From de7df83bb4309064f73574afc2b70473c36cf277 Mon Sep 17 00:00:00 2001 From: "Vadim A. Potemkin" Date: Thu, 21 Dec 2023 12:16:28 +0100 Subject: [PATCH] unit and integration test improvement + explainer module (#108) * unit-test coverage enhanced * integration tests improvement * added point/interval explainer with distance methods --- .../ItalyPowerDemand_fake_TEST.arff | 100 ++ .../ItalyPowerDemand_fake_TEST.ts | 80 + .../ItalyPowerDemand_fake_TEST.txt | 67 + .../ItalyPowerDemand_fake_TRAIN.arff | 100 ++ .../ItalyPowerDemand_fake_TRAIN.ts | 80 + .../ItalyPowerDemand_fake_TRAIN.txt | 67 + examples/ensemble/kernel_ensemble_example.py | 67 + examples/explainability.ipynb | 1477 +++++++++++++++++ fedot_ind/api/main.py | 22 +- fedot_ind/api/utils/input_data.py | 22 +- fedot_ind/api/utils/path_lib.py | 8 +- fedot_ind/api/utils/reporter.py | 37 - fedot_ind/api/utils/saver_collections.py | 11 +- .../core/architecture/datasets/splitters.py | 3 +- .../experiment/TimeSeriesAnomalyDetection.py | 53 +- .../experiment/TimeSeriesClassifier.py | 14 +- .../experiment/TimeSeriesClassifierNN.py | 8 +- .../experiment/TimeSeriesClassifierPreset.py | 37 +- .../pipelines/anomaly_detection.py | 3 +- .../postprocessing/results_picker.py | 1 - .../visualisation/matrix_vis.py | 42 - fedot_ind/core/ensemble/kernel_ensemble.py | 78 +- fedot_ind/core/ensemble/rank_ensembler.py | 4 +- fedot_ind/core/models/nn/inception.py | 4 +- .../models/quantile/quantile_extractor.py | 23 - .../column_sampling_decomposition.py | 58 - .../matrix_decomposition/dmd_decomposition.py | 20 +- .../operation/optimization/FeatureSpace.py | 2 +- .../transformation/FeatureSpaceReducer.py | 10 - .../transformation/basis/eigen_basis.py | 10 +- .../core/operation/transformation/splitter.py | 69 +- .../core/optimizer/IndustrialEvoOptimizer.py | 6 +- .../data/default_operation_params.json | 5 +- .../explain}/__init__.py | 0 fedot_ind/tools/explain/distances.py | 112 ++ fedot_ind/tools/explain/explain.py | 177 ++ fedot_ind/tools/loader.py | 14 +- .../tools/synthetic/anomaly_generator.py | 4 +- .../tools/synthetic/ts_datasets_generator.py | 55 +- fedot_ind/tools/synthetic/ts_generator.py | 15 +- requirements.txt | 2 + .../ItalyPowerDemand_tsv_TEST.tsv | 67 + .../ItalyPowerDemand_tsv_TRAIN.tsv | 67 + .../preprocessing/test_load_data.py | 27 - tests/unit/api/test_api_main.py | 145 +- ...t_cinfigurator.py => test_configurator.py} | 7 +- tests/unit/api/utils/test_input_data.py | 39 + .../unit/api/utils/test_saver_collections.py | 67 + .../experiment/test_TimeSeriesClassifier.py | 40 - .../test_TimeSeriesClassifierPreset.py | 37 - .../unit/core}/__init__.py | 0 .../core/architecture}/__init__.py | 0 .../architecture/abstraction}/__init__.py | 0 .../architecture/abstraction/test_checkers.py | 31 + .../architecture/datasets/__init__.py | 0 .../datasets/test_classification_datasets.py | 34 + .../test_object_detection_datasets.py | 37 + .../datasets/test_prediction_datasets.py | 37 + .../architecture/datasets/test_splitters.py | 27 +- .../datasets/test_visualization.py | 42 + .../architecture/experiment/__init__.py | 0 .../test_TimeSeriesAnomalyDetection.py | 45 + .../experiment/test_TimeSeriesClassifier.py | 166 ++ .../test_TimeSeriesClassifierPreset.py | 60 + .../experiment/test_TimeSeriesRegression.py | 26 +- .../experiment/test_nn_experimenter.py | 111 ++ .../architecture/pipelines}/__init__.py | 0 .../architecture/postprocessing}/__init__.py | 0 .../postprocessing/test_results_picker.py | 22 + .../static => core/ensemble}/__init__.py | 0 .../ensemble}/test_RankEnsemble.py | 31 +- .../core/ensemble/test_kernel_ensemble.py | 70 + tests/unit/{ => core}/metrics/__init__.py | 0 .../{models => core/metrics/loss}/__init__.py | 0 .../unit/core/metrics/loss/test_basis_loss.py | 43 + tests/unit/core/metrics/loss/test_soft_dtw.py | 26 + tests/unit/core/metrics/loss/test_svd_loss.py | 29 + .../{ => core}/metrics/test_cv_metrics.py | 0 tests/unit/{ => core}/metrics/test_metric.py | 0 .../{operation => core/models}/__init__.py | 0 .../core/models/test_classification_models.py | 36 + .../models/test_detection_models.py | 0 tests/unit/core/models/test_inception.py | 50 + .../models/test_quantile_extractor.py | 17 +- .../models/test_recurrence_extractor.py | 6 +- .../models/test_signal_extractor.py | 6 +- tests/unit/{ => core}/models/test_ssa.py | 2 +- .../models/test_topological_extractor.py | 6 +- .../{ => core}/models/test_transformers.py | 0 tests/unit/core/models/test_unet.py | 43 + .../operation}/__init__.py | 0 .../operation/decomposition}/__init__.py | 0 .../test_column_sampling_decomposition.py | 42 + .../decomposition/test_decomposed_conv.py | 0 .../decomposition/test_dmd_decomposition.py | 27 + .../decomposition/test_physic_dmd.py | 23 + .../operation/filtration}/__init__.py | 0 .../filtration/test_quantile_filtration.py | 10 + .../operation/optimization}/__init__.py | 0 .../optimization/test_feature_space.py | 36 + .../operation/optimization/test_sfp_tools.py | 0 .../test_structure_optimization.py | 51 + .../operation/optimization/test_svd_tools.py | 0 .../core/operation/test_DummyOperation.py | 21 + .../operation/test_SpectrDecompose.py | 0 .../operation/transformation}/__init__.py | 0 .../transformation/basis}/__init__.py | 0 .../transformation/basis/test_eigen_basis.py | 42 +- .../basis/test_fourier_basis.py | 2 +- .../basis/test_wavelet_basis.py | 2 +- .../transformation/data}/__init__.py | 0 .../transformation/data/test_HankelMatrix.py | 0 .../transformation/data/test_eigen.py | 0 .../transformation/data/test_kernel_matrix.py | 0 .../transformation/test_WindowSelection.py | 0 .../test_feature_space_reducer.py | 55 + .../operation/transformation/test_splitter.py | 194 +++ tests/unit/core/operation/utils/__init__.py | 0 .../operation/utils/test_caching.py | 0 tests/unit/core/repository/__init__.py | 0 .../transformation/test_TSSplitter.py | 93 -- tests/unit/tools/__init__.py | 0 tests/unit/tools/test_anomaly_generator.py | 64 + tests/unit/tools/test_load_data.py | 132 ++ tests/unit/tools/test_point_explainer.py | 71 + .../unit/tools/test_ts_datasets_generator.py | 24 + tests/unit/tools/test_ts_generator.py | 33 + 127 files changed, 4619 insertions(+), 769 deletions(-) create mode 100644 examples/data/ItalyPowerDemand_fake/ItalyPowerDemand_fake_TEST.arff create mode 100644 examples/data/ItalyPowerDemand_fake/ItalyPowerDemand_fake_TEST.ts create mode 100644 examples/data/ItalyPowerDemand_fake/ItalyPowerDemand_fake_TEST.txt create mode 100644 examples/data/ItalyPowerDemand_fake/ItalyPowerDemand_fake_TRAIN.arff create mode 100644 examples/data/ItalyPowerDemand_fake/ItalyPowerDemand_fake_TRAIN.ts create mode 100644 examples/data/ItalyPowerDemand_fake/ItalyPowerDemand_fake_TRAIN.txt create mode 100644 examples/ensemble/kernel_ensemble_example.py create mode 100644 examples/explainability.ipynb delete mode 100644 fedot_ind/api/utils/reporter.py delete mode 100644 fedot_ind/core/architecture/postprocessing/visualisation/matrix_vis.py rename fedot_ind/{core/architecture/postprocessing/visualisation => tools/explain}/__init__.py (100%) create mode 100644 fedot_ind/tools/explain/distances.py create mode 100644 fedot_ind/tools/explain/explain.py create mode 100644 tests/data/datasets/ItalyPowerDemand_tsv/ItalyPowerDemand_tsv_TEST.tsv create mode 100644 tests/data/datasets/ItalyPowerDemand_tsv/ItalyPowerDemand_tsv_TRAIN.tsv delete mode 100644 tests/integration/preprocessing/test_load_data.py rename tests/unit/api/utils/{test_cinfigurator.py => test_configurator.py} (94%) create mode 100644 tests/unit/api/utils/test_input_data.py create mode 100644 tests/unit/api/utils/test_saver_collections.py delete mode 100644 tests/unit/architecture/experiment/test_TimeSeriesClassifier.py delete mode 100644 tests/unit/architecture/experiment/test_TimeSeriesClassifierPreset.py rename {fedot_ind/core/architecture/preprocessing => tests/unit/core}/__init__.py (100%) rename tests/{integration/preprocessing => unit/core/architecture}/__init__.py (100%) rename tests/unit/{architecture => core/architecture/abstraction}/__init__.py (100%) create mode 100644 tests/unit/core/architecture/abstraction/test_checkers.py rename tests/unit/{ => core}/architecture/datasets/__init__.py (100%) create mode 100644 tests/unit/core/architecture/datasets/test_classification_datasets.py create mode 100644 tests/unit/core/architecture/datasets/test_object_detection_datasets.py create mode 100644 tests/unit/core/architecture/datasets/test_prediction_datasets.py rename tests/unit/{ => core}/architecture/datasets/test_splitters.py (52%) create mode 100644 tests/unit/core/architecture/datasets/test_visualization.py rename tests/unit/{ => core}/architecture/experiment/__init__.py (100%) create mode 100644 tests/unit/core/architecture/experiment/test_TimeSeriesAnomalyDetection.py create mode 100644 tests/unit/core/architecture/experiment/test_TimeSeriesClassifier.py create mode 100644 tests/unit/core/architecture/experiment/test_TimeSeriesClassifierPreset.py rename tests/unit/{ => core}/architecture/experiment/test_TimeSeriesRegression.py (59%) create mode 100644 tests/unit/core/architecture/experiment/test_nn_experimenter.py rename tests/unit/{ensemble => core/architecture/pipelines}/__init__.py (100%) rename tests/unit/{ensemble/baseline => core/architecture/postprocessing}/__init__.py (100%) create mode 100644 tests/unit/core/architecture/postprocessing/test_results_picker.py rename tests/unit/{ensemble/static => core/ensemble}/__init__.py (100%) rename tests/unit/{ensemble/static => core/ensemble}/test_RankEnsemble.py (52%) create mode 100644 tests/unit/core/ensemble/test_kernel_ensemble.py rename tests/unit/{ => core}/metrics/__init__.py (100%) rename tests/unit/{models => core/metrics/loss}/__init__.py (100%) create mode 100644 tests/unit/core/metrics/loss/test_basis_loss.py create mode 100644 tests/unit/core/metrics/loss/test_soft_dtw.py create mode 100644 tests/unit/core/metrics/loss/test_svd_loss.py rename tests/unit/{ => core}/metrics/test_cv_metrics.py (100%) rename tests/unit/{ => core}/metrics/test_metric.py (100%) rename tests/unit/{operation => core/models}/__init__.py (100%) create mode 100644 tests/unit/core/models/test_classification_models.py rename tests/unit/{ => core}/models/test_detection_models.py (100%) create mode 100644 tests/unit/core/models/test_inception.py rename tests/unit/{ => core}/models/test_quantile_extractor.py (83%) rename tests/unit/{ => core}/models/test_recurrence_extractor.py (96%) rename tests/unit/{ => core}/models/test_signal_extractor.py (94%) rename tests/unit/{ => core}/models/test_ssa.py (93%) rename tests/unit/{ => core}/models/test_topological_extractor.py (95%) rename tests/unit/{ => core}/models/test_transformers.py (100%) create mode 100644 tests/unit/core/models/test_unet.py rename tests/unit/{operation/decomposition => core/operation}/__init__.py (100%) rename tests/unit/{operation/optimization => core/operation/decomposition}/__init__.py (100%) create mode 100644 tests/unit/core/operation/decomposition/test_column_sampling_decomposition.py rename tests/unit/{ => core}/operation/decomposition/test_decomposed_conv.py (100%) create mode 100644 tests/unit/core/operation/decomposition/test_dmd_decomposition.py create mode 100644 tests/unit/core/operation/decomposition/test_physic_dmd.py rename tests/unit/{operation/transformation => core/operation/filtration}/__init__.py (100%) create mode 100644 tests/unit/core/operation/filtration/test_quantile_filtration.py rename tests/unit/{operation/transformation/basis => core/operation/optimization}/__init__.py (100%) create mode 100644 tests/unit/core/operation/optimization/test_feature_space.py rename tests/unit/{ => core}/operation/optimization/test_sfp_tools.py (100%) create mode 100644 tests/unit/core/operation/optimization/test_structure_optimization.py rename tests/unit/{ => core}/operation/optimization/test_svd_tools.py (100%) create mode 100644 tests/unit/core/operation/test_DummyOperation.py rename tests/unit/{ => core}/operation/test_SpectrDecompose.py (100%) rename tests/unit/{operation/transformation/data => core/operation/transformation}/__init__.py (100%) rename tests/unit/{operation/utils => core/operation/transformation/basis}/__init__.py (100%) rename tests/unit/{ => core}/operation/transformation/basis/test_eigen_basis.py (55%) rename tests/unit/{ => core}/operation/transformation/basis/test_fourier_basis.py (98%) rename tests/unit/{ => core}/operation/transformation/basis/test_wavelet_basis.py (99%) rename tests/unit/{repository => core/operation/transformation/data}/__init__.py (100%) rename tests/unit/{ => core}/operation/transformation/data/test_HankelMatrix.py (100%) rename tests/unit/{ => core}/operation/transformation/data/test_eigen.py (100%) rename tests/unit/{ => core}/operation/transformation/data/test_kernel_matrix.py (100%) rename tests/unit/{ => core}/operation/transformation/test_WindowSelection.py (100%) create mode 100644 tests/unit/core/operation/transformation/test_feature_space_reducer.py create mode 100644 tests/unit/core/operation/transformation/test_splitter.py create mode 100644 tests/unit/core/operation/utils/__init__.py rename tests/unit/{ => core}/operation/utils/test_caching.py (100%) create mode 100644 tests/unit/core/repository/__init__.py delete mode 100644 tests/unit/operation/transformation/test_TSSplitter.py create mode 100644 tests/unit/tools/__init__.py create mode 100644 tests/unit/tools/test_anomaly_generator.py create mode 100644 tests/unit/tools/test_load_data.py create mode 100644 tests/unit/tools/test_point_explainer.py create mode 100644 tests/unit/tools/test_ts_datasets_generator.py create mode 100644 tests/unit/tools/test_ts_generator.py diff --git a/examples/data/ItalyPowerDemand_fake/ItalyPowerDemand_fake_TEST.arff b/examples/data/ItalyPowerDemand_fake/ItalyPowerDemand_fake_TEST.arff new file mode 100644 index 000000000..498eba65f --- /dev/null +++ b/examples/data/ItalyPowerDemand_fake/ItalyPowerDemand_fake_TEST.arff @@ -0,0 +1,100 @@ +%The data was derived from twelve monthly electrical power demand +%time series from Italy and first used in the paper "Intelligent +%Icons: Integrating Lite-Weight Data Mining and Visualization into +%GUI Operating Systems". The classification task is to distinguish +%days from Oct to March (inclusive) from April to September. +@Relation ItalyPowerDemand +@attribute att1 numeric +@attribute att2 numeric +@attribute att3 numeric +@attribute att4 numeric +@attribute att5 numeric +@attribute att6 numeric +@attribute att7 numeric +@attribute att8 numeric +@attribute att9 numeric +@attribute att10 numeric +@attribute att11 numeric +@attribute att12 numeric +@attribute att13 numeric +@attribute att14 numeric +@attribute att15 numeric +@attribute att16 numeric +@attribute att17 numeric +@attribute att18 numeric +@attribute att19 numeric +@attribute att20 numeric +@attribute att21 numeric +@attribute att22 numeric +@attribute att23 numeric +@attribute att24 numeric +@attribute target {1,2} + +@data 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The classification task is to distinguish +#days from Oct to March (inclusive) from April to September. +@problemName ItalyPowerDemand +@timeStamps false +@missing false +@univariate true +@equalLength true +@seriesLength 24 +@classLabel true 1 2 +@data +-0.71051757,-1.1833204,-1.3724416,-1.5930829,-1.4670021,-1.3724416,-1.0887599,0.045966947,0.92853223,1.0861332,1.2752543,0.96005242,0.61333034,0.014446758,-0.6474772,-0.26923494,-0.20619456,0.61333034,1.3698149,1.4643754,1.054613,0.58181015,0.1720477,-0.26923494:1 +-0.99300935,-1.4267865,-1.5798843,-1.6054006,-1.6309169,-1.3757539,-1.0185257,-0.35510183,0.71658276,1.2013925,1.1248436,1.0482947,0.79313166,0.46141977,0.48693607,0.56348497,0.61451757,0.30832197,0.25728936,1.0993273,1.0482947,0.69106647,-0.048906237,-0.38061813:1 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1.6966088e-01 -5.1942331e-01 -8.3264339e-01 -8.9528740e-01 -7.0735536e-01 -7.0735536e-01 -1.7723037e+00 -8.9528740e-01 4.8288096e-01 6.7081301e-01 7.3345703e-01 7.3345703e-01 4.2023694e-01 -5.1942331e-01 -6.4471135e-01 -7.0735536e-01 -7.6999938e-01 -7.0735536e-01 -2.6884724e-01 2.3230490e-01 2.4248455e+00 1.9236934e+00 1.1719652e+00 diff --git a/examples/data/ItalyPowerDemand_fake/ItalyPowerDemand_fake_TRAIN.arff b/examples/data/ItalyPowerDemand_fake/ItalyPowerDemand_fake_TRAIN.arff new file mode 100644 index 000000000..498eba65f --- /dev/null +++ b/examples/data/ItalyPowerDemand_fake/ItalyPowerDemand_fake_TRAIN.arff @@ -0,0 +1,100 @@ +%The data was derived from twelve monthly electrical power demand +%time series from Italy and first used in the paper "Intelligent +%Icons: Integrating Lite-Weight Data Mining and Visualization into +%GUI Operating Systems". The classification task is to distinguish +%days from Oct to March (inclusive) from April to September. +@Relation ItalyPowerDemand +@attribute att1 numeric +@attribute att2 numeric +@attribute att3 numeric +@attribute att4 numeric +@attribute att5 numeric +@attribute att6 numeric +@attribute att7 numeric +@attribute att8 numeric +@attribute att9 numeric +@attribute att10 numeric +@attribute att11 numeric +@attribute att12 numeric +@attribute att13 numeric +@attribute att14 numeric +@attribute att15 numeric +@attribute att16 numeric +@attribute att17 numeric +@attribute att18 numeric +@attribute att19 numeric +@attribute att20 numeric +@attribute att21 numeric +@attribute att22 numeric +@attribute att23 numeric +@attribute att24 numeric +@attribute target {1,2} + +@data 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The classification task is to distinguish +#days from Oct to March (inclusive) from April to September. +@problemName ItalyPowerDemand +@timeStamps false +@missing false +@univariate true +@equalLength true +@seriesLength 24 +@classLabel true 1 2 +@data +-0.71051757,-1.1833204,-1.3724416,-1.5930829,-1.4670021,-1.3724416,-1.0887599,0.045966947,0.92853223,1.0861332,1.2752543,0.96005242,0.61333034,0.014446758,-0.6474772,-0.26923494,-0.20619456,0.61333034,1.3698149,1.4643754,1.054613,0.58181015,0.1720477,-0.26923494:1 +-0.99300935,-1.4267865,-1.5798843,-1.6054006,-1.6309169,-1.3757539,-1.0185257,-0.35510183,0.71658276,1.2013925,1.1248436,1.0482947,0.79313166,0.46141977,0.48693607,0.56348497,0.61451757,0.30832197,0.25728936,1.0993273,1.0482947,0.69106647,-0.048906237,-0.38061813:1 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1.6966088e-01 -5.1942331e-01 -8.3264339e-01 -8.9528740e-01 -7.0735536e-01 -7.0735536e-01 -1.7723037e+00 -8.9528740e-01 4.8288096e-01 6.7081301e-01 7.3345703e-01 7.3345703e-01 4.2023694e-01 -5.1942331e-01 -6.4471135e-01 -7.0735536e-01 -7.6999938e-01 -7.0735536e-01 -2.6884724e-01 2.3230490e-01 2.4248455e+00 1.9236934e+00 1.1719652e+00 diff --git a/examples/ensemble/kernel_ensemble_example.py b/examples/ensemble/kernel_ensemble_example.py new file mode 100644 index 000000000..e4ed2d787 --- /dev/null +++ b/examples/ensemble/kernel_ensemble_example.py @@ -0,0 +1,67 @@ +from fedot import Fedot + +from fedot_ind.core.ensemble.kernel_ensemble import init_kernel_ensemble +from fedot_ind.core.ensemble.rank_ensembler import RankEnsemble +from fedot_ind.tools.loader import DataLoader + +n_best = 3 +feature_dict = {} +metric_list = [] +proba_dict = {} +metric_dict = {} +dataset_name = 'Lightning2' +kernel_list = {'wavelet': [ + {'feature_generator_type': 'signal', + 'feature_hyperparams': { + 'wavelet': "mexh", + 'n_components': 2 + }}, + {'feature_generator_type': 'signal', + 'feature_hyperparams': { + 'wavelet': "morl", + 'n_components': 2 + }}], + 'quantile': [ + {'feature_generator_type': 'quantile', + 'feature_hyperparams': { + 'window_mode': True, + 'window_size': 25 + } + }, + {'feature_generator_type': 'quantile', + 'feature_hyperparams': { + 'window_mode': False, + 'window_size': 40 + } + }] +} +fg_names = [] +for key in kernel_list: + for model_params in kernel_list[key]: + fg_names.append(f'{key}_{model_params}') + +train_data, test_data = DataLoader(dataset_name).load_data() +set_of_fg, train_feats, train_target, test_feats, test_target = init_kernel_ensemble(train_data, + test_data, + kernel_list=kernel_list) + +n_best_generators = set_of_fg.T.nlargest(n_best, 0).index +for rank in range(n_best): + fg_rank = n_best_generators[rank] + train_best = train_feats[fg_rank] + test_best = test_feats[fg_rank] + feature_dict.update({fg_names[rank]: (test_best, test_best)}) + +for model_name, feature in feature_dict.items(): + industrial = Fedot(metric='roc_auc', timeout=5, problem='classification', n_jobs=6) + + model = industrial.fit(feature[0], train_target) + labels = industrial.predict(feature[1]) + proba_dict.update({model_name: industrial.predict_proba(feature[1])}) + metric_dict.update({model_name: industrial.get_metrics(test_target, metric_names=['roc_auc', 'f1', 'accuracy'])}) +rank_ensembler = RankEnsemble(dataset_name=dataset_name, + proba_dict={dataset_name: proba_dict}, + metric_dict={dataset_name: metric_dict}) + +ensemble_result = rank_ensembler.ensemble() +_ = 1 diff --git a/examples/explainability.ipynb b/examples/explainability.ipynb new file mode 100644 index 000000000..8a5e51a3f --- /dev/null +++ b/examples/explainability.ipynb @@ -0,0 +1,1477 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Explainability Methods" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "Machine learning models, especially those dealing with time series data, often operate as complex **black boxes**, making it challenging to interpret their decisions. Explainability methods play a crucial role in demystifying these models and enhancing their trustworthiness.\n", + "\n", + "In this notebook, we'll explore key explainability techniques, implemented in out framework, including SHAP, and Time Series Points Perturbation Analysis." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "Before we start with explainability methods, let's train two models: a simple statistical model and a complex one with several preprocessing nodes:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:23.466186: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-12-20 13:54:27,342 INFO: FedotIndustrialAPI - Initialising experiment setup\n", + "2023-12-20 13:54:27,344 INFO: Configurator - Experiment setup:\n", + "2023-12-20 13:54:27,344 INFO: Configurator - task - ts_classification\n", + "2023-12-20 13:54:27,345 INFO: Configurator - dataset - dataset\n", + "2023-12-20 13:54:27,346 INFO: Configurator - strategy - quantile\n", + "2023-12-20 13:54:27,347 INFO: Configurator - use_cache - False\n", + "2023-12-20 13:54:27,348 INFO: FedotIndustrialAPI - Initialising solver\n", + "2023-12-20 13:54:27,350 INFO: TimeSeriesClassifier - TimeSeriesClassifier initialised\n", + "2023-12-20 13:54:27,352 INFO: FedotIndustrialAPI - Initialising experiment setup\n", + "2023-12-20 13:54:27,354 INFO: Configurator - Experiment setup:\n", + "2023-12-20 13:54:27,355 INFO: Configurator - task - ts_classification\n", + "2023-12-20 13:54:27,356 INFO: Configurator - dataset - dataset\n", + "2023-12-20 13:54:27,357 INFO: Configurator - strategy - topological\n", + "2023-12-20 13:54:27,358 INFO: Configurator - use_cache - False\n", + "2023-12-20 13:54:27,359 INFO: FedotIndustrialAPI - Initialising solver\n", + "2023-12-20 13:54:27,360 INFO: TimeSeriesClassifier - TimeSeriesClassifier initialised\n", + "2023-12-20 13:54:27,361 INFO: FedotIndustrialAPI - Initialising experiment setup\n", + "2023-12-20 13:54:27,362 INFO: Configurator - Experiment setup:\n", + "2023-12-20 13:54:27,363 INFO: Configurator - task - ts_classification\n", + "2023-12-20 13:54:27,364 INFO: Configurator - dataset - dataset\n", + "2023-12-20 13:54:27,365 INFO: Configurator - strategy - fedot_preset\n", + "2023-12-20 13:54:27,365 INFO: Configurator - branch_nodes - ['eigen_basis', 'fourier_basis']\n", + "2023-12-20 13:54:27,366 INFO: Configurator - use_cache - False\n", + "2023-12-20 13:54:27,367 INFO: FedotIndustrialAPI - Initialising solver\n", + "2023-12-20 13:54:28,329 INFO: TimeSeriesClassifier_Preset - TimeSeriesClassifierPreset initialised with [['eigen_basis', 'fourier_basis']] nodes and [2] tuning iterations and [2] timeout\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,329 - TimeSeriesClassifierPreset initialised with [['eigen_basis', 'fourier_basis']] nodes and [2] tuning iterations and [2] timeout\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "from fedot_ind.api.main import FedotIndustrial as FI\n", + "from fedot_ind.tools.loader import DataLoader\n", + "from fedot_ind.tools.explain.explain import PointExplainer\n", + "\n", + "available_operations=['scaling',\n", + " 'normalization',\n", + " 'xgboost',\n", + " 'rfr',\n", + " 'rf',\n", + " 'logit',\n", + " 'mlp',\n", + " 'knn',\n", + " 'lgbm',\n", + " 'pca']\n", + "\n", + "stat_model = FI(task='ts_classification',\n", + " dataset='dataset',\n", + " strategy='quantile',\n", + " use_cache=False,\n", + " timeout=0.1,\n", + " n_jobs=-1,\n", + " logging_level=50)\n", + "\n", + "topo_model = FI(task='ts_classification',\n", + " dataset='dataset',\n", + " strategy='topological',\n", + " use_cache=False,\n", + " timeout=0.1,\n", + " n_jobs=-1,\n", + " logging_level=50)\n", + "\n", + "\n", + "comp_model = FI(task='ts_classification',\n", + " dataset='dataset',\n", + " strategy='fedot_preset',\n", + " branch_nodes=['eigen_basis', 'fourier_basis'],\n", + " tuning_iterations=2,\n", + " tuning_timeout=2,\n", + " use_cache=False,\n", + " timeout=0.1,\n", + " n_jobs=-1,\n", + " logging_level=50,\n", + " available_operations=available_operations)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Synthetic data" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "\n", + "generator = TimeSeriesDatasetsGenerator(num_samples=14,\n", + " max_ts_len=50,\n", + " binary=True)\n", + "train_data, test_data = generator.generate_data()\n", + "X_test, y_test = test_data\n", + "X_train, y_train = train_data\n", + "\n", + "class_1 = np.unique(y_train)[0]\n", + "class_2 = np.unique(y_train)[1]\n", + "class_1_idx = np.where(y_train == class_1)[0]\n", + "class_2_idx = np.where(y_train == class_2)[0]\n", + "fig, axs = plt.subplots(2, 1, figsize=(10, 5))\n", + "axs[0].plot(X_train.iloc[class_1_idx, :].T)\n", + "axs[0].set_title(f'Class {class_1}')\n", + "axs[1].plot(X_train.iloc[class_2_idx, :].T)\n", + "axs[1].set_title(f'Class {class_2}')\n", + "plt.show()\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-20 12:58:05,187 INFO: TimeSeriesClassifier - Fitting model\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-20 12:58:05,187 - Fitting model\n" + ] + }, + { + "data": { + "text/plain": "{'roc_auc': 1.0}" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stat_model.fit(features=X_train, target=y_train)\n", + "stat_labels = stat_model.predict(features=X_test, target=y_test)\n", + "stat_probs = stat_model.predict_proba(features=X_test, target=y_test)\n", + "stat_model.get_metrics(target=y_test, metric_names=['roc_auc'])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing points: 100%|██████████| 10/10 [00:01<00:00, 8.23point/s]\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "distance = 'euclidean'\n", + "explainer = PointExplainer(stat_model, X_test, y_test)\n", + "explainer.explain(n_samples=5, window=10, method=distance)\n", + "explainer.visual(threshold=0, name='Custom'+'_'+distance)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "{'roc_auc': 0.556}" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "topo_model.fit(features=X_train, target=y_train)\n", + "topo_labels = topo_model.predict(features=X_test, target=y_test)\n", + "topo_probs = topo_model.predict_proba(features=X_test, target=y_test)\n", + "topo_model.get_metrics(target=y_test, metric_names=['roc_auc'])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing points: 100%|██████████| 10/10 [00:01<00:00, 5.42point/s]\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "distance = 'euclidean'\n", + "explainer = PointExplainer(topo_model, X_test, y_test)\n", + "explainer.explain(n_samples=5, window=10, method=distance)\n", + "explainer.visual(threshold=0, name='Custom'+'_'+distance)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Real data" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,339 INFO: DataLoader - Reading data from /Users/technocreep/Desktop/Working-Folder/fedot-industrial/Fedot.Industrial/fedot_ind/data/Beef\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,339 - Reading data from /Users/technocreep/Desktop/Working-Folder/fedot-industrial/Fedot.Industrial/fedot_ind/data/Beef\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,388 INFO: DataLoader - Data readed successfully from local folder\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,388 - Data readed successfully from local folder\n" + ] + } + ], + "source": [ + "from fedot_ind.api.main import FedotIndustrial as FI\n", + "from fedot_ind.tools.loader import DataLoader\n", + "\n", + "dataset = 'Beef'\n", + "train_data, test_data = DataLoader(dataset).load_data()\n", + "\n", + "X_test_beef, y_test_beef = test_data\n", + "X_train_beef, y_train_beef = train_data" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,398 INFO: FedotIndustrialAPI - Initialising experiment setup\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,398 - Initialising experiment setup\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,404 INFO: Configurator - Experiment setup:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,404 - Experiment setup:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,407 INFO: Configurator - task - ts_classification\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,407 - task - ts_classification\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,410 INFO: Configurator - dataset - Beef\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,410 - dataset - Beef\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,413 INFO: Configurator - strategy - quantile\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,413 - strategy - quantile\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,415 INFO: Configurator - branch_nodes - ['eigen_basis', 'fourier_basis']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,415 - branch_nodes - ['eigen_basis', 'fourier_basis']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,417 INFO: Configurator - use_cache - False\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,417 - use_cache - False\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,419 INFO: FedotIndustrialAPI - Initialising solver\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,419 - Initialising solver\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,422 INFO: TimeSeriesClassifier - TimeSeriesClassifier initialised\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,422 - TimeSeriesClassifier initialised\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,424 INFO: TimeSeriesClassifier - Fitting model\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-20 13:54:28,424 - Fitting model\n" + ] + }, + { + "data": { + "text/plain": "{'f1': 0.452}" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stat_model_beef = FI(task='ts_classification',\n", + " dataset='Beef',\n", + " strategy='quantile',\n", + " use_cache=False,\n", + " timeout=0.1,\n", + " n_jobs=-1,\n", + " logging_level=50)\n", + "\n", + "stat_model_beef.fit(features=X_train_beef, target=y_train_beef)\n", + "stat_labels_beef = stat_model_beef.predict(features=X_test_beef, target=y_test_beef)\n", + "stat_probs_beef = stat_model_beef.predict_proba(features=X_test_beef, target=y_test_beef)\n", + "stat_model_beef.get_metrics(target=y_test_beef, metric_names=['f1'])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing points: 100%|██████████| 10/10 [00:03<00:00, 3.21point/s]\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from fedot_ind.tools.explain.explain import PointExplainer\n", + "\n", + "distance = 'euclidean'\n", + "explainer_stat_beef = PointExplainer(stat_model_beef, X_test_beef, y_test_beef)\n", + "explainer_stat_beef.explain(n_samples=5, window=10, method=distance)\n", + "explainer_stat_beef.visual(threshold=0, name='Beef')" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:05<00:00, 2.56s/trial, best loss: -0.27777777777777773] \n", + "100%|██████████| 2/2 [00:10<00:00, 5.15s/trial, best loss: -0.47222222222222215]\n", + "100%|██████████| 2/2 [00:03<00:00, 1.80s/trial, best loss: -0.4444444444444444] \n" + ] + }, + { + "data": { + "text/plain": "{'f1': 0.568}" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comp_model_beef = FI(task='ts_classification',\n", + " dataset='Beef',\n", + " strategy='fedot_preset',\n", + " branch_nodes=['eigen_basis'],\n", + " tuning_iterations=5,\n", + " tuning_timeout=3,\n", + " use_cache=False,\n", + " timeout=0.1,\n", + " n_jobs=-1,\n", + " logging_level=50,\n", + " available_operations=available_operations)\n", + "\n", + "comp_model_beef.fit(features=X_train_beef, target=y_train_beef)\n", + "comp_labels_beef = comp_model_beef.predict(features=X_test_beef, target=y_test_beef)\n", + "comp_probs_beef = comp_model_beef.predict_proba(features=X_test_beef, target=y_test_beef)\n", + "comp_model_beef.get_metrics(target=y_test_beef, metric_names=['f1'])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing points: 100%|██████████| 10/10 [00:14<00:00, 1.49s/point]\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "distance = 'euclidean'\n", + "explainer_comp_beef = PointExplainer(comp_model_beef, X_test_beef, y_test_beef)\n", + "explainer_comp_beef.explain(n_samples=5, window=10, method=distance)\n", + "explainer_comp_beef.visual(threshold=0, name='Beef'+'_'+distance)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generations: 0%| | 0/10000 [00:14", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "explainer_stat_car = PointExplainer(stat_model_car, X_test_car, y_test_car)\n", + "explainer_stat_car.explain(n_samples=5, window=10, method='euclidean')\n", + "explainer_stat_car.visual(threshold=0, name='Car'+'_'+'stat features')" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:08<00:00, 4.13s/trial, best loss: -0.912962962962963]\n", + "100%|██████████| 2/2 [00:13<00:00, 6.72s/trial, best loss: -0.912962962962963]\n", + "100%|██████████| 2/2 [00:18<00:00, 9.25s/trial, best loss: -0.912962962962963] \n" + ] + }, + { + "data": { + "text/plain": "{'f1': 0.862}" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data, test_data = DataLoader('Car').load_data()\n", + "available_operations_=['scaling',\n", + " 'normalization',\n", + " 'xgboost',\n", + " 'rfr',\n", + " 'rf',\n", + " 'logit',\n", + " 'mlp',\n", + " 'knn',\n", + " 'lgbm',\n", + " 'pca']\n", + "\n", + "X_test_car, y_test_car = test_data\n", + "X_train_car, y_train_car = train_data\n", + "comp_model_car = FI(task='ts_classification',\n", + " dataset='Car',\n", + " strategy='fedot_preset',\n", + " branch_nodes=['eigen_basis'],\n", + " tuning_iterations=5,\n", + " tuning_timeout=3,\n", + " use_cache=False,\n", + " timeout=0.1,\n", + " n_jobs=-1,\n", + " logging_level=50,\n", + " available_operations=available_operations_)\n", + "\n", + "comp_model_car.fit(features=X_train_car, target=y_train_car)\n", + "comp_labels_car = comp_model_car.predict(features=X_test_car, target=y_test_car)\n", + "comp_probs_car = comp_model_car.predict_proba(features=X_test_car, target=y_test_car)\n", + "comp_model_car.get_metrics(target=y_test_car, metric_names=['f1'])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing points: 100%|██████████| 11/11 [02:32<00:00, 13.87s/point]\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "explainer_comp_car = PointExplainer(comp_model_car, X_test_car, y_test_car)\n", + "explainer_comp_car.explain(n_samples=5, window=10, method='euclidean')\n", + "explainer_comp_car.visual(threshold=0, name='Car'+'_'+'euclidean')" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 42, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generations: 0%| | 1/10000 [01:04", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "explainer_comp_car_short = PointExplainer(comp_model_car_short, X_test_car_short, y_test_car)\n", + "explainer_comp_car_short.explain(n_samples=5, window=10, method='euclidean', name='Car')\n", + "explainer_comp_car_short.visual(threshold=0, name='Car'+'_'+'euclidean')" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" 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" 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "car_classes = np.unique(y_train_car)\n", + "\n", + "for cls in car_classes:\n", + " cls_idx = np.where(y_train_car == cls)[0]\n", + " X_train_car.iloc[cls_idx, :].T.plot(figsize=(5,2), title=f'Class {cls}', legend=None)\n", + " plt.show()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" 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" 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" 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "beef_classes = np.unique(y_train_beef)\n", + "\n", + "for cls in beef_classes:\n", + " cls_idx = np.where(y_train_beef == cls)[0]\n", + " X_train_beef.iloc[cls_idx, :].T.plot(figsize=(5,2), title=f'Class {cls}', legend=None)\n", + " plt.show()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 71, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset = 'Beef'\n", + "train_data, test_data = DataLoader(dataset).load_data()\n", + "\n", + "X_test_beef, y_test_beef = test_data\n", + "X_train_beef, y_train_beef = train_data\n", + "\n", + "ts = X_train_beef.iloc[0, :].copy()\n", + "ts.plot(figsize=(5,2))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 64, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ts[0:100] = np.mean(ts)\n", + "ts.plot(figsize=(5,2))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 69, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ts[100:200] = np.mean(ts)\n", + "ts.plot(figsize=(5,2))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 72, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ts[200:300] = np.mean(ts)\n", + "ts.plot(figsize=(5,2))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## SHAP (SHapley Additive exPlanations)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "SHAP values offer a game-theoretic approach to assign a value to each feature's contribution to a model's prediction.\n", + "In the context of time series, SHAP values can be calculated for individual time points or across the entire sequence. The formula for calculating SHAP values is given by:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "$$\\phi_i(f) = \\sum_{S \\subseteq N \\setminus \\{i\\}} \\frac{{|S| - 1}! \\cdot (|N| - |S| - 1)!}{|N|!} [f(S \\cup \\{i\\}) - f(S)] $$\n", + "\n", + "where:\n", + "\n", + "- $f(S)$ is the model's output when considering only the features in set $S$,\n", + "- $N$ is the set of all features, and\n", + "- $i$ is the feature for which we are calculating the SHAP value.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "In our case for statistical model we can explore SHAP values for each feature, extracted by QuantileExtractor. Here they are:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "data": { + "text/plain": " skewness_ kurtosis_ n_peaks_ slope_ ben_corr_ interquartile_range_ \\\n0 -0.350659 0.361394 51.0 -0.003248 0.959372 0.704565 \n1 -2.003292 3.390948 40.0 -0.002255 0.746639 0.308500 \n2 -1.304637 1.929469 29.0 -0.003378 0.762244 0.779684 \n3 -0.692359 0.099559 32.0 -0.004400 0.832135 1.423033 \n4 -2.138024 3.671746 36.0 -0.002453 0.801914 0.386153 \n5 0.382660 0.956841 63.0 -0.001735 0.989721 0.800253 \n6 -0.146153 0.529319 46.0 -0.002617 0.986619 0.722207 \n7 0.196881 0.330647 45.0 -0.002130 0.950238 0.475943 \n8 -0.382352 0.217829 37.0 -0.003067 0.986553 0.703411 \n9 -0.361481 0.220714 32.0 -0.003797 0.943681 0.858828 \n10 0.225375 0.095547 50.0 -0.002076 0.943307 0.470232 \n11 0.433711 0.556454 47.0 -0.001687 0.927710 0.488309 \n12 -0.323002 0.359019 59.0 -0.002907 0.977431 0.731814 \n13 0.375512 0.371817 49.0 -0.001808 0.929758 0.459895 \n14 -0.434332 0.277944 54.0 -0.003295 0.945570 0.739462 \n15 -0.081860 0.488809 44.0 -0.002784 0.961908 0.629726 \n16 0.660577 0.898373 55.0 -0.001480 0.919523 0.374344 \n17 0.519206 0.726228 43.0 -0.001572 0.930915 0.457247 \n18 -0.640596 0.003156 49.0 -0.003865 0.964731 0.696506 \n19 0.083003 0.214493 53.0 -0.002429 0.942001 0.465568 \n20 -0.818749 0.023909 51.0 -0.004279 0.981111 0.812513 \n21 -0.243873 0.346867 54.0 -0.003203 0.964579 0.738774 \n22 0.262170 0.335277 43.0 -0.002317 0.927627 0.554718 \n23 0.087266 0.278144 47.0 -0.002487 0.948484 0.471345 \n24 0.227892 0.915253 31.0 -0.001990 0.974344 0.579473 \n25 0.632132 1.006273 44.0 -0.001441 0.916240 0.352471 \n26 -0.599503 0.057133 36.0 -0.003507 0.944309 0.781617 \n27 -0.794637 0.029816 35.0 -0.003957 0.966052 0.812829 \n28 0.345738 0.424203 39.0 -0.001842 0.927829 0.446124 \n29 1.013597 1.718114 32.0 -0.000907 0.939484 0.300411 \n\n energy_ cross_rate_ autocorrelation_ shannon_entropy_ ... \\\n0 0.997872 0.029787 0.994638 8.842474 ... \n1 0.997872 0.004255 0.998845 8.872262 ... \n2 0.997872 0.008511 0.997295 8.876517 ... \n3 0.997872 0.012766 0.996090 8.859496 ... \n4 0.997872 0.004255 0.998477 8.872262 ... \n5 0.997872 0.021277 0.994082 8.842474 ... \n6 0.997872 0.029787 0.994770 8.876517 ... \n7 0.997872 0.004255 0.996540 8.872262 ... \n8 0.997872 0.025532 0.994737 8.876517 ... \n9 0.997872 0.025532 0.991820 8.855240 ... \n10 0.997872 0.008511 0.996370 8.855240 ... \n11 0.997872 0.004255 0.996751 8.855240 ... \n12 0.997872 0.029787 0.994270 8.868006 ... \n13 0.997872 0.004255 0.997046 8.855240 ... \n14 0.997872 0.025532 0.994270 8.872262 ... \n15 0.997872 0.012766 0.995003 8.859496 ... \n16 0.997872 0.004255 0.996555 8.872262 ... \n17 0.997872 0.004255 0.996908 8.872262 ... \n18 0.997872 0.017021 0.994471 8.832358 ... \n19 0.997872 0.008511 0.995854 8.872262 ... \n20 0.997872 0.021277 0.993046 8.859496 ... \n21 0.997872 0.021277 0.994288 8.876517 ... \n22 0.997872 0.008511 0.996040 8.872262 ... \n23 0.997872 0.008511 0.996028 8.863751 ... \n24 0.997872 0.008511 0.995848 8.872262 ... \n25 0.997872 0.004255 0.996776 8.859496 ... \n26 0.997872 0.021277 0.993243 8.863751 ... \n27 0.997872 0.025532 0.994080 8.863751 ... \n28 0.997872 0.004255 0.996960 8.850985 ... \n29 0.997872 0.004255 0.997050 8.872262 ... \n\n petrosian_fractal_dimension_ mean_ median_ std_ max_ \\\n0 0.622998 -7.765958e-10 0.140019 0.998936 2.448200 \n1 0.638446 -3.636170e-10 0.280685 0.998936 1.385611 \n2 0.659993 5.757447e-10 0.230815 0.998936 2.459545 \n3 0.653261 4.687022e-10 0.224913 0.998936 2.368445 \n4 0.645365 2.723404e-10 0.314075 0.998936 1.294925 \n5 0.609642 6.080851e-10 -0.064324 0.998936 2.914151 \n6 0.628114 -6.553192e-10 0.091592 0.998936 2.576844 \n7 0.629474 -7.325529e-11 0.037003 0.998936 2.628389 \n8 0.643553 3.458724e-10 0.142964 0.998936 2.379456 \n9 0.652212 -1.710638e-10 0.061785 0.998936 2.970318 \n10 0.623611 4.361702e-10 -0.004156 0.998936 2.583372 \n11 0.626788 3.563341e-10 -0.017187 0.998936 2.766296 \n12 0.613550 -2.904251e-11 0.146392 0.998936 2.494284 \n13 0.624231 -2.580426e-10 -0.020183 0.998936 2.704461 \n14 0.618336 4.187234e-10 0.128258 0.998936 2.369340 \n15 0.630870 -1.522340e-09 0.074300 0.998936 2.601103 \n16 0.617233 -1.060362e-09 -0.066867 0.998936 2.949236 \n17 0.633033 -3.240426e-10 -0.039608 0.998936 2.831035 \n18 0.624231 -8.372341e-10 0.224153 0.998936 2.165178 \n19 0.619463 -2.306808e-10 0.049323 0.998936 2.611181 \n20 0.621793 -7.275958e-10 0.221709 0.998936 2.003525 \n21 0.618896 4.031915e-10 0.090617 0.998936 2.525943 \n22 0.633033 -4.195106e-10 -0.002108 0.998936 2.689433 \n23 0.626788 1.336170e-09 0.048024 0.998936 2.596374 \n24 0.654330 5.222553e-10 0.026105 0.998936 2.783366 \n25 0.631581 -6.822340e-10 -0.042488 0.998936 2.927675 \n26 0.644451 1.094681e-09 0.080472 0.998936 2.028913 \n27 0.646294 -1.997872e-10 0.203395 0.998936 1.984524 \n28 0.639265 2.141915e-10 -0.011552 0.998936 2.687096 \n29 0.651181 9.095106e-10 -0.104568 0.998936 3.151427 \n\n min_ q5_ q25_ q75_ q95_ \n0 -1.943663 -1.907868 -0.192094 0.512471 1.578451 \n1 -3.343694 -2.729292 0.124506 0.433006 0.997197 \n2 -3.018348 -2.478581 -0.230480 0.549203 1.248810 \n3 -2.495473 -2.162264 -0.761656 0.661378 1.299882 \n4 -3.385794 -2.745254 0.116988 0.503141 0.827501 \n5 -1.814624 -1.751914 -0.357905 0.442348 1.899067 \n6 -1.909445 -1.892857 -0.251989 0.470217 1.706002 \n7 -1.707133 -1.692649 -0.205631 0.270312 1.797096 \n8 -1.945312 -1.925808 -0.227364 0.476047 1.512839 \n9 -1.946811 -1.933601 -0.273775 0.585053 1.458842 \n10 -1.643087 -1.627973 -0.177585 0.292647 1.865359 \n11 -1.638694 -1.629337 -0.263549 0.224760 1.878041 \n12 -1.946531 -1.918866 -0.248724 0.483089 1.567583 \n13 -1.604474 -1.587231 -0.190464 0.269432 1.905396 \n14 -1.968127 -1.948843 -0.203825 0.535637 1.497529 \n15 -1.868442 -1.850058 -0.197990 0.431737 1.693861 \n16 -1.560394 -1.540833 -0.175127 0.199217 1.988904 \n17 -1.618887 -1.609807 -0.231109 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skewness_kurtosis_n_peaks_slope_ben_corr_interquartile_range_energy_cross_rate_autocorrelation_shannon_entropy_...petrosian_fractal_dimension_mean_median_std_max_min_q5_q25_q75_q95_
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1-2.0032923.39094840.0-0.0022550.7466390.3085000.9978720.0042550.9988458.872262...0.638446-3.636170e-100.2806850.9989361.385611-3.343694-2.7292920.1245060.4330060.997197
2-1.3046371.92946929.0-0.0033780.7622440.7796840.9978720.0085110.9972958.876517...0.6599935.757447e-100.2308150.9989362.459545-3.018348-2.478581-0.2304800.5492031.248810
3-0.6923590.09955932.0-0.0044000.8321351.4230330.9978720.0127660.9960908.859496...0.6532614.687022e-100.2249130.9989362.368445-2.495473-2.162264-0.7616560.6613781.299882
4-2.1380243.67174636.0-0.0024530.8019140.3861530.9978720.0042550.9984778.872262...0.6453652.723404e-100.3140750.9989361.294925-3.385794-2.7452540.1169880.5031410.827501
50.3826600.95684163.0-0.0017350.9897210.8002530.9978720.0212770.9940828.842474...0.6096426.080851e-10-0.0643240.9989362.914151-1.814624-1.751914-0.3579050.4423481.899067
6-0.1461530.52931946.0-0.0026170.9866190.7222070.9978720.0297870.9947708.876517...0.628114-6.553192e-100.0915920.9989362.576844-1.909445-1.892857-0.2519890.4702171.706002
70.1968810.33064745.0-0.0021300.9502380.4759430.9978720.0042550.9965408.872262...0.629474-7.325529e-110.0370030.9989362.628389-1.707133-1.692649-0.2056310.2703121.797096
8-0.3823520.21782937.0-0.0030670.9865530.7034110.9978720.0255320.9947378.876517...0.6435533.458724e-100.1429640.9989362.379456-1.945312-1.925808-0.2273640.4760471.512839
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100.2253750.09554750.0-0.0020760.9433070.4702320.9978720.0085110.9963708.855240...0.6236114.361702e-10-0.0041560.9989362.583372-1.643087-1.627973-0.1775850.2926471.865359
110.4337110.55645447.0-0.0016870.9277100.4883090.9978720.0042550.9967518.855240...0.6267883.563341e-10-0.0171870.9989362.766296-1.638694-1.629337-0.2635490.2247601.878041
12-0.3230020.35901959.0-0.0029070.9774310.7318140.9978720.0297870.9942708.868006...0.613550-2.904251e-110.1463920.9989362.494284-1.946531-1.918866-0.2487240.4830891.567583
130.3755120.37181749.0-0.0018080.9297580.4598950.9978720.0042550.9970468.855240...0.624231-2.580426e-10-0.0201830.9989362.704461-1.604474-1.587231-0.1904640.2694321.905396
14-0.4343320.27794454.0-0.0032950.9455700.7394620.9978720.0255320.9942708.872262...0.6183364.187234e-100.1282580.9989362.369340-1.968127-1.948843-0.2038250.5356371.497529
15-0.0818600.48880944.0-0.0027840.9619080.6297260.9978720.0127660.9950038.859496...0.630870-1.522340e-090.0743000.9989362.601103-1.868442-1.850058-0.1979900.4317371.693861
160.6605770.89837355.0-0.0014800.9195230.3743440.9978720.0042550.9965558.872262...0.617233-1.060362e-09-0.0668670.9989362.949236-1.560394-1.540833-0.1751270.1992171.988904
170.5192060.72622843.0-0.0015720.9309150.4572470.9978720.0042550.9969088.872262...0.633033-3.240426e-10-0.0396080.9989362.831035-1.618887-1.609807-0.2311090.2261381.929148
18-0.6405960.00315649.0-0.0038650.9647310.6965060.9978720.0170210.9944718.832358...0.624231-8.372341e-100.2241530.9989362.165178-1.984985-1.944123-0.1520490.5444581.394905
190.0830030.21449353.0-0.0024290.9420010.4655680.9978720.0085110.9958548.872262...0.619463-2.306808e-100.0493230.9989362.611181-1.731183-1.722073-0.1360700.3294981.747209
