diff --git a/bambi/interpret/__init__.py b/bambi/interpret/__init__.py index b487826e0..5a90f2df3 100644 --- a/bambi/interpret/__init__.py +++ b/bambi/interpret/__init__.py @@ -1,11 +1,14 @@ import logging from bambi.interpret.effects import comparisons, predictions, slopes +from bambi.interpret.helpers import data_grid, select_draws from bambi.interpret.plotting import plot_comparisons, plot_predictions, plot_slopes __all__ = [ "comparisons", + "data_grid", "logger", + "select_draws", "slopes", "predictions", "plot_comparisons", diff --git a/bambi/interpret/create_data.py b/bambi/interpret/create_data.py index 9711b3200..8862d2842 100644 --- a/bambi/interpret/create_data.py +++ b/bambi/interpret/create_data.py @@ -1,66 +1,118 @@ import itertools + from typing import Union +from statistics import mode import numpy as np import pandas as pd +from pandas.api.types import ( + is_categorical_dtype, + is_float_dtype, + is_integer_dtype, + is_numeric_dtype, + is_object_dtype, + is_string_dtype, +) + +from bambi import Model from bambi.interpret.utils import ( ConditionalInfo, enforce_dtypes, - get_covariates, get_model_covariates, - make_group_panel_values, - make_main_values, - set_default_values, VariableInfo, ) +from bambi.interpret.logs import log_interpret_defaults -def _pairwise_grid(data_dict: dict) -> pd.DataFrame: - """Creates a pairwise grid (cartesian product) of data by using the - key-values of the dictionary. + +@log_interpret_defaults +def create_grid( + condition: ConditionalInfo, variable: Union[VariableInfo, None] = None, **kwargs +) -> pd.DataFrame: + """Creates a grid of data by using the covariates passed into the 'conditional' + and 'variable' argument. + + Values for the grid are either: + 1.) computed using an equally spaced grid (`np.linspace`), mean, and or mode + depending on the covariate dtype. + 2.) a user specified value or range of values if `condition.user_passed = True` Parameters ---------- - data_dict : dict - A dictionary containing the covariates as keys and their values as the - values. + condition : ConditionalInfo + Information about data passed to the conditional parameter of 'comparisons', + 'predictions', or 'slopes' related functions. + variable : VariableInfo, optional + Information about data passed to the variable of interest parameter. This + is 'contrast' for 'comparisons', 'wrt' for 'slopes', and 'None' for 'predictions'. + **kwargs : dict + Optional keywords arguments such as 'effect_type' (the effect being computed), + and 'num' (the number of values to return when computing a `np.linspace` grid). Returns ------- pd.DataFrame - A dataframe containing values used as input to the fitted Bambi model to - generate predictions. + A dataframe containing pairwise combinations of values. """ - keys, values = zip(*data_dict.items()) - data_grid = pd.DataFrame([dict(zip(keys, v)) for v in itertools.product(*values)]) - return data_grid + model, observed_data = condition.model, condition.model.data + + if condition.user_passed: + # shallow copy of user-passed data dictionary + data_dict = {**condition.conditional} + else: + data_dict = {} + # values here are the names of the covariates + for covariate in condition.covariates.values(): + x = observed_data[covariate] + num = kwargs.get("num", 50) + if is_numeric_dtype(x) or is_float_dtype(x): + values = np.linspace(np.min(x), np.max(x), num) + elif is_integer_dtype(x): + values = np.quantile(x, np.linspace(0, 1, 5)) + elif is_categorical_dtype(x) or is_string_dtype(x) or is_object_dtype(x): + values = np.unique(x) + else: + raise TypeError( + f"Unsupported data type of {x.dtype} for covariate '{covariate.name}'" + ) + + data_dict[covariate] = values + + if variable: + data_dict[variable.name] = variable.values + + # set typical values as defaults for unspecified covariates + data_dict = set_default_values(model, data_dict) + data_grid = _pairwise_grid(data_dict) + + # can't enforce dtype on 'with respect to' variable for 'slopes' as it + # may remove floating point in the epsilon + effect = kwargs.get("effect_type", None) + if effect == "slopes": + except_col = variable.name + else: + except_col = None + data_grid = enforce_dtypes(observed_data, data_grid, except_col) + + # after computing default values, fractional values may have been computed. + # Enforcing the dtype of "int" may create duplicate rows as it will round + # the fractional values. + data_grid = data_grid.drop_duplicates() + + return data_grid.reset_index(drop=True) -def _grid_level( - condition_info: ConditionalInfo, - variable_info: Union[VariableInfo, None], - user_passed: bool, - kind: str, -) -> pd.DataFrame: - """Creates a "grid" of data by using the covariates passed into the - `conditional` argument. Values for the grid are either: (1) computed - using a equally spaced grid, mean, and or mode (depending on the - covariate dtype), and (2) a user specified value or range of values. + +def _pairwise_grid(data_dict: dict) -> pd.DataFrame: + """Creates a pairwise grid (cartesian product) of data by using the + key-values of the dictionary. Parameters ---------- - condition_info : ConditionalInfo - Information about the conditional argument passed into the plot - function. - variable_info : VariableInfo, optional - Information about the variable of interest. This is `contrast` for - 'comparisons', `wrt` for 'slopes', and `None` for 'predictions'. - user_passed : bool - Whether the user passed a value(s) for the `conditional` argument. - kind : str - The kind of effect being computed. Either "comparisons", "predictions", - or "slopes". + data_dict : dict + A dictionary containing the covariates as keys and their values as the + values. Returns ------- @@ -68,69 +120,12 @@ def _grid_level( A dataframe containing values used as input to the fitted Bambi model to generate predictions. """ - covariates = get_covariates(condition_info.covariates) - - if kind == "predictions": - # Compute pairwise grid of values if the user passed a dict. - if user_passed: - data_dict = {**condition_info.conditional} - data_dict = set_default_values(condition_info.model, data_dict, kind=kind) - for key, value in data_dict.items(): - if not isinstance(value, (list, np.ndarray)): - data_dict[key] = [value] - data_grid = _pairwise_grid(data_dict) - else: - # Compute a grid of values - main_values = make_main_values( - condition_info.model.data[covariates.main], covariates.main - ) - data_dict = {covariates.main: main_values} - data_dict = make_group_panel_values( - condition_info.model.data, - data_dict, - covariates.main, - covariates.group, - covariates.panel, - kind=kind, - ) - data_dict = set_default_values(condition_info.model, data_dict, kind=kind) - data_grid = pd.DataFrame(data_dict) - else: - # Compute pairwise grid of values if the user passed a dict. - if user_passed: - data_dict = {**condition_info.conditional} - else: - # Compute a grid of values - main_values = make_main_values( - condition_info.model.data[covariates.main], covariates.main - ) - data_dict = {covariates.main: main_values} - data_dict = make_group_panel_values( - condition_info.model.data, - data_dict, - covariates.main, - covariates.group, - covariates.panel, - kind=kind, - ) - - data_dict[variable_info.name] = variable_info.values - data_dict = set_default_values(condition_info.model, data_dict, kind=kind) - data_grid = _pairwise_grid(data_dict) - - # Can't enforce dtype on numeric 'wrt' for 'slopes 'as it may remove floating point epsilons - except_col = None if kind in ("comparisons", "predictions") else {variable_info.name} - data_grid = enforce_dtypes(condition_info.model.data, data_grid, except_col) - - # After computing default values, fractional values may have been computed. - # Enforcing the dtype of "int" may create duplicate rows as it will round - # the fractional values. - data_grid = data_grid.drop_duplicates() - - return data_grid.reset_index(drop=True) + keys, values = zip(*data_dict.items()) + cross_joined_data = pd.DataFrame([dict(zip(keys, v)) for v in itertools.product(*values)]) + return cross_joined_data -def _differences_unit_level(variable_info: VariableInfo, kind: str) -> pd.DataFrame: +def _differences_unit_level(variable_info: VariableInfo, effect_type: str) -> pd.DataFrame: """Creates the data for unit-level contrasts by using the observed (empirical) data. All covariates in the model are included in the data, except for the contrast predictor. The contrast predictor is replaced with either: (1) the @@ -141,8 +136,8 @@ def _differences_unit_level(variable_info: VariableInfo, kind: str) -> pd.DataFr variable_info : VariableInfo Information about the variable of interest. This is `contrast` for 'comparisons' and `wrt` for 'slopes'. - kind : str - The kind of effect being computed. Either "comparisons" or "slopes". + effect_type : str + The type of effect being computed. Either "comparisons" or "slopes". Returns ------- @@ -153,10 +148,9 @@ def _differences_unit_level(variable_info: VariableInfo, kind: str) -> pd.DataFr """ covariates = get_model_covariates(variable_info.model) df = variable_info.model.data[covariates].drop(labels=variable_info.name, axis=1) - variable_vals = variable_info.values - if kind == "comparisons": + if effect_type == "comparisons": variable_vals = np.array(variable_info.values)[..., None] variable_vals = np.repeat(variable_vals, variable_info.model.data.shape[0], axis=1) @@ -165,11 +159,13 @@ def _differences_unit_level(variable_info: VariableInfo, kind: str) -> pd.DataFr unit_level_df_dict[f"contrast_{idx}"] = df.copy() unit_level_df_dict[f"contrast_{idx}"][variable_info.name] = value - return pd.concat(unit_level_df_dict.values()) + unit_level_df = pd.concat(unit_level_df_dict.values()) + + return unit_level_df.reset_index(drop=True) def create_differences_data( - condition_info: ConditionalInfo, variable_info: VariableInfo, user_passed: bool, kind: str + condition_info: ConditionalInfo, variable_info: VariableInfo, effect_type: str ) -> pd.DataFrame: """Creates either unit level or grid level data for 'comparisons' and 'slopes' depending if the user passed covariate values. @@ -182,10 +178,8 @@ def create_differences_data( variable_info : VariableInfo Information about the variable of interest. This is `contrast` for 'comparisons' and `wrt` for 'slopes'. - user_passed : bool - Whether the user passed a value(s) for the `conditional` argument. - kind : str - The kind of effect being computed. Either "comparisons" or "slopes". + effect_type : str + The type of effect being computed. Either "comparisons" or "slopes". Returns ------- @@ -195,14 +189,13 @@ def create_differences_data( is returned. Otherwise, a grid of values is created using the covariates passed into the `conditional` argument. """ - if not condition_info.covariates: - return _differences_unit_level(variable_info, kind) + return _differences_unit_level(variable_info, effect_type) - return _grid_level(condition_info, variable_info, user_passed, kind) + return create_grid(condition_info, variable_info, effect_type=effect_type) -def create_predictions_data(condition_info: ConditionalInfo, user_passed: bool) -> pd.DataFrame: +def create_predictions_data(condition_info: ConditionalInfo) -> pd.DataFrame: """Creates either unit level or grid level data for 'predictions' depending if the user passed covariates. @@ -211,8 +204,6 @@ def create_predictions_data(condition_info: ConditionalInfo, user_passed: bool) condition_info : ConditionalInfo Information about the conditional argument passed into the plot function. - user_passed : bool - Whether the user passed a value(s) for the `conditional` argument. Returns ------- @@ -222,9 +213,30 @@ def create_predictions_data(condition_info: ConditionalInfo, user_passed: bool) is returned. Otherwise, a grid of values is created using the covariates passed into the `conditional` argument. """ - # Unit level data used the observed (empirical) data + # unit level data uses the observed (empirical) data if not condition_info.covariates: covariates = get_model_covariates(condition_info.model) return condition_info.model.data[covariates] - return _grid_level(condition_info, None, user_passed, "predictions") + return create_grid(condition_info, None) + + +@log_interpret_defaults +def set_default_values(model: Model, data_dict: dict) -> dict: + """ + Set default values for each variable in the model if the user did not + pass them in the data_dict. + """ + # set unspecified covariates to "typical" values + unique_covariates = get_model_covariates(model) + for name in unique_covariates: + if name not in data_dict: + x = model.data[name] + if is_numeric_dtype(x) or is_integer_dtype(x) or is_float_dtype(x): + data_dict[name] = np.array([np.mean(x)]) + elif is_categorical_dtype(x) or is_string_dtype(x) or is_object_dtype(x): + data_dict[name] = np.array([mode(x)]) + else: + raise TypeError(f"Unsupported data type of {x.dtype} for covariate '{name}'") + + return data_dict diff --git a/bambi/interpret/effects.py b/bambi/interpret/effects.py index 7537b49af..cc978aeca 100644 --- a/bambi/interpret/effects.py +++ b/bambi/interpret/effects.py @@ -342,7 +342,7 @@ def get_summary_df(self, response_dim: np.ndarray) -> pd.DataFrame: for 'comparisons' and 'slopes', then a subset of the 'preds' data is used to build the summary. """ - # Scenario 1 + # scenario 1 if len(self.variable.values) > 2 and self.kind == "comparisons": summary_df = self.preds_data.drop(columns=self.variable.name).drop_duplicates() covariates_cols = summary_df.columns @@ -353,7 +353,7 @@ def get_summary_df(self, response_dim: np.ndarray) -> pd.DataFrame: contrast_values, summary_df.shape[0] // len(contrast_values), axis=0 ) contrast_values = [tuple(elem) for elem in contrast_values] - # Scenario 2 + # scenario 2 elif len(response_dim) > 1: summary_df = self.preds_data.drop(columns=self.variable.name).drop_duplicates() covariates_cols = summary_df.columns @@ -364,7 +364,7 @@ def get_summary_df(self, response_dim: np.ndarray) -> pd.DataFrame: response_dim, summary_df.shape[0] // len(response_dim) ) contrast_values = [tuple(contrast_values)] * summary_df.shape[0] - # Scenario 3 & 4 + # scenario 3 & 4 else: wrt = {} for idx, _ in enumerate(self.variable.values): @@ -455,36 +455,36 @@ def predictions( average_by: str, list, bool, optional The covariates we would like to average by. The passed covariate(s) will marginalize over the other covariates in the model. If True, it averages over all covariates - in the model to obtain the average estimate. Defaults to ``None``. + in the model to obtain the average estimate. Defaults to `None`. target : str Which model parameter to plot. Defaults to 'mean'. Passing a parameter into target only works when pps is False as the target may not be available in the posterior predictive distribution. pps: bool, optional - Whether to plot the posterior predictive samples. Defaults to ``False``. + Whether to plot the posterior predictive samples. Defaults to `False`. use_hdi : bool, optional Whether to compute the highest density interval (defaults to True) or the quantiles. prob : float, optional The probability for the credibility intervals. Must be between 0 and 1. Defaults to 0.94. - Changing the global variable ``az.rcParam["stats.hdi_prob"]`` affects this default. + Changing the global variable `az.rcParam["stats.hdi_prob"]` affects this default. transforms : dict, optional Transformations that are applied to each of the variables being plotted. The keys are the - name of the variables, and the values are functions to be applied. Defaults to ``None``. + name of the variables, and the values are functions to be applied. Defaults to `None`. sample_new_groups : bool, optional If the model contains group-level effects, and data is passed for unseen groups, whether - to sample from the new groups. Defaults to ``False``. + to sample from the new groups. Defaults to `False`. Returns ------- cap_data : pandas.DataFrame - A DataFrame with the ``create_cap_data`` and model predictions. + A DataFrame with the `create_cap_data` and model predictions. Raises ------ ValueError - If ``pps`` is ``True`` and ``target`` is not ``"mean"``. - If ``conditional`` is a list and the length is greater than 3. - If ``prob`` is not > 0 and < 1. + If `pps` is `True` and `target` is not `"mean"`. + If `conditional` is a list and the length is greater than 3. + If `prob` is not > 0 and < 1. """ if pps and target != "mean": raise ValueError("When passing 'pps=True', target must be 'mean'") @@ -505,7 +505,7 @@ def predictions( if not 0 < prob < 1: raise ValueError(f"'prob' must be greater than 0 and smaller than 1. It is {prob}.") - cap_data = create_predictions_data(conditional_info, conditional_info.user_passed) + cap_data = create_predictions_data(conditional_info) if target != "mean": component = model.components[target] @@ -601,20 +601,20 @@ def comparisons( average_by: str, list, bool, optional The covariates we would like to average by. The passed covariate(s) will marginalize over the other covariates in the model. If True, it averages over all covariates - in the model to obtain the average estimate. Defaults to ``None``. + in the model to obtain the average estimate. Defaults to `None`. comparison_type : str, optional The type of comparison to plot. Defaults to 'diff'. use_hdi : bool, optional Whether to compute the highest density interval (defaults to True) or the quantiles. prob : float, optional The probability for the credibility intervals. Must be between 0 and 1. Defaults to 0.94. - Changing the global variable ``az.rcParams["stats.hdi_prob"]`` affects this default. + Changing the global variable `az.rcParams["stats.hdi_prob"]` affects this default. transforms : dict, optional Transformations that are applied to each of the variables being plotted. The keys are the - name of the variables, and the values are functions to be applied. Defaults to ``None``. + name of the variables, and the values are functions to be applied. Defaults to `None`. sample_new_groups : bool, optional If the model contains group-level effects, and data is passed for unseen groups, whether - to sample from the new groups. Defaults to ``False``. + to sample from the new groups. Defaults to `False`. Returns ------- @@ -625,13 +625,13 @@ def comparisons( Raises ------ ValueError - If `wrt` is a dict and length of ``contrast`` is greater than 1. - If `wrt` is a dict and length of ``contrast`` is greater than 2 and - ``conditional`` is ``None``. - If ``conditional`` is None and ``contrast`` is categorical with > 2 values. - If ``conditional`` is a list and the length is greater than 3. - If ``comparison_type`` is not 'diff' or 'ratio'. - If ``prob`` is not > 0 and < 1. + If `wrt` is a dict and length of `contrast` is greater than 1. + If `wrt` is a dict and length of `contrast` is greater than 2 and + `conditional` is `None`. + If `conditional` is None and `contrast` is categorical with > 2 values. + If `conditional` is a list and the length is greater than 3. + If `comparison_type` is not 'diff' or 'ratio'. + If `prob` is not > 0 and < 1. """ contrast_name = contrast if isinstance(contrast, dict): @@ -680,16 +680,14 @@ def comparisons( conditional_info = ConditionalInfo(model, conditional) transforms = transforms if transforms is not None else {} - response_name = get_aliased_name(model.response_component.response_term) response = ResponseInfo( response_name, target="mean", lower_bound=lower_bound, upper_bound=upper_bound ) response_transform = transforms.get(response_name, identity) - # 'comparisons' not be limited to ("main", "group", "panel") comparisons_data = create_differences_data( - conditional_info, contrast_info, conditional_info.user_passed, kind="comparisons" + conditional_info, contrast_info, effect_type="comparisons" ) idata = model.predict( idata, data=comparisons_data, sample_new_groups=sample_new_groups, inplace=False @@ -751,7 +749,7 @@ def slopes( average_by: str, list, bool, optional The covariates we would like to average by. The passed covariate(s) will marginalize over the other covariates in the model. If True, it averages over all covariates - in the model to obtain the average estimate. Defaults to ``None``. + in the model to obtain the average estimate. Defaults to `None`. eps : float, optional To compute the slope, 'wrt' is evaluated at wrt +/- 'eps'. The rate of change is then computed as the difference between the two values divided by 'eps'. Defaults to 1e-4. @@ -769,29 +767,29 @@ def slopes( Whether to compute the highest density interval (defaults to True) or the quantiles. prob : float, optional The probability for the credibility intervals. Must be between 0 and 1. Defaults to 0.94. - Changing the global variable ``az.rcParams["stats.hdi_prob"]`` affects this default. + Changing the global variable `az.rcParams["stats.hdi_prob"]` affects this default. transforms : dict, optional Transformations that are applied to each of the variables being plotted. The keys are the - name of the variables, and the values are functions to be applied. Defaults to ``None``. + name of the variables, and the values are functions to be applied. Defaults to `None`. sample_new_groups : bool, optional If the model contains group-level effects, and data is passed for unseen groups, whether - to sample from the new groups. Defaults to ``False``. + to sample from the new groups. Defaults to `False`. Returns ------- pandas.DataFrame - A dataframe with the comparison values, highest density interval, ``wrt`` name, + A dataframe with the comparison values, highest density interval, `wrt` name, contrast value, and conditional values. Raises ------ ValueError - If length of ``wrt`` is greater than 1. - If ``conditional`` is ``None`` and ``wrt`` is passed more than 2 values. - If ``conditional`` is ``None`` and default ``wrt`` has more than 2 unique values. - If ``conditional`` is a list and the length is greater than 3. - If ``slope`` is not 'dydx', 'dyex', 'eyex', or 'eydx'. - If ``prob`` is not > 0 and < 1. + If length of `wrt` is greater than 1. + If `conditional` is `None` and `wrt` is passed more than 2 values. + If `conditional` is `None` and default `wrt` has more than 2 unique values. + If `conditional` is a list and the length is greater than 3. + If `slope` is not 'dydx', 'dyex', 'eyex', or 'eydx'. + If `prob` is not > 0 and < 1. """ wrt_name = wrt if isinstance(wrt, dict): @@ -831,7 +829,6 @@ def slopes( if not 0 < prob < 1: raise ValueError(f"'prob' must be greater than 0 and smaller than 1. It is {prob}.") - # 'slopes' should not be limited to ("main", "group", "panel") conditional_info = ConditionalInfo(model, conditional) grid = bool(conditional_info.covariates) @@ -847,14 +844,11 @@ def slopes( upper_bound = 1 - lower_bound transforms = transforms if transforms is not None else {} - response_name = get_aliased_name(model.response_component.response_term) response = ResponseInfo(response_name, "mean", lower_bound, upper_bound) response_transform = transforms.get(response_name, identity) - slopes_data = create_differences_data( - conditional_info, wrt_info, conditional_info.user_passed, effect_type - ) + slopes_data = create_differences_data(conditional_info, wrt_info, effect_type) idata = model.predict( idata, data=slopes_data, sample_new_groups=sample_new_groups, inplace=False ) @@ -867,7 +861,7 @@ def slopes( response_dim = np.empty(0) predictive_difference = PredictiveDifferences( - model, slopes_data, wrt_info, conditional_info, response, use_hdi, effect_type + model, slopes_data, wrt_info, conditional_info, response, use_hdi, kind=effect_type ) slopes_summary = predictive_difference.get_estimate( idata, response_transform, "diff", slope, eps, prob diff --git a/bambi/interpret/helpers.py b/bambi/interpret/helpers.py new file mode 100644 index 000000000..3715d7c07 --- /dev/null +++ b/bambi/interpret/helpers.py @@ -0,0 +1,212 @@ +from typing import Union +import warnings + +import numpy as np +import pandas as pd +import xarray as xr + +from arviz import InferenceData + +from bambi import Model +from bambi.interpret.create_data import create_grid +from bambi.interpret.utils import ConditionalInfo, VariableInfo + +warnings.filterwarnings( + "ignore", message="The group data is not defined in the InferenceData scheme" +) + + +def data_grid( + model: Model, + conditional: Union[str, list, dict], + variable: Union[str, dict, None] = None, + effect_type: Union[str, None] = None, + eps: Union[float, None] = None, + **kwargs, +): + """Create a pairwise grid of data using the covariates passed to the 'conditional' and optional + 'variable' argument. Covariates not passed to 'conditional', but are terms in the Bambi model, + are set to typical values (e.g., mean or mode). + + Parameters + ---------- + model : Model + Bambi Model object. + conditional : str, list, dict + The covariates we would like to condition on. If dict, keys are the covariate names and + values are the values to condition on. + variable: str, dict, optional + The variable of interest. This is 'contrast' for 'comparisons', 'wrt' for 'slopes', and + 'None' for 'predictions'. If dict, keys are the covariate names and values are the values. + effect_type : str, optional + The type of effect the data may be used for. This argument is useful for if the data + will be used to compute 'comparisons' or 'slopes' and a parameter is passed to 'variable' + as it determines the default 'eps' value. Defaults to None. + eps : float, optional + The epsilon value used to compute 'comparisons' or 'slopes'. If 'effect_type' is True, + 'comparisons' defaults to `0.5` and 'slopes' defaults to `1e-4`. + **kwargs : dict + Optional keywords arguments passed to 'create_grid' to determine the number of values `num` + to return when computing a `np.linspace` grid for default values. + + Returns + ------- + pd.DataFrame + A dataframe containing pairwise combinations of values based on the parameters + passed into 'conditional' and 'variable'. + + Raises + ------ + ValueError + If 'variable' and 'effect_type' not in ["comparisons", "slopes", "predictions"]. + TypeError + If 'conditional' is a dict and the values are not of type int, float, list, or np.ndarray. + If 'conditional' is a list and the elements are not of type str. + If 'variable' is a dict and there is more than one key. + + """ + if variable and effect_type not in ["comparisons", "slopes", "predictions"]: + raise ValueError( + "'If passing an argument to 'variable', the parameter 'effect_type' must be either " + f"'comparisons' or 'slopes'. Received: {effect_type}" + ) + + if isinstance(conditional, dict): + for value in conditional.values(): + if not isinstance(value, (int, float, list, np.ndarray)): + raise TypeError( + "Dictionary values must be of type int, float, list, or np.ndarray. " + f"Received: {type(value)}" + ) + + if isinstance(conditional, list): + for value in conditional: + if not isinstance(value, str): + raise TypeError(f"Elements of list must be of type str. Received: {type(value)}") + + conditional = ConditionalInfo(model, conditional) + kwargs["effect_type"] = effect_type + + if variable: + if isinstance(variable, dict): + if len(variable) > 1: + raise ValueError("Variable dictionary must have only one key.") + + if not eps and effect_type == "comparisons": + eps = 0.5 + elif not eps and effect_type == "slopes": + eps = 1e-4 + + grid = bool(conditional.covariates) + variable = VariableInfo(model, variable, kind=effect_type, eps=eps, grid=grid) + + return create_grid(conditional, variable, **kwargs) + + +def _prepare_idata(idata: InferenceData, data: xr.Dataset) -> InferenceData: + """Prepare InferenceData object for use in `select_draws` by removing the + 'observed_data' group and replacing it with another 'data' group that contains + the data used to generate predictions. + + Parameters + ---------- + idata : InferenceData + InferenceData object containing the inference data after performing `model.predict`. + data : xr.Dataset + The Dataset passed as 'data' to `model.predict` to generate predictions. + + Returns + ------- + InferenceData + A new InferenceData object with the 'observed_data' group removed and + replaced with a 'data' group that contains the 'data' used to generate + predictions. + + Raises + ------ + ValueError + If the InferenceData object does not contain a 'data' or 'observed_data' group. + """ + + if "observed_data" in idata.groups(): + coordinate_name = list(idata["observed_data"].coords) + del idata.observed_data + idata.add_groups(data=data) + else: + raise ValueError("InferenceData object does not contain a 'data' or 'observed_data' group.") + + if len(coordinate_name) > 1: + raise NotImplementedError("Only one coordinate is currently supported.") + coordinate_name = coordinate_name[0] + + # rename index to match coordinate name in other InferenceData groups + idata.data = idata.data.rename({"index": coordinate_name}) + + return idata + + +def select_draws( + idata: InferenceData, + data: pd.DataFrame, + condition: dict, + data_var: str, + group: str = "posterior", +) -> xr.DataArray: + """Select posterior or posterior predictive draws conditioned on the observation + that produced that draw by passing a `condition` dictionary. + + Parameters + ---------- + idata : InferenceData + InferenceData object containing the inference data after performing `model.predict`. + data : pd.DataFrame + The Dataframe passed as 'data' to `model.predict` to generate predictions. + condition : dict + Dictionary of variable names and values used to select draws. + data_var : str + Name of data variable in the 'group' to select draws from. + group : str, optional + Whether to select draws from the posterior or posterior predictive group. + Defaults to 'posterior'. + + Returns + ------- + xr.DataArray + A DataArray containing the selected draws. + + Raises + ------ + ValueError + If 'condition' is an empty dictionary. + If 'group' is not 'posterior' or 'posterior_predictive'. + If the InferenceData object does not contain a 'group' group. + """ + if not condition: + raise ValueError("'condition' cannot be empty an empty dictionary") + + if group not in ["posterior", "posterior_predictive"]: + raise ValueError("'group' must be either 'posterior' or 'posterior_predictive'") + if group not in idata.groups(): + raise ValueError(f"InferenceData object does not contain a '{group}' group.") + + for key, value in condition.items(): + if isinstance(value, (list, np.ndarray)): + raise ValueError(f"{key} condition value cannot be an array or list") + + idata = idata.copy() + xr_df = xr.Dataset.from_dataframe(data) + idata_new = _prepare_idata(idata, xr_df) + coordinate_name = list(idata_new["data"].coords)[0] + + # indices of draws that satisfy condition + condition_idx = np.where( + np.logical_and.reduce([idata_new["data"][key] == value for key, value in condition.items()]) + )[0] + draws = idata_new[group].isel({f"{coordinate_name}": condition_idx})[data_var] + + # for main and or parent parameters (e.g., distributional models) + if coordinate_name in draws.coords: + new_coords = np.arange(len(condition_idx)) + draws = draws.assign_coords({coordinate_name: new_coords}) + + return draws diff --git a/bambi/interpret/logs.py b/bambi/interpret/logs.py index 13ddb4116..f420de0bf 100644 --- a/bambi/interpret/logs.py +++ b/bambi/interpret/logs.py @@ -4,10 +4,9 @@ def log_interpret_defaults(func): - """ - Decorator for functions that compute default values. + """Decorator for functions that compute default values. - Logs output to console if 'bmb.config["INTERPRET_VERBOSE"] = True' and when + Logs output to console if `bmb.config["INTERPRET_VERBOSE"] = True` and when default values are computed for the variable of interest, i.e., 'contrast' or 'wrt' of 'comparisons' and 'slopes', as well as the 'conditional' parameter of 'comparisons', 'predictions', and 'slopes'. @@ -22,23 +21,23 @@ def wrapper(*args, **kwargs): arg_name = None covariate_name = None - if func.__name__ in ["set_default_values", "make_group_panel_values"]: + if func.__name__ == "set_default_variable_values": + variables = {"comparisons": "contrast", "slopes": "wrt"} + arg_name = variables.get(args[0].kind) + covariate_name = args[0].name + elif func.__name__ == "create_grid": + conditional = args[0] + if not conditional.user_passed: + covariate_name = ", ".join(conditional.covariates.values()) + arg_name = "conditional" + elif func.__name__ == "set_default_values": data_dict = kwargs.get("data_dict", args[1]) keys_before = list(data_dict.keys()) keys_after = list(func(*args, **kwargs).keys()) covariate_name = ", ".join([key for key in keys_after if key not in keys_before]) if len(covariate_name) > 0: - arg_name = "unspecified" if func.__name__ == "set_default_values" else "group/panel" - - elif func.__name__ == "make_main_values": - covariate_name = args[1] - arg_name = "main" - - elif func.__name__ == "set_default_variable_values": - variables = {"comparisons": "contrast", "slopes": "wrt"} - arg_name = variables.get(args[0].kind) - covariate_name = args[0].name + arg_name = "unspecified" if arg_name: logger.info("Default computed for %s variable: %s", arg_name, covariate_name) diff --git a/bambi/interpret/plotting.py b/bambi/interpret/plotting.py index 330c77d70..71f929894 100644 --- a/bambi/interpret/plotting.py +++ b/bambi/interpret/plotting.py @@ -337,9 +337,7 @@ def plot_comparisons( if conditional is None and average_by is None: raise ValueError("Must specify at least one of 'conditional' or 'average_by'.") - if isinstance(conditional, dict): - conditional = {key: sorted(listify(value)) for key, value in conditional.items()} - elif conditional is not None: + if conditional is not None and not isinstance(conditional, dict): conditional = listify(conditional) if len(conditional) > 3 and average_by is None: raise ValueError( @@ -475,10 +473,8 @@ def plot_slopes( wrt_name = wrt if isinstance(wrt, dict): wrt_name, wrt_value = next(iter(wrt.items())) - if not isinstance(wrt_value, (list, np.ndarray)): wrt_value = [wrt_value] - if len(wrt_value) > 2 and average_by is None: raise ValueError( "When plotting with more than 2 values for 'wrt', you must " @@ -497,9 +493,7 @@ def plot_slopes( if conditional is None and average_by is None: raise ValueError("Must specify at least one of 'conditional' or 'average_by'.") - if isinstance(conditional, dict): - conditional = {key: sorted(listify(value)) for key, value in conditional.items()} - elif conditional is not None: + if conditional is not None and not isinstance(conditional, dict): conditional = listify(conditional) if len(conditional) > 3 and average_by is None: raise ValueError( diff --git a/bambi/interpret/utils.py b/bambi/interpret/utils.py index 1ecd057f6..f11da8ba7 100644 --- a/bambi/interpret/utils.py +++ b/bambi/interpret/utils.py @@ -1,14 +1,11 @@ # pylint: disable = too-many-function-args # pylint: disable = too-many-nested-blocks from dataclasses import dataclass, field -import re -from statistics import mode from typing import Union import numpy as np from formulae.terms.call import Call import pandas as pd -from pandas.api.types import is_categorical_dtype, is_numeric_dtype, is_string_dtype import xarray as xr from bambi import Model @@ -122,7 +119,7 @@ def set_default_variable_values(self) -> np.ndarray: predictor_data = np.mean(predictor_data) if self.kind == "slopes": values = self.epsilon_difference(predictor_data, self.eps) - elif self.kind == "comparisons": + if self.kind == "comparisons": values = self.centered_difference( predictor_data, self.eps, dtype ) @@ -156,25 +153,44 @@ class ConditionalInfo: def __post_init__(self): """ - Sets the covariates attributes based on if the user passed a dictionary - or not. + Maps covariates to 'main', 'group', and 'panel' in the order they are passed + to the 'conditional' argument. + + By default, the first three elements (covariates) are mapped to 'main', 'group', + and 'panel'. If the user passes more than three covariates, the remaining + are mapped to 'covariate_4', 'covariate_5', etc. to ensure they are + not dropped due to non-unique keys. """ covariate_kinds = ("main", "group", "panel") if not isinstance(self.conditional, dict): - self.covariates = listify(self.conditional) - self.covariates = dict(zip(covariate_kinds, self.covariates)) + self.conditional = listify(self.conditional) + covariate_names = self.conditional self.user_passed = False elif isinstance(self.conditional, dict): - self.covariates = dict(zip(covariate_kinds, self.conditional)) + covariate_names = list(self.conditional.keys()) + for key, value in self.conditional.items(): + if not isinstance(value, (list, np.ndarray)): + self.conditional[key] = listify(value) + + # sort values b/c of matplotlib plotting behavior when calling `plot_categorical` + self.conditional = {key: sorted(value) for key, value in self.conditional.items()} self.user_passed = True + self.covariates = dict(zip(covariate_kinds, self.conditional)) + + # adds unique keys to the covariates dict if the user passed more than three covariates + extra_covariates = covariate_names[len(covariate_kinds) :] + if extra_covariates: + for index, extra in enumerate(extra_covariates, start=1): + self.covariates[f"covariate_{index}"] = extra + @dataclass class Covariates: """ Stores the 'main', 'group', and 'panel' covariates from the 'conditional' - argument in 'slopes' and 'comparisons'. + argument in 'plot_comparisons', 'plot_predictions', 'plot_slopes'. """ main: str @@ -219,7 +235,7 @@ def get_model_covariates(model: Model) -> np.ndarray: for term in terms.values(): if hasattr(term, "components"): for component in term.components: - # If the component is a function call, use the argument names + # if the component is a function call, use the argument names if isinstance(component, Call): covariates.append([arg.name for arg in component.call.args]) else: @@ -279,130 +295,11 @@ def enforce_dtypes( return new_df -@log_interpret_defaults -def make_group_panel_values( - data: pd.DataFrame, - data_dict: dict, - main: str, - group: Union[str, None], - panel: Union[str, None], - kind: str, - groups_n: int = 5, -) -> dict: - """ - Compute group and panel values based on original data. - """ - - # If available, obtain groups for grouping variable - if group: - group_values = make_group_values(data[group], groups_n) - group_n = len(group_values) - - # If available, obtain groups for panel variable. Same logic than grouping applies - if panel: - panel_values = make_group_values(data[panel], groups_n) - panel_n = len(panel_values) - - main_values = data_dict[main] - main_n = len(main_values) - - if kind == "predictions": - if group and not panel: - main_values = np.tile(main_values, group_n) - group_values = np.repeat(group_values, main_n) - data_dict.update({main: main_values, group: group_values}) - elif not group and panel: - main_values = np.tile(main_values, panel_n) - panel_values = np.repeat(panel_values, main_n) - data_dict.update({main: main_values, panel: panel_values}) - elif group and panel: - if group == panel: - main_values = np.tile(main_values, group_n) - group_values = np.repeat(group_values, main_n) - data_dict.update({main: main_values, group: group_values}) - else: - main_values = np.tile(np.tile(main_values, group_n), panel_n) - group_values = np.tile(np.repeat(group_values, main_n), panel_n) - panel_values = np.repeat(panel_values, main_n * group_n) - data_dict.update({main: main_values, group: group_values, panel: panel_values}) - elif kind in ("comparisons", "slopes"): - # for comparisons and slopes, we need unique values for numeric and categorical - # group/panel covariates since we iterate over pairwise combinations of values - if group and not panel: - data_dict.update({group: np.unique(group_values)}) - elif group and panel: - data_dict.update({group: np.unique(group_values), panel: np.unique(panel_values)}) - - return data_dict - - -@log_interpret_defaults -def set_default_values(model: Model, data_dict: dict, kind: str) -> dict: - """ - Set default values for each variable in the model if the user did not - pass them in the data_dict. - """ - assert kind in ( - "comparisons", - "predictions", - "slopes", - ), "kind must be either 'comparisons', 'slopes', or 'predictions'" - - unique_covariates = get_model_covariates(model) - for name in unique_covariates: - if name not in data_dict: - dtype = str(model.data[name].dtype) - if re.match(r"float*|int*", dtype): - data_dict[name] = np.mean(model.data[name]) - elif dtype in ("category", "dtype"): - data_dict[name] = mode(model.data[name]) - - if kind in ("comparisons", "slopes"): - # if value in dict is not a list then convert to a list - for key, value in data_dict.items(): - if not isinstance(value, (list, np.ndarray)): - data_dict[key] = [value] - return data_dict - - return data_dict - - -@log_interpret_defaults -def make_main_values(x: np.ndarray, _name: str, grid_n: int = 50) -> np.ndarray: - """ - Compute main values based on original data using a grid of evenly spaced - values for numeric predictors and unique levels for categoric predictors. - """ - if is_numeric_dtype(x): - return np.linspace(np.min(x), np.max(x), grid_n) - elif is_string_dtype(x) or is_categorical_dtype(x): - return np.unique(x) - raise ValueError("Main covariate must be numeric or categoric.") - - -def make_group_values(x: np.ndarray, groups_n: int = 5) -> np.ndarray: +def get_group_offset(n, lower: float = 0.05, upper: float = 0.4) -> np.ndarray: """ - Compute group values based on original data using unique levels for - categoric predictors and quantiles for numeric predictors. + When plotting categorical variables, this function computes the offset of the + stripplot points based on the number of groups ``n``. """ - if is_string_dtype(x) or is_categorical_dtype(x): - return np.unique(x) - elif is_numeric_dtype(x): - return np.quantile(x, np.linspace(0, 1, groups_n)) - raise ValueError("Group covariate must be numeric or categoric.") - - -def get_group_offset(n, lower: float = 0.05, upper: float = 0.4) -> np.ndarray: - # Complementary log log function, scaled. - # See following code to have an idea of how this function looks like - # lower, upper = 0.05, 0.4 - # x = np.linspace(2, 9) - # y = get_group_offset(x, lower, upper) - # fig, ax = plt.subplots(figsize=(8, 5)) - # ax.plot(x, y) - # ax.axvline(2, color="k", ls="--") - # ax.axhline(lower, color="k", ls="--") - # ax.axhline(upper, color="k", ls="--") intercept, slope = 3.25, 1 return lower + np.exp(-np.exp(intercept - slope * n)) * (upper - lower) diff --git a/bambi/plots/__init__.py b/bambi/plots/__init__.py index d60d42adb..66a7361a9 100644 --- a/bambi/plots/__init__.py +++ b/bambi/plots/__init__.py @@ -1,10 +1,13 @@ from bambi.interpret.effects import comparisons, predictions, slopes +from bambi.interpret.helpers import data_grid, select_draws from bambi.interpret.plotting import plot_comparisons, plot_predictions, plot_slopes __all__ = [ "comparisons", + "data_grid", "slopes", + "select_draws", "predictions", "plot_comparisons", "plot_predictions", diff --git a/docs/_quarto.yml b/docs/_quarto.yml index 4a0f4430b..0d141baf0 100644 --- a/docs/_quarto.yml +++ b/docs/_quarto.yml @@ -88,6 +88,7 @@ website: - notebooks/plot_predictions.ipynb - notebooks/plot_comparisons.ipynb - notebooks/plot_slopes.ipynb + - notebooks/interpret_advanced_usage.ipynb quartodoc: style: pkgdown diff --git a/docs/notebooks/gallery.yml b/docs/notebooks/gallery.yml index 8063b6030..e4bb954a8 100644 --- a/docs/notebooks/gallery.yml +++ b/docs/notebooks/gallery.yml @@ -129,4 +129,8 @@ - title: Slopes subtitle: Determine how the response changes href: plot_slopes.ipynb - thumbnail: thumbnails/plot_slopes.png \ No newline at end of file + thumbnail: thumbnails/plot_slopes.png + - title: Advanced interpret usage + subtitle: Create data grids and compute complex quantities of interest + href: interpret_advanced_usage.ipynb + thumbnail: thumbnails/advanced_interpret.png \ No newline at end of file diff --git a/docs/notebooks/interpret_advanced_usage.ipynb b/docs/notebooks/interpret_advanced_usage.ipynb new file mode 100644 index 000000000..053167641 --- /dev/null +++ b/docs/notebooks/interpret_advanced_usage.ipynb @@ -0,0 +1,3104 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Interpret Advanced Usage\n", + "\n", + "The `interpret` module provides a set of helper functions to aid the user in more advanced and complex analysis not covered within the `comparisons`, `predictions`, and `slopes` functions. These helper functions are `data_grid` and `select_draws`. The `data_grid` can be used to create a pairwise grid of data points for the user to pass to `model.predict`. Subsequently, `select_draws` is used to select the draws from the posterior (or posterior predictive) group of the InferenceData object returned by the predict method that correspond to the data points that \"produced\" that draw.\n", + "\n", + "With access to the appropriately indexed draws, and data used to generate those draws, it enables for more complex analysis such as cross-comparisons and the choice of which model parameter to compute a quantity of interest for; among others. Additionally, the user has more control over the data passed to `model.predict`. Below, it will be demonstrated how to use these helper functions. First, to reproduce the results from the standard `interpret` API, and then to compute cross-comparisons." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n" + ] + } + ], + "source": [ + "import warnings\n", + "\n", + "import arviz as az\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import bambi as bmb\n", + "\n", + "from bambi.interpret.helpers import data_grid, select_draws\n", + "\n", + "warnings.simplefilter(action='ignore', category=FutureWarning)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Zero Inflated Poisson\n", + "\n", + "We will adopt the zero inflated Poisson (ZIP) model from the [comparisons](https://bambinos.github.io/bambi/notebooks/plot_comparisons.html) documentation to demonstrate the helper functions introduced above. \n", + "\n", + "The ZIP model will be used to predict how many fish are caught by visitors at a state park using survey [data](http://www.stata-press.com/data/r11/fish.dta). Many visitors catch zero fish, either because they did not fish at all, or because they were unlucky. We would like to explicitly model this bimodal behavior (zero versus non-zero) using a Zero Inflated Poisson model, and to compare how different inputs of interest $w$ and other covariate values $c$ are associated with the number of fish caught. The dataset contains data on 250 groups that went to a state park to fish. Each group was questioned about how many fish they caught (`count`), how many children were in the group (`child`), how many people were in the group (`persons`), if they used a live bait and whether or not they brought a camper to the park (`camper`)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "fish_data = pd.read_stata(\"http://www.stata-press.com/data/r11/fish.dta\")\n", + "cols = [\"count\", \"livebait\", \"camper\", \"persons\", \"child\"]\n", + "fish_data = fish_data[cols]\n", + "fish_data[\"child\"] = fish_data[\"child\"].astype(np.int8)\n", + "fish_data[\"persons\"] = fish_data[\"persons\"].astype(np.int8)\n", + "fish_data[\"livebait\"] = pd.Categorical(fish_data[\"livebait\"])\n", + "fish_data[\"camper\"] = pd.Categorical(fish_data[\"camper\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [count_psi, Intercept, livebait, camper, persons, child]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [8000/8000 00:02<00:00 Sampling 4 chains, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n" + ] + } + ], + "source": [ + "fish_model = bmb.Model(\n", + " \"count ~ livebait + camper + persons + child\", \n", + " fish_data, \n", + " family='zero_inflated_poisson'\n", + ")\n", + "\n", + "fish_idata = fish_model.fit(random_seed=1234)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a grid of data\n", + "\n", + "`data_grid` allows you to create a pairwise grid, also known as a cross-join or cartesian product, of data using the covariates passed to the `conditional` and the optional `variable` parameter. Covariates not passed to `conditional`, but are terms in the Bambi model, are set to typical values (e.g., mean or mode). If you are coming from R, this function is partially inspired from the [data_grid](https://modelr.tidyverse.org/reference/data_grid.html) function in {modelr}. \n", + "\n", + "There are two ways to create a pairwise grid of data:\n", + "\n", + "1. user-provided values are passed as a dictionary to `conditional` where the keys are the names of the covariates and the values are the values to use in the grid.\n", + "2. a list of covariates where the elements are the names of the covariates to use in the grid. As only the names of the covariates were passed, default values are computed to construct the grid.\n", + "\n", + "Any unspecified covariates, i.e., covariates not passed to `conditional` but are terms in the Bambi model, are set to their \"typical\" values such as mean or mode depending on the data type of the covariate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### User-provided values\n", + "\n", + "To construct a pairwise grid of data for specific covariate values, pass a dictionary to `conditional`. The values of the dictionary can be of type `int`, `float`, `list`, or `np.ndarray`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Default computed for unspecified variable: livebait\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " camper persons child livebait\n", + "0 0 1 1 1.0\n", + "1 0 1 2 1.0\n", + "2 0 1 3 1.0\n", + "3 0 2 1 1.0\n", + "4 0 2 2 1.0\n", + "5 0 2 3 1.0\n", + "6 0 3 1 1.0\n", + "7 0 3 2 1.0\n", + "8 0 3 3 1.0\n", + "9 0 4 1 1.0\n", + "10 0 4 2 1.0\n", + "11 0 4 3 1.