From 0df3dd66990c78269b7a99f5cb78c3edeab87b57 Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 18:22:41 +0300 Subject: [PATCH 01/16] removed old hypex --- .../addons/hypex/ABTesting/__init__.py | 0 .../addons/hypex/ABTesting/ab_tester.py | 581 ----------- lightautoml/addons/hypex/__init__.py | 5 - .../addons/hypex/algorithms/faiss_matcher.py | 962 ------------------ .../algorithms/no_replacement_matching.py | 162 --- lightautoml/addons/hypex/matcher.py | 590 ----------- .../addons/hypex/selectors/base_filtration.py | 48 - .../hypex/selectors/lama_feature_selector.py | 124 --- .../addons/hypex/selectors/outliers_filter.py | 81 -- .../addons/hypex/selectors/spearman_filter.py | 72 -- lightautoml/addons/hypex/tests/__init__.py | 3 - lightautoml/addons/hypex/tests/test_aa.py | 72 -- lightautoml/addons/hypex/tests/test_ab.py | 92 -- .../addons/hypex/tests/test_matcher.py | 115 --- lightautoml/addons/hypex/utils/__init__.py | 0 lightautoml/addons/hypex/utils/metrics.py | 161 --- lightautoml/addons/hypex/utils/psi_pandas.py | 497 --------- .../hypex/utils/tutorial_data_creation.py | 158 --- lightautoml/addons/hypex/utils/validators.py | 91 -- 19 files changed, 3814 deletions(-) delete mode 100644 lightautoml/addons/hypex/ABTesting/__init__.py delete mode 100644 lightautoml/addons/hypex/ABTesting/ab_tester.py delete mode 100644 lightautoml/addons/hypex/__init__.py delete mode 100644 lightautoml/addons/hypex/algorithms/faiss_matcher.py delete mode 100644 lightautoml/addons/hypex/algorithms/no_replacement_matching.py delete mode 100644 lightautoml/addons/hypex/matcher.py delete mode 100644 lightautoml/addons/hypex/selectors/base_filtration.py delete mode 100644 lightautoml/addons/hypex/selectors/lama_feature_selector.py delete mode 100644 lightautoml/addons/hypex/selectors/outliers_filter.py delete mode 100644 lightautoml/addons/hypex/selectors/spearman_filter.py delete mode 100644 lightautoml/addons/hypex/tests/__init__.py delete mode 100644 lightautoml/addons/hypex/tests/test_aa.py delete mode 100644 lightautoml/addons/hypex/tests/test_ab.py delete mode 100644 lightautoml/addons/hypex/tests/test_matcher.py delete mode 100644 lightautoml/addons/hypex/utils/__init__.py delete mode 100644 lightautoml/addons/hypex/utils/metrics.py delete mode 100644 lightautoml/addons/hypex/utils/psi_pandas.py delete mode 100644 lightautoml/addons/hypex/utils/tutorial_data_creation.py delete mode 100644 lightautoml/addons/hypex/utils/validators.py diff --git a/lightautoml/addons/hypex/ABTesting/__init__.py b/lightautoml/addons/hypex/ABTesting/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/lightautoml/addons/hypex/ABTesting/ab_tester.py b/lightautoml/addons/hypex/ABTesting/ab_tester.py deleted file mode 100644 index d8f2a7f2..00000000 --- a/lightautoml/addons/hypex/ABTesting/ab_tester.py +++ /dev/null @@ -1,581 +0,0 @@ -import warnings -from copy import copy - -from IPython.display import display -from pathlib import Path -from sklearn.utils import shuffle -from typing import Iterable, Union, Optional, Dict, Any, Tuple - -from tqdm.auto import tqdm - -import pandas as pd -import numpy as np -from scipy.stats import ttest_ind, ks_2samp, mannwhitneyu - - -def merge_groups( - test_group: Union[Iterable[pd.DataFrame], pd.DataFrame], - control_group: Union[Iterable[pd.DataFrame], pd.DataFrame], -) -> pd.DataFrame: - """Merges test and control groups in one DataFrame and creates column "group". - - Column "group" contains of "test" and "control" values. - - Args: - test_group: Data of target group - control_group: Data of control group - - Returns: - merged_data: Concatted DataFrame - """ - test_group.loc[:, "group"] = "test" - control_group.loc[:, "group"] = "control" - - merged_data = pd.concat([test_group, control_group], ignore_index=True) - - return merged_data - - -class AATest: - def __init__( - self, - target_fields: Union[Iterable[str], str], - info_cols: Union[Iterable[str], str] = None, - group_cols: Union[str, Iterable[str]] = None, - quant_field: str = None, - mode: str = "simple", - ): - self.target_fields = [target_fields] if isinstance(target_fields, str) else target_fields - self.info_cols = [info_cols] if isinstance(info_cols, str) else info_cols - self.group_cols = [group_cols] if isinstance(group_cols, str) else group_cols - self.quant_field = quant_field - self.mode = mode - - def _preprocessing_data(self, data: pd.DataFrame) -> pd.DataFrame: - """Converts categorical variables to dummy variables. - - Args: - data: Input data - - Returns: - clean_data: Data with categorical variables converted to dummy variables - """ - # categorical to dummies - prep_data = data.copy() - init_cols = data.columns - - dont_binarize_cols = copy(self.group_cols) or [] - if self.quant_field is not None: - dont_binarize_cols.append(self.quant_field) - - # if self.group_cols is not None: - prep_data = pd.get_dummies(prep_data.drop(columns=dont_binarize_cols), dummy_na=True) - prep_data = prep_data.merge(data[dont_binarize_cols], left_index=True, right_index=True) - - # fix if dummy_na is const=0 - dummies_cols = set(prep_data.columns) - set(init_cols) - const_columns = [col for col in dummies_cols if prep_data[col].nunique() <= 1] # choose constant_columns - - # drop constant dummy columns and info columns - cols_to_drop = const_columns + (self.info_cols if self.info_cols is not None else []) - clean_data = prep_data.drop(columns=cols_to_drop) - - return clean_data - - def __simple_mode(self, data: pd.DataFrame, random_state: int = None) -> Dict: - """Separates data on A and B samples within simple mode. - - Separation performed to divide groups of equal sizes - equal amount of records - or equal amount of groups in each sample. - - Args: - data: Input data - random_state: Seed of random - - Returns: - result: Test and control samples of indexes dictionary - """ - result = {"test_indexes": [], "control_indexes": []} - - if self.quant_field: - random_ids = shuffle(data[self.quant_field].unique(), random_state=random_state) - edge = len(random_ids) // 2 - result["test_indexes"] = list(data[data[self.quant_field].isin(random_ids[:edge])].index) - result["control_indexes"] = list(data[data[self.quant_field].isin(random_ids[edge:])].index) - - else: - addition_indexes = list(shuffle(data.index, random_state=random_state)) - edge = len(addition_indexes) // 2 - result["test_indexes"] = addition_indexes[:edge] - result["control_indexes"] = addition_indexes[edge:] - - return result - - def split(self, data: pd.DataFrame, preprocessing_data: bool = True, random_state: int = None) -> Dict: - """Divides sample on two groups. - - Args: - data: Raw input data - preprocessing_data: Flag to preprocess data - random_state: Seed of random - one integer to fix split - - Returns: - result: Dict of indexes with division on test and control group - """ - if preprocessing_data: - data = self._preprocessing_data(data) - result = {"test_indexes": [], "control_indexes": []} - - if self.group_cols: - groups = data.groupby(self.group_cols) - for _, gd in groups: - if self.mode not in ("balanced", "simple"): - warnings.warn( - f"The mode '{self.mode}' is not supported for group division. Implemented mode 'simple'." - ) - self.mode = "simple" - - if self.mode == "simple": - t_result = self.__simple_mode(gd, random_state) - result["test_indexes"] += t_result["test_indexes"] - result["control_indexes"] += t_result["control_indexes"] - - elif self.mode == "balanced": - if self.quant_field: - random_ids = shuffle(gd[self.quant_field].unique(), random_state=random_state) - addition_indexes = list(gd[gd[self.quant_field].isin(random_ids)].index) - else: - addition_indexes = list(shuffle(gd.index, random_state=random_state)) - - if len(result["control_indexes"]) > len(result["test_indexes"]): - result["test_indexes"] += addition_indexes - else: - result["control_indexes"] += addition_indexes - - else: - if self.mode != "simple": - warnings.warn( - f"The mode '{self.mode}' is not supported for regular division. " f"Implemented mode 'simple'." - ) - - t_result = self.__simple_mode(data, random_state) - result["test_indexes"] = t_result["test_indexes"] - result["control_indexes"] = t_result["control_indexes"] - - result["test_indexes"] = list(set(result["test_indexes"])) - result["control_indexes"] = list(set(result["control_indexes"])) - - return result - - @staticmethod - def _postprep_data(data, spit_indexes: Dict = None) -> pd.DataFrame: - """Prepares data to show user. - - Adds info_cols and decode binary variables - - Args: - data: Raw input data - spit_indexes: Dict of indexes with separation on test and control group - - Returns: - data: Separated initial data with column "group" - """ - # prep data to show user (add info_cols and decode binary variables) - test = data.loc[spit_indexes["test_indexes"]] - control = data.loc[spit_indexes["control_indexes"]] - data = merge_groups(test, control) - - return data - - @staticmethod - def calc_ab_delta(a_mean: float, b_mean: float, mode: str = "percentile"): - """Calculates target delta between A and B groups. - - Args: - a_mean: Average of target in one group - b_mean: Average of target in another group - mode: Type of expected result: - 'percentile' - percentage exceeding the average in group A compared to group B - 'absolute' - absolute value of difference between B and A group - 'relative' - percent in format of number (absolute) exceeding the average in group A compared to group B - - Returns: - result: Delta between groups as percent or absolute value - """ - if mode == "percentile": - result = (1 - a_mean / b_mean) * 100 - return result - if mode == "absolute": - result = b_mean - a_mean - return result - if mode == "relative": - result = 1 - a_mean / b_mean - return result - - def sampling_metrics( - self, data: pd.DataFrame, alpha: float = 0.05, random_state: int = None, preprocessed_data: pd.DataFrame = None - ): - """Calculates metrics of one sampling. - - Args: - data: Raw input data - alpha: Threshold to check statistical hypothesis; usually 0.05 - random_state: Random seeds for searching - preprocessed_data: Pre-preprocessed data - - Returns: - result: Tuple of - 1) metrics dataframe (stat tests) and - 2) dict of random state with test_control dataframe - """ - data_from_sampling_dict = {} - scores = [] - t_result = {"random_state": random_state} - - split = self.split(data, preprocessed_data is None, random_state) - - a = data.loc[split["test_indexes"]] - b = data.loc[split["control_indexes"]] - - # prep data to show user (merge indexes and init data) - data_from_sampling_dict[random_state] = self._postprep_data(data, split) - - for tf in self.target_fields: - ta = a[tf] - tb = b[tf] - - t_result[f"{tf} a mean"] = ta.mean() - t_result[f"{tf} b mean"] = tb.mean() - t_result[f"{tf} ab delta"] = self.calc_ab_delta( - t_result[f"{tf} a mean"], t_result[f"{tf} b mean"], "absolute" - ) - t_result[f"{tf} ab delta %"] = self.calc_ab_delta( - t_result[f"{tf} a mean"], t_result[f"{tf} b mean"], "percentile" - ) - t_result[f"{tf} t_test p_value"] = ttest_ind(ta, tb, nan_policy="omit").pvalue - t_result[f"{tf} ks_test p_value"] = ks_2samp(ta, tb).pvalue - t_result[f"{tf} t_test passed"] = t_result[f"{tf} t_test p_value"] > alpha - t_result[f"{tf} ks_test passed"] = t_result[f"{tf} ks_test p_value"] > alpha - scores.append((t_result[f"{tf} t_test p_value"] + t_result[f"{tf} ks_test p_value"]) / 2) - - t_result["mean_tests_score"] = np.mean(scores) - result = {"metrics": t_result, "data_from_experiment": data_from_sampling_dict} - - return result - - def search_dist_uniform_sampling( - self, - data: pd.DataFrame, - alpha: float = 0.05, - iterations: int = 10, - file_name: Union[Path, str] = None, - write_mode: str = "full", - write_step: int = None, - pbar: bool = True, - ) -> Optional[Tuple[pd.DataFrame, Dict[Any, Dict]]]: - """Chooses random_state for finding homogeneous distribution. - - Args: - data: Raw input data - alpha: - Threshold to check statistical hypothesis; usually 0.05 - iterations: - Number of iterations to search uniform sampling to searching - file_name: - Name of file to save results (if None - no results will be saved, func returns result) - write_mode: - Mode to write: - 'full' - save all experiments - 'all' - save experiments that passed all statistical tests - 'any' - save experiments that passed any statistical test - write_step: - Step to write experiments to file - pbar: - Flag to show progress bar - - Returns: - results: - If no saving (no file_name, no write mode and no write_step) returns dataframe - else None and saves file to csv - """ - random_states = range(iterations) - results = [] - data_from_sampling = {} - - preprocessed_data = self._preprocessing_data(data) - - if write_mode not in ("full", "all", "any"): - warnings.warn(f"Write mode '{write_mode}' is not supported. Mode 'full' will be used") - write_mode = "full" - - for i, rs in tqdm(enumerate(random_states), total=len(random_states), disable=not pbar): - res = self.sampling_metrics(data, alpha=alpha, random_state=rs, preprocessed_data=preprocessed_data) - data_from_sampling.update(res["data_from_experiment"]) - - # write to file - passed = [] - for tf in self.target_fields: - passed += [res["metrics"][f"{tf} t_test passed"], res["metrics"][f"{tf} ks_test passed"]] - - if write_mode == "all" and all(passed): - results.append(res["metrics"]) - if write_mode == "any" and any(passed): - results.append(res["metrics"]) - if write_mode == "full": - results.append(res["metrics"]) - - if file_name and write_step: - if i == write_step: - pd.DataFrame(results).to_csv(file_name, index=False) - elif i % write_step == 0: - pd.DataFrame(results).to_csv(file_name, index=False, header=False, mode="a") - results = [] - - if file_name and write_step: - pd.DataFrame(results).to_csv(file_name, index=False, header=False, mode="a") - elif file_name: - results = pd.DataFrame(results) - results.to_csv(file_name, index=False) - return results, data_from_sampling - else: - return pd.DataFrame(results), data_from_sampling - - -class ABTest: - def __init__( - self, - calc_difference_method: str = "all", - calc_p_value_method: str = "all", - ): - """Initializes the ABTest class. - - Args: - calc_difference_method: - The method used to calculate the difference: - 'all' [default] - all metrics - 'ate' - basic difference in means of targets in test and control group - 'diff_in_diff' - difference in difference value, - performs pre-post analysis (required values of target before pilot) - 'cuped' - Controlled-Experiment using Pre-Experiment Data value, - performs pre-post analysis (required values of target before pilot) - calc_p_value_method: - The method used to calculate the p-value. Defaults to 'all' - """ - self.calc_difference_method = calc_difference_method - self.calc_p_value_method = calc_p_value_method - self.results = None - - @staticmethod - def split_ab(data: pd.DataFrame, group_field: str) -> Dict[str, pd.DataFrame]: - """Splits a pandas DataFrame into two separate dataframes based on a specified group field. - - Args: - data: - The input dataframe to be split - group_field: - The column name representing the group field - - Returns: - splitted_data: - A dictionary containing two dataframes, 'test' and 'control', where 'test' contains rows where the - group field is 'test', and 'control' contains rows where the group field is 'control'. - """ - splitted_data = { - "test": data[data[group_field] == "test"], - "control": data[data[group_field] == "control"], - } - return splitted_data - - @staticmethod - def cuped( - test_data: pd.DataFrame, control_data: pd.DataFrame, target_field: str, target_field_before: str - ) -> float: - """Counts CUPED (Controlled-Experiment using Pre-Experiment Data) in absolute values. - - Metric uses pre-post analysis of target, uses to minimize variance of effect: - ATE = mean(test_cuped) - mean(control_cuped) - , where - test_cuped = target__test - theta * target_before__test - control_cuped = target__control - theta * target_before__control - , where - theta = (cov_test + cov_control) / (var_test + var_control) - , where - cov_test = cov(target__test, target_before__test) - cov_control = cov(target__control, target_before__control) - var_test = var(target_before__test) - var_control = var(target_before__control) - - Args: - test_data: - Input data of test group - Should include target before and after pilot - control_data: - Input data of control group - Should include target before and after pilot - target_field: - Column name of target after pilot - target_field_before: - Column name of target before pilot - - Returns: - result: - Named tuple with pvalue, effect, ci_length, left_bound and right_bound - """ - control = control_data[target_field] - control_before = control_data[target_field_before] - test = test_data[target_field] - test_before = test_data[target_field_before] - - theta = (np.cov(control, control_before)[0, 1] + np.cov(test, test_before)[0, 1]) / ( - np.var(control_before) + np.var(test_before) - ) - - control_cuped = control - theta * control_before - test_cuped = test - theta * test_before - - mean_control = np.mean(control_cuped) - mean_test = np.mean(test_cuped) - - difference_mean = mean_test - mean_control - - return difference_mean - - @staticmethod - def diff_in_diff( - test_data: pd.DataFrame, control_data: pd.DataFrame, target_field: str, target_field_before: str - ) -> float: - """Counts Difference in Difference. - - Metric uses pre-post analysis and counts difference in means in data before and after pilot: - ATE = (y_test_after - y_control_after) - (y_test_before - y_control_before) - - Args: - test_data: Input data of test group - control_data: Input data of control group - target_field: Column name of target after pilot - target_field_before: Column name of target before pilot - - Returns: - did: Value of difference in difference - """ - mean_test = np.mean(test_data[target_field]) - mean_control = np.mean(control_data[target_field]) - - mean_test_before = np.mean(test_data[target_field_before]) - mean_control_before = np.mean(control_data[target_field_before]) - did = (mean_test - mean_control) - (mean_test_before - mean_control_before) - - return did - - def calc_difference( - self, splitted_data: Dict[str, pd.DataFrame], target_field: str, target_field_before: str = None - ) -> Dict[str, float]: - """Calculates the difference between the target field values of the 'test' and 'control' dataframes. - - Args: - splitted_data: - A dictionary containing the 'test' and 'control' dataframes - target_field: - The name of the target field contains data after pilot - target_field_before: - The name of the target field contains data before pilot - - Returns: - result: - A dictionary containing the difference between the target field - values of the 'test' and 'control' dataframes - """ - result = {} - if self.calc_difference_method in {"all", "diff_in_diff", "cuped"}: - if target_field_before is None: - raise ValueError( - "For calculation metrics 'cuped' or 'diff_in_diff' field 'target_field_before' is required.\n" - "Metric 'ate'(=diff-in-means) can be used without 'target_field_before'" - ) - - if self.calc_difference_method in {"all", "ate"}: - result["ate"] = ( - splitted_data["test"][target_field].values - splitted_data["control"][target_field].values - ).mean() - - if self.calc_difference_method in {"all", "cuped"}: - result["cuped"] = self.cuped( - test_data=splitted_data["test"], - control_data=splitted_data["control"], - target_field=target_field, - target_field_before=target_field_before, - ) - - if self.calc_difference_method in {"all", "diff_in_diff"}: - result["diff_in_diff"] = self.diff_in_diff( - test_data=splitted_data["test"], - control_data=splitted_data["control"], - target_field=target_field, - target_field_before=target_field_before, - ) - - return result - - def calc_p_value(self, splitted_data: Dict[str, pd.DataFrame], target_field: str) -> Dict[str, float]: - """Calculates the p-value for a given data set. - - Args: - splitted_data: - A dictionary containing the split data, where the keys are 'test' and 'control' - and the values are pandas DataFrames - target_field: - The name of the target field - Returns: - result: - A dictionary containing the calculated p-values, where the keys are 't_test' and 'mann_whitney' - and the values are the corresponding p-values - """ - result = {} - if self.calc_p_value_method in {"all", "t_test"}: - result["t_test"] = ttest_ind( - splitted_data["test"][target_field], - splitted_data["control"][target_field], - ).pvalue - - if self.calc_p_value_method in {"all", "mann_whitney"}: - result["mann_whitney"] = mannwhitneyu( - splitted_data["test"][target_field], - splitted_data["control"][target_field], - ).pvalue - - return result - - def execute( - self, data: pd.DataFrame, target_field: str, group_field: str, target_field_before: str = None - ) -> Dict[str, Dict[str, float]]: - """Splits the input data based on the group field and calculates the size, difference, and p-value. - - Args: - data: Input data as a pandas DataFrame - target_field: Target field to be analyzed - group_field: Field used to split the data into groups - target_field_before: Target field without treatment to be analyzed - - Returns: - results: - A dictionary containing the size, difference, and p-value of the split data - 'size': A dictionary with the sizes of the test and control groups - 'difference': A dictionary with the calculated differences between the groups - 'p_value': A dictionary with the calculated p-values for each group - """ - splitted_data = self.split_ab(data, group_field) - - results = { - "size": {"test": len(splitted_data["test"]), "control": len(splitted_data["control"])}, - "difference": self.calc_difference(splitted_data, target_field, target_field_before), - "p_value": self.calc_p_value(splitted_data, target_field), - } - - self.results = results - - return results - - def show_beautiful_result(self): - """Shows results of 'execute' function - dict as dataframes.""" - for k in self.results.keys(): - display(pd.DataFrame(self.results[k], index=[k]).T) diff --git a/lightautoml/addons/hypex/__init__.py b/lightautoml/addons/hypex/__init__.py deleted file mode 100644 index 0b241310..00000000 --- a/lightautoml/addons/hypex/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -"""Tools to configure resources matcher.""" -from .matcher import Matcher - - -__all__ = ["Matcher"] diff --git a/lightautoml/addons/hypex/algorithms/faiss_matcher.py b/lightautoml/addons/hypex/algorithms/faiss_matcher.py deleted file mode 100644 index f36c9123..00000000 --- a/lightautoml/addons/hypex/algorithms/faiss_matcher.py +++ /dev/null @@ -1,962 +0,0 @@ -"""Class that searches indexes.""" -import datetime as dt -import functools -import logging -import time -from typing import Any -from typing import Dict -from typing import Tuple -from typing import Union - -import faiss -import numpy as np -import pandas as pd -from scipy.stats import norm -from tqdm.auto import tqdm - -from ..utils.metrics import check_repeats -from ..utils.metrics import matching_quality - - -def timer(func): - """Decorator to measure the execution time of a function. - - Uses time.perf_counter() to determine the start and end times - of the decorated function and then prints the total execution time - - Usage Example: - - @timer - def example_function(): - ... - - Args: - func: The function whose execution time is to be measured - - Returns: - Wrapped version of the original function with added time measurement - """ - - @functools.wraps(func) - def _wrapper(*args, **kwargs): - start = time.perf_counter() - result = func(*args, **kwargs) - runtime = time.perf_counter() - start - print(f"{func.__name__} took {runtime:.4f} secs") - return result - - return _wrapper - - -faiss.cvar.distance_compute_blas_threshold = 100000 -POSTFIX = "_matched" -POSTFIX_BIAS = "_matched_bias" - -logger = logging.getLogger("Faiss hypex") -console_out = logging.StreamHandler() -logging.basicConfig( - handlers=(console_out,), - format="[%(asctime)s | %(name)s | %(levelname)s]: %(message)s", - datefmt="%d.%m.%Y %H:%M:%S", - level=logging.INFO, -) - - -class FaissMatcher: - """A class used to match instances using Faiss library.""" - - def __init__( - self, - df: pd.DataFrame, - outcomes: str, - treatment: str, - info_col: list, - features: [list, pd.DataFrame] = None, - group_col: str = None, - weights: dict = None, - sigma: float = 1.96, - validation: bool = None, - n_neighbors: int = 10, - silent: bool = True, - pbar: bool = True, - ): - """Construct all the necessary attributes. - - Args: - df: - The input dataframe - outcomes: - The target column name - treatment: - The column name with treatment - info_col: - A list with informational column names - features: - A list with names of feature using to matching. Defaults to None - group_col: - The column for stratification. Defaults to None - weights: - Dict with wight of features to matching. If you would like that matching will be more for - 1 feature and less for another one - sigma: - The significant level for confidence interval calculation Defaults to 1.96 - validation: - The flag for validation of estimated ATE with default method `random_feature` - n_neighbors: - The number of neighbors to find for each object. Defaults to 10 - silent: - Write logs in debug mode - pbar: - Display progress bar while get index - """ - self.n_neighbors = n_neighbors - if group_col is None: - self.df = df - else: - self.df = df.sort_values([treatment, group_col]) - self.columns_del = [outcomes] - if info_col: - self.info_col = info_col - else: - self.info_col = [] - - if self.info_col is not None: - self.columns_del = self.columns_del + [x for x in self.info_col if x in self.df.columns] - self.outcomes = outcomes if isinstance(outcomes, list) else [outcomes] - self.treatment = treatment - - if features is None: - self.columns_match = list( - set([x for x in list(self.df.columns) if x not in self.info_col] + [self.treatment] + self.outcomes) - ) - else: - try: - self.columns_match = features["Feature"].tolist() + [self.treatment] + self.outcomes - except TypeError: - self.columns_match = features + [self.treatment] + self.outcomes - - self.features_quality = ( - self.df.drop(columns=[self.treatment] + self.outcomes + self.info_col) - .select_dtypes(include=["int16", "int32", "int64", "float16", "float32", "float64"]) - .columns - ) - self.dict_outcome_untreated = {} - self.dict_outcome_treated = {} - self.group_col = group_col - self.weights = weights - self.treated_index = None - self.untreated_index = None - self.orig_treated_index = None - self.orig_untreated_index = None - self.results = {} - self.ATE = None - self.sigma = sigma - self.quality_dict = {} - self.rep_dict = None - self.validation = validation - self.silent = silent - self.pbar = pbar - self.tqdm = None - self.results = pd.DataFrame() - - def __getstate__(self) -> dict: - """Prepare the object for serialization. - - This method is called when the object is about to be serialized. - It removes the `tqdm` attribute from the object's dictionary - because `tqdm` objects cannot be serialized. - - Returns: - A copy of the object's dictionary with the `tqdm` attribute removed. - """ - state = self.__dict__.copy() - if "tqdm" in state: - del state["tqdm"] - return state - - def __setstate__(self, state: dict): - """Restore the object after deserialization. - - This method is called when the object is deserialized. - It adds the `tqdm` attribute back to the object's dictionary - if the `pbar` attribute is True. - - Args: - state: - The deserialized state of the object - """ - if "pbar" in state and state["pbar"]: - state["tqdm"] = None - self.__dict__.update(state) - - def _get_split(self, df: pd.DataFrame) -> (pd.DataFrame, pd.DataFrame): - """Creates split data by treatment column. - - Separate treatment column with 1 (treated) an 0 (untreated), - scales and transforms treatment column - - Args: - df: - The input dataframe - - Returns: - Tuple of dataframes - one for treated (df[self.treatment] == 1]) and - one for untreated (df[self.treatment] == 0]). Drops self.outcomes and - `self.treatment` columns - - """ - logger.debug("Creating split data by treatment column") - - treated = df[df[self.treatment] == 1].drop([self.treatment] + self.outcomes, axis=1) - untreated = df[df[self.treatment] == 0].drop([self.treatment] + self.outcomes, axis=1) - - return treated, untreated - - def _predict_outcome(self, std_treated: pd.DataFrame, std_untreated: pd.DataFrame): - """Applies LinearRegression to input arrays. - - Calculate biases of treated and untreated values, - creates dict of y - regular, matched and without bias. - - Args: - std_treated: - The dataframe of treated data - std_untreated: - The dataframe of untreated data - - """ - logger.debug("Predicting target by Linear Regression") - - start_time = dt.datetime.now() - logger.debug("start --") - - self.dict_outcome_untreated = {} - self.dict_outcome_treated = {} - df = self.df.drop(columns=self.info_col) - - for outcome in self.outcomes: - y_untreated = df[df[self.treatment] == 0][outcome].to_numpy() - y_treated = df[df[self.treatment] == 1][outcome].to_numpy() - - x_treated = std_treated.to_numpy() - x_untreated = std_untreated.to_numpy() - y_match_treated = np.array([y_untreated[idx].mean() for idx in self.treated_index]) - y_match_untreated = np.array([y_treated[idx].mean() for idx in self.untreated_index]) - x_match_treated = np.array([x_untreated[idx].mean(0) for idx in self.treated_index]) - x_match_untreated = np.array([x_treated[idx].mean(0) for idx in self.untreated_index]) - bias_coefs_c = bias_coefs(self.untreated_index, y_treated, x_treated) - bias_coefs_t = bias_coefs(self.treated_index, y_untreated, x_untreated) - bias_c = bias(x_untreated, x_match_untreated, bias_coefs_c) - bias_t = bias(x_treated, x_match_treated, bias_coefs_t) - - y_match_treated_bias = y_treated - y_match_treated + bias_t - y_match_untreated_bias = y_match_untreated - y_untreated - bias_c - - self.dict_outcome_untreated[outcome] = y_untreated - self.dict_outcome_untreated[outcome + POSTFIX] = y_match_untreated - self.dict_outcome_untreated[outcome + POSTFIX_BIAS] = y_match_untreated_bias - - self.dict_outcome_treated[outcome] = y_treated - self.dict_outcome_treated[outcome + POSTFIX] = y_match_treated - self.dict_outcome_treated[outcome + POSTFIX_BIAS] = y_match_treated_bias - - end_time = dt.datetime.now() - total = dt.datetime.strptime(str(end_time - start_time), "%H:%M:%S.%f").strftime("%H:%M:%S") - logger.debug(f"end -- [work time{total}]") - - def _create_outcome_matched_df(self, dict_outcome: dict, is_treated: bool) -> pd.DataFrame: - """Creates dataframe with outcomes values and treatment. - - Args: - dict_outcome: - A dictionary containing outcomes - is_treated: - A boolean value indicating whether the outcome is treated or not - - Returns: - A dataframe with matched outcome and treatment columns - - """ - df_pred = pd.DataFrame(dict_outcome) - df_pred[self.treatment] = int(is_treated) - df_pred[self.treatment + POSTFIX] = int(not is_treated) - - return df_pred - - def _create_features_matched_df(self, index: np.ndarray, is_treated: bool) -> pd.DataFrame: - """Creates matched dataframe with features. - - Args: - index: - An array of indices - is_treated: - A boolean value indicating whether the outcome is treated or not - - - Returns: - A dataframe of matched features - - """ - df = self.df.drop(columns=self.outcomes + self.info_col) - - if self.group_col is None: - untreated_index = df[df[self.treatment] == int(not is_treated)].index.to_numpy() - converted_index = [untreated_index[i] for i in index] - filtered = df.loc[df[self.treatment] == int(not is_treated)].values - untreated_df = pd.DataFrame( - data=np.array([filtered[idx].mean(axis=0) for idx in index]), columns=df.columns - ) # добавить дату в данные и пофиксить баги с этим (тут ломалось) - if self.info_col is not None and len(self.info_col) != 1: - untreated_df["index"] = pd.Series(converted_index) - treated_df = df[df[self.treatment] == int(is_treated)].reset_index() - else: - ids = self.df[df[self.treatment] == int(not is_treated)][self.info_col].values.ravel() - converted_index = [ids[i] for i in index] - untreated_df["index"] = pd.Series(converted_index) - treated_df = df[df[self.treatment] == int(is_treated)].reset_index() - treated_df["index"] = self.df[self.df[self.treatment] == int(is_treated)][self.info_col].values.ravel() - else: - df = df.sort_values([self.treatment, self.group_col]) - untreated_index = df[df[self.treatment] == int(not is_treated)].index.to_numpy() - converted_index = [untreated_index[i] for i in index] - filtered = df.loc[df[self.treatment] == int(not is_treated)] - cols_untreated = [col for col in filtered.columns if col != self.group_col] - filtered = filtered.drop(columns=self.group_col).to_numpy() - untreated_df = pd.DataFrame( - data=np.array([filtered[idx].mean(axis=0) for idx in index]), columns=cols_untreated - ) - treated_df = df[df[self.treatment] == int(is_treated)].reset_index() - grp = treated_df[self.group_col] - untreated_df[self.group_col] = grp - if self.info_col is not None and len(self.info_col) != 1: - untreated_df["index"] = pd.Series(converted_index) - else: - ids = ( - self.df[df[self.treatment] == int(not is_treated)] - .sort_values([self.treatment, self.group_col])[self.info_col] - .values.ravel() - ) - converted_index = [ids[i] for i in index] - untreated_df["index"] = pd.Series(converted_index) - treated_df["index"] = self.df[self.df[self.treatment] == int(is_treated)][self.info_col].values.ravel() - untreated_df.columns = [col + POSTFIX for col in untreated_df.columns] - - x = pd.concat([treated_df, untreated_df], axis=1).drop( - columns=[self.treatment, self.treatment + POSTFIX], axis=1 - ) - return x - - def _create_matched_df(self) -> pd.DataFrame: - """Creates matched df of features and outcome. - - Returns: - Matched dataframe - """ - df_pred_treated = self._create_outcome_matched_df(self.dict_outcome_treated, True) - df_pred_untreated = self._create_outcome_matched_df(self.dict_outcome_untreated, False) - - df_matched = pd.concat([df_pred_treated, df_pred_untreated]) - - treated_x = self._create_features_matched_df(self.treated_index, True) - untreated_x = self._create_features_matched_df(self.untreated_index, False) - - untreated_x = pd.concat([treated_x, untreated_x]) - - columns = list(untreated_x.columns) + list(df_matched.columns) - - df_matched = pd.concat([untreated_x, df_matched], axis=1, ignore_index=True) - df_matched.columns = columns - - return df_matched - - def calc_atc(self, df: pd.DataFrame, outcome: str) -> tuple: - """Calculates Average Treatment Effect for the control group (ATC). - - Effect on control group if it was affected - - Args: - df: - Input dataframe - outcome: - The outcome to be considered for treatment effect - - Returns: - Contains ATC, scaled counts, and variances as numpy arrays - - """ - logger.debug("Calculating ATC") - - df = df[df[self.treatment] == 0] - N_c = len(df) - ITT_c = df[outcome + POSTFIX_BIAS] - scaled_counts_c = scaled_counts(N_c, self.treated_index, self.silent) - - vars_c = np.repeat(ITT_c.var(), N_c) # conservative - atc = ITT_c.mean() - - return atc, scaled_counts_c, vars_c - - def calc_att(self, df: pd.DataFrame, outcome: str) -> tuple: - """Calculates Average Treatment Effect for the treated (ATT). - - Args: - df: - Input dataframe - outcome: - The outcome to be considered for treatment effect - - Returns: - Contains ATT, scaled counts, and variances as numpy arrays - - """ - logger.debug("Calculating ATT") - - df = df[df[self.treatment] == 1] - N_t = len(df) - ITT_t = df[outcome + POSTFIX_BIAS] - scaled_counts_t = scaled_counts(N_t, self.untreated_index, self.silent) - - vars_t = np.repeat(ITT_t.var(), N_t) # conservative - att = ITT_t.mean() - - return att, scaled_counts_t, vars_t - - def _calculate_ate_all_target(self, df: pd.DataFrame): - """Creates dictionaries of all effect: ATE, ATC, ATT. - - Args: - df: - Input dataframe - - """ - logger.debug("Creating dicts of all effects: ATE, ATC, ATT") - - att_dict = {} - atc_dict = {} - ate_dict = {} - N = len(df) - N_t = df[self.treatment].sum() - N_c = N - N_t - - for outcome in self.outcomes: - att, scaled_counts_t, vars_t = self.calc_att(df, outcome) - atc, scaled_counts_c, vars_c = self.calc_atc(df, outcome) - ate = (N_c / N) * atc + (N_t / N) * att - - att_se = calc_att_se(vars_c, vars_t, scaled_counts_c) - atc_se = calc_atc_se(vars_c, vars_t, scaled_counts_t) - ate_se = calc_ate_se(vars_c, vars_t, scaled_counts_c, scaled_counts_t) - - ate_dict[outcome] = [ - ate, - ate_se, - pval_calc(ate / ate_se), - ate - self.sigma * ate_se, - ate + self.sigma * ate_se, - ] - atc_dict[outcome] = [ - atc, - atc_se, - pval_calc(atc / atc_se), - atc - self.sigma * atc_se, - atc + self.sigma * atc_se, - ] - att_dict[outcome] = [ - att, - att_se, - pval_calc(att / att_se), - att - self.sigma * att_se, - att + self.sigma * att_se, - ] - - self.ATE, self.ATC, self.ATT = ate_dict, atc_dict, att_dict - self.val_dict = ate_dict - - def matching_quality(self, df_matched) -> Dict[str, Union[Dict[str, float], float]]: - """Estimated the quality of covariates balance and repeat fraction. - - Calculates population stability index,Standardized mean difference - and Kolmogorov-Smirnov test for numeric values. Returns a dictionary of reports. - - Args: - df_matched: - Matched DataFrame to calculate quality - - Returns: - dictionary containing PSI, KS-test, SMD data and repeat fractions - - """ - if self.silent: - logger.debug("Estimating quality of matching") - else: - logger.info("Estimating quality of matching") - - psi_columns = set(self.columns_match) - psi_columns = list(psi_columns - set([self.treatment] + self.outcomes)) - psi_data, ks_data, smd_data = matching_quality( - df_matched, self.treatment, sorted(self.features_quality), sorted(psi_columns), self.silent - ) - - rep_dict = { - "match_control_to_treat": check_repeats(np.concatenate(self.treated_index), silent=self.silent), - "match_treat_to_control": check_repeats(np.concatenate(self.untreated_index), silent=self.silent), - } - - self.quality_dict = {"psi": psi_data, "ks_test": ks_data, "smd": smd_data, "repeats": rep_dict} - - rep_df = pd.DataFrame.from_dict(rep_dict, orient="index").rename(columns={0: "value"}) - self.rep_dict = rep_df - - if self.silent: - logger.debug(f"PSI info: \n {psi_data.head(10)} \nshape:{psi_data.shape}") - logger.debug(f"Kolmogorov-Smirnov test info: \n {ks_data.head(10)} \nshape:{ks_data.shape}") - logger.debug(f"Standardised mean difference info: \n {smd_data.head(10)} \nshape:{smd_data.shape}") - logger.debug(f"Repeats info: \n {rep_df.head(10)}") - else: - logger.info(f"PSI info: \n {psi_data.head(10)} \nshape:{psi_data.shape}") - logger.info(f"Kolmogorov-Smirnov test info: \n {ks_data.head(10)} \nshape:{ks_data.shape}") - logger.info(f"Standardised mean difference info: \n {smd_data.head(10)} \nshape:{smd_data.shape}") - logger.info(f"Repeats info: \n {rep_df.head(10)}") - - return self.quality_dict - - def group_match(self): - """Matches the dataframe if it divided by groups. - - Returns: - A tuple containing the matched dataframe and metrics such as ATE, ATT and ATC - - """ - df = self.df.drop(columns=self.info_col) - groups = sorted(df[self.group_col].unique()) - matches_c = [] - matches_t = [] - group_arr_c = df[df[self.treatment] == 0][self.group_col].to_numpy() - group_arr_t = df[df[self.treatment] == 1][self.group_col].to_numpy() - treat_arr_c = df[df[self.treatment] == 0][self.treatment].to_numpy() - treat_arr_t = df[df[self.treatment] == 1][self.treatment].to_numpy() - - if self.pbar: - self.tqdm = tqdm(total=len(groups) * 2) - - for group in groups: - df_group = df[df[self.group_col] == group] - temp = df_group[self.columns_match + [self.group_col]] - temp = temp.loc[:, (temp != 0).any(axis=0)].drop(columns=self.group_col) - treated, untreated = self._get_split(temp) - - std_treated_np, std_untreated_np = _transform_to_np(treated, untreated, self.weights) - - if self.pbar: - self.tqdm.set_description(desc=f"Get untreated index by group {group}") - matches_u_i = _get_index(std_treated_np, std_untreated_np, self.n_neighbors) - - if self.pbar: - self.tqdm.update(1) - self.tqdm.set_description(desc=f"Get treated index by group {group}") - matches_t_i = _get_index(std_untreated_np, std_treated_np, self.n_neighbors) - if self.pbar: - self.tqdm.update(1) - self.tqdm.refresh() - - group_mask_c = group_arr_c == group - group_mask_t = group_arr_t == group - matches_c_mask = np.arange(treat_arr_t.shape[0])[group_mask_t] - matches_u_i = [matches_c_mask[i] for i in matches_u_i] - matches_t_mask = np.arange(treat_arr_c.shape[0])[group_mask_c] - matches_t_i = [matches_t_mask[i] for i in matches_t_i] - matches_c.extend(matches_u_i) - matches_t.extend(matches_t_i) - - if self.pbar: - self.tqdm.close() - - self.untreated_index = matches_c - self.treated_index = matches_t - - df_group = df[self.columns_match].drop(columns=self.group_col) - treated, untreated = self._get_split(df_group) - self._predict_outcome(treated, untreated) - df_matched = self._create_matched_df() - self._calculate_ate_all_target(df_matched) - - if self.validation: - return self.val_dict - - return self.report_view(), df_matched - - def match(self): - """Matches the dataframe. - - Returns: - A tuple containing the matched dataframe and metrics such as ATE, ATT and ATC - - """ - if self.group_col is not None: - return self.group_match() - - df = self.df[self.columns_match] - treated, untreated = self._get_split(df) - - std_treated_np, std_untreated_np = _transform_to_np(treated, untreated, self.weights) - - if self.pbar: - self.tqdm = tqdm(total=len(std_treated_np) + len(std_untreated_np)) - self.tqdm.set_description(desc="Get untreated index") - - untreated_index = _get_index(std_treated_np, std_untreated_np, self.n_neighbors) - - if self.pbar: - self.tqdm.update(len(std_treated_np)) - self.tqdm.set_description(desc="Get treated index") - treated_index = _get_index(std_untreated_np, std_treated_np, self.n_neighbors) - - if self.pbar: - self.tqdm.update(len(std_untreated_np)) - self.tqdm.refresh() - self.tqdm.close() - - self.untreated_index = untreated_index - self.treated_index = treated_index - - self._predict_outcome(treated, untreated) - - df_matched = self._create_matched_df() - self._calculate_ate_all_target(df_matched) - - if self.validation: - return self.val_dict - - return self.report_view(), df_matched - - def report_view(self) -> pd.DataFrame: - """Formats the ATE, ATC, and ATT results into a Pandas DataFrame for easy viewing. - - Returns: - DataFrame containing ATE, ATC, and ATT results - """ - result = (self.ATE, self.ATC, self.ATT) - - for outcome in self.outcomes: - res = pd.DataFrame( - [x[outcome] + [outcome] for x in result], - columns=["effect_size", "std_err", "p-val", "ci_lower", "ci_upper", "outcome"], - index=["ATE", "ATC", "ATT"], - ) - self.results = pd.concat([self.results, res]) - return self.results - - -def map_func(x: np.ndarray) -> np.ndarray: - """Get the indices of elements in an array that are equal to the first element. - - Args: - x: - An input array. - - Returns: - Array of indices where the elements match the first element of x. - """ - return np.where(x == x[0])[0] - - -def f2(x: np.ndarray, y: np.ndarray) -> Any: - """Index an array using a secondary array of indices. - - Args: - x: - An input array. - y: - Array of indices used for indexing x. - - Returns: - Indexed element from the input array x. - """ - return x[y] - - -def _get_index(base: np.ndarray, new: np.ndarray, n_neighbors: int) -> list: - """Gets array of indexes that match a new array. - - Args: - base: - A numpy array serving as the reference for matching - new: - A numpy array that needs to be matched with the base - n_neighbors: - The number of neighbors to use for the matching - - Returns: - An array of indexes containing all neighbours with minimum distance - """ - index = faiss.IndexFlatL2(base.shape[1]) - index.add(base) - dist, indexes = index.search(new, 20) - if n_neighbors == 1: - equal_dist = list(map(map_func, dist)) - indexes = [f2(i, j) for i, j in zip(indexes, equal_dist)] - else: - indexes = f3(indexes, dist, n_neighbors) - return indexes - - -def _transform_to_np(treated: pd.DataFrame, untreated: pd.DataFrame, weights: dict) -> Tuple[np.ndarray, np.ndarray]: - """Transforms df to numpy and transform via Cholesky decomposition. - - Args: - treated: - Test subset DataFrame to be transformed - untreated: - Control subset DataFrame to be transformed - weights: - Dict with weights for each feature. By default is 1 - - Returns: - A tuple of transformed numpy arrays for treated and untreated data respectively - """ - xc = untreated.to_numpy() - xt = treated.to_numpy() - - cov = conditional_covariance(xc, xt) - - epsilon = 1e-3 - cov = cov + np.eye(cov.shape[0]) * epsilon - - L = np.linalg.cholesky(cov) - mahalanobis_transform = np.linalg.inv(L) - if weights is not None: - features = treated.columns - w_list = np.array([weights[col] if col in weights.keys() else 1 for col in features]) - w_matrix = np.sqrt(np.diag(w_list / w_list.sum())) - mahalanobis_transform = np.dot(w_matrix, mahalanobis_transform) - yc = np.dot(xc, mahalanobis_transform.T) - yt = np.dot(xt, mahalanobis_transform.T) - - return yt.copy(order="C").astype("float32"), yc.copy(order="C").astype("float32") - - -def calc_atx_var(vars_c: np.ndarray, vars_t: np.ndarray, weights_c: np.ndarray, weights_t: np.ndarray) -> float: - """Calculates Average Treatment Effect for the treated (ATT) variance. - - Args: - vars_c: - Control group variance - vars_t: - Treatment group variance - weights_c: - Control group weights - weights_t: - Treatment group weights - - Returns: - The calculated ATT variance - - """ - N_c, N_t = len(vars_c), len(vars_t) - summands_c = weights_c ** 2 * vars_c - summands_t = weights_t ** 2 * vars_t - - return summands_t.sum() / N_t ** 2 + summands_c.sum() / N_c ** 2 - - -def calc_atc_se(vars_c: np.ndarray, vars_t: np.ndarray, scaled_counts_t: np.ndarray) -> float: - """Calculates Average Treatment Effect for the control group (ATC) standard error. - - Args: - vars_c: - Control group variance - vars_t: - Treatment group variance - scaled_counts_t: - Scaled counts for treatment group - - Returns: - The calculated ATC standard error - """ - N_c, N_t = len(vars_c), len(vars_t) - weights_c = np.ones(N_c) - weights_t = (N_t / N_c) * scaled_counts_t - - var = calc_atx_var(vars_c, vars_t, weights_c, weights_t) - - return np.sqrt(var) - - -def conditional_covariance(xc, xt): - """Calculates covariance according to Imbens, Rubin model.""" - cov_c = np.cov(xc, rowvar=False, ddof=0) - cov_t = np.cov(xt, rowvar=False, ddof=0) - cov = (cov_c + cov_t) / 2 - - return cov - - -def calc_att_se(vars_c: np.ndarray, vars_t: np.ndarray, scaled_counts_c: np.ndarray) -> float: - """Calculates Average Treatment Effect for the treated (ATT) standard error. - - Args: - vars_c: - Control group variance - vars_t: - Treatment group variance - scaled_counts_c: - Scaled counts for control group - - Returns: - The calculated ATT standard error - """ - N_c, N_t = len(vars_c), len(vars_t) - weights_c = (N_c / N_t) * scaled_counts_c - weights_t = np.ones(N_t) - - var = calc_atx_var(vars_c, vars_t, weights_c, weights_t) - - return np.sqrt(var) - - -def calc_ate_se( - vars_c: np.ndarray, vars_t: np.ndarray, scaled_counts_c: np.ndarray, scaled_counts_t: np.ndarray -) -> float: - """Calculates Average Treatment Effect for the control group (ATC) standard error. - - Args: - vars_c: - Control group variance - vars_t: - Treatment group variance - scaled_counts_c: - Scaled counts for control group - scaled_counts_t: - Scaled counts for treatment group - - Returns: - The calculated ATE standard error - """ - N_c, N_t = len(vars_c), len(vars_t) - N = N_c + N_t - weights_c = (N_c / N) * (1 + scaled_counts_c) - weights_t = (N_t / N) * (1 + scaled_counts_t) - - var = calc_atx_var(vars_c, vars_t, weights_c, weights_t) - - return np.sqrt(var) - - -def pval_calc(z): - """Calculates p-value of the normal cumulative distribution function based on z. - - Args: - z: - The z-score for which the p-value is calculated - - Returns: - The calculated p-value rounded to 2 decimal places - - """ - return round(2 * (1 - norm.cdf(abs(z))), 2) - - -def scaled_counts(N: int, matches: np.ndarray, silent: bool = True) -> np.ndarray: - """Counts the number of times each subject has appeared as a match. - - In the case of multiple matches, each subject only gets partial credit. - - Args: - N: - The length of original treated or control group - matches: - A numpy array of matched indexes from control or treated group - silent: - If true logger in info mode - - Returns: - An array representing the number of times each subject has appeared as a match - """ - s_counts = np.zeros(N) - - for matches_i in matches: - scale = 1 / len(matches_i) - for match in matches_i: - s_counts[match] += scale - - if silent: - logger.debug(f"Calculated the number of times each subject has appeared as a match: {len(s_counts)}") - else: - logger.info(f"Calculated the number of times each subject has appeared as a match: {len(s_counts)}") - - return s_counts - - -def bias_coefs(matches, Y_m, X_m): - """Computes Ordinary Least Squares (OLS) coefficient in bias correction regression. - - Constructs data for regression by including (possibly multiple times) every - observation that has appeared in the matched sample. - - Args: - matches: - A numpy array of matched indexes - Y_m: - The dependent variable values - X_m: - The independent variable values - - Returns: - The calculated OLS coefficients excluding the intercept - """ - flat_idx = np.concatenate(matches) - N, K = len(flat_idx), X_m.shape[1] - - Y = Y_m[flat_idx] - X = np.empty((N, K + 1)) - X[:, 0] = 1 # intercept term - X[:, 1:] = X_m[flat_idx] - - return np.linalg.lstsq(X, Y)[0][1:] # don't need intercept coef - - -def bias(X, X_m, coefs): - """Computes bias correction term. - - It is approximated by the dot product of the - matching discrepancy (i.e., X-X_matched) and the - coefficients from the bias correction regression. - - Args: - X: - The original independent variable values - X_m: - The matched independent variable values - coefs: - The coefficients from the bias correction regression - - Returns: - The calculated bias correction terms for each observation - """ - bias_list = [(X_j - X_i).dot(coefs) for X_i, X_j in zip(X, X_m)] - - return np.array(bias_list) - - -def f3(index: np.array, dist: np.array, k: int) -> list: - """Function returns list of n matches with equal distance in case n>1. - - Args: - index: - Array of matched indexes - dist: - Array of matched distances - k: - k of neareast neighbors with same distance - - Returns: - Array of indexes for k neighbors with same distance - """ - new = [] - for i, val in enumerate(index): - eq_dist = sorted(set(dist[i])) - unit = [] - for d in eq_dist[:k]: - unit.append(val[np.where(dist[i] == d)[0]]) - new.append(np.concatenate(unit)) - return new diff --git a/lightautoml/addons/hypex/algorithms/no_replacement_matching.py b/lightautoml/addons/hypex/algorithms/no_replacement_matching.py deleted file mode 100644 index 30e63e2b..00000000 --- a/lightautoml/addons/hypex/algorithms/no_replacement_matching.py +++ /dev/null @@ -1,162 +0,0 @@ -"""Here is the realization of Matching without replacement.""" -from typing import Tuple - -import numpy as np -import pandas as pd -from scipy.optimize import linear_sum_assignment -from scipy.spatial import distance - -from .faiss_matcher import conditional_covariance - - -class MatcherNoReplacement: - """Matching groups with no replacement. - - Realized by optimizing the linear sum of distances between pairs of treatment and - control samples. - """ - - def __init__(self, X: pd.DataFrame, a: pd.Series, weights: dict = None): - """Initialize matching. - - Args: - X: features dataframe - a: series of treatment value - weights: weights for numeric columns in order to increase matching quality. - """ - self.treatment = a - self.X = X - self.weights = weights - - def match(self): - """Function run matching with no replacement. - - Returns: - Dataframe of matched indexes. - """ - matches = {} - cov = conditional_covariance(self.X[self.treatment == 1].values, self.X[self.treatment == 0].values) - distance_matrix = self._get_distance_matrix(self.X[self.treatment == 1], self.X[self.treatment == 0], cov) - source_array, neighbor_array_indices, distances = optimally_match_distance_matrix(distance_matrix) - source_df = self.X[self.treatment == 1].iloc[np.array(source_array)] - target_df = self.X[self.treatment == 0].iloc[np.array(neighbor_array_indices)] - - matches[1] = self.create_match_df(self.treatment, source_df, target_df, distances) - matches[0] = self.create_match_df(self.treatment, target_df, source_df, distances) - - match_df = pd.concat(matches, sort=True) - return match_df - - def create_match_df( - self, base_series: pd.Series, source_df: pd.DataFrame, target_df: pd.DataFrame, distances: list - ) -> pd.DataFrame: - """Function creates matching dataframe. - - Args: - base_series: series of treatment value. - source_df: dataframe of sources indexes. - target_df: dataframe of target indexes. - distances: matrix of calculated distances. - - Returns: - Matched dataframe of indexes. - """ - match_sub_df = pd.DataFrame( - index=base_series.index, - columns=[ - "matches", - "distances", - ], - data=base_series.apply(lambda x: pd.Series([[], []])).values, - dtype="object", - ) - - # matching from source to target: read distances - match_sub_df.loc[source_df.index] = pd.DataFrame( - data=dict( - matches=[[tidx] for tidx in target_df.index], - distances=distances, - ), - index=source_df.index, - ) - - # matching from target to target: fill with zeros - match_sub_df.loc[target_df.index] = pd.DataFrame( - data=dict( - matches=[[tidx] for tidx in target_df.index], - distances=[[0]] * len(distances), - ), - index=target_df.index, - ) - return match_sub_df - - def _get_metric_dict(self, cov: np.ndarray) -> dict: - """Function calculates correct feature space and generate metrics dist for cdist calculation. - - Args: - cov: Matrix of covariations. - - Returns: - Metric dictionary - """ - metric_dict = dict(metric="mahalanobis") - mahalanobis_transform = np.linalg.inv(cov) - if self.weights is not None: - features = self.X.columns - w_list = np.array([self.weights[col] if col in self.weights.keys() else 1 for col in features]) - w_matrix = np.sqrt(np.diag(w_list / w_list.sum())) - mahalanobis_transform = np.dot(w_matrix, mahalanobis_transform) - - metric_dict["VI"] = mahalanobis_transform - return metric_dict - - def _get_distance_matrix(self, source_df: pd.DataFrame, target_df: pd.DataFrame, cov: np.ndarray) -> np.ndarray: - """Create distance matrix for no replacement match. - - Combines metric and source/target data into a - precalculated distance matrix which can be passed to - scipy.optimize.linear_sum_assignment. - - Args: - source_df: source feature dataframe. - target_df: target feature dataframe. - cov: matrix of covariations. - - Returns: - Matrix of distances. - """ - cdist_args = dict(XA=_ensure_array_columnlike(source_df.values), XB=_ensure_array_columnlike(target_df.values)) - cdist_args.update(self._get_metric_dict(cov)) - distance_matrix = distance.cdist(**cdist_args) - - return distance_matrix - - -def optimally_match_distance_matrix(distance_matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray, list]: - """Functions finds optimal neighbor with no replacement. - - Args: - distance_matrix: matrix of distances. - - Returns: - - indexes of source dataframe. - - optimal neighbors array for source array. - - distances of optimal neighbors. - """ - source_array, neighbor_array_indices = linear_sum_assignment(distance_matrix) - distances = [[distance_matrix[s_idx, t_idx]] for s_idx, t_idx in zip(source_array, neighbor_array_indices)] - return source_array, neighbor_array_indices, distances - - -def _ensure_array_columnlike(target_array: np.ndarray) -> np.ndarray: - """Function checks if array is column like and reshape it in order it is not. - - Args: - target_array: checked array. - - Returns: - column like target array. - """ - if len(target_array.shape) < 2 or target_array.shape[1] == 1: - target_array = target_array.reshape(-1, 1) - return target_array diff --git a/lightautoml/addons/hypex/matcher.py b/lightautoml/addons/hypex/matcher.py deleted file mode 100644 index df3c1407..00000000 --- a/lightautoml/addons/hypex/matcher.py +++ /dev/null @@ -1,590 +0,0 @@ -"""Base Matcher class.""" -import logging -import pickle - -import numpy as np -import pandas as pd - -from tqdm.auto import tqdm - -from .algorithms.faiss_matcher import FaissMatcher -from .algorithms.no_replacement_matching import MatcherNoReplacement -from .selectors.lama_feature_selector import LamaFeatureSelector -from .selectors.spearman_filter import SpearmanFilter -from .selectors.outliers_filter import OutliersFilter -from .selectors.base_filtration import const_filtration, nan_filtration -from .utils.validators import random_feature -from .utils.validators import random_treatment -from .utils.validators import subset_refuter -from .utils.validators import test_significance - - -REPORT_FEAT_SELECT_DIR = "report_feature_selector" -REPORT_PROP_MATCHER_DIR = "report_matcher" -NAME_REPORT = "lama_interactive_report.html" -N_THREADS = 1 -N_FOLDS = 4 -RANDOM_STATE = 123 -TEST_SIZE = 0.2 -TIMEOUT = 600 -VERBOSE = 2 -USE_ALGOS = ["lgb"] -PROP_SCORES_COLUMN = "prop_scores" -GENERATE_REPORT = True -SAME_TARGET_THRESHOLD = 0.7 -OUT_INTER_COEFF = 1.5 -OUT_MODE_PERCENT = True -OUT_MIN_PERCENT = 0.02 -OUT_MAX_PERCENT = 0.98 - -logger = logging.getLogger("hypex") -console_out = logging.StreamHandler() -logging.basicConfig( - handlers=(console_out,), - format="[%(asctime)s | %(name)s | %(levelname)s]: %(message)s", - datefmt="%d.%m.%Y %H:%M:%S", - level=logging.INFO, -) - - -class Matcher: - """Class for compile full pipeline of Matching in Causal Inference task. - - Matcher steps: - - Read, analyze data - - Feature selection via LightAutoML - - Converting a dataset with features to another space via Cholesky decomposition - In the new space, the distance L2 becomes equivalent to the Mahalanobis distance. - This allows us to use faiss to search for nearest objects, which can search only by L2 metric, - but without violating the methodology of matching, - for which it is important to count by the Mahalanobis distance - - Finding the nearest neighbors for each unit (with duplicates) using faiss. - For each of the control group, neighbors from the target group are matched and vice versa. - - Calculation bias - - Creating matched df (Wide df with pairs) - - Calculation metrics: ATE, ATT, ATC, p-value, and сonfidence intervals - - Calculation quality: PS-test, KS test, SMD test - - Returns metrics as dataframe, quality results as dict of df's and df_matched - - After receiving the result, the result should be validated using :func:`~lightautoml.addons.hypex.matcher.Matcher.validate_result` - - Example: - Common usecase - base pipeline for matching - - >>> # Base info - >>> treatment = "treatment" # Column name with info about 'treatment' 0 or 1 - >>> target = "target" # Column name with target - >>> - >>> # Optional - >>> info_col = ["user_id", 'address'] # Columns that will not participate in the match and are informative. - >>> group_col = "CatCol" # Column name for strict comparison (for a categorical feature) - >>> - >>> # Matching - >>> model = Matcher(data, outcome=target, treatment=treatment, info_col=info_col, group_col=group_col) - >>> features = model.lama_feature_select() # Feature selection via lama - >>> results, quality, df_matched = model.estimate(features=some_features) # Performs matching - >>> - >>> model.validate_result() - """ - - def __init__( - self, - input_data: pd.DataFrame, - treatment: str, - outcome: str = None, - outcome_type: str = "numeric", - group_col: str = None, - info_col: list = None, - weights: dict = None, - base_filtration: bool = False, - generate_report: bool = GENERATE_REPORT, - report_feat_select_dir: str = REPORT_FEAT_SELECT_DIR, - timeout: int = TIMEOUT, - n_threads: int = N_THREADS, - n_folds: int = N_FOLDS, - verbose: bool = VERBOSE, - use_algos: list = None, - same_target_threshold: float = SAME_TARGET_THRESHOLD, - interquartile_coeff: float = OUT_INTER_COEFF, - drop_outliers_by_percentile: bool = OUT_MODE_PERCENT, - min_percentile: float = OUT_MIN_PERCENT, - max_percentile: float = OUT_MAX_PERCENT, - n_neighbors: int = 1, - silent: bool = True, - pbar: bool = True, - ): - """Initialize the Matcher object. - - Args: - input_data: - Input dataframe - outcome: - Target column - treatment: - Column determine control and test groups - outcome_type: - Values type of target column. Defaults to "numeric" - group_col: - Column for grouping. Defaults to None. - info_col: - Columns with id, date or metadata, not taking part in calculations. Defaults to None - weights: - weights for numeric columns in order to increase matching quality by weighted feature. - By default, is None (all features have the same weight equal to 1). Example: {'feature_1': 10} - base_filtration: - To use or not base filtration of features in order to remove all constant or almost all constant, bool. - Default is False. - generate_report: - Flag to create report. Defaults to True - report_feat_select_dir: - Folder for report files. Defaults to "report_feature_selector" - timeout: - Limit work time of code LAMA. Defaults to 600 - n_threads: - Maximum number of threads. Defaults to 1 - n_folds: - Number of folds for cross-validation. Defaults to 4 - verbose: - Flag to show process stages. Defaults to 2 - use_algos: - List of names of LAMA algorithms for feature selection. Defaults to ["lgb"] - same_target_threshold: - Threshold for correlation coefficient filter (Spearman). Default to 0.7 - interquartile_coeff: - Percent for drop outliers. Default to 1.5 - drop_outliers_by_percentile: - Flag to drop outliers by custom percentiles. Defaults to True - min_percentile: - Minimum percentile to drop outliers. Defaults to 0.02 - max_percentile: - Maximum percentile to drop outliers. Defaults to 0.98 - n_neighbors: - Number of neighbors to match (in fact you may see more then n matches as every match may have more then - one neighbor with the same distance). Default value is 1. - silent: - Write logs in debug mode - pbar: - Display progress bar while get index - """ - if use_algos is None: - use_algos = USE_ALGOS - self.input_data = input_data - if outcome is None: - outcome = list() - self.outcomes = outcome if isinstance(outcome, list) else [outcome] - self.treatment = treatment - self.group_col = group_col - self.info_col = info_col - self.outcome_type = outcome_type - self.weights = weights - self.generate_report = generate_report - self.report_feat_select_dir = report_feat_select_dir - self.timeout = timeout - self.n_threads = n_threads - self.n_folds = n_folds - self.verbose = verbose - self.use_algos = use_algos - self.same_target_threshold = same_target_threshold - self.interquartile_coeff = interquartile_coeff - self.mode_percentile = drop_outliers_by_percentile - self.min_percentile = min_percentile - self.max_percentile = max_percentile - self.base_filtration = base_filtration - self.features_importance = None - self.matcher = None - self.val_dict = None - self.pval_dict = None - self.new_treatment = None - self.validate = None - self.dropped_features = [] - self.n_neighbors = n_neighbors - self.silent = silent - self.pbar = pbar - self._preprocessing_data() - - def _convert_categorical_to_dummy(self): - """Converts categorical variables to dummy variables. - - Returns: - Data with categorical variables converted to dummy variables. - """ - info_col = self.info_col if self.info_col is not None else [] - group_col = [self.group_col] if self.group_col is not None else [] - - columns_to_drop = info_col + group_col - if columns_to_drop is not None: - data = self.input_data.drop(columns=columns_to_drop) - else: - data = self.input_data - dummy_data = pd.get_dummies(data, drop_first=True) - return dummy_data - - def _preprocessing_data(self): - """Converts categorical features into dummy variables.""" - info_col = self.info_col if self.info_col is not None else [] - group_col = [self.group_col] if self.group_col is not None else [] - columns_to_drop = info_col + group_col + self.outcomes + [self.treatment] - if self.base_filtration: - filtered_features = nan_filtration(self.input_data.drop(columns=columns_to_drop)) - self.dropped_features = [f for f in self.input_data.columns if f not in filtered_features + columns_to_drop] - self.input_data = self.input_data[filtered_features + columns_to_drop] - nan_counts = self.input_data.isna().sum().sum() - if nan_counts != 0: - self._log(f"Number of NaN values filled with zeros: {nan_counts}", silent=False) - self.input_data = self.input_data.fillna(0) - - if self.group_col is not None: - group_col = self.input_data[[self.group_col]] - if self.info_col is not None: - info_col = self.input_data[self.info_col] - - self.input_data = self._convert_categorical_to_dummy() - if self.group_col is not None: - self.input_data = pd.concat([self.input_data, group_col], axis=1) - - if self.info_col is not None: - self.input_data = pd.concat([self.input_data, info_col], axis=1) - - if self.base_filtration: - filtered_features = const_filtration(self.input_data.drop(columns=columns_to_drop)) - self.dropped_features = np.concatenate( - ( - self.dropped_features, - [f for f in self.input_data.columns if f not in filtered_features + columns_to_drop], - ) - ) - self.input_data = self.input_data[filtered_features + columns_to_drop] - - self._log("Categorical features turned into dummy") - - def _apply_filter(self, filter_class, *filter_args): - """Applies a filter to the input data. - - Args: - filter_class: - The class of the filter to apply. - *filter_args: - Arguments to pass to the filter class. - """ - filter_instance = filter_class(*filter_args) - self.input_data = filter_instance.perform_filter(self.input_data) - - def _spearman_filter(self): - """Applies a filter by dropping columns correlated with the outcome column. - - This method uses the Spearman filter to eliminate features from the dataset - that are highly correlated with the outcome columns, based on a pre-set threshold - """ - self._log("Applying filter by spearman test - drop columns correlated with outcome") - self._apply_filter(SpearmanFilter, self.outcomes[0], self.treatment, self.same_target_threshold) - - def _outliers_filter(self): - """Removes outlier values from the dataset. - - This method employs an OutliersFilter. If `drop_outliers_by_percentile` is True, - it retains only the values between the min and max percentiles - If `drop_outliers_by_percentile` is False, it retains only the values between 2nd and 98th percentiles - """ - self._log( - f"Applying filter of outliers\n" - f"interquartile_coeff={self.interquartile_coeff}\n" - f"mode_percentile={self.mode_percentile}\n" - f"min_percentile={self.min_percentile}\n" - f"max_percentile={self.max_percentile}" - ) - - self._apply_filter( - OutliersFilter, self.interquartile_coeff, self.mode_percentile, self.min_percentile, self.max_percentile - ) - - def match_no_rep(self, threshold: float = 0.1) -> pd.DataFrame: - """Matching groups with no replacement. - - It's done by optimizing the linear sum of - distances between pairs of treatment and control samples. - - Args: - threshold: caliper for minimum deviation between test and control groups. in case weights is not None. - - Returns: - Matched dataframe with no replacements. - """ - a = self.input_data[self.treatment] - X = self.input_data.drop(columns=self.treatment) - if self.info_col is not None: - X = X.drop(columns=self.info_col) - - index_matched = MatcherNoReplacement(X, a, self.weights).match() - filtred_matches = ( - index_matched.loc[1] - .iloc[self.input_data[a == 1].index] - .matches[index_matched.loc[1].iloc[self.input_data[a == 1].index].matches.apply(lambda x: x != [])] - ) - - if self.weights is not None: - weighted_features = [f for f in self.weights.keys()] - index_dict = dict() - for w in weighted_features: - source = self.input_data.loc[np.concatenate(filtred_matches.values)][w].values - target = self.input_data.loc[filtred_matches.index.to_list()][w].values - index = abs(source - target) <= abs(source) * threshold - index_dict.update({w: index}) - index_filtered = sum(index_dict.values()) == len(self.weights) - matched_data = pd.concat( - [ - self.input_data.loc[filtred_matches.index.to_list()].iloc[index_filtered], - self.input_data.loc[np.concatenate(filtred_matches.values)].iloc[index_filtered], - ] - ) - else: - matched_data = pd.concat( - [ - self.input_data.loc[filtred_matches.index.to_list()], - self.input_data.loc[np.concatenate(filtred_matches.values)], - ] - ) - return matched_data - - def lama_feature_select(self) -> pd.DataFrame: - """Calculates the importance of each feature. - - This method use LamaFeatureSelector to rank the importance of each feature in the dataset - The features are then sorted by their importance with the most important feature first - - Returns: - The feature importances, sorted in descending order - """ - self._log("Counting feature importance") - - feat_select = LamaFeatureSelector( - outcome=self.outcomes[0], - outcome_type=self.outcome_type, - treatment=self.treatment, - timeout=self.timeout, - n_threads=self.n_threads, - n_folds=self.n_folds, - verbose=self.verbose, - generate_report=self.generate_report, - report_dir=self.report_feat_select_dir, - use_algos=self.use_algos, - ) - df = self.input_data if self.group_col is None else self.input_data.drop(columns=self.group_col) - - if self.info_col is not None: - df = df.drop(columns=self.info_col) - - features = feat_select.perform_selection(df=df) - if self.group_col is None: - self.features_importance = features - else: - self.features_importance = features.append( - {"Feature": self.group_col, "Importance": features.Importance.max()}, ignore_index=True - ) - return self.features_importance.sort_values("Importance", ascending=False) - - def _create_faiss_matcher(self, df=None, validation=None): - """Creates a FaissMatcher object. - - Args: - df: - The dataframe to use. If None, uses self.input_data. - validation: - Whether to use the matcher for validation. If None, determines based on whether - """ - if df is None: - df = self.input_data - self.matcher = FaissMatcher( - df, - self.outcomes, - self.treatment, - info_col=self.info_col, - weights=self.weights, - features=self.features_importance, - group_col=self.group_col, - validation=validation, - n_neighbors=self.n_neighbors, - pbar=False if validation else self.pbar, - ) - - def _perform_validation(self): - """Performs validation using the FaissMatcher.""" - if self.group_col is None: - sim = self.matcher.match() - else: - sim = self.matcher.group_match() - for key in self.val_dict.keys(): - self.val_dict[key].append(sim[key][0]) - - def _log(self, message, silent=None): - """Logs a message at the appropriate level. - - Args: - message: - The message to log. - silent: - If silent, logs will be only info - """ - if silent is None: - silent = self.silent - if silent: - logger.debug(message) - else: - logger.info(message) - - def _matching(self) -> tuple: - """Performs matching considering the presence of groups. - - Returns: - Results of matching and matching quality metrics - """ - self._create_faiss_matcher() - self._log("Applying matching") - - self.results, df_matched = self.matcher.match() - - self.quality_result = self.matcher.matching_quality(df_matched) - - return self.results, self.quality_result, df_matched - - def validate_result( - self, refuter: str = "random_feature", effect_type: str = "ate", n_sim: int = 10, fraction: float = 0.8 - ) -> dict: - """Validates estimated ATE (Average Treatment Effect). - - Validates estimated effect: - 1) by replacing real treatment with random placebo treatment. - Estimated effect must be droped to zero, p-val > 0.05; - 2) by adding random feature (`random_feature`). Estimated effect shouldn't change - significantly, p-val < 0.05; - 3) estimates effect on subset of data (default fraction is 0.8). Estimated effect - shouldn't change significantly, p-val < 0.05. - - Args: - refuter: - Refuter type (`random_treatment`, `random_feature`, `subset_refuter`) - effect_type: - Which effect to validate (`ate`, `att`, `atc`) - n_sim: - Number of simulations - fraction: - Subset fraction for subset refuter only - - Returns: - Dictionary of outcome_name (mean_effect on validation, p-value) - """ - if self.silent: - logger.debug("Applying validation of result") - else: - logger.info("Applying validation of result") - - self.val_dict = {k: [] for k in self.outcomes} - self.pval_dict = dict() - - effect_dict = {"ate": 0, "atc": 1, "att": 2} - - assert effect_type in effect_dict.keys() - - for i in tqdm(range(n_sim)): - if refuter in ["random_treatment", "random_feature"]: - if refuter == "random_treatment": - self.input_data, orig_treatment, self.validate = random_treatment(self.input_data, self.treatment) - elif refuter == "random_feature": - self.input_data, self.validate = random_feature(self.input_data) - if self.features_importance is not None and i == 0: - self.features_importance.append("random_feature") - - self.matcher = FaissMatcher( - self.input_data, - self.outcomes, - self.treatment, - info_col=self.info_col, - features=self.features_importance, - group_col=self.group_col, - validation=self.validate, - n_neighbors=self.n_neighbors, - pbar=False, - ) - elif refuter == "subset_refuter": - df, self.validate = subset_refuter(self.input_data, self.treatment, fraction) - self.matcher = FaissMatcher( - df, - self.outcomes, - self.treatment, - info_col=self.info_col, - features=self.features_importance, - group_col=self.group_col, - validation=self.validate, - n_neighbors=self.n_neighbors, - pbar=False, - ) - else: - logger.error("Incorrect refuter name") - raise NameError( - "Incorrect refuter name! Available refuters: `random_feature`, `random_treatment`, `subset_refuter`" - ) - - if self.group_col is None: - sim = self.matcher.match() - else: - sim = self.matcher.group_match() - - for key in self.val_dict.keys(): - self.val_dict[key].append(sim[key][0]) - - for outcome in self.outcomes: - self.pval_dict.update({outcome: [np.mean(self.val_dict[outcome])]}) - self.pval_dict[outcome].append( - test_significance( - self.results.query("outcome==@outcome").loc[effect_type.upper()]["effect_size"], - self.val_dict[outcome], - ) - ) - if refuter == "random_treatment": - self.input_data[self.treatment] = orig_treatment - elif refuter == "random_feature": - self.input_data = self.input_data.drop(columns="random_feature") - if self.features_importance is not None: - self.features_importance.remove("random_feature") - - return self.pval_dict - - def estimate(self, features: list = None) -> tuple: - """Performs matching via Mahalanobis distance. - - Args: - features: - List or feature_importances from LAMA of features for matching - - Returns: - Results of matching and matching quality metrics - """ - if features is not None: - self.features_importance = features - return self._matching() - - def save(self, filename): - """Save the object to a file using pickle. - - This method serializes the object and writes it to a file - - Args: - filename: - The name of the file to write to. - """ - with open(filename, "wb") as f: - pickle.dump(self, f) - - @classmethod - def load(cls, filename): - """Load an object from a file. - - This method reads a file and deserializes the object from it - - Args: - filename: - The name of the file to read from. - - Returns: - The deserialized object - """ - with open(filename, "rb") as f: - return pickle.load(f) diff --git a/lightautoml/addons/hypex/selectors/base_filtration.py b/lightautoml/addons/hypex/selectors/base_filtration.py deleted file mode 100644 index ef8e55fd..00000000 --- a/lightautoml/addons/hypex/selectors/base_filtration.py +++ /dev/null @@ -1,48 +0,0 @@ -"""Function for base filtration of data.""" -import pandas as pd -import numpy as np - - -def const_filtration(X: pd.DataFrame, threshold: float = 0.95) -> list: - """Function removes features consist of constant value on 95%. - - Args: - X: related dataset - threshold: constant fill rate, default is 0.95 - - Returns: - List of filtered columns - """ - is_const = pd.Series(0, index=X.columns, dtype=np.dtype(bool)) - for col in X.columns: - # NaNs are not counted using unique (since np.nan != np.nan). Fill them with a unique value: - cur_col = X.loc[:, col] - cur_col.loc[~np.isfinite(cur_col)] = cur_col.max() + 1 - # Get values' frequency: - freqs = cur_col.value_counts(normalize=True) - is_const[col] = np.any(freqs > threshold) - - selected_features = ~is_const - if np.sum(selected_features) == 0: - raise AssertionError("All features were removed by constant filtration.") - else: - return X.loc[:, selected_features].columns.to_list() - - -def nan_filtration(X: pd.DataFrame, threshold: float = 0.8): - """Function removes features consist of NaN value on 80%. - - Args: - X: related dataset - threshold: constant fill rate, default is 0.95 - - Returns: - List of filtered columns - """ - nan_freqs = np.mean(pd.isnull(X), axis=0) - is_sparse = nan_freqs > threshold - selected_features = ~is_sparse - if np.sum(selected_features) == 0: - raise AssertionError("All features were removed by nan filtration.") - else: - return X.loc[:, selected_features].columns.to_list() diff --git a/lightautoml/addons/hypex/selectors/lama_feature_selector.py b/lightautoml/addons/hypex/selectors/lama_feature_selector.py deleted file mode 100644 index 037a59a6..00000000 --- a/lightautoml/addons/hypex/selectors/lama_feature_selector.py +++ /dev/null @@ -1,124 +0,0 @@ -"""Feature selection class using LAMA.""" -import logging - -from typing import List - -import pandas as pd - -from ....automl.presets.tabular_presets import TabularAutoML -from ....report import ReportDeco -from ....tasks import Task - - -logger = logging.getLogger("lama_feature_selector") -console_out = logging.StreamHandler() -logging.basicConfig( - handlers=(console_out,), - format="[%(asctime)s | %(name)s | %(levelname)s]: %(message)s", - datefmt="%d.%m.%Y %H:%M:%S", - level=logging.INFO, -) - - -class LamaFeatureSelector: - """Class of LAMA Feature selector. Select top features. By default, use LGM.""" - - def __init__( - self, - outcome: str, - outcome_type: str, - treatment: str, - timeout: int, - n_threads: int, - n_folds: int, - verbose: bool, # не используется - generate_report: bool, - report_dir: str, - use_algos: List[str], - ): - """Initialize the LamaFeatureSelector. - - Args: - outcome: - The target column - outcome_type: - The type of target column - treatment: - The column that determines control and test groups - timeout: - Time limit for the execution of the code - n_threads: - Maximum number of threads to be used - n_folds: - Number of folds for cross-validation - verbose: - Flag to control the verbosity of the process stages - generate_report: - Flag to control whether to create a report or not - report_dir: - Directory for storing report files - use_algos: - List of names of LAMA algorithms for feature selection - """ - self.outcome = outcome - self.outcome_type = outcome_type - self.treatment = treatment - self.use_algos = use_algos - self.timeout = timeout - self.n_threads = n_threads - self.n_folds = n_folds - self.verbose = verbose - self.generate_report = generate_report - self.report_dir = report_dir - - def perform_selection(self, df: pd.DataFrame) -> pd.DataFrame: - """Trains a model and returns feature scores. - - This method defines metrics, applies the model, creates a report, and returns feature scores - - Args: - df: - Input data - - Returns: - A DataFrame containing the feature scores from the model - - """ - roles = { - "target": self.outcome, - "drop": [self.treatment], - } - - if self.outcome_type == "numeric": - task_name = "reg" - loss = "mse" - metric = "mse" - elif self.outcome_type == "binary": - task_name = "binary" - loss = "logloss" - metric = "logloss" - else: - task_name = "multiclass" - loss = "crossentropy" - metric = "crossentropy" - - task = Task(name=task_name, loss=loss, metric=metric) - - automl = TabularAutoML( - task=task, - timeout=self.timeout, - cpu_limit=self.n_threads, - general_params={"use_algos": [self.use_algos]}, - reader_params={ - "n_jobs": self.n_threads, - "cv": self.n_folds, - }, - ) - - if self.generate_report: - report = ReportDeco(output_path=self.report_dir) - automl = report(automl) - - _ = automl.fit_predict(df, roles=roles) - - return automl.model.get_feature_scores() diff --git a/lightautoml/addons/hypex/selectors/outliers_filter.py b/lightautoml/addons/hypex/selectors/outliers_filter.py deleted file mode 100644 index ff55cc94..00000000 --- a/lightautoml/addons/hypex/selectors/outliers_filter.py +++ /dev/null @@ -1,81 +0,0 @@ -"""Outliers filter.""" -import logging - -import pandas as pd - - -logger = logging.getLogger("outliers_filter") -console_out = logging.StreamHandler() -logging.basicConfig( - handlers=(console_out,), - format="[%(asctime)s | %(name)s | %(levelname)s]: %(message)s", - datefmt="%d.%m.%Y %H:%M:%S", - level=logging.INFO, -) - - -class OutliersFilter: - """Class of Outliers Filter. It creates a row indices that should be deleted by percentile.""" - - def __init__(self, interquartile_coeff, mode_percentile, min_percentile, max_percentile): - """Initializes the OutliersFilter. - - Args: - interquartile_coeff: - Coefficient for the interquartile range to determine outliers - mode_percentile: - If True, outliers are determined by custom percentiles - min_percentile: - The lower percentile. Values below this percentile are considered outliers. - max_percentile: - The upper percentile. Values above this percentile are considered outliers - """ - self.interquartile_coeff = interquartile_coeff - self.mode_percentile = mode_percentile - self.min_percentile = min_percentile - self.max_percentile = max_percentile - - def perform_filter(self, df: pd.DataFrame, interquartile: bool = True) -> set: - """Identifies rows with outliers. - - This method creates a set of row indices to be removed, which contains values less than - `min_percentile` and larger than `max_percentile` (if `mode_percentile` is True), or values - smaller than the 0.2 and larget than 0.8 (if `mode_percentile` is False) - - Args: - df: - The input DataFrame - interquartile: - If True, uses the interquartile range to determine outliers. Defaults to True - - Returns: - The set of row indices with outliers - """ - columns_names = df.select_dtypes(include="number").columns - rows_for_del = [] - for column in columns_names: - if self.mode_percentile: - min_value = df[column].quantile(self.min_percentile) - max_value = df[column].quantile(self.max_percentile) - elif interquartile: - upper_quantile = df[column].quantile(0.8) - lower_quantile = df[column].quantile(0.2) - - interquartile_range = upper_quantile - lower_quantile - min_value = lower_quantile - self.interquartile_coeff * interquartile_range - max_value = upper_quantile + self.interquartile_coeff * interquartile_range - else: - mean_value = df[column].mean() - standard_deviation = df[column].std() - nstd_lower, nstd_upper = 3, 3 - - min_value = mean_value - nstd_lower * standard_deviation - max_value = mean_value + nstd_upper * standard_deviation - - rows_for_del_column = (df[column] < min_value) | (df[column] > max_value) - rows_for_del_column = df.index[rows_for_del_column].tolist() - rows_for_del.extend(rows_for_del_column) - rows_for_del = set(rows_for_del) - logger.info(f"Drop {len(rows_for_del)} rows") - - return rows_for_del diff --git a/lightautoml/addons/hypex/selectors/spearman_filter.py b/lightautoml/addons/hypex/selectors/spearman_filter.py deleted file mode 100644 index cbb47bce..00000000 --- a/lightautoml/addons/hypex/selectors/spearman_filter.py +++ /dev/null @@ -1,72 +0,0 @@ -"""Spearman filter.""" -import logging - -import pandas as pd - -from scipy.stats import spearmanr - - -PVALUE = 0.05 - -logger = logging.getLogger("spearman_filter") -console_out = logging.StreamHandler() -logging.basicConfig( - handlers=(console_out,), - format="[%(asctime)s | %(name)s | %(levelname)s]: %(message)s", - datefmt="%d.%m.%Y %H:%M:%S", - level=logging.INFO, -) - - -class SpearmanFilter: - """Class to filter columns based on the Spearman correlation coefficient. - - The class is utilized to filter dataframe columns that do not exhibit a significant - correlation (based on a provided threshold) with a specified outcome column. - The significance of the correlation is determined using the Spearman correlation coefficient - and a p-value threshold of 0.05 - """ - - def __init__(self, outcome: str, treatment: str, threshold: float): - """Initialize spearman filter. - - Args: - outcome: - The name of target column - treatment: - The name of the column that determines control and test groups - threshold: - The threshold for the Spearman correlation coefficient filter - """ - self.outcome: str = outcome - self.treatment: str = treatment - self.threshold: float = threshold - - def perform_filter(self, df: pd.DataFrame) -> pd.DataFrame: - """Filters columns based on their correlation with the outcome column. - - The method tests the correlation using the Spearman correlation coefficient. - Columns that have an absolute correlation coefficient value less than the provided threshold, - and a p-value less than 0.05, are considered insignificant and are removed from the dataframe - - Args: - df: - The input DataFrame - - Returns: - The filtered DataFrame, containing only columns that - are significantly correlated with the outcome column - """ - selected = [] - columns = df.drop([self.treatment, self.outcome], 1).columns - for column in columns: - result = spearmanr(df[self.outcome].values, df[column].values) - if (abs(result[0] < self.threshold)) and (result[1] < PVALUE): - selected.append(column) - - logger.info(f"Drop columns {list(set(columns) - set(selected))}") - - columns = selected + [self.treatment, self.outcome] - df = df[columns] - - return df diff --git a/lightautoml/addons/hypex/tests/__init__.py b/lightautoml/addons/hypex/tests/__init__.py deleted file mode 100644 index d2c21a00..00000000 --- a/lightautoml/addons/hypex/tests/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from ..matcher import Matcher - -__all__ = ["Matcher"] diff --git a/lightautoml/addons/hypex/tests/test_aa.py b/lightautoml/addons/hypex/tests/test_aa.py deleted file mode 100644 index a190a63f..00000000 --- a/lightautoml/addons/hypex/tests/test_aa.py +++ /dev/null @@ -1,72 +0,0 @@ -# import pandas as pd -# import pytest - -# from lightautoml.addons.hypex.ABTesting.ab_tester import AATest -# from lightautoml.addons.hypex.utils.tutorial_data_creation import create_test_data - - -# @pytest.fixture -# def data(): -# return create_test_data(rs=52) - - -# @pytest.fixture -# def iterations(): -# return 20 - - -# @pytest.fixture -# def info_col(): -# return "user_id" - - -# def test_aa_simple(data, iterations, info_col): -# model = AATest(target_fields=["pre_spends", "post_spends"], info_cols=info_col) -# res, datas_dict = model.search_dist_uniform_sampling(data, iterations=iterations) - -# assert isinstance(res, pd.DataFrame), "Metrics are not dataframes" -# assert res.shape[0] == iterations, ( -# "Metrics dataframe contains more or less rows with random states " "(#rows should be equal #of experiments" -# ) -# assert isinstance(datas_dict, dict), "Result is not dict" -# assert len(datas_dict) == iterations, "# of dataframes is not equal # of iterations" -# assert all(data.columns) == all( -# datas_dict[0].drop(columns=["group"]).columns -# ), "Columns in the result are not the same as columns in initial data " - - -# def test_aa_group(data, iterations, info_col): -# group_cols = "industry" - -# model = AATest(target_fields=["pre_spends", "post_spends"], info_cols=info_col, group_cols=group_cols) -# res, datas_dict = model.search_dist_uniform_sampling(data, iterations=iterations) - -# assert isinstance(res, pd.DataFrame), "Metrics are not dataframes" -# assert res.shape[0] == iterations, ( -# "Metrics dataframe contains more or less rows with random states " "(#rows should be equal #of experiments" -# ) -# assert isinstance(datas_dict, dict), "Result is not dict" -# assert len(datas_dict) == iterations, "# of dataframes is not equal # of iterations" -# assert all(data.columns) == all(datas_dict[0].drop(columns=["group"]).columns), ( -# "Columns in the result are not " "the same as columns in initial " "data " -# ) - - -# def test_aa_quantfields(data, iterations, info_col): -# group_cols = "industry" -# quant_field = "gender" - -# model = AATest( -# target_fields=["pre_spends", "post_spends"], info_cols=info_col, group_cols=group_cols, quant_field=quant_field -# ) -# res, datas_dict = model.search_dist_uniform_sampling(data, iterations=iterations) - -# assert isinstance(res, pd.DataFrame), "Metrics are not dataframes" -# assert res.shape[0] == iterations, ( -# "Metrics dataframe contains more or less rows with random states " "(#rows should be equal #of experiments" -# ) -# assert isinstance(datas_dict, dict), "Result is not dict" -# assert len(datas_dict) == iterations, "# of dataframes is not equal # of iterations" -# assert all(data.columns) == all(datas_dict[0].drop(columns=["group"]).columns), ( -# "Columns in the result are not " "the same as columns in initial " "data " -# ) diff --git a/lightautoml/addons/hypex/tests/test_ab.py b/lightautoml/addons/hypex/tests/test_ab.py deleted file mode 100644 index 29e0a2b3..00000000 --- a/lightautoml/addons/hypex/tests/test_ab.py +++ /dev/null @@ -1,92 +0,0 @@ -# from lightautoml.addons.hypex.ABTesting.ab_tester import ABTest - -# import pytest -# import pandas as pd -# import numpy as np - -# DATA_SIZE = 100 - - -# @pytest.fixture -# def ab_test(): -# return ABTest() - - -# @pytest.fixture -# def data(): -# # Generate synthetic data for group A -# group_a_data = np.random.normal(loc=10, scale=2, size=DATA_SIZE) -# # Generate synthetic data for group B -# group_b_data = np.random.normal(loc=12, scale=2, size=DATA_SIZE) -# group_bp_data = np.random.normal(loc=10, scale=2, size=DATA_SIZE * 2) -# return pd.DataFrame( -# { -# "group": ["control"] * len(group_a_data) + ["test"] * len(group_b_data), -# "value": list(group_a_data) + list(group_b_data), -# "previous_value": group_bp_data, -# } -# ) - - -# @pytest.fixture -# def target_field(): -# return "value" - - -# @pytest.fixture -# def group_field(): -# return "group" - - -# @pytest.fixture -# def previous_value(): -# return "previous_value" - - -# @pytest.fixture -# def alpha(): -# return 0.05 - - -# def test_split_ab(ab_test, data, group_field): -# result = ab_test.split_ab(data, group_field) -# assert len(result["test"]) == DATA_SIZE -# assert len(result["control"]) == DATA_SIZE - - -# def test_calc_difference(ab_test, data, group_field, target_field, previous_value): -# splitted_data = ab_test.split_ab(data, group_field) -# result = ab_test.calc_difference(splitted_data, target_field, previous_value) -# assert 1 < result["ate"] < 3 -# assert 1 < result["cuped"] < 3 -# assert 1 < result["diff_in_diff"] < 3 - - -# def test_calc_difference_with_previous_value(ab_test, data, group_field, target_field, previous_value): -# ab_test.calc_difference_method = "ate" -# splitted_data = ab_test.split_ab(data, group_field) -# result = ab_test.calc_difference(splitted_data, previous_value) -# assert -1 < result["ate"] < 1 - - -# def test_calc_p_value(ab_test, data, group_field, target_field, previous_value, alpha): -# splitted_data = ab_test.split_ab(data, group_field) -# result = ab_test.calc_p_value(splitted_data, target_field) -# assert result["t_test"] < alpha -# assert result["mann_whitney"] < alpha - -# result = ab_test.calc_p_value(splitted_data, previous_value) -# assert result["t_test"] > alpha -# assert result["mann_whitney"] > alpha - - -# def test_execute(ab_test, data, group_field, target_field, previous_value, alpha): -# result = ab_test.execute(data, target_field, group_field, previous_value) -# print(result) -# assert result["size"]["test"] == DATA_SIZE -# assert result["size"]["control"] == DATA_SIZE -# assert 1 < result["difference"]["ate"] < 3 -# assert 1 < result["difference"]["cuped"] < 3 -# assert 1 < result["difference"]["diff_in_diff"] < 3 -# assert result["p_value"]["t_test"] < alpha -# assert result["p_value"]["mann_whitney"] < alpha diff --git a/lightautoml/addons/hypex/tests/test_matcher.py b/lightautoml/addons/hypex/tests/test_matcher.py deleted file mode 100644 index 4c485537..00000000 --- a/lightautoml/addons/hypex/tests/test_matcher.py +++ /dev/null @@ -1,115 +0,0 @@ -# import pandas as pd -# import sys -# from pathlib import Path - -# from lightautoml.addons.hypex import Matcher -# from lightautoml.addons.hypex.utils.tutorial_data_creation import create_test_data - -# ROOT = Path(".").absolute().parents[0] -# sys.path.append(str(ROOT)) - - -# # добавить дату в данные и пофиксить баги с этим -# # учесть если info_col передается листом из одного значения или строкой - - -# def create_model(group_col: str = None): -# data = pd.read_csv(ROOT / "Tutorial_data.csv") -# info_col = ["user_id", "signup_month"] -# outcome = "post_spends" -# treatment = "treat" - -# model = Matcher(input_data=data, outcome=outcome, treatment=treatment, info_col=info_col, group_col=group_col) - -# return model - - -# def test_matcher_pos(): -# model = create_model() -# res, quality_res, df_matched = model.estimate() - -# assert len(model.quality_result.keys()) == 4, "quality results return not four metrics" -# assert list(model.quality_result.keys()) == ["psi", "ks_test", "smd", "repeats"], "metrics renamed" - -# assert list(model.results.index) == ["ATE", "ATC", "ATT"], "format of results is changed: type of effects" -# assert list(model.results.columns) == [ -# "effect_size", -# "std_err", -# "p-val", -# "ci_lower", -# "ci_upper", -# "post_spends", -# ], "format of results is changed: columns in report" -# assert model.results["p-val"].values[0] <= 0.05, "p-value on ATE is greater than 0.1" -# assert model.results["p-val"].values[1] <= 0.05, "p-value on ATC is greater than 0.1" -# assert model.results["p-val"].values[2] <= 0.05, "p-value on ATT is greater than 0.1" - -# assert isinstance(res, tuple), "result of function estimate is not tuple" -# assert len(res) == 3, "tuple does not return 3 values" - - -# def test_matcher_group_pos(): -# model = create_model(group_col="industry") -# res, quality_res, df_matched = model.estimate() - -# assert len(model.quality_result.keys()) == 4, "quality results return not 4 metrics" -# assert list(model.quality_result.keys()) == [ -# "psi", -# "ks_test", -# "smd", -# "repeats", -# ], "metrics renamed, there should be ['psi', 'ks_test', 'smd', 'repeats']" - -# assert list(model.results.index) == [ -# "ATE", -# "ATC", -# "ATT", -# ], "format of results is changed: type of effects (ATE, ATC, ATT)" -# assert list(model.results.columns) == [ -# "effect_size", -# "std_err", -# "p-val", -# "ci_lower", -# "ci_upper", -# "post_spends", -# ], "format of results is changed: columns in report ['effect_size', 'std_err', 'p-val', 'ci_lower', 'ci_upper']" -# assert model.results["p-val"].values[0] <= 0.05, "p-value on ATE is greater than 0.1" -# assert model.results["p-val"].values[1] <= 0.05, "p-value on ATC is greater than 0.1" -# assert model.results["p-val"].values[2] <= 0.05, "p-value on ATT is greater than 0.1" - -# assert isinstance(res, tuple), "result of function estimate is not tuple" -# assert len(res) == 3, "tuple does not return 3 values" - - -# def test_matcher_big_data_pos(): -# data = create_test_data(1_000_000) -# info_col = ["user_id", "signup_month"] -# outcome = "post_spends" -# treatment = "treat" - -# model = Matcher(input_data=data, outcome=outcome, treatment=treatment, info_col=info_col) -# results, quality_results, df_matched = model.estimate() - -# assert isinstance(model.estimate(), tuple), "result of function estimate is not tuple" -# assert len(model.estimate()) == 3, "tuple does not return 3 values" -# assert len(quality_results.keys()) == 4, "quality results return not four metrics" - - -# def test_lama_feature_pos(): -# model = create_model() -# res = model.lama_feature_select() - -# assert len(res) > 0, "features return empty" - - -# def test_validate_result_pos(): -# model = create_model() -# model.estimate() -# res = model.validate_result() -# """ -# refuter: str -# Refuter type (`random_treatment` , `random_feature` default, `subset_refuter`) -# """ - -# assert len(res) > 0, "features return empty" -# assert list(model.pval_dict.values())[0][1] > 0.05, "p-value on validate results is less than 0.05" diff --git a/lightautoml/addons/hypex/utils/__init__.py b/lightautoml/addons/hypex/utils/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/lightautoml/addons/hypex/utils/metrics.py b/lightautoml/addons/hypex/utils/metrics.py deleted file mode 100644 index 9958be3f..00000000 --- a/lightautoml/addons/hypex/utils/metrics.py +++ /dev/null @@ -1,161 +0,0 @@ -"""Calculate metrics.""" -import logging - -import numpy as np -import pandas as pd - -from scipy.stats import ks_2samp - -from ..utils.psi_pandas import report - - -logger = logging.getLogger("metrics") -console_out = logging.StreamHandler() -logging.basicConfig( - handlers=(console_out,), - format="[%(asctime)s | %(name)s | %(levelname)s]: %(message)s", - datefmt="%d.%m.%Y %H:%M:%S", - level=logging.INFO, -) - - -def smd(orig: pd.DataFrame, matched: pd.DataFrame, silent=False) -> pd.DataFrame: - """Calculates the standardised mean difference to evaluate matching quality. - - Args: - orig: - Initial dataframe - matched: - Matched dataframe - silent: - If silent, logger in info mode - - Returns: - The standard mean deviation between initial and matched dataframes - """ - smd_data = abs(orig.mean(0) - matched.mean(0)) / orig.std(0) - - if silent: - logger.debug(f"Standardised mean difference:\n{smd_data}") - else: - logger.info(f"Standardised mean difference:\n{smd_data}") - - return smd_data - - -def ks(orig: pd.DataFrame, matched: pd.DataFrame, silent=False) -> dict: - """Performs a Kolmogorov-Smirnov test to evaluate matching quality per columns. - - Args: - orig: - Initial dataframe - matched: - Matched dataframe - silent: - If silent, logger in info mode - - - Returns: - dict of p-values - - """ - ks_dict = dict() - matched.columns = orig.columns - for col in orig.columns: - ks_pval_1 = ks_2samp(orig[col].values, matched[col].values)[1] - ks_dict.update({col: ks_pval_1}) - - filter_list = list(ks_dict.keys())[:3] + list(ks_dict.keys())[-3:] - dict_to_show = {key: val for key, val in ks_dict.items() if key in filter_list} - - if silent: - logger.debug(f"Kolmogorov-Smirnov test to check matching quality: \n{dict_to_show}") - else: - logger.info(f"Kolmogorov-Smirnov test to check matching quality: \n{dict_to_show}") - - return ks_dict - - -def matching_quality( - data: pd.DataFrame, treatment: str, features: list, features_psi: list, silent: bool = False -) -> tuple: - """Wraps the functionality for estimating matching quality. - - Args: - data: - The dataframe of matched data - treatment: - The column determining control and test groups - features: - The list of features, ks-test and smd accept only numeric values - features_psi: - The list of features for calculating Population Stability Index (PSI) - silent: - If silent, logger in info mode - - - Returns: - A tuple of dataframes with estimated metrics for matched treated to control and control to treated - - """ - orig_treated = data[data[treatment] == 1][features] - orig_untreated = data[data[treatment] == 0][features] - matched_treated = data[data[treatment] == 1][sorted([f + "_matched" for f in features])] - matched_treated.columns = list(map(lambda x: x.replace("_matched", ""), matched_treated.columns)) - matched_untreated = data[data[treatment] == 0][sorted([f + "_matched" for f in features])] - matched_untreated.columns = list(map(lambda x: x.replace("_matched", ""), matched_untreated.columns)) - - psi_treated = data[data[treatment] == 1][features_psi] - psi_treated_matched = data[data[treatment] == 1][[f + "_matched" for f in features_psi]] - psi_treated_matched.columns = [f + "_treated" for f in features_psi] - psi_treated.columns = [f + "_treated" for f in features_psi] - - psi_untreated = data[data[treatment] == 0][features_psi] - psi_untreated_matched = data[data[treatment] == 0][[f + "_matched" for f in features_psi]] - psi_untreated.columns = [f + "_untreated" for f in features_psi] - psi_untreated_matched.columns = [f + "_untreated" for f in features_psi] - - treated_smd_data = smd(orig_treated, matched_treated, silent) - untreated_smd_data = smd(orig_untreated, matched_untreated, silent) - smd_data = pd.concat([treated_smd_data, untreated_smd_data], axis=1) - smd_data.columns = ["match_control_to_treat", "match_treat_to_control"] - - treated_ks = ks(orig_treated, matched_treated, silent) - untreated_ks = ks(orig_untreated, matched_untreated, silent) - ks_dict = {k: [treated_ks[k], untreated_ks[k]] for k in treated_ks.keys()} - ks_df = pd.DataFrame(data=ks_dict, index=range(2)).T - ks_df.columns = ["match_control_to_treat", "match_treat_to_control"] - - report_cols = ["column", "anomaly_score", "check_result"] - report_psi_treated = report(psi_treated, psi_treated_matched, silent=silent)[report_cols] - report_psi_treated.columns = [col + "_treated" for col in report_cols] - report_psi_untreated = report(psi_untreated, psi_untreated_matched, silent=silent)[report_cols] - report_psi_untreated.columns = [col + "_untreated" for col in report_cols] - report_psi = pd.concat( - [report_psi_treated.reset_index(drop=True), report_psi_untreated.reset_index(drop=True)], axis=1 - ) - - return report_psi, ks_df, smd_data - - -def check_repeats(index: np.array, silent: bool = False) -> float: - """Checks the fraction of duplicated indexes in the given array. - - Args: - index: - The array of indexes to check for duplicates - silent: - If silent, logger in info mode - - Returns: - The fraction of duplicated index - """ - unique, counts = np.unique(index, return_counts=True) - rep_frac = len(unique) / len(index) if len(unique) > 0 else 0 - - if silent: - logger.debug(f"Fraction of duplicated indexes: {rep_frac: .2f}") - else: - logger.info(f"Fraction of duplicated indexes: {rep_frac: .2f}") - - return round(rep_frac, 2) diff --git a/lightautoml/addons/hypex/utils/psi_pandas.py b/lightautoml/addons/hypex/utils/psi_pandas.py deleted file mode 100644 index d9fd1375..00000000 --- a/lightautoml/addons/hypex/utils/psi_pandas.py +++ /dev/null @@ -1,497 +0,0 @@ -"""Calculate PSI.""" -import logging - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd - - -logger = logging.getLogger("psi_pandas") -console_out = logging.StreamHandler() -logging.basicConfig( - handlers=(console_out,), - format="[%(asctime)s | %(name)s | %(levelname)s]: %(message)s", - datefmt="%d.%m.%Y %H:%M:%S", - level=logging.INFO, -) - - -class PSI: - """Calculates population stability index for different categories of data. - - For numeric data the class generates numeric buckets, except when numeric column - includes only NULL. For categorical data: - 1. For n < 20, a bucket equals the proportion of each category, - 2. For n > 20, a bucket equals to a group of categories, - 3. For n > 100, it calculates unique_index based on Jaccard similarity, - but in case of imbalance null-good data returns PSI - - Args: - expected: - The expected values - actual: - The actual values - column_name: - The column name for which to calculate the PSI - plot: - If true, generates a distribution plot. Defaults to False - - Returns: - PSI for column - The PSI for each bucket - New categories (empty list for non-categorical data) - Categories that are absent in actual column (empty list for non-categorical data) - """ - - def __init__( - self, expected: pd.DataFrame, actual: pd.DataFrame, column_name: str, plot: bool = False, silent=False - ): - """Initializes the PSI class with given parameters. - - Args: - expected: - The expected values - actual: - The actual values - column_name: - The column name for which to calculate the PSI - plot: - If true, generates a distribution plot. Defaults to False - silent: - If True show logs. Default by False - """ - self.expected = expected[column_name].values - self.actual = actual[column_name].values - self.expected_len = len(self.expected) - self.actual_len = len(self.actual) - self.column_name = column_name - self.column_type = self.expected.dtype - self.expected_shape = self.expected.shape - self.expected_nulls = np.sum(pd.isna(self.expected)) - self.actual_nulls = np.sum(pd.isna(self.actual)) - self.axis = 1 - self.plot = plot - self.silent = silent - if self.column_type == np.dtype("O"): - self.expected_uniqs = expected[column_name].unique() - self.actual_uniqs = actual[column_name].unique() - - def jac(self) -> float: - """Calculates the Jacquard similarity index. - - The Jacquard similarity index measures the intersection between two sequences - versus the union of the two sequences - - Returns: - The Jacquard similarity index - - """ - x = set(self.expected_uniqs) - y = set(self.expected_uniqs) - - logger.info(f"Jacquard similarity is {len(x.intersection(y)) / len(x.union(y)): .6f}") - - jac_sim_index = len(x.intersection(y)) / len(x.union(y)) - - return jac_sim_index - - # в функции нет аргумента nulls, а был и испольовался далее в коде - def plots(self, expected_percents, actual_percents, breakpoints, intervals): - """Generates plots expected and actual percents. - - Args: - expected_percents: - The percentage of expected value from all expected values - actual_percents: - The percentage of actual value from all actual values - breakpoints: - The list of breakpoints - intervals: - The list of intervals - - """ - points = [i for i in breakpoints] - plt.figure(figsize=(15, 7)) - plt.bar( - np.arange(len(intervals)) - 0.15, # что такое 0.15? Может вынести в константу? - expected_percents, - label="expected", - alpha=0.7, - width=0.3, - ) - plt.bar(np.arange(len(intervals)) + 0.15, actual_percents, label="actual", alpha=0.7, width=0.3) - plt.legend(loc="best") - - if self.column_type != np.dtype("O"): - plt.xticks(range(len(intervals)), intervals, rotation=90) - else: - plt.xticks(range(len(points)), points, rotation=90) - plt.title(self.column_name) - - # plt.savefig(f"C:\\Users\\Glazova2-YA\\Documents\\data\\bip\\summary_psi_plots\\{self.column_name}.png") - plt.show() - - def sub_psi(self, e_perc: float, a_perc: float) -> float: - """Calculates the sub PSI value. - - Args: - e_perc: - The expected percentage - a_perc: - The actual percentage - - Returns: - The calculated sub PSI value. - """ - if a_perc == 0: - a_perc = 0.0001 - if e_perc == 0: - e_perc = 0.0001 - - sub_psi = (e_perc - a_perc) * np.log(e_perc / a_perc) - - logger.debug(f"sub_psi value is {sub_psi: .6f}") - - return sub_psi - - def psi_num(self): - """Calculate the PSI for a single variable. - - Returns: - PSI for column - The PSI for each bucket - New categories (empty list for non-categorical data) - Categories that are absent in actual column (empty list for non-categorical data) - - """ - buckets = 10 - breakpoints = np.arange(0, buckets / 10, 0.1) - - # Заплатка, на случай, если в актуальной таблице появились значения отличные от null - if self.expected_nulls == self.expected_len and self.actual_nulls != self.actual_len: - breakpoints = np.array(list(sorted(set(np.nanquantile(self.actual, breakpoints))))) - else: - breakpoints = np.array(list(sorted(set(np.nanquantile(self.expected, breakpoints))))) - - actual_nulls = self.actual_nulls / self.actual_len - expected_nulls = self.expected_nulls / self.expected_len - - breakpoints = np.concatenate(([-np.inf], breakpoints, [np.inf])) - - expected_percents = np.histogram(self.expected, breakpoints) - actual_percents = np.histogram(self.actual, breakpoints) - # breakpoints[0] = -np.inf - # breakpoints[-1] = np.inf - expected_percents = [p / self.expected_len for p in expected_percents[0]] - actual_percents = [p / self.actual_len for p in actual_percents[0]] - - if self.expected_nulls == 0 and actual_nulls == expected_nulls: - expected_percents = expected_percents - actual_percents = actual_percents - nulls = False - else: - expected_percents.append(expected_nulls) - actual_percents.append(actual_nulls) - nulls = True - - points = [i for i in breakpoints] - intervals = [f"({np.round(points[i], 5)};{np.round(points[i + 1], 5)})" for i in range(len(points) - 1)] - if nulls: - intervals = np.append(intervals, "empty_values") - - if self.plot: - self.plots(expected_percents, actual_percents, breakpoints, intervals) # в функции нет аргумента nulls - - psi_dict = {} - for i in range(0, len(expected_percents)): - psi_val = self.sub_psi(expected_percents[i], actual_percents[i]) - psi_dict.update({intervals[i]: psi_val}) - - psi_value = np.sum(list(psi_dict.values())) - psi_dict = {k: v for k, v in sorted(psi_dict.items(), key=lambda x: x[1], reverse=True)} - new_cats = [] - abs_cats = [] - - return psi_value, psi_dict, new_cats, abs_cats - - def uniq_psi(self): - """Calculates PSI for categorical unique counts grater than 100. - - Returns: - PSI for column - The PSI for each bucket - New categories (empty list for non-categorical data) - Categories that are absent in actual column (empty list for non-categorical data) - - """ - actual_nulls = self.actual_nulls / self.actual_len - expected_nulls = self.expected_nulls / self.expected_len - - actual_not_nulls_arr = self.actual[~np.isnan(self.actual)] - expected_not_nulls_arr = self.expected[~np.isnan(self.expected)] - - actual_not_nulls = len(actual_not_nulls_arr) / self.actual_len - expected_not_nulls = len(expected_not_nulls_arr) / self.expected_len - - expected_percents = [expected_not_nulls, expected_nulls] - actual_percents = [actual_not_nulls, actual_nulls] - - breakpoints = ["good_data", "nulls"] - if self.plot: - self.plots(expected_percents, actual_percents, breakpoints, breakpoints) # в функции нет аргумента nulls - - psi_dict = {} - for i in range(0, len(expected_percents)): - psi_val = self.sub_psi(expected_percents[i], actual_percents[i]) - if breakpoints[i] == "None": - psi_dict.update({"empty_value": psi_val}) - else: - psi_dict.update({breakpoints[i]: psi_val}) - - psi_value = np.sum(list(psi_dict.values())) - jac_metric = self.jac() - new_cats, abs_cats = [], [] - psi_dict = {k: v for k, v in sorted(psi_dict.items(), key=lambda x: x[1], reverse=True)} - - if psi_value >= 0.2: # что такое 0.2? Может перенести его в константу? - psi_value = psi_value - psi_dict.update({"metric": "stability_index"}) - else: - psi_value = 1 - jac_metric - psi_dict.update({"metric": "unique_index"}) - - logger.info(f"PSI for categorical unique >100 is {psi_value: .6f}") - - return psi_value, psi_dict, new_cats, abs_cats - - def psi_categ(self): - """Calculates PSI for categorical data excluding unique counts grater than 100. - - Returns: - PSI for column - The PSI for each bucket - New categories (empty list for non-categorical data) - Categories that are absent in actual column (empty list for non-categorical data) - - """ - expected_uniq_count = len(self.expected_uniqs) - actual_uniq_count = len(self.actual_uniqs) - # правило для категориальных > 100 - if expected_uniq_count > 100 or actual_uniq_count > 100: - psi_value, psi_dict, new_cats, abs_cats = self.uniq_psi() - - logger.info(f"PSI is {psi_value: .6f}") - - return psi_value, psi_dict, new_cats, abs_cats - - expected_dict = ( - pd.DataFrame(self.expected, columns=[self.column_name]) - .groupby(self.column_name)[self.column_name] - .count() - .sort_values(ascending=False) - .to_dict() - ) - actual_dict = ( - pd.DataFrame(self.actual, columns=[self.column_name]) - .groupby(self.column_name)[self.column_name] - .count() - .sort_values(ascending=False) - .to_dict() - ) - - breakpoints = list(expected_dict.keys() | actual_dict.keys()) - - new_cats = [k for k in actual_dict.keys() if k not in expected_dict.keys()] - abs_cats = [k for k in expected_dict.keys() if k not in actual_dict.keys()] - - expected_dict_re = {} - actual_dict_re = {} - - for b in breakpoints: - if b in expected_dict and b not in actual_dict: - expected_dict_re.update({b: expected_dict[b]}) - actual_dict_re.update({b: 0}) - elif b not in expected_dict and b in actual_dict: - expected_dict_re.update({b: 0}) - actual_dict_re.update({b: actual_dict[b]}) - elif b in expected_dict and b in actual_dict: - actual_dict_re.update({b: actual_dict[b]}) - expected_dict_re.update({b: expected_dict[b]}) - - category_names = [c for c in expected_dict_re.keys()] - groups = {} - g_counts = len(category_names) - group_num = 20 - if g_counts <= group_num: - for g_n, val in enumerate(category_names): - groups[val] = g_n - else: - group_size = np.floor(g_counts / group_num) - current_pos = 0 - reminder = g_counts % group_num - for g_n in range(group_num): - if g_n < group_num - reminder: - group_values = category_names[int(current_pos) : int(current_pos + group_size)] - current_pos += group_size - else: - group_values = category_names[int(current_pos) : int(current_pos + group_size + 1)] - current_pos += group_size + 1 - for val in group_values: - groups[val] = g_n - group_sum_exp = 0 - group_sum_act = 0 - exp_dict = {} - act_dict = {} - group_re = -1 - cat_group_name = "" - group_name_re = "" - for k, v in groups.items(): - current_group = v - if current_group == group_re: - group_re = v - exp_dict.pop(group_name_re, None) - act_dict.pop(group_name_re, None) - cat_group_name = cat_group_name + ", " + str(k) - group_sum_exp += expected_dict_re[k] - group_sum_act += actual_dict_re[k] - exp_dict.update({cat_group_name: group_sum_exp}) - act_dict.update({cat_group_name: group_sum_act}) - group_name_re = cat_group_name - else: - group_name_re = str(k) - group_re = v - cat_group_name = str(k) - group_sum_exp = expected_dict_re[k] - group_sum_act = actual_dict_re[k] - exp_dict.update({cat_group_name: group_sum_exp}) - act_dict.update({cat_group_name: group_sum_act}) - - expected_percents = [e / self.expected_len for e in exp_dict.values()] - actual_percents = [a / self.actual_len for a in act_dict.values()] - - breakpoints = [e for e in exp_dict.keys()] - - if self.plot: - self.plots( - expected_percents, actual_percents, breakpoints, breakpoints - ) # в функции plots нет аргумента nulls - - psi_dict = {} - for i in range(0, len(expected_percents)): - psi_val = self.sub_psi(expected_percents[i], actual_percents[i]) - if breakpoints[i] == "None": - psi_dict.update({"empty_value": psi_val}) - else: - psi_dict.update({breakpoints[i]: psi_val}) - psi_value = np.sum(list(psi_dict.values())) - psi_dict = {k: v for k, v in sorted(psi_dict.items(), key=lambda x: x[1], reverse=True)} - - return psi_value, psi_dict, new_cats, abs_cats - - def psi_result(self): - """Calculates PSI. - - Returns: - PSI for column - The PSI for each bucket - New categories (empty list for non-categorical data) - Categories that are absent in actual column (empty list for non-categorical data) - - """ - if len(self.expected_shape) == 1: - psi_values = np.empty(len(self.expected_shape)) - else: - psi_values = np.empty(self.expected_shape[self.axis]) - - for i in range(0, len(psi_values)): - if (self.column_type == np.dtype("O")) or ( - self.expected_nulls == self.expected_len and self.actual_nulls == self.actual_len - ): - psi_values, psi_dict, new_cats, abs_cats = self.psi_categ() - else: - psi_values, psi_dict, new_cats, abs_cats = self.psi_num() - - if self.silent: - logger.debug(f"PSI value: {psi_values: .3f}") - else: - logger.info(f"PSI value: {psi_values: .3f}") - - # если expected_shape пустой - будет ошибка - return round(psi_values, 2), psi_dict, new_cats, abs_cats - - -def report(expected: pd.DataFrame, actual: pd.DataFrame, plot: bool = False, silent: bool = False) -> pd.DataFrame: - """Generates a report using PSI (Population Stability Index) between the expected and actual data. - - Args: - expected: - The expected dataset - actual: - The new dataset you want to compare to the expected one - plot: - If True, plots the PSI are created. Defaults to False - silent: - If silent, logger in info mode - - - Returns: - A dataframe with the PSI report. The report includes the columns names, - metric names, check results, failed buckets, new categories and disappeared categories. - Anomaly score represent the PSI, metrics names indicate with metric was used for PSI calculation, - check results indicate whether the PSI is under the threshold (0.2), - and failed buckets include up to 5 buckets with the highest PSI. - - """ - if silent: - logger.debug("Creating report") - else: - logger.info("Creating report") - - assert len(expected.columns) == len(actual.columns) - - data_cols = expected.columns - score_dict = {} - new_cat_dict = {} - datas = [] - - for col in data_cols: - psi_res = PSI(expected, actual, col, plot=plot, silent=silent) - # отладка, в случае ошибки выдаст прооблемный столбец - try: - score, psi_dict, new_cats, abs_cats = psi_res.psi_result() - except: - logger.warning(f"Can not count PSIs, see column {col}") - continue - - if len(new_cats) > 0: - new_cat_dict.update({col: new_cats}) - - score_dict.update({col: score}) - check_result = "OK" if score < 0.2 else "NOK" # может 0.2 вынести в константу? - # psi_dict = {k:v for k,v in sorted(psi_dict.items(), key=lambda x: x[1], reverse=True)} - failed_buckets = list(psi_dict.keys())[:5] if score > 0.2 else [] - if "metric" in psi_dict: - new_cats = None - abs_cats = None - metric_name = psi_dict["metric"] - if metric_name == "unique_index": - failed_buckets = None - else: - metric_name = "stability_index" - data_tmp = pd.DataFrame( - { - "column": col, - "anomaly_score": score, - "metric_name": metric_name, - "check_result": check_result, - "failed_bucket": f"{failed_buckets}", - "new_category": f"{new_cats}", - "disappeared_category": f"{abs_cats}", - }, - index=[1], - ) - datas.append(data_tmp) - - data = pd.concat(datas, ignore_index=True) - - return data diff --git a/lightautoml/addons/hypex/utils/tutorial_data_creation.py b/lightautoml/addons/hypex/utils/tutorial_data_creation.py deleted file mode 100644 index a01a1a76..00000000 --- a/lightautoml/addons/hypex/utils/tutorial_data_creation.py +++ /dev/null @@ -1,158 +0,0 @@ -import numpy as np -import pandas as pd -import sys -from pathlib import Path -from typing import Iterable, Union - -ROOT = Path(".").absolute().parents[0] -sys.path.append(str(ROOT)) - - -def set_nans(data: pd.DataFrame, na_step: Union[Iterable[int], int] = None, nan_cols: Union[Iterable[str], str] = None): - """Fill some values with NaN. - - Args: - data: input dataframe - na_step: - num or list of nums of period to make NaN (step of range) - If list - iterates accordingly order of columns - nan_cols: - name of one or several columns to fill with NaN - If list - iterates accordingly order of na_step - - Returns: - data: dataframe with some NaNs - """ - if (nan_cols is not None) or (na_step is not None): - # correct type of columns to iterate - - # number of nans - if na_step is None: - na_step = [10] - print(f"No na_step specified: set to {na_step}") - elif not isinstance(na_step, Iterable): - na_step = [na_step] - - # columns - if nan_cols is None: - nan_cols = list(data.columns) - print("No nan_cols specified. Setting NaNs applied to all columns") - elif not isinstance(nan_cols, Iterable): - nan_cols = [nan_cols] - - # correct length of two lists - if len(na_step) > len(nan_cols): - na_step = na_step[: len(nan_cols)] - print("Length of na_step is bigger than length of columns. Used only first values") - elif len(na_step) < len(nan_cols): - na_step = na_step + [na_step[-1]] * (len(nan_cols) - len(na_step)) - print("Length of na_step is less than length of columns. Used last value several times") - - # create list of indexes to fill with na - nans_indexes = [list(range(i, len(data), period)) for i, period in enumerate(na_step)] - - for i in range(len(nan_cols)): - try: - data.loc[nans_indexes[i], nan_cols[i]] = np.nan - except KeyError: - print(f"There is no column {nan_cols[i]} in data. No nans in this column will be added.") - else: - print("No NaN added") - - return data - - -def create_test_data( - num_users: int = 10000, - na_step: Union[Iterable[int], int] = None, - nan_cols: Union[Iterable[str], str] = None, - file_name: str = None, - rs=None, -): - """Creates data for tutorial. - - Args: - num_users: num of strings - na_step: - num or list of nums of period to make NaN (step of range) - If list - iterates accordingly order of columns - nan_cols: - name of one or several columns to fill with NaN - If list - iterates accordingly order of na_step - file_name: name of file to save; doesn't save file if None - - Returns: - data: dataframe with - """ - if rs is not None: - np.random.seed(rs) - - if (nan_cols is not None) and isinstance(nan_cols, str): - nan_cols = [nan_cols] - # Simulating dataset with known effect size - num_months = 12 - - # signup_months == 0 means customer did not sign up - signup_months = np.random.choice(np.arange(1, num_months), num_users) * np.random.randint(0, 2, size=num_users) - - data = pd.DataFrame( - { - "user_id": np.repeat(np.arange(num_users), num_months), - "signup_month": np.repeat(signup_months, num_months), # signup month == 0 means customer did not sign up - "month": np.tile(np.arange(1, num_months + 1), num_users), # months are from 1 to 12 - "spend": np.random.poisson(500, num_users * num_months), - } - ) - - # A customer is in the treatment group if and only if they signed up - data["treat"] = data["signup_month"] > 0 - - # Simulating an effect of month (monotonically decreasing--customers buy less later in the year) - data["spend"] = data["spend"] - data["month"] * 10 - - # Simulating a simple treatment effect of 100 - after_signup = (data["signup_month"] < data["month"]) & (data["treat"]) - data.loc[after_signup, "spend"] = data[after_signup]["spend"] + 100 - - # Setting the signup month (for ease of analysis) - i = 3 - data = ( - data[data.signup_month.isin([0, i])] - .groupby(["user_id", "signup_month", "treat"]) - .apply( - lambda x: pd.Series( - { - "pre_spends": x.loc[x.month < i, "spend"].mean(), - "post_spends": x.loc[x.month > i, "spend"].mean(), - } - ) - ) - .reset_index() - ) - - # Additional category features - gender_i = np.random.choice(a=[0, 1], size=data.user_id.nunique()) - gender = [["M", "F"][i] for i in gender_i] - - age = np.random.choice(a=range(18, 70), size=data.user_id.nunique()) - - industry_i = np.random.choice(a=range(1, 3), size=data.user_id.nunique()) - industry_names = ["Finance", "E-commerce", "Logistics"] - industry = [industry_names[i] for i in industry_i] - - data["age"] = age - data["gender"] = gender - data["industry"] = industry - data["industry"] = data["industry"].astype("str") - data["treat"] = data["treat"].astype(int) - - # input nans in data if needed - data = set_nans(data, na_step, nan_cols) - - if file_name is not None: - data.to_csv(ROOT / f"{file_name}.csv", index=False) - - return data - - -# create_test_data(num_users=10_000, file_name="Tutorial_data") diff --git a/lightautoml/addons/hypex/utils/validators.py b/lightautoml/addons/hypex/utils/validators.py deleted file mode 100644 index 85ddda49..00000000 --- a/lightautoml/addons/hypex/utils/validators.py +++ /dev/null @@ -1,91 +0,0 @@ -"""Validators.""" -from typing import List - -import numpy as np -import pandas as pd -import scipy.stats as st - - -def random_treatment(df: pd.DataFrame, treatment: str): - """Replaces real treatment with a random placebo treatment. - - Args: - df: - The initial dataframe - treatment: - The columns name representing the treatment - - Returns: - The modified dataframe with the original treatment replaced - The original treatment series - A validation flag - """ - prop1 = df[treatment].sum() / df.shape[0] - prop0 = 1 - prop1 - new_treatment = np.random.choice([0, 1], size=df.shape[0], p=[prop0, prop1]) - validate = 1 - orig_treatment = df[treatment] - df = df.drop(columns=treatment) - df[treatment] = new_treatment - return df, orig_treatment, validate - - -def random_feature(df: pd.DataFrame): - """Adds a random feature to the initial dataset. - - Args: - df: - The initial dataframe - - Returns: - The modified dataframe with an additional random feature - A validation flag - """ - feature = np.random.normal(0, 1, size=len(df)) - validate = 1 - df["random_feature"] = feature - return df, validate - - -def subset_refuter(df: pd.DataFrame, treatment: str, fraction: float = 0.8): - """Returns a subset of data with given fraction (default 0.8). - - Args: - df: - The initial dataframe - treatment: - The column name representing the treatment - fraction: - The fraction of the dataset to divide random matching - - Returns: - The subset of the dataframe - A validation flag - """ - df = df.groupby(treatment, group_keys=False).apply(lambda x: x.sample(frac=fraction)) - validate = 1 - return df, validate - - -def test_significance(estimate: float, simulations: List) -> float: - """Performs a significance test for a normal distribution. - - Args: - estimate: - The estimated effect - simulations: - A list of estimated effects from each simulation - - Returns: - The p-value of the test - """ - mean_refute_value = np.mean(simulations) - std_dev_refute_values = np.std(simulations) - z_score = (estimate - mean_refute_value) / std_dev_refute_values - - if z_score > 0: # Right Tail - p_value = 1 - st.norm.cdf(z_score) - else: # Left Tail - p_value = st.norm.cdf(z_score) - - return p_value From dc18dfec747012e83dc961111f2d8387110e9959 Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 18:22:52 +0300 Subject: [PATCH 02/16] fix typo --- tox.ini | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tox.ini b/tox.ini index 1952da82..bf3f6da2 100644 --- a/tox.ini +++ b/tox.ini @@ -6,7 +6,7 @@ envlist = lint, docs, typing, - build + build, codespell [tox:.package] From cbadae8809f29bd2dd7cd46c9c5c5541e9060721 Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 18:27:35 +0300 Subject: [PATCH 03/16] removed old hypex --- lightautoml/addons/hypex/readme.md | 23 ----------------------- 1 file changed, 23 deletions(-) delete mode 100644 lightautoml/addons/hypex/readme.md diff --git a/lightautoml/addons/hypex/readme.md b/lightautoml/addons/hypex/readme.md deleted file mode 100644 index cefccc46..00000000 --- a/lightautoml/addons/hypex/readme.md +++ /dev/null @@ -1,23 +0,0 @@ -# HypEx: Hypotheses and Experiments for Causal Inference - -[![Telegram](https://img.shields.io/badge/chat-on%20Telegram-2ba2d9.svg)](https://t.me/lamamatcher) - -## Introduction -HypEx (Hypotheses and Experiments) is an addon for the LightAutoML library, designed to automate the causal inference process in data analysis. It is developed to solve matching tasks in a more effective and efficient way. This addon utilizes the Rubin's Causal Model (RCM) approach, a classic method to match closely resembling pairs, thereby ensuring a fair comparison between groups when estimating the effect of a treatment. - -The HypEx addon is designed with a fully automated pipeline to calculate Average Treatment Effect (ATE), Average Treatment Effect on the Treated (ATT), and Average Treatment Effect on the Control (ATC). It provides users with a standard interface to execute these estimations and understand the impact of interventions on different subgroups in the population. - -Key features of HypEx include automated feature selection with LightAutoML, matching using Faiss KNN for optimal pair selection, application of various data filters, and result validation. - -## Features -- Automated Feature Selection: HypEx uses the LightAutoML feature selection process to identify and use the most relevant features for causal inference. -- [Faiss](https://github.com/facebookresearch/faiss) KNN Matching: The addon leverages the power of Faiss library to perform efficient nearest neighbor searches for matching. This ensures that for each treated instance, a control instance that is closest in characteristics is selected, as per Rubin's Causal Model. -- Data Filters: The addon comes with in-built outlier detection and Spearman filters to ensure that the data being used for matching is of high quality. -- Result Validation: HypEx provides three ways for users to validate the results: random treatment validation, random feature validation, and random subset validation. -- Data Tests: HypEx also includes data testing methods such as the Standard Mean Difference (SMD) test, Kolmogorov-Smirnov (KS) test, Population Stability Index (PSI) test, and Repeats test. These tests provide additional checks to ensure the robustness of the estimated effects. - -## Quick Start -You can see the examples of usages this addons [here](https://github.com/sb-ai-lab/LightAutoML/blob/master/examples/tutorials/Tutorial_12_Matching.ipynb) - -Conclusion -The HypEx addon for LightAutoML is a powerful tool for any data analyst or researcher interested in causal inference. Its automated features, effective matching technique, and rigorous validation and testing methods make it an essential addition to the toolkit when seeking to understand cause and effect in complex datasets. From af4d098ac34fb7617a330b4966f412ed672a09f8 Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 18:37:49 +0300 Subject: [PATCH 04/16] added hypex --- pyproject.toml | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 75d41e1c..d2af2dd6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -89,7 +89,7 @@ weasyprint = {version = "52.5", optional = true} cffi = {version = "1.14.5", optional = true} # HypEx -faiss-cpu = {version = "*", optional = true} +hypex = {version = "*", optional = true} [tool.poetry.extras] @@ -116,8 +116,7 @@ afg = [ ] hypex = [ - "faiss-cpu", - "ipython" + "hypex", ] all = [ @@ -133,8 +132,7 @@ all = [ "cffi", "weasyprint", "featuretools", - "faiss-cpu", - "ipython" + "hypex", ] @@ -173,6 +171,6 @@ use_parentheses = true filter_files = true [tool.codespell] -skip = '*.git,*.csv,./lightautoml/addons/hypex/*,./lightautoml/addons/interpretation/*,*.ipynb' +skip = '*.git,*.csv,./lightautoml/addons/interpretation/*,*.ipynb' # ignore-words-list = 'LAMA,Lama,lama,MAPE,mape,splitted,fpr' From 4ec426319bc3a25c8ff92f72a6dcf55231fd6fac Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 18:55:33 +0300 Subject: [PATCH 05/16] removed old hypex --- lightautoml/addons/hypex/README.md | 23 ----------------------- 1 file changed, 23 deletions(-) delete mode 100644 lightautoml/addons/hypex/README.md diff --git a/lightautoml/addons/hypex/README.md b/lightautoml/addons/hypex/README.md deleted file mode 100644 index cefccc46..00000000 --- a/lightautoml/addons/hypex/README.md +++ /dev/null @@ -1,23 +0,0 @@ -# HypEx: Hypotheses and Experiments for Causal Inference - -[![Telegram](https://img.shields.io/badge/chat-on%20Telegram-2ba2d9.svg)](https://t.me/lamamatcher) - -## Introduction -HypEx (Hypotheses and Experiments) is an addon for the LightAutoML library, designed to automate the causal inference process in data analysis. It is developed to solve matching tasks in a more effective and efficient way. This addon utilizes the Rubin's Causal Model (RCM) approach, a classic method to match closely resembling pairs, thereby ensuring a fair comparison between groups when estimating the effect of a treatment. - -The HypEx addon is designed with a fully automated pipeline to calculate Average Treatment Effect (ATE), Average Treatment Effect on the Treated (ATT), and Average Treatment Effect on the Control (ATC). It provides users with a standard interface to execute these estimations and understand the impact of interventions on different subgroups in the population. - -Key features of HypEx include automated feature selection with LightAutoML, matching using Faiss KNN for optimal pair selection, application of various data filters, and result validation. - -## Features -- Automated Feature Selection: HypEx uses the LightAutoML feature selection process to identify and use the most relevant features for causal inference. -- [Faiss](https://github.com/facebookresearch/faiss) KNN Matching: The addon leverages the power of Faiss library to perform efficient nearest neighbor searches for matching. This ensures that for each treated instance, a control instance that is closest in characteristics is selected, as per Rubin's Causal Model. -- Data Filters: The addon comes with in-built outlier detection and Spearman filters to ensure that the data being used for matching is of high quality. -- Result Validation: HypEx provides three ways for users to validate the results: random treatment validation, random feature validation, and random subset validation. -- Data Tests: HypEx also includes data testing methods such as the Standard Mean Difference (SMD) test, Kolmogorov-Smirnov (KS) test, Population Stability Index (PSI) test, and Repeats test. These tests provide additional checks to ensure the robustness of the estimated effects. - -## Quick Start -You can see the examples of usages this addons [here](https://github.com/sb-ai-lab/LightAutoML/blob/master/examples/tutorials/Tutorial_12_Matching.ipynb) - -Conclusion -The HypEx addon for LightAutoML is a powerful tool for any data analyst or researcher interested in causal inference. Its automated features, effective matching technique, and rigorous validation and testing methods make it an essential addition to the toolkit when seeking to understand cause and effect in complex datasets. From a099ad23b99ffa73021a710cc6b7042bb304fc79 Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 18:57:48 +0300 Subject: [PATCH 06/16] added import original hypex --- lightautoml/addons/hypex/__init__.py | 38 ++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) create mode 100644 lightautoml/addons/hypex/__init__.py diff --git a/lightautoml/addons/hypex/__init__.py b/lightautoml/addons/hypex/__init__.py new file mode 100644 index 00000000..6aff62c9 --- /dev/null +++ b/lightautoml/addons/hypex/__init__.py @@ -0,0 +1,38 @@ +"""HypEx Addon for LightAutoML. + +This module forwards all imports from the official HypEx package, +maintaining the same API structure as in the original library. + +Requirements: + - Install LightAutoML with HypEx support: + `pip install lightautoml[hypex]` + +Examples: + Importing models and utilities as in HypEx: + + >>> from lightautoml.addons.hypex import AATest + >>> from lightautoml.addons.hypex.utils.tutorial_data_creation import create_test_data + + Creating test data: + >>> some_large_dataframe = create_test_data( + ... rs=52, na_step=10, nan_cols=['age', 'gender'], num_users=100_000 + ... ) + +Raises: + ImportError: If HypEx is not installed. +""" + +import importlib +import sys + +MODULE_NAME = "hypex" + +try: + hypex = importlib.import_module(MODULE_NAME) +except ImportError: + raise ImportError( + f"{MODULE_NAME} is not installed. Please install it using " + f"'pip install lightautoml[{MODULE_NAME}]'." + ) + +sys.modules["lightautoml.addons.hypex"] = hypex From 9f0417f7be02e9304bf31d773aa17c2999834411 Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 21:32:17 +0300 Subject: [PATCH 07/16] Updated link to the hypex doc --- docs/pages/modules/addons.rst | 14 +++----------- 1 file changed, 3 insertions(+), 11 deletions(-) diff --git a/docs/pages/modules/addons.rst b/docs/pages/modules/addons.rst index 02507462..154767f5 100644 --- a/docs/pages/modules/addons.rst +++ b/docs/pages/modules/addons.rst @@ -21,16 +21,8 @@ Utilization HypEx -- Hypothesises and Experiments ------------------------------------- -.. currentmodule:: lightautoml.addons.hypex +The official HypEx documentation can be found at: -.. autosummary:: - :toctree: ./generated - :nosignatures: - :template: classtemplate.rst +`HypEx Documentation `_ - ~matcher.Matcher - algorithms.faiss_matcher.FaissMatcher - algorithms.no_replacement_matching.MatcherNoReplacement - selectors.lama_feature_selector.LamaFeatureSelector - selectors.outliers_filter.OutliersFilter - selectors.spearman_filter.SpearmanFilter +For a detailed reference, visit the HypEx API documentation. \ No newline at end of file From 11785556c5905b4ae8dd0e2912b06709481cd4f5 Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 21:33:37 +0300 Subject: [PATCH 08/16] Added hypex to intersphinx --- docs/conf.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/conf.py b/docs/conf.py index 835285ae..ab11f9cc 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -67,6 +67,7 @@ "seaborn", "json2html", "faiss", + "hypex" ] # Add any paths that contain templates here, relative to this directory. @@ -161,6 +162,7 @@ "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None), "sklearn": ("https://scikit-learn.org/stable/", None), "PIL": ("https://pillow.readthedocs.io/en/stable/", None), + "hypex": ("https://hypex.readthedocs.io/en/latest/", None), } autodoc_type_aliases = { From b7be755b690036f33bb895ab4e3a8f1724914523 Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 21:52:07 +0300 Subject: [PATCH 09/16] black formatted --- docs/conf.py | 2 +- docs/pages/modules/addons.rst | 2 +- lightautoml/addons/hypex/__init__.py | 3 +-- 3 files changed, 3 insertions(+), 4 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index ab11f9cc..932a9b8f 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -67,7 +67,7 @@ "seaborn", "json2html", "faiss", - "hypex" + "hypex", ] # Add any paths that contain templates here, relative to this directory. diff --git a/docs/pages/modules/addons.rst b/docs/pages/modules/addons.rst index 154767f5..753f27a5 100644 --- a/docs/pages/modules/addons.rst +++ b/docs/pages/modules/addons.rst @@ -25,4 +25,4 @@ The official HypEx documentation can be found at: `HypEx Documentation `_ -For a detailed reference, visit the HypEx API documentation. \ No newline at end of file +For a detailed reference, visit the HypEx API documentation. diff --git a/lightautoml/addons/hypex/__init__.py b/lightautoml/addons/hypex/__init__.py index 6aff62c9..50a22fb2 100644 --- a/lightautoml/addons/hypex/__init__.py +++ b/lightautoml/addons/hypex/__init__.py @@ -31,8 +31,7 @@ hypex = importlib.import_module(MODULE_NAME) except ImportError: raise ImportError( - f"{MODULE_NAME} is not installed. Please install it using " - f"'pip install lightautoml[{MODULE_NAME}]'." + f"{MODULE_NAME} is not installed. Please install it using " f"'pip install lightautoml[{MODULE_NAME}]'." ) sys.modules["lightautoml.addons.hypex"] = hypex From 80ab26b87a98a5643e5c2f53faf24e713fae06c1 Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 22:14:04 +0300 Subject: [PATCH 10/16] Removed old hypex tutorials --- docs/pages/Tutorials.rst | 1 - .../tutorials/Tutorial_12_Matching.nblink | 6 - examples/tutorials/Tutorial_12_Matching.ipynb | 1682 ------------- .../tutorials/Tutorial_13_ABtesting.ipynb | 2120 ----------------- 4 files changed, 3809 deletions(-) delete mode 100644 docs/pages/tutorials/Tutorial_12_Matching.nblink delete mode 100644 examples/tutorials/Tutorial_12_Matching.ipynb delete mode 100644 examples/tutorials/Tutorial_13_ABtesting.ipynb diff --git a/docs/pages/Tutorials.rst b/docs/pages/Tutorials.rst index f2220e51..c3d20d42 100644 --- a/docs/pages/Tutorials.rst +++ b/docs/pages/Tutorials.rst @@ -17,4 +17,3 @@ Tutorials tutorials/Tutorial_9_neural_networks.nblink tutorials/Tutorial_10_relational_data_with_star_scheme.nblink tutorials/Tutorial_11_time_series.nblink - tutorials/Tutorial_12_Matching.nblink diff --git a/docs/pages/tutorials/Tutorial_12_Matching.nblink b/docs/pages/tutorials/Tutorial_12_Matching.nblink deleted file mode 100644 index adf2fb46..00000000 --- a/docs/pages/tutorials/Tutorial_12_Matching.nblink +++ /dev/null @@ -1,6 +0,0 @@ -{ - "path": "../../../examples/tutorials/Tutorial_12_Matching.ipynb", - "extra-media": [ - "../../../imgs" - ] -} diff --git a/examples/tutorials/Tutorial_12_Matching.ipynb b/examples/tutorials/Tutorial_12_Matching.ipynb deleted file mode 100644 index 83903a2c..00000000 --- a/examples/tutorials/Tutorial_12_Matching.ipynb +++ /dev/null @@ -1,1682 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial 12: Matching" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 0. Import libraries " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Anaconda3\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n", - "C:\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n", - "C:\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.WCDJNK7YVMPZQ2ME2ZZHJJRJ3JIKNDB7.gfortran-win_amd64.dll\n", - "C:\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.XWYDX2IKJW2NMTWSFYNGFUWKQU3LYTCZ.gfortran-win_amd64.dll\n", - " warnings.warn(\"loaded more than 1 DLL from .libs:\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'nlp' extra dependecy package 'gensim' isn't installed. Look at README.md in repo 'LightAutoML' for installation instructions.\n", - "'nlp' extra dependecy package 'transformers' isn't installed. Look at README.md in repo 'LightAutoML' for installation instructions.\n", - "'nlp' extra dependecy package 'gensim' isn't installed. Look at README.md in repo 'LightAutoML' for installation instructions.\n", - "'nlp' extra dependecy package 'transformers' isn't installed. Look at README.md in repo 'LightAutoML' for installation instructions.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\19623472\\AppData\\Roaming\\Python\\Python39\\site-packages\\lightautoml\\ml_algo\\dl_model.py:41: UserWarning: 'transformers' - package isn't installed\n", - " warnings.warn(\"'transformers' - package isn't installed\")\n", - "C:\\Users\\19623472\\AppData\\Roaming\\Python\\Python39\\site-packages\\lightautoml\\text\\nn_model.py:22: UserWarning: 'transformers' - package isn't installed\n", - " warnings.warn(\"'transformers' - package isn't installed\")\n", - "C:\\Users\\19623472\\AppData\\Roaming\\Python\\Python39\\site-packages\\lightautoml\\text\\dl_transformers.py:25: UserWarning: 'transformers' - package isn't installed\n", - " warnings.warn(\"'transformers' - package isn't installed\")\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import warnings\n", - "import numpy as np\n", - "from lightautoml.addons.hypex import Matcher\n", - "\n", - "warnings.filterwarnings('ignore')\n", - "%config Completer.use_jedi = False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Create or upload your dataset \n", - "In this case we will create random dataset with known effect size \n", - "If you have your own dataset, go to the part 2 \n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
user_idsignup_monthmonthspendtreat
0031473True
1032509True
2033441True
3034587True
4035540True
\n", - "
" - ], - "text/plain": [ - " user_id signup_month month spend treat\n", - "0 0 3 1 473 True\n", - "1 0 3 2 509 True\n", - "2 0 3 3 441 True\n", - "3 0 3 4 587 True\n", - "4 0 3 5 540 True" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Simulating dataset with known effect size\n", - "num_users = 10000\n", - "num_months = 12\n", - "\n", - "signup_months = np.random.choice(np.arange(1, num_months), num_users) * np.random.randint(0,2, size=num_users) # signup_months == 0 means customer did not sign up\n", - "df = pd.DataFrame({\n", - " 'user_id': np.repeat(np.arange(num_users), num_months),\n", - " 'signup_month': np.repeat(signup_months, num_months), # signup month == 0 means customer did not sign up\n", - " 'month': np.tile(np.arange(1, num_months+1), num_users), # months are from 1 to 12\n", - " 'spend': np.random.poisson(500, num_users*num_months) #np.random.beta(a=2, b=5, size=num_users * num_months)*1000 # centered at 500\n", - "})\n", - "# A customer is in the treatment group if and only if they signed up\n", - "df[\"treat\"] = df[\"signup_month\"]>0\n", - "# Simulating an effect of month (monotonically decreasing--customers buy less later in the year)\n", - "df[\"spend\"] = df[\"spend\"] - df[\"month\"]*10\n", - "# Simulating a simple treatment effect of 100\n", - "after_signup = (df[\"signup_month\"] < df[\"month\"]) & (df[\"treat\"])\n", - "df.loc[after_signup,\"spend\"] = df[after_signup][\"spend\"] + 100\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
user_idsignup_monthtreatpre_spendspost_spends
003True491.0517.666667
113True460.0528.000000
230False486.0424.111111
340False472.0421.777778
470False501.0427.333333
..................
546699920False498.0415.444444
546799960False464.5421.666667
546899970False447.0431.111111
546999983True494.0519.222222
547099990False481.5425.555556
\n", - "