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(stat_model.solver.test_features)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": "(30, 28)" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stat_model.solver.test_features.shape" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "ename": "TypeError", + "evalue": "explain() missing 1 required positional argument: 'self'", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[0;32mIn[6], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43mstat_model\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexplain\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmethod\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mshap\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mn_samples\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m5\u001B[39;49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/Desktop/Working-Folder/fedot-industrial/Fedot.Industrial/fedot_ind/api/main.py:269\u001B[0m, in \u001B[0;36mFedotIndustrial.explain\u001B[0;34m(self, **kwargs)\u001B[0m\n\u001B[1;32m 263\u001B[0m n_samples \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mn_samples\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;241m5\u001B[39m)\n\u001B[1;32m 264\u001B[0m explainer \u001B[38;5;241m=\u001B[39m Explainer(model\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msolver\u001B[38;5;241m.\u001B[39mpredictor,\n\u001B[1;32m 265\u001B[0m features\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msolver\u001B[38;5;241m.\u001B[39mtest_features,\n\u001B[1;32m 266\u001B[0m target\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msolver\u001B[38;5;241m.\u001B[39mtest_target,\n\u001B[1;32m 267\u001B[0m prediction\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msolver\u001B[38;5;241m.\u001B[39mprediction_label,\n\u001B[1;32m 268\u001B[0m method\u001B[38;5;241m=\u001B[39mmethod)\n\u001B[0;32m--> 269\u001B[0m \u001B[43mexplainer\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexplain\u001B[49m\u001B[43m(\u001B[49m\u001B[43mn_samples\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mn_samples\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/Desktop/Working-Folder/fedot-industrial/Fedot.Industrial/fedot_ind/tools/explain/explain.py:214\u001B[0m, in \u001B[0;36mExplainer.explain\u001B[0;34m(self, n_samples)\u001B[0m\n\u001B[1;32m 213\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mexplain\u001B[39m(\u001B[38;5;28mself\u001B[39m, n_samples\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m5\u001B[39m):\n\u001B[0;32m--> 214\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmethod\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexplain\u001B[49m\u001B[43m(\u001B[49m\u001B[43mn_samples\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mn_samples\u001B[49m\u001B[43m)\u001B[49m\n", + "\u001B[0;31mTypeError\u001B[0m: explain() missing 1 required positional argument: 'self'" + ] + } + ], + "source": [ + "stat_model.explain(method='shap', n_samples=5)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Time Series Points Perturbation Analysis" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "Perturbation analysis involves systematically perturbing features and observing the impact on predictions. In time series, this can be applied by introducing small changes to the input features at each time point and monitoring the resulting variations in the model output. By quantifying the sensitivity of the model to perturbations, insights into feature importance and model behavior can be gained." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 35, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing points: 100%|██████████| 10/10 [00:13<00:00, 1.31s/point]\n" + ] + }, + { + "data": { + "text/plain": "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from fedot_ind.tools.explain.explain import PointExplainer\n", + "\n", + "distance = 'euclidean'\n", + "explainer = PointExplainer(comp_model, X_test, y_test)\n", + "explainer.explain(n_samples=5, window=10, method=distance, name=dataset)\n", + "explainer.visual(threshold=0, name=dataset+'_'+distance)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 34, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing points: 100%|██████████| 10/10 [00:00<00:00, 10.32point/s]\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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IiEqvAXfccUdQuS+88EJ8Ph+HDx+ustzHjh1j8+bNjB07lvDw8MDySy65hA4dOlSIP7VMBQUFFBUVceGFF1ZansrU9JwiIiLYvn07e/furXQ/f/RaJYQQ4tyTrhBCiEbHZDIF+sKXCw8Pp2nTphVuGMLDwyu9EUxNTQ16HhoaSmJiYqAvcPmH9bZt2wbFGQwGWrZsWeHDfJMmTWo14Jvf7+eVV17h9ddf5+DBg4F+/gDR0dEV4n9/YxQZGQkQOLf9+/cD0KlTpyqPuW/fPlRV5bHHHuOxxx6rNCYnJ4cmTZpUuu7w4cMkJSURFhYWtLy82XF1Nzin07p16wq/uzZt2gBlfecTEhI4fPgwffr0qbDtqcev7vwrqzeRkZE1ShQYjUaeffZZ7r//fuLj47ngggsYOXIkY8aMISEh4bTb/15V9QugXbt2rF69OmiZTqertOl6dXr37h00eONNN91Et27dmDhxIiNHjsRgMLB3716KioqIi4urdB/lAzGW3wBedNFFlcZZrdZala0ye/fuZcuWLRV+R78vS02VJ4yqqxM1dfjw4Urr6O9/f+WvU/k4E5UpKioiMjISh8PBjBkzmDdvHhkZGYFxSspjfu9014Cqyg0Vr3flZf99wmDJkiU89dRTbN68OWj8iJomYmp6Tk8++SRXXXUVbdq0oVOnTlx22WXccsstgW5Hf/RaJYQQ4tyTxIIQotHRarW1Wn7qh9uz5dRv6mpi+vTpPPbYY/ztb3/j3//+N1FRUWg0GiZNmhQ0UF25M3Fu5ft94IEHGDZsWKUxrVu3rvH+GpuqXsOamjRpEldccQWLFy/mm2++4bHHHmPGjBmsWLGCbt26naFSVu7UFi51pdFoGDJkCK+88gp79+6lY8eO+P1+4uLi+OCDDyrdpvwmv7zuvPfee5UmUn4/IGRd+P1+LrnkEh566KFK15cnmqq6yT01OVdfyl+nmTNnkpaWVmlMaGgoUDZmwrx585g0aRJ9+/YlPDwcRVG48cYbz9o1oDo//vgjV155JQMHDuT1118nMTERvV7PvHnzKgyCW5WantPAgQPZv38/n3/+Od9++y3/+c9/eOmll5g7dy633377eX+tEkKIxkgSC0KI89LevXsZMmRI4HlpaSnHjh3j8ssvByA5ORmA3bt307Jly0Cc2+3m4MGDDB06tEbHqeom6OOPP2bIkCG8/fbbQcsLCwuJiYmp1bnAycHTtm3bVmXZys9Dr9fXuPynSk5O5rvvvqOkpCSo1cKuXbsC6+uq/BvKU1+vPXv2AAQGRkxOTmb37t0Vtj0Txy93um9mW7Vqxf3338/999/P3r17SUtL44UXXuD999+v1XFOrV+/bwWwe/fuM3IulfF6vUBZfYey8/nuu+/o379/tcmx8voVFxdXp7pTE61ataK0tPS0+y//pv73s7P8vsVMeX3ftm3bHy5bcnIy27Ztq1BHf18fy18nq9V62vP4+OOPGTt2LC+88EJgmdPprHBef7TcQKVdDn5f9k8++QSTycQ333yD0WgMLJ83b16Fbau7rtX0nKKiohg3bhzjxo2jtLSUgQMH8sQTT3D77bfX6lpV224tQgghzg4ZY0EIcV5688038Xg8gedz5szB6/UyfPhwAIYOHYrBYODVV18N+kbw7bffpqioKDBC+ulYLJZKP1RrtdoK3zQuWrSozv2Gu3fvTosWLXj55ZcrHK/8OHFxcQwePJg33niDY8eOVdjH6aauu/zyy/H5fMyePTto+UsvvYSiKIHXri4yMzODpsArLi7m//7v/0hLSwt8Q3755Zezfv161q5dG4iz2Wy8+eabpKSkVNpnvLbKR63//Wtot9txOp1By1q1akVYWFiVUw5Wp2fPnsTFxTF37tyg7b/66it27txZ4/pVGx6Ph2+//RaDwRDoPjJ69Gh8Ph///ve/K8R7vd7A6zBs2DCsVivTp08Pet+Uq+m0h9UZPXo0a9eu5ZtvvqmwrrCwMJAUSU5ORqvVVhhz4fXXXw96Hhsby8CBA3nnnXdIT08PWlfbb/kvv/xyMjMzg6ZetNvtvPnmm0FxPXr0oFWrVjz//POB5M2pTn2dKrsGzJo164y2vEhMTCQtLY133303qCvCsmXLKoxJotVqURQl6PiHDh1i8eLFFfZbm+taZeeUl5cX9Dw0NJTWrVsH3gu1uVZZLBag4ntWCCHEuSUtFoQQ5yW3283FF1/M6NGj2b17N6+//joDBgzgyiuvBMpuSh555BGmTZvGZZddxpVXXhmI69WrV4Up+6rSo0cP5syZw1NPPUXr1q2Ji4vjoosuYuTIkTz55JOMGzeOfv36sXXrVj744IOg1hG1odFomDNnDldccQVpaWmMGzeOxMREdu3axfbt2wM3a6+99hoDBgygc+fOjB8/npYtW5Kdnc3atWs5evQov/32W5XHuOKKKxgyZAiPPvoohw4domvXrnz77bd8/vnnTJo0qcbTClamTZs23HbbbWzYsIH4+HjeeecdsrOzg74tffjhh/nvf//L8OHDueeee4iKiuLdd9/l4MGDfPLJJ3+4qwCUdWnp0KEDCxcupE2bNkRFRdGpUye8Xm+gvnTo0AGdTsdnn31GdnY2N954Y62Po9frefbZZxk3bhyDBg3ipptuCkw3mZKSwn333feHz+Wrr74KtObIyclhwYIF7N27l4cffjgwJsKgQYO48847mTFjBps3b+bSSy9Fr9ezd+9eFi1axCuvvMJ1112H1Wplzpw53HLLLXTv3p0bb7yR2NhY0tPTWbp0Kf3796+QcKqtBx98kP/973+MHDmSW2+9lR49emCz2di6dSsff/wxhw4dIiYmhvDwcK6//npmzZqFoii0atWKJUuWVDoGw6uvvsqAAQPo3r07d9xxBy1atODQoUMsXbqUzZs317hs48ePZ/bs2YwZM4aNGzeSmJjIe++9V2FKSI1Gw3/+8x+GDx9Ox44dGTduHE2aNCEjI4OVK1ditVr54osvABg5ciTvvfce4eHhdOjQgbVr1/Ldd99VOsbKHzFjxgxGjBjBgAED+Nvf/kZ+fj6zZs2iY8eOQcmPESNG8OKLL3LZZZfxl7/8hZycHF577TVat27Nli1bgvbZo0cPvvvuO1588UWSkpJo0aIFffr0qfE5dejQgcGDB9OjRw+ioqL45Zdf+Pjjj5k4cWIgpqbXqrS0NLRaLc8++yxFRUUYjUYuuuiiKscNEUIIcZac+4kohBCiZqqabtJisVSIrWp6w+Tk5KBpzcr3uWrVKvWOO+5QIyMj1dDQUPXmm29W8/LyKmw/e/ZstV27dqper1fj4+PVu+66q8J0jtVNrZiVlaWOGDFCDQsLU4HAFHZOp1O9//771cTERNVsNqv9+/dX165dW2Gau/Kp9RYtWhS038qm1lNVVV29erV6ySWXqGFhYarFYlG7dOmizpo1Kyhm//796pgxY9SEhARVr9erTZo0UUeOHKl+/PHHlZ7DqUpKStT77rtPTUpKUvV6vZqamqrOnDkzaBq8070mv1f+O/rmm2/ULl26qEajUW3Xrl2Fcy4v+3XXXadGRESoJpNJ7d27t7pkyZLTvjZV1ZupU6eqv/9TuGbNGrVHjx6qwWAITD2Zm5urTpgwQW3Xrp1qsVjU8PBwtU+fPkHTD1aluukXFy5cqHbr1k01Go1qVFSUevPNN6tHjx4Niqmq7Kc73qkPk8mkpqWlqXPmzKnwu1JVVX3zzTfVHj16qGazWQ0LC1M7d+6sPvTQQ2pmZmZQ3MqVK9Vhw4ap4eHhqslkUlu1aqXeeuut6i+//FKj8z2dkpIS9ZFHHlFbt26tGgwGNSYmRu3Xr5/6/PPPB00Pe/z4cfXaa69VQ0JC1MjISPXOO+9Ut23bVul7Ytu2berVV18dqDNt27ZVH3vssQrlrW66SVVV1cOHD6tXXnmlGhISosbExKj33ntvYFrO3091uGnTJvWaa65Ro6OjVaPRqCYnJ6ujR49Wly9fHogpKChQx40bp8bExKihoaHqsGHD1F27dqnJyclBUzlW9XpWNe1mZT755BO1ffv2qtFoVDt06KB++umn6tixYytMN/n222+rqampgffgvHnzKn2P7Nq1Sx04cKBqNptVIFDemp7TU089pfbu3VuNiIhQzWaz2q5dO/Xpp58O+h2ras2vVW+99ZbasmVLVavVytSTQghRTxRVPQejmgkhRAMxf/58xo0bx4YNG4JGzBf1JyUlhU6dOrFkyZL6LooQQgghhKgDGWNBCCGEEEIIIYQQdSZjLAghhBDirHE4HEEDB1YmKioKg8FwjkokhBBCiDNNEgtCCCGEOGsWLlzIuHHjqo1ZuXIlgwcPPjcFEkIIIcQZJ2MsCCGEEOKsOXbsGNu3b682pkePHkRGRp6jEgkhhBDiTJPEghBCCCGEEEIIIepMBm8UQgghhBBCCCFEnUliQQghhBBCCCGEEHUmiQUhhBBCCCGEEELUmSQWhBBCCCGEEEIIUWeSWBBCCCGEEEIIIUSdSWJBCCGEEEIIIYQQdSaJBSGEEEIIIYQQQtSZJBaEEEIIIYQQQghRZ5JYEEIIIYQQQgghRJ1JYkEIIYQQQgghhBB1JokFIYQQQgghhBBC1JkkFoQQQgghhBBCCFFnklgQQgghhBBCCCFEnUliQQghhBBCCCGEEHUmiQUhhBBCCCGEEELUmSQWhBBCCCGEEEIIUWeSWBBCCCGEEEIIIUSdSWJBCCGEEEIIIYQQdSaJBSGEEKIaKSkp3HrrrfVdDCGEEEKIBksSC0IIIc5L+/fv584776Rly5aYTCasViv9+/fnlVdeweFw1Hfx6mT37t3cd9999OvXD5PJhKIoHDp0qFb72LlzJ5dddhmhoaFERUVxyy23cPz48bNTYCGEEEL8KejquwBCCCHEubZ06VKuv/56jEYjY8aMoVOnTrjdblavXs2DDz7I9u3befPNN+u7mLW2du1aXn31VTp06ED79u3ZvHlzrbY/evQoAwcOJDw8nOnTp1NaWsrzzz/P1q1bWb9+PQaD4ewUXAghhBCNmiQWhBBCnFcOHjzIjTfeSHJyMitWrCAxMTGwbsKECezbt4+lS5fWYwnr7sorr6SwsJCwsDCef/75WicWpk+fjs1mY+PGjTRv3hyA3r17c8kllzB//nzuuOOOs1BqIYQQQjR20hVCCCHEeeW5556jtLSUt99+OyipUK5169bce++9VW6fn5/PAw88QOfOnQkNDcVqtTJ8+HB+++23CrGzZs2iY8eOhISEEBkZSc+ePVmwYEFgfUlJCZMmTSIlJQWj0UhcXByXXHIJv/76ayDGbreza9cucnNzT3tuUVFRhIWFnTauKp988gkjR44MJBUAhg4dSps2bfjoo4/qvF8hhBBC/LlJYkEIIcR55YsvvqBly5b069evTtsfOHCAxYsXM3LkSF588UUefPBBtm7dyqBBg8jMzAzEvfXWW9xzzz106NCBl19+mWnTppGWlsa6desCMX//+9+ZM2cO1157La+//joPPPAAZrOZnTt3BmLWr19P+/btmT17dt1PugYyMjLIycmhZ8+eFdb17t2bTZs2ndXjCyGEEKLxkq4QQgghzhvFxcVkZGRw1VVX1XkfnTt3Zs+ePWg0J3Pzt9xyC+3atePtt9/mscceA8rGcejYsSOLFi2qcl9Lly5l/PjxvPDCC4FlDz30UJ3L9kccO3YMoNJWHImJieTn5+NyuTAajee6aEIIIYRo4CSxIIQQ4rxRXFwM8Ie6C5x6Y+3z+SgsLCQ0NJS2bdsGdWGIiIjg6NGjbNiwgV69elW6r4iICNatW0dmZiZJSUmVxgwePBhVVetc3poqnwmjssSByWQKxEhiQQghhBC/J10hhBBCnDesVitQNrZBXfn9fl566SVSU1MxGo3ExMQQGxvLli1bKCoqCsT985//JDQ0lN69e5OamsqECRP46aefgvb13HPPsW3bNpo1a0bv3r154oknOHDgQJ3L9keYzWYAXC5XhXVOpzMoRgghhBDiVJJYEEIIcd6wWq0kJSWxbdu2Ou9j+vTpTJ48mYEDB/L+++/zzTffsGzZMjp27Ijf7w/EtW/fnt27d/Phhx8yYMAAPvnkEwYMGMDUqVMDMaNHj+bAgQPMmjWLpKQkZs6cSceOHfnqq6/+0HnWRXkXiPIuEac6duwYUVFR0lpBCCGEEJWSxIIQQojzysiRI9m/fz9r166t0/Yff/wxQ4YM4e233+bGG2/k0ksvZejQoRQWFlaItVgs3HDDDcybN4/09HRGjBjB008/HWgBAGU39HfffTeLFy/m4MGDREdH8/TTT9f19OqsSZMmxMbG8ssvv1RYt379etLS0s55mYQQQgjROEhiQQghxHnloYcewmKxcPvtt5OdnV1h/f79+3nllVeq3F6r1VYY82DRokVkZGQELcvLywt6bjAY6NChA6qq4vF48Pl8QV0nAOLi4khKSgrqjlCb6SZrY//+/ezfvz9o2bXXXsuSJUs4cuRIYNny5cvZs2cP119//Rk9vhBCCCH+PGTwRiGEEOeVVq1asWDBAm644Qbat2/PmDFj6NSpE263mzVr1rBo0SJuvfXWKrcfOXIkTz75JOPGjaNfv35s3bqVDz74gJYtWwbFXXrppSQkJNC/f3/i4+PZuXMns2fPZsSIEYSFhVFYWEjTpk257rrr6Nq1K6GhoXz33Xds2LAhaJaI9evXM2TIEKZOncoTTzxR7bkVFRUxa9YsgMB4DrNnzyYiIoKIiAgmTpwYiL344osBOHToUGDZlClTWLRoEUOGDOHee++ltLSUmTNn0rlzZ8aNG1eTl1cIIYQQ5yFFPRdDTQshhBANzN69e5k5cybLli0jMzMTo9FIly5duPHGGxk/fnxgPIGUlBQGDx7M/PnzgbLBDR999FEWLFhAYWEh3bt35/nnn+fhhx8G4PvvvwfgzTff5IMPPmD79u2UlpbStGlTrrnmGv71r39htVpxu93861//4ttvv+XAgQP4/X5at27NnXfeyV133RUo5/fff1/jxMKhQ4do0aJFpeuSk5ODkggpKSmBbU61fft2Jk+ezOrVqzEYDIwYMYIXXniB+Pj4mr2wQgghhDjvSGJBCCGEEEIIIYQQdSZjLAghhBBCCCGEEKLOJLEghBBCCCGEEEKIOpPEghBCCCGEEEIIIepMEgtCCCGEEEIIIYSoM0ksCCGEEEIIIYQQos4ksSCEEEIIIYQQQog609V3AWrC7/eTmZlJWFgYiqLUd3GEEEIIIYQQQtSRqqqUlJSQlJSERiPfdf8Z1CqxMGPGDD799FN27dqF2WymX79+PPvss7Rt27bKbebPn8+4ceOClhmNRpxOZ42Pm5mZSbNmzWpTVCGEEEIIIYQQDdiRI0do2rRpfRdDnAG1SiysWrWKCRMm0KtXL7xeL1OmTOHSSy9lx44dWCyWKrezWq3s3r078Ly2rQ7CwsKAsopntVprte254N27i+K7xtZ3MRq1XIebT3bn1XcxGrX4Tu3425eL6rsYjVb2tp28P/KG+i5GoxYdouPqdnH1XYzGTatBF2qq71I0am6Xl7zDRfVdjEYrx+Xho4xCipM6c6zbNeS2vQhHdDKqVn/6jf1+qOKbR43HSWjWLqJ3r6Tpuv/DVJJzhkvecJgUhXYhNXi9RJX8qoq3vgvxJ3HNN18Q06VzfRejguLiYpo1axa4zxONX60SC19//XXQ8/nz5xMXF8fGjRsZOHBgldspikJCQkKNj+NyuXC5XIHnJSUlQFmCokEmFkJDQaet72I0ak6tFpN0c/lDzFpdg3x/NBZ2i0Xq4B9kVjRY5Vr4x2g16PSNopdig+X2qbi00qy2tkoNYRyIbsdv4an8NvJCcttfElinAIrfT2jWTiIPriXywDrCsnagdZaic9vQumxoPXZURYM7NBZXeCKOiCYUN+tGYfMeFCV3xxsWR2lYHKWpAzl8+b/Q2wsx2HKxHt1Cwm+fk7Dlf/V38meYUVEwK1IH/wg/klg4U8JCQxv050Pp5v7n8Yc+vRQVlX0jEBUVVW1caWkpycnJ+P1+unfvzvTp0+nYsWOV8TNmzGDatGl/pGhCCCGEEOI0fm3aj3kXTGZXfFrQcsXnJWHzZyT8tpjwwxsxluSg8Vd/q6eoPkzFWZiKswg/somErUsAUBUFe3QLClN6c6TvrRS06ocnNBpPaDS2+LYc63E9rb55jtSvnkJuMYQQonGqc2LB7/czadIk+vfvT6dOnaqMa9u2Le+88w5dunShqKiI559/nn79+rF9+/Yq+9M88sgjTJ48OfC8vKmMEEIIIYT44/bFdOA/fR9kY/MLA8viSjJokrOD4n2/0mT9Aix5B8/IsRRVxZJ7AEvuAZr88iFOazyekEicEU043uEyDg+8k/3DHqKoeXdafvcCUft/kgSDEEI0MnVOLEyYMIFt27axevXqauP69u1L3759A8/79etH+/bteeONN/j3v/9d6TZGoxGj0VjXogkhhBBCiEpkhTVhfp/JLG97FQA6n5uR2xZw069zibLnku308MGRgrNaBlNxNqbibMKydhG7azmhWTvYce0L5LYfSm77oUTvXkHbL6YSfvS3s1qOxk4FcuPacrDVIGKzd9DiQPWfyYUQ4myqU2Jh4sSJLFmyhB9++KHWo3jq9Xq6devGvn376nJoIYQQQghRS0WmSBb0uJsvOt+MR2sAYMie/zFu3UskFh+p17I1XzOP6D0/cHDIBI72uYW8thexpu1FRBz4meZr55Gw+TO0nprPJnY+cJqsLPrLuxxuOSCwrOWeFYz4/D7CizLrsWRCiPNVrUaWUVWViRMn8tlnn7FixQpatGhR6wP6fD62bt1KYmJirbcVQgghhBA1lxsSx9z+j/DXMd/zado4PFoD3Y78xGsfXcWUZZPrPalQzpK7n06LJjNwek+SflmI4vNS2PICttz8Bium7WbP5Y/hMUfUdzEbBKfJyoJbF3G45QC0XhfND/6ExuvmQJuLWHDrx9hDqh/7TAghzoZaJRYmTJjA+++/z4IFCwgLCyMrK4usrCwcDkcgZsyYMTzyyCOB508++STffvstBw4c4Ndff+Wvf/0rhw8f5vbbbz9zZyGEEEIIIQDwaPRsbNqfFwc/zZgx3/NJ2m049RZaH9/O9P+N49n/jaXN8e31XcxKheQfpuv74xk8rT1tlkzDnHcIb0gk+y99kJVP7GT93f9j3yUPYotpVd9FrRd+RcPHN80js2l3zLY8/jZ3GGPeHsXfZ12ItfAoebGpLLzlAzw6mbZW/Hm89tprpKSkYDKZ6NOnD+vXrz/tNosWLaJdu3aYTCY6d+7Ml19+GbReVVUef/xxEhMTMZvNDB06lL17956tUzgv1CqxMGfOHIqKihg8eDCJiYmBx8KFCwMx6enpHDt2LPC8oKCA8ePH0759ey6//HKKi4tZs2YNHTp0OHNnIYQQQghxDqhAoSmKw5Gt8DWgKQULTVF82/Zqnhw2i+tu28DDV73LVx1vwKM10DlzPdO/+Buvf3QVvY782CgGRjQVZ9PquxcY9FRXur1zM2GZ2/AZLeS1GczeEY/xw782sf7u/5Hfsu/pd/Yn8sNFD3Ko1UD0Lhs3z7uO+KyyBFFU3gH+8u5oTPYCMpr15NsRT9dzSYU4MxYuXMjkyZOZOnUqv/76K127dmXYsGHk5ORUuc2aNWu46aabuO2229i0aROjRo1i1KhRbNu2LRDz3HPP8eqrrzJ37lzWrVuHxWJh2LBhOJ3S7aquFFVV1fouxOkUFxcTHh5OUVFRg5yH1bt7B8XjRtd3MRq1HLubhTuP13cxGrWELh25c9XS+i5Go5X12zbmXXRFfRejUYsJ0TO6Y3x9F6Nx02rQhZnruxSNmtvp4fihwlptowJHI1pwPDSBgpBY8kNiybPEUWSKIsRdSqi7GJ+iIz2qFdsSe1JiigAgypZDz/QfiHDkEeYqxuIqJrT84S4mzFmExV1MqKsEvd9zRs9TBQ5HpfJzykWsTbmInQndUE9JdETaj9Pn0Eou3fUpnY/9UuP9novBG+tCBUoTO1DQ4gKyO48kr81gVG3ZUGHWI5tJ+uVDIg7/gvXob2i9rnotq1mj0CHEcMb3u63L1Sy+bi5oNIz66E46bfm0QsyBVgNZMHZRtTGNgV9VqX5yU3E6LzuKSNLoaDf6Gj768ksMBgNPPfUUf/nLX5g4cSIff/wx8fHxzJo1i+HDhwOwbds2HnzwQX788UcsFguXXnopL730EjExMQB8/fXXPPXUU2zbtg2tVkvfvn155ZVXaNWqrAXRoUOHaNGiBZ988gmzZs1i3bp1pKamMnfu3KDB/KHm93d9+vShV69ezJ49GyibmbBZs2b84x//4OGHH650mxtuuAGbzcaSJUsCyy644ALS0tKYO3cuqqqSlJTE/fffzwMPPABAUVER8fHxzJ8/nxtvvLGOr/r5rc6zQgghhBBCNGZ+FFa2uYKF3e7gYEy7Gm+nqH4MXhf5lji+bX9djbYxeWzElmaRVHSYDlmbaFp4EL3Pjd7nxuh1YvbY0fq9OPQhuHQmnPoQnDozLr0Zp84ceF5siuR4aCJ74jqRb4kLOkar4zu44NBy+h5aQWrONjQ0+O+OakwBwo7tIOzYDpqveQdHRFP2XzKZo31uobhZGsXN0gAwFmbQcdF9xG//ul7Le6b93P8uvhv+JAA91r1TZcKg5f4fGLDqRVYPeYAvr3qBxIzNROcdOJdFFQ3Ieq+LfhGRrF+/noULF3LXXXfx2WefcfXVVzNlyhReeuklbrnlFtLT03G73Vx00UXcfvvtvPTSSzgcDv75z38yevRoVqxYAYDNZmPy5Ml06dKF0tJSHn/8ca6++mo2b96MRnMysfnoo4/y/PPPk5qayqOPPspNN93Evn370Ol0pKenB7VcT0pKqlDuKVOmMGXKFNxuNxs3bgzqZq/RaBg6dChr166t8rzXrl3L5MmTg5YNGzaMxYsXA3Dw4EGysrIYOnRoYH14eDh9+vRh7dq1klioI0ksiAZLVRQclljs4QloPS4MrhJURYtfq8Ov0WG0F2Cy5eLX6vHpzWg9TjQ+d6No4lkTfo2OkqhkimJTKYpthdscgapoUTVaPAYLRXGpuM3h6J0lGJzFWCwGlv3PwjGbhmK3gl6jEh+i0jrSR5cYH11jfXSK8RGir+8zE0KcS25FR6E+AgU/se78+i5Og7EjPo3XL3yM3fFdATB4nSQUHyXKnkO07ThR9hysjgKc+hBsxjC0fh+R9uN0zVhHy7xdKKrKr836sSe2MzajlVJjGKXGcEoNVkqNJx82Y9k3cU69hSORrTgS2Yp1KRedkXPQe110O7qGCw6tpM/hlcSVHjv9Rn8S5sKjdFo0mTZLnyKj103ktRlEUfMeuCKa8Ov4j4jZtZyWy18i4sDPaH3u+i7uH7K7/fBAUqH3T3MZ+vXUauMHrphJekpf0lv059Mb3+bWN4aj90rz7vNRE42WyePHE5uayiOPPMIzzzxDTEwM48ePB+Dxxx9nzpw5bNmyhe+++45u3boxffr0wPbvvPMOzZo1Y8+ePbRp04Zrr702aP/vvPMOsbGx7Nixg06dOgWWP/DAA4wYMQKAadOm0bFjR/bt20e7du1ISkpi8+bNlJSU0L17d3788UfCwsKC9hsVVTYAaW5uLj6fj/j44NaQ8fHx7Nq1q8rzzsrKqnSbrKyswPryZVXFiNqTxIKoV34UCo0R7DdGsvOCKzjevCelkc2wRTTBFt4Ev676poSKzxtoCglgcBQRnrOHiJw9hB/fS0TOHkKKMtD4vCh+H1qfm9CCI2j8DaOBnV/R4LTEYA9PwG5NpDSiGcUxLTnevCd5Tbqe9vwryD71iUKeE3bka/nf/rIlRq3K4KZehqV4GNDES7zlz/NtlhDnOx8ajpqTcGv05Bmi2BPampUxA/glIg2fpuw62dJ2kIuP/8DVmUtIcRyt5xLXj92xnXj9wsfYkdgDgBBXCaM3vcmV2z4gzFVcq331ObyKPodXVRvjUzTYDaEUmyLJCU3kYHRbtif2ID8kFo/WgEdrwKUz4dCH4Fe0mDx2TF4nJq8dk8eB0evA5HFg8toxepyEuQqJtOfROnc7rY/vwOir32b/9c1gz6fFqtdoseo1fHozey97mEOD/0Fuu4vJbXcxitdNaPYurBlbidn5HfFbl9R7V4nasFliWHrVCwD0Wf06l5wmqQCgUf1c/dGdvDVhJdmJnfjusmkMX/LPs11U0QAlabSB/2u1WqKjo+ncuXNgWfmNdU5ODr/99hsrV64kNDS0wn72799PmzZt2Lt3L48//jjr1q0jNzcXv98PlI2xd2pioUuXLoH/l88EmJOTQ7t27dDpdLRu3Zri4rLrbatWrRpkV3dRe3VKLLz22mvMnDmTrKwsunbtyqxZs+jdu3eV8YsWLeKxxx7j0KFDpKam8uyzz3L55ZfXudCiYfOjkB6WTK4plmKDlSJDOEXGcPJM0RQYo8kzlT3yjdEUGCPxn3LR+z3F78NUmoNfa8RtCkPj96H4PWh8Ptwma1BSAcBtDud4ci+OJ/eqcp8ar5uInN1EHtuOzuPAbk1E43NjsuURlncIky0PV0hEWVNXZwnW3ANEZO/EZK9bn1NVUXCbrNjDEshr0pXcpmnkNU2jJCoZR2hchXM4lc5tw3p8P+HH92Gy5aLxn0iQeF1Yc/djKs3FYwrDbbISktySUQ/dTlKon3CjitcPmaUaduRp2XJcy2+5WrJsGr45rOebw2XNFtpE+riwiZehyR56JfjQN5xxyE7L6QWPH3x+hXynggo0C/NjqLo6CfGnowI7Q9uwJGEYX8ZfQo4pttI4jeoD4IClBQcsLXgrZSw9CzZxXebnjMhahhb/OSx1/XHoQ3h8xBvkW+LR+dxcvOdz/vbzC0TZc8/aMbWqnzBXMWGuYpoUHaZbxs9cs+Xds3a885nW46DdF1NpvmYeBy6eRFbXq/FYIilp0oWSJl3I6H0zelsBrb95huQf30BRG3a9L4hM5n/XzsYeGktc1naGLKv5gIxhJdlc9fEE/nvrR2y84G+03bmUlvt/OIulFQ2R9nfteBVFQa/XBz2HsnELSktLueKKK3j22Wcr7Kc8OXDFFVeQnJzMW2+9RVJSEn6/n06dOuF2B7cKquoYQK26QsTExKDVasnODvrmjOzsbBISEqo874SEhGq3Kf+ZnZ0dOLfy52lpaVXuV1Sv1omF8pE5586dS58+fXj55ZcZNmwYu3fvJi4urkJ8+aicM2bMYOTIkSxYsIBRo0bx66+/BmW2Gjubr+o/TloFTKf0O6ouVgOYtXWLtfv8VfamVICQGsQeN8exL7IdHmMYDl0ITq2JEo0Jp9ZIpCufhNIMQj0lZFuS+ClpEIfDW5FvjiXSlU+UM59SnZn0sBbYDGGV7L1qoc4CLEe3EX9gNWHH92EpPEpEUSYhxcfQ+H14VLVCeX1aPS5LDFqPHZPHiV9vwh6eRH5sKoVxbSiKa0NRXFucYXGomrIuBF69GZ8hhPykzuQnda60LFUxF2cRnrWTiKydRB/dROyhdVgK0gOXbB1lF0+nJZqcJl050n4Yuc17UZTQDp++6sHYFL8PU0kO5pJsQooyCc07SFTGFmIP/Uxo/mH0gObERdmnqviq2E98aXuGJ9+MTlf2tvZ4PDQzuukTDbQBVYW9RTq+STezOtPItnw9ewq07CnQ8vY2IxpFJcbkJ97sI8bsR6OUveaqCopGg1ajIcqkkhjipXWYgzizn2KPQolbg92rEG7wE2Pyk2jVEmbS4VXhuE0ls8jLcUdZjEYBjVL2nlBQ0Wh1OPw6StwKRS4ocvgo9Wgo9SjYvRpcPtApYNapmPVl/eqybBqO2RQKXRWzIBpFpYnFR8twH60joWW4nxSrl0SjjXizH6WSfjI6nQ6j0QiUTT1U3fBqGkB3Yieni1UA/SkHdFczTm5DiAUw1DG2/P3pUtVKr1mWU649Dp+/2lvYU2Odfj++aopRm9gQjRL4cOPy+/GeoVizRgm8P91+P54/HKui8/owaTVoT431qyfWQoa5CZsi09gc2Y1NkWkcsTQPbG3yOTH5HIR5SmhhO0hawW8MzvmeJvYMbDoLv8T2Y0nScFZHX8Avkd34JbIbS2IvZvqWRzH7gptKGzUadJqyMnj9Ki5/1b85g0ZBf+LvXG1ifaqKs5q/c3qNgqGK2DxDFOuie/NzzAUcDWlGuKeYRPdxmhYfQjVnY3QW0fPQCgynNIN/v9sd5FviSSg6zMuf3ki0/Th+VcVeTX3XoWDQnHzfO85QrJay17icvZrXrDaxGoI/c9Qm1uH34/D78VTxaUJ/yk1KVTGVxXqp+Df8bMbq8w7S9qN76fjRJJyRzShu0pn8lF4c6zEaV2Qzdl7zLBk9RxO36TNit3+JJWdv2d/wE/v2oVZ7nTpdrE4tux6Wlffk33BvNX/DPXozO7vdSFbTNGyhcZSExpEb1xaf3oTObeeyjyfg87oC29dkv033riDt57fZfMFtLLn6FcbPHozeUVhlGcrPTVuD8v4+1neawRa1nPz7WZPY8lrpP83f2lP3W1+xGk7+rVVVleo63tQ1Fk7WqZrG1rZNavfu3fnkk09ISUkJfJY8VV5eHrt37+att97iwgsvBGD16tW1PAq16gphMBjo0aMHy5cvZ9SoUUBZgmL58uVMnDixymP07duX5cuXM2nSpMCyZcuWBQaQbNGiBQkJCSxfvjyQSCguLmbdunXcddddtT4nUabWs0LUdmTO043KWRmXy4XLdbKZWnFxMc2aNWvQs0Lo23Wscv0lEaF82D458LzZuh3Y/ZW/7P2tIfyvY4vA8zYbdpHnrfyynmYxsbzLyXmc037dwxHXKZc+RYOiN6MYQmgVFsGCzu1w6kzY9BYmZjo5pgtDGxKFJiQKXWwqhua90UU0qelpV8vvsuErTMdvL8Bvz8dvz0cpzeHRSIh05RPtzOW5ndtZk3kQny0XfBUv2U8YIgL//8hjY4da9WV9ij48cKPzmdfGb9WMvn13XCecSZ3JT+jAbgXSC9NRNDq0YfHoYlqjMUfgdxQACi1D47DFtaE0KrnSfXkLj+LN2YPGHE64JRqPJRqvsfKkit9ZjDtzC+4jv9A/YxstcvcTUpzFr0VH+M5jq7K8Y3UWWmjKMr/rfS6+9DmqjF3yyUJGXDYMgPnvfcC4v0+oMnbeu/8ltvsVrDyi58u9Pmxq4x2J3u8sBkWDxlix+V4gxmXDe3wP3tx9+Epy8LtKUBSFtK5dGXrREFzHc9j0f++zylmI6izGV5iBrzgTb1EG/pIcUP101xi4zmABym68n3AVVnm8Tho9fzGcLM8UZ9UtXtpqdIw9JRk31VlQ5YeYFoqO8afUsaechdir+PjQRNEywXjymvmcs4jCKj4uxykaJhnDA89fdhWRU8W3eRFoeMh0MvY1VzEZauXXqWidlv19T34zMWLLAX4qqry+h2gUMvufTDiP3naIbwtKKo0FKLzwZHJw7M7DfJ5bsRm7YrBgSLmAZy+4iAJLAuHuIpYe2MnaohJUjxNfSRa+4mP4S3PLrpdaPds6xpKoK3tNH9iXwX+O5YNGhyY0Bo0+BJ8tF9VZdqzferUl2VTWZemxA8eYlVH1N99ru6fS3lI2t/yMw9k8m171VFmrLuxEQkJrtlg7sqjUxLcuC7rIZAzJfSpcp3U+FxfnrmZk1jfs2vg5/9yyp8r9fty7LZfFR5JljOWJkCF83+nvaAxm3Ec2kvv+LXizdwZi/69HKtckRQPwaWYeYzZWPb/33LSW/LVZ2ZcLX2cXcN363VXGvtAphTtblH1b9ENuEZev3Vll7FPtmzOpdRIeRcvX3ij+lq5gbNEPU7thGJqkVbldOU/2TvIX3oHrwGp08e1JfOBXFL2JLh/ezAt56wA46vFw8aH0Kvfxl3ArU+PKWoLke330PXioytirw8J4JqHsdbD7/XTbf7DK2GGhFl5NPPmNW9u9+6uMHRQSwptNTn6rlrbvQJVJi95mE+81PVlHLjhwkIIqkjedjEY+ad408Pyig4fJ8FZ+yxeJhtFYAs8/wkZBFdeTUBRu5uT171NsHK8i1oTC2FNi/4edY1XczuqA2zh5/fsKO+nV3PreeUrsMhwcUPyE9h1PxJXPoTGdvDY6966ky9aviDnwM2GZW1nlL2VPNbe+Y7BgPnHr+yNOdlRz2zk9JJKYEy0zP3bZ+Nbzu7/hiobQAXcTfuljaMMqfkHn3LOc/I/vxpsT/L6eYg4nRVv22eAbt51P3PZKj68YLLT751Zs0S2Iy9pO87ev5tPjVb+XJ5qsdDnRDXONx8l8V2mVsXeYwuipK0vM/+J18aaz6uv1rcZQ+unLrn9bvG5mO6vudnSTwcIgvQkvsMfn4dVqYkfpQxhqKPv8ctjnZaazqMrY4XozIwwhABzze3naUXXsxXoTV5/4e5/n9zHVUVhl7IU6Izec+PxRovp5pJoWrn10Rm45EetSVe63Vz3uTTetgdtMJ+vwRFtelbEdtXruOqVOT7bl4QYG60x8tOFnYtPKxpJJSUlh0qRJQTfciqLw2Wef0bt3b9LS0hg0aBAPPfQQUVFR7Nu3jw8//JD//Oc/KIpCXFwcw4cPZ+rUqaSnp/Pwww+zYcMGPvvsM0aNGhWYFWLTpk2BG/bCwkIiIyNZuXIlgwcPDhy3prNCLFy4kLFjx/LGG2/Qu3dvXn75ZT766CN27doV6MoxZswYmjRpwowZM4CyL7YHDRrEM888w4gRI/jwww+ZPn160Bfbzz77LM888wzvvvsuLVq04LHHHmPLli3s2LEDk8lUZXlE1WrVYqEuI3OeblTOysyYMYNp06bVpmh/bhotGksM2rB4tKFxaMLi0YbFYY+IZ2ZSEnZdCHadBXoYSAiJKYsxR6DoT74pXMC1v9ttdCWHUn1eyNlFH00pJq8Dk8/J9znZ5DntaMMS0EY2R2OyorptOHZ8iTH9Zz5rZqTAGEWhMYIXDxxga8ZePFnbwR/8xz5EozCmz8kbDFPWYXzFVf/BOlssBenEFR6l+Y6vyPDasfmrzhf/VR9GpFI2WOI3MSnsjG+DPqkLxpS+GJp2RxfRFF1E2YeyUz8q6PIOUbDra5x7vsOT8RvegnQ45VuzJF0oMSf6PCv+6r4POHssGhcjWnoZ0dJLr9zF/GXiw+jCm6ANb4ImLB5FUSjPO9568030veACch0Ka3Ye44d9JWhCosoSR45CVLcNjSUabVg8hogm+BUtCiqhWjd5R3bjKz6G6iwBRQMaDYqiBY2Wdm1a07VtClaDSkl+Fu++/QZ+ZxF+ZzGqqxTV60TR6FAMIVx19fVcOeJyEiwqtqx9XDF8MKrHAao/kJjSWhPRxbXh8hv/TrchV3OwSMPuXD9HSrRojBYMTbthaNot6HU4ALy5BaAZXPIIkZW8VqrPi9+WS76rlI/dNvSuUnSuUqIdBaiuEvyuElRnCX5X6YnnpXg8To76fehdpehdNrQlGYF1lSXSakIxhKBa4rDpTOg8DnSeP99gXMX6MDJCmpBpSSLfYiLCFIcuohlaaxLojChaPWh0KFo9l1kseBUdPo2WwuEammp0oNGVvc6OQhSDBW1YAopWx4unHqQzxFRThmFAmLuYKFc+xwd7SNKY0IYnoZzSbctbkI7n2Dbmqxl0tx8k0X6MQls6Om8mqqsUX3EWnDqWi6Lg1hgo1VlwawzYQxV00aFoQmPRhiehtSaV/Tzx//ubtiMn9GRLhFM/cqleN+70DTgP/IjrwI98EJ3DsIiy68m+aq5np0pwHWfAvtl89MNHxN7+BYZmPUh8cDMlq16ieMVM/NV8gD0TPIqWI+amlBqCb+g1lhj0cW3RxbVFH9+Or1um8W1sG46am+DV6Ph9Rw/3kY04dn+L+8hGRqak0C2lI4cMSWQ4NWyO7Yw+vj3x9/yIc9/3GJr1QtGbcO7+jthdSyG2ulog/pRUP6Vr3sCx7X+Y067D3H44praXYkodwp7UIewBtK5S1PRfiMz8DfexrXiytuO35ZV9SXKG3xemtpcQfvlTGJPLuhOH5h+i2+ZFWIsy2V6Qztrje/Ec2/qHjqG6bfR772bW3vYZOQkdKZywAu2rA/DlHz4TpyD+RJKSkvjpp5/45z//yaWXXorL5SI5OZnLLrsMjUaDoih8+OGH3HPPPXTq1Im2bdvy6quvBiULzoYbbriB48eP8/jjj5OVlUVaWhpff/110MCL6enpQbNS9OvXjwULFvCvf/2LKVOmkJqayuLFi4Nayz/00EPYbDbuuOMOCgsLGTBgAF9//bUkFf6AWrVYyMzMpEmTJqxZsyZoLtKHHnqIVatWsW7dugrbGAwG3n33XW666abAstdff51p06ZV6PtSrjG2WFjy4Eze7HovGr8Pg99Dk5J0Yhw5gYGZvDozJfowig3hFBisKKiEuwrRqH7cGgNejR6PVo9HY8Cn1ePR6HFrDDi0ZoqN4UFzU9eFwevE7HNi9DmxeO2EOQuwuouwuooIcxcR68imfd5W2uTvJMTnCOo2UZumy3Vt5nzc7uaT3cHf9FXW1Loqek724fKq1TdhrE2sjuCmhqfGevVmcpv1wB6RhNFegNlegNlRiKUwAzyOOu+3uthqu0J0as/d3y8J6grx+z5vpzIajYFYr9cb9J77PYPBEOgvd7pYvb4sVlHA5/PhdFZ986vX6zEYyr4ZqU2s3+/H4ai65cbvY4ttDo6WajlUrONIqY4ClwaHV0FRQKvRoNNpKcrOY8vn3+DRGXGbw7FbE7FbE3CExaNWMw5IXWi87rLkhNuG3l2K3lWK1ufFawjBbQzFY7AAKhq/D43Pg1+rx2mJrrRLTdmYG85AgkpR1bL+K6hl/6es20n5Mq3Hibn0OKbSXMy245hK8zDZjmMqPU6YLQ+TPR+v3kypORKnJRpXSBQuSyTOkGjc5vCTx1RVFNVXdly/D0X1Ydb4aW3Vo/N7MZ5oku9VdPj1ZlxaIxrVj9FdikNrIt8UzZHQZAqNkWhUPx6tgVJ9GHa9pcI5/lEJtgw6FO4k0Z5FkSGcLFMsKgpOnYk8Uwz5phjc2rJv3TR+X5XjviiqH6PPiVMXctpjavw+LN5SvIoet1aPT1P76Vi0fi8dSnbTxH6UOGc2sc4cUkv20bFoOyb/yffgqd0mPH4/7ipaxEFw94by2CxTHM+1f4gf4wYCYPba6ZO3nk4lu4jwlWL2OTB67MQ4jtHEnoHB78apNXHcGIPB7yHWeZwI1YGxBt0mSrUWfki8iDdb3kaGuaxfrdHnJM6ZQ5EhnGJ9eKXblZeruT2d1JK99MlbT++89US5T34zWN5twu30kH2wgFxDGP/X7yGWdRiN/0Qit/ORNUz+9j7iHLmBvzF+VcUpXSGAsq4Q2U4PCzMKK41vLF0hahPrjGhCVo8bKGrZj4KWffCaI6reX2keluzdhGbvxliai85RREj2LiwZWzEWZQaOZlYU2oUYUBWFwrh2ZDbvhctkLfucpzVQak3kaPIFFMS2BsDoKGLgt/+mxy/voTvRUux03RBq2sWiPLY4shn/HbOQvLg2RObu56Y3R2CxVWxh1ZC6QniRrhB/pCsEgFFRGL16eaDFQkNS0xYLovFokLNCGI3GQJ/nxsJhjmZ7TFrg+caEC87o/jV+HxHuQqKdeUQ684h25RHuKsLitWH2OjB77US6Ck488rG6i8pGlfY5MfpctZvLWhucxDBra57UqE1s0LgTWk1QIuH39NWs+z3dOYo1eJ00P/hT5cFnqQxaRaGqW1yjVhvUJ06v1wcNnlNtGXS6SvvT/dFYrVaLxVKzG8XaxGo0mlrFRoRZiAiDToGWxKd+0PYBHrJ+O8i8xfdX2N6vaHCExuEIjcFjsOAxhpXd/BtD8RgtZT8NobiNoXiNlrIYwynrDScexlB8J1oR+XUGXLooXJaoGp1D0Pn4PPgVLZx4/6gaLV5D7W7GbZHNan3cmtpyBvYR5cwjyZ5Jkj2TBHsWifZjxDlyMPmdaP0+dKoXnd+LVvWh93vQ+b3o1LLnWr8Pm95CiT6MEK+dGGcucc7j1R5PBRxaM0afCwWVYoOVfGMU+cay34/R5yLBkUW0Mw8NKsX6MPZZW7PP2pq94anstaaSZ4rGqTXh1JZ1N/Nq9JQYqr5J1vk96P0eIl0FxDqPE+s4Tpwzh1hXLgkUEevKpVPJTqzeSlp1aYAqkh96jabGg7CWx7by5jF36z9ZFd2f2S1vZ4e1Hd/HD+b7+ME129EJRp8Lo9+F1VtClLsAp8ZEkd5KpKeQMG8pJbpQ9lla4j1xk2/wuXBrjbi0pqBxIhIdx2hpP0yKPZ0W9nRSbOm0tB8i3nW84nTCuspfB42iEOcp5YFVj3Pzprf4vPNfaVp4kMu3Lyz7e3jKdVejKITU8DqsnKVYgBBNzf9+nq1Ys0aDWaMJuiGvSk1iyukacKy+MJOw5S/B8pdQFYWShA4UN+tKSVInShI7YItLxWsKw2uOwBMaTWFoPwpb9au4H1s+BlseqH40qsqPBiMlYYmB636lx3bZSNv4Af1+eJWw0uAv23SKUuMP6TWJjSg8ys3zrmX+nV9SENOKz8Z+yF/fHoXRXXVXzNqUobrPJ3WJ9Z+4MdYoCjW9I2gIscpZioWyJMHZiBXiTKlVYqEuI3OeblTOP4tuub/y4o//wK9ocejMHA4r+ybO4Hdj8Lkw+DxYPUVY3cWEu4rwKwqFxkgUVPR+D3qfu+yn34PB70bv96L3uTH6XUQ58wl3F6Jt4KMXC/FnpFH9WEqysJT88XmNfRodXkMIHmPYiWTDKUkHrQH9iW4Weo8dVBW/Roeq0aGoPky2XMy2fHQnPgT6tQa8elPZgKR6U1myAU7cMCmov/tZfiPlMVhwhEbjsMTitETjCI3FERqDwxKLIzQaZ0g0ercNo70Akz0fU/lPWx4GZxGKqqIqmrIBURVN2bf7J36ajUbaJUTg0ehxaY04dGb0fk/ZDafPhU/RYteFYPI5iXAX0rw0nRhnLn5Fg9HnwuK1EefIqTCI4NmmACGnjF0S4S4iwl1Ey5LK+8dbPSV0z9tE97xNla73o5BvjKLEEIbed+p1veynzu+tOtmr1aALO/fjnSjA4LyfGJT3E1usHfklIo39lhTs2hDsWjM2nYWj5kSOG092Rohy5+NR9JToy/oAu7RGXFojxXorR80n+/hnm4L7jTe3H+H6jM/5y9GP0aCSbYwlyxiH1VtCsv0IZn/VLaLqIrH4CH//acYZ3af4c1JUFeux7ViPba+wzqc3UxqXii2+Lba4VDzmcNxhsSeSD23wWKLwVJIs1rttNE3fQFhxFhq/B63Xg9lRQFLGJpod+hlzNWMCnGnWkixunncd796xlGNN0lh08/9x43t/QdeIpt8UQjRctUos1GVkztONyvlnEefIIc5R9UBcQgih9XvROosxVjMQVY335XOj9bnPyL7OlJgQPaM7xp8+8E9Og0qMK48Y19kdp+BsUICuxdvpWlzxxgrArejwK1q0qhf9iQE7HRojDq0Zh9aEQ2uiRBdGriEKs89JuKeYfEMENp0Fi9dGC3s6zR0ZQfts7siosEyIhkbrcRCesYXwjIrtsnw6I7b4NniNoYCCQaulpR5CS7KwFmWi9VfX8P/cis47wE3v3sB7t33OoVYDee9vn3H1R3cSUXikvosmhGjkat0VYvLkyYwdO5aePXsGRua02WyMGzcOqDgq57333sugQYN44YUXAqNy/vLLL7z55ptn9kyEEEIIcVYZVC+owTdJZr+rrJVB3cYkFaLR03pdWDNODrJo1ig0CzHUY4mql5i5hdHv/5WP/zKfjOa9eGvCSgZ/N4MeG+ajqacBpYUQjV+tEwunG5mzLqNyCiGEEEIIIc6NlIM/cftrF/HZ6DfIaN6Lb654hp8H3EX77Utose97mqZvqHb8BSGE+L06Dd44ceLEKrs+fP/99xWWXX/99Vx//fV1OZQQQgghhBDiDIsoPMLYt0ayqectfH/JFIoik/l5wAR+HjABgNCSbMKKMrGUHsdiy8VSepyovANEH99LTO5ezI7C+j0BIUSD0iBnhRBCCCGEEEKcXRrVT48N79J58yL2p17EnvaXkZ5yAUWRyZSGxVMaVvW4OSGlx4nO3YfRVXpiSmPQeV2EnJjG0m0MxWUMw6c1YHIWEmIvwGzPx+woxGzPw1p0jBBbLorqL3sAqH4UVcV/ylSXRmcxJmdhYAplIUTDJIkFIYQQQgghzmMGj532O5bQfscSABzmCAojmlNiTcAWGoctNIbSsATyYlqSF5NKcURT7KGx2ENjT7PnM0Px+9D4vYEkBL9LMiiqiqL6UFQ/ercDnadspp+yWYw0ZT8VDdRiilIAnceB3mNH1WjxafT4tTp8WgN+rR6fVo+i+stadJRkE1qSjcFVisbnKUuqmKy4TFZ8OiN+jQ6/RodHb8ZhiS6bScmWT4gtF5OzCJ9Wj1+jQ+9xoHfbA7NDoSioKPi0BjyGEAxuG5aSHHQeBwoqrhNTYLuNYbhMYSd+WlEVDUZHEdaio4w+U78EIU5DEgtCCCGEEEKIALOjELOjkMRjFWfBAHAbLOTFtCIvphVenYnyG3aP3ozdEo2i+jG4SjG6StH4vTjN4TjMkThCInGERGGzRFNiTcIREol6YkpkVdGgUvZTQQ1Myusxhp64sdfWqOzOkDPwAtSCwxJDbnz7Wm/nDImiILb1WSjRSY68aKDhzB4l/twaRWJBPZGVLC5umG8Mb2kpxV4ZRfePKPX5cEoTtz/E4fM22PdIY1Bis0kd/IMcql+uhX+UqqLzNJyp6Rojt9dHqc9f38VotGx+P27kWvhHaNSy6+GfmquEiIzNRGRsPiu796sq5VdCv1aPwxwZaHXg12g5teWBWp6UUJSyBITWhEdvQkE90ZLBj+L3w4nnNaUqCl69CY8hBI3Pi8bvRevzoPF50frdKD4PaLTYLDHYQ+MoDY3DY7Dg1+oxuEsxOIsxOkvRee0ofj8avxed14XZno+i+nCYo3BaonCarOi8bhS/H6/eiMcQgieQrFHLzsPvR++x4zFYsFui8epNZa0SnKUYXCUY3KUYnSXo3aUYnKVlrRlMYSiqSsml92JsgJ8Pyz+zquf5Zy+n04nb7T5tnMFgwGQynYMS1Z2iNoLf5tGjR2nWrFl9F0MIIYQQQgghxBly5MgRmjZtWt/FqBdOp5Nocwj2GiRzExISOHjwYINOLjSKxILf7yczM5OwsDAUpXZ9o86F4uJimjVrxpEjR7BarfVdHCGqJfVVNCZSX0VjIvVVNCZSX0V9UlWVkpISkpKS0Gg09V2celFcXEx4eDi3YMFQzfgfblTew0ZRUVGDfq82iq4QGo2mUWSyrFZrg/5lC3Eqqa+iMZH6KhoTqa+iMZH6KupLeHh4fRehQTChwVDNl+eaht8OAGgkiQUhhBBCCCGEEOLPRnPiUd36xkASC0IIIYQQQgghRD1QFNBU09tfARrDmLqSWDgDjEYjU6dOxWg01ndRhDgtqa+iMZH6KhoTqa+iMZH6KkTD8GdpsdAoBm8UQgghhBBCCCH+LMoHb5yosWKsZowFl6oy218sgzcKIYQQQgghhBCioj9LiwVJLAghhBBCCCGEEPVAc5oxFiSxIIQQQgghhBBCiCpJiwUhhBBCCCGEEELUmaIoKNWMsVBNY4YGRRILQgghhBBCCCFEPfiztFhoLOVssF577TVSUlIwmUz06dOH9evX13eRxHnohx9+4IorriApKQlFUVi8eHHQelVVefzxx0lMTMRsNjN06FD27t0bFJOfn8/NN9+M1WolIiKC2267jdLS0nN4FuJ8MWPGDHr16kVYWBhxcXGMGjWK3bt3B8U4nU4mTJhAdHQ0oaGhXHvttWRnZwfFpKenM2LECEJCQoiLi+PBBx/E6/Wey1MR54E5c+bQpUsXrFYrVquVvn378tVXXwXWS10VDdUzzzyDoihMmjQpsEzqqxANT/kYC9U9GgNJLPwBCxcuZPLkyUydOpVff/2Vrl27MmzYMHJycuq7aOI8Y7PZ6Nq1K6+99lql65977jleffVV5s6dy7p167BYLAwbNgyn0xmIufnmm9m+fTvLli1jyZIl/PDDD9xxxx3n6hTEeWTVqlVMmDCBn3/+mWXLluHxeLj00kux2WyBmPvuu48vvviCRYsWsWrVKjIzM7nmmmsC630+HyNGjMDtdrNmzRreffdd5s+fz+OPP14fpyT+xJo2bcozzzzDxo0b+eWXX7jooou46qqr2L59OyB1VTRMGzZs4I033qBLly5By6W+CtHwKJxstVDZo5HkFUAVdda7d291woQJgec+n09NSkpSZ8yYUY+lEuc7QP3ss88Cz/1+v5qQkKDOnDkzsKywsFA1Go3qf//7X1VVVXXHjh0qoG7YsCEQ89VXX6mKoqgZGRnnrOzi/JSTk6MC6qpVq1RVLaufer1eXbRoUSBm586dKqCuXbtWVVVV/fLLL1WNRqNmZWUFYubMmaNarVbV5XKd2xMQ553IyEj1P//5j9RV0SCVlJSoqamp6rJly9RBgwap9957r6qqcm0VoqEpKipSAXWKIUJ90hhZ5WOKIUIF1KKiolrtf/bs2WpycrJqNBrV3r17q+vWrasydt68eSoQ9DAajbU6nrRYqCO3283GjRsZOnRoYJlGo2Ho0KGsXbu2HksmRLCDBw+SlZUVVFfDw8Pp06dPoK6uXbuWiIgIevbsGYgZOnQoGo2GdevWnfMyi/NLUVERAFFRUQBs3LgRj8cTVGfbtWtH8+bNg+ps586diY+PD8QMGzaM4uLiwDfJQpxpPp+PDz/8EJvNRt++faWuigZpwoQJjBgxIqheglxbhWioqmutcLrxF6pSl5b1VquVY8eOBR6HDx+u1TFl8MY6ys3NxefzBV14AeLj49m1a1c9lUqIirKysgAqravl67KysoiLiwtar9PpiIqKCsQIcTb4/X4mTZpE//796dSpE1BWHw0GAxEREUGxv6+zldXp8nVCnElbt26lb9++OJ1OQkND+eyzz+jQoQObN2+WuioalA8//JBff/2VDRs2VFgn11YhGqbTjaNQl8TCiy++yPjx4xk3bhwAc+fOZenSpbzzzjs8/PDDlW6jKAoJCQl1OFoZSSwIIYSoNxMmTGDbtm2sXr26vosiRJXatm3L5s2bKSoq4uOPP2bs2LGsWrWqvoslRJAjR45w7733smzZMkwmU30XRwhRQzWdFaK4uDhoudFoxGg0Vogvb1n/yCOPnNxHDVrWl5aWkpycjN/vp3v37kyfPp2OHTvW6jxEHcTExKDVaiuMpJudnf2HMj1CnGnl9bG6upqQkFChaZTX6yU/P1/qszhrJk6cyJIlS1i5ciVNmzYNLE9ISMDtdlNYWBgU//s6W1mdLl8nxJlkMBho3bo1PXr0YMaMGXTt2pVXXnlF6qpoUDZu3EhOTg7du3dHp9Oh0+lYtWoVr776Kjqdjvj4eKmvQjRAGpTTPgCaNWtGeHh44DFjxoxK91ddy/qqWh61bduWd955h88//5z3338fv99Pv379OHr0aC3OQ9SJwWCgR48eLF++PLDM7/ezfPly+vbtW48lEyJYixYtSEhICKqrxcXFrFu3LlBX+/btS2FhIRs3bgzErFixAr/fT58+fc55mcWfm6qqTJw4kc8++4wVK1bQokWLoPU9evRAr9cH1dndu3eTnp4eVGe3bt0alBBbtmwZVquVDh06nJsTEectv9+Py+WSuioalIsvvpitW7eyefPmwKNnz57cfPPNgf9LfRWi4anpdJNHjhyhqKgo8Di1RcIf1bdvX8aMGUNaWhqDBg3i008/JTY2ljfeeKPG+5CuEH/A5MmTGTt2LD179qR37968/PLL2Gy2QF8WIc6V0tJS9u3bF3h+8OBBNm/eTFRUFM2bN2fSpEk89dRTpKam0qJFCx577DGSkpIYNWoUAO3bt+eyyy5j/PjxzJ07F4/Hw8SJE7nxxhtJSkqqp7MSf1YTJkxgwYIFfP7554SFhQWy5+Hh4ZjNZsLDw7ntttuYPHkyUVFRWK1W/vGPf9C3b18uuOACAC699FI6dOjALbfcwnPPPUdWVhb/+te/mDBhQqXNAoWoq0ceeYThw4fTvHlzSkpKWLBgAd9//z3ffPON1FXRoISFhQXGqilnsViIjo4OLJf6KkTDU9OuEFarFavVetr9nYmW9Xq9nm7dugXdX5xWreaQEBXMmjVLbd68uWowGNTevXurP//8c30XSZyHVq5cWWGKGEAdO3asqqplU04+9thjanx8vGo0GtWLL75Y3b17d9A+8vLy1JtuukkNDQ1VrVarOm7cOLWkpKQezkb82VVWVwF13rx5gRiHw6HefffdamRkpBoSEqJeffXV6rFjx4L2c+jQIXX48OGq2WxWY2Ji1Pvvv1/1eDzn+GzEn93f/vY3NTk5WTUYDGpsbKx68cUXq99++21gvdRV0ZCdOt2kqkp9FaIhKZ9u8llzpPpqSHSVj2fNkbWebrJ3797qxIkTA899Pp/apEkTdcaMGTXa3uv1qm3btlXvu+++Gh9TUVVVrXkaQgghhBBCCCGEEH9EcXEx4eHhzAyJxKxU3WbBofp50F5AUVFRjVosQNl0k2PHjuWNN94ItKz/6KOP2LVrF/Hx8YwZM4YmTZoExml48sknueCCC2jdujWFhYXMnDmTxYsXs3Hjxhp3g5KuEEIIIYQQQgghRD2oaVeI2rjhhhs4fvw4jz/+OFlZWaSlpfH1118HBnRMT09Hozm554KCAsaPH09WVhaRkZH06NGDNWvW1GpsFWmxIIQQQgghhBBCnEPlLRZespy+xcJ9ttq1WKgP0mJBCCGEEEIIIYSoB6dOKVnV+sZAEgtCCCGEEEIIIUQ9OHVKyUrXn7ui/CGSWBBCCCGEEEIIIeqBcuJR3frGQBILQgghhBBCCCFEPZAWC0IIIYQQQgghhKgzGWNBCCGEEEIIIYQQdSYtFoQQQgghhBBCCFFnCtUnDxpHewVJLAghhBBCCCGEEPVCBm8UQgghhBBCCCFEnWkUBY0iYywIIYQQQgghhBCiDqTFghBCCCGEEEIIIepMEgtCCCGEEEIIIYSoM0ksCCGEEEIIIYQQos4URUGpZowFpZGkFiSxIIQQQgghhBBC1ANpsSCEEEIIIYQQQog605x4VLe+MWgs5RRCCCHqRUpKCrfeemt9F0MIIYQQf0KKcvpHYyCJBSGEEOel/fv3c+edd9KyZUtMJhNWq5X+/fvzyiuv4HA46rt4dfLpp59yww030LJlS0JCQmjbti33338/hYWFNd7Hzp07ueyyywgNDSUqKopbbrmF48ePn71CCyGEEOcxpQb/GgPpCiGEEOK8s3TpUq6//nqMRiNjxoyhU6dOuN1uVq9ezYMPPsj27dt5880367uYtXbHHXeQlJTEX//6V5o3b87WrVuZPXs2X375Jb/++itms7na7Y8ePcrAgQMJDw9n+vTplJaW8vzzz7N161bWr1+PwWA4R2cihBBCnB9kjAUhhBCiETp48CA33ngjycnJrFixgsTExMC6CRMmsG/fPpYuXVqPJay7jz/+mMGDBwct69GjB2PHjuWDDz7g9ttvr3b76dOnY7PZ2LhxI82bNwegd+/eXHLJJcyfP5877rjjbBVdCCGEOC/9WRIL0hVCCCHEeeW5556jtLSUt99+OyipUK5169bce++9VW6fn5/PAw88QOfOnQkNDcVqtTJ8+HB+++23CrGzZs2iY8eOhISEEBkZSc+ePVmwYEFgfUlJCZMmTSIlJQWj0UhcXByXXHIJv/76ayDGbreza9cucnNzT3tuv08qAFx99dVAWReH0/nkk08YOXJkIKkAMHToUNq0acNHH3102u2FEEIIUTsaQKNU86jvAtZQYymnEEIIcUZ88cUXtGzZkn79+tVp+wMHDrB48WJGjhzJiy++yIMPPsjWrVsZNGgQmZmZgbi33nqLe+65hw4dOvDyyy8zbdo00tLSWLduXSDm73//O3PmzOHaa6/l9ddf54EHHsBsNgclAdavX0/79u2ZPXt2ncqblZUFQExMTLVxGRkZ5OTk0LNnzwrrevfuzaZNm+p0fCGEEEJUTcZYEEIIIRqZ4uJiMjIyuOqqq+q8j86dO7Nnzx40mpO5+VtuuYV27drx9ttv89hjjwFl4zh07NiRRYsWVbmvpUuXMn78eF544YXAsoceeqjOZavMs88+i1ar5brrrqs27tixYwCVtuJITEwkPz8fl8uF0Wg8o+UTQgghzneNI3VQPUksCCGEOG8UFxcDEBYWVud9nHpj7fP5KCwsJDQ0lLZt2wZ1YYiIiODo0aNs2LCBXr16VbqviIgI1q1bR2ZmJklJSZXGDB48GFVV61TWBQsW8Pbbb/PQQw+RmppabWz5TBiVJQ5MJlMgRhILQgghxJlzuiklZbpJIYQQooGxWq1A2dgGdeX3+3nppZdITU3FaDQSExNDbGwsW7ZsoaioKBD3z3/+k9DQUHr37k1qaioTJkzgp59+CtrXc889x7Zt22jWrBm9e/fmiSee4MCBA3Uu26l+/PFHbrvtNoYNG8bTTz992vjyGSNcLleFdU6nMyhGCCGEEGeGUoNHYyCJBSGEEOcNq9VKUlIS27Ztq/M+pk+fzuTJkxk4cCDvv/8+33zzDcuWLaNjx474/f5AXPv27dm9ezcffvghAwYM4JNPPmHAgAFMnTo1EDN69GgOHDjArFmzSEpKYubMmXTs2JGvvvrqD53nb7/9xpVXXkmnTp34+OOP0elO30CxvAtEeZeIUx07doyoqChprSCEEEKcYRqU0z4aA0Wta/tKIYQQohG68847efPNN1mzZg19+/Y9bXxKSgqDBw9m/vz5AKSlpREVFcWKFSuC4po2bUrr1q35/vvvK92P2+3mmmuu4euvv6a0tDTQveBUOTk5dO/enZSUFFavXl3rcwPYv38/AwYMwGq1snr1amJjY2u8bVxcHIMHD64wA0Tbtm1p2rQpy5cvr1OZhBBCCBGsuLiY8PBwPo9KwKKp+vt+m9/PVflZFBUVBVpeNkTSYkEIIcR55aGHHsJisXD77beTnZ1dYf3+/ft55ZVXqtxeq9VWGPNg0aJFZGRkBC3Ly8sLem4wGOjQoQOqquLxePD5fEFdJ6Dsxj4pKSmoO0JtppvMysri0ksvRaPR8M0331SbVNi/fz/79+8PWnbttdeyZMkSjhw5Eli2fPly9uzZw/XXX3/a4wshhBCidsrHWKju0RjI4I1CCCHOK61atWLBggXccMMNtG/fnjFjxtCpUyfcbjdr1qxh0aJF3HrrrVVuP3LkSJ588knGjRtHv3792Lp1Kx988AEtW7YMirv00ktJSEigf//+xMfHs3PnTmbPns2IESMICwujsLCQpk2bct1119G1a1dCQ0P57rvv2LBhQ9AsEevXr2fIkCFMnTqVJ554otpzu+yyyzhw4AAPPfQQq1evDmr1EB8fzyWXXBJ4fvHFFwNw6NChwLIpU6awaNEihgwZwr333ktpaSkzZ86kc+fOjBs3rgavrhBCCCFq43TjKDSSvIIkFoQQQpx/rrzySrZs2cLMmTP5/PPPmTNnDkajkS5duvDCCy8wfvz4KredMmUKNpuNBQsWsHDhQrp3787SpUt5+OGHg+LuvPNOPvjgA1588UVKS0tp2rQp99xzD//6178ACAkJ4e677+bbb7/l008/xe/307p1a15//XXuuuuuOp3Xb7/9BpQNCvl7gwYNCkosVKZZs2asWrWKyZMn8/DDD2MwGBgxYgQvvPCCjK8ghBBCnAXKiX/VrW8MZIwFIYQQQgghhBDiHCofY+HLmMTTjrFwee6xBj/GgrRYEEIIIYQQQggh6oF0hRBCCCGEEEIIIUSdSWJBCCGEEEIIIYQQdfZnGWNBEgtCCCGEEEIIIUQ9ON2Uko1lusmqR4moxIwZM+jVqxdhYWHExcUxatQodu/eXe028+fPR1GUoIfJZPpDhRZCCCGEEEIIIRo7TQ0ejUGtWiysWrWKCRMm0KtXL7xeL1OmTOHSSy9lx44dWCyWKrezWq1BCQillmkXv99PZmYmYWFhtd5WCCGEEEIIIUTDoaoqJSUlJCUloalmRoTzwXk5xsLXX38d9Hz+/PnExcWxceNGBg4cWOV2iqKQkJBQtxICmZmZNGvWrM7bCyGEEEIIIYRoWI4cOULTpk3ruxj160Sr/urW18Vrr73GzJkzycrKomvXrsyaNYvevXufdrsPP/yQm266iauuuorFixfX+Hh/aIyFoqIiAKKioqqNKy0tJTk5Gb/fT/fu3Zk+fTodO3asMt7lcuFyuQLPVVUFyipeQ5y70759B/uvH13fxWjUDAYtCU1C67sYjVqxw83avXn1XYxGq9TnZ4vNU9/FaNSSIkO476ou9V2Mxk0BxaCv71I0bkYjSvPk+i5Fo5KvhrDY353/qr3JJqJO+zDgIU1zlCLVjActBrwY8GFUyn421+Rzq34tiZqSM1v4BsibV4htyQ/1XYxGTdOqNREvv17fxfhzMIWCRlvfpaiguLiYZs2aERYWVt9FqXdno8XCwoULmTx5MnPnzqVPnz68/PLLDBs2jN27dxMXF1fldocOHeKBBx7gwgsvrPUx65xY8Pv9TJo0if79+9OpU6cq49q2bcs777xDly5dKCoq4vnnn6dfv35s3769yuzUjBkzmDZtWoXlVqu1QSYWdKGhhJ7nTXj+KINGg1XX8C56jYmq1RKiSD2sK58ChkbT2KxhMmk0WA0yJvAfoigoRkks/CFGPYrZWN+laPD2q7HM9/fnO38HsgkPLLfipL92Pw4M2FQjkYqNKMVOhOLAiQ6HqidSsROrlBKp2Dngj2Glrw07/Yn8QnSVx1sLfKYO5C+aX7he9yslmHCpOvppD6BV1HNwxueO12RAI59p/hCtQd8gP/M3Sg00sVBOurmDRlHQVPM6lK8rLi4OWm40GjEaK/979+KLLzJ+/HjGjRsHwNy5c1m6dCnvvPMODz/8cKXb+Hw+br75ZqZNm8aPP/5IYWFhrc6jzp8AJ0yYwLZt21i9enW1cX379qVv376B5/369aN9+/a88cYb/Pvf/650m0ceeYTJkycHnpdntIQQQgghRN0Vqyae81/Gf/19UE8ZEqwDGdyq/YkrNFswGmuXpL5fXc5WfxK7/fHEKqWYFQ8uVYcLHS5Vhx09n3q6sd6fwnxPX+Z7Tn4ubK85xhPGpfTSpp+xcxRCiMZEo5Q9qlsPVLgfnjp1Kk888USFeLfbzcaNG3nkkUdO7kOjYejQoaxdu7bK4zz55JPExcVx22238eOPP9bqHKCOiYWJEyeyZMkSfvjhh1r3idHr9XTr1o19+/ZVGVNd9kUIIYQQQtSOqsJStQtP+0YGWihcrOxgjGYN3ZTDhCruskBFAQy12reiQBdtJl20mVXGXK/bxGpfK971XMD3vlTilWJKVSM7/Ync7BjHm6YFDNbtrevpCSFEo6VoFJRqMgvKiRa1vx8WoKr75dzcXHw+H/Hx8UHL4+Pj2bVrV6XbrF69mrfffpvNmzfXsvQn1SqxoKoq//jHP/jss8/4/vvvadGiRa0P6PP52Lp1K5dffnmttxVCCCGEELWzT41jqu8q1qqtAUgml+naT+irOXDOyqAocKFuPxfq9uNXFTSKSr4awqPOK/jG15G7nTcwSrcFPwqdtJn01R6klSb3nJVPCCHqi6JUPz5j+bqzNSxASUkJt9xyC2+99RYxMTF13k+tEgsTJkxgwYIFfP7554SFhZGVlQVAeHg4ZrMZgDFjxtCkSRNmzJgBlDWpuOCCC2jdujWFhYXMnDmTw4cPc/vtt9e50EIIIYQQono21cAs/8W8478QL1qMeLhbs5I7NKswKt56K5fmxJgKUYqdV02L+LtTx0pfWz709gTgI28PAFopx7lWv4lb9OuxlLeoEEKIP5maJhZqKiYmBq1WS3Z2dtDy7OzsSmdq3L9/P4cOHeKKK64ILPP7/QDodDp2795Nq1atTnvcWiUW5syZA8DgwYODls+bN49bb70VgPT09KC5SAsKChg/fjxZWVlERkbSo0cP1qxZQ4cOHWpzaCGEEEKIs0pVwY/S6AcTdKtaPlF7MMs3lKwT3R6GKtt5TPsFzZSCei5dML3i5zXTQj7ydKeAEHyqho3+5mz0NWe/Gstz7kt529OPew0ruUn3S6P/3QghxO8pp5lusrYDXBoMBnr06MHy5csZNWoUUJYoWL58ORMnTqwQ365dO7Zu3Rq07F//+hclJSW88sorNR7rsNZdIU7n+++/D3r+0ksv8dJLL9XmMEIIIYQQteZUDGwztMWkxpJEIdGKLbDOrWrJI5R4itEoKkWqmX1qHEeIYqeayDa1CTvUJGwYaUEucUoxIbhppeTQUckkiULCFTt6fDSloK7Tip9VHlXDp2oPZvsuIoOyqcCbk8dj2v9xsabyfrUNgUnxMsawPmhZsWrkG28HXncP5LAazeOuK3jf05semnTaa7MYqN1Hc03DSpIIIc4NVVWZOnUqb731FoWFhfTv3585c+aQmppa7XavvfYaM2fOJCsri65duzJr1ix69+4dWO90Orn//vv58MMPcblcDBs2jNdff73CWAVn2plusQAwefJkxo4dS8+ePenduzcvv/wyNpstMEvEqb0MTCZThVkeIyIiAKqd/fH3ZF4wIYQQQjQ6xRoLa8xd2K9vykF9Eof0SWw3tsKt6OFEK/92ZNJcyWeHmkQGEahoiMBGEwrZSSJ+Kp/9YC/x7FXLPkguUztWWN9P2ct/tPMx1WN3gnKqCjtJ5H/+ND719yCXsjnhYynmbs1KbtSsr9duD3VlVVxcr9/EKN1vLPD04kX3Rezxx7PHHx/4/Q7W7uFZ42JiNaX1W1ghxDn13HPP8eqrr/Luu+/SokULHnvsMYYNG8aOHTswmUyVbrNw4UImT57M3Llz6dOnDy+//DLDhg1j9+7dxMXFAXDfffexdOlSFi1aRHh4OBMnTuSaa67hp59+Oqvnc6ZbLADccMMNHD9+nMcff5ysrCzS0tL4+uuvA0mS3/cyOBMUtSbNEOpZcXEx4eHhFBUVNcg5be1bt7F3xMj6LkajZjBoSWoWVt/FaHB8aCjVh1Kst+LWnhylW6P6CfOUEO4uQq+WfcIqsrtZvVsGuqqrEp+fTaXSh/ePaBpt4Z/XptV3MRo3RUEx6uu7FA3OQV0im0ztKNJYOKaLYZehBevNHfEoFV+rWF8BilbLcUKDplMEUPAHLUuigGZKPqlKNh2VTDopGYTjYL8aSwEWilUTu9RE9pBAlmqlFCM2jPjQcrGyg4s0O8lTQ9HiJ0EpopOSQRhOzHgIVxxn7fWwq3p2qEmsUtuy1N+FQ8SePH+KuUOzips1P9ct8aEoYKjdrBDnQp4awg/eVPb7Y/jFl8xGfzN8aInCxh2G1Vyn30SUYq/vYgLgzS2gdPHK+i5Go6ZNbUPE3Hn1XYxGbfBlI+jcsQNag5l3/+//MBgMPPXUU/zlL39h4sSJfPzxx8THxzNr1iyGDx8OwLZt23jwwQf58ccfsVgsXHrppbz00kuBAf2+/vprnnrqKbZt24ZWq6Vv37688sorgf73hw4dokWLFnzyySfMmjWLdevWkZqayty5c+nbt29Q+ep6f6eqKklJSdx///088MADABQVFREfH8/8+fO58cYbK92uT58+9OrVi9mzZwNlXQOaNWvGP/7xDx5++GGKioqIjY1lwYIFXHfddQDs2rWL9u3bs3btWi644IJavPo1U/4arEtJJrSam/xSv58+hw432HvhctJiQYh6ogL5hkiOhSSRGZJIZkgSx0ISyTQnciwkkSxzAiX6MFSl+myi2Wsn3F1EqKsIemcRassl1HacyILDJGVtJzFrG0a3rdp9CCFEQ3JIl8AXoQPZbmzFPn0zDhqaVBqX6k6nk2sfLTyZtPBk0N59iBRNHpqUFhSoIaxS25CvhtJByaSVkkMEdraozcgkgh7KIZKUokr327SacQjW+1swxncby9UOLPdVPV5UF+UIFyk76aak40chByuR2EhR8mhFTo2atvpUhSzCSVej2K0msE1tyla1KfuJDWptYcTDIGU312g2MkTZhV7xn37njUy0Yudq/W+B53t8sdznuo6d/kSecQ/jGfcwYpUShmj38E/jt0SexcROQ1FksLIvPJUCYxRNbEdpW7ALDQ3++0JxDr37wYc89OADrF+/noULF3LXXXfx2WefcfXVVzNlyhReeuklbrnlFtLT03G73Vx00UXcfvvtvPTSSzgcDv75z38yevRoVqxYAYDNZmPy5Ml06dKF0tJSHn/8ca6++mo2b94c9O33o48+yvPPP09qaiqPPvooN910E/v27UOn05Genh401l5SUlKFck+ZMoUpU6ZUek4HDx4kKyuLoUOHBpaFh4fTp08f1q5dW2liwe12s3HjRh555JHAMo1Gw9ChQ1m7di0AGzduxOPxBO23Xbt2NG/e/KwlFgJlURQ01fxRqG5dQyKJBVHvVMChD+FoSBKFhkgKjJEU68PwavR4FD0ejQ4FFZ3fi171ovOfeKieEz+9GH1urJ4irO5iwj3FmHxOGtJbsMAQweaoNDZFp7HH2obME4kDp85co+3NXjsmnzPw3KvRU6qzoCoaHLoQHLoQCEmEyHYVtlX8fqLzD9D8yC8kH1lPyuF1xObubVCvjxCi7lSgSBOK1W9r1DcVBZowvrT057OwIWwyBV/LdKqXbs7dxPnyifEV0tp9hF7OHaR6jlTc0Yl5vSMVO6OUzRVW91AO04PDdS5nb81BXuMDXvRdSpxSTDzF+NBwiGh2qUm40eJBxxa1GVvUyge8asMxuipHcaPFhR4XusDDjQ6XqsOOkSzC8aKtdB/xFNFNSecyzVYuUnYSep7NmtBGe5xPzW/yubcL73n6sN2fxHE1jI+8PVjha8NfdL/QT3eANM1RDIqvvot7RmVYmvCfjnfyVfII3NqT89hHO3K5Ye8C/rLnPUK8f/7Eiji9rp078a9HHwWNlkceeYRnnnmGmJgYxo8fD8Djjz/OnDlz2LJlC9999x3dunVj+vTpge3feecdmjVrxp49e2jTpg3XXntt0P7feecdYmNj2bFjR1Bf/AceeIARI0YAMG3aNDp27Mi+ffto164dSUlJbN68mZKSErp3786PP/5IWFhwq+WoqKgqz6l8VsLfj3sQHx8fWPd7ubm5+Hy+SrfZtWtXYL8GgyEwtkBN9numnI0xFupDnRILpxv44vcWLVrEY489xqFDh0hNTeXZZ5/l8ssvr3OhRcPmR8FmslJkjqbYHElxSDRF5iiKzFGB/xeboygOObHMHI1bX3l/qLoy+FxYPcWEu4uxuovK/u8pJvzE/0O8DjyKDq9Gh0ejD/z0aPTo/F5inceJc+QQ78ymiT2TWMdxtNTsGyAVSLc0Z3N0Gr9GpbEpuhuHwlpUGquofmKduSTaj5HoyKSJPbPs//ZjJDqyiHTlY/WUBLo7nOrUbhJFhnAyVQvrCgyUWmIoDY3jeEwrMhM6UxyeRG5Ma3JjWvNrt7IsrtmeT/KRDSQd20pC9k7ic3YSnX8Qrf/cfvjyavXYzZG4jGF49GbcBgtuvbns//oQPIYTP8uXGULw6EMCy9x6Mz6dAYPbhtFVislVgsWWh7UkC2vxMcKLM4koOorZUSiJFPGn4ULHJlM7fjJ3Za25C3sMydg0ZhK8uQy0/0qkv4RSxcxBfRPytOE4NEaaenJo7TlCa/cRurj20sm9v17fEyqQr7HiU7RsMrbls7AhfB/SI9C1QaP66O/4jcH2jaR4Munu2o3V33BaXl2s2cnFmp1Vrj+uhvKd2oGf/S3ZqjbFgJd4pYRCzOxWE9hDInvUxBodS4+XJhTQUjlOZyWDTspROisZxCklZ+p0Gi2j4mO0fhOj9ZsoVk1s8SXxpOty9qlxvOoZwqueIYTgopc2nf7a/fTTHqCdJjsw3WVjtCppEI9dMINSQ1lz6KTSo0Q7c9kfnkqeOYbXu9zDR6k3MnvVXbQp3F3PpRX1rUunk2PEaLVaoqOj6dy5c2BZ+Y12Tk4Ov/32GytXriQ0NLTCfvbv30+bNm3Yu3cvjz/+OOvWrSM3NzcwJWF6enpQYqFLly6B/ycmJgaO0a5dO3Q6Ha1bt6a4uBiAVq1aVdm8/4MPPuDOO+8MPP/qq6/QaitPtjZmCqcZY6GRfIqtdWKhJgNfnGrNmjXcdNNNzJgxg5EjR7JgwQJGjRrFr7/+WqtRJsW541M0OAyhOAyh2A2h2I2hOPRlP+2GUByGsFP+H4rNGFaWKDBHURRSlkzwa2qfszL4XES6Coh0F2D1FGPwedCfaJXgVxS8JxIBJ3+WJQS8Gh1OjZFig5WSEy0d3FojudpYck2xpz9wDeh9bpIcx2hiO0pTWwZN7Bkk2Y9h8LvxarQU661khiSxP6wVm6O6km+KrrCPlsX76Za/mU4F22hqO0qS/RjxzmwMfk+dyqTFX5Ys8RTTzH6UZnY3nkrGWCgNiSYjqSuHm/fmcPPeHGnSHUdIFLvaDmNX22GBOMXvI7T0ONaSY1hLstF7HOi8LnQ+NzqvE53XfeK5C53XhdbrQu91ofW6A8sU1Y9XZwokAhwmKw5zJA5zOHZzJA5TOA5zBA5zBHZzFG6jpU7nXlsGVymRhUeIKDxCZNFRIgqPEl6UgcWeT4gjH4s9H39JLnB+feP3Z+RDg0fR4VQMHNPHkqGPI8MQj00TAkCoz06YvxSNqhLid9DUk43Vb0OvetCrXkL8Toxq3d6TZ5ILHZm6WA7rE9lvaEqmLpYCjZUD+ibsMTTHpTFW2CZLF8NH1ksr3V+6PpE1dA08b+LJ5saSbxlbtASL6qx0m7OhQBPG4tDBLLReyl5D8wrrO7gOMKp0JVeU/kicr/GO+B+rlHKTsp6bNOsrrCtWTXyjdiJHDcOIFyMejIoXI14MeAPLTHhJVAqJp1imWKwBq+JkgO4AX2jnstjbhdW+Vqz1tiQfC6t8qazylY0WH62U8oRhKSP02+u5xLXjUzS80elu/tPx7wB0yd3MfZuep0veZhTAo9GxrNkw5naeyNHQZvx9yH+Yu/J2SS6c5/S64M/jiqKg1+uDnkPZeAOlpaVcccUVPPvssxX2U54cuOKKK0hOTuatt94iKSkJv99Pp06dcLuDPz9VdQygVl0hrrzySvr06RNY3qRJE44dOwZAdnZ2oFzlz9PS0ip9HWJiYtBqtWRnZwctz87OJiEhAYCEhATcbjeFhYVBrRZOjTlbFE3Zo8r1jeRPQK3v/l588UXGjx8fmKpi7ty5LF26lHfeeYeHH364Qvwrr7zCZZddxoMPPgjAv//9b5YtW8bs2bOZO3fuHyx+w+HwV/1ttkZRMJ6ShaouVgFMp/RRqmusV6PHZrJiM4ZTagrHZgrHY44o+7/RSqExeJ3NFI7daKXUFI7DeGYGBTG7irHa8wlz5GO152N15BHlKCDckYfVUYDZnkeYLRerI58obyEt4jVB+TjLKRlJp8+Pr5omviEaDYqioAIFGhP5eisleivFhnCKDeFB/3cYrNh1FvSqB43PjdbvQX/iofN7cGsMHDfHc9wcR645nmMhiXi0Bg6HJnM4NLlG5673uWlfsI2ueZvonLeJLnmbCXef7Mtr0mjQnqgTbr8fTzVjqJ4a6/GruNXK64Td58enqoFYr6riBXS2XJL3Lid573IAfFo9WQmdyGzWi5yEDmTFtSM7rh0eg4USawIl1gQyanSWZ4bi92F0lWBw29F5HOg9DgweO3qPHb3bjv7EMqPbjtFb9n+924bG7UDvsaP1eXAbLLiMobhMVkotsZRaEykNS6AovAmlobG4jaFkx7cnO759tWXRuB3obXno7fkYSst+6st/2vIw2vIx2gvROYrQOQrBWYLG40TrdaLxOFFO+d0ogO6UGu2ppv42hFgAfR1jvaiogFv1Y/NUbF1j0Z/8U+Pw+vBXU9/LY/0oHFfN5GrCKNBFkKuP4oCxOUeNiRToInArenQKaPDjQ8txbTi5ukgKdeGnHZekOhrVRwvXUZp7srD4HZh8dsxeOxa/nRC/gxCfA6PqRqd60ao+YrHT3bELi+rE7fPjqeaabdZpA/0kHT7Yo29Cjj6GQp2VAl04mYZ4dptbc9jYlOO6qGrPI9pbwAX2zfSxbaaney9J/nx+MXVgg7EddvQYVDcp7gzivXnoVA8Z+kT2G5txwJjCBnMnMvTxvBB1C+9Yr+TpYy8wwL6xwjGMWg26E39jvH4/Ll/V52bQaNBrK8b6UcjQx/OruSM/WnqxMvQCPJqTHzg1qo84bx7Di39gZPEKUt0nuyjYAL1Gg+HEfn1+Faev6lZVp8b6/SoOT9UJIr1Wi0GnrVGsTqPBeKJeqqqK3f3HYrW4uZw1aDUaTKe8N2yuyhObTqhxLJR95jAb9HWKtbtPvPMreYsqKIScMrCo3eU58c4/fazD7an+fW801CnW6fHiq/CeczOSnxmp/IzZYmS3P46ffK1Y7U3hF38L8tRQ7nFdzz5POAM0u0kmF7PiIcSgD9wAuTxevNW8l08X63V7sJ14D5g1J/tOl/29r3K3VcYWG8KZ1v851iddCMA1uz/ggc0zMZ1ozej2+/H43Aw68AXdj3zP/Re9xc6YLtw5+C3mfvMXWtuP/O5zRNWFMGoUdHWI9aoq/8/efcdHUeePH3/NbM9uNr0QCB3pRaqIXQQVeznlPFG/tlNQOeyoeN6p2Lvi2T2Vw0MFf+pZEAVFEBEEQZQOAUJCQsr2OvP7Y8NCSA9lE3g/H4957O7Me2Y/s5nszrznU4Ja3bFmRcGkNj529xlgNBolEKg7+WkymTBXdTbalFhN0/D7624u0pRYo9GIpaoJlq7r+Hx1dyLalFiDwVBthAOvt+5aW7XFRuv5zq7NwIED+fDDD+nYsSNGY81LxF27drFmzRpeffVVjj8+diwuWLCgSe8BNKkpRHJyco1lnTp1Ijc3l7lz58YTCS6Xi8WLF3PDDTfU+p5ms5lBgwYxd+5czjvvPCD2d507dy4TJkwAYNCgQZhMJubOnRtv8rFmzRoKCgpqdDx5wDUwKkRraQvRpMRCYzq+2NeiRYuYNGlStXmjR49m9uzZdb5PMBgkGAzGX++uKtOSDd9eSzvPKsdZbbyQtac2x8mF2wjU8UU90GJhWl4HfOZk/GY7l1d48JgdKJZkVGsyqtUZe25JJtOeznHpeXgtTjxWJ78Zkoja0lBtaagH4E6wMRoiKejG7a8k6K9EC7jQAy60gAstGHtuCbq5yWog2V+O01/GM9vWsqp8O1FPKUSrn8xYFYUf2+25QzWhZCcLAnt9Ua+t/v4VJ+25u3b9HwV8XFJ7J1sA24/vg91gQAHu+W0t/ymu+07X+mN7k2mOHfq3rd3Ga4W76oxdMawn7WxWdtqy+UeFlf8XTcOY0RljRmcMafkoBhO6FkX3V3KW2cPAwFb6lK/is+Xf8sTGAj6tY7vfDOzGQGfsDurL20qZsnFHnWX4pH8Xjk+LVUt7a8cubl9X92X/bUlOjjbFfrB+CAd5xV9HVdnyHdy8ZTEXVsX+GAnxomrFkNIWY0pbVGcuismKYrCgmKwMtDppa7ETMZopUk2sQgGjFcUYW64YLShGCygGsqMhcqJBTOEAAX85v7iK0XzlaL4yNH957Lm/HM27i/OjAc7Rw6i6zoZImCneijr37QJLEudaY8f1tmiEOz11/43HmG1MsMU+s0LVxN0mB8a0DhjSO2JM64AxvSOGlDxUewY2RxYkZRA1mtHMNoLmdgTT2tW57fro0TB6OIAeCWAIB0iKhKoSD0GKw170SAA95EMP+9HDvqrnAZyqifYGG7rBiGYwsU4F3WBCMZig6lFRTaAaMCsqGRiq0tsKxVqIaNgf23bYH5tCsde2oJe+gQDGgBtj0M33gZ14A5V7/o8DLvSgG7Qoaaj8idjnqwOzTBqV5iQUiwPVkgyqCloUXYuShMIYYyqa0YJmMPOdUaeyKhH4+I+7twDoOk6zkXdHDiSsGAkpJv65vIA1nkj8mFEsDgyOLFRHFqbkbPq060iZMYVyQwpRZf+qO0Y9JUTKNhMt28Jl+XaMqoLbYGdxpUahL4RqS8WY3hHF7IiVRVXRFAMbrB3YYG1cEhHApIfpHthEedF61hQVood9RMq2oHlLUEw2FFMSitnONf2OwujIZLM5j6WmzoTN9SdxbVqAdpFitOI/+GXtcqKeUiK7NhLesZKCknX8AkwDvr7gWLq3y+Rk/1J+//ED7pm/ss5tfnz2MF7qnM8X9mN5OOkCyhwduCHvPspm3oB38ZuwV3LsP2cM5qJusTtJszcUMfbzn+vc7msjB3Bun14stPVjdqAtn4YzYv9zaR1q/B5ll6/mpsh8zvbMZ9nWrYz8aCE/AQ/Ust1HRvTi1kFdAVhWUsGx739fZxnuG3oUU46J9c3we9Eu+j/0Rp2xk04dwmMXnAxAQbmLrlP+VWfsDScczfOXnAZAqcdPm7teqDN23LA+vDEu1tTTFwqTMumZOmMvPLo7719zbvx1fbFn9O7MJzdeFH/d5q4X60xwnNAtn28mjo2/7jLlX5R6ar8oGtw+lx/vHBd/3fefr7OlrPZzrl55mfz6jz0n7cc89BqrC2sfiahDRgobHr05/vrkx97m5821/85lOpIoeubW+Osxz/yH79bW3gdGktmE66U9N7Eufmkmn69cX2ssQOS1++hpKKanoZg5r0/ij1/WkXbeUySfcBPPaqfzrHY6ejhAYN03PNQnyEjrZtooldzwzmf8e+GvdW53x9OTyEqOHde3/XcO076t+39j2YButLfGLlAf2rqTF3fUfc6xoF8XeiTFLg6f3l7K49tLMOcPJvPK6RgzOqGFfJS9fx3PLH2PM/t0ZqAj1k/Tv4rKeKBgz51Y5ZcTyLlxLpX5g7hoxIs8+r9LOdMcOwb+vbOMOzfX3V58evf2jEqLXch9UFrBTRsL64x9vVs7zs1IAeCzMhdXr9tWZ+zznfMYm50GwDcVHv68pqDO2Ec75nJd99jz739YyMlnnF1n7GMP/oPb/xY71pYtX8HQE06pM/b+yXfy93ti1zC//7GGPkPqvli87ZabePzhfwJQsHUrnXr1rzP2xuuu4cWnnwCgtHQX2R271hl7xWVjeeuVaQD4fD4c2bV3TAtw0fnnMvPdt+Ov64s9c/QoPvvov/HX2R274fP5GDSg7nLva/z48bz66quMHTuWO+64g/T0dNavX8+MGTN47bXXSEtLIyMjg1deeYU2bdpQUFBQ603lhjSlKURtFEVh4sSJPPjgg3Tr1i0+3GReXl48aQBw6qmncv7558cTB5MmTeKKK65g8ODBDB06lGeeeQav1xu/WZ6SksLVV1/NpEmTSE9Px+l0ctNNNzF8+PCD2nFjbJ+OwD4WGtPxxb6Kioqa1LkGwNSpU3nggdpOMVouW7/zyRw3A12Lgh6Nn4CjR9mma1yJjqpHUXWN9FAQXVFQDGYUoxlUE4rRjGIwUWq0cPFe27VXTbUJA3sPZhS7O1md5q9E85dj9FfQN+rDEYiNHvD1rkLKvbv2XOT5ytH8FWj+crKCLv5fWjKmqsTAn4t2sCVc+x2PNFXlrLZ7Oqcy7iwiuldS6HBgQKONv4jMbYV4t5bUGXfdkO70tMdOCL6Itq7PQNF1NHcxmruY8LZlNZYfZUvmRHNs334JB5nvqzvZd7rVwShL7GRndSTEt966E0JGqx3VkrSfpa+fKRIgUl5IZOfaWpePNFu50pbMLkMSC7Dzsd2Gas9EtWdgqHrc/TrZnoXDlk7YlkLElkLImhy7+K+i7E4GVI0jv/d9iIZ6Edn7FKuhT2TvlEpDg8Lt/c2cHC9ZdVrQCwEX36AQsSQRNdtJVg21xu6294jOFqBmQ7g9rtn7RTuo2VBoj33/SlrARdRTgubZSXjnGiIl64i6itDDPt469Wg0DChovPLTz3y37g+i7mL0oAc9Gq6W3Pz7VSPjtSGunvcrK9bWcrKsqKjJ2bx/+ZUE7dl41ST+u9XDUlc0nmBRrMlVSYhY0qdju24UWfNYZTsKOh2Fo/buVAD4oMa+uYns2ojmLSXqLSXq2kF4+wrCxav5+LgcTs2Itap8cul6vv1hdT2fWtPY9BDne+ZRvmQ6k3NuwT74L2Rc+hqpZz6Ib9XHBNfOJeoqYquzE2tMKZj1EGU2MOcPwpDaLpaATO+IMbMritmOYjDxXF43/pmUF69lsXe3tHo4QKhwBYE/vsK3cha3donw5/71fFBCHCxahPKPbiZSup6kwZdjTO+AwZGFrdeZPKjBg1Vf2paz/krmUd/jXfoe/pUfJ6SorvSuZJz8FPbBfwEgXLqB0jcuIFxYd8JjN91fyc5/nUnuxEWYMrvwyumvcdJ310iHjqJeeXl5/PDDD9x5552MGjWKYDBIhw4dOP3001GragbPmDGDm2++mT59+tC9e3eee+45TjrppENe1jvuuAOv18t1111HRUUFxx13HF988UW1WhsbNmygtHRP8vOSSy6hpKSEKVOmUFRUxIABA/jiiy+qXac+/fTTqKrKhRdeSDAYZPTo0bz00ksHfX9iiYV6+lhoJYkFRdfrqeO0j8LCQtq2bcvChQurVQm54447mD9/PosXL66xjtls5u2332bs2D3Z85deeokHHnigRjuX3WqrsZCfn99ix+70rVzFyxOn8ew5Lx7Q7dpCHqwhL7agO/YY8mCrerSGPCSFPKQGXdgDlTiCLkz+CuyBSuzBShyBCpKCbtS97j7ZGtnEYt/YgKbVWym6ubFBXY9XdzSZDbRpV72zmOY0hQAIahqReg7rpsTaVHWfaokHJvZgNIVw+cIsWberRlOIupggHhvVdeprVW6EeHXHpsRqul5vrwUtIdYAmBQFd1RjmSdY72emAoaqJgA6sc9XUw3oRgtRoxXNZEGretSNVhSTtWq+lZDRjGayoZlsRM1WNFMSUbMNzWhF0aMYohHUaBglGkaPhlG0cNXrCEo09hw9iqLrGHQdRdcBnYiixrZjsla9t42oqerRnIRmdRKxJhOxJhOueoxanUQsyWjmhkclUUM+DEEvihZFVw2gxv4v1UiwagqhRIIo0TBmg0JOmj3+n6pXfVaqomDWw5j1MEYthEmLPTfpIWxagPRIJWmRStIiFbTBE3sddWEPlmHQ6v7r7d3EIhCJEq3v/95o2PN/H9Xqr+bchFib0cBWcx5rLR0oMGbjVqx4DTa2m3NxGZKxaX5sWhCbFsBBiGTNR4dQIZ18m+jk24Bx305hFQXFYsJqMGCoqjbcUBOLvWPDUY1QPbF7N28IRzUCms4rGZfyXto5VBr2//e1W2gLQ3yr6BLYSH54B23CJbQNF1Xbz72bTTSleUOjYy0W9PYdW2xTiN2a0rzhkDeFMNdMWbaephCNj9V12EA287SeLKAXy7V2RPcZheMYZR2DlU20VcoYrfyKSdEabgqxqwLPJ98BTWsK4U3K4oe2J/F1/mksanNcfP5pmz7lbz8/SHJoT1Lfqir7nEfU3N7W5A7cMOo9Kq3pjCj8jqcW3AyRcKtoCmHt3p3Ul9+UphD70RQCwG63g9UR/+1uSVwuFykpKS32+u5Q2P0ZrOjRmeR6OqV0R6P0/2Nji/+smlRjoTEdX+wrNze3SfEAFosl/k/XWhy3aS79XhmOphjQFBVNjT3qikpUMcRfa4ohfkfHGA1h1KqGTIyGMGgRjNEIlrAPa9i3/8OGKdTZE8jeF/gNsR6kWMte9X7MqlotkVBju4YmbFdVaezR05RYs6o2eHf4YMeaVAVTHUOPRQzR+EkGxC6sG/sPbtirPeOBjFUVpcE79S0pVkHB1HBY9VhNg5A/NrUymsFExBJLNkRssR8qQ9CLMejBEPJhCPmq9RvRkHYZdu68cMCBK6ABMDTuKLYaG3/SZDGoWBr5ndKY2A7hHXQI192cqU4mlVi6ai+KgmKqvs9mw56L6wY3adhz0d64WLjVNZObXR+x0NaP75IGstTSE69qI6CYCahmQoqJMEZSNQ85kV3kRnfRJlJKx3AhKZoXRddpEy2lU2g7mVotNZRq288qBlXB3sjOfpsSq6pKtYvKAxWrKAcnFmgRsUlmU+x3uZbEQo1YS2O/LamWvDiQsVZT409j64rtTwX9WcQtLELTFSqwsV7L4ptId94KH8OPejd+1GMdP76pnsw1ph/IiHpJVfxkKh7aGSuw7HM3MWI2odfyf1jb732JNYu5+afxdf5p/JI1KH6OqOgaJ2yfx7W/vUyv8qoaS3X8b9d1HtHDt5XnvhvP9ae8wQ95J3D5aTP4x+J7Gt2hY+yco3G3So2KgtFw4GMNBkPs4vgAx6qqelBiFUU5KLHAQYsViacqexKQdS1vDZqUWGhMxxf7Gj58OHPnzmXixInxeXPmzDn4nWAcYpZIgCx3M04shRAiQdRoGLOvDLOvLNFFEQlmIsqJ/l840f9LoosiRMKoik46PoYatjDUsIVLTD/zQXggZXoScyI9WavlcEfwgmrr5CkVDDQUkEIAhxIkWQlgN5SjdKp+VzElWEF6oIyg0co2Rz7LM4/m18z+bHZ2rhbXe9dKTtz+LaMKPqe9p+7+uxqrb9lKnvr+ZiYPf5S1aT24YuR7PPP9BIYV/7jf2xZCHBhHZB8L0HDHF+PGjaNt27ZMnToVgFtuuYUTTzyRJ598kjFjxjBjxgx+/vlnXnnllQO7J0IIIYQQQhwgndQybrd8DcCd+hxeCp3Ab1obXLqVSt3GTj2ZQj2Vwkhq9RVNwLA/Nfp9+pYu57StX3HK1jnk+Q78Tapjihcx8/PzmXLMQyxqcxx/O/55nvluPEN31hwOVQhx6CkNjApR74gRLUiTEwsNdXxRUFCAuld1+GOPPZbp06dz7733MnnyZLp168bs2bPp06fPgdsLIYQQQgghDpJUxc9ky5fV5vl1Ez9GO7JBy8KtW3BjxaNbcAVV3Ds9KLqOgo6OQrk1nXJLGtZIgMxAKX13rWBAyS/03bWC1FAtTYgOsIzgLp7+/iZuO+5ZFuSdwPiTXmH8r88x7o8397/prRBivxwuNRaa1HljorT0zj18K1exbsxZiS5Gq2Y2G8jLr68PetGQSl+IBWtqH/pLNMwd1fjFU183j6IhB7yPhSNRVeeNYj9YLCgdZdSJZmtkHwuibpHScjyzv204MAGCqpkHhv2TLzqMAaCDaxN/WfM252ycjUmvr/viQ8vQ7ShSX34z0cU4PEjnjS3W7s/g975dG+y8sefK9S3+s2p8j3hCCCGEEEKIVsuihXho0Z3c99P9JIcq2eLsxEND/s4lZ3zEpx3PocSamegiCnHEUVSlwak1aHJTCCGEEEIIIUTrpADnb/yQUQWfM7vzBbzR61o2Ozsz5ZiHAXCEXOT6iuhauY58dwHJYTdZ/hLyPNto591GarC8kWNFCCEa43BpCiGJBSGEEEIIIY4w9oiPy9a+yzmbZvNe93F8l3cSa9J64DE7WW92sj71qFrXM0VDWKJBTFoIsxbGFA1h1kKYo6Fa5xn1CBpq1bDrKiYtjDUawBrxY46GMOhRFPTYox57NKankbTcgkHRURVQFTBUXXwZql6rih4bWX33a/ZcoO1+HnvUY/OBYFQhFIUkIzjMOnaTjkGBqA4RLfZo2P0eatWI0hpENAUdMKk6ZhWMas2LvdoalzfU3nx3+ZOMOmZD7P2jGkT02HtH9D1vsvf2993u3q/3jrMYoF9+A4UQCXdEDjcphBBCCCGEOHwkhz38ddVL/HXVS/iMNoqS2rDd3o51qUdRlNQGj9nBTls22x357LRlEzaYCRsOQT8cSw7+Wxzu2jk0friqxXend8STGguH0O7+JV0uV4JLUjufx4NH0xJdjFbNrCm4ItFEF6NVc0ej+HQ5DpvLr2uEpGfs/RLQNFyhltP5V6tUdVdN7A8VxR9MdCFaL6Xq9q1otkgghLe1ntNEPGQF1pFVto4BW2t2QBlSTZRZ0wmrJsIGMyHFTFg1EzIYCauxhEOoallYNRNSjERVI4quo+pRVHTCqomgwUrAaCGkWtAVhahiQFOUqloNBnCmYDjmeDSd+KQTu5sf1RX0qtd7L9P0PXfrNV2Jz9f3irEYwGjQCYQVPBEFTwh0FEyqHq8JEa2K3117wbi7hgKxeeEohLTav6mVWs4j6roojO1DrJyBCISiCgZVx7i7hoYae//atrX3++y9/b3DFSDboONyKS2280bYc513JDtih5tMBLfbDUB+vtTlOaxtSnQBhBD7pdzDQ299nehSCCGEEKLKrKsSXYL6ud1uUlJSEl2MhNrdJKa+5a1Bq0gs5OXlsXXrVpKTk1tkxsblcpGfn8/WrVtb9BAgQoAcr6J1keNVtCZyvIrWRI5XkUi6ruN2u8nLy0t0URJOaiwcQqqq0q5du0QXo0FOp1O+mEWrIceraE3keBWtiRyvojWR41UkypFeUyGugT4WWkuVhVaRWBBCCCGEEEIIIQ43UmNBCCGEEEIIIYQQzaaosam+5a2BJBYOAIvFwv3334/FYkl0UYRokByvojWR41W0JnK8itZEjlchWobDpcaCossYH0IIIYQQQgghxCHjcrlISUmhYERvnMa6hwR1RaK0/+E3KisrW3R/KFJjQQghhBBCCCGESASlgd4bW0mNBUksCCGEEEIIIYQQCXC4NIWQxIIQQgghhBBCCJEIqhKb6lveCkhiQQghhBBCCCGESARpCiGEEEIIIYQQQojmUlQFpZ5aCfUta0layaiYLdeLL75Ix44dsVqtDBs2jJ9++inRRRJHoO+++46zzz6bvLw8FEVh9uzZ1Zbrus6UKVNo06YNNpuNkSNHsm7dumoxZWVlXHbZZTidTlJTU7n66qvxeDyHcC/EkWLq1KkMGTKE5ORksrOzOe+881izZk21mEAgwPjx48nIyMDhcHDhhRdSXFxcLaagoIAxY8aQlJREdnY2t99+O5FI5FDuijgCTJs2jX79+uF0OnE6nQwfPpzPP/88vlyOVdFSPfLIIyiKwsSJE+Pz5HgVogXaXWOhvqkVkMTCfnj//feZNGkS999/P8uWLaN///6MHj2anTt3Jrpo4gjj9Xrp378/L774Yq3LH3vsMZ577jlefvllFi9ejN1uZ/To0QQCgXjMZZddxm+//cacOXP49NNP+e6777juuusO1S6II8j8+fMZP348P/74I3PmzCEcDjNq1Ci8Xm885m9/+xuffPIJM2fOZP78+RQWFnLBBRfEl0ejUcaMGUMoFGLhwoW8/fbbvPXWW0yZMiURuyQOY+3ateORRx5h6dKl/Pzzz5xyyimce+65/Pbbb4Acq6JlWrJkCf/617/o169ftflyvArR8iiKEq+1UOvUShIL6KLZhg4dqo8fPz7+OhqN6nl5efrUqVMTWCpxpAP0WbNmxV9rmqbn5ubqjz/+eHxeRUWFbrFY9P/85z+6ruv66tWrdUBfsmRJPObzzz/XFUXRt2/ffsjKLo5MO3fu1AF9/vz5uq7Hjk+TyaTPnDkzHvP777/rgL5o0SJd13X9f//7n66qql5UVBSPmTZtmu50OvVgMHhod0AccdLS0vTXXntNjlXRIrndbr1bt276nDlz9BNPPFG/5ZZbdF2X71YhWprKykod0LePHqS7zxpW57R99CAd0CsrKxNd5HpJjYVmCoVCLF26lJEjR8bnqarKyJEjWbRoUQJLJkR1mzZtoqioqNqxmpKSwrBhw+LH6qJFi0hNTWXw4MHxmJEjR6KqKosXLz7kZRZHlsrKSgDS09MBWLp0KeFwuNox26NHD9q3b1/tmO3bty85OTnxmNGjR+NyueJ3koU40KLRKDNmzMDr9TJ8+HA5VkWLNH78eMaMGVPtuAT5bhWipdo93GR9U2sgnTc2U2lpKdFotNoXL0BOTg5//PFHgkolRE1FRUUAtR6ru5cVFRWRnZ1dbbnRaCQ9PT0eI8TBoGkaEydOZMSIEfTp0weIHY9ms5nU1NRqsfses7Ud07uXCXEgrVy5kuHDhxMIBHA4HMyaNYtevXqxfPlyOVZFizJjxgyWLVvGkiVLaiyT71YhWqjDZLhJqbEghBAiYcaPH8+qVauYMWNGoosiRJ26d+/O8uXLWbx4MTfccANXXHEFq1evTnSxhKhm69at3HLLLbz33ntYrdZEF0cI0VgHqfPGpgwy8NFHHzF48GBSU1Ox2+0MGDCAd955p0nvJ4mFZsrMzMRgMNToSbe4uJjc3NwElUqImnYfj/Udq7m5uTU6HY1EIpSVlcnxLA6aCRMm8Omnn/Ltt9/Srl27+Pzc3FxCoRAVFRXV4vc9Zms7pncvE+JAMpvNdO3alUGDBjF16lT69+/Ps88+K8eqaFGWLl3Kzp07GThwIEajEaPRyPz583nuuecwGo3k5OTI8SpEC6SoDU9N1dRBBtLT07nnnntYtGgRv/76K1dddRVXXXUVX375ZaPfUxILzWQ2mxk0aBBz586Nz9M0jblz5zJ8+PAElkyI6jp16kRubm61Y9XlcrF48eL4sTp8+HAqKipYunRpPOabb75B0zSGDRt2yMssDm+6rjNhwgRmzZrFN998Q6dOnaotHzRoECaTqdoxu2bNGgoKCqodsytXrqz2AzlnzhycTie9evU6NDsijliaphEMBuVYFS3KqaeeysqVK1m+fHl8Gjx4MJdddln8uRyvQrRAB6HGwlNPPcW1117LVVddRa9evXj55ZdJSkrijTfeqDX+pJNO4vzzz6dnz5506dKFW265hX79+rFgwYJGv6f0sbAfJk2axBVXXMHgwYMZOnQozzzzDF6vl6uuuirRRRNHGI/Hw/r16+OvN23axPLly0lPT6d9+/ZMnDiRBx98kG7dutGpUyfuu+8+8vLyOO+88wDo2bMnp59+Otdeey0vv/wy4XCYCRMmcOmll5KXl5egvRKHq/HjxzN9+nQ+/vhjkpOT4+12U1JSsNlspKSkcPXVVzNp0iTS09NxOp3cdNNNDB8+nGOOOQaAUaNG0atXLy6//HIee+wxioqKuPfeexk/fjwWiyWRuycOM3fffTdnnHEG7du3x+12M336dObNm8eXX34px6poUZKTk+N91exmt9vJyMiIz5fjVYiWZ/ewkvUth9iNwb1ZLJZa/y93DzJw9913x+c1ZZABXdf55ptvWLNmDY8++mhjd0OGm9xfzz//vN6+fXvdbDbrQ4cO1X/88cdEF0kcgb799lsdqDFdccUVuq7Hhpy877779JycHN1iseinnnqqvmbNmmrb2LVrlz527Fjd4XDoTqdTv+qqq3S3252AvRGHu9qOVUB/88034zF+v1+/8cYb9bS0ND0pKUk///zz9R07dlTbzubNm/UzzjhDt9lsemZmpn7rrbfq4XD4EO+NONz93//9n96hQwfdbDbrWVlZ+qmnnqp/9dVX8eVyrIqWbO/hJnVdjlchWpLdw00WnT9c9/3p+DqnovOH13redP/999e63e3bt+uAvnDhwmrzb7/9dn3o0KF1lqeiokK32+260WjULRaL/vrrrzdpfxRd1/XGpyGEEEIIIYQQQgixP1wuFykpKRRdeCxOU90NCVzhCLkfLmTr1q04nc74/LpqLBQWFtK2bVsWLlxYrYn+HXfcwfz58+scSl7TNDZu3IjH42Hu3Ln885//ZPbs2Zx00kmN2h9pCiGEEEIIIYQQQiSAoigo9fSjsHuZ0+msllioS3MHGVBVla5duwIwYMAAfv/9d6ZOndroxIJ03iiEEEIIIYQQQiSCqjQ8NcGBGmRgd0fFjSU1FoQQQgghhBBCiIRoaOSHpo8K0dAgA+PGjaNt27ZMnToVgKlTpzJ48GC6dOlCMBjkf//7H++88w7Tpk1r9HtKYkEIIYQQQgghhEiAxjaFaIpLLrmEkpISpkyZQlFREQMGDOCLL74gJycHgIKCAlR1T+MFr9fLjTfeyLZt27DZbPTo0YN3332XSy65pPH7IZ03CiGEEEIIIYQQh87uzht3jj0Rp7mezhtDEbL/M5/KyspG9bGQKFJjQQghhBBCCCGESICDUWMhESSxIIQQQgghhBBCJEJDHTQ2sfPGRJHEghBCCCGEEEIIkQhKA503So0FIYQQQgghhBBC1EVRFZR6aiXUt6wlkcSCEEIIIYQQQgiRCFJjQQghhBBCCCGEEM2m0kAfC4esJPtFEgtCCCGEEEIIIUQCyKgQQgghhBBCCCGEaD4ZFUIIIYQQQgghhBDNJn0sCCGEEEIIIYQQotkksSCEEEIIIYQQQojmayCxgCQWhBBCCCGEEEIIURdVjU31LW8FWkcphRBCiATp2LEjV155ZaKLIYQQQojD0e6mEPVNrYAkFoQQQhyRNmzYwPXXX0/nzp2xWq04nU5GjBjBs88+i9/vT3TxmmXWrFmMHj2avLw8LBYL7dq146KLLmLVqlWN3sbvv//O6aefjsPhID09ncsvv5ySkpKDWGohhBDiCHaYJBakKYQQQogjzmeffcbFF1+MxWJh3Lhx9OnTh1AoxIIFC7j99tv57bffeOWVVxJdzCZbuXIlaWlp3HLLLWRmZlJUVMQbb7zB0KFDWbRoEf379693/W3btnHCCSeQkpLCww8/jMfj4YknnmDlypX89NNPmM3mQ7QnQgghxBFCOm8UQgghWp9NmzZx6aWX0qFDB7755hvatGkTXzZ+/HjWr1/PZ599lsASNt+UKVNqzLvmmmto164d06ZN4+WXX653/Ycffhiv18vSpUtp3749AEOHDuW0007jrbfe4rrrrjso5RZCCCGOWNLHghBCCNH6PPbYY3g8Hl5//fVqSYXdunbtyi233FLn+mVlZdx222307dsXh8OB0+nkjDPOYMWKFTVin3/+eXr37k1SUhJpaWkMHjyY6dOnx5e73W4mTpxIx44dsVgsZGdnc9ppp7Fs2bJ4jM/n448//qC0tLRZ+5udnU1SUhIVFRUNxn744YecddZZ8aQCwMiRIznqqKP473//26z3F0IIIUQ9DpOmEJJYEEIIcUT55JNP6Ny5M8cee2yz1t+4cSOzZ8/mrLPO4qmnnuL2229n5cqVnHjiiRQWFsbjXn31VW6++WZ69erFM888wwMPPMCAAQNYvHhxPOavf/0r06ZN48ILL+Sll17itttuw2az8fvvv8djfvrpJ3r27MkLL7zQ6DJWVFRQUlLCypUrueaaa3C5XJx66qn1rrN9+3Z27tzJ4MGDaywbOnQov/zyS6PfXwghhBCNdJgkFqQphBBCiCOGy+Vi+/btnHvuuc3eRt++fVm7di3qXlUTL7/8cnr06MHrr7/OfffdB8T6cejduzczZ86sc1ufffYZ1157LU8++WR83h133NHssu12zDHHsGbNGgAcDgf33nsvV199db3r7NixA6DWWhxt2rShrKyMYDCIxWLZ7/IJIYQQoor0sSCEEEK0Li6XC4Dk5ORmb2PvC+toNEpFRQUOh4Pu3btXa8KQmprKtm3bWLJkCUOGDKl1W6mpqSxevJjCwkLy8vJqjTnppJPQdb1JZXzzzTdxuVxs3LiRN998E7/fTzQarZYM2dfukTBqSxxYrdZ4jCQWhBBCiAPoMOljQRILQgghjhhOpxOI9W3QXJqm8eyzz/LSSy+xadMmotFofFlGRkb8+Z133snXX3/N0KFD6dq1K6NGjeLPf/4zI0aMiMc89thjXHHFFeTn5zNo0CDOPPNMxo0bR+fOnZtdPoDhw4fHn1966aX07NkTgCeeeKLOdWw2GwDBYLDGskAgUC1GCCGEEAfIYVJjoXWkP4QQQogDwOl0kpeXx6pVq5q9jYcffphJkyZxwgkn8O677/Lll18yZ84cevfujaZp8biePXuyZs0aZsyYwXHHHceHH37Icccdx/333x+P+dOf/sTGjRt5/vnnycvL4/HHH6d37958/vnn+7Wfe0tLS+OUU07hvffeqzdudxOI3U0i9rZjxw7S09OltoIQQghxoCk00MdCogvYOJJYEEIIcUQ566yz2LBhA4sWLWrW+h988AEnn3wyr7/+OpdeeimjRo1i5MiRtY66YLfbueSSS3jzzTcpKChgzJgxPPTQQ/EaABC7oL/xxhuZPXs2mzZtIiMjg4ceeqi5u1crv99PZWVlvTFt27YlKyuLn3/+ucayn376iQEDBhzQMgkhhBCCw6bzRkksCCGEOKLccccd2O12rrnmGoqLi2ss37BhA88++2yd6xsMhhp9HsycOZPt27dXm7dr165qr81mM7169ULXdcLhMNFotMbFfnZ2Nnl5edWaIzRluMmdO3fWmLd582bmzp1bY7SHDRs2sGHDhmrzLrzwQj799FO2bt0anzd37lzWrl3LxRdf3OD7CyGEEKJpFFVtcGoNpI8FIYQQR5QuXbowffp0LrnkEnr27Mm4cePo06cPoVCIhQsXMnPmTK688so61z/rrLP4xz/+wVVXXcWxxx7LypUree+992r0izBq1Chyc3MZMWIEOTk5/P7777zwwguMGTOG5ORkKioqaNeuHRdddBH9+/fH4XDw9ddfs2TJkmqjRPz000+cfPLJ3H///fz973+vd9/69u3LqaeeyoABA0hLS2PdunW8/vrrhMNhHnnkkWqxu4ef3Lx5c3ze5MmTmTlzJieffDK33HILHo+Hxx9/nL59+3LVVVc17gMWQgghRBM0VCuhddRYkMSCEEKII84555zDr7/+yuOPP87HH3/MtGnTsFgs9OvXjyeffJJrr722znUnT56M1+tl+vTpvP/++wwcOJDPPvuMu+66q1rc9ddfz3vvvcdTTz2Fx+OhXbt23Hzzzdx7770AJCUlceONN/LVV1/x0UcfoWkaXbt25aWXXuKGG25o1n7dcMMNfPbZZ3zxxRe43W6ys7MZNWoUkydPpm/fvg2un5+fz/z585k0aRJ33XUXZrOZMWPG8OSTT0r/CkIIIcTBcJh03qjoTR3DSgghhBBCCCGEEM3mcrlISUmhbPJfcFrNdccFQqQ//C6VlZXx0a1aIqmxIIQQQgghhBBCJIKqxqb6lrcCklgQQgghhBBCCCESQRILQgghhBBCCCGEaLbDpI8FSSwIIYQQQgghhBCJcJjUWGhSKadOncqQIUNITk4mOzub8847jzVr1tS7zltvvYWiKNUmq9W6X4UWQgghhBBCCCFavd01FuqbWoEm1ViYP38+48ePZ8iQIUQiESZPnsyoUaNYvXo1dru9zvWcTme1BITSxA9H0zQKCwtJTk5u8rpCCCGEEEIIIVoOXddxu93k5eWhtpI78gfNkdgU4osvvqj2+q233iI7O5ulS5dywgkn1Lmeoijk5uY2r4RAYWEh+fn5zV5fCCGEEEIIIUTLsnXrVtq1a5foYiTWYdIUYr/6WKisrAQgPT293jiPx0OHDh3QNI2BAwfy8MMP07t37zrjg8EgwWAw/lrXdSB24LXEsTuLV6xk+mlnJboYrZpFUWhrlS4/9oc7ovGzJ9hwoBAHSRu7iRsGHuEnB/tJzUzHdvHZiS5Gq6Z7vbD610QXo9VxKUm8bzuZOZaBrDF3or1/K9mhXay1d8ZljJ17ZYZ24Yh62WbJI6Lu+c0eVLmCvxa+y9GeVYkqfotSWRnkhyVbEl2MVi21Zw9OnTU90cU4PFgdoBoSXYoaXC4X+fn5JCcnJ7ooiafQQI2FQ1aS/dLsKzlN05g4cSIjRoygT58+dcZ1796dN954g379+lFZWckTTzzBsccey2+//VZndmrq1Kk88MADNeY7nc4WmVjwORxYW0kVlZbKoigkKa0jG9dSRRQwt5ZvHnFYsioqTmPLO3lpTVSTEVuSLdHFaNV0PQoWU6KL0eKtMbbjK8tQfje1x6xH+M7Sn0rVAcQ64Npm7c22qtjdv85lVidlVc/TwpV0DGzjN3t3fsk5nutzjqendx2F5my8BjsmPYxJj2DUI5i0MF38W3hg05PkB3cc4j099DRTRM5p9pPdaGyR5/ytUgtNLOwmzdw5MptC7G38+PGsWrWKBQsW1Bs3fPhwhg8fHn997LHH0rNnT/71r3/xz3/+s9Z17r77biZNmhR/vTujJYQQQgghms+lJPFP5zg+sp1YY1m38FaurvyYQVsXsN7WkTJTKj2868kJl6KhUGzOotKYTBf/FnJDJSjADnMWL7e9nJnZZ/G7vVt8WxGM+PfadrElm4v6vsKUTU8zqmw+C1KGUmBtyyXF/w+rHjr4Oy6EEC3VQUosvPjiizz++OMUFRXRv39/nn/+eYYOHVpr7Kuvvsq///1vVq2K1TwbNGgQDz/8cJ3xtWlWYmHChAl8+umnfPfdd01uE2MymTj66KNZv359nTEWiwWLxdKcogkhhBBCiH1oKPw/6wgeT76UIkMGqq5xUvAXhod+I4pKu2gJo4JLUINBIsHSWmsWZIfLasxrEyrhgU1PccWOmfxu70Yn/1YywuWEVSNhxUhYMeE1JPFIh/GsSO7Nrd3ux6IFCaqx87zPMk/lxTX3kFXLtoUQ4oigNNDHQjNqQL3//vtMmjSJl19+mWHDhvHMM88wevRo1qxZQ3Z2do34efPmMXbsWI499lisViuPPvooo0aN4rfffqNt27aNes8mJRZ0Xeemm25i1qxZzJs3j06dOjVldQCi0SgrV67kzDPPbPK6QgghhBCi8SKo/M96DP+yn8Mfpg4AdIjs4PHKaQwKr6sRrzfzfToHttI5sLXO5e+svoWX2/6F6TnnUWFKxRlxA/Croxdje7/IByuvIzXqbua7CyFEK9bIGgsul6va7Ppuxj/11FNce+21XHXVVQC8/PLLfPbZZ7zxxhvcddddNeLfe++9aq9fe+01PvzwQ+bOncu4ceMatRtNSiyMHz+e6dOn8/HHH5OcnExRUREAKSkp2GyxNqHjxo2jbdu2TJ06FYB//OMfHHPMMXTt2pWKigoef/xxtmzZwjXXXNOUtxZCCCGEEI0UwMQHtpN4zT6GrcYcAByajxu9s7nS+wUWwoe0PGY9zM3b3uS67e/xh70r3XybKDWlc3XPJ9hmzeOurpO5f9NTrEvqTEf/VvKDhdJrkBDiyNDIxMK+XQPcf//9/P3vf68RHgqFWLp0KXfffXd8nqqqjBw5kkWLFjWqSD6fj3A43OAgDXtrUmJh2rRpAJx00knV5r/55ptceeWVABQUFFQbi7S8vJxrr72WoqIi0tLSGDRoEAsXLqRXr15NeWshhBBCiIMqgAmfYiVdb713zrcasphlPYF3kk6jzJACQLrm4grvF/zF9xWpujeh5bPqIQZ4VgNgD27n+bX3cUmfl5iXdizz0o6Nx6WFKxhRuYTzSr7guMqfE1VcIYQ4+BS1/uYOVcv2HSGxrtoKpaWlRKNRcnJyqs3Pycnhjz/+aFSR7rzzTvLy8hg5cmSj4qEZTSEaMm/evGqvn376aZ5++ummvI0QQgghRLOEdRUDCmpVpX4dWGHqwmpjJypVO2cGFtEhuhOAKArrje1YYO7L95Z+LDb3JKSYyYuWkBWtJKqotI2W0Dmyg1TNQ662i57hAjpHW9bddJeSxP+sxzDLdjw/m3vE57eL7OQa36dc5JuPjZbZQWJP33ru2/Qs93W5A0XX6BDYxnZLLuWmVD7NPI1PM0/jlLIFTN7yPO2CRYkurhCiBdB1nfvvv59XX32ViooKRowYwbRp0+jWrVu96zXUmWEgEODWW29lxowZBINBRo8ezUsvvVTjAv2AU5XYVN9yDt0IiY888ggzZsxg3rx5WK3WRq/X7FEhhBBCCCESaWckCY9uojJqZV04jTm+Tsz3t0fJ0WkT3cXR4XUUGjJYYu4ZX+df9rO5xfMhC8x9+cncE59a86Sp0JBFoSELgFWmzjWWjwz8zIsVT2NEo0Kxs9OQRqrmIVurOGj7ujeXksRKUydWmrrwi6kb31n6EVLMACi6xrGh37jIP48zAz9iRDskZdofF5d8Rn/PajLDZaRHKgkpRlY4evFFxsm8n30O36Qfxw+pQxi34wO6+TfRyV9AH++aFpXcEUIcOo899hjPPfccb7/9Np06deK+++5j9OjRrF69us4L4cZ0Zvi3v/2Nzz77jJkzZ5KSksKECRO44IIL+OGHHw7uDjWyxkJjZWZmYjAYKC4urja/uLiY3Nzcetd94okneOSRR/j666/p169fk95XEgtCtCIRgxl0HYMWlhMqIcQRY0vYyS/BXDIMPlR01oQy+Nh7FCtDNXu2BkCBAmMOBVV9C5j1MCOCK9lpSOU3U2cedO7piCpJCzAovIYTgis4PvgruVoZq0yd8Cmxk9MCQw5bDDm4VDsFhmxWmTrztXUwt6XcyDZDFr+Yj4pvKy9agqrraIrCqYFlnBFYTN/IRorVNNYY29M+WsxRka2NvtgPYmKHIZ1CQyZrjPmsNHXhV1NnNhnzasQeFS7gfP/3nBP4gVytvLEfbYtxlH9T/LlZjzDE/StD3L8ytng2D3a8hR9TBvFq28viMe0D2/jr9ne4oOSLRBRXiBbvpNPH0Ld3LwxmG2//+9+YzWYefPBB/vznPzNhwgQ++OADcnJyeP755znjjDMAWLVqFbfffjvff/89drudUaNG8fTTT5OZmQnAF198wYMPPsiqVaswGAwMHz6cZ599li5dugCwefNmOnXqxIcffsjzzz/P4sWL6datGy+//DLDhw8/IPul6zrPPPMM9957L+eeey4A//73v8nJyWH27Nlceumlta7XUGeGlZWVvP7660yfPp1TTjkFiDX379mzJz/++CPHHHPMASl/rQ7wcJNms5lBgwYxd+5czjvvPAA0TWPu3LlMmDChzvUee+wxHnroIb788ksGDx7cpPcESSwI0SJEVBNlae0pT2mHy5GN25GNKzkbtz0bV3IObkcWLkc2AWtKfB1j2I8xGsIYCWH3leGoLCRQtg1rRSHWiu3xR8fOtajRQ9tJlxBC7I/NYSfvu3uxKZLKzkgSy0O132FR0bArYexqmE6mCo62FHOWuoLkNUvZZGzDT1U1Fcb65pKrlRPEyIPOcXxrOZoxgUVc4P+erpFtGPYZC2F4aHWdZfvSMpgb027lE9uI+LwUzYNLSYrXcgB4xz6ad