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conditional = {\n", + " \"camper\": np.array([0, 1]),\n", + " \"persons\": np.arange(1, 5, 1),\n", + " \"child\": np.array([1, 2, 3]),\n", + "}\n", + "user_passed_grid = data_grid(fish_model, conditional)\n", + "user_passed_grid.query(\"camper == 0\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Subsetting by `camper = 0`, it can be seen that a combination of all possible pairs of values from the dictionary (including the unspecified variable `livebait`) results in a dataframe containing every possible combination of values from the original sets. `livebait` has been set to 1 as this is the mode of the unspecified categorical variable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Default values\n", + "\n", + "Alternatively, a list of covariates can be passed to `conditional` where the elements are the names of the covariates to use in the grid. By doing this, you are telling `interpret` to compute default values for these covariates. The psuedocode below outlines the logic and functions used to compute these default values:\n", + "\n", + "```python\n", + "if is_numeric_dtype(x) or is_float_dtype(x):\n", + " values = np.linspace(np.min(x), np.max(x), 50)\n", + "\n", + "elif is_integer_dtype(x):\n", + " values = np.quantile(x, np.linspace(0, 1, 5))\n", + "\n", + "elif is_categorical_dtype(x) or is_string_dtype(x) or is_object_dtype(x):\n", + " values = np.unique(x)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Default computed for conditional variable: camper, persons, child\n", + "Default computed for unspecified variable: livebait\n" + ] + }, + { + "data": { + "text/plain": [ + "((32, 4), (24, 4))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conditional = [\"camper\", \"persons\", \"child\"]\n", + "default_grid = data_grid(fish_model, conditional)\n", + "\n", + "default_grid.shape, user_passed_grid.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how the resulting length is different between the user passed and default grid. This is due to the fact that values for `child` range from 0 to 3 for the default grid." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " camper persons child livebait\n", + "0 0.0 1 0 1.0\n", + "1 0.0 1 1 1.0\n", + "2 0.0 1 2 1.0\n", + "3 0.0 1 3 1.0\n", + "4 0.0 2 0 1.0\n", + "5 0.0 2 1 1.0\n", + "6 0.0 2 2 1.0\n", + "7 0.0 2 3 1.0\n", + "8 0.0 3 0 1.0\n", + "9 0.0 3 1 1.0\n", + "10 0.0 3 2 1.0\n", + "11 0.0 3 3 1.0\n", + "12 0.0 4 0 1.0\n", + "13 0.0 4 1 1.0\n", + "14 0.0 4 2 1.0\n", + "15 0.0 4 3 1.0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "default_grid.query(\"camper == 0\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute comparisons\n", + "\n", + "To use `data_grid` to help generate data in computing comparisons or slopes, additional data is passed to the optional `variable` parameter. The name `variable` is an abstraction for the comparisons parameter `contrast` and slopes parameter `wrt`. If you have used any of the `interpret` functions, these parameter names should be familiar and the use of `data_grid` should be analogous to `comparisons`, `predictions`, and `slopes`.\n", + "\n", + "`variable` can also be passed user-provided data (as a dictionary), or a string indicating the name of the covariate of interest. If the latter, a default value will be computed. Additionally, if an argument is passed for `variable`, then the `effect_type` needs to be passed. This is because for `comparisons` and `slopes` an epsilon value `eps` needs to be determined to compute the centered and finite difference, respectively. You can also pass a value for `eps` as a kwarg." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Default computed for contrast variable: livebait\n" + ] + } + ], + "source": [ + "conditional = {\n", + " \"camper\": np.array([0, 1]),\n", + " \"persons\": np.arange(1, 5, 1),\n", + " \"child\": np.array([1, 2, 3, 4])\n", + "}\n", + "variable = \"livebait\"\n", + "\n", + "grid = data_grid(fish_model, conditional, variable, effect_type=\"comparisons\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "idata_grid = fish_model.predict(fish_idata, data=grid, inplace=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select draws conditional on data\n", + "\n", + "The second helper function to aid in more advanced analysis is `select_draws`. This is a function that selects the posterior or posterior predictive draws from the ArviZ [InferenceData](https://python.arviz.org/en/stable/getting_started/WorkingWithInferenceData.html) object returned by `model.predict` given a conditional dictionary. The conditional dictionary represents the values that correspond to that draw. \n", + "\n", + "For example, if we wanted to select posterior draws where `livebait = [0, 1]`, then all we need to do is pass a dictionary where the key is the name of the covariate and the value is the value that we want to condition on (or select). The resulting InferenceData object will contain the draws that correspond to the data points where `livebait = [0, 1]`. Additionally, you must pass the InferenceData object returned by `model.predict`, the data used to generate the predictions, and the name of the data variable `data_var` you would like to select from the InferenceData posterior group. If you specified to return the posterior predictive samples by passing `model.predict(..., kind=\"pps\")`, you can use this group instead of the posterior group by passing `group=\"posterior_predictive\"`.\n", + "\n", + "Below, it is demonstrated how to compute comparisons for `count_mean` for the contrast `livebait = [0, 1]` using the posterior draws." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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arviz.InferenceData
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      <xarray.Dataset>\n",
+       "Dimensions:       (chain: 4, draw: 1000, livebait_dim: 1, camper_dim: 1,\n",
+       "                   count_obs: 64)\n",
+       "Coordinates:\n",
+       "  * chain         (chain) int64 0 1 2 3\n",
+       "  * draw          (draw) int64 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
+       "  * livebait_dim  (livebait_dim) <U3 '1.0'\n",
+       "  * camper_dim    (camper_dim) <U3 '1.0'\n",
+       "  * count_obs     (count_obs) int64 0 1 2 3 4 5 6 7 ... 56 57 58 59 60 61 62 63\n",
+       "Data variables:\n",
+       "    Intercept     (chain, draw) float64 -2.454 -2.31 -2.91 ... -2.652 -2.887\n",
+       "    livebait      (chain, draw, livebait_dim) float64 1.629 1.58 ... 1.799 1.967\n",
+       "    camper        (chain, draw, camper_dim) float64 0.7037 0.7089 ... 0.7128\n",
+       "    persons       (chain, draw) float64 0.8707 0.8369 0.9457 ... 0.8847 0.912\n",
+       "    child         (chain, draw) float64 -1.345 -1.412 -1.418 ... -1.293 -1.573\n",
+       "    count_psi     (chain, draw) float64 0.6311 0.6201 0.6342 ... 0.6768 0.5745\n",
+       "    count_mean    (chain, draw, count_obs) float64 0.05349 0.2728 ... 0.05777\n",
+       "Attributes:\n",
+       "    created_at:                  2023-12-05T18:56:31.591639\n",
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+       "    sampling_time:               2.6078336238861084\n",
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+       "    reached_max_treedepth  (chain, draw) bool False False False ... False False\n",
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+       "    index_in_trajectory    (chain, draw) int64 6 4 -6 -5 -2 4 ... 7 1 4 4 -1 9\n",
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+       "    arviz_version:               0.16.1\n",
+       "    inference_library:           pymc\n",
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+       "    sampling_time:               2.6078336238861084\n",
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" + ], + "text/plain": [ + " mean hdi_low hdi_high\n", + "0 0.214363 0.144309 0.287735\n", + "1 0.053678 0.029533 0.077615\n", + "2 0.013558 0.006332 0.021971\n", + "3 0.003454 0.001132 0.006040\n", + "4 0.512709 0.369741 0.661034\n", + "5 0.128316 0.077068 0.181741\n", + "6 0.032392 0.015553 0.050690\n", + "7 0.008247 0.003047 0.014382\n", + "8 1.228708 0.913514 1.533121\n", + "9 0.307342 0.192380 0.426808" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_1 = select_draws(idata_grid, grid, {\"livebait\": 0}, \"count_mean\")\n", + "draw_2 = select_draws(idata_grid, grid, {\"livebait\": 1}, \"count_mean\")\n", + "\n", + "comparison_mean = (draw_2 - draw_1).mean((\"chain\", \"draw\"))\n", + "comparison_hdi = az.hdi(draw_2 - draw_1)\n", + "\n", + "comparison_df = pd.DataFrame(\n", + " {\n", + " \"mean\": comparison_mean.values,\n", + " \"hdi_low\": comparison_hdi.sel(hdi=\"lower\")[\"count_mean\"].values,\n", + " \"hdi_high\": comparison_hdi.sel(hdi=\"higher\")[\"count_mean\"].values,\n", + " }\n", + ")\n", + "comparison_df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can compare this comparison with `bmb.interpret.comparisons`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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termestimate_typevaluecamperpersonschildestimatelower_3.0%upper_97.0%
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1livebaitdiff(0, 1)0120.0536780.0295330.077615
2livebaitdiff(0, 1)0130.0135580.0063320.021971
3livebaitdiff(0, 1)0140.0034540.0011320.006040
4livebaitdiff(0, 1)0210.5127090.3697410.661034
5livebaitdiff(0, 1)0220.1283160.0770680.181741
6livebaitdiff(0, 1)0230.0323920.0155530.050690
7livebaitdiff(0, 1)0240.0082470.0030470.014382
8livebaitdiff(0, 1)0311.2287080.9135141.533121
9livebaitdiff(0, 1)0320.3073420.1923800.426808
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" + ], + "text/plain": [ + " term estimate_type value camper persons child estimate \\\n", + "0 livebait diff (0, 1) 0 1 1 0.214363 \n", + "1 livebait diff (0, 1) 0 1 2 0.053678 \n", + "2 livebait diff (0, 1) 0 1 3 0.013558 \n", + "3 livebait diff (0, 1) 0 1 4 0.003454 \n", + "4 livebait diff (0, 1) 0 2 1 0.512709 \n", + "5 livebait diff (0, 1) 0 2 2 0.128316 \n", + "6 livebait diff (0, 1) 0 2 3 0.032392 \n", + "7 livebait diff (0, 1) 0 2 4 0.008247 \n", + "8 livebait diff (0, 1) 0 3 1 1.228708 \n", + "9 livebait diff (0, 1) 0 3 2 0.307342 \n", + "\n", + " lower_3.0% upper_97.0% \n", + "0 0.144309 0.287735 \n", + "1 0.029533 0.077615 \n", + "2 0.006332 0.021971 \n", + "3 0.001132 0.006040 \n", + "4 0.369741 0.661034 \n", + "5 0.077068 0.181741 \n", + "6 0.015553 0.050690 \n", + "7 0.003047 0.014382 \n", + "8 0.913514 1.533121 \n", + "9 0.192380 0.426808 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "summary_df = bmb.interpret.comparisons(\n", + " fish_model,\n", + " fish_idata,\n", + " contrast={\"livebait\": [0, 1]},\n", + " conditional=conditional\n", + ")\n", + "summary_df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Albeit the other information in the `summary_df`, the columns `estimate`, `lower_3.0%`, `upper_97.0%` are identical." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cross comparisons\n", + "\n", + "Computing a cross-comparison is useful for when we want to compare contrasts when two (or more) predictors change at the same time. Cross-comparisons are currently not supported in the `comparisons` function, but we can use `select_draws` to compute them. For example, imagine we are interested in computing the cross-comparison between the two rows below." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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termestimate_typevaluecamperpersonschildestimatelower_3.0%upper_97.0%
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" + ], + "text/plain": [ + " term estimate_type value camper persons child estimate \\\n", + "0 livebait diff (0, 1) 0 1 1 0.214363 \n", + "1 livebait diff (0, 1) 0 1 2 0.053678 \n", + "\n", + " lower_3.0% upper_97.0% \n", + "0 0.144309 0.287735 \n", + "1 0.029533 0.077615 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "summary_df.iloc[:2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cross-comparison amounts to first computing the comparison for row 0, given below, and can be verified by looking at the estimate in `summary_df`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.2143627093182434" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cond_10 = {\n", + " \"camper\": 0,\n", + " \"persons\": 1,\n", + " \"child\": 1,\n", + " \"livebait\": 0 \n", + "}\n", + "\n", + "cond_11 = {\n", + " \"camper\": 0,\n", + " \"persons\": 1,\n", + " \"child\": 1,\n", + " \"livebait\": 1\n", + "}\n", + "\n", + "draws_10 = select_draws(idata_grid, grid, cond_10, \"count_mean\")\n", + "draws_11 = select_draws(idata_grid, grid, cond_11, \"count_mean\")\n", + "\n", + "(draws_11 - draws_10).mean((\"chain\", \"draw\")).item()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to compute the comparison for row 1." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "cond_20 = {\n", + " \"camper\": 0,\n", + " \"persons\": 1,\n", + " \"child\": 2,\n", + " \"livebait\": 0\n", + "}\n", + "\n", + "cond_21 = {\n", + " \"camper\": 0,\n", + " \"persons\": 1,\n", + " \"child\": 2,\n", + " \"livebait\": 1\n", + "}\n", + "\n", + "draws_20 = select_draws(idata_grid, grid, cond_20, \"count_mean\")\n", + "draws_21 = select_draws(idata_grid, grid, cond_21, \"count_mean\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.053678256991883604" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(draws_21 - draws_20).mean((\"chain\", \"draw\")).item()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we compute the \"first level\" comparisons (`diff_1` and `diff_2`). Subsequently, we compute the difference between these two differences to obtain the cross-comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.16068445232635978" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "diff_1 = (draws_11 - draws_10)\n", + "diff_2 = (draws_21 - draws_20)\n", + "\n", + "cross_comparison = (diff_2 - diff_1).mean((\"chain\", \"draw\")).item()\n", + "cross_comparison" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To verify this is correct, we can check by performing the cross-comparison directly on the `summary_df`." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.16068445232635978" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "summary_df.iloc[1][\"estimate\"] - summary_df.iloc[0][\"estimate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook, the interpret helper functions `data_grid` and `select_draws` were introduced and it was demonstrated how they can be used to compute pairwise grids of data and cross-comparisons. With these functions, it is left to the user to generate their grids of data and quantities of interest allowing for more flexibility and control over the type of data passed to `model.predict` and the quantities of interest computed." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Tue Dec 05 2023\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.11.0\n", + "IPython version : 8.13.2\n", + "\n", + "numpy : 1.24.2\n", + "pandas: 2.1.0\n", + "bambi : 0.13.0.dev0\n", + "arviz : 0.16.1\n", + "\n", + "Watermark: 2.3.1\n", + "\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -n -u -v -iv -w" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bambinos", + "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": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/plot_comparisons.ipynb b/docs/notebooks/plot_comparisons.ipynb index 483f2d2dc..8d1fd2495 100644 --- a/docs/notebooks/plot_comparisons.ipynb +++ b/docs/notebooks/plot_comparisons.ipynb @@ -100,20 +100,21 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "fish_data = pd.read_stata(\"http://www.stata-press.com/data/r11/fish.dta\")\n", "cols = [\"count\", \"livebait\", \"camper\", \"persons\", \"child\"]\n", "fish_data = fish_data[cols]\n", + "fish_data[\"child\"] = fish_data[\"child\"].astype(int)\n", "fish_data[\"livebait\"] = pd.Categorical(fish_data[\"livebait\"])\n", "fish_data[\"camper\"] = pd.Categorical(fish_data[\"camper\"])" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -205,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -217,7 +218,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -248,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -296,7 +297,7 @@ " persons\n", " diff\n", " (1.0, 4.0)\n", - " 0.0\n", + " 0\n", " 0\n", " 1.0\n", " 4.834472\n", @@ -308,7 +309,7 @@ " persons\n", " diff\n", " (1.0, 4.0)\n", - " 0.0\n", + " 0\n", " 1\n", " 1.0\n", " 26.423188\n", @@ -320,7 +321,7 @@ " persons\n", " diff\n", " (1.0, 4.0)\n", - " 1.0\n", + " 1\n", " 0\n", " 1.0\n", " 1.202003\n", @@ -332,7 +333,7 @@ " persons\n", " diff\n", " (1.0, 4.0)\n", - " 1.0\n", + " 1\n", " 1\n", " 1.0\n", " 6.571943\n", @@ -344,7 +345,7 @@ " persons\n", " diff\n", " (1.0, 4.0)\n", - " 2.0\n", + " 2\n", " 0\n", " 1.0\n", " 0.301384\n", @@ -356,7 +357,7 @@ " persons\n", " diff\n", " (1.0, 4.0)\n", - " 2.0\n", + " 2\n", " 1\n", " 1.0\n", " 1.648417\n", @@ -369,12 +370,12 @@ ], "text/plain": [ " term estimate_type value child livebait camper estimate \\\n", - "0 persons diff (1.0, 4.0) 0.0 0 1.0 4.834472 \n", - "1 persons diff (1.0, 4.0) 0.0 1 1.0 26.423188 \n", - "2 persons diff (1.0, 4.0) 1.0 0 1.0 1.202003 \n", - "3 persons diff (1.0, 4.0) 1.0 1 1.0 6.571943 \n", - "4 persons diff (1.0, 4.0) 2.0 0 1.0 0.301384 \n", - "5 persons diff (1.0, 4.0) 2.0 1 1.0 1.648417 \n", + "0 persons diff (1.0, 4.0) 0 0 1.0 4.834472 \n", + "1 persons diff (1.0, 4.0) 0 1 1.0 26.423188 \n", + "2 persons diff (1.0, 4.0) 1 0 1.0 1.202003 \n", + "3 persons diff (1.0, 4.0) 1 1 1.0 6.571943 \n", + "4 persons diff (1.0, 4.0) 2 0 1.0 0.301384 \n", + "5 persons diff (1.0, 4.0) 2 1 1.0 1.648417 \n", "\n", " lower_3.0% upper_97.0% \n", "0 2.563472 7.037150 \n", @@ -385,7 +386,7 @@ "5 1.140415 2.187190 " ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -422,6 +423,13 @@ "execution_count": 6, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Default computed for unspecified variable: camper\n" + ] + }, { "data": { "text/html": [ @@ -460,7 +468,7 @@ " persons\n", " diff\n", " (1, 2)\n", - " 0.0\n", + " 0\n", " 0.0\n", " 1.0\n", " 0.527627\n", @@ -472,7 +480,7 @@ " persons\n", " diff\n", " (1, 2)\n", - " 0.0\n", + " 0\n", " 1.0\n", " 1.0\n", " 2.883694\n", @@ -484,7 +492,7 @@ " persons\n", " diff\n", " (1, 2)\n", - " 1.0\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.131319\n", @@ -496,7 +504,7 @@ " persons\n", " diff\n", " (1, 2)\n", - " 1.0\n", + " 1\n", " 1.0\n", " 1.0\n", " 0.717965\n", @@ -508,7 +516,7 @@ " persons\n", " diff\n", " (1, 2)\n", - " 2.0\n", + " 2\n", " 0.0\n", " 1.0\n", " 0.032960\n", @@ -520,7 +528,7 @@ " persons\n", " diff\n", " (1, 2)\n", - " 2.0\n", + " 2\n", " 1.0\n", " 1.0\n", " 0.180270\n", @@ -532,7 +540,7 @@ " persons\n", " diff\n", " (1, 4)\n", - " 0.0\n", + " 0\n", " 0.0\n", " 1.0\n", " 4.834472\n", @@ -544,7 +552,7 @@ " persons\n", " diff\n", " (1, 4)\n", - " 0.0\n", + " 0\n", " 1.0\n", " 1.0\n", " 26.423188\n", @@ -556,7 +564,7 @@ " persons\n", " diff\n", " (1, 4)\n", - " 1.0\n", + " 1\n", " 0.0\n", " 1.0\n", " 1.202003\n", @@ -568,7 +576,7 @@ " persons\n", " diff\n", " (1, 4)\n", - " 1.0\n", + " 1\n", " 1.0\n", " 1.0\n", " 6.571943\n", @@ -580,7 +588,7 @@ " persons\n", " diff\n", " (1, 4)\n", - " 2.0\n", + " 2\n", " 0.0\n", " 1.0\n", " 0.301384\n", @@ -592,7 +600,7 @@ " persons\n", " diff\n", " (1, 4)\n", - " 2.0\n", + " 2\n", " 1.0\n", " 1.0\n", " 1.648417\n", @@ -604,7 +612,7 @@ " persons\n", " diff\n", " (2, 4)\n", - " 0.0\n", + " 0\n", " 0.0\n", " 1.0\n", " 4.306845\n", @@ -616,7 +624,7 @@ " persons\n", " diff\n", " (2, 4)\n", - " 0.0\n", + " 0\n", " 1.0\n", " 1.0\n", " 23.539494\n", @@ -628,7 +636,7 @@ " persons\n", " diff\n", " (2, 4)\n", - " 1.0\n", + " 1\n", " 0.0\n", " 1.0\n", " 1.070683\n", @@ -640,7 +648,7 @@ " persons\n", " diff\n", " (2, 4)\n", - " 1.0\n", + " 1\n", " 1.0\n", " 1.0\n", " 5.853978\n", @@ -652,7 +660,7 @@ " persons\n", " diff\n", " (2, 4)\n", - " 2.0\n", + " 2\n", " 0.0\n", " 1.0\n", " 0.268423\n", @@ -664,7 +672,7 @@ " persons\n", " diff\n", " (2, 4)\n", - " 2.0\n", + " 2\n", " 1.0\n", " 1.0\n", " 1.468147\n", @@ -676,27 +684,45 @@ "" ], "text/plain": [ - " term estimate_type value ... estimate lower_3.0% upper_97.0%\n", - "0 persons diff (1, 2) ... 0.527627 0.295451 0.775465\n", - "1 persons diff (1, 2) ... 2.883694 2.605690 3.177685\n", - "2 persons diff (1, 2) ... 0.131319 0.067339 0.195132\n", - "3 persons diff (1, 2) ... 0.717965 0.592968 0.857893\n", - "4 persons diff (1, 2) ... 0.032960 0.015212 0.052075\n", - "5 persons diff (1, 2) ... 0.180270 0.123173 0.244695\n", - "6 persons diff (1, 4) ... 4.834472 2.563472 7.037150\n", - "7 persons diff (1, 4) ... 26.423188 23.739729 29.072748\n", - "8 persons diff (1, 4) ... 1.202003 0.631629 1.780965\n", - "9 persons diff (1, 4) ... 6.571943 5.469275 7.642248\n", - "10 persons diff (1, 4) ... 0.301384 0.143676 0.467608\n", - "11 persons diff (1, 4) ... 1.648417 1.140415 2.187190\n", - "12 persons diff (2, 4) ... 4.306845 2.267097 6.280005\n", - "13 persons diff (2, 4) ... 23.539494 20.990931 26.240169\n", - "14 persons diff (2, 4) ... 1.070683 0.565931 1.585718\n", - "15 persons diff (2, 4) ... 5.853978 4.858957 6.848519\n", - "16 persons diff (2, 4) ... 0.268423 0.124033 0.412274\n", - "17 persons diff (2, 4) ... 1.468147 1.024800 1.960934\n", + " term estimate_type value child livebait camper estimate \\\n", + "0 persons diff (1, 2) 0 0.0 1.0 0.527627 \n", + "1 persons diff (1, 2) 0 1.0 1.0 2.883694 \n", + "2 persons diff (1, 2) 1 0.0 1.0 0.131319 \n", + "3 persons diff (1, 2) 1 1.0 1.0 0.717965 \n", + "4 persons diff (1, 2) 2 0.0 1.0 0.032960 \n", + "5 persons diff (1, 2) 2 1.0 1.0 0.180270 \n", + "6 persons diff (1, 4) 0 0.0 1.0 4.834472 \n", + "7 persons diff (1, 4) 0 1.0 1.0 26.423188 \n", + "8 persons diff (1, 4) 1 0.0 1.0 1.202003 \n", + "9 persons diff (1, 4) 1 1.0 1.0 6.571943 \n", + "10 persons diff (1, 4) 2 0.0 1.0 0.301384 \n", + "11 persons diff (1, 4) 2 1.0 1.0 1.648417 \n", + "12 persons diff (2, 4) 0 0.0 1.0 4.306845 \n", + "13 persons diff (2, 4) 0 1.0 1.0 23.539494 \n", + "14 persons diff (2, 4) 1 0.0 1.0 1.070683 \n", + "15 persons diff (2, 4) 1 1.0 1.0 5.853978 \n", + "16 persons diff (2, 4) 2 0.0 1.0 0.268423 \n", + "17 persons diff (2, 4) 2 1.0 1.0 1.468147 \n", "\n", - "[18 rows x 9 columns]" + " lower_3.0% upper_97.0% \n", + "0 0.295451 0.775465 \n", + "1 2.605690 3.177685 \n", + "2 0.067339 0.195132 \n", + "3 0.592968 0.857893 \n", + "4 0.015212 0.052075 \n", + "5 0.123173 0.244695 \n", + "6 2.563472 7.037150 \n", + "7 23.739729 29.072748 \n", + "8 0.631629 1.780965 \n", + "9 5.469275 7.642248 \n", + "10 0.143676 0.467608 \n", + "11 1.140415 2.187190 \n", + "12 2.267097 6.280005 \n", + "13 20.990931 26.240169 \n", + "14 0.565931 1.585718 \n", + "15 4.858957 6.848519 \n", + "16 0.124033 0.412274 \n", + "17 1.024800 1.960934 " ] }, "execution_count": 6, @@ -764,7 +790,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -772,8 +798,7 @@ "output_type": "stream", "text": [ "Default computed for contrast variable: livebait\n", - "Default computed for main variable: persons\n", - "Default computed for group/panel variable: child\n", + "Default computed for conditional variable: persons, child\n", "Default computed for unspecified variable: camper\n" ] }, @@ -816,7 +841,7 @@ " diff\n", " (0.0, 1.0)\n", " 1.000000\n", - " 0.0\n", + " 0\n", " 1.0\n", " 1.694646\n", " 1.252803\n", @@ -828,7 +853,7 @@ " diff\n", " (0.0, 1.0)\n", " 1.000000\n", - " 1.0\n", + " 1\n", " 1.0\n", " 0.422448\n", " 0.299052\n", @@ -840,127 +865,127 @@ " diff\n", " (0.0, 1.0)\n", " 1.000000\n", - " 3.0\n", + " 2\n", + " 1.0\n", + " 0.106202\n", + " 0.063174\n", + " 0.152961\n", + " \n", + " \n", + " 3\n", + " livebait\n", + " diff\n", + " (0.0, 1.0)\n", + " 1.000000\n", + " 3\n", " 1.0\n", " 0.026923\n", " 0.012752\n", " 0.043035\n", " \n", " \n", - " 3\n", + " 4\n", " livebait\n", " diff\n", " (0.0, 1.0)\n", " 1.061224\n", - " 0.0\n", + " 0\n", " 1.0\n", " 1.787412\n", " 1.342979\n", " 2.203158\n", " \n", " \n", - " 4\n", + " 5\n", " livebait\n", " diff\n", " (0.0, 1.0)\n", " 1.061224\n", - " 1.0\n", + " 1\n", " 1.0\n", " 0.445555\n", " 0.317253\n", " 0.580117\n", " \n", " \n", - " 5\n", + " 6\n", " livebait\n", " diff\n", " (0.0, 1.0)\n", " 1.061224\n", - " 3.0\n", + " 2\n", + " 1.0\n", + " 0.112006\n", + " 0.067121\n", + " 0.161160\n", + " \n", + " \n", + " 7\n", + " livebait\n", + " diff\n", + " (0.0, 1.0)\n", + " 1.061224\n", + " 3\n", " 1.0\n", " 0.028393\n", " 0.013452\n", " 0.045276\n", " \n", " \n", - " 6\n", + " 8\n", " livebait\n", " diff\n", " (0.0, 1.0)\n", " 1.122449\n", - " 0.0\n", + " 0\n", " 1.0\n", " 1.885270\n", " 1.422937\n", " 2.313218\n", " \n", " \n", - " 7\n", + " 9\n", " livebait\n", " diff\n", " (0.0, 1.0)\n", " 1.122449\n", - " 1.0\n", + " 1\n", " 1.0\n", " 0.469929\n", " 0.335373\n", " 0.609249\n", " \n", - " \n", - " 8\n", - " livebait\n", - " diff\n", - " (0.0, 1.0)\n", - " 1.122449\n", - " 3.0\n", - " 1.0\n", - " 0.029944\n", - " 0.014165\n", - " 0.047593\n", - " \n", - " \n", - " 9\n", - " livebait\n", - " diff\n", - " (0.0, 1.0)\n", - " 1.183674\n", - " 0.0\n", - " 1.0\n", - " 1.988500\n", - " 1.501650\n", - " 2.424762\n", - " \n", " \n", "\n", "" ], "text/plain": [ " term estimate_type value persons child camper estimate \\\n", - "0 livebait diff (0.0, 1.0) 1.000000 0.0 1.0 1.694646 \n", - "1 livebait diff (0.0, 1.0) 1.000000 1.0 1.0 0.422448 \n", - "2 livebait diff (0.0, 1.0) 1.000000 3.0 1.0 0.026923 \n", - "3 livebait diff (0.0, 1.0) 1.061224 0.0 1.0 1.787412 \n", - "4 livebait diff (0.0, 1.0) 1.061224 1.0 1.0 0.445555 \n", - "5 livebait diff (0.0, 1.0) 1.061224 3.0 1.0 0.028393 \n", - "6 livebait diff (0.0, 1.0) 1.122449 0.0 1.0 1.885270 \n", - "7 livebait diff (0.0, 1.0) 1.122449 1.0 1.0 0.469929 \n", - "8 livebait diff (0.0, 1.0) 1.122449 3.0 1.0 0.029944 \n", - "9 livebait diff (0.0, 1.0) 1.183674 0.0 1.0 1.988500 \n", + "0 livebait diff (0.0, 1.0) 1.000000 0 1.0 1.694646 \n", + "1 livebait diff (0.0, 1.0) 1.000000 1 1.0 0.422448 \n", + "2 livebait diff (0.0, 1.0) 1.000000 2 1.0 0.106202 \n", + "3 livebait diff (0.0, 1.0) 1.000000 3 1.0 0.026923 \n", + "4 livebait diff (0.0, 1.0) 1.061224 0 1.0 1.787412 \n", + "5 livebait diff (0.0, 1.0) 1.061224 1 1.0 0.445555 \n", + "6 livebait diff (0.0, 1.0) 1.061224 2 1.0 0.112006 \n", + "7 livebait diff (0.0, 1.0) 1.061224 3 1.0 0.028393 \n", + "8 livebait diff (0.0, 1.0) 1.122449 0 1.0 1.885270 \n", + "9 livebait diff (0.0, 1.0) 1.122449 1 1.0 0.469929 \n", "\n", " lower_3.0% upper_97.0% \n", "0 1.252803 2.081207 \n", "1 0.299052 0.551766 \n", - "2 0.012752 0.043035 \n", - "3 1.342979 2.203158 \n", - "4 0.317253 0.580117 \n", - "5 0.013452 0.045276 \n", - "6 1.422937 2.313218 \n", - "7 0.335373 0.609249 \n", - "8 0.014165 0.047593 \n", - "9 1.501650 2.424762 " + "2 0.063174 0.152961 \n", + "3 0.012752 0.043035 \n", + "4 1.342979 2.203158 \n", + "5 0.317253 0.580117 \n", + "6 0.067121 0.161160 \n", + "7 0.013452 0.045276 \n", + "8 1.422937 2.313218 \n", + "9 0.335373 0.609249 " ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -981,14 +1006,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Before we talk about the summary output, you will notice that some messages have been logged to the console. By default `interpret` is _verbose_ and logs a message to the console if a default value is computed for covariates in `conditional` and `contrast`. This is useful because unless the documentation is read, it can be difficult to tell which covariates are having default values computed for. Thus, Bambi has a config file `bmb.config[\"INTERPRET_VERBOSE\"]` where we can specify whether or not to log messages. By default, this is set to true. To turn off logging, set `bmb.config[\"INTERPRET_VERBOSE\"] = False`. From here on, we will turn off logging.