5471 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " user_id signup_month treat pre_spends post_spends\n", - "0 0 3 True 491.0 517.666667\n", - "1 1 3 True 460.0 528.000000\n", - "2 3 0 False 486.0 424.111111\n", - "3 4 0 False 472.0 421.777778\n", - "4 7 0 False 501.0 427.333333\n", - "... ... ... ... ... ...\n", - "5466 9992 0 False 498.0 415.444444\n", - "5467 9996 0 False 464.5 421.666667\n", - "5468 9997 0 False 447.0 431.111111\n", - "5469 9998 3 True 494.0 519.222222\n", - "5470 9999 0 False 481.5 425.555556\n", - "\n", - "[5471 rows x 5 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Setting the signup month (for ease of analysis)\n", - "i = 3\n", - "df_i_signupmonth = (\n", - " df[df.signup_month.isin([0, i])]\n", - " .groupby([\"user_id\", \"signup_month\", \"treat\"])\n", - " .apply(\n", - " lambda x: pd.Series(\n", - " {\n", - " \"pre_spends\": x.loc[x.month < i, \"spend\"].mean(),\n", - " \"post_spends\": x.loc[x.month > i, \"spend\"].mean(),\n", - " }\n", - " )\n", - " )\n", - " .reset_index()\n", - ")\n", - "df_i_signupmonth" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
user_idsignup_monthtreatpre_spendspost_spendsageis_maleindustry
0031491.0517.6666676501
1131460.0528.0000002002
2300486.0424.1111115012
3400472.0421.7777785702
4700501.0427.3333331801
\n", - "
" - ], - "text/plain": [ - " user_id signup_month treat pre_spends post_spends age is_male \\\n", - "0 0 3 1 491.0 517.666667 65 0 \n", - "1 1 3 1 460.0 528.000000 20 0 \n", - "2 3 0 0 486.0 424.111111 50 1 \n", - "3 4 0 0 472.0 421.777778 57 0 \n", - "4 7 0 0 501.0 427.333333 18 0 \n", - "\n", - " industry \n", - "0 1 \n", - "1 2 \n", - "2 2 \n", - "3 2 \n", - "4 1 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Additional category features\n", - "gender = np.random.choice(a=[0,1], size=df_i_signupmonth.user_id.nunique())\n", - "age = np.random.choice(a=range(18, 70), size=df_i_signupmonth.user_id.nunique())\n", - "industry = np.random.choice(a=range(1, 3), size=df_i_signupmonth.user_id.nunique())\n", - "df_i_signupmonth['age'] = age\n", - "df_i_signupmonth['is_male'] = gender\n", - "df_i_signupmonth['industry'] = industry\n", - "df_i_signupmonth['industry'] = df_i_signupmonth['industry'].astype('str')\n", - "df_i_signupmonth['treat'] = df_i_signupmonth['treat'].astype(int)\n", - "df_i_signupmonth.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['user_id', 'signup_month', 'treat', 'pre_spends', 'post_spends', 'age',\n", - " 'is_male', 'industry'],\n", - " dtype='object')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_i_signupmonth.columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Matching \n", - "### 2.0 Init params\n", - "info_col used to define informative attributes that should not be part of matching, such as user_id \n", - "But to explicitly store this column in the table, so that you can compare directly after computation" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "info_col = ['user_id', 'signup_month']\n", - "\n", - "outcome = 'post_spends'\n", - "treatment = 'treat'\n", - "weights = {'pre_spends': 10} # additional weight to feature pre_spends" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 Simple matching\n", - "This is the easiest way to initialize and calculate metrics on a Matching task \n", - "Use it when you are clear about each attribute or if you don't have any additional task conditions (Strict equality for certain features) " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1673aeeadeaa4486bafc1b93334b6047", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/5471 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
FeatureImportance
0pre_spends38600.570068
3industry38600.570068
1age29652.719604
2is_male2970.189941
\n", - "" - ], - "text/plain": [ - " Feature Importance\n", - "0 pre_spends 38600.570068\n", - "3 industry 38600.570068\n", - "1 age 29652.719604\n", - "2 is_male 2970.189941" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "features_importance = model.lama_feature_select()\n", - "features_importance" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "features = features_importance['Feature'].to_list()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a7c9cf58da6f4c9d8ed44879ad142e1f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/4 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
indexpre_spendsageis_maleindustrypre_spends_matchedage_matchedis_male_matchedindex_matchedindustry_matchedpost_spendspost_spends_matchedpost_spends_matched_biastreattreat_matched
04985472.04801471.548.00.0[1864]1522.444444421.888889100.54975310
14986494.03801493.538.00.0[1784]1515.444444423.22222292.21641910
24987492.54101493.041.00.0[927]1529.666667424.111111105.56135910
34988479.05911478.059.00.0[1128]1513.333333417.77777895.54395010
44989490.55201490.052.00.0[2196]1515.555556413.333333102.21641910
................................................
49804980502.54502501.542.01.0[310]2420.333333523.666667103.20643001
49814981477.54912478.048.01.0[244]2421.555556521.22222299.64277701
49824982488.06912488.569.00.0[233]2403.222222519.555556116.34438001
49834983505.54012503.039.01.0[376]2424.000000533.222222109.13205101
49844984504.52612504.528.00.0[347]2402.777778504.444444101.73654001
\n", - "

5414 rows × 15 columns

\n", - "" - ], - "text/plain": [ - " index pre_spends age is_male industry pre_spends_matched \\\n", - "0 4985 472.0 48 0 1 471.5 \n", - "1 4986 494.0 38 0 1 493.5 \n", - "2 4987 492.5 41 0 1 493.0 \n", - "3 4988 479.0 59 1 1 478.0 \n", - "4 4989 490.5 52 0 1 490.0 \n", - "... ... ... ... ... ... ... \n", - "4980 4980 502.5 45 0 2 501.5 \n", - "4981 4981 477.5 49 1 2 478.0 \n", - "4982 4982 488.0 69 1 2 488.5 \n", - "4983 4983 505.5 40 1 2 503.0 \n", - "4984 4984 504.5 26 1 2 504.5 \n", - "\n", - " age_matched is_male_matched index_matched industry_matched \\\n", - "0 48.0 0.0 [1864] 1 \n", - "1 38.0 0.0 [1784] 1 \n", - "2 41.0 0.0 [927] 1 \n", - "3 59.0 0.0 [1128] 1 \n", - "4 52.0 0.0 [2196] 1 \n", - "... ... ... ... ... \n", - "4980 42.0 1.0 [310] 2 \n", - "4981 48.0 1.0 [244] 2 \n", - "4982 69.0 0.0 [233] 2 \n", - "4983 39.0 1.0 [376] 2 \n", - "4984 28.0 0.0 [347] 2 \n", - "\n", - " post_spends post_spends_matched post_spends_matched_bias treat \\\n", - "0 522.444444 421.888889 100.549753 1 \n", - "1 515.444444 423.222222 92.216419 1 \n", - "2 529.666667 424.111111 105.561359 1 \n", - "3 513.333333 417.777778 95.543950 1 \n", - "4 515.555556 413.333333 102.216419 1 \n", - "... ... ... ... ... \n", - "4980 420.333333 523.666667 103.206430 0 \n", - "4981 421.555556 521.222222 99.642777 0 \n", - "4982 403.222222 519.555556 116.344380 0 \n", - "4983 424.000000 533.222222 109.132051 0 \n", - "4984 402.777778 504.444444 101.736540 0 \n", - "\n", - " treat_matched \n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "... ... \n", - "4980 1 \n", - "4981 1 \n", - "4982 1 \n", - "4983 1 \n", - "4984 1 \n", - "\n", - "[5414 rows x 15 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_matched" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Results \n", - "### 3.1 ATE, ATT, ATC" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
effect_sizestd_errp-valci_lowerci_upper
ATE100.9729680.5800320.099.836106102.109830
ATC100.9636620.5899030.099.807452102.119872
ATT101.0811020.6931780.099.722472102.439732
\n", - "
" - ], - "text/plain": [ - " effect_size std_err p-val ci_lower ci_upper\n", - "ATE 100.972968 0.580032 0.0 99.836106 102.109830\n", - "ATC 100.963662 0.589903 0.0 99.807452 102.119872\n", - "ATT 101.081102 0.693178 0.0 99.722472 102.439732" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# model.matcher.results\n", - "results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.2 SMD, PSI, KS-test, repeats" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'psi': column_treated anomaly_score_treated check_result_treated \\\n", - " 0 age_treated 0.01 OK \n", - " 1 industry_treated 0.00 OK \n", - " 2 is_male_treated 0.00 OK \n", - " 3 pre_spends_treated 0.01 OK \n", - " \n", - " column_untreated anomaly_score_untreated check_result_untreated \n", - " 0 age_untreated 0.01 OK \n", - " 1 industry_untreated 0.00 OK \n", - " 2 is_male_untreated 0.00 OK \n", - " 3 pre_spends_untreated 0.01 OK ,\n", - " 'ks_test': match_control_to_treat match_treat_to_control\n", - " age 1.0 0.440581\n", - " is_male 1.0 1.000000\n", - " pre_spends 1.0 0.308095,\n", - " 'smd': match_control_to_treat match_treat_to_control\n", - " age 0.000203 0.001464\n", - " is_male 0.000000 0.000000\n", - " pre_spends 0.002945 0.000311,\n", - " 'repeats': {'match_control_to_treat': 0.94, 'match_treat_to_control': 0.08}}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# matching quality result - SMD\n", - "model.quality_result" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
column_treatedanomaly_score_treatedcheck_result_treatedcolumn_untreatedanomaly_score_untreatedcheck_result_untreated
0age_treated0.01OKage_untreated0.01OK
1industry_treated0.00OKindustry_untreated0.00OK
2is_male_treated0.00OKis_male_untreated0.00OK
3pre_spends_treated0.01OKpre_spends_untreated0.01OK
\n", - "
" - ], - "text/plain": [ - " column_treated anomaly_score_treated check_result_treated \\\n", - "0 age_treated 0.01 OK \n", - "1 industry_treated 0.00 OK \n", - "2 is_male_treated 0.00 OK \n", - "3 pre_spends_treated 0.01 OK \n", - "\n", - " column_untreated anomaly_score_untreated check_result_untreated \n", - "0 age_untreated 0.01 OK \n", - "1 industry_untreated 0.00 OK \n", - "2 is_male_untreated 0.00 OK \n", - "3 pre_spends_untreated 0.01 OK " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# matching quality result - PSI\n", - "model.quality_result['psi']" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
match_control_to_treatmatch_treat_to_control
age1.0000000.419063
is_male0.3205750.070995
pre_spends1.0000000.510057
\n", - "
" - ], - "text/plain": [ - " match_control_to_treat match_treat_to_control\n", - "age 1.000000 0.419063\n", - "is_male 0.320575 0.070995\n", - "pre_spends 1.000000 0.510057" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# matching quality result - KS-test\n", - "\n", - "model.quality_result['ks_test']" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'match_control_to_treat': 0.94, 'match_treat_to_control': 0.08}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# matching quality result - repeats\n", - "model.quality_result['repeats']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.3 Validation\n", - "validate result with placebo treatment or random feature or random subset\n", - "our new effect size is close to zero (it should be)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "699b93dfdb404e3aa786d5c56bb829a2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/10 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
treatpre_spendspost_spendsageis_maleindustry_2user_idsignup_month
01491.0517.666667650003
11460.0528.000000200113
61513.5533.3333336810103
101485.5510.1111113601163
191504.0525.0000003710313
\n", - "" - ], - "text/plain": [ - " treat pre_spends post_spends age is_male industry_2 user_id \\\n", - "0 1 491.0 517.666667 65 0 0 0 \n", - "1 1 460.0 528.000000 20 0 1 1 \n", - "6 1 513.5 533.333333 68 1 0 10 \n", - "10 1 485.5 510.111111 36 0 1 16 \n", - "19 1 504.0 525.000000 37 1 0 31 \n", - "\n", - " signup_month \n", - "0 3 \n", - "1 3 \n", - "6 3 \n", - "10 3 \n", - "19 3 " - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "no_replacement_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(862, 8)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "no_replacement_df.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Save model" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "model.save(\"test_model.pickle\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "model2 = Matcher.load(\"test_model.pickle\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2.validate_result()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/examples/tutorials/Tutorial_13_ABtesting.ipynb b/examples/tutorials/Tutorial_13_ABtesting.ipynb deleted file mode 100644 index fbfefe41..00000000 --- a/examples/tutorials/Tutorial_13_ABtesting.ipynb +++ /dev/null @@ -1,2120 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "64e2de80", - "metadata": {}, - "source": [ - "# How to perform AA and AB tests" - ] - }, - { - "cell_type": "markdown", - "id": "9f52ff79", - "metadata": {}, - "source": [ - "## 0. Import Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "6c2c62f0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'nlp' extra dependecy package 'gensim' isn't installed. Look at README.md in repo 'LightAutoML' for installation instructions.\n", - "'nlp' extra dependecy package 'transformers' isn't installed. Look at README.md in repo 'LightAutoML' for installation instructions.\n", - "'nlp' extra dependecy package 'gensim' isn't installed. Look at README.md in repo 'LightAutoML' for installation instructions.\n", - "'nlp' extra dependecy package 'transformers' isn't installed. Look at README.md in repo 'LightAutoML' for installation instructions.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\20810012\\Desktop\\Задачи\\code\\matcher\\lightautoml\\ml_algo\\dl_model.py:41: UserWarning: 'transformers' - package isn't installed\n", - " warnings.warn(\"'transformers' - package isn't installed\")\n", - "C:\\Users\\20810012\\Desktop\\Задачи\\code\\matcher\\lightautoml\\text\\nn_model.py:22: UserWarning: 'transformers' - package isn't installed\n", - " warnings.warn(\"'transformers' - package isn't installed\")\n", - "C:\\Users\\20810012\\Desktop\\Задачи\\code\\matcher\\lightautoml\\text\\dl_transformers.py:25: UserWarning: 'transformers' - package isn't installed\n", - " warnings.warn(\"'transformers' - package isn't installed\")\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from lightautoml.addons.hypex.ABTesting.ab_tester import AATest, ABTest\n", - "from lightautoml.addons.hypex.utils.tutorial_data_creation import create_test_data\n", - "\n", - "pd.options.display.float_format = '{:,.2f}'.format\n", - "\n", - "np.random.seed(52) #needed to create example data\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "id": "2dca3eaa", - "metadata": {}, - "source": [ - "## 1. Create or upload your dataset\n", - "In this case we will create random dataset with known effect size \n", - "If you have your own dataset, go to the part 2 " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7b655d2d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Length of na_step is less than length of columns. Used last value several times\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustry
0000488.00414.44NaNME-commerce
1300501.50424.3331.00NaNLogistics
21000522.50416.2264.00ME-commerce
31200472.00423.7843.00ME-commerce
41300508.50424.2236.00FE-commerce
...........................
5365999100482.50421.8923.00FE-commerce
5366999200491.50424.0044.00ME-commerce
5367999400486.00423.7827.00FLogistics
5368999600500.50430.8956.00FE-commerce
5369999731473.00534.1156.00MLogistics
\n", - "

5370 rows × 8 columns

\n", - "
" - ], - "text/plain": [ - " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 0 0 0 488.00 414.44 NaN M \n", - "1 3 0 0 501.50 424.33 31.00 NaN \n", - "2 10 0 0 522.50 416.22 64.00 M \n", - "3 12 0 0 472.00 423.78 43.00 M \n", - "4 13 0 0 508.50 424.22 36.00 F \n", - "... ... ... ... ... ... ... ... \n", - "5365 9991 0 0 482.50 421.89 23.00 F \n", - "5366 9992 0 0 491.50 424.00 44.00 M \n", - "5367 9994 0 0 486.00 423.78 27.00 F \n", - "5368 9996 0 0 500.50 430.89 56.00 F \n", - "5369 9997 3 1 473.00 534.11 56.00 M \n", - "\n", - " industry \n", - "0 E-commerce \n", - "1 Logistics \n", - "2 E-commerce \n", - "3 E-commerce \n", - "4 E-commerce \n", - "... ... \n", - "5365 E-commerce \n", - "5366 E-commerce \n", - "5367 Logistics \n", - "5368 E-commerce \n", - "5369 Logistics \n", - "\n", - "[5370 rows x 8 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = create_test_data(rs=52, na_step=10, nan_cols=['age', 'gender'])\n", - "data" - ] - }, - { - "cell_type": "markdown", - "id": "a0402e83", - "metadata": {}, - "source": [ - "## 2. AATest \n", - "*AB-test is shown in section 3*" - ] - }, - { - "cell_type": "markdown", - "id": "b3733f84", - "metadata": {}, - "source": [ - "### 2.0 Initialize parameters\n", - "`info_col` used to define informative attributes that should NOT be part of testing, such as user_id and signup_month
" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "bc8e4ac0", - "metadata": {}, - "outputs": [], - "source": [ - "info_cols = ['user_id', 'signup_month']\n", - "target = ['post_spends', 'pre_spends']" - ] - }, - { - "cell_type": "markdown", - "id": "75c196ea", - "metadata": {}, - "source": [ - "### 2.1 Simple AA-test\n", - "This is the easiest way to initialize and calculate metrics on a AA-test (default - on 10 iterations)
\n", - "Use it when you are clear about each attribute or if you don't have any additional task conditions (like grouping)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "701d20c0", - "metadata": {}, - "outputs": [], - "source": [ - "experiment = AATest(info_cols=info_cols, target_fields=target)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a3d70bf6", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "dda8a3243b0b48128fe194d4ca767592", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/10 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
random_statepost_spends a meanpost_spends b meanpost_spends ab deltapost_spends ab delta %post_spends t_test p_valuepost_spends ks_test p_valuepost_spends t_test passedpost_spends ks_test passedpre_spends a meanpre_spends b meanpre_spends ab deltapre_spends ab delta %pre_spends t_test p_valuepre_spends ks_test p_valuepre_spends t_test passedpre_spends ks_test passedmean_tests_score
00427.85428.480.630.150.420.86TrueTrue484.63485.220.590.120.170.67TrueTrue0.53
11427.67428.650.980.230.210.54TrueTrue484.81485.040.230.050.600.93TrueTrue0.57
22428.38427.94-0.44-0.100.570.96TrueTrue484.76485.090.330.070.450.86TrueTrue0.71
\n", - "" - ], - "text/plain": [ - " random_state post_spends a mean post_spends b mean post_spends ab delta \\\n", - "0 0 427.85 428.48 0.63 \n", - "1 1 427.67 428.65 0.98 \n", - "2 2 428.38 427.94 -0.44 \n", - "\n", - " post_spends ab delta % post_spends t_test p_value \\\n", - "0 0.15 0.42 \n", - "1 0.23 0.21 \n", - "2 -0.10 0.57 \n", - "\n", - " post_spends ks_test p_value post_spends t_test passed \\\n", - "0 0.86 True \n", - "1 0.54 True \n", - "2 0.96 True \n", - "\n", - " post_spends ks_test passed pre_spends a mean pre_spends b mean \\\n", - "0 True 484.63 485.22 \n", - "1 True 484.81 485.04 \n", - "2 True 484.76 485.09 \n", - "\n", - " pre_spends ab delta pre_spends ab delta % pre_spends t_test p_value \\\n", - "0 0.59 0.12 0.17 \n", - "1 0.23 0.05 0.60 \n", - "2 0.33 0.07 0.45 \n", - "\n", - " pre_spends ks_test p_value pre_spends t_test passed \\\n", - "0 0.67 True \n", - "1 0.93 True \n", - "2 0.86 True \n", - "\n", - " pre_spends ks_test passed mean_tests_score \n", - "0 True 0.53 \n", - "1 True 0.57 \n", - "2 True 0.71 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "experiment_result.head(3)" - ] - }, - { - "cell_type": "markdown", - "id": "d9f415c2", - "metadata": {}, - "source": [ - "`dict_of_datas` is a dictionary with random_states as keys and dataframes as values.
\n", - "Result of separation can be find in column 'group', it contains values 'test' and 'control'" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "cac4e650", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustrygroup
0300501.50424.3331.00NaNLogisticstest
11000522.50416.2264.00ME-commercetest
21200472.00423.7843.00ME-commercetest
\n", - "
" - ], - "text/plain": [ - " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 3 0 0 501.50 424.33 31.00 NaN \n", - "1 10 0 0 522.50 416.22 64.00 M \n", - "2 12 0 0 472.00 423.78 43.00 M \n", - "\n", - " industry group \n", - "0 Logistics test \n", - "1 E-commerce test \n", - "2 E-commerce test " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dict_of_datas[0].head(3)" - ] - }, - { - "cell_type": "markdown", - "id": "c277b0b9", - "metadata": {}, - "source": [ - "#### - Single experiment\n", - "To get stable results lets fix `random_state`" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "01265e9e", - "metadata": {}, - "outputs": [], - "source": [ - "random_state = 11" - ] - }, - { - "cell_type": "markdown", - "id": "c4a1cd70", - "metadata": {}, - "source": [ - "To perform single experiment you can use `sampling_metrics()`" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6f1a8cf6", - "metadata": {}, - "outputs": [], - "source": [ - "experiment = AATest(info_cols=info_cols, target_fields=target)\n", - "metrics, dict_of_datas = experiment.sampling_metrics(data, random_state=random_state).values()" - ] - }, - { - "cell_type": "markdown", - "id": "4971e2e8", - "metadata": {}, - "source": [ - "The results contains the same info as in multisampling, but on one experiment" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "bad5e42e", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'random_state': 11,\n", - " 'post_spends a mean': 427.78932340161515,\n", - " 'post_spends b mean': 428.53478170908403,\n", - " 'post_spends ab delta': 0.7454583074688799,\n", - " 'post_spends ab delta %': 0.17395514653345545,\n", - " 'post_spends t_test p_value': 0.33561550504114157,\n", - " 'post_spends ks_test p_value': 0.6263469727648824,\n", - " 'post_spends t_test passed': True,\n", - " 'post_spends ks_test passed': True,\n", - " 'pre_spends a mean': 484.9912476722533,\n", - " 'pre_spends b mean': 484.8584729981378,\n", - " 'pre_spends ab delta': -0.13277467411546695,\n", - " 'pre_spends ab delta %': -0.027384212406245112,\n", - " 'pre_spends t_test p_value': 0.7577698697749305,\n", - " 'pre_spends ks_test p_value': 0.762662388584242,\n", - " 'pre_spends t_test passed': True,\n", - " 'pre_spends ks_test passed': True,\n", - " 'mean_tests_score': 0.6205986840412991}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a9c3c513", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustrygroup
0300501.50424.3331.00NaNLogisticstest
11400497.00421.7826.00MLogisticstest
22100489.00433.1130.00ME-commercetest
32831479.50527.8920.00NaNE-commercetest
42900505.00414.3330.00ME-commercetest
..............................
5365998300494.50428.3329.00FE-commercecontrol
5366998400460.00417.1166.00MLogisticscontrol
5367998500484.00411.33NaNFE-commercecontrol
5368999400486.00423.7827.00FLogisticscontrol
5369999731473.00534.1156.00MLogisticscontrol
\n", - "

5370 rows × 9 columns

\n", - "
" - ], - "text/plain": [ - " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 3 0 0 501.50 424.33 31.00 NaN \n", - "1 14 0 0 497.00 421.78 26.00 M \n", - "2 21 0 0 489.00 433.11 30.00 M \n", - "3 28 3 1 479.50 527.89 20.00 NaN \n", - "4 29 0 0 505.00 414.33 30.00 M \n", - "... ... ... ... ... ... ... ... \n", - "5365 9983 0 0 494.50 428.33 29.00 F \n", - "5366 9984 0 0 460.00 417.11 66.00 M \n", - "5367 9985 0 0 484.00 411.33 NaN F \n", - "5368 9994 0 0 486.00 423.78 27.00 F \n", - "5369 9997 3 1 473.00 534.11 56.00 M \n", - "\n", - " industry group \n", - "0 Logistics test \n", - "1 Logistics test \n", - "2 E-commerce test \n", - "3 E-commerce test \n", - "4 E-commerce test \n", - "... ... ... \n", - "5365 E-commerce control \n", - "5366 Logistics control \n", - "5367 E-commerce control \n", - "5368 Logistics control \n", - "5369 Logistics control \n", - "\n", - "[5370 rows x 9 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dict_of_datas[random_state]" - ] - }, - { - "cell_type": "markdown", - "id": "5017639b", - "metadata": {}, - "source": [ - "### 2.2 AA-test with grouping" - ] - }, - { - "cell_type": "markdown", - "id": "e3a32245", - "metadata": {}, - "source": [ - "To perform experiment that separates samples by groups `group_col` can be used" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "2fba205a", - "metadata": {}, - "outputs": [], - "source": [ - "info_cols = ['user_id', 'signup_month']\n", - "target = ['post_spends', 'pre_spends']\n", - "\n", - "group_cols = 'industry'" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b5896bf8", - "metadata": {}, - "outputs": [], - "source": [ - "experiment = AATest(info_cols=info_cols, target_fields=target, group_cols=group_cols)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "6155253f", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2db73a6134ed470c8a03f06da4c0fee4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/10 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
user_id
industrygroup
E-commercecontrol1352
test1351
Logisticscontrol1334
test1333
\n", - "" - ], - "text/plain": [ - " user_id\n", - "industry group \n", - "E-commerce control 1352\n", - " test 1351\n", - "Logistics control 1334\n", - " test 1333" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dict_of_datas[0].groupby(['industry', 'group'])[['user_id']].count()" - ] - }, - { - "cell_type": "markdown", - "id": "2fa7d9e1", - "metadata": {}, - "source": [ - "### 2.3 AA-test with grouping and quantization" - ] - }, - { - "cell_type": "markdown", - "id": "bb882251", - "metadata": {}, - "source": [ - "To perform experiment that separates samples by groups `group_col`
\n", - "and if there is group that should not be separated `quant_field` can be used" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "86f60f09", - "metadata": {}, - "outputs": [], - "source": [ - "info_cols = ['user_id', 'signup_month']\n", - "target = ['post_spends', 'pre_spends']\n", - "\n", - "group_cols = 'industry'\n", - "quant_field = \"gender\"" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "26ee60aa", - "metadata": {}, - "outputs": [], - "source": [ - "experiment = AATest(info_cols=info_cols, target_fields=target, group_cols=group_cols, quant_field=quant_field)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "6a4bdd38", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "719d36f6378947dd8736aa56854f2632", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/10 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
gender
F2477
M2356
\n", - "" - ], - "text/plain": [ - " gender\n", - "F 2477\n", - "M 2356" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data['gender'].value_counts().to_frame()" - ] - }, - { - "cell_type": "markdown", - "id": "a2fff709", - "metadata": {}, - "source": [ - "The result is in the same format as without groups\n", - "\n", - "In this regime groups equally divided on each sample (test and control):" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "6f2068ae", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
user_id
industrygroupgender
E-commercecontrolF1255
M1165
LogisticscontrolM1191
testF1222
\n", - "
" - ], - "text/plain": [ - " user_id\n", - "industry group gender \n", - "E-commerce control F 1255\n", - " M 1165\n", - "Logistics control M 1191\n", - " test F 1222" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dict_of_datas[0].groupby(['industry', 'group', 'gender'])[['user_id']].count()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "42ba288a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
user_id
industrygroup
E-commercecontrol2420
test283
Logisticscontrol1445
test1222
\n", - "
" - ], - "text/plain": [ - " user_id\n", - "industry group \n", - "E-commerce control 2420\n", - " test 283\n", - "Logistics control 1445\n", - " test 1222" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dict_of_datas[0].groupby(['industry', 'group'])[['user_id']].count()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "4a264334", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustrygroup
02831479.50527.8920.00NaNE-commercetest
13100505.00427.2254.00FLogisticstest
23600456.00419.2259.00FLogisticstest
34100451.00426.7852.00FLogisticstest
44600508.50413.0060.00NaNE-commercetest
..............................
5365999000490.00426.0018.00ME-commercecontrol
5366999100482.50421.8923.00FE-commercecontrol
5367999200491.50424.0044.00ME-commercecontrol
5368999600500.50430.8956.00FE-commercecontrol
5369999731473.00534.1156.00MLogisticscontrol
\n", - "

5370 rows × 9 columns

\n", - "
" - ], - "text/plain": [ - " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 28 3 1 479.50 527.89 20.00 NaN \n", - "1 31 0 0 505.00 427.22 54.00 F \n", - "2 36 0 0 456.00 419.22 59.00 F \n", - "3 41 0 0 451.00 426.78 52.00 F \n", - "4 46 0 0 508.50 413.00 60.00 NaN \n", - "... ... ... ... ... ... ... ... \n", - "5365 9990 0 0 490.00 426.00 18.00 M \n", - "5366 9991 0 0 482.50 421.89 23.00 F \n", - "5367 9992 0 0 491.50 424.00 44.00 M \n", - "5368 9996 0 0 500.50 430.89 56.00 F \n", - "5369 9997 3 1 473.00 534.11 56.00 M \n", - "\n", - " industry group \n", - "0 E-commerce test \n", - "1 Logistics test \n", - "2 Logistics test \n", - "3 Logistics test \n", - "4 E-commerce test \n", - "... ... ... \n", - "5365 E-commerce control \n", - "5366 E-commerce control \n", - "5367 E-commerce control \n", - "5368 E-commerce control \n", - "5369 Logistics control \n", - "\n", - "[5370 rows x 9 columns]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dict_of_datas[0]" - ] - }, - { - "cell_type": "markdown", - "id": "d87c9442", - "metadata": {}, - "source": [ - "## 3. AB-test" - ] - }, - { - "cell_type": "markdown", - "id": "0bb6fece", - "metadata": {}, - "source": [ - "### 3.0 Data\n", - "Lets correct data to see how AB-test works" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "6f5a8a1f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustrygroup
0000488.00414.44NaNME-commercetest
1300501.50424.3331.00NaNLogisticstest
21000522.50416.2264.00ME-commercetest
\n", - "
" - ], - "text/plain": [ - " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 0 0 0 488.00 414.44 NaN M \n", - "1 3 0 0 501.50 424.33 31.00 NaN \n", - "2 10 0 0 522.50 416.22 64.00 M \n", - "\n", - " industry group \n", - "0 E-commerce test \n", - "1 Logistics test \n", - "2 E-commerce test " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_ab = data.copy()\n", - "\n", - "half_data = int(data.shape[0]/2)\n", - "data_ab['group'] = ['test']*half_data + ['control']*half_data\n", - "data_ab.head(3)" - ] - }, - { - "cell_type": "markdown", - "id": "690ceec5", - "metadata": {}, - "source": [ - "### 3.1 Full AB-test\n", - "\n", - "Full (basic) version of test includes calculation of all available metrics, which are: \"diff in means\", \"diff in diff\" and \"cuped\"
\n", - "Pay attention, that for \"cuped\" and \"diff in diff\" metrics required target before pilot." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "4108a137", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'size': {'test': 2685, 'control': 2685},\n", - " 'difference': {'ate': 0.9805090006207325,\n", - " 'cuped': 0.9764245308837189,\n", - " 'diff_in_diff': 0.39224084419458904},\n", - " 'p_value': {'t_test': 0.20533212744131019,\n", - " 'mann_whitney': 0.08089945933651932}}" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model = ABTest()\n", - "results = model.execute(\n", - " data=data_ab,\n", - " target_field='post_spends', \n", - " target_field_before='pre_spends', \n", - " group_field='group'\n", - ")\n", - "results" - ] - }, - { - "cell_type": "markdown", - "id": "05487531", - "metadata": {}, - "source": [ - "To see results in more convenient way `show_beautiful_result` can be used" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "9dd905e8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
size
test2685
control2685
\n", - "
" - ], - "text/plain": [ - " size\n", - "test 2685\n", - "control 2685" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
difference
ate0.98
cuped0.98
diff_in_diff0.39
\n", - "
" - ], - "text/plain": [ - " difference\n", - "ate 0.98\n", - "cuped 0.98\n", - "diff_in_diff 0.39" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
p_value
t_test0.21
mann_whitney0.08
\n", - "
" - ], - "text/plain": [ - " p_value\n", - "t_test 0.21\n", - "mann_whitney 0.08" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model.show_beautiful_result()" - ] - }, - { - "cell_type": "markdown", - "id": "ea252142", - "metadata": {}, - "source": [ - "### 3.2 Simple AB-test\n", - "To estimate effect without target data before pilot `calc_difference_method='ate'` can be used - effect will be estimated with \"diff in means\" method" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "0ab77779", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
size
test2685
control2685
\n", - "
" - ], - "text/plain": [ - " size\n", - "test 2685\n", - "control 2685" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
difference
ate0.98
\n", - "
" - ], - "text/plain": [ - " difference\n", - "ate 0.98" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
p_value
t_test0.21
mann_whitney0.08
\n", - "
" - ], - "text/plain": [ - " p_value\n", - "t_test 0.21\n", - "mann_whitney 0.08" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = ABTest(calc_difference_method='ate')\n", - "model.execute(data=data_ab, target_field='post_spends', group_field='group')\n", - "\n", - "model.show_beautiful_result()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6193f7f0", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 66a3c6f09e6214def8a2a5162b4c17710987dfff Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 22:23:37 +0300 Subject: [PATCH 11/16] Added new HypEx tutorials --- examples/tutorials/Tutorial_12_AA_Test.ipynb | 6646 +++++++++++++++++ ...Tutorial_13_AA_Test_multigroup_split.ipynb | 458 ++ examples/tutorials/Tutorial_14_AB_Test.ipynb | 498 ++ examples/tutorials/Tutorial_15_Matching.ipynb | 2532 +++++++ ...rial_16_Matching_without_replacement.ipynb | 786 ++ ...orial_17_Modeling_Limit_Distribution.ipynb | 3478 +++++++++ .../Tutorial_18_Test_Limit_Distribution.ipynb | 438 ++ 7 files changed, 14836 insertions(+) create mode 100644 examples/tutorials/Tutorial_12_AA_Test.ipynb create mode 100644 examples/tutorials/Tutorial_13_AA_Test_multigroup_split.ipynb create mode 100644 examples/tutorials/Tutorial_14_AB_Test.ipynb create mode 100644 examples/tutorials/Tutorial_15_Matching.ipynb create mode 100644 examples/tutorials/Tutorial_16_Matching_without_replacement.ipynb create mode 100644 examples/tutorials/Tutorial_17_Modeling_Limit_Distribution.ipynb create mode 100644 examples/tutorials/Tutorial_18_Test_Limit_Distribution.ipynb diff --git a/examples/tutorials/Tutorial_12_AA_Test.ipynb b/examples/tutorials/Tutorial_12_AA_Test.ipynb new file mode 100644 index 00000000..f16a3ac9 --- /dev/null +++ b/examples/tutorials/Tutorial_12_AA_Test.ipynb @@ -0,0 +1,6646 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "64e2de80", + "metadata": {}, + "source": [ + "# AA test \n", + "\n", + "_An A/A test is a variation of an A/B test, the peculiarity of which is that the original is compared with itself, as opposed to an A/B test, which compares samples before and after exposure._" + ] + }, + { + "cell_type": "markdown", + "id": "9f52ff79", + "metadata": {}, + "source": [ + "## 0. Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6c2c62f0", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from lightautoml.addons.hypex import AATest\n", + "from lightautoml.addons.hypex.utils.tutorial_data_creation import create_test_data\n", + "\n", + "pd.options.display.float_format = '{:,.2f}'.format\n", + "\n", + "np.random.seed(42) # needed to create example data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dfdea8117d160056", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "def show_result(result):\n", + " for k, v in result.items():\n", + " print(k)\n", + " display(v)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "id": "2dca3eaa", + "metadata": {}, + "source": [ + "## 1. Create or upload your dataset\n", + "In this case we will create random dataset with known effect size \n", + "If you have your own dataset, go to the part 2 " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7b655d2d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustry
0000488.00414.44NaNME-commerce
1181512.50462.2226.00NaNE-commerce
2271483.00479.4425.00MLogistics
3300501.50424.3339.00ME-commerce
4411543.00514.5618.00FE-commerce
...........................
99959995101538.50450.4442.00MLogistics
9996999600500.50430.8926.00FLogistics
9997999731473.00534.1122.00FE-commerce
9998999821495.00523.2267.00FE-commerce
9999999971508.00475.8938.00FE-commerce
\n", + "

10000 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 0 0 0 488.00 414.44 NaN M \n", + "1 1 8 1 512.50 462.22 26.00 NaN \n", + "2 2 7 1 483.00 479.44 25.00 M \n", + "3 3 0 0 501.50 424.33 39.00 M \n", + "4 4 1 1 543.00 514.56 18.00 F \n", + "... ... ... ... ... ... ... ... \n", + "9995 9995 10 1 538.50 450.44 42.00 M \n", + "9996 9996 0 0 500.50 430.89 26.00 F \n", + "9997 9997 3 1 473.00 534.11 22.00 F \n", + "9998 9998 2 1 495.00 523.22 67.00 F \n", + "9999 9999 7 1 508.00 475.89 38.00 F \n", + "\n", + " industry \n", + "0 E-commerce \n", + "1 E-commerce \n", + "2 Logistics \n", + "3 E-commerce \n", + "4 E-commerce \n", + "... ... \n", + "9995 Logistics \n", + "9996 Logistics \n", + "9997 E-commerce \n", + "9998 E-commerce \n", + "9999 E-commerce \n", + "\n", + "[10000 rows x 8 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = create_test_data(rs=52, na_step=10, nan_cols=['age', 'gender'])\n", + "data" + ] + }, + { + "cell_type": "markdown", + "id": "a0402e83", + "metadata": {}, + "source": [ + "## 2. AATest " + ] + }, + { + "cell_type": "markdown", + "id": "b3733f84", + "metadata": {}, + "source": [ + "### 2.0 Initialize parameters\n", + "`info_col` used to define informative attributes that should NOT be part of testing, such as user_id and signup_month
" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bc8e4ac0", + "metadata": {}, + "outputs": [], + "source": [ + "info_cols = ['user_id', 'signup_month']\n", + "target = ['post_spends', 'pre_spends']" + ] + }, + { + "cell_type": "markdown", + "id": "75c196ea", + "metadata": {}, + "source": [ + "### 2.1 Simple AA-test\n", + "This is the easiest way to initialize and calculate metrics on a AA-test (default - on 2000 iterations)
Use it when you are clear about each attribute or if you don't have any additional task conditions (like grouping) \n", + "\n", + "You can also add some extra arguments to the process(): \n", + "* plot_set - types of plot, that you want to show (\"hist\", \"cumulative\", \"percentile\")\n", + "* figsize - size of figure for plots \n", + "* alpha - value to change the transparency of the histogram plot \n", + "* bins - generic bin parameter that can be the name of a reference rule, the number of bins, or the breaks of the bins \n", + "* title_size - size of title for plots" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "701d20c0", + "metadata": {}, + "outputs": [], + "source": [ + "experiment = AATest(info_cols=info_cols, target_fields=target)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a3d70bf6", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "36de7e2a221b4879812ef8611158b80f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/2000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results = experiment.process(data, iterations=2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1665bf93f36d4cff", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "experiments\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
random_statepost_spends a meanpost_spends b meanpost_spends ab deltapost_spends ab delta %post_spends t-test p-valuepost_spends ks-test p-valuepost_spends t-test passedpost_spends ks-test passedpre_spends a mean...pre_spends ks-test passedcontrol %test %control sizetest sizet-test mean p-valueks-test mean p-valuet-test passed %ks-test passed %mean_tests_score
01452.18452.15-0.03-0.010.970.56FalseFalse487.33...False50.0050.00500050000.590.490.000.000.52
12452.82451.50-1.32-0.290.090.18FalseFalse487.04...False50.0050.00500050000.440.450.000.000.45
24452.41451.92-0.50-0.110.530.06FalseFalse487.20...False50.0050.00500050000.560.360.000.000.43
35452.64451.69-0.96-0.210.230.41FalseFalse486.90...False50.0050.00500050000.260.400.000.000.35
46452.70451.63-1.07-0.240.170.53FalseFalse487.31...False50.0050.00500050000.210.350.000.000.30
..................................................................
17551993452.29452.04-0.24-0.050.760.95FalseFalse486.96...False50.0050.00500050000.610.710.000.000.68
17561994452.56451.77-0.78-0.170.320.11FalseFalse487.12...False50.0050.00500050000.610.210.000.000.34
17571995452.30452.03-0.26-0.060.740.91FalseFalse486.94...False50.0050.00500050000.570.890.000.000.79
17581996451.89452.440.550.120.480.78FalseFalse487.30...False50.0050.00500050000.380.860.000.000.70
17591997452.52451.81-0.70-0.160.370.10FalseFalse487.10...False50.0050.00500050000.660.400.000.000.49
\n", + "

1760 rows × 26 columns

\n", + "
" + ], + "text/plain": [ + " random_state post_spends a mean post_spends b mean \\\n", + "0 1 452.18 452.15 \n", + "1 2 452.82 451.50 \n", + "2 4 452.41 451.92 \n", + "3 5 452.64 451.69 \n", + "4 6 452.70 451.63 \n", + "... ... ... ... \n", + "1755 1993 452.29 452.04 \n", + "1756 1994 452.56 451.77 \n", + "1757 1995 452.30 452.03 \n", + "1758 1996 451.89 452.44 \n", + "1759 1997 452.52 451.81 \n", + "\n", + " post_spends ab delta post_spends ab delta % \\\n", + "0 -0.03 -0.01 \n", + "1 -1.32 -0.29 \n", + "2 -0.50 -0.11 \n", + "3 -0.96 -0.21 \n", + "4 -1.07 -0.24 \n", + "... ... ... \n", + "1755 -0.24 -0.05 \n", + "1756 -0.78 -0.17 \n", + "1757 -0.26 -0.06 \n", + "1758 0.55 0.12 \n", + "1759 -0.70 -0.16 \n", + "\n", + " post_spends t-test p-value post_spends ks-test p-value \\\n", + "0 0.97 0.56 \n", + "1 0.09 0.18 \n", + "2 0.53 0.06 \n", + "3 0.23 0.41 \n", + "4 0.17 0.53 \n", + "... ... ... \n", + "1755 0.76 0.95 \n", + "1756 0.32 0.11 \n", + "1757 0.74 0.91 \n", + "1758 0.48 0.78 \n", + "1759 0.37 0.10 \n", + "\n", + " post_spends t-test passed post_spends ks-test passed \\\n", + "0 False False \n", + "1 False False \n", + "2 False False \n", + "3 False False \n", + "4 False False \n", + "... ... ... \n", + "1755 False False \n", + "1756 False False \n", + "1757 False False \n", + "1758 False False \n", + "1759 False False \n", + "\n", + " pre_spends a mean ... pre_spends ks-test passed control % test % \\\n", + "0 487.33 ... False 50.00 50.00 \n", + "1 487.04 ... False 50.00 50.00 \n", + "2 487.20 ... False 50.00 50.00 \n", + "3 486.90 ... False 50.00 50.00 \n", + "4 487.31 ... False 50.00 50.00 \n", + "... ... ... ... ... ... \n", + "1755 486.96 ... False 50.00 50.00 \n", + "1756 487.12 ... False 50.00 50.00 \n", + "1757 486.94 ... False 50.00 50.00 \n", + "1758 487.30 ... False 50.00 50.00 \n", + "1759 487.10 ... False 50.00 50.00 \n", + "\n", + " control size test size t-test mean p-value ks-test mean p-value \\\n", + "0 5000 5000 0.59 0.49 \n", + "1 5000 5000 0.44 0.45 \n", + "2 5000 5000 0.56 0.36 \n", + "3 5000 5000 0.26 0.40 \n", + "4 5000 5000 0.21 0.35 \n", + "... ... ... ... ... \n", + "1755 5000 5000 0.61 0.71 \n", + "1756 5000 5000 0.61 0.21 \n", + "1757 5000 5000 0.57 0.89 \n", + "1758 5000 5000 0.38 0.86 \n", + "1759 5000 5000 0.66 0.40 \n", + "\n", + " t-test passed % ks-test passed % mean_tests_score \n", + "0 0.00 0.00 0.52 \n", + "1 0.00 0.00 0.45 \n", + "2 0.00 0.00 0.43 \n", + "3 0.00 0.00 0.35 \n", + "4 0.00 0.00 0.30 \n", + "... ... ... ... \n", + "1755 0.00 0.00 0.68 \n", + "1756 0.00 0.00 0.34 \n", + "1757 0.00 0.00 0.79 \n", + "1758 0.00 0.00 0.70 \n", + "1759 0.00 0.00 0.49 \n", + "\n", + "[1760 rows x 26 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "aa_score\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
t-test passed scoreks-test passed scoret-test aa passedks-test aa passed
post_spends0.000.000.000.00
pre_spends0.000.000.000.00
mean0.000.000.000.00
\n", + "
" + ], + "text/plain": [ + " t-test passed score ks-test passed score t-test aa passed \\\n", + "post_spends 0.00 0.00 0.00 \n", + "pre_spends 0.00 0.00 0.00 \n", + "mean 0.00 0.00 0.00 \n", + "\n", + " ks-test aa passed \n", + "post_spends 0.00 \n", + "pre_spends 0.00 \n", + "mean 0.00 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "split\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustrygroup
0000488.00414.44NaNME-commercetest
1181512.50462.2226.00NaNE-commercetest
2411543.00514.5618.00FE-commercetest
3561486.50486.5644.00ME-commercetest
4841465.50506.0066.00MLogisticstest
..............................
9995999000490.00426.00NaNMLogisticscontrol
9996999200491.50424.0029.00ME-commercecontrol
9997999600500.50430.8926.00FLogisticscontrol
9998999731473.00534.1122.00FE-commercecontrol
9999999821495.00523.2267.00FE-commercecontrol
\n", + "

10000 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 0 0 0 488.00 414.44 NaN M \n", + "1 1 8 1 512.50 462.22 26.00 NaN \n", + "2 4 1 1 543.00 514.56 18.00 F \n", + "3 5 6 1 486.50 486.56 44.00 M \n", + "4 8 4 1 465.50 506.00 66.00 M \n", + "... ... ... ... ... ... ... ... \n", + "9995 9990 0 0 490.00 426.00 NaN M \n", + "9996 9992 0 0 491.50 424.00 29.00 M \n", + "9997 9996 0 0 500.50 430.89 26.00 F \n", + "9998 9997 3 1 473.00 534.11 22.00 F \n", + "9999 9998 2 1 495.00 523.22 67.00 F \n", + "\n", + " industry group \n", + "0 E-commerce test \n", + "1 E-commerce test \n", + "2 E-commerce test \n", + "3 E-commerce test \n", + "4 Logistics test \n", + "... ... ... \n", + "9995 Logistics control \n", + "9996 E-commerce control \n", + "9997 Logistics control \n", + "9998 E-commerce control \n", + "9999 E-commerce control \n", + "\n", + "[10000 rows x 9 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "best_experiment_stat\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
a meanb meanab deltaab delta %t-test p-valueks-test p-valuet-test passedks-test passed
post_spends452.22452.11-0.11-0.020.891.00FalseFalse
pre_spends487.09487.100.000.000.991.00FalseFalse
\n", + "
" + ], + "text/plain": [ + " a mean b mean ab delta ab delta % t-test p-value ks-test p-value \\\n", + "post_spends 452.22 452.11 -0.11 -0.02 0.89 1.00 \n", + "pre_spends 487.09 487.10 0.00 0.00 0.99 1.00 \n", + "\n", + " t-test passed ks-test passed \n", + "post_spends False False \n", + "pre_spends False False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "split_stat\n" + ] + }, + { + "data": { + "text/plain": [ + "control % 50.00\n", + "test % 50.00\n", + "control size 5000\n", + "test size 5000\n", + "t-test mean p-value 0.94\n", + "ks-test mean p-value 1.00\n", + "t-test passed % 0.00\n", + "ks-test passed % 0.00\n", + "mean_tests_score 0.98\n", + "Name: 60, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "resume\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
aa test passedsplit is uniform
post_spendsnot OKOK
pre_spendsnot OKOK
\n", + "
" + ], + "text/plain": [ + " aa test passed split is uniform\n", + "post_spends not OK OK\n", + "pre_spends not OK OK" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "show_result(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2adb8cdd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['experiments', 'aa_score', 'split', 'best_experiment_stat', 'split_stat', 'resume'])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.keys()" + ] + }, + { + "cell_type": "markdown", + "id": "d9f415c2", + "metadata": {}, + "source": [ + "`results` is a dictionary with dataframes as values.
\n", + "* 'split' - result of separation, column 'group' contains values 'test' and 'control' \n", + "* 'resume' - summary of all results \n", + "* 'aa_score' - score of T-test and Kolmogorov-Smirnov test \n", + "* 'experiments' - is a table of results of experiments, which includes \n", + " - means of all targets in a and b samples, \n", + " - p_values of Student t-test and test Kolmogorova-Smirnova, \n", + " - and results of tests (did data on the random_state passes the uniform test)\n", + "* 'best_experiment_stat' - like previous point but only for the best experiment \n", + "* 'split_stat' - metrics and statistics tests for result of split\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ea319b3762723578", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
t-test passed scoreks-test passed scoret-test aa passedks-test aa passed
post_spends0.000.000.000.00
pre_spends0.000.000.000.00
mean0.000.000.000.00
\n", + "
" + ], + "text/plain": [ + " t-test passed score ks-test passed score t-test aa passed \\\n", + "post_spends 0.00 0.00 0.00 \n", + "pre_spends 0.00 0.00 0.00 \n", + "mean 0.00 0.00 0.00 \n", + "\n", + " ks-test aa passed \n", + "post_spends 0.00 \n", + "pre_spends 0.00 \n", + "mean 0.00 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results['aa_score']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9743659416932461", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
aa test passedsplit is uniform
post_spendsnot OKOK
pre_spendsnot OKOK
\n", + "
" + ], + "text/plain": [ + " aa test passed split is uniform\n", + "post_spends not OK OK\n", + "pre_spends not OK OK" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results['resume']" + ] + }, + { + "cell_type": "markdown", + "id": "c277b0b9", + "metadata": {}, + "source": [ + "### 2.2 Single experiment\n", + "To get stable results lets fix `random_state`" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "01265e9e", + "metadata": {}, + "outputs": [], + "source": [ + "random_state = 11" + ] + }, + { + "cell_type": "markdown", + "id": "c4a1cd70", + "metadata": {}, + "source": [ + "To perform single experiment you can use `sampling_metrics()`" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6f1a8cf6", + "metadata": {}, + "outputs": [], + "source": [ + "experiment = AATest(info_cols=info_cols, target_fields=target)\n", + "metrics, dict_of_datas = experiment.sampling_metrics(data=data, random_state=random_state).values()" + ] + }, + { + "cell_type": "markdown", + "id": "4971e2e8", + "metadata": {}, + "source": [ + "The results contains the same info as in multisampling, but on one experiment" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "bad5e42e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'random_state': 11,\n", + " 'post_spends a mean': 451.8546,\n", + " 'post_spends b mean': 452.4745111111112,\n", + " 'post_spends ab delta': 0.6199111111112074,\n", + " 'post_spends ab delta %': 0.13700464797208323,\n", + " 'post_spends t-test p-value': 0.43154056610193947,\n", + " 'post_spends ks-test p-value': 0.95721723072851,\n", + " 'post_spends t-test passed': False,\n", + " 'post_spends ks-test passed': False,\n", + " 'pre_spends a mean': 487.2131,\n", + " 'pre_spends b mean': 486.9744,\n", + " 'pre_spends ab delta': -0.23869999999999436,\n", + " 'pre_spends ab delta %': -0.04901695037766718,\n", + " 'pre_spends t-test p-value': 0.5271083329122467,\n", + " 'pre_spends ks-test p-value': 0.14861030130677552,\n", + " 'pre_spends t-test passed': False,\n", + " 'pre_spends ks-test passed': False,\n", + " 'control %': 50.0,\n", + " 'test %': 50.0,\n", + " 'control size': 5000,\n", + " 'test size': 5000,\n", + " 't-test mean p-value': 0.4793244495070931,\n", + " 'ks-test mean p-value': 0.5529137660176427,\n", + " 't-test passed %': 0.0,\n", + " 'ks-test passed %': 0.0,\n", + " 'mean_tests_score': 0.5283839938474595}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a9c3c513", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustrygroup
0181512.50462.2226.00NaNE-commercetest
1271483.00479.4425.00MLogisticstest
2561486.50486.5644.00ME-commercetest
36111483.50433.8928.00FLogisticstest
41141498.50516.8958.00NaNE-commercetest
..............................
9995998600494.00432.1139.00MLogisticscontrol
9996998961466.50487.4419.00FE-commercecontrol
9997999100482.50421.8943.00NaNLogisticscontrol
99989995101538.50450.4442.00MLogisticscontrol
9999999821495.00523.2267.00FE-commercecontrol
\n", + "

10000 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 1 8 1 512.50 462.22 26.00 NaN \n", + "1 2 7 1 483.00 479.44 25.00 M \n", + "2 5 6 1 486.50 486.56 44.00 M \n", + "3 6 11 1 483.50 433.89 28.00 F \n", + "4 11 4 1 498.50 516.89 58.00 NaN \n", + "... ... ... ... ... ... ... ... \n", + "9995 9986 0 0 494.00 432.11 39.00 M \n", + "9996 9989 6 1 466.50 487.44 19.00 F \n", + "9997 9991 0 0 482.50 421.89 43.00 NaN \n", + "9998 9995 10 1 538.50 450.44 42.00 M \n", + "9999 9998 2 1 495.00 523.22 67.00 F \n", + "\n", + " industry group \n", + "0 E-commerce test \n", + "1 Logistics test \n", + "2 E-commerce test \n", + "3 Logistics test \n", + "4 E-commerce test \n", + "... ... ... \n", + "9995 Logistics control \n", + "9996 E-commerce control \n", + "9997 Logistics control \n", + "9998 Logistics control \n", + "9999 E-commerce control \n", + "\n", + "[10000 rows x 9 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dict_of_datas[random_state]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "bc1297497a9c63f7", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "results = experiment.experiment_result_transform(pd.Series(metrics))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8b70cf64c1d013a9", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['best_experiment_stat', 'best_split_stat'])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "82a3bf8a9bdca25", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
a meanb meanab deltaab delta %t-test p-valueks-test p-valuet-test passedks-test passed
post_spends451.85452.470.620.140.430.96FalseFalse
pre_spends487.21486.97-0.24-0.050.530.15FalseFalse
\n", + "
" + ], + "text/plain": [ + " a mean b mean ab delta ab delta % t-test p-value ks-test p-value \\\n", + "post_spends 451.85 452.47 0.62 0.14 0.43 0.96 \n", + "pre_spends 487.21 486.97 -0.24 -0.05 0.53 0.15 \n", + "\n", + " t-test passed ks-test passed \n", + "post_spends False False \n", + "pre_spends False False " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results['best_experiment_stat']" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d168c3717090854a", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "control % 50.00\n", + "test % 50.00\n", + "control size 5000\n", + "test size 5000\n", + "t-test mean p-value 0.48\n", + "ks-test mean p-value 0.55\n", + "t-test passed % 0.00\n", + "ks-test passed % 0.00\n", + "mean_tests_score 0.53\n", + "dtype: object" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results['best_split_stat']" + ] + }, + { + "cell_type": "markdown", + "id": "5017639b", + "metadata": {}, + "source": [ + "### 2.3 AA-test with grouping" + ] + }, + { + "cell_type": "markdown", + "id": "e3a32245", + "metadata": {}, + "source": [ + "To perform experiment that separates samples by groups `group_col` can be used" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "2fba205a", + "metadata": {}, + "outputs": [], + "source": [ + "info_cols = ['user_id', 'signup_month']\n", + "target = ['post_spends', 'pre_spends']\n", + "\n", + "group_cols = 'industry'" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "b5896bf8", + "metadata": {}, + "outputs": [], + "source": [ + "experiment = AATest(info_cols=info_cols, target_fields=target, group_cols=group_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "6155253f", + "metadata": { + "jupyter": { + "is_executing": true + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f0b87388cc5e4dd28c13091a1e3eeb0c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/2000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results = experiment.process(data=data, iterations=2000)" + ] + }, + { + "cell_type": "markdown", + "id": "45f18b03", + "metadata": {}, + "source": [ + "The result is in the same format as without groups\n", + "\n", + "In this regime groups equally divided on each sample (test and control):" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "451022e7ea12f453", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "industry\n", + "Logistics 50.15\n", + "E-commerce 49.85\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results['split']['industry'].value_counts(normalize=True) * 100" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "520ed05c", + "metadata": { + "jupyter": { + "is_executing": true + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_id
industrygroup
E-commercecontrol2493
test2492
Logisticscontrol2508
test2507
\n", + "
" + ], + "text/plain": [ + " user_id\n", + "industry group \n", + "E-commerce control 2493\n", + " test 2492\n", + "Logistics control 2508\n", + " test 2507" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results['split'].groupby(['industry', 'group'])[['user_id']].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "726f57af0084ff72", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "experiments\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
random_statepost_spends a meanpost_spends b meanpost_spends ab deltapost_spends ab delta %post_spends t-test p-valuepost_spends ks-test p-valuepost_spends t-test passedpost_spends ks-test passedpre_spends a mean...pre_spends ks-test passedcontrol %test %control sizetest sizet-test mean p-valueks-test mean p-valuet-test passed %ks-test passed %mean_tests_score
00451.53452.801.270.280.110.19FalseFalse487.00...False50.0149.99500149990.360.520.000.000.47
12452.53451.80-0.73-0.160.350.83FalseFalse487.19...False50.0149.99500149990.470.920.000.000.77
23452.10452.230.130.030.870.85FalseFalse487.11...False50.0149.99500149990.900.930.000.000.92
34452.18452.15-0.03-0.010.970.30FalseFalse487.20...False50.0149.99500149990.770.470.000.000.57
47452.38451.95-0.42-0.090.590.40FalseFalse487.20...False50.0149.99500149990.580.500.000.000.53
..................................................................
17231995452.70451.63-1.07-0.240.180.14FalseFalse487.40...False50.0149.99500149990.140.160.000.000.16
17241996452.36451.96-0.40-0.090.610.81FalseFalse487.08...False50.0149.99500149990.780.690.000.000.72
17251997452.08452.250.180.040.820.59FalseFalse487.04...False50.0149.99500149990.790.660.000.000.70
17261998451.96452.360.400.090.610.44FalseFalse486.85...False50.0149.99500149990.410.540.000.000.50
17271999452.38451.95-0.42-0.090.590.54FalseFalse487.21...False50.0149.99500149990.570.620.000.000.60
\n", + "

1728 rows × 26 columns

\n", + "
" + ], + "text/plain": [ + " random_state post_spends a mean post_spends b mean \\\n", + "0 0 451.53 452.80 \n", + "1 2 452.53 451.80 \n", + "2 3 452.10 452.23 \n", + "3 4 452.18 452.15 \n", + "4 7 452.38 451.95 \n", + "... ... ... ... \n", + "1723 1995 452.70 451.63 \n", + "1724 1996 452.36 451.96 \n", + "1725 1997 452.08 452.25 \n", + "1726 1998 451.96 452.36 \n", + "1727 1999 452.38 451.95 \n", + "\n", + " post_spends ab delta post_spends ab delta % \\\n", + "0 1.27 0.28 \n", + "1 -0.73 -0.16 \n", + "2 0.13 0.03 \n", + "3 -0.03 -0.01 \n", + "4 -0.42 -0.09 \n", + "... ... ... \n", + "1723 -1.07 -0.24 \n", + "1724 -0.40 -0.09 \n", + "1725 0.18 0.04 \n", + "1726 0.40 0.09 \n", + "1727 -0.42 -0.09 \n", + "\n", + " post_spends t-test p-value post_spends ks-test p-value \\\n", + "0 0.11 0.19 \n", + "1 0.35 0.83 \n", + "2 0.87 0.85 \n", + "3 0.97 0.30 \n", + "4 0.59 0.40 \n", + "... ... ... \n", + "1723 0.18 0.14 \n", + "1724 0.61 0.81 \n", + "1725 0.82 0.59 \n", + "1726 0.61 0.44 \n", + "1727 0.59 0.54 \n", + "\n", + " post_spends t-test passed post_spends ks-test passed \\\n", + "0 False False \n", + "1 False False \n", + "2 False False \n", + "3 False False \n", + "4 False False \n", + "... ... ... \n", + "1723 False False \n", + "1724 False False \n", + "1725 False False \n", + "1726 False False \n", + "1727 False False \n", + "\n", + " pre_spends a mean ... pre_spends ks-test passed control % test % \\\n", + "0 487.00 ... False 50.01 49.99 \n", + "1 487.19 ... False 50.01 49.99 \n", + "2 487.11 ... False 50.01 49.99 \n", + "3 487.20 ... False 50.01 49.99 \n", + "4 487.20 ... False 50.01 49.99 \n", + "... ... ... ... ... ... \n", + "1723 487.40 ... False 50.01 49.99 \n", + "1724 487.08 ... False 50.01 49.99 \n", + "1725 487.04 ... False 50.01 49.99 \n", + "1726 486.85 ... False 50.01 49.99 \n", + "1727 487.21 ... False 50.01 49.99 \n", + "\n", + " control size test size t-test mean p-value ks-test mean p-value \\\n", + "0 5001 4999 0.36 0.52 \n", + "1 5001 4999 0.47 0.92 \n", + "2 5001 4999 0.90 0.93 \n", + "3 5001 4999 0.77 0.47 \n", + "4 5001 4999 0.58 0.50 \n", + "... ... ... ... ... \n", + "1723 5001 4999 0.14 0.16 \n", + "1724 5001 4999 0.78 0.69 \n", + "1725 5001 4999 0.79 0.66 \n", + "1726 5001 4999 0.41 0.54 \n", + "1727 5001 4999 0.57 0.62 \n", + "\n", + " t-test passed % ks-test passed % mean_tests_score \n", + "0 0.00 0.00 0.47 \n", + "1 0.00 0.00 0.77 \n", + "2 0.00 0.00 0.92 \n", + "3 0.00 0.00 0.57 \n", + "4 0.00 0.00 0.53 \n", + "... ... ... ... \n", + "1723 0.00 0.00 0.16 \n", + "1724 0.00 0.00 0.72 \n", + "1725 0.00 0.00 0.70 \n", + "1726 0.00 0.00 0.50 \n", + "1727 0.00 0.00 0.60 \n", + "\n", + "[1728 rows x 26 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "aa_score\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
t-test passed scoreks-test passed scoret-test aa passedks-test aa passed
post_spends0.000.000.000.00
pre_spends0.000.000.000.00
mean0.000.000.000.00
\n", + "
" + ], + "text/plain": [ + " t-test passed score ks-test passed score t-test aa passed \\\n", + "post_spends 0.00 0.00 0.00 \n", + "pre_spends 0.00 0.00 0.00 \n", + "mean 0.00 0.00 0.00 \n", + "\n", + " ks-test aa passed \n", + "post_spends 0.00 \n", + "pre_spends 0.00 \n", + "mean 0.00 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "split\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustrygroup
0000488.00414.44NaNME-commercetest
1271483.00479.4425.00MLogisticstest
2411543.00514.5618.00FE-commercetest
3561486.50486.5644.00ME-commercetest
47111496.00432.8957.00ME-commercetest
..............................
9995998300494.50428.3331.00FLogisticscontrol
9996998400460.00417.1156.00MLogisticscontrol
9997998500484.00411.3352.00ME-commercecontrol
9998999100482.50421.8943.00NaNLogisticscontrol
9999999400486.00423.7869.00FLogisticscontrol
\n", + "

10000 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 0 0 0 488.00 414.44 NaN M \n", + "1 2 7 1 483.00 479.44 25.00 M \n", + "2 4 1 1 543.00 514.56 18.00 F \n", + "3 5 6 1 486.50 486.56 44.00 M \n", + "4 7 11 1 496.00 432.89 57.00 M \n", + "... ... ... ... ... ... ... ... \n", + "9995 9983 0 0 494.50 428.33 31.00 F \n", + "9996 9984 0 0 460.00 417.11 56.00 M \n", + "9997 9985 0 0 484.00 411.33 52.00 M \n", + "9998 9991 0 0 482.50 421.89 43.00 NaN \n", + "9999 9994 0 0 486.00 423.78 69.00 F \n", + "\n", + " industry group \n", + "0 E-commerce test \n", + "1 Logistics test \n", + "2 E-commerce test \n", + "3 E-commerce test \n", + "4 E-commerce test \n", + "... ... ... \n", + "9995 Logistics control \n", + "9996 Logistics control \n", + "9997 E-commerce control \n", + "9998 Logistics control \n", + "9999 Logistics control \n", + "\n", + "[10000 rows x 9 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "best_experiment_stat\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
a meanb meanab deltaab delta %t-test p-valueks-test p-valuet-test passedks-test passed
post_spends452.18452.15-0.03-0.010.970.99FalseFalse
pre_spends487.08487.110.030.010.931.00FalseFalse
\n", + "
" + ], + "text/plain": [ + " a mean b mean ab delta ab delta % t-test p-value ks-test p-value \\\n", + "post_spends 452.18 452.15 -0.03 -0.01 0.97 0.99 \n", + "pre_spends 487.08 487.11 0.03 0.01 0.93 1.00 \n", + "\n", + " t-test passed ks-test passed \n", + "post_spends False False \n", + "pre_spends False False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "split_stat\n" + ] + }, + { + "data": { + "text/plain": [ + "control % 50.01\n", + "test % 49.99\n", + "control size 5001\n", + "test size 4999\n", + "t-test mean p-value 0.95\n", + "ks-test mean p-value 0.99\n", + "t-test passed % 0.00\n", + "ks-test passed % 0.00\n", + "mean_tests_score 0.98\n", + "Name: 1395, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "resume\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
aa test passedsplit is uniform
post_spendsnot OKOK
pre_spendsnot OKOK
\n", + "
" + ], + "text/plain": [ + " aa test passed split is uniform\n", + "post_spends not OK OK\n", + "pre_spends not OK OK" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "show_result(results)" + ] + }, + { + "cell_type": "markdown", + "id": "ef48eafa-6c17-4d40-a647-6d00d6b52a39", + "metadata": {}, + "source": [ + "### 2.4 AA with optimize group \n", + "\n", + "_If you have many columns for grouping and don't know which colun or columns will make best result, you can use parametr `optimize_group=True`.\n", + "AA-Test will choose optimal number and names of group columns._" + ] + }, + { + "cell_type": "markdown", + "id": "803a2ebca97c85d3", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "You can use `columns_labeling` to automatically name columns as target and group." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "77227253-2569-4a54-9d11-77c53e6a69eb", + "metadata": { + "jupyter": { + "is_executing": true + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'target_field': ['treat', 'pre_spends', 'post_spends', 'age'],\n", + " 'group_col': ['gender', 'industry']}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "experiment.columns_labeling(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "b6c733e6-8518-48bf-b24d-2c392a788889", + "metadata": { + "jupyter": { + "is_executing": true + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4e8e072586684390be4f5003b2943a3e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Group optimization: 0%| | 0/3 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results = experiment.process(data=data, optimize_groups=True, iterations=2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "e6c4eb05-0acd-4148-a1df-04e232ef290d", + "metadata": { + "jupyter": { + "is_executing": true + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['industry']" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "experiment.group_cols" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "86f940251f22929a", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "experiments\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
random_statepost_spends a meanpost_spends b meanpost_spends ab deltapost_spends ab delta %post_spends t-test p-valuepost_spends ks-test p-valuepost_spends t-test passedpost_spends ks-test passedpre_spends a mean...pre_spends ks-test passedcontrol %test %control sizetest sizet-test mean p-valueks-test mean p-valuet-test passed %ks-test passed %mean_tests_score
00452.60451.72-0.88-0.190.260.48FalseFalse487.38...True50.0050.00500050000.200.250.0050.000.23
11452.18452.15-0.03-0.010.970.56FalseFalse487.33...False50.0050.00500050000.590.490.000.000.52
22452.82451.50-1.32-0.290.090.18FalseFalse487.04...False50.0050.00500050000.440.450.000.000.45
33451.25453.081.830.400.020.08TrueFalse486.67...False50.0050.00500050000.020.13100.000.000.09
44452.41451.92-0.50-0.110.530.06FalseFalse487.20...False50.0050.00500050000.560.360.000.000.43
..................................................................
19951995452.30452.03-0.26-0.060.740.91FalseFalse486.94...False50.0050.00500050000.570.890.000.000.79
19961996451.89452.440.550.120.480.78FalseFalse487.30...False50.0050.00500050000.380.860.000.000.70
19971997452.52451.81-0.70-0.160.370.10FalseFalse487.10...False50.0050.00500050000.660.400.000.000.49
19981998452.27452.06-0.21-0.050.790.86FalseFalse486.73...True50.0050.00500050000.420.450.0050.000.44
19991999451.47452.861.380.310.080.02FalseTrue486.75...False50.0050.00500050000.070.360.0050.000.26
\n", + "

2000 rows × 26 columns

\n", + "
" + ], + "text/plain": [ + " random_state post_spends a mean post_spends b mean \\\n", + "0 0 452.60 451.72 \n", + "1 1 452.18 452.15 \n", + "2 2 452.82 451.50 \n", + "3 3 451.25 453.08 \n", + "4 4 452.41 451.92 \n", + "... ... ... ... \n", + "1995 1995 452.30 452.03 \n", + "1996 1996 451.89 452.44 \n", + "1997 1997 452.52 451.81 \n", + "1998 1998 452.27 452.06 \n", + "1999 1999 451.47 452.86 \n", + "\n", + " post_spends ab delta post_spends ab delta % \\\n", + "0 -0.88 -0.19 \n", + "1 -0.03 -0.01 \n", + "2 -1.32 -0.29 \n", + "3 1.83 0.40 \n", + "4 -0.50 -0.11 \n", + "... ... ... \n", + "1995 -0.26 -0.06 \n", + "1996 0.55 0.12 \n", + "1997 -0.70 -0.16 \n", + "1998 -0.21 -0.05 \n", + "1999 1.38 0.31 \n", + "\n", + " post_spends t-test p-value post_spends ks-test p-value \\\n", + "0 0.26 0.48 \n", + "1 0.97 0.56 \n", + "2 0.09 0.18 \n", + "3 0.02 0.08 \n", + "4 0.53 0.06 \n", + "... ... ... \n", + "1995 0.74 0.91 \n", + "1996 0.48 0.78 \n", + "1997 0.37 0.10 \n", + "1998 0.79 0.86 \n", + "1999 0.08 0.02 \n", + "\n", + " post_spends t-test passed post_spends ks-test passed \\\n", + "0 False False \n", + "1 False False \n", + "2 False False \n", + "3 True False \n", + "4 False False \n", + "... ... ... \n", + "1995 False False \n", + "1996 False False \n", + "1997 False False \n", + "1998 False False \n", + "1999 False True \n", + "\n", + " pre_spends a mean ... pre_spends ks-test passed control % test % \\\n", + "0 487.38 ... True 50.00 50.00 \n", + "1 487.33 ... False 50.00 50.00 \n", + "2 487.04 ... False 50.00 50.00 \n", + "3 486.67 ... False 50.00 50.00 \n", + "4 487.20 ... False 50.00 50.00 \n", + "... ... ... ... ... ... \n", + "1995 486.94 ... False 50.00 50.00 \n", + "1996 487.30 ... False 50.00 50.00 \n", + "1997 487.10 ... False 50.00 50.00 \n", + "1998 486.73 ... True 50.00 50.00 \n", + "1999 486.75 ... False 50.00 50.00 \n", + "\n", + " control size test size t-test mean p-value ks-test mean p-value \\\n", + "0 5000 5000 0.20 0.25 \n", + "1 5000 5000 0.59 0.49 \n", + "2 5000 5000 0.44 0.45 \n", + "3 5000 5000 0.02 0.13 \n", + "4 5000 5000 0.56 0.36 \n", + "... ... ... ... ... \n", + "1995 5000 5000 0.57 0.89 \n", + "1996 5000 5000 0.38 0.86 \n", + "1997 5000 5000 0.66 0.40 \n", + "1998 5000 5000 0.42 0.45 \n", + "1999 5000 5000 0.07 0.36 \n", + "\n", + " t-test passed % ks-test passed % mean_tests_score \n", + "0 0.00 50.00 0.23 \n", + "1 0.00 0.00 0.52 \n", + "2 0.00 0.00 0.45 \n", + "3 100.00 0.00 0.09 \n", + "4 0.00 0.00 0.43 \n", + "... ... ... ... \n", + "1995 0.00 0.00 0.79 \n", + "1996 0.00 0.00 0.70 \n", + "1997 0.00 0.00 0.49 \n", + "1998 0.00 50.00 0.44 \n", + "1999 0.00 50.00 0.26 \n", + "\n", + "[2000 rows x 26 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "aa_score\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
t-test passed scoreks-test passed scoret-test aa passedks-test aa passed
post_spends0.040.041.001.00
pre_spends0.050.031.000.00
mean0.040.041.000.50
\n", + "
" + ], + "text/plain": [ + " t-test passed score ks-test passed score t-test aa passed \\\n", + "post_spends 0.04 0.04 1.00 \n", + "pre_spends 0.05 0.03 1.00 \n", + "mean 0.04 0.04 1.00 \n", + "\n", + " ks-test aa passed \n", + "post_spends 1.00 \n", + "pre_spends 0.00 \n", + "mean 0.50 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "split\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustrygroup
0000488.00414.44NaNME-commercetest
1181512.50462.2226.00NaNE-commercetest
2411543.00514.5618.00FE-commercetest
3561486.50486.5644.00ME-commercetest
4841465.50506.0066.00MLogisticstest
..............................
9995999000490.00426.00NaNMLogisticscontrol
9996999200491.50424.0029.00ME-commercecontrol
9997999600500.50430.8926.00FLogisticscontrol
9998999731473.00534.1122.00FE-commercecontrol
9999999821495.00523.2267.00FE-commercecontrol
\n", + "

10000 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 0 0 0 488.00 414.44 NaN M \n", + "1 1 8 1 512.50 462.22 26.00 NaN \n", + "2 4 1 1 543.00 514.56 18.00 F \n", + "3 5 6 1 486.50 486.56 44.00 M \n", + "4 8 4 1 465.50 506.00 66.00 M \n", + "... ... ... ... ... ... ... ... \n", + "9995 9990 0 0 490.00 426.00 NaN M \n", + "9996 9992 0 0 491.50 424.00 29.00 M \n", + "9997 9996 0 0 500.50 430.89 26.00 F \n", + "9998 9997 3 1 473.00 534.11 22.00 F \n", + "9999 9998 2 1 495.00 523.22 67.00 F \n", + "\n", + " industry group \n", + "0 E-commerce test \n", + "1 E-commerce test \n", + "2 E-commerce test \n", + "3 E-commerce test \n", + "4 Logistics test \n", + "... ... ... \n", + "9995 Logistics control \n", + "9996 E-commerce control \n", + "9997 Logistics control \n", + "9998 E-commerce control \n", + "9999 E-commerce control \n", + "\n", + "[10000 rows x 9 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "best_experiment_stat\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
a meanb meanab deltaab delta %t-test p-valueks-test p-valuet-test passedks-test passed
post_spends452.22452.11-0.11-0.020.891.00FalseFalse
pre_spends487.09487.100.000.000.991.00FalseFalse
\n", + "
" + ], + "text/plain": [ + " a mean b mean ab delta ab delta % t-test p-value ks-test p-value \\\n", + "post_spends 452.22 452.11 -0.11 -0.02 0.89 1.00 \n", + "pre_spends 487.09 487.10 0.00 0.00 0.99 1.00 \n", + "\n", + " t-test passed ks-test passed \n", + "post_spends False False \n", + "pre_spends False False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "split_stat\n" + ] + }, + { + "data": { + "text/plain": [ + "control % 50.00\n", + "test % 50.00\n", + "control size 5000\n", + "test size 5000\n", + "t-test mean p-value 0.94\n", + "ks-test mean p-value 1.00\n", + "t-test passed % 0.00\n", + "ks-test passed % 0.00\n", + "mean_tests_score 0.98\n", + "Name: 69, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "resume\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
aa test passedsplit is uniform
post_spendsOKOK
pre_spendsOKOK
\n", + "
" + ], + "text/plain": [ + " aa test passed split is uniform\n", + "post_spends OK OK\n", + "pre_spends OK OK" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "show_result(results)" + ] + }, + { + "cell_type": "markdown", + "id": "6adf0fc69f8fb0a5", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "### 2.5 AA test with quantization \n", + "\n", + "_If you want make one column as parameter for quantization, you may use `quant_field`._" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "477b674bba35639d", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "info_cols = ['user_id', 'signup_month']\n", + "target = ['post_spends', 'pre_spends']\n", + "\n", + "group_cols = 'industry'\n", + "quant_field = 'gender'" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "8d2de96c1680ec35", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "experiment = AATest(info_cols=info_cols, target_fields=target, group_cols=group_cols, quant_field=quant_field)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "319e1e677e4d3b1", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "49bf42df7524434db4b8e42963481084", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/2000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABLkAAAPaCAYAAAB7/Iq3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAACG7ElEQVR4nOzdeXzddZ3v8fc5Jzn7OdmaPelC25RFEFAqqCPKIIwyXBRG5wIWGJWZcapX7CAzuI06AoMjqDOgjhtFRrxX2dRhBtwAlV0ha9M0SbNvp9mTk5wl5/zuHz+aUrpl/53l9Xw8+mjP/kmX5OTV7+/7s40b44YAAAAAAACANGa3egAAAAAAAABguYhcAAAAAAAASHtELgAAAAAAAKQ9IhcAAAAAAADSHpELAAAAAAAAaY/IBQAAAAAAgLRH5AIAAAAAAEDaI3IBAAAAAAAg7RG5AAAAAAAAkPaIXAAAwDI/3P1D5dvylW/LV1dnl6WzHJzjts/fZukcAAAAWBoiFwAAAAAAANIekQsAACDF/e7J382vNPvdk7+zehwAAICUROQCAACWufq6qzVujGvcGNeGjRusHgcAAABpjMgFAAAAAACAtEfkAgAAAAAAQNojcgEAAMsc7+yKl7z9EuXb8nXJ2y+RJPX39etTuz6ls7acpTJPmTYVbdLlF1+uX/7PLxf0Wj+5/ye65O2XaEPBBlX6K3Xe687Trf90q8bHx0/42I9c9xHl2/J1+sbTl/zxHPTUb57Sh678kM7YdIbKPGUq95brdRtepwvPvVCfufEzeuo3T83ft6uzS/m2fF36jkvnr7v0HZfOv8bBHz/c/cP522/7/G3z10vSxMSEvvzPX9afnPUnWp+/fv7+3/q3b83f78XnXjzh78GOK3Yo35avjYUbFYlETnh/AACAtZZj9QAAAAAn8tzTz+nq91ytkeGR+esikYh+84vf6De/+I3++V//WR+78WNHfezc3Jw+fNWH9chPHjns+uamZjU3NevH//ljPfKrR4762JV28ydu1je/9s0jru/t7lVvd6/+8PwfdP/u+7V/eP+KvF57a7vee9F71d3ZfcRtf/mBv9Q/3fRPikajun/3/Trn3HOO+TwjwyN67OePSZLed9X75Ha7V2Q+AACAlUTkAgAAKW1oYEhXv+dq2e12ff5fPq9z33qunE6nnv39s/ryF7+sifEJfeHmL+jCd12oU0475YjHf+bGz8wHrq3btur/3PR/9LozXqfJiUk98pNHdO937tUH//KDq/5xPPZfj80HrtPOOE0f+siHVHNKjYJ5QU2MT2hv0149+asn9dILL80/pqKyQs80PKOXXnxJH/3gRyVJd33/Lp19ztmHPXdFVcVRX/Oav7hGA30D+uuP/bXe/b/erfyCfLW3tqt6Q7UKCgt06eWX6oEfPaAH/++DuvWrt8rj8Rz1eX78wx8rHo9Lkq7+4NXL/r0AAABYDUQuAACQ0tr2tal6Q7Uef/pxVVQeijlnn3O2zj7nbL37be/W3Nycdn97t27/+u2HPbapoUnf/vdvS5Jef/br9ehTj8rv98/ffv6fnq/tb96uj1z7kVX/OB7+8cOSNP+xvHoOSfqTt/+Jrt95vcZGx+avy83N1amvO/WwFWwbNm3Qqa87dUGv2dzYrAf+5wFdcNEF89ed+YYz53+948M79MCPHtDkxKT+6+H/0vuuet9Rn+eH95iHQ77u9a/TmWefedT7AAAAWI09uQAAQMr78r9/+bDAddB5bz1Pb3zTGyVJz/7u2SNuv+db9yiZTEqSvv7trx8RliTpymuu1Dvf9c4VnvhIocGQJDO2HW2OgwoKC1bsNa+67qrDAtdrve0db9OmzZskHQpZr1X7Uq0a6xolSR/44AdWbDYAAICVRuQCAAApLS8/TxdfcvExbz+4Mqlzf+cRtz35qyclSaeefuphK5heay3iTWl5qSTpmd8+o472jlV/PUl639VHX5l1kM1mm//Yf/ub36qnu+eI+xyMX06nU++/+v0rPyQAAMAKIXIBAICUtnnrZtntx37Lkl+YL0manpo+7PpoNKr21nZJOmIPq9c6e/vxb18JV15zpSRpdGRU573uPH3wf39Q/3nPf2p/28psMn80rzvjdSe8z1XXXSWHw6FkMqkf3fujw26LRqN64P4HJEnvvuzdKiwqXJU5AQAAVgKRCwAApDSP9+iboR90MIAdPCzxoPGxcRmGIUkqLik+7nOUlJYsY8KFOf9Pz9e/3vWv8ng8ikQieuj/PaSPfvCjOnvr2Tq16lR94m8/oYa6hhV9zfyC/BPep7yiXO98t3m45v2775//PZOk//7pf8/vEcahigAAINURuQAAQMaz2WxWjyBJun7n9arvrNetX71VF737IgXzgpKk/r5+3fMf9+htZ71NX/rMl1bs9RwOx4Lud82Hr5FkHvL59G+fnr/+4KGKlVWVx93bCwAAIBUQuQAAQEbKy8+b/3VoKHTc+57o9oOrxYykcdz7zYRnTjhXcUmx/u6Gv9OPH/2xOkc79eQfn9SNn7lRefl5MgxDX7nlK3r0p4+e8HlW0sWXXKyy8jJJh8JWf1+/fvOL30iSrrz2yuMeMgoAAJAKeLcCAAAyktvt1uatmyVJL7340nHve6Lb/QHzbIgT4xPHvV/bvrZFTGjGszPPPlOf+efP6Ke//un89Y/8+JHD7rfaK9EcDoeuuu4qSdLPHviZpqen9aN7f6RkMimbzaar/+rqVX19AACAlUDkAgAAGevtF75dkrSnYY/qXq475v1++P0fHvd5NmzaIEmamppSa0vrUe8Ti8X08wd/vrRBJZ159pnze2iNDI8cdpvb7T70OtHYkl/jeHZ8aIdsNpvC4bAe+n8P6f7d90uS3vy2N2vT5k2r8poAAAAricgFAAAy1nV/c938Kqgb/voGhcPhI+7z4x/+WL/4718c93necv5b5n991x13HfU+n971afX39R/zOR76fw9pdnb2mLe//IeXNT42LulQVDuotLx0/tcd7R3HnXWpNm3epLe+/a2SpFs+c8v8mSnZcB4AAKSLHKsHAAAAWC2nv/50fXjnh/Wdu76jl//wst7xxnfo4//wcZ12+mmamJjQT3/yU+3+9m6d9caz9PIfXj7m87z+rNdr+3nb9cKzL+je79yrWCymK6+9UsG8oPa37tfub+/Wb3/zW73pzW/S8888f9Tn+Kd/+Cd94m8/oXdf9m69+W1v1paaLfL6vBobGdOzv39W3/73b0syDx08uBH8QdXrq1VZVam+3j79+1f+XRVVFdq6bev8xvLFpcUKBALL/v265sPX6HdP/E5Dg0OSpGAwqMv+4rJlPy8AAMBaIHIBAICMduudt2qwf1A/f+jn2rd3n3b+1c7Dbt+waYPu+X/36MzNZx73ee76/l265PxLdCB0QD+690f60b0/Ouz2j934MZ182snHjFySuafX0R57kMvl0p3fulNnvfGsI27b9ald+vu/+3t1dXTpqsuuOuy2u++5W1dft/x9sy69/FLlF+TPryh771++V16vd9nPCwAAsBY4XBEAAGS03Nxc3ffgffqP+/5D5/3JeQrmBeX1erXtlG36+0/9vZ7641PaeNLGEz5Pzck1euqlp/Shj3xI1Ruq5XQ6ta54nS78swv140d/rH/+138+7uN//sTP9S9f/xf9ryv+l049/VStK16nnJwcBYNBnXHWGfrYjR/T83ueP2as+tBHPqQfPPgDXXDRBSouKVZOzsr/X6Xb7dZ73vee+cscqggAANKJbdwYP/65sAEAAJA1Ln7LxXr+med18qkn67mm56weBwAAYMFYyQUAAABJUmtL6/zhlld/cPmHPwIAAKwlIhcAAAAkSV+7/WuSzMMWV2KPLwAAgLXExvMAAABZanZ2VgN9A5qZmdGjjzyq+3ffL0m69q+vVWFRocXTAQAALA57cgEAAGSp3z35O136jksPu66qukq/r/u98gvyrRkKAABgiThcEQAAIMvZbDaVV5Tr/R94v/7n9/9D4AIAAGmJlVwAAAAAAABIe6zkAgAAAAAAQNojcgEAAAAAACDtEbkAAAAAAACQ9ohcAAAAAAAASHtELgAAAAAAAKQ9IhcAAAAAAADSHpELAAAAAAAAaY/IBQAAAAAAgLRH5AIAAAAAAEDaI3IBAAAAAAAg7RG5AAAAAAAAkPaIXAAAAAAAAEh7RC4AAAAAAACkPSIXAAAAAAAA0h6RCwAAAAAAAGmPyAUAAAAAAIC0R+QCAAAAAABA2iNyAQAAAAAAIO0RuQAAAAAAAJD2iFwAAAAAAABIe0QuAAAAAAAApD0iFwAAAAAAANIekQsAAAAAAABpj8gFAAAAAACAtEfkAgAAAAAAQNojcgEAAAAAACDtEbkAAAAAAACQ9ohcAAAAAAAASHtELgAAAAAAAKQ9IhcAAAAAAADSHpELAAAAAAAAaY/IBQAAAAAAgLRH5AIAAAAAAEDaI3IBAAAAAAAg7RG5AAAAAAAAkPaIXAAAAAAAAEh7RC4AAAAAAACkPSIXAAAAAAAA0h6RCwAAAAAAAGmPyAUAAAAAAIC0R+QCAAAAAABA2iNyAQAAAAAAIO0RuQAAAAAAAJD2iFwAAAAAAABIe0QuAAAAAAAApD0iFwAAAAAAANIekQsAAAAAAABpj8gFAAAAAACAtEfkAgAAAAAAQNojcgEAAAAAACDtEbkAAAAAAACQ9ohcAAAAAAAASHtELgAAAAAAAKQ9IhcAAAAAAADSHpELAAAAAAAAaY/IBQAAAAAAgLRH5AIAAAAAAEDaI3IBAAAAAAAg7RG5AAAAAAAAkPaIXAAAAAAAAEh7RC4AAAAAAACkPSIXAAAAAAAA0h6RCwAAAAAAAGmPyAUAAAAAAIC0R+QCAAAAAABA2iNyAQAAAAAAIO0RuQAAAAAAAJD2iFwAAAAAAABIe0QuAAAAAAAApL0cqwd4rWQyqYH+AfkDftlsNqvHAQAAAAAAgIUMw9D01LTKK8pltx97vVbKRa6B/gGdVn2a1WMAAAAAAAAghTT1NKmyqvKYt6dc5PIH/JKknp4eBYNBi6cBAAAAAACAlSYnJ1VdXT3fjI4l5SLXwUMUg8EgkQsAAAAAAACSdMJtrdh4HgAAAAAAAGmPyAUAAAAAAIC0R+QCAAAAAABA2ku5PbkWKpFIKB6PWz1GVsrNzZXD4bB6DAAAAAAAgHlpF7kMw9Dg4KDGx8etHiWr5efnq6ys7ISbvgEAAAAAAKyFtItcBwNXSUmJvF4vkWWNGYahmZkZhUIhSVJ5ebnFEwEAAAAAAKRZ5EokEvOBq6ioyOpxspbH45EkhUIhlZSUcOgiAAAAAACwXFptPH9wDy6v12vxJDj4Z8C+aAAAAAAAIBWkVeQ6iEMUrcefAQAAAAAASCVpGbkAAAAAAACAV0urPbmOZ3ZWisXW7vWcTumVrakyzu7du3XDDTdwBksAAAAAAJA2MiJyzc5KP/2pNDa2dq9ZUCBddlnqhK6NGzfqhhtu0A033GD1KAAAAAAAAGsuIyJXLGYGLo9HcrtX//UiEfP1YrHUiVwLkUgkZLPZZLdzlCoAAAAAAMgsGVU73G7J51v9H0sJaclkUl/+8pe1ZcsWuVwurV+/XrfccoskqaGhQRdccIE8Ho+Kior013/915qenp5/7HXXXaf3vOc9+spXvqLy8nIVFRVp586d82c2fPvb366uri594hOfkM1mm98Ufvfu3crPz9fPfvYznXrqqXK5XOru7tbY2JiuueYaFRQUyOv16l3vepdaW1uX/wcAAAAAAABgkYyKXKns5ptv1r/8y7/os5/9rPbs2aP7779fpaWlCofDuvjii1VQUKAXX3xRP/nJT/SrX/1KH/3oRw97/BNPPKH29nY98cQTuvfee7V7927t3r1bkvTQQw+pqqpKX/ziFzUwMKCBgYH5x83MzOj222/Xd7/7XTU1NamkpETXXXed/vCHP+hnP/uZnn32WRmGoXe/+93z0QwAAAAAACDdZMThiqluampKX//613XXXXfp2muvlSRt3rxZb33rW/Wd73xHkUhEP/jBD+Tz+SRJd911ly699FLdfvvtKi0tlSQVFBTorrvuksPh0Mknn6xLLrlEv/71r3X99dersLBQDodDgUBAZWVlh712PB7XN77xDb3+9a+XJLW2tupnP/uZnn76ab35zW+WJP3whz9UdXW1HnnkEb3vfe9bq98WAAAAAACAFcNKrjXQ3NysaDSqP/3TPz3qba9//evnA5ckveUtb1EymVRLS8v8daeddpocDsf85fLycoVCoRO+ttPp1BlnnHHY6+Xk5OhNb3rT/HVFRUXatm2bmpubF/2xAQAAAAAApAIi1xrwrMDu9Lm5uYddttlsSiaTC3rtg3t0AQAAAAAAZCoi1xrYunWrPB6Pfv3rXx9x2ymnnKK6ujqFw+H5655++mnZ7XZt27Ztwa/hdDqVSCROeL9TTjlFc3Nzev755+evGxkZUUtLi0499dQFvx4AAAAAAEAqIXKtAbfbrX/4h3/QTTfdpB/84Adqb2/Xc889p+9973u6+uqr5Xa7de2116qxsVFPPPGEPvaxj2nHjh3z+3EtxMaNG/Xb3/5WfX19Gh4ePub9tm7dqssuu0zXX3+9fv/736uurk4f+MAHVFlZqcsuu2wlPlwAAAAAAIA1l1Ebz0ciqfs6n/3sZ5WTk6PPfe5z6u/vV3l5uf72b/9WXq9Xjz/+uD7+8Y/rnHPOkdfr1RVXXKE777xzUc//xS9+UX/zN3+jzZs3KxqNyjCMY973nnvu0cc//nH9+Z//uWKxmN72trfpv//7v484JBIAAAAAACBd2MaN8WPXEAtMTk5qfd56TUxMKBgMHnZbJBJRR0eHNm3aJLfbPX/97Kz0059KY2NrN2dBgXTZZdIKbLeVlo71ZwEAAAAAALCSJicnlZeXp+6J7iNa0atlxEouj8cMTrHY2r2m05m9gQsAAAAAACDVZETkkszgRHQCAAAAAADITmw8DwAAAAAAgLRH5AIAAAAAAEDaI3IBAAAAAAAg7RG5AAAAAAAAkPaIXAAAAAAAAEh7RC4AAAAAAACkPSIXAAAAAAAA0l6O1QOsmNlZKRZbu9dzOiWPZ+1eDwAAAAAAAMeUGZFrdlb66U+lsbG1e82CAumyyxYcut7+9rfrzDPP1Ne+9rUVefnrrrtO4+PjeuSRR1bk+QAAAAAAQJozDKmvT+rvl846S8rNtXqiNZUZkSsWMwOXxyO53av/epGI+XqxGKu5AAAAAACAtQzDDFuNjdK+febRZ9u2SXl5Vk+2pjJrTy63W/L5Vv/HIkPaddddp6eeekpf//rXZbPZZLPZ1NnZqcbGRr3rXe+S3+9XaWmpduzYoeHh4fnHPfDAAzr99NPl8XhUVFSkCy+8UOFwWJ///Od177336qc//en88z355JMr/JsJAAAAAABS3sCA9MtfSo88IjU1md0imbR6KktkVuRKUV//+td13nnn6frrr9fAwIAGBgYUCAR0wQUX6KyzztIf/vAHPfbYYxoaGtL73/9+SdLAwICuvPJKffCDH1Rzc7OefPJJXX755TIMQzfeeKPe//7368/+7M/mn+/Nb36zxR8lAAAAAABYM0ND0q9/LT30kLmCq7BQ2rrVjFxZKjMOV0xxeXl5cjqd8nq9KisrkyR96Utf0llnnaVbb711/n7f//73VV1drX379ml6elpzc3O6/PLLtWHDBknS6aefPn9fj8ejaDQ6/3wAAAAAACALHDhgRq29e6WZGamsTAoGrZ4qJRC5LFJXV6cnnnhCfr//iNva29t10UUX6U//9E91+umn6+KLL9ZFF12kv/iLv1BBQYEF0wIAAAAAAEsND5uHIzY3S9PTZtyqqrJ6qpRC5LLI9PS0Lr30Ut1+++1H3FZeXi6Hw6Ff/vKXeuaZZ/SLX/xC//7v/65Pf/rTev7557Vp0yYLJgYAAAAAAGtudFTas8cMXJOTZtyqrLR6qpRE5FojTqdTiURi/vLZZ5+tBx98UBs3blROztH/GGw2m97ylrfoLW95iz73uc9pw4YNevjhh7Vr164jng8AAAAAAGSQ8XEzbDU1SRMTUmmpVFFh9VQpjY3n18jGjRv1/PPPq7OzU8PDw9q5c6dGR0d15ZVX6sUXX1R7e7sef/xx/dVf/ZUSiYSef/553XrrrfrDH/6g7u5uPfTQQzpw4IBOOeWU+eerr69XS0uLhoeHFY/HLf4IAQAAAADAsk1MSM89Jz3wgPTMM5LTKW3bJrF90QktKnIlEgl96bNf0hmbzlCZp0xnbj5TX/7nL8swjPn7GIahWz53i7aVb1OZp0yXXXiZ2lvbV3zwo4pEpHB49X9EIose7cYbb5TD4dCpp56q4uJixWIxPf3000okErrooot0+umn64YbblB+fr7sdruCwaB++9vf6t3vfrdqamr0mc98RnfccYfe9a53SZKuv/56bdu2TW984xtVXFysp59+eqV/NwEAAAAAwFqZmpJeeMGMW7/9rZSTY8atoiLJZrN6urSwqMMVv3b71/T9b35f37z3mzr5tJNV+4da7fyrnQrmBfW3/+dvJUlf//LX9R//9h/65r3f1IZNG3TLZ2/R5Rdfruf3PC+3270qH4ScTrNojo1Js7Or8xqvVVBgvu4C1dTU6Nlnnz3i+oceeuio9z/llFP02GOPHfP5iouL9Ytf/GLBrw8AAAAAAFLQ9LTU0iLV1UkjI2bUOvlkwtYSLCpyvfDMC3r3Ze/WxZdcLEnasHGDHvjRA3rphZckmau4vvm1b+qTn/mkLrnsEknSt37wLdWU1ujRRx7VFf/7ihUe/xUej3TZZVIstjrPfzROp/m6AAAAAAAAixUOm3Grvl46cEAqLJRqaiQ7O0st1aIi1/Y3b9fub+9W2742banZooa6Bj33++d0y523SJK6Oro0NDik8y88f/4xeXl5esOb3qAXnn3hqJErGo0qGo3OX56anFraR+LxEJ0AAAAAAEBqm52V9u2TamulUEjKzydurZBFRa5P/OMnNDU5pXNOPkcOh0OJREKfveWzev/V75ckDQ0OSZJKSksOe1xJaYlCg6GjPuedt92p279w+1JmBwAAAAAASA+RiNTaah6WODBgxq2tWyWHw+rJMsaiItfDP35YP/nhT/Td+7+rk087WQ21Dbr5hptVVlGmq669akkD7Lp5l3bu2jl/eWpySqdVn7ak5wIAAAAAAEgp0ajU1ia9/LIZt4JB4tYqWVTk+twnP6cb/vGG+cMOTzv9NPV09eirt31VV117lUrLSiVJoaGQysrL5h8XGgrp9DNPP+pzulwuuVyupc4PAAAAAACQemIxM27V1Un9/ZLPR9xaZYs64HNmZkb21xwj6nA4lEwmJUkbNm1QaVmpnvr1U/O3T05O6o/P/1Hbz9u+AuOaDr4erMOfAQAAAAAARxGPS3v3Sg8/LP33f0vj49JJJ0mVlQSuVbaolVx/dumf6Y5b7lDV+iqdfNrJqn+5Xnffebc+8MEPSJJsNps+csNH9JUvfUWbt27Whk0bdMtnb1FZRZkuec8lyx7W6XTKbrerv79fxcXFcjqdsnFKzTVlGIZisZgOHDggu90up9Np9UgAAAAAAFgvHpc6OswN5Xt6zJPjnXSSlJtr9WRZY1GR68v//mXd8tlb9Pd/9/caDg2rrKJMf/U3f6WbPnfT/H0+ftPHFQ6HdcNf36CJ8Qmd+9Zz9eBjD8rtdi97WLvdrk2bNmlgYED9/f3Lfj4sndfr1fr1649Y2QcAAAAAQFaZmzPjVl2d1N0tuVzSpk3ELQvYxo1xw+ohXm1yclLr89ZrYmJCwWDwqPcxDENzc3NKJBJrPB0k8xDVnJwcVtEBAAAAALJXIiF1dppxq6tLysmRKiokq494mpmRRkelq66S8vKsnWWFTE5OKi8vT90T3cdsRdIiV3KlCpvNptzcXOVSRQEAAAAAwFpKJs2oVVdnruByOKTqanMFFyyVlpELAAAAAABgTSWT5l5b9fVSe7tktxO3UgyRCwAAAAAA4FiSSam391DcMgzzTIkrsPc4VhaRCwAAAAAA4LUMQ+rrkxoapNZWcw+uigrJ67V6MhwDkQsAAAAAAOAgw5AGBsy4tW+fefZE4lZaIHIBAAAAAABI0uDgobgVjUrl5ZLfb/VUWCAiFwAAAAAAyG5DQ1Jjo9TSIs3OmnErELB6KiwSkQsAAAAAAGSnAwekpiapuVkKh824VV1t9VRYIiIXAAAAAADILiMj5sqt5mZpeloqK5OqqqyeCstE5AIAAAAAANlhdFTas8dcvTU1JZWUSJWVVk+FFULkAgAAAAAAmW18/FDcmpgw41ZFhdVTYYURuQAAAAAAQGaanDTjVmOjNDYmFRdLNTWSzWb1ZFgFRC4AAAAAAJBZpqbM/bYaGsxDFNetk7ZtI25lOCIXAAAAAADIDNPTUkuLVFdnbi5fVETcyiJELgAAAAAAkN7CYWnfPqm+XhoaMuNWTY1kt1s9GdYQkQsAAAAAAKSn2VkzbtXWSqGQlJ9vrtwibmUlIhcAAAAAAEgvkYjU2moeljgwIOXlSVu3Sg6H1ZPBQkQuAAAAAACQHqJRqa3NXLk1MCAFAsQtzCNyAQAAAACA1BaLSe3tZtzq65P8fmnLFuIWDkPkAgAAAAAAqSkeN+NWXZ3U2yt5vdLmzVIOOQNH4m8FAAAAAABILXNz0v79Ztzq7pbcbmnTJik31+rJkMKIXAAAAAAAIDXMzUmdnWbc6uqSXC7iFhaMyAUAAAAAAKyVSBwet3JypA0bJKfT6smQRohcAAAAAADAGsmkGbXq6szIZbdL1dXmCi5gkYhcAAAAAABgbSWTUk+PVF9vbixvt0uVlebeW8ASEbkAAAAAAMDaMAzzLIn19VJbm3m5okLyeKyeDBmAyAUAAAAAAFaXYUh9fVJDg9Taaq7kKi+XvF6rJ0MGIXIBAAAAAIDVYRjSwMChuBWLmSu3fD6rJ0MGInIBAAAAAICVNzhoxq19+6Ro1Fy55fdbPRUyGJELAAAAAACsnFDIjFstLdLsrBm3AgGrp0IWIHIBAAAAAIDlO3BAamqSmpulcNiMW9XVVk+FLELkAgAAAAAASzcyIjU2Snv3SlNTUlmZVFVl9VTIQkQuAAAAAACweGNj5sqtpiYzbpWUmJvKAxYhcgEAAAAAgIUbHzcPSWxsNH9dWkrcQkogcgEAAAAAgBObnDTjVkODuYqruFjatk2y2ayeDJBE5AIAAAAAAMczNWXut9XQYO6/tW4dcQspicgFAAAAAACOFA6bcauuThoeNuNWTY1kt1s9GXBURC4AAAAAAHDIzIzU0iLV10tDQ1JRkblyi7iFFEfkAgAAAAAA0uystG+fuXJraEjKzyduIa0QuQAAAAAAyGaRiNTaasatgQEpL0/aulVyOKyeDFgUIhcAAAAAANkoGpXa2qTaWjNuBQLELaQ1IhcAAAAAANkkFpPa28241dcn+f3S5s1SDokA6Y2/wQAAAAAAZIN43Ixb9fVSd7fk8xG3kFH4mwwAAAAAQCabm5P27zf33OrpkVwu6aSTpNxcqycDVhSRCwAAAACATJRISB0dZtzq6pKcTmnjRuIWMhaRCwAAAACATJJImFGrttb8OSdH2rDBjFxABiNyAQAAAACQCZJJM2o1NJiHJ9rtUnW1eXgikAWIXAAAAAAApLNk0txrq6FBamuTbDapslJyu62eDFhTRC4AAAAAANKRYUi9vebZEtvazMsVFZLHY/VkgCWIXAAAAAAApBPDkPr7zZVb+/aZK7nKyyWv1+rJAEsRuQAAAAAASAeGIQ0MSI2NZtyKxcyVWz6f1ZMBKYHIBQAAAABAqhscNONWS4sUjZort/x+q6cCUgqRCwAAAACAVBUKSU1NUnOzNDtrxq1AwOqpgJRE5AIAAAAAINUcOHAoboXDZtyqrrZ6KiClEbkAAAAAAEgVIyOH4tbUlFRWJlVVWT0VkBaIXAAAAAAAWG1sTNqzx9x3a3JSKi01N5UHsGBELgAAAAAArDIxcShujY9LJSXmoYk2m9WTAWmHyAUAAAAAwFqbnDQPSWxoMFdxFRdL27YRt4BlIHIBAAAAALBWpqaklhapvl4aHiZuASuIyAUAAAAAwGoLh6W9e6W6OnNz+aIiM27Z7VZPBmQMIhcAAAAAAKtlZubQyq1QSCookGpqiFvAKiByAQAAAACw0mZnpdZWqbZWGhqS8vOJW8AqI3IBAAAAALBSIhGprc2MWwMDUl6etHWr5HBYPRmQ8YhcAAAAAAAsVzR6eNwKBIhbwBojcgEAAAAAsFSxmNTebsatvj7J55M2b5Zy+HYbWGv8qwMAAAAAYLHicWn/fvNsid3dxC0gBfCvDwAAAACAhZqbkzo6zLjV1SW53dJJJ0m5uVZPBmQ9IhcAAAAAACeSSJhxq75e6uyUnE5p40bzZwApgcgFAAAAAMCxJBLmiq26OjNuORzShg3ELSAFEbkAAAAAAHitZNLca6u+3tx7y26Xqqsll8vqyQAcA5ELAAAAAICDkkmpt9eMW+3t5nWVlebeWwBSGpELAAAAAADDMONWQ4PU1mbGrooKyeOxejIAC0TkAgAAAABkL8OQ+vvNuLVvn7kHV0WF5PVaPRmARSJyAQAAAACyj2FIg4OH4lYsZsYtn8/qyQAsEZELAAAAAJBdBgelxkappUWKRqXycsnvt3oqAMtkX8ydT994uvJt+Uf8uHHnjZKkSCSiG3feqE1Fm1Tpr9SOK3YoNBRalcEBAAAAAFiUUEh64gnp4YelujqpoEDaupXABWSIRa3keuLFJ5RIJOYvNzc26z3vfI8ue99lkqRPfeJT+sWjv9Dun+xWXl6ePvnRT2rH5Tv0+NOPr+zUAAAAAAAs1PCw1NQk7dkjhcPmyq2qKqunArDCFhW51hWvO+zyV//lq9q0eZPeev5bNTExofu+d5++e/93df4F50uS7r7nbm0/ZbtefO5FnXPuOSs3NQAAAAAAJzI6ah6W2NwsTU1JpaXELSCDLXlPrlgsph//54+1c9dO2Ww21f6xVvF4XOdfeP78fWpOrlHV+iq98OwLx4xc0WhU0Wh0/vLU5NRSRwIAAAAAQBobM1dtNTVJExNm3KqosHoqAKtsyZHr0Uce1cT4hK667ipJUmgwJKfTqfz8/MPuV1JaotDgsffluvO2O3X7F25f6hgAAAAAAJgmJsxVWw0N0vi4VFIibdsm2WxWTwZgDSw5ct33vft04bsuVHlF+bIG2HXzLu3ctXP+8tTklE6rPm1ZzwkAAAAAyCKTk4fi1ugocQvIUkuKXN1d3XryV0/qvofum7+upKxEsVhM4+Pjh63mCg2FVFJWcszncrlccrlcSxkDAAAAAJDNpqelvXul+npzc/l166STTyZuAVnKvpQH/fCeH6q4pFgXX3Lx/HVnvuFM5ebm6qlfPzV/XWtLq3q7e7X9vO3LnxQAAAAAAMk8Q+JLL0kPPCA98YR53bZtUnExgQvIYoteyZVMJvXDe36oK6+9Ujk5hx6el5enHR/aoU/v+rQKCgsUDAZ108du0vbztnNmRQAAAADA8s3MSK2tUm2tFApJBQVSTY1kX9L6DQAZZtGR68lfPane7l594IMfOOK2W796q+x2u6654hrFojFdcPEFuuMbd6zIoAAAAACALDU7eyhuDQ1J+fnS1q2Sw2H1ZABSiG3cGDesHuLVJicntT5vvSYmJhQMBq0eBwAAAABglUhEamsz49bgoBQISKWlxC3geGZmzBMwXHWVlJdn9TQrYnJyUnl5eeqe6D5uK1ry2RUBAAAAAFgVsdihuDUwIPn90pYtxC0Ax0XkAgAAAACkhnhcam+XXn5Z6uuTfD7ppJOkHL51BXBifKYAAAAAAFgrHpf275fq6qSeHsnjMeNWbq7VkwFII0QuAAAAAIA15uakjg4zbnV1SW63tGkTcQvAkhC5AAAAAABrK5GQOjvNuNXZKTmd0saN5s8AsERELgAAAADA2kgmzahVX2+u4HI4pPXrJZfL6skAZAAiFwAAAABgdSWTUne3Gbf275fsdqm6mrgFYEURuQAAAAAAqyOZlHp7zbjV1ibZbFJlpbn3FgCsMCIXAAAAAGBlGYbU13cobiWTUkWFedZEAFglRC4AAAAAwMowDKm/X2pslPbtM8+eWFEheb1WTwYgCxC5AAAAAADLYxjS4KDU0CC1tkrRqBm3fD6rJwOQRYhcAAAAAIClGxoy41ZLixm3ysslv9/qqQBkISIXAAAAAGDxDhwwD0vcu1eamZHKyqRg0OqpAGQxIhcAAAAAYOGGh6WmJqm5WZqeNlduVVVZPRUAELkAAAAAAAswOmrGrT17pKkpqbRUqqy0eioAmEfkAgAAAAAc2/i4GbeamqSJCTNuVVRYPRUAHIHIBQAAAAA40sSEeUhiY6M0NiaVlEjbtkk2m9WTAcBREbkAAAAAAIdMTh6KWyMjxC0AaYPIBQAAAAAwN5Hfu1eqrzfjVlGRdPLJxC0AaYPIBQAAAADZLByWWlrMuHXggFRYKNXUSHa71ZMBwKIQuQAAAAAgG83OSvv2SbW1UigkFRQQtwCkNSIXAAAAAGSTSMSMW/X10uCglJcnbd0qORxWTwYAy0LkAgAAAIBsEI1Kra3myq2BASkYlLZsIW4ByBhELgAAAADIZLGY1NYm1dVJ/f2Sz8fKLQAZicgFAAAAAJkoHpfa282VW729Ztw66SQph28DAWQmPrsBAAAAQCaJx6X9+82VWz09ksdjxq3cXKsnA4BVReQCAAAAgEwwNyd1dJhxq7tbcrmkTZuIWwCyBpELAAAAANJZIiF1dppxq6vLjFobNkhOp9WTAcCaInIBAAAAQDpKJs24VV9vruByOKTqanMFFwBkISIXAAAAAKSTZNI8HLGhwdxY3m4nbgGAiFwAAAAAkB6SSfMsifX1ZtwyDKmyUnK7rZ4MAFICkQsAAAAAUplhSH195sqt1lYzdpWXS16v1ZMBQEohcgEAAABAKjIMqb9famyU9u0zz55YUUHcAoBjIHIBAAAAQKoZGDi0cisaNVdu+f1WTwUAKY3IBQAAAACpYmjIXLm1dy9xCwAWicgFAAAAAFY7cEBqapKam6WZGamsTAoGrZ4KANIKkQsAAAAArDI8fChuTU+bcauqyuqpACAtEbkAAAAAYK2Njkp79piBa3LSjFuVlVZPBQBpjcgFAAAAAGtlfNwMWwfjVkmJecZEAMCyEbkAAAAAYLVNTportxobpbExqbhYqqmRbDarJwOAjEHkAgAAAIDVMjVl7rfV0GAeolhcLG3bRtwCgFVA5AIAAACAlTY9LbW0SHV10siIVFRE3AKAVUbkAgAAAICVEg6bcau+XgqFzLhVUyPZ7VZPBgAZj8gFAAAAAMs1Oyvt2yfV1ppxKz/fXLlF3AKANUPkAgAAAIClikSk1lbzsMSBATNubd0qORxWTwYAWYfIBQAAAACLFY1KbW3myq3+fikYJG4BgMWIXAAAAACwULGYGbfq6sy45fMRtwAgRRC5AAAAAOBE4nGpvd2MW729ktcrnXSSlMO3VACQKviMDAAAAADHEo9LHR3mYYk9PZLHI23aJOXmWj0ZAOA1iFwAAAAA8Fpzc2bcqquTursll4u4BQApjsgFAAAAAAclElJnpxm3urrMwxE3bJCcTqsnAwCcAJELAAAAAJJJM2rV1ZkruBwOqbraXMEFAEgLRC4AAAAA2SuZNPfaqq83N5a324lbAJCmiFwAAAAAso9hmHGroUFqazMvV1SYG8sDANISkQsAAABA9jAMqa/PjFutreZKrvJyyeu1ejIAwDIRuQAAAABkPsOQBgbMuLVvn3n2xIoK4hYAZBAiFwAAAIDMNjh4KG5Fo+bKLb/f6qkAACuMyAUAAAAgMw0NSY2NUkuLNDtrxq1AwOqpAACrhMi1RiYnzRO1+HySzWb1NAAAAEAGO3BAamqSmpulcNiMW9XVVk8FAFhlRK41YBjS449LY2PmfxxVVEjr1kl5eVJ+PuELAAAAWBEjI+bKreZmaXpaKiuTqqqsngoAsEaIXGtkdlaKxaSpKemll8yTuDgc5j6XhC8AAABgGUZHpT17zNVbU1NSSYlUWWn1VACANUbkWkNer1RcfOjy3Jw0M3N4+Dp4SCPhCwAAADiB8XFz1VZjozQxYcatigqrpwIAWITIZaGcHCkYNH8ctJjwlZdnnhSG8AUAAICsMjlpxq2GBnNPkOJiqaaGN8YAkOWIXCmG8AUAAAAcw9TUobg1Omq+Cd62jTe/AABJRK60QPgCAABAVpuellpapLo6aXiYuAUAOCoiV5oifAEAACDjzcyYcau+XhoakoqKzLhlt1s9GQAgBRG5MgjhCwAAABlhdlbat0+qrZVCIfMsTMQtAMAJELkyHOELAAAAaSMSkVpbzcMSBwbMN6Nbt0oOh9WTAQDSAJErCy01fBUVmf+JRvgCAADAiopGpbY2c+XWwID5BpS4BQBYJCIXJJ04fL38spRIEL4AAACwgmIxqb3djFt9feYbys2bzTenAAAsEl89cEyELwAAAKyKeNyMW3V1Um+v5PUStwAAy8ZXESwK4QsAAABLNjcn7d9vxq2eHsnlkjZtknJzrZ4MAJABiFxYtsWEL6/XvB/hCwAAIIvMzUmdnWbc6uoy49bGjcQtAMCKInJhVRC+AAAAoETCjFq1tebPOTnShg2S02n1ZACADETkwpohfAEAAGSJZNKMWvX1UkeH+QavutpcwQUAwCpZdOTq7+vX5//h8/rl//xSszOzOmnLSbr7nrt11hvPkiQZhqFb/+lW/eA7P9DE+ITe9JY36c5v3qnNWzev+PBIf4QvAACADJJMmntt1debG8vbbFJlpeR2Wz0ZACALLCpyjY+N6+K3XKw/ecef6IH/eUBFxUXa37pf+QX58/f5+pe/rv/4t//QN+/9pjZs2qBbPnuLLr/4cj2/53m5+eKGBSB8AQAApBnDMM+SWF8vtbWZlysqJI/H6skAAFlkUZHra7d/TVXVVfrGPd+Yv27jpo3zvzYMQ9/82jf1yc98Updcdokk6Vs/+JZqSmv06COP6or/fcXKTI2sQ/gCAABIQYYh9fdLDQ3Svn3mSq7ycvMNGQAAa2xRket/fvY/uuDiC3Tt+67V0089rfLKcn347z6sa6+/VpLU1dGlocEhnX/h+fOPycvL0xve9Aa98OwLR41c0WhU0Wh0/vLU5NRSPxZkmYWEr7k5yeEgfAEAAKwow5AGBsy41doqxWLmGy2fz+rJAABZbFGRq3N/p77/ze9r566d2vWpXXr5xZf1D//nH5TrzNVV116locEhSVJJaclhjyspLVFoMHTU57zztjt1+xduX+L4wOGWE77y8sz4RfgCAAA4jsFBqbFRammRolFz5Zbfb/VUAAAsLnIlk0md9caz9LlbPydJev1Zr9eexj2651v36Kprr1rSALtu3qWdu3bOX56anNJp1act6bmAoyF8AQAArIBQyIxbe/dKs7Nm3AoErJ4KAIB5i4pcpeWl2nbqtsOu23bKNv38wZ+bt5eVSpJCQyGVlZfN3yc0FNLpZ55+1Od0uVxycSphrDHCFwAAwAIdOCA1NUnNzVI4bMat6mqrpwIA4AiLilznvuVctbW0HXZd2742VW8wv8ht2LRBpWWleurXT+mMM8+QJE1OTuqPz/9RH/rIh1ZoZGB1EL4AAABeZWTkUNyampLKyqSqKqunAgDgmBYVuf7uE3+ni958ke649Q699/3v1R9f+KPu/fa9+tq3vyZJstls+sgNH9FXvvQVbd66WRs2bdAtn71FZRVluuQ9l6zG/MCqInwBAICsMzZmxq2mJvMNT0mJ+QYHAIAUt6jIdfY5Z+s/H/5PffHmL+rLX/yyNmzaoNu+dpvef/X75+/z8Zs+rnA4rBv++gZNjE/o3Leeqwcfe1But3vFhwesQPgCAAAZaXzcXLXV2Gj+mrgFAEgztnFj3LB6iFebnJzU+rz1mpiYUPDVFSGNGYZ0331m+CgutnoarJVEwty2Ihw2A9hrw1d5ubRuHeELAABYbHLSjFsNDeYqruJiqbCQNyYAkK5mZqTRUemqq8xvODPA5OSk8vLy1D3RfdxWtKiVXAAWzuE4csXXwfA1NSUNDRG+AACAhaamzDMlNjSY+2+tWydt28abDwBA2iJyAWvoeOFrelqqrSV8AQCAVRYOm3Grrk4aHjbfaNTUSHa71ZMBALAsRC7AYoQvAACwJmZmpJYWqb5eCoWkggJz5RZxCwCQIYhcQApaaPiy2yWfj/AFAACOY3ZWam0130AMDZlvFFi5BQDIQEQuIE0sJHwlEmbYInwBAABFImbcqquTBgbMNwRbt5pvKgAAyEBELiCNLTd85eVJgQDhCwCAjBKNSm1t5huBgQHziz1xCwCQBYhcQIYhfAEAkKViMam93fxi39dnfqHfvFnK4S0/ACA78BUPyAKELwAAMlg8Lu3fbx6W2N1N3AIAZC2+8gFZivAFAECam5s7FLd6eiSXSzrpJCk31+rJAACwBJELwDzCFwAAaSCRkDo6zLjV1SU5ndLGjcQtAEDWI3IBOC7CFwAAKSKRMKNWXZ3U2Wkejrhhgxm5AAAAkQvA4i01fBUVSfn5hC8AABYlmTT32qqvNw9PtNul6mrz8EQAADCPyAVgRRC+AABYYcmkuddWQ4N51kRJqqyU3G5r5wIAIEURuQCsGsIXAABLYBhSb6+5cqu93YxdFRWSx2P1ZAAApDQiF4A1RfgCAOAYDEPq7zdXbu3bZ35BrKiQvF6rJwMAIC0QuQBYbinhq6zM3Nye8AUASHuGIQ0MSI2NZtyKxcy45fNZPRkAAGmFyAUgJZ0ofNXVHR6+AoFDZ3UkfAEA0sbgoBm3WlqkaNT8Yub3Wz0VAABpicgFIG0cL3yFw4QvAEAaCYWkpiZp715pZsb8ghUIWD0VAABpjcgFIK0dK3zNzBC+AAApaHjYXLnV3Gx+oSovl6qqrJ4KAICMQOQCkHEcDjNcvfo/xAlfAABLjYyYK7eam6WpKXNzSeIWAAArisgFICssJHzNzUl2O+ELALCCxsakPXvMwDUxIZWWmpvKAwCAFUfkApC1CF8AgFUzMWHGrcZGaXxcKimRtm3jiwYAAKuIyAUAr7Kc8JWXZ8YvwhcAZLHJSfOQxIYGcxVXcTFxCwCANULkAoATIHwBAE5oakpqaZHq683N5YlbAACsOSIXACzBSoQvv9+8HQCQxsJhae/eQ3GrqMiMW3yCBwBgzRG5AGCFEL4AIIvMzBxauRUKSQUFUk0Nn8QBALAQkQsAVhHhCwAyzOys1Noq1dZKQ0PmJ2riFgAAKYHIBQBrjPAFAGkoEpHa2sy4NTgoBYPS1q3mJ3UAAJASiFwAkAKWEr7Kysx9jQlfALCKolEzbtXVSf395ifgLVuIWwAApCAiFwCkqBOFr/p6whcArJpYTGpvN1du9fWZn2g3b5ZyePsMAECq4qs0AKQRwhcArLJ4XNq/31y51dMjeb3ELQAA0gRfrQEgzRG+AGAFzM1JHR1m3OrqktxuadMmKTfX6skAAMACEbkAIAMRvgBggRIJM27V10udnZLTKW3caP4MAADSCpELALLEQsJXIiHZbIQvAFkgkTBXbNXXm5HL4ZDWr5dcLqsnAwAAS0TkAoAsRvgCkHWSSam72/wEt3+/+Qmsupq4BQBABiByAQAOQ/gCkJGSSam31/wk1tZmfhKrrDT33gIAABmByAUAOKHFhi+/XyovJ3wBSAGGYcathgYzbiWTUkWF5PFYPRkAAFhhRC4AwJIQvgCkNMOQ+vvNuNXaap5to6JC8nqtngwAAKwSIhcAYMUQvgBYzjCkwUEzbu3bJ8ViZtzy+ayeDAAArDIiFwBgVS01fK1bZ0YvwheABRsclBobpZYWKRo1P5n4/VZPBQAA1giRCwCw5ghfAFZUKCQ1NUl795qfSMrLD/8EAwAAsgKRCwCQEghfABZteNiMW83N5ieKsjKpqsrqqQAAgEWIXACAlEX4AnBUo6PmYYnNzdLUlFRaKlVWWj0VAACwGJELAJBWlhu+8vLMxxK+gDQ0Nibt2WOu3pqYMONWRYXVUwEAgBRB5AIApD3CF5DhJibMVVuNjWboKimRtm0z/1EDAAC8gsgFAMhISwlfZWVScTHhC0gZk5OH4tbICHELAAAcF5ELAJA1ThS+GhoIX0BKmJ42z5RYX29uLr9unXTyycQtAABwXEQuAEBWI3wBKSQcllpazLh14IBUVGSu3OIfGAAAWAAiFwAAr0H4AtbYzIzU2irV1kqhkFRQINXU8I8IAAAsCpELAIAFWEj4mpszvycnfAELFIlI+/aZK7cGB81/KFu3mv/gAAAAFonIBQDAEp0ofDU2SvE44Qs4QiQitbWZK7cGB81/CFu2ELcAAMCyELkAAFhBhC/gOGIxM27V1Un9/eY/AOIWAABYIUQuAABWGeELWS8el9rbzZVbvb3mX/STTpJyeCsKAABWDu8sAACwwGLCl9dr3o/whbQTj0v795srt3p6JI/HjFu5uVZPBgAAMhCRCwCAFEH4QsaYm5M6Osy41dUlud3Spk3ELQAAsKqIXAAApDDCF9JKIiF1dppxq7NTcjqljRvNnwEAAFYZkQsAgDRzrPA1OytNTxO+YIFk0oxa9fXmCi6HQ1q/XnK5rJ4MAABkESIXAAAZwOEwN6z3+w9dd6LwVVoqlZQQvrAMyaTU3S01NJgby9vtUnU1cQsAAFiCyAUAQIY6UfhqajJPdkf4wqIlk+ZZEuvrpbY287rKSnPvLQAAAIsQuQAAyCKELyyLYUh9febKrdZWM3aVl5t/WQAAACxG5AIAIMsRvnBChiH195vHve7bZ549saKCuAUAAFIKkQsAABxhOeErL8+MX4SvDGAY0uDgoZVb0agZt3w+qycDAAA4ApELAAAsyELCV12dZLMRvjLC0JAZt1pazLhVXn74Hz4AAECKIXIBAIAlI3xloAMHzMMS9+6VZmaksjIpGLR6KgAAgBMicgEAgBW12PDl95sdhfBlseFh8w+nudn8gyovl6qqrJ4KAABgwYhcAABg1RG+UtjoqLRnj/mHMDlp/sZXVlo9FQAAwKIRuQAAgCUIXxYbHzd/k5uapIkJ8zjSigqrpwIAAFgyIhcAAEgZhK81MDFhHpLY2CiNjUnFxdK2beZvKgAAQBojcgEAgJR2vPAVDh87fBUXm9GL8PWKqSkzbjU0mIcoErcAAECGIXIBAIC0s5DwNTdH+JJkLoFraTFL4MiIVFRE3AIAABmJyAUAADIC4es1wmEzbtXXSwcOSIWFUk1NBn2AAAAAhyNyAQCAjJWV4Wt2Vtq3T6qtlUIh84MgbgEAgCxA5AIAAFllueErL08KBlOwGUUiUmureVji4KA56Nat5gcMAACQBYhcAAAg66V1+IpGpbY26eWXpYEBc5AtW4hbAAAg6xC5AAAAjiLlw1csZsatujqpv1/y+Vi5BQAAstqiItdtn79Nt3/h9sOu27ptq17c+6IkKRKJ6DN//xk9+H8fVCwa0wUXX6A7vnGHSkpLVm5iAAAAi6RE+IrHpfZ2c8+t3l4zbp10kpTD/10CAIDstuh3Q6ecdooe+dUjh57gVW+oPvWJT+kXj/5Cu3+yW3l5efrkRz+pHZfv0ONPP74iwwIAAKSaNQtf8bjU0WHGrZ4eyeMx41Zu7ip+dAAAAOlj0ZHLkeNQaVnpEddPTEzovu/dp+/e/12df8H5kqS777lb20/Zrhefe1HnnHvO8qcFAABIAysavubmzLhVVyd1d0sul7RpE3ELAADgNRYdufa37tfJFSfL5XZp+3nb9bnbPqfq9dWq/WOt4vG4zr/w/Pn71pxco6r1VXrh2ReOGbmi0aii0ej85anJqSV8GAAAAKltseEr4E1oozpVMVynvPEuuX05cp+0QXa307oPAgAAIIUtKnK98U1v1Dd2f0Nbtm3R0MCQbv/C7XrXn7xLzzY+q9BgSE6nU/n5+Yc9pqS0RKHB0DGf887b7jxiny8AAIBscNTwFU/K3tMlT0OdEv0d2m84FM6rVq7PJfeQVFBgrvTy+cwY5vVadFZHAACAFLOoyPXOd71z/tevO+N1esOb3qAzNpyhh3/8sDwez5IG2HXzLu3ctXP+8tTklE6rPm1JzwUAAJC2kkl5hnsU7KyXd7Bdhs2u6LZqJXNd8iWlaNT80dNjHr1os5lHLrrdhC8AAABpCYcrvlp+fr4212xWR1uH3v7OtysWi2l8fPyw1VyhoZBKyo59dkWXyyWXy7WcMQAAANJXMinPSK8CnfXyDbTLkKFIYaWSTvf8Xex2c5/5V/+fYvIY4cvpNO9H+AIAANlmWZFrenpaHe0d+ssdf6kz33CmcnNz9dSvn9JlV1wmSWptaVVvd6+2n7d9RYYFAADIGIYh90ifAp0N8g+0SsmkIoXlSrq8C3o44QsAAOBwi4pcn7nxM/qzS/9M1RuqNdg/qNv+6TY5HA79xZV/oby8PO340A59etenVVBYoGAwqJs+dpO2n7edMysCAAAcZBhyjQ4o2NUgX98+2ZJzihRWLDhuHQ/hCwAAZLNFRa7+3n59+MoPa3RkVOuK1+nct56rXz33K60rXidJuvWrt8put+uaK65RLBrTBRdfoDu+cceqDA4AAJBuXKMDCnQ1yNffKns8qmhhuRJu/4kfuAyELwAAkC1s48a4YfUQrzY5Oan1ees1MTGhYDBo9TgrwjCk++4zTwteXGz1NAAAYK05x4YU6GqUv79FjuisIoXlSngCVo91mFeHr2jUfN9C+AIAIA3NzEijo9JVV5lfvDPA5OSk8vLy1D3RfdxWtKw9uQAAAHBszokD8nc3KdDTLEc0rGhBuSLrqq0e66iWsuIrP9/8QfgCAACpgMgFAACwwnInRxTobpS/p1k5kWlF88sUWVdl9ViLdqLw1dsrdXRKdsIXAABIAUQuAACAFZI7NSp/9x4FepqUMzOlaEGpokWVVo+1oghfAAAgVRG5AAAAlilnelz+nj0KdjcpZ2ZCsbwShQsrrB5rzSwnfHm9h+IX4QsAACwHkQsAAGCJcmYmzZVbXY3KDY8pllescEWNuXFVliN8AQCAtUbkAgAAWCTHzJT8vc0KdjYod3pUsWCxwpXbiFsnQPgCAACricgFAACwQI7Zafn7WhToqJNzakTxQBFxa5kIXwAAYKUQuQAAAE7AEQnL19eiYGeDnONDr8StGslGWVkNhC8AALAURC4AAIBjsEdn5e/fp8D+WrkmQor78hWu2kbcsgDhCwAAnAiRCwAA4DXssYh8/a0KdtTJPTZgxq2KrZLdYfVoeBXCFwAAeDUiFwAAwCvs8ai8A20K7q+Ve3RAc96ApolbaYXwBQBA9iJyAQCArGeLx+QbaDNXbo32KeH2K1yxhbiVIQhfAABkByIXAADIWra5uHyD7Qp01Mkz3KuEy6uZss0yHLxFynSELwAAMg/v4AAAQNaxJebkHdyvYGed3KEeJV1uzZRtkuHItXo0WGih4csmyeUifAEAkGqIXAAAIGvYEnPyDHUq2FEnz4EuGbkuzZZulJFD3MLRLSR8dXaa1xO+AACwFpELAABkvkRC3lCngp118oS6ZDhyNFuyQUaO0+rJkIYIXwAApCYiFwAAyFzJpDyhLgU76uQNdcqw2zW7rlpGrsvqyZBhCF8AAFiPyAUAADJPMinPcI+CnfXyDrbLsNkVKapU0um2ejJkkZUIXx6P5OAknwAALAiRCwAAZA7DkHu4V8Guevn622TIUKSwQkmn58SPBdYA4QsAgNVD5AIAAOnPMOQe6VOgs0G+gVbZkklFCsuVdHmtngw4IcIXAAArg8gFAADSl2HINTqgYFeDfP2tss3FzMMSiVtIcwsNXzab5HQSvgAAkIhcAAAgTbnGBuXvapS/r0X2eFTRwnIl3H6rxwJWzVLDV16eGb0IXwCATEfkAgAAacU5NqRAd5P8fXvliM4qUliuhCdg9ViAJQhfAAAcQuQCAABpwTlxQP7uJvl7mpUTDStaUK7IumqrxwJSDuELAJCtiFwAACCl5U6OKNDdKH/vXuXMTimaX6bouiqrxwLSCuELAJANiFwAACAl5U6Nyt+9R4GeJuXMTCmaX6JoYYXVYwEZg/AFAMg0RC4AAJBScqbH5e9tVrCrUbnhcUXzSxUmbgFrgvAFAEhnRC4AAJAScmYm5etpVrCrQbnTY4rlFWu6cpv53TQAyxC+AADpgsgFAAAs5ZiZkr93rxm3pkYUC65TmLgFpLRjha9YTIpECF8AAGsQuQAAgCUckbD8vXsV6KiTa2pYsQBxC0hndrvkdps/DiJ8AQDWEpELAACsKUd0Rr7eFgU76+UcH1I8UPTKYYl2q0cDsMIWEr46OiU74QsAsAKIXAAAYE3Yo7Py9+8zV26NDynuy1e4irgFZBvCFwBgtRC5AADAqrLHIvL1tyrYUSf32IDi3jyFK7ZKdr5DBWBaTvjy+iQ/4QsAICIXAABYJfZ4VN6BNgX318o9OqA5b0DTxC0AC0T4AgAsFpELAACsKFs8Jt9gu4IdtXKP9Cnh9mumfLMMB287ACwP4QsAcDy82wQAACvCNheXb7BdgY56eYa7lXD5NFNG3AKwughfAICDeNcJAACWxZaYk3dwv4KddXKHepR0ujRTdpIMR67VowHIUoQvAMhORC4AALA0iYS8Qx0KdtTJc6BLRq5Ls6UbZeQQtwCkHsIXAGQ+IhcAAFicRELeA10KdtTKE+qS4cjRbMkGGTlOqycDgEVZSvjKyzPjF+ELAFIPkQsAACxMMilPqEvBrgZ5B/fLsNs1u65aRq7L6skAYMUcL3xFo1Jfn9TZRfgCgFRE5AIAAMeXTMoz3KNAZ4O8g+2SpEhRpZJO9wkeCACZ4dXhKy/PvI7wBQCph8gFAACOzjDkHu5VsKtevoF2GUZS0cIKJZ0eqycDAMstNHzZJLlchC8AWAtELgAAcDjDkHu0X4HOBvn698mWTCpSWK6ky2v1ZACQ0ghfAGAtIhcAADAZhlyjAwp0N8rft0+2uZiihRVKuH1WTwYAaWsh4aury7ye8AUAy0PkAgAAco0Nyt/VKH9fixzxqCIF5Up4/FaPBQAZabnhy+eVvF7CFwC8FpELAIAs5hwPKXAwbsVmFSkoU8QTsHosAMg6iw1fbrcZvQhfAHAIkQsAgCzknDggf3eT/D3NyomGFS0oV2RdldVjAQBehfAFAItD5AIAIIvkTo4o0PNK3JqZUrSgTFHiFgCkDcIXABwbkQsAgCyQOz0mX/ceBbublDMzqWh+iaKFFVaPBQBYAYQvADARuQAAyGA54Qn5e/Yo2NWo3PC4onklClfWSDab1aMBAFbRUsNXXp7k8xO+AKQnIhcAABkoZ2ZSvp5mBbsalDs9pliwWNOV24hbAJDFCF8AMh2RCwCADOKYmZK/r0XBznrlTo0oFlynMHELAHAMhC8AmYTIBQBABnBEwvL37lWgo07OqRHFA0WvHJZot3o0AECaIXwBSFdELgAA0pgjOiNfr7lyyzkRUtxfQNwCAKw4wheAdEDkAgAgDdmjs/L1tyrYUSvX+JDmfPnELQDAmiJ8AUg1RC4AANKIPRaRb6BNgf218owNKO7NU7hiq2TnOwQAgPUIXwCsROQCACAN2ONReQfaFNxfK/fogOa8AU0TtwAAaWAp4Ssvz4xfhC8Ai0HkAgAghdniMfkG2xXsqJV7pE8Jt08z5ZtlOPgSDgBIX8cKX/G4FIlI/f1Sd7d5PeELwELxDhkAgBRkm4vLO7hfwY46eYa7lXD5NFNG3AIAZC673QxaLhfhC8DS8E4ZAIAUYkvMyTvUoUBHnTyhbiWdLs2UnSTDkWv1aAAArDnCF4DFIHIBAJAKEgl5hzoU7KyXJ9QpI8ep2dINMnKcVk8GAEBKIXwBOBYiFwAAVkok5D3QZR6WGOqUYXdotoS4BQDAYiwrfPnMH4QvIP0RuQAAsEIyKc+BbgU76+Ud3C/DbldkXbWSuS6rJwMAICMsJnw5XZKH8AWkPSIXAABrKZmUZ6RXgY56eQfbJUmRokolnW6LBwMAIPMtJHx1dUs2Eb6AdETkAgBgLRiG3MO9CnQ1yD/QJsNIKlpYoaTTY/VkAABkNcIXkDmIXAAArCbDkHu0X4HOBvn698mWTChSWKGky2v1ZAAA4BgWG77cLjN6Eb4AaxG5AABYDYYh19iguXKrb59sczFFCyuUcPusngwAACwB4QtIfUQuAABWmGtsUP6uRvn7WuSIRxUpKFfC47d6LAAAsMIIX0BqIXIBALBCnOMhBbqb5O/dK0dsRpGCckU8AavHAgAAa4jwBViHyAUAwDI5J4fl72qSv2ePcqJhRQvKFVlXZfVYAAAgRRC+gLVB5AIAYIlyp0YV6G6Uv6dZOTNTihaUKUrcAgAAC0D4AlYekQsAgEXKnR6Tr3uPgt1NypmZUDS/VNHCCqvHAgAAaY7wBSwPkQsAgAXKCU/I37NHga4mOcNjiuaVKFy5TbLZrB4NAABkKMIXsHBELgAATiBnZlK+nmYFuxqUOzWqWF6JpolbAADAIosNXy6XVJBP+ELmI3IBAHAMjtlp+Xv3KthZr9zJYcXyihWuOpm4BQAAUg7hC5Dsy3nwV//lq8q35esfb/jH+esikYhu3HmjNhVtUqW/Ujuu2KHQUGjZgwIAsFYckbCC7S+p/OkHVNTwhCQpXLVN8eA6AhcAAEgbB8NXXp5UWipVVUrl5VIwIBlJM3zV1UvPPiv99nfSE09Kf/yj1N4uDQ5KU1NSImH1RwEs3JJXcr304ku65z/u0WlnnHbY9Z/6xKf0i0d/od0/2a28vDx98qOf1I7Ld+jxpx9f9rAAAKwmR3RGvr59CnTUyTURUtxfoHBljWRb1v8JAQAApAxWfCGTLSlyTU9P6/qrr9e/feff9K9f+tf56ycmJnTf9+7Td+//rs6/4HxJ0t333K3tp2zXi8+9qHPOPWdlpgYAYAXZo7Py9bcq2FEr1/iQ5nz5Cldsley8ewMAAJmP8IVMsaTIdePOG3XRJRfp7Re+/bDIVfvHWsXjcZ1/4fnz19WcXKOq9VV64dkXjhq5otGootHo/OWpyamljAQAwKLZYxH5BtoU2F8r99ig5rxB4hYAAIAIX0hPi45cD/7fB1X/Ur1+8+JvjrgtNBiS0+lUfn7+YdeXlJYoNHj0fbnuvO1O3f6F2xc7BgAAS2aPR+UdaFewo1bu0QHNefwKV2whbgEAABzHQsOXdOh+hC+spUVFrt6eXv3jx/9RD//yYbnd7hUZYNfNu7Rz1875y1OTUzqt+rTjPAIAgKWxxWPyDbYr0FEnz0ivEm6fZspOkuHgZMMAAABLQfhCKlnUu/raP9bqQOiAzj/70OGIiURCz/z2GX3nru/ooccfUiwW0/j4+GGruUJDIZWUlRz1OV0ul1wu19KmBwBgAWxzcXkH9yvYUSfPcI8SLs8rcSvX6tEAAAAyztHCl2FIsRjhC6trUZHr/D89X880PHPYdTv/aqe2nrxVN/zDDaqsrlRubq6e+vVTuuyKyyRJrS2t6u3u1fbztq/c1AAALIAtMSfvUIe5civUpaTTrZnSTTJyiFsAAABryWZbXvjyes2fCV84nkVFrkAgoFNfd+ph13l9XhUWFc5fv+NDO/TpXZ9WQWGBgsGgbvrYTdp+3nbOrAgAWDuJhLyhTgU76+QZ6pSR49Rs6UYZOU6rJwMAAMArCF9YaSu+CcmtX71Vdrtd11xxjWLRmC64+ALd8Y07VvplAAA4UjIpb6hTgY56eUMdMuwOzRavl5HLYfEAAADpgPCF5bCNG+OG1UO82uTkpNbnrdfExISCwaDV46wIw5Duu0+am5OKi62eBgAyUDIpz4FuBTvr5R3aL8NmV7SwQkniFgAAQEZ6dfiKRKT4nHn9wfCVnycVFGRp+JqZkUZHpauuOlQK09zk5KTy8vLUPdF93FbE6aQAAOkrmZRnpFeBjnr5Btpk2GyKFFYq6VyZMwADAAAgNZ1oxdfAgNTdY15P+MoeRC4AQPoxDLlH+hTorJd/oE2GkVSkqEJJp8fqyQAAAGARwheIXACA9GEYco/2K9DVKF/fPtmSc4oUVijp8lo9GQAAAFIQ4Su7ELkAAKnPMOQaG1Sgq0G+/lbZ41FFCyuUcPusngwAAABp5kTha3CQ8JWuiFwAgJTmGhuUv6tR/r4WOeJRRQrKlfD4rR4LAAAAGeTV4esgwlf6IXIBAFKSczykQHeT/L175YjNKJpfpog3M866CwAAgNS30PBlk+R0Si434ctqRC4AQEpxTg7L390kf0+zciLTihaUK7KuyuqxAAAAAMJXiiNyAQBSQu7UqPzdTQr07FHOzJSiBaWKFlVaPRYAAABwXISv1EHkAgBYKnd6TL7uPQp2NylnZkKx/FKFCyusHgsAAABYMsKXNYhcAABL5IQn5O9pVqCrUbnhccXyihWu3Ga+IwAAAAAyDOFr9RG5AABrKmdmUr6eZgW7GpU7NaJYXonClTXELQAAAGSdpYSvvKBUWEj4OhoiFwBgTThmp+Xv3atgZ71yp0YUDxQpXHUycQsAAAB4lROFr6Ehqaf3OOFLki8pZWP3InIBAFaVIxKWr69FwY56OScPmHGrskay2a0eDQAAAEgLiwlfXknr7NKpV0iePKsmtgaRCwCwKhzRGfn6WxXYXyvXREhxfwFxCwAAAFghxwpfkVFpeswMYB7rxrMEkQsAsKLssYh8ffsU7KyXa3xQc948hSu2SvZsXDANAAAArB2bTcrNleJWD2IRIhcAYEXY49H5lVvusUHNeQMKl28hbgEAAABYE0QuAMCy2OIx+QbaFOyok3u0X3Mev8IVxC0AAAAAa4vIBQBYEttcXL7BdgX218oz0quE26eZspNkOPjSAgAAAGDt8Z0IAGBRbHNxeQf3K9hRJ89wjxIuzytxK9fq0QAAAABkMSIXAGBBbIk5eYc6FOiok+dAt4xcl2ZKN8nIIW4BAAAAsB6RCwBwfImEvKFOBTvr5Al1yXDkarZkg4wcp9WTAQAAAMA8IhcA4OiSSXlDnQp01Msb6pBhd2h2XbWMXJfVkwEAAADAEYhcAIDDJZPyHOhWsKtB3sF2GTa7IuuqlSRuAQAAAEhhRC4AgCmZlGekV4HOevkG2mXIUKSwUkmn2+rJAAAAAOCEiFwAkO0MQ+6RPgU6G+QfaJVhJBUpKFfS5bV6MgAAAABYMCIXAGQrw5B7tF+Brkb5+vbJlpxTpLCCuAUAAAAgLRG5ACALuUYHFOhqkK+/VfZ4VNHCCiXcPqvHAgAAAIAlI3IBQBZxjg0p0NUof3+LHLGIIgXlSnj8Vo8FAAAAAMtG5AKALOCcOGDGrd69csRmFM0vU2RdtdVjAQAAAMCKIXIBQAZzTg7L390kf0+zciLTr8StKqvHAgAAAIAVR+QCgAyUOzUqf/ceBXqalDMzqWhBmaJFlVaPBQAAAACrhsgFABkkZ3pc/u4mBXv2KGdmQrG8EoULK6weCwAAAABWHZELADJATnhC/p5mBboalRseUyyvWOGKGslms3o0AAAAAFgTRC4ASGOOmSn5e5sV7GxQ7vSoYsFihSu3EbcAAAAAZB0iFwCkIcfstPx9LQp01Mk5NaJ4oIi4BQAAACCrEbkAII04ImH5+loU7GyQcyKkuL9Q4coayWa3ejQAAAAAsBSRCwDSgD06K3//PgX218o1eUBxbx5xCwAAAABehcgFACnMHovI19+qYEed3GMDivvyFS7fItkdVo8GAAAAACmFyAUAKcgej8o70Kbg/lq5R/s15w1qumIrcQsAAAAAjoHIBQApxBaPyTfQZq7cGu1Xwu1TmLgFAAAAACdE5AKAFGCbi8s32K5AR508w71KuLyaKTtJhoNP0wAAAACwEHz3BAAWss3F5R3qULCjVp4DPUq4PJop2yTDkWv1aAAAAACQVohcAGABW2JO3qEOc+XWgW4ZuS7NlG6SkUPcAgAAAIClIHIBwFpKJOQNdSrYWSdPqEuGI0ezJRtk5DitngwAAAAA0hqRCwDWQjIpT6hLwY46eUMdMuwOza6rlpHrsnoyAAAAAMgIRC4AWE3JpDzDPQp21ss72C7DZldkXbWSxC0AAAAAWFFELgBYDYYhz3CPAl0N8vW3yZChSGGFkk6P1ZMBAAAAQEYicgHASjIMuUf6FOhskG+gVbZkUpHCciVdXqsnAwAAAICMRuQCgJVgGHKNDijY1SBff6tsibi5cou4BQAAAABrgsgFAMvkGhs0D0vs2yd7PKpoYbkSbr/VYwEAAABAViFyAcASOceGFOhqlL+/RY7orCKF5Up4AlaPBQAAAABZicgFAIvknDggf3eT/D3NyomGFS0oV2RdtdVjAQAAAEBWI3IBwALlTo4o0N1oxq3ItKL5ZYquq7J6LAAAAACAiFwAcEK5U6Pyd+9RoKdJOTNTiuaXKFpUafVYAAAAAIBXIXIBwDHkTI/L39usYFejcmYmFMsrUbiwwuqxAAAAAABHQeQCgNfImZk0V251Nyp3ekyxvGKFK2okm83q0QAAAAAAx0DkAoBXOGamzJVbnQ3KnR5VLLhO4cptxC0AAAAASANELgBZzzE7LX9fiwIddXJOjSgeKCJuAQAAAECaIXIByFqOSFi+vn0KdtbLOT70StyqkWx2q0cDAAAAACwSkQtA1rFHZ+Xv36dAR51c40OK+/IVrtpG3AIAAACANEbkApA17LGIfP2tCnbUyT02oLg3T+GKrZLdYfVoAAAAAIBlInIByHj2eFTegTYF99fKPTqgOW9A08QtAAAAAMgoRC4AGcsWj8k32K5gR63cI31KuP0KV2whbgEAAABABiJyAcg4trm4fIPtCnTUyzPco4TLq5myzTIcfMoDAAAAgEzFd3wAMoYtMSfv4H4FO+vkDvUo6XRppmyTDEeu1aMBAAAAAFYZkQtA2rMl5uQZ6lSwo06eA10ycl2aLd0oI4e4BQAAAADZgsgFIH0lEvIe6FKwo1aeUJcMR45mSzbIyHFaPRkAAAAAYI0RuQCkn2RSnlCXgp318g51yLDbNbuuWkauy+rJAAAAAAAWIXIBSB/JpDzDPWbcGmyXIZsiRZVKOt1WTwYAAAAAsBiRC0DqMwy5h3sV7KqXr79NhgxFCiuUdHqsngwAAAAAkCKIXABSl2HIPdqvQGeDfP37ZEsmFSksV9LltXoyAAAAAECKIXIBSD2GIdfogIJdDfL1t8o2F1O0sEIJt8/qyQAAAAAAKYrIBSCluMYG5e9qlL+vRfZ4VNHCciXcfqvHAgAAAACkOCIXgJTgHA8p0NUof99eOaKzihSWK+EJWD0WAAAAACBNELkAWMo5cUD+7ib5e5qVEw0rWlCuyLpqq8cCAAAAAKQZ+2Lu/L1vfk9vPuPNqg5WqzpYrXee90798n9+OX97JBLRjTtv1KaiTar0V2rHFTsUGgqt+NAA0l/u5IgKm36rsmcfUn7rH5TwBBSu3KY5b9Dq0QAAAAAAaWhRkauiqkKf/5fP68k/Pqkn/vCE3nbB23TVZVepualZkvSpT3xKj/38Me3+yW49+tSjGuwf1I7Ld6zK4ADSU+70mAqafq/yZx5QfssLSjg9Cldt05wvz+rRAAAAAABpbFGHK77r0ncddvmzt3xW3/vm9/Ticy+qoqpC933vPn33/u/q/AvOlyTdfc/d2n7Kdr343Is659xzjvqc0WhU0Wh0/vLU5NRiPwYAaSBnelz+3mYFuxqVGx5XNL9U4cIKq8cCAAAAAGSIJe/JlUgk9MhPHtFMeEbbz9uu2j/WKh6P6/wLz5+/T83JNapaX6UXnn3hmJHrztvu1O1fuH2pYwBIcTkzk/L1NCvY1aDc6THF8oo1XblNstmsHg0AAAAAkEEWHbmaGpp00XkXKRKJyOf36T8f/k+dfOrJaqhtkNPpVH5+/mH3LyktUWjw2Pty7bp5l3bu2jl/eWpySqdVn7bYsQCkGMfMlPy9e824NTWiWHCdwsQtAAAAAMAqWXTk2rptq35X+ztNTkzqpw/8VB+59iN69KlHlzyAy+WSy+Va8uMBpBZHJCx/714FOurkmhpWLLBO4coaybaoLQABAAAAAFiURUcup9Opk7acJEk68w1n6qUXX9K3vv4tvfcv36tYLKbx8fHDVnOFhkIqKStZsYEBpCZHdEa+3hYFO+vlnAgp7i985bBE4hYAAAAAYPUt+7vPZDKpaDSqM99wpnJzc/XUr5+av621pVW93b3aft725b4MgBRlj84q2FGnsqcf0Lq6X8k2F1O4skax/BICFwAAAABgzSxqJdcXbv6CLnzXhapaX6XpqWk9cP8D+v2Tv9dDjz+kvLw87fjQDn1616dVUFigYDComz52k7aft/2Ym84DSF/2WES+/lYFO+rkHhtQ3JuncMVWye6wejQAAAAAQBZaVOQ6EDqgv73mbzU0MKRgXlCnnXGaHnr8Ib3jne+QJN361Vtlt9t1zRXXKBaN6YKLL9Ad37hjVQYHYA17PCrvQJuC+2vlHh3QnDegaeIWAAAAAMBii4pcd33vruPe7na79ZW7v6Kv3P2VZQ0FIPXY4jH5BtsV7KiVe6RPCbdfM+WbZTgWvbUfAAAAAAArju9OARyXbS4u32C7Ah318gx3K+HyaaaMuAUAAAAASC18lwrgqGyJOXkH9yvYWSd3qEdJp0szZSfJcORaPRoAAAAAAEcgcgE4XCIh71CHgh118hzokpHj1GzpRhk5xC0AAAAAQOoicgEwJRLyHugy41aoU4YjR7MlG2TkOK2eDAAAAACAEyJyAdkumZQn1KVgV4O8g/tl2O2KrKtWMtdl9WQAAAAAACwYkQvIVsmkPMM9CnQ2yDvYLkmKFFUq6XRbPBgAAAAAAItH5AKyjWHIPdyrYFe9fAPtMoykooUVSjo9Vk8GAAAAAMCSEbmAbGEYco/2K9DZIF//PtmSSUUKy5V0ea2eDAAAAACAZSNyAZnOMOQaHVCgu1H+vn2yzcUULaxQwu2zejIAAAAAAFYMkQvIYK6xQfm7GuXva5EjHlWkoFwJj9/qsQAAAAAAWHFELiADOcdDCnQ3yd+7V47YrCIFZYp4AlaPBQAAAADAqiFyARnEOXFA/u4m+XualRMNK1pQrsi6KqvHAgAAAABg1RG5gAyQOzmiQM8rcWtmStGCMkWJWwAAAACALELkAtJY7vSYfN17FOxuUs7MhKL5pYoWVlg9FgAAAAAAa47IBaShnPCE/D17FOxqVG54XNG8EoUrt0k2m9WjAQAAAABgCSIXkEZyZibl62lWsKtBudNjigWLNU3cAgAAAACAyAWkA8fMlPx9LQp21it3clixvGJWbgEAAAAA8CpELiCFOSJh+Xv3KtBZL+fksOKBIoWrtkk2u9WjAQAAAACQUohcQApyRGfk69unYEednBMhxf0FClfWELcAAAAAADgGIheQQuzRWfn6WxXsqJVrfEhzvnziFgAAAAAAC0DkAlKAPRaRb6BNgf21co8Nas4bVLhiq2R3WD0aAAAAAABpgcgFWMgej8o70KZgR53cI/2a8wYUrthC3AIAAAAAYJGIXIAFbPGYfIPtCnbUyj3Sp4Tbp5nyzTIc/JMEAAAAAGAp+I4aWEO2ubi8g/sV7KiTZ7hHCZdXM2XELQAAAAAAlovvrIE1YEvMyTvUoUBHnTyhLiWdbs2UbpKRk2v1aAAAAAAAZAQiF7CaEgl5hzoU7KyXJ9QpI8ep2dKNMnKcVk8GAAAAAEBGIXIBqyGRkPdAlwId9fKGOmXY7Zot2UDcAgAAAABglRC5gJWUTMpzoFvBznp5h/bLsDkUWVelZK7L6skAAAAAAMhoRC5gJSST8oz0KtBRL99AmwybTZHCSiWdbqsnAwAAAAAgKxC5gOUwDLmHexXoapB/oE2GkVSkqEJJp8fqyQAAAAAAyCpELmApDEPu0X4FOhvk62+VLTmnSGGFki6v1ZMBAAAAAJCViFzAYhiGXGODCnSZccsejypaWKGE22f1ZAAAAAAAZDUiF7BArrFB+bsa5e9rkSMeVaSgXAmP3+qxAAAAAACAiFzACTnHQwp0N8nfu1eO2IwiBeWKeAJWjwUAAAAAAF6FyAUcg3NyWP7uJvl7muWIhBUrKFNkXZXVYwEAAAAAgKMgcgGvkTs1Kn93kwI9e5QzM6VoQamiRZVWjwUAAAAAAI6DyAW8Ind6TL7uPQp2NylnZkKx/FKFCyusHgsAAAAAACwAkQtZLyc8IX9PswJdjXKGxxTNK1G4cptks1k9GgAAAAAAWCAiF7JWzsykfD3NCnY1KndqRLG8Ek0TtwAAAAAASEtELmQdx+y0/L17FeysV+7UiOKBIoWrTiZuAQAAAACQxohcyBqOSFi+vhYFO+rlnDxgxq3KGslmt3o0AAAAAACwTEQuZDxHdEa+/lYF9tfKNRFS3F9A3AIAAAAAIMMQuZCx7NFZ+fpbFeysk2t8SHPePIUrtkp2h9WjAQAAAACAFUbkQsaxxyLyDbQpsL9W7rFBzXkDCpdvIW4BAAAAAJDBiFzIGLZ4TL6BNgU76uQe7decx69wBXELAAAAAIBsQORC2rPNxeUbbFdgf608I71KuH2aKTtJhoO/3gAAAAAAZAsqANKWbS4u7+B+BTvq5BnuUcLleSVu5Vo9GgAAAAAAWGNELqQdW2JO3qEOBTrq5Al1yXC6NVO6SUYOcQsAAAAAgGxF5EL6SCTkDXUq2Fknz1CnjBynZks3yshxWj0ZAAAAAACwGJELqS+ZlDfUqUBHvbyhDhl2h2aL18vIdVk9GQAAAAAASBFELqSuZFKeA90KdjXIO9guw2ZXZF21ksQtAAAAAADwGkQupJ5kUp6RXgU66+Xrb5NhkyKFlUo63VZPBgAAAAAAUhSRC6nDMOQe6VOgs17+gTYZRlKRwnIlXV6rJwMAAAAAACmOyAXrGYbco/0KdDXK17dPtuScIoUVxC0AAAAAALBgRC5YxzDkGhtUoKtBvv5W2eNRRQsrlHD7rJ4MAAAAAACkGSIXLOEcG1Kgq0H+vhY54lFFCsqV8PitHgsAAAAAAKQpIhfWlHPigAJdjfL37pUjNqNofpki3qDVYwEAAAAAgDRH5MKacE4Oy9/dJH9Ps3Ii04oWlCuyrsrqsQAAAAAAQIYgcmFV5U6Nyt+9R4GeJuXMTClaUKpoUaXVYwEAAAAAgAxD5MKqyJkel7+7ScHuJuXMTiqWV6JwYYXVYwEAAAAAgAxF5MKKyglPyN/TrEBXo3LDY2bcKqiRbDarRwMAAAAAABmMyIUVkTMzKV9Ps4JdjcqdHlUsWKxw5TbiFgAAAAAAWBNELiyLY3Za/t69CnTWyzk1onigiLgFAAAAAADWHJELS+KIhOXra1Gwo17OyQOK+wsVrqyRbHarRwMAAAAAAFmIyIVFsUdn5e/fp8D+WrkmQor7C4hbAAAAAADAckQuLIg9FpGvb5+CnfVyjQ9qzpuncMVWye6wejQAAAAAAAAiF47PHo/K19+qwP5auccGNOcNKly+hbgFAAAAAABSCpELR2WLx+QbaFOwo07u0X4l3D5WbgEAAAAAgJRF5MJhbHNx+QbbFdhfK89InxJur2bKTpLh4K8KAAAAAABIXZQLSDLjlndwv4KddfIc6FHC5dFM2SYZjlyrRwMAAAAAADghIleWsyXm5B3qUKCjTp4D3TJyXZop3SQjh7gFAAAAAADSB5ErWyUS8oY6zZVboS4ZjhzNlmyQkeO0ejIAAAAAAIBFI3Jlm2RSnlCXgh118oY6ZNgdml1XLSPXZfVkAAAAAAAAS0bkyhbJpDwHuhXsapB3sF2Gza7IumoliVsAAAAAACADELkyXTIpz0ivAp318g20y5ChSGGlkk631ZMBAAAAAACsGPti7nznbXfqHee8Q1WBKm0p2aKr3nOVWltaD7tPJBLRjTtv1KaiTar0V2rHFTsUGgqt6NBYAMOQe7hXxS89rrLnHpGvv02RgjLNlm0mcAEAAAAAgIyzqMj19FNP68M7P6xfPvdLPfzLhzUXn9N7L3qvwuHw/H0+9YlP6bGfP6bdP9mtR596VIP9g9px+Y4VHxzHYBhyjfSr+OVfqOzZh+Xv26tIfqlmyjcr6fJaPR0AAAAAAMCqWNThig8+9uBhl7+x+xvaUrJFtX+s1Vve9hZNTEzovu/dp+/e/12df8H5kqS777lb20/Zrhefe1HnnHvOEc8ZjUYVjUbnL09NTi3l44Ak1+iAAl0N8vW3yh6PKlpYroTbb/VYAAAAAAAAq25Ze3JNTkxKkgoKCyRJtX+sVTwe1/kXnj9/n5qTa1S1vkovPPvCUSPXnbfdqdu/cPtyxsh6zrEhBboa5e9vkSMWUaSgXAkPcQsAAAAAAGSPJUeuZDKpm2+4Wee+5Vyd+rpTJUmhwZCcTqfy8/MPu29JaYlCg0ffl2vXzbu0c9fO+ctTk1M6rfq0pY6VVZwTB+TvblKgp1mOaFjRgnJF1lVbPRYAAAAAAMCaW3LkunHnjdrTuEeP/f6xZQ3gcrnkcrmW9RzZJndyRIHuRvl7mpUTmVY0v0yRdVVWjwUAAAAAAGCZJUWuT370k3r8vx7Xo799VJVVlfPXl5SVKBaLaXx8/LDVXKGhkErKSpY9bLbLnRqVv3uPAj1NypmZUrSgVNGiyhM/EAAAAAAAIMMt6uyKhmHokx/9pP7r4f/Sz37zM23ctPGw2898w5nKzc3VU79+av661pZW9Xb3avt521dk4GyUMz2u/OZnVP7MgyrY95ySTo/CVds058u3ejQAAAAAAICUsKiVXDfuvFE/uf8nuv+n98sf8GtocEiSFMwLyuPxKC8vTzs+tEOf3vVpFRQWKBgM6qaP3aTt520/6qbzOL6cmUlz5VZXo3LDY4rlFStcUSPZbFaPBgAAAAAAkFIWFbm+983vSZL+/O1/ftj1d99zt66+7mpJ0q1fvVV2u13XXHGNYtGYLrj4At3xjTtWaNzs4JiZkr+3WcHOBuVOjyoWLFa4chtxCwAAAAAA4BgWFbnGjfET3sftdusrd39FX7n7K0udKWs5Zqfl72tRoKNOzqkRxQNFxC0AAAAAAIAFWPLZFbFyHJGwfH0tCnY2yDk+9ErcqpFsi9oyDQAAAAAAIGsRuSxkj87K379Pgf21ck0eUNybp3DVNuIWAAAAAADAIhG5LGCPReTrb1Wwo07usQHFffkKl2+R7A6rRwMAAAAAAEhLRK41ZI9H5e9uU3B/rdyjA5rzBjRdsZW4BQAAAAAAsExErjUSGGpTQduLKo73K+H2KVzByi0AAAAAAICVQuRaC4ahdW3PKndiRDMnnSTDwW87AAAAAADASmKH87ViGIp7ggQuAAAAAACAVUDkAgAAAAAAQNojcgEAAAAAACDtEbkAAAAAAACQ9ohcAAAAAAAASHtELgAAAAAAAKQ9IhcAAAAAAADSHpELAAAAAAAAaY/IBQAAAAAAgLRH5AIAAAAA/P/27jXayrrQ9/hvIjcRuWgCcrfkIgim5q0io7SOkOmwtIt21J1lXjL16NYk04xMzTCvodmuyDq77RZzp6XbGImapijeNbwAgpdExcUCVFRY50WntSPUva015+N/8vmMwRhr/p+5nL8XvvqO+TwLoHgiFwAAAADFE7kAAAAAKJ7IBQAAAEDxRC4AAAAAiidyAQAAAFA8kQsAAACA4olcAAAAABRP5AIAAACgeCIXAAAAAMUTuQAAAAAonsgFAAAAQPFELgAAAACKJ3IBAAAAUDyRCwAAAIDiiVwAAAAAFE/kAgAAAKB4IhcAAAAAxRO5AAAAACieyAUAAABA8UQuAAAAAIoncgEAAABQPJELAAAAgOKJXAAAAAAUT+QCAAAAoHgiFwAAAADFE7kAAAAAKJ7IBQAAAEDxRC4AAAAAiidyAQAAAFA8kQsAAACA4olcAAAAABRP5AIAAACgeCIXAAAAAMUTuQAAAAAonsgFAAAAQPFELgAAAACKJ3IBAAAAUDyRCwAAAIDiiVwAAAAAFE/kAgAAAKB4IhcAAAAAxRO5AAAAACieyAUAAABA8UQuAAAAAIoncgEAAABQPJELAAAAgOKJXAAAAAAUT+QCAAAAoHgiFwAAAADFE7kAAAAAKJ7IBQAAAEDxRC4AAAAAiidyAQAAAFA8kQsAAACA4olcAAAAABRP5AIAAACgeCIXAAAAAMUTuQAAAAAonsgFAAAAQPHecuT6/Y2/z6f2/FRGDxydPrU+ufqXV691va2tLd/6+rcyavNRGbDhgOy121557JHHOmwwAAAAAPyttxy5Xlz5YsZtMy7fufA7r3v93LPOzcXnXZxp06flt7f9Nj026pF9PrpPXn755X94LAAAAAC8ns5v9Rd232P37L7H7q97ra2tLd//3vdz/NeOz+S9JidJps+YnpH9R+aaX16TT3z6E+v8zqpVq7Jq1ar218tbl7/VSQAAAACs5zr0mVyPL3g8z/zpmey6267tZ7179872O22f22+9/XV/Z9q3p2Vo76Ht/8YOGduRkwAAAABYD3Ro5HrmT88kSfr177fWeb/+/bLkT0te93eO/eqxWbRsUfu/BxY/0JGTAAAAAFgPvOXbFTtat27d0q1bt6pnAAAAAFCwDv0mV/8B/ZMkS55Z+1tbS55Zkn4D+r3erwAAAADAP6xDI9ewLYal/4D+mT1rdvtZa2tr7rztzuy4y44d+VEAAAAA0O4t3664YsWKzH90fvvrxxc8nnvvvjd9N+mbIUOH5LCjD8vZU8/Ou0a8K8O2GJZvnfytDBg4IJP3ntyhwwEAAADgL95y5Lrrjruy58Q9219POXZKkuQzB34m3//x9/OVf/5KVq5cmaO/eHSWtSzLzu/fOVdce0W6d+/ecasBAAAA4K+85cg14YMT0tLW8obXa7Vappw2JVNOm/KP7AIAAACA/7EOfSYXAAAAAFRB5AIAAACgeCIXAAAAAMUTuQAAAAAonsgFAAAAQPFELgAAAACKJ3IBAAAAUDyRCwAAAIDiiVwAAAAAFE/kAgAAAKB4IhcAAAAAxRO5AAAAACieyAUAAABA8UQuAAAAAIoncgEAAABQPJELAAAAgOKJXAAAAAAUT+QCAAAAoHgiFwAAAADFE7kAAAAAKJ7IBQAAAEDxRC4AAAAAiidyAQAAAFA8kQsAAACA4olcAAAAABRP5AIAAACgeCIXAAAAAMUTuQAAAAAonsgFAAAAQPFELgAAAACKJ3IBAAAAUDyRCwAAAIDiiVwAAAAAFE/kAgAAAKB4IhcAAAAAxRO5AAAAACieyAUAAABA8UQuAAAAAIoncgEAAABQPJELAAAAgOKJXAAAAAAUT+QCAAAAoHgiFwAAAADFE7kAAAAAKJ7IBQAAAEDxRC4AAAAAiidyAQAAAFA8kQsAAACA4olcAAAAABRP5AIAAACgeCIXAAAAAMUTuQAAAAAonsgFAAAAQPFELgAAAACKJ3IBAAAAUDyRCwAAAIDiiVwAAAAAFE/kAgAAAKB4IhcAAAAAxRO5AAAAACieyAUAAABA8UQuAAAAAIoncgEAAABQPJELAAAAgOKJXAAAAAAUT+QCAAAAoHgiFwAAAADFE7kAAAAAKJ7IBQAAAEDxRC4AAAAAiidyAQAAAFA8kQsAAACA4olcAAAAABRP5AIAAACgeHWLXD+48AcZN3xc+nfvnw/v9OHcefud9fooAAAAANZzdYlcM38xM1OOnZITTjkhs+fOztbbbJ19PrpPnl3ybD0+DgAAAID1XF0i14XTLsyBXzgwBxx8QEaPGZ1zpp+THj165LJ/uaweHwcAAADAeq7DI9crr7ySu++8O7vutut/fUinTtl1t11z+623r/P+VatWpbW1tf3f8tblHT0JAAAAgCbXuaP/g88/93xWr16dfv37rXXer3+/PPLHR9Z5/7RvT8uZ3zizo2e87dQ61fLq00uy+rkXqp4CAAAANKs1a1Lr27vqFZXo8Mj1Vh371WNzxLFHtL9e3ro8Y4eMrXBRx6t1qmXIvrtk1QsvVj0FAAAAaHKdumyQjQduXPWMhuvwyLXpOzbNBhtskCXPLFnrfMkzS9JvQL913t+tW7d069ato2e87Qz78JZVTwAAAABoWh3+TK6uXbvm3du/O7NnzW4/W7NmTW6cdWN23GXHjv44AAAAAKjP7YpHHHtEDjvwsGz7nm2z/Y7b5/vf+35WrlyZ/Q/evx4fBwAAAMB6ri6Ra59P7ZPnnn0up3/99Cz505KMe/e4XHHtFes8jB4AAAAAOkKtpa2lreoRf621tTVDew/NsmXL0qtXr6rnAAAAAFCh1tbW9O7dO4uWLXrTVtThz+QCAAAAgEYTuQAAAAAonsgFAAAAQPFELgAAAACKJ3IBAAAAUDyRCwAAAIDiiVwAAAAAFE/kAgAAAKB4IhcAAAAAxRO5AAAAACieyAUAAABA8UQuAAAAAIoncgEAAABQPJELAAAAgOKJXAAAAAAUT+QCAAAAoHgiFwAAAADFE7kAAAAAKJ7IBQAAAEDxRC4AAAAAiidyAQAAAFA8kQsAAACA4olcAAAAABRP5AIAAACgeCIXAAAAAMUTuQAAAAAonsgFAAAAQPE6Vz3gb7W1tSVJWltbK14CAAAAQNX+0oj+0ozeyNsucq1YviJJMmTIkIqXAAAAAPB2sWL5ivTu3fsNr9da2lrePIM12Jo1a/L0U0+n58Y9U6vVqp7TIZa3Ls/YIWPzwOIHsnGvjaueAwAAADSpZmwQbW1tWbF8RTYfuHk6dXrjJ2+97b7J1alTpwwaPKjqGXWxca+N06tXr6pnAAAAAE2u2RrEm32D6y88eB4AAACA4olcAAAAABRP5GqAbt265YRTTki3bt2qngIAAAA0sfW5QbztHjwPAAAAAG+Vb3IBAAAAUDyRCwAAAIDiiVwAAAAAFE/kAgAAAKB4IhcAAAAAxRO5AAAAACieyAUAAABA8USuBmptbc3Vv7w68x6aV/UUAAAAoMm88soreWTeI3nttdeqnlIJkauODtrvoFxywSVJkpdeeikT3zMxB+93cN43/n256oqrKl4HAAAANIMXX3wxR37+yGzeY/PsPHbnPLHoiSTJ8V8+PueccU7F6xpH5KqjW268JbtM2CVJcvWVV6etrS2PtzyeM887M2dPPbvidQAAAEAz+MZXv5H777k/V99wdbp3795+/sHdPpiZv5hZ4bLGErnqqHVZa/pu0jdJ8ttrf5uPf+Lj6dGjRz4y+SOZ/8j8itcBAAAAzeCaX16T71zwnezy/l1Sq9Xaz7cau1UWPrawumENJnLV0aAhgzLn1jlZuXJlZl07KxM/MjFJ0vJCS7p171bxOgAAAKAZPP/s89ms32brnK9cuXKt6NXsRK46Ouzow/KF/b+QMYPHZMDAAZnwwQlJ/nwb45hxYypeBwAAADSDd7/n3bnumuv+6+D/d60Zl87IDrvsUM2oCnSuekAzO+TwQ7L9jtvnicVPZOLuE9Op05+b4vB3Ds/Xpn6t4nUAAABAM/j66V/Pvnvsm3kPzstrr72W6edOz7wH5+X2W27PNbOvqXpew9Ra2lraqh4BAAAAwN9vwWMLcs4Z5+T+e+7PyhUrs8122+QrJ3wlY8eNrXpaw4hcdfS5T3wu2++4fY4+4ei1zs8969zMnTM3P7n8J9UMAwAAAGgynslVR7fceEt2n7T7Oue77bFbbrnxlgoWAQAAAM3mP3/9n5l13ax1zmddNyvX/+b6ChZVQ+Sqo5UrVqZr167rnHfp0iXLW5dXsAgAAABoNqeeeGpWr169znlbW1tOPfHUxg+qiMhVR2PGjcnMX8xc5/yKf70io8aMqmARAAAA0GzmPzL/dTvDiNEjsuDRBRUsqoa/rlhHx598fD63z+ey4LEF+cCHPpAkmT1rdq74v1fkx5f/uNpxAAAAQFPo1btXFs5fmGHDh611vuDRBemxUY+KVjWeB8/X2XXXXJdpp0/LfXffl+4bds/Y8WNzwikn5P27vr/qaQAAAEATOPrQo3P7rbfnZ1f+LFu8a4skyfxH5+dzn/hcttthu5x/6fkVL2wMkQsAAACgYMuWLcsn/9cnc9cdd2Xg4IFJkqeeeCq7TNglP5350/Tp06fagQ0icgEAAAAUrq2tLb+7/ne5/5772+8ke98H3lf1rIYSuTrY8E2G586H78ym79g0w/oOS61We8P3Lly6sHHDAAAAgKbz6quvZsCGA3LT3TdlzNZjqp5TKQ+e72Cnn3N6em7cs/3nN4tcAAAAAP+ILl26ZPDQwVm9enXVUyrnm1wAAAAABZvxwxm5eubVufinF6fvJn2rnlMZkauONtlgk8x7el4267fZWudLn1+aLfttmaWrl1a0DAAAAGgWE7adkAWPLsirr76aIcOGpMdGPda6fuPcGyta1lhuV6yjtrbX74erVq1K165dG7wGAAAAaEaT955c9YS3BZGrDqafNz1JUqvVMuPSGdmo50bt11avXp1bbrwlI0aPqGoeAAAA0EROPOXEqie8LbhdsQ7GbzE+SbL48cUZNHhQOm3Qqf1a165dM3T40Jx02kl5z07vqWoiAAAA0ERaWlryH//+H1nw2IIcdfxR6btJ39w99+70698vAwcNrHpeQ4hcdfSxiR/LZTMvS5++faqeAgAAADSp+++9P3vvtnd69e6VRQsX5Y55d2T4O4dn6temZvGixbl4xsVVT2yITv/9W/h7Xf27q9cKXKtXr869d9+blhdaKtsEAAAANJcpx07JZw/6bOY+Mjfdu3dvP9990u655cZbKlzWWCJXHZ149ImZ8cMZSf4cuCZ9YFJ23W7XjB0yNjfdcFPF6wAAAIBmcNecu3LwoQevc775oM2z5E9LKlhUDZGrjq66/KqM22ZckuQ3v/pNFi1clDl/nJPDjjksU6dMrXgdAAAA0Ay6duua1tbWdc4fe/ixvGOzd1SwqBoiVx0tfX5p+g3olyS5/tfXZ69998qWI7fMAf90QB6878GK1wEAAADNYI+P75GzTjsrr7766p8PasniRYtzygmnZM9P7FntuAYSuepos/6bZd6D87J69erMunZWJu4+MUny0osvrfUXFwEAAAD+XlO/OzUrV6zMlv22zEsvvZSP7fqxbLfldum5cc+c/K2Tq57XMJ2rHtDM9j94/xy030EZsPmApJZ8cLcPJknuuO2OjBw9stpxAAAAQFPo3bt3fnn9L3PrzbfmgXsfyMoVK7PNdtu0d4j1Ra2lraWt6hHN7Kp/vypPLH4ie++7dwYNHpQk+flPfp7efXpn8l6TK14HAAAA0BxELgAAAIDCzZ0zNzf97qY8u+TZrFmzZq1rp087vaJVjeV2xQ42/bzpOeiLB6V79+6Zft70N33vl476UoNWAQAAAM3qu6d/N1O/NjUjRo3IZv03S61Wa7/21z83O9/k6mDjtxifG+64IZtsuknGbzH+Dd9Xq9Vyz/x7GrgMAAAAaEYj+o/IqWeemv0P2r/qKZXyTa4Odu+Ce1/3ZwAAAIB66NSpU3Z+385Vz6hcp6oHAAAAAPD3O/yYw/ODC39Q9YzKuV2xjk469qTXPa/VaunevXu22HKLTN5rcvpu0rfBywAAAIBmsWbNmuw3eb88+vCjGTVmVLp06bLW9ctmXlbRssYSueroYxM/lnvn3pvVq1dny1FbJkkee/ixdNqgU0aOHplH5j2SWq2Wa2++NqPHjK54LQAAAFCi4488PjMunZEJEyes8+D5JLnoRxdVtKyxRK46uuh7F+XWm27NhT+6ML169UqSLFu2LEcdclR2fv/OOfALB+aQzx6Sl196OTOvm1nxWgAAAKBEgzcenB/+6w/z0ckfrXpKpUSuOtpq0Fa58vor1/mW1kMPPJR9PrJPHnryodw99+7s85F9Mv+5+RWtBAAAAEq29bCtM/O6mRk5emTVUyrlwfN11LqsNc8ueXad8+eefS7LW5cnSfr06ZNXX3m10dMAAACAJnHiqSfm26d8Oy+++GLVUyrVueoBzWzSXpNy5D8dmanfnZrtdtguSTJ3ztycfNzJmbT3pCTJnbffmXeNfFeVMwEAAICCXXzexVn42MKM7D8yQ4cPTecua+eeG+feWNGyxhK56uici8/JSceclM9/+vN57bXXkiSdO3fOZw78TE4/5/QkyYjRI3LepedVORMAAAAo2OS9J1c94W3BM7kaYMWKFVk4f2GSZPg7h6dnz57VDgIAAABoMr7J1QA9e/ZM3036tv8MAAAAUA8rVqzImjVr1jrr1atXRWsay4Pn62jNmjU587QzM7T30IwbNi7jho3L0D5Dc9Y3z1rnfzgAAACAv8fCBQuz3+T9MnCjgRnae2iG9x2e4X2HZ1ifYRned3jV8xrGN7nq6JtTvpmf/vCnOeWMU7LT+3ZKkvzh5j/kjFPPyKqXV+Xkb51c8UIAAACgdIcecGja2tpywb9ckM36b5ZarVb1pEp4JlcdjR44OtOmT8ukj09a6/yaq67JcYcfl4eefKiiZQAAAECzGNRzUG6484aMGDWi6imVcrtiHb2w9IWMHD1ynfORo0fmhaUvVLAIAAAAaDbb7rBtnlz8ZNUzKud2xTraeputc8kFl+Ss885a6/ySCy7J2PFjK1oFAAAANJPzLz0/x3zpmDz15FMZs/WYdO6ydu7ZevzWFS1rLLcr1tHNs2/OpyZ/KoOHDs4Ou+yQJJlz65w8ufjJ/Nuv/y3vnfDeihcCAAAApZvzhzk55LOHZNHCRe1ntVotbW1tqdVqWbp6aYXrGkfkqrOnn3o6l154aR7+48NJklFbjcqBXzwwZ089O+decm7F6wAAAIDS7TRmp4zcamS+8s9fed0Hzw8dNrSiZY0lclXgvnvuy67b7brelFQAAACgfgZuNDA333Nz3rnlO6ueUikPngcAAAAo2IQPTch999xX9YzKefA8AAAAQMH22HOPTDlmSh6878GMGTcmXbp0Wev6pI9PqmhZY7ldsQJuVwQAAAA6St9Ofd/w2vr04Hnf5KqDA/Y54E2vL2tZ1qAlAAAAQLN7Yc0LVU94WxC56qBX717/7fVP/+9PN2gNAAAAQPNzuyIAAABA4W6efXPOP/v8PPzQw0mSUWNG5ajjj8p7J7y34mWN468rAgAAABTsF5f9Invvtnd69OiRQ486NIcedWg23HDD7PXhvXL5zy+vel7D+CYXAAAAQMF23GrHHPjFA3PEMUesdX7BtAsy4wczcvtDt1e0rLF8kwsAAACgYAvnL8wee+6xzvmkj0/K4wser2BRNUQuAAAAgIINGjIos2fNXuf8ht/ekEFDBlWwqBr+uiIAAABAwY78P0fmhKNOyH1335cd37tjkuS239+Wn//45znj3DMqXtc4nskFAAAAULhfXfmrXPjdCzPvoXlJklFbjcqXj/9yJu81ueJljSNyAQAAAFA8z+QCAAAAKNjcOXNzx213rHN+x2135K477qpgUTVELgAAAICCHXfEcXly8ZPrnD/15FM57ojjKlhUDZELAAAAoGDzHpyXbbbbZp3z8duOz7wH51WwqBoiFwAAAEDBunbrmiXPLFnn/Jmnn8kGnTeoYFE1RC4AAACAgn3oIx/KN776jSxbtqz9rKWlJaeddFom7j6xwmWN5a8rAgAAABTsqSefyqQPTMrS55dm/LbjkyT33X1f+vXvlyuvvzKDhwyueGFjiFwAAAAAhVu5cmUu/9nluf+e+9N9w+4ZO35sPvmZT6ZLly5VT2sYkQsAAACA4nkmFwAAAECTGNJrSBbOX1j1jEqIXAAAAABNoq1t/b1hT+QCAAAAoHgiFwAAAECT2O+A/bJxr42rnlEJkQsAAACgQPtO2jfLli1rf33OGefk69/+ejZ9x6ZJkqXPL81OY3aqal7DiVwAAAAABZp13ay8suqV9tfTTp+WlqUt7a9fe+21PDLvkQqWVUPkAgAAACjQ3z5kfn1+6HwicgEAAADQBEQuAAAAgALVarXUarW/Oaxmy9tB56oHAAAAAPDWtbW15fCDDk/Xbl2TJC+//HKO/dKx6bFRjyRZ63ld64NaS1vL+n3DJgAAAECBDj/48P/R+y760UV1XvL2IHIBAAAAUDzP5AIAAACgeCIXAAAAAMUTuQAAAAAonsgFAAAAQPFELgAAAACKJ3IBAAAAUDyRCwAAAIDi/T+Wz1wag8PfmQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "result = experiment.process(data=data, iterations=2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "6ffeaefb9285e2a1", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "gender industry group \n", + "F E-commerce test 2261\n", + " Logistics control 2305\n", + "M E-commerce control 2240\n", + " Logistics control 2194\n", + "Name: user_id, dtype: int64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result['split'].groupby(['gender', 'industry', 'group'])['user_id'].count()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "6411dc57079f1316", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "experiments\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
random_statepost_spends a meanpost_spends b meanpost_spends ab deltapost_spends ab delta %post_spends t-test p-valuepost_spends ks-test p-valuepost_spends t-test passedpost_spends ks-test passedpre_spends a mean...pre_spends ks-test passedcontrol %test %control sizetest sizet-test mean p-valueks-test mean p-valuet-test passed %ks-test passed %mean_tests_score
00452.05452.460.410.090.640.91FalseFalse486.90...False72.2327.77722327770.370.620.000.000.53
12452.05452.460.410.090.640.91FalseFalse486.90...False72.2327.77722327770.370.620.000.000.53
27452.05452.460.410.090.640.91FalseFalse486.90...False72.2327.77722327770.370.620.000.000.53
38452.05452.460.410.090.640.91FalseFalse486.90...False72.2327.77722327770.370.620.000.000.53
415452.05452.460.410.090.640.91FalseFalse486.90...False72.2327.77722327770.370.620.000.000.53
..................................................................
6431981452.05452.460.410.090.640.91FalseFalse486.90...False72.2327.77722327770.370.620.000.000.53
6441982452.05452.460.410.090.640.91FalseFalse486.90...False72.2327.77722327770.370.620.000.000.53
6451984452.05452.460.410.090.640.91FalseFalse486.90...False72.2327.77722327770.370.620.000.000.53
6461988452.05452.460.410.090.640.91FalseFalse486.90...False72.2327.77722327770.370.620.000.000.53
6471998452.05452.460.410.090.640.91FalseFalse486.90...False72.2327.77722327770.370.620.000.000.53
\n", + "

