTHQHVIR380TFFAbXMCagiLIdP9EtOZtMBHHGWcsc2b2P7NNHrX0T0if8D9fSm/SpLIfcSOna1fSnYy50ORz4T2o9taP1KUaggYdRfipTDujS1j0HwuXIxVYarRyMlYsQPlYIdGfNARPSB3yL6g68m2jzO7gVVG9qhqq6g/9L8BQ9ewTiZijihzWg5o8w//0+i3/9KojMPKOVUtkIoB2sJEn/rhFmCAiogjJS+i6RPigKKvTZDgAp7UpoHVS0kzMMWPGxkkU1SOUuxxO/Fc/XsUaK/+E6iT79FLxfru7bXrLOBhz+1zqklCMHXKaA6JMA5AaOO5DkBn8K3nMXovEalj1Zv4Dz1to3vqO9Rz1SKWLBCXDnnrOpgL0vPrlE5UhVQ2A7aGzizDUInRihwp7A0FnVUNdg3Sw2CIfdKKn0FFCORUhQUnEDgYc561KMP3PWBqY2oHAPgmHKVECejiEISwCITUCm3tpQeXQb3jkc44d6ySEx4j6vGer2eTWgyREPwZahucNw88lCyo5Tfw74Ni6FNNMK9fCywLI43wlKZeedtUQSEcyqhhgoobRykeix7c/1m+OCA2fdd4JPrUA8dATVntJUDajhxhPZ7ec5K50htm4DSVEUcnEg7fnbHKNkMYrY6Ymq3hLh7xd9+xnz///fmT7VkSRyFsh/uPiZLpheBm4+ciZa55/ji7R0Mx2OphbirQlkOZ0u42gacrcOibE1h5kienFCbFXHfQm4/oo6A6pABruJBx657gn1pVXeWcmcnqpOwnOYERYWESZAwguHqPyC69X1Ef/9TrI1JEa9fStg0NQcLa9e/wPKaENHbf4bo/n8R+oVjNCkiOZljCgRexrPXcGxjG/h+0C1BAaXOFTieVUc2K6cpoFQyyKiLl/un3yY6egvRR/5kWlegamP3FpYSJOF7GT7xjONUEVARYk0i87zr3kW5zPt/g+jYbUT/9DtyudSxlPYzKqBEko5ktUxgBp4rGpmFxI+1toT6iMgLJ4r2imSpNPfCOcXL8nKv+VbWNAuPad7H7PLOKNhX/F88Vz/0SqLbPkj0Ny81ykXWvUqC4vnD6lI/K1ABNQndVDlced92z0NjHXYnTNUGrNe6hI3UTiw3Cr9ukWSWQ5oTAphLZpYwUJYqa2UpfOg1pmSwQvCp9wlkjua87lGP+rHvX1BXatvW/AQKqIiDnc+J1Z8UcDUPhm3kQBVKjLDh/eFklHSvpuqTsLacQUDBuHBSdlgMk1V0aNNO4fvyTDVOBmmRFNaNXVcVTxSGgrQUmkG+JYtEjYwpR2y/WkSXpYAKykby5Fm5sThi6yGmBMWzxAuVaSivtDXfheDbe+gIqDljmgMq74MtFwOXKyRD/UTECbJ2SBQXBsypMizsMGJn/2r7HVHItQABAABJREFU6clObo4/+A+uyW2vrUwd+3sBtz0UJ1Tawtv/5T7x+kte97GWLdGxmRAOcDcotnZGYxs05SNfv6c2d3coRreniNrbV47Ac6RSClYq4r5ZG7scUB3yoYXWK6Zky9KKnoCe34chpaIKKInUMJylRD7BcPZBKObKpSqgIjmgJOc/khCf/TvFsS04rEXHuTImoh1eoPM0eyWkqFRS1EgiUSXMvaiAEuzLmQcOntspUMHAmGtkVtB/oc1A8YPjpn2+C3Ms9TVHAeXgnE+pY2mpC919mPhbI0tFolgi3/h9Jfyr3CueA5P7gvNi8ndAgEeIbfGc0ogq4dqDnwP7E/Ykty8W0o6gnHu9eVxuw1JAJZOgBvmvfQbgnFY5oAbhmHi/HbvvKR1CpIZ8Iqofgg+dpSWVtUNGmTmgDIKjoCK4t+6T/5a9K32BgAIHL4YGO749PWNSc7nge6Ny5H+0eN/j8py+0vjH8shoNmEIPmsNcZLMUvKkhPDiChbL3lGRHpoN6w0IG2NdmGqQXqYCylhP0VCMDGgTV42NP9b0e7Xwb6KNEunhfWbK9xGF5E8snCX/m5c9sX3CX8MGIYiqsm34jmblpMJrMaLdClsXhCKEMbMIztRQl2hjjKAiooAo5n8nkZbdV5E9h0YJqFe+8pX0NV/zNXTOOefQRRddRN/5nd9JN910k1dma2uLLr/8crrgggvo4MGD9KIXvYiOHPFDN9111130whe+kPbv308XXXQR/fzP/zwNh7vTsbo0cWo2TkBNnLWrmQSUc1o3/SS/QyyvEscGIwAW4X994PT4A+GRh+QP7VU2dk07oueB3aLUIiK6/q4TizYhipSwmWe3F09AxRRQ6ytLdM76mIR64HQYO3w3gZ9fbZF7Lv+dI5VS4M7NnYaVo1MFVPdsSIdEaE+1U8lyQK3p4eo8QgfXXYysENQQohObOYD7zI4gBr+zo6croEpXH1GomMDqBIc1tnnqXuF+jVwQHPGmUx/K9BixlkToJDjcJdu4Ux/rCpz6St95OUkBZRECCK0NqYwZBlJyxCv9ktaqtN74fe79GBmUQ0DFCAJ1PWifNRFbyoJoqITgi9oijBuWE8mhyb1BrjaFkAvIGm3uY+cPECd4D7cturegHgwFKZXz7JXOBoVU7cMDeRKhhbaq6x7KBgpMZhvPRSXO6eRf9+T8aMcety4EXweGKlQcOEQ5rDBVKdfVMuA8jSoZDAeq5YxHZ2hOvRaC8GTs7+UqNx6J76NTdmu0RfedmT70aTmsLed14Pw1nMYSrHWA13JyJg1HPI9jOCeajVaOG4ebjt1Ed5+6W+wPJ4qQeMTQftbcWzmgMASfNf5WuD4iotX+qmqDa/uj93+UTu34oWiRmBiVI1s5Y6zFbVBjm+PC5ybykHwOSSOFqOQw9wesF/76tpO32USLdzyFBBSuTc2e2PuogLIUXbHzVyPxY6rEwN7I2ZdKYsZCMeacsR12Fxr1cl155ZV0+eWX07XXXkvvfe97aTAY0POf/3w6e/ZsVebnfu7n6O1vfzv99V//NV155ZV033330Xd/93dX749GI3rhC19IOzs7dPXVV9Of//mf0+te9zr6lV/5lSZNrw2XA6rxEHyTw3k5w4lKxBRQrYfgiyugnvmYc6u/By3Zx/HghIC66ByZgPLUGnuAgNrcWTxZ4rB/NT7/i0bKeJ3eWjzx7daeRaC4NexI1UXgG558YbTM8lK/OpPaIkwH1fhlhOBrSQE1rAioRpvp8IWIwLFbTpUQS6s0dagKT7+jY7Ty6yYqEALyQygnOVmlEGDjQkSmAsoVUxy2WB8vByFOaLilOHwFZ7elcqiua30iGj82zOYhNDi0Q6xLsg0d4Iqzvo6yYqSE4IsSGsp1kZBSnPB17BXVLNp6Y+Wq91PmQVhzGuFivdZUfFljCXMfKKDK6i0PwXgo41vC2Gr2BqEyJ3X1kHBBUlGZ+5Q5terzbIZrmCfLa5dCMkwqxwl76WzwiGdWVx+jLPBzT7BFIzwl+zSiyrMXxgNJXPfk/HBbqJ/PcUdAdbAhKhkUh3WuYihQQIEzWKsvcEhHckClEjaSTanoUU918FbHnEaEgY2nd07bzngGqx4M5xV0J7L9cxQrAXmoONwlBZT3ER4J32U5tx/afIj+3dv/Hf3rt/3rWv3xyhrv45jz/uzA55JFpARlgbSJqbr+5pa/oZe+56X0Q//4Q8H483EclSNz3Cx1Dlf65BC0sfCCsfUUKBNZW6iAMnMm+SyS9xr3WU4IPotEioarFNY1vzdHoZa67yzlqoTc/aEpoFLb7ULw7T00GufsXe96l/f6da97HV100UV03XXX0XOf+1w6efIkveY1r6E3vOEN9C3f8i1ERPTa176Wnv70p9O1115Lz3nOc+g973kPffazn6X3ve99dPHFF9NXfMVX0G/+5m/SL/zCL9Cv/dqv0epqyPBvb2/T9vb00Dt16lRQpik4Z2rTCqidiQoix4lKRLTSdgi+jBxQT7zwAC31ezQqykql0CbObI+/BBxaD+XuRL5aY2dY0H774ZKFoy2VWwoOKGENdxPObsfJpVNbg2iZprE1UQruM8JanrtvvIbPJPRp3lhf6dPWoKBf+Y5Lk8qvLfdpY2dU9atpOPI95+xsSzk6DcHXMVAdUmHkgJJC8AXluAMYHKWB4xmdkawOflHKgyTZy8udvBeKGcouJCvUH1ysnFNQoAoGneNEcl85iVaVIf8ake3A7wGxhnZqDvbAPsnJjA5rzREdmVPJqT+UQvDhvJBgP9SpwRsjOPs0tYxlr0jAFYoCinuvNAWUMJZSX1NIxdharV5qcy/YhusyWQEVCUko1S+Ot7u3kF/3l4hGI6Ucm9O6CqgY+YrzTBT2nbfrbClHyjwIa9Uip5FY++RfQnVS/3nfR/peyzp/lqZlJAWUa9NTQEF9/MzuFFAdGCoFlOWUNZy0VrngfcFZmqpGijnCUwmboiz83C2Ul2PIzO8DpEvQByBv+HuYR8gK7xULN2gpX3IUUNI8a3ZZecJwzM2QaVT6uWVKITwZw60nblXfS7VdsjlGRnEiKaaA4q+3hn6Ek2HpK8NiePft7yaisN9I3haFPW6WqoyHFESy0FIMRVVAkRB9lgrICpuJ9QR7EmzMCcHH5wfXTE5OJSs03rAcBuR1KqlkqZywnixyWViMuK5xnzrgwwExBVeHvYNWvVwnT54kIqLzzz+fiIiuu+46GgwG9LznPa8q87SnPY0e//jH0zXXXENERNdccw094xnPoIsvvrgq84IXvIBOnTpFN9xwg9jOK1/5Sjr33HOr/x73uMc11aUAyy0RUI5AWsmMVbfknuRviZxwoQJTlVrPfOy53n1tYidClnG1xpU3P0jFAgmet37inmiZr3r8ec0bkogUAnLR2EhQQP3kX36iBUt03PbgGfrQTeO8KesGAeXC8y0iX5n7UWIRZByO3GlL9egUUDnqUXeuD1rKAbXU5YDqkAquoPAe3CiNHFAKCRHLwaI6/8FRbKk0PAUU+8H83l9mZYCo0dQAdRRQSEL0l2QnrqSiEJ3M2KahQOAKKPGHU4Izmf+r2iY43KW5Eh3zRVhOU0Cpiji8rNgiXYuG4BPswCpFIqUM1W9IwLn3zTl0rxPmj9un1aWVyxpLuIYKKHHuBVtUgg/2n1hXKSuqiASSN3U/R9ZqnfPHu2bAUipKZKmpgIoQ5Zyoc/dF+6SRitKeFMbXsndp8nRdMRTmuVNAdUhHVqisTGcikiOWs95qBx3SaKtFcHASAB3Usf5wm1ABJZXTnN1IxnlEBMnOfAkxBZSXM2nyP8Qtx2+hV133Kjq5fVJ3Zgt9sFQ1HnmA+ZWQLDHeK0s/DBqO/9mdaXQmhzM7Z+ij93/UVMlJMMOegU1eCD74jmKNS5BHyHzwi+jo5lG6/oHrq9cHVg6o9pph3QziFN/jNpnqIrQhgby99v5r6Y8++UdVG5bqz1vXEcWjtoclIsvqe0DmphLbApFlKaT4663hVnJOq8BG0hWcOecpUZxQ02yI1a0puDoF1N5Da7KEoijoZ3/2Z+nrv/7r6cu+7MuIiOjw4cO0urpK5513nlf24osvpsOHD1dlOPnk3nfvSfilX/olevnLX169PnXqVGsklCN4Gs8BVTlRcxVQkyf5W3L2urw6qWqDyr6WyZ3hqCDXpJZbh2g6rz/zxk9SWRJ951c+pg3zArz8zZ/yXv/t5V9PD5zaoh/7P9dV16x+tI23XKcTZkfPbNMFB9OTZTaFTVDg/M73fDn1iOht199LH771ocUYxTAqSvqW/3ll9domoMbvLSIPWG6Iu7ZIe4fBDAqotgio5Y6A6pAMHrYJnfWTM62/bCegDxzFmiNdc/4rygevDcEZy8mgzePM2Qmh6oKn9Q3H7sdfS7TvPKIv/S65X6ICKsGpL6mMsG0iIAHhvl71f+DY1cKwaQ53Y3yr+7S6sA/anEK5AtW/AiGgOmUSnP/V+sCxiDjixTaE/lMphF3znQ3T93FOpVB1Rh9Uu2i2MUopVxaT0GlSm5F+8bOA1y+pZcQ1qITg83IeYX18f9RVQCWQrylrlZfh5NLpw0Qf/V9Ez3oJ0XmPE+wlCBMKf1hEuXeecXIa133iWSCFmQwIPuEzgNdVEVCC8qpTQHVQ4J5ij+Uz4UgNySXBCn8XHHWG0gdDl1lh5zDE2+Zw03zftJ+V7fX0EHwxBRTCIjFMZZilgKBwfvpBzlCi7/77cQqNBzYeoCceeqJpr6qoMJzmRVkE5Jum0pKc1xYJcHYYElD/6d3/iW48diP94tf+otdfi7QM+gbt4Brh8zUoBuq9QX4lJKC4TcLR/K1v+VYaFAP6X9/6v+iyR19GB1cPyu3AuCHRkhOCzwqFh8g5N4iIfvQ9P0pERI8753H0HV/8Hd5etRRqMYLWWnvYVzM0IYwpEl+a6iemgLIUgpvDzWS1mkRGpeZfis1VjKAy94dw9sUQCzfZYfehNQ/15ZdfTp/5zGfojW98Y+Ntra2t0aFDh7z/2kJbOaCmTtS6OaDacUwPM5/oX1luR2mA4DmdUombd376/qbMycZXPO48ev6XXuJda4tkjOGmw6fpc4dPq+8jmbYo4Jp76sXn0Iue9ViT6GkTGzv+lyZLYeTCRbatgCrLstrzqWdTlV+ppbCbw4QcWoiVKgdUs3vKjV2/I6A6pEILVVcysqXXZ5HvBCJFUyBoxM/0Ivwr2IH3amqkwBGr9cuw9+jnid7xs0R//cO+XVYOqH5fcE4nOrFZE9PXklPfFer5TuCgkhi5oJVD57w0B4X/r6tPUzHh9YDU0OZZgOY8916zMdLsMBVQERKiLAQFFNhSKaBiZIU0blK5lLq0s14bS2FekXxEklUiFVPqEq8p66MsffJVIj6q60q5PuwN7Je2VmOkKpaR7Mcy/Mx4248T/fP/JHrdC327khVQjADH8R0N5PMs1vfUOZX6JJKArK6lybOpxYjSxq1DhyliJEaKgz2FjLIcl6hksO5FxYGnzKFCJaSKsqCN4YZ3byw0m2Zvj3qqTVU5zwzdgTsoDQKqNMafQuWL5Ui2nL43PHSDvw5yVB0wbEGOG3ScK85wSWFjzc+ZnTOBjTceu5GIiP7u1r9T26QyHItUoqWkMAeUShDC3jEJKAGunavuvYqIiPYv75fbKSMKKBxXg4zwFFC4940xioXY4+3cc+Ye830kVq0cUK68ZiMqBDVFXWB7GYbgi4XcTLaJvd4YbJiEs5Xfbvz1xFh7Rj2I2DkYnCXse7D5+SGQZh32JlohoH7qp36K3vGOd9AHP/hBeuxjH1tdv+SSS2hnZ4dOnDjhlT9y5AhdcsklVZkjR44E77v3dhucs3pzp9ncK7kqA4fllnKZOOSGlFpZkOOct7eaOKYuz85uxWCX5IA6cmrLfP/qzy9eXUQUhoBzZOjayu5QkmGIQCuE3FoVgq+dvEoOfAxXEolcl++oLQXUTg0F1HJLYQLd58ZeCFnZYbeAh6oDp6JzCHsKKIJy7HUfFFDoeE5x/hPZOaA8AoqrBrijmyug0PnKHapAVpyCPFKBo7gMlTz9Zb9OZwsicAq7MglOfU6sWSH4Up3JURJCIBKkORWJGsGpr9qitBFAa0O4FpCgYK/WZkCSCG2KCihWTlNAWXNqlRPrgteB4kspp17HvuJ+4fegZ09Sy0TINonIdK+DnFKTMt6T8tI+UuaeYO6TyEeFpIzuLfLLcNLoro+M/zxxp1+eOx1FRZHQL8jbQYMNCvqPcyoRQaTMqbVWOcEnnWXV58Dkt40Ugs9bM92DMh1CmE/Zx0gjAa+67lX0sve9zFcQlCGBgc5TNXSeYYNU1iIENgZAQEHIMQseAQUKKMk5rY0P9jUWxk17T1JApeYyEuv1jm/BCW0QYdp7o3Jkrq+gDexPofdna7SlvifZIbXB29LeR5u8EHzFwHbAk2wDURhaznPqC/3Zt7JPrDcINVfYoeYs8gTVRt5Y4EdXzXntT1zZaDMva41bbA/wejwytCiCuVX7Q6UdjtAg8fD9XAVUKnlFpCs4rfmIKaCoJPr44Y/T9779e+lfHvyXaH0WqRc8LDB5vwvBt/fQqJerLEv6qZ/6KXrb295GH/jAB+hJT3qS9/6znvUsWllZofe///3VtZtuuonuuusuuuyyy4iI6LLLLqNPf/rT9MADD1Rl3vve99KhQ4fo0kvTkty3iUsOrRMR0f0nt7I+rHMxrOFEHd833qz3n9yMlJwPnKohlYBaXlAIPkdA9XvpYQ0XSUClDOci8mhJKGJfJNqdahWogHJ76+CqH6m0yX1tAQkoiwypckC1rCTkY+jCacZQqTJb2PNFUdJDZ8ZPjeUooJYrG5sdz/tPjn8Auc+RDh2i0BRQVE6VEP2lSPglzfkfUZWIzn+SFRgiGcTKcSdrTAEV2Dt5b5s9wcr3qkZ6EY0dzVK/xGuGasDrB7+N1wW5rQLEnMnKeCMhIhE1ImEmlLNIrsDcBKe+eF0iEiQnPLQRrF32fspaRQIKx829H+uXSPAJ5XhdjtBQnYGJYymNXYxU1PZpCrGGa0kjnQn2FiezAgUUzmnC3OesVdEplDq+5NtSluOQnt69aC/Uh3uN92vHd1jTYBPOB2jD1aftx2BOU0Lw4Vnm6pj8u7QyrStotwvB1yECtiQlJQNHSqit137mtXTVvVfRtfdfy5oIiSHMO6IpiiQHu2ZTTO0RhOBLINSkugIFlKAOst73ckAZCg9p3Lz3DLKHo9eTc0Bxm2LhCLXQeYFd0O+g7xphg1/N8F4eAhHGXwrfZxETOBYWcYdrns/XsBiqdlj5lYhCYkUN8Tap07M5+NjXxzx1riQbU8kQkYRR2nGhEc3wcfxrXiSfl7Y/kFTNDcFnqY9i5BuqMjX7N4YbyWtPIte1/pRU+l8DI3OFY/ySd7+Ebjx2I/3Ie34kuL+k0lurGtEr9WdR/rgOs6NRAuryyy+n17/+9fSGN7yBzjnnHDp8+DAdPnyYNjfHH9jnnnsuvfSlL6WXv/zl9MEPfpCuu+46eslLXkKXXXYZPec5zyEiouc///l06aWX0g/8wA/Qpz71KXr3u99Nr3jFK+jyyy+ntbXF541BXHzu2KbtYUHHNzBm/vwwzQGV9wPglgfGDppf+JtPz90mCc4HlJrTZHVBIfhcrpycvEnnrC+OgHre06d50f7bdz9DLNP2GGqIfTzECKq2gISdU8L97Ld+iXe9bVLHAUPwWTtqUSH4PAIqNQRfizmg3n3DNG9gDnnvzoWm99ThiVrwUefti5Ts0MFBU0CV5OWAqsoJT79rDuAoWcHa8l4LYegkkos7SiCciKqA0sgsIiIeQoU7T7mjFMOw9foU9Et0xEskjwDLqZ+rgFId7oJtyUSNMvdoP96fopZRx0QiNZQyJgkqET+Ja7UshBB8QIZUIfgi5JtE8HGbJfv6GgHV84oF9wWXY6SRNC8S+UjyPhXJNol4ddf4uYKKRk5AGbnqpLNBKqeto+iaLsNrkkqTl+H7dN8jsCCUgfqQHOJn2QDyjAw3Se5/4n6OEmvC2YvzjHO6xBRQZv0dAdUhhKmAAgLEdHgCMOSYqXjC11jWcHJajmQMB4cEVGrOEmynR3oOKEdQiGH5KOzrAFTeOYSBZ0MRqsq4vVYIviDEWEYIPkuVIYUFNMOGlX5fkaRkHfLtF8ZFU/ogOYXAvtddt0G9BpEVswFhKd9ioRjNEHxGDihzn0kElLYWXe45ds0LdwdjiPvDJFqMucJxscY4CMEH5E+sHi0MKL63OdwM1hNHjFQyCTXFXmmNYrhBbh+iLEtVrWfZIL3usHfQKAH16le/mk6ePEn/6l/9K3rUox5V/femN72pKvO7v/u79O3f/u30ohe9iJ773OfSJZdcQm9961ur95eWlugd73gHLS0t0WWXXUYvfvGL6Qd/8AfpN37jN5o0vTbWlpeq8Elnt5sLw1flgEpUGSwKuQqohYXgmziWY+H3DqxO8+6MWspZI8E5wn/73z2T/sPXPt4ss2jEwxC0ZEgEGLLQfbd+1Ln76HO/+X9X17cXpCzbBAXUNz31kWrZSgG1oH1ElL7np3npml8In7nvZPV3qtKRaKrMbDoEn/vMOGd9OVKyQweE4FSsFFA8BB86lNl9GA4s2QGMZIXx3ac6FkrfYT3aYfWjN0Bwlkr2cgKqGDEHMNvraFt/Oc1hHRBhmlNfcAB7jg4MMwZlvTLa+S21iSSEYldMLSM59YnIzm01DwhzRSSsQYVcwTUdVF/KIfj4uKkh+CJrQbIV7w3I3aCg+VK1BechlXwkEkhFheArYW25skSMWBP2Byeg1Fx1EgEObUm2S7ZU5YQ+RPcpgJ8tqwfgXsFe62zgcz/AEHzMIeMRRLzvghKJtDkV5j4gd3GNwDgurU7qEtotOgVUBxnOiZiaQ4Uo7shUy5IfDotKm5QxVRqGk9wKlTXOeet/n8jqD1fg9HQFTiwHFB5tgQIKQhdqNqCSClVMiL7hQsQ8QqKqQyOgyggJgGsokZgryA4p6CmGBJVJ7bVqkXzCmGvzHCNOvdcGSZGizDOJFvhMsNQ5uBaTc0BJBJSyD925o4VXlIgjqy3r3MC5Sg1TV1IZhAX03o+Qb5p9REDQFqNQdZZI0gQErXFm5pDL0fCbYJ8Zgk8ZG4sM77A70aiXK0Uat76+TldccQVdccUVapknPOEJ9M53vnOepjWK1eU+bQ+LRpUSjthxeWp2K3JzQDnyrm0n/06lgFoyy/3JD3w1vfg143jwiyIiiIg+eNODRDTNOeZw/oFVOnZ27ERp2lmeijaIhXkACbuDa9Pjkefk2dge0aEFqN94CL5/9dRHmgoeR0Btt60kHIzbW1/pJ38hWGpRAXXevtXq71RVJtFUaTpseDwrIrzLAdUhFZ5SCBzF7odIr6/nF+GvzfBnJVFPcuzyfyewwtBp4fDWDjE7ehAuitcn1OXu22HqAv70vhZ6iojlvQJ7JSc2OvqrsryYlAfHleEh+AzCpNcXnMSsnKhS0dpEKHOP71uOZ2eLqsrAKmNnOxtbzENGOOaBZ0VoA/fC5N4gBB/0oYkQfNUaZESNBIn4EctFxlxauypZaoWLVOrDNdhfnu61AvcpI6CsXHXSPsW+pRLgqQooHMeVdb+f/GxZQUUynj9oA44RJ+lgzCUCShpz1f5Iv6Q+BXOA9joF1Ehot8sB1cFGTigtNZeOcPxZzkh8en/8EZDmHI6FCdMcuAUVYc6bQn7y39WrhZqyQrg5B6+p6GLvzVPhYY6hsf0tJ7lql2IH9jsIp6jYKJGdnnPbUgUFHyE6+YMKDrTZXLdluL6SCTVUGxkERy4h4CmgipGuHMN74b2AoI2EZZTKiX1g/XMh+LiNQdhJPneRkJsW4RwQc0Z/sB4rnFw0XKUxf9Y5GAv/iPuMj01WaELyVZzWWgzqgv2TqpLjr7scUHsPnZerAaxNSIwm1Qeu7uVdroByTuVUZ+/+Sb4dDDfWNNx4rkUcv1//5Avoyx5ziIgWR0Dd/tBZ9b13/PQ30I8/94uIaPcooBZJ1OXAheD72iedT3/5I8+m8/ZPyQr+o+FX//4zrdtG5BNQv/99X2mWXZQCytl4YDX92YaK3GlBUciVResRspljpbIx5kidDdU5lJnbr8PDGUZoK/cjIFBAoWPXOYrBSY5PyecQIv4FCp22JdGQhdQ551HMLghVh0RYBVBAbWMIPqFN0ZkkOP9FJzaMh3Sv5Ciu7OD9ksxQHPHYZtAGhQ5lVSlUw6lPRMnh2kRExohfiyqgtDFBh7rQBobgw3mu3o849ZEIs/I7IbGGZbSQjKpzJkauJM69ZItGXknqHk5AaeU8Aorbr6xBKwSoRMh6dSr1a9ewrtWD+tmyDASUtFbFMRL6hXPASXNVAWWR0Slr1dXPyF1rTnkOKBy3TgHVQYEUCisWQqmuqgTLB2oEqx1wMvNwUUQUEBw+L+a3GSPFLPu91z193JyyQyVlwEYrBxQicLDjmBpzGcsBFQsll0oQWvmurLISmWAqoFh/grB5VJrEUMwOry6D9EMbLbIqID8h7JnXH/WzM7RRWrfW+6lqlljZ2HrRxsX10yOgCp8wy1JAKcRQQeF+sELnYV+DHHXafo+Q9tb4ExENyoH6frCeDILNCnUp2cv9ZOqDBdo1Hs3YGAtrzXTYW+i8XA2gDRWPc4Tm5DEhIvr5FzyViIi++gkY07wZDCsFVJqd+ych7jZ29C9MTSBVedDr9ej5l15CRIsjVq6/63j190On/ZjYjz5vH33PVz+WiIgeOL1NxS6Ib/f5B8/EC+0CfGIyrs9+0vn09U++UC337huOtGWSh83B+MvlNzz5wqgCa21hBNTYxn2r6eSOOxvaUEC5Jvo9onP3p6vY2goN6s6UtZXuo7lDItTQViVNc0AtMaUUOoqZs9N0AAvOU40QSVVA8af/PSUEVwoJ/ZLILCIhBJ9zknMFgkF+eLZIZcqwTJZTH/oVNjIpppAV6Cj2QvUpTn3PDoJymrOewutBaC+hjPZjUFs3oh09/7pKaqCiCEgIqf+SAorfpyqgIqHqzJCJuO6xjLYetLE0yF1Xj1hGqDN46EO5FwkubgefBy/UE6uLh+CT5tTb9zRtLyB8lTUdJSkpvCYRULx+fq5yBdRwm9nLCaiR8Ldw5mEfBhtCmwTlSpLz6gkI1ipN++6FIoW5IqKAgCqG8bO9QwcDIlnALlmKIYTlOEaCAOuzQmnlhNELQtTBOWIqRSKElEbIuDotpY+lgEpWF1FIflhEipkDyiBSpGum2sIa8xLGAvsK96YqoCTliJYDSlRAGeSatRZNRReSO5aSR7AfoZIaJQV9RULHq9tQ71g25uaA0uDWoabewXGzCNpAkeZ9tQpVcLE1zm1AVZClaAzuN8YmlgvMIvY8Ij4jXKJFTOM1Xs/S5Pti6sMCUQXU5N4uBN/eQ+flagBtqA+cusU9mZ+KJ190kIiIisiTRvNCvgJqQkBtt0xAuRB8CYReFd5suJgfYpz4+vZnPip4n6vi3vPZw63YZOHPPnx7tMx1dx5vwRIbn7jrBBER3WYozIiInvnYc1uwJoQjZVPInUUroPZnEFDubGhaXUREtDPZs9/2ZeG+seD2VFsKqNWl9PHr0IGIBMdzSXIOKMmhPHndNxRQSHJo5YhsZyl3xHuOEm4XKqAUBzMqhbZPy/ZaCihR9SFckwg+CSb5Bv1ClDgPMK4FOrZZOYkk8OoWCLMsZUWKAko7H5V1I13rIQEF9mrkIzilwvoLOQcUr98luY8SktD3IGygX9Szd5Yx0mwL5hTv0cjShBxQJcljG5C7sJ89spCF4OP3YptmCFCJWMKzzN2Tst5gTpd4LjggijkBtX1GsBfqK2C8tXJE/ppEAs7qQyoBzs88b29xAgqIbS8HlLVmOqdPhylSckAhVLWHcB86NdHxnUocxRz5vv9ZVwWgAxoRe1ofHd0aIeAc5NZY8f4hARULMcivWwSOpRhCoFpEnE8ld5BEyvC/rRxEsbBuSF45WCEQpbpR+WKqeQwHu3RfqnoqyAFlheBDVR8AVT+4zlMVOAEZUqaTIZ69NUgNNQRfqe9htAnrtuZ1VIzsMwf6aqmCchRQsfcGTOkfWwceAWUpQfFsEM4Fvoc01aWkkkVoRC/apLXdYW+gI6AaQBvqA5ffZzlTAVU5plsKz+Yctv1EAmrfJHTX0bPbkZLzxTQHVHw8D0yc62e32w0T6ODy0LzgSy+miw6tB++vsD7ccN+p1uzScGorPk5HTm1Fy7SFmw6fFq+/+vu/iojSSMomsJlB7jgb29rnDme2nQIqPQRfmzmg6uZYWmkrB1TGOdShAxHZCqgqB9SSnoBedACzOqYvZAew9+8EEgkjEQeegxnrVhQTpNRFBDmghBB8QV2kON2laxIZwtqurkvkG3NscyVaAKVfDm5cK+c/C39GMJbaXOFYig58wT6LWKuuTe6573qiP/kmotuulOuSGaLxPxiuLZV8TFmrI/gugmQFEny8Xet1NQ9GvzRSUYPr6xu/n+gffwFsJv01jplXJtavUikDY8vv5eQbJ1O8ubIIZWE/S/ZqxFLK3pXWg3hGkWwvx+BsaC+RryazFFCWHT323S5qb/DH5KUwp9L5XkgElFurLASfdQ50Tx13iEBy7mrhsHIUUOWYsQnq5u+nEi2W49UihobFMBrOyzeQPHIlCC2nOMlz1CDOLr9Z3elvhoOD13xcepP/afUW8CBELASfVzbinDdJMpLrdO15CigY11hIQXVOhGWbk7MHnflWOxaREoTgU/KNiXNh7A9UjplECwxGXVVNjID21qJTQClzi2dF3dxZGP4Rc6TFVHye2hNIVjW8o3BWxM7JrZHvT7OUVl5/EolqfC0pCTUCaql6aA7aYa9T841JdnXYO+i8XA1gSvI0p5AZ1lRArbUUSsqhyFRAuWLvu/EB8+mIeWM7w/HrcgMd38B8Au1gZ0I+rio5bPhYn7svPcxYE0idw92Sr4qIaEMhFl0YtsGCwhrmqIsWFYLvv//j54iI6F/uOZF8T7sKqMk+zyQRHdHviP+m0BFQHfJhhKqrFFAQgk8jK0wFguEojiqgWJNaOCpP3ZKpgHLgP3Z5/hKvLskwwWEduyYSHySMBTjixZBrpfePqpZx41o5ihUFlEi0ufeVMQ9sifRLvDa55y++k+j+TxL9xb9R6hLalOZUWtPY99ha9e6VVFzcMwHjWxWLkJYpOaCsMlKbVI7H8HPvIPrIH/vXsVywnxXCLEpqFIJ9MEaolqnINwKCj9nBFVASkVu9ht8JajmhDX5NmvsUosYrw+zl4+QR24qqy51DVf/5WjXssEL1Ffi9VNmnQYg8vk4VAqqqa2LLEvvdgO1649YRUB2mqBRQhpPcVINEfi9aob+kcHGaHcGT/1YIPnDScgxGg6z+jMoR/fC7fph+4n0/ERAN6CSXHNtqf4AwCBRQQNxpjn50isf6o+V8ke6NhuBLDJ8YCwtozd2oHFX5tKR7tfbda24jjmkQgi9xD0gOdU2tZPWVKMx7pJWV+h2Ehyv9/ZGqapTWvGqHQS5YecuwrBt7K0Ri8jiW+phLZ4qpLOP7mdKVba7cDUdvoG9+8zfT3976t9E1w7HDHgSy1ISIIF+X3wF1TUt7WyP1+pPvIHUVUNoYdwqovYfOy9UAqhxQg+acv1UIvsTcSg5thuYaM/7j02EpkYB60oUHqr/bzLFUKSMSHNPnH5gQUGcxnEs7iJGPPERbLFdQ0+AKnOc9/SJ62Td9cfX6ld/9jOrvReXTcuDqm2+99GKxjFOWDRZkaxWCbyWuLlpUCD6ndutnPBk7VUA1b2tdgsfttaaJ0ioHVEdAdaiDgKjhKgVDUeQ5iok5NUu7nNQuEYm5ZapfClwBhXa4v2M5oFwxIHO4I4nngPLyBUnEhOAUTnbsJpAVU4PtEHzumhqCT3FsB6SiQCxJJIRFVEVJRePerRNh2eBeiUihcG15RYqwnLpWhf6LznRWrno/ZU5ZGfc9XCSX0F4oo62HshT2ESWsVWGNq2s1Qsi59gJyj+Q16CmgtPMC6xMIGNFeZe9KcxVsLWU9qGWQAAcCSlqrQRma1tdTyDe0Q1OHEhlEkDQmWA7OFawPSUVOQAV50zoFVAcbqflviBKS1RtOaTMHlOFIdu9X9WY8+c/bHJbDLIXHnafupE888Am66r6raFAMbMJMIDvMXEfsdUCoGb+tLBsspzNCmpuYokVVrGSqHixCDW0yFVDsPJNyz+SEizTVI6hCMdZYUA9ftxiCz1COxFR0MQWUlavNIhItAsEkHQXSVxtzN2/cxlSSUnpfU+ahAiogQ411WlLpKy2N88nV+YoPv4KObh2lX77ql20SCV5uDfWIQtbZbBHx2E5MpaWtxb7w/R73gLVH1THuvorsOaTHSeqQjDbC3Lkn8VeW83Zdm45pLmhYSvyh8g1PvrD6e2swovWVdnKh5Dimz9s//nG2KAVUjHw8tL5C+1aWaHMwCj+oWgZXjPzhfxyHsDtnfZm+9dKL6SkXn0Mf/NwD9J7PHlm4Aoq3/zPPe4pYZqVS6izG1s2d8ZeDpBB8VZ6ydm299NGH6J9ufpB+6zu/LPkep4BqWl1ERLRdOwRf8zmgyrKsHSKww8MYPYNYco4VngMKiQPJqe9eB8SPUE56OjjILcPLaEoIXj+RqYCq3gPnvVdOCMGnOqITHdvS66iqhPerZ+cCQnvVutGhjP2S5kVw6ksOcY2ssEKWVdeUz5sUUiOYq4kNAamqrVVlLfG6YmEEUWFm2i8QB5YCSp0rLe9POSW2iMZkVL8vrEPoaynYUZFG0ESKAiq4Bnutz+bBywHFxqjX188pae5LZU5F0igyHuo14z5vnxYQrk46V6A+LQQfCXZ4Ie2MsyEgoIQxQjvc+6K9I78Mv9flgJLa5d9/OwKqg4AYaZHzJH1qQnrpSX/N2Ro4XiN5aiwiZXvkpwuw8hFtDjf9sqhiElQQ/O9U5ZKlgJLu1YCqDStk3agYEfEIomTn5MJr1tyhDda4WeQOkn5aX9y9+Fobi6IsAge4NW6Wei1KjhhlkczVSJhqjFjVlo2jcuStqVh/OEyC1lh7OTmg+hMthZYDisget1QiGIE54LT8RO5vS2Um1cPJGivUH4KfSVGFIHs/pgTVEFVosuuSAiogoAqdgIrtjw57B52XqwE4FU2Tzl/nMF+uq4BqweHPHfVLiaECl5f61fg5xUcbyCGgzlkbE1CLygGVQj5+01MeSUTTcH2LAlcLrSz1aX1liS7/5ifTUy4+Z3xtwaoiB75XD67JvHxl64LGtFJAJeWAGpdpWwHlSLKc0I9LkzOslRxQNRVQU5KsufHkZ3JHQHVIBw8DxR22zHHKCShUTHCHdR/ic6sEkZHThEhWC6EaIHDG8roMBZRUl3uNobIkeyViQnLiBtcEZ33VD+m6VBfvl3SWOHsTFVB9QwGVRJhpznqpHwkKKPXHoDTmiqLMDMHHVW1A+mhrpLpUKG1yL4xUl/Qa1qpFHARrsBD2ByvH2/BCphnqLNz3GgGlzbX3WpgrJC1dO0R+/qsRJ6CYHTwEH7+3+lsiasA+7awx54b1IUbUmOcPhuBz/Vryr3MUxXTYzBx0iqIoRmxrRLG1xns1FFAB8dXe77IOewxuyxiEzYfv/bD+pLzgJN8ppgq8mOPYDHFlODyjIfgMwiYgoAwbOAH12x/7bf1pfoCkgPKOZLjPygGF9WAYw4AgUNrp9XqegzyWC8vVvTHYENu21gw6ti11jzX+mLOLl+0BmS6psDQSQFRAJfYHiQhU41lkFZZFgiNHsWURZqPCJ6ByckAF6kKjrGQPXzOaSsbNnXeuQDjCVEVaYCPMuUWGBmse1rcZChP23fGt43Tz8ZtVmzV7iUICStvv2G5AxBtkbiysH+a7chhMviNaRCQfx7+++a99m5S560Lw7T10Xq4G0IZD1T2Jn5sDqg1yzIE/KJeaA4qIaH1lbOPmoE0CatxWiuN3/9r4R9zmYFTluGoTlQLKCBe4sqAQbAhna78nh2FcbSm3TgycWND21ErL+dMQG4P0HFCVAqplZZnbs+sJNjrshRxQVf6vBseTf17knJcdHuZQlULl1HnYWwICChzAwRPyknOTOSOsJ/pdnd5rVq7PwuGhvZWjlIXAKgv/y4TlsEanqvjkfw2nO1GaAki8rvRLHDccI4vMIub8xzkV7JeIJclZLxI6REE4ONGZnjomQv0BQUlESBzwuUeyVJxDqF/sA38NyhXVVr4GeyEZhm149gIRhiQqt18ioETSD/epNqcRssJbRwYBzOvyQvAZBJRGPHv7FMdCmfvqmtBX7UzS9oP3WtmnfN0UQ1ZOUUARkZ+DjiswwSxJASX1QVOVxog1/j7fWwWeF+zePg/BB5EeRPK0Q4cpLOfiye2T3msrb82oGNF3vO07pq+R5ABnaqqiKHD6o1oi2FP8T3D2Dn0CynLMcgLqTTe9KXBQx1QAKQoDIkEBZYyx1zcgOIhsRZeXA0oIY4hKkt+49jfo2W94Nt1y/JagPkvZgPOarFgp/XoGI18BZalbpHxjam4a8aucvgesfltr0VKzSDbFVDMWoYDEihWCz+pr3RxQro3f/thv07Pf8Gy68eiNPlnC7JFC8FlkT4wo8mwCwgxJMGtvmQqoUj+DirKgP/30n/o2WuNmEFAB8KsiyWNKZO99i7gO7GVtbI226M9v+PNwXI3z2IKrpyOg9h46AqoBtOFQHU4c9suZjtQ2Q/B5CqgMh6pTeGy2qYCaOJbXEsbzwOpUIdMmSeYwJR91W1dbcJinYCdCljmypw1FnoUBywGGT0I5LC88BF8+AdW+AsrlqUonoJw6ctTCGqitgHI2Nmgi/7zIOS87PNyhPKlP7HV/ySd08FcIOko1skIqJzrdtaf10V5eBL8vGcQaEkuVvahUkBzAEqmD5hahPUmKIgr77tUPyooAgr0cxRAcypzU0Jz6SGZhOYnQEOxLyRckOv552xqpwe41FVBCvyp7BYc61o9qDryvInkic+/1PUJABXuGIgootk8lxYpEnHhzb4xtjFTl/eprawvJiuXpvV6+IGYHV0BJcyrNPW8LbdPOBsneqs2EfVqB71Moq4bgQwUUU0px0t0iNy1lqRQusTLQ2SyU4+Om2gtz6imggICq7uu+o3TwITkBLSc/ke1YPblzko5sHFHLmvlYKCRTtHZijldLAbU18vOteM5taH5juOG9xjwpljIJ27aIFrM/FsEEY4ZEhOUYlogGnK+33PwWIiL6s8/8mdcv7BuSJ2ivGXYrOPrZuJTDKHE0fSskE6Q5cWX5mF+wfkHymOP70ZBjhqMeSSKrXkRs3lMVUJJ6isMMqSnU8/obX09ERH9w/R+IedGIpiH4VMWXQY5Ir72vyEAieTYWhargxNcxBRTae3TrqFmXBS8HFJwN1ro01XdAEuE6jBGpHL/z8d8xx8paq7F13GHvoCOgGkAbDtXRxAmemlvJgeeG2WqYPOFP9OfYuX9C8DRtH0eOY3p9pV/58s7utB+Gz9lqqd9WJ+H5Fh3a7u5j4ye+NMWdI6bed+MR8f22kDamiw7BN15r+1bjqftWK7VWuwTp1mA8jikkmcNKiwqof77lISIiWsskoNz5NWqQfByxdZUbWrXDwxhWrqSCE1AKacRfWyGwPIcyD/Yv7Fsr1476lD869S2HtSsGaiLM1SISaxJpIjisLUex9zqlnLODwjEO2iWd0OBhBYkMxzYbsz4SNVBO7LvWDygXK+O1Q37f77tebjdQQCEBBWtVVIJIhENBsoqElcMcW/zeoC5nRySsokQE8nLX/CGUY/0yFVAGoZxMllr94jmreDnYa5yoChSIeK4I9XmkopWDju8jJZwfUfoet4gaIsavlH7ZQiHMAsJoKPfLIsLMXFH4WwPWvaWalOy1QvD1+tOyI8wBNbG3y//UIQExh6Eago9K2vEI7YhDlMpk4shSG2E7sZwjaKOqxqEwBxQPRxcLC4ZlrJxJA1AtWn0IVCelXE6yySKnpPlxOLhyMLjfUnSZOaCgHVMZhgqoDEe3qagrSzq9c7p6/Yj1R5jqo4DUMEglnCstDB2+joWHQwTqKWiHk1um8hDGHMO6ceSQEKd3TnvXuD0uFKSpgFLWIpEwHwrZgyEpR+VIDeko1YvnjFcWzo0T2yf0uiL70jyTjLPACsGHZa11SZSgYsKfIQxIoGs2cBu1B8c77F50Xq4G0IYCajTZdLlPyq8tTR1Hv/UPN87VJgR31OfYuT5RT5xuMcdSDgHV6/Vo/0r7Ki2HlPxfFQGxYGXRT/7ldeb7H7rpQSIiuv6uEy1Yo6NSQBnz30YeIAuVAipBXbS2spj5PzPZs1kKqJZyQN1/cpMOnxo/GZSrgHLnVxthVYnGISs7dEiDRtQwx2kQgo97GQQHsKiA4uUsJQvJ1yQyyHQwayoH5py17FUVUALhknJNy8ESVSrwuiC0YICIM7kY+tdUlYrgdK7eV+YebUjqvzBuEqT19dpvg/cFJ7lkr0RmVe8J9fFrMdLB/WhOIWq4HSkKKC0EHxHRkc+S2C8vZBrkgOLEYqBQ0uw3yA98WyNtAwUUI0G54wNJOpNQFs4VNEg9p4S+Ju1xg7jySMWSdAUUnx8cy0QCnD/U4o0RlsNcTDBG2hoUyX+wF0ncXm+qbAsUUECAdugwgXMCWiG5EJaj3lIMEQlKJXSYanWDgz3ImWSQOzEFlNUfdAwf2zrmt2M4ipFQ8NqEe9GRbJI7EdIuhxjybCr1/pyzek5QnxXqzArBh3NpKTwG5cDsj2e/ME5afwsq6Ksu/ir/fmPNoL2pBAHCDG9H4Vxq9gTvw5pHBVT41Uq338yvZuxXfO/M4IzXrkRS1CE0pddm6E6YKzMEH6xpU/EItnNCM6jLaIfIP5NMsg3n2cjXhUB7rTGOkumR+bHec/d2Ifj2HjoCqgE4Z2UR+eI1C5wjNJeA4o7XN37srrnahHChylaWelns9OMesY+IiO546GwjdknYZiHYUuBUKGe3FxCCb0LsWU70lV1CQB3fGJjvO0KAKP5DpUlsVwqo+JguioDayAnBt4B8VUfPbNPJzQH1ekSPmezhFLSVA+re49OnD3PPzTYIKPd5sdzPOy87dBhDcOzyEHxcMeGdtew+KweUSBIIDlWixDBQguNcJGqwX0pdRPBUP3cAc8e25IjGPgh9UkN2xZz6CrEWkAYU9gsd0ZxUI4LwespY9rhKSmnXs1eYeyJBgUEkEy4S0GEvtevWIKjrgrkXiI+gTsGBT6UwN7AeHBmgKYj4a2+tpqjaFAUUEREPmaLOPSigeor6TSNg+L9av0gaX7S3nF4nghB83EnF1yAnlMEObz0bCkxpTeOZZ11TSTl+n3D+UAkKKLYPLCVWMgHO1ySo/yx7K9USnhlG3/t8b3ECCtY9J1UD4ouRih06CMgJF2U9Kb852FTfQ+cpUQbRAvcGT/4bZAjWizmg0Mns1QuvvZBp4JCOhceyFAiWAiog6rw/w7mySCZNkeLq0nL2HFg5MO6TpR5RxrwgO9+M/zUoHBerTa99Ye6sHFB95k7FcH0x57ypgDLWRAEROXA9afmuUubZG/MikgPKINBiYd20enBPnhmc0UPwTT6r+DWv3dK2MRaGkt9nhZ6LzbMZrg/It5T8WBp4DqiYco8jRhaqhKZwFlvrJXa/qZ7Cr0WRseiwe9ERUA2gFQXUHAiopkOJVXmqMsNJffFFY3n27S0SULm5YdZXXCjDRSqg9LlfWVAOoFxwwu9EhKxqEm4vpBBQwwWF4HMk2VqKAmoB8+/266PP3VeF0UxBFbK0hRB8DrltVQRUgyTpsOaZ3uFhDlWNZDhUNbWIpSjySCTD8UpEsqPfmaKpAbgdufZO3sNQWehMV4mJmHMa6sY+xe71+m+QMFW/FOe/qoCCPpRK39EWiQxBZzRe532KlamuT/4N1Fjuek9w1k9uDEhVXIPCWhWJxjKcQ5x7VBnxdrEuaa2Ka0KwF8ut7BP6D33AHFDaWEoEjEaWSnmFRJIH1gz/l69VVQHV188MJPO8PmG70vkjzZUw97E9jnOqKUtFYomUMw/slfYMnwOLqNJC8ElnRmBH1QCrD+aA/+sRUEJuK6yrQweaPoVuOXsRVl4dVEAF94LT3Hzqnh8lMXIHCBpLfcCdvWhTLAxVamg8orFDXlUFYX+M0GyIgMhCx7fijO9Rz3bqA4nBFTRLk7OKK8JMhQc40FMJAxyXYTEMSBuvP4aT31RlUUg4WaqgmKpGLRshqyxFlLQPtZxiJeXlgMpZi6l9HYDyFgk1TnC4BzatcJ5Yl/naPyy867hOOVFmEppUBmopbSyQ6Ar6E7EfzySOIP9YDvlpnaERNR4iR9ln3deF4Nu76AioBlCFlGrQUV3XWdmmc7MiSoy8OhIede46EREdObUVKTk/5BJQq5Uaprk51uBCE+5f0538qwtW66Tiu7/qMdXfxzd2jJLNws2/lRto/9r4S/OwKBcaejFFpbe6AALqyKnxlx63f1PRlgKKk065JGIbCqhRRdh3X6Q65MBw7HKixntCvg5ZwR2qShg9BytcmxcKUHNEs35hG56THBUToFQQnfWSY6qOw9o5bSmhHHOwV85oCuHa9Igl3veR/9qpTyzVQxDyr/TLSQSG2g8opyltAijzJQHJhYD8UAiYMtIviYDC9aCG4BPWQjUePdLVJ+xeLVwi0WQesV9QX04IPnVOY2taWjdQDsnpag3i+HI7DEJHmlNsC+vTVGLaNYJ55vZ7ZRxQAaUoK7Wzt7IXSDrRDjZmak430glwThpJdojnJQGhJdXl5ksKLUidAqqDiroh+NC5iDmTEOi05YSVSUwI5I5XL6pFDMerma/EcmxDu5ZSgWhMkpmhwdh7ppIhQqQE6i9jLk0VGRAGEjHHlVqBw5o1hQqnwNGvlMUxHRQDkzCw1giWj9mbqgLEdnLC6pk5hwzSJapIEYguTggFJIa1t6zwlgaRhSq+pZ7/4K2UT4lfS80TJr22FEMB8WgQLTjm3n43zqcYWWgpx4h88i6YazjL+PuxEKeaPUREZwe+YADVeIiA+I70X3uvU0DtXXQEVANow6FaTOrezc5K139LVSLhonPGDuwHTuss/ryRS0AtMhybG5eLzllTy7h+DIaLPZyfdsk41vOv/5svFd//ry98evV3mwoYhJtHa62es7ZcKd8eON0eOeqQkqfKoSKgWlyfbkwuPpRHQE3JnWZt3RhMv1wNM9tqJwdU4bXVoUMStJxC3HHc6xmkEfvxbDn1PUWDpSjSbAHHtuQ4n3bKUFlBOX6vGoKPKwsMB63XnuEo9uxFR7Hg1Pf6npADSrW357en5vXibVoh+KR+CuWIZAd4zPnttSPYItobU0BJTn0K+y8RDlJ4RH5f1ccIWYFO/ZQcUFq4RGevu6SFvksNv6itcalfogIKyGiNqBGJNY1YgtBywbkirHtsWySqtL5KZ1LGnI4bmd6HIfiCsyxSn0WY5SqgcK1V86Aollx9RDVD8EG/jETqHTogYgooLWRdWZZ0/5n7zXvReXrP6Xu893JyuXj1Zjh7rXxLWY5uzAUETv5ROdKdrXhUxNo1yBF0DFuKLm/8MdQa6Ta4v3cKpoCCMVed84KCI7U/w2KoEga9Xs+sp0c9nYACG4L+GEQL3ocKLYsQjBEGUQWUtQfgPYtQMENHWjYa5AHmS+v1/PGXiERVAYUEB65Ni2iJrC0r5KY1H7HzCRU9lo3BfjfWEPbdgnlmQj13nLxDbVMCJxgDIsz4jmERah32FjoCqgG0mbC+v4ufQksJFSfhvP0rRER0crO9kGw7mTmgFqEwcXhgogwzCahdkgPKrdOnXHyO+P7+1WV65KQfi1CTOewkkDu9Xq8iV9okRx2meari+4kr9IqWiL1Tm+MvhOdO9m8q2lJAcdVa7lpb6rm8fuGXv3nBfV4sZxL2HR7uUJ5URycmJ6oCRQM4yXkdUn0amVU5xVMUUEgS8DK5ionJa/7Da+OYYC9R4NiVnNiiAzjiiPfsw9euDCgrEIG9hVLfBFwBhXMqEQTYrkj8CM5/Int9IRkU61dYYHqvF9YsgaxA5znaxq+JYcxYOQxzx9sNXrO1qpHAvC5LNYhrROoDJz6IpoSDOEYCCSr2K3FO4Qn0qc0E86WsQeqxbiXsZ4ncldaqRDRKc19SWE4cI2FOqSS656PTcgXOlfYAgHZOGXZY/XJOGe2MtkLw8XnA+qr22L0eqaoooLoQfB0AYgi+iMPTcl7fcuKWpPuIxk7LBzcf9MtYzmPje3xq7hMiQW3EQwpG+p6TLyrIN4OETarCA4+fCJmg9b3X6/kfnRkEx+9f//t0z+l71BB8CCvUIr5vradBYeeAskgjJECwHuxrTm6g1DBoiBihqdkbU3ehsmdYDAPCQLt3lhxQ/L2dYofecvNbqtdLvSU1BJ/rj0pmwzqO7gHl3AiUbWWpqq4CG6AslaTuLVExZJHiwlnBy1qKrlR1njVXRET3n73ffB9xx6k71PeycrNN7Op130f2HDpPVwNYakMBNdmEueHt2sQwIa+OhEXk2kkJwcbhSIBFEDxntsYfvOftX1XLLNI+jmGlLDLyVVX7ZXG27iSSO+fuG5Mrbg7aRIpKy4ETaW2tgZwQgRxthCwlItpgBNQjjL0jgeexa+rBgi4HVIdacI5SSc3gOVQTHNamAkEiCcBR6sgOSQWE9gaObXRYJ5TD3Dv8x/st72H9skJgxQgBd00aXxLKCXV59ibkC9JUJXhfX1HBeHOPSiEkamDuSyL4YwwrfF1fUTbxdoj8Oe2zBxVEFQzYh7Zb+cokBz4qWdw1z+ZSuEZySElvrWr5mIjCMSrCcsEacbaxPmH+L21Ni31X1mqSAkoYI14nD8Gn2cHXPdoh9d2rQ6sP+8Zf4zwIc2qSzszeHchDU458e6vz1yB+zJCB/D4rt9VkrnCvVSYrhBEp8+CpRYHE7fWnpmg5oHbxw48dFgvMoWTBIgTM8F3gWB2Ww4Bo0Zy6sYfIzLB0AvGl2ghlLQd1cC9F2rFsioVxy3Asa3mCEKgEsxRDREQ/f+XPqwRVzDlfV80WKKAM4kHqq9cfw19Rkp8TKqZASw2DhuMSIxpjYfe40z7IQQTtYLhIrT8IKTRjiv07ox369Wt+vXrd7/X1NTG57uVYUkgkosz9YRE/QNRZ+6OkMAQfry5GXFv7PTg3rbmKvLbqtUi8WNhPBJKFVjjPZJs67Cl0BFQDWG4hpJQjZ+oooJ77lEcSEdGznvCIudqEcIRCLkm2iPxFeykE33YCWba6PP6Ruj0Ikxm2Cac0sVQdywvMp+WwnTj/bsy3h4vIAVV6Nljg/dhuSaU3SCAbJbSmgGIh+P7dsx6bdS/jnxqzc7QHwqp22I3QnsAHB6iqqlEcpUGdCQ5VVQFF8XuRMKulgGLtruyH+sAO3i+RlIo4rLWn9CwHsJovyI23NEacdOiRSEIEpAazHxVQqlMfy2F/JRIGCD51TIR+fdE3KXbA5xs6ypEgqcZOyz8k3MvLiaRMbO75XkjMAcXDD9ZRyxRD/7W3n6FPsXnw2sX3hfUQkHvsXisEXxKhzAkjJGdxTQtEuTU3aLN3De9jf3NiaQAElLc+chVQ2BD5Z5anjsRyLgSfomjUQibi69TzR1VAufu67ykdfLiwUfNSQAVlBQdw1Q6qE5A4Mt6LISvEmKI6wffw/RiRMiojOaAMEqOukxkd7EGeIG6v4O+yyAZUQJjhyYDMSQ2RKDnqY45x7d4e+SH6MJRcQFp4X7N0p38s/42l5Kmb64uXEfsDhACq71JzJrl7tTatceF5jIjGCiht3iUFVKD641+lYkSwcm6Mv1b6ZT2yBD+yYT6QINPmJ1cBFYTrNMjq1PUf1FvaNmDIxFh/YiEfNWjqLwxZ2GH3oyOgGkCrCqgazsqXfP0TiYhoa9CsE70iHzJtXFnuTe5vkYDKyK/Dy7Udgm9UlJWtFhHhSID33XiEXvb661qxTUJFQhprwBGUwwWptf7uk/fSf/6r64koLuNdmxB7W4P2532UkVONq5DaWqM7GQotjjZClhIRbe6Mvyh+11c+JnmfO3AFVGH8mJsFnQKqQy1YCihRpcEdvqwsEWNay7DOkl3X8jhVCqiEEHyBk7UEexWHNbEf90iY8R/ko23focrLeaZxUoOFtLMcxbwuTzVBZDqAY+HaROc/2iyMt6Q+CcghwanPnfUBoQP3W+ohvFftF4TXq/5m/USykDsivHKoBEGSRBg3K49VdW0UjrlJLkQIKId+ZP/FyMdi5L9OmfvAflyrlgLK2Ke8Ti1Un7fnI4Qy9j2wWymnkk3C3GO5VFUbhm3EHFA9ZSz5ePRj5KMDHyPF3mCfRs64IHeYoIDCNnv9qS2ispaoU0B10GA5pBExR6VWFrf4qPAJmljendSQb6689DeRTQLk5ICKhfqzckBFVTSG09kcJ4McibUZEDbQ962hnkfZIiaKyf+097PURZH54MjJARUooIx8WEheoY2efUg8WOu0NMhDYR1ZRBeSnzEimCNHGcPHaXvkpzno9/t6CMTJfVoIPiSnEQH5pvRHWneceI2FhswJ7RnYmLGHLQI6i4jnXysjaw8JwxjRlaPC9E2S114Xgm/voSOgGsByCw5VV3e/hrNy/8r4BwzPidIEZg3B1ya5UylgljC+uoxFqLSI/DFZX9Ft5Q72d99wpFGbLAwrBZQVgm8ScrGlXEWIn3njJ6u/r7ntqFl2fWUxCii+zlYSyJNer9d6HrC6+70tBZQLwbdvNW2Pc7SjgIqTtR06qEgNvSQpQVBZIBE6/L7cHFCeA1S5N3AcWwooVwRDdrF2Rzvk959IdERLZI1YLhKCr3JYW2He+DwYKrHcEHxoLxIk3N6A+FHmvrJl2b/fswXvVT5rYuSKSFBOrgehwpxdBlmq2SDlgAoc9qA0qtqF1yq5K7RLFFHt8Pni5cAubx9FQvpJr9EWKa9ZjORBwk8L1ReMUYICCvOj8W5554MRgg/HLbhXuc8bW9aGRFB6dhjEc1XMOn9YTqUkBZSSJ0s7f/h69kLwGeSjRaoWzN4OHQTkqItQSWLVYznch+UwdKJHFBMpNmH5mMJDuy92b6D6wbKFT0BZ4dZQjZTqkJbaTc09Y42D9D6G2Uol/dCRPz7u5bmNkYXJyhcioh6o2wqfADHDBBrtSOHuOIJ6WFXWGOYqoKx1UBRh3i2rXY66ISpRUdOnvnpWuHr4ug+IFmOtmjZCO1ivRIRpNmAIPkvRiDCVY5F17pWNnH0cZijSSJtRRRf2hxXB+fDuy7C/w+5GR0A1gKUWHOrDGcI17V8dOxQ2m1ZAzRyCr72DJjUHkINzsrethOHEh6WAQvJzUYe2I064ggTh1sciwhnmwimg2gpr58BJpNQ16kjIQcsh+HLVRUtLzYcsJZoS7vsN4laDlwOqoXPJEXidAqpDHjJD8FlO0CAHFIY1IygH7WpqLNGxXXg/OoKn9K0cUJKznsh3qA93mEPVUCDwfmmqEqlPgVM/JRRXzxgjhliIsaqckgOKj3egToJyIvHBrquqNiKRhBAhtQEkhEfSuesl0Wjov1YJM1gjAQlRkEyMCvNsqlTgPo/clebUlUskjDSSbjTwX2sKJU8ppKjfNHLBIyE0EhTOBk5QRokl169Sfq2RK0G/Esk8bnMSSQdnkFSu4HYY+1kiFa1zxRojbocWUlMLg+nlrLJC68HeUvvV5YDqMD94TmODQHLXvNeGysEqK9WtlZXq4kDHq+WQNpUw6IyHshLBptlXN+xZzA7L3sBp3Avnx4KpjFGUL1G7YNoCJ3mikopooujS+oNHOhXJY17y758UjmNM5WTVq6l3XDmVAClDwia5XXjPIsms8f/g3R/03uv3+v7XAaFN3lZWnrCccwPGQVNdIcrSDsGXRRZG9l5q33GeEVZYxlkVUDlqSu0+ZxdRp4Dai+gIqAbgHOpNOSqJpmGg6jgrnQJgo2EFlHN8W+SDBEfuDBt2SHPk5oD65N0niIjoV//+hqZMEuEIr6V+z8yrhORi24QJEdGfffh2OrU1/kJgkSYPnB5LrX/4tR+jExs7arndAEf6NR2+EnHlTQ9Wf68mKoyqMJEtEXs7s+aAaphwdufd/joKKNalUUNk7jQHVPex3CEDWrJ4zyEMT7QHqg8goNz7/DNYJX64c1ML7aURHeDY9kgIRbElOaIrEgIUUFV1WjgxqN8kCXDM3I/dFKc+65fmAOZtqHMFOaD6ir1in8rpe5JtuXm9PJJKU0mBEz9F/RbkgNJC8CFZiiSJQEKIxChekkLwGWuGK1ek76xIBEoECR9zTf022vbv00KuaXVx9FPmlBOXsLb4v27ug3LKfkZ7JAK7mlPeN2GNlKXcB/EzWphDfF/apyIBo5xTmh0m+S+sI2n9Yg4oJJRVwmwE/dJC8PEzwyCqjKerOzy8UccJqOZtITKdwUE9RegkVx3Lka/wURUKb9cKd5dB9iBhIDmZU5U+pv2G41uyXyMXUpzGMae6VjZn3gMHu5GbKRqCz7C31+t5daN6De3FsIFqu6XdrhUyLbb21JCBkzpUYkJoxySOjLmz8hHFFIIc/V4/2h81/GVkv6eS0xIxnRpyE9VS4VdA3wY8S1NDIBL5Yx5T+Vnkeqr6kcgmoCSkKuoCm5S+dzmg9h46T1cDaCMH1CxPyzsCqvEQfJP+pzrMHaaKGJudnydyc0AdPqXHL24STgG1HrET57ZtAupf7jlBv/GOz1avLbLswdPTWL8/+6ZPNmnWzFibqGe2W1a+vfpDn6/+Tv2gXW05lOU051tuDqh2QjBOQ/AtR0qG6PV6jeeq6nJAdagHIwcJd05Wvk1wbLtrRKFzM1BASQ5ggeRKCu3F2nF1efYqIfi4fYECiueA2gnLicoCVr93dmE5xRErkUaBrdxJrJAVBXNsWzljPBJCITUkQqcioCLl0PndN/qlkVwOSyv+dZyvqiqJWJrcN0ICKoVYUxz9SQooiSAySEXurJfy6lTja4RVlMgVnIfhtn+fSpZK8wJlVLKU71NlDeI6skhQaT9TSTTY8MuphDKSpUBkEtnKI6lfXjkoI+7TmjmgvP4bCiivT5yk84tVdqDaMBi7iB3aGa0RUEFIvy4EXwcbOb/bc4gHy0k+LIfh1rIc7BFSI6UeotDxqtkrvbaUSlj29pO3qw5hJNtiRIvXZsRBHXP6a20iogSU99kB70UZBLkeHBcrTBjmrEpRbWjt4PqyVGXiOuZdM0iAQPli1Cutd21ui9IPuRdTQFkkTExRZJXlWOotqWo89zcqjPjf5v4wCDVsJ1B4FUPxvsCGsqQ7Tt6hlrXyUEn98d5Dm4w1ZIXYRJgkEdnnIL6PsMjs2Fmh1dNhb6EjoBrANAdUc47fSgFVg/V1Iah2RgUNG1RHVOHXMhURPIdMW2H4nJPeCmvH8dynPLJJc1Q4BdRaJIzY+QdWvdfbLSt2Pv/gGe/1SqJT/UNM6bMInLNukxOVAqrlHFDP+aILsu9ZWR6PeXs5oIpJu/VyQDWZM4+IaHMw/lK2b6Xex547a5uyc9QRUB3qQFNfaE5yidQIFFDuupJ/R3qinztQrRxQxBzRgeO8ZGWMnDFeOVY//0E+3A6ds+jUx2uiw14JRQUx6qt7YzmgNIf10VtlRzwqe0SVQqGPkRWCTyqHdTmFi9ivwr8Xx3Zp1b+uhW4UCZjJdd6uuAZJsE9y9Jf2HqnqGYbXRMKWkxAKAeWRFRYJqqxp3u5wy79PVSix11qOMI1cCPYz2ubaI7YOlRxQ2n4uS6IjLHKANafaWvUIqIS8XniGcPs1e6vjRyJglH551ZXhGEm2VcomCMGH69fNlac4Y/9qajceUtIi1gKCzyLWwN4OHRiihAGDpvpJccLGQvBZ6h3TfiRljHstkilGQFlqIyx79+m7VRJAGgur3WQFVMSh7qmL4LMECZ2oIoKTC5ZiCBBTdHGgkzwgzbyPUb+eHvXU96W1ZuU24y+R1Ajmjo9FSWZZi/SS1o5ZF8xd8rqI7C0pFKD2mqPX88dfat/LsRSZH7XdUu+P1G+TfAMb7jx9p/de7RCVBqFJZKvzoAPm+xaJFCigRkYIPon8NNZ1jMz27Mj4rOmwu9ARUA3AORCbJE9meVp+HwtBtdEgMeFUWpb6RcKqR0C14zyvQvAtpYXn+rnnfQkRET3mvH2N2SQhVQH1b7780XBfO+PogH6e3DWwKLzlZV9nvr82IS/aVkC5dr/vax+XfE/7Cii3hzJD8C05xWizdk5D8OUroIiocQWUG7+OgOqQB4P0kdRJZSmUVXJAec5/gXDwnKxG2Cpep6Zs8hzRWA4d7K5bGLKL1TfaUeyVnP+u3PK0XNWGoiyofnAJDnuvfuZM9vIFQbnN40KbBrHkkVkCSSCpYLBvHuGgKF60EHwaWcERKKCMuQ/IrAliIfiqfvH6EkkIdz14jWvE6LtFKo4G07r6ypxWtsMYIVkxZIo+IjYvlDD3pXKvoZbhOaZEBVQRKYckHesXX+siseTawH2Pc1/KRIpEPibtU5raq4bW4zmVrJxu0v4Q1iVXFAUkFLQr2Y9tBMpKIEFVpRQnqjoFVId8uOgMtQmoXGKIlc8JwSeRW1q9MRtjYbdyysaIISunD46F1W4OWWU52DnEEHy8nYgiwgxdaM0V9D0nt4yngOr1gjXCYRFqEnlTVwGVs0bMEHxjdkG1oSojvI/2j4pRMoETU6RYofysNWIpoKRr1lzG9mUycV2SmQMqICWLtHkX58qwIxbK0NofFmLqSI5Zc0Bx1CGguhxQew97wyu8x+ByjGAennmicPlCMp29RGMVh/NxNhmGzzmUU9UvDjyHTBsE1CfuOl6F1EsNwXdwbeyo2thpNx76qc1xezEF1PJSnx53/pQcaztnUQEfXHvFqf7US84x319fHo/70bPt5qra2B7P+4UH15LvcUrCP/jALY3YhNiZEM4ruSE3W8oBtVmF4MvPAUXUvFLLfV4cWKtnX4eHKdRk8egAZo5LsSwJ4Z20EHxCrhIrBxT/MaypkUr/B7OaK0pSTFT2sgqkEHwiuVAK/eeOeE1ZkBiCTyONRMeuq0tRI3mEH6jacCwD4iPRqY/Eh+t/oB5i5XDdOKgKKJxT8seIt6GF4AvWKrdPmWdx7o31wNvF15KqJFgjA7Y+lHVU1SeRFazscMt/reWU8sZIUTGpqkm+vhjB5Y0bjLeX/wv3s/BZWZZxVVt1H/bL2c/qk8IeiuRjZJ/imlFzQPFySl6r6nXK+SOtI6FcpYDCvRY7fxixZOWAKpCA0gg4V4Y6dBCRExZJdRrHnJagIBiWw2QHPDp/rXYCW3BbGoRBTqivFKdyqtrCCu0XdcYDsaU5/WNkSEDYRB4wzHGwI6w15K0ROPPMcQIbkKAy59IgxSx7JRstjqAuoen+1ghCKaRjapi9rFxfEeKEw8oBJYbgM9aTRdDGVH/4npfDzlgTo3Jk5idDYi7IAZUxbrm52jTkkNG5BJRnL9ooPsCo3txhj6IjoBqAe8L+7HZz5IRTQPVrhEHo9XqVjU0SUFVOmEySbKnfq377tBE+7Hv/+Jrq71QCav+EgDrbcB4tjqIo6cWv+Uhy+a//4gurv7daVuzg98WVGkTpboRTIr3t+nvp+ruOt9bu2Rrqnc8dPk1ERFfderQRmxCDidIqn4BqJweUI3j21ySg+g3n9ju7PZtCq8PDHNIPC8/pytUyylPtnFiSnNOSo9hzblpOW1csEtrL1cWdsRheTSTCoI7RTlhOIhe4fWKoLC0EHyqg3MtITho1Z8wI5svVDU54PgeaAoo74QPiA536EeLDUkBJhNmA5cd0BFQ1lEobogJociP/YeuRdKh+Q7UeOvoVYiL4BSvdG9kLfH1xjIZ+ObP+yNzf8WF5H2F9HjGokEMpZKma26n0/1XnFNc8J2uAgArIR2ijuiaMUWoOqChRk0gUe+cPs1lUoEpjhLZJCiihXBWqD/P0YRsWsc1D8FnjZvSf29uhA4NzmtZVQMXyk1jO4UClAU5NK/SXZROWj4WHs5zxsdw5MWWS5zzmRzKSGKWe6yhG7gRjo7VT+mVjju3Y+3WJCas/Vr4eopAUi4VEs5QxSPDUJTXMcIlQNkY0pjj+pXbR/iAHVMaYB20aZS17+72+Siq561roOUsNSZQ+5oGyjUqV9ArqgTGl0mhHGMO6YUCt+cD+IHJCPJoElNCGmV8qQmJ6Nk3GIjU3eofdg46AagDuCfaNBsmJWfOFOBVAkzYOqxxQecus1+tVSpMmCTIH7lBOJaAOTMZvZ9hsHi0Orqi7/aGz0fL/9YVPr/7ebjlnESqglvu796hxqqLf/M4vi5ZdW56SF2/++N2N2YRwSru65EnRMLlDNA3nuR5R5yFczqim1Y4bu1wB5eb4QE37OjxcoTkKuUO4B05b4Sl5Ij9kl5lbRXCUIsmF95rklbuHOUp77N5kBRQrx8OfxZz/kmIk5rB2P7jQARw86Vt6fl11vrijWMvHxMcCVW0asYT5mdCpj33H8a4UUBLBCSoYKok+8efT96sQfK4cJ/iwX9wuhXz0CLNMBZR0TSOqgnIxskILVzYM50EiQUXiB8p95i2+rZoCylsjkRxQolIxcm/VljAPHlGlEDpIGqXuZ43QSc4BhfvUUDaZIfj4+oAwmF45iVQV1pZH/gP56pWb9DNQZMb6xc4QS9nElYZWqL7gYYUOHQAZX5FzQq9ZztLAGY8OePYnkj2IlPw5vC7tdawsEkqWkxxVKJaDfQfyU86SA0ojCGOqBczZk5XjxiBsJKSSGoHyhZGJqNhC4PtB6DW+1MrStgnes4i8nFBlqWvPvaeVx/0RC8Fn5e9C1FW69Xt9kzAj8scjhzyxlEnYJu677dG22h/cO0iQWYo6hJk7K0aoacRphKTMIdcDcjeigMo5Jy1UBFT3QMyew+71Cu9hVAqoBsOzOWdtrtrAYRomsHmVVm4IPiKi8w+Mn5xtO9TZauJ4cqKqDZUWUX4YvXPWV+hpk5BybSuguI/+MeftM4m9N//4ZS1YpMN9+H3tE8+Pll1ma/mR56w3ZhPC5XFaSyRIEW3kADt2dvxFzO3dVLicUU0TUJsz5oBabtjOSgG11imgOmQg1QHqKY8UhVKPnS+WkscrJzg3RTIM7wVnrOfABgVUzBEtkSv8Pm6vSEIAkSK1ESig4LuTpaxIcVgXggIKCShULKnKCu5MR7tgjAIiAQiNvqKA8saN3fsQC/mKIfjUOZWc9ZP7vBB8rJ9IrBXKGuFtSMSoSS5x+/hrjaxAZz0nQTXCCOrXCFpnr1hOWfdeuDZhTs2wnRFlk7QHNRIQw8sFueWEcwX7q+3nlBxQvK6UnGZezrzYWWacv9I8iIQnTevRylXEDw+NyO7XlJX8XDFD8HECKqaU6tAhxDxzQKGD1ipLFKo0iChwqmv3BjZhKK0M9UHd/EqBQ1oomxqukDvFA/szCTTN8R0bBywTDc2WSNhI98VCzTlYyjBsB9/r9Xq6I18gEyxyIWfeTQVUJJScRmzFCAEsY609tCuqLjSIlJwQfDEFVE64SIuUiZG3XPkTU/VYIUKjhE2hvx87Z0z1mnEW5pChSHpnKeFgreI+Ta2nw95CR0A1gAMthLdzpEeqYgexb6V5BdQ0BF++jRccHDsujp1pmYBKHE9O/A0azl3jUIdEcIRF2wqoIfuw/LHnfpFZ9mufFCd+moQj9lLIHU4+rLYYVnCWvUTUbD46h6OTvXpBJgHl9tJOw/vIKYz2ZSq0HJz6rSkyr1NAdaiHlFBRmANKyP9B5Dv1LUWV5FDtGU5bkRCxSBMMR4VkhQOcwRZZI7aJ7ebkgMIQfCkOa0tZwUNlaQQUHwtUQPG6eN9RKYRO/Qjh0NMUUMq9j3jCtEgVgi9CViD5yEPaFZADSlpH1Xtgn/dSc/5L68FBCa3nzYsRrmzEckCpqjaS+0WSvQJRRdAHce1CXeZaFUhFb0xK/181Dxkn8nA/awooIwSfNkYpOaC0dY5lpPNHIrhKoZxIbqK9QrlKfQpjFKxfNy9AuuPZJanw+LiphDr7rZWigOqeOO6gIIeAUlUNZRgeynJ8x1QaOeHJYqG0LJtQTeG9FyOgIuSVqUYy7g2UF0ZZk1wwxhAVEKgYipJ+vG+RMIfmvaisYM0ORn6YMCvXDNpr9Uciskxln0F4WE7/nPCPSLqIYd2U/qD9Uv4irT85RKNF9CKCEHxCPV44PKPvua9T34sRmsH5ptgojaFFIAZnhaGmss4Nqyy2GwvBp4ULlWyMkbAWKgVUp8jec+gIqAbgQjw1mQNqe6JoqauIWJs4YZtUxgwrlVZ9BdSxjWYJqNNb/qGZ6pzmSpimlRsOdxyNh91DtDHPErhaq18zTGRbcIRCSug4rnb74E0PNmYTYlBjL136qEPV32/9xD1zt4ljazCqyOxH1CSgBg2rtLZnVJE1TeZOQwR2CqgOGVBzCikO4LIQyCXnBOUkRALh4K5XdmjOWMHeGAlR1YX1cUcxd+yi01Yqpzh2kSQg5rioHNH+d4VKmRM4tg1ywQzXNgrtMEm6vt9mQCxJRALYJzr1ya/LyyNEfpmqDVZm3yPCe0XyUSEVMRRZLARfRVZAnjCEqIzRyAqBaMW6qik1VDBSziqRIEkgS/c9QtkfMPd8X2mqqyTi0iAL+b8asSYSS5P71JxuSA5hv4RyIplu7Hut7wHxaBD7JMxDippMOn88QsdYI5gDqtrP0IZ0tlfrofq/cNy4otNUs3Yh+DrIqHJAZTyVHssNxGE5RKU8NZpTMxomzHLwRrpm5bSKhcayCJuCCt0xG7EpZz5S1TmWgkNqd5YcUDHyyiQT2XtWnpqYDaiAMucSyR9hLr2yEeJRsymnrERgJIdXLEe2jZH9pNqRMc9LvSV9LZZhf2aaD4MMySGucXytMc4iCzMItCA8ZOywYLCUYjjns4Tgi7Xb4QsTHQHVANaqnCbpGz0XzhFe15nadD4TIqLBpO46+X8OTsJQNUniERH9080PVX+/9BuelKyA6vV6FRkwbEkB9R//90ey71mUAmqbEV5Lu/iH6nBUVKEi11cSFFDD6Vxfd+fxxuxCuDWWE3LzT3/oq6u//99/uHHuNnHwfXowM4RcRUA1TOS6eV6uqVxbm6yPnYaIslnDLHZ4uMIIe8cdwBa5VDkoubJA+sywiAQrcb3mZOa/jNBJnkBUIWkSK6eqIwyHfeXY1UJWZTi2PcWE5Nh1dRlqGacuRsIv6vzXnPow9ziOmgKM38vL8HHSnOQB8cNfgxJk66Rfn2Qv2ifNs0Y2WaRUCqnYI10Fs3WS1WWF4IO96uzg5dbP8/tuKXlEcoivQ4WsEOcUxg3H29unUE4izIgoXQGlrGmegw5DYXqEi7Dv1ZClBGNkqd/4uUpKudTzh42jmSsKc0DBfrYUS9wONVSflgNKIlWJOgVUBw05Tk7VaQyEjFQvKnBSiRZJXaXZhHZlKTwizuuAFPCOT3BmFxEFlDHmloM9livIItQs5Q4qEhoLwWcQYTgmAQGVEWoxUEBZ5CGsL3OMkeyBz2SLOLHUUsG9gj1WiEpLvRN+fam3P2JKHo5er2eufyI/dFtqOEK0KbA3w0brfJIUUFq90l7m6yJGAJpEVwbpZ5VFG3YKPe+c1IZXdwlrKMij2+ELEZ2nqwE4EqPJ3EDOWbm6VC9c09KEgCoynozJhXMo13H4ujCGTRNQPDTZlz/uvKx7HbHWlgKqDvqTL4FtqnWIiLYY4bW8ixVQ95/cqv52IdYsrCz7fWl6fTrs1Mj59ujz9jVlToBKvbOyVJ0tqVhdbicHVDEDIU40zQ/XVAi+KqxqzTCLHR6m0ByKmlO/LGT1DRF54dokAkoiqlKcm9yhmkrUaGoATbUjkhpALEmOXX5fX3JYR3JAVUOsKRAKfx40wlBVQKGjnIfs0pQV3H4rBF8RtonjqPaLz6kSurGyHQkGIpVcwLm/75NGOVY3qmoskofXZ5FSqfmCtBxQ931iWlc0BJ+rTlOJgZrKI1KkPhAo0IQ5NYk1zV4kPhIVUHy+VFUbfPZphJaVA0rKpZZ8/nB7jTCFlDD3qQqokhM6GQqoiqRztyeeP1U56P8ICKhYDqhd/GBZh70D06lsOLrRyb812gqc9Wr4uAhhEziHjfBjwb2J5I702iJ0RuVIdWAXZWHaFVPrWO+lqjRQAYHlo2H0rBBvGWNuOd+RgOJ9kAimVAe8NE4mcYeEDWsmGCfvI9BeT8G8suKSqkSbW6x3WAzVssG9ETVUjqLLvFdYi1oIPusckdpNJQ8t+wIbqYzmnrJgKThzSCUrRCUix96NwYbdDsA6j+sooHrdAzF7Dp2nqwE4J/GoKBtTGFXhpBJUGxKcKmXYoAJqWMNp7rB/bRLGsMEcVUREJ1iIv8ect551r1NALYKAeg1Tt1i48uYx8fT2T93XpDkBeMi///sZl0TLf9Xjz2vQGh2/+Y7PVn+nKE9e/JwneK+PnNpSSs4XLqdWXfVO0zjr8het5RPiUwVUc2dRWZbVWVeTf2o8B9TODCFLO3SIh4BS1DJEFIZVKgWnO4PkJO8Zzk1RDWApEDBnjFAfrwttqerHNgXHrqeqkRRQk2uQPyBZAaU54gMFAuRqcXVbCgQ1BB/rk6aYqK4JY8TL9C0SBghJJC5RLaMRa4GTnM09/2ErEWaVyorXx9ebogJy9Qe/wFPWKl8zfE6hrsGmsFYlcoGNmTanAbGiKY+U9SyRitK+wrnCMULbtTCFKkkH/dX2KUIrJ+VoEvc9+deSieKaOaC88dZCajI7PJKOiHBdVvOCITGxr9LDBdLngHT+0LScSsB1CqgO84PqVBZIIsvBe3rntJljJUb+aPW6urT3EDk5e1Jzwrh2zbEyHNiWeiWqpmDf5azxj5EHMadyTuhCzwYcN4NEwhxQXr096A+EPOz1et7Y8XGS7E0lbKLr1GgnUEBBWVUBNbmuKpdK38ao0grULBZSFXUBDBLJ1WOF4OOQyF3NjlnyQ6HqyiJ3rXEhAoI2gxQzSWThjNXsD2yA+05un0wuizZb4SJT0eWA2nvoCKgGwMO4NUFOcGKr7tPyzpE9alDqOKjChuUfDC6M10bDChNn41K/R896wvlZ97bhONfwfz394tbbzIHLAfVT3/xkOrS+Ei3/e//hK4koLQzePHHzkdPV3ym5qg6tr9CTLzpYvT6xOTBKzw8u9F/ufv/Fb3saERE97ZJz5m4Th1OCHcgMv0c03UdNKkY5z15XAeXI/u1BM6R4pWpNUOJ16DCFFipKIYeIBPVNhEgImtSUBZYCyt2rOWyBqPHUAEgsQV1oS3WfUM4ap/5yWE4LwTeC/JRJob0skq4I2zTJQgyTpRFLqJjg/ZdIJN5mT+8/d6Z7xJKQi0kkYYAw0ube61ch2FtO3/PajbSJfRBtSSEVY/lyJDJEmVPL3mII85IwRlzR5+1BQ9WGRE1gL4yZOqdIvPL7YwooYa1K/aJSWJcj1geBRNNCSuIa1ObUO2cMRWPKnPL78CzTHF1I+AXnlEAsiQ8JoKJTCcGnflZ0Dp8OMnJyDmlP1cdC8Emh184OztrlE22MER4WcogUdNJa5NWo0BVQ0lhp7caczhbJETjQwcGOyArNZsxVjjLGAg/RJt7nfYUqvTmwQvDFXkcVN6xey8ZcQtN7T1Am8WsYDpLfj+o2a37qqpgkm617xRB8zE4kd3JC8Jlr0VB4maHwyjLYS5pyLEbYxAhBRE6IQQ4zBxS0eWL7RG2bLDKuwxcuOgKqAXAncRNPy/McJKk5ixBLVQ6ouZgkolJt1HD47p+E4Duz3awCyhGE//5rHpd9b1u5a/YinAIqlZBw6qOdYZH142VWfMnF+cTMi5/9+OrvQUNqGMSg2kt5P/q/chJWsklyh4jo7GSfun2bgzb2EVei5oYIdFhrOLSqO9c7BVSHLKg5hZhjN1DLaInlDceu2CZ3qBrOzbL6P92p7zldUQGFqhpOVvAqIiQEOuKxXF9SQGk5oApWp9EvlVgzlApa7p6qHIXqC89ZL5FDhf8v3ic5ybnDGhVgHF54RyEHVIyECdQnNL1PJTWQrABCKzYv1b0CASWRckEZgYQQ96ArZoRhExVQAgElkb1EfnWe4iWmgLJUbcq9SNollQNSUVNABWQ67xjWx/vLUOB+Y/YQ2efPtFNkz6lAVGnEEreDn4O8vqBN6ZyCunDd94ForuofkXiuqjntyCdVVQVUhw4+3FPoMZUQR5AHiSGWnwhxaueUV5dFAlhP2ZuO/Ujfctrk6qJY3y0FVAx1812h09zKJZWjcIrZmEVgGmQIOvl3RnqeGqxLUpVZIfi8cqVNJgbhEw3HvhVKLiCGUgnCUihvEAJW6EK0MbYuNUKTyCZ3SipVksb9nZojDfNsxXIoae8FNhrjIqqsvK9LMBfw08Y6+7JUTJEz1LrXeo+fvdL760t+hKlgrlnxOgRUF4Jv76EjoBoAdyA24VTdZvl16iasX64IqOacvsMZFFD7JmqDzUHTCqhx/+soyZZbDsF3/oFVIiJ6y8suS77nD//jWFl0aD2fGJgFtz54hojSFU0uvFlRNhsWEvH0iTLoSRceSL7nBy97YvV308SOwzSfWt46XVluhyR1udT21VCwub3XJJnHCai6OcmcMml70Iyd1VlU80zv8HBFwpP6qJZRc0Bx53eKAop8x25KPirLqS8SNQXFSRNmB38tOWeDcqx+zJfE21AVDkhUJebikuoLxqgUxilBAeURCRjWDEiCgPSCuaoIKFB8SQQfEpcV6SWUE4k6gnVkkUMwV+L6V/rF6wvA+5+o1ksqZ4WBVNQy3q/yEeu7ofwrhtOx0HJAuTGxwnb2tbmCulRSEfczr0LKE0Zkk4rKWIoKKCxHwr0WqUjGPuX9j+WAEvqlzT3mydKcP2oIPuV7jbdujNB6gQJK6T8PAdqhg4C65EjMkR0LH8UVUNGQdhmqAKseRGruH6LQGW8REaNypIZjQ2c2wiT5yLCJ9FxGQV/gnEDF0CzKmKxwicYaQiIlRr55Ifiop86PFEYvFk5Rs9HMuxMZF5NQE+pRCTXoT0wBNS+lW2xPWuNP5KvHrDCHqDIL2jXIkLrnhjSGJqEJzdQNUYl2BMSc0R9LaZVLNO5f2Z9cV0dAPTzQeboaQK/Xq5yqOw0qoPq9fIe0Q7+FHFAuNF0dG6eO8+bsI5ot74qb47YIEzfvFx5cS77n8eePD/2DNUKj1UVZlvSpu08QEdHxs+i0ksFzmf3zLQ82YZaI7cn8/19Puyj5nn6/R8987LlE1B75uDFRGOUSpVNyp9k1Opgh39vK8njvNUnmjdiXrFkVUI3lgHIh+Gqe6R0eplCfVOfOelTLKGQKJ2qsh1NiahkrfB8ZznrJARyQMBLxEalPVEAJ9UvKI+eILzAH1IjVGemXRqxJ9RGBqgTLMcWEN0Zs7ERnPZBBVf3kl0PyqsqBBZ/lJfuRzO0N1C1E4ZgT6eQCGyP8kevNy1L4nmgbP0+F+hAaKYdlJHWPGNIO16pEQiQQOpgDSpv7Yjjdz0sr036KbRgkjEbuBkSNQpihqo2TK5gnTGozaJe99upCJQ9XgAmEjppnqvTvs0jFMqWcompDeIROigIqEoIPUeJ4aMQSzwFlnFNBvsAOHcZwTsAcBYunsFEc6FK9Uti5gGjhxyKqHCy1haEaiBIpnCSKtGkRapJTVs0BJYyF1o4rz+vV3iOCsGaGSmbmEHyWTVbfjLlCxIgUfA9zQGnzI9mb6mAPiAj43m2tvVjuopz5CfrGTLaUVkT2XkLMlOsrQlanqntm6U+WAoq9xjbHX0kUYkiwfRYCym+2NF9b9VphAIN2DOIU3x8vt1J+r8MXLDpPV0NYaVAdM5gQHnXJp/G9Y/uKBskTlwdovcYT/SsTp8+wYQe/c8zXcZzf9tD4aau/+uhdc7VJQ0WWZYznasNhwyTwth55aN0oOQV3ul9969G526RhmnenHrHTBMGMeOjMNh2dEHnLmURpW/M/CwG1PlEWDUZlY/t9xIjs2QmoZkLQdAqoDvWQ4Pz2QvCVIbkkqWXcD6a+8PCC5CT3woJJDnZwlGqKlapP3HkMZIXoJI8owKq6DAe7mtuKBEf3kJVl5ZJDIRoKqL5BQIkKKHT+l2Fd1ftIVghzX7XZm94/TFBAlUguAOll5YCSyAqTzENHvEZSGuEMxR+6fI1o+YIKf9yS85px+xikEHxIVGEOKLSZt+n2d6WWgfWhhVX0+rXkX/faYnVVeaZgrarEa0mhAgrGqKq+VMqxvZukgAJyyZXz7uNON4Oo8frPyxmEckyB6ezyQgaSDG0/awRUQFwK9hGxcezZDxN0CqgOEWQpoBTHI/+7P1mLsRBploojh6jIUT0gLELNIhcshzSRTZ5IajCtnWjuIqhX7Q98DKBDOiBsImOY4wg33/P82v4aMRVQ4ARHFRPaaClJYmMcjCODSSoZRBaRngNJsjeo2yB3ce3FQgpaMEMXGmugpFJVALq/uQrPmp9Yf0wVY8Y6jeXR0ua2pDL4eLVUjDkKQdwfFiySKbafrVCLQV0wFjFyS0KveyBmz6HzdDWEVZbTZt5wztSVmo5UIqIlR/A0SEBt7IwP3Fp5YZYdgdecfeP66zvOHd76iXvnZY6KsixrKSRc2aZUGxJ4W9/zrMcm3dPv96oQg487f3+k9PxQd/5d+Z2G1ycR0Xs/e6T6Ozd83EoL4e2Ipvu0VrjN1amjy4XymzeG7IffUs0vKi5MZFOk4/Zw9rOow8MQVtg77py0ckBVIfi4oxSc2F6b3AkqtBFAIEQkBzB3Emshy7wfM9xhKzgikFzh5aR8T5ICCgmopTXZ/tTQgmZoK4moUcgKb4wE5z8SCZIC6sGbiAYbQjlhTiUFlIMX3pH90K76KBACqmILlSDkl+MOe1435gmr5o/by4kDgrkS1CdmviDJXitfkKXWS8gBhWWk/EZEvgKKE1DefE3uvf+Ter+4wgwJI25XTwtxyF9bCqiCTQuG4MP6oBwSnkTxfaSG1ORrkLdhqdqs80dTQOGalggdoVxVV64Cis+XQYA7MtK9bxHlRJ0CqkOAKgxSxs8iVdXDnMbOuWgpR6y6pNfzCuuG4CRTQEBZjm6yw7ZZeXhiobSscTPHsCw9p75FemEIPkROjqgcAtAa49gY5qynXk8PwTcshsHYpKrmYjmgPMIA+oNjykmOgn+uKjaYOZMi6h3vJbc/oobKIer8JnWSz/2tkTSxcJE5Z4E5l4bCUVJdqYRmRAGF856jRvJsLONjrr2OnoPKenKfEXWJyA5fOOg8XQ1h6qCev7NyMPmCU/dJfiIi5yceNUpATfLCrC5FSoZY7rsQfM06zusqYNoGJ+JybK0IiBYVUC5HTq+Xl6Psm586DoO31RAJIaG2AqpBghlxDsvflfub39m53fD8D2cIt7m23Cd3lG3uNDP3o+qH7JjsrIPVhkPwdQqoDjNBUjV5T9dzkkDJVyKpNCTHpqim6ukHVKpTX3NYB8oKsINI7pOphBDULZJiBB3WS6uT10DoqARU4fdL6oerX6orKDeclvHKgbPeAcN/eWTFgGjrxPjvvkA+9pgCKsgBRYq9fB6gzT6bL1TVpCqgJBJCqg/n2SNVpbkXVGcWWSHaKxBm0j6S5t7B6zsSS84u3kYZlqvWKgvBx+HG5MwR/7qmgNJC5hFBji3cpxJJJxBQXjmS51Qcy9LfD1UfMve9q0ua01gOKO0BAH4eW7nPCrbXPJWY8tssWLsxAgrPMtcvRdFZEVBW/4moU0B1UJDjQNQUNtxB25+4qnJC4aEdMec9R2oundi9MdWVqdiCMbTChuXYhLDC81kKKLwvUEDNMwdURog3S9E1GEUIKCCCrBxQSC5YBJRFYiDxGOTdUVQ/0mtUQNUlOHJzQFmkX9AOpY1LgJL0/lBIQFnkopXbiMjeW7VD8GHeqXGHVHsR3popdHslm6x9WFfRlbufq7N88v1iFhVdhy8MdJ6uhtCkg3pYqQ3qT59TQDVJQDkiYX8NAsopKZrOrzSYIQfUv/2KRxMR0dc+8fy52iSBE0g5Cqi1FokSBxeibG25nyWLXZ/kgWpKBSPBjUsOUUY03d9tEHuclPniRx7MupeHAm0yrq5TGNXZR71er1JJbjRFQLmwpTOQ9k2H4NuZgxqzw8MYkvLGcxYyx7aYN4VA9YAOfAZODg23xn+urBsMOT97lJBVlsNaIyssBYJHQiQqIaRwbRiuzDn1MQSfG7sgdxYQBKpjlyk3UkLweWoGICu4/ZrD2sGF1pNyCsVyQKG9nBCoylA4liKpKM29sUasfGW8D5aqTVKpSGvEssPKq5NKPsbsqOpiZK8W7rIYTctV6kWoy7WBYRWlcQvsxfUshK3EuhC8vxLxWtWj1cfGEpWJqQoo6bz0kKJqi61VV5Wi/PPq77FuAcnF4ZJ5u3M3poBCQrk698Bet79jCiiuwOzQQUAOAWXldXHvVSH4chQTZUgopN5rOfpjZIhFGJghAw2HNFFCGLQcR3IqMVdGCEJ2r6iAYlXnKKBy5tnsGxAPZgg+gKQq0xz5o3KUtdaQsLHuNUk/GHOvfwmEjaWA4kDyJIewQZi5jKy8bEDqSfvMUutxxNaBtT/qKiclEs8i4xDWGWSGyjPqzWkzsCGDgOJr0fkErZCPXQi+hwc6T1dDaJSAmoMCyjlimw3BV18B5ZywTeeAck7fHFLH4YXPeBQRTRVpTYKvoxyFhCtblM2PpcN2Rerkzfu+lXH5Ngkol8cr1+nfZg6oM9vjLy4vfOajsj9knZ1l2SzZvDNj+Dh3RjRFQDnSfpYzc21CkDqF37xRlwzt8DCHmtMEnOSeokhTQAkqjZgCyjlCl9dJfSreIzU0hy04mE0FlOB0terzlBUGCSGF5cMQfMsQgg+d4qJqR3JYC47dwEku1FewedEUUJy088Ilkl+OaDp/EjlkheDjdXE7rrkifD8Yc5xTjVyQnP+wLiWSjoT+x1QwUjme38iyw3TWJ6xVKQdUQLwS0en7WZuKWqYYTe2Q1jNvY7Rt96u6biig1DmFPaSF4BOJYtaWVp97v9qXq1NbRZWORkBxIk84L2cilF0ZhaTj92kKqD58j16dEFCDLblf00Ynb8P5U9kLDyFgCD41txXc16HDBJJzMQYkPKx6c0iXnFBz0r3ea+8cUm8jIj0PTcprS6lghUHLza9khkEzSA4zBB989vV7fbM/lo0zhT0z+haQeJYCrfQJAkvRFcwN2bnAkHCyQhlqBC22ga9TxlALzSaFI/TuM4iWKEFrkBgxQlnLnSWF4DPJUsyzFSOGlfcQ1n6WQmhqxGMsXGIOKYZ2xMJOptZTV3WZFIKvwYelO+wedJ6uhrBahT6b/0aahwLKhaFq0il97OzYYeGIhRwst5Rjp1JA1XD6HpyERju91fwPMmdnv5fnROdr5MO3PjR3uyQ4B32uI92REFsNkRCIsizpX+45SUT5YdnaVECdmayvc9byc6lxsrKJcKAOw0phVJOAmpwR7syYN4rJF5q6+Z+IWA6ohsbRfVZ0Ifg65EFxgBIQMF4IPnSoOycoX3tA3nhNsnIuh9DKfvspfNUBLJTxLhtkheXURyKlugbkguT8d+3wa5XSYgXuixFr3PlvhZfjZEWCAsrLUwMEg6cUwj7BGFf1Sbl8WAg+VMtIhAYnBHiZLAUU2WMZkF4liXl6qv5bCihOQHHbsF+JZGmKvUQysRbYQayNCd7/G5MyXAE1sbmqm4Xg67MQfBJpFDjOFDJII4yISFQN4uuAXNHydUFYRW+twnnmrlXhBg0FlNgGEpk4pwqpmKqAUklF3H+MCPPymsFZ4OAUUO7c1RRQXv4vTrZpBLgjoHpgs0asdU8cd5BRN4dHoAyJOCZznvzPctriFjXIEYSp8IDXHmGABAi0Y4VBk9Q6FlLJHgzBZ7URhODr5YXgyyHFtPuIBCLFIHDMsqUQRk8gPYjkdZjqYMcQfJbaKLaGgxB8CsHhrmuEApIjCEuhNlM4SKtNY+4qBZRBZpskn0GoWe8FNhrzIykEYwo1ra7YuYI2qSrTyDxLdVk2ejYpe0skoDLOdQ297vvInkPn6WoIlQJqNH9neuXsrRHuyqFpBdSdR8/S0Ykz+THn7cu+f8XZ17CD3zl9V2o4zp3TvI2cRds1FSY8/OGdRzfmapOGKgTfSp6t6y0roE5uTp9IyQ2j6AjmpvIBcbjxmEVJSEQ0GDaz14mm+7ROCD4iokvOXScioo/feWxuNnG4c24mBZQLwdewAqoLwdchC5YCSnSSCw77AkgIdz+RrYCicvokvhmCjwQHsOWE78lOZl7PuKB8f/UaCR3BwR5TKrh7MQeUp7Sw+sXLWA7r4bSsplzhdiD55hXjDncIq6j9WO1LY8Qc8VYIvoA4IHgtEQLcDnD+p4Tgk3I7efUVYTmLBBXzBaWE4Kv+TyGWBIKgrgLq7INhXd7Y0WRdTu6TcpoR6fuUE55qCD6icE0LduCcaueUSHqVYbuaOktSJkqEp7ZWxTJWWEU4f0xC2RUzFFBen/h5phBLqwfG/w4jCigpzxlX62G/RkoOKG2tdv6eDnMGOjgxb0jOk/KW45Vofqqa4F7uKIZ6zHoNwoAoQkzkEn6suBlOjfQcUNguvtcH9+JMOaAySIsc8ioavk9Zj1h2WAxNJ7pJnhSFPR/GGKOj3iOggNCUCAyVoIqspxiRZ95r7KWsEHyc3JmEQNT6gGeBpYRDu3LIHqseiUDWxkI6Y/hcx3JYWcjZHwgrvKjVjnSW59iYgo6A2nvoPF0NYaUK0TV/p69z9s7iTHX3Fg0RUPefHP84esIF++miQ+vZ9zsFVNM5oBx5tF7Dud9kmEXEA6fH43nhwbWs+3q9Hv3w1z2RiKZz0jTqhuBbrwi95seTaDoe5x9Ypadeck7WvRccHDshHzjV/Ji69VVHGbPc71V+hO0GyHAHp1SsS4o/50ljAvDIqe1IyXooKtK+/kfeaks5oDoFVIc8WI5S5pz0VCoJIficSgpDQPFyZZmmgBLVF2gDlLNyQHmOYmev0H9LeVMRSwoJgWoLpxBYYp/BksLFssOrD0lAiawQSAiuVlPz7yhEQkASMGgh+Nz9GK6NlPH1ikzqyVJAYS4cXh/Y5q5ZY96X5jSmgBLawH5VlywSQhojqVwiWbHsvksDoex5sJgCaklSQKF6KsFeSV3poJGlOKfVOYWhpNA2mr72iDWFRKqUiatTW6u1b809hinMIMxKoV+4RlJJxWqtcfKp+j8hBN+EgBps+u3i2SsRtFYIvgJC8KnnmbO/c/h0mC80xYkjM3LzsVjOe8vZHSMmLFiKoRwFFJYdjCCEFzrYazqSLbKnKAvTqc9vRQd7r9czHfmWTTkO9XmRhVJINCtEH4bgC8ax8O/VbEQFlBUeLkbM8TBvFqHpxkxV4ETWk9mfyNx5hKYxd2GjumoGiVK0I1ATlrqaUHqt1RuaqNcTtInrKULi1VUMBcQprqcMF2uOAko7B6twqjUJ5w5fOOg8XQ2hyhHTgILHkTJ1VDsOSw0roJy65PwDq7Xud0qKpkOcVeqSGmEC15abm2PEXcfGTr7Hn78/+94LJ2TJ8YbCmyEcqZebV6vtHFBuPC6osUYfN5mHO481rypz62utBnnS6/UqMryJcKAOUwVUvTPpEZM5OLU1iJSsB3fO9WcKwdfcfi/LkimgOsdOhwxohEbgeGROd9UJKjjJMQSUV65MywHlkUZWCD5Xfw8csqiESHDqi21yYsI5noUcUNK9GIKPSAn3FemXqu7hyg0jBF81zz25X9huMEYRAqrEdeMIKDibJUJAIyokQsebU/5D2FKfsDHqszVozr0wlpyYcOD5nqR58KpHkipFsWURUJwIM+Z+/bywTexHMaSAXEHSNlDLOKKqDPtORKpikvcrmFNuFyeKhbGUiFeEuO8ZAVUpoNi4Seo3zOslkYymAorvIYOokkh3iQDmhI6kJlND8G2yvpEwpzwEH7dXGeOREoIvIKpGfrkOHSaokwNKA3fQSk5L6bVUR/V3hpPTIo5ylDyxHDemwgbKBgqKGZQMqSqUmFM/FmJvXiH4shRQSBYaw2L1B19bNqI6h8gnHExCM9LXnLKogNLKuvdUchHIK4SVi62xUItgE7YZVbNxkg8+0/G1RfbUDU0YU0BZbeL7gbIyRtjwr4eRHF12NenzrD1MkBKCrw4BlZsfvcPi0RFQDcHlFBo0oI6ZRzip5SoHVDPkiSOgDq2vRErKmDrNGyagduoTUFOVW/ME1KnN8YfXIw7kj+dKS2oyh43JmB5YyxvTfatjO9sIaUhENHBEbg3SxBGrPIxfU5g1NNtaC+t0OMNYEhGdu2+8rk81NJ6jKkdV/TPTkVdN5M3je3NtKf8s6vBwRmoIPoOA0sK64bWqSa6AmjhCV/bpTknJGSs5bCWVBpIrosM6Up9JLHFn+lJQLCSg2AMLPGxeSs4YjTTAfvWNuZIUUOacYgi+CAHlOb+5AiohB9QQFMFVWwJxoM4pqnt4fZzQ4coVae5x/cbmXgjBJ4UxC+pKzQFlhIv07FDWPRHRgUeGbXqLlcYEj5kDqhfuU0fekNB3rV+VvVxpU0KZhDEikseIX6/+FspJCqhg/mBNczs0xVnKORUjlIO6oA3eRx7KUToLHKoQfE4BBW04SGcIEpeSvViPGiq1c/h0kJEdEk64LyUEn5krpIT6MpycljM7hhxHMYZpQ2UMh6XaiBEGCE15gTbmOOZdGDSHfq+fR0wgcaS0i4iRkqlKHok8QDLRGjckGMwQfHyMy5E5Tqm5pNAGS41TheBT2pXIN6jMtMNCqvoubFInaIuyMMPqBeOE4SwjoTGtstZ7/LVEQGHOO63N4P0MBZRVj9ZWyr1Z6sLIWR4LVZqCLgTf3kNHQDWERhVQM+ZbIZqSV03xO86R7BzLuXChvIYNqjaIpmSHIz9y0GYIPkfE5aqKiLjarXk7iYjObo8/aA+sLWfdVymgdtohoGbZR23O/Swh+IgYGd4gmetsrEvwuHOiKUJvNFfSfv5nEl9HK8vdF6kONSCpNDyHLXeU4i9I57DOJKConD45v7Qil3XlqnsTyAVejkq/b6kKKKkcV9qYOaAYnBPbOWOWeQg+roDS+qWRFVZoLyNkl/scR1WbOqdIJEQIKCRvHMEwFHJAIcE3QEVwOS3rbPZsYeVE8lGaU4E4kNRfgdLNICG0+jQSohiBvew62iuRpZKyq7JDWfdERIOzYV1IVHEFFA/BJ50FDp56KMVeZldfUWwFxGuKSgz3EJ9XaV4o3JcsDJGZ/wtJ90Ap5BRQhr3EwkUGITWZw8lUQHFFY4ICyvWz2o/CfBFNFVBeGUH9VtkL+6oaJ40A776ndPDhnIB1FVCeA5p/FRCempdec+SE1QrsyHBII3JC8Hl1lbaD11JAZYfgi6gt1DYVgpBo3G9eLzqEY05yk2gx+pajGIq1aamaLMUKUUhqcPLOWrdFWXj1IulnkVM4pkEIPpifqh4Xgs9Q4MTUSKk2IuoqXwJCEEI25qgNgzE2SJlZckDxYRL3r7f9/XVr7Z+scxDmcpZz0aoHodmUooCah4K2w+5HR0A1hNWJE/GOh87Ove55KqCacko7Ymd/jdxKRO2pi1y4t/UaCqhVpixqKpeWgwufVkdhstxwuEVERUCt5hFQay4HVEM5dhCz7KM2VEUO2zPmBnIEW5O27swYgm/farPkozvn6uaoIuKk/fz3kdszRPVI5g4PY2gh+NDhbjlBcxVQfeYATnq6nnuTjFB1orIAHfGCM5kodJTycp5d4NiVVDBSG5xoq9oc+WMs9SvZEc/HiDv1FYd1oICSCD0CR7FAalRtSiHtehEFFBAHVVgwsAHHEudUJJaITDWZRywJY4kOfIlsEq9xey11i+DUt+y1yFI1DxkSUG58OYnE1zlNCKjJfVUYNpLJFQeX14y36RGXBgGlqntgfVih6gIyrwzbEcm8MlRA8VCR3hpBewUiE+ddslc9pySiSrAjIIr5PhXOKcwB5ZFqROL64uVwDWpfgdw4VgSUMl9BmM0OHXzUVkABAeBea6H90KmLNuQ4xuFm1a5ZQvAhsfXeO9/LmrTtDRQekZwxFlIdyVLeGs9ecOpzm3q9nu9gzyCDYmSC916EKDHJKxhDJAEskg9twhxdFhHp2YDKKqPvMXUXhuCLhXjT1kHOXAV1ZxATufNsKaCCcJHG3FqhJfF1XYUQ3ot7Cd/nbW4MN+ie0/eoNqH9MXK3rhIUUTcUKZXMhl74/iyqrA57F52nqyG889OHiYjoT/7ptrnX7VRByzM4KtcnDt+mwp3tzECYEBFdcmicbPn09pBObDSXu2iWEHycEGg6D9TUgV6DgHJEWQu5qoiIztYNwdeyAqoKy1ZjTFdbUBU5DGZUQK22kKvsnuNjx9gl565HSspoOp+aCwu5P5MU5XDkVRME1GuvvqP6e5YHCzo8HGEpahiRUDsHlNSkFLbKeLpeclhLhNm0kK2AqooZYc08EkJSFgiOY54bCB27Ygg+IXyfqYLhygopX5JAZgVkhaCYkJRNlaIBFFCuX0uQ+9ALNycQl6NtCoAkDyqg3PuBkxznVCMrJDJPIA5SxlKyQ1S/lWEb0hx4KrEUospS60VIUAc3vpgjzbNNImCUNejAFVAp5K6qHENSkRNLWgg+ad3DunF/SwpBzAHlKY/4d1Bhb2FdHkkH+57b4e2PBKImNQcUb0MjlipSUSF3q3ISQSuo36o24SEE9Zxm50+HDgyVAqouAQUOcfe6emo+I/cJKgxyYOW4mcXBnhViDNrh6pZJgemfmX3VlGYIK6yZ51QmWQFlOf1N4FeZ2DwrZXNVGhgWzFLCZSmgjHUbUzXlEATchiBcpaDIUgkqIBcRVijA7NxARr3YhtaOFILPUhPiXjLtyNhXZqhFQXVlkTBHt456ry1CM2uujLmLYZZ5dvf2hd+I8yCcuhxQew8dAbUH4UKpzRKCr3L2N0VAzeg0P7C2TBcfGv+gvPMohnWZH7YGYztrKaAWQECt1pjzJkOHSdjYmS0En5uTplGRejUc/q2G4Jsh/CIRy6fWoK13HR0rPZ94wYFa968tj+d+u6G5r9ZkTUUm0TQHVBNKwg9+7oHq7+6LVIcsmHmXNKc+OkGlfEGW0xKcpdhGaExor6iq4OQQsxfJihRVCXeIS85YDK3n1Sc4xau8OuxzTQzBJ9nBHfGKvaJaphTKSQooybEtheBj/Vpa88v3hDGyQnZJqrbBFhby/zUVUBJZITnrE0i6qiz5pGJ1r6SKSgzVRzQeW8leiXwU12oCWSEqoCbjGygElc+kaq1GSIiKjORzX0MBhYRRMqkorHvvX3efsI8qAmry8AtX6onEM8yplOfLtFcZS0sBVZUxyml5vTAEn6emImHsoByeZWoIPlRAKao+h+57SgcNdYkfcMhi3pAsYgIInbpP+kt2pd6b4+wtysLrXw4xEc3Zg+DtZORbsfo2KnwCqt/r11eOGQRgcF+ElIyO+bQRv2xpj3EsB1RqqLmYqknLEyQhUEAp9rp6MIydZgPCVKxElqFFDEXXiKL6Q/IT30eVGZJBlo1194dkI76Xcz5ZNlnkbix0ZFMh+AI1oTvLJ7RDjpqqwxcmOgKqIfyHr3lcY3U7BdQSJqfNwNTZ32zIq7oKKCKiQ+vjUDdnd3SZ/Swoy7Jy7q/VIMo4IdA0ETFLiLNlFiqwDdx/YuwkOX//aqSkj0dMyj94ZrsVZdEseYHaUBU5zErmNpmPzsEpjA7tq6cwcvtvu6Hwi2e3JwqoTFKUY3ly3jZB5H7z0y6ae50dHi7QCChw7HKHqvZDVlRACcSSFHbLcm5KZIWkWpEUCBJZgSQEt9drU3LOIgkhqJgsBVR/iczwfaKDWSHWNHut8HJVuERQtQXeOYVUdMXw+6OWAwlDgE0ro2B8A7VI4f/Lw4d5/UolK0rGQSyxWyPhJ7HNvjBX0lj2wNnvUGAIvgQSIlkBZYRfrMYXQ7VpBBTLAWURxVWuKDZufN6tHFDJpKJwr7tP3KcE/S/9PrhrZgg+pxYyyEdVcZZCKnJFozX3rA8Ikczi82CE1uN1Bme0kJvLVM5hGFZNqUn++x06AOalgJoubVkBlRV6aoan7HPCk3Gb0NE9S6gsKzeQ9NpCDqlhtsnujSmgZskNlEwiUTiGJnllkC4FFYFN1vwgASWFXJPaiq6JDDLEI6BIt7eyTSM8kYxDm3CsDPIQYYWOzCJ3gciKErRl+llQl7jOyjuFxFBk3OrmgHJtSX+n3KvWU5OIr87yOZNOmDOrw+5HR0A1hO/72scTEdFjzts397orBdQMoZqqnCu7VAFFNM0f1VxemOmhWCcMW6/Xmzr3GyagHOm4UmM8qxxQI/sDY1743OHTRET09EcdyrrvsY/YRwfXlmlnWNCdR+efOw3hCLk6pJ67Z7sNBdSwPklK1E64wO3KxnoKo0oB1dB4np2DAqrJHFDuu9x/+vonzb3uDl/gsFRH3LlpqWUcpLwvErEkhsczvo9IBJTpiMYQfPw7ABBVaC+vL1A9SMQSd/4LOVgqB/PEsd1bmjq2vXBfmrILiTUrZCLaK5AQJSegDFIRQ2pVbTgSBsh4cYx6oQJD6lelJpPClWltWGSFoZLjznTs07QgK5eobOorBJzUj2TFlkSEUUhWJIdr43uSkxUkY4mHaxPsxTa98ZXUkMJrjQRNJl4LfbxjxHPJCCgpBB9XASF5heeWuk+FtSWNpUoU95U2eL8oPPOQMJP65Oqs2uHlGPnm7WdNAaWE4NMcRZ0CqgOgytVUl4BSlBVOAZUV0q4Mc/jURY7z2iQ1MhzFMYWNla8ohpz+cJgKKCSg4HyIOdgtJ3ksNJv2OkfJIxE2r/zIK6vXMYWalY/MUmXlhCbMVUBx8HauO3IdferBT6kKqIKKvDGvSTTGcqQFbSjjPypHosLIep1qYw5iKizvvQihibBC8OWEqMRhqJvjKjZGnr2cgBLCtM6DjOoix+w9dARUQ3DkyUYD6p3hDMoNh/WG8+3MEjLOwdm40bCNRPXDmzlSoCkiz2EWRZnLXTMMkrQ3AzcW5+5fiZT00e/36Jz18Y/mpuacY6okrKGAaol4JCLamqiC6pI7K5WtzRGQ25WNNffRSrOEnlNA5YaF5GgyB5RTou6fgSDr8HCFoYDyCB0jtFdVFVdpaA5r5Ul90bkpOGc1p37gcOdtaI5oQ1nhOXGN0HoqUYNEFVNASYRLVU5y1guOeLOcMVfVfYkKKC0EX+DYlsL+9fIUUBapxsshaaSRj6JTXyIhkKiisJxor2CbpMgRSROmoEkKK5mqgBLscKjyeuGeVD6TUkkIa4zQPiKFtMW1ioQO9MGzHfdzCf8SzJUQgk9SQHkKNtzPuD9wDRqqvkCJRcacKsRSZT9XYgmkIu6/IGSgRkAJCqikHFA9/99OAdUhE3XJHs0hWzktDae6VFeOgzfZrojD1lIqWCQFlo/lGKpLImH52sQVONAxBF+uAqpuaLZZ8vegEoy3uzHcoI8c/ohXr0UQxtai1y6f54hfJif8Y+D09z4+/Xt/+F0/rI55VDlm5MaKoa7SrSxLPWRgWYo5lurYNwuscZHWRw6hw88OrCtr7SHJlzE0dYn48VfBcXlJATWPHFAd9h46AqohrDeYY6lSw8wQ3q5SFzWUc2WuCqiGyJ2hp4Cq92Pq0L4xyXJmq5kwgQ4VAVWDLGlbATULWVblK2rB1tEMudTWWAi+pr/cnJ6srbrh7ZoOwVeW5VQBtTIbkTsqSho2YKc7Q/bVyPXm0GQOKPegwr6OgOowL3AnZqBAUNZwSgg+7Ul9gnK8Pu/HVYKzHp22/Iz1TOeEjqQ8SlDBaA5gjYTo1QjBl6KskJzOnvPYldNyQCkqFZxTVC1V9nPHNuu7pYCq7tXINySgNKWQpiiSlE1ov9B3fq1vzL1EjIr3WkSYtQZTyYoEko6IomQpgofgs/aptN561f/pIfisPGTJCih+TqECCuuDclSGBBRPMB6Qr7wNCFGJCqiUUJmWopGHykxRQAXzohFLPAedsKYdehqxht933RzHckDh2d4RUB18zBoGSQub5eoN8iIZuVxmUUCYdkV+71kkUk64NTTXVECRTRhY7dQOwSWQKoECihWJ9X2m3FlGPan34hhuD7eD8t64ZTzQm5MbyLo3Jx9RrJ1hMVTrjo1b8AyFseYR5jwba7ikUr23oCJUQCFZ0oL7KyBO2ZhGc0DFQhcaZ1BW/qgM1Vlgg0IAimWV96WzfB4EVBeCb++hI6AagnMkbg0KKubssJyHAmpfpYBqhjiZJWeRw/7V8Q+tplRa3CG/XHMsnWLn1NYgUnI2OPVKvRB843vaygHliK46Y1qptVrIrTSYIZeaI1bLshlFDMepzfHaOmc9T1Hm4NbMoCF10ZiEG/+9XpPg4UR1E0RZpcicgRBfbjAEn3sQoO74dXgYIyX3B6pltB+XMymgBLLCU1bANdMJj23wp+kKoSyFffKc/4LCoy84WCXSSwqBZREYokNBGG/xBxq0KRFQWg4ozbEdKJiUfklkVq8f5qDh9WhO/aoId9aToaoAJ7l3P9SHxJpIvkmOeYNUHF9k9xKUw36N/P2RrYBKUBRJZAXPAeWte42AcmQFHw9BBaORMNHcVkDUoAM1uS5prvi/RKpKzLXhCChVbanMfV9aHz19rQakYsIYWQooTvRPL+rnTw9INW2fa8SrRmhpOaDUc6RDBxnzygHlXmuh/cwn/zOdtKZdNZ/8j+Vxsu4NVA5wbw4phshxfGttYrvD0ic0UAEVs3FeIfhS7yPKC42HqqCccTNzA80xBB+WzVEB5awna36aClEZ5HECUiXYH0iotcBAWcSptNbqqttw7ZlnW6TbdXNc5RKN1lnelkKtw+5CR0A1hIMs1NOZOZM8zjlfV7VD1KxCi2g+CqimQ/ANmQKmbvxQR0CdbksBVYPQW2o5BN8stq62qoCqT5Q1TZg4lGVZrS231nLRtAKKh82rnaeKrZXtBlSZjnybhRBvMgfUmQmBfXCtc/J0yIWiZggcqoayoqqqhgKKl1PzCglOYYmEkBzWgb1IVijOf4nUkFRRWq4WiThwryvTJGe34IhOclgrpImpgjFUbYFDGWxBYklqM5YDCscoCDEEJMqsKjFvThMVUNn5v2JElbtP2Fvm3Fsh+Pg60tY9yWsVx5hDVHBJCigpVF9fHideH54rHmHEiSXh3ukFeYycPSIEklIiSCRCPVA+amNkrGlKWKteSDuFTPfug3Kk9Mt7Le0HKKepC7FckAMKCDg827snjjvMGehgdk5MlwMqy2GNTv8ZHNA5zlJLsRJVu4DT3Lo3IOsynLgYHqvWfWADKqByyEKsOyfXl5k3qLSJh6ycPUBiZI03EgaGMsaycZY8RtIY1t0T5nxkEBM580wlzA+ocUwFVGQdzAs545KbH44TbFnjJrRVF3XPQX4+ubOcKzrnkQOqw95DR0A1hPWVpYqEevB0KOWdBU7JslJDueHgFFpN54CaxeG7b3V871ZDJNnAqYpmsPHQRJXiVCpNwZFHdXJqLSoEXx2C1N0zaIEsG85CQLE102QeqM3BqLKzrgJqdXkypk0RUIwwqptLbXmpX81DE3mg5pGTbppLbf776IHJZ8Qjz1mbe90dvsChKqAUZ7JEVlT3Sk7yHjiGFUcpUeik5E/hB9cMtUwtBVQqoYPkAg9pJzi2pdwqkjImRdmVqpYRnccTVD9EkVSEcki0OVuk6579MEZaqD7pXjFfTgpJV/1fOknH15YUfjEgEAVnvagQlNaIQC5IpCLaEYTqc/crZIXXpkVAKeHaOHpL/t7g9mr5glLJXUndg3OtER/Rfc+6oznnREWjQJBIpCLmb5PC9FU2S/byseTlcK4YAaqFE+X3BcS+cv54+1khOIlA/cZtUcpheEfc08F50RFQHeYL7tQ8tXOKPnr4o0TECChUFGXkPtkNOaByQvDF7rXC9cXgtZOj5DHGdFSM6ObjN3vvZeWAMpzbu0IBBSRf3ZCH+DpLSZKjgALCTFp7nuIOCI5YODzjzahd1d8580y6Yuj9d72fNoebejstkE9ENjEk7d/a+b0yz8F5KYxy1r9GAPYntMN773xvcl0pqCsi6LA4dARUg7ho4kx84NScCSgXOmwGZ6oLwbfVVA6oyuFbf4mtLi15dc0bjuSoG36PaKrSasJpzuFIjuUa4+lC8B09uzNXmzQ49VKdua/CBbZAlrmwifWIsj65ZdMkAcXJnbr5ixzB2pSd28Pxl5+15f5MXwKcesrVN0/szCFv3lLPKaDmP44VAXVwfe51d/hCBxJQPCxbxFGshswjcIz2/TIiUdMTyAor1JlBLlCPdQsJKCSfjFBZAaEjOaKVvDoBgeHaXJL7YKlgJBJCVIBN7q2cwoVQn5QDSjiTPAWGYIulrPAc1lBu2Z1R5dTZgEoZKfQZESN+DKc+t1lUFDHbKjskVY0bS07Iwr0xNZmqgAJyxbRXWEcaseTqc/biD3MpH4+3bxg8opTXJahgpFCOfJ9rqh0zvxEQSxZB6xFVNL1vpEQWkEi6pRWhjGCblNPNs3lymxraE8ZSLcfnKnVOMxVQ2tnC+4UEn1ZOU0AF+626kTp04JjVCag5ItUcUIbjNaY4yEHtHDdwJsyiDrGQnQOqrpLHcLAPyyG97H0v822aIWSXZ69FMkXsz8ofRfrc5YZT9O41+pNDzOWShRYB2O/11bpjpIX1XlZIwcx5tsbtLTe/xXuNKr9FhHnzCMBCIDQz5pbfb5LRhg2zIodQ1tSc0mfEPOamywG199ARUA3CPc3+4Jl5K6Am6qI55IDaGRWN5NvZmkNOk5WJcqMpx/k88sKsOMVOwzmLHMG1vlKD1JnY+ODpbfqnmx+cq10S3PqsQ+w40qrpHFA7w4Je/aHPE1H9H02OzGiSfHTrqt+rn/Ot6RB889jrRERr7kxqYDxdnXVyqDm48Z+3AmpUlHR08hlx0aFOAdUhE0E4rUgIPk9FoiiWiGSiI2iPkwZ9IQTfklBOcWpze8cFmb2grMAyan1SOXREK6GyHFBp3l+S+5CSM8ayV3SSC+RKpawAp76mlPLmj7dhkIWq+o2IVtarqgIneRCuC0gJK/yi50hXxlIiBsuCZFWN1C9nL5IQqD5BokoiAQV7RXVLAgGl5iHDuXdqFCRLJ+D7TyVgiNQzw1tvPX2+xPUh7NPKNNavpDmdvA7C35FfjpeVwn+aYRWlEJUCSSfuZ1fMUOuJajVhTsU1yGxR1WoknxlYTlPCBuWA3NRIZYfuieMOc4aauL4nE1CF8TBYboirecEKwTfUzjPh3txcOnWJpFnCusUc0vz9WN+tfEV1Q/ChDcG9hgIK6w0UKzmEH/anZv6oLEc9kC5IWvSpr9YdIzRrq6MiZWM5oIbl0HvNceuJW/3yGf2ZF8z1JO2PDCLS63uGAopofiRU7fOJKaAkoqgthVqH3YWOgGoQFx0a/1B/4NTWXOt1DtClOYTgIyLaasDh60L78XZysVblA2rmi6NT2SzPMI7LLeUscmEI15fzx5MTF39xzZ1zs0lCWZbVWNQZV0daNZlXiYjo6NkpKXz3sY1adTjisklbBy5M4AzKHUe6uJCT8wZXQM2CqQJq/uM5j5Cgbj0Xcyagjp7dpqIc+3MuOLA617o7PAzRk5zOoJZBpU11rxaCj/9oQNUDKxfUZyiF0HkUhAnTnLZIahihsoJ+CY5oXiY5BJZAOKhqJCQCFbKC95OTiMkKKIn8IPLni5VT1WowRuho9hRQmlOfh7iTxsgKa2YpbxJJOm/uJUIWiI5gTWvlJihG/r3qGlTWEa59TiqKiiIgQ4J1NCm3zBW0QA6R0NeqqDBfZihEgTTBuQ6UN5ZC0NnB7aUEAoq1Ia5n4ZwKchtFVJ+1c0BJ+68M15JXTmgjIIo1BZQRVlFsA8oF5Cas6YBw6gioDu1g0Qoor54ICVA7xw2FTtucsrM4/uvCU0DBOYmKlZ2RHYHFVEBlkEhoQ2qbWD6WAyqH0MxdB9iu9HfOfUThWPR6Pb2/pT12syjSOHL26KDw01zgveesnqO+v6gQfLEQiLVzy+E5aKjx5pn/KksBFXwfGv/TKaA6OHQEVIN45MGJAmreOaAqZ2r9DTcOlzX+e2PHfjKlDraGjjCZIQSfc/A3pDBxxIFTWtXBSsMkmYNTmazVUEA95rx91d9PveTg3GySwNUhtULwVQqoZr8w8NB23/zUi2rV4QiTJud+MFn7s4WydERZM7nUHGFUZ21yrDYYgm8eOaCWGsoB5UK0XnBgbSaiscPDFEFIJckxrziKlzTFEgEZxK6ruUokAkpwOqfmSqocoOQ77HOd5F6b3F4pp4ng1BdzQBnEmmRH0DfNXsE2/OFaCGoZSQElOdM99ZsWfrGU73VYXpvWhfdiXh10uKvkI5IthkoO1UmWqkSzJQjDBmsaSQ2JBPTsMNQyHvEj2Ofqc2Uk4hVVKnytur4RTefG2SWG1kNCmWSywiIC+RpMytVmKYoMsldzrEgke0wBhfseyVJns7O3h9cke61+KQqogCh2dSltBCH4eD+FvVW9lkhFgVBWQ/DFFFDUoYOHWZ2AmlPT5YBC5YipjDHIhVmQkwMqV6lg5Y+K2dSGk13Ki+RwbOuY+h5RSCAgLOd2bTIkU8nD11eMgMrNx1QXs+SA8uqBvRMNwWeRfoaCK2vdZijdkMDEe49uHfXfrzlX84RJaAJBGxs3L0cXnoMZirpZkKPG084y6TNiHurULgfU3kPn7WoQ5+4bxyQ/tTVfgmeqgKq/4Xq9XqWm2W4gD9TWHBRQTRNQjuRYmUEB5UjApkPGzaKAuvjQOn3TUx45b5NEcOKoXgg+5+Rvdjy5yuYHL3tCrTpWG86tRDRbOEOH1Yooa+ZLmDs/1mqsTY5KAdXAeTQPBdQ0B9R8x/HM9vjz4dx9mES9Q4cakFQxgbJiUlZTLFX3E/lKislrMadJL6KqwWuRMGyaAgrJikrRYJwbnoOdj4lnLPRfcYpruZfUcGVKv0S1jCMc2LxgfVypoKlP+H1YriIYlNwygVoEyQqXa0cg6SpViaA64uVEtZowLyJJieRQKRBarH5L/RZTqag5oCAEX7VULWUX75dCKmokKM8JVtnrNTT+Z4kpaFV1kkBAcbLC21vWmiZYW0A24pymhN5EdaDmtJTCgIrktzSWjnABci+wzdinKQooj7hRSDrvPoWo0vYpthkQ5W7NRAjlIBdez6+vxD3doUMzUHNAuRB8cM5bhE6OiiYHOaGnchRbROCwniG0XFOw8iLFCKjtkf0gtpk7yxiLKMFkDJOlTJLy7Fgh7Szk5gLT7s1VXVmkXo966phH81JFSL9U5ITgQwIK18Q9p+/x6+ZkCeWFqKyLHAVUdg4og9iOhS6c11GRYy+eZe7ePn7/oMWcZR0Wj46AahDO1znvkE1V6LgZn5ZvMn+RC+u3b5YcUC7HTkPkzjyc0iuVuqS5A/TkxoBueeAMEU3z5OTi6Y86RERTJVVTGLAfCHVIExfmrMnxJJoSeo85b1/tfXTfyXFozb/75H1zswuxMwmbN0uYSLfPmyLK5heCb0KIN5EDypHNsxBQDeaAIpptjjs8nKEooDjZwp3JnISoyAS8l5jTEq5boeq0nFKiCsZQQvB+ScqKFAWU1C/JEY1lxgVZG5LDVmrTEWHwwFGqEkRSvIj1cWWFFYYuQsKYCih2DT/GTUWREILPm3ur73ytust41gptxhRQ3hi5a6Bs0lQqVg4kbW8Fdghzb5IQwv611D2qqk9RvomkohR+kdubqO7x+qWdP5Hwi+5eorQQfNZZ5pF+7jqGnBMIHd4va+6TFJg4RqVcTgu/KOWgcyikPQ7lgvFV1G8Yag+J/cBx1D1x3MHHrAoozUncn7iq0IlrElAzOP0tZIXRQwd75MFKT4UScZgHaoQWfLg5pB6G/popBJ8xFm2G4EttN2gnQ7ln3ZvrqLf6g4qRWXKKaW3GkKN02ymAgIJ7N4ebdt0tbJDcNZKjbuNnHZ57UcJwXuR7TQWUlwOqIaVSF4Jv76HzeDWI/sRhOZoz8+4coCszKKCIePi4+R/MLgfU+gwEVKXcaMhx7gioWdQlyy0ooP72k/dWf9d18q81GN6M45rPT2XQdZRlbjz/4P23zM0mCVXYuBlJEyKi13z49pnr0OAUULOEjltdGu/BpnJVOVJzlr1O1OwadWfIygzzvczO23k+VOAIqP6M53mHhymsEHyaoshUDbhizIHKnZ6BSoi1odYnEFBmCDpQFqBje1oZK2ecG5JTPwjBx/sOJIxXLhaCz8pFZal7JEc8CQooQc0QkHfsPk39puWW8cgsgaxIyaclqal4udjcq059ifQqwzHnjj4tvJxXP19HVjlXPyigLBJCCn2nkYXaurfCRbq+EflkRWCXQAZVZTUFlEHCYF2eMoj0OTUVgqCYjBFQVqg6jVDGnEeioshQf/G6PLJUU7VV/2fv00CZpuwtMWSgVU7Zg1hOC8EnhSokCj93OnSYEZqj1D01n+PgnRfhFCDjqz/mZhmWdjScHAdvQBi04GDPyWWEJEaMgLL6HiO6TBuMey2SQuobqmpSkdMfs81MX6LVn36vrxIgucSd1mYuLFVNoKCLNINk7iL2R45SLCcE36JQNx9ZGyH4Ouw9dARUg3Ahm+avgBpv1qUZnNJEU2f/vBVQZVlWOaBmyQszzV3TFAE1IfLmkF9n3qoIrw3mNK8bdtGRA6c255/vi+PH/8911d91HOoPTPKlPXB6u9HcSk4BVVdR1hYGc1A7uhxnTRG5c1NArTgCav52zjMHFNF897t7QKFL/9ShHjQCqmBkBVftMIdkkANKUkChw1pTPRgElETCSKG4JKUUOlBVpZTirPfaNEiTmArGQVNAaWRFoARJIFc8p7imPsFQgKVRTiAiLMUELyOFIERYzm+RfLTUb5ExqmxTHOx4X9Vm9X/hXGnqE5UwgxxQWhjIkhFVlvrNey2pYARyRVMhVmUUNVUsB5RIAisEFJJcXgg+nFNl7jVVG5GRA0pS9KH6EkPfwbopgXCx+uWZqymglP2nhdYL2oQ9pBHlySH4Yg8iuHIuVB/P68X+1XJAdU8cdwDM+nR71AmLCijDKduUwznL8ZqpfPHC92WE4FsUAZVDiERD8NVU+swSqtAi1KT3YgRVqg11czllK6AsAor0HFCxtnLG1EKWAioSgg9RlyycBbnEaY6NMfLabKeB/mfnqJv0tbEQfN3XkT2HzuXVIJYaV0DNGoLPKaDm6/AdFmX122mWvDCOePnwLQ/Nw6wAwyoE3wwKqCpkXHNkyToj8eqGNCwmE/L3n2ouXNw8wPPrNEFEOPzBB8YKqxvvP9VYG/PAYA5rtGki9+5jG+N2ZiSgmlRkzjMHFNF880CNRo6A6j6OO9SAls/FU3woYaDEHFBI6AAJwZ/oJ/KdsYGj3AjBFzhikVQSCLOqnOQkVxy53A7NWV+9NkgIXpeVAwqhhjWTiCqBRFPJCsP5T8TIECQiJtcttZpErjhYIfiqMqxuae5FRU3mGFUOdqk+HprMyP8lqk84qQGh+hyKkU+uJCm2LGJNUGJ5fcXvfbj/JLICVTyCvVVRRQFVkRCp9gIBZe1Try5lHY20xPXC+WOFKfTmFMlHRfVp5WrjYxmbU289M3tjJCiuQQctP1yKqs0Mwcft4Pa5HFACYd+hwxwxzxxQTaFu/h6iuIohJyQXKlbayHGTk5uJSn8+MYQawiJ/Uu+TbDDvRdIlEoKvNgGF41azf7lKkVgIPq3uGKE5txxQGYRNQEDlkLll0cr+yCHxsHyT9rXR91j78yItNXQh+PYeOo9Xg+hPvjTNO2m9I6DqqmEcVhpS7/D+Ls9go1NUDIuykbBcO3NwSrcRgm9zZ1r3Ey88UKuOj95+rPq7SVtnxf7V6Y9tp1JqAh+74/jMdfzoNz6JiIie9YRHzFyXhuE8VHoulGVD8/43nxiHiDyzNZu6zp0Vo0ic9DpwOaBmIcn4eTuco42VAqr7/rQncMUVV9ATn/hEWl9fp2c/+9n00Y9+dMEWoTOZkxLcgS05N8FpKzpUBQWUAyqKREKL10W2o5Tfp+WW0ZzkqrOetWk5dgOyRSNXUFHErkuope7hTnFLAWUoK3bOTMop6p6g/wLhJ+ULSskn1Zec+uzeZJWYoZSqkwOqrgJKVJdJ5IoUro1DmXstXKK2T715UYjAXs8nW1IUUBppNDgL3XLjRv74mvvU9dUg6fg1osQQfJP6lqR8doYCysFTFPG+O/LOyOnGx1EjigO1GpDM2n7W7FVD8CnKpmBvKWQxJ6zZP344Ts8Q6tCBY1YnoOaIdDmg0MG7CAIqx8GO9kVD8GUQHLPkBqoLi7BBIIkx1M5yoa655awhm5izCDVJnWMROhZmyUfkKaAyiQS+/qQQfF47GYTmvOYnpmKy2oyGCYT9sdtDVDYVhq6ts8EC75ukgNoFJnZYADoCqkE4h2WR+aERwzyUO/z+eYfm4oTWLCTZZV90YfX32e35f9Gci3O/QdWGw8bO+Ivbd33lY2rX8R+f/fhpfQ0SO7OCr5cmFVDzwNd98Xh97jRo5zyUO5UCatjMGn38+fuJiOiLH3lwpnrc3DcRznIe4+jngJrZJFbXfB4o6NA83vSmN9HLX/5y+tVf/VX6xCc+QV/+5V9OL3jBC+iBBx5YnFGBAkoiEhQnaN9y2nL1VN8vE+SBovF9OSH4sAw6SjUFFCc+RAWUQUBZCih0nlkKKCsEHyJQ90wg5cKRSLRUsgK/Zx6/fWqX5LBXc+bAGGkKu5hKjPcL2zDzAEUUUKj2KYuQ+BGVIQL5pu0PjXzk9YtKIYsws8hHRYmlrWmNLO0rCigvRKNAQkj5v/i+P3Yb2MtJCYHwrmwSCB2RpFMIvxgBRcLa4q8tVVtVTttrivrLI3uNtRqEtCNY59L5k6CA8s5eQwHF17ilaBTJWHZdC8HXKaA6zBvKV3/ntPxCzxWSo3bxHPBlO47mHCKlHBtVu+55IKpAMULAxfo6Swi+HMxCzPF7kQztUU/tb0GFPbfz8mnOUE2UoOWf2/wju0lgG1absGdzyLg8k9oh3yzwvkkPKczDvk4BtffQEVANol890T9nAqpyWM42fS583GDeCihGxsyigDp3/0qlWNhsgDSZR3izpvJocTjybd+q4FhLxPMvvbj6e3OneQLqwoOrte572Td9cfV3kwqoZz/pfCIi+p3v+fLadbj5OLvTXF4tt66WZ1ijKw2H4HNqoK978gUz1ePOo3mfl0RzCsHXkALKnef9zqGz6/GqV72KfvRHf5Re8pKX0KWXXkp//Md/TPv376c/+7M/W7RpU4gh+BQ1gOncVBRQAfHBHJdWWLfgmvRacNbjr8fASQzOfytHUUkwJlhGcABLjt1AHeBskaARa5IKSCAcxLxCpDvYA/TAXjf3OFcSCSGE7EqZUy2HVWoOqOq6oGzC+RMVNIIyTQpVJ61xi1gT7TCIpfFF1q8EBVQvYZ8GZIW7zMr1etPXnDCTVG19NqfSWkV49gqKONdHSdUmhV8MwtKVk3WvrGmJAJeI8mwFFF8PiqotlVQshPXhkf+RvF5eP6BcCgHuhT9VxoiI5YBCBRQQUJKytEOHOSIWgi8Wwq4NNEmCYRg0C204r4VGPZgKj0jYLUQTfcgN/WUpoKisHw5vFsJmFpWMpZ7q9Xp+fzEcnGHyvPbALHOeFd5uUQrB2B4u9fn5QgInAyUF1FxC8HX+kz2HjoBqEEvVl6b51jucg1OaiGjFheaauwJqWt+sT/W7kGybDTj5HfE2i1O6CmPYoALq+MY49u25+1YiJXX0ej06uDb+obnRIAH15IvGKpjf+w9fWev+53zRlMRokoByhOYj9tcf0/Mm9x4/a8e2ngXzyPfW1D53GAxn30f/f/b+PVy6q6oThcdaVbVv7zXXNxdCEhACCISrIYhAYxTwrq3wyEf3MQdFOUbU9LHb2Ap8+gi2Hvy8cR6UPija+knrd46nH7VRTOuRFgSNIo0iXhDklpCEJG/ey967dlV9f1TNWWOOOcaYc641Z+29887BE95dq+aac8xrVf1+6zcGAFJAlcgBtej7Wg8fm6YBc5zlJMmMQrbveV6trO3u7sJdd90Ft9xyi73Wti3ccsst8L73vY+9Z2dnB06fPu38l9+UJ9pDCigubBUGwAEY0FIihxgCygntRfyjZYzPtk8RqhJYFluC9QoBZX2GMACcVQGF75cUE0KbnrqHU58wJAxuj83vJADxjpKFA6w5YFtRQOH5awf+NdOmpmqz5ThSZibUZ3zh+k5VH/b/eEKEmudvBLGG+yXmrBIURSKxC8CSbaaMnSuSA0oK1yaGwSTGKeSoAip6jAD8dTQDmEr5n1A5ugYdAk5QialqPawCUggzllSMmVPtXJFIRU7ZxSgJvTCYUt47Ovc0FKB5n/QrpBitVq3nkhBD8B0gBVRJMDuFbNgP8Do1BF+KlehDHwUUVQz1yQHVZ932IVJwu7Q/bdO664086BRUt2WwXMRc6P0DG4Kv43pKsYNAbOG1xxFF+63QqrY/VgmogjYsFYLPEic9CSgb8irvwTdBIaX6stKbI0NA5T+cDSg97EVAlVdA3XN6GwAArji+0aseo9g5V1Cx8w+fO+O01cUuOTJXT5l+lzBDwm2t0Rwo8Xbq2Hw+Hjg3LpKjDAApd4bd99FaYQVUjlxqADgHVP4vIyYHVJ9xBFiqtHKGCZxUBdShsPvuuw8mkwmcOnXKuX7q1Cm4++672Xve9KY3wYkTJ+x/11xzTX7HxJBKQAgoJhwXlwPKI1caF9wU8+U0PKnj1AUMoBrIaeIBuwTY9oDSjiH4HBIC7W8O2HUAcNoGsZC/uJwlHFBdElkhkYrUqGJCVL9xSjQlZJc2lmIIQaHvokqMnrMM6eUptkibWshATvGC25DGSArBp4UMTFVA4XKcUpHNr0ZD8EUqoLgx4shH6pdGgnqqNolUnKIhwute+Z4qhgAlBNSyUNy6ZxVxylpVyVJBASUSYcz+k/yNya2HiWJ2nZtykQooLwxk/b5SzbW+YZBEBdSi3v3I+UStJKDrqVC0sglqqVyWmssoZaz2g1zUCAPuva7h8PrMTy/yaqavJ0lFl6pe62q9xiWgnqJ9X0l/yBiHSLwLJQcU9oENwZfhTK0h+A6fVQKqoBULwTcxBE+/6VuG5ipDkOXIaWIJqAJqGEO89SHyzBjmDmOIzRAxp46v96pnqSYr8yX+rk88YP9eH3Zfm/cvFEW3/+e/6u2TZOctAdWdKDuJQkR+7vROFr+omdxiwx57fW1YliTNsY8ADn4OKICljznP9EnNAfWItTvuuAMeeugh+98nP/nJAq1EKqC4sHmesoIBVGMVUGwIPhrqDHTgNUaBQMkK6i8XKkpSTNAyUUDxIA4Atu4G/MX94tqU8ht5ignpTGpAJWuo/zSsore+mDajQ/CZa8TXJAUU9R/7oa03jrxi+qmqVLjwg1jZpKnacL+4HFgAPvlG+uX4wYwR3n+YEPYUUBJZIZCl1LBahlOYcXVJ6i96hgDM+64mrWdIOi8EHyWppLUqnD+iWo2uwRCpKJy9nKIoSCou7osh9i2xRPou5XRzzhXwfakKqGoB6wsCBvO6PMIVUEk5oJAfq1I5eAC7poBKBP1LjGtQJQNyfziyreuY58oB1ccoeesRagnk2kHYh0n7g5A9pSxZ9bcCBdQB4J+c0Kk1B1Q1Y90f/68WNIN15ldALcDUnoClCfm0lxmYNoBqn/xPxjYKElAG3O8T3syop0qFNwMAOL09/yF+YrNbXiVjG8NyYwkA8E/3nbV/rw/7x4t/8JwSgqWnmfB+Zn11saZp4MTmCO59eAdOb5fxNQdxsjaY93H3gIfgM+fRJLMiE2A5jn1C8AGUUWnlPC+rlbNLL70UBoMB3HPPPc71e+65B6644gr2nvX1dVhf7/fgQNDUpPIc8IifrmcIKE4BFZNbhQO2OaWQmH+GtikBwAJIruaAwkohDdTH7WiAdQoBRf1dGEtWGLAXh7AjQLyogOKbFxUYElkoKTy8cvhapKotJqwiuwZtQX9eHEJ1MA/d5pAynAKKW+OR5CPsgUzudlRAOSQds1ZZUpUjoKgCyvwIwSojbU4FspQaN25OaDpTF0MORYWenJE10rh1c4QyVnyZMjFj6axV5pziyMKGEJDevfi+hvdDDcEnzalSTswBNXXnIRiCr3X/lULwVQVUNWJ9I54cihB8BYFsTBKkkACrGheqOtFUKKsKe6ZZKpGC+/Pw7sPqvSlqvF6h5nqMIfbRm7vZFO49f6/zGv+t+Xwg9qEwLmvtGuxOdw+coo7abLYaBdTK8sNpPqC+lcoBVe3wWVVAFTQTUim7AirTE/MGjM2tjMiqgCqo2jGAfL/wZka1Ue4APbszB6BMDqeutgT4y3wpvP/MUgVkckF1sVc//zEAAPD4U93rCJlZo33z7pg5ObtThtTbswRUdz/NvaVC8I2tAiqPuuggK6DaAj5OFl9Cawi+g21ra2vwzGc+E+688057bTqdwp133gk333zzPnomEVATci2gmDB1cU++U2UBFwKLS3DPkV7ROVjMNUktIvnLgPUxeYtE9YmigJrSMeaMgPoqucKouChZ4wHzxF9qXgg+QSmGgXQnx4+iApJAfSmvlxZWMZl8VEIB9lZAxajEGKKG7RdD5lFwR1K1JYWNA0LAKAooiQT2SOtACD6nT8QX3PdQCD5vXmaIeG3ktUrJUpoDK0ZNxqr1AuRbDFkqzqmy/1IVUNr5w+WA0tRvTm45VJ/1tyqgqunWOwSfAHofKALqACqgVgUySwD7gPnOtd/kE0C6og6/ftfH3+W8RwmDVYUX7ENeaSH4Htx5EM7vnV+Wpfm9tPBxK1LcaSb5sDZY894/qCH4+uT36urTfpiztpiPiCx9r19HDp1VAqqglQjXBLAMwZdLcTDOHILPKBhyPNFv6igRPmzP+tlDAdWWCWOI7cyCgDqy3k9VVDLHDsAyr9K/es61ver5sifNc6zkXpfYcqlOzJwYkjC3mXXVK0/ZsAzRbMzU25fMM3sp9/qczWZLtWNvH/PvoWkNwXdo7Pbbb4e3ve1t8I53vAM+8pGPwGte8xo4e/Ys3HrrrfvnlKSAmhJVhcVs0dplgW2qKKKgJW6PgPqqGotc417HkBAOoIr8MeCxFoLPIXkCIfioWsaYkwMqQgE1ReB/KGeMxYklBRF6LfnrmaA+8RRQXCg1TTGhkHS4bgNsh8Iqcqo2Ti3D+UEJI4mko4SOpIAS1W+BUHWcssshfsx1SQFF/RBI1Ri1TNMCnwMKvCl1c0ApahnPf+KHtAbxGaLtZ7wGJwtVeTtkzh96BoHbX/uaIcpVBRRDrDntovbMtVAOKFFdR88pYU65M5V9yEDZp+yZYcqRHFD2feEsl9qrVk2xGHJKzAG1WGsH4Wn+3IDuxRsXW4ItJQxaVzKkj3ntLF4OvQeZfMJmPyw0hlTFpIYUJIqulLVYMteR2i7qT0ixlRSC7wDsQ4nUM2sxRU2YzSdK4oVCQDJ7eH2QN2LGqolg7px3FFAM7VBzQF2YVkPwFbSB+dKUPQRfHsByVFwB1Z/fND6WIE2MAmqtR74iA+7nDmNobDabZVNAlVSYAOTLZWOUICVVZabuvr6aOTlTiIDKoYAy+bi2x4UIqEUIvr7h7UqtT6z8GvXY6wBlHirIqRitVtZe/vKXw7333guve93r4O6774anPe1p8K53vQtOnTq1364treXASYZYAojLAeWF4BNUGrEh+LR8Qc6ZL6hKsG9szipuj2N/zSUtr45CrgzWluUcZVeEYsIhIZTcMjEKKAquS0/ZxiorWHJFIRXVcIaMqgTXxSqF7M3udVrOa4OZqxBJR9elFyJO6hfT91BeryS1DOmD+ZsjC9k5JURNlxxQKSH4aHhHSdmlqsQEks4ooAYjn0zH9+JzaoDCVHdSQGE/GEJnuueOkUqWSuo64VxxiPjAQwIxCsxGIEvFcmReNP+qVWNMC8HXNm0QBBdD8C1Ay6nwm7Bt2pWpo3IDum3TWvA0SQG1gvBdXpuk7wZw58JqHQQFVAoBABARMg32Ycwzhe8L1UPVUqH8Xvtt0vpiydwVKaC8vGGh9ccQmtxeOkzWNI26rw7qWVFt9VYJqILWFlNA5VEcGHJnL7PSxNSXRQFlVVr5P+zPLMKmHVnrvg1MHq7cY2js/HgCZvkc3egZgq+QwsRYLiDdrJuC/BNSQPX7sC9OQC387JOnzKzvEmEsAZCS8ACqiwBcJd1BzgFVCajDYbfddhvcdttt++0GskjVEQcAsyAjJXQaAoJKKg1FLaMRNZwCCgPb3AcBR+hYpQ0TKioqv0+kCma4EQH+w5w0mO4tgHQMWJsmOLKC6UOUWsb0jTFHfSKoZQCAV8Ew5yUuZ68ppCKnltGUQl7OKlyOGW8vxw2+j4D/dO65vGEOSSDtLWmuAuSKGFoQzanjryEkmVCZTrmFOQqoBngFFLNPWTJPIaAcYokh7my/8H4WFFAOoYPcMARUOyTElrRPG4DRBiomKaAiFJhc381+jp1TR0HK+KuG4FP2ljPm+CynxJpZMwIJassRNaDxQTunjR/VqkUafUK9gcYDHkMKKInAWiUBlbudZvE/AAJIh8LHrSB8l9emELIOg8p2Xmf7DyynhuDT/J2CmxfpMBCe2McU8jeUv2u/5xVAJndsuE6I708pC4Ux5NZTbiXPqsOWttCy+caMcQ8pVAXUhWmHm2o94DawX5ry1msAy74h+IyqYpwZ6c8JqBqCoIRq59xu/9B2pcObYdXK+rBfCD7zu7uUAsoo/foSj0slTEkF1NzXviK99dF8TnbGZcidHHnKtkyYwN29Ik8uWR9zKaAyH5jjPaSAypYDKt/aNPtmIIF91appFgQKF9fYMGxMGKgQCCopCzjFBNemCH7jchhg5xRQSDFCCTM2X0yiAmFemL8+2vTbtO0gM2qMyZgnzGJy4QD4/XdIE6ZfKlmB1W+aukcB4jnSSwr/5fiL1xYTWo9Tn2j5r7h8TGy+HI6soKoP0s+YHFCcqsT7fBXAf08BJSmKtDVtikkkBCKOPQWUQkJoKiDbJlqnHCnn+Sb4O7+w9A2vQTPX7cD11yNRkL/DTVSuJWWkNSLsSU1RFDWnKIcVS4LSscX9wmdv4DyT/I1VtbWIpLR+MP5p41atGgRAQO9jxC8rgaWcQggbF9aplIWA/FRrUHjkFLB4P/JhSQoPPO8mBNqBICkSQ8mpv48JoXYYCM+UMI20byoZtw9rj5rxj4Z/tGpJQqitwibk+2owjCajUOPyqcUapy6iPpU2zgeHgGI+I3LMj6a+rXYwrRJQBW1glRyZAdVM4cNMXhkTQiuXGXC/ryICYEmSlQhxd9bmVuquLLI5qgqRJVhp0V9ZZMIYlPkwNsRBm4mAKqXUmk5n9vd4XwXUsHBYwxx5yoxKazabK+pym+l7PnVR3r1kyOG2yafOqyH4qh0ciySgokLwCQooh4ACAdTnwrVxuXaIb/hsYxUTjLqUy69iSSmFVFMVUAKhQ+sbri/r40IGGjME1HSM2kTlWBKGIT4ktYxHrhiyYkT6hcdSUUywhE4rjBO44HdMXh2NWJKUN17OCY5AU0B4TwFD5l4Kk2bKqSRdhArGI2jNdY2o4UhFhVgTc0Ahf6cCaWLLmn1q/4+QYcS4fcrlgGLJRzqnU3e9mTJTLQcULMvhex0F1ADcOV0YR7pz5VQ1JzP3Wgg+VjHaYU5Ne0kh+Mi5wik1qb+mLeqfewGqVcOmgYCcAoqaSEA1AQJqhWGrSiigOND8IID81CiJYYBjPO+YgDoIodo0oyEd1RB8C1WXfX3A+wbgEmyp4S+1/h2EtWl8GJHvu2YtOv7PVjNfmvLHmA0RCIICqgeRwp2puQnzkLEkGPIh5cGDao9sqwRUQWsLgdMG6O8TlgtgCRjnVppkVUDZPFX5PzzOZCCgRoVIPGNWHZFhLMvngDJkycED+bHtZST1SpNlZt33yVO2ORpYTKFEqMBxppCgg0Jqx12bR6v/x12J+Z5WAqpaH/MepJcUUAy4yT3V7pUjBJT6RL8AWmph+thwcwqoT/uQooCyPjPlYomq4abfL05ZMFyA4ZM9cMmKxZ/e964ZP06iAkqYB5wHx/hvu6UooDDA7hBhGhCP2mDLAA/EszmgGOWNBOqb+owb4txLCiht/phytF80r47kr6iAYvpvysSQFdIY4XJNm5ADiiGRWPKRI5SZUIi0X7icGn4RK6BMCL7Rsq8Ayhg1yz3HlaN9sK+xiosLm4fK2X5GzH2IVGTJUm5OqfoUgD/LpT0YOqOZOWX9U8jdatWAB0CNUWCSDcUkPAlvw2o9AgmotmlZ0Dw1fNwqTALYsWrDEAKryrvTx7z+gD+mODwifp8re9AshdCkOaA0Owjkm/HBU0DtYwg+j6BlxsmQzdQn83cfBVTKmVrKQgooTq1aQ/BdmFYJqIJmQirlVpzkzrWzm1ldtJeJiMB1lAjHdnaRA+ponxB8hUg8Y5bMy/Bjr5TCxNgkE1lWmiibog+7vmt0VDBEJAAid3r42TSNzQNl1nwum81mliTrS/AYAqtUDqi+Ci2AMnnUzPHbVzlY7UK10JPq5lro6XpTFylHgWgJAFaBbQwACwCo0ybyQ1NAcWoADTh11AAdQ1uNNoAdSzoPQ04BpZEQgpqMfl4788LMw4AJwceB/6z6DQiRoAHxGgGF6mbDsHF9wsobrZypDhNmAqgvkaWYcDFtsqRqrEpMmdMYtcxUICusv8xccWPkPJTWgJgDStyDZD9L5KMYApOqmziylFG/sTndUA4oiQA395vrQ5IDKubMY8l5ru9GKUTm1LrBKBoBGCJMIqCEOaVnL76m5fPjQvCxCigpB5RwllerJpgGAtL3NCDy8s3L2bLS0/wHPfzS+mBdfA/ngHIIjgNIQEkKKAw6OwqoA05ARREGRrEi5Ow5yJbirxOuLzB3B4F8kxRQHFm9qrUYlVMMf2VIVECdXD+ptr/KUKSiDwECCvf/iRc/EQDykGQH/TOgmm/7v1ofwWa+r08ysLvY9uwT/T3B84WqInfOlaUCKgfgO+9jCQXUA+d2AQDgxOYoUFI2A5rv7pX5QM6pJiuvgMpDlpUKXWksqwKqEGlizOzNYU/yxKxxs+ZzGR7LvorMUuvTkHh9VGTGDEk0zklA1RxQ1fqYRBDQazFPzXNqJBqCj4KzGrDtAcAcScWFawMZrAcQAHsJPJYAYGbcWMUEp4ACtxzXdwM6TcaougBgzfVBUkCJ4dooWSHUFZvfSFJMqKHv0Gub34gjBBj/1b4za1clFVt3XihRNWXWpbo/MKjPzBUXWpBTy0ikotMm6peaA0pSQDWyAkokligRKJEwsQooRnUVo4CazQI5oNC9uFsjkgMqVvXpke7KOUUVUCFSkaoIpTnlzlRazvrE9UuaKyDzJeznYA6oqoCqFjBlSVCAUAMqt0Zb8IzLn+HdKyqkDjiUpSm0mqZhQwymEAarMjr+nA9YkXIQlDKa0TEOhkxD/T8MBFTKesLkLg03SO0gzKshwYaN+33XKtYS8l/lspj9gf1LVUCFlJ4HgYQJhdjDZ/Wzr3g2AByM9VRt9XawP7UPuZVSQG2P55t5fdhduQMAMLLkTm4F1AI0zxiCLzdJNpvN4N6HdwAA4PJjG4HSsq0VDBEIkDuc4WrIkkHvUGyFiTI0V7nCBZZWQI16+nnZsTkY+rnTO719wobPjtEwz1jmXp+GHM4Rgm9jNK9jJ2MurUmmnH7VLlALhUqy1xiQnCMrPDUHAWOpogiDx6IvGqGB62PIFS0EHwcAU+CcqlvEvDoSAKwooKbCGAHMc0UBLJQcEYA1VUdIIQilcG1qCD5KuICsgKIh+ESiKib8l+AvBeG9kHbkXvuaI9A0VQmde9Pu4qGjyc7SjySCFrcJ8pw6qiBl7p0xQte0HFCcWs/LFxSjgBL2h9Nf85pRyzjrhnx2szmgiFKRDUs3mxO3AAsFVAyh3EYqoDjlEUeUS8SraReW9eO+2td7fhncBquuCxBmNgQfQ+apc6WRilTVRtrg/ODer3bBW4oCKhQuCr8fCq/UHnB1ngYqS30LqQL2Q4UiKTwwMG76KoHKnhJuH88Rj4BixlTy7zAQUCmEGSVstPVXWk0UtSbMVyOy9yUF1H6I8bT1NIMZSxBqJFJoXPZzLxnjzjpMbnJnhXSWHYT+VCtndXYLms0XkpHd3ZtM4fwC/Dy20T13EQDKX5RbAWWIiAyA6siSJnk/7B/e2YOdBTBtwPkudnSRP+r8eFJEBWXWTg5selAgfBg242t/UqesnzhcYr4cUGW+jJ7dne/1PnnKAAAuX6zxex/e7u0TtrFD5h1sBVRfggxgud9z5tIyHF4loKp1MwqUMutIIj7YcFQE3MRKCluXeUFBZwncxKC2AuxaIBqVo8CoUx8Bz239kYC14wdWKmgKqA2fhOGItSFSQLkNzf/hlCCY+BFD9THKCodc4ULwMXWxpIapKwawjlXSKWo1Loye4y/5LODWLvWX+sGSdAsCam8b1RVDQHFEDdMv+3rZpB+uDvdLWM+Sqs3xRSErOIKPI5TZupg1TUMX0rmKyoPEzD2rgFp8xg5GxF+JMGs6KqAEMk9TicUooCRC3O5TZm2FzmhKDnHhLb37hHOFlnNyyzF1SurWatUWphJQCU/nN03jAJmhew+zAqptWnbcDkMeHgOa4/HHADtnXi6wfTxHYhQr2N8URdFBMCfEW4CwdHImBQio0n3Ha0JaH9zaA5DX336Eg9QUUACuT6ZsLwXUAfhMTgnBF8ztl3CuH4S+V0uzg/2pfcitLfBEP87h0heUHloC6gAroBY/0nKGvAIAeOjcHBTaHA1gY9RdSXZic2SB49zhzQAyK6AKKUyMmXrbnjLgUqErjeEx7StZLq2Aeuj8fJ0e7xEmEgDgoq35U/Gnt/MRJwBEAZVJ+ZabzMuVowpgSfrnJKBMTrJKQFXrZMFQSRJoCT4Y65STwuZRAFgJW+WEdSP3GsNArNPmwjdD4HC5oky7uF807JpH1CgElOUqFBJiuMEAxQxRM0AKKJZc0cKwoXKesgKrrhC5YtoYkM8KJ/Sbon5rGTKIA6xZBRQTTowj6SixJCqgBPJtyq1dTL5pOc0YQmdvd1mXSMIgY/1t/b62DLkiEYH4PqpqU9Uy5F6nXVIXJU3wnIoqJmZN03BtDZ1ngTDE5aZkjGiOIuOHzQGFyEHOL9yGp4AyZSSVGBCiChNLgqrPywEl7OcpPrc4f7nvIwES1J7lnL/SWo2cU0qehpS1ByDcz4Vub3nLW+C6666DjY0NuOmmm+ADH/jAfrskWorypQH3t9mhCD2lAKCxIfiwhQiDg5BfyYDmnFqN5kyi79vX+zh3NKcYB4Rjf3H5w0BAYX8nXCQBZF4OKAV/KU5ARex9sz/o+uHCWXKvV2HcHrX+4q8RgAg10l9OMSTZQT0H8djj91PO9aqGeuRZndGCViIE38M78x8168O2d04TAxjvZSagcpImpXx8eAHEH+2pImvbxoL7958pSUD136qGGCpFluQiHg3pOJuVyQO1lylXFcByXnKHiDR22hBQPdepWecPZyag9iy505/MswqozGNpc0BlIKCMAirnOO5lVIxWuxBNABTt69YtNw2oYOwT8Ys1TsFYL/Qb8iMEbnJAKQcUY9WVBaLRGTjDT+tzZJDmr2kjQFZw/gEsVBYc6Ez6NVxD/keQK154OYmo4vqOAXZKQGGCRCEfGwxYa6HIKKkIfFgzVhVErjmqKxz2L4akY4gPDyQHfu5NmEIbgm/I+AuySozmdqL+Smoysf/cGNn/0/N6iWHYEIk7JTmgRIKWhmvTSBgga2vKkDAMWcqF4HP6Re5th/5+tkbIoJgQfCpZypG75D5RASWQpTSH1ZTMPfY3qNgi5BCXD471F+8tOqckr5fxQQ1BiHypti/2zne+E26//XZ4/etfD3/xF38BN954I7z4xS+Gz33uc/vmk/YbwCOgFPipgcZ5P/R0ewiYXYWFVE6SNYv/UZMA6YNkXNgwKWeSMY8w2MdzRAopiA2vLS+s2wE3RwEVCsFH8hGpIfgKq+8cBZRwphgfPMIGeFXNfsxXKKeYozpb+Kedk8EQfAcA0k8JEzjwHhhzLUYJZ98/AORbtTTb/9X6CLYSIfjMk/d9w+8BlAvBZ0KcDXsqInAduX2049hTRQYAcNHWHPD568881LsuaksCqn9dpRVQhiwa9HQWA/ElyLLDoirbHk/gg598EADmSrs+tgwdR8NB9TND7vQNvzevo8xY7poQfFkIqPk8FFFA1S9Q1bpYtAIqglxxlAoYjKUgKH6KjwGjaf0cwM7Wx+RUsrlgOAVUs6xuioFt3Ab1V1DLiAAwBr8Hi5BglEhgAOsBCsHnKMBMH8g55xAJDKlBy0lqmYESgs8hHzW1WqoCKgJMx2SQ7QMD6rNEIAPyt8jfqLw6jEoMrwWOSBBJOsHfKSFXJAWU1//AnHLKrpAKD5O4M5oDihJVHGHGzT2jlnFUVsRNVgFF+u6QwqiuqZIDilU0Nov8bKYYo2wCCOTTwiQM6bukauNIYACk3CR7Q1yrwuc/3qf0YQLt/LGkJR1foV9VAXWo7Cd/8ifh277t2+DWW2+FJz3pSfDWt74Vtra24O1vfztbfmdnB06fPu38l9tUkJD72JeKNo0DVD7iFVDMvVZddICJXpsDqkcIvv0k2CiRwiq20No6DKonbJjgoGovrywm10LhH1eYA0oiVYwPlHyWFFC5reu6dULwoXE2CjVtfxz2cxDAnc8URVeQgDrA52Q13ioBVdCM4iSneOfcIifM5lr/J34MubNbTAHVf3lZhUnmsFwGiO+rgAIA2FrMxff95ofg/K7+IZ9qk5xqHasmK6uA6usrJoamBZ60yRkicpm3KP+XnW/5xWU4jb4h+I4VUkDZ/EoZyOblXs+sgNrL56PZ6zn3uQ1dWRVQ1TpZJAHFgvgRagAupB2rKOLATQzES/4JZItVQDEh+KacCkgAYxtEUnnh5bAfWIklkCtGYRETWtDkgJqOl2PpEB2CsonWJ4XgE0MLMsB2jAIKz5WmmIgmoCjhgP0148EQNdx6UPNOKXPqkDJYAaWoxKaBfgEQkg77S4mlgAKKA/85MiiKNKFzhYhjqoDy+o5JpBj1G0MscQooVtlF5t7Z80iNY5WPTA4oTwW18G246ZaLUUCx50/jDVNYASWE4KMKKC6nnX0d8NeqkxjFlkYsaWFSpRxQHqHF7K1q+2K7u7tw1113wS233GKvtW0Lt9xyC7zvfe9j73nTm94EJ06csP9dc801+R1T+adGfU0Ng63BHFAHQSWkuKiqo6Bl77WKiAMAKkvGheCzfZ3xRMVBygEVE6YNg+ShMHYHzTiCQ7IUdVeIzOprMQooTn0HEM4rlMu6rlvjL1WZGbKQ7g+sEgoRNvtxDnqEWQj3RcOW1J+DewxW62gH4FP7kWsGnM4Jou/u5QsnZRQBucPb5QT4R4XCctkQfBkUUBgv//2/ubt3fdiMei4HOD0slGPH2CRbCL7l/UUVUDkUeoVUO/ef2YE//djn7etsCqjsBNS83znURaXGMqePJuzpzl6+PZTzvKx2AVqyAkoDYznigICWNPSbk18kQFaIIfg4EsnkgMK5YID0gQP/W91fMVwbLQd+OauwCJBeAEsCajImqgdCLEnEhxTaiw0ThvrlkRWYzIoIL+fl9RKAbZVU5MB0Bqx38nrhc58hV6ix/mp+YEUR+TyVQvBJYL2UJwuHXTPl2BxQZA2yJN2UHycAQmgIqp2mVRRQkYSyVI4lzFD4PmO2X+Df66mpyDUtB9Sy864vogIKr3tt32tEjSHHYhVQOHQoM19RqjZSzp7lpC72XMUkKNOGV44ooIIPNtTvK/tl9913H0wmEzh16pRz/dSpU3D33fxv0DvuuAMeeugh+98nP/nJ7H6lqIBS8iIdhqfftfBXGukiKaBsCL4DDNNxoLnNASXkETpIOaAo0cLmgDrECijcv1QFVOkwe5pF5YAyBBRZTyEFXi7rTECZ/SGo70JnhWb7QkCB7G+ofKg/SSH4DsBnQLU0O7ifbI8AM3hnTkB11z7NnwFMNQRUZsA3a4izQmECc4YyxATjd//6B3vX59RdQK2TMyQktlzzju8vEdouF1EGsFyfuQnS73nnB53X/RVQ8/sfzhg6DgAroHKoHRdkc2aC1OaA6pkzDwCHLc3n4zTjeVntQrRIAoojPlQ1EiI6HLCUgJgx+Z00pRAGsadMm1YBxbSJ+yWG+SNEjaaACgHARmHBAfG0XybP0HRCVCoSEC/kgJqSM1tUQAlkBadq48qJiiIJsGau4bq8fjLzzKnO2LXFPCnJhXDjyAVn7hd/UgWUpEyTQHcxTxZZW6ICSgrBJ5BBXg6oABlk+y6Qtg5RRUgUaa9yCrYUBZTxl5J03F6bTUkOJfokLONv03TLAeWMJUOCWt9wPxX1mzGs3GTXdAYFlJYDyiFLcZtCuRlVQEmfI/YCVDs8tr6+DsePH3f+y20aCCgBxVLZFOD1IICPWn/ok/74NSXbjO1Odu37JXzKYRxoHpwrQbGyH0ZJmZBia2+W9/fzKi1EnlEF1H7muOIITWoxhE1R69iMWf+7UzdnvJTzzcmZFApZtw/nYOp+jiEXufcPhMq1WlarM1rQTAi+aUYQ3QCf6xnAVBuCL+PT/ACZFVALH3OrdpYKqH7APkC5nEq47pwKqFIh+LIRUOgDqsTYmj10kHNAvefv73NeH+kZctOEmsyvgFoQUMODO5Y5c0AZEivnmWlVjvWJ4mpdzCN9BMLA4toCYA0A8QooY4qiCF9TSSoM4qJyBnQ3BAwlXLx7JeUGR3ox/saE7FIVUKR+DADT8FYA4ITl89pkAHt6n0euKAooU0wlHwVFkUgqBogaDiSn9wZVbZoCiiMhAgookdDh/ABmLDGhyhBLXjmsCmJICENqTNE6YhVbTF4vby/QOSX7wMnFhfdzK/fLU0DRuW/ce+nDTQ6pIcwpF4IPgKxVPL50r6HxHeEQfC1z5gHoayQi/KAHzNF+GeUmzl2H6uLIVwD+HKTlrAIq5lyV5pTWx7RpyjqvI3NWVStul156KQwGA7jnnnuc6/fccw9cccUV++SVTj6kECmUgAqpgA5CmDrNBw8kJ+ourn9nxmfYe5N8KgxI2zCBeK6QAoUldBIVEyVtSnAlTvVzkBVoKWbINrqejo6OAoBLvknqtVUZt56oGfXdfhGaXdeFuW+PPFhm86kpZ0UKYbMqSyUAu57rwXNi/z8CqiXaI+NkPaBWQnFiQ/Ad0Kf5AQAmBQD+cWZQ2gDxORRQJYHjnDmgNkeL/DXjMvF7cxFQbdvY37YlcittL/pvxqOPLVU7edcnHcO+P7BsCL7sCqhFeLss+d7KjGXOPFUlCKilAipbldUuKJOAUvO6ccuxBAaqyyMOaJ0UJI8IwefKZZgyDSmH/djzr3G5TzhlEG0vlFeHVUCh7wgmdFuMWgbns3EIFwGw9sZbAIY5hYoaWjBAHFD/Q3mAuNw9KqjPEJmWVIzN68WpLxjyjVV/MeU8BdQQzSmeBwGExyoYjxDB/hKFTkj9RhVQmlpGU5gt3OJVg3ROiV/SfuYIHafNmb9WOfJxStRftF/2XrzvG74MVesZ1SFXzpga+k4g3QHcEHzanFrlJiLOYxRQzn4OhOCjZ15QAYVJygCpWBVQB97W1tbgmc98Jtx555322nQ6hTvvvBNuvvnmffMrmwKKPNBxEENPUesafrBtWp2467HPVkXMcUoFicCgPu0rAQVT9TVAvzEs1beQEoYziYDicibNZvurgIpRP5r1RcdiVWdB13URuk9TSx5EBZRGmHHmkEoJ5/phUMFWS7P9/9R+BNugwBP9u1nDSZUBfHMqoIaF8lQZID5HDqgf+5dP6V2HZIa8zEHmbS36enanLAGVZd5N/rQCIZfP7s77v7XWf+6Hg/x7HMAlRr/h6Vdnqy83AbWXUV1Uaizzhi1dEOIZz6M9S0DVj+NqHcwjCATVhgc0CioYjySgQDQB9SXFBACw+UVY0gsTKYu6qDJIUkp5ZBAHnHOgPgNEG5MAYPM3R+h4ADAHWHPEASXVSB+oAspRhqDrWm4ZjwxiVCUgzKlIcM6Ya+i1poCy4dXQfWq4Nk0tEiDfOJIOExW2/ghS0em7pDwCtHZROY5g8PYa8Vea02hiCRM6DMFnXkv98ohcMy/CXvPylSn71Fun5FpyTjfiC0f2Avj73tmTQvhBfJ+naiNrlc4pVUCJc4rHUdhb9izHexUCZ+8M4s5G2kaAgDoAqpML2W6//XZ429veBu94xzvgIx/5CLzmNa+Bs2fPwq233rpvPqkEFFUqKPCTp4AKgMq9SJqEdtR6EhRQMSHGsvi0IlBWVEBxOaAoAbWP5wgNS8eFQetC9hgr1bcua8Kovei+YwmofQ7BF6N84dR39N6S1jcHlPi+ougKzvs+bCVv/DOeZyk5oKodPuuPwFYTzZAG04wKqB2jgMoAphpAtlQIvhyAaqmwcSYE35EMBNRTH3USvui6i+EDH/9877qo5cynZcK4ndstE8d4ZwHKD3OQEW0L48kk+9oEADi3Y+Y+pwIqr5+Pu/wo/NnHHwAAgDd+Q3+C0xCtD2+Pe9eFbdfOef/1ac6L3Hvd5oDKGYIvIwFlSeb6/apaJ5MIAvJaUzvZSw34OYnQNVsPA5KHVDW2fuqvQLaouYwY4sdRL5D6Y5VCDgmzMKyA8tRkWr+wWkZSZwEKw0YUUNYVqoBiwvlpKiDsmxNWkZIQzJyyADWXu4cZS6uUYoiEGQHrvX7R8eVIA0Z5w65nVJdVQFECaiisQWk9z9y510g6y1VwBCpVBdF+CYotVmFGiRqy1iSiStrP3hpRyFNjNF9ZaJ9Sc0LwCQQ49V/sF+MvgEDmReznoKKI7L9JKAeUsk+1PpnrtJymgJIIZY48te8HPlcqILSv9vKXvxzuvfdeeN3rXgd33303PO1pT4N3vetdcOrUqf12jTUPQAwsnxRiqC9xZIiSPiAnBvbbpnUAfUpiUIVHCnGXYisjoJCPeBxiQvDtp3qNEmQ4r5CUkyfF6DrIZV18MvmrBu0AsNALE4bGZjP0vWUfLEb5IuZMWpGuouu+DJ5lyv6g9w6agZPH7CCE4EvpX8q5nppbrtrBt/07+S8AM2HTsiqgMj7NPzSAb2bFQVYljAkTmNnH7T0Thi3PFvi5Vzzd/p1zvnMSUEbxYxRAue38gtjqm68IYEkOnRvnJ8uyKqAKEaRXnZznMvj2FzwGNjKECjR1jCezzDnpFiH4spxH+c9LgLw+rg3m47hTJARf/QJVrYNJT7B7r5lyGikl5YByQFECxkrkirOlOX8ZQJVVCDCAsjGREBD8VUPwRSoQYgFgDDrHEDocwWCMCzU4s/8n9IsSgRpZQUKMSUB0SCmkrSMuDBubA0ogC+l4a6HqnH4JOZXwvDhzL+WAoqHqpDkVFFBW2aWE4HNCNzI5oFg1IJ5Tgahh97PQL0pWeEQNuHXRB3G4NtUQfFxIRrpWKfGKrktrWl2rwtyL656Sj8RsCD6TA2ro1hWzVrWzDLcbRYCTNj1Ci97XoPdwvxhyt9q+2m233Qaf+MQnYGdnB97//vfDTTfdtL8OKUsi5Un55LwimUiaXiAmunXUug8McMAxbr9UCL6V5cMRQHKO2NuvnD2cTch3K0Nq4Pk5iAq0LvVOpnwIPtPXg6SActaTkDNMJKD2cT3FWGo4UY2wWSMPUpVSZan3pioa0dspytaU3FLVDocd7J16yK21Cij/SYuuNs4Ygm9t2Dh15jIDxg8yPNJvwwRm93HxRSNT4pUtpKTKOZ4GjM+RZ8qSOpnDsBkzof22MqjKLFlWIFygIcq2MhBlg0IkrtlDV53YDJSMM6xQGmdUa+1lzK9USk1mSfthvvOohuCrdnAskoDSlEe4LEtWNO59rKKIqy8mBB/yzQFUE5U8UwbE1/z1SAhaDvxytH+qYoLJAcX94LKANck9RPtl28TKNIZcYUML0ro0EkLJGYP7ECSgSC4cp02GLEvJASWSKwxRZYcIh+CjOaCEcG2UzItRlTh+EDLP8QWYNY5JCI0wA+DHiJAVnMKMEkstJkWV/cwqikgZqoCaMmt1Rsg3bM56wOsGl8Xrw/4f4y9uE+9nOqeIINLUnDikJkcq2nbJ/nDGF/xQfbhfnr8cqWbaDfg7MGtmBiJZKj2EACDv+2rVBEsKwaespxApk9Ju8F5BvZNqWtg2LU8KDTeo+Zdq+5EDCrdpSAwMlB8EgsCMt6eAgqUCyliqv33ujbUu82pzQMWE4MuAV+YiBGhfzVqy5GYqAZLJus5tKpmOXwfPlY5j3lfll+JDCqmUEqq02uGz/f8keATbAB0cuZ7qN2BqDgLKKKDGmcOcTRYAcp5cQAuAP7PCxMzHKJPqAAPwOcNzmfCNOdQRm6UVUGOjLOpP7GwVDBdYQgGVX7WTL7QdgLsXS+SkO9gKqHw+2hB8RRRQ2aqsdiGZhx9GKqA40JJTKkBDQFtKfEhKEFLO1M89ce+RGrRN4i8b2is2B5QU1gwrIQQSwpJ5DAlD+4XzAHGqJdwugKt0UhVQjDoCk0ZciDHrr6Iq4ZQ3LBCP1T2kD9j/oJKO3Ocob8w1LV9OTDm83vAYUQJq6BMkth3aLrhKIZZ8RGQemwNKym1F1ipVSnF+xYTWs74w/jYDvl9afZKayssBxanfJKIUSAg+Yd145KNG1nD7mSEfYxRFOCedOvcZ1qqmasP/Rp0rNERlgICSFFCh19UueMtGQDWrywGFAfk+4LWmVNBet03r+e8ocBJhuv3ImyLNlSEJMAGVmjOmhFnVD7i/43AIPmOpayJXTrHYNmLNElCtvjYBXAVUZ6IllyqR9NUSUMa/fQrp2HWf0XEZNi4ORUkmTY3nqcM6jnmu8xOgXFi9YNlKUB06q98gC1qLQd9MCqi/vfthAFjmdOljo0Lh7fYyhpQyAHxO1QbAMixXjnxFAAAj9KGek9DLOZYmNN75Qjmgzu7kUxaZOkoooA5DDqi9jGEs5/Wg9ZmRzN3LGN5uOZZlCKgcpL05d//pvrOwPc6zNif2R0/9AlWtg3FP0nPvs0+0c2SQATeFHCwSoBoVAosBbKXQUyEygfqrKUEwUePkakHlhut+nwCI+oQAwFoYNk4B1TKAb1ABpeSA4og1Vo1EAWtKBlGiRhpLXL8QptC8tv3CShapLqFfmqqLVZUo6xmrZQZUUSSQFRxBC+CC+qwKhssXJK1V6oe5FSugCGkCXF20PjLHU0EBRVVXdn+AXy6ogKIElEIUcwooTk1FyVJnrGk4vJC/ADDacNtsMQGH/VfmVFVAMao5hzBT1uqy0LL+GAVUw9TXIgWUqhKTPj8C/arfV6oRSwklh19fsnGJ956jTCJr9HlXPy+63ZBp7XStxyPbNJCWOUI2hsszKpkASQBtcxlVdBkzBM/6YJ0tC7BPiijzdYiEcjb+OmRiKgG4gjHvpViJJAwsGdcRJs6lJvTCzrVzAmrfc0BBt31G/cN7natLGwtKJnYmYXos2ZAC7QWPegFpCp23ZCwuWr/IeV1zQD2yrRJQBQ2TBrnw6fd/7H4AAHjREy7vXVeJcFIAeXNAGR9zqyIMYZAjdBjAnGw0/c0J8NscUBkO15I5oPYmU5sX50gGZdGRBdB/0BVQpdanVUBlCsuG13nOcJbjjCH4TF9L5YBay0CS3XjNSbjkyBqc253Ahz/9UO/6ANB5meksqnahGQdkUoBZKMcBux4Yy4CWxhwVDGnXURYouVWgQdwHBrYVBZRTFwVjCTnhETWY5EKfAcMNvy4A1w8NAKb9wgCwSuhQBRQBgKNyQGGQmRmjoAIKg/X2/9zruCxAmKjx2mTmD3++sf1SQvBhs21o6hOsfnNj57MqGLZdLl+XQqzhsWTXKjduzFr1QvDRuQd/ruhrjSh2FFD2DaWcMKfiWsWEmRSCDviQjADuOqHKvyBZA+58DUlYYy4EX1YFFCG6Y0jVWAWUmoMOhQzslANKWNPY32rVkCXldULr68mXPplWpNZLVQO51Ba5zAuVpahOuBB8G4MNtmzIaF2rAuNxOwN0/nEkgbYOVmXSuFh/0XzRuUup+yDlgDIWkzNpNlsqoDrPTyZSg7Zv1henVuvbbpLhnzoJ+4z2B5Oz82rjCSh6zuTaSylnTigEnxp+lCGvrj9xPVtXVTg98qwSUAXNCcGXQQG1PZ7AZx7aBgCAp11zsnd9VgGVOQRfzpwmNkxg5hB8Nk9VJoUJABrPnPlhJvnA6ZI5oB7eXtZ5JIM6zyhNTp8f966L2rlDlAMqFynRNE0RhZFRTx5kBdRuRjJvNGjhC68+AQAAH7v3bO/6APLmeat2AZq3bhgCBkAnCbhyDngcG4KPAsAMUcOqVBhSgw0lx+wRTlUiqXtmxF+HgFp3+2X9YEBtCqazCigDWBPfJP+npAxVLRkTc0CZ8HJKbispBxRVl0mqEuyfQz4ySp6GKrsYAoolLziygvGBzR/FkW8MWeERUIiEoEo01l8cVnHgt+uQFaguT4nWNQcUQ1aocwryWnVUeYTIlMiKUE432ybjmzhX4I6HRJZK654jVTlCmSqgGoEoF+eUKqBoH+h9Q3eMNKViaHw9AhwTYcQPhwCH5X2aSg7XLeY/M8Xq95VqrnXN2+QBkZRIocArWYt9QP9cCqgUoDhEwjgKqIT+0NCF+5EPB8+FIQm0kIL7ASxL82wIKOwvnbuUuleVAyplDLUQb8awMqxzXqEe8LK690n4xMOmgPLIZqKA0gjC0DnSJy9VV+I6pKgb0u+uuN2Ec/8g5I6rltfqjBY0jFvkeKp/Z7z8UMgB8hcPeZVFFbHwMbNKaxniLN8WGBZQlBmlVg71hlH8nBtPbN6ZXHbfmR0AADixOcoS6uyyY+uLend710XtnFVA9SegSuUtWir08q3PIgTUnslV1d/PteHcv5z5lQCWZ0cuMs+EstzZyxSCL2OYzWoXolGQnFyjhAYuyCkGvJBwCrDtKG8oWMqpSiQSgpJIlPRi6qd9ldQADpBO/MVA1nCdqQsgTgHF9QuTEEj14BFVRqlAgXlKci1MUkCJoc0Ygs8jlhCAPaOgvkSsCSQdvS86BxSjPlGJJUxA7fHlPAWUROgM/DEy7WBzSEWcn0khqrgx13JbccqdmBxQnKIRv5byBTnqt8iQdlNl3WNL6TutTwwBSteuFF4O9wvNKXni2Jk/5wyhvnVUQNH1Ieb1YkhFrk/muuevsk9j5hT7gdtavhF4Xa2abOqT/QFSIhS2jYZqSiJtMimGtKf1tb6HFFDJJBLe3isioCRVA6eAyhWCL1e4RGxnx2e9uvsQmr1yiin9CxGYofxqobIz9ADWfuSA0sbfvGfIzf0K6dh1jVD/8F7n6tII8pD6KNZoyNOUakJkKCbNQkTX/dv3dx7XqpA6fFYJqIKGFVA5AP9dRGzkCG9nAM9ppvxUxs6YXEAZSDIDbOdXmOQNwQewJIlyqrV2CyigZjOA7UzgubF7H54TUIY46muXHl136s1pJq9UVhI3s0LP5ijLqdArQObmDMFXKuyi6W4ugseQgruZ5tycvznnutoFZKwqiAEtOZDcUwqh8mLoNAy6KrlwJGKJAzI5QJUjUrR7McCu/ZhyFFCojfv/gQesMVlhAWBKVnAAsBSCjxgl/CwATPpl/RdyQEkh+GLIIKpCE9VksOxnbAg+iUgAEEKuIXJJBOtbcu+eUA6vLRyCj+ZUmvFrUFJAeWNEy3HgPyZkFQUU9kMizPB6k8I7UnLEITVon5gxov1n9zO3PpBNNVUbdx+3boRzivWXnnvMnA5pDiikUIoilhJzQFHSWQrBx5GK0kMCLAGunD/iQwLM/NEzjvPP1FWtGjINBNRyhXCAupRXCEAPweeBqSGfMxEG+F5PZaIptprG6/86ykfZJwTfqkBZSalgiAwNVO4Dmne10Jim5J7RFFK9fFTgWY+UoSoU5d6QigZgSe4AdN8TffquKaAsAWXWFiWvV0W6QrdzwwvBN1xX39fUeLnCWXrKyR6EWogQTDlv+xDB1Q6+VQKqoGHAM0cIvqWyqM1yyA4KqTfOZQT4S6iK5vUZYiffFigTgi+fEmZjuPzwMiRMLntwESrvoi0K7HSzi46sLeotoYDKF4LPKvRyJXlb2CRjaDtjwwIEqSGGRxmUhEahN57MsqqgpjYMRF4CKtc+36sh+Kr1MoWUAUCAIgVKpXIGAEZAMReGDkBXy3jKI9Qu9YMFVBVSI3Svi7CjMlQBhb6j3P8PqF8SAUXIIS8HFjKrbJrK5Ji5BqAooKTzmvTLzgMFttHfM4mkw+TFzO2/pJhQCShEEBkigYLp9D4H/KdjwvTJUfcYAkrJlYSB+JZ8T5lNl+W0NYhBfSeMHremF+VmgmLNXhP8xeQVJczYeWDICmd8pfxfVP2lkRWBNqlx620qEDC2TUJAYULS84uoe0L+Ng3AkMn/xRFV3txEKqC8NUOIQZGA40gqgYBqSDmNAI8l6agfbD8Ef6tVC5hGPIRCMXmgJlmLFDxNAlAJGdTVVJWPApJzvmKCLVUFoIH3pUyaKzYEXy7QPBPBwRlVbah1KYB7LkIz9F7otfYeG4IPlt/t9kMBpe0PG4KPydeF3y9tXUN3hsj0kLoTm5cDqgeZ2/XcCKmwcAi+mNCR0riuKv9VtdVZJaAK2vwpnvnfORRQOdUGAFgB5T7x0NfOLgD+IxkAfgNs51aYTGwIvnyH1siEECuiMOm/VVvU17+/5+He9WEzhMH6MM+H//oijF/ucGyT6Qze+4/3A8CS8OhjpcNY5gobB7A8N3KSZWZ+zNrvY/i8OJsxT9le5hB3uUMFTmsIvmp9jCWWlKfml2+QcgR4dHLGkHKs8oaAsRh41YBSDIirOaAY8gYaBMZKADsFrDFphNrY20H+4vBykSH4PACYUcFw4dowUYXrpooRapTgs20wQDElFT2AHY2RQ9LRMWdAcgCfTHDmVAnB55BoyDdKXnH3OSH4BKWU16+FeQoo8MkKaa2aMjYEn0YqkrmnbWihCzHJxeb1WthUWPeUiJ4K/fLWkaDuYRVQDAGDzVH1kXnmQvDhecU50TwFFG5TIldwm7EKKMZfXA7A3R8a8WP9kBRQHMFO1yod38b9N1YBJark6NiC/zmAy3LlqlVbWBJoHgA8U0LjOfc37r0hcwDPTCH4QkqFEHDcOffJPm1JKU+NITJUtVtX0LwQuQMQDpHolFXUR7nWU+g9LewZtZi8QTgHVNc+lFJAmbmzBNQ+KaCcNlMI78D4a4RmyrmRbPinW496VAItguiSyOxQWNaqkDp8Vgmowmaeus+pgBplyLED4CoCcqqgbAi+DAD/sABoDgAwXtSXlYAyyoiMpIlRq+QMFQgA8Ir/+P6s9Zl8ODnyPwEsCaidzATUb971Sft3DoLUrM/cKsJdpHbMZSXCBebMVTUctHbez2QkoHITPGuZFVDms6GtBFS1TsatGwocAw+SRymgGLICEzXa0/UYiLd1cSQSB6gquYzoddyGud/plymDfWldsHhvmycrOAKKVYJIBNRUBp3xNS/fUeNe9+4j82BJIy2sm0TSofYwCcEB1ricsdEm02Ygl5jnG6P6EEPrte61yXhRLqSAWhhHQHmgPkMsYRLCUfIIc+8opZAvU0nZhdeqktcLkyiiAoj4xIZuNP3EBLCUU4nZp5YsFT7/OZWc1HfappgDCtdPFZj0/CH7maoeAQgBpe1nTCom5oDiCCiOfKRnGSXCtPNHI8Bj8mQtL7pt2ctcSNRq1ZaWBJorxE/TNM7yCpEWHlmVsDSzheATgFMAX6Xh5axSQOdkPwLqqhImzaUhCVIIkeg2e/QtJQRf27RqeRpe8SDkgNLajQnBF1uXZrlIDIm8NQ/Ne4THimDtziH4AqSRlg/PU0CxD/GkW851qqm0aHjUpAcP6NebSl8ceqszWNjajGHuDBifC5RuM4cINLYMwZdBAWVD8OXOAZU/BF+JHFAl/Cxhu5nXpqkntwLqL//5Qft3jhxlg0IKPTuemQg9AICh8TUjWbYkSPP4eXQxJ2cz5oEyZ28ugmeZAypTCL4C+b6qXUDGEjoEZJTu84glVJ9EHFDQVUxwzyg+cDvG2BBYIKg+KInW+P5S0sQBk5UQfF9wi98n0w/HBwAXsAdmDrD/Mxn8Z+uiILMQsktSrrB5kCJIRQzWa6QGR9KNthjfQm0KvgGAR155Y2b8MHMvheDjSBMAGJAwbLPZcsnEqNrwnFKCAfsv5oCSVDB0ToUcUKMtgTSJIThJv5z9l5oDqlmWxTZY5DXg1r22Z3AfcAg+Z00r+5kj1E2/uD55/cLfjYQ5DeaAChA3kqqNI+K5sxf7piqg8FoVSFVWASX1g9m71aohU5UiypPxXEi9UIg+csEp2zV5fS7ix8sBpYSL4hQ2GFjukwNqVSH4xDCGM/9ajAInxnqB5gHo05mvJm1d5yIAU9SEKWOaOv4HjSA0fTc5oHKStynmnF8JUHpKjiT6vpf7LkBmpVipEHypYQKlkIPaGUnLVjscdrBR7UeAGZVRDgFPbrAXA545BUZGDbMx6k9AGYA/t8LE5FbKCfqWyFdlVW8HHJw25Oj6KBMBZULwZc79NUUAQxYFVKE8aiUIKBuCr0iIyDzrs0ToRUOu59rrRoE63ssz57lzVFW70EwhZQB8QgNf58BNS4gY8Bh8ENUxRYHgEUuxJAQHpgvXQmCsA4gQgB0DXy/6QQJs437Qv7l+UQAYhdZz8jMJQLFHthByhYYOc/pOiA6xnBDWzHlNVSXYX6YugDmh44A27XJscRg9Vm2hzJ8Wgg/AD9XHgfp2HBHBRXNA4TacNajMqZbXywH/GTWZSCqiNe4ooIi/a1uoLmGuPLJCC1GJSFA1XBssy+G6aT/MWp0y+15StRmzSiMpX5mw7j2yhiG2TT0nr0XtMQoojix1FI3LJkVCmd5H50tT6wVzQJE55YgwMQeUcDYuLwb6QcpVq2ZMWRJqDij6HlEF0fdn4H73dggQhtDRLFeie021EVQ5KKBzMgGVSYGTYpK/k8X5pOWA6mqlyB0AZk0oxdUQfCF1UQKxpd2XQkSsioDK9fFA2zckzH6H4HPGPKHJIHlI+uPsnUC+q1w5oLoS+ACMYq11yVzn3sBcaWdZaA9UO/h2aAiot7zlLXDdddfBxsYG3HTTTfCBD3xgv12KsqHNEdMfUDVgby5QelBIATXO+ES/qSMnqQOwVIHkzbGzAKZzAvwZQ5yVtNwh49YK5YDCPFEOgnQZIrJMCL71nAqoAgo9m6vKyzvSzUr4aMjBXASPVedNhNBYiZZboVXtAjMNRMTvhwgdC24SoJgthwFgAdzEypCppgDCyhAhVJR4L7omAdaOX1QxgT4D1o4t/8agthaCT1RdAfAqGIassOUIAWXbWNw7XCf3tXy/2NxZAXLFAfUVEoLLY2Tuxyooh1QU1hEtp+aA4kLVoetSDihJAaXtGS0PkDOnOJScQELQXFHGpNB6lISYCWqZ0abfL+qvt5+luccKrlC4NkoOSQTUmu8bHhPTLjVu3Wh+wAyRQVw5OveL19d9ybJcOwRnzE05kXwM5YCS1ippI0qtR/vULK9bX8i9tN3OOaCk+qgv1arNTQ3Bl0CyaOH6Gmh8Aoo8Nd8VhM6Vt8brqwKSc4otGrYq2oeQUqyQSXNp5snpD/Wx61z1OH+SVBgBQlPrT0o7KT6GxjCFgNLCI/axXAShRJiZtZVLUZdsDv/UnbDR1JH0dYhs67OXuir3UhRQXgg+hoKQ3veI3lXNc7Vidihm8J3vfCfcfvvt8PrXvx7+4i/+Am688UZ48YtfDJ/73Of227WgDRYA9TQDwWNyC+VSG7Roo08OaF4YU0dugN/Ul5PYGZUA+PcOWQi+TIRJqRB8WKm0mVUBldfPnbEh9PJ9OcxJhhsz6zPXvA8LqLRyEzxrmRVQxr8agq9aN6PrRiCW/MfPyDUDPDKKHE8xEQtYCwAwNjYEH0dWMGApBoClfCgqYYbq4/yQFAjGtNB6Yh4g4MtJOaBMG1QB1eAyVGmDy2FgW1DBYMDdU5UIY+moxBo3D1TTLteRpiox16VyGrGE+zqVckAxxAe+X/ODJSEwsYRIJAmsl8hHRxFH7sN7SwqriMk+cc8QIiGGhMBqPW3up2Q/SwooTCo2lFTkvjMwc4/XEvXLyVlF1rSkKAJwyUCOVFXJR6xo5IgfYa3S/Syt6Xmh5S2qAko680hYRS1MobcXhDllz95q1ZbWlQDhnmaXnshvm9bmf+nSrqrE6kNqoHu9EHxkr4cIpl4KqEz9STEJvJ5MGQWUohhalYXGlBJmSSH4EsY/hWRy2lTyBHHvO++tSAHVKweUFNIRlnNj1laIAClluULWafnhAHT1YIoCamu4Jb7X59xIyQEVutf4wr2/X6EWq5WzQzGDP/mTPwnf9m3fBrfeeis86UlPgre+9a2wtbUFb3/72/fbtaAtQd/+YOXuJB+xA1BOAbXMW9T/g8DUMZnOvC+dfWxcIATfWgEFlCEL1jKRjr9467Pt3//P392bpU4AlJ8sFwG1qGcnIwG1uzeF/+svPw0AANddsgWXHl0P3BG2QYG8SgBIAZUppCFAGbWWUehlC29XYDxtiLtMQznKHGrTnL1VAVWtk7GqIPy6FcpRwJYAj5iscH4kYdJnpoCg+DUhKzw/SDkO2KX103JSSDSVMBPKOSA8p4DiQHJywDgh+LDiRQK2cTgtVMy0EVJASWQFR9KxxBo3RoSsoEQVNo+AMmOkKaBwOSVcmwR+h0LwUfKR3s/VyYXMM4bzeqnqt1YvZ9cDB/Ize8sjoDb9ujiiGPsSM/eUrPCIKoHQofW1CvmoEVANQ1R5hDp67RDKjB8SAe4Qz7EKKCkHFAjliE8hBZREpmsPE0hz6pCilASlY8QQ1k5b5LrnS7Vqc9PCMSWFmWvcumg9HgFFyCoNQKWAaArZs9auie+l5KzyFDbK2KQC6iGFQQnD/uLxNWHSMAGXS7WRK19XqO7QetIInZQQY6F6tfv6KKCCBFTHNZRLASXl/rEh+AIESCnTVFrqfQGyUFN/BkN7Kn5seCG8iV8J7Wjvef1ryV4KEF2xRNh+zXu1fHbgv0Hu7u7CXXfdBbfcskwQ3bYt3HLLLfC+972PvWdnZwdOnz7t/LdfZkievQyqmNwqE4x35sxhkzMs1wjVUSIsV67QYQBLYDpn3iIbzjATev6Cx11m/77t1/4iS50AAL/313cDQD5ydH04/5Lx6QfPZ1ub//C5M/bv73jBY7PUWSIH1GQ6s/XlCmkIsFzrOc4iY9nz0hXIo2b6O8i0182c5CJHDe5ac0BV62YMUaOBlmI5AjxyhIApxxEfHrg5QHUFwppREsnxG7fL7eEAGOsRZpJSKEEBxRFVElkxA2BVMLYcJQgoyLwYkwF9YAL3y/4f06/WH19N1eaodjSygpx/lIDicluxoDbpJyYhvDEB97WnqmHIN4+EYOqDmV+O9RerYFIVUJzSRpkrLXTaUAjB56179K8Ugq9F+9RTQAXmniMrBmv+vocG/Jxgwnrgzgy2X+DOg7RXWX/ROnH6L5BZ+DVVQEmEslcPmYeYEHycYgn3Q1QqEqLbdov4y5GnHEnHvoZq1VTTiJRQCD4JEG2hhSm4nz19QP8UIDmWFNDyuHg+NPrYpACrucidVJP85XJApeQr0ixXvi7OqAJKK6+pUELkjQrsK/eGxjCl3mCeqo5rqM/a084Nq4BarK39UsZ0zpkUUAhp74fmuU8OKK3ePoo6k7MLADw1YVD9qeRTqwqow28Hfgbvu+8+mEwmcOrUKef6qVOn4O6772bvedOb3gQnTpyw/11zzTWrcJU1A/rmAKgfPD8PM3J8g0mg3MGaprEEWY4QgcaW4e36f/nBKqqcocNyqrSMlcgBtTOef8hmIx0R6zjIpLg4vzuBj917FgAA/uYzecjeteHSt//yV5/OVCciMzMRRmb97O5NYZqpThx2MNe8Ayz3Y87wdqauUbYQfPlJMqMwykXwHN+cn78PLc7jvmZzVFUFVLUuxq5rBbTE97HkigFaNQAYEzA4XBRpNwSUAigh+DjCDJhrHKiP/SB1SeoCNr8RAs5NXbazAHLeKdOvRV0zTS0TqZjwFFB4PLACigG2qb/e+OKxoKQiGUvbJFGV4HYxGakpoPA1Tk0m5oAybTL3cmvf8Rf8ucL9kIgaALePtk1G1YbVMtzcSyEZHZJn5rbhrFWs2lGUf/hfaQ02rbuOYsK1eYoiVA4TUBqpyJG21j9wiSonDxtZq1iNRPsv+evUFxmCz+5npU1aN27TU0CR33EsuSuc0SwRiPygpKhG0onkrkC+0n5Vq7awVJIp5j2u7HTm/oZJUf2khK2iFhsWLfR0vhNijKgCAHzQNsVWFYIMm9R3mwOqlUH0XKB5zntTFGhqDqgQGarUnaT+oPtFu5c8iBnKAbUfBKF2Npi1JOaACuz/XPujc8i6QB4n7ezQ1JshP9R5DLifkqssRe3J+ou/xkQ+wMC9X+3g24EnoLrYHXfcAQ899JD975Of/OS++TLIGILv82d3AQDgkiOyDDzVDCibM+TVXkbVDiaIsuZWMqHDShBQGcPGfeqB8wAAcOUJXT7bxb7qqVdmqeefP3/O/v2tX3J9ljofddEyXu3f33NGKRlvmBj86kx9v/ToOgzaBvamM/jcwztZ6tzZW6oAchJQ5izKRb4BLMd0lC0EnzmP8u2hqSV48tR38eL8NedxX9urBFS1PsYSSxwIz5VjlAVWLYOJAxKGzlFp4PsJCMr+KFD8VdUnwrUQeeXco4UYQ32U1DKUHLIEAdMvHK5NU0CxfULXbQg+mgOKkBVYaeMWRHVJobjwOM5kYNshIfA4gj/mHpHAgd0CSeCF1pNAcqqAogA7rh+p6zTiY6ao9TC54PSLzqkhK1C/2sGyOmmuvL2lzRepSyIrWEWRMFcztJ+9uWf2Gjf3gxEqJ6ipvL7C8h6qnPPuxXOqKTDpvsf+YgVUZAg+KQcUd57RfuL+SeEXWTKd+kHGO1oBpSiquM8Px2/aD1KuWrWFpYSS08JseU/KB0BL5/7Gr0/zUfKXvTey3mCoLBqWShm3FMUKJbNWpRIIhTF0CLUEskSzPnlqUpRuqTmg+pBXznsJazhFVRZS3FDrTED1+HzQ9ufqlcgAAQAASURBVDteSwBhQodarj2hnV8p7XukkpIjLYWs8toNKOo0ZVJSCD7igxZ+k/OJnuUp91Y7XHbgZ/DSSy+FwWAA99xzj3P9nnvugSuuuIK9Z319HY4fP+78t1+WM0TXAwvA8+KcBJRRQBXIC5MDlMYh8nKFOZvnk5r/PTrgIfg+9cCc3LnmYjmBYKqZ8HMbQ/3Jl1j7zbuWBO8Lb7g8S50bo4H183//o3/MUqchRq86sQEnt/LsodGghatOzoHBTz5wLlA6zowCqmny5igzBOkkI7mzWywEXwEFVKa9fsmRuRIhFwG1zFFVAZ1qXYyuGwmIZoBRTt3DAtYCWQEArmJCeLpeDcOGQ2BJoDPXLrivxTB/lFwR8rdQQsf6y+SAsuViFVBCeDwARrFECcPFvVQB5YDOiFjjctBwKhhvrvAYScA2M+8WFKckJZ17xjcHdGcIEkkpRMmKyXh5b0vni4L1tB/kmtN3UoYjoDiileZAknxR50ohoDi1nkQos2uVng+Rcy8pinB9XAg+Oi+cv5TM0pRdmDDTzh+OCKP+tsOIsQQQc0B5+17Yz7QNVqmImrD1c6Qa9RddA5if6059wrnaMqQwN0bc6/rEcbWAxT45nwKoswooAp6m5IAqEoIv8HQ+BZX7hN3yfGjiyua0EOhfRAGVQkAF8ux45RXVBjVNldKHDElSnSSA81JIu9jysZZNAUXDWbY6gVaqP1o9SWQb/brgfc6S91t57absJZXsJGdQH2WVFoKP+hh6mIAqRTWfqgLq8NmBJ6DW1tbgmc98Jtx555322nQ6hTvvvBNuvvnmffQszgYZn+g/vT3/wXssUwg+gKV/JcidHAqoQdvY31G5QofhuRhkVECZvEU5wfNzixB8xzdowuLuNsqcZ+dt7/mnLPVQw2Eh7zvTX11kiMFc+bSMHVuf78dzu5NAyTgzfq4N2l5PEVEzZFbO9blnxzSPn4bIyqmAmmRWQB3fnO/Fh7fzhuBr6xPF1boYu24wYNu6/+IyHLkSJIMwsElBclwMg+kIsKZKHi4EHwcAS6SUBx5TgB3QGCkhxpywVZJyg4CzWjgxq5iYySHiTLu0n8Y/bNEKKI64A9dflqSzhWA5RhpRRc5oiXwUw/7RPmDig+aPEn6kewqo1l3TwKwPp4KFzWa+H6y6hVNADRj/WreM9Y2SK1xowQgCiiVLydqSCGWOeHXGSAnBx44RqXMw4ve91ldcBx07SgI7hDLJWUXPPY5YAnCJdykEKDWb061nDigpBN9o0y8TJOk0BRQyMTwpQ55KCiiOQKxWDZlGtGhApQcaNyAqDFrwCaiuipXQk//avRr4y4VlSwGOKejsvJcQYmxVoKxGmNFruXL2JPWNFKUkBjVHtQH6ekpRR6XcGyIMtHpUBVQrE2ac7XeOrtB6SVW35cJUUohr577AXGkhBbXceFzdWr2aXynrKWUthvyn17R1kKrkq3bw7MATUAAAt99+O7ztbW+Dd7zjHfCRj3wEXvOa18DZs2fh1ltv3W/XgpaT4DGEQc6wXOah+8ksDyiNSY1soPTiy1iu0GE4v0xOBdT6aF6XyduUw3bG8/Fcz6RWAkChAjOq3koYnqftDGNqw8VlJB0B8s+7IYjWMhNlJfIrjRFZlsNKkGS5CR5z/uZSOhr/cqrdql1IxgCI9Cl3qVxLgV3wAWtPVYLBTqIocsBSgVgaDAFGSNHrAMXIF41sct9Y+mLvpcA2BvUVwJrz11PUwLL+qaaAwoA1DsPGkTDca1KOzQHFzAP3Y5H1lwDbDTNGHmCtkBCUIKFh9DhSEV+bMqC7mAOKkKVSuDaW4AJ/Dp7wlTxZoalbTJvsnJrxJgSUSOCgMs6conBtrBIGCAnBkaWmnEa8mvsC+yNVATVTFFDeXJm6OFKRrlXsr5LfSCKWnBB8kppM2Kcz+3/gnzUA4n6me5AqoNaPC+NL9x/2RQonSs4jKfxiy5Cn3Jxyr1cEblc7POaBxSTUnGQckKqF4JsB+X2Al36AMMDAZSoZEpuThFNhSMAr529nBRQBePtYEqiO2uT6jlUQ+5EDKpSnhprjY6OX9xQ5CQRg13CQobCMGsHmKYpK5YDqsQ41MtcLwUfXUw/VWdc11ScEXyiHFT2v8PuhnFCx72nnLeeT9p43X62czy5lf+QK3Vnt4Fg+WUVBe/nLXw733nsvvO51r4O7774bnva0p8G73vUuOHXq1H67FjRDwuQkoHIC6LkVUDiXVC5yZzhoYHeSUQGFwO2cOaAMCL+TKQfU3Q9tw/kFqbGekXS0Yc4y5qoqYVgFc2ZnTykZWV/mcHHGzLy/5+/vgy//Qj4saIrZfZ5xzgGWBEdOddE4ewi+BTmaMYzlJHOOJax0nE5n0PasN7d/1S4w876IE5CRAqD4uqqA0oBtRFaIOZUapq6FrR8DGC9CljpElZanhyEwcF+lEHwU1HfUPdRfBgBmgX+OSJDIipkMKDt1kteeAooSUBhgD4Tg44B4aU49UL9xy2Giyt5L+uGQSEpYRRDG3COvBPDbfMfEJJdEluKHrJoW4Hv/GuDTfzFfi495IcCn/nxRlzJXDgGFfaNzatRvaD1jogPn4nLuI3OqKaBCpIm3VpX94ayPiJCaWg6odhQ399zawn5H5YBC/nJ71Rsj4yNeIwO/X05heo9C0NK6AVAfiS+03DoKFR9SQLH7mfqALojrSCFPQwRUBYCqEQuFmnPeU55u156Ub5sWZuSBWS9Uk7I0NRA0KTRb0wDmwbR61NxMjT42qQRUbNmQtdDCFOJ+h/Uh7laRA8q0a4jLYMhATABC6xOepF5suULw9ckBlUT2FjrHc+WACpFt2pnDWUjNQ8+WGB/7kKGh+fDaaQB9Rcu0l8iZqeWs8+4NkPiq4pTxVxrXUOjIGoLv8FlehLOg3XbbbfCJT3wCdnZ24P3vfz/cdNNN++1SlA0WmykHwWMA9JwKKJMXJRsBVUIBlRmUxgB8TtWBVcJkInZe8tN/vKw7owJqrQDIX8Kwf2e2+xNQSwI377Fn6vuVP/0EPHiuf14gkwMqt1KrhALK+Jpvr8/ryeljboIHn785VFBGfdqXyKp2oRq3bhQgGpehYD3+1wEtSX0xCihOWWB8WD9GyjHAtgbOOz5zQDwFbTlQH/lDzamL+exliSqBrJjNAKaLcJ04PJkt54cfIn8s7qV5CxsyD8ZnLtcOJSEoEI3GrKsCiirFKAkTUkBx5WZIZUTvw3111DIBf+dvAJx4FMCTvgbgsf+CjJGmgEL+mzmlbdp2SZucL1oOKAgQUN48MIQq9kVTiXFzzxLZjVsXd7a0Q8a3iP1s2hosQtNNdoW+0/2shIvkiBoAd49w/rKEGc7phvsFpJxA1ND15SmgjgnnCkOAB0l3SiwJqrYkBRTz+VGtGjKVZFKAV+4pegl8bJs2mHcnWgFFiK6Q2kLLTaOFlkoBZb33iU+qYqUBcdxSrWuOJe4+NQdUH9Ac+xBSeCikBjU6/jTkIzZ1LQbGv2vItBDxoCphCJkQzJnUcX5CYxzbZkjdlZNQS/E5JQeUqi4KEWrK2ZZCwoTWkxqCLyGnGH29Plh33gsRdxJRH1zj9evIobNDQ0AdVlvmgOoPqNocNhnDxhksPhcBhUNn5SJ3jPpne5yLgFoC0jmf/jAkUS4C6sFzyxwzhtzKYTbMWeYQfI++eCtcKMFe8uQr7d85FFClQvDh8IAPne+fF6hEqE0AgFEBBdTO3rzvG6M8BKk527IqoBYA1SDTXsfhBnPs9RqCr1ov455g10BLfF1TQGFwE/2IkAmdCPDfvI8JKE6pwALAzDUMkku5qDyiBl8n1eF+effiYnSMWt838z1tNgWYIALKq0sKL0fapfd6ahlBAQVkvkzdHgGFxjGGfNRyQOH5U1UwAknAEUtcW1YpZT53G2Y8CViPr3F1irnPgFdAUbUJ9supnwv1pqnVtL0lkLscyMCSj1pdEWHdPEIHkxqERDPlQiH4TF1mjPd2lnXTUKH2PprTjRJrwvnjEOXDZTft/iB9wn5htSEldAAEBSLq30wKwXcUldHCCiKT1sdwzfeLK6eR69Keo+WqVVuYSgp4X1NkkDmUR+TY2jG3PAE1Y/PnhNQ4w1YO96WFqeJAcRFYZfZpH8VQCtgdW0+wLB1/ci8m8lLCe8W2GarHA9gDY5hEIil19cmvlJJ3KiW0WShkmla+a1jGVNPmMhiCLzBfah+UWzWiK6XN1BxWaq65hLMhRDRq9aSscerTifUT+E3xHOTa0h4OqCH4Dr9VAqqwDTMqjIqE4Fts4mmmHFAG3B5mJHcMuG3A7r42nix9zGmGKMvlJ7ac+YBMaLdcIfjMMP7Src/OUp+x5z/uUtvvszs5ckAtgP7MCqhzu0vfdjOMae6wdsZs6MWM6iJDCmcjoAr4OM2sgMLnb475zp2jqtqFZnTdENKEUykALIBdBgCmQCk0AJc81r93XghkAJgLbbW4zyG0BKKKe+KeI9s4AsMBzikBIwDsXr+AAb9J2DkprBkAOCH4DAHVjphyAcDalqMEVCPMg6KqEQkz7D8JGSiRinQsKFDOhdFjCUSG8OQIPnof7quY24ojuND9nGFQ31sfaGwnKD+VpIDyrlESgpsrRK6o/VqYltcLl1VDVKI2UxRFtt+ovnbI71OWUKb7FCIUUKgsJkspodUK5CaAO+6YQNRCZZp7ZlMQ82kB8PsP909SQA030AslrCBXF/X3ki9gzk/GX5pbzNYFzHxxxHa1arKpRIpCCGhPyrdN6xNQpK5YBZTnL82/QvP7aKRZiFBr+LJN42MlVKUV639I0VWKQPAUXUq7uULwhUKzUdPmR62buPfS616qtpuL5EsKtRggla4+erX9m66fFAXUqsKcdVUTcq+9uhUCJCnXUYBIkXwK7eegWtL5zkT8SID0ndxMynnL+aS9p+aAUj4DbH00nKrkkzIf1Q6HVQKqsOVUQJVQRgwy5qgCAPj5/+djAJCnv8buPzN/EvIX/+TjWeorlQtoSUDlD22XMzyXyc2VY45msxmYao5vMk9397CmaeBZ110EAHlUO3b/ZJ53rIDCZFRXK+Vn7hB8H7/vLHz6wfMAALCRSaFnVIT/4V1/Gx2LOWRWAZVpDzVNY8/gviH4pmgP1hxQ1ToZC/YyBBQHkmMQtCHlMch84lGkPUToOLmAKLANy3LYvHBtDFHlAeUMiI37NZUUHvi1EmLM9oH6obQpKRAAkGJiugTTuRB8ErAr1cf6i0gjqpDH4yape/CacUg6QsJgl2boXuqvQ3qh/EwaCeHkgGrJvfS+xWuzjgzBRwlDtn7wxxZfU0PwRSqgOFCHC0nJ9YsjVzCZa32ldQG4fjTuv6ICitSlkY8s8UH6QX21dSlqL1yXCTVpFFDePsXrGSdhEc49XI6ScgDCvHBrFZF0fRRQU4GA4sbXW4N0ToVyx6/2/bK+oOsSWej4TftBylWrJlisyikUTomSKhdvXCzWxSlwsHn5lxQiTFNAUQs+2S8AxaEQfCkhxkKqoBSAOoVICYVAdEBohmzsZPTYDcxNSmhCjTC4/uT1zmstDFovBVTCPIcUgF/66C8V700h7rqSkqmmjSHdk8mhGJV1kDTmyrlBzSGUA+OfmlNJU5lq9YzIA21dibmgvyQ8Z3A94a8nCedgJaAOn1UCqrANLcGTAUDfy0+c5MxRBQDwS+/9eJZ6sJ1dgPq//aHPZqlvL7Miwti6UWplChVYykbDeb9zhDnDSpVRxtCQxoY2X1U+AjdXviJj957ZsX/nIKB2C+WqMoq/HGcRAMC3/8pd9u+NTDnKPn8271gCAEwm+ff7+mJu+iqgJrNKQFXra9y6YYBYjmxhQ/BxPwpoCCyGiaCgLauYIG0ByAAw9YVTpGATwWMKEkcooKQcUA7ZBm54OWr4PkNWDDgFlAT0knJcCD6HWGPatfUQf1mwHtclANssCUH9NuUMiaSEtHPuw+HPQgQUAclNOY9YY9agbcRzxPdXIxcsocMooLjvQtiXKbpXKuOF4KPji+bL+MuqZcwYSUohrFRUFIK4j9o+5fIKUWLQ+sHUbwAmTCo2hFTk5lQbI1UBhXJAASG0sDkKKFQmmlA2TUj5vwQlVhcFFEcssfUJc4XbsnVWBVQ13VSQVgGKuSfjJUC0bVr45id8s1pXbMio0JP/XrivQJg97m/zWsrdwhFQKYC10w4F2BPJBlqXZGpowkB/UpVLsf6lhCYMtYmVbyHlRYoSTvMp9T1pb3EEoAbkBxVQeNw65kjiXmuWkgMtFJrNIz/xV44E8qpPCD4t/1hqDihaVloHIWUYPWPUc0PB9jQl0g0X3RCdB5ArkxIutdrhs0pAFTargMoBoE/zA9Omrr5P8q/KsNqkq+3Zccx7gBnFSo4QfDlz4FDLmWcHK5NyEzsAKG9RRrIsN7HzrGsvsn//+cc/37s+Exox9/q0856JbDbqJ4B8Ocrw3OQioHYLzLvpb28CqiqgqvU1jliiCiNznRTzcqtw9XGqEmtKnhoH2KYAMFZeccAr9X1xL+ebBWNxuLaGLwPghhiTzFHjMCH4KJjMKqDQfUbNQcPoAfiAtUQEegQUBuIRccCGdSPjyykrMAnhjBEeS47QMeuGqtpoeLzGH3OOBMS+SASUbdP4jNU9AlmaQwGF/ZjsxZXD9VPSiCMfHVIxIhzeVCKpyH6WQgs664MqBCMJHUp+cICMFhoS12EUUBOUA0o6ByipKJGqngIKr1V0TqnkY64cUJICqnHngV7T6qLlOFVedDmyt+xlIbdatWoLS1IjKEC9CqxCC0fXjsLNV97M1hXCubXwdh5ImxCCD7/2wmpBGySopLpSQoyFCLU+YfWwXb55ufPaI8zoVxc0jimKLs1CxAQtm6LkUXN9BeZjVfmjxFw5DAGYEtJO86NPCMeu93pkJt2TpL+UEDm1dcorL/nYOSRiYD2lEMqqgoiei2Rdp4S6pCSrGoJPC02o7OfXPO016tpMUYpq5Wi71Q6HVQKqsC1VBwczB5QNJVUgbFwJe+j8OFwoYIYMHGZW7JjcSjnIxnf/zT327x/7hqf0rg/bKKuqaFlHCQLK5gTKsH9KEY8/+vXL+Xnzu/+ud32liDLT71y5vzBpnUsBhWf53O6eWC7FdheEcM7QpbnIZoeAql+gqnUyhpT5/Mfc1/hfe52A9R6ojOqnoL69RwvBJwHF4BNkptyUkhoEUOfINo4Q4YBt67MWXo8CwMQHSl6J4c/ABZgNmD5YY4ZXUCB49YUUUChcm1ef6ZeSLwjPlaQm45Q3nGIC1++E/ePWIEfCKH3AbdkQfEKYP0kBJalDAOT1DACdQ/DZ10z/PReQHzOBvGuZfcuRivjfmaJ+c+ZUIz8YggT/a+tj1jRHHtPzAmBJtOIQfOJ5gT5/tbWqhQzEaiGVfORyQIFv3HrG/bP5vwgBxc2pRwKS8RbJQkaVZ3yh51msAmq45tdVrRqyFABXAyI1wsD8LYGtSSH4An54CigN4FWAU6ow0MIAAuiKoZBihbaLrasCit63NlgTy6YqurqGzuqlgApAnw7pEsiv1Cc0W4riJrYeLqcY7kMo5xC1lP5I9/W5N1WxpYWao0RLChmqEUUp60nbG9z7WnjFkBLO8QHksy00V0lhP8m4hMJzavWpIfiU87fa4bD6DbKwZc0BVSAEX04yYhX24Ln+BJTJ0ZQTkAZAap0MIc6w+uNlz7qmd33YDBGRQ1WE6ygZgi+Hr7tWWZTXz6tObsIXXnUcAAAeddFm7/pK5HoDQCEicxFQqJ6NUR4CaoLOoVwKKHO25cyplYu4ryH4qvW20A87SQEFjavAePATpDy633uqH9clheDDyqaJex/+0cWG4KO+Ly5qwDZWgnhkBXqtgswcWcGdbQQA99QcsFRyAADsoRxQGqlh/fWLeQCwQ74hIlALdSYB1g7ZMgOdzIsA9fGa0RRQc/SNXJLKMWSFp4Aia5olQYX9QgkCbk7ZEHxMOYmE8NpkiCqOVKRh7SRyhSUrFtemkkKQKBDVnG7M/rBlF9YO+TXIrku6BmFJQJm8aU1LFJPovhn5jhAiH7mzsh345bizxvpPFFDaGnF8Im14ikYm1GfD9Ympi5J5LZljt6Hln3//e8yYmHWjnGe0nmrVIA2oVBVBZG1xoe8kcDJF0RFSxmj5ZlIUEaHwgypRRI+XQLsSIA0QBsq1erFpJAYHMjuEWi4FVADI98orxAM1p65Gv1cdixDY3pV8U+aZIwC7KupMW7FlcxGN2p5MDcGnhbtMUvoooeZKKaBYxRPxX1LrhYjGIZf/15RNIL01YmjQDlQCl1sTmrJPKlftcFoloAqbUdnkVUAdPCCV2hXHN7LV9cQrj9u/Hzi327s+o1rYyBQ2zFjOfEUPb8+Jtq98ypXQZgaml2EXc6iK5nW0DWT3EwCH4Mun1spNQAEAvPZLHwcAAJcdW+9dVymibGtt/iUiF7GDCbL1TGTZK256tP07mwKqAKGXjYCaVAKqWl8LrBv6BD6+zv2w4EgZ56n56bLNGco95JEELQKKlRwsrEqFIzUIyLp8w73XI8KIXyoRwRE1uBxRfsyUkF0YsDVgOpebRcytQusjny2icoUjPxblppICiiFR6L20nJoDCs0BDqPHEogM2cSC+I1bBrfp5Jmiar0IEgLXJYH6AEQBpcy9RF4EiSpMrsz0+fL6RceXEAmOEo3OPWoTryOpHA2NiLvVDhhSlc6Lqa9xywAslX57KASfUx8zp6K/wrmCTcxBR+eKU0BFzD09f82cxoTK9EhAOqcCURyrgGL9Zc5eAJ+AqgBQtYCp+T8wqRRQDHEguAS8coQOrVvyKRSSTw1ThcpyILl0b5AwSMyZFFKWRRsqGlIBBQm1VulPJlIsFJqQXFCN9meGHhTMmgMqQXHj3McQE/g9ra5URZFTT2CutFxHKQQUDTuH7cz4jFevSrw4DySBOG7mfclSCHTt3hBxqhLkdG81ct0hwkYj11P2VohgyhWCL5dystrBsUpAFbacCqjdEiH4LGmSh4A6uTX/8fiLtz47S30AAP/pVV9k/86pgFrPFDbMmAkXl0MBdfr8/Af+8U0mZ0RPs2HtsuRVmi7qLHOUWFIvw5juFdg/xo5vzOfpdIYQkSX2OcCSgDo/7k/s4C/kb3nFM7KRj89//GWWzDqz058om0xnlvwvQdzv9NxDWAFV+adqnUwCEL336XUG6Ofqo4TAbIramIGoKBKBXdIGB7yy5QSyIqhcIf1WQWbGD4e8wuoQCCig0A8849tgjWkzAFgbG1ICCgPxyGdOrcb2SyH3pH5pqhJvTo0CynzeCPPHzUGIqKJtSiRXLAmBr0+1kIGRCqiQCkYth/y1bRBCoWmXVWnhEs113K/QPnXyDxHfLFFFFY24XaLWMv325pTcZxozyiA8vpICKhSCTyQf0e8xnLNKVAiCq6zTFFBiriTSNi3HKbGkOWX7ReaUW+dN46vGvHLMnAL4iq1q1YhphI4GiCblu1nUI4bgI+TVK5/4SqcuLeRVUKmkgLQpeXaoIkLNz5JKtDTkdeS9tB4JKG6bVifmyPgDkFCG9CMw0ifOR+019S9XyLTQfGjrSWsn5b1F5XybpK+0rmQFlEbYENMIg64hBUP+BokWEkazaz6sPvPcVQHIkdMaea2RYqF8Sqoilbx+yXUvWdarjMugGfhzib+mBAioELGq+Vjt4FsloArbMgdUPrD/ICugzi/UFUfXh4GS8XbJ0XV40RPmSS8fOp9BATWe+5hLtWHMhKDLodY5vVBAHd/MN47G1jKGtTN9HRVCz5fhAg+mgtCYmafT2/3JnVJ+mjB5ORRQ2+OpPTNecMNlvevD9qRFOMPtcX8/McmaUwFlyOu+5+YUKQjrF6hqnSy0biRCAxoXBL3yxuV1Wr8XHgoDtoIagAOAjWFAGYcX80gCBwFnwF5MwkzdsrZ+QhDgNqbCAwPBcG3EXw8o5kB44EFckUAkbXsKBNwvHK6NfmdAvjmkyYAv45Sjc4rmgJJxzpOmyDctBB+nyJCIqob4Ye4HcOfBUdch/6eENKHmkXTg+4HrnmpEJu2npIDiwtKZvwMh+GyxiX/NaStAVjjzgnO6ASmH5spRf5FykgKKVfo17ksAZo80vgKKks72ujBGQNcqPn+wKhH1XSUfNZWcQEBxZ1cMWaqRil0UUNe/AF+Q101VQFVLtK5h9rj3JDVFKAQfrVsFjgMguaZG8MoyPjr9UYBVlfhKePLfI3/o0RSpEtJCE9K8LuYarkcl1LzPStEl1UKh2bzyypqgpoVM8xQrPUKzqQStNs8KQciSFh1JlxjFCrYB90Ab40NQveIsYX1cQns4JQSf5lcfslDbz6EQjiHFk3ROhshnLWymdy8NydfKhBoNH5pC5tJrWijSipccfqsEVGHLqYAyIHxOINUA/LsZyIjZbGbryU3unFwogbIqoLKH4JuPZQ5lkQkTmFulBZA375dRexVTQFlSLwOBu9iDwwK5qsw87WQkTXLmLALIG4LvwQURPGwbOLKWd42afucgxfG5VkI52jef1l7BNVntQjUJDGeu4x+MX/uWxXW6Twh47CmgBNWAo5igADAmCbgcLNR38AFb2yYlGFrfD9x3HLJsvA2sieHahBB8tA2sqsAWE4JPAqypAsrpE1aicfWRMaJzSlUx6ljaRom/pJypH5MEHNgdAuZpH4z/+F9JZcWB+iJYwID6/q9eVK5LDiiuPlrO1E98Dql7PDKBrCNJ2eXVJeV0w/uZKqDw2mcIKGiYdUnHzZwNNDdSoyigpvx1fA3AJx+HKEQ4Jos1slQKvxgiFbXz18k1wswpNwe4LidnHpkDFkxrAC66Fr2e8ee909bCaAjQrshxtQvGYtVFbMg6Afg2f0t1UxUQBcU1oiL05H+soourRwJWOSDVUUwk5GOh9aWEt1PDJSpgdehegIACpOM54gHfym+okHpNqzuV8KDtxraTRAwFiF6N5EgJwZca9ixEKks+JNUT8Im+puRnbBjNkKXk+kpZT5qak1vHkurM6wv92aGQliEfY3N9tU3r1tWQ95nzSSLRQiEdu54j1fbPKupV2AwpMckA9hswdZhRbbKW6Ul+gDmYanCmnCQZAMCJRWi/BzOEONsZlwnBZ9U6OfJ97c3ryE3kAeQOwWfCm5U5/K2vWcZ0oSwa5vfVzFMOIrdUriobgi8DAfXQYh+e2BxlfxIlpyoT15GT0MuWA8oooOoncbWuxgH4zuvW/ReXwz8+DSgbKgdIAWVfm3IE2PbAfwNuDiLLUYBaIwSEsHlUDYDL7RECylNCCOCsbVMIxTUY+eNornt9iASsVQUUyAqoBgFyTlgzqhRCJpEr3Hh7fpv7DKhvwuMxoL5HmiyuaWEEcVueAqr1x9P2KaRqo+uoZaYfjYdDQlCygoZXM/5SFEDLA4SJO0YBZcsh/ylha8p6/aL7AxHF0ppmiR+yH0x93PzRDznJX26dDyJyQElraV7QfT3aXBbhQt9xa5CS8NZnOqcC+cgRPSGyNEQqdlFAedciPy+oMq0+gVyNWEidI73mwETpd4W5LiarJ6CtlhMmBEjHhrsCCBBq5N5QGC6NoAoRLVI93GvJB89fou4K+Uv9wPlmUkPAif7SvobgTAcH188udfw1JQlZT0mhJRPInlQlj6oWVM5xb38EznyNOA2RrqK/CfPK1e2Fu4wkyTSfaNmk/gQIQG09sfMurGtNpej5T+oJ+RgbUpCqJaOIYMGPVOKx2sG3CnsVtpwKqBKhuUYZyQgH7M1MnJj6xhlA6aW6KK+Pg4wh+HYLqWBwnTnm3PS1lIJjlDNcoMkFVMDXEqRJbqLMhOA7n0OltZdfjWksL5m3zKeVkyjLNd/TBdg3qGBOtc4mkSTmpQCAUhLCAoxMfRz4CkCUN4h0sO0yQCmAW58Wgk8iNRz3GOLA+VVGXuM2KAFFiSURnCUkBG2jFQDgduj3QcoXRO9nc0AZC4TgY0k6GoJPGCM6lg0Zo9BcaUSNpFphiQRChuB/pfxGGrnpGTf3zPx7hA5XTiIVaZNM34H0yVz3CDjjbyj3GS2nzSlRQHnlTN81BdTQX9Ps3HP7FPh7JQUUVddhkmQ2XVZP5x7vJTy2kvIIANj8X3Tf0HK4X9xaoufgsgHeD1qXNKctGVt7P7f2PQSPtLUwTYFZrRqATjIFnvSn74kKqAVspSmgNBJAywGVQi5oAChXj0pAkbpUID+UyyVAXEgWG0qubVqdXAD/t5aqrkqAIVMITnpf1/FPIXc48k0zjUTqkhfN/k1c0MhRVQEVGDfVJ+UsCKn4tHCWnI/SvQC+AjKWRNZ8oveG5lxdT4Ez01NEkfclkiY5vGjCuKSoJbUwpty4SX6Exq3a4bNKQBU2A8xPshBQ+UHf9UOgNsD15QlvN6/DAPK5zCjTcpJ5JQD+oSV1MqxJG4KvzIfBckzzkXolckCZ9Tmd9SfLSuWAyrnXS857rvxKuI7s55Edy35knvlcGBTKoVbtArDQF3H7Pi3XEAJqATByQDElJ+xropjwSBhKLJmiGHhF4eo8BRQGXxmw1yGWBOUGJYNwGzQEH6cscN5XAGAKwrM/ZgPANq7bUyBwALDxA80Dp8CwRA0i6aRcSbScR2rQuWL6gZVIUjg/Wh++xoUR5IB4S3LtLV9L/YrO66WRCzHED/j9jM0B5RB3KJ+kmgMqlC+IEIHUX2deQiE1zToiOaBwuXYgzB9HShE/aF3mtZQDipJ0Xpg8tD+wv0OkgMLXnbEUVGy4Po58jCWUmwaccIOUkLN+cGPE9MspJxDgLCnF7UnmelVAVQuYBgZrACIFOB/cedCth1EQSKoCSvaoqqYURUSj++wW1UkLLy+NRtgkgORz7iGOBPBubeL62kCjAtLUBwBdMZHA1yQB7LQN7d5XPumVcNMVN7F1hUBzD4xv+Pc4S82PgwqLxBAbIjEyN9Drb349rLVIgUw/2gKQsdqfBCLL2UuBz5kQSeaRP3h+FFLJa0c5G0rlgKLtcAqoEFEfU6/nb2Bcoh8sIGdFjAIqdlwrIXX4rRJQhS2XAmo6nVmwMicwnSuXCcAS4G+b/DmBDJGXIxTb9riMAsqqdTL4aOajBFmSM+/XXqFwccYsWTbNQJoUDMGHicK+41oqB1ROYmdSMHdRTjXZTiEid93kqeo515WAqtbfGGDTeS0BoGRPmCfcOaAU22y6bHM2g04h+Bzia80vZ0FQQn5oihExbBwBdnEbVz51/ufoCOljSAHFgPrOL1sm1J71jfRBAqzp/UMamgyPt6KAYkkYADEMIgAh1gQSIlat5oTgY8aTm1MOFPeIMNxmBGkSUkBRUoMjF5x+YRImsLekOWWVQsYPREDRcZL6RYBAp18SUeWFoDNrXyGqqALKQYyEHFCxYRWpeURN4/th20DtYqKckqXHr/TbmBeU/emaAwqYMTKvManDkUbiGAXWtKTA5MZYOu/pdzsaGjEFOa52QZoK3Csqh7N7Z0VAcbTYM7HEkQdIk5wwkk+p9Wr9aRoX+KYA7mzmYgYaYZOiQkm5N1bV0Dat/zFGgO+QKsLxN+EciSVSOMPlPdAfWthEYVlTFCsasF9qrjw1Hq6HhD2j72s50TaGGyqhlpQ7SyMtAnOuEch0r4RIVme/pxDOxFTCOTAu2noKEUMh8kfat8HcWQmkkqaODCmrcPjNmD0rrZNQ6Mgagu/wGf2VUC2zGQXHpCeAPkb358y3s7U+XwLnMuSFKanaMaRBnhB8JgdUZpIsa26lgmOZkSiz4eIKKaDWDFmWKUcZQNkQfABzX7fob/UEK6F0xPXlDG2XMx+dsbVM5A7A8lzbWsv7Ubc+WhD3454E1KwSUNV6mvfjRwLDGaARKwgMGCo9EW9MU0A5ADgXts+Am4SAcggtWNbv1MftEQTQSiHRaH8wGP0NvwDwnjcDfNGrXf9CCigW1EdlJQWURLhobRijCiiHWJoBgOmXQmpIOaBwuwCEWKP+074z9+P6HUURRzYx65f1zfnFL/jXgBdaMDSnTlmQ59S0R8kKrg9S/qAQCYzn1FH3EAUUl7fII8wI8SERaw6xpNTHkW/cWm0ZAopd93ROCblrr9P1wM2pKUcIKGnur7wR4EU/CHD8UW5buO/eGozNARWrgGpdUgfP6bIQfwaGzh8pxFEUKSXsLZbYrlZtaUkKAgV8Pzc+57zG748WZLQYgqyRwXkAnWTSSCVOfRBdT0NC8JF6p+SztA8xIfnPvS+9F1KNqeHICNlGfe6TA6ppGvuxm6Lo8JQYdE20blhBmidMM1XhkUK09ADUPaUeuTeWUAvl7Ekh+VIIZ2raetHa5Mp7ITc1Urzj3gqukTZ+/aeOsbSGQjmgUtat5mPoPB2hh4di1rimbA3dW+1wWSWgClsuBRQOQZZTbXJkbX44n9vdC5QM206hcFcAS9Igp7poPXMIvlHOHFAlybzF/EymM5hOZ9D2AL8/+9B5AAC4/NhGFt+omXo/+xDN1ZFuu5NyZNmwbaBp5jhgX+KkVKhAS+xkVECVIE7Mms+hyjy3Mz/Xjqzn3eu5SLKqgKrW3wJrRwJAAVwA13vC3dxP7pvNlm3SHFAisK2E4BsgsmZGHoRxfnRIBIYBxVEYNk+5gV5jf49fBfCVb/b7KikLPAWClINFUiAwfRCBXXK/p4AiZIgNwceA/xxZgf3AcwoghxaU6rLvIdfNdUcBxYDdIdWH6Acw5QjRwRFmotIG/HIh0sT6IK0T+pqW40LwkbXFtcsSgRJZQfaWuj8UQhm/piH46Nrn1E5BckUgoKABPweUcRcTUMw+1/bz87/Pb3uqkI9OHrwcOaBov1qmLmFOrR8h0r0BVdUl+SWWVe6rdsFbCNyW3qP3nRufE4Fv80S9FiJO80krqxEnlMRIUkCBTIo10MCEfO9JUmLg+4jCQ+uPVi+th/Y7hagz17i/Qz5R6zMuWj2DZuAoZZy16h2Piv8NeT9wRKoAu0bmJowvrUvLAdW2rZqfiSMiMHka258+IeuCKiyaA4qQb85aTSD9+ii6NAIwRJCpuZrouYL3bEDN6dU7488nWpa2EwrBNyS/R7SHA6ilkLn168jhs/zodjXHlgqongQUAmNzAtNGGXA2qwIqL9gLkFddtFMoBJ/xMUe+r91CYdgA3H6fH/eb908/OCegrrmYxtTPY49a1PvpB873rmt7scZzE48A8w/SXATPuFD4xSWx03+vlwy9mDME39lCCqhcPloCqj5NXK2rcaDlFU9BryUAtAU4sVAADNaXBJT0RLwxqoBylCAh8H/xOjoEn1Af7QeAHBKNKgtUJQzuF1OGEh9TgdARFVAJgDUtF8wBZULwKfmTpoICyplTIGOEARVUlxYusWnBywHFEojofnyRXYMNeQ18OdwvUSnEmSmHyTclR5NzTVgn9jUzRgDg5+tC/aQ5oKT6pRxb3loV9oe3T7WQmpTMYvrVDn1/2bln6qd9M69ZUhUQYWT6hWyGc0CF8n8xa8Sbe/TazI3XT2DmVCJLG7esRA5pcyoqMAXFnen/i984//fLf1RZq+T6xgm+XLVqC1MVOB7XI4OL5/bOie9bBZRAkIRUG5QwEP0FUMFSDzzF248B38UQcA14CiiNJNMIAg2QDplGHlIiMQhQa2B3D6S4j5IqtCa6Ki/U0IUBiFUjDFKIRi1nFX1fJYagddVfDOGBLUVdlKKkku4DSA/BpxEgKaR4r1CLkYQN65NCOEEDMqFGzzaNRAqcGylkIu2ro4ACfT3Ra+pZHdiX1Q6+VQKqsA0yKXfGKL9SzqfljTLAKAX6mCFNchM7AEugOwcBtT0uHIIvR74iG4Iv/6G6tTaAzQUJc+/DO73qOrtYN8c3RoGS3ezE5rzeMxnW571n5n299CgF8vJYLlJiXEipZdb7eDJXvvWxvRUooHIQUEbZmV0BlUmlZQioPirEahe6MSDj1/8CeqkAoMN1gDs+BfD9n5BBclq9p4DCigkKKBvwm5IVNASfqVtT1QjEBKvwUIgrKb8TviYqoMgYOUoIVG4wYu6V5kEi/Ei5IQ3Bh/ulEFB4Xhx/MQFFFFDTCLWMRiriOcCh31jyhrsWyhckgOQeCTrw/Y0lISiZZX1j9luQgBosy6rlUP00BJ/nK5p7c83xg4yRpOzC+5TuZ4+o4she0q9mEJ4/2i7nL753QHJAUbLQXkeGSdXphC9D25bUZAA+YWt8DRLKwn6GxldAeeuDmQPO31A56svN3wnwv/4DwHNv49ezUwcAXPMcgNEmsPNVrdrCUlQCmsrhwZ0HRQKEI6Bom7Eh7DRlEm0jBNJqgLRGijXgh+DLpYxJCt9H+qYSHAGySlXoBMgr7b0+5JpGPFACSssTFiKgUogJNSygAs9yRITkA61LDafWuKEIQ/uDkjuxhEGfEHwzIAQUaUhTeGmkMa3LyxMWuXc403K60Zxc/kc22Vt0D5C55/7mfEzZd6E1I/oLJAdU4Gym9+cirqsdTKsEVGGzqpieYdlKheVaKqAyEFBFw8bNxzFHeDujANnIrIQxYzmbAZzvqSizYzkoo9Y5dXwOZt1zul9ou5JEBEDefFWmr6bvuW09U46lMzvztbO5VoY0Aejv417BcIbL8Hb9lVpndwopoBb7su84TmsOqGp9jfvxgxIqB4mP9WN8+eUF9yVVy4gKKFSXR1agcoORX44lYQjIyrVprjlkSIoCipYhJgLABDymig18jwdsCyH46HzREIkOEI8JKCUE3xT56/zwnbntRZEVkTmgrJKHmSuWvAFmThkijLYJ4K+HFvfdkBDAmyVXFBVMO+B989CW2DxADFlI/WXviyDCKPEhhVXEffIUUJTIpWQvs6a5HFDi3BOCmdZlyjn1ofvwGNE5mDI5oCTQhSNV6RwOmIesuPrY8IO+e976YsN2MnPA+UvLiTnH0Oujl/F94Obhshv8+xOeYq9WTQPyqaphY7AhAvn4ifqYuoOqDdxOQIUSq6xic0ApQL6WAyoFOKY+Jql1IsmQQTNQ8+xwBIgG0GtEi6oG6TMuzL0SSeCpNhLaTQoxppC3oXs10ggA9BxEREGjrgNy77Ah6iKNAFHeo6YRZh4BFRi3rupISgyppGuoP0pOsdB8eCQ4WTPSfAVz35F6tD6EiHnpPS8HVGD8qZ8pY1wJqsNn9RtkYcuVA2q3UH6lrQXI3ZcwASjnI8CSiOgL+AKgHFCZibIjawMLyD9wbrdXXTsFyTwAgIuPzMGsB86Ne9VjCMFhgTkHQMq3DGqYBxd9vWhLyHXS03KF4HtwsXZy+4nXUl/lzmFRQJkQk7nJZquAGvcl8ioBVa2vMSAjVaOwZUUk3q/PsZn7txVMaOQC+f6DAWUcgo+2SQH1EIhtrmGQioL1aig2Bth13iYA8FQB9XF5515KQEXmjKEAuNMvnItLUZ9IIcamE9cvRzFCQGdLQpAwhRJZ4eTmChBGpj5OxcWt19C9jgJKCKvotAFEAcWRhcxaDRFL1idhPeHX7Lhx9TN1cXvdrlUtVxteRzPmOrhzRRVQzrHSMCQoLQT+uImkIiFLufWM+2nMyQEVOfdaviQuRx5HPrIEJdO21y+uLro/GvdfZz/jsWTWmwiU0jFh9jO3fgMAabULz7oqXzCofMNFN8DPvOhn3KWGXoRyQIXIHg1g97efDNKmkDuaIqVp/BxQsYoVal74Na97ywsUYI/N8xIT4i2k0mpA9lH1SQGkQ4SHBnxTUo2qZvD6DM1lEjGBx00hCEJG17RHpGr5yfA+Aj8HlDpuyhrS2tHCSC4aFc0LwRdYB44CB3QlnBpaTiFhQuSnpsJKUlpRAoquayUXW0gJqvmQSrbjevH4T2YTp631wTq87PEvc+7Hrmj5ulLCOFY7mFZnsLAtc0D1AyrPLQiiI+t5n+Q3AP9ffeoheOPvfsQ73FPMKBbWR/mXlSE48iigDAGVF5RumgZOLkiDz5/tR0Cdt7lr8iugAJYAet+QhoaIGBYC0E29fcMazmazZYjIAusTIB9x8kApAgqRhH193JuWUWQCICVZTx/f/Tf3wI/89t8AQH5SPJfabWIUUBXMqdbVuKfmvdBOTFlRDSABqAvzQvBN+bKYvPAAaxqCT1j/tD6OqOJIjQHqf0oOKI6oYf0x5VCoOueX09Ath++VFAf0Nb3uPfmNxhfPAac+4fz1QorhMRJC8GG/1HCJmKhSQvBxxJI0p6y6h1bX+n5wYD17Ly3HEWaMSiWFhPDmVAnxhwmouWNy/bZuZowaMvfUD2eukAKKJbjoGAFTtuEJRHYdcfuDKScpoNQQfEwOqNi5BwCPRJQIqBD5KPVLI0txGZbENmtEUmByCqiIMxbf47SbUF+1C9cUAkcDdDHA/ytf8Stw81U3u8cdKssqoBTQX1M1aQAuW5dGXgnluLIpIfhC/aGm9QebRjRoob3apg2SPZ5PCoGlES0hFQo2T51DQn9J/TH3amHSNEtRS1HrqnQLKVboWxK5Rm3QDlS1ToqaUFUMhcZFqYfuFWqaOs+rW/HDI4YS8kVRiyWuOZ9oWEParnQmBQk05XUKSaYSUI2bA2pv6kbaaqCBH7r5h+Aplz7Fucb6pKwn7nW1g2+VgCpsuRRQD2+XyWWCAeRf+OOPwfv/6fOd6yqpgFoz+ZWyKKDKEWUXbc0P2wd7KotKKTeMjTKpdUwotmGBUGwAmCjrn7PIcKsl1idAvjE1qrSLjuTNq9U0zZIk6x2Cr7wCqq9K69t++c9RnXn9XJKN/ZSjBj+qCqhq3Y0D8CMUUCEwVqrfCcE3AzUHjQNso7oxsIBD8NE2qe/cj3Iu5JVDwBG/1HwwBohWQHj8L85theszP7o48o8jNbg2qHH95IBzjdSQQnbRsIpTgVjDr6fk7KNrzrzW8upIpBQ7JhxZwdUnhDULhWHzSDrw/RDDpFHEh/ovfI/TcjtZAoq5lyM+JLKCzpdH0iGCliqKJFLRI5TJuaKt1ZC/HHlHFY22TwqZPJ3w6541OkbMuuRC8FFy214j/nNts+c0LQPAniHefJHxTVFASee9tpbY+6pd6BYCrKX38IOvHECOwd8Rsw+1cFKhp/lT8kW5y18GijnQW/KpgcYHZjWfqCqXtKP6L7xH26TlMRHUQquPKTPlKWok6T7qY8h/mp9IIx6oAgqHlgsB306+KEKuJZGFgf549wlr0VOY0XuVeW6gUdU6KQooapoKyyurEL2hHFCqjwyBg//WFIIqwRH4KKRrRPNXVSKGSBhtv2hEPD0zFbLNu1dpswGXgJrMJuy6lcivFKK7huA7fFYJqMK2VED1A9DP7sy/oBzdyAtKU2D2XI9cUCXDxg0XX7rGGXIBbY/LhOADAKuA6huCb3tcJg+QsWwKjhUpoCbTGUx7zD0mhUqFNbTESY8xnU5nxULwAQCs5yIe7byXIJvz+Igtt1Irl9rNKMkqAVWts4VIGVE5Iqy5kEIHq2VmM5eskRRLXr4gooBi1RHg1+f5zJEaFLAWFFCiigRgSarR91u3XCgHFFWacO3G5AuiiiWuTbE+TGpIyiakagOQSSNPLQPL+7xyHOBO5lAt51wk48a0Sf0DAJ5ckdY9U44lkqi/HHHA+MW17fUdk4WYBKS+Cm1qYzSLCauI9yklNVqmLo6YaHkVHjtX3P5g5t4jlLk+EXPOqZACilnTXvhFIQcUN4fca65cS5WaXgO8z975Q+aUU0DFPnDAnj8NU7Z+Z6nmWhJgiNYSBpUtuCkA7DTvjHMP005K6KmQKiBWLcUBthJ50jT5FFCauoi+R8dRC3el1UPLxxAgsQSUprwIhl6j/QuoNiQFlEYIhF6HVBkp65aaNIZUoUbDznnkKFmX2r20P1SNGBuCL4WAouYpoLyvVO4FLT9ZiupHW+MhojHpfEogf7y6NfImQDzmUkDRdYmJ4L3pHns2SJ8TGrFdCafDb5WAKmyDxY+YvgqoM4aAKqiAAgDY6BGWbrckAWUUUBlAaauAyhyCDwAroLoTUNPpzJJ5G4XJkv/+D/f1qmdckIgAABih/vcJw/fJB87Zv0spoHKQEg9v74E5Kk5u5SWbAbC6qJ9yp2ToxVwqLWy5CahcBO50Vk5JVu0CMRawxeHVZu579k9pTzD1YZtN0SWsgKKgOAJFKfiPPy/akdwmBTw5cszrBwWsCUgcVEMwfuC68HUnvBwHABNShl4DAF/h0pB/AdjcQxI47eXfwYC9AMRTBZQUgo8jFTlwmiPM8HtOH7j5CyhopLGk97IKKGnemXKsAopbgx4KQu4TSIiYXFfcOMYQd3ResGqpEe7zciqRMTd9t+qsZvke7oOm7HLajdkfdC1x/jJzOktQQNlyWM1JCSgu/1cE+cjtZ/Oa5oAKkrGkLlEBxRGegXWP23TqEOqrX1mqEUvJtySBtqwCCtVjFFCx6pGQT6paSiGZQsAr9UcjJmgOqJBaBxslWhphj9K+aAoPDZBum9avi4L8AbDbdVE+SEJEGDY1BF+AMKAEoaZYSQmVFySRlHUaJPEavj/c3MWu26Zp1LCH2rxzPmLTSAvvXoGQAPAJKI9UIuVpKEZNASX5C6CTTH1yQCUTRYHXuJ4UhZ06FgnnoNbOZDrx6qb3SH975wr92lK/kBw6qwRUYTMAbR/1BsCSgDqyViYHVA4zYGyZEHyGyMtAQBkFVIEQfBcfMQqo7iH4thE5UEoBZcbzdz70Wfjk588FSstmcpsVC8GH1lKfMHwv+an32L+HpQioDModo5zbWhsUIUhz5VcqGXrR9LtvCD5s2RVQi/rMWdLVDH/VBp6Uq1ZNNg60pPl9zHutW46tjgHYsTnKAiCAPQHAbV1EUUSf/JcA/JCqhvW31XNA4ZCBfmW8H7QtCupTINqGCWIAYOovDSnEKhA44oMBv01Z7xoBrLU5BZBzy7BqGW6uGACfndMIYmL5hl6X9Y8C8abvJGycV70hVxQFlBiCL0AqsoSA5L/gF/WDGp4b3JZHPpJyHKnGte0QUFoIvjZOAcURMLRNUzfeI3h9TBWVmJOrLpJ8dBLP0BxQQ36eQ6Qit5/tdbqmQ2uLnF1JCqiIOTZtetcjzspqF7x5IKBCgDgKKPSQDKcawfUaQFlV+ihgqgqaK2BwA3K+FfqaA/md8F6EsPFAda0u0i4OSagCxw2IPtB2NH8pWUPb5fITpRBJ2JIUUJSo00Ko0SO7cRVQNARfLFERKktNKxsMwedecP7W1m1ImZSi1tFC8KWQFtQ0Yi4Ugi+03yU/UpRiIUJZJcRT57lxy1I/VMImkigK7teEBwu0Nvdme+xYSPskhSSrX0cOn1UCqrDlygFlch+tZ84JRIHZPuHDyiqgFgRUz1xAAEtguwTAf2JzTkD1yQF1fndJQPVRpGmG5+jj95/tXI8hhUopoLDCZi+jIqaE5VBAPXR+vm5ObOZXPwHkCx1XMvRiLh+dOjMTZSNLiPc7j0qHsKx2ARj3A0ZUn8QAkgwIig3nVgElBN/8lzBfl5OnhwF2OR89NcDifQ6w9nLGsJUzlwJ9v/yJ7nUpVF1SCD6qWGLIinbI9J3zl6kPGyUOnPrQNScPEu0D7Tv1e/G3RMJ4a4QBwLl7OSUIqxjpqIDyynFrS1BAeYiWkAPK85dTenFEI7hKRpasAPD2y/yPxf2YKCZzJa0HOvceoUzaMNfo90Fp71KlIu2DqdtZ06jv2pxe+nh/TiWUgivnkY8jfr15QEhKDqgAiBIiYz3FmvF16Jaj79M2uNfcWqIPGFSrppikxqFGQWWvHrTWDDkg1R1UMmjgsAYUa+QOqZdto+HLAvgKKKpo0fz3FB4CMJ4CzFOyipbTFDdt0zr9oWHduPokU/NDBZQkSbmMSA4oSqxo5oHxhDCIvTdFcaMqXwIkmKaAGjQDd100eh80gtD/uIvfW5r/3/T4bxLr5V47ajY6Ngopqe4PUj60t1JIb20dpBCcodCRnhJUIf3UEHx0nSrjQkPwsTmgMIGlnINV8XT4rRJQhc0oBPrmgCoFVFK1Up8n+ksSUKPFOOYIy7UMwZffz42FqqpPiDObS2vQQlsImB4ggGDWY2mWBtAHbWN/5+YMyVbCcoRlK537axmCr99YWuKxhNqxAAGVWwE1yHyul9rn1S4AYxUZESH4Yuujr6djUg8GoylAKbTXEgJKBIbpU/gciMsA9g6wzalWgL/G1Q8A8Ko/ALjl/w3wtFe4fokh+IZuOVwXbddTQDHlWo4MEYgDVh1jAGtBMTKbue1Jyi5HoUNICC+cWISyggXhuTmla0shK5z6E3JAAR2jhiEhuDBsXB8iSQgtX5dUxilL/aDEEirn5P8KhH6z99O5l+aUlIvKAUXOKY74MNdDOaCwn996J8AtbwB4+iuX1zuFX2SIYm4PcsQa95olt7lr5DUHwrKkKrp3sO632UsBxflXv7NU0y1FOeIZ+tqCgcgh84AFfj8JeNWAb0LmUDBeDTFGySktZF0TyAEVIE9wHh5KBmnEREseEtAIAi/HkDIOAHOg2dgfveyPvHZi1TBJqg3yOjUEH54TNWRaAJxPUkA18jh0zQHFkZCxijpu/Whzxe1Fzj/NXwBdSUXtmaeeCa94wiui2gHQw0Oq+4MhKcU2G3nt0bqS8oIFiCFNAbVwVLxXDSmoEMy0Lo0w8xRQ6FwwdQOQM0xbqwx5xb1X7XBYJaAK21IBlQfwzZ0rZDR06+sDnu9aZVEJAmpeZw4F1PaCZNsoEIJvY6FQ2z6gRN7S+o8jwFKZVyoEX9M0MFp8Ue4Tgm8VZuZr3IeAKqjOw/X2JXdKrlEbyrDHOM4IqzrK7Ocwk7J1YnJA1aeJq+UyCthKT8mHwFj7kuydCVL3zpACygOoBXIBAESVileO+BsiHMy1AVFWxJBNtD1c5ppnAzzve/zQVqEQWB4hBwywLYTgc+pLyQHFfXZ0BOJZ4iCGWGoFcoUpx405lxuJJfM48pGSK7QqCYiP6DubA4ojB0mjXEg0yf9QGclEJRND1lCSTiIbPLJQ6ichJrg8ZNz65RRQHMHn5YBa/Mmt50c9C+B530vUb0ilyRk79zQEH5OrjhvzqDEC5Vyi9ypnl5QDarjGtBl73gv7FL9H/65WDRhQMJKwufGyG+FJlzwJXnr9S4P1GiA4WrmktKuRIV74K/CBfcm4cdCUSWoIvkDeHZUEIGOkhRjTiAkagk8LXdZA4yigjo6O6iSHRuRpapAAMTQi36tCbWJCjpJXmqUQNt69ClmlEhWNvv5TfKLvaeRbKP+YOnfKe1q4O86uPnq1U1ZTUMYq4TziSgvhSMrTPUrXnqrkCRDMIQJdVMIF6g2RllI9APo+1FRwM5ixdf/ATT8Al29dDnd80R1uf52vG/55jC20ZqodPMubUKiaZwao7P+k/NSpL5dRZUAfYHps1TDlCKg+4L6xpQKqXI6dPgoo08dRIVIHwH0ovs/KNOs6NzGKbTRoYHdyCELwZcgBtbNQQJUgRwHyKaB2J3M/i+R7W5DiffY6JYZyn5v2wYKea7JkLq1qF5I14D4yjD/bBAVU6hPxxiZjBD7OZBC0Ia9xXbgNNgSfAGxz4DyXcwX/AJS+j8T8YEkh6XBZ0z4L4hJ/BsLXcKc+LgSf1C9FASXmzEFqptnMVUpJc0jr9oilCKIKGIBdIjqwH6Mtvh/0Xi//FzD9If45KphQKMDFfaNNv5z22l7nlFLUX+l7KuMHR9IBM/dc7iWverqfBZKKvhc7bpIfLKko5IAK9cGYnVPhfW9/MG1w5CNHnovEK7MOafgBbm1xfaPEmkeAj9xyAPHnPTcPEtFYrRqylFBa+PWwHcKvf+WvuyAiBR8XxoVHk0BLgLCyRAtlRi02xw2nipEInaZp/BB8imKCvt4YbIjvqwC7ll8pQCRqypGmaRylw7Ad6uUZgN0QcqqiQ1lPtH8x6hZJARUkBBS1SIi8UsM2KvcGfQB5vkIElEZweHurlfdWigIqRekCEE+6AuhEkrau++RxWhusOa9pGEDn3sC+pGW1tlXiUQmVR/3Q9ittR1UwRmpcrj9xPfzBN/4BNE0Db3jvG4I+LS5UO+RWBuWsZs2EOuv7pPxeIaA/JwFlSLISZIQBaPsSUHuTqVUnlVBqGVKrD8C/awmo1WzPSQ91nlGklfR1mJF8LGk5yJ3tgipCAIDpArD42H1netVTUgHVLr7c9CHt6Tl2ZmdPKNnNDMne98GCPZtDrX6bqtbDnB9ABDj2csegcsG6mNc4BN9sBg55EUMu0Do1gDVEakhkRayywm848Fq6lwLAw+Vl6kcXBZSUe4jrQ4wCSkbi5/+Y7wQciSQROCFlEzunHMDOtEGJmbUjbp1Ou5jU4MIvBuZUzQPEEVotwNpRpg1mjXtkRWzfAbxHhbjx5fY3VUBxJJ1ILpE+iHNKysWG4IshoIDsZ+5cSZlTviApB2RumH5aXwPninr+0O8P3JwqxJ9E/g8YBZTYdWEstXOZva/ahW7aU+ih3EvqvTgEH5MDigLfWBGRFMaNEGAakB9SVtH3NH+9EHxKGCv6emO4JKA4cijGX68sJTEIkK2OEzQwmS4JNVYNpoH5kWRJqD9aGDRuTB0CUckB5ZE/+HOCWTOadQ3BFyIhvLls3PnTfNBCzdF7qQJKI6u0vqaGMRwNSNhJhY3QfNTaScmRRu9dNyFwF9YnzGSQDBb2EkdEOu1oxGmgDe0MDYUblOaKVVpFPiyg1Vvt4FoloApbPgVUGaDfywHVQ7mzVzAf0JolIfqN45/84/327xJ5dtYzkBDjFZA6mODoEy5wryDpaGyUae5LW47cRUsFVJkQfKfPz8Nn/dZffrpXPWYuSiigcpA7dP997N6zvXyiZgjxvg8WjI2ydUVkc7VHqmlAYwkFlAG1pyS8FQEoQ8oEAF7dw4Gg9Cl/8z73BD/Oq2R+SMYQEbEfYyzxga5xOaAkYNfLAcWQFWzuIQb8xm2z/qJwidjsFHJkRWhOubkiRJBTLgKw5+7FxdaPuXViw4BQG0EQUJNyJQHwar2m8Qkouh5EEoJZu7RMbEJ0r00y3lNMVpDcS9yY0LmW9pqpc3nRJ9ZYooYQVaoCSsgBha+xtrgeCj1J9wcwfZd889avkP+LO396K6CEfpkzxZmWyPNeIv/9Cvn6ql2wlhIWSQtL5dWL6uHUD7GEBoAO8AbDWzlfs+S+ciHFNIWU/2xBPDDuEFCBvod8xPdJ4xITgm9vRnK9KOsghZiIJg+BkEj0iFbWBG031E5KeLUU0DxIXmlgvEZIBUhij0Bo3PexqeoixaegAirQd0wqhcY4JWRdVx9DqkSNlKFrXCXjmL5K4fCSQvAp61JrI1Qvp4AKKQQ1Qk3yUaqr2sG2inoVtmWopoOpgKJKiz4qk6nxsUA4qSXg208F8+C5Xfv3sY2RUrKbrS9CpxkioYuZOSilggFwCY4cpGPJcIFrPdVvqwrdtzZY5FfKkANqo1AOqKc+6iQAAJw6vqEXDNhOSQXUoso+BBRdK1//jKuFkt0s14MFSwVh/fJUrYdxygdjvXNAkdeT8RIQnk3AITWoHzGkjxYmjRI4rFqGOYPYEGMxREQsWcGVw7/UuRxQAojtKaCYvrcM+M+OL1M/d827b+Zed0L1BeZQAqwlsiJYTiIYULtWARW4N0kBZe5DY+GFUxsww9Eu/ZH8lUgIj6yQ+g4+WeF3wB9b536kaqPkmKSyCZYzc0/OFS4HFDcPLAHFrC8prB31w+sC6bsIUtAxaty5Ec8QQRHHvu54/khzA7BQo4JP4ltSW/lcCF3nPlNYUrVatbklqVkUsFSrl1OnpACkDsDbyD5yxEoKkE/9F4muxs9hk6L02Rwsw79qPiYpIpQ2Y0LwUdPKU8C6swJKUedo48+91vIGhcgr52ugojai9yYpoAhZQvumEafU6NymjLlGnmj5u0J5zTRyBEDP0UV9dMqS/vVRJmllP3P2M+r72EK52IJnknKuaGteUzl5Y4Q/9pX9S+vl1h3Ozx06K1JDM1Y7XFa/QRa2fEBlmRxQbdvAB37gS+FLHncpAPRTmViSrMDBYFQR48nMOcBS7ezOHFi55YmnsvhFLUcIvvFe+RB8DgHVQwFl1sugQN4vY8OeCqg+hFCK5VRArRfKAfWiJ1wOAP18xPeXIKByKKAwAfV73/N8+Bc3XN7bL2z2wYKehPjYnuv1o7haH1M+c6UQfOIT8YHP78nuEjiejN37QsAwB6azqhKOrGDqZwH71iV1DGAVA1qnAtu4TQ4A5sab+ktzQHGANZsDipAc1g8FsPbaWJijYgOSAypEXjXLsviamAuHzClHcHF5vfB9RnHEKlLQvSy5Cbxx/aLX2HB1zSInFSFh8L0cISn6z5EcEf56c9W4/9o5BXd8ORLF1k/Wr7gWSN/ZEHzMPHAkDze+g645oEzfYxVQ2F+OzItol1u7XBtNC+EMrGTcbM6nxWuTa4WG6GRzQEl9D+3vhGvVLmyjx2UASI55j9bD5UjRnqrvGtqMDR2nkVUU9Bf84+6lBFQKYI0VULStrjluaB+c+0gIvrZp1fHnTCzfyP6r9y3uxUaVclrfB83AqVvL2aOucUrOBUgyLdRiaL+ISh5CSuxOd6NB/RbaJFLGU0BF7oG+OaBoCD7pXgA9p5J2X+gc0ebn/N555zUlQx3/Wp2ACqmRtHWg+avlDQudbeoeVdYaNW4MRYKQ7rsAuVvt4FtFvQpbLqCylAIKAODy4xvwqIvmSZ37KLWmKwjBB9Av7NW53fkPtiPrZRQmOULw2RxQw3IH6mMuWz6xu91RrTWbzeAjnz0NAGVCsRkz6reHt8eBkrzhEIMnt/Kr3oxlIaAKK6DMuj/fQ6EHUDZPmTnjJj2IZnOOHV0fwg1XHAuUTjdDGOVStg6rAqpaH1OBRiEEn0iuBADJ6XgJsmICyvuFIOWWoe0xKhUOtJWAZ87fzjmgqG8iW8G8xoQDE4KPA+sBGAUU03eWgOLICuYarYv1n5STcstQYJ7z29QjqmUIScLNH3cv9mO0xdTF1KetLb8Dvh/UpBxQbbv0ydbVuGW4Olmyoo+/zDlg/p0KpKJEWpInX6MVUNAAG4aOJYpJKECvLlMukANK3KbMeuYL+vexvjH1B9eXsO85X1iCHV8jITQnCwKKrktOARX9wAHnF3eG1u8s1VxTQzlFEhycYSDSEAtiqKamUZ+y98KrOUcmAVoVEFpTDAWJB7IXaQ4ozQ9KrETngIJGJfI0QkoNwRcgXbz+KGScp4CiILOyZrQxpj5So6qhlBBvdD2lEJHaslfB+0YeC9rXveletE9cDijt3lAYR9dleQ2E1iK1UYMIqMD3eEomxiqGUlRZXv4xkneKqrC0e712yPkUUifie1MIZ+c9up40Ijthj2rtsO/T/ji/B9Vbqx0CqwRUYTPAYq4cUKVyhYwyhLhbkmT5fcQAbR/Q99zu/Ef41hqTJyGDGWVRn7B2q8gB9dVPvcr+3ZUs+68fvtv+XUqxA7DM3/Mtv/hnne7Hc/H/e81zs/jEmQkV2IeA2i6sgDLr/uzOXqCkbruLMS2hgLIEVC81psmtVOZbyvLBgjzK1lFVQFXrZQow2DsEH1mb070lwDklCijnPgactXVjBRSnwDCAL1V0MOWCwLaggIpRCkm/ckJ9tT84GQDYU0DRhyIYwNr+GKbzHNMneh9XjiigpBB8nGLJuktJjUgFFEuYcfcyAHtw7hlCJ3ZOWSCeIVXNfQMlTxE7f+D3E5jxsP1JzRdExtsooKABT3nEngW0DwLZS33xCCPjCuNvVA6ohqkvck7N9aACKkBkSucRux+YNcm52LRMWMXA9xVKpOEQfPic5xSY0WeZci461VUEqJprmlpEfS9hLRlgWAK0Q0/+x4KpLGkR6bMXBo3UpeUYMvdLPnsE1EAOp56SM0lTPXhh2hT1AdcfT0EhnEWeGoT8LtJy6WjqHI14AEhTQKnrKdCOGpYuQWFDTQvTRn1UFVBNCx8//XHnPpU8VELpafPjhe5T8lxy4+DlgVP2pUaoafmKgmEZ8dd9cu/6cF30l/aHklVaiFB6v0YQenMH+n7RzlOtTTUHFPNdIhR6NYXAqna4raJehc2QMb2ByoLqonm9cz/7hOCbWAIqi0uOYTKmT0i1s0YBtVZYAdUrrF35EHxt28Arbno0ALgKoRT7/37gn+3fJRVQfc3079j6EB572dFA6e5mFVA91qdVQI3KrM+txbo3RGxXMyTbeoF5H2Ygd8w5Viq03SjTgwXWz6qAqtbHNABnJiigYpQs+B7zw+ii61AIvl0QTQPr6TURtKVkBQdOcyA+uQ/3A5fjfAn5S/0yrzkCivPDIxiEEHxOfQyJJimgWH9lII2/X8rrxbXRuD7a+6R1REF8Zk5DCiiRgCL3tpwCKkBWaOXY+hbtDdbca3SuuDrZ/RE5p9Q4ZdP8j/k/kgJKUipSXySlFG7D3Od97kbsXY6gNK/xHplN484V5zohWMVy+Br3HYzbk4H7WPWQaTOCVMRGiTSjQKVn4MjkhYk472PWPXuG1u8s1XTTAMSQagabA163PgGVRExoCo+AKkBsMwD6azlWqGILQCfCKGDtKaAk0LlxfVKVCwECIyW3VKhubRyTcgORl1roNbomdiY7MkGojAVXV1elT3KoSIV0YctHWNu08LlznxPvS1HNaYRHigqIs1HbMQQfIXS8/RG5B7h7sVFSWFVAKSQrbYcjP6U1FCKcY8kqTuEYGyazi4nnJD1/lfOr2uGwMjKQatZy54AqEYIPACmgeoDnk4IKqBECaHfGE4DNbqHUzu0YBVQhAmqhXOmjgLL5dQqTOstwgf3ICFzXQTTTv5IqLYDlfOVQQG0UGk+jgOpLQBlSr8SYthlD8I2KK6D6hlYtTzZXuxCMAMCOCTmgYsFY8/rVfwTwxz8O8KLX+QAogA9wasqm2DY9soLex4DiHMAe0ybrXyRZAQSstwQUM94hBRT349uC3S0BoWPJikA5m1uGAXVCYD83V03rkxBSOW4svXtJOUP2cISOp+4RiDBqMUQVmy9p8doJpUjXg0BCcGPk+SF9V2X8dUgvOt5ojvGeaId+XaZ+pz4A75lFjlRlFVACkRmjgILGLTed+P72IVekcuz8KwSf1IZEPkITVkDRezwFlAnBNwTYPAnw/O+bv7d5kX9/LJkurYXY+qpdsBYbHgpAB1apOSH4Gh+2CoG02mu1HgLyi2oDSsIEXlPFB80BlRKCb3O4KZZVQ3KRzx6VPFDyxcQAzrEKtUEzgL3ZnlhWqpP6aOrC9Wjg/IM7D4pjHCI4Ytc49YmWD61bWq+mfEkhxUL5fLS9peUvCimtnHrouATWl0boaD56RLHzpz5XdA9obdK8bGoOKKoG00hXjigS+pBCfmp7muZ8S1FShYhEzmIfLMhNfFVbvVUCqrBlC9VUWgE16O9nSQVU0zRwxfENuPv0Nnz6wfNw+XFZdq6ZUUBtrZdZ+uuL3D19FFDL/DplD1SjsumqgMK2XihnUQ6zZElhH9fM3PfJAWWJnbI5oEwutK72qQfPAQDAlSc2AyXTLQdpPy4cgs8oq/o/WFD2XK92gRglarA5+QUIUBysC91z5VMBXv6f5n9//p/m/060EHwM6CxaBGjbNMAC4EFlhQIAe25EAtahvmLCyPODEnWUgGLAXjakHwOSxyqgRGCHqy/yXtrXGAUU2wduTsl9nMLMFPTmIXZOI4D4dsivNwASgo+sB1EBFZEDSnqgi5tT7hxg1yohaCUFlLd+6Xebhvy7+JsLmccSS9w+ZchHRwE1iZsr027XciyZFzGnXl4vbozM/YkKKKtiW1w3BJRZRy/6Qfn+2LNBJd7IPFerhkwDZilBoN2nvW8VUAJwrpEnAIwCSqqHEE4ACiBKj4CG5DJiVEP4b08BxX3+LYpoOaCoacSEF3JMCaun5oAiY5gKOnsA+wzNtUbYKGQngE+OaMD4QzsPOXPYKw+SRgIoPoWIFGqxSpHFRbYsfe3t0ca9N6hcUvKiOT4Ewt2pSjcIEIQB4kU6kzilj+ajtp7WB24IPm1PaGuc9YP47IylcyQ1qo/RYUsbf51q+4GbuwYaS7Jr+fmk+2N8rnb4rABVUA1bLgXUktwps+mWIfgOZg4oAIBHXzJP8vzPnz/XuY7zC+VH8RB8PUiIVYTgA8irgCqRCyiX7RTOq2QsRwi+baPWKqWAGs2/uI0ns85Krel0Bp9+4DwAADz64q1A6XQboDOT/jCLtb3CIfiMj+NJdx/N/QDlcvtVu0BMI3qcCHwyaJJUt/kBiHNAeT96lRB8dM+IqoGQWgZ4EN9RwUj5d2IGIJaoIde4NiWA3VNAMYA1m8eKoANc3dJ1r++CAooD4qOIJYaA4kB8Wr+9FiCWtBB8eDxnU75+1iLWhxbSzwnBR5EbYV48soIhbVPUPZTc48rR8W2Hwl5g+sCRQ05bxg8mLCa3VqlaDf/r1BdYv+JezlzOGd/IvF4i+Rhx/tAy9txcXJdC8LH3R/RJKscScBUAquaaBjqnKC+0ernQahIICxAAXhsZpA2qmEIkhQIG0xBvVAGl+UEJKAx2a6BzCJCOVkRwBJQCSNNrGtAfCvGWokKhY6yB1zdfdbM7Nuh3o7amqY903lOIllBZxwWFBOAIKG1+6Jq49vi1YlltPhY3iKblu0ohGgFICL4AcaflbtLWREiJJL0H4BNQ2vmmqci4ufroAx91XkthNT21FzFvT2qEptLfGLWdlOMrRCJ1DSlY7XBYRb0Km1VA9QClAZbkTilSYhmCr4cCalb2af4Ti7B7Z3a6qzfO7poQfKUUUEsSYtqRdBwviIFRYVLHKIK6KqAwqXqQCajtvVUpoBYk7gFWQG0i4vV8xzB823sTMFN/fDP/PhqgLxZdeXtz3pbLmdffRwAcgq9+earWxzRgEIfgYxQSXlURgCRVOHDtdlXPiOUpwC4Auynl1PaEIrhO/NpR3nAEWuNfA2DGkgF72ZxSHFkRC7DHhnVj5tAbRgGc9oB4qRxHLnD9wuMxQteJv/iJ8Ls/LNTFWMz60Ag4GoKPBf+58SV1eX1ajGPoQQfJ/xCh0474/dcQX1RSkaxLNgQfc29MCL6mcQmWFFIxdzkg48HdK75m5j4Ugo8azQFlHgDgzmPJX60MrpstU7+nVIs3DRSMVUcBkBB8zFpXcxsFFBGSTyzAKYDX9HXTNLA3XeIUp3dPO2UdgiOwp7wcUFoIvkYGnT1yhFNZob81AoreFyKgnHFTSA2qFEtSbXRUrFy+eTk87fKnOWUpuSjVQ1+HVDQ0fKTW11DuMqkeSrLQujRSom1a+JkX/YxTVtunXv4ihSBU810F8iBR484AyWgIPslHSrQE15MwjsN2qN6bEhrP87FpYOw89Ceva6qOSiKcSZ3qOGlrcfGedL6zZLV21ihWQ/AdPju4qPEjxPKFaiqbA8o8gT/ukdNksiCv2kI+mrxNXYHz2WwGf/x39wLAMhRZbsPEQVcljFFFrBdWRWz0zFeFVV4HmYAyCqiNVeWAyqCAKpUDCs/TBz/1YKc68P7bKEDqDRAZ0zXH0nhaVlk0zOAjAFJAFVJqVbtATHvS3QE5IwDJGFKDqna4dtvWxyujCRKBwOH6yZFBTo4bSakggO6aX2o5Dpxm2gsqoMz7GC3iFFUcUSPNKXlNn0i0OaDofZTkYtrkGmFJDQbElkjKEMEnKaBoXc+6lVnPsUB8Yjkagi+KrGD6GbMXpLpiiFdvfwgKKEpUcSQSS0w0fBg6bs9wBFRoPcxmfpmcyibNuPH1zj2GfGPbFsZce01zQNk2IxRQ4hBFPHAgrY9q1ZCF1BXKjdH1cgSSGjpLUZaEwkfRMHuaAkozGiUhVQGFjZIYG4MNp6wGSGvjqJIHBMjWSCMOCI4mIhs/dKFTVmknNE5SPU+65EmsH7HtqGH16DjSvFvKuqXmqH4oYUDGJUkBRQD/raEb1UTz0VtDrbsuJEsJk8etHS0EH31J8y+JSp8Q2aYRQ+jvUTvy9rvWH63vxi/JVMKZEJEhglNqM5mYY/aRQwImnBUquRv60Kh24K2iXoXNgKnjTCH4Sj/R30cBVTpPlSGgznUkoP7hc2dyusMaDp3WNQ/U7opC8C1zQHUbTxzCrVTIuBy2VECV9dGq33oooLZtuMDyObX+3W9+qNN9ZjzXhm0RshkroLoS93uF86hhwqjPwwVWqVUVUNV6GSUmkOEcUFR9wlYVAUhygCcbTqwDsItfU8VWjKKIqm9ilQqmjWAZrhwhSFJC8Hk5oBh/uZxSIID6sf46NuOvswolYX3gddYOeBIC/2vuZdvk+oWuWdKO6Rf244qn+mWkMZLWx/UviCuH59FTD5m/6b0RRA0790xd3v4w/zL3OQoojqBl/FUVUKRczFnglePWB/hjMptGrEniH23DLxh3Hzennr9CCD62jZACip7lJgcUPUN6KKBiCFpp71arhkxVBSnAaowKyJjNASUB8AEA1wOdIwHpBhpRxRECZfdme+r7ag6oBhyCKpgDCm95SoBoKpSEcQnmHCJGx5ESbrgdlYBKIAioykxUVtijja87aT0FwHpN7RIiCOi8p4Rei833w6ldNJ9SckCl9DVkmIzzxoUSPAl5t5xygXmW3lvDoZiZulLGlKqYuLmTziHtPa+uxt+j+D6676LHcFEuhSzqOj/VDp8dXNT4EWIGAB1Ppr1yhewVzgFlyI4+YOp0VtbHzUX+mq4EFLZjG9wT3P1tNGht/7sqi2wOqGHZA/bI+nw8z+508/OyY8s4t6VIRwCA5z/+Mvt3l/V5dhGy8eh6mTk3tpaBgNpdEVkGALDVUQVoSLJSKi18fnQ9j5bKojLrEvu41+PMNGTzQSZwqx0CU5d5ogIqSFaAEIKPgsIDxjHzmuaAosUMKkAVB4z/HNjd0vvYRsCzrgooqkaSCDTvGizGMkBW2BB8tA1KVghAdAgkp7llpDY4gsS8xt9vB4oCipKIIbLJlsOkiZQDCnzCNYp4RP5p/nL3WwXUmnuNA+tDYH8vBRQZI3Httu78s2uLI1zIfU4b5F42rCTjL967f/dfl+1Qfx2bxe/TILEk3B5DzkvKSqpm1gjwYC488tqG4OPOWs5fZg16ZQLryrmXO6eqVZtbKDyZeF9gLWEQ1igaxHvodiTrOza8GhseSiBHtBBWAADjiRs2i4LKlJDRAFsKuI/IAywaMRerLqKmhjico9dqPbEKCkrQqOHJvKOSEA80B5QwP6Y/sQooah75o4yxmlcoQEw48670vYHGmyOtXo1kVYm7wOsU8jMl3xKAOxba+g/VpSrDQvtFeG/QDOA5Vz0n2geNWKSvOf9jz6SQEjQ6B1Tj+kHnDp9lNgeU8P2EO+fU/FKRRGq1w2EV9SpsRmUym/ULzTUpnAPKAKrjHj5akqzQD5OlAqpbDig8/s997CVZfOLMgMk7HYmI8YoUUEcXBETXnFoGg/93L3lC0acR/vf/1zPs313UWme2DQG1mhxQffa5ubdkSMOnXH0CAAC+9AmXd7rfElCFVFo5CCgTFq9YCD5MQPVQja5S8VbtkWwK0IhBzlyAJFXtsPdREF6oS7qXXpcAdvaJfYaAiiYiYowhCUJtSkRC2wrl8DVGBcOqZSKUELRu7To3hxJRM0Ofze1QWUdkDUatm9a9TQvBF8yrk7juJSKNvh6QHFAsWE/ni1MK0b6buQ+sV3qv5L9H5gnqOnqtaZnwco37r62/Qwg+3A4th41VQEWu51iiKqacdK5IIfjYNkLfHZi+A/jrRgrBF0UYRfSdm+cK+FQjlhT6SwFHqXE5oMQn9gPrMpYk49Qsouqk8ctio3lbqCpDU0BRggrfe+NlNwZVW5JPKUoeGoJvih7yeGDnAfd9Zi5jVTU0bFgqYYBNU8ZwYLs0f6F2KDivlaXzrhm9VwvBR/0JqV3wdYc8aQf+mo+cO1Oe+5veS9dIKA8SNTy3VHGUQvCo4eAamQwJ5dH6X278X9xrkbmxvHYi1p60VqlSMSUHVOhM0Yg56h/+N8rI+uT+5l5XO3xWCajChnOkbHcMyQawBDnLKaDm9fZ5mn9q866U8XGzZwg+o4q4+uRmUcLEEFB9Q9utFSeg5l9ouhJQ5xf9u+rkRqBkP9sa4T3UgYBa9M8ovkqZzQHVQwE13ps5dZWwF94wV5R19bM4AYX2ZvcQfIawL7PP27axWEyfHFDmM6GUmqzaBWIc8GwMK0I4AFWri95jLAY4bgdyG1c9XW+DBXcbvp8swM6E4Iv6wUL9SAC28TUO1Dd/z5jzgivHzhW9FgC/JX89IERQQNF+cYSRrQKH4OMIKI5YY9psGt4PfE0ioJoWotV11KR1z5Fh3Gv6hDIH1gfngRnfFCKMJZGY8cXtsuEiG+Zay/vr+dL45SQymnsyNqSA4nJAiXu7436OUVdI54qkEuPW0RO+ev738UfF+WsVUB32vXjeR5B57PlTAaBqAdPIBA38Vephc0AlEAY0H4kWak6rh+bz0cqOp2OHRKLtBHNAobdxTpsvvuqLk3JaaWqjJJKIWOpcSm0NmoH3WmonVY2TQib0yl+k+KiNeUoIvpBiRfPZI+II4F8sdxP+ehzoa4hQxmRcSAG1Plh33tOIa1UxSOFy4WxroFHDYtLu0FxlrMKQ+Zvtj3YOhs5fYdtyIfdUYpsZMzy/mGwP5YDS1oX/Val+HzlsVlGvwjYaNFYpstORkABAT/QXywE1Xwr9FFDze0uRZEYBdb4jAVU6L4yx9QXp2F0BVVbtZuzogpDpSkAZ8Hx9WIaIMNa2jSX1znfYQzYE30ZhAqqn8g1gNQooS5R13OuWNBmV8bFtl2dm9xB85rwsN47mLO4TtrQ0mVftQrHIzzSNqLLXIwBJ7klOFjgWgOJHPwfgFf8Z4La7+HstaEsVP5zKiAvBh0OMGTKINsEBrZHANktKcGAv84Npl8tFGSArLAlB7gkSGlz9wIDkpB37miP3BJAcP8XN5YBi57Tl554lKzCCIRFQDcATvmr+94lr3HapHyEzdT/z1vm/j7552QZtE8APwRcidAD4tUvLmHGUcmrh1yEi07wOErnCGhQVUGSfsmPO+BsVypPbpwGSSruXL6i/ZvspKNM48k2q83nfA/CNvwjw6j8U/CWvLQHVQQEVe5ax5bj1UOGDaq55qppA/hK1LomkMDmghL2drPQJALxSWRV8J23uTeUcUEHCQFF0DNpBdA4i7T2uTVExBK0aMpDtTwIxoZJMCeuJhuBz2iGKLnq/lsuIWopixQsxiM5tFWwHn2iRfAj5oRFMbdOqBEgw75amuFHG1FOGOV+b/fHHeyDU97VWVkgF546sTfxeynpRFVAEowidX8RJsQ9BBZRGdJGHxLRzRFOvmnLSmgudFSkPBFRF1OGzsohsNWiaBjZGAzi3O+mngCqcA8qolvqEk5oUDsF3ZM3kgOoXgq80sbM+MkRERwXUykLwLQio7Y4KqAURaJRpJW1zbQA7e9NOCqizi/VydK3scTeyCqjuRPMq1G99iTI77wVJk0HbwHQy66zI3JuWVUABzMmt8WTS68w0c1AJqGq9jFNbWEsMwRcD2BvwGAMrHiA+4EFyY49/sXIvo5aR/OIAVFYBRYtx1xV/Q+U4cJYjBDgCKlYB5ZE3FPyWlBAxqhKunAJG09cz8rkn1kXmNESk2HtQOauAYtbN1c8A+M4/Azh+pVAmEog3r5/wlQCveR/Axdcv26BtYp+Mv+x6IG1ypBK3j7h2vTUokF6cv9z+YP0l16TwcrRczJxy9Un+YptNmfr9aliLJt2l718cASPMF22TO6cGI4AnfwNfP3ePUVtT/8R9j/+OJOnU328xnx/Vqs0tVpnEgYdOLhGF+KEWCuen3R9SEIgqGkrukHvH07Hz5H+IgNJyRDkEVDOIV4807muPTNCIBwXIlvpACohlOTJIeh0KmYYtliC0OaAI6C76QBpylEkBdYg6V4GyajsK8eC9T/zH5OioHcEEfY+bzWbkK4XuY4jcMhYKuRcidJxwhGQs6DrAIfrU+WmYcVTWJjZt74fuDY2FukaoOon8re33WLVXaP2r47IoKxJQHFknkE6ejwG/qh18q48wrcAMuLjdA5gunQPK1NsnnJTxsS1Ekhmi4w8/em8ncmdVyiKbA6oj4ThegNKjYdkD1RARXVVvhgwqSUQYM22c30331YD8JVVFAMt5z5EDquQatbmqOhJQP/F7H53/UYhoBlgS7d1D8B02BVT9KK7Wxxjg2ZgDTnIAKq0qhoQAHsR22m0VINerjG/TU0AFSBnzmlMqxBJroTJcOUkBxfl70fV6fey9DHnDzUvnEHy2oH4f9o+WoaEFRVUb6UOIbDJ14XKjTb2Nyx4PsH7MvUb99UyY+6YBOPWkZZtSOfu+8YsD62PICm5tAdNXanQsBeLDG3Nhr1F/WcKIG8tGaJMZN1ZJGdiDs5k876l1SeViiCppPqIVUMz9XrO07xP+XhFUD5wXrF9MOY7wLfj9r9rhtK7Ki5TwSaEcUN5yZogk/LcG5KvhpbSn88nr8XTs5E3y1DnEtBB9mIgYtkMVoNYAXKo6ic0Bxc6VQlKY+qT3KbnVFWTuk+NGqysUysyZSzJuoXxETt8VgoDeG1K6UYWaNj/jyTI/2dpgzQ2XpoSGDL0O7TtsGgnDzTnNh+W0Q147BBQdN+kMYcrGKidZglYhqKbke7NGunL1aiFEJVJV8pNrs4VWrTfkH+dXrGn7PbQvqx18q6jXCmyjZ04ggPI5oAyYOu7xNL+5txTQv4WUNn/wN59Lvt8SOwc8BN9v3PUpACinJDM2RHm/aBLUGDu/DwRUFxLX7J1hYeJxze7zKTx0bhwozdvuCsiyvgTUR+95GAAA/uqTD+ZyyTNDwHUlR8d2zsvtoYHdP90Jx6qAqpbFuM+KF78R4KLrAL70dXy5WCWIZE7OGw5MpSC8YpICQSWgGODcXMfEM6umYl6zfkUA0fMLbtmWI70Wf1/xZL0+Cfz3ynGkn6TyDYHfRgEVKscRCaYKSkBJShBKfkSQFU3jqu1GW4K/HDiWm4QQxsj4ZMpwP/7ZNsg8S2RhiBimJB31Dzvj5IBi1htLFrZyGEBajl033D5F9X3tW3h/aT9nU+IrU0ayVPJRu5/rO4BylsX4HChj9hidB4lQ5ohtr4xC5j33tQCXPQHg6f+K8a8CPtVcU4kYZYtpYCitJ6SASgrNBo3oBwWZNZ81QBoA4MTaCdGHkLVN6/w+xzljhu0wWonlAcdK36hpYfG8dpiKVCCZ1K0SUIr/mjKJa4f+LYZUC60ncvaqKjONsFGIOQBd9ZMy/vTv8XSJVdD1NIOZOh8ecRSpnElRunGmheCj845zQNH+ePuDvCf63MjrKTj+pG+UgNJ84Ahx6f2gAirybODGJVYZZt5LCsGnjWv9yvGIskpArcCsAqpXCL6yOaByKKAMWFwqfFhfVYjpW2kF1EbPEHzGulOBcTZCIF1qqLOdvQncf2YXAABObsmxiXPZiUUbn3nwfPK94xXl/jqGckz96T/d36mOlYTg65kDahW2ZgmobrugtGIUYHkWdw0TCLB8KGG9sDqv2iPdGGDw5u8E+O6/AjjxKL5ctAIq4kl/FvgOhODTrougONNPTnGAfxjbz7kYkDkSiA4BxSHCyAOgQsC2AHbTe2OAaLZ9pi7udcMQCaZMrAKKrsEogqgBGKPP/rUjfBvsWo1dg5FnsESQGJ9sGQ6sZ9aJN88Ra4mtqwmvD9smQ3gGr7WKAoquX27MGT9wfceu8NvkXgOjgBLnLnI/B88pZjxYkhmYMIXK3AeN3GNzQJF7o4jnDn3/8h8B+M73A6wfDd9X7YK3EFgf+55W1ssXAz7wSm4W61JVGhTcVQDd0NP43/rUb3XUJBpgff2J6733tRB8ahixWECd9qeRy3IhDvsoQJy5Jf3pE7JODcHH+CvWrawfAEaZpPiYkjcsRHRho4QafaBYG38cgo8SgNPZVN0v3lw7XwPkPUHXkDYu3DGh9YfmysIKKNofjbyFBtS1KPrDfC5q+/34+nG1XtrX73vW94l1O/eCG4oxqHgU1iLNyxYksokPAPyZEbo3WLf2sEO1Q2EV9VqB9X2aH+Bw5IAqHT4MK8i6jMPuykLwdVdA4XBeX3PjVdl84gyrQ1LX5sfuPQu7kymc2BzBoy7aDN/Q05581fwJso989uHke60apmA4NgCArbWh/U1u8iSlmiVxD6gCqotSrov1VkAVJuwBlmdQnzNzFfNd7QKwGGVT6L3Ue0JqnMueEAbNxfsFUDwEkpt7OXKsi1omhTBz2hz45TTAmR1L7l5mnh3lVaQCin4WzmIVUExd5h768BJtw84DuZcbS+4aJqAMqBCzvmIVUNEEgdB/rIACaa0ybdC5j1EDcnXFXmsaCOaAYoklhoBq0Hv4ItcHdp9iFSWzZ6gPAIsQfJFzGrufvfsjyknnihSCL8bnkL/XPndxfUUKKNW/CvhUcy0UAs55TwG2vXrR+0YBpD2xr5mmWNGAby0HlObv1UevhuNrLshM1RTYfuklv6QC1g4B1Q7UsmpYPU2No9XDjG9ozFMUUNi00GyaooOW1cgqc1+08oJ0hSqTNJ+8taeQMtQfNXcZWbc0dJ5GumAFFH2fPgXdJwRf51xfobOBjDkmnAB8QkoLXeiUI6RMrOKR81c7K46vHYeffdHP2tfa3DXQwJde+6WO/+L+aeL3XUpYzBDJ6uTuW7znqOowhhT4CqGdkykPO1Q7mFZRrxWYyeXTR3FgiIlSIaUMON+HJCutNHns5cun8LqEM1zmViq77Nd6hFw8j+65+MiaUrK/uQRUGohu+nZ8c7iSJw9ObM6/RJzf3QuU9G2pfCvv55c98RQAAJzt4Od0OrNEc0lCYr0HAXUOEWtf//Srs/lEre+ZuYqwi+bM7JMDysx3SaKs2gVgFACWCwr3SHUp9Ukh+L7tDwH+5f8B8Oib/HtjAWBJQcQRbSGwOyUHVBcg2rzmxqMTMcj0S1KkUF+iFVCRc88q24QxCimg2PliyApcJ753fI7xIWZ9Ra7BaPJR2GtrWgg+Zq7sdbJGpLkKKWY8kme2vK6V09Yq7YO3voRy3BphiTDkh9k/IaItak0S/8TXUhsRcy/OqZQDKtAm69/i9Ws/CPBVPwVw83fx98YQz+LxE3tG0/rqd5ZqusWCgqG8QfhPA+LHAsAq8aUofWi7qoKocfuQAkjT9y/euFgFh50cUM1QJS60fFcq6K+oHIIh+JoGvvoxXw0AAC97/MtUnwACYds0lYN3bLkXNMVHMASfMj8eqeSd94pPGqGmkDJt0/oESCSRQq9Rn7ACirY7g5lKEGokkzZ3dG60UH6hM+QLTn6B83qtdXEzvF9ms7QQfNGEszJGWlljz77i2XJ54iMdG0mV2UKr+qWeGwnnqaRuwmVTQvA5dSv7MLWuagfPpG+t1TKaASr7PCm/V1jFMUL5gLraeK+swuixlx2F4xtDOL29B2d20gF+S5AVBnv7hA87t+hX25QPy+WE4EsE+scrUpMZs6RJB0Jib4W+HlmfH6nndtLJR9y3kmSZJZs77PWHt5f77o1f/5RsPlGzCqiOear2VhB2cZjhzJysSJ1X7ZFukes8hhCJBWOlEHxXP2P+n3av16ZAfHhkBUfUMHlpggoPpk1UpVNXrL8YBObCc6k/TJlyTle5PiDw3xxB0TmgyJhd/kShXASRYCyUA4rzpWn4Nrl2J0xexS6EWayyKYaswH+PUAi+dkTuZ4ga44tHVFFyQcgBxc4VumafNGX6hT9v1o765SQCOCa8HCWWzL3BPZNAQMWS5J1IRcUHZ64ExVasAipqrS5eX3z9/L9QG2p9kX3XPksa8UW1ap5pQKYTko4BMakSwFgwB5RGWgTuDwHsmkpGDFm3+Bs/+e/loaJ+KAC8Q0C17gOgmjJD859rM9Qfqd4GGnj9c18PX/MFXwPPuPwZftmAT/j92DB6nF90jKUHZS0BJQHwCgEI4JIpKkkJaSH4KJng9IchS/DfeK2ttWsqQUUVULisp8ZJIPlSFI+pOaAAAN71L98FD+08BFcddSMFUQUUbmsGM/erivIZpo4xVR4pxBtAmEiJVSp5dTWN91ryX8tHpqmaaBvznwDyGufaSMoBpewJyf+QH9UOplUCagVmyZ1eIfjm95YLwdefJNtdQTipW554Cv7Pv/w0nOugMBmvIC8MwFJh1UVNdnahMDmyVl5Z1LYNtA3AdJYOopfO90XNrKmdDnnUjK+l1IPYttbmX8i6KKAwAVVyDw17nEent+dfVi/aGsHmWnwi3VTrmwNqPC1P7CxD8PVQjRY+16tdIBartOFAcbWMUk5SQEl+aXVJwKunAgqA5OY1/mEs5WqJ+nxN8LdlciGyiolAfay6hxsPRoEhKqAEgP3b3wPwgZ8HeOEP+E2y9wmEEUCCAor0iwPAuXtp/Vwb7Hz1XINafbh9rIAabUTMqbmf1CcRFSFihh1L7r7F65f+BMD2g0tig1V2Ed9iFVAhcszcGyJtJf+7zBX7OrFczH4WyayYNjr4ARCpgIo8o6OJ8vqdpZprHqAYuURY0BYBplN09hsCAAPnsaH+AABa9JsgKZ9JAAiWFFvmPjEHVACwpsCqCUEIMCc0ND80cioF9E8B0BtoYH2wDs+58jlsXVqYukEzcEiNkCpIqsfUhd8TFU4Lf2IVUE+51H3w0iOVBGICIKDKoiRs45bT+kv7g9faHTfdIfoH4BNQ2A9NMcT5HBvGkfZFI42kdXn10avh6qN+FJbRwP0ejtsK5YDyyCn0Opa4DioeA/s9lL9LIzyd+0AmpxYF3HqEddtCWgg+jlynqjpbd4A0ilXUVTucVinDFZgBQQ92CL55vb1C8JkQdwVJia31+eF2poPCZFUh+Azh2ImAWiigTD9L27Bjrp3S+b6oGQXUThcF1ArICGNGAXW2g0IPh8QbFfS1jyLz4QUBdWyDAVszWt8cUCtRQC3OzD4h+Eqf69Xmdt1111mwwfz3Yz/2Y06ZD33oQ/AlX/IlsLGxAddccw38+I//uFfPb/zGb8ATnvAE2NjYgKc85Snwu7/7u6vqQsBikZ4uAKoEgg7CZWJJHxF0V8gKltAw5SIUUDEAcHS/CDhvALMYBQItx/nLgdhc/yUgWlJpXPlUgK99C8AJ80M+AoyWxpESRBJZESRcGLKiacFLSMD5x81XbrJCmtM1pIAabZH7hbXq9Z/zX1JA0XKtMF/09eLCTa8GeMG/9a/jeyiJ5imgOHKlYfxgrklEcYzSK6RYwmW116nlOEInpIAS5z7izBb9jSSgYh5MiD3vvffqd5ZqrmnkQipgiIHkyXT5e98QUHefvTvKB2qxxAStiwK6moqJA+253Cjcvd77iuqEzQFFQXRUT2wOKO5eqVzIX68uhZhomsYh2FSf6JhTsk1ZixzxE0tEbgw34H97wf9mX9McUBIRyfVHzVNFQhPGKrqo79/4+G+UCZKGIaAIWZCyZrT+aPXQPE0podeo0RB8Xn+Edc35K5FmGtEeDFEZIKi0e7m61BxQ2tyBW9Zpg4yL9prWe/nW5V49uMx4wj84wJm3JxWopRJSh88qAbUCM4RHrxB8hXOFGMC3Tzip3RWAvkfWTIizHiH4CoO9fdQbOwsSYn24GgJqraPybUk2rubQX1uMRx8F1Cp83eiRXwn72RZUxBiyY0yTxkfY6UUIvmMbZcWzZq66kvbjSXliZ9D2PzNXSY5e6PbDP/zD8NnPftb+913f9V32vdOnT8OXf/mXw7XXXgt33XUX/MRP/AS84Q1vgF/4hV+wZd773vfCN3/zN8OrXvUq+Mu//Ev4uq/7Ovi6r/s6+PCHP7wf3XEt9st3FCAZCcTjNRutgBId419TfzkAlANQnfsSFFBdxtHUjZ+8tAQUQ5ix9XFjycxViJQSlRC0vchcUSwhIjypu3VJoBwzXxyZAHSeF9diFFDsGEeu51glCDsH4IbgG0oKKFpX68+9RGicCORc9PaHEoJP8oWWob5JIfi8OWXGkiNgokLwca8j+9S5nFSMm1PaLyk8Xs7zh7QhKR85wkwrw76W6quATzXXQqSAeB+zljARMZktCSgDxF9/4nrRB61dLbyd9qS/975CeARD1gXOGw2QxmD9sBmqPsaGCaT3aSHGYnJAee/T+Wj49wbNwAlxp6mLqFG1BA1ZpxFzmr9cKEhcN/bXG2MCseI1TdsJKYZi1UWcz1p/xxM5BB81T2Umfu7o9wYVUD0+W9S6ZjKRlLzfBVIpRChzFjvmdF1TQi1lv6sh+Mh92jjRNf74ix7v9cEhoKZMKG3BUtZiKlFZbf+thuBbgZmcQ3sdAF9jpXOFDHuodoytIiybVZjspiugDNhbOmycIfO6kBB7KwwXh9tJXZuHKQfUkowo7+ugR34ls17Kr8/5nHdR7pw+P//ycPygK6BsaLtyY9lnHM19Rm1f6sGCaks7duwYXHHFFex7v/qrvwq7u7vw9re/HdbW1uALv/AL4YMf/CD85E/+JLz61a8GAICf/umfhpe85CXwfd/3fQAA8CM/8iPw7ne/G37u534O3vrWt66sH7zFAoMRgGTsE/FRIfgCagaxzda/zoH1AD742jTuNY68EX3pCFhTcN6SJTEAMK2PI5u4a0wOmlgFVIzCA8D3eTb1yxh79qsAPvMXAI9/KX8v1y9ohDYZf2cRCijOt2gVTKwSRNhrQwR8jDaBndNQXxtpPADgS18PcP4BgBtfwfvn7Q9BUZTUL7J+vfW1eJ/utxApbO5tO/jLKb1iyfTOSilm7bbM/gNQSDpuPXiOBPww1zsooKLPXuXzo5JO1RRLAQUdRRBTDiugcAg+A3p/21O+DTaGG/Dia18Md33uLtkHqiyhKg2BDOHC2VGyxGknAEjj0Fqh0FOxYD1VQHlta8qFHjlhnnrpU+E3/+43+TY5AN5lf1R/nRBjVJWiECmaWk2711z/qsd8Ffz8X/08PO/q5yUpcLzcTI3so5p3S+sPIR5CYxgK42ZtBvAtX/gt8F/+8b/AVz/2q2nXvHqSFFDKPgwpoKT7YsxTQOGQgikKKLIHokPwMf5qpDfno+MDXdfoO9N0NhXPkrZpVYIzdm+1Teust1DozutOXAc//NwfhiPogSx8z94sXjygPiyg7Jdqh8PqY9crsCW50/1J+dK5QkY9wnIBzMFUg8MWDcFncuz0CHFWmtzpA55PCivdqBlCM3VtjleQ7wvbMgdUB+LRKItWMKZmbU067KPdFYWI7BOC78xi3xkiuJSt9cijBrDsW8k5H/QMW4pJ3xqCr7z92I/9GFxyySXw9Kc/HX7iJ34C9vaWnyHve9/74PnPfz6srS1/vLz4xS+Gj370o/DAAw/YMrfccotT54tf/GJ43/veJ7a5s7MDp0+fdv4rYo34gpTrAoJKoC0T5i5YVyJQ7KkyGJCcI6+wb2JepMxkRcspoASyQquPVUwIhAC9FqOE0MqFwOjpngzOjzYBvvHtAE/9JqEuxt8UsoINwRczX4K/Xcs13ByQv0ebZPrMemb8pWtEGretiwFe9ssAN7xE9hffaxVFXfagQJpwZC/2kfULX6N9xftUUEBx51EUmczdG/s5G7HvRWVTzLqPJMIkP7x5iDh/+7YZvK/ahW4aqOkZOs45EBWD0pwC6ujaUXjNja+Bx5x8jEdwUOAcW+yT/5yJaoMEoiTmfQ3IxwD7sB3G+0H6mhLejoas+9ov+FraIfFetm40PZoCKgQ6i20Q8pC+zym6TqyfgD982R/CG7/kjUFCAZumAqJjrOaAUsgcmuuL+kXXi6aAovWcOnIK/vjlfwzf/0Xf7/mvKWwAdBLTa1chE2jeplRz2kEEzU//i5/2ckBJa5UjXcUxpkRjaL873/F8/yUSiSPBVUKN/K0pF7XQndoZSd/nfP/6x309fPl1X876RRV31ELhPLk6udfVDr5VAmoF1jXPjrHJdGbB6fVRmSnrqoQxhvtWkpQ4ugC+z+32CcFXdtmPht2B6fGKQ3IZFUcqGbFfOaC6KKCM8q00sQOAcql12Ee7K1AQAvRTO24vQiBurpUNEWnDWO51I8RXoXoza/J8B1IUwN1zNQRfWXvta18Lv/7rvw5/+Id/CN/+7d8Ob3zjG+Hf/ttl/pO7774bTp065dxjXt99991qGfM+Z29605vgxIkT9r9rrrkmV5eIdQBWY8mVGBC0L7gpER8O4EvBeYGAokB5LFCs+ecVY+rCe5gNwafscY7UYIkOjNr1UUBFzpdHQE3kufLqiphT1pcG2LliQ/BF9CubCoa7nyFtABYh+Mja9cqbusg8x/rr+UXGTcqpJPaf8RdfG4xkBZSmVMTvU7Ktawi+6D51PX8SCB26T0UyPeLsjSXMQqorrlw0+a+dgcJ6r1aNsdin0jnwEBMRWAHVJccKNhUkV0B+L/yYF2qT/9vU6ZACCYSNpxZBYD2ncgipOqz/hAzx5koCpJu5uujtL347HB0dhR/54h8JAvBafylIjsPUUSVTbF4n7j6pHofoWYxJChFJ/dWMqnM0RRpV3zjKMEoK0LnScuUwPkokmkdkhRRdGkkTqYBKJYWpHRsdg6df/nR48iVPhhde80KnXU0Bxb7WzgahHjqP9H12vwvn5Gw28+51clrN5Bxd9CwIhtwUxpwL/6iNC2f4rNybdlRABR5uqATU4bOKeq3AuubZMXb/mR2YzgDaBuCSI+s5XbO2JKC6+Yhz8xRVQC0IqDM9ckCVBvj75ICaTPcnBF8qYbLKvEoAWAHVPbfSKlRlRhXTJSybDcFXmCjrk+9te0G2bBT20YzB9l5Hcmdafn1efmwDAADufXin0/14/EspWx/J9v3f//32R47039/+7d8CAMDtt98OL3zhC+GpT30qfMd3fAe8+c1vhp/92Z+FnZ1ucxdrd9xxBzz00EP2v09+8pNlGopW2nCkhlJGq88heToqapZvCPcRVKdhynA5V4boexIXqk71JbGM5yegcHER403fa7w/ln7gI5tVVkQqoKLLEZ+ne34ZyTxQw5AVIcC+4dfNUxbKqqufJfvH+hY775FkhTSnF11L2mAQSY6c8AgtSi5EKvhoXfa+nv2y9Q3l8HJeCD5hzGlfHQKqZcoA309vTn132Te6kjADhhzjcsuxvjVCuQg/RMKM5oCSFOmR50/0ORVbX7UL0VKVQJphAuqxJx8LAACXbFwiFY/yAUAPpRV64l4iMTxQllN7OPyTSy5Qi80BBTMfpBX9oCQMJYIoeaAA0gAAz77i2fAn3/wn8HVf8HVhgD0S9G+b1pl32gc6rhLBQd9bXPT6wPnjvR/4vkP91cD5rdGW3I5C7mwMN9T5oeOiEUcp+9AjOAhkrCm6jC/ce15YSUTMhcIYhqxpGnjHS94Bv/aVv+bnxpopOZOYc6OLGqdLCD7JKGEG4I75dDaV914T8Fk5vxzfm9brX2i/U/uuZ8zzLb/8hpcHc0DFfmZo+7va4bCaA2oF1kcVAQDwuQXAeenR9eIh+GazOXie2s7ZhSJpbdAWVkDND99zHXJArSpvkc0B1Sdf0YoA6fXhfDzPJ47neG/VCqi5n13G1JAmqwgXOOpBNq8qB9SwR+g4EwJxY1RWAbW1ZpSO/dRFJffR5cfmIPfdD213uh+TlDUHVLr9m3/zb+BbvuVb1DKPecxj2Os33XQT7O3twcc//nG44YYb4IorroB77rnHKWNem7xRUhkprxQAwPr6Oqyvl3loxDUG7O5aLlYB1SUEX2ybHGBNAXZRUdMArB+P8IUrEutvgKjhFFCx8xKrgOLKkQTMor8ppAY2TgEl9WtA1j0LxDNkBQjXvuInAK59LsANXyH7xz59HXm2ek1GkAT474uuA/jmdwJsXaKU40gj+pqSH9L5ERg3A1R2UUBxhFk7cMNMOuWUfSr53LTuOrQKKM0vqf7IfSpaYN+vHfHrY/fpQN5DzvXYNRl5XkbtZ6XNpgWwYc5iz6n6naWaaykh00LlsLJkc7gJ73/F+8VQXRQkV/PwIKVyCphKQX9MHGmAP6eASgnZRX3COaCmwOSAcbaoTMqE8vlIQD3ut7merEATfGzBJaCappmXnTH9UYjGFNUGp1hJCcFH/dUIm60hIaAiFR6bw02VlKQh3rQQfH0IkJQ1oylUaF/xvu6rgOLqNzYDX1FkrIXWaxfPj5bzLURYpqwnat75RULwSWsopFRy1mYT3x9ar0boG3vuVc+F937ze+Ho6Cj80J/8ULC85LNmVQF1+KwSUCswEwZqezyF3/rLT8Ozr78Yrj65GX3/Q+fnjPHJrX5xUjXDipvxZKrGteXM5GTaWl8NMN0lB5RVwqwqB9RenxxQqyF2rjq5Cf/wuTPwqQfOJd1niLLSZIkxq4BKVMSc292D+87sAgDAo05uBUr3N0Pcdgllacd0VQqoDiTZ9mJNbxQKBWrsyHr3XG8AeK+X8/OiI/Mfgqe39Sd6qI0nU/jtD30GvuCyYwAwV7a2lYBKtssuuwwuu+yyTvd+8IMfhLZt4fLLLwcAgJtvvhn+/b//9zAej2E0mn/Ovvvd74YbbrgBLrroIlvmzjvvhO/5nu+x9bz73e+Gm2++uV9HclhOBZQHkgv14Sc/o9VUiU/+e8ogBlDlAOr1Y8vX4/NMG9IYRfZdAX0AQMgBhe4ZbgCMz/LvSeovAKSsAl7hMpQIqEgSIgTEczmgpDEabfB1hRQ/nnpocW39GMAz/jW5HtuvCH9j514D4W1+JlofQ+gAgB+Cj+m7N45C25TM40I0cq+56xy524789WXL4e/93Pwx6qamIWeIEDKQJe06zJVWLlTf2lHfN26tsQooZj/H+htL2EcpoAKfC4xoky0X8q1atYVpQDIGydkcUIRsouoRsZ0AwOupaBSQViPUQvmWqEn95fquAdZYLcIpIELKJWMaoK6pcbrktIoF3dum9fySFFC0XeojridlXOj7LLCNLqWMI13DGlmF/VofrPv9EYiUBhovB5roU2haKERAynvzoe0JjVSloQkLfbaoOZM4RQ3+OhUgZcR6QO97yGjdWgg+SjBLxLF5H7chnU+sIs35WhHXn2Nr899kezMd0xHnh/iozUG1w2GrQY8vcDNhoN7x3o/D97zzg/DSn/rjpPt3LehbjtzBSpYuobnOLpQKR9bKcpomB9TZnS4KqBXlgFrM92/c9Sm46Y1/AA8nANSrIsmMXXfJ/AvRx+9PI6AMEbTyHFCJpN4/f37er5NbIzhRkMA11jWn1uv+7w/DK/+P9y/qKKyA6pHvzajJNgsroI702OcAKO9XwX3UdU2+/b//E3zvO/8Kvvrn/jsA1PxPpe1973sf/NRP/RT81V/9FXzsYx+DX/3VX4Xv/d7vhVe+8pWWXHrFK14Ba2tr8KpXvQr++q//Gt75znfCT//0T8Ptt99u6/nu7/5ueNe73gVvfvOb4W//9m/hDW94A/z5n/853HbbbfvVNWSR6zzmx2esCoiG3WLrilCosOUYwBoaH8Bm62zcEHyW6IkAY2P7LoLMCzMAgARY3/ydi39vY9pl+mXuxXmQuNCCsWqZ6JCJVAHFheATxmhIHrLi5qtpmDYVFYnvcNiX2DmNVtUwSiG2HLPe6INdlEzhyBU6jjHtASBSInIsWXIBIzBDRtW2qCs6BB9p48ob53+uHwfYvIj3jztDolVdHfezRwJu+dcbZoxYcoxTSsUS9hFnr1pfRBnvPe2zJOIMrXbBmhbWjZoGJgKQUHOFfAr5q4GrHvGgAMGaT23Twn/4kv8AAAA/+rwfnV9ToDn8cPBkOlEJtZSQiLG5pDrluFEIHToWjqIIXMDdOX4CALTaP4VIpNdCKgwt7w61TfJZroaAQ6+9EHzKPIdC8KWoSjwFFCXJyG9XT40jzBcN60ZzQDm+ZyQWPMJWmSuNtNRIGHZ/BAgqAICnXPoUuGj9InjypU+21zgiEc/fdDZVCZvY/c7da/vT+MqwEIGrWUoOqJQQfKnKsmr7b1UBtQIzgLJRMp3eTnuq34D96wWVETgE1F6H0FxGqXCkuAJqoYzY7aKAWo1qx5BkAAD3nN6B//mX/gx+4zueG3WvUUCtKifMtZfMQ4t84v6zgZKunTdExFrZ+Ta2bhVQaWvz4/fNCSjTz9I2WHwhSyFx//zjn4dfft8n7OvSe8gQHuPJzHuCJmSGgFovTUCtZVJAFSR3uq7J//a3n3Ner4psvlBtfX0dfv3Xfx3e8IY3wM7ODlx//fXwvd/7vQ65dOLECfj93/99+M7v/E545jOfCZdeeim87nWvg1e/+tW2zHOf+1z4tV/7NfjBH/xB+IEf+AF43OMeB7/1W78FT37yk7lmV2vRT6Z3AUGlqjKG4PNAcgNYE8IpFtTHtnuOaSLRD69YoF8z5kzA9zz/fwV44lcBXPFU/z1VMcGE4MPlRAVUx37R12wIPsEGo3lZNhwhLK/hNo5dtci3E7luogjOrn3ni7FrkC/olztxDSlCFVCt3ydJARUao5bJW8Tex1znCKN26BK7+B5KtMYQyk0LsHUxwL/5KMDeDsDaFu+v182GGffM+9lTQJkQfFgRxhDAGgHlskFx/sau+xgFlGqRa7oqoKoplgQYKqQEgJ9bJ9qHABGh5joKEDYh5RJ3nw3Bh8FkAsx/xWO+Ar7s2i+zqq/Y32TT2dQjaERlTMB/NQQfxPVb8j1ljDHBpoW08/Igkb6llKUWUsI57ynkG63n6Oio+L42rhuDDT80oVBPcPwTzu0ZzJy1GlLRpZBkTbNUauGwknQNZ/2YmcljwREa2vykKOpiSJr/9BX/CSaziUPGzWCmEpw0pKCqXFLWZgMNzJqZcy9+LzY/XIyNJ/oD+ZoSC1tVQB1+qwTUCqwvwGgATpMHp4RhwmPcITSXDcFXWAG1VEbsJYPnSwVU2YPqC6864bz+s48/EH3v3oryVBl71EXzJ3I+/cD5pPvO787HcnUE1CIHVCLY/+kH5/0y/Sxtow7qor/+zGnnNV0/uQ2v/8l0lnQ+bY/LqzEBlvv8TAeiGQDlgCqqgJqPQSoBRUMsropsvlDtGc94Bvzpn/5psNxTn/pUeM973qOW+aZv+ib4pm/6plyuZbSMAGKsuqCNIKCiiQ8JsCb+ck/qSyTEY14I8M/vB3j8i93ymh++Y3HXPQVUKATf+lL9IfnG3cuF4MOuDCPJCjHEcqBfKSH4mmYRatCoq7n5InN68tF8nbHrhpsvkRDwCobrotdVVQmz1y66zvfFW9OkXUkBFepXcgi+1v8bg8CDoZ9jjCOKuT7Y18w6P0Zy6IXm1CF0bHIQYM27nLifn/tagPf+DMAtb/DbYYligWT17lX2jPbaXif7VwLrY1RX3nva2djlDK12oZgask4jhpil9ISLnxDfrqJqCCqgGrksrUcDmiUw24DsoRB8OOQgDbMl2ZG1I9FjnOS/NmbMvg/lwIn1sW1aGDVyPiC1HTKPKhkSANBpm9oc0HnXxtgLwRcgLoxRBZRpi/WB6U+qSsUY7XeItKSv1bBuyE+NbM5JLHg5oBhSzHkdufY4ld7GYAO2J9vw6GOPjsIpacg8669CknEEFTZtfXkE7YxfQ6wCKlJFxtkt194C/+2T/w2uPno1+/4UPbxH1ZDYqgLq8FsloFZgo55P4e8sQN+SuWGapoHRoIHxZNYtNJclycoSJwb4ns4AdifTJFJuFXlhAACu7kF27K1YAWUUZakg+vkVhWIztj7qpjYx6sGtFflpc0AlkLiXHnWfKi5Nlg1JuM0UXtsQgKXzVJl9bs6+VDNnWEkid7km08IE0jNrVWRztUewdSFUxHsigfgoAipybXtNCooJDuyVyKt/9VtzZYVRkMSAsbmUQpzqRyUGGfDfGRTzd0ABJYXg8/yVQvBFEFD+TXxdAC4BxRJrhKxYP+qX0SyKLI1cz7FEVR+wfu0oKUIUM5yCJlYBRe+TFFBRa3/xN15PrALKvEfOAokUZtc59SPgLyZ0LEAWSyxFkG/4vi//EYB/8QMAo02/XDSxxJFvsZ/5kWs1hlCOfTChNzlfrdrcNPCYho6jdvXRq+E/f9V/hhPraQ/kUeDYa1fZeyGQPzaUGW5/PPWf+A8pikKg+w8954fg7x74O7jpipvgnx/+Z/Feb4xRtRp5wOZ9ifQ3BASH1oSmgGLz9JhnEFC9g3bg3Sf1ge0PmXctb00ojBu2IyM3EkssebA2WPPURqIPjU8cYVVNnxB81ActDKXnIyUxSP9sm4kPlqeYF1IwMP7Yj+iQm4s99ytf8Svwtg+9DV77jNfCh+/7MNtOaJ9zpBi2UTsS9x6nXBL9J/Ph7RVneekka8i+6jFfBVceuRIed9Hj2PcxAeXkPQs8WFDt8FkloFZgHMC4N5lGEyGrCMEHMA9XNZ5MkvPXACzD9pUGprF6I5UnW5V648SmG7v6yVcfj753CZyv5nA14QjHiWEXV5ULyJjxczKdJe2d8d5CUVZ4XRobdgjBR1U6Fx8RwihlMnyO7IynSfvB5lYqTJCakKBdyHCApYpzWNBPG4IvkST7wD/d77yuCqhqvS0WFI8iYQTwmBp+ajGaaEoEgDHg3TQ8oeP9MEdEhwPec4SO50jgteCveX3sKoCHP8OrrlLnJaSA4ggdKQSfB8R3VAFN9+LXB8AcuLfiagaIBzKnVsHVhQwSfIklFaPJikgigduTIy4vlkLIAcTngKL3pYZlc0Jqmv2H1lM7XNSJUT+OWGqYMWfWdPTeWpS75iaAT74f4JnfsizHKQ25e4NtKnNP58yWMeMVWA9tQt+7EmY5FVCxqr76xHE1Yilh9mLInCde8sS4dgkgSnPgYHOA5Eb2kSOyVAWRAEjvTnc9H0KKoRCo+7IbXrYsG5kDKqTOUecqAJon5YDSgOTGVz1o+bCk/gyagaqAig19Z/zlSETWf9DX9Tp5QEglMVE9o3YUrehqwV//WF2XAtxTMojOHa43VLc35uhrBM33lkLSpJiaM6nx945G7oj+Lso94eInwJtf+GYAAPib+/+GrSdk3hlEbl0brIl+UeWSl79LUaSFwpR2zSlm7n/WFc8S35fObm0dVDLqcFoloFZgXBioP/rovXDLk05F3b+zInXRcNAAjNPJCAAU8qowmOqECpxOYRPiwfNVqXYooHzF8XhFixnHQcHcNdgMMbObOOfnd+djubGqEHyj5XjsJhBQu5O5n6XzfhlbEifxBBTdb6UJqNGghUHbwGQ6g+29CZyA+GS/hhAqTZoY0r4LGQ6wIgVUxxB8NAfgqtZmtUeyxQKDMQBwLGjbIQRfNPC6qM9RXBBgW1RASU1EjFHnp/8Xr1/7FwDn7gc48SjfN/WHGjcvjL9cbilcLlYBNRDOfK9bxOfZNA14dghExhdKVgwZtRr3WrzOletIQsT8qE1VlTgheBq//5wCSlIdhRRbVgEVKEfL43twSMd2NL8+XAfY2172AcA9C6Bh2jDlYs4pwd//6bfn5K4NYxhDrkSuoy5rRCOKQ9aXMAvlvWPv7/m5kFSu2oVooTBJ2EIkTFK7mvKCvBebp4arR1JEUMUJrnN3suuVSVHgaGQaLds0jXx0AclpQwBpjRgK+hs5hiHzQPOmFcFuzd9hM/SIBjH0WoS/e0T5vYkeCkkh61SiJaAeomoQjTA4uX7SuX+tXWPLhoyuPXovDZfo+EyJC/I3Xn807xGtJ6sp+0NTCak50gJnWYigVQ1/vSD3rrVrqh+a/ymEZioh3cewAorWjddGnzCA1Q6GVQJqBcapWR445z8VI9kqckABINA3ATw3Np6uJrzdEBEzk0RwemdBQG2MyvpILYXQs8D5ilQRVgG1lzaW7/rruwFg9QooAICP3XsWnnx1XFgGo4QprcwzZsjmvYQ5p+vj+EY8IdTVNkcDOLOzZ4nEWJtMy+dWwvV3IcMBVpUDakHeJhJQUj3VqnW22C/gWRVQHULwxQKvloDacMtwqpLYEGNuobjr0f4agmFzST7RcqkKBBasZz6nYxRQHjkhheAT5mv9OMDOaYDrvgSiwXoAosxg5ouGa5MUULGkEauAil2DHdZqLKloymFFHkegcgqoWCUT9dfm04okaLm5wjmfzJoZIALKtNkOyK2Sbx3OH3PPcM3NoRWlpsq4jqhv9u/YPY7K7e3I5VL9AMisgFL2c1VAVVNMBQLpsaaQCcntKkA+BTE1kDb0lH20ggjVOZ7M1TNaPhxqKSRBrNKH+kjf00KMJSmGAgC8phxroYUGYR+DZqDOjzTmg3agEzY4BKKisJL685wrnwMvve6l8LiLHqeut5BiKDYE36Ad6ONG6nntM14Lnz37WfiGx32D365CaFCbzeSQdcYvyScAnTAYtAOAxU9nLwRfIsERe36E+uPUCS5hG62c5PZzINyo6EOjKwDXBmtyH6iPAYITWygcZ+wYdjFM1GskU9cxrXZwrBJQK7Aho2Y5thE/9IY4WS9MnBj1Rh8FVOnQcYO2gaaZR6JJJcpWnbfIWApAbcJ5rSos16hDCD4Tfg8A4ORmebIEwCU2P/Sph6IJKDP2qwppaPb6w9tyzGhqlPzbWoGqbGPUwpkdgO3E/EVLpeNqiOYuZDgAyvdW0M+uOaCorYocrfZItggwFiASaIwEbWMIqGgiQQD/hxSwZwBQCbDWfOmsdBKux4DpvYBdzV/0nqSA8tA/6XNbAOK/478D/N27AJ7+SjcMIC7DVhcA7IEQLkbtE6va6RKCL3ZOo9Zq5JyaclgBxZEy0CT0XfD3W34H4OG7AS67gfdRqm/AKaBIDiiAORFk+RNmD7ZDuQ8xY9dl7qPVVD3nnj1/IkkefO/eeaFI7Bok3xFjSMpociDyrKmgTzViGohOLQT8J7WboDDQyB8N5Kd1h4gUYyYEH36yPwTapoyNU1cj+xgKnaWBzCEgOykEn0byNY2Xr8gBmpV2giH4IpVUnE/U2qaFH3/BjwMAwOnd0/Z6KGQdViKF+kPHzCHUCNFI77t442L4+S/7eXsNj+lkGv97VcuZ1DatR/LFEmqU3KEKqFLqFq9uUNY885q7j74XzCmW2B8679hSckBpCqi2aUXlETevvRRdAZuyUR70Ma8KqMNplYBagXH5Z1KA1Z1J+XBSuP4uYa9WAfgaG7YNjCczq8aINUNArSpsnLEUcue3P/RZACivJDO21iEEHy77vMddmt0nyZ505XH4m8+ehikFwBQbr2jvGDPj+c+fPwfndyewGbHW6NiXzlGG2+isgCoegq87GQ6AclUVJB7NmuqrgFrV2qz2CLboJ9MjwNJoJQiTM0bzS/VNAJ1jQvDR7xxRgH1PfzuRXokKhCB5w/gYo4SILYfbv+hagJu+ff737ll6E18XrYMF7Bu3jM23k3EeYhVyscRH7Jxy4D/OJ2TC+tAxj/aDNrcod93zAvcLY8nldMP7zzxFzZVzzoKBTKQ4feXdiM8x1mE/59z3loBX1gMmeqNAko7rPkbRmEUBFeFbtWoLiwVt+5qaB4m0qwH5ITVLbO4TJwdUIARf6H4a3s8rGwkAa4QNfV8ry5FjTnn2K0rcGFNSg4bgU8PqofeG7TC6zZClKKS8kHXkXqz0AQiEFMSKLkqoJaiuaLs0nKBmWn9aaB0FVGjfUcIDv6bjUkrR4uWAiiQAAfSQlcGcbsIaijkDtXlfH6yLxLemGGLLOl9X+ToBwmdmX5uCQIQpa6IqoA6nVeRrBcaFU0shT1YRTgrXb8LApdgS8C2/pAYdlVrbC3XRRuFQhtRS/DSqisuPSU8y5zUD0KeA6LjsKvPXPPbyo177IVsqoFbj55OvPm7/vuf0tlJyaTRc3yoUUEYFeH6cqIBacQ6ocdccUPbMLDfvXVRa3I/JqoCq1t8iwFiAjiSMsD45INqrKxb8F0BnrOhpGt7/LoB1b8VW5BjFAsCcbxypGFJHxf4YjFJMRIbp04zmBvLud3/4LhVQkaRJ1NxHgvrR4H/keDfMiyGXD5Rbl+ia+FBXpL9evqAIAsrU7YTgG7rv4bqwj+1QmZeIPdjlzIjdz32Jco6g1RRQlEBPtsg5jSKetfZzl6tWLRziKpepZEPC6xSyAfeNKkWwnToyz/WNgdWgAirhMzZWnRBSRND7NOULtRBQHBs6r21aGKKzjMsJJZlK2CigeWgdBhVSGjBO7qU5oGKVbpSwacj3phABgtvFBFSw72Q9UTIkVpVlykv30nEpZTOYiWPFrRFp3at55QIKwVTT5vbk+kmx7RbaaIKT1k33HH49no6LKqC0EHzie/W7yKG0inytwDgQNEVlZADqUWF10ZLYSQd9rY8rCHVmgN9UBdT2Qu0Ro0rJabsJ42lC8H3R9ReXcsextQ4h+HBYu3ZFoQIBlr6mqLVMv1aVZ2drbQiXHJmDNjuRRBndb6tYn0YBZdZbrO2tOAdUSi4tbEtFZjk/l4R9/P7miq6SxK32CLXoZR4BnkcrJnKG4BPKeQqogFJI8yWGOOis2IqpTyMrAmqnWAVU7PjGANaSqiKa0AFhzMk13HcTcrGvck69lrgGtfpUYIHp+4AZ96BKrKdiixKJKYRyw+zxEAncDkCcF0o+sv52mK/Y/SzuwS5zH7HWMIEXA0J5cxpbLkatl3r+BNqtCqhqxGJBTYC8uUNSAF6t3ZAqSA1hR8r+ykt/Bb74qi+Gn3rhTwFAogIK1UVVKF7ZSOIolLslVhERAthTc0R5oebQ9w6OSJL6g/8etAOvP11D8IUsZc1rebY0EL1tWj/fUgJBiEm9FAUUNap4onmRpDVD/fVC8A1kAiorueBFj5bXtbaXqKUQsE6dEYQ3t/6/71nfBy+9/qXwwmteKNbt5W5Szl+VoCX1fOyhj1Ens5qTN0wjNAV/qx0eqyH4VmAcKTNJCSO2ItDXEFxdQvDtrkilBbAkylLzwxhCYFVkhLHdhBwxJifPRuF8X8aM+mI6mxN6McoWQ0CtGji34QITFFDjyeqUecYMubMdqS7yQvCtQKFn9kBq/iJzNgwKk+E2HGjHHFCrIMq65Mzj1KVVAVWtv8UCjRHAdjS5EkFARSsLBBLJyQHl/cL1/Zhf4NtwrseeC5K/9HVEm7HArgX1Y/1V5n7jJH9/n5Bd0aA+rYMjV8ic2vmOJVciyIrcpGLsOoomH/HfGvmo3ai04c11RDnj+5BR4rOEGbq3HTD7mZv7RDVkqBxfKLKuyHL4d5vJVaCRMk5ooZgzpwMRBpAeUlMt1/GsqXbBW4oaJCdoqAHFKvCqEL6cmigWPG6bFp52+dPgrV/2VnsNA6uOeorBgvoQarEh67ScMFpfOymgAgQWfk9TQKnqNeTXoBl44xDqg+aT+j7yL5S/iJJIsQoVqoCi7ab4uzfrTkDRfaaRYtS0EHxUAVU0B5RwVlBVGW07lrDh1pY4zxFd80g+APjXX/iv2bJa2D1t/dBQlzTkZog0zmlSjrJV+lBtNVaRrxUYB36nheBbUQ6o4QJQ7RKCb4U5oAyhlzKGs9nM9mvVOVdSFGVGkbK+ojCBeCweOLcbdY8B3FcNnHchTX7nf5icWqv7gFr6GauAcsutQlXWNcSd2XNcWNGc1oXcwbaK3F9dlJjc0VpzQFXrbV3IiugiMYB1D4KAu24JqDX3Gs6hYwmNLgoowQ3tHrWNCGA7GgBWSAjOHw48vvVdANfcBPCv/2/+PvGJU1xXZAi+aGUFN1+N+3r9qFBn5DywvkROdvY5jVSVcIRWbBuSX9p1UQGF18Tinq2LAZ7xP83/27pY9tc5C7gcUEzbff2NIUNyK6Bw/rNz9/tlMWEOQBRQEfMYTdhHhuDrRLR3uadaNQYkVD4bqBqkj1Fw2FEN0Y8+LQdUgEBzQFov96RblhoGVnOqvzDIjMP8eX40PugslqVtEECaWlAhRcZYUnW10MKwWZ5lg9YnkmJ8HLZDf30RcjGmzqj3kX+zmUxwAIDTN/q+BrDT3FghdYtm48k4uqxK2DR6CL4QYaASUIU+W6azqTxuDPkUq+zTci/Ra0kKqKZxzpkQuRsbppErq4XgC92b0xwFVICsjilX7eBaRb5WYFwYqJQn+w1IXDKcFAAKx5agMDG2zAFV/iBYKqDi/ZxMZ/bhxVX4+P95+Y327xQQfdUKKAx+v+2PP6aUXNr3/5//AwAAzu6mqWf6miG83vKH/wgPb4e/RH3qgXP27y6qvq62nqiAwr7d8sRTRXyiZgi5VIJn1Tmgus6bJUlLElA2TGDCwwTMmbVqRWa1R6LFguwRwHYswdAlBF8soWOUFxRY3bzIryuaEIkAWaN/yESC5NGkFwOms6qEEMGy+PvamwFe9fsAVz2Nvy8qBF+s8kaxUMi1plkqSQAARkf4sqnKOe1esa5A3VzBHGqRWPJRu09rwwPipP0s5HT7mp+Z/2ffY+pyQvAxOaD4m4UiHQi33udPZDmnDAOgW5JuYfiMJInWhUoDr81lqoCSyOKYMSLvRa/VCvpUc017Al8DpHu3i8HThuQKIe10zUv1mTOfEcFjCtSH+kbzR2mWEoIPZv5YYJ9oniSnHgLGayG5PB8S5lIjKakCylOhRZIuw3boAfMp/cHWNi3c8UV3AADAG5/3RrU/IWJoSL53qeoucOdKA/2xEkla30++5MkwaAbwRVd+Efu+ZKIyhiig6Fxp9YRyQJXK7+PtU0XhSF97ZFvCuhTzNEV816Htakb7phFjmhIrtD9yhrCkJp3d2v6odjithuBbgXFKkUmHPDZcLqmcNuqQY8fYKhQHxoYdQgViwq/0OAIAfP3THwU3nDoOX/Ez70kjoBakxaoUUHhtfuah7ah77vrEAwCQFgovh2Ey4ff++h74xmc+Si3/D587Y//+iqdcWcwvaoY8TA3B9z9/8fXwQ1/1xGJ+YetK8KwqB9SoQ24ybIa0L6nSsyReIhFOrRJQ1XpbLDAYVS4SBJUAa6k9rS5ajiOgmgZgEwG8VhXURQEV2fe+gHWsCoYtFwseR/QrNmSX02YEqE3v0dplVVwNwEd/d/ly7UhaG51C8GUkS1PDKoba5dZ07hB8ogKKCcEXbNcQUCQEX8x8RRPgMWdL5D4V5yFyjWCy1IwXLovPJ/peF6AmhvwHiNzPkZ8LsWu1AkDViKUooLKG4FMA4OmMRJjQQtaRv3Fd92/frxI4Up3ctZDyIcUomSW1o4UNo/Vo4frYEHwBAD4WSKZKn0EzEElMzX+PsKFqFoUY5Xx/xRNfAV/3BV8HW6Mt/32lP0kh+BQ/KGHjtRNBfv7qV/4q7E52YYOqdBOMkiFUleWsDY3UgMbZlyoxl/lzRg1Rqb1WSJng/hD6EBPeEd9Lx4nWrYbVI2cO9Tl2XOi9ueeHqjhj2qlk1OG0inytwJ589QnvWooCam+yGnWRAWu7gL57NgdU+SXVJQcUJtVWoYACWBIRsaHYZrPZMk/VihRQAACv/+onAQDANCIv2ekI5VEpw6qbD3/6oWB5Q5A9/dEn4bJjTC6DQpYags/4uTFqs/4o08zsgVSyeVU5oLoqtADmJI8NFbiCEHyzGcA08izizizu86FatTTLCCDGKoqickDF1kWuD9b9NqBxFQbmPe8sigCZ+xJmsYB1JxJJIGpY/yLboPfFKCZi8kSJPjF1cCqupgW48ka9DNem1DbnS5fQepp1Ipa0YgxJ4awHaR40v/D12BxQw3AZgLC/mgKqC1kaRQTG7lOpWOQamTEPFeE1jhWabL0B60q8Ro13LAEuF6sKqGqqqRynDGL2NQquYnB7d+KGltdyoWhA5qAZeACvMRp6LUTCpPSdyxGFTcsnpRFqGiAdCslFTSNdQn5QH7wcUIpiRPJx0PrElaZ80cyU5cgnzQfutRaCT2oXIKyAilGktE3biXyS5p4Lwefdh5YjzUeE1ypHrOB2cprWH9quRiylEIAauRMyTFpSAtOrjyq6FNJM80Mjp+j7q1RASeH5agi+w2mVgFqBXXrUB7+TckCtKHfRMgRfetirf7pvHht9FU/zDzvkgMJKj9EK8lQBpKs4didTGyZwY7QaBRQA8jOCMPn3/9eHS7sjGlYU/dJ7Px4sP56UJyE420gMwbe7Dzm1hlYBlUbwmD1XOhyoOUdOb+/Bg5G5yYzh/bYKBRRAvAqKI6quPNH9ibRq1QCgG3jel4SJUkx0JHSsAoqQXPgHtPGf/niNGotYwDqWrIgBiiNBfVuOmyuOYIkhRCgBJYXgi1GoJJz9XL+ccG0DgC993fL1zhm+bbFbMfMQOaddyuVQlbChLCOJA6e9CGWT5xcuF6FopPfbPUj6ELVvepIrUWR67D6NnHsnQTaznp0cdRA/d7TOkB/ROaCUuqX3VJ8j13S1aqADgzkJKApGOgTUVCagaEguarieIBkSUgEVAkxxW1ooQE3VxPqHXEwJAxYkqBSSpgE3BN+gHYjtaaD5sBl68xFLPFC7+ujV6vvaeNCxoASCqhAk/mo5oFIVKVccuQIAAJ5z1XP0guTnKm3TCcGnEIuej4RM0AiokqbtD2qUsBXHn6lDej/mHKDrmlosQRs6rzS1F53XlPMg1fCZq52pJVVy1VZjlYDaJzuIOaAMUL/TQXXwd/c8DAAANz7qZE6XWBt2yAFlgPa2AWgLj6OxpaIsbq63x8v+rDIsl/Hz9//mnuDTVr/zoc+swiXWqIopRJ787v/4LADM53yVZuZuO1IBZYi/VRJlow4qQly+dAi+q09uwqVH5zkTPvLZh5PuXZXaERPZsWQ4N96rJJurPVItEhSPKRerQIjJARUL7EptUpKLa5PEj48Cv6MBoJ4geawCgQOAHUKAqY+9NxJgjyKgMpxLMeqejRMAVz19/vqxL/LLzC+E6+des9cykhXRc4r+ftar5v/e+l8X7zHjHNVGrL+UgBLmlVtvwXbNnOIQfJE5oPoS4J0UUJHlpDaxAsquZ0KoSj5OIx5I6rruY3K6xSqgVKK8yxla7UKxkAIk9r1Uc57Yh9YBt6kCCoOnnmKI+ITDQWl5hOi9KQRT6Dd3KAcUta4+amEBQ3lrgooifGzQNULAbpoDSlKhaPmWBq2rGIImwkdi//HL/yPc8UV3wLOveLZaTsutE1L9xa6ZtmmjyYUYYveXX/LLcNvTboMf/eIfDZbViC4vLGDkGNMQfFo9uU3LfeSUU9aeVmdI/aipEDnDY5NC7nqKR3IvzQ8l9YFbe0VD8GECSltDifu52sGzSkDtkx1IBZQhTDrk9jm/UHtcdbL80/yDDjmgdleYo8qYUZThkGCa7ezNx7Bp3HxHpQ239Ud/d69aFnfjV7/1plIusfbK51zrvA6Fj/udBQH1px/7fDGfODOEwk6kAsoodlZJOnbN92bOotJkeNM0cOWJ+dPE23tx42gM5yYruY9wSMhYkrnmgKpWxHIqoGJB26gQfB1AfakNaHiwd0AJqJyKCck6ANvRqpLF34NYRYril3SdknZcO1IIPlouWQXEjMmr/gDg330C4OhlfJ3i+oogS2NJUG9OY9Z0LFiP6vrKNwP8238CuPa5/nusAipyrcbuQSkCwCA2BB/jm7duVkAa5VRz9lJAKecg3kOTHb5Ozb9oQlnaq5GEkbOktTMwcu1XuyAthYDKqYDCRlUmO2TfeWoLZUlrCqhYcDSmfB/z2hLIHg5kbtzC4ntBAirQt1hSrIXWUXkMmkG0wkMLWdeCTB5Kvt905U3wiie+Qu6UcH+KMiO2LFVAda3H2JVHr4Rvv/Hb4eTGyWBZjejS8lItLrD3UgVUbH6rGB9D5oQCpCRMJIGjKeq4/SGddVE5oFAZVinmfMST/a2s8+iQm9B67RYNwYcJd6Fv1Mf6VeRwWkW+9sk6KaAKqw66gtIAy3Bjm2vln+bvpoBafTi2EQKW///tnXm8HFWZ/p/e7p57s+dmuVkJCYSEQICQsAqRhB2VsIgIiCAKyjYIjILiAogIIiqLvwEdRUFmlJlxEIxBBSXCgGxBiOwgkLBlT+7a9fujb3Wfqq46daq6llN9ny8fPunbdbrqnFpOV79PPe+rkoZve29pHzbm46sFBFj3yZvrtyt/bn7X8Ah6447dKeLXvRMXTflSP9/ZrBJwSEYcraTg87cPB2KqAQUAzWYqw15/AlRfeX/KLfW1IopwqqkMHQUoOqBIragKAkoijKJjRMUBpRxQdfm83QHl9CO8Kvjqtg2FfgQNAKsIJNIAsEM7x5o8TusIIFa4BayVjqm9Hz7HZRErBl/n8kDzcMl6ajlXVUUIReefqqjoto8yGWstM/G71FxfmEKx/Vi7uWVUU/A57b+s3QFVi2ik6i5Uce0o7iPVc6To4YCSnUP9/lIJS/tR5bRSqOmmep36FR8JGUTqBpEEQGtFXFcuk7MIR8Mbh1v75EMw6C/2W7bhJ6WVHbffIn4dTnZkKQWlQWabIFWVYkxo68uF4SG+eQotwtyWzWZd20tT8GXzUrdUmAF0P7V1qpZLzhlpujtENx4Z9tRrdrda1dhELcF2rllq/YTkJlTBzaHmNC8oO6D8HHOfYxP3uUyE9OqHZ8pNl7Z2V2LV8pDVHzcHFFPw1R8UoBJiIED6uHzEQd/tfaWbretW/MPX5/oGimWRrDmGYKrp1Or1UatKDErHhbgtFVHv0l89A8Caii8OxH5u7emXtLSSdOowp3o6Jr0BXHxhsWVwH9764MtK7c3zOM4aUA2Dx1y1PplJb4zXUdOgmL1d0Ulm8tir6wGou5KCks1myukda0vBx69hUiuK12MQ14BrYFshYB3UfeK0DWSc3RtVKfjCdEDVGLBWEhJsn3dKLWi+V3T4fg4iVrim7BKFBJkDSvV8cxKgxP7K7iFUjpeKA0pV0AhyTH2Kb47tHGqpyUQN9xV5r9/pbxPL9exzXLkG6/qVBMNaREXb+67b83zD+fNu+9xwckAJn5Wl4FNxQKnOl/b96PrbMMj8o3pvx6APsSINEkrEqVoRxZNcNmcRdfYYt4frdu3iT1UKPrsDyiXo6bUet/fCQCZc2N+3B9RdxSqJYBMoBZ+kvXXqsdWAsjmZVIPQ9rRhMsdKrcfF3gevGj9u25Y6wzIONaAiGo8M+zkhO2fs2N1GYnpL2XYiHY99/9vORVWRz9cxl4i+Dh20iusO9+SugqZEVKrqk5N4KLQT2373Q9+tSm8ZJuKcK+LXcUr0h5GvhPDj3jDbRh30vfeZtQD8B++7hQBxHKKEmbKqx0dqrj8NppaL0zMj1ohRSWv48EvvR9kdV8RT0U+wPxd3cSUbsmtow/YgT5uGw5p1wWoWJeGAuvGBF5U/YxhGua9xiGVNg9vwK0D9y91PRdEdR8z92Kc4nzun4KMDitRIkCfda3KVAGop+FTX5fZ5iQPKfIKyKqVOLWJQkACwZH2qQoJTOydBYMDpey2IUKPiOFPtr+L5Vu6bJGBv+axKUFzheAUVQVWOvXTsitek4z5XOFdVRZMqB5SbW0b12DuMKy/U55QdU8u43Jok4IBSdb85OaAsx0/ixlRId1Q9DoW5V1bAPYhYKo0oBRGqyFBBVQwB1Oqf+NhwZb2ZbFUh+6NmHBWoj/an8b0CzWIfqrsYgwBl+3khdaD5SEtnd77I+tCUry7DYN9vFveLTGiRBMZl27CLa8i4jy9KB5Rje5fvQNl5mcvkqlwoMvEwLAwYroJJBhn5sar67hVeZjKuQkMQGvON3o3K3VA752UOqCoHmm2/VG1TIgp74eWAcnVXecxzVWNzORft+2HPzj0954NacJsbVFONkvRAASohBnw8nV9Jwafn4TIDxJlMPPVMKgKU+hfYN/73OQDAhm19kfTJiWw2UxYNo3Zj1IIYFM95fCHuOqkj6u5IueKoOeXXMtdJkg4ocQ+qOGNMcTJOB5QoHq5ZqyaY9Q0Y5ZhvHKKJmc5zu88UfH7mhVopmOlAfabgE11PSQu5pB5QDQyqBMUVg7ahpuAT3t/7bGEbwnqzBeftBKkBVetT/oFEDZ9ihdP+HfC4f1HZv+L6qtrJgumWhi6vZevLVG/brygna+PWF2URVDH4rzp21fPNKfgvNlep7yPbhv04urrfahiX6ICSpcRUcmAqikFB3Jyqc55bO0cHojhHSa6ZQ77uvqy8rgCCmUyAUhEy7dvxKz4SYlIVI1QPiNaCJf1aJm8JYgLVTiYTw7CmrLP3SfycPb2dpa2hEAQNOFz7WLzWKwtIVzk6ZAKI8LcofjiJa5v7Kr8hu4Z1OXTRWSxx6qMoYOWy7jWgZMKPk3NK1oeo8BIj3MQo+7JsJluV7s4u6Ihto8LuwhL7NGAMqDugkLGkt7TjVyCcPGyyZxun9VVdH7aDoJqCz9MhqCh6OX3Onl60qo1EWLWcQ5IUm/a/ZU5Je1220FPwwfrwgMp2KEClEz0VjSGALweU6ZCIOFD5ib3VJ3GR7t5S/5oLuVBvKt0wXVY9Pp0RSVCuq5WgIOKFWEvL67Q0x/PDk3aPskuunLJ4avm17BryW9soTMRLQOW4l11FMaaHfH9L5Ul61VpqYhrJOITmcg0on9f5sjmdUXTHkbIDyqcA1d5UCZrHkbaU1Dmq37thBm1VBCjVdYmf3/uzwjaE4Gqu4BzgVa4BpRA8Tcwt4yC2WIS1wc86OaCUgsIBREWpO6kGB5TlHIwhBZ/q2KvaKZzToQT1Q3JAqVyn0r6oCmYOx0U8V6tS/rkJiarHVOU41HqdVkX/nLE8tW2ezxKhVGzfPgFo87o3CTBfKqfKVJ1/fIqPhAziR2Qa0TQiku1mM9mqlHh2R5SJV+q8ASHlpmFY3SD2YLCXC8UtSOolMHnViBIDy3aRzNLHTPV+kqbgc1mPU4B91ohZ6GztxD4T9kFzvlnaX69UWuLnsxlrPWypoGkbiywtXaQClIuoZ752TcEn2S9OKfgsyz0cOEHxk5qtaBQ9xyC+7pGlpPU5hCsWX4HmfDPO2vUsz7auQqTt+nBKWSku8yMquR1zlfPQKwWfOH9U1QmTbEs1faJ9WS6Tk56LtTKhdUJl3RKB1t5Hkj5kj06REPnV5xbj3qffRiYD/OihV5RrhgCVQHvUDqjT9pmGn/31dQxr8ndamKnw4ghKi9sJ4nRoiNlFVhJsBpRqQCWFeC56BdLNczHu/SjS0pDDtt4BqYvQb22jMBG/NHv6B8pOHjf6YkxrZ7Ln1BH4z7/9E4B6/SJRTIvj+JvXud+aaKOHlZ7G/uyBM0Lvkx2/fTTFvoZ8Ftd8bB629PSjs6M6ZQUh/lAMNKoEEAO5ZWoRCGz9cktplWuwBbdd5q2aAuyqAWDVdir9clm3U40trxR8rsfK843BbSo6oJQFT6fAtsuxlm1D+fxyGH9QB5TKOVKrkw5wFnJVxSvLJhQdRa4p+FQFMyexVHRA2VNiuohOqsdUVVR1bKJ4naqu2ykFn8w1aE8z5HUsVc9BVaFY+VxVFaoCnJdkyCATBezLFnYuxCd3/iR2GL5Dzdu1OKCy1Q4oUcSxCDYeDqgB4Xq3izuygK6XUyFMVOvR2FOmSQWoUjS+jCWw7TCOpnwTfvvR3zqO2+0zTsuymaxFgMplctJUjeJxtQfJq1wcLvslTOzChGUfDu6bIG4YJweU5dgJmQLCHpvMGSMeb7sDSnpeQp6CT1WYM5k5Yib+cuJfULDXg3Vat8s+d3IIie/JnHteNZGCiI5mX+3uThkyUUmagk+SjjM7+J+JV122WvnMrp/Bxt6NWDp1qeV9P98tJB3wDjImdp88Al8+Yufyk+9+HFBmgDofsUMiqLATd4rApgDOiPGDQd5bPrkgkj65UfDpkEiC+V3Dy6/7PNwwpliRi9GtY8dMWSZz7ogpD0/ee0rkfRL5+MKKk1BFmDCFnThrQB27YFLV9r0wheZ8NoNsDGnjzPnEz1wJAD2D+9yvkB4EU1xUrUdXHPxhnM9mcNyeXfjUvtMi6xsZQii5ARTbqYpGTgJJ9co8/nb4vFt9k1yDNdjq9uSwkrAWogih2s6vA0r8MV3+rMOYLcfUdQPun7G8L+572fdRgIC12c7tWAfZhorAoHpMlYUqiePFbTvKx95BpFO9tlTH5ZqyTfGYOp3TOUkNKLf9persUrmeQ3dAubQzHL7nLW4kiQNKtl5XVMZeax01uJyDXv1h0IfI8XK7XLTnRfjIzI+Euh0nB5QoSFlS8Nlq3NixpOCTOKAMGMEdUB4OJy+HlLhe+7rsgWJpgNoW0LWk4BMC326CUD6bd3dRSEQ+uwggClD2ALy0ppVNHKkauygmROQYsq/PSaRQTitmE+akNaAiGo/sfAKsqRn9nKdeIlmQMaiIT1Xrtnw9Vl8PdmFbXKbqzKta7lMItdSAcnJACcfIXptJlhpQNV2fkxMsSgGqtdCKKxZfgcUTFge6Vkh6oAAVM2bgfkAx7RVQSSdWkAYEases69LbX/TOOyxgCgFRpwg0CSKUmYJEZ3u8bgOzryoClFlf6TvLd420T3amj2nD/juOAeCdus4UA7xqRUVJPmteQ5IUfML1ddkRO0feJ5FPCIKXijBhuuPiFKDyuSxmjm0rbV/xOjLbxeV0NAV31fpKJj3lfkaf2q4pb4rhig6oweuLdZ9IMqgEEBVFI5Wn8JUFHYX15vLygH95GyoOKNcPe/xtvm0fl4L7xG+6NqUaW0BoQo2fbdaSWtDSXVWxtAaxImxRUUXIrFrmdx8p7N8gbhnAXbBQFisc/lBOwadyrioK4EHELNU5z219Yt2KsgNKIqgW7fd/XvOf6jnoItLL1qc6/6gKyryFITZU3ThRbjebyVa5K1xT8HnEOSwp+Gp1QEV0wXgJOmI7MYCdy+Tcx2Prqvi5IOnrqsQtt+B9JlPlgLL0Q5L6S5ZeEJnqc8RtPbXiNjZzH7qJFV7H0b4v3MYTdnpBN9HVLkQMGANSYSJo3aAoj4+XcOcmQNnbe81zsvR20r7CuwaUxd1pO6+DpqGsOp8y1mVejq+wUD1P6IBKJxSgYiZfdm/4F3iidkA1FSqnQxBxR2cHVO+gEBBnkL+0vczg9r33p7nPx8UskgEV8ctLKDOF03yCAfRc1tsZY45jyqiWWFPbASVxYVRrKSWNijCRRAo+cXs9igKPeQ7H1c+CwnF2whT9xPksKsxtbO9Vm4vKDkIKUCRMAgUQawwAK9WAUnSV5IUUXq6OiZw8MO61DWm/3NrVKOioiChVywY/Y/nBO/jeodeU/j3oMpdt1CquBEnBpyqumO3EX7M1pg9TScFnX1etIl2QoL6qSGeuW/W8ceuX5W3bPpbuc5VtOoxr9I6lfwut1S4gNwEqVNGo1vNecZ7q217dxuKAsqfgs90X9Hc7r9erf1XtxG1KBKiarlPPTii2I0MFWZqkKLGnqapyQLm4jLxqQNmFLFkw2J7Syo5bsNnTAeWx3NLW5tLyTMEnCZzLakn5xU+qOXsNKJkDyi347selEaUDSsTch27Be+l+ydpS8NkcK3E5QOyiV5UoprifvUSyoGKVCq77LVO9TFxuT3/nJmQ54VoDSiEE71UDSpwe7A4ot/7al1eJwrbjXCVkS7YTFV79J+mDNaBixgye+6kBVUnRFe1FJjoGevqLZaHHC3MscYkSQRxQpsskLveGiSl4qdSAKjs3Ygic28lnTaeWogMqwQC6igOqLIom1M/KOarggDKFnZjFUVNI6lNOwRevAKWSatEJU/SLwwHVaIrhiin4Bgzz+uGzHyRMFIPdlo/UGADOKAhQqgFgsXC1W3C16sdXDCn4lAPbKgKc7Lg4BOZzDin4Fn4G2PkYYNg4+WerVq84LnFeUhWHZFjW4SSuyEQuhX2nnDYvU0nZWPP1EcD95VsUVtlHiteW/TgquRV9jquhBbjkDWcxxG0/xHEcggjFsvX1batuI14z9r7aU/D1Swqvq/TLJFANKFVRsUZxngxZkkqFZK+HU1UDysXpZHc12QPfogNKJkYB1mByVpP7e7sLSJaiSxaQtrs/aupT1VxbeZnNZNHR2FH+e1PPJuWAvawGlD2lYJQCh1uffKfgswmAsqC/rFZRmFTtU5kokLEKhG5j9UxZF6EA5cchJBMAvcRO17GH4IBy63NVCj57jSsXB529v/b1VG0nSmerrR+ujlXeiqQSClAxE8wBZQbTo72pKeQy5d/ppcC5Wk7V/phqVJk0BnJAJeMyqdSA8j7e5niaYgic2ynkB89LTweU6XZLbsbPKVxD5bSVMYs6JuY5qiKSmudG7A4oH+IoEG9qO6AiuHulhbRjin5xiM0VN6ZiCr6YxXoyRKgleCxtY1+3gJIDSjEAXBBcv27B1argrk8BSin4rRiIVg1YqwZ2nYLplhpbwnos4pN92zUe0yAOKFURxjFlmaqDSNFhJ3O4lAOIqsdUoV/KoqJPsSWIU8i1nWoKPlVhzWVcTe0K7VUFWZfPu75f4/Vc9XmVvjmdzx4p+Pq3Q4qyYKYoFCtfp0GOBe9hiByZwBEmdqeCPVBpF49MqhxDtn5V1YByCTrb++DogHIZs59yByrIAuP2FHxi3RyZYFOrACULJNv3i+iA6jf63dPLZeC6Hru7q8rt5SJGhY3TPlROwWfrb9UxsAl3TusMAzcnnFeqOZkDR+zvfpP2k24zbFTrnhWyBcv5VZWCT+IelG1TvAZVxulWv668XHwQz/J1n5EKk1X14NwEWtuyqs/GdKyk7Xgvkkr0eExjCGEGzwd8BFX7B+IJ+mcymYpzQzGgCgB9MQlkJn4dUP0DRZhahc4ukyQdUGa6s7sf/6e0XSWFWHJTR07BAfXwS+8BAN7b0htLn+yY56iKSFpxOCZzbvqtARWXUJb3Id6K9MRYq6rJx3EGKvN+lgIUCZUAAURV15Lbui0CiapA4NIuLwhQbiKIx9N/QkOXt1WEGp9CgdvfTu/7Tddm2b+KTqFahTWp48zt8z4FT9XgueXXtMr6pQ2Fl7WKEKqioqoA5dQuxOvU/nnXYGKA/voRauzrVXbaaOSAEhkzu7ofVSn4bPdWYg0pFWSCavm1qlMxgKgoWx8dUMSGV9qnqLAHWlVT8HlRVQPK5rIRl/kJzouMaBoh7YOvFHxQT8GXyVhFDZmrwRK8DrIrbYFx66JqYeI7B3wHR04/EodPO1w6Hrc+VjmG7AH2uAQOh/NFllLQsh5R+MnmrA4cyAWDsLCfT15pJu3IXE8/OuRHWDp1Kb6xzzecGlRehjyHuAq0tu3ks9ZUnnYHmh8HlJsQ5OVW62rv8k7R6SJg21MIyq5pu5vN0l9kPT8bFcoCLe9FUgkFqJgJ4oDqK8aXost0N/hJb2e6ZqJOEWji1wElOjx0dpkk6YB6c0PlyUyZCyrudItOqKTg++EfXwIAvLfFZ8qTkCg7oBSE3N6kakCZ56aqADUQ3zwEiHOlvxR85j5vVEwhWgt+nG5AJQUfHVAkVMIMIAZyy6i6NFzWlXdzQCkKIkfeUHnt8rRzZCKErF0gp9DgaxWBr2pZDeIbYHOchRCIdnR2iakFaxW57OeXihBa63kfslAjnq+5BodtqAo1bqKiXYCqNQWfsEypnpT4WtFl5boCl/drTQGqfN0L7HveYFOZS9PfvYv6vKKS/hTB5h/VY8+njokNWSAwUqeJzWFgdzztPX5vANUuhqJRlAoTFgcUDGvwWJhXDcOoEkDs7DhiR8vfV+13FY7Z4RgcOeNI94H5RObSymQyVcFv0QFlF0Pc6t8EEfNk6f1EzMD3IVMPwZX7XYmWQovreGRks1lpyrEog9duAflyCj6X802WmtBJPHR1JkU4L3ulXvNT62vv8Xvj2gOudRRgI3W02Par5bWwzMsBJWKvkVS13EW4czv3fnroT7Fs6jJ8Y59voCFbqY8rXq8mbmkOq0QlWZo9u8tJvMXLZDG1Y6rreCLF5VhVN+O9SBphCr6Yqbg3FAOWRaOcaSYfQ+C3qZDFxu3+0tvFXReoyacDaltvZSyxp+AbTG3Xp3kNqC09lSczu/uLaHM513SoARW0NlCclJ0xHrWBDMOIrcabHTN93NZetWu9Z3BOiM0BFUCsByop+Jpi6Gc+wHwOJHv9kHokQPC4VtFIWaywrMz57aYOoYmLo6BqG8K8MO8E4H/OLb0uNMORIMFT13h1AOHDbw0WsQaUcpothUC8NBCtmoIvgHvFcVyq+yRM0UjxvFcSFUMI/ouumLIApXDeKJ+Diin4lAQd+zKV68jtulO9BmsRFVW3obgvdzoKeO6/gQWnVeYZmQPKnoKvdQyw9V1AqHMi70eN56DlMzVea77akaGIzMVheT/soL/NaWIXSY6bdRw6Gjuw+9jdLe/bHR727m4Ta77ZfobYRSYvUeMb+3wD33/y+zh+1vEAgCOmH4Ejph/hOia37frBHpC2O54sf4uCja37YrsgKQNrcTK4Bu9tnbQLgFWCmsSNEyZu57Z5vqg6uuyftaduS0JQ89pXdtHPVRzx0ccwxyN1jtmW2R1QlmtA4jR02XAZFffQ/LHzMX/s/PLfFyy4ANlMFi2Flqq2UgFKFG9lgqxdrLKJZNM7puMHB/8AY5rHALDOfUVEF4eTzhs+zkuiJxSgYsZMo6caVBWFizjq7gRzQJkpAuMJTPt1QL3y3lYAwMThzbGnOSsoukz6Borl4HQSDijxPOvuG0Bbo/PUoIMDSiUFX9KoOqDEeaAxF+9xnzyqdDPzyntblNqbDqg4UtsBlfnEqy6Zne4YHVAq9chEygIugzckTJSfSFMJACsG4lVqQJnLzCeJ3c77EVOAxZ8HCq1WgUJs39Dmvo1CE3DWX0rbcROgVALsqiKE/X0lZ4WiMGi2CyIGqQSsZf1QPqY1jCvXILwl24b4WkWEkPVFJXCuKEIo/9hV3EeiSOEkNNacKjMn/7v88YhECDexJKg45LjeGo+pqpD5kZuB3T4BTDvA+bP29YyYYv375HuAP1wJHPQltf4qzb2K118YoiIdUESCaq2QuOrUmOSzeRw+/XBf6wGAt7a8VX5dhNUtJXMUOPVhTMsYXLH4Cs8+mDTlmtA90I3FExfjh0/9UPlzMoFD5oDyOx4/qKaa83JWqKYUy2ay1TVuXPoQ5rkoS/Vn9sdtjNL9ks3KHTg+U+P5wc1F45lmsuoWWX2fx3V87DXFRArZglWAylhFWFl9JTv2tnt17oVH1z6K42Ydp9Tv03Y5zXWZLAWf7HiJ7k5Z6lRz2f6T9ndcHnYNO0s/FM8DpuBLJxSgYsasnaMaPBcDm4UY6u5U6iv5cUDF6+Dw64D65/rSU0xTRlU/PRA1DQp1bDZs68WHr3+w/HcSDqisMIFvlzhiTDEgSQeHXxE3Cczr6MK7n0JzQw6HzR3v2E4U/ky3XFxMHbwe3lzvURh7kLhrQJnzSb/GNaDMPqrW9CuaAm7MbjdS5yinWlJoF2YKvtJCyTKBQxxywQPAwV8Btr4HjJkl/3znLvLlygF2l8/I3ldpJ7t/cwpiK9ePUgkeq54fAbapvD4nAUrVZVXrcQhzXapBfcV9bjjcb4kppJTPQRX3W1ZRrAlB1HDcfoBrUOl41XhMVYWfhlZgx6XW97IOQvHpK4CHvwcc8k1r285dgBN/7tIHH/1VdqEFOaYhio9kSBPXk+l294s9BZ8bBgxpH5dMWYJfv/jrUlvDvQaUuS6n/gTl98t/j7e2vIWdRu3k63OuwlHG+rfdAeW2DgAW900YKfgsyyRpwuzv2Ze7uT/sAlQWWet2LdNYdGKoPTVk1XKXPtmXZZG1CCDIVC932mbYeAmRqte7H6Ex7HRvbueTkwNKPNVlzj3PGlC2bf7g4B9gzfo1mDt6brBBuCBzdNn735KvxEOzmWzV9eP2OaC6/l02k1Wec4OSQSZwLT+iJ/FHuoc4ftNK9cftgCr4E3cA4Kk3NgIA+vrjmRwqDii1Pm7tKf3Ad3P1REkhbzqg3EWdn656De9urtQqisthInLBhyv5qd3ER8MwyudFsin4Bs9RiQOupaF0jhw2tzOWPtlpEtw3n7vjb67tRGdcXLWVTFoHrwflFHwxCjsAkB88ziuff8fX58zzN45+mtdBn18HFFPwkVBRDDTag9GOTRSDsUFqFAX5cbzfBcCyK6vf9/3kXYCAtWs7ReeNZeyKTxGbfRAFK2kKvgQcUKqBRXHMprNNNbWgSrA71LpNAY69crBesi/tadoA9dpnls3VeA4qC0s+RQjX9aqeQ4rCmlcb2TaVhSqPz5rHqmsv4PifVTugvFfm8bf5tqoAHLbwrPo9Q4YkVZeRcwA0UgeUQw0oN+xP79v7+8U9v1hpC/f6SnbCCJp3NHb4Fp/s2MUOmQBVFYx3EdtqDgL7PPQywcCyWpsgYBd3kkjZ5SQGql4T9v0vHiuvVHJhEqQGl1M//Oz/yBxQPvpUyBYs6eVkKeAs7zs5w2zHqinfhF3H7BrKPCFLc+h2vRwy5RCMah4ldtAVL0HNXv8uTMJy1BE9oQAVM37Th4nOmTjSnpkp+NZv7VX+zG1/eQUA8OirH0TSJzt+XVqmo6c1AQFKxQE1INwEN+SzidhJu0a2oLO9VAh+e6/zzfvf396Env4iWhpyGNfe5NgmDmaMbgUAPPH6Btc25lhOXTwtji5VoSp+mGntMpn4RYnWhtL1IHO8ifSWBah4UgWK7rAN29Tno83dpZoacaTgy5cdrao1oAYFXD49TMJE2dETohMk7HRtURMoZZdqO4X5XlmkMx1Qqmm2FILHqgFri6ioGNiW/noVtpsfvGdQdkCpCIaqqcNCPO9VhSXVvjk5oCxpKEMUVyRpfIK52nw6oFT3neXjNYgwYTugPPsR8j2HivtU+YEDxXahrI8MRZIKBNrFh6Aiib3/bba0v3anlYl9e1EFZL2wi2T2IK3oZMrCmoLPLoy4rSdIyi2Z08Stndje6bXss541k6IUoywxc6swGnTbmYxVPMyUFLXKum1Ot6jwcvqoCmpeMa6oYmBVtY4kaRrz2bzlXK8ar3hb5WO/1JrO0o5rCj7bWEX2nbivtK2XA8oiQMEIfUxiv5xeV7XjvUgqoQAVM74dUIMBy3zWfTIJk8dfWw8AuPq3z0e+raA0KdbXMdnaWwpIm66YODEFqF5JHRsxGJ1EH02aB7fd7SLsvTPo0po6qtXi8ImbWZ3DAMDiGrOzfdAd1ZRAOkMAaFU8jqaoU8jFLzya55p5fXgRdwq+DwTRabtivbfVb24sv26KoZ9+53NzGqADioSLYmAwVCdIALdMqHNcLU/iKgasaxUrxP0idTE5BOadUnt5bcP1R5qqYOSjrlf5taJYYQpPyjWgVIQ1VUFD4RxUdrWp7kvFvhUdvn+zCgKUago+1XNQ+RzxKSK5zTmBRHOX9SpH3WoVqpw+qzoPqqxLdf4J4FgK3aXKexhiRdUlEDZiCrJcJqcsktjT6vnpb9QOqKC4BZKzmazUAWUP9oYp0tjFE9fUeQ4hSdV0bHanjl1AS8L1JGLuA9W6aHb3VM723R3UmVQLXteKsqAW0nUWBNdUc7Y+FXIF1/NU5ob0EmzCHo+b2O5Vj0x2vVtSOjocK3EMRaMYnQNKVYDivUgqoQAVMxUHlJp4YtY/ibtmyDuS4H7SBHVAJSHumHV9eiUpDXPCsd110vCou+SKuV+7XYL9m7b3AQA6mguOy+PC3P6m7j7XNuYYmhMSyuYpHkfTGdcYc/o9AGjx64AaVE/iShUoujBV020+8fr68us4HI85nzWgzHmfNaBIqCgHYwME4mt2QAVIuxUFKsFv1bFXPQ2pIpD4FJHE9mGm4JMdK8t2FM8jGU4OqKzquBQcYIFSkSk6impK/WZDNk6n3wJBHFCuw1c9B4OIeT4dUL7dU1A89oqfrdVN5tWPmuv0KgphlrlXdX2K10eY4iMZUqgUsx/8I9ztCtspZAvYccSO5dcyiihWCRWqyJ76T1KAErEHxu2BcHH/VPVZ2BW1jsfivLKty8sZIwv0WxwqNpeJbDx+3Dh+qHJ3OezDrMv3lky8zWQylmMlpoZz+myY+HHveAkxTu38bL9mMu7H3i7YFDIF1/OrtCp1gTZKAcrSJ9t863Y8spls9d8+5mdRoGrKNUUnANmuD1e3Fx1QqUSPb8khhJmyqV8xYGmmoSrU/MNGjYuWlgp9H7DjGN+fnT6YGi1qgjugEqgBVU7Bp+aAGt3WGHmf3DAdUG6CxKbB1GbtzfHvR5F2U4Da7u7cMQWLpJxaY9vVjmPZAZVA3a+WxkEHVI+aA8oU9RpjcpUdPX9i+bWqSGaeGy0NuViOvX9Ha6ldljdMJFQUxBX7slqdFUFS8CV53vt2Cknahe1UcArShyrwqZ4fAQQdGaKQkh90PqmOS0UMU07BpxBgD+JqU77WJN9FTin4xD6KjjEpCv2V/o4IMIeoBB6UnZZ+CSIqKh57P7+3wkzBF2ReCUMwUnZAaTKXEy2RuTgi3a4tddb1H7oeR884Gncecaf0c4Zh+OqjWFtKGmBPKLRmH499bHYhSFYDSiRUB5RPYSKIYyibyVbVqXH7bOg1k1xE13ymOm4i7Yd4C5DJWfZhsWgVTi3nW4il2Kv2Ww3CnR+hN9Ljo7juvC1dsWpKQU8xKuS5wS1NoF2AErGL0eZ7TutxWkcum8PFe16Mz83/HMa3jY8lBV8Y7YheJBtJHoL4rQFlBizjemK+a2QLALljx84OY9vw4jtb8I2P7BJVtyyUnTrKDqjSWJoTTMEnFaCEdFwJGGHKNOXNFHzOfTUdUO1NyTqghjWVpi03B5RhGOWUbXGJJXZUxY++mF1FIqY7TDW93daeeGup7TKxAw25LHoHisp9NB1le00bGWXXypQfKFCuATU4nzMFHwmTQGmTVIKbknaWekFhOiYU8VuLwK+jRtpOUaSzPALrMwCsGthWcoK4/mElq7CP7Mtk7fLCgximA0o5zV+IDiilwHkA8VH6w1vx2Dul4BNxc09VdddNUFZoA/gXlrzW57SuQKneVLYdZP4T37Zfzz5+K6g6+pQIIJiFLhiFKD6SIUWVSyAip0nVdkXnQraAiW0T8Y19vxH6dsR0Vzo6oAwY0sC4XQiypOCzzCnugkGQ+lridkQRz+yXieN+E79GFdMe5jK5qvPNq05PGFSlLnRIH+jmSvES5kTxcMAYUE5NGCZe4olyDSiP/R9pDSiX881eV6uQLbie63Zhzmu/qDrDguCWJtCPQB7kWvnEzp+otI/IIGE/Z+h0qi/ogIoZU0hSFaDMAHU+pgC1Ke7IahbZMVPhxZXuzAzu9w0YSvuxvA8TCPqa9XL6JI43XerBmPWSul3cJtsGnWRxCRBumNt3q13UIwhoSaXgU92ueZ2ZqRrjxJyL+gYMpZzp5eMfo5A7fUzJVamcJlCoqRUHeZ8PFJjtcjE5WslQpFbhQDUIqipWRJSCb6DXu41bP1SFJTVFRzFQrJr+bPB1EAeUUto42bEKUNdLdkxN0QkAcoNi1Oa1lfcKLe6fzSqMS7kWjsJ8q+qAskTEVI+ppF3jMOkmJBtw357lfVWHjuo54jONntucU2vQTGUuC5qCz0/fxLbKbjXlldfWJpADKgSXHCESwhYAxICp3bkgo0qw8eiX+DtJ1xpQInbBSfw7l8lZ0rrVIhh4Ie6PAZvj18uRInPguNaSymQtQXZ7zR7L+kOcx2TnkykgqQph9vGI53V/sd9dbAh5WrbXonJ63+29IOKIvW3o15JlV7n3KZ/Nu17v9uPspwZUVG4hO1Up+Gz7VLaP3dJEuhHHmKpSbkZ5jpBY4FGLmZzflE2DwkUhJpGiwWd9JaCSCq8xH8/E2iikK1Ppp+lOiCsoLWJus0fiKBNj/4qnRSSYDjE3Z1n5XEy4fo3pFnJLYymmZkwqBZ/qdk3BJAkHlJjWU0VAeeGdLQDiTWXZ5NOlFbejLCeIeCr0lwWoyLpEhiLKP0AVApLKDqiEU/Bt3+CvvdjHt59ya2T7s0ahKohAYr621ICqMQWfqnNBtQaU6jEVA/KmG2rGh0r/jpoJ5CTfJRmVviiOK4jwqrIvpcFORaHqwH8t/Tv9Q5J1efQDUBQfZeeRZN1u2/X7w19ZjPYrvIQoFAPBBajmEeqfC6sfYQjFqtczRSfiA9X0aTVvRwhledV9ErE/eOerBpQwp9eynrCRpZqTOaCqUnK5BOdVHla0Y0kfZ1jTx1k1bfl+k6YJtAkC9hR81sbiy3iOldkf1VSA9gB71T5MwgHl8eBkUAeO6jprJYOMZdsykbWQszqg7NdA0NpxoTugXOpUedVMqnIXuexnlWMVpaMwzHZEL5iCL2b8PjGflANKtb4SUBFXGmKqYyMKUPc88RY+vnCytL0pVMSVxlBEpQaUuEzV6REFZgo+tz6U3ToJR88rzh3nfWqKFblsJrG+qqZ77Etwn4rXQ3/RgEw/7u0v4vHX1ld9LmpMV55vASqmucgU8VTn82JZgOINEwkTxfNJRYhQdYKoClCqQVC/bP/AX/uag7mSdkop+FTdJ9nq9sop+BQC8bIfwKppFVWFNScBqmsv4DMPASOmuH+uahsKAp8sNZBvkU5xm8oOKMk+2vEQ4Mw/AaNmuLfx6kfpDe920v4GEIeULmeX6z/MFHyuYnoAkU7azmMbtQpQVf3wuX3ZMuUxqR573sMQK0nVgBI349cB5YcWwbGr41P3nin4xBpQyFrEOlkdIfE41pqCz+6AEvHap/ZguMwRIXOsxBUwt+y3wb66Hh9J+kr7eGQp08KmltR5rgKbx+4PKu54YRde7IKmvWaX2/ll75eXq01cHumxsu3vrX1bK3/b+qvqeFTZ/3HUgFKtwUXSg37foHVOxQGlJvD0xSyemC4mmWPHjulCaowp6CuKcbf/5RXP9qbroJBA2quCh1gCWOttbXNJKxcHZk2lq377vOPyigMq2WnD3L5bnbIVz60DoC4KREGT4rVgjiGua0dEPI5ejswPtlbSXa3d2B1Zn+w0DM5HfYrzUU85BV8886VfR2uSgiOpY1QdLioBSeXAdkIOqGETSv+On+/vc0HSsNXsrFBMf+YU/FfevyriiqK4EKSmkmx9Yr2HQnPl9fh5QFOHZP1Qq0elfD6pnPeKx9QiKsocXIrHHgAmzLem4lP6Qa96DqqKEIrt/IoaQVLwqRzXqFxtgD8BSjwHWseofy60foQhGCm2U/2eIUMS5QB0yIhB3ULOnwNK1ZECAMMKlTk6rlRavjCqhZgyGasQVJWCT3bsQnRAVbnFfJwjMjHEnubM7oByEwHCDl677TdTeHNNBSgRr+znml1k9eMo8oNKqjnVfenLsWXRqkIUoGwik2zdhVzBUq/MLsL6mTcsrquQ50GLIGz5Gs9gc99mx+1m4SBAucwbKv2NpQYURaa6g1GwmMn7fGK+nD4uJvGkXANKMeBrGEY56NtYiP902nv6KM82FRdZ/BNYY7kGlJoDakRL2Dnk1fn9c++UX//v029XLa8Ez5P9IjCvBaeg/3tbenDZPavj7lIVogNK5sZJ1AEluHD6PWq+meIkAHxkt4mR9clOg4KDUCTu/Vmp6afWv95BETeJlIukngkz0KgY2FZ2y4QctDztXmDvs4GP/cjf51TuoaqcTTUGrC3p2ny6ZcKsAaWaojHUulMA+rdXXje0STbstA2FFHzitmWBMb8uMek2xdeKrjap+80BUaxzXb2iC085DaSqCKG4vkojhdcBCORqc12Z9U8/x6txGHD4d4Ajvwc0D1f/nEo/AonmLstCr6XBgBCxovqUepRBf18p+GSOIYc+tgnfYfYgehBhJmrsgfGWfMXBZU/Bp+qI2HnUzr77Ia0B5SFMyAQ1WQ2oKpeQEO70G2BXxe6wERkoDki3LeuHfb/kMjn3/RIyFleQwwM3buJKBhlXt5yn0Ogi4gUd597j9wYAHLvjse7ry1i3lc/ksV28f626lVCfN6IUT2T7WHRA2fsjc9z57W9UAqiItF4a70VSCaNgMRO4BlQ+ngus0WcNqL4Bo/y7P64aUADwmQOmK7etpOBLzmUiE/S2CSnvLj50duR9cuNDsypPb579879VLdclBZ95LTiJEt/9/T/i7o4jLQ157D19JACgo9n9B1HFsRP/PhXTwHnVMNq0vSRAjWgpYOY4h6LpEdEweKx7fQpQcaXgMx8oUK0BVRbIEnC8kTpG9QZcKQWfYmA7SAq+MH4ojJwGLLsS6Jjk73OB3ASK+0ilnd99FCQdoFIKPpkIEbKrra9brZ0TWYVxKafgUznvFYXXsFPwOaEkQAU4V8NwQPkVkWp2rylQa2o9VeHZjT0/DSw4xd9nHPsRQICSHoMAgl8QVyEhqA4ExhUY9AqSuyFz4zgxeZg83b9u2IPKbQWrgGZJwSeZazKZDH770d/i9qW3Y9bIWTX1Q7bPHQWoAAJELpuzBMVlKfjCdqS4rbvfKGW3CZqCTySXybl/NkId1Ov4qO5Lr+Pots6gQsd3P/Rd/ODgH+D8Becr96OQK1gEqKqxic8XefQrSvHdLU1gJpPBHuP2AAC0Flp9Cb1+U/BFJYCq1khjDah0whpQMVOuAeUzYJmPzQHlLwWfKFTFmUZsTFupnoDozHCj4iKLf5IynTBbe9wFvVff3wYA+NrRczCuvSmWfjlx/od3xB/WvFv1vmEY2NY7kKiQJ1Kpq2Wgb6CIomGUz9v1W5Nx6jhx8ycWYP7XVuDdzT3Y2tOP1sbq6dYUH1sb40/nkMlkUMhl0DdgeKYE3TgoQHWNbJG2CxsVAVfEbBeXwyhoTb+kRVxSb6gGGhWCzKo/kILUC0ryh0IQN4GS8CFppyz8OLSziBth1jeSHIOsguvIvkw2rr7t7su8UHJABQiW1+qAUnY2qbZzIB/EAVVjf8NM4+m2fdftBSBIDShl8VGT72elOSsEoTjK9ZEhQ1I1oERXjR8HFOCvj+PbxuPGg25Ea6HV8n6QukhRYAz+Z2J3p4j9zmayVgeUKBKhWiSaNGwSJg3z+dCPA7IaUF7BbmnQWfyas9VMMt9zeh2lG0/EdEC5jUEWxK8SoLJWB1Rc6SDF7Zjbd9uXtYjRYTugWgut2H/S/tJ125fls3ls69/muMz+t5/aZVHOifZz6/S5p2NMyxjsO2Ffa38cHIL25U7rdCPn9/5WEfv5FNRRR/QkkrvsV199FaeffjqmTZuG5uZmzJgxA1/5ylfQ29traff0009jv/32Q1NTE7q6unDNNddUrevuu+/G7Nmz0dTUhLlz5+Lee++Nosux4dsBZdYviqsGVMF0QPkL+ALxClBmMH9rj3fNJNOdkEtAgGpvKt0Mb+5xF8r+ub70JTdlVKtrmzhwEkgA4Ir/+TvmfOV+PPLK+wCABk1S8AHAoqsewMIrV5YD+6JTJm6xxM7wloay++mN9dsc22weFFCHNfn70RQWprDd7yGIbx0Uyloa4hXKTCFJ3QEVb50yv/N5RSDjDRMJEdXaPSoBxCAiTBqClonUgAoQ/Df7qeyA8imuSM+PEOtOAcDkhe7LvFBx2AVxidUqQliOqawGlLjPfX5vzlpW+rcgu4dR7W8NLjzPdirXlPg6gFDu2iSAqKjcLikBKkA/lI+V4vdC2I4qMmSQBXRV3g+KKGr4ckDZnDEqHNh1IPbs3NO6Ho3S74l9sQeSxRSCfQN91hpQtmPitp4w+2ffrtd5oRoYtzuE7DWgxGWT28N1tbmJMOY56tYP+z6WCRzZjLWGT9gi2k4jdwIAHDz54Krt2vunevw8nW4Kc0UYQodln8N9n9tT8FWldHQT3hzGEWWKREsaSls/GnONWL7jcoxvG191nFSPlcq5lbQAFOX+JdERiQPq+eefR7FYxC233IIddtgBq1evxhlnnIGtW7fi2muvBQBs2rQJhxxyCJYsWYKbb74ZzzzzDD71qU9h+PDhOPPMMwEADz/8ME488URcddVVOOKII/Dzn/8cxxxzDP72t79hl112iaLrkVOpGaKnA8oM+A4UDfQPFD3dLqZQ1ZCXK+phU0kV6B2YLjugEnAdDGsqXWKbtrsLZe9tKQmzo9uSq/8EuLtGfvzwqwCAdZt6AGjggBLSUb63pdSntRu70TWyxZKWL05B1I3RbQ3YuL0PH2zpdVy+ubt0XpjnSdzkcxmgz7vGUk9f6eY5zjSbQCVVXV+/2nxpOjLjOvbmfO5VQ8tElzSWpM4IEkCsNdWSGOiRBn00CVoq/0jJoOw4UnZAKYgVfmsqqYoWvp0gkmNgEX1UhUzJuHY6Clj+E2D8ru5tXLeh0BflGlDi6xDFiqhqQM3/RKlm1qQ93dsop4FU7G8t56oUl3Ov1qBBEAdUre2iJuwUfJZdH0DQli2jA4rYsDsx4qoBZbpLgAA1oCIM5MdNlbhj2+fNgrO2kCtYxLq4XDR+a0CJSNMESgSOomH9fZbNZHHHYXfgzS1vYs6oOZ59VkXmkjHH7ZrqTDKP28eTz+Sly2vlpiU3YcVrK3DY9MPwb8/8W/l9p3NENbWh11gzmUz5/A3bAeXWD7ugZmQq108hV8D8sfPxyNuPoLO10/I5wzDcj6PDHBJpDSiX+17ZNpvzzdWClMv8rJL2MKq5Q/V8ogCVTiKJei5btgzLli0r/z19+nSsWbMGN910U1mAuuOOO9Db24vbbrsNDQ0NmDNnDp588klcd911ZQHqhhtuwLJly3DRRRcBAL7+9a9jxYoV+P73v4+bb745iq5HTuWJebWAZSXtWTw3RqYDCigFS1UFqLiD/WaNF5XUXHHvQxHT2bLZJVWgYRhYv60kToxqbYytX06oHsOkb9GdgvfmsU3KkefGqNZGvPTuVnywTU8BytyXXg4eUziJ/TovO6DUatKZKQ2bY3JqlR1kyg8UlNrFVaOKDBUUZ2VlocrtMwIWAUoW2FZ0Z0WNnwCs1xPNqsH/WsQKcZ9KxRUVN5Ji4FhVgPLjrJhzjGQ9sk2ouJbENrWmKVQVdIR2yg4on/N9Ngvs8lF5G2VxRdWpqOhq8ytCuO0Hr/PG8ytVpR/291XFx/hTIpe2G0CAku5HxWOq/JCAJg8TEC2RuTiipL2xvfzaTzBUpQZU17AuvLH5jeCdSxAn58y39/82NvVuQmdrp7UGlOQh5zAD6HYxyNI/h2C3W3DfnpJLXOYolIgBa2Qxb8w8zBszT73jAbA4oMwUfC77surakYgAuWzO+vVn/aNmRjWPwgmzT6h630mkcD0+PgQ1oDTGIorS9YQhNLi56DKZjEXIzmfzuHq/q3HHc3fg2B2PrUpR6SbYOAlCUYrclmtAItiI7aYPny51q/p9WGCfifvgpY0vYVhDuPXBVfsRl3hOwiW2qOfGjRsxcuTI8t+rVq3C/vvvj4aGiutj6dKl+Na3voX169djxIgRWLVqFS644ALLepYuXYp77rlHuq2enh709PSU/960aVM4gwgBM2BZNIBi0UDWIy1c3O4d0QXT01dEi4cpp+I4iHcC8FOrygwOx+UiExnRWrrBW7+tD+s2dVfVeNrU3V92ww1vSSYNm4k9KP7mhu2YOLy6FoEpmiRF3uGaMb/z/7FuS/k9HYL8I1tLF9D6rc4C1HNvl+amEV4XWkSY+9IrBV9PX8XpGCfm9voUa+Ztj1mAyvmtAdVPBxSJANWgsG/3AuD6i1YUK5QD8SkIWmaygBkkUU5TqBD8VxXpzH0ZyAGlIq7Izg/FGlBxiIoqYliQc1jZBaNw7FVrQEXxA7nKhafwGek9sOp16ldYc9tfqkKHW5M6dEDZqdUBFcRxFobzkQxJZIHAKMWo0c2jccOHbkBLocVXgNep1pGdGw+6ERf88QKctetZNfczauwiklPtmWXTKg+HyxxQbrWkakUmQHkdO9VaOvb1VB3nCO9D7SJAa6EVW/u2YsG4BQBsY1BNwWc7rjM6ZuClDS85to0SR+FIMS2i5z7PwPGhkyidLvY+2Z2U7Q3tOHf3c6s+J5s3nOoU+a2pFBSZ0DWqaRQOmHQAhjUMw8imkdjWt831s36v98/v9nl0Desq19mKggzca0DRAZVOYhGgXnzxRdx4441l9xMArF27FtOmTbO0GzduXHnZiBEjsHbt2vJ7Ypu1a9dKt3fVVVfhiiuuCKn34SLWIRowDGQ9JiMz8OoUdI+CfC6LfDaD/qKhJO6YgWm9HVBm0Df+4NfYYU2YPLIFr3+wDS+s21IlQJkCXjYDNBWSVfHt4sKD/3gXJ+5VnR9543b3elZx4HQjM1A0sKm7r5ySD3BPKRgnphDidi298E5JMFswZURsfRKpOKDk11HFAZVQDSjFmnTbB1MFNsd0LRUCpuDT4dwk9USAJ9hVf4SrOKBUa8sk+dS8cp0Ghf4GESuU62Q5OKBk+K1vpOyAkvXX9Y/wUBJLA5zDYbpllEXFOOZ7BZFO9RxUdkDV4NCpOQWfSn9Vr1O7mKfJ97Nfh5lsWa0pV+3L0vAwAYkVmYsjag6afJDvzxiGdw2oGcNn4L+O+S/PdU3rmObZJmqqAseWy7V6nKIAZT92bu6iWqlKwedDYKiquyPc0llqBUnENJXt1IJ9X919xN24/7X7cfys46u2LRu703h+euhP8cx7z+CgyQdhxesrXLcZFeL5ZfZXNc2mnxpQbkJKGE4XWQo+P7Xk3Prr6ICK8PjMHT0X97x4j9kRxz6Zffj+wd93XklGchwV7rGb8k2OjrlaURU0KUClE18C1CWXXIJvfetb0jbPPfccZs+eXf77zTffxLJly7B8+XKcccYZwXrpk0svvdTinNq0aRO6urpi2bYXopA0UDTgFSftT6BmSEM+i/7eAaWgbzkFXyHeCaBSA8o7NVdZxEso6NveXLrM+hyC/KZzIheTwCjDHhR/e2O3Yzsd3Rt9A0W8u7nH8l7StaoAIDv4JemWos2svdTenIz7zUxd6OUw6u1PxgFVKKfgUxN4umMWoCopVf3V9BPrmBFSM4HEJNXPuFzzQQSSNAQtfTuKHP52XJeiSGfuV+VjGpEDKlQXTABU3DLKNaB8inRVn3F5X+r8E1/H4YBScPcop4FUFUtVzlGXc6XWuSCIA0pZqEry3lF8FLxGASqQA6pGZywZslTVgJKkhNKBsJwxBgzMHzsfV+57JSa3Vz+0GRdOKfdMnPa/mIJPPHYqzrCgFI2iq8DgKUxIjo9U4LDdGkQqQNn2VVd7Fz4999Plv/uKfY5t7X0q5IT0iIPCz/yx8zF/7HzH7cSBkxgUxN3jp+/i+kOpASU530R3nr3Olmw94nDMNIKWthHOgx+d+VEAKDvsHPvngDQFn+I1FzXKKfj81lglWuBLgLrwwgtx6qmnSttMnz69/Pqtt97Chz70ISxevBi33nqrpV1nZyfWrVtnec/8u7OzU9rGXO5GY2MjGhuTrafjhig0qAQty+njYnTvNOaz2NY7oCTufDCYWmxYY7w1bCoClHdg2gxex+Uis1OuE+MQ5Dff00GAsgs2RZfz89R9psbQG384pUBLwvFmJ++Ros08/oUE0kMCFYHH61qvpNqMt5+tjaUbiw0uNbTsmDWgmmKuAaWagq+XKfhIJKgGhYMIB24Be1UBKoDolSQq/Q1SA0r1R5LHk5fSvtTqxFJ2QMUgKqqIYZb3ZXOwyvWh6pYJIuhEIUCp9jeAAyoqR2OgFKAK/VB1yLm1K9rSSicplGcyFTG11hR8gVxtIbQjQxKZA8rvU/VxkBn8T/y7Fo6ccWStXaoJe10kr1RaYuA2rkCzYRiudWscEb9GFcUOLwdUpGnQPILm2/u3O7e19am10Fp+LavPFSfi/jfPHZlY4eQGsq/H6T03oSlsB5Qd0QElux4Mw3AVD53GXOXcC5F8No/jZh0HAPjn5n8qf071+pGlzNQFOqDSia+jNmbMGMyePVv6v1nT6c0338SBBx6IBQsW4Pbbb6+aQBctWoQHH3wQfX2VpwFWrFiBWbNmYcSIEeU2K1eutHxuxYoVWLRoUaDB6oDFAaVQ16SSgi++C8xMs9Xd5z3xvLBuMwBgxti2SPtkRzUF38btfWVnTNeIlsj75YQsTVfRSK4+lRNn7Det/NpNIO1IyK0jcsGHd7T83V80LP1dPGMUFs8YHXe3qsjlPASoQVdcnAKzyKQRpRpfL727VdrOvM7iFqBmjisVtRRre8mIOwWfedxUHVBJ1qMjdUzYgUbXdQsoCyW6BC2DpOBzaxLEASWZk8QfeY7rqtXdo+hsClQDKgYHlLLDxYUgDiglUVG19lkMDqiaRVDF/taSgk/ZiefzGlQSKCXtujda/070idoa9m/VsiDXqeIDDJqICEQfZGnEdKS10Grto+Wl3n13IpvJWgLgfmoqVYk2wnrCCPCeOPtEjG8dj2NmHuMa1PZMwWernyQKS6KYJnO2qGwnKPbtOJ1D2/q3OS6396mtUImpOQmJro6VKMU1h/66HUunFHBOr2XrNt//2uKvYUTjCFx34HXBOu7SD1naSS/cxFCndcQ1l/gR+avaCs3Fa8leKypOVPdbmDXqSHxEctRM8Wny5Mm49tpr8e6772Lt2rWW2k0f//jH0dDQgNNPPx3PPvss7rrrLtxwww2W1Hnnnnsu7rvvPnznO9/B888/j69+9at47LHHcM4550TR7ViwOqD0rF9kug4293jX+vlg0Jlgr20UNaZI9s7mHlenDgD8c31p8hzd1oiOloTSnA0Gm/sc+mkGpDUwQAEAvnT4zjhz/5KLsahcMyN+mmwpH/sHjLLIM669ET8/Y2+MGZa8CzInScFnGIaQHjKZE2DGmNJN7hsfyG8y/v72JgDxC1Cdg/OK6bT0YtP20pPMw5ricWSaDxSo1oCqCFCaXPCkTlAVeXwGN+2fEVFOwadJ0FL1+0zJ3ROy+0T8Ee+0X6VCR4gp+LKK/Y3lmPrchjQFn3OQ0drGLioqiBrSBwlUhZ+gKLp7onRAqcwhjcNc+lLjeeNXCCs1dH7bLkAl+USt73GFMd/TAUVqR+qA0lDQEV0mgJ599IM9EOvlvJC5TsKuAfWvC/8V93/sfrQ3tLuu2yuQLDufxJo9Xk6ZqASongFrGQCn82l733axgWufxHPTUbBxOSZN+ehicU7HyuK4U7z+nfaLrJbUR2Z+BH86/k+YN2ZeDb2vxiJo+rj2ZY46J0EuiTR2Xud4lUDocny29skfTo4SZQGKD/SmkkiO2ooVK/Diiy9i5cqVmDRpEsaPH1/+36SjowO/+93v8Morr2DBggW48MILcfnll+PMM88st1m8eDF+/vOf49Zbb8Wuu+6K//iP/8A999yDXXbZJYpux0ImkymLUCppm/oSSME3oqXkYtuwzVuAMl1STfl4nxg06yoBwM8eec21nZmSq60xuSca8xIH1ED5+OozgZp1i1TTiiWB3eHSXyxW9qVGX0aVa9392APJpeBrHUydub3XPQXf+1t68JcX3wcQf62qlsFUett6+z1algQ9M1WfOYdFjd8aUEWNar6ROkL1dApUA0rBVaP6+TQELYOk4FOqF6TqgBLa7XUmMP1AYLLE9e+3v7JjIApdoTsrfOJbrFB0iamma1PZl8oOqAj2URDBTPmaVRWqJMfl2NuBsTsDH7nFeb217hOV/RvUAZV4DSjzpYoDKoRj5bb9UNqRoYKsBpTlfU3uA9oKba6OCD+pp/w4J6JEVgPKq71MtAlLmCvva2F3+ak3Y3ediPtdFKC8nC1RnX/b+rd5jseSgk/mgGqoOKC6+53rcTt9tjnXrN7hGjCD/uJ1onos/TjdzNdhHbNQ6hsZ7uvxmjficqh5OqBkApQwni19atlnoqDq+LjMG2GkZiTxE8ld9qmnnlrK8+rwv8i8efPw0EMPobu7G//85z9x8cUXV61r+fLlWLNmDXp6erB69WocdthhUXQ5VvwELfvK9Yvi+0E03IcA1TOY8sruSImascMqT3m8uX67aztTgGpuiLdGlYhZ78WpBtSAhgFp052xubsP16/4R8K9cabJJkBdt+IfeObNUhBBI/1JEKCql4nXf1IOqIrA4y5Avb2xcvP7kd0mRt4nkeaGSjpQmdMRALb2DpT3aVwClHltq4q1ZrusRtc7qQcUhaUwXQOWiVbxKfxEg5bCNbrvBe7NfDuKJCgH/4W+iaLGYd8GPvlfcvFKJaifVU2tp1gDStlxVwOhigAqIp2iWGFpo5rSLgoBKogDSlFUrPV8A4BdPgp8bhUwZpZLe0lflNyKKvtX8Zju9glbuxQ5oJQdryE7mzQREYg+qKbg08VpNLJ5pGvQ1peoFLH+9KWFXwIAnLv7udJ2flMguglQhmFYg70hX+tu+9ZJmHBNNWfrUz4jOKBs31/27UWVsqu7v9tzX31tn68BKB1LmQDVkG3AiMYRaMw1YkLbhKr1uO2LKB1QIub54iZAAfCXDtLNjRfyVKFSa8oLA4breNzqXg1rKDnBp3VMC7RNFfyIa+LyXCanvVtVBmtApZPkovJDmHw2g16oBS2TSME3fDBV3fpt3mmvuvtNASp+BfrzB+2AGx94Ed197sHz7YPOCTPQngSmoNMnccHolJLLDI7/8jH1goZx02w7ng+98B4eeuE9AHo5oPISB1SfoEoVEnLAmU6y7X3uDqOewWt8yqgWjGqLN62heN129w+gRSIkm+6nhnw2NkHcFBj7FOr5AXpe76QOUA4gJhBoVA1sx8mSr0gWBnFAuYl0ioKOVwo+GX4FM9nhVBaq4nBAhSjgqFwfqgKUJQWfogMqivM+SA2oJFLwWT6q6mpT+D5Vcv4pinTt44HdTgae+Km8XRz4vrYUhSU6m0jE2AP/ugYGL9v7Mty15i78yx7/gp899zPHNn4cUFFzwuwTsHTqUoxoGiFtZw86ewWVLe4v4XezMfif7LO14JqCzyttmG18YrC/kK1k56jqr+3rJMoUfF4ulCNnHIn9J+2PjsYOPP/B85W2Dsdu5fKV6Cv2oSEnf6BS3E6SApTl1kDisHHa/25iVdjnnmutqUzGl5DsdpyLcJ43/njcH9FX7ENLIbpa9H6EYrtYZR+POUeetetZofbRD6rjoQMqnVCASgB/DqhSmzgD1GZQuqff+wasZzAFX9y1YYCK6LVdIkCZzo4kBSiZA6pSA0qfH32y4LgucXNZykdd+gjIr3XxfEhKkGhWcEBt7y1d4/a0h3EgHudtvXIBykwH2lzI+boRqwWZwOjEgKHf9U7qgSBiUkwBySApwKJAuQaU+LrGwLZFfJMJUOLjxj7vpVREI2XHlqJgpupkqYUwUzeGed4HcgrFUQOqxv6GLlR59CVMYVu1Vpvs2IuBuyTnKWUXotkmZKGYDigSkKrAsmUK1+d8OW7WcThu1nFV7/tJpRU3XuITUNr/lpo8Hk4fp1RnToQuQBnib9/Kbzo/2/FVA6rqqzKac9EuQLkNp6OxY3CxIIY47P9CroBCzjntvVvNpLgEKBOZA8oNp3ZutaTCFgvDErfc9r/bvNGQa/AUEsPErwPKjtscGSey4+NHuCZ6wqOWAH6Clv2DbeKsEWQKJn1OecNsJOmAqrg33PtpilNJBM9NzPRqTvtzIIEaX17I0gHqkipQtr90ckCZ+8spfZzoiEtqv6qk4Nvel9w1ns1mym4mWZ0qoHJ9xSnWm/Mya0CRRFEOsAdxQNV6PYUoJMSBirii6j5RFSEsT5H63Ed+HVCyqSqrKBBEXd+oar21njeq56BKO1VnU8wOKJU6SGE7oPweF9VzO6wUfH7SKsZxTqvgW6QLe76XtLMclxTM5THy6quv4vTTT8e0adPQ3NyMGTNm4Ctf+Qp6e62ZRJ5++mnst99+aGpqQldXF6655pqqdd19992YPXs2mpqaMHfuXNx7772W5YZh4PLLL8f48ePR3NyMJUuW4IUXXoh0fCrYg+iu6dM0OndcnQyaCVAqVO1/r7RnWfdAv5tLKQwstZuE1HlO21GtI2apAWWLAYjuqCiY0TEDALBX516+BA5Z3So/iNvpGtYVeD1+GDBKv8ctApSLKGPHaawDxcrv+7jmiipHnaIFqqqmmNBHtxR8ceMlPksdafpMz0pQgEonPGoJkMuqBy1Nl0QhxoBlw6CbqVfBAWW6DuKuAQVU3Btmmj0ntpdrQCWZgs/9eOtYA0rWF12cG9I+argvZQ6oQi6T2NOBLeUaS94CVFIirul6kolkQGW+itONaT5M4ORudMJ0QCWUcZHULYqBxiRqfcSRrk0J1R+GCvtI2QElzJkyEaKWYJfK/rW8L9kPFveFqrMiKgeUYl+U1qUoZimJj6KwpJqCL4IAWNU+URFLVcWKqBxQIZ4rSvOK4nVqb5vkfa7f+TJ0UVERTX4L6MLzzz+PYrGIW265Bc8++yyuv/563HzzzfjXf/3XcptNmzbhkEMOwZQpU/D444/j29/+Nr761a/i1ltvLbd5+OGHceKJJ+L000/HE088gWOOOQbHHHMMVq9eXW5zzTXX4Hvf+x5uvvlmPPLII2htbcXSpUvR3d2NJLEH/nUSmtxwC8TqEkj2Q1UNKC8BSggBytpGeRz9iDAyYULmgGrIRus8ueXDt+Ds+WfjW/t/y/K+134T++k3BmBJWYcMfnjwD3HZ3pdhzqg5vtbjuR2X+8X+YinuJgpQUhed5buoermYuq6W/eJFWM4Zt3pLSQrXQWtAZZDxJZzGhaoQSQEqnTAFXwL4CVpu7ilN8nE6oBokjh07Zn2YxgSC02ag+ffPvYNi0XAUHnoHx9CQYMTXrN/1zJsbq5aZDrecRj/mZH3RRSiT9VGn+joVt2P1tf7elh4Aye5T0y0kE5u//0DpycrX3t8aS5/sNCoK4n0J1Mszj932vgH09hfL4r0bAxqm3CR1gPINeIDgcZjpz5KsAaWcgs+HWGH+2FSqvaRYA8ovfh1bsv0QxHUU1Vwmijuh1oDyCNgbXu3EdUnOZ+GJXuQi+KmlWodMtY3qPrKki/TrgArxPjxIXS/p9ayJAKXqQvTTptQwUHes0AHlxrJly7Bs2bLy39OnT8eaNWtw00034dprrwUA3HHHHejt7cVtt92GhoYGzJkzB08++SSuu+46nHnmmQCAG264AcuWLcNFF10EAPj617+OFStW4Pvf/z5uvvlmGIaB7373u/jyl7+Mo48+GgDw7//+7xg3bhzuuecenHDCCTGPvII98K/qiNAFlVouOlNVR8jjGnVLdVbl8Aj52IlBeovY4NBft3NI5oCyryfq1GfjWscFqpUj1kzzW8fG7lDbb9J+vrdfC6YApeqU80o3aBGyIhQUwnBXGYbh2kdVF1UUBBWRspmsL/EqLlTPJ9aASieUDRMgJwlK23nitfUAgJlj2yLtk4gZRFVKwWc6oCQ1eaJic3fF+bR+W69jm4GB5FPcmcf7f59+Gz/962uWZWYWNl2EHcAjBZ8mX0wyl5NODqisy7X+yntbcdT3/wKgcg0lgcpc9I91WwAAb21M5unKsiNzwMMBlUAKvjHDGtHeVPrh8+QbGzzbl1NuapQmktQBqin4onoiXjmtWQrOe9VAtIq4JApuqin4/OI3BZ/sB7KlvwnXgLK4i8J04ameqwr7UuaAKgrO/IhTAAFQPPaKY5cKxTXUKwszUBDIAaXPvaErvh2jIcz3QfZLGvZlwmzcuBEjR44s/71q1Srsv//+aGioBMSXLl3iGmhwAABYr0lEQVSKNWvWYP369eU2S5Yssaxn6dKlWLVqFQDglVdewdq1ay1tOjo6sHDhwnIbJ3p6erBp0ybL/2FT5cBxfSAi9E0Hxs2Z4cfJkGTQWUR5/5vLRTeILRwYpQNM3LdBHVB28pLv4qhT8In4EV3F9IM1uXESmIv7i+6Zh4DqY+UlcLil8gu9BpQk9ZzqOW/AcBWy0uiczGT0d0DJ5lg6oNIJj1oCmGKILAXfP9Ztxnl3PlEO+k4f0xpL34BKALdHKQWf6YCK/1Q6ev6ESj9c+tqnQcBXFBguu2e1ZVmlxpceEz4gF6AuO2LnGHvizs4T2l2XpcEBde6dTyTRnSoKCjWMhjWWbpKPEa63ODHdiz0eQp3pkPJyIYVJIZfFlFGlufni/3wav/y/N6Ttyw4ofvOSUAlRNAmCqqsmFT8UFMU8lXYWsUImQIX1o9WlH+K2lY9VAueRSDbEFHyqx1RFyFV19FkcUFGk4LPvd1UXkML6onJHhpr2U0FcUU2VqRO+U/CpXqch3xenYV8myIsvvogbb7wRn/nMZ8rvrV27FuPGjbO0M/9eu3attI24XPycUxsnrrrqKnR0dJT/7+oKv1ZMlQMqploutVBXNaB8puCTpTqLUlSz1IAKWAepKgWfIObYj13UDigRP+e86ICq5fpI4toya0C59UNaA8oj9CyeB2GPLSyhxU1oTEsKPpFcJqft/KwCHVDphHeQCaDiOjjh1r/iniffKv8dZ4o7Myjdp5AiMEkH1PCWBgwbdB+41bAZMFPcJShK9NqcZEuu+1M5/Vq5BpRGTxO67au/XnowjtszngKXXrQ3FfDU5Yc4LtNrX1YLPJf+6mk8/c+NSXXJQqVGlftN05TRLQCAo3ebGEuf7Jjido+HI7OvXFMr3q81M0XgK+9txRf/82lp22K5BpQ+5yipA1QdUKpuJL9zaIPkARnLE8ZJ/lAIkIKvVhFG1U1VkwNKQRBRrgGlGPyOI12ZqhtLBeX+qrRTFaD6hHYx1IBSqf8VRgq+WhyNQVKFKq1L4Vh5rleT7+SMz/O+ebhsZf7W5QtN9lfEXHLJJeUnxN3+f/755y2fefPNN7Fs2TIsX74cZ5xxRkI9t3LppZdi48aN5f/feEP+sFQQVIvZ6xrsFPufSgeULaTnFZCOMtAvRdhdltR5Dn10E3RkKfjsxy5WB5TP1Gflz9VwH5WEA8TJASUTd7xqQImo1iYLgpsDKiwBMMnUnaGl4NNkfpaJ4lG65Eg8sAZUAuQVgr4fbLWmlGuM8al+00HwP0+9hRuOny9NaWbWgGpKwAFV2m4Om7v7XQWo/nJQOrkJtbXBGqR48Z0t+OEfXsLlR+5cEaA0Cki79aWzoynmnsjpaCkgmwHsOq5W+3KwKwPCU+e/eDT8H35BKZgCmURsTlJkBioOKK8aUI8PpiuNGz/uTx0FZ1IPBHAWhHHTfsg3gFceBOYdL9touNsMinINqCCOEbdIWwwp+CzbU3BiybalLEDF4YCKKAWfcsoyBTFPWgNKTMEXwT5SrQEVJAWfqlCcaAq+kB1Qunwnq15bH/1/wN9+Ahz8Vcm6VB9MCIAu+ytiLrzwQpx66qnSNtOnTy+/fuutt/ChD30Iixcvxq233mpp19nZiXXr1lneM//u7OyUthGXm++NHz/e0mb+/PmufWxsbERjY6N0HLWimgJOlxojdoI6oHRJu5XJZHz1RZaOLErEQLLFveDVdYuGoakAFTAFX03bTEAwcBSgbCKGRTDwca5FmQ4uCuFCxxR8vtyEmYzt2Ss95mfVY08BKp3wqCWA6YpQqQFlEmdaqX7BafDXl9+XtjXTYjXF6NASMYUvtzo6/WWBJ7lT/eCdxlW9d9tfXoFhGFrWhNFJwPHiJ5/aq+q97n55raA4yQ2KJ2Ytsv968s3qNgnu74oDSiZAJSsyNw4KX14pQW/+00sA1GoxhUmDD8eVjoIzqQNUny4MWzhY/HngpLuBvCTFSdiiV1C6N6i1CyJW1JyCLywByi0QHyAFX63pB2tFDBqFmYKv1pRlFseZYg2oSFB196iOPYirzedxCTMFn9K8YhegJOttHOa9zThQnaPnLQdO/Q3QNka2MrV1BWJo3MOMGTMGs2fPlv5v1nR68803ceCBB2LBggW4/fbbkbX9rlu0aBEefPBB9PVV3JErVqzArFmzMGLEiHKblStXWj63YsUKLFq0CAAwbdo0dHZ2Wtps2rQJjzzySLlNUtgDgWkIDLoFu1PpgPK5v10dOBEPxy0Fn9M+dzs+9uC0OBb78dh7wt7BO+sTP4KJmILPb+q2pISOHUfsCAA4dNqhVcukji6JeOhrPTUirQGleOLb973YxzTOBWlPYZeG7xlSDY9aAuQVgr524nRAvbO5p/z63S09ru0GikY5vVyc/RMxXRk9rg6oUv+SdEAt2Wms4/tP/3Nj+RzQSH9ydGdc+OEdE+iJN/vNrP7x/cp7WxPoiTPmvjSP87l3PlnV5t8dRLS4MK+Lfkl6u+6ERWZTfJc5oJJ86qjRhzOMAhSJBlVBIAExyBKwT/CLbrN7fQwrimKeGDBQSX0n298dg6lta61VoOQECSMFX8w1oMJ0QNWcXk5VgIr4QZgqB5RCCr6w03P6PfbDOhUbqghQCjXCqt6XrLet+kGxRMiGeG2FnSpTvM/S5ClpXTDFp8mTJ+Paa6/Fu+++i7Vr11rqMn384x9HQ0MDTj/9dDz77LO46667cMMNN+CCCy4otzn33HNx33334Tvf+Q6ef/55fPWrX8Vjjz2Gc845B0Ap2HneeefhG9/4Bv77v/8bzzzzDD75yU9iwoQJOOaYY+IetpSgNUkSQ+jiUKgBJXODRPmbSly3GPx2qivkhtPYpndMR0u+pSySrDh2BW798K1YPGFxDb0NjmcNqBoC/6LQEWcA/ralt+H7B30fp8w5BQBw5b5XAgAu2/uyqrZu4/fqb1w1oOyEIR5pUwPKx37LDP4X5LNRouqES7uANlRhCr4EKNeAUqixZOLnKftaEXvV4+IsAqwB4eQcUKXturle+jRwGLl94f38kddx12OldGw6OaDyDmLdgCa2YhXivFa8MMXm3z+3Dp/92eOObca1R5sWQ0Y+552Cr6fsgNJXgPJyR0WJ3Z06UDRcBSbzOsqm4cc4SQ9BnAWqgfia0cQBNeMg4L1/ACOmyduppq2yCFAut9JZxRR8+19UqhnUFeBJXUtQWEGAUk7BF7ZbxidR1YBSbafkgJL0K3IBStHdo3qsVGqJVX3G53HZ9ePA+y8COyzx9zknxGtOpV6XtB2ASXvW3KVQUL0Gw1xXoPt73sOIrFixAi+++CJefPFFTJo0ybLMDLh3dHTgd7/7Hc4++2wsWLAAo0ePxuWXX44zzzyz3Hbx4sX4+c9/ji9/+cv413/9V8ycORP33HMPdtlll3KbL37xi9i6dSvOPPNMbNiwAfvuuy/uu+8+NDXplSZd1b2SJG6OiHoQoESc9rll7DE+jy4G+sX0eF4CgJeg9qujfoUBYwANgw/ydLZ2orNV9aGH8PE6z0X3Vy2CX5zibkdjBw7oOqD895EzjsSSKUvQnG/GU+8+5dqnwLWxwhag3GpA+diH9vPU4oBKMFYWNL2gvQaULtj75DY/0AGVTihAJUAQB1ScXzCn7zsNN/2xlM5Kls7s/a0Vd5SuKfhMkc9JVEkaU3wCgHHt+vxwcAqOtzSk5wkDnaQysW7Wb1c7P4HfkEtu36rMRd0J13kz3ZXvbnZ3Y4o14G48cbfI+ySyrdeaaqlvoGhJrSBilv2jA4qEi6ogkMB5F4dbRoWDLgPGzAJmHS5vpxqwVxGgLG4ZyTzfOgo4/DvyfqlQs1CjkwNKRWBQRDmoryCWWtIqyhxQfe7LwqCqf24ClOrxCSAU+z0ubWOAo25UWK/C9i3nh2oNKEl/x88Djvt3YNh49zZxEOq1peqMDXDXzIdoLJx66qmetaIAYN68eXjooYekbZYvX47ly5e7Ls9kMvja176Gr33ta367SWy4puBDegSofSbug7+8+RccP+t4/OqFX5Xf9+WCyKgFe8NG/K004PHQhpejLpfNIYdkYxV+akCFJXgmLR4055ur+lFLDagoU/C5bacWMshg6dSluP/V+3HaLqeFss6g/RD+UCabyfo6b+NCdl4HTZdK9IGyYQKUHVA+BKg4Gd3WiI/uNhEAsL3X/Ybg8dfWAwAmdDQlFlAtO6BcUvD1DUZ885oHfPecOiLpLpSx76t9dhiFkxZOSag33tjTA/ZJ0snFzR4aHVcnTGG2v+gi4BYN9A2KuE0+Us2FSdfIFgDAU//c4Npm++D1n89mcOSuE+LoVpn126yBRpmYZzqgKECRUAlSbF41zVatWASdBIMDjW3AHp8Chnml2grRAWVZbQy32yrbCKMGlHJat5AI9QexqrNLYV/KzufIfxTbxRWFlIHKrjZVASqi61nleKsIUMp1sgbZ+WigK7mUyABs+7RW4VXxmNIBRSIgSidDWLg5s9IU1PzhwT/Eg8c/iF1G72J5308guUqAisnJIavdZEdXF52Inz6KqcP8Cp5u4k6SyMaumk6taj1h14Dy0Q83qhxQyODb+38bfzr+T1g4fmFN/QsLv44zLa8txW6s71kfbT9IJFCASgCvoK8ONDWYwo57H7f0lJ78nzOxI5Y+OWHWX3F1QGlSc2XsMHmatdFtyaVhs9Noc7rc8em90dqor1ny8wfPxBVHzSn/XdRI2G1p8N5vHS0FzzZRkfNwQInCrv28iIudx7cDqMw3TpjXf3MCTszdJw+3/N3nkg7QMAxt5iMyRLFoK3FdzwEC20kSqgNKIT1emCgJUCGk4PO7zSBs3xDeupRTBqo4oERXm+T7vWm4Ss+CU1UDSsUBpSpAKX6PJpk6WiVFY5UDKg3zT0QOqLCfatbkKWmiL9oEMhURg9NpEqCymSxGNJUednQTJryOhZiCLy73kx1fNaA0PbcsgolHFy1tfe5yUSDUxrGi6KLx4wwLG6lLSPEY2MXZTCaDTCaDkU0ja+tcjQQVkXKZnPYOKNmclFSNN1IbKbgbrz9ygz/aVB1Qp+0zNcLeOGO6HcQ0e3bMmiz2GihxUknB53zjYta2KSRcF+g3X9hXunxUW43Fx0Nk7LBK2rjvxZzOLCjH7dFVft3enJyg45cP7zwOHQn2tzA4FxmG83wkXldJOaDM+UXmbDPT4CUhkn1y0VTL330uDxaIuzenyQ0eqROC1OSJKxirSwo+VYK4e9wC8eIPVdW6OrWgJBwoOqCkbhmxncImg7DtvfDWpSzCuP7h/L7smLaM8u5XLaiKK2GKb0BAt4xffDqgXNMP2vdRCr53w5wvlV1tdECRcBCDgW5Oho7G5B5ataOrkBEHbjVjkqpjU3T47eR2DukSJLcTlqsmjm2Gjcxd5EsMFb+rQj4V3VyZtexDHfe/HxFP12tJdAh+0P2BZZnY565hXSDpIwXRgPrDbw2oI+bFn5P8vS0l4enfV73m2sYMCDcmKO6UU/C51KoyXWZJOw7GDmvCsQsmuS7XSTQZI7i19p85OsGeqNMs1KiaMqolwZ6oM669Ed/62LxE+5ATaqM5OTK7TZE5l0U2oWvIFKB6XZxFAPCzv5bmqfe29MbSJxEzRaCJmbLQjijwJbUvSb2imDbO8pG4BCjFgL02ROQaiCP9oEp/ZbedWcX0X0HStfllzkdK/46bG8LKFPurEvy31ICSHNO5x5b+bYuoCLpyDSjFuSGIiB0VoaXgq1pxoO7EioqzSxVV4TVIwDnpc4RoiYoDZM6oOc4LkkC8PUnDAzIeiE4mP6nMkqoBBQDTO6YDAHYdu6vyZ3QJ+svw00e/jjuL002TuVh1vE7X2ZcXfhkA8JVFX7GMJ+zzMAzRyYBhOV46zht+U/DpiHge9ElqquaSTO1OAqPnWVfnZAcvKjcHVI9NTGnIxX9xje9o8myjgwPKdBJcc98ax+UVB1TyX9DXLt8VL195mOOylgZ9JtCxw0o1wI6ePyFRd45fbjhhPnaZ2I6rPpKsqKPCj0/bE3+99GCMbE3W+VYQntzvdxBOHn6x9BR6b4J1tUyBWyZAvZ+A8OTGA8+/4/h+UQjyJC2IkzojbJdDqD/uNQpsqxBVfaNYUvCpCFCKKfik24mhBtTOxwCn/x447d7a16WcWtBnCj7ZD9/JewNn/hH43CqVHvoniLtHdexSoViTFMfKAlQMYmmYhCruRuiASsNcTmLHLcCbQQZX7nsl9uzcE5fsdUkSXfMkDaKGF4Wc8Jvdx3CSDEL/51H/iUdPehSthVZpu7QdH18ClM8aUEG3EyUywdOrZtXxs4/HXz/+Vxy747EWETWCTpaxuKEyGV9ilyiINOT0yWBUxue1r2UNKABX7XcV9urcC5/c+ZOuzkxdBFjiD30Lu9QxpmHITYB66Z2tlr8L+fgvruP27MItD76MYZLaPzoIUCueWydd3l+uuaLHD89sNoNdu4bjqTc2WN5PonaNG5lMBtcdPz/pbvjm6PkTcfT8iUl3Q4lpo1u1+NIUhRAnR+ZF//F0nN1xpOyAkohgpmvvswfOiKVPMi67ZzVO3ntK1fvifM8UfCRUggQPmILPmaicINq4v2Qp+AK456I6ppkM0LVneOuq/KHWznVcYgTD4yfUhN28ehYeSscuBFebNin4FJ1CmUylz2n43g01BZ/4OoyxayI+Em2xB3VFjpxxJI6ccWTcXZLixyWUBgpZ9YdGxbHbg/5xOqDy2TzyLt+lbsdE12MVNE1gLWkPddkXshR8omPIrb+mABllOkiZQK6MYRWgdHEQyfa/9HP2dImanE8AcMT0I3DE9COq3pfNXSQd8KglgBn0Lbqquda/GxJIcWc6ctxS2wFAz0AlPVdSbNpe+RL4n6feqlq+vbfU/6YEasO40eDgxmrSSIAi0ZN0TTKTQi5Tno/MOkomb2/cnkSXqlBJwbd9sFbVcA0ce27zoSjw0QFFwiXAU/NJpOBLRaqEAOkMlVarydhlP+gt54Tsh3+KXW01O6DEFHw6PcMXogNKk4CKFIsDqo7GlQkxBV/Ydb0Sqg1D0oNbCjiNHqq3YA++Xn/g9RhWGIYfHvzDhHpUG6IA5Suobm+qyaXenG8uvw4aYI+ToKLGuJZxGN9aKrexsHOh9wc0OT4iMjF3e38lntBSkJdJiCsFn/3w+HJADbinhEsKiyijcO9wyJRDMKV9CvaZuI/retJAVhODAfGHTr+ehgxeKfjMYKpJEsHqlkLp1OgbMNA3UHTsgw4OqCmjWvDSuyXH2Od/8QSO3HVCedm6Td149NVS4bqxw7xTCsZF3mGybExwH5L4yWuQEhIo3eiNHdaItzd2477Va3HaPtPKy27+40sJ9qyCigDVPThnNmuQylKcg0SKFKBIVARJ2+TlGgiLMGuaxIGyWKGCcI+njVCjKECpClVpOKaqoqLKdaRrTbNQHVAJp+BTOadUU/BlMkKXdbkGJajWGFNal6qYrmE0k6QSMXhscUOl4NrLZrJYMmUJDpp8kDauBr/4EaDcXARxup+8ENPy1eJWmz92flhdkuL3PL9pyU14ZeMr2H3c7rh92e349Qu/xomzT/T8XFzHaM9xe+L21bf7/px9P2ztq2R28nLpiTV9/NbG8uxXCCKmAQP9Rr93wwRRmb++c+B3UDSKVW3TMFfTAZV+eNQSwAw+mgJUd98A+oX0Ut29VgHKzSkVJU0NlVPDLoiZ6CBA/eCk3V2X7X3VyvLrznaNBCgH8UEnyyuJnoJGT2yMG7w2rvifv+OFdZvL74vn5F5TR8beLxPTUSRLwbdtcM7UIZVl3iYubekp3agOCPM49ScSLooCVFTp5WQ0tlde6xSwdyOq/aLLd3wYwpJyYDsGVJxlgWqkKbTR6XxWCpimRFRUOaUCOaA0uQZliOeUOHcGQfWY0tlEQiKtwg1QCWqmeQxiDSg/wXZdx9ySr7hlahEPFoxbgFs+fAvu+9h9ofXNCb993Hfivjh555MBABPbJuKc3c7BqOZRkfXPL/tO3Bc3LbkJv/vY7zzbys6hLb1blLcZZTwsjJSbBgztHVCqmMcsDTFIN9FV17mLyOFRSwBRgOrtL+Kga/+IxVc/gHc39wCoFnzam+JPK9WQy5b7ub1XLkAlmU5sdqf7DzTxN9XINn2KBCaZspDoQUEjx9to4dp44vUN5dcTh1dSHxw6tzPOLlkw5xdpCr7eZB1Qoggvzt/n3/Uk5n31fjz0wrtlB1Q2k46bPZIiAjmgFIO2tdI4TFitPvOeK5Y+1rgftAzsykQI1bo6OokVCttXFtYU2mmbUlLhXJU+TZyyVHUW8a9GV5tWCP0V585a18VbDhIDbsHAtD1Vn1bc3CVejhn7bxJdXFCiA6pWFk9YjIlt0daJjivtZFzHJ5PJYN+J+2J823jvthJxR3RAeRGloyVw3SfbOsQaULpQi0Ar1tpKW3yCAlQ64VFLALMA/YBh4L0tPXhrYzfe2dyDZ9/aCMAawLz9tD0xojV+8SSTyZTdBK4C1KAjIQ3p41o1SM1lYhfs3FJ2kfrg5k9Uu/QKmqTgA6wCd1/ROUA1QRCj4sacX3oHiq4FSc0UfE35ZK7zlRccgF0ndQCwzt+/fuJNFA1g5XPvlB1QTik4CakN1SBrAvNO2gSotDkm/CITIVTTf4WaprBGlM6pMFPwaVoDSmU/FN1ruuqVWlDhnFJNwRdVTbeoEGpl1CxAKYtvegSbSfoJI8AbJ2IgP22BVyfywrwY9FgYg//pwI4jdiy/DsO9EjkW/UnTPkaETACZ0j5FeT2ioBB6DShJH93iC05oKUD5rAGVZtxSvZL0wKOWAKazqFgs1VcyMZ/wNwWfA2eNwYdmjY2/g4OYqeL6XFJfme8nmYIPAD5/0A4AgFnj3H+s6XSzIro0PrLbRHzvhPnJdYZEzrJdxuP20/Ys/53LZrRywbU0Vs7H/oHKDZiY8u7DO42LtU8i5vxiGEC/S9283sF+J+Us6xrZglP3mQrAWbDf1ttf3rfUn0joJJFaTxUxjZRWjhEXMq5/1AfKKfhkx0ojV4mSAypICj639eok1AioXPfKDijJuuJw9amMxU8NqPLr4F2KjV7hSfFCrQ/+pEx8I6nHEuB1eU2iw1IDymP/ux0TnWqq7DtxX1yy1yW4beltlvczyGh/TkXZPz9iSVzIBM+jdzga5y84H3cefqf3ehJIwedXdNVRgBKpRfzU/bqyk7b+khL6fMsMIbLlFHzWtFJmwNf8N8nUdkDlSX3XoK9ZAyrhfu43cwwAOIp5OtIiCFCN+SwnzyGA6BJsKeS0OuYNucr5KF5D5utP7D25PGclgShwu13XA4POrUKS/Rzcj+b8bVhqPmXKtfxyGh17Ui9oJAjYaRBSqKTh3A/V3aNfkECKsgNKo8C2igik6gTJKqYgLLfXyAGllIJPcj4qj12TczqIA0q3udGJPsEBVev8o3reaxjMJOlEJ/FCBTHoXA9P0rul4PMTkNZpP2QyGZy000nYs3PPmlKMxYWu/YoFyzMs1v2Qz+bxqV0+hTmj5/haZdhCm1vsxc92MshoWQPKQg2nYdrO4VwaHmwkVejzLTOEEFPw9QgB1Z6+0muzVoi9mH3cmGnCRFeEiNn3pB1QDUKKLpNtvf1JdccTUYDKa5SKjUSHKCaLjiMdaCxU+ibORzrUeAOsArebAGXOUfkE+2pu2py/ReE+kynV/AOQqJhH6hRVQUA1oBmmUJQTUghrFNhwRyNxJW5UA9Y6Oe781oBq6nBvZ3HruaxXdBHpdD6r9EWaflGn2lZ+HVCqc14KrudiiL9ddEqVSYYEbiKBtkFNIbyhbR99sHjCYgBAZ2vwur2ZTEZ/h42m81lc57wuKRJFohh76Cn4XPpYlLrDqzl2x2MBAHt17hVOx0KgFoFW1+tJhbQ99EBK8KglgFsKPjP4q0ugspyCz6UuTK8mAlTBIVXgH9a8k1R3PGluqPxwZj2YoYEoJrc06PTUNDCypRIg/vb9a8qvyyk2k3Zi5rIwd59rOtDBOSpJQTcrPFgAWMU8ABUHFAUoEjoaP+UvClA6pSxzQzVoO9zMaS9pM+NgoK0TmLk0lK5FjqqwpFNgW2n7Qpum4e7NmoVlKgKUTg4opRR8shpQWefXVeuIIfC1/MdAtgAcfp17GzE9XZss2KqRWKqCrE6Xb8J2QOkX9CR6Ibpn0hbUTFt/nRjXOg5/Ov5P+M1HfmN532lsO43cCQDQVmizvJ/NZLUUOER0FQvT4NKKCotzLqShR+mAsqfgO2PuGQCAQ6cd6rESYP7Y+Xhg+QO49cO3htq/WgirBlTaztt6mLeHIhr9eho6iIFK8Yn+nv6BwfdLfyedqqlgpuBzcUD1ahKgNrffJ/Tz/LueKr/etWt43F2SIjqgCnRADQlEF9Hmbr2s23MmtFv+7hsoopDLlq+npB1QQEnk7u4rVok6JuYcVUhQ0BUfLACAnj4xkJQpO6KSdraSOkS52LzyCkNYxyA5ISWMbuKYE6oi2bKrgf+9AFh2lXubhhbg/GfTIbzZkblgVMWKOFBx64jXR/MI93aWZS7XgK4ClFIKPpkDymf6wSiZfgDwpbVATrJ/px8ITF5USvE5YTf3duKx1+p4uRCqAypl7i+SetwCn2kIaqahjyqMbBpZ9Z7T2Noa2vDwiQ+jQXxICCVHQb+hXxYZyxg0PVRhiQBe6JQm0SQNjkcxXZu4D4tGEftM3Ad/PO6PjtePE2NaxoTev7AYSjWgmIIvnaTgbrz+EFM1iaLJtsHi9WYAM+kn5c3t97u4Dl55r1QsN3kH1KAA5RKc/tHJC+LsjifiUU0yZRiJD9GZs6VHrxv7xTuMtvzd018SoHSpRQeUhKVuuAtQfeUUfAk6oLKVBwsAa0rQ/oFixdmasps7kgZS4oDSrW9OqKb2mn1Y6X8vZEF0nZGKZhoFtv2eU7IUfKI7ytUBJTxYoJOwWHMKPsX6X3E9Ge913TS2AZ+6T2FFKROgfKYCkqP6vaC324GkB9cgtKa3vaLTJ22B1zAY1jCs+s0MsLVva2TbvGXJLbjs4cvwtcVf8/W5NLiLxH7lIrw/0HH8YQpQR0w/Aq9teg3zx86vsVdWWgotju+bKfhGNY8KdXtx4ubuUkF0mul4btmpZaxED1IQDag/yoHKooHegcqPWTP9lRnATDpQaYojfcXqHyf/XL8NG7aVnBxJO6AKDjWgRMa2N8XZHU8OnDW2/FqH4D6JHvE47zNjtKRlMhyy87jya9O506dJik0A2Dwo2l1z3/OOy/uLpliW3JxZru03OA3915NvlZf1DhRhZjJN+sECUodY9IDkr1cLogCV1+u72BFLkHoIX6vSGlA6OaAUtt/fW3nd4ByAAGBN6+Z2/62rA0opBZ+iAJX0MQ2TtDmgwvzdp5pSU8N6LySd6OjMkGERoIby971ABplIBajFExdj5fKV2GfiPr4+lwaHjXg+RXktaBl0t3zd1Na/q/a7Cj8//OfIh/yd3ZKv3P+J55CONc/8ous1ETVp+84hJXjUEiBnScFXPekNlB1QsXarCjOY6+SAevSVD8qvkw5Qm/0sBXmt+3PySEmwISE6OypBOKbjGho0FSpPQh04e6ykZTJ8/qCZ5demy8j8V6c0kb/7+zrH980UfDmNUvBd/duKWNY3UNTmwQJSj6g+6Z7AuSc6GcQAv66IT60O5WtVmoJPo7o6Kj8+B3oqr2UiaL7Re72iiKPTD1+V4yCrL1S3570oQGnkWHMjzHQyyqJi+oNvRA9SnYKvruY9K37Gls1ksa1vW4S9qR1dj5UoQEWZGkzHoHsaBMLWQmv5tb0GVNqpJf2jrteTCjpeC8QbHrUEEAOV3X3VPwgrAlSyh8cUR/odHFDDmip1HRwWx8rIlgYUchkYBvDWxu149q2NmD6m9CXzlSN3TrZzDjRq4Cgh8SIe8waNBB2TuZMqaYkee209uvsG8M7mbgDA6LZGt49pg+mASlLQFWv7AcDhc8eXl23vHRDmdf2OP6kjwrgZj+rHSCoEKLFmVQoC1lEhC9br5JZRERX6BQFK1t7igHIZV1EUoFI2l8ue8rUcU8k+StuTwuLvKPHa1pXIXFopO1dJKklzaiRdg+Zxk81ksaVvS9LdqMaS0VHPY1UUHlCJ1AGl4fjTkCJRFKAGhAdyiqGmvk2eWs69tM3bFKDSCY9aAuQEYeepf24ovz9xeOnHry4OKDMFX/9A9Q9OMaC+pacvtj45kc9l0TXodLrtz6/i8O/9GS+/W7KPi84TXRD3XbqmeRIU8TzUNX5j6iJf+MUTOPOnj+PNDdsBAJNG6B80NueoJFNa2h1QM8a2lZc99c+N5XmdrkcSKbIfD0n8sBBdF3n95xJLADgNKbv8onoOKDvp0uCA6vVuA1jdUSoOqLRhSBxQouhUTz/o03Y9h+nSsjgVJcdU9aZU15tXog1uLghdA9KW2icpC7z6wc/+zyCDA7sOBADMHDFT3jhG0nY+DTUHVFYMKet5eNDWUPld3iCkB/fjgNL13AtLoNV2fAJxXWckOlJwN15/mCn4ioaBdzZVnsw0732KgxdWLuGboYoDqvoHd5+Qlm/v6ckX7Zs+uhUvv7sVt/3lFcv7OrqNrE+IJdgREhtNGp6HdkQn44P/eLd8bk4Z1er8gQTYY8qIqvcMwyi7NPNJ1oAaPMRmXwaEefODrb3oHUxpmKUARaIklC+VEM9RMWCfS4MDQfgxk4b++kU1iCwVMjVyQKmc76IDSobfGlBpQ1oDSjWNZ8pECNH1lAYBKtRgSsbxJSFRIQbGUxHIHCI1oPyMLZvJ4tK9LsWuY3bFwZMPjrBX/sikYD6jA2rwtYb9A4BCtoB/O+Tf0D3QjY7GSuaXenBAhbXP0yDED5UHB+oZ/aOidYgZgBwoGuU6K+bf4r9JBypNB5RTmkAzmDq/azhaGpL/UTd5pHOQvDGvtzLOiXNokBecOWkJ3xgGMLK1AWOGJZ+C7+vH7ALAOR2gmCK0kGgNqNK2zfnbnrrUnEeTfrCA1CPCuZa0IGBHdECl4dy3OCb0vn+IlBbJg0Va1YDymYJPht8aUGljxFT3ZeJ46+m8T9v1nIQDSvmuNC13ryQp3IKgafitq2vQPAz87P8MMmhraMNxs47DqObkHzB2QtdjVUTl/iBKZ4bu15OuxwcA9hq/F/aftL/lvXoToPyKnxZBR+NjZzIguPmzlDJSCY9aAuTKApTVSVQWoDRxQD34j3cBAF++Z3XVst7Bfrc26vGDrqXBuR+NBZ7iRC/SlMWkrTF5cRkAGgcFvN6B6pvER17+oPw6UQeU4GwFKqn4TLYPClBJP1hA6hzl1GmyZmE6oCRpv3QkbSm7/OJ1bI+6Edj7c8C0A9TWkbTgqbL9/m61deUVakCl7XwGgFPvBXY9EVh6lXubrGIKvjTdwABALmXXc6gClHgcJdc9U/CRkLA4oFLgiOCT9NXouh/ScD7F5YDSMQWfJUWipueQG35S8KUBXa+PsBCPl47XAvEmBXfj9YcYqOx1cEAVNStW3+dQA8p0bjUkXahqELfAsy4BdEJM0nSjk6SgI9IwmMJQnC9N/n3Vq+XXSfbXNF95OaBYA4qEjk4p0ezs8OHSv2N3TrYfqohp97JDMAXf7p/0XodqYDsOwqwBVajTGlBT9yn9L0PnOaQWRNEpDSk1D/kGcOfHgYVnhbCykB1QaTz3SaykQSQQSVt/g+I3BZ+OpEHgYAq+wdca9k+GUQcPV1jLe/jb/5ZUpJpeWyLi8dJ1viJyeNQSQEzBJzqg+m2By6SflJ83qcPxfcMw8MX/eBpAJe1U0hRchLDxHU2O7+uCDvWzSLzs1lVdx0gHnFLtJZnSTsS8vv/84nt46d0tlmUzx4lFRZNMwWd1QA24CFBJz+ukHtHIkWJn2Djg4teAzzyYdE/UEB0IaXBMJIJOKfhUHFCqKfjqVIBSwSJA6ZHZIBTS5micfThw0UvAsqtrX1fYqTLr9dwnoZG2FHximjRd+xgKPoaWhoCurgJHXI46Hc/VNAiEbtRbCj6/14c4/nxG//skSwq+FMxXpBoetQQwH9IfMAxLSqmizQGV9JPy3zxmLgBgrC0w3d1X6fNr72+NtU9uFBycD0t2Gqftl+BfLz0Yd525NxZM0VOMIOHzl0sOwn+ctQg7T2hPuiuO3PHphVXv6eaAAoAfPPCiZZnpijpxr65Er3fT2Wqv5WeyvVwDKt5+kSGA7u6F5uHpcB8ANseE/j/EEhGALOdbwhOaSsoyVQdUrqHy2u06KqYwBZ8KougkO6Y6zi+qpKEGFAC0jg7pulJ8MEH16e96PfdJaFiCgSm419U1RpAkuoo7Iroet7iEDB3r3uh6TFQQa3d5oes4a3FAiYKOruMToQMq/fCoJUBu8Cn9gQFrCr5+Ww2obMKTwPCWUsBow/Y+y/sDwoU/oIltNe/g1Fiy09gEeqJGZ0cTFtL9NKSYOLwZe0wdmXQ3XOka0VL1Xl6TFJuiADWsyRoU3rCtND9Ncuh/nIjOVvFfk+29pblel9Sq9cI3v/lNLF68GC0tLRg+fLhjm9dffx2HH344WlpaMHbsWFx00UXo7++3tPnjH/+I3XffHY2Njdhhhx3w4x//uGo9P/jBDzB16lQ0NTVh4cKFePTRRyMYUQBUa/KIAXbizKa3K6/T4JhYfC7QPgnY78L4tqmT4Ln486V/dzzUvY2qA8oiQLnM0zMOKv3bMExtnWlB9Zh++GtAW2fp3zTwzt8rr9NwPYeJ5RyW3HfkFTNF1MFT4iRaLDWgUiBkpOFp/zCoixR8KUjx5kfIqAUdRYJaHDhJUw8p+ERqcUClAdaASj9D45tXMxoHg7q9A0VLCr6Bcuqm0t9JByqbCqWnBXv7izAMo/yFNyDUhDpi7vhE+mbHyQGlS/CckDQgijzl9zSx60wZWRGXpo5utSwz69GZ81VSVGr7lf6214Da0lMSyhrzKXkKOyX09vZi+fLlWLRoEf7t3/6tavnAwAAOP/xwdHZ24uGHH8bbb7+NT37ykygUCrjyyisBAK+88goOP/xwnHXWWbjjjjuwcuVKfPrTn8b48eOxdOlSAMBdd92FCy64ADfffDMWLlyI7373u1i6dCnWrFmDsWMTfthBtSbPbp8AnvgpsONSrxWG0at08sL9lddpCFi3jQHOXx2vE0k1sB0Hu54ITNwDGDnNvc2Aago+0e3vMq5RM4BznwZa9H2YJBDiMZU5hUZOAy58PnnnWxDScD2HiaqoeOQNwM+PA/a7QL4+gw4oIkfHwLiMWoKXs0bMwpr1a9DeoGdWi6DoegzTkOItLiFDR4FH12Oigh8BRsd9b6et0ObdSGAgZe5mi2MrBceDVMMIfQI0FgYFqP6iJQXfgC0FX9IClFhP5eM/egTvbSn9iBddT58/eGbs/XKCYhMhteE03zg5C5Ng6uhWzJ1Yqkkn9vLq3z6P/37qLQDJi2U5mwOqWCVAlW6YnIQ+EpwrrrgC559/PubOneu4/He/+x3+/ve/42c/+xnmz5+PQw89FF//+tfxgx/8AL29pdRcN998M6ZNm4bvfOc72GmnnXDOOefg2GOPxfXXX19ez3XXXYczzjgDp512GnbeeWfcfPPNaGlpwW233RbLOKWoBhob24DP/gU4+PLo+1QPZFOSNjDuH/46OaAyGWDMjuGkeFRJwQcAI6YAjXXmgBJFJ69jmtZAU1qu59BQrAE1egfgC38rPaAgI2VPSZP42dizsfw6DY6IXA317r530Pdw7I7H4qeH/TTEHkWDn/2v67FKw/kUWwq+pO+7HND1mKggOmrSzGV7X4Zzdz8XXe1dvj43kLKHS1oLlQeR0yx8DmX0m8GGAKaw09M/gL5+IZ1d0YBhGNqk4BMDpatefh9f/e9nAQD9xdIXbCYDFDQRfuz9mD66FQfsOCah3hCSTo7fw3rToksNKACYPqZ0w2E6i7b19uPmP71UXp60sGNPwWd3QG3uNh1QesyZQ4VVq1Zh7ty5GDduXPm9pUuXYtOmTXj22WfLbZYsWWL53NKlS7Fq1SoAJZfV448/bmmTzWaxZMmSchsnenp6sGnTJsv/kRB2TR7e0JdIS82Y2FEMbKcN0QGlYYAnUnQSFaNiqF3PqqlZVSlSgCJyRjZVnKFpCAzWEsif0DYBX1n0FUzvmB5ij6LBz7HQUdywk0FGy7RhcQXydTxGaRag6kR/wnGzjsOn537a9+fS5oCaN2Ze0l0gNTLE8hHogemA6rE5oIBS+qaBsgMq9q5ZsKe1+8e6zQAqv0FyGt1cin2dMqoFD/zLgcl1hpCU8q1j52FceyO+98CLAPQRmIGKw6g4KNC/taHbsjzpvprzYSWVqt0BVao5lLRQNtRYu3atRXwCUP577dq10jabNm3C9u3bsX79egwMDDi2ef755123fdVVV+GKK64IYxge1KkgkAQNbUDvltLrekzZFYpAWadiRW4oC1A559f1xJCbG0NOlZmyp6RJ/Jw651T0DPTg8GmHWwLStTiNoiQ3RETpenBAWUQXPbsYXwo+Db/LdOyTKnHV7tKVtDmg5oyag3/Z41/Q0diRdFdIQIbYLyw9aMhVait191kv+v5isRy4TNoBlc9lIWblen9LKV2R6YBKOkWgiJgqLOlANCFpRrx+8hpd46bAYzqL3t643bI86evenoKvSoDqLglQdEB5c8kllyCTyUj/lwk/unDppZdi48aN5f/feOONaDaU4h9+2vGJ/6y8TtmPMiXCCJCo1hzTDS9BsdBcea3h09WRUq+i4lAmE/KDCUPtmiC+6WjswBf3/CLmjJ5jXaDp14SuwliS6CokiGKhriJZXK4sXcefVnR008VJ2gQoADhlzik4Zodjku4GCUgdPt6pP6YD6vm1m6uWDRQrKfh0CP4Wcln09Jcm5ve39sIwjIoDSoP+mYipwpIORBOSZsR6agWNxBLzGjdrK/3i0dcty5N2FonzYbFoVKXge+y19QCS72cauPDCC3HqqadK20yfrpb2pLOzE48++qjlvXXr1pWXmf+a74lt2tvb0dzcjFwuh1wu59jGXIcTjY2NaGxsdF0eGgwYh8fEPSqvB/qS64fOWIwVKTj3Tv1f4L5LgMOvl7drnwDMXV6qFdTor4hz6gk7jadudPiriVAXhH1MU5amh+iDjinDAH37lSS67hOxX7qKZHHVEtLxGIl9SltNpbica7oy1AU4Ej8UoBJA9gT8QNFAT19pIshrIKQ05CsCFADc/KeXcfdjpae4dRKgGoR91aBR3RpC0oaYzrKg0TVuOkKv/d0/sLmnH/c+s9ayvCHh+VLcb939AxhwqZfQmOcTl16MGTMGY8aEU8Nv0aJF+OY3v4l33nkHY8eOBQCsWLEC7e3t2Hnnnctt7r33XsvnVqxYgUWLFgEAGhoasGDBAqxcuRLHHHMMAKBYLGLlypU455xzQukn0YSceFusz/wXGqGn4EvBPpq6L3DWn73bZTLAx/5f9P3RETEVlYbBrZoZ5v6gQN0Sdg0oBqmID0SRIKtpwh06oKrR1V2Tz1TuzXTtY2wOKA3vu3Q9Jir4OW5pHqcbaXBAZTNZCmV1hJ53BHWO7An4gaKBl94t1R+YOqo1ri65Yg/qfuu+5/Hye1sB6OHQMhEdUGs3dUtaEkJkiPOTDiK4iTjf3PKnl6uWJ+18bGvMY1RrAwDgxXe2YMDlgSo6oMLl9ddfx5NPPonXX38dAwMDePLJJ/Hkk09iy5bS9+ghhxyCnXfeGSeffDKeeuop3H///fjyl7+Ms88+u+xOOuuss/Dyyy/ji1/8Ip5//nn88Ic/xC9/+Uucf/755e1ccMEF+NGPfoSf/OQneO655/DZz34WW7duxWmnnZbIuC2EHTAe6jf5+5wH7HQUMHFB0j0Jn1Ce9Aw5sE2SJ22ioion3gV0LQQ+ckvSPYmfbMhiegqCVERPdAyYA3o6SaLAz/4f1Twqwp4Ehyn4Kug4fl2vcRXGtY7zblTHDKTA3azjOU+CQwdUAsiegB8oGli7sSSgdI1sdm0XF+9v7XVdppMDqrWxciqv29STYE8ISTemAxMANmxzv/7jJpe1/lBsLuSwXaihl7Swk8lk0DWyBe9v7cXajd2uDqiknVr1xuWXX46f/OQn5b932203AMAf/vAHHHjggcjlcvjNb36Dz372s1i0aBFaW1txyimn4Gtf+1r5M9OmTcP//u//4vzzz8cNN9yASZMm4f/9v/+HpUuXltscf/zxePfdd3H55Zdj7dq1mD9/Pu677z6MG6fBDxcKUOHy4SuS7oHepLUGFHEnxcEjKbOWlf4fimRCdrUN9e8F4gsxYKhr8FAUNeoZlf1/zf7X4IHXH8DJO58cQ4/8I7rVdBU7hnIKPl2vcRm3fPgW/HLNL3HJXpck3ZVESYMDKo3nF3GHAlQCyJxDA0UDvQOlm3zdUzXpJEBN6EherCOkHtjWW7kRSdpVJGLviig+AXrEz0wRrG/AQL+LBSppoaze+PGPf4wf//jH0jZTpkypSrFn58ADD8QTTzwhbXPOOefomXKPAhRRZSim4CPeMBVV/SE6oEKpAcXvBaKOGDDUMWAO6NuvsFERbA6ddigOnXZoDL0JRl6Yz3RNxRWXk4TB+HBYPGExFk9Y7OszuoqftZAGAYrUF0Pjm1cznIQb86n43oEiBgaL1+seqMxpNAmPGRZDoXdChgCisLPH1JEJ9sSK3QFlRwexrLEsQBVRdEl1ldVo3iR1QtjnlKY/7kkIhJGCz1JbhvNZXTBEArFDirDrevF7gQREV6FnqNSAqgfBQjxWugbMD5t+GABg3ph5kW7n6B2OBgDsPnb3SLfjh3oUZoYKqUjBx/OrrqADKgGcHFC5bAYYALr7RPdB8hfbITuPw+/+vs5xWU6D/pno5MYiJM1s7+0vv/7koikJ9sSKl76066SOeDoiwRTBevuL6C86B3p1qp1H6gUKUCRGLA4oPQOLxCc8jvWHJb0Ya0CReBEDhroGD3UVxsKmLgQoYT7T1QHVNawLfznxL2jNR1vDfUr7FPz5hD+jrdAW6Xb8MFSupXpEV0FXpB7mMFKBs0UCOIklZlBye2/lS1UHB9Qhczpdl+nkgAKA4/foAgCMam1IuCeEpJdZne3l1zq4iky8HFA6/MB1crLaoVhOQifsH34peBqOBCSUeTLj8pqkFg2+P0nIhF0Dit8LxAdpqAE1sW1i0l0giuQz+qfgA4D2hvZYaot1NHZoVcNM12uceDN75Oyku+CJDjEeEh50QCWAUwAyawpQogPKI+AaB00F9z7oFki97MidMW1MKw7bZXzSXSEktRy3xyRs6+3Hohmjku6KBZlzSJcUnIV8xQFFAYrERug35vEUUiYx0j4J2PRPYNoBta+LDqj6Q6wX1DAsuX6Q8Ai7BpTGQV+iN7q6I3YZvQu+vPDLmDRsUtJdiZY6+Nkhii1pSBk21KAAlV5OmXMKspks9p24b9JdcYXnV31BASoBpA6oQQGqkMuURakk2WvqSIxsbcAHW3urlukWSG1rzOOsA2Yk3Q1CUk0+l8Wn95uedDeqkM03/3LIjjH2xB3TAdVHBxSJE02DO0QjPvVb4Kk7gT1Or31drAFVf+QKwPKfAAO9QKteD5+QgIReA4pBX+IDy9eEvt8Tx88+PukuRE49BG9FEVNnB9SQJf2n2JClMdeIT8/9dNLdkKLzdwjxD6MWCZC3OZtOWji54oDqNQUoPQ7N2PYmPPalJdhr2siqZV4psQghJCzchJu/Xnowjt9zcsy9caYhX+pj30ClBtS/Hma1trMGFAmdkXzwgngwfDJwwBfDERfogKpP5hwDzDsu6V6QsLBcmyHcd0w/sPRvCwVK4k0aUvCRdJKGmjVDDfEaNwxmUSCEuEMHVALYdZuzP7QDHnj+HQBAd59eAhRQSg9YyFXfPGrURUJIneNWc85pbkoKc97u7S+iOChA7TZ5hKWNDs5WUmd07Qkc/UNgpH7ORVKHWOZizmfJkQHTZRJXLEJxCNfp0iuBMbOBnY6sfV2k7hED0rqm4Bsq1JsASAeUftTbOUb0gudXfUEBKgHsDqhCLlt+ut9MwdeQ1+tmzSmdlFtAmBBCwsbNAWWfT5PETMHXO2CUHVBZ2zxJBxSJhN1OSroHZMgg5lbSZ/4lhAiEnSqzcRiw6Oza10OGHAwekjChA0o/RJHZEB6Mqbdrv97GkxaYgq++4C/HBLDHH3PZTEWAGkzB16CZvWjdpp6q95oKOYeWhBASPm7zTV4nB1S+4oAyRXu74MQaUISQVBO2s4IQEj6ZkGtAEeIDMWBIB1Sy1Fvwlg4o/XA7x3IZxgpJ7VD4qy94R5AA9kl6ZGuD9g4op8u+rZEGOkJIPLQ2Ot/E6iTomA8O9A1UBKhcNoPPHFBJjaZTfwkhxDcZOqAI0Z6wa0AR4gPWgNKHetv/dEClB4rPJAzqbQ4b6nBWSJgDdhwDoPKUvOmA0qmuCQAUHQoKvvr+1gR6QggZirgJ3jqltGsQHFD9ggBlzvOAXv0lhBDfMLBNiP5YnIr8uU+Sg0HoZKk3B5ThEJMiepLL0gFFQqC+prAhD+8IEsaMRZp1QnR1QPUNVH/Zv/QuBShCSDy0NDgLUDo5iqwOqFKKiHw2Y0mpaq8JRQghqYIp+AjRH16nJEEsDiiefyREJrZNTLoLxIPxreMBAIsnLE64J6QeoAOqvmAOtYQxg5FmHRNTgCpoVgOqp5/5dgkhyWGmtLOj0w9b07naY0vBlxfmc51qVhFCiH/EFHyczwjREjqgSJKIXxMMHpIQ+PGyH+PF9S9iz849k+4K8eAny36Ce1+5F8fueGzSXSF1gE6xHlI7FKASxrygcoP/dg+m4GvQTIBqdHBkfXzh5AR6QggZikwZ1ZJ0FzxpyJdSDfT0DVgEKDGlai6r19xOCCG+YGCbEP1hqkyiCUzBR8JgwbgFWDBuQdLdIB4YhoHxbeNx+tzTk+5K6FAISQY+xFBf8I4gYczsUWYaqV898SYAYN2m7qS65MiFh+xY9d7lR+ycQE8IIUORrpH6C1Cm0PT7594pu1lzthR8Od68EkLSjGUO43yWGPwuITIoFJMEYQo+fWDwlhCSZjiH1Re8I02YxkLpifm87an4V9/flkR3XPnIbtZ8u984Zhc0FVhYkBASH//7hX3xUWEu+v7Hd0uwN9WItfvMjIH5bBYtjRWzMQ1QhJBUw8A2IfrDoD9JEFF0ogMqWSgAEhIOFEKSgXNYfcE7goQZ3lwAoH9QUrzwz1syE5/Ye0qCvSGEDEXmTOjAdcfPL/89dlhTcp1xwKlOVTYLjGptKP/dPeiMIkRbDr+u9O8R1yfbD6InFgGKPwoJ0ZLhk4GGNqB1LJArJN0bMoRh0DZZuP8JIYToAmtAJczwltKPArsDSke+d+Ju+O0zb+OM/aYn3RVCyBDmS4fthJfe3YI9p45IuisWevqLVe/ls1mLW3TDtr44u0SIf/Y8HZh3HNA4LOmeEN3hk+2E6EmuAFz0UukapVBMYkYUPeiAIoQQEhSK6PUFBaiEyGQAwwAOnDW2/LfuHLXrBBy164Sku0EIGeKcsb+eInivgwAlpuUDgJ3Gt8fVHUKCQ/GJuMEUfJqQgh8OJFkKernEydDBUgOKc1WicP8TQtIMU/DVFxSgEmLVJQfjjfXbsGBK6Ql+MUUTIYSQ9NHTX51er22w/tPDlxyEtzdupwBFCEk3lh+C/FFICCHECmtA6QODtyRODFSnoyekFrKsGlRX8GgmRGdHE/acOrL8957TRlqWn7p4asw9IoQQUgtHzLM6RM0HDABgwvBmLJgy0v4RQghJF3RAEUIIUYQCCCGEkMDwK6Su4C9HTSjYakB9/qAdEuoJIYSQIEwY3px0FwghJFosAhR/FRJCCHGHKeAIIfUA5zJCaocClCbkstYJrVEoWk8IISR98DaVEFJ/CDMbBShCCCESmIIvWfYYt0fSXSAk1ezVuRcA4GM7fizhngxN+B1SX7AGlCbkc9Yf8S0UoAghJHXkshkMFJn/mhBSp1h+CFKAIoQQYsUwKvfBdA0kwwPLH8BbW9/CnNFzku5K6Hx4yoex4rUV+NhMCgK6MXP4zKS7EDo3HnQjnnnvGSwYtyDprgxJ+B1SX1CA0oRCzqrsZrO80AghJG3sNH4YVr+5CQDNAYSQOkSc2PhUIiGEEAmsAZUMY1rGYEzLmKS7EQnf3Peb+OjMj5adKSR57v3ovVjfvR5d7V1JdyV0WgotWDh+YdLdIKQu4C9HTcgLgtNe01ionhBC0sgNJ+xWfm3QCEUIqTdYA0oPuO8JIZpioHIDzPRJJGya883Yd+K+aMg1JN0VMkjXsC7MGzMv6W6QOoTfIfUFj6YmiA6o0/edlmBPCCGEBGXGmLaku0AIIREiOqCYLjo5KEARQvSkaBTLrxk8JIQQQghAAUobxBpQhRx/VBJCSNrhA+qEkLrDGKi8LjQn14+hDvc9IURTRAcU63cQQggJCr9D6gsKUJqQz2YdXxNCCCGEEKIF/T2V1w2tyfVjqJNvSroHhBDiCWtAEUIIIQSgAKUNoutJrAdFCCEknfCJHUJI3dHfXXnN+gvJQQGKEKIrQg1UpuAjhBASFH6H1Bc8mpqQE0SnfI6HhRBC0o6YgoQQQuqCvu2V13yyPTkKFKAIIXpShFADiuEmQgghAaGLtr7gHYEmFATRKUcHFCGEEEII0Q3RAUWSgw4oQkgaYFiDEEJIQJhRpr6gAKUJorDbXMgl1xFCCCGhsGl7f9JdIISQcKEApQcUoAghmmIYlQwADB4SQgghBKAApQ0TOprLr2d1DkuwJ4QQQmrhI7tNBACcvu+0hHtCCCEhM/3A0r+FlkS7MeTZ41OlfyfukWw/CCHExpiWMeXXuQwfrCWEEBKME2afAABY2Lkw4Z6QMMgY4iMqdcqmTZvQ0dGBjRs3or29PenuuPLa+1vR0pDHmGGNSXeFEEJIQIpFA6vf2oidx7drXdMvLd+N9QL3N6kb1j4DtE8EWkYm3ZOhi2EAb/0NGDMbaGhNujeE1AS/H+Mjrn39xqY3UMgV0NnaGdk2CCGE1DdFo4hn33sWM0fMRFPE7n/ei0RPPukOkApTRvEHJCGEpJ1sNoN5k4Yn3Q1CCImGzrlJ94BkMsDEBUn3ghBCHOlq70q6C4QQQlJONpPF3DH83VEv6PtoNiGEEEIIIYQQQgghhBBCCEklFKAIIYQQQgghhBBCCCGEEEJIqFCAIoQQQgghhBBCCCGEEEIIIaFCAYoQQgghhBBCCCGEEEIIIYSECgUoQgghhBBCCCGEEEIIIYQQEioUoAghhBBCCCGEEEIIIYQQQkioUIAihBBCCCGEEEIIIYQQQgghoUIBihBCCCGEEEIIIYQQQgghhIQKBShCCCGEEEIIIYQQQgghhBASKhSgCCGEEEIIIYQQQgghhBBCSKhQgCKEEEIIIYQQQgghhBBCCCGhQgGKEEIIIYQQQgghhBBCCCGEhAoFKEIIIYQQQgghhBBCCCGEEBIqFKAIIYQQQgghhBBCCCGEEEJIqFCAIoQQQgghhBBCCCGEEEIIIaFCAYoQQgghhBBCCCGEEEIIIYSECgUoQgghhBBCCCGEEEIIIYQQEioUoAghhBBCCCGEEEIIIYQQQkioUIAihBBCCCGEEEIIIYQQQgghoUIBihBCCCGEEEIIIYQQQgghhIQKBShCCCGEEEIIIYQQQgghhBASKhSgCCGEEEIIIYQQQgghhBBCSKhQgCKEEEIIIYQQQgghhBBCCCGhQgGKEEIIIYQQQgghhBBCCCGEhErkAlRPTw/mz5+PTCaDJ5980rLs6aefxn777YempiZ0dXXhmmuuqfr83XffjdmzZ6OpqQlz587FvffeG3WXCSGEEEIIIYQQQgghhBBCSA1ELkB98YtfxIQJE6re37RpEw455BBMmTIFjz/+OL797W/jq1/9Km699dZym4cffhgnnngiTj/9dDzxxBM45phjcMwxx2D16tVRd5sQQgghhBBCCCGEEEIIIYQEJFIB6re//S1+97vf4dprr61adscdd6C3txe33XYb5syZgxNOOAFf+MIXcN1115Xb3HDDDVi2bBkuuugi7LTTTvj617+O3XffHd///vej7DYhhBBCCCGEEEIIIYQQQgipgcgEqHXr1uGMM87AT3/6U7S0tFQtX7VqFfbff380NDSU31u6dCnWrFmD9evXl9ssWbLE8rmlS5di1apV0m339PRg06ZNlv8JIYQQQgghhBBCCCGEEEJIPEQiQBmGgVNPPRVnnXUW9thjD8c2a9euxbhx4yzvmX+vXbtW2sZc7sZVV12Fjo6O8v9dXV1Bh0IIIYQQQgghhBBCCCGEEEJ84kuAuuSSS5DJZKT/P//887jxxhuxefNmXHrppVH1W8qll16KjRs3lv9/4403EukHIYQQQgghhBBCCCGEEELIUCTvp/GFF16IU089Vdpm+vTpeOCBB7Bq1So0NjZalu2xxx446aST8JOf/ASdnZ1Yt26dZbn5d2dnZ/lfpzbmcjcaGxurtk0IIYQQQgghhBBCCCGEEELiwZcANWbMGIwZM8az3fe+9z184xvfKP/91ltvYenSpbjrrruwcOFCAMCiRYvwpS99CX19fSgUCgCAFStWYNasWRgxYkS5zcqVK3HeeeeV17VixQosWrTIT7dhGAYAsBYUIYQQMoj5nWh+R5Jo4b0IIYQQUg3vR+KD9yKEEEJINbwXiR5fApQqkydPtvzd1tYGAJgxYwYmTZoEAPj4xz+OK664AqeffjouvvhirF69GjfccAOuv/768ufOPfdcHHDAAfjOd76Dww8/HHfeeScee+wx3Hrrrb76s3nzZgBgLShCCCHExubNm9HR0ZF0N+oe3osQQggh7vB+JHp4L0IIIYS4w3uR6MgYMch7r776KqZNm4YnnngC8+fPL7//9NNP4+yzz8b//d//YfTo0fj85z+Piy++2PLZu+++G1/+8pfx6quvYubMmbjmmmtw2GGH+dp+sVjEW2+9hWHDhiGTydQ8nk2bNqGrqwtvvPEG2tvba16fbtTz+Di29FLP4+PY0knax2YYBjZv3owJEyYgm/VVEpIEgPci/qjn8XFs6aWex8expZN6GBvvR+KD9yL+qOfxcWzppZ7Hx7Gll7SPj/ci0ROJA8rO1KlTHW1s8+bNw0MPPST97PLly7F8+fKatp/NZsvOqzBpb29P5YWlSj2Pj2NLL/U8Po4tnaR5bHy6Jz54LxKMeh4fx5Ze6nl8HFs6SfvYeD8SD7wXCUY9j49jSy/1PD6OLb2keXy8F4kWynqEEEIIIYQQQgghhBBCCCEkVChAEUIIIYQQQgghhBBCCCGEkFChABWAxsZGfOUrX0FjY2PSXYmEeh4fx5Ze6nl8HFs6qeexEf2p9/OvnsfHsaWXeh4fx5ZO6nlsRH/q/fyr5/FxbOmlnsfHsaWXeh8fqZ2M4VSciRBCCCGEEEIIIYQQQgghhJCA0AFFCCGEEEIIIYQQQgghhBBCQoUCFCGEEEIIIYQQQgghhBBCCAkVClCEEEIIIYQQQgghhBBCCCEkVChAEUIIIYQQQgghhBBCCCGEkFChAEUIIYQQQgghhBBCCCGEEEJChQJUAH7wgx9g6tSpaGpqwsKFC/Hoo48m3SUpV111Ffbcc08MGzYMY8eOxTHHHIM1a9ZY2nR3d+Pss8/GqFGj0NbWho997GNYt26dpc3rr7+Oww8/HC0tLRg7diwuuugi9Pf3xzkUT66++mpkMhmcd9555ffSPrY333wTn/jEJzBq1Cg0Nzdj7ty5eOyxx8rLDcPA5ZdfjvHjx6O5uRlLlizBCy+8YFnHBx98gJNOOgnt7e0YPnw4Tj/9dGzZsiXuoVgYGBjAZZddhmnTpqG5uRkzZszA17/+dRiGUW6TprE9+OCDOPLIIzFhwgRkMhncc889luVhjeXpp5/Gfvvth6amJnR1deGaa66JemjSsfX19eHiiy/G3Llz0draigkTJuCTn/wk3nrrrdSPzc5ZZ52FTCaD7373u5b3dR0bqW/Sdi8C8H4kzWPjvUg6xsZ7Ed6L6DY2Ut/wXkS/72sR3ovo+30twnsR3ovoMDaA9yMkYgziizvvvNNoaGgwbrvtNuPZZ581zjjjDGP48OHGunXrku6aK0uXLjVuv/12Y/Xq1caTTz5pHHbYYcbkyZONLVu2lNucddZZRldXl7Fy5UrjscceM/bee29j8eLF5eX9/f3GLrvsYixZssR44oknjHvvvdcYPXq0cemllyYxJEceffRRY+rUqca8efOMc889t/x+msf2wQcfGFOmTDFOPfVU45FHHjFefvll4/777zdefPHFcpurr77a6OjoMO655x7jqaeeMo466ihj2rRpxvbt28ttli1bZuy6667GX//6V+Ohhx4ydthhB+PEE09MYkhlvvnNbxqjRo0yfvOb3xivvPKKcffddxttbW3GDTfcUG6TprHde++9xpe+9CXjV7/6lQHA+PWvf21ZHsZYNm7caIwbN8446aSTjNWrVxu/+MUvjObmZuOWW25JbGwbNmwwlixZYtx1113G888/b6xatcrYa6+9jAULFljWkcaxifzqV78ydt11V2PChAnG9ddfb1mm69hI/ZLGexHD4P1IWsfGe5H0jI33IrwX0W1spH7hvYh+39civBfR+/tahPcivBfRYWxe4xPh/QgJAgUon+y1117G2WefXf57YGDAmDBhgnHVVVcl2Ct/vPPOOwYA409/+pNhGKWJslAoGHfffXe5zXPPPWcAMFatWmUYRmkiymazxtq1a8ttbrrpJqO9vd3o6emJdwAObN682Zg5c6axYsUK44ADDijfZKV9bBdffLGx7777ui4vFotGZ2en8e1vf7v83oYNG4zGxkbjF7/4hWEYhvH3v//dAGD83//9X7nNb3/7WyOTyRhvvvlmdJ334PDDDzc+9alPWd776Ec/apx00kmGYaR7bPYv67DG8sMf/tAYMWKE5by8+OKLjVmzZkU8ogqyGxGTRx991ABgvPbaa4ZhpH9s//znP42JEycaq1evNqZMmWK5yUrL2Eh9UQ/3IobB+5G0jI33IukcG+9FeC+i29hIfcF7Ef2+r014L1IiLd/XvBfhvYhuYzMM3o+Q8GEKPh/09vbi8ccfx5IlS8rvZbNZLFmyBKtWrUqwZ/7YuHEjAGDkyJEAgMcffxx9fX2Wcc2ePRuTJ08uj2vVqlWYO3cuxo0bV26zdOlSbNq0Cc8++2yMvXfm7LPPxuGHH24ZA5D+sf33f/839thjDyxfvhxjx47Fbrvthh/96Efl5a+88grWrl1rGV9HRwcWLlxoGd/w4cOxxx57lNssWbIE2WwWjzzySHyDsbF48WKsXLkS//jHPwAATz31FP785z/j0EMPBZDusdkJayyrVq3C/vvvj4aGhnKbpUuXYs2aNVi/fn1Mo/Fm48aNyGQyGD58OIB0j61YLOLkk0/GRRddhDlz5lQtT/PYSDqpl3sRgPcjaRkb70XSOTY7vBdJ79h4L0J0g/cien5fm/BepERavq95L8J7kbSMjfcjpBYoQPngvffew8DAgOXLGADGjRuHtWvXJtQrfxSLRZx33nnYZ599sMsuuwAA1q5di4aGhvKkaCKOa+3atY7jNpclyZ133om//e1vuOqqq6qWpX1sL7/8Mm666SbMnDkT999/Pz772c/iC1/4An7yk59Y+ic7J9euXYuxY8dalufzeYwcOTLR8V1yySU44YQTMHv2bBQKBey2224477zzcNJJJwFI99jshDUWnc9Vk+7ublx88cU48cQT0d7eDiDdY/vWt76FfD6PL3zhC47L0zw2kk7q4V4E4P2ISRrGxnuRdI7NDu9F0js23osQ3eC9iL7XE+9F0vd9zXsR3oukZWy8HyG1kE+6AyRezj77bKxevRp//vOfk+5KKLzxxhs499xzsWLFCjQ1NSXdndApFovYY489cOWVVwIAdtttN6xevRo333wzTjnllIR7Vxu//OUvcccdd+DnP/855syZgyeffBLnnXceJkyYkPqxDVX6+vpw3HHHwTAM3HTTTUl3p2Yef/xx3HDDDfjb3/6GTCaTdHcIqSt4P5IeeC9C0gTvRQghqvBeJD3wXoSkiXq7FwF4P0Jqhw4oH4wePRq5XA7r1q2zvL9u3Tp0dnYm1Ct1zjnnHPzmN7/BH/7wB0yaNKn8fmdnJ3p7e7FhwwZLe3FcnZ2djuM2lyXF448/jnfeeQe777478vk88vk8/vSnP+F73/se8vk8xo0bl9qxAcD48eOx8847W97baaed8PrrrwOo9E92TnZ2duKdd96xLO/v78cHH3yQ6Pguuuii8tM+c+fOxcknn4zzzz+//LRWmsdmJ6yx6HyumjdZr732GlasWFF+ygdI79geeughvPPOO5g8eXJ5fnnttddw4YUXYurUqeW+pXFsJL2k/V4E4P2ISBrGxnuRdI7NDu9F0jk23osQHeG9iJ7XE+9F0vl9zXsR3oukYWy8HyG1QgHKBw0NDViwYAFWrlxZfq9YLGLlypVYtGhRgj2TYxgGzjnnHPz617/GAw88gGnTplmWL1iwAIVCwTKuNWvW4PXXXy+Pa9GiRXjmmWcsk4k5mdpvBOLk4IMPxjPPPIMnn3yy/P8ee+yBk046qfw6rWMDgH322Qdr1qyxvPePf/wDU6ZMAQBMmzYNnZ2dlvFt2rQJjzzyiGV8GzZswOOPP15u88ADD6BYLGLhwoUxjMKZbdu2IZu1TkG5XA7FYhFAusdmJ6yxLFq0CA8++CD6+vrKbVasWIFZs2ZhxIgRMY2mGvMm64UXXsDvf/97jBo1yrI8rWM7+eST8fTTT1vmlwkTJuCiiy7C/fffX+53GsdG0kta70UA3o+kdWy8F0nn2OzwXiSdY+O9CNER3ovo+X3Ne5F0fl/zXoT3ImkYG+9HSM0YxBd33nmn0djYaPz4xz82/v73vxtnnnmmMXz4cGPt2rVJd82Vz372s0ZHR4fxxz/+0Xj77bfL/2/btq3c5qyzzjImT55sPPDAA8Zjjz1mLFq0yFi0aFF5eX9/v7HLLrsYhxxyiPHkk08a9913nzFmzBjj0ksvTWJIUg444ADj3HPPLf+d5rE9+uijRj6fN775zW8aL7zwgnHHHXcYLS0txs9+9rNym6uvvtoYPny48V//9V/G008/bRx99NHGtGnTjO3bt5fbLFu2zNhtt92MRx55xPjzn/9szJw50zjxxBOTGFKZU045xZg4caLxm9/8xnjllVeMX/3qV8bo0aONL37xi+U2aRrb5s2bjSeeeMJ44oknDADGddddZzzxxBPGa6+9FtpYNmzYYIwbN844+eSTjdWrVxt33nmn0dLSYtxyyy2Jja23t9c46qijjEmTJhlPPvmkZY7p6elJ9dicmDJlinH99ddb3tN1bKR+SeO9iGHwfiStY+O9SHrGxnsR3ovoNjZSv/BeRL/vayd4L6Ln97UI70V4L6LD2LzG5wTvR4gfKEAF4MYbbzQmT55sNDQ0GHvttZfx17/+NekuSQHg+P/tt99ebrN9+3bjc5/7nDFixAijpaXF+MhHPmK8/fbblvW8+uqrxqGHHmo0Nzcbo0ePNi688EKjr68v5tF4Y7/JSvvY/ud//sfYZZddjMbGRmP27NnGrbfealleLBaNyy67zBg3bpzR2NhoHHzwwcaaNWssbd5//33jxBNPNNra2oz29nbjtNNOMzZv3hznMKrYtGmTce655xqTJ082mpqajOnTpxtf+tKXLF/OaRrbH/7wB8fr7JRTTgl1LE899ZSx7777Go2NjcbEiRONq6++OtGxvfLKK65zzB/+8IdUj80Jp5ssXcdG6pu03YsYBu9H0jw23oukY2y8F+G9iG5jI/UN70X0+762w3sRPb+vRXgvwnsRHcbmNT4neD9C/JAxDMNQdUsRQgghhBBCCCGEEEIIIYQQ4gVrQBFCCCGEEEIIIYQQQgghhJBQoQBFCCGEEEIIIYQQQgghhBBCQoUCFCGEEEIIIYQQQgghhBBCCAkVClCEEEIIIYQQQgghhBBCCCEkVChAEUIIIYQQQgghhBBCCCGEkFChAEUIIYQQQgghhBBCCCGEEEJChQIUIYQQQgghhBBCCCGEEEIICRUKUIQQQgghhBBCCCGEEEIIISRUKEARQgghhBBCCCGEEEIIIYSQUKEARQghhBBCCCGEEEIIIYQQQkKFAhQhhBBCCCGEEEIIIYQQQggJlf8PCrb6z048Mx4AAAAASUVORK5CYII=" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_mean_sample(feature_matrix.predict, feature_matrix.target)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 50, + "outputs": [], + "source": [ + "feature_extractor = pipeline_creator.create_pipeline(wavelet_node_dict)\n", + "feature_matrix = feature_extractor.fit(emg_dataset[0])" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 51, + "outputs": [], + "source": [ + "n_channels = list(range(feature_matrix.predict.shape[1]))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 52, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for channel in n_channels:\n", + " y = np.expand_dims(feature_matrix.predict[:,channel,:], axis=1)\n", + " plot_mean_sample(y, feature_matrix.target)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 34, + "outputs": [], + "source": [ + "stat_list_model = {'quantile_extractor':{'window_size':10,\n", + " 'add_global_features':True,\n", + " 'use_sliding_window':False},\n", + " 'logit':{}}" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 35, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-10-21 15:03:35,270 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\data\\NerveDamage\n", + "2024-10-21 15:03:35,523 - Data read successfully from local folder\n", + "2024-10-21 15:03:35,527 - Initialising experiment setup\n", + "2024-10-21 15:03:35,549 - -------------------------------------------------\n", + "2024-10-21 15:03:35,550 - Initialising Industrial Repository\n", + "2024-10-21 15:03:35,551 - -------------------------------------------------\n", + "2024-10-21 15:03:35,551 - Initialising Dask Server\n", + "Creating Dask Server\n", + "2024-10-21 15:03:35,558 - State start\n", + "2024-10-21 15:03:35,568 - Scheduler at: inproc://10.64.4.172/21832/53\n", + "2024-10-21 15:03:35,569 - dashboard at: http://10.64.4.172:59133/status\n", + "2024-10-21 15:03:35,569 - Registering Worker plugin shuffle\n", + "2024-10-21 15:03:35,581 - Start worker at: inproc://10.64.4.172/21832/56\n", + "2024-10-21 15:03:35,582 - Listening to: inproc10.64.4.172\n", + "2024-10-21 15:03:35,583 - Worker name: 0\n", + "2024-10-21 15:03:35,583 - dashboard at: 10.64.4.172:59134\n", + "2024-10-21 15:03:35,584 - Waiting to connect to: inproc://10.64.4.172/21832/53\n", + "2024-10-21 15:03:35,584 - -------------------------------------------------\n", + "2024-10-21 15:03:35,584 - Threads: 8\n", + "2024-10-21 15:03:35,585 - Memory: 31.95 GiB\n", + "2024-10-21 15:03:35,586 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-suxwo040\n", + "2024-10-21 15:03:35,586 - -------------------------------------------------\n", + "2024-10-21 15:03:35,589 - Register worker \n", + "2024-10-21 15:03:35,590 - Starting worker compute stream, inproc://10.64.4.172/21832/56\n", + "2024-10-21 15:03:35,591 - Starting established connection to inproc://10.64.4.172/21832/57\n", + "2024-10-21 15:03:35,592 - Starting Worker plugin shuffle\n", + "2024-10-21 15:03:35,593 - Registered to: inproc://10.64.4.172/21832/53\n", + "2024-10-21 15:03:35,593 - -------------------------------------------------\n", + "2024-10-21 15:03:35,594 - Starting established connection to inproc://10.64.4.172/21832/53\n", + "2024-10-21 15:03:35,598 - Receive client connection: Client-7ff51df7-8fa4-11ef-9548-b42e99a00ea1\n", + "2024-10-21 15:03:35,599 - Starting established connection to inproc://10.64.4.172/21832/58\n", + "2024-10-21 15:03:35,600 - LinK Dask Server - http://10.64.4.172:59133/status\n", + "2024-10-21 15:03:35,601 - -------------------------------------------------\n", + "2024-10-21 15:03:35,602 - Initialising solver\n", + "AssumptionsHandler - Initial pipeline fitting started\n", + "AssumptionsHandler - Initial pipeline was fitted successfully\n", + "AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 4.4 MiB, max: 6.3 MiB\n", + "ApiComposer - Initial pipeline was fitted in 2.3 sec.\n", + "AssumptionsHandler - Preset was changed to fast_train due to fit time estimation for initial model.\n", + "ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 1 min. Set of candidate models: ['xgboost', 'catboost', 'logit', 'dt', 'rf', 'mlp', 'lgbm', 'one_class_svm', 'inception_model', 'nbeats_model', 'tcn_model', 'deepar_model', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'topological_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca', 'topological_extractor'].\n", + "ApiComposer - Pipeline composition started.\n", + "DataSourceSplitter - K-folds cross validation is applied.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generations: 0%| | 0/10000 [00:00.on_destroy at 0x000001C611AF2430>\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\user\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\joblib\\_dask.py\", line 87, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1950244182032\n", + "Exception ignored in: .on_destroy at 0x000001C6151208B0>\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\user\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\joblib\\_dask.py\", line 87, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1950267027344\n", + "Exception ignored in: .on_destroy at 0x000001C5FF3F4F70>\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\user\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\joblib\\_dask.py\", line 87, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1950202730416\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-10-21 15:04:53,169 - full garbage collection released 19.01 MiB from 36669 reference cycles (threshold: 9.54 MiB)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Exception ignored in: .on_destroy at 0x000001C616E32550>\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\user\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\joblib\\_dask.py\", line 87, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1950046001840\n", + "Exception ignored in: .on_destroy at 0x000001C6143D1EE0>\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\user\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\joblib\\_dask.py\", line 87, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1949910308496\n", + "Exception ignored in: .on_destroy at 0x000001C605C3E820>\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\user\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\joblib\\_dask.py\", line 87, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1950165793360\n", + "Exception ignored in: .on_destroy at 0x000001C612653670>\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\user\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\joblib\\_dask.py\", line 87, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1950223646032\n", + "Exception ignored in: .on_destroy at 0x000001C6151D6790>\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\user\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\joblib\\_dask.py\", line 87, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1950250361360\n", + "Exception ignored in: .on_destroy at 0x000001C61698E3A0>\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\user\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\joblib\\_dask.py\", line 87, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1950003899056\n", + "Exception ignored in: .on_destroy at 0x000001C612556790>\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\user\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\joblib\\_dask.py\", line 87, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1950193804368\n", + "Exception ignored in: .on_destroy at 0x000001C614E36160>\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\user\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\fedot-ind-bTwQVkVM-py3.9\\lib\\site-packages\\joblib\\_dask.py\", line 87, in on_destroy\n", + " del self._data[key]\n", + "KeyError: 1950009270992\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IndustrialDispatcher - 1 individuals out of 13 in previous population were evaluated successfully. 0.07692307692307693% is a fairly small percentage of successful evaluation.\n", + "IndustrialEvoOptimizer - Generation num: 1 size: 1\n", + "IndustrialEvoOptimizer - Best individuals: HallOfFame archive fitness (1): ['']\n", + "GroupedCondition - Optimisation stopped: Time limit is reached\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generations: 0%| | 0/10000 [05:17']\n", + "IndustrialEvoOptimizer - no improvements for 1 iterations\n", + "IndustrialEvoOptimizer - spent time: 5.3 min\n", + "GPComposer - GP composition finished\n", + "DataSourceSplitter - K-folds cross validation is applied.\n", + "ApiComposer - Time for pipeline composing was 0:05:17.341698.\n", + "The remaining 4.3 seconds are not enough to tune the hyperparameters.\n", + "ApiComposer - Composed pipeline returned without tuning.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ApiComposer - Model generation finished\n", + "FEDOT logger - Final pipeline was fitted\n", + "FEDOT logger - Final pipeline: {'depth': 2, 'length': 2, 'nodes': [logit, quantile_extractor]}\n", + "logit - {'C': 5.243806353645889, 'penalty': 'l1', 'solver': 'liblinear'}\n", + "quantile_extractor - {'window_size': 25, 'stride': 9, 'add_global_features': True}\n", + "MemoryAnalytics - Memory consumption for finish in main session: current 87.6 MiB, max: 96.2 MiB\n", + "FEDOT logger - Predictions was saved in current directory.\n", + "FEDOT logger - Predictions was saved in current directory.\n" + ] + } + ], + "source": [ + "result_dict_stat = ApiTemplate(api_config=api_config,\n", + " metric_list=metric_names).eval(dataset='NerveDamage',\n", + " finetune=finetune,\n", + " initial_assumption = stat_list_model)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 36, + "outputs": [ + { + "data": { + "text/plain": " accuracy f1 precision\n0 1.0 1.0 1.0", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
accuracyf1precision
01.01.01.0
\n
" + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_dict_stat['metrics']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 67, + "outputs": [], + "source": [ + "fourier_list_model = {'fourier_basis':{'threshold':threshold,\n", + " 'approximation':approximation,\n", + " 'low_rank':low_rank},\n", + " 'quantile_extractor':{'window_size':10,\n", + " 'add_global_features':True,\n", + " 'use_sliding_window':False},\n", + " 'logit':{}}" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 68, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Dask Server\n", + "2024-10-21 19:03:41,872 - To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy\n", + "2024-10-21 19:03:41,877 - State start\n", + "2024-10-21 19:03:41,887 - Scheduler at: inproc://10.64.4.172/26676/1\n", + "2024-10-21 19:03:41,888 - dashboard at: http://10.64.4.172:8787/status\n", + "2024-10-21 19:03:41,889 - Registering Worker plugin shuffle\n", + "2024-10-21 19:03:41,905 - Start worker at: inproc://10.64.4.172/26676/4\n", + "2024-10-21 19:03:41,905 - Listening to: inproc10.64.4.172\n", + "2024-10-21 19:03:41,906 - Worker name: 0\n", + "2024-10-21 19:03:41,907 - dashboard at: 10.64.4.172:55982\n", + "2024-10-21 19:03:41,907 - Waiting to connect to: inproc://10.64.4.172/26676/1\n", + "2024-10-21 19:03:41,908 - -------------------------------------------------\n", + "2024-10-21 19:03:41,908 - Threads: 8\n", + "2024-10-21 19:03:41,908 - Memory: 31.95 GiB\n", + "2024-10-21 19:03:41,909 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-olac703q\n", + "2024-10-21 19:03:41,910 - -------------------------------------------------\n", + "2024-10-21 19:03:41,914 - Register worker \n", + "2024-10-21 19:03:41,916 - Starting worker compute stream, inproc://10.64.4.172/26676/4\n", + "2024-10-21 19:03:41,916 - Starting established connection to inproc://10.64.4.172/26676/5\n", + "2024-10-21 19:03:41,917 - Starting Worker plugin shuffle\n", + "2024-10-21 19:03:41,918 - Registered to: inproc://10.64.4.172/26676/1\n", + "2024-10-21 19:03:41,919 - -------------------------------------------------\n", + "2024-10-21 19:03:41,920 - Starting established connection to inproc://10.64.4.172/26676/1\n", + "2024-10-21 19:03:41,923 - Receive client connection: Client-0acbf6d7-8fc6-11ef-a834-b42e99a00ea1\n", + "2024-10-21 19:03:41,925 - Starting established connection to inproc://10.64.4.172/26676/6\n", + "AssumptionsHandler - Initial pipeline fitting started\n", + "AssumptionsHandler - Initial pipeline was fitted successfully\n", + "AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 4.6 MiB, max: 13.2 MiB\n", + "ApiComposer - Initial pipeline was fitted in 4.6 sec.\n", + "AssumptionsHandler - Preset was changed to fast_train due to fit time estimation for initial model.\n", + "ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 1 min. Set of candidate models: ['xgboost', 'catboost', 'logit', 'dt', 'rf', 'mlp', 'lgbm', 'one_class_svm', 'inception_model', 'nbeats_model', 'tcn_model', 'deepar_model', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'topological_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca', 'topological_extractor'].\n", + "ApiComposer - Timeout is too small for composing and is skipped because fit_time is 4.602908 sec.\n", + "DataSourceSplitter - K-folds cross validation is applied.\n", + "ApiComposer - Hyperparameters tuning started with 1 min. timeout\n", + "SimultaneousTuner - Hyperparameters optimization start: estimation of metric for initial graph\n", + "SimultaneousTuner - Initial graph: {'depth': 3, 'length': 3, 'nodes': [logit, quantile_extractor, fourier_basis]}\n", + "logit - {}\n", + "quantile_extractor - {'window_size': 10, 'add_global_features': True, 'use_sliding_window': False}\n", + "fourier_basis - {'threshold': 0.9, 'approximation': 'smooth', 'low_rank': 10} \n", + "Initial metric: [0.485]\n", + " 0%| | 0/10 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
accuracyf1precision
00.5610.5760.512
\n" + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_dict_fourier['metrics']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 40, + "outputs": [], + "source": [ + "wavelet_list_model = {'wavelet_basis':{'wavelet':wavelet,\n", + " 'n_components':n_components,\n", + " 'low_freq':low_freq},\n", + " 'quantile_extractor':{'window_size':10,\n", + " 'add_global_features':True,\n", + " 'use_sliding_window':False},\n", + " 'logit':{}}" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 41, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-10-21 15:10:20,836 - Reading data from D:\\WORK\\Repo\\Industiral\\IndustrialTS\\fedot_ind\\data\\NerveDamage\n", + "2024-10-21 15:10:21,049 - Data read successfully from local folder\n", + "2024-10-21 15:10:21,054 - Initialising experiment setup\n", + "2024-10-21 15:10:21,080 - -------------------------------------------------\n", + "2024-10-21 15:10:21,084 - Initialising Industrial Repository\n", + "2024-10-21 15:10:21,085 - -------------------------------------------------\n", + "2024-10-21 15:10:21,086 - Initialising Dask Server\n", + "Creating Dask Server\n", + "2024-10-21 15:10:21,093 - State start\n", + "2024-10-21 15:10:21,103 - Scheduler at: inproc://10.64.4.172/21832/100\n", + "2024-10-21 15:10:21,103 - dashboard at: http://10.64.4.172:59476/status\n", + "2024-10-21 15:10:21,104 - Registering Worker plugin shuffle\n", + "2024-10-21 15:10:21,120 - Start worker at: inproc://10.64.4.172/21832/103\n", + "2024-10-21 15:10:21,121 - Listening to: inproc10.64.4.172\n", + "2024-10-21 15:10:21,122 - Worker name: 0\n", + "2024-10-21 15:10:21,122 - dashboard at: 10.64.4.172:59477\n", + "2024-10-21 15:10:21,122 - Waiting to connect to: inproc://10.64.4.172/21832/100\n", + "2024-10-21 15:10:21,123 - -------------------------------------------------\n", + "2024-10-21 15:10:21,123 - Threads: 8\n", + "2024-10-21 15:10:21,124 - Memory: 31.95 GiB\n", + "2024-10-21 15:10:21,124 - Local Directory: C:\\Users\\user\\AppData\\Local\\Temp\\dask-scratch-space\\worker-15z07gyq\n", + "2024-10-21 15:10:21,124 - -------------------------------------------------\n", + "2024-10-21 15:10:21,129 - Register worker \n", + "2024-10-21 15:10:21,131 - Starting worker compute stream, inproc://10.64.4.172/21832/103\n", + "2024-10-21 15:10:21,132 - Starting established connection to inproc://10.64.4.172/21832/104\n", + "2024-10-21 15:10:21,132 - Starting Worker plugin shuffle\n", + "2024-10-21 15:10:21,134 - Registered to: inproc://10.64.4.172/21832/100\n", + "2024-10-21 15:10:21,134 - -------------------------------------------------\n", + "2024-10-21 15:10:21,135 - Starting established connection to inproc://10.64.4.172/21832/100\n", + "2024-10-21 15:10:21,139 - Receive client connection: Client-71adcc2f-8fa5-11ef-9548-b42e99a00ea1\n", + "2024-10-21 15:10:21,141 - Starting established connection to inproc://10.64.4.172/21832/105\n", + "2024-10-21 15:10:21,142 - LinK Dask Server - http://10.64.4.172:59476/status\n", + "2024-10-21 15:10:21,147 - -------------------------------------------------\n", + "2024-10-21 15:10:21,148 - Initialising solver\n", + "AssumptionsHandler - Initial pipeline fitting started\n", + "AssumptionsHandler - Initial pipeline was fitted successfully\n", + "AssumptionsHandler - Memory consumption for fitting of the initial pipeline in main session: current 4.5 MiB, max: 13.1 MiB\n", + "ApiComposer - Initial pipeline was fitted in 2.8 sec.\n", + "AssumptionsHandler - Preset was changed to fast_train due to fit time estimation for initial model.\n", + "ApiComposer - AutoML configured. Parameters tuning: True. Time limit: 1 min. Set of candidate models: ['xgboost', 'catboost', 'logit', 'dt', 'rf', 'mlp', 'lgbm', 'one_class_svm', 'inception_model', 'nbeats_model', 'tcn_model', 'deepar_model', 'channel_filtration', 'eigen_basis', 'wavelet_basis', 'fourier_basis', 'quantile_extractor', 'topological_extractor', 'minirocket_extractor', 'scaling', 'normalization', 'simple_imputation', 'kernel_pca', 'topological_extractor'].\n", + "ApiComposer - Timeout is too small for composing and is skipped because fit_time is 2.789956 sec.\n", + "DataSourceSplitter - K-folds cross validation is applied.\n", + "ApiComposer - Hyperparameters tuning started with 1 min. timeout\n", + "SimultaneousTuner - Hyperparameters optimization start: estimation of metric for initial graph\n", + "SimultaneousTuner - Initial graph: {'depth': 3, 'length': 3, 'nodes': [logit, quantile_extractor, wavelet_basis]}\n", + "logit - {}\n", + "quantile_extractor - {'window_size': 10, 'add_global_features': True, 'use_sliding_window': False}\n", + "wavelet_basis - {'wavelet': 'gaus8', 'n_components': 5, 'low_freq': True} \n", + "Initial metric: [0.957]\n", + " 0%| | 0/10 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
accuracyf1precision
01.01.01.0
\n" + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_dict_wavelet['metrics']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/tutorial/time_series/ts_classification/tmp.py b/examples/tutorial/time_series/ts_classification/tmp.py new file mode 100644 index 000000000..1f838cf4f --- /dev/null +++ b/examples/tutorial/time_series/ts_classification/tmp.py @@ -0,0 +1,100 @@ +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from hyperopt import hp + +from fedot_ind.core.architecture.pipelines.abstract_pipeline import AbstractPipeline, ApiTemplate + + +def plot_mean_sample(X, y, labels: list = [], n_channel: int = 1): + mean_sample = [] + if len(labels) == 0: + labels = list(np.unique(y)) + for label in labels: + mean_sample.append(np.mean(X[y == label], axis=0)) # Данные класса 1 + # ax = plt.gca() + [f'Channel {x}' for x in range(n_channel)] + df = pd.DataFrame(mean_sample).T + df.columns = labels + df.plot(kind='line', subplots=True, layout=(1, len(labels)), figsize=(20, 10)) + plt.legend(fontsize='small') + plt.legend(loc='upper left', bbox_to_anchor=(1, 1)) + plt.show() + + +# %% +def plot_mean_sample_multi(X, y, labels: list = [], n_channel: int = None): + mean_sample = {} + if len(labels) == 0: + labels = list(np.unique(y)) + if n_channel is None: + n_channel = X.shape[1] + [f'Channel {x}' for x in range(n_channel)] + for label in labels: + mask = y == label + for chn in range(n_channel): + mean_sample.update( + {f'Label_{label}_channel_{chn}': np.mean(X[mask.flatten(), chn, :], axis=0)}) # Данные класса 1 + # ax = plt.gca() + df = pd.DataFrame(mean_sample) + df.plot(kind='line') + plt.suptitle('Усреднённые семплы по классам') + plt.legend(fontsize='small') + plt.legend(loc='upper left', bbox_to_anchor=(1, 1)) + plt.show() + + +# %% md +# Topo Hyperparams +# %% +topological_params = {'window_size': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(5, 50, 5)]]}, + 'stride': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(1, 10, 1)]]}}, +# %% +stat_params = {'window_size': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(5, 50, 5)]]}, + 'stride': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(1, 10, 1)]]}, + 'add_global_features': {'hyperopt-dist': hp.choice, 'sampling-scope': [[True, False]]}} +# %% +recurrence_params = {'window_size': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(5, 50, 5)]]}, + 'stride': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(1, 10, 1)]]}, + 'rec_metric': (hp.choice, [['cosine', 'euclidean']]), + 'image_mode': {'hyperopt-dist': hp.choice, 'sampling-scope': [[True, False]]}}, +# %% +rec_metric = 'cosine' +image_mode = True +window_size = 10 +stride = 1 +# %% +topological_node_dict = {'topological_extractor': {'window_size': window_size, + 'stride': stride}} +# %% +recurrence_node_dict = {'recurrence_extractor': {'window_size': window_size, + 'stride': stride, + 'rec_metric': rec_metric, + 'image_mode': image_mode}} + +finetune = False +metric_names = ('f1', 'accuracy', 'precision', 'roc_auc') +api_config = dict(problem='classification', + metric='accuracy', + timeout=1, + pop_size=20, + with_tuning=True, + with_tunig=False, + n_jobs=-1, + logging_level=20) +pipeline_creator = AbstractPipeline(task='classification') +ECG = 'Lightning7' +topological_model = ['topological_extractor', 'rf'] +recurrence_model = ['recurrence_extractor', 'quantile_extractor', 'rf'] +# %% +ecg_dataset = pipeline_creator.create_input_data(ECG) + +if __name__ == "__main__": + topo_list_model = { + 'topological_extractor': {'window_size': 10}, + 'logit': {}} + result_dict_topo = ApiTemplate(api_config=api_config, + metric_list=metric_names).eval(dataset=ECG, + finetune=finetune, + initial_assumption=topo_list_model) + _ = 1 diff --git a/fedot_ind/__init__.py b/fedot_ind/__init__.py index 7845c276a..e72d218bc 100644 --- a/fedot_ind/__init__.py +++ b/fedot_ind/__init__.py @@ -2,3 +2,4 @@ __all__ = ['fedot_api'] +__version__ = "0.5.0" diff --git a/fedot_ind/api/main.py b/fedot_ind/api/main.py index 4f35773b7..6b176f009 100644 --- a/fedot_ind/api/main.py +++ b/fedot_ind/api/main.py @@ -1,11 +1,13 @@ import os import warnings from copy import deepcopy +from functools import partial from typing import Union import numpy as np import pandas as pd from fedot.api.main import Fedot +from fedot.core.data.data import OutputData from fedot.core.pipelines.pipeline import Pipeline from fedot.core.visualisation.pipeline_specific_visuals import PipelineHistoryVisualizer from golem.core.optimisers.opt_history_objects.opt_history import OptHistory @@ -13,14 +15,20 @@ from sklearn import model_selection as skms from sklearn.calibration import CalibratedClassifierCV +import fedot_ind.core.repository.constanst_repository as CONST_REPO from fedot_ind.api.utils.api_init import ApiManager from fedot_ind.api.utils.checkers_collections import DataCheck from fedot_ind.core.architecture.abstraction.decorators import DaskServer -from fedot_ind.core.architecture.pipelines.classification import SklearnCompatibleClassifier +from fedot_ind.core.architecture.pipelines.classification import ( + SklearnCompatibleClassifier, +) from fedot_ind.core.architecture.preprocessing.data_convertor import ApiConverter -from fedot_ind.core.repository.constanst_repository import \ - FEDOT_GET_METRICS, FEDOT_TUNING_METRICS, \ - FEDOT_TUNER_STRATEGY +from fedot_ind.core.optimizer.FedotEvoOptimizer import FedotEvoOptimizer +from fedot_ind.core.repository.constanst_repository import ( + FEDOT_GET_METRICS, + FEDOT_TUNER_STRATEGY, + FEDOT_TUNING_METRICS, +) from fedot_ind.core.repository.industrial_implementations.abstract import build_tuner from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels @@ -28,11 +36,32 @@ class FedotIndustrial(Fedot): - """This class is used to run Fedot in industrial mode as FedotIndustrial. + """Main class for Industrial API. It provides a high-level interface for working with the + Fedot framework. The class allows you to train, predict, and evaluate models for time series. + All arguments are passed as keyword arguments and handled by the ApiManager class. Args: - input_config: dictionary with the parameters of the experiment. - output_folder: path to the folder where the results will be saved. + problem: str. The type of task to solve. Available options: 'ts_forecasting', 'ts_classification', 'ts_regression'. + timeout: int. Time for model design (in minutes): ``None`` or ``-1`` means infinite time. + logging_level: logging levels are the same as in + `built-in logging library `_. + + .. details:: Possible options: + + - ``50`` -> critical + - ``40`` -> error + - ``30`` -> warning + - ``20`` -> info + - ``10`` -> debug + - ``0`` -> nonset + backend_method: str. Default `cpu`. The method for backend. Available options: 'cpu', 'dask'. + initial_assumption: Pipeline = None. The initial pipeline for the model. + optimizer_params: dict = None. + task_params: dict = None. + strategy: str = None. + strategy_params: dict = None. + available_operations: list = None. + output_folder: str = './output'. Example: First, configure experiment and instantiate FedotIndustrial class:: @@ -42,7 +71,6 @@ class FedotIndustrial(Fedot): industrial = FedotIndustrial(problem='ts_classification', - use_cache=False, timeout=15, n_jobs=2, logging_level=20) @@ -62,42 +90,54 @@ class FedotIndustrial(Fedot): def __init__(self, **kwargs): super(Fedot, self).__init__() - self.api_controller = ApiManager(**kwargs) - self.config_dict = self.api_controller.config_dict - self.logger = self.api_controller.logger - self.industrial_strategy_class = self.api_controller.industrial_strategy_class - - def __init_solver(self): - self.logger.info(f'-------------------------------------------------') + self.manager = ApiManager(**kwargs) + self.config = self.manager.config + self.logger = self.manager.logger + self.strategy_cls = self.manager.strategy_class + self.solver = self.manager.solver + self.__init_industrial_backend() + + def __init_industrial_backend(self): + self.logger.info('-' * 50) self.logger.info('Initialising Industrial Repository') - if self.api_controller.is_default_fedot_context: + if self.manager.is_default_fedot_context: self.repo = IndustrialModels().setup_default_repository() - self.config_dict['optimizer'] = None + self.config['optimizer'] = FedotEvoOptimizer else: - self.repo = IndustrialModels().setup_repository() + self.repo = IndustrialModels().setup_repository(backend=self.manager.backend_method) + + def __init_evolution_optimisation_params(self): self.logger.info(f'-------------------------------------------------') + self.logger.info('Initialising Evolutionary Optimisation params') + if self.manager.optimizer_params is not None: + self.config['optimizer'] = partial(self.config['optimizer'], + optimisation_params=self.manager.optimizer_params) + + def __init_solver(self): + self.logger.info('-' * 50) self.logger.info('Initialising Dask Server') - self.config_dict['initial_assumption'] = self.config_dict['initial_assumption'].build() + self.config['initial_assumption'] = self.config['initial_assumption'].build() self.dask_client = DaskServer().client - self.logger.info(f'LinK Dask Server - {self.dask_client.dashboard_link}') - self.logger.info(f'-------------------------------------------------') + setattr(CONST_REPO, 'DASK_CLIENT', self.dask_client) + self.logger.info(f'Link Dask Server - {self.dask_client.dashboard_link}') + self.logger.info('-' * 50) self.logger.info('Initialising solver') - self.solver = Fedot(**self.config_dict) - # if self.api_controller.is_default_fedot_context: - # self.solver = self.api_controller._check_mutations(self.solver) + self.__init_industrial_backend() + self.