\n", + "Before we talk about the summary output, you should have noticed that messages are being logged to the console. By default `interpret` is _verbose_ and logs a message to the console if a default value is computed for covariates in `conditional` and `contrast`. This is useful because unless the documentation is read, it can be difficult to tell which covariates are having default values computed for. Thus, Bambi has a config file `bmb.config[\"INTERPRET_VERBOSE\"]` where we can specify whether or not to log messages. By default, this is set to true. To turn off logging, set `bmb.config[\"INTERPRET_VERBOSE\"] = False`. From here on, we will turn off logging.\n", "\n", "As `livebait` was encoded as a categorical dtype, Bambi returned the unique levels of $[0, 1]$ for the contrast. `persons` and `child` were passed as the first and second element and thus act as the main and group variables, respectively. It can be see from the output above, that an equally spaced grid was used to compute the values for `persons`, whereas a quantile based grid was used for `child`. Furthermore, as `camper` was unspecified, the mode was used as the default value. Lets go ahead and plot the commparisons." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -997,12 +1022,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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g4Oc//3n5PidBEARBzBDGGHaeGMWvdvdC1nR0NZROgDHG8PyhQTzx+imoOsP5rSHccVXXvBJgAFnCpsblA9xFWrDU7LS6p1IpXHjhhbjjjjtwyy23FHdvgiAIgigDms7w8tFhvHBkCDVuJ5pC3pJdW9V1/PzNXrxxcgwAcPXyBrx7jiVhPVdIhFUZGzduxMaNGys9DIIgCIKYEEnV8PzBQfyhZxQNNR7U+t0lu3ZG1vD//uEEjg2nIAC46cI2XLG0vmTXrzYq6o7cvHkzLrvsMgSDQTQ1NeHmm2/GoUOH8vowxvDlL38ZbW1t8Pl82LBhA/bt21f+wWmKschFLEr5x0kQBEEQs0RKUvH07n68dmwELSFvSQXYaErGv25/C8eGU3A7RXxs/ZJ5LcCAClvCtm/fjrvvvhuXXXYZVFXFF7/4Rdx4443Yv38/AoEAAOAf/uEf8K1vfQuPPfYYVqxYga985St45zvfiUOHDiEYLGOCtlf+uTTXueYzpbkOQRAEQVSQaFrG03v6cbA/XvIyRCdHUvjxayeQkjWEfS58bH0nWsO+kl2/WqmoCHvmmWfy9h999FE0NTVh586dePvb3w7GGB5++GF88YtfxPvf/34AwA9/+EM0Nzfj8ccfx5133lmJYRMEQRDEgiKalvHLXb14ayiJpQ01cDtL50izz4Bsi3jxsSvmTwqKs1FVMWGxGC/EWVdXBwDo6elBf38/brzxRquPx+PBtddei1deeWVCESZJEiQpNxsxHo/PbDBXforPcHQHZnY+AMgpQE7O/HyCIAiCqDB2AbassQbOEiVhZYxh++EhPLt/AACwqiWID162uKQCr9qpGhHGGMOmTZtw9dVXY82aNQCA/v5+AEBzc3Ne3+bmZpw4cWLC62zevBkPPfRQ8QNyuIylCH+3Qy5+HARBEARRIWIZBb/eXXoBpuo6frGrFztP8BmQVy2rx8YLWuflDMipqBoRds8992D37t146aWXxh0TCn4ojLFxbSYPPPAANm3aZO3H43F0dHSUdrBlJJlM4ujRo9Z+T08Pdu3ahbq6OixevLiCIyMIgiAWErGMgl//qRdHBpJYWkIBlpRUPP6HEzg+koYA4L9d2Ib1sxSAn1U0DCezaA37UMKk/jOmKkTYvffei1/+8pd44YUX0N7ebrW3tLQA4Bax1tZWq31wcHCcdczE4/HA45m79aR27NiB6667zto3BeVtt92Gxx57rEKjIgiCIBYS8Sy3gB0aSGBZCetA9kYz+PFrJxDLKPA4RfzFZR1Y2RIqybWnQtF09EYzUHWGVa0hXLmsoWSishgqKsIYY7j33nuxdetWbNu2DV1dXXnHu7q60NLSgueeew4XX3wxAECWZWzfvh3f+MY3KjHksrNhwwYwxio9DIIgCGKBksgqeGp3Hw71l1aA/el0FD974zQUjaGhxo2/vKITTcHSJXmdCJ0x9MeySEoKOusDWNdVj5Utpa1tWQwVFWF33303Hn/8cfziF79AMBi0YsDC4TB8Ph8EQcB9992Hr33ta+ju7kZ3dze+9rWvwe/348Mf/nD5B6hkKns+QRAEQcwiSUnFU7v7cKAvjqUNpRFgOmN4bv8Ath8eAgCsaK7BBy9dXNYSRIwxjKZkDCUltIS8uO68RqxuC8Prqq6yRxUVYVu2bAHArT92Hn30Udx+++0AgPvvvx+ZTAZ33XUXxsbGsG7dOjz77LPlzRHm9PDC25nRaZcdGoevjl+PIAiCIKqYlKTi6T05AVaKWYpZRcOTr5/CoYEEAODt3Y24cXVzWQPw41kFfbEswj4nbjivCRd31iLkrc6UFwKb576veDyOcDiMWCyGUCjf75zNZtHT04Ouri54vQUmUSk5rcLbk+L0AJ4i609OwZSfgSAIgiDOgZSk4qk9fdh3JoauEgmwoYSEH792AsNJCU5RwC1va8eFHZHiBzsJsqrj5GgKLqeICxaFcfmSupLWtJwOU2kPO1URmF+VeGrKKp4IgiAIohpIy9wCtvdMDF0NgZIIsEP9CTy54ySyio6wz4W/XNeJRbXly4CfllScHE3jvNYQrlxWj856/6RZFKoJEmEEQRAEsUAZSUp4bv8A9vfF0VUfgMdZXMwUYwwvHhnGb/f1gwHorPPjw+sWI1hGd2A0LWMwIeGyrlq8Y1VLWWPNSg2JMIIgCIJYgBwdTOK5AwPoj2bQ1VC8AMsqGn72xmns7eWVai5bUoubLmyDUyxfKoiBeBYpScWGFY24urs60k5MBxJhBEEQBLGAUDUdrx8fxYtHhqFqDN3NwaID5fvjWTz+hxMYTspwCALeu7YV67rqyuYSZIzh1FgGogBsvKAFb1tcOyfcj4WQCCMIgiCIBUJSUvH8wQG8cSKKWr8bDbXFz95/8+QYfr7rDBSNIexz4UOXL8biOn8JRjsxms5wfDiJkN+Fd69uxcqWMmZLKDMkwiYhraQhacXPjvQ4PPC7yvcyEgRBEMS50BvN4Ln9A3hrKInFtX74PcVJAFXT8dSePvyhZxQAsLypBh+4tAM1RV53KhRNx7GhJNpr/Xj3mhZ0lFHszQYkwiYgraTxH4f/A1EpWvS1Ip4Ibl1xKwkxgiAIoiIwxrCvN47fHxxALK1geQnqQI6lZfx/fzyJ02M8Kfn15zXh+vOaypr/KyNrODGawormIDauaUF9zdzPwUkibAIkTUJUisLr8MLrnHmOkayaRVSKQtKkcxZhmzdvxs9+9jMcPHgQPp8PV155Jb7xjW9g5cqVMx4HQRAEsTCRVA0vHx3Bq28Nw+1wYFljTdGxU4cHEnjy9VPIKBp8Lgc+cGlH2V2C8ayCvmgWF3fU4p2rm8tqbZtN5senKBNep7doC1ZWm17G/e3bt+Puu+/GZZddBlVV8cUvfhE33ngj9u/fj0AgUNRYCIIgiIXDWErG7w4MYO+ZOJpDHkT87qKupzOG/zo4iOcPDoIBWBTx4cPrFqO2yOuejcFEFrGMgquW12PDyqaS5DGrFkiEVRnPPPNM3v6jjz6KpqYm7Ny5E29/+9srNCqCIAhiLnFkIIH/OjiI3mgGnfWBomsmJiUV/77jFI4OJgEA67rq8N4LWsuaEkLVdZwYScPndmDjmlZc2lkLsUoKb5cKEmGToOoqZE2GU5z5I5I1uehxxGIxAEBdXV3R1yIIgiDmN7Kq47Vj3P2o6cDypiAcRQqXt4aS+Pcdp5DIqnA5BNx80SJcvLi2RCOemKSk4vRYGl0NAdxwXjMW18/PuGoSYZPw2L7HSnKdT1zwiRmfyxjDpk2bcPXVV2PNmjUlGQ9BEAQxPxlOSvivg4PY1xtDY40XdYHi3ISazvD7AwPYfngIDEBj0IMPXb4YLWWsx8gYQ18si4ysYV1XPa5d2Thv4r8mYv5+snnAPffcg927d+Oll16q9FAIgiCIKoUxhoP9CTx/cBAD8SyW1AfgKdL9OJaS8eSOUzg5mgbAs9+/94K2ssZjyaqOEyMphP0u3HRRG9YuCs8792MhJMIm4fbVtyPkDhUVmJ9W0kir6Rmde++99+KXv/wlXnjhBbS3t894DARBEMT8JatoeOWtEbx2bAQOQShJ9vs9Z2LY+uZpZBUdXpeImy9ahLXtkdIMeBJiGQX9sQy6m4O4YVUTWsPlK/ZdTZAImwSn6ITb4YbbMXNzrqqr0xZhjDHce++92Lp1K7Zt24aurq4Z358gCIKYvwzGs3juwAAO9yfQHPIWPftRVnU8tacXrx8fAwB01PrwF5ctRm2Rbs2p0BnDmbEMNMZwTXcDru5uLHoSwVyCRFiVcffdd+Pxxx/HL37xCwSDQfT39wMAwuEwfL6F8T8DgiAIYnIYY9h7Jo7nDw1iNCWjq6GmaDdhfyyLJ14/icGEBAHA21c04h2rmosO6p+KrKLh+EgKLSEvNqxswqrW4Jys/1gMJMKqjC1btgAANmzYkNf+6KOP4vbbb5/9AREEQRBVQyyt4NVjw9hxYgwehwPdTcUlX2WM4Q89o3h6Tx9UnSHoceLWSzuwvKmmhKMez3BCwmhaxppFYVx/XhMa5kH2+5lAIqzKYIxVeggEQRBElaFoOvaeieHVt0bQH8+iLeJDyOsq6pqJrIKfv3kGB/oTAICVzUHcckl7WWcjqrqOkyNpeF0O3Hh+My5dUjevkq9OFxJhU5BVp5ftvtTnEwRBEMSp0TReOjqMw/0J+D1OrChB8P3+3hi2vnkGKVmDQxTwrvObceXyhrLWfkxkFZyJZtDVEMB1K5uwpIGqwJAImwCPw4OIJ4KoFJ122aFCIp4IPI6FaWYlCIIgZk4iq+CPPaPYeWIMWUXD4jp/0aknsoqGX+/uwxsnefB9S8iLWy9tL+tsRJ0x9EYzkDUdVy6rx9Xd8zv313SgpzABfpcft664FZImFX0tj8NTdP1JgiAIYuGgajr298Xxylsj6I1m0Bzyor22+O+RY8NJ/OfO04imFQgAruluwDtWNZe19FBW0XBiJIXGoAfvWdmK81tD8z7313QgETYJfpefxBNBEAQxq5yJZvDK0WEc6IvD63KguwRlhxRNx+/2D+Clo8NgAGr9Lvw/l3Sgq8zuwOGkhNGUjNULPPh+KkiEEQRBEESFycga/tgzgtdPjCGVVdFe64fPXXy+rL5YBv++4xQG4tyzc2lnLd57QWvRbs2psAffv2t1My7pXNjB91NBIowgCIIgKsjRwSRePDKEnuEUGms8aGsOFn1NnTG8eGQYv9s/AI0xBNwOvP9t7VjVGirBiCcnllHQF8tgSX0AN6xqQmc9Bd9PBYkwgiAIgqgAiayCV98awc6TY9B1huWNNSWJzxqMZ/HTN07j1FgGALCqNYT3XbyorMHwms5wajQNQQCuWd6AK5c3IEDB92eFnhBBEARBzCKMMRwaSODFw8M4OZpGW8SHsK+4nF8AF0IvHhnC7w8OQtMZPE4R772gFZd01pY1E30iq6A3mkF7nR8bVjRieZEJZBcSJMImQU+loMty0dcR3W6IATLHEgRBEEA0LePloyN489QYHIKAFc3FB94DQG80g5+9cRq9MZ5WaUVzDW6+aFHR9SSnQtMZzkQz0HQd65fV46rlDQgWmUB2oUEibAL0VApjT/47tFis6Gs5wmHUfvADJMQIgiAWMLrOsK83jhePDKE/nsWiiK8kgkXVdDx/aAjbDw9CZ4DP5cB/W9uKizoiZbVGJSUVp8e4Fe/aFY04r2Xh1X0sBSTCJkCXZWixGASPB6LXO/PrZLPQYjHosnzOImzLli3YsmULjh8/DgBYvXo1vvSlL2Hjxo0zHgdBEARROYaTEl46Mow9Z6LwOB0lyXgP8Ez6P33jNAYTfObj+a0h/PlFbWW1RlmJV1Udl3fV4ZruxpK4UhcqM4oAvP766xGNRse1x+NxXH/99ed8nRdeeAE33XQT2traIAgCfv7zn+cdv/322yEIQt5yxRVXzGTIM0L0eiEGAjNfZiDg2tvb8fWvfx07duzAjh07cP311+PP//zPsW/fvjJ8QoIgCKJcqJqON0+O4fE/nMSbJ8fQGvKhvdZftABTNB2/2duHf93+FgYTEgJuBz50+WJ8ZN3isgqwtKzi8EACNR4nbr54Ed6zppUEWJHMyBK2bds2yBPES2WzWbz44ovnfJ1UKoULL7wQd9xxB2655ZYJ+7z73e/Go48+au273eXzb1cDN910U97+V7/6VWzZsgWvvfYaVq9eXaFREQRBENNhKCHhhSND2HsmhoDbie4SWb96hlP42RunMZLi38EXtofx39a2lX0m4nBCwlhaxtsW1+LaFY2oDczv7+LZYlo/td27d1vb+/fvR39/v7WvaRqeeeYZLFq06Jyvt3HjxrO62TweD1paWqYzzJLAFIUvRQTnM0UpagyapuE//uM/kEqlsH79+qKuRRAEQZQfVdOx+0wMLx4ZwmhSxuK6QEmSrqYkFc/s7cdOo+ZjyOvEn1+0qOx5v3TGcHIkDadDwI2rm3HZkrqyljlaaExLhF100UWWW3Ait6PP58O//Mu/lGxwALe6NTU1IRKJ4Nprr8VXv/pVNDU1TdpfkiRIUq7mYzwen9F9R3/wgxmdV0j9nXdO+5w9e/Zg/fr1yGazqKmpwdatW3H++eeXZDwEQRBEeRhMZPHi4SHs7Y2jxuPEiubig9UZY3jjZBS/2duHtKwBAC5bUod3r24pibibCknVcHw4hZaID+9c1YzlTTVlvd9CZFoirKenB4wxLF26FH/84x/R2NhoHXO73WhqaoLDUbqXYuPGjbj11lvR2dmJnp4ePPjgg7j++uuxc+dOeDwT16DavHkzHnrooZKNoRKsXLkSu3btQjQaxU9/+lPcdttt2L59OwkxgiCIKkTVdPzpdBQvHhnGWFrG4trSWL8GE1n8YlcveoZTAIDmkAc3X7RoVrLQxzIK+mMZrGoN4Z3nN6Oe6j6WBYExxio9CAAQBAFbt27FzTffPGmfvr4+dHZ24oknnsD73//+CftMZAnr6OhALBZDKJRvts1ms+jp6UFXVxe8tiB6dWwMI//2PTjCYYj+mRfx1tNp6Ok06m6/Dc7a2hlf5x3veAeWLVuG7373u+OOTfYZCIIgiPIzGM9i++Eh7O+NI+h1oTnkKdr6pWg6th0axAuHh6ExBpdDwA3nNeOq5Q0lySk2FYwx9MWyyKoaruiqw9XdjfCWsc7kfCUejyMcDk+oPezMOJLv8OHD2LZtGwYHB6Hret6xL33pSzO97JS0trais7MTR44cmbSPx+OZ1Eo2HQSXiy9FTAQQiowJM2GM5QlLgiAIorJkFQ17z8Tw0lFu/eqsC5RErBwdTOIXu85Ygfcrm4O46cI21M1CILyq6zg+nELI58KfrW7DBYvClPurzMxIhP3bv/0b/uZv/gYNDQ1oaWnJ+yEJglA2ETYyMoJTp06htbW1LNevBr7whS9g48aN6OjoQCKRwBNPPIFt27bhmWeeqfTQCIIgFjyKpuNQfwJ/7BnBydEMQj4XVjQVH/uVyCp4ek8f/nSaJwkPeZ1479o2rGkLzYoQSssqTo2msaQhgBtXt2BRxFf2exIzFGFf+cpX8NWvfhWf+9znirp5MpnE0aNHrf2enh7s2rULdXV1qKurw5e//GXccsstaG1txfHjx/GFL3wBDQ0NeN/73lfUfauZgYEBfPSjH0VfXx/C4TDWrl2LZ555Bu985zsrPTSCIIgFi6YzHB1M4vXjI3hrKAWPQ0RXQwCuImcKajrDq8dG8PsDA5BUHQKAK5bW453nN8+KG5AxhqGEhFhWwds6a3H9eU1UemgWmZEIGxsbw6233lr0zXfs2IHrrrvO2t+0aRMA4LbbbsOWLVuwZ88e/OhHP0I0GkVrayuuu+46PPnkkwgGg0Xf+1zQs9lZP//73/9+UfckCIIgSgdjDD3DKbx+fBRHBpIQBGBxnR8eZ2lcj7/a3YshI+P9oogPf35RG9prZx6LPB3M4Ptavxs3nk/pJyrBjETYrbfeimeffRaf/OQni7r5hg0bMNW8gN/+9rdFXX+miG43HOEwtFgMWpGxWI5wGOI8TzBLEAQxHzk1msaO42M40B+Hpuloi/hLMutxLCXj6b192NfLUyj53Q68a3ULLumsLUlC17ORkTWciabhcYq4vKsel3fVoTFIsx8rwYxE2PLly/Hggw/itddewwUXXACXK990+alPfaokg6sUYiCA2g9+AHoRiVqta7ndVLybIAhiDjEQz2LH8THs640hLWtoi/hQU4KM9IqmY/vhIbxweAiqziAKwLquerxjVXPZc36Z9++NZqBqDOe1hrCuq25W0l0QkzOjFBVdXV2TX1AQcOzYsaIGVUqmmiY6H9I7zIfPQBAEUQ3EswreOD6GnSfHEM8oaAn7SlIbkTGGfb1xPL23D9E0nzXf1RDATWvb0BIu/99tnTEMxLOIZxV01gVwxdJ6rGwJlj3dxUKmrCkqenp6ZjwwgiAIgqgmZFXH3t4YXntrBP3xLBprPFjZUppyQAPxLH69uxdvDfGEq2GfCxvXtMxa+ofRlIzBRBZNQQ/+21qedoLyflUP5a34SRAEQRBViq4zHBtO4pWjIzg2nELA40R3U2ksRImsgt8fGMTrx0fBADhFAdd0N+DaFU1wO8sf/J7IKuiNZRD0urBhZRMuWVyLsJ9mPVYbMxJhH//4x6c8/oMS1V2cLaqkaMCMmMtjJwiCqBT9sSxeOzaCfb08L9eS+kBJxJGi6Xj56DC2Hx6CpPJE5ue3hvCeC1pnJeGqGXTvcop42+JaXN5Vh9Yw5fyqVmacosKOoijYu3cvotHohIW9qxVzQkE6nYbPNzdf0nQ6DQDjJkcQBEEQ44lnFbx5Ygw7TowhnlWwKOIvSdC9zhj+dCqKZ/cPIJbhcV+LIj6854JWdDWUP/hdVnnQvcYYVjQHsW5pPZbU+ynjfZUzozdv69at49p0Xcddd92FpUuXFj2o2cLhcCASiWBwcBAA4PfPnReWMYZ0Oo3BwUFEIpGSFk4nCIKYb0iqhn298fy4r+bSxH31DKfw9J4+nIlmAPC4r3etbsba9kjZU05oOkN/LIuUrGJJvR/rltZjRTMF3c8VSlrA+9ChQ9iwYQP6+vpKdcmiOdsMBcYY+vv7EY1GZ39wJSASiYwrHUUQBEFwNJ3hUH8Cf+gZwYmRNAIeJ1pC3pKIlOGkhGf29mN/H8/35XGKuHZFI65a3lB0Jv2zoRuZ7qNpGa0RH9Z11eH8tlBJksgSxVP2At4T8dZbb0FV1VJesuwIgoDW1lY0NTVBKVHB7dnC5XKRBYwgCGICzEz3f+gZxdGBJByiULK4r0RWwbZDQ/hDzwh0BggALuuqwztWNZfEtTkVOmOIphUMJrKor3Hj3WtasLY9gkCZ70uUhxn91MzyQiaMMfT19eGpp57CbbfdVpKBzTYOh4MEDUEQxDzgTDSD13tGsb8vDk1nWFTrK0lahqyi4cUjQ3j56AhkjQfdr2wO4t1rWtAcKm++L1XXeY3HjIJavxtv727AJZ11qJ2FYH+ifMxIhL355pt5+6IoorGxEd/85jfPOnOSIAiCIMrBcFLCjuOj2H06hrSkoa22dJnuXzs2gm2HhpBRNABAe60PN57fguVNNUVffyqyioaBeBaSqqMp6MGVy+qxsiU0KzMtifIzo7fz+eefL/U4CIIgCGJGxDIKdp0cwxsno4imZTSHvCUpgq3pDG+cGMPvDw4gnuWhNo1BD248vxnnt4bKGosbyygYSmQhCgIW1/lx0eIIupuCs1LeiJg9ivovwtDQEA4dOgRBELBixQo0NjaWalwEQRAEMSUpScXeMzG8fnwUgwkJDTUerGgOFi2OdMaw90wMz+0fwEiK1xCO+Fy4YVUzLl5cvhmPms4wkpIwmpIR9Liwtj2CC9rDWFIfoNmO85QZibBUKoV7770XP/rRj6Dr3C/ucDjwsY99DP/yL/8Cv7/4/4EQBEEQxERkZA37emP4Y88o+uNZRHxurGgOFi2OGGM4PJDEc/v70RvLAgACbgc2rGzCuq46OMs041HVdAwkJCSzCupr3LjhvCac1xoqe5wZUXlmHJi/fft2/OpXv8JVV10FAHjppZfwqU99Cp/5zGewZcuWkg6SIAiCILKKhgN9cbx+fBSnxzIIeV0lKTPEGMORwSR+f2AAp8Z4ri+PU8TV3Q24elkDPGWqtSirOvrjWWQVDa1hLzasbMR5LUEEvZR8e6EwozxhDQ0N+M///E9s2LAhr/3555/HBz7wAQwNDZVqfEVzrrk6CIIgiOpEVnUc7Ofi69RoBn63Ay1hL5xicZapicSXyyFgXVc9rl3RWLa0DxlZQ388C03X0V7rxyWdtVjZEqTC2vOIsuYJS6fTaG5uHtfe1NRkldEhCIIgiGJQNB2HBxJ4vWcUJ0bT8Dod6GoIFJ0IdSrxdU13Q9ksUUlJxUAsA0EQ0NUYwMUdtVjeVDMrBb2J6mRGImz9+vX4u7/7O/zoRz+C18t91plMBg899BDWr19f0gESBEEQCwtZ1XFkMIE3Tozh2HAKboeIzrriE60yxnB0MInfHxzEyVGj7u4siK9YRsFgPAu3S8R5rSFcvLgWXQ0UbE/MUIQ9/PDD2LhxI9rb23HhhRdCEATs2rULHo8Hzz77bKnHSBAEQSwAMrKGg/1xvHFyDKfHMnCKIhbX+YsuxTOR+HKKAtZ11eHtKxrLKr764xkE3E5cvDiCizpq0VHnozJzhMWMa0dmMhn85Cc/wcGDB8EYw/nnn4+PfOQj8Pl8pR5jUVBMGEEQRHUTzyo40MvF10A8C5/LieaQt2jLl84Y9vfGsf3wkFVce7bF1+o2bvlqi1TXdyNRXsoaE7Z582Y0Nzfjr/7qr/Laf/CDH2BoaAif+9znZnJZgiAIYgExkpSw90wMfzodxUhSRsjnwtLGmqID7jWdYdepKF44PIShpASAux0vX1KHa1Y0IjQL4uvyJfW4eHGExBcxJTMSYd/97nfx+OOPj2tfvXo1/uIv/oJEGEEQBDEpfbEM9pyOYW9vDLG0gtqAG90lyPMlqzp2nBjFS0eGEc0oAACvS8T6pQ24cll92WY7xjIK+mMZ1HhcuHxJPd7WGUFrmMQXcXZm9Eb29/ejtbV1XHtjYyP6+vqKHhRBEAQxv2CM4fRYBm+eHMOh/iSSkoLGoBcrmr1Fx0hlFQ2vHRvBy0eHkZJ5bccajxNXL2/A5V11ZUn9wBhDLKNgIJ7l4quLxBcxfWYkwjo6OvDyyy+jq6srr/3ll19GW1tbSQZGEARBzH10neH4SAq7TkVxaCABSdHRHPJiUW3xYiWeUfDqsRG8dmwEksqrt9T6XbimuxGXdNYWncpiImRVx3BSQjyrIOR1Yd1S7nYk8UXMhBmJsE984hO47777oCgKrr/+egDA73//e9x///34zGc+U9IBEgRBEHMPVdNxbDiFN06M4a2hJDSdoSXsQ00JXIL9sSxeOjqEP52KQTPmljUFPbh2RSPWtkdKnvqBMYZ4VsVwIgsIQFPIi6uW12NZUw2aglRaiJg5M/ptuP/++zE6Ooq77roLssyLm3q9Xnzuc5/DAw88UNIBEgRBEHMHM8fXmyfHcHyYp4NoCXvhdxcnvsw0Ey8dHcaRwaTV3lnvxzXLG3Fea/ExZYWYVq9EVkHQ58IF7RGc3xZCZ33xaTMIAigiRQUAJJNJHDhwAD6fD93d3fB4PKUcW0mgFBUEQRDlJ6toONSfwBsnx3ByNA2nKKI17C06HkvVdew+FcNLR4fRH+dFtQUAqxeFcc3yBnTU+Usw+hxmrNdwUoIAAc1hDy5YFMHypho0BqvvO46oTsqaosKkpqYGl112WTGXIAiCIOYww0kJh/rj+NOpGAYTErwuR0my26dlFX/sGcWrx0aQyKoAALdDxKVLanHlsgbUBdylGL6FzhiGkxJGUzJCXhfWGlavJfXFfxaCmIzyzNc9R1544QX84z/+I3bu3Im+vj5s3boVN998s3WcMYaHHnoIjzzyCMbGxrBu3Tr83//7f7F69erKDZogCGKBo+kMJ0ZS2N8Xx6H+BOIZhef4agjAWWQwfH88i9feGsGbp8agaNxRE/I6sX5ZAy5fUgefu7RuQEXTMRiXkJAUNNS4ccN5TVjVFqJYL2JWqKgIS6VSuPDCC3HHHXfglltuGXf8H/7hH/Ctb30Ljz32GFasWIGvfOUreOc734lDhw4hGAxWYMQEQRALl7Ss4shAEn86FcWpsTRUjaEx6EFLqLg0EzpjONAXx6tvjeDYcMpqbwl5cXV3A9a2h4tO4FpIRtbQH89C1XW0hX3YcF4jVrYEy5bIlSAmoqIibOPGjdi4ceOExxhjePjhh/HFL34R73//+wEAP/zhD9Hc3IzHH38cd95552wOlSAIYsEyGM/iYH8Ce07HMJSU4HGKaAn5irZKpWUVO46P4bWeEUTTPLmqAOD8thDWL6tHV32g5HUW41leTFsUBSypD+CiDh7vVY5cYgRxNioqwqaip6cH/f39uPHGG602j8eDa6+9Fq+88sqkIkySJEiSZO3H4/Gyj5UgCGK+Iakajg+ncaAvjqODSSSyCiJ+N5Y2Boq2SvXFMnj1rRHsOhWFqnOXo9/twGVL6rCuqw4Rf+njvUaTMkZSEgIeJy5YFMHajjCW1AdKns6CIKZD1Yqw/v5+AEBzc3Nee3NzM06cODHpeZs3b8ZDDz1U1rERBEHMVwYTWbw1mMLuM1EMxLIQADQEvWgNF+dyVDUd+3rj+EPPKI6P5FyOrWEv1i+tx4UdkZInV5VUDYNxCRlFRcTvxjXdDVi9KEyJVYmqoWpFmEnhLz1jbMo/BA888AA2bdpk7cfjcXR0dJRtfARBEHMd0+q1vzeOt4a41SvodWFxCWY5jiQl/PH4KHaeGEPaKCkkCsDqtjDWL61HZ72/5C7HWEbBUCILURDQFvHiwo4mdDdTvBdRfVStCGtpaQEwvk7l4ODgOOuYHY/HU5X5ygiCIKqNwUQWRweS2HMmhoF4FoIgoKHGU7TVS9N5oP0fe0ZxdCiXWDXkdeKyJXW4dEkdwr7SCiJN5ykmxtK5FBNrFoXQWR8oS/kigigFVSvCurq60NLSgueeew4XX3wxAECWZWzfvh3f+MY3Kjw6giCIuUlaVtEznMLB/gR6hlKW1asUYmUsLeP146PYeXwMCYnn9hIAdDfXYF1XPVY0B0seg5WRNQwmspBUHY01HrxjVTPOawmiKUQpJojqp6IiLJlM4ujRo9Z+T08Pdu3ahbq6OixevBj33Xcfvva1r6G7uxvd3d342te+Br/fjw9/+MMVHDVBEMTcQtV0nB7L4OhgAgf6EhhNy3AIAupLYPVSdR0H+xLYeWIMhwcSMEuw1HicuLSzFpctqUNtiROrMsYQTfOs9k6HgM56P9a281mOxZZHIojZpKJv644dO3DddddZ+2Ys12233YbHHnsM999/PzKZDO666y4rWeuzzz5LOcIIgiDOgcFEFj1DKeztjaE/loWi6Yj43ehqKH6GY388i53HR/HmqagV6wUAyxtrcFlXHc5vDZXc6qVovJZjLKMg7HPh0iW1WN0WRkedn2Y5EnOSompHzgWodiRBEAuJRFbBiZE09vfFcXIkjURWQcDjREONp+hcWBlZw+4zUew8MYbTYxmrPeh14m2La3FJZy0aakofk5uSVAwkstA0huawFxe2h7GiOYj6MtyLIErBrNSOJAiCICpPRtZwcjSNo4NJHBlMIJpW4BRLE2SvM4ae4RR2nhjD3jMxK6+XKACrWkO4pLMW3U2lj/XSGcNYSsZwitejXNZYg7XtYSxrpMSqxPyBRBhBEMQcRFZ1nBpL49hgEocGEhhJyRAA1PrdWNZYU7QoGk5KePNkFLtOjWHMyGYPAE1BDy5dUoeLOiKo8ZT+KyQpqRhOSJA1HRGfC1cua8D5rSEsivggksuRmGeQCCMIgpgjqJqO3mgWPcNJ7O9PYDghQdMZIn4XuuqLL56dklTsPhPDrpNjOGVzN3qcIi7siODSzlosivhKntdLVnmsVyKrwO9xorPBj9VtYXQ1BEqeyoIgqgkSYQRBEFWMpGroi2ZxJprGwb4EBuLcShTyutBR6y86maqi6TjYn8Cuk2M4NJCA4W2EKADLm2pwcUctVrWGir5PIZrOEE3LGE3JEEUBzSEP1i+rw9LGmqILghPEXIFEGEEQRJURzyo4M5bBqdE0jgwmMZaWoWo6Ah4XWsLeomOidMZwYiSNXafGsOdMDFlFt461Rby4uKMWa9vDCJYhw3wiq2A4KRszNV24vKsO3c1BLK4rXlASxFyDRBhBEESF0XWG4ZSEM2MZHBtK4dRoGrEsj8MKeV1ojxQvUBhj6I1msft0FLvPxBDL5OK8wj4XLuqI4KKOCJrLkOTUdDfGswpqPE4sbwpgVWsIXQ2Bsgg9gpgrkAgjCIKoALrOMJDI4vhwGof64xhMSEhLKpxOEbU+N5Y2FB9cDwADcUN4nY5hJCVb7R6niNVtYVy8OIKuhgDEErv/dMYQSysYSUkQIKDJcDcubwqiKeghdyNBgEQYQRDErDKclHBiJIX9vXH0RjNIKxoCbicifnfJgt5HkhJ2n4lh9+koBuKS1e5yCDivJYS1Rp6tctRUzMgahpISMrKKiN+NixfX4ryWIDrriy8GThDzDRJhBEEQZSaWUXBihNdrNBOo+lwO1NV40FGiNA8jSQn7euPYcyaGM9HczEaHIGBFcw3WtkdwXmsQHmfpc2wpmo7RlIxoWobH5UB7xIfVi8JY1hhAxF/akkUEMZ8gEUYQBFEGUpKKk6NpHB5I4NhQCtGMDLfDgfqAu+gEqiaDiSz29cax90wMfbGs1S4AWNZUg7WLwljdFobPXR7hNZaWEUsrEEUB9QE3rl3ZhO6mGsrpRRDnCIkwgiCIEjGaknFmLIOe4SSODacsgVLnd6O7KVh03BVjDANxCXt7Y9h7JobBRM7VKApAV0MAq9vCWLMoXJZEqqquI5pSMJbhiWHrAm5cuaweXY01aK/1USZ7gpgmJMIIgiBmiKYzDMSzOBPN4MhAEr2xDBJZBS5RRNjnQldj8YWydcbQG81gX28c+3pjGE7mgusdgoBlTQGsaQtjVWsIgTIILzOf11haBgPPyL+ui+fzWlznJ+FFEEVAIowgCGIaZBUNvdFcDq+hpISsrMHnciDid6Ml5C3a4qVqOo4Np7C/L46DfXHEs6p1zCkK6G6qwZpFYZzXEiqLq1HVdETTCqIZLrzCPhcu6azFsiYuvPxu+uogiFJAv0kEQRBnYSwl40w0g5OjaRwbSmIsrUDTGWo8TjTVeEsihDKyhkMDcezvS+DIQAKSmkug6naKWNEcxOq2EM5rDsJTBuuTrPIYr3hGgSAAEb8bly2pw5KGADrq/GVxbxLEQod+qwiCIApQNR398Sx6o1kcHUyiL5ZBIqvCIQAhn7sk5YIAHkN2sD+O/X1xHB9OWSWDACDodWJVSwirWkNY1lh8XciJkBQNY2kF8awMhyhaMV5LGgJor/WXxcpGEEQOEmEEQRAAkpJquRmPDiYxmpKRVTX4nA6E/W40l8DNqOkMJ0ZSONSfwMGBBIZsgfUA0BT04PxWLrwW1fpKnkAV4LM2x9Iy0rIKl0NEfY0Hb1vchMX1frRFKLieIGYTEmEEQSxYYmkFp8a4i/HYcIqX8mFA0OtCc6j4Go0AF3eHDdF1dDCRV6dRFIDFdQGsag3i/NYQ6ms8Rd+vEJ0xxDMKxtIKZFWH3+NAY40HK1vq0V7LhVc5krYSBHF2SIQRBLGgGElKODWWwdHBBE6OphHPqHCIAmr9bnQ1lG4246GBBA71J3BmLAOblxF+twMrm4NY2RJEd1OwbDm8omkFsYwMnfHA+u6mAJY1BdFe60NjjYfyeBELDyULxM8A0VOAnABWvhco8ve9WEiEEQQxr9F1hqGkhNNjaRzqT6I3mkFSUuB0iKjzu9HUVLybMZ5VcGQgiSODCRwdTCIta3nH28JerGwJYmVLCO1lcDMyxpCSNcQyClISF5URvwuXLqlDZ70f7RE/wn4qlE0sQNKjQOw0MNoDjBwBMqOAnAaCbUD3uwCxshUdSIQRBDHvSMsq+mJZ9EUzeGsoiYG4hJSkwutyoNbvRku4OOGlajqOj6RxZDCBIwNJ9Mezecc9ThHLGmu48GoOIuQrvQBSNR2xjIJYRoGq6/C7nWioceOKpXVoi/iwiOK7iIWIrgGJPi68hg7xdTYOCALgiwCRTkBKALp+1kvNBiTCCIKY86iajsGEhL5YFidGUjg1xt2Mms7gdzuKLo7NGMNAQsJbg0kcHUzi2HASipZzMgoA2iI+dDfXoLspiMV1fjjK4O5LSSpiGQVJSYFDEBHyO7FmURhLGgJoC3vRQG5GYiGSjefcjMOHgdQQoKQBpxfw1QHBFkCozrhHEmEEQcw5GGOIZRT0xbI4PZrGseEURtMysrIOl1NAxOfG4jp/UQHn0bSMo4NJvDWUxFtDKSQlNe940OO0RNfyppqSZ6tnjCEta0hkVSQkBYwx+NxONATcuGxJLRbV+tEW8VLiVGLhoSlAop8Lr+EjhrUrBoAB7hog0AS4/ZUe5TlBv70EQVQ9jDFE0woGExL6Yxn0DKcwkpItYRT0uIpOmpqWVLw1nOKiazCJkZScd9zlELCkPoBljTXobq5BS6g0RbhNdMaQklQksipSsgrGeBB/2O/C+W1BtEV8aA55KaieWJikR7noGjsJjBwG0mOAmuHWLm8EqF8OiHPP/U4ijCCIqsMUXQOJLAZiWfQMpzBsii5DnISMNBIzje1KyyqOD6fRM5xEz3AKfbFs3ixGUQDaa/1Y1hjg5Xpq/SVNmGpauqJGML0AIOBxotbvwkWLI2gJedEU8qA+4CmLa5MgqhopyWO74r3c2pXsB7IJ7vv3hLmL0eWr9CiLhkQYQRAVR9MZRlMyhhISBuNZy72YyqpgAAJuJ4JeJ5qD3hkLErvoOjacQn+B6AJ4stTlTTVY1liDroZAyQPbNZ3n7IpmFMiaBr/bicYaNy5fUovmMLdy1frdZOkiFh5KlouuRB8w8ha3emWiANMBlx/whoFga9XGds0UEmEEQcw6WUXDcFLCUEJCfyyLk6NpxDKKldoh4OGiq5hi2ElJxfHhFI6PpNAziehqDHrQ1RDA0oYAuhoCCHpLP4sxq3BrVyKjAAIQ9rpwXksQXY0BLIpQzi5igaJKQHKQx3aN9QBjx3lcl6YATg8XXXXLAEcJZYqu8vixRD+/dhVAIowgiLKTyPJ4ruEET5TaF80gIamQVA1OUTTccG60RRwzEl2McUva8ZE0Toxw4TWclMf1awx6LMFVLtGlajoSWRXxrAJJ1eB2OlAbcGPtohA66gJoi3jLcl+CqGqkJJAc4MIregKInuSpItQsILq46Ap3cAFWKlSJi7vRt4DRY8DYCUBXAG8tcMVdpbtPEZAIIwiipJgzF4cSEgYTEnqGUhhKSkhkVWhMh8fhQNDrRGvIC88M3X06Y+iPZXF8JGUJr0RWHdevOeTBkvryii5F05E0RFfWEJVBj9O6Z1vEh5awl0oDEQsHxoDMGBdcyX5g5BgXYFKc5/FyuAFPCAi18cD6UiEleFJWU3TFz3B3ph1XAKhp5FaxKoBEGEEQRaHrDGNpGYNGPJd95iJjgM/FRVd9g3/GJYHSsopTo2mcHE3jxGgap8cykNX8P64OQcCiWh+W1AewpN6PzvpA2UoCJbIqEnbR5XWiqzGAJfUBNAU9aA55S56ygiCqEsa4GzE9wpdEP7dyZaPc+sV0HkDvCQK1XYCjRP8RYnrOlTlquDPTw+P7+Wq567FuKV9qmrhApGStZ+fLX/4yHnrooby25uZm9Pf3V2hEBEEksgpGUzJGjED606NpjKV5AlFAsILom2YYRK8zhqGEhJOjaZwc4cJrKCmN6+dxiuis92NJfQCd9QG015anELWkGrm6sioUTYPDEF1LG/l9m0NeNAU9JLqI+c+kgisGyEmAaQBEwFPD83XVtJQubYSS5u5EM34seoK7GwsJtuYEV91SLsKqmKr/q7F69Wr87ne/s/YdjrmXB4Qg5iopSc0JrngWp8YyRsZ2FZquwyGKlqVrpqWAkpKK06NpnBrL4PRYGqfG0sgq4/+X2lDDE7B21PnRWRdAU8hT8hqMAJCRNSSyChKSClXPuU/Pawmio86PpqAHjSS6iIWApvLs88kBLrhGe4zaiwWCyxXgCVJLFURvlh6KnuDL2Enu1izE6eFliGq7gNolQG0nn0k5h6j6vyJOpxMtLS2VHgZBzHuSkoqxlIzRlIxho+D1WEpBUlahagyCAPjdTgTcDtQFZpaNXlZ19EYzODXGXYqnx7gVrRCXQ0B7rR+L6/zoNIRXuURPVuGFrxNZBTpj8Lh4DrKLmiJoi/gs0UV1GIl5j5wy4rgG+SzC6HEeZyWnee1Fd00uI32pBBdjXNiZwfpjJ/i99fF/F+BvAOpMwdVV1eWIzpWqF2FHjhxBW1sbPB4P1q1bh6997WtYunTppP0lSYIk5UyU8Xh8NoZJEHOKRFbBWErBaDonuKKW4NIhQIDP7UCNx4l2vx9u5/T/0Km6joG4hDNjGZyJctE1EM9CL8gTIQBoCHrQUetHe60PHXV+tIRmng/srOPSdMSzvAajrGrwuBwI+1y4bEkdFtX60Bj0oKHGQ4H0xPxGU7hLMTXMY6nGjnNrl5TgAkhw8jgufwMQ9nMRVgqycSB2ii/RU1x8ycnx/ZxebuWKLOYWrkgnH888o6pF2Lp16/CjH/0IK1aswMDAAL7yla/gyiuvxL59+1BfXz/hOZs3bx4XR0YQC5WMzK085jKS5EWuY0aWdlVnEAXAZ1i4Ziq4NJ1hIJ7FmWjGEF0Z9Mez0AoVF3jNxfY6PzpqfWg3hFc5rUw6Y0hKKuJGHjKHICDo4+7Fzno/WsJeNAW9M/rcBDEn0FRubTIFV+w0nzkopwA5w/u4vFzkRBbz2YulQEoYgus0t3LFThk1HgsQHHymZKQTqF3M14HGOW/lOhcExtj4v5JVSiqVwrJly3D//fdj06ZNE/aZyBLW0dGBWCyGUCg0W0MliFklLas5sZVWMJySMBDLIp5VkVE0yEaMlSgK8Lkc8LsdCHicM7L2KJqOgXgWfdEsemOG4IploU4guHwuBxZFfGiL+LCo1oeOWh/CPldJay4WIqkaUpKGpKQiq6gQBAEBjxNNNR4sbQygNeJDC81eJOYrZnqI1DCP54qd4oJLSvLgdgbA6c65Fl2+4q1cZsB+/ExO4MVO8XGMQwBqmoFIBxBu54IrtKh0sybPhfQInx159X38WZSBeDyOcDh8Vu0xp/4KBQIBXHDBBThy5MikfTweDzyeEiZ7I4gqQtN5Dq5oWsZYmlu2emMZRNMKMooGSdEhCAyiwAPmfUaNRY9TnJHwycga+mIZ9May6Itm0BvLYCghjXMpAoDXJaIt4kN7xIdFtX4sivhQ6y+v4NJ0XvQ6KZlFrxncDgcCXgc66/3oqPWjvsaN5pC37GMhiIqgZLnYSg3lUjZkxnLpIRxuHjzvrwdc7cVbl5gOpEaA+GlDcJ0GYmcmdikCPH4s0sETsYYN4VXKhKzniq5xESoluVUw2Dz7Y5iAOSXCJEnCgQMHcM0111R6KARRdlKSagguLrr641kMJCSksirSsgad5VyJfpcDoeDMxZbOGMZS/B59sSz6Y1n0xTITBs0DvIB2W8SHtrAXrYbwqgu4yy640rKKlKwhbXOlmkWv17aH0RTyoj7gRkONpyw5wgiioshpnn8rE+VuxehJXuBaSvLM84IIuAPcrViK9BCqlCuiHe8FEr3cyjVRagjTwhVeBITaudgKt1emyDbTc4JLTgGabDwbPy/+3XQet8CVyQo2HapahH32s5/FTTfdhMWLF2NwcBBf+cpXEI/Hcdttt1V6aARRMjKyhmhGzrkSkxIG4oYrUdYgGUlJXQ4RfjdPl9BcROB6VtG4O9EQW/1xvhQmPzWp9bvQGvahLeJFW9iH1ogPIa+zrIJL1XQutmQVKckUnAL8Hv75u5tq0Bzyoi7gRmONByFfecdDELMGYzyWKhvjgisbM2osDgBSDFAy3PoFxoPXPTVAqLW4zPPmDMV4HxdZCUN0pYb5fQoRnUCwjQuucDt3J4baShdLNh1MC5ecsgkugaeq8ASB+mV8fIEGPsnAVwvMMGl0OahqEXb69Gl86EMfwvDwMBobG3HFFVfgtddeQ2dnZ6WHRhDTgpnB4VkeIJ7IqhhJSuiP8yB57krUIAjIdyWGZm7dUjTdEnQDcXOdndS65RQFNIU8aA3xMjstYS66ym1RkhQNadlYFJ5l3yEK8LsdCPlcOL81hKaQB7V+N+oCboS8Lip4Tcx9dJ2X8THFViaay8clJ7nY0mQukEQntyi5/EBNiLvzZupWlJNcbCX6uZXLXNTsxP09QS64QubSzrPOlyoJ63RQpXzBxXSbhSvIk7OG2rjrNdDIBVcpC4CXgaoe3RNPPFHpIRDEtFA0HfGMYomteJbXUBxKSkhLGg+SV3UAzEp06ivSlajpDCNJCQOJnNAaiEsYTU0cuwUAYZ8LLSGvJbZaQ17U13jKlhYC4C7PjCm2ZNWy8HmcInxuB5pCbiyKhFFX40Gd343agLvsFjeCKDu6ZgiticRWiosKjVebgMOVE1u+uuJip5QsT3BqCS1jW0pM3F9w8DipYFvOshVqq0xaCNO6paS5C1bNAhAAp8tIDNsAtF3E48389Vxs+SKVEYZFUtUijCCqFXuuqVhG4fFUsSyGUxKyso6sqkE3FJDbKcJjiK2I3wW3ozjLFq/RKGEwkcVQQsJwcnKx5XM50BzitQybQ140hTxoCXnhd5f3V1/VdaSlnOBSdZ7s1edyIOB2YnlTDdoiPkT8LtT63aj1uymGi5i7MMYFlWXZihsFrPt5ELua4ZYts2i0w83FltsQFMW48eQkd1Um+421sT1RKggTfz0v7xNs5QlPg62GdWuWJQHTDfeqKbaMdBkQuHXLHQAaV3Ix6IsYgquOu2DnCSTCCGIKNJ0hkVXyxNZAQsJQQkJG5pYtTWcQAHgNF2LE74LXNfOYrbSsYtiwng0lZEtsjabkiaIzAABuh4gmm9hqNopIB2fBkiSrei5g3pih6BDN+DUXVjTXoMmYnVgbcCPic8FJiVCJuYim2KxasVy8VmrQSAGRATQzaF3gliyXD3AHjSzzM0zDwPScBS05CKQGcoJrslmJAOAJ8XixoG2paZ792Ym6aogtY7HEFrjVz+Xj+clCi4CAYdnyRuasdWs6kAgjCHAhEc8qiNtybfUnshhJyjaxxTPJe5wivEa8UlPIA+cMgjw1nc9GHEpyS5bpshxOSEjJ2qTn+VwOq4xOk1E4uinoQcjnKksdRRPGGCRVR1bhzyKjaFA1w9JnTBhorOHuxPqg4U70uxH0Oil+i5hbMMaFjWnRysaA9Ch352VGuGtMyfDaiQxc0Di9XEh4I3zG3UzjtVSJCzqzdFBywNgfmriMj4mvjourYItt3TS7dRR1jT8bVTIWI6YNjLs6XT6+1HZyMeivA7zhnNiqRNqKKoBEGLFgYIwhZRRnjmdUxLNcbA3EsxhNc7GVVXTojFu2TLEV9DrRFPRM23qjM4Z4RsFwUsZIigusEaMu41hKgTZFnuSwz4XGGg8agm40BnNiq8ZTXsuWzhiyCn8OGUVD1hCfALhL1clL/KxoDqLBmJUY9nGXIiU/JeYU1izEKLcyZaM565KUGG/VMkWEt5aLiJm67jSVp5dIDXOBZeb4Sg1N7UIUHDzYvKbJWAzBVdM0OwKG6TaRZRNbjAFg3GLl9PKxeGqAuiV8jD5DaHkjgDc0u0lZ5wD0V5OYd2Rkni09keWzEAuD47MqT2oK8JnMXid3I9b63fC6HNNyI5pCa8QofD2S5CJrJCVhJClPmEXexOUQ0FDD6xQ2Bj1oNNYNNZ6yl9CRbVYtSeFpMBgYBAjwuh3wuUQ0GklO621iK+R1kdgi5haawi1aVrxWLH8WomwGxrNcrJbLx60zDs/MsslrKreamWWCzOz1qSFuVZs0sAA8Dqqmmbsva5py2/662XHN6Vouhs1czPE6vYbVz89nSQYaubDyGNn3PcHSZeFfINBfU2LOYZalMTOlJyUVCUMIjaUVpGXVEBk5q5bLwa1aXqeIsG96MxFVXUc0rRgii8dmjRjLWGpqoSUKQJ2RPLQ+4Ea9IboaatxldSGqug5Z0SGpOiRNh2wTWoAAl0PgMWwuBxbV+tBU40HY70LQ60LI50TI6yprPUeCKCmaykWWFDcEV4KXpkkOAdlRIw5JKpiFaMQizXQWoioZBbCHxq8zUUwptBwew6rVyNf2xR2Y4UOYBky3WbSyfCalKgECkGf1C7Vz12agPuc69AT5GElklQQSYURVomo6ElnVsmTFswpGkzIGExKSkmpZclSNWeEXbocIj9MBj1NETcAJj/PcrFpmDq+xlIzRtIzRlGJtj6V4EtWpCqyKAlDrd6O+xo26ABdY9cY64neXJe0DYwyypkNS+HPIqjokRQMEZuTZEuFxinA7RQQ8DnTU8oz2Qa8LAQ8vZRTyuRD0UMwWMUdQJS6uJENkSQljBuIgXytZIw5JRb57zIzV8k7PFaZr3EWZHuHWq/QIt2qZ21MFxAPcqmYmCA00cGuWKbQ8wfKLGHPmoV1oaXJOaFlWrUCuYLY9TssbroqM8vMdEmHErGIGeEuqDlnVIamaZbVKyVwIDSUkjKUVy12mGZYmp8itN14XD4pvDHrOqQC1GQtm1lscS8mIZmSMpRSMpWWMpWUo2tR17F0OAfUBD+oCbtQH3KgzhFZdwI2wz1U2oaVoDFmVx2aZgguCOSYutHwuB1oiXh6jZYisGo8TAY8TAbcTXtfMUmIQxKyiKVxYmYk45RQgJ3iKh9QQF19mULxuTF6xhJbXKNXTeO7pHphupJIY5cIqbz3ChR2buIqEhctvCK1Gbi2yRFcjd8vNxu9d3sxDM6cWAyBya5bTsPY1NPLxeUL8WZkLWbUqCokwoqRIqpYLes/w2YbJrIqkzOsdZmQNqq5D1RhUnUHVdKg6t94AgCgI8LpEeJ3G7MNzCIi3p5GIphVErQLXMqJp5ZxElgAeDF8b4FnZzezsfNtVtoB4RTNchoa7UFJ1qJoGZigtt5MLLa/TgZZ6LxqCHoR9LgS9TgS9fFxkzSLmBErGyKVlE1pSgoue1AgXXKrMg+E1OXee6MwJLd80guJ1lbsFM2PGMsrXptA6F5ElOgBfPbcQ+Rt4nip/nbGun526iLpqPJfCRTK0ln3m4RKebNVfm7NmecOAq4iSRkRZIRFGTBtdZ8gompHSgQuukSRPIBrNGBYsWbMCvR0OAU5RhFMU4DS2PR6B74sinA5h0tgoxnjRZjNPlym0YobQiqa5q3KKsCwAXGQFvU5E/FxUmQlCI34Xt2b5XTNKNTHlc2IMihmTZVn+dKiabrk3Teue2ymiLuAyhJ8HNV6nZc2qMZZyZrMniKLQdVs5maSxpLilKT3CRY+Z+VyTuctQgJHiwc2tV04Pt8w4jeSlU/2nx0ojES0QWrZFSmDKuCyAp5Lw1XJLkd9c1xnCq54Hnc803cTZYIynnVAN0WkXoKY4ZAAcDv48HG7uTvU1cWEVaOSTB2jm4ZyGRBhhkTVq+JmpCXKLzuseZhQkJBUZSYOs8azwssIFhWnB8rkcqPO74QmfPR6LGWVsRlJczHGRJSOWUY01F1tns2IBPC4r7HMh4ufJQMM2oVXrdyFcpgShpktVUvjzkFTdypTPACsuy+0U0VDjtpKV1nid8LvzhVa5Z0QSxIxQpVx5HTltKyeTMgRPlLsKNcUQE1nDXShwoeX08EB0p4cLHId36np+TLfl6YrmRFY2ZqyjfNvMPj8VossQWbW50jZm1nUz/qkcIkvXcs/DbrlSjbxZJg6XIa6MtA7edi4GPSHu6nT7jQkEtu15nrx0oUEibIGgarqV0TwlcUtVSlaRzKoYNSxKGUWFovKAb0U1rDXG/1ZFQYDbIcDpEI0A+KlnGaqajrG0kl9HMaMgZrOexTPKlDML7fjdDkR8PJjcFFoRf267xussy0xDnTFIiiG0DEuWpPJ4FMaMyQCG+3RRxIf6GjfCPjcCHgf8bmdu7XZQlnii+lCy+QWRlRQXWtk4n1WYiRkpHKScmABgTLLNWWccbiMuyyguPZlQ0OSciLLXU8zbjvNEqGdF4JYz0xpkWrTsoqvU8U5MN8SVYnsmhjUrb8yC8UwMC5Y3zMfpqzPisPw8IN5tLC4/uQwXKCTC5jiymkuqaWZ2zygaskaurFhGQSKrIC0bQfAaT10Aw1koCgK31Di4tcbn49uTuQhVTTdycKk4E80gnjXycRnCypzNmJ4i63shfjdPAFq4hHwuS3idSwD+TNAZs9yEpkVL1nSjFBEDBJ4h32PkEltU60NDgKeXMF2GQQ8PhieRRVQVmpJzC8rpfBdhdhRIGzMKNZuL0LTSCGLOBeb0AK5wTnBNZDnSZO7+Sw5wMSXFCnJzGdtK+hwHLxiWoUguo7q5NkvaeEOlrXXI9JxLUJXyrVh27NYrVwAIhoyxhfm+WRPSTIHh8tMsQ2JSSIRVGbrOLEGQMWbEZYygbZ7RXUMiqyKZVZCQVUiKDkXjwkHVmDWTEMhZr1xOES6HiIDHiVpDbNkFlm64BZOSitGUjKSkWOkhkhIXVUljP6Ocu7hyCAKCRs6pkM+FsNeJkCGqQl6XFWBeLoEFcNFozcS0xWbx/GFcZLmNwHePU0RT0Jzx6DZchbmZhj6XgwLgicqia7m0A/Y6fGZ6BinFBVAmxgPdrVijLADBsGAJNhehG3DV8rXoyrcaMZ2LNynBk41aObjitlQRRpuaPffPILq4gLIHjlvla8xcVKHSu92sAHezrI5kBLfbgvNNt6nTDXjNmKtaLgjzxJXhHnR6aWYhURQkwmYBzQhkN0WUpPI4KzPeKiVza1Iyy12FijF70FybBaJND4DTIcJluAZdomCJK5dDtOKwNJ0hJau5hKbZXGLTvG2J9zlHryAAwCEKCHqcCBqiKujlQivotW874Xc7ypYaQdF0Y2G5bVWHohtC1HhgTlGwYrICbgfaIl7U+90I+lwIuJ3wGyLL73Yg4KZZhsQsoqm5zORqlgsENZuLHTJzO5kWLDMWy4w10g23mAkzcmOJLm6tcXp44Wi/J2fB0rVc4LwUB+KJXM4tKc6Fm5Tgxajl5NlnD9oRnVxMeUJGFvWwIbbMbeNYqbOp62rOLWi3XqkyfybmrUQHF1mm6zTUagTfh3k6CXdNzj3orpk6bo0gSgS9ZSVAVnX0RjNIy7wkzkSuQNNapRgWK8D4O8QAURTgErmocjoEuBwi/G7RanOIAjSdzxJMyTxT/GhKsbZTNjGVkvi9p2OxMvG5HKjxOi0hVWMIraDXiRqPy9r2uconrgAj27ttNqGsmi5CXkCbCYBT4BY+tyE+a/05ERg0sr2bwsrvcVC+LGJ2MMVTXjZyWw4nyZjRJyV4/JUVX6QUFGgWAOgARC6gRGduMWvzOdy8n5rNiSZzdqKUMNZmuyGsztkdaMMVALxBI7+Usdj3vUbeKWcJxZUZe2UJLLvIMkoMwViJdtepm8ddWQlHgznLlekaNIUW/S0gqgASYSXg8EACT+3uRVbRucWfFbgCRRFel8NyvYkCIKvcUpWWeP6sqBE0nzaFlbG2AulnIKoEwJp5Z7rVajxO1BgCi2/njpcyRYMZayWrPL5KYwy6uWbc7aozbrXSGZ8paT47h2G98jhEeF0imkPcRRjyueB3c6uVz0ja6jNKEVE8FlFyCgWVOfPP3ial8sWPZaFScwKCsdwXvj3WSnRx4eIxUgsIDm4FK0xWKtvvUZACYiaiyoy3smr9Bfm+JzhBIs+a0rgFLVFlPBtNNp6P0aarOVO/6TJ1OA2rnvG8fHVcVHmNgHszN5YprsyFLFjEBKi6ClmTIWkSJE2CU3SiwddQ6WGRCCsFSUlBbzSLhqDHSkhqCiq+n9s226fh/bMQAPgM64456y6X4sBhE1x87XM7ylab0Mzmnjdr0CwjJPDBmgH/ZpC/25WbWWlasTwuHvTudojwufOtVzWe8lvdiAWArhsuPrvLT87fV7M5S9WkgkrJubcYYJXGEV05wSA6Aaef/2U1LTimFUxOG7MPU7kZiXkzE9PTc/9ZCDkLjymu3DXGdjBfcHlqeN9i0jIwxt2auvFcTBGlq8azUvKflYnDzZ+Pw82flydijNm0pHmNDO8eLk6dnlyby88tXgSBfEElazJf9Ny+pEnIqlmklJS1yJoMVVehMAWarqHWW4u/PP8v4RIrm1uNRFgJeP34GH7xp95pn+dxivC781MZ+E0h4nFa7Vx08WOlFlU6Y0b2eh5fZWaw13Rmrbm1SocgCFZmewiAWxThNtIzNBsB7bUBd54oDHicXIgZrlWCKBmaasRJZWzrTE7YmOkOpEROTDE15+YyLS9AQTyVIaggwDDT5kSHKuVis6x8Wenc/c3tPNfiNHF4eNC3GaNkpTLwFwgsI3apmNxRjOWElGYTVJptzYycX5YLkNlcpLZn5goAAWNs3rARuO7jqRdMUeXy5YQV5bta0DDGoOiKJZoUXYGiKZD1nKiSNRmKpiCtppFRM0graaTVNBRNgaqrUJlqrXWd/wdGEATr+8opOvki8LXH6UFADCCpJCGZFQcqDImwEhD2ueB1iQh6XfAbsUh+t9Oy7Ni3AzahVQ5RwsWTDs0sC2QrDWQGsuss/81zGXFoLsNq5Xc7rBqNAbcDXrcDXifP6m729ThF+D1O1BhCkQQWMW10PWctKoz9sbKIm6kCJFuahYwRTyXZYqoKsrAL4CKBMS4izOSZuplQVM7Fa5nB8XliLj0+NcF0EUTbTLpALtmmJaz8ubUpptyB4rKeM91mlVLyxZRlodLz46GseDMjoN/hyuXYMl2STq8xo9IIane6bTMJjZmW5AZckOhMHyeaTKuUXVRl1SxSagoZJYOMlkFWyVoiSmMaNF2DylRoOq+PKzAhl6dSFOEQHHmiyu10wy/6rX3HWUS9znRk1SwkTUJSTiLsCc/OAzoL9FtTAi7qiOBDly1Gd3Ow6GuZcVLaBNYoVdfHtZvfOIKxFkUBDlGwlQjibsCwi7v3QsasQJ+buwG52BIt0UXxVcRZ0fXcF7o9YHpcm222mplCwZ5eQZO5OGKma6tQLBXMdrO7wCy3l3kvKRenZd6rWBFlYneJFcYfuc22gG3bEFdOz7kFf1vPQM/POM903s703L75DMz+QL7LD4Lh8jOFlSGaAo1G3FeIiytTVLlsbj9rOcdxE3MenemWBUrR+aLqXBgVbpuLpEpIq2mkFW6dymrZnIjSc5apvBg/8G2n6MwTUw7BAb/Lb7U5BMc4McUYg8Y0y8UoaRJSWorHdqmSFeOV1bKW2MtqWeuYua/YrNMRTwQfWfWRWXvOU0EirAyYQsp086k6s1mm9HEiShB4/h4rCbVo1FsURDgcsGosBjy5WX9elwN+o+agxyXC7XBYxZ7NlAzWtoNmBRLIuZ4KxdNE0/vNNqVQOBnpE3SNu/ZMcaApXPioEqAqALOJKFUx+tosNBOlFTDzNqkFuZuKxeE23GK2ZcJ9b4HQMlxnU/0P2xJIWr5QkpM8X1de7JQ6yecyhJMg8nvZ14IxO9IdyFmeLAHos9UUtC12K5XLd/Y6jMScxLQcmQLKLpzsgsp0+ZkCJa2mLWEia7JlfdKYBp3p0HQNOvhasCt8gQsiURC5YBIdlgXKKTjhdXmtbVEQoTEtL27LFEuFsVyFxyfa10v498AUe9UCibASwQAcGUjYcnlxK5TDjIcSRQQ9XDj5jNl9PndOOJnuQHv2epeRD8xtzLCkHFYLCCtWpyA+52zxO6YFScli3Ew+TcpZmuzCyuxjv7Z91polJGz3zRNyUn7agJIh5Adnm1aavH1vLubIWvvy+xSKqMmEk93CpGa4kDL7MT03a8/8HxOMfdHBZzaOW7tsbkab69HMQm/PSG/FWLmMOCun0Wbsk4ia0zDG8mOYDBecKZIsK5LtuCmi7IIkq2Yh6ZIVZG5aoOwCyty2BJQhngRBsCxNDsFhbYsQAREQdAEqVDCBgTEGBpZzMdqEnBWzNcm+Ob5y4Ha44XF44HV44Xa44XV44XF64HHkFq/Ta/XxOAvWDg8cogNj2bGSCrtiIBFWAhbX+fHeta1WrJTbPvPPsFC5neJZC1oTcwgrWLtwZph9KbSCaAUuNSUniEwLkFnLT0kZJWWMbOeqVBArZQggNpFQU8bf2259KusfH5twsscL5bm8JtouWLu8hgVnEtc4Y7b4J7s4NNZSHEiPjI9/MoXURMLJDDJ328Zit5KZ5WrssVP2QH5z1p+5LTppRt8cxBQziq5YQsnuZisUUYUWKFmVuTvMZtFRdAU60/m1DZFk39aYxoUScnFQpobSmW6JIp3pYGDcWlVwjbwx2SxkdgFl3zfX2jnV6Zw5bocbHtFjCSi3w523bV+by0T7bocbYjmKrVcYEmElIOJ347IldZUeBgHYLEjaeDHE7G06phRMpnBRJcMqkspPJaBmDCFkutIUQJ8gPkpXAM3mujOFGisUbTZr02whiPnla6xs4va1rYyLY6KAbHe+4DJL3zA9t+i22KaJFus4y6WKSJvWKrOW4bgfdM7aZAkfZy7flTvA0zO4DWuYJZhcU4go0/I0//7QzydMkaQyNU98mFYg07JkCpJxx5iWFzxuBpNLumTNuisUSKbg0ZkORVOgg99X13XuziuwSJmCyb5oTMvrZ36GPJFXEH9lirfZQIDAxY7IBY/L4YJbzK3tx6ylcN/W5nF44BJdsxIKYxepUy7g65SSQsgdKvu4zgUSYUTp0fWclcJy+Wi5L9s8V5C93S6YjC9hVeKWINO9ZqUDyAJqerzbLW+2nS05pD3fU56brTAwvFC0aajYPGbBYSsW7C7YNmN/XDz42toviAuy75t5rBzunIvLEkETBIGPE0vGs9A1fq5pkZOTEw2eixlB5J9DEPk51rYDEAU+JpfL9jlcgNPFBZ/LVyCWzPHb3HSFFjWaoTdrmAHT41xi9tiiCcRMoevMFEt2C5QpkiwxUmDVMd10Zt6nieKhrPvbYpys8RYIq0KhZIkr24w9KxVCBd1Ypihyik6+Lbrgcrj4WnRx8WRsm+1mm/1YYZvb4YZDKE1ORsZyYkjW5bx9uxCyt9sFlGXxM4/DqJRiWAoFQQADg8D42j6DUgB3uYqCaLlfRYgQRAEOOOASXHA6nIh4Iqj31p91RuVsQH+x5hP6RAJHy5+mb4+DYboR/2NLZKmZa3uRWzm3bZVjsQVRjwvoLnTLFVh8JhNcE429GhK52CkUAxNaVgpEheXGsqcCMLcdtrgfR65NcIy3KJkuOOg5dyhY7hgwceyQ6fYUGICCoG8rCNwMCBcLBJw5fk+uZI4p4qzx29bWZ7Afs+87xveheKdpYX1ZMWaJi8msABMKIN1wfxluLbs7y5wtJ2syMmoGkiohq2eRVbPjgqgVTcnL06Tq+aLFskZNYCnKG5+uQ0NuXPYva7uQM49VE6IgwiVyUWSuzaWw3b6eaNvat4kq+/65iqRx7kubmCncNsVsQk6AgY1ze5r79nb7bMfCGZAMXCiJoggRIkRBtASRKBhttmNu0c2D+Y00E9ZnFQ3BJPI2M4bNmhRQENsmiqI1SaBwpqW9vVRCs5SQCCsFSgYYPc7dUWbsjn3Wl+maUu37tgBn3ThmtRuWHLNNlW2xL7bcP/ZYJGv2mW5s29c2a1Petl7m+KBSY59+77IJGGeuXXAY64mEgMN23BQEYoHwsbeLuX0YgsUUPfbprED+th3rD5YhgATBuI5gsw6JEyxGu2iLQbI+pwv5Vi3zmRQKIRHjhJ099mkysbQArUkTCZi8/50bgsfcNmN4zDZTvKiaClmX891KBbPXClMBmMHNhWkCCtf2WKRC99ZEQqtQxEzmmpnoc88VREG0EnGOS39gfAFPdMyewPOsbaZAMISQ9YUuOiBAyBM3pmDJ24Y+TsxM2c54Pqs00rycm03giIKY9/Mx729ahUxLkSAIXAwJorW2BFGBSDJFoCVYJgjedwpOS+iYz6RQENnPNfvbBZHZ3/yZOURHyWO8GGP8+03jv59QNTCdAboE6Lz+MDTeJrhdcNbWlvT+M2FO/LX9zne+g3/8x39EX18fVq9ejYcffhjXXHNNpYeV44//Bjz3YKVHUSIKgpYLv8DzRMok+5a7SSzYtll48qwxjvECxLwvCq9vG+qEwsdwg5mCx9oXjH2Ru8Gs4wX74yw3dnFim8HmcNk+U+GzEPKfi/3zFbrorOdbIAatlAXVF5+kM32cu6YwrmVcG1OhaqplGVE0xRIH9gDoQvePBi3PqmLvV+jKyjtPH2+ByYsPsllerDgeQ1xNJsTOJtDmKwKE8RaGCSwRDqHA6jCBFcL84rV/CRfO1BNFEQ44LKFgnm8XEqZVBQLOWdxMZOnh3qz8NAx2VJ2/t3Zrz2Qip7DN7GdZhCBCdOQ+k/kcREHMe0Z2wWgXNHbLkvncxu2bz8z2M7E/Y1NIWW2ig///UOdhCVz0cSHDdHPyC7e0WwInrx8DU1UwVQPTNUDlxgNdVbnYUVVrgapCUzWomgZZ5bGyumrEyaqqsc2voWuadT5UFUxV+D0UhbdpKnRFNfobx4xzGWOG0DLGqmnG58mFXghuD0IbN6Luo38JwVlZGVT1IuzJJ5/Efffdh+985zu46qqr8N3vfhcbN27E/v37sXjx4koPj2M3b+Z94RaKC/sXsf2Y3RoygSCZylJiCom8+9ktORMJoYJ2s1iuXVBYokWwWW9gu1/BceMP4oTjybMy2axLRjsTRDBRhC6IYIIDuugAEwToggAGgbdDABMAHQJ0kaem1SFCFwBAgC6KMOxTvI8Afg0IRjtv0420tnyb99cAXlhcyFk48qwNutEG08IgQWcZaOrE7hV7rIw9JmWcRcLmhrFmPNncS4VjmciSYVo6GFjuejYXxLlaQOxfXhPGZphfbPNccJQa80vZdINMKCYmEBeF51junIIvX3sMTJ7bp+B6dnfQRNuWaLBZTcw283MYGxybG8qM0clrN7eNcxjLt95AgHUPILctABCYABGAwACRAYIuQNBhtOkQmMbHrgMCGEQmQIQAkQlwCCIczAkHE+BgAkQGOJgAJxPhYICoC3AAcDKRn6cziIzx6+uAwBhEBogag6AziDr/IKIOCLoOgQGCbvQHA3RA0Mx2nR9jCh+brgM6g2Bcn4d/6Dkho2tgmi20QNPBjD52wcD7FKwtgaFyIWRYeMB06Br/28Js98tdj+W2C8UVM4WXKb5YTmxZfVieUIPt5zqXEEMh6Ok0rITHFURgrLqf4rp16/C2t70NW7ZssdpWrVqFm2++GZs3bz7r+fF4HOFwGLFYDKFQeWZDHDr8a/znnkeNLyr+t4cZf5YYAGb8b00HcscFQxgYRmSzry4gd17hYvyx042/frnzTfHB8u/Nctt5x5h9fGZfltfGhQHfhm7879H4n6MO/odFZ7YzmHkd3fqDqzM+UvOLnJ9r/CLbrmtHKJwMV7Av5Hcf379U15mi/0R9J+xXzLmT9Ju0j2176nPYxOcUXPds9y0cszCur/HFaH6xGv9Ea438fSZwXZ/3RSxY+yIECCy3DcHeB7lrWM/Adl8G/qXNjC9ZwbxW7ss+dw3k3Ucw9wWjHzPFgMC/hJl5Tu6eIrPf13guOst7joLxC2o9S2ObP0PDQsPMtXHcvBazHcf4fmZfweojcCFg/SExj+fOyx/D+HvxY7ltgH8mvm1+UedfI28xrg/d3t92PXs/Yn4hml4GI2m4sS/Y2vOOTdXP+M+/IIrjjxceM4+bx8zrGtv+Sy9Fw199AoLbXZaPfa7ao6otYbIsY+fOnfj85z+f137jjTfilVdemfAcSZIgSZK1H4/HyzpGADj06h9x4z/tt/Yn+iIGAEzQXigGJmo72zUmO2/K8djaqs/hRRAEMQ1Mb4R9bdvOC8a2fWGPO17Qbn2xW9uA6YHIO8cUAkCuL8BDHax+4vhjKLi+/XoABOv8/PsL5rY91KLwOmYog2j896DgWoJtzDmRkvt8efcSBQhWqIj9fCOcQxgveHgf8372n5P9Z4F8K6v1cyr4eZnkXU/IPaPCNuta+e3Wz08U4ayrAyrsigSqXIQNDw9D0zQ0NzfntTc3N6O/v3/CczZv3oyHHnpoNoZn0RAHarKzektiBli6Uxi3YdsVCvZtG+MOFbYLsPwxU5w7YVvhjJ1xM3iESfsLEx0vvEfe9YSCpoLxCuPbJu1rrcw+E3wZTjTmSdon3LbGk/+ZhMnuVXierU2YalyCcUXrC8j2hZH3TGz9hcK2wrFOdW1zPEKufYJzJ/0iGyckjPPNpNCCESJgxRXmviQF84vTGpOQv2196ZrNYt6x3Jcu8oWAQ7SNJ9ev8Jnmj0OwfWk7cuMQ860gdutIzsoh2h6JeT/7Z8rdV7A+L/h1Yfs8yB9j3s/Fek8meG8Lf68K+ud+xvnvQh4TCQ9M0TTR+eOubf952s4r/Dlb4y7oM8Hxs/3ujP88/NycIMLE1zZvMdHnKHjGee+A9S4hvy3vd83WVoXxtUCVizCTwpfOLMEwEQ888AA2bdpk7cfjcXR0dJR1fJd/6FNILlsPpinWF4P1Sw5zNdHLmn+d/Jek4BcDGB+kbf9DU0jeC43xL/K4+9n6iGL+vv36tqz/eecXfslNcCxvrAXVA6xfLvu1Jjw//7NY+xMcm/Ev3STv1oTP+VwvMdk1p9t+1j7nPsaJTpnwD/w53XeG/cd1PcfnUWw/FL5/kw5o6k4zGNe4HsXco/D3jiCIOUVVi7CGhgY4HI5xVq/BwcFx1jETj8cDj8czG8OzcDY0IPKejbN6T4IgCIIg5jbVaZ8zcLvduOSSS/Dcc8/ltT/33HO48sorKzQqgiAIgiCI4qlqSxgAbNq0CR/96Edx6aWXYv369XjkkUdw8uRJfPKTn6z00AiCIAiCIGZM1YuwD37wgxgZGcHf//3fo6+vD2vWrMHTTz+Nzs7OSg+NIAiCIAhixlR9nrBimY08YQRBEARBECbnqj2qOiaMIAiCIAhivkIijCAIgiAIogKQCCMIgiAIgqgAVR+YXyxmyNtslC8iCIIgCIIwNcfZwu7nvQhLJBIAUPas+QRBEARBEHYSiQTC4fCkx+f97Ehd19Hb24tgMFi20h5maaRTp07RDMwioOdYGug5lgZ6jqWBnmPpoGdZGmbjOTLGkEgk0NbWBnGKEnrz3hImiiLa29tn5V6hUIh+MUoAPcfSQM+xNNBzLA30HEsHPcvSUO7nOJUFzIQC8wmCIAiCICoAiTCCIAiCIIgKQCKsBHg8Hvzd3/0dPB5PpYcyp6HnWBroOZYGeo6lgZ5j6aBnWRqq6TnO+8B8giAIgiCIaoQsYQRBEARBEBWARBhBEARBEEQFIBFGEARBEARRAUiEEQRBEARBVAASYWfhhRdewE033YS2tjYIgoCf//znZz1n+/btuOSSS+D1erF06VL867/+a/kHWuVM9zlu27YNgiCMWw4ePDg7A65SNm/ejMsuuwzBYBBNTU24+eabcejQobOeR+9kPjN5jvROjmfLli1Yu3atlfRy/fr1+M1vfjPlOfQuTsx0nyW9j2dn8+bNEAQB991335T9KvlOkgg7C6lUChdeeCH+z//5P+fUv6enB+95z3twzTXX4M0338QXvvAFfOpTn8JPf/rTMo+0upnuczQ5dOgQ+vr6rKW7u7tMI5wbbN++HXfffTdee+01PPfcc1BVFTfeeCNSqdSk59A7OZ6ZPEcTeidztLe34+tf/zp27NiBHTt24Prrr8ef//mfY9++fRP2p3dxcqb7LE3ofZyY119/HY888gjWrl07Zb+Kv5OMOGcAsK1bt07Z5/7772fnnXdeXtudd97JrrjiijKObG5xLs/x+eefZwDY2NjYrIxprjI4OMgAsO3bt0/ah97Js3Muz5HeyXOjtraWfe9735vwGL2L02OqZ0nv4+QkEgnW3d3NnnvuOXbttdeyT3/605P2rfQ7SZawEvPqq6/ixhtvzGt717vehR07dkBRlAqNau5y8cUXo7W1FTfccAOef/75Sg+n6ojFYgCAurq6SfvQO3l2zuU5mtA7OTGapuGJJ55AKpXC+vXrJ+xD7+K5cS7P0oTex/HcfffdeO9734t3vOMdZ+1b6Xdy3hfwnm36+/vR3Nyc19bc3AxVVTE8PIzW1tYKjWxu0draikceeQSXXHIJJEnCj3/8Y9xwww3Ytm0b3v72t1d6eFUBYwybNm3C1VdfjTVr1kzaj97JqTnX50jv5MTs2bMH69evRzabRU1NDbZu3Yrzzz9/wr70Lk7NdJ4lvY8T88QTT+CNN97A66+/fk79K/1OkggrA4Ig5O0zoyhBYTsxOStXrsTKlSut/fXr1+PUqVP43//7fy/oPzB27rnnHuzevRsvvfTSWfvSOzk55/oc6Z2cmJUrV2LXrl2IRqP46U9/ittuuw3bt2+fVDzQuzg503mW9D6O59SpU/j0pz+NZ599Fl6v95zPq+Q7Se7IEtPS0oL+/v68tsHBQTidTtTX11doVPODK664AkeOHKn0MKqCe++9F7/85S/x/PPPo729fcq+9E5OznSe40TQOwm43W4sX74cl156KTZv3owLL7wQ3/72tyfsS+/i1EznWU7EQn8fd+7cicHBQVxyySVwOp1wOp3Yvn07/vmf/xlOpxOapo07p9LvJFnCSsz69evxq1/9Kq/t2WefxaWXXgqXy1WhUc0P3nzzzQXvrmCM4d5778XWrVuxbds2dHV1nfUceifHM5PnOBH0To6HMQZJkiY8Ru/i9JjqWU7EQn8fb7jhBuzZsyev7Y477sB5552Hz33uc3A4HOPOqfg7OSvh/3OYRCLB3nzzTfbmm28yAOxb3/oWe/PNN9mJEycYY4x9/vOfZx/96Eet/seOHWN+v5/9j//xP9j+/fvZ97//feZyudh//ud/VuojVAXTfY7/9E//xLZu3coOHz7M9u7dyz7/+c8zAOynP/1ppT5CVfA3f/M3LBwOs23btrG+vj5rSafTVh96J8/OTJ4jvZPjeeCBB9gLL7zAenp62O7du9kXvvAFJooie/bZZxlj9C5Oh+k+S3ofz43C2ZHV9k6SCDsL5jTgwuW2225jjDF22223sWuvvTbvnG3btrGLL76Yud1utmTJErZly5bZH3iVMd3n+I1vfIMtW7aMeb1eVltby66++mr21FNPVWbwVcREzxAAe/TRR60+9E6enZk8R3onx/Pxj3+cdXZ2MrfbzRobG9kNN9xgiQbG6F2cDtN9lvQ+nhuFIqza3kmBMSMCjSAIgiAIgpg1KDCfIAiCIAiiApAIIwiCIAiCqAAkwgiCIAiCICoAiTCCIAiCIIgKQCKMIAiCIAiiApAIIwiCIAiCqAAkwgiCIAiCICoAiTCCIAiCIIgKQCKMIAiCIAiiApAIIwhiwaNpGnRdr/QwCIJYYJAIIwhizrFhwwbcc889uOeeexCJRFBfX4+//du/hVmFTZZl3H///Vi0aBECgQDWrVuHbdu2Wec/9thjiEQi+PWvf43zzz8fHo8HJ06cwLZt23D55ZcjEAggEongqquuwokTJ6zztmzZgmXLlsHtdmPlypX48Y9/nDcuQRDwve99D+973/vg9/vR3d2NX/7yl9bxsbExfOQjH0FjYyN8Ph+6u7vx6KOPlvdhEQRRtZAIIwhiTvLDH/4QTqcTf/jDH/DP//zP+Kd/+id873vfAwDccccdePnll/HEE09g9+7duPXWW/Hud78bR44csc5Pp9PYvHkzvve972Hfvn2oq6vDzTffjGuvvRa7d+/Gq6++ir/+67+GIAgAgK1bt+LTn/40PvOZz2Dv3r248847cccdd+D555/PG9dDDz2ED3zgA9i9ezfe85734CMf+QhGR0cBAA8++CD279+P3/zmNzhw4AC2bNmChoaGWXpiBEFUG1TAmyCIOceGDRswODiIffv2WSLp85//PH75y1/iV7/6Fbq7u3H69Gm0tbVZ57zjHe/A5Zdfjq997Wt47LHHcMcdd2DXrl248MILAQCjo6Oor6/Htm3bcO21146751VXXYXVq1fjkUcesdo+8IEPIJVK4amnngLALWF/+7d/i//1v/4XACCVSiEYDOLpp5/Gu9/9bvzZn/0ZGhoa8IMf/KBsz4YgiLkDWcIIgpiTXHHFFZYAA4D169fjyJEj2LFjBxhjWLFiBWpqaqxl+/bteOutt6z+brcba9eutfbr6upw++23413vehduuukmfPvb30ZfX591/MCBA7jqqqvyxnDVVVfhwIEDeW32awYCAQSDQQwODgIA/uZv/gZPPPEELrroItx///145ZVXSvMwCIKYk5AIIwhi3uFwOLBz507s2rXLWg4cOIBvf/vbVh+fz5cn4gDg0Ucfxauvvoorr7wSTz75JFasWIHXXnvNOl7YnzE2rs3lcuXtC4JgBf1v3LgRJ06cwH333Yfe3l7ccMMN+OxnP1uSz0wQxNyDRBhBEHMSuzgy97u7u3HxxRdD0zQMDg5i+fLleUtLS8tZr3vxxRfjgQcewCuvvII1a9bg8ccfBwCsWrUKL730Ul7fV155BatWrZrWuBsbG3H77bfjJz/5CR5++OE89yZBEAsLZ6UHQBAEMRNOnTqFTZs24c4778Qbb7yBf/mXf8E3v/lNrFixAh/5yEfwsY99DN/85jdx8cUXY3h4GP/1X/+FCy64AO95z3smvF5PTw8eeeQR/Nmf/Rna2tpw6NAhHD58GB/72McAAP/zf/5PfOADH8Db3vY23HDDDfjVr36Fn/3sZ/jd7353zmP+0pe+hEsuuQSrV6+GJEn49a9/PW0RRxDE/IFEGEEQc5KPfexjyGQyuPzyy+FwOHDvvffir//6rwFwt+JXvvIVfOYzn8GZM2dQX1+P9evXTyrAAMDv9+PgwYP44Q9/iJGREbS2tuKee+7BnXfeCQC4+eab8e1vfxv/+I//iE996lPo6urCo48+ig0bNpzzmN1uNx544AEcP34cPp8P11xzDZ544omingNBEHMXmh1JEMScY8OGDbjooovw8MMPV3ooBEEQM4ZiwgiCIAiCICoAiTCCIAiCIIgKQO5IgiAIgiCICkCWMIIgCIIgiApAIowgCIIgCKICkAgjCIIgCIKoACTCCIIgCIIgKgCJMIIgCIIgiApAIowgCIIgCKICkAgjCIIgCIKoACTCCIIgCIIgKgCJMIIgCIIgiArw/wM6hw6RFpeisQAAAABJRU5ErkJggg==", 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" ] @@ -1031,14 +1056,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -1069,14 +1094,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "True\n" + "False\n" ] }, { @@ -1118,7 +1143,7 @@ " diff\n", " (0.0, 1.0)\n", " 0.0\n", - " 0.0\n", + " 0\n", " 1.0\n", " 0.864408\n", " 0.627063\n", @@ -1130,7 +1155,7 @@ " diff\n", " (0.0, 1.0)\n", " 1.0\n", - " 0.0\n", + " 0\n", " 1.0\n", " 1.694646\n", " 1.252803\n", @@ -1141,118 +1166,128 @@ " livebait\n", " diff\n", " (0.0, 1.0)\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " 0.864408\n", - " 0.627063\n", - " 1.116105\n", - " \n", - " \n", - " 3\n", - " livebait\n", - " diff\n", - " (0.0, 1.0)\n", - " 1.0\n", " 1.0\n", + " 1\n", " 2.0\n", " 1.009094\n", " 0.755449\n", " 1.249551\n", " \n", " \n", - " 4\n", - " livebait\n", - " diff\n", - " (0.0, 1.0)\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " 0.864408\n", - " 0.627063\n", - " 1.116105\n", - " \n", - " \n", - " 5\n", + " 3\n", " livebait\n", " diff\n", " (0.0, 1.0)\n", " 1.0\n", - " 2.0\n", + " 2\n", " 4.0\n", " 1.453235\n", " 0.964674\n", " 1.956434\n", " \n", " \n", - " 6\n", + " 4\n", " livebait\n", " diff\n", " (0.0, 1.0)\n", " 0.0\n", - " 1.0\n", + " 1\n", " 3.0\n", " 1.233247\n", " 0.900295\n", " 1.569891\n", " \n", " \n", - " 7\n", + " 5\n", " livebait\n", " diff\n", " (0.0, 1.0)\n", " 0.0\n", - " 3.0\n", + " 3\n", " 4.0\n", " 0.188019\n", " 0.090328\n", " 0.289560\n", " \n", " \n", - " 8\n", + " 6\n", " livebait\n", " diff\n", " (0.0, 1.0)\n", " 1.0\n", - " 2.0\n", + " 2\n", " 3.0\n", " 0.606361\n", " 0.390571\n", " 0.818549\n", " \n", " \n", - " 9\n", + " 7\n", + " livebait\n", + " diff\n", + " (0.0, 1.0)\n", + " 0.0\n", + " 1\n", + " 4.0\n", + " 2.958183\n", + " 2.180928\n", + " 3.736781\n", + " \n", + " \n", + " 8\n", " livebait\n", " diff\n", " (0.0, 1.0)\n", - " 1.0\n", " 0.0\n", + " 0\n", + " 3.0\n", + " 4.950441\n", + " 3.897761\n", + " 5.942056\n", + " \n", + " \n", + " 9\n", + " livebait\n", + " diff\n", + " (0.0, 1.0)\n", " 1.0\n", - " 1.694646\n", - " 1.252803\n", - " 2.081207\n", + " 0\n", + " 4.0\n", + " 23.283362\n", + " 19.883794\n", + " 26.575537\n", " \n", " \n", "\n", "" ], "text/plain": [ - " term estimate_type value ... estimate lower_3.0% upper_97.0%\n", - "0 livebait diff (0.0, 1.0) ... 0.864408 0.627063 1.116105\n", - "1 livebait diff (0.0, 1.0) ... 1.694646 1.252803 2.081207\n", - "2 livebait diff (0.0, 1.0) ... 0.864408 0.627063 1.116105\n", - "3 livebait diff (0.0, 1.0) ... 1.009094 0.755449 1.249551\n", - "4 livebait diff (0.0, 1.0) ... 0.864408 0.627063 1.116105\n", - "5 livebait diff (0.0, 1.0) ... 1.453235 0.964674 1.956434\n", - "6 livebait diff (0.0, 1.0) ... 1.233247 0.900295 1.569891\n", - "7 livebait diff (0.0, 1.0) ... 0.188019 0.090328 0.289560\n", - "8 livebait diff (0.0, 1.0) ... 0.606361 0.390571 0.818549\n", - "9 livebait diff (0.0, 1.0) ... 1.694646 1.252803 2.081207\n", + " term estimate_type value camper child persons estimate \\\n", + "0 livebait diff (0.0, 1.0) 0.0 0 1.0 0.864408 \n", + "1 livebait diff (0.0, 1.0) 1.0 0 1.0 1.694646 \n", + "2 livebait diff (0.0, 1.0) 1.0 1 2.0 1.009094 \n", + "3 livebait diff (0.0, 1.0) 1.0 2 4.0 1.453235 \n", + "4 livebait diff (0.0, 1.0) 0.0 1 3.0 1.233247 \n", + "5 livebait diff (0.0, 1.0) 0.0 3 4.0 0.188019 \n", + "6 livebait diff (0.0, 1.0) 1.0 2 3.0 0.606361 \n", + "7 livebait diff (0.0, 1.0) 0.0 1 4.0 2.958183 \n", + "8 livebait diff (0.0, 1.0) 0.0 0 3.0 4.950441 \n", + "9 livebait diff (0.0, 1.0) 1.0 0 4.0 23.283362 \n", "\n", - "[10 rows x 9 columns]" + " lower_3.0% upper_97.0% \n", + "0 0.627063 1.116105 \n", + "1 1.252803 2.081207 \n", + "2 0.755449 1.249551 \n", + "3 0.964674 1.956434 \n", + "4 0.900295 1.569891 \n", + "5 0.090328 0.289560 \n", + "6 0.390571 0.818549 \n", + "7 2.180928 3.736781 \n", + "8 3.897761 5.942056 \n", + "9 19.883794 26.575537 " ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1272,7 +1307,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1310,7 +1345,7 @@ " 0.0\n", " 0.0\n", " 1.0\n", - " 0.0\n", + " 0\n", " \n", " \n", " 1\n", @@ -1318,7 +1353,7 @@ " 1.0\n", " 1.0\n", " 1.0\n", - " 0.0\n", + " 0\n", " \n", " \n", " 2\n", @@ -1326,7 +1361,7 @@ " 1.0\n", " 0.0\n", " 1.0\n", - " 0.0\n", + " 0\n", " \n", " \n", " 3\n", @@ -1334,7 +1369,7 @@ " 1.0\n", " 1.0\n", " 2.0\n", - " 1.0\n", + " 1\n", " \n", " \n", " 4\n", @@ -1342,7 +1377,7 @@ " 1.0\n", " 0.0\n", " 1.0\n", - " 0.0\n", + " 0\n", " \n", " \n", " 5\n", @@ -1350,7 +1385,7 @@ " 1.0\n", " 1.0\n", " 4.0\n", - " 2.0\n", + " 2\n", " \n", " \n", " 6\n", @@ -1358,7 +1393,7 @@ " 1.0\n", " 0.0\n", " 3.0\n", - " 1.0\n", + " 1\n", " \n", " \n", " 7\n", @@ -1366,7 +1401,7 @@ " 1.0\n", " 0.0\n", " 4.0\n", - " 3.0\n", + " 3\n", " \n", " \n", " 8\n", @@ -1374,7 +1409,7 @@ " 0.0\n", " 1.0\n", " 3.0\n", - " 2.0\n", + " 2\n", " \n", " \n", " 9\n", @@ -1382,7 +1417,7 @@ " 1.0\n", " 1.0\n", " 1.0\n", - " 0.0\n", + " 0\n", " \n", " \n", "\n", @@ -1390,19 +1425,19 @@ ], "text/plain": [ " count livebait camper persons child\n", - "0 0.0 0.0 0.0 1.0 0.0\n", - "1 0.0 1.0 1.0 1.0 0.0\n", - "2 0.0 1.0 0.0 1.0 0.0\n", - "3 0.0 1.0 1.0 2.0 1.0\n", - "4 1.0 1.0 0.0 1.0 0.0\n", - "5 0.0 1.0 1.0 4.0 2.0\n", - "6 0.0 1.0 0.0 3.0 1.0\n", - "7 0.0 1.0 0.0 4.0 3.0\n", - "8 0.0 0.0 1.0 3.0 2.0\n", - "9 1.0 1.0 1.0 1.0 0.0" + "0 0.0 0.0 0.0 1.0 0\n", + "1 0.0 1.0 1.0 1.0 0\n", + "2 0.0 1.0 0.0 1.0 0\n", + "3 0.0 1.0 1.0 2.0 1\n", + "4 1.0 1.0 0.0 1.0 0\n", + "5 0.0 1.0 1.0 4.0 2\n", + "6 0.0 1.0 0.0 3.0 1\n", + "7 0.0 1.0 0.0 4.0 3\n", + "8 0.0 0.0 1.0 3.0 2\n", + "9 1.0 1.0 1.0 1.0 0" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1438,7 +1473,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1476,9 +1511,9 @@ " livebait\n", " diff\n", " (0.0, 1.0)\n", - " 3.649691\n", - " 2.956185\n", - " 4.333621\n", + " 3.804287\n", + " 3.067686\n", + " 4.535191\n", " \n", " \n", "\n", @@ -1486,10 +1521,10 @@ ], "text/plain": [ " term estimate_type value estimate lower_3.0% upper_97.0%\n", - "0 livebait diff (0.0, 1.0) 3.649691 2.956185 4.333621" + "0 livebait diff (0.0, 1.0) 3.804287 3.067686 4.535191" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1515,19 +1550,19 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "estimate 3.649691\n", - "lower_3.0% 2.956185\n", - "upper_97.0% 4.333621\n", + "estimate 3.804287\n", + "lower_3.0% 3.067686\n", + "upper_97.0% 4.535191\n", "dtype: float64" ] }, - "execution_count": 13, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1555,7 +1590,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1595,9 +1630,9 @@ " diff\n", " (0.0, 1.0)\n", " 1.0\n", - " 1.374203\n", - " 1.011290\n", - " 1.708711\n", + " 1.279527\n", + " 0.939933\n", + " 1.598656\n", " \n", " \n", " 1\n", @@ -1605,9 +1640,9 @@ " diff\n", " (0.0, 1.0)\n", " 2.0\n", - " 1.963362\n", - " 1.543330\n", - " 2.376636\n", + " 1.910381\n", + " 1.499571\n", + " 2.314881\n", " \n", " \n", " 2\n", @@ -1615,9 +1650,9 @@ " diff\n", " (0.0, 1.0)\n", " 3.0\n", - " 3.701510\n", - " 3.056586\n", - " 4.357385\n", + " 3.202820\n", + " 2.614381\n", + " 3.795517\n", " \n", " \n", " 3\n", @@ -1625,25 +1660,29 @@ " diff\n", " (0.0, 1.0)\n", " 4.0\n", - " 7.358662\n", - " 6.047642\n", - " 8.655654\n", + " 5.833529\n", + " 4.723662\n", + " 6.934235\n", " \n", " \n", "\n", "" ], "text/plain": [ - " term estimate_type value ... estimate lower_3.0% upper_97.0%\n", - "0 livebait diff (0.0, 1.0) ... 1.374203 1.011290 1.708711\n", - "1 livebait diff (0.0, 1.0) ... 1.963362 1.543330 2.376636\n", - "2 livebait diff (0.0, 1.0) ... 3.701510 3.056586 4.357385\n", - "3 livebait diff (0.0, 1.0) ... 7.358662 6.047642 8.655654\n", + " term estimate_type value persons estimate lower_3.0% \\\n", + "0 livebait diff (0.0, 1.0) 1.0 1.279527 0.939933 \n", + "1 livebait diff (0.0, 1.0) 2.0 1.910381 1.499571 \n", + "2 livebait diff (0.0, 1.0) 3.0 3.202820 2.614381 \n", + "3 livebait diff (0.0, 1.0) 4.0 5.833529 4.723662 \n", "\n", - "[4 rows x 7 columns]" + " upper_97.0% \n", + "0 1.598656 \n", + "1 2.314881 \n", + "2 3.795517 \n", + "3 6.934235 " ] }, - "execution_count": 14, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1661,7 +1700,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -1789,20 +1828,28 @@ "" ], "text/plain": [ - " term estimate_type value ... estimate lower_3.0% upper_97.0%\n", - "0 livebait diff (0.0, 1.0) ... 0.864408 0.627063 1.116105\n", - "1 livebait diff (0.0, 1.0) ... 1.694646 1.252803 2.081207\n", - "2 livebait diff (0.0, 1.0) ... 1.424598 1.078389 1.777154\n", - "3 livebait diff (0.0, 1.0) ... 2.344439 1.872191 2.800661\n", - "4 livebait diff (0.0, 1.0) ... 2.429459 1.871578 2.964242\n", - "5 livebait diff (0.0, 1.0) ... 4.443540 3.747840 5.170052\n", - "6 livebait diff (0.0, 1.0) ... 3.541921 2.686445 4.391176\n", - "7 livebait diff (0.0, 1.0) ... 10.739204 9.024702 12.432764\n", + " term estimate_type value persons camper estimate lower_3.0% \\\n", + "0 livebait diff (0.0, 1.0) 1.0 0.0 0.864408 0.627063 \n", + "1 livebait diff (0.0, 1.0) 1.0 1.0 1.694646 1.252803 \n", + "2 livebait diff (0.0, 1.0) 2.0 0.0 1.424598 1.078389 \n", + "3 livebait diff (0.0, 1.0) 2.0 1.0 2.344439 1.872191 \n", + "4 livebait diff (0.0, 1.0) 3.0 0.0 2.429459 1.871578 \n", + "5 livebait diff (0.0, 1.0) 3.0 1.0 4.443540 3.747840 \n", + "6 livebait diff (0.0, 1.0) 4.0 0.0 3.541921 2.686445 \n", + "7 livebait diff (0.0, 1.0) 4.0 1.0 10.739204 9.024702 \n", "\n", - "[8 rows x 8 columns]" + " upper_97.0% \n", + "0 1.116105 \n", + "1 2.081207 \n", + "2 1.777154 \n", + "3 2.800661 \n", + "4 2.964242 \n", + "5 5.170052 \n", + "6 4.391176 \n", + "7 12.432764 " ] }, - "execution_count": 15, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1828,12 +1875,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1865,7 +1912,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1882,7 +1929,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1929,7 +1976,7 @@ "\n", "