648 rows × 26 columns

\n", + "
" + ], + "text/plain": [ + " random_state post_spends a mean post_spends b mean \\\n", + "0 0 452.05 452.46 \n", + "1 2 452.05 452.46 \n", + "2 7 452.05 452.46 \n", + "3 8 452.05 452.46 \n", + "4 15 452.05 452.46 \n", + ".. ... ... ... \n", + "643 1981 452.05 452.46 \n", + "644 1982 452.05 452.46 \n", + "645 1984 452.05 452.46 \n", + "646 1988 452.05 452.46 \n", + "647 1998 452.05 452.46 \n", + "\n", + " post_spends ab delta post_spends ab delta % post_spends t-test p-value \\\n", + "0 0.41 0.09 0.64 \n", + "1 0.41 0.09 0.64 \n", + "2 0.41 0.09 0.64 \n", + "3 0.41 0.09 0.64 \n", + "4 0.41 0.09 0.64 \n", + ".. ... ... ... \n", + "643 0.41 0.09 0.64 \n", + "644 0.41 0.09 0.64 \n", + "645 0.41 0.09 0.64 \n", + "646 0.41 0.09 0.64 \n", + "647 0.41 0.09 0.64 \n", + "\n", + " post_spends ks-test p-value post_spends t-test passed \\\n", + "0 0.91 False \n", + "1 0.91 False \n", + "2 0.91 False \n", + "3 0.91 False \n", + "4 0.91 False \n", + ".. ... ... \n", + "643 0.91 False \n", + "644 0.91 False \n", + "645 0.91 False \n", + "646 0.91 False \n", + "647 0.91 False \n", + "\n", + " post_spends ks-test passed pre_spends a mean ... \\\n", + "0 False 486.90 ... \n", + "1 False 486.90 ... \n", + "2 False 486.90 ... \n", + "3 False 486.90 ... \n", + "4 False 486.90 ... \n", + ".. ... ... ... \n", + "643 False 486.90 ... \n", + "644 False 486.90 ... \n", + "645 False 486.90 ... \n", + "646 False 486.90 ... \n", + "647 False 486.90 ... \n", + "\n", + " pre_spends ks-test passed control % test % control size test size \\\n", + "0 False 72.23 27.77 7223 2777 \n", + "1 False 72.23 27.77 7223 2777 \n", + "2 False 72.23 27.77 7223 2777 \n", + "3 False 72.23 27.77 7223 2777 \n", + "4 False 72.23 27.77 7223 2777 \n", + ".. ... ... ... ... ... \n", + "643 False 72.23 27.77 7223 2777 \n", + "644 False 72.23 27.77 7223 2777 \n", + "645 False 72.23 27.77 7223 2777 \n", + "646 False 72.23 27.77 7223 2777 \n", + "647 False 72.23 27.77 7223 2777 \n", + "\n", + " t-test mean p-value ks-test mean p-value t-test passed % \\\n", + "0 0.37 0.62 0.00 \n", + "1 0.37 0.62 0.00 \n", + "2 0.37 0.62 0.00 \n", + "3 0.37 0.62 0.00 \n", + "4 0.37 0.62 0.00 \n", + ".. ... ... ... \n", + "643 0.37 0.62 0.00 \n", + "644 0.37 0.62 0.00 \n", + "645 0.37 0.62 0.00 \n", + "646 0.37 0.62 0.00 \n", + "647 0.37 0.62 0.00 \n", + "\n", + " ks-test passed % mean_tests_score \n", + "0 0.00 0.53 \n", + "1 0.00 0.53 \n", + "2 0.00 0.53 \n", + "3 0.00 0.53 \n", + "4 0.00 0.53 \n", + ".. ... ... \n", + "643 0.00 0.53 \n", + "644 0.00 0.53 \n", + "645 0.00 0.53 \n", + "646 0.00 0.53 \n", + "647 0.00 0.53 \n", + "\n", + "[648 rows x 26 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "aa_score\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
t-test passed scoreks-test passed scoret-test aa passedks-test aa passed
post_spends0.000.000.000.00
pre_spends0.000.000.000.00
mean0.000.000.000.00
\n", + "
" + ], + "text/plain": [ + " t-test passed score ks-test passed score t-test aa passed \\\n", + "post_spends 0.00 0.00 0.00 \n", + "pre_spends 0.00 0.00 0.00 \n", + "mean 0.00 0.00 0.00 \n", + "\n", + " ks-test aa passed \n", + "post_spends 0.00 \n", + "pre_spends 0.00 \n", + "mean 0.00 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "split\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustrygroup
0819300494.50427.1140.00FE-commercetest
1819500494.00416.2241.00FE-commercetest
2411543.00514.5618.00FE-commercetest
3820300472.50412.6752.00FE-commercetest
4820500460.00408.2266.00FE-commercetest
..............................
9995999000490.00426.00NaNMLogisticscontrol
9996999200491.50424.0029.00ME-commercecontrol
9997999400486.00423.7869.00FLogisticscontrol
99989995101538.50450.4442.00MLogisticscontrol
9999999600500.50430.8926.00FLogisticscontrol
\n", + "

10000 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 8193 0 0 494.50 427.11 40.00 F \n", + "1 8195 0 0 494.00 416.22 41.00 F \n", + "2 4 1 1 543.00 514.56 18.00 F \n", + "3 8203 0 0 472.50 412.67 52.00 F \n", + "4 8205 0 0 460.00 408.22 66.00 F \n", + "... ... ... ... ... ... ... ... \n", + "9995 9990 0 0 490.00 426.00 NaN M \n", + "9996 9992 0 0 491.50 424.00 29.00 M \n", + "9997 9994 0 0 486.00 423.78 69.00 F \n", + "9998 9995 10 1 538.50 450.44 42.00 M \n", + "9999 9996 0 0 500.50 430.89 26.00 F \n", + "\n", + " industry group \n", + "0 E-commerce test \n", + "1 E-commerce test \n", + "2 E-commerce test \n", + "3 E-commerce test \n", + "4 E-commerce test \n", + "... ... ... \n", + "9995 Logistics control \n", + "9996 E-commerce control \n", + "9997 Logistics control \n", + "9998 Logistics control \n", + "9999 Logistics control \n", + "\n", + "[10000 rows x 9 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "best_experiment_stat\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
a meanb meanab deltaab delta %t-test p-valueks-test p-valuet-test passedks-test passed
post_spends452.05452.460.410.090.640.91FalseFalse
pre_spends486.90487.600.710.150.090.32FalseFalse
\n", + "
" + ], + "text/plain": [ + " a mean b mean ab delta ab delta % t-test p-value ks-test p-value \\\n", + "post_spends 452.05 452.46 0.41 0.09 0.64 0.91 \n", + "pre_spends 486.90 487.60 0.71 0.15 0.09 0.32 \n", + "\n", + " t-test passed ks-test passed \n", + "post_spends False False \n", + "pre_spends False False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "split_stat\n" + ] + }, + { + "data": { + "text/plain": [ + "control % 72.23\n", + "test % 27.77\n", + "control size 7223\n", + "test size 2777\n", + "t-test mean p-value 0.37\n", + "ks-test mean p-value 0.62\n", + "t-test passed % 0.00\n", + "ks-test passed % 0.00\n", + "mean_tests_score 0.53\n", + "Name: 0, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "resume\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
aa test passedsplit is uniform
post_spendsnot OKOK
pre_spendsnot OKOK
\n", + "
" + ], + "text/plain": [ + " aa test passed split is uniform\n", + "post_spends not OK OK\n", + "pre_spends not OK OK" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "show_result(result)" + ] + }, + { + "cell_type": "markdown", + "id": "cffcad06fd42968", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "### 2.6 Unbalanced AA test\n", + "\n", + "_If you want to perform AA test with unbalanced groups, you can use parametr `test_size` to define sizes of test group and control group_" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "ec010ddeab79c96a", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "info_cols = ['user_id', 'signup_month']\n", + "target = ['post_spends', 'pre_spends']\n", + "\n", + "group_cols = 'industry'" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "91943d71e588c78", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "experiment = AATest(info_cols=info_cols, target_fields=target, group_cols=group_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "8e177ae5712350f4", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a5608550db0b4fe5befe6e3d79db8dae", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/2000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "result = experiment.process(data=data, test_size=0.3, iterations=2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "166b6a3a05f173c4", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "group\n", + "control 0.70\n", + "test 0.30\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result['split']['group'].value_counts(normalize=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "52f1f22bd39ecfa1", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "experiments\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
random_statepost_spends a meanpost_spends b meanpost_spends ab deltapost_spends ab delta %post_spends t-test p-valuepost_spends ks-test p-valuepost_spends t-test passedpost_spends ks-test passedpre_spends a mean...pre_spends ks-test passedcontrol %test %control sizetest sizet-test mean p-valueks-test mean p-valuet-test passed %ks-test passed %mean_tests_score
01451.99452.570.570.130.500.76FalseFalse487.11...False70.0129.99700129990.690.660.000.000.67
12452.16452.170.010.000.990.64FalseFalse487.00...False70.0129.99700129990.730.720.000.000.72
23452.22452.03-0.20-0.040.820.59FalseFalse487.18...False70.0129.99700129990.660.480.000.000.54
34451.90452.780.880.190.310.24FalseFalse487.13...False70.0129.99700129990.540.600.000.000.58
45452.64451.05-1.59-0.350.060.14FalseFalse487.20...False70.0129.99700129990.230.140.000.000.17
..................................................................
17051995452.12452.280.160.040.850.63FalseFalse487.10...False70.0129.99700129990.890.640.000.000.73
17061996452.00452.540.540.120.530.85FalseFalse486.94...False70.0129.99700129990.370.720.000.000.60
17071997452.29451.88-0.40-0.090.640.60FalseFalse487.04...False70.0129.99700129990.650.440.000.000.51
17081998451.78453.071.290.280.130.33FalseFalse487.12...False70.0129.99700129990.470.660.000.000.60
17091999452.25451.96-0.29-0.060.740.77FalseFalse487.17...False70.0129.99700129990.640.710.000.000.69
\n", + "

1710 rows × 26 columns

\n", + "
" + ], + "text/plain": [ + " random_state post_spends a mean post_spends b mean \\\n", + "0 1 451.99 452.57 \n", + "1 2 452.16 452.17 \n", + "2 3 452.22 452.03 \n", + "3 4 451.90 452.78 \n", + "4 5 452.64 451.05 \n", + "... ... ... ... \n", + "1705 1995 452.12 452.28 \n", + "1706 1996 452.00 452.54 \n", + "1707 1997 452.29 451.88 \n", + "1708 1998 451.78 453.07 \n", + "1709 1999 452.25 451.96 \n", + "\n", + " post_spends ab delta post_spends ab delta % \\\n", + "0 0.57 0.13 \n", + "1 0.01 0.00 \n", + "2 -0.20 -0.04 \n", + "3 0.88 0.19 \n", + "4 -1.59 -0.35 \n", + "... ... ... \n", + "1705 0.16 0.04 \n", + "1706 0.54 0.12 \n", + "1707 -0.40 -0.09 \n", + "1708 1.29 0.28 \n", + "1709 -0.29 -0.06 \n", + "\n", + " post_spends t-test p-value post_spends ks-test p-value \\\n", + "0 0.50 0.76 \n", + "1 0.99 0.64 \n", + "2 0.82 0.59 \n", + "3 0.31 0.24 \n", + "4 0.06 0.14 \n", + "... ... ... \n", + "1705 0.85 0.63 \n", + "1706 0.53 0.85 \n", + "1707 0.64 0.60 \n", + "1708 0.13 0.33 \n", + "1709 0.74 0.77 \n", + "\n", + " post_spends t-test passed post_spends ks-test passed \\\n", + "0 False False \n", + "1 False False \n", + "2 False False \n", + "3 False False \n", + "4 False False \n", + "... ... ... \n", + "1705 False False \n", + "1706 False False \n", + "1707 False False \n", + "1708 False False \n", + "1709 False False \n", + "\n", + " pre_spends a mean ... pre_spends ks-test passed control % test % \\\n", + "0 487.11 ... False 70.01 29.99 \n", + "1 487.00 ... False 70.01 29.99 \n", + "2 487.18 ... False 70.01 29.99 \n", + "3 487.13 ... False 70.01 29.99 \n", + "4 487.20 ... False 70.01 29.99 \n", + "... ... ... ... ... ... \n", + "1705 487.10 ... False 70.01 29.99 \n", + "1706 486.94 ... False 70.01 29.99 \n", + "1707 487.04 ... False 70.01 29.99 \n", + "1708 487.12 ... False 70.01 29.99 \n", + "1709 487.17 ... False 70.01 29.99 \n", + "\n", + " control size test size t-test mean p-value ks-test mean p-value \\\n", + "0 7001 2999 0.69 0.66 \n", + "1 7001 2999 0.73 0.72 \n", + "2 7001 2999 0.66 0.48 \n", + "3 7001 2999 0.54 0.60 \n", + "4 7001 2999 0.23 0.14 \n", + "... ... ... ... ... \n", + "1705 7001 2999 0.89 0.64 \n", + "1706 7001 2999 0.37 0.72 \n", + "1707 7001 2999 0.65 0.44 \n", + "1708 7001 2999 0.47 0.66 \n", + "1709 7001 2999 0.64 0.71 \n", + "\n", + " t-test passed % ks-test passed % mean_tests_score \n", + "0 0.00 0.00 0.67 \n", + "1 0.00 0.00 0.72 \n", + "2 0.00 0.00 0.54 \n", + "3 0.00 0.00 0.58 \n", + "4 0.00 0.00 0.17 \n", + "... ... ... ... \n", + "1705 0.00 0.00 0.73 \n", + "1706 0.00 0.00 0.60 \n", + "1707 0.00 0.00 0.51 \n", + "1708 0.00 0.00 0.60 \n", + "1709 0.00 0.00 0.69 \n", + "\n", + "[1710 rows x 26 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "aa_score\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
t-test passed scoreks-test passed scoret-test aa passedks-test aa passed
post_spends0.000.000.000.00
pre_spends0.000.000.000.00
mean0.000.000.000.00
\n", + "
" + ], + "text/plain": [ + " t-test passed score ks-test passed score t-test aa passed \\\n", + "post_spends 0.00 0.00 0.00 \n", + "pre_spends 0.00 0.00 0.00 \n", + "mean 0.00 0.00 0.00 \n", + "\n", + " ks-test aa passed \n", + "post_spends 0.00 \n", + "pre_spends 0.00 \n", + "mean 0.00 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "split\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustrygroup
0000488.00414.44NaNME-commercetest
1819300494.50427.1140.00FE-commercetest
2819200487.50436.7820.00ME-commercetest
3271483.00479.4425.00MLogisticstest
4820051486.00495.00NaNMLogisticstest
..............................
9995999400486.00423.7869.00FLogisticscontrol
99969995101538.50450.4442.00MLogisticscontrol
9997999731473.00534.1122.00FE-commercecontrol
9998999821495.00523.2267.00FE-commercecontrol
9999999971508.00475.8938.00FE-commercecontrol
\n", + "

10000 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 0 0 0 488.00 414.44 NaN M \n", + "1 8193 0 0 494.50 427.11 40.00 F \n", + "2 8192 0 0 487.50 436.78 20.00 M \n", + "3 2 7 1 483.00 479.44 25.00 M \n", + "4 8200 5 1 486.00 495.00 NaN M \n", + "... ... ... ... ... ... ... ... \n", + "9995 9994 0 0 486.00 423.78 69.00 F \n", + "9996 9995 10 1 538.50 450.44 42.00 M \n", + "9997 9997 3 1 473.00 534.11 22.00 F \n", + "9998 9998 2 1 495.00 523.22 67.00 F \n", + "9999 9999 7 1 508.00 475.89 38.00 F \n", + "\n", + " industry group \n", + "0 E-commerce test \n", + "1 E-commerce test \n", + "2 E-commerce test \n", + "3 Logistics test \n", + "4 Logistics test \n", + "... ... ... \n", + "9995 Logistics control \n", + "9996 Logistics control \n", + "9997 E-commerce control \n", + "9998 E-commerce control \n", + "9999 E-commerce control \n", + "\n", + "[10000 rows x 9 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "best_experiment_stat\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
a meanb meanab deltaab delta %t-test p-valueks-test p-valuet-test passedks-test passed
post_spends452.15452.190.040.010.960.99FalseFalse
pre_spends487.09487.09-0.00-0.001.000.99FalseFalse
\n", + "
" + ], + "text/plain": [ + " a mean b mean ab delta ab delta % t-test p-value ks-test p-value \\\n", + "post_spends 452.15 452.19 0.04 0.01 0.96 0.99 \n", + "pre_spends 487.09 487.09 -0.00 -0.00 1.00 0.99 \n", + "\n", + " t-test passed ks-test passed \n", + "post_spends False False \n", + "pre_spends False False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "split_stat\n" + ] + }, + { + "data": { + "text/plain": [ + "control % 70.01\n", + "test % 29.99\n", + "control size 7001\n", + "test size 2999\n", + "t-test mean p-value 0.98\n", + "ks-test mean p-value 0.99\n", + "t-test passed % 0.00\n", + "ks-test passed % 0.00\n", + "mean_tests_score 0.99\n", + "Name: 1472, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "resume\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
aa test passedsplit is uniform
post_spendsnot OKOK
pre_spendsnot OKOK
\n", + "
" + ], + "text/plain": [ + " aa test passed split is uniform\n", + "post_spends not OK OK\n", + "pre_spends not OK OK" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "show_result(result)" + ] + }, + { + "cell_type": "markdown", + "id": "906d845f", + "metadata": {}, + "source": [ + "## MDE \n", + "this is the boundary value of the effect, for which it makes sense to introduce some changes. " + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "33cf7c57", + "metadata": { + "jupyter": { + "is_executing": true + } + }, + "outputs": [], + "source": [ + "info_cols = ['user_id', 'signup_month']\n", + "target = ['post_spends', 'pre_spends']\n", + "\n", + "group_cols = 'industry'\n", + "mde_target = 'post_spends'" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "24bc3a9e", + "metadata": { + "jupyter": { + "is_executing": true + } + }, + "outputs": [], + "source": [ + "experiment = AATest(info_cols=info_cols, target_fields=target, group_cols=group_cols)" + ] + }, + { + "cell_type": "markdown", + "id": "820a55cd", + "metadata": {}, + "source": [ + "Single experiment of data splitting for MDE calculation. \n", + "\n", + "_P.s. [None] is the number of random state. You can change it like sampling_metrics(data, random_state=42) and get result with [42] instead of [None]_ " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "14352eba", + "metadata": { + "jupyter": { + "is_executing": true + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustrygroup
0000488.00414.44NaNME-commercetest
1271483.00479.4425.00MLogisticstest
2561486.50486.5644.00ME-commercetest
37111496.00432.8957.00ME-commercetest
4941470.00512.1154.00MLogisticstest
..............................
9995999400486.00423.7869.00FLogisticscontrol
99969995101538.50450.4442.00MLogisticscontrol
9997999600500.50430.8926.00FLogisticscontrol
9998999731473.00534.1122.00FE-commercecontrol
9999999971508.00475.8938.00FE-commercecontrol
\n", + "

10000 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 0 0 0 488.00 414.44 NaN M \n", + "1 2 7 1 483.00 479.44 25.00 M \n", + "2 5 6 1 486.50 486.56 44.00 M \n", + "3 7 11 1 496.00 432.89 57.00 M \n", + "4 9 4 1 470.00 512.11 54.00 M \n", + "... ... ... ... ... ... ... ... \n", + "9995 9994 0 0 486.00 423.78 69.00 F \n", + "9996 9995 10 1 538.50 450.44 42.00 M \n", + "9997 9996 0 0 500.50 430.89 26.00 F \n", + "9998 9997 3 1 473.00 534.11 22.00 F \n", + "9999 9999 7 1 508.00 475.89 38.00 F \n", + "\n", + " industry group \n", + "0 E-commerce test \n", + "1 Logistics test \n", + "2 E-commerce test \n", + "3 E-commerce test \n", + "4 Logistics test \n", + "... ... ... \n", + "9995 Logistics control \n", + "9996 Logistics control \n", + "9997 Logistics control \n", + "9998 E-commerce control \n", + "9999 E-commerce control \n", + "\n", + "[10000 rows x 9 columns]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "splitted_data = experiment.sampling_metrics(data)['data_from_experiment'][None]\n", + "splitted_data" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "e6b66b61", + "metadata": { + "jupyter": { + "is_executing": true + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGdCAYAAAAMm0nCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAuqklEQVR4nO3df3RU9Z3/8Vd+DgSYxIDJkBJiFAUiP4UaxipFCAmYdVVyTv1BwbUoixvcQlxE9osQoBZLVfwVZbv+iJ7KKnSLq0AhAywgEn6lpgq6VChubMkku9Iw/JwM5H7/8GSWgZAwIUPmM3k+zpkD997PfOb95s65eXHn3kyUZVmWAAAADBLd3gUAAAAEiwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADBObHsXECoNDQ06fPiwunXrpqioqPYuBwAAXALLsnTs2DGlpaUpOvri51kiNsAcPnxY6enp7V0GAABohW+++Ua9evW66PaIDTDdunWT9N0/gN1ub+dqWubz+VRWVqbc3FzFxcW1dzkh01H6lOg1UnWUXjtKnxK9hhuPx6P09HT/z/GLidgA0/ixkd1uNybAJCQkyG63h+2bqi10lD4leo1UHaXXjtKnRK/hqqXLP7iIFwAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4se1dAK6Ma55c094lSJJsMZaW3CwNKF4v79nmvypdkr5+Jv8KVAUAMA1nYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwTlAB5rXXXtOgQYNkt9tlt9vldDr1u9/9zr/99OnTKiwsVPfu3dW1a1cVFBSopqYmYI6qqirl5+crISFBKSkpmjVrls6cORMwZvPmzbrppptks9nUp08flZaWtr5DAAAQcYIKML169dIzzzyjiooK7dmzR6NHj9Zdd92lffv2SZJmzpypjz76SCtXrtSWLVt0+PBhTZgwwf/8s2fPKj8/X/X19dq+fbvefvttlZaWat68ef4xhw4dUn5+vm6//XZVVlZqxowZevjhh7V+/fo2ahkAAJguNpjBd955Z8Dy008/rddee007duxQr1699MYbb2j58uUaPXq0JOmtt95S//79tWPHDo0YMUJlZWX64osvtGHDBqWmpmrIkCFatGiRZs+ereLiYsXHx2vZsmXKzMzUc889J0nq37+/tm3bpqVLlyovL6+N2gYAACZr9TUwZ8+e1XvvvacTJ07I6XSqoqJCPp9POTk5/jH9+vVT7969VV5eLkkqLy/XwIEDlZqa6h+Tl5cnj8fjP4tTXl4eMEfjmMY5AAAAgjoDI0mff/65nE6nTp8+ra5du2rVqlXKyspSZWWl4uPjlZSUFDA+NTVVbrdbkuR2uwPCS+P2xm3NjfF4PDp16pQ6d+7cZF1er1der9e/7PF4JEk+n08+ny/YNq+4xhpDVastxgrJvMGyRVsBf7bEhH13MaHep+GEXiNPR+lTotdwc6m1BR1g+vbtq8rKSh09elS/+c1v9OCDD2rLli1BF9jWFi9erAULFlywvqysTAkJCe1QUeu4XK6QzLvk5pBM22qLhjdc0ri1a9eGuJLQC9U+DUf0Gnk6Sp8SvYaLkydPXtK4oANMfHy8+vTpI0kaNmyYdu/erRdffFH33nuv6uvrVVdXF3AWpqamRg6HQ5LkcDi0a9eugPka71I6d8z5dy7V1NTIbrdf9OyLJM2ZM0dFRUX+ZY/Ho/T0dOXm5sputwfb5hXn8/nkcrk0duxYxcXFtfn8A4rD4yJoW7SlRcMb9NSeaHkbolocv7fY3OueQr1Pwwm9Rp6O0qdEr+Gm8ROUlgQdYM7X0NAgr9erYcOGKS4uThs3blRBQYEkaf/+/aqqqpLT6ZQkOZ1OPf3006qtrVVKSoqk71Kg3W5XVlaWf8z5/+t2uVz+OS7GZrPJZrNdsD4uLi5sd1JTQlWv92zLYeFK8jZEXVJNJu27izHtPXg56DXydJQ+JXoNF5daV1ABZs6cORo/frx69+6tY8eOafny5dq8ebPWr1+vxMRETZkyRUVFRUpOTpbdbtdjjz0mp9OpESNGSJJyc3OVlZWlSZMmacmSJXK73Zo7d64KCwv94WPatGl65ZVX9MQTT+gnP/mJNm3apBUrVmjNmjVB/hMAAIBIFVSAqa2t1eTJk1VdXa3ExEQNGjRI69ev19ixYyVJS5cuVXR0tAoKCuT1epWXl6dXX33V//yYmBitXr1ajz76qJxOp7p06aIHH3xQCxcu9I/JzMzUmjVrNHPmTL344ovq1auXXn/9dW6hBgAAfkEFmDfeeKPZ7Z06dVJJSYlKSkouOiYjI6PFCzNHjRqlTz/9NJjSAABAB8J3IQEAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADBOUAFm8eLF+v73v69u3bopJSVFd999t/bv3x8wZtSoUYqKigp4TJs2LWBMVVWV8vPzlZCQoJSUFM2aNUtnzpwJGLN582bddNNNstls6tOnj0pLS1vXIQAAiDhBBZgtW7aosLBQO3bskMvlks/nU25urk6cOBEw7pFHHlF1dbX/sWTJEv+2s2fPKj8/X/X19dq+fbvefvttlZaWat68ef4xhw4dUn5+vm6//XZVVlZqxowZevjhh7V+/frLbBcAAESC2GAGr1u3LmC5tLRUKSkpqqio0MiRI/3rExIS5HA4mpyjrKxMX3zxhTZs2KDU1FQNGTJEixYt0uzZs1VcXKz4+HgtW7ZMmZmZeu655yRJ/fv317Zt27R06VLl5eUF2yMAAIgwQQWY8x09elSSlJycHLD+3Xff1a9//Ws5HA7deeedeuqpp5SQkCBJKi8v18CBA5Wamuofn5eXp0cffVT79u3T0KFDVV5erpycnIA58/LyNGPGjIvW4vV65fV6/csej0eS5PP55PP5LqfNK6KxxlDVaouxQjJvsGzRVsCfLTFh311MqPdpOKHXyNNR+pToNdxcam2tDjANDQ2aMWOGfvCDH2jAgAH+9Q888IAyMjKUlpamzz77TLNnz9b+/fv129/+VpLkdrsDwosk/7Lb7W52jMfj0alTp9S5c+cL6lm8eLEWLFhwwfqysjJ/eDKBy+UKybxLbg7JtK22aHjDJY1bu3ZtiCsJvVDt03BEr5Gno/Qp0Wu4OHny5CWNa3WAKSws1N69e7Vt27aA9VOnTvX/feDAgerZs6fGjBmjgwcP6rrrrmvty7Vozpw5Kioq8i97PB6lp6crNzdXdrs9ZK/bVnw+n1wul8aOHau4uLg2n39AcXhcP2SLtrRoeIOe2hMtb0NUi+P3Fpv7kWGo92k4odfI01H6lOg13DR+gtKSVgWY6dOna/Xq1dq6dat69erV7Njs7GxJ0oEDB3TdddfJ4XBo165dAWNqamokyX/djMPh8K87d4zdbm/y7Isk2Ww22Wy2C9bHxcWF7U5qSqjq9Z5tOSxcSd6GqEuqyaR9dzGmvQcvB71Gno7Sp0Sv4eJS6wrqLiTLsjR9+nStWrVKmzZtUmZmZovPqayslCT17NlTkuR0OvX555+rtrbWP8blcslutysrK8s/ZuPGjQHzuFwuOZ3OYMoFAAARKqgAU1hYqF//+tdavny5unXrJrfbLbfbrVOnTkmSDh48qEWLFqmiokJff/21PvzwQ02ePFkjR47UoEGDJEm5ubnKysrSpEmT9Ic//EHr16/X3LlzVVhY6D+DMm3aNP3pT3/SE088of/6r//Sq6++qhUrVmjmzJlt3D4AADBRUAHmtdde09GjRzVq1Cj17NnT/3j//fclSfHx8dqwYYNyc3PVr18/Pf744yooKNBHH33knyMmJkarV69WTEyMnE6nfvzjH2vy5MlauHChf0xmZqbWrFkjl8ulwYMH67nnntPrr7/OLdQAAEBSkNfAWFbzt76mp6dry5YtLc6TkZHR4t0lo0aN0qeffhpMeQAAoIPgu5AAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOLHtXQDQnGueXNPeJQTt62fy27sEAIh4nIEBAADGIcAAAADjBBVgFi9erO9///vq1q2bUlJSdPfdd2v//v0BY06fPq3CwkJ1795dXbt2VUFBgWpqagLGVFVVKT8/XwkJCUpJSdGsWbN05syZgDGbN2/WTTfdJJvNpj59+qi0tLR1HQIAgIgTVIDZsmWLCgsLtWPHDrlcLvl8PuXm5urEiRP+MTNnztRHH32klStXasuWLTp8+LAmTJjg33727Fnl5+ervr5e27dv19tvv63S0lLNmzfPP+bQoUPKz8/X7bffrsrKSs2YMUMPP/yw1q9f3wYtAwAA0wV1Ee+6desClktLS5WSkqKKigqNHDlSR48e1RtvvKHly5dr9OjRkqS33npL/fv3144dOzRixAiVlZXpiy++0IYNG5SamqohQ4Zo0aJFmj17toqLixUfH69ly5YpMzNTzz33nCSpf//+2rZtm5YuXaq8vLw2ah0AAJjqsu5COnr0qCQpOTlZklRRUSGfz6ecnBz/mH79+ql3794qLy/XiBEjVF5eroEDByo1NdU/Ji8vT48++qj27dunoUOHqry8PGCOxjEzZsy4aC1er1der9e/7PF4JEk+n08+n+9y2rwiGmsMVa22GCsk8wbLFm0F/BmJzt+XJrz/Lhe9Rp6O0qdEr+HmUmtrdYBpaGjQjBkz9IMf/EADBgyQJLndbsXHxyspKSlgbGpqqtxut3/MueGlcXvjtubGeDwenTp1Sp07d76gnsWLF2vBggUXrC8rK1NCQkLrmmwHLpcrJPMuuTkk07baouEN7V1CyKxduzZgOVT7NBzRa+TpKH1K9BouTp48eUnjWh1gCgsLtXfvXm3btq21U7SpOXPmqKioyL/s8XiUnp6u3Nxc2e32dqzs0vh8PrlcLo0dO1ZxcXFtPv+A4vC4fsgWbWnR8AY9tSda3oao9i4nJPYWf/cxZ6j3aTih18jTUfqU6DXcNH6C0pJWBZjp06dr9erV2rp1q3r16uVf73A4VF9fr7q6uoCzMDU1NXI4HP4xu3btCpiv8S6lc8ecf+dSTU2N7HZ7k2dfJMlms8lms12wPi4uLmx3UlNCVa/3bHiFBW9DVNjV1FbO33+mvQcvB71Gno7Sp0Sv4eJS6wrqLiTLsjR9+nStWrVKmzZtUmZmZsD2YcOGKS4uThs3bvSv279/v6qqquR0OiVJTqdTn3/+uWpra/1jXC6X7Ha7srKy/GPOnaNxTOMcAACgYwvqDExhYaGWL1+u//iP/1C3bt3816wkJiaqc+fOSkxM1JQpU1RUVKTk5GTZ7XY99thjcjqdGjFihCQpNzdXWVlZmjRpkpYsWSK32625c+eqsLDQfwZl2rRpeuWVV/TEE0/oJz/5iTZt2qQVK1ZozRrzfq08AABoe0GdgXnttdd09OhRjRo1Sj179vQ/3n//ff+YpUuX6m/+5m9UUFCgkSNHyuFw6Le//a1/e0xMjFavXq2YmBg5nU79+Mc/1uTJk7Vw4UL/mMzMTK1Zs0Yul0uDBw/Wc889p9dff51bqAEAgKQgz8BYVsu3vnbq1EklJSUqKSm56JiMjIwL7tQ436hRo/Tpp58GUx4AAOgg+C4kAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxgk6wGzdulV33nmn0tLSFBUVpQ8++CBg+9/93d8pKioq4DFu3LiAMUeOHNHEiRNlt9uVlJSkKVOm6Pjx4wFjPvvsM912223q1KmT0tPTtWTJkuC7AwAAESnoAHPixAkNHjxYJSUlFx0zbtw4VVdX+x//9m//FrB94sSJ2rdvn1wul1avXq2tW7dq6tSp/u0ej0e5ubnKyMhQRUWFfvnLX6q4uFi/+tWvgi0XAABEoNhgnzB+/HiNHz++2TE2m00Oh6PJbV9++aXWrVun3bt3a/jw4ZKkl19+WXfccYeeffZZpaWl6d1331V9fb3efPNNxcfH68Ybb1RlZaWef/75gKADAAA6pqADzKXYvHmzUlJSdNVVV2n06NH62c9+pu7du0uSysvLlZSU5A8vkpSTk6Po6Gjt3LlT99xzj8rLyzVy5EjFx8f7x+Tl5ekXv/iF/vrXv+qqq6664DW9Xq+8Xq9/2ePxSJJ8Pp98Pl8o2mxTjTWGqlZbjBWSeYNli7YC/oxE5+9LE95/l4teI09H6VOi13BzqbW1eYAZN26cJkyYoMzMTB08eFD//M//rPHjx6u8vFwxMTFyu91KSUkJLCI2VsnJyXK73ZIkt9utzMzMgDGpqan+bU0FmMWLF2vBggUXrC8rK1NCQkJbtRdyLpcrJPMuuTkk07baouEN7V1CyKxduzZgOVT7NBzRa+TpKH1K9BouTp48eUnj2jzA3Hffff6/Dxw4UIMGDdJ1112nzZs3a8yYMW39cn5z5sxRUVGRf9nj8Sg9PV25ubmy2+0he9224vP55HK5NHbsWMXFxbX5/AOK17f5nK1hi7a0aHiDntoTLW9DVHuXExJ7i/MkhX6fhhN6jTwdpU+JXsNN4ycoLQnJR0jnuvbaa9WjRw8dOHBAY8aMkcPhUG1tbcCYM2fO6MiRI/7rZhwOh2pqagLGNC5f7Noam80mm812wfq4uLiw3UlNCVW93rPhFRa8DVFhV1NbOX//mfYevBz0Gnk6Sp8SvYaLS60r5L8H5s9//rO+/fZb9ezZU5LkdDpVV1eniooK/5hNmzapoaFB2dnZ/jFbt24N+BzM5XKpb9++TX58BAAAOpagA8zx48dVWVmpyspKSdKhQ4dUWVmpqqoqHT9+XLNmzdKOHTv09ddfa+PGjbrrrrvUp08f5eV9d1q9f//+GjdunB555BHt2rVLn3zyiaZPn6777rtPaWlpkqQHHnhA8fHxmjJlivbt26f3339fL774YsBHRAAAoOMKOsDs2bNHQ4cO1dChQyVJRUVFGjp0qObNm6eYmBh99tln+tu//VvdcMMNmjJlioYNG6aPP/444OOdd999V/369dOYMWN0xx136NZbbw34HS+JiYkqKyvToUOHNGzYMD3++OOaN28et1ADAABJrbgGZtSoUbKsi98Cu359yxeLJicna/ny5c2OGTRokD7++ONgywMAAB0A34UEAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOEEHmK1bt+rOO+9UWlqaoqKi9MEHHwRstyxL8+bNU8+ePdW5c2fl5OToq6++Chhz5MgRTZw4UXa7XUlJSZoyZYqOHz8eMOazzz7Tbbfdpk6dOik9PV1LliwJvjsAABCRgg4wJ06c0ODBg1VSUtLk9iVLluill17SsmXLtHPnTnXp0kV5eXk6ffq0f8zEiRO1b98+uVwurV69Wlu3btXUqVP92z0ej3Jzc5WRkaGKigr98pe/VHFxsX71q1+1okUAABBpYoN9wvjx4zV+/Pgmt1mWpRdeeEFz587VXXfdJUl65513lJqaqg8++ED33XefvvzyS61bt067d+/W8OHDJUkvv/yy7rjjDj377LNKS0vTu+++q/r6er355puKj4/XjTfeqMrKSj3//PMBQQcAAHRMQQeY5hw6dEhut1s5OTn+dYmJicrOzlZ5ebnuu+8+lZeXKykpyR9eJCknJ0fR0dHauXOn7rnnHpWXl2vkyJGKj4/3j8nLy9MvfvEL/fWvf9VVV111wWt7vV55vV7/ssfjkST5fD75fL62bDMkGmsMVa22GCsk8wbLFm0F/BmJzt+XJrz/Lhe9Rp6O0qdEr+HmUmtr0wDjdrslSampqQHrU1NT/dvcbrdSUlICi4iNVXJycsCYzMzMC+Zo3NZUgFm8eLEWLFhwwfqysjIlJCS0sqMrz+VyhWTeJTeHZNpWWzS8ob1LCJm1a9cGLIdqn4Yjeo08HaVPiV7DxcmTJy9pXJsGmPY0Z84cFRUV+Zc9Ho/S09OVm5sru93ejpVdGp/PJ5fLpbFjxyouLq7N5x9QvL7N52wNW7SlRcMb9NSeaHkbotq7nJDYW5wnKfT7NJzQa+TpKH1K9BpuGj9BaUmbBhiHwyFJqqmpUc+ePf3ra2pqNGTIEP+Y2tragOedOXNGR44c8T/f4XCopqYmYEzjcuOY89lsNtlstgvWx8XFhe1Oakqo6vWeDa+w4G2ICrua2sr5+8+09+DloNfI01H6lOg1XFxqXW36e2AyMzPlcDi0ceNG/zqPx6OdO3fK6XRKkpxOp+rq6lRRUeEfs2nTJjU0NCg7O9s/ZuvWrQGfg7lcLvXt27fJj48AAEDHEvQZmOPHj+vAgQP+5UOHDqmyslLJycnq3bu3ZsyYoZ/97Ge6/vrrlZmZqaeeekppaWm6++67JUn9+/fXuHHj9Mgjj2jZsmXy+XyaPn267rvvPqWlpUmSHnjgAS1YsEBTpkzR7NmztXfvXr344otaunRp23QNAIhY1zy5JqjxthhLS27+7qP29joz/PUz+e3yuiYLOsDs2bNHt99+u3+58bqTBx98UKWlpXriiSd04sQJTZ06VXV1dbr11lu1bt06derUyf+cd999V9OnT9eYMWMUHR2tgoICvfTSS/7tiYmJKisrU2FhoYYNG6YePXpo3rx53EINAAAktSLAjBo1SpZ18Vtgo6KitHDhQi1cuPCiY5KTk7V8+fJmX2fQoEH6+OOPgy0PAAB0AHwXEgAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcSLmu5AAAG2vPX+5G9AczsAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgnNj2LgAAWuOaJ9e0y+vaYiwtuVkaULxe3rNRQT3362fyQ1QV0PFwBgYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHG4CwloY413x1zO3SpXGnfHADANAQYArpD2uvW7NRoDOBCu+AgJAAAYhwADAACMQ4ABAADG4RoYAJd9bYZJFywDiAycgQEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYJw2DzDFxcWKiooKePTr18+//fTp0yosLFT37t3VtWtXFRQUqKamJmCOqqoq5efnKyEhQSkpKZo1a5bOnDnT1qUCAABDheQ26htvvFEbNmz4vxeJ/b+XmTlzptasWaOVK1cqMTFR06dP14QJE/TJJ59Iks6ePav8/Hw5HA5t375d1dXVmjx5suLi4vTzn/88FOUCAADDhCTAxMbGyuFwXLD+6NGjeuONN7R8+XKNHj1akvTWW2+pf//+2rFjh0aMGKGysjJ98cUX2rBhg1JTUzVkyBAtWrRIs2fPVnFxseLj40NRMgAAMEhIroH56quvlJaWpmuvvVYTJ05UVVWVJKmiokI+n085OTn+sf369VPv3r1VXl4uSSovL9fAgQOVmprqH5OXlyePx6N9+/aFolwAAGCYNj8Dk52drdLSUvXt21fV1dVasGCBbrvtNu3du1dut1vx8fFKSkoKeE5qaqrcbrckye12B4SXxu2N2y7G6/XK6/X6lz0ejyTJ5/PJ5/O1RWsh1VhjqGq1xVghmTdYtmgr4M9IRq+RqaP02lH6lMKj1yv1cyrUP2vawqXW1uYBZvz48f6/Dxo0SNnZ2crIyNCKFSvUuXPntn45v8WLF2vBggUXrC8rK1NCQkLIXretuVyukMy75OaQTNtqi4Y3tHcJVwy9RqaO0mtH6VNq317Xrl17RV8vVD9r2sLJkycvaVzIvwspKSlJN9xwgw4cOKCxY8eqvr5edXV1AWdhampq/NfMOBwO7dq1K2COxruUmrquptGcOXNUVFTkX/Z4PEpPT1dubq7sdnsbdhQaPp9PLpdLY8eOVVxcXJvPP6B4fZvP2Rq2aEuLhjfoqT3R8jZE9nfm0Gtk6ii9dpQ+pfDodW9x3hV5nVD/rGkLjZ+gtCTkAeb48eM6ePCgJk2apGHDhikuLk4bN25UQUGBJGn//v2qqqqS0+mUJDmdTj399NOqra1VSkqKpO+Sot1uV1ZW1kVfx2azyWazXbA+Li4ubHdSU0JVb7h9wZ63ISrsagoVeo1MHaXXjtKn1L69XumfU+H8s/FS62rzAPNP//RPuvPOO5WRkaHDhw9r/vz5iomJ0f3336/ExERNmTJFRUVFSk5Olt1u12OPPSan06kRI0ZIknJzc5WVlaVJkyZpyZIlcrvdmjt3rgoLC5sMKAAAoONp8wDz5z//Wffff7++/fZbXX311br11lu1Y8cOXX311ZKkpUuXKjo6WgUFBfJ6vcrLy9Orr77qf35MTIxWr16tRx99VE6nU126dNGDDz6ohQsXtnWpAADAUG0eYN57771mt3fq1EklJSUqKSm56JiMjIwrfkETAAAwB9+FBAAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHFi27sAE13z5Jo2n9MWY2nJzdKA4vXyno1q8/kBAIgknIEBAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjBPb3gUAANDRXfPkmivyOrYYS0tulgYUr5f3bNRlzfX1M/ltVFXrcAYGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGCcsA4wJSUluuaaa9SpUydlZ2dr165d7V0SAAAIA2EbYN5//30VFRVp/vz5+v3vf6/BgwcrLy9PtbW17V0aAABoZ2EbYJ5//nk98sgjeuihh5SVlaVly5YpISFBb775ZnuXBgAA2llYfpVAfX29KioqNGfOHP+66Oho5eTkqLy8vMnneL1eeb1e//LRo0clSUeOHJHP52vT+mLPnGjT+SQptsHSyZMNivVF62zD5f1653DWUfqU6DVSdZReO0qfEr221rfffttGVQU6duyYJMmyrOYHWmHoL3/5iyXJ2r59e8D6WbNmWTfffHOTz5k/f74liQcPHjx48OARAY9vvvmm2awQlmdgWmPOnDkqKiryLzc0NOjIkSPq3r27oqLCP1F7PB6lp6frm2++kd1ub+9yQqaj9CnRa6TqKL12lD4leg03lmXp2LFjSktLa3ZcWAaYHj16KCYmRjU1NQHra2pq5HA4mnyOzWaTzWYLWJeUlBSqEkPGbreH7ZuqLXWUPiV6jVQdpdeO0qdEr+EkMTGxxTFheRFvfHy8hg0bpo0bN/rXNTQ0aOPGjXI6ne1YGQAACAdheQZGkoqKivTggw9q+PDhuvnmm/XCCy/oxIkTeuihh9q7NAAA0M7CNsDce++9+p//+R/NmzdPbrdbQ4YM0bp165SamtrepYWEzWbT/PnzL/gYLNJ0lD4leo1UHaXXjtKnRK+mirKslu5TAgAACC9heQ0MAABAcwgwAADAOAQYAABgHAIMAAAwDgHmCnnmmWcUFRWlGTNm+NedPn1ahYWF6t69u7p27aqCgoILfnlfVVWV8vPzlZCQoJSUFM2aNUtnzpy5wtUH5/xejxw5oscee0x9+/ZV586d1bt3b/3jP/6j//uqGkVCr+eyLEvjx49XVFSUPvjgg4BtpvV6sT7Ly8s1evRodenSRXa7XSNHjtSpU6f8248cOaKJEyfKbrcrKSlJU6ZM0fHjx69w9cFpqle3261JkybJ4XCoS5cuuummm/Tv//7vAc8zodfi4mJFRUUFPPr16+ffHknHpOZ6jbRjUkv7tVEkHZOkML6NOpLs3r1b//Iv/6JBgwYFrJ85c6bWrFmjlStXKjExUdOnT9eECRP0ySefSJLOnj2r/Px8ORwObd++XdXV1Zo8ebLi4uL085//vD1aaVFTvR4+fFiHDx/Ws88+q6ysLP33f/+3pk2bpsOHD+s3v/mNpMjp9VwvvPBCk19jYVqvF+uzvLxc48aN05w5c/Tyyy8rNjZWf/jDHxQd/X//L5o4caKqq6vlcrnk8/n00EMPaerUqVq+fPmVbuOSXKzXyZMnq66uTh9++KF69Oih5cuX60c/+pH27NmjoUOHSjKn1xtvvFEbNmzwL8fG/t+PgUg7Jl2s10g8JjW3XxtFyjHJry2+fBEXd+zYMev666+3XC6X9cMf/tD66U9/almWZdXV1VlxcXHWypUr/WO//PJLS5JVXl5uWZZlrV271oqOjrbcbrd/zGuvvWbZ7XbL6/Ve0T4uxcV6bcqKFSus+Ph4y+fzWZYVeb1++umn1ve+9z2rurrakmStWrXKv82kXpvrMzs725o7d+5Fn/vFF19Ykqzdu3f71/3ud7+zoqKirL/85S+hLLtVmuu1S5cu1jvvvBMwPjk52frXf/1Xy7LM6XX+/PnW4MGDm9wWacek5nptisnHpEvpNVKOSefiI6QQKywsVH5+vnJycgLWV1RUyOfzBazv16+fevfurfLycknf/Q934MCBAb+8Ly8vTx6PR/v27bsyDQThYr025ejRo7Lb7f7/JURSrydPntQDDzygkpKSJr+7y6ReL9ZnbW2tdu7cqZSUFN1yyy1KTU3VD3/4Q23bts0/pry8XElJSRo+fLh/XU5OjqKjo7Vz584r1sOlam6f3nLLLXr//fd15MgRNTQ06L333tPp06c1atQoSWb1+tVXXyktLU3XXnutJk6cqKqqKkmReUy6WK9NMf2Y1FyvkXRMOhcfIYXQe++9p9///vfavXv3Bdvcbrfi4+Mv+MLJ1NRUud1u/5jzf/Nw43LjmHDRXK/n+9///V8tWrRIU6dO9a+LpF5nzpypW265RXfddVeT203ptbk+//SnP0n67rP3Z599VkOGDNE777yjMWPGaO/evbr++uvldruVkpIS8LzY2FglJyeHVZ9Sy/t0xYoVuvfee9W9e3fFxsYqISFBq1atUp8+fSTJmF6zs7NVWlqqvn37qrq6WgsWLNBtt92mvXv3Rtwxqbleu3XrFjDW9GNSS71GyjHpfASYEPnmm2/005/+VC6XS506dWrvckIqmF49Ho/y8/OVlZWl4uLiK1NgG2qp1w8//FCbNm3Sp59+2g7VtZ2W+mxoaJAk/f3f/73/+8mGDh2qjRs36s0339TixYuvaL2X41Lev0899ZTq6uq0YcMG9ejRQx988IF+9KMf6eOPP9bAgQOvcMWtN378eP/fBw0apOzsbGVkZGjFihXq3LlzO1bW9prrdcqUKf5tph+TpOZ7vfrqqyPimNQUPkIKkYqKCtXW1uqmm25SbGysYmNjtWXLFr300kuKjY1Vamqq6uvrVVdXF/C8mpoa/yk+h8NxwR0AjctNnQZsLy31evbsWUnSsWPHNG7cOHXr1k2rVq1SXFycf45I6dXlcungwYNKSkryb5ekgoIC/8cNJvR6Ke9fScrKygp4Xv/+/f2nrh0Oh2prawO2nzlzRkeOHAmbPqWWez148KBeeeUVvfnmmxozZowGDx6s+fPna/jw4SopKZFkTq/nS0pK0g033KADBw7I4XBEzDGpKef22igSjklNObfXTZs2RcQxqSkEmBAZM2aMPv/8c1VWVvofw4cP18SJE/1/j4uL08aNG/3P2b9/v6qqquR0OiVJTqdTn3/+ecCB0eVyyW63X/CDoz211GtMTIw8Ho9yc3MVHx+vDz/88IL/6UZKr//v//0/ffbZZwHbJWnp0qV66623JJnRa0t9XnvttUpLS9P+/fsDnvfHP/5RGRkZkr7rs66uThUVFf7tmzZtUkNDg7Kzs69oP81pqdeTJ09KUsDdVZIUExPjPxNlSq/nO378uA4ePKiePXtq2LBhEXNMasq5vUqKmGNSU87t9cknn4yIY1KT2vsq4o7k/Dsbpk2bZvXu3dvatGmTtWfPHsvpdFpOp9O//cyZM9aAAQOs3Nxcq7Ky0lq3bp119dVXW3PmzGmH6oNzbq9Hjx61srOzrYEDB1oHDhywqqur/Y8zZ85YlhU5vTZF513xb2qv5/e5dOlSy263WytXrrS++uora+7cuVanTp2sAwcO+MeMGzfOGjp0qLVz505r27Zt1vXXX2/df//97VB9cM7ttb6+3urTp4912223WTt37rQOHDhgPfvss1ZUVJS1Zs0a/3NM6PXxxx+3Nm/ebB06dMj65JNPrJycHKtHjx5WbW2tZVmRdUxqrtdIOya1tF/PFynHJALMFXT+D4BTp05Z//AP/2BdddVVVkJCgnXPPfdY1dXVAc/5+uuvrfHjx1udO3e2evToYT3++OP+2/zC2bm9/ud//qclqcnHoUOH/M+JhF6bcv7BwrLM7LWpPhcvXmz16tXLSkhIsJxOp/Xxxx8HbP/222+t+++/3+ratatlt9uthx56yDp27NgVrLp1zu/1j3/8ozVhwgQrJSXFSkhIsAYNGnTBbdUm9HrvvfdaPXv2tOLj463vfe971r333hsQOCPpmNRcr5F2TGppv54vUo5JUZZlWVf+vA8AAEDrcQ0MAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMb5/5i7dlCCwYS9AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "splitted_data[mde_target].hist()" + ] + }, + { + "cell_type": "markdown", + "id": "b71fd1b4b45f9fd5", + "metadata": {}, + "source": [ + "You can evaluate minimum detectable effect for your data. This will be the smallest true effect obtained from the changes, which the statistical criterion will be able to detect with confidence " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "4f6fc4aa", + "metadata": { + "jupyter": { + "is_executing": true + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.88, 0.02)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mde = experiment.calc_mde(data=splitted_data, group_field=\"group\", target_field=mde_target)\n", + "mde" + ] + }, + { + "cell_type": "markdown", + "id": "5306416f71359a5a", + "metadata": {}, + "source": [ + "You can also calculate the amount of data you need to have in order to determine the minimum effect of the test." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "2b541941", + "metadata": { + "jupyter": { + "is_executing": true + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1949.4372012485414" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "experiment.calc_sample_size(data=splitted_data, target_field=mde_target, mde=5)" + ] + }, + { + "cell_type": "markdown", + "id": "1a794b76b1b997d9", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Chi2 Test" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "20a71bf1bc0501f2", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "target = ['post_spends', 'pre_spends'] \n", + "treated_field = 'treat'" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "2e0c0b4550bed569", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "experiment = AATest(target_fields=target)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "c7d27ccbc0f9fbd0", + "metadata": { + "collapsed": false, + "jupyter": { + "is_executing": true, + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'post_spends': 4.2708618195357307e-129, 'pre_spends': 0.3904626181767134}" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "experiment.calc_chi2(data, treated_field)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/tutorials/Tutorial_13_AA_Test_multigroup_split.ipynb b/examples/tutorials/Tutorial_13_AA_Test_multigroup_split.ipynb new file mode 100644 index 00000000..4fe0bb08 --- /dev/null +++ b/examples/tutorials/Tutorial_13_AA_Test_multigroup_split.ipynb @@ -0,0 +1,458 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5a568d0e-bfbd-4bba-b2bb-f026d3673f08", + "metadata": {}, + "source": [ + "## AA test multigroup split simple\n", + "Provides simple approach to split into 2 and more groups. \n", + "Uses memory more efficiently than old method \"process\"" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0a39f551-bd63-4b0f-aab4-1fe25387f132", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from lightautoml.addons.hypex import AATest\n", + "from lightautoml.addons.hypex.utils.tutorial_data_creation import create_test_data\n", + "\n", + "pd.options.display.float_format = '{:,.2f}'.format\n", + "\n", + "np.random.seed(42) # needed to create example data" + ] + }, + { + "cell_type": "markdown", + "id": "e78274b1-2f25-48c2-83f6-f7f21c08c905", + "metadata": {}, + "source": [ + "### Prepare some large data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "78111984-665f-42ba-bce0-80fa9e0d789c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustry
0000478.00422.89NaNMLogistics
1100472.50407.2268.00NaNE-commerce
2200485.00426.1144.00FLogistics
3381485.00466.1159.00FE-commerce
4411539.00522.7854.00ME-commerce
...........................
999959999500473.50428.5622.00FLogistics
999969999600497.00421.8965.00ME-commerce
999979999731485.00517.5620.00MLogistics
999989999800458.50410.7869.00ME-commerce
999999999900494.00416.8944.00FE-commerce
\n", + "