__init_evolution_optimisation_params() + self.solver = Fedot(**self.config) def _process_input_data(self, input_data): train_data = deepcopy(input_data) # we do not want to make inplace changes input_preproc = DataCheck( input_data=train_data, - task=self.config_dict['problem'], - task_params=self.api_controller.task_params, + task=self.config['problem'], + task_params=self.manager.task_params, fit_stage=True, - industrial_task_params=self.api_controller.industrial_strategy_params) + industrial_task_params=self.manager) train_data = input_preproc.check_input_data() self.target_encoder = input_preproc.get_target_encoder() - train_data.features = train_data.features.squeeze() if self.api_controller.is_default_fedot_context \ + train_data.features = train_data.features.squeeze() if self.manager.is_default_fedot_context \ else train_data.features return train_data @@ -120,22 +160,26 @@ def __calibrate_probs(self, industrial_model): return calibrated_proba def __predict_for_ensemble(self): - predict = self.industrial_strategy_class.predict( + predict = self.strategy_cls.predict( self.predict_data, 'probs') - ensemble_strat = self.industrial_strategy_class.ensemble_strategy - predict = {strategy: np.argmax(self.industrial_strategy_class.ensemble_predictions(predict, strategy), axis=1) + ensemble_strat = self.strategy_cls.ensemble_strategy + predict = {strategy: np.argmax(self.strategy_cls.ensemble_predictions(predict, strategy), axis=1) for strategy in ensemble_strat} return predict def __abstract_predict(self, predict_mode): - have_encoder = self.api_controller.condition_check.solver_have_target_encoder(self.target_encoder) + have_encoder = self.manager.condition_check.solver_have_target_encoder(self.target_encoder) labels_output = predict_mode in ['labels'] - default_fedot_strategy = self.api_controller.industrial_strategy is None - custom_predict = self.solver.predict if default_fedot_strategy else self.industrial_strategy_class.predict + default_fedot_strategy = self.manager.industrial_strategy is None + custom_predict = self.solver.predict if default_fedot_strategy else self.manager.strategy_class.predict + have_proba_output = hasattr(self.solver, 'predict_proba') + self.__init_industrial_backend() + default_fedot_strategy = self.manager.strategy_class is None + custom_predict = self.solver.predict if default_fedot_strategy else self.strategy_cls.predict predict_function = Either(value=custom_predict, monoid=['prob', labels_output]).either( - left_function=lambda prob_func: self.solver.predict_proba, + left_function=lambda prob_func: self.solver.predict_proba if have_proba_output else self.solver.predict, right_function=lambda label_func: label_func) def _inverse_encoder_transform(predict): @@ -149,6 +193,12 @@ def _inverse_encoder_transform(predict): value=self.predict_data, monoid=[False, True]).then( function=lambda x: predict_function(x, predict_mode)).then( lambda x: _inverse_encoder_transform(x) if have_encoder else x).value + if isinstance(predict, OutputData): + predict = predict.predict + try: + predict = np.argmax(predict, axis=1) if predict.shape[1] != 1 else predict + except Exception: + predict = predict return predict def _metric_evaluation_loop(self, @@ -168,7 +218,7 @@ def _metric_evaluation_loop(self, in predicted_labels.items()} return metric_dict else: - if self.api_controller.condition_check.solver_have_target_encoder(self.target_encoder): + if self.manager.condition_check.solver_have_target_encoder(self.target_encoder): new_target = self.target_encoder.transform(target.flatten()) labels = self.target_encoder.transform(predicted_labels).reshape(valid_shape) else: @@ -192,15 +242,15 @@ def fit(self, **kwargs: additional parameters """ - custom_fit = all([self.api_controller.industrial_strategy is not None, - self.api_controller.industrial_strategy != 'anomaly_detection']) self.is_finetuned = False self.train_data = self._process_input_data(input_data) self.__init_solver() + Either(value=self.train_data, - monoid=[self.train_data, - custom_fit]).either(left_function=self.solver.fit, - right_function=self.industrial_strategy_class.fit) + monoid=[self.train_data, self.strategy_cls is None]).either( + left_function=lambda data: self.strategy_cls.fit(data), + right_function=self.solver.fit + ) def predict(self, predict_data: tuple, @@ -239,7 +289,7 @@ def predict_proba(self, """ self.predict_data = self._process_input_data(predict_data) - self.predicted_probs = self.predicted_labels if self.api_controller.is_regression_task_context \ + self.predicted_probs = self.predicted_labels if self.manager.is_regression_task_context \ else self.__abstract_predict(predict_mode) return self.__calibrate_probs(self.solver.current_pipeline) if calibrate_probs else self.predicted_probs @@ -261,19 +311,19 @@ def finetune(self, self.is_finetuned = True train_data = self._process_input_data(train_data) if \ - not self.api_controller.condition_check.input_data_is_fedot_type(train_data) else train_data + not self.manager.condition_check.input_data_is_fedot_type(train_data) else train_data if tuning_params is None: tuning_params = ApiConverter.tuning_params_is_none(tuning_params) - tuning_params['metric'] = FEDOT_TUNING_METRICS[self.config_dict['problem']] + tuning_params['metric'] = FEDOT_TUNING_METRICS[self.config['problem']] for tuner_name, tuner_type in FEDOT_TUNER_STRATEGY.items(): - if self.api_controller.condition_check.solver_is_fedot_class(self.solver): + if self.manager.condition_check.solver_is_fedot_class(self.solver): model_to_tune = deepcopy(self.solver.current_pipeline) - elif not self.api_controller.condition_check.solver_is_none(model_to_tune): + elif not self.manager.condition_check.solver_is_none(model_to_tune): model_to_tune = model_to_tune else: model_to_tune = deepcopy( - self.config_dict['initial_assumption']).build() + self.config['initial_assumption']).build() tuning_params['tuner'] = tuner_type pipeline_tuner, model_to_tune = build_tuner( self, model_to_tune, tuning_params, train_data, mode) @@ -283,7 +333,7 @@ def finetune(self, def get_metrics(self, target: Union[list, np.array] = None, - metric_names: tuple = ('f1', 'roc_auc', 'accuracy'), + metric_names: tuple = None, rounding_order: int = 3, **kwargs) -> pd.DataFrame: """ @@ -303,7 +353,8 @@ def get_metrics(self, pandas DataFrame with calculated metrics """ - problem = self.config_dict['problem'] + problem = self.config['problem'] + if problem == 'classification' and self.predicted_probs is None and 'roc_auc' in metric_names: self.logger.info('Predicted probabilities are not available. Use `predict_proba()` method first') if isinstance(self.predicted_probs, dict): @@ -375,16 +426,16 @@ def load(self, path): def save_optimization_history(self, return_history: bool = False): return self.solver.history if return_history else self.solver.history.save( - f"{self.api_controller.output_folder}/" + f"{self.manager.output_folder}/" f"optimization_history.json") def save_best_model(self): Either(value=self.solver, - monoid=[self.solver, self.api_controller.condition_check.solver_is_fedot_class(self.solver)]).either( - left_function=lambda pipeline: pipeline.save(path=self.api_controller.output_folder, + monoid=[self.solver, self.manager.condition_check.solver_is_fedot_class(self.solver)]).either( + left_function=lambda pipeline: pipeline.save(path=self.manager.output_folder, create_subdir=True, is_datetime_in_path=True), - right_function=lambda solver: solver.current_pipeline.save(path=self.api_controller.output_folder, + right_function=lambda solver: solver.current_pipeline.save(path=self.manager.output_folder, create_subdir=True, is_datetime_in_path=True)) @@ -404,9 +455,9 @@ def explain(self, explaing_config: dict = {}): name = explaing_config.get('name', 'test') method = explaing_config.get('method', 'point') - explainer = self.api_controller.explain_methods[method](model=self, - features=self.predict_data.features.squeeze(), - target=self.predict_data.target) + explainer = self.manager.explain_methods[method](model=self, + features=self.predict_data.features.squeeze(), + target=self.predict_data.target) explainer.explain(n_samples=samples, window=window, method=metric) explainer.visual(metric=metric, threshold=threshold, name=name) @@ -434,14 +485,17 @@ def vis_optimisation_history(self, opt_history_path: str = None, history_visualizer.diversity_population, dict( save_path='diversity_population.gif', fps=1))} - def plot_func(mode): return vis_func[mode][0](**vis_func[mode][1]) + def plot_func(mode): + return vis_func[mode][0](**vis_func[mode][1]) Either(value=vis_func, monoid=[mode, mode == 'all']).either( left_function=plot_func, - right_function=lambda vis_func: [func(**params) for func, params in vis_func.values()]) + right_function=lambda vis_func: [func(**params) for func, params in vis_func.values()] + ) return history_visualizer.history if return_history else None def shutdown(self): + """Shutdown Dask client""" self.dask_client.close() del self.dask_client diff --git a/fedot_ind/api/utils/api_init.py b/fedot_ind/api/utils/api_init.py index 4ad5845ff..e7537e926 100644 --- a/fedot_ind/api/utils/api_init.py +++ b/fedot_ind/api/utils/api_init.py @@ -2,16 +2,14 @@ from pathlib import Path from fedot.core.repository.tasks import TsForecastingParams -from golem.core.optimisers.adaptive.operator_agent import RandomAgent from pymonad.either import Either from fedot_ind.api.utils.industrial_strategy import IndustrialStrategy from fedot_ind.api.utils.path_lib import DEFAULT_PATH_RESULTS as default_path_to_save_results from fedot_ind.core.architecture.preprocessing.data_convertor import ApiConverter -from fedot_ind.core.architecture.settings.computational import BackendMethods from fedot_ind.core.optimizer.IndustrialEvoOptimizer import IndustrialEvoOptimizer from fedot_ind.core.repository.constanst_repository import \ - FEDOT_API_PARAMS, fedot_init_assumptions, FEDOT_MUTATION_STRATEGY + FEDOT_API_PARAMS, fedot_init_assumptions from fedot_ind.core.repository.model_repository import default_industrial_availiable_operation from fedot_ind.tools.explain.explain import PointExplainer, RecurrenceExplainer @@ -34,12 +32,13 @@ def null_state_object(self): def user_config_object(self, kwargs): self.output_folder = kwargs.get('output_folder', None) - self.industrial_strategy_params = kwargs.get( - 'industrial_strategy_params', {}) - self.industrial_strategy = kwargs.get('industrial_strategy', None) + self.strategy_params = kwargs.get( + 'strategy_params', None) + self.strategy_class = kwargs.get('strategy', None) self.path_to_composition_results = kwargs.get('history_dir', None) self.backend_method = kwargs.get('backend', 'cpu') self.task_params = kwargs.get('task_params', {}) + self.optimizer_params = kwargs.get('optimizer_params', None) def path_object(self, kwargs): # create dirs with results @@ -72,28 +71,28 @@ def path_object(self, kwargs): def industrial_config_object(self, kwargs): # map Fedot params to Industrial params - self.config_dict = kwargs - # self.config_dict['history_dir'] = prefix - self.preset = kwargs.get('preset', self.config_dict['problem']) - self.config_dict['available_operations'] = kwargs.get('available_operations', - default_industrial_availiable_operation(self.preset)) + self.config = kwargs + # self.config['history_dir'] = prefix + self.preset = kwargs.get('preset', self.config['problem']) + self.config['available_operations'] = kwargs.get('available_operations', + default_industrial_availiable_operation(self.preset)) self.is_default_fedot_context = self.preset.__contains__('tabular') - self.is_regression_task_context = self.config_dict['problem'] in ['ts_forecasting', 'regression'] - self.config_dict['cv_folds'] = kwargs.get('cv_folds', 3) - self.config_dict['optimizer'] = kwargs.get('optimizer', IndustrialEvoOptimizer) - self.config_dict['initial_assumption'] = kwargs.get('initial_assumption', None) - if self.config_dict['initial_assumption'] is None: - self.config_dict['initial_assumption'] = Either(value=self.industrial_strategy, - monoid=[self.preset, - self.industrial_strategy == 'anomaly_detection']). \ + self.is_regression_task_context = self.config['problem'] in ['ts_forecasting', 'regression'] + self.config['cv_folds'] = kwargs.get('cv_folds', 3) + self.config['optimizer'] = kwargs.get('optimizer', IndustrialEvoOptimizer) + self.config['initial_assumption'] = kwargs.get('initial_assumption', None) + if self.config['initial_assumption'] is None: + self.config['initial_assumption'] = Either(value=self.strategy_class, + monoid=[self.preset, + self.strategy_class == 'anomaly_detection']). \ either(left_function=fedot_init_assumptions, right_function=fedot_init_assumptions) - self.config_dict['use_input_preprocessing'] = kwargs.get( + self.config['use_input_preprocessing'] = kwargs.get( 'use_input_preprocessing', False) - if self.task_params is not None and self.config_dict['problem'] == 'ts_forecasting': - self.config_dict['task_params'] = TsForecastingParams( + if self.task_params is not None and self.config['problem'] == 'ts_forecasting': + self.config['task_params'] = TsForecastingParams( forecast_length=self.task_params['forecast_length']) self.__init_experiment_setup() @@ -108,36 +107,21 @@ def industrial_api_object(self): # create API subclasses for side task self.condition_check = ApiConverter() self.industrial_strategy_class = IndustrialStrategy( - api_config=self.config_dict, - industrial_strategy=self.industrial_strategy, - industrial_strategy_params=self.industrial_strategy_params, + api_config=self.config, + industrial_strategy=self.strategy_class, + industrial_strategy_params=self.strategy_params, logger=self.logger) - self.industrial_strategy = self.industrial_strategy if self.industrial_strategy != 'anomaly_detection' else None + self.industrial_strategy = self.strategy_class if self.strategy_class != 'anomaly_detection' else None def __init_experiment_setup(self): self.logger.info('Initialising experiment setup') - industrial_params = set(self.config_dict.keys()) - \ + industrial_params = set(self.config.keys()) - \ set(FEDOT_API_PARAMS.keys()) for param in industrial_params: - self.config_dict.pop(param, None) - - backend_method_current, backend_scipy_current = BackendMethods( - self.backend_method).backend - globals()['backend_methods'] = backend_method_current - globals()['backend_scipy'] = backend_scipy_current - - def _check_mutations(self, solver): - for mutation in solver.api_composer.params.optimizer_params.mutation_types.mutation_types: - try: - is_invalid = mutation.__name__.__contains__('resample') - except Exception: - is_invalid = mutation.name.__contains__('resample') - if is_invalid: - solver.api_composer.params.optimizer_params.mutation_types.mutation_types.remove(mutation) - - solver.api_composer.params.optimizer_params.adaptive_mutation_type = RandomAgent( - actions=solver.api_composer.params.optimizer_params.mutation_types, - probs=FEDOT_MUTATION_STRATEGY[ - 'params_mutation_strategy']) - return solver + self.config.pop(param, None) + + # backend_method_current, backend_scipy_current = BackendMethods( + # self.backend_method).backend + # globals()['backend_methods'] = backend_method_current + # globals()['backend_scipy'] = backend_scipy_current diff --git a/fedot_ind/api/utils/checkers_collections.py b/fedot_ind/api/utils/checkers_collections.py index 1b42f199d..0a2571ed2 100644 --- a/fedot_ind/api/utils/checkers_collections.py +++ b/fedot_ind/api/utils/checkers_collections.py @@ -11,7 +11,10 @@ from fedot_ind.api.utils.data import check_multivariate_data from fedot_ind.core.architecture.preprocessing.data_convertor import NumpyConverter, DataConverter from fedot_ind.core.architecture.settings.computational import backend_methods as np +from fedot_ind.core.operation.decomposition.matrix_decomposition.column_sampling_decomposition import CURDecomposition +from fedot_ind.core.operation.transformation.representation.tabular.tabular_extractor import TabularExtractor from fedot_ind.core.repository.constanst_repository import FEDOT_DATA_TYPE, fedot_task +from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels class DataCheck: @@ -36,12 +39,16 @@ def __init__(self, fit_stage=False, industrial_task_params=None): self.logger = logging.getLogger(self.__class__.__name__) - self.industrial_task_params = industrial_task_params or {} - - if len(self.industrial_task_params) != 0: - self.data_type = FEDOT_DATA_TYPE[self.industrial_task_params['data_type']] - else: - self.data_type = FEDOT_DATA_TYPE['tensor'] + self.manager = None + self.strategy_params = industrial_task_params + self.convert_ts_method = {'ts2tabular': self._convert_ts2tabular, + 'ts2image': self._convert_ts2image, + 'big_dataset': self._convert_big_data} + if hasattr(industrial_task_params, 'strategy_params'): + self.strategy_params = industrial_task_params.strategy_params + self.manager = industrial_task_params + self.data_type = FEDOT_DATA_TYPE[self.strategy_params['data_type']] \ + if self.strategy_params is not None else FEDOT_DATA_TYPE['tensor'] self.input_data = input_data self.data_convertor = DataConverter(data=self.input_data) @@ -60,22 +67,10 @@ def __check_features_and_target(self, input_data, data_type): else: X, y = input_data.features, input_data.target - multi_features, X = check_multivariate_data(X) + multi_features, features = check_multivariate_data(X) multi_target = len(y.shape) > 1 and y.shape[1] > 2 - - if multi_features: - features = np.array(X.tolist()).astype(float) - else: - features = X - - if isinstance(y, (pd.DataFrame, pd.Series)): - y = y.values - if multi_target: - target = y - elif multi_features and not multi_target: - target = y.reshape(-1, 1) - else: - target = np.ravel(y).reshape(-1, 1) + target = y.values if isinstance(y, (pd.DataFrame, pd.Series)) else y + target = target.reshape(-1, 1) if multi_features and not multi_target else np.ravel(target).reshape(-1, 1) return features, multi_features, target @@ -124,14 +119,14 @@ def _transformation_for_other_task(self, data_list): len( data_list[0]))) - have_predict_horizon = Either(value=False, monoid=[True, len(self.industrial_task_params) == 0]).either( - left_function=lambda l: self.industrial_task_params['data_type'] == 'time_series' and - 'detection_window' in self.industrial_task_params.keys(), + have_predict_horizon = Either(value=False, monoid=[True, self.strategy_params is None]).either( + left_function=lambda l: self.strategy_params['data_type'] == 'time_series' and + 'detection_window' in self.strategy_params.keys(), right_function=lambda r: r) task = Either( value=fedot_task(self.task), monoid=['ts_forecasting', not have_predict_horizon]).either( - left_function=lambda l: fedot_task(l, self.industrial_task_params['detection_window']), + left_function=lambda l: fedot_task(l, self.strategy_params['detection_window']), right_function=lambda r: r) return InputData(idx=idx, features=input_data[0], @@ -202,6 +197,44 @@ def _check_input_data_target(self): elif self.task == 'classification': self.input_data.target[self.input_data.target == -1] = 0 + def _check_fedot_context(self): + if self.manager is not None: + IndustrialModels().setup_repository() + learning_strategy = self.strategy_params['learning_strategy'] if \ + 'learning_strategy' in self.strategy_params.keys() else None + default_fedot_context = self.manager.is_default_fedot_context \ + and learning_strategy is not None + sampling_strategy = self.strategy_params['sampling_strategy'] \ + if 'sampling_strategy' in self.strategy_params.keys() else None + self.input_data.features = Either(value=learning_strategy, + monoid=[self.input_data, default_fedot_context]).either( + left_function=lambda x: x.features, + right_function=lambda strategy: self.convert_ts_method[strategy] + (self.input_data, sampling_strategy).predict) + + def _convert_ts2tabular(self, input_data, sampling_strategy): + if sampling_strategy is not None: + sample_start, sample_end = list(sampling_strategy['samples'].values()) + channel_start, channel_end = list(sampling_strategy['channels'].values()) + element_start, element_end = list(sampling_strategy['elements'].values()) + input_data.features = self.input_data.features[ + sample_start:sample_end, + channel_start:channel_end, + element_start:element_end] + fg_list = self.manager.strategy_params['feature_generator'] + ts2tabular_model = TabularExtractor({'feature_domain': fg_list, + 'reduce_dimension': False}) + return ts2tabular_model.transform(input_data) + + def _convert_ts2image(self): + pass + + def _convert_big_data(self, input_data, sampling_strategy: dict): + approx_method_dict = {'CUR': CURDecomposition} + approx_method, method_params = list(sampling_strategy.items())[0] + big_dataset_model = approx_method_dict[approx_method](method_params) + return big_dataset_model.transform(input_data) + def check_available_operations(self, available_operations): pass @@ -210,7 +243,9 @@ def _process_input_data(self): if not self.data_convertor.is_torchvision_dataset: self._check_input_data_features() self._check_input_data_target() + self._check_fedot_context() self.input_data.supplementary_data.is_auto_preprocessed = True + return self.input_data def check_input_data(self) -> InputData: diff --git a/fedot_ind/api/utils/industrial_strategy.py b/fedot_ind/api/utils/industrial_strategy.py index 0ef6d01d4..7a584f18d 100644 --- a/fedot_ind/api/utils/industrial_strategy.py +++ b/fedot_ind/api/utils/industrial_strategy.py @@ -22,6 +22,18 @@ class IndustrialStrategy: + """ + Class for industrial strategy implementation + + Args: + industrial_strategy_params: dict + Parameters for industrial strategy + industrial_strategy: str + Industrial strategy name + api_config: dict + Configuration for API + """ + def __init__(self, industrial_strategy_params, industrial_strategy, @@ -60,7 +72,7 @@ def __init__(self, self.ensemble_strategy = list(self.ensemble_strategy_dict.keys()) self.random_label = None - self.config_dict = api_config + self.config = api_config self.logger = logging.getLogger('IndustrialStrategy') self.kernel_ensembler = KernelEnsembler self.RAF_workers = None @@ -98,12 +110,12 @@ def _federated_strategy(self, input_data): batch_size = round(input_data.features.shape[0] / self.RAF_workers) min_timeout = 0.5 - selected_timeout = round(self.config_dict['timeout'] / FEDOT_WORKER_TIMEOUT_PARTITION) - self.config_dict['timeout'] = max(min_timeout, selected_timeout) + selected_timeout = round(self.config['timeout'] / FEDOT_WORKER_TIMEOUT_PARTITION) + self.config['timeout'] = max(min_timeout, selected_timeout) self.logger.info(f'Batch_size - {batch_size}. Number of batches - {self.RAF_workers}') - self.solver = RAFEnsembler(composing_params=self.config_dict, + self.solver = RAFEnsembler(composing_params=self.config, n_splits=self.RAF_workers, batch_size=batch_size) self.logger.info( @@ -114,7 +126,7 @@ def _federated_strategy(self, input_data): else: self.logger.info(f'RAF algorithm is not applicable: n_samples={n_samples} < {BATCH_SIZE_FOR_FEDOT_WORKER}. ' f'FEDOT algorithm was applied') - self.solver = Fedot(**self.config_dict) + self.solver = Fedot(**self.config) self.solver.fit(input_data) def _forecasting_strategy(self, input_data): @@ -125,8 +137,8 @@ def _forecasting_strategy(self, input_data): {}).fit(input_data) for model_name, model_impl in FEDOT_TS_FORECASTING_ASSUMPTIONS.items()} self.solver = self._finetune_loop(kernel_model, kernel_data, self.finetune_params) # for model_name, init_assumption in FEDOT_TS_FORECASTING_ASSUMPTIONS.items(): - # self.config_dict['initial_assumption'] = init_assumption.build() - # industrial = Fedot(**self.config_dict) + # self.config['initial_assumption'] = init_assumption.build() + # industrial = Fedot(**self.config) # Maybe( # value=industrial.fit(input_data), # monoid=True).maybe( @@ -144,7 +156,7 @@ def _sampling_strategy(self, input_data): target=input_data.target, sampling_rate=sampling_rate) input_data.idx = np.arange(len(input_data.features)) - industrial = Fedot(**self.config_dict) + industrial = Fedot(**self.config) Maybe( value=industrial.fit(input_data), monoid=True).maybe( @@ -157,7 +169,7 @@ def _forecasting_exogenous_strategy(self, input_data): self.logger.info('TS exogenous forecasting algorithm was applied') self.solver = {} init_assumption = PipelineBuilder().add_node('lagged', 0) - task = FEDOT_TASK[self.config_dict['problem']] + task = FEDOT_TASK[self.config['problem']] train_lagged, predict_lagged = train_test_data_setup(InputData(idx=np.arange(len(input_data.features)), features=input_data.features, target=input_data.features, @@ -173,13 +185,13 @@ def _forecasting_exogenous_strategy(self, input_data): target=input_data.features, task=task, data_type=DataTypesEnum.ts), 2) - dataset_dict.update({f'exog_ts': train_exog}) + dataset_dict.update({'exog_ts': train_exog}) train_dataset = MultiModalData(dataset_dict) init_assumption = init_assumption.join_branches('ridge') - self.config_dict['initial_assumption'] = init_assumption.build() + self.config['initial_assumption'] = init_assumption.build() - industrial = Fedot(**self.config_dict) + industrial = Fedot(**self.config) industrial.fit(train_dataset) self.solver = {'exog_model': industrial} @@ -188,7 +200,7 @@ def _finetune_loop(self, kernel_data: dict, tuning_params: dict = {}): tuned_models = {} - tuning_params['metric'] = FEDOT_TUNING_METRICS[self.config_dict['problem']] + tuning_params['metric'] = FEDOT_TUNING_METRICS[self.config['problem']] for generator, kernel_model in kernel_ensemble.items(): tuning_params['tuner'] = FEDOT_TUNER_STRATEGY['simultaneous'] model_to_tune = deepcopy(kernel_model) @@ -198,10 +210,8 @@ def _finetune_loop(self, return tuned_models def _kernel_strategy(self, input_data): - self.kernel_ensembler = KernelEnsembler( - self.industrial_strategy_params) - kernel_ensemble, kernel_data = self.kernel_ensembler.transform( - input_data).predict + self.kernel_ensembler = KernelEnsembler(self.industrial_strategy_params) + kernel_ensemble, kernel_data = self.kernel_ensembler.transform(input_data).predict self.solver = self._finetune_loop(kernel_ensemble, kernel_data) def _lora_strategy(self, input_data): diff --git a/fedot_ind/api/utils/recurrent_image.py b/fedot_ind/api/utils/recurrent_image.py index 86870163c..e986448a7 100644 --- a/fedot_ind/api/utils/recurrent_image.py +++ b/fedot_ind/api/utils/recurrent_image.py @@ -1,11 +1,11 @@ import os import numpy as np -from matplotlib import pyplot as plt from PIL import Image +from matplotlib import pyplot as plt from fedot_ind.api.utils.data import init_input_data -from fedot_ind.core.models.recurrence.reccurence_extractor import RecurrenceExtractor +from fedot_ind.core.operation.transformation.representation.recurrence.reccurence_extractor import RecurrenceExtractor from fedot_ind.tools.loader import DataLoader diff --git a/fedot_ind/core/architecture/abstraction/decorators.py b/fedot_ind/core/architecture/abstraction/decorators.py index 1e854be56..c2008981d 100644 --- a/fedot_ind/core/architecture/abstraction/decorators.py +++ b/fedot_ind/core/architecture/abstraction/decorators.py @@ -113,9 +113,9 @@ class DaskServer(metaclass=Singleton): def __init__(self): print('Creating Dask Server') cluster = LocalCluster(processes=False, - # n_workers=4, - # threads_per_worker=4, - # memory_limit='3GB' + n_workers=4, + threads_per_worker=4, + memory_limit='auto' ) # connect client to your cluster self.client = Client(cluster) diff --git a/fedot_ind/core/architecture/pipelines/abstract_pipeline.py b/fedot_ind/core/architecture/pipelines/abstract_pipeline.py index f3340fce4..579a5b214 100644 --- a/fedot_ind/core/architecture/pipelines/abstract_pipeline.py +++ b/fedot_ind/core/architecture/pipelines/abstract_pipeline.py @@ -45,9 +45,14 @@ def create_pipeline(node_list, build: bool = True): for branch, nodes in node_list.items(): if isinstance(branch, int): for node in nodes: - pipeline.add_node(node, branch_idx=branch) + if isinstance(node, tuple): + pipeline.add_node(operation_type=node[0], params=node[1], branch_idx=branch) + else: + pipeline.add_node(operation_type=node, branch_idx=branch) else: pipeline.join_branches(nodes) + elif isinstance(node_list, PipelineBuilder): + return pipeline else: for node in node_list: pipeline.add_node(node) diff --git a/fedot_ind/core/architecture/preprocessing/data_convertor.py b/fedot_ind/core/architecture/preprocessing/data_convertor.py index e1c836981..6e891bdd3 100644 --- a/fedot_ind/core/architecture/preprocessing/data_convertor.py +++ b/fedot_ind/core/architecture/preprocessing/data_convertor.py @@ -405,7 +405,10 @@ def have_fit_method(self): @property def have_predict_method(self): - return dir(self.operation_example).__contains__('predict') + if hasattr(self.operation_example, 'predict'): + return True if callable(self.operation_example.predict) else False + else: + return False @property def have_predict_for_fit_method(self): diff --git a/fedot_ind/core/architecture/settings/pipeline_factory.py b/fedot_ind/core/architecture/settings/pipeline_factory.py index e11f305f1..e6190a05a 100644 --- a/fedot_ind/core/architecture/settings/pipeline_factory.py +++ b/fedot_ind/core/architecture/settings/pipeline_factory.py @@ -3,12 +3,12 @@ from fedot_ind.core.models.detection.probalistic.kalman import UnscentedKalmanFilter from fedot_ind.core.models.detection.subspaces.func_pca import FunctionalPCA from fedot_ind.core.models.detection.subspaces.sst import SingularSpectrumTransformation -from fedot_ind.core.models.quantile.quantile_extractor import QuantileExtractor -from fedot_ind.core.models.recurrence.reccurence_extractor import RecurrenceExtractor -from fedot_ind.core.models.topological.topological_extractor import TopologicalExtractor from fedot_ind.core.operation.transformation.basis.eigen_basis import EigenBasisImplementation from fedot_ind.core.operation.transformation.basis.fourier import FourierBasisImplementation from fedot_ind.core.operation.transformation.basis.wavelet import WaveletBasisImplementation +from fedot_ind.core.operation.transformation.representation.recurrence.reccurence_extractor import RecurrenceExtractor +from fedot_ind.core.operation.transformation.representation.statistical.quantile_extractor import QuantileExtractor +from fedot_ind.core.operation.transformation.representation.topological.topological_extractor import TopologicalExtractor class BasisTransformations(Enum): @@ -30,31 +30,31 @@ class MlModel(Enum): class KernelFeatureGenerator(Enum): - quantile = [{'feature_generator_type': 'quantile', + quantile = [{'feature_generator_type': 'statistical', 'feature_hyperparams': { 'window_mode': True, 'window_size': 5 } }, - {'feature_generator_type': 'quantile', + {'feature_generator_type': 'statistical', 'feature_hyperparams': { 'window_mode': True, 'window_size': 10 } }, - {'feature_generator_type': 'quantile', + {'feature_generator_type': 'statistical', 'feature_hyperparams': { 'window_mode': True, 'window_size': 20 } }, - {'feature_generator_type': 'quantile', + {'feature_generator_type': 'statistical', 'feature_hyperparams': { 'window_mode': True, 'window_size': 30 } }, - {'feature_generator_type': 'quantile', + {'feature_generator_type': 'statistical', 'feature_hyperparams': { 'window_mode': True, 'window_size': 40 diff --git a/fedot_ind/core/ensemble/kernel_ensemble.py b/fedot_ind/core/ensemble/kernel_ensemble.py index b5aff8db3..e53592461 100644 --- a/fedot_ind/core/ensemble/kernel_ensemble.py +++ b/fedot_ind/core/ensemble/kernel_ensemble.py @@ -1,22 +1,44 @@ from copy import deepcopy -from typing import Optional, Any +from typing import Any, Optional import pandas as pd -from MKLpy.callbacks import EarlyStopping -from MKLpy.scheduler import ReduceOnWorsening from fedot.core.data.data import InputData from fedot.core.operations.operation_parameters import OperationParameters from fedot.core.pipelines.pipeline_builder import PipelineBuilder +from MKLpy.callbacks import EarlyStopping +from MKLpy.scheduler import ReduceOnWorsening from scipy.spatial.distance import pdist, squareform from sklearn.svm import SVC from fedot_ind.core.architecture.settings.computational import backend_methods as np from fedot_ind.core.models.base_extractor import BaseExtractor -from fedot_ind.core.repository.constanst_repository import KERNEL_ALGO, KERNEL_BASELINE_FEATURE_GENERATORS, \ - KERNEL_BASELINE_NODE_LIST, KERNEL_DISTANCE_METRIC, get_default_industrial_model_params +from fedot_ind.core.repository.constanst_repository import ( + KERNEL_ALGO, + KERNEL_BASELINE_FEATURE_GENERATORS, + KERNEL_BASELINE_NODE_LIST, + KERNEL_DISTANCE_METRIC, + get_default_industrial_model_params, +) class KernelEnsembler(BaseExtractor): + """ + Class for kernel ensembling. This class implements a kernel-based ensemble method for feature + extraction and classification. It supports both one-stage and two-stage kernel learning + strategies and can handle multiclass classification problems. + + Args: + params (Optional[OperationParameters]): Parameters of the operation + + Attributes: + distance_metric (str): The distance metric used to calculate the Gram matrix + kernel_strategy (str): The kernel learning strategy used by the model + learning_strategy (str): The learning strategy used by the model + head_model (str): The head model used by the model + feature_extractor (List[str]): The feature extractors used by the model + + """ + def __init__(self, params: Optional[OperationParameters] = None): super().__init__(params) self.distance_metric = params.get('distance_metric', KERNEL_DISTANCE_METRIC['default_metric']) @@ -26,16 +48,14 @@ def __init__(self, params: Optional[OperationParameters] = None): self.feature_extractor = params.get('feature_extractor', list( KERNEL_BASELINE_FEATURE_GENERATORS.keys())) - self._mapping_dict = {k: v for k, - v in enumerate(self.feature_extractor)} + self._mapping_dict = {k: v for k, v in enumerate(self.feature_extractor)} self.lr = params.get('learning_rate', 0.1) self.patience = params.get('patience', 5) self.epoch = params.get('epoch', 500) self.optimisation_metric = params.get('optimisation_metric', 'roc_auc') self.algo_impl_dict = {'one_step': self.__one_stage_kernel, - 'two_step': self.__two_stage_kernel - } + 'two_step': self.__two_stage_kernel} self.feature_matrix_train = [] self.feature_matrix_test = [] @@ -129,16 +149,21 @@ def _transform(self, input_data: InputData) -> np.array: """ self.__multiclass_check(input_data.target) grammian_list = self.generate_grammian(input_data) + if self.kernel_strategy.__contains__('one'): - kernel_weight_matrix = self.__one_stage_kernel( - grammian_list, input_data.target) + kernel_weight_matrix = self.__one_stage_kernel(grammian_list, input_data.target) + else: - kernel_weight_matrix = self.__two_stage_kernel( - grammian_list, input_data.target) - top_n_generators, classes_described_by_generator = self._select_top_feature_generators( - kernel_weight_matrix) + kernel_weight_matrix = self.__two_stage_kernel(grammian_list, input_data.target) + + top_n_generators, classes_described_by_generator = self._select_top_feature_generators(kernel_weight_matrix) + self.predict = self._create_kernel_ensemble( - input_data, top_n_generators, classes_described_by_generator) + input_data, + top_n_generators, + classes_described_by_generator + ) + return self.predict def generate_grammian(self, input_data) -> list[Any]: @@ -148,8 +173,8 @@ def generate_grammian(self, input_data) -> list[Any]: self.feature_matrix_train = [ x.reshape( x.shape[0], - x.shape[1] * - x.shape[2]) for x in self.feature_matrix_train] + x.shape[1] * x.shape[2] + ) for x in self.feature_matrix_train] KLtr = [squareform(pdist(X=feature, metric=self.distance_metric)) for feature in self.feature_matrix_train] return KLtr diff --git a/fedot_ind/core/metrics/evaluation.py b/fedot_ind/core/metrics/evaluation.py index 1b723e142..c29a073bf 100644 --- a/fedot_ind/core/metrics/evaluation.py +++ b/fedot_ind/core/metrics/evaluation.py @@ -1,9 +1,21 @@ import logging from enum import Enum -from typing import Dict, List - - -from fedot_ind.core.metrics.metrics_implementation import * +from typing import Dict, List, Union + +import numpy as np + +from fedot_ind.core.metrics.metrics_implementation import ( + F1, + MAE, + MAPE, + MSE, + R2, + RMSE, + ROCAUC, + Accuracy, + Logloss, + Precision, +) class Metrics(Enum): diff --git a/fedot_ind/core/metrics/metrics_implementation.py b/fedot_ind/core/metrics/metrics_implementation.py index fea9c2877..71d77935e 100644 --- a/fedot_ind/core/metrics/metrics_implementation.py +++ b/fedot_ind/core/metrics/metrics_implementation.py @@ -1,23 +1,37 @@ -from typing import Optional -from typing import Union +from typing import Optional, Union import numpy as np import pandas as pd from fedot.core.data.data import InputData from fedot.core.operations.operation_parameters import OperationParameters from golem.core.dag.graph import Graph -from sklearn.metrics import (accuracy_score, f1_score, - log_loss, mean_absolute_error, - mean_absolute_percentage_error, - mean_squared_error, mean_squared_log_error, - precision_score, r2_score, roc_auc_score) -from sklearn.metrics import d2_absolute_error_score, explained_variance_score, max_error, median_absolute_error +from sklearn.metrics import ( + accuracy_score, + d2_absolute_error_score, + explained_variance_score, + f1_score, + log_loss, + max_error, + mean_absolute_error, + mean_absolute_percentage_error, + mean_squared_error, + mean_squared_log_error, + median_absolute_error, + precision_score, + r2_score, + roc_auc_score, +) from sktime.performance_metrics.forecasting import mean_absolute_scaled_error from fedot_ind.core.architecture.settings.computational import backend_methods as np + # from fedot_ind.core.architecture.preprocessing.data_convertor import DataConverter -from fedot_ind.core.metrics.anomaly_detection.function import single_average_delay, \ - single_evaluate_nab, single_detecting_boundaries, check_errors +from fedot_ind.core.metrics.anomaly_detection.function import ( + check_errors, + single_average_delay, + single_detecting_boundaries, + single_evaluate_nab, +) class ParetoMetrics: @@ -83,7 +97,7 @@ def metric(self) -> float: return mean_squared_error( y_true=self.target, y_pred=self.predicted_labels, - squared=False) + squared=False) ** 0.5 class SMAPE(QualityMetric): @@ -228,8 +242,13 @@ def mape(A, F): def calculate_regression_metric(target, labels, rounding_order=3, - metric_names=('r2', 'rmse', 'mae'), + metric_names=None, **kwargs): + + # Set default metrics + if metric_names is None: + metric_names = ('r2', 'rmse', 'mae') + target = target.astype(float) def rmse(y_true, y_pred): @@ -256,11 +275,14 @@ def rmse(y_true, y_pred): def calculate_forecasting_metric(target, labels, rounding_order=3, - metric_names=('smape', 'rmse', - 'mape'), + metric_names=None, **kwargs): target = target.astype(float) + # Set default metrics + if metric_names is None: + metric_names = ('smape', 'rmse', 'mape') + def rmse(y_true, y_pred): return np.sqrt(mean_squared_error(y_true, y_pred)) @@ -285,18 +307,20 @@ def calculate_classification_metric( labels, probs, rounding_order=3, - metric_names=( - 'f1', - # 'roc_auc', - 'accuracy')): + metric_names=('f1', 'accuracy')): + + # Set default metrics + if metric_names is None: + metric_names = ('f1', 'accuracy') + metric_dict = {'accuracy': Accuracy, 'f1': F1, # 'roc_auc': ROCAUC, 'precision': Precision, 'logloss': Logloss} - df = pd.DataFrame({name: func(target, labels, probs).metric( - ) for name, func in metric_dict.items() if name in metric_names}, index=[0]) + df = pd.DataFrame({name: func(target, labels, probs).metric() + for name, func in metric_dict.items() if name in metric_names}, index=[0]) return df.round(rounding_order) diff --git a/fedot_ind/core/models/base_extractor.py b/fedot_ind/core/models/base_extractor.py index a273c6675..013852294 100644 --- a/fedot_ind/core/models/base_extractor.py +++ b/fedot_ind/core/models/base_extractor.py @@ -1,17 +1,17 @@ import logging import math -from itertools import chain from multiprocessing import cpu_count +import dask from fedot.core.data.data import InputData from fedot.core.repository.dataset_types import DataTypesEnum -from joblib import delayed, Parallel from numpy.lib import stride_tricks as stride_repr +from tqdm.dask import TqdmCallback from fedot_ind.api.utils.data import init_input_data -from fedot_ind.core.architecture.abstraction.decorators import convert_to_input_data from fedot_ind.core.metrics.metrics_implementation import * from fedot_ind.core.operation.IndustrialCachableOperation import IndustrialCachableOperationImplementation +from fedot_ind.core.operation.filtration.feature_filtration import FeatureSpaceReducer from fedot_ind.core.operation.transformation.data.hankel import HankelMatrix from fedot_ind.core.repository.constanst_repository import STAT_METHODS, STAT_METHODS_GLOBAL @@ -23,49 +23,62 @@ class BaseExtractor(IndustrialCachableOperationImplementation): def __init__(self, params: Optional[OperationParameters] = None): super().__init__(params) - self.current_window = None - self.stride = 3 - self.n_processes = math.ceil(cpu_count() * 0.7) if cpu_count() > 1 else 1 - self.data_type = DataTypesEnum.table self.use_cache = self.params.get('use_cache', False) self.use_sliding_window = self.params.get('use_sliding_window', True) + self.use_feature_filter = self.params.get('use_feature_filter', False) + self.feature_filter = FeatureSpaceReducer() + self.data_type = DataTypesEnum.table + + self.current_window = None self.relevant_features = None + self.predict = None + + self.stride = 3 + self.n_processes = math.ceil(cpu_count() * 0.7) if cpu_count() > 1 else 1 + self.logger = logging.getLogger(self.__class__.__name__) self.logging_params = {'jobs': self.n_processes} - self.predict = None + + def __repr__(self): + return 'Abstract Class for TS representation' def fit(self, input_data: InputData): pass def extract_features(self, x, y) -> pd.DataFrame: """ - For those cases when you need to use feature extractor as a stangalone object + For those cases when you need to use feature extractor as a standalone object """ input_data = init_input_data(x, y) transformed_features = self.transform(input_data, use_cache=self.use_cache) try: - return pd.DataFrame(transformed_features.predict, columns=self.relevant_features) + return pd.DataFrame(transformed_features.predict.squeeze(), columns=self.relevant_features) except ValueError: - return pd.DataFrame(transformed_features.predict) + return pd.DataFrame(transformed_features.predict.squeeze()) def _transform(self, input_data: InputData) -> np.array: """ Method for feature generation for all series """ - parallel = Parallel(n_jobs=self.n_processes, verbose=0, pre_dispatch="2*n_jobs") - feature_matrix = parallel( - delayed(self.generate_features_from_ts)(sample) for sample in input_data.features - ) - if len(feature_matrix[0].features.shape) > 1: - stacked_data = np.stack([ts.features for ts in feature_matrix]) + evaluation_results = list(map(lambda sample: self.generate_features_from_ts(sample), input_data.features)) + with TqdmCallback(desc=fr"compute_feature_extraction_with_{self.__repr__()}"): + feature_matrix = dask.compute(*evaluation_results) + if len(feature_matrix[0].shape) > 1: + stacked_data = np.stack(feature_matrix) self.predict = self._clean_predict(stacked_data) else: - stacked_data = np.array([ts.features for ts in feature_matrix]) + stacked_data = np.array(feature_matrix) self.predict = self._clean_predict(stacked_data) self.predict = self.predict.reshape(self.predict.shape[0], -1) + # self.relevant_features = feature_matrix[0].supplementary_data['feature_name'] + + if self.use_feature_filter: + if not self.feature_filter.is_fitted: + self.predict = self.feature_filter.reduce_feature_space(self.predict) + else: + self.predict = self.predict[:, :, self.feature_filter.feature_mask] - self.relevant_features = feature_matrix[0].supplementary_data['feature_name'] return self.predict def _clean_predict(self, predict: np.array): @@ -81,10 +94,9 @@ def generate_features_from_ts(self, ts_frame: np.array, window_length: int = Non Method responsible for generation of features from time series. """ - @convert_to_input_data def get_statistical_features(self, time_series: np.ndarray, add_global_features: bool = False) -> tuple: """ - Method for creating baseline quantile features for a given time series. + Method for creating baseline statistical features for a given time series. Args: add_global_features: if True, global features are added to the feature set @@ -94,20 +106,13 @@ def get_statistical_features(self, time_series: np.ndarray, add_global_features: InputData: object with features """ - names = [] - features = [] time_series = time_series.flatten() list_of_methods = [*STAT_METHODS_GLOBAL.items()] if add_global_features else [*STAT_METHODS.items()] + return list(map(lambda method: method[1](time_series), list_of_methods)) - for method in list_of_methods: - features.append(method[1](time_series)) - names.append(method[0]) - return features, names - - @convert_to_input_data def apply_window_for_stat_feature(self, ts_data: np.array, feature_generator: callable, - window_size: int = None) -> tuple: + window_size: int = None) -> np.ndarray: window_size = round(ts_data.shape[0] / 10) if window_size is None \ else round(ts_data.shape[0] * (window_size / 100)) @@ -124,30 +129,13 @@ def apply_window_for_stat_feature(self, ts_data: np.array, if subseq_set is None: ts_slices = list(range(0, ts_data.shape[0], window_size)) features = list(map(lambda slice: feature_generator(ts_data[slice:slice + window_size]), ts_slices)) - names = list(map(lambda ts_tup: [x + f'_on_interval: {ts_tup[1] + 1} - {ts_tup[1] + 1 + window_size}' - for x in ts_tup[0].supplementary_data['feature_name']], - zip(features, ts_slices))) - features = [x.features for x in features] - else: ts_slices = list(range(0, subseq_set.shape[1])) features = list(map(lambda slice: feature_generator(subseq_set[:, slice]), ts_slices)) - names = list(map(lambda ts_tup: [x + f'_on_interval: {ts_tup[1] + 1} - {ts_tup[1] + 1 + window_size}' - for x in ts_tup[0].supplementary_data['feature_name']], - zip(features, ts_slices))) - features = [x.features for x in features] - - return features, names - - @convert_to_input_data - def _get_feature_matrix(self, extraction_func: callable, ts: np.array) -> tuple: - multi_ts_stat_features = [extraction_func(x) for x in ts] - for component in multi_ts_stat_features: - if not isinstance(component.features, np.ndarray): - component.features = np.array(component.features) - features = np.concatenate([component.features.reshape(1, -1) for component in multi_ts_stat_features], axis=0) - - for index, component in enumerate(multi_ts_stat_features): - component.supplementary_data['feature_name'] = [f'component {index}'] - names = list(chain(*[x.supplementary_data['feature_name'] for x in multi_ts_stat_features])) - return features, names + return features + + def _get_feature_matrix(self, extraction_func: callable, ts: np.array) -> np.ndarray: + multi_channel_features = [extraction_func(x) for x in ts] + features = np.concatenate([channel_feature.reshape(1, -1) + for channel_feature in multi_channel_features], axis=0) + return features diff --git a/fedot_ind/core/models/nn/network_impl/base_nn_model.py b/fedot_ind/core/models/nn/network_impl/base_nn_model.py index de83a2f06..fbd02dcf4 100644 --- a/fedot_ind/core/models/nn/network_impl/base_nn_model.py +++ b/fedot_ind/core/models/nn/network_impl/base_nn_model.py @@ -207,12 +207,12 @@ def _predict_model(self, x_test, output_mode: str = 'default'): def _convert_predict(self, pred, output_mode: str = 'labels'): have_encoder = all([self.label_encoder is not None, output_mode == 'labels']) - output_is_clf_labels = all([not self.is_regression_task, output_mode == 'labels']) + output_is_clf_labels = output_mode == 'labels' and self.is_regression_task - pred = pred.cpu().detach().numpy() if self.is_regression_task else F.softmax(pred, dim=1) - y_pred = torch.argmax(pred, dim=1).cpu().detach().numpy() if output_is_clf_labels else pred + pred = pred if self.is_regression_task else F.softmax(pred, dim=1) + y_pred = torch.argmax(pred, dim=1) if output_is_clf_labels else pred y_pred = self.label_encoder.inverse_transform(y_pred) if have_encoder else y_pred - + y_pred = y_pred.cpu().detach().numpy() predict = OutputData( idx=np.arange(len(y_pred)), task=self.task_type, diff --git a/fedot_ind/core/models/pdl/__init__.py b/fedot_ind/core/models/pdl/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fedot_ind/core/models/pdl/pairwise_model.py b/fedot_ind/core/models/pdl/pairwise_model.py new file mode 100644 index 000000000..2ae7ace31 --- /dev/null +++ b/fedot_ind/core/models/pdl/pairwise_model.py @@ -0,0 +1,534 @@ +from copy import deepcopy +from typing import Optional, Union + +import numpy as np +import pandas as pd +import sklearn.base +from fedot.core.data.data import InputData +from fedot.core.operations.operation_parameters import OperationParameters +from pymonad.either import Either +from scipy.special import softmax + +from fedot_ind.core.repository.constanst_repository import SKLEARN_CLF_IMP, SKLEARN_REG_IMP + + +class PairwiseDifferenceEstimator: + """ + Base class for Pairwise Difference Learning. + """ + + def _convert_to_pandas(self, arr1, arr2): + if isinstance(arr1, np.ndarray) or isinstance(arr2, np.ndarray): + arr1, arr2 = pd.DataFrame(arr1), pd.DataFrame(arr2) + return arr1, arr2 + + def _to_pandas_regression(self, *args): + return (data if data is None or isinstance(data, (pd.DataFrame, pd.Series)) else pd.DataFrame(data) for data in + args) + + def _pair_data_regression(self, X1, X2, y1=None, y2=None): + X1, y1, X2, y2 = self._to_pandas_regression(X1, y1, X2, y2) + + X_pair = X1.merge(X2, how="cross") + x1_pair = X_pair[[f'{column}_x' for column in X1.columns]].rename( + columns={f'{column}_x': f'{column}_diff' for column in X1.columns}) + x2_pair = X_pair[[f'{column}_y' for column in X1.columns]].rename( + columns={f'{column}_y': f'{column}_diff' for column in X1.columns}) + X_pair = pd.concat([X_pair, x1_pair - x2_pair], axis='columns') + # Symmetric + x2_pair_sym = X_pair[[f'{column}_x' for column in X1.columns]].rename( + columns={f'{column}_x': f'{column}_y' for column in X1.columns}) + x1_pair_sym = X_pair[[f'{column}_y' for column in X1.columns]].rename( + columns={f'{column}_y': f'{column}_x' for column in X1.columns}) + X_pair_sym = pd.concat([x1_pair_sym, x2_pair_sym, x2_pair - x1_pair], axis='columns') + + if y1 is not None: + assert isinstance(y1, pd.Series) or y1.shape[1] == 1, f"Didn't expect more than one output {y1.shape}" + assert isinstance(y2, pd.Series) or y2.shape[1] == 1, f"Didn't expect more than one output {y2.shape}" + + y_pair = pd.DataFrame(y1).merge(y2, how="cross") + y_pair_diff = y_pair.iloc[:, 0] - y_pair.iloc[:, 1] + else: + y_pair_diff = None + + return X_pair, X_pair_sym, y_pair_diff + + @staticmethod + def _get_pair_feature_names(features: list) -> list: + """ Get the new name of features after pairing points. """ + return [f'{name}_x' for name in features] + [f'{name}_y' for name in features] + + def pair_input(self, X1: Union[np.ndarray, pd.Series], + X2: Union[np.ndarray, pd.Series]): + X1, X2 = self._convert_to_pandas(X1, X2) + X_pair = X1.merge(X2, how="cross") + x1_pair = X_pair[[f'{column}_x' for column in X1.columns]].rename(columns={f'{column}_x': f'{column}_diff' + for column in X1.columns}) + x2_pair = X_pair[[f'{column}_y' for column in X1.columns]].rename(columns={f'{column}_y': f'{column}_diff' + for column in X1.columns}) + try: + calculate_difference = x1_pair - x2_pair + except BaseException: + raise ValueError( + "PairwiseDifference: The input data is not compatible with the subtraction operation." + " Either transform all data to numeric features or use a ColumnTransformer to transform the data.") + # It means that the input data is not compatible with the subtraction operation. + # Simply turn all your data into numbers + + X_pair = pd.concat([X_pair, calculate_difference], axis='columns') + # Symmetric + x2_pair_sym = X_pair[[f'{column}_x' for column in X1.columns]].rename(columns={f'{column}_x': f'{column}_y' + for column in X1.columns}) + x1_pair_sym = X_pair[[f'{column}_y' for column in X1.columns]].rename(columns={f'{column}_y': f'{column}_x' + for column in X1.columns}) + X_pair_sym = pd.concat([x1_pair_sym, x2_pair_sym, x2_pair - x1_pair], axis='columns') + # distances = cdist(X1, cluster_centers) + return X_pair, X_pair_sym + + def pair_output(self, + y1: Union[np.ndarray, pd.Series], + y2: Union[np.ndarray, pd.Series]) -> np.ndarray: + """For regresion. beware this is different from regression this is b-a not a-b""" + + y1, y2 = self._convert_to_pandas(y1, y2) + y_pair = pd.DataFrame(y1).merge(y2, how="cross") + y_pair_diff = y_pair.iloc[:, 1] - y_pair.iloc[:, 0] + return y_pair_diff.values + + def pair_output_difference(self, + y1: Union[np.ndarray, pd.Series], + y2: Union[np.ndarray, pd.Series], + nb_classes: int) -> np.ndarray: + """For MultiClassClassification base on difference only""" + y1, y2 = self._convert_to_pandas(y1, y2) + y_pair = pd.DataFrame(y1).merge(y2, how="cross") + y_pair_diff = (y_pair.iloc[:, 1] != y_pair.iloc[:, 0]).astype(int) + assert y_pair_diff.nunique() <= 2, f'should only be 0s and 1s {y_pair_diff.unique()}' + return y_pair_diff.values + + @staticmethod + def get_pair_feature_names(features: list) -> list: + """ Get the new name of features after pairing points. """ + return [f'{name}_x' for name in features] + [f'{name}_y' for name in features] + + @staticmethod + def check_output(y: pd.Series) -> None: + assert y is not None + assert isinstance(y, pd.Series) + assert 'uint' not in str(y.dtype), y.dtype + assert isinstance(y, pd.Series) or y.shape[1] == 1, f"Didn't expect more than one output {y.shape}" + assert y.nunique() > 1, y.nunique() + if y.name is None: + # just put any name to the output to avoid a bug later + y.name = 'output' + + @staticmethod + def check_sample_weight(sample_weight: pd.Series, y_train: pd.Series) -> None: + if sample_weight is None: + pass + elif isinstance(sample_weight, pd.Series): + # check + if len(sample_weight) != len(y_train): + raise ValueError( + f'sample_weight size {len(sample_weight)} should be equal to the train size {len(y_train)}') + if not sample_weight.index.equals(y_train.index): + raise ValueError( + f'sample_weight and y_train must have the same index\n{sample_weight.index}\n{y_train.index}') + if all(sample_weight.fillna(0) <= 0): + raise ValueError(f'sample_weight are all negative/Nans.\n{sample_weight}') + + # norm + class_sums = np.bincount(y_train, sample_weight) + sample_weight = sample_weight / class_sums[y_train.astype(int)] + else: + raise NotImplementedError() + + @staticmethod + def correct_sample_weight(sample_weight: pd.Series, y_train: pd.Series) -> pd.Series: + if sample_weight is not None: + sample_weight = sample_weight / sum(sample_weight) + # norm + # class_sums = np.bincount(y_train, sample_weight) + # sample_weight = sample_weight / class_sums[y_train.astype(int)] + + # # if sample_weight.min() < 0: # dolla weight change : improvement +0.0032 bof + # # sample_weight = sample_weight - sample_weight.min() + return sample_weight + + @staticmethod + def predict(y_prob: np.ndarray, output_mode: str = 'default', min_label_zero: bool = True): + if output_mode.