\n", " \n", - " 100.00% [4000/4000 00:08<00:00 Sampling 4 chains, 0 divergences]\n", + " 100.00% [4000/4000 00:09<00:00 Sampling 4 chains, 0 divergences]\n", "
\n", " " ], @@ -1974,7 +2021,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -2012,22 +2059,22 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Mon Nov 06 2023\n", + "Last updated: Fri Dec 01 2023\n", "\n", "Python implementation: CPython\n", "Python version : 3.11.0\n", "IPython version : 8.13.2\n", "\n", - "numpy : 1.24.2\n", "pandas: 2.1.0\n", "bambi : 0.13.0.dev0\n", + "numpy : 1.24.2\n", "arviz : 0.16.1\n", "\n", "Watermark: 2.3.1\n", diff --git a/docs/notebooks/plot_predictions.ipynb b/docs/notebooks/plot_predictions.ipynb index 6a1db66ae..32bf9e9a7 100644 --- a/docs/notebooks/plot_predictions.ipynb +++ b/docs/notebooks/plot_predictions.ipynb @@ -70,7 +70,15 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -105,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -205,14 +213,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Default computed for main variable: hp\n", + "Default computed for conditional variable: hp\n", "Default computed for unspecified variable: cyl, gear, wt\n" ] }, @@ -248,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -257,12 +265,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -288,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -328,9 +336,9 @@ " high\n", " A\n", " 3.21725\n", - " 21.876109\n", - " 15.133244\n", - " 27.922724\n", + " 21.772689\n", + " 15.295918\n", + " 28.579925\n", " \n", " \n", " 1\n", @@ -338,9 +346,9 @@ " high\n", " A\n", " 3.21725\n", - " 21.630976\n", - " 15.038827\n", - " 28.092181\n", + " 21.654470\n", + " 15.208969\n", + " 27.820442\n", " \n", " \n", " 2\n", @@ -348,9 +356,9 @@ " high\n", " A\n", " 3.21725\n", - " 21.505488\n", - " 15.294205\n", - " 27.963085\n", + " 21.466219\n", + " 15.319086\n", + " 28.015167\n", " \n", " \n", " 3\n", @@ -358,9 +366,9 @@ " high\n", " A\n", " 3.21725\n", - " 21.164276\n", - " 15.054589\n", - " 27.239663\n", + " 21.209564\n", + " 14.923899\n", + " 27.175027\n", " \n", " \n", " 4\n", @@ -368,9 +376,9 @@ " high\n", " A\n", " 3.21725\n", - " 21.060886\n", - " 14.989347\n", - " 27.026034\n", + " 21.017896\n", + " 15.155415\n", + " 27.031430\n", " \n", " \n", " 5\n", @@ -378,9 +386,9 @@ " high\n", " A\n", " 3.21725\n", - " 20.878214\n", - " 15.214564\n", - " 27.106655\n", + " 20.880792\n", + " 14.942705\n", + " 26.726247\n", " \n", " \n", " 6\n", @@ -388,9 +396,9 @@ " high\n", " A\n", " 3.21725\n", - " 20.646827\n", - " 14.528253\n", - " 26.313486\n", + " 20.594125\n", + " 15.106361\n", + " 26.719041\n", " \n", " \n", " 7\n", @@ -398,9 +406,9 @@ " high\n", " A\n", " 3.21725\n", - " 20.339827\n", - " 14.846441\n", - " 26.226122\n", + " 20.395551\n", + " 14.541060\n", + " 26.177808\n", " \n", " \n", " 8\n", @@ -408,9 +416,9 @@ " high\n", " A\n", " 3.21725\n", - " 20.198725\n", - " 14.666220\n", - " 25.962922\n", + " 20.191636\n", + " 14.669791\n", + " 25.720162\n", " \n", " \n", " 9\n", @@ -418,9 +426,9 @@ " high\n", " A\n", " 3.21725\n", - " 20.057896\n", - " 14.665662\n", - " 25.687646\n", + " 20.020732\n", + " 14.498038\n", + " 25.572835\n", " \n", " \n", "\n", @@ -428,19 +436,19 @@ ], "text/plain": [ " hp cyl gear wt estimate lower_3.0% upper_97.0%\n", - "0 52 high A 3.21725 21.876109 15.133244 27.922724\n", - "1 57 high A 3.21725 21.630976 15.038827 28.092181\n", - "2 63 high A 3.21725 21.505488 15.294205 27.963085\n", - "3 69 high A 3.21725 21.164276 15.054589 27.239663\n", - "4 75 high A 3.21725 21.060886 14.989347 27.026034\n", - "5 80 high A 3.21725 20.878214 15.214564 27.106655\n", - "6 86 high A 3.21725 20.646827 14.528253 26.313486\n", - "7 92 high A 3.21725 20.339827 14.846441 26.226122\n", - "8 98 high A 3.21725 20.198725 14.666220 25.962922\n", - "9 103 high A 3.21725 20.057896 14.665662 25.687646" + "0 52 high A 3.21725 21.772689 15.295918 28.579925\n", + "1 57 high A 3.21725 21.654470 15.208969 27.820442\n", + "2 63 high A 3.21725 21.466219 15.319086 28.015167\n", + "3 69 high A 3.21725 21.209564 14.923899 27.175027\n", + "4 75 high A 3.21725 21.017896 15.155415 27.031430\n", + "5 80 high A 3.21725 20.880792 14.942705 26.726247\n", + "6 86 high A 3.21725 20.594125 15.106361 26.719041\n", + "7 92 high A 3.21725 20.395551 14.541060 26.177808\n", + "8 98 high A 3.21725 20.191636 14.669791 25.720162\n", + "9 103 high A 3.21725 20.020732 14.498038 25.572835" ] }, - "execution_count": 20, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -459,12 +467,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -477,8 +485,7 @@ "bmb.interpret.plot_predictions(\n", " model=model, \n", " idata=idata, \n", - " conditional=[\"hp\", \"wt\"],\n", - " pps=False,\n", + " conditional={\"hp\": np.linspace(50, 350, 50), \"wt\": np.linspace(1, 6, 5)},\n", " legend=False,\n", " subplot_kwargs={\"main\": \"hp\", \"group\": \"wt\", \"panel\": \"wt\"},\n", " fig_kwargs={\"figsize\": (20, 8), \"sharey\": True}\n", @@ -496,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -528,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -564,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -602,6 +609,16 @@ " 0\n", " 100\n", " 1.5\n", + " high\n", + " A\n", + " 26.555116\n", + " 21.760939\n", + " 31.063823\n", + " \n", + " \n", + " 1\n", + " 100\n", + " 1.5\n", " low\n", " A\n", " 27.270087\n", @@ -609,7 +626,7 @@ " 30.732663\n", " \n", " \n", - " 1\n", + " 2\n", " 100\n", " 1.5\n", " medium\n", @@ -619,17 +636,17 @@ " 29.935737\n", " \n", " \n", - " 2\n", + " 3\n", " 100\n", - " 1.5\n", + " 3.5\n", " high\n", " A\n", - " 26.555116\n", - " 21.760939\n", - " 31.063823\n", + " 19.062014\n", + " 15.640371\n", + " 22.270361\n", " \n", " \n", - " 3\n", + " 4\n", " 100\n", " 3.5\n", " low\n", @@ -639,7 +656,7 @@ " 23.345664\n", " \n", " \n", - " 4\n", + " 5\n", " 100\n", " 3.5\n", " medium\n", @@ -649,30 +666,86 @@ " 20.512842\n", " \n", " \n", - " 5\n", - " 100\n", + " 6\n", + " 120\n", + " 1.5\n", + " high\n", + " A\n", + " 25.276504\n", + " 20.725179\n", + " 29.302010\n", + " \n", + " \n", + " 7\n", + " 120\n", + " 1.5\n", + " low\n", + " A\n", + " 25.991476\n", + " 21.783040\n", + " 29.916805\n", + " \n", + " \n", + " 8\n", + " 120\n", + " 1.5\n", + " medium\n", + " A\n", + " 24.252924\n", + " 19.938226\n", + " 28.508014\n", + " \n", + " \n", + " 9\n", + " 120\n", " 3.5\n", " high\n", " A\n", - " 19.062014\n", - " 15.640371\n", - " 22.270361\n", + " 18.447689\n", + " 15.571493\n", + " 21.136173\n", + " \n", + " \n", + " 10\n", + " 120\n", + " 3.5\n", + " low\n", + " A\n", + " 19.162660\n", + " 15.550823\n", + " 22.847000\n", + " \n", + " \n", + " 11\n", + " 120\n", + " 3.5\n", + " medium\n", + " A\n", + " 17.424109\n", + " 14.884559\n", + " 20.048618\n", " \n", " \n", "\n", "" ], "text/plain": [ - " hp wt cyl gear estimate lower_3.0% upper_97.0%\n", - "0 100 1.5 low A 27.270087 23.200397 30.732663\n", - "1 100 1.5 medium A 25.531536 21.180258 29.935737\n", - "2 100 1.5 high A 26.555116 21.760939 31.063823\n", - "3 100 3.5 low A 19.776986 16.419739 23.345664\n", - "4 100 3.5 medium A 18.038434 15.527548 20.512842\n", - "5 100 3.5 high A 19.062014 15.640371 22.270361" + " hp wt cyl gear estimate lower_3.0% upper_97.0%\n", + "0 100 1.5 high A 26.555116 21.760939 31.063823\n", + "1 100 1.5 low A 27.270087 23.200397 30.732663\n", + "2 100 1.5 medium A 25.531536 21.180258 29.935737\n", + "3 100 3.5 high A 19.062014 15.640371 22.270361\n", + "4 100 3.5 low A 19.776986 16.419739 23.345664\n", + "5 100 3.5 medium A 18.038434 15.527548 20.512842\n", + "6 120 1.5 high A 25.276504 20.725179 29.302010\n", + "7 120 1.5 low A 25.991476 21.783040 29.916805\n", + "8 120 1.5 medium A 24.252924 19.938226 28.508014\n", + "9 120 3.5 high A 18.447689 15.571493 21.136173\n", + "10 120 3.5 low A 19.162660 15.550823 22.847000\n", + "11 120 3.5 medium A 17.424109 14.884559 20.048618" ] }, - "execution_count": 23, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -687,7 +760,7 @@ " \"cyl\": [\"low\", \"medium\", \"high\"]\n", " },\n", ")\n", - "summary_df.head(6)" + "summary_df" ] }, { @@ -710,7 +783,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -807,7 +880,7 @@ "4 high A 175 3.440 16.908354 15.261489 18.666662" ] }, - "execution_count": 7, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -823,7 +896,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -902,7 +975,7 @@ "4 high A 175 3.440" ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -930,7 +1003,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -975,7 +1048,7 @@ "0 20.060735 17.838142 22.28725" ] }, - "execution_count": 26, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1001,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1041,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1131,7 +1204,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1155,7 +1228,7 @@ "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()" ] }, - "execution_count": 12, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1166,7 +1239,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1201,7 +1274,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1236,12 +1309,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -1311,7 +1384,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1411,7 +1484,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -1457,7 +1530,7 @@ "\n", "
\n", " \n", - " 100.00% [8000/8000 00:02<00:00 Sampling 4 chains, 25 divergences]\n", + " 100.00% [8000/8000 00:02<00:00 Sampling 4 chains, 10 divergences]\n", "
\n", " " ], @@ -1472,8 +1545,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n", - "There were 25 divergences after tuning. Increase `target_accept` or reparameterize.\n" + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n", + "There were 10 divergences after tuning. Increase `target_accept` or reparameterize.\n" ] } ], @@ -1493,7 +1566,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -1519,7 +1592,7 @@ "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()" ] }, - "execution_count": 17, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -1538,12 +1611,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -1568,12 +1641,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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bAschL4P3o48+ir/4i7+A1WrFa6+9hu9+97sIBAJ45JFHRr2eRCKZ1OeAZNjfuHHjoGMtLS3YsGHDuOsmIiKi0hMXEjjb7cX5Hi83w5kGkZiA91rteO+SfdAJqotnluMPFs+ApVydw+omJi+D95w5czBnzhwAwFe/+lUAwA9+8AN84xvfQFVV1bDXMZvNw65qO51OABh2NXwgi8UCi8WSSdlERERUYjqdQZy+4uKW79MgLiTwQbsTb1+wIhC9dv/eUKlD05JqzDZpc1jd5ORl8L7e7bffjv/9v/83Ll++PGLwXrp0Kc6cOTPkeP+x+vr6aa2RiIiISocvHMPHHS50u8O5LqXoJEQRn3a6cfR8H1zBWPp4TYUaf7CkGjdZ9GN2MuSrggjeb7/9NqRSKebNmzfiZe69915s2bIFp06dwooVKwAkxwkePHgQK1asQE1NTbbKJSIioiIlJESc7fbgfI8XAsdyT6mEKOJstxdHzvfB5oukj5t0Snxl8Qwsra2AtEADd7+8Ct5/9Vd/hfLyctx+++2YMWMG7HY7Dh8+jJdffhnf//7306vdmzdvxoEDB9Da2oq6ujoAwEMPPYQ9e/Zg48aN2L17NywWC/bu3YuLFy/iyJEjubxZREREVAS63CF81O5kW8kUE0URF3t9eOt8H3o81/6CUKaS465FFnxxrgkyaWEH7n55FbxXrlyJffv24cCBA3C73dDr9WhoaMDPf/5zbNq0KX05QRAgCAIG7v2jUqlw9OhRbN26FQ8//DCCwSBuueUWvP7669y1koiIiCbNH4njo3Yn20qmmCiKaLUF8Na5XnS6QunjWqUMqxdUYcUNZijlBbHlzLgVxM6VucKdK4mIiEqXkBBxrtuLcz0etpVMsQ5HAP9+rm/QBkNqhRR3zK/Cl280Q6WQTfhrrrvZkvUJJxPNinm14k1ERESUDzqdQXzS6YY/zJncU+mqK4gj5/vwed+1DYaUMim+dKMZd9xUCa2yuKNpcd86IiIionESEiLa7AFc6PVyE5wp1usJ48j5Ppzr8aaPyaUSrLjBhNULLdCrSiOSlsatJCIiIhpBOCagxerH530+hGPsKZlKvd4wfnfBiuYuT/qYTCLB8rlGrFloQYVGkcPqso/Bm4iIiEqSLxzDhV4f2mwBxLnl5JQaLnBLANw6x4i7Fllg0ilzV1wOMXgTERFRSbH6wrjQ40OXOwSOmJhafQMCd/9dKwFwy2wD7lpoQWWZKpfl5RyDNxERERU9URRx1RXC+R4v7P5orsspOgzc48PgTUREREXNFYjiRKsDnlBs7AvThDBwTwyDNxERERWt8z1e/L7TDbZwT62RAndDKnBXMXAPi8GbiIiIik4oKuDkZcegLcgpc93uEN6+aMXZ7mtjARm4x4/Bm4iIiIpKlzuEk60OROIcDThVrjiDeOeiFRd6feljDNwTx+BNRERERUFIiDh9xYVLA3ZFpMxctvvxzgUbWmzX7lOpJNnDvXoBA/dEMXgTERFRwXMHozjewhMop4Ioimix+vH2RSvaHcH0cZlUgtvmGLFqQVXJzuHOFIM3ERERFbSLvT582umCwM6SjIiiiAu9Prx90YqrrlD6uFwqwe03mHDnTVUlt9PkVGPwJiIiooIUjiVPoOx28wTKTCREEWe7vXj7ghW93mv3pVIuReMNZnx5vhllagbuqcDgTURERAWnyx3CqcsOhGNc5p6suJDAJ51uvPu5DY7AtU2F1AopvnRjJb40zwytilFxKvHeJCIiooLhCcXwyRUXV7kzEIkJ+KDdifda7PCF4+njWqUMd8yvROM8M9QKWQ4rLF4M3kRERJT3wjEBzV0etFj93AxnkvyRON5vteP96/5SUKFR4M6bKrG8zgSlXJrDCosfgzcRERHlrURCxMU+H5q7PIgJTNyT4QpG8d4lOz7qcA66D6vKVFh9UxWWza6AXMrAnQ0M3kRERJSXrjiC+PSqG/4B7RA0fn3eMN793IbfX3UP+ivBLKMGaxZUYdHMckglktwVWIIYvImIiCivOPwRnL7ihs0XyXUpBUcURVxxBvHu5zacH7DLJADcZNFj1YIqzKvUQcLAnRMM3kRERJQXApE4ft/pHrRpC41PQhRxrtuL/7hkQ+eAGdwSAEtqK7D6pirUGjW5K5AAMHgTERFRjoVjAi72+nCx14c4z5yckJiQwMcdLhxvsQ8aCSiTSvCF2QasuqkKldzWPW/kVfD+3e9+h4MHD+LEiRPo7OyEwWDA8uXLsX37dtx2222jXnf//v148MEHh/1cT08Pqqurp6NkIiIimiRvOIaLvT602QIM3BMUiMRx8rID7192IBgV0sc1ChlW3GDCyhu56U0+yqvg/dOf/hQOhwN/8zd/g8WLF8Nms+HZZ59FY2Mj3nzzTdx9991jfo19+/Zh0aJFg46ZzebpKpmIiIgmyOoL40KPD13uEETm7Qlx+CN4r8WO01dcgyaUGLUKfHl+JW6rM0Il5wzufJVXwXvPnj2wWCyDjjU1NWH+/Pn44Q9/OK7gXV9fj+XLl09XiURERDQJoiii0xnC+V4vHP7o2FegQa44g/iPSzac6/Zi4O8qNQY17rypCvU1FZBJecJkvsur4H196AYAvV6PxYsXo7OzMwcVERERUSbiQgKttgAu9HoRiAhjX4HShISIcz1eHG+x44pz8AmnC2bocedNnFBSaDIO3u+++y5+9KMf4fz58wiFQoM+J5FI0NramtHX93g8OH369LhWuwHgnnvugc1mQ0VFBdasWYMnn3wS9fX1GdVAREREExOKCrjY50OL1Y9oPDH2FSgtHBPwYbsT7192wB2MpY/LJBI0zDbgjpsqUV2uzmGFNFkZBe/33nsPa9euxZo1a3D+/Hk0NTXB5/Ph/fffx7x58/DlL3854wK/853vIBAIYNu2baNerrq6Gtu2bUNjYyPKy8tx5swZ7N69G42NjTh+/DgaGhpGvb7VaoXNZht0rKWlJeP6iYiISoknGMP5Xi/a7QFu7T5BzkAUJ1rt+KjDNeiXFY1ChttvMKFxnhkVGp4wWcgyCt5PPPEEHnzwQfz0pz+FQqHAU089hVtvvRWfffYZmpqacN9992VU3OOPP45Dhw7hxz/+8ZhTTZqamtDU1JT+96pVq7B+/XosXboU27dvx6uvvjrq9ffu3YudO3dmVC8REVGpsnrDONfjRbc7nOtSCoooimh3BHG8xY7zPYP7tyv1Knx5vhlfmG2EUs4t3YtBRsG7ubkZjzzySLq3SBCSvVvLli3D448/jieffBJ/9Ed/NKmvvXPnTjz11FP4+7//e3z3u9+d1NeYO3cu7rjjDpw8eXLMy27ZsgUbN24cdKylpQUbNmyY1PcmIiIqdjxhcvKEhIgzXR4cb7Gjyz24VXd+lR5fnm/GTTPKuKV7kckoeAeDQej1ekilUqhUKtjt9vTnFi1ahHPnzk3q6+7cuRM7duzAjh078Oijj2ZSIkRRhFQ69m+JFotl2JM7iYiIaLC4kECbPYDzvT74w/Fcl1NQ/JE4Pmp34uRlB7wD7juZVIJbZhnwpflmzKzgDpPFKqPgPWfOHPT19QEAFi9ejN/+9rf4wz/8QwDAsWPHJjU/e9euXdixYwcee+wxPPHEE5mUh7a2Nhw/fhzr1q3L6OsQERFR8qS/S31+fN7nQ4QnTE5IlzuE91sd+Oyqe9BmQTqlDCvmmbHiBhM3vCkBGQXvNWvW4J133sGf/Mmf4C//8i+xZcsWnD9/HiqVCv/+7/+O//7f//uEvt6zzz6L7du3o6mpCevXrx/SItLY2AgA2Lx5Mw4cOIDW1lbU1dUBANatW4dVq1Zh2bJl6ZMrn376aUgkEuzatSuTm0lERFTSQlEBzd0e7jA5QUJCxNluD95vdaDjunGA1eVqfOlGMxpmG6CQsX+7VGQUvHfu3Amn0wkA+Na3voVgMIhDhw5BIpHgscceG3MSyfVee+01AMAbb7yBN954Y8jnxdT2VoIgQBCE9L8BYOnSpXj55ZfxzDPPIBQKwWKx4O6778bjjz+OBQsWTPYmEhERlaxEQsTFPh/OdHkQFxi4x8sfiePDdidOXddOIgGwuKYcX7qxEnPNWs7fLkESUeRmrSM5e/Ys6uvr0dzcjCVLluS6HCIioqzp8YTwcYcL3hB7uMcr2U5ix2dXPYP+MtA/DnDFDSYYtMocVljc1t1sgSXL880nmhXzaudKIiIiyi1/JI7THS5cdYXGvjAhnkjgbLcXJ9lOQuOQcfB+77338Itf/AIdHR3D7lx59OjRTL8FERERTbO4kMD5Hh/O93jZxz0O7mAUH7Q78WG7C4EI20lofDIK3vv27cPmzZthMpmwYMECqFSqQZ9nFwsREVH+63QGcfqKC4GIkOtS8lpCFNFq8+PkZScuXLfZjVYpw/I6ExrnsZ2ERpZR8H766afxta99DQcOHBgSuomIiCi/eUIxnO5wocfD3SZHE4oK+PiKC6cuO+AIDN4oaLZRg8Z5ZtTXVrCdhMaUUfDu6OjAj3/8Y4ZuIiKiAhKKCjjf68XnvT6wq2RkXe4QTl124PdX3YgNmOqikEnQMMuAFfPMqDVwsxsav4yC980335zeQIeIiIjyl5AQ0eUK4bLdjx5PGOwGHV40nsCZLg8+aHOg87oTTM06JRrnmXHrHCM0SlmOKqRCllHw/uEPf4hHHnkEa9asQW1t7VTVRERERFPE7o+gzR5AhyOIKHebHFGfN4wP2p345IoL4di1+0kC4OaZ5Vgxz4Qbq/SQ8mRJysCEg/d//s//edC/PR4PFixYgFtuuWXIFvESiQSvvvpqZhUSERHRhISiAtrsAbTZA/CEYrkuJ2/FhASauzz4oN2JDsfgUYB6lRzL64y4nbO3aQpNOHh/9tlng0bjyGQyWCwWdHd3o7u7e9BlOUKHiIgoO/pbSVrtfvSylWRUVl8YH7Y5cfqKG6HY4Eku86v0+OINJtw8swxyKU+WpKk14eDd3t4+DWUQERHRZHhCMbRYfWizs5VkNHEhgbM9XnzQ5kSbPTDoczqlDLfVGfHFuSaY9RwYQdOHO1cSEREVGFEUcdUVwiWrD72eSK7LyWtWXxgft7vw8RUXgtHBq9vzKnW4/QYTFs8sh5yjACkLMg7egiDgl7/8Jd5++204HA6YzWbcdddd2LhxI+Ry5noiIqKpEo4JaLH60Wrzc7ObUfRPJvmo3TlkG3etUoZb5xhx+1wTKsu4uk3ZlVEyttvtaGpqwunTpyGXy2E2m+FwOPDCCy/gmWeewZtvvonKysqpqpWIiKgk2XwRXOrz4YozyLnbIxBFEV3uED5qd+H3V92IXNd2c0OlDl+ca8KSmnJudEM5k1Hw/tu//VtcvHgRhw4dwte+9jXIZDIIgoCXX34Z3/rWt/C3f/u3+PnPfz5VtRIREZWMuJBAuyOIFqsPzgAnk4wkGI3j0043Pmp3odc7eAfOMpUct9YZcVudEZXs3aY8kFHwfu211/DUU0/h/vvvTx+TyWR44IEHYLVasWPHjkzrIyIiKimRuIALPT5csvp5suQIEqKINnsAH7U7cbbbi/iAPwNIACysLsPyOhMWVpdBJuWENcofGQVvURSxZMmSYT9XX18PkbOMiIiIxiUcE3Ch14fP+3yIC/z5ORxXMIrTV1w43eGCKzj4rwAmnRLL64z4whwjKjSKHFVINLqMgve6detw5MgRrFu3bsjn3nrrLaxZsyaTL09ERFT0GLhHF40ncK7Hg487XLhsC2DgPSSTSrCkphxfnGvCDZU67ipJeS+j4P3444/jvvvugyAIeOCBB1BdXY3e3l4cOnQIv/nNb/Cb3/wGTqczfXmTyZRxwURERMUgHBNwvseLS33+Qa0SlPyLeqcziI+vuPDZVc+QEyVrKtS4tc6IW2YZoFVxghoVjoyerbfeeisA4Nlnn8X//J//M328v8XktttuG3R5QeDoIyIiKm3hmIBzPV60MHAP4Q3F8EmnG6c7XLD5B88n1ypluGW2AbfVGTGzQpOjCokyk1Hw3r59O7eFJyIiGgcG7uHFhAQu9PpwusOFz/t8g1pJpBJgwYwy3FZnxMJqbuFOhS+j4M2pJURERKNzB6O4ZPWjzRZg4E4RRRFXnEGcvuLGmS43wrHBrSSWMhVuqzPiltkGlKl5oiQVDzZGERERTbG4kMAVZxCXrH44/NFcl5M3HP4IPu1045NON5yBwfeLWiHFslkG3DbHiFlGDf+iTkVpwsH7//yf/zOhy//5n//5RL8FERFRQfKEYmix+tBmD3IGd0ooKuBMlwefXHEN2b69v5XkC3OMWFRdxh0lqehNOHh/85vfHPdlJRLJhIL37373Oxw8eBAnTpxAZ2cnDAYDli9fju3btw85UXM4VqsVW7duxb/+678iGAyioaEBTz31FNauXTvuGoiIiCZCSCQncFyy+mHzRca+QgkQEiIu9flwutONCz3eIS02tQYNvjDHgGWzDNBzKgmVkAk/29va2qajDgDAT3/6UzgcDvzN3/wNFi9eDJvNhmeffRaNjY148803cffdd4943UgkgrVr18LtduO5556DxWLBnj170NTUhCNHjmD16tXTVjcREZUebziGllTv9vXj7kpRf9/2p51unOnyIBgdPMmsXC3HLbON+MIcA2aUq3NUJVFuTTh419XVTUcdAIA9e/bAYrEMOtbU1IT58+fjhz/84ajB+8UXX0RzczNOnDiBlStXAgDuuusuNDQ0YOvWrTh16tS01U1ERKUhkRBx1RVCi82HXg9XtwHA6gvj951u/P6qZ0jftkImQX1NBb4wx4h5Vdzghiiv/r5zfegGAL1ej8WLF6Ozs3PU677yyitYuHBhOnQDgFwux6ZNm/Doo4+iq6sLtbW1U14zEREVP38kjharH5dt/iETOEqRNxTDZ1fd+PSqG93u8KDPSQDMt+jRMNuAJTXlUMlluSmSSkK5Ro6ZFRrUGNSo1KtyXc6YMg7ely5dws9+9jOcP38eoVBo0OckEgmOHj2a0df3eDw4ffr0qKvdANDc3Iw777xzyPFly5YBAM6ePcvgTURE4yaK/avbfvR6whBLfBJgOCbgbLcHn3a6h2zdDgCzjBo0zDJg2awKjgCkaaOQSTCjXI0agxozKzTQFdg5AhlV29zcjMbGRtTW1qKlpQXLli2D3W5HV1cXZs+ejRtvvDHjAr/zne8gEAhg27Zto17O4XAMuyV9/zGHwzHq9a1WK2w226BjLS0tE6yWiIgKXTAaR6s1gFabf0ifcqmJCQlc7PXh91fduNjrG3KSpFmnRMNsA26ZbSiI1UbKLpkUMGiVMOuUMOmUUClkCEUFhGMCQjEBwaiQ/nc4JmCkMfcmnQIzKzSYaVCjUqeCVFq4LUsZBe9HH30U/+k//Se8/PLLUCqVePHFF3Hrrbfit7/9LR566CE89dRTGRX3+OOP49ChQ/jxj388rqkmo838HGse6N69e7Fz584J10hERIVPFEV0e8JosfrR7Q6V9Op2PJFAq9WP31/14FyPd8hYRJ1KjmWzKnDLLAPnbVOaVAJUaBQw6ZQw65Uw6VQwaBTjDsmiKCISTyAUFRCMJQO5XCpBdYUaakXxtCtlFLxPnz6NvXv3QprawjWRSL44169fj0ceeQQ/+MEPcOzYsUl97Z07d+Kpp57C3//93+O73/3umJc3m83Drmo7nU4AGHY1fKAtW7Zg48aNg461tLRgw4YN4y+aiIgKijsYRZs9gCvOIAKR0l3dTogi2uwBfHbVjeYuL0KxwfeFSi7F4pnlaJhtwI1VesgKeMWRpkaZWo5KvSodtI1aZUbPC4lEArVCBrVCBuMU1plvMgreLpcLJpMJUqkUCoUCLpcr/bnly5fjySefnNTX3blzJ3bs2IEdO3bg0UcfHdd1li5dijNnzgw53n+svr5+1OtbLJZhT+4kIqLiEozG0W4Pot0RgDsYy3U5OSOKIjpdIfz+qhvNVz3wReKDPi+XSrBoZjmW1VZgITe3KXkGrQKWMhUsZWpUlamgURbPKnQ2ZRS8a2trYbfbAQDz58/Hu+++i6985SsAgM8++wx6vX7CX3PXrl3YsWMHHnvsMTzxxBPjvt69996LLVu24NSpU1ixYgUAIB6P4+DBg1ixYgVqamomXAsRERWHmJBApzMZtvu8kZJtJRFFEd3uMM50eXCmyw3Xdb949O8kuWxWBW6uLoeqiP7ET+MnlQBGnRKWMhWqUm+cTjM1Mgred9xxB06cOIENGzbg61//Op544gn09PRAqVRi//792LRp04S+3rPPPovt27ejqakJ69evx8mTJwd9vrGxEQCwefNmHDhwAK2trem54g899BD27NmDjRs3Yvfu3bBYLNi7dy8uXryII0eOZHIziYioACUSInq8YbTbA+hyhYacGFgq+vvXm7s8ONM1dNa2BMC8Kh2WzUqO/9MqC2tKBGVOrZDCqE22jFjK1KjUKyHnXzimRUavrm3btqG7uxsA8Hd/93fo7e3FoUOHIJFI8LWvfQ3PPPPMhL7ea6+9BgB444038MYbbwz5vJhaohAEAYIgpP8NACqVCkePHsXWrVvx8MMPIxgM4pZbbsHrr7/OXSuJiEpIIBLHhV4f2u2lu6OkKIro8fSvbA8N2wAwx6TFslkVWFrL8X+lQiJJ9mYbtUoYtAoYtcnebLaNZI9EFEv1D25jO3v2LOrr69Hc3IwlS5bkuhwiIhqFJxTD+R4v2u2BEceSFbOBYbu5ywPHCGG7vrYC9TXlMGiVOaiSskUmRXKySCpgG7QKGDQKrmRPsYlmRf49iYiICpozEMXZbg+uukpvDGB/G8nZ1Mr2cGF7tlGDpbUVqK+tYNguAQatAjdW6XFDpQ5KOUN2vmHwJiKigtTnDeNctxc9nvDYFy4iCVFEpzOIs91enO32DDlBEmDYLjVyqQSzTVrMt+hRVcaNjPIZgzcRERWUq65k6HT4h67uFishIaLdEcDZbg/OdXvhDceHXGbWgLBtZNguCUatAjda9Jhr5up2oWDwJiKivJdIiOhwBnGu2wtPqDRmb8cTCVy2BdDcldxB8vrt6yUA6sxaLKmpwBL2bJcMuVSCOrMWN1r0qNRzdbvQMHgTEVHeEUURrmAMNl8k+eYPIxQt/gkl0XgCl6w+nOv24nyvF+HY4NsslQDzKvVYUluOxTPLOY2kRMilEph0StSZtZhbqeNmRgWMwZuIiHIuLiTgCEQHBO0I4kJpnCkZjMRxvteHcz1etFh9iF13u2VSCeZX6VFfW46bq8uhVfFHd7HTKKWo0qtRWaZMbsuuVUKawXbslD/46iUioqwLxwTYfBFYU0HbHYyW1AhAVzCK8z1enO32osMxdPyhQibBghllWFJTgUXVZVBzB8miJZEke7Ur9SpU6pO7ROr4y1XR4iNLRERZ4QvH0OkM4aorCHsJnRgJJFtn+nwRnOtO9mt3u4dOYtEqZVhUXY4lNeW4sUrPk+WKlEImQWWZClWpkG3WcZfIUsLgTURE08YZiOKqK4irrhDcw4y9K2ZCQkSHM4ALPT6c7/EOO2PboFFgcU2yX7vOrIOM7QRFR6+Wp0K2ElV6NSq07MsvZQzeREQ0ZURRhM0XQWcqbAciwthXKiLhmIBLVj/O93hxsdeHUGzo7a8uV+PmmcmV7ZkVakgkDNvFQioBTDrloBVttgnRQAzeRESUkf6tyq84g+hyhRCJF//0kYHcwSjO9/pwoceLy7YAhOu2z+wf+3fzzOTKtpkj4IqCQiaBIbUVu1GrQIVGCZNOyb9a0KgYvImIaFJCUQEtVj9abf4hM6aLmSiK6HaHcb7Xi/M9w++cqZRJcdMMPW6eWY6FM8p4slwBk0qAMrUCBm3/mxIGjYKPKU0KnzVERDQhPZ4QLvX50e0OlcwkkkhcQKs1gAu9Xlzs88E3zM6R5Wo5bp5ZjptnluMGzlrOW3KpBAq5BAqZFEqZFAp56r1MCoUsdTx1rEKjQIVGwVF+NGUYvImIaEzhmIDLtgBabH74hwmdxcgZiOJirxcXen24bA9AGOa3jJkVyX7tm6vLUWNgv3a+kUqAGeVqzDZpUWvQQCWXMkRTTjF4ExHRiKy+MFr6/Oh0BSEUeeu2kBBxxRlMh22rLzLkMnKpBDdW6bGwugyLqsu4TXsekkqA6go15pi0qDVqoJLz5EbKHwzeREQ0iC8cQ7c7jFabv+hHAPojcVzq8+Finw+X+vzDTiEpV8uxqLocC6vLOF87T8mkwMwKTXplm48R5SsGbyKiEheKCuj1htGXeivmEYAJUUSXK4SLfT583udDlyuE6xtIJABmGTVYWF2ORdVlHPmXp+RSCWYakivbNQYNe+qpIDB4ExGVmHBMgNUbQZ8vGbS9oeLu2Q5G4vjc6sfnqbA93AQWlVyKmyx6LKoux4LqMug5sSKvSCVAhUYBky45ss+oU8Ko5eg+Kjz8n4WIqMhF4gLs/ih6PWFYvWG4irx9JCGK6HaH8HmfDxd7fbg6zKo2kNzIZsGMMiysLsMck5YhLk9IJUjNxlamg7aBIZuKBIM3EVEREUURnlAMdn8Udn8Edn+k6Fe0gWRf+iWrH5f6fLhkHX6uuEouxXyLHgtmlGHBjDJUaLh1d7bJpIBKLoNKLoVKIYVKLoNSLoVKLoVWKYNRy5BNxY3Bm4iogEXjCTgCEdh914J2TCj+4drxRAIdjmA6aA+3iQ0AzChXYWEqaM8xayGXsg94uihkEpRrFChXK1CmlkOvkqfDtSoVruXsw6YSx+BNRFRA+k+EtHrDsPuj8ISKu22knyiKcASi6aB92RZAdJj5hmqFFDdW6bHAUoabZug57m+KSSWATiVHuSYZrsvVCpRrku/VCo7tIxoLgzcRUR6LxJMnQvZPHSmFtpF+oaiAVpsfLVY/Wmx+OAPRIZeRAKg1anCTpQwLZugxy8he7akklQCWchVmG7WwlKtRppJzAxqiDORd8Pb5fNi1axc+/fRTfPLJJ7Db7XjiiSewY8eOMa+7f/9+PPjgg8N+rqenB9XV1VNcLRHR1IoJCVh9keRoP0/xnwg5UDyRwBVnMBm0rf5hR/0BQJlanl7Rnl+lh5YTSKaUTJrc7ZEb0BBNvbz738rhcOD5559HQ0MDNmzYgBdeeGHCX2Pfvn1YtGjRoGNms3mqSiQimjKJhAh7IIJeTxi9njCcgSiG2Zm8KImiCKsvkg7abfbh20fkUgnmmnW4aYYeN1nKMKNcxbnaU0wuk6CmQoPZJg1nYhNNo7wL3nV1dXC5XJBIJLDb7ZMK3vX19Vi+fPk0VEdElDl/JI4edwg9nmT7SCmcDNnPG4ql20dabX54w8O3zsysUGO+RY/5Fj3mmnUMgtNAKZei1pAM2zMrNGzRIcqCvAveXMUgomITFxLo80XSYds3QtgsRqGogMv2ZMhutQZg80eGvVyFRoH5VcmgfaNFzw1sMiSRABqFDBqlDNrUm0YhT75PvemV7Ncmyrai/J/tnnvugc1mQ0VFBdasWYMnn3wS9fX1uS6LiEqIKxBFtyeEXk8YNl+kZNpHYkIC7Y4AWq0BtNr86HYP36etlEsxr1KXXtWu0rN9ZKIUMgkqNApUaBQo1yigV8lTIVsOtULK+5MoDxVV8K6ursa2bdvQ2NiI8vJynDlzBrt370ZjYyOOHz+OhoaGEa9rtVphs9kGHWtpaZnukomowCUSItyhGFzBKFyBKFzB5MfxEmkfERIirrqCaLUlg/YVZxDCML9lyKQSzDFpcWOVDjdWcfrIRKgV0nS4rkjNya7QKKBR8qRHokJTVMG7qakJTU1N6X+vWrUK69evx9KlS7F9+3a8+uqrI15379692LlzZzbKJKICFY0n4A4mw7UzEIU7mJyjXSqr2UAyaHe7Q7hsD+CyzY8OR3DYEyIlAGoMmnTQrjProJSzT3ssaoUUVWUqWMrUMOqSAZtTRYiKR1EF7+HMnTsXd9xxB06ePDnq5bZs2YKNGzcOOtbS0oINGzZMY3VElM984Rj6vJHkZjWBKPwl1JvdLyGK6PGEcdmW3LSm3RFAJD40aANApV6VDtrzqnTQKov+R0zGdCpZKmirUFWm5jb2REWuJP5XFEUR0jG2CbZYLLBYLFmqiIjyUSAST87P9kZg9YURiAi5LinrEqKIPm8Yl20BXLYH0Gb3IxwbPmgbtQrMq9JjXqUO86r0DI3jUKaWw1KmgqVcDUuZCjqeREpUUor+Fd/W1objx49j3bp1uS6FiPJMOCagz5ucn93ni5TuirY7jDZ7co52uyOIUGz4XzgqNIp0yJ5XpYOR27GPSquUwahTwqhVwKhVoqpMxW3ViUpcXgbv119/HYFAAD6fDwBw7tw5/OpXvwIAfPWrX4VWq8XmzZtx4MABtLa2oq6uDgCwbt06rFq1CsuWLUufXPn0009DIpFg165dObs9RJR7QkJM9WdH4QzEYPNF4AmVzq6Q/fp7tNvsgVTQHrl1pFwtH7SibdQqOCljBGVqOUw6JQxaBUw6JYxaJUM2EQ2Rl8H729/+Njo6OtL/Pnz4MA4fPgwguYI9d+5cCIIAQRAgitfOalq6dClefvllPPPMMwiFQrBYLLj77rvx+OOPY8GCBVm/HUSUG3EhkZ4u4gwkp42U2kmQ/eJCAl0DgvZIJ0MCyRXtGyp1uMGsww2VOpj1Sgbt60gkQLk6Ga5NOiWMuuRqNjf4IaLxyMvg3d7ePuZl9u/fj/379w869o//+I/TUxAR5a1IXIAnGIMzHbJj8IZjEEswZANAJCagwxlEuyOAdnsQV11BxEf4jcOoTQXtSh1uqOSK9nB0KhlMOiXMOhXM+mTYZsgmosnKy+BNRHS9REKENxyDOxhLz832BGMIRkvvBMiB/JE42u0BdDiS/dkjbVgDACadckDQZo/29ZRyKcw6Jcx6Jcx6Fcw6tosQ0dRi8CaivBOOCakNaWJwh6JwB2PwlmiryECiKMIZiCZXtFMnQtpH2IJdAmBGuRp1Zi3mVuow16zj1JEB5FIJjKl2kf6wXabm/UNE04vBm4hyKi4k4AxE4QhE4fBH4QhESnKM33D6T4TscAbR4Uj2Z/sjw09ekUqAWoMGN6RCdp1Zx50NU6SSZP+6SXdtJdvAthoiygEGbyLKGlEU4QnFYPdH4fBH4Eid9Fiq/djXC0UFXHEG0eFMhuyrriBiI2w9r5RJMcekRV2lFnPNOsw2arkzZIpcJkFNhQaVZckVbZNWCTn7sokoDzB4E9G0CccE2HzJgG33ReAMRhEfIUiWmoFtI1ccQVxxBtHnDY/Yn12ulmOOWYc6kxZ1Zi1mVmggk3LFtp9SLkWtQYPZJg3vGyLKWwzeRDQlEgkR7lAMdn8Edl8ENj9bRgaKxpNj/a44AqlV7eCIJ4b292fPMWtTQVvHiSPDUCukmGXUYrZJgxllakgZtokozzF4E9GkhGNCMmT7U6vZgeiIY+tKTX9LTYczuZJ9xRFEjyc04smhCpkEs4zJlew6kw5zTFr2Z49Ap5JhllGD2UYtqspU/GWEiAoKgzcRjSkSF+AKxOAIROAKJGdml+L26iPpX83udAbR6Qqi0xmEd5T7x6hVYLYpuZo9x6RDdYWarRHXUcql0Cpl0ChkUCtk0KvkmGlQo1KvynVpRESTxuBNRIP0j/Jz+KPpnR/ZMnKNKIpw+KO4kgrYnc4ger3hEVezZVIJag0azDFpk29mLcpLfGydWiGFQauARiGHRikbFLA1qY/5iwgRFSMGb6ISFY4J8Efi8Ifj8EfiyV0fgwzZ1wtG47jqGriaHUIoNvJ9VKFRYLZRk17RrjFoSnqihlYpS87L1ia3VzfplNAq+aOHiEoT//cjKlKJhIhAND4oXA/8eKQxdaUsJiTQ4w6h0xXCVVcQV10hOALRES+vkElQa0ie3DfbqMVsk7akN6nRq+WDArZRy50fiYgGYvAmKgK+1FbqrmAUrmAMnlAMgUic87FHkRBF2HwRXB0Qskc7ARIAKvUqzDFpMMuYbBuZUV7avdn987JnGTWYaVBDJWfIJiIaDYM3UQGJCQm4gzG4g1G4QzG4Asn3nI09OlEU4Q7GcNWdDNldrhC63CFE4okRr6NTyjDbpMUsYzJozzJq2CKBZH92rUGDWSYtqkv8Fw8iooniTxGiPCSKIrzhODyp1Wt3KLmSzUki4+MNx9CVWsnucodw1RUacWY20N8yci1gzzZquaX4AHq1PPULiAZVeo7wIyKaLAZvohwLROJwh2LwBJMB2xOMwRuOQRh5MZYGCETi6HKH0gG7yzX6KD+pJLk5Ta0hGbBnmTSwlHHldiCJJDnysP8XEYNWmeuSiIiKAoM3UZaEY0Jy9bp/FTsYhScU40mOE+CPxNGdCtldrhC63SG4Q7ERLy9Bsi97llGDWqMGswwaVFdooJSX7pSR62mVMlRoFTBoFDBolTBoFCjXKPiLCBHRNGDwJppi4ZgAb6i/RSSWbhcZrZ+YhvKFY6mQHU6Hbc8oIRu4tkqbbBvRoMag4VSNFIVMgor+cJ0K2hVaBU+IJCLKIgZvokkKRuPwhuLwhZOtIf0r2eEYA/ZE9G+v3u0Oo9uTXMXudodGbRcBkiG7xqBB7YA3raq0/0uTSgCtSo5ytRxlagXK1XKUaxQoU8t5YigRUR7g/8REo4gJCfjCcXhDyXDd/7EvHEd8tLlzNKyEKMLpj6LLE0KPO5QO26Od+AgkQ3Z/uK4xalBbUdohWyGTwKhVomxAsC5TK1CmkkPKFhEiorxVuj+5iFJiQiK9qYwvnFzB9kfi8IZjCEW5ej1ZcSEBqy+CHs+1gN3jCSM6RsuNWafEzAGr2DUGdUmv1sqlEhi0Cpj1Sph1Kpj0ypLfcp6IqFCV7k8zKin926P7wsmdG32RWDpsszUkc8FoHD2eMHrcyXDd4wnD6guPuhmNVAJYytSoMagxsyLZjz2zQl3SPdlSCWDQKmDSqWDSKWHWKVGhUXAVm4ioSDB4U9EIx4RBK9b9H/vC3B59qiREEa5ANB2ue1Kr2GOd9CiXSlBdoUZNKmDXGNSYUa6GQlba00U0Simq9GpUlilRqVfBqFVymggRURFj8KaCwnCdPZGYgF5vMmD3esPoTb0fq1VEq5SlV69nViTfV+pVJR8oJRKgQqNAVZkKVXoVKstU0JdwnzoRUSnKu//1fT4fdu3ahU8//RSffPIJ7HY7nnjiCezYsWNc17dardi6dSv+9V//FcFgEA0NDXjqqaewdu3a6S2cpkw0nkiH6f5g7U29Z7ieegNXsQcGbGcgOuZ1+/uxayrU6aBdppZzZ0MAcpkElfrkSnZVmQpmnYrzw4mISlzeBW+Hw4Hnn38eDQ0N2LBhA1544YVxXzcSiWDt2rVwu9147rnnYLFYsGfPHjQ1NeHIkSNYvXr1NFZO4yWKIoJRAYFoHIGIgMB1K9ecdz19ApE4er1h9KUCdp83jD5vBNExtslUyqWoLlejOhWwq8uTb6oS7sceSCWXwqhLzsg2apUwahWo0HDLeSIiGizvgnddXR1cLhckEgnsdvuEgveLL76I5uZmnDhxAitXrgQA3HXXXWhoaMDWrVtx6tSp6SqbBkgkRARjyUDtj8QRjCRPbAxGk/8ORYVRT7qjzMWEBGy+SHr1us+bfO8bYzY2AJh0SlSXpwJ2ahXboFVAyhAJANCr5TBqFcmArUuG7FKeukJEROOXdz8tMlkheuWVV7Bw4cJ06AYAuVyOTZs24dFHH0VXVxdqa2unosySF4omw3R/uB74cTAqQGSwzgohIcIRiKDPG0mtXodh9UbgCETG/OVGrUiuYs9IrWRzFXsopVyKqjIVLGUqmPXJ1exSPyGUiIgmL++Cdyaam5tx5513Djm+bNkyAMDZs2cZvMcpEhfSbSDJlpBkO0j/MW4ek10JUYQ7GEuH6z5vGFZfBFZfBMIYj4VMIkFVmQrVFamQXa7CjHI1WyGGoUoF7RnlaljKVDBoeR8REdHUKarg7XA4YDKZhhzvP+ZwOEa8rtVqhc1mG3SspaVlagvMI9F4Ir1C3R+s/f1BO8IJIbmSEEV4gjH0+ZIr11ZfKmCPow8bSO7wOKN/FbtcjRkValRxosiIVHJpMmSXq1JBW5nrkoiIqIgVVfAGRm9VGe1ze/fuxc6dO6ejpGkXjgmIxBOIxAVE44nkm5BAJJZ8338sEu+/XAJxBuuc6l/Btg4I2H3eCGy+8QXscrU8HbBnpFawq8pUUMnZJjIShUySOvkxeRJklV6FCi13gCQiouwpquBtNpuHXdV2Op0AMOxqeL8tW7Zg48aNg461tLRgw4YNU1rjRIiiiHAsgUA0eUJiMCogmPo4kPo4HBMwjpxGOSIkRDgDUdh811pDbL7xB2ydUgbLgHA9oywZtjVKBuzR6NVyGDTJEyANWgWMOiVnZhMRUc4V1U+ipUuX4syZM0OO9x+rr68f8boWiwUWi2XaapuI8z1efN7n4/SPAhITErD7I+m2kP6g7fBHIYzjTFOdSg5LmQozylWwlPW3PqgZFsegkElQoUmuYBu0imTI5gmQRESUp4rqp/q9996LLVu24NSpU1ixYgUAIB6P4+DBg1ixYgVqampyXOH4JPushVyXQcMIROLJFWv/tZVrmz8CVyCK8fyOVKaSoyrVT9wfsGeUqaFjwB6VXCpBuUaRCtnX3nOMHxERFZK8/Kn1+uuvIxAIwOfzAQDOnTuHX/3qVwCAr371q9Bqtdi8eTMOHDiA1tZW1NXVAQAeeugh7NmzBxs3bsTu3bthsViwd+9eXLx4EUeOHMnZ7aHC0r+T46BwnQrYwejYvxBJABi0CljK1OlRdJYyFarK2CIyHnq1HGadMh2wKzQKlKnZi01ERIUvL4P3t7/9bXR0dKT/ffjwYRw+fBgA0NbWhrlz50IQBAiCAHHAn/FVKhWOHj2KrVu34uGHH0YwGMQtt9yC119/nbtW0hDBSBw2fwR2fwQ2XxT21MeOQHTMEX0AIJMmtwSv0qtQ2b+CXaZCpZ5bg4+XUi6FWa+EWaeEWa+CWaeEmnPEiYioSOVl8G5vbx/zMvv378f+/fuHHJ8xYwYOHDgw9UVRQYoLCTgCqVDti8Duj6bD9nhWrwFAo5ClVqxTb/rke6NOyd0cJ0AmBQxaJSr1Sph1Kpj0SpRzJZuIiEpIXgZvoonoH83Xv2Jt90fhSH3sDsbG1XstlSS3Sq/UJ1es+8N1VZmK/deTpFPJUKVXJVey9UqYtEpIOU+ciIhKGBMFFYSEKMIXjsPhT04KsQeSAdvuj8A5ztYQIDmerzK1al2ZCteVehVMOiU3mcmAXCaBOfWLi1mffM+WESIiosEYvClviKIIb3+4DvSvWkfhDEThCETGvZumQiZJr1xX6vvDYDJs8+TGzEkkQLlakQ7YlXolt58nIiIaBwZvyqqEKMITiiXDtD8KZ6A/ZE8sXMskEph0ygHh71rILlPLGQKnUP+UEZMueRKkUcc52URERJPB4E1TLp5IwB2IJQN1Klg7/VE4AlG4guNvC5FKAKP2WvvCwMkXBi1bQ6aDRimFSadK3ddKGLWcMkJERDRVGLxpUkJRId0C4gokQ7UzmGwL8YzzhEYguXJt0CoYrrNMq5ShTC2HXiVHmVqBMrUcZr2SG9IQERFNI/6UpWEJiWRLiCsVpq9/C8XGv7OmQiZJjo9LtSqYUuPkzDolKrQKjuSbJlqlLBWsr4Xr/rAtZ6sIERFR1jF4lyhRFBGICnClVqpdqUDdH7Q9oRjG2RECIBnyTKk+4HTA1iVXsctU7LmeTnKZBEZtcqdHo1YBg1YJg0bBcE1ERJRnGLyLWDgmwBWMwhVIrly7UgHbFYzBGYwiGk+M+2tJJUCFRgGzLrlxjHlAyDZxt8Gs0alkMGqV6aBt0HI7dSIiokLB4F3AInEB7mB/qI6lQnU0HbYn0g4CXFu1NmqV171XsN86y+RSCSq0Chg0Chh1qZCtUXIreiIiogLG4J3H+les+8P19e/Hu+V5P0WqJcGoTY6EM2kVyWCt4/SKXNKr5TBoFKlWESVXsYmIiIoUg3ceevG9Nrx90YpwbPytIAAgk0pgTIW35JsiHaqNOiV0Shl7rXNIKZfCoFGkV7INqZDNmdhERESlgcE7Tw0XuuVSCQzaa60fA98btUro1XJOCMkDcqkE5Ro5KjTX+rANGiV3zSQiIipxDN55qL62HM5AdMCkiuR7PaeD5JX+gF2mVqBcnQzYFVoFp7gQERHRsBi889CKG8wwalW5LoOQnOaiS83CLtcoUD5gJjY3myEiIqKJYHKgkieRXNvJ8dpGM8n3eqUcUk5zISIioinA4E0l4/pwrVfJUa5WQK+Wc1QiERERTTsGbyoqcpkE5epkoE62hlzbKp07ORIREVEuMXhTQdKpZKlwfe3kxnIN+66JiIgofzGlUN6SSTEoVF9bxebqNRERERUeBm/KObVCmgrYqckhqXDN8YlERERUTBi8adpJUiP59CoZ9KrkPPL+kxv1ajl3biQiIqKSwOBNU0IulSTDdepExrLUx3qVHDqO5CMiIiLKv+Dt9/vx2GOP4Ze//CWcTicWLVqE//E//gf+63/9r6Neb//+/XjwwQeH/VxPTw+qq6uno9ySIpdJ0oG6TH1t5ZqbyRARERGNLe/S0n333YcPP/wQu3fvxoIFC/CLX/wC999/PxKJBB544IExr79v3z4sWrRo0DGz2Txd5RYdtUKabgEpUynSq9ZlajnUClmuyyMiIiIqWHkVvP/t3/4Nb731VjpsA8Bdd92Fjo4OfP/738ef/umfQiYbPfzV19dj+fLl2Si34EgkgEImhVIuZb81ERERUZblVfB+5ZVXoNfrsXHjxkHHH3zwQTzwwAM4deoUvvSlL+WouvwilQBalRw6pQxapRxKuRQquRQKmRQKmQRKuRTKVMhWDHhPRERERLmRV8G7ubkZN998M+TywWUtW7Ys/fmxgvc999wDm82GiooKrFmzBk8++STq6+vH/N5WqxU2m23QsZaWlgnegqmRXJlOnqyoGxCu9So5tCoZdEo5NEq2fRAREREVkrwK3g6HA/PmzRty3GQypT8/kurqamzbtg2NjY0oLy/HmTNnsHv3bjQ2NuL48eNoaGgY9Xvv3bsXO3fuzOwGTJEvzDbitjpTrssgIiIioimUV8EbwKgbpoz2uaamJjQ1NaX/vWrVKqxfvx5Lly7F9u3b8eqrr476fbds2TKkxaWlpQUbNmwYX+FTiKP3iIiIiIpPXgVvs9k87Kq20+kEcG3le7zmzp2LO+64AydPnhzzshaLBRaLZUJfn4iIiIhovPLqbLulS5fi/PnziMfjg46fOXMGAMbVq309URQhlebVzSQiIiKiEpRXifTee++F3+/Hr3/960HHDxw4gJqaGqxYsWJCX6+trQ3Hjx9HY2PjVJZJRERERDRhedVq8od/+If4yle+gm9/+9vwer2YP38+XnrpJbzxxhs4ePBgeob35s2bceDAAbS2tqKurg4AsG7dOqxatQrLli1Ln1z59NNPQyKRYNeuXbm8WURERERE+RW8AeA3v/kNtm3bhu3bt6e3jH/ppZcGbRkvCAIEQYAoiuljS5cuxcsvv4xnnnkGoVAIFosFd999Nx5//HEsWLAgFzeFiIiIiChNIg5MrzTI2bNnUV9fj+bmZixZsiTX5RARERFRHploVsyrHm8iIiIiomLF4E1ERERElAUM3kREREREWcDgTURERESUBXk31SSfRCIRAMmt44mIiIiIBurPiP2ZcSwM3qPo7OwEAGzYsCG3hRARERFR3urs7MStt9465uU4TnAUbrcbx44dw+zZs6FSqab1e7W0tGDDhg34l3/5F8yfP39avxeNjo9FfuHjkT/4WOQXPh75g49F/sj2YxGJRNDZ2YnVq1fDYDCMeXmueI/CYDDgj//4j7P6PefPn8+Z4XmCj0V+4eORP/hY5Bc+HvmDj0X+yOZjMZ6V7n48uZKIiIiIKAsYvImIiIiIsoDBm4iIiIgoCxi880RVVRWeeOIJVFVV5bqUksfHIr/w8cgffCzyCx+P/MHHIn/k+2PBqSZERERERFnAFW8iIiIioixg8CYiIiIiygIGbyIiIiKiLGDwJiIiIiLKAgbvHPD5fNi6dSv+4A/+AFVVVZBIJNixY8e4r79//35IJJJh33p7e6ev8CKV6eMBAFarFd/85jdRWVkJrVaLlStX4ujRo9NTcJHz+/343ve+h5qaGqjVatxyyy34v//3/47runxtTE4m9zmf+1Nrso8Fn/vTI9OfD3x9TJ1MHot8en1wy/gccDgceP7559HQ0IANGzbghRdemNTX2bdvHxYtWjTomNlsnooSS0qmj0ckEsHatWvhdrvx3HPPwWKxYM+ePWhqasKRI0ewevXqaaq8ON1333348MMPsXv3bixYsAC/+MUvcP/99yORSOCBBx4Y19fga2NiJnuf87k/9TJ9/vO5P7Uy+fnA18fUmorslBevD5GyLpFIiIlEQhRFUbTZbCIA8Yknnhj39fft2ycCED/88MNpqrC0ZPp47NmzRwQgnjhxIn0sFouJixcvFm+//fapLreo/fa3vxUBiL/4xS8GHf/KV74i1tTUiPF4fNTr87UxcZnc53zuT61MHgs+96dHJj8f+PqYWpk8Fvn0+mCrSQ70/3mD8kOmj8crr7yChQsXYuXKleljcrkcmzZtwgcffICurq6pKLMkvPLKK9Dr9di4ceOg4w8++CC6u7tx6tSpHFVWvDK5z/ncn1p8/uefTH4+8PUxtYolOzF4F7B77rkHMpkMJpMJ9913H5qbm3NdUklqbm7GsmXLhhzvP3b27Nlsl1SwmpubcfPNN0MuH9wF139fjvc5ztfG+GVyn/O5P7Wm4vnP537+4Osj/+TD64M93gWouroa27ZtQ2NjI8rLy3HmzBns3r0bjY2NOH78OBoaGnJdYklxOBwwmUxDjvcfczgc2S6pYDkcDsybN2/I8fHel3xtTFwm9zmf+1Mrk8eCz/38w9dH/sin1weDd4beeecd3HXXXeO67CeffIJbbrkl4+/Z1NSEpqam9L9XrVqF9evXY+nSpdi+fTteffXVjL9HocrF4wFg1D9/FcOfxiZjso9FJvclXxuTk8l9zuf+1Jrs/cnnfn7i6yM/5NPrg8E7QwsXLsQ//dM/jeuyc+bMmbY65s6dizvuuAMnT56ctu9RCHLxeJjN5mFXLpxOJwAMu+JRCibzWEzHfcnXxugyuc/53J9aU31/8rmfW3x95LdcvT4YvDM0c+ZM/MVf/EWuywAAiKIIqbS02/Zz8XgsXboUZ86cGXK8/1h9fX1W68kXk3ksli5dipdeegnxeHxQn2um9yVfGyPL5D7nc39qTcfzn8/93OHrI//l4vXBV2ORaGtrw/Hjx9HY2JjrUkrOvffeiwsXLgyaOBCPx3Hw4EGsWLECNTU1OayusNx7773w+/349a9/Pej4gQMHUFNTgxUrVkz4a/K1MbpM7nM+96fWVD//+dzPLb4+8lvOXh+5nWZYuv7t3/5NPHz4sPjP//zPIgBx48aN4uHDh8XDhw+LgUAgfbmHHnpIlMlkYnt7e/rY2rVrxZ07d4qvvPKKePToUfF//a//JdbU1IhlZWXimTNncnFzCl4mj0c4HBaXLFkizp49Wzx06JD41ltviffee68ol8vFd955Jxc3p6B95StfEY1Go/j888+Lv/vd78S//Mu/FAGIBw8eHHQ5vjamznjucz73s2OyjwWf+9NnPD8f+PrIjsk+Fvn0+mDwzpG6ujoRwLBvbW1t6ct94xvfGHLse9/7nrh48WKxrKxMlMvlYk1Njbhp0ybx4sWL2b8hRSKTx0MURbG3t1f88z//c9FkMolqtVpsbGwU33rrrezeiCLh8/nE//bf/ptYXV0tKpVKcdmyZeJLL7005HJ8bUyd8dznfO5nx2QfCz73p894fj7w9ZEdk30s8un1IRFFUZzOFXUiIiIiImKPNxERERFRVjB4ExERERFlAYM3EREREVEWMHgTEREREWUBgzcRERERURYweBMRERERZQGDNxERERFRFjB4ExERERFlAYM3EREREVEWMHgTEREREWUBgzcRERERURYweBMRERERZQGDNxERjSgcDuMLX/gC5s+fD4/Hkz7e29uL6upqrFmzBoIg5LBCIqLCweBNREQjUqvV+OUvfwmr1YqHHnoIAJBIJPD1r38doijipZdegkwmy3GVRESFQZ7rAoiIKL/ddNNNeOGFF/Cnf/qneO655+B0OvHOO+/gjTfewMyZM3NdHhFRwZCIoijmuggiIsp/W7ZswQsvvABBEPDoo49i165duS6JiKigMHgTEdG4fPTRR/jiF78IpVKJq1evoqqqKtclEREVFAZvIiIaUyAQwPLly5FIJNDX14fVq1fj1VdfzXVZREQFhSdXEhHRmL71rW/hypUr+M1vfoMXX3wR/+///T/84z/+Y67LIiIqKAzeREQ0qhdeeAEHDx7Enj17sGTJEvyX//Jf8N3vfhd/93d/hw8++CDX5RERFQy2mhAR0YjOnDmDFStW4Gtf+xr279+fPh6JRPDlL38ZDocDn3zyCQwGQ85qJCIqFAzeRERERERZwFYTIiIiIqIsYPAmIiIiIsoCBm8iIiIioixg8CYiIiIiygIGbyIiIiKiLGDwJiIiIiLKAgZvIiIiIqIsYPAmIiIiIsoCBm8iIiIioixg8CYiIiIiygIGbyIiIiKiLGDwJiIiIiLKAgZvIiIiIqIs+P+4ddHkWSCo3QAAAABJRU5ErkJggg==", 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", "text/plain": [ "
" ] @@ -1590,14 +1663,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Mon Nov 06 2023\n", + "Last updated: Sun Nov 26 2023\n", "\n", "Python implementation: CPython\n", "Python version : 3.11.0\n", @@ -1605,8 +1678,8 @@ "\n", "bambi : 0.13.0.dev0\n", "pandas : 2.1.0\n", - "numpy : 1.24.2\n", "matplotlib: 3.7.1\n", + "numpy : 1.24.2\n", "\n", "Watermark: 2.3.1\n", "\n" diff --git a/docs/notebooks/plot_slopes.ipynb b/docs/notebooks/plot_slopes.ipynb index 264a08bb1..5ff89d863 100644 --- a/docs/notebooks/plot_slopes.ipynb +++ b/docs/notebooks/plot_slopes.ipynb @@ -95,17 +95,29 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n" + ] + } + ], "source": [ "import arviz as az\n", + "import numpy as np\n", "import pandas as pd\n", "import warnings\n", "\n", "import bambi as bmb\n", "\n", - "warnings.simplefilter(action=\"ignore\", category=FutureWarning)" + "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" ] }, { @@ -127,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -224,7 +236,7 @@ "5 1 1.10 40.874001 1 14 0.40874 3.5" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -239,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -371,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -510,8 +522,8 @@ "" ], "text/plain": [ - " term estimate_type value dist100 educ4 estimate lower_3.0% \n", - "0 arsenic dydx (1.5, 1.5001) 0.2 1.0 -0.110797 -0.128775 \\\n", + " term estimate_type value dist100 educ4 estimate lower_3.0% \\\n", + "0 arsenic dydx (1.5, 1.5001) 0.2 1.0 -0.110797 -0.128775 \n", "1 arsenic dydx (1.5, 1.5001) 0.2 1.2 -0.109867 -0.126725 \n", "2 arsenic dydx (1.5, 1.5001) 0.2 2.0 -0.105618 -0.122685 \n", "3 arsenic dydx (1.5, 1.5001) 0.5 1.0 -0.116087 -0.134965 \n", @@ -533,7 +545,7 @@ "8 -0.096476 " ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -568,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -691,7 +703,7 @@ "5 -0.093209 " ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -728,7 +740,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -797,7 +809,7 @@ "source": [ "well_model_interact = bmb.Model(\n", " \"switch ~ dist100 + arsenic + educ4 + dist100:educ4 + arsenic:educ4\",\n", - " data,\n", + " data=data,\n", " family=\"bernoulli\"\n", ")\n", "\n", @@ -812,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -850,99 +862,99 @@ " \n", " \n", " Intercept\n", - " -0.097\n", - " 0.122\n", - " -0.322\n", - " 0.137\n", + " -0.117\n", + " 0.177\n", + " -0.450\n", + " 0.222\n", + " 0.005\n", " 0.003\n", - " 0.002\n", - " 2259.0\n", - " 2203.0\n", - " 1.0\n", + " 1317.0\n", + " 1366.0\n", + " 1.00\n", " \n", " \n", " dist100\n", - " 1.320\n", - " 0.175\n", - " 0.982\n", - " 1.640\n", - " 0.004\n", - " 0.003\n", - " 2085.0\n", - " 2457.0\n", - " 1.0\n", + " 1.500\n", + " 0.243\n", + " 1.082\n", + " 1.997\n", + " 0.008\n", + " 0.006\n", + " 949.0\n", + " 1080.0\n", + " 1.01\n", " \n", " \n", " arsenic\n", - " -0.398\n", - " 0.061\n", - " -0.521\n", - " -0.291\n", - " 0.001\n", - " 0.001\n", - " 2141.0\n", - " 2558.0\n", - " 1.0\n", + " -0.514\n", + " 0.090\n", + " -0.690\n", + " -0.349\n", + " 0.003\n", + " 0.002\n", + " 1209.0\n", + " 1269.0\n", + " 1.00\n", " \n", " \n", " educ4\n", - " 0.102\n", - " 0.080\n", - " -0.053\n", - " 0.246\n", + " 0.118\n", + " 0.111\n", + " -0.087\n", + " 0.331\n", + " 0.003\n", " 0.002\n", - " 0.001\n", - " 1935.0\n", - " 2184.0\n", - " 1.0\n", + " 1170.0\n", + " 1102.0\n", + " 1.00\n", " \n", " \n", " dist100:educ4\n", - " -0.330\n", - " 0.106\n", - " -0.528\n", - " -0.136\n", - " 0.002\n", - " 0.002\n", - " 2070.0\n", - " 2331.0\n", - " 1.0\n", + " -0.364\n", + " 0.148\n", + " -0.628\n", + " -0.075\n", + " 0.005\n", + " 0.004\n", + " 855.0\n", + " 1076.0\n", + " 1.01\n", " \n", " \n", " arsenic:educ4\n", - " -0.079\n", - " 0.043\n", - " -0.161\n", - " -0.000\n", - " 0.001\n", + " -0.062\n", + " 0.060\n", + " -0.172\n", + " 0.055\n", + " 0.002\n", " 0.001\n", - " 2006.0\n", - " 2348.0\n", - " 1.0\n", + " 1078.0\n", + " 1258.0\n", + " 1.00\n", " \n", " \n", "\n", "" ], "text/plain": [ - " mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk \n", - "Intercept -0.097 0.122 -0.322 0.137 0.003 0.002 2259.0 \\\n", - "dist100 1.320 0.175 0.982 1.640 0.004 0.003 2085.0 \n", - "arsenic -0.398 0.061 -0.521 -0.291 0.001 0.001 2141.0 \n", - "educ4 0.102 0.080 -0.053 0.246 0.002 0.001 1935.0 \n", - "dist100:educ4 -0.330 0.106 -0.528 -0.136 0.002 0.002 2070.0 \n", - "arsenic:educ4 -0.079 0.043 -0.161 -0.000 0.001 0.001 2006.0 \n", + " mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk \\\n", + "Intercept -0.117 0.177 -0.450 0.222 0.005 0.003 1317.0 \n", + "dist100 1.500 0.243 1.082 1.997 0.008 0.006 949.0 \n", + "arsenic -0.514 0.090 -0.690 -0.349 0.003 0.002 1209.0 \n", + "educ4 0.118 0.111 -0.087 0.331 0.003 0.002 1170.0 \n", + "dist100:educ4 -0.364 0.148 -0.628 -0.075 0.005 0.004 855.0 \n", + "arsenic:educ4 -0.062 0.060 -0.172 0.055 0.002 0.001 1078.0 \n", "\n", " ess_tail r_hat \n", - "Intercept 2203.0 1.0 \n", - "dist100 2457.0 1.0 \n", - "arsenic 2558.0 1.0 \n", - "educ4 2184.0 1.0 \n", - "dist100:educ4 2331.0 1.0 \n", - "arsenic:educ4 2348.0 1.0 " + "Intercept 1366.0 1.00 \n", + "dist100 1080.0 1.01 \n", + "arsenic 1269.0 1.00 \n", + "educ4 1102.0 1.00 \n", + "dist100:educ4 1076.0 1.01 \n", + "arsenic:educ4 1258.0 1.00 " ] }, - "execution_count": 11, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -965,23 +977,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 21, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Default computed for wrt variable: arsenic\n", - "Default computed for main variable: dist100\n", - "Default computed for group/panel variable: educ4\n" - ] - }, { "data": { - "image/png": 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", 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" + "
" ] }, "metadata": {}, @@ -993,9 +996,12 @@ " well_model_interact,\n", " well_idata_interact,\n", " wrt=\"arsenic\",\n", - " conditional=[\"dist100\", \"educ4\"],\n", + " conditional={\n", + " \"dist100\": np.linspace(0, 4, 50),\n", + " \"educ4\": np.arange(0, 5, 1)\n", + " },\n", " subplot_kwargs={\"main\": \"dist100\", \"group\": \"educ4\", \"panel\": \"educ4\"},\n", - " fig_kwargs={\"figsize\": (16, 4), \"sharey\": True},\n", + " fig_kwargs={\"figsize\": (16, 6), \"sharey\": True, \"tight_layout\": True},\n", " legend=False\n", ")" ] @@ -1011,7 +1017,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1020,14 +1026,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": 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ZYOL1FRioGHRXEhgBlwWjXlpICSGEEItSdg7mzsDUYa1NlFIBux8Cy0En+wMhhBBisUmX0kTzUWKFGNPZaaaz02RKGfKVPDp0WI1W7CY7bY42zAbzVc9NlVI1ilqSGkIIIURDKVUU5jJFZtNFZjNFxmM5ErkymWKFqqKg1+lxWAw4zJLAEEIIIRa9Ygays9pXbERLaBQTYHaBpwuM1lpHKIQQQog7RFEVYoUY0XyUSC7CeHqceCFOtpKlqlQx6o04zU5arC1YjVZ0ddySWpIaQgghRB0rlKvzCYyJWI7xeJ5UoUyxXEWv02EzG3GYDbQ47JhkiLcQQgixeFXLWiVGdlYb7h0fheylId/VsjbM2xEAdwfU8UkKIYQQQiyMslImlo8xl59jJjfDWGqMZClJvpwHmG8j1WJrwahvrDRBY0UrhBBCNLlCuUokVSSSLjAezzERy5MuVChWqxj1elxWIyGXFavJUOtQhRBCCFFL1QpkZiA1BfGLkByHQgrKeS1pYbKD2QneHnhDuwghhBBCNJ+yUiaajzKTm2E6M81EZoJ0KU2hUtC6OpgceCwe2h3tdV2FcSMkqSGEEELUUKmiMJspEk4WGI/lGIvlSBXKVKoqJoOWxGj3WrEYJYkhhBBCLGqqqg30Tk1CYhyiZyGfgHIO9CawusERBJNNKjGEEEKIRaCiVIgVYkRyEaYz04ymR0kVUxSrRQx6A06Tk1ZbK1ZDfbeSuhWS1BBCCCHuoEpVIZotEU4WmIjnGInmSOXLFCpVTAY9bquJLq8ds1FaSQkhhBCLXj4B6bBWjTF3FrIzUMoCOrC4wRnUKjKEEEIIsSjkyjlGUiOEs2FGU6Mki0nylTwGnQGn2UnAHsC6CGZmSVJDCCGEuA1UVSVVqJDMlUnmy8RzRcKpIrOpIulimUK5ikGvx2UxEnJLOykhhBBCAIWklsRIT0P0PGTCUEiDqmitpKwecHeCTi5+EEIIIRaTqlLlbOIse6f3Mp2ZRqfTafMwGmCo9+0gSQ0hhBBiAZQqCpOJPOFkgUi6QDhVIFOokC9VqSgKOnRYjHpsZiN+pwW7WZZgIYQQYtErZrQqjPQ0RM9pMzIKKS2JYbJrSYzWkDbkWwghhBCLUjgbZu/0XoZjw5gMJvo8fRgW+d5AzqgIIYQQt6hQrjKZyDMyl+XMTJpopkRFUTAa9NhNRuyXEhgmg1xNKYQQQohLcjFIjGmVGNFzUIiDomizMKweaA2AXn5VF0IIIRa7XDnHwchBDkUOkS1l6XB1YDPaah1WXZCdkhBCCHETCuUqE/EcF2eznIlkiGVLVBUVt81El09mYVyWKVa4OJdlZC6Ly2oEWmodkhBCCFEbiqJVYCTGYG5YG/JdSIFeDzYftAxKEkMIIYQQ86pKleH4MK9Nv8ZkdhK/zU+br63WYdUV2TkJIYQQbyNbrDARz3NxLsPZmQzxXAlVBbfNRLckMgBI5ctcjGa5OKd9zaaL8/ctC7lqGJkQQghRA9UyJCe0REbklJbUKGXAaAVbC7jaZC6GEEIIId5kOjPN3um9nImfwWQwMegZXPStpq6lYZIa8XicZ599lh/+8IcAPPXUU3zlK1/B6/Ve9zmqqvLcc8/xjW98g3g8zsaNG/na177GqlWrAIjFYnz605/mhRdeYHx8HL/fz6/8yq/wp3/6p3g8nvnv09fXx+jo6FXf+w//8A/5whe+sPBvVAghRF1I5stMxHNcmM1yYS5DIlcGwGMz0dvqWPQtpdKFMhdms5yfzXBxLks0W3rTY9rcVvr8djxWUw0iFEIIIe6wUlarwoiPwOwpyEahWgKzA+wt4O2pdYRCCCGEqEOxQozx9Djn4ueYyEyQL+fpdHViNVprHVrdapikxtNPP83ExATPP/88AB/+8If50Ic+xI9+9KPrPudLX/oSf/7nf863v/1tli5dymc/+1kef/xxhoeHcblcTE1NMTU1xf/4H/+DlStXMjo6ym//9m8zNTXF97///au+15/8yZ/wzDPPzN92Op23540KIYSomVi2xEQ8x/lIlpFolmS+jEGvw2sz0e93YNQv3kRGvlTl4lyG85cSGZHXVWIA6IB2r5X+Vgf9fgd9rQ7sFm2bcS6SqUHEQgghxB0wPx/jgjYfIx/ThnxbveDp1CozhBBCCCHeIFFIMJ4e53ziPOOZcdKlNBaDBZ/VR4ezo9bh1b2GSGqcOnWK559/nj179rBx40YAvvnNb7Jp0yaGh4dZtmzZm56jqipf/vKX+aM/+iPe//73A/BXf/VXhEIhvve97/GRj3yE1atX8w//8A/zzxkcHOTP/uzP+Lf/9t9SqVQwGq/8eFwuF21t0rtMCCGaTaFc5cJslhNTScZiOVKFMia9Hp/dzGDAiUGvq3WINVGuKoxEs5yPaImMqUQe9XX364B2j5WBgJOBgJbEsJqkJFYIIcQioFS1Id+TByB2AQpJ0BuuzMcwNMSv2UIIIYS4gxRVIVlMMpWZ4lziHONpLZFhMpjwWXyE7CF0usV5/uFWNMRu69VXX8Xj8cwnNADuv/9+PB4Pu3fvvmZS4+LFi4TDYbZt2zZ/zGKxsHnzZnbv3s1HPvKRa75WMpnE7XZfldAA+OIXv8if/umf0t3dzQc+8AH+4A/+ALPZvEDvUAghxJ0WSRc4O5Pm6GSKSKqAQa+j1WEh5LaiX4QbCVVVmUkXOTeT5mxEaylVUdSrHhNwWhgIOBgMOBnwX6nEEEIIIRaFahlmh2HiNS2poSrgCMp8jMvKBS3JY5LqFCGEEItbRamQKCZIFBIkigkiuQjhXJhsKUumnMGoN+Kz+gjag5LIuEUNcTYiHA4TDAbfdDwYDBIOh6/7HIBQKHTV8VAo9Kb5GJdFo1H+9E//9E0Jj49//ONs2LABn8/Ha6+9xqc+9SkuXrzI//2///e6MReLRYrFK605UqnUdR8rhBDizihVtOqDk1MpzkUypAtl3DYTfYt0RkamWOFcJMO5iJbISBcqV93vthpZEnQxGHAwEHDisclsjGuRNV8IIZpcuQCRkzD+mjYvQ28AdweY7LWOrLZUBZKTMHtamyESHwFVhfueedunNjJZ94UQQrxRrpxjOjvNXH6OcDbMTG6GfCVPvpJHVVWMeiN2kx2n2UnQEUQvF0O8YzVNanzmM5/hueeee8vH7Nu3D+CaWStVVd82m/XG+6/3nFQqxXve8x5WrlzJpz/96avu+8QnPjH/57Vr1+Lz+fjVX/1VvvjFL9La2nrN1/385z//tu9NCCHEnRHNFDkXyXB0Mkk4UQBU/C4r7R7roroqQlFVJuN5hmfSDIfTTCbyV91vMujo9ztYEnQxFHQSdFkW1c/nVsmaL4QQTaqYgZnjWjIjOQkmC/j6wGipdWS1U0xr1Sqzp7T/lt4wN8vu1x7TxGTdF0IIAcy3khpNjTKSGiFZTKKoCmaDGbvJjtfipc3RJgmM26SmSY2PfexjfPCDH3zLx/T19XH06FFmZmbedN/s7OybKjEuuzz/IhwO097ePn88Eom86TnpdJonnngCp9PJD37wA0ymt74S9f777wfg3Llz101qfOpTn+L3f//352+nUim6u7vf8vsKIYRYOIVyldFojlPTKc7PZkjlyzitJrpb7JiNi2dTkStVOBvJcCacZngmTa5Uver+do+VJUEnQ0EXva32RVmx8k7Jmi+EEE0mNa2dsJ/cD5kZMLugdRAMi7Bi8XI1RuQEzJyE5NjV9xss4B+CwHLty+KC1GRtYr1DZN0XQojFSVEVovkok5lJLiYvMpmZJFPKoNfp8Vg89Lh7MOoboilSU6jpT9rv9+P3+9/2cZs2bSKZTPLaa69x3333AbB3716SySQPPPDANZ/T399PW1sb27dvZ/369QCUSiV27tzJF7/4xfnHpVIpfumXfgmLxcIPf/hDrNa37/956NAhgKuSJW9ksViwWBbxFTxCCFEDqqoSThU4F8lwfCrFbKqADmh1WmhzL46qjMuzMU5PpxgOpxmL5a4a8G0x6hkKOlnW5mIo5MJtXYQnaBaYrPlCCNEEygWInoPwUe2/xTRYveBfprWbWkwqBZg9o7XcipyE4hvaK7k7tQRGcIVWufL6EziVIs1O1n0hhFhcykqZs/GzHIkcIZKPkC1nsRgseCweAvaAVGLUSEOkj1asWMETTzzBM888w9e//nUAPvzhD/Pe9773qiHhy5cv5/Of/zzve9/70Ol0/N7v/R6f+9znGBoaYmhoiM997nPY7XaefvppQKvQ2LZtG7lcju985zukUqn5fpiBQACDwcCrr77Knj172Lp1Kx6Ph3379vGJT3yCp556ip6enjv/wxBCCPEmmWKFC7MZTkylGIvlyJUquK0mehfJrIyqojISzXJ6OsXJ6RTxXPmq+4MuC8vaXCwLuehtdWDQN39yRwghhLgh6bA2E2LqEKRnQKcDZwg8i+zK+1xUa7U1cxJi50B5XWWnwXwpibFSS2RYPbWLUwghhLhDFFXhYvIi+8P7GUmNYDKYaLG20O5oXxQXTNa7hkhqAHz3u9/l2WefZdu2bQA89dRTfPWrX73qMcPDwySTyfnbn/zkJ8nn8/zO7/wO8XicjRs38sILL+ByuQA4cOAAe/fuBWDJkiVXfa+LFy/S19eHxWLh//2//8dzzz1HsVikt7eXZ555hk9+8pO38+0KIYR4G4qiMhHPc2ZGO5Efy5YxGfQEXBa6fc0/uLNQrnJmJs2p6RTDM2kKZWX+PqNex2DAyfJ2F0tDLnx2cw0jFUIIIepMpahVY0wfg+gZKKTB5oGWgcXTYkpVtTZR4WMwcwxSU1ffb2+F4CoIrYKWQTA0zKkDIYQQ4h0bT49zIHyAs4mz6NDR5erCbJDfq1+vVC1RqBRq9voNszNpaWnhO9/5zls+RlXVq27rdDo+85nP8JnPfOaaj9+yZcubnvNGGzZsYM+ePTcVqxBCiNsnmS9zfjbD8YkkE4k8xUqVFruFwYCz6SsQ0oUyJ6dTnJxKcWE2S/V1a5jdbGB5m5sV7S6Ggq5FNTdECCGEuCGZ2UtVGQe1uRk6PTiD4O7SKjSanVKF2IUriYx8/HV36rSkTmg1hFaCI7g4fiZCCCHE60RyEQ7OHORU7BSlaol2Rzs2k63WYdWNXDnHmfgZTsdOcyFxgaUtS1njX1OTWBomqSGEEGLxqlQVxuN5hsMpTofTJHIlrEYDIZcVm7m5+1wnciVOTKU4MZVkNHr1fAy/08KKdhcr2910t9jRy8kHIYQQ4mrV8pUT+ZHTUEyCxQ0t/VpbpWZXLcPcMEwfgZkTUM5duU9v0tpKta3WKjLMztrFKYQQQtRQopDg0Owhjs8dJ11K0+Zow2V21TqsupAsJhmODXM6dprR1Cjq685KxAqxmsUlSQ0hhBB1K5opcnEuy9GJJFPJPJWqSqvDzJKgq6lP4M+lixyfSnJiKsVkIn/VfV0+G6va3azs8BBwyZBKIYQQ4ppyMZgd1qoykpOgKpeqMjqavwKhWoLIKS2RETlx9fBus0NLYITWQGDZ4kjsCCGEENdQrBYZT41zPnme8/HzJEoJ/DY/bb62WodWc7F8jJOxk5yOnmYqe3WLypA9xPKW5SxvWU6xWrzOd7j9JKkhhBCirmSLFW3odTjNxbks6XwZu9lIh8eG1dS8VRnRTJGjk0mOTSQJp670pdQBva0OVnW4WdXhxivzMYQQQohrUxSIX4Twce1kfj6uVR94e8DY5BcCVIoQOXkpkXFSS2xcZvVC+1poW6dVqOikRaUQQojFqapUmc5OM5Ia4XTsNLF8DBUVn9XHEu+SRT0A/HIi4+TcScK58FX3dbu65xMZPqtv/vh4evxOhzlPkhpCCCFqrlxVGI/lOBfJcDqcIp4rY9DpaHVaaHdbm3ZjEcuWODaZ5NhEgqnklUSGXgeDASerOjysaHfhsi6SoaVCCCHErShlr1RlxEegUtKqMgLLm/sEfrUEMye19x05BUr5yn22Fmhfp315e5r75yCEEEK8jWg+ymhqlFOxU4SzYUrVEm6Lm253N0b94j09fr1Ehg4d/Z5+VrSuYJlvGc46bFG5eD81IYQQNTeTKnAukubkVJpwqkBVUWmxmxnwN+/Q70SuxPHJJEcnk0zEr7SWupzIWNPpYWWHG7tZlmghhBDiLaWmtZP604cgE9GqMVztYLLXOrLbR6lcSeCEj8Pr2z7Y/ZcSGXeBZ5EMPxdCCCGuo6pUGU2PcmLuBCPJEdLlNHajnYA9gM24eId/JwoJjkePXzeRsbJ1JctblmOv8/2UnDERQghxx03EcxwZT3JqOkW6UMZlNdHls2ExNmd7qVypwrHJJEfGE4xErwzo1AH9AQdrO72s6nDjsMiyLIQQQrylahmi57U2S3PDUExrVQn+paBvzn0EShWi52DqEISPQPl187ZsLdCxXvtyd0oiQwghxKJXrBY5nzjPsdljjKXHUFQFv81PyBFq2i4QbydbznIyepLjc8evahnVaImM15OzJ0IIIe4IVVUZi+U4NJZgeCZNvlQh6LLS4W3OKyTKVYXT4TSHxxOcCaepqipwZUbG2i4Pqzrc0lpKCCGEeDulLCTGtdZSc8OQvnRVoTMEnu6ahnbbqCokx2Fiv1aVUcpcuc/iho67oGMDeHslkSGEEEIAmVKGs4mzHIkcIZwLY9KbaHO0YTVaax1aTRQrRU7HT3N87jgXEhdQUefv63P3sdq/uuESGa8nSQ0hhBC3laKoXIxmOTwW50wkQ6msEPJY6fY15sL5VhRV5cJslsPjCU5MJSlWlPn72j1W7ur2srbLi8cmiQwhhBDiLeVikBjTqjKi5yEfA1UBq0c7kd+sg79zUZg8oCUzspErx00OrbVUx3poHZQZGUIIIcQl0XyU0zHt5H20EMVhctDr7l2UszKqSpVziXMcmzvGmdgZKmpl/r4ORwer/atZ5V+Fy+yqYZQLY/F9ukIIIe6ISlXhwlyWg6Nxzs9mqCoqbR4bziZssTSTKnBoLM6h8QTpwpVNg9duYl2Xl7u6vYTci+/qkFyxQjhVQK8Hm7lJW4IIIYRYOOkZiF+E2dOQnIBCGm0R8UHLIBiabw8BQCmntdOa3AexC1eO603Qtho679GGnjdrey0hhBDiFoSzYY7PHed07DSpUgqvxcugdxD9Ikv8q6rKdHaao7NHOT53nFzlSsvrVmsrq/2rWe1fTauttYZRLrwm3RUKIYSolUK5ypmZNIfHEozFtMW0zWNtusHXuVKFoxNJDo7Frxr4bTMZWNPp4a5uLz2tdvSLsCVEulBmJlXAaNCztM3F+m4v/X5HrcMSQghRjyolbV5E+PilGRkpMNq0RIarvXkrEpSqlrwZfw0ix7XbAOigdQl03QNt68C0+C6KEEIIId7KdGaaY3PHOBU7Ra6cw2/zs8S7ZNHNy0gWkxybO8bR2aPM5efmjztNTlb7V7MmsIY2e1vT/lya6wyTEEKImknmypwOpzg0niCcLGAx6unw2rCamueqwqqicjaS5uBonFPhNFVF60mp18GyNjcberwsa3Nh1DfpCZi3kciVmEkXsJsMrOn0clePl94WO3p9c26ihBBCvAOZWS2JMXkQ0tPaMUew+YddZ2ZgfK/WXqqYunLc1a5VZHTeDTZvzcITQggh6pGqqkxkJubbKuUreYL2IB3OjlqHdkeVqiVOxU5xdPYoF5MX548bdUaWtyxnbWAtA96BRVGtIkkNIYQQ70g4WeDEVJJjk0li2RJuq4kBvwOjoXkW0Ui6wIERrb1UpnilvVS7x8qGHh/rur1N2VbrRiiqSiJXZjZdwGU1cV9fK+u6PXR6bU17RYgQQohbVClp7ZVmjkPkNBST2tBrXx8YzLWO7vYp52HqkJbMSIxeOW52aImM7vu0ZI4QQgghrqKoCuPpcY7MHuFc/BylaomgI0ina/Gsm6qqMpmZ5HDkMMejxylVS/P39bp7WRtYy8qWlViadd7YdSzOMzBCCCHeEVVVGYnmODqR4Ew4Q6ZYptVhYWnI1TTtlkoVheOTSfaNxhiNXulJ6TAbuKvby4ZeH+0eWw0jrJ2qopLMl4nnSlQVBY/NzMNDftZ0Lc7ZIUIIId6GqkLkFFzYoc3KUBVwBsHd0bxVGaqqtdUa36vNy1DK2nGdHoIroGsjhFbCIhxiKoQQQtyI8dQ4B2YOcD55nopSIWgP4jQ7ax3WHZMtZzk6e5TDkcPM5mfnj/ssPtYF17HWvxav1Vu7AGtMdlBCCCFuSiJX4hfnohydSFCqKgRdVjp9zXNyfzKRZ/9IjMPjCYoVBQAdsKzNxT29LSxrc2FYhO2UKlWFRK5MIl9CBTxWE2s6PQwEHPS02PHam/gKWyGEELeumIYLO7WT+wDeHmjmKwmLaW1OxtirkLvS3xpnm1aR0XkPWN21i08IIYSoc3P5OQ7MHOBk9CSlaok2Rxt2k73WYd0RiqpwLnGOw5HDnImfQVG1cxJGvZGVLSu5K3gXve5e6YqAJDWEEELcoEpV4fhUit3n5phJF+jw2HBZTbUOa0EUylUOjyfYPxpjKlGYP+6zm7i3r4X1PT48tuZ4rzejVFGI50ok82X0OvDZzdzT10Jfq4Pulub5/IUQQtwmkdNw7mcQHwFPF1g9tY7o9lAVmDsLY7shfEy7DVrypmMDdG8Eb2/zVqUIIYQQCyBdSnNk9giHI4dJlVK0OdpwmV21DuuOSBaTHIoc4lDkEOlSev54h7OD9cH1rGpdhdUoXRFeT5IaQggh3tZMqsCuM7OcnE5hNxsZCjZHm6nJRJ7XLkY5Mp6kVNVOQBj0OlZ1uLmnt4WBgKMp3ufNqFQVYrkSyVwZvV5Hq8PMQ0ta6W110OWzYzM3z+B3IYQQt0kxAxdf1qoVVAUCy0HfhOtHIalVZYzvgVz0ynFvL/Rsgo71zV2VIoQQQiyAYrXIyehJ9of3M5ufpdXayhLvkqavRrhclXFg5gDn4udQUQGwGW2sDazlrsBdhByhGkdZvySpIYQQ4roK5SqHxuLsuRAjkS/R43M0/EntclXh6ESSvRejTMTz88eDLotWldHtxb7Ihn5XFZVErkQsWwIdtDrMPDDYSn/ASXeLDYuxsT9zIYQQd9DcOTi3HWLnwdUBNl+tI1pYqgrRszDyijbwfL4qwwpd92rJDHdHbWOshWIGsrNQKYAz1NyD34UQQiyIilLhXOIc+8P7GU+P4zQ7GfQOotfpax3abZUqpuarMlKl1PzxXncvd4fuZnnLcowyc+ttyU9ICCHENY3MZXn57CznZjP47GaWBl0NfaXEbLrIaxejHBiLUyhfqsrQ6VjV6WZjfyt9rfaGfn836/