100000 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 0 0 0 478.00 422.89 NaN M \n", + "1 1 0 0 472.50 407.22 68.00 NaN \n", + "2 2 0 0 485.00 426.11 44.00 F \n", + "3 3 8 1 485.00 466.11 59.00 F \n", + "4 4 1 1 539.00 522.78 54.00 M \n", + "... ... ... ... ... ... ... ... \n", + "99995 99995 0 0 473.50 428.56 22.00 F \n", + "99996 99996 0 0 497.00 421.89 65.00 M \n", + "99997 99997 3 1 485.00 517.56 20.00 M \n", + "99998 99998 0 0 458.50 410.78 69.00 M \n", + "99999 99999 0 0 494.00 416.89 44.00 F \n", + "\n", + " industry \n", + "0 Logistics \n", + "1 E-commerce \n", + "2 Logistics \n", + "3 E-commerce \n", + "4 E-commerce \n", + "... ... \n", + "99995 Logistics \n", + "99996 E-commerce \n", + "99997 Logistics \n", + "99998 E-commerce \n", + "99999 E-commerce \n", + "\n", + "[100000 rows x 8 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "some_large_dataframe = create_test_data(rs=52, na_step=10, nan_cols=['age', 'gender'], num_users=100_000)\n", + "some_large_dataframe" + ] + }, + { + "cell_type": "markdown", + "id": "bcd56a07-020d-489b-85da-04139e5470ca", + "metadata": {}, + "source": [ + "### Process split" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d73c71a9-affc-4f05-9098-792de285b27a", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "44d99595a6b040d5bac894b96f22e0a2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Generating random divisions: 0%| | 0/2000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABM0AAARTCAYAAAB4RtTLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAADNV0lEQVR4nOzdeZzNdf//8eeZwSzMDGMYMxgjYSYpRLKUbUqbiAq5NJYWV6SSFkUTJe1p76JQXUp1la66EpUsoZAoZdcwkqUjTMwYzLx+f/jN+TpnFrOcmTPL4367ndvN+ZzP5/15vc/nLO95+pz3x2FmJgAAAAAAAAAufr4uAAAAAAAAAChrCM0AAAAAAAAAD4RmAAAAAAAAgAdCMwAAAAAAAMADoRkAAAAAAADggdAMAAAAAAAA8EBoBgAAAAAAAHggNAMAAAAAAAA8EJoBAAAAAAAAHgjNAKAAYmNj5XA4XDc/Pz+FhISoQYMG6tatm8aOHatVq1bl20bXrl3lcDi0ePHi0in6DLL7tGPHDrflZa1OSRoyZIgcDodmzZrl61JKxGeffaaLL75YoaGhrtdYWXr+UbHNmjVLDodDQ4YM8XUpZcLMmTPVtm1bVa9e3fV+9PycLG8q+mdoebR7924NHjxY0dHRqlKlitt70NvHq6jf64888ogcDoceeeQRr9QBAOVRFV8XAADlSadOnXT22WdLktLT0+V0OrV27VotXrxYzz77rLp06aIZM2borLPOKrEaYmNjtXPnTiUnJys2NrbE9lNaZs2apaFDhyoxMbFS/kG3bt069evXT1lZWerevbuioqLkcDhUr149X5cGVDqff/65hg0bpsDAQCUkJKh27dqSpBo1avi4srJn8eLF6tatm7p06VLhQ35v99XM1LdvX61atUrnnHOOunXrpqpVq6pz587FLxYA4FWEZgBQCDfffHOOszHMTF988YXuuusuLVmyRB07dtR3332nxo0bu6339ttvKy0tTTExMaVYcd4WLlyoEydOqH79+r4u5YymTJmiBx54QFFRUb4uxes++eQTnThxQg8++KAmT57s63KASu3DDz+UJL344ou65ZZbfFwNKqqdO3dq1apViomJ0U8//aQqVdz/JKvI33kAUN4QmgFAMTkcDl155ZXq2LGjLrzwQm3dulU333yzFi5c6LZeWQnLsjVp0sTXJRRYVFRUhf3jISUlRZLUtGlTH1cCgPcjSkP266xx48Y5AjOpYn/nAUB5w5xmAOAlNWvW1NSpUyVJ33zzjdasWeP2eF5zimRkZOjpp5/WBRdcoJCQEFWrVk316tVTu3btdN999+mvv/6S9H/zDu3cuVPSqcH26fOsZbe7ePFiORwOde3aVWlpaXr44YcVHx+v4OBgt59z5jWn2emWLFmiyy67TOHh4QoODtaFF16od955J9d1zzRnSm5zo8TGxmro0KGSpLfeesutP127dnWtd6b5XebMmaMePXooPDxcAQEBatSokYYNG6YtW7bkuv7pfV+0aJEuu+wy1apVS0FBQWrTpo3efvvtPJ+T/Jw8eVKvv/66OnbsqLCwMAUGBqpp06YaPXq0du/enevzMXPmTEnS0KFDc+17flatWqX77rtPF154oerVq6dq1aopMjJSvXr10tdff51j/XHjxsnhcGjEiBF5tvnLL7/I4XAoMjJSJ06ccHvsjz/+0JgxY1yvp5CQELVr104vv/yyTp48maOt04/bL7/8ov79+ysqKkr+/v6u18GJEyf073//W4MGDVJcXJxCQ0MVFBSk5s2ba/To0frjjz/yrPXAgQMaPXq0YmJiXMf9rrvu0qFDh874mlm4cKH69u2rqKgoVatWTXXr1tW1116r7777Ls/95eX019PcuXPVuXNnhYaGKiQkRF27dtW8efMK1d6//vUvORwOXX755Xmuc+DAAQUEBKhatWr6888/Xcu//vpr3XHHHWrVqpUiIiIUEBCgBg0aqH///lq9enWh6jjTXGc7duyQw+HI82fiBw8eVFJSklq1aqWQkBAFBwerZcuWeuyxx5SWlpZj/aysLE2bNk2dOnVSzZo1VbVqVdWtW1fnn3++7rjjjkLPK5aWlqYnnnhCbdq0ce2/RYsWGj9+vA4ePOi2bvbrZdGiRZKkbt26ud6PhZnrrbB9/vvvvzV9+nT17dtXTZs2VfXq1VW9enW1bNlSDz30kA4dOpTnvk6ePKkZM2YoISHB7VgnJCTopZdeynO75ORkDR48WPXq1VNAQICaNGmi8ePHKyMjo8D97Nq1q7p16ybp1PfE6Z/dp78e/vzzT7344ou68sor1bhxYwUFBSk0NFRt27bVk08+qWPHjuXafnZb0qk55jp06KCwsLAc31nLli3T5Zdfrpo1a6pGjRpq166d6/P79DY8paen69lnn9VFF12kmjVrKjAwUM2bN9d9992nAwcOFKmvBZH9nunSpUuu7WX37UyfX2vWrNGgQYNcn33h4eHq2bNnoT9rpFPPxSOPPKKmTZsqICBAUVFRSkxMdAV7AFDpGQDgjBo1amSSbObMmfmul5WVZeHh4SbJpkyZ4vZYly5dTJItWrTItSwzM9N69Ohhkiw0NNSuuOIKGzhwoCUkJLj2uXbtWjMz+/bbby0xMdGqV69ukqxfv36WmJjoum3cuNHMzBYtWmSSrH379tauXTurXr26XXHFFda/f39LSEjI0afk5ORc6xw9erT5+fnZOeecYwMGDLBLLrnE/Pz8TJKNGTMmR99z69/pkpKSTJIlJSW5lt1zzz3WqVMnk2RNmjRx68/pz19iYmKuz39WVpbddNNNJsmqVKli3bt3twEDBlizZs1MkgUHB9sXX3yRo5bsvk+YMMEcDoddcMEFNmDAALvoootMkkmy559/Ptd+5OXYsWOWkJBgkiwwMND1nDds2NAkWUREhK1Zs8a1/ty5cy0xMdGaNGlikqxTp0659j0/PXr0MD8/P2vZsqVdeeWVdv3111ubNm1cfZg6darb+ps3bzZJVrNmTUtPT8+1zTFjxuR6jJcsWWK1atUySRYbG2vXXHON9ezZ07Xssssus+PHj7ttk33cbrnlFgsICLDY2Fi74YYbrFevXvbMM8+YmdmuXbtMkoWFhdlFF11k119/vV155ZUWHR1tkqxOnTq2devWHHX+8ccfrucuPDzc+vbta3369LFatWpZ8+bNrU+fPnm+Z++55x6TZH5+fnbhhRfa9ddfb+3btzeHw2H+/v42Y8aMAj3/2bJfT3fffbdJsrZt29rAgQPtwgsvdB2LF198scDtHTp0yIKCgszPz89+//33XNd58cUXTZL17dvXbXmTJk2sWrVq1rp1a7vmmmusb9++ds4557jeI//5z39ytDVz5kyTZImJiQVani05OdkkWaNGjXI89uuvv7pe+1FRUXb55Zdbr169LDIy0iRZq1at7NChQ27bDB061PX+SUhIsIEDB1rPnj2tadOmJsnmzp2b53Pm6cCBA9aqVSvXZ+s111xj/fr1s4iICJNkjRs3dvvsmz59uiUmJrrq69mzp+v9OH369ALtsyh9/vbbb12v886dO1v//v3tsssus9q1a5skO/vss83pdObY16FDh6xz584myapWrWpdunSxgQMHWrdu3axOnTrmOcTPfi/eeeedFhoaao0aNbIbbrjBEhISLCgoyCRZnz59Cvz8TpkyxXr27GmSLDIy0u2z+5577nGt984775gkq1+/vnXp0sUGDBhgPXr0sBo1apgk69Chgx07dixH+9nvm1GjRpmfn5917tzZBg4caO3bt7cdO3aYmdl7773n+k5q2bKlDRw40PU9df/997va8LR7925r2bKl67MjISHBrr32Wtf7ODY21rWPwvS1IP78809LTEzMs70///zTzPL+zjMzmzp1qqvfrVq1suuuu846d+5s1apVM0k2ceLEHNvk9f189OhR1/de9erV7eqrr7brr7/eIiMjrXbt2q7v19O/twGgsiE0A4ACKGhoZmau4OQf//iH2/LcBq1LliwxSda6dWtLTU3N0dbq1atz/MGUV9iVLTs0k2TnnXee7dmzJ98+5RWaSbLHH3/c7bHFixe7/sCaP3/+Gft3utxCM7Mz/2FulvcfEK+99porkMoOF81OhWnZ+6tZs6bt378/175XrVrVPvvss1zrCQsLs7S0tDxr8pT9R1qTJk3cntPjx4/b8OHDXX+oZ2RkFKhvBTFv3jz7448/cixfsWKFhYaGWtWqVXOELtkh5XvvvZdjuxMnTljdunVNkq1fv961fM+ePVa7dm1zOBz26quvWmZmpusxp9Np3bt3z/WPtey+SbIHHnjAbbtsqamp9t///jfH83L8+HEbN26cSbIrr7wyx3bXXnutSbKuXbva4cOHXcsPHjzoChNye16nTZvmCiN++uknt8eWLFliISEhVq1aNduyZUuOfeYl+/XkcDjs3//+t9tjc+bMMYfDYVWqVHF7Ts9k0KBBuYbv2Vq3bm2Scrx+586da3/99VeO9efOnWtVqlSx2rVr53hdezs0S0tLcwWa48ePdzu2R48etYEDB5okGzp0qGv5zp07TZI1aNAg18+sDRs22M6dO3OtIzf9+/d3/efB6Z+hf//9t11xxRUmyTp27JhjuzN9juWlKH02OxUaf/311zneG0ePHnUFFrfffnuO/fXt29f13eH5GX7ixAn75JNP3Jad/l586KGH7OTJk67H1q9f7/rPmBUrVhS4z9nfNV26dMlznQ0bNth3332XY/lff/1ll112mUmyp556Ksfj2bWGhobmuv3u3btdwdsLL7zg9tiSJUtc/fEMzbKyslyfgcOHD3f73j1x4oQrUO/WrVuh+1oYZ2ovr++F+fPnm8PhsIiICFuyZInbYz///LM1aNDAJNnixYvdHsvrdT127FiTZHFxcbZ7927X8qNHj1rv3r1dzyGhGYDKjNAMAAqgMKHZgAEDTJJdccUVbstzG7R+8MEHJp06q6uwtRQkNFu6dGmh28mus3Xr1rlul/1HxaWXXprrdqUZmmX/kZrbWTxZWVl23nnnmSSbPHmy22PZfc/tjDkzs7i4uDM+f6dLT093/QH36aef5nj86NGjrrNNZs+eXaC+FVd24PTKK6+4LX/zzTdNOnVmmKdPPvnEpFNnSp0uOxAcNWpUrvv6/fffrWrVqlanTh3LyspyLc/uW7Nmzdz+SC+M6Oho8/Pzc/vjdseOHeZwOMzPz891huXp1q9fbw6HI8fzmpmZ6TqD7Ycffsh1f0899ZRJKtQZJNmvp7zO1OnXr59Jp864K6iFCxeaJGvevHmOx9atW2eSrF69enbixIkCt5kd3Hz++eduy70dmmWH2VdffXWu2/39999Wt25dq1KliivgW7VqlUmya665psD9ycvOnTvNz8/PHA5HjmDU7NTrNTAw0CTZ8uXL3R4ramhWlD6fydGjR61KlSpWp04dt+XZxz8wMDDPMxE9Zb8XL7jgArf3aLYRI0aYJJs0aVKB2jMrfpCUfeZru3btcjyW/R2WVz2TJk0y6dSZarnJDoM8Q7MvvvjCdYZWbu+dzMxMO/fcc3P8x0FZCc3at29vknI9Y9Ts/8YU/fr1c1ue2+s6LS3NQkJCTFKuZ2Tv2bPH9T4hNANQmXEhAADwsqysLEnKcy6V07Vp00b+/v6aMWOGmjVr5ppjyRvq1q2riy++uMjb33TTTbkuT0xM1LPPPqtly5YpMzNT/v7+Rd5Hcfz+++/avn27qyZPDodDQ4cO1d13361FixbpwQcfzLFOr169cm07Pj5emzZtyjEPWV5++OEHHTlyROHh4bm2GRwcrAEDBuiFF17QokWLdOONNxao3YI4cOCAPv/8c/3yyy86ePCgax6yrVu3SpI2b97stv4NN9yg0aNH6+uvv9bvv/+uBg0auB7Lnl9t2LBhbtt8/vnnkqT+/fvnWkP9+vXVtGlTbdiwQVu3blWzZs3cHu/Tp88ZXyc//fSTFi5cqOTkZB09etT1Pjp58qSysrK0bds2tW7dWpL07bffysx0wQUXKC4uLkdb5557rs477zz99NNPbsvXrl2rP/74Q02aNNEFF1yQax3Z88mtWLEi33pzk9vrMHv5Rx99lOd8f7np1q2bYmNjtXnzZn333Xfq0KGD67Hs43TTTTflOon4H3/8oc8//1ybNm3S4cOHXfPN/frrr5JOvSauvPLKAtdSWGd6vdSoUUNt27bVvHnztHr1al122WWKi4tTSEiI5s2bp8mTJ+vGG2/McQXiglq6dKmysrLUpk0bnXfeeTker1+/vnr27Kn//ve/WrRokTp27Fik/ZyuKH0+3YoVK/Ttt98qJSVFaWlpMjNJcs1Zd/DgQdWqVUuSNH/+fEnSVVddVeirH1999dW5fjfFx8dLUoE/8wojMzNTixcv1ooVK7Rnzx6lp6fLTv3HvaScn1Gnu+6663JdvmTJEknSoEGDcn180KBBeuaZZ3Iszz5O/fr1y/W94+fnp0suuUS//PKLVqxYoXPPPTf/zpUip9OpVatWKSgoKM/vrsJ8fv3444/6+++/FRERkev8ifXq1dNll12mTz/9tFh1A0B5R2gGAF7mdDolSeHh4Wdct0mTJnr++ed17733atSoURo1apQaNWqkDh066Oqrr9b111+vatWqFamOwk5Q7CmvP1izl6enp+vAgQOqW7dusfZTVNl/3NWuXVuhoaG5rpN9hdC8/hDM64qm2e3lNUl1XrXk90f+mWopiunTp+vuu+/W0aNH81wnNTXV7X6NGjV0/fXXa9asWXr77bddYeL+/fv1+eefKzAwUAMHDnTb5rfffpOkAoWwf/75Z47QLL/X4tGjRzV48GDNnTs333ZP78fvv/9+xnZjY2NzhGbZ/di+ffsZQ+3TJ9cvqDO9Z7LrlqQ33nhDy5Yty7HuAw88oLi4ONcE9I888ohrInTp1IUTZs+eLUmui2icbuLEiZo8eXKOiziczvM14W3Zz/PgwYM1ePDgfNfNfp5DQkI0c+ZMDR06VOPHj9f48eMVFRWliy66SJdffrluvPFG1ahRo0D798X7sSh9lk697/r165fra+F0qamprtAs+2IwuQXGZ+Ktz7yC2rp1q6699lpXYJub/F6Peb3Hz/QZkNfy7OM0YcIETZgwIc/9SkX7DChJycnJMjOlp6crICAg33ULUntBPkeLGlwDQEVCaAYAXmRmWrt2rSSpZcuWBdrmjjvu0A033KBPP/1Uy5Yt07JlyzRnzhzNmTNHSUlJ+vbbb4t09llQUFChtyms7DMFCiL7zKGyxM+v/F5Ees2aNbrtttvk7++vJ598Ur169VJMTIyCg4PlcDg0bdo03Xbbbbkeo2HDhmnWrFl66623XKHZv//9b508eVLXXXedatas6bZ+9rG77rrrVL169Xzrql27do5l+b0Wx40bp7lz5youLk5PPPGE2rVrp4iICFdY3LFjR3333Xe59iO/4Cu3x7L7Ua9ePfXs2TPffkREROT7eFGc3odly5bprbfeyrHOkCFDXGHIkCFDNHHiRH3wwQd64YUXFBQUpM8++0xOp1MXXXRRjtDk448/1iOPPKIaNWro5ZdfVvfu3RUdHa2goCA5HA49+OCDmjJlSqHet/nJ6z2dvfzyyy9XZGRkvm00atTI9e9+/fopISFBn376qb799lstX75cc+fO1dy5c/Xwww/rq6++KvDnamkrap9vvvlmLVu2TB06dNDEiRN1/vnnq1atWqpataokKTo6Wnv27PHaMSvtz7zrrrtOv/76q66++mrdd999OueccxQaGqqqVavq+PHjZwx/zvQ9ltdnQF7Ls49T586dXcFpXlq0aJHv46Utu/YaNWqoX79+Pq4GACoPQjMA8KJ58+bp4MGDkpTj5zf5iYyM1C233KJbbrlFkrRp0yYNGzZM3333nR544IFc/7guacnJybku37FjhyQpMDDQLSDJDjn+/vvvXLfLPjvCW7J/lnTgwAGlpqbmerZZ9lkFhf0JU1Fryes5K4laPvzwQ5mZ7rjjDt133305Hs/+eWZuLr74Yp199tnasmWLli9frk6dOmnWrFmScv40U5IaNmyorVu36v7771fbtm29Un+2Dz74QJL0/vvv5/pTutz6kf0cZr8Wc5PbYw0bNpR0KtjL7q83JScn6/zzz8+zltN/Cjtr1qwz1tCoUSN1795dCxcu1Mcff6xBgwble5yyn8vJkyfr1ltvzfF4fq+J3BT1Pd2wYUNt2rRJw4cPz/PndXkJCwtzO1tr165duuOOO/Tf//5Xo0aNcv0sLz/Zr4/s91xuvP1+LEqfjx49qnnz5snPz0/z5s3LEVYfPXpUe/fuzbFd9tlimzZtKnbdJWnTpk36+eefVbduXc2dOzfHzyEL+3o8Xf369bV58+Y8PwPyWp79GdC7d2+NHTu2yPv3hezaHQ6HZsyYUewAtKifowBQ2ZTf/2IHgDLm8OHDuvvuuyVJl156qVq1alXktuLi4nT//fdLktatW+f2WPYfstnzFJWUf//737kuf/vttyWd+p/60/8Iyh6Ab9y4Mcc2aWlpWrRoUa7tFbU/DRo0cJ0pkFv4YGau5d26dStU24XVtm1b1ahRQ3/99Veu87+kp6drzpw5Xq3lr7/+kuR+1kq2Y8eO6aOPPsp3++yf9s2aNUtr1qzR+vXr1bBhQ/Xo0SPHuldccYWk/wtlvCm/fixYsMD1c+fTXXzxxXI4HFqzZo22bNmS4/ENGzbk+GmmJNdZbBs2bMj352JF9c477+S6PPs9kz3fUGFkh2OzZs3Svn379MUXXygoKCjXubPyey7379+vr776qlD7zn5P5xXOZM8P5cmbr5eGDRtq4sSJknJ+FublkksukZ+fn9atW5fr62DPnj2uecG89X4sSp8PHz6szMxMhYaG5gjMpFOfwbmdYZY9/9S8efP0xx9/FK1gLzjTZ3f26zE6OjrX+cPy+o4piEsuuUSS9N577+X6+Lvvvpvr8uzjlP2fDgVVWt+7+YmOjtZ5552nv//+2/X6LY4LLrhANWrUkNPp1Jdffpnj8X379uW6HAAqG0IzACgmM9MXX3yhCy+8UFu3blVUVJSmT59eoG2/+eYbzZs3L8f8Q2am//3vf5Jy/gGcfbZKSfzRf7o1a9boqaeeclu2bNkyvfLKK5LkCgizJSQkSJJeeeUVt3mCjh49qltvvVW7du3KdT/Z/dmwYUOha8w+U+DRRx91++PYzPTYY49p3bp1qlmzpusMvpISGBiokSNHSpLuuecetzNwTpw4oTvvvFN79+5V48aNC33mTV6yJ+5+66233M4EOnbsmG6//fZ8z3qTTk1O7+fnpw8++MB1TLOXebr33ntVs2ZNPffcc3r22Wd1/PjxHOskJycX6Y/g7H689NJLbss3b96sESNG5LpNbGysevXqpaysLP3zn/906//hw4f1z3/+M9c/iKtWraqkpCSZma699tpc55HKzMzUN998o++//77QfZk7d64rHM32n//8Rx999JGqVKmiO+64o9Bt9u3bVzVr1tQ333yjyZMn6+TJk+rXr1+uZ1ZmP5fTpk1zO0aHDx9WYmKiDh8+XKh9X3jhhQoNDdWGDRtyBIIffvihXnzxxVy3u/XWW9WoUSN9+OGHuv/++3M9U23v3r1un5Nr167V+++/r/T09BzrfvbZZ5JyDwNzExMTo+uvv15mpttuu00HDhxwPZb9eXTs2DF17NjRKxcBkIrW58jISNWqVUuHDh3K8fx+//33GjduXK77atWqlXr37q309HT17t1bKSkpbo+fPHmyVCZvz/7s3rp1a65z6DVr1kz+/v5av359jotgfPbZZ3r++eeLvO/hw4crODjY7Tsp2/Lly/Xqq6/mul3v3r3Vrl07rVq1SkOHDs117q+DBw/q9ddfdwvIztTX0vLYY49JOvWfHtnvi9OZmVauXFmgsCsoKMh1Rurdd9+tPXv2uB5LT0/XP//5z1zfjwBQ6ZTilToBoNxq1KiRSbJOnTpZYmKiJSYm2oABAywhIcHCw8Ndl7bv2rWr/fbbb7m2kdsl359//nmTZKGhoda1a1e78cYb7dprr3XtLywszNauXevWzssvv2ySrEaNGta3b18bPny4DR8+3DZt2mRmZ76UvWefkpOTc61z9OjR5ufnZy1atLCBAwdaly5dzM/PzyTZnXfemaO948ePW9u2bV11X3XVVXbFFVdYnTp1rH79+jZs2LBcL12fkZFh0dHRJslat25tN910kw0fPtyeeuop1zqJiYkmyWbOnOm2bVZWlg0ePNgkWZUqVaxHjx42cOBAa968uUmyoKAgmzdvXoH7fqb95efYsWPWo0cP136vvPJK69+/v8XExJgkq127tv3www9e2ZeZ2cGDB139qF27tvXp08f69etndevWtZCQELvzzjtNkiUmJubZxuWXX+567TocDtu+fXue6y5ZssQiIiJMktWtW9e6d+9ugwYNsquvvtqaNGlikqx9+/aF7ttHH31kDofDJFnLli1twIAB1r17d6tatap1797dOnbsmON9Y2a2e/dui42NdfW/b9++du2111p4eLg1bdrUrrnmGpNks2fPzrHPe++919XvFi1aWO/evW3AgAHWtWtXq1mzpkmy1157Lc+aPWUfh7vuusskWbt27ezGG2+09u3bu/bz3HPPFbg9TyNGjHC1I8m++eabXNf77bffXPXXr1/f+vXrZ9dcc42FhYVZVFRUnu/BmTNn5vlayf6MkmQdOnSw6667zlq0aGEOh8MmTJhgkqxRo0Y5tvvll19cx6dmzZp2ySWX2I033mh9+vSxc845xxwOh0VGRrrWnzt3ruu906lTJxswYIBdd911rvdytWrV7Isvvijwc+Z0Ou388893fR716dPHrrvuOqtTp45JssaNG+f6/s/tc7qgCttnM/fnt3379jZw4EDr1KmTORwOGzx4cJ6fVX/99ZdddNFFrucm+/uje/furj6e7kzvxfxeA/nJ/sxv3ry5DRo0yIYPH27333+/6/HszyE/Pz/r0qWLDRw40Nq0aWOSbPz48a6+e8pr+eneeecd13fSeeed5/Y9NXbsWJNkVatWzbHd7t27rVWrVibJqlevbh07drQBAwZY3759rVWrVubv72+SLD09vVB9LYwzfU/nd7xeeOEFq1Klikmys88+26666iq78cYb7dJLL7W6deuapBx15fW6PnLkiF144YWuMUWvXr3s+uuvt3r16lnt2rXtpptuyvUzAwAqE0IzACiA7D9cTr9Vr17doqOjrUuXLnbPPffYqlWr8m0jt0Hrtm3b7JFHHrEePXpYTEyMBQYGWq1atey8886zBx54wHbt2pWjnczMTJsyZYq1aNHCAgMDXfVkt+ut0GzRokW2cOFC69Gjh4WFhVlQUJC1bdvWZs2alWebBw8etFGjRlmDBg2satWqVr9+fbv11ltt3759lpSUlOfge/369XbNNddYnTp1XH8EnV7/mf7ge/fdd12BR9WqVa1hw4Y2ZMgQV5BY0L4XdH95OXHihL366qt20UUXWUhIiFWrVs2aNGlid9xxh/3+++9e3ZeZ2Z9//mm33367NWnSxAICAiw6Otr+8Y9/2NatWwv0R/AHH3zgev2c6fViZrZv3z6bMGGCtWnTxtW/Bg0aWMeOHS0pKcl+/vnnIvVt6dKl1qNHD4uIiLDg4GA799xzbfLkyZaRkZFviLF//34bOXKkNWjQwKpVq2YNGza0kSNH2oEDB6x79+4myRYsWJDrPpcvX26DBg2yRo0aWUBAgIWEhFizZs2sT58+9sYbb9hff/11xucj2+mvpw8++MA6dOhgNWrUsOrVq9vFF19sn332WYHbys2qVatcxyk2NtaysrLyXDc5OdkGDRpkMTExFhAQYI0aNbIRI0bY3r1783wPnum18tZbb1mbNm0sMDDQQkNDrXv37vbVV19ZcnJynqGZmVlqaqo99dRT1qFDB9d7Myoqytq1a2f33nuvrVixwrXunj177IknnrArr7zSGjdubMHBwRYaGmrnnHOOjRw5Ms/3cn6OHj1qU6ZMsVatWllwcLAFBgZafHy8Pfjgg3ke3+KEZoXtc7ZPPvnEOnbsaDVr1rQaNWpY27Zt7dVXX7WsrKx8P6syMjLstddes4svvthq1qzpej9eeuml9sorr7itW1Kh2c6dO+3GG2+0qKgoV5Bz+ushKyvL3nzzTbvgggusRo0aFhYWZp07d7Y5c+aYWd7hWEFCMzOzxYsX26WXXmqhoaEWHBxsbdq0sTfffNNSUlJMkkVFReW63bFjx+z111+3bt26We3ata1KlSpWt25da9WqlY0cOTLXz40z9bUwihOamZ36zrz11lutadOmFhgYaMHBwXbWWWdZz5497cUXX7Tdu3e7rZ/f6/ro0aM2YcIEa9KkiVWrVs0iIyNt0KBBlpycnO/3NgBUFg4zL12OBwAAoAw4dOiQzjrrLB0+fFj79u0rkSthni42NlY7d+5UcnKyYmNjS3RfAM7s7bffVmJionr16lUqP1UFAFRczGkGAADKpVWrVuVY9ueffyoxMVEHDx7U1VdfXeKBGQDfSElJyfXqosuXL3fNd5l9wRMAAIoq56VsAAAAyoH27durQYMGio+PV+3atbV7926tXbtWR44cUUxMjF5++WVflwighHzzzTcaPny4zj//fMXExMjf31/bt293XRRm6NChuvbaa31cJQCgvCM0AwAA5dL48eO1cOFC/fTTTzp48KCqVaumJk2a6Oqrr9aYMWNUu3ZtX5cIoIRcdNFFGjp0qL799lstXrxYR48eVc2aNZWQkKBhw4Zp4MCBpVLH2LFj5XQ6C7Ru586ddfPNN5dwRQAAb2JOMwAAAAAoguw5DQsiMTFRs2bNKtmCAABeRWgGAAAAAAAAeOBCAAAAAAAAAIAHQjMAAAAAAADAA6EZAAAAAAAA4IHQDAAAAAAAAPBAaAYAAAAAAAB4IDQDAAAAAAAAPBCaAQAAAAAAAB4IzQAAAAAAAAAPhGYAAAAAAACAB0IzAAAAAAAAwAOhGQAAAAAAAOCB0AwAAAAAAADwQGgGAAAAAAAAeCA0AwAAAAAAADwQmgEAAAAAAAAeCM0AAAAAAAAAD4RmAAAAAAAAgAdCMwAAAAAAAMADoRkAAAAAAADggdAMAAAAAAAA8EBoBgAAAAAAAHggNAMAAAAAAAA8EJoBAAAAAAAAHgjNAAAAAAAAAA+EZgAAAAAAAIAHQjMAAAAAAADAA6EZAAAAAAAA4IHQDAAAAAAAAPBAaAYAAAAAAAB4IDQDAAAAAAAAPBCaAQAAAAAAAB4IzQAAAAAAAAAPhGYAAAAAAACAB0IzAAAAAAAAwAOhGQAAAAAAAOCB0AwAAAAAAADwQGgGAAAAAAAAeCA0AwAAAAAAADwQmgEAAAAAAAAeCM0AAAAAAAAAD4RmAAAAAAAAgAdCMwAAAAAAAMADoRkAAAAAAADggdAMAAAAAAAA8EBoBgAAAAAAAHggNAMAAAAAAAA8EJoBAAAAAAAAHgjNAAAAAAAAAA+EZgAAAAAAAIAHQjMAAAAAAADAA6EZAAAAAAAA4IHQDAAAAAAAAPBAaAYAAAAAAAB4IDQDAAAAAAAAPBCaAQAAAAAAAB4IzQAAAAAAAAAPhGYAAAAAAACAB0IzAAAAAAAAwAOhGQAAAAAAAOCB0AwAAAAAAADwQGgGAAAAAAAAeCA0AwAAAAAAADwQmgEAAAAAAAAeCM0AAAAAAAAAD4RmAAAAAAAAgAdCMwAAAAAAAMADoRkAAAAAAADggdAMAAAAAAAA8EBoBgAAAAAAAHggNAMAAAAAAAA8EJoBAAAAAAAAHgjNAAAAAAAAAA+EZgAAAAAAAIAHQjMAAAAAAADAA6EZAAAAAAAA4IHQDAAAAAAAAPBAaAYAAAAAAAB4IDQDAAAAAAAAPBCaAQAAAAAAAB4IzQAAAAAAAAAPhGYAAAAAAACAB0IzAAAAAAAAwAOhGQAAAAAAAOCB0AwAAAAAAADwQGgGAAAAAAAAeCA0AwAAAAAAADwQmgEAAAAAAAAeCM0AAAAAAAAAD4RmAAAAAAAAgAdCMwAAAAAAAMADoRkAAAAAAADggdAMAAAAAAAA8EBoBgAAAAAAAHggNAMAAAAAAAA8EJoBAAAAAAAAHnwami1dulS9evVSdHS0HA6HPvnkE7fHzUwPP/ywoqKiFBQUpISEBG3dutU3xQIAAAAAAKDS8GlodvToUZ1//vl65ZVXcn38qaee0osvvqjXX39dK1euVPXq1dWzZ08dO3aslCsFAAAAAABAZeIwM/N1EZLkcDg0d+5c9enTR9Kps8yio6N1zz33aOzYsZKkw4cPKzIyUrNmzdKAAQMK1G5WVpb++OMPhYSEyOFwlFT5AACgAjEz/f3334qOjpafH7NZlFWM8wAAQGEVZpxXpZRqKrTk5GTt3btXCQkJrmVhYWFq3769vvvuuzxDs4yMDGVkZLju7969W+ecc06J1wsAACqeXbt2qUGDBr4uA3n4448/1LBhQ1+XAQAAyqGCjPPKbGi2d+9eSVJkZKTb8sjISNdjuZkyZYomTpyYY/muXbsUGhrq3SIBAECFlJqaqoYNGyokJMTXpSAf2ceHcR4AACiowozzymxoVlTjxo3TmDFjXPezn4zQ0FAGUwAAoFD4yV/Zln18GOcBAIDCKsg4r8xO0lGvXj1J0r59+9yW79u3z/VYbgICAlwDJwZQAAAAAAAAKIoyG5o1btxY9erV08KFC13LUlNTtXLlSnXo0MGHlQEAAAAAAKCi8+nPM48cOaJt27a57icnJ2vdunUKDw9XTEyM7rrrLj322GNq2rSpGjdurAkTJig6Otp1hU0AAAAAAACgJPg0NPvhhx/UrVs31/3sucgSExM1a9Ys3XfffTp69KhuvfVWHTp0SJ07d9b8+fMVGBjoq5IBAAAAAABQCTjMzHxdRElKTU1VWFiYDh8+zPxmAACgQBg/lA8cJwAAUFiFGT+U2TnNAAAAAAAAAF8hNAMAAAAAAAA8EJoBAAAAAAAAHgjNAAAAAAAAAA+EZgAAAAAAAIAHQjMAAAAAAADAA6EZAAAAAAAA4IHQDAAAAAAAAPBAaAYAAAAAAAB4IDQDAAAAAAAAPBCaAQAAAAAAAB6q+LoAAKUjJSVFTqfTK21FREQoJibGK20BAACgbPPmOFJiLAmg/CA0AyqBlJQUxcXFKz09zSvtBQUFa9OmjQx2AAAAKjhvjyMlxpIAyg9CM6AScDqdSk9PU/thSQqNii1WW6l7dmjljIlyOp0MdAAAACo4b44jJcaSAMoXQjOgEgmNilV4THNflwEAAIByhnEkgMqICwEAAAAAAAAAHjjTDAAAAADgM1xoAEBZRWgGAAAAAPAJLjQAoCwjNAMAAAAA+AQXGgBQlhGaAQAAwOuWLl2qp59+WmvWrNGePXs0d+5c9enTx/W4mSkpKUnTp0/XoUOH1KlTJ7322mtq2rSp74oG4DNcaABAWURoBgAAAK87evSozj//fA0bNkx9+/bN8fhTTz2lF198UW+99ZYaN26sCRMmqGfPntqwYYMCAwN9UDFQfnlzTjDmAwOA/0NoBgAAAK+74oordMUVV+T6mJlp6tSpGj9+vHr37i1JevvttxUZGalPPvlEAwYMKM1SgXLN23OCMR8YAPwfQjMAAACUquTkZO3du1cJCQmuZWFhYWrfvr2+++67PEOzjIwMZWRkuO6npqaWeK1AWefNOcGYDwwA3BGaAQAAoFTt3btXkhQZGem2PDIy0vVYbqZMmaKJEyeWaG1AecWcYADgfX6+LgAAAAAoiHHjxunw4cOu265du3xdEgAAqMAIzQAAAFCq6tWrJ0nat2+f2/J9+/a5HstNQECAQkND3W4AAAAlhdAMAAAApapx48aqV6+eFi5c6FqWmpqqlStXqkOHDj6sDAAA4P8wpxkAAAC87siRI9q2bZvrfnJystatW6fw8HDFxMTorrvu0mOPPaamTZuqcePGmjBhgqKjo9WnTx/fFQ0AAHAaQjMAAAB43Q8//KBu3bq57o8ZM0aSlJiYqFmzZum+++7T0aNHdeutt+rQoUPq3Lmz5s+fr8DAQF+VDAAA4IbQDAAAAF7XtWtXmVmejzscDk2aNEmTJk0qxaoAAAAKjjnNAAAAAAAAAA+EZgAAAAAAAIAHfp4JwCtSUlLkdDq90lZERIRiYmK80hYAAAAAAEVBaAag2FJSUhQXF6/09DSvtBcUFKxNmzYSnAEAAAAAfIbQDCgDvHmWllT6Z2o5nU6lp6ep/bAkhUbFFqut1D07tHLGRDmdTkIzAAAAAIDPEJoBPubts7Qk352pFRoVq/CY5qW6TwAAAAAASgKhGeBj3jxLS+JMLQAAAAAAvIHQDCgjOEsLAAAAAICyw8/XBQAAAAAAAABlDaEZAAAAAAAA4IHQDAAAAAAAAPBAaAYAAAAAAAB4IDQDAAAAAAAAPBCaAQAAAAAAAB4IzQAAAAAAAAAPhGYAAAAAAACAB0IzAAAAAAAAwAOhGQAAAAAAAOCB0AwAAAAAAADwQGgGAAAAAAAAeCA0AwAAAAAAADxU8XUBAAAAAACUpJSUFDmdTq+0FRERoZiYGK+0BaBsIzQDAAAAAFRYKSkpiouLV3p6mlfaCwoK1qZNGwnOgEqA0AwAAAAAUGE5nU6lp6ep/bAkhUbFFqut1D07tHLGRDmdTkIzoBIgNAMAAAAAVHihUbEKj2nu6zIAlCNcCAAAAAAAAADwQGgGAAAAAAAAeCA0AwAAAAAAADwQmgEAAAAAAAAeCM0AAAAAAAAAD4RmAAAAAAAAgAdCMwAAAAAAAMADoRkAAAAAAADggdAMAAAAAAAA8EBoBgAAAAAAAHggNAMAAAAAAAA8EJoBAAAAAAAAHgjNAAAAAAAAAA+EZgAAAAAAAIAHQjMAAAAAAADAA6EZAAAAAAAA4IHQDAAAAAAAAPBQpkOzzMxMTZgwQY0bN1ZQUJCaNGmiRx99VGbm69IAAAAAAABQgVXxdQH5efLJJ/Xaa6/prbfeUosWLfTDDz9o6NChCgsL0+jRo31dHgAAAAAASklJkdPp9EpbERERiomJ8UpbAIqnTIdmK1asUO/evXXVVVdJkmJjY/Xee+9p1apVPq4MAAAAAIBTgVlcXLzS09O80l5QULA2bdpIcAaUAWU6NOvYsaOmTZumLVu2qFmzZvrpp5+0bNkyPffcc74uDQAAAAAAOZ1Opaenqf2wJIVGxRarrdQ9O7RyxkQ5nU5CM6AMKNOh2QMPPKDU1FTFxcXJ399fmZmZmjx5sgYNGpTnNhkZGcrIyHDdT01NLY1SUcF583RriVOui6IiHANO2wcAAKi4QqNiFR7T3NdlAPCiMh2affDBB5o9e7beffddtWjRQuvWrdNdd92l6OhoJSYm5rrNlClTNHHixFKuFBWZt0+3ljjlurAqwjHgtH0AAAAAKF/KdGh277336oEHHtCAAQMkSS1bttTOnTs1ZcqUPEOzcePGacyYMa77qampatiwYanUi4rJm6dbS5xyXRQV4Rhw2j4AAAAAlC9lOjRLS0uTn5+f2zJ/f39lZWXluU1AQIACAgJKujRUQpxu7XsV4RhUhD4AAAAAQGVQpkOzXr16afLkyYqJiVGLFi20du1aPffccxo2bJivSwMAAAAAAEAFVqZDs5deekkTJkzQ7bffrv379ys6Olq33XabHn74YV+XBgAAAAAAgAqsTIdmISEhmjp1qqZOnerrUgAAAAAAAFCJ+J15FQAAAAAAAKByITQDAAAAAAAAPBCaAQAAAAAAAB4IzQAAAAAAAAAPhGYAAAAAAACAB0IzAAAAAAAAwAOhGQAAAAAAAOCB0AwAAAAAAADwQGgGAAAAAAAAeCA0AwAAAAAAADwQmgEAAAAAAAAeCM0AAAAAAAAAD4RmAAAAAAAAgAdCMwAAAAAAAMADoRkAAABKXWZmpiZMmKDGjRsrKChITZo00aOPPioz83VpAAAAkqQqvi4AAAAAlc+TTz6p1157TW+99ZZatGihH374QUOHDlVYWJhGjx7t6/IAr0pJSZHT6fRKWxEREYqJifFKWwCA/BGaAQAAoNStWLFCvXv31lVXXSVJio2N1XvvvadVq1b5uDLAu1JSUhQXF6/09DSvtBcUFKxNmzYSnAFAKSA0AwAAQKnr2LGjpk2bpi1btqhZs2b66aeftGzZMj333HN5bpORkaGMjAzX/dTU1NIoFSgWp9Op9PQ0tR+WpNCo2GK1lbpnh1bOmCin00loBgClgNAMAAAApe6BBx5Qamqq4uLi5O/vr8zMTE2ePFmDBg3Kc5spU6Zo4sSJpVgl4D2hUbEKj2nu6zIAAIXAhQAAAABQ6j744APNnj1b7777rn788Ue99dZbeuaZZ/TWW2/luc24ceN0+PBh123Xrl2lWDEAAKhsONMMAP4/JukFgNJz77336oEHHtCAAQMkSS1bttTOnTs1ZcoUJSYm5rpNQECAAgICSrNMAABQiRGaAYCYpBcASltaWpr8/Nx/9ODv76+srCwfVQQAAOCO0AwAxCS9AFDaevXqpcmTJysmJkYtWrTQ2rVr9dxzz2nYsGG+Lg0AAEASoRkAuGGSXgAoHS+99JImTJig22+/Xfv371d0dLRuu+02Pfzww74uDQAAQBKhGQAAAHwgJCREU6dO1dSpU31dCgAAQK64eiYAAAAAAADggdAMAAAAAAAA8EBoBgAAAAAAAHggNAMAAAAAAAA8cCEAAAAAAADKsJSUFDmdTq+1FxERoZiYGK+1B1RUhGYAAAAAAJRRKSkpiouLV3p6mtfaDAoK1qZNGwnOgDMgNAMAAAAAoIxyOp1KT09T+2FJCo2KLXZ7qXt2aOWMiXI6nYRmwBkQmgEAAAAAUMaFRsUqPKa5r8sAKhUuBAAAAAAAAAB4IDQDAAAAAAAAPBCaAQAAAAAAAB4IzQAAAAAAAAAPhGYAAAAAAACAB0IzAAAAAAAAwAOhGQAAAAAAAOCB0AwAAAAAAADwQGgGAAAAAAAAeCA0AwAAAAAAADwQmgEAAAAAAAAeCM0AAAAAAAAAD4RmAAAAAAAAgAdCMwAAAAAAAMADoRkAAAAAAADggdAMAAAAAAAA8EBoBgAAAAAAAHggNAMAAAAAAAA8EJoBAAAAAAAAHgjNAAAAAAAAAA+EZgAAAAAAAIAHQjMAAAAAAADAA6EZAAAAAAAA4IHQDAAAAAAAAPBAaAYAAAAAAAB4qOLrAgAAAAAAgO+kpKTI6XR6rb2IiAjFxMR4rT3AVwjNAAAAAACopFJSUhQXF6/09DSvtRkUFKxNmzYSnKHcIzQDAAAAAKCScjqdSk9PU/thSQqNii12e6l7dmjljIlyOp2EZij3CM0AAAAAAKjkQqNiFR7T3NdlAGUKFwIAAAAAAAAAPBCaAQAAAAAAAB4IzQAAAAAAAAAPhGYAAAAAAACAB0IzAAAAAAAAwAOhGQAAAAAAAOCB0AwAAAAAAADwQGgGAAAAAAAAeCA0AwAAAAAAADwQmgEAAAAAAAAeCM0AAAAAAAAAD2U+NNu9e7f+8Y9/qHbt2goKClLLli31ww8/+LosAAAAAAAAVGBVfF1Afg4ePKhOnTqpW7du+uKLL1SnTh1t3bpVtWrV8nVpAAAAAAAAqMDKdGj25JNPqmHDhpo5c6ZrWePGjX1YEQAAAAAAACqDMv3zzE8//VRt27bV9ddfr7p166p169aaPn16vttkZGQoNTXV7QYAAAAAAAAURpkOzX777Te99tpratq0qRYsWKB//vOfGj16tN566608t5kyZYrCwsJct4YNG5ZixQAAAAAAAKgIyvTPM7OystS2bVs9/vjjkqTWrVvrl19+0euvv67ExMRctxk3bpzGjBnjup+amkpwVgmkpKTI6XR6pa2IiAjFxMR4pS2gNPE+AAAAAADvKdOhWVRUlM455xy3ZfHx8froo4/y3CYgIEABAQElXRrKkJSUFMXFxSs9Pc0r7QUFBWvTpo0EBihXeB8AAAAAgHeV6dCsU6dO2rx5s9uyLVu2qFGjRj6qCGWR0+lUenqa2g9LUmhUbLHaSt2zQytnTJTT6SQsQLnC+wAAAAAAvKtMh2Z33323OnbsqMcff1w33HCDVq1apWnTpmnatGm+Lg1lUGhUrMJjmvu6DMCneB8AAFA43pzeQGKKAwCoSMp0aNauXTvNnTtX48aN06RJk9S4cWNNnTpVgwYN8nVpAAAAAMo5b09vIDHFAQBUJGU6NJOkq6++WldffbWvywAAAABQwXhzegOJKQ4AoKIp86EZAAAAAJQkpjcAAOTGz9cFAAAAAAAAAGUNoRkAAAAAAADggdAMAAAAAAAA8EBoBgAAAAAAAHjgQgAAgAJJSUmR0+n0WnsRERFcWQwAAABAmUVoBgA4o5SUFMXFxSs9Pc1rbQYFBWvTpo0EZwAAAADKJEIzAMAZOZ1Opaenqf2wJIVGxRa7vdQ9O7RyxkQ5nU5CM6AS2717t+6//3598cUXSktL09lnn62ZM2eqbdu2vi4NAACA0AwAUHChUbEKj2nu6zIAVAAHDx5Up06d1K1bN33xxReqU6eOtm7dqlq1avm6NAAAAEmEZgAAAPCBJ598Ug0bNtTMmTNdyxo3buzDigAAANxx9UwAAACUuk8//VRt27bV9ddfr7p166p169aaPn26r8sCAABwITQDAABAqfvtt9/02muvqWnTplqwYIH++c9/avTo0Xrrrbfy3CYjI0OpqaluNwAAgJJSpNDsrLPO0oEDB3IsP3TokM4666xiFwUAAADfKK1xXlZWltq0aaPHH39crVu31q233qpbbrlFr7/+ep7bTJkyRWFhYa5bw4YNvVYPAACApyKFZjt27FBmZmaO5RkZGdq9e3exiwIAAIBvlNY4LyoqSuecc47bsvj4eKWkpOS5zbhx43T48GHXbdeuXV6rBwAAwFOhLgTw6aefuv69YMEChYWFue5nZmZq4cKFio2N9VpxAAAAKB2lPc7r1KmTNm/e7LZsy5YtatSoUZ7bBAQEKCAgwGs1AAAA5KdQoVmfPn0kSQ6HQ4mJiW6PVa1aVbGxsXr22We9VhwAAABKR2mP8+6++2517NhRjz/+uG644QatWrVK06ZN07Rp07y2DwAAgOIoVGiWlZUl6dTlwFevXq2IiIgSKQoAAAClq7THee3atdPcuXM1btw4TZo0SY0bN9bUqVM1aNCgEt0vAABAQRUqNMuWnJzs7ToAAABQBpTmOO/qq6/W1VdfXWr7AwAAKIwihWaStHDhQi1cuFD79+93/c9kthkzZhS7MAAAAPgG4zwAAIAihmYTJ07UpEmT1LZtW0VFRcnhcHi7LgAAAPgA4zwAAIBTihSavf7665o1a5YGDx7s7XoAAADgQ4zzAAAATvErykbHjx9Xx44dvV0LAAAAfIxxHgAAwClFOtPs5ptv1rvvvqsJEyZ4ux4AQCWWkpIip9PplbYiIiIUExPjlbaAyoRxHgAAwClFCs2OHTumadOm6euvv9Z5552nqlWruj3+3HPPeaU4AEDlkZKSori4eKWnp3mlvaCgYG3atJHgDCgkxnkAAACnFCk0+/nnn9WqVStJ0i+//OL2GJPFAgCKwul0Kj09Te2HJSk0KrZYbaXu2aGVMybK6XQSmgGFxDgPAADglCKFZosWLfJ2HQAASJJCo2IVHtPc12UAlRbjPAAAgFOKdCGAbNu2bdOCBQuUnp4uSTIzrxQFAAAA32KcBwAAKrsihWYHDhxQjx491KxZM1155ZXas2ePJGn48OG65557vFogAAAASg/jPAAAgFOKFJrdfffdqlq1qlJSUhQcHOxa3r9/f82fP99rxQEAAKB0Mc4DAAA4pUhzmn355ZdasGCBGjRo4La8adOm2rlzp1cKAwAAQOljnAcAAHBKkc40O3r0qNv/PGb766+/FBAQUOyiAAAA4BuM8wAAAE4pUmh28cUX6+2333bddzgcysrK0lNPPaVu3bp5rTgAAACULsZ5AAAApxTp55lPPfWUevTooR9++EHHjx/Xfffdp19//VV//fWXli9f7u0aAQAAUEoY5wEAAJxSpDPNzj33XG3ZskWdO3dW7969dfToUfXt21dr165VkyZNvF0jAAAASgnjPAAAgFOKdKaZJIWFhemhhx7yZi0AAAAoAxjnAQAAFPFMs5kzZ+rDDz/MsfzDDz/UW2+9VeyiAAAA4BuM8wAAAE4pUmg2ZcoURURE5Fhet25dPf7448UuCgAAAL7BOA8AAOCUIoVmKSkpaty4cY7ljRo1UkpKSrGLAgAAgG8wzgMAADilSKFZ3bp19fPPP+dY/tNPP6l27drFLgoAAAC+wTgPAADglCKFZgMHDtTo0aO1aNEiZWZmKjMzU998843uvPNODRgwwNs1AgAAoJQwzgMAADilSFfPfPTRR7Vjxw716NFDVaqcaiIrK0s33XQTc10AAACUY4zzAAAATil0aGZm2rt3r2bNmqXHHntM69atU1BQkFq2bKlGjRqVRI0AAAAoBYzzAAAA/k+RQrOzzz5bv/76q5o2baqmTZuWRF0AAAAoZYzzAAAA/k+h5zTz8/NT06ZNdeDAgZKoBwAAAD7COA8AAOD/FOlCAE888YTuvfde/fLLL96uBwAAAD7EOA8AAOCUIl0I4KabblJaWprOP/98VatWTUFBQW6P//XXX14pDgAAAKWLcR4AAMApRQrNpk6d6uUyAAAAUBYwzgMAADilSKFZYmKit+sAAABAGcA4DwAA4JQizWkmSdu3b9f48eM1cOBA7d+/X5L0xRdf6Ndff/VacQAAACh9jPMAAACKGJotWbJELVu21MqVK/Xxxx/ryJEjkqSffvpJSUlJXi0QAAAApYdxHgAAwClFCs0eeOABPfbYY/rqq69UrVo11/Lu3bvr+++/91pxAAAAKF2M8wAAAE4pUmi2fv16XXvttTmW161bV06ns9hFAQAAwDcY5wEAAJxSpNCsZs2a2rNnT47la9euVf369YtdFAAAAHyDcR4AAMApRQrNBgwYoPvvv1979+6Vw+FQVlaWli9frrFjx+qmm27ydo0AAAAoJYzzAAAATilSaPb4448rPj5eMTExOnLkiM455xxdcskl6tixo8aPH+/tGgEAAFBKGOcBAACcUqUwK2dlZenpp5/Wp59+quPHj2vw4MHq16+fjhw5otatW6tp06YlVScAAABKEOM8AAAAd4UKzSZPnqxHHnlECQkJCgoK0rvvvisz04wZM0qqPgAAAJQCxnkAAADuChWavf3223r11Vd12223SZK+/vprXXXVVXrjjTfk51ekX3oCAACgDGCch7IqJSXFa1dujYiIUExMjFfaAgBUfIUKzVJSUnTllVe67ickJMjhcOiPP/5QgwYNvF4cAAAASgfjPJRFKSkpiouLV3p6mlfaCwoK1qZNGwnOAAAFUqjQ7OTJkwoMDHRbVrVqVZ04ccKrRQEAAKB0Mc5DWeR0OpWenqb2w5IUGhVbrLZS9+zQyhkT5XQ6Cc0AAAVSqNDMzDRkyBAFBAS4lh07dkwjRoxQ9erVXcs+/vhj71UIAACAEsc4D2VZaFSswmOa+7oMAEAlU6jQLDExMceyf/zjH14rBgAAAL7BOA8AAMBdoUKzmTNnllQdAAAA8CHGeQAAAO64FBIAAAAAAADggdAMAAAAAAAA8EBoBgAAAAAAAHggNAMAAAAAAAA8EJoBAAAAAAAAHgjNAAAAAAAAAA+EZgAAAAAAAICHKr4uAACA0pKSkiKn0+mVtiIiIhQTE+OVtgAAAACUPYRmAIBKISUlRXFx8UpPT/NKe0FBwdq0aSPBGQAAAFBBEZoBACoFp9Op9PQ0tR+WpNCo2GK1lbpnh1bOmCin00loBgAAAFRQ5So0e+KJJzRu3Djdeeedmjp1qq/LAQCUQ6FRsQqPae7rMgAAACoVpslAeVRuQrPVq1frX//6l8477zxflwIAAAAAAAqIaTJQXpWL0OzIkSMaNGiQpk+frscee8zX5QAAAAAAgAJimgyUV+UiNBs5cqSuuuoqJSQknDE0y8jIUEZGhut+ampqSZcHAAAAAADOgGkyUN6U+dBszpw5+vHHH7V69eoCrT9lyhRNnDixhKsCAAAAAABARebn6wLys2vXLt15552aPXu2AgMDC7TNuHHjdPjwYddt165dJVwlAAAAAAAAKpoyfabZmjVrtH//frVp08a1LDMzU0uXLtXLL7+sjIwM+fv7u20TEBCggICA0i4VAAAAAAAAFUiZDs169Oih9evXuy0bOnSo4uLidP/99+cIzAAAAAAAAABvKNOhWUhIiM4991y3ZdWrV1ft2rVzLAcAAAAAAAC8pUzPaQYAAAAAAAD4Qpk+0yw3ixcv9nUJAAAAAAAAqOA40wwAAAAAAADwQGgGAAAAn3viiSfkcDh01113+boUAAAASYRmAAAA8LHVq1frX//6l8477zxflwIAAOBCaAYAAACfOXLkiAYNGqTp06erVq1avi4HAADAhdAMAAAAPjNy5EhdddVVSkhIOOO6GRkZSk1NdbsBAACUlHJ39UwAAABUDHPmzNGPP/6o1atXF2j9KVOmaOLEiSVcFQAAwCmcaQYAAIBSt2vXLt15552aPXu2AgMDC7TNuHHjdPjwYddt165dJVwlAACozDjTDAAAAKVuzZo12r9/v9q0aeNalpmZqaVLl+rll19WRkaG/P393bYJCAhQQEBAaZcKAAAqKUIzAAAAlLoePXpo/fr1bsuGDh2quLg43X///TkCMwAAgNJGaAYAAIBSFxISonPPPddtWfXq1VW7du0cywEAAHyBOc0AAAAAAAAAD5xphlKRkpIip9PplbYiIiIUExPjlbYAAEDZsXjxYl+XAAAA4EJohhKXkpKiuLh4paeneaW9oKBgbdq0keAMAAAAAACUGEIzlDin06n09DS1H5ak0KjYYrWVumeHVs6YKKfTSWgGAAAAAABKDKEZSk1oVKzCY5r7ugwAAAAAAIAz4kIAAAAAAAAAgAdCMwAAAAAAAMADoRkAAAAAAADggdAMAAAAAAAA8EBoBgAAAAAAAHggNAMAAAAAAAA8EJoBAAAAAAAAHgjNAAAAAAAAAA+EZgAAAAAAAIAHQjMAAAAAAADAA6EZAAAAAAAA4KGKrwsAAKCiSElJkdPp9Fp7ERERiomJ8Vp7AAAAAAqO0AwAAC9ISUlRXFy80tPTvNZmUFCwNm3aSHAGAAAA+AChGQAAXuB0OpWenqb2w5IUGhVb7PZS9+zQyhkT5XQ6Cc0AAAAAHyA0AwDAi0KjYhUe09zXZQAAAAAoJi4EAAAAAAAAAHggNAMAAAAAAAA8EJoBAAAAAAAAHpjTDAAAAECRpaSkyOl0eqWtiIgILn4CACgzCM0AAAAAFElKSori4uKVnp7mlfaCgoK1adNGgjMAQJlAaAYAAACgSJxOp9LT09R+WJJCo2KL1Vbqnh1aOWOinE4noRkAoEwgNAMAAABQLKFRsQqPae7rMgAA8CouBAAAAAAAAAB4IDQDAAAAAAAAPBCaAQAAAAAAAB4IzQAAAAAAAAAPhGYAAAAAAACAB0IzAAAAAAAAwAOhGQAAAAAAAOCB0AwAAAAAAADwQGgGAAAAAAAAeCA0AwAAAAAAADxU8XUBAAAAAAAAxZGSkiKn0+mVtiIiIhQTE+OVtlC+EZoBAAAAAIByKyUlRXFx8UpPT/NKe0FBwdq0aSPBGQjNAAAAAABA+eV0OpWenqb2w5IUGhVbrLZS9+zQyhkT5XQ6Cc1AaAYAAAAAAMq/0KhYhcc093UZqEC4EAAAAAAAAADggdAMAAAAAAAA8EBoBgAAAAAAAHggNAMAAAAAAAA8EJoBAAAAAAAAHgjNAAAAAAAAAA+EZgAAAAAAAIAHQjMAAAAAAADAA6EZAAAAAAAA4IHQDAAAAAAAAPBQxdcFwPdSUlLkdDq91l5ERIRiYmK81h4AAAAAAEBpIzSr5FJSUhQXF6/09DSvtRkUFKxNmzYSnAEAAAAAgHKL0KySczqdSk9PU/thSQqNii12e6l7dmjljIlyOp2EZgAAAAAAoNwiNIMkKTQqVuExzX1dBgAAAAAAQJnAhQAAAAAAAAAAD4RmAAAAAAAAgAdCMwAAAAAAAMADoRkAAAAAAADggdAMAAAAAAAA8EBoBgAAAAAAAHggNAMAAAAAAAA8VPF1AQAAAAAAAGVZSkqKnE6n19qLiIhQTEyM19pDySjTodmUKVP08ccfa9OmTQoKClLHjh315JNPqnnz5r4uDQAAAAAAVAIpKSmKi4tXenqa19oMCgrWpk0bCc7KuDIdmi1ZskQjR45Uu3btdPLkST344IO67LLLtGHDBlWvXt3X5QEAAAAAgArO6XQqPT1N7YclKTQqttjtpe7ZoZUzJsrpdBKalXFlOjSbP3++2/1Zs2apbt26WrNmjS655BIfVQUAAAAAACqb0KhYhcfwy7fKpEyHZp4OHz4sSQoPD89znYyMDGVkZLjup6amlnhdAAAAAAAAqFjKzdUzs7KydNddd6lTp04699xz81xvypQpCgsLc90aNmxYilUCAAAAAACgIig3odnIkSP1yy+/aM6cOfmuN27cOB0+fNh127VrVylVCAAAAAAAgIqiXIRmo0aN0v/+9z8tWrRIDRo0yHfdgIAAhYaGut0AAABQ9kyZMkXt2rVTSEiI6tatqz59+mjz5s2+LgsAAEBSGZ/TzMx0xx13aO7cuVq8eLEaN27s65IAAADgJVwpvXSkpKTI6XR6pa2IiAiu9AYAqDTKdGg2cuRIvfvuu/rvf/+rkJAQ7d27V5IUFhamoKAgH1cHAACA4uBK6SUvJSVFcXHxSk9P80p7QUHB2rRpI8EZAKBSKNOh2WuvvSZJ6tq1q9vymTNnasiQIaVfEAAAAErMma6UzlXSC8/pdCo9PU3thyUpNCq2WG2l7tmhlTMmyul0EpoBACqFMh2amZmvSwAAAEApKMiV0qdMmaKJEyeWcmUVQ2hUrMJjmvu6DAAAypVycSEAAAAAVGwFuVI6V0kHAAClqUyfaQYAAICKL/tK6UuXLs33SukBAQEKCAgoxcoAAEBlRmgGAAAAn+BK6QAAoCwjNAMAAIBPcKV0AABQljGnGQAAAHzitdde0+HDh9W1a1dFRUW5bu+//76vSwMAAOBMMwAAAPgGV0oHAABlGWeaAQAAAAAAAB4IzQAAAAAAAAAPhGYAAAAAAACAB0IzAAAAAAAAwAOhGQAAAAAAAOCB0AwAAAAAAADwQGgGAAAAAAAAeCA0AwAAAAAAADwQmgEAAAAAAAAeCM0AAAAAAAAAD4RmAAAAAAAAgIcqvi4AAAAUTEpKipxOp9fai4iIUExMjNfaAwAAACoSQjMAAMqBlJQUxcXFKz09zWttBgUFa9OmjQRnAAAAQC4IzQAAKAecTqfS09PUfliSQqNii91e6p4dWjljopxOJ6EZAAAAkAtCMwAAypHQqFiFxzT3dRkAAABAhUdoBgAAXLw5bxpzpgEAAKA8IzQDAACSvD9vGnOmAQAAoDwjNAMAAJK8O28ac6YBAACgvCM0AwAAbpg3DQAAAJD8fF0AAAAAAAAAUNYQmgEAAAAAAAAeCM0AAAAAAAAAD4RmAAAAAAAAgAdCMwAAAAAAAMADoRkAAAAAAADggdAMAAAAAAAA8EBoBgAAAAAAAHggNAMAAAAAAAA8EJoBAAAAAAAAHgjNAAAAAAAAAA+EZgAAAAAAAIAHQjMAAAAAAADAA6EZAAAAAAAA4KGKrwsAAAAAAACozFJSUuR0Or3WXkREhGJiYrzWXmVFaAYAAAAAAOAjKSkpiouLV3p6mtfaDAoK1qZNGwnOionQDAAAAAAAwEecTqfS09PUfliSQqNii91e6p4dWjljopxOJ6FZMRGalQOcpgkAAAAAQMUWGhWr8Jjmvi4DpyE0K+M4TRMAAAAAAKD0EZqVcZymCQAAAAAAUPoIzcoJTtMEAAAAAAAoPYRmXuDNOceYbwwAUJHxnQkAAIDygtCsmLw95xjzjQEAKiq+MwEAAFCeEJoVkzfnHGO+MQBARcZ3JgAAAMoTQjMvYc4xAAAKhu9MAAAAlAeEZgAAAICPeHOeP4m5/gAA8CZCMwAAAMAHvD3Pn8RcfwAAeBOhGQAAAOAD3pznT2KuPwAAvI3QDAAAAPAh5vkDAKBsIjQDAAAA8uDNOceYbwwAgPKF0AwAAADIhbfnHGO+MQAAyhdCMwAAACAX3pxzjPnGAAAofwjNAAAAgHww5xgAAJUToRkAAAAAAEAFxzydhUdoBgAAAAAAUIExT2fREJoBAAAAAABUYMzTWTSEZgAAAAAAAJUA83QWjp+vCwAAAAAAAADKGkIzAAAAAAAAwAOhGQAAAAAAAOCB0AwAAAAAAADwwIUAAAAAAAAAUCwpKSlyOp1eaSsiIqJMXJmT0AwAAAAAAABFlpKSori4eKWnp3mlvaCgYG3atNHnwRmhGQAAAAAAAIrM6XQqPT1N7YclKTQqtlhtpe7ZoZUzJsrpdBKaAQAAAAAAoPwLjYpVeExzX5fhNVwIAAAAAAAAAPBAaAYAAAAAAAB4IDQDAAAAAAAAPJSL0OyVV15RbGysAgMD1b59e61atcrXJQEAAMALGOcBAICyqsyHZu+//77GjBmjpKQk/fjjjzr//PPVs2dP7d+/39elAQAAoBgY5wEAgLKszIdmzz33nG655RYNHTpU55xzjl5//XUFBwdrxowZvi4NAAAAxcA4DwAAlGVVfF1Afo4fP641a9Zo3LhxrmV+fn5KSEjQd999l+s2GRkZysjIcN0/fPiwJCk1NbVEajxy5Igk6a+dm3UyI71YbaXuTXG1mV2vN9vPbR8l3X72v721j/Lefm774BiUbvulsQ/a9/0+ynv7ue2DY3Dm9r0pu00z83rbOIVxHp+tFa390thHeW8/t31wDEq3/dz2wTEo3fZz2wfH4Mzte1OhxnlWhu3evdsk2YoVK9yW33vvvXbhhRfmuk1SUpJJ4saNGzdu3LhxK/Zt165dpTHkqZQY53Hjxo0bN27cfHkryDivTJ9pVhTjxo3TmDFjXPezsrL0119/qXbt2nI4HEpNTVXDhg21a9cuhYaG+rBSZOOYlE0cl7KJ41L2cEzKpuIeFzPT33//rejo6BKoDkV1pnFebirre7Qy9rsy9lmi3/S7cqiM/a6MfZZKp9+FGeeV6dAsIiJC/v7+2rdvn9vyffv2qV69erluExAQoICAALdlNWvWzLFeaGhopXrhlQcck7KJ41I2cVzKHo5J2VSc4xIWFublanC6khzn5aayvkcrY78rY58l+l3Z0O/KozL2WSr5fhd0nFemLwRQrVo1XXDBBVq4cKFrWVZWlhYuXKgOHTr4sDIAAAAUB+M8AABQ1pXpM80kacyYMUpMTFTbtm114YUXaurUqTp69KiGDh3q69IAAABQDIzzAABAWVbmQ7P+/fvrzz//1MMPP6y9e/eqVatWmj9/viIjI4vUXkBAgJKSknKc2g/f4ZiUTRyXsonjUvZwTMomjkv54O1xXm4q62uhMva7MvZZot/0u3KojP2ujH2Wyl6/HWZcSx0AAAAAAAA4XZme0wwAAAAAAADwBUIzAAAAAAAAwAOhGQAAAAAAAOCB0AwAAAAAAADwUOFCs1deeUWxsbEKDAxU+/bttWrVqnzX//DDDxUXF6fAwEC1bNlS8+bNK6VKK5fCHJfp06fr4osvVq1atVSrVi0lJCSc8TiiaAr7fsk2Z84cORwO9enTp2QLrIQKe0wOHTqkkSNHKioqSgEBAWrWrBmfYyWgsMdl6tSpat68uYKCgtSwYUPdfffdOnbsWClVWzksXbpUvXr1UnR0tBwOhz755JMzbrN48WK1adNGAQEBOvvsszVr1qwSrxOlozDv0V9//VX9+vVTbGysHA6Hpk6dWnqFelFlHVsVpt8ff/yx2rZtq5o1a6p69epq1aqV3nnnnVKs1nsq65itMP2eNWuWHA6H2y0wMLAUq/WeyjoeLEy/u3btmuN4OxwOXXXVVaVYcfFV1jFmYfp94sQJTZo0SU2aNFFgYKDOP/98zZ8/v/SKtQpkzpw5Vq1aNZsxY4b9+uuvdsstt1jNmjVt3759ua6/fPly8/f3t6eeeso2bNhg48ePt6pVq9r69etLufKKrbDH5cYbb7RXXnnF1q5daxs3brQhQ4ZYWFiY/f7776VcecVW2OOSLTk52erXr28XX3yx9e7du3SKrSQKe0wyMjKsbdu2duWVV9qyZcssOTnZFi9ebOvWrSvlyiu2wh6X2bNnW0BAgM2ePduSk5NtwYIFFhUVZXfffXcpV16xzZs3zx566CH7+OOPTZLNnTs33/V/++03Cw4OtjFjxtiGDRvspZdeMn9/f5s/f37pFIwSU9j36KpVq2zs2LH23nvvWb169ez5558v3YK9oLKOrQrb70WLFtnHH39sGzZssG3bttnUqVPL5fu+so7ZCtvvmTNnWmhoqO3Zs8d127t3bylXXXyVdTxY2H4fOHDA7Vj/8ssv5u/vbzNnzizdwouhso4xC9vv++67z6Kjo+3zzz+37du326uvvmqBgYH2448/lkq9FSo0u/DCC23kyJGu+5mZmRYdHW1TpkzJdf0bbrjBrrrqKrdl7du3t9tuu61E66xsCntcPJ08edJCQkLsrbfeKqkSK6WiHJeTJ09ax44d7Y033rDExMRyOQArywp7TF577TU766yz7Pjx46VVYqVU2OMycuRI6969u9uyMWPGWKdOnUq0zsqsIKHZfffdZy1atHBb1r9/f+vZs2cJVobSUJxxRqNGjcplaFZZx1bF7beZWevWrW38+PElUV6JqaxjtsL2e+bMmRYWFlZK1ZWcyjoeLO77+/nnn7eQkBA7cuRISZXodZV1jFnYfkdFRdnLL7/stqxv3742aNCgEq0zW4X5eebx48e1Zs0aJSQkuJb5+fkpISFB3333Xa7bfPfdd27rS1LPnj3zXB+FV5Tj4iktLU0nTpxQeHh4SZVZ6RT1uEyaNEl169bV8OHDS6PMSqUox+TTTz9Vhw4dNHLkSEVGRurcc8/V448/rszMzNIqu8IrynHp2LGj1qxZ4zrN/LffftO8efN05ZVXlkrNyB3f+RWTN8YZ5U1lHVsVt99mpoULF2rz5s265JJLSrJUr6qsY7ai9vvIkSNq1KiRGjZsqN69e+vXX38tjXK9prKOB73xufbmm29qwIABql69ekmV6VWVdYxZlH5nZGTk+Kl1UFCQli1bVqK1ZqtSKnspBU6nU5mZmYqMjHRbHhkZqU2bNuW6zd69e3Ndf+/evSVWZ2VTlOPi6f7771d0dHSOP3ZQdEU5LsuWLdObb76pdevWlUKFlU9Rjslvv/2mb775RoMGDdK8efO0bds23X777Tpx4oSSkpJKo+wKryjH5cYbb5TT6VTnzp1lZjp58qRGjBihBx98sDRKRh7y+s5PTU1Venq6goKCfFQZisMb44zyprKOrYra78OHD6t+/frKyMiQv7+/Xn31VV166aUlXa7XVNYxW1H63bx5c82YMUPnnXeeDh8+rGeeeUYdO3bUr7/+qgYNGpRG2cVWWceDxf1cW7VqlX755Re9+eabJVWi11XWMWZR+t2zZ08999xzuuSSS9SkSRMtXLhQH3/8cakFwxXmTDNUTE888YTmzJmjuXPnltuJPCuCv//+W4MHD9b06dMVERHh63Lw/2VlZalu3bqaNm2aLrjgAvXv318PPfSQXn/9dV+XVqktXrxYjz/+uF599VX9+OOP+vjjj/X555/r0Ucf9XVpAFDpxlYhISFat26dVq9ercmTJ2vMmDFavHixr8sqMZV5zNahQwfddNNNatWqlbp06aKPP/5YderU0b/+9S9fl1aiGA+eOsusZcuWuvDCC31dSomqrGPMF154QU2bNlVcXJyqVaumUaNGaejQofLzK504q8KcaRYRESF/f3/t27fPbfm+fftUr169XLepV69eodZH4RXluGR75pln9MQTT+jrr7/WeeedV5JlVjqFPS7bt2/Xjh071KtXL9eyrKwsSVKVKlW0efNmNWnSpGSLruCK8l6JiopS1apV5e/v71oWHx+vvXv36vjx46pWrVqJ1lwZFOW4TJgwQYMHD9bNN98sSWrZsqWOHj2qW2+9VQ899FCpfcHDXV7f+aGhoZxlVo4VZ5xRXlXWsVVR++3n56ezzz5bktSqVStt3LhRU6ZMUdeuXUuyXK+prGM2b7y3q1atqtatW2vbtm0lUWKJqKzjweIc76NHj2rOnDmaNGlSSZbodZV1jFmUftepU0effPKJjh07pgMHDig6OloPPPCAzjrrrNIoueKcaVatWjVdcMEFWrhwoWtZVlaWFi5cqA4dOuS6TYcOHdzWl6Svvvoqz/VReEU5LpL01FNP6dFHH9X8+fPVtm3b0ii1UinscYmLi9P69eu1bt061+2aa65Rt27dtG7dOjVs2LA0y6+QivJe6dSpk7Zt2+YaDEvSli1bFBUVVS4GSOVBUY5LWlpajkFL9kDWzEquWOSL7/yKqajjjPKsso6tvHWss7KylJGRURIllojKOmbzxvHOzMzU+vXrFRUVVVJlel1lHQ8W53h/+OGHysjI0D/+8Y+SLtOrKusYszjHOjAwUPXr19fJkyf10UcfqXfv3iVd7imlcrmBUjJnzhwLCAiwWbNm2YYNG+zWW2+1mjVrui41PHjwYHvggQdc6y9fvtyqVKlizzzzjG3cuNGSkpKsatWqtn79el91oUIq7HF54oknrFq1avaf//zH7TLCf//9t6+6UCEV9rh4Kq9XYirLCntMUlJSLCQkxEaNGmWbN2+2//3vf1a3bl177LHHfNWFCqmwxyUpKclCQkLsvffes99++82+/PJLa9Kkid1www2+6kKF9Pfff9vatWtt7dq1Jsmee+45W7t2re3cudPMzB544AEbPHiwa/3ffvvNgoOD7d5777WNGzfaK6+8Yv7+/jZ//nxfdQFeUtj3aEZGhuu1ExUVZWPHjrW1a9fa1q1bfdWFQqusY6vC9vvxxx+3L7/80rZv324bNmywZ555xqpUqWLTp0/3VReKpLKO2Qrb74kTJ9qCBQts+/bttmbNGhswYIAFBgbar7/+6qsuFEllHQ8W9XXeuXNn69+/f2mX6xWVdYxZ2H5///339tFHH9n27dtt6dKl1r17d2vcuLEdPHiwVOqtUKGZmdlLL71kMTExVq1aNbvwwgvt+++/dz3WpUsXS0xMdFv/gw8+sGbNmlm1atWsRYsW9vnnn5dyxZVDYY5Lo0aNTFKOW1JSUukXXsEV9v1yuvI6ACvrCntMVqxYYe3bt7eAgAA766yzbPLkyXby5MlSrrriK8xxOXHihD3yyCPWpEkTCwwMtIYNG9rtt99eal/slcWiRYty/a7IPhaJiYnWpUuXHNu0atXKqlWrZmeddZbNnDmz1OtGySjMezQ5OTnX147n66Wsq6xjq8L0+6GHHrKzzz7bAgMDrVatWtahQwebM2eOD6ouvso6ZitMv++66y7XupGRkXbllVfajz/+6IOqi6+yjgcL2+9NmzaZJPvyyy9LuVLvqaxjzML0e/HixRYfH28BAQFWu3ZtGzx4sO3evbvUanWYlZPz+AAAAAAAAIBSUmHmNAMAAAAAAAC8hdAMAAAAAAAA8EBoBgAAAAAAAHggNAMAAAAAAAA8EJoBAAAAAAAAHgjNAAAAAAAAAA+EZgAAAAAAAIAHQjMApeaRRx5Rq1atfF0GyoCuXbvqrrvu8nUZAACgiIYMGaI+ffr4ugyUAbGxsZo6daqvywBKBKEZUMYtXrxYDodDhw4d8nUpKIRZs2apZs2aBVrX4XDok08+8er+GbwAAAB4R0H/43fHjh1yOBxat26dV/dfEmNFAAVDaAYAAAAAJeD48eO+LgEAUAyEZkAxde3aVaNGjdKoUaMUFhamiIgITZgwQWbmWufgwYO66aabVKtWLQUHB+uKK67Q1q1bXY/v3LlTvXr1Uq1atVS9enW1aNFC8+bN044dO9StWzdJUq1ateRwODRkyJAz1vSf//xHLVu2VFBQkGrXrq2EhAQdPXpU0v+dSj9x4kTVqVNHoaGhGjFihNugLisrS1OmTFHjxo0VFBSk888/X//5z39cj2ef/bZw4UK1bdtWwcHB6tixozZv3uxWxxNPPKHIyEiFhIRo+PDhOnbsmNvjixcv1oUXXqjq1aurZs2a6tSpk3bu3Jlrn7L/5+6DDz7QxRdfrKCgILVr105btmzR6tWr1bZtW9WoUUNXXHGF/vzzT7dt33jjDcXHxyswMFBxcXF69dVX3R6///771axZMwUHB+uss87ShAkTdOLECdfj2f+7+M477yg2NlZhYWEaMGCA/v7771xrXbx4sYYOHarDhw/L4XDI4XDokUceyXXd2NhYSdK1114rh8Phui9J//3vf9WmTRsFBgbqrLPO0sSJE3Xy5ElJkpnpkUceUUxMjAICAhQdHa3Ro0dLOvWa3Llzp+6++27X/vPicDj02muv6YorrlBQUJDOOusst2Pt6csvv1RgYGCOMx/vvPNOde/eXZJ04MABDRw4UPXr11dwcLBatmyp9957L882s+vw/B/UmjVratasWa77u3bt0g033KCaNWsqPDxcvXv31o4dO/JtFwAAbynImC82NlaPPvqobrrpJoWGhurWW2+VJC1btsw1fmnYsKFGjx7tGpudyauvvqqmTZsqMDBQkZGRuu666wpVU0ZGhsaOHav69eurevXqat++vRYvXux6PPvs+AULFig+Pl41atTQ5Zdfrj179rjWyczM1JgxY1SzZk3Vrl1b9913n9s+pPzHn56yx5ILFixQ69atFRQUpO7du2v//v364osvFB8fr9DQUN14441KS0tzbXemMWpmZqaGDx/uerx58+Z64YUX3PadPRZ+5plnFBUVpdq1a2vkyJFuY7/TzZo1SxMnTtRPP/3kGledPj45XePGjSVJrVu3lsPhUNeuXV2P5TcePX78uEaNGqWoqCgFBgaqUaNGmjJliqT8x4qnyx4rz5kzRx07dlRgYKDOPfdcLVmyJNf1JWnatGmKjo5WVlaW2/LevXtr2LBhkqTt27erd+/eioyMVI0aNdSuXTt9/fXXebaZ29l2hw4dksPhcHvd/fLLL7riiitUo0YNRUZGavDgwXI6nXm2C/iMASiWLl26WI0aNezOO++0TZs22b///W8LDg62adOmuda55pprLD4+3pYuXWrr1q2znj172tlnn23Hjx83M7OrrrrKLr30Uvv5559t+/bt9tlnn9mSJUvs5MmT9tFHH5kk27x5s+3Zs8cOHTqUbz1//PGHValSxZ577jlLTk62n3/+2V555RX7+++/zcwsMTHRatSoYf3797dffvnF/ve//1mdOnXswQcfdLXx2GOPWVxcnM2fP9+2b99uM2fOtICAAFu8eLGZmS1atMgkWfv27W3x4sX266+/2sUXX2wdO3Z0tfH+++9bQECAvfHGG7Zp0yZ76KGHLCQkxM4//3wzMztx4oSFhYXZ2LFjbdu2bbZhwwabNWuW7dy5M9d+JScnmyRXXRs2bLCLLrrILrjgAuvatastW7bMfvzxRzv77LNtxIgRru3+/e9/W1RUlH300Uf222+/2UcffWTh4eE2a9Ys1zqPPvqoLV++3JKTk+3TTz+1yMhIe/LJJ12PJyUlWY0aNaxv3762fv16W7p0qdWrV8/tOTtdRkaGTZ061UJDQ23Pnj22Z88e1/Pvaf/+/SbJZs6caXv27LH9+/ebmdnSpUstNDTUZs2aZdu3b7cvv/zSYmNj7ZFHHjEzsw8//NBCQ0Nt3rx5tnPnTlu5cqXrNXfgwAFr0KCBTZo0ybX/vEiy2rVr2/Tp023z5s02fvx48/f3tw0bNuS6/smTJy0yMtLeeOONPJf9/vvv9vTTT9vatWtt+/bt9uKLL5q/v7+tXLnStU2XLl3szjvvdKtj7ty5bvsKCwuzmTNnmpnZ8ePHLT4+3oYNG2Y///yzbdiwwW688UZr3ry5ZWRk5Nk/AAC8pSBjvkaNGlloaKg988wztm3bNtetevXq9vzzz9uWLVts+fLl1rp1axsyZMgZ97l69Wrz9/e3d99913bs2GE//vijvfDCC4Wq6eabb7aOHTva0qVLbdu2bfb0009bQECAbdmyxczMZs6caVWrVrWEhARbvXq1rVmzxuLj4+3GG290tfHkk09arVq17KOPPrINGzbY8OHDLSQkxHr37m1mZx5/esoeS1500UVuY7guXbrYZZddZj/++KMtXbrUateubU888YRruzONUY8fP24PP/ywrV692n777TfX8/H++++72khMTLTQ0FAbMWKEbdy40T777LMcz9np0tLS7J577rEWLVq4xlVpaWm5rrtq1SqTZF9//bXt2bPHDhw4YGZnHo8+/fTT1rBhQ1u6dKnt2LHDvv32W3v33XfNLO+xoqfssXKDBg3sP//5j23YsMFuvvlmCwkJMafTmes2f/31l1WrVs2+/vpr17IDBw64LVu3bp29/vrrtn79etuyZYuNHz/eAgMD3cbsjRo1sueff96tjrVr17oeP3jwoEmyRYsWue7XqVPHxo0bZxs3brQff/zRLr30UuvWrVuudQK+RGgGFFOXLl0sPj7esrKyXMvuv/9+i4+PNzOzLVu2mCRbvny563Gn02lBQUH2wQcfmJlZy5YtXWGIp+xBxcGDBwtUz5o1a0yS7dixI9fHExMTLTw83I4ePepa9tprr1mNGjUsMzPTjh07ZsHBwbZixQq37YYPH24DBw50q+n0L9jPP//cJFl6erqZmXXo0MFuv/12tzbat2/vCs0OHDhgklyDnDPJ/gI+Pax57733TJItXLjQtWzKlCnWvHlz1/0mTZq4Bh3ZHn30UevQoUOe+3r66aftggsucN1PSkqy4OBgS01NdS279957rX379nm2MXPmTAsLCytQ33ILjHr06GGPP/6427J33nnHoqKizMzs2WeftWbNmrmCV0+nD17OtO/TQ0azU8fpn//8Z57b3Hnnnda9e3fX/QULFlhAQEC+r9GrrrrK7rnnHtf9woZm77zzjjVv3tztfZaRkWFBQUG2YMGCfHoIAIB3nGnMZ3bq+7dPnz5u2w0fPtxuvfVWt2Xffvut+fn5ucZNefnoo48sNDTUbQxSmJp27txp/v7+tnv3brftevToYePGjTOzU2MWSbZt2zbX46+88opFRka67kdFRdlTTz3lun/ixAlr0KCBKzQ70/jTU25jySlTppgk2759u2vZbbfdZj179jQzK9AYNTcjR460fv36ue4nJiZao0aN7OTJk65l119/vfXv3z/PNpKSklxj2PzkFhiZnXk8escdd1j37t3djuPpchsn5bXv00PG7ON0+n8Ge+rdu7cNGzbMdf9f//qXRUdHW2ZmZp7btGjRwl566SXX/cKGZo8++qhddtllbm3u2rXLdaIAUJbw80zACy666CK3n8B16NBBW7duVWZmpjZu3KgqVaqoffv2rsdr166t5s2ba+PGjZKk0aNH67HHHlOnTp2UlJSkn3/+uci1nH/++erRo4datmyp66+/XtOnT9fBgwdzrBMcHOxW75EjR7Rr1y5t27ZNaWlpuvTSS1WjRg3X7e2339b27dvd2jnvvPNc/46KipIk7d+/X5K0ceNGtz5n7ydbeHi4hgwZop49e6pXr1564YUX3H4GkJfT9xkZGSlJatmypduy7BqOHj2q7du3a/jw4W59eeyxx9z68v7776tTp06qV6+eatSoofHjxyslJcVtv7GxsQoJCXHrb/Z+Curxxx93q8NzH6f76aefNGnSJLf1b7nlFu3Zs0dpaWm6/vrrlZ6errPOOku33HKL5s6d6/rpZmGdflyy72e/NrNPm69Ro4ZatGghSRo0aJAWL16sP/74Q5I0e/ZsXXXVVa4LH2RmZurRRx9Vy5YtFR4erho1amjBggX59vdMfvrpJ23btk0hISGuesLDw3Xs2LEcr0sAAEpKfmO+bG3btnXb5qefftKsWbPcvtN79uyprKwsJScn57u/Sy+9VI0aNdJZZ52lwYMHa/bs2W4/VzxTTevXr1dmZqaaNWvmtv8lS5a4fX8GBwerSZMmrvunj3MOHz6sPXv2uI3rqlSp4tbPgow/c+M5rsueKuP0Zdl1FHSM+sorr+iCCy5QnTp1VKNGDU2bNi3HGKRFixby9/fPtb8FNWLECLc68lKQ8eiQIUO0bt06NW/eXKNHj9aXX35ZqFpOd/q4Lvs4ZY/rWrRo4dr/FVdcIenUuO6jjz5SRkaGpFPjugEDBsjP71RUcOTIEY0dO1bx8fGqWbOmatSooY0bNxZ7XLdo0SK35yMuLk6SGNehzKni6wIASDfffLN69uypzz//XF9++aWmTJmiZ599VnfccUeh2/L399dXX32lFStW6Msvv9RLL72khx56SCtXrnTNs5CfI0eOSJI+//xz1a9f3+2xgIAAt/tVq1Z1/Tt7sOY5J0J+Zs6cqdGjR2v+/Pl6//33NX78eH311Ve66KKL8twmt316LsuuIbsv06dPzxHgZQ+UvvvuOw0aNEgTJ05Uz549FRYWpjlz5ujZZ5/Nc7+e+ymoESNG6IYbbnDdj46OznPdI0eOaOLEierbt2+OxwIDA9WwYUNt3rxZX3/9tb766ivdfvvtevrpp7VkyZIctRbHG2+8ofT0dEn/9xy0a9dOTZo00Zw5c/TPf/5Tc+fOdZvb4+mnn9YLL7ygqVOnqmXLlqpevbruuuuufCdDdjgcOeZGOX1ukSNHjuiCCy7Q7Nmzc2xbp06d4nQRAACvql69utv9I0eO6LbbbnPNPXq6mJiYfNsKCQnRjz/+qMWLF+vLL7/Uww8/rEceeUSrV68u0FW6jxw5In9/f61Zs8YtJJLkFvTkNs7x/F7OT1HHn55juPzGWwUZo86ZM0djx47Vs88+qw4dOigkJERPP/20Vq5cmed+PfdTUJMmTdLYsWPPuF5BxqNt2rRRcnKyvvjiC3399de64YYblJCQkO88s0Uxb9481/gqKChIktSrVy+ZmT7//HO1a9dO3377rZ5//nnXNmPHjtVXX32lZ555RmeffbaCgoJ03XXX5Tmuyw7bTn/9eM4Xd+TIEfXq1UtPPvlkju2z/yMeKCsIzQAv8Pwi/v7779W0aVP5+/srPj5eJ0+e1MqVK9WxY0dJpyZK37x5s8455xzXNg0bNtSIESM0YsQIjRs3TtOnT9cdd9yhatWqSZLb/2CeicPhUKdOndSpUyc9/PDDatSokebOnasxY8ZIOvW/O+np6a4vy++//141atRQw4YNFR4eroCAAKWkpKhLly5Ffk7i4+O1cuVK3XTTTW7Pi6fWrVurdevWGjdunDp06KB3330339CsMCIjIxUdHa3ffvtNgwYNynWdFStWqFGjRnrooYdcy/K6GEFhVKtWLccxCw8PV3h4eI51q1atmmPdNm3aaPPmzTr77LPz3EdQUJB69eqlXr16aeTIkYqLi9P69evVpk2bXPefl++//z7HcWrdurUk5RiUZhs0aJBmz56tBg0ayM/PT1dddZXrseXLl6t37976xz/+IelUkLplyxa317unOnXquJ1puHXrVrf/SW/Tpo3ef/991a1bV6GhoQXqFwAA3pbfmC8vbdq00YYNG/L9Ts9PlSpVlJCQoISEBCUlJalmzZr65ptvXP+xll9NrVu3VmZmpvbv36+LL764SPsPCwtTVFSUVq5cqUsuuUSSdPLkSa1Zs0Zt2rRxrXem8WdxnXPOOWccoy5fvlwdO3bU7bff7lrmjTOXchtX1a1bV3Xr1s2xnuQ+bi/IeFSSQkND1b9/f/Xv31/XXXedLr/8cv31118KDw/PdayYl++//z7HcRo1apQkqVGjRjnWDwwMVN++fTV79mxt27ZNzZs3dzuuy5cv15AhQ3TttddKOhV45Xchpuz/zNyzZ49rPHn6RQGkU++Jjz76SLGxsapShUgCZRuvUMALUlJSNGbMGN1222368ccf9dJLL7nOVGratKl69+6tW265Rf/6178UEhKiBx54QPXr11fv3r0lSXfddZeuuOIKNWvWTAcPHtSiRYsUHx8v6dSXm8Ph0P/+9z9deeWVCgoKyvcU8JUrV2rhwoW67LLLVLduXa1cuVJ//vmnqz3p1BV6hg8frvHjx2vHjh1KSkrSqFGj5Ofnp5CQEI0dO1Z33323srKy1LlzZx0+fFjLly9XaGioEhMTC/Sc3HnnnRoyZIjatm2rTp06afbs2fr1119dp9wnJydr2rRpuuaaaxQdHa3Nmzdr69atbuGNN0ycOFGjR49WWFiYLr/8cmVkZOiHH37QwYMHNWbMGDVt2lQpKSmaM2eO2rVrp88//1xz584t9n5jY2N15MgRLVy40PVz2NN/Euu57sKFC9WpUycFBASoVq1aevjhh3X11VcrJiZG1113nfz8/PTTTz/pl19+0WOPPaZZs2YpMzNT7du3V3BwsP79738rKCjINRiKjY3V0qVLNWDAAAUEBCgiIiLPWj/88EO1bdtWnTt31uzZs7Vq1Sq9+eab+fZv0KBBeuSRRzR58mRdd911bmchNm3aVP/5z3+0YsUK1apVS88995z27duXb2jWvXt3vfzyy+rQoYMyMzN1//33u/0v8KBBg/T000+rd+/emjRpkho0aKCdO3fq448/1n333acGDRrkWy8AAN6Q35gvL/fff78uuugijRo1SjfffLOqV6+uDRs26KuvvtLLL7+c77b/+9//9Ntvv+mSSy5RrVq1NG/ePGVlZal58+YFqqlZs2YaNGiQbrrpJj377LNq3bq1/vzzTy1cuFDnnXee23965efOO+/UE088oaZNmyouLk7PPfec25W0CzL+LK6CjFGbNm2qt99+WwsWLFDjxo31zjvvaPXq1QX6tUV+YmNjlZycrHXr1qlBgwYKCQnJ8QsM6VSQFhQUpPnz56tBgwYKDAxUWFjYGcejzz33nKKiotS6dWv5+fnpww8/VL169VxnE+Y2VszLK6+8oqZNmyo+Pl7PP/+8Dh486LoSZl4GDRqkq6++Wr/++qvrPz2zNW3aVB9//LF69eolh8OhCRMm5HtWXlBQkC666CI98cQTaty4sfbv36/x48e7rTNy5EhNnz5dAwcO1H333afw8HBt27ZNc+bM0RtvvJFvCA2UOt9OqQaUf126dLHbb7/dRowYYaGhoVarVi178MEH3Sby/Ouvv2zw4MEWFhZmQUFB1rNnT9cVi8zMRo0aZU2aNLGAgACrU6eODR482O0qN5MmTbJ69eqZw+GwxMTEfOvZsGGD/b/27jw8yvLeH/8ngISwhC2CoRLigiR1w0pFoYpWLMeq1fac1qpHcamtFauWX11wQ9zQ1iqttXqkX0V7QG2rWKvWpVa0akUU1wqu4Pi1oB2rRiQiy/37w4v5moGwZZIJ4fW6rrku55l57uczuQfnk3eeee5Ro0alzTffPJWWlqbtttuuwYU6R48enQ4++OB03nnnpd69e6euXbum448/Pn3yySe556xYsSJNmjQpDRo0KG222WZp8803T6NGjUoPP/xwSmn1ixM888wzKSLSvHnzctsuvvjiVFFRkbp27ZpGjx6dTj/99NxFVBcuXJgOOeSQVFlZmTp27JgGDBiQzjvvvEYvOrq6i4quro7VXYB/6tSpafDgwaljx46pZ8+eaa+99kq333577vHTTjst97M49NBD05VXXtlgjNVd/PXKK69MAwYMWG2tK51wwgmpd+/eKSLS+PHjG33enXfembbddtvUoUOHBmPee++9adiwYamsrCyVl5en3XbbLbey0/Tp09PQoUNTeXl56tKlS9p9990bXEz373//e9ppp51SaWlpWtP/6iMiXX311Wm//fZLpaWlqbq6usEKU2uy2267pYhIf/3rXxtsf++999LBBx+cunbtmvr06ZPOOeecdNRRR+UuFpzSqgsBvP322+lrX/ta6tKlSxo4cGC65557GiwEkFJKCxYsSEcddVSqqKhIpaWlaeutt07HH398+vDDD9epXgBoinXp+RpbiOfJJ59M++23X+ratWvq0qVL2mmnndLFF1+81mP+7W9/SyNGjEg9e/ZMZWVlaaeddmrwOb0uNa1cUbK6ujptttlmqbKyMn3zm99Mzz//fEpp9b3T9OnTG/QPS5cuTaecckoqLy9PPXr0SGPHjm3w2b62/jPfuvZw+T3Y2nrUTz75JB199NGpe/fuqUePHumHP/xhOvPMMxuMsbIX/rxTTjkljRgxotF6P/nkk/Sf//mfqUePHrmVLBszefLk1L9//9SuXbsGY66pH73uuuvS4MGDU5cuXVJ5eXnad9990+zZs3P7NtYrft7KXnnatGlpt912Sx07dkxf/OIXV+nTVmf58uWpsrJylYUYVo67zz77pLKystS/f//0q1/9apU+Lv99/9JLL6U99tgjlZWVpcGDB6f777+/wUIAKX22WNo3v/nN1KNHj1RWVpZqamrSqaee2uhiCFAsJSmtx5fVgVXsvffeMXjw4Jg0aVKxS1knRx99dHzwwQdxxx13FLsUWoGSkpKYPn16HHLIIcUuBQBatdbY87XGmiiO+fPnx1ZbbRXPPPNMDB48uNjlQJth9UwAAAAAyCM0g41MJpNpsDxz/q0pyz8DANBy/va3v62xrwOguHw9EzYyy5YtW+OKNVahAQDYONTX18fbb7/d6OMbuuImAIUhNAMAAACAPL6eCQAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkKdDsQtobitWrIh//vOf0a1btygpKSl2OQDARiClFB999FH069cv2rXzN8bWSp8HAKyv9enz2nxo9s9//jP69+9f7DIAgI3QW2+9FVtuuWWxy6AR+jwAYEOtS5/X5kOzbt26RcRnP4zy8vIiVwMAbAzq6uqif//+uT6C1kmfBwCsr/Xp89p8aLbyVP3y8nLNFACwXnzlr3XT5wEAG2pd+jwX6QAAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAK7pFHHomDDjoo+vXrFyUlJXHHHXfkHlu6dGmcccYZseOOO0aXLl2iX79+cdRRR8U///nP4hUMAJBHaAYAQMF9/PHHsfPOO8fVV1+9ymOLFy+O2bNnx7nnnhuzZ8+O22+/PV5++eX4xje+UYRKAQBWr0OxCwAAoO3Zf//9Y//991/tY927d48HHnigwbZf/epXsdtuu0Umk4mqqqqWKBEAYI2EZsBGIZPJRDabLdh4FRUVfikDaEU+/PDDKCkpiR49ejT6nCVLlsSSJUty9+vq6lqgMqC56fOA1kpoBrR6mUwmampqo75+ccHGLCvrHHPnztFQAbQCn3zySZxxxhlx2GGHRXl5eaPPmzhxYkyYMKEFKwOamz4PaM2EZkCrl81mo75+cQw9dnyUV1Y3eby6BfNj5vUTIpvNaqYAimzp0qXxne98J1JKcc0116zxuePGjYuxY8fm7tfV1UX//v2bu0SgGenzgNZMaAZsNMorq6NX1aBilwFAgawMzN58883461//usazzCIiSktLo7S0tIWqA1qSPg9ojYRmAAC0uJWB2auvvhoPPfRQ9O7du9glAQA0IDQDAKDgFi1aFK+99lru/rx58+LZZ5+NXr16RWVlZfzXf/1XzJ49O+66665Yvnx5LFy4MCIievXqFR07dixW2QAAOUIzAAAK7qmnnop99tknd3/ltchGjx4d559/ftx5550RETF48OAG+z300EOx9957t1SZAACNEpoBAFBwe++9d6SUGn18TY8BALQG7YpdAAAAAAC0NkIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPB2KXQAAAAA0p0wmE9lstiBjVVRURFVVVUHGAlo3oRkAAABtViaTiZqa2qivX1yQ8crKOsfcuXMEZ7AJEJoBAADQZmWz2aivXxxDjx0f5ZXVTRqrbsH8mHn9hMhms0Iz2AQIzQAAAGjzyiuro1fVoGKXAWxELAQAAAAAAHmEZgAAAACQR2gGAAAAAHmEZgAAAACQx0IAQEFkMpnIZrMFGauiosJqRAAAABSV0AxoskwmEzU1tVFfv7gg45WVdY65c+cIzgAAACgaoRnQZNlsNurrF8fQY8dHeWV1k8aqWzA/Zl4/IbLZrNAMAACAohGaAQVTXlkdvaoGFbsMAAAAaDILAQAAAABAHqEZAAAAAOQRmgEAAABAHqEZAAAAAOQRmgEAAABAnlYdmi1fvjzOPffc2GqrraKsrCy22WabuPDCCyOlVOzSAAAAAGjDOhS7gDW57LLL4pprrokbb7wxtt9++3jqqafimGOOie7du8fJJ59c7PJgo5LJZCKbzRZkrIqKiqiqqirIWK2JnxEAAAArterQ7PHHH4+DDz44DjjggIiIqK6ujptvvjmefPLJIlcGG5dMJhM1NbVRX7+4IOOVlXWOuXPntKlQyM8IAACAz2vVodmwYcPiuuuui1deeSW22267eO655+LRRx+NK664otF9lixZEkuWLMndr6ura4lSoVXLZrNRX784hh47Psorq5s0Vt2C+THz+gmRzWbbVCDkZwQAAMDnterQ7Mwzz4y6urqoqamJ9u3bx/Lly+Piiy+OI444otF9Jk6cGBMmTGjBKmHjUV5ZHb2qBhW7jFbNzwgAAICIVr4QwO9+97uYOnVqTJs2LWbPnh033nhjXH755XHjjTc2us+4cePiww8/zN3eeuutFqwYAAAAgLagVZ9pdtppp8WZZ54Z3/3udyMiYscdd4w333wzJk6cGKNHj17tPqWlpVFaWtqSZQIAAADQxrTqM80WL14c7do1LLF9+/axYsWKIlUEAAAAwKagVZ9pdtBBB8XFF18cVVVVsf3228czzzwTV1xxRRx77LHFLg0AAAAi4rOV2LPZbEHGqqiosJgUtBKtOjS76qqr4txzz40TTzwx3n333ejXr1/84Ac/iPPOO6/YpQEAAEBkMpmoqamN+vrFBRmvrKxzzJ07R3AGrUCrDs26desWkyZNikmTJhW7FAAAAFhFNpuN+vrFMfTY8VFeWd2kseoWzI+Z10+IbDYrNINWoFWHZgAAbJweeeSR+NnPfhZPP/10LFiwIKZPnx6HHHJI7vGUUowfPz4mT54cH3zwQQwfPjyuueaaGDhwYPGKBmiC8srq6FU1qNhlAAXUqhcCAABg4/Txxx/HzjvvHFdfffVqH//pT38av/zlL+Paa6+NmTNnRpcuXWLUqFHxySeftHClAACr50wzAAAKbv/994/9999/tY+llGLSpElxzjnnxMEHHxwRETfddFP07ds37rjjjvjud7/bkqUCAKyW0AxagUKuthNhxR0AWrd58+bFwoULY+TIkblt3bt3j6FDh8bf//73RkOzJUuWxJIlS3L36+rqmr1WAGDTJTSDIiv0ajsRVtwBoHVbuHBhRET07du3wfa+ffvmHludiRMnxoQJE5q1NgCAlYRmUGSFXG0nwoo7ALRd48aNi7Fjx+bu19XVRf/+/YtYEQDQlgnNoJWw2g4Am4otttgiIiLeeeedqKyszG1/5513YvDgwY3uV1paGqWlpc1dHgBARFg9EwCAFrbVVlvFFltsEQ8++GBuW11dXcycOTP22GOPIlYGAPD/ONMMAICCW7RoUbz22mu5+/PmzYtnn302evXqFVVVVXHqqafGRRddFAMHDoytttoqzj333OjXr18ccsghxSsaAOBzhGYAABTcU089Ffvss0/u/sprkY0ePTqmTJkSp59+enz88cfx/e9/Pz744IP4yle+Evfee2906tSpWCUDADQgNAMAoOD23nvvSCk1+nhJSUlccMEFccEFF7RgVQAA6841zQAAAAAgjzPNANqITCYT2Wy2IGNVVFREVVVVQcYCAADYGAnNANqATCYTNTW1UV+/uCDjlZV1jrlz5wjOAACATZbQDKANyGazUV+/OIYeOz7KK6ubNFbdgvkx8/oJkc1mhWYAAMAmS2gG0IaUV1ZHr6pBxS4DAABgo2chAAAAAADIIzQDAAAAgDxCMwAAAADIIzQDAAAAgDwWAgBoIZlMJrLZbEHGqqiosLIlAABAMxKaAbSATCYTNTW1UV+/uCDjlZV1jrlz5wjOAAAAmonQDKAFZLPZqK9fHEOPHR/lldVNGqtuwfyYef2EyGazQjMAAIBmIjQDaEHlldXRq2pQscsAAABgLSwEAAAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5hGYAAAAAkEdoBgAAAAB5OhS7AAAAAKBxmUwmstlswcarqKiIqqqqgo0HbZXQDAAAAFqpTCYTNTW1UV+/uGBjlpV1jrlz5wjOYC2EZgAAANBKZbPZqK9fHEOPHR/lldVNHq9uwfyYef2EyGazQjNYC6EZAAAAtHLlldXRq2pQscuATYqFAAAAAAAgj9AMAAAAAPIIzQAAAAAgj9AMAAAAAPIIzQAAAAAgj9UzAVgnmUwmstlswcarqKiwzDkAbAT0AMCmSmgGwFplMpmoqamN+vrFBRuzrKxzzJ07R9MMAK2YHgDYlAnNAFirbDYb9fWLY+ix46O8srrJ49UtmB8zr58Q2WxWwwwArZgeANiUCc0AWGflldXRq2pQscsAAFqYHgDYFFkIAAAAAADyCM0AAAAAII/QDAAAAADyCM0AAAAAII/QDAAAAADyCM0AAAAAII/QDAAAAADyCM0AAAAAII/QDAAAAADydCh2AQAAAEDxZDKZyGazBRuvoqIiqqqqCjYeFIvQDAAAADZRmUwmampqo75+ccHGLCvrHHPnzhGcsdETmgEAAMAmKpvNRn394hh67Pgor6xu8nh1C+bHzOsnRDabFZqx0ROaAQAAwCauvLI6elUNKnYZ0KoIzWAd+I4/AAAAbFqEZrAWvuMPAAAAmx6hGayF7/gDAADApkdoBuvId/wBAABg09Gu2AUAAAAAQGsjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAABa3PLly+Pcc8+NrbbaKsrKymKbbbaJCy+8MFJKxS4NACAirJ4JAEARXHbZZXHNNdfEjTfeGNtvv3089dRTccwxx0T37t3j5JNPLnZ5AABCMwAAWt7jjz8eBx98cBxwwAEREVFdXR0333xzPPnkk0WuDADgM0IzAFqNTCYT2Wy2IGNVVFREVVVVQcYCCm/YsGFx3XXXxSuvvBLbbbddPPfcc/Hoo4/GFVdc0eg+S5YsiSVLluTu19XVtUSpAMAmSmgGQKuQyWSipqY26usXF2S8srLOMXfuHMEZtFJnnnlm1NXVRU1NTbRv3z6WL18eF198cRxxxBGN7jNx4sSYMGFCC1YJAGzKhGYAtArZbDbq6xfH0GPHR3lldZPGqlswP2ZePyGy2azQDFqp3/3udzF16tSYNm1abL/99vHss8/GqaeeGv369YvRo0evdp9x48bF2LFjc/fr6uqif//+LVUyALCJEZoB0KqUV1ZHr6pBxS4DaGannXZanHnmmfHd7343IiJ23HHHePPNN2PixImNhmalpaVRWlrakmUCAJuwdsUuAACATc/ixYujXbuGrWj79u1jxYoVRaoIAKChVh+avf322/Hf//3f0bt37ygrK4sdd9wxnnrqqWKXBQBAExx00EFx8cUXx9133x3z58+P6dOnxxVXXBHf/OY3i10aAEBEtPKvZ77//vsxfPjw2GeffeLPf/5zbL755vHqq69Gz549i10aAABNcNVVV8W5554bJ554Yrz77rvRr1+/+MEPfhDnnXdesUsDAIiIVh6aXXbZZdG/f/+44YYbctu22mqrIlYEAEAhdOvWLSZNmhSTJk0qdikAAKvVqr+eeeedd8aQIUPi29/+dvTp0yd22WWXmDx5crHLAgAAAKCNa9Wh2RtvvBHXXHNNDBw4MO6777744Q9/GCeffHLceOONje6zZMmSqKura3ADAAAAgPXRqr+euWLFihgyZEhccsklERGxyy67xIsvvhjXXntto0uRT5w4MSZMmNCSZQIAAADQxrTqM80qKyvji1/8YoNttbW1kclkGt1n3Lhx8eGHH+Zub731VnOXCQAAAEAb06rPNBs+fHi8/PLLDba98sorMWDAgEb3KS0tjdLS0uYuDQAAAIA2rFWfafbjH/84nnjiibjkkkvitddei2nTpsV1110XY8aMKXZpAAAAALRhrTo0+/KXvxzTp0+Pm2++OXbYYYe48MILY9KkSXHEEUcUuzQAAAAA2rBW/fXMiIgDDzwwDjzwwGKXAQAAAMAmpFWfaQYAAAAAxSA0AwAAAIA8QjMAAAAAyCM0AwAAAIA8QjMAAAAAyCM0AwAAAIA8QjMAAAAAyCM0AwAAAIA8QjMAAAAAyCM0AwAAAIA8QjMAAAAAyCM0AwAAAIA8HYpdAAAAABsuk8lENpstyFgVFRVRVVVVkLEANnZCMwAAgI1UJpOJmpraqK9fXJDxyso6x9y5cwRnACE0AwAA2Ghls9mor18cQ48dH+WV1U0aq27B/Jh5/YTIZrNCM4AQmgEAAGz0yiuro1fVoGKXAdCmWAgAAAAAAPIIzQAAAAAgj9AMAAAAAPI0W2i29dZbx3vvvbfK9g8++CC23nrr5josAABNoIcDAPhMs4Vm8+fPj+XLl6+yfcmSJfH2228312EBAGgCPRwAwGcKvnrmnXfemfvv++67L7p37567v3z58njwwQejurq60IcFAKAJ9HAAAA0VPDQ75JBDIiKipKQkRo8e3eCxzTbbLKqrq+PnP/95oQ8LAEAT6OEAABoqeGi2YsWKiIjYaqutYtasWVFRUVHoQwAAUGB6OACAhgoemq00b9685hoaAIBmoocDAPhMs4VmEREPPvhgPPjgg/Huu+/m/nq50vXXX9+chwYAYAPp4QAAmjE0mzBhQlxwwQUxZMiQqKysjJKSkuY6FAAABaKHAwD4TLOFZtdee21MmTIljjzyyOY6BAAABaaHAwD4TLvmGvjTTz+NYcOGNdfwAAA0Az0cAMBnmi00+973vhfTpk1rruEBAGgGejgAgM8029czP/nkk7juuuviL3/5S+y0006x2WabNXj8iiuuaK5DAwCwgfRwAACfabbQ7Pnnn4/BgwdHRMSLL77Y4DEXlAUAaJ30cAAAn2m20Oyhhx5qrqEBAGgmejgAmkMmk4lsNluQsSoqKqKqqqogY8GaNFtottJrr70Wr7/+euy1115RVlYWKSV/pQQAaOX0cAAUSiaTiZqa2qivX1yQ8crKOsfcuXMEZzS7ZgvN3nvvvfjOd74TDz30UJSUlMSrr74aW2+9dRx33HHRs2fP+PnPf95chwYAYAPp4QAotGw2G/X1i2PoseOjvLK6SWPVLZgfM6+fENlsVmhGs2u20OzHP/5xbLbZZpHJZKK2tja3/dBDD42xY8dquAAAWiE9HADNpbyyOnpVDSp2GbDOmi00u//+++O+++6LLbfcssH2gQMHxptvvtlchwUAoAn0cAAAn2nXXAN//PHH0blz51W2//vf/47S0tLmOiwAAE2ghwMA+EyzhWZ77rln3HTTTbn7JSUlsWLFivjpT38a++yzT3MdFgCAJtDDAQB8ptm+nvnTn/409t1333jqqafi008/jdNPPz3+8Y9/xL///e947LHHmuuwAAA0gR4OAOAzzXam2Q477BCvvPJKfOUrX4mDDz44Pv744/jWt74VzzzzTGyzzTbNdVgAAJpADwcA8JlmO9MsIqJ79+5x9tlnN+chAAAoMD0cAEAznml2ww03xO9///tVtv/+97+PG2+8sbkOCwBAE+jhAAA+02yh2cSJE6OiomKV7X369IlLLrmkuQ4LAEAT6OEAAD7TbKFZJpOJrbbaapXtAwYMiEwm01yHBQCgCfRwAACfabbQrE+fPvH888+vsv25556L3r17N9dhAQBoAj0cAMBnmm0hgMMOOyxOPvnk6NatW+y1114REfHwww/HKaecEt/97neb67AAADSBHg4KL5PJRDabLchYFRUVUVVVVZCxAFizZgvNLrzwwpg/f37su+++0aHDZ4dZsWJFHHXUUa6HAQDQSunhoLAymUzU1NRGff3igoxXVtY55s6dIzgDaAHNEpqllGLhwoUxZcqUuOiii+LZZ5+NsrKy2HHHHWPAgAHNcUgAAJqopXu4t99+O84444z485//HIsXL45tt902brjhhhgyZEjBjwXFks1mo75+cQw9dnyUV1Y3aay6BfNj5vUTIpvNCs0AWkCzhWbbbrtt/OMf/4iBAwfGwIEDm+MwAAAUUEv2cO+//34MHz489tlnn/jzn/8cm2++ebz66qvRs2fPZjsmFFN5ZXX0qhpU7DIAWA/NEpq1a9cuBg4cGO+9957ADABgI9GSPdxll10W/fv3jxtuuCG3bXWrdgIAFEuzrZ556aWXxmmnnRYvvvhicx0CAIACa6ke7s4774whQ4bEt7/97ejTp0/ssssuMXny5DXus2TJkqirq2twAwBoLs22EMBRRx0Vixcvjp133jk6duwYZWVlDR7/97//3VyHBgBgA7VUD/fGG2/ENddcE2PHjo2zzjorZs2aFSeffHJ07NgxRo8evdp9Jk6cGBMmTCjI8QEA1qbZQrNJkyY119AAADSTlurhVqxYEUOGDMmtyLnLLrvEiy++GNdee22jodm4ceNi7Nixuft1dXXRv3//FqkXANj0NFto1lizAwBA69VSPVxlZWV88YtfbLCttrY2brvttkb3KS0tjdLS0uYuDQAgIprxmmYREa+//nqcc845cdhhh8W7774bERF//vOf4x//+EdzHhYAgCZoiR5u+PDh8fLLLzfY9sorr8SAAQMKdgwAgKZottDs4Ycfjh133DFmzpwZt99+eyxatCgiIp577rkYP358cx0WAIAmaKke7sc//nE88cQTcckll8Rrr70W06ZNi+uuuy7GjBlTsGMAADRFs4VmZ555Zlx00UXxwAMPRMeOHXPbv/rVr8YTTzzRXIcFAKAJWqqH+/KXvxzTp0+Pm2++OXbYYYe48MILY9KkSXHEEUcU7BgAAE3RbNc0e+GFF2LatGmrbO/Tp09ks9nmOiwAAE3Qkj3cgQceGAceeGBBxwQAKJRmO9OsR48esWDBglW2P/PMM/GFL3yhuQ4LAEAT6OEAAD7TbKHZd7/73TjjjDNi4cKFUVJSEitWrIjHHnssfvKTn8RRRx3VXIcFAKAJ9HAAAJ9pttDskksuidra2qiqqopFixbFF7/4xdhrr71i2LBhcc455zTXYQEAaAI9HADAZwp+TbMVK1bEz372s7jzzjvj008/jSOPPDL+8z//MxYtWhS77LJLDBw4sNCHBACgifRwAAANFTw0u/jii+P888+PkSNHRllZWUybNi1SSnH99dcX+lAAABSIHg4AoKGCfz3zpptuil//+tdx3333xR133BF/+tOfYurUqbFixYpCHwoAgALRwwEANFTw0CyTycTXv/713P2RI0dGSUlJ/POf/yz0oQAAKBA9HABAQwX/euayZcuiU6dODbZtttlmsXTp0kIfCgDWSyaTiWw2W5CxKioqoqqqqiBjQWughwMAaKjgoVlKKY4++ugoLS3Nbfvkk0/ihBNOiC5duuS23X777YU+NAA0KpPJRE1NbdTXLy7IeGVlnWPu3DmCM9oMPRwAQEMFD81Gjx69yrb//u//LvRhAGC9ZLPZqK9fHEOPHR/lldVNGqtuwfyYef2EyGazQjPaDD0cAEBDBQ/NbrjhhkIPCQAFU15ZHb2qBhW7DGh19HAAAA0VfCEAAAAAANjYCc0AAAAAII/QDAAAAADyCM0AAAAAII/QDAAAAADybFSh2aWXXholJSVx6qmnFrsUAAAAANqwjSY0mzVrVvzP//xP7LTTTsUuBQAAAIA2bqMIzRYtWhRHHHFETJ48OXr27FnscgAAAABo4zaK0GzMmDFxwAEHxMiRI4tdCgAAAACbgA7FLmBtbrnllpg9e3bMmjVrnZ6/ZMmSWLJkSe5+XV1dc5UGAAAAQBvVqs80e+utt+KUU06JqVOnRqdOndZpn4kTJ0b37t1zt/79+zdzlQAAAAC0Na06NHv66afj3XffjS996UvRoUOH6NChQzz88MPxy1/+Mjp06BDLly9fZZ9x48bFhx9+mLu99dZbRagcAAAAgI1Zq/565r777hsvvPBCg23HHHNM1NTUxBlnnBHt27dfZZ/S0tIoLS1tqRIBAAAAaINadWjWrVu32GGHHRps69KlS/Tu3XuV7QAAAABQKK3665kAAAAAUAyt+kyz1ZkxY0axSwCA1cpkMpHNZgs2XkVFRVRVVRVsPAAAYN1tdKEZALRGmUwmampqo75+ccHGLCvrHHPnzhGcAQBAEQjNAKAAstls1NcvjqHHjo/yyuomj1e3YH7MvH5CZLNZoRkAwFoU8ox/Z/uzktAMAAqovLI6elUNKnYZAACbjEKf8e9sf1YSmgEAAAAbrUKe8e9sfz5PaAYAAABs9JzxT6G1K3YBAAAAANDaCM0AAAAAII/QDAAAAADyCM0AAAAAII/QDAAAAADyCM0AAAAAII/QDAAAAADydCh2AVAImUwmstlsQcaqqKiIqqqqgowFAAAAbJyEZmz0MplM1NTURn394oKMV1bWOebOnSM4AwAAgE2Y0IyNXjabjfr6xTH02PFRXlndpLHqFsyPmddPiGw2KzQDAACATZjQjDajvLI6elUNKnYZAAAAQBsgNAMAADZZhbw2boTr4wK0JUIzAABgk1Toa+NGuD4uQFsiNAMAADZJhbw2boTr4wK0NUIzAABgk+bauACsTrtiFwAAAAAArY3QDAAAAADyCM0AAAAAII/QDAAAAADyCM0AAAAAII/QDAAAAADyCM0AAAAAII/QDAAAAADyCM0AACi6Sy+9NEpKSuLUU08tdikAABEhNAMAoMhmzZoV//M//xM77bRTsUsBAMgRmgEAUDSLFi2KI444IiZPnhw9e/YsdjkAADlCMwAAimbMmDFxwAEHxMiRI9f63CVLlkRdXV2DGwBAc+lQ7AIAANg03XLLLTF79uyYNWvWOj1/4sSJMWHChGauCgDgM840AwCgxb311ltxyimnxNSpU6NTp07rtM+4cePiww8/zN3eeuutZq4SANiUOdMMAIAW9/TTT8e7774bX/rSl3Lbli9fHo888kj86le/iiVLlkT79u0b7FNaWhqlpaUtXSoAsIkSmgEA0OL23XffeOGFFxpsO+aYY6KmpibOOOOMVQIzAICWJjQDAKDFdevWLXbYYYcG27p06RK9e/deZTsAQDG4phkAAAAA5HGmGS0ik8lENpstyFgVFRVRVVVVkLEAgNZjxowZxS4BACBHaEazy2QyUVNTG/X1iwsyXllZ55g7d47gDAAAAGg2QjOaXTabjfr6xTH02PFRXlndpLHqFsyPmddPiGw2KzQDAAAAmo3QjBZTXlkdvaoGFbsMAAAAgLWyEAAAAAAA5BGaAQAAAEAeX88EgI1EIVcijrAaMQAArInQDAA2AoVeiTjCasQAALAmQjMA2AgUciXiCKsRAwDA2gjNAGAjYiViAABoGRYCAAAAAIA8QjMAAAAAyCM0AwAAAIA8QjMAAAAAyCM0AwAAAIA8QjMAAAAAyCM0AwAAAIA8QjMAAAAAyCM0AwAAAIA8QjMAAAAAyCM0AwAAAIA8QjMAAAAAyCM0AwAAAIA8QjMAAAAAyCM0AwAAAIA8QjMAAAAAyCM0AwAAAIA8HYpdAAAAAEBrlslkIpvNFmy8ioqKqKqqKth4NA+hGQAAAEAjMplM1NTURn394oKNWVbWOebOnSM4a+WEZgAAAACNyGazUV+/OIYeOz7KK6ubPF7dgvkx8/oJkc1mhWatnNAMAAAAYC3KK6ujV9WgYpdBC7IQAAAAAADkEZoBAAAAQB6hGQAAAADkcU0zACCnkMupW0odAICNmdAMAIiIwi+nbil1AAA2ZkIzACAiCrucuqXUAQDY2LXq0GzixIlx++23x9y5c6OsrCyGDRsWl112WQwaZIlXAGgullMHAIBWvhDAww8/HGPGjIknnngiHnjggVi6dGl87Wtfi48//rjYpQEAAADQhrXqM83uvffeBvenTJkSffr0iaeffjr22muvIlUFAAAAQFvXqs80y/fhhx9GRESvXr2KXAkAAAAAbVmrPtPs81asWBGnnnpqDB8+PHbYYYdGn7dkyZJYsmRJ7n5dXV1LlAcAAABAG7LRnGk2ZsyYePHFF+OWW25Z4/MmTpwY3bt3z9369+/fQhUCAAAA0FZsFGeanXTSSXHXXXfFI488EltuueUanztu3LgYO3Zs7n5dXZ3gbC0ymUxks9mCjVdRURFVVVUFGw8AAACgpbXq0CylFD/60Y9i+vTpMWPGjNhqq63Wuk9paWmUlpa2QHVtQyaTiZqa2qivX1ywMcvKOsfcuXMEZwAAAMBGq1WHZmPGjIlp06bFH//4x+jWrVssXLgwIiK6d+8eZWVlRa6ubchms1FfvziGHjs+yiurmzxe3YL5MfP6CZHNZoVmAAAAwEarVYdm11xzTURE7L333g2233DDDXH00Ue3fEFtWHlldfSqGlTsMgAAAABahVYdmqWUil0CAAAAAJugjWb1TAAAAABoKUIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPEIzAAAAAMgjNAMAAACAPB2KXQAAAADApiyTyUQ2my3YeBUVFVFVVVWw8TZVQjMAAACAIslkMlFTUxv19YsLNmZZWeeYO3eO4KyJhGYAABTFxIkT4/bbb4+5c+dGWVlZDBs2LC677LIYNGhQsUsDgBaTzWajvn5xDD12fJRXVjd5vLoF82Pm9RMim80KzZpIaAYAQFE8/PDDMWbMmPjyl78cy5Yti7POOiu+9rWvxUsvvRRdunQpdnm0EoX8ypKvKwGtWXlldfSq8oej1kRoBgBAUdx7770N7k+ZMiX69OkTTz/9dOy1115FqorWpNBfWfJ1JQDWh9AMAIBW4cMPP4yIiF69eq328SVLlsSSJUty9+vq6lqkLoqnkF9Z8nUlANaX0AwAgKJbsWJFnHrqqTF8+PDYYYcdVvuciRMnxoQJE1q4MloDX1kCoBjaFbsAAAAYM2ZMvPjii3HLLbc0+pxx48bFhx9+mLu99dZbLVghALCpcaYZAABFddJJJ8Vdd90VjzzySGy55ZaNPq+0tDRKS0tbsDIAYFMmNAMAoChSSvGjH/0opk+fHjNmzIitttqq2CUBAOQIzQAAKIoxY8bEtGnT4o9//GN069YtFi5cGBER3bt3j7KysiJXBwBs6lzTDACAorjmmmviww8/jL333jsqKytzt1tvvbXYpQEAONMMAIDiSCkVuwQAgEYJzQAAgA2WyWQim80WZKyKioqoqqoqyFgA0FRCMwAAYINkMpmoqamN+vrFBRmvrKxzzJ07R3AGQKsgNAMAADZINpuN+vrFMfTY8VFeWd2kseoWzI+Z10+IbDYrNAOgVRCaAQAATVJeWR29qgYVuwwAKCirZwIAAABAHqEZAAAAAOQRmgEAAABAHqEZAAAAAOQRmgEAAABAHqEZAAAAAOQRmgEAAABAng7FLgAA2HRkMpnIZrMFGauioiKqqqoKMhYAAOQTmgEALSKTyURNTW3U1y8uyHhlZZ1j7tw5gjMAAJqF0AwAaBHZbDbq6xfH0GPHR3lldZPGqlswP2ZePyGy2azQDACAZiE0AwBaVHlldfSqGlTsMgAAYI0sBAAAAAAAeYRmAAAAAJBHaAYAAAAAeVzTbCOQyWQim80WbLyKigoXTQYAAABYA6FZK5fJZKKmpjbq6xcXbMyyss4xd+4cwRkAAABAI4RmrVw2m436+sUx9NjxUV5Z3eTx6hbMj5nXT4hsNis0AwAAAGiE0GwjUV5ZHb2qBhW7DAAAAIBNgoUAAAAAACCP0AwAAAAA8gjNAAAAACCP0AwAAAAA8gjNAAAAACCP0AwAAAAA8gjNAAAAACBPh2IXAAAAAEDzymQykc1mCzJWRUVFVFVVFWSs1kxoBgAAANCGZTKZqKmpjfr6xQUZr6ysc8ydO6fNB2dCMwAAAIA2LJvNRn394hh67Pgor6xu0lh1C+bHzOsnRDabFZoBAAAAsPErr6yOXlWDil3GRsNCAAAAAACQR2gGAAAAAHl8PbMArEABAAAA0LYIzZrIChQAAAAAbY/QrImsQAEAQGvmWxEAsGGEZgViBQoAAFob34oAgA0nNAMAgDbKtyIAYMMJzQAAoI3zrQgAWH/til0AAAAAALQ2QjMAAAAAyCM0AwAAAIA8QjMAAAAAyGMhAAAAAACaJJPJRDabLchYFRUVrWKlZqEZAAAAABssk8lETU1t1NcvLsh4ZWWdY+7cOUUPzoRmAAAAAGywbDYb9fWLY+ix46O8srpJY9UtmB8zr58Q2WxWaAYAAADAxq+8sjp6VQ0qdhkFIzQDAIAiKeT1XyJazzVgAKAtEJoBAEARFPr6LxGt5xowANAWCM0AAKAICnn9l4jWdQ0YAGgLhGYAAFBEbe36LwDQVrQrdgEAAAAA0NoIzQAAAAAgj9AMAAAAAPJsFKHZ1VdfHdXV1dGpU6cYOnRoPPnkk8UuCQCAAtDnAQCtVatfCODWW2+NsWPHxrXXXhtDhw6NSZMmxahRo+Lll1+OPn36FLs8AAA20MbQ52UymchmswUZq6KiwqqWALARafWh2RVXXBHHH398HHPMMRERce2118bdd98d119/fZx55plFrg4AgA3V2vu8TCYTNTW1UV+/uCDjlZV1jrlz5wjOAGAj0apDs08//TSefvrpGDduXG5bu3btYuTIkfH3v/99tfssWbIklixZkrv/4YcfRkREXV1ds9S4aNGiiIj495svx7Il9U0aq25hJjfmynoLOf7qjtHc46/870IdY2Mff3XHMActO35LHMP4xT/Gxj7+6o5hDtY+fiGtHDOlVPCx+czG0OfNnz8/6usXx6D9Do/Ovfo2aazF/34nXn5gWsyfPz969OgREf7f2tbGb4ljbOzjr+4Y5qBlx1/dMcxBy46/umOYg7WPX0jr1eelVuztt99OEZEef/zxBttPO+20tNtuu612n/Hjx6eIcHNzc3Nzc3Nr8u2tt95qiZZnk6TPc3Nzc3NzcyvmbV36vFZ9ptmGGDduXIwdOzZ3f8WKFfHvf/87evfuHSUlJQ2eW1dXF/3794+33norysvLW7pUVsOctE7mpXUyL62TeWl9NmROUkrx0UcfRb9+/Zq5OtbH+vR5rBv/z2p7zGnbY07bHnNaXOvT57Xq0KyioiLat28f77zzToPt77zzTmyxxRar3ae0tDRKS0sbbFt5CnxjysvLvVFbGXPSOpmX1sm8tE7mpfVZ3znp3r17M1ZDS/V5rBv/z2p7zGnbY07bHnNaPOva57Vr5jqapGPHjrHrrrvGgw8+mNu2YsWKePDBB2OPPfYoYmUAADSFPg8AaO1a9ZlmERFjx46N0aNHx5AhQ2K33XaLSZMmxccff5xbZQkAgI2TPg8AaM1afWh26KGHxr/+9a8477zzYuHChTF48OC49957o2/fpq1gFPHZKf7jx49f5TR/isectE7mpXUyL62TeWl9zEnr1Zx9HuvGv4+2x5y2Pea07TGnG4+SlKylDgAAAACf16qvaQYAAAAAxSA0AwAAAIA8QjMAAAAAyCM0AwAAAIA8bT40u/rqq6O6ujo6deoUQ4cOjSeffHKNz//9738fNTU10alTp9hxxx3jnnvuaaFKNx3rMyeTJ0+OPffcM3r27Bk9e/aMkSNHrnUO2TDr+29lpVtuuSVKSkrikEMOad4CN1HrOy8ffPBBjBkzJiorK6O0tDS22247/x9rBus7L5MmTYpBgwZFWVlZ9O/fP3784x/HJ5980kLVtn2PPPJIHHTQQdGvX78oKSmJO+64Y637zJgxI770pS9FaWlpbLvttjFlypRmrxOKRe/V9ujb2h49X9ujX2wjUht2yy23pI4dO6brr78+/eMf/0jHH3986tGjR3rnnXdW+/zHHnsstW/fPv30pz9NL730UjrnnHPSZpttll544YUWrrztWt85Ofzww9PVV1+dnnnmmTRnzpx09NFHp+7du6f/+3//bwtX3rat77ysNG/evPSFL3wh7bnnnunggw9umWI3Ies7L0uWLElDhgxJX//619Ojjz6a5s2bl2bMmJGeffbZFq68bVvfeZk6dWoqLS1NU6dOTfPmzUv33XdfqqysTD/+8Y9buPK265577klnn312uv3221NEpOnTp6/x+W+88Ubq3LlzGjt2bHrppZfSVVddldq3b5/uvffelikYWpDeq+3Rt7U9er62R7/YdrTp0Gy33XZLY8aMyd1fvnx56tevX5o4ceJqn/+d73wnHXDAAQ22DR06NP3gBz9o1jo3Jes7J/mWLVuWunXrlm688cbmKnGTtCHzsmzZsjRs2LD0m9/8Jo0ePVrz1QzWd16uueaatPXWW6dPP/20pUrcJK3vvIwZMyZ99atfbbBt7Nixafjw4c1a56ZqXUKz008/PW2//fYNth166KFp1KhRzVgZFIfeq+3Rt7U9er62R7/YdrTZr2d++umn8fTTT8fIkSNz29q1axcjR46Mv//976vd5+9//3uD50dEjBo1qtHns342ZE7yLV68OJYuXRq9evVqrjI3ORs6LxdccEH06dMnjjvuuJYoc5OzIfNy5513xh577BFjxoyJvn37xg477BCXXHJJLF++vKXKbvM2ZF6GDRsWTz/9dO6U/DfeeCPuueee+PrXv94iNbMqn/dsKvRebY++re3R87U9+sW2pUOxC2gu2Ww2li9fHn379m2wvW/fvjF37tzV7rNw4cLVPn/hwoXNVuemZEPmJN8ZZ5wR/fr1W+WXHTbchszLo48+Gv/n//yfePbZZ1ugwk3ThszLG2+8EX/961/jiCOOiHvuuSdee+21OPHEE2Pp0qUxfvz4lii7zduQeTn88MMjm83GV77ylUgpxbJly+KEE06Is846qyVKZjUa+7yvq6uL+vr6KCsrK1JlUFh6r7ZH39b26PnaHv1i29JmzzSj7bn00kvjlltuienTp0enTp2KXc4m66OPPoojjzwyJk+eHBUVFcUuh89ZsWJF9OnTJ6677rrYdddd49BDD42zzz47rr322mKXtkmbMWNGXHLJJfHrX/86Zs+eHbfffnvcfffdceGFFxa7NIA10ntt/PRtbZOer+3RL7ZebfZMs4qKimjfvn288847Dba/8847scUWW6x2ny222GK9ns/62ZA5Wenyyy+PSy+9NP7yl7/ETjvt1JxlbnLWd15ef/31mD9/fhx00EG5bStWrIiIiA4dOsTLL78c22yzTfMWvQnYkH8vlZWVsdlmm0X79u1z22pra2PhwoXx6aefRseOHZu15k3BhszLueeeG0ceeWR873vfi4iIHXfcMT7++OP4/ve/H2effXa0a+fvVy2tsc/78vJyZ5nRpui92h59W9uj52t79IttS5v9yXfs2DF23XXXePDBB3PbVqxYEQ8++GDsscceq91njz32aPD8iIgHHnig0eezfjZkTiIifvrTn8aFF14Y9957bwwZMqQlSt2krO+81NTUxAsvvBDPPvts7vaNb3wj9tlnn3j22Wejf//+LVl+m7Uh/16GDx8er732Wq4Zjoh45ZVXorKyUvNUIBsyL4sXL16l0VnZ5KaUmq9YGuXznk2F3qvt0be1PXq+tke/2MYUdx2C5nXLLbek0tLSNGXKlPTSSy+l73//+6lHjx5p4cKFKaWUjjzyyHTmmWfmnv/YY4+lDh06pMsvvzzNmTMnjR8/Pm222WbphRdeKNZLaHPWd04uvfTS1LFjx/SHP/whLViwIHf76KOPivUS2qT1nZd8VmFqHus7L5lMJnXr1i2ddNJJ6eWXX0533XVX6tOnT7rooouK9RLapPWdl/Hjx6du3bqlm2++Ob3xxhvp/vvvT9tss036zne+U6yX0OZ89NFH6ZlnnknPPPNMioh0xRVXpGeeeSa9+eabKaWUzjzzzHTkkUfmnv/GG2+kzp07p9NOOy3NmTMnXX311al9+/bp3nvvLdZLgGaj92p79G1tj56v7dEvth1tOjRLKaWrrroqVVVVpY4dO6bddtstPfHEE7nHRowYkUaPHt3g+b/73e/Sdtttlzp27Ji23377dPfdd7dwxW3f+szJgAEDUkSschs/fnzLF97Gre+/lc/TfDWf9Z2Xxx9/PA0dOjSVlpamrbfeOl188cVp2bJlLVx127c+87J06dJ0/vnnp2222SZ16tQp9e/fP5144onp/fffb/nC26iHHnpotZ8VK+dh9OjRacSIEavsM3jw4NSxY8e09dZbpxtuuKHF64aWovdqe/RtbY+er+3RL7YNJSk51w8AAAAAPq/NXtMMAAAAADaU0AwAAAAA8gjNAAAAACCP0AwAAAAA8gjNAAAAACCP0AwAAAAA8gjNAAAAACCP0AxoFueff34MHjy42GW0iClTpkSPHj2KXcZGr6SkJO64445ilwEA5Dn66KPjkEMOKXYZLWJT6mGby/z586OkpCSeffbZYpcCTSY0g1ZkxowZUVJSEh988EGxS6GImuN9oHkBAGh5zfHHVb8zQMsRmgEAADTRp59+WuwSACgwoRmsh7333jtOOumkOOmkk6J79+5RUVER5557bqSUcs95//3346ijjoqePXtG586dY//9949XX3019/ibb74ZBx10UPTs2TO6dOkS22+/fdxzzz0xf/782GeffSIiomfPnlFSUhJHH330Wmv6wx/+EDvuuGOUlZVF7969Y+TIkfHxxx9HxP87lX7ChAmx+eabR3l5eZxwwgkNmroVK1bExIkTY6uttoqysrLYeeed4w9/+EPu8ZV/yXrwwQdjyJAh0blz5xg2bFi8/PLLDeq49NJLo2/fvtGtW7c47rjj4pNPPmnw+IwZM2K33XaLLl26RI8ePWL48OHx5ptvrvY1rTwr6ne/+13sueeeUVZWFl/+8pfjlVdeiVmzZsWQIUOia9eusf/++8e//vWvBvv+5je/idra2ujUqVPU1NTEr3/96waPn3HGGbHddttF586dY+utt45zzz03li5dmnt85Sn5v/3tb6O6ujq6d+8e3/3ud+Ojjz5a61ys9K9//SuGDBkS3/zmN2PJkiXx/vvvxxFHHBGbb755lJWVxcCBA+OGG25o9LU39j5Y21yt6ThbbbVVRETssssuUVJSEnvvvfdqj79yvu++++7YaaedolOnTrH77rvHiy++2OjrPeuss2Lo0KGrbN95553jggsuiIiIWbNmxX777RcVFRXRvXv3GDFiRMyePbvRMVf3F9Rnn302SkpKYv78+bltjz76aO490r9//zj55JNz738A2FDr0vNVV1fHhRdeGEcddVSUl5fH97///Yho2mfTr3/96xg4cGB06tQp+vbtG//1X/+1XjUtWbIkfvKTn8QXvvCF6NKlSwwdOjRmzJiRe3zlWU/33Xdf1NbWRteuXeM//uM/YsGCBbnnLF++PMaOHRs9evSI3r17x+mnn97gGBFr7j/zrfxMv++++2KXXXaJsrKy+OpXvxrvvvtu/PnPf47a2tooLy+Pww8/PBYvXpzbb219z/Lly+O4447LPT5o0KD4xS9+0eDYK3vhyy+/PCorK6N3794xZsyYBr3f2rz++uux9dZbx0knnRQppUZ7+cZe+zHHHBMffvhhlJSURElJSZx//vkRsfa5KsTvDCvn+4477si9r0aNGhVvvfVWo6/38MMPj0MPPbTBtqVLl0ZFRUXcdNNNERFx7733xle+8pXce+TAAw+M119/vdExV3e23R133BElJSUNtv3xj3+ML33pS9GpU6fYeuutY8KECbFs2bJGx4UWkYB1NmLEiNS1a9d0yimnpLlz56b//d//TZ07d07XXXdd7jnf+MY3Um1tbXrkkUfSs88+m0aNGpW23Xbb9Omnn6aUUjrggAPSfvvtl55//vn0+uuvpz/96U/p4YcfTsuWLUu33XZbioj08ssvpwULFqQPPvhgjfX885//TB06dEhXXHFFmjdvXnr++efT1VdfnT766KOUUkqjR49OXbt2TYceemh68cUX01133ZU233zzdNZZZ+XGuOiii1JNTU2699570+uvv55uuOGGVFpammbMmJFSSumhhx5KEZGGDh2aZsyYkf7xj3+kPffcMw0bNiw3xq233ppKS0vTb37zmzR37tx09tlnp27duqWdd945pZTS0qVLU/fu3dNPfvKT9Nprr6WXXnopTZkyJb355purfV3z5s1LEZGr66WXXkq777572nXXXdPee++dHn300TR79uy07bbbphNOOCG33//+7/+mysrKdNttt6U33ngj3XbbbalXr15pypQpuedceOGF6bHHHkvz5s1Ld955Z+rbt2+67LLLco+PHz8+de3aNX3rW99KL7zwQnrkkUfSFlts0eBnlu+GG25I3bt3TymllMlk0qBBg9Lo0aPTsmXLUkopjRkzJg0ePDjNmjUrzZs3Lz3wwAPpzjvvXO1Ya3ofrG2u1nScJ598MkVE+stf/pIWLFiQ3nvvvdUef+V819bWpvvvvz89//zz6cADD0zV1dW593C+F198MUVEeu2111bZ9uqrr6aUUnrwwQfTb3/72zRnzpz00ksvpeOOOy717ds31dXV5faJiDR9+vQGdbz//vu5x5955pkUEWnevHkppZRee+211KVLl3TllVemV155JT322GNpl112SUcffXRjUwUA62Rder4BAwak8vLydPnll6fXXnstd9vQz6ZZs2al9u3bp2nTpqX58+en2bNnp1/84hfrVdP3vve9NGzYsPTII4+k1157Lf3sZz9LpaWl6ZVXXkkpfdazbLbZZmnkyJFp1qxZ6emnn061tbXp8MMPz41x2WWXpZ49e6bbbrst95ndrVu3dPDBB6eU1t5/5lv5mb777rs36OFGjBiRvva1r6XZs2enRx55JPXu3Ttdeumluf3W1vd8+umn6bzzzkuzZs1Kb7zxRu7nceutt+bGGD16dCovL08nnHBCmjNnTvrTn/60ys8s3/jx43M97HPPPZe22GKLdPbZZ+ceb6yXX50lS5akSZMmpfLy8rRgwYK0YMGC3M9pbXNViN8ZVs73kCFD0uOPP56eeuqptNtuuzXo4/PdddddqaysrMF8/ulPf0plZWW5vu0Pf/hDuu2229Krr76annnmmXTQQQelHXfcMS1fvjyl9P96+WeeeSZXx8peeaXp06enz8cRjzzySCovL09TpkxJr7/+err//vtTdXV1Ov/88xutFVqC0AzWw4gRI1JtbW1asWJFbtsZZ5yRamtrU0opvfLKKyki0mOPPZZ7PJvNprKysvS73/0upZTSjjvu2Oj//FcXFKzJ008/nSIizZ8/f7WPjx49OvXq1St9/PHHuW3XXHNN6tq1a1q+fHn65JNPUufOndPjjz/eYL/jjjsuHXbYYQ1q+stf/pJ7/O67704Rkerr61NKKe2xxx7pxBNPbDDG0KFDcw3He++9lyIi1+SszcoP2t/85je5bTfffHOKiPTggw/mtk2cODENGjQod3+bbbZJ06ZNazDWhRdemPbYY49Gj/Wzn/0s7brrrrn748ePT507d24Q5px22mlp6NChjY6xshGYO3du6t+/fzr55JMbvEcOOuigdMwxx6zlVf8/q3sfrMtcrek4+c3L2o59yy235La99957qaysrEETmm/nnXdOF1xwQe7+uHHj1vgzW758eerWrVv605/+lNu2vqHZcccdl77//e83GPdvf/tbateuXe69CQAbYm09X0qfhWaHHHJIg/2a8tl02223pfLy8gY9yPrU9Oabb6b27dunt99+u8F+++67bxo3blxK6bOeJf8PXVdffXXq27dv7n5lZWX66U9/mru/dOnStOWWW+ZCs7X1n/lW10tOnDgxRUR6/fXXc9t+8IMfpFGjRqWU1q3vWZ0xY8ak//zP/8zdHz16dBowYEDuD5kppfTtb387HXrooY2OsTI0e+yxx1LPnj3T5Zdf3uDxNfXyq7O6wGhd5qoQvzOsnO8nnngit23OnDkpItLMmTNXu8/SpUtTRUVFuummm3LbDjvssDX+zP71r3+liEgvvPBCSmnDQrN99903XXLJJQ2e89vf/jZVVlau8TVCc/P1TFhPu+++e4NTiffYY4949dVXY/ny5TFnzpzo0KFDg6+q9e7dOwYNGhRz5syJiIiTTz45Lrroohg+fHiMHz8+nn/++Q2uZeedd4599903dtxxx/j2t78dkydPjvfff3+V53Tu3LlBvYsWLYq33norXnvttVi8eHHst99+0bVr19ztpptuWuUU65122in335WVlRER8e6770ZExJw5c1b5et4ee+yR++9evXrF0UcfHaNGjYqDDjoofvGLXzT4GkBjPn/Mvn37RkTEjjvu2GDbyho+/vjjeP311+O4445r8FouuuiiBq/l1ltvjeHDh8cWW2wRXbt2jXPOOScymUyD41ZXV0e3bt0avN6Vx2lMfX197LnnnvGtb30rfvGLXzR4j/zwhz+MW265JQYPHhynn356PP7447nH9t9//1yt22+/faPjr8tcrek46yt//j7/Hv788U844YSIiDjiiCNi2rRpERGRUoqbb745jjjiiNwY77zzThx//PExcODA6N69e5SXl8eiRYtW+dmvj+eeey6mTJnSoJ5Ro0bFihUrYt68eRs8LgBErLnnW2nIkCEN9mnKZ9N+++0XAwYMiK233jqOPPLImDp1aoOvK66tphdeeCGWL18e2223XYPjP/zwww16oc6dO8c222yTu//5PufDDz+MBQsWNOjrOnTo0OB1rkv/uTr5fd3KS2V8ftvKOta1R7366qtj1113jc033zy6du0a11133Sq9xfbbbx/t27df7ettTCaTif322y/OO++8+P/+v/+vwWNr6uW33377XK37779/o+Ovy1wV6neGDh06xJe//OXc/ZqamujRo0fMmTMnMplMg+Nfcskl0aFDh/jOd74TU6dOjYjPeuw//vGPDfq6V199NQ477LDYeuuto7y8PKqrq3M/tw313HPPxQUXXNCgnuOPPz4WLFiwyr8DaEkdil0AbGq+973vxahRo+Luu++O+++/PyZOnBg///nP40c/+tF6j9W+fft44IEH4vHHH4/7778/rrrqqjj77LNj5syZuWtYrcmiRYsiIuLuu++OL3zhCw0eKy0tbXB/s802y/33ymZtxYoV61zrDTfcECeffHLce++9ceutt8Y555wTDzzwQOy+++6N7rO6Y+ZvW1nDytcyefLkVQK8lY3S3//+9zjiiCNiwoQJMWrUqOjevXvccsst8fOf/7zR4+YfpzGlpaUxcuTIuOuuu+K0005r8PPcf//9480334x77rknHnjggdh3331jzJgxcfnll8dvfvObqK+vX+1xP29d5mpNxymkz6/AWV5eHhERhx12WJxxxhkxe/bsqK+vj7feeqvB9TBGjx4d7733XvziF7+IAQMGRGlpaeyxxx6NXjS5XbvP/qaTPncNlfzrjyxatCh+8IMfxMknn7zK/lVVVRv8+gBgXXXp0qXB/aZ8NnXr1i1mz54dM2bMiPvvvz/OO++8OP/882PWrFnrtPriokWLon379vH00083CIkiPvuD10qr63NS3jXL1mRD+8/8Hm5N/da69D233HJL/OQnP4mf//znsccee0S3bt3iZz/7WcycObPR4+YfpzGbb7559OvXL26++eY49thjc/1OxJp7+XvuuSfXr5SVlTU6/rrMVSF/Z2hMv379GvR1vXr1iojP/hg6YsSIePfdd+OBBx6IsrKy+I//+I/c8w466KAYMGBATJ48Ofr16xcrVqyIHXbYYY19Xf57bHV93YQJE+Jb3/rWKvt36tRpQ18iNJnQDNZT/gfxE088EQMHDoz27dtHbW1tLFu2LGbOnBnDhg2LiIj33nsvXn755fjiF7+Y26d///5xwgknxAknnBDjxo2LyZMnx49+9KPo2LFjRESDv2CuTUlJSQwfPjyGDx8e5513XgwYMCCmT58eY8eOjYjP/mpTX1+f++B+4oknomvXrtG/f//o1atXlJaWRiaTiREjRmzwz6S2tjZmzpwZRx11VIOfS75ddtkldtlllxg3blzsscceMW3atDWGZuujb9++0a9fv3jjjTca/CXs8x5//PEYMGBAnH322bltjS1GsL7atWsXv/3tb+Pwww+PffbZJ2bMmBH9+vXLPb755pvH6NGjY/To0bHnnnvGaaedFpdffvkqjWBErPZ98MUvfnGd5qqx46zve+uJJ57INffvv/9+vPLKK1FbWxsREdtuu+0qz99yyy1jxIgRMXXq1Kivr4/99tsv+vTpk3v8sccei1//+tfx9a9/PSIi3nrrrchms2t8HRERCxYsiJ49e0ZEw7AuIuJLX/pSvPTSS6utBwCaak09X2Oa+tnUoUOHGDlyZIwcOTLGjx8fPXr0iL/+9a+5IGFNNe2yyy6xfPnyePfdd2PPPffcoON37949KisrY+bMmbHXXntFRMSyZcvi6aefji996Uu5562t/2yqdel7HnvssRg2bFiceOKJuW1ruhj9+igrK4u77rorvv71r8eoUaPi/vvvb/AthMZ6+QEDBqwyVseOHVfpv9Z1rgrxO8OyZcviqaeeit122y0iIl5++eX44IMPora2Njp06LDa9+qwYcOif//+ceutt8af//zn+Pa3v50LH1f+bjN58uRc7Y8++ugaa9h8883jo48+io8//jgXNK+ur3v55Zf1dbQ6QjNYT5lMJsaOHRs/+MEPYvbs2XHVVVflzlQaOHBgHHzwwXH88cfH//zP/0S3bt3izDPPjC984Qtx8MEHR0TEqaeeGvvvv39st9128f7778dDDz2UCyMGDBgQJSUluQ/psrKyBn8ZzDdz5sx48MEH42tf+1r06dMnZs6cGf/6179y40V8tvz5cccdF+ecc07Mnz8/xo8fHyeddFK0a9cuunXrFj/5yU/ixz/+caxYsSK+8pWvxIcffhiPPfZYlJeXx+jRo9fpZ3LKKafE0UcfHUOGDInhw4fH1KlT4x//+EfulPt58+bFddddF9/4xjeiX79+8fLLL8err77aIGQrhAkTJsTJJ58c3bt3j//4j/+IJUuWxFNPPRXvv/9+jB07NgYOHBiZTCZuueWW+PKXvxx33313TJ8+vWDHb9++fUydOjUOO+yw+OpXvxozZsyILbbYIs4777zYddddY/vtt48lS5bEXXfd1WCO8q3ufbAuc7Wm4/Tp0yfKysri3nvvjS233DI6deoU3bt3b7SGCy64IHr37h19+/aNs88+OyoqKuKQQw5Z4+s/4ogjYvz48fHpp5/GlVde2eCxgQMHxm9/+9sYMmRI1NXVxWmnnbbGv8Buu+220b9//zj//PPj4osvjldeeWWVMwLPOOOM2H333eOkk06K733ve9GlS5d46aWX4oEHHohf/epXa6wVANZmTT1fY5ry2XTXXXfFG2+8EXvttVf07Nkz7rnnnlixYkUMGjRonWrabrvt4ogjjoijjjoqfv7zn8cuu+wS//rXv+LBBx+MnXbaKQ444IB1et2nnHJKXHrppTFw4MCoqamJK664osFq1uvSfzbVuvQ9AwcOjJtuuinuu+++2GqrreK3v/1tzJo1a52+bbEuunTpEnfffXfsv//+sf/++8e9994bXbt2XWMvvzrV1dWxaNGiePDBB3OXTVmXuSrU7wybbbZZ/OhHP4pf/vKX0aFDhzjppJNi9913z4VojTn88MPj2muvjVdeeSUeeuih3PaePXtG796947rrrovKysrIZDJx5plnrnGsoUOHRufOneOss86Kk08+OWbOnBlTpkxp8JzzzjsvDjzwwKiqqor/+q//inbt2sVzzz0XL774Ylx00UVrHB+aVXEvqQYblxEjRqQTTzwxnXDCCam8vDz17NkznXXWWQ0uyPrvf/87HXnkkal79+6prKwsjRo1KrcKTkopnXTSSWmbbbZJpaWlafPNN09HHnlkymazuccvuOCCtMUWW6SSkpI0evToNdbz0ksvpVGjRqXNN988lZaWpu222y5dddVVucdHjx6dDj744HTeeeel3r17p65du6bjjz8+ffLJJ7nnrFixIk2aNCkNGjQobbbZZmnzzTdPo0aNyq0CtC4XZE8ppYsvvjhVVFSkrl27ptGjR6fTTz89txDAwoUL0yGHHJIqKytTx44d04ABA9J5552XW2En3+ouWr+6OlZ3UdGpU6emwYMHp44dO6aePXumvfbaK91+++25x0877bTcz+LQQw9NV155ZYMxPr9i0kpXXnllGjBgwGprXV0dS5cuTd/61rdSbW1teuedd9KFF16YamtrU1lZWerVq1c6+OCD0xtvvNHoeCmt/n2wtrla23EmT56c+vfvn9q1a5dGjBix2uOu/Dn/6U9/Sttvv33q2LFj2m233dJzzz23xnpTSun9999PpaWlqXPnzqusoDV79uw0ZMiQ1KlTpzRw4MD0+9//Pg0YMCBdeeWVuefE5xYCSCmlRx99NO24446pU6dOac8990y///3vV3nfPfnkk2m//fZLXbt2TV26dEk77bRTuvjii9daKwCsybr0fPmfYytt6GfT3/72tzRixIjUs2fPVFZWlnbaaacGi/CsS00rV5Ssrq5Om222WaqsrEzf/OY30/PPP59SWrcLsi9dujSdcsopqby8PPXo0SONHTs2HXXUUbmFANbWf+Zb1x4uvwdbW9/zySefpKOPPjp179499ejRI/3whz9MZ555ZoMxVvbCn3fKKac02getro6PPvooDRs2LO21115p0aJFa+3lV+eEE05IvXv3ThGRxo8fn1Ja+1wV4neGlT/n2267LW299daptLQ0jRw5stEV7D/vpZdeShGRBgwY0OA9llJKDzzwQKqtrU2lpaVpp512SjNmzGjQx62ul58+fXradtttU1lZWTrwwAPTddddl/LjiHvvvTcNGzYslZWVpfLy8rTbbrutcaVTaAklKa3HF9hhE7f33nvH4MGDY9KkScUuZZ0cffTR8cEHH8Qdd9xR7FLYSMyYMSP22WefeP/999fp+ikA0Ba1xp6vNdZE6zZlypQ49dRTG5wpCKwfq2cCAAAAQB6hGbRi+ctA59+asqwzAAAt529/+9sa+zoAWh9fz4RWbNmyZTF//vxGH6+uro4OHaznAQDQ2tXX18fbb7/d6ONWDQRofYRmAAAAAJDH1zMBAAAAII/QDAAAAADyCM0AAAAAII/QDAAAAADyCM0AAAAAII/QDAAAAADyCM0AAAAAII/QDAAAAADy/P/l1eXwXTasiwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABM0AAAx7CAYAAACgVh33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeZgU5bk/7mfYZmEVEAFlUUTFBUVRAqio4C5qxA1RUYnGIxojJipqgh4XTM6JMYvRaCLoOSrRRPMzrolG3KJRUdx32RRcwAWQRYT394ff6cPUzMAMzEwPM/d9XX1dM9XVVU/VW1399qdrKUgppQAAAAAAcprkuwAAAAAAqG+EZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmkCc9e/aMgoKC3KNJkybRunXr2GyzzWLvvfeOH/3oR/Hss8+ucRp77bVXFBQUxNSpU+um6LUoXaaZM2eWGV7f6oyIOOmkk6KgoCAmT56c71Jqxd/+9rfYY489ok2bNrltrD6t/9pyySWXREFBQVxyySX5LmWNvvjiixg7dmz06NEjWrRoEQUFBbHXXnvlu6x6a+bMmVFQUBA9e/bMdylsINZ1XzB16tQN5v1of9cw2d/VT5MmTYr+/ftHy5Ytc/2qmTNn1nh7rc8+qLQuoGY1y3cB0NgNHjw4ttxyy4iIWLp0acyfPz9efPHFmDp1avziF7+IIUOGxE033RRbbLFFrdXQs2fPmDVrVsyYMaNBdNImT54cJ598cowePbrBhmJrMn369BgxYkSsWrUq9tlnn+jSpUsUFBRE586d810a/89pp50Wd955Z/Ts2TOOOOKIKCoqim222SbfZQH1xNSpU2PvvfeOIUOGbPA/eNjf1Q977bVXPPbYY/Hoo482+NCyppf1vvvui1NOOSWKiopi2LBh0aFDh4iIaNWqVSxevHi9pw/Ub0IzyLPvfe97cdJJJ5UZllKKBx54IH74wx/GY489FoMGDYqnn346Nt988zLj3XLLLbFkyZLo3r17HVZcuUceeSRWrFgRm266ab5LWauJEyfGBRdcEF26dMl3KTXur3/9a6xYsSIuvPDCuOKKK/JdDhkrVqyIu+++O4qKiuKll16KNm3a5Lsk4P/Zbbfd4o033oiSkpJ8l7JWZ555Zhx77LHRsWPHfJdSKfs7GoI777wzIiJ+/etfx6mnnlrmubZt28Ybb7wRzZs3z0dpQB0QmkE9VFBQEAcddFAMGjQodtttt3jnnXfie9/7XjzyyCNlxqsvYVmpXr165buEKuvSpUuDDMwiImbPnh0REb17985zJVRk3rx58c0338Smm27qCyTUMyUlJRvMUVAdO3as14FZhP0dDcOa+lXNmzffYPYZwLpxTTOox9q1axfXXHNNRET885//jGnTppV5vrJrhS1fvjz+67/+K3bZZZdo3bp1tGjRIjp37hy77rprnHfeefHZZ59FxLenMRYUFMSsWbMiImLzzTcvc5210umufn2FJUuWxE9/+tPo06dPlJSUlDmds7Jrmq3usccei/322y/at28fJSUlsdtuu8X//M//VDju2q6FVtH1XHr27Bknn3xyRETcfPPNZZZn9UP013ZNsylTpsTQoUOjffv2UVhYGD169IhTTjkl3n777QrHX33ZH3300dhvv/1io402iuLi4th5553jlltuqXSdrMk333wT119/fQwaNCjatm0bRUVF0bt37/jBD34QH374YYXrY9KkSRERcfLJJ1e47Guy+vUwbrzxxthll12iZcuW0a5duzjooIPimWeeqVb948ePj4KCgjj99NMrHefVV1+NgoKC2GSTTWLFihW54XfddVd873vfi+233z422mijKCoqis033zxOOeWUeOutt6pVx9qu/bO2a4jMnTs3xo0bl9vuW7duHbvuumv89re/jW+++abKdRQUFESPHj0iImLWrFkVvt8iqtfuq0+7tO0mTZoUAwcOjLZt2671PZn1yCOPxBFHHBFdunSJFi1aRKdOneK73/1uPP300xWO/+yzz8Z5550Xu+22W3Tu3DlatGgRm2yySQwfPjwefvjhNc7r7bffjjPOOCO23nrrKCkpiTZt2sS2224bZ5xxRrz66qsVvialFDfccENu22zbtm3st99+lda3NrNmzYqTTjopOnfunFvPEyZMiGXLllW6D1p9+BNPPBHDhw+PjTfeOJo0aVJmn/LBBx/EWWedFb17946ioqJo27ZtDB48OH7/+9/HypUry9Wyrttpdh994YUXxpZbbhlFRUXRtWvXGDNmTKXbzdosWbIkrrnmmth9991jo402yu0Phw8fHrfddluF41911VWx8847R+vWraOkpCS22267uPjii+Pzzz8vN/7q1wNatWpV/PrXv46+fftGSUlJdOnSJU4//fTcZ9by5cvjsssui2222SaKi4uja9eucfbZZ8dXX321xmWYNWtWnHjiidGlS5coKiqKrbbaKi655JJYunRpuXErW8er11ndbbC675G99tor9t5774j49jNz9f3E6p+5a9teHnrooTjkkEOiU6dO0aJFi+jatWscc8wx8fzzz1c4/urb9fTp0+OII46Ijh07RmFhYWy77bbxi1/8IlJKFb62IvZ3ZeVrf1e6TT/22GMREbH33nuXaYvV91kPP/xwnHXWWbHTTjvl2n6zzTaLY445Jp577rkKp7/6djh79uwYM2ZMdOvWLZo3b17mTIqvvvoqfvKTn0Tv3r2jsLAwunbtGqecckp8+OGHa92Wp02bFqNGjYru3btHYWFhtG/fPvbff/+4//7713lZq6K0r/joo4+Wm17psq3tmmZLly6NX/ziF/Gd73wn2rVrF0VFRbH11lvHeeedFwsWLKhWPRERTz/9dBx44IHRrl27aNWqVfTv3z9uuummak8HqIYE5EWPHj1SRKRJkyatcbxVq1al9u3bp4hIEydOLPPckCFDUkSkRx99NDds5cqVaejQoSkiUps2bdKBBx6YRo4cmYYNG5ab54svvphSSumJJ55Io0ePTi1btkwRkUaMGJFGjx6de7zxxhsppZQeffTRFBFpwIABadddd00tW7ZMBx54YDrmmGPSsGHDyi3TjBkzKqzzBz/4QWrSpEnadttt07HHHpv23HPP1KRJkxQRady4ceWWvaLlW92ECRNSRKQJEybkhp177rlp8ODBKSJSr169yizP6utv9OjRFa7/VatWpRNPPDFFRGrWrFnaZ5990rHHHpu22mqrFBGppKQkPfDAA+VqKV32n/zkJ6mgoCDtsssu6dhjj03f+c53UkSkiEi//OUvK1yOyixbtiwNGzYsRUQqKirKrfNu3bqliEgdO3ZM06ZNy41/9913p9GjR6devXqliEiDBw+ucNnXpLTWc845JxUUFKTdd989jRw5Mm2//fa5dXLXXXdVeRneeuutFBGpXbt2aenSpRWOM27cuAq3gaZNm6aSkpLUv3//dMQRR6RDDz00bbHFFikiUsuWLdNTTz1VbloVbRNrGl6qdBsfMmRIuecee+yxtNFGG6WISD179kyHHnpo2n///XPD9ttvv/T1119XaX2MHj06jRgxIrcMFb3fqtvupUrb7swzz0xNmjTJtd2AAQPSzJkzq1TfueeemyIiNWnSJO22227pqKOOSgMGDEgFBQWpadOm6aabbir3mqFDh6YmTZqkHXbYIR100EHpqKOOSjvvvHOunmuuuabCed16662psLAwRUTq3r17GjFiRPrud7+bdtxxx1RQUFCmrWbMmJEiIvXo0SONHj06NW/ePO2zzz7p6KOPzr03CwsL0zPPPFOl5Sz12muvpY4dO6aISF27dk1HH310Ovjgg1PLli3T7rvvngYNGlThPqh033TGGWeU2aftt99+6bbbbksppfTss8/m9t3du3dPxxxzTDrggANSUVFRioi0//77p+XLl5eZ7rpup6XDBw4cmL7zne+kkpKSXFt06dIlRUTq3Llzevvtt6u1fmbPnp223Xbb3L5v3333Tccee2zaY489Utu2bVOPHj3KjL9gwYK000475T5/Dj300DRixIjcOt58883LfT6s3rYjR45MxcXF6YADDkiHH3546tSpU4qI1K9fv7R48eK0++6756Z7yCGHpLZt26aISAceeGC52kvX5Yknnpg6dOiQNtlkk3TUUUelQw45JPeZN3jw4HL7pcrW8fpsg9V9j0ycODHtv//+KSLSJptsUmY/ce6555Zbxoq2l4svvjhFRCooKEiDBw9OI0eOzLVN06ZN0x//+Mdyryndri+44ILUokWL1KdPn3TsscemIUOGpKZNm6aISGeffXa511XG/u7/5HN/98Ybb6TRo0enTTbZJLfvWb0tnnjiidy4vXr1Si1atEj9+vVLhx56aDriiCNy+4BmzZqlP//5z+WmX7odHnfccal9+/apc+fOacSIEemII47Iba+LFy9Ou+66a4qI1KpVq3TIIYfk9k+dOnVKJ510UqXb8jXXXJPrK+60007pyCOPTLvvvntq0aJFioh06aWXrtOyVsWNN95Y6fRuvPHGlFLZ9sr68MMP0w477JAiIrVv3z4NGzYsffe73831GXv27Flue11Tf+SOO+7IvRe33377NHLkyLT77rungoKCXF/K13uoed5VkCdVDc1SSrkO5fHHH19meEWh0mOPPZb7krFw4cJy03ruuefS/PnzK6wl+2WmVOkHeESkvn37pnnz5q1xmSoLzSIiXXnllWWemzp1aiouLk4RkR588MG1Lt/qKvvCMGnSpBQRafTo0RW+LqXKQ7Prrrsu11EvDRdT+jZMK51fu3bt0ieffFLhsjdv3jz97W9/q7Cetm3bpiVLllRaU9b555+fC/9WX6dff/11GjNmTO5LaPaLd2XLVhWl7VRcXJweeeSRMs/9/Oc/zy3Hxx9/XOVploaYt99+e7nnVqxYkfti/Morr5R5bsqUKWnx4sVlhq1atSpde+21KSLSdtttl1atWlXm+ZoOzebNm5c6dOiQCgoK0u9+97u0cuXK3HPz589P++yzT7lO+9qsqYOd0rq3e2nbtWnTJj399NNVrqfUDTfckCIibbnllumll14q89xjjz2WWrdunVq0aFEueLn//vvT3Llzy03vX//6V2rTpk1q3rx5+uCDD8o89/zzz6fmzZungoKC9Otf/7rMek0ppZkzZ6bnn38+93/pOitdb2+99VbuuW+++SadcsopuQCzOkq/7B577LFp2bJlueEffPBB2nrrrXPzrCw0i4h07bXXlpvusmXLcvuE008/vUyo+t5776WePXumiEgXXnhhmdetb2hW2n6zZs3KPbd06dJccPGd73ynimvm2x9g+vfvn1uv2X3e0qVL03333Vdm2DHHHJMivv2BZfXPmUWLFqUDDzwwRUQaNGhQmdes3ra9evUq8wVy/vz5qXfv3iki0g477JB22223MtN9//33c+H1k08+WWa6pesyItJhhx1WZt87Z86cXPhwwQUXlHnd2kKzddkG1+U9sqYvztllzG4vDzzwQC6E+vvf/17muT/84Q+5z6pXX321zHOrb9fXX399meceeeSRXJg0Z86cSmvKsr+rP/u7tfWpUvr2x7fPPvuswuHNmjVLHTp0KNePWf29dvzxx5fZl5Y655xzUkSkbbfdtsz6W7p0aTryyCNzr89uyw8++GAqKChIHTt2TI899liZ515++eW02WabpYhIU6dOrfayVseaplfZNr5q1apc/2fMmDFl+uQrVqzIhbZ77713mdetqT/SunXrFBHp6quvLvPcww8/nPtBRmgGNc+7CvKkOqHZsccemyLK/5pe0Yf4HXfckSK+PaqrurVUJTR7/PHHqz2d0jr79etX4etKOw777rtvha+ry9Cs9CitX//61+Ves2rVqtS3b98UEemKK64o81zpsld0xFxKKW2zzTZrXX+rW7p0aWrVqlWKiHTPPfeUe/6rr77K/fJ56623VmnZqqK0nX/4wx9W+Hzpl+js8q/JH//4x0o7+H/9619TRKT+/ftXq86BAwemiEivvfZameE1HZqVfqE788wzK3zdBx98kJo3b5423njjcgFeZdb0JXJ92r207f7zP/+zSnWsbuXKlalr164pIsp8eVtdaWi6+pEuazN+/PgKg6XDDz88RUQ666yzqjSd1b9EVrRe5s2blyK+Pfqiqkf9Pf744yni26MeFixYUO75e++9d62h2T777FPhtP/nf/4nRXx79FpFXyD//Oc/p4hIrVu3LnOkU02EZn/961/Lve7jjz9OJSUlKSIqPEKzIqXvzS5duqRFixatdfxZs2alJk2apIKCgnIhRErfvldKv9StXsPqbZsN4VJK6eqrr04R3x4xlQ3WU0rprLPOqjC4Ll2XxcXFFf7Q87e//S0XuqzeBlUJzWpqG0yp8vfI+oRmpUebV/Z5dMghh6SISKeeemqZ4aXb9RFHHFHh6w444IAUEemWW25Z+4L9P/Z39WN/l9L6B0kjR46s8H1auh22b98+ffHFF+Vet2TJklw7P/TQQ+We/+STT3L7p+y2PGDAgBQRFR7hltL/9XlHjBhRZnh9CM1Kw+uddtoprVixotzrVq5cmTuKf/V9W2Xv/csvvzxFVP7jx9lnny00g1rimmawAVi1alVERO76HWuy8847R9OmTeOmm26Ka6+9NubNm1djdXTq1Cn22GOPdX79iSeeWOHw0aNHR0TEk08+WeF1furKBx98EO+9916ZmlZXUFCQu15a6fUtsoYPH17h8D59+kREVPm6Qs8//3wsXrw42rdvX+E0S0pK4thjj11jLeujouWP+L82rOw6cxU5+uijo2XLlvHwww/HBx98UOa50uuvnXLKKRW+9t13343f/va38cMf/jDGjBkTJ510Upx00knx8ccfR0RU+9pm1XXfffdFRMQxxxxT4fObbrpp9O7dOz799NN455131nt+NdHuRx55ZLXn++KLL8bcuXOjV69escsuu1Q4Tuk1nv71r3+Ve27BggVxyy23xHnnnRennnpqrp1KryuzejutXLky/vGPf0RExGmnnVatOps1axYHHHBAueGdO3eOjTbaKJYvX17la8SU1nbAAQdE+/btyz1/8MEHR7t27dY4jcrWden749hjj43CwsJyzx9xxBGx0UYbxaJFi8pdq3J9tGvXLg499NBywzt16pRbb1V97z744IMREXHcccdFq1at1jr+448/HqtWrYp+/fpF3759yz2/6aabxv777x8RFW+7zZo1i/3226/c8NILb3fv3j223377Sp+fO3duhXXtt99+0blz53LDDznkkOjQoUMsXLgwXnjhhTUsWfk612UbrM57ZH1888038dRTT0VElLszd6kxY8ZERO1/jq2N/d2a1eT+rjrmzp0bN954Y5x77rm5O7yfdNJJ8dprr0VE5dvqsGHDom3btuWGT5s2LRYvXhwdO3as8D2+8cYbx7777ltu+Pz58+PZZ5+N4uLiSrfJNbVTvpX2H0aMGBHNmpW/916TJk1izz33jIiq1V+67x41alSFz1fWbwPWn7tnwgZg/vz5EREVfrHL6tWrV/zyl7+MH//4x3HmmWfGmWeeGT169IiBAwfGIYccEkcddVS0aNFineqo7CKnVbX55puvcfjSpUtjwYIF0alTp/Waz7oq/SLQoUOHSu/yVXqH0Mq+NFR2R9PS6S1btqxatVS2zqpSy/pYW1utHn5dddVV8eabb5Yb97//+7+jY8eO0apVqzjqqKNi8uTJccstt8SFF14YERGffPJJ3HfffVFUVBQjR44s89qVK1fGmWeeGb///e/XeOHphQsXVnvZquP999+PiKhSWPzpp5/GVltttV7zq4l2X5f3aelyvvfee2sN5z/99NMy/994441xzjnnrPFi7Ku304IFC3Ljbr311tWqs0uXLtG8efMKn2vTpk18/vnnVX6PlW7Da1pfPXr0iC+++KLS5yt77drasaCgIDbffPP4/PPPa/T9W3pDkopU9N5dk9IbxFT1rnDru+126dKlwi+WpYFdZfvW1q1bR0Tl+9Y11dOzZ89YsGBBlddJaZ3V3Qar+x5ZHwsWLMjNv7Jlr6vPsbWxv1uzmtzfVdWll14aV1xxRZmb8mRVtq1W1hZV2ddW9NyMGTMipRRLly6t8MeH1WXbqT4o3c5+8pOfxE9+8pM1jluV+kvX49r6Z0DNE5pBPZdSihdffDEiInbYYYcqveass86Ko48+Ou6555548skn48knn4wpU6bElClTYsKECfHEE09Ely5dql1LcXFxtV9TXWsKSLJKj8CrT5o0aRwH8K7eTg8++GDuF/bVXXLJJdGxY8eI+PZIssmTJ8fNN9+cC83+93//N7755ps48sgjyx3R86tf/Squv/766Ny5c1x99dUxaNCg2GSTTaKoqCgivj365fbbb6/W9rImlW1LpcOPPPLIaNmy5Rqn0aFDhxqpZX2ty/u0dDk7d+6cOxqoMqVtGvHtEQTf//73o2nTpvGzn/0shg8fHt27d4+SkpIoKCiIG264Ib7//e/XWDvVxvtrTV+a1/aFui72iauriX1eTbVFTVtb29bmvrU666S6ddT1e6QmbEifY/Z3Neeuu+6KSy65JFq1ahW//e1vY5999omuXbtGcXFxFBQUxIUXXhgTJ06sdPnW1hbV3deWtlOrVq1ixIgR1ViS+qG0/t133z0X/lZmu+22q4uSgHUkNIN67v7774/PP/88IqLCw9ors8kmm8Spp54ap556akREvPnmm3HKKafE008/HRdccEHcfPPNtVLvmsyYMaPC4aW3hy8qKioTPJQeEbdo0aIKX1d6JERN2XTTTSPi21+GFy5cWOHRZqW/HJaOW1tKp1/ZOqvtWmbMmBE77bRTueGlbbXZZpvlhlXldK899tgjttxyy3j77bfjqaeeisGDB+du/V7RqZl33HFHRET8/ve/r/B0s+qeCrmu21K3bt3inXfeifPPPz/69+9frXmui3y1e7du3SLi2+CvtF2q4s4774yUUpx11llx3nnnlXu+onbq0KFDlJSUxJIlS+Ktt96q8JS7ulC6/kq36Yqs6z6mdNqlbVWR0jZevR3Xd5+3pmWp6L27JqVHG1V0FGlFqrLMdbX/XN2a3kvVXSfrYl3eI+ujQ4cOUVhYGMuXL4/333+/wlNl89EOFbG/q19KP3evuOKKCk8lXddttSr72oqeK22ngoKCuOmmmzaoMDfi/+o/7LDD4kc/+tF6T2/TTTeNN998s9L1uKb1C6yfDWvvA43Ml19+Geecc05EROy7774VhhhVtc0228T5558fERHTp08v81zpF7VvvvlmnadfFf/7v/9b4fBbbrklIr79NW7103NKO1pvvPFGudcsWbKk0mucrOvybLbZZrlfAyvqSKeUcsP33nvvak27uvr37x+tWrWKzz77LO65555yzy9dujSmTJlSa7X8z//8zxqHl15HpDpKrwc3efLkmDZtWrzyyivRrVu3GDp0aLlxP/vss4j49vS4rNdee63cNrw2a9qWIv7v2iNZBx54YET835eJ2pavdt91112jY8eO8frrr+euW1MVa2qnZcuWxV/+8pdyw5s2bZq7fs2NN964jhWvv9JryTz44IO5HyZW98ADD1Q4vCpK3x9/+tOfKjx96u67747PP/88WrduXeaaSuu6nZb64osv4m9/+1u54Z9++mnuGmVVfe+WXkvp9ttvX+OpaKX23HPPaNKkSUyfPj1eeumlcs/PmzcvV0Nt7z9X9/e//z0++eSTcsPvv//+WLBgQbk2qGnr8h6JWPfPsWbNmsXuu+8eERV/jkVE3HTTTRFRt+1QEfu7urW2bWpNy/fJJ5/krs1WXbvsskuUlJTEp59+Gg8//HC55+fPn1/htLt27Rp9+/aNRYsW5fYdVVVX/do1Ke0/lIat62vIkCEREXHrrbdW+HxpXxqoeUIzqIdSSvHAAw/EbrvtFu+880506dKlyp2tf/7zn3H//feXux5FSinuvffeiCjfISr9lb06ncd1MW3atPj5z39eZtiTTz4Z1157bURELiAsNWzYsIiIuPbaa8tcz+Srr76K0047LebMmVPhfEqX5/XXX692jaW/Bl522WVlvvillOLyyy+P6dOnR7t27XJH8NWWoqKiGDt2bEREnHvuuWWOMFmxYkWcffbZ8dFHH8Xmm2++ThdCXpvrrruu3BFkv/zlL+PZZ5+N1q1b5y4kXR2jR4+OJk2axB133JFr89JhWaUXnL722mvLnJI2b968OPHEE6vdEd5nn32iSZMm8dBDD5U5lTSlFL/+9a8r/eL64x//ONq1axdXX311/OIXv4ivv/663DgzZsyoNBCurny1e/PmzWPChAmRUorvfve78eSTT5YbZ+XKlfHPf/4znnnmmdyw0na6+eabyxwdtWzZsjjjjDMqPYLkoosuimbNmsVvf/vb+N3vflfuC8WsWbNq9AL5Fdlzzz1jxx13jEWLFsVZZ51Vpm3nzp0b55577jpP+6ijjoru3bvH3LlzY9y4cWW21xkzZuSmfdZZZ+VOOY5Y9+10deeee26Za3QtX748xo4dG1999VXstttuMXjw4Cotw6GHHhr9+vWLuXPnxlFHHVXuguPLli2LBx54IPd/9+7d46ijjoqUUnz/+98vM37pPnvZsmUxaNCgGDRoUJVqqAlLly6N//iP/4ilS5fmhq3evqeffnqZNqhp6/oeKf0ce+edd9Z4famKlC7bddddF4888kiZ5yZPnhz33HNPNG/ePM4+++xqTbem2d99qy72dxFr7+uVLt8NN9xQZn/45ZdfxujRo+PLL79cp/mWlJTE9773vYj4tp9XeiOfiG/3T2eeeWalwfzll18eEd/+6FbRDwIppfj3v/8df//738sMr6t+7Zocdthhseuuu8azzz4bJ598coXXLfv888/j+uuvr1KfZsyYMdGqVat4+umn49e//nWZ56ZOnRrXX399jdUOZNTNTTqBrB49eqSISIMHD06jR49Oo0ePTscee2waNmxYat++fe620XvttVd6//33K5xGRbfA/uUvf5kiIrVp0ybttdde6bjjjkvf/e53c/Nr27ZtevHFF8tM57e//W2KiNSqVat0xBFHpDFjxqQxY8akN998M6VU+e2vK1umGTNmVFjnD37wg9SkSZO03XbbpZEjR6YhQ4akJk2apIhIZ599drnpff3116l///65ug8++OB04IEHpo033jhtuumm6ZRTTqnwFuXLly/P3U6+X79+6cQTT0xjxoxJP//5z3PjjB49OkVEmjRpUpnXrlq1Kp1wwgkpIlKzZs3S0KFD08iRI9PWW2+dIiIVFxen+++/v8rLvrb5rcmyZcvS0KFDc/M96KCD0jHHHJO6d++eIiJ16NAhPf/88zUyr1Kl290Pf/jDVFBQkPbcc880cuTItMMOO6SISE2bNk133nlntadb6oADDsjNo6CgIL333nsVjvfMM8+kFi1apIhIW265ZTr66KPTAQcckIqLi9N2222Xvvvd71a4jBMmTKhwm0jp/27H3rRp07TXXnulI444IvXq1Ss1b948XXDBBZVu44899ljq2LFjiojUqVOntM8++6RRo0alQw45JPXq1StFRBowYECV10Flt6cvta7tXrpe18ePf/zj3HS22267dNhhh6Vjjz027bXXXqldu3YpItJ1112XG//zzz/PbfsdOnRIhx9+eBoxYkTq1KlTat26dW6djx49uty8br755tS8efPcujjyyCPTEUcckXbaaadUUFBQpg3Xts5SWvt7sCKvvPJKbn+76aabpqOPPjodcsghqWXLlmnw4MFp4MCBKSLSU089VeZ1Fe17s5599tnctHv06JGOOeaYdNBBB6WioqIUEWn//fdPy5cvL/e6ddlOS/fRAwcOTAMGDEglJSXpkEMOSUcffXRuX9ipU6fcPr2qZs6cmdv3lZSUpP322y+NHDky7bnnnqlt27bl2mP+/Plpxx13zO2zDz/88HTkkUemjTfeOEVE2nzzzcu1z9radm2fP5MmTapwGyvdF5x44ompffv2qXPnzumoo45Kw4cPTy1btsytryVLllRpfuu6Da7Pe6T082/rrbdOo0aNSmPGjEnnn39+uWWsaH938cUX5/azu+++ezruuOPSzjvvnNu2/vjHP5Z7zdq26zXNrzL2d9+qD/u7e++9N0VEatGiRTrkkEPSKaecksaMGZPbv73//vu55d50003TiBEj0qGHHpratm2bunTpUmmfqyrbxaJFi9Iuu+yS62seeuihuf1Tx44dc/2WK664otxrf/WrX6VmzZrl+gMHH3xwOu6449K+++6bOnXqlCKizPuiKstaXWt6b6ypvT788MO00047pYhILVu2TIMGDUrHHntsru2bNm2aIiItXbo095o17fNuv/323Gt22GGH3P64oKAgnXPOOTXyvgDK866CPCnt8Kz+aNmyZeratWsaMmRIOvfcc9Ozzz67xmlU9CH+7rvvpksuuSQNHTo0de/ePRUVFaWNNtoo9e3bN11wwQVpzpw55aazcuXKNHHixLTddtvlvtCtPt2aCs0effTR9Mgjj6ShQ4emtm3bpuLi4tS/f/80efLkSqf5+eefpzPPPDNtttlmqXnz5mnTTTdNp512Wvr444/X2FF75ZVX0qGHHpo23njjXDC3ev1rC5Zuu+22XMe5efPmqVu3bumkk06q9EtnbYRmKaW0YsWK9Lvf/S595zvfSa1bt04tWrRIvXr1SmeddVb64IMPanReKZX9InLdddelnXbaKRUXF6c2bdqkAw44YJ07nKXuuOOO3DzWtj29/PLL6dBDD01dunRJRUVFqXfv3um8885LCxcurHQZ17RNrFq1Kv3iF79Iffr0SS1atEjt27dPw4cPT9OmTVvrNv7xxx+nn/zkJ2nnnXfOtcNmm22WBg0alCZMmJBefvnlKq+DqnwhWpd2r6nO8lNPPZVGjRqVevTokQoLC1Pr1q3TVlttlQ4//PD0hz/8IX322Wdlxv/000/TGWeckXr16pUKCwtT165d0/HHH5/eeeedSgONUq+99loaM2ZM2nzzzVNhYWFq27Zt2nbbbdOZZ56ZXnvttdx4tfUlsnTaJ5xwQurUqVNuPV944YVpyZIlaYsttkgRkd56660yr6lKaJZSSrNnz05jx45NW2yxRWrRokVq3bp1GjhwYLruuuvSihUrKnzNumynqw9fvHhx+vGPf5w233zz1KJFi7TJJpukk046Kc2ePbta66XUokWL0s9+9rO06667ptatW6fCwsLUo0ePdOihh6YpU6aUG/+rr75KEydOTDvttFMqKSlJRUVFqU+fPunCCy8st+2kVPuh2YQJE9L777+fRo4cmTbZZJPUokWLtOWWW6af/vSn6auvvqry/NZnG1zX98isWbPScccdl7p06ZILDVaf/9rCigceeCAddNBBqUOHDqlZs2a54PDf//53hePnIzRLyf6uLvd3N954Y9p5551TSUlJbh2u/jk6Y8aMNGrUqNS9e/fce/30009PH330UaXtX9XtYtGiRenCCy/M7Q87d+6cTjjhhDRr1qxcIPf73/++wte+8sor6bTTTku9e/dORUVFqaSkJG2xxRZp//33T7/+9a/Thx9+WO1lrY51Dc1S+jYYvv7669Pee++dey926tQp7bTTTmns2LHpoYceKjP+2vZ5TzzxRNp///1TmzZtUklJSerXr19uvQnNoHYUpFSPbtcDQF6V3sHKRwON3YwZM2LLLbeM1q1bx2effVavL0I9derU2HvvvWPIkCFVujEHQH2xYsWK2H777ePtt9+OadOmxc4775zvkgDKqL89QACAWvTVV19VeM2bWbNmxahRo2LVqlWVXncPgKqbNm1amWuURkQsXrw4zjzzzHj77bejb9++AjOgXmq29lEAABqeTz/9NLbffvvo1atXbLXVVtGmTZuYPXt2vPDCC7F8+fLYcccd47LLLst3mQAbvBEjRsSSJUtihx12iE6dOsUnn3wS06dPj88++yzat29f6d1eAfJNaAYANEodO3aMH/3oR/HPf/4znnvuufjiiy+ipKQk+vbtGyNGjIizzjorSkpK8l0mwAZv3Lhxcffdd8frr78eTz31VDRp0iR69OgRxx9/fPzoRz+Kbt261XoNTz75ZPzhD3+o8vj//d//HR07dqzFioANgWuaAQAA0KBNnjw5Tj755CqPP2PGjOjZs2ftFQRsEIRmAAAAAJDhyrYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkNEs3wXUtlWrVsXcuXOjdevWUVBQkO9yAIANQEopFi1aFF27do0mTfzGWF/p5wEA1VWdfl6DD83mzp0b3bp1y3cZAMAGaM6cObHZZpvluwwqoZ8HAKyrqvTzGnxo1rp164j4dmW0adMmz9UAABuChQsXRrdu3XL9COon/TwAoLqq089r8KFZ6aH6bdq00ZkCAKrFKX/1m34eALCuqtLPc5EOAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAADkxYcffhjHH398dOjQIYqLi2OHHXaI559/Pt9lAQBERESzfBcAAEDj8/nnn8fgwYNj7733jgceeCA23njjeOedd2KjjTbKd2kAABEhNAMAIA9+9rOfRbdu3WLSpEm5YZtvvnkeKwIAKEtotgGYPXt2zJ8/v8am17Fjx+jevXuNTQ8AoLruueee2H///eOoo46Kxx57LDbddNM444wz4tRTT630NcuXL4/ly5fn/l+4cGFdlEqe1WRfWD8YgOoQmtVzs2fPjm226RNLly6psWkWF5fEm2++ocMAAOTN+++/H9ddd12MGzcuLrzwwnjuuefiBz/4QbRo0SJGjx5d4WsmTpwYl156aR1XSj7VdF9YPxiA6hCa1XPz58+PpUuXxIBTJkSbLj3Xe3oL582Mf990acyfP19nAQDIm1WrVkX//v3jyiuvjIiIfv36xauvvhrXX399paHZ+PHjY9y4cbn/Fy5cGN26dauTesmPmuwL6wcDUF1Csw1Emy49o333rfNdBgBAjejSpUtsu+22ZYb16dMn/vKXv1T6msLCwigsLKzt0qiH9IUByIcm+S4AAIDGZ/DgwfHWW2+VGfb2229Hjx498lQRAEBZQjMAAOrcOeecE88880xceeWV8e6778Ztt90WN9xwQ4wdOzbfpQEARITQDACAPNh1113j7rvvjttvvz223377uOyyy+Kaa66JUaNG5bs0AICIcE0zAADy5JBDDolDDjkk32UA1HuzZ8+O+fPn19j0Onbs6IYYUAVCMwAAAKinZs+eHdts0yeWLl1SY9MsLi6JN998Q3AGayE0AwAAgHpq/vz5sXTpkhhwyoRo06Xnek9v4byZ8e+bLo358+cLzWAthGYAAABQz7Xp0jPad98632VAo+JGAAAAAACQUe9Dsw8//DCOP/746NChQxQXF8cOO+wQzz//fL7LAgAAAKABq9enZ37++ecxePDg2HvvveOBBx6IjTfeON55553YaKON8l0aAAAAAA1YvQ7Nfvazn0W3bt1i0qRJuWGbb755HisCAAAAoDGo16dn3nPPPdG/f/846qijolOnTtGvX7+48cYb810WAAAAAA1cvQ7N3n///bjuuuuid+/e8dBDD8V//Md/xA9+8IO4+eabK33N8uXLY+HChWUeAAAAAFAd9fr0zFWrVkX//v3jyiuvjIiIfv36xauvvhrXX399jB49usLXTJw4MS699NK6LBMAAACABqZeH2nWpUuX2HbbbcsM69OnT8yePbvS14wfPz6+/PLL3GPOnDm1XSYAAAAADUy9PtJs8ODB8dZbb5UZ9vbbb0ePHj0qfU1hYWEUFhbWdmkAAAAANGD1+kizc845J5555pm48sor4913343bbrstbrjhhhg7dmy+SwMAAACgAavXodmuu+4ad999d9x+++2x/fbbx2WXXRbXXHNNjBo1Kt+lAQAAANCA1evTMyMiDjnkkDjkkEPyXQYAAAAAjUi9PtIMAAAAAPJBaAYAAAAAGfX+9EwAqmb27Nkxf/78GplWx44do3v37jUyLQAAgA2R0AygAZg9e3Zss02fWLp0SY1Mr7i4JN588w3BGQAA0GgJzQAagPnz58fSpUtiwCkTok2Xnus1rYXzZsa/b7o05s+fLzQDAAAaLaEZQAPSpkvPaN9963yXAQAAsMETmgEAAFCpmrxuaoRrpwIbDqEZAAAAFarp66ZGuHYqsOEQmgEAAFChmrxuaoRrpwIbFqEZAAA0YDV5ap3T6hov100FGiOhGQAANFA1fWqd0+oAaEyEZgAA0EDV5Kl1TqsDoLERmgEAQAPn1DoAqL4m+S4AAAAAAOoboRkAAAAAZDg9EwAAKuHOkwDQeAnNAACgAu48CQCNm9AMAAAq4M6TANC4Cc0AAGAN3HkSABonNwIAAAAAgAxHmkEj4ULGAAAAUHVCM2gEXMgYAAAAqkdoBo2ACxkDAABA9QjNoBFxIWMAAACoGjcCAAAAAIAMR5oBAECe1OSNeiLcrAdovGr7xmf2142T0AwAAPKgpm/UE+FmPUDjVNs3PrO/bryEZgAAkAc1eaOeCDfrARqv2r7xmf114yU0AwCAPHKjHoCaUdv7U/vrxkdoBkCjUdvXugAAABoOoRkAjUJtX+sCAABoWIRmADQKtX2tCwAAoGERmkEVuL0wNJz3gWtRAAAAVSE0g7Vwe2HwPgAAABofoRmshdsLg/cBAADQ+AjNoIqc0gXeBwAAQOPRJN8FAAAAAEB9IzQDACAvLrnkkigoKCjz2GabbfJdFgBARDg9EwCAPNpuu+3i4Ycfzv3frJnuKQBQP+iVAACQN82aNYvOnTvnuwwAgHKEZgAA5M0777wTXbt2jaKiohg4cGBMnDix0rvqLl++PJYvX577f+HChXVVJgDUqtmzZ8f8+fNrbHodO3Z0l/oaIDQDACAvBgwYEJMnT46tt9465s2bF5deemnsscce8eqrr0br1q3LjT9x4sS49NJL81ApANSe2bNnxzbb9ImlS5fU2DSLi0vizTffEJytJ6EZAAB5ceCBB+b+7tu3bwwYMCB69OgRd9xxR4wZM6bc+OPHj49x48bl/l+4cGF069atTmoFgNoyf/78WLp0SQw4ZUK06dJzvae3cN7M+PdNl8b8+fOFZutJaAYAQL3Qrl272GqrreLdd9+t8PnCwsIoLCys46oA1q4mT61zWl3j1aZLz2jffet8l8FqhGY1wA4SAGD9LV68ON5777044YQT8l0KQJXV9Kl1TquD+kNotp7sIAEA1s2PfvSjGD58ePTo0SPmzp0bEyZMiKZNm8bIkSPzXRpAldXkqXVOq4P6RWi2nuwgAQDWzQcffBAjR46MBQsWxMYbbxy77757PPPMM7HxxhvnuzSAanNqHTQ8QrMaYgcJGz6nWgPUrSlTpuS7BACiZvvBEfrCNBxCM4BwqjUAAI1TTfeDI/SFaTiEZgDhVGsAABqnmuwHR+gL07DU69DskksuiUsvvbTMsK233jrefPPNPFUEtcPh0PWHU60BAGiM9INZXw3xcjf1OjSLiNhuu+3i4Ycfzv3frFm9LxmqxeHQAAAAbMga6uVu6n0C1axZs+jcuXO+y4Ba43BoAAAANmQN9XI39T40e+edd6Jr165RVFQUAwcOjIkTJ65xpS1fvjyWL1+e+3/hwoV1USasN4dDAwAADVVDPHWP8hra99p6HZoNGDAgJk+eHFtvvXXMmzcvLr300thjjz3i1VdfjdatW1f4mokTJ5a7DhoAAACQHw311D0avnodmh144IG5v/v27RsDBgyIHj16xB133BFjxoyp8DXjx4+PcePG5f5fuHBhdOvWrdZrBQAAAMprqKfu0fDV69Asq127drHVVlvFu+++W+k4hYWFUVhYWIdVAXXBHUahbjh1AgCoLQ3t1D0avg0qNFu8eHG89957ccIJJ+S7FKAOucMo1A2nTgAAwP+p16HZj370oxg+fHj06NEj5s6dGxMmTIimTZvGyJEj810aUIfcYRTqhlMnAADg/9Tr0OyDDz6IkSNHxoIFC2LjjTeO3XffPZ555pnYeOON810akAcO54a64b0GAAD1PDSbMmVKvksAAAAAoBFqku8CAAAAAKC+qddHmgEADYu7c0LD4329dtbRmrlLOlBfCc0AgDrh7pzQ8Hhfr511tGbukg7UZ0IzAKBOuDsnNDze12tnHa2Zu6QD9ZnQDIdDA1Cn3J0TGh7v67WzjtasttePU2SBdSE0a+QcDg0AADRkTpEF1pXQrJFzODQAANCQOUUWWFdCMyLC4eKsP4e8AwBQn/nOA1SX0AxYbw55BwAAoKERmgHrzSHvAAAANDRCM6DGOOQdAACAhkJoBgAANFo1eV3WCNdmBWhIhGYAAECjVNPXZY1wbVaAhkRoBlBH3GEUAOqXmrwua4RrswI0NEIzgDrgDqMAUH+5LisAFRGaAdQBdxgFAADYsAjNAOqQX7IBAAA2DE3yXQAAAAAA1DdCMwAAAADIcHomdaK27xroroRAY1CT+7oI+zsAAFgToRm1rrbvGuiuhEBjUNP7ugj7OwAAWBOhGbWutu8a6K6EQGNQk/u6CPs7AABYG6EZdaa27xroroRAY2BfBwAAdcONAAAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAICMZvkuAAAAAIDaNXv27Jg/f36NTKtjx47RvXv3GplWfSY0AwAAAGjAZs+eHdts0yeWLl1SI9MrLi6JN998o8EHZ0IzAAAAgAZs/vz5sXTpkhhwyoRo06Xnek1r4byZ8e+bLo358+cLzQAAAKi/nHIFVFWbLj2jffet813GBkNoBkC9odMPANXjlCuA2iM0A6Be0OkHgOpzyhVA7RGaAVAv6PQDwLpzyhVAzROaAVCv6PQDAAD1QZN8FwAAAAAA9Y3QDACAvLvqqquioKAgfvjDH+a7FACAiBCaAQCQZ88991z8/ve/j759++a7FACAHKEZAAB5s3jx4hg1alTceOONsdFGG+W7HACAHKEZAAB5M3bs2Dj44INj2LBhax13+fLlsXDhwjIPAIDa4u6ZAADkxZQpU+KFF16I5557rkrjT5w4MS699NJargoA4FuONAMAoM7NmTMnzj777Lj11lujqKioSq8ZP358fPnll7nHnDlzarlKAKAxc6QZAAB1btq0afHJJ5/EzjvvnBu2cuXKePzxx+O3v/1tLF++PJo2bVrmNYWFhVFYWFjXpQIAjZTQDACAOjd06NB45ZVXygw7+eSTY5tttonzzz+/XGAGAFDXhGYAANS51q1bx/bbb19mWMuWLaNDhw7lhgMA5INrmgEAAABAxgZ1pNlVV10V48ePj7PPPjuuueaafJcDAGXMnj075s+fX2PT69ixY3Tv3r3Gpgf13dSpU/NdAgBAzgYTmj333HPx+9//Pvr27ZvvUgCgnNmzZ8c22/SJpUuX1Ng0i4tL4s033xCcAQBAHmwQodnixYtj1KhRceONN8bll1+e73IAoJz58+fH0qVLYsApE6JNl57rPb2F82bGv2+6NObPny80AwCAPNggQrOxY8fGwQcfHMOGDVtraLZ8+fJYvnx57v+FCxfWdnkAkNOmS89o333rfJcBAACsp3ofmk2ZMiVeeOGFeO6556o0/sSJE+PSSy+t5aoAAAAAaMjq9d0z58yZE2effXbceuutUVRUVKXXjB8/Pr788svcY86cObVcJQAAAAANTb0+0mzatGnxySefxM4775wbtnLlynj88cfjt7/9bSxfvjyaNm1a5jWFhYVRWFhY16UCAAAA0IDU69Bs6NCh8corr5QZdvLJJ8c222wT559/frnADAAAAABqQr0OzVq3bh3bb799mWEtW7aMDh06lBsOAAAAADWlXl/TDAAAAADyoV4faVaRqVOn5rsEAAAAABo4R5oBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIKPWQrMtttgiFixYUG74F198EVtssUVtzRYAgFqmnwcANAa1FprNnDkzVq5cWW748uXL48MPP6yt2QIAUMv08wCAxqBZTU/wnnvuyf390EMPRdu2bXP/r1y5Mh555JHo2bNnTc8WAIBapp8HADQmNR6aHX744RERUVBQEKNHjy7zXPPmzaNnz57xi1/8oqZnCwBALdPPAwAakxoPzVatWhUREZtvvnk899xz0bFjx5qeBQAAeaCfBwA0JjUempWaMWNGbU0aAIA80s8DABqDWgvNIiIeeeSReOSRR+KTTz7J/TJZ6qabbqrNWQMAUIv08wCAhq7WQrNLL700/vM//zP69+8fXbp0iYKCgtqaFQAAdUg/DwBoDGotNLv++utj8uTJccIJJ9TWLAAAyAP9PACgMWhSWxP++uuvY9CgQbU1eQAA8kQ/DwBoDGotNPve974Xt912W21NHgCAPNHPAwAag1o7PXPZsmVxww03xMMPPxx9+/aN5s2bl3n+6quvrq1ZAwBQi/TzAIDGoNZCs5dffjl22mmniIh49dVXyzznYrEAABsu/TwAoDGotdDs0Ucfra1JAwCQR/p5AEBjUGvXNCv17rvvxkMPPRRLly6NiIiUUm3PEgCAOqCfBwA0ZLUWmi1YsCCGDh0aW221VRx00EExb968iIgYM2ZMnHvuubU1WwAAapl+HgDQGNRaaHbOOedE8+bNY/bs2VFSUpIbfswxx8SDDz5YW7MFAKCW6ecBAI1BrV3T7O9//3s89NBDsdlmm5UZ3rt375g1a1ZtzRYAgFqmnwcANAa1dqTZV199VeaXx1KfffZZFBYW1tZsAQCoZfp5AEBjUGuh2R577BG33HJL7v+CgoJYtWpV/PznP4+99967tmYLAEAt088DABqDWjs98+c//3kMHTo0nn/++fj666/jvPPOi9deey0+++yzeOqpp2prtgAA1DL9PACgMai1I8223377ePvtt2P33XePww47LL766qs44ogj4sUXX4xevXrV1mwBAKhl+nkAQGNQa0eaRUS0bds2LrrootqcBQAAeaCfBwA0dLV2pNmkSZPizjvvLDf8zjvvjJtvvrm2ZgsAQC3TzwMAGoNaC80mTpwYHTt2LDe8U6dOceWVV9bWbAEAqGX6eQBAY1Brodns2bNj8803Lze8R48eMXv27NqaLQAAtUw/DwBoDGotNOvUqVO8/PLL5Ya/9NJL0aFDh9qaLQAAtUw/DwBoDGotNBs5cmT84Ac/iEcffTRWrlwZK1eujH/+859x9tlnx7HHHltbswUAoJbp5wEAjUGthWaXXXZZDBgwIIYOHRrFxcVRXFwc++23X+yzzz6udQEAsAGrqX7eddddF3379o02bdpEmzZtYuDAgfHAAw/UYuUAAFXXrDYmmlKKjz76KCZPnhyXX355TJ8+PYqLi2OHHXaIHj161MYsAQCoAzXZz9tss83iqquuit69e0dKKW6++eY47LDD4sUXX4ztttuulpYAAKBqai0023LLLeO1116L3r17R+/evWtjNgAA1LGa7OcNHz68zP9XXHFFXHfddfHMM88IzQCAvKuV0zObNGkSvXv3jgULFtTG5AEAyJPa6uetXLkypkyZEl999VUMHDiwwnGWL18eCxcuLPMAAKgttXZNs6uuuip+/OMfx6uvvlpbswAAIA9qsp/3yiuvRKtWraKwsDBOP/30uPvuu2PbbbetcNyJEydG27Ztc49u3bqt9/wBACpTK6dnRkSceOKJsWTJkthxxx2jRYsWUVxcXOb5zz77rLZmDQBALarJft7WW28d06dPjy+//DL+/Oc/x+jRo+Oxxx6rMDgbP358jBs3Lvf/woULBWcAQK2ptdDsmmuuqa1JAwCQRzXZz2vRokVsueWWERGxyy67xHPPPRe/+tWv4ve//325cQsLC6OwsLDG5g0AsCa1FpqNHj16vadx3XXXxXXXXRczZ86MiIjtttsufvrTn8aBBx643tMGAGDd1EQ/rzKrVq2K5cuX19r0AQCqqtauaRYR8d5778XFF18cI0eOjE8++SQiIh544IF47bXXqvT60tuQT5s2LZ5//vnYZ5994rDDDqvy6wEAqB3r28+L+PZ0y8cffzxmzpwZr7zySowfPz6mTp0ao0aNqq2yAQCqrNZCs8ceeyx22GGH+Pe//x133XVXLF68OCIiXnrppZgwYUKVpjF8+PA46KCDonfv3rHVVlvFFVdcEa1atYpnnnmmtsoGAGAtaqKfFxHxySefxIknnhhbb711DB06NJ577rl46KGHYt99962t0gEAqqzWQrMLLrggLr/88vjHP/4RLVq0yA3fZ5991in0qsptyAEAqH011c/74x//GDNnzozly5fHJ598Eg8//LDADACoN2rtmmavvPJK3HbbbeWGd+rUKebPn1+t6QwcODCWLVsWrVq1WuNtyCMili9fXuY6GAsXLqxe4QAArFFN9fMAAOqzWjvSrF27djFv3rxyw1988cXYdNNNqzyd0tuQ//vf/47/+I//iNGjR8frr79e6fgTJ06Mtm3b5h5uQw4AULNqqp8HAFCf1Vpoduyxx8b5558fH330URQUFMSqVaviqaeeih/96Edx4oknVnk6pbch32WXXWLixImx4447xq9+9atKxx8/fnx8+eWXucecOXNqYnEAAPh/aqqfBwBQn9VaaHbllVdGnz59onv37rF48eLYdtttY88994xBgwbFxRdfvM7TXdttyAsLC6NNmzZlHgAA1Jza6ucBANQnNX5Ns1WrVsV//dd/xT333BNff/11nHDCCTFixIhYvHhx9OvXL3r37l3laY0fPz4OPPDA6N69eyxatChuu+22mDp1ajz00EM1XTYAAGtRk/08AID6rsZDsyuuuCIuueSSGDZsWBQXF8dtt90WKaW46aabqj2t0tuQz5s3L9q2bRt9+/Z1G3IAgDypyX4eAEB9V+Oh2S233BK/+93v4vvf/35ERDz88MNx8MEHxx/+8Ido0qR6Z4P+8Y9/rOnyAABYRzXZzwMAqO9qvHcze/bsOOigg3L/Dxs2LAoKCmLu3Lk1PSsAAOqQfh4A0JjUeGj2zTffRFFRUZlhzZs3jxUrVtT0rAAAqEP6eQBAY1Ljp2emlOKkk06KwsLC3LBly5bF6aefHi1btswNu+uuu2p61gAA1CL9PACgManx0Gz06NHlhh1//PE1PRsAAOqYfh4A0JjUeGg2adKkmp4kAAD1gH4eANCYuM0RAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAeTFx4sTYddddo3Xr1tGpU6c4/PDD46233sp3WQAAESE0AwAgTx577LEYO3ZsPPPMM/GPf/wjVqxYEfvtt1989dVX+S4NACCa5bsAAAAapwcffLDM/5MnT45OnTrFtGnTYs8998xTVQAA3xKaAQBQL3z55ZcREdG+ffsKn1++fHksX7489//ChQvrpC4AoHFyeiYAAHm3atWq+OEPfxiDBw+O7bffvsJxJk6cGG3bts09unXrVsdVAgCNSb0OzVwcFgCgcRg7dmy8+uqrMWXKlErHGT9+fHz55Ze5x5w5c+qwQgCgsanXoZmLwwIANHxnnnlm3HvvvfHoo4/GZpttVul4hYWF0aZNmzIPAIDaUq+vaebisAAADVdKKc4666y4++67Y+rUqbH55pvnuyQAgJx6HZplre3isBEuEAsAsKEYO3Zs3HbbbfH//X//X7Ru3To++uijiIho27ZtFBcX57k6AKCxq9enZ66uKheHjXCBWACADcV1110XX375Zey1117RpUuX3ONPf/pTvksDANhwjjQrvTjsk08+ucbxxo8fH+PGjcv9v3DhQsEZAEA9lFLKdwkAAJXaIEKz0ovDPv7442u8OGzEtxeILSwsrKPKAAAAAGiI6nVo5uKwAAAAAORDvQ7NXBwWAAAAgHyo1zcCcHFYAAAAAPKhXh9p5uKwAAAAAORDvT7SDAAAAADyQWgGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAB58fjjj8fw4cOja9euUVBQEH/961/zXRIAQI7QDACAvPjqq69ixx13jGuvvTbfpQAAlFPvQzO/QAIANEwHHnhgXH755fHd734336UAAJRT70Mzv0ACABARsXz58li4cGGZBwBAbWmW7wLW5sADD4wDDzww32UAAJBnEydOjEsvvTTfZQAAjUS9P9KsuvwCCQDQMI0fPz6+/PLL3GPOnDn5LgkAaMAaXGg2ceLEaNu2be7RrVu3fJcEAEANKCwsjDZt2pR5AADUlgYXmvkFEgAAAID1Ve+vaVZdhYWFUVhYmO8yAABYi8WLF8e7776b+3/GjBkxffr0aN++fXTv3j2PlQEANMDQDACADcPzzz8fe++9d+7/cePGRUTE6NGjY/LkyXmqCgDgW/U+NPMLJABAw7TXXntFSinfZQAAVKjeh2Z+gQQAAACgrtX70MwvkAAAAADUtQZ390wAAAAAWF9CMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADI2CBCs2uvvTZ69uwZRUVFMWDAgHj22WfzXRIAADVAPw8AqK/qfWj2pz/9KcaNGxcTJkyIF154IXbcccfYf//945NPPsl3aQAArAf9PACgPqv3odnVV18dp556apx88smx7bbbxvXXXx8lJSVx00035bs0AADWg34eAFCfNct3AWvy9ddfx7Rp02L8+PG5YU2aNIlhw4bF008/XeFrli9fHsuXL8/9/+WXX0ZExMKFC2ulxsWLF0dExGez3opvli9dr2kt/Gh2bpql9dbk9CuaR21Pv/TvmprHhj79iuahDep2+nUxD9PP/zw29OlXNA9tsPbp16TSaaaUanzafEs/z761oU2/LuaxoU+/onlog7qdfkXz0AZ1O/2K5qEN1j79mlStfl6qxz788MMUEelf//pXmeE//vGP02677VbhayZMmJAiwsPDw8PDw8NjvR9z5sypiy5Po6Sf5+Hh4eHh4ZHPR1X6efX6SLN1MX78+Bg3blzu/1WrVsVnn30WHTp0iIKCghqZx8KFC6Nbt24xZ86caNOmTY1Mk+rRBvmnDeoH7ZB/2iD/aqMNUkqxaNGi6Nq1a41Mj5qhn9d4aIf80wb5pw3yTxvkX777efU6NOvYsWM0bdo0Pv744zLDP/744+jcuXOFryksLIzCwsIyw9q1a1cr9bVp08YbJ8+0Qf5pg/pBO+SfNsi/mm6Dtm3b1ti0KE8/j6rQDvmnDfJPG+SfNsi/fPXz6vWNAFq0aBG77LJLPPLII7lhq1atikceeSQGDhyYx8oAAFgf+nkAQH1Xr480i4gYN25cjB49Ovr37x+77bZbXHPNNfHVV1/FySefnO/SAABYD/p5AEB9Vu9Ds2OOOSY+/fTT+OlPfxofffRR7LTTTvHggw/GJptskreaCgsLY8KECeVOD6DuaIP80wb1g3bIP22Qf9pgw6WfR2W0Q/5pg/zTBvmnDfIv321QkJJ7qQMAAADA6ur1Nc0AAAAAIB+EZgAAAACQITQDAAAAgAyhGQAAAABkCM0qce2110bPnj2jqKgoBgwYEM8+++wax7/zzjtjm222iaKiothhhx3i/vvvr6NKG67qtMGNN94Ye+yxR2y00Uax0UYbxbBhw9baZqxddd8HpaZMmRIFBQVx+OGH126BjUB12+CLL76IsWPHRpcuXaKwsDC22mor+6MaUN12uOaaa2LrrbeO4uLi6NatW5xzzjmxbNmyOqq2YXn88cdj+PDh0bVr1ygoKIi//vWva33N1KlTY+edd47CwsLYcsstY/LkybVeJxsW/bz808+rH/T18k9fL//08/Kr3vf1EuVMmTIltWjRIt10003ptddeS6eeempq165d+vjjjysc/6mnnkpNmzZNP//5z9Prr7+eLr744tS8efP0yiuv1HHlDUd12+C4445L1157bXrxxRfTG2+8kU466aTUtm3b9MEHH9Rx5Q1Hddug1IwZM9Kmm26a9thjj3TYYYfVTbENVHXbYPny5al///7poIMOSk8++WSaMWNGmjp1apo+fXodV96wVLcdbr311lRYWJhuvfXWNGPGjPTQQw+lLl26pHPOOaeOK28Y7r///nTRRRelu+66K0VEuvvuu9c4/vvvv59KSkrSuHHj0uuvv55+85vfpKZNm6YHH3ywbgqm3tPPyz/9vPpBXy//9PXyTz8v/+p7X09oVoHddtstjR07Nvf/ypUrU9euXdPEiRMrHP/oo49OBx98cJlhAwYMSN///vdrtc6GrLptkPXNN9+k1q1bp5tvvrm2Smzw1qUNvvnmmzRo0KD0hz/8IY0ePVpHaj1Vtw2uu+66tMUWW6Svv/66rkpsFKrbDmPHjk377LNPmWHjxo1LgwcPrtU6G4OqdKTOO++8tN1225UZdswxx6T999+/FitjQ6Kfl3/6efWDvl7+6evln35e/VIf+3pOz8z4+uuvY9q0aTFs2LDcsCZNmsSwYcPi6aefrvA1Tz/9dJnxIyL233//SsdnzdalDbKWLFkSK1asiPbt29dWmQ3aurbBf/7nf0anTp1izJgxdVFmg7YubXDPPffEwIEDY+zYsbHJJpvE9ttvH1deeWWsXLmyrspucNalHQYNGhTTpk3LHdr//vvvx/333x8HHXRQndTc2PlMZk308/JPP69+0NfLP329/NPP2zDV9edys1qZ6gZs/vz5sXLlythkk03KDN9kk03izTffrPA1H330UYXjf/TRR7VWZ0O2Lm2Qdf7550fXrl3LvZmomnVpgyeffDL++Mc/xvTp0+ugwoZvXdrg/fffj3/+858xatSouP/+++Pdd9+NM844I1asWBETJkyoi7IbnHVph+OOOy7mz58fu+++e6SU4ptvvonTTz89LrzwwrooudGr7DN54cKFsXTp0iguLs5TZdQH+nn5p59XP+jr5Z++Xv7p522Y6rqv50gzGpyrrroqpkyZEnfffXcUFRXlu5xGYdGiRXHCCSfEjTfeGB07dsx3OY3WqlWrolOnTnHDDTfELrvsEsccc0xcdNFFcf311+e7tEZl6tSpceWVV8bvfve7eOGFF+Kuu+6K++67Ly677LJ8lwawwdPPyw99vfpBXy//9PMaH0eaZXTs2DGaNm0aH3/8cZnhH3/8cXTu3LnC13Tu3Lla47Nm69IGpf77v/87rrrqqnj44Yejb9++tVlmg1bdNnjvvfdi5syZMXz48NywVatWRUREs2bN4q233opevXrVbtENzLq8D7p06RLNmzePpk2b5ob16dMnPvroo/j666+jRYsWtVpzQ7Qu7fCTn/wkTjjhhPje974XERE77LBDfPXVV3HaaafFRRddFE2a+L2qNlX2mdymTRtHmaGfVw/o59UP+nr5p6+Xf/p5G6a67utp0YwWLVrELrvsEo888khu2KpVq+KRRx6JgQMHVviagQMHlhk/IuIf//hHpeOzZuvSBhERP//5z+Oyyy6LBx98MPr3718XpTZY1W2DbbbZJl555ZWYPn167nHooYfG3nvvHdOnT49u3brVZfkNwrq8DwYPHhzvvvturhMbEfH2229Hly5ddKLW0bq0w5IlS8p1mEo7t99e35Ta5DOZNdHPyz/9vPpBXy//9PXyTz9vw1Tnn8u1cnuBDdyUKVNSYWFhmjx5cnr99dfTaaedltq1a5c++uijlFJKJ5xwQrrgggty4z/11FOpWbNm6b//+7/TG2+8kSZMmOBW5Oupum1w1VVXpRYtWqQ///nPad68ebnHokWL8rUIG7zqtkGWOyqtv+q2wezZs1Pr1q3TmWeemd5666107733pk6dOqXLL788X4vQIFS3HSZMmJBat26dbr/99vT++++nv//976lXr17p6KOPztcibNAWLVqUXnzxxfTiiy+miEhXX311evHFF9OsWbNSSildcMEF6YQTTsiNX3ob8h//+MfpjTfeSNdee22t3oacDY9+Xv7p59UP+nr5p6+Xf/p5+Vff+3pCs0r85je/Sd27d08tWrRIu+22W3rmmWdyzw0ZMiSNHj26zPh33HFH2mqrrVKLFi3Sdtttl+677746rrjhqU4b9OjRI0VEuceECRPqvvAGpLrvg9XpSNWM6rbBv/71rzRgwIBUWFiYtthii3TFFVekb775po6rbniq0w4rVqxIl1xySerVq1cqKipK3bp1S2eccUb6/PPP677wBuDRRx+tcP9eus5Hjx6dhgwZUu41O+20U2rRokXaYost0qRJk+q8buo3/bz808+rH/T18k9fL//08/Krvvf1ClJyDCEAAAAArM41zQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRk0QpdccknstNNO+S6jQarLdfvII49Enz59YuXKlXUyv5o2c+bMKCgoiOnTp+e7lCp58MEHY6eddopVq1bluxQAqJR+Xu3Rz6u6Da2fd/3118fw4cPzXQb1kNAMqmjq1KlRUFAQX3zxRb5LyavqfADutdde8cMf/rBG53/SSSfF4YcfXqPT3FCdd955cfHFF0fTpk3rbJ51vf579uwZ11xzTY1Os7Lt8gc/+EHssssuUVhYWGGH+IADDojmzZvHrbfeWqP1AJB/+nnf0s+rP/Tz1k1F2+VLL70UI0eOjG7dukVxcXH06dMnfvWrX5UZ55RTTokXXnghnnjiiRqthw2f0AxgA/Tkk0/Ge++9FyNGjMh3KRVasWJFvktYJ6ecckocc8wxlT5/0kknxa9//es6rAgAaGz082rWtGnTolOnTvG///u/8dprr8VFF10U48ePj9/+9re5cVq0aBHHHXecfh7lJWgghgwZksaOHZvGjh2b2rRpkzp06JAuvvjitGrVqtw4n332WTrhhBNSu3btUnFxcTrggAPS22+/nXt+5syZ6ZBDDknt2rVLJSUladttt0333XdfmjFjRoqIMo/Ro0evtaY777wzbb/99qmoqCi1b98+DR06NC1evDillNLo0aPTYYcdli655JLUsWPH1Lp16/T9738/LV++PPf6lStXpiuvvDL17NkzFRUVpb59+6Y777wz9/yjjz6aIiI9/PDDaZdddknFxcVp4MCB6c033yxTx8SJE1OnTp1Sq1at0imnnJLOP//8tOOOO5aZzq677ppKSkpS27Zt06BBg9LMmTMrXKbsehgyZEiF440ePbrcuDNmzEgppfTKK6+kAw44ILVs2TJ16tQpHX/88enTTz9d63qbMGFCuWk++uijFc6/KtvD6r788stUVFSU7r///jLD77rrrtSqVav01VdfpZRSOu+881Lv3r1TcXFx2nzzzdPFF1+cvv7669z4EyZMKLNuhwwZks4+++wy0zzssMPKbD/Lli1L5557buratWsqKSlJu+22W6XLVWrs2LHpyCOPLDf8nnvuSf3790+FhYWpQ4cO6fDDD889t7btf9KkSalt27bpwQcfTNtss01q2bJl2n///dPcuXNzy1bR+i99f0yZMiXtueeeqbCwME2aNCmtXLkyXXrppWnTTTdNLVq0SDvuuGN64IEHcvMrfd2LL75Y4TIOGTKk3PxKPfHEE2n33XdPRUVFabPNNktnnXVW7r2VUkrXXntt2nLLLVNhYWHq1KlTGjFiREppzdtlqWwbrm7WrFkpItK7775bccMAUCv08/TzVqefp5+3rv28UmeccUbae++9ywx77LHHUosWLdKSJUsqaRkaI6EZDcaQIUNSq1at0tlnn53efPPN9L//+7+ppKQk3XDDDblxDj300NSnT5/0+OOPp+nTp6f9998/bbnllrkPw4MPPjjtu+++6eWXX07vvfde+tvf/pYee+yx9M0336S//OUvKSLSW2+9lebNm5e++OKLNdYzd+7c1KxZs3T11VenGTNmpJdffjlde+21adGiRSmlb3fqrVq1Ssccc0x69dVX07333ps23njjdOGFF+amcfnll6dtttkmPfjgg+m9995LkyZNSoWFhWnq1Kkppf/rTA0YMCBNnTo1vfbaa2mPPfZIgwYNyk3jT3/6UyosLEx/+MMf0ptvvpkuuuii1Lp169wH/ooVK1Lbtm3Tj370o/Tuu++m119/PU2ePDnNmjWrwuV69tlncx24efPmpQULFlQ43hdffJEGDhyYTj311DRv3rw0b9689M0336TPP/88bbzxxmn8+PHpjTfeSC+88ELad999cx9aa1pvixYtSkcffXQ64IADctNcvfNZ3e0h68gjj0zHH398mWEjRowoM+yyyy5LTz31VJoxY0a655570iabbJJ+9rOf5Z5fl87U9773vTRo0KD0+OOPp3fffTf913/9VyosLCzT0cnq27dvuuqqq8oMu/fee1PTpk3TT3/60/T666+n6dOnpyuvvDL3/Nq2/0mTJqXmzZunYcOGpeeeey5NmzYt9enTJx133HEppVTp+i/tFPXs2TP95S9/Se+//36aO3duuvrqq1ObNm3S7bffnt5888103nnnpebNm+eWa22dqQULFqTNNtss/ed//mdufiml9O6776aWLVumX/7yl+ntt99OTz31VOrXr1866aSTUkopPffcc6lp06bptttuSzNnzkwvvPBC+tWvfpVSqny7XN2aQrOUUtpkk03SpEmTKn0egJqnn6efV93tIUs/Tz9vdaNGjcqFbaW++uqr1KRJk7WGmjQuQjMajCFDhqQ+ffqU+YXp/PPPT3369EkppfT222+niEhPPfVU7vn58+en4uLidMcdd6SUUtphhx3SJZdcUuH0Szsun3/+eZXqmTZtWoqISn/JGz16dGrfvn3ul62UUrruuutSq1at0sqVK9OyZctSSUlJ+te//lXmdWPGjEkjR44sU9PDDz+ce/6+++5LEZGWLl2aUkpp4MCB6YwzzigzjQEDBuQ+8BcsWJAiItdBW5u1fQCurqKOxGWXXZb222+/MsPmzJmT66hWZb0ddthhVZr3mraHitx9991lfm0s/VVy9V/Nsv7rv/4r7bLLLrn/q9uZmjVrVmratGn68MMPy4wzdOjQNH78+Ern27Zt23TLLbeUGTZw4MA0atSoCsevyvY/adKkckdRXXvttWmTTTbJ/V/R+i/dJq655poyw7t27ZquuOKKMsN23XXX3PZYlW2pR48e6Ze//GWZYWPGjEmnnXZamWFPPPFEatKkSVq6dGn6y1/+ktq0aZMWLlxY4TQrapPVrS0069evX6X7CQBqh37et/Tz/m/e+nn/Rz/v/6ytn5dSSk899VRq1qxZeuihh8o9t9FGG6XJkyev8fU0Lq5pRoPyne98JwoKCnL/Dxw4MN55551YuXJlvPHGG9GsWbMYMGBA7vkOHTrE1ltvHW+88UZEfHsh8MsvvzwGDx4cEyZMiJdffnmda9lxxx1j6NChscMOO8RRRx0VN954Y3z++eflxikpKSlT7+LFi2POnDnx7rvvxpIlS2LfffeNVq1a5R633HJLvPfee2Wm07dv39zfXbp0iYiITz75JCIi3njjjTLLXDqfUu3bt4+TTjop9t9//xg+fHj86le/innz5lVrWZ944okyNa7pQukvvfRSPProo2XG32abbSIi4r333qvSequqNW0PV155ZZkaZs+eHQcddFA0b9487rnnnoiI+Mtf/hJt2rSJYcOG5abxpz/9KQYPHhydO3eOVq1axcUXXxyzZ89ep/oiIl555ZVYuXJlbLXVVmXqeeyxx8q18+qWLl0aRUVFZYZNnz49hg4dWuH4Vdn+IyJKSkqiV69euf+7dOmS25bWpn///rm/Fy5cGHPnzo3BgweXGWfw4MFl5rcuXnrppZg8eXKZ9bX//vvHqlWrYsaMGbHvvvtGjx49YosttogTTjghbr311liyZMl6zXN1xcXFNTo9AKpGP08/b3X6ef9HP6/q/bJXX301DjvssJgwYULst99+5Z7XzyNLaAar+d73vhfvv/9+nHDCCfHKK69E//794ze/+c06Tatp06bxj3/8Ix544IHYdttt4ze/+U1svfXWMWPGjCq9fvHixRERcd9998X06dNzj9dffz3+/Oc/lxm3efPmub9LOw+rVq2qcq2TJk2Kp59+OgYNGhR/+tOfYquttopnnnmmyq/v379/mRoPPfTQNS7X8OHDy4w/ffr0eOedd2LPPfdc7/VWVaeffnqZ+Xft2jVatGgRRx55ZNx2220REXHbbbfFMcccE82aNYuIiKeffjpGjRoVBx10UNx7773x4osvxkUXXRRff/11pfNp0qRJpJTKDFv94qmLFy+Opk2bxrRp08rU88Ybb5S7q8/qOnbsWK6TWVxcXO31kLX6thTx7faUrb8yLVu2XO/5V8XixYvj+9//fpn19dJLL8U777wTvXr1itatW8cLL7wQt99+e3Tp0iV++tOfxo477lhjd0T77LPPYuONN66RaQFQd/Tz9PP08xp3P+/111+PoUOHxmmnnRYXX3xxhePo55ElNKNB+fe//13m/2eeeSZ69+4dTZs2jT59+sQ333xTZpwFCxbEW2+9Fdtuu21uWLdu3eL000+Pu+66K84999y48cYbI+LbO6pERKxcubLK9RQUFMTgwYPj0ksvjRdffDFatGgRd999d+75l156KZYuXVqm3latWkW3bt1i2223jcLCwpg9e3ZsueWWZR7dunWrcg19+vSpcL1k9evXL8aPHx//+te/Yvvtt891KLIqWg/FxcVl6mvdunVu3Oz62nnnneO1116Lnj17lluu0g/jNa23iqZZmTVtD+3bty8z79IO06hRo+LBBx+M1157Lf75z3/GqFGjcq//17/+FT169IiLLroo+vfvH717945Zs2atsYaNN964zC+6K1eujFdffTX3f79+/WLlypXxySeflFsfnTt3rnS6/fr1i9dff73MsL59+8YjjzxS4fhV3f7Xpqrrv02bNtG1a9d46qmnygx/6qmn1nt+O++8c7z++uvl1teWW26Z2z6bNWsWw4YNi5///Ofx8ssvx8yZM+Of//xntZahIsuWLYv33nsv+vXrt06vB2Dd6eeVp59Xdrn18/TzKluG1157Lfbee+8YPXp0XHHFFRXW895778WyZcv08yhDaEaDMnv27Bg3bly89dZbcfvtt8dvfvObOPvssyMionfv3nHYYYfFqaeeGk8++WS89NJLcfzxx8emm24ahx12WERE/PCHP4yHHnooZsyYES+88EI8+uij0adPn4iI6NGjRxQUFMS9994bn376ae4Xwsr8+9//jiuvvDKef/75mD17dtx1113x6aef5qYXEfH111///+zdeXxU9b0//jdrCJKACFSoLC4IuFCrthSXuqHUWqu216q1iq21VXGlLkW9IlbFtretXaxVbxW9Vy/WW7HeuuBSUeuKC24IKIKhCqXRSmQxKPn8/uiP+ZpDAgFmMpPk+Xw85vFgzpz5nHfOOWTeec1Z4sQTT4xZs2bFPffcExMmTIjTTjst2rdvHxUVFXHOOefE2WefHTfddFPMmzcvnn/++fj1r38dN910U5PXyZlnnhk33HBD3HjjjTF37tyYMGFCvPrqq7nX58+fH+PHj48nn3wy3nrrrbj//vvj9ddfr1fnJ/Xp0yfKy8vjvvvui7///e+xdOnSRpc9aNCgePrpp2PBggVRXV0ddXV1MXbs2HjvvffimGOOiRkzZsS8efNi2rRp8e1vfztWr1693vU2aNCgeOmll2LOnDlRXV29zlter2t/aMwXv/jF2HLLLePYY4+Nrbfeut5h7oMHD46qqqqYMmVKzJs3L371q1/Va44bsv/++8fdd98dd999d8yePTtOOeWUet+Ebb/99nHsscfG8ccfH3fccUfMnz8/nnnmmZg0aVLcfffdjY47evTo+Otf/1pv2oQJE+J//ud/YsKECfHaa6/Fyy+/HD/+8Y9zta9v/2+KDVn/5557bvz4xz+O2267LebMmRM//OEPY+bMmevdBtnlPfroo/H2229HdXV1REScf/758cQTT8Rpp52W+/b6T3/6U5x22mkREfHnP/85fvWrX8XMmTPjrbfeiptvvjnq6upiyJAhuTGz+2VExBtvvBEzZ86MxYsXx8qVK3Pfbn7yG+annnoqysrK6p36AkDz0OetTZ+nz1tTuz6v8T7vlVdeif322y8OOuigGDduXCxevDgWL14c//jHP+rV89hjj8U222xT7xRWcCMAWo199tknnXrqqenkk09OlZWVafPNN08XXHBBg7ci7969eyovL0+jR4+ud+ea0047LW277baprKws9e7dOx133HGpuro69/qll16attxyy9SuXbv13op81qxZafTo0al3796prKwsbb/99unXv/517vU1F9q8+OKL0xZbbJG6deuWTjrppPThhx/m5qmrq0tXXXVVGjJkSOrUqVPq3bt3Gj16dHrkkUdSSg1ftPaFF15Y6/bKl19+eerVq1fq1q1bGjNmTDrvvPNyFzFdvHhxOvzww1Pfvn1T586d08CBA9PFF1+cVq9e3ejPdv3116f+/fun9u3bN3or8pRSmjNnTvrCF76QysvL69U0d+7cdMQRR+RuiT106NB01llnpbq6uvWutyVLlqQDDzwwdevWbb23Il/f/tCY8847L0VEuvjii9d67dxzz81tr6OOOir94he/SN27d8+9nr1A7KpVq9Ipp5ySevbsmfr06ZMmTZq01l2VVq1alS6++OI0aNCg1KlTp9S3b990xBFHpJdeeqnRGt99993UpUuXtW47/8c//jHtsssuqXPnzqlXr17pa1/7Wu619e3/a25F/klTp06tdwvwhtZ/Yxd6Xb16dbrkkkvSpz/96dSpU6cm3Yp84MCBacKECbnnTz75ZBo+fHgqKyurV8czzzyTq2OzzTZLw4cPz12M9rHHHkv77LNP2nzzzVN5eXkaPnx4uu2223LvbWy/bOjW59n/S9/73vfS97///YY3CgAFo8/7F33ev+jz9Hkb0udNmDChwR5v4MCB9X6mgw46KE2aNKnhjUKb1S6lJp7EDCVu3333jV122SWuuuqqYpfSJCeccEK8//77ceeddxa7lFappe0PG+Pcc8+NmpqauPbaa4tdSl6sWLEitthii7j33ntj3333LXY5a6muro4hQ4bEs88+G1tvvXWxywFoU1ra57o+r7Ba2v6wMfR5zevVV1+N/fffP+bOnRvdu3cvdjmUEKdnArRQF154YQwcOHCDLgZcyh5++OHYf//9S7KRiohYsGBB/Pa3vxWYAQAFp89rXosWLYqbb75ZYMZaOha7AGipqqqq1nmhy1mzZsWAAQOasSLamh49esQFF1xQ7DLy5pBDDolDDjmk2GU0avfdd693u3UAWi99HsWmz2teo0aNKnYJlCinZ8JG+vjjj2PBggWNvj5o0KDcnXoAAGg59HkARAjNAAAAAGAtrmkGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGR2LXUCh1dXVxTvvvBMVFRXRrl27YpcDALQAKaX44IMPol+/ftG+ve8YS5U+DwDYUBvS57X60Oydd96J/v37F7sMAKAFWrhwYWy11VbFLoNG6PMAgI3VlD6v1YdmFRUVEfGvlVFZWVnkagCAlqCmpib69++f6yMoTfo8AGBDbUif1+pDszWH6ldWVmqmAIAN4pS/0qbPAwA2VlP6PBfpAAAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZJR+avf322/Gtb30rtthiiygvL4+dd945nn322WKXBQDAJtLnAQClrGOxC1iXf/7zn7HnnnvGfvvtF/fee2/07t07Xn/99dh8882LXRoAAJtAnwcAlLqSDs1+/OMfR//+/ePGG2/MTdt6662LWBEAAPmgzwMASl1Jh2Z33XVXjB49Oo488sh45JFH4tOf/nSceuqpcdJJJzX6ntra2qitrc09r6mpaY5SAciDqqqqqK6uzstYvXr1igEDBuRlLCD/9HkApSOfPViEPozWo6RDszfffDOuueaaGDduXFxwwQUxY8aMOOOMM6Jz584xZsyYBt8zadKkmDhxYjNXCsCmqqqqiqFDh8XKlSvyMl55edeYPfs1DRuUKH0eQGnIdw8WoQ+j9WiXUkrFLqIxnTt3jt133z2eeOKJ3LQzzjgjZsyYEU8++WSD72noG8j+/fvH0qVLo7KysuA1A7Bxnn/++dhtt91ixHcmRGXfQZs0Vs2iBfH0DRPjueeei1133TU/BdKm1NTURPfu3fUPBaTPAygN+ezBIvRhlL4N6fNK+kizvn37xg477FBv2rBhw+KPf/xjo+8pKyuLsrKyQpdGiXFKF7QelX0HRc8BQ4pdBlBg+jyA0qIHg7WVdGi25557xpw5c+pNmzt3bgwcOLBIFVGKnNIFAC2PPg8AKHUlHZqdffbZsccee8QVV1wR3/jGN+KZZ56J6667Lq677rpil0YJqa6ujpUrV+T1lK7q6mqhGQAUkD4PACh1JR2afe5zn4upU6fG+PHj49JLL42tt946rrrqqjj22GOLXRolyOHEANBy6PMAgFJX0qFZRMRXvvKV+MpXvlLsMgAAyDN9HgBQytoXuwAAAAAAKDVCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZHQsdgEAAADFUlVVFdXV1Xkbr1evXjFgwIC8jQdA8QjNAACANqmqqiqGDh0WK1euyNuY5eVdY/bs1wRnAK2A0AwAAGiTqqurY+XKFTHiOxOisu+gTR6vZtGCePqGiVFdXS00A2gFhGYAAECbVtl3UPQcMKTYZQBQYtwIAAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGR2LXQAAAFA4VVVVUV1dnZexevXqFQMGDMjLWAD8P/n8XR3h93W+CM0AAKCVqqqqiqFDh8XKlSvyMl55edeYPfs1f4gB5FG+f1dH+H2dL0IzAABopaqrq2PlyhUx4jsTorLvoE0aq2bRgnj6holRXV3tjzCAPMrn7+oIv6/zSWgGAACtXGXfQdFzwJBilwHAOvhdXXrcCAAAAAAAMoRmAAAAAJDh9EyA/5+7iwEAALCG0Awg3F0MAACA+oRmAOHuYgAAANQnNKNZOO2NlsIdawAAAIgQmtEMnPYGAAAAtDRCMwrOaW8AAABASyM0o9k47Q0AAIBCcEkgCkFoBgAAALRYLglEoQjNAAAAgBbLJYEoFKEZtBEOVwYAAFozlwQi34Rm0AY4XBkAAAA2jNAM2gCHKwMAAMCGEZpBG+JwZQAAAGgaoVkLkM9rUUW4HlUpso0BAACgtAjNSly+r0UV4XpUpcY2BgAAgNIjNCtx+bwWVYTrUZUi2xgAAABKj9CshXAtqtbPNmZT5fM0X6f4AgBruJQI0FYJzQBagXyf5usUXwAgwqVEgLZNaAbQCuTzNF+n+AIAa7iUCNCWCc1wuHUTWEe0FE7zBaC1cfmB0qDHWDf7KbROQrM2zuHW62cdAQAUh8sP0BLYT6H1Epq1cQ63Xj/rCACgOFx+gJbAfgqtl9CMiHC4dVNYRwAAxdHS+zCn7rUNLX0/BdYmNAMAACgQp+4BtFxCMwAAgAJx6h5AyyU0AwAAKDCn7gG0PO2LXQAAAAAAlBqhGQAAAABklPTpmZdccklMnDix3rQhQ4bE7Nmzi1QRQNuVzzt/Rbj7F6DXAwBKW0mHZhERO+64Yzz44IO55x07lnzJAK1Ovu/8FeHuX8C/6PUAgFJV8l1Jx44dY8sttyx2GQBtWj7v/BXh7l/A/6PXAwBKVcmHZq+//nr069cvunTpEiNHjoxJkyat8w+s2traqK2tzT2vqalpjjIB2gR3/gLybUN6vdbY5zn1HWgKvyvIh3zuR21lHyrp0GzEiBExefLkGDJkSCxatCgmTpwYe++9d7zyyitRUVHR4HsmTZq01rUxAAAoPRva67W2Ps+p70BT+F1BPuR7P2or+1BJh2YHH3xw7t/Dhw+PESNGxMCBA+MPf/hDnHjiiQ2+Z/z48TFu3Ljc85qamujfv3/BawUAYMNsaK/X2vo8p74DTeF3BfmQz/2oLe1DJR2aZfXo0SO23377eOONNxqdp6ysLMrKypqxKgAA8mF9vV5r7fOc+g40hd8V5IP9aMO0qNBs2bJlMW/evDjuuOOKXQqQ4fx4ADaVXg8AKCUlHZqdc845ceihh8bAgQPjnXfeiQkTJkSHDh3imGOOKXZpwCc4Px6AjaHXAwBKWUmHZn/729/imGOOiXfffTd69+4de+21Vzz11FPRu3fvYpcGfILz4wHYGHo9AKCUlXRoNmXKlGKXAGwA58cDsCH0egBAKWtf7AIAAAAAoNQIzQAAAAAgo6RPz2wp3DUQAAAAaMtaYzYiNNtE7hoIAAAAtGWtNRsRmm0idw0EAAAA2rLWmo0IzfLEXQMByOch6RGlc1g6AAA0RWvLRoRmAJAH+T4kPaJ0DksHAIC2SGgGAHmQz0PSI0rrsHQAAGiLhGYAkEet7ZD0fGuNd1UCYNM0x+UNfP4AG0NoBgA0i9Z6VyUANl5zXN7A5w+wsYRmAECzaK13VQJg4zXH5Q18/gAbS2gGADQrp7ACkNUcnw0+f4AN1b7YBQAAAABAqRGaAQAAAECG0zMBoIVojruLAdDyuDMkQGEIzQCgBWiOu4sB0PK4MyRA4QjNAKAFaI67iwHQ8rgzJEDhCM0AoAVx5y8AGuLzASD/hGYAANAI14oCgLZLaAYAAA1wrSgAaNuEZgAA0ADXigKAtk1oBgAA6+BaUQDQNrUvdgEAAAAAUGqEZgAAAACQ4fRMgGbiDmwAAAAth9AMoBm4AxsAAEDLIjQDaAbuwAYAANCyCM0AmpE7sAEAALQMbgQAAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADI6FrsAgKaoqqqK6urqvI3Xq1evGDBgQN7GAwAAoHURmgElr6qqKoYOHRYrV67I25jl5V1j9uzXBGcAAAA0SGgGlLzq6upYuXJFjPjOhKjsO2iTx6tZtCCevmFiVFdXC80AAABokNAMaDEq+w6KngOGFLsMAOAT8nkJBZdPAKCUCM0AAICNku9LKLh8AgClRGgGAABslHxeQsHlEwAoNUIzAABgk7iEAgCtUftiFwAAAAAApUZoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMlrU3TOvvPLKGD9+fJx55plx1VVXFbscAADyRJ8HQCmrqqqK6urqvI3Xq1evGDBgQN7GozBaTGg2Y8aMuPbaa2P48OHFLgUAgDzS5wFQyqqqqmLo0GGxcuWKvI1ZXt41Zs9+TXBW4lpEaLZs2bI49thj4/rrr4/LLrus2OUAAJAn+jwASl11dXWsXLkiRnxnQlT2HbTJ49UsWhBP3zAxqqurhWYlrkWEZmPHjo1DDjkkRo0atd5mqra2Nmpra3PPa2pqCl0eAAAbSZ8H0Dbk8/TGYp3aWNl3UPQcMKTZl0vxlHxoNmXKlHj++edjxowZTZp/0qRJMXHixAJXBQDAptLnAbQN+T690amNNJeSDs0WLlwYZ555ZjzwwAPRpUuXJr1n/PjxMW7cuNzzmpqa6N+/f6FKBABgI+jzANqOfJ7e6NRGmlNJh2bPPfdcLFmyJHbdddfctNWrV8ejjz4av/nNb6K2tjY6dOhQ7z1lZWVRVlbW3KUCALAB9HkAbY/TG2lpSjo0O+CAA+Lll1+uN+3b3/52DB06NM4///y1GikAAFoGfR4AUOpKOjSrqKiInXbaqd60zTbbLLbYYou1pgMA0HLo8wCAUte+2AUAAAAAQKkp6SPNGjJ9+vRilwAArVZruB08LZc+DwAoJS0uNAMACsPt4AEA4P8RmgEAEeF28AAA8ElCMwCgHreDBwAANwIAAAAAgLUIzQAAAAAgw+mZALQZ7gwJAAA0ldAMgDbBnSEBAIANITQDoE1wZ0gAAGBDCM0AaFPcGRIAAGgKNwIAAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyChaabbPNNvHuu++uNf3999+PbbbZplCLBQCgwPR5AEBbULDQbMGCBbF69eq1ptfW1sbbb79dqMUCAFBg+jwAoC3omO8B77rrrty/p02bFt27d889X716dTz00EMxaNCgfC8WAIAC0+cBAG1J3kOzww8/PCIi2rVrF2PGjKn3WqdOnWLQoEHxs5/9LN+LBQCgwPR5AEBbkvfQrK6uLiIitt5665gxY0b06tUr34sAAKAI9HkAQFuS99Bsjfnz5xdqaAAAikifBwC0BQULzSIiHnrooXjooYdiyZIluW8m17jhhhsKuWgAAApInwcAtHYFC80mTpwYl156aey+++7Rt2/faNeuXaEWBQBAM9LnAQBtQcFCs9/97ncxefLkOO644wq1CAAAikCfBwC0Be0LNfCqVatijz32KNTwAAAUiT4PAGgLChaaffe7341bb721UMMDAFAk+jwAoC0o2OmZH374YVx33XXx4IMPxvDhw6NTp071Xv/5z39eqEUDAFBA+jwAoC0oWGj20ksvxS677BIREa+88kq911wsFgCg5dLnAQBtQcFCs4cffrhQQwMAUET6PACgLSjYNc3WeOONN2LatGmxcuXKiIhIKRV6kQAANAN9HgDQmhUsNHv33XfjgAMOiO233z6+/OUvx6JFiyIi4sQTT4wf/OAHhVosAAAFps8DANqCgoVmZ599dnTq1Cmqqqqia9euuelHHXVU3HfffYVaLAAABabPAwDagoJd0+z++++PadOmxVZbbVVv+uDBg+Ott94q1GIBACgwfR4A0BYU7Eiz5cuX1/vmcY333nsvysrKCrVYAAAKTJ8HALQFBQvN9t5777j55ptzz9u1axd1dXXxk5/8JPbbb79CLRYAgALT5wEAbUHBTs/8yU9+EgcccEA8++yzsWrVqjjvvPPi1Vdfjffeey8ef/zxQi0WAIAC0+cBAG1BwY4022mnnWLu3Lmx1157xWGHHRbLly+Pr33ta/HCCy/EtttuW6jFAgBQYPo8AKAtKNiRZhER3bt3jwsvvLCQiwAAoAj0eQBAa1ewI81uvPHGuP3229eafvvtt8dNN91UqMUCAFBg+jwAoC0oWGg2adKk6NWr11rT+/TpE1dccUWhFgsAQIHp8wCAtqBgoVlVVVVsvfXWa00fOHBgVFVVFWqxAAAUmD4PAGgLChaa9enTJ1566aW1pr/44ouxxRZbFGqxAAAUmD4PAGgLChaaHXPMMXHGGWfEww8/HKtXr47Vq1fHX/7ylzjzzDPj6KOPLtRiAQAoMH0eANAWFCw0+9GPfhQjRoyIAw44IMrLy6O8vDwOOuig2H///Zt8rYtrrrkmhg8fHpWVlVFZWRkjR46Me++9t1AlAwDQBPno8yL0egBAaetYiEFTSrF48eKYPHlyXHbZZTFz5swoLy+PnXfeOQYOHNjkcbbaaqu48sorY/DgwZFSiptuuikOO+yweOGFF2LHHXcsROkAAKxDvvq8CL0eAFDaChaabbfddvHqq6/G4MGDY/DgwRs1zqGHHlrv+eWXXx7XXHNNPPXUUxopAIAiyFefF6HXAwBKW0FCs/bt28fgwYPj3Xff3aRG6pNWr14dt99+eyxfvjxGjhzZ6Hy1tbVRW1ube15TU5OX5QMAUJg+L6JpvZ4+DwBoTgW7ptmVV14Z5557brzyyiubNM7LL78c3bp1i7Kysjj55JNj6tSpscMOOzQ6/6RJk6J79+65R//+/Tdp+QAA1JevPi9iw3o9fR4A0JwKcqRZRMTxxx8fK1asiM985jPRuXPnKC8vr/f6e++916RxhgwZEjNnzoylS5fG//7v/8aYMWPikUceabSZGj9+fIwbNy73vKamRkMFAJBH+erzIjas19PnAQDNqWCh2VVXXZWXcTp37hzbbbddRETstttuMWPGjPjlL38Z1157bYPzl5WVRVlZWV6WDQDA2vLV50VsWK+nzwMAmlPBQrMxY8YUZNy6urp617IAAKB5FarPi9DrAQClo2ChWUTEvHnz4sYbb4x58+bFL3/5y+jTp0/ce++9MWDAgCbdEWn8+PFx8MEHx4ABA+KDDz6IW2+9NaZPnx7Tpk0rZNkAAKzHpvZ5EXo9AKC0FexGAI888kjsvPPO8fTTT8cdd9wRy5Yti4iIF198MSZMmNCkMZYsWRLHH398DBkyJA444ICYMWNGTJs2LQ488MBClQ0AwHrko8+L0OsBAKWtYEea/fCHP4zLLrssxo0bFxUVFbnp+++/f/zmN79p0hi///3vC1UeAAAbKR99XoReDwAobQU70uzll1+OI444Yq3pffr0ierq6kItFgCAAtPnAQBtQcFCsx49esSiRYvWmv7CCy/Epz/96UItFgCAAtPnAQBtQcFCs6OPPjrOP//8WLx4cbRr1y7q6uri8ccfj3POOSeOP/74Qi0WAIAC0+cBAG1BwUKzK664IoYNGxYDBgyIZcuWxQ477BBf/OIXY4899oiLLrqoUIsFAKDA9HkAQFuQ9xsB1NXVxU9/+tO46667YtWqVXHcccfF17/+9Vi2bFl89rOfjcGDB+d7kQAANAN9HgDQluQ9NLv88svjkksuiVGjRkV5eXnceuutkVKKG264Id+LAgCgGenzAIC2JO+nZ958883x29/+NqZNmxZ33nln/N///V/ccsstUVdXl+9FAQDQjPR5AEBbkvfQrKqqKr785S/nno8aNSratWsX77zzTr4XBQBAM9LnAQBtSd5Ds48//ji6dOlSb1qnTp3io48+yveiAABoRvo8AKAtyfs1zVJKccIJJ0RZWVlu2ocffhgnn3xybLbZZrlpd9xxR74XDQBAAenzAIC2JO+h2ZgxY9aa9q1vfSvfiwEAoJnp8wCAtiTvodmNN96Y7yEBACgB+jwAoC3J+zXNAAAAAKClE5oBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZJR2aTZo0KT73uc9FRUVF9OnTJw4//PCYM2dOscsCACAP9HoAQCkr6dDskUceibFjx8ZTTz0VDzzwQHz00Udx0EEHxfLly4tdGgAAm0ivBwCUso7FLmBd7rvvvnrPJ0+eHH369InnnnsuvvjFLxapKgAA8kGvBwCUspIOzbKWLl0aERE9e/ZsdJ7a2tqora3NPa+pqSl4XQAAbLr19Xr6PACgOZX06ZmfVFdXF2eddVbsueeesdNOOzU636RJk6J79+65R//+/ZuxSgAANkZTej19HgDQnFpMaDZ27Nh45ZVXYsqUKeucb/z48bF06dLcY+HChc1UIQAAG6spvZ4+DwBoTi3i9MzTTjst/vznP8ejjz4aW2211TrnLSsri7KysmaqDACATdXUXk+fBwA0p5IOzVJKcfrpp8fUqVNj+vTpsfXWWxe7JAAA8kSvBwCUspIOzcaOHRu33npr/OlPf4qKiopYvHhxRER07949ysvLi1wdAACbQq8HAJSykr6m2TXXXBNLly6NfffdN/r27Zt73HbbbcUuDQCATaTXAwBKWUkfaZZSKnYJAAAUiF4PAChlJX2kGQAAAAAUg9AMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgIySD80effTROPTQQ6Nfv37Rrl27uPPOO4tdEgAAeaDPAwBKWcmHZsuXL4/PfOYzcfXVVxe7FAAA8kifBwCUso7FLmB9Dj744Dj44IOLXQYAAHmmzwMASlnJh2Ybqra2Nmpra3PPa2pqilgNAAD5os8DAJpTyZ+euaEmTZoU3bt3zz369+9f7JIAAMgDfR4A0JxaXWg2fvz4WLp0ae6xcOHCYpcEAEAe6PMAgObU6k7PLCsri7KysmKXAQBAnunzAIDm1OqONAMAAACATVXyR5otW7Ys3njjjdzz+fPnx8yZM6Nnz54xYMCAIlYGAMCm0OcBAKWs5EOzZ599Nvbbb7/c83HjxkVExJgxY2Ly5MlFqgoAgE2lzwMASlnJh2b77rtvpJSKXQYAAHmmzwMASplrmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgIwWEZpdffXVMWjQoOjSpUuMGDEinnnmmWKXBABAHujzAIBSVfKh2W233Rbjxo2LCRMmxPPPPx+f+cxnYvTo0bFkyZJilwYAwCbQ5wEApazkQ7Of//zncdJJJ8W3v/3t2GGHHeJ3v/tddO3aNW644YZilwYAwCbQ5wEApaxjsQtYl1WrVsVzzz0X48ePz01r3759jBo1Kp588skG31NbWxu1tbW550uXLo2IiJqamoLUuGzZsoiIeO+tOfFx7cpNGqtmcVVuzDX15nP8hpZR6PHX/Dtfy2jp4ze0DNugecdvjmUYv/jLaOnjN7QM22D94+fTmjFTSnkfm3/R5/nd2trGb45ltPTxG1qGbdC84ze0DNugecdvaBm2wfrHz6cN6vNSCXv77bdTRKQnnnii3vRzzz03ff7zn2/wPRMmTEgR4eHh4eHh4eGxyY+FCxc2R8vTJunzPDw8PDw8PIr5aEqfV9JHmm2M8ePHx7hx43LP6+rq4r333ostttgi2rVrl5dl1NTURP/+/WPhwoVRWVmZlzHZMLZB8dkGpcF2KD7boPgKsQ1SSvHBBx9Ev3798jIe+aHPaztsh+KzDYrPNig+26A05Hs7bEifV9KhWa9evaJDhw7x97//vd70v//977Hllls2+J6ysrIoKyurN61Hjx4Fqa+ystJ/nCKzDYrPNigNtkPx2QbFl+9t0L1797yNxdr0eTSF7VB8tkHx2QbFZxuUhnxuh6b2eSV9I4DOnTvHbrvtFg899FBuWl1dXTz00EMxcuTIIlYGAMCm0OcBAKWupI80i4gYN25cjBkzJnbffff4/Oc/H1dddVUsX748vv3tbxe7NAAANoE+DwAoZSUfmh111FHxj3/8Iy6++OJYvHhx7LLLLnHffffFpz71qaLVVFZWFhMmTFjr9ACaj21QfLZBabAdis82KD7boOXS59EY26H4bIPisw2KzzYoDcXcDu1Sci91AAAAAPikkr6mGQAAAAAUg9AMAAAAADKEZgAAAACQITQDAAAAgAyhWSOuvvrqGDRoUHTp0iVGjBgRzzzzzDrnv/3222Po0KHRpUuX2HnnneOee+5ppkpbrw3ZBtdff33svffesfnmm8fmm28eo0aNWu82Y/029P/BGlOmTIl27drF4YcfXtgC24AN3Qbvv/9+jB07Nvr27RtlZWWx/fbb+32UBxu6Ha666qoYMmRIlJeXR//+/ePss8+ODz/8sJmqbV0effTROPTQQ6Nfv37Rrl27uPPOO9f7nunTp8euu+4aZWVlsd1228XkyZMLXictiz6vNOj1ik+vV3x6veLT5xVXyfd6ibVMmTIlde7cOd1www3p1VdfTSeddFLq0aNH+vvf/97g/I8//njq0KFD+slPfpJmzZqVLrrootSpU6f08ssvN3PlrceGboNvfvOb6eqrr04vvPBCeu2119IJJ5yQunfvnv72t781c+Wtx4ZugzXmz5+fPv3pT6e99947HXbYYc1TbCu1odugtrY27b777unLX/5y+utf/5rmz5+fpk+fnmbOnNnMlbcuG7odbrnlllRWVpZuueWWNH/+/DRt2rTUt2/fdPbZZzdz5a3DPffcky688MJ0xx13pIhIU6dOXef8b775ZuratWsaN25cmjVrVvr1r3+dOnTokO67777mKZiSp88rDXq94tPrFZ9er/j0ecVX6r2e0KwBn//859PYsWNzz1evXp369euXJk2a1OD83/jGN9IhhxxSb9qIESPS97///YLW2Zpt6DbI+vjjj1NFRUW66aabClViq7cx2+Djjz9Oe+yxR/rP//zPNGbMGI3UJtrQbXDNNdekbbbZJq1ataq5SmwTNnQ7jB07Nu2///71po0bNy7tueeeBa2zLWhKI3XeeeelHXfcsd60o446Ko0ePbqAldGS6PNKg16v+PR6xafXKz59XmkpxV7P6ZkZq1atiueeey5GjRqVm9a+ffsYNWpUPPnkkw2+58knn6w3f0TE6NGjG52fdduYbZC1YsWK+Oijj6Jnz56FKrNV29htcOmll0afPn3ixBNPbI4yW7WN2QZ33XVXjBw5MsaOHRuf+tSnYqeddoorrrgiVq9e3Vxltzobsx322GOPeO6553KH9r/55ptxzz33xJe//OVmqbmt85nMuujzSoNer/j0esWn1ys+fV7L1Nyfyx0LMmoLVl1dHatXr45PfepT9aZ/6lOfitmzZzf4nsWLFzc4/+LFiwtWZ2u2Mdsg6/zzz49+/fqt9Z+JptmYbfDXv/41fv/738fMmTObocLWb2O2wZtvvhl/+ctf4thjj4177rkn3njjjTj11FPjo48+igkTJjRH2a3OxmyHb37zm1FdXR177bVXpJTi448/jpNPPjkuuOCC5ii5zWvsM7mmpiZWrlwZ5eXlRaqMUqDPKw16veLT6xWfXq/49HktU3P3eo40o9W58sorY8qUKTF16tTo0qVLsctpEz744IM47rjj4vrrr49evXoVu5w2q66uLvr06RPXXXdd7LbbbnHUUUfFhRdeGL/73e+KXVqbMn369Ljiiivit7/9bTz//PNxxx13xN133x0/+tGPil0aQKug12t+er3SoNcrPn1e2+NIs4xevXpFhw4d4u9//3u96X//+99jyy23bPA9W2655QbNz7ptzDZY4z/+4z/iyiuvjAcffDCGDx9eyDJbtQ3dBvPmzYsFCxbEoYcemptWV1cXEREdO3aMOXPmxLbbblvYoluZjfl/0Ldv3+jUqVN06NAhN23YsGGxePHiWLVqVXTu3LmgNbdGG7Md/v3f/z2OO+64+O53vxsRETvvvHMsX748vve978WFF14Y7dv7vqqQGvtMrqysdJQZ+rwSodcrPr1e8en1ik+f1zI1d69ni2Z07tw5dtttt3jooYdy0+rq6uKhhx6KkSNHNviekSNH1ps/IuKBBx5odH7WbWO2QUTET37yk/jRj34U9913X+y+++7NUWqrtaHbYOjQofHyyy/HzJkzc4+vfvWrsd9++8XMmTOjf//+zVl+q7Ax/w/23HPPeOONN3JNbETE3Llzo2/fvpqojbQx22HFihVrNUxrmtt/Xd+UQvKZzLro80qDXq/49HrFp9crPn1ey9Tsn8sFub1ACzdlypRUVlaWJk+enGbNmpW+973vpR49eqTFixenlFI67rjj0g9/+MPc/I8//njq2LFj+o//+I/02muvpQkTJrgV+Sba0G1w5ZVXps6dO6f//d//TYsWLco9Pvjgg2L9CC3ehm6DLHdU2nQbug2qqqpSRUVFOu2009KcOXPSn//859SnT5902WWXFetHaBU2dDtMmDAhVVRUpP/5n/9Jb775Zrr//vvTtttum77xjW8U60do0T744IP0wgsvpBdeeCFFRPr5z3+eXnjhhfTWW2+llFL64Q9/mI477rjc/GtuQ37uueem1157LV199dUFvQ05LY8+rzTo9YpPr1d8er3i0+cVX6n3ekKzRvz6179OAwYMSJ07d06f//zn01NPPZV7bZ999kljxoypN/8f/vCHtP3226fOnTunHXfcMd19993NXHHrsyHbYODAgSki1npMmDCh+QtvRTb0/8EnaaTyY0O3wRNPPJFGjBiRysrK0jbbbJMuv/zy9PHHHzdz1a3PhmyHjz76KF1yySVp2223TV26dEn9+/dPp556avrnP//Z/IW3Ag8//HCDv9/XrPMxY8akffbZZ6337LLLLqlz585pm222STfeeGOz101p0+eVBr1e8en1ik+vV3z6vOIq9V6vXUqOIQQAAACAT3JNMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzIOeSSy6JXXbZpdhltErNuW4feuihGDZsWKxevbpZlteazJo1K7baaqtYvnx5sUsBgCY54YQT4vDDDy92Ga1Sc67b3//+93HQQQc1y7IKYfr06dGuXbt4//33i11Kk/zud7+LQw89tNhl0AIIzWATtbQPiEJZsGBBtGvXLmbOnLneeffdd98466yz8rp8DeP/c95558VFF10UHTp0iIjCBHaTJ0+OHj16rDX9jjvuiIMOOii22GKLJu8Phdh2Df3M7733Xpx++ukxZMiQKC8vjwEDBsQZZ5wRS5cuzc2zww47xBe+8IX4+c9/ntd6AKAUbUgfO2jQoLjqqqvyuvxC9IQt0Ycffhj//u//HhMmTGjW5Tb3+m/Xrl3ceeedeR2zof3yww8/jBNOOCF23nnn6NixY4N95ne+8514/vnn47HHHstrPbQ+QjOAVuSvf/1rzJs3L77+9a8XZfnLly+PvfbaK3784x8XZfnr8s4778Q777wT//Ef/xGvvPJKTJ48Oe6777448cQT68337W9/O6655pr4+OOPi1QpAPm2atWqYpcAjfrf//3fqKysjD333LPYpawlpdTieqLVq1dHeXl5nHHGGTFq1KgG5+ncuXN885vfjF/96lfNXB0tToJWbp999kljx45NY8eOTZWVlWmLLbZIF110Uaqrq8vN895776Xjjjsu9ejRI5WXl6cvfelLae7cubnXFyxYkL7yla+kHj16pK5du6Yddtgh3X333Wn+/PkpIuo9xowZs96abr/99rTTTjulLl26pJ49e6YDDjggLVu2LKWU0pgxY9Jhhx2WLrnkktSrV69UUVGRvv/976fa2trc+1evXp2uuOKKNGjQoNSlS5c0fPjwdPvtt+def/jhh1NEpAcffDDttttuqby8PI0cOTLNnj27Xh2TJk1Kffr0Sd26dUvf+c530vnnn58+85nP1Bvnc5/7XOratWvq3r172mOPPdKCBQsa/Jmy62GfffZpcL4xY8asNe/8+fNTSim9/PLL6Utf+lLabLPNUp8+fdK3vvWt9I9//GO9623ChAlrjfnwww83uPym7A+ftHTp0tSlS5d0zz331Jt+xx13pG7duqXly5enlFI677zz0uDBg1N5eXnaeuut00UXXZRWrVqVm3/ChAn11u0+++yTzjzzzHpjHnbYYfX2nw8//DD94Ac/SP369Utdu3ZNn//85xv9udYYO3Zs+rd/+7fc8xtvvHGtdXPjjTemlFL65z//mU488cTcfrbffvulmTNn5t47c+bMtO+++6Zu3bqlioqKtOuuu6YZM2bk9q9PPiZMmFCvjjX/N1544YV11ruubVdVVZWOPPLI1L1797T55punr371q7l9JaXG9891/cxZf/jDH1Lnzp3TRx99lJtWW1ubysrK0oMPPrjO2gEojqZ8lg8cODBdeuml6bjjjksVFRW5z9fHHnss7bXXXqlLly5pq622SqeffnquB1ufq6++Om233XaprKws9enTJ33961/foJrW97l+4403pu7du6f77rsvDR06NG222WZp9OjR6Z133snN8/HHH6ezzz47de/ePfXs2TOde+656fjjj0+HHXZYbp519ZmftCF97D777LPWvGusb502tt7W1RNmrdmeRx99dOratWvq169f+s1vftPgvCmlNGfOnBQR6bXXXqs3/ec//3naZpttcuvyO9/5Tq6f3n777dNVV11Vb/41ffkn6/jFL35Rb57PfOYz9fqg9fVXDTnkkEPSOeecs9b03//+92mHHXZInTt3TltuuWUaO3Zs7rW33norffWrX02bbbZZqqioSEceeWRavHhx7vU1vefNN9+cBg4cmCorK9NRRx2Vampqcj9bQ+t/TZ93zz33pF133TV16tQpPfzww+nDDz9Mp59+eurdu3cqKytLe+65Z3rmmWdyy1vzvn/+858N/owDBw6st6yBAwfmXrvzzjvTZz/72VRWVpa23nrrdMkll+R6s7q6ujRhwoTUv3//1Llz59S3b990+umnp5TWvV+ukd2Gn/TII4+kzp07pxUrVjS8YSClJDSj1dtnn31St27d0plnnplmz56d/vu//zt17do1XXfddbl5vvrVr6Zhw4alRx99NM2cOTONHj06bbfddrnQ45BDDkkHHnhgeumll9K8efPS//3f/6VHHnkkffzxx+mPf/xjiog0Z86ctGjRovT++++vs5533nkndezYMf385z9P8+fPTy+99FK6+uqr0wcffJBS+tcv9m7duqWjjjoqvfLKK+nPf/5z6t27d7rgggtyY1x22WVp6NCh6b777kvz5s1LN954YyorK0vTp09PKf2/D60RI0ak6dOnp1dffTXtvffeaY899siNcdttt6WysrL0n//5n2n27NnpwgsvTBUVFblg56OPPkrdu3dP55xzTnrjjTfSrFmz0uTJk9Nbb73V4M/1zDPP5IK6RYsWpXfffbfB+d5///00cuTIdNJJJ6VFixalRYsWpY8//jj985//TL17907jx49Pr732Wnr++efTgQcemPbbb7/1rrcPPvggfeMb30hf+tKXcmN+MmTc0P0h69/+7d/St771rXrTvv71r9eb9qMf/Sg9/vjjaf78+emuu+5Kn/rUp9KPf/zj3OsbE5p997vfTXvssUd69NFH0xtvvJF++tOfprKysnqBbtbw4cPTlVdemXu+YsWK9IMf/CDtuOOOuXWzpjEYNWpUOvTQQ9OMGTPS3Llz0w9+8IO0xRZb5LbdjjvumL71rW+l1157Lc2dOzf94Q9/SDNnzky1tbXpqquuSpWVlbkx1+y/azQ1NGts261atSoNGzYsfec730kvvfRSmjVrVvrmN7+ZhgwZkmpra9e5f67rZ866/vrrU69evdaaPmLEiLWCQABKQ1M+y9eEBP/xH/+R3njjjdxjs802S7/4xS/S3Llz0+OPP54++9nPphNOOGG9y5wxY0bq0KFDuvXWW9OCBQvS888/n375y19uUE3r+1y/8cYbU6dOndKoUaPSjBkz0nPPPZeGDRuWvvnNb+bG+PGPf5w233zz9Mc//jHNmjUrnXjiiamioiIXCqyvz/ykDelj33333bTVVlulSy+9NPfZmlJa7zpd13prrCdsyMCBA1NFRUWaNGlSmjNnTvrVr36VOnTokO6///5Gt9nuu++eLrroonrTdtttt9y0VatWpYsvvjjNmDEjvfnmm7ltdtttt+Xm35jQbH39VUO6d++epkyZUm/ab3/729SlS5d01VVXpTlz5qRnnnkmt+zVq1enXXbZJe21117p2WefTU899VTabbfd6n1pPWHChNStW7f0ta99Lb388svp0UcfTVtuuWXub4rG1v+avyOGDx+e7r///vTGG2+kd999N51xxhmpX79+6Z577kmvvvpqGjNmTNp8881zP9f6QrMlS5bkvshctGhRWrJkSUoppUcffTRVVlamyZMnp3nz5qX7778/DRo0KF1yySUppX+FwJWVlemee+5Jb731Vnr66adz/68a2y8/aV2h2fLly1P79u3X+6U0bZvQjFZvn332ScOGDav3Td/555+fhg0bllJKae7cuSki0uOPP557vbq6OpWXl6c//OEPKaWUdt5559wv7qz1fUBkPffccykiGj1ia8yYMalnz565I5hSSumaa65J3bp1S6tXr04ffvhh6tq1a3riiSfqve/EE09MxxxzTL2aPnmkzN13350iIq1cuTKllNLIkSPTqaeeWm+MESNG5IKdd999N0VELohbn6aGJCk1HBj96Ec/SgcddFC9aQsXLsw1ck1Zb419IGaXva79oSFTp06td1TZmqPP7r333kbf89Of/jTttttuuecbGpq99dZbqUOHDuntt9+uN88BBxyQxo8f3+hyu3fvnm6++eZ607LLTulf3wpXVlamDz/8sN70bbfdNl177bUppZQqKirS5MmTG1zOmm/DG7Mh+0ND2+6//uu/0pAhQ+ptp9ra2lReXp6mTZu23v2zoZ856x//+EcaMGBAvUB6jSOOOKJJf0QB0Pya8lk+cODAdPjhh9d734knnpi+973v1Zv22GOPpfbt2+f6o8b88Y9/TJWVlbmjdDa0pqZ8rq85UvqNN97IvX711VenT33qU7nnffv2TT/5yU9yzz/66KO01VZb5T5H19cvZW1IH9tQYLS+ddqU9Zbthxpb9pe+9KV604466qh08MEHN/qeX/ziF2nbbbfNPW/s6LNPGjt2bL0jCDc0NGtKf5X1z3/+M0VEevTRR+tN79evX7rwwgsbfM/999+fOnTokKqqqnLTXn311RQRuaO/JkyYkLp27Vpv3Z977rlpxIgRuecNrf81+8Sdd96Zm7Zs2bLUqVOndMstt+SmrVq1KvXr1y+3PzZlX4qINHXq1HrTDjjggHTFFVfUm/Zf//VfqW/fvimllH72s5+l7bffvt4ZHJ/U0Db5pPX9jbD55ps32u9CSim5phltwhe+8IVo165d7vnIkSPj9ddfj9WrV8drr70WHTt2jBEjRuRe32KLLWLIkCHx2muvRUTEGWecEZdddlnsueeeMWHChHjppZc2upbPfOYzccABB8TOO+8cRx55ZFx//fXxz3/+c615unbtWq/eZcuWxcKFC+ONN96IFStWxIEHHhjdunXLPW6++eaYN29evXGGDx+e+3ffvn0jImLJkiUREfHaa6/V+5nXLGeNnj17xgknnBCjR4+OQw89NH75y1/GokWLNuhnfeyxx+rVeMsttzQ674svvhgPP/xwvfmHDh0aERHz5s1r0nprqnXtD1dccUW9GqqqquLLX/5ydOrUKe66666IiPjjH/8YlZWV9a6RcNttt8Wee+4ZW265ZXTr1i0uuuiiqKqq2qj6IiJefvnlWL16dWy//fb16nnkkUfW2s6ftHLlyujSpct6x3/xxRdj2bJlscUWW9Qbf/78+bnxx40bF9/97ndj1KhRceWVV65zufn24osvxhtvvBEVFRW52nr27BkffvhhzJs3b5P3z5qamjjkkENihx12iEsuuWSt18vLy2PFihV5/IkAyKd1fZavsfvuu9d7z4svvhiTJ0+u97k3evToqKuri/nz569zeQceeGAMHDgwttlmmzjuuOPilltuWetzYl01NfVzvWvXrrHtttvmnvft2zfXuy1dujQWLVpUr3/r2LFjvZ8zH/3SLbfcUq/GdV0ofX3rtCnrrak+2aeueb6mVz/55JPr1RARcfTRR8eCBQviqaeeyv1cu+66a66/jIi4+uqrY7fddovevXtHt27d4rrrrtuk/q0p/VXWypUrIyLq9W9LliyJd955Jw444IAG3/Paa69F//79o3///rlpO+ywQ/To0SO3TiL+dZH8ioqK3PNP7k/r88n9at68efHRRx/Vu+Zap06d4vOf/3y95W2MF198MS699NJ66+ukk06KRYsWxYoVK+LII4+MlStXxjbbbBMnnXRSTJ06Na/XWNPzsT4di10AtATf/e53Y/To0XH33XfH/fffH5MmTYqf/exncfrpp2/wWB06dIgHHnggnnjiibj//vvj17/+dVx44YXx9NNPx9Zbb73e9y9btiwiIu6+++749Kc/Xe+1srKyes87deqU+/eaJq6urq7Jtd54441xxhlnxH333Re33XZbXHTRRfHAAw/EF77whSa9f/fdd69398RPfepTjc67bNmyOPTQQxu8gHzfvn03eb011cknnxzf+MY3cs/79esXHTt2jH/7t3+LW2+9NY4++ui49dZb46ijjoqOHf/1K/TJJ5+MY489NiZOnBijR4+O7t27x5QpU+JnP/tZo8tp3759pJTqTfvoo49y/162bFl06NAhnnvuudxdMNdY0ww2pFevXk1qjpctWxZ9+/aN6dOnr/XamrtiXnLJJfHNb34z7r777rj33ntjwoQJMWXKlDjiiCPWO/6mWrZsWey2224NBq29e/eOiI3fPz/44IP40pe+FBUVFTF16tR6/0/WeO+99+r90QJAy7PZZpvVe75s2bL4/ve/H2ecccZa8w4YMGCdY1VUVMTzzz8f06dPj/vvvz8uvvjiuOSSS2LGjBkN3k06q6mf69nPpHbt2q3VL6xLPvqlr371q/WCuWy/+UnrW6edO3fepPXWVJdeemmcc8459aZtueWWsf/++8ett94aX/jCF+LWW2+NU045Jff6lClT4pxzzomf/exnMXLkyKioqIif/vSn8fTTTze6nKb0b+vrr7LW3HH8k/1beXn5un7cJmtof2rq3wLZ/z+FsmzZspg4cWJ87WtfW+u1Ll26RP/+/WPOnDnx4IMPxgMPPBCnnnpq/PSnP41HHnmkwR5uQ7333nu53hIaIjSjTch++D311FMxePDg6NChQwwbNiw+/vjjePrpp2OPPfaIiIh333035syZEzvssEPuPf3794+TTz45Tj755Bg/fnxcf/31cfrpp0fnzp0jIup9s7k+7dq1iz333DP23HPPuPjii2PgwIExderUGDduXET86xuXlStX5j4wn3rqqejWrVv0798/evbsGWVlZVFVVRX77LPPRq+TYcOGxdNPPx3HH398vfWS9dnPfjY++9nPxvjx42PkyJG5xiOrofVQXl4e2223XYPzZtfXrrvuGn/84x9j0KBBuTAqa13rraExG7Ou/aFnz57Rs2fPtd5z7LHHxoEHHhivvvpq/OUvf4nLLrss99oTTzwRAwcOjAsvvDA37a233lpnDb179653ZNTq1avjlVdeif322y8i/rXeV69eHUuWLIm99967ST/XmvfNmjWr3rTG1vfixYujY8eOMWjQoEbH23777WP77bePs88+O4455pi48cYb44gjjtig9b0+jdV32223RZ8+faKysrLR9za2fzZWX01NTYwePTrKysrirrvuavSovFdeeSX+7d/+bdN+MAAKZl2f5Y3ZddddY9asWQ32Jk3RsWPHGDVqVIwaNSomTJgQPXr0iL/85S+5P/bXVdPGfq5/Uvfu3aNv377x9NNPxxe/+MWIiPj444/jueeei1133TU33/r6zE9qqH+rqKiod3TSJ+dt6PN6fet0XettQ/qJbJ/61FNPxbBhwyIiok+fPtGnT5+13nPsscfGeeedF8ccc0y8+eabcfTRR+dee/zxx2OPPfaIU089NTdtfUfVZ/u3mpqaekcpNrW/+qTOnTvHDjvsELNmzYqDDjooIv61DQYNGhQPPfRQrjf8pGHDhsXChQtj4cKFuaPNZs2aFe+//369v1+asuymrP9tt902OnfuHI8//ngMHDgwIv4VFs6YMSPOOuusJi+vU6dODe5Dc+bMWec+VF5eHoceemgceuihMXbs2Bg6dGi8/PLLseuuu25STzpv3rz48MMP47Of/exGvZ+2wemZtAlVVVUxbty4mDNnTvzP//xP/PrXv44zzzwzIiIGDx4chx12WJx00knx17/+NV588cX41re+FZ/+9KfjsMMOi4iIs846K6ZNmxbz58+P559/Ph5++OHch/TAgQOjXbt28ec//zn+8Y9/5I4Ea8zTTz8dV1xxRTz77LNRVVUVd9xxR/zjH//IjRfxr9uin3jiiTFr1qy45557YsKECXHaaadF+/bto6KiIs4555w4++yz46abbop58+bF888/H7/+9a/jpptuavI6OfPMM+OGG26IG2+8MebOnRsTJkyIV199Nff6/PnzY/z48fHkk0/GW2+9Fffff3+8/vrr9er8pD59+kR5eXncd9998fe//z2WLl3a6LIHDRoUTz/9dCxYsCCqq6ujrq4uxo4dG++9914cc8wxMWPGjJg3b15MmzYtvv3tb8fq1avXu94GDRoUL730UsyZMyeqq6vrfeuXta79oTFf/OIXY8stt4xjjz02tt5663rfwA4ePDiqqqpiypQpMW/evPjVr34VU6dOXed4+++/f9x9991x9913x+zZs+OUU06J999/P/f69ttvH8cee2wcf/zxcccdd8T8+fPjmWeeiUmTJsXdd9/d6LijR4+Ov/71r/WmDRo0KObPnx8zZ86M6urqqK2tjVGjRsXIkSPj8MMPj/vvvz8WLFgQTzzxRFx44YXx7LPPxsqVK+O0006L6dOnx1tvvRWPP/54zJgxo976XrZsWTz00ENRXV2dO6z9vffei5kzZ+aCuzlz5sTMmTNj8eLFjdbc0LY79thjo1evXnHYYYfFY489FvPnz4/p06fHGWecEX/729/Wu3829DPX1NTEQQcdFMuXL4/f//73UVNTE4sXL47FixfXa7YWLFgQb7/9dqO3KAeg+Dbms/z888+PJ554Ik477bSYOXNmvP766/GnP/0pTjvttPUu789//nP86le/ipkzZ8Zbb70VN998c9TV1cWQIUOaVNPGfq5nnXnmmXHllVfGnXfeGbNnz45TTz21Xv/QlD7zkzakjx00aFA8+uij8fbbb0d1dXVErH+drm+9NdQTNubxxx+Pn/zkJzF37ty4+uqr4/bbb1/vNv/a174WH3zwQZxyyimx3377Rb9+/XKvDR48OJ599tmYNm1azJ07N/793/89ZsyYsc7x9t9///iv//qveOyxx+Lll1+OMWPG1Atq19dfNaah/u2SSy6Jn/3sZ/GrX/0qXn/99Vy/v2Y5O++8cxx77LHx/PPPxzPPPBPHH3987LPPPmudlrwuTV3/m222WZxyyilx7rnnxn333RezZs2Kk046KVasWBEnnnjiBi3voYceisWLF+eOrLv44ovj5ptvjokTJ8arr74ar732WkyZMiUuuuiiiIiYPHly/P73v49XXnkl3nzzzfjv//7vKC8vz4V3De2XEf8KEWfOnBnvvfdeLF26NGbOnFnvDJiIf11KZptttnF2AetW5GuqQcHts88+6dRTT00nn3xyqqysTJtvvnm64IIL6l2o9b333kvHHXdc6t69eyovL0+jR4+ud4fC0047LW277baprKws9e7dOx133HGpuro69/qll16attxyy9SuXbtGb9W9xqxZs9Lo0aNzt2vefvvt069//evc62suVnnxxRenLbbYInXr1i2ddNJJ9S4oWldXl6666qo0ZMiQ1KlTp9S7d+80evTo9Mgjj6SUGr4Q5wsvvLDWrbwvv/zy1KtXr9StW7c0ZsyYdN555+Uunr548eJ0+OGHp759+6bOnTungQMHposvvjitXr260Z/t+uuvT/3790/t27evd/eerDlz5qQvfOELqby8vF5Nc+fOTUcccUTq0aNHKi8vT0OHDk1nnXVWqqurW+96W7JkSTrwwANTt27dUkQ0ehecpuwPjTnvvPNSRKSLL754rdfOPffc3PY66qij0i9+8Yt6F8rPXph+1apV6ZRTTkk9e/ZMffr0SZMmTVrr7plr7uo0aNCg1KlTp9S3b990xBFHpJdeeqnRGt99993UpUuXNHv27Ny0Dz/8MH39619PPXr0yN21KKWUampq0umnn5769euXOnXqlPr375+OPfbYVFVVlWpra9PRRx+du713v3790mmnnVbvQsknn3xy2mKLLVJE5C6Au+YixtnHJ+8qNWbMmHr7R2PbbtGiRen4449PvXr1SmVlZWmbbbZJJ510Ulq6dOl698+GfuY1/y8aenzy/8UVV1yRRo8e3eg6BqC4mvJZ3tjFwZ955pncZ85mm22Whg8fni6//PL1LvOxxx5L++yzT9p8881TeXl5Gj58eL27LDalpvV9rjd0k52pU6emT/7J9tFHH6UzzzwzVVZWph49eqRx48al448/Pneh8/X1Sw1pah/75JNPpuHDh6eysrJ6Na1rna5vvTXWE2YNHDgwTZw4MR155JGpa9euacstt6x399J1+cY3vpEiIt1www31pn/44YfphBNOSN27d089evRIp5xySvrhD39Yr1/LXkR+6dKl6aijjkqVlZWpf//+afLkyWvdPXNd/VVjXn311VReXr7W3Ut/97vf5fr9vn37ptNPPz332ltvvZW++tWvps022yxVVFSkI488Mi1evDj3ekM3RfrFL36RBg4cmHve0Ppv7IL+K1euTKeffnquL9tzzz1zNx1IqeG/Pz7Zd6aU0l133ZW222671LFjx3p13HfffWmPPfZI5eXlqbKyMn3+85/P3SFz6tSpacSIEamysjJtttlm6Qtf+EK9m501tl8OHDiwwZ7vkw466KA0adKkBOvSLqUNOEkeWqB99903dtlll7jqqquKXUqTnHDCCfH+++/HnXfeWexSWqWWtj9sjHPPPTdqamri2muvLXYpDdpnn31iv/32a/AC/MW2atWqGDx4cNx66631LnYLQOkoxc/yUqypNRk0aFCcddZZG3QqYEtz5JFHxq677hrjx48vdil5MX/+/Nh+++1j1qxZMXjw4GKXs5ZXX3019t9//5g7d25079692OVQwpyeCdDKXHjhhTFw4MANuulDc1m6dGnMmzdvrYv1loqqqqq44IILBGYAQLP66U9/us6bPbU099xzT3zve98rycAsImLRokVx8803C8xYLzcCgDyrqqpa5wU4Z82atd47NMGm6NGjR1xwwQXFLqNB3bt3j7/97W/FLqNR22233UZfIBqAluuxxx6Lgw8+uNHX13fNWthUgwYNitNPP73YZeTN2LFji13COrl2LU3l9EzIs48//jgWLFjQ6OvrujskAADNb+XKlfH22283+rovVADaJqEZAAAAAGS4phkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgIyOxS6g0Orq6uKdd96JioqKaNeuXbHLAQBagJRSfPDBB9GvX79o3953jKVKnwcAbKgN6fNafWj2zjvvRP/+/YtdBgDQAi1cuDC22mqrYpdBI/R5AMDGakqf1+pDs4qKioj418qorKwscjUAQEtQU1MT/fv3z/URlCZ9HgCwoTakz2v1odmaQ/UrKys1UwDABnHKX2nT5wEAG6spfZ6LdAAAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAABF8fbbb8e3vvWt2GKLLaK8vDx23nnnePbZZ4tdFgBARER0LHYBAAC0Pf/85z9jzz33jP322y/uvffe6N27d7z++uux+eabF7s0AICIEJoBAFAEP/7xj6N///5x44035qZtvfXWRawIAKA+oRnNoqqqKqqrq/MyVq9evWLAgAF5GQsAKI677rorRo8eHUceeWQ88sgj8elPfzpOPfXUOOmkkxp9T21tbdTW1uae19TUNEep0Obls5eP0M8DLYfQjIKrqqqKoUOHxcqVK/IyXnl515g9+zUftADQgr355ptxzTXXxLhx4+KCCy6IGTNmxBlnnBGdO3eOMWPGNPieSZMmxcSJE5u5Umjb8t3LR+jngZZDaEbBVVdXx8qVK2LEdyZEZd9BmzRWzaIF8fQNE6O6utqHLAC0YHV1dbH77rvHFVdcERERn/3sZ+OVV16J3/3ud42GZuPHj49x48blntfU1ET//v2bpV5oq/LZy0fo54GWRWhGs6nsOyh6DhhS7DJooZwWANC69O3bN3bYYYd604YNGxZ//OMfG31PWVlZlJWVFbo0oAF6eWB9WuNlmYRmQMlzWgBA67PnnnvGnDlz6k2bO3duDBw4sEgVAQAbq7VelkloBpQ8pwUAtD5nn3127LHHHnHFFVfEN77xjXjmmWfiuuuui+uuu67YpQEAG6i1XpZJaAa0GE4LAGg9Pve5z8XUqVNj/Pjxcemll8bWW28dV111VRx77LHFLg0A2Eit7W82oRkAAEXxla98Jb7yla8UuwwAgAa1L3YBAAAAAFBqhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACDD3TMBoIWoqqqK6urqvI3Xq1evGDBgQN7GAwCA1kRoBgAtQFVVVQwdOixWrlyRtzHLy7vG7NmvCc4AAKABQjMAaAGqq6tj5coVMeI7E6Ky76BNHq9m0YJ4+oaJUV1dLTQDAIAGCM0AoAWp7Dsoeg4YUuwyAACg1XMjAAAAAADIEJoBAAAAQIbQDAAAAAAyXNOMVqGqqiqqq6vzMlavXr1cFBsAAADaOKEZLV5VVVUMHTosVq5ckZfxysu7xuzZrwnOAAAAoA0TmtHiVVdXx8qVK2LEdyZEZd9BmzRWzaIF8fQNE6O6ulpoBgAAAG2Y0IxWo7LvoOg5YEixy9go+Ty9NMIppgAAALCphGZQZPk+vTTCKaYAAACwqYRmUGT5PL00wimmAAAAkA9CMygRLfn0UgAAAGht2he7AAAAAAAoNUIzAAAAAMgQmgEAAABAhmuaAbQSVVVVUV1dnZexevXq5UYSAABAmyY0A2gFqqqqYujQYbFy5Yq8jFde3jVmz35NcAYAtAq+XAQ2htAMoBWorq6OlStXxIjvTIjKvoM2aayaRQvi6RsmRnV1tYYQAGjxfLkIbCyhWQuQz29FInwzAq1ZZd9B0XPAkGKXAQBQMny5CGwsoVmJy/e3IhG+GQEAANoeXy6yKRzM0jYJzUpcPr8VifDNCAAAAGwIB7O0XUKzFsK3IgAAAND8HMzSdgnNAGgz3DkLAICN5WCWtkdoBkCb4M5ZAC2TLzygdfB/mZZIaAZAm+DOWQAtjy88oHXwf5mWqqRDs0suuSQmTpxYb9qQIUNi9uzZRaoIaIxvjmgpHFZfXH5XABvCFx7QOvi/TEtV0qFZRMSOO+4YDz74YO55x44lXzK0Ob45AprC7wpgY/nCA1oH/5dpaUo+gerYsWNsueWWxS4DWAffHAFN4XcFAAAtScmHZq+//nr069cvunTpEiNHjoxJkyatszmura2N2tra3POamprmKBMI3xytj9PS4F/8rgAAoCUo6dBsxIgRMXny5BgyZEgsWrQoJk6cGHvvvXe88sorUVFR0eB7Jk2atNZ10ACKzWlpADQkn1+oRPhSBQDyqaRDs4MPPjj37+HDh8eIESNi4MCB8Yc//CFOPPHEBt8zfvz4GDduXO55TU1N9O/fv+C1AqyL09IAyMr3FyoRvlQBgHwq6dAsq0ePHrH99tvHG2+80eg8ZWVlUVZW1oxVATSd09IAWCOfX6hE+FIFAPKtRYVmy5Yti3nz5sVxxx1X7FJoY5w6AQAUii9UgGLz9w40rKRDs3POOScOPfTQGDhwYLzzzjsxYcKE6NChQxxzzDHFLo02xKkTAABAa+XvHWhcSYdmf/vb3+KYY46Jd999N3r37h177bVXPPXUU9G7d+9il0Yb4tQJAACgtfL3DjSupEOzKVOmFLsEyHHqBAAA0Fr5ewfW1r7YBQAAAABAqRGaAQAAAEBGSZ+eSfNwpxQAAACA+oRmbZw7pQAAAACsTWjWxrlTCgAAAMDahGZEhDulAAAAQLG4bFJpEprlQT53bjs2hWI/BQAAKD0um1S6hGabKN87tx2bQrCfAgAAlCaXTSpdQrNNlM+d245NodhPAQAASpvLJpUeoVme2LlpCeynAJSSSy65JCZOnFhv2pAhQ2L27NlFqmhtLm8AhedaTkCpEpoBAFA0O+64Yzz44IO55x07lk576vIGUHiu5QSUstLpSgAAaHM6duwYW265ZbHLaJDLG0DhuZYTUMqEZgAAFM3rr78e/fr1iy5dusTIkSNj0qRJjf6hW1tbG7W1tbnnNTU1zVKjyxtA4fl/BpSi9sUuAACAtmnEiBExefLkuO++++Kaa66J+fPnx9577x0ffPBBg/NPmjQpunfvnnv079+/mSsGANoSoRkAAEVx8MEHx5FHHhnDhw+P0aNHxz333BPvv/9+/OEPf2hw/vHjx8fSpUtzj4ULFzZzxQBAW+L0TAAASkKPHj1i++23jzfeeKPB18vKyqKsrKyZq2J93GG0+GwDgMIQmgFQMjT90LYtW7Ys5s2bF8cdd1yxS6GJ3GG0+GwDgMIRmgFQEjT90Pacc845ceihh8bAgQPjnXfeiQkTJkSHDh3imGOOKXZpNJE7jBafbQBQOEIzAEqCph/anr/97W9xzDHHxLvvvhu9e/eOvfbaK5566qno3bt3sUtjA7nzYfHZBsD6OKtjwwnNACgpLbnpz2cjElGcZkQzRXOaMmVKsUsAgDbBWR0bR2gGAHmQ70YkovmbEc0UAEDr5KyOjSM0A4A8yGcjElGcZkQzBa2TI0gBWKMln9VRDEIzAJqkNZx62BxaQyPSGn4G4F8cQQoAG09oBsB6tYZTDwHaIkeQAsDGE5oBsF6t4dRDgLbMEaQAsOGEZgA0mT+6AACAtqJ9sQsAAAAAgFIjNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGR2LXQBAqaiqqorq6uq8jNWrV68YMGBAXsYCAACg+QnNAOJfgdnQocNi5coVeRmvvLxrzJ79muAMAACghRKaAUREdXV1rFy5IkZ8Z0JU9h20SWPVLFoQT98wMaqrq4VmAAAALZTQDOATKvsOip4DhhS7DAAAAIpMaAYAALRZ+bymaYTrmgK0JkIzAACgTcr3NU0jXNcUoDURmgEAAG1SPq9pGuG6pgCtjdAMAABo01zTFICGtC92AQAAAABQaoRmAAAAAJDh9EwAAIACyucdOt2dE6D5CM0AAAAKJN936HR3ToDmIzQDAAAokHzeodPdOQGal9AMAACgwNyhE6DlcSMAAAAAAMgQmgEAAABAhtAMAAAAADJaVGh25ZVXRrt27eKss84qdikAAAAAtGItJjSbMWNGXHvttTF8+PBilwIAAABAK9ciQrNly5bFscceG9dff31svvnmxS4HAAAAgFauRYRmY8eOjUMOOSRGjRq13nlra2ujpqam3gMAAAAANkTHYhewPlOmTInnn38+ZsyY0aT5J02aFBMnTixwVQAAAAC0ZiV9pNnChQvjzDPPjFtuuSW6dOnSpPeMHz8+li5dmnssXLiwwFUCAAAA0NqU9JFmzz33XCxZsiR23XXX3LTVq1fHo48+Gr/5zW+itrY2OnToUO89ZWVlUVZW1tylAgAAANCKlHRodsABB8TLL79cb9q3v/3tGDp0aJx//vlrBWYAAAAAkA8lHZpVVFTETjvtVG/aZpttFltsscVa0wEAAAAgX0r6mmYAAAAAUAwlfaRZQ6ZPn17sEgAAAABo5RxpBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAU3ZVXXhnt2rWLs846q9ilAABEhNAMAIAimzFjRlx77bUxfPjwYpcCAJAjNAMAoGiWLVsWxx57bFx//fWx+eabF7scAIAcoRkAAEUzduzYOOSQQ2LUqFHFLgUAoJ6OxS4AAIC2acqUKfH888/HjBkzmjR/bW1t1NbW5p7X1NQUqjSADVJVVRXV1dV5GatXr14xYMCAgo3f2DJaukJvA9omoRkAAM1u4cKFceaZZ8YDDzwQXbp0adJ7Jk2aFBMnTixwZQAbpqqqKoYOHRYrV67Iy3jl5V1j9uzXcqFNvsdvaBktXaG3AW2X0AwAgGb33HPPxZIlS2LXXXfNTVu9enU8+uij8Zvf/CZqa2ujQ4cO9d4zfvz4GDduXO55TU1N9O/fv9lqBmhIdXV1rFy5IkZ8Z0JU9h20SWPVLFoQT98wMaqrq3OBTT7Hb2wZLV2htwFtl9AMAIBmd8ABB8TLL79cb9q3v/3tGDp0aJx//vlrBWYREWVlZVFWVtZcJQJskMq+g6LngCEtdvzWwDoi34RmAAA0u4qKithpp53qTdtss81iiy22WGs6AEAxuHsmAAAAAGQ40gwAgJIwffr0YpcAAJDjSDMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyChYaLbNNtvEu+++u9b0999/P7bZZptCLRYAgALT5wEAbUHBQrMFCxbE6tWr15peW1sbb7/9dqEWCwBAgenzAIC2oGO+B7zrrrty/542bVp0794993z16tXx0EMPxaBBg/K9WAAACkyfBwC0JXkPzQ4//PCIiGjXrl2MGTOm3mudOnWKQYMGxc9+9rN8LxYAgALT5wEAbUneQ7O6urqIiNh6661jxowZ0atXr3wvAgCAItDnAQBtSd5DszXmz59fqKEBACgifR4A0BYULDSLiHjooYfioYceiiVLluS+mVzjhhtuKOSiAQAoIH0eANDaFSw0mzhxYlx66aWx++67R9++faNdu3aFWhQAAM1InwcAtAUFC81+97vfxeTJk+O4444r1CIAACgCfR4A0Ba0L9TAq1atij322KNQwwMAUCT6PACgLShYaPbd7343br311kINDwBAkejzAIC2oGCnZ3744Ydx3XXXxYMPPhjDhw+PTp061Xv95z//eaEWDQBAAenzAIC2oGCh2UsvvRS77LJLRES88sor9V5zsVgAgJZLnwcAtAUFC80efvjhQg0NAEAR6fMAgLagYNc0W+ONN96IadOmxcqVKyMiIqVU6EUCANAM9HkAQGtWsNDs3XffjQMOOCC23377+PKXvxyLFi2KiIgTTzwxfvCDHxRqsQAAFJg+DwBoCwoWmp199tnRqVOnqKqqiq5du+amH3XUUXHfffc1aYxrrrkmhg8fHpWVlVFZWRkjR46Me++9t1AlAwDQBPno8wAASl3Brml2//33x7Rp02KrrbaqN33w4MHx1ltvNWmMrbbaKq688soYPHhwpJTipptuisMOOyxeeOGF2HHHHQtRNgAA65GPPg8AoNQVLDRbvnx5vW8e13jvvfeirKysSWMceuih9Z5ffvnlcc0118RTTz0lNAMAKJJ89HkAAKWuYKdn7r333nHzzTfnnrdr1y7q6uriJz/5Sey3334bPN7q1atjypQpsXz58hg5cmSj89XW1kZNTU29BwAA+ZPvPg8AoBQV7Eizn/zkJ3HAAQfEs88+G6tWrYrzzjsvXn311Xjvvffi8ccfb/I4L7/8cowcOTI+/PDD6NatW0ydOjV22GGHRuefNGlSTJw4MR8/AgAADchXnwcAUMoKdqTZTjvtFHPnzo299torDjvssFi+fHl87WtfixdeeCG23XbbJo8zZMiQmDlzZjz99NNxyimnxJgxY2LWrFmNzj9+/PhYunRp7rFw4cJ8/DgAAPz/8tXnAQCUsoIdaRYR0b1797jwwgs3aYzOnTvHdtttFxERu+22W8yYMSN++ctfxrXXXtvg/GVlZa6lAQBQYPno8wAASlnBjjS78cYb4/bbb19r+u233x433XTTRo9bV1cXtbW1m1IaAACboFB9HgBAKSlYaDZp0qTo1avXWtP79OkTV1xxRZPGGD9+fDz66KOxYMGCePnll2P8+PExffr0OPbYY/NdLgAATZSPPg8AoNQV7PTMqqqq2HrrrdeaPnDgwKiqqmrSGEuWLInjjz8+Fi1aFN27d4/hw4fHtGnT4sADD8x3uQAANFE++jwAgFJXsNCsT58+8dJLL8WgQYPqTX/xxRdjiy22aNIYv//97wtQGQAAmyIffR4AQKkr2OmZxxxzTJxxxhnx8MMPx+rVq2P16tXxl7/8Jc4888w4+uijC7VYAAAKLF993jXXXBPDhw+PysrKqKysjJEjR8a9995bwMoBAJquYEea/ehHP4oFCxbEAQccEB07/msxdXV1cfzxx7vWBQBAC5avPm+rrbaKK6+8MgYPHhwppbjpppvisMMOixdeeCF23HHHQpUPANAkBQnNUkqxePHimDx5clx22WUxc+bMKC8vj5133jkGDhxYiEUCANAM8tnnHXroofWeX3755XHNNdfEU089JTQDAIquYKHZdtttF6+++moMHjw4Bg8eXIjFAADQzArV561evTpuv/32WL58eYwcObLBeWpra6O2tjb3vKamJi/LBgBoSEGuada+ffsYPHhwvPvuu4UYHgCAIsl3n/fyyy9Ht27doqysLE4++eSYOnVq7LDDDg3OO2nSpOjevXvu0b9//7zUAADQkILdCODKK6+Mc889N1555ZVCLQIAgCLIZ583ZMiQmDlzZjz99NNxyimnxJgxY2LWrFkNzjt+/PhYunRp7rFw4cJNXj4AQGMKdiOA448/PlasWBGf+cxnonPnzlFeXl7v9ffee69QiwYAoIDy2ed17tw5tttuu4iI2G233WLGjBnxy1/+Mq699tq15i0rK4uysrJNKx4AoIkKFppdddVVhRoaAIAiKmSfV1dXV++6ZQAAxVKw0GzMmDGFGhoAgCLKV583fvz4OPjgg2PAgAHxwQcfxK233hrTp0+PadOm5WV8AIBNUbBrmkVEzJs3Ly666KI45phjYsmSJRERce+998arr75ayMUCAFBg+ejzlixZEscff3wMGTIkDjjggJgxY0ZMmzYtDjzwwEKVDQDQZAULzR555JHYeeed4+mnn4477rgjli1bFhERL774YkyYMKFQiwUAoMDy1ef9/ve/jwULFkRtbW0sWbIkHnzwQYEZAFAyChaa/fCHP4zLLrssHnjggejcuXNu+v777x9PPfVUoRYLAECB6fMAgLagYKHZyy+/HEccccRa0/v06RPV1dWFWiwAAAWmzwMA2oKChWY9evSIRYsWrTX9hRdeiE9/+tOFWiwAAAWmzwMA2oKChWZHH310nH/++bF48eJo165d1NXVxeOPPx7nnHNOHH/88YVaLAAABabPAwDagoKFZldccUUMGzYsBgwYEMuWLYsddtghvvjFL8Yee+wRF110UaEWCwBAgenzAIC2oGO+B6yrq4uf/vSncdddd8WqVaviuOOOi69//euxbNmy+OxnPxuDBw/O9yIBAGgG+jwAoC3Je2h2+eWXxyWXXBKjRo2K8vLyuPXWWyOlFDfccEO+FwUAQDPS5wEAbUneT8+8+eab47e//W1MmzYt7rzzzvi///u/uOWWW6Kuri7fiwIAoBnp8wCAtiTvoVlVVVV8+ctfzj0fNWpUtGvXLt555518LwoAgGakzwMA2pK8h2Yff/xxdOnSpd60Tp06xUcffZTvRQEA0Iz0eQBAW5L3a5qllOKEE06IsrKy3LQPP/wwTj755Nhss81y0+644458LxoAgALS5wEAbUneQ7MxY8asNe1b3/pWvhcDAEAz0+cBAG1J3kOzG2+8Md9DAgBQAvR5AEBbkvdrmgEAAABASyc0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABARkmHZpMmTYrPfe5zUVFREX369InDDz885syZU+yyAAAAAGjlSjo0e+SRR2Ls2LHx1FNPxQMPPBAfffRRHHTQQbF8+fJilwYAAABAK9ax2AWsy3333Vfv+eTJk6NPnz7x3HPPxRe/+MUiVQUAAABAa1fSoVnW0qVLIyKiZ8+ejc5TW1sbtbW1uec1NTUFrwsAAACA1qWkT8/8pLq6ujjrrLNizz33jJ122qnR+SZNmhTdu3fPPfr379+MVQIAAADQGrSY0Gzs2LHxyiuvxJQpU9Y53/jx42Pp0qW5x8KFC5upQgAAAABaixZxeuZpp50Wf/7zn+PRRx+Nrbbaap3zlpWVRVlZWTNVBgAAAEBrVNKhWUopTj/99Jg6dWpMnz49tt5662KXBAAAAEAbUNKnZ44dOzb++7//O2699daoqKiIxYsXx+LFi2PlypXFLg0AgE00adKk+NznPhcVFRXRp0+fOPzww2POnDnFLgsAICJKPDS75pprYunSpbHvvvtG3759c4/bbrut2KUBALCJHnnkkRg7dmw89dRT8cADD8RHH30UBx10UCxfvrzYpQEAlP7pmQAAtE733XdfveeTJ0+OPn36xHPPPRdf/OIXi1QVAMC/lHRoBgBA27F06dKIiOjZs2eDr9fW1kZtbW3ueU1NTbPUBQC0TSV9eiYAAG1DXV1dnHXWWbHnnnvGTjvt1OA8kyZNiu7du+ce/fv3b+YqAYC2RGgGAEDRjR07Nl555ZWYMmVKo/OMHz8+li5dmnssXLiwGSsEANoap2cCAFBUp512Wvz5z3+ORx99NLbaaqtG5ysrK4uysrJmrAwAaMuEZgAAFEVKKU4//fSYOnVqTJ8+PbbeeutilwQAkCM0AwCgKMaOHRu33npr/OlPf4qKiopYvHhxRER07949ysvLi1wdANDWuaYZAABFcc0118TSpUtj3333jb59++Yet912W7FLAwBwpBkAAMWRUip2CQAAjXKkGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAAAAZAjNAAAAACBDaAYAAAAAGUIzAAAAAMgQmgEAAABAhtAMAAAAADKEZgAAAACQITQDAAAAgAyhGQAAAABkCM0AAAAAIENoBgAAAAAZQjMAAAAAyBCaAQAAAECG0AwAAAAAMoRmAAAAAJAhNAMAAACADKEZAAAAAGQIzQAAAAAgQ2gGAAAAABlCMwAAAADIEJoBAAAAQIbQDAAAAAAyhGYAAAAAkCE0AwAAAIAMoRkAAP8fe3ceZmVd/4//NWzDgMwAAjroAK6AO2oSouGCkRlqfVIzUzTTTFySLENNRE1MyywzTEuoTypWih8z3AMxFXcUBVFkGVJQxwVkcVjm/fvDH+frHIZ9Zs6Zmcfjuu7r8tznPu/7dc77DPPyOfcCAEAWoRkAAAAAZBGaAQAAAEAWoRkAAAAAZBGaAQAAAEAWoRkAAAAAZBGaAQAAAEAWoRkAAAAAZMn70Gzy5MkxePDg6Nq1axQUFMS9996b65IAAAAAaOTyPjRbunRp7L333nHTTTfluhQAAAAAmogWuS5gQ4488sg48sgjc10GAAAAAE1I3odmm6qysjIqKyszjxcvXpzDagAAAABoiPL+9MxNNWrUqCgpKcksZWVluS4JAIAauHYtAJDPGl1oNnz48Fi0aFFmmT9/fq5LAgCgBq5dCwDks0Z3emZhYWEUFhbmugwAADbAtWsBgHzW6EIzAAAaJ9euBQDqU96fnrlkyZKYOnVqTJ06NSIi5syZE1OnTo3y8vLcFgYAQL1y7VoAoD7lfWj2/PPPR58+faJPnz4RETFs2LDo06dPXHbZZTmuDACA+uTatQBAfcr70zMPOeSQSCnlugwAAHLMtWsBgPqU90eaAQAAAEB9y/sjzQAAaJyWLFkSs2bNyjxec+3ajh07Rrdu3XJYGQCA0AwAgBx5/vnn49BDD808HjZsWEREDBkyJMaOHZujqgAAPiM0AwAgJ1y7FgDIZ65pBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkKVBhGY33XRT9OjRI1q3bh19+/aNZ599NtclAQBQC/R5AEC+yvvQ7K677ophw4bFiBEj4sUXX4y99947Bg0aFO+9916uSwMAYAvo8wCAfJb3odn1118fZ5xxRpx22mmx2267xc033xxt2rSJ2267LdelAQCwBfR5AEA+a5HrAtZnxYoV8cILL8Tw4cMz65o1axYDBw6Mp59+usbXVFZWRmVlZebxokWLIiJi8eLFdVLjkiVLIiLiw3kzY1Xl8i0aa/HC8syYa+qtzfFr2kddj7/mv2trHw19/Jr2YQ7qd/z62Ifxc7+Phj5+TfswBxsevzatGTOlVOtj8xl9nn9bG9v49bGPhj5+TfswB/U7fk37MAf1O35N+zAHGx6/Nm1Sn5fy2Ntvv50iIj311FPV1v/4xz9OBxxwQI2vGTFiRIoIi8VisVgsli1e5s+fXx8tT5Okz7NYLBaLxZLLZWP6vLw+0mxzDB8+PIYNG5Z5XFVVFR9++GFsvfXWUVBQUCv7WLx4cZSVlcX8+fOjuLi4VsZk05mH3DMHuWcOcs8c5F5dzEFKKT755JPo2rVrrYxH7dDnNR3mIffMQe6Zg9wzB7mX6z4vr0OzTp06RfPmzePdd9+ttv7dd9+NbbfdtsbXFBYWRmFhYbV17du3r5P6iouL/eDkAfOQe+Yg98xB7pmD3KvtOSgpKam1sVibPo+NYR5yzxzknjnIPXOQe7nq8/L6RgCtWrWK/fbbLx577LHMuqqqqnjssceiX79+OawMAIAtoc8DAPJdXh9pFhExbNiwGDJkSOy///5xwAEHxA033BBLly6N0047LdelAQCwBfR5AEA+y/vQ7IQTToj3338/Lrvssli4cGHss88+8eCDD8Y222yTs5oKCwtjxIgRa50eQP0yD7lnDnLPHOSeOcg9c9Bw6fNYF/OQe+Yg98xB7pmD3Mv1HBSk5F7qAAAAAPB5eX1NMwAAAADIBaEZAAAAAGQRmgEAAABAFqEZAAAAAGQRmq3DTTfdFD169IjWrVtH375949lnn13v9n//+9+jV69e0bp169hzzz1jwoQJ9VRp47Upc3DrrbfGwQcfHB06dIgOHTrEwIEDNzhnbJxN/VlYY9y4cVFQUBDHHnts3RbYBGzqHHz88ccxdOjQKC0tjcLCwth11139m7SFNnUObrjhhujZs2cUFRVFWVlZXHDBBfHpp5/WU7WNy+TJk2Pw4MHRtWvXKCgoiHvvvXeDr5k0aVLsu+++UVhYGDvvvHOMHTu2zuukYdHn5Z4+Lz/o83JPn5d7+rzcyvteL7GWcePGpVatWqXbbrstvfbaa+mMM85I7du3T++++26N2z/55JOpefPm6dprr03Tp09Pl156aWrZsmWaNm1aPVfeeGzqHHz7299ON910U3rppZfSjBkz0qmnnppKSkrSf//733quvHHZ1HlYY86cOWm77bZLBx98cDrmmGPqp9hGalPnoLKyMu2///7pq1/9avrPf/6T5syZkyZNmpSmTp1az5U3Hps6B7fffnsqLCxMt99+e5ozZ0566KGHUmlpabrgggvqufLGYcKECemSSy5J99xzT4qINH78+PVuP3v27NSmTZs0bNiwNH369HTjjTem5s2bpwcffLB+Cibv6fNyT5+XH/R5uafPyz19Xu7le68nNKvBAQcckIYOHZp5vHr16tS1a9c0atSoGrc//vjj01FHHVVtXd++fdP3v//9Oq2zMdvUOci2atWq1K5du/TnP/+5rkpsEjZnHlatWpUOPPDA9Mc//jENGTJEM7WFNnUORo8enXbccce0YsWK+iqx0dvUORg6dGg67LDDqq0bNmxY6t+/f53W2RRsTCP1k5/8JO2+++7V1p1wwglp0KBBdVgZDYk+L/f0eflBn5d7+rzc0+fll3zs9ZyemWXFihXxwgsvxMCBAzPrmjVrFgMHDoynn366xtc8/fTT1baPiBg0aNA6t2f9NmcOsi1btixWrlwZHTt2rKsyG73NnYcrrrgiunTpEqeffnp9lNmobc4c3HfffdGvX78YOnRobLPNNrHHHnvE1VdfHatXr66vshuVzZmDAw88MF544YXMof2zZ8+OCRMmxFe/+tV6qbmp8zuZ9dHn5Z4+Lz/o83JPn5d7+ryGqb5/L7eok1EbsIqKili9enVss8021dZvs8028frrr9f4moULF9a4/cKFC+uszsZsc+Yg20UXXRRdu3Zd64eJjbc58/Cf//wn/vSnP8XUqVProcLGb3PmYPbs2fHvf/87TjrppJgwYULMmjUrzj777Fi5cmWMGDGiPspuVDZnDr797W9HRUVFHHTQQZFSilWrVsVZZ50VF198cX2U3OSt63fy4sWLY/ny5VFUVJSjysgH+rzc0+flB31e7unzck+f1zDVd6/nSDManWuuuSbGjRsX48ePj9atW+e6nCbjk08+iZNPPjluvfXW6NSpU67LabKqqqqiS5cuccstt8R+++0XJ5xwQlxyySVx880357q0JmPSpElx9dVXx+9///t48cUX45577ol//etfceWVV+a6NIAGT5+XG/q8/KDPyz19XtPjSLMsnTp1iubNm8e7775bbf27774b2267bY2v2XbbbTdpe9Zvc+ZgjV/+8pdxzTXXxKOPPhp77bVXXZbZ6G3qPLz11lsxd+7cGDx4cGZdVVVVRES0aNEiZs6cGTvttFPdFt3IbM7PQmlpabRs2TKaN2+eWde7d+9YuHBhrFixIlq1alWnNTc2mzMHP/vZz+Lkk0+O733vexERseeee8bSpUvjzDPPjEsuuSSaNfP3qrq0rt/JxcXFjjJDn5cH9Hn5QZ+Xe/q83NPnNUz13euZ0SytWrWK/fbbLx577LHMuqqqqnjssceiX79+Nb6mX79+1baPiHjkkUfWuT3rtzlzEBFx7bXXxpVXXhkPPvhg7L///vVRaqO2qfPQq1evmDZtWkydOjWzHH300XHooYfG1KlTo6ysrD7LbxQ252ehf//+MWvWrEwjGxHxxhtvRGlpqUZqM2zOHCxbtmythmlNc/vZ9U2pS34nsz76vNzT5+UHfV7u6fNyT5/XMNX77+U6ub1AAzdu3LhUWFiYxo4dm6ZPn57OPPPM1L59+7Rw4cKUUkonn3xy+ulPf5rZ/sknn0wtWrRIv/zlL9OMGTPSiBEj3Ip8C23qHFxzzTWpVatW6R//+EdasGBBZvnkk09y9RYahU2dh2zuqrTlNnUOysvLU7t27dI555yTZs6cme6///7UpUuXdNVVV+XqLTR4mzoHI0aMSO3atUt33nlnmj17dnr44YfTTjvtlI4//vhcvYUG7ZNPPkkvvfRSeumll1JEpOuvvz699NJLad68eSmllH7605+mk08+ObP9mtuQ//jHP04zZsxIN910U53ehpyGR5+Xe/q8/KDPyz19Xu7p83Iv33s9odk63Hjjjalbt26pVatW6YADDkhTpkzJPDdgwIA0ZMiQatv/7W9/S7vuumtq1apV2n333dO//vWveq648dmUOejevXuKiLWWESNG1H/hjcym/ix8nmaqdmzqHDz11FOpb9++qbCwMO24447p5z//eVq1alU9V924bMocrFy5Ml1++eVpp512Sq1bt05lZWXp7LPPTh999FH9F94ITJw4scZ/39d85kOGDEkDBgxY6zX77LNPatWqVdpxxx3TmDFj6r1u8ps+L/f0eflBn5d7+rzc0+flVr73egUpOYYQAAAAAD7PNc0AAAAAIIvQDAAAAACyCM0AAAAAIIvQDAAAAACyCM0AAAAAIIvQDAAAAACyCM0AAAAAIIvQDAAAAACyCM2gCTj11FPj2GOPzXUZjVJ9frZ/+tOf4stf/nK97KsuTJo0KQoKCuLjjz/OdSkb5ac//Wmce+65uS4DADabHrDu6AEbhgcffDD22WefqKqqynUpNFBCM2C9NiXo6NGjR9xwww21uv9DDjkkfvjDH9bqmA3Rp59+Gj/72c9ixIgR9brf+v78CwoK4t57763VMWv6Xk6aNCmOOeaYKC0tjbZt28Y+++wTt99+e7VtLrzwwvjzn/8cs2fPrtV6AKAh0APmh5p6wLoI7C6//PLYZ5991lp/yy23xCGHHBLFxcUb/X2oi7mr6T3PnTs3Tj/99Nhhhx2iqKgodtpppxgxYkSsWLEis81XvvKVaNmy5Vp9HmwsoRn8/z7/jyvkm3/84x9RXFwc/fv3z3Upa0kpxapVq3JdxiZ56qmnYq+99oq77747XnnllTjttNPilFNOifvvvz+zTadOnWLQoEExevToHFYKQF3TA5LPct0DLlu2LL7yla/ExRdfnJP9r8/rr78eVVVV8Yc//CFee+21+PWvfx0333zzWrWeeuqp8dvf/jZHVdLgJWiEBgwYkIYOHZqGDh2aiouL09Zbb50uvfTSVFVVldmme/fu6Yorrkgnn3xyateuXRoyZEhKKaUnnngiHXTQQal169Zp++23T+eee25asmTJRu33pptuSjvvvHMqLCxMXbp0Sf/zP/+zSTV9+umn6Uc/+lHq2rVratOmTTrggAPSxIkTM8+PGTMmlZSUpAcffDD16tUrtW3bNg0aNCi98847mW1WrVqVLrjgglRSUpI6duyYfvzjH6dTTjklHXPMMZlt/v73v6c99tgjtW7dOnXs2DEdfvjhNb7HOXPmpIiotqz5nGr6zLO3XWNDn+m6PrchQ4asNeacOXNq3P+a+fzWt76V2rRpk7p27Zp+97vf1bhtSinNnDkzRUSaMWNGtfXXX3992nHHHTOf5Xe/+93Uo0eP1Lp167TrrrumG264odr2Q4YMqfbZdu/ePf3617+uts3ee++dRowYkXn80UcfpdNPPz116tQptWvXLh166KFp6tSp66w1pZSOOuqodOGFF661/k9/+lPabbfdUqtWrdK2226bhg4dmnlu3rx56eijj05t27ZN7dq1S8cdd1xauHBh5vkRI0akvffeO/3lL39J3bt3T8XFxemEE05Iixcvzry3mj7/iRMnpohIEyZMSPvuu29q2bJlmjhxYvr000/Tueeemzp37pwKCwtT//7907PPPpvZ35rXffTRRzW+x+7du1fbV/fu3TPP3XvvvalPnz6psLAw7bDDDunyyy9PK1euTCmlVFVVlUaMGJHKyspSq1atUmlpaTr33HNTSuv/Xmb76le/mk477bRq6/785z+n7bfffp2vASC/6AH1gI29BxwxYsRan82a70p5eXk67rjjUklJSerQoUM6+uijq31uEydOTF/4whdSmzZtUklJSTrwwAPT3Llz05gxY9Yac8yYMdXq2FAf9/nPZV1zN23atPSVr3wltW3bNnXp0iV95zvfSe+//37mtev6fq7vPWe79tpr0w477FBt3bx581JEpFmzZq23dqiJ0IxGacCAAWmrrbZK559/fnr99dfTX//619SmTZt0yy23ZLZZExL88pe/TLNmzcosbdu2Tb/+9a/TG2+8kZ588snUp0+fdOqpp25wn88991xq3rx5uuOOO9LcuXPTiy++mH7zm99sUk3f+9730oEHHpgmT56cZs2ala677rpUWFiY3njjjZTSZw1Ty5Yt08CBA9Nzzz2XXnjhhdS7d+/07W9/OzPGL37xi9ShQ4d09913p+nTp6fTTz89tWvXLvNL/Z133kktWrRI119/fZozZ0565ZVX0k033ZQ++eSTtd7TqlWr0t13350iIs2cOTMtWLAgffzxxzW+/w8++CBtv/326YorrkgLFixICxYsSCmlDX6m6/vcPv7449SvX790xhlnZMZctWpVjfvv3r17ateuXRo1alSaOXNm+u1vf5uaN2+eHn744XXO2f77758uvfTSauv222+/zLoVK1akyy67LD333HNp9uzZmTm76667MttvTsM0cODANHjw4PTcc8+lN954I/3oRz9KW2+9dfrggw/WWWtJSUkaN25ctXW///3vU+vWrdMNN9yQZs6cmZ599tnMvlevXp322WefdNBBB6Xnn38+TZkyJe23335pwIABmdePGDEibbXVVukb3/hGmjZtWpo8eXLadttt08UXX5xSWvfnv6Zp2muvvdLDDz+cZs2alT744IN03nnnpa5du6YJEyak1157LQ0ZMiR16NAh87421Gy99957mSZtwYIF6b333ksppTR58uRUXFycxo4dm95666308MMPpx49eqTLL788pfRZg1VcXJwmTJiQ5s2bl5555pnMz9W6vpc16d+/f/rRj35Ubd2MGTPW26gDkF/0gHrAxt4DfvLJJ+n4449PX/nKVzKfTWVlZVqxYkXq3bt3+u53v5teeeWVNH369PTtb3879ezZM1VWVqaVK1emkpKSdOGFF6ZZs2al6dOnp7Fjx6Z58+alZcuWpR/96Edp9913z4y5bNmyanVsbGi2rrn76KOPUufOndPw4cPTjBkz0osvvpiOOOKIdOihh6aU1v/9XNd7rskll1yS9ttvv7XWb7PNNmsFgbAxhGY0SgMGDEi9e/eu9he8iy66KPXu3TvzuHv37unYY4+t9rrTTz89nXnmmdXWPfHEE6lZs2Zp+fLl693n3XffnYqLizNH6WxqTfPmzUvNmzdPb7/9drXXHX744Wn48OEppZT5K9Dn/0py0003pW222SbzuLS0NF177bWZxytXrkzbb7995pf6Cy+8kCIizZ07d73vZ42N/QWZUs3NwoY+04353M4///yN2vdXvvKVautOOOGEdOSRR67zNb/+9a/TTjvtlHm8rr88ft7QoUOr/fV4UxumJ554IhUXF6dPP/202jY77bRT+sMf/lDjPj/66KMUEWny5MnV1nft2jVdcsklNb7m4YcfTs2bN0/l5eWZda+99lqKiMzRXyNGjEht2rSp9tn/+Mc/Tn379s08runzX/OduPfeezPrlixZklq2bJluv/32zLoVK1akrl27Zr6PG/Ndiog0fvz4ausOP/zwdPXVV1db97//+7+ptLQ0pZTSr371q7TrrrumFStW1DhmTXOS7a677kqtWrVKr776arX1ixYtShGRJk2atN7XA5Af9IB6wJQafw+Yve+UPuuNevbsWe17VllZmYqKitJDDz2UPvjgg/X2NGvOQFiXTfk+1DR3V155Zfryl79cbd38+fMzweyGvp81vedsb775ZiouLq4WSK/Rp0+fzB9cYVO4phmN1he/+MUoKCjIPO7Xr1+8+eabsXr16sy6/fffv9prXn755Rg7dmxstdVWmWXQoEFRVVUVc+bMWe/+jjjiiOjevXvsuOOOcfLJJ8ftt98ey5Yt2+iapk2bFqtXr45dd9212v4ff/zxeOuttzKvadOmTey0006Zx6WlpfHee+9FRMSiRYtiwYIF0bdv38zzLVq0qPY+99577zj88MNjzz33jOOOOy5uvfXW+Oijj9b73rLdfvvt1Wp84okn1rnthj7TjfncNla/fv3WejxjxoyIiDjrrLOq1RAR8a1vfSvmzp0bU6ZMybyvfffdN3r16pUZ46abbor99tsvOnfuHFtttVXccsstUV5evln1RXz2eSxZsiS23nrravXMmTOn2jx/3vLlyyMionXr1pl17733Xrzzzjtx+OGH1/iaGTNmRFlZWZSVlWXW7bbbbtG+ffvMZxLx2YV727Vrl3n8+e/Thnz+e/XWW2/FypUrq11vo2XLlnHAAQdU29/mePnll+OKK66o9nmdccYZsWDBgli2bFkcd9xxsXz58thxxx3jjDPOiPHjx2/SNdYmTpwYp512Wtx6662x++67V3uuqKgoImKzv5MA1D894Gf0gI2zB1zf+LNmzYp27dplxu7YsWN8+umn8dZbb0XHjh3j1FNPjUGDBsXgwYPjN7/5TSxYsGCz38+mevnll2PixInV3vuaz/utt97a4u/n22+/HV/5ylfiuOOOizPOOGOt54uKivRzbJYWuS4Acqlt27bVHi9ZsiS+//3vx3nnnbfWtt26dVvvWO3atYsXX3wxJk2aFA8//HBcdtllcfnll8dzzz0X7du332AtS5YsiebNm8cLL7wQzZs3r/bcml/wEZ8FEZ9XUFAQKaUNjr9G8+bN45FHHomnnnoqHn744bjxxhvjkksuiWeeeSZ22GGHjRrj6KOPrtaUbbfdduvcdkOfaatWrbboc9tYV1xxRVx44YXV1m277bZx2GGHxR133BFf/OIX44477ogf/OAHmefHjRsXF154YfzqV7+Kfv36Rbt27eK6666LZ555Zp37adas2VrzsXLlysx/L1myJEpLS2PSpElrvXZd73frrbeOgoKCao3DmjBnS9X0fdrYW3Jn//zUlSVLlsTIkSPjG9/4xlrPtW7dOsrKymLmzJnx6KOPxiOPPBJnn312XHfddfH444+v9f6yPf744zF48OD49a9/Haeccspaz3/44YcREdG5c+faeTMA5AU9oB6wofaA67JkyZLYb7/9arxL5Jo+ZsyYMXHeeefFgw8+GHfddVdceuml8cgjj8QXv/jFDY6/pZYsWRKDBw+OX/ziF2s9V1paukXfz3feeScOPfTQOPDAA+OWW26pcZsPP/xQP8dmEZrRaGX/UpsyZUrssssuazUjn7fvvvvG9OnTY+edd96sfbZo0SIGDhwYAwcOjBEjRkT79u3j3//+d+Z/9tdXU58+fWL16tXx3nvvxcEHH7xZ+y8pKYnS0tJ45pln4ktf+lJERKxatSpeeOGF2HfffTPbFRQURP/+/aN///5x2WWXRffu3WP8+PExbNiwtcZs1apVRES1v862a9eu2tFJn9/289tFbNxnur7PraYx12XNXws//7h3794REdGlS5fo0qXLWq856aST4ic/+UmceOKJMXv27PjWt76Vee7JJ5+MAw88MM4+++zMunX9JXCNzp07V/ur3eLFi6v9hXrfffeNhQsXRosWLaJHjx4b9b5atWoVu+22W0yfPj2+/OUvR8Rnc9CjR4947LHH4tBDD13rNb1794758+fH/PnzM0ebTZ8+PT7++OPYbbfdNmq/a/a9MZ//TjvtFK1atYonn3wyunfvHhGfNYrPPffcJt1yvGXLljV+h2bOnLne71BRUVEMHjw4Bg8eHEOHDo1evXrFtGnTYt99913ne5g0aVJ87Wtfi1/84hdx5pln1jjuq6++Gi1btlzrCDQA8pceUA/YmHvANetr+rzvuuuu6NKlSxQXF69zzD59+kSfPn1i+PDh0a9fv0xwuCmf98bUXVN9d999d/To0SNatKg5hljf93Nd9b399ttx6KGHxn777RdjxoyJZs3WPpluzdF2ffr0qZX3R9Pi9EwarfLy8hg2bFjMnDkz7rzzzrjxxhvj/PPPX+9rLrroonjqqafinHPOialTp8abb74Z//d//xfnnHPOBvd3//33x29/+9uYOnVqzJs3L/7yl79EVVVV9OzZc6Nq2nXXXeOkk06KU045Je65556YM2dOPPvsszFq1Kj417/+tdHv+/zzz49rrrkm7r333nj99dfj7LPPjo8//jjz/DPPPBNXX311PP/881FeXh733HNPvP/++5nGIlv37t2joKAg7r///nj//fdjyZIl69x3jx49YvLkyfH2229HRUVFRGz4M93Q59ajR4945plnYu7cuVFRUbHeo6CefPLJuPbaa+ONN96Im266Kf7+979vcM6/8Y1vxCeffBI/+MEP4tBDD42uXbtmnttll13i+eefj4ceeijeeOON+NnPfhbPPffcesc77LDD4n//93/jiSeeiGnTpsWQIUOqNekDBw6Mfv36xbHHHhsPP/xwzJ07N5566qm45JJL4vnnn1/nuIMGDYr//Oc/1dZdfvnl8atf/Sp++9vfxptvvhkvvvhi3HjjjZn97LnnnnHSSSfFiy++GM8++2yccsopMWDAgLVOSVmfjf3827ZtGz/4wQ/ixz/+cTz44IMxffr0OOOMM2LZsmVx+umnb9L+HnvssVi4cGHmr6qXXXZZ/OUvf4mRI0fGa6+9FjNmzIhx48bFpZdeGhERY8eOjT/96U/x6quvxuzZs+Ovf/1rFBUVZcK7mr6XEydOjKOOOirOO++8+J//+Z9YuHBhLFy4MHNk2RpPPPFEHHzwwbV2ZB8AdU8PqAds7D1gjx494pVXXomZM2dGRUVFrFy5Mk466aTo1KlTHHPMMfHEE0/EnDlzYtKkSXHeeefFf//735gzZ04MHz48nn766Zg3b148/PDD8eabb2bmv0ePHjFnzpyYOnVqVFRURGVlZURELFy4MKZOnRqzZs2KiIhp06bF1KlT1+qZsuvLnruhQ4fGhx9+GCeeeGI899xz8dZbb8VDDz0Up512WqxevXqD38+a3vPbb78dhxxyC9/7XAAA6iVJREFUSHTr1i1++ctfxvvvv5/p6T5vypQpUVhYuNZpvLBRcntJNagbAwYMSGeffXY666yzUnFxcerQoUO6+OKL17rdeE0XB3/22WfTEUcckbbaaqvUtm3btNdee6Wf//znG9znE088kQYMGJA6dOiQioqK0l577VXtDjsbU9OaO/X06NEjtWzZMpWWlqavf/3r6ZVXXkkp/b/bjX/e+PHjq93ae+XKlen8889PxcXFqX379mnYsGHVbjc+ffr0NGjQoNS5c+dUWFiYdt1113TjjTeu971dccUVadttt00FBQXrvN14Sik9/fTTaa+99kqFhYXValrfZ7qhz23mzJnpi1/8YioqKtrg7cZHjhyZjjvuuNSmTZu07bbbVrtz1focf/zxKSLSbbfdVm39p59+mk499dRUUlKS2rdvn37wgx+kn/70p9Uukpp9UdJFixalE044IRUXF6eysrI0duzYte6ctHjx4nTuueemrl27ppYtW6aysrJ00kknVbtof7bXXnstFRUVrXXnqptvvjn17Nkz830599xzM8/NmzcvHX300alt27apXbt26bjjjksLFy7MPF/TBV9//etfp+7du2ce1/T5r+tCsMuXL0/nnntu6tSpUyosLEz9+/fP3HQgpZovIBtZtzS/77770s4775xatGhRrY4HH3wwHXjggamoqCgVFxenAw44IHOR1/Hjx6e+ffum4uLi1LZt2/TFL34xPfroo5nX1vS9rOl26BFR7e6iKaXUs2fPdOedd641HwDkJz2gHrAp9IDvvfde5nONiDRx4sSUUkoLFixIp5xySqYX23HHHdMZZ5yRFi1alBYuXJiOPfbYVFpamlq1apW6d++eLrvssrR69erMe/6f//mf1L59+2r92YgRI2rsmT7fvw0YMKDa92Ndc/fGG2+kr3/966l9+/apqKgo9erVK/3whz9MVVVVG/x+1vSe19wgo6bl884888z0/e9/f52fMaxPQUqbcCI8NBCHHHJI7LPPPnHDDTfkupSMfKypMenRo0f88Ic/3KRTARua4447Lvbdd98YPnx4rkupFXPmzIldd901pk+fHrvsskuuy1nLAw88ED/60Y/ilVdeWedpBADkl3zst/KxpsZED5h73bt3j5EjR8app56a61LWUlFRET179oznn39+o6/dB5/n9EyABuK6666rdkHghm7ChAlx5pln5mVgFhGxdOnSGDNmjMAMAMipfO4BX3vttSgpKanxhkr5YO7cufH73/9eYMZm838CsJGeeOKJOPLII9f5/Pqu8wC1oUePHnHuuefmuoxaM3To0FyXsF7f/OY3c10CAHlAD0iu5XMPuPvuu8crr7yS6zLWaf/999+k6/lCNqdnwkZavnx5vP322+t8fnPvtgQAQP7SAwI0XUIzAAAAAMjimmYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZWuS6gLpWVVUV77zzTrRr1y4KCgpyXQ4A0ACklOKTTz6Jrl27RrNm/saYr/R5AMCm2pQ+r9GHZu+8806UlZXlugwAoAGaP39+bL/99rkug3XQ5wEAm2tj+rxGH5q1a9cuIj77MIqLi3NcDQDQECxevDjKysoyfQT5SZ8HAGyqTenzGn1otuZQ/eLiYs0UALBJnPKX3/R5AMDm2pg+z0U6AAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACBLi1wXAADkj/Ly8qioqKiVsTp16hTdunWrlbEAAMhvjbGPFJoBABHxWaPTq1fvWL58Wa2MV1TUJl5/fUZeNDwAANSdxtpHCs0AgIiIqKioiOXLl0Xf746I4tIeWzTW4gVz45nbRkZFRUXOmx0AAOpWY+0jhWYAQDXFpT2iY7eeuS4DAIAGprH1kW4EAAAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgBATvTo0SMKCgrWWoYOHZrr0gAAokWuCwAAoGl67rnnYvXq1ZnHr776ahxxxBFx3HHH5bAqAIDPCM0AAMiJzp07V3t8zTXXxE477RQDBgzIUUUAAP+P0AwAgJxbsWJF/PWvf41hw4ZFQUFBjdtUVlZGZWVl5vHixYvrqzwAoAnK+2uavf322/Gd73wntt566ygqKoo999wznn/++VyXBQBALbr33nvj448/jlNPPXWd24waNSpKSkoyS1lZWf0VCAA0OXkdmn300UfRv3//aNmyZTzwwAMxffr0+NWvfhUdOnTIdWkAANSiP/3pT3HkkUdG165d17nN8OHDY9GiRZll/vz59VghANDU5PXpmb/4xS+irKwsxowZk1m3ww475LAiAABq27x58+LRRx+Ne+65Z73bFRYWRmFhYT1VBQA0dXl9pNl9990X+++/fxx33HHRpUuX6NOnT9x6663rfU1lZWUsXry42gIAQP4aM2ZMdOnSJY466qhclwIAkJHXodns2bNj9OjRscsuu8RDDz0UP/jBD+K8886LP//5z+t8jWtdAAA0HFVVVTFmzJgYMmRItGiR1ydBAABNTF6HZlVVVbHvvvvG1VdfHX369IkzzzwzzjjjjLj55pvX+RrXugAAaDgeffTRKC8vj+9+97u5LgUAoJq8/nNeaWlp7LbbbtXW9e7dO+6+++51vsa1LgAAGo4vf/nLkVLKdRkAAGvJ6yPN+vfvHzNnzqy27o033oju3bvnqCIAAAAAmoK8Ds0uuOCCmDJlSlx99dUxa9asuOOOO+KWW26JoUOH5ro0AAAAABqxvA7NvvCFL8T48ePjzjvvjD322COuvPLKuOGGG+Kkk07KdWkAAAAANGJ5fU2ziIivfe1r8bWvfS3XZQAAAADQhOT1kWYAAAAAkAt5f6QZ0DCUl5dHRUVFrYzVqVOn6NatW62MBQAAAJtDaAZssfLy8ujVq3csX76sVsYrKmoTr78+Q3AGAABAzgjNgC1WUVERy5cvi77fHRHFpT22aKzFC+bGM7eNjIqKCqEZAAAAOSM0A2pNcWmP6NitZ67LAAAAgC3mRgAAAAAAkEVoBgAAAABZnJ4JAA1Ebd6lNsKdagEAYH2EZgDQANT2XWoj3KkWAADWR2hGo1CbR1848gLIR7V5l9qI3N2p1r/XAAA0FEIzGrzaPvrCkRdAPmvId6n17zUAAA2J0IwGrzaPvsjVkRcATYF/rwEAaEiEZjQaDfnoC4CmxL/XAAA0BM1yXQAAAAAA5BuhGQAAAABkEZoBAAAAQBahGQAAAABkEZoBAAAAQBZ3zwRgo5SXl0dFRUWtjdepU6fo1q1brY0HAABQm4RmAGxQeXl59OrVO5YvX1ZrYxYVtYnXX58hOAMAAPKS0AyADaqoqIjly5dF3++OiOLSHls83uIFc+OZ20ZGRUWF0AwAAMhLQjMANlpxaY/o2K1nrssAAACoc24EAAAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAOTE22+/Hd/5zndi6623jqKiothzzz3j+eefz3VZAAAREdEi1wUAAND0fPTRR9G/f/849NBD44EHHojOnTvHm2++GR06dMh1aQAAESE0AwAgB37xi19EWVlZjBkzJrNuhx12yGFFAADVCc2ABqG8vDwqKipqbbxOnTpFt27dam08ADbNfffdF4MGDYrjjjsuHn/88dhuu+3i7LPPjjPOOGOdr6msrIzKysrM48WLF9dHqQBAEyU0A/JeeXl59OrVO5YvX1ZrYxYVtYnXX58hOAPIkdmzZ8fo0aNj2LBhcfHFF8dzzz0X5513XrRq1SqGDBlS42tGjRoVI0eOrOdKAYCmSmgG5L2KiopYvnxZ9P3uiCgu7bHF4y1eMDeeuW1kVFRUCM0AcqSqqir233//uPrqqyMiok+fPvHqq6/GzTffvM7QbPjw4TFs2LDM48WLF0dZWVm91AsAND1CM6DBKC7tER279cx1GbBOTiOGjVdaWhq77bZbtXW9e/eOu+++e52vKSwsjMLCwrouDQAgIoRmAFArnEYMm6Z///4xc+bMauveeOON6N69e44qAgCoTmgGALXAacSwaS644II48MAD4+qrr47jjz8+nn322bjlllvilltuyXVpAAARITQDgFrlNGLYOF/4whdi/PjxMXz48Ljiiitihx12iBtuuCFOOumkXJcGABARQjMAAHLka1/7Wnzta1/LdRkAADUSmgEAAADkkBtK5ae8Ds0uv/zyGDlyZLV1PXv2jNdffz1HFQHkr9r8ReuXLAAA1A83lMpfeR2aRUTsvvvu8eijj2Yet2iR9yUD1Lva/kXrlywAANQPN5TKX3mfQLVo0SK23XbbXJcBkNdq8xetX7IAAFD/3FAq/+R9aPbmm29G165do3Xr1tGvX78YNWrUev8nrrKyMiorKzOPFy9eXB9lAuQFv2gBAABqR7NcF7A+ffv2jbFjx8aDDz4Yo0ePjjlz5sTBBx8cn3zyyTpfM2rUqCgpKcksZWVl9VgxAAAAAI1BXodmRx55ZBx33HGx1157xaBBg2LChAnx8ccfx9/+9rd1vmb48OGxaNGizDJ//vx6rBgAAACAxiDvT8/8vPbt28euu+4as2bNWuc2hYWFUVhYWI9VsTHc1Q8AAABoSBpUaLZkyZJ466234uSTT851KWwCd/UDAAAAGpq8Ds0uvPDCGDx4cHTv3j3eeeedGDFiRDRv3jxOPPHEXJfGJnBXPwAAAKChyevQ7L///W+ceOKJ8cEHH0Tnzp3joIMOiilTpkTnzp1zXVo1Tj3cOO7qR77zs5x75gAAAMgXeR2ajRs3LtclbJBTD6Fx8LOce+YAAADIJ3kdmjUETj2ExsHPcu6ZAwAAIJ8IzWqJUw+hcfCznHvmAAAAyAfNcl0AAAAAAOQbR5oBAAAAdcoNn2iIhGawEWrzH/gI/8gDAABNhxs+0VAJzWADavsf+Aj/yAMAAE2HGz7RUAnNYANq8x/4CP/IAwA0NU5Lg8+44RMNjdAMNpJ/4NlSGmYAaHqclgbQcAnNAOqBhhkAmianpQE0XEIzgHqgYQaAps1ZCwANj9AMoB5pmAEAABoGoRkAAADQoLl+MHVBaAYAAAA0WK4fTF0RmgEAAAANlusHU1eEZgAAAECD5/rB1LZmuS4AAAAAAPKN0AwAAAAAsgjNAAAAACCLa5oBAACwTuXl5VFRUVFr43Xq1MkF1oEGQWgGAABAjcrLy6NXr96xfPmyWhuzqKhNvP76DMEZkPeEZgAAANSooqIili9fFn2/OyKKS3ts8XiLF8yNZ24bGRUVFUIzIO8JzQAAAFiv4tIe0bFbz1yXAVCv3AgAAAAAALI40gwAAMhbtXkRehegB2BTCM0aAHerAQCgKarti9C7AD0Am0JolufcrQYAgKaqNi9C7wL0AGwqoVmec7caAACaOhehByAXhGYNhEYBAAAAoP64eyYAADlx+eWXR0FBQbWlV69euS4LACAiHGkGAEAO7b777vHoo49mHrdooT0FAPKDrgQAgJxp0aJFbLvttrkuAwBgLUIzAABy5s0334yuXbtG69ato1+/fjFq1Kh13qyosrIyKisrM48XL15cX2UC0MSVl5dHRUVFrY3XqVMnN+drAIRmAADkRN++fWPs2LHRs2fPWLBgQYwcOTIOPvjgePXVV6Ndu3ZrbT9q1KgYOXJkDioFoCkrLy+PXr16x/Lly2ptzKKiNvH66zMEZ3lOaAYAQE4ceeSRmf/ea6+9om/fvtG9e/f429/+Fqeffvpa2w8fPjyGDRuWebx48eIoKyurl1qBuuMIntwzB+tXUVERy5cvi77fHRHFpT22eLzFC+bGM7eNjIqKikb1OTVGQjMAAPJC+/btY9ddd41Zs2bV+HxhYWEUFhbWc1VAXXIET+6Zg41XXNojOnbrmesyqEdCMwAA8sKSJUvirbfeipNPPjnXpQD1xBE8uWcOYN2EZgAA5MSFF14YgwcPju7du8c777wTI0aMiObNm8eJJ56Y69Ialdo87aqxnXJF/nAET+6ZA1ib0AwAgJz473//GyeeeGJ88MEH0blz5zjooINiypQp0blz51yX1mjU9mlXjfWUKwCoidAMAICcGDduXK5LaPRq87Qrp1wBNGyOPN50QjMAAGjknHYF0LQ58njzCM0AAAAAGjFHHm8eoRkATYZD0gEAaMocebxphGYANAkOSQcAADaF0AyAJsEh6QAAwKYQmkET4bQ0+IxD0gEAgI0hNIMmwGlpAAAAsGmEZtAEOC0NAKgrjmYHoLFqUKHZNddcE8OHD4/zzz8/brjhhlyXA7WmNpvNiHU3nE5LAwBqk6PZAWjMGkxo9txzz8Uf/vCH2GuvvXJdCtSq2m42IzScAED9aAxHs9fXHy8BaHgaRGi2ZMmSOOmkk+LWW2+Nq666KtflQK2qzWYzwumTAED9a6hHs/vjJQDr0yBCs6FDh8ZRRx0VAwcO3GBoVllZGZWVlZnHixcvruvyoFY01GYTAKCh8sdLANYn70OzcePGxYsvvhjPPffcRm0/atSoGDlyZB1XBQAANBYN/Y+XbsYAUDfyOjSbP39+nH/++fHII49E69atN+o1w4cPj2HDhmUeL168OMrKyuqqRAAAgJxxMwaAupPXodkLL7wQ7733Xuy7776ZdatXr47JkyfH7373u6isrIzmzZtXe01hYWEUFhbWd6kAAAD1rjHcjKExcLQfNE55HZodfvjhMW3atGrrTjvttOjVq1dcdNFFawVmAAAATVFDP8W0IXO0HzReeR2atWvXLvbYY49q69q2bRtbb731WusBAACgvjnaDxqvvA7NAAAgl+r6lKvaHH9d+wDqh6P9oPFpcKHZpEmTcl0CAABNQF2fclXb49e0DwBg8zW40AwAAOpDXZ9yVZvjr2sfAMDmE5oBAMB61PUpV07pAoD81CzXBQAAAABAvhGaAQAAAEAWoRkAAAAAZKmz0GzHHXeMDz74YK31H3/8cey44451tVsAAOqYPg8AaArqLDSbO3durF69eq31lZWV8fbbb9fVbgEAqGP6PACgKaj1u2fed999mf9+6KGHoqSkJPN49erV8dhjj0WPHj1qe7cAANQxfR4A0JTUemh27LHHRkREQUFBDBkypNpzLVu2jB49esSvfvWr2t4tAAB1TJ8HADQltR6aVVVVRUTEDjvsEM8991x06tSptncBAEAO6PMAgKak1kOzNebMmVNXQwMAkEP6PACgKaiz0Cwi4rHHHovHHnss3nvvvcxfJte47bbb6nLXAADUIX0eANDY1VloNnLkyLjiiiti//33j9LS0igoKKirXQEAUI/0eQBAU1BnodnNN98cY8eOjZNPPrmudgEAQA7o8wCApqBZXQ28YsWKOPDAA+tqeAAAckSfBwA0BXV2pNn3vve9uOOOO+JnP/tZXe2CWlJeXh4VFRW1Nl6nTp2iW7dutTYeAJBf9HkAQFNQZ6HZp59+Grfccks8+uijsddee0XLli2rPX/99dfX1a7ZBOXl5dGrV+9YvnxZrY1ZVNQmXn99huAMABopfR4A0BTUWWj2yiuvxD777BMREa+++mq151wsNn9UVFTE8uXLou93R0RxaY8tHm/xgrnxzG0jo6KiQmgGAI2UPg8AaArqLDSbOHFiXQ1NHSgu7REdu/XMdRkAQAOgzwMAmoI6uxHAGrNmzYqHHnooli9fHhERKaW63iUAAPVAnwcANGZ1Fpp98MEHcfjhh8euu+4aX/3qV2PBggUREXH66afHj370o7raLQAAdawu+rxrrrkmCgoK4oc//GEtVgoAsPnqLDS74IILomXLllFeXh5t2rTJrD/hhBPiwQcfrKvdAgBQx2q7z3vuuefiD3/4Q+y11161WSYAwBaps9Ds4Ycfjl/84hex/fbbV1u/yy67xLx58+pqtwAA1LHa7POWLFkSJ510Utx6663RoUOH2iwTAGCL1FlotnTp0mp/eVzjww8/jMLCwrraLQAAdaw2+7yhQ4fGUUcdFQMHDtzgtpWVlbF48eJqCwBAXamz0Ozggw+Ov/zlL5nHBQUFUVVVFddee20ceuihdbVbAADqWG31eePGjYsXX3wxRo0atVHbjxo1KkpKSjJLWVnZJtcOALCxWtTVwNdee20cfvjh8fzzz8eKFSviJz/5Sbz22mvx4YcfxpNPPllXuwUAoI7VRp83f/78OP/88+ORRx6J1q1bb9Rrhg8fHsOGDcs8Xrx4seAMAKgzdXak2R577BFvvPFGHHTQQXHMMcfE0qVL4xvf+Ea89NJLsdNOO9XVbgEAqGO10ee98MIL8d5778W+++4bLVq0iBYtWsTjjz8ev/3tb6NFixaxevXqtV5TWFgYxcXF1RYAgLpSZ0eaRUSUlJTEJZdcUpe7AAAgB7a0zzv88MNj2rRp1daddtpp0atXr7jooouiefPmW1oiAMAWqbPQbMyYMbHVVlvFcccdV2393//+91i2bFkMGTKkrnYNAEAdqo0+r127drHHHntUW9e2bdvYeuut11oPsKXKy8ujoqKiVsbq1KlTdOvWrVbGAvJbnYVmo0aNij/84Q9rre/SpUuceeaZQjMAgAZKnwc0JOXl5dGrV+9YvnxZrYxXVNQmXn99huAMmoA6C83Ky8tjhx12WGt99+7do7y8vK52CwBAHaurPm/SpElbUBVAzSoqKmL58mXR97sjori0xxaNtXjB3HjmtpFRUVEhNIMmoM5Csy5dusQrr7wSPXr0qLb+5Zdfjq233rqudgsAQB3T5wENUXFpj+jYrWeuywAakDq7e+aJJ54Y5513XkycODFWr14dq1evjn//+99x/vnnx7e+9a262i0AAHVMnwcANAV1dqTZlVdeGXPnzo3DDz88WrT4bDdVVVVxyimnxNVXX11XuwUAoI7p8wCApqBOQrOUUixcuDDGjh0bV111VUydOjWKiopizz33jO7du9fFLgEAqAf6PACgqaiz0GznnXeO1157LXbZZZfYZZdd6mI3AADUM30eANBU1Elo1qxZs9hll13igw8+0EgBADQi+jyA+ldeXh4VFRW1Nl6nTp3c/RM2Qp1d0+yaa66JH//4xzF69OjYY4896mo3AADUM30eQP0pLy+PXr16x/Lly2ptzKKiNvH66zMEZ7ABdRaanXLKKbFs2bLYe++9o1WrVlFUVFTt+Q8//LCudg0AQB3S5wHUn4qKili+fFn0/e6IKC7tscXjLV4wN565bWRUVFQIzWAD6iw0u+GGG+pqaAAAckifB1D/ikt7RMduPXNdBjQpdRaaDRkypK6GBgAgh/R5AEBT0KwuB3/rrbfi0ksvjRNPPDHee++9iIh44IEH4rXXXqvL3QIAUMf0eQBAY1dnodnjjz8ee+65ZzzzzDNxzz33xJIlSyIi4uWXX44RI0bU1W4BAKhj+jwAoCmos9Dspz/9aVx11VXxyCOPRKtWrTLrDzvssJgyZUpd7RYAgDqmzwMAmoI6C82mTZsWX//619da36VLl6ioqKir3QIAUMf0eQBAU1BnoVn79u1jwYIFa61/6aWXYrvttqur3QIAUMf0eQBAU1Bnodm3vvWtuOiii2LhwoVRUFAQVVVV8eSTT8aFF14Yp5xySl3tFgCAOqbPAwCagjoLza6++uro3bt3dOvWLZYsWRK77bZbfOlLX4oDDzwwLr300o0aY/To0bHXXntFcXFxFBcXR79+/eKBBx6oq5IBANgItdHnAQDkuxa1PWBVVVVcd911cd9998WKFSvi5JNPjv/5n/+JJUuWRJ8+fWKXXXbZ6LG23377uOaaa2KXXXaJlFL8+c9/jmOOOSZeeuml2H333Wu7dAAA1qM2+zwAgHxX66HZz3/+87j88stj4MCBUVRUFHfccUeklOK2227b5LEGDx681tijR4+OKVOmCM0AAOpZbfZ5AAD5rtZPz/zLX/4Sv//97+Ohhx6Ke++9N/75z3/G7bffHlVVVVs07urVq2PcuHGxdOnS6Nev3zq3q6ysjMWLF1dbAADYcnXV5wEA5KNaD83Ky8vjq1/9aubxwIEDo6CgIN55553NGm/atGmx1VZbRWFhYZx11lkxfvz42G233da5/ahRo6KkpCSzlJWVbdZ+AQCorrb7PACAfFbrodmqVauidevW1da1bNkyVq5cuVnj9ezZM6ZOnRrPPPNM/OAHP4ghQ4bE9OnT17n98OHDY9GiRZll/vz5m7VfAACqq+0+DwAgn9X6Nc1SSnHqqadGYWFhZt2nn34aZ511VrRt2zaz7p577tmo8Vq1ahU777xzRETst99+8dxzz8VvfvOb+MMf/lDj9oWFhdX2DQBA7ajtPg8AIJ/Vemg2ZMiQtdZ95zvfqbXxq6qqorKystbGAwBg49R1nwcAkE9qPTQbM2ZMrY01fPjwOPLII6Nbt27xySefxB133BGTJk2Khx56qNb2AQDAxqnNPg8AIN/VemhWm95777045ZRTYsGCBVFSUhJ77bVXPPTQQ3HEEUfkujQAAAAAGrG8Ds3+9Kc/5boEAAAAAJqgWr97JgAAAAA0dEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAICdGjx4de+21VxQXF0dxcXH069cvHnjggVyXBQAQEUIzAAByZPvtt49rrrkmXnjhhXj++efjsMMOi2OOOSZee+21XJcGABAtcl0AAABN0+DBg6s9/vnPfx6jR4+OKVOmxO67756jqgAAPiM0AwAg51avXh1///vfY+nSpdGvX78at6msrIzKysrM48WLF9dXeQBAE+T0TAAAcmbatGmx1VZbRWFhYZx11lkxfvz42G233WrcdtSoUVFSUpJZysrK6rlaAKApEZoBAJAzPXv2jKlTp8YzzzwTP/jBD2LIkCExffr0GrcdPnx4LFq0KLPMnz+/nqsFAJoSp2cCAJAzrVq1ip133jkiIvbbb7947rnn4je/+U384Q9/WGvbwsLCKCwsrO8SAYAmypFmAADkjaqqqmrXLQMAyBVHmgEAkBPDhw+PI488Mrp16xaffPJJ3HHHHTFp0qR46KGHcl0aAIDQDACA3HjvvffilFNOiQULFkRJSUnstdde8dBDD8URRxyR69IAAIRmAADkxp/+9KdclwAAsE6uaQYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJAlr0OzUaNGxRe+8IVo165ddOnSJY499tiYOXNmrssCAAAAoJHL69Ds8ccfj6FDh8aUKVPikUceiZUrV8aXv/zlWLp0aa5LAwAAAKARa5HrAtbnwQcfrPZ47Nix0aVLl3jhhRfiS1/6Uo6qAgAAAKCxy+vQLNuiRYsiIqJjx47r3KaysjIqKyszjxcvXlzndQEAAADQuOT16ZmfV1VVFT/84Q+jf//+sccee6xzu1GjRkVJSUlmKSsrq8cqAQAAAGgMGkxoNnTo0Hj11Vdj3Lhx691u+PDhsWjRoswyf/78eqoQAAAAgMaiQZyeec4558T9998fkydPju2333692xYWFkZhYWE9VQYAAABAY5TXoVlKKc4999wYP358TJo0KXbYYYdclwQAAABAE5DXodnQoUPjjjvuiP/7v/+Ldu3axcKFCyMioqSkJIqKinJcHQAAAACNVV5f02z06NGxaNGiOOSQQ6K0tDSz3HXXXbkuDQAAAIBGLK+PNEsp5boEAAAAAJqgvD7SDAAAAAByQWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAEBOjBo1Kr7whS9Eu3btokuXLnHsscfGzJkzc10WAEBECM0AAMiRxx9/PIYOHRpTpkyJRx55JFauXBlf/vKXY+nSpbkuDQAgWuS6AAAAmqYHH3yw2uOxY8dGly5d4oUXXogvfelLOaoKAOAzQjMAAPLCokWLIiKiY8eONT5fWVkZlZWVmceLFy+ul7oAgKbJ6ZkAAORcVVVV/PCHP4z+/fvHHnvsUeM2o0aNipKSksxSVlZWz1UCAE2J0AwAgJwbOnRovPrqqzFu3Lh1bjN8+PBYtGhRZpk/f349VggANDVOzwQAIKfOOeecuP/++2Py5Mmx/fbbr3O7wsLCKCwsrMfKAICmTGgGAEBOpJTi3HPPjfHjx8ekSZNihx12yHVJAAAZQjMAAHJi6NChcccdd8T//d//Rbt27WLhwoUREVFSUhJFRUU5rg4AaOpc0wwAgJwYPXp0LFq0KA455JAoLS3NLHfddVeuSwMAcKQZAAC5kVLKdQkAAOvkSDMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyJL3odnkyZNj8ODB0bVr1ygoKIh777031yUBAAAA0MjlfWi2dOnS2HvvveOmm27KdSkAAAAANBEtcl3Ahhx55JFx5JFH5roMAAAAAJqQvA/NNlVlZWVUVlZmHi9evDiH1QAAAADQEOX96ZmbatSoUVFSUpJZysrKcl0SAAAAAA1MowvNhg8fHosWLcos8+fPz3VJAAAAADQwje70zMLCwigsLMx1GQAAAAA0YI3uSDMAAAAA2FJ5H5otWbIkpk6dGlOnTo2IiDlz5sTUqVOjvLw8t4UBALBFJk+eHIMHD46uXbtGQUFB3HvvvbkuCQAgI+9Ds+effz769OkTffr0iYiIYcOGRZ8+feKyyy7LcWUAAGyJpUuXxt577x033XRTrksBAFhL3l/T7JBDDomUUq7LAACglh155JFx5JFH5roMAIAa5X1oBgAAERGVlZVRWVmZebx48eIcVgMANHZ5f3omAABERIwaNSpKSkoyS1lZWa5LAgAaMaEZAAANwvDhw2PRokWZZf78+bkuCQBoxJyeCQBAg1BYWBiFhYW5LgMAaCIcaQYAAAAAWRxpBgBATixZsiRmzZqVeTxnzpyYOnVqdOzYMbp165bDygAAhGYAAOTI888/H4ceemjm8bBhwyIiYsiQITF27NgcVQUA8BmhGQAAOXHIIYdESinXZQAA1Mg1zQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgS4MIzW666abo0aNHtG7dOvr27RvPPvtsrksCAKAW6PMAgHyV96HZXXfdFcOGDYsRI0bEiy++GHvvvXcMGjQo3nvvvVyXBgDAFtDnAQD5LO9Ds+uvvz7OOOOMOO2002K33XaLm2++Odq0aRO33XZbrksDAGAL6PMAgHzWItcFrM+KFSvihRdeiOHDh2fWNWvWLAYOHBhPP/10ja+prKyMysrKzONFixZFRMTixYvrpMYlS5ZERMSH82bGqsrlWzTW4oXlmTHX1Fub49e0j7oef81/19Y+Gvr4Ne3DHNTv+PWxD+Pnfh8Nffya9mEONjx+bVozZkqp1sfmM/o8/7Y2tvHrYx8Nffya9mEO6nf8mvZhDup3/Jr2YQ42PH5t2qQ+L+Wxt99+O0VEeuqpp6qt//GPf5wOOOCAGl8zYsSIFBEWi8VisVgsW7zMnz+/PlqeJkmfZ7FYLBaLJZfLxvR5eX2k2eYYPnx4DBs2LPO4qqoqPvzww9h6662joKCgVvaxePHiKCsri/nz50dxcXGtjMmmMQe5Zw5yzxzkB/OQe3UxByml+OSTT6Jr1661Mh61Q5/XdJiH3DMHuWcOcs8c5F6u+7y8Ds06deoUzZs3j3fffbfa+nfffTe23XbbGl9TWFgYhYWF1da1b9++TuorLi72g5Nj5iD3zEHumYP8YB5yr7bnoKSkpNbGYm36PDaGecg9c5B75iD3zEHu5arPy+sbAbRq1Sr222+/eOyxxzLrqqqq4rHHHot+/frlsDIAALaEPg8AyHd5faRZRMSwYcNiyJAhsf/++8cBBxwQN9xwQyxdujROO+20XJcGAMAW0OcBAPks70OzE044Id5///247LLLYuHChbHPPvvEgw8+GNtss03OaiosLIwRI0asdXoA9ccc5J45yD1zkB/MQ+6Zg4ZLn8e6mIfcMwe5Zw5yzxzkXq7noCAl91IHAAAAgM/L62uaAQAAAEAuCM0AAAAAIIvQDAAAAACyCM0AAAAAIIvQbB1uuumm6NGjR7Ru3Tr69u0bzz777Hq3//vf/x69evWK1q1bx5577hkTJkyop0obr02Zg1tvvTUOPvjg6NChQ3To0CEGDhy4wTljwzb152CNcePGRUFBQRx77LF1W2ATsKlz8PHHH8fQoUOjtLQ0CgsLY9ddd/Xv0Rba1Dm44YYbomfPnlFUVBRlZWVxwQUXxKefflpP1TY+kydPjsGDB0fXrl2joKAg7r333g2+ZtKkSbHvvvtGYWFh7LzzzjF27Ng6r5OGRZ+Xe/q8/KDXyz29Xu7p9XKnQfR5ibWMGzcutWrVKt12223ptddeS2eccUZq3759evfdd2vc/sknn0zNmzdP1157bZo+fXq69NJLU8uWLdO0adPqufLGY1Pn4Nvf/na66aab0ksvvZRmzJiRTj311FRSUpL++9//1nPljcemzsEac+bMSdttt106+OCD0zHHHFM/xTZSmzoHlZWVaf/9909f/epX03/+8580Z86cNGnSpDR16tR6rrzx2NQ5uP3221NhYWG6/fbb05w5c9JDDz2USktL0wUXXFDPlTceEyZMSJdcckm65557UkSk8ePHr3f72bNnpzZt2qRhw4al6dOnpxtvvDE1b948Pfjgg/VTMHlPn5d7+rz8oNfLPb1e7un1cqsh9HlCsxoccMABaejQoZnHq1evTl27dk2jRo2qcfvjjz8+HXXUUdXW9e3bN33/+9+v0zobs02dg2yrVq1K7dq1S3/+85/rqsRGb3PmYNWqVenAAw9Mf/zjH9OQIUM0UltoU+dg9OjRaccdd0wrVqyorxIbvU2dg6FDh6bDDjus2rphw4al/v3712mdTcXGNFM/+clP0u67715t3QknnJAGDRpUh5XRkOjzck+flx/0ermn18s9vV7+yNc+z+mZWVasWBEvvPBCDBw4MLOuWbNmMXDgwHj66adrfM3TTz9dbfuIiEGDBq1ze9Zvc+Yg27Jly2LlypXRsWPHuiqzUdvcObjiiiuiS5cucfrpp9dHmY3a5szBfffdF/369YuhQ4fGNttsE3vssUdcffXVsXr16voqu1HZnDk48MAD44UXXsgc1j979uyYMGFCfPWrX62XmvE7mfXT5+WePi8/6PVyT6+Xe3q9hicXv5Nb1NnIDVRFRUWsXr06ttlmm2rrt9lmm3j99ddrfM3ChQtr3H7hwoV1VmdjtjlzkO2iiy6Krl27rvUDxcbZnDn4z3/+E3/6059i6tSp9VBh47c5czB79uz497//HSeddFJMmDAhZs2aFWeffXasXLkyRowYUR9lNyqbMwff/va3o6KiIg466KBIKcWqVavirLPOiosvvrg+SibW/Tt58eLFsXz58igqKspRZeQDfV7u6fPyg14v9/R6uafXa3hy0ec50oxG55prrolx48bF+PHjo3Xr1rkup0n45JNP4uSTT45bb701OnXqlOtymqyqqqro0qVL3HLLLbHffvvFCSecEJdcckncfPPNuS6tyZg0aVJcffXV8fvf/z5efPHFuOeee+Jf//pXXHnllbkuDaBR0Oflhl4vP+j1ck+v1/Q40ixLp06donnz5vHuu+9WW//uu+/GtttuW+Nrtt12203anvXbnDlY45e//GVcc8018eijj8Zee+1Vl2U2aps6B2+99VbMnTs3Bg8enFlXVVUVEREtWrSImTNnxk477VS3RTcym/NzUFpaGi1btozmzZtn1vXu3TsWLlwYK1asiFatWtVpzY3N5szBz372szj55JPje9/7XkRE7LnnnrF06dI488wz45JLLolmzfytqq6t63dycXGxo8zQ5+UBfV5+0Ovlnl4v9/R6DU8u+jwzmqVVq1ax3377xWOPPZZZV1VVFY899lj069evxtf069ev2vYREY888sg6t2f9NmcOIiKuvfbauPLKK+PBBx+M/fffvz5KbbQ2dQ569eoV06ZNi6lTp2aWo48+Og499NCYOnVqlJWV1Wf5jcLm/Bz0798/Zs2alWliIyLeeOONKC0t1URths2Zg2XLlq3VLK1pbFNKdVcsGX4nsz76vNzT5+UHvV7u6fVyT6/X8OTkd3Kd3WKgARs3blwqLCxMY8eOTdOnT09nnnlmat++fVq4cGFKKaWTTz45/fSnP81s/+STT6YWLVqkX/7yl2nGjBlpxIgRbkW+hTZ1Dq655prUqlWr9I9//CMtWLAgs3zyySe5egsN3qbOQTZ3VNpymzoH5eXlqV27dumcc85JM2fOTPfff3/q0qVLuuqqq3L1Fhq8TZ2DESNGpHbt2qU777wzzZ49Oz388MNpp512Sscff3yu3kKD98knn6SXXnopvfTSSyki0vXXX59eeumlNG/evJRSSj/96U/TySefnNl+za3If/zjH6cZM2akm266qc5vRU7Dos/LPX1eftDr5Z5eL/f0ernVEPo8odk63Hjjjalbt26pVatW6YADDkhTpkzJPDdgwIA0ZMiQatv/7W9/S7vuumtq1apV2n333dO//vWveq648dmUOejevXuKiLWWESNG1H/hjcim/hx8nkaqdmzqHDz11FOpb9++qbCwMO24447p5z//eVq1alU9V924bMocrFy5Ml1++eVpp512Sq1bt05lZWXp7LPPTh999FH9F95ITJw4scZ/39d87kOGDEkDBgxY6zX77LNPatWqVdpxxx3TmDFj6r1u8ps+L/f0eflBr5d7er3c0+vlTkPo8wpScgwhAAAAAHyea5oBAAAAQBahGQAAAABkEZoBAAAAQBahGQAAAABkEZoBAAAAQBahGQAAAABkEZoBAAAAQBahGQAAAABkEZpBI3f55ZfHPvvss1mvnTt3bhQUFMTUqVNrtaam5pBDDokf/vCH9bKvn/3sZ3HmmWfWy77qwtixY6N9+/a5LmOj/fSnP41zzz0312UA0ETp83JPn7fxGlqf961vfSt+9atf5boMckxoBjWYNGlSFBQUxMcff5zrUhqdgoKCuPfee2t1zB49esQNN9xQq2M2RAsXLozf/OY3cckll9Trfuvz86+rBr+m7+WCBQvi29/+duy6667RrFmzGhviCy+8MP785z/H7Nmza7UeAOqOPq/u6PPqjj5v89X0vbznnnviiCOOiM6dO0dxcXH069cvHnrooWrbXHrppfHzn/88Fi1aVKv10LAIzQAaiT/+8Y9x4IEHRvfu3XNdylpWr14dVVVVuS5jk1RWVkbnzp3j0ksvjb333rvGbTp16hSDBg2K0aNH13N1AEBTos+rXZMnT44jjjgiJkyYEC+88EIceuihMXjw4HjppZcy2+yxxx6x0047xV//+tccVkquCc1okA455JA455xz4pxzzomSkpLo1KlT/OxnP4uUUmabjz76KE455ZTo0KFDtGnTJo488sh48803M8/PmzcvBg8eHB06dIi2bdvG7rvvHhMmTIi5c+fGoYceGhERHTp0iIKCgjj11FM3WNM//vGP2HPPPaOoqCi23nrrGDhwYCxdujQiIk499dQ49thjY+TIkZm/Zpx11lmxYsWKzOurqqpi1KhRscMOO0RRUVHsvffe8Y9//CPz/Jq/ij722GOx//77R5s2beLAAw+MmTNnVqvjmmuuiW222SbatWsXp59+enz66afVnp80aVIccMAB0bZt22jfvn30798/5s2bt1Gf++rVq+O73/1u9OrVK8rLyyOlFJdffnl069YtCgsLo2vXrnHeeeet8/U9evSIiIivf/3rUVBQkHkcEfF///d/se+++0br1q1jxx13jJEjR8aqVasiIta7n0MOOSTmzZsXF1xwQRQUFERBQcE6919QUBCjR4+OI488MoqKimLHHXes9hlne/jhh6N169Zr/SX6/PPPj8MOOywiIj744IM48cQTY7vttos2bdrEnnvuGXfeeef6PsYa/9rVvn37GDt2bObx/Pnz4/jjj4/27dtHx44d45hjjom5c+eud9xx48bF4MGDq62rqqqKa6+9NnbeeecoLCyMbt26xc9//vPM89OmTYvDDjss870988wzY8mSJZnn13x3f/nLX0ZpaWlsvfXWMXTo0Fi5cmVErPvzX3P4/X333Re77bZbFBYWRnl5+QZ/Ljdkhx12iIiIPn36REFBQRxyyCGZ5/74xz9G7969o3Xr1tGrV6/4/e9/n3luxYoVcc4550RpaWm0bt06unfvHqNGjYqIdX8ve/ToEb/5zW/ilFNOiZKSknXWNHjw4Bg3btxGvwcA1k+fp8/T561Nn1e7fd4NN9wQP/nJT+ILX/hC7LLLLnH11VfHLrvsEv/85z+r1aTPIxI0QAMGDEhbbbVVOv/889Prr7+e/vrXv6Y2bdqkW265JbPN0UcfnXr37p0mT56cpk6dmgYNGpR23nnntGLFipRSSkcddVQ64ogj0iuvvJLeeuut9M9//jM9/vjjadWqVenuu+9OEZFmzpyZFixYkD7++OP11vPOO++kFi1apOuvvz7NmTMnvfLKK+mmm25Kn3zySUoppSFDhqStttoqnXDCCenVV19N999/f+rcuXO6+OKLM2NcddVVqVevXunBBx9Mb731VhozZkwqLCxMkyZNSimlNHHixBQRqW/fvmnSpEnptddeSwcffHA68MADM2PcddddqbCwMP3xj39Mr7/+errkkktSu3bt0t57751SSmnlypWppKQkXXjhhWnWrFlp+vTpaezYsWnevHk1vq85c+akiEgvvfRS+vTTT9PXv/711KdPn/Tee++llFL6+9//noqLi9OECRPSvHnz0jPPPFNtDrK99957KSLSmDFj0oIFCzLjTJ48ORUXF6exY8emt956Kz388MOpR48e6fLLL9/gfj744IO0/fbbpyuuuCItWLAgLViwYJ37j4i09dZbp1tvvTXNnDkzXXrppal58+Zp+vTpNW6/atWqtM0226Q//vGP61z33//+N1133XXppZdeSm+99Vb67W9/m5o3b56eeeaZzGsGDBiQzj///Gp1jB8/vtq+SkpK0pgxY1JKKa1YsSL17t07ffe7302vvPJKmj59evr2t7+devbsmSorK2us9YMPPkgFBQVpypQp1db/5Cc/SR06dEhjx45Ns2bNSk888US69dZbU0opLVmyJJWWlqZvfOMbadq0aemxxx5LO+ywQxoyZEjm9UOGDEnFxcXprLPOSjNmzEj//Oc/q/2srevzHzNmTGrZsmU68MAD05NPPplef/31tHTp0g3+XI4ZMyaVlJTU+B5TSunZZ59NEZEeffTRtGDBgvTBBx+klFL661//mkpLS9Pdd9+dZs+ene6+++7UsWPHNHbs2JRSStddd10qKytLkydPTnPnzk1PPPFEuuOOO1JK6/5efl72HH7ejBkzUkSkOXPmrLNuADaePk+fp8+rTp9Xt31eSimtXr06lZWVpRtvvLHa+gceeCC1atUqffrpp+usm8ZNaEaDNGDAgNS7d+9UVVWVWXfRRRel3r17p5RSeuONN1JEpCeffDLzfEVFRSoqKkp/+9vfUkop7bnnnplf1tnWNC4fffTRRtXzwgsvpIhIc+fOrfH5IUOGpI4dO6alS5dm1o0ePTpttdVWafXq1enTTz9Nbdq0SU899VS1151++unpxBNPrFbTo48+mnn+X//6V4qItHz58pRSSv369Utnn312tTH69u2baaY++OCDFBGZBm1D1jRTTzzxRDr88MPTQQcdVK2x/NWvfpV23XXXzC/CjVFTI3H44Yenq6++utq6//3f/02lpaUbtZ/u3bunX//61xu177POOqvaur59+6Yf/OAH63zN+eefnw477LDM44ceeigVFhau97tx1FFHpR/96EeZx5vaTP3v//5v6tmzZ7Xvd2VlZSoqKkoPPfRQjft86aWXUkSk8vLyzLrFixenwsLCTPOU7ZZbbkkdOnRIS5Ysyaz717/+lZo1a5YWLlyYUvrsu9u9e/e0atWqzDbHHXdcOuGEEzKPa/r8x4wZkyIiTZ06NbNuY34uN9RMfb7B/7yddtop0xytceWVV6Z+/fqllFI699xz02GHHVbtM/28mubk89YXmi1atGiTfq4AWD993mf0eZ/R5+nz6rrPSymlX/ziF6lDhw7p3Xffrbb+5ZdfXu/PP42f0zNpsL74xS9WO0S7X79+8eabb8bq1atjxowZ0aJFi+jbt2/m+a233jp69uwZM2bMiIiI8847L6666qro379/jBgxIl555ZXNrmXvvfeOww8/PPbcc8847rjj4tZbb42PPvporW3atGlTrd4lS5bE/PnzY9asWbFs2bI44ogjYquttsosf/nLX+Ktt96qNs5ee+2V+e/S0tKIiHjvvfciImLGjBnV3vOa/azRsWPHOPXUU2PQoEExePDg+M1vfhMLFizY4Ps78cQTY+nSpfHwww9XO03tuOOOi+XLl8eOO+4YZ5xxRowfPz5zqP3VV19d7b2Ul5evc/yXX345rrjiimrbn3HGGbFgwYJYtmzZevezqT7/eax5vOY7ceSRR2b2v/vuu0dExEknnRSTJk2Kd955JyIibr/99jjqqKMyd/5ZvXp1XHnllbHnnntGx44dY6uttoqHHnpove93Q15++eWYNWtWtGvXLlNPx44d49NPP13r+7DG8uXLIyKidevWmXUzZsyIysrKOPzww2t8zYwZM2LvvfeOtm3bZtb1798/qqqqqp0Osvvuu0fz5s0zj0tLSzPfufVp1apVte/rxvxcbo6lS5fGW2+9Faeffnq179BVV12V+bxOPfXUmDp1avTs2TPOO++8ePjhhzd7f9mKiooiImLZsmW1NiZAU6fP0+dtDn1eVHten7dx7rjjjhg5cmT87W9/iy5dulR7Tp+H0Iwm63vf+17Mnj07Tj755Jg2bVrsv//+ceONN27WWM2bN49HHnkkHnjggdhtt93ixhtvjJ49e8acOXM26vVrri3wr3/9K6ZOnZpZpk+fvta1GFq2bJn57zXN5KZceHPMmDHx9NNPx4EHHhh33XVX7LrrrjFlypT1vuarX/1qvPLKK/H0009XW19WVhYzZ86M3//+91FUVBRnn312fOlLX4qVK1fGWWedVe29dO3adb3vf+TIkdW2nzZtWrz55pvRunXr9e6nNv3xj3/M7H/ChAkREfGFL3whdtpppxg3blwsX748xo8fHyeddFLmNdddd1385je/iYsuuigmTpwYU6dOjUGDBlW7jkm2goKCatdliYhq72XJkiWx3377Vfs8pk6dGm+88UZ8+9vfrnHMTp06RURUa+LX/JLfUp//zq2pf2O+c0VFReu99khtWfPzc+utt1b7vF599dXMd3vfffeNOXPmxJVXXhnLly+P448/Pr75zW/Wyv4//PDDiIjo3LlzrYwHwJbT5+nzsunzatbU+7xx48bF9773vfjb3/4WAwcOXOt5fR5CMxqsZ555ptrjKVOmxC677BLNmzeP3r17x6pVq6pt88EHH8TMmTNjt912y6wrKyuLs846K+6555740Y9+FLfeemtEfPaXk4jP/rq0sQoKCqJ///4xcuTIeOmll6JVq1Yxfvz4zPMvv/xy5q9Ea+rdaqutoqysrNoFNHfeeedqS1lZ2UbX0Lt37xo/l2x9+vSJ4cOHx1NPPRV77LFH3HHHHesd9wc/+EFcc801cfTRR8fjjz9e7bmioqIYPHhw/Pa3v41JkybF008/HdOmTYuOHTtWex8tWrSIiM9+MWd/rvvuu2/MnDlzrfe+8847R7Nmzda7n4jP5mtj5yr785gyZUr07t07IiK22267zH4/f2eik046KW6//fb45z//Gc2aNYujjjoq89yTTz4ZxxxzTHznO9+JvffeO3bcccd444031ltD586dq/3l980336z216t999033nzzzejSpctan8e6Lki/0047RXFxcUyfPj2zbpdddomioqJ47LHHanxN79694+WXX85cyHjN+2nWrFn07Nlzve/h8zb289/Yn8sN7Sui+s/mNttsE127do3Zs2ev9XmtuaBsRERxcXGccMIJceutt8Zdd90Vd999d6YRqul7ubFeffXVaNmyZeav1gBsOX3e2vR5G6bP+3/0eRvu8+6888447bTT4s4776w275/36quvxvbbb58JLml6WuS6ANhc5eXlMWzYsPj+978fL774Ytx4443xq1/9KiI++yVyzDHHxBlnnBF/+MMfol27dvHTn/40tttuuzjmmGMiIuKHP/xhHHnkkbHrrrvGRx99FBMnTsz8Uu3evXsUFBTE/fffH1/96lejqKgottpqq3XW8swzz8Rjjz0WX/7yl6NLly7xzDPPxPvvv58ZL+Kzu7qcfvrpcemll8bcuXNjxIgRcc4550SzZs2iXbt2ceGFF8YFF1wQVVVVcdBBB8WiRYviySefjOLi4hgyZMhGfSbnn39+nHrqqbH//vtH//794/bbb4/XXnstdtxxx4iImDNnTtxyyy1x9NFHR9euXWPmzJnx5ptvximnnLLBsc8999xYvXp1fO1rX4sHHnggDjrooBg7dmysXr06+vbtG23atIm//vWvUVRUtN5bYffo0SMee+yx6N+/fxQWFkaHDh3isssui6997WvRrVu3+OY3vxnNmjWLl19+OV599dW46qqrNrifHj16xOTJk+Nb3/pWFBYWrveX2t///vfYf//946CDDorbb789nn322fjTn/603vd+0kknxeWXXx4///nP45vf/GYUFhZmnttll13iH//4Rzz11FPRoUOHuP766+Pdd99db3Nw2GGHxe9+97vo169frF69Oi666KJqf+U76aST4rrrrotjjjkmrrjiith+++1j3rx5cc8998RPfvKT2H777dcas1mzZjFw4MD4z3/+E8cee2xEfHYI/0UXXRQ/+clPolWrVtG/f/94//3347XXXovTTz89TjrppBgxYkQMGTIkLr/88nj//ffj3HPPjZNPPjm22Wab9X4mn7exn//G/FxuSJcuXaKoqCgefPDB2H777aN169ZRUlISI0eOjPPOOy9KSkriK1/5SlRWVsbzzz8fH330UQwbNiyuv/76KC0tjT59+kSzZs3i73//e2y77baZ0y9q+l5GREydOjUiPvsr5/vvvx9Tp06NVq1aVZvfJ554Ig4++OBa+4svAPq8mujz9Hn6vNrr8+64444YMmRI/OY3v4m+ffvGwoULI+KzAPfz4eUTTzwRX/7ylzf686IRyvVF1WBzDBgwIJ199tnprLPOSsXFxalDhw7p4osvrnbxxw8//DCdfPLJqaSkJBUVFaVBgwalN954I/P8Oeeck3baaadUWFiYOnfunE4++eRUUVGRef6KK65I2267bSooKKh2l5maTJ8+PQ0aNCh17tw5FRYWpl133bXanVeGDBmSjjnmmHTZZZelrbfeOm211VbpjDPOqHYXlqqqqnTDDTeknj17ppYtW6bOnTunQYMGpccffzylVPNFa9dcFPTzd+37+c9/njp16pS22mqrNGTIkPSTn/wkc4HYhQsXpmOPPTaVlpamVq1ape7du6fLLrssrV69usb3VdPFOH/1q1+ldu3apSeffDKNHz8+9e3bNxUXF6e2bdumL37xi9UuYFuT++67L+28886pRYsWqXv37pn1Dz74YDrwwANTUVFRKi4uTgcccEDmzj0b2s/TTz+d9tprr1RYWJjW989aRKSbbropHXHEEamwsDD16NEj3XXXXeutd40DDjggRUT697//XW39Bx98kI455pi01VZbpS5duqRLL700nXLKKemYY47JbJN9gdi33347ffnLX05t27ZNu+yyS5owYUK1C8SmlNKCBQvSKaeckjp16pQKCwvTjjvumM4444y0aNGiddY4YcKEtN1221Wbz9WrV6errroqde/ePbVs2TJ169at2sV4X3nllXTooYem1q1bp44dO6YzzjgjczewlP7fd/fzzj///DRgwIDM45o+/3Vd6HVDP5fZr1vzHZw4cWJm3a233prKyspSs2bNqtVx++23p3322Se1atUqdejQIX3pS19K99xzT0rps4vh7rPPPqlt27apuLg4HX744enFF1/MvHZd38uIWGv5/PMppdSzZ8905513rvVeAdg8+rzP6PM+o8/7jD6vdvu8AQMG1Njnff7fg+XLl6eSkpL09NNPr/VeaToKUso64RoagEMOOST22WefuOGGG3JdykY59dRT4+OPP457770316U0aQUFBTF+/PjMX+gam5RS9O3bNy644II48cQTc11OrZg4cWJ84xvfiNmzZ2eO/sonDzzwQPzoRz+KV155JXNqCgBbRp/H5tDnNTz53ueNHj06xo8fX6s3kKLhcU0zgEaioKAgbrnlls2+41Q+mjBhQlx88cV52UhFfHZHpzFjxgjMAIA6pc+rfy1bttzsG4jQeOjyYSOUl5ev99oF06dPj27dutVjRVCzffbZJ/bZZ59cl1FrrrvuulyXsF61dQdOAHJHn0dDoc+rX9/73vdyXQJ5wOmZsBFWrVoVc+fOXefzPXr0cKQJAEADpM8DYF2EZgAAAACQxTXNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsrTIdQF1raqqKt55551o165dFBQU5LocAKABSCnFJ598El27do1mzfyNMV/p8wCATbUpfV6jD83eeeedKCsry3UZAEADNH/+/Nh+++1zXQbroM8DADbXxvR5jT40a9euXUR89mEUFxfnuBoAoCFYvHhxlJWVZfoI8pM+DwDYVJvS5zX60GzNofrFxcWaKQBgkzjlL7/p8wCAzbUxfZ6LdAAAAABAFqEZAAAAAGQRmgEAAABAFqEZAAAAAGQRmgEAAABAFqEZAAAAAGQRmgEAAABAFqEZAAC1bvLkyTF48ODo2rVrFBQUxL333pt5buXKlXHRRRfFnnvuGW3bto2uXbvGKaecEu+8807uCgYAyCI0AwCg1i1dujT23nvvuOmmm9Z6btmyZfHiiy/Gz372s3jxxRfjnnvuiZkzZ8bRRx+dg0oBAGrWItcFAADQ+Bx55JFx5JFH1vhcSUlJPPLII9XW/e53v4sDDjggysvLo1u3bvVRIgDAegnNAADIuUWLFkVBQUG0b99+ndtUVlZGZWVl5vHixYvroTIAoKlyeiYAADn16aefxkUXXRQnnnhiFBcXr3O7UaNGRUlJSWYpKyurxyoBgKZGaAYAQM6sXLkyjj/++EgpxejRo9e77fDhw2PRokWZZf78+fVUJQDQFDk9EwCAnFgTmM2bNy/+/e9/r/cos4iIwsLCKCwsrKfqAICmTmgGAEC9WxOYvfnmmzFx4sTYeuutc10SAEA1QjMAoN6Ul5dHRUVFrYzVqVMnd1nMY0uWLIlZs2ZlHs+ZMyemTp0aHTt2jNLS0vjmN78ZL774Ytx///2xevXqWLhwYUREdOzYMVq1apWrsgGAzdQY+zyhGQBQL8rLy6NXr96xfPmyWhmvqKhNvP76jLxoqFjb888/H4ceemjm8bBhwyIiYsiQIXH55ZfHfffdFxER++yzT7XXTZw4MQ455JD6KhMAqAWNtc8TmgEA9aKioiKWL18Wfb87IopLe2zRWIsXzI1nbhsZFRUVOW+mqNkhhxwSKaV1Pr++5wCAhqWx9nlCMwCgXhWX9oiO3XrmugwAAGpZY+vzmuW6AAAAAADIN0IzAAAAAMiS96HZ22+/Hd/5zndi6623jqKiothzzz3j+eefz3VZAAAAADRieX1Ns48++ij69+8fhx56aDzwwAPRuXPnePPNN6NDhw65Lg0AAACARiyvQ7Nf/OIXUVZWFmPGjMms22GHHXJYEQAAAABNQV6HZvfdd18MGjQojjvuuHj88cdju+22i7PPPjvOOOOMdb6msrIyKisrM48XL15cH6UCQJSXl0dFRUWtjdepU6ec32YbAACaqrwOzWbPnh2jR4+OYcOGxcUXXxzPPfdcnHfeedGqVasYMmRIja8ZNWpUjBw5sp4rBaCpKy8vj169esfy5ctqbcyiojbx+uszBGcAAJADeR2aVVVVxf777x9XX311RET06dMnXn311bj55pvXGZoNHz48hg0blnm8ePHiKCsrq5d6AWi6KioqYvnyZdH3uyOiuLTHFo+3eMHceOa2kVFRUSE0AwCAHMjr0Ky0tDR22223aut69+4dd9999zpfU1hYGIWFhXVdGgDUqLi0R3Ts1jPXZQAAAFuoWa4LWJ/+/fvHzJkzq6174403onv37jmqCAAAAICmIK9DswsuuCCmTJkSV199dcyaNSvuuOOOuOWWW2Lo0KG5Lg0AAACARiyvQ7MvfOELMX78+Ljzzjtjjz32iCuvvDJuuOGGOOmkk3JdGgAAAACNWF5f0ywi4mtf+1p87Wtfy3UZAAAAADQheX2kGQAAAADkgtAMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALK0yHUBAMDGKS8vj4qKilobr1OnTtGtW7daGw8AABoToRkANADl5eXRq1fvWL58Wa2NWVTUJl5/fYbgDAAAaiA0A4AGoKKiIpYvXxZ9vzsiikt7bPF4ixfMjWduGxkVFRVCMwAAqIHQDAAakOLSHtGxW89clwEAAI2eGwEAAAAAQBahGQAAAABkcXomwP+vNu9M6K6EAAAADZvQDCBq/86E7koIAADQsAnNAKJ270zoroQAAAANn9AMNkJtnrYX4dS9fObOhAAAAEQIzWCDavu0vQin7gEAAEC+E5rBBtTmaXsRTt0DAACAhkBoBhvJaXsAAADQdDTLdQEAAAAAkG+EZgAAAACQxemZAOSN2rxTrbvUAgAAW0JoBkBeqO071bpLLQAAsCWEZgDkhdq8U6271AIAAFtKaAZNhNPeaCjcqRbg/2PvzuOjqu/98b8DSghLIohoqMSgIuCu1VLAukGlVBFvW62WKmqrteLKdaNVETfcL61at1bQW6nWKl6vdeeKuOK+IigKhmtBb1yIQIwCn98f/JivGcI+ySTh+Xw85vFgzsz5nHfmTJh3XnPO+QAAjYHQDDYATnsDAACAtSM0gw2A094AAABg7QjNYAPitDcAAABYMy3yXQAAAAAANDZCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAcm7KlCkxePDg6NKlSxQUFMR9991X6/GUUpx//vlRWloaRUVFMWDAgHjvvffyUywAQB2EZgAA5NzChQtjl112ieuvv77Ox6+44or44x//GDfeeGNMnTo12rZtGwMHDoyvvvqqgSsFAKjbRvkuAACA5mfQoEExaNCgOh9LKcXYsWPj3HPPjSFDhkRExO233x6bb7553HfffXH44Yc3ZKkAAHVypBkAAA1q1qxZMW/evBgwYEBmWUlJSfTu3Tuee+65la5XU1MTVVVVtW4AAPVFaAYAQIOaN29eRERsvvnmtZZvvvnmmcfqMmbMmCgpKcncunbtWq91AgAbNqEZAABNwsiRI2P+/PmZ25w5c/JdEgDQjLmmGdAkVFRURGVlZc7G69SpU5SVleVsPADW3BZbbBERER9//HGUlpZmln/88cex6667rnS9wsLCKCwsrO/yAAAiQmgGNAEVFRXRs2evqK5elLMxi4raxPTp7wjOAPKgW7duscUWW8SkSZMyIVlVVVVMnTo1fvvb3+a3OACA/5/QDGj0Kisro7p6UfQ+dlQUl5av93hVc2fH1FtHR2VlpdAMoJ4sWLAgZs6cmbk/a9aseO2116Jjx45RVlYWp512Wlx88cXRvXv36NatW5x33nnRpUuXOOSQQ/JXNADAtwjNcNobTUZxaXl0LOuR7zIAWAMvvfRS7Lfffpn7I0aMiIiIYcOGxfjx4+Oss86KhQsXxvHHHx9ffPFF7LXXXvHwww9H69at81UyAEAtQrMNnNPeAID6sO+++0ZKaaWPFxQUxIUXXhgXXnhhA1YFALDmhGYbOKe9AQAAAKyoUYdmF1xwQYwePbrWsh49esT06dPzVFHz5bQ3AAAAgP+nUYdmERE77LBDPP7445n7G23U6EsGAAAAoIlr9AnURhttFFtssUW+ywAAAABgA9Ii3wWsznvvvRddunSJrbfeOoYOHRoVFRWrfH5NTU1UVVXVugEAAADA2mjUoVnv3r1j/Pjx8fDDD8cNN9wQs2bNih/84Afx5ZdfrnSdMWPGRElJSebWtWvXBqwYAAAAgOagUYdmgwYNikMPPTR23nnnGDhwYDz44IPxxRdfxN///veVrjNy5MiYP39+5jZnzpwGrBgAAACA5qDRX9Ps2zbZZJPYbrvtYubMmSt9TmFhYRQWFjZgVQCNQ0VFRVRWVuZkrE6dOkVZWVlOxgIAAGiKmlRotmDBgnj//ffjyCOPzHcpAI1KRUVF9OzZK6qrF+VkvKKiNjF9+juCMwAAYIPVqEOzM844IwYPHhxbbbVV/Otf/4pRo0ZFy5Yt44gjjsh3aQCNSmVlZVRXL4rex46K4tLy9Rqrau7smHrr6KisrBSaAQAAG6xGHZr97//+bxxxxBHx6aefxmabbRZ77bVXPP/887HZZpvluzSARqm4tDw6lvXIdxkAAABNXqMOze688858lwAAAADABqhRz54JAAAAAPkgNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALBvluwA2DBUVFVFZWZmTsTp16hRlZWU5GQsAAACgLkIz6l1FRUX07NkrqqsX5WS8oqI2MX36O4IzAAAAoN4Izah3lZWVUV29KHofOyqKS8vXa6yqubNj6q2jo7KyUmgGAAAA1BuhWQ449XDNFJeWR8eyHvkuA/LG/xUAAABNh9BsPTn1EFgT/q8AAABoWoRm68mph8Ca8H8FAABA0yI0yxGnHgJrwv8VNHZOIwYAgGWEZgBARDiNGAAAvk1oBgBEhNOIAQDg24RmNAtOJ8o/+wCaD6cRAwCA0IxmwOlE+WcfAAAA0NwIzWjynE6Uf/YBAAAAzY3QjGbD6UT5Zx8AAADQXLTIdwEAAAAA0NgIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgy0b5LgCIqKioiMrKypyN16lTpygrK8vZeNBc5PJ3ze8ZAAA0b0IzyLOKioro2bNXVFcvytmYRUVtYvr0d/xBD9+S6981v2cAANC8Cc0gzyorK6O6elH0PnZUFJeWr/d4VXNnx9RbR0dlZaU/5uFbcvm75vcMAACaP6FZE+DUvQ1DcWl5dCzrke8yoNnzuwYAAKwJoVkj59Q9AAAAgIYnNGvknLoHAAAA0PCEZk2E04kAAAAAGk6LfBcAAAAAAI2N0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAgAa3ZMmSOO+886Jbt25RVFQU22yzTVx00UWRUsp3aQAAERGxUb4LAABgw3P55ZfHDTfcELfddlvssMMO8dJLL8UxxxwTJSUlccopp+S7PAAAoRkAAA3v2WefjSFDhsSBBx4YERHl5eXxt7/9LV544YU8VwYAsIzTMwEAaHB9+/aNSZMmxbvvvhsREa+//no8/fTTMWjQoJWuU1NTE1VVVbVuAAD1xZFmAAA0uHPOOSeqqqqiZ8+e0bJly1iyZElccsklMXTo0JWuM2bMmBg9enQDVgkAbMgcaQYAQIP7+9//HnfccUdMmDAhXnnllbjtttviqquuittuu22l64wcOTLmz5+fuc2ZM6cBKwYANjSONAMAoMGdeeaZcc4558Thhx8eERE77bRTfPjhhzFmzJgYNmxYnesUFhZGYWFhQ5YJAGzAHGkGAECDW7RoUbRoUbsVbdmyZSxdujRPFQEA1OZIMwAAGtzgwYPjkksuibKysthhhx3i1VdfjWuuuSaOPfbYfJcGABARTexIs8suuywKCgritNNOy3cpAACsh2uvvTZ+9rOfxYknnhi9evWKM844I37zm9/ERRddlO/SAAAiogkdafbiiy/GTTfdFDvvvHO+SwEAYD21b98+xo4dG2PHjs13KQAAdWoSR5otWLAghg4dGrfcckt06NAh3+UAAAAA0Mw1idBs+PDhceCBB8aAAQNW+9yampqoqqqqdQMAAACAtdHoT8+8884745VXXokXX3xxjZ4/ZsyYGD16dD1XBQAAAEBz1qiPNJszZ06ceuqpcccdd0Tr1q3XaJ2RI0fG/PnzM7c5c+bUc5UAAAAANDeN+kizl19+OT755JPYfffdM8uWLFkSU6ZMieuuuy5qamqiZcuWtdYpLCyMwsLChi4VAAAAgGakUYdm/fv3jzfffLPWsmOOOSZ69uwZZ5999gqBGQAAAADkQqMOzdq3bx877rhjrWVt27aNTTfddIXlAAAAAJArjfqaZgAAAACQD436SLO6TJ48Od8lAAAAANDMOdIMAAAAALI0uSPNAMiPioqKqKyszNl4nTp1irKyspyNBwAAkEtCMwBWq6KiInr27BXV1YtyNmZRUZuYPv0dwRkAANAoCc0AWK3Kysqorl4UvY8dFcWl5es9XtXc2TH11tFRWVkpNAMAABoloRkAa6y4tDw6lvXIdxkAAAD1zkQAAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWTbKdwEAAAAA1K+KioqorKzMyVidOnWKsrKynIzVmAnNAAAAAJqxioqK6NmzV1RXL8rJeEVFbWL69HeafXAmNAMAAABoxiorK6O6elH0PnZUFJeWr9dYVXNnx9RbR0dlZaXQDAAAAICmr7i0PDqW9ch3GU2GiQAAAAAAIIvQDAAAAACyCM0AAAAAIEu9hWZbb711fPrppyss/+KLL2Lrrbeur80CALAe9HAAAMvUW2g2e/bsWLJkyQrLa2pq4qOPPqqvzQIAsB70cAAAy+R89sz7778/8+9HHnkkSkpKMveXLFkSkyZNivLy8lxvFgCA9aCHAwCoLeeh2SGHHBIREQUFBTFs2LBaj2288cZRXl4eV199da43CwDAetDDAQDUlvPQbOnSpRER0a1bt3jxxRejU6dOud4EAAA5pocDAKgt56HZcrNmzaqvoQEAqCd6OACAZeotNIuImDRpUkyaNCk++eSTzLeXy9166631uWkAANaRHg4AoB5Ds9GjR8eFF14Ye+yxR5SWlkZBQUF9bQoAgBzRwwEALFNvodmNN94Y48ePjyOPPLK+NgEAQI7p4QAAlmlRXwN//fXX0bdv3/oaHgCAeqCHAwBYpt5Cs1//+tcxYcKE+hoeAIB6oIcDAFim3k7P/Oqrr+Lmm2+Oxx9/PHbeeefYeOONaz1+zTXX1NemAQBYR3o4AGh4FRUVUVlZmbPxOnXqFGVlZTkbb0NVb6HZG2+8EbvuumtERLz11lu1HnNBWQCAxkkPBwANq6KiInr27BXV1YtyNmZRUZuYPv0dwdl6qrfQ7IknnqivoQEAqCd6OABoWJWVlVFdvSh6HzsqikvL13u8qrmzY+qto6OyslJotp7qLTRbbubMmfH+++/H3nvvHUVFRZFS8i0lAEAjp4cDgIZVXFoeHct65LsMvqXeJgL49NNPo3///rHddtvFj3/845g7d25ERPzqV7+Kf//3f6+vzQIAsB70cAAAy9RbaHb66afHxhtvHBUVFdGmTZvM8p///Ofx8MMP19dmAQBYD3o4AIBl6u30zEcffTQeeeSR2HLLLWst7969e3z44Yf1tVkAANaDHg4AYJl6O9Js4cKFtb6dXO6zzz6LwsLC+tosAADrQQ8HALBMvYVmP/jBD+L222/P3C8oKIilS5fGFVdcEfvtt199bRYAgPWghwMAWKbeTs+84ooron///vHSSy/F119/HWeddVa8/fbb8dlnn8UzzzxTX5sFAGA96OEAAJaptyPNdtxxx3j33Xdjr732iiFDhsTChQvjJz/5Sbz66quxzTbb1NdmAQBYD3o4AIBl6u1Is4iIkpKS+P3vf1+fmwAAIMf0cAA0NRUVFVFZWZmTsTp16hRlZWU5GYumrd5Cs3HjxkW7du3i0EMPrbX87rvvjkWLFsWwYcPqa9MAAKwjPRwATU1FRUX07NkrqqsX5WS8oqI2MX36O4Iz6i80GzNmTNx0000rLO/cuXMcf/zxGi4AgEZIDwdAU1NZWRnV1Yui97Gjori0fL3Gqpo7O6beOjoqKyuFZtRfaFZRURHdunVbYflWW20VFRUV9bVZAADWQ0P2cB999FGcffbZ8dBDD8WiRYti2223jXHjxsUee+yR0+0AsGEoLi2PjmU98l0GzUi9TQTQuXPneOONN1ZY/vrrr8emm25aX5sFAGA9NFQP9/nnn0e/fv1i4403joceeiimTZsWV199dXTo0CFn2wAAWB/1dqTZEUccEaecckq0b98+9t5774iIePLJJ+PUU0+Nww8/vL42CwDAemioHu7yyy+Prl27xrhx4zLL6jrCDQAgX+otNLvoooti9uzZ0b9//9hoo2WbWbp0aRx11FFx6aWX1tdmAQBYDw3Vw91///0xcODAOPTQQ+PJJ5+M73znO3HiiSfGcccdt9J1ampqoqamJnO/qqoqZ/UAAGSrl9AspRTz5s2L8ePHx8UXXxyvvfZaFBUVxU477RRbbbVVfWwSAID11JA93AcffBA33HBDjBgxIn73u9/Fiy++GKecckq0atVqpZMNjBkzJkaPHp3TOgAAVqbeQrNtt9023n777ejevXt07969PjYDAEAONWQPt3Tp0thjjz0yR6/ttttu8dZbb8WNN9640tBs5MiRMWLEiMz9qqqq6Nq1a73VCABs2OplIoAWLVpE9+7d49NPP62P4QEAqAcN2cOVlpbG9ttvX2tZr169VjlDZ2FhYRQXF9e6AQDUl3qbPfOyyy6LM888M9566611HuOGG26InXfeOdMU9enTJx566KEcVgkAwLfloodbE/369YsZM2bUWvbuu++6lAcA0GjU20QARx11VCxatCh22WWXaNWqVRQVFdV6/LPPPlvtGFtuuWVcdtll0b1790gpxW233RZDhgyJV199NXbYYYf6Kh0AYIOVix5uTZx++unRt2/fuPTSS+Owww6LF154IW6++ea4+eabczI+AMD6qrfQbOzYses9xuDBg2vdv+SSS+KGG26I559/XmgGAFAPctHDrYk999wzJk6cGCNHjowLL7wwunXrFmPHjo2hQ4c2yPYBAFan3kKzlV3AdV0tWbIk7r777li4cGH06dNnpc8zFTkAwLrLdQ+3KgcddFAcdNBBDbY9AIC1UW+hWUTE+++/H+PGjYv3338//vCHP0Tnzp3joYceirKysjU+UuzNN9+MPn36xFdffRXt2rWLiRMnrnDR2G8zFTkAwPrJRQ8HAN9WUVERlZWVORmrU6dOUVZWlpOxYFXqLTR78sknY9CgQdGvX7+YMmVKXHLJJdG5c+d4/fXX4y9/+Uv84x//WKNxevToEa+99lrMnz8//vGPf8SwYcPiySefXGlwZipyAIB1l6seDgCWq6ioiJ49e0V19aKcjFdU1CamT39HcEa9q7fQ7JxzzomLL744RowYEe3bt88s33///eO6665b43FatWoV2267bUREfPe7340XX3wx/vCHP8RNN91U5/MLCwujsLBw/YoHANhA5aqHA4DlKisro7p6UfQ+dlQUl5av11hVc2fH1FtHR2VlpdCMeldvodmbb74ZEyZMWGF5586d1+uQzKVLl9a6ZhkAALlTXz0cABSXlkfHsh75LgPWWL2FZptssknMnTs3unXrVmv5q6++Gt/5znfWaIyRI0fGoEGDoqysLL788suYMGFCTJ48OR555JH6KBkAYIOXix4OAJqbXF6TLcJ12ZqKegvNDj/88Dj77LPj7rvvjoKCgli6dGk888wzccYZZ8RRRx21RmN88skncdRRR8XcuXOjpKQkdt5553jkkUfihz/8YX2VDQCwQctFDwcAzUmur8kW4bpsTUW9hWaXXnppnHTSSVFWVhaLFy+O7bffPpYsWRK/+MUv4txzz12jMf7yl7/UV3kAANQhFz0cADQnubwmW4TrsjUlOQ/Nli5dGldeeWXcf//98fXXX8eRRx4ZP/3pT2PBggWx2267Rffu3XO9SQAA1pMeDgBWzTXZNjw5D80uueSSuOCCC2LAgAFRVFQUEyZMiJRS3HrrrbneFAAAOaKHAwCorUWuB7z99tvjT3/6UzzyyCNx3333xX//93/HHXfcEUuXLs31pgAAyBE9HABAbTkPzSoqKuLHP/5x5v6AAQOioKAg/vWvf+V6UwAA5IgeDgCgtpyHZosXL47WrVvXWrbxxhvHN998k+tNAQCQI3o4AIDacn5Ns5RSHH300VFYWJhZ9tVXX8UJJ5wQbdu2zSy79957c71pAADWkR4OAKC2nIdmw4YNW2HZL3/5y1xvBgCAHNLDAQDUlvPQbNy4cbkeEgCAeqaHAwCoLefXNAMAAACApk5oBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZhGYAAAAAkEVoBgAAAABZNsp3AQAAADReFRUVUVlZmbPxOnXqFGVlZTkbD6C+CM0AAACoU0VFRfTs2SuqqxflbMyiojYxffo7gjOg0ROaAQAAUKfKysqorl4UvY8dFcWl5es9XtXc2TH11tFRWVkpNAMaPaEZAAAAq1RcWh4dy3rkuwyABmUiAAAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCwmAgAAAKhHFRUVUVlZmZOxOnXqZNZJgAYiNAMAAKgnFRUV0bNnr6iuXpST8YqK2sT06e80q+Asl6FihGARyB2hGQAAQD2prKyM6upF0fvYUVFcWr5eY1XNnR1Tbx0dlZWVzSYUynWoGNE8g0UgP4RmAAAA9ay4tDw6lvXIdxmNTi5DxYjmGSwC+SM0AwAAIK+EikBjZPZMAAAAAMjiSDMAAABYD2ZIheZJaAYAAADryAyp0HwJzQAAAGAdNcQMqbk8ki3C0WywpoRmAAAAsJ7qazKDXB/JFuFoNlhTQjMAAABopHJ5JFvEyo9mA1YkNAMAAIBGrr6OZItw+iesjNAMAAAANlBO/4SVE5oBAADABsrpn7ByQjMAAADYwNXn6Z/QVLXIdwEAAAAA0NgIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAAAAgi9AMAAAAALIIzQAAyLvLLrssCgoK4rTTTst3KQAAERGxUb4LAABgw/biiy/GTTfdFDvvvHO+SwGaqYqKiqisrMzJWJ06dYqysrKcjAU0bkIzAADyZsGCBTF06NC45ZZb4uKLL853OUAzVFFRET179orq6kU5Ga+oqE1Mn/6O4Aw2AEIzAADyZvjw4XHggQfGgAEDVhua1dTURE1NTeZ+VVVVfZfHGnAED41dZWVlVFcvit7Hjori0vL1Gqtq7uyYeuvoqKys9F6FDYDQDACAvLjzzjvjlVdeiRdffHGNnj9mzJgYPXp0PVfF2nAED01JcWl5dCzrke8ygCakUYdmY8aMiXvvvTemT58eRUVF0bdv37j88sujRw//0QEANGVz5syJU089NR577LFo3br1Gq0zcuTIGDFiROZ+VVVVdO3atb5KZA04ggeA5qxRh2ZPPvlkDB8+PPbcc89YvHhx/O53v4sDDjggpk2bFm3bts13eQAArKOXX345Pvnkk9h9990zy5YsWRJTpkyJ6667LmpqaqJly5a11iksLIzCwsKGLpU14AgeAJqjRh2aPfzww7Xujx8/Pjp37hwvv/xy7L333nmqCgCA9dW/f/948803ay075phjomfPnnH22WevEJgBADS0Rh2aZZs/f35ERHTs2HGlz3GBWACAxq99+/ax44471lrWtm3b2HTTTVdYnk8ucg8AG64mE5otXbo0TjvttOjXr98qGykXiAUAIBdc5H7DkMtgNEI4CtCcNJnQbPjw4fHWW2/F008/vcrnuUAsAEDTNHny5HyXUIuL3Dd/uQ5GI4SjAM1JkwjNTjrppHjggQdiypQpseWWW67yuS4QCwBALrnIffOVy2A0QjgK0Nw06tAspRQnn3xyTJw4MSZPnhzdunXLd0kAAEAzIxgFoC6NOjQbPnx4TJgwIf7rv/4r2rdvH/PmzYuIiJKSkigqKspzdQAAAAA0Vy3yXcCq3HDDDTF//vzYd999o7S0NHO766678l0aAAAAAM1Yoz7SLKWU7xIAAAAA2AA16tAMAADYsFVUVERlZWVOxurUqZML9AOwxoRmAABAo1RRURE9e/aK6upFORmvqKhNTJ/+juAMgDUiNAMAABqlysrKqK5eFL2PHRXFpeXrNVbV3Nkx9dbRUVlZKTQDYI0IzQAAgEatuLQ8Opb1yHcZAGxgGvXsmQAAAACQD0IzAAAAAMji9EwAAIAmzAyjAPVDaAYAANBEmWEUoP4IzQAAAJooM4wC1B+hGQAAQBNnhlGA3DMRAAAAAABkEZoBAAAAQBahGQAAAABkEZoBAAAAQBahGQAAAABkEZoBAAAAQJaN8l0AAABQfyoqKqKysjInY3Xq1CnKyspyMhYANHZCMwAAaKYqKiqiZ89eUV29KCfjFRW1ienT3xGcAbBBEJoBAEAzVVlZGdXVi6L3saOiuLR8vcaqmjs7pt46OiorK4VmAGwQhGYAANDMFZeWR8eyHvkuAwCaFBMBAAAAAEAWoRkAAAAAZHF6JgAA5EkuZ7aMMLslAOSS0AwAAPIg1zNbRpjdEgBySWgGAAB5kMuZLSPMbgkAuSY0AwCAPDKzJQA0TiYCAAAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAAAIAsQjMAAAAAyCI0AwAgL8aMGRN77rlntG/fPjp37hyHHHJIzJgxI99lAQBEhNAMAIA8efLJJ2P48OHx/PPPx2OPPRbffPNNHHDAAbFw4cJ8lwYA0PhDsylTpsTgwYOjS5cuUVBQEPfdd1++SwIAIAcefvjhOProo2OHHXaIXXbZJcaPHx8VFRXx8ssv57s0AIDGH5otXLgwdtlll7j++uvzXQoAAPVo/vz5ERHRsWPHOh+vqamJqqqqWjcAgPqyUb4LWJ1BgwbFoEGD8l0GAAD1aOnSpXHaaadFv379Yscdd6zzOWPGjInRo0c3cGUAwIaq0R9ptrZ8AwkA0PQMHz483nrrrbjzzjtX+pyRI0fG/PnzM7c5c+Y0YIUAwIam2YVmY8aMiZKSksyta9eu+S4JAIBVOOmkk+KBBx6IJ554IrbccsuVPq+wsDCKi4tr3QAA6kuzC818AwkA0DSklOKkk06KiRMnxv/8z/9Et27d8l0SAEBGo7+m2doqLCyMwsLCfJcBAMBqDB8+PCZMmBD/9V//Fe3bt4958+ZFRERJSUkUFRXluToAYEPX7I40AwCgabjhhhti/vz5se+++0ZpaWnmdtddd+W7NACAxn+k2YIFC2LmzJmZ+7NmzYrXXnstOnbsGGVlZXmsDACA9ZFSyncJAAAr1ehDs5deein222+/zP0RI0ZERMSwYcNi/PjxeaoKAAAAgOas0Ydm++67r28hAQAAAGhQrmkGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQRWgGAAAAAFmEZgAAAACQpUmEZtdff32Ul5dH69ato3fv3vHCCy/kuyQAAHJAnwcANFaNPjS76667YsSIETFq1Kh45ZVXYpdddomBAwfGJ598ku/SAABYD/o8AKAxa/Sh2TXXXBPHHXdcHHPMMbH99tvHjTfeGG3atIlbb70136UBALAe9HkAQGO2Ub4LWJWvv/46Xn755Rg5cmRmWYsWLWLAgAHx3HPP1blOTU1N1NTUZO7Pnz8/IiKqqqrqpcYFCxZERMRnH86IxTXV6zVW1byKzJjL683l+HVto77HX/7vXG2jqY9f1zbsg4YdvyG2Yfz8b6Opj1/XNuyD1Y+fS8vHTCnlfGyW0ef5v7W5jd8Q22jq49e1DfugYcevaxv2QcOOX9c27IPVj59La9XnpUbso48+ShGRnn322VrLzzzzzPS9732vznVGjRqVIsLNzc3Nzc3Nbb1vc+bMaYiWZ4Okz3Nzc3Nzc3PL521N+rxGfaTZuhg5cmSMGDEic3/p0qXx2WefxaabbhoFBQU52UZVVVV07do15syZE8XFxTkZk7VjH+SffZB/9kHjYD/kX33sg5RSfPnll9GlS5ecjEdu6PM2DPZB42A/5J99kH/2Qf7lu89r1KFZp06domXLlvHxxx/XWv7xxx/HFltsUec6hYWFUVhYWGvZJptsUi/1FRcX+8XJM/sg/+yD/LMPGgf7If9yvQ9KSkpyNhYr0uexOvZB42A/5J99kH/2Qf7lq89r1BMBtGrVKr773e/GpEmTMsuWLl0akyZNij59+uSxMgAA1oc+DwBo7Br1kWYRESNGjIhhw4bFHnvsEd/73vdi7NixsXDhwjjmmGPyXRoAAOtBnwcANGaNPjT7+c9/Hv/3f/8X559/fsybNy923XXXePjhh2PzzTfPW02FhYUxatSoFU4PoOHYB/lnH+SffdA42A/5Zx80Xfo86mIfNA72Q/7ZB/lnH+RfvvdBQUrmUgcAAACAb2vU1zQDAAAAgHwQmgEAAABAFqEZAAAAAGQRmgEAAABAFqEZAAAAAGQRmq3E9ddfH+Xl5dG6devo3bt3vPDCC6t8/t133x09e/aM1q1bx0477RQPPvhgA1XafK3NPrjlllviBz/4QXTo0CE6dOgQAwYMWO0+Y/XW9vdguTvvvDMKCgrikEMOqd8CNwBruw+++OKLGD58eJSWlkZhYWFst912/j9aT2u7D8aOHRs9evSIoqKi6Nq1a5x++unx1VdfNVC1zc+UKVNi8ODB0aVLlygoKIj77rtvtetMnjw5dt999ygsLIxtt902xo8fX+910rTo8/JPn9c46PXyT6+Xf3q9/GkSfV5iBXfeeWdq1apVuvXWW9Pbb7+djjvuuLTJJpukjz/+uM7nP/PMM6lly5bpiiuuSNOmTUvnnntu2njjjdObb77ZwJU3H2u7D37xi1+k66+/Pr366qvpnXfeSUcffXQqKSlJ//u//9vAlTcfa7sPlps1a1b6zne+k37wgx+kIUOGNEyxzdTa7oOampq0xx57pB//+Mfp6aefTrNmzUqTJ09Or732WgNX3nys7T644447UmFhYbrjjjvSrFmz0iOPPJJKS0vT6aef3sCVNx8PPvhg+v3vf5/uvffeFBFp4sSJq3z+Bx98kNq0aZNGjBiRpk2blq699trUsmXL9PDDDzdMwTR6+rz80+c1Dnq9/NPr5Z9eL7+aQp8nNKvD9773vTR8+PDM/SVLlqQuXbqkMWPG1Pn8ww47LB144IG1lvXu3Tv95je/qdc6m7O13QfZFi9enNq3b59uu+22+iqx2VuXfbB48eLUt2/f9Oc//zkNGzZMI7We1nYf3HDDDWnrrbdOX3/9dUOV2Oyt7T4YPnx42n///WstGzFiROrXr1+91rmhWJNm6qyzzko77LBDrWU///nP08CBA+uxMpoSfV7+6fMaB71e/un18k+v13g01j7P6ZlZvv7663j55ZdjwIABmWUtWrSIAQMGxHPPPVfnOs8991yt50dEDBw4cKXPZ9XWZR9kW7RoUXzzzTfRsWPH+iqzWVvXfXDhhRdG586d41e/+lVDlNmsrcs+uP/++6NPnz4xfPjw2HzzzWPHHXeMSy+9NJYsWdJQZTcr67IP+vbtGy+//HLmsP4PPvggHnzwwfjxj3/cIDXjM5lV0+flnz6vcdDr5Z9eL//0ek1PPj6TN6q3kZuoysrKWLJkSWy++ea1lm+++eYxffr0OteZN29enc+fN29evdXZnK3LPsh29tlnR5cuXVb4hWLNrMs+ePrpp+Mvf/lLvPbaaw1QYfO3Lvvggw8+iP/5n/+JoUOHxoMPPhgzZ86ME088Mb755psYNWpUQ5TdrKzLPvjFL34RlZWVsddee0VKKRYvXhwnnHBC/O53v2uIkomVfyZXVVVFdXV1FBUV5akyGgN9Xv7p8xoHvV7+6fXyT6/X9OSjz3OkGc3OZZddFnfeeWdMnDgxWrdune9yNghffvllHHnkkXHLLbdEp06d8l3OBmvp0qXRuXPnuPnmm+O73/1u/PznP4/f//73ceONN+a7tA3G5MmT49JLL40//elP8corr8S9994b//znP+Oiiy7Kd2kAzYI+Lz/0eo2DXi//9HobHkeaZenUqVO0bNkyPv7441rLP/7449hiiy3qXGeLLbZYq+ezauuyD5a76qqr4rLLLovHH388dt555/oss1lb233w/vvvx+zZs2Pw4MGZZUuXLo2IiI022ihmzJgR22yzTf0W3cysy+9BaWlpbLzxxtGyZcvMsl69esW8efPi66+/jlatWtVrzc3NuuyD8847L4488sj49a9/HRERO+20UyxcuDCOP/74+P3vfx8tWviuqr6t7DO5uLjYUWbo8xoBfV7joNfLP71e/un1mp589Hn2aJZWrVrFd7/73Zg0aVJm2dKlS2PSpEnRp0+fOtfp06dPredHRDz22GMrfT6rti77ICLiiiuuiIsuuigefvjh2GOPPRqi1GZrbfdBz549480334zXXnstczv44INjv/32i9deey26du3akOU3C+vye9CvX7+YOXNmpomNiHj33XejtLRUE7UO1mUfLFq0aIVmaXljm1Kqv2LJ8JnMqujz8k+f1zjo9fJPr5d/er2mJy+fyfU2xUATduedd6bCwsI0fvz4NG3atHT88cenTTbZJM2bNy+llNKRRx6ZzjnnnMzzn3nmmbTRRhulq666Kr3zzjtp1KhRpiJfT2u7Dy677LLUqlWr9I9//CPNnTs3c/vyyy/z9SM0eWu7D7KZUWn9re0+qKioSO3bt08nnXRSmjFjRnrggQdS586d08UXX5yvH6HJW9t9MGrUqNS+ffv0t7/9LX3wwQfp0UcfTdtss0067LDD8vUjNHlffvllevXVV9Orr76aIiJdc8016dVXX00ffvhhSimlc845Jx155JGZ5y+fivzMM89M77zzTrr++uvrfSpymhZ9Xv7p8xoHvV7+6fXyT6+XX02hzxOarcS1116bysrKUqtWrdL3vve99Pzzz2ce22effdKwYcNqPf/vf/972m677VKrVq3SDjvskP75z382cMXNz9rsg6222ipFxAq3UaNGNXzhzcja/h58m0YqN9Z2Hzz77LOpd+/eqbCwMG299dbpkksuSYsXL27gqpuXtdkH33zzTbrgggvSNttsk1q3bp26du2aTjzxxPT55583fOHNxBNPPFHn/+/LX/dhw4alffbZZ4V1dt1119SqVau09dZbp3HjxjV43TRu+rz80+c1Dnq9/NPr5Z9eL3+aQp9XkJJjCAEAAADg21zTDAAAAACyCM0AAAAAIIvQDAAAAACyCM0AAAAAIIvQDAAAAACyCM0AAAAAIIvQDAAAAACyCM1gA3XBBRfErrvuuk7rzp49OwoKCuK1117LaU0bmn333TdOO+20BtnWeeedF8cff3yDbKu5ufHGG2Pw4MH5LgMAIiLi6KOPjkMOOWSd1p08eXIUFBTEF198kdOaNjTl5eUxduzYBtnWkUceGZdeemmDbKs+rM/fHPlw+OGHx9VXX53vMmhEhGawFjQa9aegoCDuu+++nI7ZkA1NYzZv3rz4wx/+EL///e8zy+ojsFtZE3/JJZdE3759o02bNrHJJpus0Vj1se/q+plff/31OOKII6Jr165RVFQUvXr1ij/84Q+1nnPsscfGK6+8Ek899VRO6wGApq6+vkitj76wKXr99dfjwQcfjFNOOaVBt9uQr399/H21svfl22+/HT/96U+jvLw8CgoK6uw1zz333Ljkkkti/vz5OauHpk1oBtDM/fnPf46+ffvGVlttlZftf/3113HooYfGb3/727xsf1Vefvnl6Ny5c/z1r3+Nt99+O37/+9/HyJEj47rrrss8p1WrVvGLX/wi/vjHP+axUgDWxtdff53vEmC9XXvttXHooYdGu3bt8l3KCpri79iiRYti6623jssuuyy22GKLOp+z4447xjbbbBN//etfG7g6Gq0Ezcg+++yThg8fnoYPH56Ki4vTpptums4999y0dOnSzHM+++yzdOSRR6ZNNtkkFRUVpR/96Efp3XffzTw+e/bsdNBBB6VNNtkktWnTJm2//fbpn//8Z5o1a1aKiFq3YcOGrbamu+++O+24446pdevWqWPHjql///5pwYIFKaWUhg0bloYMGZIuuOCC1KlTp9S+ffv0m9/8JtXU1GTWX7JkSbr00ktTeXl5at26ddp5553T3XffnXn8iSeeSBGRHn/88fTd7343FRUVpT59+qTp06fXqmPMmDGpc+fOqV27dunYY49NZ599dtpll11qjbPnnnumNm3apJKSktS3b980e/bsOn+m5a/Fq6++mlJKafHixemYY45JPXr0SB9++GFaunRpGjVqVOratWtq1apVKi0tTSeffPJKX6Otttqq1uu61VZbZR6777770m677ZYKCwtTt27d0gUXXJC++eablFJa5Xb22WefFfbXykRE+tOf/pR+9KMfpdatW6du3brVeo2zPfLII6mwsDB9/vnntZafcsopab/99ksppVRZWZkOP/zw1KVLl1RUVJR23HHHNGHChFrP32effdKpp55aq46JEyfWek5JSUkaN25c5n5FRUU69NBDU0lJSerQoUM6+OCD06xZs1Zaa0op7bDDDum6667L3B82bNgKr83yMd588830ox/9KLVt2zZ17tw5/fKXv0z/93//l1l3Ze/nUaNGrTDmE088UauOcePGpZKSklXWuvx1Wdm+e+qpp9Jee+2VWrdunbbccst08sknZ36fUkrp+uuvT9tuu20qLCxMnTt3Tj/96U9X+zNnO/HEEzP7cbknn3wytWrVKi1atGi19QOQW2vS32211VbpwgsvTEceeWRq3759pkdb3efGqqzsM2VNa/rqq6/Sv//7v6cuXbqkNm3apO9973u1PhuXfy4+/PDDqWfPnqlt27Zp4MCB6V//+lfmOYsXL06nn356KikpSR07dkxnnnlmOuqoo9KQIUMyz1lVr5lted+4vIdZuHBh+tGPfpT69u2bPv/881RTU5OGDx+etthii1RYWJjKysrSpZdeutLXKPuzdZ999sk8dsstt6SePXumwsLC1KNHj3T99ddnHlvVdlbVF37b8n70b3/7W+rTp08qLCxMO+ywQ5o8efJK673ppptSaWlpWrJkSa3lBx98cDrmmGNSSinNnDkzHXzwwalz586pbdu2aY899kiPPfZYredvtdVW6T/+4z9q1bG8L04ppc8//3yFXmh1PVa2xYsXp5KSkvTAAw/UWv7VV1+ls846K2255ZapVatWaZtttkl//vOfM49Pnjw57bnnnqlVq1Zpiy22SGeffXamd05p2Xv35JNPTmeeeWbq0KFD2nzzzdOoUaNq/Wx1vf6jRo1Ku+yyS7rllltSeXl5KigoSCml9OGHH6aDDz44tW3bNrVv3z4deuihad68eZnxlq9Xl1X9fbW6v4E+++yz9Itf/CJ16tQptW7dOm277bbp1ltvTSmt+n357Z9z+T7MNnr06LTXXnvV+RgbHqEZzco+++yT2rVrl0499dQ0ffr09Ne//jW1adMm3XzzzZnnHHzwwalXr15pypQp6bXXXksDBw5M2267bfr6669TSikdeOCB6Yc//GF644030vvvv5/++7//Oz355JNp8eLF6Z577kkRkWbMmJHmzp2bvvjii1XW869//StttNFG6ZprrkmzZs1Kb7zxRrr++uvTl19+mVJa9od8u3bt0s9//vP01ltvpQceeCBtttlm6Xe/+11mjIsvvjj17NkzPfzww+n9999P48aNS4WFhZmGYHnz07t37zR58uT09ttvpx/84Aepb9++mTHuuuuuVFhYmP785z+n6dOnp9///vepffv2mQ+wb775JpWUlKQzzjgjzZw5M02bNi2NHz8+ffjhh3X+XN9uDr766qv0b//2b2m33XZLn3zySUppWfNWXFycHnzwwfThhx+mqVOn1toH2T755JMUEWncuHFp7ty5mXGmTJmSiouL0/jx49P777+fHn300VReXp4uuOCC1W7n008/TVtuuWW68MIL09y5c9PcuXNXuv2ISJtuumm65ZZb0owZM9K5556bWrZsmaZNm1bn8xcvXpw233zzWg1K9rL//d//TVdeeWV69dVX0/vvv5/++Mc/ppYtW6apU6dm1lnb0Ozrr79OvXr1Sscee2x644030rRp09IvfvGL1KNHj1pB67d9+umnqaCgID3//POZZV988UXq06dPOu644zKvzeLFi9Pnn3+eNttsszRy5Mj0zjvvpFdeeSX98Ic/zARIq3o/f/nll+mwww5LP/rRjzJjZte0pqHZyvbdzJkzU9u2bdN//Md/pHfffTc988wzabfddktHH310SimlF198MbVs2TJNmDAhzZ49O73yyivpD3/4wyp/5roMHTq01h9GKS37o6JFixYrBIEA1L816e+22mqrVFxcnK666qo0c+bMzG1VnxursqrPlDWt6de//nXq27dvmjJlSpo5c2a68sorU2FhYebL2nHjxqWNN944DRgwIL344ovp5ZdfTr169Uq/+MUvMmNcfvnlqUOHDumee+5J06ZNS7/61a9S+/btM6HZ6nrNbN8OzT7//PPUt2/fdMABB6SFCxemlFK68sorU9euXdOUKVPS7Nmz01NPPbXCl37f9sILL2S+vJ07d2769NNPU0op/fWvf02lpaXpnnvuSR988EG65557UseOHdP48eNXu52V9YXZlvejW265ZfrHP/6Rpk2bln7961+n9u3bp8rKyjrX+eyzz1KrVq3S448/nln26aef1lr22muvpRtvvDG9+eab6d13303nnntuat26da2+eG1Ds9X1WHV55ZVXUkTUCqBSSumwww5LXbt2Tffee296//330+OPP57uvPPOlNKy/rNNmzbpxBNPTO+8806aOHFi6tSpU61QbJ999knFxcXpggsuSO+++2667bbbUkFBQXr00UdX+fqPGjUqtW3bNv3oRz9Kr7zySnr99dfTkiVL0q677pr22muv9NJLL6Xnn38+ffe7360VUq0qNFvV31er+xto+PDhadddd00vvvhimjVrVnrsscfS/fffn1Ja+fvy21YVmj300EOpVatW6auvvlrp/mHDITSjWdlnn31Sr169an3Ld/bZZ6devXqllFJ69913U0SkZ555JvN4ZWVlKioqSn//+99TSinttNNOmVAmW/a3c6vz8ssvp4hY6RFbw4YNSx07dsw0KimldMMNN6R27dqlJUuWpK+++iq1adMmPfvss7XW+9WvfpWOOOKIWjV9+8P/n//8Z4qIVF1dnVJKqU+fPunEE0+sNUbv3r0zH2CffvppiohVfjP3bcubg6eeeir1798/7bXXXrUCxKuvvjptt912mSByTdQVGPXv33+Fbzf/8z//M5WWlq7Rdlb1YZi97RNOOKHWst69e6ff/va3K13n1FNPTfvvv3/m/sqOPvu2Aw88MP37v/975v7ahmb/+Z//mXr06FHr/V1TU5OKiorSI488Uuc2X3311RQRqaKiotby7G2nlNJFF12UDjjggFrL5syZk2lk1uT9/O1vvrOtaWiWUt377le/+lU6/vjjay176qmnUosWLVJ1dXW65557UnFxcaqqqqpzzLp+5mzPPPNM2mijjep8PTt06JBp9gFoOKvr71Ja9rlxyCGH1FpvdZ8bq7ImnymrqunDDz9MLVu2TB999FGt9fr3759GjhyZUlr2uRgRaebMmZnHr7/++rT55ptn7peWlqYrrrgic/+bb75JW265ZebzdnWfzdmW943vvPNO2nnnndNPf/rTWl9ynXzyyWn//fev9XOtSl2BUUopbbPNNiuEbRdddFHq06fPGm2nrp5oZdu+7LLLMsuWvz6XX375StcbMmRIOvbYYzP3b7rpptSlS5cVjj77th122CFde+21mftrG5qtrseqy8SJE1PLli1rvUYzZsxIEbHCkW/L/e53v1uhV7z++uszf1uktOy9m30U1Z577pnOPvvszP26Xv9Ro0aljTfeuFaI+eijj6aWLVvW6jPffvvtFBHphRdeyKy3stAspbr/vlqTv4EGDx6cOTow28rel9+2qr8TXn/99bX6vaJ5c00zmp3vf//7UVBQkLnfp0+feO+992LJkiXxzjvvxEYbbRS9e/fOPL7ppptGjx494p133omIiFNOOSUuvvji6NevX4waNSreeOONda5ll112if79+8dOO+0Uhx56aNxyyy3x+eefr/CcNm3a1Kp3wYIFMWfOnJg5c2YsWrQofvjDH0a7du0yt9tvvz3ef//9WuPsvPPOmX+XlpZGRMQnn3wSERHvvPNOrZ95+XaW69ixYxx99NExcODAGDx4cPzhD3+IuXPnrvbnO+KII2LhwoXx6KOPRklJSWb5oYceGtXV1bH11lvHcccdFxMnTozFixdHRMSll15a62epqKhY6fivv/56XHjhhbWef9xxx8XcuXNj0aJFq9zO2vr267H8/vL3xKBBgzLb32GHHSIiYujQoTF58uT417/+FRERd9xxRxx44IGZC90vWbIkLrroothpp52iY8eO0a5du3jkkUdW+fOuzuuvvx4zZ86M9u3bZ+rp2LFjfPXVVyu8H5arrq6OiIjWrVuv0fhPPPFErde7Z8+eERHx/vvvr9H7uT69/vrrMX78+Fr1DRw4MJYuXRqzZs2KH/7wh7HVVlvF1ltvHUceeWTccccdsWjRojUe/6233oohQ4bEqFGj4oADDljh8aKiorUaD4DcWVV/t9wee+xRa53VfW6sypp8pqyqpjfffDOWLFkS2223Xa3tP/nkk7U+s9u0aRPbbLNN5n5paWmmf5s/f37MnTu3Vg+30UYb1fo51/Wz+Yc//GFsu+22cdddd0WrVq0yy48++uh47bXXokePHnHKKafEo48+mnnshBNOqPWzrMzChQvj/fffj1/96le1nn/xxRdnfvZVbWdtfbuHW/76LO/hdthhh8z2Bw0aFBHLerh77rknampqImJZD3f44YdHixbL/jResGBBnHHGGdGrV6/YZJNNol27dvHOO++sdw+3qh6rLtXV1VFYWFjrPfbaa69Fy5YtY5999qlznXfeeSf69OlTa51+/frFggUL4n//938zy779d0NE7ffdqmy11Vax2Wab1dpe165do2vXrpll22+/fWyyySaZfbAu1uRvoN/+9rdx5513xq677hpnnXVWPPvss+u8vWxFRUUREfo+IiJio3wXAI3Nr3/96xg4cGD885//jEcffTTGjBkTV199dZx88slrPVbLli3jsccei2effTYeffTRuPbaa+P3v/99TJ06Nbp167ba9RcsWBAREf/85z/jO9/5Tq3HCgsLa93feOONM/9e/kG5dOnSNa513Lhxccopp8TDDz8cd911V5x77rnx2GOPxfe///2VrvPjH/84/vrXv8Zzzz0X+++/f2Z5165dY8aMGfH444/HY489FieeeGJceeWV8eSTT8YJJ5wQhx12WOa5Xbp0Wen4CxYsiNGjR8dPfvKTFR5r3br1Krfz7ddjff35z3/OhE/Lx91zzz1jm222iTvvvDN++9vfxsSJE2P8+PGZda688sr4wx/+EGPHjo2ddtop2rZtG6eddtoqL5paUFAQKaVay7755pvMvxcsWBDf/e5344477lhh3W83MN/WqVOniIj4/PPPV/qcb48/ePDguPzyy1d4rLS0dL3fz+trwYIF8Zvf/KbOGaTKysqiVatW8corr8TkyZPj0UcfjfPPPz8uuOCCePHFF1c7a+e0adOif//+cfzxx8e5555b53M+++yz1b6GAORP27Zta91f3efGqrRv336dP1OWb7tly5bx8ssvR8uWLWs99u3AKbtfqasXWJV1/Ww+8MAD45577olp06bFTjvtlFm+++67x6xZs+Khhx6Kxx9/PA477LAYMGBA/OMf/4gLL7wwzjjjjNXWtLx/veWWW1b40nb5a7Gq7eTSgw8+mOmllgchgwcPjpRS/POf/4w999wznnrqqfiP//iPzDpnnHFGPPbYY3HVVVfFtttuG0VFRfGzn/1spT3c8rDt2/vt2/1bxOp7rLp06tQpFi1aFF9//XUm2Fz+M6yvut53a/J3Q/bvWH1Zk7+BBg0aFB9++GE8+OCD8dhjj0X//v1j+PDhcdVVV6339j/77LOIWHl/zYZFaEazM3Xq1Fr3n3/++ejevXu0bNkyevXqFYsXL46pU6dG3759IyLi008/jRkzZsT222+fWadr165xwgknxAknnBAjR46MW265JU4++eTMB9a3v9VcnYKCgujXr1/069cvzj///Nhqq61i4sSJMWLEiIhY9s1TdXV15kPw+eefj3bt2kXXrl2jY8eOUVhYGBUVFSv9RmlN9OrVK6ZOnRpHHXVUrdcl22677Ra77bZbjBw5Mvr06RMTJkxYZWj229/+Nnbcccc4+OCD45///GetGouKimLw4MExePDgGD58ePTs2TPefPPN2H333aNjx44rjLXxxhuv8LruvvvuMWPGjNh2221XWsOqttOqVas13lfPP//8Cq/PbrvtFhGxwof1ckOHDo077rgjttxyy2jRokUceOCBmceeeeaZGDJkSPzyl7+MiGUB5rvvvlvrfZZts802q3WE33vvvVfrG67dd9897rrrrujcuXMUFxev0c+1zTbbRHFxcUybNi222267zPK6Xpvdd9897rnnnigvL4+NNqr742FV7+e1eb1XZ2X1TZs2bZXvh4022igGDBgQAwYMiFGjRsUmm2wS//M//xM/+clPVlrf22+/Hfvvv38MGzYsLrnkkjrHff/99+Orr77KvCcAaFir6u9WZk0+N1ZlVZ8pq6tpt912iyVLlsQnn3wSP/jBD9Zp+yUlJVFaWhpTp06NvffeOyIiFi9eHC+//HLsvvvumeetrtesy2WXXRbt2rWL/v37x+TJk2v1J8XFxfHzn/88fv7zn8fPfvaz+NGPfhSfffZZdO7cOTp37lxrnLp648033zy6dOkSH3zwQQwdOnSlNaxsOx07dqyzL1yZ559/foXX56STToqIqHPm8NatW8dPfvKTuOOOO2LmzJnRo0ePWq/nM888E0cffXT827/9W0QsC3Bmz5690u0vD1bmzp2b6RNee+21Ws9Zkx4r26677hoRy77YW/7vnXbaKZYuXRpPPvlkDBgwYIV1evXqFffcc0+klDJfoj/zzDPRvn372HLLLddouxF19+V16dWrV8yZMyfmzJmTOdps2rRp8cUXX6yy5/22ut5D22+//Rr9DbTZZpvFsGHDYtiwYfGDH/wgzjzzzLjqqqvW6W+2b3vrrbdiyy23zHz5zIZNaEazU1FRESNGjIjf/OY38corr8S1114bV199dUREdO/ePYYMGRLHHXdc3HTTTdG+ffs455xz4jvf+U4MGTIkIiJOO+20GDRoUGy33Xbx+eefxxNPPBG9evWKiGUfvAUFBfHAAw/Ej3/84ygqKlrl4elTp06NSZMmxQEHHBCdO3eOqVOnxv/93/9lxotYNl3zr371qzj33HNj9uzZMWrUqDjppJOiRYsW0b59+zjjjDPi9NNPj6VLl8Zee+0V8+fPj2eeeSaKi4tj2LBha/SanHrqqXH00UfHHnvsEf369Ys77rgj3n777dh6660jImLWrFlx8803x8EHHxxdunSJGTNmxHvvvVcrRFqZk08+OZYsWRIHHXRQPPTQQ7HXXnvF+PHjY8mSJdG7d+9o06ZN/PWvf42ioqI6G5flysvLY9KkSdGvX78oLCyMDh06xPnnnx8HHXRQlJWVxc9+9rNo0aJFvP766/HWW2/FxRdfvNrtlJeXx5QpU+Lwww+PwsLCVX7w3X333bHHHnvEXnvtFXfccUe88MIL8Ze//GWVP/vQoUPjggsuiEsuuSR+9rOf1Tr6r3v37vGPf/wjnn322ejQoUNcc8018fHHH6+ygdh///3juuuuiz59+sSSJUvi7LPPrvVN4NChQ+PKK6+MIUOGxIUXXhhbbrllfPjhh3HvvffGWWedVWcz1KJFixgwYEA8/fTTccghh9R6vadOnRqzZ8/OnOY5fPjwuOWWW+KII46Is846Kzp27BgzZ86MO++8M/785z/HSy+9tMr3c3l5eTzyyCMxY8aM2HTTTaOkpCQ23njjqKioiM8++ywqKipiyZIlmUZy2223XenvT1377uyzz47vf//7cdJJJ8Wvf/3raNu2bUybNi0ee+yxuO666+KBBx6IDz74IPbee+/o0KFDPPjgg7F06dLo0aPHSn/madOmxf777x8DBw6MESNGxLx58yJi2Tfh3/528amnnoqtt9661ik0ADScVfV3K7O6z41VWd1nyupq2m677WLo0KFx1FFHxdVXXx277bZb/N///V9MmjQpdt5551pftK3KqaeeGpdddll07949evbsGddcc0188cUXmcfXpNdcmauuuiqWLFkS+++/f0yePDkzfmlpaey2227RokWLuPvuu2OLLbZY6dF1nTt3jqKionj44Ydjyy23jNatW0dJSUmMHj06TjnllCgpKYkf/ehHUVNTEy+99FJ8/vnnMWLEiNVup66+cGWuv/766N69e/Tq1Sv+4z/+Iz7//PM49thjV/mzDx06NA466KB4++23M19wLte9e/e49957Y/DgwVFQUBDnnXfeKo/CKioqiu9///tx2WWXRbdu3eKTTz5Z4aj11fVYdYW/m222Wey+++7x9NNPZ0Kz8vLyGDZsWBx77LHxxz/+MXbZZZf48MMP45NPPonDDjssTjzxxBg7dmycfPLJcdJJJ8WMGTNi1KhRMWLEiMwRcWtiTV//AQMGxE477RRDhw6NsWPHxuLFi+PEE0+MffbZZ4XTpVemrr+v1uRvoPPPPz+++93vxg477BA1NTXxwAMPZN73K3tffv311zFt2rSIWPY32EcffRSvvfZatGvXrla4/tRTT9V5qQ42UHm9ohrk2D777JNOPPHEdMIJJ6Ti4uLUoUOH9Lvf/a7WxTA/++yzdOSRR6aSkpJUVFSUBg4cmJnFKKWUTjrppLTNNtukwsLCtNlmm6Ujjzyy1gw8F154Ydpiiy1SQUFBZkrklZk2bVoaOHBg2myzzVJhYWHabrvtal1EdPmF088///y06aabpnbt2qXjjjuu1kwtS5cuTWPHjk09evRIG2+8cdpss83SwIED05NPPplSqvvimcsv/j5r1qzMsksuuSR16tQptWvXLg0bNiydddZZmYtyzps3Lx1yyCGptLQ0tWrVKm211Vbp/PPPX+kFUeu6uObVV1+d2rdvn5555pk0ceLE1Lt371RcXJzatm2bvv/979eaqKAu999/f9p2223TRhttVGtq8Ycffjj17ds3FRUVpeLi4vS9730vMzPV6rbz3HPPpZ133jkVFhamVf13FxHp+uuvTz/84Q9TYWFhKi8vT3fdddcq613ue9/7XoqI9D//8z+1ln/66adpyJAhqV27dqlz587p3HPPXWGK+OwL03/00UfpgAMOSG3btk3du3dPDz74YK2JAFJKae7cuemoo45KnTp1SoWFhWnrrbdOxx13XJo/f/5Ka3zwwQfTd77znVr7c8aMGen73/9+KioqqvVeeffdd9O//du/pU022SQVFRWlnj17ptNOOy0tXbp0te/nTz75JP3whz9M7dq1q3Xx22HDhq0w9fe3H09p2cVYvz2z08r23QsvvJDZRtu2bdPOO++cLrnkkpTSsos777PPPqlDhw6pqKgo7bzzzrX2Y10/86hRo+qsLXt6+wMOOCCNGTNmpa8xAPVnTfq7lV3Ue1WfG6uyus+UNanp66+/Tueff34qLy9PG2+8cSotLU3/9m//lt54442UUt0T5EycOLHW594333yTTj311FRcXJw22WSTNGLEiFr9xOo+m7PV1TeefPLJqbS0NM2YMSPdfPPNadddd01t27ZNxcXFqX///umVV15Z5Wt1yy23pK5du6YWLVrUmjXxjjvuSLvuumtq1apV6tChQ9p7773Tvffem1JKq93OyvrCb1vej06YMCF973vfS61atUrbb7/9Cj1ZXZYsWZJKS0tTRKT3339/hXH322+/VFRUlLp27Zquu+66FXq27PfbtGnTUp8+fVJRUVHadddd06OPPrpCr7OqHmtl/vSnP6Xvf//7tZZVV1en008/PdO3b7vttunWW2/NPD558uS05557platWqUtttginX322embb77JPF7XxEhDhgyp9XdNXa//yi7o/+GHH6aDDz44tW3bNrVv3z4deuihtWb8zF5v+Xvw23+n1PX31er+BrroootSr169UlFRUerYsWMaMmRI+uCDDzJj1vW+XP6eyb59+31bXV2dSkpK0nPPPbfCz8qGqSCltThpHhq5fffdN3bdddcYO3ZsvktZI0cffXR88cUXcd999+W7lA1aQUFBTJw4sdaRWM1JSil69+4dp59+ehxxxBH5LmcFixYtik033TQeeuih2HffffNdzgqWn7757rvv1prwAoCG0Rj7u8ZY04Zm9uzZ0a1bt3j11VczR2I1N9XV1dGjR4+46667Vpi0qqkaN25cXHrppTFt2rScXoM4V2644YaYOHHiek1OQfNi9kyAZq6goCBuvvnmdZ5ZtL498cQTsf/++zfKwCxi2TVKbr/9doEZANCgioqK4vbbb4/Kysp8l5IzDz74YFx66aWNMjCLWHY9t2uvvTbfZdCIuKYZrIeKiopVXqNq2rRpq52dCRrCrrvu2mi/hT3wwAPX+Nou+VDXhXYBaNqeeuqpGDRo0EofXz57H+RbY/1ScV3dfffd+S5hlX7961/nuwQaGadnwnpYvHjxKmfTWZsZcgAAaBjV1dXx0UcfrfTxdZ1xE4DmRWgGAAAAAFlc0wwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsgjNAAAAACCL0AwAAAAAsmyU7wLq29KlS+Nf//pXtG/fPgoKCvJdDgDQBKSU4ssvv4wuXbpEixa+Y1wXU6ZMiSuvvDJefvnlmDt3bkycODEOOeSQzOMppRg1alTccsst8cUXX0S/fv3ihhtuiO7du6/xNvR5AMDaWps+r9mHZv/617+ia9eu+S4DAGiC5syZE1tuuWW+y2iSFi5cGLvssksce+yx8ZOf/GSFx6+44or44x//GLfddlt069YtzjvvvBg4cGBMmzYtWrduvUbb0OcBAOtqTfq8gpRSaqB68mL+/PmxySabxJw5c6K4uDjf5QAATUBVVVV07do1vvjiiygpKcl3OU1eQUFBrSPNUkrRpUuX+Pd///c444wzImJZz7b55pvH+PHj4/DDD1+jcfV5AMDaWps+r9kfabb8UP3i4mLNFACwVpzyVz9mzZoV8+bNiwEDBmSWlZSURO/eveO5555baWhWU1MTNTU1mftffvllROjzAIC1tyZ9not0AADQoObNmxcREZtvvnmt5ZtvvnnmsbqMGTMmSkpKMjenZgIA9UloBgBAkzBy5MiYP39+5jZnzpx8lwQANGNCMwAAGtQWW2wREREff/xxreUff/xx5rG6FBYWZk7FdEomAFDfhGYAADSobt26xRZbbBGTJk3KLKuqqoqpU6dGnz598lgZAMD/0+wnAgAAoOEtWLAgZs6cmbk/a9aseO2116Jjx45RVlYWp512Wlx88cXRvXv36NatW5x33nnRpUuXzAybAAD5JjQDACDnXnrppdhvv/0y90eMGBEREcOGDYvx48fHWWedFQsXLozjjz8+vvjii9hrr73i4YcfjtatW+erZACAWgpSSinfRdSnqqqqKCkpifnz57vuBQCwRvQPTYP9BACsrbXpH1zTDAAAAACyCM0AAAAAIIvQDAAAAACyCM0AAAAAIIvQDAAAAACyCM0AAAAAIIvQDAAAAACybJTvAgCADUdFRUVUVlbmZKxOnTpFWVlZTsYCAGD9NMc+T2gGADSIioqK6NmzV1RXL8rJeEVFbWL69HcaRUMFALAha659ntAMAGgQlZWVUV29KHofOyqKS8vXa6yqubNj6q2jo7KyMu/NFADAhq659nlCMwCgQRWXlkfHsh75LgMAgBxrbn2eiQAAAAAAIIvQDAAAAACyCM0AAAAAIEteQ7MpU6bE4MGDo0uXLlFQUBD33XdfrcdTSnH++edHaWlpFBUVxYABA+K9997LT7EAAAAAbDDyGpotXLgwdtlll7j++uvrfPyKK66IP/7xj3HjjTfG1KlTo23btjFw4MD46quvGrhSAAAAADYkeZ09c9CgQTFo0KA6H0spxdixY+Pcc8+NIUOGRETE7bffHptvvnncd999cfjhhzdkqQAAAABsQBrtNc1mzZoV8+bNiwEDBmSWlZSURO/eveO5555b6Xo1NTVRVVVV6wYAAAAAa6PRhmbz5s2LiIjNN9+81vLNN98881hdxowZEyUlJZlb165d67VOAAAAAJqfRhuarauRI0fG/PnzM7c5c+bkuyQAAAAAmphGG5ptscUWERHx8ccf11r+8ccfZx6rS2FhYRQXF9e6AQAAAMDaaLShWbdu3WKLLbaISZMmZZZVVVXF1KlTo0+fPnmsDAAAAIDmLq+zZy5YsCBmzpyZuT9r1qx47bXXomPHjlFWVhannXZaXHzxxdG9e/fo1q1bnHfeedGlS5c45JBD8lc0AAAAAM1eXkOzl156Kfbbb7/M/REjRkRExLBhw2L8+PFx1llnxcKFC+P444+PL774Ivbaa694+OGHo3Xr1vkqGQAAAIANQF5Ds3333TdSSit9vKCgIC688MK48MILG7AqAAAAADZ0jfaaZgAAAACQL0IzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAAACALEIzAAAAAMgiNAMAoMEtWbIkzjvvvOjWrVsUFRXFNttsExdddFGklPJdGgBARERslO8CAADY8Fx++eVxww03xG233RY77LBDvPTSS3HMMcdESUlJnHLKKfkuDwBAaAYAQMN79tlnY8iQIXHggQdGRER5eXn87W9/ixdeeCHPlQEALOP0TAAAGlzfvn1j0qRJ8e6770ZExOuvvx5PP/10DBo0aKXr1NTURFVVVa0bAEB9caQZAAAN7pxzzomqqqro2bNntGzZMpYsWRKXXHJJDB06dKXrjBkzJkaPHt2AVQIAGzJHmgEA0OD+/ve/xx133BETJkyIV155JW677ba46qqr4rbbblvpOiNHjoz58+dnbnPmzGnAigGADY0jzQAAaHBnnnlmnHPOOXH44YdHRMROO+0UH374YYwZMyaGDRtW5zqFhYVRWFjYkGUCABswR5oBANDgFi1aFC1a1G5FW7ZsGUuXLs1TRQAAtTnSDACABjd48OC45JJLoqysLHbYYYd49dVX45prroljjz0236UBAESE0AwAgDy49tpr47zzzosTTzwxPvnkk+jSpUv85je/ifPPPz/fpQEARITQDACAPGjfvn2MHTs2xo4dm+9SAADq5JpmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJClUYdmS5YsifPOOy+6desWRUVFsc0228RFF10UKaV8lwYAAABAM7ZRvgtYlcsvvzxuuOGGuO2222KHHXaIl156KY455pgoKSmJU045Jd/lAQAAANBMNerQ7Nlnn40hQ4bEgQceGBER5eXl8be//S1eeOGFPFcGAAAAQHPWqE/P7Nu3b0yaNCnefffdiIh4/fXX4+mnn45BgwatdJ2ampqoqqqqdQMAAACAtdGojzQ755xzoqqqKnr27BktW7aMJUuWxCWXXBJDhw5d6TpjxoyJ0aNHN2CVAAAAADQ3jfpIs7///e9xxx13xIQJE+KVV16J2267La666qq47bbbVrrOyJEjY/78+ZnbnDlzGrBiAAAAAJqDRn2k2ZlnnhnnnHNOHH744RERsdNOO8WHH34YY8aMiWHDhtW5TmFhYRQWFjZkmQAAAAA0M436SLNFixZFixa1S2zZsmUsXbo0TxUBAAAAsCFo1EeaDR48OC655JIoKyuLHXbYIV599dW45ppr4thjj813aQAAAAA0Y406NLv22mvjvPPOixNPPDE++eST6NKlS/zmN7+J888/P9+lAQAAANCMNerQrH379jF27NgYO3ZsvksBAAAAYAPSqK9pBgAAAAD5IDQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIslG+CwAAAACgflVUVERlZWVOxurUqVOUlZXlZKzGTGgGAAAA0IxVVFREz569orp6UU7GKypqE9Onv9PsgzOhGQAAAEAzVllZGdXVi6L3saOiuLR8vcaqmjs7pt46OiorK4VmAAAAADR9xaXl0bGsR77LaDJMBAAAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBFaAYAAAAAWYRmAAAAAJBlo3wXAAAAALAhq6ioiMrKypyN16lTpygrK8vZeBsqoRkAAABAnlRUVETPnr2iunpRzsYsKmoT06e/IzhbT0IzAAAAgDyprKyM6upF0fvYUVFcWr7e41XNnR1Tbx0dlZWVQrP1JDQDACAvPvroozj77LPjoYceikWLFsW2224b48aNiz322CPfpQFAgysuLY+OZT3yXQbfIjQDAKDBff7559GvX7/Yb7/94qGHHorNNtss3nvvvejQoUO+SwMAiAihGQAAeXD55ZdH165dY9y4cZll3bp1y2NFAAC1Cc0AAGhw999/fwwcODAOPfTQePLJJ+M73/lOnHjiiXHcccetdJ2ampqoqanJ3K+qqmqIUgHIgVzODmlmSBqK0AwAgAb3wQcfxA033BAjRoyI3/3ud/Hiiy/GKaecEq1atYphw4bVuc6YMWNi9OjRDVwpAOsr17NDmhmShiI0AwCgwS1dujT22GOPuPTSSyMiYrfddou33norbrzxxpWGZiNHjowRI0Zk7ldVVUXXrl0bpF4A1l0uZ4c0MyQNSWgGAECDKy0tje23377Wsl69esU999yz0nUKCwujsLCwvksDoJ6YHZKmpkW+CwAAYMPTr1+/mDFjRq1l7777bmy11VZ5qggAoDahGQAADe7000+P559/Pi699NKYOXNmTJgwIW6++eYYPnx4vksDAIgIoRkAAHmw5557xsSJE+Nvf/tb7LjjjnHRRRfF2LFjY+jQofkuDQAgIlzTDACajFxO1R5R93TtpoOnIR100EFx0EEH5bsMAIA6Cc0AoAnI9VTtEStO1246eAAA+H+EZgDQBORyqvaIuqdrNx08AAD8P0IzAGhCGmKqdtPBAwCAiQAAAAAAYAWNPjT76KOP4pe//GVsuummUVRUFDvttFO89NJL+S4LAAAAgGasUZ+e+fnnn0e/fv1iv/32i4ceeig222yzeO+996JDhw75Lg0AAACAZqxRh2aXX355dO3aNcaNG5dZ1q1btzxWBAAAAMCGoFGfnnn//ffHHnvsEYceemh07tw5dtttt7jllltWuU5NTU1UVVXVugEAAADA2lin0GzrrbeOTz/9dIXlX3zxRWy99dbrXdRyH3zwQdxwww3RvXv3eOSRR+K3v/1tnHLKKXHbbbetdJ0xY8ZESUlJ5ta1a9ec1QMA0Nw1VJ8HANDYrdPpmbNnz44lS5assLympiY++uij9S5quaVLl8Yee+wRl156aURE7LbbbvHWW2/FjTfeGMOGDatznZEjR8aIESMy96uqqgRnADlQUVERlZWVORuvU6dOUVZWlrPxgNxoqD4PAKCxW6vQ7P7778/8+5FHHomSkpLM/SVLlsSkSZOivLw8Z8WVlpbG9ttvX2tZr1694p577lnpOoWFhVFYWJizGgBYFpj17NkrqqsX5WzMoqI2MX36O4IzaCQaus8DAGjs1io0O+SQQyIioqCgYIUjvTbeeOMoLy+Pq6++OmfF9evXL2bMmFFr2bvvvhtbbbVVzrYBwOpVVlZGdfWi6H3sqCguLV/v8armzo6pt46OyspKoRk0Eg3d5wEANHZrFZotXbo0IpbNYPniiy9Gp06d6qWo5U4//fTo27dvXHrppXHYYYfFCy+8EDfffHPcfPPN9bpdAOpWXFoeHct65LuMRssprDRlDd3nAQA0dut0TbNZs2bluo467bnnnjFx4sQYOXJkXHjhhdGtW7cYO3ZsDB06tEG2DwBryimsNBcN1ecBADR26xSaRURMmjQpJk2aFJ988knmm8nlbr311vUubLmDDjooDjrooJyNBwD1wSmsNCcN1ecBADRm6xSajR49Oi688MLYY489orS0NAoKCnJdFwA0SU5hpanT5wEALLNOodmNN94Y48ePjyOPPDLX9QAAkEf6PACAZVqsy0pff/119O3bN9e1AACQZ/o8AIBl1ulIs1//+tcxYcKEOO+883JdDwDUm1zObmlmS5orfR4AwDLrFJp99dVXcfPNN8fjjz8eO++8c2y88ca1Hr/mmmtyUhwA5EquZ7c0syXNlT4PAGCZdQrN3njjjdh1110jIuKtt96q9ZiLxQLQGOVydkszW9Kc6fMAAJZZp9DsiSeeyHUdANAgzG4Jq6bPA6ApchkO6sM6hWbLzZw5M95///3Ye++9o6ioKFJKvoEEAGgG9HkANBUuw0F9WafQ7NNPP43DDjssnnjiiSgoKIj33nsvtt566/jVr34VHTp0iKuvvjrXdQIA0AD0eQA0NQ1xGY5cHskW4Wi2pmKdQrPTTz89Nt5446ioqIhevXpllv/85z+PESNGaKYAAJoofR4ATVV9XYYj10eyRTiaralYp9Ds0UcfjUceeSS23HLLWsu7d+8eH374YU4KA/g23+wANAx9HgDUlssj2SJMKtWUrFNotnDhwmjTps0Kyz/77LMoLCxc76IAvs03OwANR58HAHUzodSGZ51Csx/84Adx++23x0UXXRQRy6YfX7p0aVxxxRWx33775bRAAN/sADQcfR4AwDLrFJpdccUV0b9//3jppZfi66+/jrPOOivefvvt+Oyzz+KZZ57JdY0AEeGbHYCGoM8DAFhmnUKzHXfcMd5999247rrron379rFgwYL4yU9+EsOHD4/S0tJc1wgAQAPR5wGsvVxef9e1d6HxWKfQLCKipKQkfv/73+eyFgAAGgF9HsCay/X1d117FxqPdQrNxo0bF+3atYtDDz201vK77747Fi1aFMOGDctJcQAANCx9HpDNLOarlsvr77r2LjQu6xSajRkzJm666aYVlnfu3DmOP/54zRQAQBOlzwO+zSzma871d6H5WafQrKKiIrp167bC8q222ioqKirWuygAAPJDnwd8m1nMgQ3ZOoVmnTt3jjfeeCPKy8trLX/99ddj0003zUVdAADkgT4PqIujqIAN0TqFZkcccUSccsop0b59+9h7770jIuLJJ5+MU089NQ4//PCcFggAQMPR50HTY+bG5s915SA/1ik0u+iii2L27NnRv3//2GijZUMsXbo0jjrqqLj00ktzWiBAc6GhBZoCfR40LWZubP5cVw7yZ61Ds5RSzJs3L8aPHx8XX3xxvPbaa1FUVBQ77bRTbLXVVvVRI0CTp6EFmgJ9HjQ9Zm5s/lxXDvJnnUKzbbfdNt5+++3o3r17dO/evT7qAmhWNLRAU6DPg6bLNceaP/sYGt5ah2YtWrSI7t27x6effqqRAlhLmh2gMdPnAQD8Py3WZaXLLrsszjzzzHjrrbdyXQ8AAHmkzwMAWGadJgI46qijYtGiRbHLLrtEq1atoqioqNbjn332WU6KAwCgYenzIPdMBgTQNK1TaDZ27NgclwEAQGOgz4PcMhkQQNO1TqHZsGHDcl0HAACNgD4PcstkQABN1zqFZhER77//fowbNy7ef//9+MMf/hCdO3eOhx56KMrKymKHHXbIZY0AADQgfR7knsmAAJqedQrNnnzyyRg0aFD069cvpkyZEpdcckl07tw5Xn/99fjLX/4S//jHP3JdJwAADUCfB7DhyeV19yJce4/mY51Cs3POOScuvvjiGDFiRLRv3z6zfP/994/rrrsuZ8UBANCw9HkAG5ZcX3cvwrX3aD7WKTR78803Y8KECSss79y5c07TaQAAGpY+D2DDksvr7kW49h7NyzqFZptssknMnTs3unXrVmv5q6++Gt/5zndyUhgAAA1PnwewYXLdPVhRi3VZ6fDDD4+zzz475s2bFwUFBbF06dJ45pln4owzzoijjjoq1zUCANBA9HkAAMusU2h26aWXRq9evaKsrCwWLFgQ22+/fey9997Rt2/fOPfcc3NdIwAADUSfBwCwzFqdnrl06dK48sor4/7774+vv/46jjzyyPjpT38aCxYsiN122y26d+9eX3UC1LtczhpkxiCgqdHnAQDUtlah2SWXXBIXXHBBDBgwIIqKimLChAmRUopbb721vuoDaBC5njXIjEFAU6PPAwCoba1Cs9tvvz3+9Kc/xW9+85uIiHj88cfjwAMPjD//+c/RosU6nekJ0CjkctYgMwYBTZE+DwCgtrUKzSoqKuLHP/5x5v6AAQOioKAg/vWvf8WWW26Z8+IAGppZg4ANlT4PAKC2tfracPHixdG6detayzbeeOP45ptvcloUAAANS58HAFDbWh1pllKKo48+OgoLCzPLvvrqqzjhhBOibdu2mWX33ntv7ioEAKDe6fMAAGpbq9Bs2LBhKyz75S9/mbNiAADID31e82V2aABYN2sVmo0bN66+6gAAII/0ec2T2aEBYN2tVWgGAAA0HWaHpinI5dGQEXUfEemIS2BdCM0AaDQ0tAD1w+zQNFa5PhoyYsUjIh1xCawroRkAjYKGFgA2PLk8GjKi7iMiHXEJrCuhGQCNgoYWADZcDXE0pCMugbUlNAOgUdHQAgAAjUGLfBcAAAAAAI2N0AwAAAAAsgjNAAAAACCLa5oBAJB3l112WYwcOTJOPfXUGDt2bL7LyaioqIjKysqcjNWpU6dmOTmJ1wiA5kpoBgBAXr344otx0003xc4775zvUmqpqKiInj17RXX1opyMV1TUJqZPf6dZhUJeIwCaM6EZDcI3kFD//J4BTdGCBQti6NChccstt8TFF1+c73JqqaysjOrqRdH72FFRXFq+XmNVzZ0dU28dHZWVlc3q/1evEQDNmdCMeucbSKh/fs+Apmr48OFx4IEHxoABA1YbmtXU1ERNTU3mflVVVX2XFxERxaXl0bGsR72MncsvPCLy96VHfb5GAJAvQjPqnW8gNwyOcsovv2dAU3TnnXfGK6+8Ei+++OIaPX/MmDExevToeq6q4eT6C48IX3oAQC4JzWgwvoFsvhzl1Hj4PQOaijlz5sSpp54ajz32WLRu3XqN1hk5cmSMGDEic7+qqiq6du1aXyXWu1x+4RHhSw8AyDWhWRPQXA7br0+OcsovRzkBsLZefvnl+OSTT2L33XfPLFuyZElMmTIlrrvuuqipqYmWLVvWWqewsDAKCwsbutR65wsPAGichGaNnMP2V89RTo2Hph+ANdW/f/948803ay075phjomfPnnH22WevEJgBADQ0oVkj57D91XOUEwA0Pe3bt48dd9yx1rK2bdvGpptuusJyAIB8EJo1EY7gWT2vEQAAAJArLfJdwNq47LLLoqCgIE477bR8lwIAQI5Nnjw5xo4dm+8yAAAiogmFZi+++GLcdNNNsfPOO+e7FAAAAACauSYRmi1YsCCGDh0at9xyS3To0CHf5QAAAADQzDWJa5oNHz48DjzwwBgwYEBcfPHFq3xuTU1N1NTUZO5XVVXVd3mw3ioqKqKysjJn43Xq1MlEBgAAALAeGn1oduedd8Yrr7wSL7744ho9f8yYMTF69Oh6rgpyp6KiInr27BXV1YtyNmZRUZuYPv0dwRkAAACso0Ydms2ZMydOPfXUeOyxx6J169ZrtM7IkSNjxIgRmftVVVXRtWvX+ioR1ltlZWVUVy+K3seOiuLS8vUer2ru7Jh66+iorKwUmgEAAMA6atSh2csvvxyffPJJ7L777pllS5YsiSlTpsR1110XNTU10bJly1rrFBYWRmFhYUOXCuutuLQ8Opb1yHcZAAAAQDTy0Kx///7x5ptv1lp2zDHHRM+ePePss89eITDLl1xej8q1qAAAAADyr1GHZu3bt48dd9yx1rK2bdvGpptuusLyfMn19ahciwoAAAAg/xp1aNYU5PJ6VK5FBQAAANA4NLnQbPLkyfkuoU6uRwUAAADQfLTIdwEAAAAA0NgIzQAAAAAgS5M7PRPyIZczpEaYJRUAAAAaO6EZrEauZ0iNMEsqAAAANHZCM1iNXM6QGmGWVAAAAGgKhGY49XANmSEVAAAANhxCsw2cUw8BAAAAViQ028A59RAAAABgRUIzIsKphwAANE65vJRIc72MCAD1Q2gGAAA0Srm+lIjLiACwNoRmAABAo5TLS4m4jAgAa0toBgAANGouJQJAPgjNAACADVYur5kW4bppAM2J0AwAANgg5fqaaRGumwbQnAjNAACADVIur5kW4bppAM2N0AwAANiguWYaAHVpke8CAAAAAKCxEZoBAAAAQBahGQAAAABkEZoBAAAAQBYTAcAGoqKiIiorK3MyVqdOncwIBQAAQLMmNIMNQEVFRfTs2SuqqxflZLyiojYxffo7gjMAAACaLaEZbAAqKyujunpR9D52VBSXlq/XWFVzZ8fUW0dHZWWl0AwAAIBmS2gGG5Di0vLoWNYj32UAAABAo2ciAAAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAAAADIIjQDAAAAgCxCMwAA8mLMmDGx5557Rvv27aNz585xyCGHxIwZM/JdFgBARAjNAADIkyeffDKGDx8ezz//fDz22GPxzTffxAEHHBALFy7Md2kAALFRvgsAAGDD9PDDD9e6P378+OjcuXO8/PLLsffee+epKgCAZYRmAAA0CvPnz4+IiI4dO9b5eE1NTdTU1GTuV1VVNUhdAMCGyemZAADk3dKlS+O0006Lfv36xY477ljnc8aMGRMlJSWZW9euXRu4SgBgQ9KoQzMXhwUA2DAMHz483nrrrbjzzjtX+pyRI0fG/PnzM7c5c+Y0YIUAwIamUYdmLg4LAND8nXTSSfHAAw/EE088EVtuueVKn1dYWBjFxcW1bgAA9aVRX9PMxWEBAJqvlFKcfPLJMXHixJg8eXJ069Yt3yUBAGQ06tAs2+ouDhvhArEAAE3F8OHDY8KECfFf//Vf0b59+5g3b15ERJSUlERRUVGeqwMANnSN+vTMb1uTi8NGuEAsAEBTccMNN8T8+fNj3333jdLS0sztrrvuyndpAABN50iz5ReHffrpp1f5vJEjR8aIESMy96uqqgRnAACNUEop3yUAAKxUkwjNll8cdsqUKau8OGzEsgvEFhYWNlBlAAAAADRHjTo0c3FYAAAAAPKhUYdmLg4LAAAAQD406okAXBwWAAAAgHxo1EeauTgsAAAAAPnQqI80AwAAAIB8EJoBAAAAQBahGQAAAABkEZoBAAAAQBahGQAAAABkEZoBAAAAQBahGQAAAABkEZoBAAAAQBahGQAAAABkEZoBAAAAQBahGQAAAABkEZoBAAAAQBahGQAAAP9fe3cfV2V9P378Dcg5HBEEpuhxEuQdoJPwJhVvopLUVWit1JUZmmlNMidpduPEWTksSzfHatFStyydIa0ZOW8SNTVtombBcAiiNdBKTFFCgffvD3+cr+dwuFW4AF/Px+N6PDzXzefzvq73hbwf73M4FwDAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMABTTMAAAAAAADAAU0zAAAAAAAAwAFNMwAAAAAAAMBBs2iaJSYmSlBQkHh4eMjAgQNl3759RocEAACAa4A6DwAANFVNvmm2du1aiYuLk/j4eElPT5ebbrpJRo4cKadOnTI6NAAAAFwF6jwAANCUNfmm2WuvvSZTp06VyZMnS8+ePeWNN96Q1q1by9tvv210aAAAALgK1HkAAKApa2V0ANW5ePGi7N+/X5599lnbOldXV4mKipI9e/Y4PaakpERKSkpsr3/44QcRETl79myDxFhUVCQiIqfzsqS0pPiqxjpbcNw2ZkW813J8Z3M09PgV/75WczT38Z3NQQ4ad/zGmIPxjZ+juY/vbA5yUPP411LFmKp6zcfGZdR5/N/a0sZvjDma+/jO5iAHjTu+sznIQeOO72wOclDz+NdSneo8bcK++eYbFRHdvXu33fo5c+bogAEDnB4THx+vIsLCwsLCwsLCctXLiRMnGqPkuS5R57GwsLCwsLAYudSmzmvSnzSrj2effVbi4uJsr8vLy+X06dPyk5/8RFxcXK7JHGfPnpWAgAA5ceKEeHt7X5MxUTfkwHjkwHjkoGkgD8ZriByoqpw7d046dep0TcbDtUGdd30gB8YjB00DeTAeOTCe0XVek26atWvXTtzc3OTkyZN260+ePCkdO3Z0eozZbBaz2Wy3zsfHp0Hi8/b25gfHYOTAeOTAeOSgaSAPxrvWOWjbtu01GwuVUeehJuTAeOSgaSAPxiMHxjOqzmvSDwIwmUzSr18/2bp1q21deXm5bN26VSIiIgyMDAAAAFeDOg8AADR1TfqTZiIicXFxEhMTI/3795cBAwbIsmXL5Pz58zJ58mSjQwMAAMBVoM4DAABNWZNvmo0fP16+/fZbmT9/vhQUFEh4eLhs3LhROnToYFhMZrNZ4uPjK/15ABoPOTAeOTAeOWgayIPxyEHzRZ0HZ8iB8chB00AejEcOjGd0DlxUeZY6AAAAAAAAcKUm/Z1mAAAAAAAAgBFomgEAAAAAAAAOaJoBAAAAAAAADmiaAQAAAAAAAA5omlUhMTFRgoKCxMPDQwYOHCj79u2rdv9169ZJSEiIeHh4SO/evSU1NbWRIm256pKDpKQkGTZsmPj6+oqvr69ERUXVmDPUrK4/BxXWrFkjLi4ucs899zRsgNeBuubgzJkzEhsbK1arVcxms/To0YP/j65SXXOwbNkyCQ4OFovFIgEBATJr1iz58ccfGynalmfHjh0SHR0tnTp1EhcXF/nggw9qPCYtLU369u0rZrNZunXrJitXrmzwONG8UOcZjzrPeNR5xqPOaxqo9YzTLOo8RSVr1qxRk8mkb7/9tn711Vc6depU9fHx0ZMnTzrdf9euXerm5qYvv/yyZmRk6Lx589Td3V0PHz7cyJG3HHXNwYMPPqiJiYl64MABzczM1EmTJmnbtm3166+/buTIW4665qBCbm6u/vSnP9Vhw4bpmDFjGifYFqquOSgpKdH+/fvrnXfeqZ9++qnm5uZqWlqaHjx4sJEjbznqmoPVq1er2WzW1atXa25urv7rX/9Sq9Wqs2bNauTIW47U1FR9/vnndf369SoimpKSUu3+OTk52rp1a42Li9OMjAxdvny5urm56caNGxsnYDR51HnGo84zHnWe8ajzmgZqPWM1hzqPppkTAwYM0NjYWNvrsrIy7dSpk/7ud79zuv+4ceP0rrvusls3cOBAfeyxxxo0zpasrjlwVFpaql5eXrpq1aqGCrHFq08OSktLdfDgwfrWW29pTEwMxdRVqmsOXn/9de3SpYtevHixsUJs8eqag9jYWL399tvt1sXFxemQIUMaNM7rRW2Kqaefflp79eplt278+PE6cuTIBowMzQl1nvGo84xHnWc86rymgVqv6WiqdR5/nung4sWLsn//fomKirKtc3V1laioKNmzZ4/TY/bs2WO3v4jIyJEjq9wf1atPDhxduHBBLl26JH5+fg0VZotW3xwsXLhQ/P39ZcqUKY0RZotWnxx8+OGHEhERIbGxsdKhQwf52c9+JosWLZKysrLGCrtFqU8OBg8eLPv377d9rD8nJ0dSU1PlzjvvbJSYwe9kVI86z3jUecajzjMedV7TQK3X/BjxO7lVg43cTH333XdSVlYmHTp0sFvfoUMH+c9//uP0mIKCAqf7FxQUNFicLVl9cuBo7ty50qlTp0o/UKid+uTg008/lb/85S9y8ODBRoiw5atPDnJycuSTTz6RCRMmSGpqqmRnZ8v06dPl0qVLEh8f3xhhtyj1ycGDDz4o3333nQwdOlRUVUpLS+Xxxx+X5557rjFChlT9O/ns2bNSXFwsFovFoMjQFFDnGY86z3jUecajzmsaqPWaHyPqPD5phhYnISFB1qxZIykpKeLh4WF0ONeFc+fOycSJEyUpKUnatWtndDjXrfLycvH395c333xT+vXrJ+PHj5fnn39e3njjDaNDu26kpaXJokWL5E9/+pOkp6fL+vXr5aOPPpIXXnjB6NAAoEWgzmt81HlNA3Ve00Ctd/3hk2YO2rVrJ25ubnLy5Em79SdPnpSOHTs6PaZjx4512h/Vq08OKixZskQSEhJky5YtEhYW1pBhtmh1zcHRo0fl2LFjEh0dbVtXXl4uIiKtWrWSrKws6dq1a8MG3cLU5+fAarWKu7u7uLm52daFhoZKQUGBXLx4UUwmU4PG3NLUJwe/+c1vZOLEifLoo4+KiEjv3r3l/PnzMm3aNHn++efF1ZX3qhpaVb+Tvb29+ZQZqPOaAOo841HnGY86r2mg1mt+jKjzyKgDk8kk/fr1k61bt9rWlZeXy9atWyUiIsLpMREREXb7i4hs3ry5yv1RvfrkQETk5ZdflhdeeEE2btwo/fv3b4xQW6y65iAkJEQOHz4sBw8etC2jR4+W2267TQ4ePCgBAQGNGX6LUJ+fgyFDhkh2dratkBUROXLkiFitVgqpeqhPDi5cuFCpWKooblW14YKFDb+TUR3qPONR5xmPOs941HlNA7Ve82PI7+QGe8RAM7ZmzRo1m826cuVKzcjI0GnTpqmPj48WFBSoqurEiRP1mWeese2/a9cubdWqlS5ZskQzMzM1Pj6eR5FfpbrmICEhQU0mk77//vuan59vW86dO2fUKTR7dc2BI56qdPXqmoPjx4+rl5eXPvHEE5qVlaUbNmxQf39/ffHFF406hWavrjmIj49XLy8vfe+99zQnJ0c3bdqkXbt21XHjxhl1Cs3euXPn9MCBA3rgwAEVEX3ttdf0wIEDmpeXp6qqzzzzjE6cONG2f8WjyOfMmaOZmZmamJjY4I8iR/NCnWc86jzjUecZjzqvaaDWM1ZzqPNomlVh+fLlesMNN6jJZNIBAwboZ599ZtsWGRmpMTExdvv//e9/1x49eqjJZNJevXrpRx991MgRtzx1yUFgYKCKSKUlPj6+8QNvQer6c3Aliqlro6452L17tw4cOFDNZrN26dJFX3rpJS0tLW3kqFuWuuTg0qVLumDBAu3atat6eHhoQECATp8+XQsLCxs/8BZi27ZtTv9/r7juMTExGhkZWemY8PBwNZlM2qVLF12xYkWjx42mjTrPeNR5xqPOMx51XtNArWec5lDnuajyGUIAAAAAAADgSnynGQAAAAAAAOCAphkAAAAAAADggKYZAAAAAAAA4ICmGQAAAAAAAOCAphkAAAAAAADggKYZAAAAAAAA4ICmGQAAAAAAAOCAphnQwkyaNEnuueeeeh2blpYmLi4ucubMmWsa0/UmKChIli1b1ihzTZw4URYtWtQoczWEBQsWSHh4uNFh1NqgQYMkOTnZ6DAAAKiEGtB41IDNwzPPPCMzZswwOgw0EzTNADSYY8eOiYuLixw8ePCajuvi4iIffPDBNR2zOTp06JCkpqbKk08+2ajzNub1b4givqr7MikpSYYNGya+vr7i6+srUVFRsm/fPrt95s2bJ88884yUl5dfs3gAAGhpqAEblrMasCEadrfeeqv8+te/rrT+ySeflH79+onZbK71m58NkTtn55yWliZjxowRq9Uqnp6eEh4eLqtXr7bbZ/bs2bJq1SrJycm5pvGgZaJphuvSxYsXjQ4BuGrLly+XsWPHSps2bYwOpZLm+DOWlpYmDzzwgGzbtk327NkjAQEBMmLECPnmm29s+/z85z+Xc+fOyccff2xgpACA+mqOv58AR02hBnzkkUdk/Pjxhs1fld27d0tYWJgkJyfLF198IZMnT5aHH35YNmzYYNunXbt2MnLkSHn99dcNjBTNhgLNXGRkpMbGxmpsbKx6e3vrT37yE503b56Wl5fb9gkMDNSFCxfqxIkT1cvLS2NiYlRVdefOnTp06FD18PDQzp0764wZM7SoqKhW8yYmJmq3bt3UbDarv7+/3nfffXWK6ccff9SnnnpKO3XqpK1bt9YBAwbotm3bbNtXrFihbdu21Y0bN2pISIh6enrqyJEj9X//+59tn9LSUp01a5a2bdtW/fz8dM6cOfrwww/rmDFjbPusW7dOf/azn6mHh4f6+fnp8OHDqzzHbdu2qYhoYWGhqqqeP39eR40apYMHD9bCwkItKSnR2NhY7dixo5rNZr3hhht00aJFVV4jEbFbIiMjbduSkpI0JCREzWazBgcHa2Jiom1bdfMEBgbajRkYGOh07tzcXBURfe+99zQiIkLNZrP26tVL09LSqoz3z3/+s1qtVi0rK7NbP3r0aJ08ebKqqmZnZ+vo0aPV399fPT09tX///rp582a7/QMDA3Xp0qV2cRw4cMC2vbCwUEXELt+HDx/WUaNGqaenp/r7++tDDz2k3377bZWxlpaWatu2bXXDhg1263/88Ud9+umntXPnzmoymbRr16761ltv2banpaXpzTffrCaTSTt27Khz587VS5cu2bZHRkbqjBkzdM6cOerr66sdOnTQ+Ph4u3Nzdv3j4+P1pptu0qSkJA0KClIXFxdVVc3Ly9PRo0erp6enenl56dixY7WgoMA2XsVxzlRcuyuXip/dsrIyXbRokQYFBamHh4eGhYXpunXrbMeePn1aH3zwQW3Xrp16eHhot27d9O2331bV6u9Lx2vs5eWlq1atsls/efJkfeihh5weAwBoPNSA1IDOXI81YGRkZKVrXqGme72q+zkmJqbSmLm5uXaxVFfHOV6XqnL3wQcfaJ8+fdRsNuuNN96oCxYssNWm5eXlGh8frwEBAWoymdRqteqMGTNqPGdHd955py2PFVatWqWdO3euMXaAphmavcjISG3Tpo3OnDlT//Of/+g777yjrVu31jfffNO2T2BgoHp7e+uSJUs0Ozvbtnh6eurSpUv1yJEjumvXLu3Tp49OmjSpxjk///xzdXNz03fffVePHTum6enp+vvf/75OMT366KM6ePBg3bFjh2ZnZ+srr7yiZrNZjxw5oqqXCyZ3d3eNiorSzz//XPfv36+hoaH64IMP2sZYvHix+vr6anJysmZkZOiUKVPUy8vLVjD973//01atWulrr72mubm5+sUXX2hiYqKeO3fO6XldWTAVFhbq4MGDdcSIEXr+/HlVVX3llVc0ICBAd+zYoceOHdOdO3fqu+++W+V12rdvn4qIbtmyRfPz8/X7779XVdV33nlHrVarJicna05OjiYnJ6ufn5+uXLmyxnlOnTqlIqIrVqzQ/Px8PXXqlNO5KwqVzp076/vvv68ZGRn66KOPqpeXl3733XdOjzl9+rSaTCbdsmWLbd33339vt+7gwYP6xhtv6OHDh/XIkSM6b9489fDw0Ly8PNsxdS2YCgsLtX379vrss89qZmampqen6x133KG33XZbldc2PT1dRcSuAaWqOm7cOA0ICND169fr0aNHdcuWLbpmzRpVVf3666+1devWOn36dM3MzNSUlBRt166dXVMsMjJSvb29dcGCBXrkyBFdtWqVuri46KZNm6q9/vHx8erp6amjRo3S9PR0PXTokJaVlWl4eLgOHTpU//3vf+tnn32m/fr1syucqyu2SktLNTk5WUVEs7KyND8/X8+cOaOqqi+++KKGhIToxo0b9ejRo7pixQo1m822gjg2NlbDw8P1888/19zcXN28ebN++OGHqlr1feno7Nmz6uHhof/85z/t1r/++utVFuoAgMZDDUgN6Mz1WAN+//332rlzZ124cKHm5+drfn6+qmqN93p19/OZM2c0IiJCp06dahuztLTULpbaNs2qyt2OHTvU29tbV65cqUePHtVNmzZpUFCQLliwQFUvN369vb01NTVV8/LydO/evbafparO2ZkhQ4boU089ZbcuMzPTaSMQcETTDM1eZGSkhoaG2r2DN3fuXA0NDbW9DgwM1HvuucfuuClTpui0adPs1u3cuVNdXV21uLi42jmTk5PV29tbz549W6+Y8vLy1M3NTb/55hu744YPH67PPvusql4umEREs7OzbdsTExO1Q4cOttdWq1Vffvll2+tLly5p586dbQXT/v37VUT02LFj1Z5PhYqCKTMzU8PCwvS+++7TkpIS2/YZM2bo7bffbnde1XFWLKiqdu3atVKh9cILL2hERESt5hERTUlJqdXcCQkJtnUV12fx4sVVHjdmzBh95JFHbK///Oc/a6dOnSq983ilXr166fLly22v61owvfDCCzpixAi7MU+cOGFrFjmTkpKibm5udtcoKytLRaTSu54VnnvuOQ0ODrY7JjExUdu0aWM7v8jISB06dKjdcTfffLPOnTvX9trZ9Y+Pj1d3d3e7AnbTpk3q5uamx48ft6376quvVER03759tuOqK7Yc3/lWvfwOfevWrXX37t12+06ZMkUfeOABVVWNjo6u9I5iharuS0e/+tWvtEuXLpX+P/jHP/6hrq6u1d4TAICGRw1IDVjd3NdTDeg4d4Wa7vXa3M8zZ86s4uxr3zRTdZ674cOHV/rE4t/+9je1Wq2qqvrqq69qjx499OLFi07HdHbOjtauXasmk0m//PJLu/U//PCDiki1n0AEVFX5TjO0CIMGDRIXFxfb64iICPnvf/8rZWVltnX9+/e3O+bQoUOycuVKadOmjW0ZOXKklJeXS25ubrXz3XHHHRIYGChdunSRiRMnyurVq+XChQu1junw4cNSVlYmPXr0sJt/+/btcvToUdsxrVu3lq5du9peW61WOXXqlIiI/PDDD5Kfny8DBw60bW/VqpXded50000yfPhw6d27t4wdO1aSkpKksLCw2nOrOL9u3brJ2rVrxWQy2dZPmjRJDh48KMHBwfLkk0/Kpk2bbNsef/xxu3Opyvnz5+Xo0aMyZcoUu/1ffPFF27lXN09dRURE2P5dcX0yMzNFRKRXr162+X/+85+LiMiECRMkOTlZSkpKRERk9erV8stf/lJcXS//d1lUVCSzZ8+W0NBQ8fHxkTZt2khmZqYcP3683jEeOnRItm3bZnc9QkJCRETs7ocrFRcXi9lstrvHDh48KG5ubhIZGen0mMzMTImIiLA7ZsiQIVJUVCRff/21bV1YWJjdcVfed9UJDAyU9u3b280XEBAgAQEBtnU9e/YUHx8fWw7qIzs7Wy5cuCB33HGH3TX761//artev/rVr2TNmjUSHh4uTz/9tOzevbtOcyQkJMiaNWskJSVFPDw87LZZLBYpLy+33SMAAONQA15GDVjZ9VQDVjd+dfd6be7nhnTo0CFZuHChXXxTp06V/Px8uXDhgowdO1aKi4ulS5cuMnXqVElJSZHS0tJaj79t2zaZPHmyJCUlSa9evey2WSwWEZFGPV80T62MDgBoLJ6ennavi4qK5LHHHnP65MEbbrih2rG8vLwkPT1d0tLSZNOmTTJ//nxZsGCBfP755+Lj41NjLEVFReLm5ib79+8XNzc3u21XFhvu7u5221xcXERVaxy/gpubm2zevFl2794tmzZtkuXLl8vzzz8ve/fulRtvvLHK4+666y5JTk6WjIwM6d27t2193759JTc3Vz7++GPZsmWLjBs3TqKiouT999+XhQsXyuzZs2uMqaioSEQuP6nwymKvIt6a5rmWUlNT5dKlSyLyf784o6OjRVXlo48+kptvvll27twpS5cutR0ze/Zs2bx5syxZskS6desmFotF7r///iq/WLii0LoybxVzVigqKpLo6GhZvHhxpeOtVqvTcdu1aycXLlyQixcv2orainO4Ws7uu9o8LdLxZ6yhVNxDH330kfz0pz+122Y2m0Xk8hf25+XlSWpqqmzevFmGDx8usbGxsmTJkhrHX7JkiSQkJMiWLVsqNRBFRE6fPi2enp7X7HoDABoWNSA1oKOWVgNWpaZ73WQyXdX9fLWKiorkt7/9rfziF7+otM3Dw0MCAgIkKytLtmzZIps3b5bp06fLK6+8Itu3b6/0M+Jo+/btEh0dLUuXLpWHH3640vbTp0+LiNi94Qs4Q9MMLcLevXvtXn/22WfSvXv3SsXIlfr27SsZGRnSrVu3es3ZqlUriYqKkqioKImPjxcfHx/55JNPbP/pVxdTnz59pKysTE6dOiXDhg2r1/xt27YVq9Uqe/fulVtuuUVEREpLS2X//v3St29f234uLi4yZMgQGTJkiMyfP18CAwMlJSVF4uLiqhw7ISFB2rRpI8OHD5e0tDTp2bOnbZu3t7eMHz9exo8fL/fff7+MGjVKTp8+Lf7+/uLv7283TsUv8ivf7e3QoYN06tRJcnJyZMKECVXGUNU8fn5+4u7ubjdmdT777LNK1+eJJ54QkcufjHLk4eEhv/jFL2T16tWSnZ0twcHBdtdz165dMmnSJLn33ntF5PIv+2PHjlU5f8Uv4vz8fOnTp4+ISKXHr/ft21eSk5MlKChIWrWq3X/LFY/3zsjIsP27d+/eUl5eLtu3b5eoqKhKx4SGhkpycrKoqu3dyV27domXl5d07ty5VvOKSK2vf2hoqJw4cUJOnDhh+7RZRkaGnDlzxu6eqo6ze6hnz55iNpvl+PHjVX6qTuTytY+JiZGYmBgZNmyYzJkzR5YsWeJ0zAovv/yyvPTSS/Kvf/2r0icTKnz55Ze2XAIAjEUNSA1YleupBhS5fM0dr01t7vXq7mdnY9aXs9z17dtXsrKyqo3PYrFIdHS0REdHS2xsrISEhMjhw4elb9++VcaXlpYmd999tyxevFimTZvmdNwvv/xS3N3dK30CDXBE0wwtwvHjxyUuLk4ee+wxSU9Pl+XLl8urr75a7TFz586VQYMGyRNPPCGPPvqoeHp6SkZGhmzevFn++Mc/Vnvshg0bJCcnR2655Rbx9fWV1NRUKS8vl+Dg4FrF1KNHD5kwYYI8/PDD8uqrr0qfPn3k22+/la1bt0pYWJjcddddtTrvmTNnSkJCgnTv3l1CQkLktddekzNnzti27927V7Zu3SojRowQf39/2bt3r3z77bcSGhpa49hLliyRsrIyuf322yUtLc02vtVqlT59+oirq6usW7dOOnbsWOU7Uf7+/mKxWGTjxo3SuXNn8fDwkLZt28pvf/tbefLJJ6Vt27YyatQoKSkpkX//+99SWFgocXFxNc4TFBQkW7dulSFDhojZbBZfX98qzyMxMVG6d+8uoaGhsnTpUiksLJRHHnmk2nOfMGGC3H333fLVV1/JQw89ZLete/fusn79eomOjhYXFxf5zW9+U+2nsCwWiwwaNEgSEhLkxhtvlFOnTsm8efPs9omNjZWkpCR54IEH5OmnnxY/Pz/Jzs6WNWvWyFtvveW08G/fvr307dtXPv30U1vBFBQUJDExMfLII4/IH/7wB7npppskLy9PTp06JePGjZPp06fLsmXLZMaMGfLEE09IVlaWxMfHS1xcnO3d0Nqo7fWPioqS3r17y4QJE2TZsmVSWloq06dPl8jIyCobUo4CAwPFxcVFNmzYIHfeeadYLBbx8vKS2bNny6xZs6S8vFyGDh0qP/zwg+zatUu8vb0lJiZG5s+fL/369ZNevXpJSUmJbNiwwXbfV3VfLl68WObPny/vvvuuBAUFSUFBgYhIpT832blzp4wYMaLW1wsA0HCoAakBq3I91YAV12bHjh3yy1/+Usxms7Rr167Ge72m+zkoKEj27t0rx44dkzZt2oifn5+4urpKdna2FBUVSUFBgRQXF9uagT179qzy02/Ocjd//ny5++675YYbbpD7779fXF1d5dChQ/Lll1/Kiy++KCtXrpSysjIZOHCgtG7dWt555x2xWCy2pqezc962bZvcfffdMnPmTLnvvvts9ZzJZBI/Pz9bPDt37pRhw4bxlwOomYHfpwZcE5GRkTp9+nR9/PHH1dvbW319ffW5556r9LhxZ18SuW/fPr3jjju0TZs26unpqWFhYfrSSy/VOOfOnTs1MjJSfX191WKxaFhYmK5du7ZOMV28eFHnz5+vQUFB6u7urlarVe+991794osvVPX/Hjd+pZSUFLvHKV+6dElnzpyp3t7e6uPjo3FxcXaPG8/IyNCRI0dq+/bt1Ww2a48ePey+rNSRsy9dnzFjhlqtVs3KytI333xTw8PD1dPTU729vXX48OGanp5e7bVKSkrSgIAAdXV1tXtq4urVqzU8PFxNJpP6+vrqLbfcouvXr1dVrXGeDz/8ULt166atWrWq8XHj7777rg4YMEBNJpP27NlTP/nkk2rjVVUtKytTq9WqIqJHjx6tNO5tt92mFotFAwIC9I9//GOlL0l1vN8yMjI0IiJCLRaLhoeH66ZNmyo9bvzIkSN67733qo+Pj1osFg0JCdFf//rX1X7h7p/+9CcdNGiQ3bri4mKdNWuWWq1WNZlM2q1bN3377bdt29PS0vTmm29Wk8mkHTt21Llz59oe663q/Atfx4wZozExMbbXzq5/VV8Em5eXp6NHj1ZPT0/18vLSsWPH2j3tyfG4invwyicZLVy4UDt27KguLi62OMrLy3XZsmUaHBys7u7u2r59ex05cqRu375dVS9/sW5oaKhaLBb18/PTMWPGaE5Ojm1MZ/el4+PQK5Yrny769ddfq7u7u544caLSuQIAGhc1IDWgM9drDbhnzx4NCwtTs9lsd69Ud6/XdD9nZWXpoEGD1GKx2NVnkZGRTmumK+s3+f9Py6xQVe42btyogwcPVovFot7e3jpgwADbEzJTUlJ04MCB6u3trZ6enjpo0CC7J5w6O+eYmBinsV15D6qqBgcH63vvvVflNQYquKjW4Y/jgSbo1ltvlfDwcFm2bJnRodg0xZiuN8eOHZMbb7xRDhw4YPcuXEtSXFwswcHBsnbtWrsvu23OVqxYIYsWLZKMjIwav6vCCHPnzpXCwkJ58803jQ4FAK57TbHeaooxXW+oAY2Xm5srPXr0kIyMDOnevbvR4VTy8ccfy1NPPSVffPFFrf8sFtcv7hAAaKYsFov89a9/le+++87oUK6Z1NRUWbRoUZNsmIlc/nOT6r4LBgAAoKE19RowNTVVpk2b1iQbZiKXn+S6YsUKGmaoFe4SwImdO3faHj/tTMXTfwCj3XrrrUaHcE2tW7fO6BCq9dRTTxkdAgCgAVEDorloyjVgbGys0SFU6/777zc6BDQj/Hkm4ERxcbF88803VW6v79OWAAAA0HRRAwIArkTTDAAAAAAAAHDganQAAAAAAAAAQFND0wwAAAAAAABwQNMMAAAAAAAAcEDTDAAAAAAAAHBA0wwAAAAAAABwQNMMAAAAAAAAcEDTDAAAAAAAAHBA0wwAAAAAAABw8P8AW6eCDixV63IAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "target = ['post_spends', 'pre_spends']\n", + "# Dict \n", + "# key - group name\n", + "# value - share\n", + "# The sum of the shares is 1\n", + "groups = {'test1': 0.3, 'test2': 0.2, 'control': 0.5 }\n", + "\n", + "aa_test = AATest()\n", + "results = aa_test.process_split(df=some_large_dataframe, target_fields=target, groups=groups)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9d383167-895a-4f1c-ad49-8d679d230dbc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['best metric', 'best split', 'all metrics', 'all splits', 'best split DataFrame', 'get_resume'])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.keys()" + ] + }, + { + "cell_type": "markdown", + "id": "22700a2c-3d1c-4911-9460-6366b7586046", + "metadata": {}, + "source": [ + "`results` is a dictionary with dataframes as values.
\n", + "* 'best split' - result of best separation\n", + "* 'best metric' - metrics of best split \n", + "* 'all metrics' - metrics of all experiments (i.e. of random splits) \n", + "* 'all splists' - results of all random splits \n", + "* 'best split DataFrame' - pandas DataFrame with column 'group' (or defined by 'group_column_name' parameter) contains values defined as keys in 'groups' parameter \n", + "* 'get resume' - function that plots p-values distribution of all experiments\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ea1ebdec-2743-4acc-a8a4-7eb3b17c1179", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "control post_spends mean 451.86\n", + "test1 post_spends mean 451.93\n", + "test2 post_spends mean 451.90\n", + "post_spends mean delta (control - test1) -0.06\n", + "post_spends mean delta% (control - test1)/test1 -0.01\n", + "post_spends t-test p-value (control,test1) 0.83\n", + "post_spends ks-test p-value (control,test1) 0.98\n", + "post_spends mean delta (control - test2) -0.04\n", + "post_spends mean delta% (control - test2)/test2 -0.01\n", + "post_spends t-test p-value (control,test2) 0.91\n", + "post_spends ks-test p-value (control,test2) 1.00\n", + "post_spends mean delta (test1 - test2) 0.02\n", + "post_spends mean delta% (test1 - test2)/test2 0.01\n", + "post_spends t-test p-value (test1,test2) 0.95\n", + "post_spends ks-test p-value (test1,test2) 0.97\n", + "post_spends mean t-test p-value 0.89\n", + "post_spends mean ks-test p-value 0.98\n", + "post_spends t-test passed True\n", + "post_spends ks-test passed True\n", + "control pre_spends mean 487.31\n", + "test1 pre_spends mean 487.35\n", + "test2 pre_spends mean 487.27\n", + "pre_spends mean delta (control - test1) -0.04\n", + "pre_spends mean delta% (control - test1)/test1 -0.01\n", + "pre_spends t-test p-value (control,test1) 0.75\n", + "pre_spends ks-test p-value (control,test1) 0.77\n", + "pre_spends mean delta (control - test2) 0.03\n", + "pre_spends mean delta% (control - test2)/test2 0.01\n", + "pre_spends t-test p-value (control,test2) 0.83\n", + "pre_spends ks-test p-value (control,test2) 0.94\n", + "pre_spends mean delta (test1 - test2) 0.08\n", + "pre_spends mean delta% (test1 - test2)/test2 0.02\n", + "pre_spends t-test p-value (test1,test2) 0.66\n", + "pre_spends ks-test p-value (test1,test2) 0.51\n", + "pre_spends mean t-test p-value 0.83\n", + "pre_spends mean ks-test p-value 0.88\n", + "pre_spends t-test passed True\n", + "pre_spends ks-test passed True\n", + "mean of means t-test p-value 0.86\n", + "mean of means ks-test p-value 0.93\n", + "t-test passed % 100.00\n", + "ks-test passed % 100.00\n", + "mean_test_score 0.91\n", + "experiment_index 909\n", + "Name: 908, dtype: object" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results['best metric']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb0ab274-6385-4052-9e8e-217b116d49e8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/tutorials/Tutorial_14_AB_Test.ipynb b/examples/tutorials/Tutorial_14_AB_Test.ipynb new file mode 100644 index 00000000..689c4246 --- /dev/null +++ b/examples/tutorials/Tutorial_14_AB_Test.ipynb @@ -0,0 +1,498 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "64e2de80", + "metadata": {}, + "source": [ + "# AB test\n" + ] + }, + { + "cell_type": "markdown", + "id": "9f52ff79", + "metadata": {}, + "source": [ + "## 0. Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6c2c62f0", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:22:44.312194100Z", + "start_time": "2024-03-05T13:22:40.794306600Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from lightautoml.addons.hypex import ABTest\n", + "from lightautoml.addons.hypex.utils.tutorial_data_creation import create_test_data\n", + "\n", + "pd.options.display.float_format = '{:,.2f}'.format\n", + "\n", + "np.random.seed(42) # needed to create example data" + ] + }, + { + "cell_type": "markdown", + "id": "2dca3eaa", + "metadata": {}, + "source": [ + "## 1. Create or upload your dataset\n", + "In this case we will create random dataset with known effect size \n", + "If you have your own dataset, go to the part 2 " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7b655d2d", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:23:00.204737400Z", + "start_time": "2024-03-05T13:22:44.315225300Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustry
0000488.00414.44NaNME-commerce
1181512.50462.2226.00NaNE-commerce
2271483.00479.4425.00MLogistics
3300501.50424.3339.00ME-commerce
4411543.00514.5618.00FE-commerce
...........................
99959995101538.50450.4442.00MLogistics
9996999600500.50430.8926.00FLogistics
9997999731473.00534.1122.00FE-commerce
9998999821495.00523.2267.00FE-commerce
9999999971508.00475.8938.00FE-commerce
\n", + "