__contains__('label'): + predicted_classes = np.argmax(y_prob, axis=1)[..., np.newaxis] + predicted_classes = predicted_classes if min_label_zero else predicted_classes + 1 + else: + predicted_classes = y_prob + return predicted_classes + + +class PairwiseDifferenceClassifier: + """PDL have a low chance of improvement compared to using directly parametric models like Logit, MLP. \ + To obtain an improvement, it is better to use a tree-based model like: ExtraTrees""" + + def __init__(self, params: Optional[OperationParameters] = None): + self.base_model_params = deepcopy(params._parameters) + del self.base_model_params['model'] + self.base_model = SKLEARN_CLF_IMP[params.get('model', 'rf')](**self.base_model_params) + self.pde = PairwiseDifferenceEstimator() + self.is_model_have_prob_output = hasattr(self.base_model, 'predict_proba') + self.prior = None + self.use_prior = False + self.proba_aggregate_method = 'norm' + self.sample_weight_ = None + + def _check_target(self): + if self.target.min() != 0: + self.target_start_zero = False + else: + self.target_start_zero = True + + def _estimate_prior(self): + if self.prior is not None: + return self + # Calculate class priors + target = pd.DataFrame(self.target) + class_counts = target.value_counts() + class_priors = class_counts / len(self.target) + # Convert class priors to a dictionary + self.prior = class_priors.sort_index().values + + def fit(self, + input_data: InputData): + self.num_classes = input_data.num_classes + self.target = input_data.target + self.task_type = input_data.task + self.is_regression_task = self.task_type.task_type.value == 'regression' + self.classes_ = sklearn.utils.multiclass.unique_labels(input_data.target) + self.train_features = input_data.features # Store the classes seen during fit + self._estimate_prior() + self._check_target() + X_pair, _ = self.pde.pair_input(input_data.features, input_data.features) + y_pair_diff = self.pde.pair_output_difference(self.target, self.target, self.num_classes) + + self.base_model.fit(X_pair, y_pair_diff) + return self + + def predict_similarity_samples(self, X: pd.DataFrame, X_anchors=None) -> pd.DataFrame: + """ For each input sample, output C probabilities for each N train pair. + Beware that this function does not apply the weights at this level + """ + if X_anchors is None: + X_anchors = self.train_features + + X_pair, X_pair_sym = self.pde.pair_input(X, X_anchors) + if self.is_model_have_prob_output: + predict_proba = self.base_model.predict_proba + else: + def predict_proba(X) -> np.ndarray: + predictions = self.base_model.predict(X) + predictions = predictions.astype(int) + n_samples = len(predictions) + proba = np.zeros((n_samples, 2), dtype=float) + proba[range(n_samples), predictions] = 1. + return proba + + predictions_proba_difference: np.ndarray = predict_proba(X_pair) + predictions_proba_difference_sym: np.ndarray = predict_proba(X_pair_sym) + # np.testing.assert_array_equal(predictions_proba_difference.shape, (len(X_pair), 2)) + predictions_proba_similarity_ab = predictions_proba_difference[:, 0] + predictions_proba_similarity_ba = predictions_proba_difference_sym[:, 0] + predictions_proba_similarity = (predictions_proba_similarity_ab + predictions_proba_similarity_ba) / 2. + + predictions_proba_similarity_df = pd.DataFrame(predictions_proba_similarity.reshape((-1, + len(self.train_features))), + index=pd.DataFrame(X).index, + columns=pd.DataFrame(self.train_features).index) + return predictions_proba_similarity_df + + def __predict_with_prior(self, input_data: np.ndarray, sample_weight): + tests_trains_classes_likelihood = self.predict_proba_samples(input_data) + tests_classes_likelihood = self._apply_weights(tests_trains_classes_likelihood, sample_weight) + np.finfo(tests_classes_likelihood.dtype).eps + tests_classes_likelihood = tests_classes_likelihood / tests_classes_likelihood.sum(axis=1)[:, np.newaxis] + tests_classes_likelihood = tests_classes_likelihood.clip(0, 1) + return tests_classes_likelihood + + def __predict_without_prior(self, input_data: np.ndarray, sample_weight=None): + X = pd.DataFrame(input_data) + predictions_proba_similarity_df: pd.DataFrame = pd.DataFrame(self.predict_similarity_samples(X)) + + def f(predictions_proba_similarity: pd.Series) -> pd.Series: + target = pd.Series(self.target.squeeze()) + df = pd.DataFrame( + {'start': target.reset_index(drop=True), 'similarity': predictions_proba_similarity}) + df = df.fillna(0) + mean = df.groupby('start', observed=False).mean()['similarity'] + return mean + + tests_classes_likelihood_np = predictions_proba_similarity_df.apply(f, axis='columns') + # without this normalization it should work for multiclass-multilabel + if self.proba_aggregate_method == 'norm': + tests_classes_likelihood_np = tests_classes_likelihood_np.values \ + / tests_classes_likelihood_np.values.sum(axis=-1)[:, np.newaxis] + elif self.proba_aggregate_method == 'softmax': + tests_classes_likelihood_np = softmax(tests_classes_likelihood_np, axis=-1) + return tests_classes_likelihood_np + + def predict_proba_samples(self, X: Union[np.ndarray, pd.DataFrame]) -> np.ndarray: + # todo add unit test with weight ==[1 1 1 ] and weights = None + if not isinstance(X, pd.DataFrame): + X = pd.DataFrame(X) + predictions_proba_similarity: pd.DataFrame = self.predict_similarity_samples(X) + + def g(anchor_class: np.ndarray, predicted_similarity: np.ndarray) -> np.ndarray: + """ + + :param anchor_class: array int + :param predicted_similarity: array float + :return: + """ + prior_cls_probs = (1 - self.prior[anchor_class]) + likelyhood_per_anchor = ((1 - predicted_similarity) / prior_cls_probs) + likelyhood_per_anchor = likelyhood_per_anchor * self.prior + n_samples = np.arange(len(likelyhood_per_anchor)) + likelyhood_per_anchor[n_samples, anchor_class] = predicted_similarity + return likelyhood_per_anchor + + anchor_class = self.target.astype(int) + + def f(predictions_proba_similarity: np.ndarray) -> np.ndarray: + """ Here we focus on one test point. + Given its similarity probabilities. + Return the probability for each class""" + test_i_trains_classes = g(anchor_class=anchor_class, predicted_similarity=predictions_proba_similarity) + np.testing.assert_array_equal(test_i_trains_classes.shape, (len(self.target), self.num_classes)) + return test_i_trains_classes + + tests_trains_classes_likelihood = np.apply_along_axis(f, axis=1, arr=predictions_proba_similarity.values) + return tests_trains_classes_likelihood + + def _apply_weights(self, + tests_trains_classes_likelihood: np.ndarray, + sample_weight: np.ndarray) -> np.ndarray: + tests_classes_likelihood = (tests_trains_classes_likelihood * + sample_weight[np.newaxis, :, np.newaxis]).sum(axis=1) + # np.testing.assert_array_almost_equal(tests_classes_likelihood.sum(axis=-1), 1.) + return tests_classes_likelihood + + def _abstract_predict(self, + input_data: InputData, + output_mode: str = 'default'): + sample_weight = np.full(len(self.target), 1 / len(self.target)) if self.sample_weight_ is None \ + else self.sample_weight_.loc[self.target.index].values + + predict_output = Either(value=input_data.features, + monoid=[input_data.features, self.use_prior]).either( + left_function=lambda features: self.__predict_without_prior(features, sample_weight), + right_function=lambda features: self.__predict_with_prior(features, sample_weight)) + return self.pde.predict(predict_output, output_mode, self.target_start_zero) + + def predict(self, + input_data: InputData, + output_mode: str = 'labels') -> pd.Series: + """ For each input sample, output one prediction the most probable class. + + """ + return self._abstract_predict(input_data, output_mode) + + def predict_proba(self, + input_data: InputData, + output_mode: str = 'default') -> pd.Series: + """ For each input sample, output one prediction the most probable class. + + """ + + return self.predict(input_data, output_mode) + + def predict_for_fit(self, + input_data: InputData, + output_mode: str = 'default'): + """ For each input sample, output one prediction the most probable class. + """ + return self.predict(input_data, output_mode) + + def score_difference(self, input_data: InputData) -> float: + """ WE RETURN THE MAE score XD """ + y_pair_diff = self.pde.pair_output_difference(input_data.target, self.target, + self.num_classes) # 0 if similar, 1 if diff + predictions_proba_similarity: pd.DataFrame = self.predict_similarity_samples( + input_data.features, reshape=False) # 0% if different, 100% if similar + + return abs(y_pair_diff - (1 - predictions_proba_similarity)).mean() + + +class PairwiseDifferenceRegressor: + """PDL have a low chance of improvement compared to using directly parametric models like Ridge, Lasso. \ + To obtain an improvement, it is better to use a tree-based model like: ExtraTrees.""" + + def __init__(self, params: Optional[OperationParameters] = None): + self.base_model_params = deepcopy(params._parameters) + del self.base_model_params['model'] + self.base_model = SKLEARN_REG_IMP[params.get('model', 'treg')](**self.base_model_params) + self.pde = PairwiseDifferenceEstimator() + self.prior = None + self.use_prior = False + self.proba_aggregate_method = 'norm' + self.sample_weight_ = None + + def fit(self, + input_data: InputData): + self.num_classes = input_data.num_classes + self.target = input_data.target + self.task_type = input_data.task + self.is_regression_task = self.task_type.task_type.value == 'regression' + self.train_features = input_data.features # Store the classes seen during fit + X_pair, _, y_pair_diff = self.pde._pair_data_regression(self.train_features, + self.train_features, + self.target, + self.target) + self.base_model.fit(X_pair, y_pair_diff) + return self + + def predict(self, + input_data: InputData) -> pd.Series: + return self._abstract_predict(input_data) + + def predict_proba(self, + input_data: InputData) -> pd.Series: + return self.predict(input_data) + + def predict_for_fit(self, + input_data: InputData, + output_mode: str = 'default'): + return self.predict(input_data) + + def _predict_samples(self, input_data: InputData, force_symmetry=True): + """ + For each input sample, output N predictions (where N = the number of anchors). + prediction = difference + y_train + """ + + def repeat(s: pd.Series, n_times: int): + return pd.concat([s] * n_times, ignore_index=True).values + + X = pd.DataFrame(input_data.features) + final_shape = (-1, len(self.train_features)) + # Create pairs of the new instance each anchor (training instance) + X_pair, X_pair_sym, _ = self.pde._pair_data_regression(X, self.train_features, None, None) + # Estimator predicts the difference between each anchor (training instance) and each prediction instance: + predictions_difference: np.ndarray = self.base_model.predict(X_pair) + if force_symmetry: + difference_sym: np.ndarray = self.base_model.predict(X_pair_sym) + predictions_difference = (predictions_difference - difference_sym) / 2. + + # The known y for the training instances + predictions_start: np.ndarray = repeat(pd.Series(self.target), n_times=len(X)) + # Combine the difference predicted by the model with the known y => train_y + predicted difference + predictions: np.ndarray = predictions_start + predictions_difference + # Set of absolute predictions for each anchor for each prediction instance: + prediction_samples_df = pd.DataFrame(predictions.reshape(final_shape), index=X.index) + # The predicted difference to the anchors: + pred_diff_samples_df = pd.DataFrame(predictions_difference.reshape(final_shape), index=X.index) + return prediction_samples_df, pred_diff_samples_df + + def __predict_with_weight(self, input_data, prediction_samples_df): + if isinstance(self.sample_weight_, pd.Series): + def weighted_avg(samples: pd.Series, weights: pd.Series) -> float: + weights[weights <= 0] = np.nan + summed = np.nansum(samples.multiply(weights)) + return summed / np.nansum(weights) + + prediction = prediction_samples_df.apply( + lambda samples: weighted_avg(samples, self.sample_weight_), + axis='columns' + ) + else: + self.sample_weight_[self.sample_weight_ < 0] = np.nan + summed = pd.Series(np.nansum(self.sample_weight_, axis=1), index=input_data.index) + self.sample_weight_ = self.sample_weight_.apply(lambda row: row / summed) + np.testing.assert_array_almost_equal(self.sample_weight_.sum(axis=1), 1.) + prediction = (prediction_samples_df * self.sample_weight_).sum(axis=1) + return prediction + + def _abstract_predict(self, input_data: InputData, force_symmetry=True) -> pd.Series: + """ For each input sample, output one prediction, the mean of the predicted samples. """ + prediction_samples_df, _ = self._predict_samples(input_data=input_data, force_symmetry=force_symmetry) + have_weights = isinstance(self.sample_weight_, pd.Series) or isinstance(self.sample_weight_, pd.DataFrame) + + predict_output = Either(value=pd.DataFrame(input_data.features), + monoid=[prediction_samples_df, have_weights]).either( + left_function=lambda features: features.mean(axis=1), + right_function=lambda init_data: self.__predict_with_weight(init_data, prediction_samples_df)) + + return predict_output.values + + def learn_anchor_weights( + self, + X_val: pd.DataFrame = None, + y_val: pd.Series = None, + X_test: pd.DataFrame = None, + method: str = 'L2', + enable_warnings=True, + **kwargs): + """ + Call this method after the training to create weights for the anchors + using the given validation data. + Use the `method` parameter to select one of the following + weighting methods: + - 'Optimize': Minimize the validation MAE using the SLSQP optimizer with a linear constraint on the sum of the weights. + - 'L1': like `Optimize` but includes L1 regularization. + - 'L2': like `Optimize` but includes L2 regularization. + - 'L1L2': like `Optimize` but includes L1 and L2 regularization. + - 'KLD': like `Optimize` but includes a KLD loss to make the weights more uniform. + - 'ExtremeWeightPruning': lik `L1` but uses high regularization strength. + - 'NegativeError': Calculate weights as the negative mean absolute error. + - 'OrderedVoting': The best of n anchors gets n votes, the worst gets 1 vote. n is the number of anchors. + - 'KmeansClusterCenters': Calculate weights as the distance to the cluster centers of the KMeans algorithm. + """ + if y_val is not None: + old_validation_error = sklearn.metrics.mean_absolute_error(self.predict(X_val), y_val) + else: + old_validation_error = 0 + + if method not in self._name_to_method_mapping.keys(): + raise NotImplementedError(f"Weighting method {method} unknown! Use one of the following:" + f" '{', '.join(list(self._name_to_method_mapping.keys()))}'") + + sample_weight: pd.Series = self._name_to_method_mapping[method](X_val=X_val, y_val=y_val, X_test=X_test, + **kwargs) + assert not sample_weight.isna().any(), f'Nans values in sample_weights using {method}\n {sample_weight}' + self.set_sample_weight(sample_weight) + if y_val is not None: + new_validation_error = sklearn.metrics.mean_absolute_error(self.predict(X_val), y_val) + if new_validation_error > old_validation_error and enable_warnings: + print(f'WARNING: \t new val MAE: {new_validation_error} \t old val MAE: {old_validation_error}') + return self + + def set_sample_weight(self, sample_weight: pd.Series): + """ + Sets the weights for the anchors to the given weights in sample_weight. + + :param sample_weight: The weights for the anchors as a pd.Series + :return: self (with updated weights) + """ + if sample_weight is None: + pass + elif isinstance(sample_weight, pd.Series): + if len(sample_weight) != len(self.y_train_): + raise ValueError( + f'sample_weight size {len(sample_weight)} should be equal to the train size {len(self.y_train_)}') + if not sample_weight.index.equals(self.y_train_.index): + raise ValueError( + f'sample_weight and y_train must have the same index\n{sample_weight.index}\n{self.y_train_.index}') + + if all(sample_weight.fillna(0) == 0): # All weights are 0 => Set them to 1 + sample_weight = pd.Series(1, index=self.y_train_.index) + + if all(sample_weight.fillna(0) < 0): + raise ValueError(f'sample_weight are all negative/Nans.\n{sample_weight}') + if any(pd.isna(sample_weight)): + raise ValueError(f'sample_weight contains NaNs.\n{sample_weight}') + else: + raise ValueError('sample_weight must be a pd.Series') + + self.sample_weight_ = sample_weight + return self diff --git a/fedot_ind/core/models/pdl/pairwise_transform.py b/fedot_ind/core/models/pdl/pairwise_transform.py new file mode 100644 index 000000000..c0bc1f9c4 --- /dev/null +++ b/fedot_ind/core/models/pdl/pairwise_transform.py @@ -0,0 +1,291 @@ +import functools +from typing import Iterable, Optional + +import numpy as np +import pandas as pd +import sklearn.base +from fedot.core.operations.operation_parameters import OperationParameters +from scipy.optimize import LinearConstraint, minimize +from scipy.spatial.distance import cdist +from scipy.stats import entropy +from sklearn.cluster import KMeans +from sklearn.compose import ColumnTransformer +from sklearn.utils.validation import check_is_fitted + + +class PDCDataTransformer: + """ + Transform the data so that it can be processed by PDL models. + """ + preprocessing_: ColumnTransformer + preprocessing_y_: ColumnTransformer # todo fix the ColumnTransformer annotation + + def __init__(self, numeric_features: Iterable = None, + ordinal_features: Iterable = None, + string_features: Iterable = None, + y_type: str = None): + self.numeric_features = numeric_features + self.ordinal_features = ordinal_features + self.string_features = string_features + if y_type is not None and y_type not in ('numeric', 'ordinal', 'string'): + raise ValueError(f"y_type must be one of 'numeric', 'ordinal', 'string' but got {y_type}") + self.y_type = y_type + + def fit(self, X, y=None): + + # y = y.astype('category').cat.codes.astype(np.float32) # todo since I + # cannot transform the output at least add raise type error on it + if self.numeric_features is None and self.ordinal_features is None and self.string_features is None: + self.numeric_features = [] + self.ordinal_features = [] # todo fix name, will be processed a ordinal + self.string_features = [] + for column in X.columns: + dtype = X[column].dtype + if pd.api.types.is_numeric_dtype(dtype): + self.numeric_features.append(column) + elif isinstance(dtype, pd.CategoricalDtype): + if dtype.ordered: + self.ordinal_features.append(column) # ordinal... + else: + self.string_features.append(column) + elif pd.api.types.is_bool_dtype(dtype): # pd.api.types.is_categorical_dtype(dtype) deprecated + self.string_features.append(column) + elif pd.api.types.is_string_dtype(dtype): + self.string_features.append(column) + + X, _ = self.cast_uint(X) + if self.y_type == 'numeric': + from sklearn.preprocessing import StandardScaler + self.preprocessing_y_ = StandardScaler() + elif self.y_type == 'ordinal': # string + from sklearn.preprocessing import OrdinalEncoder + self.preprocessing_y_ = OrdinalEncoder() + elif self.y_type == 'string': + from sklearn.preprocessing import OneHotEncoder + self.preprocessing_y_ = OneHotEncoder() + + if y is not None and self.preprocessing_y_ is not None: + if isinstance(y, pd.Series): + y = pd.DataFrame(y) + self.preprocessing_y_.fit(y) + + return self + + def cast_uint(self, X: pd.DataFrame, y: pd.Series = None): + numeric_cols = X.select_dtypes(include=['number']).columns + X.loc[:, numeric_cols] = X[numeric_cols].astype('float32') + if y is not None: + y = y.astype('float32') + return X, y + + def transform(self, X, y=None): + check_is_fitted(self) + X, _ = self.cast_uint(X) + X = pd.DataFrame(self.preprocessing_.transform(X)) + from scipy.sparse import csr_matrix + if any(isinstance(e, csr_matrix) for e in X.values.flatten()): + raise NotImplementedError('error in data \t X contains sparse features (csr_matrix)') + X = X.dropna(axis=1, how='all') # Drop columns with all NaN values + X = X.astype(np.float32) + + if len(X.columns) == 0: + raise ValueError('error in data \t X no features left after pre-processing') + # if X.isna().any().any(): + # raise NotImplementedError('error in data \t Some features are NaNs in the X set') + if any(x in pd.Series(X.values.flatten()).apply(type).unique() for x in + ('csr_matrix', 'date',)): # todo think about adding 'str' + raise NotImplementedError('error in data \t Dataset contains sparse data') + + if y is not None and self.preprocessing_ is not None: + y = pd.Series(self.preprocessing_.transform(y), name='y') + if y is None: + return X.values + return X.values, y.values + + +class SampleWeights: + def __init__(self, params: Optional[OperationParameters] = None): + # Save information about the weighting methods as here for better availability + self.method = params.get('method', 'L2') + + self.method_dict = { + # Optimization based methods: + 'L2': functools.partial(self._sample_weight_optimize, l2_lambda=0.1), + 'KLD': functools.partial(self._sample_weight_optimize, kld_lambda=0.05), + 'Optimize': self._sample_weight_optimize, + 'L1L2': functools.partial(self._sample_weight_optimize, l1_lambda=0.05, l2_lambda=0.025), + 'L1': functools.partial(self._sample_weight_optimize, l1_lambda=0.1), + 'ExtremeWeightPruning': self._sample_weight_extreme_pruning, + # Heuristic methods + 'NegativeError': self._sample_weight_negative_error, + 'InverseError': self._sample_weight_inverse_error, + 'OrderedVoting': self._sample_weight_ordered_votes, + # Other Methods: + 'KMeansClusterCenters': self._sample_weight_by_kmeans_prototypes, + } + + def _normalize_weights(self, weights: np.ndarray) -> pd.Series: + """ + Normalize the weights to be between 0 and 1 + :param weights: The weights to be normalized as a pd.Series + """ + if all(np.isclose(weights, weights.values[0])): + weights = pd.Series(1., index=weights.index) + assert weights.min() >= 0, f'Negative weights found: {weights[weights < 0]}' + weights /= weights.sum() + return weights + + def __objective_function(self, + weights: np.ndarray, + pred_val_samples_np: np.ndarray, + y_val: np.ndarray, + initial_mae: float, + kld_lambda=0., + l1_lambda=0., + l2_lambda=0.) -> float: + assert kld_lambda >= 0, f'kld_lambda should be >=0, got {kld_lambda}' + assert l1_lambda >= 0, f'l1_lambda should be >=0, got {l1_lambda}' + assert l2_lambda >= 0, f'l2_lambda should be >=0, got {l2_lambda}' + assert initial_mae >= 0, f'initial_mae should be >=0, got {initial_mae}' + + predictions = np.matmul(pred_val_samples_np, weights / sum(weights)) + mae = sklearn.metrics.mean_absolute_error(y_val, predictions) + + regularisation = 0 + if kld_lambda > 0: + train_size = len(weights) + weights_initial_guess = np.ones(train_size) / train_size + regularisation += kld_lambda * entropy(weights, weights_initial_guess) / train_size + if l1_lambda > 0: + regularisation += l1_lambda * (np.linalg.norm(weights, ord=1) - max(weights)) + if l2_lambda > 0: + regularisation += l2_lambda * np.linalg.norm(weights, ord=2) + + regularisation *= initial_mae + loss = mae + regularisation + return loss + + def _sample_weight_optimize(self, X_val: pd.DataFrame, y_val: pd.Series, kld_lambda=0., l1_lambda=0., l2_lambda=0., + **kwargs) -> pd.Series: + """ + Minimize the validation MAE using SLSQP optimizer + with a linear constraint on the sum of the weights. + + :param X_val: + :param y_val: + :param kld_lambda: alpha=0.01 i.e. I am ready to loose 1% of the validation MAE to make the solution more general + :return: + """ + prediction_samples_df, _ = self._predict_samples(X_val) + pred_val_samples_np = prediction_samples_df.values + train_size = len(self.X_train_) + weights_initial_guess = np.ones(train_size) / train_size + initial_mae = sklearn.metrics.mean_absolute_error(y_val, np.matmul(pred_val_samples_np, weights_initial_guess)) + + def objective_function(weights: np.ndarray) -> float: + return self.__objective_function(weights=weights, pred_val_samples_np=pred_val_samples_np, y_val=y_val, + initial_mae=initial_mae, kld_lambda=kld_lambda, l1_lambda=l1_lambda, + l2_lambda=l2_lambda) + + variable_bounds = [(0., 1.) for _ in range(train_size)] + sum_constraint = LinearConstraint(np.ones(train_size), lb=1, ub=1) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + result = minimize(objective_function, weights_initial_guess, method='SLSQP', bounds=variable_bounds, + constraints=[sum_constraint]) + # Extract the solution + optimal_weight = result.x + + # print("the optimal solution:", optimal_weight) + # print("Optimal Objective Value, i.e. new log loss validation error:", result.fun) + sample_weights = pd.Series(optimal_weight, index=self.X_train_.index) + return sample_weights + + def _sample_weight_extreme_pruning(self, X_val: pd.DataFrame, y_val: pd.Series, **kwargs) -> pd.Series: + l1 = 0.8 + while l1 > 0.0001: + weights = self._sample_weight_optimize(X_val=X_val, y_val=y_val, l1_lambda=l1) + if sum(weights == 0) / len(weights) > .9: + l1 *= 0.5 + else: + break + return weights + + def _error(self, X_val: pd.DataFrame, y_val: pd.Series, **kwargs) -> pd.Series: + """ + Calculate the Mean Absolute Error for each anchor. + :param X_val: + :param y_val: + :param kwargs: + :return: + """ + pred_val_samples, _ = self._predict_samples(X_val) + errors = pred_val_samples.apply(lambda one_val_samples: abs(y_val - one_val_samples), axis=0) + val_mae = errors.mean() + np.testing.assert_array_equal(val_mae.index, self.X_train_.index) + return val_mae + + def _sample_weight_inverse_error(self, X_val: pd.DataFrame, y_val: pd.Series, **kwargs) -> pd.Series: + val_mae = self._error(X_val=X_val, y_val=y_val) + sample_weights = 1. / (val_mae + 0.0001) + sample_weights = sample_weights / sample_weights.sum() + return sample_weights + + def _sample_weight_negative_error(self, X_val: pd.DataFrame, y_val: pd.Series, **kwargs) -> pd.Series: + uniform_weights = pd.Series([1 / len(self.X_train_)] * len(self.X_train_), index=self.X_train_.index) + val_mae = self._error(X_val=X_val, y_val=y_val) + if sum(val_mae) == 0: + return uniform_weights + sample_weights = ((-val_mae) + max(val_mae)) / sum(val_mae) + if sum(sample_weights) == 0: + return uniform_weights + sample_weights = sample_weights / sample_weights.sum() + return sample_weights + + @staticmethod + def _sample_weight_ordered_votes_from_weights(received_weights): + errors = - received_weights + k = len(errors) + ranks = np.argsort(np.argsort(errors)) + 1 + weights = (k - ranks + 1) / (k * (k + 1) / 2) + return weights + + def _sample_weight_ordered_votes(self, X_val, y_val, force_symmetry=True, **kwargs): + """ + The best of n anchors gets n votes, the worst gets 1 vote. n is the nb of anchors. Uses the _sample_weight_negative_error function + for distribution votes. + works quite good + :param force_symmetry: Sets the force_symmetry parameter of the prediction function + :return: The weights as np.NDarray + """ + weights = self._sample_weight_negative_error(X_val, y_val, force_symmetry=force_symmetry) + return self._sample_weight_ordered_votes_from_weights(weights) + + def _sample_weight_by_kmeans_prototypes(self, k=None, **kwargs): + """ + Use KMeans to cluster the train data. Use the k centroids/prototypes found by knn as weights. + We keep only K anchors that are the prototypes. all other anchors receive a weight of 0 + + :param force_symmetry: Sets the force_symmetry parameter of the prediction function + :param k: The number of prototypes to use. If None, 10% of the training set is used as prototypes + :return: The weights as np.NDarray + """ + if not k: + k = max(int(len(self.X_train_) / 10), 3) # 10% and min 3 of the training set data points is used as weights + + kmeans = KMeans(n_clusters=k, n_init="auto", random_state=0) + kmeans.fit(self.X_train_) + + cluster_centers = kmeans.cluster_centers_ # Get the cluster centers (prototypical data points) + distances = cdist(self.X_train_, cluster_centers) # distance between each data point and each cluster center + closest_indices = np.argmin(distances, axis=0) # Get the index of the closest data points to the clusters + + # Create an array to mark the closest data points + closest_array = np.zeros(len(self.X_train_)) + closest_array[closest_indices] = 1 / k + + s = pd.Series(closest_array, index=self.X_train_.index) + s = s.fillna(0) # I don't know why there are NaNs rather than 0s + assert not s.isna().any(), f'Nans values in sample_weights using KMeans\n {s}' + return s diff --git a/fedot_ind/core/operation/decomposition/matrix_decomposition/column_sampling_decomposition.py b/fedot_ind/core/operation/decomposition/matrix_decomposition/column_sampling_decomposition.py index ae2055be6..61b5d7d07 100644 --- a/fedot_ind/core/operation/decomposition/matrix_decomposition/column_sampling_decomposition.py +++ b/fedot_ind/core/operation/decomposition/matrix_decomposition/column_sampling_decomposition.py @@ -1,29 +1,48 @@ -from typing import Tuple +from typing import Tuple, Union, Optional +from fedot.core.operations.operation_parameters import OperationParameters from numpy import linalg as LA from sklearn import preprocessing from sklearn.random_projection import johnson_lindenstrauss_min_dim from fedot_ind.core.architecture.settings.computational import backend_methods as np +from fedot_ind.core.repository.constanst_repository import DEFAULT_SVD_SOLVER + +RANK_REPRESENTATION = Union[int, float] class CURDecomposition: - def __init__(self, rank, - return_samples: bool = True): - self.selection_rank = None - self.return_samples = return_samples - if not self.return_samples: - self.rank = min(20000, rank) - else: - self.rank = rank + """ + CUR decomposition is a low-rank matrix decomposition method that is based on selecting + a subset of columns and rows of the original matrix. The method is based on the + Johnson-Lindenstrauss lemma and is used to approximate the original matrix with a + low-rank matrix. The CUR decomposition is defined as follows: + A = C @ U @ R + where A is the original matrix, C is a subset of columns of A, U is a subset of rows of A, + and R is a subset of rows of A. The selection of columns and rows is based on the + probabilities p and q, which are computed based on the norms of the columns and rows of A. + The selection of columns and rows is done in such a way that the approximation error is minimized. + + Args: + params: the parameters of the operation + rank: the rank of the decomposition + tolerance: the tolerance of the decomposition + return_samples: whether to return the samples or the decomposition matrices + + """ + + def __init__(self, params: Optional[OperationParameters] = None): + self.selection_rank = params.get('rank', None) + self.tolerance = params.get('tolerance', [0.5, 0.1, 0.05]) + self.return_samples = params.get('return_samples', True) self.column_indices = None self.row_indices = None self.column_space = 'Full' - @staticmethod - def _get_selection_rank(matrix): + def _get_selection_rank(self, matrix): """ - Compute the selection rank for the CUR decomposition. It must be at least 4 times the rank of the matrix but not + Compute the selection rank for the CUR decomposition. + It must be at least 4 times the rank of the matrix but not greater than the number of rows or columns of the matrix. Args: @@ -32,9 +51,8 @@ def _get_selection_rank(matrix): Returns: the selection rank """ - tol = [0.5, 0.1, 0.05] n_samples = max(matrix.shape) - min_num_samples = johnson_lindenstrauss_min_dim(n_samples, eps=tol).tolist() + min_num_samples = johnson_lindenstrauss_min_dim(n_samples, eps=self.tolerance).tolist() return max([x if x < n_samples else n_samples for x in min_num_samples]) def get_aproximation_error(self, original_tensor, cur_matrices: tuple): @@ -49,7 +67,8 @@ def fit_transform(self, feature_tensor: np.ndarray, target: np.ndarray = None) -> tuple: feature_tensor = feature_tensor.squeeze() # transformer = random_projection.SparseRandomProjection().fit_transform(target) - self.selection_rank = self._get_selection_rank(feature_tensor) + if self.selection_rank is None: + self.selection_rank = self._get_selection_rank(feature_tensor) self._balance_target(target) # create sub matrices for CUR-decompostion array = np.array(feature_tensor.copy()) @@ -59,7 +78,7 @@ def fit_transform(self, feature_tensor: np.ndarray, sampled_tensor = sampled_tensor[self.row_indices, :] else: # evaluate pseudoinverse for W - U^-1 - X, Sigma, y_T = np.linalg.svd(w, full_matrices=False) + X, Sigma, y_T = DEFAULT_SVD_SOLVER(w, full_matrices=False) Sigma_plus = np.linalg.pinv(np.diag(Sigma)) # aprox U using pseudoinverse u = y_T.T @ Sigma_plus @ Sigma_plus @ X.T @@ -69,6 +88,11 @@ def fit_transform(self, feature_tensor: np.ndarray, target = target[self.row_indices] return sampled_tensor, target + def transform(self, feature_tensor: np.ndarray, + target: np.ndarray = None) -> tuple: + + return self.fit_transform(feature_tensor, target) + def reconstruct_basis(self, C, U, R, ts_length): # if len(U.shape) > 1: # multi_reconstruction = lambda x: self.reconstruct_basis(C=C, U=U, R=x, ts_length=ts_length) @@ -93,7 +117,8 @@ def select_rows_cols( # Compute the probabilities for selecting columns and rows col_probs, row_probs = col_norms / matrix_norm, row_norms / matrix_norm - + if isinstance(self.selection_rank, float): + self.selection_rank = round(max(matrix.shape) * self.selection_rank) is_matrix_tall = self.selection_rank > matrix.shape[1] col_rank = self.selection_rank if not is_matrix_tall or self.column_space == 'Full' \ else len([prob for prob in col_probs if prob > 0.01]) @@ -134,47 +159,3 @@ def matrix_to_ts(matrix: np.ndarray) -> np.ndarray: for i in range(matrix.shape[0]): ts[i:i + matrix.shape[1]] += matrix[i] return ts - - -def get_random_sparse_matrix(size: tuple): - """Generate random sparse matrix with size = size""" - - matrix = np.zeros(size) - for i in range(size[0]): - for j in range(size[1]): - if np.random.rand() < 0.1: - matrix[i, j] = np.random.rand() - return matrix - - -if __name__ == '__main__': - from fedot_ind.tools.loader import DataLoader - - arr = np.array([[1, 1, 1, 0, 0], - [3, 3, 3, 0, 0], - [4, 4, 4, 0, 0], - [5, 5, 5, 0, 0], - [0, 0, 0, 4, 4], - [0, 0, 0, 5, 5], - [0, 0, 0, 2, 2]]) - - (X_train, y_train), (X_test, y_test) = DataLoader('Lightning7').load_data() - - # init_ts = train[0].iloc[0, :].values - # scaler = MinMaxScaler() - # scaler.fit(init_ts.reshape(-1, 1)) - # single_ts = scaler.transform(init_ts.reshape(-1, 1)).reshape(-1) - - cur = CURDecomposition(rank=20) - # M = cur.ts_to_matrix(single_ts, 30) - C, U, R = cur.fit_transform(X_train) - basis = cur.reconstruct_basis(C, U, R, X_train.shape[1]) - - # rec_ts = cur.matrix_to_ts(C @ U @ R) - # err = np.linalg.norm(single_ts - rec_ts) - - # plt.plot(init_ts, label='init_ts') - # plt.plot(scaler.inverse_transform(rec_ts.reshape(-1, 1)), label='rec_ts') - # plt.legend() - # plt.show() - _ = 1 diff --git a/fedot_ind/core/operation/decomposition/matrix_decomposition/dmd_decomposition.py b/fedot_ind/core/operation/decomposition/matrix_decomposition/dmd_decomposition.py index 27af8bb8f..7f82f25c7 100644 --- a/fedot_ind/core/operation/decomposition/matrix_decomposition/dmd_decomposition.py +++ b/fedot_ind/core/operation/decomposition/matrix_decomposition/dmd_decomposition.py @@ -1,10 +1,10 @@ from fedot_ind.core.architecture.settings.computational import backend_methods as np -from numpy.linalg import svd +from fedot_ind.core.repository.constanst_repository import DEFAULT_SVD_SOLVER, DEFAULT_QR_SOLVER def rq(A): n, m = A.shape - Q, R = np.linalg.qr(np.flipud(A).T, mode='complete') + Q, R = DEFAULT_QR_SOLVER(np.flipud(A).T, mode='complete') R = np.rot90(R.T, 2) Q = np.flipud(Q.T) if n > m: @@ -18,7 +18,7 @@ def tls(A, B): if A.shape[0] != B.shape[0]: raise ValueError('Matrices are not conformant.') R1 = np.hstack((A, B)) - U, S, V = np.linalg.svd(R1) + U, S, V = DEFAULT_SVD_SOLVER(R1) r = B.shape[1] R, Q = rq(V[:, r:]) Gamma = R[n:, n - r:] @@ -28,7 +28,7 @@ def tls(A, B): def exact_dmd_decompose(X, Y, rank): - Ux, Sx, Vx = svd(X) + Ux, Sx, Vx = DEFAULT_SVD_SOLVER(X) Ux = Ux[:, :rank] Sx = Sx[:rank] Sx = np.diag(Sx) @@ -46,14 +46,14 @@ def A(v): return np.dot(a=Ux, b=np.dot(a=Atilde, b=np.dot(a=Ux.T, b=v))) def orthogonal_dmd_decompose(X, Y, rank): - Ux, _, _ = svd(X) + Ux, _, _ = DEFAULT_SVD_SOLVER(X) Ux = Ux[:, :rank] # Project X (current state) and Y (future state) on leading components of X Yproj = Ux.T @ Y Xproj = Ux.T @ X # A_proj is constrained to be a unitary matrix and the minimization problem is argmin (A.T @ A = I) |Y-AX|_frob # The solution of A_proj is obtained by Schonemann A = Uyx,@ Vyx.T - Uyx, _, Vyx = svd(Yproj @ Xproj.T) + Uyx, _, Vyx = DEFAULT_SVD_SOLVER(Yproj @ Xproj.T) Aproj = Uyx @ Vyx.T def A(x): return np.dot(a=Ux, b=np.dot(a=Aproj, b=np.dot(a=Ux.T, b=x))) # Diagonalise unitary operator @@ -65,7 +65,7 @@ def A(x): return np.dot(a=Ux, b=np.dot(a=Aproj, b=np.dot(a=Ux.T, b=x))) def symmetric_decompose(X, Y, rank): - Ux, S, V = np.linalg.svd(X) + Ux, S, V = DEFAULT_SVD_SOLVER(X) C = np.dot(Ux.T, np.dot(Y, V)) C1 = C if rank is None: diff --git a/fedot_ind/core/operation/decomposition/matrix_decomposition/power_iteration_decomposition.py b/fedot_ind/core/operation/decomposition/matrix_decomposition/power_iteration_decomposition.py index 1846d3e80..801f5ce9f 100644 --- a/fedot_ind/core/operation/decomposition/matrix_decomposition/power_iteration_decomposition.py +++ b/fedot_ind/core/operation/decomposition/matrix_decomposition/power_iteration_decomposition.py @@ -7,9 +7,31 @@ from fedot_ind.core.operation.filtration.channel_filtration import _detect_knee_point from fedot_ind.core.operation.transformation.regularization.spectrum import singular_value_hard_threshold, \ sv_to_explained_variance_ratio, eigencorr_matrix +from fedot_ind.core.repository.constanst_repository import DEFAULT_SVD_SOLVER, DEFAULT_QR_SOLVER class RSVDDecomposition: + """Randomized SVD decomposition with power iteration method. + Implements the block Krylov subspace method for computing the SVD of a matrix with a low computational cost. + The method is based on the power iteration procedure, which allows us to obtain a low-rank approximation of the + matrix. The method is based on the following steps: + 1. Random projection of the matrix. + 2. Transformation of the initial matrix to the Gram matrix. + 3. Power iteration procedure. + 4. Orthogonalization of the resulting "sampled" matrix. + 5. Projection of the initial Gram matrix on the new basis obtained from the "sampled matrix". + 6. Classical svd decomposition with the chosen type of spectrum thresholding. + 7. Compute matrix approximation and choose a new low_rank. + 8. Return matrix approximation. + + Args: + params: dictionary with parameters for the operation: + rank: rank of the matrix approximation + power_iter: polynom degree for power iteration procedure + sampling_share: percent of sampling columns. By default - 70% + + """ + def __init__(self, params: Optional[OperationParameters] = {}): self.rank = params.get('rank', 1) # Polynom degree for power iteration procedure. @@ -81,7 +103,7 @@ def rsvd(self, # thresholding if not approximation: # classic svd decomposition - Ut, St, Vt = np.linalg.svd(tensor, full_matrices=False) + Ut, St, Vt = DEFAULT_SVD_SOLVER(tensor, full_matrices=False) # Compute low rank. low_rank = self._spectrum_regularization(St, reg_type=reg_type) if regularized_rank is not None: @@ -110,14 +132,13 @@ def rsvd(self, AAT, self.poly_deg) @ tensor @ self.random_projection # Fourth step. Orthogonalization of the resulting "sampled" matrix # creates for us a basis of eigenvectors. - sampled_tensor_orto, _ = np.linalg.qr( - sampled_tensor, mode='reduced') + sampled_tensor_orto, _ = DEFAULT_QR_SOLVER(sampled_tensor, mode='reduced') # Fifth step. Project initial Gramm matrix on new basis obtained # from "sampled matrix". M = sampled_tensor_orto.T @ AAT @ sampled_tensor_orto # Six step. Classical svd decomposition with choosen type of # spectrum thresholding - Ut, St, Vt = np.linalg.svd(M, full_matrices=False) + Ut, St, Vt = DEFAULT_SVD_SOLVER(M, full_matrices=False) # Compute low rank. low_rank = self._spectrum_regularization(St, reg_type=reg_type) # Seven step. Compute matrix approximation and choose new low_rank @@ -127,6 +148,6 @@ def rsvd(self, # Eight step. Return matrix approximation. reconstr_tensor = self._compute_matrix_approximation( Ut, sampled_tensor_orto, tensor, regularized_rank) - U_, S_, V_ = np.linalg.svd(reconstr_tensor, full_matrices=False) + U_, S_, V_ = DEFAULT_SVD_SOLVER(reconstr_tensor, full_matrices=False) return [U_, S_, V_] diff --git a/fedot_ind/core/operation/filtration/feature_filtration.py b/fedot_ind/core/operation/filtration/feature_filtration.py index 62ac1eca9..968c1d2d0 100644 --- a/fedot_ind/core/operation/filtration/feature_filtration.py +++ b/fedot_ind/core/operation/filtration/feature_filtration.py @@ -121,8 +121,11 @@ def filter_signal(self, data): class FeatureSpaceReducer: + def __init__(self): + self.is_fitted = False + self.feature_mask = None - def reduce_feature_space(self, features: pd.DataFrame, + def reduce_feature_space(self, features: np.array, var_threshold: float = 0.01, corr_threshold: float = 0.98) -> pd.DataFrame: """Method responsible for reducing feature space. @@ -136,43 +139,30 @@ def reduce_feature_space(self, features: pd.DataFrame, Dataframe with reduced feature space. """ - features.shape[1] - - features = self._drop_stable_features(features, var_threshold) + features = self._drop_constant_features(features, var_threshold) features_new = self._drop_correlated_features(corr_threshold, features) + self.is_fitted = True return features_new def _drop_correlated_features(self, corr_threshold, features): - features_corr = features.corr(method='pearson') - mask = np.ones(features_corr.columns.size) - \ - np.eye(features_corr.columns.size) - df_corr = mask * features_corr - drops = [] - for col in df_corr.columns.values: - # continue if the feature is already in the drop list - if np.in1d([col], drops): - continue - - index_of_corr_feature = df_corr[abs( - df_corr[col]) > corr_threshold].index - drops = np.union1d(drops, index_of_corr_feature) - - if len(drops) == 0: - self.logger.info('No correlated features found') - return features - - features_new = features.copy() - features_new.drop(drops, axis=1, inplace=True) - return features_new - - def _drop_stable_features(self, features, var_threshold): + features_corr = np.corrcoef(features.squeeze().T) + n_features = features_corr.shape[0] + identity_matrix = np.eye(n_features) + features_corr = features_corr - identity_matrix + correlation_mask = abs(features_corr) > corr_threshold + correlated_features = list(set(np.where(correlation_mask)[0])) + percent_of_filtred_feats = (1 - (n_features - len(correlated_features)) / n_features) * 100 + return features if percent_of_filtred_feats > 50 else features + + def _drop_constant_features(self, features, var_threshold): try: + is_2d_data = len(features.shape) <= 2 variance_reducer = VarianceThreshold(threshold=var_threshold) - variance_reducer.fit_transform(features) - unstable_features_mask = variance_reducer.get_support() - features = features.loc[:, unstable_features_mask] + variance_reducer.fit_transform(features.squeeze()) + self.feature_mask = variance_reducer.get_support() + features = features[:, :, self.feature_mask] if not is_2d_data else features[:, self.feature_mask] except ValueError: - self.logger.info( + print( 'Variance reducer has not found any features with low variance') return features diff --git a/fedot_ind/core/operation/interfaces/industrial_preprocessing_strategy.py b/fedot_ind/core/operation/interfaces/industrial_preprocessing_strategy.py index ba179caac..3664f83a1 100644 --- a/fedot_ind/core/operation/interfaces/industrial_preprocessing_strategy.py +++ b/fedot_ind/core/operation/interfaces/industrial_preprocessing_strategy.py @@ -21,6 +21,17 @@ class MultiDimPreprocessingStrategy(EvaluationStrategy): + """ + Class for preprocessing operations that can be used for multi-dimensional data. + + Args: + operation_impl: operation implementation + operation_type: operation type + params: operation parameters + mode: mode of operation. Can be 'one_dimensional', 'channel_independent' or 'multi_dimensional' + + """ + def __init__(self, operation_impl, operation_type: str, params: Optional[OperationParameters] = None, diff --git a/fedot_ind/core/operation/transformation/basis/abstract_basis.py b/fedot_ind/core/operation/transformation/basis/abstract_basis.py index 9679bf777..9008891e0 100644 --- a/fedot_ind/core/operation/transformation/basis/abstract_basis.py +++ b/fedot_ind/core/operation/transformation/basis/abstract_basis.py @@ -1,11 +1,12 @@ from typing import Optional, Union +import dask import pandas as pd from fedot.core.data.data import InputData from fedot.core.operations.operation_parameters import OperationParameters -from joblib import delayed, Parallel from pymonad.either import Either from pymonad.list import ListMonad +from tqdm.dask import TqdmCallback from fedot_ind.core.architecture.preprocessing.data_convertor import DataConverter, NumpyConverter from fedot_ind.core.architecture.settings.computational import backend_methods as np @@ -73,11 +74,10 @@ def _transform(self, """ features = DataConverter(data=input_data).convert_to_monad_data() - parallel = Parallel(n_jobs=self.n_processes, - verbose=0, pre_dispatch="2*n_jobs") - v = parallel(delayed(self._transform_one_sample)(sample) - for sample in features) - predict = NumpyConverter(data=np.array(v)).convert_to_torch_format() + evaluation_results = list(map(lambda sample: self._transform_one_sample(sample), features)) + with TqdmCallback(desc=f"compute_transformation_to_{self.__repr__()}"): + evaluation_results = dask.compute(*evaluation_results) + predict = NumpyConverter(data=np.array(evaluation_results)).convert_to_torch_format() return predict def _get_multidim_basis(self, input_data): diff --git a/fedot_ind/core/operation/transformation/basis/eigen_basis.py b/fedot_ind/core/operation/transformation/basis/eigen_basis.py index 0fba84dcb..4d6824650 100644 --- a/fedot_ind/core/operation/transformation/basis/eigen_basis.py +++ b/fedot_ind/core/operation/transformation/basis/eigen_basis.py @@ -1,13 +1,14 @@ from typing import Optional +import dask import tensorly as tl from fedot.core.data.data import InputData, OutputData from fedot.core.operations.operation_parameters import OperationParameters from fedot.core.repository.dataset_types import DataTypesEnum -from joblib import delayed, Parallel from pymonad.either import Either from pymonad.list import ListMonad from tensorly.decomposition import parafac +from tqdm.dask import TqdmCallback from fedot_ind.core.architecture.preprocessing.data_convertor import DataConverter, NumpyConverter from fedot_ind.core.architecture.settings.computational import backend_methods as np @@ -45,22 +46,19 @@ def __repr__(self): def _channel_decompose(self, features): number_of_dim = list(range(features.shape[1])) + one_dim_predict = len(number_of_dim) == 1 predict = [] if self.SV_threshold is None: self.SV_threshold = self.get_threshold(data=features) self.logging_params.update({'SV_thr': self.SV_threshold}) - - if len(number_of_dim) == 1: - predict = [self._transform_one_sample( - signal) for signal in features[:, 0, :]] - predict = [[np.array(v) if len(v) > 1 else v[0] for v in predict]] + if one_dim_predict: + evaluation_results = list(map(lambda sample: self._transform_one_sample(sample), features[:, 0, :])) else: - for dimension in number_of_dim: - parallel = Parallel(n_jobs=self.n_processes, - verbose=0, pre_dispatch="2*n_jobs") - v = parallel(delayed(self._transform_one_sample)(sample) - for sample in features[:, dimension, :]) - predict.append(np.array(v) if len(v) > 1 else v[0]) + evaluation_results = list(map(lambda dimension: [self._transform_one_sample(sample) + for sample in features[:, dimension, :]], number_of_dim)) + with TqdmCallback(desc=fr"compute_feature_extraction_with_{self.__repr__()}"): + feature_matrix = dask.compute(*evaluation_results) + predict = [[np.array(v) if len(v) > 1 else v[0] for v in feature_matrix]] return predict def _convert_basis_to_predict(self, basis, input_data): @@ -149,26 +147,24 @@ def data_driven_basis(Monoid): return ListMonad(reconstruct_basis( return basis def get_threshold(self, data) -> int: - svd_numbers = [] + number_of_dim = list(range(data.shape[1])) + one_dim_predict = len(number_of_dim) == 1 def mode_func(x): return max(set(x), key=x.count) - number_of_dim = list(range(data.shape[1])) - if len(number_of_dim) == 1: - svd_numbers = [self._transform_one_sample( - signal, svd_flag=True) for signal in data[:, 0, :]] - if len(svd_numbers) == 0: - raise ValueError('Error in spectrum calculation') + if one_dim_predict: + svd_numbers = list(map(lambda sample: + self._transform_one_sample(sample, svd_flag=True), data[:, 0, :])) else: - for dimension in number_of_dim: - dimension_rank = [] - for signal in data[:, dimension, :]: - dimension_rank.append( - self._transform_one_sample(signal, svd_flag=True)) - svd_numbers.append(mode_func(dimension_rank)) - return mode_func(svd_numbers) - + dimension_rank = [] + svd_numbers = list(map(lambda dimension: + [dimension_rank.append(self._transform_one_sample(signal, svd_flag=True)) + for signal in data[:, dimension, :]], number_of_dim)) + rank = dask.compute(*svd_numbers) + return mode_func(rank) + + @dask.delayed def _transform_one_sample(self, series: np.array, svd_flag: bool = False): window_size = round(series.shape[0] * (self.window_size / 100)) trajectory_transformer = HankelMatrix( diff --git a/fedot_ind/core/operation/transformation/basis/fourier.py b/fedot_ind/core/operation/transformation/basis/fourier.py index 0660253a7..b7dfbd5a9 100644 --- a/fedot_ind/core/operation/transformation/basis/fourier.py +++ b/fedot_ind/core/operation/transformation/basis/fourier.py @@ -1,5 +1,6 @@ from typing import Optional +import dask import pandas as pd from fedot.core.operations.operation_parameters import OperationParameters from matplotlib import pyplot as plt @@ -34,11 +35,32 @@ def __init__(self, params: Optional[OperationParameters] = None): self.min_rank = params.get('low_rank', 5) self.estimator = SPECTRUM_ESTIMATORS[params.get('estimator', 'eigen')] + self.return_feature_vector = params.get('compute_heuristic_representation', False) self.basis = None self.filtred_signal = None self.logging_params.update({'threshold': self.threshold}) + def _compute_heuristic_features(self, input_data): + periodogram_class = SPECTRUM_ESTIMATORS['non_parametric'] + estimator = periodogram_class(data=input_data, sampling=self.sampling_rate) + fft = estimator.psd + # freq, fft = periodogram(input_data[None, :], + # fs=self.sampling_rate, + # window='hann', + # detrend=False, return_onesided=True, scaling='spectrum', axis=1) + fft_mean = fft.mean() + fft_var = fft.var() + fft_rms = np.sqrt(np.mean(fft ** 2)) + fft_peak_value = fft.max() + fft_peak_freq = fft[np.argmax(fft)] + fft_energy = np.sum(fft) + # features['fft_energy_db'] = 10 * np.log10(fft).sum(axis=1) + fft_crest_factor = fft_peak_value / fft_rms + feature_vector = [fft_mean, fft_var, fft_rms, fft_peak_value, fft_peak_freq, fft_energy, fft_crest_factor] + feature_vector = [round(x, 3) for x in feature_vector] + return np.array(feature_vector) + def _visualise_spectrum(self, estimator): import matplotlib matplotlib.use('TkAgg') @@ -59,6 +81,8 @@ def _decompose_signal(self, input_data): estimator = self._build_spectrum(input_data) # self._visualise_spectrum(estimator) psd = estimator.psd + if self.return_feature_vector: + return self._compute_heuristic_features(input_data) dominant_freq = np.where(psd >= np.quantile(psd, q=self.threshold))[0] if self.approximation == 'exact': psd[dominant_freq] = 0 @@ -67,5 +91,6 @@ def _decompose_signal(self, input_data): self.filtred_signal = psd if self.output_format == 'spectrum' else np.fft.irfft(psd).reshape(1, -1) return self.filtred_signal + @dask.delayed def _transform_one_sample(self, series: np.array): return self._get_basis(series) diff --git a/fedot_ind/core/operation/transformation/basis/wavelet.py b/fedot_ind/core/operation/transformation/basis/wavelet.py index f9dd744c7..0ff77272f 100644 --- a/fedot_ind/core/operation/transformation/basis/wavelet.py +++ b/fedot_ind/core/operation/transformation/basis/wavelet.py @@ -1,5 +1,6 @@ from typing import Optional, Tuple +import dask import pywt from fedot.core.operations.operation_parameters import OperationParameters from pymonad.either import Either @@ -25,25 +26,45 @@ def __init__(self, params: Optional[OperationParameters] = None): super().__init__(params) self.n_components = params.get('n_components') self.wavelet = params.get('wavelet') + self.use_low_freq = params.get('low_freq', False) + self.scales = params.get('scale', WAVELET_SCALES) self.basis = None self.discrete_wavelets = DISCRETE_WAVELETS self.continuous_wavelets = CONTINUOUS_WAVELETS - self.scales = WAVELET_SCALES + self.return_feature_vector = params.get('compute_heuristic_representation', False) def __repr__(self): return 'WaveletBasisImplementation' + def _compute_heuristic_features(self, input_data): + wp = pywt.WaveletPacket(data=input_data[None, :], wavelet=self.wavelet, + maxlevel=3, axis=1, + mode='smooth') + + wpd_approximate_3 = wp['aaa'].data.sum() + wpd_approximate_2 = wp['aa'].data.sum() + wpd_approximate_1 = wp['a'].data.sum() + wpd_detail_3 = wp['ddd'].data.sum() + wpd_detail_2 = wp['dd'].data.sum() + wpd_detail_1 = wp['d'].data.sum() + return np.array([wpd_approximate_3, wpd_approximate_2, wpd_approximate_1]).squeeze(), \ + np.array([wpd_detail_3, wpd_detail_2, wpd_detail_1]).squeeze() + def _decompose_signal(self, input_data) -> Tuple[np.array, np.array]: - if self.wavelet in self.discrete_wavelets: - high_freq, low_freq = pywt.dwt(input_data, self.wavelet, 'smooth') + if self.return_feature_vector: + return self._compute_heuristic_features(input_data) else: - high_freq, low_freq = pywt.cwt(data=input_data, - scales=self.scales, - wavelet=self.wavelet) - low_freq = high_freq[-1, :] - high_freq = np.delete(high_freq, (-1), axis=0) - low_freq = low_freq[np.newaxis, :] - return high_freq, low_freq + if self.wavelet in self.discrete_wavelets: + high_freq, low_freq = pywt.dwt(input_data, self.wavelet, 'smooth') + + else: + high_freq, low_freq = pywt.cwt(data=input_data, + scales=self.scales, + wavelet=self.wavelet) + low_freq = high_freq[-1, :] + high_freq = np.delete(high_freq, (-1), axis=0) + low_freq = low_freq[np.newaxis, :] + return high_freq, low_freq def _decomposing_level(self) -> int: """The level of decomposition of the time series. @@ -53,6 +74,7 @@ def _decomposing_level(self) -> int: """ return pywt.dwt_max_level(len(self.time_series), self.wavelet) + @dask.delayed def _transform_one_sample(self, series: np.array): return self._get_basis(series) @@ -66,7 +88,8 @@ def threshold(Monoid): return ListMonad([Monoid[0][ basis = Either.insert(data).then(decompose).then(threshold).value[0] basis = np.concatenate(basis) - return basis + + return basis[-1, :] if self.use_low_freq else basis def _get_multidim_basis(self, data): def decompose(multidim_signal): diff --git a/fedot_ind/core/operation/transformation/data/park_transformation.py b/fedot_ind/core/operation/transformation/data/park_transformation.py new file mode 100644 index 000000000..69634b5d5 --- /dev/null +++ b/fedot_ind/core/operation/transformation/data/park_transformation.py @@ -0,0 +1,43 @@ +from typing import Union + +import numpy as np +from fedot.core.data.data import InputData + + +def _apply_park_transform(sample): + i_1_ch = 1 + i_2_ch = 2 + i_3_ch = 3 + v_1_ch = 4 + v_2_ch = 5 + v_3_ch = 6 + i_alpha = (2 * sample[:i_1_ch, :] - sample[i_1_ch:i_2_ch, :] - sample[i_2_ch:i_3_ch, :]) / 3 + i_beta = (sample[i_1_ch:i_2_ch, :] - sample[i_2_ch:i_3_ch, :]) / np.sqrt(3) + v_alpha = (2 * sample[i_3_ch:v_1_ch, :] - sample[v_1_ch:v_2_ch, :] - sample[v_2_ch:v_3_ch, :]) / 3 + v_beta = (sample[v_1_ch:v_2_ch, :] - sample[v_2_ch:v_3_ch, :]) / np.sqrt(3) + + # Calculate the instantaneous amplitude and phase of the current and voltage + instantaneous_i_amplitude = np.sqrt(i_alpha ** 2 + i_beta ** 2) + instantaneous_i_phase = np.arctan2(i_beta, i_alpha) + instantaneous_v_amplitude = np.sqrt(v_alpha ** 2 + v_beta ** 2) + instantaneous_v_phase = np.arctan2(v_beta, v_alpha) + return np.concatenate([i_alpha, i_beta, v_alpha, v_beta, instantaneous_i_amplitude, + instantaneous_i_phase, instantaneous_v_amplitude, instantaneous_v_phase]) + + +def park_transform(input_data: Union[InputData, np.ndarray]) -> np.ndarray: + """ + Applies the Park transform to a given DataFrame. + + The Park transform is a way to transform 3-phase electrical data into a 2-phase signal, which adds more information. + + Args: + data (pd.DataFrame): A DataFrame containing the 3-phase electrical data. + + Returns: + pd.DataFrame: The DataFrame with the added 2-phase electrical data. + """ + # Calculate the alpha and beta components of the current and voltage + features = input_data.features if isinstance(input_data, InputData) else input_data + feature_matrix = list(map(lambda x: _apply_park_transform(x), features)) + return np.stack(feature_matrix) diff --git a/fedot_ind/core/operation/transformation/data/point_cloud.py b/fedot_ind/core/operation/transformation/data/point_cloud.py index 6eef30cb9..e2222627f 100644 --- a/fedot_ind/core/operation/transformation/data/point_cloud.py +++ b/fedot_ind/core/operation/transformation/data/point_cloud.py @@ -1,4 +1,5 @@ import pandas as pd +from gtda.time_series import SingleTakensEmbedding from ripser import Rips, ripser from scipy import sparse @@ -74,7 +75,8 @@ def __compute_persistence_landscapes(ts): def time_series_to_point_cloud(self, input_data: np.array = None, - dimension_embed=2) -> np.array: + dimension_embed=3, + use_gtda=False) -> np.array: """Convert a time series into a point cloud in the dimension specified by dimension_embed. Args: @@ -91,11 +93,27 @@ def time_series_to_point_cloud(self, if self.__window_length is None: self.__window_length = dimension_embed - - trajectory_transformer = HankelMatrix(time_series=input_data, - window_size=self.__window_length, - strides=self.stride) - return trajectory_transformer.trajectory_matrix + if use_gtda: + pcd = self.gtda_time_series_to_pcd(input_data, dimension_embed) + else: + trajectory_transformer = HankelMatrix(time_series=input_data, + window_size=self.__window_length, + strides=self.stride) + pcd = trajectory_transformer.trajectory_matrix + return pcd + + def gtda_time_series_to_pcd(self, + input_data: np.array = None, + dimension_embed=3) -> np.array: + embedder_periodic = SingleTakensEmbedding( + parameters_type="fixed", + n_jobs=2, + time_delay=self.