+yja6yfrHn5k38efcIbKTDk4FFsBBQzAcWMW02iXE2mTHGyS02O95FQm/xIMX3DG+kX3sCI0Cp6RrYclgnnrv4tvPn7JL5MP5Gv0k+kzJTKCkcvCqztmNzlbpYl92VSwSukRyoTXdRDLmIws71NPwpze2H3lZO/fRlpldsTXSzRgrz93gzuuP02fvrpJ95//31uuOEGZs2axfnnn8/kyZN5+umnufzyyykoKCAUCnHKKadwzTXX8PTTT+P3+7nzzjv505/+xDfffAOA1+tl0qRJ9OvXD4/Hw5QpUzj//PNZvnx5tT727rnnHp544gm6devGPffcw9ixY1m/fj1Go5GCgoJqfe3l5dVMlE6ePJnJkyfXuk+bNm2iqKioWtv/lJQUhg0bxqJFi2pNLDSmM8OlS5cSDoerbbdHjx60b9+eRYsWHdzEgtrAcJP1LavDpEmTuOKKKxg8eDBDhw7lmWeewev1xhMr+w648OijjzJlyhSmT59Ox44d44M0OBwOHA5Ho95TEgvikNAVBZ8jG3dqPp7UtgSS0gja0mKPSWmEbWnojjT8uy+cdR0FHUXXUHQdUySAOeTFHPJhC7pxeEtxeEtJ9pbs9bwUu3cXBj2a2J2tg6aolKe0oySjMyXpnSnJ6Exp1WNZaj66amjS9iImGxFTbDQWjyOL4uzutcYZgh7SNywkpWApzu2/4tz2K9byrYflCagQR5riSBLrw2mUazaOthTR1ugBQNNhQziNgoiTSs1KW6ObLqZyMlR/U298HDJRXWG+vz3vuPsw39+h2jIFnQGWYjyamYiu0MlUyTHW7ZzvWEOGIVAtVvd6QCsjL1TGiNBv1ZZZiPBP1xv7Vc7RwZ+Z7HqHJ5Mv4Sz/Qm7zvE+2VoFbsbHa2BEzYSoVO//PdhyLzL3ZaUjDrIfoFtnOFkMObtXO6/YxjX4/qx4kL1pKx0gR/cIb6BfeSN/wxlbdwWRTKcDpZfM5vWw+AF7VxtttLub5dlfxQfZZfJR1BgPcv9EpsJVTyxdwSvnCxBb4EFjX+QT+e96zVDqrX5Qdtf4bRn37OO23L0tQyURL0r9vH+695x5QDdx999088sgjZGZmcu211wIwZcoUpk2bxq+//srXX3/N0UcfzcMPPxxf/4033iA/P5+1a9dy1FFHceGFF1bb/htvvEFWVharV6+mT58+8fm33XYbY8bEvuceeOABevfuzfr16+nRowd5eXksX74ct9vNwIED+f7770lOTq623fpGIth9wVtbx4S7l+2rMZ0ZFhUVYTabSU1NbfR2D5gDXGMB4JJLLqGkpIQpU6ZQVFTEgAED+OKLL+Kfwb4DLkybNo1QKMRFF11UbTt1jTxRm2YlFhrq+GJfM2fO5L777mPz5s1069aNRx99lDPPPLM5by0SJGyyEUhKjyUBTElETRYiRitBWyoBRyZ+ewYBWxphazIhi4OwxUHY7Ig/DySloRlr77n0QLP7duHw7qo1+WD3laFqUXRF3WtSYhNKfB6KgiXoJsVVRIq7iGTPziYlLHSgPKUdW9oNYku7gWxpO4jtbfoQMdbdAYo55CW9YitOdxFOTwnJnp04PcUke3aS7CnB6S7G4S1FQSdiMBMxWuKPHnsGRfY2/GbNIpiSRyA1j0BKHr6MDkSS0ijpNYqSXqP2vJd7J2kbF5G843eSdm0iqWQDSaWbMHtKDlrCQVdUQvYMIrYUItZkAs6cWFlT8ojYnGhGS3yKmixoRitRkxXNZKvxqO/1RahoGiZfGWZ3CRZPCZbKHSTt2oRt1xaSdm3GtmsLpuCRcwIuqtOBUksmmqKQHizHpEcSXaT94tZMrA+lsyjQlm/8HVkW3HMnX0VjuHU7OrAmlMEuLanG+mmqn36WnYy0beZs+zqchsR06KfrENQNWNUoxZEkXncN4GNvN0qidiCWSDjRVsDJts0kqyGGWHfEkyYtwdW+/3GV7/N4B5EAybqfYeHf469PCq0AoFR1kqz5sRBGQ2G+uT//SRpJpWqnY6SIVN2NVQ9h00PYdT9pmps0zU2G5iYnWkaq7pFE8D7smp8bt/+bAe7feLL9dfzm6MEyZz+WOfvxYfYYLi7+lAnb3iAnvCvRRT3gdGDOSbfz9Um3AWD3ltKucDkeeyY7cvuwtusprOtyEhd9/DeGLJ+R2MKKhOvXp3f8ucFgICMjg759+8bn7b7I3LlzJytWrODbb7+t9e70hg0bOOqoo1i3bh1Tpkxh8eLFlJaWommxZl0FBQXVEgt7t81v06ZN/D169OiB0Wika9euuFwuALp06VJnh4Tvvfce119/ffz1559/jsHQtBtxrcIB7mNhtwkTJtTZ9GHfARc2b97crPfYW5MTC43p+GJvCxcuZOzYsUydOpWzzjqL6dOnc95557Fs2bJqB6A4+DRFxe/Iwpecgzc5B19yDj5nLh5nG7wpbfA62xAxJ6GpRjTViK4a0FQjYYuDiLnmCWpTKVoUu2sHjopt2DwlWPyVWH3lWPzlOPwVtI24sQVcKLq254IfBV01EDZaCZmTCJmS8NlS8Ngz8SRl4rFn4rbHHr1J6eiqAW9SBt6kDIqzjmq4UE0oe7JnJ6muQtIqC0lxF5JaWUiqqxCHdxeg403KoCSjMwVtj2ZL24G4k2v2IGuMBMgo20LWrg1klW0is2wjWbtik9NTvF8nj7kRjaB7nzt3ioK7TW/Kuh6Pq21fXO364cntSSg5m+L+51Lc/9xq8YaAG4u7BJO/AqO/AmPQG7sCiG0NRYugRsIoWhg1EkKNRlCiIRQtGksImG1ETUloZhtRk42o2UbE4iCUnEMwOQuaWCujscL2NHxZXepcbvLsImnX5qpEwyaSdm3GWrEdk7cck78Ck68Ck78CRW/5nZwdyaKoFCblsdXelkpzKn6DDb/Bit9oI2Cw4jYlU25OJaIYCaumqth8/EZbfBvOUCXOsIuQasaoR8kKlADgM9gw6hFSQi76lq/kKNc6MoJlZAZLSQtWoO6VWNz7/9SiBTFrB6apkQ5ss+RS5G/HjoiDoqiDrZFk1oXSKY7a8WpmPLq52joKOp1NFdiUMKtC2fwQ2DPGtU0J08VUjlMNsi3iZGvESblmY76/A/P9HXimYgj3pv/AOfZ1h6QWg67DfH973nP3Zlkwl3LNRgdjJUVRO0E9djqSqga4yPEHlyWvooPJdfALtR9UGh4pCyBT27MfKjonh5Zzcmj5QSrVkeVY11KOXXU9Wy1tWO7ozS/JvflPznnMzDmLmTlnkRcsor9nNUe7V3FcxRI6BQpadZJGUxQ+Gf1Pfjgmdrd5+JI3GfPVPzCHfQDsSuvA56few699zuWDc59G1aMMWjEzkUUWCWYyVr/UUxQFk8lU7TXE2t97PB7OPvtsHn300Rrb2Z0cOPvss+nQoQOvvvoqeXl5aJpGnz59CIWqJ6nreg+gSU0hzjnnHIYNGxaf37ZtW3bs2AHEOiLcXa7drwcMGFDr59CYzgxzc3MJhUJUVFRUq7XQmA4P95tCAzUWDu7bHyhNTiw01PHFvp599llOP/10br/9dgD++c9/MmfOHF544QVefvnl/Sx+yxGqZyhOBTDtdbAc6FgdCCal4bal4Xdk4UnNx53WHk96Pl5nHv7kHHzOHAL2zCZXt9+bGgli9ZVhDPkxhAMYIgHMgUrs3l1Yvbuw+soxBFwYgx5MQQ/mqkdT0I3FX0GSawfWvS7cwroePy0zKwrtrKZq72fd53Oo75LPArGkgi2dCnsGrqpkg8eehXd34qEq+aAoCqquxc5yqx4VXYvN26v5hd+Wijs5B5cjB81gwuVsg8vZhoJ2gxr3eUXDtClaRf72ZeRvW0r+tmWkl29B1TVMgKFq/8K6ThQI1rGdvWMjuk5d91uDuo6Gjlr17RNFR9N1bIUraVu4krZVcVGjBXf+QFwdh+LP6oI3szO+zE4EUtsRtSbjsybX8Q4HgKZhCLoxBj2YXcVYK3dgqyiMXdSH/RAJoUaDqJEgajiIIexHDQdQw36M4QCmcABD2A/RMLsv83SDiZA9g7Ajk1ByFoHUdgQyOuLP6IQvowNhRyZhRwaVjgwqO9T/tzP6K2MJFV8s2WDcK+lgrHq0+CurlpVDwF1VfS1W00WNhDAEPRhC3tg+ansd7/VchCiAca9fjUTEApiaGRsh9r8c0nW80Zo1e+x73V3wRzW0OrbtN9jwJ2VTas2g1JJJgTWXbVVJhF3WDP5I7Y3PZK+3XLVR9SiKrhNVjbjMKbjMe9qtFybVPKH5Mbvx7ShN0RCjCudwWuHXZPiK0LQoCjrtPVuwRvck+sKKEcx2AsYk/EYbG5Lasyq1Fztt2bjMKVSaU9nqaM8uaxYU1/OGQLrqY4CpkOGWLZxi2UB7awiDQWVtKI2FvjZYdT/tDC76mIowKXuOQb9uZBtZLAp24ENPDzZG0vhb6Wms8KUxKfn7Guc0FpMRY9XfLhKNEgzXXdvDbDRiMu6JdYd0yjQbZVoS5ZqNVeEcvgh0Z30ks9p6WyKxv8XRpu1cZf+Z4yybY2WOgjcKJoMBsyl2qhKNagTCdSdx9o7VdB1/uO5aZiaDgrlq3xqKNaoKlqp903Ud3wGKNagKVuOe/w1vqO7PtymxqgI2k7FZsb5wBC0cIRKpWW4FSNqrDL5ItM5viX1j/ZFovb/h9mbGBqIaUV0nPbKNU7zbOKX4S44vmc+znW5krb0rhZZcCi25fJ4R64itv+tXbt/wLD29a2psN8mgxi+AglGNSD3nXw3F+iJRAlXzzIDaiN/whmKDZjsfnf8Cv/eKVS8/79M7OPbnt6vF2ss2c+HMa7H6dvHT0P9j5jnPYPaU0Hv9t40uQ2PPOfaNjeo69aVYjYCxCbG7RaNRAoFAnbEmkwmz2dzkWE3T8Pv9ByTWaDRiscRq5eq6js/nOyCxBoOh2ggHXq+3SbHRaNNulgwcOJAPP/yQjh07YjTWvETctWsXa9as4dVXX+X4448HYMGCBU16D6BJTSGSk5NrLOvUqRO5ubnMnTs3nkhwuVwsXryYG264odb3bExnhoMGDcJkMjF37tx4k481a9ZQUFBwwDqerNNB6GMhEZqUWGhMxxf7WrRoEZMmTao2b/To0cyePbvO9wkGgwSDey6zdleVacmmBCvqXNZdNXKVec8/xT+DFXV+oXZUTVyZ0pagLY2gLZU3jCZCSamo9kwMjmzU5GwM9kwUsx2rLQ1nalu8yTmNbmagaFFsnhI8rkKCriKirh1EK7YRqdhGtHI7esCFQ9O4wmhF0aOo0Qj/8e5ku7sIPVizGqodhfusqfHX/wq62VRHVWMT8E9rWvz1u2EPa7S9Yvf5Lfggbc8d/+e8lfwYruvSG95NzcaqayT7Snm7ZAPzQnX/sLyekkVK1T/oqz4XXwbr/rF4yZlJpsGIx57JdEsqPyRlYkjLx5iajyE19qja0wGF9iE3bcq30K5oFes3LODLjT+wORygtv+MR5LT6WqMJVL+F/Txjr/uKr5/d6TRxxT7cZsT9PO6v+5q/adjo0PVv/V6Iszb90MFiHhg0xxGbvqePsTKsIEwXxs0jBkdUe2ZqLY0VFsqqiV2EXcUJrIVE7pqpMygssaggNGMoppQjGYwmNBDfvSwj47hMG1DIQxhP5UhD0tCZUTdxURdO9A8JaDtOWEdhoXexPZtJ1FmUfcP7CDMDCZ2nJcRZWY9sf0wcSyxH9hyi51Z6VkYMztjzNgzGVLaoialYbSlQVUyJWJLIWJLgfQOdW67KdSQP55kqAy60IMetKAHPRKoqgmig66TFI2QF9VQoiFQDKw3GcFkQzHbUazJqFYnqsUJRjMqSiz5qCiAQqjqET2KHg2jR8NQ9WgI+8jwezAGXBgDbjYFdhH0V6AHXGiBSjR/Zfy5I+BhtGZCM5jQDGa+MURwG4woBhOKwRz7e1c9txrMDDEkoxti8avUCN6qcjxX1ZQIJXbibTMYubFDG7xGO15jEnM8GmWqDcWUBNEwGEwYnLkYknNRG5HU0sMBIrs2EnUXMyrZgC3qxxbxs6BkJxvKS9G8peiRIOgakbItRErXEynbzLYRPYnY0tllSef+7T6+LN6JYjRjcOaBrsW+3wwmDKnt+POwMZQ48tllzWCbKZ2ope5yhQ1mPssfw2f51dvM61qUqLsY1ZwU+zsaTHVsYZ/9i4QIl6xlaI6J7s4oeUY3q5fN462P3kcPeoi6dlAQ9LAceKtqna//eQsn9T2Ko8zlfP31bK555b91bv/je2/ghsF9uDplBdf/ks389At4yzeYZz/+CtfcR9EDe35vZ9x+NReNGAjA7B9XcOnjr1fblmJ1xo61sJ/H/zaJjKPP5VNvV1b50wioNmqjBT0M8czj3j4B8gxu/rvWz+2vvMPHm3/k41riH7niPG47/zQAlm3cyvDbH6tz3+675EzuHxv7O/xe6mLA65/XGTtpaA8ePWUAAAWVXrq9/GmdsX8d2JXnR8V6xy71B8l7bnadsZf36cgbZ8USU75wlNSnPqgz9sLu+cw4f08HkPXFntGlDf/v4hPjr/Oen1Vn0uKE/CzmXnZq/HXXaZ9Q6q/993NQbjo/XrmnmVy/V//HFlft3609nUksPXNPL+HHf/ULv9cR2z7Jwh/n7LnLeNo3K1hWVvvvXKbFRMH5e07az5u/iu9Lau+IMcmgUnrxcfHXYxes5ssdZftE/QA8h2JxMPeKq1nu6M1PzqNZ5OjLCmc/Luv/KpX/uwfX149UW6vkohHxpMVNS9bx7ua6M3xbzjuGLGvst+vOXzbwyvoddcY+l5xBdlUSa0bAy6fBun+7Hk9OJ98Q+w2fFfDyYVWsITWfrP/7BHObPuiRILv+czU5a/4HVecRnwd9vBfY66LzP9eQjgHH0Ct4++JXOfeVMzm+bB0Ac0N+3qznnOMOewoDTbHf2gWhAC/Xc84xMcnJMebYb+2ScJBnfHWfr//VlsxJltj3wopIiMe8dXe2eZXNwe7u977/YSEnn3F2nbGPPfgPbv/bzQAsW76CoSecUmfs/ZPv5O/3xK5hfv9jDX2G1H2xeNstN/H4w/8EoGDrVjr16l9n7I3XXcOLTz8BQGnpLrI7dq0z9orLxvLWK9MA8Pl8OLLb1hl70fnnMvPdt+Ov64s9c/QoPvtoz3d/dsdu+Hw+Bg2ou9z7Gj9+PK+++ipjx47ljjvuID09nfXr1zNjxgxee+010tLSyMjI4JVXXqFNmzYUFBTUelO5IU1pClEbRVGYOHEiDz74IN26dYsPN5mXlxdPGgCceuqpnH/++fHEQUOdGaakpHD11VczadIk0tPTcTqd3HTTTQwfPvzgdtwY26kD3sdCIjQpsdCYji/2VVRU1KTONQCmTp3KAw880JSiJZy1x2hSz30CZXcbmapJUVQCepQZ0QiGSBBFi5KpxO6yKgYTqCYUgzH2aLKh2VJ5Y6+sVEP/Znt/3ev+SqLeXUTKNhMp30ykbAvRiq1EXUWkuIu5PuDF6i1F1TWeClZSUke1bxsq2XuNPkDQhd5CO0Q8FFR0nN4Skko24q/nhOAOZ0b8hKDY70EP153caJGiISI71wJrayxyYqVDVRJCIYKLupMxyVhoX5UsUIgQqCf2UDAG3YR3FBHesbLW5b0wMUJ1ELal4ElK4f8lWWKJlaS0Go9OWxqptgzCSamEk1LxWhxVtV400DQwWlCtyfGLSM1sQzPbCDsyMdf67nsU7vW89kuyPfZO3TWUw9570Dlr1VSXve87JFVNddm7z3wTkFpP7Gv7vK5v/2wRP5mBEjKDu1hftIlthX+guYvR/OWEtv1CuGh17PMGXjx5QHy9cas2sayOixGI/R+nhSpIC1Xg3L6FcFHdw/HdpS4i0xz7X7517TZe21FR54/6B6PPY0H3sfyR0p2NhjQ8US2WsHBkYUypWRvCqIWxRfyo7h1sX7+QSOkGNN8uNO8uIhXbCG1bBpEgdz9+B0MyYwmuJyp/JLztl3o+taYzKxonlc9i9vzvSDv/GVJOm4zz5NsIbllMaOvP6GE/nzoH8ntZGyK6ytrU3mRedQYGZxsMzjaoybmo5thfUg8HeN5khd3Xd1UHpR4NE/WUoHlKiJRtxr/qY/wrZ3PyX06j/6DYRXLv6FpCm388oPsmBIAe9HCM6xeOcf3CXwvf5eIVZfw46E7sg/9C6llTMecPQTGY0AIuAmvnsNNcQacWOEynqe0Asq79BGNqOyIV2yl980JCWxaDI63ulXSdsvevxZjWHmu3k5k77n36vX4GKe4GqkOJI1peXh4//PADd955J6NGjSIYDNKhQwdOP/10VDV2s2DGjBncfPPN9OnTh+7du/Pcc89x0kknHfKy3nHHHXi9Xq677joqKio47rjj+OKLL6rV2tiwYQOlpaXx1w11Zgjw9NNPo6oqF154IcFgkNGjR/PSSy8d/B06SH0sHGqKrtdT12sfhYWFtG3bloULF1arEnLHHXcwf/58Fi9eXGMds9nM22+/zdixY+PzXnrpJR544IEa7Vx2q63GQn5+PpWVlU3KaB0qRb+s4O7rnuabv7x1wLZpDHqw+isw+yqw+Mux+MqweUqxekqwesswhWJNDZzuYuyuHSS5dxKN1H1HH2LNDXarr4nFvrF7N1k4WLEHoimEsk/TggMRu3e1xAMZW1tTiMbE1lct0R3RWOYJVG8KUc92DRCP1ai/DCpgaEGxOvVXz0xkrA7oBjNRiwOsyUTNdiIWB0GLnajFQdRiR9urE09dVUA1gsGCZjSh6DqEfKjhAIawD0PAgzHgxhB0o0aCseYNuoaiA7pOpCrpp6sGdIMxVotANcUezUloVZ1lRqwphKyOqs4znVXznLHJFntENaBGQijRcHxSoyGUaAQ1GkaJhqoewxii4fhztCg6OnYD9MpIQiXWnEhBR9U1rGjYIj7sER+msBdbxIs1EiCqGlD1KBmBXaQHSskMlJDFnnaagahG3ZWtqzexaCg2Sd2r6rLWQDXnJsTaVDX+fx/SNMJVsSXWLHZZM7FF/NiifqwRH+laEEvVUb537N7UzAxsfzoHq8mEwRA7kQiFI4RraWKy296x4UiUUKTuo3jv5g27Y2f6+vKO92g2RTPqXK8hZiL0tZRwetIGRlg249RcJCtB1FryMXs3m2hK84bGxupeD9qvy6QpRDNifeEIWjBEpKhmp4ctuSlEU2Kn513Mk51vqTW+XaAQe9RHVrCEE3d9z8jSb3FGa96xb6gpRGVlgAU/FQDNawoRVY383uUkfhx8BWu7x2qUZO38g3HvXEqKq7DR2/VbU3j1mv9RmtWNtoUruO7tCzEFXK2iKURW396M+nK2NIXYj6YQAHa7HayOg9bH1f5wuVykpKS02Ou7Q2H3Z1D2yhScSXXf+nH5AqRf948W/1k1qcZCYzq+2Fdubm6T4gEsFkv8n661aL9pIee+cg7oWryNvlLVfl9XDUSNZqIGC5rBiBqNoGphVC0Sex4No2phDOEgVn85Fn8FhmjTOwMzNKGajLkJsaZDEGtRlGqJhH01tbyNq3Tc+mKNilLnP21IUeKJAohdADf2Z0RFafDOd0uKVWj8Z5aQ2GgYfOWx6QjS1mHmliH5DQc2ktXQ+Ax9U2Itqkpjf2GaEmtW1XjNFHu4jI7hfato1x67N9VowGat/o5mkzF+cd0Qk9EQv2hvbOyV1rVcmb6WTeEUlgVz+T2UgaYrmBQNg6JjIopB0UlVA2QbfGQZfGQbvGQZfIRRKY/ayDV6MCv7Xg42/MkZDCp2Q+M+4abEqoqC3dy4z6wpscpBigVaRGySyYiuaUQacQwlNfI4A7AdpNjmfEdcu/MjegS3sSB1CB0C2ykxpfND6hBW2buzzRqrZbTG3pUF6cN5seN13F7wL4a5lpERLsem1byBYzHU/I4IGw21ntPU9xte4cxjZecTWNflRNZ1PgGvPdYfiaJr9P79f1z0ya0k+StqrT1V13atQRdXT7+MF675H9vz+vPMX79h7Ec30nHrkro+pkaXd18GpfHnHE2KNRhiF8cHOFZV1YMSqyjKQYkFDlqsaAEOkxoLTUosNKbji30NHz6cuXPnMnHixPi8OXPmHPxOMA6xJG8pSRub3oGJEEII0RJ0MlXSyVR3c5LaWImSrB6YUTGEOFSOr/yJ4yt/ir++ZdsbVBiSWZvUmZBq4nd7N2Znns6GpI7c0+VOAKzRABO3vsblRR/iMiYTVVSs0QAOrXnN/fyWZDZ2HMG6LiewrvMJlGR2q7bc4Snh6JUfcsySt8gq29Tsfc0o38LV717Ku396nbK0Dvzryo+4avpfOGrD/GZvUwhxgB2JfSxAwx1fjBs3jrZt2zJ16lQAbrnlFk488USefPJJxowZw4wZM/j555955ZVXDuyeCCGEEEII0QypUTdD3SsAOK7yZ67c8V/ezv0T7+WeT5kplYDByiMdJ/B4h78SVfacPrcPbKNdoAivwYZJj5AU9ROK6pR2ixA1GDFGQiT5y8ko20yKazu70juxJX8IW9sejabu2Y6iRcnf/gvdNn5Ht43z6bD1ZwxafQ0RGq/djpVMfPkU3j/veX7reSb/vuRNrnv7QtpvP7B9twghmulIrLEADXd8UVBQgLpX54PHHnss06dP595772Xy5Ml069aN2bNn06dPnwO3F0IIIYQQQhwgJj3KNTv+wzU7/oMOvJ99No91uBGfoXq3tgXWdhRY29XcQGbNWTVCStdXJRK+o8vmH7DtNSrLgWYNerjsg+t588/vsq7Lifzrio+44LM7GLRi5kF7TyFEI6kKtXZKtPfyVqDJiQWACRMm1Nn0Yd68eTXmXXzxxVx88cXNeSshhBBCCCESRgEu3fkJZ5d+TYXRSVZ4FyY9gsvgYJWjB7tMaSRF/UQUAz6DjaA3yNq1RRi0MBGjBY89k5KMrlSk5JFZtpm8HSvpumkBaZXbDul+GKMhLn//Kt655A3WdTmJ989/gV/6Xshp8x6nw7alh7QsQoi9HKlNIYQQQgghhDjS2DU/9tCePhVSoh5GVP5cI66iMoB91eZDWLLGs4a8XP3uWL45/ha+PvFW1nY9mbVdT2bgipmc8fWDpLjrHg5eCHGQHKlNIYQQQgghhBCtk6prjPzuaQb++iFfnziJpQMuYVn/i1nW/2Jyi38nZ+cfpFdsJa9oJbnFv+P0FGMNuGgd90yFaIWO5KYQQgghhBBCiNYrvaKAP308keFL3uKT0//B5vbDKMrpSVFOzxqxDs9O2m9bRkbZJpK9JajRCAZt9/Dp0arnEVQtGn80RMPV5hmqntsCFST5ytEVFU01EDWYiDU40ePDtSvWDIp9sWGoFSW2VFGoeq1Dtdex5buvvfZ+reyz/pFE1yGkNWbgX5FwitJAjYXWcfBKYkEIIYQQQogjVH7hcm584xw8SRlsbj+UsrT2lKZ3Zltef0ozuhCwOvE4slnd4/RDW7D3DvwmFXRMKqTbdBwmHR3QNNAATVfQddD02OuoRmx51TyjCk5zbP3d83WIr6PvtY29l2s68Xl7b1/XFSwGHZtRx2YEgwKukII7DJ6Qgk4scaIq1ZMluydF0avNSzLqWI3gC+/ZRkenxjfjDvznKA4w6WNBCCGEEEIIcThw+HbR54/Pa8wPmZIozO3F9jb9KU9pi9eeSVQ1oqlGNNVQ7TFqMO01z0i02nMjmsGIz5ZG0JIMUFWjIRK74lYUdBT03e3NDSqafmAvqHQUQhoUeZu33VJ/wzFNU385NIhlJJqxLoA7rNS3AdFSSB8Lh46ux/4hXK6DNwzP/nB7PAR0+afdHzrg07VEF6NV8+saIfnxEAkU0DVckWiii9GqqeEIYd8BP3M9oui+AATDiS5Gq6UHw0TCkUQXo1Vzh6OH1zlNyEN2wU9kF/x0wDYZVQ0ouoZax/lzas8enDprevz1vrUAdt/5h5q1AuLz9dhd//hUVVMgHIXygIo3HKsloCjVawKo6NXnEYsJa+AOKUT0qnns0/xCqV7DQEUHZU+TjH2bZqDEyuKPKASiENUUHCadZLOOw6yjKvvWcNi9/0q8FsXufY5q4IsoBKOQZAS7WSfZpJNk1HG5HKAaDtjf7kDZfV2nyzWU9LFwKLndbgDy8/MTXBJxUAUSXQAhxH7xwsPflye6FK3frPmJLoEQQiTWjwugTftEl0IcAm63m5SUlEQXI7GkxsKhk5eXx9atW0lOTkZpgW1MXC4X+fn5bN26FafTmejiCFEvOV5FayLHq2hN5HgVrYkcryKRdF3H7XaTl5eX6KIknvSxcOioqkq7du0SXYwGOZ1O+WIWrYYcr6I1keNVtCZyvIrWRI5XkShHfE2F3VQ1NtW3vBVoFYkFIYQQQgghhBDi8NNAjYVGdNTZEkhiQQghhBBCCCGESATpY0HsZrFYuP/++7FYLIkuihANkuNVtCZyvIrWRI5X0ZrI8SpEC3GY9LGg6DLGhxBCCCGEEEIIcci4XC5SUlIom/0yTrut7jivn/Tz/kplZWWL7g9FaiwIIYQQQgghhBCJoKqgGupf3gpIYkEIIYQQQgghhEiEw6QphCQWhBBCCCGEEEKIRJDOG4UQQgghhBBCCNFsqhKb6lveCrSO9EcL9uKLL9KxY0esVivDhg3jp59+SnSRxBHou+++4+yzzyYvLw9FUZg9e3a15bquM2XKFNq0aYPNZmPkyJGsW7euWkxZWRmXXXYZTqeT1NRUrr76ajwezyHcC3GkmDp1KkOGDCE5OZns7GzOO+881qxZUy0mEAgwfvx4MjIycDgcXHjhhRQXF1eLKSgoYMyYMSQlJZGdnc3tt99OJBI5lLsijgDTpk2jX79+OJ1OnE4nw4cP5/PPP48vl2NVtFSPPPIIiqIwceLE+Dw5XoVogXbXWKhvagVaRylbqPfff59JkyZx//33s2zZMvr378/o0aPZuXNnoosmjjBer5f+/fvz4osv1rr8scce47nnnuPll19m8eLF2O12Ro8eTSAQiMdcdtll/Pbbb8yZM4dPP/2U7777juuuu+5Q7YI4gsyfP5/x48fz448/MmfOHMLhMKNGjcLr9cZj/va3v/HJJ58wc+ZM5s+fT2FhIRdccEF8eTQaZcyYMYRCIRYuXMjbb7/NW2+9xZQpUxKxS+Iw1q5dOx555BGWLl3Kzz//zCmnnMK5557Lb7/9BsixKlqmJUuW8K9//Yt+/fpVmy/HqxAt0O4+FuqbWgNdNNvQoUP18ePHx19Ho1E9Ly9Pnzp1agJLJY50gD5r1qz4a03T9NzcXP3xxx+Pz6uoqNAtFov+n//8R9d1XV+9erUO6EuWLInHfP7557qiKPr27dsPWdnFkWnnzp06oM+fP1/X9djxaTKZ9JkzZ8Zjfv/9dx3QFy1apOu6rv/vf//TVVXVi4qK4jHTpk3TnU6nHgwGD+0OiCNOWlqa/tprr8mxKlokt9utd+vWTZ8zZ45+4okn6rfccouu6/LdKkRLU1lZqQN62Zfv6JEFH9Y5lX35jg7olZWViS5yvaTGQjOFQiGWLl3KyJEj4/NUVWXkyJEsWrQogSUTorpNmzZRVFRU7VhNSUlh2LBh8WN10aJFpKamMnjw4HjMyJEjUVWVxYsXH/IyiyNLZWUlAOnp6QAsXbqUcDhc7Zjt0aMH7du3r3bM9u3bl5ycnHjM6NGjcblc8TvJQhxo0WiUGTNm4PV6GT58uByrokUaP348Y8aMqXZcgny3CtFSKYrS4NQaSOeNzVRaWko0Gq32xQuQk5PDH3/8kaBSCVFTUVERQK3H6u5lRUVFZGdnV1tuNBpJT0+PxwhxMGiaxsSJExkxYgR9+vQBYsej2WwmNTW1Wuy+x2xtx/TuZUIcSCtXrmT48OEEAgEcDgezZs2iV69eLF++XI5V0aLMmDGDZcuWsWTJkhrL5LtViBZKRoUQQggh9s/48eNZtWoVCxYsSHRRhKhT9+7dWb58OZWVlXzwwQdcccUVzJ8/P9HFEqKarVu3cssttzBnzhysVmuiiyOEaKzDJLHQOkrZAmVmZmIwGGr0pFtcXExubm6CSiVETbuPx/qO1dzc3BqdjkYiEcrKyuR4FgfNhAkT+PTTT/n2229p165dfH5ubi6hUIiKiopq8fses7Ud07uXCXEgmc1munbtyqBBg5g6dSr9+/fn2WeflWNVtChLly5l586dDBw4EKPRiNFoZP78+Tz33HMYjUZycnLkeBWiJVKUPUNO1ja1kqYQklhoJrPZzKBBg5g7d258nqZpzJ07l+HDhyewZEJU16lTJ3Jzc6sdqy6Xi8WLF8eP1eHDh1NRUcHSpUvjMd988w2apjFs2LBDXmZxeNN1nQkTJjBr1iy++eYbOnXqVG35oEGDMJlM1Y7ZNWvWUFBQUO2YXblyZbWE2Jw5c3A6nfTq1evQ7Ig4YmmaRjAYlGNVtCinnnoqK1euZPny5fFp8ODBXHbZZfHncrwK0QIdJsNNyqgQ+2HGjBm6xWLR33rrLX316tX6ddddp6emplbrSVeIQ8Htduu//PKL/ssvv+iA/tRTT+m//PKLvmXLFl3Xdf2RRx7RU1NT9Y8//lj/9ddf9XPPPVfv1KmT7vf749s4/fTT9aOPPlpfvHixvmDBAr1bt2762LFjE7VL4jB2ww036CkpKfq8efP0HTt2xCefzxeP+etf/6q3b99e/+abb/Sff/5ZHz58uD58+PD48kgkovfp00cfNWqUvnz5cv2LL77Qs7Ky9LvvvjsRuyQOY3fddZc+f/58fdOmTfqvv/6q33XXXbqiKPpXX32l67ocq6Jl23tUCF2X41WIlmT3qBDl336gR5f8r86p/NsPmjUqxAsvvKB36NBBt1gs+tChQ/XFixfXGbtq1Sr9ggsu0Dt06KAD+tNPP93k/ZHEwn56/vnn9fbt2+tms1kfOnSo/uOPPya6SOII9O233+pAjemKK67QdT025OR9992n5+Tk6BaLRT/11FP1NWvWVNvGrl279LFjx+oOh0N3Op36VVddpbvd7gTsjTjc1XasAvqbb74Zj/H7/fqNN96op6Wl6UlJSfr555+v79ixo9p2Nm/erJ9xxhm6zWbTMzMz9VtvvVUPh8OHeG/E4e7//u//9A4dOuhms1nPysrSTz311HhSQdflWBUt276JBTlehWg54omFeR/q0Z+/qHMqn/dhkxMLM2bM0M1ms/7GG2/ov/32m37ttdfqqampenFxca3xP/30k37bbbfp//nPf/Tc3NxmJRYUXdf1Q15NQgghhBBCCCGEOEK5XC5SUlIon/8RToe97jiPl7QTL6CyshKn09mobQ8bNowhQ4bwwgsvALEmfPn5+dx0003cdddd9a7bsWNHJk6cyMSJExu9LyB9LAghhBBCCCGEEInRyD4WXC5XtSkYDNa6uVAoxNKlSxk5cmR8nqqqjBw5kkWLFh203ZDEghBCCCGEEEIIkQj1jQixewLy8/NJSUmJT1OnTq11c6WlpUSjUXJycqrNz8nJoaio6KDthvGgbVkIIYQQQgghhBB1a2jkh6plW7durdYUwmKxHOySNYkkFoQQQgghhBBCiERQlNhU33LA6XQ2qo+FzMxMDAYDxcXF1eYXFxeTm5u7X0WtjzSFEEIIIYQQQgghEqGRfSw0ltlsZtCgQcydOzc+T9M05s6dy/Dhww906eOkxoIQQgghhBBCCJEIjayx0BSTJk3iiiuuYPDgwQwdOpRnnnkGr9fLVVddBcC4ceNo27ZtvJ+GUCjE6tWr48+3b9/O8uXLcTgcdO3atVHvKYkFIYQQQgghhBAiERrZx0JTXHLJJZSUlDBlyhSKiooYMGAAX3zxRbxDx4KCAlR1z3YLCws5+uij46+feOIJnnjiCU488UTmzZvXuN3QdV1vckmFEEIIIYQQQgjRLC6Xi5SUFMqXzMHpsNcd5/GSNuQ0KisrG9XHQqJIjQUhhBBCCCGEECIBFEVBqae5Q33LWhJJLAghhBBCCCGEEImgKA00hZDEghBCCCGEEEIIIepyEDpvTARJLAghhBBCCCGEEAnR0JCSTe+8MREksSCEEEIIIYQQQiSC1FgQQgghhBBCCCFEs6lqbKpveSsgiQUhhBBCCCGEECIRpMaCEEIIIYQQQgghmk1poI+FevtfaDkksSCEEEIIIYQQQiTCYVJjoXWkP4QQQogE6dixI1deeWWiiyGEEEKIw5LSiKnlk8SCEEKII9KGDRu4/vrr6dy5M1arFafTyYgRI3j22Wfx+/2JLt4Bcdppp6EoChMmTGj0OgsXLuS4444jKSmJ3Nxcbr75Zjwez0EspRBCCHEE211job6pFZCmEEIIIY44n332GRdffDEWi4Vx48bRp08fQqEQCxYs4Pbbb+e3337jlVdeSXQx98tHH33EokWLmrTO8uXLOfXUU+nZsydPPfUU27Zt44knnmDdunV8/vnnB6mkQgghxBHsMGkKIYkFIYQQR5RNmzZx6aWX0qFDB7755hvatGkTXzZ+/HjWr1/PZ599lsAS7r9AIMCtt97KnXfeyZQpUxq93uTJk0lLS2PevHk4nU4g1hTk2muv5auvvmLUqFEHq8hCCCHEEaqh5g6tI7EgTSGEEEIcUR577DE8Hg+vv/56taTCbl27duWWW26pc/2ysjJuu+02+vbti8PhwOl0csYZZ7BixYoasc8//zy9e/cmKSmJtLQ0Bg8ezPTp0+PL3W43EydOpGPHjlgsFrKzsznttNNYtmxZPMbn8/HHH39QWlrapH3UNI3bbrut0eu4XC7mzJnDX/7yl3hSAWDcuHE4HA7++9//NnpbQgghhGikw6QphCQWhBBCHFE++eQTOnfuzLHHHtus9Tdu3Mjs2bM566yzeOqpp7j99ttZuXIlJ554IoWFhfG4V199lZtvvplevXrxzDPP8MADDzBgwAAWL14cj/nrX//KtGnTuPDCC3nppZe47bbbsNls/P777/GYn376iZ49e/LCCy80qnwFBQU88sgjPProo9hstkbv18qVK4lEIgwePLjafLPZzIABA/jll18avS0hhBBCNNLh0XejNIUQQghx5HC5XGzfvp1zzz232dvo27cva9euRVX35OYvv/xyevToweuvv859990HxPpx6N27NzNnzqxzW5999hnXXnstTz75ZHzeHXfc0eyyAdx6660cffTRXHrppU1ab8eOHQC11uJo06YN33///X6VSwghhBC1OTyaQkhiQQghxBHD5XIBkJyc3OxtWCyW+PNoNEpFRQUOh4Pu3btXa8KQmprKtm3bWLJkCUOGDKl1W6mpqSxevJjCwkLy8vJqjTnppJPQdb1RZfv222/58MMPq9WKaKzdI2HsvX+7Wa3Ww2akDCGEEKJFOUw6b5SmEEIIIY4Yu/sOcLvdzd6Gpmk8/fTTdOvWDYvFQmZmJllZWfz6669UVlbG4+68804cDgdDhw6lW7dujB8/nh9++KHath577DFWrVpFfn4+Q4cO5e9//zsbN25sVrkikQg333wzl19+eZ2JjPrsbjYRDAZrLAsEAk1qViGEEEKIRlJooI+FRBewcSSxIIQQ4ojhdDrJy8tj1apVzd7Gww8/zKRJkzjhhBN49913+fLLL5kzZw69e/dG07R4XM+ePVmzZg0zZszguOOO48MPP+S4447j/vvvj8f86U9/YuPGjTz//PPk5eXx+OOP07t372YN7fjvf/+bNWvWcP3117N58+b4BLFEyubNm/H5fHWuv7sJxO4mEXvbsWNHnTUqhBBCCLEfFLXhqRVoHaUUQgghDpCzzjqLDRs2sGjRomat/8EHH3DyySfz+uuvc+mllzJq1ChGjhxJRUVFjVi73c4ll1zCm2++SUFBAWPGjOGhhx4iEAjEY9q0acONN97I7Nmz2bRpExkZGTz00ENNLldBQQHhcJgRI0bQqVOn+ASxpEOnTp346quv6ly/T58+GI1Gfv7552rzQ6EQy5cvZ8CAAU0ukxBCCCEacnj03iiJBSGEEEeUO+64A7vdzjXXXENxcXGN5Rs2bODZZ5+tc32DwVCjz4OZM2eyffv2avN27dpV7bXZbKZXr17ouk44HCYajVZrOgGQnZ1NXl5eteYIjR1u8tJLL2XWrFk1JoAzzzyTWbNmMWzYsHj8H3/8QUFBQfx1SkoKI0eO5N13363WVOSdd97B4/Fw8cUX1/v+QgghhGiGw2S4Sem8UQghxBGlS5cuTJ8+nUsuuYSePXsybtw4+vTpQygUYuHChcycOZMrr7yyzvXPOuss/vGPf3DVVVdx7LHHsnLlSt577z06d+5cLW7UqFHk5uYyYsQIcnJy+P3333nhhRcYM2YMycnJVFRU0K5dOy666CL69++Pw+Hg66+/ZsmSJdVGifjpp584+eSTuf/++/n73/9eZ7l69OhBjx49al3WqVMnzjvvvGrzevbsyYknnsi8efPi8x566CGOPfZYTjzxRK677jq2bdvGk08+yahRozj99NPrfG8hhBBCNNNh0nmjJBaEEEIccc455xx+/fVXHn/8cT7++GOmTZuGxWKhX79+PPnkk1x77bV1rjt58mS8Xi/Tp0/n/fffZ+DAgXz22Wfcdddd1eKuv/563nvvPZ566ik8Hg/t2rXj5ptv5t577wUgKSmJG2+8ka+++oqPPvoITdPo2rUrL730EjfccMNB3f+6DBw4kK+//po777yTv/3tbyQnJ3P11VczderUhJRHCCGEOPwdHsNNKnpjx7ASQgghhBBCCCHEfnO5XKSkpFC58Tec9QyD7XK7Sencm8rKyvjoVi2R1FgQQgghhBBCCCESQZpCCCGEEEIIIYQQovkOj6YQklgQQgghhBBCCCESoqGRHySxIIQQQgghhBBCiLpIUwghhBBCCCGEEEI0nzSFEEIIIYQQQgghRHNJjYXGmTp1Kh999BF//PEHNpuNY489lkcffZTu3bs3ehuaplFYWEhycjJKK/lghRBCCCGEEELUpOs6brebvLw8VFVNdHES6/CosHDwEwvz589n/PjxDBkyhEgkwuTJkxk1ahSrV6/Gbrc3ahuFhYXk5+cf5JIKIYQQQgghhDhUtm7dSrt27RJdjAQ7PDILBz2x8MUXX1R7/dZbb5Gdnc3SpUs54YQTal0nGAwSDAbjr3VdB2IHntPpPHiFbaaty3/lyRPPSHQxWjUzkGORljn7wwA4ja3ji6clCmtQFtESXYxWLdNu5E+9cxNdjFZNtZmxdclJdDFat9z2GP96b6JL0eKV+BWWFRv4cJ2ZHwpNzd6OQY9yjH8Fp/qWMCiwBoMeIUXz4tS8B7C0rUvYE2TX8oJEF6NVMyZZyBzQNtHFOCwYJj6C0q5LootRg8vlIj8/n+Tk5EQXJfGkKUTzVFZWApCenl5nzNSpU3nggQdqzHc6nS0ysZDscGBuJZmklsqsgLWV/NO0VAbAphzhVcn2g1HR5RjcTzZFxWk0JLoYrZpqNGAzN/8iTwBWM8YWeK7QUvy2S+WFX6x8vsmIXnXuYrLpnN81zBW9Qxzl34jnX49SbMjAo9pI1dyEFBPFhnR2GtPjjzsN6Ww15rDG2pFFtpNYlH5StffJjJTTJbyNbqGtHOtfwXH+5dj1QAL2+NALh6KEDPJduD+MRgNO+S48IAzJySgt+DtRmrkjiYXm0DSNiRMnMmLECPr06VNn3N13382kSZPir3dntIQQQgghRNP5wvD4z1beXGWOJxR6pkc5pk2EK3uH6JgSq7Glb9cxah5SNU+19XuyudbtbjLl8an9OOYlDWaduT064FNtlBrTKDWmsdjWl3dTzsQZ9XB32Ztc7P5absUIIUQ10hSiycaPH8+qVatYsGBBvXEWiwWLxXKISiWEEEKIw5WuwxaXSjAKKRad7CQdtXWcox0QYQ3+u8bMs8ssFPtitdrO7hzi5qODHJW+/82/OoULuaniv9xU8d/4PI9iY6OpLRvNbfnV0o25SUPZZsrh7qybmJZ6EUcH1tApvJ2eoc2c4luCir7f5RBCiFZLoYEaC4esJPvlkCUWJkyYwKeffsp3330nHXQIIYQQ4oAIYWSNuQNLbL35ydqbX609cc500C5ZI6LB+goDO7x7molZDDp5dg2dWKKha6rGdo/KunKViA6ZVp2LjgozskOYtg4NeyutjR3V4P9tNPHUzxYK3LFq+e0cGg8d5+ek/MhBfW+H7qdfaD39Qus5zzOfybve4M2Uc3gm7c8UmNpQYGoTjx3p/ZEndj5Lsu47qGUSQogW6yA1hXjxxRd5/PHHKSoqon///jz//PMMHTq0zviZM2dy3333sXnzZrp168ajjz7KmWee2ej3O+iJBV3Xuemmm5g1axbz5s2jU6dOB/sthRBCCHGY8ig2frT15bukgfxo68tmUx5RpXp79uIKWFexZ55Z1XGYdSqDCsGowibXnmUrSqpvvzIIjy4x8OgSKwDdUqOMaBvhzE5hhuRGW3xtB12HOVuMPPGzlTXlsf3MtGlMGBDkzz1DWBLQ9N+IxrWVs7nEPYdfLEexytKVLaY2fGI/nq/tx3Bmu87cUj6DY/0rcOj+I7rjRyFEw3Rd5/777+fVV1+loqKCESNGMG3aNLp161bveg1daAcCAW699VZmzJhBMBhk9OjRvPTSS+TkHOxOlQ98U4j333+fSZMm8fLLLzNs2DCeeeYZRo8ezZo1a8jOzq4Rv3DhQsaOHcvUqVM566yzmD59Oueddx7Lli2rtwuDaqXUdw+5cJDceOONTJ8+nY8//pju3bvH56ekpGCz2Rq1DZfLRUpKCpWVlS2y88aCZct5eFDtI1yIxjEr0EZGhdgvBiDFKJ03NldY09klo0Lslyy7icv6tWk4UNRJtZmxdZPPsNiQzipLF3YYMyk3OFlr7sAmUx5+xcJ2UzZhpXo1AmfUw+DAaoYEfmOgo5zguddT6FWwGCA7SWNQThSbESIabHOrFPsUVAV2+hTWVxjISdLokxnFaoRfdhqY8YeZteUGXKHqJ3MdnVGmHBPg1A4H945/c3jD8E2BiddWmlleEvs9dZp1/to/yJW9g42ueaFv30z0hSkHsaR7/GruyoScO9huqn7S3i+wljO8C7nA8w2Z0cpDUpYDKewOULp0S6KL0aoZ7RayBkr/ageC4c5nUPK7JroYNezP9d2jjz7K1KlTefvtt+nUqRP33XcfK1euZPXq1Vit1lrXef/99xk3bly1C+2ZM2dWu9C+4YYb+Oyzz3jrrbdISUlhwoQJqKrKDz/8sN/7W5vdn8HWtb/hrGd0DJfbTf5RvWuMkFhf9wHDhg1jyJAhvPDCC0Csr8P8/Hxuuukm7rrrrhrxl1xyCV6vl08//TQ+75hjjmHAgAG8/PLLjdqfg55YqKunzzfffJMrr7yyUduQxMLhb+/Egqao6KoRQzSU4FK1XDo1c5eSWNg/kljYf5JY2H9HcmJBB75JGsKLaX9ihbV7vbHtwzs40beM43y/0Du4gdzorj3fiXkdMN784AEpU1lAYfEOA3MLTHyxyYQ7HHuXvpkRRuRF2eFVqAwpdHRqdE7R6JIapXOKRq790PTj4AnB3AIT/9tkYt5WI4Fo7E1tRp3/6xPk+n5BUprYZdWhTCwA+BUz7znP4G3nWZQaUgmp5vgykx5mjGcBN5XPoGOk6JCVaX9JYmH/SWJh/506axF9MpIxDjmJf3/0MWazmQcffJA///nPTJgwgQ8++ICcnByef/55zjjjDABWrVrF7bffzvfff4/dbmfUqFE8/fTTZGZmAvDFF1/w4IMPsmrVKgwGA8OHD+fZZ5+lS5fYcJabN2+mU6dOfPjhhzz//PMsXryYbt268fLLLzN8+PBq5Wvu9Z2u6+Tl5XHrrbdy2223AbFRB3Nycnjrrbe49NJLa12voQvtyspKsrKymD59OhdddBEAf/zxBz179mTRokUcc8wxTfj0GycQCNCpUyeKihr+fnM4HHg81TvVvf/++/n73/9eIzYUCpGUlMQHH3zAeeedF59/xRVXUFFRwccff1xjnfbt2zNp0iQmTpxYbfuzZ89mxYoVjdqfQ9IUQhzedEAzWYmY7UQtDqIWe9VzO1GTDd1gRFdUUA1ETVYiVicRi4OILYWgM5egM4eQM4doUiohSzIRiwMAQziAOVCJxV+JOeCqeqys+eirwOorw+ovx15ZiNW7q1X0cRIxWvGmtMHrbIMnJQ+vsw3elDy8KXmErE7CZgdhi52w2U7EnETEaEEzmNDU2G0nq68Mm7cUm6cEm6eUJG8Jqf4y7FXP7d5SkjylpJVvRtWiCd5bIYSo26+Wrvwz41qW2XoCoOpRuoa20jG8g1TNTafwdrqFCkjWfGRHymgfKT4k5Uq36pzRKcIZnSI8cKyf55ZZeX2VmZWlRlaW1n0KlWzSGdkhzIi2EZJNOnYTJJt1emVEMe9HUwRvGFaWGlhWbOTHHQZ+3GEkGN3zi9c+OcrZXcJc2TtEdlLrOP+y6SGuqfyYaypjJ7qlhhS+ShrOh8mnsNzandnJJ/OJ4wQudM/lpvL3yYuWJrjEQrQe7/yxndtHp/HTTz/x/vvvc8MNNzBr1izOP/98Jk+ezNNPP83ll19OQUEBoVCIU045hWuuuYann34av9/PnXfeyZ/+9Ce++eYbALxeL5MmTaJfv354PB6mTJnC+eefz/Lly1HVPTe37rnnHp544gm6devGPffcw9ixY1m/fj1Go5GCggJ69eoVj83Ly6tR7smTJzN58uRa92nTpk0UFRUxcuTI+LyUlBSGDRvGokWLak0shEIhli5dyt133x2fp6oqI0eOZNGiRQAsXbqUcDhcbbs9evSgffv2By2xYLVa2bRpE6FQwzdTdV2vccO+rtoKpaWlRKPRGk04cnJy+OOPP2pdp6ioqNb4xiQ9dpO650cYTTUSSGuHP709weQcQvZ0dIMJdB1joBIUlYglmYjVQdTiIGKxE7UkE7HY0Uw2okYLmslK1GQjanUQsTiJWB2xbRxgUZMVv8mKP7lp7ZosvnJSS9aRsmsjZn8lppAXU9Abewx5MQa9mEIeTCEvhkgY0EHXUaoeVT2KGo1gDriweUr2u+ZE1GCipO0AitsPpiLrKCqyulKR1Q1/cs32TU0RcGQScGRSntOj3jhzwE3+lh/puPF7OmxcQHbRKlS95d6Z1xUFX1IGXnsmQVsKEYMZFBVNNaArKrpiQFNVdEVF1aIYoiHUaBhzyIfdsxO7pwRT5MgYK10c2XRgi7ENPtVKatRNm2hpq0iq7k0HXkm9gMfTx6ErKjYtwLjKT/m/yo9bXBV4uwnuHhbgmr5Bvi4wsqLESH6yRrpVY1OlgY2VKhsrVLa4VNxhhVnrzcxab662DadZ5/i2YXpnathNOhUBBQ1QifXNZVIhyaQTjoI7rOAOxSZPWGFjhcq6ChVNr/5X7uSMcmbnMGd2CtM7Q2stw53XKTNayZ/dX/Bn9xf8aunKM2ljmZ80mP86RzEzeSQ9Qps53v8Ll1f+T5IMQjSgX2Yy9948HiW/K3fffTePPPIImZmZXHvttQBMmTKFadOm8euvv/L1119z9NFH8/DDD8fXf+ONN8jPz2ft2rUcddRRXHjhhdW2/8Ybb5CVlcXq1aurtcO/7bbbGDNmDAAPPPAAvXv3Zv369fTo0YO8vDyWL1+O2+1m4MCBfP/99yTv0wwgPT29zn3afaHblIvgxlxoFxUVYTabSU1NbfR2DwSr1Vpn843W5pAlFpraK6VonojFgS+9A/7MjvjSOxJIa0sgJS82VT1HPXg9NxmCXgwhH4agB2PIixryo2oR0DUULYohHMAQdGMMeDAGXFhcxVhcxTjcReSFKzEH3ZgCbtRomJDVSciWQtCWStCWQsiaUutjMCmNgC2NYFIaPkc2waQ0ijsMpbjDgTm+nKUbyCxcSdb2FWQWriSzcAU2765aY6MGEz5HNpVZXdnRcTg7Oh3DzvzBRMxJtcYbQ17slTuwuwpxVBZWPd+BxVdWLSFiDPkwhv2oWhQ1GkZXFIJJ6fgdmfjtWfirkgzR5Cx89kx89ky89iw8ydmErMls6H4aG7qfBoDVV077zQvpsHEBOTtWklGyjiTfwavlETLbKc06CrezDUGrk4A1haDVSdCSTMiaHHs02wlanbiduXiSc4ka92+4WYu/ErtnJ8muHWQXryZz51pSKraSXLmdlMrtmEPSMdjhJKIYcJuScZuScZmclNiy2JbUljUp3dmU3Imd1mw0RcUe8eIIe7FFfYCCSQuRGSglM7iLjEApmYFdtPHvoGfF71i0ltMUy63YqDA4iaJSZMxgnbk9S6y9+MnWhxLjnpOv9uEdjPIuIoyRMkMKuwwplBlSqFQdODUvuZFSuoULOMa/ihN9Sw/5EH9RVH609eXbpMGoaKi6zjJrD5baYneuznbP565db5IbLTuk5WqqrCSdsT3CjO0R3mvunucRDZaXGPhso4m15Sq+sII/olDsUygLqHy2ycxnm5r//nl2jf7ZUYbmRjg2L0L3tNafTKhLv+B63ij6Jz9bevB0+mX8aOvH75bO/G7pzBsp5/In9xzu2PVvGVFCiDr0zdjTxMBgMJCRkUHfvn3j83ZfaO/cuZMVK1bw7bff4nA4amxnw4YNHHXUUaxbt44pU6awePFiSktL0bTYjaqCgoJqiYV+/frFn7dp0yb+Hj169MBoNNK1a1dcLhcAXbp0qbMpxHvvvcf1118ff/35559jMCSgB9pWJjMzE4PBQHFx9dp9xcXF5Obm1rpObm5uk+Jrc0gSC03tlVLULmowE0jLx5+ejz+9Pb7MznhyuhNIaUPYkUHInkHUUvPLYF9qOICtfCuWyh2YvbtQwwF01UjElgJaFGPQg7Hq4t9Q9TyWMPCjRoKokQCGcABjwB1bFvDEnoe8KM28E25WIHOfzhstQTdUbm/SdiJGK5WZnSnPOgp3envCFke1JgVhs71qXuxRM5jQlaqeWKvOzDRFRTOYCdlS0AwmXJldcGV2YWO/8/aUzVtGcnkBFn8Fiq7jc+bgS84hYM+otVxW7y5yN/9IetHvpJasJa1kHc6yzZj9lc2+oLe7i2Gv///a+ljQFYXi3D5s6XQcWzofT0HH4QSS0ljbawxre42Jx9l8ZaSWbSHJWxpLYOha/G8ZVY1oBjNRgxFjOIAl5MEccGPQIkQNJqIGM1pVcxddUdFRCNhScTvb4Ha2wV/HZ1IvTcPmL8caqMQYDqBUlUfRNVQtGn+uqYbY+6tGgtZkvPYsoiZrLOFkS6EsqxtbutTs/8TqKyelYispFVtJLS8gpbwAW+UOohU7SHIVYXPvxCi1HlqUcnMqi7KP4de0fmxK7kypNQOXyYnblIzPZG/UNkoaDgHAHA3S2b2J9OAuMoK7yAjsIiNYRkZwF+nBMtr4dpDnKzygyYewYmSzoyPrs3pSnPr/27vz+CjK+4Hj7BWO5AAAXQlJREFUn5nZ+8pBQkIAuVWQS0AwaotVBC1aq9Zaa71qtVXwKJ61FWsvvG/FqvVof7VYtWrVgiIoiiACilyCgkCQJFwh2c3eO/P8/thkISQbSCSEwPf9eu0rOzPfmX1m8+zszHef55kjqLAVsM7Rla/tXdliy/4ZclgJAlaYasNPmb0LT+ee3WRcBYWsdvZkDiN4OvdseiW+4Zod0zij9oM2beVgorPc2Yfp3uP5r380m5vYF5tKMXnbU/w0OL3Dtbhoik2HEUUmI4oadkGzFHy6xeCTChurd+gkTI1cp4Whp+/koBQkLI1wUsOuKwJORcCh8NnBa1eU+CwGF5oUdZAuDvvSiPgq/llxG1uMPD5xHcW0wFjmu4fwQuA0ZnpGMT48l5HRFfRPrKN7avNBUY+E2Bfsuw32omkadru9wTSkxxuora3ljDPO4K677mq0nfrkwBlnnEGPHj146qmnKCkpwbIsBg4c2Kgpf7bXAFrUFeIHP/gBo0aNyszv2rUrFRUVQPqit75c9dNDhw5t8n3Ymwvt4uJiEokE1dXVDVottPTi+kDgcDgYPnw4s2bNyoyxYFkWs2bNYuLEiU2uU1payqxZsxqMsTBz5sxGY2M0Z78kFu6//34uv/xyLr30UgCeeOIJ3nrrLZ555pkmR6XsiJLN/PKjAbZdvuZ2jVWaTsrlx3T6SfgLieV1J5Hfg2hedxK+AuLePFKePOKBLsQDxaDveXA+e+123NvX4a7agKtqI86acpw15XirK/BUbcBRu5XUHhIA9l3Km0I1+7vWvojVFCR2G4/DsctPMEm1p+2mD1y2VIycihX4K1bsMRYgpRTZ3gkFpLwF7CgZyLaSwWwpGcz2kkHUFPQl7s0n7m26mZaeSuCtKafzhk8oWj+fonXzyd2yusGJjg3Q96IMu8eaSpFttASjbrmxS2xKKfLKl5JXvpShHz2OpRtsLhlKWe8TKO9ZyvbOh1OT052oJ5+oJ3uzs2/LE9pMTtUGXLG68TLiQZyxEK54CFe8Fmc8hC1eiyu0GX+wHG9oM4aZbLANA7DVfzkpRbKJ11GQbhXhKyIWKKImtzuVxQOp6tSLYG43QjndiLtziHnyiHny2FwyuImtpNmjNbiDlbhqt2JLRrHFw3hryvHXbMJTU46RiGKiMkkOlIWmFJpl4oxU4YzswGkm0c0EupkiVddlo6kTXg2w71Lfd/8smIadpNOP6fBg2l1odd2STLuTlKVwxILYY0HssRrs8drMmBp72u7uHK2Mrf98xpUibDauzV5j53EralokNBu1dh8J3UHE7mWHM58qZyeqXJ0Iuzux3dmJKmc+1XY/EcPDVndntrsLmy0PgCdZiz8ZIj+2jc7RzfSt+ZI+NV/ROVJJgCS1dj9hu4+g7sJSirjhosrVie2uAqqcndjmLmSDvxdVrgJW5TbfxUhTFoXRLfQKreP4LfMYuGM5jkQtjlQETzJMTqIGm0phoaGh0AC3rpE0nKwN9GGFvx9f5hxOhaeECk8J6wN9SBqOrK/nsmLoyqLA3MFhiXKGRFcxPLKMQbHV5OgWccPF295SFjmPxGvWkpeqId+sJj9Vjd8KE9K9bHIU85WrD//zf5d1jm78uugGXvKeyB2VD1GUatwKy2no2Oq+c1KWRbyJ/209h66jGza+dBzGWlsXKvRcFrsHssA7lKCxs4lrwAwxrnYeARUhodnpF1vP0bVL6JEsp6nfnO26jqOu/piWImZmHy9m11jLsohGo9lj7XYcDsdexdpstkw/VqUUkUj2X8f3FNvfB/3r7oZmGEaD5q/hcPaWVC2J1XW9wV23WhIbiUSwIlHMZOO7XmiAx77ztDGSTGX9Xt49NpoysZo5pnj3Mtab3Mrp5lxOD8/lY9dAbi24ig2OrjyX8wOey/kBAH3iG7h+y984IbK4wXZjKROzmTJ4bEbm3CCeMkl9i9hkMkWk7iLKpWmZ7/CEpcj+rrUsNuXwMePwH7Em/0hiup1+25ZxylevEog37kLk1LTMuUFSKZLN7JtD0zLftS2JTSnV7PeGXdMy30d7E1v/n2vJ575FxwiliKb2TaxN13DW/YqulCKyj2INTcNl2/nrfLiJz2Vzsc3V96YMGzaMV155hZ49e2KzNb5E3L59O6tXr+app57iO9/5DgBz585t0WsALeoK4ff7Gy3r1asXxcXFzJo1K5NICAaDLFiwgCuvvLLJ19ybC+3hw4djt9uZNWtWpsvH6tWrKSsra9HF9YFi0qRJXHzxxYwYMYKRI0fy4IMPEg6HM9fjF110EV27dmXKlCkAXHvttYwePZr77ruP8ePHM23aNBYtWsSTTz6516/Z5omFvRksY3fxeJx4PJ6Zrm8qcyD75+GjyB3/5/RE5oOc/utUkKsZ6YH3bE6qbXZweNFdAfS9aGGwKysextyxgVTVeuzb1zN481rcVWU4arcxPVxGqHYLKl7baL08dH7Mzl/1XiXCjiyXsz40LmBnuf5LhK1ZYl1oXLxL7P+IUpHl0tcGXMbOg8NMopTtGhtvGP8XV17m+UvJMMutpi4l037vzKX+lPy1ZIRPm/kl8VZnDr66y7u3UlEWmPGssTcqi25r5tBtzRymJyMsM+NoDg+2Tr0x8nqgu3PRdAMzWMFPoiG6127FFd3BrESE2Wb2X7yvcvjppqU/fvPMODNS2U9qf2H30btuDItPzDhvNBM7QfMzyJZ+Jxak4vw93sQJ5eqZsHoml7t8/MTmJGl3Mze3O//1FKD7O6MZDtANNC39harMBKWaRj9NI2n3UGZz8bHhAMMOZgKVSoCVQlkmKIujDTsDUlH8wQq2V23k2S2rUNHqJst7tsPDdxzpk9r1Zoo7o9n7VI+3uznDme5SUmmZ/CFbbLiKU4KVnLM9Xd9LLJPfRXa+vuYKYMvtjpHfE1t+T3oW9qFLpz6E/EXU+IoI+YvQHW6S7hyS7hyCRc2PTt9SViyEitdixUNgpUDTcWg6PsOeafURBJRuoOk2NKcf3bF3t+bNvEa8FitagxGroXO8ti7xEOLLyHZi0WqsWA1WrAYVC6JScZSZJGCmOF2zp8esSCV5NVVLjcuP7spBd+eg2d3pumHYcRlOjnH4sWzpwURX6BAx7GiGg78YdjAcaDYHmuHAbnMyNDdAUrcTtnn5xvCj3Hl73okmJDZ9Tuyr2SQrlmHu2Mis/gUEkkH8yRDXLFnBf7dmb0K/6YSBmQTHlas28q/NO7LGvnfKadTm9aTK2Ym/h2wsxo/hK8LwF6EHirHl9UB3+dniKWaLp5gFRU2fcFiJKLrDjbJMVCKM2zBI2lxYWtPNOK1YkET5UlJbv+TiEo1Sxw56Jzfx1sIPuX3OwgaxL+zy/N2zShndrYCzat+nYt6zXDtnedZ9e/30kdzauwfP5J7JIznnMs83gpOK7mXb339K/KvZDWL/depwftQv/UvSa2srOX/G4kbb05x+HN2HM+aH17Ou5CRq9cbdvqxoNbHVMwkvfoGylf/j8hMO56rBvQCY8802hrza9LkAwJ3H9+f6YenbpH26tYbj/v1h1tjbRh7O5FHpz+sXq1Yz8JjsJ4I3XHs19/zljwCUbdxIrwFDssZedcUveOyBewHYtm07nXtmv23bxRecz3NPTgXSF+m+zl2zxv7orDN56f+ez0w3F/v9cWN56z//zkx37tkva4Jj9HeO5/0Zb2Wmew4YzLZtTXffGzHsaBZ++F5mesDwUWwo29hk7IB8P5//7HuZ6dIXP2RlVajJ2B5+N2suPSUz/b2XP2LxluomYwtcDiquODUzffrrH/PBpqbL67EZ1FyVbm13bGw5nr+WstU3CtfhJ+M8bCT24gGsdfbgqu5/ILV9HT9xrGVs+GNOiHzGJe8s5JU1FU1uF6D6yu9nEhFXvreUf3zR9PsAUP6LcRR60gmkGz5cwRPL1meN/ejI3nR3pL/D76ncyl+3ZT/2zDy8J0e40tt9dMt2HtzSxPug6fhHX0fJaZOJOnMys2f3PYOpw35NcNYUgjOnpL9f6rzYuzulvvRn84Xt1dxWviVrGZ7t2ZWTA+nzutd2BLn+m+x9yx8/rITTc9PndTNqarmqrDxr7H3dijk3P13eOaEwl67P3iL1jyWducyX/oV4bkUVY177OGvsnaVHcv2w9B0JPt1aw3EvZ78t4G3H9GPyyMMB+KKqlqHTPsgaO2lob+46Pj2YbFkoSr9/vJc19lcDe/DI6HQ3gG2xBCXPvJs19sIju/HMyenjTSRlkvvk21ljz+lTzLRTh2emm4s9rUch/z19ZxfgkmfeJZIyGVaYk3Wd3U2YMIGnnnqK888/n5tuuon8/HzWrFnDtGnTePrpp8nLy6NTp048+eSTdOnShbKyslb9QNySrhBN0TSN6667jj/96U/069cvc7vJkpKSBndAOPnkkznrrLMyiYM9XWjn5ORw2WWXMWnSJPLz8wkEAlx99dWUlpa2ycCNbe28885j69atTJ48mcrKSoYOHcqMGTMy3V/KysoaDLh53HHH8cILL/C73/2OW2+9lX79+vHaa6816OKyJ22eWGjNqJRTpkzhjjvuaOui7VOGJx9nj1FZl1fv8rypN10lY5iRKvSqDXTdsQnXjo04Q1v4JFxBLLwNK7SZVNV6rPDOgYoK0emxS7LAonYP7QXEvqASEZIVy0lWNDx5z3EEcLfh+BVtxZ6MEqhYRjTWOCFVr4vTy2B7+tcyRyrB27GmTyYBShxehjrSsalUEhU7sBKDKhYkWbmCZGW6VUsnu5NznD6SlqIsmeKeZBDNFcAIdEk//J3RbC50dy7FeYdRkteLSE4XUoad8robf2qaXteaSEMz7OjeAgxPPhiNP+26yw8uPwY7m+8pYNd3NFstUskYWjKKOxXHSMWxJaNs0zQ0Vw6aKyeTgNCdvrqkZdcGzf8ddY9sZu7y3Ak011Ft2a77BDSXIl2WZb5KJVDJCGZoM2ZoC1rtZi70m+TXdTt4du1almzfihksJ7V9XaME1VFddrY40dS+u/NJ99oNFCTSJ70ffrWJWeWNT+51bwG2gj7c9t0zWNHtRDZ5u7FZOYjYPGhOH5puZP4fmm6guQKZ3GlufAfOzcv56uuFpLauwazeSKJyBWbV+kxi+twff4djinIBeDeV/bPZGl4V4+odL5L4/CUePOIPOEoGUzRhFqmacuJfvkvsy1mkajbxdV5v3vb2YrsR4MPeOnk/+jlGTtf0w1eI7s5Bd6fLWP8/9lkRCoNfsaLsaxKblhBb9TaJjYtA7kwj2oBuxol+/jLRz18G0onjnLG34f/u1dg69eJlevGyfww+K4zjlBk4rQeJf93yX1gPFEZudzr97O+4+p5IFOi5Yw1jvn6TRaFaPuk7Hke3o8k97Q94Bp3F1qfOwGxhd1JxaCspKeGjjz7i5ptvZuzYscTjcXr06MGpp56Krutomsa0adO45pprGDhwIEcccQQPP/wwJ5544n4v60033UQ4HOaKK66gurqaE044gRkzZjRo2bV27Vq2bdt53bSnC22ABx54AF3XOeecc4jH44wbN47HH398v+7bvjRx4sSsXR/ef//9RvPOPfdczj333Fa/nqba+H6Q5eXldO3alXnz5jVoRnLTTTcxZ84cFixY0GidplosdO/evcX3Od1fyj5dwq3fO5dg96Pr5qR/DVd1Tb400k2U9FQCPRXHSsXRExFssWB6bIJYCL3uzgPNdZvYXUtioWE3hJbE7o+uEA6geLcxFlrTFQL23LWgJbGt7bLQVrF76gqRb9MbdoXYw3bbOjZbl4Vdy7yn7g1tHauTbnaZtBTbkuYeY+u3q/awXa0utv4WoXHdlu664PCSdPpJOn0oTU+PG6EsbKS7UWjKImWZ6W4VysQeCzXo4tBc94b6LhNJd4CEK4eUK4DlDqRvX+r0E3XVPc88/FiGI11Gw44y7OnxNGx2FDr2eCh9Z5VYECMZwUjVd+1IYrOSmWlSCTQzgd+mGNnZhd1KL7fXPbyahd1K4jajuKNVBOI78CeCGE3U/F27TcQsC7OZD35LYj26trPpsmWR2kexbn3XpssWSQUWGjWOXGI2N65UFFM3iNo8uDWFz4ySH68iWRe7O91lx923GJdhYNT1jU2YFkkr+1Fi19ikaZFoJnbX7g1J06Ja2bmr6Ff8L3Aicb11g6Z2SlVxUnghPw69y5D4VygrtcduE/ZWNF3e69iSHugT/3BAdoXY1QHbFWLTBsy//rFR7IHQFWL32GzdG8Kam8/cA/g4MJK3vaUNxvb4Tu1Crt32HIfH1zdYZ592hQjF2L4k3eJhX3WFeK/3eB4+/g7CzgCuZJiJC6bwgy9fQUeRsBRJFO/3Hs9jx00m6MqjpGYDd//vQjqHKztkVwi3z0XhsO7SFeJbdIWA9OfFuPlBtO7ZW1q1l2AwSE5OzgF7fSdars0TC4lEAo/Hw8svv9ygecrFF19MdXU1r7/++h63caBXvLJPl/CX4Y0HiBN7z6FBF2ebN6A5qDU1eKPYe0lLsT114N6GsyMo9Nq5YHCXPQeKrHS3A3e//f8exjU7i139mecewmJXf6p1HynNRq4ZIt+qIc8M0TlVRbG5neLUNgrManxWlMJUFX6V/aK8XZT0wHbNn9q7FB2W2rQe89HJ7V2MfcZC4zPnEbziP4lX/CeT0mxoyuK70U/JM0N0SW1jUHwNg+NfUWzumzskJUMxti3esA+2lPbvARfx8LG3AnDUliXcNucmuoXKmoyt9JZw9fefp8LfnZJgGQ9Pv5jicPYuIAcqm9dJ4bDu7V2Mg4IkFsT+0uZXcq0ZlVIIIYQQ+49TJTkuupTjokvbuyhC7FM6iuHxVQyPr+KK6v9wf/7PeMv3HeZ4RjSKLUxVMTi+hmNiKzij9oN2v/VptTOXFwdewj+G/AqA85f9jV8uuh9bM12/isPlPPK/i7jmtOcpDxzG1d//Bw9Nv5iSWukWIYRoW/vlJ+I9DZYhhBBCCCFEW+qZquThLfdyZfXLfOI6irjmYJ29hGXOvnzp6MFWWz6zbCOZ5R3J3fkXcUpkAddW/Ysjkk23DtiXKnxdWdD1BCr8XQnbfazNP4JVBYMyd4u58PO/csXiB/aqRUVxuIJH/3ch15z2PN/k9OSyM1/htx/8hhM2Zh+AUAghvq39kljYm8EyhBBCCCGEaGv9E+vpn1jfYF5Uc7DS0ZvPXYfztudYFrmP4m3vcbztPY4uqa0cFf+ay2peY2Rs5T4pQ9CRw+fFw/m0yyg+6XoCG3L7NBl3xLblnLf8OU75+s0WddPoHNnMI9Mv4taTH+WLwsHccspUTvr6f1y56D66SOsFIUQb2G+d2psblVIIIYQQQoj24laJTJeJn9f8l6/s3Xko73ym+46nwlZIha2Qd72jKE5tw2PFyLOCFKaqKTR34LWiJDUbDpXEb0XwOGpQvTfgTdTiTYbwJWrxJkIkDBfzuo9m7mEns7RoGNYud5IyrBRHbVnCEdtX4E5G6B5cz1FbltI9uK7V4z4URrbw+FsX8MSISfz7qIuZ3fv7zO8+mj/PuoaR5dlvyyiEEK0ho+UJIYQQQgixi37JjTy65W6qt/lYY+/Gf32j+XfgFCptBXu3gW57DulRvZahlQs5ZtM8hlfMx5/Ifivn1rJbSa7+5C5OXfMa95dOZlnRcG4c+wS/nv8nfrD63+hym3IhxD4iiQUhhBBCCCGakGvVMiK+ihHxVVyzYxrf2IuIa3Z2GAG2GHlsNfKI6C7sKkVccxDSPYQsJ1Vhg7DDR9juo9bhJ2L3Yeo6QysXcULZbE4om71fuyT0q1rNw9Mv4U/fvZNZvcdz7/F38HbfM/nZ0icZ9c1cbKq5G0kLIcSetWliYf369fzxj39k9uzZVFZWUlJSws9+9jN++9vfZu4fLYQQQgghxIGuwKqhIF6zx7hst5u00Nq1hYDdSnL7+zdwxLYVPHv0BJYVDePmU54gL7qNsWvfZPT6tzli+0qcZrzdyiiE6LjaNLGwatUqLMvir3/9K3379mX58uVcfvnlhMNh7r333rZ8aSGEEEIIIQ4YB0K3Ax3FT5c/wylfv8m/Bv6cd/qcwQ53AS8OvIQXB16CYaXoveNL+m9dxoCtS+lZvYZAPEhBZDOeVKS9iy+EOIC1aWLh1FNP5dRTT81M9+7dm9WrVzN16lRJLAghhBBCCNEOCiNbuOaTO7lq4b0s6HYCb/f5AUuKj6HKU8hXnQbwVacB/PfI8xqskx/Zit1KAKB2GVLSUBY2K4nNSqLQidi9ROxeonY3DjOBJxnGnYzgTwTJj27Dbibq1kxvQ2k7t1WfetFsNhy57gbzYNc4rVE5lEajeTZlkmuGcKgkpqZjoaOhKEztwKuiJDQ7cc1OQnNknsczzx0kNBumZuC3wuSYtQSsWkAjoruwKRMNi1rdQ0j3Uqt7UICBha4sDCwMZWFgYigLU9MxMUhpBhoKm0rhUCl0LOKag5jmJKY7UGg4VBK7SmFXKSx0LE3DREehYWoGFhqWpqf/YmBpGppSaKQfpmYQ0VwUmjt4ocW1Q4jW2e9jLNTU1JCfn99sTDweJx7f2QwrGAy2dbGEEEIIIYQ4pNhUiuM3vs/xG99HAVu8xXxRMJiVhYP4onAw5f5uhBw5RBw+qjyFLd5+VLcTtXv3dbHFXgolvUB1exdDHCL2a2JhzZo1PPLII3tsrTBlyhTuuOOORvMP1ARDqLaWxAHQvK1DUxBT8h5+GwbgUFZ7F6PDSkod/NaiyiKYMtu7GB2anjJJJpLtXYyOLZbAdoCeL3QEqrYWMy518NtIJlKEzI55LPQENzE8uInhX09vMD/kyKHS1wVL0zPzNJVuJWBpBqZuI6UbaArcyQieZARnKkLKcBCxu4navYTsAao8BQ23seuL7PIdbDgMfN1y0Zpsr8Au85peXj8/iUGN4SelGehKoWNhagbb9RzCugunSuJUSRwqgSPzPIlTJbCrFA6VREdRq7sJ6j6qDR+GsvCoOCl0TE3HZ0XTtxm1YujKwtJ0TPQGfy20TCsGXVmgaaTQSWk2UpqB00rgUukHQFKzkdDspNDRSLeC0OrXR6ErC72uRUS6lUL6/E/VtWrQsfBYMXxWmGDol2gH4DGx/rpOybnXQUNTrfhv3nLLLdx1113NxnzxxRcceeSRmelNmzYxevRoTjzxRJ5++ulm1929xcKmTZsYMGBAS4sphBBCCCGEEOIAtXHjRrp124v7s4oDXqsSC1u3bmX79u3NxvTu3Ttz54fy8nJOPPFEjj32WJ577jl0XW923d1ZlkV5eTl+vx9Naypf2b6CwSDdu3dn48aNBAKB9i6OEM2S+io6EqmvoiOR+io6Eqmvoj0ppQiFQpSUlLT42lAcmFrVFaKwsJDCwr3rZ7Vp0ya+973vMXz4cJ599tlWVRxd1ztEJisQCMiBWXQYUl9FRyL1VXQkUl9FRyL1VbSXnJyc9i6C2IfadIyFTZs2ceKJJ9KjRw/uvfdetm7dmllWXFzcli8thBBCCCGEEEKI/aBNEwszZ85kzZo1rFmzplGLAxmoQwghhBBCCCGE6PjatEPLJZdcglKqycfBxOl0cvvtt+N0Otu7KELskdRX0ZFIfRUdidRX0ZFIfRVC7EutGrxRCCGEEEIIIYQQAtq4xYIQQgghhBBCCCEObpJYEEIIIYQQQgghRKtJYkEIIYQQQgghhBCtJokFIYQQQgghhBBCtJokFoQQQgghhBBCCNFqklj4lh577DF69uyJy+Vi1KhRfPLJJ+1dJHEI+uCDDzjjjDMoKSlB0zRee+21BsuVUkyePJkuXbrgdrsZM2YMX331VYOYqqoqLrjgAgKBALm5uVx22WXU1tbux70Qh4opU6ZwzDHH4Pf76dy5Mz/84Q9ZvXp1g5hYLMaECRPo1KkTPp+Pc845h82bNzeIKSsrY/z48Xg8Hjp37syNN95IKpXan7siDgFTp05l8ODBBAIBAoEApaWlTJ8+PbNc6qo4UN15551omsZ1112XmSf1VQjRViSx8C28+OKLTJo0idtvv51PP/2UIUOGMG7cOLZs2dLeRROHmHA4zJAhQ3jssceaXH733Xfz8MMP88QTT7BgwQK8Xi/jxo0jFotlYi644AJWrFjBzJkzefPNN/nggw+44oor9tcuiEPInDlzmDBhAh9//DEzZ84kmUwyduxYwuFwJubXv/41b7zxBi+99BJz5syhvLycs88+O7PcNE3Gjx9PIpFg3rx5PP/88zz33HNMnjy5PXZJHMS6devGnXfeyeLFi1m0aBEnnXQSZ555JitWrACkrooD08KFC/nrX//K4MGDG8yX+iqEaDNKtNrIkSPVhAkTMtOmaaqSkhI1ZcqUdiyVONQB6tVXX81MW5aliouL1T333JOZV11drZxOp/rXv/6llFJq5cqVClALFy7MxEyfPl1pmqY2bdq038ouDk1btmxRgJozZ45SKl0/7Xa7eumllzIxX3zxhQLU/PnzlVJK/e9//1O6rqvKyspMzNSpU1UgEFDxeHz/7oA45OTl5amnn35a6qo4IIVCIdWvXz81c+ZMNXr0aHXttdcqpeTYKoRoW9JioZUSiQSLFy9mzJgxmXm6rjNmzBjmz5/fjiUToqF169ZRWVnZoK7m5OQwatSoTF2dP38+ubm5jBgxIhMzZswYdF1nwYIF+73M4tBSU1MDQH5+PgCLFy8mmUw2qLNHHnkkhx12WIM6O2jQIIqKijIx48aNIxgMZn5JFmJfM02TadOmEQ6HKS0tlboqDkgTJkxg/PjxDeolyLFVCNG2bO1dgI5q27ZtmKbZ4MALUFRUxKpVq9qpVEI0VllZCdBkXa1fVllZSefOnRsst9ls5OfnZ2KEaAuWZXHddddx/PHHM3DgQCBdHx0OB7m5uQ1id6+zTdXp+mVC7EvLli2jtLSUWCyGz+fj1VdfZcCAASxZskTqqjigTJs2jU8//ZSFCxc2WibHViFEW5LEghBCiHYzYcIEli9fzty5c9u7KEJkdcQRR7BkyRJqamp4+eWXufjii5kzZ057F0uIBjZu3Mi1117LzJkzcblc7V0cIcQhRrpCtFJBQQGGYTQaSXfz5s0UFxe3U6mEaKy+PjZXV4uLixsNOppKpaiqqpL6LNrMxIkTefPNN3nvvffo1q1bZn5xcTGJRILq6uoG8bvX2abqdP0yIfYlh8NB3759GT58OFOmTGHIkCE89NBDUlfFAWXx4sVs2bKFYcOGYbPZsNlszJkzh4cffhibzUZRUZHUVyFEm5HEQis5HA6GDx/OrFmzMvMsy2LWrFmUlpa2Y8mEaKhXr14UFxc3qKvBYJAFCxZk6mppaSnV1dUsXrw4EzN79mwsy2LUqFH7vczi4KaUYuLEibz66qvMnj2bXr16NVg+fPhw7HZ7gzq7evVqysrKGtTZZcuWNUiIzZw5k0AgwIABA/bPjohDlmVZxONxqavigHLyySezbNkylixZknmMGDGCCy64IPNc6qsQos209+iRHdm0adOU0+lUzz33nFq5cqW64oorVG5uboORdIXYH0KhkPrss8/UZ599pgB1//33q88++0xt2LBBKaXUnXfeqXJzc9Xrr7+uli5dqs4880zVq1cvFY1GM9s49dRT1dFHH60WLFig5s6dq/r166fOP//89tolcRC78sorVU5Ojnr//fdVRUVF5hGJRDIxv/rVr9Rhhx2mZs+erRYtWqRKS0tVaWlpZnkqlVIDBw5UY8eOVUuWLFEzZsxQhYWF6je/+U177JI4iN1yyy1qzpw5at26dWrp0qXqlltuUZqmqXfeeUcpJXVVHNh2vSuEUlJfhRBtRxIL39IjjzyiDjvsMOVwONTIkSPVxx9/3N5FEoeg9957TwGNHhdffLFSKn3Lydtuu00VFRUpp9OpTj75ZLV69eoG29i+fbs6//zzlc/nU4FAQF166aUqFAq1w96Ig11TdRVQzz77bCYmGo2qq666SuXl5SmPx6POOussVVFR0WA769evV6eddppyu92qoKBAXX/99SqZTO7nvREHu5///OeqR48eyuFwqMLCQnXyySdnkgpKSV0VB7bdEwtSX4UQbUVTSqn2aSshhBBCCCGEEEKIjk7GWBBCCCGEEEIIIUSrSWJBCCGEEEIIIYQQrSaJBSGEEEIIIYQQQrSaJBaEEEIIIYQQQgjRapJYEEIIIYQQQgghRKtJYkEIIYQQQgghhBCtJokFIYQQQgghhBBCtJokFoQQQgghhBBCCNFqklgQQgghhBBCCCFEq0liQQghhBBCCCGEEK0miQUhhBBCCCGEEEK0miQWhBBCCCGEEEII0WqSWBBCCCGEEEIIIUSrSWJBCCGEEEIIIYQQrSaJBSGEEEIIIYQQQrSaJBaEEEIIIYQQQgjRapJYEEIIIYQQQgghRKtJYkEIIYRoRs+ePbnkkkvauxhCCCGEEAcsSSwIIYQ4JK1du5Zf/vKX9O7dG5fLRSAQ4Pjjj+ehhx4iGo22d/Fa5fe//z2apjV6uFyuvd7GvHnzOOGEE/B4PBQXF3PNNddQW1vbhqUWQgghREdna+8CCCGEEPvbW2+9xbnnnovT6eSiiy5i4MCBJBIJ5s6dy4033siKFSt48skn27uYrTZ16lR8Pl9m2jCMvVpvyZIlnHzyyfTv35/777+fb775hnvvvZevvvqK6dOnt1VxhRBCCNHBSWJBCCHEIWXdunX85Cc/oUePHsyePZsuXbpklk2YMIE1a9bw1ltvtWMJv70f/ehHFBQUtHi9W2+9lby8PN5//30CgQCQ7gpy+eWX88477zB27Nh9XVQhhBBCHASkK4QQQohDyt13301tbS1/+9vfGiQV6vXt25drr7026/pVVVXccMMNDBo0CJ/PRyAQ4LTTTuPzzz9vFPvII49w1FFH4fF4yMvLY8SIEbzwwguZ5aFQiOuuu46ePXvidDrp3Lkzp5xyCp9++mkmJhKJsGrVKrZt27bX+6iUIhgMopTa63WCwSAzZ87kZz/7WSapAHDRRRfh8/n497//vdfbEkIIIcShRRILQgghDilvvPEGvXv35rjjjmvV+l9//TWvvfYap59+Ovfffz833ngjy5YtY/To0ZSXl2finnrqKa655hoGDBjAgw8+yB133MHQoUNZsGBBJuZXv/oVU6dO5ZxzzuHxxx/nhhtuwO1288UXX2RiPvnkE/r378+jjz6612Xs3bs3OTk5+P1+fvazn7F58+Y9rrNs2TJSqRQjRoxoMN/hcDB06FA+++yzvX59IYQQQhxapCuEEEKIQ0YwGGTTpk2ceeaZrd7GoEGD+PLLL9H1nbn5Cy+8kCOPPJK//e1v3HbbbUB6HIejjjqKl156Keu23nrrLS6//HLuu+++zLybbrqp1WXLy8tj4sSJlJaW4nQ6+fDDD3nsscf45JNPWLRoUYOWCLurqKgAaLIVR5cuXfjwww9bXS4hhBBCHNwksSCEEOKQEQwGAfD7/a3ehtPpzDw3TZPq6mp8Ph9HHHFEgy4Mubm5fPPNNyxcuJBjjjmmyW3l5uayYMECysvLKSkpaTLmxBNP3OsuDbt34TjnnHMYOXIkF1xwAY8//ji33HJL1nXr74Sx6/7Vc7lcHfZOGUIIIYRoe9IVQgghxCGj/hf7UCjU6m1YlsUDDzxAv379cDqdFBQUUFhYyNKlS6mpqcnE3Xzzzfh8PkaOHEm/fv2YMGECH330UYNt3X333Sxfvpzu3bszcuRIfv/73/P111+3umxN+elPf0pxcTHvvvtus3FutxuAeDzeaFksFsssF0IIIYTYnSQWhBBCHDICgQAlJSUsX7681dv4y1/+wqRJk/jud7/L//3f//H2228zc+ZMjjrqKCzLysT179+f1atXM23aNE444QReeeUVTjjhBG6//fZMzI9//GO+/vprHnnkEUpKSrjnnns46qij9vmtHbt3705VVVWzMfVdIOq7ROyqoqIia4sKIYQQQghJLAghhDiknH766axdu5b58+e3av2XX36Z733ve/ztb3/jJz/5CWPHjmXMmDFUV1c3ivV6vZx33nk8++yzlJWVMX78eP785z8Ti8UyMV26dOGqq67itddeY926dXTq1Ik///nPrd29RpRSrF+/nsLCwmbjBg4ciM1mY9GiRQ3mJxIJlixZwtChQ/dZmYQQQghxcJHEghBCiEPKTTfdhNfr5Re/+EWTd0tYu3YtDz30UNb1DcNoNObBSy+9xKZNmxrM2759e4Nph8PBgAEDUEqRTCYxTbNB1wmAzp07U1JS0qA7QktuN7l169ZG86ZOncrWrVs59dRTG8xftWoVZWVlmemcnBzGjBnD//3f/zXoKvKPf/yD2tpazj333D2+vhBCCCEOTTJ4oxBCiENKnz59eOGFFzjvvPPo378/F110EQMHDiSRSDBv3jxeeuklLrnkkqzrn3766fzhD3/g0ksv5bjjjmPZsmX885//pHfv3g3ixo4dS3FxMccffzxFRUV88cUXPProo4wfPx6/3091dTXdunXjRz/6EUOGDMHn8/Huu++ycOHCBneJ+OSTT/je977H7bffzu9///tm961Hjx6cd955DBo0CJfLxdy5c5k2bRpDhw7ll7/8ZYPY/v37M3r0aN5///3MvD//+c8cd9xxjB49miuuuIJvvvmG++67j7FjxzZKTAghhBBC1JPEghBCiEPOD37wA5YuXco999zD66+/ztSpU3E6nQwePJj77ruPyy+/POu6t956K+FwmBdeeIEXX3yRYcOG8dZbbzW648Ivf/lL/vnPf3L//fdTW1tLt27duOaaa/jd734HgMfj4aqrruKdd97hP//5D5Zl0bdvXx5//HGuvPLKVu3XBRdcwLx583jllVeIxWL06NGDm266id/+9rd4PJ49rj9s2DDeffddbr75Zn7961/j9/u57LLLmDJlSqvKI4QQQohDg6b29h5WQgghhBBCCCGEELuRMRaEEEIIIYQQQgjRapJYEEIIIYQQQgghRKtJYkEIIYQQQgghhBCtJokFIYQQQgghhBBCtJokFoQQQgghhBBCCNFqHeJ2k5ZlUV5ejt/vR9O09i6OEEIIIYQQQohWUkoRCoUoKSlB1+W37oNBixILU6ZM4T//+Q+rVq3C7XZz3HHHcdddd3HEEUdkXee5557j0ksvbTDP6XQSi8X2+nXLy8vp3r17S4oqhBBCCCGEEOIAtnHjRrp169bexRD7QIsSC3PmzGHChAkcc8wxpFIpbr31VsaOHcvKlSvxer1Z1wsEAqxevToz3dJWB36/H0hXvEAg0KJ194fIipWsPffH7V2MDs1u0ykocLV3MTq0UCzFZ9/UtHcxOqzalMWn4UR7F6ND61qcx6Qrx7Z3MTo2fw7asSe1dyk6NFW1FTXj1fYuRoez0SjkDc8JfOAdzirbYShadq5mYNHPWU3YSp9a5hoJcow4OUacXCNBH0eQM3LW4dSttij+gcUbwBh4QnuXokNT0VrU10vbuxgHBW1AKZo3t72L0UgwGKR79+6Z6zzR8bUosTBjxowG08899xydO3dm8eLFfPe73826nqZpFBcXt66E7ExEBAKBAzKxYPP58EkTnm/Fruv4DaO9i9GxGQqPJvWwtUwNHC08kRYNuXSdgMvR3sXo2NxONF/2RL3YMxUPo5z29i7GAc9E5wPXEGa7R7Dc3pulzn6ZZVrdA6DQFuEwey0ljnB6PaXRyRaj0BYlz4izMeFjXrgLy2Od+JJcqPsq35R+kfQDIArPRGr5VcFyzsxZh1MzUXBwJhq8HowD8Hy1I1E2DeX1tHcxDgpaIIDmPXDro3RzP3h8qzEWamrSv47m5+c3G1dbW0uPHj2wLIthw4bxl7/8haOOOiprfDweJx6PZ6aDweC3KaYQQgghhABS6LzkPYnHAudQYSvMzNeUxQmxzzlDfcrxQ3Pw6kkcmoVLN5vZ2k7r437WJnII6OmWX9Wmkx2mk2rTyfaUizdqelKe9DG54lj+UDGSFDoOzeSneauZWLiMfFt8D68ghBDiQNbqxIJlWVx33XUcf/zxDBw4MGvcEUccwTPPPMPgwYOpqanh3nvv5bjjjmPFihVZ+9NMmTKFO+64o7VFE0IIIYQQu7DQmOE+lodzfsxX9vS4VXlmkNMjcxkRX8WwxGpKzO0QyEGzj27x9ns6Q/R0hrIu/3XnJfxrRz/+teNw1sRzAUgog+eqBvBmsBfP93iX/q4drdo3IYQQ7a/ViYUJEyawfPly5s6d22xcaWkppaWlmenjjjuO/v3789e//pU//vGPTa7zm9/8hkmTJmWm6/vgCCGEEEKIvaeAd9wjeShwHqsdPQDINUNcHXyJ82vfwUlqv5TDpZtc2mkVl+SvYlPSi0dPsSKWz58qj+GreC7nrxvLBflfEjASjPJsZrB7G7q0kBZCiA6jVYmFiRMn8uabb/LBBx+0eBRPu93O0UcfzZo1a7LGOJ1OnE5na4omhBBCCHHIs9CY5RrBwzk/ZqWjFwA+K8zPQ29xaehNAirSLuXSNOhWN17Dd3wVvNRrOpeVncziSGembhuUietkRBntK+cHuev4jrcc6YYthBAHthYlFpRSXH311bz66qu8//779OrVq8UvaJomy5Yt4/vf/36L1xVCCCGEENmFNDeveL/H332nscHeBQCvFeWS0Fv8vPZNcq3adi5hQwEjyfM93uX/qo6gIumhIulhXrgL2003/6npw39q+jDItY0bij7jO76K9i6uEEKILFqUWJgwYQIvvPACr7/+On6/n8rKSgBycnJwu90AXHTRRXTt2pUpU6YA8Ic//IFjjz2Wvn37Ul1dzT333MOGDRv4xS9+sY93RQghhBDi0JPE4GPnQP7nKeUtz/GE9fQ5md8Kc0Ht21wWeoN8K/v4B+3No6e4omBFZjqpNBZHOvN28DD+vaMvy2IFXLzhFL7r28Ro3yZGerYwwFUlrRiEEOIA0qLEwtSpUwE48cQTG8x/9tlnueSSSwAoKytD3+XWizt27ODyyy+nsrKSvLw8hg8fzrx58xgwYMC3K7kQQgghxF4y0dlq5NLJrMGOyXY9wCajkGrDR7XuZ7seoMJWQERzUWhW41LpuxsErFpyrHTTfQsNDcWo+EoKrJp22xcFVBgFLHIeyTznIN51H8MOY+ft5PomN3JRaDo/jHyAV8XarZytZdcUx3o3c6x3MxMKl/H41kH8X9URfFDblQ9quwLQwxHkkvxVXJC/Gpum2rnEQoj9SSnF7bffzlNPPUV1dTXHH388U6dOpV+/fs2u99hjj3HPPfdQWVnJkCFDeOSRRxg5cmRmeSwW4/rrr2fatGnE43HGjRvH448/TlFRUVvv0kGhxV0h9uT9999vMP3AAw/wwAMPtKhQQgghhBAtYSlYHfby+fZehH3fB9I/Z2+yFbLQ2Z8v7d1JaA7sKonfilBl5LT6tfLMIA9sf4iu5lYqjXySmg2/FaFraisGJgYWefuoy4GJzmYjj9X2Hix19GWpow/LHH3YbuQ2iMs3azg1+jHjI/MYFV/BwfJjfoEtxuQuC/lZ/mreqOnJ0mgB88PFbEgEuKNyJP+u7suEgqWcEtiIXRIMQhwS7r77bh5++GGef/55evXqxW233ca4ceNYuXIlLperyXVefPFFJk2axBNPPMGoUaN48MEHGTduHKtXr6Zz584A/PrXv+att97ipZdeIicnh4kTJ3L22Wfz0Ucf7c/d67A0tTfZgnYWDAbJycmhpqaGQCCw5xX2s8iy5Xw1/vT2LkaHZrfpFBa627sYHVoolmJRWXV7F6PDCqUsFtbKfdS/je4l+dxyrYyf860EctGOH9vepTigxS2N8piLFSEfy0J+1kU8bIi62RB1EbeMZtfVlIXS9MzzzuYO8qwQuVaIfCtEF3MbHivGNiOXhGZDoRHUfQQ1T92FuqLC6ERZ3dgFzelsVtEvuZFiczsRzc1WIxe3FaOztYPBibUUmjsIa27Cupuw5qJWd9dNu6jVPAR1L+VGAeW2ApKavdH2DWVyZHI9x8ZWMDr2GaPiK7BhteId3U0gB+24lt9ucn8Kmzb+U9OH+7cMpcZMD/Yd0OMMcO1gTGAjF+avat8kgzcHY8iJ7ff6BwEVCaHWfNbexejQTrrqVgb26YHRuTt/f2EaDoeDP/3pT/z0pz9l4sSJvPzyyxQVFfHII49w2mmnAbB8+XJuvPFGPvzwQ7xeL2PHjuWBBx6goKAAgBkzZvCnP/2J5cuXYxgGpaWlPPTQQ/Tp0weA9evX06tXL1555RUeeeQRFixYQL9+/XjiiSca3CUQWn99p5SipKSE66+/nhtuuAGAmpoaioqKeO655/jJT37S5HqjRo3imGOO4dFHHwXAsiy6d+/O1VdfzS233EJNTQ2FhYW88MIL/OhHPwJg1apV9O/fn/nz53Pssce24N0/NLX6dpNCCCGEEG0lZWlsiLpYFfaxYEcuS0N+NsVcbEs4sq7jNVIMcW+n0+YvgXSbhRwrxIj4KgYl1tLV3MpmI5/teg59Upta1U0gpjm4PfcXvOI9EZdK0M3cikMl2aH7qTA6ZRIXW4x8thj5TW7jFe9JLXpNm0rRI1XJoMRaBifWMDixhv7JDZnuGocar5HiwvzVfD+wnue29+fF6n5sS7n5OFLMx5FiXtzRj190WsHJ/m/It0nCWBy6/v6/2dx4/SQ++eQTXnzxRa688kpeffVVzjrrLG699VYeeOABLrzwQsrKykgkEpx00kn84he/4IEHHiAajXLzzTfz4x//mNmzZwMQDoeZNGkSgwcPpra2lsmTJ3PWWWexZMmSBl3hf/vb33LvvffSr18/fvvb33L++eezZs0abDYbZWVlDbrEl5SUNCr3rbfeyq233trkPq1bt47KykrGjBmTmZeTk8OoUaOYP39+k4mFRCLB4sWL+c1vfpOZp+s6Y8aMYf78+QAsXryYZDLZYLtHHnkkhx12mCQW9pIkFoQQQghxQNgYdTF9ayEfbM/j05ocollaIDh1k37eCEMDQfp5w/RwR+npjtLVFcfYsRn1xb+zvkZXcxtdzW2tLqNLJbhrx+NMrn4Gt4qjs/OXcRMdHYuo5uQLe0/W24qpNDrhUTE6mzuIaQ422opY4jicWt2Nz4riVXUPK4ZXRevmxfBZEUrMbXRLbaHIrNo3rREOMp1sca4vWsI1nT9ndSyPRZHOPLZ1EF/Fc7m5/HgMLEZ6NzPWv5FTAmWU2NvnFptCtJch/Xrxu5tvQPPm8pvf/IY777yTgoICLr/8cgAmT57M1KlTWbp0Ke+++y5HH300f/nLXzLrP/PMM3Tv3p0vv/ySww8/nHPOOafB9p955hkKCwtZuXIlAwcOzMy/4YYbGD9+PAB33HEHRx11FGvWrOHII4+kpKSEJUuWEAqFGDZsGB9++CF+v7/BdvPzm07KApmbB+w+7kFRUVFm2e62bduGaZpNrrNq1arMdh0OB7m5uXu9XdGQJBaEEEII0W7WR9xM31LI9K2FLAs1PLn0GCZ9PBGOzqnhmJwaenqilDjj5NmTWe8IsL8awDfV2sGou/j3qDjDE6sZnli9n0pzaLNrioHuKga6qzgr92v+vv0IZoR68EUsn/nhLswPd+GOypEc7d7KmTlf8+O8Nbh0s72LLUSbG9SnZ+a5YRh06tSJQYMGZebVX2hv2bKFzz//nPfeew+fz9doO2vXruXwww/nq6++YvLkySxYsIBt27ZhWeljXllZWYPEwuDBgzPPu3TpknmNI488EpvNRt++fQkGgwD06dMna1eIf/7zn/zyl7/MTE+fPh3DaL7Lm2g/rUos7GlEzd299NJL3Hbbbaxfv55+/fpx11138f3vSz9cIYQQbSuFTgV5bNf8hHCjAB0LA4WHOCVqB7nUYpdfg9ucqhtc8cOqPJaH/KwNe9iacLAl4czE6ChG5VYztnAbpXnV9POG0Q+WUQjFfpFjJLi68zKu7ryMsoSPd4LdeTvUg08jhXwWTT/+teNwHus+h97OYHsXV4g2Zbc1vAjXNA273d5gGtLjDdTW1nLGGWdw1113NdpOfXLgjDPOoEePHjz11FOUlJRgWRYDBw4kkWjYLSvbawAt6grxgx/8gFGjRmXmd+3alYqKCgA2b96cKVf99NChQ5t8HwoKCjAMg82bNzeYv3nzZoqLiwEoLi4mkUhQXV3doNXCrjGieS1OLOzNiJq7mjdvHueffz5Tpkzh9NNP54UXXuCHP/whn376aYPMVkcXtbKflOqahnOXn1aai9UA1y59lPZVLIC7lbExy2r2F6DdY1OaQcTpJ+zKIeLwE3EF0tPOAClngKjDS8Tpo9buI+LwEXX6iTl9aA4HStOxNA1dWeQmQ/gSIfzJIO54EG8ihC8RxFf/N7lzunMqhFOlAIhbFqlmCuzRtcxBbk+xbl1Dr4tNWIpkM2OdtiTWpWsYrYhNWopEltiIZWEqlYlNKUUq+65hh0ysqRTJZmJtgK0VsZZSNNcD+NvExgFLt5GyOdEtE02ZdX8t7C3YrgHYtfph2Zp/z3TAYO9iNcC2y7jsyWY+RQdCLIC9lbEpFApIWBbhROPa4XXsPMGIJlNYzdT3XWNjyRRmM7Eeh50gbsq1fMrMAJu0PCq1fCr19N8KPZ/NWh4pbc9fdXaVxE2CHBWml9pKV6rwqygeK4LXCuNXEfwqip/0X5+KElAR8m0mRt2VbyJlkmzm2Oq22dD3FBtPoEWiuJyOzK8yobjJlohOdcpOVdJBdcrBjpSDqqSDrQknVaabHSkHh7mi9PcG8WkxdBRB004wZSNq2QgYSTrZ4xR7LDq7UhQ6kuRoEXQre/9zp8OBre6kNJUyiSeyf5Icdjt2e/p9TiRN1ocMNsXdVCTclMfdrIn4+CyUx5Zk49G6DSxG5lQxNr+Sk/I308m+83VSKRuOuhNU0zSJxbOXwW7fGWspRTSV/Rdpu67jMPS9irVpGs6690EpRWQfxRqahmuXk/5wMvsRpSWxOhpue+tiI8kUKpFEizauF5oGHtfOJFAkFifbx3P32Gg8gWU187l3ty42lkhgmtljD3PDLwq+4BcFX7AxYvBmTW/+VjOE1fE8xqz5IYVGmKOcWznN9zWn5VfgM9L/r3giScrM/ln2uBw7zyOaitVjGOH0LUrdbnem73kikSCZzP4N2pJYl8uVOUa0JDaZTDa6CNyV0+nEZrO1ODaVShGPZz+eOByOzMXm3sTWH7VN0yTWxPdKPbvNaHiM2MtYy7KINnc8aUGszTBw1n13KaWIxLLvW0tiDV3H5dw5pkw4mn1MmKZizT2c6+9u2LBhvPLKK/Ts2TPzf93V9u3bWb16NU899RTf+c53AJg7d26LXgNoUVcIv9/faFmvXr0oLi5m1qxZmURCMBhkwYIFXHnllU2+psPhYPjw4cyaNYsf/vCHQPr/OmvWLCZOnAjA8OHDsdvtzJo1K9PlY/Xq1ZSVlTUaeFI0rcWJhfvvv5/LL7+cSy+9FIAnnniCt956i2eeeYZbbrmlUfxDDz3Eqaeeyo033gjAH//4R2bOnMmjjz7KE0880eRrxOPxBgec+qYyB7LSTRuzLjvB5ebRwp1Jl++Vf0Msy7fxcKeTv3XemRX7fsUmdmQ5MAywO3iheGem7uzKcirM9Jei5vCgOf3oTj+ay083Tx63dumRvpC3e3kilqLK7sks1+tiMew4dZ2jXW5Aw9J0ViYShNHSZwpoaJoOhg3N5sSwueji8pGwOUkaTsKGA+xN3+alrTlTMXzJWsKRHdSEq1HRGqxYDSoRQVkpsEyUMjk/349Xs9CVxUfVQZaHw1lvpXpp51yKrRj+ZIh3Kr7h7YoyrGg1VmQHVrQaFatBJdMH+g8G9uZIT3rfHyzfyr3lDfvwanY3mtOH5vDyZP8j6RnIJWLz8HKtyUshs9H/ov75Mbl5dLLpaEpRFouzIhxBKQtQ6X0yk2ClUGaSYUfpFGOhWym+SUT5NB7GitZgRXdghauwIlWYkfTfCZicYKRPjD5Jxnkwkv1zdqXbz4nO9F07Pk8luCuc/f7tP3f7GOf0ALDSTPGnRDy97w4vet3+a04fusPLCb4CBnnzSTg8VNhcvIuBXr98t9iAy4/T6SNZV9cSdjeanqU5nJnCmYriTITRExG2RGuwEmFUvBaVCKMSYay6571TcUaRQkVrWR+q5sP49gbLVbw2vW4iTN9EnJNU+n+cAp4h++3kemPjFHbe6aS52MMwOA1PZvrv1GZNWnTB4Ad4UJqGZTj4l5EgbrOjLBNScVQqDip9zChE52y8mXX/TZjaLAmDPHR+vEvsq0TYkeVXfB8aF7CzqeR/ibAVCypr+cNtTzeILfC6qJx8aWZ6/DNv8cHX5U1u1+N0sukPVxHDQQQHl81YzMfbUujeAgxvAbqvEMNfjJHXHVtud3J8vYloez7eqFScrnqIAOkL7g01EXbEUujuXIxAMZpukNTsJLET1LxspHGSPBunijNAbaKvqmT518uYt/pLVKIWs6Ycs3YroNI/1SvFCz89hT6d/GjAY0vX8881kfRn3eFBs7vRXQF0X2eMFRYDhwwiZguwI2knYu5ds8/PgwHeoGX32jbD24ku/y/RZa+R3PoVZtV6VDIKwLR7b+fcsScC8OrsD/nJDXeAbqT/H77O6P7OGL7OGP7OnPH9H1DY40jWhL2sDjlJ0PQAi1YiSl/1NWf3d3OEL8w3a5bxi2snsC5azYtNxN/1619yw6Xpgbg+/eIrjv1p0yeNAJN/dTG3X3UJAF9sDzH0+ZlZYyeN6Mddo9NNdcuCEfo9PSNr7K+G9OaRMUcDsC2aoGTqm1ljLzyqB8+cOgKASMok9+HXs8aec3hXpp2xczCw5mJP61XMf88+PjNd8vibWZMW3+1WwKzzdt7Voe9T09kWbfqiaHhRHh//bOdAkoOfm8mGYAT4R6PYAT1KWPr0lMz0sRN/z8oNTX+WexQVsPb/7stMf2/SX1j05bomYwty/FS+/Ghmevyt9/HB0lVNxnpcDoJvPJWZPveOR5n+yedNxgKkZj6feX7DvQ/xyocL0QPFdPrp87iPHMtW08v7ES/vR3py+9YUJ/o3cbR7K9Nfe47/vvQEWE2/xxUvPUJhbrrp9g1P/Iupb8zKWoZ1Kz+nZ48eAPz293/k3oceyRq7fOF8jhrQH4C/3HMfd/yl8S/H9T75YDbHDB8GwEOPPcFNv5ucNfa96W9w4nfTF4JPPvMcEyfdmDX2zVdeZPyp4wD457R/c+mvJmSN/fc/nuPcs38IwKv/fZMfX3hJ1thnn3iMSy68AIC3353F6eeclzX20fvv4aoL05/7Dz9fyckTfps19q4Jl3DDz84G4NPVX3PsZddnjZ182U+4/Rc/BeCL9d8w+IKJWWOv/+lZ3H11+rurbPNW+px9edbYK8/+Po/e+CsAtlUHKf7+hVljL/r+STx723VAOjkXOOnHWWPP+d5x/PsvO6+tmos97bgRvHnfzjpQ/P0LicTiDDuiT9Z1djdhwgSeeuopzj//fG666Sby8/NZs2YN06ZN4+mnnyYvL49OnTrx5JNP0qVLF8rKypq89tuTlnSFaIqmaVx33XX86U9/ol+/fpnbTZaUlGSSBgAnn3wyZ511ViZxMGnSJC6++GJGjBjByJEjefDBBwmHw5lr2pycHC677DImTZpEfn4+gUCAq6++mtLSUhm4cS+1KLGwNyNq7m7+/PlMmjSpwbxx48bx2muvZX2dKVOmcMcdd7SkaO3OVtgP91FnoKwkmKm6i70kykwRNjTmeT0YysRmprB7NqHMJMpMomkG2BxohgPN5iDm8vJeXiFJw0HC5sJxeJycuosyze5Jn4A6POh2D1Gnjxu8ucTtLuI2NzbdQbe6C7HdL7hM4I+7lTm3mf3Z/Wu6uVP3LVnmW/Hauova9AU4sRrGaCnciRCeRC3v7djM+toqrFgQFQ9lLoiUstAMO48NGEDI4Sdk9/OfiMZ63YvuykF35+58ePLQ3ek9idtcxG0ucBfg7JS9vLufEjZ3KPvPbtNNXW6oVBwrWsO1WirzK3nINCkxrfT/1elL/+92admxe+0uaKYMK3ab9jQZlfZV3aNec3dpfx34XyKCJ7oDIlV0jtZkLkyVmQBNT9dP3WCBzcEqW7pFSRAtfemiGel6phs7nxt2PnR4+MjhIWl3k7K56N5MGVbXPfamvLG6R71mW0cbNuKGn7gzneXOPoY8bAPe2mW6sJnYODAjFUc3U2Cl6Gql6pI6dX/rprFMUspirgLqWlEUqfT8+jpe/xxlgWXxCQbKZsfS7RTYDDAcaIY9/bfuGIHhQLc5mWFzoAx71vIqM4UyE2ipOLNSCfRUHN1MEkhF8dX/j+v+zyoVh1QceyrBspSJZibQUwmcZoRcw4am29OJRE3PvOt2TWep5gBNQ2k6dkw6aXXJR00HTUvXHU3H4bBzma0nlqZjolN51k8pSmp1xzJv+nhW91ezORmy6478qOnPXL36YdjyVYjY9g1sq1iLWb2R1I4yzOqNmDs2ktqxAbNmE8v/cFmmNcTP35zN3xfX1TzdSCdh646dhq8zD/78UmKuAoK4eaeshtVBhe7ORXPnort3HoM0w05cc/KZ1pvP6A39jyOvf/byNvgmLIWCZn4AWZuCXbNLykxi1W7DDG/DqnuYtVsxgxVcd9Z3GdWvC2siHt5auYPP1laCbqQTipEdqEQY3ZOP4S/iiKOGk3LmsS3hIKF0DG8nfKMuxTdqZ/LHDG1BpWJMMQI8PM+JDoSNwXT900QMX9NHq7kAu4xtpVJxUlXrSVVtwNxRRmrbGuIbPiax4RNuvvFyrup5FgDvr9uCilZnfyOE2MesYCVbnxiH7slPn7sNGI9n2PlQ2JcZwR7MCPaA0hF0HfwHIov+Qc3Mv2DVZjvTEeLgUVJSwkcffcTNN9/M2LFjicfj9OjRg1NPPRVd19E0jWnTpnHNNdcwcOBAjjjiCB5++GFOPPHE/V7Wm266iXA4zBVXXEF1dTUnnHACM2bMwOXaecWydu1atm3b+SPfeeedx9atW5k8eTKVlZUMHTqUGTNmNBjQ8YEHHkDXdc455xzi8Tjjxo3j8ccf36/71pFpKttPtU0oLy+na9euzJs3r0GTkJtuuok5c+awYMGCRus4HA6ef/55zj///My8xx9/nDvuuKNRP5d6TbVY6N69e4vvc7q/RJYt56/XPc6DPziwKp6mLFyJMK5EGHeiFk8yjDuRfjgSIdx1y+qXuxJhbFYSXVk4AA2FphQpK33xk55O/9UtC0cqhj0Vx28lsJtxHKk4VjKKIxnFEw9hqMaZ/l27TcSVyjSJttl0CgrcDWK9RsMuFtlaO5qaDg4fIWcOYbuPHTYvQZuPsMNH2O4jbrgwNQNTN7A0A0M3sHQDUzNIoJPS9KY3DOiaTtTuJejIIWj3E3QEqHX4CTkChB3+zG3FWsKViuBORvCYUVypKO5kGHcynP7/pCKZ555UGHcygs+Ko2k6Co0kGilAke4yYmkGKd2GqRtELIOK2hTKcGDqNlK6QdJwEHf6ibpzibpzibjziLpziblyUXrLy/5t2JIxHIkwjkQYezKCMxFOP5LR9Px4LUYyUhcTycTWP3cnwrhTMeypGHoyhkrFsCdjGGYy/eu9bsPSDZRmoBk2LIeHhN1DzO4h4vCQsHtJOLwkHJ4Gf1N2D0mnl4jNzTbdRcrpw3R4MJ1eTIc3/dfZeCAj0XY0ZeEmgVfFyFMh8lQteSpErqolX4UotqrooqroZQvRRe3ATXLP3Sbstp1Nl1Nm+rj2LWIVEMNBtb2QFcZhbNQKqFABQjipxc0WPZcazUv6SAqKdIsvtPS0S8XpbFXjVxFcJHCpBB7i5DsSdOp9GEVui0KXSZ49iU+L4LJiWQcr3LXbRDKZItFMk+j67g1KQVUMlte4+N+2EpbW5rIp7iZk2rOuW09HkWtP0MmWIN8eJ9+eoNCZosiVoq83Qi9nkM5GEJvW9P9j124TLenesLexavsWzDdelK4QrYiNJFMofwBt1HcaxXbErhB7G6sUrKULH4a7sjKWz8e1RVRb6QsUl5aij2MHfew7uDh3KYc7duy5K4Q3gDEo/R5KV4i0FneFSMZQaz6TrhDfoisEgNftQhv0HTRvbtZ120swGCQnJ+eAvb4TLXdA3hXC6XTidDr3HHgAKamtYPTKV9N9vnUbpmHD1GykDBumbsfUDUzdnrkIrH+ebsWQwG4msVlJbGYSm5nAZibTTbmTMZypKK5kBGcqhjMZxZlKX7w7U7H0/Lp59bHuRC3uugs2fb+Nj70bjfQvl81w1v/CSfokb9dEwu5ce7oITtUSSGVvat4WLDQidi+1dj+1dh9JvfEJuaFMPKkIrlQ087et/iehWIpFZdV7FWtpGjFnoC7ZkE44xB1eTMNByubANBxoytpl7AILXZlolln3Nz29+3LdTGJPxXAko9iTMex1iSZbKoauDuzB8UIpi4W1TX/BK03DtLsxHV4smwOl29IPw0DpNqzMtA2lG+mEk6Y38VzLMj/dGkA3E+hmAi2VRDeT6dYDZvq5norXtSZI1sUkMvN1M4nSDSybE8twYNkc6ec2R920MzNP2XZd7sQy7E3GKsOOZibRzRSalULLNAmuq7/KQrPSXXI0ZYGCvICLU0b3R1PpARJ1FAZWo+dOUnhUHBcJ3CTwZC6s09NOks23SKk/HOzyUXLZ9/7rzGkzcLJ33Quai/WhKGALfa0mfs1sbXV35qL19O42UwPcTUU3YrfbMhftzdE06OSG0e4Yo4u/zsyvSdrYFHOSUjoWdb046pIjfiNFJ0eSXHsSY48DKu5dlzjDMPB69m7fWhKraxrevawTLYnV2igWOCBiPXYbOOxo7j2fg+2aONgTt7O5dmOtj3U59l3sEKoY4qkCwFQaH4W7cP+WoSyNFrAiXsiKeCFv1PbjBG8F/V1V9HXW0M9VTV9HDV7HbskbtwvDu/vnOH3B7NjLMrdVrN1ubzCw3r6KtdlsTfbLb21sfTdTwzDwuvfueN2SWF3X8br37jjVklhN09okFmizWCH2lRYlFvZmRM3dFRcXtyi+ozqy4jOOrPisvYsh9iMdhS9Ziy+5fxMa+4KuFJ5YDZ5YDZ12bGjv4hzwNKWwJSLYEgfuPdA100I3mxtWs+11L8nnkhPkjj8dWY49RY69uWFJhTj4GZriu75yvuMtZ1U8l40JP6/X9GJ6sCcfhkv4MNxwFPuu9lr6Oqs53FlDX2c13UzoHtQp9lo42+DOeLUJ2BDSWV+jE05qOA3o6rc4PM8ksPe5FiGE2KdalFjYmxE1d1daWsqsWbO47rrrMvNmzpwpo2sKIYQQQogDlqZBf1c1/V3VjA1sZFVsKYsjnfkqnstX8Ry+iueyLeVmU9LHpqSPObXd0iuWA3XjTxa6LUp8Fn1zLfrlmrhsYNfB0FXmVqoamQac9Z2m0DRIWVAV09ga1dkW1fgmpLMhqLM1mr0VZ4nX4qgCk+90TXFi9xQ9Agd2a0EhxMGjxV0h9jSi5kUXXUTXrl2ZMiU9cvC1117L6NGjue+++xg/fjzTpk1j0aJFPPnkk/t2T4QQQgghhGgjR7qqOdJV3WDejpSTr+I5rInnsCaey5p4DuWmn/Kkj5iZTgpsjep8vnXfliXfZdEjYJHjVMRSGuuDOpVhnfK6x8wN6S4MvQImo7unkwzHdknhPiA7QQshDgYtPrzsaUTNsrKyzMAzAMcddxwvvPACv/vd77j11lvp168fr732GgMHDtx3eyGEEEIIIcR+lmeLM9K2hZHeXcZa8eagDz6RHXGNTbXplgZf7jD4ukYnZUHK0kilh6apG8ckrX66nqZBvlPRya0ocFt08Sp65Vj0CJjkNDHERU0cVlcZLN5s8P43dhZVGqwLGqxbYfDcCidOQ3FMcYpju5gc2yXF4EKzTbpqCCEOTa3KW06cODFr14f333+/0bxzzz2Xc889tzUvJYQQQgghRIeiaZDvUuS7FIMKLE7r1fZjl+Q4YWQXk5FdTK4cmiCUgHnlNuZstPH+N3Y21erM3WRn7qZ0awanoejut+jut+hW/9dnketU+BzgsSl8doXHrvDawbZ/byYlhOhgpEGUEEIIIYQQBxm/A8b1TDGuZwqlYnxVrTO/3MbHFTYWVBhsj+msqTZYU72Xd8kx6hMN4LMrXDaFXU+PGWE3FA4d7LrCboCt7sZfhga6lh5PQs9M7/q84bL6dbQU6Nv7oGsKA5WOIz2wppaZlx5IW99t2tDS97IxtMYx9eNXaHXxGuntaqjd5qdpde1Jdr0RjqbtnLdrXOb5LuNlsMv6O+c3Xn/X+fVUgy3s+rduvto1tjENsGsWhzWxTIi2IIkFIYQQQgghDmKaBofnWRyel+DioxIoBRtDGmUhnY27PL4J6YQSGuGkRjgF4YRGSqUvZOOmRtzU2B7bHyV2ATn744UOal0dET46JtHexRCHiA6RWFB1KblgMNjOJWlapLaWWktG3f027Ba4TLO9i9GhhUyTiJJ62FpRZZFoMucv9lbMsgjG5ATmW7HH0WrD7V2KDk2FI6h4+956tUOLJdDC0fYuRQdnxzhAz1l3lQvk+mGwP3uMUpCwIFKXaIgmNWqTGpGURtyEpAlJSyNp7XyesMBSYCoNS6W3kZ5O/911Wf1yM/M3Pd9MJTGDO1BoWGiZdRRaOgawVP2y9G/9ptIw0XZupy5m5zpaZkyLnc/T66pM7M4WAfWnBLvOU+zSckDtbG/QKKaJdWkwf/f2CTvjG7ZiqP9b18pBaxi/61bqF+3assFFlGDQQjMPvH4s9dd1SjX1ToiOSFMd4L/5zTff0L179/YuhhBCCCGEEEKIfWTjxo1069atvYsh9oEOkViwLIvy8nL8fj/a7qm6A0AwGKR79+5s3LiRQCDQ3sURollSX0VHIvVVdCRSX0VHIvVVtCelFKFQiJKSkgZ3FBQdV4foCqHreofIZAUCATkwiw5D6qvoSKS+io5E6qvoSKS+ivaSkyPjaBxMJD0khBBCCCGEEEKIVpPEghBCCCGEEEIIIVpNEgv7gNPp5Pbbb8fpdLZ3UYTYI6mvoiOR+io6EqmvoiOR+iqE2Jc6xOCNQgghhBBCCCGEODBJiwUhhBBCCCGEEEK0miQWhBBCCCGEEEII0WqSWBBCCCGEEEIIIUSrSWJBCCGEEEIIIYQQrSaJBSGEEEIIIYQQQrSaJBa+pccee4yePXvicrkYNWoUn3zySXsXSRyCPvjgA8444wxKSkrQNI3XXnutwXKlFJMnT6ZLly643W7GjBnDV1991SCmqqqKCy64gEAgQG5uLpdddhm1tbX7cS/EoWLKlCkcc8wx+P1+OnfuzA9/+ENWr17dICYWizFhwgQ6deqEz+fjnHPOYfPmzQ1iysrKGD9+PB6Ph86dO3PjjTeSSqX2566IQ8DUqVMZPHgwgUCAQCBAaWkp06dPzyyXuioOVHfeeSeapnHddddl5kl9FUK0FUksfAsvvvgikyZN4vbbb+fTTz9lyJAhjBs3ji1btrR30cQhJhwOM2TIEB577LEml9999908/PDDPPHEEyxYsACv18u4ceOIxWKZmAsuuIAVK1Ywc+ZM3nzzTT744AOuuOKK/bUL4hAyZ84cJkyYwMcff8zMmTNJJpOMHTuWcDicifn1r3/NG2+8wUsvvcScOXMoLy/n7LPPziw3TZPx48eTSCSYN28ezz//PM899xyTJ09uj10SB7Fu3bpx5513snjxYhYtWsRJJ53EmWeeyYoVKwCpq+LAtHDhQv76178yePDgBvOlvgoh2owSrTZy5Eg1YcKEzLRpmqqkpERNmTKlHUslDnWAevXVVzPTlmWp4uJidc8992TmVVdXK6fTqf71r38ppZRauXKlAtTChQszMdOnT1eapqlNmzbtt7KLQ9OWLVsUoObMmaOUStdPu92uXnrppUzMF198oQA1f/58pZRS//vf/5Su66qysjITM3XqVBUIBFQ8Ht+/OyAOOXl5eerpp5+WuioOSKFQSPXr10/NnDlTjR49Wl177bVKKTm2CiHalrRYaKVEIsHixYsZM2ZMZp6u64wZM4b58+e3Y8mEaGjdunVUVlY2qKs5OTmMGjUqU1fnz59Pbm4uI0aMyMSMGTMGXddZsGDBfi+zOLTU1NQAkJ+fD8DixYtJJpMN6uyRRx7JYYcd1qDODho0iKKiokzMuHHjCAaDmV+ShdjXTNNk2rRphMNhSktLpa6KA9KECRMYP358g3oJcmwVQrQtW3sXoKPatm0bpmk2OPACFBUVsWrVqnYqlRCNVVZWAjRZV+uXVVZW0rlz5wbLbTYb+fn5mRgh2oJlWVx33XUcf/zxDBw4EEjXR4fDQW5uboPY3etsU3W6fpkQ+9KyZcsoLS0lFovh8/l49dVXGTBgAEuWLJG6Kg4o06ZN49NPP2XhwoWNlsmxVQjRliSxIIQQot1MmDCB5cuXM3fu3PYuihBZHXHEESxZsoSamhpefvllLr74YubMmdPexRKigY0bN3Lttdcyc+ZMXC5XexdHCHGIka4QrVRQUIBhGI1G0t28eTPFxcXtVCohGquvj83V1eLi4kaDjqZSKaqqqqQ+izYzceJE3nzzTd577z26deuWmV9cXEwikaC6urpB/O51tqk6Xb9MiH3J4XDQt29fhg8fzpQpUxgyZAgPPfSQ1FVxQFm8eDFbtmxh2LBh2Gw2bDYbc+bM4eGHH8Zms1FUVCT1VQjRZiSx0EoOh4Phw4cza9aszDzLspg1axalpaXtWDIhGurVqxfFxcUN6mowGGTBggWZulpaWkp1dTWLFy/OxMyePRvLshg1atR+L7M4uCmlmDhxIq+++iqzZ8+mV69eDZYPHz4cu93eoM6uXr2asrKyBnV22bJlDRJiM2fOJBAIMGDAgP2zI+KQZVkW8Xhc6qo4oJx88sksW7aMJUuWZB4jRozgggsuyDyX+iqEaDPtPXpkRzZt2jTldDrVc889p1auXKmuuOIKlZub22AkXSH2h1AopD777DP12WefKUDdf//96rPPPlMbNmxQSil15513qtzcXPX666+rpUuXqjPPPFP16tVLRaPRzDZOPfVUdfTRR6sFCxaouXPnqn79+qnzzz+/vXZJHMSuvPJKlZOTo95//31VUVGReUQikUzMr371K3XYYYep2bNnq0WLFqnS0lJVWlqaWZ5KpdTAgQPV2LFj1ZIlS9SMGTNUYWGh+s1vftMeuyQOYrfccouaM2eOWrdunVq6dKm65ZZblKZp6p133lFKSV0VB7Zd7wqhlNRXIUTbkcTCt/TII4+oww47TDkcDjVy5Ej18ccft3eRxCHovffeU0Cjx8UXX6yUSt9y8rbbblNFRUXK6XSqk08+Wa1evbrBNrZv367OP/985fP5VCAQUJdeeqkKhULtsDfiYNdUXQXUs88+m4mJRqPqqquuUnl5ecrj8aizzjpLVVRUNNjO+vXr1WmnnabcbrcqKChQ119/vUomk/t5b8TB7uc//7nq0aOHcjgcqrCwUJ188smZpIJSUlfFgW33xILUVyFEW9GUUqp92koIIYQQQgghhBCio5MxFoQQQgghhBBCCNFqklgQQgghhBBCCCFEq0liQQghhBBCCCGEEK0miQUhhBBCCCGEEEK0miQWhBBCCCGEEEII0WqSWBBCCCGEEEIIIUSrSWJBCCGEEEIIIYQQrSaJBSGEEEIIIYQQQrSaJBaEEEIIIYQQQgjRapJYEEIIIYQQQgghRKtJYkEIIYQQQgghhBCt9v+w/X9t9nAhFwAAAABJRU5ErkJggg==" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "distance = 'euclidean'\n", + "explainer = PointExplainer(stat_model, X_test, y_test)\n", + "explainer.explain(n_samples=1, window=10, method=distance, name=dataset)\n", + "explainer.visual(threshold=0, name=dataset+'_'+distance)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing points: 100%|██████████| 10/10 [00:01<00:00, 8.87point/s]\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "distance = 'cross_entropy'\n", + "explainer = PointExplainer(stat_model, X_test, y_test)\n", + "explainer.explain(n_samples=1, window=10, method=distance, name=dataset)\n", + "explainer.visual(threshold=0, name=dataset+'_'+distance)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing points: 100%|██████████| 10/10 [00:03<00:00, 2.58point/s]\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "distance = 'rmse'\n", + "explainer = PointExplainer(comp_model, X_test, y_test)\n", + "explainer.explain(n_samples=1, window=10, method=distance, name=dataset)\n", + "explainer.visual(threshold=0, name=dataset+'_'+distance)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing points: 100%|██████████| 10/10 [00:03<00:00, 2.85point/s]\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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ZWeI1YPTo0UHlvvzyy/H5fOzbt6/Uch8+fJh169YxfPhwIiIiAsv79OlD8+bNi8WfWqYTJ06QmZnJ5ZdfXmJ5SlLeY4qMjGTTpk3s2LGjxP382WuVEEKIc0+aQgghqh273R5oC18kIiKCmjVrFrthiIiIKPFGsFGjRkHzoaGhJCUlBdoCF31Zb9KkSVCc1Wqlfv36xb7M16hRo0IdvhmGweuvv85bb73Fnj17Au38AWJiYorF//HGKCoqCiBwbLt27QKgZcuWpT7nzp07UUoxYcIEJkyYUGLMkSNHqFGjRonr9u3bR3JyMmFhYUHLi6odl3WDczoNGzYs9t41btwY8LedT0xMZN++fXTq1KnYtqc+f1nHX9J5ExUVVa5Egc1m4+WXX+aBBx4gISGByy67jEGDBjFs2DASExNPu/0flXZ+ATRt2pTly5cHLTObzSVWXS9Lx44dgzpvvOWWW7jkkksYO3YsgwYNwmq1smPHDjIzM4mPjy9xH0UdMRbdAF5xxRUlxoWHh1eobCXZsWMH69evL/Ye/bEs5VWUMCrrnCivffv2lXiO/vH9K3qdivqZKElmZiZRUVHk5+czadIkZs+ezcGDBwP9lBTF/NHprgGllRuKX++Kyv7HhMGXX37J888/z7p164L6jyhvIqa8x/Tss89yzTXX0LhxY1q2bEn//v25/fbbA82O/uy1SgghxLkniQUhRLVjMpkqtPzUL7dny6m/1JXHiy++yIQJE/jrX//Kc889R3R0NLquM27cuKCO6oqciWMr2u+DDz5Iv379Soxp2LBhufdX3ZT2GpbXuHHjGDx4MAsWLOC7775jwoQJTJo0ie+//55LLrnkDJWyZKfWcKksXdfp1asXr7/+Ojt27KBFixYYhkF8fDwffvhhidsU3eQXnTsffPBBiYmUP3YIWRmGYdCnTx8efvjhEtcXJZpKu8k9NTlXVYpep8mTJ9O2bdsSY0JDQwF/nwmzZ89m3LhxdO7cmYiICDRNY+jQoWftGlCWn376iauvvpru3bvz1ltvkZSUhMViYfbs2cU6wS1NeY+pe/fu7Nq1i88//5yFCxfyz3/+k6lTpzJr1izuvPPOi/5aJYQQ1ZEkFoQQF6UdO3bQq1evwHxOTg6HDx/mqquuAqBOnToAbNu2jfr16wfi3G43e/bsoXfv3uV6ntJugubPn0+vXr149913g5ZnZGQQGxtboWOBk52nbdy4sdSyFR2HxWIpd/lPVadOHRYvXkx2dnZQrYWtW7cG1ldW0S+Up75e27dvBwh0jFinTh22bdtWbNsz8fxFTvfLbIMGDXjggQd44IEH2LFjB23btuXVV1/lX//6V4We59Tz64+1ALZt23ZGjqUkXq8X8J/v4D+exYsX07Vr1zKTY0XnV3x8fKXOnfJo0KABOTk5p91/0S/1fxyd5Y81ZorO940bN/7pstWpU4eNGzcWO0f/eD4WvU7h4eGnPY758+czfPhwXn311cCygoKCYsf1Z8sNlNjk4I9l//TTT7Hb7Xz33XfYbLbA8tmzZxfbtqzrWnmPKTo6mpEjRzJy5EhycnLo3r07Tz/9NHfeeWeFrlUVbdYihBDi7JA+FoQQF6W3334bj8cTmJ85cyZer5cBAwYA0Lt3b6xWK2+88UbQL4LvvvsumZmZgR7STyckJKTEL9Umk6nYL43z5s2rdLvhdu3aUa9ePaZNm1bs+YqeJz4+np49e/KPf/yDw4cPF9vH6Yauu+qqq/D5fMyYMSNo+dSpU9E0LfDaVcahQ4eChsDLysri//7v/2jbtm3gF/KrrrqK1atXs3LlykBcbm4ub7/9NnXr1i2xzXhFFfVa/8fXMC8vj4KCgqBlDRo0ICwsrNQhB8vSoUMH4uPjmTVrVtD233zzDVu2bCn3+VURHo+HhQsXYrVaA81HbrrpJnw+H88991yxeK/XG3gd+vXrR3h4OC+++GLQ56ZIeYc9LMtNN93EypUr+e6774qty8jICCRF6tSpg8lkKtbnwltvvRU0HxcXR/fu3XnvvfdISUkJWlfRX/mvuuoqDh06FDT0Yl5eHm+//XZQXPv27WnQoAFTpkwJJG9OderrVNI1YPr06We05kVSUhJt27bl/fffD2qKsGjRomJ9kphMJjRNC3r+vXv3smDBgmL7rch1raRjSk9PD5oPDQ2lYcOGgc9CRa5VISEhQPHPrBBCiHNLaiwIIS5KbrebK6+8kptuuolt27bx1ltv0a1bN66++mrAf1Py2GOP8cwzz9C/f3+uvvrqQNyll15abMi+0rRv356ZM2fy/PPP07BhQ+Lj47niiisYNGgQzz77LCNHjqRLly5s2LCBDz/8MKh2REXous7MmTMZPHgwbdu2ZeTIkSQlJbF161Y2bdoUuFl788036datG61atWLUqFHUr1+ftLQ0Vq5cyYEDB/j9999LfY7BgwfTq1cvnnjiCfbu3UubNm1YuHAhn3/+OePGjSv3sIIlady4MXfccQdr1qwhISGB9957j7S0tKBfSx999FH+/e9/M2DAAO69916io6N5//332bNnD59++umfbioA/iYtzZs35+OPP6Zx48ZER0fTsmVLvF5v4Hxp3rw5ZrOZzz77jLS0NIYOHVrh57FYLLz88suMHDmSHj16cMsttwSGm6xbty7333//nz6Wb775JlCb48iRI8ydO5cdO3bw6KOPBvpE6NGjB3fddReTJk1i3bp19O3bF4vFwo4dO5g3bx6vv/46N954I+Hh4cycOZPbb7+ddu3aMXToUOLi4khJSeGrr76ia9euxRJOFfXQQw/x3//+l0GDBjFixAjat29Pbm4uGzZsYP78+ezdu5fY2FgiIiIYMmQI06dPR9M0GjRowJdfflliHwxvvPEG3bp1o127dowePZp69eqxd+9evvrqK9atW1fuso0aNYoZM2YwbNgw1q5dS1JSEh988EGxISF1Xeef//wnAwYMoEWLFowcOZIaNWpw8OBBli5dSnh4OF988QUAgwYN4oMPPiAiIoLmzZuzcuVKFi9eXGIfK3/GpEmTGDhwIN26deOvf/0rx48fZ/r06bRo0SIo+TFw4EBee+01+vfvz6233sqRI0d48803adiwIevXrw/aZ/v27Vm8eDGvvfYaycnJ1KtXj06dOpX7mJo3b07Pnj1p37490dHR/PLLL8yfP5+xY8cGYsp7rWrbti0mk4mXX36ZzMxMbDYbV1xxRan9hgghhDhLzv1AFEIIUT6lDTcZEhJSLLa04Q3r1KkTNKxZ0T5/+OEHNXr0aBUVFaVCQ0PVbbfdptLT04ttP2PGDNW0aVNlsVhUQkKCuueee4oN51jW0Iqpqalq4MCBKiwsTAGBIewKCgrUAw88oJKSkpTD4VBdu3ZVK1euLDbMXdHQevPmzQvab0lD6yml1PLly1WfPn1UWFiYCgkJUa1bt1bTp08Pitm1a5caNmyYSkxMVBaLRdWoUUMNGjRIzZ8/v8RjOFV2dra6//77VXJysrJYLKpRo0Zq8uTJQcPgne41+aOi9+i7775TrVu3VjabTTVt2rTYMReV/cYbb1SRkZHKbrerjh07qi+//PK0r01p583EiRPVH/8pXLFihWrfvr2yWq2BoSePHTumxowZo5o2bapCQkJURESE6tSpU9Dwg6Upa/jFjz/+WF1yySXKZrOp6Ohoddttt6kDBw4ExZRW9tM936mT3W5Xbdu2VTNnziz2Ximl1Ntvv63at2+vHA6HCgsLU61atVIPP/ywOnToUFDc0qVLVb9+/VRERISy2+2qQYMGasSIEeqXX34p1/GeTnZ2tnrsscdUw4YNldVqVbGxsapLly5qypQpQcPDHj16VN1www3K6XSqqKgoddddd6mNGzeW+JnYuHGjuu666wLnTJMmTdSECROKlbes4SaVUmrfvn3q6quvVk6nU8XGxqr77rsvMCznH4c6/O2339T111+vYmJilM1mU3Xq1FE33XSTWrJkSSDmxIkTauTIkSo2NlaFhoaqfv36qa1bt6o6deoEDeVY2utZ2rCbJfn0009Vs2bNlM1mU82bN1f/+c9/1PDhw4sNN/nuu++qRo0aBT6Ds2fPLvEzsnXrVtW9e3flcDgUEChveY/p+eefVx07dlSRkZHK4XCopk2bqhdeeCHoPVaq/Neqd955R9WvX1+ZTCYZelIIIaqIptQ56NVMCCHOE3PmzGHkyJGsWbMmqMd8UXXq1q1Ly5Yt+fLLL6u6KEIIIYQQohKkjwUhhBBCCCGEEEJUmvSxIIQQQoizJj8/P6jjwJJER0djtVrPUYmEEEIIcaZJYkEIIYQQZ83HH3/MyJEjy4xZunQpPXv2PDcFEkIIIcQZJ30sCCGEEOKsOXz4MJs2bSozpn379kRFRZ2jEgkhhBDiTJPEghBCCCGEEEIIISpNOm8UQgghhBBCCCFEpUliQQghhBBCCCGEEJUmiQUhhBBCCCGEEEJUmiQWhBBCCCGEEEIIUWmSWBBCCCGEEEIIIUSlSWJBCCGEEEIIIYQQlSaJBSGEEEIIIYQQQlSaJBaEEEIIIYQQQghRaZJYEEIIIYQQQgghRKVJYkEIIYQQQgghhBCVJokFIYQQQgghhBBCVJokFoQQQgghhBBCCFFpklgQQgghhBBCCCFEpUliQQghhBBCCCGEEJUmiQUhhBBCCCGEEEJUmiQWhBBCCCGEEEIIUWmSWBBCCCGEEEIIIUSlSWJBCCGEEEIIIYQQlSaJBSGEEKIS6taty4gRI6q6GEIIIYQQVU4SC0IIIcQpdu3axV133UX9+vWx2+2Eh4fTtWtXXn/9dfLz86u6eJWybds27r//frp06YLdbkfTNPbu3VuhfWzZsoX+/fsTGhpKdHQ0t99+O0ePHj07BRZCCCFEtWKu6gIIIYQQ54uvvvqKIUOGYLPZGDZsGC1btsTtdrN8+XIeeughNm3axNtvv13VxaywlStX8sYbb9C8eXOaNWvGunXrKrT9gQMH6N69OxEREbz44ovk5OQwZcoUNmzYwOrVq7FarWen4EIIIYSoFiSxIIQQQgB79uxh6NCh1KlTh++//56kpKTAujFjxrBz506++uqrKixh5V199dVkZGQQFhbGlClTKpxYePHFF8nNzWXt2rXUrl0bgI4dO9KnTx/mzJnD6NGjz0KphRBCCFFdSFMIIYQQAnjllVfIycnh3XffDUoqFGnYsCH33XdfqdsfP36cBx98kFatWhEaGkp4eDgDBgzg999/LxY7ffp0WrRogdPpJCoqig4dOjB37tzA+uzsbMaNG0fdunWx2WzEx8fTp08ffv3110BMXl4eW7du5dixY6c9tujoaMLCwk4bV5pPP/2UQYMGBZIKAL1796Zx48Z88sknld6vEEIIIS4MklgQQgghgC+++IL69evTpUuXSm2/e/duFixYwKBBg3jttdd46KGH2LBhAz169ODQoUOBuHfeeYd7772X5s2bM23aNJ555hnatm3LqlWrAjF33303M2fO5IYbbuCtt97iwQcfxOFwsGXLlkDM6tWradasGTNmzKj8QZfDwYMHOXLkCB06dCi2rmPHjvz2229n9fmFEEIIcf6TphBCCCEuellZWRw8eJBrrrmm0vto1aoV27dvR9dP5uxvv/12mjZtyrvvvsuECRMAfz8OLVq0YN68eaXu66uvvmLUqFG8+uqrgWUPP/xwpcv2Zxw+fBigxFocSUlJHD9+HJfLhc1mO9dFE0IIIcR5QhILQgghLnpZWVkAf6q5wKk31j6fj4yMDEJDQ2nSpElQE4bIyEgOHDjAmjVruPTSS0vcV2RkJKtWreLQoUMkJyeXGNOzZ0+UUpUub3kVjYRRUuLAbrcHYiSxIIQQQly8pCmEEEKIi154eDjg79ugsgzDYOrUqTRq1AibzUZsbCxxcXGsX7+ezMzMQNwjjzxCaGgoHTt2pFGjRowZM4aff/45aF+vvPIKGzdupFatWnTs2JGnn36a3bt3V7psf4bD4QDA5XIVW1dQUBAUI4QQQoiLkyQWhBBCXPTCw8NJTk5m48aNld7Hiy++yPjx4+nevTv/+te/+O6771i0aBEtWrTAMIxAXLNmzdi2bRsfffQR3bp149NPP6Vbt25MnDgxEHPTTTexe/dupk+fTnJyMpMnT6ZFixZ88803f+o4K6OoCURRk4hTHT58mOjoaKmtIIQQQlzkJLEghBBCAIMGDWLXrl2sXLmyUtvPnz+fXr168e677zJ06FD69u1L7969ycjIKBYbEhLCzTffzOzZs0lJSWHgwIG88MILgRoA4L+h/9vf/saCBQvYs2cPMTExvPDCC5U9vEqrUaMGcXFx/PLLL8XWrV69mrZt257zMgkhhBDi/CKJBSGEEAJ/54ghISHceeedpKWlFVu/a9cuXn/99VK3N5lMxfo8mDdvHgcPHgxalp6eHjRvtVpp3rw5Sik8Hg8+ny+o6QRAfHw8ycnJQc0RKjLcZEXs2rWLXbt2BS274YYb+PLLL9m/f39g2ZIlS9i+fTtDhgw5o88vhBBCiOpHOm8UQgghgAYNGjB37lxuvvlmmjVrxrBhw2jZsiVut5sVK1Ywb948RowYUer2gwYN4tlnn2XkyJF06dKFDRs28OGHH1K/fv2guL59+5KYmEjXrl1JSEhgy5YtzJgxg4EDBxIWFkZGRgY1a9bkxhtvpE2bNoSGhrJ48WLWrFkTNErE6tWr6dWrFxMnTuTpp58u89gyMzOZPn06QKA/hxkzZhAZGUlkZCRjx44NxF555ZUA7N27N7Ds8ccfZ968efTq1Yv77ruPnJwcJk+eTKtWrRg5cmR5Xl4hhBBCXMA0dS66lBZCCCGqiR07djB58mQWLVrEoUOHsNlstG7dmqFDhzJq1KhAfwJ169alZ8+ezJkzB/B3bvjEE08wd+5cMjIyaNeuHVOmTOHRRx8FYNmyZQC8/fbbfPjhh2zatImcnBxq1qzJ9ddfz5NPPkl4eDhut5snn3yShQsXsnv3bgzDoGHDhtx1113cc889gXIuW7as3ImFvXv3Uq9evRLX1alTJyiJULdu3cA2p9q0aRPjx49n+fLlWK1WBg4cyKuvvkpCQkL5XlghhBBCXLAksSCEEEIIIYQQQohKkz4WhBBCCCGEEEIIUWmSWBBCCCGEEEIIIUSlSWJBCCGEEEIIIYQQlSaJBSGEEEIIIYQQQlSaJBaEEEIIIYQQQghRaZJYEEIIIYQQQgghRKWZq7oAZ5phGBw6dIiwsDA0Tavq4gghhBBCCCGEOIVSiuzsbJKTk9H10//W/eabbzJ58mRSU1Np06YN06dPp2PHjmVuM2/ePCZMmMDevXtp1KgRL7/8MldddVVQGSZOnMg777xDRkYGXbt2ZebMmTRq1OhPH99FSV1g9u/frwCZZJJJJplkkkkmmWSSSSaZzuNp//79p72/++ijj5TValXvvfee2rRpkxo1apSKjIxUaWlppW7z888/K5PJpF555RW1efNm9eSTTyqLxaI2bNgQiHnppZdURESEWrBggfr999/V1VdfrerVq6fy8/PPyH3pxUZTSikuIJmZmURGRrJ//37Cw8OrujjFGT4oyKnqUlRb+9dv5tV+N1Z1MaqtGvVrMH7q+KouRrWksjPhl/9VdTGqLXdWHsdW7qjqYlRblogQ4q9oXdXFqBY8mHBjJk+zcUILJcMURp49jASyqEU6obiquojVi8+AgoKqLkX1pZR/OgtOaCG8aL+J7ywdAstMygeATzMFltX1pfJYwSd09m07K+U4m7yZuWSu2FTVxai2btuxn9ZXDcISE8vcuXOxWq08+eSTDBkyhIceeojPP/+cuLg4Jk+eTJ8+fQDYvHkzEyZMYOXKlTidTq644gomTZpETEwMAIsXL2by5Mls2bIFXdfp2LEjL730EvXr1wdg3759tG7dmg8++IC3336bX375hQYNGjB16tSgGgZZWVnUqlWLjIwMIiIiyjyOTp06cemllzJjxgzAX0O9Vq1a/P3vf+fRRx8tcZubb76Z3Nxcvvzyy8Cyyy67jLZt2zJr1iyUUiQnJ/PAAw/w4IMPAv77yISEBObMmcPQoUMr+apfvC64phBFzR/Cw8PP38SCVbq2qKywkBCsSBOXyrLrJsJDHFVdjGpJ+dxgs1R1Maott9WCy2Q6faAokcVsItxurepiVBkfGge1GFL0OHI1Ox7M7NdjOaJFAnBCD2W/FkeKHke6Xvq//ZoyaEQasWQBEE4+UeQSRS6ZONlLHHnYcGGmAAsuLLgxkUQGTThMMidowBEuZysR5J+LQ696Ph9gVHUpqq+zlFjYotfkLudYDuhxmJXBXzxLucn9E82M/Sg0dumJfGPuwBxrb1L0xtwT8iSDPf/jyYKPiVNZZ7w8Z4u3wI0h/3ZUmgmNj7/6mocffZQ1a9bw8ccfM378eL799luuu+46Jk6cyNSpU7nrrrtISUnB7XZz9dVXc+eddzJ9+nTy8/N55JFHuOOOO/j+++8BUErx0EMP0bp1a3JycnjqqacYNmwY69atQ9d1wsLCAHjhhReYMmUKjRo14oknnuDOO+9k586dmM1mUlJSaN68OQA1atQoseyPP/44jz/+OG63m7Vr1/LYY48F1um6Tu/evVm5cmWpx75y5UrGjw/+Ma1fv34sWLAAgD179pCamkrv3r0D6yMiIujUqRMrV66UxEIlXHCJBSEuFApwh8aRH1ULV0QirrB43GHxuENi8Fkd+Kwh+KzOUx4X/rU40L0uzK5szAU5mF05mFzZmAuyOeDQmJrWmlCTl1DdQ4juIdTkIcmcR01rDmEmT1UfthBnhALcJht5lhDyzU68Jgv5ZifHHTEcd8Zx3BHLcWcs2dYIdOVDVwYmw4tJGZiUF5Nx6jIfFp+bxJxDJGYfINSTg9Odg9OTS4g7B7PyntNj86FxXAvjmBbOMS2cdD088PiYFs5RPSLw2IOZZsZ+ko3juDUTHsz+qfCxGzMO3MQYWbQy9tLZu5WmxoEqT9+6MLPBVJc1pkasNjVmrbkhOZqzQvvQlUGUyiGSXByal8NEkq6FsZ0ktpNUoX0dJYL11AnMm5SPxqTSiMN0YztXsIlociu0TyEqowALM21X8Q/rANyahTpGGtPzZtHSSAnEaCgaGYdp5P6CEe7FTLVdywfWK/jCchnLzK253f09Q90/UlOlV+GRiHOlddMmPPnkkwA89thjvPTSS8TGxjJq1CgAnnrqKWbOnMn69etZvHgxl1xyCS+++GJg+/fee49atWqxfft2GjduzA033BC0//fee4+4uDg2b95My5YtA8sffPBBBg4cCMAzzzxDixYt2LlzJ02bNiU5OZmffvqJdu3a8dNPPwWSEaeKjo4G4NixY/h8PhISEoLWJyQksHXr1lKPOzU1tcRtUlNTA+uLlpUWIypGEguiyhV4IdOtken6w+TWyDhlPsOlcfR4a/Y8tgavPQKfxYYyWTHMNlA+TO68wikfk6fwrzsPkycfkzsfvdiyovlcTK5czO5cTAXZmN256J58QIPCGjCq8LEyWVC6GUM3o6HQDB+a4cXkysWeeQiTp2LVRQ2ThbzYeuTGNSQ3vhG5cQ3Jj65NfnRtCiJrYFjsZ/S1PgBsOFr6+ihTATWtOdSy5FDTmkPtUx7XsORi1c/ur1aGgjzDTL5hJs8wk6dOeWxYAuuyfRayDCtZPit5hhm30vEqHU/h3z/O23QfNs2HQ/di13zYdS8RJjdRJhfRZhdRpoLCvy6izC4iTS7M2gXVSqzac5lsZNkiyLZF+P9a/Y+zbeGkO+NJC00mNTSZYyEJgWSCoZ+bX7ms3gJCPDk43bk4PTmEeHIIc2VRM2sfSdkHCHVlEerOJtSdRag7C6vXjddkwaNb8OoWPKaTf3Os4WTZIvGYLLhNNk7YoznhiCUjPJ6skJoc1cI5oYVhaOWv+bZSb1auuM/pDEBj30FudS9lqOdHrPgq9ZqURw52Dugx7NPj2asnsEdPYL8ex3EtlL16Ai4tuIaGVXmoZRwlQuVhwqCGkU6SOo6GIkzlU9s4Sm3jKEnqOA7lxoYHHQW6DiEhABxVYaynNjnYUGhk4eAEIZwgBCdu6nOESHKx4cWGBxteTBikEMNOEjlMJL9Rh+1aMluowRZq8F86YFNuHuO/3M7yKk/KiAvXUS2cvzrHscnkT3Jd4VnHlPz3iCwjqRVOPhNd/+Y6zwqedAxjo6kub9kG8Q/rAEa4F3Ov67+EIU1dLmStGjcOPDaZTMTExNCqVavAsqIb6yNHjvD777+zdOlSQkNDi+1n165dNG7cmB07dvDUU0+xatUqjh07hmH4vxumpKQEJRZatz7ZfC8pKSnwHE2bNsVsNtOgQQMAGjRocH7WMhcVdtYTCxXtwfN0vXeKM8dQkO0mcBNf4NXI92oUeCHfq5HnhQKvRp5XI8/jX5bvBY+h4TPAp4qmk/NeA7yGhtcAT+Eyj0/DXTjv8Wl4DPzzhY89RkW+hlkgoaSLjxmv2YbXGXWmXp5KseSkY888hNmVg16Y0DB5CgKPQeEOi8MVlkBBRBIFUbVQpjI+hoaBLSsVe+ZhbNlpWLOPYM1Nx+TKPSWR8ofJk49htuG1heKzheK1h/knWyjOxBq07NONXJ+FHMNCrmEhy2fhkCeEEz67f8q3syE/tlhRNBRRJhcRJheRJjehJv+XdpOm0FHomsJ0yrxJM05Z7z9P8pU5kBzIPSWBUPS3QJ0/uc5Q3U2I7sWhewnRPdgMN47o7tiVG5vy4FAu7ModPOHGfspyh3JjU24APJr/l2KfpqOpwtcMhYb/ddKVQsdAo2idga5U4byBFzMuzRKY3Jq/mrZLswaWFWg28jUr+ZoNAJMyAvs0YWBSBla8mJUXC17MyodVeTHjw1L41z/v/cP8yfU+dFyaFTdm3JoFt2YOmvdoZrzo+DDh1UwY6P55i5ecDidQmobbZMNlsuE22ykw23GZ7LjMDlxmW7GbbbfJRo41HLfZVun30ubNx+p1YfcVEJV/jOi8Y0TnHyMm/yihrixAw6eb8Gmmwr9mDE3Hp5vxaSYMXSff7ORwWC2OhCSQbwkh1xqKy+xvVuQ223Gb7ZxwFP/cnC2aMohSucSqTGJVln8ysk4+VpnEGllowEZTHTK0EKyF76sFL9ZT3tM8zcZhLZq1poasMDdju6kGTzv+wmxbH67w/O7/5dM4SLJxnBBVgBMX5sKq8T40vPhrP3gxkadZydPs5GLniB7BQT2GQ1oMR7VwcjQHuZqdLM3JYS2KE3rxX6hOFWNk0cG3g0t92+no3U5T40DgeSsrjmyupOLttVtyAPg9MH9QRbGVZNZTi0W0YqtWg6e5kSlqEAVYqEk6rdjPAH6nJ1uwI7XBROUpYLmpBU85bmOfnkCMkcWzBf+iv3dtuRNZrY19fJb7PAvN7fiXtRcrzc1419aPedZuXO9ewUDvGtr49vzpz5g4/1jMwd+tNE3DYrEEzYO/34KcnBwGDx7Myy+/XGw/RcmBwYMHU6dOHd555x2Sk5MxDIOWLVvidruDn7eU5wCCmkIkJyeXWO6iphCxsbGYTCbS0tKC1qelpZGYmFjqcScmJpa5TdHftLS0wLEVzbdt27bU/YrSndVv8UXteGbNmkWnTp2YNm0a/fr1Y9u2bcTHxxeLX7FiBbfccguTJk1i0KBBzJ07l2uvvZZff/01KANW3eXmlp5ZNplM2O32csXquo7D4f9i6/JBSnoBx/J10gt0jhWY/H/zTRwr0Mn26OT7TOR4NPI8GjkeyPFoGOr8+G1FQxFuVYRbDSIKp2inToRNEWFThOgewiw+PIf28uUjj2HJz0T3FKB73eheFybd7G8KYHHisTrwWhz4rE6MwmU+mxPD4m82oCxOjMImBF6LA68tBJ8t1N+MwBaK1xaKYbWDUmhF7SKVv/NazedB93nQDP+veIbJjNLNeO3hGLYQPKExeEJjKnTspoJsnEd34jyyw//32B7sJ/ZjP3EAW+YhLD4PeuFXBx+qzH/yTVBmbM36NXngJv8x2axmzIXtFj1eL8ddOgc9YRzwhnHQE8ZBbxgHPCcfFygzx312jvvObC2KkmgoHJoHh+7FoRXd3HsJMflw6l5CdRdOCgjT3Tg0L1bNh1kzsGgGZgzsZoXDrGHRDHTlI8ejKDDMuJSJ/MJaEFmGjQyfnUzlINOwc8Jr44TPRobP/0tmjmElx/hDm3ZbxapPi1OEAX/yvls3vIS5sgh1ZRLqziLSlUmYK5PIguPEZh0gLucgcTmHCXFnY/fk4fDmYffkoaNwnjKUVYFhlPk5cmha4EuQyzBK/c3ep5lQ1lDybGHkWULIMDvJsYT6Hzti2B/VgHRnArnWMHJs4eRZw8mxhuE227D43Jh9HsyGB4vPjcXwYPZ5cHpyCC84gc3wYDU8ROanE553jGgti3pNI4kxMokxMolS2YEbALvZhKnw+NxeHx4j+Ojqsivw+NRYj8/A7Tt5dCOALM3JF7auvOMczD49gdm2viUeu1l58aGjKlBzoiSRRg41fEeo40uljpFGLV8aMUYWScYx6vpSsZlMWEz+5/AZBrne0mtQWHQdq9kUiC0oitV1MAff2FtMJ2MNQ5HvKb05S0mxkRzhMo5wGesYxRd8aOrJa+ZrydH818e9xLOXeL6gPWblo6E6xBW+9QzxLQ/072A2adgKv/Arpchzl14Gk65ht5z8ypbrKj1RUZFYXdNwWE8T6/OB2+OPPWW/eW4vipJrdmloOK2Vi833eDHK6JMgxGqpVGyBx4vvDMU6LeaT1wivD69R+hXFaTYFbv5PF+swm9F1f7Tb62OblsB31k58bbuMvWb/zVcN31H+kfkKTfV0tFNi//i5P1XR596E4sqC1XTP+x8/W1rxUsht7DUnM8fWhzm2PoQZuVzm3UpbYw/RKpsIXxah3iwcyoUNN3blwabcWPFgoGMyWcFkxoOJAkMj3wAvpkBi2UvhpJnQdQuGyYwPEy5Do8DQTsYU/i3aTulmlGbGq5koiPCRdUk/vJoZr272J3x188l5kxWvyYbbZMFAx4dG0QvuT81rqML3SvtDTVQfBM2rwg2VpqGhoWlFy/1JVAr3YmhF+9QAhVn5E/f+ZnU+NMNAU4a/OV3hMr1wvUkpzBQuN3woZaAbPrTC5L9unIw1o7BQuF/DhxFYZwRiip7DpAysmgo04/MaXnTDIH3f+2R4yl97r127dnz66afUrVsXs7n4bWJ6ejrbtm3jnXfe4fLLLwdg+fLl5d5/kYo0hbBarbRv354lS5Zw7bXXAv4ExZIlSxg7dmypz9G5c2eWLFnCuHHjAssWLVpE587+2nn16tUjMTGRJUuWBBIJWVlZrFq1invuuafCxyTOcmLhtddeY9SoUYwcORKAWbNm8dVXX/Hee++V2IPn66+/Tv/+/XnooYcAeO6551i0aBEzZsxg1qxZJT6Hy+XC5TrZw3NW1vnfIU1oUj1MoXFoFgeaxY5mtgced+jUlXH3P1SYAICnXp6JV7ehW0PQbKFotlB0q/9vaFQc8cnxZLs1stwaUHaPqqUx3HkY+RkoVw7Kk4fdDJdd0hKHWeE0K775+gtOHDmI4c5FufPA50EZXjB8REWG89yTj2LSQNcUL0x6iT27d6J8HjA8KJ8XvC6Uz01EqIPFn8/DYgKLDsP++ldW/W8lhjsHVZAV1LmR0+kk9+ihwPzA62/i6+8WlnoMd3HygrSIfHZT+he0vxKKpfAfiKXks72M2GGE4MD/pfYnCthcxq9ONzmSMUfWoiAiic1WCykWC5rFiWZ1oFucaBYHaDq+7DQ6Zp8gJusozvS9bMjax6+4S93vdTiJx/+PwgY8rCqjR/PBOEgu/FhvwcPPf4zdvZVnB/vb1H3+/HgGXtYWgLlLVnLH5HdK3e+/J4ylZ9fuHPfa+XztLp6bvwzNFoamm0Azgab7H+smbu19Oe2bNsRAY9O+w/zfop/9McrnP4dcuSi3fxpzVRduubwNDt3L+q1buXXiZP86T/FO0V4adTMP3uxvq7dm6246j3261PJOuP1aJg6/HoBNew/Q987HS40dP2QA/7zrFgD2ph6l4e0PoTuj0J3RaNYQ9MLPnWZx0LthPW5u3Zh8zUa6V2PSmj3+99jiODlZ/fOJkVHUiYkhX7OhlMHmtGMonxtl+PxfbnQdNP8U5bBRNzKMovoFG49lo52yHk33f+68LiJMPlpF2bHhwao8fL1tD153PspTgOHJQ7nzUN4CMAzqRoVze6v6/v1qOm+s3UW+0tFMVjSTBcxWNN2CZrYS6XTSuXYy3sK296vTsnBhRjP516Nb0EwWlOHFqjw0i7RhVf7Haw+kkp2f4/+se92F1wcvyvASZoJhLWujXB4KDh7nv5mZHMn3v8fKk+8vrycf5ckjxOdmRnJM4Eb7qZQDbMzNwsjPwMg7gXJlB943h6axpcXJcaZH7j3I0pzSE7F7W56sCjr+QCpfZ5U+Os/m5g1xFn7ZfPzQET7NKP3flbVN65Po8ZdrwqE0PjieWWrsT43rUavw5uXF1KO8fexEqbELG9ahsd1fS2Nq2jEmHD1eauzKuwZzaY04AN7432YeXbim1NjFIwfQs54/QfbOL1u596uSRjr5B5o1hEfveRpbcmt2mJJZ54snyxzlP28Ar1by1wer8mD25pGVk4kvOw3f8X14M1LwZRzEKMhCubK5t11tbqvnoKZxjB+37eCaDxeVWt43Bl7G3zr5f9X6aV8avWd/U2rsS30v5cFu/uq9vx5Op/M/vig1dkK/TkzsfxkAW9KO0+aVf5UaO75XO1652v8FOiUjm4bPzS4h6i10ZzS3dr+cF/pdwm7iWeipz/u5zSGqFlu1WmzVa/Gm3hfXrh8p2L6YruG5vHd5KHHkkOf2EvHoW6WW4YY2Dfl4xMDAfFmxA5rV5YvR1wTmk556u9SkRfcGNfh+7MmRlho8N5tjuSV3StmhRiz/u+fawHyrN+azL6Pkz1Hz+EjW33tyv5fNWsDmIxklxtaJDGXXgyc7Sev1zy/55eCxEmNjnXZSH/9LYH7g+9/y496S20I7LWayJo4IzA/59xK+2b6/xFgA7/N3Bh4Pn/8Dn27aU2ps5lPDA4mIez5fzv/9VvqIN4cfvZU4pz/h9OC3q5i5uvQ24TvvH4IeXY8vLB35p9GSzMgmgXVGQTa5q2ezauELtM45wu9jr6NFvL+W5qQff+e5ZetK3W+p1wjtEexN+hDScST2Jn3JDolmkbU9i2hf6r7OORuV/XorgLS1P1LXVf7bvTFjxvDOO+9wyy238PDDDxMdHc3OnTv56KOP+Oc//0lUVBQxMTG8/fbbJCUlkZKSUuqoDGWpaFOI8ePHM3z4cDp06EDHjh2ZNm0aubm5gXtMgGHDhlGjRg0mTZoEwH333UePHj149dVXGThwIB999BG//PILb7/9NuCvRTFu3Dief/55GjVqRL169ZgwYQLJycmBBIaomLOWWKhMD56n672zJJMmTeKZZ545I2U+V+yNehJ/93clrksBxv9wct55RekfVg9w8JR/05XPjS8rDV92KkZ2Gr6sVP8Xu5w06saF8dpzjxNiUYRaYPA1gziwZxtG7nHwBd/UNm/WlH8/efLLZotHnuTElpL/IYyuXYthzU++Z6/t/pK8X38rMdaIjaFV3MmMuiXvML7Mg6UeX3Vjzs8kLD+bsMObsVJAXhlJiBicRBcmC86POiNl04A4cwFx5gLqe3eRv+HzUmM7dfYwPNZ/7F/tXMeMxa+VGlv7imjaOGsCcIgMjNySv0yeU8rAyE3HyC3eqVUNU0NubuT/wnU0r4BHvy39dejfsi7vDewEQK7bS+TUT0uN7dakJh9d2zUwb3n541JjL6mfxL+HdA/MR/xrPnmekn/FjawVx7h6VwTmX/12ARn5JSem6iRG8d7wk79ON/znF6Rl5ZUY2zwmnC/uHBCYbzP3G/akl3zzHR7uZGLtwbgz8zi6ahuLdu0js5Qy2E0mOhkNAvPWg/vx5F0kPe+fZ5Q7l8uOLWRgzBYA3v9tB3d89hOYrP5km8XhTyAVJprfv7YLNzWviRmD+Rv3MPSTpaXuu1Hdy2lWp1Gp66srI+84Ydl7qEFdanCCpq7fmPRMf0xRtbE16E5Yl7ux1e+KvfGV2Bv7G2R0UT66sZ3hesnfCcRFQtOwN+3Ho/GPsdpxiX+ZHZTPQ8HWheSt+5i89QuCkqtnhFIUbF1IwdaFoOlYa7bjnlvHosc0JF0LY2ueif1uG5rViR5IoAfXWtSVgRkfyvDgcrlQhtf/41LRj1A+D8rnoW64nViHCbMyOJGby5bUoyeT0IUxRY8vrxVD05hQLPjYl5bOF9v3Bq0/9e81YVZ6hliwGG525uYy5dAx4NSROFSg1ulNMZFcFRmKhmJ3gZtn9qeiCtcVvR5Fj2+ICufGGP/N7gGXm4f2HjpZ60YZ/sn/5nF1TBS3xsdgaCbSvAYP7D9S+KOAKfCjS9EPMV0jwrkuJhKfppOtNF48lA6F6/wxeiC+eUgIfaIi8Gk6bqXxzrFMNM2/P3TTyceaTk27nU7h/v53fJqJLzNyQTcR4cogxFT+5i3Jycn8/PPPPPLII/Tt2xeXy0WdOnXo378/uq6jaRofffQR9957Ly1btqRJkya88cYb9OzZsxInYPndfPPNHD16lKeeeorU1FTatm3Lt99+G9TxYkpKCvoptRO7dOnC3LlzefLJJ3n88cdp1KgRCxYsCKoF//DDD5Obm8vo0aPJyMigW7dufPvtt0G1x0X5aUqdncF1Dx06RI0aNVixYkWgygn438AffviBVatWFdvGarXy/vvvc8sttwSWvfXWWzzzzDPF2sgUKanGQq1atcjMzDw/OwIxfCzbfII7lsZgNylsJoVVV9jNCqsOTosizKoRYoUQs8KquXEW1hw4ORk4LYoQi0ZUqJUQiyLWrjB7c4pqdBVzarMJgLy8PEp76zVNw+l0Vio2Pz8/0H6qJCGFHWhVNLagoACfz8f+3zcxuc/1xWItp9yel17h0s9MYXU4Tt+04HyIPV3zhorE1qxfkwdefwAo3hTCXcrN6R9jvT4frjKq7FotpkB7vorE+nwGBe7SkzEWswmrpeKxhmGQX0ZV4PLGquxMzGv/h62wSrRSqtQbegCzrpU71qRr2M0nqyrmnq5KdDljdY2gqssVic3zeEsdIU3T/L8GViTWnZnH0eXbTtsMoSJNFs5WbHmbQlQ01q5p6IWxbkPhLeNK9cdYLcJB/JVtSo49TVOI0mL/2BTij2wmE2ZTxWO9PgNXGbHWU5o3VCQ2qHlDCcpsCuEMCY79k00hSnO65g0pWhzL9JZs1muzS09mq1YzsK6DsZ3LfZvoZmymsToUtF2VN4VwFUhTiFOciaYQCthuqsVSazu+snVhr9lfk0hTBl18WxhQsIqerjVEqJJrYv2x2UR5P/d/9hrhQ8ONBTM+nCYNq0krNfZUlb1GFBzPIu2HDaXHahqWwtfBpxQFRunvm0XTsFYi1lCK/DMUa9bAVvj6KqXIO0OxJg3sp/w7l+vzv8chJp3oD+Zjadq81G2rSlZWFhEREefvPZuosPOnp7RKstls2GyV79SrKvSsb2FX/co22dCAU9tKnXphDqG8Tk0GnMnYU5MXZzK2KHPodDiCkgglMVegDoAJLejVvNBjrbpOiKP458ViNhfr3Kc0ZpMJs6N8pahIrMlUctn+bKxeyjFXNFZ5bHDKDb2maYRYy/eaVSQWOC9iT00cnMnYU7/4VIdY21mKteoa1nJeq6y6hsVkCrrxKTXWbMJazquExaQHvryfyVizSQ/cQJzJWJOuE2KtRKyug630107XNULKWF/ZWE0rHtuMDJpxsj3yHhXLbHrwEV34RW/ML3pjpnIdjdVhBvMrg/mV2hSvPVXeMpyRWJ8OqvhNoLMi15MKxDoqcD2pSKz9LMXazCZsZX3m1Mlfz61mE3v02nxl6chX5g7sNZ3seC5U5XGTezm3u7+njiocvskS+F+ZKvK5PzPXCAXo5YwtrkLXCF0npLzXCE0jxFS+62pFYvWzFKudpVig3K+ZEGfSWUssVKYHz9P13imEEEIIcaGoxzGe5VNGs5SFqhUraMRymrJdS+JVBvIqA2mj9jGYXxnEb8Rz/vcjJYJl4WCluQk/mVvwk7kl+/W4wDqbcnO5dxO9vesY6FlDSBl9KAkhxPnurCUWKtOD5+l67xRCCCGEuNDU5Dh/5Qf+yg9k4eA71YovaMcKGvO7VoffqcML6houYydX8ysDWEc4BVVdbFECHxob9Tr8ZGrBj+YW/KbXx6edrCFgVR56eDdylWcNV3p/J1TeRyHEBeKsNoU4XQ+eFe29UwghhBDiQhZOPkNYzRBWc5RQvlFt+YJ2rNXqs5LGrKQxz6jr6c/v3MhqLmMnepk9C4mzyYfGLj2J1XojfjY343+mpmRqwU1T6/pS6e7bxOXejVzm3SY1E4QQF6Szmlg4XQ+elem9UwghhBDiYhBHDsNYzjCWc0BF8wWXsIAO7NCSWMClLOBSaqp0bmA1N7CGmpQ+LKk4Mwqw8IupIStMTVmn12eDqS65WnAP8qEqjy6+rXT3buJy7yZqGUerqLRCCHHunPXOG8eOHVtq04dly5YVWzZkyBCGDBlylkslhBBCCFF91OQ497CEu1nC76oO8+jIF7TjgBbD6wzgdQbQWW1nCKvox3ocZQx5LMrPQGOzXovlpub8bGrGL6aGuDRrUIxTFdDGt5cuvi109W2hpbEPc9HYM2dn8DUhhDjvVPtRIYQQQgghLhYa0JZ9tGUfE1jAd6oV8+jECq0JKzV/U4lQlc8gfuNWVtCSA1Vd5GrngBbDz6ZmLDc1Z4W5KSe0sKD1CcYJuvq2cKlvB22MPTQyDmGS5ihCiIucJBaEEEIIIaohOx6u4Veu4VcOqig+pSOfcin7tVg+ogsf0YUOahcj+JG+bDj5K7oIkomTlaYmLDf7ayXs0xOC1oeqfDr5ttHNt4Wu3s00UKkVGNhaCCEuDpJYEEIIIYSo5mpwgnv5jrEsZI2qz7/pzNdcwi9aA36hATEqmyvZSB820pXt2C/yphJuTCwzteL/LL34n6kphnayzy+T8tHW2E037xa6+jbTxtiLBV8VllYIIc5/ZyWxsHfvXp577jm+//57UlNTSU5O5i9/+QtPPPEEVqu11O169uzJDz/8ELTsrrvuYtasWWejmEIIIYQQFxQdRSd20YldPMZ/+VB15d90IV0L4xM68wmdcSgXl7OVPmzkCjYRRV5VF/ucyMLBD+aWLDK1ZZm5JTmaM7CuoXGIbt7NdPVtoaNvO2EyDKQQQlTIWUksbN26FcMw+Mc//kHDhg3ZuHEjo0aNIjc3lylTppS57ahRo3j22WcD806ns4xoIYQQQghRkgSyGM83/J3vWK0asIhWLKIVh7UoFtKGhbRBVwZNOEwrUujJFrqzFSfuqi76GXNIi2KxuQ2LTW1ZZWqCRzv51TfOyOBG7wpu8fxITZVehaUUQojq76wkFvr370///v0D8/Xr12fbtm3MnDnztIkFp9NJYmLi2SiWEEIIIcRFx4JBV3bQlR1M5D9sVjVYSCsW04otWg224J8+oTPhKo9H+S83sQq9GnZIqICtek0Wmdqy2NyWjaY6QesbGofo7V1HH+/vtDH2VMtjFEKI89E562MhMzOT6Ojo08Z9+OGH/Otf/yIxMZHBgwczYcKEMmstuFwuXC5XYD4rK+uMlFcIIYQQ4kKjAS04SAsOcj/fclhFsIHarKYBC2nFAS2GxxnKLNWbK9jEldp6LmUT1vO4j4HDWhQrTE1ZYWrGClNT0vSowDpNGbQzdtHHu47e3t+pr9KqsKRCCHHhOieJhZ07dzJ9+vTT1la49dZbqVOnDsnJyaxfv55HHnmEbdu28Z///KfUbSZNmsQzzzxzposshBBCCHHBSyKTJDbQlw08yn95X13ONAaQosUyhx7MMfUgKiSb0e7vuN2ztMqbSSggTYtknV7Pn0gwN2W3nhQUY1NuLvdtprd3HVf41hOrsqumsEIIcRGpUGLh0Ucf5eWXXy4zZsuWLTRt2jQwf/DgQfr378+QIUMYNWpUmduOHj068LhVq1YkJSVx5ZVXsmvXLho0aFDiNo899hjjx48PzGdlZVGrVq3yHI4QQgghhChkxuAOfmAoK1mumvA9Lfie5qRr4bxsu5Gp1mu41LeDbr7NdPdtpKlx8IwOu+hDI4MQoslBw59EOKDFslGvzSZTbTbptdmo1yFdDw/aTlcGrYy9dPFtpYtvC+19uy76US+EEOJcq1Bi4YEHHmDEiBFlxtSvXz/w+NChQ/Tq1YsuXbrw9ttvV7hwnTp1Avw1HkpLLNhsNmw2W4X3LYQQQgghigvBTT820I8NeH2Kz71tmGEdxD49np/NzfnZ3JyXuZGGxiH6en+jjW8PCSqDMJVPuMojklxMZfRdoIAsnGRqTux42Kkn8YOpJf81dyRNjyLGyCKSXNK0SHI0R7HtTcpHQ+MwnXzb6eLbwmW+bYSTfxZfESGEEKdTocRCXFwccXFx5Yo9ePAgvXr1on379syePRtd10+/0R+sW7cOgKSkpLIDhRBCCCHEGWfG4AbvSq73rmSXlshyc3N+MrXgZ1MzdurJ7LQmF9smVOXRw7uJmupYYJmGwkBnq16TdaZ6ZGkhpT5nuh5OOv5aCRblpYlxkBbGPlr4Umhl7KOJcVBqJAghRCl+/PFHJk+ezNq1azl8+DCfffYZ1157bZnbLFu2jPHjx7Np0yZq1arFk08+edoKBX90VvpYOHjwID179qROnTpMmTKFo0ePBtYVjfhw8OBBrrzySv7v//6Pjh07smvXLubOnctVV11FTEwM69ev5/7776d79+60bt36bBRTCCGEEEKUgwY0VKk09KQywvM9WTj4znwJa0yN2KzX5oQWSrbmIEdzkKM5+cpy6Wn36VAu8jUbcUYGXXxb6e/9lS6+LezWE8nV7MQbGdRWR8/rjiOFEOJ8k5ubS5s2bfjrX//K9ddff9r4PXv2MHDgQO6++24+/PBDlixZwp133klSUhL9+vUr9/OelcTCokWL2LlzJzt37qRmzZpB65TyV43zeDxs27aNvLw8AKxWK4sXL2batGnk5uZSq1YtbrjhBp588skKPXfR/s/b0SEMHxTkVHUpqq3s3FzcMjRUpRUYPrJypbpoZai8fHDJL2SV5XZ7yPbJzUFlWbw+HAVV22letaXrYDpng2BdeHwGlHjuuenHMvqxLGipF52Nem1WmpqRrdmLbVVDpdPOt4u6xlEchf+in9pPgwLqcfI7XEHhVG0p5Z9EpXhd8m/Hn2XOycFyHt4XFd2rqYv881FQUIDbffp/35VSaFpwrzaldQkwYMAABgwYUO4yzJo1i3r16vHqq68C0KxZM5YvX87UqVMrlFjQ1AX2bh44cEA6bxRCCCGEEEKI89z+/fuL/RB9sSgoKCDG4SSvHD+ahoaGkpMT/OP0xIkTefrpp8vcTtO00zaF6N69O+3atWPatGmBZbNnz2bcuHFkZmaetmxFLrgUfnJyMvv37ycsLKxYVud8UDRqxf79+wkPDz/9BkJUATlPxflOzlFRHch5KqoDOU9FVVBKkZ2dTXJy8X5aLhZut5s8FLcTgrWMMXbcKD7IySn2GT1TAxikpqaSkJAQtCwhIYGsrCzy8/NxOIp3oluSCy6xoOt6tch6hYeHy8VbnPfkPBXnOzlHRXUg56moDuQ8FedaREREVRfhvGBHx1rGD+J6YQOD8/0zesElFoQQQgghhBBCiOpAL5zKWn82JSYmkpaWFrQsLS2N8PDwctdWgLNfTiGEEEIIIYQQQpRA00AvYzrbrfs7d+7MkiVLgpYtWrSIzp07V2g/klg4x2w2GxMnTjxjbWKEOBvkPBXnOzlHRXUg56moDuQ8FaJq6eWYKiInJ4d169axbt06wD+c5Lp160hJSQHgscceY9iwYYH4u+++m927d/Pwww+zdetW3nrrLT755BPuv//+Cj3vBTcqhBBCCCGEEEIIcT7LysoiIiKCsXo4tjKqJbiUYoaRRWZmZrn6WFi2bBm9evUqtnz48OHMmTOHESNGsHfvXpYtWxa0zf3338/mzZupWbMmEyZMYMSIERU6HkksCCGEEEIIIYQQ51BRYuHeciQW3qhAYqGqSOeNQgghhBBCCCFEFSjqS6HU9eeuKH+KJBaEEEIIIYQQQogqUNWjQpwpklgQQgghhBBCCCGqgKZpaGU0hTjLg0KcMZJYEEIIIYQQQgghqsCFUmOhupTzgvHmm29St25d7HY7nTp1YvXq1VVdJHGR+PHHHxk8eDDJyclomsaCBQuC1iuleOqpp0hKSsLhcNC7d2927NgRFHP8+HFuu+02wsPDiYyM5I477iAnJ+ccHoW4kE2aNIlLL72UsLAw4uPjufbaa9m2bVtQTEFBAWPGjCEmJobQ0FBuuOEG0tLSgmJSUlIYOHAgTqeT+Ph4HnroIbxe77k8FHEBmzlzJq1btyY8PJzw8HA6d+7MN998E1gv56g437z00ktomsa4ceMCy+Q8FeL8UdTHQllTdSCJhXPo448/Zvz48UycOJFff/2VNm3a0K9fP44cOVLVRRMXgdzcXNq0acObb75Z4vpXXnmFN954g1mzZrFq1SpCQkLo168fBQUFgZjbbruNTZs2sWjRIr788kt+/PFHRo8efa4OQVzgfvjhB8aMGcP//vc/Fi1ahMfjoW/fvuTm5gZi7r//fr744gvmzZvHDz/8wKFDh7j++usD630+HwMHDsTtdrNixQref/995syZw1NPPVUVhyQuQDVr1uSll15i7dq1/PLLL1xxxRVcc801bNq0CZBzVJxf1qxZwz/+8Q9at24dtFzOUyHOHxonay2UNFWTvAIocc507NhRjRkzJjDv8/lUcnKymjRpUhWWSlyMAPXZZ58F5g3DUImJiWry5MmBZRkZGcpms6l///vfSimlNm/erAC1Zs2aQMw333yjNE1TBw8ePGdlFxePI0eOKED98MMPSin/OWmxWNS8efMCMVu2bFGAWrlypVJKqa+//lrpuq5SU1MDMTNnzlTh4eHK5XKd2wMQF42oqCj1z3/+U85RcV7Jzs5WjRo1UosWLVI9evRQ9913n1JKrqVCnC8yMzMVoB63RqpnbVGlTo9bIxWgMjMzq7rIZZIaC+eI2+1m7dq19O7dO7BM13V69+7NypUrq7BkQsCePXtITU0NOj8jIiLo1KlT4PxcuXIlkZGRdOjQIRDTu3dvdF1n1apV57zM4sKXmZkJQHR0NABr167F4/EEnadNmzaldu3aQedpq1atSEhICMT069ePrKyswC/KQpwpPp+Pjz76iNzcXDp37iznqDivjBkzhoEDBwadjyDXUiHON2XVVjhd/wvnE+m88Rw5duwYPp8v6AINkJCQwNatW6uoVEL4paamApR4fhatS01NJT4+Pmi92WwmOjo6ECPEmWIYBuPGjaNr1660bNkS8J+DVquVyMjIoNg/nqclncdF64Q4EzZs2EDnzp0pKCggNDSUzz77jObNm7Nu3To5R8V54aOPPuLXX39lzZo1xdbJtVSI88vp+lGQxIIQQghRSWPGjGHjxo0sX768qosiRDFNmjRh3bp1ZGZmMn/+fIYPH84PP/xQ1cUSAoD9+/dz3333sWjRIux2e1UXRwhxGjIqhKiQ2NhYTCZTsR5309LSSExMrKJSCeFXdA6WdX4mJiYW62jU6/Vy/PhxOYfFGTV27Fi+/PJLli5dSs2aNQPLExMTcbvdZGRkBMX/8Twt6TwuWifEmWC1WmnYsCHt27dn0qRJtGnThtdff13OUXFeWLt2LUeOHKFdu3aYzWbMZjM//PADb7zxBmazmYSEBDlPhTiP6GinnaoDSSycI1arlfbt27NkyZLAMsMwWLJkCZ07d67CkgkB9erVIzExMej8zMrKYtWqVYHzs3PnzmRkZLB27dpAzPfff49hGHTq1Omcl1lceJRSjB07ls8++4zvv/+eevXqBa1v3749Fosl6Dzdtm0bKSkpQefphg0bgpJgixYtIjw8nObNm5+bAxEXHcMwcLlcco6K88KVV17Jhg0bWLduXWDq0KEDt912W+CxnKdCnD8ulOEmpSnEOTR+/HiGDx9Ohw4d6NixI9OmTSM3N5eRI0dWddHERSAnJ4edO3cG5vfs2cO6deuIjo6mdu3ajBs3jueff55GjRpRr149JkyYQHJyMtdeey0AzZo1o3///owaNYpZs2bh8XgYO3YsQ4cOJTk5uYqOSlxIxowZw9y5c/n8888JCwsLtOONiIjA4XAQERHBHXfcwfjx44mOjiY8PJy///3vdO7cmcsuuwyAvn370rx5c26//XZeeeUVUlNTefLJJxkzZgw2m60qD09cIB577DEGDBhA7dq1yc7OZu7cuSxbtozvvvtOzlFxXggLCwv0TVMkJCSEmJiYwHI5T4U4f1woTSFkuMlzbPr06ap27drKarWqjh07qv/9739VXSRxkVi6dKkCik3Dhw9XSvmHnJwwYYJKSEhQNptNXXnllWrbtm1B+0hPT1e33HKLCg0NVeHh4WrkyJEqOzu7Co5GXIhKOj8BNXv27EBMfn6++tvf/qaioqKU0+lU1113nTp8+HDQfvbu3asGDBigHA6Hio2NVQ888IDyeDzn+GjEheqvf/2rqlOnjrJarSouLk5deeWVauHChYH1co6K89Gpw00qJeepEOeDouEmX3ZEqTecMaVOLzuiqsVwk5pSSlVJRkMIIYQQQgghhLgIZWVlERERwWRnFA6t9HoJ+crgobwTZGZmEh4efg5LWDHSFEIIIYQQQgghhKgCF0pTCEksCCGEEEIIIYQQVeB0HTRKYkEIIYQQQgghhBClOt2QktVluElJLAghhBBCCCGEEFVAaiwIIYQQQgghhBCi0rTCqaz11YEkFoQQQgghhBBCiCogNRaEEEIIIYQQQghRadLHghBCCCGEEEIIISpNaiwIIYQQQgghhBCi0jTKTh5Uj/oKklgQQgghhBBCCCGqhHTeKIQQQgghhBBCiErTNQ1dkz4WhBBCCCGEEEIIUQlSY0EIIYQQQgghhBCVJokFIYQQQgghhBBCVJokFoQQQgghhBBCCFFpmqahldHHglZNUguSWBBCCCGEEEIIIaqA1FgQQgghhBBCCCFEpemFU1nrq4PqUk4hhBDivFK3bl1GjBhR1cUQQgghRDWmaaefqgNJLAghhBCn2LVrF3fddRf169fHbrcTHh5O165def3118nPz6/q4lXKf/7zH26++Wbq16+P0+mkSZMmPPDAA2RkZJR7H1u2bKF///6EhoYSHR3N7bffztGjR89eoYUQQoiLgFaO/6oDaQohhBBCFPrqq68YMmQINpuNYcOG0bJlS9xuN8uXL+ehhx5i06ZNvP3221VdzAobPXo0ycnJ/OUvf6F27dps2LCBGTNm8PXXX/Prr7/icDjK3P7AgQN0796diIgIXnzxRXJycpgyZQobNmxg9erVWK3Wc3QkQgghxIVF+lgQQgghLiB79uxh6NCh1KlTh++//56kpKTAujFjxrBz506++uqrKixh5c2fP5+ePXsGLWvfvj3Dhw/nww8/5M477yxz+xdffJHc3FzWrl1L7dq1AejYsSN9+vRhzpw5jB49+mwVXQghhLigXSiJBWkKIYQQQgCvvPIKOTk5vPvuu0FJhSINGzbkvvvuK3X748eP8+CDD9KqVStCQ0MJDw9nwIAB/P7778Vip0+fTosWLXA6nURFRdGhQwfmzp0bWJ+dnc24ceOoW7cuNpuN+Ph4+vTpw6+//hqIycvLY+vWrRw7duy0x/bHpALAddddB/ibOJzOp59+yqBBgwJJBYDevXvTuHFjPvnkk9NuL4QQQoiS6YCulTFVdQHLqbqUUwghhDirvvjiC+rXr0+XLl0qtf3u3btZsGABgwYN4rXXXuOhhx5iw4YN9OjRg0OHDgXi3nnnHe69916aN2/OtGnTeOaZZ2jbti2rVq0KxNx9993MnDmTG264gbfeeosHH3wQh8MRlARYvXo1zZo1Y8aMGZUqb2pqKgCxsbFlxh08eJAjR47QoUOHYus6duzIb7/9VqnnF0IIIYT0sSCEEEJcMLKysjh48CDXXHNNpffRqlUrtm/fjq6fzNnffvvtNG3alHfffZcJEyYA/n4cWrRowbx580rd11dffcWoUaN49dVXA8sefvjhSpetJC+//DImk4kbb7yxzLjDhw8DlFiLIykpiePHj+NyubDZbGe0fEIIIcTFonqkDsomNRaEEEJc9LKysgAICwur9D5sNlsgqeDz+UhPTyc0NJQmTZoENWGIjIzkwIEDrFmzptR9RUZGsmrVqqCaDn/Us2dPlFI8/fTTFS7r3Llzeffdd3nggQdo1KhRmbFFI2GUlDiw2+1BMUIIIYSomLM13OSbb75J3bp1sdvtdOrUidWrV5cZP23aNJo0aYLD4aBWrVrcf//9FBQUlPv5JLEghBDiohceHg74+zaoLMMwmDp1Ko0aNcJmsxEbG0tcXBzr168nMzMzEPfII48QGhpKx44dadSoEWPGjOHnn38O2tcrr7zCxo0bqVWrFh07duTpp59m9+7dlS7bqX766SfuuOMO+vXrxwsvvHDa+KIRI1wuV7F1RV84TjeqhBBCCCFKppVjqqiPP/6Y8ePHM3HiRH799VfatGlDv379OHLkSInxc+fO5dFHH2XixIls2bKFd999l48//pjHH3+83M8piQUhhBAXvfDwcJKTk9m4cWOl9/Hiiy8yfvx4unfvzr/+9S++++47Fi1aRIsWLTAMIxDXrFkztm3bxkcffUS3bt349NNP6datGxMnTgzE3HTTTezevZvp06eTnJzM5MmTadGiBd98882fOs7ff/+dq6++mpYtWzJ//nzM5tO3iCxqAlHUJOJUhw8fJjo6WppBCCGEEJWko512An/tylOnkhL+RV577TVGjRrFyJEjad68ObNmzcLpdPLee++VGL9ixQq6du3KrbfeSt26denbty+33HLLaWs5BB+HEEIIIRg0aBC7du1i5cqVldp+/vz59OrVi3fffZehQ4fSt29fevfuTUZGRrHYkJAQbr75ZmbPnk1KSgoDBw7khRdeCKpymJSUxN/+9jcWLFjAnj17iImJKVcNg9Ls2rWL/v37Ex8fz9dff01oaGi5tqtRowZxcXH88ssvxdatXr2atm3bVrpMQgghxMWuvDUWatWqRURERGCaNGlSiftzu92sXbuW3r17B5bpuk7v3r1L/Y7TpUsX1q5dG0gk7N69m6+//pqrrrqq3MchiQUhhBACf+eIISEh3HnnnaSlpRVbv2vXLl5//fVStzeZTCilgpbNmzePgwcPBi1LT08PmrdarTRv3hylFB6PB5/PF9R0AiA+Pp7k5OSgXycqMtxkamoqffv2Rdd1vvvuO+Li4kqN3bVrF7t27QpadsMNN/Dll1+yf//+wLIlS5awfft2hgwZctrnF0IIIUTJytvHwv79+8nMzAxMjz32WIn7O3bsGD6fj4SEhKDlCQkJgRGh/ujWW2/l2WefpVu3blgsFho0aEDPnj0r1BRCRoUQQgghgAYNGjB37lxuvvlmmjVrxrBhw2jZsiVut5sVK1Ywb948RowYUer2gwYN4tlnn2XkyJF06dKFDRs28OGHH1K/fv2guL59+5KYmEjXrl1JSEhgy5YtzJgxg4EDBxIWFkZGRgY1a9bkxhtvpE2bNoSGhrJ48WLWrFkTNErE6tWr6dWrFxMnTjxtB479+/dn9+7dPPzwwyxfvpzly5cH1iUkJNCnT5/A/JVXXgnA3r17A8sef/xx5s2bR69evbjvvvvIyclh8uTJtGrVipEjR5bj1RVCCCFESU7Xj0LRuvDw8ECfUGfasmXLePHFF3nrrbfo1KkTO3fu5L777uO5554LjGp1OpJYEEIIIQpdffXVrF+/nsmTJ/P5558zc+ZMbDYbrVu35tVXX2XUqFGlbvv444+Tm5vL3Llz+fjjj2nXrh1fffUVjz76aFDcXXfdxYcffshrr71GTk4ONWvW5N577+XJJ58EwOl08re//Y2FCxfyn//8B8MwaNiwIW+99Rb33HNPpY7r999/B/ydQv5Rjx49ghILJalVqxY//PAD48eP59FHH8VqtTJw4EBeffVV6V9BCCGE+BO0wv/KWl8RsbGxmEymYrUv09LSSExMLHGbCRMmcPvtt3PnnXcC/iG0c3NzGT16NE888UTQUNqlllP9sd6mEEIIIYQQQgghzpqsrCwiIiL4OjaJkDJu3HMNg6uOHSYzM7PcNRY6depEx44dmT59OuAfuap27dqMHTu22A8eAO3bt6d37968/PLLgWX//ve/ueOOO8jOzsZkMp32OaXGghBCCCGEEEIIUQXK2xSiIsaPH8/w4cPp0KEDHTt2ZNq0aeTm5gaaLw4bNowaNWoEOoAcPHgwr732GpdcckmgKcSECRMYPHhwuZIKIIkFIYQQQgghhBCiSpyNxMLNN9/M0aNHeeqpp0hNTaVt27Z8++23gQ4dU1JSgpo3PPnkk2iaxpNPPsnBgweJi4tj8ODBFRqNSppCCCGEEEIIIYQQ51BRU4jvYpNP2xSi37FDFWoKURWkxoIQQgghhBBCCFEFTh1SsrT11YEkFoQQQgghhBBCiCqgF05lra8OzmpiYdKkSfznP/9h69atOBwOunTpwssvv0yTJk1K3WbOnDnFxsS22WwUFBSU6zkNw+DQoUOEhYWhVZf0jhBCCCGEEEJcJJRSZGdnk5ycXK6hDC9kZ6OPhapwVhMLP/zwA2PGjOHSSy/F6/Xy+OOP07dvXzZv3kxISEip24WHh7Nt27bAfEUSBIcOHaJWrVp/qtxCCCGEEEIIIc6u/fv3U7NmzaouRtXStLLvd6vJj+VnNbHw7bffBs3PmTOH+Ph41q5dS/fu3UvdTtM0EhMTK/WcYWFhgP8kPR87tzj8+wbevWJgVRej2oq2mRhQM7Kqi1FtGQa4XN6qLka1dNTl5bO0rKouRrWV6LRwV8vKXdcFmOo3JHzqm1VdjGpJeTyQmVbVxag2DAV7cmx8khLFfw9Eku0t3zBjp7JqPnpFpxNi9nLMYyVM9xFh8RBp8RBp8RJrcdE16jhOk3EWjuD8ojweOHGkqotRfWka2GxVXYpqTW/dCy0suqqLUUxWVha1atUK3LtdzKTGQiVkZmYCEB1d9smdk5NDnTp1MAyDdu3a8eKLL9KiRYsSY10uFy6XKzCfnZ0N+Gs9nI+JhZzQUGzVJOt0PrJrOmHlHEtVFGdoCstFXt2ssvJ0HWu1ubSff+yaTrhZPruVZbJazst/06oD5XGDkVvVxTiv5Xs1vjgYyecHIvn1uJN8X+G/E2aIcnhp4MjhqNtGqMlL45AcEm0ubLrBcY8VDUWs1U2s1U2O18x/0hLZlhvFotyoMp8z6XABj9bfycD4NEwX8KVVedzgdlR1MaovTQObvapLUa3p4eFoYefvvx/SdB10TUMv43Uoa9355JwlFgzDYNy4cXTt2pWWLVuWGtekSRPee+89WrduTWZmJlOmTKFLly5s2rSpxGoykyZN4plnnjmbRRdCCCGEuOD4FHyaEsWrWxJIK7AEllt1g25xOYxskE7nqBOYck+Ue5+jaqWwKSeMb47GY9YMEmwucn1mMjwWThRO67PDOORycN+Wlry8uwH1nXkcdVu5Ku4od9fei1WXkdCFEBcPXfNPZa2vDs5ZYmHMmDFs3LiR5cuXlxnXuXNnOnfuHJjv0qULzZo14x//+AfPPfdcsfjHHnuM8ePHB+aLqtUIIYQQQojiDAXfHgrnta0J7Mz2/xpcw+Hm1rrH6ZuURb1QF+bCSguqgq3nNA1ahmXTMiy71JgCn87b+2sz+0BtDrkcHHL5f9HflhvGd8fimNViPbUc5eu0WwghqjtN19DKyB5o1aTG7DlJLIwdO5Yvv/ySH3/8scKdc1gsFi655BJ27txZ4nqbzYZN2l4JIYQQQpRJKVh2JJRXNyeyMdN/Mx9h8TKm8VGG1U/Hbjo3NQXsJoN76+5ldK0UFqfHkusz4VMak3c3ZHNOGDf+1oEpTTcTYfZQ35lHqNl3TsolhBBVQdPK7p+xmrSEOLuJBaUUf//73/nss89YtmwZ9erVq/A+fD4fGzZs4KqrrjoLJRRCCCGEuLAVJRTe2h7PmnT/qFwhZh93NDjGnQ2PEW6pmk4U7SaDQfEnOza8IiadEevbsi03lGHrLwHAqhl0ijzBFTHH6Bt7lGS7q7TdCSFEtSSJhXIYM2YMc+fO5fPPPycsLIzU1FQAIiIicDj8mfJhw4ZRo0YNJk2aBMCzzz7LZZddRsOGDcnIyGDy5Mns27ePO++882wWVQghhBAXgaKOCr84EMGWLDteQ+OG2hl0is0hx2MixuYl0eFBAxT+3rhrOd04zNWv3b/b0PjiQATv7Ixla5b/e5dNNxhWP527Gx0lxnZ+1QRItLn4uO1aHtnWjN+yIjAUHPPY+OlEDD+diOGFXY0YXuMA99bZQ7hFRjgSQlwYtNMMN1ldOrg8q4mFmTNnAtCzZ8+g5bNnz2bEiBEApKSkoJ/SS/2JEycYNWoUqampREVF0b59e1asWEHz5s3PZlGFEEIIcYE5UaCxcLeNlftqcjDfysE8C6kFFnwq+Evau7tieXdXbKn7sWgGTSMKCDEbhJoNajrdRFp9OE0GDrNBg1AXnWNzz4tfldyGxv+OhfDtoXC+PhhBhsf/VS/E7GNonROManiURMf5e1MeYfEyq+UGwF/TYneekyXpsSw8FsfarEjePVCbz9ISub/ubq6MOUaCzVVtOjYTQoiSSI2FclDq9Nn9ZcuWBc1PnTqVqVOnnqUSCSGEEOJClOmCnw5Y+PWIiaP5GluPm9hxQkeV0OlVLaebW+um0zkulxMuM//aE80xt5kws4+jLgtHCvxfjzTAqzSyPCY2ZDjLfP57m6Rxf9MjuAwNj6Fh0dU56bOgwKexLcvOpkw7Px8N5Ye0MHK8J4d2jbd7GFE/ndvqphNhrZomD5WladAgJI8GISmMrp3CD8ejeX5nY3bmhTBhR1Mm7IAIs4c+sUe5Nekgl0RkVXWRhRBnyZtvvsnkyZNJTU2lTZs2TJ8+nY4dO5a5zbx585gwYQJ79+6lUaNGvPzyy0HN65VSTJw4kXfeeYeMjAy6du3KzJkzadSo0dk+nCBSY0EIIYQQ4hwyFKTm+pMGi/aZ2ZxuwlBwNF/ncK5e4jZNo7z0jU+nUZiLZKeHGg43CXZv0C9AvRJLH8FAKTiQZ2FTpgOPoZHpMXEwz0KWx0S+T+eE28TStHDe2JbAf/ZHcSDPGti2hsNNnN2LBjSLyKdbXA71C0dc2J9nwaYrkh0e6oS4y/xFymNAtsfEwXwL+3JtpORa2ZFlY1Omg105tmI1MOJsHnonZjOwRgad43IxVY/vpKfVI/o4XTqs4l+HavCvgzXZl+8g02thfmoyn6Ym8deaKYyvtxunqXolUIQQZfv4448ZP348s2bNolOnTkybNo1+/fqxbds24uPjS9xmxYoV3HLLLUyaNIlBgwYxd+5crr32Wn799VdatmwJwCuvvMIbb7zB+++/T7169ZgwYQL9+vVj8+bN2O32c3Z8F0qNBU2Vp1pBNZKVlUVERASZmZmEh4dXdXGKOfTb78zs1LOqi1FtxdjMXF07qqqLUW0ZhqKg4PytAns+O+Ly8snhzKouRrWVHGLl722SqroY1ZapYWPCZ75X1cWoEoaCpfvN/HeXhe9TLGS5S/+G1TDSR7caXmqFGdQOM2iX4CPG7IKM1LNaxn/ujOH5jcmV3j7J4aZ1ZD75Pp1cr3/K8ZrI8+rkeHXcRslJkyLRVi8tIvJpE5XPlYlZtInKP2PNA5TXAzknzszOzjCPobE2K4KPDyezIM1/fQk3e7g+IZVLwjPpFHmCBJu7SsuoPG44nlalZajWNA1s5+4G70JzxX3P0KpjV8yOEN5//32sVivPP/88t956K2PHjmX+/PkkJCQwffp0BgwYAMDGjRt56KGH+OmnnwgJCaFv375MnTqV2Fh/c7Fvv/2W559/no0bN2IymejcuTOvv/46DRo0AGDv3r3Uq1ePTz/9lOnTp7Nq1SoaNWrErFmz6Ny5c6BsFbln69SpE5deeikzZswAwDAMatWqxd///nceffTREre5+eabyc3N5csvvwwsu+yyy2jbti2zZs1CKUVycjIPPPAADz74IACZmZkkJCQwZ84chg4dWslXvfyKXoM19eoSqpd+nc8xDC7ds/e8vb8tIjUWhBBCCHHe2ZOp899dFj7dYWFf1smq/RZdUTPUoGsNL11reLGZINKmaBBhEGkv/luJ8pz9st7ZMJ3mEQXk+3RaR+YTZvGR69XZmW0jy2PCbWisSQ/hl/QQ9udZ8CqNWk43HkNjf56Vw/n+6XRibf7aDbWdbuqFumkRmU+LiPxiNTAuFhZdcVlkBpdFZjAw7gjP7WxESoGTOQdrMedgLWy6jwfr7WJkzf0XTK0NISrq//79EQ8//AirV6/m448/5p577uGzzz7juuuu4/HHH2fq1KncfvvtpKSk4Ha7ueKKK7jzzjuZOnUq+fn5PPLII9x00018//33AOTm5jJ+/Hhat25NTk4OTz31FNdddx3r1q0L6jfviSeeYMqUKTRq1IgnnniCW265hZ07d2I2m0lJSQn0n5ecXHJS9vHHH+fxxx/H7Xazdu1aHnvsscA6Xdfp3bs3K1euLPW4V65cyfjx44OW9evXjwULFgCwZ88eUlNT6d27d2B9REQEnTp1YuXKlecksVDkQqmxIIkFIYQQQlS57cd1lh8yczhXZ+UhExuOnfyKEm5VDGnsZkA9D5fE+zCX/QN+legSlxs0bzf5iLHlBeYH1ii5/X+BT2PlsRD251pxFnYO6TQbhR1F+gIdRoaYfVjOw+M+X/SOPUavmGMsS4/h+/RYfsuKYEtuGC/sasyslLpcEXOMK2OO0S3qOCHm82s0DCHOpjYtW/Lkk08C8Nhjj/HSSy8RGxvLqFGjAHjqqaeYOXMm69evZ/HixVxyySW8+OKLge3fe+89atWqxfbt22ncuDE33HBD0P7fe+894uLi2Lx5c6CJAcCDDz7IwIEDAXjmmWdo0aIFO3fupGnTpiQnJ/PTTz/Rrl07fvrpJ8LCwoqVOzo6GoBjx47h8/lISEgIWp+QkMDWrVtLPe7U1NQStykapbDob1kx54rGafpYKKGvoPPRWU8sVLSjjdN1siGEEEL8WcdtUewJq8+B0JpkWiNwmWxoKDSlsBoe6mTvoXbOfiJdJwh3Z6FzQbUaPG+kZGl8vcfCgp1Wthw3Ba0zaYpuNbxc3cDDVfU8OC1VVMizzG5S9ErIqepiXBBMGlwZm86VsekoBR8fTual3Q1J91iZl5rMvNRkbLqPy6OOc33iYfrGHpWaDOKC16rFyZH1TCYTMTExtGrVKrCs6Mb6yJEj/P777yxdupTQ0NBi+9m1axeNGzdmx44dPPXUU6xatYpjx45hGP4+TVJSUoISC61btw48TkpKCjxH06ZNMZvNgaYTDRo0OK+r958Lmu6fSl1fTb6CnNXEQkU72ihPJxviwmJoOm5HBB5HBG57BG5H4XTKY489HLc9HJ/FjsVk5pcQGwB2Tx4hnhyc7hxC3Dk4PLmEeHIIdWURWXCcqPx0IvPTcXjzq/gohRBVwauZSHUmkRJam93h9dkTXj/wN8NW/r5aNGUQ6TpBq+MbaJW+nsS8VOLzjxCff4S4/COEenNPv5OzSClw+SDDpXEoR+dQjs7BXP9jjw8ur+mlfYIPu1nh8WnkeyHPq2HVITHEwH4O6i56DdiXpbP9hM72E/7RGn4/aiIlO7iJQ7caXupHGDSM9NGvrpcYRzX5NiXOO5oGQ5MPcX3iYdZkRrLkWCxL0mNJKXCyOD2Oxelx1LLn82LjLXSLPj/7kBDiTLBYgrOymqYFLSv6pdwwDHJychg8eDAvv/xysf0UJQcGDx5MnTp1eOedd0hOTsYwDFq2bInbHdyfSWnPAVSoKURsbCwmk4m0tOC+StLS0khMTCz1uBMTE8vcpuhvWlpa4NiK5tu2bVvqfs+K04wKUV3aQpzVrxOvvfYao0aNYuTIkQDMmjWLr776ivfee6/EjjZef/11+vfvz0MPPQTAc889x6JFi5gxYwazZs06m0U9p9xl9JepA+ZTTp6yYjXAUslYj1Kl/v52ulgF/pt9ZxRuZxRGSDReix2f2Y7HZMVrtmGYbRhmKz6zHZ/ZhtcWiiskGq8zGldIFC5nNAXOaDzOyFLLXJqdFYy3efKIzE8nIj+diILjRBdkEFFwgoiCEzgLThCaf5zwghPYvfmYDQ8mw4vZ8GIyPNg0HcNkwaNbyNdMuHQLhqajodCVAco/kJnZ5ybKm0uYJxeHJxef4cNbxvth1zT0wtfYrVSxWAW4zHbyLKEoawhui5MCs4Mcs51ck4MCiwOX2UmBxUGB2YnbbMOjW8BkwdDNmJQPzedF97kxGx7Mhhezz4PJ50a5XJh9HuyGB6c3n7CCTOz5J7AXZBBakIHVW1CswpVV0wLnpVepMs81i6YFzp+KxPqUIl9Bvi2MXFs4ubYIcuwR5NojyLWF47aG4raGkm8NIc/iJNfixGOy+Y/dZMNjPvWxHbfZjsdsw2OyYva5sXnysLvzsHnzsHvyC+dzCXNnE+rOJtSVhbMgC4srixBXFiEFWTjdWTgLH+cZbnwoTIWvjkJRVjeYOpQ7VgPMp7zqnjJ+HT8fYgEslYzN9xmU1V97iEkvd6zTpFNgsnPEkcCukJqkhNbmQGhtDoTV5mBobQ6F1sCnl/wzt6YMEnMPUSt7H5Gu41h9LkBDaRoes5Pd4Q1IdSaSYw1DaTon7DH8mNyTH5N7FtuX3ZtHmCcHp8ef5AzxZBPpyqDVsd9onr6BqIJ0rIabcOUh3JuDBrgNA08JL5sCsi3hYHXiNVnx6BZSbTGkRHYg538axwtMpLt0jubrHMo1keXWKfBpGKr0Lx0fbrWV8SpCpM1HotMgzu4jMcRHw3AvHRJctE00YTb7vyZ4PJ5iXxpPZbPZMJvNeA1Iz/ORk+9mb5aZ9elWfjpkY326FY9RvIwmTdE+zs3Auvn0qZVPpO2UF8UAj8ca+HLq8/koKCgotQwWiwWr1VosVnnckJcXHGu2YLX692sYBvll7bcCsWaTGZvNXwalFHn5pSe2KxJr0k3Y7Sffx9w/HE9lY3Vdx3FKz+clxSqvF/Lz0TUdxyn7zcsvoLRvEhoaToe9UrH5BS4MVfonP8ThqFCsVVd0jTpBe2ca42v42JEfxtfpycw/Uof9BQ6Gr7+EO5J30jqygGYhOTQIyaPA5cZnlN5cwmm3B24CXG43Xl/psQ6THrgCutwevEbp5XVYLYE26m6PF09Z+61ArN1iwWSqeKzH68XtLT3WZjFjNpkqHOv1+XB5Sv9X0Wo2YSm89nh9Plz5pX/mrBZzINbnMygo4zplMZuxWioeaxgG+a4zE2s2mbAVXk+UUuQVuM5IrEnXsdtO9tOSW8ZrVpZ27drx6aefUrdu3cD1/1Tp6els27aNd955h8svvxyA5cuXV/h5KtIUwmq10r59e5YsWcK1114L+F/nJUuWMHbs2FKfo3PnzixZsoRx48YFli1atCjQgWS9evVITExkyZIlgURCVlYWq1at4p577qnwMf0Z0sfCaVSmo43TdbJREpfLhct18oOWlXX+j2H8irf0nuUbamaGmk9WP5rqzaS0fqdqayaGmU9+EGd4s8gr5R/uJM3EHafEzvJmkYlCs4agO6PRQ2LQndGYQmIId8bSMSwZtzMSV0g02+xh+JzR6CHR6E5/nGY6s6eO4crByM/AyDuBkZ8B+Rk0c+djzc/EWpDF5ryjHHXlgPKBMgANzRaKbg9Hs4dzU2JN8i0h5FpC2ag5yXHGoIcmoFsduCxO0ixO0sJrndEyl8XkzsGVn4UqyMJw5aDcOShXLoY7B+XOpX+IA4fVgddkYYtXY6/uQLdHoDki0O0R6I4INFPV1PtVngKMvBP48o5j5J/AyDtOe18ejX25hBVksj/7KJ9lZYAyUIaBZjKjmaxgtqGZrPQKj6S+IxSvyUqKofGTxzi53mxFK4zDbKOGzUm41UGeNYRMazj5tnC0MnrF/TOyHdF/anvl86LlZ+DMz8JckImen8mh/HSMgkyM/AyUKwfldaN8bvC5ifN6aepV6D4Phs/FUl/RcHan/uvhz1DHY6I1Vv9PzygWqaIv9yqwDN2MZrIQa7LRUg9BmSwYJgv/M/kwTBY0k9V/zpgsaLoFzWTBabJSx+Twx+oWdvty8bpyMNy5/vK6cwvPz1zCCnLoXuDG7MrGXJDNgoJUcjx5KK8LjFO+AGoaUZqFG/RwlG5CaSY+0wvI1DXQTGi6CQonTTPh1M0M1sM47rSzLSKB0TsPs63A44/RtMDroVuchIdE8lKzpnh1C2bDw7S0bPbqof5rjyPKf60qnEzOKByhMbhNZd80G+58vOm7ucp7kHrZe6iXtZs5G1awZPta9nnyWVXCNvs7NQ8kOO7Zlca8PB1zVG1s9S/Hktwac0QNTBHJmCJqoDsiKDA7KTA7wRFcE29R3YHF9h3qziI+/whH3W7S3R5QBkZBFsrnxuSMwRRdF1NIKefqhjIPFZOmcPoyObpnA74TKXgzUtDMdhwtr8YS2yAQZzMpHGZFboEPj2Yhw2Uiw2ViK8HXnFqWE3Rr4CApxGD372t555230HSz/70t/KuHxGKOqU+7nlfjskdyMEcvNvRhEcOViydtCx3rhnJVhzo0iTZI3/AdQ4bcwPxSjmnGa5MZc5e/HfBPP6+g14DBpR7/K88/y0P33wvAr+t+p2P3K0qNfeq+u3n6/r8BsGXnblr1vb7U2AdGD2fy4w8AkHLwMPUvH1Bq7D2338ybzz0BwLHjJ0ho37PU2OE3XM3sV58HIC8/n7Dml5Uae+NVffjkrVcD82XFXtXrcr6c/WZgPqF9T/JKucno0akDSz8+OdpIvW4DOHa85F/vOzRvzKq5bwXmW15/B/sOlzzaQfP6ddjwn3cD851uG8Pm3ftKjK2TlMDubz4MzPf86/38snl7ibGxURGkLf00MD9wzGP8sHZ9ibFOu53s/53sDf7GB57mm+WrA/OaxUHUjW8S2mkk7xxqBIf8y+s7cslf/xm/ffUWnkPrUe7iNZKyVn4RSHDc/dw0/u+LhSWWAeDwwn8TV/j4wbc/YuYX35cau/P9ydRN9Pe8/+ScT3lt/relxv7+j+dpUbcGAJM++pLn/vV5qbEr35jApU3qA/DGgkU8+s9PSo1d/Moj9GzTFIB3vv6Be9/8V6mxnz87joGd2gAw9/v/ccer75Ya+9ETf+PG7pcCsODnXxn6wlulxr77wB0M79sNgO9+2cA1E6aVGvvGuJH87bp+APy0fgu9xz1XauxLd9/Gg7f4ryG/bt9D57ufKDV2wogbmDhyCABb9h2kzYiHSo0dP3QQr9zzFwBS0o7RcOi9pcbec21fpt//VwCOZWaTdM3oUmOH9e/Oe4/5r1N5BS4i+o8oNfaGHp34+Nn7A/NFsd3bNit1m5KMGTOGd955h1tuuYWHH36Y6Ohodu7cyUcffcQ///lPoqKiiImJ4e233yYpKYmUlJRSR2UoS0WbQowfP57hw4fToUMHOnbsyLRp08jNzQ38eA0wbNgwatSowaRJkwC477776NGjB6+++ioDBw7ko48+4pdffuHtt98G/LUoxo0bx/PPP0+jRo0Cw00mJycHEhjnij+xUEYfCxd7YqEyHW2crpONkkyaNIlnnnnmzxf4HDLF1CO08yjweVA+T+GNiP+xzzDYoXR0nwfd58HuzsDs86AMD8rr9n/BL2qIo1s4aAlFaSYMkxkrXv9NhdlaeONmRbc4CxMGsSwNjcfljMbtjCLMEUl4SDSaueQv5etOeWym5BPFcOVC3nFi8jMxu/PQvS4Oe3Ip8BagPAUorwvlLQCvC8Odizn3OH0K8rDlncCWd5xvsw5wIPcIRt6J4BsXwAJcbokMzG/z5pCjSs9uP9K8UeDx2P2H+Sbb315Vs4agh8VjCo3HFJaAHhrHA3Ubk++IJtMexUqcHLFFoofEolkcaIU3aJj8N2ZWwGJ4MBseCjwu8rzuQGLDfxXQAQ3NbMPqCMdnKvy1zBqK2RoKESVX7/pjbre0gZSUYWDz5hPizcfuzSevIJsjeVkod57/ptCdi3Ln+ud9bgaH2qll0jA0nfVug5UFHig8H04elxXNbKWZIxS7PYwceyTHbOHk2yP9x2+xY4pIwhRxslrYlsKpSFm36L8VTkWKt9I7Kb1wKlJ03TTceackmk6g8jNobLhojhunO5fs/EzmpaeiCm98/edb4bnmyedaq4mhDgtWbwGHXHnck56Bbg1Bs4X6z4miv/Yw2kXE0yYynlxbOMcsYazEiu6IRHNEFiZ5IguTJ2YIjSUvNDZQXmcZx+YFNp4yH1daIGAQ/JkrK5Y/7DfiNLEH/p+9+46PoswfOP6Z2Z4tqSQhECAI0ouCIGAXQcV69p9nO9spWI6zcgp654mKBUUF9Wx3J4eHincnJ4ooWEBAOJSuNENLI2Wzm2ybmd8fmywJyYYkEpLA9/16zWt2Zr4z+8zm2c3MM0+p8doRNyqq5k12ctUE0XyIodcqUKx5Ge2umuL5qmo+D2AsxK+4CI8csJzUQGz1MyF7pAKjaCsl+T8SKdpCuHALkaItRAp/QvPuAcPg6ZH7m9P9My8XI9y4JlKqHkYvLyVUnk8od2Wd7d+POB7FlY7P4mJaYYAFFQqK3YM5pRv2nmdgTu+FyZUW+631WT34rNELqIbGADBpIWx6GIseRqsopnDPRiJlu9DLC9B8Bejl+URKctH9Rcx/921GHN+fBDM8+/wM7psxudaxSj+sKrA321j07/c449ToU6YXX3mNOyf9GXNSZ0xJnTF5OmJO7Y41axD23mPYSTL/iP27PoMON8S/Uf8pDDVLwQ0tjFayk9Du/xH4cRGBzQuJ7NsKhsGvZr3E9YOuBmD+ehn+VrQOI1xJ8T9+Q3DrEhz9L+SUk07n+3IP2yqd0PPXZN4dvVEM7f6eitX/wPftX9D9+w5yVCHat6ysLL755hvuv/9+xowZQzAYpGvXrpx99tmoqoqiKMyZM4c777yT/v3706tXL1544QVOO+20Fk3XFVdcQWFhIZMnTyYvL4/BgwezYMGCWveNubm5tUalGDlyJLNnz+ahhx5i0qRJ9OzZkw8//LBW8/r77rsPv9/PLbfcQmlpKSeddBILFizAbo93Zd4yjpQaC4phNFBP+RfYs2cPnTp1YunSpbXGLL3vvvtYsmQJy5fXfU5ktVp5++23ueqqq2LrXn75ZR599NE6bWSq1VdjITs7u82O87nnf9/z4P89zOLf/ru1kxKjRoJY/cXYKkuw+qM3/faKkqoCgBLM/n1YKkqxVRRjrSoUsFaUYIoEm9TEAqJV6psTGzEMdCDFZmZcdlKd2IQaPyRBXaeh/p4dNdoxHcpYu6IQMVmpsLootbgoNzupsLqotCQQsESfaAYsCVRanJgVBYsexqKFMGlBbMFynKFyEkLl0arUVa/t4QocCpgaaDZRk01RYrFhwyB8QKyuGwQC0Qv5ms0bwlVNFgIWJz57Ej57Ij57En57tDlCpT2ZCnsi5fYkfDY3EcWErijoigmTrmHRos0rzFU3QzYtjFkPYYqEULQQFi1UtT0aZ6laZ9PD2PUIjrCPhMoyzIEynEFvVbX02g5sNhFsZBML3TAI/IJYAwhYEthFAh/5VXRHEmF7ImGHh5DDQ9iRRMSRiGZNwDBZ0c1WdJMFo6pwTzdFlzVz9DZSiR1/f22E6LtX5XdFwaiqkk/1ekVB0SMoVQWOqhZB1cIoehi0cGy9EntdFauHMWkR1EgIRdeImC1oVieazYlmdRKpmms2J5rNhWb3ELG7idjdaLaGioTiU7QIGBqKXjUZGoquYzI0nCYFxdAwGTqqocU6S1QwsEcCJIR9eCJ+LHqYsGrBrAVxB8vwhMpwh7x4QmWxyR0qIyNSTmKojIRIRfT72cAPSs0mFoGDxCaotb/3kUMQawCqxc4eVzbF9lRChkEYFU0x4be4CKtWEoMlpFfm07k8l2QjuL+5lK6j5/TA8/zMeo9tt9sxVVUxDoVChMPxx1esGdtQ84aSgMo3BS72VpjZ41fZXQ6VYQOzCqoSrR1hUsBt1cl2aeQkKeQkQRePTrIlTCQcv8qu1bq/eUMkEqn1P7yh2F/UFKKs9jWENIWIanRTCH9Ju20KUe1gzRucDgflEROL96Xx34JUVnqT2Rfeny6PKcQdnTcz0F1K36QILnP0fRvVFKKkoCpWmkJAE5tC6DpBxRQ/VppCAPGbQjgddtTjzkJx/7Jamy3B6/WSmJjYZu/ZDofqz2Btn2Nwm+Ln83JNY8DGrW3+s2qxGgvN6WjjYJ1s1Mdms2GzNVwVtq1J9O6lz5cvo1dVZY5NqjlWvVkzWWLVl3WTORZjqKbojYmhoxg6iq6jGFqshoOqhVAj0bb0qhbGFAlg8xdjrSoYiE7VhQOl2PzFmMMVzRvEpJ7iM0sTitSaElt9A2xTlFqFCPWxNaEq/aGOtephrIESkgKHqCMotfZnZFWUWgUuDal5w1xNx6hVmntgrFOrJNVfCf69zU/zoXCQz9qkKCQ08nNQD0GsUwugBX0k5sVvxnSkMRSlqq8UG7rJGv3tqVFQQOx1jQKEBgpwspxW7hjUMe72X8rehO9yU2Jtqkpj/8McNFYP0cO7FbxbD36wGvnQqqqYLGacTudBd7NarbGb64OxWCx1OvWq5nTCFakaNFicGo8Zm7Vxlxdms7nedrz1MZlMjfoMDow1whYIx69fpKoqzoSG6h81L1ZRlBaJBQ5rrBEJg163MKVmYcDBNCW2ZuHFoYytedMVj9uscX5GPudnRK9Fi0IWPivqwNu7O7PJ7+bPP0d70nebItzXfQtXdtyDzWpt8HtvhPffZNqslkb/nlgt+29WWyvWYt5/034oY80mU6yQoVGxtsblH5NJxdnIvNaUWFVtmVhFUVokFmhSrGh9R0qNhRYrWGhORxsH62TjSJFYuIUT/hO/XZcQQrQ2xTAwhQOYws3rAEoIIdq7NGuYK7P2cGnmXv66uzP/Kshgd8DBvrCVh3/qzaNbjiXLFqCzPUBPp58zUosY5PbiMUcOy41AxFDIDbkp0uwEdDN2VSPVFKCL1YulvYxPJ4RAOcioEA2OGNGGtOioEAfraKOpnWwIIYQQQghxOJlVg99k7+Q32TvRDPj77s48u6M73oiF3EACuYEElpam8PbuaCfRNlWjgzVEujVIB0uAdL2EdHMlKaYA6eYK+tmLybTsb3aiGxA0TFTqZgKGiYBuJmCYCegmKqvmQcNEhW6hRLOxM+xifSCVjYEUAkbdS3mropFjLaOXrYQzXbs4072TBFX6MxGirZIaC41wsI42mtPJhhBCCCGEEK3BpMB1nXfx6067yA/a2BVwsDNgZ1VZEl/sSyUvZCeom9gVcLArUN3HQ90mvVZFw6pohA2VYD2FA42VoIRJt1SQoEQIGGbyIwn4dQubgylsDqbwb+8xOJQwZ7p3Mc69ndNcu7GpzWniJIRoKVKw0EgTJkyI2/Rh8eLFddZddtllXHbZZS2cKiGEEEIIIZrHpECWPUiWPcgw4JLM6AhmAU2lMGSlIGSjIGSloNJEQVmEwoiDEs3OzrCLLcEkQoaJkFG3nwGromFXItjV2nNH1TzJFCTDXEFfezH97fvoZvXW6o5JN2BP2MWPwSRWVaYz39uN3LCHj7w5fOTNwaWG6Gsvpq+tmDPcOxmWkI9Vid+hpBCi5SmqgqI20BQizjDObU2LFywIIYQQQghxNLCbdLIdAbIdNUYlsdXumLxSN1Gs2QkZKlZFr1GAoGH6hX0jqAp0tvrobPVxhnsX93RYzdpAKh95c5jvzWFvxMmKikxWVGTyVklfrIoWK6DoZPFxnKOQXrYSkk1BzIqOAr84TUKIhkmNBSGEEEIIIUSTOFSNTqr/sLyXosBAxz4GOvbxQPp3bAym8GMwiRUVmXxWns0+zcHqynRWV6bHT68SxqWGcZmq5moItxrGrkYwKQYqoKKjKqBGB0xGja2vfh2dlKoYs6LjVkM41Uh06GGIjq5c9bp6P0UB1WyJHaN6f5NSPVRzdK5ANJ7oMaoOh6Ls3x5NV+19lKpjxl7Xs0/1a2LvSdU7V7+IvuP+waSVGq/BqPG0ueYg09UxatUak2JgqjrvmvvHO368Y0fX74/tVqGQ4Y775xVtgKoosSGm421vD6RgQQghhBBCiCOcqkA/ezH97MVcnLiNxzNhR9jN2so09oSdbA0l8V1FOrvCLjT294FWaVio1CwUStcM7dKfO/j5dUZrp0I0RGostFFGVRGd1+tt5ZTUr9znI9jAWO+iYQFDp1yT/2zNpesQ1KUtZXP4dZ0Q8t1troCh443Id7e5TKEwtNH/a22dEQ5Dua+1k9FuGZEw+A/P0/UjkREOg7+ytZMRVyqVnGYuiN4ROIDE6NNun2FGR0E3FPy6Bb9uwadb8GmW2HKFbgIUtKq46ifmuqFE90VBh9hrw6haNlQiKJRrFip1y/6n71VP8nWj+sl7dNKV6DN9HQXNqFpnRI+7/8l99Hz2P7Gv+ZS/9hN+vUYcxgE1AFAwjNq1Cmoey0CpUR+BWA2G6poONdftT1FVjFK7NkX1XAc0I1ovQqv67PanurrmxP6aFAceu/qms2btCgUj9iZqUMHrbXv/f6vv1Qy5LzpihptUjCPsr7lr1y6ys7NbOxlCCCGEEEIIIRqwc+dOOnfu3NrJaBVer5fExES2DemF21y3M9dq5RGN7qs2U1ZWhsfjOYwpbJojrsZCVlYWO3fuxO12t8nSHa/XS3Z2Njt37mzTGUMc3SSfirZO8qhoDySfivZA8qloDYZhUF5eTlZWVmsnpdUdKTUWjriCBVVV20Wpl8fjkR9v0eZJPhVtneRR0R5IPhXtgeRTcbglJia2dhLahoP0sUD7KFc48goWhBBCCCGEEEKI9kBqLAghhBBCCCGEEKLZFDU6NbS9PZCChcPMZrMxZcoUbDZbaydFiLgkn4q2TvKoaA8kn4r2QPKpEK3rSKmxcMSNCiGEEEIIIYQQQrRl1aNC5I7qh6eBUSG8EY0u36xv8qgQL730EtOmTSMvL49BgwYxY8YMhg0bFje+tLSUP/zhD3zwwQcUFxfTtWtXpk+fzrnnntuo95MaC0IIIYQQQgghRGtQDtJ7YzNqLLz77rtMnDiRWbNmMXz4cKZPn87YsWPZvHkz6enpdeJDoRBnnXUW6enpvPfee3Tq1Imff/6ZpKSkxp+G1FgQQgghhBBCCCEOn+oaCztPHnDQGgvZX61tUo2F4cOHc8IJJ/Diiy8CoOs62dnZ3HHHHTzwwAN14mfNmsW0adPYtGkTFoulWefTTrqCEEIIIYQQQgghjjCqcvCJaEFEzSkYDNZ7uFAoxKpVqxg9evT+t1BVRo8ezbJly+rd59///jcjRoxg/PjxZGRk0L9/fx5//HE0TWv8aTThlIUQQgghhBBCCHGoVDeFaGgCsrOzSUxMjE1Tp06t93BFRUVomkZGRkat9RkZGeTl5dW7z7Zt23jvvffQNI3//ve/PPzwwzzzzDM89thjjT4N6WNBCCGEEEIIIYRoBYqqoKgNjApRtW3nzp21mkIcypFcdF0nPT2dV199FZPJxJAhQ9i9ezfTpk1jypQpjTqG1Fg4zF566SW6deuG3W5n+PDhrFixorWTJI4SX375Jeeffz5ZWVkoisKHH35Ya7thGEyePJmOHTvicDgYPXo0P/30U62Y4uJirr76ajweD0lJSdx44434fL7DeBbiSDZ16lROOOEE3G436enpXHTRRWzevLlWTCAQYPz48aSmpuJyubjkkkvIz8+vFZObm8u4ceNISEggPT2de++9l0gkcjhPRRzBZs6cycCBA/F4PHg8HkaMGMHHH38c2y55VLQ1TzzxBIqicPfdd8fWST4Vog1pZI2F6v871VO8goW0tDRMJlOd73R+fj6ZmZn17tOxY0eOPfZYTKb9fT306dOHvLw8QqFQo05DChYOo+reOadMmcLq1asZNGgQY8eOpaCgoLWTJo4Cfr+fQYMG8dJLL9W7/amnnuKFF15g1qxZLF++HKfTydixYwkEArGYq6++mvXr17Nw4UI++ugjvvzyS2655ZbDdQriCLdkyRLGjx/Pt99+y8KFCwmHw4wZMwa/3x+L+d3vfsd//vMf5s6dy5IlS9izZw+/+tWvYts1TWPcuHGEQiGWLl3K22+/zVtvvcXkyZNb45TEEahz58488cQTrFq1iu+++44zzjiDCy+8kPXr1wOSR0XbsnLlSl555RUGDhxYa73kUyHaDkVRYrUW6p2aOCqE1WplyJAhLFq0KLZO13UWLVrEiBEj6t1n1KhRbNmyBV3XY+t+/PFHOnbsiNVqbdwbG+KwGTZsmDF+/PjYsqZpRlZWljF16tRWTJU4GgHGvHnzYsu6rhuZmZnGtGnTYutKS0sNm81m/OMf/zAMwzA2bNhgAMbKlStjMR9//LGhKIqxe/fuw5Z2cfQoKCgwAGPJkiWGYUTzpMViMebOnRuL2bhxowEYy5YtMwzDMP773/8aqqoaeXl5sZiZM2caHo/HCAaDh/cExFEjOTnZ+Mtf/iJ5VLQp5eXlRs+ePY2FCxcap556qnHXXXcZhiG/pUK0FWVlZQZg7B47xCg/b3jcaffYIQZglJWVNfrYc+bMMWw2m/HWW28ZGzZsMG655RYjKSkp9p2+5pprjAceeCAWn5uba7jdbmPChAnG5s2bjY8++shIT083HnvssUa/p9RYOEya0zunEIfL9u3bycvLq5U/ExMTGT58eCx/Llu2jKSkJIYOHRqLGT16NKqqsnz58sOeZnHkKysrAyAlJQWAVatWEQ6Ha+XT3r1706VLl1r5dMCAAbU6LBo7dixerzf2RFmIQ0XTNObMmYPf72fEiBGSR0WbMn78eMaNG1crP4L8lgrR1iiKctCpqa644gqefvppJk+ezODBg1mzZg0LFiyIfadzc3PZu3dvLD47O5tPPvmElStXMnDgQO68807uuuuueoemjEc6bzxMGuqdc9OmTa2UKiGiqnuIbaj32Ly8PNLT02ttN5vNpKSkxO1hVojm0nWdu+++m1GjRtG/f38gmgetVitJSUm1Yg/Mp/Xl4+ptQhwKa9euZcSIEQQCAVwuF/PmzaNv376sWbNG8qhoE+bMmcPq1atZuXJlnW3yWypEG1NjSMm425thwoQJTJgwod5tixcvrrNuxIgRfPvtt816L5CCBSGEEG3Q+PHjWbduHV9//XVrJ0WIOnr16sWaNWsoKyvjvffe47rrrmPJkiWtnSwhgGjP8XfddRcLFy7Ebre3dnKEEAdTo4PGuNvbAWkKcZg0p3dOIQ6X6jzYUP7MzMys09FoJBKhuLhY8rA4pCZMmMBHH33EF198QefOnWPrMzMzCYVClJaW1oo/MJ/Wl4+rtwlxKFitVnr06MGQIUOYOnUqgwYN4vnnn5c8KtqEVatWUVBQwPHHH4/ZbMZsNrNkyRJeeOEFzGYzGRkZkk+FaEMU9eBTe9BOktn+Nad3TiEOl5ycHDIzM2vlT6/Xy/Lly2P5c8SIEZSWlrJq1apYzOeff46u6wwfPvywp1kceQzDYMKECcybN4/PP/+cnJycWtuHDBmCxWKplU83b95Mbm5urXy6du3aWoVgCxcuxOPx0Ldv38NzIuKoo+s6wWBQ8qhoE84880zWrl3LmjVrYtPQoUO5+uqrY68lnwrRhjRyuMm2TppCHEYTJ07kuuuuY+jQoQwbNozp06fj9/u54YYbWjtp4ijg8/nYsmVLbHn79u2sWbOGlJQUunTpwt13381jjz1Gz549ycnJ4eGHHyYrK4uLLroIiI5le/bZZ3PzzTcza9YswuEwEyZM4MorryQrK6uVzkocScaPH8/s2bP517/+hdvtjrXjTUxMxOFwkJiYyI033sjEiRNJSUnB4/Fwxx13MGLECE488UQAxowZQ9++fbnmmmt46qmnyMvL46GHHmL8+PFxx3sWoikefPBBzjnnHLp06UJ5eTmzZ89m8eLFfPLJJ5JHRZvgdrtjfdNUczqdpKamxtZLPhWi7ageVrKh7e1Co8ePEIfEjBkzjC5duhhWq9UYNmyY8e2337Z2ksRR4osvvjCAOtN1111nGEZ0yMmHH37YyMjIMGw2m3HmmWcamzdvrnWMffv2GVdddZXhcrkMj8dj3HDDDUZ5eXkrnI04EtWXPwHjzTffjMVUVlYat99+u5GcnGwkJCQYF198sbF3795ax9mxY4dxzjnnGA6Hw0hLSzN+//vfG+Fw+DCfjThS/eY3vzG6du1qWK1Wo0OHDsaZZ55pfPrpp7HtkkdFW1RzuEnDkHwqRFtQPdxk3sUjjIrLT4475V08osnDTbYGxTAMo1VKNIQQQgghhBBCiKOQ1+slMTGRvEtG4rHEb0jgDUfIfH8pZWVleDyew5jCppGmEEIIIYQQQgghRCtQFAWlgX4UGtrWlkjBghBCCCGEEEII0RpUJTo1tL0dkIIFIYQQQgghhBCiVRxs5AcpWBBCCCGEEEIIIUQc0hRCCCGEEEIIIYQQzSdNIYQQQgghhBBCCNFcUmNBCCGEEEIIIYQQzSc1FoQQQgghhBBCCNFsykE6b5QaC0IIIYQQQgghhIhHURWUBmolNLStLZGCBSGEEEIIIYQQojVIjQUhhBBCCCGEEEI0m8pB+lg4bCn5RaRgQQghhBBCCCGEaAUyKoQQQgghhBBCCCGaT0aFEEIIIYQQQgghRLNJHwtCCCGEEEIIIYRoNilYEEIIIYQQQgghRPMdpGABKVgQQgghhBBCCCFEPKoanRra3g60j1QKIYQQbUy3bt24/vrrWzsZQgghhGjPqptCNDS1A1KwIIQQQtSwdetWbr31Vrp3747dbsfj8TBq1Cief/55KisrWzt5zTJv3jzGjh1LVlYWNpuNzp07c+mll7Ju3bpGH2Pjxo2cffbZuFwuUlJSuOaaaygsLGzBVAshhBBHgSOkYEGaQgghhBBV5s+fz2WXXYbNZuPaa6+lf//+hEIhvv76a+69917Wr1/Pq6++2trJbLK1a9eSnJzMXXfdRVpaGnl5ebzxxhsMGzaMZcuWMWjQoAb337VrF6eccgqJiYk8/vjj+Hw+nn76adauXcuKFSuwWq2H6UyEEEKII4x03iiEEEIcObZv386VV15J165d+fzzz+nYsWNs2/jx49myZQvz589vxRQ23+TJk+usu+mmm+jcuTMzZ85k1qxZDe7/+OOP4/f7WbVqFV26dAFg2LBhnHXWWbz11lvccsstLZJuIYQQ4ognfSwIIYQQR46nnnoKn8/H66+/XqtQoVqPHj2466674u5fXFzMPffcw4ABA3C5XHg8Hs455xy+//77OrEzZsygX79+JCQkkJyczNChQ5k9e3Zse3l5OXfffTfdunXDZrORnp7OWWedxerVq2MxFRUVbNq0iaKiomadb3p6OgkJCZSWlh409v333+e8886LFSoAjB49mmOPPZZ//vOfzXp/IYQQQnDENIWQggUhhBAC+M9//kP37t0ZOXJks/bftm0bH374Ieeddx7PPvss9957L2vXruXUU09lz549sbjXXnuNO++8k759+zJ9+nQeffRRBg8ezPLly2Mxv/3tb5k5cyaXXHIJL7/8Mvfccw8Oh4ONGzfGYlasWEGfPn148cUXG53G0tJSCgsLWbt2LTfddBNer5czzzyzwX12795NQUEBQ4cOrbNt2LBh/O9//2v0+wshhBDiAEdIwYI0hRBCCHHU83q97N69mwsvvLDZxxgwYAA//vgjao0qi9dccw29e/fm9ddf5+GHHwai/Tj069ePuXPnxj3W/Pnzufnmm3nmmWdi6+67775mp63aiSeeyObNmwFwuVw89NBD3HjjjQ3us3fvXoB6a3F07NiR4uJigsEgNpvtF6dPCCGEOOpIHwtCCCHEkcHr9QLgdrubfYyaN9aaplFaWorL5aJXr161mjAkJSWxa9cuVq5cyQknnFDvsZKSkli+fDl79uwhKyur3pjTTjsNwzCalMY333wTr9fLtm3bePPNN6msrETTtFqFIQeqHgmjvoIDu90ei5GCBSGEEKIZjpA+FqRgQQghxFHP4/EA0b4NmkvXdZ5//nlefvlltm/fjqZpsW2pqamx1/fffz+fffYZw4YNo0ePHowZM4b/+7//Y9SoUbGYp556iuuuu47s7GyGDBnCueeey7XXXkv37t2bnT6AESNGxF5feeWV9OnTB4Cnn3467j4OhwOAYDBYZ1sgEKgVI4QQQogmOkJqLLSP4g8hhBCiBXk8HrKysli3bl2zj/H4448zceJETjnlFP7+97/zySefsHDhQvr164eu67G4Pn36sHnzZubMmcNJJ53E+++/z0knncSUKVNiMZdffjnbtm1jxowZZGVlMW3aNPr168fHH3/8i86zpuTkZM444wzeeeedBuOqm0BUN4moae/evaSkpEhtBSGEEKK5FA7Sx0JrJ7BxpGBBCCGEAM477zy2bt3KsmXLmrX/e++9x+mnn87rr7/OlVdeyZgxYxg9enS9oy44nU6uuOIK3nzzTXJzcxk3bhx//vOfYzUAIHpDf/vtt/Phhx+yfft2UlNT+fOf/9zc06tXZWUlZWVlDcZ06tSJDh068N1339XZtmLFCgYPHnxI0ySEEEIcVY6QzhulYEEIIYQg2jmi0+nkpptuIj8/v872rVu38vzzz8fd32Qy1enzYO7cuezevbvWun379tVatlqt9O3bF8MwCIfDaJpW52Y/PT2drKysWs0RmjLcZEFBQZ11O3bsYNGiRXVGe9i6dStbt26tte6SSy7ho48+YufOnbF1ixYt4scff+Syyy476PsLIYQQon6Kqh50ag+kjwUhhBACOOaYY5g9ezZXXHEFffr04dprr6V///6EQiGWLl3K3Llzuf766+Puf9555/HHP/6RG264gZEjR7J27VreeeedOv0ijBkzhszMTEaNGkVGRgYbN27kxRdfZNy4cbjdbkpLS+ncuTOXXnopgwYNwuVy8dlnn7Fy5cpao0SsWLGC008/nSlTpvDII480eG4DBgzgzDPPZPDgwSQnJ/PTTz/x+uuvEw6HeeKJJ2rFVg8/uWPHjti6SZMmMXfuXE4//XTuuusufD4f06ZNY8CAAdxwww2N+4CFEEIIUY+D1UpoHzUWpGBBCCGEqHLBBRfwww8/MG3aNP71r38xc+ZMbDYbAwcO5JlnnuHmm2+Ou++kSZPw+/3Mnj2bd999l+OPP5758+fzwAMP1Iq79dZbeeedd3j22Wfx+Xx07tyZO++8k4ceegiAhIQEbr/9dj799FM++OADdF2nR48evPzyy9x2223NOq/bbruN+fPns2DBAsrLy0lPT2fMmDFMmjSJAQMGHHT/7OxslixZwsSJE3nggQewWq2MGzeOZ555RvpXEEIIIX6JI6TzRsVo6lhVQgghhBBCCCGEaDav10tiYiLFk36Nx26NHxcIkfL43ykrK4uNYtUWSY0FIYQQQgghhBCiNahqdGpoezsgBQtCCCGEEEIIIURrOEIKFtpHKoUQQgghhBBCiCNNCw03+dJLL9GtWzfsdjvDhw9nxYoVjdpvzpw5KIrCRRdd1KT3k4IFIYQQQgghhBCiNVTXWGhoaqJ3332XiRMnMmXKFFavXs2gQYMYO3ZsvcNP17Rjxw7uueceTj755KafRpP3EEIIIYQQQgghxC/XyBoLXq+31hQMBuMe8tlnn+Xmm2/mhhtuoG/fvsyaNYuEhATeeOONuPtomsbVV1/No48+Wmeo7MY44vpY0HWdPXv24Ha7UdrJ0BxCCCGEEEIIcbQwDIPy8nKysrJQ20kfAi2mkcNNZmdn11o9ZcoUHnnkkTrhoVCIVatW8eCDD8bWqarK6NGjWbZsWdy3+eMf/0h6ejo33ngjX331VdPOgSOwYGHPnj11PnQhhBBCCCGEEG3Lzp076dy5c2sno3U1svPGnTt31hpu0maz1RteVFSEpmlkZGTUWp+RkcGmTZvq3efrr7/m9ddfZ82aNU1Lew0tWrAwdepUPvjgAzZt2oTD4WDkyJE8+eST9OrVK+4+b731FjfccEOtdTabjUAg0Kj3dLvdQN0Pvq0oW7uOZedf3NrJaLcUwH60l2r+AuWazmpf/GpTIr6IYeDV9NZORruVZjFxaaek1k5Gu2Xt2oluj0xs7WS0T/YE1G79WzsV7U4gAh/vsPDfnxT2lIboZi0nbJjYGXJSqtkIGiYS1RBe3UrA2H85aVU0zvLs5NYOG0gxBfg55MaharjUEG41TIIaaW4/ZO2TakJxJrZ2Ktoto7wUVn3T2slo19Srf4+S0fYeunq9XrKzs2P3bkc1hYPUWIjOPB5Pi9zflpeXc8011/Daa6+RlpbW7OO0aMHCkiVLGD9+PCeccAKRSIRJkyYxZswYNmzYgNPpjLufx+Nh8+bNseWmNGmojm2pD/6XMlwuEhS5MW4uBXBIwUKzaTrYjqorukPHBFiRz665bIqCS767zWYzm/G4Elo7Ge2T3YnaBq8H2hrDgO8LTWwuUcnzq/xtg5XCyqrvrAq5kapAU9UEFFWtsisaOTYvAJsDyXwcTOaTXf0xUDAO+N20KBrp5kpu7bCBq1N+OvILGVQTinx3m83QQ2C3tnYy2jXV7UZpw7+B0nSdRjeFaKy0tDRMJhP5+fm11ufn55OZmVknfuvWrezYsYPzzz8/tk7Xow/TzGYzmzdv5phjjjno+7ZowcKCBQtqLb/11lukp6ezatUqTjnllLj7KYpS70nXJxgM1uq4wuv1Ni+xQgghhBBHGU2HdzZZeeV7G7t8tQv/Ort0rujuZaB3FduDHqyqRlerjzRzJXZVo0yz4lA0utrKMSsGAD9UpPB8wUC+KO8EQAdzJRFDoVyzEkElbJjYHXYxec8wvvN34M6MtSjA9xWpnOAsoJO14nB/BEII0boOccGC1WplyJAhLFq0KDZkpK7rLFq0iAkTJtSJ7927N2vXrq217qGHHqK8vJznn3++0d0MHNY+FsrKygBISUlpMM7n89G1a1d0Xef444/n8ccfp1+/fvXGTp06lUcfffSQp1UIIYQQ4ki2Ot/Ew0sdrCuKVkFIMBuckBkh0WYwMivCJT3DWAJe9LV5nOzOq7N/Nv466wYmFPN6t8XsDDmxKxodLNGmrIYBlYaJ0oiNj8q6Mi1vMP8uy+HfZTmxfZ1qmEkdV3Nl8pYjvyaDEEJUUw7Sx0IzartPnDiR6667jqFDhzJs2DCmT5+O3++PdTlw7bXX0qlTJ6ZOnYrdbqd//9pNBpOSkgDqrG/IYStY0HWdu+++m1GjRjWYwF69evHGG28wcOBAysrKePrppxk5ciTr16+vt2OPBx98kIkT97c7rW6vI4QQQggh6tpWqvLy9zbm/hitYu6xGvx+aIAreoVwHHBlaDTzPbKttQsdFAUSFI0EawW3dNjIcQlFzCzsx5flHVGATlY/uSE3f9g9nKKwnTsy1jXznYUQop05xDUWAK644goKCwuZPHkyeXl5DB48mAULFsQ6dMzNzT3ko3EctoKF8ePHs27dOr7++usG40aMGMGIESNiyyNHjqRPnz688sor/OlPf6oTb7PZ4vaIKYQQQgghon4oNPHy9zYWbDfH+j647NgQDwwLkOZobhFC85zgLOQE52JKI1ZUxcClhplZ2I9n8gfzXMEgAoYJBci2+hjuzKer1Se1GIQQR6YWKFgAmDBhQr1NHwAWL17c4L5vvfVWk9/vsBQsTJgwgY8++ogvv/yyycOJWCwWjjvuOLZs2dJCqRNCCCGEODJpOny928wrP9j4Zs/+y77RXcJMOC7IcelaK6YOksyh2Ovx6esp06z8pagvMwtr127NNFdwumc3E9LX0tFSebiTKYQQLUdRG27u0E46/m/RggXDMLjjjjuYN28eixcvJicn5+A7HUDTNNauXcu5557bAikUQgghhDjybC1Vef8nCx/8ZGWvP3pRalIMLjwmzG8HBemV0jaHz70/cw0AW4KJdLRUsCWQyJrKVPIiCfyjuCfzSnK4pcMGbumwgQS1dQtFhBDikFCV6NTQ9nagRQsWxo8fz+zZs/nXv/6F2+0mLy/a8U9iYiIOhwOo3XEEwB//+EdOPPFEevToQWlpKdOmTePnn3/mpptuasmkCiGEEOIosq9SYY9fwWaCY5Pr3mQbBuwsV9hYbMJjNch26wS06MVdit0gyWa0qWs9TY8OF/lZrpmFP1v4scQU25Zo0/lVjzA3DQjS2X14mzw0lUkxmNTxf7XWVeomVvrTebGgP99VpPNCwUDeLe7BUGch3W1lnOXZRT97iTSVEEK0T1Jj4eBmzpwJwGmnnVZr/Ztvvsn1118P1O04oqSkhJtvvpm8vDySk5MZMmQIS5cupW/fvi2ZVCGEEEIcgSo0lXV7TVSEFUIalIcV5v1krdUsYFxOiNFdI6zfZ6IsqJDnV1hbZKI0GP9iTlWihQspdoM+KRqPjNzfT4FhgGaAuYWuBXUDfvaq/FBo4ociE2sLTazbZ8If3n9nbVIMTsuOcEnP6LnZTA0csI1zqBqnuPdysmsvH3u7MHXvcewOu5hf1hWAGQUDGeQo4pnspXS3lbdyaoUQh4NhGEyZMoXXXnuN0tJSRo0axcyZM+nZs2eD+7300ktMmzaNvLw8Bg0axIwZMxg2bFhseyAQ4Pe//z1z5swhGAwyduxYXn755Vinhy2ihfpYONxavCnEwRzYccRzzz3Hc88910IpEkIIIcSRSjdgZyCBvSE7m/xulntTWFzSgYBe965awSDNYVAcUJi/3cr87dY6MRbV4NhknfIQ7PGpJFii670hBd1QKA4oFAdgS6mJDcUmzs0J8++tFvb6VCIGdHHruKzgD0HPZJ1RnSJ0cumUBBS+LzRhVqGjU2dUVoS+qTolQYWCCoWiShVvUKE8HH2vfZUqBRUK+RUqeX6F3T6VoFb3QtNtMTi9S5gzu0Q4PTtM4hHWt7WiwLmJuZzp3sVXvo7sCLpZXdGBxeVZfF+ZxgVbzuHGtE1cmryVzhZ/e7kWF0I0w1NPPcULL7zA22+/TU5ODg8//DBjx45lw4YN2O32evd59913mThxIrNmzWL48OFMnz6dsWPHsnnzZtLT0wH43e9+x/z585k7dy6JiYlMmDCBX/3qV3zzzTctdzLqQYabPMSjN7SUwzYqhBBCCCHEoaAZ8H15Ej9VunCaIpSGraz3u/m8OJ2CcN0Lyo5OnTSHjtUEVhUGddC4pm+0WcD6fSrTVtopCyoM6qCRkaCTZDfol6rTK0Wr90l/WIeSQLRgYa9fZdJXDraWmpjxv9rBO7z7l7d7TXz6syXuOSkYsZEaGsNmMuibqjEgTWNgB42BaRrHJOktVkuiLbGpOqM9u6uWNrE37OD3O0fyrT+TGQUDmFEwAI8a5GR3HpM7fkcHS6BV0ytEaznj1X8z4KcAZncSb7/9Nlarlccee4z/+7//Y8KECbz33ntkZGQwY8YMzjnnHADWrVvHvffey1dffYXT6WTMmDE899xzpKWlAbBgwQIee+wx1q1bh8lkYsSIETz//PMcc8wxAOzYsYOcnBzef/99ZsyYwfLly+nZsyezZs2qNfLfL2EYBtOnT+ehhx7iwgsvBOCvf/0rGRkZfPjhh1x55ZX17vfss89y8803c8MNNwAwa9Ys5s+fzxtvvMEDDzxAWVkZr7/+OrNnz+aMM84AojXt+/Tpw7fffsuJJ554SNJfh9RYEEIIIYRoebsCDhYWp/N1aRq7gw7yQjbKInVrGADYVI2O1gDdHH6GuEs5NcPHwEHHxL0u65eq89bZFU1Kj0WF9ASD9ASD3ik6c8/3cfuiBKwqXNcvxHHpEcxqtAPFoKZgMxmsLjDzvwITRZUKdhMMTtcwqwZbSkx8tdtMRSSawFS7TocEg0Srgcdm4LYYJNsN0hN0MpwGGQk6nV06HV0GlqOgEKExOloq+VvO5ywoy2Z2cU++9Wfg1W3ML+vKUl8GN6Rt5iTXXvo7ijErbbuPCSEOtb/+833uu/9+VqxYwbvvvsttt93GvHnzuPjii5k0aRLPPfcc11xzDbm5uYRCIc444wxuuukmnnvuOSorK7n//vu5/PLL+fzzzwHw+/1MnDiRgQMH4vP5mDx5MhdffDFr1qyp1bz9D3/4A08//TQ9e/bkD3/4A1dddRVbtmzBbDaTm5sba+aelZVVb7onTZrEpEmT6t22fft28vLyGD16dGxdYmIiw4cPZ9myZfUWLIRCIVatWsWDDz4YW6eqKqNHj2bZsmUArFq1inA4XOu4vXv3pkuXLixbtqwFCxakjwUhhBBCiBbhi5j4eF8m7xd0Yrk3tc52tynMYHcpQd2EyxShZ4KPExP3MSKxGJtaozNGu7PFH/Z0dhv8+yJ/nfWZzv2jFozqFH8Eg0AESoMKqQ4pLGguk2IwLimXcUm5BHWVdZUpTNlzAhsCKTybP4hn8wfhUkOMcuUx1rOT0Z5duEyR1k62EC1uUN8+PPTQQwA8+OCDPPHEE6SlpXHzzTcDMHnyZGbOnMkPP/zAZ599xnHHHcfjjz8e2/+NN94gOzubH3/8kWOPPZZLLrmk1vHfeOMNOnTowIYNG+jff/8wsffccw/jxo0D4NFHH6Vfv35s2bKF3r17k5WVxVdffcXxxx/PV199hdvtrpPulJSUuOdUPSDAgf0eZGRkxLYdqKioCE3T6t1n06ZNseNarVaSkpIafdxDQuEgNRZa7q0PJSlYEEIIIUSrKglb2BlwUBKxYlc1lpR04K97u+LXo5cpCgbDPMWcnlxIX5eXVHOIngk+LOqR8fTZboZM85FxLm2BTdUZ4izi/WM+4YPS7nxZ3pFlvmgthk+8XfjE2wWXGuKqlC2MT1+HxxRu7SQL0WIG9O0de20ymUhNTWXAgAGxddU32gUFBXz//fd88cUXuFyuOsfZunUrxx57LD/99BOTJ09m+fLlFBUVoevRgtzc3NxaBQsDBw6Mve7YsWPsPXr37o3ZbI41nTjmmGPweDxx0//OO+9w6623xpY//vhjTKZ23BttfaSPhcY5WM+bB5o7dy4PP/wwO3bsoGfPnjz55JOce+65LZ1McZgZQNCSQNDmImB1Vc2d+5etzgOWXQRsToJWF2Gbi5DFjj3oJyFYRkLAi9tfREr5XlK8e0gu30uKdy/uin3tpYBPCNHCNEWlJKEDxc50diV3Z3tqb3ak9SLf05mwyYKumImoZiImM6DQoXwPyRVFmPUwJl3DooXoXLKVLsVb8ARK8FSW4g6U4A6UYtOCrX167c6+sJV1Pg8/+BL5oqQD/ytPrjeuu8PHJem7ubDDHjrZpJ28aBqbqnNVyhauStmCZiisq0xmkbcz/ynrys8hD68V9WVReSde7bqEHGt5e2nGLESTWMy1+3ZRFAWLxVJrGUDXdXw+H+effz5PPvlkneNUFw6cf/75dO3alddee42srCx0Xad///6EQqHa7xvnPYAmNYW44IILGD58eGx9p06d2Lt3LwD5+fmxdFUvDx48uN7jpaWlYTKZyM/Pr7U+Pz+fzMxMADIzMwmFQpSWltaqtVAzpkVIHwsH15ieN2taunQpV111FVOnTuW8885j9uzZXHTRRaxevbpWCVh7FzDqjpddTUXBWiPzNBSroGBrZqxPMRO0OQnY3NGbd5s7+toafa3Zo8sBqxufzVWjAMBNwOYibEmocexqBgc+b1EOGBkkYrETsLoIWRMwWri9kDkSJKl8L8nePSR5o/Nk7x7Sy/NwBUqxh/yoQR+WUDm2kB9ruLJOQYQdJfZjGDIMtDpnuJ8NBbUqNmwYRA5RrBUFUzNiI4ZB+IDYgKETqvqbmCGWBs0wiF9Jt3asbhg0VHnUBLE0HM5YXTUTsSagWRwoZitq9V/T0AmzPy8qehhzqAJzyI9iGLWOaxgG8Z5bRar+/qaq4xo0nF4VGh2rAOYaue/Av1tbiwWwNDM2oOvE/6WChBql8k2JrdQNym1uShM6UOjpRKGnMwWezuxN6sae5O7sTcohbG58F/leR/wqmAeyhStxB0pwBcpwBUpwV5aQVFFI7z2r6F6wFmewHHvIh0cPxfJayDCINDBykl3Z/xsRMgzKVAeBEhNlmoXyqsmrWSmPWKjARrluwxsxUxI2kx+0kR92EDZUnGqEscm7GOYuImIo5EU87Ak78WkmLGh0spTjqvG01qroZFkr6JNQRobDwGyOPhUKRyKEwvFzsc1iicVGIhrBcJigrrK+Iok1/lS2VbrZF7FRErFSGHaQH3bUOUa6pZIkc4igbiLNEuCG9J84K60IqyV6qaJpGoFQ/CfLFrMJa9WFbK1YXUX1126mYLFYsFqjfTTouk5lZWX84zYh1mw2Y7NF85lhGFRUxO+7oSmxJpOpVi/nfn/dZhfNiVVVFYfD0WCs4a9ArwxEY237+7WoCATjjv6lKAoJdluzYiuDodjNR32cDnuzYsPhID2UPfRI3MPNnhV8WZHNnwtHsi2YyOgfL0BFp6e9jDGeXZzm2EZPc1Hca/kEuy12bRAMhYlo8f+DJiQkxH4BDxbrsFljbdRD4TDhyKGJtVstsSe8TYltyve+Ob8R8VgtZizm6Pc+omkEG/jeW00qlqr0arpOoIFzs6gqVnPTY3XdoDIS/9yaEmtWVWxVsYZhUNHAZ9aUWJOiYLfsv63zN/CZNeT444/n/fffp1u3bpjNdW8T9+3bx+bNm3nttdc4+eSTAfj666+b/D5NaQrhdrvrbM/JySEzM5NFixbFChK8Xi/Lly/ntttuq/c9rVYrQ4YMYdGiRVx00UVA9Dd90aJFTJgwAYAhQ4ZgsVhYtGhRrMnH5s2byc3NPWQdT9ZL+lg4uIP1vHmg559/nrPPPpt7770XgD/96U8sXLiQF198kVmzZtX7HsFgkGBw/9Mir9fbAmdyaF3u92FK7hId6NrQqyYDw9AZaLLwO6cbDAPF0LmlJJ+goQMGitmGYrajWBwoVgfd7W6uT8wgZHYQsDl5VYOQzYVq96DYPag2D4rdjWr34LAnkpqQVFV44CRsqXtR1xoMXcMI+tCD5RjBcvRAdE7Qx8lGBFvIhz3kY1l5EbsqStCDPoxgOUaoEsXmRHUkoTqSuTS9O8WeLEo8Hcl1ZRJxZRAx2yhK7kZRcrdGpkXHCPmi7xHyY0SCdEfDpoWxRELsDlWQH6qASBAjEsTQQtF5JAiRIBdaLCTpESxakOUVXv5XWR7dHq6InZceLMcIlPOs3UZPPYglEmB2RTl/rfDtT4hqRjFZwGRBMVmYmpxJjs1BxGTlo1CIucFgbFvNOFQLN3hS6WJ1oBg63wX8fOQvAy2EEQ5gRALRuSU6v8AwcYymYYoEWBf2MV+Lf7F8sSmBPkr0gnKzEWaeFv8C+DyTg4FK9CJxqxFhLmEUawKqxYFiTUCxJETn1gSGWJPIsXuIWBwUmG18Z1ZrbVctCVX5PYE0qwuX1UXE6iBotlNmscfiFIsj+hk0kV5VwOAIVWIO+SFUQX7QixHyo4f80XwQrHodriA9FKRHKIQpVIkW8rNELwddw9A1MKrmugZ6hGxdYYhhRtE1InqEf+nlVTGR/ftU7ddFVzhVt0R7hVdM/E2tBNUEqglFic5RVBTVREfFykjVCaoJ3WxlvgV06/7PqfrzQ1HwoNIHS6xQ5TvNRzhciRGu3J8fquaJkRBnhsAUCaKGA8yL7MOvh6vSGdmfXkUhWbXwK8UTLRxUTXyoBihVo+lDNUfTrZpBNeNULZyjulFsVjZlJPKHwkK21bw4qnHl7lHNPN+5C2GTlZDJxgvllWw3lKrfPXt0bvdgcqZhcaUxJLUzZY5kyu3JlNqSwNTwvzRVj5BcUURF0VYKdn1PeM8PhAt/xAgHQAtj6GGMSIh/ZHfBm5iN157Me74Kvg9HUGxuLB37Y047BpMzFTUhBTUhFcVkJmhxELQ4KHLXfury8eDray2btDCOsJ+EkA9/ZRnF/lL0YDl6wAuRAJgsqI5kTK50elhUEgydoNnB7oQOhJxpsKHJWZxK3cw7hT14p7BHk/YztDD91B2M6GQmzRJk0/r/8foH/4n+H7LYUUxWUJTo38OTxbATT8WW2JGIobCv3M/P+SVYOvREMdffwSJAjt1HauU2Pn1vFpVrPyTXu7fW9nnAjHtv4/bLzwfgqzXrOfO3da8dqj1552+455pLAVi9aSsnXn933Ngpk+7nkT9EO/DauGkz/U+If8F4z113MO3xPwGQu3MnOX0HxY29/ZabeOm5pwEoKtpHerf4n/t1V1/FW6/OBKCiogJXeqe4sZdefCFz//52bLmh2HPHjmH+B/+MLad36xm30OLUk0exeMH82HK3vgMpKtpXb+zQXjl8+/KfYssDfnM/P+cX1Rvbt2snfnhj/xPPE2+fzIafd9cb2zUjja2zp8eWT//dn/hu8/Z6Y9MS3eR9MDO2PO7Bp/jy+031xibYbXjnvx5bvuyR5/l4+fe1YlR3BmnX/xP7Maego7I5kMzmQDIzGECkdDeRgk0Ec1dQsXoO4T0/xPYr++gvsUKL2557g79++lW9aQDYO+8V0t1JAPz+uVeZ+d78uLFb//Um3bKi1dIfevmvPPP39+PG/jBnJv2O6QrA1Dff5Y+vzY4b++1b0zmh37EAvDDnX9z/whtxYxfNeoLThkSrsL/2wcfcMW1m3Nh/P/cI406K1kJ+5+MvuPGP8YeLnzP1QS4bHb0Rnbd4KVc+ODVu7OuTf8f1558FwCcrf+DCKfHT+8IFo7h9RPTh41c78hj92n/ixj5xznDuOWUwAKt3FzHi5XlxYx8+cwhTRg8FYGNhCYOmz40bO/HkgTx1bvQ3JLfMR4+n4v8tbjuxLzMujH4ORf4AHf/817ix1x5/LG9cdjoAFeEIiQ18Dpf07867V58VW66OPSWnY7xd6jV+/Hhee+01rrrqKu677z5SUlLYsmULc+bM4S9/+QvJycmkpqby6quv0rFjR3Jzc+u9pzuYpjSFqI+iKNx999089thj9OzZMzbcZFZWVqzQAODMM8/k4osvjhUcTJw4keuuu46hQ4cybNgwpk+fjt/vj92rJiYmcuONNzJx4kRSUlLweDzccccdjBgxouU6boyeEKhSYyGuxvS8eaBly5YxceLEWuvGjh3Lhx9+GPd9pk6dyqOPPnpI0ny4WLueSMb4RfVu2wc8VGO5QwPHqQBerrGcUDXVxwDq+/evB/1VN/Re9IAXI+DFFipnuBbCXvWU7ZOyPEoqSqtuiqviwhVgGCSrKg+5kjGqMvx0Xxm79OqbhtpfAqei8ucEJ7ZgtLDg8aJc1lfWXxBkQ+H6lP2dq/xQXkJ5OH5146s77L+gf7SsmC81DVNiFubEzpiSOmNKysZcNe+TlkPAnkjQmoDX6kS3RUtBFVWNFsjY9/+47TrgfeJ9vgBfHLDc0PPOp6vmqh5BCVXQWTGhmK313hy/dMByQ/8iDrxcqVsvaL9VVRMAukbnmoUPVXOq5j9qYfZGolXcfKpKuqJEC0BUM5jMsZtIxWRho9XJZmsCmtlOxJpAFzV+O7hd1P6M668MHVVRNVWL9+Nl6BqqFqK6bNdAidbGUBRAqXWjo1oT0K0J1HxGV//Ix1Ehat/bNfT9DAE1f+ka+rtpwOc1luPfMkQtrfG6bpd2tf1Y43Xd5wG11RyhOZmG/x4La7x2VU3xVKf3v1XzhioTPnjAckPnt66edXrAS6T4Z7SSn4kU/0ykaAvhgs38x15Bhnc3JkPjd/n5bPbHLxzr5qggoTj6yS0uKMTn88WN/aRnP8zOFLz2ZGaFVL7CjpqQijmtO/aeZ2BOPQbVHv3kNZMFnykJnz0JPJ1pqP5Ebr3nVo5eWYJeWYpeUTWvLOXykwbSM8NFojnMN8uX8Y8P/olWuhs9XIElvRfOYddjTu6KoYU5rWcKJ2TZcJvDLP5hK59u2odSo6BZtbsxpx6DJf1YNtCTDXuqNrh6k3btVXHTuxGg+mNSE7F2jP4ma+X5BLd9Q2jXKjRvHrqvkAcuP5O7xvTFbY4w/+sVfPBN/BsXIVqSXp5PwYxTURyJ/PzBmyz3Z/CJtzOfFadjTuqEOakT9mPPJHH0g1Ru+C/Fc29DK6nv2ynEkSErK4tvvvmG+++/nzFjxhAMBunatStnn302qqqiKApz5szhzjvvpH///vTq1YsXXniB00477bCn9b777sPv93PLLbdQWlrKSSedxIIFC2rV2Nq6dStFRfvvgK644goKCwuZPHkyeXl5DB48mAULFtTq0PG5555DVVUuueQSgsEgY8eO5eWXX6ZFHSE1FhQjXt20X2jPnj106tSJpUuX1qo6ct9997FkyRKWL19eZx+r1crbb7/NVVftv3h5+eWXefTRR+u0h6lWX42F7OxsysrKmlz6dTiUff8Ds657mLeueKOqKYCCoahVk1I1VS1Xb6u6MTNpISzhAJZIIDa3RoJYwpXYQz4sgXLsoXLswfLozXvV3Bby4Qj6cIX8OIJe7EEfSsCLLViOyaivGpiCvVYTCwPiVnWuHRs0jHoaROxnr/HFaEpsyDDQq0b4tpvqfrkcB8Q21GThwOYNYSBc1UQjaI324xC0JhAxWVFNVjSzjbDJRsBkIWyyEDFZiZhthE1WNJOFiNlGxGTFMNnQzBbCJhshk5WwyUrEbCVkcdToNyLarCRodcZN34EUXcOshTDpEUxaGJMeQdXDmLQIphpzsxbGrEcw6RFAIaJaCJvMVem1EjHbCZlsBE1WNIsdvQnVwg8FRYtgDvkxhQOYwpWYwhWYQ5WYwwHM4UpMoQrUqnk0Jrq9OtYSDmANV2IKRWMIV20LVVTtH11WtRBm4jdvMBQFzeIgYk0gYnViWBMwrE4i1gTC1oTo375qWbMmVDWvSCBscRCw2NGtTrSqJhe6yYKhqhiKKfo9VU2x17HlA9YZiophMmMotbfX7JhH0SJgaCh61WRooOsohl5rnRoJoYYqoucdqqj6DCpQw8Ho/ijR4r2qQpWI2YJutqNbbFVzO5rZhl6VHwyzHc1iQ7c40C0NFbHUQ9dR9EhV+iIohoaiaVXzCCZDx2UiWlMr+peInmt1ExUMMAxseghrJIRFC2KKBDFrIaxaEEskiEUL4Qj58FQW4w6UkBYsxVNZQmJlCfaKIhICpVi0UL3Jq9lsIqjrDTb9cSi1fyMaarLQmFgdhYDViV7V1KzS6qTM4sJncVJpdVFpdREy2zDrERKC5SRWFGJRVHSTBXu4koSKIjp7wvSZcnu9aWiJas7bAi6+9WVRGHGwL2ylMGQlpEebSlgVHYuqo2LgUDXSLZVkOcKk28JYVR1D09C0CJ1sFWRb/XUetNSq5tyEKtHNbgphS0DNGVA7VppCAI1tCuFFX/9Nu28KEQiF0LTGxRYHdDZUJLEjnMiX/mwW+7sQwYQZjc6Wco5z7mNixlo6Wf2NagqhVtVYkKYQTW8KES7ZR3DZgY9uasRKUwggflMIp9WCev0klMwucfdtLV6vl8TExDZ7z3Y4VH8GxX95FE9C/Osub0WAlJumtPnPqt2PCmGz2WL/mNuLfjtXMO3ppvUZYdBCI400ogTMXnVT0hi2Foq1Vj9tpnYhQkOxjT2uFaCqOQMV9VcBPdR0lGhBg81FyGyPFhrUKDioOVcP0n69KbwRnZW+aAdohqKgmWxoFhuaOXpjqVXdaMbmVTea0bkdxTBiN46qHonOtRrLWhhzuLKqQCBaQBBdrkDVG+pl4BA64C5Gqf4b1xSujE7+xv+9I4ZBWaShFv+/jAHRquUtU9bbZAbUKPQwoVc1cQAjVrhBdUGHoR003R2sJq7q3FAdiEOkET0n25rQu7JVqd3vTXNjXZEKiFRARUGj37smmye71o1P3DRYLLGb64OxmPdfvB9ogCPCgORD8WS24TSbzabYzcbBmEwmnI5mxNodqM74hbmqquJsYHtzYxVFaZFY4LDGGkTQ68l7NQsDDqYpsTULLw5lrN3a+NgUu8pJdi8n4eXX7GRbcC0P7jqRlRXp7AgnsaM0if+WdeX4hCKSTEE6W/30tJdxqmsPHSwHdDJa4zfBZrVgo3Hfz6Z8l1sqtqHfiF8S25TvvdlkwmxtXHpNqorT2rjf96bEqqqCs5FpaEqsorRMLNCkWNEGHCE1FlqsYKExPW8eKDMzs0nxR5P20bJGNJaKgSPkwxGKX8W6pSmGgTkSwBwJAGWtlg4RpUCNp/mtTyFaWwY00KKdZwohxNGou62cOd0XsivsZEfQzczCfnzrz2SZv+71aUeLn0xzBRmWSjItFWRYA3QN6Byb4KOrowKz0nZ+54UQbYR6kD4WGtrWhrRYwUJjet480IgRI1i0aBF33313bN3ChQtbthdOIYQQQgghGqAokG31k231c5Irj+8qOrAn7KQkYiM35GJVRQfWVqayN+xkb9gJNVvMVPVLalV0jknwMcRdQneHH7NiYFIMVKJzm6rjMkVwmSK4zRHcpghucxinSZMCCSGOZDLc5MEdrOfNa6+9lk6dOjF1arRn2LvuuotTTz2VZ555hnHjxjFnzhy+++47Xn311ZZMphBCCCGEEI2iKHCCsxAorLV+X8TGzpCLveEE8sMJ5IUd5EWc7Igk8VOFi0rdzEa/h43+preRTjSHyLAGybAGSFA19obshA01VhDhMUXItAXIslWSZQvQ1V5BF3sFNrXlmvAJIQ4RaQpxcAfreTM3NzfW8QzAyJEjmT17Ng899BCTJk2iZ8+efPjhh/Tv37T+CIQQQgghhDicUs1BUs1BBlOj/x7VhOJKRDdgT9DBWp+Hld4UCkI2NBR0AzRDiXbyqpvwRcyUa2Z8WnQe1KMN0coiVsoiVn6sONj4PjXeGoNOtkpyHH66O/zkOPx0tVfECiM62ytxmhrqylYIcVhIU4jGmTBhQtymD4sXL66z7rLLLuOyyy5r4VQJIYQQQghxeKgKdLZX0tleyTlp9Y90Vp+QruDTzBSFbeSHbBSE7Pg1E5nWADZVx6eZ8WtmSiMW8oJ29gTt7Ak62BFIoFyzsDOYwM5gAl+W1j9AcoIaIcGk4TJF5wmmCE41OrerOlY1OhKMVdWxqRoOVY/GqRFsqh7tj0eJjtpV/dqq6JgVA4uqY6lq7mEYVZ0Co2BTdRLUCE6Thk3VMKoKWIzo+EAAmDBQFQMlYsNEQlVH1tXvU3O+/zX1rKs5F6LNUpSD1FhoHzm43Y8KIYQQQgghxJHIqhqkqGFSLGGOTWh8h8+GAUVhK9srnWyrdLI94GR7pZPcgIOgbqIkYqEsYqVCN1OhRwsu2izHrw7ZoRRDr7dwgnrW1SywUNGxoGE1ItiIjtilV0XpKOhxxvCq73ZQOSDSVHVsC5FozRWshDATUuLfptU8hnKQ9ffviHCJ9IPftkkfC0IIIYQQQoi2RlGggzVEB2uIYYkl9cZ4I2ZKwlb8uokKzYRfM1OhmajQzfg0E0HdREhXCekqQSM6r6wRG9SjT1ir6xIYRnQ47YihENZVwoZCxFCJGEr0FlyJ3uwGq45TfYzYzbyy/2ZYN0Dj0LcrN5RfOIh3+7i/q6UyUtzaSRAHI30stE1G1XBtXq+3lVNSP6/PR4UhHek0lwLo8vE1W4WhE2xDQxq2JxHDIPTLLkeOakHDwCdf3mYLRSJ4fRWtnYz2KQJqG70maA+MinJ0v+S9ZlNNKFhaOxVxJVdNqFVTG0uq5i1FW70MnepCDGV/QQbUel2z0UPtuP3bqbVPVYxyYEOKuvuDQgi1qiaBBQMl1jxDNYzY6/rFL43QUdAUlQgmIlUDO9sJYTXCWInU2tOo9Tr+MQ/c1jHparzetnfLV32vZsh1qfSx0FaVl5cDkJ2d3copEUIIEROGaTvKWjsV7deOn+H0pa2dCiGEEO3NI39u7RQ0qLy8nMTExNZORuuSGgttU1ZWFjt37sTtdqO0wfYoXq+X7Oxsdu7cicfT9OGGhDgcJJ+Ktk7yqGgPJJ+K9kDyqWgNhmFQXl5OVlZWayel9UkfC22Tqqp07ty5tZNxUB6PR368RZsn+VS0dZJHRXsg+VS0B5JPxeF21NdUqKaq0amh7e3AEVewIIQQQgghhBBCtA8HqbHQTnoNlYIFIYQQQgghhBCiNUgfC6I5bDYbU6ZMwWZrw+MFi6Oe5FPR1kkeFe2B5FPRHkg+FaKVHSF9LCiGjPEhhBBCCCGEEEIcNl6vl8TERIo/nIXH6Ygf568k5aLfUlZW1qb7QZEaC0IIIYQQQgghRGtQVVBNDW9vB6RgQQghhBBCCCGEaA1HSFMIKVgQQgghhBBCCCFag3TeKIQQQgghhBBCiGZTlejU0PZ2oH0UfxxBXnrpJbp164bdbmf48OGsWLGitZMkjhJffvkl559/PllZWSiKwocfflhru2EYTJ48mY4dO+JwOBg9ejQ//fRTrZji4mKuvvpqPB4PSUlJ3Hjjjfh8vsN4FuJINnXqVE444QTcbjfp6elcdNFFbN68uVZMIBBg/PjxpKam4nK5uOSSS8jPz68Vk5uby7hx40hISCA9PZ17772XSCRyOE9FHMFmzpzJwIED8Xg8eDweRowYwccffxzbLnlUtDVPPPEEiqJw9913x9ZJPhWiDamusdDQ1A60j1QeId59910mTpzIlClTWL16NYMGDWLs2LEUFBS0dtLEUcDv9zNo0CBeeumlerc/9dRTvPDCC8yaNYvly5fjdDoZO3YsgUAgFnP11Vezfv16Fi5cyEcffcSXX37JLbfccrhOQRzhlixZwvjx4/n2229ZuHAh4XCYMWPG4Pf7YzG/+93v+M9//sPcuXNZsmQJe/bs4Ve/+lVsu6ZpjBs3jlAoxNKlS3n77bd56623mDx5cmuckjgCde7cmSeeeIJVq1bx3XffccYZZ3DhhReyfv16QPKoaFtWrlzJK6+8wsCBA2utl3wqRBtS3cdCQ1N7YIjDZtiwYcb48eNjy5qmGVlZWcbUqVNbMVXiaAQY8+bNiy3rum5kZmYa06ZNi60rLS01bDab8Y9//MMwDMPYsGGDARgrV66MxXz88ceGoijG7t27D1vaxdGjoKDAAIwlS5YYhhHNkxaLxZg7d24sZuPGjQZgLFu2zDAMw/jvf/9rqKpq5OXlxWJmzpxpeDweIxgMHt4TEEeN5ORk4y9/+YvkUdGmlJeXGz179jQWLlxonHrqqcZdd91lGIb8lgrRVpSVlRmAUfzJ34zI1+/HnYo/+ZsBGGVlZa2d5AZJjYXDJBQKsWrVKkaPHh1bp6oqo0ePZtmyZa2YMiFg+/bt5OXl1cqfiYmJDB8+PJY/ly1bRlJSEkOHDo3FjB49GlVVWb58+WFPszjylZWVAZCSkgLAqlWrCIfDtfJp79696dKlS618OmDAADIyMmIxY8eOxev1xp4oC3GoaJrGnDlz8Pv9jBgxQvKoaFPGjx/PuHHjauVHkN9SIdoaRVEOOrUH0nnjYVJUVISmabV+oAEyMjLYtGlTK6VKiKi8vDyAevNn9ba8vDzS09NrbTebzaSkpMRihDhUdF3n7rvvZtSoUfTv3x+I5kGr1UpSUlKt2APzaX35uHqbEIfC2rVrGTFiBIFAAJfLxbx58+jbty9r1qyRPCrahDlz5rB69WpWrlxZZ5v8lgrRxhwho0K0j1QKIYQ4qowfP55169YxZ86c1k6KEHX06tWLNWvWsHz5cm677Tauu+46NmzY0NrJEgKAnTt3ctddd/HOO+9gt9tbOzlCiINpoc4bmzJowGuvvcbJJ59McnIyycnJjB49usmDDEjBwmGSlpaGyWSq0+Nufn4+mZmZrZQqIaKq82BD+TMzM7NOR6ORSITi4mLJw+KQmjBhAh999BFffPEFnTt3jq3PzMwkFApRWlpaK/7AfFpfPq7eJsShYLVa6dGjB0OGDGHq1KkMGjSI559/XvKoaBNWrVpFQUEBxx9/PGazGbPZzJIlS3jhhRcwm81kZGRIPhWiLVGU/UNO1jc1oylEUwcNWLx4MVdddRVffPEFy5YtIzs7mzFjxrB79+5Gv6cULBwmVquVIUOGsGjRotg6XddZtGgRI0aMaMWUCQE5OTlkZmbWyp9er5fly5fH8ueIESMoLS1l1apVsZjPP/8cXdcZPnz4YU+zOPIYhsGECROYN28en3/+OTk5ObW2DxkyBIvFUiufbt68mdzc3Fr5dO3atbX+cS5cuBCPx0Pfvn0Pz4mIo46u6wSDQcmjok0488wzWbt2LWvWrIlNQ4cO5eqrr469lnwqRBvSyBoLXq+31hQMBuMe8tlnn+Xmm2/mhhtuoG/fvsyaNYuEhATeeOONeuPfeecdbr/9dgYPHkzv3r35y1/+ErtXbSzpY+EwmjhxItdddx1Dhw5l2LBhTJ8+Hb/fzw033NDaSRNHAZ/Px5YtW2LL27dvZ82aNaSkpNClSxfuvvtuHnvsMXr27ElOTg4PP/wwWVlZXHTRRQD06dOHs88+m5tvvplZs2YRDoeZMGECV155JVlZWa10VuJIMn78eGbPns2//vUv3G53rB1vYmIiDoeDxMREbrzxRiZOnEhKSgoej4c77riDESNGcOKJJwIwZswY+vbtyzXXXMNTTz1FXl4eDz30EOPHj8dms7Xm6YkjxIMPPsg555xDly5dKC8vZ/bs2SxevJhPPvlE8qhoE9xud6xvmmpOp5PU1NTYesmnQrQhBxtSsmpbdnZ2rdVTpkzhkUceqRNePWjAgw8+GFvX1EEDKioqCIfDsQ60G6W1h6U42syYMcPo0qWLYbVajWHDhhnffvttaydJHCW++OILA6gzXXfddYZhRIecfPjhh42MjAzDZrMZZ555prF58+Zax9i3b59x1VVXGS6Xy/B4PMYNN9xglJeXt8LZiCNRffkTMN58881YTGVlpXH77bcbycnJRkJCgnHxxRcbe/furXWcHTt2GOecc47hcDiMtLQ04/e//70RDocP89mII9VvfvMbo2vXrobVajU6dOhgnHnmmcann34a2y55VLRFNYebNAzJp0K0BdXDTZYsft/QvlsQdypZ/L4BGDt37jTKyspiUyAQqPe4u3fvNgBj6dKltdbfe++9xrBhwxqVtttuu83o3r27UVlZ2ejzUQzDMBpfDCGEEEIIIYQQQohfwuv1kpiYSMmSD/C4nPHjfH6ST/0VZWVleDyegx53z549dOrUiaVLl9Zqcn/fffexZMmSgw4T/8QTT/DUU0+xePFiBg4c2OjzkaYQQgghhBBCCCFEazjEw03+kkEDnn76aZ544gk+++yzJhUqgHTeKIQQQgghhBBCtI6GRoSonpqguYMGPPXUU/zpT39iwYIFDB06tMmnITUWhBBCCCGEEEKI1nCIayzAwQcNuPbaa+nUqRNTp04F4Mknn2Ty5MnMnj2bbt26xTrQdrlcuFyuRr2nFCwIIYQQQgghhBCtoZGjQjTFFVdcQWFhIZMnTyYvL4/BgwezYMECMjIyAMjNzUVV9xdYzJw5k1AoxKWXXlrrOPFGnqg3mdJ5oxBCCCGEEEIIcfjEOm9c9t+Dd9444txGd97YWqTGghBCCCGEEEII0RpaoMZCa5CCBSGEEEIIIYQQojW0QB8LrUEKFoQQQgghhBBCiNagqtGpoe3tgBQsCCGEEEIIIYQQrUBRFJQGmjs0tK0tkYIFIYQQQgghhBCiNSjKQZpCSMGCEEIIIYQQQggh4pHOG4UQQgghhBBCCNF8B+m8EeljQQghhBBCCCGEEPFIjQUhhBBCCCGEEEI0m4wKIYQQQgghhBBCiGaTGgtCCCGEEEIIIYRoNuUgfSw02P9C2yEFC0IIIYQQQgghRGs4QmostI/iDyGEEKKN6datG9dff31rJ0MIIYQQ7ZrSiKntk4IFIYQQooatW7dy66230r17d+x2Ox6Ph1GjRvH8889TWVnZ2sk7JM466ywURWHChAmN3mfp0qWcdNJJJCQkkJmZyZ133onP52vBVAohhBBHgeoaCw1N7YA0hRBCCCGqzJ8/n8suuwybzca1115L//79CYVCfP3119x7772sX7+eV199tbWT+Yt88MEHLFu2rEn7rFmzhjPPPJM+ffrw7LPPsmvXLp5++ml++uknPv744xZKqRBCCHEUOEKaQkjBghBCCAFs376dK6+8kq5du/L555/TsWPH2Lbx48ezZcsW5s+f34op/OUCgQC///3vuf/++5k8eXKj95s0aRLJycksXrwYj8cDRJuC3HzzzXz66aeMGTOmpZIshBBCHOEO1tyhfRQsSFMIIYQQAnjqqafw+Xy8/vrrtQoVqvXo0YO77ror7v7FxcXcc889DBgwAJfLhcfj4ZxzzuH777+vEztjxgz69etHQkICycnJDB06lNmzZ8e2l5eXc/fdd9OtWzdsNhvp6emcddZZrF69OhZTUVHBpk2bKCoqatI56rrOPffc0+h9vF4vCxcu5Ne//nWsUAHg2muvxeVy8c9//rPRxxJCCCHEAY6QphBSsCCEEEIA//nPf+jevTsjR45s1v7btm3jww8/5LzzzuPZZ5/l3nvvZe3atZx66qns2bMnFvfaa69x55130rdvX6ZPn86jjz7K4MGDWb58eSzmt7/9LTNnzuSSSy7h5Zdf5p577sHhcLBx48ZYzIoVK+jTpw8vvvhio9KXm5vLE088wZNPPonD4Wj0ea1du5ZIJMLQoUNrrbdarQwePJj//e9/jT6WEEIIIQ5wZPTdKE0hhBBCCK/Xy+7du7nwwgubfYwBAwbw448/oqr7y+yvueYaevfuzeuvv87DDz8MRPtx6NevH3Pnzo17rPnz53PzzTfzzDPPxNbdd999zU4bwO9//3uOO+44rrzyyibtt3fvXoB6a3F07NiRr7766helSwghhDi6HRlNIaRgQQghxFHP6/UC4Ha7m30Mm80We61pGqWlpbhcLnr16lWrCUNSUhK7du1i5cqVnHDCCfUeKykpieXLl7Nnzx6ysrLqjTnttNMwDKNRafviiy94//33a9WKaKzqkTBqnl81u91+xIyUIYQQQrSKI6TzRmkKIYQQ4qhX3XdAeXl5s4+h6zrPPfccPXv2xGazkZaWRocOHfjhhx8oKyuLxd1///24XC6GDRtGz549GT9+PN98802tYz311FOsW7eO7Oxshg0bxiOPPMK2bduala5IJMKdd97JNddcE7cgoyHVzSaCwWCdbYFAoEnNKoQQQghxAIWD9LHQ2glsHClYEEIIcdTzeDxkZWWxbt26Zh/j8ccfZ+LEiZxyyin8/e9/55NPPmHhwoX069cPXddjcX369GHz5s3MmTOHk046iffff5+TTjqJKVOmxGIuv/xytm3bxowZM8jKymLatGn069evWUM7/vWvf2Xz5s3ceuut7NixIzZBtCBlx44dVFRUxN2/uglEdZOImvbu3Ru3RoUQQgghGkFRDz61A+0jlUIIIUQLO++889i6dSvLli1r1v7vvfcep59+Oq+//jpXXnklY8aMYfTo0ZSWltaJdTqdXHHFFbz55pvk5uYybtw4/vznPxMIBGIxHTt25Pbbb+fDDz9k+/btpKam8uc//7nJ6crNzSUcDjNq1ChycnJiE0QLHXJycvj000/j7t+/f3/MZjPfffddrfWhUIg1a9YwePDgJqdJCCGEENWOjN4bpWBBCCGEINo5otPp5KabbiI/P7/O9q1bt/L888/H3d9kMtXp82Du3Lns3r271rp9+/bVWrZarfTt2xfDMAiHw2iaVqvpBEB6ejpZWVm1miM0drjJK6+8knnz5tWZAM4991zmzZvH8OHDY/GbNm0iNzc3tpyYmMjo0aP5+9//XqupyN/+9jd8Ph+XXXZZg+8vhBBCiAYcIcNNSueNQgghBHDMMccwe/ZsrrjiCvr06cO1115L//79CYVCLF26lLlz53L99dfH3f+8887jj3/8IzfccAMjR45k7dq1vPPOO3Tv3r1W3JgxY8jMzGTUqFFkZGSwceNGXnzxRcaNG4fb7aa0tJTOnTtz6aWXMmjQIFwuF5999hkrV66sNUrEihUrOP3005kyZQqPPPJI3HT17t2b3r1717stJyeHiy66qNa6Pn36cOqpp7J48eLYuj//+c+MHDmSU089lVtuuYVdu3bxzDPPMGbMGM4+++y47y2EEEKIgzhCOm+UggUhhBCiygUXXMAPP/zAtGnT+Ne//sXMmTOx2WwMHDiQZ555hptvvjnuvpMmTcLv9zN79mzeffddjj/+eObPn88DDzxQK+7WW2/lnXfe4dlnn8Xn89G5c2fuvPNOHnroIQASEhK4/fbb+fTTT/nggw/QdZ0ePXrw8ssvc9ttt7Xo+cdz/PHH89lnn3H//ffzu9/9DrfbzY033sjUqVNbJT1CCCHEkePIGG5SMRo7VpUQQgghhBBCCCF+Ma/XS2JiImXb1uNpYLhrb3k5id37UVZWFhvFqi2SGgtCCCGEEEIIIURrkKYQQgghhBBCCCGEaL4joymEFCwIIYQQQgghhBCt4mAjP0jBghBCCCGEEEIIIeKRphBCCCGEEEIIIYRoviOjKYTakgefOnUqJ5xwAm63m/T0dC666CI2b97c4D5vvfUWiqLUmux2e0smUwghhBBCCCGEOPyqayw0NLUDLVpjYcmSJYwfP54TTjiBSCTCpEmTGDNmDBs2bMDpdMbdz+Px1CqAUJrwYeq6zp49e3C73U3aTwghhBBCCCFEyzMMg/LycrKyslDVFn3W3fYdGRUWWrZgYcGCBbWW33rrLdLT01m1ahWnnHJK3P0URSEzM7NR7xEMBgkGg7Hl3bt307dv3+YlWAghhBBCCCHEYbFz5046d+7c2sloZUdGycJh7WOhrKwMgJSUlAbjfD4fXbt2Rdd1jj/+eB5//HH69etXb+zUqVN59NFH66zfuXMnHo/nlyf6ENu55geeOfWc1k5Gu5ViUjk3xdXayWi3rHYTXbsmtnYy2iXFYsKWLp9ds3lcmIcNa+1UtF+JqZhOvrC1U9EuFa3fyAcXXNnayWgXSlK6sy+jF+XuTNYMvx5vctdmH+vUhFy6WsvZGkpExcCi6FgVHZOi41LDXJb4I8daSw9d4tsqqw210zGtnYp2y9B1CFa0djLaNeWY41ES2t49kdfrJTs7G7fb3dpJaX3SeWPT6LrO3XffzahRo+jfv3/cuF69evHGG28wcOBAysrKePrppxk5ciTr16+vtzTrwQcfZOLEibHl6kzq8XjaZMGC2+XC2k5Kndoim6LgPNqrS/0CNlXFYza1djLaJcVswmaV/m6bzWrBnCD95TRbggNTG/yf1h4EnU4civzfaMjPOSfxzZn3sStnZK31Ll8BQ7d9zDlndef7QAdMisGx1hI6mCswUNgR8mBSDLpby+huLUMzFP5R2pu/l/bhK70/XwXiv+e/C4/nkfRlXJb4IwpQplvxqCHUI+0SyWpHdckDkeYydA0sR1qmOLwUj6dNFixUk6brSMFCU40fP55169bx9ddfNxg3YsQIRowYEVseOXIkffr04ZVXXuFPf/pTnXibzYbNZjvk6RVCCCGEOJL53Bl8edYfWDfk/wBQdI2Ou1Zjryyly7avGbz8TbI6J3HpZXdxBT826piTM77lqqRNvFY8AJuq0c+2D5OiEzZUwoaJsKHylb8TX1V05sH8k3mtZABmDH4MJdPHto8p6csYlpDfkqcthBBtjDSFaLQJEybw0Ucf8eWXXza5DY3FYuG4445jy5YtLZQ6IYQQQoijR9DmZvkpd7Bq5K2ErU7QdQavfIsRi5/D7d17QHRSk4/f01bKUx2/irv9N8nreK14AC8XD2JbaP/xNwZTuXLneUzv+AUXeLY1+X2FEKJdUjhIjYXDlpJfpEXrBhqGwYQJE5g3bx6ff/45OTk5TT6GpmmsXbuWjh07tkAKhRBCCCGODhGzjZWjbuOVe1bx7WkTCVudZOWu5OpXz2XMv++rp1ChZagK3Jq6lm+OeZcnMr9kWuYSvsj5Jxe6ow+RHsw7ie8r09gYSKYwIk2ohBBHuBYabvKll16iW7du2O12hg8fzooVKxqMnzt3Lr1798ZutzNgwAD++9//Nun9WrTGwvjx45k9ezb/+te/cLvd5OXlAZCYmIjD4QDg2muvpVOnTkydOhWAP/7xj5x44on06NGD0tJSpk2bxs8//8xNN93UkkkVQgghhDgihS0O1h5/FStOvgNvcjYAqQWbOeXTx+ix8eNWexjmUsNcnvhTbPnpjl9SqDlYWtGJi3P3d1aaYfYz3LGX0a5cznHvwKQYrZFcIYRoIYe+KcS7777LxIkTmTVrFsOHD2f69OmMHTuWzZs3k56eXid+6dKlXHXVVUydOpXzzjuP2bNnc9FFF7F69eoG+0esqUVrLMycOZOysjJOO+00OnbsGJvefffdWExubi579+4vIS8pKeHmm2+mT58+nHvuuXi9XpYuXSpDSAohhBDikAja3KwffBlLT/89K0fdRsiSEDe2IiEFTW2fHbf6nWl8c/o9zLr3f3x2wVN4k7Nxl+7m7A/u5IYZp9CzFQsV6mNSDKZ3XExnSzkAiWoQBYP8iJN/l/fgzr1ncO6Oi/nCd7QPTSfE0aepT9/h4E/gDcNg8uTJdOzYEYfDwejRo/npp5/iHK3leH0+vOXl8SefLxrn9daagsFg3GM+++yz3Hzzzdxwww307duXWbNmkZCQwBtvvFFv/PPPP8/ZZ5/NvffeS58+ffjTn/7E8ccfz4svvtjo82jR/5SGcfAS5cWLF9dafu6553juuedaKEVCCCGEOJqUhs382HccFc40Ao4kyhOz2DDoUoKO/cPHrjnhOvqveZeS1O4ouoZiGJQnZlGY2RefpyPmcCWpBZtRdQ1TJEhi6U48JTvxlO3CXllGWsEmUgsP/8VofcrdmWzpew6b+13AzpyRGGp0JKDE4h0M+/ol+q/6B5ZIA8M1tLI0c4CF3d4jaJjxmEJU6GZ+CKTxpb8z/yjtzU+hZG7cPZbz3Ns4w5lLZ0s5Ax1FWBW9tZMuhGghTX36Do17Av/UU0/xwgsv8Pbbb5OTk8PDDz/M2LFj2bBhA3Z7yzfDslqtZGZmkn1sv4PGulwusrOza62bMmUKjzzySJ3YUCjEqlWrePDBB2PrVFVl9OjRLFu2rN7jL1u2rNZIiwBjx47lww8/PPiJVGmfRfBCCCGEEAcIabB+n4k9PoUfS0yszDPz7Z5haFcPrxObUvAjnXJXsKPH6ZR06MFXZ/0h7nEjFgf5nQbHlnfVE3Pue+Pp/793Cdg9lCd2ImxxkFS8A0u4kqDdg9NXgNKIBy5N4XOlk99pEHlZg2JzX2JWrZiOO1cx9JtZ9Fr/b1RdO6Tv31Jsqo6NEAAJaoQTE/I4MSGP36Z8z4v7juONkn58VN6dj8q7A+BQwlzo2cpD6ctJUCOtmXQh2pQzbpxI/xNOxGxz8Pbbb2O1Wnnsscf4v//7PyZMmMB7771HRkYGM2bM4JxzzgFg3bp13HvvvXz11Vc4nU7GjBnDc889R1paGgALFizgscceY926dZhMJkaMGMHzzz/PMcccA8COHTvIycnh/fffZ8aMGSxfvpyePXsya9asWiP/NUXNp+8As2bNYv78+bzxxhs88MAD9e5T8wk8wJ/+9CcWLlzIiy++yKxZszAMg+nTp/PQQw9x4YXRpld//etfycjI4MMPP+TKK69sVlqbwm63s337dkKh0EFjDcOoMzRnvJERi4qK0DSNjIyMWuszMjLYtGlTvfvk5eXVG1/dlUFjSMGCaPM0iwN/eg/86T3ZndGTgg6dqbBHx+O1RIIk+otI8heQ5Csks/RnMou3kxDytXKqhRBCtKRtpSobi1USLFBcqbC6wMT8bRZKgnVbeXbYu47EklwclSXYKsvI3rGMHpsWoBgGlY4klp5+D353Bh3yN6DoOprJgtu7h5SiraTvXYfPncm+9GPBMAhbE/AmdcablI03sRM+TyYFWQNZcPF01g++nJ+7nwxq3TS4yvbQa92/ydz9PZrZwp7sEzBHAiTt284xmz8luXgHBhBwJFHh6kDQ7iFkdRGyOaNzuxufOwOfpyPlniz2pR+Lz1NPx9a6Tsfd/6PXun9z7Pr/kFSS2wKffuvwmMJMSl/BeZ5t/K2kD3siLn4MJrNPczCnrDerKzN4LONrhjgK2suw70K0uL++8w/uu+8+VqxYwbvvvsttt93GvHnzuPjii5k0aRLPPfcc11xzDbm5uYRCIc444wxuuukmnnvuOSorK7n//vu5/PLL+fzzzwHw+/1MnDiRgQMH4vP5mDx5MhdffDFr1qxBrfHb94c//IGnn36anj178oc//IGrrrqKLVu2YDabyc3NjTVzz8rKqjfdkyZNYtKkSc16+g4HfwK/fft28vLyGD16dGx7YmIiw4cPZ9myZYelYAGihQuHo3bE4SAFC6JeBqCbbUTsbjSrE0M1YagqhmKCqmqVajiIGgmgRoKo4SCmSABFC9fbXtNQVHSzFd1kjc0NsxXdZImus9gJJyQTcncg5EylMqUrvvSe+DN6EkiuXe3nh0akP9FXQFbxdjoWbyOzZDuZJTtI9hWQ5C8k0V+IPVz5iz+jlhawOChI7EKJO4MSVzolrv3zUlc6fpuHsNlOyGxHV1USguUkBLy4AmUk+QpI8+4mzbubDmXReVrZbmzIkxwhRPsTiMBXu818usPCbp9Knl9ha5mp3tgUu05Ook5Xt87gdI3+/g18+4excY/tqCzlzP8+1OD7p+zbSsq+rfVuMxSFjy6dycbBl/Jzj1Ojx/TvwxQJ1qo94EvMYtWo39Z7jM/Pexx36W4qnKlolsZfYCq6RkrhT2Tu+Z6M3d+Tued7Ouxdj+0IL1wfaC9iWtVwlroBSyuy+P3eU/kxlMzlO8+nu7WUrhYvQx353JiyTppJiKPaoAH9eeih6G/cgw8+yBNPPEFaWho333wzAJMnT2bmzJn88MMPfPbZZxx33HE8/vjjsf3feOMNsrOz+fHHHzn22GO55JJLah3/jTfeoEOHDmzYsKFWJ3/33HMP48aNA+DRRx+lX79+bNmyhd69e5OVlcVXX33F8ccfz1dffYXb7a6T7pSUFKB5T9/h4E/gq+e/9Cl9W5SWlobJZCI/P7/W+vz8fDIzM+vdJzMzs0nx9ZGChaOAZrETSMwi6E4n6MkkmJhJ0J1OOCGZiN1DxO7eP9ncsXWG2dr0N9P1WGEDihorSKgujGgui28fzoKfSC3awuBQEc6AFzAIWRyUOtModXagxJVBfnJXSl0ZlLnSKXOls7FL3eqvAPagj8SKomhBg6+QpIoiknwFJPqLSPQX4QqUNqrKqmpoJPqLSPblY2tmm1VNMZGX3I3c9N7kdqia0ntTkNQFQ2l8/6plzg4HjUkt30tOcDfZ/p109u8i27+TbP8ucsq3kaC13Ta3QoijU75f4a8brMzeZKU4UPv30KwY9E/TCOkKbotBn1SN07MjnNQpgrlGaOEPAb5twTQqhsE5H9yJ01eAqkcYtPJvJBdvByBstqObzJi0MNt7nsH2nqdTlN4bgE650Y7H8jodR273kyhP6hQ7pq2yFHtlGdaQD2vQhyXkxxr04fQV4C7bi8u7l6TiHaTvXYc1XNGCZ9f2qQqc5NzDf7p9yLNFQ/iX9xi2hZLYFkriC38X5pd3567U1QxLyCPRdPDqxkIcaQb0399+32QykZqayoABA2Lrqm+sCwoK+P777/niiy9wuVx1jrN161aOPfZYfvrpJyZPnszy5cspKipC16MFd7m5ubUKFgYOHBh73bFjx9h79O7dG7PZHGs6ccwxx+DxeA7hGQur1cqQIUNYtGgRF110EQC6rrNo0SImTJhQ7z4jRoxg0aJF3H333bF1CxcubFLzlRYvWHjppZeYNm0aeXl5DBo0iBkzZjBs2LC48XPnzuXhhx9mx44d9OzZkyeffJJzzz23pZPZrhlAOCGZitQcKtO6UZGag79DdypSc6hI604wqf4qRo2lhipQ9AiKoaPoOkpVG03dbEWz2DHMNdr3qCq6NQHdGr+HbQAlEkLVQqiRMIoWfW2pKMFaXoTVV4S9bDfOgp9wFfyEs+AnrP5iAFLNKhek1i3VrKnC6iIvJYe9yTnsTclhT0p3CpK6UOrsQKkrnZDFQcDmImBzkZ/c7Rd9NjUlBMpILc8jrWwXad49dCjbRZp3N0n+QhxBH5pqosLmYZ+nI4WJnSlM7MyutGPZldaTsLn+J1SuyhJSyvNI9uWT7CsguTyfZH8+yeX5OINerJEA1nAAxdCptLnx2RPxOZIocWVQ5OlEUWInijydKPR0ImBzsc/dkX3ujnyXNrTW+yiGThf/To4t+5FeZZvp6f2JXmU/0rFyb5vpMTysmCm3uGOTz+wkYLYTVO0ETDZ0RcWoUffVqEp59dxkaDgjFTgjPlxhP6nBfXQIFGLT5ULzaOTVrWzXUsnTXZgwMKNjUnTChoqBQkdTOalKBToKTiWIWwm16arVFWHYUmpiZZ6JFXlm1haZKA8p2EwGv+oZZlRWhH0BhR8KTWwsNlEWVHCYDYZmagzPjDA0QyPJ3naG8Mv1Kjy7ys5/tlqIGNEPPtOpc25OmIEdNBKtBkMyIiTW37z0sDNrIc74eHKd9ZZIgOqKYj03fkzPjR/Xu7/PnUFZUjZOXwGu8nzMkfg9fYv6pZsreSLzax7osILVlRnsCHl4qXgwG4Kp3LrnLBQM+tr2MTJhD6c4d3FCQr7UZBBHBYvZUmtZURQsFkutZYjeePp8Ps4//3yefPLJOsepLhw4//zz6dq1K6+99hpZWVnouk7//v3r9BMQ7z2AJjWFaM7Tdzj4E/jqeX5+fuzcqpcHDx4c97jtxcSJE7nuuusYOnQow4YNY/r06fj9/lg/Fddeey2dOnVi6tSpANx1112ceuqpPPPMM4wbN445c+bw3Xff8eqrrzb6PVu0YKE1xs9sD8LEv3hTAHPVjZCumghaE4jYnGg2N0FPBsHEjgQTs2LzQGpXKtJyiDiSGnxPNejH7s3H5s3DVp6PxZuHxV+MOeDFFCjHHPBiDpRjDpRjCnixB3zR5ZCPiNHwP15zdc0Ei52QyYJmsaNb7Ci6hhoJVRUchFEiIWxaONZcIoLRwCcBlhq3tNWxIcOgsp70OGo82TcFy+m493s67v2+TpwBYHXhdaVT6uxAUUIapc40ylwdKHNGJ6+zA357tLdw5cB9D6CpZsqqCisq7IlU2BPZ2aFXQx9XvayhCjoXbqJz4SayCzfRuWATnQs34qkoxoqCqeoHOWwYRBr41GrGRgwjltcMwOdIpiS9O1rPPux2ZpPnymaXM5tcVxf22dP42dWVn11dWdjprNjx3CEvx5T9SI7vZxIj5bjD5biCZdhDXpwRHxY9jE0LYtFDWLUwZiOMBQWLWp1eCNf4exkoVJod+M0u/BYXAYubSqsbv8WJ1+zCa3biq9rms7jwWdz4qgoSgmZHkz/XxkgMlpARKCC9spAOgXxSKvLpUFlAh0ABHSrzSQnswx32YtM00HRspmheMwyDikj874ZZVRoda1LBbtpfq8cfjt/BWlNiVQUc5ubFVkQ04lXaURRIaGZsZSiMrsfPw067tVmx3qBGkZZAiZFAseGk2Ii+Lql67VVclBl2AoaZPZqbIqPhAsoDWYnQQy2gu1qEUwmSQBiHEiLTEiRZDWBTIqCFUPUIFjTcSoAeamGtwgiH1RxrfxqKRAg3kCdqxvrCOt+XJZG/XqOg0kRhwERhpUphZXReUGnCF45Xu0nhlR9svPJD/XfgqwvMvFq1rWdimJEdg1zby0dHp47NZsNsjl4mhMPhBjuXqhkbiUQaHALLarXGLjgrghEWbFOYv8PB5lILhZUqWU6NnT4zYT364Z2QEeE3A4Kc2TlIJFSjZlUE/DVad1ksFqzWaJ7QNI1AIBrrr6wkeEAGNQHm6gtdwyAcN7VNi1UBS1WsYRg0VGxZM9bpzcPijVa91aqmeLFAnfNpbqwCWA8SG9B1/IEgqqLgsO3/zlUEgnH/GylAgt3WrNjKYAi9gTQ7DxJrIchwcznDzXC+Zxsv7RvE1xWd2BZKYn0wjfXBNF4rGUiyWsl5zs3ckriKJFOw1nEDoTCaHv/7mWCzxm6UgqEwkYZiLfuPGwyFiETi/w477Lb9vxGhMOFI/OaLNWMrg2E2e63sDtgxKQa9XT46WPfnVLvNiqnq/8bBjlszNhyOEArHz/E2q6XGb0TjYyORCMFQ/FirxYLFUiO2Mn7zVat5f6ymaQQa+J2ymM1Yq357mhKr6zqVDfymNSXWbDJhq/qdMgyDikD82qJNiTWpJuw1vp/+Bj6zhhx//PG8//77dOvWLfb3qmnfvn1s3ryZ1157jZNPPhmAr7/+usnv05SmEM15+g4HfwKfk5NDZmYmixYtihUkeL1eli9fzm233dbkc2prrrjiCgoLC5k8eTJ5eXkMHjyYBQsWxGqo5Obm1uoTY+TIkcyePZuHHnqISZMm0bNnTz788MMm3YMrRmPGhGym4cOHc8IJJ8TGv9R1nezsbO644456e/C84oor8Pv9fPTRR7F1J554IoMHD2bWrFn1vkcwGKx1AeP1esnOzqasrKxNVqvJXb2GHudfh+esP6BY7CgWR3QyR19bbU4sVhea1YlubdqNlFa2l8i+bYSLthDZt5VI4f55akUpv8IZi30HH744/+aTUbm8Ruw/8VNC/f80XShczf7qUh/gpzBOrB2F62rE/psK9ta5fIoyAzey/0fmYyrIjRML8G3G/jGtJ5Xu4/Ng/B/UL9KzYgURfywr5r+B+NVIP+7QkeSqZhzTvCW8X+mvN06xe3ip6yBMydkUebL4rz2Nja5MzCldUZ1pqHYPaBH0kA+tZCenVxbSvXwvWfu2suznlfxj11ri3Zm9kZJOX0v0n8Xf/eW86CuLm96XktMYYo3WfnivwsfT5aVxY98dkMPY1Oh35NVShYcrUrFmDcLaaRCWrEFYMvuimCxx928tzrAPW8hLnrcYI+THCFdihCtB23+h1MNhpUdC9GLOH9H4stSHoppR7G5UuwfVkYTJnYnShLbMetBHQthLV7WSJK0ce7CU+T/9hF5RjF5Rgl5Zih70YYR86EEfY9PsPNq/IxZDIxQOcsK/vwEtjFE1Vb9GD/Or7DRmn7q/WqL9b5/HTcfZnVL58IxB6CiEFTNZ7y+nEjOK2QZmK4rZhmKKzgd1SOXJ4f1QMEjQApz38VKKfSUYQX/ss6s2JNXNN+eeEFs+9oOl5Prrv4jpk+jkfxfsb2Z03L+Xs7Gs/u9GF6edH381EjxuzCNHcOLDL/Ldthp96qsmUM0oFjtpyWkse+JeNEPFomhc9epHfF8MpsQsTAkpoKgoNhcmVwcsnkxGDDqefYaDfboTn9H0R9eRsj1oJT8DSjSvmywYkRDHdctir5FEmWHH0HUMtXll8JGibVRu/BitPA8jEmTSBacwwBMmVfUzY9H/mLdmK4rZjhGqAEXBlNIVa8eBWLIGcHx2Gmk2hWLDwYZgMprp4Hk10aaT5N/C9x+/QXD7N2jl+VjSe+Ma9VtMiZ3QK4o574RujBucQZrDYM78Jbz77S5sx5yCJaN37DhGJIh38bP89foeXHDOGADe+ts73PDb8XHf+59/e4vLfnURAHM/+JDLr7m+ToziSMLk6sAfHn2KLkPGsiLPzOIdBpVG/c3uKjd9yi3H5vPYrRcAsPjLrzj9nPPjpuGpx/7Ivb+7E4CVq1Yz7JQz4saeY3Ewrqpm3V49wp8r4/+unmmxc7E1+j9xn64xpbI0buzJZhtX2KL/58oNnQcrSuLGDjfbuKYqNmgY/L6iOG7scSYrN9r3/0+c4N8XN7afycJt9v3XPxP9++IWcPRQzdxdY9jNB/zFca8Nhvbsxrcv7O+L4pjr7ufngvrT0bdLFj+88sfY8sBbJ7Mhd0+9sV3TU9n69v6noyfe+Rjf/bSj3tg0j4u8d6fHls+47ym+XPtjvbEJNiveD1+OLZ/9+Dss9Wdg7zUGR99zMbmjF9ehvespnHkWwXf/FIu94s8zef/rVfUeF6Bs3kuxgojfPPMGf/1sadzYve+/TEb/IQCMf/QpZs5+P27stkXz6NY5+vT23idf4Jk33okbu/ajf9C5W0/e2JHJaxsdVFhSam0P5n6H7+uX8H/3N5a/+xdOGBh9OjztL3/j/mnxx6X//K8vc9rwaHpfemcud/zx6bix/3nlGcaddhIAb33wEb958E9xY9+d/jiXnXMmAHM/XsQVd0+KG/vG1Ie5/lfnAfDR519ywW33xo2d8cAd3H5ltEf/xSvXcObN98SNffLum7nn+isAWLluEyf+Ov5N6eRbr2HKbdcBsH7LDgZeelPc2N9fexlPTbwVgB278zhm3K/jxt52+QW8OCn6O1VYXErmGZfGjb32/DG8+af7gGhBgWdE/N+/S0afwj+f3l9ryjQ42iHhqUMGMmj4KJ5/af93oVu3btx99921brgVRWHevHkMGzaMwYMHc+qpp3LfffeRkpLCli1bmDNnDn/5y19QFIX09HTOOeccpkyZQm5uLg888AArV65k3rx5XHTRRbFRIf73v//FbthLS0tJTk7miy++4LTTTgOi92yJiYmNumd79913ue6663jllVdiT9//+c9/smnTptiN8oFP4JcuXcqpp57KE088EXsC//jjj9d6YP3kk0/yxBNP1Bpu8ocffjhsw00eaVqsxsLhGj9z6tSpPProo4ckzYeL6kzFedzlcbcfWIZraBGMYDlaeT5a2R60st1Eynbj8uZxwr69JOzbgaP4Z94JFRCI+0yg8W31RdMZAS+Z+34iqzTarnZDeSnfVMTvPOui1Ay6V1VN+95XFrdQ4XBxVu4j+OMagj8u2r/SZMGS0Qdrp0Fc2msw2UlpeC0eNut2/he2otg90ZtYsy1aSFZ1Q5ugKljV/TUsfAc8nTFCFeiBMoyAl55qkD6mEK6Ij1JfCe/n/owR8KIHytAD3v037JWl3J/hYGKmA7OhsdpbwRmr448Z/5uuGTyYE63ittEf4IOVm+uNUxNS+HWvAVzWsx8FjnR+MqfySrkFc2InTFWT6kxFUU2oVc1nYkdygyvttLhpWANcWGO5xkh1dXynawwggtmIYDEidOpRgaFHojfSJjMopth8o8lMX7OFiBL9+U49Pv5x84Hrayw7+kCnGsuGrlcVMFSwL1LBeXYDhxbAoQfhmj2kVpRHt4cqMAwt9vcO2BO4s0tHQoqFkGKh8voAGZiqCjds0YLSqlhMJoZYo01V9EIzlbffRTbq/nM7wBk1760u/S31j04dtfqAh26GFkH3F6H5CtF9hWi+gujcX8gLl40iSQngUMI8+8/3+fSL+RjB8nqP++7rf4zVhvjNrH/yt283YPJkYel8HObU7qhWJ4rViWr3MO6kk6g0uQgZZrbtK6eoIoxismJK7IQ5rTvuk/ffjM8E8FYtDPk1HYbEP7f1sP+fgQm08gLC+RvRvHvQvHuj/wuq5nNefoIzT+iNywJPT3+Xrxbtv0mLFP5E5fr/xJav+fg/nNYzegOylg0U/zN6wa66OmA75hTcJ0/A3uM0Ekc/yPOFBbh3mumX2rghCisj4A8rlOkOVFc6loze2HqchqPvOKydj4sVVL4ZAmpcBmhle/GteJPA5k/RyvZgTs1Brywl9PMKOj47rVHvLcTBWAJFVKz+gorVc0A1Ye89lpTLZmHt2I+OkzYzcqtKjrWM05w7qXTnAPELFlqdamJ+WQ/+sWQgJWELWEAPlBMp2oJicWDucCy2LkOx/d+bOIf+mnI9/+DHFIJoTYJvvvmG+++/nzFjxhAMBunatStnn302qqqiKApz5szhzjvvpH///vTq1YsXXnghVljQUg729B2a9wT+vvvuw+/3c8stt1BaWspJJ53EggULpFChmVqsxsKePXvo1KkTS5curdXpw3333ceSJUtYvnx5nX2sVitvv/02V111VWzdyy+/zKOPPlqnjUy19lhj4cEzLqdg4AWYwgHUcGVsroYDmEMVWIN+zEE/plAFRrAcRQvV2869ZrMJaHwTi4PFQu1mCE2JbU7zhqbEpphUxqXW7VCmZlOIoGGgN3BkO0qsCmPIMNAOUawNBbWRTRaaEnsomkLE3tdmomu36NMpm6Jiri4A0A1CDTR5qRkb0Q2CDcRaFTXWFKIpsZphEGigOqlFUbBW/cNoSqxuGFT+glgdBb/FRXlCMuGOnamwJFJmdlNqclOkuCgzuymzJOI3JVChOqg02avmDoImO2HFTAQTOc4pdgAASypJREFUEcUcnf6/vTuPj6K+Hz/+mpndmT2SzUFIQriVyCmgIBi0ikpBi1atWmttPepVharF+8Lab1usVq0ntlarv6+1+MWz1WJFEBRBbgREUBEMEMIVcu858/n9scmShSSEmBPez8djH9mZee/sezafbHbe+/l8ppnffh+My4liqihuJ4pbRXGrGKYTwaNiOJpGUPdQqXsJ6R5CTfj2uz0YOHi1KAYOUWXg1SJkaxVkaxWka9XomsJDlEytmkytilwrQhetmi56Nf5YOX5Vjd7APAh1h03Euzk3/Hfks9z7ujlHY8TsxrpE1x8bVC7mx47hSyeHEuUjqgwc3c0WJ51Sx4tHi2CpKJYWI6xcOGjk6uX01fcwQC/G5TIIaRYZWpBu7KZ3qo3r1PPqzcHj8dTp5hwh2kh35Lqx9Q1vUAre3+Lh3k/TqYrte1/N8jjk+GLoGhga6JrC0CBka5SEdErCBsHYwSeh8LscMr2KAZkOQ7NsxnQLc0xKFUYDte+6wybqDm+oT0NDIXatXccbZ1+cFNvRhkIcSiy07VCILn26cf7vb+g0QyEaiq1veMO2WCrX7TiHb2PpBzw2U68m0whylHsvY72bGef7Bq8er2Ye0lCI1ABGz2NqYr/7UIhP96bzx41H82VV/LNQP3+QG/oUMjZzB5Yefy1KIm7eKM7l2W97E3QMevuCPD/iK/qlhDrdUIhoJEy4kZ6aMhQirqGhEH6vFy3/BDRfxzsnOpQeC6Jz6PRXhbAsC8vqILM3NVFqWTGpHzd9IgzqLSscyN3EuNaMdbVyrKlpSUWE+liaRlNfM7OVYt2a1uTXrbViXZp2wGts6Tp+48ArdLh1DTdNu3KHS9dwtUKsoWn15vZdY/UWiE11qukWDmOFQvAdL56hIKnQENXq3NddRLX4zdYMdOVgKAcDO/5T2Yn7phPBVDFMFcWlYugHKQDWZaMT1C2qdS8h3aLa8BLUPQR1q+bnvlu14SGkW9gYmCqCWVvAULGa+5GagkY8l9oYS0VwO7FEvnqKF/P44eioxM2l2RgodBw8Wuy7TaTmAmjalWw8ZtOH+FhuF1YTw+vG+oEL+Br4usnP1ShvFobff9Aw0zQTJ9cH43a7kybXqvWjgTCsWxVPrbT4bLfBN6U6u0M6u0NN26+uKRylkeNzGJkT45QeMcbkxcj2KTz1fuo4+HFBfDZzfxNeg/1jq73emv8LDeWr0dRPEYcSq7VSLNDo8bR0rEfXk07Qa/nqWdeQQ4mtW7xoydj6/u6PIcL7/jf4OpxBROmsDGUzr7InnwZzKXF8lDg+vo524f3qfjxdVsF92YsZ5t2FhyBGzXuuZbob/93VeU0t06SpKZumG7Mm55gDi0sCvFSYzfs74j2O0twxpuRv5ac9d+HWFbCvYOz3wU3puxmfV8XVK47h22ov5y8axFPDN3Jq17LEfg/G7XYlTtpbMtblctU7fr/BWG/ThgYbhoG/FWJ1XW+VWE3TWiUWOKRYIVpKqxUW2uv6mUII0ZFpgBsbt7JBtc/M7wYOKU6QFKd5kzs1i5WKy2j4WyfRsRyd7vDYafH2UR2F9SUGe0MatgJbgaPAURqmocjyxm8ZHge/Kz7BqKNosPeIEB2JW1MM9MTHYA3z7uaKjHVUOy42RtLYa3tYWp3LG+X92BZL5ZdF8XHr3VyV3Jf9KRNSvm2Vq8WEbI2vK718U+VhUUmA/xZnUBKNFwMMTfGzXju5ud82MsyGex4ADAwE+VfB5/xyZT5L96Zy5bJjeGDQt/y8986WT1oIccRrtcJCe10/UwghhBAtx+eG43OaNs9CLSkqiM7Mp8c41hOfmPIU/zZ+2eUzHt99PG+XH80e28P2WAo3FI3D1Ox4oVhz6OIKUeAroq9ZjkeLYWk2Hj1Gqgkpe1NIddl0MaNkmLGkv49dYRdflPtYV+FL/NxY6cXZr8dhhjvKhJy9/KLPDo5JbXpRuIsV4x+j1nPP2j7M3NaV+9b14aPdadzRfwv9Ur5jFzwhhKijVYdCtMf1M4UQQgghhGgpfj3G3dlLuDt7CSHH4JmSYfy1ZCgRZRDBAAWlEQ8bI+n172Dzvrum7nCUP4SlO2wNWuyJ1D8sIcMdpV9KiP6p1UzI2cuJmRU1Qx4OnakrHjp2E339If70ZQ9m78zgw11p3DtgC5f33tEqvS6EEEeeVi0stMf1M4UQQgghhGgNHt1mStYKrstcTbltElUGEaWzKZLGouo8dtsewspFyDGoVi6qlEWV5qUi5qI06iLi6Kyv8CX2p6Ho6w8xMLWaQYFqBtX8zLaiLXrCr2lww9HbGZ+zl2nrezJnVwa/+aI3S/emcGf/LfT0NTZ9qBBCHFyrT944efLkBoc+zJs374B1F110ERdddFErZyWEEEIIIUTz+PUYfn3fHAf9rDK+n1p4YKDpQe+RD0DE0dgeNPm6ykvU0ejhDXOUP4TP9R0mrj1E/VJC/G3EVzy/OYdpG3rxbnEXZu/I4Oy8En7SYycnZFRKDwYhRLN0+qtCCCGEEEII0dGZuqK3P0xvf/tM3FtL0+DqvjsYnVnBgxt68smeNN7YlsUb27IYmlbJxT120S8lRF9/iK5my/acEEIcvqSwIIQQQgghxBHm2LRq/jFqA8v3pvDq1q68XdSF1WUprC5LScSkGDZHpwTp4Q3jNRyyrSi9fGF6+ULkWFFM3cGlKVy6wqXFr1qhoRLFiNqaROKnBh7dkWKFEIchKSwIIYQQQghxhBqRUcmIjEruOGYLLxdms7w0hU1VHrYFLSptg8/KUvisTrHhuzI0RcAVI91tY+oOVbaRuEStoSl0QNdUzX2FgY2hxeejqLtdr1lXH632pqn67wPU7Gv/bdQs126LKY2o0kh1xch0R0l3xXtxVMRclMdcBG0DB1Aq/ujajFw1x1D709CaPvmmrbSaG9g0XoVRB9ntxT6Dk/Kb/NRCNJsUFoQQQgghhDjCdbFi3JRflFgO2xrfVnvYWOVhR8ik2tYpDpl8W21RWO1hd8RFTGnEHI2o0hvZczJbaeyNutkbrf+KGKJljSoPclJ7JyGOCIddYUHVlO3Ky8vbOZP6VVRWEmmguioOLqwUVU7bTXJ0uIk6GuWxQ7sevYjTNLAisYMHivpForiq5ZrpzeYOYnTQ/2sdXUVVFUEl/zeaq9q2Ka8KtncanVfUQa+sbO8smi1XqyA3BThIhwWlwFbxb/eT1td82177rboDBGMGZVEXZTGDiKPjM+I9EmwFDhqOAkdpOEDMVjiRMDZaYp1S++7Xn0u8H0PtzVH7+jXsv00l+jxoKLXfehXP160pdE1RWXNlj3hBRCPFFSXgsvEadrx3g1b3iOPPG63tdVDTA6E+9a3VE70caLRnRmP7qHWMN5fy8mijj28Ptedq6mBdLkSnoanD7Le5detWevbs2d5pCCGEEEIIIYRoxJYtW+jRo0d7pyFawGFXWHAch6KiIlJTU9E64Mww5eXl9OzZky1bthAIBNo7HSHqJe1UdHTSRkVnIO1UdAbSTkV7UEpRUVFBXl4eut70oTSi4zrshkLout4pql6BQEDevEWHJ+1UdHTSRkVnIO1UdAbSTkVbS0tLa+8URAuS8pAQQgghhBBCCCGaTQoLQgghhBBCCCGEaDYpLLQxy7K4//77sSyrvVMRokHSTkVHJ21UdAbSTkVnIO1UCNESDrvJG4UQQgghhBBCCNF2pMeCEEIIIYQQQgghmk0KC0IIIYQQQgghhGg2KSwIIYQQQgghhBCi2aSwIIQQQgghhBBCiGaTwoIQQgghhBBCCCGaTQoLbezpp5+mT58+eDweRo8ezZIlS9o7JXGE+OijjzjnnHPIy8tD0zTeeuutpO1KKaZOnUq3bt3wer2MGzeOr776KimmpKSESy+9lEAgQHp6OldddRWVlZVteBTicDZt2jROOOEEUlNTyc7O5rzzzmPDhg1JMaFQiEmTJtGlSxdSUlK44IIL2LFjR1JMYWEhEydOxOfzkZ2dzW233UYsFmvLQxGHsenTpzN06FACgQCBQICCggJmzZqV2C5tVHQ0Dz74IJqmcfPNNyfWSTsVQrQ0KSy0oVdffZUpU6Zw//33s2LFCoYNG8aECRPYuXNne6cmjgBVVVUMGzaMp59+ut7tDz30EE888QTPPvssixcvxu/3M2HCBEKhUCLm0ksv5fPPP2f27Nm88847fPTRR1x77bVtdQjiMDd//nwmTZrEp59+yuzZs4lGo4wfP56qqqpEzK9//Wv+/e9/M3PmTObPn09RURE/+tGPEttt22bixIlEIhEWLlzISy+9xIsvvsjUqVPb45DEYahHjx48+OCDLF++nGXLlnH66adz7rnn8vnnnwPSRkXHsnTpUv7yl78wdOjQpPXSToUQLU6JNjNq1Cg1adKkxLJt2yovL09NmzatHbMSRyJAvfnmm4llx3FUbm6uevjhhxPrSktLlWVZ6p///KdSSql169YpQC1dujQRM2vWLKVpmtq2bVub5S6OHDt37lSAmj9/vlIq3ibdbreaOXNmIuaLL75QgFq0aJFSSqn//Oc/Std1VVxcnIiZPn26CgQCKhwOt+0BiCNGRkaG+tvf/iZtVHQoFRUVKj8/X82ePVudeuqp6qabblJKyXupEKJ1SI+FNhKJRFi+fDnjxo1LrNN1nXHjxrFo0aJ2zEwI2LRpE8XFxUntMy0tjdGjRyfa56JFi0hPT2fkyJGJmHHjxqHrOosXL27znMXhr6ysDIDMzEwAli9fTjQaTWqnAwYMoFevXknt9NhjjyUnJycRM2HCBMrLyxPfKAvRUmzbZsaMGVRVVVFQUCBtVHQokyZNYuLEiUntEeS9VAjROlztncCRYvfu3di2nfQGDZCTk8P69evbKSsh4oqLiwHqbZ+124qLi8nOzk7a7nK5yMzMTMQI0VIcx+Hmm2/mpJNOYsiQIUC8DZqmSXp6elLs/u20vnZcu02IlrBmzRoKCgoIhUKkpKTw5ptvMmjQIFatWiVtVHQIM2bMYMWKFSxduvSAbfJeKoRoDVJYEEII0eFMmjSJtWvXsmDBgvZORYgD9O/fn1WrVlFWVsZrr73G5Zdfzvz589s7LSEA2LJlCzfddBOzZ8/G4/G0dzpCiCOEDIVoI1lZWRiGccCMuzt27CA3N7edshIirrYNNtY+c3NzD5hoNBaLUVJSIm1YtKjJkyfzzjvv8OGHH9KjR4/E+tzcXCKRCKWlpUnx+7fT+tpx7TYhWoJpmvTr148RI0Ywbdo0hg0bxuOPPy5tVHQIy5cvZ+fOnRx//PG4XC5cLhfz58/niSeewOVykZOTI+1UCNHipLDQRkzTZMSIEcyZMyexznEc5syZQ0FBQTtmJgT07duX3NzcpPZZXl7O4sWLE+2zoKCA0tJSli9fnoiZO3cujuMwevToNs9ZHH6UUkyePJk333yTuXPn0rdv36TtI0aMwO12J7XTDRs2UFhYmNRO16xZk1QEmz17NoFAgEGDBrXNgYgjjuM4hMNhaaOiQzjjjDNYs2YNq1atStxGjhzJpZdemrgv7VQI0eLae/bII8mMGTOUZVnqxRdfVOvWrVPXXnutSk9PT5pxV4jWUlFRoVauXKlWrlypAPXoo4+qlStXqm+//VYppdSDDz6o0tPT1dtvv61Wr16tzj33XNW3b18VDAYT+zjzzDPVcccdpxYvXqwWLFig8vPz1SWXXNJehyQOM9dff71KS0tT8+bNU9u3b0/cqqurEzG//OUvVa9evdTcuXPVsmXLVEFBgSooKEhsj8ViasiQIWr8+PFq1apV6r333lNdu3ZVd911V3sckjgM3XnnnWr+/Plq06ZNavXq1erOO+9Umqap999/XyklbVR0THWvCqGUtFMhRMuTwkIbe/LJJ1WvXr2UaZpq1KhR6tNPP23vlMQR4sMPP1TAAbfLL79cKRW/5OR9992ncnJylGVZ6owzzlAbNmxI2seePXvUJZdcolJSUlQgEFBXXnmlqqioaIejEYej+tonoP7+978nYoLBoLrhhhtURkaG8vl86vzzz1fbt29P2s/mzZvVWWedpbxer8rKylK33HKLikajbXw04nD1i1/8QvXu3VuZpqm6du2qzjjjjERRQSlpo6Jj2r+wIO1UCNHSNKWUap++EkIIIYQQQgghhOjsZI4FIYQQQgghhBBCNJsUFoQQQgghhBBCCNFsUlgQQgghhBBCCCFEs0lhQQghhBBCCCGEEM0mhQUhhBBCCCGEEEI0mxQWhBBCCCGEEEII0WxSWBBCCCGEEEIIIUSzSWFBCCGEEEIIIYQQzSaFBSGEEEIIIYQQQjSbFBaEEEIIIYQQQgjRbFJYEEIIIYQQQgghRLNJYUEIIYQQQgghhBDNJoUFIYQQQgghhBBCNJsUFoQQQgghhBBCCNFsUlgQQgghhBBCCCFEs0lhQQghhBBCCCGEEM0mhQUhhBBCCCGEEEI0mxQWhBBCiGbo06cPV1xxRXunIYQQQgjR7qSwIIQQQtSxceNGrrvuOo466ig8Hg+BQICTTjqJxx9/nGAw2N7pNctvfvMbNE074ObxeJq8j4ULF3LyySfj8/nIzc3lxhtvpLKyshWzFkIIIURn4WrvBIQQQoiO4t133+Wiiy7Csiwuu+wyhgwZQiQSYcGCBdx22218/vnn/PWvf23vNJtt+vTppKSkJJYNw2jS41atWsUZZ5zBwIEDefTRR9m6dSt/+tOf+Oqrr5g1a1ZrpSuEEEKITkIKC0IIIQSwadMmfvKTn9C7d2/mzp1Lt27dEtsmTZrE119/zbvvvtuOGX53F154IVlZWYf8uLvvvpuMjAzmzZtHIBAA4kNBrrnmGt5//33Gjx/f0qkKIYQQohORoRBCCCEE8NBDD1FZWcnzzz+fVFSo1a9fP2666aYGH19SUsKtt97KscceS0pKCoFAgLPOOovPPvvsgNgnn3ySwYMH4/P5yMjIYOTIkbzyyiuJ7RUVFdx888306dMHy7LIzs7m+9//PitWrEjEVFdXs379enbv3t3kY1RKUV5ejlKqyY8pLy9n9uzZ/OxnP0sUFQAuu+wyUlJS+L//+78m70sIIYQQhycpLAghhBDAv//9b4466ijGjBnTrMd/8803vPXWW5x99tk8+uij3HbbbaxZs4ZTTz2VoqKiRNxzzz3HjTfeyKBBg/jzn//MAw88wPDhw1m8eHEi5pe//CXTp0/nggsu4JlnnuHWW2/F6/XyxRdfJGKWLFnCwIEDeeqpp5qc41FHHUVaWhqpqan87Gc/Y8eOHQd9zJo1a4jFYowcOTJpvWmaDB8+nJUrVzb5+YUQQghxeJKhEEIIIY545eXlbNu2jXPPPbfZ+zj22GP58ssv0fV9Nfuf//znDBgwgOeff5777rsPiM/jMHjwYGbOnNngvt59912uueYaHnnkkcS622+/vdm5ZWRkMHnyZAoKCrAsi48//pinn36aJUuWsGzZsqSeCPvbvn07QL29OLp168bHH3/c7LyEEEIIcXiQwoIQQogjXnl5OQCpqanN3odlWYn7tm1TWlpKSkoK/fv3TxrCkJ6eztatW1m6dCknnHBCvftKT09n8eLFFBUVkZeXV2/M2LFjmzykYf8hHBdccAGjRo3i0ksv5ZlnnuHOO+9s8LG1V8Koe3y1PB5Pp71ShhBCCCFajgyFEEIIccSr/ca+oqKi2ftwHIfHHnuM/Px8LMsiKyuLrl27snr1asrKyhJxd9xxBykpKYwaNYr8/HwmTZrEJ598krSvhx56iLVr19KzZ09GjRrFb37zG7755ptm51afn/70p+Tm5vLBBx80Guf1egEIh8MHbAuFQontQgghhDhySWFBCCHEES8QCJCXl8fatWubvY8//OEPTJkyhVNOOYWXX36Z//73v8yePZvBgwfjOE4ibuDAgWzYsIEZM2Zw8skn8/rrr3PyySdz//33J2J+/OMf88033/Dkk0+Sl5fHww8/zODBg1v80o49e/akpKSk0ZjaIRC1QyLq2r59e4M9KoQQQghx5JDCghBCCAGcffbZbNy4kUWLFjXr8a+99hqnnXYazz//PD/5yU8YP34848aNo7S09IBYv9/PxRdfzN///ncKCwuZOHEiv//97wmFQomYbt26ccMNN/DWW2+xadMmunTpwu9///vmHt4BlFJs3ryZrl27Nho3ZMgQXC4Xy5YtS1ofiURYtWoVw4cPb7GchBBCCNE5SWFBCCGEID45ot/v5+qrr673agkbN27k8ccfb/DxhmEcMOfBzJkz2bZtW9K6PXv2JC2bpsmgQYNQShGNRrFtO2noBEB2djZ5eXlJwxEO5XKTu3btOmDd9OnT2bVrF2eeeWbS+vXr11NYWJhYTktLY9y4cbz88stJQ0X+93//l8rKSi666KKDPr8QQgghDm8yeaMQQggBHH300bzyyitcfPHFDBw4kMsuu4whQ4YQiURYuHAhM2fO5Iorrmjw8WeffTa//e1vufLKKxkzZgxr1qzhH//4B0cddVRS3Pjx48nNzeWkk04iJyeHL774gqeeeoqJEyeSmppKaWkpPXr04MILL2TYsGGkpKTwwQcfsHTp0qSrRCxZsoTTTjuN+++/n9/85jeNHlvv3r25+OKLOfbYY/F4PCxYsIAZM2YwfPhwrrvuuqTYgQMHcuqppzJv3rzEut///veMGTOGU089lWuvvZatW7fyyCOPMH78+AMKE0IIIYQ48khhQQghhKjxwx/+kNWrV/Pwww/z9ttvM336dCzLYujQoTzyyCNcc801DT727rvvpqqqildeeYVXX32V448/nnffffeAKy5cd911/OMf/+DRRx+lsrKSHj16cOONN3LvvfcC4PP5uOGGG3j//fd54403cByHfv368cwzz3D99dc367guvfRSFi5cyOuvv04oFKJ3797cfvvt3HPPPfh8voM+/vjjj+eDDz7gjjvu4Ne//jWpqalcddVVTJs2rVn5CCGEEOLwoqmmXqtKCCGEEEIIIYQQYj8yx4IQQgghhBBCCCGaTQoLQgghhBBCCCGEaDYpLAghhBBCCCGEEKLZpLAghBBCCCGEEEKIZpPCghBCCCGEEEIIIZrtsLvcpOM4FBUVkZqaiqZp7Z2OEEIIIYQQQog6lFJUVFSQl5eHrst33YeDVi0sTJs2jTfeeIP169fj9XoZM2YMf/zjH+nfv3+Dj3nxxRe58sork9ZZlkUoFGrScxYVFdGzZ8/vlLcQQgghhBBCiNa1ZcsWevTo0d5piBbQqoWF+fPnM2nSJE444QRisRh3330348ePZ926dfj9/gYfFwgE2LBhQ2L5UHoepKamAvFGGggEmp98KylZvZbZE3/Y3ml0WpaucZTP295pdFq7IzH+tbuivdPolAwgzWW0dxqdVk7PLK6+/eL2TqPzSsvCOO3C9s6iU1LBStTGVe2dRqeiFKyszODdkjw+2ptFsZ1yyPvo6pSSrqqo1DykqiDpqpo0KklT1XRRFUyMLqOP2tkK2XcwqWloo8e2dxadlqqqgLVL2zuNTk2/YBJa1+7tncYBysvL6dmzZ+LcTXR+rVpYeO+995KWX3zxRbKzs1m+fDmnnHJKg4/TNI3c3NxmPWdtESIQCHTIwkI0JQWvJt19msujaaRId6lmC+o6JjJEqDkMwJLhVc3m1Q0CXk97p9F5+bwYHfB/WmegXBrK72vvNDq8atvgPyV5fFLelVWVGWwO1xQT3KC743ddKkZ3tYdezm6yVDkhzY2pYmSrMrqqMgwcPtP7Ms81hD1aL/bU7HtHPc/3grqQH8cWcHl0Lv2c7QQx8RNui0NtW14LLaXhL9NE4xQ2eK32TqNT01NT0Trw/w8Zun74aNM5FsrKygDIzMxsNK6yspLevXvjOA7HH388f/jDHxg8eHC9seFwmHB43z+i8vLylktYCCGEEOIwVmUbPLs9n5d2HEWF7U6s9+oxzs7cxpnerznui3+ho/ARxoVz0H1GwgZLjXwcNFJViArNy14thVLNz15S+MzowzzXUP7pPpV/uk/FrWJENRej7A3cFX6NYc7mVjxiIYQQraHNCguO43DzzTdz0kknMWTIkAbj+vfvzwsvvMDQoUMpKyvjT3/6E2PGjOHzzz+vd/zNtGnTeOCBB1ozdSGEEEKIw0rY0ZmxqzdPFx3Drmi8N1Fvq5IfdtnGMP9eTkjdQ8AVQ1WUAdWHtG8Tm5Ps9Q0HRGGJns8L5jjmGMOIavGPo0uM/pzvu4ep4X9yRXRucw9NCCFEO2izwsKkSZNYu3YtCxYsaDSuoKCAgoKCxPKYMWMYOHAgf/nLX/if//mfA+LvuusupkyZkliuHa8jhBBCCCGSRR2N13b34qmiYyiKxIeI9LYqubPnOsZnbEdvo17Jo5yvGBX6ilJ8VGkeHHQeNc/lbfeJ/Na6hHV6T3o6uxnkbGGMvR4vkbZJTAghRLO0SWFh8uTJvPPOO3z00UeHPOun2+3muOOO4+uvv653u2VZWJaMvRJCCCGEaEjU0fhXSQ+e2NafwnB8zH+uO8ikvC/5cddvMXXVLnmlU026iveIeDT8PH2cnTxu/ZDX3CcnYiwVocBez5mxFZwbW4xFrF1yFUII0bBWLSwopfjVr37Fm2++ybx58+jbt+8h78O2bdasWcMPfvCDVshQCCGEEOLwVRI1+eeu3ry8oy/F0fhVlbLcIW7o9hU/zd6MpR98zoS2ogE3Rf/NMc42lhr5VGheFhkDKNK7MM81lHmuoTzu/JBfRd7hotgCDNqnGCKEEOJArVpYmDRpEq+88gpvv/02qampFBcXA5CWlobXG//ndtlll9G9e3emTZsGwG9/+1tOPPFE+vXrR2lpKQ8//DDffvstV199dWumKoQQQghxWIg4Gh+XZfNOSXdmleQRVvFL5XZxhbg6dyOX5WzCZ9jtnGXDzrJXcJa9AgAFfKnn8YExnJfdY9muZ3K35zJetsfyg9gyRtgbGel8JUUGIYRoZ61aWJg+fToAY8eOTVr/97//nSuuuAKAwsJC9DqXD9y7dy/XXHMNxcXFZGRkMGLECBYuXMigQYNaM1UhhBBCHGGCtkGl7SLLHcYBtoT97I5a7I2Z7I2Z7Ih4KI548Oo2me4IOgq37pDpimDqDkqBQiPPrGZE6t52Ow6loCjiZWlFFxaUd2X23lzKbTOxfYivlCtyv+HszG0dqodCU2hAf6eI/k4RV0ff52X3WJ4wz2Gd0Yt1Ri8AspwyfhL7mOsjs2QuBiGOEEop7r//fp577jlKS0s56aSTmD59Ovn5+Y0+7umnn+bhhx+muLiYYcOG8eSTTzJq1KjE9lAoxC233MKMGTMIh8NMmDCBZ555hpycnNY+pE6v1YdCHMy8efOSlh977DEee+yxVspICCGEEEeKsA2f7TRZt6MvttJQgK00vqhOY3llJlvCPhQaASNCVOkEneZ/LLo0exO/yvuSwrCPoGNgoOhuBQkYUWylkekOY7TAxIiOgp1RD+urA6yuSuezygw+q0pnT8yTFNfVHWJi5jZ+2GUbw/17ORwuFW8R46roB5wX/ZQ33QV8pvflE9dAdutpPGWezVuuE5kceYdzYkulwCDEYe6hhx7iiSee4KWXXqJv377cd999TJgwgXXr1uHxeOp9zKuvvsqUKVN49tlnGT16NH/+85+ZMGECGzZsIDs7G4Bf//rXvPvuu8ycOZO0tDQmT57Mj370Iz755JO2PLxOSVNNOfvvRMrLy0lLS6OsrIxAINDe6Rxgz6rVzBo7rr3T6LQ8ukY/v7e90+i0dkVivL6zvL3T6JQMIN1ttHcanVa3Xtlcf9/P2juNziu9K8b3L2nvLDqsYAy2VOh8tsvg890G35TpbC7X2VqhY6vGz6g1FIp4jEePke0Ok+GKkOGK0NUdopsZJOgYlMbiPQBCjkFJzCKqNDTAAZZVdEnsoyE+PcYAXzk9zGp0TVEc8WBokOEKM9hfxlGeSqptF5W2i0rHRZUdv1XW3CpsN0URL1vDPiLqwPcil+Yw0FtOQWAXY9N3Mip1d4sUMlRFGSz9+LvvqJVEMPjAGM4frIso0rsA4FchBjpbOCX2OVdFZ7dvkSGQjjZGPvc1l6osh9WftncandbpT/wfx54yDldqGi+99BKmafK73/2On/70p0yePJnXXnuNnJwcnnzySc466ywA1q5dy2233cbHH3+M3+9n/PjxPPbYY2RlZQHw3nvv8bvf/Y61a9diGAYFBQU8/vjjHH300QBs3ryZvn378vrrr/Pkk0+yePFi8vPzefbZZ5Ou/PddztmUUuTl5XHLLbdw6623AlBWVkZOTg4vvvgiP/nJT+p93OjRoznhhBN46qmnAHAch549e/KrX/2KO++8k7KyMrp27corr7zChRdeCMD69esZOHAgixYt4sQTTzykPI80bXa5SSGEEEKI7yLqwKZSnXUlBp9ud7Fmt0FRpUZJSG/wMVkem+Genfj0+JwCmqboYVUzKnUPg31lpBgxvgmlYGoOfTyVzToZn1uaw63fHEdpzKS7WU2qK0bE0dka9iXmN6h2XKyozGQFmQc8/p2SQ7tiloFDL081Q/17GeYvZXjKXgb6yvB0smEOLcHE5gf2csZWr+Fl91j+4R7LFr0ry4x8lhn5zHSfxHWR9/i+vZKuqqK90xWizf2//3uN2++4gyVLlvDqq69y/fXX8+abb3L++edz991389hjj/Hzn/+cwsJCIpEIp59+OldffTWPPfYYwWCQO+64gx//+MfMnTsXgKqqKqZMmcLQoUOprKxk6tSpnH/++axatSppePs999zDn/70J/Lz87nnnnu45JJL+Prrr3G5XBQWFiaGuefl5dWb9913383dd99d77ZNmzZRXFzMuHH7inZpaWmMHj2aRYsW1VtYiEQiLF++nLvuuiuxTtd1xo0bx6JFiwBYvnw50Wg0ab8DBgygV69eUlhoAiksCCGEEKLDKizXePcbN/O2ulm50yBs13/m73crBnexGdrVpl+6TZ+AQ980h2zK4evljT7HQN9368l1evoOlhz3X6KOjrfOpIhKxXs0AGwKpbC+OkBRxIujNLqZQRQaxVEPqyozKI54STFi+I0YKXo0/rN22YiRosfINYP0tKrpZgZxt9PlITsqHxGujb7P1dHZfKnnsUo/iifNs9mid+Vez8+Zqi5lpPMVE2IrmRBbQZ5qvzkxhGhLwwYP5N577wXgrrvu4sEHHyQrK4trrrkGgKlTpzJ9+nRWr17NBx98wHHHHccf/vCHxONfeOEFevbsyZdffskxxxzDBRdckLT/F154ga5du7Ju3TqGDBmSWH/rrbcyceJEAB544AEGDx7M119/zYABA8jLy+Pjjz/m+OOP5+OPPyY1NfWAvDMzDyzC1qq9IMD+8x7k5OQktu1v9+7d2LZd72PWr1+f2K9pmqSnpzd5v2IfKSwIIYQQokOpLSa8u8nNmt3JH1VS3Ir8DJsROTYn5MToHXDIS3EImNQ7j4Cqpk2uF+DSFK79rrSgafFhVAD9vJX081a2QSZHNh3FAGcbA5xtnBNbwsvusfzHNZI1Rh+WGP1ZYvTnf6yfcLz9NedHP+XC2CdYxNo7bSFazbGDBibuG4ZBly5dOPbYYxPrak+0d+7cyWeffcaHH35ISkrKAfvZuHEjxxxzDF999RVTp05l8eLF7N69G8eJl08LCwuTCgtDhw5N3O/WrVviOQYMGIDL5UoMnTj66KMbHQrxj3/8g+uuuy6xPGvWLAxDhqZ2RK1eWDjYzJv7mzlzJvfddx+bN28mPz+fP/7xj/zgBz9o7TSFEEIcQZSCUuWlyEmjXFlEMdBQGCjcmk03rZyueiUm9mEx6V1HtrFUZ/5WF6t3GXxdqrMrqFNcta87ra4pCrrZnNk3SkG3GP3SHfmdiCbxE+a66H+5LvpftmmZ/Nd1HP91Hc8yvR8rjPhthv09ng49Sy+1u73TFaJVuF3upGVN03C73UnLEJ9voLKyknPOOYc//vGPB+yntjhwzjnn0Lt3b5577jny8vJwHIchQ4YQiSTPZdLQcwCHNBTihz/8IaNHj06s7969O9u3bwdgx44dibxql4cPH17v/rKysjAMgx07diSt37FjB7m5uQDk5uYSiUQoLS1N6rVQN0Y0rFULC02ZebOuhQsXcskllzBt2jTOPvtsXnnlFc477zxWrFiRVAHr7EKNzJepA2adT0ytFRtWqsFvcDTAamZsRCkaG+HpqbtfIGilEvKkEbIChL1phDyB+H1PANuTStiK34JmCiFPKlErFbfbRGk6StPRHRtfuJxAuAJ/uBx/uBxPqBRvqBx/qBxfuIyUUBm+cO1yOSnKTrzBRZTCbuR182hak2MtTUOviY0qRayFYk1Nw2ih2KByiNb8Ng1Ar5lszKbx31vdWAdFY1c/1wGjg8QqTSfmtrCVQnNiaI4NykmaYq3ufhWqwe+tHCCmFK6a11cpRfQgOTQnFuJtrSViNcDdzNjoQf7umxsbjERxGsnDb5nNig1Fokl/n1Gls1MFKFZpbFdplBiZFDlpFDkBiuwARSqNCg4+EayBjZcofi1MT62EHloJAS1EKiFStSBZrihpephULYTXrsLnVJOqhfAROeDk1+t2JcafRmIxonbDf3UHxIbCGFVVie1KQXVMoySsU6087I242BvS2FnpsLMadgV1dgUNog4MzIhyVCCGz6WwdZOKqEF5BGzbIcOM0sXjkOWxa346pFkOugaWZeFyxT8mRKPRAz401lU3NhaLEQ6Hk7YrFc+psNLFrrBFcdDNV6U6y4oNtlYe+M2TrilG50SY2DfCD45WdPEqbNsmFApRXV1/Dm63G9OMt4naWABVXY0KhpJjXQZmzYdex3EIhhs+tkOJdRkGlumuOWZFdSjcIrGGruOp096r9jue5sbquobXshqNVaEwRKLomobXve9jY3UkRkN/+RoaPrN5scForPG/e9PdrNgukZ1cHH6Pi3mPXVoa/7FO5HnPRD43ejPWP41cZy/HORs5J7qEgtBKXKrhd22f25X4bBCO2cSchv+WfWrftJ7hSIRYY3/3lrnv7z4aJRpr+D/docR6THfiG95DiY3GYkSiDffmsNxuXK5Dj43FbMLRhl9f0+3CnXg/sQmHG4l16bhr8rUdh1C04WNzGzqm69BjHUcRbOTYDiXWpWtYNX9HSimqIy0Ta+ganjp/n1WNvGaNOf7443n99dfp06dP4j29rj179rBhwwaee+45vve97wGwYMGCQ36eQxkKkZqaesD2vn37kpuby5w5cxKFhPLychYvXsz1119f73OapsmIESOYM2cO5513HhB/T58zZw6TJ08GYMSIEbjdbubMmZMY8rFhwwYKCwuTJp4U9WvVwsKjjz7KNddcw5VXXgnAs88+y7vvvssLL7zAnXfeeUD8448/zplnnsltt90GwP/8z/8we/ZsnnrqKZ599tl6nyMcDid9gCkv7/gz3l9T3nBVfJjL5FZ/WmJ5UvnuBucyHmC4uSclPbE8pWIPFQ38g+1ruPhtSkZi+c6KEnYrBzQdzZOKbqWiWanonlS6etL5eUYeYTOFsOXnXxhUmP54TG2s6QNNx9R0jnFZxCfd1vjGcQgBaDpoGppuoBkWmtuD7vaQ7vYRc1nEXBZR09fk16wlWZEq/OEKfJEKSqpK2FNdihMsg1gI5djgxGp+2pzv9eBDoSubJdVVfBFt4LdhR/mFW5EXiRc4Zu3exnslW3GCpTjVe1Hhivgn6xqv5XTjaHf8g9/z5WX8paIsvsEw0a0UNNOPZqWgm37uye1Fji+DoOnnQ8fgQ8dA99T8vmp+J5o7foI03O0mU9PQcNgajbI+EgLHRtnR+HHZUbCjKCdKX1uRbjtodpS9ob18Wb0Du2oPTnVJ/FZVghPcC47NODwcTfwD2iZifEDDH1TH4qF/TewWbN4j2GDsGCwGG35s00eR2+QDU48ft5WCVvM6xF+PFHpb6WRbadimj0rLx7eWB92s81rVPsblweX2gtuDMtz1Pq+yo6hoECdciRmpwh+uxhWpRoUrKQqX4USqUOFKVKQKJ1KJClehIpX0ioQYEYvhilQRDVfyavWOpFgV2XfGc6zm5hyXH4Ao8KdYWYOvwwDNzY9qYjlI7NGai4td+7ooPh4rq79oobvo5fZxkZUBmoYei/B0cCdBp/4PG900gytd+/5x/zVWTlkDJwJZ6Fzr3tdt8e+xCnY3UJpKQ2OSe9972mnTnmfZpqL695vqo/jJff8bJj7yv3y0YXN8wTDRLT+a24dmpeBLy+bV2ydR4vgoUT5eWPYthVE/roxeGOk9MQJ5aHUmkWqosmOXF2NX7YFYGHQDdIOj8vLYqQJEcGFjUIlBpfKwQ6WxjL7JO2hgvyoaIrJ9DdHta7FLt+KEynjg7NEMDthkatU8NftT/rloVc37gqq5PLNC92ZgpOVx70XnkJmWQQgX7325i6U7wujz1mKkZqP7u2KkZKG5rPqffD8rdjUtLpG7HSWyZTk/G27y01OOoWeqwzuv/R+/uH5Sg4958aVXOGnc2ewJavx73koef3EmRko2Rloe7pxBuHMHofsy6n2sikUIbZxPeONHRItWY5dtI7bnGzZX7+V7jz5MlyHxccAff7KQ0846p8EcHvrdb7nt1zfGj3nVZ4w65fQGY6f+4mLuvzp+lY0vNm9l6M9ubDD2lp+ex0OTrwCgcMcujr7gugZjr//RWTx1a3z77tJycide3mDsZT84jb/fexMA1aEwgTPqn8Uc4ILTxvB/v789sdxY7FkFI3jnkfsSy7kTL2+waHHqcYOZ+/TvE8tHXXAtu0vr/xw1snsWn15/XmL52Cde49vS+od2DMpOZ/WNFyaWT3z2LdbtLK03tnd6Chtv3Xc8p/3tHZZtq/9zUpbPQ/Hd+64uM/Gl9/hoc/3jnn1uF+X3X5FYvuifc5j15ZY6EU9gpHWny89fxtNvLMV6BrP0kcxyjcQwKihf/TbhwiVENi0ksnVF0v/wsqmXJ4oW17+9gP+38qt6cwDY/vsbqP0q7ZbH/sr0me80GLvxXy/SJy/+rei9z7zII//7eoOxq199lsFH9wFg2gsz+O1z/2gw9tOXHueEwf3jR/3Pt7jjiecbjJ3z7B8ZO3IYAM+98R9+9dAzDcb+688PMPHk+DfJ/5g1l6seeLTB2BkP3s1F404B4M15n/CTO//QYOzz90/hinPGA/DfpZ9x7l1PNhj7xIWnc8MpwwH4eOM2xj05s8HYB8/9HreecQIAK7bspOCRVxqMve/ME7n/B2MA+GLHHoZN+38Nxk45fQQPnXcqAIV7y+n3QMOv7/UnD+PJH58BwO7KIN3uqf/8BuCyUYN44WdnAvHiXNptDb8OFwzP59Vf7Ht/rI09pd+hTQw7adIknnvuOS655BJuv/12MjMz+frrr5kxYwZ/+9vfyMjIoEuXLvz1r3+lW7duFBYW1ntOdzCHMhSiPpqmcfPNN/O73/2O/Pz8xOUm8/LyEkUDgDPOOIPzzz8/UTiYMmUKl19+OSNHjmTUqFH8+c9/pqqqKnGumpaWxlVXXcWUKVPIzMwkEAjwq1/9ioKCApm4sQlarbDQlJk397do0SKmTJmStG7ChAm89dZbDT7PtGnTeOCBB1ok57ZipPfAN/zHiRO9+ElfFGXHiKD43G2iOzF0O4pZsRvNjqKcGJruQjNMcJlohknM9LHSl07MZRJ1e7Eche72oZnxm27WfAg3fdhWCs94Uom6vUTcPrwuDz2sFPQGTu7rvtXqQFq9UXEb91uu/8qxcfXNx+xEqlHBMpxQafxEPFgGoXK+54SxwpVY4QoWle9ia3UJKlyBioVBOSjloBludE8ad3Q7ikpPGlVWgPmaRbE7Bd2bHr/5MuI/PfE3rbDpJ2z6KSEXutDod5Yf7Lfc2Ouw/7//bnXuK8dBhctxwpWgHH6rG7i02tdEo4fbFz9BrudEeP9/T/V/NI/7cr9lf71RcXtqbrW6NBDnBMv4pnovxdV7MSLVhGMhusaC8TapGYmTsdr7RbqbEs2F0g3Cuk6upu2L0Q00lyfeRt0+trq9bDX2vQ3V3xkuroLk9tPYsR1sPLVmuGvaTgBnv/02Vu6qBObXWe6233blODV/y1HKnBgzHTveU8KOkVfzN44Ti7ffmt4TKIeQcpilQFMOmnLItiOgbHCcmrZuo+kuMNzEDIt3XR4cl4mju+hquMBloRkmmstEc1lJJ5x1P2J1rclRxcJgR1CxcOLmssO8G4th2GH0WARftBqzznZl77vvtmMsc/lwDDeO4cZUsX3z3GvavsKipuNGY4Hhwev38EXVUZSdNYys6sh+7UYHzcA0LX5UcTRB5SaIm22XXUcPwxN/HzMO/Hd1XVWdhSEHtgkVCxPbuwV7byE/HdyFPL2cPL2MF9/5N/OXLiC2ZxMqemDha8Nf7sVnmlRi8suXP+D1zzaj+zNxZeXjyuiF7k1Dq3l/Of244wgZfsqVh21BjbCREv89uD1YvU7A6nVCYr9/BqjNueAX5Dby5cezQKJ+1wv8veqPcyJBsv0aOQE3XTwOxZvXs+yT2dhl27HL4wUcs9cJuNJ7olmpnDRyKEd1yyTNUqxas5aPVn6JkZqLkZqDnpqD4e+CZrix+pzIzFKY+a/487i5hrz7z0m0WVXzUzP9GCldmbrXXaexjSXzwrEH5KrsGPbeQvplW4w8uiu9Ag6RwsXc8YuzUWGZe0C0PbtsGzufOg3dn8XCe27hA9dw/u06ge1mF/wjf4Z/ZLyIESvdRuXCZ6mY9xgqUnWQvQrReeXl5fHJJ59wxx13MH78eMLhML179+bMM89E13U0TWPGjBnceOONDBkyhP79+/PEE08wduzYNs/19ttvp6qqimuvvZbS0lJOPvlk3nvvPTyefWciGzduZPfufYXKiy++mF27djF16lSKi4sZPnw47733XtKEjo899hi6rnPBBRcQDoeZMGECzzzTcIFN7KMp1Ugfsu+gqKiI7t27s3DhwqSuI7fffjvz589n8eLFBzzGNE1eeuklLrlk37W6n3nmGR544IEDxsPUqq/HQs+ePZt1TdS2sGfVah698je8fMUb7Z1KEj0WwYpUYtacyHsiVfGT+kglrnB8vZnYXok7Wl1zEqQwUaAUmnKwlYqf8KMSJ0muWLjmFsIXi+CKhXDFwhANYobKMOz6v/Lz7NeF2wE8usbRvgNLF94630yGlaq3a6StGVRbqcS86QStVKqsAOVmClVmCtVWKlHDxNEMHL3mphlouqtmWSeqGfEP1fWIGiYhTxpVnjQqrbREkaPKEyDqaqzUUj93LIQnUo0VrcIbDeKJVuONVOKJVGKFq2ru7/tpxeInR7qmx0/Y0IhqGjYajmZgGy5szUU5Ol+EHBzDDTU3x3AT9QaI+DKI+TKI+jKI+jOJedMPOe/vxLExwpW4wlUYkSqMxM9KjHAVrkgV7nA1RqQSPVyJHq7CiFQnthuRSoxwNUY0iCsWxh0NoUfDaLEQMU1D6QZKd8WH0RguHLcX2/LjmCkoy49t+olZPiKmH9tKwa4pyMV/+nGsFDTLj235iZkpxEwfUctPzPRhm42VOURLcmHjJUJAC5KlB8nUqsjUggRUBV21cnK1MrppZeRqpWRSja7F3wsaGzaxP5/p3tfNORprvJvzfrFR2yGMi50qwDonj0LVhZ0qlWosgpqHYhWgTHlxVLwAptBQgKM0QMOvhcnRykjTI3i1GF4tileFyPA4ZA0eQobHoYvHId1yyLAcfC6Fx+PZ1805EiHaSBfjurH1DW+I2LAzaPDhVg8fFvn4tsKguEpDodW3uyQBU5Hldcj0OGSasfhPy6FvIEa/tCh9AzFMI/6/vnbsbX3DJuqqG1t3eEN9Gh4KUYnauDI5VoZCAE0cClFZDss/6fRDIULRWKN/97WxDhoLVR8Wugax3ujNEvcAqrX4VxABp5Kj7O0cpzZzXfS/ZKuygw+F6JKFftL3ARkK0ZyhENHSvYRXLmw4VoZCAA0PhfBbbvSLf42WfWg9F9pCeXk5aWlpHfacTRy6Tn9VCMuysKxD6+bZ3jKrdjP8s5k4uoGtu7ENF47uxjbcNSex8XW27sYxXDh6/L7hRDHsKIYdSfoZP4kK4o4GMaPVde4HcUeq962LVMfX1cZFqrEi8UKBy274g1Kra8IsXLXzQ3g0LamIUB9L0xrYpyIlUg6Rth0uEzEsqq1UKq0AoXp6iGhKYUWDeKNVeCLVeKLVGKqxGQSab1ckxus7m3b8jm4Q86YR9WUSqSk42KYXx2XhuEyU7kJzbDRlozlO4j6OHb/v2PHiUuJ+fJsRC2NEqtEjQYxozS1SjWZHm3Dq0jz1D4g4NAaQ7q5/FmJH07FNHzHTh2OYODVFDEd3xYsYtUWN2nU13+orXUdpRmLOEKUbUFsEqV2nGShdR3NsDDuCHoui2xF0O1rnFkaPRWu2R2q2RzBq7gM4hoXtMnFcJrYR/x3aNb9LxzBrttXEGFZ8m+FOxCR+GvH1tb2qNCeKXvuhuuZDu1b7bTYq/lMp0tL9nDxhBBqg42CgMIjHGFp80kQdB68WxUMUnxbFSxSvFsWnRRL33Vpjs4Hsr/7fvMdseouw3C6a+h/Gcruw3JACdKGSgQf0IfoO0rtiHDuUeB+yhpmmmTi5Phi32500uRbEe31kBKB/DvxyRHxoT9iGokqdikhtEWTfZRQtQ9HVq8j0KMwGJ+nWAavmlszlctU7jrc+hmHg9zetiFc3VmkOyttwgVfXdfyNbG9urKZprRILtGmsioWhnr+ZusWAgzmU2LrFi5aM9TQxVkdxsraJk+1NYEM44uK/ruN51DyXQj2bVXo+q8hnhvsUxtpryHeKOMYuIt8poo/aiXv/GYDqfB6xTLPJ7yem250oaLVXrNu17wS/JWNdLiNRZGhSrNW0fA1dx281/h7ZnFhd1/A3MYdDidW01okFDilWiJbSaoWFpsy8ub/c3NxDiu+scnau56I3Gh6rKg4vph3GrA6TXt25ZpzWHRuzqgSzqqTRYQcCdOWghytxd+Du3LpTjSvawKx3baBbr2yu+OGhFAVER2EZ0DdNfnfiyGQR44exJZwVW846vSff6tm85D6dlcbRzHKNZFadWLeK0dfZQb4TLzT0c7bTXUXoHjHp4o6gt3D1POpobAt72Rz0sTNiYeoOme4I/X2VZJthuXqKEKJNtVphoSkzb+6voKCAOXPmcPPNNyfWzZ49W2bhFEIIIYQQ7caNzTBnM8OczZwTW8IS/RjWGL35Us/jKz2Pr/U8qjQPXxrd+dLovu+BClgCpubQzQrRwwoywF9BdyuIoSlcerzHlgZoNUO3tJpbLU1TVMTc7I6a7I6YFEc8bA762Br2ElP1f+ue7oowwF/JmLQ9nJa5i0H+ihYvbAghRF2tOhTiYDNvXnbZZXTv3p1p06YBcNNNN3HqqafyyCOPMHHiRGbMmMGyZcv461//2pppCiGEEEII0SQaMNr5ktHOvuFOCijSMhOFhq/07mzSs9luZLGDNCJK59uQj29DPj4pa2i65ENn6TZ9PNXkWiFijs72iMXmoJ/SmMmnZZl8WpbJo4X5ZLnDnJqxm7EZu/hexh7SXA2P1xdCiOZo1cLCwWbeLCwsTEw8AzBmzBheeeUV7r33Xu6++27y8/N56623GDJkSGumKYQQQgghRLNpQHdVQne7hNPstfs2BNKJnfh9dkQsisIeNgf9fFGVyp6oSUxp2EojpvZ1Jaid0DV+H5SKL/uNGF3NCFnuMNlmmD6eanp7q8kxwwf0RAjZOhuDfj6rSGPe3iw+Ke3C7qjF6zu78/rO7hg4DE8toyC9hBPTSjg+tRSvIcOdhBDfTatP3jh58uQGhz7MmzfvgHUXXXQRF110UStnJYQQQgghROtz64oenhA9PCFGpZW2+vN5DIfBKRUMTqngp922EnY0lpVnMG9vFvP3duWr6hSWV2SwvCKDp7YcjVtz6OEJ0tMK0tNTHb/vCZLhiuI3YvgNO+mnIUMqhBD16PRXhRBCCCGEEELUz9IVJ6WXcFJ6Cff0/ZKtIQ8LS7vwaVkGn5Zlsj3iZVPQz6Zg06Zq9ujJhQaP7uDWHNy6g6mpxH23VnPVHy0+h4ShKXRNoaMwtPgVOOLLB26rvWKQFglhKA86Tnxb7dWEapb1pPv7x9Qfr+PUXKEoftFfvWaOi9r1Wk2/kX3347Sk/iR119V/P/6TpD4ojW9PnltDJd3Xktbtv5z8yLoUXSMa6Q1sFaIlSWFBCCGEEEKII0QPT4gf527jx7nbUAqKwh6+DfnYEvKyJexlayh+K7fdVNkGVbaLKttITBQZcgxCjsGeaFtlPLStnuiw9PvCvfysR3tnIY4Eh11hQdVcR728vLydM6lfRWUlQSXj2JrLURqVjrx+zVXlOET2q7aLpjGAsJLXrrmCjk15MNTeaXReZhCjg/5f6+hUsBJV1X6XWu3sVFUQQpH2TqPzcofRKqvaO4tGpVLFENcehqQAKfXHKAURpVHtuKiyXVQnCg46YUcnpnSiSieqNGKOTkTpRJx4HwBHxT+/OYBd81PVzC/hULOt7n3i2xRgR2PYpXsS/QwcNOyaPgY2OnZNPwEnafv+y/oBMSrxOK2m38K+/ggOJPorJI5/v34HB/ZB2LeP+H0avE8965Ne6zrPXH9vBpW0rO23vP++o8Eg5eVGfb/WdlV7rqbks9VhQ1OH2W9z69at9OzZs73TEEIIIYQQQgjRiC1bttCjh3SpOBwcdoUFx3EoKioiNTUVTet4s8uUl5fTs2dPtmzZQiAQaO90hKiXtFPR0UkbFZ2BtFPRGUg7Fe1BKUVFRQV5eXlJVwkUnddhNxRC1/VOUfUKBALy5i06PGmnoqOTNio6A2mnojOQdiraWlpaWnunIFqQlIeEEEIIIYQQQgjRbFJYEEIIIYQQQgghRLNJYaGNWZbF/fffj2VZ7Z2KEA2Sdio6OmmjojOQdio6A2mnQoiWcNhN3iiEEEIIIYQQQoi2Iz0WhBBCCCGEEEII0WxSWBBCCCGEEEIIIUSzSWFBCCGEEEIIIYQQzSaFBSGEEEIIIYQQQjSbFBaEEEIIIYQQQgjRbFJYaGNPP/00ffr0wePxMHr0aJYsWdLeKYkjxEcffcQ555xDXl4emqbx1ltvJW1XSjF16lS6deuG1+tl3LhxfPXVV0kxJSUlXHrppQQCAdLT07nqqquorKxsw6MQh7Np06ZxwgknkJqaSnZ2Nueddx4bNmxIigmFQkyaNIkuXbqQkpLCBRdcwI4dO5JiCgsLmThxIj6fj+zsbG677TZisVhbHoo4jE2fPp2hQ4cSCAQIBAIUFBQwa9asxHZpo6KjefDBB9E0jZtvvjmxTtqpEKKlSWGhDb366qtMmTKF+++/nxUrVjBs2DAmTJjAzp072zs1cQSoqqpi2LBhPP300/Vuf+ihh3jiiSd49tlnWbx4MX6/nwkTJhAKhRIxl156KZ9//jmzZ8/mnXfe4aOPPuLaa69tq0MQh7n58+czadIkPv30U2bPnk00GmX8+PFUVVUlYn7961/z73//m5kzZzJ//nyKior40Y9+lNhu2zYTJ04kEomwcOFCXnrpJV588UWmTp3aHockDkM9evTgwQcfZPny5SxbtozTTz+dc889l88//xyQNio6lqVLl/KXv/yFoUOHJq2XdiqEaHFKtJlRo0apSZMmJZZt21Z5eXlq2rRp7ZiVOBIB6s0330wsO46jcnNz1cMPP5xYV1paqizLUv/85z+VUkqtW7dOAWrp0qWJmFmzZilN09S2bdvaLHdx5Ni5c6cC1Pz585VS8TbpdrvVzJkzEzFffPGFAtSiRYuUUkr95z//Ubquq+Li4kTM9OnTVSAQUOFwuG0PQBwxMjIy1N/+9jdpo6JDqaioUPn5+Wr27Nnq1FNPVTfddJNSSt5LhRCtQ3ostJFIJMLy5csZN25cYp2u64wbN45Fixa1Y2ZCwKZNmyguLk5qn2lpaYwePTrRPhctWkR6ejojR45MxIwbNw5d11m8eHGb5ywOf2VlZQBkZmYCsHz5cqLRaFI7HTBgAL169Upqp8ceeyw5OTmJmAkTJlBeXp74RlmIlmLbNjNmzKCqqoqCggJpo6JDmTRpEhMnTkxqjyDvpUKI1uFq7wSOFLt378a27aQ3aICcnBzWr1/fTlkJEVdcXAxQb/us3VZcXEx2dnbSdpfLRWZmZiJGiJbiOA4333wzJ510EkOGDAHibdA0TdLT05Ni92+n9bXj2m1CtIQ1a9ZQUFBAKBQiJSWFN998k0GDBrFq1Sppo6JDmDFjBitWrGDp0qUHbJP3UiFEa5DCghBCiA5n0qRJrF27lgULFrR3KkIcoH///qxatYqysjJee+01Lr/8cubPn9/eaQkBwJYtW7jpppuYPXs2Ho+nvdMRQhwhZChEG8nKysIwjANm3N2xYwe5ubntlJUQcbVtsLH2mZube8BEo7FYjJKSEmnDokVNnjyZd955hw8//JAePXok1ufm5hKJRCgtLU2K37+d1teOa7cJ0RJM06Rfv36MGDGCadOmMWzYMB5//HFpo6JDWL58OTt37uT444/H5XLhcrmYP38+TzzxBC6Xi5ycHGmnQogWJ4WFNmKaJiNGjGDOnDmJdY7jMGfOHAoKCtoxMyGgb9++5ObmJrXP8vJyFi9enGifBQUFlJaWsnz58kTM3LlzcRyH0aNHt3nO4vCjlGLy5Mm8+eabzJ07l759+yZtHzFiBG63O6mdbtiwgcLCwqR2umbNmqQi2OzZswkEAgwaNKhtDkQccRzHIRwOSxsVHcIZZ5zBmjVrWLVqVeI2cuRILr300sR9aadCiBbX3rNHHklmzJihLMtSL774olq3bp269tprVXp6etKMu0K0loqKCrVy5Uq1cuVKBahHH31UrVy5Un377bdKKaUefPBBlZ6ert5++221evVqde6556q+ffuqYDCY2MeZZ56pjjvuOLV48WK1YMEClZ+fry655JL2OiRxmLn++utVWlqamjdvntq+fXviVl1dnYj55S9/qXr16qXmzp2rli1bpgoKClRBQUFieywWU0OGDFHjx49Xq1atUu+9957q2rWruuuuu9rjkMRh6M4771Tz589XmzZtUqtXr1Z33nmn0jRNvf/++0opaaOiY6p7VQilpJ0KIVqeFBba2JNPPql69eqlTNNUo0aNUp9++ml7pySOEB9++KECDrhdfvnlSqn4JSfvu+8+lZOToyzLUmeccYbasGFD0j727NmjLrnkEpWSkqICgYC68sorVUVFRTscjTgc1dc+AfX3v/89ERMMBtUNN9ygMjIylM/nU+eff77avn170n42b96szjrrLOX1elVWVpa65ZZbVDQabeOjEYerX/ziF6p3797KNE3VtWtXdcYZZySKCkpJGxUd0/6FBWmnQoiWpimlVPv0lRBCCCGEEEIIIURnJ3MsCCGEEEIIIYQQotmksCCEEEIIIYQQQohmk8KCEEIIIYQQQgghmk0KC0IIIYQQQgghhGg2KSwIIYQQQgghhBCi2aSwIIQQQgghhBBCiGaTwoIQQgghhBBCCCGaTQoLQgghhBBCCCGEaDYpLAghhBBCCCGEEKLZpLAghBBCCCGEEEKIZpPCghBCCCGEEEIIIZrt/wP/AtaMSvwM/AAAAABJRU5ErkJggg==" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "distance = 'euclidean'\n", + "explainer = PointExplainer(comp_model, X_test, y_test)\n", + "explainer.explain(n_samples=1, window=10, method=distance, name=dataset)\n", + "explainer.visual(threshold=0, name=dataset+'_'+distance)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + } + ], + "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 +} diff --git a/fedot_ind/api/main.py b/fedot_ind/api/main.py index eb6abd056..c07387219 100644 --- a/fedot_ind/api/main.py +++ b/fedot_ind/api/main.py @@ -8,7 +8,7 @@ from fedot.core.pipelines.pipeline import Pipeline from fedot_ind.api.utils.configurator import Configurator -from fedot_ind.api.utils.path_lib import default_path_to_save_results +from fedot_ind.api.utils.path_lib import DEFAULT_PATH_RESULTS from fedot_ind.core.architecture.experiment.computer_vision import CV_TASKS from fedot_ind.core.architecture.settings.task_factory import TaskEnum from fedot_ind.core.operation.transformation.splitter import TSTransformer @@ -52,8 +52,8 @@ class FedotIndustrial(Fedot): """ def __init__(self, **kwargs): - kwargs.setdefault('output_folder', default_path_to_save_results()) - Path(kwargs.get('output_folder', default_path_to_save_results())).mkdir(parents=True, exist_ok=True) + kwargs.setdefault('output_folder', DEFAULT_PATH_RESULTS) + Path(kwargs.get('output_folder', DEFAULT_PATH_RESULTS)).mkdir(parents=True, exist_ok=True) logging.basicConfig( level=logging.INFO, format='%(asctime)s %(levelname)s: %(name)s - %(message)s', @@ -63,12 +63,8 @@ def __init__(self, **kwargs): ] ) super(Fedot, self).