10000 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 0 0 0 488.00 414.44 NaN M \n", + "1 1 8 1 512.50 462.22 26.00 NaN \n", + "2 2 7 1 483.00 479.44 25.00 M \n", + "3 3 0 0 501.50 424.33 39.00 M \n", + "4 4 1 1 543.00 514.56 18.00 F \n", + "... ... ... ... ... ... ... ... \n", + "9995 9995 10 1 538.50 450.44 42.00 M \n", + "9996 9996 0 0 500.50 430.89 26.00 F \n", + "9997 9997 3 1 473.00 534.11 22.00 F \n", + "9998 9998 2 1 495.00 523.22 67.00 F \n", + "9999 9999 7 1 508.00 475.89 38.00 F \n", + "\n", + " industry \n", + "0 E-commerce \n", + "1 E-commerce \n", + "2 Logistics \n", + "3 E-commerce \n", + "4 E-commerce \n", + "... ... \n", + "9995 Logistics \n", + "9996 Logistics \n", + "9997 E-commerce \n", + "9998 E-commerce \n", + "9999 E-commerce \n", + "\n", + "[10000 rows x 8 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = create_test_data(num_users=10000, rs=52, na_step=10, nan_cols=['age', 'gender'])\n", + "data" + ] + }, + { + "cell_type": "markdown", + "id": "d87c9442", + "metadata": {}, + "source": [ + "## 2. AB-test" + ] + }, + { + "cell_type": "markdown", + "id": "0bb6fece", + "metadata": {}, + "source": [ + "### 2.0 Data\n", + "Let's correct data to see how AB-test works" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6f5a8a1f", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:23:00.294937300Z", + "start_time": "2024-03-05T13:23:00.200596300Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustrygroup
0000488.00414.44NaNME-commercetest
1181512.50462.2226.00NaNE-commercetest
2271483.00479.4425.00MLogisticstest
\n", + "
" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 0 0 0 488.00 414.44 NaN M \n", + "1 1 8 1 512.50 462.22 26.00 NaN \n", + "2 2 7 1 483.00 479.44 25.00 M \n", + "\n", + " industry group \n", + "0 E-commerce test \n", + "1 E-commerce test \n", + "2 Logistics test " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_ab = data.copy()\n", + "\n", + "half_data = int(data.shape[0] / 2)\n", + "data_ab['group'] = ['test'] * half_data + ['control'] * half_data\n", + "data_ab.head(3)" + ] + }, + { + "cell_type": "markdown", + "id": "690ceec5", + "metadata": {}, + "source": [ + "### 3.1 Full AB-test\n", + "\n", + "Full (basic) version of test includes calculation of all available metrics, which are: \"diff in means\", \"diff in diff\" and \"cuped\"
\n", + "Pay attention, that for \"cuped\" and \"diff in diff\" metrics required target before pilot." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4108a137", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:23:00.405321800Z", + "start_time": "2024-03-05T13:23:00.232414600Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'size': {'test': 5000, 'control': 5000},\n", + " 'difference': {'ate': 1.108044444444488,\n", + " 'medain_diff': 0.16666666666668561,\n", + " 'cuped': 0.897496915890514,\n", + " 'diff_in_diff': 0.610344444444479},\n", + " 'p-value': {'t-test': 0.15973563889393272,\n", + " 'mann_whitney': 0.11494755666097989}}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = ABTest()\n", + "results = model.execute(\n", + " data=data_ab,\n", + " target_field='post_spends',\n", + " target_field_before='pre_spends',\n", + " group_field='group'\n", + ")\n", + "results" + ] + }, + { + "cell_type": "markdown", + "id": "ea252142", + "metadata": {}, + "source": [ + "### 2.2 Simple AB-test\n", + "To estimate effect without target data before pilot `calc_difference_method='ate'` can be used - effect will be estimated with \"diff in means\" method" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0ab77779", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:23:31.806379600Z", + "start_time": "2024-03-05T13:23:31.442627700Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'size': {'test': 5000, 'control': 5000},\n", + " 'difference': {'ate': 1.108044444444488},\n", + " 'p-value': {'t-test': 0.15973563889393272,\n", + " 'mann_whitney': 0.11494755666097989}}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = ABTest(calc_difference_method='ate')\n", + "model.execute(data=data_ab, target_field='post_spends', group_field='group')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/tutorials/Tutorial_15_Matching.ipynb b/examples/tutorials/Tutorial_15_Matching.ipynb new file mode 100644 index 00000000..20198896 --- /dev/null +++ b/examples/tutorials/Tutorial_15_Matching.ipynb @@ -0,0 +1,2532 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Matching" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0. Import libraries " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:37:59.927516500Z", + "start_time": "2024-03-05T13:37:55.056727900Z" + } + }, + "outputs": [], + "source": [ + "from lightautoml.addons.hypex import Matcher" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Create or upload your dataset \n", + "In this case we will create random dataset with known effect size \n", + "If you have your own dataset, go to the part 2 \n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:37:59.952659200Z", + "start_time": "2024-03-05T13:37:59.931623200Z" + } + }, + "outputs": [], + "source": [ + "from lightautoml.addons.hypex.utils.tutorial_data_creation import create_test_data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:18.298652Z", + "start_time": "2024-03-05T13:37:59.946835700Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustry
0000504.5422.777778NaNFLogistics
1141500.0506.33333351.0NaNE-commerce
2200485.0434.00000056.0FLogistics
3381452.0468.11111146.0ME-commerce
4400488.5420.11111156.0MLogistics
...........................
9995999521482.0501.66666731.0MLogistics
9996999600453.0406.88888953.0MLogistics
9997999700461.0415.11111152.0FE-commerce
99989998101491.5439.22222222.0ME-commerce
9999999921481.0517.22222253.0ME-commerce
\n", + "