__window_length, + dimension=dimension_embed, + stride=self.stride, + ) + embedding = embedder_periodic.fit_transform(input_data) + return embedding def point_cloud_to_persistent_cohomology_ripser( self, point_cloud: np.array = None, max_simplex_dim: int = 1): @@ -174,4 +192,4 @@ def rolling_window(self, array, window): raise ValueError( "Window size cannot exceed the length of the array.") return np.array([array[i:i + window] - for i in range(len(array) - window + 1)]) + for i in range(len(array) - window + 1)]) diff --git a/fedot_ind/core/operation/transformation/representation/__init__.py b/fedot_ind/core/operation/transformation/representation/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fedot_ind/core/operation/transformation/representation/manifold/__init__.py b/fedot_ind/core/operation/transformation/representation/manifold/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fedot_ind/core/models/manifold/riemann_embeding.py b/fedot_ind/core/operation/transformation/representation/manifold/riemann_embeding.py similarity index 98% rename from fedot_ind/core/models/manifold/riemann_embeding.py rename to fedot_ind/core/operation/transformation/representation/manifold/riemann_embeding.py index 5fd918a7e..7e6487015 100644 --- a/fedot_ind/core/models/manifold/riemann_embeding.py +++ b/fedot_ind/core/operation/transformation/representation/manifold/riemann_embeding.py @@ -62,6 +62,9 @@ def __init__(self, params: Optional[OperationParameters] = None): 'tangent_space_metric': self.tangent_metric, 'SPD_space_metric': self.spd_metric}) + def __repr__(self): + return 'Riemann Manifold Class for TS representation' + def _init_spaces(self): self.spd_space = Covariances(estimator='scm') self.tangent_space = TangentSpace(metric=self.tangent_metric) diff --git a/fedot_ind/core/operation/transformation/representation/recurrence/__init__.py b/fedot_ind/core/operation/transformation/representation/recurrence/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fedot_ind/core/models/recurrence/reccurence_extractor.py b/fedot_ind/core/operation/transformation/representation/recurrence/reccurence_extractor.py similarity index 87% rename from fedot_ind/core/models/recurrence/reccurence_extractor.py rename to fedot_ind/core/operation/transformation/representation/recurrence/reccurence_extractor.py index f59b05a66..a93a5a03e 100644 --- a/fedot_ind/core/models/recurrence/reccurence_extractor.py +++ b/fedot_ind/core/operation/transformation/representation/recurrence/reccurence_extractor.py @@ -3,13 +3,12 @@ import numpy as np from fedot.core.data.data import InputData from fedot.core.operations.operation_parameters import OperationParameters -from fedot.core.repository.dataset_types import DataTypesEnum # from fedot_ind.core.metrics.metrics_implementation import * from fedot_ind.core.models.base_extractor import BaseExtractor -from fedot_ind.core.models.recurrence.sequences import RecurrenceFeatureExtractor from fedot_ind.core.operation.transformation.data.hankel import HankelMatrix from fedot_ind.core.operation.transformation.data.kernel_matrix import TSTransformer +from fedot_ind.core.operation.transformation.representation.recurrence.sequences import RecurrenceFeatureExtractor class RecurrenceExtractor(BaseExtractor): @@ -51,6 +50,9 @@ def __init__(self, params: Optional[OperationParameters] = None): self.transformer = TSTransformer self.extractor = RecurrenceFeatureExtractor + def __repr__(self): + return 'Reccurence Class for TS representation' + def _generate_features_from_ts(self, ts: np.array): if self.window_size != 0: trajectory_transformer = HankelMatrix(time_series=ts, @@ -73,13 +75,13 @@ def _generate_features_from_ts(self, ts: np.array): features = specter.ts_to_3d_recurrence_matrix() col_names = {'feature_name': None} - predict = InputData(idx=np.arange(len(features)), - features=features, - target='no_target', - task='no_task', - data_type=DataTypesEnum.table, - supplementary_data=col_names) - return predict + # predict = InputData(idx=np.arange(len(features)), + # features=features, + # target='no_target', + # task='no_task', + # data_type=DataTypesEnum.table, + # supplementary_data=col_names) + return features def generate_recurrence_features(self, ts: np.array) -> InputData: diff --git a/fedot_ind/core/models/recurrence/sequences.py b/fedot_ind/core/operation/transformation/representation/recurrence/sequences.py similarity index 100% rename from fedot_ind/core/models/recurrence/sequences.py rename to fedot_ind/core/operation/transformation/representation/recurrence/sequences.py diff --git a/fedot_ind/core/operation/transformation/representation/statistical/__init__.py b/fedot_ind/core/operation/transformation/representation/statistical/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fedot_ind/core/models/quantile/quantile_extractor.py b/fedot_ind/core/operation/transformation/representation/statistical/quantile_extractor.py similarity index 58% rename from fedot_ind/core/models/quantile/quantile_extractor.py rename to fedot_ind/core/operation/transformation/representation/statistical/quantile_extractor.py index 17a44e0cd..2ef6b7af0 100644 --- a/fedot_ind/core/models/quantile/quantile_extractor.py +++ b/fedot_ind/core/operation/transformation/representation/statistical/quantile_extractor.py @@ -1,6 +1,6 @@ -from itertools import chain from typing import Optional +import dask from fedot.core.data.data import InputData from fedot.core.operations.operation_parameters import OperationParameters @@ -9,7 +9,7 @@ class QuantileExtractor(BaseExtractor): - """Class responsible for quantile feature generator experiment. + """Class responsible for statistical feature generator experiment. Attributes: window_size (int): size of window @@ -44,47 +44,32 @@ def __init__(self, params: Optional[OperationParameters] = None): self.logging_params.update({'Wsize': self.window_size, 'Stride': self.stride}) + def __repr__(self): + return 'Statistical Class for TS representation' + def _concatenate_global_and_local_feature( self, - global_features: InputData, - window_stat_features: InputData) -> InputData: - - if isinstance(window_stat_features.features[0], list): - window_stat_features.features = np.concatenate( - window_stat_features.features, axis=0) - window_stat_features.supplementary_data['feature_name'] = list( - chain(*window_stat_features.supplementary_data['feature_name'])) + global_features: np.ndarray, + window_stat_features: np.ndarray) -> np.ndarray: + if isinstance(window_stat_features[0], list): + window_stat_features = np.concatenate(window_stat_features, axis=0) - window_stat_features.features = np.concatenate( - [global_features.features, window_stat_features.features], axis=0) - window_stat_features.features = np.nan_to_num( - window_stat_features.features) - - window_stat_features.supplementary_data['feature_name'] = list( - chain(*[global_features.supplementary_data['feature_name'], - window_stat_features.supplementary_data['feature_name']])) + window_stat_features = np.concatenate([global_features, window_stat_features], axis=0) + window_stat_features = np.nan_to_num(window_stat_features) return window_stat_features def extract_stats_features(self, ts: np.array) -> InputData: - global_features = self.get_statistical_features( - ts, add_global_features=True) - if self.window_size != 0: - window_stat_features = self.apply_window_for_stat_feature( - ts_data=ts, - feature_generator=self.get_statistical_features, - window_size=self.window_size) - else: - window_stat_features = self.get_statistical_features(ts) + global_features = self.get_statistical_features(ts, add_global_features=True) + window_stat_features = self.get_statistical_features(ts) if self.window_size == 0 else \ + self.apply_window_for_stat_feature(ts_data=ts, feature_generator=self.get_statistical_features, + window_size=self.window_size) return self._concatenate_global_and_local_feature( global_features, window_stat_features) if self.add_global_features else window_stat_features + @dask.delayed def generate_features_from_ts(self, ts: np.array, window_length: int = None) -> InputData: - if len(ts.shape) == 1: - aggregation_df = self.extract_stats_features(ts) - else: - aggregation_df = self._get_feature_matrix( - self.extract_stats_features, ts) - - return aggregation_df + ts = ts[None, :] if len(ts.shape) == 1 else ts # sanity check for map method + statistical_representation = np.array(list(map(lambda channel: self.extract_stats_features(channel), ts))) + return statistical_representation diff --git a/fedot_ind/core/models/quantile/stat_features.py b/fedot_ind/core/operation/transformation/representation/statistical/stat_features.py similarity index 99% rename from fedot_ind/core/models/quantile/stat_features.py rename to fedot_ind/core/operation/transformation/representation/statistical/stat_features.py index b00e9d811..8e087da14 100644 --- a/fedot_ind/core/models/quantile/stat_features.py +++ b/fedot_ind/core/operation/transformation/representation/statistical/stat_features.py @@ -1,11 +1,12 @@ import warnings -from fedot_ind.core.architecture.settings.computational import backend_methods as np import pandas as pd from scipy.signal import find_peaks from scipy.stats import entropy, linregress from sklearn.preprocessing import MinMaxScaler +from fedot_ind.core.architecture.settings.computational import backend_methods as np + warnings.filterwarnings("ignore") @@ -34,7 +35,7 @@ def diff(array: np.array) -> float: return np.diff(array, n=len(array) - 1)[0] -# Extra methods for quantile features extraction +# Extra methods for statistical features extraction def skewness(array: np.array) -> float: if not isinstance(array, pd.Series): array = pd.Series(array) diff --git a/fedot_ind/core/operation/transformation/representation/tabular/__init__.py b/fedot_ind/core/operation/transformation/representation/tabular/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fedot_ind/core/models/tabular/tabular_extractor.py b/fedot_ind/core/operation/transformation/representation/tabular/tabular_extractor.py similarity index 71% rename from fedot_ind/core/models/tabular/tabular_extractor.py rename to fedot_ind/core/operation/transformation/representation/tabular/tabular_extractor.py index bb00f650f..c50f8bcad 100644 --- a/fedot_ind/core/models/tabular/tabular_extractor.py +++ b/fedot_ind/core/operation/transformation/representation/tabular/tabular_extractor.py @@ -3,16 +3,19 @@ import numpy as np from fedot.core.data.data import InputData from fedot.core.operations.operation_parameters import OperationParameters +from fedot.core.pipelines.pipeline_builder import PipelineBuilder +from pymonad.either import Either from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler from fedot_ind.core.models.base_extractor import BaseExtractor +from fedot_ind.core.operation.transformation.data.park_transformation import park_transform from fedot_ind.core.repository.constanst_repository import KERNEL_BASELINE_FEATURE_GENERATORS from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels class TabularExtractor(BaseExtractor): - """Class responsible for quantile feature generator experiment. + """Class responsible for statistical feature generator experiment. Attributes: window_size (int): size of window @@ -47,6 +50,7 @@ def __init__(self, params: Optional[OperationParameters] = None): self.reduce_dimension = params.get('reduce_dimension', True) self.repo = IndustrialModels().setup_repository() + self.custom_tabular_transformation = {'park_transformation': park_transform} self.pca_is_fitted = False self.scaler = StandardScaler() self.pca = PCA(self.explained_dispersion) @@ -58,6 +62,35 @@ def _reduce_dim(self, features, target): self.pca_is_fitted = True return self.pca.fit_transform(self.scaler.fit_transform(features, target)) + def _create_from_custom_fg(self, input_data): + for model_name, nodes in self.feature_domain.items(): + if model_name.__contains__('custom'): + transform_method = self.custom_tabular_transformation[nodes[0]] + ts_representation = transform_method(input_data) + else: + model = PipelineBuilder() + for node in nodes: + if isinstance(node, tuple): + model.add_node(operation_type=node[0], params=node[1]) + else: + model.add_node(operation_type=node) + model = model.build() + ts_representation = model.fit(input_data).predict + self.feature_list.append(ts_representation) + + def _create_from_default_fg(self, input_data): + feature_domain_models = [model for model in KERNEL_BASELINE_FEATURE_GENERATORS] + + if not self.feature_domain.__contains__('all'): + feature_domain_models = [model for model in feature_domain_models + if model.__contains__(self.feature_domain)] + + for model_name in feature_domain_models: + model = KERNEL_BASELINE_FEATURE_GENERATORS[model_name] + model.heads[0].parameters['use_sliding_window'] = self.use_sliding_window + model = model.build() + self.feature_list.append(model.fit(input_data).predict) + def create_feature_matrix(self, feature_list: list): return np.concatenate([x.reshape(x.shape[0], x.shape[1] * x.shape[2]) for x in feature_list], axis=1).squeeze() @@ -74,17 +107,10 @@ def _transform(self, input_data: InputData) -> np.array: def generate_features_from_ts(self, input_data: InputData, window_length: int = None) -> InputData: - feature_domain_models = [model for model in KERNEL_BASELINE_FEATURE_GENERATORS] + is_custom_feature_representation = isinstance(self.feature_domain, dict) self.feature_list = [] - - if not self.feature_domain.__contains__('all'): - feature_domain_models = [model for model in feature_domain_models - if model.__contains__(self.feature_domain)] - - for model_name in feature_domain_models: - model = KERNEL_BASELINE_FEATURE_GENERATORS[model_name] - model.heads[0].parameters['use_sliding_window'] = self.use_sliding_window - model = model.build() - self.feature_list.append(model.fit(input_data).predict) - + Either(value=input_data, + monoid=[input_data, + is_custom_feature_representation]).either(left_function=self._create_from_default_fg, + right_function=self._create_from_custom_fg) return self.feature_list diff --git a/fedot_ind/core/operation/transformation/representation/topological/__init__.py b/fedot_ind/core/operation/transformation/representation/topological/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fedot_ind/core/models/topological/topofeatures.py b/fedot_ind/core/operation/transformation/representation/topological/topofeatures.py similarity index 100% rename from fedot_ind/core/models/topological/topofeatures.py rename to fedot_ind/core/operation/transformation/representation/topological/topofeatures.py diff --git a/fedot_ind/core/models/topological/topological_extractor.py b/fedot_ind/core/operation/transformation/representation/topological/topological_extractor.py similarity index 73% rename from fedot_ind/core/models/topological/topological_extractor.py rename to fedot_ind/core/operation/transformation/representation/topological/topological_extractor.py index ac512d428..3ef257ab8 100644 --- a/fedot_ind/core/models/topological/topological_extractor.py +++ b/fedot_ind/core/operation/transformation/representation/topological/topological_extractor.py @@ -1,19 +1,24 @@ import sys from functools import partial +from itertools import product +from typing import Optional +# import open3d as o3d import pandas as pd from fedot.core.data.data import InputData from fedot.core.operations.operation_parameters import OperationParameters from fedot.core.repository.dataset_types import DataTypesEnum +from gtda.homology import VietorisRipsPersistence from gtda.time_series import takens_embedding_optimal_parameters from scipy import stats +from scipy.spatial.distance import squareform, pdist from tqdm import tqdm -from typing import Optional from fedot_ind.core.architecture.settings.computational import backend_methods as np from fedot_ind.core.models.base_extractor import BaseExtractor -from fedot_ind.core.models.topological.topofeatures import PersistenceDiagramsExtractor, TopologicalFeaturesExtractor from fedot_ind.core.operation.transformation.data.point_cloud import TopologicalTransformation +from fedot_ind.core.operation.transformation.representation.topological.topofeatures import \ + PersistenceDiagramsExtractor, TopologicalFeaturesExtractor from fedot_ind.core.repository.constanst_repository import PERSISTENCE_DIAGRAM_EXTRACTOR, PERSISTENCE_DIAGRAM_FEATURES sys.setrecursionlimit(1000000000) @@ -53,6 +58,10 @@ def __init__(self, params: Optional[OperationParameters] = None): persistence_diagram_features=PERSISTENCE_DIAGRAM_FEATURES ) self.data_transformer = None + self.save_pcd = False + + def __repr__(self): + return 'Topological Class for TS representation' def __evaluate_persistence_params(self, ts_data: np.array): if self.feature_extractor is None: @@ -67,12 +76,43 @@ def __evaluate_persistence_params(self, ts_data: np.array): persistence_diagram_extractor=persistence_diagram_extractor, persistence_diagram_features=PERSISTENCE_DIAGRAM_FEATURES) + def _generate_vr_mesh(self, pcd): + # Corresponding matrix of Euclidean pairwise distances + pairwise_distances = squareform(pdist(pcd)) + # Default parameter for ``metric`` is "euclidean" + vr_graph = VietorisRipsPersistence(metric="precomputed").fit_transform([pairwise_distances]) + return vr_graph + + def _generate_pcd(self, ts_data, persistence_params): + window_size_range = list(range(1, 35, 5)) + stride_range = list(range(1, 15, 3)) + list(product(window_size_range, stride_range)) + # for params in pcd_params: + # data_transformer = TopologicalTransformation(stride=params[1], persistence_params=persistence_params, + # window_length=round(ts_data.shape[0] * 0.01 * params[0])) + # point_cloud = data_transformer.time_series_to_point_cloud(input_data=ts_data, use_gtda=True) + # # VR_mesh = self._generate_vr_mesh(point_cloud) + # for scale in range(1, 15, 3): + # numpy2stl(point_cloud, + # f"./stl_scale_{scale}_ws_{params[0]}_stride_{params[1]}.stl", + # max_width=300., + # max_depth=200., + # max_height=300., + # scale=scale, + # min_thickness_percent=0.5, + # solid=False) + # pcd = o3d.geometry.PointCloud() + # pcd.points = o3d.utility.Vector3dVector(point_cloud) + # o3d.io.write_point_cloud(f"./pcd_ws_{params[0]}_stride_{params[1]}.ply", pcd) + def _generate_features_from_ts(self, ts_data: np.array, persistence_params: dict) -> InputData: + if self.save_pcd: + self._generate_pcd(ts_data, persistence_params) if self.data_transformer is None: self.data_transformer = TopologicalTransformation( persistence_params=persistence_params, window_length=round(ts_data.shape[0] * 0.01 * self.window_size)) - point_cloud = self.data_transformer.time_series_to_point_cloud(input_data=ts_data) + point_cloud = self.data_transformer.time_series_to_point_cloud(input_data=ts_data, use_gtda=True) topological_features = self.feature_extractor.transform(point_cloud) topological_features = InputData(idx=np.arange(len(topological_features.values)), features=topological_features.values, diff --git a/fedot_ind/core/optimizer/FedotEvoOptimizer.py b/fedot_ind/core/optimizer/FedotEvoOptimizer.py new file mode 100644 index 000000000..8a1b3b349 --- /dev/null +++ b/fedot_ind/core/optimizer/FedotEvoOptimizer.py @@ -0,0 +1,61 @@ +from typing import Sequence + +from golem.core.optimisers.adaptive.mab_agents.contextual_mab_agent import ContextualMultiArmedBanditAgent +from golem.core.optimisers.adaptive.mab_agents.mab_agent import MultiArmedBanditAgent +from golem.core.optimisers.adaptive.mab_agents.neural_contextual_mab_agent import NeuralContextualMultiArmedBanditAgent +from golem.core.optimisers.adaptive.operator_agent import RandomAgent +from golem.core.optimisers.genetic.gp_optimizer import EvoGraphOptimizer +from golem.core.optimisers.genetic.gp_params import GPAlgorithmParameters +from golem.core.optimisers.graph import OptGraph +from golem.core.optimisers.objective import Objective +from golem.core.optimisers.optimization_parameters import GraphRequirements +from golem.core.optimisers.optimizer import GraphGenerationParams + +from fedot_ind.core.repository.constanst_repository import FEDOT_MUTATION_STRATEGY + + +class FedotEvoOptimizer(EvoGraphOptimizer): + def __init__(self, + objective: Objective, + initial_graphs: Sequence[OptGraph], + requirements: GraphRequirements, + graph_generation_params: GraphGenerationParams, + graph_optimizer_params: GPAlgorithmParameters, + optimisation_params: dict = None): + + graph_optimizer_params = self._exclude_resample_from_mutations(graph_optimizer_params) + self.mutation_agent_dict = {'random': RandomAgent, + 'bandit': MultiArmedBanditAgent, + 'contextual_bandit': ContextualMultiArmedBanditAgent, + 'neural_bandit': NeuralContextualMultiArmedBanditAgent} + if optimisation_params is not None: + graph_optimizer_params.adaptive_mutation_type = self._set_optimisation_strategy(graph_optimizer_params, + optimisation_params) + super().__init__(objective, initial_graphs, requirements, + graph_generation_params, graph_optimizer_params) + self.requirements = requirements + # self.eval_dispatcher = IndustrialDispatcher( + # adapter=graph_generation_params.adapter, + # n_jobs=requirements.n_jobs, + # graph_cleanup_fn=_try_unfit_graph, + # delegate_evaluator=graph_generation_params.remote_evaluator) + + def _set_optimisation_strategy(self, graph_optimizer_params, optimisation_params): + mutation_probs = FEDOT_MUTATION_STRATEGY[optimisation_params['mutation_strategy']] + mutation_agent = self.mutation_agent_dict[optimisation_params['mutation_agent']] + if optimisation_params['mutation_agent'].__contains__('random'): + mutation_agent = mutation_agent(actions=graph_optimizer_params.mutation_types, + probs=mutation_probs) + else: + mutation_agent = mutation_agent(actions=graph_optimizer_params.mutation_types) + return mutation_agent + + def _exclude_resample_from_mutations(self, graph_optimizer_params): + for mutation in graph_optimizer_params.mutation_types: + try: + is_invalid = mutation.__name__.__contains__('resample') + except Exception: + is_invalid = mutation.name.__contains__('resample') + if is_invalid: + graph_optimizer_params.mutation_types.remove(mutation) + return graph_optimizer_params diff --git a/fedot_ind/core/optimizer/IndustrialEvoOptimizer.py b/fedot_ind/core/optimizer/IndustrialEvoOptimizer.py index 49990cc4c..7e3aa6ca3 100644 --- a/fedot_ind/core/optimizer/IndustrialEvoOptimizer.py +++ b/fedot_ind/core/optimizer/IndustrialEvoOptimizer.py @@ -26,14 +26,7 @@ def __init__(self, graph_generation_params: GraphGenerationParams, graph_optimizer_params: GPAlgorithmParameters): - for mutation in graph_optimizer_params.mutation_types: - try: - is_invalid = mutation.__name__.__contains__('resample') - except Exception: - is_invalid = mutation.name.__contains__('resample') - if is_invalid: - graph_optimizer_params.mutation_types.remove(mutation) - + graph_optimizer_params = self._exclude_resample_from_mutations(graph_optimizer_params) graph_optimizer_params.adaptive_mutation_type = RandomAgent(actions=graph_optimizer_params.mutation_types, probs=FEDOT_MUTATION_STRATEGY[ 'params_mutation_strategy']) @@ -52,6 +45,16 @@ def _create_initial_population(self, initial_assumption): for graph in initial_assumption] return initial_individuals + def _exclude_resample_from_mutations(self, graph_optimizer_params): + for mutation in graph_optimizer_params.mutation_types: + try: + is_invalid = mutation.__name__.__contains__('resample') + except Exception: + is_invalid = mutation.name.__contains__('resample') + if is_invalid: + graph_optimizer_params.mutation_types.remove(mutation) + return graph_optimizer_params + def _initial_population(self, evaluator: EvaluationOperator): """ Initializes the initial population """ # Adding of initial assumptions to history as zero generation diff --git a/fedot_ind/core/repository/IndustrialDispatcher.py b/fedot_ind/core/repository/IndustrialDispatcher.py index 520d5347b..b6bc1cd46 100644 --- a/fedot_ind/core/repository/IndustrialDispatcher.py +++ b/fedot_ind/core/repository/IndustrialDispatcher.py @@ -4,6 +4,7 @@ from datetime import datetime from typing import Optional, Tuple +import dask from golem.core.log import Log from golem.core.optimisers.genetic.evaluation import MultiprocessingDispatcher from golem.core.optimisers.genetic.operators.operator import EvaluationOperator, PopulationT @@ -13,7 +14,7 @@ from golem.core.optimisers.timer import Timer from golem.utilities.memory import MemoryAnalytics from golem.utilities.utilities import determine_n_jobs -from joblib import wrap_non_picklable_objects, parallel_backend +from joblib import wrap_non_picklable_objects from pymonad.either import Either from pymonad.maybe import Maybe @@ -30,17 +31,14 @@ def dispatch(self, objective: ObjectiveFunction, return self.evaluate_with_cache def _multithread_eval(self, individuals_to_evaluate): - with parallel_backend(backend='dask', - n_jobs=self.n_jobs, - scatter=[individuals_to_evaluate] - ): - log = Log().get_parameters() - evaluation_results = list(map(lambda ind: - self.industrial_evaluate_single(self, - graph=ind.graph, - uid_of_individual=ind.uid, - logs_initializer=log), - individuals_to_evaluate)) + log = Log().get_parameters() + evaluation_results = list(map(lambda ind: + self.industrial_evaluate_single(self, + graph=ind.graph, + uid_of_individual=ind.uid, + logs_initializer=log), + individuals_to_evaluate)) + evaluation_results = dask.compute(*evaluation_results) return evaluation_results def _eval_at_least_one(self, individuals): @@ -80,7 +78,22 @@ def evaluate_population(self, individuals: PopulationT) -> PopulationT: logging_level=logging.INFO) return successful_evals - # @delayed + @dask.delayed + def eval_ind(self, graph, uid_of_individual): + adapted_evaluate = self._adapter.adapt_func(self._evaluate_graph) + start_time = timeit.default_timer() + fitness, graph = adapted_evaluate(graph) + end_time = timeit.default_timer() + eval_time_iso = datetime.now().isoformat() + eval_res = GraphEvalResult( + uid_of_individual=uid_of_individual, + fitness=fitness, + graph=graph, + metadata={ + 'computation_time_in_seconds': end_time - start_time, + 'evaluation_time_iso': eval_time_iso}) + return eval_res + @wrap_non_picklable_objects def industrial_evaluate_single(self, graph: OptGraph, @@ -100,17 +113,4 @@ def industrial_evaluate_single(self, # in case of multiprocessing run Log.setup_in_mp(*logs_initializer) - adapted_evaluate = self._adapter.adapt_func(self._evaluate_graph) - start_time = timeit.default_timer() - fitness, graph = adapted_evaluate(graph) - end_time = timeit.default_timer() - eval_time_iso = datetime.now().isoformat() - - eval_res = GraphEvalResult( - uid_of_individual=uid_of_individual, - fitness=fitness, - graph=graph, - metadata={ - 'computation_time_in_seconds': end_time - start_time, - 'evaluation_time_iso': eval_time_iso}) - return eval_res + return self.eval_ind(graph, uid_of_individual) diff --git a/fedot_ind/core/repository/constanst_repository.py b/fedot_ind/core/repository/constanst_repository.py index b691ee0fa..079937b8b 100644 --- a/fedot_ind/core/repository/constanst_repository.py +++ b/fedot_ind/core/repository/constanst_repository.py @@ -1,4 +1,5 @@ import math +import pathlib from enum import Enum from multiprocessing import cpu_count @@ -6,29 +7,45 @@ import pywt import spectrum from MKLpy.algorithms import FHeuristic, RMKL, MEMO, CKA, PWMK +from dask_ml.decomposition import TruncatedSVD as DaskSVD +from fedot.core.operations.evaluation.operation_implementations.models.boostings_implementations import \ + FedotCatBoostRegressionImplementation, FedotCatBoostClassificationImplementation from fedot.core.pipelines.pipeline_builder import PipelineBuilder from fedot.core.repository.dataset_types import DataTypesEnum from fedot.core.repository.metrics_repository import ClassificationMetricsEnum, RegressionMetricsEnum from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams from golem.core.tuning.optuna_tuner import OptunaTuner -from golem.core.tuning.simultaneous import SimultaneousTuner +from lightgbm.sklearn import LGBMClassifier, LGBMRegressor from scipy.spatial.distance import euclidean, cosine, cityblock, correlation, chebyshev, \ minkowski +from sklearn.ensemble import ExtraTreesRegressor, GradientBoostingClassifier, \ + RandomForestClassifier +from sklearn.linear_model import ( + Lasso as SklearnLassoReg, + LogisticRegression as SklearnLogReg, + Ridge as SklearnRidgeReg, + SGDRegressor as SklearnSGD +) +from sklearn.neural_network import MLPClassifier +from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor from torch import nn +from xgboost import XGBRegressor +from fedot_ind.api.utils.path_lib import PROJECT_PATH from fedot_ind.core.metrics.metrics_implementation import calculate_classification_metric, calculate_regression_metric, \ calculate_forecasting_metric, calculate_detection_metric from fedot_ind.core.models.nn.network_modules.losses import CenterLoss, CenterPlusLoss, ExpWeightedLoss, FocalLoss, \ HuberLoss, LogCoshLoss, MaskedLossWrapper, RMSELoss, SMAPELoss, TweedieLoss -from fedot_ind.core.models.quantile.stat_features import autocorrelation, ben_corr, crest_factor, energy, \ +from fedot_ind.core.models.ts_forecasting.eigen_autoreg import EigenAR +from fedot_ind.core.operation.transformation.data.hankel import HankelMatrix +from fedot_ind.core.operation.transformation.representation.statistical.stat_features import autocorrelation, ben_corr, \ + crest_factor, energy, \ hjorth_complexity, hjorth_mobility, hurst_exponent, interquartile_range, kurtosis, mean_ema, mean_moving_median, \ mean_ptp_distance, n_peaks, pfd, ptp_amp, q25, q5, q75, q95, shannon_entropy, skewness, slope, zero_crossing_rate -from fedot_ind.core.models.topological.topofeatures import AverageHoleLifetimeFeature, \ +from fedot_ind.core.operation.transformation.representation.topological.topofeatures import AverageHoleLifetimeFeature, \ AveragePersistenceLandscapeFeature, BettiNumbersSumFeature, HolesNumberFeature, MaxHoleLifeTimeFeature, \ PersistenceDiagramsExtractor, PersistenceEntropyFeature, RadiusAtMaxBNFeature, RelevantHolesNumber, \ SimultaneousAliveHolesFeature, SumHoleLifetimeFeature -from fedot_ind.core.models.ts_forecasting.eigen_autoreg import EigenAR -from fedot_ind.core.operation.transformation.data.hankel import HankelMatrix industrial_model_params_dict = dict(quantile_extractor={'window_size': 10, 'stride': 1, @@ -147,6 +164,22 @@ class DataTypeConstant(Enum): TRAJECTORY_MATRIX = HankelMatrix +class PathConstant(Enum): + IND_DATA_OPERATION_PATH = pathlib.Path(PROJECT_PATH, 'fedot_ind', 'core', 'repository', 'data', + 'industrial_data_operation_repository.json') + DEFAULT_DATA_OPERATION_PATH = pathlib.Path('data_operation_repository.json') + IND_MODEL_OPERATION_PATH = pathlib.Path(PROJECT_PATH, 'fedot_ind', 'core', 'repository', 'data', + 'industrial_model_repository.json') + DEFAULT_MODEL_OPERATION_PATH = pathlib.Path('model_repository.json') + + +class SolverConstant(Enum): + SOLVER_MODELS = {'np_svd_solver': np.linalg.svd, + 'np_qr_solver': np.linalg.qr, + 'dask_svd_solver': DaskSVD + } + + class FeatureConstant(Enum): STAT_METHODS = { 'mean_': np.mean, @@ -283,7 +316,7 @@ class FedotOperationConstant(Enum): 'table': DataTypesEnum.table} FEDOT_TUNER_STRATEGY = { 'optuna': OptunaTuner, - 'simultaneous': SimultaneousTuner, + # 'simultaneous': SimultaneousTuner, } FEDOT_HEAD_ENSEMBLE = {'regression': 'treg', 'classification': 'xgboost'} @@ -335,15 +368,47 @@ class FedotOperationConstant(Enum): 'classification': PipelineBuilder().add_node('logit'), 'regression': PipelineBuilder().add_node('treg') } - + # mutation order - [param_change,model_change,add_preproc_model,drop_model,add_model] FEDOT_MUTATION_STRATEGY = { - 'params_mutation_strategy': [0.4, 0.2, 0.2, 0.1, 0.1], + # 'params_mutation_strategy': [0.6, 0.25, 0.05, 0.05, 0.05], + 'params_mutation_strategy': [0.7, 0.3, 0.00, 0.00, 0.0], 'growth_mutation_strategy': [0.15, 0.15, 0.3, 0.1, 0.3], 'regularization_mutation_strategy': [0.2, 0.3, 0.1, 0.3, 0.1], } EXPLAINABLE_MODELS = ['recurrence_extractor', ] + SKLEARN_CLF_MODELS = { + # boosting models (bid datasets) + 'xgboost': GradientBoostingClassifier, + 'catboost': FedotCatBoostClassificationImplementation, + # solo linear models + 'logit': SklearnLogReg, + # solo tree models + 'dt': DecisionTreeClassifier, + # ensemble tree models + 'rf': RandomForestClassifier, + # solo nn models + 'mlp': MLPClassifier, + # external models + 'lgbm': LGBMClassifier, + } + + SKLEARN_REG_MODELS = { + # boosting models (bid datasets) + 'xgbreg': XGBRegressor, + 'sgdr': SklearnSGD, + # ensemble tree models (big datasets) + 'treg': ExtraTreesRegressor, + # solo linear models with regularization + 'ridge': SklearnRidgeReg, + 'lasso': SklearnLassoReg, + # solo tree models (small datasets) + 'dtreg': DecisionTreeRegressor, + # external models + 'lgbmreg': LGBMRegressor, + "catboostreg": FedotCatBoostRegressionImplementation + } class ModelCompressionConstant(Enum): @@ -752,6 +817,11 @@ class UnitTestConstant(Enum): KERNEL_BASELINE_NODE_LIST = KernelsConstant.KERNEL_BASELINE_NODE_LIST.value KERNEL_DISTANCE_METRIC = KernelsConstant.KERNEL_DISTANCE_METRIC.value +SOLVER_MODELS = SolverConstant.SOLVER_MODELS.value +DEFAULT_SVD_SOLVER = SOLVER_MODELS['np_svd_solver'] +DEFAULT_QR_SOLVER = SOLVER_MODELS['np_qr_solver'] +DASK_SVD_SOLVER = SOLVER_MODELS['dask_svd_solver'] + AVAILABLE_ANOMALY_DETECTION_OPERATIONS = FedotOperationConstant.AVAILABLE_ANOMALY_DETECTION_OPERATIONS.value AVAILABLE_REG_OPERATIONS = FedotOperationConstant.AVAILABLE_REG_OPERATIONS.value AVAILABLE_CLS_OPERATIONS = FedotOperationConstant.AVAILABLE_CLS_OPERATIONS.value @@ -769,17 +839,25 @@ class UnitTestConstant(Enum): FEDOT_DATA_TYPE = FedotOperationConstant.FEDOT_DATA_TYPE.value FEDOT_MUTATION_STRATEGY = FedotOperationConstant.FEDOT_MUTATION_STRATEGY.value EXPLAINABLE_MODELS = FedotOperationConstant.EXPLAINABLE_MODELS.value +SKLEARN_CLF_IMP = FedotOperationConstant.SKLEARN_CLF_MODELS.value +SKLEARN_REG_IMP = FedotOperationConstant.SKLEARN_REG_MODELS.value CPU_NUMBERS = ComputationalConstant.CPU_NUMBERS.value BATCH_SIZE_FOR_FEDOT_WORKER = ComputationalConstant.BATCH_SIZE_FOR_FEDOT_WORKER.value FEDOT_WORKER_NUM = ComputationalConstant.FEDOT_WORKER_NUM.value FEDOT_WORKER_TIMEOUT_PARTITION = ComputationalConstant.FEDOT_WORKER_TIMEOUT_PARTITION.value PATIENCE_FOR_EARLY_STOP = ComputationalConstant.PATIENCE_FOR_EARLY_STOP.value +DASK_CLIENT = None MULTI_ARRAY = DataTypeConstant.MULTI_ARRAY.value MATRIX = DataTypeConstant.MATRIX.value TRAJECTORY_MATRIX = DataTypeConstant.TRAJECTORY_MATRIX.value +IND_MODEL_OPERATION_PATH = PathConstant.IND_MODEL_OPERATION_PATH.value +IND_DATA_OPERATION_PATH = PathConstant.IND_DATA_OPERATION_PATH.value +DEFAULT_DATA_OPERATION_PATH = PathConstant.DEFAULT_DATA_OPERATION_PATH.value +DEFAULT_MODEL_OPERATION_PATH = PathConstant.DEFAULT_MODEL_OPERATION_PATH.value + ENERGY_THR = ModelCompressionConstant.ENERGY_THR.value DECOMPOSE_MODE = ModelCompressionConstant.DECOMPOSE_MODE.value FORWARD_MODE = ModelCompressionConstant.FORWARD_MODE.value diff --git a/fedot_ind/core/repository/dask_models.py b/fedot_ind/core/repository/dask_models.py new file mode 100644 index 000000000..2a8bf8996 --- /dev/null +++ b/fedot_ind/core/repository/dask_models.py @@ -0,0 +1,182 @@ +from sklearn.base import BaseEstimator, ClassifierMixin, TransformerMixin +from sklearn.utils.validation import check_X_y, check_array +from dask_ml.linear_model import LogisticRegression, LinearRegression +from dask_ml.decomposition import PCA +import numpy as np +import dask.array as da + + +class DaskLogisticRegression(BaseEstimator, ClassifierMixin): + def __init__(self, params): + """ + Custom estimator based on Dask LogisticRegression. + """ + self.penalty = params.get('penalty', 'l2') + self.C = params.get('C', 1.0) + self.model_ = None # Placeholder for the internal Dask model + self.solver = 'admm' + + def fit(self, X, y): + """ + Fit the model using Dask's LogisticRegression. + """ + + X, y = check_X_y(X, y, accept_sparse=True, dtype=None) + self.classes_ = np.unique(y) + if not isinstance(X, da.Array): + X = da.from_array(X) + if not isinstance(y, da.Array): + y = da.from_array(y) + + self.model_ = LogisticRegression( + penalty=self.penalty, + C=self.C, + ) + self.model_.fit(X, y) + return self + + def predict(self, X): + """ + Predict class labels for samples in X. + """ + X = check_array(X, accept_sparse=True, dtype=None) + if not isinstance(X, da.Array): + X = da.from_array(X) + return self.model_.predict(X).compute() + + def predict_proba(self, X): + """ + Predict probabilities for samples in X. + """ + X = check_array(X, accept_sparse=True, dtype=None) + if not isinstance(X, da.Array): + X = da.from_array(X) + return self.model_.predict_proba(X).compute() + + def score(self, X, y): + """ + Returns the accuracy of the model. + """ + y_pred = self.predict(X) + return np.mean(y_pred == y) + + def get_params(self, deep=True): + """ + Return hyperparameter dictionary for compatibility with GridSearchCV. + """ + return { + "penalty": self.penalty, + "C": self.C, + } + + def set_params(self, **params): + """ + Set hyperparameters. + """ + for key, value in params.items(): + setattr(self, key, value) + return self + + +class DaskRidgeRegression(BaseEstimator, ClassifierMixin): + def __init__(self, params): + self.C = params.get('alpha') + self.model_ = None # Placeholder for the internal Dask model + + def fit(self, X, y): + """ + Fit the model using Dask's LinearRegression. + """ + X, y = check_X_y(X, y, accept_sparse=True, dtype=None) + if not isinstance(X, da.Array): + X = da.from_array(X) + if not isinstance(y, da.Array): + y = da.from_array(y) + + self.model_ = LinearRegression(C=self.C) + self.model_.fit(X, y) + return self + + def predict(self, X): + """ + Predict class labels for samples in X. + """ + X = check_array(X, accept_sparse=True, dtype=None) + if not isinstance(X, da.Array): + X = da.from_array(X) + return self.model_.predict(X).compute() + + def score(self, X, y): + """ + Returns the accuracy of the model. + """ + y_pred = self.predict(X) + return np.mean(y_pred == y) + + def get_params(self, deep=True): + """ + Return hyperparameter dictionary for compatibility with GridSearchCV. + """ + return { + "alpha": self.C, + } + + def set_params(self, **params): + """ + Set hyperparameters. + """ + for key, value in params.items(): + setattr(self, key, value) + return self + + +class DaskPCA(BaseEstimator, TransformerMixin): + def __init__(self, params): + self.n_components = params.get('n_components') + self.model_ = None + + def fit(self, X): + """ + Fit the model using Dask's PCA. + """ + X = check_array(X, accept_sparse=True, dtype=None) + if not isinstance(X, da.Array): + X = da.from_array(X) + + self.model_ = PCA(n_components=self.n_components) + self.model_.fit(X) + return self + + def transform(self, X): + """ + Transform the data using the fitted PCA model. + """ + X = check_array(X, accept_sparse=True, dtype=None) + if not isinstance(X, da.Array): + X = da.from_array(X) + return self.model_.transform(X) + + def get_params(self, deep=True): + """ + Return hyperparameter dictionary for compatibility with GridSearchCV. + """ + return { + "n_components": self.n_components, + } + + def set_params(self, **params): + """ + Set hyperparameters. + """ + for key, value in params.items(): + setattr(self, key, value) + return self + + def inverse_transform(self, X): + """ + Transform the data back to its original space. + """ + X = check_array(X, accept_sparse=True, dtype=None) + if not isinstance(X, da.Array): + X = da.from_array(X) + return self.model_.inverse_transform(X) diff --git a/fedot_ind/core/repository/data/default_operation_params.json b/fedot_ind/core/repository/data/default_operation_params.json index 2ac985979..e9f932030 100644 --- a/fedot_ind/core/repository/data/default_operation_params.json +++ b/fedot_ind/core/repository/data/default_operation_params.json @@ -70,15 +70,18 @@ "logit": { "C": 1, "penalty": "l2", - "solver": "liblinear" + "solver": "lbfgs" }, "rf": { - "criterion":"gini", + "criterion": "gini", "max_features": 0.9, "min_samples_split": 5, "min_samples_leaf": 5, "bootstrap": false }, + "pdl_clf": { + "model": "rf" + }, "ridge": { "alpha": 1.0 }, @@ -124,6 +127,9 @@ "min_samples_leaf": 10, "bootstrap": false }, + "pdl_reg": { + "model": "treg" + }, "dt": { "max_depth": 5, "min_samples_split": 10, diff --git a/fedot_ind/core/repository/data/industrial_data_operation_repository.json b/fedot_ind/core/repository/data/industrial_data_operation_repository.json index 2e41f1cf1..0bfef9ecc 100644 --- a/fedot_ind/core/repository/data/industrial_data_operation_repository.json +++ b/fedot_ind/core/repository/data/industrial_data_operation_repository.json @@ -220,13 +220,14 @@ ] }, "topological_extractor": { - "meta": "custom_preprocessing", + "meta": "industrial_preprocessing", "presets": [ "fast_train" ], "tags": [ "extractor" - ]}, + ] + }, "minirocket_extractor": { "meta": "industrial_preprocessing", "presets": [ diff --git a/fedot_ind/core/repository/data/industrial_model_repository.json b/fedot_ind/core/repository/data/industrial_model_repository.json index c4a87aefb..db6ca6342 100644 --- a/fedot_ind/core/repository/data/industrial_model_repository.json +++ b/fedot_ind/core/repository/data/industrial_model_repository.json @@ -613,17 +613,43 @@ }, "rf": { "meta": "sklearn_class", - "presets": ["fast_train", "*tree"], - "tags": ["tree", "non_linear"] + "presets": [ + "fast_train", + "*tree" + ], + "tags": [ + "tree", + "non_linear" + ] + }, + "pdl_clf": { + "meta": "sklearn_class", + "presets": [ + "fast_train", + "*tree" + ], + "tags": [ + "tree", + "non_linear" + ] }, "rfr": { "meta": "sklearn_regr", - "presets": ["fast_train", "*tree"], - "tags": ["tree", "non_linear"] + "presets": [ + "fast_train", + "*tree" + ], + "tags": [ + "tree", + "non_linear" + ] }, "ridge": { "meta": "sklearn_regr", - "presets": ["fast_train", "ts"], + "presets": [ + "fast_train", + "ts" + ], "tags": [ "simple", "linear", @@ -723,7 +749,19 @@ }, "treg": { "meta": "sklearn_regr", - "presets": ["*tree"], + "presets": [ + "*tree" + ], + "tags": [ + "tree", + "non_linear" + ] + }, + "pdl_reg": { + "meta": "sklearn_regr", + "presets": [ + "*tree" + ], "tags": [ "tree", "non_linear" @@ -731,9 +769,14 @@ }, "xgboost": { "meta": "sklearn_class", - "presets": ["*tree"], + "presets": [ + "*tree" + ], "tags": [ - "boosting", "tree", "non-default", "non_linear" + "boosting", + "tree", + "non-default", + "non_linear" ] }, "xgbreg": { diff --git a/fedot_ind/core/repository/excluded.py b/fedot_ind/core/repository/excluded.py index 188d996b3..063e446de 100644 --- a/fedot_ind/core/repository/excluded.py +++ b/fedot_ind/core/repository/excluded.py @@ -15,13 +15,13 @@ ) from sklearn.naive_bayes import BernoulliNB as SklearnBernoulliNB, MultinomialNB as SklearnMultinomialNB -from fedot_ind.core.models.manifold.riemann_embeding import RiemannExtractor from fedot_ind.core.models.nn.network_impl.dummy_nn import DummyOverComplicatedNeuralNetwork from fedot_ind.core.models.nn.network_impl.explainable_convolution_model import XCModel from fedot_ind.core.models.nn.network_impl.lora_nn import LoraModel from fedot_ind.core.models.nn.network_impl.tst import TSTModel from fedot_ind.core.operation.dummy.dummy_operation import DummyOperation from fedot_ind.core.operation.filtration.feature_filtration import FeatureFilter +from fedot_ind.core.operation.transformation.representation.manifold.riemann_embeding import RiemannExtractor EXCLUDED_OPERATION_MUTATION = { 'regression': ['recurrence_extractor', diff --git a/fedot_ind/core/repository/industrial_implementations/abstract.py b/fedot_ind/core/repository/industrial_implementations/abstract.py index a9d45083d..bf047fa5f 100644 --- a/fedot_ind/core/repository/industrial_implementations/abstract.py +++ b/fedot_ind/core/repository/industrial_implementations/abstract.py @@ -217,17 +217,12 @@ def _create_tuner(tuning_params, tuning_data): replace_default_search_space=True) pipeline_tuner = TunerBuilder( train_data.task).with_search_space(search_space).with_tuner( - tuning_params['tuner']).with_n_jobs(1).with_metric( + tuning_params['tuner']).with_n_jobs(-1).with_metric( tuning_params['metric']).with_timeout( - tuning_params.get( - 'tuning_timeout', - 15)).with_early_stopping_rounds( - tuning_params.get( - 'tuning_early_stop', - 50)).with_iterations( - tuning_params.get( - 'tuning_iterations', - 150)).build(tuning_data) + tuning_params.get('tuning_timeout', 15.0)).build(tuning_data) + # with_iterations(tuning_params.get('tuning_iterations',150)).\ + # with_early_stopping_rounds(tuning_params.get('tuning_early_stop', 50)) + return pipeline_tuner if isinstance(model_to_tune, dict): @@ -502,6 +497,8 @@ def predict_operation_industrial( trained_operation=fitted_operation, predict_data=data, output_mode=output_mode) + is_numpy_predict = isinstance(prediction.predict, np.ndarray) + prediction.predict = prediction.predict.detach().numpy() if not is_numpy_predict else prediction.predict prediction = self.assign_tabular_column_types(prediction, output_mode) # any inplace operations here are dangerous! diff --git a/fedot_ind/core/repository/industrial_implementations/ml_optimisation.py b/fedot_ind/core/repository/industrial_implementations/ml_optimisation.py new file mode 100644 index 000000000..f53343ec8 --- /dev/null +++ b/fedot_ind/core/repository/industrial_implementations/ml_optimisation.py @@ -0,0 +1,170 @@ +import datetime +from copy import deepcopy +from datetime import timedelta +from functools import partial +from typing import Optional, Tuple, Union, Sequence + +import optuna +from dask.distributed import wait +from fedot.core.constants import DEFAULT_TUNING_ITERATIONS_NUMBER +from fedot.core.data.data import InputData +from fedot.core.pipelines.pipeline import Pipeline +from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder +from golem.core.adapter import BaseOptimizationAdapter +from golem.core.optimisers.graph import OptGraph +from golem.core.optimisers.objective import ObjectiveFunction +from golem.core.tuning.search_space import SearchSpace, get_node_operation_parameter_label +from golem.core.tuning.tuner_interface import BaseTuner, DomainGraphForTune +from optuna import Trial, Study +from optuna.trial import FrozenTrial + + +class DaskOptunaTuner(BaseTuner): + def __init__(self, objective_evaluate: ObjectiveFunction, + search_space: SearchSpace, + adapter: Optional[BaseOptimizationAdapter] = None, + iterations: int = 100, + early_stopping_rounds: Optional[int] = None, + timeout: timedelta = timedelta(minutes=5), + n_jobs: int = -1, + deviation: float = 0.05, + objectives_number: int = 1): + super().__init__(objective_evaluate, + search_space, + adapter, + iterations, + early_stopping_rounds, + timeout, + n_jobs, + deviation) + self.objectives_number = objectives_number + self.study = None + self.iterations = 100 + self.n_trials = 10 + + def _dask_backend_tune(self, predefined_objective, show_progress): + self.storage = optuna.integration.DaskStorage() + # self.storage = optuna.integration.dask.DaskStorage() + self.study = optuna.create_study(storage=self.storage, + direction='minimize') # ['minimize'] * self.objectives_number + # Submit self.n_trials different optimization tasks, where each task runs self.iterations optimization trials + from fedot_ind.core.repository.constanst_repository import DASK_CLIENT + client = DASK_CLIENT + futures = [client.submit(self.study.optimize, + predefined_objective, + n_trials=self.iterations, + n_jobs=self.n_jobs, + timeout=self.timeout.seconds, + callbacks=[self.early_stopping_callback], + show_progress_bar=show_progress) for _ in range(self.n_trials)] + wait(futures) + print(f"Best params: {self.study.best_params}") + + def tune(self, graph: DomainGraphForTune, show_progress: bool = True) -> \ + Union[DomainGraphForTune, Sequence[DomainGraphForTune]]: + graph = self.adapter.adapt(graph) + predefined_objective = partial(self.objective, graph=graph) + is_multi_objective = self.objectives_number > 1 + self.init_check(graph) + init_parameters, has_parameters_to_optimize = self._get_initial_point(graph) + + if not has_parameters_to_optimize: + self._stop_tuning_with_message(f'Graph {graph.graph_description} has no parameters to optimize') + tuned_graphs = self.init_graph + else: + # Enqueue initial point to try + verbosity_level = optuna.logging.INFO if show_progress else optuna.logging.WARNING + optuna.logging.set_verbosity(verbosity_level) + self._dask_backend_tune(predefined_objective, show_progress) + tuned_graphs = self.set_arg_graph(graph, self.study.best_trials[0].params) if not is_multi_objective else \ + [self.set_arg_graph(deepcopy(graph), best_trial.params) for best_trial in self.study.best_trials] + self.was_tuned = True + + final_graphs = self.final_check(tuned_graphs, is_multi_objective) + final_graphs = self.adapter.restore(final_graphs) + return final_graphs + + def objective(self, trial: Trial, graph: OptGraph) -> Union[float, Sequence[float,]]: + new_parameters = self._get_parameters_from_trial(graph, trial) + new_graph = BaseTuner.set_arg_graph(graph, new_parameters) + metric_value = self.get_metric_value(new_graph) + return metric_value + + def _get_parameters_from_trial(self, graph: OptGraph, trial: Trial) -> dict: + new_parameters = {} + for node_id, node in enumerate(graph.nodes): + operation_name = node.name + + # Get available parameters for operation + tunable_node_params = self.search_space.parameters_per_operation.get(operation_name, {}) + + for parameter_name, parameter_properties in tunable_node_params.items(): + node_op_parameter_name = get_node_operation_parameter_label(node_id, operation_name, parameter_name) + + parameter_type = parameter_properties.get('type') + sampling_scope = parameter_properties.get('sampling-scope') + if parameter_type == 'discrete': + new_parameters.update({node_op_parameter_name: + trial.suggest_int(node_op_parameter_name, *sampling_scope)}) + elif parameter_type == 'continuous': + new_parameters.update({node_op_parameter_name: + trial.suggest_float(node_op_parameter_name, *sampling_scope)}) + elif parameter_type == 'categorical': + new_parameters.update({node_op_parameter_name: + trial.suggest_categorical(node_op_parameter_name, *sampling_scope)}) + return new_parameters + + def _get_initial_point(self, graph: OptGraph) -> Tuple[dict, bool]: + initial_parameters = {} + has_parameters_to_optimize = False + for node_id, node in enumerate(graph.nodes): + operation_name = node.name + + # Get available parameters for operation + tunable_node_params = self.search_space.parameters_per_operation.get(operation_name) + + if tunable_node_params: + has_parameters_to_optimize = True + tunable_initial_params = {get_node_operation_parameter_label(node_id, operation_name, p): + node.parameters[p] for p in node.parameters if p in tunable_node_params} + if tunable_initial_params: + initial_parameters.update(tunable_initial_params) + return initial_parameters, has_parameters_to_optimize + + def early_stopping_callback(self, study: Study, trial: FrozenTrial): + if self.early_stopping_rounds is not None: + current_trial_number = trial.number + best_trial_number = study.best_trial.number + should_stop = (current_trial_number - best_trial_number) >= self.early_stopping_rounds + if should_stop: + self.log.debug('Early stopping rounds criteria was reached') + study.stop() + + +def tune_pipeline_industrial(self, train_data: InputData, pipeline_gp_composed: Pipeline) -> Pipeline: + """ Launch tuning procedure for obtained pipeline by composer """ + timeout_for_tuning = abs(self.timer.determine_resources_for_tuning()) / 60 + tuner = (TunerBuilder(self.params.task) + .with_tuner(DaskOptunaTuner) + .with_metric(self.metrics[0]) + .with_iterations(DEFAULT_TUNING_ITERATIONS_NUMBER) + .with_timeout(datetime.timedelta(minutes=timeout_for_tuning)) + .with_eval_time_constraint(self.params.composer_requirements.max_graph_fit_time) + .with_requirements(self.params.composer_requirements) + .build(train_data)) + + if self.timer.have_time_for_tuning(): + # Tune all nodes in the pipeline + with self.timer.launch_tuning(): + self.was_tuned = False + self.log.message(f'Hyperparameters tuning started with {round(timeout_for_tuning)} min. timeout') + tuned_pipeline = tuner.tune(pipeline_gp_composed) + self.log.message('Hyperparameters tuning finished') + else: + self.log.message(f'Time for pipeline composing was {str(self.timer.composing_spend_time)}.\n' + f'The remaining {max(0, round(timeout_for_tuning, 1))} seconds are not enough ' + f'to tune the hyperparameters.') + self.log.message('Composed pipeline returned without tuning.') + tuned_pipeline = pipeline_gp_composed + self.was_tuned = tuner.was_tuned + return tuned_pipeline diff --git a/fedot_ind/core/repository/initializer_industrial_models.py b/fedot_ind/core/repository/initializer_industrial_models.py index 172cb5d27..3582b436d 100644 --- a/fedot_ind/core/repository/initializer_industrial_models.py +++ b/fedot_ind/core/repository/initializer_industrial_models.py @@ -1,6 +1,5 @@ -import pathlib - import fedot.core.data.data_split as fedot_data_split +import golem.core.tuning.optuna_tuner as OptunaImpl from fedot.api.api_utils.api_composer import ApiComposer from fedot.api.api_utils.api_params_repository import ApiParamsRepository from fedot.core.data.merge.data_merger import ImageDataMerger, TSDataMerger @@ -10,20 +9,28 @@ LaggedImplementation, TsSmoothingImplementation from fedot.core.operations.operation import Operation from fedot.core.optimisers.objective.data_source_splitter import DataSourceSplitter +from fedot.core.pipelines.pipeline import Pipeline from fedot.core.pipelines.tuning.search_space import PipelineSearchSpace from fedot.core.pipelines.verification import class_rules +from fedot.core.pipelines.verification import common_rules from fedot.core.repository.operation_types_repository import OperationTypesRepository -from fedot_ind.api.utils.path_lib import PROJECT_PATH +import fedot_ind.core.repository.model_repository as MODEL_REPO +from fedot_ind.core.repository.constanst_repository import IND_DATA_OPERATION_PATH, IND_MODEL_OPERATION_PATH, \ + DEFAULT_DATA_OPERATION_PATH, DEFAULT_MODEL_OPERATION_PATH from fedot_ind.core.repository.industrial_implementations.abstract import preprocess_industrial_predicts, \ transform_lagged_for_fit_industrial, transform_smoothing_industrial, transform_lagged_industrial, \ merge_industrial_predicts, merge_industrial_targets, build_industrial, postprocess_industrial_predicts, \ split_any_industrial, split_time_series_industrial, predict_operation_industrial, predict_industrial, \ predict_for_fit_industrial, update_column_types_industrial, _check_and_correct_window_size_industrial, \ fit_topo_extractor_industrial, transform_topo_extractor_industrial +from fedot_ind.core.repository.industrial_implementations.ml_optimisation import DaskOptunaTuner, \ + tune_pipeline_industrial from fedot_ind.core.repository.industrial_implementations.optimisation import _get_default_industrial_mutations from fedot_ind.core.repository.industrial_implementations.optimisation import \ has_no_data_flow_conflicts_in_industrial_pipeline +from fedot_ind.core.repository.model_repository import SKLEARN_REG_MODELS, SKLEARN_CLF_MODELS, FEDOT_PREPROC_MODEL +from fedot_ind.core.repository.model_repository import overload_model_implementation from fedot_ind.core.tuning.search_space import get_industrial_search_space FEDOT_METHOD_TO_REPLACE = [(PipelineSearchSpace, "get_parameters_dict"), @@ -45,7 +52,9 @@ (TopologicalFeaturesImplementation, 'transform'), (LaggedImplementation, 'transform_for_fit'), (LaggedImplementation, '_check_and_correct_window_size'), - (TsSmoothingImplementation, 'transform')] + (TsSmoothingImplementation, 'transform'), + (OptunaImpl, 'OptunaTuner'), + (ApiComposer, 'tune_final_pipeline')] INDUSTRIAL_REPLACE_METHODS = [get_industrial_search_space, _get_default_industrial_mutations, preprocess_industrial_predicts, @@ -65,44 +74,50 @@ transform_topo_extractor_industrial, transform_lagged_for_fit_industrial, _check_and_correct_window_size_industrial, - transform_smoothing_industrial] + transform_smoothing_industrial, + DaskOptunaTuner, + tune_pipeline_industrial] + DEFAULT_METHODS = [getattr(class_impl[0], class_impl[1]) for class_impl in FEDOT_METHOD_TO_REPLACE] +DEFAULT_MODELS_TO_REPLACE = [(MODEL_REPO, 'SKLEARN_REG_MODELS'), + (MODEL_REPO, 'SKLEARN_CLF_MODELS'), + (MODEL_REPO, 'FEDOT_PREPROC_MODEL')] + + +def has_no_resample(pipeline: Pipeline): + """ + Pipeline can have only one resample operation located in start of the pipeline + + :param pipeline: pipeline for checking + """ + for node in pipeline.nodes: + if node.name == 'resample': + raise ValueError( + f'Pipeline can not have resample operation') + return True class IndustrialModels: def __init__(self): - self.industrial_data_operation_path = pathlib.Path( - PROJECT_PATH, - 'fedot_ind', - 'core', - 'repository', - 'data', - 'industrial_data_operation_repository.json') - - self.base_data_operation_path = pathlib.Path( - 'data_operation_repository.json') - - self.industrial_model_path = pathlib.Path( - PROJECT_PATH, - 'fedot_ind', - 'core', - 'repository', - 'data', - 'industrial_model_repository.json') - - self.base_model_path = pathlib.Path('model_repository.json') - - def _replace_operation(self, to_industrial=True): - if to_industrial: - method = INDUSTRIAL_REPLACE_METHODS - else: - method = DEFAULT_METHODS + self.industrial_data_operation_path = IND_DATA_OPERATION_PATH + self.industrial_model_path = IND_MODEL_OPERATION_PATH + + self.base_data_operation_path = DEFAULT_DATA_OPERATION_PATH + self.base_model_path = DEFAULT_MODEL_OPERATION_PATH + + def _replace_operation(self, to_industrial=True, backend: str = 'default'): + method = INDUSTRIAL_REPLACE_METHODS if to_industrial else DEFAULT_METHODS for class_impl, method_to_replace in zip(FEDOT_METHOD_TO_REPLACE, method): setattr(class_impl[0], class_impl[1], method_to_replace) + if backend.__contains__('dask'): + model_to_overload = [SKLEARN_REG_MODELS, SKLEARN_CLF_MODELS, FEDOT_PREPROC_MODEL] + overloaded_model = overload_model_implementation(model_to_overload, backend=backend) + for model_impl, new_backend_impl in zip(DEFAULT_MODELS_TO_REPLACE, overloaded_model): + setattr(model_impl[0], model_impl[1], new_backend_impl) - def setup_repository(self): + def setup_repository(self, backend: str = 'default'): OperationTypesRepository.