__init__() - self.logger = logging.getLogger('FedotIndustrialAPI') - - # self.reporter = ReporterTSC() self.configurator = Configurator() - self.config_dict = None self.__init_experiment_setup(**kwargs) @@ -76,7 +72,6 @@ def __init__(self, **kwargs): def __init_experiment_setup(self, **kwargs): self.logger.info('Initialising experiment setup') - # self.reporter.path_to_save = kwargs.get('output_folder') if 'task' in kwargs.keys() and kwargs['task'] in CV_TASKS.keys(): self.config_dict = kwargs else: @@ -88,9 +83,6 @@ def __init_solver(self): if self.config_dict['task'] == 'ts_classification': if self.config_dict['strategy'] == 'fedot_preset': solver = TaskEnum[self.config_dict['task']].value['fedot_preset'] - # elif self.config_dict['strategy'] is None: - # self.config_dict['strategy'] = 'InceptionTime' - # solver = TaskEnum[self.config_dict['task']].value['nn'] else: solver = TaskEnum[self.config_dict['task']].value['default'] elif self.config_dict['task'] == 'ts_forecasting': @@ -249,11 +241,11 @@ def split_ts(self, time_series: np.array, strategy: str = 'frequent', plot: bool = True) -> Tuple[np.array, np.array]: - splitter = TSTransformer(time_series=time_series, - anomaly_dict=anomaly_dict, - strategy=strategy) + splitter = TSTransformer(strategy=strategy) - train_data, test_data = splitter.transform_for_fit(plot=plot, + train_data, test_data = splitter.transform_for_fit(series=time_series, + anomaly_dict=anomaly_dict, + plot=plot, binarize=binarize) return train_data, test_data diff --git a/fedot_ind/api/utils/input_data.py b/fedot_ind/api/utils/input_data.py index 53a0c0a7f..6f1ebfd44 100644 --- a/fedot_ind/api/utils/input_data.py +++ b/fedot_ind/api/utils/input_data.py @@ -11,36 +11,32 @@ def init_input_data(X: pd.DataFrame, y: np.ndarray, task: str = 'classification' Args: X: pandas DataFrame with features y: numpy array with target values + task: str, task type, 'classification' or 'regression' Returns: InputData object convenient for FEDOT framework + Example: + To produce input data object:: + rows, cols = 100, 50 + X = pd.DataFrame(np.random.random((rows, cols))) + y = np.random.randint(0, 2, rows) + + input_data = init_input_data(X, y) + """ is_multivariate_data = True if isinstance(X.iloc[0, 0], pd.Series) else False if is_multivariate_data: input_data = InputData(idx=np.arange(len(X)), features=np.array(X.values.tolist()), target=y.reshape(-1, 1), - # task=Task(TaskTypesEnum.classification), task=Task(TaskTypesEnum(task)), data_type=DataTypesEnum.image) else: input_data = InputData(idx=np.arange(len(X)), features=X.values, target=np.ravel(y).reshape(-1, 1), - # task=Task(TaskTypesEnum.classification), task=Task(TaskTypesEnum(task)), data_type=DataTypesEnum.table) return input_data - - -if __name__ == '__main__': - rows, cols = 100, 50 - - X = pd.DataFrame(np.random.random((rows, cols))) - y = np.random.randint(0, 2, rows) - - input_data = init_input_data(X, y) - - _ = 1 diff --git a/fedot_ind/api/utils/path_lib.py b/fedot_ind/api/utils/path_lib.py index f37734b96..33903d984 100644 --- a/fedot_ind/api/utils/path_lib.py +++ b/fedot_ind/api/utils/path_lib.py @@ -7,10 +7,6 @@ PATH_TO_DEFAULT_PARAMS = os.path.join(PROJECT_PATH, 'fedot_ind/core/repository/data/default_operation_params.json') # For results collection -DS_INFO_PATH = os.path.join(PROJECT_PATH, 'core', 'architecture', 'postprocessing', 'ucr_datasets.json') +DS_INFO_PATH = os.path.join(PROJECT_PATH, 'fedot_ind', 'core', 'architecture', 'postprocessing', 'ucr_datasets.json') - -def default_path_to_save_results() -> str: - path = PROJECT_PATH - save_path = os.path.join(path, 'results_of_experiments') - return save_path +DEFAULT_PATH_RESULTS = os.path.join(PROJECT_PATH, 'results_of_experiments') diff --git a/fedot_ind/api/utils/reporter.py b/fedot_ind/api/utils/reporter.py deleted file mode 100644 index e8e98e411..000000000 --- a/fedot_ind/api/utils/reporter.py +++ /dev/null @@ -1,37 +0,0 @@ -import os - -import pandas as pd - - -class ReporterTSC: - def __init__(self, path_to_save: str = None): - self._report = [] - self.path_to_save = path_to_save - - @property - def report(self): - return self._report - - def add_to_report(self, dataset_name, generator, launch_num, result): - self._report.append({'dataset': dataset_name, - 'generator': generator, - 'launch_num': launch_num, - 'result': result}) - - def get_summary(self): - report_df = pd.DataFrame(self.report) - report_path = os.path.join(self.path_to_save, 'summary.csv') - report_df.to_csv(report_path, index=False) - return report_df - - -if __name__ == '__main__': - reporter = ReporterTSC() - reporter.add('beef', 'quantile', 1, 'good') - reporter.add('beef', 'quantile', 2, 'bad') - reporter.add('beef', 'wavelet', 1, 'good') - reporter.add('beef', 'wavelet', 2, 'bad') - reporter.add('lightning', 'quantile', 1, 'good') - reporter.add('lightning', 'quantile', 2, 'bad') - - print(reporter.get('dataset_name')) diff --git a/fedot_ind/api/utils/saver_collections.py b/fedot_ind/api/utils/saver_collections.py index f4cb01213..07c2339d7 100644 --- a/fedot_ind/api/utils/saver_collections.py +++ b/fedot_ind/api/utils/saver_collections.py @@ -3,7 +3,7 @@ import pandas as pd -from fedot_ind.api.utils.path_lib import default_path_to_save_results +from fedot_ind.api.utils.path_lib import DEFAULT_PATH_RESULTS class ResultSaver: @@ -15,12 +15,11 @@ def __init__(self, dataset_name: str, generator_name: str, output_dir: str = Non self.save_method_dict = {'labels': self.save_labels, 'probs': self.save_probs, 'metrics': self.save_metrics, - 'baseline_metrics': self.save_baseline_metrics - } + 'baseline_metrics': self.save_baseline_metrics} def __init_save_path(self, dataset_name, generator_name, output_dir): if output_dir is None: - self.output_dir = default_path_to_save_results() + self.output_dir = DEFAULT_PATH_RESULTS else: self.output_dir = os.path.abspath(output_dir) path = os.path.join(self.output_dir, generator_name, dataset_name) @@ -37,12 +36,12 @@ def save(self, predicted_data, prediction_type: str): def save_labels(self, label_data): df = pd.DataFrame(label_data, dtype=int) - df.to_csv(os.path.join(self.path, 'predicted_labels.csv')) + df.to_csv(os.path.join(self.path, 'labels.csv')) def save_probs(self, prob_data): df_preds = pd.DataFrame(prob_data.round(3), dtype=float) df_preds.columns = [f'Class_{x + 1}' for x in df_preds.columns] - df_preds.to_csv(os.path.join(self.path, 'predicted_probs.csv')) + df_preds.to_csv(os.path.join(self.path, 'probs.csv')) def save_metrics(self, metrics: dict): df = pd.DataFrame(metrics, index=[0]) diff --git a/fedot_ind/core/architecture/datasets/splitters.py b/fedot_ind/core/architecture/datasets/splitters.py index 81ef6481a..906712aac 100644 --- a/fedot_ind/core/architecture/datasets/splitters.py +++ b/fedot_ind/core/architecture/datasets/splitters.py @@ -1,7 +1,7 @@ """ This module contains functions for splitting a torch dataset into parts. """ -from typing import List, Tuple, Generator, Optional, Dict +from typing import Dict, Generator, List, Optional, Tuple import numpy as np from torch.utils.data import Dataset, Subset @@ -44,6 +44,7 @@ def k_fold(dataset: Dataset, n: int) -> Generator[Tuple[Subset, Subset], None, N train_ds = Subset(dataset, train_indices) yield train_ds, test_ds + def split_data(dataset: Dataset, n: int, verbose: bool = False) -> List[np.ndarray]: """ Splits the data into n parts, keeping the proportions of the classes. diff --git a/fedot_ind/core/architecture/experiment/TimeSeriesAnomalyDetection.py b/fedot_ind/core/architecture/experiment/TimeSeriesAnomalyDetection.py index c8125f806..f3f0ef0c3 100644 --- a/fedot_ind/core/architecture/experiment/TimeSeriesAnomalyDetection.py +++ b/fedot_ind/core/architecture/experiment/TimeSeriesAnomalyDetection.py @@ -1,6 +1,6 @@ import logging from pathlib import Path -from typing import List, Union, Dict +from typing import Dict, List, Union from typing import Optional import matplotlib.patches as mpatches @@ -13,12 +13,11 @@ from fedot.core.pipelines.pipeline_builder import PipelineBuilder from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder from fedot.core.repository.dataset_types import DataTypesEnum -from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum from fedot.core.repository.tasks import Task, TaskTypesEnum from golem.core.tuning.simultaneous import SimultaneousTuner from matplotlib import pyplot as plt -from fedot_ind.api.utils.path_lib import default_path_to_save_results +from fedot_ind.api.utils.path_lib import DEFAULT_PATH_RESULTS from fedot_ind.api.utils.saver_collections import ResultSaver from fedot_ind.core.metrics.evaluation import PerformanceAnalyzer from fedot_ind.core.operation.transformation.splitter import TSTransformer @@ -53,7 +52,7 @@ def __init__(self, params: Optional[OperationParameters] = None): self.dataset_name = params.get('dataset') self.tuning_iterations = params.get('tuning_iterations', 30) self.tuning_timeout = params.get('tuning_timeout', 15.0) - self.output_folder = params.get('output_folder', default_path_to_save_results()) + self.output_folder = params.get('output_folder', DEFAULT_PATH_RESULTS) self.saver = ResultSaver(dataset_name=self.dataset_name, generator_name='fedot_preset', @@ -137,10 +136,12 @@ def _build_pipeline(self): for index, (basis, extractor) in enumerate(zip(self.branch_nodes, self.extractors)): pipeline_builder.add_node(basis, branch_idx=index) pipeline_builder.add_node(extractor, branch_idx=index) - pipeline_builder.join_branches('mlp', params={'hidden_layer_sizes': (256, 128, 64, 32), - 'max_iter': 300, - 'activation': 'relu', - 'solver': 'adam', }) + pipeline_builder.join_branches('rf') + + # pipeline_builder.join_branches('mlp', params={'hidden_layer_sizes': (256, 128, 64, 32), + # 'max_iter': 300, + # 'activation': 'relu', + # 'solver': 'adam', }) return pipeline_builder.build() @@ -156,9 +157,9 @@ def _tune_pipeline(self, pipeline: Pipeline, train_data: InputData): tuned pipeline """ if train_data.num_classes > 2: - metric = ClassificationMetricsEnum.f1 + metric = 'f1' else: - metric = ClassificationMetricsEnum.ROCAUC + metric = 'roc_auc' pipeline_tuner = TunerBuilder(train_data.task) \ .with_tuner(SimultaneousTuner) \ @@ -186,12 +187,10 @@ def fit(self, features, with IndustrialModels(): self.train_data = self._init_input_data(features, anomaly_dict) - self.pre_pipeline = self._tune_pipeline(self.predictor, - self.train_data) - - self.pre_pipeline.fit(self.train_data) - # train_data_preprocessed = self.generator.root_node.predict(self.train_data) - train_data_preprocessed = self.pre_pipeline.root_node.predict(self.train_data) + self.preprocessing_pipeline = self._tune_pipeline(self.predictor, + self.train_data) + self.preprocessing_pipeline.fit(self.train_data) + train_data_preprocessed = self.preprocessing_pipeline.root_node.predict(self.train_data) train_data_preprocessed.predict = np.squeeze(train_data_preprocessed.predict) train_data_preprocessed = InputData(idx=train_data_preprocessed.idx, @@ -202,18 +201,12 @@ def fit(self, features, metric = 'roc_auc' if train_data_preprocessed.num_classes == 2 else 'f1' self.model_params.update({'metric': metric}) - self.auto_model = Fedot(available_operations=['scaling', - 'normalization', - 'fast_ica', - 'xgboost', - 'rfr', - 'rf', - 'logit', - 'mlp', - 'knn', - 'lgbm', - 'pca'], - **self.model_params) + if self.model_params.get('available_operations') is None: + self.auto_model = Fedot(available_operations=['scaling', 'normalization', 'fast_ica', 'xgboost', + 'rfr', 'rf', 'logit', 'mlp', 'knn', 'lgbm', 'pca'], + **self.model_params) + else: + self.auto_model = Fedot(**self.model_params) self.auto_model.fit(train_data_preprocessed) self.predictor = self.auto_model.current_pipeline @@ -229,7 +222,7 @@ def predict(self, features: pd.DataFrame) -> np.array: """ test_data = self._init_input_data(features, is_fit_stage=False) - test_data_preprocessed = self.pre_pipeline.root_node.predict(test_data) + test_data_preprocessed = self.preprocessing_pipeline.root_node.predict(test_data) if test_data.features.shape[0] == 1: test_data_preprocessed.predict = np.squeeze(test_data_preprocessed.predict).reshape(1, -1) @@ -247,7 +240,7 @@ def predict(self, features: pd.DataFrame) -> np.array: def predict_proba(self, features) -> np.array: test_data = self._init_input_data(features, is_fit_stage=False) - test_data_preprocessed = self.pre_pipeline.predict(test_data) + test_data_preprocessed = self.preprocessing_pipeline.predict(test_data) test_data_preprocessed.predict = np.squeeze(test_data_preprocessed.predict) test_data_preprocessed = InputData(idx=test_data_preprocessed.idx, features=test_data_preprocessed.predict, diff --git a/fedot_ind/core/architecture/experiment/TimeSeriesClassifier.py b/fedot_ind/core/architecture/experiment/TimeSeriesClassifier.py index 4b29636ba..d35ea3ed5 100644 --- a/fedot_ind/core/architecture/experiment/TimeSeriesClassifier.py +++ b/fedot_ind/core/architecture/experiment/TimeSeriesClassifier.py @@ -55,9 +55,9 @@ def __init__(self, params: Optional[OperationParameters] = None): self.logger.info('TimeSeriesClassifier initialised') - def fit(self, features: Union[np.ndarray, pd.DataFrame], + def fit(self, features: pd.DataFrame, target: np.ndarray, - **kwargs) -> object: + **kwargs) -> Fedot: baseline_type = kwargs.get('baseline_type', None) self.logger.info('Fitting model') @@ -122,16 +122,16 @@ def _fit_baseline_model(self, features: pd.DataFrame, target: np.ndarray, baseli return baseline_pipeline def predict(self, features: np.ndarray, **kwargs) -> np.ndarray: - self.prediction_label = self.__predict_abstraction(test_features=features, mode='labels', **kwargs) + self.prediction_label = self._predict_abstraction(test_features=features, mode='labels', **kwargs) return self.prediction_label def predict_proba(self, features: np.ndarray, **kwargs) -> np.ndarray: - self.prediction_proba = self.__predict_abstraction(test_features=features, mode='probs', **kwargs) + self.prediction_proba = self._predict_abstraction(test_features=features, mode='probs', **kwargs) return self.prediction_proba - def __predict_abstraction(self, - test_features: Union[np.ndarray, pd.DataFrame], - mode: str = 'labels', **kwargs): + def _predict_abstraction(self, + test_features: Union[np.ndarray, pd.DataFrame], + mode: str = 'labels', **kwargs): self.logger.info(f'Predicting with {self.strategy} generator') if self.test_features is None: diff --git a/fedot_ind/core/architecture/experiment/TimeSeriesClassifierNN.py b/fedot_ind/core/architecture/experiment/TimeSeriesClassifierNN.py index fb546ac79..53a8ca018 100644 --- a/fedot_ind/core/architecture/experiment/TimeSeriesClassifierNN.py +++ b/fedot_ind/core/architecture/experiment/TimeSeriesClassifierNN.py @@ -5,8 +5,8 @@ import torch from fedot.core.operations.operation_parameters import OperationParameters +from fedot_ind.api.utils.path_lib import DEFAULT_PATH_RESULTS from fedot_ind.core.architecture.experiment.TimeSeriesClassifier import TimeSeriesClassifier -from fedot_ind.api.utils.path_lib import default_path_to_save_results from fedot_ind.core.models.nn.inception import InceptionTimeNetwork TSCCLF_MODEL = { @@ -22,13 +22,13 @@ def __init__(self, params: Optional[OperationParameters] = None): self.num_epochs = params.get('num_epochs', 10) def _init_model_param(self, target: np.ndarray) -> Tuple[int, np.ndarray]: - self.model_hyperparams['models_saving_path'] = os.path.join(default_path_to_save_results(), 'TSCNN', + self.model_hyperparams['models_saving_path'] = os.path.join(DEFAULT_PATH_RESULTS, 'TSCNN', '../../models') - self.model_hyperparams['summary_path'] = os.path.join(default_path_to_save_results(), 'TSCNN', + self.model_hyperparams['summary_path'] = os.path.join(DEFAULT_PATH_RESULTS, 'TSCNN', 'runs') self.model_hyperparams['num_classes'] = np.unique(target).shape[0] if target.min() != 0: target = target - 1 - return self.num_epochs, target \ No newline at end of file + return self.num_epochs, target diff --git a/fedot_ind/core/architecture/experiment/TimeSeriesClassifierPreset.py b/fedot_ind/core/architecture/experiment/TimeSeriesClassifierPreset.py index d5a343d05..a459148ac 100644 --- a/fedot_ind/core/architecture/experiment/TimeSeriesClassifierPreset.py +++ b/fedot_ind/core/architecture/experiment/TimeSeriesClassifierPreset.py @@ -11,11 +11,10 @@ from fedot.core.pipelines.pipeline import Pipeline from fedot.core.pipelines.pipeline_builder import PipelineBuilder from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder -from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum from golem.core.tuning.sequential import SequentialTuner from fedot_ind.api.utils.input_data import init_input_data -from fedot_ind.api.utils.path_lib import default_path_to_save_results +from fedot_ind.api.utils.path_lib import DEFAULT_PATH_RESULTS from fedot_ind.api.utils.saver_collections import ResultSaver from fedot_ind.core.metrics.evaluation import PerformanceAnalyzer from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels @@ -48,7 +47,7 @@ def __init__(self, params: Optional[OperationParameters] = None): self.dataset_name = params.get('dataset') self.tuning_iterations = params.get('tuning_iterations', 30) self.tuning_timeout = params.get('tuning_timeout', 15.0) - self.output_folder = params.get('output_folder', default_path_to_save_results()) + self.output_folder = params.get('output_folder', DEFAULT_PATH_RESULTS) self.saver = ResultSaver(dataset_name=self.dataset_name, generator_name='fedot_preset', @@ -106,9 +105,9 @@ def _tune_pipeline(self, pipeline: Pipeline, train_data: InputData): tuned pipeline """ if train_data.num_classes > 2: - metric = ClassificationMetricsEnum.f1 + metric = 'f1' else: - metric = ClassificationMetricsEnum.ROCAUC + metric = 'roc_auc' pipeline_tuner = TunerBuilder(train_data.task) \ .with_tuner(SequentialTuner) \ @@ -137,11 +136,9 @@ def fit(self, features, with IndustrialModels(): self.train_data = self._init_input_data(features, target) - self.preprocessing_pipeline = self._tune_pipeline(self.preprocessing_pipeline, self.train_data) self.preprocessing_pipeline.fit(self.train_data) - self.baseline_model = self.preprocessing_pipeline.nodes[0] self.preprocessing_pipeline.update_node(self.baseline_model, PipelineNode('cat_features')) self.baseline_model.nodes_from = [] @@ -189,24 +186,30 @@ def predict(self, features: pd.DataFrame, target: np.array) -> dict: test_data_preprocessed.predict = np.squeeze(test_data_preprocessed.predict).reshape(1, -1) else: test_data_preprocessed.predict = np.squeeze(test_data_preprocessed.predict) - self.test_data_preprocessed = InputData(idx=test_data_preprocessed.idx, - features=test_data_preprocessed.predict, - target=test_data_preprocessed.target, - data_type=test_data_preprocessed.data_type, - task=test_data_preprocessed.task) + test_data_preprocessed = InputData(idx=test_data_preprocessed.idx, + features=test_data_preprocessed.predict, + target=test_data_preprocessed.target, + data_type=test_data_preprocessed.data_type, + task=test_data_preprocessed.task) - self.prediction_label_baseline = self.baseline_model.predict(self.test_data_preprocessed).predict - self.prediction_label = self.predictor.predict(self.test_data_preprocessed) + self.prediction_label_baseline = self.baseline_model.predict(test_data_preprocessed).predict + self.prediction_label = self.predictor.predict(test_data_preprocessed) return self.prediction_label def predict_proba(self, features, target) -> dict: test_data = self._init_input_data(features, target) test_data_preprocessed = self.preprocessing_pipeline.root_node.predict(test_data) - self.test_data_preprocessed.predict = np.squeeze(test_data_preprocessed.predict) + test_data_preprocessed.predict = np.squeeze(test_data_preprocessed.predict) + + test_data_preprocessed = InputData(idx=test_data_preprocessed.idx, + features=test_data_preprocessed.predict, + target=test_data_preprocessed.target, + data_type=test_data_preprocessed.data_type, + task=test_data_preprocessed.task) - self.prediction_proba_baseline = self.baseline_model.predict(self.test_data_preprocessed, 'probs').predict - self.prediction_proba = self.predictor.predict_proba(self.test_data_preprocessed) + self.prediction_proba_baseline = self.baseline_model.predict(test_data_preprocessed, 'probs').predict + self.prediction_proba = self.predictor.predict_proba(test_data_preprocessed) return self.prediction_proba def get_metrics(self, target: Union[np.ndarray, pd.Series], metric_names: Union[str, List[str]]) -> dict: diff --git a/fedot_ind/core/architecture/pipelines/anomaly_detection.py b/fedot_ind/core/architecture/pipelines/anomaly_detection.py index 1bdbc5b07..b2e5a46cd 100644 --- a/fedot_ind/core/architecture/pipelines/anomaly_detection.py +++ b/fedot_ind/core/architecture/pipelines/anomaly_detection.py @@ -3,6 +3,7 @@ from pymonad.list import ListMonad from pymonad.either import Right from fedot_ind.core.architecture.pipelines.abstract_pipeline import AbstractPipelines +from fedot_ind.core.operation.transformation.basis.eigen_basis import EigenBasisImplementation class AnomalyDetectionPipelines(AbstractPipelines): @@ -66,7 +67,7 @@ def __multi_functional_pca(self): def __functional_pca_pipeline(self, decomposition: str = 'svd', **kwargs): feature_extractor, detector, lambda_func_dict = self._init_pipeline_nodes(model_type='functional_pca', **kwargs) - data_basis = DataDrivenBasis() + data_basis = EigenBasisImplementation() if kwargs['component'] is None: kwargs['component'] = [0 for i in range(self.train_features.shape[1])] if decomposition == 'tensor': diff --git a/fedot_ind/core/architecture/postprocessing/results_picker.py b/fedot_ind/core/architecture/postprocessing/results_picker.py index ca4066903..f9b07bbe6 100644 --- a/fedot_ind/core/architecture/postprocessing/results_picker.py +++ b/fedot_ind/core/architecture/postprocessing/results_picker.py @@ -30,7 +30,6 @@ def __init__(self, path: str = None, launch_type: Union[str, int] = 'max'): self.launch_type = launch_type self.logger = logging.getLogger(self.__class__.__name__) - def __get_results_path(self, path): if path: return path diff --git a/fedot_ind/core/architecture/postprocessing/visualisation/matrix_vis.py b/fedot_ind/core/architecture/postprocessing/visualisation/matrix_vis.py deleted file mode 100644 index 38f19a195..000000000 --- a/fedot_ind/core/architecture/postprocessing/visualisation/matrix_vis.py +++ /dev/null @@ -1,42 +0,0 @@ -from matplotlib import pyplot as plt - -plt.rcParams['figure.figsize'] = (10, 8) -plt.rcParams['font.size'] = 14 -plt.rcParams['image.cmap'] = 'plasma' -plt.rcParams['axes.linewidth'] = 2 - -cols = plt.get_cmap('tab10').colors - -def plot_2d(m, title=""): - plt.imshow(m) - plt.xticks([]) - plt.yticks([]) - plt.title(title) - -def plot_wcorr(minimum=None, maximum=None): - """Plots the w-correlation matrix for the decomposed time series. - - """ - if minimum is None: - minimum = 0 - if maximum is None: - maximum = d - - if Wcorr is None: - calc_wcorr() - - ax = plt.imshow(Wcorr) - plt.xlabel(r"$\tilde{F}_i$") - plt.ylabel(r"$\tilde{F}_j$") - plt.colorbar(ax.colorbar, fraction=0.045) - ax.colorbar.set_label("$W_{i,j}$") - plt.clim(0, 1) - - # For plotting purposes: - if maximum == d: - max_range = d - 1 - else: - max_range = maximum - - plt.xlim(minimum - 0.5, max_range + 0.5) - plt.ylim(max_range + 0.5, minimum - 0.5) \ No newline at end of file diff --git a/fedot_ind/core/ensemble/kernel_ensemble.py b/fedot_ind/core/ensemble/kernel_ensemble.py index f206faf63..a850772fa 100644 --- a/fedot_ind/core/ensemble/kernel_ensemble.py +++ b/fedot_ind/core/ensemble/kernel_ensemble.py @@ -1,6 +1,5 @@ import numpy as np import pandas as pd -from fedot.api.main import Fedot from MKLpy.algorithms import FHeuristic, RMKL from MKLpy.callbacks import EarlyStopping from MKLpy.scheduler import ReduceOnWorsening @@ -8,8 +7,6 @@ from fedot_ind.core.architecture.pipelines.classification import ClassificationPipelines from fedot_ind.core.architecture.settings.pipeline_factory import KernelFeatureGenerator -from fedot_ind.core.ensemble.rank_ensembler import RankEnsemble -from fedot_ind.tools.loader import DataLoader class KernelEnsembler(ClassificationPipelines): @@ -52,8 +49,8 @@ def transform(self, kernel_params_dict: dict = None, feature_generator: str = No for specified_params in kernel_params: feature_extractor, classificator, lambda_func_dict = self._init_pipeline_nodes(**specified_params) - self.feature_matrix_train.append(feature_extractor.extract_features(self.train_features)) - self.feature_matrix_test.append(feature_extractor.extract_features(self.test_features)) + self.feature_matrix_train.append(feature_extractor.extract_features(self.train_features, self.train_target)) + self.feature_matrix_test.append(feature_extractor.extract_features(self.test_features, self.test_target)) return def __one_stage_kernel(self, kernel_params_dict: dict = None, feature_generator: str = None): @@ -99,74 +96,3 @@ def init_kernel_ensemble(train_data, test_target = kernels.test_target return set_of_fg, train_feats, train_target, test_feats, test_target - - -if __name__ == '__main__': - n_best = 3 - feature_dict = {} - metric_list = [] - proba_dict = {} - metric_dict = {} - dataset_name = 'Lightning2' - kernel_list = {'wavelet': [ - {'feature_generator_type': 'wavelet', - 'feature_hyperparams': { - 'wavelet': "mexh", - 'n_components': 2 - }}, - {'feature_generator_type': 'wavelet', - 'feature_hyperparams': { - 'wavelet': "morl", - 'n_components': 2 - }}], - 'quantile': [ - {'feature_generator_type': 'quantile', - 'feature_hyperparams': { - 'window_mode': True, - 'window_size': 25 - } - }, - {'feature_generator_type': 'quantile', - 'feature_hyperparams': { - 'window_mode': False, - 'window_size': 40 - } - }] - } - fg_names = [] - for key in kernel_list: - for model_params in kernel_list[key]: - fg_names.append(f'{key}_{model_params}') - - train_data, test_data = DataLoader(dataset_name).load_data() - set_of_fg, train_feats, train_target, test_feats, test_target = init_kernel_ensemble(train_data, - test_data, - kernel_list=kernel_list) - - n_best_generators = set_of_fg.T.nlargest(n_best, 0).index - for rank in range(n_best): - fg_rank = n_best_generators[rank] - train_best = train_feats[fg_rank] - test_best = test_feats[fg_rank] - feature_dict.update({fg_names[rank]: (test_best, test_best)}) - - for model_name, feature in feature_dict.items(): - industrial = Fedot( - # available_operations=['fast_ica', 'scaling','normalization', - # 'xgboost', - # 'rf', - # 'logit', - # 'mlp', - # 'knn', - # 'pca'], - metric='roc_auc', timeout=5, problem='classification', n_jobs=6) - - model = industrial.fit(feature[0], train_target) - labels = industrial.predict(feature[1]) - proba_dict.update({model_name: industrial.predict_proba(feature[1])}) - metric_dict.update({model_name: industrial.get_metrics(test_target, metric_names=['roc_auc', 'f1', 'acc'])}) - rank_ensembler = RankEnsemble(dataset_name=dataset_name, - proba_dict=proba_dict, - metric_dict=metric_dict) - ensemble_result = rank_ensembler.ensemble() - _ = 1 diff --git a/fedot_ind/core/ensemble/rank_ensembler.py b/fedot_ind/core/ensemble/rank_ensembler.py index b5da4d69a..a6cff6b41 100644 --- a/fedot_ind/core/ensemble/rank_ensembler.py +++ b/fedot_ind/core/ensemble/rank_ensembler.py @@ -71,7 +71,7 @@ def _deep_search_in_dict(self, obj, key): if item is not None: return item - def _create_models_rank_dict(self, prediction_proba_dict, metric_dict): + def _create_models_rank_dict(self, prediction_proba_dict, metric_dict) -> dict: """ Method that returns a dictionary with the best metric values of base models @@ -96,7 +96,7 @@ def _create_models_rank_dict(self, prediction_proba_dict, metric_dict): model_rank.update({model: current_metrics[self.metric]}) return model_rank - def _sort_models(self, model_rank): + def _sort_models(self, model_rank) -> dict: """ Method that returns sorted dictionary with models results `` diff --git a/fedot_ind/core/models/nn/inception.py b/fedot_ind/core/models/nn/inception.py index 61b125a93..44191fb29 100644 --- a/fedot_ind/core/models/nn/inception.py +++ b/fedot_ind/core/models/nn/inception.py @@ -62,7 +62,8 @@ def __init__(self, in_channels, padding=kernel_sizes[2] // 2, bias=False ) - self.max_pool = nn.MaxPool1d(kernel_size=3, stride=1, padding=1, return_indices=return_indices) + self.max_pool = nn.MaxPool1d( + kernel_size=3, stride=1, padding=1, return_indices=return_indices) self.conv_from_maxpool = nn.Conv1d( in_channels=in_channels, out_channels=n_filters, @@ -316,4 +317,3 @@ def network_architecture(self): nn.Linear(in_features=4 * 32 * 1, out_features=4) ) return network - diff --git a/fedot_ind/core/models/quantile/quantile_extractor.py b/fedot_ind/core/models/quantile/quantile_extractor.py index 14958c7fa..676e181d8 100644 --- a/fedot_ind/core/models/quantile/quantile_extractor.py +++ b/fedot_ind/core/models/quantile/quantile_extractor.py @@ -47,29 +47,6 @@ def __init__(self, params: Optional[OperationParameters] = None): 'Stride': self.stride, 'VarTh': self.var_threshold}) - def _drop_features(self, predict: pd.DataFrame, columns: Index, n_components: int): - """ - Method for dropping features with low variance - """ - # Fill columns names for every extracted ts component - predict = pd.DataFrame(predict, - columns=[f'{col}{str(i)}' for i in range(1, n_components + 1) for col in columns]) - - if self.relevant_features is None: - reduced_df, self.relevant_features = self._filter_by_var(predict, threshold=self.var_threshold) - return reduced_df - else: - return predict[self.relevant_features] - - def _filter_by_var(self, data: pd.DataFrame, threshold: float): - cols = data.columns - filtrat = {} - - for col in cols: - if np.var(data[col].values) > threshold: - filtrat.update({col: data[col].values.flatten()}) - - return pd.DataFrame(filtrat), list(filtrat.keys()) def _concatenate_global_and_local_feature(self, global_features: InputData, window_stat_features: InputData) -> InputData: 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 d0d3596c4..848f2be12 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 @@ -1,8 +1,6 @@ from typing import Tuple -import matplotlib.pyplot as plt import numpy as np -from sklearn.preprocessing import MinMaxScaler class CURDecomposition: @@ -40,10 +38,6 @@ def fit_transform(self, matrix: np.ndarray) -> tuple: return c, u, r 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) - # TS_comps = list(map(multi_reconstruction, R)) - # else: rank = U.shape[1] TS_comps = np.zeros((ts_length, rank)) for i in range(rank): @@ -109,55 +103,3 @@ def get_random_sparse_matrix(size: tuple): if np.random.rand() < 0.1: matrix[i, j] = np.random.rand() return matrix - - -if __name__ == '__main__': - from fedot_ind.tools.loader import DataLoader - from tqdm import tqdm - - 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 - - # ranks = list(range(5, 20)) - # cur_errors = [] - # with tqdm(total=len(ranks), desc='cur') as pbar: - # for rank in ranks: - # cur = CURDecomposition(rank=rank) - # C, U, R = cur.fit_transform(M) - # cur_errors.append(np.linalg.norm(M - C @ U @ R)) - # pbar.update(1) - - # f,a = plt.subplots(2, 1, figsize=(10, 10)) - # # a[0].plot(ranks, svd_errors, label='svd') - # a[1].plot(ranks, cur_errors, label='cur') - # a[0].set_title('svd') - # a[1].set_title('cur') - # 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 ace5c4b7a..0fcf42567 100644 --- a/fedot_ind/core/operation/decomposition/matrix_decomposition/dmd_decomposition.py +++ b/fedot_ind/core/operation/decomposition/matrix_decomposition/dmd_decomposition.py @@ -3,7 +3,7 @@ def rq(A): - n, m = A.shape() + n, m = A.shape Q, R = np.linalg.qr(np.flipud(A).T, mode='complete') R = np.rot90(R.T, 2) Q = np.flipud(Q.T) @@ -19,18 +19,14 @@ def tls(A, B): raise ValueError('Matrices are not conformant.') R1 = np.hstack((A, B)) U, S, V = np.linalg.svd(R1) - r = A.shape[1] - R = rq(V[:, r:]) + r = B.shape[1] + R, Q = rq(V[:, r:]) Gamma = R[n:, n - r:] Z = R[:n, n - r:] Xhat = -np.dot(Z, np.linalg.inv(Gamma)) return Xhat -def dmd_decompose(): - pass - - def exact_dmd_decompose(X, Y, rank): Ux, Sx, Vx = svd(X) Ux = Ux[:, :rank] @@ -76,10 +72,10 @@ def symmetric_decompose(X, Y, rank): r = np.linalg.matrix_rank(X) Ux = Ux[:, :rank] Yf = np.zeros((rank, rank)) - for i in range(r): - Yf[i, i] = np.real(C1[i, i]) / S[i, i] - for j in range(i + 1, r): - Yf[i, j] = (S[i, i] * np.conj(C1[j, i]) + S[j, j] * C1[i, j]) / (S[i, i] ** 2 + S[j, j] ** 2) + for i in range(rank): + Yf[i, i] = np.real(C1[i, i]) / S[i] + for j in range(i + 1, rank): + Yf[i, j] = (S[i] * np.conj(C1[j, i]) + S[j] * C1[i, j]) / (S[i] ** 2 + S[j] ** 2) Yf = Yf + Yf.T - np.diag(np.diag(np.real(Yf))) # elif method == 'skewsymmetric': # for i in range(r): @@ -92,7 +88,7 @@ def symmetric_decompose(X, Y, rank): def hankel_decompose(X, Y, rank): - nx, nt = X.shape() + nx, nt = X.shape # J = np.eye(nx) J = np.fliplr(np.eye(nx)) # Define the left matrix diff --git a/fedot_ind/core/operation/optimization/FeatureSpace.py b/fedot_ind/core/operation/optimization/FeatureSpace.py index 8c9d9dddb..67b8bc457 100644 --- a/fedot_ind/core/operation/optimization/FeatureSpace.py +++ b/fedot_ind/core/operation/optimization/FeatureSpace.py @@ -6,7 +6,7 @@ class VarianceSelector: """ Class that accepts a dictionary as input, the keys of which are the names of models and the values are arrays - of data in the np.array format.The class implements an algorithm to determine the "best" set of features and the + of data in the np.ndarray format.The class implements an algorithm to determine the "best" set of features and the best model in the dictionary. """ diff --git a/fedot_ind/core/operation/transformation/FeatureSpaceReducer.py b/fedot_ind/core/operation/transformation/FeatureSpaceReducer.py index 68ef6fd92..4bce2cdbd 100644 --- a/fedot_ind/core/operation/transformation/FeatureSpaceReducer.py +++ b/fedot_ind/core/operation/transformation/FeatureSpaceReducer.py @@ -28,9 +28,6 @@ def reduce_feature_space(self, features: pd.DataFrame, final_feature_space_size = features_new.shape[1] - if init_feature_space_size != final_feature_space_size: - self.logger.info(f'Feature space reduced from {init_feature_space_size} to {final_feature_space_size}') - return features_new def _drop_correlated_features(self, corr_threshold, features): @@ -47,7 +44,6 @@ def _drop_correlated_features(self, corr_threshold, features): 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() @@ -63,9 +59,3 @@ def _drop_stable_features(self, features, var_threshold): except ValueError: self.logger.info('Variance reducer has not found any features with low variance') return features - - def validate_window_size(self, ts: np.ndarray): - if self.window_size is None or self.window_size > ts.shape[0] / 2: - self.logger.info('Window size is not defined or too big (> ts_length/2)') - self.window_size, _ = WindowSizeSelector(time_series=ts).get_window_size() - self.logger.info(f'Window size was set to {self.window_size}') diff --git a/fedot_ind/core/operation/transformation/basis/eigen_basis.py b/fedot_ind/core/operation/transformation/basis/eigen_basis.py index 6fc51ebf4..0e9a25521 100644 --- a/fedot_ind/core/operation/transformation/basis/eigen_basis.py +++ b/fedot_ind/core/operation/transformation/basis/eigen_basis.py @@ -85,11 +85,11 @@ def _transform(self, input_data: InputData) -> np.array: def get_threshold(self, data) -> int: svd_numbers = [] - with tqdm(total=len(data), desc='SVD estimation') as pbar: - for signal in data: - svd_numbers.append( - self._transform_one_sample(signal, svd_flag=True)) - pbar.update(1) + # with tqdm(total=len(data), desc='SVD estimation') as pbar: + for signal in data: + svd_numbers.append( + self._transform_one_sample(signal, svd_flag=True)) + # pbar.update(1) return self._mode(svd_numbers) # return stats.mode(svd_numbers).mode if scipy.__version__ > '1.7.3' else stats.mode(svd_numbers).mode[0] diff --git a/fedot_ind/core/operation/transformation/splitter.py b/fedot_ind/core/operation/transformation/splitter.py index f46e4f126..42896fab9 100644 --- a/fedot_ind/core/operation/transformation/splitter.py +++ b/fedot_ind/core/operation/transformation/splitter.py @@ -12,7 +12,7 @@ class TSTransformer: Class for transformation single time series based on anomaly dictionary. Args: - strategy: strategy for splitting time series. Available strategies: 'frequent' and `unique`. + strategy: strategy for splitting time series. Available strategies: 'frequent'. Attributes: @@ -36,12 +36,6 @@ class TSTransformer: splitter = TSSplitter(strategy='frequent') train, test = splitter.transform_for_fit(series=ts, anomaly_dict=anomaly_d_uni, plot=False, binarize=True) - In case of `unique` strategy, the splitting will be based on unique anomalies and hence - the output of `split` method will be tuple of lists `unique_classes`, `unique_train`, `unique_test` - where every element of every list is corresponding to unique anomaly. Important fact is that plotting - function is now available for this case yet:: - unique_cls, unique_train, unique_test = splitter.split(strategy='unique', binarize=False) - """ def __init__(self, strategy: str = 'frequent'): @@ -69,27 +63,7 @@ def transform(self, series: np.array): def _unique_strategy(self, series: np.array, anomaly_dict: Dict, plot: bool = False, binarize: bool = False) -> tuple: - """ - Split time series into train and test parts based on unique anomalies. - - Args: - plot: if True, plot time series with anomaly intervals. Available only for univariate time series. - binarize: if True, target will be binarized. Recommended for classification task if classes are imbalanced. - - Returns: - tuple with train and test parts of time series ready for classification task with FedotIndustrial. - - """ - unique_classes, unique_trains = [], [] - for cls, list_of_inters in anomaly_dict.items(): - features, target = self.get_features_and_target(series=series, - classes=[cls], - transformed_intervals=[list_of_inters], - binarize=binarize) - unique_trains.append((pd.DataFrame(features), np.array(target))) - unique_classes.append(cls) - - return unique_classes, unique_trains + raise NotImplementedError def _frequent_strategy(self, series: np.array, anomaly_dict: Dict, plot: bool = False, binarize: bool = False) -> tuple: @@ -238,8 +212,6 @@ def balance_with_non_anomaly(self, series, target, features, non_anomaly_interva ts = series.copy() counter = 0 taken_slots = pd.Series([0 for _ in range(len(ts))]) - # for non_anom in non_anomaly_intervals: - # taken_slots[non_anom[0]:non_anom[1]] = 0 while len(non_anomaly_ts_list) != number_of_anomalies and counter != number_of_anomalies * 100: seed = np.random.randint(1000) @@ -311,40 +283,3 @@ def _transform_test(self, series: np.array): transformed_data.append(series_part) transformed_data = np.stack(transformed_data) return transformed_data - - -if __name__ == '__main__': - uni_ts = np.random.rand(800) - anomaly_d_uni = {'anomaly1': [[40, 50], [60, 80], [200, 220], [410, 420], [513, 524], [641, 645]], - 'anomaly2': [[130, 170], [300, 320], [400, 410], [589, 620], [715, 720]], - 'anomaly3': [[500, 530], [710, 740]], - 'anomaly4': [[77, 90], [98, 112], [145, 158], [290, 322]]} - - ts1 = np.arange(0, 100) - multi_ts = np.array([ts1, ts1 * 2, ts1 * 3]).T - anomaly_d_multi = {'anomaly1': [[0, 5], [15, 20], [22, 24], [55, 63], [70, 90]], - 'anomaly2': [[10, 12], [15, 16], [27, 31], [44, 50], [98, 100]], - 'anomaly3': [[0, 3], [15, 18], [19, 24], [55, 60], [85, 90]]} - - splitter_multi = TSTransformer() - train_multi, test_multi = splitter_multi.transform_for_fit(series=multi_ts, - anomaly_dict=anomaly_d_multi, plot=False, binarize=True) - - splitter_uni = TSTransformer() - train_uni, test_uni = splitter_uni.transform_for_fit(series=uni_ts, - anomaly_dict=anomaly_d_uni, plot=True, binarize=True) - - unique_ts = np.random.rand(800) - anomaly_unique = { - 'class1': [[0, 10], [20, 30], [50, 60], [70, 80], [100, 110], [120, 130], [160, 170], 200, 210, 310, 330, 350, - 370, 410, 430, 460, 480, 500, 520, [540, 560], [590, 610], [630, 650], [680, 700], [720, 740], - [760, 780], [80, 100], [320, 340]], - 'class2': [[0, 20], [50, 70], [100, 120], [140, 160], [190, 210], [230, 250], [270, 290], [240, 250], - [270, 280], [330, 340], [360, 370], [400, 410], [440, 450], [480, 490], [520, 530], [570, 580], - [610, 620], [660, 670], [700, 710]]} - - # splitter_unique = TSTransformer(strategy='unique') - # unique_cls, unique_train, unique_test = splitter_unique.transform_for_fit(series=unique_ts, - # anomaly_dict=anomaly_unique, plot=True, - # binarize=False) - _ = 1 diff --git a/fedot_ind/core/optimizer/IndustrialEvoOptimizer.py b/fedot_ind/core/optimizer/IndustrialEvoOptimizer.py index 9ad02e0da..856057cf1 100644 --- a/fedot_ind/core/optimizer/IndustrialEvoOptimizer.py +++ b/fedot_ind/core/optimizer/IndustrialEvoOptimizer.py @@ -10,8 +10,7 @@ from golem.core.optimisers.populational_optimizer import _try_unfit_graph from fedot_ind.core.repository.IndustrialDispatcher import IndustrialDispatcher -from fedot_ind.core.repository.initializer_industrial_models import \ - has_no_data_flow_conflicts_in_industrial_pipeline +from fedot_ind.core.repository.initializer_industrial_models import has_no_data_flow_conflicts_in_industrial_pipeline class IndustrialEvoOptimizer(EvoGraphOptimizer): @@ -24,10 +23,9 @@ def __init__(self, graph_optimizer_params.mutation_types.remove(MutationTypesEnum.single_drop) graph_generation_params.verifier._rules.append(has_no_data_flow_conflicts_in_industrial_pipeline) - #graph_generation_params.verifier._rules.remove(has_no_conflicts_with_data_flow) super().__init__(objective, initial_graphs, requirements, graph_generation_params, graph_optimizer_params) 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) \ No newline at end of file + delegate_evaluator=graph_generation_params.remote_evaluator) diff --git a/fedot_ind/core/repository/data/default_operation_params.json b/fedot_ind/core/repository/data/default_operation_params.json index 6ecbdfc8e..7fbe18b47 100644 --- a/fedot_ind/core/repository/data/default_operation_params.json +++ b/fedot_ind/core/repository/data/default_operation_params.json @@ -161,9 +161,8 @@ "threshold": 20000 }, "quantile_extractor": { - "window_size": null, - "window_mode": false, - "var_threshold": 0.01 + "window_size": 0, + "stride": 1 }, "recurrence_extractor": { "window_size": 20, diff --git a/fedot_ind/core/architecture/postprocessing/visualisation/__init__.py b/fedot_ind/tools/explain/__init__.py similarity index 100% rename from fedot_ind/core/architecture/postprocessing/visualisation/__init__.py rename to fedot_ind/tools/explain/__init__.py diff --git a/fedot_ind/tools/explain/distances.py b/fedot_ind/tools/explain/distances.py new file mode 100644 index 000000000..241ff7f6f --- /dev/null +++ b/fedot_ind/tools/explain/distances.py @@ -0,0 +1,112 @@ +import numpy as np +from scipy.spatial.distance import cosine, euclidean +from scipy.stats import energy_distance +from scipy.stats import entropy + + +def kl_divergence(probs_before: np.ndarray, probs_after: np.ndarray) -> float: + """ + KL Divergence measures the information lost when one probability distribution is used to approximate another. + + Args: + probs_before: The probability distribution before some event. + probs_after: The probability distribution after the same event. + + """ + return entropy(probs_before, qk=probs_after) + + +def jensen_shannon_divergence(probs_before: np.ndarray, probs_after: np.ndarray) -> float: + """ + Jensen-Shannon Divergence is a symmetric and smoothed version of KL Divergence, measuring + the similarity between two distributions. + + Args: + probs_before: The probability distribution before some event. + probs_after: The probability distribution after the same event. + + """ + p = 0.5 * (probs_before + probs_after) + return 0.5 * (entropy(probs_before, qk=p) + entropy(probs_after, qk=p)) + + +def total_variation_distance(probs_before: np.ndarray, probs_after: np.ndarray) -> float: + """ + Total Variation Distance measures the discrepancy between two probability distributions. + It is also half the absolute area between the two curves + + Args: + probs_before: The probability distribution before some event. + probs_after: The probability distribution after the same event. + + """ + return 0.5 * np.sum(np.abs(probs_before - probs_after)) + + +def energy_distance_measure(probs_before: np.ndarray, probs_after: np.ndarray) -> float: + """ + Energy Distance measures the distance between the characteristic functions of two distributions. + + Args: + probs_before: The probability distribution before some event. + probs_after: The probability distribution after the same event. + + """ + return energy_distance(probs_before, probs_after) + + +def hellinger_distance(probs_before: np.ndarray, probs_after: np.ndarray) -> float: + """ + Hellinger Distance measures the similarity between two probability distributions. + + Args: + probs_before: The probability distribution before some event. + probs_after: The probability distribution after the same event. + + """ + return np.sqrt(np.sum((np.sqrt(probs_before) - np.sqrt(probs_after)) ** 2)) / np.sqrt(2) + + +def cosine_distance(probs_before: np.ndarray, probs_after: np.ndarray) -> float: + """ + Cosine Distance measures the cosine of the angle between two vectors, indicating their similarity. + + Args: + probs_before: A vector. + probs_after: Another vector. + + """ + return cosine(probs_before, probs_after) + + +def euclidean_distance(probs_before: np.ndarray, probs_after: np.ndarray) -> float: + """ + Euclidean Distance is the straight-line distance between two points in space. + + Args: + probs_before: A vector. + probs_after: Another vector. + + """ + return euclidean(probs_before, probs_after) + + +def cross_entropy(p, q): + return -sum([p[i] * np.log2(q[i]) for i in range(len(p))]) + + +def rmse(p, q): + return np.sqrt(np.mean((p - q) ** 2)) + + +DistanceTypes = dict( + cosine=cosine_distance, + euclidean=euclidean_distance, + hellinger=hellinger_distance, + energy=energy_distance_measure, + total_variation=total_variation_distance, + jensen_shannon=jensen_shannon_divergence, + kl_div=kl_divergence, + cross_entropy=cross_entropy, + rmse=rmse +) diff --git a/fedot_ind/tools/explain/explain.py b/fedot_ind/tools/explain/explain.py new file mode 100644 index 000000000..0352424d9 --- /dev/null +++ b/fedot_ind/tools/explain/explain.py @@ -0,0 +1,177 @@ +import math + +# import lime +# import lime.lime_tabular +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +# import shap +from matplotlib.cm import ScalarMappable +from matplotlib.colors import Normalize +from tqdm import tqdm + +from fedot_ind.tools.explain.distances import DistanceTypes + + +class PointExplainer: + def __init__(self, model, features, target): + self.picked_target = None + self.picked_feature = None + self.model = model + self.features = features + self.target = target + + self.scaled_vector = None + self.window_length = None + + def explain(self, n_samples: int = 1, window: int = 5, method: str = 'rmse'): + self.picked_feature, self.picked_target = self.select(self.features, + self.target, + n_samples_=n_samples) + self.scaled_vector, self.window_length = self.importance(window=window, + method=method) + + def visual(self, threshold: int = 90, name='dataset'): + self.plot_importance(thr=threshold, name=name) + + def importance(self, window=None, method='euclidean'): + model = self.model + part_feature_ = self.picked_feature + part_target_ = self.picked_target + distance_func = DistanceTypes[method] + base_proba_ = self.predict_proba(model, part_feature_, part_target_) + + if not window: + window_length = 0 + n_parts = part_feature_.shape[1] + + iv_scaled = self.get_vector(base_proba_, distance_func, model, n_parts, + part_feature_, part_target_, window_length) + + else: + window_length = part_feature_.shape[1] * window // 100 + n_parts = math.ceil(part_feature_.shape[1] / window_length) + iv_scaled = self.get_vector(base_proba_, distance_func, model, n_parts, + part_feature_, part_target_, window_length) + + return pd.DataFrame(iv_scaled), window_length + + def get_vector(self, base_proba_, distance_func, model, n_parts, part_feature_, part_target_, window_length): + importance_vector_ = {cls: np.zeros(n_parts) for cls in np.unique(part_target_)} + with tqdm(total=n_parts, desc='Processing points', unit='point') as pbar: + for part in range(n_parts): + feature_ = part_feature_.copy().values + feature_ = self.replace_values(feature_, window_len=window_length, i=part) + proba_new = self.predict_proba(model, feature_, part_target_) + + distance_dict = {} + for idx, cls in enumerate(part_target_): + if cls not in distance_dict: + distance_dict[cls] = [] + distance_dict[cls].append(distance_func(base_proba_[idx], proba_new[idx])) + for cls in distance_dict: + importance_vector_[cls][part] = np.mean(distance_dict[cls]) + pbar.update(1) + return importance_vector_ + + @staticmethod + def replace_values(features: np.ndarray, window_len: int, i: int): + if window_len: + for idx, ts in enumerate(features): + mean_ts = ts.mean() + features[idx, i * window_len:(i + 1) * window_len] = mean_ts + else: + for idx, ts in enumerate(features): + mean_ts = ts.mean() + features[idx, i] = mean_ts + return features + + @staticmethod + def predict_proba(model, features, target): + if hasattr(model, 'solver'): + model.solver.test_features = None + base_proba_ = model.predict_proba(features=pd.DataFrame(features), target=target) + else: + base_proba_ = model.predict_proba(X=features) + return base_proba_ + + @staticmethod + def select(features_, target_, n_samples_: int = 3): + selected_df = pd.DataFrame() + selected_target = np.array([]) + df = features_.copy() + df['target'] = target_ + for class_label in np.unique(target_): + class_samples = df[df['target'] == class_label].sample(n=n_samples_, replace=False) + selected_df = pd.concat([selected_df, class_samples.iloc[:, :-1]]) + selected_target = np.concatenate([selected_target, class_samples['target'].to_numpy()]) + + return selected_df, selected_target + + def plot_importance(self, thr=90, name='dataset'): + feature, target = self.picked_feature, self.picked_target + vector_df = self.scaled_vector + window = self.window_length + # filter by threshold value for each class + threshold_ = {cls: np.percentile(vector_df[cls], thr) for cls in np.unique(target)} + importance_vector_filtered_ = {cls: np.where(vector_df[cls] > threshold_[cls], vector_df[cls], 0) for cls in + np.unique(target)} + vector_df = pd.DataFrame(importance_vector_filtered_) + n_classes = len(np.unique(target)) + fig, axs = plt.subplots(n_classes, 1, figsize=(10, 5 if n_classes < 6 else 5 * n_classes // 2)) + fig.suptitle(f'Importance of points for {name} dataset') + + # Color bar definition + cbar_ax = fig.add_axes([1, 0.3, 0.01, 0.5]) + cmap = plt.get_cmap('Reds') + norm = Normalize(vmin=vector_df.min().min(), vmax=vector_df.max().max()) + + scal_map = ScalarMappable(norm=norm, cmap='Reds') + + for idx, cls in enumerate(np.unique(target)): + copy_vec = vector_df[cls].copy() + if not window: + # every 10% of length + x_ticks = np.arange(0, len(feature.iloc[idx, :]), len(feature.iloc[idx, :]) // 10) + for dot_idx, dot in enumerate(copy_vec): + axs[idx].axvline(dot_idx, color=cmap(norm(dot))) + axs[idx].set_xticks(x_ticks) + + else: + # ticks with window step + x_ticks = [*np.arange(0, len(feature.iloc[idx, :]), window), len(feature.iloc[idx, :])] + for span_idx, dot in enumerate(copy_vec): + left = span_idx * window + right = span_idx * window + window if span_idx * window + window < len(feature.iloc[idx, :]) \ + else len(feature.iloc[idx, :]) + axs[idx].axvspan(left, right, color=cmap(norm(dot))) + top = axs[idx].get_ylim()[1] + axs[idx].text(left, top, f'{dot:.2f}', fontsize=8, color='white') + axs[idx].set_xticks(x_ticks) + + mean_value = feature.iloc[idx, :].mean() + axs[idx].plot([0, len(feature.iloc[idx, :])], [mean_value, mean_value], color='black', linestyle='--', + label='mean') + class_indexes = np.where(target == cls)[0] + for class_idx in class_indexes: + axs[idx].plot(feature.iloc[class_idx, :], color='dodgerblue', label=f'class-{cls}') + axs[idx].text(len(feature.iloc[idx, :]) - 1, mean_value, f'mean={mean_value:.2f}', fontsize=10) + axs[idx].set_title(f'Class: {cls}') + plt.colorbar(scal_map, cax=cbar_ax) + plt.tight_layout() + plt.show() + + +# class ShapExplainer: +# def __init__(self, model, features, target, prediction): +# self.model = model +# self.features = features +# self.target = target +# self.prediction = prediction +# +# def explain(self, n_samples: int = 5): +# X_test = self.features +# +# explainer = shap.KernelExplainer(self.model.predict, X_test, n_samples=n_samples) +# shap_values = explainer.shap_values(X_test.iloc[:n_samples, :]) +# shap.summary_plot(shap_values, X_test.iloc[:n_samples, :], plot_type="bar") diff --git a/fedot_ind/tools/loader.py b/fedot_ind/tools/loader.py index 648e2dbd3..85c107ba1 100644 --- a/fedot_ind/tools/loader.py +++ b/fedot_ind/tools/loader.py @@ -16,11 +16,12 @@ class DataLoader: - """Class for reading data from ``tsv`` files and downloading from UCR archive if not found locally. - At the moment supports only ``.txt`` and ``.arff`` formats, but not relational ``.arff`` or ``.ts`` files. + """Class for reading data files and downloading from UCR archive if not found locally. + At the moment supports ``.ts``, ``.txt``, ``.tsv``, and ``.arff`` formats. Args: dataset_name: name of dataset + folder: path to folder with data Examples: >>> data_loader = DataLoader('ItalyPowerDemand') @@ -729,8 +730,8 @@ def read_arff_files(self, dataset_name, temp_data_path): """Reads data from ``.arff`` file. """ - train = loadarff(temp_data_path + dataset_name + f'/{dataset_name}_TRAIN.arff') - test = loadarff(temp_data_path + dataset_name + f'/{dataset_name}_TEST.arff') + train = loadarff(temp_data_path + '/' + dataset_name + f'/{dataset_name}_TRAIN.arff') + test = loadarff(temp_data_path + '/' + dataset_name + f'/{dataset_name}_TEST.arff') data_train = np.asarray([train[0][name] for name in train[1].names()]) x_train = data_train[:-1].T.astype('float64') @@ -787,8 +788,3 @@ def extract_data(self, dataset_name: str, data_path: str): return (x_train, y_train), (x_test, y_test) else: return (pd.DataFrame(x_train), y_train), (pd.DataFrame(x_test), y_test) - - -if __name__ == '__main__': - data_loader = DataLoader('AppliancesEnergy') - _train_data, _test_data = data_loader.load_data() diff --git a/fedot_ind/tools/synthetic/anomaly_generator.py b/fedot_ind/tools/synthetic/anomaly_generator.py index 17b33e262..5d8d90077 100644 --- a/fedot_ind/tools/synthetic/anomaly_generator.py +++ b/fedot_ind/tools/synthetic/anomaly_generator.py @@ -138,8 +138,8 @@ def plot_anomalies(self, initial_ts, modified_ts, anomaly_intervals_dict): label=cls) for cls in anomaly_intervals_dict.keys()] for anomaly_class, intervals in anomaly_intervals_dict.items(): - for interval in intervals.transform_for_fit(', '): - start_idx, end_idx = map(int, interval.transform_for_fit(':')) + for interval in intervals: + start_idx, end_idx = interval ax.axvspan(start_idx, end_idx, alpha=0.3, color=color_dict[anomaly_class]) # Put a legend to the right of the current axis diff --git a/fedot_ind/tools/synthetic/ts_datasets_generator.py b/fedot_ind/tools/synthetic/ts_datasets_generator.py index a5b5da4f6..650ec665f 100644 --- a/fedot_ind/tools/synthetic/ts_datasets_generator.py +++ b/fedot_ind/tools/synthetic/ts_datasets_generator.py @@ -1,6 +1,7 @@ import numpy as np import pandas as pd from sklearn.model_selection import train_test_split + from fedot_ind.tools.synthetic.ts_generator import TimeSeriesGenerator @@ -11,25 +12,37 @@ class TimeSeriesDatasetsGenerator: Args: num_samples: The number of samples to generate. max_ts_len: The maximum length of the time series. - n_classes: The number of classes. - test_size (float): The proportion of the dataset to include in the test split. + binary: Whether to generate binary classification datasets or multiclass. + test_size : The proportion of the dataset to include in the test split. + multivariate: Whether to generate multivariate time series. Example: Easy:: generator = TimeSeriesGenerator(num_samples=80, max_ts_len=50, - n_classes=5) + binary=5, + test_size=0.5, + multivariate=False) train_data, test_data = generator.generate_data() """ - def __init__(self, num_samples: int = 80, max_ts_len: int = 50, n_classes: int = 3, test_size: float = 0.5): + def __init__(self, + num_samples: int = 80, + max_ts_len: int = 50, + binary: bool = True, + test_size: float = 0.5, + multivariate: bool = False): self.num_samples = num_samples self.max_ts_len = max_ts_len - self.n_classes = n_classes self.test_size = test_size - self.ts_types = None + self.multivariate = multivariate + + if binary: + self.selected_classes = ['sin', 'random_walk'] + else: + self.selected_classes = ['sin', 'random_walk', 'auto_regression'] def generate_data(self): """ @@ -39,9 +52,31 @@ def generate_data(self): Tuple of train and test data, each containing tuples of features and targets. """ + if self.multivariate: + n_classes = len(self.selected_classes) + features = self.create_features(self.num_samples * n_classes, self.max_ts_len, self.multivariate) + target = np.random.randint(0, n_classes, self.num_samples * n_classes) + X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=self.test_size, random_state=42, shuffle=True) + return (X_train, y_train), (X_test, y_test) - ts_frame = pd.DataFrame(np.random.rand(self.num_samples, self.max_ts_len)) - labels = np.random.randint(self.n_classes, size=self.num_samples) - - X_train, X_test, y_train, y_test = train_test_split(ts_frame, labels, test_size=self.test_size, random_state=42) + ts_frame = pd.DataFrame() + labels = np.array([]) + for idx, ts_class in enumerate(self.selected_classes): + for sample in range(self.num_samples): + label = idx + params = {'ts_type': ts_class, + 'length': self.max_ts_len} + ts_gen = TimeSeriesGenerator(params) + ts = ts_gen.get_ts() + ts_frame = ts_frame.append(pd.DataFrame(ts).T) + labels = np.append(labels, label) + ts_frame.reset_index(drop=True, inplace=True) + X_train, X_test, y_train, y_test = train_test_split(ts_frame, labels, test_size=self.test_size, random_state=42, shuffle=True) return (X_train, y_train), (X_test, y_test) + + def create_features(self, n_samples, ts_length, multivariate): + features = pd.DataFrame(np.random.random((n_samples, ts_length))) + # TODO: add option to select dimentions + if multivariate: + features = features.apply(lambda x: pd.Series([x, x, x]), axis=1) + return features diff --git a/fedot_ind/tools/synthetic/ts_generator.py b/fedot_ind/tools/synthetic/ts_generator.py index 37709902d..b1779fbb1 100644 --- a/fedot_ind/tools/synthetic/ts_generator.py +++ b/fedot_ind/tools/synthetic/ts_generator.py @@ -32,7 +32,7 @@ def __init__(self, params: dict): self.ts_types = {'sin': SinWave, 'random_walk': RandomWalk, 'auto_regression': AutoRegression, - 'smooth_normal': SmoothNormal} + 'smooth_normal': SmoothNormal} self.params = params def __define_seed(self): @@ -89,7 +89,8 @@ def __init__(self, params: dict): def get_ts(self): time_index = np.arange(0, self.ts_length) sine_wave = self.amplitude * np.sin(2 * np.pi / self.period * time_index) - return sine_wave + noise = np.random.normal(0, 1, self.ts_length) + return np.array(sine_wave + noise) class RandomWalk(DefaultTimeSeries): @@ -100,7 +101,8 @@ def __init__(self, params: dict): def get_ts(self): time_index = pd.Series(np.arange(0, self.ts_length)) random_walk = pd.Series(np.cumsum(np.random.randn(self.ts_length)) + self.start_val, index=time_index) - return np.array(random_walk) + noise = np.random.normal(0, 1, self.ts_length) + return np.array(random_walk + noise) class AutoRegression(DefaultTimeSeries): @@ -122,8 +124,8 @@ def get_ts(self): else: ar_process[i] = np.sum(self.ar_params * ar_process[i - len(self.ar_params):i].ravel(), axis=0) + np.random.normal(0, 1) - # return np.array(pd.Series(ar_process[:, 0], index=time_index)) - return ar_process + noise = np.random.normal(0, 1, self.ts_length) + return np.array(ar_process + noise) class SmoothNormal(DefaultTimeSeries): @@ -139,7 +141,8 @@ def __check_window_size(self): def get_ts(self): normal_ts = np.random.normal(0, 1, self.ts_length) + 100 - return self.savitzky_golay(y=normal_ts, window_size=self.window_size, order=3) + noise = np.random.normal(0, 1, self.ts_length) + return np.array(self.savitzky_golay(y=normal_ts, window_size=self.window_size, order=3) + noise) @staticmethod def savitzky_golay(y: np.array, window_size: int, order: int = 3, deriv: int = 0, rate: int = 1): diff --git a/requirements.txt b/requirements.txt index b81359be1..5c7ee6ef7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -29,5 +29,7 @@ torchvision==0.15.1 tensorboard>=2.12.0 statsforecast==1.5.0 +opencv-python + chardet tqdm \ No newline at end of file diff --git a/tests/data/datasets/ItalyPowerDemand_tsv/ItalyPowerDemand_tsv_TEST.tsv 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-1.5062846 -1.5062846 -1.0555261 -1.0104502 -0.063857444 1.1081146 1.3785697 1.3334938 1.1081146 0.74750781 0.61228026 1.0630387 0.9278112 0.61228026 0.25167348 0.16152179 0.026294249 0.65735611 0.20659763 -0.19908499 +2 0.98403309 0.16966088 -0.51942331 -0.83264339 -0.8952874 -0.70735536 -0.70735536 -1.7723037 -0.8952874 0.48288096 0.67081301 0.73345703 0.73345703 0.42023694 -0.51942331 -0.64471135 -0.70735536 -0.76999938 -0.70735536 -0.26884724 0.2323049 2.4248455 1.9236934 1.1719652 diff --git a/tests/integration/preprocessing/test_load_data.py b/tests/integration/preprocessing/test_load_data.py deleted file mode 100644 index 2177de8c3..000000000 --- a/tests/integration/preprocessing/test_load_data.py +++ /dev/null @@ -1,27 +0,0 @@ -from fedot_ind.tools.loader import DataLoader - - -def test_load_multivariate_data(): - train_data, test_data = DataLoader('Epilepsy').load_data() - x_train, y_train = train_data - x_test, y_test = test_data - assert x_train.shape == (137, 3) - assert x_test.shape == (138, 3) - assert y_train.shape == (137,) - assert y_test.shape == (138,) - - -def test_load_univariate_data(): - train_data, test_data = DataLoader('DodgerLoopDay').load_data() - x_train, y_train = train_data - x_test, y_test = test_data - assert x_train.shape == (78, 288) - assert x_test.shape == (80, 288) - assert y_train.shape == (78,) - assert y_test.shape == (80,) - - -def test_load_fake_data(): - train_data, test_data = DataLoader('Fake').load_data() - assert train_data is None - assert test_data is None diff --git a/tests/unit/api/test_api_main.py b/tests/unit/api/test_api_main.py index 35af56c04..89ce3cc48 100644 --- a/tests/unit/api/test_api_main.py +++ b/tests/unit/api/test_api_main.py @@ -1,3 +1,6 @@ +import os.path + +import numpy as np import pytest from fedot_ind.api.main import FedotIndustrial @@ -5,57 +8,80 @@ from fedot_ind.core.architecture.experiment.TimeSeriesClassifier import TimeSeriesClassifier from fedot_ind.core.architecture.experiment.TimeSeriesClassifierPreset import TimeSeriesClassifierPreset from fedot_ind.core.models.topological.topological_extractor import TopologicalExtractor +from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator @pytest.fixture() def tsc_topo_config(): - config = dict(task='ts_classification', - dataset='Chinatown', - strategy='topological', - timeout=0.5, - logging_level=40, - use_cache=False) - return config + return dict(task='ts_classification', + dataset='Chinatown', + strategy='topological', + timeout=0.1, + logging_level=40, + use_cache=False) @pytest.fixture() def tsc_fedot_preset_config(): - config = dict(task='ts_classification', - dataset='Chinatown', - strategy='fedot_preset', - timeout=0.5, - logging_level=40, - use_cache=False) - return config + return dict(task='ts_classification', + dataset='Chinatown', + strategy='fedot_preset', + timeout=0.5, + logging_level=40, + use_cache=False) @pytest.fixture() def none_tsc_config(): - config = dict(task='ts_classification', - dataset='Chinatown', - strategy=None, - timeout=0.5, - logging_level=40, - use_cache=False) - return config + return dict(task='ts_classification', + dataset='Chinatown', + strategy=None, + timeout=0.5, + logging_level=40, + use_cache=False) @pytest.fixture() def anomaly_detection_fedot_preset_config(): - config = dict(task='anomaly_detection', - dataset='custom_dataset', - strategy='fedot_preset', - use_cache=False, - timeout=0.5, - n_jobs=1, - logging_level=20) - return config + return dict(task='anomaly_detection', + dataset='custom_dataset', + strategy='fedot_preset', + use_cache=False, + timeout=0.5, + n_jobs=1, + logging_level=20) + + +@pytest.fixture() +def decomposition_config(): + return dict(task='anomaly_detection', + dataset='custom_dataset', + strategy='decomposition', + use_cache=False, + timeout=0.5, + n_jobs=1, + logging_level=20) + + +@pytest.fixture() +def ts_config(): + return dict(random_walk={'ts_type': 'random_walk', + 'length': 1000, + 'start_val': 36.6}) + + +@pytest.fixture() +def anomaly_config(): + return {'dip': {'level': 20, + 'number': 2, + 'min_anomaly_length': 10, + 'max_anomaly_length': 20} + } def test_main_api_topo(tsc_topo_config): industrial = FedotIndustrial(**tsc_topo_config) - assert type(industrial) is FedotIndustrial assert type(industrial.solver) is TimeSeriesClassifier assert industrial.solver.strategy == 'topological' assert industrial.config_dict['task'] == 'ts_classification' @@ -66,7 +92,6 @@ def test_main_api_topo(tsc_topo_config): def test_main_api_fedot_preset(tsc_fedot_preset_config): industrial = FedotIndustrial(**tsc_fedot_preset_config) - assert type(industrial) is FedotIndustrial assert type(industrial.solver) is TimeSeriesClassifierPreset assert industrial.solver.extractors == ['quantile_extractor', 'quantile_extractor', 'quantile_extractor'] assert industrial.solver.branch_nodes == ['eigen_basis', 'fourier_basis', 'wavelet_basis'] @@ -76,8 +101,64 @@ def test_main_api_fedot_preset(tsc_fedot_preset_config): def test_main_api_anomaly_detection_fedot_preset(anomaly_detection_fedot_preset_config): industrial = FedotIndustrial(**anomaly_detection_fedot_preset_config) - assert type(industrial) is FedotIndustrial + assert type(industrial.solver) is TimeSeriesAnomalyDetectionPreset assert industrial.solver.extractors == ['quantile_extractor', 'quantile_extractor', 'quantile_extractor'] assert industrial.solver.branch_nodes == ['eigen_basis', 'fourier_basis', 'wavelet_basis'] assert industrial.config_dict['task'] == 'anomaly_detection' + + +def test_api_tsc(tsc_topo_config): + tsc_topo_config.update({'output_folder': '.'}) + industrial = FedotIndustrial(**tsc_topo_config) + train_data, test_data = TimeSeriesDatasetsGenerator(num_samples=50, + max_ts_len=30, + binary=True, + test_size=0.5).generate_data() + model = industrial.fit(features=train_data[0], target=train_data[1]) + labels = industrial.predict(features=test_data[0], target=test_data[1]) + probs = industrial.predict_proba(features=test_data[0], target=test_data[1]) + metrics = industrial.get_metrics(target=test_data[1], metric_names=['roc_auc', 'accuracy']) + + for name, predict in zip(('labels', 'probs'), (labels, probs)): + industrial.save_predict(predicted_data=predict, kind=name) + industrial.save_metrics(metrics=metrics) + + expected_results_path = industrial.solver.saver.path + + for result in (model, labels, probs, metrics): + assert result is not None + for s in ('labels', 'probs', 'metrics'): + filepath = expected_results_path + f'/{s}.csv' + assert os.path.isfile(filepath) + + +def test_generate_ts(tsc_topo_config, ts_config): + industrial = FedotIndustrial(**tsc_topo_config) + ts = industrial.generate_ts(ts_config=ts_config) + + assert isinstance(ts, np.ndarray) + assert ts.shape[0] == 1000 + + +def test_generate_anomaly_ts(tsc_topo_config, ts_config, anomaly_config): + industrial = FedotIndustrial(**tsc_topo_config) + init_synth_ts, mod_synth_ts, synth_inters = industrial.generate_anomaly_ts(ts_data=ts_config, + anomaly_config=anomaly_config) + assert len(init_synth_ts) == len(mod_synth_ts) + for anomaly_type in synth_inters: + for interval in synth_inters[anomaly_type]: + ts_range = range(len(init_synth_ts)) + assert interval[0] in ts_range and interval[1] in ts_range + + +def test_split_ts(tsc_topo_config): + anomaly_dict = {'anomaly1': [[40, 50], [60, 80]], + 'anomaly2': [[130, 170], [300, 320]]} + industrial = FedotIndustrial(**tsc_topo_config) + train_data, test_data = industrial.split_ts(time_series=np.random.rand(1000), + anomaly_dict=anomaly_dict, + plot=False) + + assert train_data is not None + assert test_data is not None diff --git a/tests/unit/api/utils/test_cinfigurator.py b/tests/unit/api/utils/test_configurator.py similarity index 94% rename from tests/unit/api/utils/test_cinfigurator.py rename to tests/unit/api/utils/test_configurator.py index 1c6312e94..f16d1342d 100644 --- a/tests/unit/api/utils/test_cinfigurator.py +++ b/tests/unit/api/utils/test_configurator.py @@ -1,5 +1,4 @@ -import pytest -from fedot_ind.api.utils.configurator import Configurator, IndustrialConfigs +from fedot_ind.api.utils.configurator import Configurator from fedot_ind.core.models.quantile.quantile_extractor import QuantileExtractor TASKS = ['ts_classification', 'ts_regression', 'anomaly_detection'] @@ -58,7 +57,3 @@ def test_init_experiment_setup_preset(): assert setup['tuning_iters'] == 5 assert setup['tuning_timeout'] == 15 assert setup['output_folder'] == 'output_folder' - - -def test_get_generator_class(): - pass \ No newline at end of file diff --git a/tests/unit/api/utils/test_input_data.py b/tests/unit/api/utils/test_input_data.py new file mode 100644 index 000000000..89ad7e0ba --- /dev/null +++ b/tests/unit/api/utils/test_input_data.py @@ -0,0 +1,39 @@ +import pytest + +from fedot_ind.api.utils.input_data import init_input_data +import numpy as np +import pandas as pd + + +@pytest.fixture +def sample_univariate(): + rows, cols = 100, 50 + X = pd.DataFrame(np.random.random((rows, cols))) + y = np.random.randint(0, 2, rows) + return X, y + + +@pytest.fixture +def sample_multivariate(): + rows, cols = 100, 50 + X = pd.DataFrame(np.random.random((rows, cols))) + X = X.apply(lambda x: pd.Series([x, x]), axis=1) + y = np.random.randint(0, 2, rows) + return X, y + + +def test_init_input_data_uni(sample_univariate): + x, y = sample_univariate + input_data = init_input_data(X=x, y=y) + + assert np.all(input_data.features == x.values) + assert np.all(input_data.target == y.reshape(-1, 1)) + + +def test_init_input_data_multi(sample_multivariate): + x, y = sample_multivariate + input_data = init_input_data(X=x, y=y) + + assert input_data.features.shape[0] == x.shape[0] + assert input_data.features.shape[1] == x.shape[1] + assert np.all(input_data.target == y.reshape(-1, 1)) diff --git a/tests/unit/api/utils/test_saver_collections.py b/tests/unit/api/utils/test_saver_collections.py new file mode 100644 index 000000000..6ea309a69 --- /dev/null +++ b/tests/unit/api/utils/test_saver_collections.py @@ -0,0 +1,67 @@ +import os.path +import shutil +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest + +from fedot_ind.api.utils.path_lib import DEFAULT_PATH_RESULTS +from fedot_ind.api.utils.saver_collections import ResultSaver + +CUSTOM_PATH = './results' + + +@pytest.fixture +def sample_results(): + labels = [[np.random.randint(0, 5)] for _ in range(10)] + probs = np.random.rand(10, 5).round(3) + metrics = {'roc_auc': np.random.rand(1).round(3), + 'f1': np.random.rand(1).round(3), + 'accuracy': np.random.rand(1).round(3)} + baseline_metrics = {'roc_auc': np.random.rand(1), + 'f1': np.random.rand(1), + 'accuracy': np.random.rand(1)} + return {'labels': labels, 'probs': probs, 'metrics': metrics, 'baseline_metrics': baseline_metrics} + + +@pytest.mark.parametrize('path', [CUSTOM_PATH, DEFAULT_PATH_RESULTS]) +def test_init_result_saver(path): + dataset_name = 'name' + generator_name = 'generator' + saver = ResultSaver(dataset_name=dataset_name, generator_name=generator_name, output_dir=path) + ds_folder = os.path.abspath(os.path.join(saver.output_dir, generator_name, dataset_name)) + gen_folder = os.path.abspath(os.path.join(saver.output_dir, generator_name)) + + assert os.path.abspath(saver.output_dir) == os.path.abspath(path) + assert os.path.isdir(saver.output_dir) + assert os.path.isdir(ds_folder) + assert os.path.isdir(gen_folder) + + # Keep your test folder clean! + if path != DEFAULT_PATH_RESULTS: + shutil.rmtree(Path(saver.output_dir)) + + +@pytest.mark.parametrize('prediction_type', ('labels', 'probs', 'metrics', 'baseline_metrics')) +def test_save(prediction_type, sample_results): + results = sample_results + dataset_name = 'name' + generator_name = 'generator' + output_dir = './results' + expected_file_path = os.path.join(output_dir, generator_name, dataset_name, f'{prediction_type}.csv') + + saver = ResultSaver(dataset_name=dataset_name, generator_name=generator_name, output_dir=output_dir) + saver.save(predicted_data=results[prediction_type], prediction_type=prediction_type) + + assert os.path.isfile(expected_file_path) + saved_data = pd.read_csv(expected_file_path, index_col=0) + + if prediction_type in ('metrics', 'baseline_metrics'): + for m in ['roc_auc', 'f1', 'accuracy']: + assert saved_data[m][0], results[prediction_type][m][0] + elif prediction_type in ('labels', 'probs'): + arr = saved_data.values + assert np.allclose(arr, results[prediction_type], rtol=1.e-3) + # Keep your test folder clean! + shutil.rmtree(Path(saver.output_dir)) diff --git a/tests/unit/architecture/experiment/test_TimeSeriesClassifier.py b/tests/unit/architecture/experiment/test_TimeSeriesClassifier.py deleted file mode 100644 index efbbb8119..000000000 --- a/tests/unit/architecture/experiment/test_TimeSeriesClassifier.py +++ /dev/null @@ -1,40 +0,0 @@ -import pytest - -from fedot_ind.core.architecture.experiment.TimeSeriesClassifier import TimeSeriesClassifier - - -@pytest.fixture -def params(): - return dict(task='ts_classification', - dataset='Ham', - strategy='quantile', - use_cache=False, - timeout=1, - n_jobs=-1, - window_mode=True, - window_size=20) - - -@pytest.fixture -def classifier(params): - return TimeSeriesClassifier(params) - - -def test_init(classifier): - assert classifier.strategy == 'quantile' - assert classifier.model_hyperparams is None - assert classifier.generator_runner is None - assert classifier.dataset_name == 'Ham' - assert classifier.output_folder is None - assert classifier.saver is not None - assert classifier.logger is not None - assert classifier.datacheck is not None - assert classifier.prediction_proba is None - assert classifier.test_predict_hash is None - assert classifier.prediction_label is None - assert classifier.predictor is None - assert classifier.y_train is None - assert classifier.train_features is None - assert classifier.test_features is None - assert classifier.input_test_data is None - assert classifier.logger.name == 'TimeSeriesClassifier' diff --git a/tests/unit/architecture/experiment/test_TimeSeriesClassifierPreset.py b/tests/unit/architecture/experiment/test_TimeSeriesClassifierPreset.py deleted file mode 100644 index a63d0f05f..000000000 --- a/tests/unit/architecture/experiment/test_TimeSeriesClassifierPreset.py +++ /dev/null @@ -1,37 +0,0 @@ -import pytest -from fedot.core.pipelines.pipeline import Pipeline - -from fedot_ind.core.architecture.experiment.TimeSeriesClassifierPreset import TimeSeriesClassifierPreset -from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator - - -@pytest.fixture -def dataset(): - (X_train, y_train), (X_test, y_test) = TimeSeriesDatasetsGenerator(num_samples=30, - max_ts_len=50, - n_classes=2, - test_size=0.5).generate_data() - return X_train, y_train, X_test, y_test - - -@pytest.fixture -def params(): - return dict(branch_nodes=['eigen_basis'], - dataset='FordA', - model_params={'task': 'classification', - 'n_jobs': -1, - 'timeout': 1}, - output_folder='.') - - -@pytest.fixture -def classifier(params): - return TimeSeriesClassifierPreset(params) - - -def test_init(classifier): - assert classifier.branch_nodes == ['eigen_basis'] - assert classifier.tuning_iterations == 30 - assert classifier.tuning_timeout == 15.0 - assert isinstance(classifier.preprocessing_pipeline, Pipeline) - assert classifier.output_folder == '.' diff --git a/fedot_ind/core/architecture/preprocessing/__init__.py b/tests/unit/core/__init__.py similarity index 100% rename from fedot_ind/core/architecture/preprocessing/__init__.py rename to tests/unit/core/__init__.py diff --git a/tests/integration/preprocessing/__init__.py b/tests/unit/core/architecture/__init__.py similarity index 100% rename from tests/integration/preprocessing/__init__.py rename to tests/unit/core/architecture/__init__.py diff --git a/tests/unit/architecture/__init__.py b/tests/unit/core/architecture/abstraction/__init__.py similarity index 100% rename from tests/unit/architecture/__init__.py rename to tests/unit/core/architecture/abstraction/__init__.py diff --git a/tests/unit/core/architecture/abstraction/test_checkers.py b/tests/unit/core/architecture/abstraction/test_checkers.py new file mode 100644 index 000000000..d344f3731 --- /dev/null +++ b/tests/unit/core/architecture/abstraction/test_checkers.py @@ -0,0 +1,31 @@ +import pytest + +from fedot_ind.core.architecture.abstraction.сheckers import parameter_value_check + + +@pytest.fixture +def valid_data(): + parameter = 'custom' + value = 1.35 + valid_values = {1.35, 4, 1} + return parameter, value, valid_values + + +@pytest.fixture +def invalid_data(): + parameter = 'custom' + value = 1.35 + valid_values = {1.354, 4, 1} + return parameter, value, valid_values + + +def test_valid_parameter_value_check(valid_data): + parameter, value, valid_values = valid_data + assert parameter_value_check(parameter, value, valid_values) is None + + +def test_invalid_parameter_value_check(invalid_data): + parameter, value, valid_values = invalid_data + with pytest.raises(ValueError) as execution_info: + parameter_value_check(parameter, value, valid_values) + assert str(execution_info.value) == f"{parameter} must be one of {valid_values}, but got {parameter}='{value}'" diff --git a/tests/unit/architecture/datasets/__init__.py b/tests/unit/core/architecture/datasets/__init__.py similarity index 100% rename from tests/unit/architecture/datasets/__init__.py rename to tests/unit/core/architecture/datasets/__init__.py diff --git a/tests/unit/core/architecture/datasets/test_classification_datasets.py b/tests/unit/core/architecture/datasets/test_classification_datasets.py new file mode 100644 index 000000000..f7313bd82 --- /dev/null +++ b/tests/unit/core/architecture/datasets/test_classification_datasets.py @@ -0,0 +1,34 @@ +import numpy as np +import pytest +from torch import Size, Tensor + +from fedot_ind.core.architecture.datasets.classification_datasets import NumpyImageDataset + +N_SAMPLES = 5 +IMAGE_SIZE = (50, 50) + + +@pytest.fixture +def images_n_target(): + images = np.array([np.random.randint(low=0, high=255, size=IMAGE_SIZE) for _ in range(N_SAMPLES)]) + target = np.array([[np.random.randint(0, 3)] for _ in range(N_SAMPLES)]) + return images, target + + +def test_get_item(images_n_target): + images, target = images_n_target + dataset = NumpyImageDataset(images=images, targets=target) + for sample_idx in range(N_SAMPLES + 1): + if sample_idx == N_SAMPLES: + with pytest.raises(IndexError) as execution_info: + dataset.__getitem__(sample_idx) + assert str( + execution_info.value) == f'index {sample_idx} is out of bounds for dimension 0 with size {N_SAMPLES}' + else: + item = dataset.__getitem__(sample_idx) + img, target_value = item[0], item[1] + assert isinstance(item, tuple) + assert isinstance(img, Tensor) + assert isinstance(target_value, Tensor) + assert np.all(np.array(img.size()) == IMAGE_SIZE) + assert target_value.size() == Size((1,)) diff --git a/tests/unit/core/architecture/datasets/test_object_detection_datasets.py b/tests/unit/core/architecture/datasets/test_object_detection_datasets.py new file mode 100644 index 000000000..403ae2d07 --- /dev/null +++ b/tests/unit/core/architecture/datasets/test_object_detection_datasets.py @@ -0,0 +1,37 @@ +import os + +import pytest +from torchvision import transforms + +from fedot_ind.api.utils.path_lib import PROJECT_PATH +from fedot_ind.core.architecture.datasets.object_detection_datasets import COCODataset, YOLODataset + +yolo_path = os.path.join(PROJECT_PATH, 'tests', 'data', 'datasets', 'minerals', 'minerals.yaml') +coco_path = os.path.join(PROJECT_PATH, 'tests', 'data', 'datasets', 'ALET10', 'test.json') +coco_img_path = os.path.join(PROJECT_PATH, 'tests', 'data', 'datasets', 'ALET10', 'test') + + +@pytest.fixture +def synthetic_coco_dataset(): + return COCODataset(coco_img_path, coco_path, transform=transforms.ToTensor()) + + +@pytest.fixture +def yolo_dataset(): + return YOLODataset(yolo_path, transform=transforms.ToTensor()) + + +def test_coco_dataset_sample(synthetic_coco_dataset): + sample = synthetic_coco_dataset[0] + image, label = sample + assert len(synthetic_coco_dataset) == 3 + assert image is not None + assert label is not None + + +def test_yolo_dataset_sample(yolo_dataset): + sample = yolo_dataset[0] + image, label = sample + assert len(yolo_dataset) == 47 + assert image is not None + assert label is not None diff --git a/tests/unit/core/architecture/datasets/test_prediction_datasets.py b/tests/unit/core/architecture/datasets/test_prediction_datasets.py new file mode 100644 index 000000000..e610277bd --- /dev/null +++ b/tests/unit/core/architecture/datasets/test_prediction_datasets.py @@ -0,0 +1,37 @@ +import os + +import numpy as np +import pytest +from torch import Tensor +from torchvision.transforms import transforms + +from fedot_ind.api.utils.path_lib import PROJECT_PATH +from fedot_ind.core.architecture.datasets.prediction_datasets import PredictionFolderDataset, PredictionNumpyDataset + +IMAGE_SIZE = (50, 50) +N_SAMPLES = 5 +yolo_path = os.path.join(PROJECT_PATH, 'tests', 'data', 'datasets', 'ALET10', 'test') + + +@pytest.fixture +def images(): + return np.array([np.random.randint(low=0, + high=255, + size=IMAGE_SIZE) for _ in range(N_SAMPLES)]) + + +def test_prediction_numpy_dataset(images): + dataset = PredictionNumpyDataset(images=images) + sample = dataset[0] + assert len(dataset) == N_SAMPLES + assert isinstance(sample, tuple) + assert isinstance(sample[0], Tensor) + + +def test_prediction_folder_dataset(): + dataset = PredictionFolderDataset(image_folder=yolo_path, + transform=transforms.ToTensor()) + tensor, filename = dataset[0] + assert len(dataset) == 3 + assert isinstance(tensor, Tensor) + assert isinstance(filename, str) diff --git a/tests/unit/architecture/datasets/test_splitters.py b/tests/unit/core/architecture/datasets/test_splitters.py similarity index 52% rename from tests/unit/architecture/datasets/test_splitters.py rename to tests/unit/core/architecture/datasets/test_splitters.py index 6757a6a6c..c2f23f02c 100644 --- a/tests/unit/architecture/datasets/test_splitters.py +++ b/tests/unit/core/architecture/datasets/test_splitters.py @@ -5,7 +5,7 @@ from torchvision.datasets import ImageFolder from torchvision.transforms import ToTensor -from fedot_ind.core.architecture.datasets.splitters import k_fold, split_data +from fedot_ind.core.architecture.datasets.splitters import k_fold, split_data, undersampling, dataset_info, get_dataset_mean_std, train_test_split from fedot_ind.api.utils.path_lib import PROJECT_PATH DATASETS_PATH = os.path.abspath(PROJECT_PATH + '/tests/data/datasets') @@ -18,8 +18,13 @@ def dataset(): yield ImageFolder(root=path, transform=ToTensor()) +def test_train_test_split(dataset): + train_ds, test_ds = train_test_split(dataset, p=0.2) + assert len(train_ds) + len(test_ds) == len(dataset) + + def test_split_data(dataset): - fold_indices = split_data(dataset, n=3) + fold_indices = split_data(dataset, n=3, verbose=True) assert np.array_equal(np.sort(np.concatenate(fold_indices)), np.arange(len(dataset))) assert fold_indices[0].size == 21 assert fold_indices[1].size == 20 @@ -29,3 +34,21 @@ def test_split_data(dataset): def test_k_fold(dataset): for train_ds, val_ds in k_fold(dataset, 3): assert len(train_ds) + len(val_ds) == len(dataset) + + +def test_undersampling(dataset): + balanced = undersampling(dataset=dataset, n=3, verbose=True) + assert len(balanced) == 9 + + +def test_dataset_info(dataset): + result = dataset_info(dataset=dataset, verbose=True) + assert isinstance(result, dict) + + +def test_get_dataset_mean_std(dataset): + mean, std = get_dataset_mean_std(dataset=dataset) + assert isinstance(mean, tuple) + assert isinstance(std, tuple) + assert len(mean) == 3 + assert len(std) == 3 \ No newline at end of file diff --git a/tests/unit/core/architecture/datasets/test_visualization.py b/tests/unit/core/architecture/datasets/test_visualization.py new file mode 100644 index 000000000..6779e0f79 --- /dev/null +++ b/tests/unit/core/architecture/datasets/test_visualization.py @@ -0,0 +1,42 @@ +import os + +import pytest +from matplotlib import pyplot as plt +from torchvision.transforms import transforms + +from fedot_ind.api.utils.path_lib import PROJECT_PATH +from fedot_ind.core.architecture.datasets.object_detection_datasets import COCODataset +from fedot_ind.core.architecture.datasets.visualization import draw_sample_with_bboxes, draw_sample_with_masks + +coco_path = os.path.join(PROJECT_PATH, 'tests', 'data', 'datasets', 'ALET10', 'test.json') +coco_img_path = os.path.join(PROJECT_PATH, 'tests', 'data', 'datasets', 'ALET10', 'test') + + +@pytest.fixture +def synthetic_coco_dataset(): + return COCODataset(coco_img_path, coco_path, transform=transforms.ToTensor()) + + +@pytest.fixture +def sample_prediction(): + return { + 'boxes': [[0.0, 0.0, 1.0, 1.0]], + 'labels': [1], + 'scores': [0.9] + } + + +def test_draw_sample_with_bboxes(synthetic_coco_dataset, sample_prediction): + sample = synthetic_coco_dataset[0] + image, label = sample + figure = draw_sample_with_bboxes(image=image, target=label, prediction=sample_prediction) + + assert isinstance(figure, plt.Figure) + + +def test_draw_sample_with_masks(synthetic_coco_dataset): + sample = synthetic_coco_dataset[0] + image, _ = sample + figure = draw_sample_with_masks(image=image, target=image) + + assert isinstance(figure, plt.Figure) diff --git a/tests/unit/architecture/experiment/__init__.py b/tests/unit/core/architecture/experiment/__init__.py similarity index 100% rename from tests/unit/architecture/experiment/__init__.py rename to tests/unit/core/architecture/experiment/__init__.py diff --git a/tests/unit/core/architecture/experiment/test_TimeSeriesAnomalyDetection.py b/tests/unit/core/architecture/experiment/test_TimeSeriesAnomalyDetection.py new file mode 100644 index 000000000..2f320a049 --- /dev/null +++ b/tests/unit/core/architecture/experiment/test_TimeSeriesAnomalyDetection.py @@ -0,0 +1,45 @@ +from fedot_ind.core.architecture.experiment.TimeSeriesAnomalyDetection import TimeSeriesAnomalyDetectionPreset +from fedot_ind.tools.synthetic.ts_generator import TimeSeriesGenerator + +import pytest + + +@pytest.fixture() +def time_series(): + ts_config = {'ts_type': 'random_walk', + 'length': 1000, + 'start_val': 36.6} + ts = TimeSeriesGenerator(ts_config).get_ts() + return ts + + +@pytest.fixture() +def anomaly_dict(): + anomaly_d = {'anomaly1': [[40, 50], [60, 80], [200, 220]], + 'anomaly2': [[300, 320], [400, 420], [600, 620]]} + return anomaly_d + + +@pytest.fixture() +def detector(): + params = dict(branch_nodes=['eigen_basis'], + dataset='test', + tuning_iterations=1, + tuning_timeout=1, + model_params=dict(problem='classification', + timeout=0.5, + n_jobs=1, + logging_level=50)) + detector = TimeSeriesAnomalyDetectionPreset(params) + return detector + + +# def test_fit_predict(detector, time_series, anomaly_dict): +# try: +# detector.fit(time_series, anomaly_dict) +# except Exception as ex: +# detector.fit(time_series, anomaly_dict) +# labels = detector.predict(time_series) +# proba = detector.predict_proba(time_series) +# metrics = detector.get_metrics(time_series, metric_names=['f1', 'roc_auc']) +# assert detector.auto_model.current_pipeline.is_fitted is True diff --git a/tests/unit/core/architecture/experiment/test_TimeSeriesClassifier.py b/tests/unit/core/architecture/experiment/test_TimeSeriesClassifier.py new file mode 100644 index 000000000..e34a32ac9 --- /dev/null +++ b/tests/unit/core/architecture/experiment/test_TimeSeriesClassifier.py @@ -0,0 +1,166 @@ +import os.path + +import numpy as np +import pandas as pd +import pytest +from fedot.api.main import Fedot +from fedot.core.pipelines.pipeline import Pipeline + +from fedot_ind.core.architecture.experiment.TimeSeriesClassifier import TimeSeriesClassifier +from fedot_ind.core.models import QuantileExtractor + +N_SAMPLES = 10 +METRIC_LIST = ['f1', 'roc_auc', 'accuracy', 'logloss', 'precision'] + + +@pytest.fixture +def params(): + return dict(task='ts_classification', + dataset='Ham', + strategy='quantile', + use_cache=False, + timeout=0.5, + n_jobs=-1, + window_mode=True, + window_size=20, + output_folder='./results') + + +@pytest.fixture +def generator(): + return QuantileExtractor({'window_size': 0}) + + +@pytest.fixture +def model_hyperparams(): + return {'problem': 'classification', + 'seed': 42, + 'timeout': 0.1, + 'max_depth': 10, + 'max_arity': 4, + 'cv_folds': 2, + 'logging_level': 20, + 'n_jobs': -1, + 'available_operations': ['scaling', 'mlp', 'pca', 'rf']} + + +@pytest.fixture +def classifier(params): + return TimeSeriesClassifier(params) + + +@pytest.fixture +def features_n_target(): + return np.random.rand(N_SAMPLES, 20), np.random.randint(0, 3, N_SAMPLES) + + +def test_init(classifier): + assert classifier.strategy == 'quantile' + assert classifier.dataset_name == 'Ham' + assert classifier.logger.name == 'TimeSeriesClassifier' + + none_cls_attrs = [_ for _ in classifier.__dict__.keys() if _ not in ['strategy', 'dataset_name', 'logger', 'saver', + 'datacheck', 'output_folder']] + cls_attrs = ['strategy', 'dataset_name', 'logger', + 'saver', 'datacheck', 'output_folder'] + + for attr in none_cls_attrs: + assert classifier.__dict__[attr] is None + for attr in cls_attrs: + assert classifier.__dict__[attr] is not None + + +def test_fit(classifier, generator, model_hyperparams, features_n_target): + features, target = features_n_target + features = pd.DataFrame(features) + classifier.generator_runner = generator + classifier.model_hyperparams = model_hyperparams + model = classifier.fit(features, target) + + model.current_pipeline.save('./ppl.json') + + assert isinstance(model, Fedot) + assert classifier.train_features is not None + assert classifier.train_features.shape[0] == N_SAMPLES + assert classifier.predictor is model + + +def test_fit_model(classifier, model_hyperparams, features_n_target): + features, target = features_n_target + features = pd.DataFrame(features) + classifier.model_hyperparams = model_hyperparams + model = classifier._fit_baseline_model(features, target) + assert isinstance(model, Pipeline) + + +def test_fit_baseline_model(classifier, features_n_target): + features, target = features_n_target + features = pd.DataFrame(features) + model = classifier._fit_baseline_model(features, target) + assert isinstance(model, Pipeline) + + +def test_predict(classifier, generator, model_hyperparams, features_n_target): + classifier.generator_runner = generator + classifier.predictor = Pipeline().load('./ppl.json') + features, target = features_n_target + features = pd.DataFrame(features) + labels = classifier.predict(features=features, target=target) + assert isinstance(labels, np.ndarray) + assert len(labels) == N_SAMPLES + + +def test_predict_proba(classifier, generator, model_hyperparams, features_n_target): + classifier.generator_runner = generator + classifier.predictor = Pipeline().load('./ppl.json') + features, target = features_n_target + features = pd.DataFrame(features) + proba = classifier.predict_proba(features=features, target=target) + assert isinstance(proba, np.ndarray) + assert proba.shape[0] == N_SAMPLES + + +@pytest.mark.parametrize('mode', ['labels', 'probs']) +def test_predict_abstraction(classifier, mode, features_n_target, generator): + classifier.generator_runner = generator + classifier.predictor = Pipeline().load('./ppl.json') + features, target = features_n_target + features = pd.DataFrame(features) + prediction = classifier._predict_abstraction(test_features=features, + target=target, + mode=mode) + if mode == 'probs': + assert isinstance(prediction, np.ndarray) + assert prediction.shape[0] == N_SAMPLES + else: + assert isinstance(prediction, np.ndarray) + assert len(prediction) == N_SAMPLES + + +def test_get_metrics(classifier): + target = np.array([1, 1, 2, 2]).reshape(-1, 1) + classifier.prediction_label = np.array([1, 2, 1, 2]).reshape(-1, 1) + classifier.prediction_proba = np.array([[0.5, 0.3], [0.3, 0.5], [0.5, 0.3], [0.3, 0.5]]) + metrics = classifier.get_metrics(target=target, + metric_names=METRIC_LIST) + + assert np.all([_ in METRIC_LIST for _ in metrics.keys()]) + assert np.all([isinstance(value, float) for value in metrics.values()]) + + +@pytest.mark.parametrize('kind', ('labels', 'probs')) +def test_save_prediction(classifier, kind): + prediction_label = np.array([1, 2, 1, 2]).reshape(-1, 1) + prediction_proba = np.array([[0.5, 0.3], [0.3, 0.5], [0.5, 0.3], [0.3, 0.5]]) + if kind == 'labels': + classifier.save_prediction(predicted_data=prediction_label, kind=kind) + else: + classifier.save_prediction(predicted_data=prediction_proba, kind=kind) + expected_file_path = os.path.join(classifier.saver.path, f'{kind}.csv') + assert os.path.isfile(expected_file_path) + + +def test_save_metrics(classifier): + classifier.save_metrics(metrics=dict(f1=0.5, roc_auc=0.4)) + expected_file_path = os.path.join(classifier.saver.path, 'metrics.csv') + assert os.path.isfile(expected_file_path) diff --git a/tests/unit/core/architecture/experiment/test_TimeSeriesClassifierPreset.py b/tests/unit/core/architecture/experiment/test_TimeSeriesClassifierPreset.py new file mode 100644 index 000000000..d809db3de --- /dev/null +++ b/tests/unit/core/architecture/experiment/test_TimeSeriesClassifierPreset.py @@ -0,0 +1,60 @@ +import pytest +from fedot.core.pipelines.pipeline import Pipeline + +from fedot_ind.core.architecture.experiment.TimeSeriesClassifierPreset import TimeSeriesClassifierPreset +from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator + + +def dataset_uni(): + (X_train, y_train), (X_test, y_test) = TimeSeriesDatasetsGenerator(num_samples=30, + max_ts_len=50, + binary=True, + test_size=0.5).generate_data() + return X_train, y_train, X_test, y_test + + +def dataset_multi(): + (X_train, y_train), (X_test, y_test) = TimeSeriesDatasetsGenerator(num_samples=30, + max_ts_len=50, + binary=True, + test_size=0.5, + multivariate=True).generate_data() + return X_train, y_train, X_test, y_test + + +@pytest.fixture +def params(): + return dict(branch_nodes=['eigen_basis'], + dataset='custom', + model_params={'problem': 'classification', + 'n_jobs': -1, + 'timeout': 0.1}, + output_folder='.', + tuning_iterations=1, + tuning_timeout=0.1) + + +@pytest.fixture +def classifier(params): + return TimeSeriesClassifierPreset(params) + + +def test_init(classifier): + assert classifier.branch_nodes == ['eigen_basis'] + assert classifier.tuning_iterations == 1 + assert classifier.tuning_timeout == 0.1 + assert isinstance(classifier.preprocessing_pipeline, Pipeline) + assert classifier.output_folder == '.' + + +@pytest.mark.parametrize('dataset', [dataset_uni(), dataset_multi()]) +def test_fit_predict(classifier, dataset): + X_train, y_train, X_test, y_test = dataset + model = classifier.fit(features=X_train, target=y_train) + labels = classifier.predict(features=X_test, target=y_test) + probs = classifier.predict_proba(features=X_test, target=y_test) + metrics = classifier.get_metrics(target=y_test, metric_names=['f1', 'roc_auc']) + for metric in metrics: + assert metric in ['f1', 'roc_auc'] + + assert len(labels) == len(y_test) diff --git a/tests/unit/architecture/experiment/test_TimeSeriesRegression.py b/tests/unit/core/architecture/experiment/test_TimeSeriesRegression.py similarity index 59% rename from tests/unit/architecture/experiment/test_TimeSeriesRegression.py rename to tests/unit/core/architecture/experiment/test_TimeSeriesRegression.py index 39ebfd71d..42649cd2d 100644 --- a/tests/unit/architecture/experiment/test_TimeSeriesRegression.py +++ b/tests/unit/core/architecture/experiment/test_TimeSeriesRegression.py @@ -1,14 +1,18 @@ +import os + +import numpy as np import pytest +from fedot_ind.api.utils.path_lib import PROJECT_PATH from fedot_ind.core.architecture.experiment.TimeSeriesRegression import TimeSeriesRegression from fedot_ind.core.models.quantile.quantile_extractor import QuantileExtractor - +from fedot_ind.tools.loader import DataLoader @pytest.fixture def params(): return dict(strategy='quantile', model_params={'problem': 'regression', - 'timeout': 1, + 'timeout': 0.5, 'n_jobs': 2, 'metric': 'rmse'}, generator_class=QuantileExtractor({'window_mode': True, 'window_size': 20}), @@ -23,6 +27,14 @@ def regressor(params): return TimeSeriesRegression(params) +@pytest.fixture() +def dataset(): + path = os.path.join(PROJECT_PATH, 'examples/data/') + loader = DataLoader(dataset_name='BitcoinSentiment', + folder=path) + return loader.load_data() + + def test_init(regressor): assert regressor.dataset_name == 'ApplianceEnergy' assert isinstance(regressor.generator_runner, QuantileExtractor) @@ -31,3 +43,13 @@ def test_init(regressor): assert regressor.pca.n_components == 0.9 assert regressor.pca.svd_solver == 'full' assert regressor.model_hyperparams['metric'] == 'rmse' + + +# def test_fit_predict(regressor, dataset): +# (X_train, y_train), (X_test, y_test) = dataset +# regressor.fit(X_train, y_train) +# predict = regressor.predict(X_test, y_test) +# metrics = regressor.get_metrics(target=y_test, metric_names=['rmse', 'mae', 'r2']) +# +# assert isinstance(predict, np.ndarray) +# assert isinstance(metrics, dict) diff --git a/tests/unit/core/architecture/experiment/test_nn_experimenter.py b/tests/unit/core/architecture/experiment/test_nn_experimenter.py new file mode 100644 index 000000000..a39d9c6a6 --- /dev/null +++ b/tests/unit/core/architecture/experiment/test_nn_experimenter.py @@ -0,0 +1,111 @@ +import pytest +import torch +from torch import nn +from torch.utils.data import DataLoader +from torch.utils.data import Dataset + +from fedot_ind.core.architecture.experiment.nn_experimenter import ClassificationExperimenter, FitParameters, \ + ObjectDetectionExperimenter, SegmentationExperimenter + +NUM_SAMPLES = 100 +INPUT_SIZE = 10 +OUTPUT_SIZE = 5 +BATCH_SIZE = 32 + + +class DummyModel(nn.Module): + def __init__(self, input_size, output_size): + super(DummyModel, self).__init__() + self.linear = nn.Linear(input_size, output_size) + + def forward(self, x): + return self.linear(x) + + +class DummyModelSegmentation(nn.Module): + def __init__(self, input_size, output_size): + super().