10000 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 0 0 0 504.5 422.777778 NaN F \n", + "1 1 4 1 500.0 506.333333 51.0 NaN \n", + "2 2 0 0 485.0 434.000000 56.0 F \n", + "3 3 8 1 452.0 468.111111 46.0 M \n", + "4 4 0 0 488.5 420.111111 56.0 M \n", + "... ... ... ... ... ... ... ... \n", + "9995 9995 2 1 482.0 501.666667 31.0 M \n", + "9996 9996 0 0 453.0 406.888889 53.0 M \n", + "9997 9997 0 0 461.0 415.111111 52.0 F \n", + "9998 9998 10 1 491.5 439.222222 22.0 M \n", + "9999 9999 2 1 481.0 517.222222 53.0 M \n", + "\n", + " industry \n", + "0 Logistics \n", + "1 E-commerce \n", + "2 Logistics \n", + "3 E-commerce \n", + "4 Logistics \n", + "... ... \n", + "9995 Logistics \n", + "9996 Logistics \n", + "9997 E-commerce \n", + "9998 E-commerce \n", + "9999 E-commerce \n", + "\n", + "[10000 rows x 8 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = create_test_data(num_users=10000, rs=42, na_step=45, nan_cols=['age', 'gender'])\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:18.418303900Z", + "start_time": "2024-03-05T13:38:18.298652Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['user_id', 'signup_month', 'treat', 'pre_spends', 'post_spends', 'age',\n", + " 'gender', 'industry'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:18.477754900Z", + "start_time": "2024-03-05T13:38:18.336440500Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "treat\n", + "0 5002\n", + "1 4998\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['treat'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:18.619416200Z", + "start_time": "2024-03-05T13:38:18.362065400Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "223" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['gender'].isna().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Matching \n", + "### 2.0 Init params\n", + "info_col used to define informative attributes that should not be part of matching, such as user_id \n", + "But to explicitly store this column in the table, so that you can compare directly after computation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:18.634761700Z", + "start_time": "2024-03-05T13:38:18.408068600Z" + } + }, + "outputs": [], + "source": [ + "info_col = ['user_id']\n", + "\n", + "outcome = 'post_spends'\n", + "treatment = 'treat'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Simple matching\n", + "This is the easiest way to initialize and calculate metrics on a Matching task \n", + "Use it when you are clear about each attribute or if you don't have any additional task conditions (Strict equality for certain features) " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:18.660452500Z", + "start_time": "2024-03-05T13:38:18.428454200Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[18.12.2024 22:05:15 | hypex | INFO]: Number of NaN values filled with zeros: 446\n" + ] + } + ], + "source": [ + "# Standard model with base parameters\n", + "model = Matcher(input_data=df, outcome=outcome, treatment=treatment, info_col=info_col,\n", + " algo='fast')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "**Feature selection** models the significance of features for the accuracy of target approximation. However, it does not\n", + "rule out the possibility of overlooked features, the complex impact of features on target description, or the\n", + "significance of features from a business logic perspective. The algorithm will not function correctly if there are data\n", + "leaks. \n", + "Points to consider when selecting features:\n", + "\n", + "* Data leaks - these should not be present.\n", + "* Influence on treatment distribution - features should not affect the treatment distribution.\n", + "* The target should be describable by features.\n", + "* All features significantly affecting the target should be included.\n", + "* The business rationale of features.\n", + "* The feature selection function can be useful for addressing these tasks, but it does not solve them nor does it\n", + " absolve the user of the responsibility for their selection, nor does it justify it.\n", + "\n", + "[Link to ReadTheDocs](https://hypex.readthedocs.io/en/latest/pages/modules/selectors.html#selector-classes)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:28.156006400Z", + "start_time": "2024-03-05T13:38:18.489973100Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tikhomirov/PycharmProjects/Sber_New/LightAutoML/.venv/lib/python3.10/site-packages/hypex/selectors/feature_selector.py:42: UserWarning: FeatureSelector does not rule out the possibility of overlooked features, the complex impact of features on target description, or the significance of features from a business logic perspective.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
rank
signup_month1
pre_spends2
age3
gender_F4
gender_M5
industry_Logistics6
\n", + "
" + ], + "text/plain": [ + " rank\n", + "signup_month 1\n", + "pre_spends 2\n", + "age 3\n", + "gender_F 4\n", + "gender_M 5\n", + "industry_Logistics 6" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selected_features = model.feature_select()\n", + "selected_features" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:28.228174700Z", + "start_time": "2024-03-05T13:38:28.149105600Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['signup_month', 'pre_spends', 'age', 'gender_F'], dtype='object')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chosen_features = selected_features[:4].index\n", + "chosen_features" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:30.559294800Z", + "start_time": "2024-03-05T13:38:28.180655600Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d8df4c3cc13a49da8f0ad88547e701b3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/10000 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
effect_sizestd_errp-valci_lowerci_upperoutcome
ATE82.6367642.3392830.078.05177087.221757post_spends
ATC101.6875664.5714390.092.727546110.647585post_spends
ATT63.5707140.6947260.062.20905164.932378post_spends
\n", + "" + ], + "text/plain": [ + " effect_size std_err p-val ci_lower ci_upper outcome\n", + "ATE 82.636764 2.339283 0.0 78.051770 87.221757 post_spends\n", + "ATC 101.687566 4.571439 0.0 92.727546 110.647585 post_spends\n", + "ATT 63.570714 0.694726 0.0 62.209051 64.932378 post_spends" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "### Variable quality_results contains:\n", + "\n", + "* results of psi test\n", + "* resulnt of Kolmogorov-Smirnov test\n", + "* results of smd\n", + "* number of repeats \n", + "\n", + "**PSI (Population Stability Index)** \n", + "shows the difference between treated and untreated populations\n", + "\n", + "**Rules:**\n", + "\n", + "* PSI < 0.1 - No change. You can continue using existing model.\n", + "* PSI >=0.1 but less than 0.2 - Slight change is required.\n", + "* PSI >=0.2 - Significant change is required. Ideally, you should not use this model any more. \n", + "\n", + "**SMD (Standardized Mean Differences)** \n", + "helps to check if the balance of the groups has been reached\n", + "\n", + "**Rules:**\n", + "\n", + "* Smaller than 0.1. For a randomized trial, the smd between all the covariates should typically fall into this bucket.\n", + "* 0.1 - 0.2. Not necessarily balanced, but small enough that people are usually not too worried about them.\n", + "* 0.2. Values that are greater than this threshold are considered seriously imbalanced.\n", + "\n", + "**Repeats** \n", + "shows the fraction of duplicated indexes" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:30.892217300Z", + "start_time": "2024-03-05T13:38:30.629494400Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['psi', 'ks_test', 'smd', 'repeats'])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quality_results.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "**Kolmogorov-Smirnov test**¶\n", + "the distribution of one sample is compared with the distribution of the second sample and it is decided whether the samples have the same or different distribution.\n", + "\n", + "Table shows the p-value results of the test. If p-value < 0.05 we reject the null hypothesis and we have enough evidence to say that the sample data do not have the same distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:31.100377500Z", + "start_time": "2024-03-05T13:38:30.776682700Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
match_control_to_treatmatch_treat_to_control
age1.418144e-012.209790e-07
pre_spends2.348715e-2643.829212e-19
signup_month0.000000e+000.000000e+00
\n", + "
" + ], + "text/plain": [ + " match_control_to_treat match_treat_to_control\n", + "age 1.418144e-01 2.209790e-07\n", + "pre_spends 2.348715e-264 3.829212e-19\n", + "signup_month 0.000000e+00 0.000000e+00" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quality_results['ks_test']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:31.164918500Z", + "start_time": "2024-03-05T13:38:30.915062300Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
indexsignup_monthpre_spendsagegender_Fgender_Mindustry_Logisticssignup_month_matchedpre_spends_matchedage_matchedgender_F_matchedgender_M_matchedindustry_Logistics_matchedindex_matchedpost_spendspost_spends_matchedpost_spends_matched_biastreattreat_matched
09568487.022.00100.0506.023.00.01.01.0[6352]462.222222408.33333354.00416510
179665471.569.00010.0483.569.00.01.01.0[5349]505.222222415.22222290.06866710
272314487.062.01010.0496.562.01.00.01.0[8654]503.555556428.77777874.83213910
314431517.036.01010.0520.536.01.00.01.0[7881]526.111111423.111111103.02002810
4797310501.065.01000.0525.065.01.00.00.0[7333]440.444444416.66666723.91511210
............................................................
499729260486.045.00002.0479.546.00.01.01.0[971]421.777778509.88888989.94851201
499816560465.539.01012.0460.040.01.00.01.0[6730]417.666667502.22222286.44323101
499928120451.053.01012.0441.557.01.00.01.0[6610]417.333333521.555556106.00229101
500028130504.569.00101.0514.065.00.01.00.0[9319]422.888889528.555556107.08612701
500112970495.543.01011.0496.538.01.00.00.0[3004]415.111111532.555556118.40541201
\n", + "