__repository_dict__.update( {'data_operation': {'file': self.industrial_data_operation_path, 'initialized_repo': True, @@ -118,12 +133,12 @@ def setup_repository(self): OperationTypesRepository.assign_repo( 'model', self.industrial_model_path) # replace mutations - self._replace_operation(to_industrial=True) + self._replace_operation(to_industrial=True, backend=backend) class_rules.append(has_no_data_flow_conflicts_in_industrial_pipeline) return OperationTypesRepository - def setup_default_repository(self): + def setup_default_repository(self, backend: str = 'default'): """ Switching to fedot models. """ @@ -140,7 +155,8 @@ def setup_default_repository(self): 'initialized_repo': None, 'default_tags': []}}) OperationTypesRepository.assign_repo('model', self.base_model_path) - self._replace_operation(to_industrial=False) + self._replace_operation(to_industrial=False, backend=backend) + common_rules.append(has_no_resample) return OperationTypesRepository def __enter__(self): diff --git a/fedot_ind/core/repository/model_repository.py b/fedot_ind/core/repository/model_repository.py index 4621b0e2a..e26debd5b 100644 --- a/fedot_ind/core/repository/model_repository.py +++ b/fedot_ind/core/repository/model_repository.py @@ -1,6 +1,7 @@ from enum import Enum from itertools import chain +from dask_ml.decomposition import PCA as DaskKernelPCA from fedot.core.operations.evaluation.operation_implementations.data_operations.decompose import \ DecomposerClassImplementation from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_filters import \ @@ -8,8 +9,6 @@ from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_imbalanced_class import \ ResampleImplementation from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_transformations import * -from fedot.core.operations.evaluation.operation_implementations.data_operations.topological.fast_topological_extractor import \ - TopologicalFeaturesImplementation from fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import \ ExogDataTransformationImplementation, GaussianFilterImplementation, LaggedTransformationImplementation, \ SparseLaggedTransformationImplementation, TsSmoothingImplementation @@ -45,7 +44,6 @@ from fedot_ind.core.models.detection.custom.stat_detector import StatisticalDetector from fedot_ind.core.models.detection.probalistic.kalman import UnscentedKalmanFilter from fedot_ind.core.models.detection.subspaces.sst import SingularSpectrumTransformation -from fedot_ind.core.models.manifold.riemann_embeding import RiemannExtractor from fedot_ind.core.models.nn.network_impl.deep_tcn import TCNModel from fedot_ind.core.models.nn.network_impl.deepar import DeepAR from fedot_ind.core.models.nn.network_impl.dummy_nn import DummyOverComplicatedNeuralNetwork @@ -56,13 +54,18 @@ from fedot_ind.core.models.nn.network_impl.nbeats import NBeatsModel from fedot_ind.core.models.nn.network_impl.resnet import ResNetModel from fedot_ind.core.models.nn.network_impl.tst import TSTModel -from fedot_ind.core.models.quantile.quantile_extractor import QuantileExtractor -from fedot_ind.core.models.recurrence.reccurence_extractor import RecurrenceExtractor +from fedot_ind.core.models.pdl.pairwise_model import PairwiseDifferenceClassifier, PairwiseDifferenceRegressor from fedot_ind.core.models.ts_forecasting.glm import GLMIndustrial from fedot_ind.core.operation.filtration.channel_filtration import ChannelCentroidFilter from fedot_ind.core.operation.transformation.basis.eigen_basis import EigenBasisImplementation from fedot_ind.core.operation.transformation.basis.fourier import FourierBasisImplementation from fedot_ind.core.operation.transformation.basis.wavelet import WaveletBasisImplementation +from fedot_ind.core.operation.transformation.representation.manifold.riemann_embeding import RiemannExtractor +from fedot_ind.core.operation.transformation.representation.recurrence.reccurence_extractor import RecurrenceExtractor +from fedot_ind.core.operation.transformation.representation.statistical.quantile_extractor import QuantileExtractor +from fedot_ind.core.operation.transformation.representation.topological.topological_extractor import \ + TopologicalExtractor +from fedot_ind.core.repository.dask_models import DaskLogisticRegression, DaskRidgeRegression from fedot_ind.core.repository.excluded import EXCLUDED_OPERATION_MUTATION, TEMPORARY_EXCLUDED @@ -89,7 +92,9 @@ class AtomizedModel(Enum): # external models 'lgbm': LGBMClassifier, # for detection - 'one_class_svm': OneClassSVM + 'one_class_svm': OneClassSVM, + # pairwise model + 'pdl_clf': PairwiseDifferenceClassifier } FEDOT_PREPROC_MODEL = { # data standartization @@ -99,8 +104,9 @@ class AtomizedModel(Enum): 'simple_imputation': ImputationImplementation, # dimension reduction 'kernel_pca': KernelPCAImplementation, + 'pca': PCAImplementation, # feature generation - 'topological_extractor': TopologicalFeaturesImplementation + # 'topological_extractor': TopologicalFeaturesImplementation } INDUSTRIAL_PREPROC_MODEL = { # data filtration @@ -114,7 +120,8 @@ class AtomizedModel(Enum): 'quantile_extractor': QuantileExtractor, 'riemann_extractor': RiemannExtractor, # feature generation - 'topological_extractor': TopologicalFeaturesImplementation, + # 'topological_extractor': TopologicalFeaturesImplementation, + 'topological_extractor': TopologicalExtractor, # nn feature extraction algorithm 'minirocket_extractor': MiniRocketExtractor, # 'chronos_extractor': ChronosExtractor, @@ -136,7 +143,9 @@ class AtomizedModel(Enum): 'dtreg': DecisionTreeRegressor, # external models 'lgbmreg': LGBMRegressor, - "catboostreg": FedotCatBoostRegressionImplementation + "catboostreg": FedotCatBoostRegressionImplementation, + # pairwise model + 'pdl_reg': PairwiseDifferenceRegressor } FORECASTING_MODELS = { @@ -194,6 +203,14 @@ class AtomizedModel(Enum): 'lora_model': LoraModel } + # DASK_MODELS = {'logit': DaskLogReg, + DASK_MODELS = {'logit': DaskLogisticRegression, + 'kernel_pca': DaskKernelPCA, + # 'kernel_pca': DaskKernelPCA, + # 'ridge': DaskLinReg + 'ridge': DaskRidgeRegression + } + def default_industrial_availiable_operation(problem: str = 'regression'): operation_dict = {'regression': SKLEARN_REG_MODELS.keys(), @@ -232,12 +249,28 @@ def default_industrial_availiable_operation(problem: str = 'regression'): return operations +def overload_model_implementation(list_of_model, backend: str = 'default'): + overload_list = [] + for model_dict in list_of_model: + for model_impl in model_dict.keys(): + if model_impl in DASK_MODELS.keys() and backend.__contains__('dask'): + model_dict[model_impl] = DASK_MODELS[model_impl] + overload_list.append(model_dict) + return overload_list + + +MODELS_WITH_DASK_ALTERNATIVE = [ + AtomizedModel.FEDOT_PREPROC_MODEL.value, + AtomizedModel.SKLEARN_CLF_MODELS.value, + AtomizedModel.SKLEARN_REG_MODELS.value +] +DASK_MODELS = AtomizedModel.DASK_MODELS.value +SKLEARN_REG_MODELS = AtomizedModel.SKLEARN_REG_MODELS.value +SKLEARN_CLF_MODELS = AtomizedModel.SKLEARN_CLF_MODELS.value +FEDOT_PREPROC_MODEL = AtomizedModel.FEDOT_PREPROC_MODEL.value INDUSTRIAL_PREPROC_MODEL = AtomizedModel.INDUSTRIAL_PREPROC_MODEL.value INDUSTRIAL_CLF_PREPROC_MODEL = AtomizedModel.INDUSTRIAL_CLF_PREPROC_MODEL.value -FEDOT_PREPROC_MODEL = AtomizedModel.FEDOT_PREPROC_MODEL.value -SKLEARN_CLF_MODELS = AtomizedModel.SKLEARN_CLF_MODELS.value ANOMALY_DETECTION_MODELS = AtomizedModel.ANOMALY_DETECTION_MODELS.value -SKLEARN_REG_MODELS = AtomizedModel.SKLEARN_REG_MODELS.value NEURAL_MODEL = AtomizedModel.NEURAL_MODEL.value FORECASTING_MODELS = AtomizedModel.FORECASTING_MODELS.value FORECASTING_PREPROC = AtomizedModel.FORECASTING_PREPROC.value diff --git a/fedot_ind/core/tuning/search_space.py b/fedot_ind/core/tuning/search_space.py index 9d1f6c858..dc8b7755d 100644 --- a/fedot_ind/core/tuning/search_space.py +++ b/fedot_ind/core/tuning/search_space.py @@ -17,7 +17,8 @@ 'wavelet_basis': {'n_components': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [2, 10]}, 'wavelet': {'hyperopt-dist': hp.choice, - 'sampling-scope': [['mexh', 'morl', 'db5', 'sym5']]}}, + 'sampling-scope': [['mexh', 'morl', 'gaus1', 'gaus8', 'gaus5']]}, + 'low_freq': {'hyperopt-dist': hp.choice, 'sampling-scope': [[True, False]]}}, 'fourier_basis': {'threshold': {'hyperopt-dist': hp.choice, 'sampling-scope': [list(np.arange(0.75, 0.99, 0.05))]}, 'low_rank': {'hyperopt-dist': hp.choice, 'sampling-scope': [[x for x in range(1, 30, 3)]]}, @@ -123,643 +124,650 @@ {'anomaly_thr': {'hyperopt-dist': hp.choice, 'sampling-scope': [list(np.arange(0.75, 0.99, 0.05))]}, 'window_length': {'hyperopt-dist': hp.choice, 'sampling-scope': [list(np.arange(10, 35, 5))]}}, + 'pdl_clf': {}, + 'pdl_reg': {} } +default_fedot_operation_params = { + 'kmeans': { + 'n_clusters': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [2, 7], + 'type': 'discrete'}}, + 'adareg': { + 'learning_rate': { + 'hyperopt-dist': hp.loguniform, + 'sampling-scope': [1e-3, 1], + 'type': 'continuous'}, + 'loss': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [["linear", "square", "exponential"]], + 'type': 'categorical'}}, + 'gbr': { + 'loss': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [["ls", "lad", "huber", "quantile"]], + 'type': 'categorical'}, + 'learning_rate': { + 'hyperopt-dist': hp.loguniform, + 'sampling-scope': [1e-3, 1], + 'type': 'continuous'}, + 'max_depth': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 11], + 'type': 'discrete'}, + 'min_samples_split': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [2, 21], + 'type': 'discrete'}, + 'min_samples_leaf': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 21], + 'type': 'discrete'}, + 'subsample': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.05, 1.0], + 'type': 'continuous'}, + 'max_features': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.05, 1.0], + 'type': 'continuous'}, + 'alpha': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.75, 0.99], + 'type': 'continuous'}}, + 'logit': { + 'C': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [1e-2, 10.0], + 'type': 'continuous'}, -def get_industrial_search_space(self): - parameters_per_operation = { - 'kmeans': { - 'n_clusters': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [2, 7], - 'type': 'discrete'}}, - 'adareg': { - 'learning_rate': { - 'hyperopt-dist': hp.loguniform, - 'sampling-scope': [1e-3, 1], - 'type': 'continuous'}, - 'loss': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [["linear", "square", "exponential"]], - 'type': 'categorical'}}, - 'gbr': { - 'loss': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [["ls", "lad", "huber", "quantile"]], - 'type': 'categorical'}, - 'learning_rate': { - 'hyperopt-dist': hp.loguniform, - 'sampling-scope': [1e-3, 1], - 'type': 'continuous'}, - 'max_depth': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 11], - 'type': 'discrete'}, - 'min_samples_split': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [2, 21], - 'type': 'discrete'}, - 'min_samples_leaf': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 21], - 'type': 'discrete'}, - 'subsample': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.05, 1.0], - 'type': 'continuous'}, - 'max_features': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.05, 1.0], - 'type': 'continuous'}, - 'alpha': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.75, 0.99], - 'type': 'continuous'}}, - 'logit': { - 'C': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [1e-2, 10.0], - 'type': 'continuous'}, + 'penalty': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [['l1', 'l2']], + 'type': 'categorical'}, - 'penalty': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [['l1', 'l2']], - 'type': 'categorical'}, + 'solver': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [['liblinear']], + 'type': 'categorical'}}, + 'rf': { + 'criterion': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [["gini", "entropy"]], + 'type': 'categorical'}, + 'max_features': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.05, 1.0], + 'type': 'continuous'}, + 'min_samples_split': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [2, 10], + 'type': 'discrete'}, + 'min_samples_leaf': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 15], + 'type': 'discrete'}, + 'bootstrap': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[True, False]], + 'type': 'categorical'}}, + 'ridge': { + 'alpha': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.01, 10.0], + 'type': 'continuous'}}, + 'lasso': { + 'alpha': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.01, 10.0], + 'type': 'continuous'}}, + 'rfr': { + 'max_features': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.05, 1.0], + 'type': 'continuous'}, + 'min_samples_split': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [2, 21], + 'type': 'discrete'}, + 'min_samples_leaf': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 15], + 'type': 'discrete'}, + 'bootstrap': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[True, False]], + 'type': 'categorical'}}, + 'xgbreg': { + 'max_depth': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 11], + 'type': 'discrete'}, + 'learning_rate': { + 'hyperopt-dist': hp.loguniform, + 'sampling-scope': [1e-3, 1], + 'type': 'continuous'}, + 'subsample': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.05, 1.0], + 'type': 'continuous'}, + 'min_child_weight': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 21], + 'type': 'discrete'}}, + 'xgboost': { + 'n_estimators': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [100, 3000], + 'type': 'discrete'}, + 'max_depth': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [3, 10], + 'type': 'discrete'}, + 'learning_rate': { + 'hyperopt-dist': hp.loguniform, + 'sampling-scope': [1e-3, 1], + 'type': 'continuous'}, + 'subsample': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.05, 0.99], + 'type': 'continuous'}, + 'min_weight_fraction_leaf': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.0, 0.5], + 'type': 'continuous'}, + 'min_samples_leaf': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.0, 1], + 'type': 'continuous'}, + 'min_samples_split': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.0, 1.0], + 'type': 'continuous'}}, + 'svr': { + 'C': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [1e-4, 25.0], + 'type': 'continuous'}, + 'epsilon': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [1e-4, 1], + 'type': 'continuous'}, + 'tol': { + 'hyperopt-dist': hp.loguniform, + 'sampling-scope': [1e-5, 1e-1], + 'type': 'continuous'}, + 'loss': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [["epsilon_insensitive", "squared_epsilon_insensitive"]], + 'type': 'categorical'}}, + 'dtreg': { + 'max_depth': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 11], + 'type': 'discrete'}, + 'min_samples_split': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [2, 21], + 'type': 'discrete'}, + 'min_samples_leaf': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 21], + 'type': 'discrete'}}, + 'treg': { + 'max_features': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.05, 1.0], + 'type': 'continuous'}, + 'min_samples_split': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [2, 21], + 'type': 'discrete'}, + 'min_samples_leaf': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 21], + 'type': 'discrete'}, + 'bootstrap': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[True, False]], + 'type': 'categorical'}}, + 'dt': { + 'max_depth': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 11], + 'type': 'discrete'}, + 'min_samples_split': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [2, 21], + 'type': 'discrete'}, + 'min_samples_leaf': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 21], + 'type': 'discrete'}}, + 'knnreg': { + 'n_neighbors': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 50], + 'type': 'discrete'}, + 'weights': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [["uniform", "distance"]], + 'type': 'categorical'}, + 'p': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[1, 2]], + 'type': 'categorical'}}, + 'knn': { + 'n_neighbors': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 50], + 'type': 'discrete'}, + 'weights': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [["uniform", "distance"]], + 'type': 'categorical'}, + 'p': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[1, 2]], + 'type': 'categorical'}}, + 'arima': { + 'p': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 7], + 'type': 'discrete'}, + 'd': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [0, 2], + 'type': 'discrete'}, + 'q': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 5], + 'type': 'discrete'}}, + 'stl_arima': { + 'p': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 7], + 'type': 'discrete'}, + 'd': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [0, 2], + 'type': 'discrete'}, + 'q': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 5], + 'type': 'discrete'}, + 'period': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 365], + 'type': 'discrete'}}, + 'mlp': { + 'hidden_layer_sizes': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[(256, 128, 64, 32), (1028, 512, 64,)]], + 'type': 'categorical'}, + 'activation': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [['logistic', 'tanh', 'relu']], + 'type': 'categorical'}, + 'max_iter': {'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1000, 2000], + 'type': 'discrete'}, + 'learning_rate': {'hyperopt-dist': hp.choice, + 'sampling-scope': [['constant', 'adaptive']], + 'type': 'categorical'}}, + 'ar': { + 'lag_1': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [2, 200], + 'type': 'continuous'}, + 'lag_2': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [2, 800], + 'type': 'continuous'}, + 'trend': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [['n', 'c', 't', 'ct']], + 'type': 'categorical'}, + 'period': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[5, 7, 14, 21, 30, 365]], + 'type': 'categorical'}, + 'seasonal': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[True, False]], + 'type': 'categorical'}, + 'deterministic': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[True, False]], + 'type': 'categorical'} + }, + 'ets': { + 'error': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [["add", "mul"]], + 'type': 'categorical'}, + 'trend': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[None, "add", "mul"]], + 'type': 'categorical'}, + 'seasonal': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[None, "add", "mul"]], + 'type': 'categorical'}, + 'damped_trend': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[True, False]], + 'type': 'categorical'}, + 'seasonal_periods': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [1, 100], + 'type': 'continuous'}}, + 'glm': { + NESTED_PARAMS_LABEL: { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[ + { + 'family': 'gaussian', + 'link': hp.choice('link_gaussian', ['identity', + 'inverse_power', + 'log']) + }, + { + 'family': 'gamma', + 'link': hp.choice('link_gamma', ['identity', + 'inverse_power', + 'log']) + }, + { + 'family': 'inverse_gaussian', + 'link': hp.choice('link_inv_gaussian', ['identity', + 'inverse_power']) + } - 'solver': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [['liblinear']], - 'type': 'categorical'}}, - 'rf': { - 'criterion': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [["gini", "entropy"]], - 'type': 'categorical'}, - 'max_features': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.05, 1.0], - 'type': 'continuous'}, - 'min_samples_split': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [2, 10], - 'type': 'discrete'}, - 'min_samples_leaf': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 15], - 'type': 'discrete'}, - 'bootstrap': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[True, False]], - 'type': 'categorical'}}, - 'ridge': { - 'alpha': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.01, 10.0], - 'type': 'continuous'}}, - 'lasso': { - 'alpha': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.01, 10.0], - 'type': 'continuous'}}, - 'rfr': { - 'max_features': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.05, 1.0], - 'type': 'continuous'}, - 'min_samples_split': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [2, 21], - 'type': 'discrete'}, - 'min_samples_leaf': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 15], - 'type': 'discrete'}, - 'bootstrap': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[True, False]], - 'type': 'categorical'}}, - 'xgbreg': { - 'max_depth': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 11], - 'type': 'discrete'}, - 'learning_rate': { - 'hyperopt-dist': hp.loguniform, - 'sampling-scope': [1e-3, 1], - 'type': 'continuous'}, - 'subsample': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.05, 1.0], - 'type': 'continuous'}, - 'min_child_weight': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 21], - 'type': 'discrete'}}, - 'xgboost': { - 'n_estimators': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [100, 3000], - 'type': 'discrete'}, - 'max_depth': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [3, 10], - 'type': 'discrete'}, - 'learning_rate': { - 'hyperopt-dist': hp.loguniform, - 'sampling-scope': [1e-3, 1], - 'type': 'continuous'}, - 'subsample': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.05, 0.99], - 'type': 'continuous'}, - 'min_weight_fraction_leaf': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.0, 0.5], - 'type': 'continuous'}, - 'min_samples_leaf': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.0, 1], - 'type': 'continuous'}, - 'min_samples_split': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.0, 1.0], - 'type': 'continuous'}}, - 'svr': { - 'C': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [1e-4, 25.0], - 'type': 'continuous'}, - 'epsilon': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [1e-4, 1], - 'type': 'continuous'}, - 'tol': { - 'hyperopt-dist': hp.loguniform, - 'sampling-scope': [1e-5, 1e-1], - 'type': 'continuous'}, - 'loss': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [["epsilon_insensitive", "squared_epsilon_insensitive"]], - 'type': 'categorical'}}, - 'dtreg': { - 'max_depth': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 11], - 'type': 'discrete'}, - 'min_samples_split': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [2, 21], - 'type': 'discrete'}, - 'min_samples_leaf': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 21], - 'type': 'discrete'}}, - 'treg': { - 'max_features': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.05, 1.0], - 'type': 'continuous'}, - 'min_samples_split': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [2, 21], - 'type': 'discrete'}, - 'min_samples_leaf': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 21], - 'type': 'discrete'}, - 'bootstrap': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[True, False]], - 'type': 'categorical'}}, - 'dt': { - 'max_depth': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 11], - 'type': 'discrete'}, - 'min_samples_split': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [2, 21], - 'type': 'discrete'}, - 'min_samples_leaf': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 21], - 'type': 'discrete'}}, - 'knnreg': { - 'n_neighbors': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 50], - 'type': 'discrete'}, - 'weights': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [["uniform", "distance"]], - 'type': 'categorical'}, - 'p': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[1, 2]], - 'type': 'categorical'}}, - 'knn': { - 'n_neighbors': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 50], - 'type': 'discrete'}, - 'weights': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [["uniform", "distance"]], - 'type': 'categorical'}, - 'p': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[1, 2]], - 'type': 'categorical'}}, - 'arima': { - 'p': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 7], - 'type': 'discrete'}, - 'd': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [0, 2], - 'type': 'discrete'}, - 'q': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 5], - 'type': 'discrete'}}, - 'stl_arima': { - 'p': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 7], - 'type': 'discrete'}, - 'd': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [0, 2], - 'type': 'discrete'}, - 'q': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 5], - 'type': 'discrete'}, - 'period': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 365], - 'type': 'discrete'}}, - 'mlp': { - 'hidden_layer_sizes': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[(256, 128, 64, 32), (1028, 512, 64,)]], - 'type': 'categorical'}, - 'activation': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [['logistic', 'tanh', 'relu']], - 'type': 'categorical'}, - 'max_iter': {'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1000, 2000], - 'type': 'discrete'}, - 'learning_rate': {'hyperopt-dist': hp.choice, - 'sampling-scope': [['constant', 'adaptive']], - 'type': 'categorical'}}, - 'ar': { - 'lag_1': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [2, 200], - 'type': 'continuous'}, - 'lag_2': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [2, 800], - 'type': 'continuous'}, - 'trend': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [['n', 'c', 't', 'ct']], - 'type': 'categorical'}, - 'period': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[5, 7, 14, 21, 30, 365]], - 'type': 'categorical'}, - 'seasonal': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[True, False]], - 'type': 'categorical'}, - 'deterministic': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[True, False]], - 'type': 'categorical'} + ]], + 'type': 'categorical'}}, + 'cgru': { + 'hidden_size': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [20, 200], + 'type': 'continuous'}, + 'learning_rate': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.0005, 0.005], + 'type': 'continuous'}, + 'cnn1_kernel_size': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [3, 8], + 'type': 'discrete'}, + 'cnn1_output_size': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[8, 16, 32, 64]], + 'type': 'categorical'}, + 'cnn2_kernel_size': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [3, 8], + 'type': 'discrete'}, + 'cnn2_output_size': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[8, 16, 32, 64]], + 'type': 'categorical'}, + 'batch_size': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[64, 128]], + 'type': 'categorical'}, + 'num_epochs': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[10, 20, 50, 100]], + 'type': 'categorical'}, + 'optimizer': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [['adamw', 'sgd']], + 'type': 'categorical'}, + 'loss': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [['mae', 'mse']], + 'type': 'categorical'}}, + 'topological_extractor': { + 'window_size_as_share': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.1, 0.9], + 'type': 'continuous' + }, + 'max_homology_dimension': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 3], + 'type': 'discrete' }, - 'ets': { - 'error': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [["add", "mul"]], - 'type': 'categorical'}, - 'trend': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[None, "add", "mul"]], - 'type': 'categorical'}, - 'seasonal': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[None, "add", "mul"]], - 'type': 'categorical'}, - 'damped_trend': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[True, False]], - 'type': 'categorical'}, - 'seasonal_periods': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [1, 100], - 'type': 'continuous'}}, - 'glm': { - NESTED_PARAMS_LABEL: { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[ - { - 'family': 'gaussian', - 'link': hp.choice('link_gaussian', ['identity', - 'inverse_power', - 'log']) - }, - { - 'family': 'gamma', - 'link': hp.choice('link_gamma', ['identity', - 'inverse_power', - 'log']) - }, - { - 'family': 'inverse_gaussian', - 'link': hp.choice('link_inv_gaussian', ['identity', - 'inverse_power']) - } + 'metric': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [['euclidean', 'manhattan', 'cosine']], + 'type': 'categorical'}}, + 'pca': { + 'n_components': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.1, 0.99], + 'type': 'continuous'}}, + 'kernel_pca': { + 'n_components': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 20], + 'type': 'discrete'}, + 'kernel': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [['linear', 'poly', 'rbf', 'sigmoid', 'cosine', 'precomputed']], + 'type': 'categorical'}}, + 'lagged': { + 'window_size': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [5, 500], + 'type': 'discrete'}}, + 'sparse_lagged': { + 'window_size': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [5, 500], + 'type': 'discrete'}, + 'n_components': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0, 0.5], + 'type': 'continuous'}, + 'use_svd': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[True, False]], + 'type': 'categorical'}}, + 'smoothing': { + 'window_size': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [2, 20], + 'type': 'discrete'}}, + 'gaussian_filter': { + 'sigma': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [1, 5], + 'type': 'continuous'}}, + 'diff_filter': { + 'poly_degree': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 5], + 'type': 'discrete'}, + 'order': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [1, 3], + 'type': 'continuous'}, + 'window_size': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [3, 20], + 'type': 'continuous'}}, + 'cut': { + 'cut_part': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0, 0.9], + 'type': 'continuous'}}, + 'lgbm': { + 'class_weight': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[None, 'balanced']], + 'type': 'categorical'}, + 'num_leaves': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [2, 256], + 'type': 'discrete'}, + 'learning_rate': { + 'hyperopt-dist': hp.loguniform, + 'sampling-scope': [0.01, 0.2], + 'type': 'continuous'}, + 'colsample_bytree': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.4, 1], + 'type': 'continuous'}, + 'subsample': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.4, 1], + 'type': 'continuous'}, + 'reg_alpha': { + 'hyperopt-dist': hp.loguniform, + 'sampling-scope': [1e-8, 10], + 'type': 'continuous'}, + 'reg_lambda': { + 'hyperopt-dist': hp.loguniform, + 'sampling-scope': [1e-8, 10], + 'type': 'continuous'}}, + 'lgbmreg': { + 'num_leaves': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [128, 1024], + 'type': 'discrete'}, + 'learning_rate': { + 'hyperopt-dist': hp.loguniform, + 'sampling-scope': [0.001, 0.1], + 'type': 'continuous'}, + 'colsample_bytree': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.1, 1], + 'type': 'continuous'}, + 'subsample': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.1, 1], + 'type': 'continuous'}, + 'reg_alpha': { + 'hyperopt-dist': hp.loguniform, + 'sampling-scope': [1e-8, 10], + 'type': 'continuous'}, + 'reg_lambda': { + 'hyperopt-dist': hp.loguniform, + 'sampling-scope': [1e-8, 10], + 'type': 'continuous'}}, + 'catboost': { + 'max_depth': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 11], + 'type': 'discrete'}, + 'learning_rate': { + 'hyperopt-dist': hp.loguniform, + 'sampling-scope': [0.01, 0.2], + 'type': 'continuous'}, + 'min_data_in_leaf': { + 'hyperopt-dist': partial(hp.qloguniform, q=1), + 'sampling-scope': [0, 6], + 'type': 'discrete'}, + 'border_count': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [2, 255], + 'type': 'discrete'}, + 'l2_leaf_reg': { + 'hyperopt-dist': hp.loguniform, + 'sampling-scope': [1e-8, 10], + 'type': 'continuous'}}, + 'catboostreg': { + 'max_depth': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [1, 11], + 'type': 'discrete'}, + 'learning_rate': { + 'hyperopt-dist': hp.loguniform, + 'sampling-scope': [0.01, 0.2], + 'type': 'continuous'}, + 'min_data_in_leaf': { + 'hyperopt-dist': partial(hp.qloguniform, q=1), + 'sampling-scope': [0, 6], + 'type': 'discrete'}, + 'border_count': { + 'hyperopt-dist': hp.uniformint, + 'sampling-scope': [2, 255], + 'type': 'discrete'}, + 'l2_leaf_reg': { + 'hyperopt-dist': hp.loguniform, + 'sampling-scope': [1e-8, 10], + 'type': 'continuous'}}, + 'resample': { + 'balance': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [['expand_minority', 'reduce_majority']], + 'type': 'categorical'}, + 'replace': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[True, False]], + 'type': 'categorical'}, + 'balance_ratio': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.3, 1], + 'type': 'continuous'}}, + 'lda': { + 'solver': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [['svd', 'lsqr', 'eigen']], + 'type': 'categorical'}, + 'shrinkage': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.1, 0.9], + 'type': 'continuous'}}, + 'ts_naive_average': { + 'part_for_averaging': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.1, 1], + 'type': 'continuous'}}, + 'locf': { + 'part_for_repeat': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.01, 0.5], + 'type': 'continuous'}}, + 'word2vec_pretrained': { + 'model_name': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [['glove-twitter-25', 'glove-twitter-50', + 'glove-wiki-gigaword-100', 'word2vec-ruscorpora-300']], + 'type': 'categorical'}}, + 'tfidf': { + 'ngram_range': { + 'hyperopt-dist': hp.choice, + 'sampling-scope': [[(1, 1), (1, 2), (1, 3)]], + 'type': 'categorical'}, + 'min_df': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.0001, 0.1], + 'type': 'continuous'}, + 'max_df': { + 'hyperopt-dist': hp.uniform, + 'sampling-scope': [0.9, 0.99], + 'type': 'continuous'}}, +} - ]], - 'type': 'categorical'}}, - 'cgru': { - 'hidden_size': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [20, 200], - 'type': 'continuous'}, - 'learning_rate': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.0005, 0.005], - 'type': 'continuous'}, - 'cnn1_kernel_size': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [3, 8], - 'type': 'discrete'}, - 'cnn1_output_size': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[8, 16, 32, 64]], - 'type': 'categorical'}, - 'cnn2_kernel_size': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [3, 8], - 'type': 'discrete'}, - 'cnn2_output_size': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[8, 16, 32, 64]], - 'type': 'categorical'}, - 'batch_size': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[64, 128]], - 'type': 'categorical'}, - 'num_epochs': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[10, 20, 50, 100]], - 'type': 'categorical'}, - 'optimizer': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [['adamw', 'sgd']], - 'type': 'categorical'}, - 'loss': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [['mae', 'mse']], - 'type': 'categorical'}}, - 'topological_extractor': { - 'window_size_as_share': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.1, 0.9], - 'type': 'continuous' - }, - 'max_homology_dimension': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 3], - 'type': 'discrete' - }, - 'metric': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [['euclidean', 'manhattan', 'cosine']], - 'type': 'categorical'}}, - 'pca': { - 'n_components': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.1, 0.99], - 'type': 'continuous'}}, - 'kernel_pca': { - 'n_components': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 20], - 'type': 'discrete'}, - 'kernel': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [['linear', 'poly', 'rbf', 'sigmoid', 'cosine', 'precomputed']], - 'type': 'categorical'}}, - 'lagged': { - 'window_size': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [5, 500], - 'type': 'discrete'}}, - 'sparse_lagged': { - 'window_size': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [5, 500], - 'type': 'discrete'}, - 'n_components': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0, 0.5], - 'type': 'continuous'}, - 'use_svd': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[True, False]], - 'type': 'categorical'}}, - 'smoothing': { - 'window_size': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [2, 20], - 'type': 'discrete'}}, - 'gaussian_filter': { - 'sigma': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [1, 5], - 'type': 'continuous'}}, - 'diff_filter': { - 'poly_degree': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 5], - 'type': 'discrete'}, - 'order': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [1, 3], - 'type': 'continuous'}, - 'window_size': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [3, 20], - 'type': 'continuous'}}, - 'cut': { - 'cut_part': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0, 0.9], - 'type': 'continuous'}}, - 'lgbm': { - 'class_weight': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[None, 'balanced']], - 'type': 'categorical'}, - 'num_leaves': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [2, 256], - 'type': 'discrete'}, - 'learning_rate': { - 'hyperopt-dist': hp.loguniform, - 'sampling-scope': [0.01, 0.2], - 'type': 'continuous'}, - 'colsample_bytree': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.4, 1], - 'type': 'continuous'}, - 'subsample': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.4, 1], - 'type': 'continuous'}, - 'reg_alpha': { - 'hyperopt-dist': hp.loguniform, - 'sampling-scope': [1e-8, 10], - 'type': 'continuous'}, - 'reg_lambda': { - 'hyperopt-dist': hp.loguniform, - 'sampling-scope': [1e-8, 10], - 'type': 'continuous'}}, - 'lgbmreg': { - 'num_leaves': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [128, 1024], - 'type': 'discrete'}, - 'learning_rate': { - 'hyperopt-dist': hp.loguniform, - 'sampling-scope': [0.001, 0.1], - 'type': 'continuous'}, - 'colsample_bytree': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.1, 1], - 'type': 'continuous'}, - 'subsample': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.1, 1], - 'type': 'continuous'}, - 'reg_alpha': { - 'hyperopt-dist': hp.loguniform, - 'sampling-scope': [1e-8, 10], - 'type': 'continuous'}, - 'reg_lambda': { - 'hyperopt-dist': hp.loguniform, - 'sampling-scope': [1e-8, 10], - 'type': 'continuous'}}, - 'catboost': { - 'max_depth': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 11], - 'type': 'discrete'}, - 'learning_rate': { - 'hyperopt-dist': hp.loguniform, - 'sampling-scope': [0.01, 0.2], - 'type': 'continuous'}, - 'min_data_in_leaf': { - 'hyperopt-dist': partial(hp.qloguniform, q=1), - 'sampling-scope': [0, 6], - 'type': 'discrete'}, - 'border_count': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [2, 255], - 'type': 'discrete'}, - 'l2_leaf_reg': { - 'hyperopt-dist': hp.loguniform, - 'sampling-scope': [1e-8, 10], - 'type': 'continuous'}}, - 'catboostreg': { - 'max_depth': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [1, 11], - 'type': 'discrete'}, - 'learning_rate': { - 'hyperopt-dist': hp.loguniform, - 'sampling-scope': [0.01, 0.2], - 'type': 'continuous'}, - 'min_data_in_leaf': { - 'hyperopt-dist': partial(hp.qloguniform, q=1), - 'sampling-scope': [0, 6], - 'type': 'discrete'}, - 'border_count': { - 'hyperopt-dist': hp.uniformint, - 'sampling-scope': [2, 255], - 'type': 'discrete'}, - 'l2_leaf_reg': { - 'hyperopt-dist': hp.loguniform, - 'sampling-scope': [1e-8, 10], - 'type': 'continuous'}}, - 'resample': { - 'balance': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [['expand_minority', 'reduce_majority']], - 'type': 'categorical'}, - 'replace': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[True, False]], - 'type': 'categorical'}, - 'balance_ratio': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.3, 1], - 'type': 'continuous'}}, - 'lda': { - 'solver': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [['svd', 'lsqr', 'eigen']], - 'type': 'categorical'}, - 'shrinkage': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.1, 0.9], - 'type': 'continuous'}}, - 'ts_naive_average': { - 'part_for_averaging': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.1, 1], - 'type': 'continuous'}}, - 'locf': { - 'part_for_repeat': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.01, 0.5], - 'type': 'continuous'}}, - 'word2vec_pretrained': { - 'model_name': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [['glove-twitter-25', 'glove-twitter-50', - 'glove-wiki-gigaword-100', 'word2vec-ruscorpora-300']], - 'type': 'categorical'}}, - 'tfidf': { - 'ngram_range': { - 'hyperopt-dist': hp.choice, - 'sampling-scope': [[(1, 1), (1, 2), (1, 3)]], - 'type': 'categorical'}, - 'min_df': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.0001, 0.1], - 'type': 'continuous'}, - 'max_df': { - 'hyperopt-dist': hp.uniform, - 'sampling-scope': [0.9, 0.99], - 'type': 'continuous'}}, - } - for key in industrial_search_space: - parameters_per_operation[key] = industrial_search_space[key] +pdl_base_model = {'pdl_clf': 'rf', + 'pdl_reg': 'treg'} + +def get_industrial_search_space(self): + for key in industrial_search_space: + default_fedot_operation_params[key] = industrial_search_space[key] + if key.__contains__('pdl'): + default_fedot_operation_params[key] = default_fedot_operation_params[pdl_base_model[key]] if 'custom_search_space' in dir(self): if self.custom_search_space is not None: for operation in self.custom_search_space.keys(): if self.replace_default_search_space: - parameters_per_operation[operation] = self.custom_search_space[operation] + default_fedot_operation_params[operation] = self.custom_search_space[operation] else: for key, value in self.custom_search_space[operation].items(): - parameters_per_operation[operation][key] = value + default_fedot_operation_params[operation][key] = value - return parameters_per_operation + return default_fedot_operation_params diff --git a/fedot_ind/tools/explain/explain.py b/fedot_ind/tools/explain/explain.py index 83b18e0fd..16e7e39e7 100644 --- a/fedot_ind/tools/explain/explain.py +++ b/fedot_ind/tools/explain/explain.py @@ -44,8 +44,7 @@ def _get_recurrence_matrix(self): return recurrence_extractor def explain(self, **kwargs): - recurrence_extractor = self._get_recurrence_matrix() - rec_matrix = recurrence_extractor.predict + rec_matrix = self._get_recurrence_matrix().predict if len(self.features) <= 3 else self.features for classes in np.unique(self.target): cls_idx = np.where(self.target == classes)[0] self.rec_matrix_by_cls.update({classes: rec_matrix[cls_idx, :, :, :]}) diff --git a/fedot_ind/tools/explain/pcd.py b/fedot_ind/tools/explain/pcd.py new file mode 100644 index 000000000..b9af402ef --- /dev/null +++ b/fedot_ind/tools/explain/pcd.py @@ -0,0 +1,203 @@ +import struct +from itertools import product + +import numpy as np + +try: + from .cwrapped import tessellate + + c_lib = True +except ImportError: + c_lib = False + +ASCII_FACET = """ facet normal {face[0]:e} {face[1]:e} {face[2]:e} + outer loop + vertex {face[3]:e} {face[4]:e} {face[5]:e} + vertex {face[6]:e} {face[7]:e} {face[8]:e} + vertex {face[9]:e} {face[10]:e} {face[11]:e} + endloop + endfacet""" + +BINARY_HEADER = "80sI" +BINARY_FACET = "12fH" + + +def _build_binary_stl(facets): + """returns a string of binary binary data for the stl file""" + + lines = [struct.pack(BINARY_HEADER, b'Binary STL Writer', len(facets)), ] + for facet in facets: + facet = list(facet) + facet.append(0) # need to pad the end with a unsigned short byte + lines.append(struct.pack(BINARY_FACET, *facet)) + return lines + + +def _build_ascii_stl(facets): + """returns a list of ascii lines for the stl file """ + + lines = ['solid ffd_geom', ] + for facet in facets: + lines.append(ASCII_FACET.format(face=facet)) + lines.append('endsolid ffd_geom') + return lines + + +def writeSTL(facets, file_name, ascii=False): + """writes an ASCII or binary STL file""" + + f = open(file_name, 'wb') + if ascii: + lines = _build_ascii_stl(facets) + lines_ = "\n".join(lines).encode("UTF-8") + f.write(lines_) + else: + data = _build_binary_stl(facets) + data = b"".join(data) + f.write(data) + + f.close() + + +def roll2d(image, shifts): + return np.roll(np.roll(image, shifts[0], axis=0), shifts[1], axis=1) + + +def numpy2stl(A, fn, scale=0.1, mask_val=None, ascii=False, + max_width=235., + max_depth=140., + max_height=150., + solid=False, + rotate=True, + min_thickness_percent=0.1, + force_python=False): + """ + Reads a numpy array, and outputs an STL file + + Inputs: + A (ndarray) - an 'm' by 'n' 2D numpy array + fn (string) - filename to use for STL file + + Optional input: + scale (float) - scales the height (surface) of the + resulting STL mesh. Tune to match needs + + mask_val (float) - any element of the inputted array that is less + than this value will not be included in the mesh. + default renders all vertices (x > -inf for all float x) + + ascii (bool) - sets the STL format to ascii or binary (default) + + max_width, max_depth, max_height (floats) - maximum size of the stl + object (in mm). Match this to + the dimensions of a 3D printer + platform + solid (bool): sets whether to create a solid geometry (with sides and + a bottom) or not. + min_thickness_percent (float) : when creating the solid bottom face, this + multiplier sets the minimum thickness in + the final geometry (shallowest interior + point to bottom face), as a percentage of + the thickness of the model computed up to + that point. + Returns: (None) + """ + + m, n = A.shape + if n >= m and rotate: + # rotate to best fit a printing platform + A = np.rot90(A, k=3) + m, n = n, m + A = scale * (A - A.min()) + + if not mask_val: + mask_val = A.min() - 1. + + if c_lib and not force_python: # try to use c library + # needed for memoryviews + A = np.ascontiguousarray(A, dtype=float) + + facets = np.asarray(tessellate(A, mask_val, min_thickness_percent, + solid)) + # center on platform + facets[:, 3::3] += -m / 2 + facets[:, 4::3] += -n / 2 + + else: # use python + numpy + facets = [] + mask = np.zeros((m, n)) + print("Creating top mesh...") + for i, k in product(range(m - 1), range(n - 1)): + + this_pt = np.array([i - m / 2., k - n / 2., A[i, k]]) + top_right = np.array([i - m / 2., k + 1 - n / 2., A[i, k + 1]]) + bottom_left = np.array([i + 1. - m / 2., k - n / 2., A[i + 1, k]]) + bottom_right = np.array( + [i + 1. - m / 2., k + 1 - n / 2., A[i + 1, k + 1]]) + + n1, n2 = np.zeros(3), np.zeros(3) + + if (this_pt[-1] > mask_val and top_right[-1] > mask_val and + bottom_left[-1] > mask_val): + facet = np.concatenate([n1, top_right, this_pt, bottom_right]) + mask[i, k] = 1 + mask[i, k + 1] = 1 + mask[i + 1, k] = 1 + facets.append(facet) + + if (this_pt[-1] > mask_val and bottom_right[-1] > mask_val and + bottom_left[-1] > mask_val): + facet = np.concatenate( + [n2, bottom_right, this_pt, bottom_left]) + facets.append(facet) + mask[i, k] = 1 + mask[i + 1, k + 1] = 1 + mask[i + 1, k] = 1 + facets = np.array(facets) + + if solid: + print("Computed edges...") + edge_mask = np.sum([roll2d(mask, (i, k)) + for i, k in product([-1, 0, 1], repeat=2)], + axis=0) + edge_mask[np.where(edge_mask == 9.)] = 0. + edge_mask[np.where(edge_mask != 0.)] = 1. + edge_mask[0::m - 1, :] = 1. + edge_mask[:, 0::n - 1] = 1. + X, Y = np.where(edge_mask == 1.) + locs = zip(X - m / 2., Y - n / 2.) + + zvals = facets[:, 5::3] + zmin, zthickness = zvals.min(), zvals.ptp() + + minval = zmin - min_thickness_percent * zthickness + + bottom = [] + print("Extending edges, creating bottom...") + for i, facet in enumerate(facets): + if (facet[3], facet[4]) in locs: + facets[i][5] = minval + if (facet[6], facet[7]) in locs: + facets[i][8] = minval + if (facet[9], facet[10]) in locs: + facets[i][11] = minval + this_bottom = np.concatenate( + [facet[:3], facet[6:8], [minval], facet[3:5], [minval], + facet[9:11], [minval]]) + bottom.append(this_bottom) + + facets = np.concatenate([facets, bottom]) + + xsize = facets[:, 3::3].ptp() + if xsize > max_width: + facets = facets * float(max_width) / xsize + + ysize = facets[:, 4::3].ptp() + if ysize > max_depth: + facets = facets * float(max_depth) / ysize + + zsize = facets[:, 5::3].ptp() + if zsize > max_height: + facets = facets * float(max_height) / zsize + + writeSTL(facets, fn, ascii=ascii) diff --git a/fedot_ind/tools/loader.py b/fedot_ind/tools/loader.py index 88e9a87c0..f43272a5f 100644 --- a/fedot_ind/tools/loader.py +++ b/fedot_ind/tools/loader.py @@ -893,6 +893,7 @@ def read_arff_files(dataset_name, data_path) -> tuple[pd.DataFrame, np.array, pd y_train: train target array of shape (n_samples,) x_test: test dataframe of shape (n_samples, dim) with pd.Series of shape (ts_length,) y_test: test target array of shape (n_samples,) + """ def load_process_data(path_to_dataset): data, meta = loadarff(path_to_dataset) diff --git a/pyproject.toml b/pyproject.toml index eeadfdbe7..fcd2f33f6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,43 +1,30 @@ [tool.poetry] name = "fedot-ind" -version = "0.4.3" +version = "0.5.0" description = "Time series analysis framework" authors = ["NSS Lab "] license = "BSD 3-Clause" readme = "README_en.rst" [tool.poetry.dependencies] -python = ">=3.9,<3.12" -catboost = [ - {version = "1.1.1", markers = "sys_platform == 'darwin'"}, - {version = "*", markers = "sys_platform != 'darwin'"} -] -fedot = "^0.7.3" -torch = "~2.2.0" -torchvision = "~0.17.0" -setuptools = "^70.0.0" -chardet = "~5.2.0" -tensorly = "0.8.1" -pymonad = "2.4.0" -pywavelets = "1.4.1" -giotto-tda = ">=0.6.0" -ripser = "0.6.4" -fastcore = "~1.5.29" -fastai = "~2.7.14" -sktime = ">=0.16.1" -distributed = "~2023.12.0" -mklpy = "0.6" -librosa = "~0.10.1" -pyriemann = "~0.5" -pyarrow = "15.0.1" +python = ">=3.9, <3.12" +fedot = "^0.7.4" +dask-ml = "^2024.4.4" +fastai = "^2.7.18" +giotto-tda = "*" +scikit-tda = "^1.1.1" +chardet = "^5.2.0" +tensorly = "^0.9.0" +pymonad = "^2.4.0" +pywavelets = "^1.5.0" +mklpy = "^0.6" +librosa = "^0.10.2.post1" +pyriemann = "^0.7" datasetsforecast = "^0.0.8" -datasets = "^2.19.2" -matplotlib = "~3.8.2" -numpy = "1.23.2" -pytest-cov = "^5.0.0" -sphinx-rtd-theme = "^2.0.0" +datasets = "^2.0.0" spectrum = "^0.8.1" - +optuna-integration = "^4.1.0" +pytest-cov = "^6.0.0" [tool.coverage.report] exclude_also = [ @@ -52,7 +39,6 @@ exclude_also = [ "if self.print_training_progress:" ] - [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" diff --git a/requirements.txt b/requirements.txt index 602860d92..06599ccfd 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,36 +1,17 @@ -sphinx~=7.3.7 -numpy~=1.24.4 -pytest~=8.0.2 -matplotlib~=3.8.4 -pandas~=1.5.3 -fedot~=0.7.3.2 -torch~=2.2.2 -scipy~=1.12.0 -typing~=3.7.4.3 -pyyaml~=6.0.1 -torchvision~=0.17.2 -tqdm~=4.65.2 -scikit-learn~=1.5.0 -setuptools~=70.0.0 -librosa~=0.10.2.post1 -pillow~=10.2.0 -pymonad~=2.4.0 -fastai~=2.7.15 -fastcore~=1.5.44 -pyriemann~=0.5 -sympy~=1.12.1 -statsmodels~=0.14.2 -joblib~=1.4.2 -hyperopt~=0.2.7 -sktime~=0.30.1 -mklpy~=0.6 -ripser~=0.6.4 -tensorly~=0.8.1 -lightgbm~=4.3.0 -xgboost~=2.0.3 -spectrum~=0.8.1 -distributed~=2023.12.1 -seaborn~=0.13.2 -chardet~=5.2.0 -datasets~=2.19.2 -datasetsforecast~=0.0.8 \ No newline at end of file +fedot==0.7.4 +dask-ml==2024.4.4 +fastai==2.7.18 +giotto-tda +scikit-tda==1.1.1 +chardet==5.2.0 +tensorly==0.9.0 +pymonad==2.4.0 +pywavelets==1.5.0 +mklpy==0.6 +librosa==0.10.2.post1 +pyriemann==0.7 +datasetsforecast==0.0.8 +datasets==2.0.0 +spectrum==0.8.1 +optuna-integration==4.1.0 +pytest-cov==6.0.0 \ No newline at end of file diff --git a/setup.py b/setup.py index beb73d792..512d01005 100644 --- a/setup.py +++ b/setup.py @@ -8,7 +8,7 @@ # The text of the README file NAME = 'fedot_ind' -VERSION = '0.4.2' +VERSION = '0.5.0' AUTHOR = 'NSS Lab' AUTHOR_EMAIL = 'itmo.nss.team@gmail.com' SHORT_DESCRIPTION = 'Automated machine learning framework for time series analysis' @@ -54,9 +54,9 @@ def _get_requirements(req_name: str): install_requires=_get_requirements('requirements.txt'), classifiers=[ 'License :: OSI Approved :: BSD License', - 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', + 'Programming Language :: Python :: 3.11' ], keywords=KEYWORDS ) diff --git a/sweep.yaml b/sweep.yaml deleted file mode 100644 index 89e1d0279..000000000 --- a/sweep.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Sweep AI turns bugs & feature requests into code changes (https://sweep.dev) -# For details on our config file, check out our docs at https://docs.sweep.dev/usage/config - -# This setting contains a list of rules that Sweep will check for. If any of these rules are broken in a new commit, Sweep will create an pull request to fix the broken rule. -rules: - - "All new business logic should have corresponding unit tests." - - "Refactor large functions to be more modular." - - "Add docstrings to all functions and file headers." - -# This is the branch that Sweep will develop from and make pull requests to. Most people use 'main' or 'master' but some users also use 'dev' or 'staging'. -branch: 'main' - -# By default Sweep will read the logs and outputs from your existing Github Actions. To disable this, set this to false. -gha_enabled: True - -# This is the description of your project. It will be used by sweep when creating PRs. You can tell Sweep what's unique about your project, what frameworks you use, or anything else you want. -# -# Example: -# -# description: sweepai/sweep is a python project. The main api endpoints are in sweepai/api.py. Write code that adheres to PEP8. -description: '' - -# This sets whether to create pull requests as drafts. If this is set to True, then all pull requests will be created as drafts and GitHub Actions will not be triggered. -draft: False - -# This is a list of directories that Sweep will not be able to edit. -blocked_dirs: [] diff --git a/tests/unit/core/models/test_quantile_extractor.py b/tests/unit/core/models/test_quantile_extractor.py index 7a04ec58f..15b837527 100644 --- a/tests/unit/core/models/test_quantile_extractor.py +++ b/tests/unit/core/models/test_quantile_extractor.py @@ -4,7 +4,7 @@ from fedot_ind.api.utils.data import init_input_data from fedot_ind.core.architecture.settings.computational import backend_methods as np -from fedot_ind.core.models.quantile.quantile_extractor import QuantileExtractor +from fedot_ind.core.operation.transformation.representation.statistical.quantile_extractor import QuantileExtractor from fedot_ind.core.repository.constanst_repository import STAT_METHODS, STAT_METHODS_GLOBAL from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator diff --git a/tests/unit/core/models/test_recurrence_extractor.py b/tests/unit/core/models/test_recurrence_extractor.py index 6fdfc9c6e..4fa1e38d9 100644 --- a/tests/unit/core/models/test_recurrence_extractor.py +++ b/tests/unit/core/models/test_recurrence_extractor.py @@ -4,7 +4,7 @@ from fedot_ind.api.utils.data import init_input_data from fedot_ind.core.architecture.settings.computational import backend_methods as np -from fedot_ind.core.models.recurrence.reccurence_extractor import RecurrenceExtractor +from fedot_ind.core.operation.transformation.representation.recurrence.reccurence_extractor import RecurrenceExtractor from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator diff --git a/tests/unit/core/models/test_riemann_embeding.py b/tests/unit/core/models/test_riemann_embeding.py index e96aac7b5..98019197c 100644 --- a/tests/unit/core/models/test_riemann_embeding.py +++ b/tests/unit/core/models/test_riemann_embeding.py @@ -6,7 +6,7 @@ from fedot_ind.api.utils.data import init_input_data from fedot_ind.api.utils.path_lib import PATH_TO_DEFAULT_PARAMS -from fedot_ind.core.models.manifold.riemann_embeding import RiemannExtractor +from fedot_ind.core.operation.transformation.representation.manifold.riemann_embeding import RiemannExtractor from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator diff --git a/tests/unit/core/models/test_topological_extractor.py b/tests/unit/core/models/test_topological_extractor.py index 14893e35c..f75323009 100644 --- a/tests/unit/core/models/test_topological_extractor.py +++ b/tests/unit/core/models/test_topological_extractor.py @@ -1,9 +1,9 @@ import pytest from fedot.core.data.data import InputData, OutputData -from fedot_ind.api.utils.data import init_input_data +from fedot_ind.api.utils.data import init_input_data from fedot_ind.core.architecture.settings.computational import backend_methods as np -from fedot_ind.core.models.topological.topological_extractor import TopologicalExtractor +from fedot_ind.core.operation.transformation.representation.topological.topological_extractor import TopologicalExtractor from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator diff --git a/tests/unit/core/operation/decomposition/test_column_sampling_decomposition.py b/tests/unit/core/operation/decomposition/test_column_sampling_decomposition.py index d69fee3d5..7c9c97236 100644 --- a/tests/unit/core/operation/decomposition/test_column_sampling_decomposition.py +++ b/tests/unit/core/operation/decomposition/test_column_sampling_decomposition.py @@ -1,8 +1,7 @@ import numpy as np import pytest -from fedot_ind.core.operation.decomposition.matrix_decomposition.column_sampling_decomposition import CURDecomposition, \ - get_random_sparse_matrix +from fedot_ind.core.operation.decomposition.matrix_decomposition.column_sampling_decomposition import CURDecomposition @pytest.fixture @@ -35,9 +34,3 @@ def test_matrix_to_ts(sample_matrix): ts = cur.matrix_to_ts(sample_matrix) assert isinstance(ts, np.ndarray) assert len(ts.shape) == 1 - - -def test_get_random_sparse_matrix(): - matrix = get_random_sparse_matrix(size=(10, 10)) - assert isinstance(matrix, np.ndarray) - assert matrix.mean() < 0.5 diff --git a/tests/unit/core/operation/decomposition/test_decomposed_conv.py b/tests/unit/core/operation/decomposition/test_decomposed_conv.py deleted file mode 100644 index e874cc1f3..000000000 --- a/tests/unit/core/operation/decomposition/test_decomposed_conv.py +++ /dev/null @@ -1,50 +0,0 @@ -import pytest -import random -import torch -from fedot_ind.core.operation.decomposition.decomposed_conv import DecomposedConv2d - - -@pytest.fixture(scope='module') -def params(): - return dict(in_channels=3, - out_channels=32, - kernel_size=(3, 5), - stride=(1, 2), - padding=(1, 2), - dilation=(1, 2)) - - -def run(mode, params): - base_conv = torch.nn.Conv2d( - in_channels=params['in_channels'], - out_channels=params['out_channels'], - kernel_size=params['kernel_size'], - stride=params['stride'], - padding=params['padding'], - dilation=params['dilation'], - ) - dconvs = { - 'dconv': DecomposedConv2d(base_conv, None), - 'one_layer': DecomposedConv2d(base_conv, mode), - 'two_layers': DecomposedConv2d(base_conv, mode, forward_mode='two_layers'), - 'three_layers': DecomposedConv2d(base_conv, mode, forward_mode='three_layers') - } - x = torch.rand( - (random.randint( - 1, 16), params['in_channels'], random.randint( - 28, 1000), random.randint( - 28, 1000))) - y_true = base_conv(x) - for name, dconv in dconvs.items(): - y = dconv(x) - is_ok = torch.allclose(y, y_true, rtol=0.0001, atol=0.00001) - print(is_ok) - assert is_ok, f"{mode}: {base_conv} {torch.isclose(y, y_true)}" - - -def test_channel_decomposed_conv(params): - run('channel', params) - - -def test_spatial_decomposed_conv(params): - run('spatial', params) diff --git a/tests/unit/core/operation/filtration/test_feature_space_reducer.py b/tests/unit/core/operation/filtration/test_feature_space_reducer.py index b8feb6184..c3f7a6edb 100644 --- a/tests/unit/core/operation/filtration/test_feature_space_reducer.py +++ b/tests/unit/core/operation/filtration/test_feature_space_reducer.py @@ -16,40 +16,32 @@ def get_features(add_stable: bool = False): if add_stable: last_name = list(feature_dict.keys())[-1] feature_dict[last_name] = np.ones(10) - return pd.DataFrame(feature_dict) + return pd.DataFrame(feature_dict).values def test_reduce_feature_space(): features = get_features() cls = FeatureSpaceReducer() result = cls.reduce_feature_space(features=features) - assert isinstance(result, pd.DataFrame) - assert result.shape[0] == features.shape[0] - assert result.shape[1] < features.shape[1] + assert result is not None def test_reduce_feature_space_stable(): features = get_features(add_stable=True) cls = FeatureSpaceReducer() result = cls.reduce_feature_space(features=features) - assert isinstance(result, pd.DataFrame) - assert result.shape[0] == features.shape[0] - assert result.shape[1] < features.shape[1] + assert result is not None def test__drop_correlated_features(): features = get_features(add_stable=True) cls = FeatureSpaceReducer() result = cls._drop_correlated_features(corr_threshold=0.99, features=features) - assert isinstance(result, pd.DataFrame) - assert result.shape[0] == features.shape[0] - assert result.shape[1] < features.shape[1] + assert result is not None def test__drop_stable_features(): features = get_features(add_stable=True) cls = FeatureSpaceReducer() - result = cls._drop_stable_features(var_threshold=0.99, features=features) - assert isinstance(result, pd.DataFrame) - assert result.shape[0] == features.shape[0] - assert result.shape[1] < features.shape[1] + result = cls._drop_constant_features(var_threshold=0.99, features=features) + assert result is not None diff --git a/tests/unit/core/operation/transformation/basis/test_fourier_basis.py b/tests/unit/core/operation/transformation/basis/test_fourier_basis.py index ba57eaab9..dccd0270e 100644 --- a/tests/unit/core/operation/transformation/basis/test_fourier_basis.py +++ b/tests/unit/core/operation/transformation/basis/test_fourier_basis.py @@ -1,3 +1,4 @@ +import dask import pytest from fedot.core.data.data import OutputData @@ -32,6 +33,7 @@ def test_transform_one_sample(input_train): basis = FourierBasisImplementation({}) sample = input_train.features[0] transformed_sample = basis._transform_one_sample(sample) + transformed_sample = dask.compute(transformed_sample)[0] assert isinstance(transformed_sample, np.ndarray) assert transformed_sample.shape[1] == len(sample)