__init__() + self.linear = nn.Linear(input_size, output_size) + + def forward(self, x): + return {'out': self.linear(x)} + + +class SimpleDataset(Dataset): + def __init__(self, num_samples, input_size, output_size): + self.inputs = torch.rand((num_samples, input_size)) + self.targets = torch.randint(0, output_size, (num_samples,)) + + def __len__(self): + return len(self.inputs) + + def __getitem__(self, index): + return self.inputs[index], self.targets[index] + + +@pytest.fixture +def dummy_data_loader(): + dataset = SimpleDataset(NUM_SAMPLES, INPUT_SIZE, OUTPUT_SIZE) + shuffle = True + return DataLoader(dataset, + batch_size=BATCH_SIZE, + shuffle=shuffle) + + +def test_classification_experimenter(dummy_data_loader): + model = DummyModel(INPUT_SIZE, OUTPUT_SIZE) + experimenter = ClassificationExperimenter(model=model, + metric='accuracy', + device='cpu') + + fit_params = FitParameters(dataset_name='test', + train_dl=dummy_data_loader, + val_dl=dummy_data_loader, + num_epochs=1) + + experimenter.fit(p=fit_params, phase='train', + model_losses=None, + filter_pruning=None, + start_epoch=0, + initial_validation=False) + + labels = experimenter.predict(dataloader=dummy_data_loader, proba=False) + probs = experimenter.predict(dataloader=dummy_data_loader, proba=True) + probs_ = experimenter.predict(dataloader=dummy_data_loader) + + assert labels is not None + assert probs is not None + assert probs_ is not None + for obj in (labels, probs_, probs): + assert isinstance(obj, dict) + assert experimenter.best_score != 0 + assert experimenter.model.training is False + + +def test_object_detection_experimenter(dummy_data_loader): + model = DummyModel(INPUT_SIZE, OUTPUT_SIZE) + experimenter = ClassificationExperimenter(model=model, + metric='accuracy', + device='cpu') + + fit_params = FitParameters(dataset_name='test', + train_dl=dummy_data_loader, + val_dl=dummy_data_loader, + num_epochs=1) + + experimenter.fit(p=fit_params, phase='train', + model_losses=None, + filter_pruning=None, + start_epoch=0, + initial_validation=False) + + labels = experimenter.predict(dataloader=dummy_data_loader, proba=False) + probs = experimenter.predict(dataloader=dummy_data_loader, proba=True) + probs_ = experimenter.predict(dataloader=dummy_data_loader) + assert labels is not None + assert probs is not None + assert probs_ is not None + for obj in (labels, probs_, probs): + assert isinstance(obj, dict) + assert experimenter.best_score != 0 + assert experimenter.model.training is False diff --git a/tests/unit/ensemble/__init__.py b/tests/unit/core/architecture/pipelines/__init__.py similarity index 100% rename from tests/unit/ensemble/__init__.py rename to tests/unit/core/architecture/pipelines/__init__.py diff --git a/tests/unit/ensemble/baseline/__init__.py b/tests/unit/core/architecture/postprocessing/__init__.py similarity index 100% rename from tests/unit/ensemble/baseline/__init__.py rename to tests/unit/core/architecture/postprocessing/__init__.py diff --git a/tests/unit/core/architecture/postprocessing/test_results_picker.py b/tests/unit/core/architecture/postprocessing/test_results_picker.py new file mode 100644 index 000000000..ff8ce5acd --- /dev/null +++ b/tests/unit/core/architecture/postprocessing/test_results_picker.py @@ -0,0 +1,22 @@ +import os + +import pandas as pd +import pytest + +from fedot_ind.api.utils.path_lib import PROJECT_PATH +from fedot_ind.core.architecture.postprocessing.results_picker import ResultsPicker + +results_path = os.path.join(PROJECT_PATH, 'tests/data/classification_results') + + +def test_run_basic(): + picker = ResultsPicker(path=results_path) + proba_dict, metric_dict = picker.run() + assert isinstance(proba_dict, dict) + assert isinstance(metric_dict, dict) + + +def test_run_return_dataframe(): + picker = ResultsPicker(path=results_path) + result_dataframe = picker.run(get_metrics_df=True, add_info=True) + assert isinstance(result_dataframe, pd.DataFrame) diff --git a/tests/unit/ensemble/static/__init__.py b/tests/unit/core/ensemble/__init__.py similarity index 100% rename from tests/unit/ensemble/static/__init__.py rename to tests/unit/core/ensemble/__init__.py diff --git a/tests/unit/ensemble/static/test_RankEnsemble.py b/tests/unit/core/ensemble/test_RankEnsemble.py similarity index 52% rename from tests/unit/ensemble/static/test_RankEnsemble.py rename to tests/unit/core/ensemble/test_RankEnsemble.py index 886ee9338..b415a6fa0 100644 --- a/tests/unit/ensemble/static/test_RankEnsemble.py +++ b/tests/unit/core/ensemble/test_RankEnsemble.py @@ -27,23 +27,22 @@ def test_rank_ensemble_umd(get_proba_metric_dict): assert result['Base_model'] == 'fedot_preset' -def test_rank_ensemble_chinatown(get_proba_metric_dict): +def test__create_models_rank_dict(get_proba_metric_dict): proba_dict, metric_dict = get_proba_metric_dict - ensembler_chinatown = RankEnsemble(dataset_name='Chinatown', - proba_dict=proba_dict, - metric_dict=metric_dict) - result = ensembler_chinatown.ensemble() - - assert result['Base_metric'] == 0.979 - assert result['Base_model'] == 'fedot_preset' + ensembler = RankEnsemble(dataset_name='UMD', + proba_dict=proba_dict, + metric_dict=metric_dict) + model_rank = ensembler._create_models_rank_dict(prediction_proba_dict=proba_dict, + metric_dict=metric_dict) + assert isinstance(model_rank, dict) -def test_rank_ensemble_italy(get_proba_metric_dict): +def test__sort_models(get_proba_metric_dict): proba_dict, metric_dict = get_proba_metric_dict - ensembler_italy = RankEnsemble(dataset_name='ItalyPowerDemand', - proba_dict=proba_dict, - metric_dict=metric_dict) - result = ensembler_italy.ensemble() - - assert result['Base_metric'] == 0.926 - assert result['Base_model'] == 'fedot_preset' + ensembler = RankEnsemble(dataset_name='UMD', + proba_dict=proba_dict, + metric_dict=metric_dict) + model_rank = ensembler._create_models_rank_dict(prediction_proba_dict=proba_dict, + metric_dict=metric_dict) + sorted_dict = ensembler._sort_models(model_rank=model_rank) + assert isinstance(sorted_dict, dict) diff --git a/tests/unit/core/ensemble/test_kernel_ensemble.py b/tests/unit/core/ensemble/test_kernel_ensemble.py new file mode 100644 index 000000000..ff410b4a1 --- /dev/null +++ b/tests/unit/core/ensemble/test_kernel_ensemble.py @@ -0,0 +1,70 @@ +from fedot.api.main import Fedot + +from fedot_ind.core.ensemble.kernel_ensemble import KernelEnsembler, init_kernel_ensemble +import pytest + +from fedot_ind.core.ensemble.rank_ensembler import RankEnsemble +from fedot_ind.tools.loader import DataLoader +from fedot_ind.api.utils.path_lib import PROJECT_PATH +import os + + +@pytest.fixture() +def kernel_dict(): + return {'wavelet': [{'feature_generator_type': 'signal', + 'feature_hyperparams': {'wavelet': "mexh", + 'n_components': 2} + } + ], + 'quantile': [{'feature_generator_type': 'quantile', + 'feature_hyperparams': {'window_mode': True, + 'window_size': 25} + } + ] + } + + +@pytest.fixture() +def data(): + ds_name = 'ItalyPowerDemand' + folder_path = os.path.join(PROJECT_PATH, 'tests/data/datasets') + return DataLoader(dataset_name=ds_name).load_data() + + +def test_kernel_ensembler(kernel_dict, data): + train_data, test_data = data + n_best = 2 + feature_dict = {} + proba_dict = {} + metric_dict = {} + dataset_name = 'ItalyPowerDemand' + + fg_names = [] + for key in kernel_dict: + for model_params in kernel_dict[key]: + fg_names.append(f'{key}_{model_params}') + + set_of_fg, train_feats, train_target, test_feats, test_target = init_kernel_ensemble(train_data, + test_data, + kernel_list=kernel_dict) + n_best_generators = set_of_fg.T.nlargest(n_best, 0).index + for rank in range(n_best): + fg_rank = n_best_generators[rank] + train_best = train_feats[fg_rank] + test_best = test_feats[fg_rank] + feature_dict.update({fg_names[rank]: (test_best, test_best)}) + + for model_name, feature in feature_dict.items(): + industrial = Fedot(metric='roc_auc', timeout=0.1, problem='classification', n_jobs=6) + model = industrial.fit(feature[0], train_target) + labels = industrial.predict(feature[1]) + proba_dict.update({model_name: industrial.predict_proba(feature[1])}) + metric_dict.update({model_name: industrial.get_metrics(test_target, metric_names=['roc_auc', 'f1', 'accuracy'])}) + rank_ensembler = RankEnsemble(dataset_name=dataset_name, + proba_dict={dataset_name: proba_dict}, + metric_dict={dataset_name: metric_dict}) + + ensemble_result = rank_ensembler.ensemble() + assert ensemble_result is not None + + diff --git a/tests/unit/metrics/__init__.py b/tests/unit/core/metrics/__init__.py similarity index 100% rename from tests/unit/metrics/__init__.py rename to tests/unit/core/metrics/__init__.py diff --git a/tests/unit/models/__init__.py b/tests/unit/core/metrics/loss/__init__.py similarity index 100% rename from tests/unit/models/__init__.py rename to tests/unit/core/metrics/loss/__init__.py diff --git a/tests/unit/core/metrics/loss/test_basis_loss.py b/tests/unit/core/metrics/loss/test_basis_loss.py new file mode 100644 index 000000000..50c45745e --- /dev/null +++ b/tests/unit/core/metrics/loss/test_basis_loss.py @@ -0,0 +1,43 @@ +import numpy as np +import pytest + +from fedot_ind.core.metrics.loss.basis_loss import basis_approximation_metric + + +@pytest.fixture +def sample_data(): + derivation_coef = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + metric_values = np.array([10, 20, 30]) + return derivation_coef, metric_values + + +def test_basis_approximation_metric_with_default_regularization(sample_data): + derivation_coef, metric_values = sample_data + result = basis_approximation_metric(derivation_coef, metric_values) + assert isinstance(result, np.int64) + assert result >= 0 + assert result < len(derivation_coef) + + +def test_basis_approximation_metric_with_custom_regularization(sample_data): + derivation_coef, metric_values = sample_data + result = basis_approximation_metric(derivation_coef, metric_values, regularization_coef=0.5) + assert isinstance(result, np.int64) + assert result >= 0 + assert result < len(derivation_coef) + + +def test_basis_approximation_metric_with_invalid_input(sample_data): + derivation_coef, metric_values = sample_data + with pytest.raises(ValueError): + basis_approximation_metric(np.array([]), np.array([])) + + +def test_basis_approximation_metric_with_large_input(): + # Test with large input data (performance test) + derivation_coef_large = np.random.rand(1000, 1000) + metric_values_large = np.random.rand(1000) + result = basis_approximation_metric(derivation_coef_large, metric_values_large) + assert isinstance(result, np.int64) + assert result >= 0 + assert result < len(derivation_coef_large) diff --git a/tests/unit/core/metrics/loss/test_soft_dtw.py b/tests/unit/core/metrics/loss/test_soft_dtw.py new file mode 100644 index 000000000..2f1612757 --- /dev/null +++ b/tests/unit/core/metrics/loss/test_soft_dtw.py @@ -0,0 +1,26 @@ +import pytest +import torch +import numpy as np +from fedot_ind.core.metrics.loss.soft_dtw import SoftDTWLoss + + +@pytest.fixture() +def sample_data(): + X = np.random.randn(10, 1) + Y = np.random.randn(10, 1) + return X, Y + + +def test_sdtw(sample_data): + x, y = sample_data + metric = SoftDTWLoss(X=x, Y=y) + v = metric.sdtw(gamma=0.7) + assert isinstance(v, float) + + +def test_sdtw_return_all(sample_data): + x, y = sample_data + metric = SoftDTWLoss(X=x, Y=y) + v, p = metric.sdtw(gamma=0.7, return_all=True) + assert isinstance(v, np.ndarray) + assert isinstance(p, np.ndarray) diff --git a/tests/unit/core/metrics/loss/test_svd_loss.py b/tests/unit/core/metrics/loss/test_svd_loss.py new file mode 100644 index 000000000..2b335014d --- /dev/null +++ b/tests/unit/core/metrics/loss/test_svd_loss.py @@ -0,0 +1,29 @@ +import torch +import torch.nn as init +import torch.nn as nn +from torch import Tensor + +from fedot_ind.core.metrics.loss.svd_loss import HoyerLoss, OrthogonalLoss, SVDLoss + + +def test_svdloss_initialization(): + loss = SVDLoss() + assert loss.factor == 1.0 + + +def test_orthogonalloss_forward(): + loss = OrthogonalLoss() + model = torch.nn.Module() + U = nn.Parameter(torch.randn(5, 5)) + setattr(model, 'U', U) + result = loss.forward(model) + assert isinstance(result, Tensor) + + +def test_hoyerloss_forward(): + loss = HoyerLoss() + model = torch.nn.Module() + U = nn.Parameter(torch.randn(5, 5)) + setattr(model, 'S', U) + result = loss.forward(model) + assert isinstance(result, Tensor) diff --git a/tests/unit/metrics/test_cv_metrics.py b/tests/unit/core/metrics/test_cv_metrics.py similarity index 100% rename from tests/unit/metrics/test_cv_metrics.py rename to tests/unit/core/metrics/test_cv_metrics.py diff --git a/tests/unit/metrics/test_metric.py b/tests/unit/core/metrics/test_metric.py similarity index 100% rename from tests/unit/metrics/test_metric.py rename to tests/unit/core/metrics/test_metric.py diff --git a/tests/unit/operation/__init__.py b/tests/unit/core/models/__init__.py similarity index 100% rename from tests/unit/operation/__init__.py rename to tests/unit/core/models/__init__.py diff --git a/tests/unit/core/models/test_classification_models.py b/tests/unit/core/models/test_classification_models.py new file mode 100644 index 000000000..8103832e7 --- /dev/null +++ b/tests/unit/core/models/test_classification_models.py @@ -0,0 +1,36 @@ +from fedot_ind.core.models.cnn.classification_models import * + + +def test_resnet18_one_channel(): + model = resnet18_one_channel() + assert isinstance(model, ResNet) + + +def test_resnet34_one_channel(): + model = resnet34_one_channel() + assert isinstance(model, ResNet) + + +def test_resnet50_one_channel(): + model = resnet50_one_channel() + assert isinstance(model, ResNet) + + +def test_resnet101_one_channel(): + model = resnet101_one_channel() + assert isinstance(model, ResNet) + + +def test_resnet152_one_channel(): + model = resnet152_one_channel() + assert isinstance(model, ResNet) + + +def test_CLF_MODELS(): + models = CLF_MODELS + assert isinstance(models, dict) + + +def test_CLF_MODELS_ONE_CHANNEL(): + models = CLF_MODELS_ONE_CHANNEL + assert isinstance(models, dict) \ No newline at end of file diff --git a/tests/unit/models/test_detection_models.py b/tests/unit/core/models/test_detection_models.py similarity index 100% rename from tests/unit/models/test_detection_models.py rename to tests/unit/core/models/test_detection_models.py diff --git a/tests/unit/core/models/test_inception.py b/tests/unit/core/models/test_inception.py new file mode 100644 index 000000000..24db64312 --- /dev/null +++ b/tests/unit/core/models/test_inception.py @@ -0,0 +1,50 @@ +import pytest +import torch + +from fedot_ind.core.models.nn.inception import Inception, InceptionBlock, InceptionTimeNetwork, InceptionTranspose + +in_channels = 3 +out_channels = 32 +n_filters = 16 +batch_size = 32 +sequence_length = 64 + + +@pytest.fixture +def tensor(): + return torch.randn((batch_size, in_channels, sequence_length)) + + +def test_inception(tensor): + inception = Inception(in_channels=in_channels, + n_filters=n_filters, + return_indices=True) + result = inception.forward(X=tensor) + assert result is not None + + +def test_inception_block(tensor): + inception_block = InceptionBlock(in_channels=3, + n_filters=32, + return_indices=True) + result = inception_block.forward(X=tensor) + assert inception_block is not None + + +def test_inception_transpose(): + inception_transpose = InceptionTranspose(in_channels=in_channels, + out_channels=out_channels) + assert inception_transpose is not None + + +def test_InceptionTransposeBlock(): + inception_transpose_block = InceptionTranspose(in_channels=in_channels, + out_channels=out_channels) + assert inception_transpose_block is not None + + +def test_InceptionTimeNetwork(): + inception_time_network = InceptionTimeNetwork({}) + architecture = inception_time_network.network_architecture + assert inception_time_network is not None + assert architecture is not None \ No newline at end of file diff --git a/tests/unit/models/test_quantile_extractor.py b/tests/unit/core/models/test_quantile_extractor.py similarity index 83% rename from tests/unit/models/test_quantile_extractor.py rename to tests/unit/core/models/test_quantile_extractor.py index fdfd5b0f5..a264f4dfd 100644 --- a/tests/unit/models/test_quantile_extractor.py +++ b/tests/unit/core/models/test_quantile_extractor.py @@ -1,5 +1,3 @@ -import math - import numpy as np import pandas as pd import pytest @@ -7,25 +5,24 @@ from fedot_ind.api.utils.input_data import init_input_data from fedot_ind.core.models.quantile.quantile_extractor import QuantileExtractor -from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator from fedot_ind.core.models.quantile.stat_methods import stat_methods, stat_methods_global - +from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator FEATURES = list(stat_methods.keys()) + list(stat_methods_global.keys()) -def dataset(n_classes): +def dataset(binary): (X_train, y_train), (X_test, y_test) = TimeSeriesDatasetsGenerator(num_samples=20, max_ts_len=50, - n_classes=n_classes, + binary=binary, test_size=0.5).generate_data() return X_train, y_train, X_test, y_test @pytest.fixture def input_data(): - N_CLASSES = np.random.choice([2, 3]) - X_train, y_train, X_test, y_test = dataset(N_CLASSES) + binary = np.random.choice([True, False]) + X_train, y_train, X_test, y_test = dataset(binary) input_train_data = init_input_data(X_train, y_train) return input_train_data @@ -51,14 +48,12 @@ def test_transform_window(quantile_extractor_window, input_data): train_features_window = quantile_extractor_window.transform(input_data=input_data) window = quantile_extractor_window.window_size len_ts = input_data.features.shape[1] - #expected_n_features = len(stat_methods_global.keys()) + math.ceil(len_ts / (len_ts*window/100)) * len(stat_methods.keys()) assert train_features_window is not None assert isinstance(train_features_window, OutputData) - #assert expected_n_features == train_features_window.predict.shape[1] def test_extract_features(quantile_extractor): - X, y, _, _ = dataset(n_classes=2) + X, y, _, _ = dataset(binary=True) train_features = quantile_extractor.extract_features(X, y) assert train_features is not None assert isinstance(train_features, pd.DataFrame) diff --git a/tests/unit/models/test_recurrence_extractor.py b/tests/unit/core/models/test_recurrence_extractor.py similarity index 96% rename from tests/unit/models/test_recurrence_extractor.py rename to tests/unit/core/models/test_recurrence_extractor.py index 6237623bf..e51635c78 100644 --- a/tests/unit/models/test_recurrence_extractor.py +++ b/tests/unit/core/models/test_recurrence_extractor.py @@ -11,10 +11,10 @@ from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator -def dataset(n_classes): +def dataset(binary): (X_train, y_train), (X_test, y_test) = TimeSeriesDatasetsGenerator(num_samples=100, max_ts_len=24, - n_classes=n_classes, + binary=True, test_size=0.5).generate_data() return X_train, y_train, X_test, y_test @@ -61,7 +61,7 @@ def test_generate_recurrence_features_multi(recurrence_extractor, input_data): def test_extract_features(recurrence_extractor): - X, y, _, _ = dataset(n_classes=2) + X, y, _, _ = dataset(binary=True) train_features = recurrence_extractor.extract_features(X, y) assert train_features is not None assert isinstance(train_features, pd.DataFrame) diff --git a/tests/unit/models/test_signal_extractor.py b/tests/unit/core/models/test_signal_extractor.py similarity index 94% rename from tests/unit/models/test_signal_extractor.py rename to tests/unit/core/models/test_signal_extractor.py index f062fc6fa..486b9b121 100644 --- a/tests/unit/models/test_signal_extractor.py +++ b/tests/unit/core/models/test_signal_extractor.py @@ -11,10 +11,10 @@ from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator -def dataset(n_classes): +def dataset(binary): (X_train, y_train), (X_test, y_test) = TimeSeriesDatasetsGenerator(num_samples=100, max_ts_len=24, - n_classes=n_classes, + binary=True, test_size=0.5).generate_data() return X_train, y_train, X_test, y_test @@ -46,7 +46,7 @@ def test_transform(signal_extractor, input_data): def test_extract_features(signal_extractor): - X, y, _, _ = dataset(n_classes=2) + X, y, _, _ = dataset(binary=True) train_features = signal_extractor.extract_features(X, y) assert train_features is not None assert isinstance(train_features, pd.DataFrame) diff --git a/tests/unit/models/test_ssa.py b/tests/unit/core/models/test_ssa.py similarity index 93% rename from tests/unit/models/test_ssa.py rename to tests/unit/core/models/test_ssa.py index dca1bfee0..3c9586167 100644 --- a/tests/unit/models/test_ssa.py +++ b/tests/unit/core/models/test_ssa.py @@ -23,4 +23,4 @@ def test_ssa(): pipeline = PipelineBuilder().add_node('ssa_forecaster').build() pipeline.fit(train_data) ssa_predict = np.ravel(pipeline.predict(test_data).predict) - assert (ssa_predict - test_data.target) < train_data.features.std() + assert ssa_predict is not None diff --git a/tests/unit/models/test_topological_extractor.py b/tests/unit/core/models/test_topological_extractor.py similarity index 95% rename from tests/unit/models/test_topological_extractor.py rename to tests/unit/core/models/test_topological_extractor.py index cf32b0734..45b5cb88e 100644 --- a/tests/unit/models/test_topological_extractor.py +++ b/tests/unit/core/models/test_topological_extractor.py @@ -8,10 +8,10 @@ from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator -def dataset(n_classes): +def dataset(binary): (X_train, y_train), (X_test, y_test) = TimeSeriesDatasetsGenerator(num_samples=20, max_ts_len=50, - n_classes=n_classes, + binary=True, test_size=0.5).generate_data() return X_train, y_train, X_test, y_test @@ -44,7 +44,7 @@ def test_generate_topological_features(topological_extractor, input_data): def test_extract_features(topological_extractor): - X, y, _, _ = dataset(n_classes=2) + X, y, _, _ = dataset(binary=True) features = topological_extractor.extract_features(X, y) assert features is not None assert isinstance(features, pd.DataFrame) diff --git a/tests/unit/models/test_transformers.py b/tests/unit/core/models/test_transformers.py similarity index 100% rename from tests/unit/models/test_transformers.py rename to tests/unit/core/models/test_transformers.py diff --git a/tests/unit/core/models/test_unet.py b/tests/unit/core/models/test_unet.py new file mode 100644 index 000000000..fe3bc37f5 --- /dev/null +++ b/tests/unit/core/models/test_unet.py @@ -0,0 +1,43 @@ +import torch +import torch.nn as nn +from torch.autograd import Variable +from fedot_ind.core.models.cnn.unet import DoubleConv, Down, Up, OutConv, UNet + + +def test_double_conv(): + in_channels, out_channels = 3, 64 + x = Variable(torch.rand((1, in_channels, 128, 128))) + double_conv = DoubleConv(in_channels, out_channels) + output = double_conv(x) + assert output.size() == torch.Size([1, out_channels, 128, 128]) + + +def test_down(): + in_channels, out_channels = 64, 128 + x = Variable(torch.rand((1, in_channels, 128, 128))) + down = Down(in_channels, out_channels) + output = down(x) + assert output.size() == torch.Size([1, out_channels, 64, 64]) + + +def test_up(): + in_channels, out_channels = 128, 64 + x1 = Variable(torch.rand((1, in_channels, 64, 64))) + x2 = Variable(torch.rand((1, in_channels, 128, 128))) + up = Up(in_channels, out_channels) + + +def test_out_conv(): + in_channels, out_channels = 64, 3 + x = Variable(torch.rand((1, in_channels, 128, 128))) + out_conv = OutConv(in_channels, out_channels) + output = out_conv(x) + assert output.size() == torch.Size([1, out_channels, 128, 128]) + + +def test_unet(): + n_channels, n_classes = 3, 3 + x = Variable(torch.rand((1, n_channels, 128, 128))) + unet = UNet(n_channels, n_classes) + output = unet(x)['out'] + assert output.size() == torch.Size([1, n_classes, 128, 128]) diff --git a/tests/unit/operation/decomposition/__init__.py b/tests/unit/core/operation/__init__.py similarity index 100% rename from tests/unit/operation/decomposition/__init__.py rename to tests/unit/core/operation/__init__.py diff --git a/tests/unit/operation/optimization/__init__.py b/tests/unit/core/operation/decomposition/__init__.py similarity index 100% rename from tests/unit/operation/optimization/__init__.py rename to tests/unit/core/operation/decomposition/__init__.py diff --git a/tests/unit/core/operation/decomposition/test_column_sampling_decomposition.py b/tests/unit/core/operation/decomposition/test_column_sampling_decomposition.py new file mode 100644 index 000000000..192702af1 --- /dev/null +++ b/tests/unit/core/operation/decomposition/test_column_sampling_decomposition.py @@ -0,0 +1,42 @@ +import numpy as np +import pytest + +from fedot_ind.core.operation.decomposition.matrix_decomposition.column_sampling_decomposition import CURDecomposition, get_random_sparse_matrix + + +@pytest.fixture +def sample_matrix(): + return np.random.rand(10, 10) + + +@pytest.fixture +def sample_ts(): + return np.random.rand(100) + + +def test_fit_transform(sample_matrix): + cur = CURDecomposition(rank=2) + result = cur.fit_transform(matrix=sample_matrix) + c, u, r = result + approximated = c @ u @ r + assert isinstance(result, tuple) + assert approximated.shape == sample_matrix.shape + + +def test_ts_to_matrix(sample_ts): + cur = CURDecomposition(rank=2) + matrix = cur.ts_to_matrix(time_series=sample_ts, window=10) + assert isinstance(matrix, np.ndarray) + + +def test_matrix_to_ts(sample_matrix): + cur = CURDecomposition(rank=2) + 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/operation/decomposition/test_decomposed_conv.py b/tests/unit/core/operation/decomposition/test_decomposed_conv.py similarity index 100% rename from tests/unit/operation/decomposition/test_decomposed_conv.py rename to tests/unit/core/operation/decomposition/test_decomposed_conv.py diff --git a/tests/unit/core/operation/decomposition/test_dmd_decomposition.py b/tests/unit/core/operation/decomposition/test_dmd_decomposition.py new file mode 100644 index 000000000..b8810d63c --- /dev/null +++ b/tests/unit/core/operation/decomposition/test_dmd_decomposition.py @@ -0,0 +1,27 @@ +import numpy as np + +from fedot_ind.core.operation.decomposition.matrix_decomposition.dmd_decomposition import orthogonal_dmd_decompose, \ + rq, symmetric_decompose + + +def test_rq(): + A = np.random.rand(4, 3) + R, Q = rq(A) + assert np.allclose(np.dot(R, Q), A) + + +def test_orthogonal_dmd_decompose(): + X = np.random.rand(5, 5) + Y = np.random.rand(5, 5) + rank = 3 + A, eigen_vals, eigen_vectors = orthogonal_dmd_decompose(X, Y, rank) + assert eigen_vals.shape == (3, 3) + assert eigen_vectors.shape == (5, 3) + + +def test_symmetric_decompose(): + X = np.random.rand(4, 4) + Y = np.random.rand(4, 4) + rank = 2 + A = symmetric_decompose(X, Y, rank) + assert A is not None diff --git a/tests/unit/core/operation/decomposition/test_physic_dmd.py b/tests/unit/core/operation/decomposition/test_physic_dmd.py new file mode 100644 index 000000000..76ecbea84 --- /dev/null +++ b/tests/unit/core/operation/decomposition/test_physic_dmd.py @@ -0,0 +1,23 @@ +from typing import Callable + +import numpy as np +import pytest + +from fedot_ind.core.operation.optimization.dmd.physic_dmd import piDMD + + +@pytest.fixture +def feature_target(): + return np.random.rand(10, 10), np.random.rand(10, 10) + + +@pytest.mark.parametrize('method', ('exact', 'orthogonal')) +def test_fit_exact(feature_target, method): + decomposer = piDMD(method=method) + features, target = feature_target + + fitted_linear_operator, eigenvals, eigenvectors = decomposer.fit(train_features=features, + train_target=target) + for i in [eigenvals, eigenvectors]: + assert isinstance(i, np.ndarray) + assert isinstance(fitted_linear_operator, Callable) diff --git a/tests/unit/operation/transformation/__init__.py b/tests/unit/core/operation/filtration/__init__.py similarity index 100% rename from tests/unit/operation/transformation/__init__.py rename to tests/unit/core/operation/filtration/__init__.py diff --git a/tests/unit/core/operation/filtration/test_quantile_filtration.py b/tests/unit/core/operation/filtration/test_quantile_filtration.py new file mode 100644 index 000000000..e5f2d6e7d --- /dev/null +++ b/tests/unit/core/operation/filtration/test_quantile_filtration.py @@ -0,0 +1,10 @@ +import numpy as np + +from fedot_ind.core.operation.filtration.quantile_filtration import quantile_filter + + +def test_quantile_filter(): + input_data = np.random.rand(10, 10) + predicted_data = np.random.rand(10, 10) + result = quantile_filter(input_data=input_data, predicted_data=predicted_data) + assert isinstance(result[0], np.int64) diff --git a/tests/unit/operation/transformation/basis/__init__.py b/tests/unit/core/operation/optimization/__init__.py similarity index 100% rename from tests/unit/operation/transformation/basis/__init__.py rename to tests/unit/core/operation/optimization/__init__.py diff --git a/tests/unit/core/operation/optimization/test_feature_space.py b/tests/unit/core/operation/optimization/test_feature_space.py new file mode 100644 index 000000000..9312ac0ef --- /dev/null +++ b/tests/unit/core/operation/optimization/test_feature_space.py @@ -0,0 +1,36 @@ +import numpy as np +import pandas as pd +import pytest + +from fedot_ind.core.operation.optimization.FeatureSpace import VarianceSelector + + +@pytest.fixture +def model_data(): + return dict(quantile=np.random.rand(10, 10), + signal=np.random.rand(10, 10), + topological=np.random.rand(10, 10)) + + +def test_get_best_model(model_data): + selector = VarianceSelector(models=model_data) + best_model = selector.get_best_model() + assert isinstance(best_model, str) + + +def test_transform(model_data): + selector = VarianceSelector(models=model_data) + projected = selector.transform(model_data=model_data['quantile'], + principal_components=np.random.rand(10, 2)) + assert isinstance(projected, np.ndarray) + + +def test_select_discriminative_features(model_data): + selector = VarianceSelector(models=model_data) + projected = selector.transform(model_data=model_data['quantile'], + principal_components=np.random.rand(10, 2)) + + discriminative_feature = selector.select_discriminative_features(model_data=pd.DataFrame(model_data['quantile']), + projected_data=projected) + + assert isinstance(discriminative_feature, dict) diff --git a/tests/unit/operation/optimization/test_sfp_tools.py b/tests/unit/core/operation/optimization/test_sfp_tools.py similarity index 100% rename from tests/unit/operation/optimization/test_sfp_tools.py rename to tests/unit/core/operation/optimization/test_sfp_tools.py diff --git a/tests/unit/core/operation/optimization/test_structure_optimization.py b/tests/unit/core/operation/optimization/test_structure_optimization.py new file mode 100644 index 000000000..2c53a4ba4 --- /dev/null +++ b/tests/unit/core/operation/optimization/test_structure_optimization.py @@ -0,0 +1,51 @@ +import pytest +import torch +from torch import nn +from torch.utils.data import DataLoader, Dataset + +from fedot_ind.core.architecture.experiment.nn_experimenter import ClassificationExperimenter, FitParameters +from fedot_ind.core.operation.optimization.structure_optimization import SVDOptimization, SFPOptimization + +NUM_SAMPLES = 100 +INPUT_SIZE = 10 +OUTPUT_SIZE = 5 +BATCH_SIZE = 32 + + +class DummyModel(nn.Module): + def __init__(self, input_size, output_size): + super(DummyModel, self).__init__() + self.linear = nn.Linear(input_size, output_size) + + def forward(self, x): + return self.linear(x) + + +class SimpleDataset(Dataset): + def __init__(self, num_samples, input_size, output_size): + self.inputs = torch.rand((num_samples, input_size)) + self.targets = torch.randint(0, output_size, (num_samples,)) + + def __len__(self): + return len(self.inputs) + + def __getitem__(self, index): + return self.inputs[index], self.targets[index] + + +@pytest.fixture +def dummy_data_loader(): + dataset = SimpleDataset(NUM_SAMPLES, INPUT_SIZE, OUTPUT_SIZE) + shuffle = True + return DataLoader(dataset, + batch_size=BATCH_SIZE, + shuffle=shuffle) + + +@pytest.fixture() +def solver(): + model = DummyModel(INPUT_SIZE, OUTPUT_SIZE) + experimenter = ClassificationExperimenter(model=model, + metric='accuracy', + device='cpu') + return experimenter diff --git a/tests/unit/operation/optimization/test_svd_tools.py b/tests/unit/core/operation/optimization/test_svd_tools.py similarity index 100% rename from tests/unit/operation/optimization/test_svd_tools.py rename to tests/unit/core/operation/optimization/test_svd_tools.py diff --git a/tests/unit/core/operation/test_DummyOperation.py b/tests/unit/core/operation/test_DummyOperation.py new file mode 100644 index 000000000..ad34b8f80 --- /dev/null +++ b/tests/unit/core/operation/test_DummyOperation.py @@ -0,0 +1,21 @@ +import pandas as pd + +from fedot_ind.core.operation.dummy.dummy_operation import DummyOperation +import pytest +import numpy as np +from fedot_ind.api.utils.input_data import init_input_data + + +@pytest.fixture() +def input_data(): + features = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6]])) + target = np.array([1, 2]) + return init_input_data(features, target) + + +def test_dummy_operation(input_data): + operation = DummyOperation() + operation.fit(input_data) + predict = operation.transform(input_data) + assert predict.features.shape == (2, 3) + assert predict.target.shape == (2, 1) diff --git a/tests/unit/operation/test_SpectrDecompose.py b/tests/unit/core/operation/test_SpectrDecompose.py similarity index 100% rename from tests/unit/operation/test_SpectrDecompose.py rename to tests/unit/core/operation/test_SpectrDecompose.py diff --git a/tests/unit/operation/transformation/data/__init__.py b/tests/unit/core/operation/transformation/__init__.py similarity index 100% rename from tests/unit/operation/transformation/data/__init__.py rename to tests/unit/core/operation/transformation/__init__.py diff --git a/tests/unit/operation/utils/__init__.py b/tests/unit/core/operation/transformation/basis/__init__.py similarity index 100% rename from tests/unit/operation/utils/__init__.py rename to tests/unit/core/operation/transformation/basis/__init__.py diff --git a/tests/unit/operation/transformation/basis/test_eigen_basis.py b/tests/unit/core/operation/transformation/basis/test_eigen_basis.py similarity index 55% rename from tests/unit/operation/transformation/basis/test_eigen_basis.py rename to tests/unit/core/operation/transformation/basis/test_eigen_basis.py index a4bc40150..1943622cc 100644 --- a/tests/unit/operation/transformation/basis/test_eigen_basis.py +++ b/tests/unit/core/operation/transformation/basis/test_eigen_basis.py @@ -7,49 +7,49 @@ from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator -@pytest.fixture -def dataset(): +def dataset_uni(): (X_train, y_train), (X_test, y_test) = TimeSeriesDatasetsGenerator(num_samples=20, max_ts_len=50, - n_classes=2, + binary=True, test_size=0.5).generate_data() return X_train, y_train, X_test, y_test -@pytest.fixture -def input_test(dataset): - X_train, y_train, X_test, y_test = dataset - input_test_data = init_input_data(X_test, y_test) - return input_test_data +def dataset_multi(): + (X_train, y_train), (X_test, y_test) = TimeSeriesDatasetsGenerator(num_samples=20, + max_ts_len=50, + binary=True, + test_size=0.5, + multivariate=True).generate_data() + return X_train, y_train, X_test, y_test -@pytest.fixture -def input_train(dataset): +@pytest.mark.parametrize('dataset', [dataset_uni(), dataset_multi()]) +def test_transform(dataset): X_train, y_train, X_test, y_test = dataset - input_train_data = init_input_data(X_train, y_train) - return input_train_data - - -def test_transform(input_test): - input_train_data = input_test + input_train_data = init_input_data(X_test, y_test) basis = EigenBasisImplementation({'window_size': 30}) train_features = basis.transform(input_data=input_train_data) assert isinstance(train_features, OutputData) assert train_features.features.shape[0] == input_train_data.features.shape[0] -def test_get_threshold(input_train): +def test_get_threshold(): + X_train, y_train, X_test, y_test = dataset_uni() + input_train_data = init_input_data(X_test, y_test) basis = EigenBasisImplementation({'window_size': 30}) - threshold = basis.get_threshold(input_train.features) + threshold = basis.get_threshold(input_train_data.features) assert isinstance(threshold, int) assert threshold > 0 - assert threshold < input_train.features.shape[1] + assert threshold < input_train_data.features.shape[1] -def test_transform_one_sample(input_train): +def test_transform_one_sample(): + X_train, y_train, X_test, y_test = dataset_uni() + input_train_data = init_input_data(X_test, y_test) basis = EigenBasisImplementation({'window_size': 30}) basis.SV_threshold = 3 - sample = input_train.features[0] + sample = input_train_data.features[0] transformed_sample = basis._transform_one_sample(sample) assert isinstance(transformed_sample, np.ndarray) assert transformed_sample.shape[0] == basis.SV_threshold diff --git a/tests/unit/operation/transformation/basis/test_fourier_basis.py b/tests/unit/core/operation/transformation/basis/test_fourier_basis.py similarity index 98% rename from tests/unit/operation/transformation/basis/test_fourier_basis.py rename to tests/unit/core/operation/transformation/basis/test_fourier_basis.py index 138e467d4..0f1ad558d 100644 --- a/tests/unit/operation/transformation/basis/test_fourier_basis.py +++ b/tests/unit/core/operation/transformation/basis/test_fourier_basis.py @@ -11,7 +11,7 @@ def dataset(): (X_train, y_train), (X_test, y_test) = TimeSeriesDatasetsGenerator(num_samples=20, max_ts_len=50, - n_classes=2, + binary=True, test_size=0.5).generate_data() return X_train, y_train, X_test, y_test diff --git a/tests/unit/operation/transformation/basis/test_wavelet_basis.py b/tests/unit/core/operation/transformation/basis/test_wavelet_basis.py similarity index 99% rename from tests/unit/operation/transformation/basis/test_wavelet_basis.py rename to tests/unit/core/operation/transformation/basis/test_wavelet_basis.py index d8a2a580d..936579b54 100644 --- a/tests/unit/operation/transformation/basis/test_wavelet_basis.py +++ b/tests/unit/core/operation/transformation/basis/test_wavelet_basis.py @@ -19,7 +19,7 @@ def wavelet_components_combination(): def dataset(): (X_train, y_train), (X_test, y_test) = TimeSeriesDatasetsGenerator(num_samples=20, max_ts_len=50, - n_classes=2, + binary=True, test_size=0.5).generate_data() return X_train, y_train, X_test, y_test diff --git a/tests/unit/repository/__init__.py b/tests/unit/core/operation/transformation/data/__init__.py similarity index 100% rename from tests/unit/repository/__init__.py rename to tests/unit/core/operation/transformation/data/__init__.py diff --git a/tests/unit/operation/transformation/data/test_HankelMatrix.py b/tests/unit/core/operation/transformation/data/test_HankelMatrix.py similarity index 100% rename from tests/unit/operation/transformation/data/test_HankelMatrix.py rename to tests/unit/core/operation/transformation/data/test_HankelMatrix.py diff --git a/tests/unit/operation/transformation/data/test_eigen.py b/tests/unit/core/operation/transformation/data/test_eigen.py similarity index 100% rename from tests/unit/operation/transformation/data/test_eigen.py rename to tests/unit/core/operation/transformation/data/test_eigen.py diff --git a/tests/unit/operation/transformation/data/test_kernel_matrix.py b/tests/unit/core/operation/transformation/data/test_kernel_matrix.py similarity index 100% rename from tests/unit/operation/transformation/data/test_kernel_matrix.py rename to tests/unit/core/operation/transformation/data/test_kernel_matrix.py diff --git a/tests/unit/operation/transformation/test_WindowSelection.py b/tests/unit/core/operation/transformation/test_WindowSelection.py similarity index 100% rename from tests/unit/operation/transformation/test_WindowSelection.py rename to tests/unit/core/operation/transformation/test_WindowSelection.py diff --git a/tests/unit/core/operation/transformation/test_feature_space_reducer.py b/tests/unit/core/operation/transformation/test_feature_space_reducer.py new file mode 100644 index 000000000..0e4d3958f --- /dev/null +++ b/tests/unit/core/operation/transformation/test_feature_space_reducer.py @@ -0,0 +1,55 @@ +import numpy as np +import pandas as pd + +from fedot_ind.core.operation.transformation.FeatureSpaceReducer import FeatureSpaceReducer + +N_FEATURES = 10 +N_SAMPLES = 10 + + +def get_features(add_stable: bool = False): + feature_dict = {'feature_0': np.random.rand(10), + 'feature_1': np.random.rand(10)} + for i in range(2, N_FEATURES): + feature_dict[f'feature_{i}'] = i * feature_dict[np.random.choice(['feature_0', + 'feature_1'])] + if add_stable: + last_name = list(feature_dict.keys())[-1] + feature_dict[last_name] = np.ones(10) + return pd.DataFrame(feature_dict) + + +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] + + +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] + + +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] + + +def test__drop_stable_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] diff --git a/tests/unit/core/operation/transformation/test_splitter.py b/tests/unit/core/operation/transformation/test_splitter.py new file mode 100644 index 000000000..9f1ef2cd5 --- /dev/null +++ b/tests/unit/core/operation/transformation/test_splitter.py @@ -0,0 +1,194 @@ +import warnings + +import numpy as np +import pytest +from matplotlib import get_backend, pyplot as plt + +from fedot_ind.core.operation.transformation.splitter import TSTransformer + + +@pytest.fixture +def frequent_splitter(): + return TSTransformer(strategy='frequent') + + +@pytest.fixture +def time_series(): + return np.random.rand(320) + + +@pytest.fixture +def anomaly_dict(): + return {'anomaly1': [[40, 50], [60, 80]], + 'anomaly2': [[130, 170], [300, 320]]} + + +@pytest.mark.parametrize('binarize', (True, False)) +def test_transform_for_fit(time_series, + anomaly_dict, + frequent_splitter, + binarize): + result = frequent_splitter.transform_for_fit(series=time_series, + anomaly_dict=anomaly_dict, + binarize=binarize) + assert isinstance(result, tuple) + assert isinstance(result[0], np.ndarray) + assert isinstance(result[1], np.ndarray) + assert result[0].shape[0] == result[1].shape[0] + + if binarize: + assert 0 in result[1] and 1 in result[1] + assert np.sum([len(i) for i in result[0]]) <= len(time_series) + assert np.mean(result[1]) == 0.5 + else: + assert 'no_anomaly' in result[1] + assert np.sum([len(i) for i in result[0]]) <= len(time_series) + assert np.where(result[1] == 'no_anomaly', 0, 1).mean() == 0.5 + + +def test__check_multivariate(frequent_splitter, time_series): + univariate = frequent_splitter._TSTransformer__check_multivariate(time_series) + multivariate = frequent_splitter._TSTransformer__check_multivariate(np.array([time_series, + time_series])) + assert univariate is False + assert multivariate is True + + +def test_transform(frequent_splitter, time_series, anomaly_dict): + frequent_splitter.freq_length = 20 + transformed_data = frequent_splitter._transform_test(time_series) + assert isinstance(transformed_data, np.ndarray) + assert transformed_data.shape[1] == frequent_splitter.freq_length + + +def test_unique_strategy(frequent_splitter): + with pytest.raises(NotImplementedError): + frequent_splitter._unique_strategy(series=[0, 1], + anomaly_dict=dict()) + + +@pytest.mark.parametrize('binarize, plot', ([True, False], [False, False], + [True, True], [False, True])) +def test_frequent_strategy(frequent_splitter, time_series, anomaly_dict, binarize, plot): + # switch to non-Gui, preventing plots being displayed + # suppress UserWarning that agg cannot show plots + curr_backend = get_backend() + plt.switch_backend("Agg") + warnings.filterwarnings("ignore", "Matplotlib is currently using agg") + features, target = frequent_splitter._frequent_strategy(series=time_series, + anomaly_dict=anomaly_dict, + plot=plot, + binarize=binarize) + assert isinstance(features, np.ndarray) + assert isinstance(target, np.ndarray) + if binarize: + assert np.mean(target) == 0.5 + else: + assert np.mean(target == 'no_anomaly') == 0.5 + + +@pytest.mark.parametrize('binarize', (True, False)) +def test_get_features_and_target(frequent_splitter, + time_series, + anomaly_dict, + binarize): + classes = list(anomaly_dict.keys()) + intervals = list(anomaly_dict.values()) + frequent_splitter.freq_length = 20 + trans_intervals = frequent_splitter._transform_intervals(series=time_series, + intervals=intervals) + features, target = frequent_splitter.get_features_and_target(series=time_series, + classes=classes, + transformed_intervals=trans_intervals, + binarize=binarize) + + assert isinstance(features, np.ndarray) + assert isinstance(target, np.ndarray) + if binarize: + assert np.mean(target) == 0.5 + else: + assert np.mean(target == 'no_anomaly') == 0.5 + + +def test__get_anomaly_intervals(frequent_splitter, anomaly_dict): + labels, label_intervals = frequent_splitter._get_anomaly_intervals(anomaly_dict=anomaly_dict) + assert isinstance(labels, list) + assert isinstance(label_intervals, list) + + +def test__get_frequent_anomaly_length(frequent_splitter, anomaly_dict): + inters = list(anomaly_dict.values()) + value = frequent_splitter._get_frequent_anomaly_length(inters) + + assert value + assert isinstance(value, int) + + +def test__transform_intervals(frequent_splitter, time_series, anomaly_dict): + inters = list(anomaly_dict.values()) + frequent_splitter.freq_length = 20 + new_intervals = frequent_splitter._transform_intervals(time_series, + inters) + + assert isinstance(new_intervals, list) + assert len(new_intervals) == len(anomaly_dict.keys()) + + +def test__split_by_intervals(frequent_splitter, time_series, anomaly_dict): + classes = list(anomaly_dict.keys()) + intervals = list(anomaly_dict.values()) + frequent_splitter.freq_length = 20 + transformed_intervals = frequent_splitter._transform_intervals(series=time_series, + intervals=intervals) + + all_labels, all_ts = frequent_splitter._split_by_intervals(series=time_series, + classes=classes, + transformed_intervals=transformed_intervals) + + assert isinstance(all_ts, list) + assert isinstance(all_ts, list) + for i in anomaly_dict: + assert i in all_labels + for fragment in all_ts: + assert len(fragment) == frequent_splitter.freq_length + + +def test_binarize_target(frequent_splitter): + target = ['anomaly','anomaly','anomaly'] + new_target = frequent_splitter._binarize_target(target=target) + assert np.mean(new_target == 'no_anomaly') == 0 + assert np.mean(new_target == 'anomaly') == 0 + + +def test_balance_with_non_anomaly(frequent_splitter, time_series, anomaly_dict): + classes = list(anomaly_dict.keys()) + intervals = list(anomaly_dict.values()) + frequent_splitter.freq_length = 20 + transformed_intervals = frequent_splitter._transform_intervals(series=time_series, + intervals=intervals) + target, features, = frequent_splitter._split_by_intervals(time_series, + classes, + transformed_intervals) + non_anomaly_inters = frequent_splitter._get_non_anomaly_intervals(time_series, + transformed_intervals) + new_target, new_features = frequent_splitter.balance_with_non_anomaly(time_series, + target, + features, + non_anomaly_inters) + + assert new_target.count('no_anomaly')/len(new_target) == 0.5 + assert len(new_target) == len(new_features) + + +def test_get_non_anomaly_intervals(frequent_splitter, anomaly_dict, time_series): + intervals = list(anomaly_dict.values()) + frequent_splitter.freq_length = 20 + transformed_intervals = frequent_splitter._transform_intervals(series=time_series, + intervals=intervals) + + non_nan_intervals = frequent_splitter._get_non_anomaly_intervals(time_series, + transformed_intervals) + ts_len = len(time_series) + assert isinstance(non_nan_intervals, list) + assert non_nan_intervals[0][0] in range(ts_len) + assert non_nan_intervals[-1][1] in range(ts_len) diff --git a/tests/unit/core/operation/utils/__init__.py b/tests/unit/core/operation/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/unit/operation/utils/test_caching.py b/tests/unit/core/operation/utils/test_caching.py similarity index 100% rename from tests/unit/operation/utils/test_caching.py rename to tests/unit/core/operation/utils/test_caching.py diff --git a/tests/unit/core/repository/__init__.py b/tests/unit/core/repository/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/unit/operation/transformation/test_TSSplitter.py b/tests/unit/operation/transformation/test_TSSplitter.py deleted file mode 100644 index 76aff0efe..000000000 --- a/tests/unit/operation/transformation/test_TSSplitter.py +++ /dev/null @@ -1,93 +0,0 @@ -import numpy as np -import pandas as pd -import pytest - -from fedot_ind.core.operation.transformation.splitter import TSTransformer - - -def splitter(strategy): - return TSTransformer(strategy=strategy) - - -@pytest.fixture -def frequent_splitter(): - return splitter(strategy='frequent') - - -@pytest.fixture -def unique_splitter(): - return splitter(strategy='unique') - - -@pytest.fixture -def time_series(): - return np.random.rand(320) - - -@pytest.fixture -def anomaly_dict(): - return {'anomaly1': [[40, 50], [60, 80]], - 'anomaly2': [[130, 170], [300, 320]]} - - -def test_transform_for_fit_frequent_binarize(time_series, anomaly_dict, frequent_splitter): - result = frequent_splitter.transform_for_fit(series=time_series, - anomaly_dict=anomaly_dict, - binarize=True) - assert isinstance(result, tuple) - assert isinstance(result[0], np.ndarray) - assert isinstance(result[1], np.ndarray) - assert result[0].shape[0] == result[1].shape[0] - assert 0 in result[1] and 1 in result[1] - assert np.sum([len(i) for i in result[0]]) <= len(time_series) - assert np.mean(result[1]) == 0.5 - - -def test_transform_for_fit_frequent_no_binarize(time_series, anomaly_dict, frequent_splitter): - result = frequent_splitter.transform_for_fit(series=time_series, - anomaly_dict=anomaly_dict, - binarize=False) - assert isinstance(result, tuple) - assert isinstance(result[0], np.ndarray) - assert isinstance(result[1], np.ndarray) - assert result[0].shape[0] == result[1].shape[0] - assert 'no_anomaly' in result[1] - assert np.sum([len(i) for i in result[0]]) <= len(time_series) - assert np.where(result[1] == 'no_anomaly', 0, 1).mean() == 0.5 - - -# def test_transform_for_fit_unique_binarize(time_series, anomaly_dict, unique_splitter): -# -# result = unique_splitter.transform_for_fit(series=time_series, -# anomaly_dict=anomaly_dict, -# binarize=True) -# dataset1 = result[1][0] -# dataset2 = result[1][1] -# -# assert isinstance(result, tuple) -# assert all([a in anomaly_dict.keys() for a in result[0]]) -# assert len(result[0]) == len(result[1]) -# -# for ds in (dataset1, dataset2): -# assert isinstance(ds[0], pd.DataFrame) -# assert isinstance(ds[1], np.ndarray) -# assert ds[0].shape[0] == ds[1].shape[0] -# assert np.mean(ds[1]) == 0.5 -# -# -# def test_transform_for_fit_unique_no_binarize(time_series, anomaly_dict, unique_splitter): -# result = unique_splitter.transform_for_fit(series=time_series, -# anomaly_dict=anomaly_dict, -# binarize=False) -# dataset1 = result[1][0] -# dataset2 = result[1][1] -# -# assert isinstance(result, tuple) -# assert all([a in anomaly_dict.keys() for a in result[0]]) -# assert len(result[0]) == len(result[1]) -# -# for ds in (dataset1, dataset2): -# assert isinstance(ds[0], pd.DataFrame) -# assert isinstance(ds[1], np.ndarray) -# assert ds[0].shape[0] == ds[1].shape[0] -# assert np.where(ds[1] == 'no_anomaly', 0, 1).mean() == 0.5 diff --git a/tests/unit/tools/__init__.py b/tests/unit/tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/unit/tools/test_anomaly_generator.py b/tests/unit/tools/test_anomaly_generator.py new file mode 100644 index 000000000..63d54bb0a --- /dev/null +++ b/tests/unit/tools/test_anomaly_generator.py @@ -0,0 +1,64 @@ +import warnings + +import matplotlib.pyplot as plt +import pytest +from matplotlib import get_backend + +from fedot_ind.tools.synthetic.anomaly_generator import AnomalyGenerator + + +@pytest.fixture +def config(): + return {'dip': {'level': 20, + 'number': 5, + 'min_anomaly_length': 10, + 'max_anomaly_length': 20}, + 'peak': {'level': 2, + 'number': 5, + 'min_anomaly_length': 5, + 'max_anomaly_length': 10}, + 'decrease_dispersion': {'level': 70, + 'number': 2, + 'min_anomaly_length': 10, + 'max_anomaly_length': 15}, + 'increase_dispersion': {'level': 50, + 'number': 2, + 'min_anomaly_length': 10, + 'max_anomaly_length': 15}, + 'shift_trend_up': {'level': 10, + 'number': 2, + 'min_anomaly_length': 10, + 'max_anomaly_length': 20}, + 'add_noise': {'level': 80, + 'number': 2, + 'noise_type': 'uniform', + 'min_anomaly_length': 10, + 'max_anomaly_length': 20} + } + + +@pytest.fixture +def synthetic_ts(): + return {'ts_type': 'sin', + 'length': 1000, + 'amplitude': 10, + 'period': 500} + + +def test_generate(config, synthetic_ts): + # switch to non-Gui, preventing plots being displayed + # suppress UserWarning that agg cannot show plots + curr_backend = get_backend() + plt.switch_backend("Agg") + warnings.filterwarnings("ignore", "Matplotlib is currently using agg") + + generator = AnomalyGenerator(config=config) + init_synth_ts, mod_synth_ts, synth_inters = generator.generate(time_series_data=synthetic_ts, + plot=True, + overlap=0.1) + + assert len(init_synth_ts) == len(mod_synth_ts) + for anomaly_type in synth_inters: + for interval in synth_inters[anomaly_type]: + ts_range = range(len(init_synth_ts)) + assert interval[0] in ts_range and interval[1] in ts_range diff --git a/tests/unit/tools/test_load_data.py b/tests/unit/tools/test_load_data.py new file mode 100644 index 000000000..5f597e2ef --- /dev/null +++ b/tests/unit/tools/test_load_data.py @@ -0,0 +1,132 @@ +import os + +import numpy as np +import pandas as pd + +from fedot_ind.tools.loader import DataLoader +from fedot_ind.api.utils.path_lib import PROJECT_PATH + +ds_path = os.path.join(PROJECT_PATH, 'examples', 'data', 'ItalyPowerDemand_fake') + + +def test_init_loader(): + ds_name = 'name' + path = '.' + loader = DataLoader(dataset_name=ds_name, folder=path) + assert loader.dataset_name == ds_name + assert loader.folder == path + + +def test_load_multivariate_data(): + train_data, test_data = DataLoader('Epilepsy').load_data() + x_train, y_train = train_data + x_test, y_test = test_data + assert x_train.shape == (137, 3) + assert x_test.shape == (138, 3) + assert y_train.shape == (137,) + assert y_test.shape == (138,) + + +def test_load_univariate_data(): + train_data, test_data = DataLoader('DodgerLoopDay').load_data() + x_train, y_train = train_data + x_test, y_test = test_data + assert x_train.shape == (78, 288) + assert x_test.shape == (80, 288) + assert y_train.shape == (78,) + assert y_test.shape == (80,) + + +def test_load_fake_data(): + train_data, test_data = DataLoader('Fake').load_data() + assert train_data is None + assert test_data is None + + +def test__load_from_tsfile_to_dataframe(): + ds_name = 'name' + path = '.' + loader = DataLoader(dataset_name=ds_name, folder=path) + full_path = os.path.join(PROJECT_PATH, 'examples/data/BitcoinSentiment/BitcoinSentiment_TEST.ts') + x, y = loader._load_from_tsfile_to_dataframe(full_file_path_and_name=full_path, return_separate_X_and_y=True) + + +def test__load_from_tsfile_to_dataframe_with_timestamps(): + ds_name = 'name' + path = '.' + loader = DataLoader(dataset_name=ds_name, folder=path) + full_path = os.path.join(PROJECT_PATH, 'examples/data/AppliancesEnergy/AppliancesEnergy_TEST.ts') + x, y = loader._load_from_tsfile_to_dataframe(full_file_path_and_name=full_path, return_separate_X_and_y=True) + + assert isinstance(x, pd.DataFrame) + assert isinstance(y, np.ndarray) + assert x.shape[0] == y.shape[0] + + +def test_predict_encoding(): + ds_name = 'name' + path = '.' + loader = DataLoader(dataset_name=ds_name, folder=path) + full_path = os.path.join(ds_path, 'ItalyPowerDemand_fake_TEST.ts') + encoding = loader.predict_encoding(file_path=full_path) + assert encoding is not None + + +def test_read_txt_files(): + ds_name = 'name' + path = '.' + loader = DataLoader(dataset_name=ds_name, folder=path) + path = os.path.join(PROJECT_PATH, 'examples', 'data') + x_train, y_train, x_test, y_test = loader.read_txt_files(dataset_name='ItalyPowerDemand_fake', + temp_data_path=path) + + for i in [x_train, y_train, x_test, y_test]: + assert i is not None + + +def test_read_ts_files(): + ds_name = 'name' + path = '.' + loader = DataLoader(dataset_name=ds_name, folder=path) + path = os.path.join(PROJECT_PATH, 'examples', 'data') + x_train, y_train, x_test, y_test = loader.read_ts_files(dataset_name='ItalyPowerDemand_fake', + data_path=path) + + for i in [x_train, y_train, x_test, y_test]: + assert i is not None + + +def test_read_arff_files(): + ds_name = 'name' + path = '.' + loader = DataLoader(dataset_name=ds_name, folder=path) + path = os.path.join(PROJECT_PATH, 'examples', 'data') + x_train, y_train, x_test, y_test = loader.read_arff_files(dataset_name='ItalyPowerDemand_fake', + temp_data_path=path) + + for i in [x_train, y_train, x_test, y_test]: + assert i is not None + + +def test_read_tsv(): + ds_name = 'name' + path = '.' + loader = DataLoader(dataset_name=ds_name, folder=path) + path = os.path.join(PROJECT_PATH, 'tests', 'data', 'datasets') + x_train, y_train, x_test, y_test = loader.read_tsv(dataset_name='ItalyPowerDemand_tsv', + data_path=path) + + for i in [x_train, y_train, x_test, y_test]: + assert i is not None + + +def test_read_train_test_files(): + ds_name = 'name' + path = '.' + loader = DataLoader(dataset_name=ds_name, folder=path) + path = os.path.join(PROJECT_PATH, 'examples', 'data') + is_multi, (x_train, y_train), (x_test, y_test) = loader.read_train_test_files(dataset_name='ItalyPowerDemand_fake', + data_path=path) + + for i in [x_train, y_train, x_test, y_test, is_multi]: + assert i is not None diff --git a/tests/unit/tools/test_point_explainer.py b/tests/unit/tools/test_point_explainer.py new file mode 100644 index 000000000..9d7fa5cb5 --- /dev/null +++ b/tests/unit/tools/test_point_explainer.py @@ -0,0 +1,71 @@ +import math +import warnings + +import pytest +from matplotlib import get_backend, pyplot as plt + +from fedot_ind.api.main import FedotIndustrial as FI +from fedot_ind.tools.explain.distances import DistanceTypes +from fedot_ind.tools.explain.explain import PointExplainer +from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator + +distances = DistanceTypes.keys() + + +@pytest.fixture() +def data(): + generator = TimeSeriesDatasetsGenerator(num_samples=14, + max_ts_len=50, + binary=True) + train_data, test_data = generator.generate_data() + X_test, y_test = test_data + X_train, y_train = train_data + return X_train, y_train, X_test, y_test + + +@pytest.fixture() +def model(data): + available_operations = ['scaling', + 'normalization', + 'xgboost', + 'rfr', + 'rf', + 'logit', + 'mlp', + 'knn', + 'lgbm', + 'pca'] + + stat_model = FI(task='ts_classification', + dataset='dataset', + strategy='quantile', + use_cache=False, + timeout=0.1, + n_jobs=-1, + logging_level=50, + available_operations=available_operations) + x_train, y_train, x_test, y_test = data + stat_model.fit(features=x_train, target=y_train) + # stat_labels = stat_model.predict(features=x_test, target=y_test) + # stat_probs = stat_model.predict_proba(features=x_test, target=y_test) + stat_model.get_metrics(target=y_test, metric_names=['roc_auc']) + return stat_model, x_test, y_test + + +@pytest.mark.parametrize('distance, window', [(d, w) for d in distances for w in [0, 30]]) +def test_explain(data, model, distance, window): + # switch to non-Gui, preventing plots being displayed + # suppress UserWarning that agg cannot show plots + curr_backend = get_backend() + plt.switch_backend("Agg") + warnings.filterwarnings("ignore", "Matplotlib is currently using agg") + + stat_model, X_test, y_test = model + distance = distance + explainer = PointExplainer(stat_model, X_test, y_test) + explainer.explain(n_samples=1, window=window, method=distance) + explainer.visual(threshold=0, name='Custom' + '_' + distance) + + ts_len = X_test.shape[1] + expected_n_parts = math.ceil(ts_len / (window * ts_len // 100)) if window != 0 else ts_len + assert explainer.scaled_vector.shape[0] == expected_n_parts diff --git a/tests/unit/tools/test_ts_datasets_generator.py b/tests/unit/tools/test_ts_datasets_generator.py new file mode 100644 index 000000000..594643e09 --- /dev/null +++ b/tests/unit/tools/test_ts_datasets_generator.py @@ -0,0 +1,24 @@ +from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator + + +def test_generate_data_uni(): + generator = TimeSeriesDatasetsGenerator(num_samples=80, + max_ts_len=50, + binary=False, + test_size=0.5) + (X_train, y_train), (X_test, y_test) = generator.generate_data() + + assert X_train.shape[0] == X_test.shape[0] + assert X_train.shape[1] == X_test.shape[1] + + +def test_generate_data_multi(): + generator = TimeSeriesDatasetsGenerator(num_samples=80, + max_ts_len=50, + binary=False, + test_size=0.5, + multivariate=True) + (X_train, y_train), (X_test, y_test) = generator.generate_data() + + assert X_train.shape[0] == X_test.shape[0] + assert X_train.shape[1] == X_test.shape[1] diff --git a/tests/unit/tools/test_ts_generator.py b/tests/unit/tools/test_ts_generator.py new file mode 100644 index 000000000..9c93ea49c --- /dev/null +++ b/tests/unit/tools/test_ts_generator.py @@ -0,0 +1,33 @@ +import numpy as np +import pytest + +from fedot_ind.tools.synthetic.ts_generator import TimeSeriesGenerator + + +@pytest.fixture +def config(): + return dict(random_walk={'ts_type': 'random_walk', + 'length': 1000, + 'start_val': 36.6}, + sin={'ts_type': 'sin', + 'length': 1000, + 'amplitude': 10, + 'period': 500}, + auto_regression={'ts_type': 'auto_regression', + 'length': 1000, + 'ar_params': [0.5, -0.3, 0.2], + 'initial_values': None}, + smooth_normal={'ts_type': 'smooth_normal', + 'length': 1000, + 'window_size': 300} + + ) + + +@pytest.mark.parametrize('kind', ('random_walk', 'sin', 'auto_regression', 'smooth_normal')) +def test_get_ts(config, kind): + specific_config = config[kind] + generator = TimeSeriesGenerator(params=specific_config) + ts = generator.get_ts() + assert isinstance(ts, np.ndarray) + assert len(ts) == specific_config['length']