10000 rows × 19 columns

\n", + "
" + ], + "text/plain": [ + " index signup_month pre_spends age gender_F gender_M \\\n", + "0 956 8 487.0 22.0 0 1 \n", + "1 7966 5 471.5 69.0 0 0 \n", + "2 7231 4 487.0 62.0 1 0 \n", + "3 1443 1 517.0 36.0 1 0 \n", + "4 7973 10 501.0 65.0 1 0 \n", + "... ... ... ... ... ... ... \n", + "4997 2926 0 486.0 45.0 0 0 \n", + "4998 1656 0 465.5 39.0 1 0 \n", + "4999 2812 0 451.0 53.0 1 0 \n", + "5000 2813 0 504.5 69.0 0 1 \n", + "5001 1297 0 495.5 43.0 1 0 \n", + "\n", + " industry_Logistics signup_month_matched pre_spends_matched \\\n", + "0 0 0.0 506.0 \n", + "1 1 0.0 483.5 \n", + "2 1 0.0 496.5 \n", + "3 1 0.0 520.5 \n", + "4 0 0.0 525.0 \n", + "... ... ... ... \n", + "4997 0 2.0 479.5 \n", + "4998 1 2.0 460.0 \n", + "4999 1 2.0 441.5 \n", + "5000 0 1.0 514.0 \n", + "5001 1 1.0 496.5 \n", + "\n", + " age_matched gender_F_matched gender_M_matched \\\n", + "0 23.0 0.0 1.0 \n", + "1 69.0 0.0 1.0 \n", + "2 62.0 1.0 0.0 \n", + "3 36.0 1.0 0.0 \n", + "4 65.0 1.0 0.0 \n", + "... ... ... ... \n", + "4997 46.0 0.0 1.0 \n", + "4998 40.0 1.0 0.0 \n", + "4999 57.0 1.0 0.0 \n", + "5000 65.0 0.0 1.0 \n", + "5001 38.0 1.0 0.0 \n", + "\n", + " industry_Logistics_matched index_matched post_spends \\\n", + "0 1.0 [6352] 462.222222 \n", + "1 1.0 [5349] 505.222222 \n", + "2 1.0 [8654] 503.555556 \n", + "3 1.0 [7881] 526.111111 \n", + "4 0.0 [7333] 440.444444 \n", + "... ... ... ... \n", + "4997 1.0 [971] 421.777778 \n", + "4998 1.0 [6730] 417.666667 \n", + "4999 1.0 [6610] 417.333333 \n", + "5000 0.0 [9319] 422.888889 \n", + "5001 0.0 [3004] 415.111111 \n", + "\n", + " post_spends_matched post_spends_matched_bias treat treat_matched \n", + "0 408.333333 54.004165 1 0 \n", + "1 415.222222 90.068667 1 0 \n", + "2 428.777778 74.832139 1 0 \n", + "3 423.111111 103.020028 1 0 \n", + "4 416.666667 23.915112 1 0 \n", + "... ... ... ... ... \n", + "4997 509.888889 89.948512 0 1 \n", + "4998 502.222222 86.443231 0 1 \n", + "4999 521.555556 106.002291 0 1 \n", + "5000 528.555556 107.086127 0 1 \n", + "5001 532.555556 118.405412 0 1 \n", + "\n", + "[10000 rows x 19 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_matched" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:31.487579500Z", + "start_time": "2024-03-05T13:38:31.046548300Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
indexsignup_monthpre_spendsagegender_Fgender_Mindustry_Logisticssignup_month_matchedpre_spends_matchedage_matchedgender_F_matchedgender_M_matchedindustry_Logistics_matchedindex_matchedpost_spendspost_spends_matchedpost_spends_matched_biastreattreat_matched
09568487.022.00100.0506.023.00.01.01.0[6352]462.222222408.33333354.00416510
581087482.054.00110.0499.055.00.01.00.0[1742]478.333333411.22222267.21494210
672287493.035.01010.0511.036.01.00.00.0[5653]492.555556419.44444473.22066510
7796810512.564.01000.0540.063.01.00.01.0[4470]436.777778418.66666718.26192010
813966479.027.00000.0493.527.00.01.01.0[6721]485.666667408.88888976.86075010
............................................................
499029240493.032.01011.0496.538.01.00.00.0[3004]425.000000532.555556108.98500401
49913130484.035.01002.0480.534.01.00.01.0[7816]426.000000510.77777886.70367501
499229190490.520.00112.0483.518.00.01.00.0[2781]420.333333517.55555698.94099701
499729260486.045.00002.0479.546.00.01.01.0[971]421.777778509.88888989.94851201
500112970495.543.01011.0496.538.01.00.00.0[3004]415.111111532.555556118.40541201
\n", + "

4964 rows × 19 columns

\n", + "
" + ], + "text/plain": [ + " index signup_month pre_spends age gender_F gender_M \\\n", + "0 956 8 487.0 22.0 0 1 \n", + "5 8108 7 482.0 54.0 0 1 \n", + "6 7228 7 493.0 35.0 1 0 \n", + "7 7968 10 512.5 64.0 1 0 \n", + "8 1396 6 479.0 27.0 0 0 \n", + "... ... ... ... ... ... ... \n", + "4990 2924 0 493.0 32.0 1 0 \n", + "4991 313 0 484.0 35.0 1 0 \n", + "4992 2919 0 490.5 20.0 0 1 \n", + "4997 2926 0 486.0 45.0 0 0 \n", + "5001 1297 0 495.5 43.0 1 0 \n", + "\n", + " industry_Logistics signup_month_matched pre_spends_matched \\\n", + "0 0 0.0 506.0 \n", + "5 1 0.0 499.0 \n", + "6 1 0.0 511.0 \n", + "7 0 0.0 540.0 \n", + "8 0 0.0 493.5 \n", + "... ... ... ... \n", + "4990 1 1.0 496.5 \n", + "4991 0 2.0 480.5 \n", + "4992 1 2.0 483.5 \n", + "4997 0 2.0 479.5 \n", + "5001 1 1.0 496.5 \n", + "\n", + " age_matched gender_F_matched gender_M_matched \\\n", + "0 23.0 0.0 1.0 \n", + "5 55.0 0.0 1.0 \n", + "6 36.0 1.0 0.0 \n", + "7 63.0 1.0 0.0 \n", + "8 27.0 0.0 1.0 \n", + "... ... ... ... \n", + "4990 38.0 1.0 0.0 \n", + "4991 34.0 1.0 0.0 \n", + "4992 18.0 0.0 1.0 \n", + "4997 46.0 0.0 1.0 \n", + "5001 38.0 1.0 0.0 \n", + "\n", + " industry_Logistics_matched index_matched post_spends \\\n", + "0 1.0 [6352] 462.222222 \n", + "5 0.0 [1742] 478.333333 \n", + "6 0.0 [5653] 492.555556 \n", + "7 1.0 [4470] 436.777778 \n", + "8 1.0 [6721] 485.666667 \n", + "... ... ... ... \n", + "4990 0.0 [3004] 425.000000 \n", + "4991 1.0 [7816] 426.000000 \n", + "4992 0.0 [2781] 420.333333 \n", + "4997 1.0 [971] 421.777778 \n", + "5001 0.0 [3004] 415.111111 \n", + "\n", + " post_spends_matched post_spends_matched_bias treat treat_matched \n", + "0 408.333333 54.004165 1 0 \n", + "5 411.222222 67.214942 1 0 \n", + "6 419.444444 73.220665 1 0 \n", + "7 418.666667 18.261920 1 0 \n", + "8 408.888889 76.860750 1 0 \n", + "... ... ... ... ... \n", + "4990 532.555556 108.985004 0 1 \n", + "4991 510.777778 86.703675 0 1 \n", + "4992 517.555556 98.940997 0 1 \n", + "4997 509.888889 89.948512 0 1 \n", + "5001 532.555556 118.405412 0 1 \n", + "\n", + "[4964 rows x 19 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_matched[df_matched['industry_Logistics'] != df_matched['industry_Logistics_matched']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Matching with a fixed variable \n", + "Used when you have categorical feature(s) that you want to compare by strict equality \n", + "group_col is used for strict comparison of categorical features. \n", + "In our case there is only one attribute \n", + "If there are several such attributes, you should make one of them and use it" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:32.786324Z", + "start_time": "2024-03-05T13:38:31.287765900Z" + } + }, + "outputs": [], + "source": [ + "group_col = \"industry\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "Also group_col might be the list. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:33.069805500Z", + "start_time": "2024-03-05T13:38:31.361722200Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[18.12.2024 22:05:18 | hypex | INFO]: Number of NaN values filled with zeros: 446\n" + ] + } + ], + "source": [ + "model = Matcher(input_data=df, outcome=outcome, treatment=treatment,\n", + " info_col=info_col, group_col=group_col)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:39.817868900Z", + "start_time": "2024-03-05T13:38:31.480582100Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tikhomirov/PycharmProjects/Sber_New/LightAutoML/.venv/lib/python3.10/site-packages/hypex/selectors/feature_selector.py:42: UserWarning: FeatureSelector does not rule out the possibility of overlooked features, the complex impact of features on target description, or the significance of features from a business logic perspective.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
rank
signup_month1
pre_spends2
age3
gender_F4
gender_M5
\n", + "
" + ], + "text/plain": [ + " rank\n", + "signup_month 1\n", + "pre_spends 2\n", + "age 3\n", + "gender_F 4\n", + "gender_M 5" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selected_features = model.feature_select()\n", + "selected_features" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:39.920722400Z", + "start_time": "2024-03-05T13:38:39.824871400Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['signup_month', 'pre_spends', 'age', 'gender_F'], dtype='object')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chosen_features = selected_features[:4].index\n", + "chosen_features" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:43.485335300Z", + "start_time": "2024-03-05T13:38:39.853989400Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "14cbd6d6dc204f57ad1127bfe3f70b03", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/4 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
effect_sizestd_errp-valci_lowerci_upperoutcome
ATE81.1923921.6310280.077.99557784.389208post_spends
ATC98.9454153.1166050.092.836869105.053961post_spends
ATT63.4251620.6523490.062.14655964.703765post_spends
\n", + "" + ], + "text/plain": [ + " effect_size std_err p-val ci_lower ci_upper outcome\n", + "ATE 81.192392 1.631028 0.0 77.995577 84.389208 post_spends\n", + "ATC 98.945415 3.116605 0.0 92.836869 105.053961 post_spends\n", + "ATT 63.425162 0.652349 0.0 62.146559 64.703765 post_spends" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:43.843138800Z", + "start_time": "2024-03-05T13:38:43.552239500Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
indexsignup_monthpre_spendsagegender_Fgender_Mindustrysignup_month_matchedpre_spends_matchedage_matchedgender_F_matchedgender_M_matchedindustry_matchedindex_matchedpost_spendspost_spends_matchedpost_spends_matched_biastreattreat_matched
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [index, signup_month, pre_spends, age, gender_F, gender_M, industry, signup_month_matched, pre_spends_matched, age_matched, gender_F_matched, gender_M_matched, industry_matched, index_matched, post_spends, post_spends_matched, post_spends_matched_bias, treat, treat_matched]\n", + "Index: []" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_matched[df_matched['industry'] != df_matched['industry_matched']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Results \n", + "### 3.1 ATE, ATT, ATC" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:44.131617100Z", + "start_time": "2024-03-05T13:38:43.805857500Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
effect_sizestd_errp-valci_lowerci_upperoutcome
ATE81.1923921.6310280.077.99557784.389208post_spends
ATC98.9454153.1166050.092.836869105.053961post_spends
ATT63.4251620.6523490.062.14655964.703765post_spends
\n", + "
" + ], + "text/plain": [ + " effect_size std_err p-val ci_lower ci_upper outcome\n", + "ATE 81.192392 1.631028 0.0 77.995577 84.389208 post_spends\n", + "ATC 98.945415 3.116605 0.0 92.836869 105.053961 post_spends\n", + "ATT 63.425162 0.652349 0.0 62.146559 64.703765 post_spends" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 SMD, PSI, KS-test, repeats" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:44.682946400Z", + "start_time": "2024-03-05T13:38:44.007221600Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['psi', 'ks_test', 'smd', 'repeats'])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quality_results.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:44.852815200Z", + "start_time": "2024-03-05T13:38:44.132618700Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
column_treatedanomaly_score_treatedcheck_result_treatedcolumn_untreatedanomaly_score_untreatedcheck_result_untreated
0age_treated0.01OKage_untreated0.08OK
1gender_F_treated0.00OKgender_F_untreated0.00OK
2industry_treated0.00OKindustry_untreated0.00OK
3pre_spends_treated0.61NOKpre_spends_untreated0.16OK
4signup_month_treated16.14NOKsignup_month_untreated0.00OK
\n", + "
" + ], + "text/plain": [ + " column_treated anomaly_score_treated check_result_treated \\\n", + "0 age_treated 0.01 OK \n", + "1 gender_F_treated 0.00 OK \n", + "2 industry_treated 0.00 OK \n", + "3 pre_spends_treated 0.61 NOK \n", + "4 signup_month_treated 16.14 NOK \n", + "\n", + " column_untreated anomaly_score_untreated check_result_untreated \n", + "0 age_untreated 0.08 OK \n", + "1 gender_F_untreated 0.00 OK \n", + "2 industry_untreated 0.00 OK \n", + "3 pre_spends_untreated 0.16 OK \n", + "4 signup_month_untreated 0.00 OK " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quality_results['psi']" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:44.968735600Z", + "start_time": "2024-03-05T13:38:44.251169500Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
match_control_to_treatmatch_treat_to_control
age1.292517e-012.721265e-04
pre_spends4.164710e-2631.725086e-24
signup_month0.000000e+000.000000e+00
\n", + "
" + ], + "text/plain": [ + " match_control_to_treat match_treat_to_control\n", + "age 1.292517e-01 2.721265e-04\n", + "pre_spends 4.164710e-263 1.725086e-24\n", + "signup_month 0.000000e+00 0.000000e+00" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quality_results['ks_test']" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:45.035373500Z", + "start_time": "2024-03-05T13:38:44.332403300Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'match_control_to_treat': 0.42, 'match_treat_to_control': 0.07}" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quality_results['repeats']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Save model" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:39:02.572116900Z", + "start_time": "2024-03-05T13:39:02.402954500Z" + } + }, + "outputs": [], + "source": [ + "model.save(\"test_model.pickle\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:39:02.657280200Z", + "start_time": "2024-03-05T13:39:02.525218900Z" + } + }, + "outputs": [], + "source": [ + "model2 = Matcher.load(\"test_model.pickle\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:39:02.725146Z", + "start_time": "2024-03-05T13:39:02.591885500Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
effect_sizestd_errp-valci_lowerci_upperoutcome
ATE81.1923921.6310280.077.99557784.389208post_spends
ATC98.9454153.1166050.092.836869105.053961post_spends
ATT63.4251620.6523490.062.14655964.703765post_spends
\n", + "
" + ], + "text/plain": [ + " effect_size std_err p-val ci_lower ci_upper outcome\n", + "ATE 81.192392 1.631028 0.0 77.995577 84.389208 post_spends\n", + "ATC 98.945415 3.116605 0.0 92.836869 105.053961 post_spends\n", + "ATT 63.425162 0.652349 0.0 62.146559 64.703765 post_spends" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model2.results" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:39:02.786620200Z", + "start_time": "2024-03-05T13:39:02.626950900Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
effect_sizestd_errp-valci_lowerci_upperoutcome
ATE81.1923921.6310280.077.99557784.389208post_spends
ATC98.9454153.1166050.092.836869105.053961post_spends
ATT63.4251620.6523490.062.14655964.703765post_spends
\n", + "
" + ], + "text/plain": [ + " effect_size std_err p-val ci_lower ci_upper outcome\n", + "ATE 81.192392 1.631028 0.0 77.995577 84.389208 post_spends\n", + "ATC 98.945415 3.116605 0.0 92.836869 105.053961 post_spends\n", + "ATT 63.425162 0.652349 0.0 62.146559 64.703765 post_spends" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/tutorials/Tutorial_16_Matching_without_replacement.ipynb b/examples/tutorials/Tutorial_16_Matching_without_replacement.ipynb new file mode 100644 index 00000000..3c4d3e45 --- /dev/null +++ b/examples/tutorials/Tutorial_16_Matching_without_replacement.ipynb @@ -0,0 +1,786 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Matching without replacement" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0. Import libraries " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:37:59.927516500Z", + "start_time": "2024-03-05T13:37:55.056727900Z" + } + }, + "outputs": [], + "source": [ + "from lightautoml.addons.hypex import Matcher" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Create or upload your dataset \n", + "In this case we will create random dataset with known effect size \n", + "If you have your own dataset, go to the part 2 \n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:37:59.952659200Z", + "start_time": "2024-03-05T13:37:59.931623200Z" + } + }, + "outputs": [], + "source": [ + "from lightautoml.addons.hypex.utils.tutorial_data_creation import create_test_data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:18.298652Z", + "start_time": "2024-03-05T13:37:59.946835700Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_idsignup_monthtreatpre_spendspost_spendsagegenderindustry
0000504.5422.777778NaNFLogistics
1141500.0506.33333351.0NaNE-commerce
2200485.0434.00000056.0FLogistics
3381452.0468.11111146.0ME-commerce
4400488.5420.11111156.0MLogistics
...........................
9995999521482.0501.66666731.0MLogistics
9996999600453.0406.88888953.0MLogistics
9997999700461.0415.11111152.0FE-commerce
99989998101491.5439.22222222.0ME-commerce
9999999921481.0517.22222253.0ME-commerce
\n", + "

10000 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 0 0 0 504.5 422.777778 NaN F \n", + "1 1 4 1 500.0 506.333333 51.0 NaN \n", + "2 2 0 0 485.0 434.000000 56.0 F \n", + "3 3 8 1 452.0 468.111111 46.0 M \n", + "4 4 0 0 488.5 420.111111 56.0 M \n", + "... ... ... ... ... ... ... ... \n", + "9995 9995 2 1 482.0 501.666667 31.0 M \n", + "9996 9996 0 0 453.0 406.888889 53.0 M \n", + "9997 9997 0 0 461.0 415.111111 52.0 F \n", + "9998 9998 10 1 491.5 439.222222 22.0 M \n", + "9999 9999 2 1 481.0 517.222222 53.0 M \n", + "\n", + " industry \n", + "0 Logistics \n", + "1 E-commerce \n", + "2 Logistics \n", + "3 E-commerce \n", + "4 Logistics \n", + "... ... \n", + "9995 Logistics \n", + "9996 Logistics \n", + "9997 E-commerce \n", + "9998 E-commerce \n", + "9999 E-commerce \n", + "\n", + "[10000 rows x 8 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = create_test_data(num_users=10000, rs=42, na_step=45, nan_cols=['age', 'gender'])\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:18.418303900Z", + "start_time": "2024-03-05T13:38:18.298652Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['user_id', 'signup_month', 'treat', 'pre_spends', 'post_spends', 'age',\n", + " 'gender', 'industry'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:18.477754900Z", + "start_time": "2024-03-05T13:38:18.336440500Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "treat\n", + "0 5002\n", + "1 4998\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['treat'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:18.619416200Z", + "start_time": "2024-03-05T13:38:18.362065400Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "223" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['gender'].isna().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Matching without replacement\n", + "### 2.0 Init params\n", + "info_col used to define informative attributes that should not be part of matching, such as user_id \n", + "But to explicitly store this column in the table, so that you can compare directly after computation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:18.634761700Z", + "start_time": "2024-03-05T13:38:18.408068600Z" + } + }, + "outputs": [], + "source": [ + "info_col = ['user_id']\n", + "\n", + "outcome = 'post_spends'\n", + "treatment = 'treat'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Matching\n", + "This is the easiest way to initialize and calculate metrics on a Matching task \n", + "Use it when you are clear about each attribute or if you don't have any additional task conditions (Strict equality for certain features) " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:18.660452500Z", + "start_time": "2024-03-05T13:38:18.428454200Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[18.12.2024 22:04:52 | hypex | INFO]: Number of NaN values filled with zeros: 446\n" + ] + } + ], + "source": [ + "# Standard model with base parameters\n", + "model = Matcher(input_data=df, outcome=outcome, treatment=treatment, info_col=info_col)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-05T13:38:30.559294800Z", + "start_time": "2024-03-05T13:38:28.180655600Z" + } + }, + "outputs": [], + "source": [ + "df_matched = model.match_no_rep()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
signup_monthtreatpre_spendspost_spendsagegender_Fgender_Mindustry_Logisticsuser_idsignup_month_matchedtreat_matchedpre_spends_matchedpost_spends_matchedage_matchedgender_F_matchedgender_M_matchedindustry_Logistics_matcheduser_id_matched
000504.5422.7777780.0101041522.5509.7777780.01014095
141500.0506.33333351.0000100488.0420.33333350.00003916
200485.0434.00000056.0101221492.5510.77777856.01016057
381452.0468.11111146.0010300453.5415.22222242.00105255
400488.5420.11111156.0011481488.0472.11111159.00116874
.........................................................
999121482.0501.66666731.0011999500474.0435.11111128.00118397
999200453.0406.88888953.0011999641459.0518.77777857.00018416
999300461.0415.11111152.0100999771459.5472.11111151.01005727
9994101491.5439.22222222.0010999800492.5411.55555625.00105675
999521481.0517.22222253.0010999900471.5427.00000053.00106361
\n", + "

9996 rows × 18 columns

\n", + "
" + ], + "text/plain": [ + " signup_month treat pre_spends post_spends age gender_F gender_M \\\n", + "0 0 0 504.5 422.777778 0.0 1 0 \n", + "1 4 1 500.0 506.333333 51.0 0 0 \n", + "2 0 0 485.0 434.000000 56.0 1 0 \n", + "3 8 1 452.0 468.111111 46.0 0 1 \n", + "4 0 0 488.5 420.111111 56.0 0 1 \n", + "... ... ... ... ... ... ... ... \n", + "9991 2 1 482.0 501.666667 31.0 0 1 \n", + "9992 0 0 453.0 406.888889 53.0 0 1 \n", + "9993 0 0 461.0 415.111111 52.0 1 0 \n", + "9994 10 1 491.5 439.222222 22.0 0 1 \n", + "9995 2 1 481.0 517.222222 53.0 0 1 \n", + "\n", + " industry_Logistics user_id signup_month_matched treat_matched \\\n", + "0 1 0 4 1 \n", + "1 0 1 0 0 \n", + "2 1 2 2 1 \n", + "3 0 3 0 0 \n", + "4 1 4 8 1 \n", + "... ... ... ... ... \n", + "9991 1 9995 0 0 \n", + "9992 1 9996 4 1 \n", + "9993 0 9997 7 1 \n", + "9994 0 9998 0 0 \n", + "9995 0 9999 0 0 \n", + "\n", + " pre_spends_matched post_spends_matched age_matched gender_F_matched \\\n", + "0 522.5 509.777778 0.0 1 \n", + "1 488.0 420.333333 50.0 0 \n", + "2 492.5 510.777778 56.0 1 \n", + "3 453.5 415.222222 42.0 0 \n", + "4 488.0 472.111111 59.0 0 \n", + "... ... ... ... ... \n", + "9991 474.0 435.111111 28.0 0 \n", + "9992 459.0 518.777778 57.0 0 \n", + "9993 459.5 472.111111 51.0 1 \n", + "9994 492.5 411.555556 25.0 0 \n", + "9995 471.5 427.000000 53.0 0 \n", + "\n", + " gender_M_matched industry_Logistics_matched user_id_matched \n", + "0 0 1 4095 \n", + "1 0 0 3916 \n", + "2 0 1 6057 \n", + "3 1 0 5255 \n", + "4 1 1 6874 \n", + "... ... ... ... \n", + "9991 1 1 8397 \n", + "9992 0 1 8416 \n", + "9993 0 0 5727 \n", + "9994 1 0 5675 \n", + "9995 1 0 6361 \n", + "\n", + "[9996 rows x 18 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_matched" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/tutorials/Tutorial_17_Modeling_Limit_Distribution.ipynb b/examples/tutorials/Tutorial_17_Modeling_Limit_Distribution.ipynb new file mode 100644 index 00000000..b6dac3fe --- /dev/null +++ b/examples/tutorials/Tutorial_17_Modeling_Limit_Distribution.ipynb @@ -0,0 +1,3478 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# Modeling Limit Distribution" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AP4lCsm0VVYg" + }, + "source": [ + "## Modeling real data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9zBoPRULWmlz" + }, + "source": [ + "This part provides the optimal method for tests in which you need to determine a statistically significantly better sample" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-22T06:17:08.790842Z", + "start_time": "2024-02-22T06:16:56.980631Z" + } + }, + "outputs": [], + "source": [ + "import plotly.graph_objects as go\n", + "from scipy.stats import bernoulli, norm\n", + "from statsmodels.stats.proportion import proportions_ztest\n", + "\n", + "import numpy as np \n", + "\n", + "from lightautoml.addons.hypex.abn_test import min_sample_size\n", + "from lightautoml.addons.hypex.abn_test import quantile_of_marginal_distribution\n", + "from lightautoml.addons.hypex.abn_test import test_on_marginal_distribution" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EeWJwNgKWIYv" + }, + "source": [ + "## Help functions for easy solution and construction of confidence intervals\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-22T06:17:13.833237Z", + "start_time": "2024-02-22T06:17:13.499920Z" + }, + "id": "FbjH0D7_Ye2S" + }, + "outputs": [], + "source": [ + "def min_sample_size_2(d, alpha=0.05, beta=0.2, p=1 / 2):\n", + " l = 2 * p * (1 - p) * (((norm.ppf(1 - alpha) - norm.ppf(beta)) / d) ** 2)\n", + " return int(l)\n", + "\n", + "\n", + "def confidence_interval_bern(p, cnt, c=0.95):\n", + " se = np.sqrt(p * (1 - p) / cnt)\n", + " q = norm.ppf(1 - (1 - c) / 2)\n", + " return p - q * se, p + q * se" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OqaRZ7ezWTbx" + }, + "source": [ + "## Comparison of simple and optimal solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XMz0yIyGZNtw" + }, + "source": [ + "Consider the size of a single sample depending on the number of samples." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-22T13:12:37.370079Z", + "start_time": "2024-02-22T13:12:37.034432Z" + } + }, + "outputs": [], + "source": [ + "k_list = [2, 3, 4, 5, 6] # Number of samples\n", + "alpha = 0.05 # Significance level\n", + "beta = 0.2 # 1 - power\n", + "p_true = 0.3 # Bernoulli distribution parameter\n", + "d_relative_pred = 0.1 # I assume that the conversion rate will increase by d_relative_pred * p\n", + "\n", + "n_1 = [] # limit method\n", + "n_2 = [] # method 2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-22T06:17:49.641022Z", + "start_time": "2024-02-22T06:17:17.360316Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 517 + }, + "id": "kBb35WlHWarp", + "outputId": "bacafc45-d238-43a8-c5a9-f808d07c6b93" + }, + "outputs": [], + "source": [ + "for k in k_list:\n", + " alpha_ = alpha / k\n", + " beta_ = beta / (k - 1)\n", + "\n", + " c_1 = quantile_of_marginal_distribution(num_samples=k, quantile_level=1 - alpha_)\n", + " c_2 = quantile_of_marginal_distribution(num_samples=k, quantile_level=beta)\n", + " n_1 += [min_sample_size(number_of_samples=k,\n", + " minimum_detectable_effect=p_true * d_relative_pred,\n", + " variances=p_true * (1 - p_true),\n", + " significance_level=alpha,\n", + " power_level=beta,\n", + " quantile_1=c_1,\n", + " quantile_2=c_2)]\n", + " n_2 += [min_sample_size_2(d=p_true * d_relative_pred, p=p_true,\n", + " alpha=alpha_, beta=beta_)]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-22T06:17:50.505125Z", + "start_time": "2024-02-22T06:17:49.643243Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "lines", + "name": "Optimal solution", + "type": "scatter", + "x": [ + 2, + 3, + 4, + 5, + 6 + ], + "y": [ + 3605, + 3369, + 3431, + 3351, + 3548 + ] + }, + { + "mode": "lines", + "name": "Simple solution", + "type": "scatter", + "x": [ + 2, + 3, + 4, + 5, + 6 + ], + "y": [ + 3662, + 5425, + 6536, + 7359, + 8016 + ] + } + ], + "layout": { + "autosize": true, + "font": { + "size": 16 + }, + "height": 500, + "legend": { + "x": 0.005, + "xanchor": "left", + "y": 0.99, + "yanchor": "top" + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "font": { + "size": 18 + }, + "text": "Modeling the minimum sample size" + }, + "width": 725, + "xaxis": { + "title": { + "text": "Number of samples" + } + }, + "yaxis": { + "range": [ + 0, + 10500 + ], + "title": { + "text": "The size of a single sample" + } + } + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure()\n", + "fig.add_trace(go.Scatter(x=k_list, y=n_1,\n", + " mode=\"lines\", name=f\"Optimal solution\"))\n", + "fig.add_trace(go.Scatter(x=k_list, y=n_2,\n", + " mode=\"lines\", name=f\"Simple solution\"))\n", + "\n", + "fig.update_layout(title={\n", + " \"text\": \"Modeling the minimum sample size\",\n", + " \"font\": {\n", + " \"size\": 18\n", + " }\n", + "},\n", + " xaxis={\n", + " \"title_text\": \"Number of samples\"\n", + " },\n", + " yaxis={\n", + " \"title_text\": \"The size of a single sample\",\n", + " \"range\": [0, 10500]\n", + " },\n", + " legend={\n", + " \"yanchor\": \"top\",\n", + " \"y\": 0.99,\n", + " \"xanchor\": \"left\",\n", + " \"x\": 0.005\n", + " },\n", + " width=725,\n", + " height=500,\n", + " autosize=True,\n", + " font={\n", + " \"size\": 16\n", + " })\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sP9yLos1doPQ" + }, + "source": [ + "Consider the probability of an error of the first kind - this is the probability of rejecting\n", + "a hypothesis if it is valid. In order not to make mistakes too often, we want to control this value.\n", + "\n", + "The value is theoretical, so we will model the frequency of incorrect rejection of the hypothesis, close to the true probability of error of the first kind with a sufficient number of simulations." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-22T13:13:03.972111Z", + "start_time": "2024-02-22T13:13:03.949470Z" + } + }, + "outputs": [], + "source": [ + "d_relative_pred = 0.05 # Assume that the conversion rate will increase by d_relative_pred * p\n", + "iter_size = 1000 # Number of test iterations\n", + "\n", + "reject_stat_1 = [] # limit method\n", + "reject_stat_2 = [] # pairwise comparisons" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-22T19:32:21.745231Z", + "start_time": "2024-02-22T13:13:05.422928Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 517 + }, + "id": "MX6x4I3ZZkYz", + "outputId": "a9872c55-96fc-44d4-d738-83607ad18ab7" + }, + "outputs": [], + "source": [ + "for k in k_list:\n", + " alpha_ = alpha / k\n", + " beta_ = beta / (k - 1)\n", + "\n", + " c_1 = quantile_of_marginal_distribution(num_samples=k, quantile_level=1 - alpha_)\n", + " c_2 = quantile_of_marginal_distribution(num_samples=k, quantile_level=beta)\n", + " n_1 = min_sample_size(number_of_samples=k,\n", + " minimum_detectable_effect=p_true * d_relative_pred,\n", + " variances=p_true * (1 - p_true),\n", + " significance_level=alpha,\n", + " power_level=beta,\n", + " quantile_1=c_1,\n", + " quantile_2=c_2)\n", + " n_2 = min_sample_size_2(d=p_true * d_relative_pred, p=p_true,\n", + " alpha=alpha_, beta=beta_)\n", + "\n", + " success_cnt_1 = bernoulli.rvs(p_true, size=[iter_size, k, n_1])\n", + " success_cnt_2 = bernoulli.rvs(p_true, size=[iter_size, k, n_2])\n", + " # limit method\n", + " reject_cnt_0 = 0\n", + " for i in range(iter_size):\n", + " hyp = test_on_marginal_distribution(success_cnt_1[i],\n", + " significance_level=alpha,\n", + " quantiles=c_1)\n", + " if hyp == 0:\n", + " reject_cnt_0 += 1\n", + " reject_stat_1.append(1 - reject_cnt_0 / iter_size)\n", + "\n", + " # pairwise comparisons\n", + " reject_cnt_0 = 0\n", + " for i in range(iter_size):\n", + " hyp = 0 # if nothing is rejected, then H(0) is valid\n", + " for s in range(k):\n", + " all_rejected_flg = True\n", + " for t in range(k):\n", + " if s != t:\n", + " if proportions_ztest([np.sum(success_cnt_2[i][s]), np.sum(success_cnt_2[i][t])], [n_2, n_2],\n", + " alternative=\"larger\")[1] > alpha_:\n", + " all_rejected_flg = False\n", + " break\n", + " if all_rejected_flg:\n", + " hyp = s + 1\n", + " break\n", + " if hyp == 0:\n", + " reject_cnt_0 += 1\n", + " reject_stat_2.append(1 - reject_cnt_0 / iter_size)\n", + "reject_stat_1 = np.array(reject_stat_1)\n", + "left_side_1, right_side_1 = confidence_interval_bern(reject_stat_1, iter_size, c=1 - alpha)\n", + "\n", + "reject_stat_2 = np.array(reject_stat_2)\n", + "left_side_2, right_side_2 = confidence_interval_bern(reject_stat_2, iter_size, c=1 - alpha)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-22T20:45:23.748568Z", + "start_time": "2024-02-22T20:45:19.076066Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "lines", + "name": "Significance level", + "type": "scatter", + "x": [ + 2, + 3, + 4, + 5, + 6 + ], + "y": [ + 0.05, + 0.05, + 0.05, + 0.05, + 0.05 + ] + }, + { + "error_y": { + "array": [ + 0.01185024761974595, + 0.012848606648945646, + 0.013885483775132178, + 0.012432414773866297, + 0.01413010340392374 + ], + "arrayminus": [ + 0.01185024761974595, + 0.012848606648945646, + 0.013885483775132185, + 0.012432414773866293, + 0.014130103403923747 + ], + "symmetric": false, + "type": "data", + "visible": true + }, + "mode": "lines", + "name": "Optimal solution", + "type": "scatter", + "x": [ + 2, + 3, + 4, + 5, + 6 + ], + "y": [ + 0.038000000000000034, + 0.04500000000000004, + 0.05300000000000005, + 0.04200000000000004, + 0.05500000000000005 + ] + }, + { + "error_y": { + "array": [ + 0.01376112473691514, + 0.006464613717023735, + 0.0019589837574297123, + 0, + 0 + ], + "arrayminus": [ + 0.01376112473691514, + 0.006464613717023733, + 0.0019589837574297123, + 0, + 0 + ], + "symmetric": false, + "type": "data", + "visible": true + }, + "mode": "lines", + "name": "Simple solution", + "type": "scatter", + "x": [ + 2, + 3, + 4, + 5, + 6 + ], + "y": [ + 0.052000000000000046, + 0.01100000000000001, + 0.0010000000000000009, + 0, + 0 + ] + } + ], + "layout": { + "height": 500, + "legend": { + "x": 0.995, + "xanchor": "right", + "y": 0.99, + "yanchor": "top" + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Modeling the probability of a first-kind error" + }, + "width": 725, + "xaxis": { + "title": { + "text": "Number of samples" + } + }, + "yaxis": { + "range": [ + -0.005, + 0.07 + ], + "tickformat": ".0%", + "title": { + "text": "The frequency of incorrect rejection of the hypothesis" + } + } + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure()\n", + "fig.add_trace(go.Scatter(x=k_list, y=len(k_list) * [alpha],\n", + " mode=\"lines\", name=\"Significance level\"))\n", + "fig.add_trace(go.Scatter(x=k_list,\n", + " y=reject_stat_1,\n", + " mode=\"lines\",\n", + " name=f\"Optimal solution\",\n", + " error_y={\n", + " \"type\": \"data\",\n", + " \"symmetric\": False,\n", + " \"array\": reject_stat_1 - left_side_1,\n", + " \"arrayminus\": right_side_1 - reject_stat_1,\n", + " \"visible\": True\n", + " }))\n", + "\n", + "fig.add_trace(go.Scatter(x=k_list,\n", + " y=reject_stat_2,\n", + " mode=\"lines\",\n", + " name=f\"Simple solution\",\n", + " error_y={\n", + " \"type\": \"data\",\n", + " \"symmetric\": False,\n", + " \"array\": reject_stat_2 - left_side_2,\n", + " \"arrayminus\": right_side_2 - reject_stat_2,\n", + " \"visible\": True\n", + " }))\n", + "\n", + "fig.update_layout(title=\"Modeling the probability of a first-kind error\",\n", + " xaxis={\n", + " \"title_text\": \"Number of samples\"\n", + " },\n", + " yaxis={\n", + " \"tickformat\": \".0%\",\n", + " \"title_text\": \"The frequency of incorrect rejection of the hypothesis\",\n", + " \"range\": [-0.005, 0.07]\n", + " },\n", + " legend={\n", + " \"yanchor\": \"top\",\n", + " \"y\": 0.99,\n", + " \"xanchor\": \"right\",\n", + " \"x\": 0.995\n", + " },\n", + " width=725,\n", + " height=500)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2DA__F6ci1G6" + }, + "source": [ + "Consider the probability of a second kind of error - this is the probability of accepting a hypothesis if it is unfair.\n", + "\n", + "We will model the frequency of incorrect hypothesis acceptance, close to the true probability of a second-kind error with a sufficient number of simulations." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-22T20:45:39.130929Z", + "start_time": "2024-02-22T20:45:39.105257Z" + } + }, + "outputs": [], + "source": [ + "d_relative_pred = 0.1 # Assume that the conversion rate will increase by d_relative_pred * p\n", + "iter_size = 1000 # Number of test iterations\n", + "\n", + "reject_stat_1 = []\n", + "reject_stat_2 = []" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-23T00:24:47.065346Z", + "start_time": "2024-02-22T20:45:42.369567Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 517 + }, + "id": "xFZx_z9LZmKz", + "outputId": "64bc4d49-52b5-4573-aff0-89f9da7d603e" + }, + "outputs": [], + "source": [ + "for k in k_list:\n", + " alpha_ = alpha / k\n", + " beta_ = beta / (k - 1)\n", + "\n", + " c_1 = quantile_of_marginal_distribution(num_samples=k, quantile_level=1 - alpha_)\n", + " c_2 = quantile_of_marginal_distribution(num_samples=k, quantile_level=beta)\n", + " n_1 = min_sample_size(number_of_samples=k,\n", + " minimum_detectable_effect=p_true * d_relative_pred,\n", + " variances=p_true * (1 - p_true),\n", + " significance_level=alpha,\n", + " power_level=beta,\n", + " quantile_1=c_1,\n", + " quantile_2=c_2)\n", + " n_2 = min_sample_size_2(d=p_true * d_relative_pred, p=p_true,\n", + " alpha=alpha_, beta=beta_)\n", + "\n", + " success_cnt_2 = []\n", + " for i in range(k - 1):\n", + " success_cnt_2 += [bernoulli.rvs(p_true, size=[iter_size, n_2])]\n", + " success_cnt_2 += [bernoulli.rvs(p_true * (1 + d_relative_pred), size=[iter_size, n_2])]\n", + "\n", + " # limit method\n", + " reject_cnt_0 = 0\n", + " for i in range(iter_size):\n", + " success_cnt_1 = []\n", + " for _ in range(k - 1):\n", + " success_cnt_1 += [bernoulli.rvs(p_true, size=n_1)]\n", + " success_cnt_1 += [bernoulli.rvs(p_true * (1 + d_relative_pred), size=n_1)]\n", + " hyp = test_on_marginal_distribution(success_cnt_1,\n", + " significance_level=alpha,\n", + " quantiles=c_1)\n", + " if hyp == k:\n", + " reject_cnt_0 += 1\n", + " reject_stat_1.append(1 - reject_cnt_0 / iter_size)\n", + "\n", + " # pairwise comparisons\n", + " reject_cnt_0 = 0\n", + " for i in range(iter_size):\n", + " hyp = 0\n", + " for s in range(k):\n", + " all_rejected_flg = True\n", + " for t in range(k):\n", + " if s != t:\n", + " if proportions_ztest([np.sum(success_cnt_2[s][i]), np.sum(success_cnt_2[t][i])], [n_2, n_2],\n", + " alternative=\"larger\")[1] > alpha_:\n", + " all_rejected_flg = False\n", + " break\n", + " if all_rejected_flg:\n", + " hyp = s + 1\n", + " break\n", + " if hyp == k:\n", + " reject_cnt_0 += 1\n", + " reject_stat_2.append(1 - reject_cnt_0 / iter_size)\n", + "\n", + "reject_stat_1 = np.array(reject_stat_1)\n", + "left_side_1, right_side_1 = confidence_interval_bern(reject_stat_1, iter_size, c=1 - alpha)\n", + "reject_stat_2 = np.array(reject_stat_2)\n", + "left_side_2, right_side_2 = confidence_interval_bern(reject_stat_2, iter_size, c=1 - alpha)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-23T00:24:47.721138Z", + "start_time": "2024-02-23T00:24:47.128043Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "lines", + "name": "The probability of an error of the 2nd kind", + "type": "scatter", + "x": [ + 2, + 3, + 4, + 5, + 6 + ], + "y": [ + 0.2, + 0.2, + 0.2, + 0.2, + 0.2 + ] + }, + { + "error_y": { + "array": [ + 0.024838165092145092, + 0.02502122601517809, + 0.025548113622789442, + 0.024508485612926317, + 0.02411663805940567 + ], + "arrayminus": [ + 0.024838165092145092, + 0.02502122601517809, + 0.025548113622789442, + 0.024508485612926317, + 0.02411663805940567 + ], + "symmetric": false, + "type": "data", + "visible": true + }, + "mode": "lines", + "name": "Optimal solution", + "type": "scatter", + "x": [ + 2, + 3, + 4, + 5, + 6 + ], + "y": [ + 0.20099999999999996, + 0.20499999999999996, + 0.21699999999999997, + 0.19399999999999995, + 0.18600000000000005 + ] + }, + { + "error_y": { + "array": [ + 0.02493017176152068, + 0.022893100890139823, + 0.023061373007090824, + 0.021311487930913864, + 0.02182303501567906 + ], + "arrayminus": [ + 0.02493017176152068, + 0.022893100890139823, + 0.023061373007090824, + 0.021311487930913864, + 0.02182303501567906 + ], + "symmetric": false, + "type": "data", + "visible": true + }, + "mode": "lines", + "name": "Simple solution", + "type": "scatter", + "x": [ + 2, + 3, + 4, + 5, + 6 + ], + "y": [ + 0.20299999999999996, + 0.16300000000000003, + 0.16600000000000004, + 0.137, + 0.14500000000000002 + ] + } + ], + "layout": { + "height": 500, + "legend": { + "x": 0.995, + "xanchor": "right", + "y": 0.99, + "yanchor": "top" + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Modeling the probability of a second kind of error" + }, + "width": 725, + "xaxis": { + "title": { + "text": "Number of samples" + } + }, + "yaxis": { + "range": [ + 0, + 0.25 + ], + "tickformat": ".0%", + "title": { + "text": "The frequency of hypothesis acceptance" + } + } + } + }, + "image/png": "iVBORw0KGgoAAAANSUhEUgAABF4AAAH0CAYAAAAE++nQAAAAAXNSR0IArs4c6QAAIABJREFUeF7snQd4VMXXh39pkEDoEDqISJGmFAFBBEFRkCJI772DgDQpAtKk9yodBOm9KgIiTVDhLwoWpEgPPSEB0r7vzOYum2Sz2U225GZ/8zw+JNl75859z+ya++bMGY+oqKgosJEACZAACZAACZAACZAACZAACZAACZAACdidgAfFi92ZskMSIAESIAESIAESIAESIAESIAESIAESUAQoXjgRSIAESIAESIAESIAESIAESIAESIAESMBBBCheHASW3ZIACZAACZAACZAACZAACZAACZAACZAAxQvnAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAk4iADFi4PAslsSIAESIAESIAESIAESIAESIAESIAESoHjhHCABEiABEiABEiABEiABEiABEiABEiABBxGgeHEQWHZLAiRAAiRAAiRAAiRAAiRAAiRAAiRAAhQvnAMkQAIkQAIkQAIkQAIkQAIkQAIkQAIk4CACFC8OAstuSYAESIAESIAESIAESIAESIAESIAESIDihXOABEiABEiABEiABEiABEiABEiABEiABBxEgOLFQWDZLQmQAAmQAAmQAAmQAAmQAAmQAAmQAAlQvHAOkAAJkAAJkAAJkAAJkAAJkAAJkAAJkICDCFC8OAgsuyUBEiABEiABEiABEiABEiABEiABEiABihfOARIgARIgARIgARIgARIgARIgARIgARJwEAGKFweBZbckQAIkQAIkQAIkQAIkQAIkQAIkQAIkQPHCOUACJEACJEACJEACJEACJEACJEACJEACDiJA8eIgsOyWBEiABEiABEiABEiABEiABEiABEiABCheOAdIgARIgARIgARIgARIgARIgARIgARIwEEEKF4cBJbdkgAJkAAJkAAJkAAJkAAJkAAJkAAJkADFC+cACZAACZAACZAACZAACZAACZAACZAACTiIAMWLg8CyWxIgARIgARIgARIgARIgARIgARIgARKgeOEcIAESIAESIAESIAESIAESIAESIAESIAEHEaB4cRBYdksCJEACJEACJEACJEACJEACJEACJEACFC+cAyRAAiRAAiRAAiRAAiRAAiRAAiRAAiTgIAIULw4Cy25JgARIgARIgARIgARIgARIgARIgARIgOKFc4AESIAESIAESIAESIAESIAESIAESIAEHESA4sVBYNktCZAACZAACZAACZAACZAACZAACZAACVC8cA6QAAmQAAmQAAmQAAmQAAmQAAmQAAmQgIMIULw4CCy7JQESIAESIAESIAESIAESIAESIAESIAGKF84BEiABEiABEiABEiABEiABEiABEiABEnAQAYoXB4FltyRAAiRAAiRAAiRAAiRAAiRAAiRAAiRA8cI5QAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIOIkDx4iCw7JYESIAESIAESIAESIAESIAESIAESIAEKF44B0iABEiABEiABEiABEiABEiABEiABEjAQQQoXhwElt2SAAmQAAmQAAmQAAmQAAmQAAmQAAmQAMWLA+dA6NPnWLv1OxQqkAdVKpSy6UrXb93FvkM/oULpYihe5CV17rPnYQgLC4evbyp4e3nZ1J8jD5Yxrdq0Hy/lzYnqlUs78lIu6XvH/mN4HByClg3ftcv1beFlLuY7vz2OB4+C0LpRTTWe8IgIPH36HKl8vJEqlY9dxmivTmRsB478gotXbiAyIhLlXi+CimWK2at7t+nn+Onfcf6fK2hUpxrS+6exeN/L1+1F9myZUKt6BYvHhYQ+RWRkFPzT+tmNoy3jtOWinEe20OKxJEACJEACJEACJEACyY2AW4iXDTsPYdSU5Yr9+oWjjCJDC4Y8CFdv3A/3HwbhzXLFsXjKQLvE6e79R6ja8BN89MFbGDekk019ygNMpwGTMbRPK+MD/4hJS7F59w9YMPFTVKlQ0qb+7HHw0VPncOLnP9Dy43eRI1tmY5fBT0JR4cPueL9aeUwb1cMel0pWfTTtOhr/Xr2JU3sW2GVctvAyF/NWvcbh13N/4/dDhjktYmjI+EXo3LIO+nZupH4WX6zscgNWdhIVFYWO/Sfh5K/njWc0rV8dn/drY2UPPEwjMH7W1/h687fY8/Uk5MsdYBFM8WrtrPock8+824EP8NPuBUibxtcusG0Zp7UX5DyylhSPIwESIAESIAESIAESSK4E3EK8rN9+EKOnrVAxkL8CT/m8e4x47D5wEgPHzFc/q1D6VSydPtgu8bK3eFm1cT+Onf4dvdo3iCOP7DLgBDqZt2Ib5i7bgnULR6JEkQLGo20RCc4Yp72v4UrxYi7mscXLiV/+wIr1+1Czajk0qFVF3X58sbI3G0v9nT77J9p+MgEf1qiIAd2bIUum9AgOCUWGdGmdOYwUcS1bhIa14mXYl4uVbJ4+uid8U6eyCydbxmntBTmPrCXF40iABEiABEiABEiABJIrAbcSL2n8fCHp9XvXTELeXC/+atyo80hcuXZbvWZJvMhfXj08PBKMpXacNeIlvj7NZbwkeGEAto7Rmj61Y2wRL44chy1jtvZYS+N1pXgxN/7Y4sXcMclBvGza9QM+n7xUZZBJJllybNbOU3uP3dbr2iI0rBUv1tyTI8dpzfXlmKTOI1vvwdpxacc5un9bx8PjSYAESIAESIAESIAEkh8BtxIvn3ZrgqkL1qNlw/cwtE9LFY1ffvsLrXuPx4BuTTFlwbo44kXqtMxbvhUHfvxZyZlXC+VH/fcrqz48PWNKmF0HTmDl+n049+clVWPhjdeLQupxxF5qJPUKVm3Yj70Hf1LH5smZDW9XLIU+HT9Guuj6DebEiywp2f39STV2EUdPnz1Hv5FzUaZkIRR+OS9WrN+rlnXItVs0eBcdmtWOMcbnz8OwaPVO7P7+hLqX/Hmy463yJfHfjUC0aVTT4sPxxp2H8dXXO3HtZiBKFSuIjOn9Fb9m9aujbKnCxqVGwiahcVhz//G9VUzvOXeObFi/4yBOnbmAgvlzoVWjmmhSt5rx1C17jmD/4dMY0be1ut+Dx36F1M5p2/gDlC9dFNbGVhMvS6cNwqLVO3Dil/PwTe2DejUro2+XxvDxNtTbkborE2Z9jQv/XMXNO/eVyCv8ch40qlMVTepVNx73IkPoDVSvXAbrth9U81DuoV3TD9Cw9tvGe4gdc3khtng5//cVzFqyGU3qVcM7lUrDUqzO/vEPzv99FcM+aaXmnWmT8w78+At6tv8oRkaTuVjIeEXunP39omJRtlQR9O/aGPlyZ1eHy7yfvXRznPkya0xv+Ph4mw3v//64iPkrt+PfKzfUeSJKSxYtgNaNa6r7sqZJPZm12w7gz3+uqsOl7lCNKmXUPNWyOh4FPcGcpZvVciyZFxIjyRSS97SXl6fxMhKnhat24PjPv8d4vzT7qLpxqZ3EefrC9eoYyR6R97ws+ar8RgljP7//eRlzlm1B4zpV1fzbtu8oJGYS70+7NUXVN1+LcWv3HjzGtIXr8cOJs6rPMiULI/TpM3VOYpca/XnxP8z4aiP8fFNh9ID26nNm8rxv1HhmfNFLXd/Z49Q+f+09j6Rfa2Js6fNBll5JzGTe5M0dgJ3fHsPf/17Dm+VKoEWDGoqXLGHduOOw8TNc4vhJp0Yxlm0l9BlkzZzmMSRAAiRAAiRAAiRAAvon4FbiRf7yvmzdHvXA9eO22ciUIR36jJiF46f/wK5VX+KdRn1jiJew8Ai06jlW/WItD2dFXsmHX3/7Wz0UxpYpIiXkwUaa/HVfColqtS1Mj5W/jvb4bIZ6qJIHS3koPPrTb+oBS5bvrJ3/uZIl5sTLnKVbMH/lNmz8arQSQNoDvDYNpb+X8+VU45X25dAuqFuzkvo6IiIS7fp+qR7wRcyUKFoAt+88MB47/rPOSijF11Zu2KfEi4xTHtg1QdS55Yeo/EZJJV6sGYe19x/fOMzdswgkeSiVNnZwR+Nym+mLNmDxml1KFMlDvdbGDOqAujUrWx1bES8aU+lD4qR9bxrbq9dvo1bLwYqvxEce4mVuiYARCSbiT1pC92Aai9gxl/Nji5fYc8VSrIKfPFVZKN3b1EevDg2MTESGvfNxXzx9FqbeG6ktFOn97sjP+GTEbHXu+9XeUAJL5rO0LUvHqvfK+h2HMHvJpjjzZe28EfGKF3lIHT5xiTo/f54cipu8V6XNm9AvjqCIPUdE9gwet1C9r94sVwwiGn/+39+qn/3fTEHuHFkhUqNxl5GqtonM49eKFYQIU2mmMTI9TgSJzLEzv/+j7kckbftmtXD1+h183Olz1b+IlvTp0uLw8bPqe9MYHjn5P3QbPM04XJkf/mn8VMFhaaYyRfqv326ouo5cs0C+nPjn0nX1mRP72PjeI7EzXi5dvYkmXUerca2aPVSJHHPzyNnjdNQ8sjbGlj4fsmXJqGIm72Pts0WYyeepfK5OmrsWKzbsQ+aM6VC5fElcunJTfSZIzDYtHqMElzRL1zAVrPHFkj8nARIgARIgARIgARJIGQTcTrxERkWhy8Ap6N2hIWrXqKAelKUgaZN676BS3Z4xxItkAIycsky9NqJvGyVE5CGz+5BpKstizbwR6sHt5u17eLfpp+qXcBEnWiaBPCy933xgDEkjOxX1HzUPUmR0SM/mahcaeegdPXWFKpw7e9wnamcgW8SLPGhKwdIP331TjVFqfkhRUynAK4V41QNx9EOtPChPGNrF+GAtf30fOuGrGA+K8U3thJYaWTMOa+8/vjFo0kJYjx7YwbiL0i+//Y3Wvccp9rtXT1TSQ3vokXEN7N4UFcsWQ+pUqdS9y0OfNbGVcWjiRR64W338nhIHsoysSddR6gFee6iXuXH9ZiBeKZDbOHz5y3ud1kOU0NCK88Z3D1IwV6SK3IM8jEssEyNelKiIpx7Pk5CnKF+7m5qr32+cYczCkWygXkNnomvruirzKr4muyzVajlI3ffOlROUGJAmwqHHZ9NjzDl5MJUH1GXTh6gMo4Sa9ClL+QKyZjQeKlkYwtmaws3Ne4xRgs10XBKTddu/V1JRROuY6Svxzbbv1XyvV7OSup4ICW254eHNM5E1cwY1N+T9/0mnj9GlVV01HtkBaNu+H9WOYvIAPmjMAiVtJo3opurYSJMMko/aD1dfH9w4Xe0YpAkNEThjBndUnxkqRsu3Yu7yrUrIifSRNm7mKqzZcgDd2tRTtZy0pY1akWVbM17ks6llr7EqXlK7SpZSai22wHPmOB05j6yNsaXPh98u/GuUZZ1afIgP3imPgKyZ8DwsHCEhoajXbpiSMsumDzZKaMlSWrJ2d4x4WrpGxgyGrEE2EiABEiABEiABEiCBlE/A7cSLPHzXbzdMLQWRX6ZFdhzdNgcenh5xxIsIGvmLu/Ywpk0HTWx0bF4b/bs2wdqtBzB2xiq0afw+Bvdsbpw15mq8dB8yXWUH7Fs7GTkDshiPlT7lej3bN0CPtvVtEi+xH0olq6R87e7IliWDkhDSeg+bie+P/orv1k1FzuwvrvvtD6fR9/M5dhEv1ozD2vuP761nqZBvt8FTceTkb/hu/TTkDMhsFC+SZSFZL6bN2tjKOfHVeFn6zW61dG3yiO5K4mlNlkP9c/m6yii6/+ixWlYm2Q3HdsxVhWWtuYcDG6ap5Sz2Fi8yxvGzVuPrzd9h1pg+KuNKmsZOk0jx8dfkkOlyvdgP8sd3zlNbHtsqXrR+RFZdvHwdgfceqswPeW9JFoxk01hqsmRQLYGKJztGxEnJ6u2V2Nq1+kt44MVSwXkrtmLByu1KEpUpVQiv1ehoPM7c1u0iS+UYkSnbV4yPMSxNqGjj0ISGyFERrlqT5T8NO45QS1eGfdJa/fiNWt2UCPphyyxVjFhrianxIpkZLXuOVdkyiyYPiLH8SfqNT7w4Y5yOmkfWxlhEoCZFzH0+aDHTsptMAyxZdHKuLNF67+1yxpe097UIGclKlGbpGin/1wveIQmQAAmQAAmQAAmQgEbA7cSLLAPSsjwEgvYAKQ97sTNeZLtV2Wr6yFbDsgqtaUJFHlrl4VV7KIr9wGdOvEgGjLZswNw0lHogUoPBlowXc9kAcp2w8HB8v2G6uox8Lw+xsbdEdqR40a4bexzW3H98b1FL0kJq9Cz7Zo8xE0l76Nm6bCwKFcgTo0trYysnxSdeDh07g55DZxiXnshyroWrd6idn8w1EXzyV25r7kF7GHSEeNEe+GV5jDyQa5lZkmklGVeWmrZ1tSzXir1UQnsfbFr8BYq+ks9m8SLvQcn8kqyo2M0a8aIVYZVzZcmH1J15p3JpvFPpdZU5IrL13Sb9Ld7fxGFdUbpkIdRsNkBlsUg2i7kmmS1yjLb0xPQY7T0lMkWkSnziRRuP9p7XPi9MH9y1fm0VL8JL2l//XsPMMb3xbpWycW7DWvHiiHE6ah5ZG+M6771plCLmPh/ii5lA1LKPTDOrNLi1Ww1W9YC0rd4tfQZZnIh8kQRIgARIgARIgARIIEURcEvxImnu8gAmImLP1xNVQVBz4kX++pzO388oL7TIa8dqS3mkroTUl9D60o4zJ160v2iLXDHXXsqbA+VeK2J38VLlo97qfs8eWKKWSmjN2eLF2vuP711mSVpodRe07a4tPfRYG1sZR0LiZWD3ZqooriZJJAtCCqwWejmPWrYi45IlKbaIl2/mf46Sr77skIwXuSftoVt2+Nq296iqHWTN7kNSUHTUlOUx6gdpsZJircvX74UmjWzNeNHGVK3S66og8Ut5ciBzpvRqqZZwTCjjRcYhwlJqEWn1leRnUpNn9dzhkBo89doOVd83NinCbDrXJBNCZKssJRGxJILJXJMMJunL3DHasi0tWyK+h/g7dx+qulKaeJFaLHXafBanfpRc31bxYjpmqUMktY9i78hmrXhxxDgdNY+0uCQUY/nMt/T5YEm8aEvMzGWHNegwXMmucweXKd4ULynq9yXeDAmQAAmQAAmQAAkkmoBbihehJTUp7tx7gMZ1DLvgmBMvWs2IX/Z/FaPYaOwlAtov16aFK6VPc+JFe9g5tUeKgKaON3D2znjRrqtlIyRFvGi1bbQ+LMmQ2Jk31t5/YsSLZJ9IFoomOCw99FgbW0viRVtipi3ZkQdx0yVF2j1IDR3JsrJGvPQfNRf7Dp1SWVZShyWpGS+xY6WNSStEK7VFtu49ogrSanVlLH2aaPNSaiRJHRLTpo394MYZqk6LLeJFdoR6q35vJUVEnJk2kYbWihftPCmMLfVeZA7IshbJ7JHdt8q+3wWlSxTC6jnD4r1NqQtT7gPLx2nHyC5Gy2cMidGXNi+mjeqhatNYK16095G5Le1tFS8SzyVTB2LSvG/U/Uudkn5dGscYZ2LFiz3G6ah5JMv8rImxgEiseJHdumRZ2oqZnylJrjXJeKtYp0eMJZ4UL4n+3YQnkgAJkAAJkAAJkECKIuC24iV2FM2JF6nfIXU8JDtF/iqtNa1GhrZrkPYQK0V4R/ZvazxOKzhquvPNrCWb1Ba1Wn0Y03HcuHVXCSBZamBv8SLLX6TgasuG7+Kz3i3VX2NlV5axM1aqOjYJ7Wok45Sin1L8U3ug1MZui3ix9v7je5fFdy3Zwll2mDFXX8HcUgJrYyvjMJfxIlkRcj0RLVo9Fi2b58TOecaCm4+DQ9B10FQlARISL//duIMPWgxSy2S02jyJFS/xxUrjKuLg7QZ9VD0RaUP7tFJzI6GmyUTZmUdEjbb70a3A+6jRuL/a0enA+mlqftkiXqSmi2SZxJYismxDlm9Ys9Ro94GTeK9qOWPBYLkXqWUj71fZgl2WFWrCbcHE/qhSoVSM2xVBIZkQUltFy1wQQSNj0prsmCMFlKVmkBTklR1vpMaLZDlJk9ovjTuPVFkPkk0k275bK17kfFkCZ1qwWX4mMZLCx5LFY2tx3YePgtGi5xi1/GVIrxZo3aim8V4SK17sMU5HzSMZm7UxTqx40T7XZbmSLE3Tmmxd32/knBhZUBQvCX2i8HUSIAESIAESIAEScA8CFC/RcTYnXuQhSx5OpfVs9xEKvpQLJ345j/XbD6oHrc1Lx6hlO6ZLl2pVr4AiBfOqrWcl+0KaqXgRcSC7wsiyn7crvqZqUMhDvOyiIXUPtAdge4sXySiQjAy5rvw1XJZQyQOe1qwRL9rOQfJw3b5pLXXfxQu/pJbEyHbS1tSasfb+43v7mW7FLDJMHsjlPmQ3EWmmO+hYeuixNrbSp7arkeyMIxkOsk3x5t1H1PaxplsQy25VUp9EHtQlrvJwufPbY4q5tNjiReLQrH51dQ+B9x+q+jRyrGmtoMSKl/hiJTWOtKbJJ/leK/xrzceeJs9EPsj4Zf5KcVqJg6mUs0W8SB8iHeT+pbZKsSIv4e9/r2Hr3h/VkKwRLyK+JEvoo1pvqW3VpQ6LMJUdpWQZoGTNaLskSZ8ydtlWXYr4nj77pxKQ2lbtsmuZbL8uTYpdi5D589//sG7bQfW9bCctx0uRZrmmFMVO6+erdg8TQSJ9j+jXRp1vi3jRsmXkPdaoTjUEB4dgh8kcslW8yPVF6DbtNlqxNS0EnRTxYo9xOmIeyf1aG+PEihcpXt6i51glU6XGT9WKr6k6STO+2qjibboEieLFmk8UHkMCJEACJEACJEACKZ+Ae4iXHYcweupyLJk6SG0pbK5JZsKbdXpAHkyl1oXW5C/Xg8cuUH/B1prUdhk7uJN6kNOaLD+SbaY1mSEP1V1a1VG/jDeoVUXVWNCa1EyYunCdqgtj2mSJgWxtLQ+0J37+Ax0/naR2O5ECndK0rJXNS8YouaNtDWxOeEiWgKS+y+5JptdduGo7zl24hAzp06JMycLIkjm9qtkxd3xfSG2NhJo8yMp2vFqB3FED2qF29Ypqi2JbxpHQ/cc3DtOtmOUYTWrIg+roAR3UdsZaE/ZS72PbsnExtni2NbaaeJHdcEwLA4t06dPpY2OGhcRVdo8SIaM1kQgiYORh/Nj2uYq76ZbOIgW0rBM5R/6CLn9J11rsmMvPYz8wm5srcpy5WGlL6+R1TRzEztRKaA5IVsei1TtjFBHWthKXB1GtrdywDxPnro2zJCO+/kUWfTJiljGmcpwIz2Xr9iJPzqwJ1ngRkSRz05SnZA/JVvCmwkni8+XsNWoJjmmTWA3u1cK4m5DwkQwvyRbRmiyFGtK7hTELRmokDZ2wOMY1Rcr06dBQbRVvylmy4YS16ftRaryY8he20xduULVytKa9L0Xkalk0lmJUvFq7OJ9j8vkk80bYrJw1VC27ij2PtPngrHE6ah4JG2tibOnzIT4WGvdHj59g9LTlalmg1uTzYcrIHihZtIDVn0EJvdf4OgmQAAmQAAmQAAmQQMog4BbixR6hkodn+ct47pzZ1Fa55ppsZSrLRSIjI9VfyL28PC1eWh48bt6+BzlPxIFv6lT2GKpNfWgPH5rMseZk+YuvCAj/tH7IlCGdNaeYPSYx92+61GjyiG5Kanh6eiBbloyJHoc1sZXORWRdvxWoxEn+PDnM1ujR5oA84ObKnlWJFktNsoak6Kunhwfy580Ro/Bxom/I5ERLsRr25WKVUaJledh6PclSkbF7e3urrZcTmu/W9C88NNEhy3T8fG17T8j9SjaT/CeZKFkyZVDzw1yTeiA3bt+DX+pUyJY1Y7zsJRvu/oPHyJI5g9n3vsRc3g/Sn4gebfmVNfcb3zFBwSGqz1w5sqotyJNrs8c4HTGPNF7WxjixfOXzSD7zZZ5JXSM2EiABEiABEiABEiABEjBHgOLFjeaF7K7zRumiShp4eXqqLAzJBJJlHBu/+sIuD86Oxmmpnoyjr52S+pfMLFnak1Ch2ZR0z7wXEiABEiABEiABEiABEiABEnAFAYoXV1B30TVlCULsJpkKM77opYrS6qFRvNgnSlp9DdOaH/bpmb2QAAmQAAmQAAmQAAmQAAmQAAmYEqB4caP5IDuwSK2aBw+D1BKOPLkCVK0HVyxxSix2WZ60fd9RtQSjYhnz9XoS27c7nXfgyC94FBSMOu++aaxF4k73z3slARIgARIgARIgARIgARIgAWcRoHhxFmlehwRIgARIgARIgARIgARIgARIgARIwO0IULy4Xch5wyRAAiRAAiRAAiRAAiRAAiRAAiRAAs4iQPHiLNK8DgmQAAmQAAmQAAmQAAmQAAmQAAmQgNsRoHhxu5DzhkmABEiABEiABEiABEiABEiABEiABJxFgOLFWaR5HRIgARIgARIgARIgARIgARIgARIgAbcjQPHidiHnDZMACZAACZAACZAACZAACZAACZAACTiLAMWLs0jzOiRAAiRAAiRAAiRAAiRAAiRAAiRAAm5HgOLF7ULOGyYBEiABEiABEiABEiABEiABEiABEnAWAYoXZ5HmdUiABEiABEiABEiABEiABEiABEiABNyOAMWL24WcN0wCJEACJEACJEACJEACJEACJEACJOAsAhQvziLN65AACZAACZAACZAACZAACZAACZAACbgdAYoXtws5b5gESIAESIAESIAESIAESIAESIAESMBZBChenEWa1yEBEiABEiABEiABEiABEiABEiABEnA7AhQvbhdy3jAJkAAJkAAJkAAJkAAJkAAJkAAJkICzCFC8OIs0r0MCJEACJEACJEACJEACJEACJEACJOB2BChe3C7kvGESIAESIAESIAESIAESIAESIAESIAFnEaB4cRZpXocESIAESIAESIAESIAESIAESIAESMDtCFC8uF3IecMkQAIkQAIkQAIkQAIkQAIkQAIkQALOIkDx4izSvA4JkAAJkAAJkAAJkAAJkAAJkAAJkIDbEaB4cbuQ84ZJgARIgARIgARIgARIgARIgARIgAScRYDixVmkeR0SIAESIAESIAESIAESIAESIAESIAG3I0Dx4nYh5w2TAAmQAAmQAAmQAAmQAAmQAAmQAAk4iwDFi7NI8zokQAIkQAIkQAIkQAIkQAIkQAIkQAL1/GTzAAAgAElEQVRuR4Dixe1CzhsmARIgARIgARIgARIgARIgARIgARJwFgGKF2eR5nVIgARIgARIgARIgARIgARIgARIgATcjgDFi9uFnDdMAiRAAiRAAiRAAiRAAiRAAiRAAiTgLAIUL84izeuQAAmQAAmQAAmQAAmQAAmQAAmQAAm4HQGKF7cLOW+YBEiABEiABEiABEiABEiABEiABEjAWQQoXpxFmtchARIgARIgARIgARIgARIgARIgARJwOwIUL24Xct4wCZAACZAACZAACZAACZAACZAACZCAswhQvDiLNK9DAiRAAiRAAiRAAiRAAiRAAiRAAiTgdgQoXtwu5LxhEiABEiABEiABEiABEiABEiABEiABZxGgeHEWaV6HBEiABEiABEiABEiABEiABEiABEjA7QhQvLhdyHnDJEACJEACJEACJEACJEACJEACJEACziJA8eIs0rwOCZAACZAACZAACZAACZAACZAACZCA2xGgeHG7kPOGSYAESIAESIAESIAESIAESIAESIAEnEWA4sVZpHkdEiABEiABEiABEiABEiABEiABEiABtyNA8eJ2IecNkwAJkAAJkAAJkAAJkAAJkAAJkAAJOIsAxYuzSPM6JEACJEACJEACJEACJEACJEACJEACbkeA4sXtQs4bJgESIAESIAESIAESIAESIAESIAEScBaBFCleHgU9wbNnYQjImtFmjs+ehyEyMgp+vqlsPpcnkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIApgRQlXu7ef4Q2fcbjyrXb6h4L5s+Fzi3roG7NSur7+w+DUOWj3nFmwJJpg1CxTDGs2XIAi9fsVK+3aPAuOrX4UH1978FjfNBiEHaunIDs2TJxBpEACZAACZAACZAACZAACZAACZAACZCAVQRSlHi5c/chtu49gnrvV0ZaP1+s2rgfy9btxQ9bZqkMFhEobzfogwUTP0W+3AFGQAFZMyF1Kh9UbdgHi6cOgp9vatRqOQhnvl0MHx9vTFmwDhERkRjcs7lVUHkQCZAACZAACZAACZAACZAACZAACZAACQiBFCVeYof02s1AvN98IFbNHooyJQsbxYtkrhTIlzPG4dqxp/cuUhKmZPX22LpsLNL7p0WdNp9h9+ovkS2L7UuXOM1IgARIgARIgARIgARIgARIgARIgATcl0CKFi9b9hzB8IlLcGTrbGTOmM4oXqpXLo0M6f1R+OU8qP/BW8iQLq3KaKlYpwfWzhuBNH6p8V6zASrjZdK8tUjj54t+XRpDMmrSpvFV/7GRAAmQAAmQAAmQAAmQAAmQAAmQAAmQQEIEUqx4+fvSNbToMRZtG7+PXh0aKA7BT0Ixc/FGyNKioOAQiJjJmjkD1i0YiVSpfLB4zS6sWL9XHdusfnUlZRp0GIH930zGrCWbcfj4GYSFhaNX+wZoWr96Qmz5OgmQAAmQAAmQAAmQAAmQAAmQAAmQgJsTSJHi5fqtu2jdexzeeL0oxg/pDC8vT7NhvnT1plpGJFkupYoVVMc8Dg5BVGQUMqRPixGTliJn9ixo8VENVK7fC6f2LMQff13G55OXYvfqier4G/dCdTuFMvqnwvOwCIQ8i9DtPbjTwAMy+uJ+0DOER0S5023r8l49PIAcmfxw875+Px90CT6Rg07t4wl/Px/ce/wskT3wNGcSSOfnDXh4ICgkzJmX5bUSSSBL+tQIDg3Ds7DIRPbA05xJIGdmP9y6Hwr+puFM6om7lreXBzKnS407D58mrgOe5VQCaXy9kcrLAw+f6Pf/Xbmy+DmVWUq7WIoTL/9cuo72/b5E9bfKYES/NvD28oo3Zk9CnqJ87W5YOn0wKpR+NcZxImWadB2NAxum4fzfVzBg9Dy1ZOnm7Xt4t+mnOLVngVqCRPGS0t4Syfd+KF6Sb2xij4ziRT+xkpFSvOgrXhQv+ooXxYu+4kXxop94UbzoJ1YyUooXfcXLEaNNUeLlz4v/oWHHEfiwRkX07tgQnp6GTBep2ZIpQzocPn4WT589Q8WyxeHj7YUZX21Sy42+Wz9V1XkxbYPGLEDhgnnVltKPgp6gUt2eOLFzHn7/8zLGz1qN7SvGq8MpXhwxLdmnOQIUL/qZFxQv+okVxYu+YiWjpXjRV8woXvQVL4oX/cSL4kU/saJ40VesHDXaFCVe9nx/EgO+mB+HVd2alfDl0C749ofTGDphMUJCDSl5UnB38ojuqFi2WIxzLl6+jmbdx+Dw5hkqq0WabCm9be+Panvpfp0bQ/qkeHHUtGS/FC/6ngMUL/qKHzNe9BUvihd9xYvi5UW8mKavr7nL0ZKAOxMwl1zAz7CkzYgUJV6sQREeEYF79x+rQwOyZoSHPCFZ2aQgr2/qVEq+aI0ZL1bC42FJJsCMlyQjdFoHFC9OQ22XC1G82AWj0zqheHEaartciOKF4sUuE4mdkAAJOJUAxYv9cbudeLE3QooXexNlf/ERoHjRz9ygeNFPrGSkFC/6ihfFi77iRfFC8aKvGcvRkgAJCAGKF/vPA4qXJDKleEkiQJ5uNQGKF6tRufxAiheXh8CmAVC82ITL5QdTvLg8BDYNgOKF4sWmCcODSYAEkgUBihf7h4HiJYlMKV6SCJCnW02A4sVqVC4/kOLF5SGwaQAULzbhcvnBFC8uD4FNA6B4oXixacLwYBIggWRBgOLF/mGgeEkiU4qXJALk6VYToHixGpXLD6R4cXkIbBoAxYtNuFxysM/Olcbrpvb2BDyAZ2GRxp+F1WnjknHxogkToHiheEl4lvAIEiCB5EaA4sX+EaF4SSJTipckAuTpVhOgeLEalcsPpHhxeQhsGgDFi024XHJwmu7vWbxuyPxvXTIuXjRhAhQvFC8JzxIeQQIkkNwIULzYPyIUL0lkSvGSRIA83WoCFC9Wo3L5gRQvLg+BTQOgeLEJl0sONs148dm1So0h7MPWxrEw48UlYbHqohQvFC9WTRQeRAIkkKwIULzYPxwUL0lkSvGSRIA83WoCFC9Wo3L5gRQvLg+BTQOgeLEJl8sP1rJfmOXi8lBYNQCKF4oXqyYKDyIBEkhWBChe7B8OipckMqV4SSJAnm41AYoXq1G5/ECKF5eHwKYBULzYhMvlB1O8uDwENg2A4iXliJd/r97E3XuPUL50UZvmgDMOTsrYTp/9E5ky+KPgS7njDDUsPAIRERHwTZ1Kvbbv0CmUe60IsmRKj6fPnsPL0xM+Pt7OuEX88ddl/HnxP7zxelHkyZnNKddMzheR2Pz063nce/AI71YpizR+vhaH++jxExw7fQ61qlew2209CnqCZ8/CEJA1Y6L7jIyMwr5DP6HSGyWQIV3aOP1cvX4b12/exZvlisd5LSIiEs+ehyGNX2qbr3/3/iP88tvfqFm1nNlzKV5sRprgCRQvCSKyfADFSxIB8nSrCVC8WI3K5QdSvLg8BDYNgOLFJlwuP5jixeUhsGkAFC/6ES9Nu47GuT8vmY1vjSplUKZEYfx46jcsnjLQpjngjIOXr9ub6LF1HzIdZUoWQueWdeIMdc7SLTjw48/YsnSseq14tXZYOWsoypYqjFa9xqHUqy9jUM/mOHz8LH47/y96dWjgkNsdMn4Rjv70G8qWKoJ671dG9cqlHXIdvXQaHhGBms0GwD+NH/LnyY7PerdErhxZYwx/8LiF6NTiQxQqkEf9XOLTrPsXOHdwGTzkF7UkNJEWbfqMx5Vrt1UvBfPnUvOnbs1KNvcaFhaO19/rhI1fjcarhfLHOX/99oPYsvdHrJ03Is5rx0//jk4DJuPotjnImMHfpmv/9OsFtO/3JX4/tNzseRQvNuG06mCKF6swxX8QxUsSAfJ0qwlQvFiNyuUHUry4PAQ2DYDixSZcLj+Y4sXlIbBpABQvL3DlyuJnEztnH3zj1l3113Np42atRtbMGdC1VV31fdo0fth94ESi5Yaj78VR4uXO3YcICn5izIYxFS+Xrt6En19q5MiWGV9v/g57D/6EVbOH2v1WQ0Kf4o1a3ZT8KfyyQSK4ezt15gJ6fDYDJ3bOg5eXp1kcEqtl04cYM7TsKV5kXmzde0RJsLR+vli1cT+WrduLH7bMgp+vITvK2pYU8RL8JFTJnyKv5IW3l5e1l1THUbzYhMsuB1O8JBEjxUsSAfJ0qwlQvFiNyuUHUry4PAQ2DYDixSZcLj+Y4sXlIbBpABQvL3Ald/FiGtj+o+Yie7bMGNyzufHHIjd2fnccrxUriO37j6HoK/lUhkeF0q+qY0KfPsfMxRux67vjyJQhHZrWfwcNa1c1+yC6Y/8xHDx2BmnT+CphkTljOgzv2xpVKpRSfTXvMQZdWtXBkZO/4fzfVzB2cEclgibNXYv9h08jnb8fGtWppo6RB04Z27Z9P6JY4ZfU69Lf5/3bovIbJVR/kv1w7NQ53H8YpLITerZvgPervaFek4wXeVh+HBwCySAoXaIQJgztjLy5ArDrwAn8/L+/8Hk/w5b1puJFxvJKgdwqC6VVr7Gq7xJFCqjjendsiAUrt2PR5AHGZSCSFbNq034smjQAnp4xMy5kycjSb3Zj7dYDCAoOhWQYfdarJTKkT4vWvcfjl9/+UtkQsrRp7fzP45xvePDfg9uBD9S9N/+oBrq3ra8yO4T14RNn1TIWc3GL/Ya2FMcv56xBvtzZ8SgoGMdO/a6uI0uxzvz+D14rXhA7vz2uMkz6d20S7/3EF1+Ji2mTZTzm4n0n8AFa9R6n7lV4Fy9awBgf7fxpC9djydrdaklWxvT+aFC7CooXfkllvAzs0QxrtxxQh3ZsXhtN6r1j8/yNzezazUC833ygEm9lShaGcPL29sLFyzcgS9neqfS6mhMyp6TJPJsw+2tcvHIDpYoVxP/+uGh1xsu6bd9jz8GfMGl4NwQ9CcHQ8V9hzbwRSkDJ+0auJe8BETLN6ldHj3YfqfkdFRWFlRv3Y8X6vYqdSLy//r3GjBeb/o+WtIMpXpLGDxQvSQTI060mQPFiNSqXH0jx4vIQ2DQAihebcLn8YIoXl4fApgFQvLzAZU68XPkvCk+f2oTULgfnz+cBXwtlIeITL5Pnf4P2zWrhrfIlsef7k/j9z8vqgVHaqCnLlSTp17WxeuAfPXU5urepb3b5hYgS6atbm3oo9WpBrN9xUD18Htk6W/UlgkNay4bvIVeOLHi/WnlMX7geF/65qh7q7z98jAmz16Bv50Zo2fBdJV5M+/tm2/f48+JVfL9huurn683f4pUCeZAlY3ocOn4G0xdtwLHtc5XYEPFy7sK/6NW+gVquMXPxJiVTxgzqgJUb9uHQsTNYOn2wcVzaUqOeQ2eosbduVBPTF63HyV/OY0S0oBEBVO3jvkom1X+/sjq3Q7+JKFG0gBp/7LZh5yFMmvuNkgI5AzKrMch9zxrTB7sPnMTAMfMxb0I/JaqkxkzsJg/a8qCfN1c2/Hf9DnoPn6WOr/rma0Y28cUtdl+W4iisfjhxVsVDREvJoi+ruAl7EQhSayVnQBY8CQ2N937ii6/ct2kbNGaB2Xg3rP02Zi/ZhAM//qKEXDr/NEoCmra/L13DR+2Hq2VgxQrlR46AzHj4KFiJF1mmJbLlvxuBGDdzFY7tmKuklC3zNzazLXuOYPjEJWr+ivgSTiJc+nb+WM27aQvWo0KZV1Xs/7txBx+0GKTmhcjDW3fuq/has9RIu87qOcOUIJT3X5Ouo3D2wBIlIOV9IwKrW5v6SvgNHLMA00b1UEJTm0c9232Et998Dd8ePo3Fa3ZRvNjlE9W6TiherOMU71EUL0kEyNOtJkDxYjUqlx9I8eLyENg0AIoXm3C5/GCKF5eHwKYBULy8wGVOvIydGo7LV6NsYmqPg4d96o0C+eKvcxGfeDGt8SJLbeq0+UwJjFSpfFDugy4Y9klrlC7xihri5t0/4PbdB0oexG6xlwbJ0o13GvXF7tUTVc0OeYBcMPFTVKlQUp2qLbeZPKI7atcwFEeVrIKTv/yhluDE7k9qcFRt+An2fD1RZWhIRomIGBE3cq3ZSzdj3cKRKmMido0XkRhjpq9QD9HWiJeureuaXWokWRciY+Q6Uvy3bpvPsOfrSciX25D1YNokU0Hkwcj+bdWPvzvyMz4ZMVuxvffwsTr35K758E8b/3K1i5ev44+/riDw/kMs+2YPOrWsg7aN34/DxjRuIp5Mm2S7WIqjsCpSMK8SXloT9vsOn8LXc4YbM3Es3Y9cM3Z8Y/NIKN4bdx7Ghh2HFNv4mjVLjap81BtfDOqAimWK2zR/Ta8pkqdFj7GKtVbjJ/ac2rTrB6zetF/N1YWrdqivZVmSCEprlxq1a/IB5H25YuZnRvlmTrxoUkbGKJleWTNlUEJPxF9Atkz4cmgXNXwuNbLHJ6ltfVC82MYrztEUL0kEyNOtJkDxYjUqlx9I8eLyENg0AIoXm3C5/GCKF5eHwKYBULy8wGVOvKxaF4Fbd5wvXlo18UbO7PGH0hrxosmSAxumITT0mZIwshxG2wFIepfdXqaN6hnnQuZqskgdk7GDO6hsCnloNn2A1GSBJmakQ1nWMnraCpzasyCOXJDXtf7eKl8K3QZPU9Kl+lulVUbGV1/vVMVKJUsj9kOyLL9o0GE4Dm6cgb0HTyaY8RKfeJHdaGq1HKwyGWSsl6/dwtzxfc1CFwEg2RANalVRr9+8fQ/vNv0Um5eMUbsmJSReRELJciPJ5sifN4eqx9P645oqOyk+ySVxk/o0pk3jHF8czRUiNhdLS/cj4iZ2fGNDSSje9hIvtVsNRq/2DfFqoXw2zV9tvNdv3UXr3uPUTlPjh3Q21puJzUl2LZq2cAP2rZ2sMmOe//9ORJNGdFPdWCNeZJ5L++iDtzBuSCcjroTEi2T0hEdEKqEnMfmkUyM0qlNVnU/xYtP/yuxysMPFy4NHQTh49FfIxKxeuQyKF3lJrZeUbdgqlilml5twZScUL66k717XpnjRT7wpXvQTKxkpxYu+4kXxoq94UbxYFi/JNZq2ihc/39SoVLcnNiwapeqsJNRiP6zLc4LsUrN8xhD1EBv7wVy2Aq5Ur6cSF9Uqva66lx2Hdn9/QmXJxO7vVuB91GjcXxVXDQoOQZ8Rs4xLSuRc6T8+8aItyfhl/1eQehoJLTUS8bJmywElO0QWmbYuA6cgc6b0OHDkFyWgtAye2HxE9FQuXxIDujVVL2m71Yj8CQ4JtShe7j14jLcb9FHLobR6O90GT0WF0sVsFi9SV8VSHK0VL5buR2RcQuIloXhbK16WTBtkfN40V1xXEy+Vy5ewaf5KjP65dF3tClT9rTJqiZlpcVtL4mXFhn1qmY82V6wRL5Pnr1NZK7J8T+oNNa1fXc0TW8SLLI179ZX8xqwcipeEPqXs/7pDxcvNO/dRr+1QlR4oTVKbZJutqQvWq0rQBzfNsLkCs/0RJK1Hipek8ePZ1hOgeLGelauPpHhxdQRsuz7Fi228XH00xYurI2Db9Sle3EO8SOaELGUIC49Qf8mXQriytEcK08oSjNhNK4a7cNIAPHv+HHOXb1XbJe//ZqoqBGruwVy2b/ZP64uR/dtB/rDbb+Rc1Kz6Bj7t1kSJFyn8K/JBsgnmr9iG/YdPqQyD//3xLzp+Okllj8g45Q/AkglgKl7y5MyKQT2aq4Kkkl2QPl0aJXmsXWokxW+7DpqmljZJkVMp6CrLSL7/8RdVb0WKvMoyo9hFdTUuIpE27/kBM0b3UkWNx85YCXmOEpF16b9bFsWLFAV+s04PVe9EeEhtkQFfzEePtvVtFi8yHktxtFa8WLof4ZKQeJFxWIq3NeJF7uON0kXRqUUdhIQ8hWQgxd5OWhMvsnzNlvn758X/0LDjCHxYo6IqmuvpadhZSeqqSGFpS+JFMq8+7vQ5ZNlc+dJFVTaU1MixpsaLLIPrN3KOknhSHNoW8SJyUArrThjaBdmyZMCcZVvUtbmdtG3/T0vK0Q4VL/OWb8X3R3/FzDG9laGr+14lJV60SbJ3zSRjdeek3IQrz6V4cSV997o2xYt+4k3xop9YyUgpXvQVL4oXfcWL4iUFiZf1e9XOQLJTj7TAew9VAVkpYJs9Wya1U8qoqctV8VWtSTZIn44fmxUv8rCpNRETk0d0U0t/pJl7MJflJ1L3RHaCkSaZL/JHXSmuunz9XsxdttX4x940fr5KnMiDbWRklKqN8e0Pp9V5shxHnk++mf85Sr76srEQqvaHYllmM3tsH+TMnkUt35HMfdPiutrONb2HzVTnd2lVF+EREeg1dIbahUna6b2LlEASEfX6ux0xpFcLVYQ3vibXHjphsXGMUudGxlDwpdzQlt38tHuBKq5rrskOPlJTRpoUV5VtwWXHoXZNP1BsLMUtdn+W4ihCoWypwujU4kPjabH7lxcs3U988Y09Dkvxlpop67cftFjjRbKMRk1dpnabkiLPVSu9jmbdRuPcwWVKikkT8dK7Q0PUql7BpvkrhaVFbsVu8pwrczI2p32HTqn4iAiU+Tho7AJVnFqbx5JVtWnxF3GKBMvr63ccghTVFVEoTXa+Gjtjlar1IqKncZeYxXVNl+iJYIyIjFJZMlL3qPOAyWonI2my49fRU+coXuJ9V9r/BYeKl+qN+6FzyzrqjS+pdpp40dLHvlkwEiWLGrZd02ujeNFr5PQ3booX/cSM4kU/sZKRUrzoK14UL/qKF8XLi3jpaTvppMyyp8+eQ37Xz5I5fbyZ7drSoPkT+iHoSajaCcbaJrVlUqf2UTvRxG4hoc9w78EjJU1Ml37IcfLgKdkokpFgrkkWzdNnYWpXocQ2WaqTysfHuIX2yV/Pq0wKbeechPqV858+fa5Elq3tSchTtSV2UsZvek1r4pjQGJNyP1rfluKd0PWlqLLEVUpcaLIloXPscd8JXUObj1K/x9w8tub8xBwjW0rLLkqZMqaLUYvJXF/mnnHd5TMsMWytOceh4kUqWpcpUUitSTMVL6fOXEC7vl/i8OaZKhVRz43iRc/R09fYKV70Ey+KF/3ESkZK8aKveFG86CteFC8v4sWHlhcszBVk1dfMtm60UlcjIGsm425F1p3Fo0jA9QQoXuwfA4eKF9kbXLbMkjWHUpxK0q9eeSm32toqQ3p/Y8qU/W/LeT1SvDiPtbtfieIlec8Arz9Ow/PfP9QgJYHV388bQaHhxkFHFiuHiJf1X1A8eUchcaOjeEkcN1edRfHiKvKJuy7FC8WLuZkjyx1kSUt8xWYTN9uS11mSbbF9/1FV8DZXjqzJa3AcDQkkQIDixf5TxKHiRdY7Dhm3yLiGTRu+rOOcN6GvWreo90bxovcI6mf8FC/JO1Y+GxfC58DGeAcZ9nFXhL3bKHnfhJuOjuJFX4GneNFXvCheKF70NWM5WhIgASFA8WL/eeBQ8aIN99yfl3Dh76sIfhKKfHmy482yxY1rH+1/S87tkeLFubzd+WoUL8k7+qYZL17nJfvlPCJeLYvI6CwXZrwk3/hRvCTf2JgbGcWLvuJF8ULxoq8Zy9GSAAlQvDhmDjhUvJz/+4rKdmlUpxry5Q4w3sGi1TuQLUtGNKhVxTF35cRe9SReHkQ8RefAQ0Y63l6eqrJ2ZFSU+llmz9RYFPCOE+nxUrYQoHixhZZrj021aQG8v9uEsIZdEPZeY9cOhldPkADFS4KIktUBFC/JKhwJDobiheIlwUnCA0iABJIdAWa82D8kDhUvw75cjD/+uoyNX32hqohrTfYRl+2ttK3W7H9bzutRT+LldngIylwzbDVnruXw8sPPeZs6Dx6vZBMBihebcLn0YIoXl+K3+eIULzYjc+kJFC8uxW/zxSleKF5snjQ8gQRIwOUEKF7sHwKHipd6bYei3vuVY+z1LrcQeO8hqn3cF5uXjEGRgnntf1dO7FFP4uV5VCROP7uj6DyIeIYugQdVlsvC6CyX1B6eKJv6RWaSEzHyUlYQoHixAlIyOYTiJZkEwsphULxYCSqZHEbxkkwCYeUwKF4oXqycKjyMBEggGRGgeLF/MBwqXpp2HY1iRV6Ks4Xaz//7C236jMeOlRPwcr6c9r8rJ/aoJ/Hi8fAe/D5rpujc9PdDsZ4NkSM4BOfnblE/i0qfGaET1zmRHi9lCwGKF1toufZYihfX8rf16hQvthJz7fEUL67lb+vVKV4oXmydMzyeBEjA9QQoXuwfA4eKl4lz12Llhn1YM28EShQpoJYb3bn7EJ9PXoKf//c3jm2fAx8fb/vflRN7pHhxImw3vxTFi34mAMWLfmIlI6V40Ve8KF70FS+KF/2Kl3sPHsPT0wOZMqRL0qSTbZWfPQ9DGr/USerH3Mn7Dp1CudeKIEum9HbrOyl9OvJe7XaD7IgErCBA8WIFJBsPcah4efT4CRp0HI7bgQ+Qxs8XeXJmxV//XlND/HJoF9StWcnG4Sa/w/UkXkzp3b5/DWUef4ccIU/xc7FuyQ8sRxSHAMWLfiYFxYt+YkXxoq9YyWgpXvQVM4oX/YkX2Rhj/KzVuP8wSA0+e7ZMGP5Ja1R/q4xVk2/J2t3qd/73q5VXxx8//Ts6DZiMo9vmIGMGf6v6sPag4tXaYeWsoShbqrC1pyR4nC19Dh63UJVUKFQgj8PvNcGB8wASsCMBihc7wozuyqHiRa4REvoU67YdxG8XLiH06TO8lDcH6rz7JooXecn+d+OCHileXADdTS9J8aKfwFO86CdWFC/6ihXFi/7iRfGiL/Fy+PhZ9PhsOgb3bK52H5WdL9dvP4gZX23E8hlD8MbrRROchH1GzELRV/KjR9v66tjgJ6G4cu02irySF95eXgmeb8sBtkgSa/u1pU85dtn0IShf2sDFkfdq7fh5HAnYgwDFiz0oxuzD4eLF/kNOXj1SvCSveKTk0VC86Ce6FC/6iRXFi75iRfGiv3hRvOhLvDTqPBJFX8mHsYM7xphs/UfNReC9R1g1eyj+uXQdQ8Yvwntvl8O67d8jKDgUXVrVQeeWdbDv0E8YPt1qKu0AACAASURBVHEpfFP7IFf2rCj0ch60b1YLQ8d/pUoPSNmBL+esgZenJy5euY4jJ3/Dm+WKY0jPFvhqzU58/+OvSmL06fix2oDj4aNgdP9surqmNPnD7We9Wxo357AkSf67cUdd66dfL6jxvFm2uLqvVKl8cPDYr5i+cAMuXrmBMiULY0S/Nij8siFrxbTPUVOWo0KZV1GregX1mpy39+BPmDisK6YtXA9Ddk82ZEzvjwa1qygxZXqv0v+4Gatw8tfzKJg/F3p1aIiaVcupvmRs3t5euHj5Bk6f/RPvVHodvTs2RN5c3OhCf590KW/EFC/2j6nDxYssMzp66jdcvW7YTce0dWtTD76pU9n/rpzYI8WLE2G7+aUoXvQzAShe9BMrihd9xYriRX/xonixLF4i/r2AqNAQpwfW++WigF+aGNcNC4/A6+92xNzxfVGt0usxXtt94CQGjpmPcweX4dyFS2jW/Qt8WKOiKhsgUmHZN3uw5+uJSJ0qFT4dPQ/5cgeojBn/tH6IjIxCk66jcPbAEpXx0n3IdCUa+ndtjAJ5c2LklGW4djNQiRuRMFIfMp1/GlWW4FHQE2zZcwRlShRSwmTp2t349+pNbPxqdBxJEhtil4FT4OXlhb6dG+Fx0BNs3HUYn/dri5u376F++2Hqem9XLIXVm77FqTMXsG/tFFWHxlS8tO49XkmXFg1qqO5lLDK+LUvH4u9L1/BR++EY1LM5ihXKjxwBmfE4KMR4r1LvpVbLQShe+CW0bfIBfvr1POYu36rG/mqh/EYOfTt/jFcK5MG0BeuV5OnftYnT5wMvSAKxCVC82H9OOFS8iPXuP2qeGnXmjOniFNLdtmyc+mDVc6N40XP09DV2ihf9xIviRT+xonjRV6woXvQXL4oXy+Il6LNOiLh4wemBTTf+K3i98mqM68oGGO806ou180agVLGCMV7T6rQc3zkPV/67pcSLSBgPDw91XO1Wg5XIENkSe6nR739ejiNeypQspI6XJsuYRGKI8JEmWSWfT1qKI1tnq+9Dnz7H/85fxOWrN1XpApEfvx9arl6zlPHSqtc4ZM2cAUP7tEJA1ozG+5m1ZBN2fXcC+9ZOVj+TIsJvN+iDOeM/wTuVSlstXrTrmy41Mr3Xk7+ch8if79ZPQ86AzOpa9doORZUKpTCwRzMlXkw5bNr1A1Zv2q+kDhsJuJoAxYv9I+BQ8SLbSadN44s54/s6pJK5/XHY3iPFi+3MeEbiCFC8JI6bK86ieHEF9cRfk7saJZ6dK85kcV1XUE/8NSleXrDLlcUvDsiQRZMReeNq4gEn8sw0nQfAM3f+GGdrGS+agDB9cdeBExg0ZoESHr+d/zeOeJGlSLL7kSzZsVW8LFq9A2f/uGgULyd++QMd+09S15IlRu37fan+UCvLeGR3pB37j1klXmSJ0ZDxC9UmH7IcqFPLD9G4TjW1TEqaZNRorXrjfkoENf+oht3Ey/Z9RzF90QajQJJrSXZPUHAIpo3qGUe8yB+spy3cYBRCiQwtTyMBuxCgeLELxhidOFS8iNX9oHoFY3Et+w/f9T1SvLg+Bu4yAooX/USa4kU/sZKRUrzoK14UL/qKF8WLZfGS3KIpNV6k1sn4zzrHGJrIlODgUCydPtiseBFx0bhuNXRvU98gXgrmQ492H6k+Esp4+errnTjz+z9mxcvEuWtx/u8rWDJ1kKoPI4KmRY8xVokXubYs97l09Sa+PXIac5Zuwc6VE7Bx52EcO33OmFnyJOQpytfuhmmjeqidmEyzaDr0m4i3K76Gdk0/UPdiutRIvpdjl0wbhIplisW51yMn/4deQ2fi2Pa5yJA+rXpdsnBeLZQPwz5pTfGS3CY/xxODAMWL/SeEQ8XLlAXrcObcP1g9Z5j9R55MeqR4SSaBcINhULzoJ8gUL/qJFcWLvmIlo6V40VfMKF70JV60XY0Gdm+Ghh++jaioKKzdcgCzl27GipmfodxrRYziRZbEBGTJiM17fsDUBeuxeckYVfRWMlikhsvscZ9ApIbUVIld48V0iY0l8TJ32RYcPHYG87/sh/DwCFUjxdqlRjKmRnWqqnozF/65CpFKUl9FCvbK9tYiWiqVK6FqtsxbsQ2HNs1AtiwZY4gX+bnUZpk5pjeu3wzEhNlrEPwkxChtRMy8UbooOrWog5CQp6pWjXavktlSs9lANP+oOjq1rIPTZy6g9/BZmDehH6q++RrFi74+ytxutBQv9g+5Q8XLtn1HMXTCV6qaec6ALHFG37hOVVUoS8+N4kXP0dPX2Cle9BMvihf9xIriRV+xonjRX7woXvQlXmS0e74/ifGzVuP+wyA1eKnTOHpgB1SvXFp9ry01kp9rx4wZ1AENa7+tXpcME1l69Ne/11C6RCEM7dMSjbvELK5btlRhdGrxoTo+tniRYr2SKXJqzwLcvHMfvYfNVFkv0qpUKKl2QjKt8SI7LcnORLGbnPf90V/Vj7Nny4QWDd41XnP+ym0qA0Z9pvj5qmVHNaqUUd9LFovWp+m9yHFlSxXC3fuPjcV9Dxz5BaOmLlMcJNun+lulY9yriKwBX8xHSOhT1bdsLNK7Q0P1tdR4MeWw79AptVOSVntGf+92jjglEaB4sX80HSpe+n4+B9/+cDreUR/bMRcZ0hlS7/TaKF70Gjn9jZviRT8xo3jRT6xkpFxqlHzi5RH8CB5PHsPjSTA8QoKAJ0HwCHkMBD+O/v4xvE4dhJTzDHu3EaKy5kRUtlyIypYTkdlyJ58b4UiMBCheXkwGczVekvNUuXv/kSqemyVT+hjD1MSL7FL06PETtZWyLAOK3aRobfp0aeHj7ZXk27xx6y4yZkhnc83Ip8+eq52GTIvraoOR1+QeZTci2W3JUrsVeF8V6jV3nCxnevAoSHHSig2b9iWvy/mZM6aHn6++d3NNciDZgW4IULzYP1QOFS/2H27y65HiJfnFJKWOiOJFP5GleNFPrCheHBMrj8cP4KFJE/lXiZTHBpHyJPrfEBEs8n0QokIew9MOW+pGZcmByIBciMySEwjIrcRMpIiZgFyISuXrmJtlrxYJULzoV7zEF1hzxXX5NiABEkhZBChe7B9PipckMqV4SSJAnm41AYoXq1G5/ECKF5eHwKYBMOMlflyej+4paSJixCBSguAR/BiQbBTJQpGfhRp+ZsxQeRZqE3/Tg6PS+CMqTTpEpZX/0gNp0wFp0yPKzx+I3rbWZ9cqdUrYh63g8eAuPO7ehEfgDXg+vGvxulH+GRAZkAdRWbMjSv0r2TI5EZk1F6LSZ0r0mHmiZQIULylPvMiymsPHz6ito9lIgARSJgGKF/vH1eHi5eipczh15gKehMT9Rax/16a6T7mjeLH/pGSP5glQvOhnZlC86CdWMtKULl48wp8bZYlhGU8QEBJsyDR5Ikt7DFknL16L/v65oSaBzc3TC0qgKHmSTokUEShRadID/ulffC9iRZMsaeTnaY1yxdI14yuu6xEeBo+7t+Bx9wY8A28Ad24Yv/a4exseEWHxdhuV2u9Fdky2nEC2XErIRAbkRFSmAMAz6UslbOaYQk6geEl54iWFTE3eBgmQgAUCFC/2nx4OFS+7DpzAoDELVNEqKSqVP092pE7lo4ptSUGuPV9Pgn9aP/vflRN7pHhxImw3vxTFi34mAMWLfmKlJ/Hi8Sw0hjTxCDFIE1m+A6NQMUgT9b1kp4hQCXueqIBESc0DEzGi5Im/GYEi0iStQbRAvvZzbO22RO1qFBUFzweBhuwY+e/OdXgE3oTn3RsqW8Yj9En8UsbLC1GZcyAyWshITRlZvgQRM9lyIMondaL4ustJFC8UL+4y13mfJJCSCFC82D+aDhUv7fp+qQTLyE/boVLdnvj2mynIlSMrZny1EVKxfO28Efa/Iyf3SPHiZOBufDmKF/0En+JFP7FyhXjxkFomJvVNNEHyYvmOybIdrS6KSJTw8ESBjfJOFZ194q/EiCH7xJCNgrQZgGhpEpU2g0mGij+iUqdJ1PUcfVKixEsCgxJBJVkyki1jEDPX4HnnJnD3JmS5laUWmSELILVkJDsmm6GujJIzsoTJP2ZRUkezSY79U7xQvCTHeckxkQAJWCZA8WL/GeJQ8fJ+84Ho3LKO2l6uZPX2WDNvBF4rVlBlvDToMBw7V05AgXw57X9XTuyR4sWJsN38UhQv+pkAFC/6iVWixUtUlMqSMNY1URknWtHY4OhdeKKX9UQXmTXUSAkGIiMSBUgyKwxZJYb6J4ZlPP7q4d5DCZQXQuXFcelSXFFZR4gXSwGRjCHPQJEwhiVMkiEDqSkTeBMe92/BIyL+eEb6pTFkxkQvW/LIltvwdTZZwpTNqqVViZosyegkiheKl2Q0HTkUEiABKwlQvFgJyobDHCpe6rUdqgpvtW9WC406j0St6hXQsXlt/PHXZbXHvSZibBhvsjuU4iXZhSTFDojiRT+hpXjRT6wQGYnUYSFIGxGCR7fvGcSIqoESLU1UAVlNqEQv3ZHXZGlKZGSiblTqicSQJGqpjqEOiiokqwkUESrGZTzpEeXtk6jrpbSTnC1eLPKLjITHgztKwsiyJcgSJsmSUYLmJmR5WHwtytsbahemrDmVnInKHp0lI1+LmEkh8aZ4oXhJaZ9BvB8ScAcCFC/2j7JDxUvPoTPUiOeO74t5K7Zh7rItaNP4fZz4+Xfcvf8IBzfNgLesIddxo3jRcfB0NnSKF/0EjOLFBbGKjFDSxFAk9lH0tsWGXXjU7jvyr7aVsVb7RLJULNT2SOguJJvBIEZeFJHV6pwYisgaap9ohWWNO/Xo/P97CXFx9OvJSrwkcLMewY/gqYr8Sm2ZG8baMqoA8OMHlp1OxqxqyZJauiRbY2s7MMnXDq6jY88YUrxQvNhzPrEvEiAB5xCgeLE/Z4eKl/N/X8Gduw9R9c3X8Px5GEZMXoqd3x5HmZKF0aNtfbxZrrj978jJPVK8OBm4G1+O4kU/wad4SXyspI6JR3RdE6l5YtzCWLJOgh4Zd+ORY7TXVCHZp4ncwli2KPZLCw//9AiXJTzRO/CozJM0/tG78ERnpJhsc6xe4043iQ90Es7Uk3ixdJseYc8MhX3v3DBky6jlS4ZMGc/7dywuSVNLzLQlSyZiRv0sY9ZktYSJ4kWf4iUqKgqB9x4hfbo08E2dyngTYeERiIiIiPGzJLydjac+CnqCY6fO4YN3ysMjeut4e/RrqY99h06h3GtFkCWT7bWYIiIi8ex5GNL4sbi2o+PE/l1DgOLF/twdKl7uPXgMHx9vpPd/UaAvMjIKz8PCIK/lyp7FaR+u9kdn6JHixVFk2W9sAhQv+pkTFC+AYQtjbcmOhS2MtSU92pbGid7C2BNRfrKzzovaJzG2MI7+uYdpHZToHXhSp/aGv58P7j1+pp9J5sYjTSnixWIIIyOUfFHLlQKvx5QzsmW2hfeJKqacNXu0mMmlsmUM22PnQFQWWcLk7dTZQ/GiL/Fy/2EQ5izbgm8Pn4J8LU12JR3Rt436g+mcpVtw4MefsWXpWLvOo9//vIwmXUfh7IElTsuGL16tHVbOGoqypQoneC+Dxy1EpxYfolCBPOrY46d/R6cBk3F02xxkzOCf4Pk8gAT0RoDixf4Rc6h46T1sJooVeQnd29SPMfJ/Ll1H/fbD8N26qciZPYv978qJPVK8OBG2m1+K4kU/EyAliZc4WxhHb1MsWSYO28JYCRRZvhNdMDZalhiW7xhqoRiW8UR/LVLFN02i/8qf2seT4kU/by+4hXhJIB6ej+8bsmU0MSP/arVlgh/Ff7aHhyrqK8V9pcivKvYbYPha7cjk62f3mUDxoi/x0n/UXMjv6ROGdsHL+XPhxq1A7Pj2OHIGZEbT+tVVJntQ8BMUfCm3XedKchcvImmWTR+C8qWLqvsOfhKKK9duo8greZ0miuwKnJ2RQAIEKF7sP0VcIl5u3r6Hd5t+is1LxqBIwbz2vysn9kjx4kTYbn4pihf9TIDkKF7UUpwnj6JroBhqn8hSndhbGBt+ZlJMNrFbGHv5GGufeGhLdKJ34xGBouSJiUAxCBURKPZ/8Eto5lC8JEQoeb1O8WI5Hh7PQgzLlaJryyDwulrC5Cl1Zh4EWiwKLRljkVJXRor7RssYETNqu2zZNjsRjeJFX+LljVrd0K1NPbUZhrm268AJ/Py/v/B5vzZK0AwZvwi1a1TA6k3fIiwsHP27NkGqVD5YuHI7HjwKQutGNdGlVV3V1Y79x3Dw2BmkTeOLvQd/QuaM6TC8b2tUqVAKscVL6NPnmLl4I3Z9dxyZMqRD0/rvoGHtqvDzfbHsSRvffzfu4Ms5a/DTrxfgm9oHb5YtjrGDO6pxHDz2K6Yv3ICLV26oUgcj+rVB4ZcNWSumGS+jpixHhTKvqo1ApMl5MsaJw7pi2sL1WLJ2N/LkzIaM6f3RoHYVvPF6UQwd/5XaKMTLy1P1P27GKpz89TwK5s+FXh0aombVcqovGZu3txcuXr6B02f/xDuVXkfvjg2RN1dAIt5RPIUEnEOA4sX+nB0iXtZt+x4hoc+wYech5MqRFZXLlTCOPCw8HAeO/IIbt+/i0KaZ6sNKz43iRc/R09fYKV70Ey+HiZc4WxiLQHkcXTzWUVsYpzLusmPYcSe69knadGoLY+MOPNFLeRC9vXFUKl/dBIziRTehUgOleEl8vFQNpfu3lISBiJloIaPqy4iYCXseb+eynXlkthyG7bGVnNH+y4nIzDmAeIpGU7xYFi8/hwQiKCIs8UFN5Jll02ZDOs+4O6WNnrZC1WPs3rYeypUqglcK5IlRx2Tlhn04dOwMlk4fjN/O/4tm3b/Ae2+XQ+O61XD2j4tqI41XC+VXsiU8PAIDx8zHzpUTUCBfTixftxeT53+jxE6pVwti/Y6D+N8fF3Fk6+w44kVEiNSK7Ne1sSpLMHrqcpVBX7dmpTh33GXgFHh5eaFv50Z4HPQEG3cdxuf92kL+0CsZ9p1b1sHbFUspOXTqzAXsWztF3ZOpeGnde7ySLi0a1FD9b9lzBHKvsqTq70vX8FH74RjUszmKFcqPHAGZ8TgoxLg0Suq91Go5CMULv4S2TT7AT7+ex9zlW7Hxq9GKRfch05Vw6dv5Y8Vz2oL1SvKIpGIjgeRKgOLF/pFxiHh5v/lAXLsZaHa0afx8lelt+OHbqFimmP3vyMk9Urw4GbgbX47iRT/BT1C8yBa0sgOPsUCsmS2MlVB5kZmijk3iFsaGHXeil/GY7MZjKlDUlsbaa/7pIPUiUnqjeNFXhCleHBSvqCh4ProPyI5LImFuG7bGVjswybImyYaLr3l6IipTgHEJE2TnJcmSyZYLmV5+CcFR3ngWlrjt1x10ty7pNleWuBl95c9vxKkQ878zO3KQP736Md5IEzfjQorcrt64H8vX70NI6FM1BJERPds1ULVMzImXcweXKTkix0vGzPqFo1C8yEvq3AYdhqsdTRvUqqLEy4+nfsPiKQPVa7Js6Z1GfbF79US1dEer8RIWFoFyH3TBsE9ao3SJV9Sxm3f/gNt3H2DWmD5xsLTqNQ5ZM2fA0D6tEJA1o/H1WUs2Ydd3J7Bv7WT1M6kv+XaDPpgz/hO8U6m01eJFzo291Mg0Q+fkL+ch8ue79dPUkixp9doOVZk8A3s0U+KlTMlCSgBJ27TrB6zetN/udXIcOV/Yt/sRoHixf8wdIl60YU6auxYF8udE4zrV7D/yZNIjxUsyCYQbDIPixfFBlnomCI8AIsLgER4GRIQD0f/K91ER4fCIiEhwIN4/bIfXzz8gskAxRGYOiBYsBrliFCgJ9mL+gKjUftE1TtIhSmqe+Bn+VcJELd8x1EERyWLY6tg/0UsEEjlE3Z1G8aKvkFG8uCZeHqEh8LhzTe3A5BF4S33tIYV+ZSnTw7sWBxWVLoOhjozswKRlygQYvo9Mb3hQdYdmTrx0u3IYfz576PTbn5+vKor6vpAUsQcgm2Fcu3lHLZ2ZNPcbtGz4rsoosSReJPOjVI0OWLdwJEoUKaC6FClSu0ZFJW9iixd5XUTN2MEdkCdngFG8/Hf9Duq0+Uxli5juqCRSZdqonnFYyRKjIeMX4nbgA7UcqFPLD9WzhyyDkvbl0C7Gc6o37qcESPOPathNvGzfdxTTF21QmTtaGzllGYKCQ9R4Y4uXfYd+wrSFG4xCyOnB5wVJwAoCFC9WQLLxEIeKF20ssiXdo8dP1LcprfI3xYuNM46HJ5qA3sWLqdQQmeEhqdVSP0RkRthzg9SQNPiIcETJ6yI81Ovytfz73ChFEBaGKNk1x+zx0o9IEzlfJIrhOEOf0r+cG32M/ExkiwtalF9aVSDWIEv8Tb6O/plIE/+YhWSj0sX/S7ILbiHFXJLiRV+hpHhJnvHyvHkVnvdExFwDAm/D485/KnPG8/Y1iwOO8kmFqOx5EJUlByKjM2UM9WUMGTMpqZkTL8nt/qS2Suw6KsMnLlGZ7MtnDLEoXkTWlKze3mrxcv3WXdRsNkD1KxnxWsbLk5CnqFS3JzYsGoVihQ2ZMwk1kT6Xrt7Et0dOq52XZHnTxp2Hcez0OWNmifRbvnY3TBvVA+9XKx9DvHToNxFvV3wN7Zp+oC5lutRIvpeMlyXTBhmz9U0zXo6c/B96DZ2JY9vnIkP6tOp8EU6vFsqnsnYoXhKKHl9PjgQoXuwfFYeKF/kQnLdiK1Zu2G9MV5QPVtmOTT7YUqeKu7bUHrcoaZLPnoXFSDc07ff58zA8eBSsXpfUSNP27HkY5H8c5op3mRsbxYs9IsY+rCFgSbxoUsMj4rlBKmhSI1pgGKWGZG0o6SGZHAap8UKCGDI94kiNsOcmx8t5pt+HwUMkiJIjESZSQyRJuMukhjU8zR2jltV4ewGyHau3NzykQKy3N6K8vOEhP/PyBrx9YhwDL/neG1HePvDw9lHHeF48B6/LfyKiVCVElHn7RYaKCBbJSPHPkNgh8jwHEKB4cQBUB3ZJ8eJAuA7oWtV4uX4d4Teuq2wZqSfjcUeWM90wLGsKDbF41UiVJRMtYdS22LJFdnR9GZ/UDhix47pM7uJFfj+u0aQ/+nZujIpliyGdfxqc/f0iug2eip7tG6BH2/pJFi/b9v2IhZMG4Nnz56oOytGffsP+b6bi3ys3YmwnLSIkLDwCk0Z0U8uI/rx4VRX1bdv4/TgBmrpgPRrVqYp8uQNw4Z+raNR5pKqv8vBRsNryWURLpXIl1NjnrdiGQ5tmIFuWjDHEi/xcarPMHNMb128GYsLsNQh+EmKUNjKeN0oXRacWdRAS8lSJKE0USWZLzWYD0fyj6ujUsg5On7mA3sNnYd6Efqj65msUL457S7FnBxKgeLE/XIeKl683f4vxs75G5TdKoHzpV+Hj460+YI+eOqdSAEcNaGfXO7p7/xHa9BmvtneTJlXFJZ1QK8QlmTfzV25Xhb+kSTX1OeP74rViBdX3a7YcwOI1O9XXLRq8qwSRNFkT+kGLQcqeZ8+WKcaY9S5efMMjMDygEsr7ZUdxH/dJ97V64kVEwCP8RabFi0yK6EwNTWSYZl4oeWGaqfEicwNaZkeYQX7EkCBaloe5TI2IcHhFhiPyuUGUGARJtGCx+mZceKCs/9ekhZdBUihh4eOt/hW5oX0f5fVCYCjJEX28h3aeHOuTynC8t5eqQeIRLT6UINH6064RfYzIEU2SSL+GMYhI8YYUjbRnS7DGiz0vxr6STIDiJckIndoBxYtTcSf5YgkV15XaMaqGjCxhil1b5uE9y1ImfebopUsiZ3KrGjNqByZZzpQMBXdyFy/h//87z+ipK9RuPlp9FwmAyI5+XRqr3+NXbdyPg0d/NRTXvXAJzbqNhlbjxVzGixStlV2PZGmPVlxXC6osC5o8ohtKFSuIP/66jMZdRuHsgSVqe2ZZNjRq6nL8cOKscQ50bV0XfTp+HGdO9B42E98f/VX9XH5PN/0dfv7KbSoDRpr88VeWHdWoUkZ9L1ksq2YPVbsdSbaMbKX917/X1HFlSxXC3fuPlcCRJhuDjJq6DPcfBqkiv9XfKh1jvIePn8WAL+YbuUkB4d4dGqpzJeOlbKnCxueKfYdOqZ2StNozSX6TsQMScAABihf7Q3WoeJF1lNkyZ1Qph6ZN25bt2I65yJDOkJJnjyZFurbuPYJ671dGWj9f9T+HZev24octs1QGy6/n/lapf/IhW7Loy5i1ZDN2HTiO79ZNU5ev2rAPFk8dBD/f1Ko6+ZlvF6v/yUxZsA6SvTO4Z/M4w9S7eDG9Ialu/0bqAFTwzYHyvgEonSobfDwct+uUYfmIthzENPMinuUnMTIvzGVqSLaHtpwk9nIV0+UnL6SIYbmKaaZGtNjQskbsMTEd3YenV7TUELEgEkGkhEEoGKVGdCbHC6lhyNLQpIYhi8PwvZIa0d8bjtdEiWl/cnx0pkd0xochS8QrWnxEixUlNVJ+cdbYIaZ4cfSkt2//FC/25eno3iheHE3Yvv0nJF4sXU2WoSoZo+26FHhD7cakRM39WxZrbsnW9LJkSSSMCBkPJWYkYyYnojJlAzwd9/tNfPeU3MWLNm75Q6Vkhj8PC1O/x9trB1Ktxsv8Cf0Q9CRU/QE0ofb02XNVriBL5vRKyMTX5DjZaci0uK52rLwmf5yV3Ygs9SHH3wq8rzJszB0nzwKyRXaWTOnjZMzLufK6nJ85Y3qrM+cTun++TgKuIkDxYn/yDhUvTbuOxpvliqtiXKbt4uXrqNduGDYvGYMiBfPa/66ie5Q0QNlhSbPZkop4/p8rcaqpi82WdEo59vTeRWoJlKxR3bpsLNL7p1UFvnav/lKlJcZuehcv6Z6H4S3PzDjpFYL7HnGLhlYM9cSbwUD5oEhUCAqD/zNDrY0X0sQgSSSTQy1fkcKjIj9i1OzQlrcYJIuramokdqKp9efGZSeaUHixHMXcUpMX0sMH8WdqRGd2RC9PUYJDSYzo/7wM2RxaEZpvHwAAIABJREFUX1kyp8Wjp5EIk+0fHZSpkVhGPC8mAYoXfc0Iihd9xYviRV/xSop4SehOPe7dhGfgLXgGXjcsYQq8aagzc/saPJ4bduSJr0VGixhkMxT8lfoyyJITEbnyJ3TZRL+uF/GS6BtM4ERzxXUddS32SwIkkDQCFC9J42fubIeKl6Xf7FaFrbavGB/DHJ/94yJa9BiD4zvnIb1/GvvfVXSPUhhLCoJJlXGx6pICmCmDvyp0pTVJM5Q1mG+VL4mKdXpg7bwRSOOXGu81G6AyXibNW6tSDiXFUjJq0qbxVf9pTe/iJUdwCM7PNaRg/pM5PY7nDcCxPNlwIncALmfyjxEbz6hIlLj9EBWvB6LStTuofPU2soY8S3z8JFPDJNPC8HXMTIwXNTaiMy9iLE+JFhA+2r9apoehzoah3ob5ehyGpSbR9Tqil6YYl6hEXyO5baOr9+K6iZ8o+juT4kVfMaN40Ve8KF70FS9HihdLJDyCH8HzTvTyJVnGdOe6EjNqe+zHDyxCjMyYRWXLqF2Yshu2xtayZ2THuMQ2dxcvsoxHlhBVqVAysQh5HgmQgJMIULzYH7RDxYvUUpFiVbJ2MlPGFw/xl6/ewsUrN4xrLDNnSG/3ei9/X7qGFj3GqnWpvTo0UOS6DJyCIgXz4dNuTYwkZRs7qTXzYY2KWLxmF1as36tea1a/Oup/8BYadBiB/d9MVsuSDh8/g7CwcPRq3wBN61dXxwWFhts/Kk7o8ebd/1Dkzl5kDw7F1sX/IQI+CPfwUf9GeHj/H3vnASZVdb7xd+rObGEbLEsVG02NHRCTqNgNiBgrRo0mCrH91VgSiIUQe4kYJaCoscTeMfaaxALYC80oFhSWtsvW6fN/zp2ZZXfZhZnde+693+47z+OD7J77nfe870XkxznfQdTlR3W+Fx8P9OGLfh4s6efCt72TSLZpRtxnoxfbrcrHtlWF2KaqGMW1BUjAa9TK1Iu5vEgY9b3N86j6/NCB7urAhNrZ2K/hMcwvmoq3Co/vrsvkuuiALQ7cuOoAY96L+r1hy/ycVL4D/mQY5bGVKIuvRkV8JUpjP6I89gN6x39EeXzVFhfY5MrHOs8gbPBWYp1nADZ4+2OddwDWe/qjxlOxxWfnzdJzqYT8RLgCOkAHnOZAe3/GLQp6nSZTlB6t4EU1tPp08ddbNUTtRrnqD7/d6rhsB6jr6U4+9yrsvdtwXP2HM5rPpqodL2quaef9qrlUZseL6jquPrX1jUgmksZ1cJddfzf69S3H5KMOxL4Tz8GiF+Yazb8uv+FuPP/AdSnw0hjNVpajxhngZe1LqKgPYcKT52SlLeqLoqqiCqv7rEJV3yqsLV+LuLf18aRgYwB911aick1/VFb1RVl1GVzJ1jdHZTUZB9EBwQ4QvAgOj9Id7wDBi+MjEi3QjThKY2tQHv8RvWM/oCz+Q/rff0RZ/EfkJTs+whSDD+s9lVjv7Y/1BowZaAAZ9fMNnkrMuVXfLm/RplM8HaADjnOgvT/jFuUTHnclKK3gpSvCOvvs/1b8gNMuuBbjfroHLrvglFZHnFSPF3Ud3R03XGSUV0eHDjjmfKNj+YgdW5/pVd3Nj5syA689djOWfPktLpox2ziytKpqPQ46/vdY9MIc4wiS+KNGjSF8MHJqp+yOJhP4JLoOC5vW4L3QarwfXoONiUirWkUuL/bMq8CY/H4YlVeB3fN6w4+Om6N1SkgPeYhHjeQEzaNGcrJSSnnUSFZePGokKy+7jhrpcslVW21chW0cWzKOL6WOMxkNf+s3djyty4WSR/6jSxbr0gE6QAdMdYBHjUy10yimHbyo7t/q2jm1C2Xcvntgp2FD8K/X3jM6go/ZY6SpK1r21fc4+jeXGceGzv3N0XCnO9arni2lxUUtbjWajl1GbIdZ8x7H86+9Z9xq5Ha33pVxycw5GLr9IOPqt411DRg74Wy899xsfLHsG1x96wNG3xr16cngpW14SQDLotVYGFqDBaHVxo8/xhtaDfPDjV39vTEqWIExgUrsndcX6jYlfrbuAMHL1j1yygiCF6ckkZ0OgpfsfHLKKIIXpySRnY7uBl62tGp1eYBxA1O6twzW/mAAGbe6mal6LUoe/nd2pnEUHaADdMBmBwhezA9AK3hZtWYDjjx1WvOd9tdOOxMTDhkLtfNEXfv8xhO3bPVat1yW/MLrC4wGum0/ak41t7oe77Z7nsKc+541hqgdK3fc8HvsvvOOrR5Rty6d8LuZeOvJW4wx6qOulH7mxf8a10tfcMaxxjoIXraezspYPRaF1+C9ptVYGK7Cl9GNUIAm81GXOQ73lWJ0oC9GBftibKAfers3NS/e+gw9ZwTBi5ysCV7kZKWUErzIyovgRVZePQm8bBHKxGLo17fzjXllpU61dIAOSHeA4MX8BLWCl9n/eBqvv/0RZs08FzNu+gcmHDzWABZq18hxU67Eiw9ej0H9t9yIzPwlA6FwBBuqa1FZUb7ZTpctzVdX34hAnt+AL5kPd7zklpA6iqQAzIKm1VgQXoNPI+sRSyZaFRnsKcLoYAVGByoxOlCB7bzFuU3STUcTvMgJluBFTlYEL7KyUmoJXmRlRvCyKa+efquRrDeXaulAz3aA4MX8/LWCl3HHXoAzThqPE4860LhRKANeNtY2YOyRZ+PhOVdgl+Hbmr8qCysSvHTN7Aji+CC0LgVjQqvwQWgt6pOtb4pSO2BGBfpidLAvRuX1xc7+MrjR8xr2Erx07V2z8mmCFyvd7vpc3PHSdQ+trEDwYqXbXZ+L4IXgpetvESvQATpgtQMEL+Y7rhW8nHjWTOyx8464+KwTWoGXRR8vxa/PvxZvPTkLvctk72YgeDH3pUwgiS8iGzYdTwpVYW2i9Q0Chaphb6APRgf6GTti9sjr0yMa9hK8mPuu6axG8KLTXfNrE7yY76nOigQvOt01vzbBi/metq3oXv19qo/M2pVwVf0IrE81+3VXrdzi5ElfHhJ9KoHe/ZHo0x/u9avh+fhtxA48BpFjpugXzhm65IDX40JZUR7W1HR801aXJuDDpjqQ/4fjgY0b0HTNQ0iW9Da1tlXFuGuva05rBS/zHvwX5t4/H3+59Dd45JnXjWNGOwwZgEuvmoviXoV4aPZlXVPvgKcJXvSH8E2sFgvDa7CgUfWJWYOvY7WtJvW53NjV1xujFIwJ9sPobtqwl+BF/7tm1gwEL2Y5aU0dghdrfDZrFoIXs5y0pg7BizU+dzSLa8MaA8ooEAN189KaHw3A4lrzPVyhppzEJQuKgLwgEnlB40fkBYwfk4Egknn5qZ8HCwB/XmpcQH1NjVM/5iGZfk6NTRYU5jQ3B2/uAMGLrLeC4EVWXjrUagUvsXgcf7jqDqimty0/A/v1wexrzsf2QwboWJOlNQleLLXbmKw6Hsa7odVYEKoyjiipHTLxFi171SGkYb4So2Gv6hMzNlCJPp6g9UJNnpHgxWRDNZYjeNForobSBC8aTNVYkuBFo7kaShO8aDDVpJKu+tr0TpkfjCuxPZ+8Dfe3XyJZVIJksBAINcK4qSmcG6DJRZ4CMAaIaQtzFNBRXwuof9JQx/iaGhsAAgXNMKcZ6Cgw1IM+BC+ywiZ4kZWXDrVawUtG8OfLVmDpl9+hvqEJgwf2xT577oRgwK9jPZbXJHix3PLNJmxKxrAotBYLFYwJr8ZH4fVQX2v5GeQtxKi8CowJVmJUXiV28PWyX3iOCghecjTMxuEELzaa34mpCV46YZqNjxC82Gh+J6YmeOmEaTY94nvlUfievLPdo0auaBgIh4Bwo7FTxoAxoVDqx/Q/7lATkqHGTePCm77fcpwa71a1Eq0vVzBj2UmPDwikduI078xR8MaANoFNO3MUzEl/fdMOntQunszuHQPyBIOA22OGNNNrELyYbqnWggQvWu0VUVwreFny5bfGbpdjxu+PwQM23V50xwPz0ae8BJMO/5kIk7YkkuDFeRGqW5I+i6xPN+ytwqLQGmxIhFsJLXfnYe9AX4xR11gHKo2GvR6HN+wleHHeu9aRIoIXOVkppQQvzs8rA1s6Utr491ecv4geqpDgRU7wWwIvOlbRCuYoSKOOPmWgThrmuBSgUTBHfU9Bn2aYk4E6CvSk4I82mOP1po9MbeWYVYudOZuOWWVgTnoHj4kwh+BFx1uprybBiz5vpVTWCl6mXzsPi5d/g8fv/DM8HnezJw8+9RqumnU/3n/xDvE7XwheZLzqX0Y3Nl9jrfrEfB+rbyW8wOU1mvSODlYaPWJU8948OOtvOAheZLxrSiXBi5ysCF5kZEXwIiOn9lQSvMjJzmrwosMZVzSSBjFpSGMAGwVp0jtxjONTaZjTAu640jt1UjtzUmNTx6zUzpy46VKTBsxJ7a7J/ZhV6uiVJz+I4rISrAu7kQzmO3ZnjunmCS1I8CI0OBNlawUvR546DUceui9+O/kXrSSvXV+D/X95Pp68ayaGbT/IxOVYX4rgxXrPzZhxbbwJ74RWY2FIXWNdhWXRarTc8Op1ufETfzlG51UYO2JUv5hit73H4whezEjemhoEL9b4bNYs3PFilpPW1CkKegGXC3WNUWsm5CxdcoDgpUv2WfpwdwAvOgzbBHOa4A6ljlu1gjkZQNPU0Lz7ZtPOnKbUTh6LYU4K6mQaIKfgTivAo0BNy5456bHJQGsQlPT6dFjaI2sSvPTI2FstWit4OX7KDIwcNgRXXHhqq0k/+HQ5Tjnvasy/7xpsN7if6BQIXkTH1yy+LhHFonAKwigY83F4HSItUIxq2Lujr9ho1ptq2NsXfT35li6e4MVSu7s0GcFLl+yz/GGCF8stz3nCAd/8Y4vP/DDk1znX5APWOEDwYo3PZsxC8GKGi9nVcMUi6eNTGZizqVdOph9OameOgjkK9qQATsteOer77kgTkk3pXTw6duZ4POmbq1K7bFrDnEw/nDTgSd9ilYI3qT47qZuv8pEwGiKna/RQmEPwkt2vje48Sit4ue72h3DfYy/hwdmXYedh2xrHjdasq8HlN9yFDz79Eu88ext8Pq9ofwleRMfXofhoMoGPIqph7xosCK3G++E1qE20/tvVgd4CjMpTNyel+sQM9RVrNYPgRau9phYneDHVTu3FCF60W9zlCQheumyhbQUIXmyzPueJCV5ytszWB9r2eEnBnBSkUY2OM71vDGjTotFxK5iT2a1jHLXKgJ0WTZHjGo5ZZWBOi102GUCTaoCcua48c6NVardO6jarzA1XaZhj3HwVQNJr7670bF4EgpdsXOreY7SCl421DZj0mz+ham018oMBDOzXG8u/Xmk4eu20MzHhkLHi3SV4ER9hVgtIAlgarU7tiGmqwoJwFVbHG1s9W+r2Y5RxhXVfjM6rxC555aY27CV4ySoqRwwieHFEDFmLIHjJ2ipHDMxAGO5ycUQcWxVB8LJVixwzgODFMVFkJcSK5rqtYE76VqtUo+O2MGdTk+Pm3jiZ41Wt+uc0wWUFzMnAm/Tum1bHrIyryFvAnBZjjZ05mmAOwUtWr3W3HqQVvCjnGptCeOSZN/DZ0hVoCoUxZFAlxh+0D3YaNqRbGEvw0i1i7NQivo/XY2FTakfMwvBq/C9aCwVoMp+gy4s983qndsUEK7FXXh8EXJ3f4UXw0qmYbHmI4MUW2zs9KcFLp62z5UGCF1ts7/SkBC+dts7yBwleLLe8SxNaAV66JLCDh12xaOr4VKgxdRNVpvFxBuakd98Yt1lljlk178bZ1Pi4uc+OOoKlAeaoa8SNHTiZI1ItduIYO3DS15Ebu3UCBc3Hq1rt2FHXmqevNw/OPAOorUbTNQ8hWdJbh7Xaa/YvD2qfoztPoBW8xOJxNDaFkR/Mg1dtK0t/6uob4fV6xd9opJZD8NKdf3nktraNiQjeyzTsDVfhs8gGqKutMx8vXNhZNexN74oZE6jMqWEvwUtueVg9ekb1Ityx8YsOp728dC9MKd7ZalmcLwsHCF6yMMlBQwheHBRGFlIIXrIwySFDCF4cEkSWMqSClyyXl9OwZpiTBjSZW6o27cxJ78hpag/mZHbwtL4ByxWL5aQhq8HJJJqufZjgJSuzut8greDlH4++iBtmP4yXHroBA/v1aXbvd3/4K9TNRo/fOUO8owQv4iPUtoAw4vggtNY4lrQwtNr494bkpv+Iq4a9O/h6YVReJcYEKzEqrwIDvYUd6iF40RaVKYUJXkyx0ZYiBC+22N7pSQleOm2dLQ8SvNhie6cmJXjplG22PUTwotn6RBwuA9Sket+kGhu3bHScOV6VuZ48BAV8NjVATvfbSV9R7mqoNwRzx4vm3BxcXit4Of2C67DNoMrNbjX6ZPFXmHzWTLz22M2o7FPmYHu2Lo3gZesecUTKgTiS+CKyAQvV7UlNVVgUqsLaRKiVPf09BRgdqEjfntQXO/pKoACN+hC8yHmT/rJgHv7e14sr1/pwxt4nyRHeQ5USvMgKnuBFVl4EL3LyIniRk5VSSvAiKy/2eJGVlw61WsHLEb+6FMdNOAC/Pv6wVtrVzUYHHHM+Hp17pfheLwQvOl7LnlNzRawWC0JrsLBptbEz5ptYXavFl7j92DuvAqOD/XBY74HYNtYLSGRQTM/xSdpKCV5kJUbwIisvghdZeRG8yMmL4EVOVgQvsrJSagle5GVmtmKt4OXsabfgx9Xr8NTdf2mlO3ME6d9P3Yry0l5mr8nSegQvltrd7SdbnwgZfWLeUztiwlVYHKk2dspkPgF4sEegt3Fr0uhAJfYK9IFq4suPsxwgeHFWHltTQ/CyNYec9X2CF2flsTU1BC9bc8g53yd4cU4W2SjhjpdsXHLOGIIX52RhlxKt4OWNdz7COdNm4Wejd8G4ffdA77JivL3oczz78jvYa9dh+Pu1F9i1btPmJXgxzUoWaseBhmQU76s+MaHV+Di+DgvqqxBCvHmkati7k7/MuDlJ9YlRDXvVLhl+7HWA4MVe/3OdneAlV8fsHU/wYq//uc5O8JKrY/aNJ3ixz/vOzEzw0hnX7HuG4MU+750ys1bwohb56LNv4Ia/P2JcK535jNt3d1zx+18bIEb6h+BFeoJy9KseL1W1TfioaR0WhFPHk1S/mJpEpNUiUg17+xo7YkYFKzDYUyRnkd1EKcGLrCAJXmTlRfAiKy+CFzl5EbzIyUopJXiRlRfBi6y8dKjVDl6U6HAkiu9/WGPAl0EDKlBa3H3+IEjwouO1ZM32HOioue7yaA0WhqqMI0qLwmuwMtbQ6vFKTz5GKxATVDCmL4b5Spsb9tJp8xxwrfkRrpo1RsGZq/+D2YOLMOObJpzZfx/ja8k+A5As3XS7m3kzs1JXHSB46aqD1j5P8GKt312djeClqw5a9zzBi3VemzETwYsZLlpXg+DFOq+dOpMl4KW+oQlNofBmHqgdLy6X7EahBC9OfbW7n65sbzWqijfi3dBqLAipa6yrsCxa06JLDFCcadib3hGzm683vC539zPM4hX5Hp8L32uPG7P+adweuH3vEZj5+gc4Z9FS42uRX05F7KBfWqyK02XjAMFLNi45ZwzBi3OyyEYJwUs2LjljDMGLM3LIVgXBS7ZOOWMcwYszcrBThVbwUrW2Guf96VZ8vmxFu2t8Z/7tKC4qsHP9XZ6b4KXLFrJAlg5kC17alqtLRI0jScaOmKY1+Di6DtFkonlYHjzYPa9P8zXWqmFvgcuXpSoOyzjg/fd8eD940/jpZbv0w+wdeuPPn/2Is/+33vha9OdHIr7nfjTMgQ4QvDgwlC1IIniRlRfBi5y8CF7kZKWUErzIyovgRVZeOtRqBS8zbr4Xr/77fZxx0nhcd/tD+MulvzGOGd0891FUVpTh9msugM/r0bEuy2pKAi+r443Y8/tHO/SmryeIDwcdb5l3nCg3BzoLXtrOEkEcH4XXYWF4Dd5rXIUPImuh4Ezm44ELI/2lqYa9gUrsE6hEqScvN7E9fPSfqxdh7sYvcHnpXphSvHMPd8P5yyd4cX5GLRUSvMjKi+BFTl4EL3KyIniRlZVSS/AiLzOzFWsFL5NO/xPGHzwWJ//yYOx+yBl49t6rsf02/fHWu5/grD/+FQufn4OC/IDZa7K0HsGLpXb36MnMAi9tTUwgiSXRaixsqkodTwpXoSre1GrYdt5e6R0x/YwfB3u7T58mHS8VwYsOV/XVJHjR562OygQvOlzVV5PgRZ+3ZlcmeDHbUb31uONFr79mVyd4MdtRefW0gpdDT7wYv5n8Cxw3YX/sffhUXH/ZFBwwdnesXLUW6nsPzr4Mu47cXp5rLRRLAi+uUCO8r6Z6UCDcCN+rTwCBIKIHHpP6Wl4Q0YOPFZ1HdxavC7y059l3sTosDK1JN+ytwv+ita2Gqd1RqZuTVNPeSoxgw95W/hC8yPqVSPAiKy+CF1l5EbzIyYvgRU5WSinBi6y8CF5k5aVDrVbwcuJZM7H7TjvgkrNPxIVX3o6ajfW46cqzMP/ld4yjR68+ejP6VZTpWJdlNUWBl5r1CP7xhA69SfYqQ9N1j1jmHSfKzQErwUtbZerKatUjRu2IWRBajS8iGxBr0bK3yO3D3nkVxtGkUYG+2M3fG74e3LCX4CW3d9vu0QQvdieQ2/wEL7n5Zfdoghe7E8h+foKX7L1ywkiCFyekkL0GgpfsvequI7WCl1vvegLLvvoet199Pj5Z/BUmnzWz2cdD998bN195tnhfRYGXljteAAT8HsTjCUTjyVQO3PHi6PfRTvDS1phQMob3w2uNPjELmlbhw/A6NCZjzcNUw97d8npjlIIx+ZXYK68vCl1eR/trpjiCFzPd1F+L4EW/x2bOQPBippv6axG86PfYrBkIXsxy0po6BC/W+GzWLAQvZjkpt45W8NLWli9XrMR7HyzGsO0HY+/dhom/SlqtTxJ4aZtHSaEfkWgcjeG43De4Byl3Enhpa3scSXweWW9cX/1eqAqLQlVYn9h0hby6rHqkr8zYDTM62Ndo2Fvult3faUuvHsGLrF+YBC+y8iJ4kZUXwYucvAhe5GSllBK8yMqL4EVWXjrUWgpedCzA7poEL3Yn0HPmdzJ4aS+Fr6K1RqNedTRJ9Yv5NlbXatgQbxFGqz4x+ZUYnVeBId5e3SZMghdZURK8yMqL4EVWXgQvcvIieJGTFcGLrKyUWoIXeZmZrZjgpYuOErx00UA+nrUD0sBL24WtS4TwbmgVFjatMWCMukkp0WJQH3cAowOVxo4Y1bhXXWnthitrf5w0kODFSWlsXQvBy9Y9ctIIghcnpbF1LQQvW/fIKSMIXpySRHY6uOMlO5+cMorgxSlJ2KeD4KWL3hO8dNFAPp61A9LBS9uF1ht9YqqwoHE1FoTX4OPwOoSx6dhbkcuLPY0eMf2MXjG75/WGH56s/bJzIMGLne7nPjfBS+6e2fkEwYud7uc+N8FL7p7Z9QTBi13Od25egpfO+WbXUwQvdjnvnHkJXrqYBcFLFw3k41k70N3AS9uFx5IJfBxd17wjZlF4DTYmIs3D/HBjV39vjAqmbk/aO68v1G1KTvwQvDgxlY41EbzIyovgRVZeBC9y8iJ4kZOVUkrwIisvghdZeelQS/DSRVcJXrpoIB/P2oHuDl7aGqHu2loeqcGCcBUWGldZr8GP8YbmYaph73BfKUY3N+zth94OadhL8JL1a+2IgQQvjoghaxEEL1lb5YiBBC+OiCErEQQvWdnkmEEEL46JIishBC9Z2dStB2kFL9FoDI2hMArzg/B43IjF4/jw0y8RDOZhl+HbdgtjCV66RYwiFtHTwEt7ofwQa0g37E017f0yuhHpy9CN4YM9RRgdrEj1iglUYDtvsS3ZErzYYnunJyV46bR1tjxI8GKL7Z2elOCl09ZZ8mAGtnQ0WfTAYxA9ZoolWjhJbg4QvOTml92jCV7sTsD++bWCl9n3PoN7Hn4Brz56E3oV5uOEqX/G58tWGKu+cMpx+M2JR9jvQBcVELx00UA+nrUDBC+bW1WXiOK98GosbEr1ifk0sh7R5KaWvWoHjLrCeky6Ye9O/jJLGvYSvGT9WjtiIMGLI2LIWgTBS9ZWOWIgwYsjYuhQBMGLs/PZkjqCF1nZEbzIykuHWq3g5eRzr8bOw7fFpWefiHff/wK/vegGzLjoNFRvrMNDT7+G1x/7q441WVqT4MVSu3v0ZAQvW48/gjg+DK81jiUpGPNBeA3qkrHmBwtVw95AH4wO9DN2xOyR10dLw16Cl61n5aQRBC9OSmPrWghetu6Rk0YQvDgpja1r6VcWxOoNTa12k279KY6wwwGCFztc7/ycBC+d9667PKkVvBx64sWYesqRmHT4z5DZ/fLec7PR0BTCPuPPwvMPXIdtBvYV7SXBi+j4RIkneMk9rgSSWBypTh1PaqoyflwTb2ou5HO5sasv1bDXOJ5kUsNegpfcs7LzCYIXO93PfW6Cl9w9s/MJghc73c99boKX3D2z6wmCF7ucz37euRs/h7rFU318rz4GhJoQO+gYJAP5xtem9toJBQ69KKK9VfYvD2a/eI7czAGt4EXteBmx4zb447mTMfHX0w3I8rer/g+r127AgcdeiCfvmolh2w8SHQvBi+j4RIkneDEnrm9itcaxpPebqvBuqAorYrWtCg/zlWBsoBJ7BSqwb6Af+nhy/02G4MWcrKyqQvBijtNNyRhiySTUDWUxpP6JIoG48bVk6mvqe8b3019rMVaNiyKOWBKbajSPV88A8WQC19R8ACRdeKzyUKjjg8VuvzkLYBUtDhC8aLFVW1GCF23Wml6Y4MV0S00vuMf3j6CqxV/4tZ3go4HHocKbgjASPgQvXUtJK3h5/Lm3cMWN9yA/GEBjUwjzbrwY++y1Ex599g3MuPleLHx+DgryA11bgc1PE7zYHEAPmp7gRU/Y1fEw3g2thrq++r3QaqNPTMvPIG8hRuVVYEwwdYX1jr6tN+wleNGTla6qusFLGHFEk0lNpI5CAAAgAElEQVQDGmyCDxkQkQYVri1Aiebn0rCixdg4kogm44gnYUCOWELNlUBcQQ81p/p+GnikvqZAiBqrnlHPJhB3pcc2A5EMHFE/ZkBKep50PfVsY4tjfLqyyaau6uU0zF+Ckb4yDMsrhYKnw/2lyHd5s3mcYzQ7QPCi2WCTyxO8mGyoxnIELxrNNal0yx0vd6xdhHqfD1PztkN+sJcxA3e8mGS0kDJawUsymcQT//o33v90GcbutROOPGRfw5bp185DeWkvo8Gu9A/Bi/QE5egneLEmK/W39otCa7EotNpo3PtBaB3UH5wznzJ3ntGwd1SgAqPzKrFbXu/NhBG8ZJ9VrrskDHiQSEOLzA4K48eOd0lkYEcikTCybAYOBrRIIOlKAh6gMRpLQ4s0bEgDjxQ0Se3GSP3YEmy0hBMZeJJEqMU7k70bskfmwQOfywWPyw0f3PC4XFDH+Txwp76ufkx/3WuM8XRqwQqQqs+ueeVYFqnp0OuB3gLjyvkRvlIDyOzoK8HO/rJOzcmHOu8AwUvnvbPjSYIXO1zv3JwEL53zza6n9vhiDqoKAvio10GoKBtol4wuzcsdL12yD1rBS9ekyXia4EVGTt1BJcGLPSmqHQOfhddjgbo9STXtDVVhQyLcLKbA5TWa9I4OpnrEqOa911V/iLkbv8DlpXthSvHOOQtXc27anZD+g36bIxypoxvpHQvJTUc6MrsdmncqGLsjWh7zaOffVZ14DDFXm+Mfxu6ITfNvAg4Z8JDZPaF+TEGHzNESA1YYOylS32t75EStsSd8PFDAIQ0gFIgwwIMCEW54km54Xa7UPwacSH/PGIMW0CIFL9SYluMVvEh9Lf31pKrlhs/taTG25XOpfzf0bAZG0qCkhT6vob0FNDH0bf6sqmflp2WPF/UWfR+rw9JIDZbFarAktAFLo9X4KlZrvI9tP2pN2/l6YZivFMPzSg0wM9xfgm28vSxehZWO2TsXwYu9/uc6O8FLro7ZN57gxT7vOzMzwUtnXOtez5gOXr769kfMf/kdnHrcofjo8/9h5Y9rOnTs+InjkOf3iXaU4EV0fKLEE7w4J67l0RosCFUZR5M+CK/F97H6VuL6eoKoijdikKcIZZ487pLYQnQBeNIwIQ0FtrBLQu2QUGDBgBItAUYGbDQDhTTEQApEKFihdmO43WlIYUAMNEOLgNeDQr8PoVC8GYI0Q42WYEOBiAw0yQCJNIgwAEiLsUEec9H2Czbb5rqLo9X4UgGZSDUWRzZgWWQjvovXdahrV385dvQXY7i/HCN9pRjqL0Y/T4G2dfSUwgQvspImeJGTF8GLnKyUUoIXWXnpUGs6eHnr3U9w1h//atxYNGveE3jpzYUd6n5n/u0oLpL9PzUELzpeS9ZszwGCF+e+F2vjTViQuTkpVIXPoxtMEat6VDTvMDDggtrJsAkmZP6gv2kXQnp3QjKze0LtTvAYoKLl7ggDWhj1Mjsa3PC6PWmY0Xqs8VybnRkp+OFOwQxXsgVwyIxN78BI79DYdPQkpU8956SP7h4vTlprd9CSLXhpb62hZAxLItVYFt2I5RkgE61pddtZy+eKXF6M9JdjqL/E+Gcnvzq6VIZebOib9atE8JK1VY4YSPDiiBiyEkHwkpVNtg7yfL0YyVjE0LBb9D3jqNEnNUNQkV9ifC2x3U6AV84mBB416trrZDp4iccTCEeiCAb8cLms3X7cNSs69zTBS+d841O5O0Dwkrtndj1xWfUC3F27BKcVDscvC7dP77po0euCuyTsiqbdeQleHBXHVsV0Bbx0VLw2EcGS6AZ8EanG8vQuGXVkqTYRbfeRPu6A0cB3pL8MQ/3qyJJq6FuCAHc6beYXwctWX2lHDSB4cVQcWxRD8OL8rIJ/OAGujalLG4afPQlVhflYctuTqGxoMr7WdO3DSBaXO38haYUEL12LynTwsjU5jU1h+Hxe+LzO+hvPrenu6PsEL511js/l6gDBS66O2TeezXXt874zMxO8dMY1+57RAV46Ws3qeCOWRtVxpQ2pnTKRGgPMtNc8Wf1V02BvkQFghnlLMDyvDCP8pdjO28uArz31Q/AiK3mCFzl5Ebw4P6u8O2bA1VBrCB15+HBUBXz44vklqAzHjK+FzrwCKEjdcCThQ/DStZS0ghfV6+Xex14yrpEuKS7EjXMewT0Pv2Aonn3NBdhvn127pt4BTxO8OCCEHiKB4EVO0AQvcrJSSgleZOVlJXhpzxnV0PfbWK3R0FftilkaVkeXqvF1tNZoeN32o6DL9t5eqRuWAmUpKOMvwSBvUY9o6EvwIuvXF8GLnLwIXuRkpZTu8f0jqIo34cOBx6GvN1+W+LRagpeuxaYVvEy99CYU9yrEddOnYPnXKzHp9D/h6CN+jo119ahaU41H5l7RNfUOeJrgxQEh9BAJBC9ygiZ4kZMVwYusrJRau8FLR46pW5S+jG1MARmjmW9qh8z38fp27/BSPZx29BUb112rG5aG+coMIFPhCcoLZQuKCV5kxUnwIicvghc5WRG8yMpKl1qt4OXQEy/Gb048AscdeQD+8eiLuGH2w1j0wlzUNzThgGPOx7+fuhXlpXK2V7UXAsGLrleTdds6QPAi550geJGTFcGLrKycDF46crIxGTMgjDqylNkdszRSjbWJULuPlLr9zRBG9ZEZ5i8xGvoWueU0YGy5MIIXZ/8ae7ZhBe6rW9Ys0u91IxLbdBX7kQXb4pSiYc5eRA9VR/AiK3jueJGVlw61WsHL8VNm4OD99sJvJ/8CZ158I5pCEdz/t2nYWNeAsRPONna87DxsWx3rsqwmwYtlVvf4iQhe5LwCBC9ysiJ4kZWVRPDSkcMbExEsVkCmRf+YZbFq1HXQ0LefJx/DjCa+pRjhLzOAjPrH77Bbwtqul+DF2b/G5tR+jpkb3u9Q5JReO+Hysr2dvYgeqo7gRVbwBC+y8tKhVit4ufWuJzD3/vn4xYFj8K/X3sOVF/0ax47fH2++8zHOnnYLd7zoSDSHmiWFfkSicTSG4zk8xaF2OUDwYpfzuc9L8JK7Z3Y+wR4vdrqf+9xOPWqU+0raf+LHWAOWRWuMZr6pI0s1+DK6EWFs/nu1atm7jbdXeldM5shSKbbz9TKujHfCh+DFCSl0rEG9b9/E6owBavfL/XXLcGTBEJxcNNz4Wn9PAYb4ipy9iB6qjuBFVvAEL7Ly0qFWK3hpaAxhxk3/wLsffIH99tnNAC9ejwfHnHEFPG43e7zoSDSHmgQvOZjlgKEELw4IIUsJBC9ZGuWQYQQvDgkiSxndHby0Z0MCSayI1qWOLMXUkaUNRkNf9bV4Ox1k/HBjB19xCsgYu2NSR5YGeQqzdNm8YQQv5nmpu1Jm98vUXjvhMu5y0W13l+sTvHTZQksLELxYarcjJ9MKXhy5YpNF8aiRyYayXIcOELzIeTkIXuRkpZQSvMjKqyeCl44SiiYTWB5T112rHTIboHrHqN0yP8Qa2n2k0OXFUAVhfCUYkVeK4b7UkaXe7oC2l4DgRZu1phcmeDHdUq0FCV602mt6cYIX0y0VV1A7eFnw0RI89cJ/8O3KKkw9+UjjCml1rXR5SS+cdsLh4gxrK5jgRXyEYhZA8CImKhC8yMmK4EVWVkotwcvWM2tIRpshzJKwauybOrK0PhFu9+Fyd54BYBSIUUAm00umwNX1hr4EL1vPyykjCF6ckkR2OghesvPJKaMIXpyShH06tIKXL5Z9g+OmXIm+fUpRV9+Eyy84BRMOGYsHn3oNV826Hx+8dAcCeX4tq4/F43C73HC7czvjHI5EkUgkEQxkp4vgRUt8LNqOAwQvcl4Lghc5WRG8yMqK4KVrea1LhAwAo0CMAjJqd8zySDXqk7F2Cw/wFmC4L32zUrqh71BvCXwu1V0muw/BS3Y+OWEUwYsTUsheA8FL9l45YSTBixNSsFeDVvBy2fV3Y2NdPWb9+VxMueQmTDh4rAFeVny3CuNP+SOe/cdV2H7IANMdULcnHT/lSpz5qwkYf/A+zfU31NThZ0edu9l8d918CcbsMdIAQvMefM74/uRJBxm3ManP+upaHDb5Ejx33zUGRGr5IXgxPT4W7MABghc5rwbBi5ysCF5kZUXwoiev7+P1BpBRPWTUkSX17/+LbkQEm64VzsysmvZu6yvCMJ9q5luG4V51u1Kp8TV3Ow19CV70ZKajKsGLDlf11SR40eetjsoELzpclVVTK3hRkOOCM4/F0Uf83LhOOgNeMgDk8TtnYMSO25jqmDrGdM/DLxg1r5s+pRV4UQDl55POw5zrfo/BAyqa563oXYo8vw/7HX0e5t10CYKBPBx+0iX4+JV58Pm8xtGoeDyBS88+cTOtBC+mxsdiW3CA4EXO60HwIicrghdZWRG8WJeXatr7dbTWaOK7NFyNJdFqA8h8G6ttB8cAefBgx3RD3+H+MoxI95LZpawM9U1RhKObQxzrVsOZsnGA4CUbl5wzhuDFOVlko4TgJRuXuvcYreDltxfdgPLSXgYAaQlennvlXVx61Vy899xsFBXmm+pwzcZ6hCIRTD5rJi4887h2wYvaubLt4H6t5l25ai0OPfFivP/iHQaE2WXcaXj6nr+gV2GBsTvn+QeuRZ/yEoIXU9NisVwcIHjJxS17xxK82Ot/rrOzuW6ujtk7nj1e7PU/gnh6d0zrhr6r4o3tCiv2+DHUVwJ1REnBGAVlRvpLUezO7ki3vavtWbMTvMjKm+BFVl4EL7Ly0qFWK3h55d/v4/zLb8PkSQdiwYdLsP/Y3VBW0gs3/P1hHHXYT3HVH36rY01GTQVRzj396HbBy7h9d0dxr0IM3W4gJh72UxQXFRg7WsaMPwsPzb4M+cE8HHzCRcaOl+tnP4T8YMDYubNmXQ0K8gPGP5kPd7xoi5CF2zhA8CLnlSB4kZOVUkrwIisvghdn5lWXiGJJuomvul1pqTq6FN2A6kSkXcF93AEMN3bFqCNL6oalEuPnQZfXmQvsAaoIXmSFTPAiKy+CF1l56VCrFbwowY/OfxM3zH4YjU2hZv2/OHAMpp9/sgE8dH3aAy/1DU2YNe9xqKNFdfWNxm1LvcuK8cicK+D3+zDvwX/h3kdfNCSdMHGcAWUmnX4ZXn74Btx615N4692PEY3GcM5pk3D8xHHGuLqm9hvS6VqXmXUDfo8BnKLxpJllWUuTAwUBL5rCMSQYlyaHzSs7veo9/G39Z7iq72icW/4T8wqzkhYHvG4X/D43GsNxLfVZ1FwHei2+0yhYO/IMcwuzmhYHNrrD+KR+PT5rWo/FoQ1YrI4tRarRmNj8/5/UdQiDfYUYmVeKEXnl2ClYZvz7UH9uDX21LKQHFL11/af4U9UCnFe+C/7Sd0wPWLHsJar7Q4J5XjSE5P5ZRHYCuakfuvwBrI41YfmOJ6HSZ+6Jj9yUdH50UZBgvPPuAdrBixIXiUSxcvU6A74MrOyDkuLCrmjO6tn2wEvbBzNNftUul5+M3D71P3L1jUgmkijuVQDVHLhf33JMPupA7DvxHCx6YS4WL/8Gl99wN55/4DpjfF1jNCs9ThxkgJdEEtEYz107MZ+2mgzwEokbt27x42wHpq1ZgNsUeKkYRfDi7KgMdR6PC36vxwCb/DjfgV5L5qV+vx6hb9es812Qo1D9wTASiyPe4i951O9i30bqmiHM503rjVuWvoxuRDS5+f+TeOHCDv5i46rrnQIKxpRhZEA19O3VTjtfOd44TakBXtYsxLnlu+CqitFOk0c9bRxQN7cG/R6CFyFvxtAv/5kGL5NR6RUKXvJ9Qtx2pkxLwIv6g2JTKLyZAy2P7JhtTzbgpaExhFFHTMXdf70Uo3cf0UqCgjLHTZmB1x67GUu+/BYXzZiN/zz9N6yqWo+Djv89Fr0wxziCxKNGZifHeh05wKNGct4NHjWSk5VSyqNGsvLiUSNZeeVyq1EsmcDXsVosjVZjSWgDlsVqjCNL38fq2m3oq44lqYa+w/2pY0rD/GXGkaVKj8w/1NidLI8a2Z1AbvPzqFFuftk9mkeN7E7A/vm1ghfVE2Xu/c/i5bcWQd1k1PbzzvzbTT9uFIvHjR0rqiHu1FOOxPiD9jFuJlKft979BKFwGGP23Ak+rwe33PmEcdzo1Udv2kzHJTPnYOj2g4wrpTfWNWDshLONZsBfLPsGV9/6AJ6992qjJsGL/S9xT1FA8CInaYIXOVkRvMjKSqkleJGVWS7gpaOVhZIxLIvWYKn6J1xt9I5RQKYq3tTuI6px73CfAjEpIKPAzEhfOYrc/NvaLb09BC+yfm0RvMjKi+BFVl461GoFLwpQ/PPJV3H2aZMwoLI3vF5PqzUc8vO9mqGIWYu78MrZeOnNha3KZW4xUs1+p10zr7nfTFlJEW647HcYs+fIVuO/+uYHnPC7mXjryVuMXS3qo66UfubF/xp6LzjjWEw4ZCzBi1mhsU5WDhC8ZGWTIwYRvDgihqxFcMdL1lY5YiDBiyNiyFqEGeClo8lqExGjoa+CMKqhr7ruWu2W2dhBQ9++nqABYYb5ytINfVVz32IE2NDXsJjgJevX2hEDCV4cEUPWIghesraq2w7UCl5+dtS5OHbC/jjvN790jIFqR8z6DbWGnoreJXC5VCu37D6qIW8gz98KFnHHS3becVTXHSB46bqHVlUgeLHKaXPmIXgxx0erqhC8WOW0OfPoBC8dKVwdbzR2xyyLbDAa+SogszxSgxA2b6DtBjDIW5Q6rtR8w1IptvP2gtelvttzPgQvsrImeJGVF8GLrLx0qNUKXqZeehMG9a/A9P87WYd2R9QkeHFEDD1CBMGLnJgJXuRkpZQSvMjKi+BFVl52gJeOHFoRqzUgjDq2tDi8wTiy9GU09Zdx7X2G+ooxwleGEYEyDPWqXjKl2MZbJCuAHNQSvORglgOGErw4IIS0hIZk1LiprT4ZRUMihszP1Y8NyRgaElFcV/2h8f0PBxyHvkKb6/YvDzrHdIFKtIKXtxd9jvMvvw0v/PM649rm7vgheOmOqTpzTQQvzsylPVUEL3KyIniRlZVSS/AiKzMngZf2nFMNfb+MbUwfV9qAZekdMt/H69HeHYL5Li+G+tTumBLjuJI6tjTCX4I+Hvl/ICF4kfVri+Clc3m1B0UakzHUJzZBEgOixCPN0CQDT9Q4BVFSgCUK9XP1Ty73jX44kOClc8nJf8p08NJej5WObNLRXNfqSAherHa8585H8CIne4IXOVkRvMjKiuBFXl5OBy8dOar+MGX0jVFHlsLVWBxVUKYG6xKhdh8pcfsxzFeKnfxlGKr6yPhLjN0ykhr6ErzI+vXVE8BLvQIbBhCJGmCkscWOkoZE3Pi5+np9ImLsODF2mKgf1TOZnzfXSEESHZ8APChwe1Hg8qHA7Uv96PEg3/h56uuP139lgByCFx0JyKhpOnh57T8f4vsf12S1+hMnHYg8v+wO8wQvWUXNQSY4QPBigokWlSB4schok6bhUSOTjNRYJrPLpaMpfhjya42zs3RXHJAKXjpas2rcuzhSjaUt+scsi1WjLhFt95F+nnwM85VghHG7UpkBZNQ/frS+cKIrHpv1LMGLWU5aU8dJ4EXt+GgNRVoft6lPKOiR2lGidpIYkKTNzhEDlqR3lKgfm3LcSZKN66qzp7qGvsDlRb4BSDKwxJsCJmlIku/2otDtT/9c/ehJQxVvM0wpdPmba2TTMZQ9XrJJqHuPMR28tLRrfXWt0Yi2V2F+KxdD4QjU9/r3Lc+pua0ToyB4cWIq3VMTwYucXAle5GSllBK8OD8vghfnZ9SRwu4GXjpa54+xBuNGpdQNS6ndMV9GNyLcQUPfId5eqeuu0w191W6Z7Xy94EE2f4TT8z4QvOjxVVfVzoIXBUkU2MjsJGneGZLeWZLZSZKBJCkgsmknyabjNpka1kCSQgVJ3D6o434tIYn690KPPwVTjB0mGUiS3nnSDFNSoMWuX2EEL7p+JcipqxW8nDt9FkYOG4LfnTKxlSP/W/EDJp42Ha8+chP69S2X41Y7SgleRMcnSjzBi5y4CF7kZEXwIisrpbYo6AVcLtQ1tr/DQN6KurfingJe2ksxgSRWROuMvjFLYzVYajT0rTa+Fm+nK4QfbuzgK04dU/KXGc181W6Zgd5CS14SghdLbM5pkraQpOVxm5ArBvhdWN3QmNNxG107SRQQaYYiLSCJsXMk83O1kyQNSVJHclI7TtQOk9QxHS8K0z9XEMUuSJJTSFkOJnjJ0qhuPMwW8LKqaj0OOv73ePKumRi2/SDR9hK8iI5PlHiCF2fH9U20Dj/GGwyR99ctxbMN3+DUomEYX7Ct8bUhviL09xQ4exE9VB13vMgKnuBFVl49Gbx0lFQEcWM3jNoVsySyobmXzA+x1O8hbT/qb/qHpiHMyLwyo5eMgjO93QFTXwaCl67ZmYEkDQnVlDW1QyRz/CZz203zz9M9SZq/3qYnSebYjYIkZn8UzGgJSRToaLWTJLOjxJWCJAYUae9YTgto0t0gidmeq3oELzpclVVTC3h55JnX0dgUxmPPvYn+lb2x7147N7sSjcWg+sD8WLUObz4xCx6PW5ZjbdQSvIiOT5R4ghdnx/XnDYswt/aLDkVeUbo3zizeydmL6KHqCF5kBU/wIisvgpfs81J/WM809F0SrsbSdEPf9Ylwu0XK3XnpXTGlGGHcsFRi/FztHOjMpyeBFwVJmnePtGrYuqlBawaS1LVp3Goc0WlxdXCmd4kOSKL+lGRAkkzD1hY7SYo8PpT58+COuY2dIxlIUpiBJC2P5RjPZY7jeDvzevCZLjpA8NJFA7vB41rAy6EnXoyVq9a2a09+MIADxu6Go3/xc4zZY6R4CwlexEcoZgEEL86O6r66ZXi2YUWzSL/XjUgs0fzzU4uGY0LBEGcvooeqI3iRFTzBi6y8CF66npe6SSkFZKqhgIy6aWl5pBrqxpf2PgO9Bc27YjJHlnb0FsPn2vJfdjoVvLSEJKpJq3EdcAewZLPbbZIRNMTjrXegpBu3dj2Z1hXaQpJC96ZGrMYxmjRAUTuYDEiiYIgnfdwmA1bS0CTT+FWN6ejT2R4vZq+b9bJzgOAlO5+68ygt4CVj2PW3P4Rtt+mHY8fv3209JHjpttE6bmEEL46LpENBLhdQWRrEqg1NckT3YKUEL7LCJ3iRlRfBi768vo/XG8eVVA8ZdWRJ/fv/ohsRwSbon5ldNe3d1ldkAJnheWUY4UvdrqSa/LrTnTTMAC+qr82mZq2tIUlqh8mmHSUZSFKv4EjmWE4zJNnU0DXUToPirrqqIMmmm21SvUUyV/+q22pUg1bj5y0gSaZXSX4rWLLpGI66LcfKD8GLlW53fS6Cl657KL2CVvDS0hx1i1E4HEFFn1J4Pc67Qq+zQRK8dNY5PperAwQvuTpm33iCF/u878zMBC+dcc2+Zwhe7PO+MzMTvHTGta49szy6EV9Fa/B5eL1x9fXyaA2+idV1WHQXfxmG+kpQk4zgtcaVODx/MMYXDElBkvQVv+oK4JZX/aodJ6FEHOp67eZxiVi7tzh1bTWpp9UukcxxG7WTpMC4yjd9e036e73U0ZrM8Zou7CQxQ68VNQherHDZvDkIXszzUmol7eDlqRf+g5vnPooNNZv+g3/ckQfg/DOOQXGR/EaTBC9SX315ugle5GRG8CInK6WU4EVWXgQvsvIieHFOXp9E1mO5sUNmA5aom5aiNVgdbzRdYFHLniQtIEnLnSRFLSCJ03aSmG6IpoIEL5qM1VSW4EWTsYLKagUvz73yLi69ai723m049t17Z5SV9MKCDxfjX6+9h5+P2RWzrzkfLvUnFMEfghfB4QmTTvAiJzCCFzlZEbzIykqpJXiRlRnBi7PzqktEsTjdxPep+q+wMLQGg31F2M3fu/m4jYIkqjFr85W/Li96efwItNlxoo7lWH3cxtnu6lVH8KLXX7OrE7yY7ai8elrBy6/Oucpw5IHbprdyRt12dOWN/8ArD99o3Hok+UPwIjk9WdoJXuTkRfAiJyuCF1lZEbzIy4vgRU5mZvR4kbNa+UoJXmRlSPAiKy8darWCl58ddS5OO+FwnH7CEa20r1qzAQcddyH+ccsfjN0wkj8EL5LTk6Wd4EVOXgQvcrIieJGVFcGLvLwIXuRkRvAiJyullOBFVl4EL7Ly0qFWK3iZeulN+HH1ejx9z1VwuzcdKbrjgfmYNe8JvPH4LajoXaJjXZbVJHixzOoePxHBi5xXgOBFTlYEL7KyIniRlxfBi5zMCF7kZEXwIisrpZbgRV5mZivWCl4++HQ5TjnvapSVFGHfUbugd1kx3l74GZZ/vRJHH/FzzLzkdLPXY3k9ghfLLe+xExK8yIme4EVOVgQvsrIieJGXF8GLnMwIXuRkRfAiKyuCF3l56VCsFbwowR9+thyz730Gn3zxFRqbQth+m/44dsL+OGHiOPh81t53r8NAghcdrrJmew4QvMh5Lwhe5GRF8CIrK4IXeXkRvMjJjOBFTlYEL7KyIniRl5cOxdrBS0vRyWRS/C1GbUMgeNHxWrImwYvsd4DgRVZ+vE5aVl681UhWXgQvcvIieJGTFcGLrKwIXuTlpUOxdvBSvbEOL725CN+trDL0DxncD4fuvzeKiwp0rMfymgQvllveYyfkjhc50RO8yMlKKSV4kZUXwYusvAhe5ORF8CInK4IXWVkRvMjLS4direDl25VVOOJXl26mOz8YwNzrL8QeuwzVsSZLaxK8WGp3j56M4EVO/AQvcrIieJGVlVJL8CIrM4IXOXkRvMjJiuBFVlYEL/Ly0qFYK3g58+Ib8faiz/HAbdOx87Bt4XK78NmSr3HVrAdQtXYD3nxiFjwet451WVaT4MUyq3v8RAQvcl4Bghc5WRG8yMqK4EVeXgQvcjIjeJGTFcGLrKwIXuTlpUOxVvAy7tgLcOBP98D0/zu5lfZX/v0+zr/8Njx33zI5mxgAACAASURBVDXYdnA/HeuyrCbBi2VW9/iJCF7kvAIEL3KyIniRlRXBi7y8CF7kZEbwIicrghdZWRG8yMtLh2Kt4OXCK2fD7/fi2mlnttK+ctVaHHrixXjhn9dh8IC+OtZlWU2CF8us7vETEbzIeQUIXuRkRfAiKyuCF3l5EbzIyYzgRU5WBC+ysiJ4kZeXDsVawcvLb72PC664DXfccBHKSoqa9avjR3Pvn497Z/3BuOXI5/Vih20H6Fif9poEL9ot5gRpBwhe5LwKBC9ysiJ4kZUVwYu8vAhe5GRG8CInK4IXWVkRvMjLS4direDl3Omz8PrbH21Vd98+pXj9sb9udZwTBxC8ODGV7qmJ4EVOrgQvcrIieJGVFcGLvLwIXuRkRvAiJyuCF1lZEbzIy0uHYq3gRd1qVFvXsFXdPp8Xw3cYvNVxThxA8OLEVLqnJoIXObkSvMjJiuBFVlYEL/LyIniRkxnBi5ysCF5kZUXwIi8vHYq1gpdVazagX0WZDt2OqUnw4pgour0Qghc5ERO8yMmK4EVWVgQv8vIieJGTGcGLnKwIXmRlRfAiLy8dirWCF3XUSDXS/fXxh+OQ/fZGMODXsQZbaxK82Gp/j5qc4EVO3AQvcrIieJGVFcGLvLwIXuRkRvAiJyuCFxlZnbnmDWxIhA2xH4TXIJJMYM+8PvC7PMbX7qw4AKXuPBmLAdC/PChGqxOFagUv73+yDHc99Dz+/d4nyA8GcPIxB+PY8fujX99yJ3rRKU0EL52yjQ91wgGCl06YZtMjBC82Gd/JafN8bhQGfVhfm/qfI36c7UBR0Au4XKhrjDpbKNUZDhC8yHkRCF7kZEXwIiOrPb9/BKvjTR2K/Wjgcajw5stYDMFLl3PSCl4y6r77oQqPP/cWHnr6dTQ2hXDo/ntj8qSDsOdPhhq3Gkn+ELxITk+WdoIXOXkRvMjJSikleJGVF8GLrLwIXuTkRfAiJyuCFxlZqV0u4WTCEBvwe+B1u1AfijWL3yuvAn6XW8ZiCF66nJMl4CWjsmZjPc7906348LPlxpe236Y/fn38YTh83Bixx5AIXrr8DrJAlg4QvGRplAOGEbw4IIQcJBC85GCWA4YSvDgghBwkELzkYJbNQwlebA4gx+m9HhfKivKwpiaU45McbocD+QEv/B4Xahrk7tbkUaOuvTmWgBfV50XtePnnk68aO15+MnJ7HHXovvjvws+M66a3GdgXzz9wXddWYtPTBC82Gd8DpyV4kRM6wYucrJRSghdZeRG8yMqL4EVOXgQvcrJSSgleZOVF8CIrLx1qtYKXDz5djrsffh5vvvOxoV31dzl+4gEYseM2zWtRV06/9OZCnPmrCTrWp70mwYt2izlB2gGCFzmvAsGLnKwIXmRlpdQSvMjKjOBFTl4EL3KyIniRlZVSS/AiLzOzFWsFL+pWo+Vfr8RJRx+EiYf+FMW9CszWb3s9ghfbI+gxAghe5ERN8CInK4IXWVkRvMjLi+BFTmYEL3KyIniRlRXBi7y8dCjWCl5WfLcKgwf0hccjp2lQriYTvOTqGMd31gGCl846Z/1zBC/We96VGXnUqCvuWf8sd7xY73lXZiR46Yp71j5L8GKt312djUeNuuqgtc9zx4u1fjtxNq3gJbNgBWBWrlq32fr32WskvJ7UPeZSPwQvUpOTp5vgRU5mBC9yslJKCV5k5UXwIisvghc5eRG8yMlKKSV4kZUXwYusvHSo1QpePl+2Ar+/cjZUc932Pu/Mvx3FRbKPHxG86HgtWbM9Bwhe5LwXBC9ysiJ4kZWVUkvwIiszghc5eRG8yMmK4EVWVkotwYu8zMxWrBW8ZHq8/PmS09Gvohw+b+vdLX37lMHtdpm9JkvrEbxYanePnozgRU78BC9ysiJ4kZUVwYu8vAhenJ1ZBrZ0pHJKr51wednezl5ED1XHHS+ygid4kZWXDrVawcu4Yy/AsRP2x+9OmahDuyNqErw4IoYeIYLgRU7MBC9ysiJ4kZUVwYu8vAhenJ0ZwYuz89mSOoIXWdkRvMjKS4dareDl0qvmIhqN4+Yrz9Kh3RE1CV4cEUOPEEHwIidmghc5WRG8yMqK4EVeXgQvsjLrVxbE6g1NSMqS3SPVErzIip3gRVZeOtSaDl6+/3EN6huaDK1ff7cKl8ycg9uu/j9U9inbTP/Q7QaJv/GI4EXHa8ma7TlA8CLnvSB4kZMVwYusrAhe5OVF8CIrM4IXOXkRvMjJSikleJGVlw61poMX1dfl9bc/ykorm+tmZZO2QSWFfkSicTSG49rmYGHzHCB4Mc9L3ZUIXnQ7bG593mpkrp+6q7G5rm6Hza1P8GKun7qrEbzodti8+gQv5nlpRSWCFytcdvYcpoOXb1dWobauIatVjxi6Da+TzsopPYMIXvT4qqsqwYsuZ82vS/Bivqc6KxK86HTX/NoEL+Z7qrMiwYtOd82vTfBivqe6KhK86HJWT12CFz2+SqpqOniRtHgztPKokRkuskY2DhC8ZOOSM8YQvDgjh2xVELxk65QzxhG8OCOHbFUQvGTrlDPGEbw4I4dsVBC8ZOOSc8YQvDgnC7uUELx00XmCly4ayMezdoDgJWurbB9I8GJ7BDkJIHjJyS7bBxO82B5BTgIIXnKyy/bBBC+2R5C1AIKXrK1yxECCF0fEYKsIgpcu2k/w0kUD+XjWDhC8ZG2V7QMJXmyPICcBBC852WX7YIIX2yPISQDBS0522T6Y4MX2CLIWQPCStVWOGEjw4ogYbBVB8NJF+wleumggH8/aAYKXrK2yfSDBi+0R5CSA4CUnu2wfTPBiewQ5CSB4ycku2wcTvNgeQdYCCF6ytsoRAwleHBGDrSJMBy+xeOqGHK/HY+vCrJqc4MUqpzkPwYucd4DgRU5WSinBi6y8CF5k5UXwIisvghc5eRG8yMlKKSV4kZWXDrWmg5ezp92Cyj5luOyCUzD/5XfQp7wEY/YcqUO7I2oSvDgihh4hguBFTswEL3KyIniRlZVSS/AiKzOCF1l5EbzIyYvgRU5WBC+ystKl1nTwMvXSmzB4QCWmnXcSzp0+CyOHDcHvTpmoS7/tdQlebI+gxwggeJETNcGLnKwIXmRlRfAiLy+CF1mZEbzIyYvgRU5WBC+ystKl1nTw8vhzb2HWvMdx3m9/iWdefBuDB1TgqMN+1q7+PX6yo/gjSQQvul5N1m3rAMGLnHeC4EVOVgQvsrIieJGXF8GLrMwIXuTkRfAiJyuCF1lZ6VJrOnjZWNeAa/72T+OY0dY+78y/HcVFBVsb5ujvE7w4Op5uJY7gRU6cBC9ysiJ4kZUVwYu8vAheZGVG8CInL4IXOVkRvMjKSpda08FLRmg4EsXvLr0ZO2w7ACcdfXC7+gf1r4Db7dKyNtXk1+1yt1s/EomiemM9KnqXwKX+hNTio3QnEkkEA/6sdBG8ZGUTB5ngAMGLCSZaVILgxSKjTZqGzXVNMtKiMuzxYpHRJk1D8GKSkRaVIXixyGgTpiF4McFEC0uwua6FZjt0Km3gRa3XrhuOmkIRHD/lSpz5qwkYf/A+zdYnk0n8/b5ncfs9TxlfKyspwm1Xn49dR25v/PzBp17DvAefM/598qSD8NvJvzD+fX11LQ6bfAmeu+8a9O1T2ipKgheHvtndUBbBi5xQCV7kZKWUErzIyovgRVZeBC+y8iJ4kZMXwYucrJRSghdZeelQqxW8KMFfffsj7vznc1i87BvUNzZhu2364+jDf47DDhilZbfLjXMewT0Pv2B4dd30Ka3Ay0eff4lfnXMV7v/bNOwyfDvceteT+Ndr7+LVR242xu939HmYd9MlCAbycPhJl+DjV+bB5/NC1YzHE7j07BM3y4DgRcdryZrtOUDwIue9IHiRkxXBi6yslFqCF1mZEbzIyovgRU5eBC9ysiJ4kZWVLrVawctnS1fghKkzDO377LUTyoqL8O4HX2BDTR3OOGk8zj/jGNPXVbOxHqFIBJPPmokLzzyuFXi5ac6jWPK/bzHvxouNedesq8EBx5yPx++cgaLCfBx64sV4/8U7kOf3YZdxp+Hpe/6CXoUFGH/KH/H8A9caV2O3/RC8mB4hC3bgAMGLnFeD4EVOVgQvsrIieJGXF8GLrMwIXuTkRfAiJyuCF1lZ6VKrFbycPe0W/G/FD3j6nquae6ao4z5/veMx3PXQ83j7mdtQUlyoZW0Kopx7+tGtwMtFf/47SosLMf3/Tm6ec6f9f43Z11yAn47aBWPGn4WHZl+G/GAeDj7hImPHy/WzH0J+MIALzjzWADUF+QHjn8yH4EVLfCzajgMEL3JeC4IXOVkRvMjKiuBFXl4EL7IyI3iRkxfBi5ysCF5kZaVLrVbw8rOjzsUpxx5q7G5p+flh9ToccsJFuP9v07HHLjtqWVt74OXMi2/EsO0H4/dTj2uec+/Dp+LKi36NXxw4BvMe/BfuffRF43snTByHiYf9FJNOvwwvP3yDcSzprXc/RjQawzmnTcLxE8cZ4+qaYlr0W1E04PcYR6ii8aQV03GOLjpQEPCiKRxDgnF10Un9j6uW3YVBr+j/Puh3yTkzeN0u+H1uNIbjzhFFJR06kOd1Ay4gHE3QJQEO5Od5EIkmEONvXgLSSh3lq2+Kgf+r4fy41P0kwTwvGkJy/yzifJfNU+jzuuFxASHBv3cZR3356bQDWsGL6qeido/cccNFrQSqq6b/cPUdmH/fNdhucL9Oi9/Sgx3teFENdaed96vmRzM7XvbbZ1fja7X1jUgmkijuVYDLrr8b/fqWY/JRB2Lfiedg0QtzsXj5N7j8hrvx/APXpcBLY1SLfiuKGuAlkUQ0xv95tcLvrs5hgJdI3Lh1ix+HO+ACCgM+1DfJ/e+Dwx02VZ7H44Lf6zHAJj/Od0BBMkVeIlGCMuenlfqDYSQWR5x/ySMhLhQG+XuXiKAAo1dm0O8heBESmAFe3C6EInJ/7yrK9wlx25kytYKXx557E1fe+A9jN4nq8VJaXIRFHy/Fsy+/jf59e+PhOZdvdp2zWTa1B15Uj5dlX33XDIJa9ngZseM2raZe8d0qHDdlBl577GYs+fJbXDRjNv7z9N+wqmo9Djr+91j0whzjCBKPGpmVGOtszQEeNdqaQ875Po8aOSeLbJTwVqNsXHLOGDbXdU4W2SjhUaNsXHLOGB41ck4WW1PCo0Zbc8hZ3+etRs7Kww41WsGL6ueiju/ccufjrdY2bt/d8afzT9nsamYzDFBXWKsdK6oh7tRTjsT4g/YxbiZSn023Gk3HLiO2w6x5j+P5194zbjVS1Ljl55KZczB0+0HGldIb6xowdsLZeO+52fhi2Te4+tYH8Oy9VxvDCV7MSI01snGA4CUbl5wxhuDFGTlkq4LgJVunnDGO4MUZOWSrguAlW6ecMY7gxRk5ZKOC4CUbl5wzhuDFOVnYpUQreMksqikUwQ+r1hq3DfWrKEd5aS9t673wytl46c2Freo/d9812HZwPygQdNs9T2HOfc8a31c7Vu644ffYfefWfWa++uYHnPC7mXjryVuMMeqjrpR+5sX/GhDngjOOxYRDxhK8aEuRhdtzgOBFzntB8CInK6WU4EVWXgQvsvIieJGVF8GLnLwIXuRkZfy5M+CF3+NCTYPcY+j9y4OyTHeYWkvAi8PWjFA4gg3VtaisKN9sp8uWtNbVNyKQ52/eQaPGcseL09LtvnoIXuRkS/AiJyuCF1lZKbUEL7IyI3iRlRfBi5y8CF7kZEXwIisrXWp7JHgx00yCFzPdZK0tOUDwIuf9IHiRkxXBi6ysCF7k5UXwIiszghc5eRG8yMmK4EVWVrrUErx00VmCly4ayMezdoDgJWurbB9I8GJ7BDkJ4FGjnOyyfTB3vNgeQU4CCF5yssv2wQQvtkeQtQCCl6ytcsRAHjVyRAy2iiB46aL9BC9dNJCPZ+0AwUvWVtk+kODF9ghyEkDwkpNdtg8meLE9gpwEELzkZJftgwlebI8gawEEL1lb5YiBBC+OiMFWEQQvXbSf4KWLBvLxrB0geMnaKtsHErzYHkFOAghecrLL9sEEL7ZHkJMAgpec7LJ9MMGL7RFkLYDgJWurHDGQ4MURMdgqQit4+ccjL2LIoEr8dPQu8Ho8ti5U1+QEL7qcZd22DhC8yHknCF7kZKWUErzIyovgRVZeBC+y8iJ4kZMXwYucrJRSghdZeelQqxW8zLj5Xjz67Bvo26cUpx53GI469Kco7lWgYx221SR4sc36HjcxwYucyAle5GRF8CIrK6WW4EVWZgQvsvIieJGTF8GLnKwIXmRlpUutVvCiRH+25Gs8/MzrePrF/xprOO7IA3DCxHEYtv0gXWuytC7Bi6V29+jJCF7kxE/wIicrghdZWRG8yMuL4EVWZgQvcvIieJGTFcGLrKx0qdUOXjLCN9TU4ZkX/4v7n3gZVWursfduw3HyLw/BfmN3FX0MieBF16vJum0dIHiR804QvMjJiuBFVlYEL/LyIniRlRnBi5y8CF7kZEXwIisrXWotAy8baxvw7Mtv455HXjDAS34wgMamEMpKijD1lIk46eiDdK1Ra12CF632sngLBwhe5LwOBC9ysiJ4kZUVwYu8vAheZGVG8CInL4IXOVkRvMjKSpda7eDl82Ur8Mgzb+DJ5/9trGHcvrtj8qSDMHqPkVj21Xe4//GX8d6Hi/H6Y3/VtUatdQletNrL4gQvIt8BghdZsbG5rqy82ONFVl4EL7LyIniRkxfBi5ysCF5kZaVLrVbwkmmuq3a3qB0tx07YHwMqe2+2lo11DSguktl0l+BF16vJum0d4I4XOe8EwYucrJRSghdZeRG8yMqL4EVWXgQvcvIieJGTFcGLrKx0qdUKXv5+3zMYWNkHB++3FwJ5fl1rsLUuwYut9veoyQle5MRN8CInK4IXWVkptQQvsjIjeJGVF8GLnLwIXuRkRfAiKytdarWClyVffosXXl+AY8bvj8EDKprXcMcD89GnvASTDv+ZrnVZVpfgxTKre/xEBC9yXgGCFzlZEbzIyorgRV5eBC+yMiN4kZMXwYucrAheZGWlS61W8DL92nlYvPwbPH7nn+HxuJvX8OBTr+GqWffj/RfvQDAgeycMwYuuV5N12zpA8CLnnSB4kZMVwYusrAhe5OVF8CIrM4IXOXkRvMjJiuBFVla61GoFL0eeOg1HHrovfjv5F630r11fg/1/eT6evGsmhm0/SNfaLKlL8GKJzZwEAMGLnNeA4EVOVgQvsrIieJGXF8GLrMwIXuTkRfAiJyuCF1lZ6VKrFbwcP2UGRg4bgisuPLWV/g8+XY5Tzrsa8++7BtsN7qdrbZbUJXixxGZOQvAi6h0geBEVF5vryoqLPV6E5UXwIiswghc5eRG8yMmK4EVWVrrUagUv193+EO577CU8OPsy7DxsW+O40Zp1Nbj8hrvwwadf4p1nb4PP59W1NkvqErxYYjMnIXgR9Q4QvIiKi+BFVlwEL8LyIniRFRjBi5y8CF7kZEXwIisrXWq1gpeNtQ2Y9Js/oWptNdSV0gP79cbyr1caa7l22pmYcMhYXeuyrC7Bi2VW9/iJeNRIzitA8CInK6WU10nLyou3GsnKi+BFVl4EL3LyIniRkxXBi6ysdKnVCl6U6MamEB555g18tnQFmkJhDBlUifEH7YOdhg3RtSZL6xK8WGp3j56M4EVO/AQvcrIieJGVlVJL8CIrM4IXWXkRvMjJi+BFTlYEL7Ky0qVWO3jRJdwpdQlenJJE99dB8CInY4IXOVkRvMjKiuBFXl4EL7IyI3iRkxfBi5ysCF5kZaVLrXbwoo4Zvb3oM3z3w5rN1jD1lCMRyON10rrC3VrdkkI/ItE4GsPxrQ3l9x3gAMGLA0LIUgLBS5ZGOWQYjxo5JIgsZXDHS5ZGOWQYwYtDgshSBsFLlkY5YBjBiwNCyEFCfsALv8eFmoZoDk85a2j/8qCzBAlToxW8vPTmQlx45WzDkrKSos0a6T5zz1UoKswXZllrudzxIjo+UeIJXuTERfAiJyullOBFVl4EL7LyIniRlRfBi5y8CF7kZKWUErzIykuHWq3gRV0nXZAfwG1Xn4/8YJ4O/bbXJHixPYIeI4DgRU7UBC9ysiJ4kZWVUkvwIiszghdZeRG8yMmL4EVOVgQvsrLSpVYreDny1Gk4bNxonHXqRF36ba9L8GJ7BD1GAMGLnKgJXuRkRfAiKyuCF3l5EbzIyozgRU5eBC9ysiJ4kZWVLrVawcuNcx7Bx5//Dw/cNl2XftvrErzYHkGPEUDwIidqghc5WRG8yMqK4EVeXgQvsjIjeJGTF8GLnKwIXmRlpUutVvDyzEtvY9o1d+K0Ew5Hv4ryzdZw7Pj94Pf7dK3NkroEL5bYzEkAELzIeQ0IXuRkRfAiKyuCF3l5EbzIyozgRU5eBC9ysiJ4kZWVLrVawcv5l9+GV/79fofa35l/O4qLCnStzZK6BC+W2MxJCF5EvQMEL6LiYnNdWXGxx4uwvAheZAVG8CInL4IXOVkRvMjKSpdareBFl2gn1SV4cVIa3VsLd7zIyZfgRU5W3PEiKyvueJGXF8GLrMwIXuTkRfAiJyuCF1lZ6VJrGXhpCkXg9Xrg83p0rcWWugQvttjeIycleJETO8GLnKwIXmRlRfAiLy+CF1mZEbzIyYvgRU5WBC+ystKlVit4icXjuOOB5/DQU69iQ00drp12JiYcMhZTL73J6O1y68zzdK3LsroEL5ZZ3eMnIniR8woQvMjJiuBFVlYEL/LyIniRlRnBi5y8CF7kZEXwIisrXWq1gpc33/kYZ0+7BUcf8XMs/GgJzjltkgFeXn7rfVxwxW1gjxddsWZXt6TQj0g0jsZwPLsHOMpWBwhebLU/p8kJXnKyy/bBeT43CoM+rK8N266FArbuQFHQC7hcqGuMbn0wR9juAMGL7RHkJIDgJSe7bB1M8GKr/TlPnh/wwu9xoaZB7u9d/cuDOa+bD2xyQCt4UTtbBvWvwPT/OxlnXnwjJhw81gAvq9duwIHHXojH75yBETtuIzoP7ngRHZ8o8QQvcuIieJGTlVJK8CIrL4IXWXkRvMjKi+BFTl4EL3KyUkoJXmTlpUOtVvAy7tgL8LtTJ+LY8fu3C16evfdqbL9Nfx3rsqwmwYtlVvf4iQhe5LwCBC9ysiJ4kZWVUkvwIiszghdZeRG8yMmL4EVOVgQvsrLSpVYreFHXSdfU1uPumy81+rpkdrzcetcTmHv/fHz48p3I8/t0rc2SugQvltjMSXidtKh3gOBFVFzc8SIrLoIXYXkRvMgKjOBFTl4EL3KyIniRlZUutVrBy7KvvsfRv7kM2wzsi7r6Ruy20w6IxRP493uf4PwzjsEZJ43XtS7L6hK8WGZ1j5+IO17kvAIEL3Ky4o4XWVlxx4u8vAheZGVG8CInL4IXOVkRvMjKSpdareBFiVbwRe1wWfjRUjQ2hTB0u4E46eiDjYa7brdL17osq0vwYpnVPX4ighc5rwDBi5ysCF5kZUXwIi8vghdZmRG8yMmL4EVOVgQvsrLSpVY7eGkpPJlMwqX+RNKNPgQv3ShMhy+F4MXhAbWQR/AiJyuCF1lZEbzIy4vgRVZmBC9y8iJ4kZMVwYusrHSp1Qpevvr2R9TWNXSofZcR28Hr8ehamyV1CV4ssZmTsMeLqHeA4EVUXOzxIisu9ngRlhfBi6zACF7k5EXwIicrghdZWelSqxW8nDt9Fl5/+6MOtb8z/3YUFxXoWpsldQleLLGZkxC8iHoHCF5ExUXwIisughdheRG8yAqM4EVOXgQvcrIieJGVlS61WsHLqqr1aGgMbaZ9+rXzMGhABa6bPgUej1vX2iypS/Biic2chOBF1DtA8CIqLoIXWXERvAjLi+BFVmAEL3LyIniRkxXBi6ysdKnVCl46Ev2fBZ9i6qU3473nZqOoMF/X2iypS/Biic2chOBF1DtA8CIqLoIXWXERvAjLi+BFVmAEL3LyIniRkxXBi6ysdKm1Bbx890MVDj/pUjxw23TsvvOOutZmSV2CF0ts5iQEL6LeAYIXUXERvMiKi+BFWF4EL7ICI3iRkxfBi5ysCF5kZaVLrVbwsnZ9DZpC4Vba6+qb8OBTr+LpF/8L9njRFWt2dUsK/YhE42gMx7N7gKNsdYC3Gtlqf06TE7zkZJftg/N8bhQGfVhf2/r3K9uFUUC7DhQFvYDLhbrGKB0S4ADBi4CQWkgkeJGTF8GLnKwIXmRlpUutVvDSUXPd/GAA55w+Cacee6iudVlWlzteLLO6x09E8CLnFSB4kZOVUkrwIisvghdZeRG8yMqL4EVOXgQvcrIieJGVlS61WsHLsq++R3VNXSvtBfkBjBi6jfhrpDOLInjR9WqyblsHCF7kvBMEL3KyIniRlZVSS/AiKzOCF1l5EbzIyYvgRU5WBC+ystKlVit40SXaSXUJXpyURvfWQvAiJ1+CFzlZEbzIyorgRV5eBC+yMiN4kZMXwYucrAheZGWlS61W8PLhZ8uh+rxs7ZPn92P/sbttbZgjv0/w4shYuqUoghc5sRK8yMmK4EVWVgQv8vIieJGVGcGLnLwIXuRkRfAiKytdarWCl456vLRdTN8+pXj9sb/qWqPWugQvWu1l8RYOELzIeR0IXuRkRfAiKyuCF3l5EbzIyozgRU5eBC9ysiJ4kZWVLrVawctl19+NcDiC6eef3Er/BZffhsqKMlx6zmTj626XC0WF+brWqLUuwYtWe1mc4EXkO0DwIis2NteVlRd7vMjKi+BFVl4EL3LyIniRkxXBi6ysdKnVCl4OPfFinHbC4Thh4rhW+v+z4FNMvfRmLPjX31FYENS1NkvqErxYYjMnAcAdL3JeA4IXOVkppQQvsvIieJGVF8GLrLwIXuTkRfAiJyuCF1lZ6VKrFbwceeo0DBpQgduvPr+V/pfeXIgLr5yNJ+b9GcN3GKxrbZbUJXixxGZOQvAiK1E0cQAAIABJREFU6h0geBEVF8GLrLh4q5GwvAheZAVG8CInL4IXOVkRvMjKSpdareDl1ruewNz75+OSs0/ET0ftgt6lxfh82QpcNet+hMIR/Ov+6xAM+HWtzZK6BC+W2MxJCF5EvQMEL6LiIniRFRfBi7C8CF5kBUbwIicvghc5WRG8yMpKl1qt4CUcieKPV9+Bl95ctJn+u266BGP2HKlrXZbVJXixzOoePxGPGsl5BQhe5GSllPKokay8eNRIVl4EL7LyIniRkxfBi5ysCF5kZaVLrVbwkhG95MtvsfR/36G2vhGVfcoM4FJcVKBrTV2qq2BRIpHMeicOwUuX7ObDOThA8JKDWTYPJXixOYAcpyd4ydEwm4cTvNgcQI7TE7zkaJjNwwlebA4gh+kJXnIwywFD8wNe+D0u1DREHaCmcxL6l8vuzdq5VZv3lHbwkkwm8c33q7F6zQZst01/qKujv/uhCvnBAHqXFZu3kiwqbaipw8+OOnezkXfdfAnG7DESDz71GuY9+Jzx/cmTDsJvJ//C+Pf11bU4bPIleO6+awz9LT8EL1kYzyGmOEDwYoqNlhQheLHEZtMmIXgxzUpLChG8WGKzaZMQvJhmpSWFCF4ssdmUSQheTLHRsiIEL5ZZ7diJtIKXhsaQcXvRh58tNwy4dtqZmHDIWJx32a345rvVePbeqy01RgGUn086D3Ou+z0GD6honruidyny/D7sd/R5mHfTJQgG8nD4SZfg41fmwefz4sY5jyAeT+DSs0/cTC/Bi6UR9ujJCF7kxE/wIicrpZTgRVZeBC+y8iJ4kZUXwYucvAhe5GSllBK8yMpLh1qt4OXR+W/ib3c9gUvOOhEPPPEKfvXLgw3wsvCjpTjtgmvxxuO3oKJ3iY51tVszA17UzpVtB/drNWblqrVQ11+//+IdBoTZZdxpePqev6BXYQHGn/JHPP/AtehTvrlWghfL4uvxExG8yHkFCF7kZEXwIisrpZbgRVZmBC+y8iJ4kZMXwYucrAheZGWlS61W8DLp9D/h0P1HYeopR+LMi2/EhIPHGuAlc+Tn4TlXYJfh2+pa22Z1M+Bl3L67o7hXIYZuNxATD/up0W9G7WgZM/4sPDT7MuQH83DwCRcZO16un/2QcSzqgjOPxZp1NSjIDxj/ZD4EL5bF1+MnIniR8woQvMjJiuBFVlYEL/LyIniRlRnBi5y8CF7kZEXwIisrXWq1gpcjT52Gow7/KU4/4YhW4OWrb37Akb+ejpcfvhEDKnvrWttmdesbmjBr3uNQR4vq6hvx1Av/MfrMPDLnCvj9Psx78F+499EXjedOmDjOgDKTTr8MLz98A26960m89e7HiEZjOOe0STh+4jhjXF1TzDL9Zk8U8HsM4BSNJ80uzXoaHCgIeNEUjiHBuDS4a25JF4DCoFf0fx/MdcTZ1bxuF/w+NxrDcWcLpTrDgTyvG3AB4WiCjghwID/Pg0g0gRh/8xKQVmpHWX1TDPxfDefH5XYBwTwvGkJy/yzifJfNU+jzuuFxASHBv3cZO0756bQDWsHLzL/eh/8u/Az33vpHXH793caOlwN/ticunvl3fLr4K7z5xCx4PO5Oi+/qgyu+W2UcI1K7XH4ycnujnLp5KZlIorhXAS67/m7061uOyUcdiH0nnoNFL8zF4uXf4PIb7sbzD1yXAi+NcjtTG+AlkUQ0xv957eq7ZMXzBniJxI1bt/hxuAMuoDDgQ32T3P8+ONxhU+V5PC74vR4DbPLjfAcUJFPkJRIlKHN+Wqk/GEZiccT5lzwS4kJhkL93iQgKgNvtQtDvIXgREpgBXtwuhCJyf+8qyvcJcduZMrWCl+qNdfjlby9H1dpqY/UD+/Uxjhk1NoVw29X/hwPG7m6rK6r576gjpuLuv16K0buPaKVFQZnjpszAa4/dDHUd9kUzZuM/T/8Nq6rW46Djf49FL8wxjiDxqJGtEfaoyXnUSE7cPGokJyullM11ZeXFHi+y8uJRI1l58aiRnLx41EhOVkopm+vKykuHWq3gRQluCkXw6Pw38MXSFahraMK2gyox6YifYcdtB+pYzxZrvvXuJwiF/7+9MwHXamr/8NM8ax4UhYgQ4TNFpDRIM2nShGgmTdTXR1IkzUqTogxJSYNKFKEyliFDEiIyNs/J+V9r+b+vU52cvc/77nft57z3vq7v+uqctdd+1v3bOufcZw0H5LKLzpEc2bPJqMlz7HKj12YNt/u8pL76DJogFcqfbI+U3rFrj1Sp30XeWThePlv/nQwZ83T0RCbES8JjTNoHIl70RI940ZMV4kVXVqZaxIuuzBAvuvJCvOjJC/GiJyvEi66sgqo2UPEyY/ZS+X3rDrsxbRiuV9/8QPo9NMXOuDFXkUIFZNiATnLZRWcfUZ7Zg6Z5p0Gy4sVRdlaLucyR0vOWvG2Pl+7RoandJNhciJcwJJscNSBe9OSMeNGTFeJFV1aIF315IV50ZYZ40ZMX4kVPVogXXVkFVW2g4sXMGtm+c7dMGtYrqPp99/vn4cPyx9ad9j5zlHUW8xOSx8tsyJs7V04rXyIX4sUjPJrFTADxEjPChHWAeEkY6rg8iKVGccGYsE6Y8ZIw1HF5EOIlLhgT1gniJWGoY34Q4iVmhAntgKVGCcUdyocFKl5mzlsuwyfMktULx0n2bNlCCSDWohAvsRLkfq8EEC9eSblvh3hxn4GfChAvfmi5b4t4cZ+BnwoQL35ouW+LeHGfgdcKEC9eSYWjHeIlHDm4rCJQ8bJx00/SvOMD0r75dXJNlcrHjLPCaSc7PdUoHuARL/GgSB9eCCBevFAKRxvESzhy8FoF4sUrqXC0Q7yEIwevVSBevJIKRzvESzhy8FIF4sULpfC0QbyEJwtXlQQqXrr1Hy3LV6497thWLRh3zKa2rkBk9LmIl4yS4z6/BBAvfom5a494ccc+I09GvGSEmrt7EC/u2GfkyYiXjFBzdw/ixR17v09GvPgl5rY94sUt/zA8Pe7ixeyhYi6ztGjT5l9k5649xx1nxQrl1C9BQryE4TVOjhoQL3pyRrzoycpUinjRlRfiRVdeiBddeSFe9OSFeNGTlakU8aIrryCqjbt46dJvlJQqXkQG9GgjC5aukuJFCx1zalAQA3HVJ+LFFfnkey7iRU/miBc9WSFedGVlqkW86MoM8aIrL8SLnrwQL3qyQrzoyiqoauMuXjr2HS5ly5SSft1biVlqdPaZp0inNg2Dqt95v4gX5xEkTQGIFz1RI170ZIV40ZUV4kVfXogXXZkhXvTkhXjRkxXiRVdWQVUbd/Eye+EKGT1ltnS/7QaZt2SllC1TQhrVqZpm/ReedwZLjYJK1kO/hfLnlIOHDsveA38vD+MKNwHES7jzSV0d4kVPVogXXVkhXvTlhXjRlRniRU9eiBc9WSFedGUVVLVxFy87du2Rh8Y+Y5cZpXexuW56hIL9POIlWL7x7h3xEm+iwfWHeAmObRA9s8dLEFSD65OlRsGxDaJnxEsQVIPrE/ESHNt494x4iTfRYPtjj5dg+WroPe7iJTLoAwcPSae+I+T0U8tIqyY102RxcukSkjVrFg2cjlsjS41Ux6eqeMSLnrgQL3qyMpUiXnTlhXjRlRfiRVdeiBc9eSFe9GRlKkW86MoriGoDEy+m2NQnHAVRfBj6RLyEIYXkqAHxoidnxIuerBAvurIy1SJedGWGeNGVF+JFT16IFz1ZIV50ZRVUtYGKl6CKDlO/iJcwpZG5a0G86MkX8aInK8SLrqwQL/ryQrzoygzxoicvxIuerBAvurIKqlrES4xkES8xAuR2zwQQL55ROW+IeHEega8CWGrkC5fzxsx4cR6BrwIQL75wOW+MeHEegecCEC+eUYWiIUuNQhGD0yIQLzHiR7zECJDbPRNAvHhG5bwh4sV5BL4KQLz4wuW8MeLFeQS+CkC8+MLlvDHixXkEngtAvHhGFYqGiJdQxOC0CMRLjPgRLzEC5HbPBBAvnlE5b4h4cR6BrwIQL75wOW+MeHEega8CEC++cDlvjHhxHoHnAhAvnlGFoiHiJRQxOC0iYeJl3/6Dkj17NsmRPZvTAcf74YiXeBOlv+MRQLzoeTcQL3qyMpUiXnTlhXjRlRfiRVdeiBc9eSFe9GRlKkW86MoriGoDFS/mVKNJTy+U5+a+Jlu375KH+90u9WtVkY59h0vOnDlkzKDuQYwpoX0iXhKKO6kfhnjREz/iRU9WiBddWZlqES+6MkO86MoL8aInL8SLnqwQL7qyCqraQMXLG6s+ki79RkmTulfJe2u/kK7tG1vxsnTFB9Ljvsdk1YJxUrBAvqDGlpB+ES8JwcxDRATxouc1QLzoyQrxoisrxIu+vBAvujJDvOjJC/GiJyvEi66sgqo2UPFiZracXLqE9L+ztdze+1GpX7OKFS8//7ZVajS9W2ZPHigVzygX1NgS0i/iJSGYeQjiRdU7gHhRFRdLjXTFxYwXZXkhXnQFhnjRkxfiRU9WiBddWQVVbaDipXrTHtKpbUNpWq9amuJl/lNDpHy50kGNLSH9Il4SgpmHIF5UvQOIF1VxIV50xYV4UZYX4kVXYIgXPXkhXvRkhXjRlVVQ1QYqXu7632OyfedumTqir93XJTLjZcwTc2TijAWyZulkyZUzR1BjS0i/iJeEYOYhiBdV7wDiRVVciBddcSFelOWFeNEVGOJFT16IFz1ZIV50ZRVUtYGKl/Ubf5Amtw6QcieVlF2790rlc06XPw//JW++87Hc1eFG6dCqXlDjSli/iJeEoU76B7HHi55XAPGiJytTKaca6cqLzXV15YV40ZUX4kVPXogXPVkhXnRlFVS1gYoXU7SRL2aGy3trv5S9+/ZLhdNOklZNatoNd7NmzRLUuBLWL+IlYaiT/kGIFz2vAOJFT1aIF11ZmWoRL7oyQ7zoygvxoicvxIuerBAvurIKqtrAxUvqwlNSUiSL+YkkE12Il0wUZsiHgngJeUCpykO86MkK8aIrK8SLvrwQL7oyQ7zoyQvxoicrxIuurIKqNiHi5dvvt8jmLb8fM4bL/3O2ZM+WLaixJaRfxEtCMPMQ9nhR9Q4gXlTFxVIjXXEx40VZXogXXYEhXvTkhXjRkxXiRVdWQVUbqHhZt/5b6Xn/eNm85bc061+1YJwULJAvqLElpF/ES0Iw8xDEi6p3APGiKi7Ei664EC/K8kK86AoM8aInL8SLnqwQL7qyCqraQMVLt/6j5atvNssDfW6RE0sUlRzZj5zdUrJ4EfX7vCBegno16fdoAiw10vNOIF70ZGUqZXNdXXmxx4uuvBAvuvJCvOjJC/GiJyvEi66sgqo2UPFSvWkPaVq/mnRq0zCo+p33i3hxHkHSFIB40RM14kVPVogXXVmZahEvujJDvOjKC/GiJy/Ei56sEC+6sgqq2kDFS9/BE+XQocMy4v7OQdXvvF/Ei/MIkqYAxIueqBEverJCvOjKCvGiLy/Ei67MEC968kK86MkK8aIrq6Cqjbt4+eGnX2X3nn223m++3yJ9Bk2Qx4bcKaWKFzlmDBVOO1myZcsa1NgS0i/iJSGYeQh7vKh6BxAvquJiqZGuuJjxoiwvxIuuwBAvevJCvOjJCvGiK6ugqo27eDH7uixfudZTvWyu6wlTYI0K5c8pBw8dlr0HDgf2DDqOHwFmvMSPZdA9IV6CJhzf/tnjJb48g+6NpUZBE45v/4iX+PIMujfES9CE49c/4iV+LBPRU97c2SVntiyyfc+hRDwukGeULponkH6TpdO4i5dNm3+Rnbv2eOJXsUI5jpP2RCqYRoiXYLgG1SviJSiy8e8X8RJ/pkH2iHgJkm78+0a8xJ9pkD0iXoKkG/++ES/xZxpUj4iXoMgG0y/iJRiumnqNu3j56effZe7it6R987qSN08uTSwyVCtLjTKEjZsyQADxkgFojm5BvDgCn8HHIl4yCM7RbYgXR+Az+FjESwbBOboN8eIIfAYei3jJADSHtyBeHMIPyaPjLl7WfLpBWncbLCteHC3FihQMyTCDKwPxEhxbej6SAOJFzxuBeNGTlakU8aIrL8SLrrwQL7ryQrzoyQvxoicrUyniRVdeQVSLeImRKuIlRoDc7pkA4sUzKucNES/OI/BVAOLFFy7njREvziPwVQDixRcu540RL84j8FwA4sUzqlA0RLyEIganRSBeYsSPeIkRILd7JoB48YzKeUPEi/MIfBWAePGFy3ljxIvzCHwVgHjxhct5Y8SL8wg8F4B48YwqFA0RL6GIwWkRgYmXIoUKpDuwl58eKifkz5tuuzA3QLyEOZ3MVRviRU+eiBc9WZlKES+68kK86MoL8aIrL8SLnrwQL3qyMpUiXnTlFUS1gYmXRnWuTHdz3bvvaCZ5cucMYlwJ6xPxkjDUSf8gxIueVwDxoicrxIuurEy1iBddmSFedOWFeNGTF+JFT1aIF11ZBVVtYOKFzXWDiix+/XKcdPxYJqInxEsiKMfnGYiX+HBMVC/MeEkU6fg8B/ESH46J6gXxkijS8XkO4iU+HBPRC+IlEZTj9wxmvMSPpdaeEC8xJseMlxgBcrtnAogXz6icN0S8OI/AVwGIF1+4nDdGvDiPwFcBiBdfuJw3Rrw4j8BzAYgXz6hC0RDxEooYnBaBeIkRP+IlRoDc7pkA4sUzKucNES/OI/BVAOLFFy7njREvziPwVQDixRcu540RL84j8FwA4sUzqlA0RLyEIganRcRdvOzZu1++/u5HObvCKZIjezang0vEwxEviaDMMwwBxIue9wDxoicrUyniRVdeiBddeSFedOWFeNGTF+JFT1amUsSLrryCqDbu4iWIIsPcJ+IlzOlkrtoQL3ryRLzoyQrxoisrUy3iRVdmiBddeSFe9OSFeNGTFeJFV1ZBVYt4iZEs4iVGgNzumQDixTMq5w0RL84j8FUAM1584XLeGPHiPAJfBSBefOFy3hjx4jwCzwUgXjyjCkVDZryEIganRSBeYsSPeIkRILd7JoB48YzKeUPEi/MIfBWAePGFy3ljxIvzCHwVgHjxhct5Y8SL8wg8F4B48YwqFA0RL6GIwWkRiJcY8SNeYgTI7Z4JIF48o3LeEPHiPAJfBSBefOFy3hjx4jwCXwUgXnzhct4Y8eI8As8FIF48owpFQ8RLKGJwWgTiJUb8iJcYAXK7ZwKIF8+onDdEvDiPwFcBiBdfuJw3Rrw4j8BXAYgXX7icN0a8OI/AcwGIF8+oQtEQ8RKKGJwWgXiJET/iJUaA3O6ZAOLFMyrnDREvziPwVQDixRcu540RL84j8FUA4sUXLueNES/OI/BcAOLFM6pQNES8hCIGp0UgXmLEj3iJESC3eyaAePGMynlDxIvzCHwVgHjxhct5Y8SL8wh8FYB48YXLeWPEi/MIPBeAePGMKhQNES+hiMFpEYiXGPEjXmIEyO2eCSBePKNy3hDx4jwCXwUgXnzhct4Y8eI8Al8FIF584XLeGPHiPALPBSBePKMKRUPESyhicFoE4iVG/IiXGAFyu2cCiBfPqJw3RLw4j8BXAYgXX7icN0a8OI/AVwGIF1+4nDdGvDiPwHMBiBfPqELREPESihicFoF4iRE/4iVGgNzumQDixTMq5w0RL84j8FUA4sUXLueNES/OI/BVAOLFFy7njREvziPwXADixTOqUDREvIQiBqdFIF5ixI94iREgt3smgHjxjMp5Q8SL8wh8FYB48YXLeWPEi/MIfBWAePGFy3ljxIvzCDwXgHjxjCoUDREvoYjBaRGIlxjxI15iBMjtngkgXjyjct4Q8eI8Al8FIF584XLeGPHiPAJfBSBefOFy3hjx4jwCzwUgXjyjCkVDxEsoYnBaRFKKl4MHD8m2HbulRLFCksX8hJTqOnDwkPz1V4rkyZ3TUzCIF0+YaBQHAoiXOEBMUBeIlwSBjtNjEC9xApmgbhAvCQIdp8cgXuIEMkHdIF4SBDoOj0G8xAFiArtAvCQQdkgflVTiJSUlRR6fPl/GTZtr4yhSqIA8NuQuOf/s8vbvz85dJlOeXWj/3LLxtXJby+vtn//YtlPqtOwjC6c/JCWLFz4iSsRLSN/sTFgW4kVPqIgXPVmZShEvuvJCvOjKC/GiKy/Ei568EC96sjKVIl505RVEtUklXtau2yA3dx0sM8b2k0pnnSZjnnhRXl62Wl57foRle3WT7jJleB/JkzuXXNeqj3z06hTJkSO7PDrheTl8+C/p26XFMRkgXoJ4LekzLQKIFz3vBeJFT1aIF11ZmWoRL7oyQ7zoygvxoicvxIuerBAvurIKqtqkEi/DJ8ySL77eJFMe7W15/vr7drnmxrtk9uSBUiB/Xqndord8sGSS5MqZQypVby8vTXtQTsifT+q1uVcWPf2wFC9aCPES1JtIv+kSQLykiyg0DRAvoYnCUyHMePGEKTSNEC+hicJTIYgXT5hC0wjxEpoo0i0E8ZIuolA1YMZLqOJwUkxSiZdeDzwuhQvml/53to7CPqdaOxn/UA+58pJKclm9zvLc+AGSN08uqdm8l53x8sj45yRvntzS4/amVtTky5vb/i9yMePFyXublA9FvOiJHfGiJytTKeJFV16IF115IV505YV40ZMX4kVPVqZSxIuuvIKoNqnEy+29H5Uzy5eVnh1virK8+LqOcn+vdnJ9jctkyrMvy1OzltjPNW9YXRrWuVIa3zJAls4cZpclrVj9kRw69Kd0bd9YmjWsHkQe9AkBCEAAAhCAAAQgAAEIQAACEIBAJiKQVOLFzHgxG+r2635zNMLIjJerLz/ffmzn7r2S8leKFDwhnwx4ZKqcWLKotGxUQ65o2FXeXzxRPv/qO/nfsKmy6Omhmeg1YCgQgAAEIAABCEAAAhCAAAQgAAEIBEEgqcSL2eNl/cbvZdKwXpZl6j1eKp5R7gi+336/RW66Y6Ase2GEfLFhk/QaOF7eemmsbPnlD7m2WU95f/EEuwSJCwIQgAAEIAABCEAAAhCAAAQgAAEIHI9AUomXf0416i+VKp4mo6fMlkXL3rGnGmXNmuUIRn0GTZAK5U+2R0rv2LVHqtTvIu8sHC+frf9Ohox5WuY/NYS3CgIQgAAEIAABCEAAAhCAAAQgAAEI/CuBpBIvKSkp8ti0uTJh+nwLxcxYmTSsp1xw7hlHQNr43Y/SvNMgWfHiqOisFnOk9Lwlb9vjpXt0aCr1a1VR/Wrt239Qtm3fKaVKFD1GOqkemPLijeQ7cOCQlCh27AlayoeWVOXv3rNPtu3YJUUKnXDEZtxJBSFkgzX//m/bsVtMNiWLF7an13FBAAIQgAAEIACBRBEwe4X++sd2KV6koOTk+5BEYQ/Nc5JKvESo7z9wULZu8y8ddu3eK7lz5bTyRfPVrf9oWb5yrR2C2fOmUZ2qR2w4rHlsWmv/fesOadN9iGza/IsdQvlypaVDq3r/KvgatO0nGzf9dMSQu7RrJJ3bNdKKQX3de/ftl1ZdHpSvvtkcHUvLxjXknq6tJFu2rOrHp3UAn3y+Ubr0GyVbt++yQzDSvV/3VtL4uqppDsm0q9qo2zGfe2JEH7nswrO1YshUdY+c9ILdEH/1wvFyQv68mWpsWgdz8OAhubXnMNm3/4DMnjzwuMMYOu45mf7CK0d83vwC7OnH+msdeqaom+8pwhej369FftuHb8SZtyKzhcX/hk2TNZ9+ZQc5oEcbe5ALV3IRSErxklwRHzvax6bOlVrVLpayZUrIOx9+bn8gmfn4/+zyKy43BMx+Qy8teUsa1L5C8uXJLTNmL5Vpzy+RN+eOkTy5c6ZZlPkm6fprL5c611wS/XzBAvmkUMH8bgbBU+1siiefXyIN61whpUsWk1UfrJOOfUfIjLH95MJKFSDkiMDHn2+UDd9slupXXigF8ueVCdPn2ZmPa5ZOTnPmyx/bdspVjbvLhKE97b+TkatEscLH/e/R0dCS8rFzF78l/x36hB074iUcr4CZUWYyeWnJ22L2zPs38fLwY8/KDz/9Kn06t4gWnytXDilVvEg4BpOkVfA9RfiC9/u1yG/78I04c1b0y2/bpHrTHnJd9UvF/DKu4hmnyP4DB6RwwQKZc8CM6rgEEC+8HPYfA2Ndb7+5PjRCQmDzlt+kdove//oDu/kmqV2zOtKk7lUhqZoyjiZgli02aNdf5k0bLKefWgZAISEwa8EbMvaJObJ89ijJkT3bMVVFvnldOP0hObXsiSGpmjIMgfc/+lI63ztKHujdXsxJhYiXcLwXk59ZaPfMq1eziixe/m664mX7zt3ycL/bw1E8VVgCfE8RvhfB79civ+3DN+LMWdEj456TBa+uktfnjJLs2Y79niNzjppRpUUA8ZLk74VZ2lL35r4y/qEeEjlSO8mRhGL4kd/ompO0zHKwtC7zTVK+fHnssqTSJYtKvZqXS9kyJUNRf7IXYcTZrPmvy2tvfSh1q18mXW9pnOxIQjH+Dz/5SuYvXSlvvfuJ9OzYTK6vcVmadUW+ea1+xQVS8IT8UuG0k6RhnSvFzCjjckfAfL26scN9MuqBrlKyWGFp2L4/4sVdHNEnL13xgQwa+ZS8MHmgvLn6YzFiM70ZL0tXvG+X7Znf+JqZaBedx4xA11HyPYXrBI59vt+vRX7bh2/EmbMi899Wnty55MSSRe3puGZWYMe2DZjllznj/tdRIV6SMPTIkPfs3S83d31Q8ufLK0+Ouoc9KELyLmz4drO07PygtG1a+19/YB83ba5kzZZVUlJElr+9xu4PM2fKQORLCHI0R9BPnLFAPvxkvVx9eWW57+626veGCgHWmEtY+OpqeXnZO7Luy2+kY5sG0qpJzTT7NEvGzKl3ZmmR2dvLiNBiRQrK8xPuYzO8mFPIWAc7du6Rm+64X9reVMdO1f762x8RLxlDGde7Pv3yW7mlx1CZOrKvVDrrVCuc0xMvC5auku82/2yX+a1b/60se2uNjLi/s9Su9s+y2bgWSWeeCPA9hSeKUmpgAAAWCklEQVRMCW3k92uR3/YJHUwSP+ycau3k0gsq2n3lcubMLpOfeVnMnoBmNrT2fUOTONYMDR3xkiFs+m8ypxrdOWCM/PzrVpk+ph/7goQk0h9//l1adxssF1c+S4bc08GzDDO7pNdu2Vta31BL2je/LiSjoQxzStW1N/WUAT1aS4NaVwAkJATMzBezmfWSZx+Rk0v/s4fL8cozm+LVa3OvPDd+gJx3dvmQjCK5ynjljffk7vvHS5umtSWLiGzdsUvMD/DNGlaXpvWutr9B5Eo8gUEjp8vqDz+TapdXtg//fMMm+Wz9dzaTTm0b2j2V0rvuGTJJtu/YZfdU4goHAb6nCEcOR1fh92uR3/bhHLX+qox4GTOou9SoeqEdTCSXF58YJGeWP1n/ABmBZwKIF8+oMk/Dnbv3Svf/jpF9+w7IxEd6Il1CEq35DW77Hg/baddmt3O/60Cb3TFQrq5SWTq3bRiSEVGGIWCW8pnfcphTqrjCQcCcInZ1kzvtKSrmNJX0LjM78JK6He1v9c1vrbgST8Dsl7Ts7TXRB5sMn3nxNbmjdX27ZKz8KeyhlPhUxC7bMzP8IpfZyNqcItb6xlpy8w017Qli6V2jJs8WI0PNJuRc4SHA9xThySJSid+vRX7bh2/EmaMis0TWfJ2K/GI0sv/fzAn32ZmCXMlDAPGSPFnbke7dd0Cadxwofx4+LCMHdpX8+fLYj2fNmlVOLMGJAq5eh/Ubf5Amtw6w/zB3u7WJzcNcefPksmvgzXKH9j2Gyq0t6tpd0b//8Rd7JLg50aho4YLyyuvvSd/BE+3sJdbKu0pRZO26DfLFhu/l2qoXSaET8tllLeakD3Jxl4l5slkqZPZnuej8MyVrliwycvJsO1ti+Qsj7G/kn5y1xC53iPzgt2L1x/bEgcsuOsduvjtq8hzbx2uzhrPPi9soo09nqVFIgjiqjKOXGh39tcs0N0eBN6hVRcqeVErWb/xe2t81VG5reb2VaFxuCPA9hRvu6T01va9FZrNxczz78Ps6S7mTSkp67dN7Hp8PhsDUmYtk2szFYkSL+blr5MQXZNnbH8rSmcM5KTEY5KHtFfES2miCKSxypNnRvZsNXM1GrlxuCJhTIMwJHUdf9WtVsSc/mP0NqjToIv+9q7W0aFTDipd2dz0sJs/I1bdLCzsNn8sdgU+/+EY63ztStm7fRS7uYjjmyWbPiYHDn4x+vGTxwnYp32UXnW0/Nmz8TLsvxfuLJ9i/v/rmB9LvoSl2Dba5zL+PwwZ0irYP0dCSthTESzijP1q8HP21y1RtZlKYvV0iV6M6V9pZnrlz5QznoJKgKr6nCGfI6X0ten3VWunab7RElqyk1z6co8z8VR08eEj6PTzFnvhmLvM9yKiBXVm6nPmjP2aEiJckDJ0hZw4CKSkp9gd888Oh2Snd79KkzEEhfKMwuZijUs0md6VKFE3zuOLwVZ35KzKz/P7YulNSJEVKFC0sWbOanUKOf0XamxYlihWSLFn+vX3mJ8gIIRA/AmYmzLYdu6R40cL8xjd+WGPqie8pYsIX2M1+vxb5bR9Y4XR8DAGz1cMe+71hEb6nSNL3A/GSpMEzbAhAAAIQgAAEIAABCEAAAhCAAASCJ4B4CZ4xT4AABCAAAQhAAAIQgAAEIAABCEAgSQkgXpI0eIYNAQhAAAIQgAAEIAABCEAAAhCAQPAEEC/BM+YJEIAABCAAAQhAAAIQgAAEIAABCCQpAcRLkgbPsCEAAQhAAAIQgAAEIAABCEAAAhAIngDiJXjGPAECEIAABCAAAQhAAAIQgAAEIACBJCWAeEnS4Bk2BCAAAQhAAAIQgAAEIAABCEAAAsETQLwEz5gnQAACEIAABCAAAQhAAAIQgAAEIJCkBBAvSRo8w4YABCAAAQhAAAIQgAAEIAABCEAgeAKIl+AZ8wQIQAACEIAABCAAAQhAAAIQgAAEkpQA4iVJg2fYEIAABCAAAQhAAAIQgAAEIAABCARPAPESPGOeAAEIQAACEIAABCAAAQhAAAIQgECSEkC8JGnwDBsCEIAABCAAAQhAAAIQgAAEIACB4AkgXoJnzBMgAAEIQAACEIAABCAAAQhAAAIQSFICiJckDZ5hQwACEIAABCAAAQhAAAIQgAAEIBA8AcRL8Ix5AgQgAAEIQCAmArt275X3P/rS9nH5f86VPLlzRvv75PON9s/nnV0+pmekd/PadRvkkfEzZeyD3aVYkYLpNU/I5w8f/kuWr1wj819ZKZs2/yLdb7tBrq16UUKeHY+H3PfoNClTqpjcfnP9eHRHHxCAAAQgAAEIhJQA4iWkwVAWBCAAAQhAIELgiw2b5MYO99m/9ux4k9zSvG4UTvcBY+yfxwzqHiiwt979RDr2HSGvzRohJ5YoEuizvHb+3tovpX2Ph+XGelfLaeVKy7lnnioXnVfB6+3O27XoPEhOP6WMDOpzi/NaKAACEIAABCAAgeAIIF6CY0vPEIAABCAAgbgQiIiX8uVKy5Zft8prs4ZLwQL5bN/JLF4efuxZeXfN5zJ36oNx4ZzoThAviSbO8yAAAQhAAAJuCCBe3HDnqRCAAAQgAAHPBCLiZezgO6Vb/9FyR+v60v3WG9IUL70eeNzO+mjRqEa0/yFjnpFCBfNL57YN7ccGPDJVihY+QQ4ePCQLXl1lP9b6xlrSpO5VMnLSC/LmOx9L6ZLFpM1NteX6GpfZz0dmvPS/s7UsWvaOmKVHZobJgLvb2P+PXG+9+6lMnDHffv6kE4tLwzpXSodW9SRH9mzy8ecbZdj4mTKwd3vbh/l79SsulJaN/6k1NZTP1n8nwx6faZdZmb7q1bxcOrZuIDlyZJcnZy2RcdNess3PLH+y5MyRXaaO7Jsm0/lLV8r0F5ba5UhFChWwfHrc3lSKFy0kqz/4TIZPnGU/t3fffqlw2knSvvl10qDWFbavSM3NG1aXmfOW23FdXPkseaB3e1n35Xfy1Kwl8s33W6TxdVWlfbM6cmLJova+5+ctl5UfrJP/nHemzF64QjZu+kmqX3GB3NezXXSp1tHixSwpG/PEHFn29hr55bdtcukFFaVPlxZy1ullo7WMmzZX1q77WnLnyiHnnnWadGzTQM4PeJmZ5xeVhhCAAAQgAAEIpEkA8cKLAQEIQAACEAg5gYh4mT15oCxe/q488dwieWPOKCsOjp7xUrtFb6l19cV2SVLkanfXw1KiaCF5ZEBH+yGzbMn0aQRCzasukk+//FYWLP1bwFS9tJJUvfR8eXft57LsrTXy5twxVtJExEvePLmlRaPqkiVLFpny7Mti/m5qyZc3d7RN/VpV7F4rZv8ZU2tkeVSkD/McM3unYoVycv7Zp6cpXr7/8Ve5rlUfKXdSSWnTtLat1wiMmxpcI/fd3dbKGLNHSq6cOezHsmXNav//6MuIldt6DbOfu/LiSvLTL7/Lcy8tk8H33CYXnHuGvPLGe/LOmi+k8jmnS+5cOe2eMQtfXS0zxvaXCyudER2T6ffWFnWlZPHCMmH6fNm6fZcde+sba8oJBfJZCXTD9VfJPV1b2hJGTJxlx27qN1LGiBfD+PL/nCNTHu1t26QWL2a/mpadB8n2nbulZZNrpUjBAvL0nFet1Fn+wgj58/BhubJhN5uZ4b9n735ZuuJ9+c/5Z8ltLa8P+RtMeRCAAAQgAIHkJoB4Se78GT0EIAABCCggkFq8nFiiqNRs3ksa1blCzOyTjIqXsmVKyPD7OluBcujPw1L52lujUsMgMbMvLqvXWYYN6CR1a1waFRDznxws5U8pY6m98+HncmvPR6JtGt/yXyuDJg3rFaV69/3j5Otvf5T5Tw2J9vFQvw7RGSXHwz949Ax5du4yWbVgXHRZ1aMTnpdpMxfL67NHSYliheT23o9K/nx5ZMT9XY6b4tSZi2T4hFnRe0xDIzn++usvO3MmcqWkpMjOXXvlj+07pX6be6VXx2Z25ktEFr34xCA7s8ZckT6XvTBCShX/e78bM1NoyevvySvPDYuKl7mL35LlL4yMPmfs1BettHnt+eF2Zkxq8fL6qrXStd9oeXb8gOgMlq++2SyG6ehB3SxXI2ZG3N9Zale7JFr3vv0Hj9hsWcHrTIkQgAAEIACBpCOAeEm6yBkwBCAAAQhoI5BavFQ8o5xMfmahjJo8WxY/84g8OmGmHU5kc12vM14qVTzNzhyJXFUbdZMbrr9a7upwY/Rj51Rrd4yASL257s7de+Xyep3tPe1uqiOVa95ml/KU/H8ZYTqKLOH57I0noxLDywa9rbsNsUuhnp/496bC5opIkGkj75FLLjjLk3hZv/EHaXLrADs7pXa1i+3MFiOSzN/NtW3HLnn08edl6YoP7FKjyNWlfWO7NCutTYXN0qV7h0yW9xZNsDN9zDVj9lIxe86YcZrLzHh55Y33oyImdf0zxvaTCytVOEK8GCFjxIzJN3IdPnxYjHzp26WFXTpWvWkPO9OmRtUL7Tiuu+bS6NImbe809UIAAhCAAASSiQDiJZnSZqwQgAAEIKCSwNHixQiCq5vcZX8Aj8iC1OLFLPPp3bl5dKxpLTU6WryYH+rNviYZES9mvxQjBi6p21Ga1qtm6zryymKXMPk5GanZHQOt1Ei9b4tZXmTGYpbqmCU7Xma8mDq+/X6LPPfSclnz6Vd2yZKRLmbmTmTWyeaffpV7urWSSmedKsWKFJLaLXpJi8bXHle8mKVIfQdPPEK8mNk5ZpbOv4mXN1Z9JF36jYrOakk948WINCPUJgy9+5h3tNxJpcTMUNqxa4888+Jr8t7aL6LHiz825E65psoFKt9rioYABCAAAQgkCwHES7IkzTghAAEIQEAtgaPFixmI2eh10MjpdobJBZXOiM54MT/Mn1b2RLuHSeQys0fMEdCp93iJh3hZvnKt3ezXSB8jW8ysmYsrV7TLYVJfZhmPWdLkR7zcM2SS3RPlgyWToktpxj81T8zmsoufGSply5T0JF7MsqJs2bJGy4ks3zF7sZi9Vy69vpPdaDf1PilmHEGIF7PJ8TMvvipvzxsrhQsWOGLGy7xXVkq/hybLvGmD5fRT/17KFbkMv7/+SjliHDt27pEWnR+we+WYTZe5IAABCEAAAhAILwHES3izoTIIQAACEICAJZCWeDHLcOq06mNPvzHSIzLjxcyamPT0Qhk2oKPkzp1T5r+yUswP9eZ0oniIF7OvzBUXnysff/613a9k/4GDdsmT2eQ2MuvDbEJrNtg9ePBP+eizDbJi9cd23xc/4mXNpxukdbfBdj+TtjfVFrNkaOwTc+xSnMgeMl5mvJi9V/btPyD1rr3cnib05rufWGE1bshdUq1KZbvRsNmYt2fHZmKW9sxZ9KbdwPjflhp5nfFiZtk82PcWe0KUWcpk9oa5sd7VMrBXe5tr6hkvZrPc+m3vtRv89u3SUk45uZR898PPMu+Vty3LLJJFZs5bJm2b1pFTyp4omzb/LLfe/Yjdh8bsR8MFAQhAAAIQgEB4CSBewpsNlUEAAhCAAASOEC9zpjwQPVrYfCIiAFKLl59+/l3uH/6krHx/nb3XfM5sbmtmuAztf4f9mFnGc/aZpxyxx8vxlhr17tRc2jWrE5Um5lQfI3vMZY54HvNg9+ims2Z2iZnRMXbq3CP2SzEi5u47bor2kXpT2n+L+MVFb9qjryOXWV405J4OdmNdc3XsO9wuG/q3zXUXLXtXHhr7tN0bxVxmhogRGeaIa3MZTg+MeEo2b/nN/t0cWW24dr2lsXRq888eL6lrfnnZO9Jn0AR5f/GE6F4xaS01MqcamRlJkWc3rP33hsiRfWGOPk7anGD04Mjp8u7aL6JjNqLJzF4yM4Z6DRxvT0cyl+m3xpUXSZ8uzaM18J8LBCAAAQhAAALhJIB4CWcuVAUBCEAAAhCIicCvv2+XXLlyRE8EiqmzVDebJS9bfv3DfqR0yaJWCBx9maUxv2/dISkpYo+iTr3Ux28d5hhlI5NOyJ9PChXM7/d2297UY+SHEUMRaZO6I/N5M7ukSOET4sYrsrnuoqeHyh/bdkqB/Hk9nz5kZhEZfmY5UkTSROo1p02ZvV7KlCqWJvsMAeImCEAAAhCAAAQCJYB4CRQvnUMAAhCAAAQgkIwE0jrVKBk5MGYIQAACEIAABEQQL7wFEIAABCAAAQhAIM4EzNKj1R+sY+PbOHOlOwhAAAIQgIBGAogXjalRMwQgAAEIQAACEIAABCAAAQhAAAIqCCBeVMREkRCAAAQgAAEIQAACEIAABCAAAQhoJIB40ZgaNUMAAhCAAAQgAAEIQAACEIAABCCgggDiRUVMFAkBCEAAAhCAAAQgAAEIQAACEICARgKIF42pUTMEIAABCEAAAhCAAAQgAAEIQAACKgggXlTERJEQgAAEIAABCEAAAhCAAAQgAAEIaCSAeNGYGjVDAAIQgAAEIAABCEAAAhCAAAQgoIIA4kVFTBQJAQhAAAIQgAAEIAABCEAAAhCAgEYCiBeNqVEzBCAAAQhAAAIQgAAEIAABCEAAAioIIF5UxESREIAABCAAAQhAAAIQgAAEIAABCGgkgHjRmBo1QwACEIAABCAAAQhAAAIQgAAEIKCCAOJFRUwUCQEIQAACEIAABCAAAQhAAAIQgIBGAogXjalRMwQgAAEIQAACEIAABCAAAQhAAAIqCCBeVMREkRCAAAQgAAEIQAACEIAABCAAAQhoJIB40ZgaNUMAAhCAAAQgAAEIQAACEIAABCCgggDiRUVMFAkBCEAAAhCAAAQgAAEIQAACEICARgKIF42pUTMEIAABCEAAAhCAAAQgAAEIQAACKgggXlTERJEQgAAEIAABCEAAAhCAAAQgAAEIaCSAeNGYGjVDAAIQgAAEIAABCEAAAhCAAAQgoIIA4kVFTBQJAQhAAAIQgAAEIAABCEAAAhCAgEYCiBeNqVEzBCAAAQhAAAIQgAAEIAABCEAAAioIIF5UxESREIAABCAAAQhAAAIQgAAEIAABCGgkgHjRmBo1QwACEIAABCAAAQhAAAIQgAAEIKCCAOJFRUwUCQEIQAACEIAABCAAAQhAAAIQgIBGAogXjalRMwQgAAEIQAACEIAABCAAAQhAAAIqCCBeVMREkRCAAAQgAAEIQAACEIAABCAAAQhoJIB40ZgaNUMAAhCAAAQgAAEIQAACEIAABCCgggDiRUVMFAkBCEAAAhCAAAQgAAEIQAACEICARgKIF42pUTMEIAABCEAAAhCAAAQgAAEIQAACKgggXlTERJEQgAAEIAABCEAAAhCAAAQgAAEIaCSAeNGYGjVDAAIQgAAEIAABCEAAAhCAAAQgoIIA4kVFTBQJAQhAAAIQgAAEIAABCEAAAhCAgEYCiBeNqVEzBCAAAQhAAAIQgAAEIAABCEAAAioIIF5UxESREIAABCAAAQhAAAIQgAAEIAABCGgkgHjRmBo1QwACEIAABCAAAQhAAAIQgAAEIKCCAOJFRUwUCQEIQAACEIAABCAAAQhAAAIQgIBGAogXjalRMwQgAAEIQAACEIAABCAAAQhAAAIqCPwf1EvVSpG3sEQAAAAASUVORK5CYII=", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure()\n", + "fig.add_trace(go.Scatter(x=k_list, y=len(k_list) * [beta],\n", + " mode=\"lines\", name=\"The probability of an error of the 2nd kind\"))\n", + "fig.add_trace(go.Scatter(x=k_list,\n", + " y=reject_stat_1,\n", + " mode=\"lines\",\n", + " name=f\"Optimal solution\",\n", + " error_y={\n", + " \"type\": \"data\",\n", + " \"symmetric\": False,\n", + " \"array\": reject_stat_1 - left_side_1,\n", + " \"arrayminus\": right_side_1 - reject_stat_1,\n", + " \"visible\": True\n", + " }))\n", + "\n", + "fig.add_trace(go.Scatter(x=k_list,\n", + " y=reject_stat_2,\n", + " mode=\"lines\",\n", + " name=f\"Simple solution\",\n", + " error_y={\n", + " \"type\": \"data\",\n", + " \"symmetric\": False,\n", + " \"array\": reject_stat_2 - left_side_2,\n", + " \"arrayminus\": right_side_2 - reject_stat_2,\n", + " \"visible\": True\n", + " }))\n", + "\n", + "fig.update_layout(title=\"Modeling the probability of a second kind of error\",\n", + " xaxis={\n", + " \"title_text\": \"Number of samples\"\n", + " },\n", + " yaxis={\n", + " \"tickformat\": \".0%\",\n", + " \"title_text\": \"The frequency of hypothesis acceptance\",\n", + " \"range\": [0, 0.25]\n", + " },\n", + " legend={\n", + " \"yanchor\": \"top\",\n", + " \"y\": 0.99,\n", + " \"xanchor\": \"right\",\n", + " \"x\": 0.995\n", + " },\n", + " width=725,\n", + " height=500)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/tutorials/Tutorial_18_Test_Limit_Distribution.ipynb b/examples/tutorials/Tutorial_18_Test_Limit_Distribution.ipynb new file mode 100644 index 00000000..f6722e42 --- /dev/null +++ b/examples/tutorials/Tutorial_18_Test_Limit_Distribution.ipynb @@ -0,0 +1,438 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d9fe77410b4b4d28", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# How to do experiment via Limit Distribution? (ABn Test)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7f5730c16e20fe3e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-19T07:40:30.120314Z", + "start_time": "2024-02-19T07:40:22.040562Z" + } + }, + "outputs": [], + "source": [ + "from scipy.stats import bernoulli\n", + "\n", + "from lightautoml.addons.hypex.abn_test import min_sample_size\n", + "from lightautoml.addons.hypex.abn_test import test_on_marginal_distribution\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "3f990ce477eadc58", + "metadata": {}, + "source": [ + "### Initialize random state" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ec6339fd4b9bfbe9", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-19T07:50:19.576582Z", + "start_time": "2024-02-19T07:50:19.565297Z" + } + }, + "outputs": [], + "source": [ + "seed = 42 # You can choose any number as the seed\n", + "random_state = np.random.RandomState(seed)" + ] + }, + { + "cell_type": "markdown", + "id": "56a30e356af1c7ed", + "metadata": {}, + "source": [ + "## Multiple testing for best sample selection" + ] + }, + { + "cell_type": "markdown", + "id": "a75d120b056c6172", + "metadata": {}, + "source": [ + "### Number of samples and parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3cbf54ea3ce36deb", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-19T07:50:24.608488Z", + "start_time": "2024-02-19T07:50:24.597353Z" + } + }, + "outputs": [], + "source": [ + "num_samples = 10 # Number of samples\n", + "minimum_detectable_effect = 0.05 # MDE\n", + "assumed_conversion = 0.3 # Assumed conversion rate\n", + "significance_level = 0.05 # Significance level\n", + "power_level = 0.2 # Power level (1 - beta)" + ] + }, + { + "cell_type": "markdown", + "id": "f519dc7b24067462", + "metadata": {}, + "source": [ + "### Calculate the minimum sample size" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "67b353030a5f7356", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-19T07:50:28.734673Z", + "start_time": "2024-02-19T07:50:26.677119Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample size = 1313\n" + ] + } + ], + "source": [ + "sample_size = min_sample_size(\n", + " num_samples,\n", + " minimum_detectable_effect,\n", + " variances=assumed_conversion * (1 - assumed_conversion),\n", + " significance_level=significance_level,\n", + " power_level=power_level,\n", + " equal_variance=True,\n", + ")\n", + "print(f\"Sample size = {sample_size}\")" + ] + }, + { + "cell_type": "markdown", + "id": "8ce92cbd46e2e66f", + "metadata": {}, + "source": [ + "### Testing samples with equal conversion rate" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ac90451bc36c6044", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-19T07:51:05.235352Z", + "start_time": "2024-02-19T07:50:32.706689Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Samples with equal conversion rate\n", + "\tAccepted hypothesis H(0)\n", + "\tAccepted hypothesis H(0)\n", + "\tAccepted hypothesis H(0)\n", + "\tAccepted hypothesis H(0)\n", + "\tAccepted hypothesis H(0)\n" + ] + } + ], + "source": [ + "print(\"\\nSamples with equal conversion rate\")\n", + "for _ in range(5):\n", + " samples = bernoulli.rvs(\n", + " assumed_conversion, size=[num_samples, sample_size], random_state=random_state\n", + " )\n", + " hypothesis = test_on_marginal_distribution(\n", + " samples, significance_level=significance_level\n", + " )\n", + " print(f\"\\tAccepted hypothesis H({hypothesis})\")" + ] + }, + { + "cell_type": "markdown", + "id": "6cddf5b6661f161c", + "metadata": {}, + "source": [ + "### Testing where the last sample has a higher conversion rate by MDE" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6f61bafb73e0eaad", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-19T07:51:42.685919Z", + "start_time": "2024-02-19T07:51:05.238615Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Last sample has higher conversion by MDE\n", + "\tAccepted hypothesis H(10)\n", + "\tAccepted hypothesis H(10)\n", + "\tAccepted hypothesis H(10)\n", + "\tAccepted hypothesis H(10)\n", + "\tAccepted hypothesis H(10)\n" + ] + } + ], + "source": [ + "print(\"\\nLast sample has higher conversion by MDE\")\n", + "for _ in range(5):\n", + " samples = [\n", + " bernoulli.rvs(assumed_conversion, size=sample_size, random_state=random_state)\n", + " for _ in range(num_samples - 1)\n", + " ]\n", + " samples.append(\n", + " bernoulli.rvs(\n", + " assumed_conversion + minimum_detectable_effect,\n", + " size=sample_size,\n", + " random_state=random_state,\n", + " )\n", + " )\n", + " hypothesis = test_on_marginal_distribution(\n", + " samples, significance_level=significance_level\n", + " )\n", + " print(f\"\\tAccepted hypothesis H({hypothesis})\")" + ] + }, + { + "cell_type": "markdown", + "id": "5cda8f5b9ac6f05c", + "metadata": {}, + "source": [ + "## Multiple testing for best client income sample (conversion * price)" + ] + }, + { + "cell_type": "markdown", + "id": "1c248f764c4a24b8", + "metadata": {}, + "source": [ + "### Parameters for different samples" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "14b5c6bdd7967b6f", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-19T07:41:52.147726Z", + "start_time": "2024-02-19T07:41:52.133629Z" + } + }, + "outputs": [], + "source": [ + "num_samples = 5 # Number of samples\n", + "minimum_detectable_effect = 2.5 # MDE\n", + "prices = [100, 150, 150, 200, 250] # Tariff prices\n", + "conversions = [0.15, 0.1, 0.1, 0.075, 0.06] # Tariff conversions\n", + "significance_level = 0.05\n", + "power_level = 0.2\n", + "variances = [\n", + " price ** 2 * conversion * (1 - conversion)\n", + " for price, conversion in zip(prices, conversions)\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "378d5887ef93b9e0", + "metadata": {}, + "source": [ + "### Calculate minimum sample size for unequal variances" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "13b3d7fa50a129a", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-19T07:42:09.825024Z", + "start_time": "2024-02-19T07:41:52.150025Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample size = 7200\n" + ] + } + ], + "source": [ + "sample_size = min_sample_size(\n", + " num_samples,\n", + " minimum_detectable_effect,\n", + " variances=variances,\n", + " significance_level=significance_level,\n", + " power_level=power_level,\n", + " equal_variance=False,\n", + ")\n", + "print(f\"Sample size = {sample_size}\")" + ] + }, + { + "cell_type": "markdown", + "id": "43298f88aa5666a2", + "metadata": {}, + "source": [ + "### Testing samples with equal ARPU (Average Revenue Per User)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "55423e5fcc7dd753", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-19T07:42:25.335995Z", + "start_time": "2024-02-19T07:42:17.533344Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Samples with equal ARPU\n", + "\tAccepted hypothesis H(0)\n", + "\tAccepted hypothesis H(0)\n", + "\tAccepted hypothesis H(4)\n", + "\tAccepted hypothesis H(0)\n", + "\tAccepted hypothesis H(0)\n" + ] + } + ], + "source": [ + "print(\"\\nSamples with equal ARPU\")\n", + "for _ in range(5):\n", + " samples = [\n", + " price * bernoulli.rvs(conversion, size=sample_size)\n", + " for price, conversion in zip(prices, conversions)\n", + " ]\n", + " hypothesis = test_on_marginal_distribution(\n", + " samples, significance_level=significance_level\n", + " )\n", + " print(f\"\\tAccepted hypothesis H({hypothesis})\")" + ] + }, + { + "cell_type": "markdown", + "id": "b7b9e1039797e997", + "metadata": {}, + "source": [ + "### Testing where the last sample has higher ARPU by MDE" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-19T07:42:33.114314Z", + "start_time": "2024-02-19T07:42:25.337517Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Last sample has higher ARPU by MDE\n", + "\tAccepted hypothesis H(5)\n", + "\tAccepted hypothesis H(5)\n", + "\tAccepted hypothesis H(5)\n", + "\tAccepted hypothesis H(0)\n", + "\tAccepted hypothesis H(5)\n" + ] + } + ], + "source": [ + "print(\"\\nLast sample has higher ARPU by MDE\")\n", + "for _ in range(5):\n", + " samples = [\n", + " price * bernoulli.rvs(conversion, size=sample_size)\n", + " for price, conversion in zip(prices, conversions[:-1])\n", + " ]\n", + " samples.append(\n", + " prices[-1]\n", + " * bernoulli.rvs(\n", + " conversions[-1] + minimum_detectable_effect / prices[-1], size=sample_size\n", + " )\n", + " )\n", + " hypothesis = test_on_marginal_distribution(\n", + " samples, significance_level=significance_level\n", + " )\n", + " print(f\"\\tAccepted hypothesis H({hypothesis})\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d54ff8d1-c226-421f-bd86-914851dab222", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 4bbaf636a62162de7a5f82e7975cd7f3e3be8503 Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 22:23:49 +0300 Subject: [PATCH 12/16] Added nblink for new HypEx tutorials --- docs/pages/tutorials/Tutorial_12_AA_Test.nblink | 6 ++++++ .../tutorials/Tutorial_13_AA_Test_multigroup_split.nblink | 6 ++++++ docs/pages/tutorials/Tutorial_14_AB_Test.nblink | 6 ++++++ docs/pages/tutorials/Tutorial_15_Matching.nblink | 6 ++++++ .../Tutorial_16_Matching_without_replacement.nblink | 6 ++++++ .../Tutorial_17_Modeling_Limit_Distribution.nblink | 6 ++++++ .../tutorials/Tutorial_18_Test_Limit_Distribution.nblink | 6 ++++++ 7 files changed, 42 insertions(+) create mode 100644 docs/pages/tutorials/Tutorial_12_AA_Test.nblink create mode 100644 docs/pages/tutorials/Tutorial_13_AA_Test_multigroup_split.nblink create mode 100644 docs/pages/tutorials/Tutorial_14_AB_Test.nblink create mode 100644 docs/pages/tutorials/Tutorial_15_Matching.nblink create mode 100644 docs/pages/tutorials/Tutorial_16_Matching_without_replacement.nblink create mode 100644 docs/pages/tutorials/Tutorial_17_Modeling_Limit_Distribution.nblink create mode 100644 docs/pages/tutorials/Tutorial_18_Test_Limit_Distribution.nblink diff --git a/docs/pages/tutorials/Tutorial_12_AA_Test.nblink b/docs/pages/tutorials/Tutorial_12_AA_Test.nblink new file mode 100644 index 00000000..21630094 --- /dev/null +++ b/docs/pages/tutorials/Tutorial_12_AA_Test.nblink @@ -0,0 +1,6 @@ +{ + "path": "../../../examples/tutorials/Tutorial_12_AA_Test.ipynb", + "extra-media": [ + "../../../imgs" + ] +} diff --git a/docs/pages/tutorials/Tutorial_13_AA_Test_multigroup_split.nblink b/docs/pages/tutorials/Tutorial_13_AA_Test_multigroup_split.nblink new file mode 100644 index 00000000..fbac0a24 --- /dev/null +++ b/docs/pages/tutorials/Tutorial_13_AA_Test_multigroup_split.nblink @@ -0,0 +1,6 @@ +{ + "path": "../../../examples/tutorials/Tutorial_13_AA_Test_multigroup_split.ipynb", + "extra-media": [ + "../../../imgs" + ] +} diff --git a/docs/pages/tutorials/Tutorial_14_AB_Test.nblink b/docs/pages/tutorials/Tutorial_14_AB_Test.nblink new file mode 100644 index 00000000..e0ae4463 --- /dev/null +++ b/docs/pages/tutorials/Tutorial_14_AB_Test.nblink @@ -0,0 +1,6 @@ +{ + "path": "../../../examples/tutorials/Tutorial_14_AB_Test.ipynb", + "extra-media": [ + "../../../imgs" + ] +} diff --git a/docs/pages/tutorials/Tutorial_15_Matching.nblink b/docs/pages/tutorials/Tutorial_15_Matching.nblink new file mode 100644 index 00000000..1121c23c --- /dev/null +++ b/docs/pages/tutorials/Tutorial_15_Matching.nblink @@ -0,0 +1,6 @@ +{ + "path": "../../../examples/tutorials/Tutorial_15_Matching.ipynb", + "extra-media": [ + "../../../imgs" + ] +} diff --git a/docs/pages/tutorials/Tutorial_16_Matching_without_replacement.nblink b/docs/pages/tutorials/Tutorial_16_Matching_without_replacement.nblink new file mode 100644 index 00000000..3bd21c4c --- /dev/null +++ b/docs/pages/tutorials/Tutorial_16_Matching_without_replacement.nblink @@ -0,0 +1,6 @@ +{ + "path": "../../../examples/tutorials/Tutorial_16_Matching_without_replacement.ipynb", + "extra-media": [ + "../../../imgs" + ] +} diff --git a/docs/pages/tutorials/Tutorial_17_Modeling_Limit_Distribution.nblink b/docs/pages/tutorials/Tutorial_17_Modeling_Limit_Distribution.nblink new file mode 100644 index 00000000..5278a5d3 --- /dev/null +++ b/docs/pages/tutorials/Tutorial_17_Modeling_Limit_Distribution.nblink @@ -0,0 +1,6 @@ +{ + "path": "../../../examples/tutorials/Tutorial_17_Modeling_Limit_Distribution.ipynb", + "extra-media": [ + "../../../imgs" + ] +} diff --git a/docs/pages/tutorials/Tutorial_18_Test_Limit_Distribution.nblink b/docs/pages/tutorials/Tutorial_18_Test_Limit_Distribution.nblink new file mode 100644 index 00000000..3743ecff --- /dev/null +++ b/docs/pages/tutorials/Tutorial_18_Test_Limit_Distribution.nblink @@ -0,0 +1,6 @@ +{ + "path": "../../../examples/tutorials/Tutorial_18_Test_Limit_Distribution.ipynb", + "extra-media": [ + "../../../imgs" + ] +} From 618271072a631116086109469c93b446c1552bee Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 22:24:28 +0300 Subject: [PATCH 13/16] Reorganized Tutorials.rst structure --- docs/pages/Tutorials.rst | 53 +++++++++++++++++++++++++++++++++++++++- 1 file changed, 52 insertions(+), 1 deletion(-) diff --git a/docs/pages/Tutorials.rst b/docs/pages/Tutorials.rst index c3d20d42..b683c015 100644 --- a/docs/pages/Tutorials.rst +++ b/docs/pages/Tutorials.rst @@ -1,15 +1,30 @@ Tutorials ========= +This section contains tutorials for both **LightAutoML** and **HypEx**, covering a wide range of use cases from basic model training to advanced hypothesis testing. + +LightAutoML Tutorials +--------------------- + +Core Features +~~~~~~~~~~~~~ .. toctree:: :maxdepth: 1 - :caption: Contents + :caption: Core Features tutorials/Tutorial_1_basics.nblink tutorials/Tutorial_2_WhiteBox_AutoWoE.nblink tutorials/Tutorial_3_sql_data_source.nblink tutorials/Tutorial_4_NLP_Interpretation.nblink + +Advanced Topics +~~~~~~~~~~~~~~~ + +.. toctree:: + :maxdepth: 1 + :caption: Advanced Topics + tutorials/Tutorial_5_uplift.nblink tutorials/Tutorial_6_custom_pipeline.nblink tutorials/Tutorial_7_ICE_and_PDP_interpretation.nblink @@ -17,3 +32,39 @@ Tutorials tutorials/Tutorial_9_neural_networks.nblink tutorials/Tutorial_10_relational_data_with_star_scheme.nblink tutorials/Tutorial_11_time_series.nblink + +--- + +HypEx Tutorials +--------------- + +A/B and A/A Testing +~~~~~~~~~~~~~~~~~~~ + +.. toctree:: + :maxdepth: 1 + :caption: A/B and A/A Testing + + tutorials/Tutorial_12_AA_Test.nblink + tutorials/Tutorial_13_AA_Test_multigroup_split.nblink + tutorials/Tutorial_14_AB_Test.nblink + +Matching +~~~~~~~~~~~~~~~~~~~~~~ + +.. toctree:: + :maxdepth: 1 + :caption: Matching + + tutorials/Tutorial_15_Matching.nblink + tutorials/Tutorial_16_Matching_without_replacement.nblink + +Modeling and Testing Limits +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. toctree:: + :maxdepth: 1 + :caption: Modeling and Testing Limits + + tutorials/Tutorial_17_Modeling_Limit_Distribution.nblink + tutorials/Tutorial_18_Test_Limit_Distribution.nblink From 847cfb20ab679216ae64b385d3fd0630f05e4bcb Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 22:31:08 +0300 Subject: [PATCH 14/16] Added docs to ignore in codespell --- tox.ini | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tox.ini b/tox.ini index bf3f6da2..1b2aeb4e 100644 --- a/tox.ini +++ b/tox.ini @@ -79,7 +79,7 @@ commands = deps = codespell == 2.3.0 commands = - codespell + codespell --skip="docs,_build,imgs" # example: # tox -e exp -- --dataset_project=Datasets_with_metadata --tags=binary openml From e551483c7df3359e93c4628fca8e76fd2ff85079 Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 22:41:37 +0300 Subject: [PATCH 15/16] Removed duplicates --- docs/pages/Tutorials.rst | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/docs/pages/Tutorials.rst b/docs/pages/Tutorials.rst index b683c015..7c0290f8 100644 --- a/docs/pages/Tutorials.rst +++ b/docs/pages/Tutorials.rst @@ -6,8 +6,6 @@ This section contains tutorials for both **LightAutoML** and **HypEx**, covering LightAutoML Tutorials --------------------- -Core Features -~~~~~~~~~~~~~ .. toctree:: :maxdepth: 1 @@ -18,8 +16,6 @@ Core Features tutorials/Tutorial_3_sql_data_source.nblink tutorials/Tutorial_4_NLP_Interpretation.nblink -Advanced Topics -~~~~~~~~~~~~~~~ .. toctree:: :maxdepth: 1 @@ -38,8 +34,6 @@ Advanced Topics HypEx Tutorials --------------- -A/B and A/A Testing -~~~~~~~~~~~~~~~~~~~ .. toctree:: :maxdepth: 1 @@ -49,8 +43,6 @@ A/B and A/A Testing tutorials/Tutorial_13_AA_Test_multigroup_split.nblink tutorials/Tutorial_14_AB_Test.nblink -Matching -~~~~~~~~~~~~~~~~~~~~~~ .. toctree:: :maxdepth: 1 @@ -59,8 +51,6 @@ Matching tutorials/Tutorial_15_Matching.nblink tutorials/Tutorial_16_Matching_without_replacement.nblink -Modeling and Testing Limits -~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. toctree:: :maxdepth: 1 From f6b842fe6a12e143656b9b9c70d0cb25b72cac53 Mon Sep 17 00:00:00 2001 From: Dmitry Tikhomirov Date: Wed, 18 Dec 2024 22:43:16 +0300 Subject: [PATCH 16/16] Removed line --- docs/pages/Tutorials.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/pages/Tutorials.rst b/docs/pages/Tutorials.rst index 7c0290f8..502340bd 100644 --- a/docs/pages/Tutorials.rst +++ b/docs/pages/Tutorials.rst @@ -29,7 +29,6 @@ LightAutoML Tutorials tutorials/Tutorial_10_relational_data_with_star_scheme.nblink tutorials/Tutorial_11_time_series.nblink ---- HypEx Tutorials ---------------