XDvhVVxWs3cW9/C4MBJz0tUpEhhBDiJpVyMHKpOqNaBv+y5hqCXc7B+D4Y/QVkI1eO+/ovVWXctfhO5JcLkJvV5oiYneDrhbY10DIIjtZaRyeEEKJOKarCaGqUgzMHOZ84j9FgpM/T19Qn8lVV5XzyPAfCBzgTP3NVVca6wDo2hDbgt/lrHGVjad7/W4QQQtyS2XSRw2NxDo4nKFUU+ludmI2NeaVEVVE5HU7x6oUoF2az88d9dhP39bVwd18LzkVUlaGoKql8mWi2RFVR8dpMrO3yMBR00dNqX1Q/CyGEEAtEVWF2GC7sgNg5cLaDvaXWUS2c5IRWlTF1EKol7ZjBolVl9D4I7vbaxnenVctaq618XEviuNpgYCu0DGgVKovoAhEhhBA3bzIzyYGZA5yNn6WqVGl3tjf1rIhCpcDh2cPsD+8nVojNH+9x9XB36G5WtK5o6mTO7SQ/NSGEEACEkwWOTCQ4PpkkVSgTdFnx+RrzisNcscK+0Th7L0RJ5MsA6IBlbS429rcyFHIumlkZqqqSLlSIZkuUKgpum5GVHW6WhVx0t9gX5QB0IYQQCyQT0WZnTB3SbrcuBUMTrCvVCkwfhtFXtCHnl7natURG1z1au6nFolKEfAzyCW02isMPSx6F1iXa/BCDnFYQQgjx1mZzsxyOHOZE9ASFSoE2ZxsOk6PWYd02M9kZ9oX3cWzuGGVFOydhMVhYF1jH3aG7CdgDNY6w8cnuQwghFjFVVZmI5zk6keDUdJr0pWRGW9DakK2YppN5Xj0f5fB4goqilXPazQbu7WthY38LXntjJmluRaZYIZopUqhUcVlMLAk6WNbmpq/Vvqh+DkIIIW6Dch4m9msVDPkYeLrB4qp1VO9cIaW1lxrbrbVUAtAZoH0t9D6kVSM04P7olpQL2mdbSGpJC7tfq8hoHQBvD5hstY5QCCFEA0gUEhyZPcKxuWOkSilC9hCdrs5ah3VbVJUqp2On2Rfex1h6bP540Bbk3vZ7WeNfg3mxtaq8jSSpIYQQi5CqqoxGcxweTzA8kyZXqtDmttHhbbxfUKuKysnpFK+ejzISvdJiqsNjZdNgK2u7vJgMjdk+62blilpFRrZUwWkx0tNiZ3m7m95WO36npdbhCSGEaHSqCrOn4cJObRC4rQUCKxr/RH9iDC7u0ipO1Kp2zOrRqjK67weru7bx3SmVojYvpJgBoxkcQejeCC394OkCo+wlhBBC3JhsOcux2WMcmj1EvBDHb/OzxLukIS+efDvZcpYDMwfYH95PppwBQIeOFa0ruLftXnpcPU35vmtNkhpCCLGIqKrKSDTH/pEY5yIZylWFkNtKt89e69BuWr5UZf9ojN3noyQvtZjS62BVh4dNA630LpLB34VylWimRKZYxmY20O6xsqI9SG+rnaDLsih+BkIIIe6ATOTSif/D2u3WocZuNaVUIXxUe0/xi1eO+/qh/2FoW6e1WloMqhVITWhJDW839D+itZXydDX2ZyyEEOKOK1fLnIydZH94P+FcGJ/F17TJjJnsDK+FX+Po7FGqly6KcJqcbAhtYENwA27LIrkookYkqSGEEIvEXKbI3gsxjk4mKFcU2j02HA04GDqWLbH7/Bz7R+OUKgqgtZi6r7+Fjf2ti2JGRKmiEM0WSeXLWEwGQm4LDw356W210+a2otc334ZRCCFEjWSjED4G43sgF9NOdDdyq6lSFsZe1VpnFRLaMZ0BOtZfOpnfU9Pw7ihVgXRY+zl4e6HvQQitlkSGEEKIm6aoChcSF9gX3sdoahS7yc4S7xL0uubqmqCqKmcTZ9k7vZeLySsXRXQ4OtjYvpGVrSsxLJaLImqs8c5mCSGEuCn5UpVDY3FeuxgjnivR7rXhtjbWL6uqqjIWy/HKuTlOTqVQLx0Puiw8tMTPuu7mbzGlqCrJXJlotogOHUG3hXv7Wuj3O2j3WDE2+fsXQghxhyUnYfqI9pWPXWo1tbxxW01l5+DiThjfC9WSdszs1FpM9T6gtZtaLFQVsrPal6sNVr0fOu6SORlCCCFuyVRmiv0z+xmODaNDR4+7B1OTJchL1RKHI4d5LfwasUIMuNJiamP7RrqcXU1ZjVLPJKkhhBBNqqqonA6n2H0+ykQsh89hZmnI1VALbVVROTGV5JVzc0zE8/PHh4JOHlriZ0nQ2VDv51YUylVm00VypQpeu5n1PT6Wt7nobXVgNkoiQwghxAJSFK0V09QhmDmhVTU4ApeSGQ265sQvwvkdWqupy5dFuDpgYAt0bNCGYC8m+QSkpsDug6VPQvc9iyuhI4QQYsHEC3EORQ5xbO4Y+XKedmc7dlPjtbZ+K+lSmr3Tezkwc4BitQiAxWBhQ2gD97bdi9firW2ANZIupYnmo1SVas2Gny+yHZwQQiwO47Ecr16IMhxOYTIYGAw6Meob52REqaKwfzTGK+fmSOS0eRlGvY67ur08sMRPm9ta4whvr6qiEs+WiOaKWAwGOrxW1nQFGfA78Tlqs2EQQgjRxKplmDsDE/shek677Qw1bismVdFaZl14CeIjV44HlsPAVvAvbdyKk1uhKpCPa5UZJrs2M6TnfnAGax2ZEEKIBpQr5zgxd4L9kf3E8jFCjhAdzo5ah7WgIrkIr069yrG5Yyiq1va6xdrCxvaNrAusq9mJ/FoqVUtE81EypQwOs4Mh7xBLW5bS7+mvSTwNk9SIx+M8++yz/PCHPwTgqaee4itf+Qper/e6z1FVleeee45vfOMbxONxNm7cyNe+9jVWrVo1/5gtW7awc+fOq573a7/2a/zt3/7tO3ptIYSohUSuxGsXYxweT5AvV+ny2rGZG6efY6ZYYc+FKK+ej5Iva4O2HGYD9w+0snGgFWcDzgC5GYVylZlUgWKlSovDzEODfoZCLrpb7BhkToYQQoiFVspB5JSWzEiMaNUYrnYwO2od2a2plmBsL1zcAbmodkxvgM57tMoMV3sto7uzVAUKKS2RoVTB5oWu+6DrHvD11jo6IYQQDShbznI6dppDkUNEchG8Fi9DvqGm6Z6gqiojqRFenXqVc4lz88d7XD1s6tjEUt/SpnmvN0pRFRKFBPFCHIPeQNAeZFPHJvrcffht/pr+PBrm7NDTTz/NxMQEzz//PAAf/vCH+dCHPsSPfvSj6z7nS1/6En/+53/Ot7/9bZYuXcpnP/tZHn/8cYaHh3G5rgy3e+aZZ/iTP/mT+ds229W9RG/ltYUQ4k4qVqocm0iy50KUSLpIm9tKl69xyj5j2RKvnJvlwGicclVrDdHiMPPwkJ8NPb6mnpehqiqJfJm5dBGjQUe3z87abi+DAQeuBpt9IoQQokHkYlolw+QBbVC0yaoNijZaah3ZrSllYfQX2syMUlY7ZrJr8zL6Hgaru7bx3SmqCsWUltCpFLX3HVoFoZXg69cSG0IIIcRNypQynIqd4nDkMLO5WVwWFwOegaYZiK2oCiejJ3l16lWms9OANi9jectyNnVsosvVVeMI77xMKUO0EKVcLeO1erkndA+DvkG6XF2Y9PVxnqIhkhqnTp3i+eefZ8+ePWzcuBGAb37zm2zatInh4WGWLVv2pueoqsqXv/xl/uiP/oj3v//9APzVX/0VoVCI733ve3zkIx+Zf6zdbqetrW3BXlsIIe4UVVU5F8nw6vkoF+ayuKxGloZc6Bvk6oGpRJ5dZ2c5NpGcH/7d6bXxyNIAqzrcDfM+bkW5qjCXKZLMl/HYTNzT52NFu5veVodUZQghhFh4qgrJCZg+CuEjWmLD5gP/EOgb4tfCN8sntKqM0d1Xhn/bWmBwq1aV0KhJmptVzkNmBso5sLigdYmWzPD1g6O11tEJIYRoUOlSmlPRUxyJHGG2MIvb7GbQN4i+UedsvUG5Wubw7GF2T+0mWUwCYNQbWR9Yz8aOjbRYW2oc4Z1VqpaIFi61lzI56Hf3s6xlGb3uXlxm19t/gzusIXavr776Kh6PZz6pAHD//ffj8XjYvXv3NRMLFy9eJBwOs23btvljFouFzZs3s3v37quSGt/97nf5zne+QygU4sknn+TTn/70fCXHrby2EELcCZFUgVcvRDk+qS2+fQ00OPriXJYdwxHORjLzx4aCTh5ZGmDA72jakk5VVckUK8ymi1RVlZDbygODrQyFXPidi+TEixBCiDurWoHYeZg8qM3NuDz8O7iicYd/Z2bg/M9h4gCoWrtKXB2w5FFov0trOdXsXj8nQ2/S5p+0r9USGc7g4poZIoQQYkGlSilORbXKjLnCHF6Ll0Fv8yQzCpUC+8L72Du9l1wlB4DdaOe+9vu4J3RP0w07fyuKqhAvxEkUEhj0BgK2ABvbNtLn7iNoD9b1uZmGSGqEw2GCwTcPMQsGg4TD4es+ByAUCl11PBQKMTo6On/73/ybf0N/fz9tbW0cP36cT33qUxw5coTt27ff8msDFItFisXi/O1UKvUW71AIIW5ctljhwGic/aMxkrkynT57Q8yaUFWVs5EMLw1HGI1qGwe9DtZ0enh4KECH1/Y236ExVRWVVKFMPFeiXFFwWkwsCTpZ0+VhMODEaloEJ16anKz5Qoi6pKowd1ZryRS/qN12tjXu8G+AxBic+5nWOutyjWfLoJbMCKxYHCfyK0XIRLQ2U/YW6HkA2laDr29xJHPqgKz7QohmVVEqnIie4LWp15jNz+KxeljiXdI0yYxMKcOe6T0cmDlAsar9O+61eNnUsYm7AndhMtRHW6U7IVvOMpefo1Kt4LF6uKftHga99dVe6u3U9CzYZz7zGZ577rm3fMy+ffsArpkZUlX1bTNGb7z/jc955pln5v+8evVqhoaGuOeeezh48CAbNmy45df+/Oc//7bvTQghbkZVUTk1nWL3+Tkm43laHRaWhlx1nTkHUFSVk1MpdpyJMJUoAGDQ67i7x8cjSwO0OMw1jnDhlasKiVyZZL6ECritJpa3uRgMOOn02Qg4LXX/uYkbJ2u+EKLu5GJw8WVtZoZaBXe3NjejUUXPw7kXYHb4yrHQai2Z4euvXVx3iqpCIalVqOh04O6EoccgsFxLbIg7StZ9IUQzCmfDvDr1KsPxYRwmR1O1mYoX4uye2s3hyGGqlyo8A7YAD3Y+yKrWVU0zG+TtKKpCrBAjUUhgNVrpd/ezvHU5fe4+HCZHrcO7aTVNanzsYx/jgx/84Fs+pq+vj6NHjzIzM/Om+2ZnZ99UiXHZ5RkZ4XCY9vb2+eORSOS6zwHYsGEDJpOJs2fPsmHDBtra2m76tQE+9alP8fu///vzt1OpFN3d3dd9vBBCvJWpRJ5fnJvj1HQKi9HAkqCr7ucuVBWVoxMJdpyZZTatXQVhMujY2N/Kg0v8eGyNkf2/UVVFJZotksiVMOj1eG0m7ulroa/VQafP1nTvV1wha74Qom5USjB1CC7ugmxEO/lt9dQ6qlujqlq7rLMvaO2zQGuX1Xk3DD4KrmvPRGwq1ZJWlVFIgdUFHRu0FlMtA2BsvotCGoWs+0KIZlKsFjkcOcy+8D4ypQxdri4sTTKTai4/x8sTL3N87jjqpQrPTmcnD3U+xFLf0kVzoWG+kmcuP0exUqTF2sKDHQ8y5BuizdHW0D+DmiY1/H4/fr//bR+3adMmkskkr732Gvfddx8Ae/fuJZlM8sADD1zzOZdbSm3fvp3169cDUCqV2LlzJ1/84hev+1onTpygXC7PJ0Ju5bVBm99hsTTHPwJCiNrJFivsH4mxbzROplCh22fHZq7vqwgqisKhsQQ7hiPEc2UArCY9mwZaeWDQj6MBWmXdjFJFIZIukCtVaHGYeXgoQJ/fQafXJq2lFglZ84UQdSF6Xms1FTmtJTICyxtzZoaqQuSElsxIjGnH9Abo2qhVZtibfPD15VkZuTlAryVvBjaDfxm4rn9RnbhzZN0XQjSLsdQYuyd3czF1Ea/Vy6BvsNYhLYiZ7AwvT77MyejJ+WODnkEe7HyQXndvQ5/Iv1GqqhIvxonlY5gNZjqdnaxqXcWAd6AhqzKupSHOLK1YsYInnniCZ555hq9//esAfPjDH+a9733vVYO6ly9fzuc//3ne9773odPp+L3f+z0+97nPMTQ0xNDQEJ/73Oew2+08/fTTAJw/f57vfve7vPvd78bv93Py5En+43/8j6xfv54HH3zwpl5bCCEWkqKonAqn2H0+yngsR8BpoSPkqnVYb6miKBwYjbPzzCyJS8kMh9nAQ0v8bBxobboT/JlihUiqgKKqdHptPLoiyNKQC5dVKjKEEELcQfkEjPwCJvdplRotA9CIV1iqCkwf1dpMpaa0Y3oT9G6CgXeBzVvT8G67ck6ryijnwOaDrvsgtEprryVVGUIIIRZQtpxlf3g/hyKHKCtlet29TTFPYiozxcsTLzMcv9KucplvGQ93PUyHs6OGkd05xWqRudwcuUoOr8XLvW33ssy3jE5XZ9O0E7usIZIaAN/97nd59tln2bZtGwBPPfUUX/3qV696zPDwMMlkcv72Jz/5SfL5PL/zO79DPB5n48aNvPDCC7hc2olBs9nMz3/+c/7X//pfZDIZuru7ec973sOnP/1pDAbDTb22EEIslJlUgV+cm+PkVAqjXseSoBOjvn4Xn0pVYf+lZEYyryUzXBYjDy8NcF9fC2Zj/cZ+sxRVJZErM5cpYDEZWNrmYm2XhwG/s6nepxBCiAZQrcD0EbiwA9LTWqspr7fWUd28y8mMs89DOqwdM1ig7yEY2AKW+r6o4x1RFchFIRvVEheeLmi/C/xDMitDCCHEgqsqVc4lzrFneg8T6QlCjhAeS4O2qXyd8fQ4L0+8zLnEufljK1tX8nDnw4QczV/lqKoqyVKSWD6GXqen3dHOav9q+j39TfH5Xo9OVVW11kEsBqlUCo/HQzKZxO121zocIUQdypeqHByLsfdijFS+TJfPjt1cv7nn8qVkxq7XJzOsRh4ZCnBffwsmQ/Oc5K9UFWYzRZL5Ml67iZXtblZ1eOjy2RZF6Wo9qud1tZ5jE0I0ifQMnP+5lgww27WERqNdfXetZIbRCv2bof8RMDdHa4RrUipaVUY+rrXTal8HweXg7dVabYmr1Pu6Wu/xCSGEoiqMpEY4OHOQC8kLGHQGOp2dDT8geyw1xs6JnVxMXgRAh47V/tU81PkQAXugxtHdfuVqmbn8HNlyFpfZxaB3kGUty+hx9WDU1++5pLdzo+tq475DIYRoEqqqcmYmwy/OzTEazdLqsDAUdNXtyfJyVWHfSIxdZ2ZJFSoAuK1GNi8NcE9fcyUzcqUKM6kiFUUh6LLwwGAry9rctDikDYQQQogaqJZh8oBWnZGLga8XTPZaR3VzrpfMGNiiJTMa7f3cjEpRq6op57VZGf0PQ2i1VGUIIYS4LVRVZSI9wcHIQc7Gz6Ki0u5ox2q01jq0d2Q8Pc6O8R3zyQy9Ts9a/1oe6nyIFlvzr6n5Sp5wRttDBe1BHuh4gAHvAC3W5n/vrydJDSGEqKG5TJHd5+Y4NpkEdAwGnBjrNClweWbGjuErlRkem4nNSwPc3etrmmSGeqnF1GymiNmop89vZ12XlyVBZ9PNBRFCCNFAkpNadUb4xOsGgdfnBRDXtJiTGaWMlsxQVa0ao+teCK7QqmyEEEKI22A6M82h2UOcjp6mrJRpc7Rhb/C1diI9wY7xHVxIXgC0ZMZdgbt4qPMhvFZvTWO7U2Zzs6RKKZb7lrPKv4o+d19TzEO5FZLUEEKIGihWqhydSPKLc3PEsyU6fXaclvr8J7mqqBwai/PScIT4pQHgbquRLcuC3NPrq9skzM16Y4up+/pbWNXhpttnR69voJNGQgghmkulBON7YWQX5JPa4GhTA11hqaoQPgpnfrK4khmqCvkYZGe1GSGBFdB5tzYvY5GefBBCCHH7zeZmORw5zMnYSXLlHG2ONpxmZ63Dekcm05PsmNjB+cR5QEtmrAus4+HOhxdNMqOqVBlLjWEz2Xi893HWBdY1dIuphbC4370QQtTAhVmt1dT52Sxum4mhkAt9HV5pqagqR8YTvHg6QjRbAsBpMbJlWYB7m6jNVKWqEE4VyJWqhDxWaTElhBCifsRHteqMyGmtRVFwRa0junGqCpFTcObHkJzQji2GZMbleRmFBFi90PMAtK8FX19jVdYIIYRoKNlyloMzBzkye4RUKUXIHqLD2VHrsN6RqcwUO8Z3zA8A16HTkhldD+Oz+mob3B2ULWeZTE/S6+5lc/dmul3dtQ6pLkhSQwgh7pBkrsyrF+Y4PJ6gUlXpa3VgNtZfYkBRVU5MpfjZqRlm00UA7GYDm5cG2NjfWpcx34qKohBOasmMTq+NbataWN7mkhZTQgghai+fgIn9MLYHyhloGQCjpdZR3bi5MzD8Y4iPaLcNFhjYrCU0mjWZUc5rlSjVIjhD0PegNi/D4a91ZEIIIZpYVakyHB9m7/RepjJTBOwBhnxDtQ7rHZnJzrBjfAfD8WFAS2asDazl4a6HF9XcCFVVieQi5Mo57m27lwc7H8RhctQ6rLohSQ0hhLjNKlWF41Mpdp+bI5wq0O6x4bHVX9sBVVUZnkmz/eQM08kCADaTgYeH/GwabMVibI6T/RVFYSZVJFMs0+G18djKECvb3ZLMEEIIUXulHEwfgdFfaCfInSHwdNY6qhsXu6AlM6LaFZXoTdD3MCx5FzR464vryicgOwM6g1aN0Xm3Nu9E5mUIIYS4zcLZMHun9zIcG8ZkMDHoHcSgb9zfa6P5KDvGd3AiegLQkhlr/Gt4pOuRRTEA/PUqSoWx1Bgus4tf6v8l1vjXoNc1xwWmC0WSGkIIcRuNx3L84twcwzNp7GYjS+u01dTFuSwvnAgzGssBYDHqeWiJnweX+JvmZH9VUZlJFUgXKrR7rbxreYCV7R5s5uZ4f0IIIRpYtQwzx2HkF5AY1doWBVdAo/zymhiH4X+B2dPabb0Beh6EJY+B1V3b2G6XfFwb/m1xQec90L5Oq6hp4JNJQgghGkOunONw5DAHIwdJl9J0ujqxGW21DuuWJYoJdo3v4sjsEVRUAFa2rmRz12YC9kCNo7vz0qU009lpBj2DbOneQpujrdYh1SVJagghxG2QLpTZNxLnwEicXLlCt89el8mByUSe7SfDnJnJAGDU63hgsJVHhgLY63Rw+c2qVBUi6SLpQpk2j5UtywKs7HBjNzfH+xNCCNHAFAWiZ2HkFZg7q7WYal0KhgZZozIzWmXG9BHttk4P3RthaBvYmrTXdSkLyXGtjVbfw9B1L7jbax2VEEKIRUBRFc7Gz7J3ei8TmQlara0s8S2pdVi3LF1K88rkKxyYOYCiKgAM+YbY2r11UZ7Iz5QyRHIRjHojm9o3saljU0Mnq263BtktCyFEY6gqKqemU/zi/ByT8Twhl5VOX/0tQrPpIttPzXB8MgmAXgf39LXwrmVB3HXYGutWFCtVZpIFChWFNo+VR5YGWNXhxtEkyRohhBANLj4Ko69qFRqo4OvVBmk3gnwczvwUxvcCKqDT2i4tfaJ5Z0hUiloyA6B9PfRu0j4zIYQQ4g6YzkxzMHKQk9GTGPQGBjwDDdtqKl/J84vJX/Ba+DUqSgWAfk8/W7q3LLoh2KqqkigmmMvPYTfaWdG6gjX+NfS5+9DVYZePevKOzuyUSiUikQiKolx1vKen5x0FJYQQjWg6mWf3uTlOTqcwGwwMBV0Y9PW1CCVyJX5+OsLB0fjlUxCs6/by6PIgrc4GGkD6FnKlCuFUAVWFbp+N9T0+lskAcHEDZF8jhLjtFEWbOzF1ECKnoJwDT1fjzJsoZeDcz7TKkksnIQithmXvad5qBaUCqSnts2pdolVn+JeCvkFag4lrkjVfCNEowtkwR2ePcip2ilw5R4ezA7upMec2laolXgu/xi8mf0GxWgSgy9nF1p6t9Hv6axzdnaWoCnP5OZKFJG6Lm43tG1nZspI2R5skM27QLSU1zp49y2/91m+xe/fuq46rqopOp6NarS5IcEII0QgyxQqHRuO8NhIjXdBaTdXbnIZcqcLOM7O8ej5KRdF6VK5oc/H4yjbaPA1yVejbSOXLzKQKmIx6lgScrO/xMRBwYDLISQfx1mRfI4S47SpFmB2Gyf0QvQBqBZxt4G2QE6iVAlzYARde0t4LQMsgrHgv+Jr0JISqQCYCuaj2Oa14L4TWgNFc68jEOyBrvhCiUYSzYY7NHuNk7CS5co6gPUiHs6PWYd2SqlLlUOQQuyZ2kSlrra+D9iDv6nkXQ96hRXUSv1wtM5ufJVfO0WptZWvPVpa1LKPFurgGoS+EW0pq/OZv/iZGo5F//ud/pr29fVH9zyeEEJdlixVOTKXYPxIjnCrgd1pYGnLVOqyrlKsKu89H2XkmQqGsXYnW1+rgiVUhelodNY7unVNUlXi2xFymiMNi5K4eL+u6vPS02NHXWZWMqF+yrxFC3DbFNMychIl9kJwAvRFc7WBukCsslQqM/gLObteqNADcXbD8PRBYDs3476WqQHYWslGtldbKp7TWWubG3zcJWfOFEPUvkotwdPYoJ6MnyZazDZ3MUFWVE9ETvDT2EvFiHACvxcvW7q2s9q9eNP8GFytFEsUE6VIag85AyBFic9dmhnxDOEyyv7hVt5TUOHz4MAcOHGD58uULHY8QQtS9XElLZuy7GGMmVcBtM9Vdq6mqonJoLM7PTs2QKmjtIdrcVn5pVYilIVfDbx4qisJsukgqX8ZnN/PwkJ9VnR7aPfU3v0TUP9nXCCEWXHYOpo/C5AHIRrT2Ui39YGiQq/xVBaYOaUPAc1HtmCMAy94N7eu0geDNRqlqn1U+BvYALHsSOu4Cu1w52UxkzRdC1Ku5/BxHZo9wMnqSTClDwB6g3dmYrR1VVeV84jwvjr1IOBcGwGFy8EjXI2wIbmjYWSA3I1vOkiwmyZazWAwWfBYfa/1r6XJ10eXqwtwoe8I6dktJjZUrVzI3N7fQsQghRF3Ll6qcnE6ybyTOdCKPy2piSZ0lM1RV5dR0mp+eDDOb1tpDeG0mHlsZ4q5uL/oGT2a8fvh30GXhgcFWVrS78dplQyBunexrhBALKnIKTv2z1rrI3gL+ZdBIv7zPnYFTP7oyFNvihqW/BN33N9b7uFFKFTIzUEiAIwjL/z9oXws2X60jE7eBrPlCiHp0Onaal8ZfIl6IE7AHaHO01TqkWzaZmeRnoz9jNDUKgMVg4YGOB9jYvrHpT+RnShlihRilagmb0UbAHmBT+yY6nB2EHCFMelOtQ2wqN5zUSKVS83/+4he/yCc/+Uk+97nPsWbNGkymqz8Ut9u9cBEKIUSNFcpVTk5rlRlTyTxOi4nBoBNjnQ2IHI1m+cnxMGOxHAA2k4GtywJsHGht+LkSmWKFmVQBuDL8e2nIVXezS0TjkH2NEGLBqapWmTH8Y61tU3BFY7VnSk7A6R9psz8AjBYYfBT6N2t/bjZKBdJhrUWYqw0GtkDbGrDKv/nNRtZ8IUS9UlSFA+EDvDz5Mga9gSXeJQ3bVSFWiPHi2IucjJ4EwKAzcG/bvTzU+VDDDja/EYqqEM1HSRQTOEwO+tx9DHoH6XB24Lf50TdjdWuduOGkhtfrveovlqqqPProo1c9RoZrCSGaSbmqcHo6zZ6LUSbjeRxmI4OB+ktmzGWK/PREmBNT2i9sJoOOBwb9PDIUaOiT/pfnZUSzRawmA8vbXKzr9jLgd2Bs8CSNqD3Z1wghFpRShQu74PzPwWQDX3etI7pxuaiWiJk8oN3WGaD3QRjaBhZnbWO7HaplSE9rM0LcnTD0OIRWN+d7FYCs+UKI+lSsFnl54mX2z+zHZ/HRYmvMdofZcpZdE7s4MHMARdXmeK4LrGNL9xY8Fk+No7t9ipUikXyEQqVAq7WVhzsfZsg3RMgeatjEVKO54aTGSy+9dDvjEEKIuqEoKhfmMuy5EOP8bAar0VCXJ9IzxQovnY6w92IURQUdcHevj0dXhPDYGressVRRiKQLZIsVfHYz9w+0sqrDQ5fPJpsDsWBkXyOEWDCVIpx9AUZ+oQ2WtrfWOqIbU8rBue0wsktLygB0bNDmZjj8tY3tdqiWIDUF5QJ4u7T3GVrVOEPbxS2TNV8IUW9SpRQvjmpVDe3Odpzmxkusl6ol9kzvYffUbkrVEgCD3kEe63mMkCNU4+huD1VVSZVSRPNR9Do9nc5O1vjXMOAdkIHfNXDDSY3NmzffzjiEEKIujMdy7BuJcXI6hQ7oabFjMdZXtUO5qvCLc3PsPDNLsaJdCbE05OSJ1e20ua01ju7WpfJlZjNFUKHDa2Xr8iBDQafMyxC3hexrhBALopjR5mdMHQRPF1hctY7o7VUrMPoynN0OZa1lJf6l2iwJbwNVmNyoShFSk1qFhq8Xuu+D4CowNe6eSdwcWfOFEPVkJjvD9tHtjKXH6PH0YDE0VotHRVU4FDnEzvGdZMoZANod7TzW+xj9nv4aR3f7xAtx5nJzuCwu1gbWsqJlBV2uLoz6WxpXLRbALf3kv/Wtb+F0OvnABz5w1fG///u/J5fL8Ru/8RsLEpwQQtwpc5ki+y7GODaZJF+q0um1YbfU1+KkqCqHxxJsPzVDMl8GoMNj5YnV7SwJNt6VHQBVRWUuUySRL+E0m1jV4WZ1p4e+VgdmY31VxojmJfsaIcQtyUbh1D/BzClo6dfaTtUzVYXpw3D6n7WWUwCudljxFASWN9b8jxtRLmjJDLUKvgHouQ8CK8AoF0ssZrLmCyFq6XziPC+OvkisGGPAM4BBX18XUL4VVVU5mzjLz0Z/xlx+DgCvxcu7et7FqtZVTdtVoVQtMZGewGa08XDXw6xsXUmrrUGqcpvcLZ2x+8IXvsD/+T//503Hg8EgH/7wh2UjIIRoGOlCmUNjCQ6MxknkSrR5bHT56q8NwblIhp8cn2Y6qQ3L9tpMPL4yxLpuL/oG3DxUFZVIukAqXybosvKuZUGWt7sJNXCliWhcsq8RQty05ASc+EeIj4J/CRjq/ER57AKc/CdIjGq3LW6t/VL3fdBsAyxLOUhPgor22XTdB4FlYGjc1pxi4ciaL4SoBVVVOTJ7hF0Tu6ioFfo9/Q2VBJjOTLN9dDsjqREAbEYbj3Q9wj2hexoqMXMzVFVlNj9Luphm0DvIA50P0OnsrHVY4nVuKakxOjpKf/+bS4p6e3sZGxt7x0EJIcTtVihXOTGVZO+FGDOpAq0OC0tDrrrbWETSBZ4/HuZ0OA2A1aRny9IgmwZbMdXZjI8boahaZUY8WyLktvLI0gAr29046qwqRiwusq8RQtwwVYXISTj9Y63aIbAM6vmX+UxEq8wIH9VuG8ww+C4Y2ArGxmp38bZKWW1mhk4H/mXQvRH8Q/X9+Yg7TtZ8IcSdliql2B/ez4GZAzhMDtrt7bUO6YYli0leHHuRY3PHADDoDGxs38hDnQ9hNTbvBYn5cp6JzAQ+i49tfdtY41+DSS6OqDu3dBYpGAxy9OhR+vr6rjp+5MgRWlulBEcIUb8qVYXhmTR7L8QYi2VxWkwsCbow6OsrmZEpVnjx9AyvXYyhqKDXwcb+Vt61PNiQCQBVVYnnykTSBVocZratCrG2y4vLKhsDUXuyrxFC3JD0DIy8DFOHteoG/9L6bdlUysKZn8LoK6AqgA567oelT4LVXevoFlYxA+kp0Bu1wd9d90LrEtA33sUf4vaTNV8IcadUlAqnoqfYG95LJBuhzdmGy9wAs7eAQqXAK5OvsHd6L1W1CsAa/xq29mzFa/HWNrjbSFEVprPTlColVvtX80DHA/ht/lqHJa7jls6MffCDH+TZZ5/F5XLxyCOPALBz504+/vGP88EPfnBBAxRCiIWgqioj0Rx7L0Q5G0lj1Ovp9zvrrtqhXFV49XyUl4Yj80PAV7S5eGJ1OwFXY15RmcyXCSfzuG0mtiwLsr7bi89R5206xKIi+xohxFsqZWF8H4zthnwcPN31OxB8fgj4C1DOa8eCK7W5Ga622sa20AopSE9r1Sdta7VWWr5+SWaItyRrvhDiTpjMTLJnag/nEuewGq0M+gbRN0C7x6pS5cDMAXZN7CJXyQHQ6+7l8d7H6XB21Di62ytdShPOhgnag2zq3cRy3/Kmba3VLG4pqfHZz36W0dFRHn30UYxG7VsoisK/+3f/jj/7sz9b0ACFEOKdCicLvHYxyonpFJWKQqfPjtVUX4uTqqocm0zy0xNh4rkrQ8CfXNPOYKAxh4CnC2WmkwXsZgP3D7Ryd5+PoKt5S1RF45J9jRDimpQqzJyAi7u0WRT2Vm3QdD1WZ6iq1mLq1I8gpw3vxNUBK39Za5HVLFQFCklIh7XB7J0boOseLZlRj5+LqDuy5gshbqdMKcOBmQMcnj1MoVKg09mJpQHaPV4eAr59ZDvRQhQAv83Poz2PstS3tO7adC8UVVVJlpLM5eawGq3cG7qX+9rvw2Px1Do0cQN0qqqqt/rks2fPcvjwYWw2G2vWrKG3t3chY2sqqVQKj8dDMpnE7W6ykm8h6lQiV+LgWIKDY3HS+TIdXltdtjsaj+X4l2PTjMW0KyHcViPbVrZxV09jDgFPF8qEkwUsJj3L29zc299Cp9dW67BEk7kd6+pC7WtkzReiCSTG4OLLWlLDYAJPl9beqB4lxuDkP2rDwKH5hoCrKhRTkJ2DaklrnxVYoSUzvD2SzFgE6nnNv13xCSEaS0WpMBwbZu/0XsK5MAFbAK/VW+uwbkg4G+aFkRfmh4DbjXa2dG9hQ2hDQ1SX3ApFVYjmoySKCdxmN8t8y1jRuoJOZ2fTJnAayY2uq7e0M/+TP/kT/tN/+k8MDQ0xNDQ0fzyfz/Pf//t/54//+I9v5dsKIcSCSObLHB1PcGAsTjxbIuCy0tFWfyfVE7kSL5yc4fB4AgCTQccjSwM8vCSA2dh4m4dMscJ0Io/ZpGddt5cNPT66W2yyKRB1T/Y1Qoh5+TiM7YWJ16CUA283mOy1jura8nE4/S8wuV+7rTfB4FYYfLTxh4CrKhTTWtVJpaAlagJLIbgKfH3gkNkH4tbImi+EWGjhbJhXp17lTPwMFqOFQW9jtJpKl9K8NPYSh2cPA4tjCHhFqRDJRciWs7RaW9nctZllLctkbkaDuqVKDYPBwPT0NMFg8Krj0WiUYDBItVpdsACbhVy9IcTtlylWODaR4MBonEi6SIvDjN9pqbtqh2Klyq4zc7xybpZyVfsneEOPj20rQ7ht9VdJ8nYyxQrTyTxmo57lITd390oyQ9x+C7muLvS+RtZ8IRpQPg6Th7RkRi4KzpDWbqoeVYpw/udw/iVQtJaVdN0Ly94DNm9NQ3vHSjnIRqCc0+aWeLq14d++fnAGah2dqJF6XvMXOj4hROMoK2WOzh5lz9Qe0qU0na7OhkgGlKolXp16ld1Tuylf2kesal3Foz2PNkx1yc0qVArM5GaoKBXa7G2sC65jyDuE09yYrb6b3W2t1FBV9Zonq44cOUJLS8utfEshhLhl+VKV41NJ9l2MMZMq4LGbWRpy1V0yQ1FVDo8leOFkmFShAkBfq533rOmg01d/lSRvJ1OsEE7mMRn1rOn0cHevj54WuyQzRMORfY0Qi9jrkxnZOXD4IbC8Pts2qQpM7NOqM4op7VjLAKx8n1ZR0sjyCcjMgMGoVWKEVl9KZASlvZRYULLmCyEWQiQX4ZXJVxiODeOxeBj0DdY6pLelqipH547y4tiLpEtpALqcXWzr20aXq6vG0d0ehUqBcDYMQI+rh7WBtQx4B7AYGryiVQA3mdTw+XzodDp0Oh1Ll149KKZarZLJZPjt3/7tBQ8SIB6P8+yzz/LDH/4QgKeeeoqvfOUreL3e6z5HVVWee+45vvGNbxCPx9m4cSNf+9rXWLVqFQAjIyP09/df87l/93d/xwc+8AEA+vr6GB0dver+P/zDP+QLX/jCArwzIcStKpSrnJxOsW8kxlS8gMtqZEnQhUFff7/8XpzL8uNj00wm8gD47CaeXN3Oqg53wyUBKorCeCyPDljd6WFDj4/eVklmiMZTy32NEKLGrpXMCK6oz2QGQPS8NjcjOa7dtrfCiqegbW3jnvRXFcjOapUxFpdWbdJxl5bM0Nfp5yAalqz5QoiFUFbKHJ87zqtTr5Iqpuh2dzfECfKx1BgvjLzAVHYKAK/Fy6M9j7KydWVT/h6fr+QJZ8Po0NHv6Wd9cD197j4MekOtQxML6KaSGl/+8pdRVZXf+q3f4rnnnsPjuTIN3mw209fXx6ZNmxY8SICnn36aiYkJnn/+eQA+/OEP86EPfYgf/ehH133Ol770Jf78z/+cb3/72yxdupTPfvazPP744wwPD+Nyueju7mZ6evqq53zjG9/gS1/6Ek8++eRVx//kT/6EZ555Zv620yklSkLUSrmqMBxOs/dilPFYHrvZwGDQgbEOfwGOZ0v85ESY45NJACxGPVuXBXlgsBWjof7ifTuJXIlwqkBvq51HhgIsCTqbchMkFoda7muEEDXSaMmMXBRO/RCmj2i3jRYY+iXoe0SramhE1RKkZ6CUBnsAlmyD9jXgaqt1ZKKJyZovhHin5vJzvDLxCqfjp3GZXQx4B+r+d+F4Ic7PRn/GqdgpAMwGMw93PszG9o0Y9Q26j3gL+fKlZIZOx6BnkLuCd0kyo4nd1P/Bv/EbvwFAf38/DzzwACbTnen9furUKZ5//nn27NnDxo0bAfjmN7/Jpk2bGB4eZtmyZW96jqqqfPnLX+aP/uiPeP/73w/AX/3VXxEKhfje977HRz7yEQwGA21tV2+ef/CDH/Brv/Zrb0pauFyuNz1WCHFnKYrK+dkMey/GOD+bwWLQ0+93YKrD5ECxXGXnmVleOTdHRVHRAff2tfDYyhBOS+NtHipVhbF4DpNex+alAe4faMXRgO9DiNer1b5GCFED5TxMHoCR3drMhnpPZpQLcG47XNwBShXQQc8mWPakVtXQiKolSIyDUgFPJyzdpn0GVs/bP1eId0jWfCHEraooFa06Y/pVEoUE3a5uLMb6rs4oVoq8PPkye6f3UlWr6NCxPrieLd1bmnKOxOVkhl6nZ9A7qFVmePoaYmC7uHU3fEYqlUrND+dYv349+XyefD5/zccu9HCsV199FY/HM5/QALj//vvxeDzs3r37mkmNixcvEg6H2bZt2/wxi8XC5s2b2b17Nx/5yEfe9JwDBw5w+PBhvva1r73pvi9+8Yv86Z/+Kd3d3XzgAx/gD/7gDzCbzdeNuVgsUiwW52+nUqkbfr9CiDcbi+bYNxLlVFjr/djTYsdirL9su6KqHBpL8MKJMOmiNjdjIODgPWvaafc03twMgFi2xGy6QJ/fwealAQYCzbcJEovPQu5rZM0Xoo4pCkROwsVdEL8INl99JzNUBcb3anMzShntmH8prPwVcHfUNLR3JDunJZP8S6F7IwSWaVUnQtwBC30uQ9Z9IRaHfCXPeHqck3MnOR0/jdPkZNA7WNfVGYqqcChyiB3jO8iWswD0e/rZ1ruNkCNU2+Bug0KlwHRmGr1Oz5BviHXBdfS5JZmxWNxwUsPn8zE9PU0wGMTr9V7zL/HloVvVanVBgwyHwwSDwTcdDwaDhMPh6z4HIBS6+i9tKBR603yMy/7yL/+SFStW8MADD1x1/OMf/zgbNmzA5/Px2muv8alPfYqLFy/yf//v/71uzJ///Od57rnn3vJ9CSHe3kyqwP6ROMcnExQqCp0eG/Y6rRAYmcvyL6+bm9HiMPPu1e2saHfV9cbnespVhbFYDotRz7uWB7mvvxWbuf4SSULcioXc18iaL0SdSozBxZdh5gTojdC6tL5bNkXPwYkfQGpSu+0IwMpfhuCqxp2bUa1oySSjBZY+Cb0PgMla66jEIrPQ5zJk3ReieZWqJSYyE4wkRzgbP0u8GEeHjk5nJ1Zjfa9fF5IXeGHkBSK5CACt1lYe73ucIe9QQ56PeCvFapFwJoyKqlVmhNZLMmMRuuFd/YsvvkhLS8v8nxfiL8RnPvOZt90M7Nu3D+AtNx5v5Y33X+85+Xye733ve/y3//bf3nTfJz7xifk/r127Fp/Px6/+6q/yxS9+kdbW1mu+7qc+9Sl+//d/f/52KpWiu7v7LWMVQlyRyJU4MBrn8HiCVKFCu9tKt60+y8Tj2RLPnwhz7HVzM961PMimgcacm6GoKrFMiWi2yJKgk81Lg/S02msdlhALaiH3NbLmC1Fn8nEY3QOT+6CUA083mOt4HcvOaXMzwke120YrLH0C+h7SkjGNKh+H1BT4l8CSx6F1sNYRiUVqoc9lyLovRHOpKBWms9OMpkYZjg0TLURRFAWP1UOvu7fuZ09E81G2j27nTPwMAFaDlc3dm7kndE/TzZIoV8tMZ6epKlV63b1sCG1gwDPQdO9T3Jgb/pu5efPm+T9v2bJlQV78Yx/7GB/84Aff8jF9fX0cPXqUmZmZN903Ozv7pkqMyy7PvwiHw7S3t88fj0Qi13zO97//fXK5HP/u3/27t437/vvvB+DcuXPXTWpYLBYsFimpFuJmqarKyekUO4dnmUkVCLisLAvVZ9umYuXS3IyzV+Zm3NPXwuMNOjejqqjMZYokciV8djOPrQhxb38LVpNsEETzWch9jaz5QtSJShGmDsPIy9ogalebltCoV9eam9H7ICx7Ahq537VShfgI6A2w5DHofxjMjlpHJRaxhT6XIeu+EM2hUClwbO4Yp6KniOQilJUyLrOLLmcXJkN9XlD5eoVKgV0Tu3gt/BqKqqBDx71t9/JI1yPYTXV8McctqCgVZrIzFKoFupxd3NN2D0u8S+o+4SRur1v69B988EE2b97Mli1bePDBB3E4bm2T6vf78fv9b/u4TZs2kUwmee2117jvvvsA2Lt3L8lk8k2toi7r7++nra2N7du3s379egBKpRI7d+7ki1/84pse/5d/+Zc89dRTBAKBt43n0KFDAFclS4QQ71y2WOHls7PsH41j0usZCrnQ12GZpKKqHB7X5makCpfmZvgdvGdtY87NKFcVIukimUKZgMvC4ytDrOzw0OK4/twgIZrJQu1rhBA1oqowdxYu7tT+a3VDcHmdz814DYb/BYrarDBtbsb7wN3gv18Uklr7LG8vDD2uzc4Qoo7Imi+EUFWVi8mL7J7azVh6DKfJSZujre6Hf1+mqAoHZw6yY3wHuUoOgCXeJTze+zgB+9uf02wkiqowk50hW87S4ezg7tDdLPMta4ikk7j9bimp8d73vpedO3fy1a9+lUKhwN133z2/MXjooYdwOhf2yqIVK1bwxBNP8Mwzz/D1r38dgA9/+MO8973vvWpI+PLly/n85z/P+973PnQ6Hb/3e7/H5z73OYaGhhgaGuJzn/scdrudp59++qrvf+7cOXbt2sWPf/zjN732q6++yp49e9i6dSsej4d9+/bxiU98gqeeeoqenp4FfZ9CLGaj0Swvno5wcS5Lh8eGu05bTY1Fs/zzsWkm4lfmZjy5uo2V7e6G61NZrFQJJwuUqgrtHitblwVY3u5uyCoTId6JO72vEUIsoFxMm5sxeQDUqtbiyFDHSfno+UtzMya0246ANgQ8uLJx52aAVp2RHNf+2/cwDGzRkktC1BlZ84VY3BKFBK+FX+PY3DFUVaXf099QV/ufT5znhZEXmM3PAuC3+dnWu40lviU1jmxhqapKrBAjlo8RcoR4pOsRlrcsr/u5JuLO0qmqqt7qk6vVKvv27WPHjh3s2LFjvj9lsVhcyBgBiMViPPvss/zwhz8E4KmnnuKrX/0qXq93/jE6nY5vfetb/OZv/iag/SV47rnn+PrXv048Hmfjxo187WtfY/Xq1Vd97//yX/4Lf/M3f8Po6Ch6/dVXdB08eJDf+Z3f4fTp0xSLRXp7e/ngBz/IJz/5Sez2Gy/nSqVSeDwekskkbrds8IW4rFRR2D8SY/f5KIVylZ4We13OoUjmyzx/fJojE9rcDLNRz9ZlQR4cbLy5GflSlelkHlWFnlY763u8LA25pM2UaCi3Y11dqH2NrPlC3AGVEkwdgou7IBsBdydYPbWO6vpyUW1uxvQR7bbRCkt/SUsANNDJlGu6PDvD2wOD74JQAw82F3Wpntf82xWfEGJhlZUyJ6Mn2Tu1l7nCHO2OdpwN1OpxLj/H9tHtnI2fBcBmtLGlewsbghuabp5EtpxlKjOF2+xmfXA9dwXvwmGSqrrF5EbX1XeU1Dh9+jQ7d+5kx44d7Ny5k1KpxMMPP8wPfvCDW/2WTUs2OkK8WSRd4KXTEU5Np2lxmPE766/cs1RRePncLLvOzFKuanMzNvT62LYyhMtan9Uk11OqKEwm8qioLAk4Wd/jYzDgaLikjBBwe9bVhdrXyJovxG0WPa+1mooMa9UArrb6bTVVKcC5n8GFHaBU0OZmPADLnmzsuRkA1RLER8Foga77oO9Bqc4Qt0U9r/m3Kz4hxMKZzEyyZ2oPZxNncZgchOyhhumykK/k2TWxi33hfSiqgl6nn5+bYTM2Xuvrt1KqlpjKTKHX6VnRuoJ7Q/c2XTstcWNudF29pcuCfu3Xfo1du3ahKAqPPPIIjzzyCJ/61KdYu3btLQcshFg8FEXl+FSSHcOzxLJFelscWOqsSkBVVY5OJnn+eJhkvgxAb6ud967poNPXWJuHqqISThXIFiv0+x3cP9DKUNCJXt8YGzkhbjfZ1wjRIPIJGPkFTO7ThoK39Gsn1OuRqsDEPjj9L1BMacf8S7VWU+6Omob2jqkqZGa0Co3AUhjYqrX9EqIByJovxOKRLWc5OHOQQ5FD5Ct5ulxdWAx1um94A0VVODBzgB3jO8hXtNbXQ74hHu99HL/t7WcTNxJFVQhnw+Qrefrd/dzbfi/97v6GSTyJ2rmlpMbf//3f4/f7+c3f/E22bt3Kww8/LL0nhRA3ZCKeY9/FOMenklhNBpYE628Y+EQ8xz8fnWYspg3d8tpMPLG6jTWdnoZaWFVVZS5TIpot0u6x8uiKIKs63FiM9ZVAEqLWZF8jRJ0r5yF8HEZehtS0Nkzb66t1VNcXu6DNzUiOa7ftflj5yxBa3fhtmUpZSIyCvRVW/Qp03l2/iSUhrkHWfCGaX66c41TsFIdmDjGTnyFgC9DubK91WDfsXOIc20e2z8/NCNgCbOvbxqC3uS4geOPcjHd1v4vlLctlCLi4YbeU1IjFYuzatYsdO3bwX//rf+XEiROsW7eOLVu2sGXLFp588smFjlMI0eDmMkX2j8Q4OpEkV6rS6bXhqLOB1Kl8mRdOhjk4lgDAZNCxeWmQh4f8mBqsRVMyX2Y6mafFbubxFSHW9/pkALgQ1yH7GiHqVLkAMydgbDckxsHihODy+m01lYvCqR/B9GHtttEKQ9ug7xEwNPgafHkQeLUMXfdA/xZwhWodlRA3rZHW/NOx0+h1enrdvQ1zdbkQtZQr5xiOD3Nw5iCRXASn2ckS7xL09bpveIO5/BwvjLzAucQ5QJubsbV7KxtCGxrmPdwIVVWJF+PM5ebwWrxs7t7MusC6hppxIurDO5qpcdn58+f57Gc/y3e+8x0URaFarS5EbE1F+myKxSpVKHNoNM7BsQTxXIk2txWv3VzrsK5Srir84twcO4ZnKVUVANZ3e9m2qg2PrbGuEsiVKkwm8lhNBtZ2eri3v6UuZ5UI8U7dznX1ne5rZM0X4h2qFC8lM17VZjaYHeDqqN/EQKV4aW7GS1fmZvTcD8veDRZXraN7ZypFSIehnANPFwxs0SpO9M1zckXUv3pe829nfH994q+ZykwRsAdY2bKSfm9/Q80CEOJOyVfyDMe0ZMZMbgan2UnAFmiYAdr5cp6dEzvZP7N/fm7GfW338UjXI1iN1lqHt2BUVSVRTDCXn8Nj9rDav5rV/tW02lprHZqoM7d1pkYsFpsfqrVjxw5OnDhBS0sLv/zLv8zWrVtvOWghRPPIl6ocn0qy90KUSLqI32FhWchVV5twVVU5PpXiJ8enSeS0uRndPhvvXdtBd4u9xtHdnNcPAV/Z7ub+gdaGew9C1Irsa4SoE5USRE7A2B6Ij2iVDq1LoF7bEFxrbkbrElj1PnB31ja2d6qY1pIZqODp1tpMhVY2fpJGLHqNtOYrqoLVZCVfyfPi+Is4wg66Xd0sb11Ov7sfu0n2+mJxK1QKDMeHOTRziKnsFE6Tk35Pf8MkM6pKlf0z+9k5vpNCtQDAUt9SHu99vKlO9L8+meE2u9nUsYm1/rVN9R5FbdxSUiMQCOD3+3n44Yd55pln2LJlC6tXr17o2IQQDShfqnI2kmbvxRgTsTxum5GlofqbmzEZz/Mvx6YYiWpzM9xWI0+sbmNdl7euEi9vp6qohJMFsiUZAi7ErZJ9jRA1Vs7D3BktmRG7oM1oaBkAQ31Vdl7lmnMznoLQmsadm6EqkI1Cbk6rjgmtgo71WqLGWMefhRA3odHWfJPeRNAeJOQIkSlluJC8wNn4WXxWH8taljHoHaTd0Y5RX6eVbELcBlWlynB8mH3hfUxnprGZbAx4BhommaGqKmcTZ9k+sp1oIQpA0B5kW+82BrwDNY5u4VwrmbHGv6bpBp2L2rmlle/QoUMMDAzMD9QaHR3ly1/+MitXrmTbtm0LGqAQov4l82Um4jlG5rKcn80Qz5axmgwMBhwY62wWRapQZvuJGQ6OxVHR5mY8PBTgkaEAZmN9xfpWVFVlNlMkli3R7rXx2Mogqzo8DfUehKgXsq8RogbKBW3g9Nw5mD0F2TmtIqPekxlvmpthgaFfauy5GdUSpGeglNYGgA9shbbVWrupRk3QCHEdjbzmO81OnGYnVaVKvBhn99RuDswcwG/zM+QdosvVRbujXYbsiqYWzobZO72X4dgwJoOJXk9vQyX1IrkIL4y8wIXkBQDsRjtbe7ayPri+aeZmqKpKsphkNj+rJTPaN7EmIMkMsfBu6W/+f/pP/4n3v//9/PZv/zaJRIKNGzdiMpmYm5vjz//8z/noRz+60HEKIepMNFNkPJ7n/GyGsWiOZL6MQQceu5l+f/0lM8pVhVfOzbHzdXMz7ur2sm1lqO5mfLydRK7ETKqAz2Hml1aFWNctQ8CFeCdkXyPEHXI5kRE9D5GTWoJAVcDmu5TMqOMTceUCnNsOF3c2x9wMVYVCAjIRLXHh7oShx7VB7DZfraMT4rZphjXfoDfgt/nx2/wUKgUSxQQ7J3Zi1BtptbYy5NMSHB3ODhkwLppGrpzjcOQwByMHSZfSdLo6sRlttQ7rhmXLWXaM7+DgzEFUVAw6AxvbN/JQ50NNMzfjjcmM+9vvZ41/DQF7oNahiSZ1S2fBDh48yF/8xV8A8P3vf59QKMShQ4f4h3/4B/74j/+4ITYCQoibVyhXOTGVYjicZiqRJ12oYDLo8NnNDAacGOqw5ZGqqhydTPLT42ES+caem5EvVZmI57CZDWwabOXevhZaZQi4EO+Y7GuEuI0UBWLntYqMyIkriQyrF3z99Z3IAC3Wsb0w/C9QymjHWodg1a805tyMSlFLZJTSYPVA1z3a4O+WAWkxJRaFZlvzrUYrbcY2cECpWiJeiPPK5CsY9AZ8Fh+D3kH63H30uHsa6mp2IS5TVIWz8bPsnd7LRGaCVmsrS3xLah3WDasoFfZO7+WVyVcoVosArGhZwWO9j+GzNsdFBFKZIWrllla1XC6Hy6VdkfTCCy/w/ve/H71ez/3338/o6OiCBiiEqL2qonI6nGLPhShj0TxWkx6f3Uybx1p3szJebzyW41+OTTMW0+ZmeGwmnljVxtouT0PNzagoClPxPGVFZVWnm439MgRciIUk+xohbpP4KIz+AmZOai2ObD7w9dV3e6nXmzsDJ/4R0lPabUcAVvyyNmuigfYRqArk49qsDHRaW6klj0JgGTjkhINYXJp5zTcbzIQcIUKOEOVqmUQxwb7wPg7MHKDd0c6awBoGPYM4zc5ahyrEDZnLz7F3ei8noyfR6/QNNzfjZPQkPx/7OYliAoB2Rzvb+rbR6+6tbXALRJIZotZuKamxZMkS/vEf/5H3ve99/PSnP+UTn/gEAJFIBLfbvaABCiFqazyW49ULUYbDaYx6XV3OyXijRK7ECydnODyeALS5GZuXBnhoSePNzZjLlIhli3S12HlgsJXlbe66rIgRopE1zL4mF9Pa9ni7wRlqrJOqYnHJzMLYqzB1SBsC7unSBk83ikwETv0TzJzQbptsl+ZmPASNdKWzqkB2Vktm2HzQdd+lqowGqJAR4jZpmDX/HTIZTATsAQL2AKVqidncLD++8GN8Vh+rWlcx5BuizdFW6zCFuKZkMcnp2GkOhA+QKCXodHZiNzXORX3j6XG2j2xnIjMBgMvs4l3d72JtYG1DXVx5PaqqEi/GieajkswQNXVLu/I//uM/5umnn+YTn/gEjz76KJs2bQK0Kx3Wr1+/oAEKIWojli3x2sUoRyaSFMtVOr12bOb6viqiWK6y8+wsr5ydo6KoAKzv9rJtVRseW2P98p4pVpiM5/DazWxbFWJ9jw+7uYFOpAjRQBpmX5MYhRP/PzBatZPEodXaVe/OoCQ4RH0opmF8P4zvgXwMXB3g7al1VDeulIWzP4WRV7SEgE4PvQ/C0icaKykDWmVGelob/L38vdC2FuwttY5KiJprmDV/AZkNZjpdnSiqQqwQ45XJVzgYOUi/p5+VLSvp9fRi0jfW70qi+SiqwlRmiuH4MMOxYeLFOC3WFpZ4lzRMIiBRSPDzsZ9zIqpdFGHSm3iw80E2tW/C1AQXE1SVKnP5OVKlFF6zl00dm1jjl2SGqB2dqqrqrTwxHA4zPT3NunXr0Ou1K59fe+013G43y5cvX9Agm0EqlcLj8ZBMJpvqChDRfPKlKofH47w2EiOWLdHmttV9QkBRVQ6MxNl+aoZMsQJAv9/Bu1e30+lrnOFhoA00n4hr7bJWd3rYNNhK0NUcg8OEWEgLva4u5L7mtq35U4fgyN9qffyzc1DJawOKvT0QXHUpwSGD+EQNVIowfRRGX4HkpNbSyNFAybZqBUZfhrMvaJUlAMGVsPKXtaqoRlLMQGpCS8J03K0NM3e01joqId6Rel7zb0d8l337+LfJVrIE7cEF+56XpUtp5nJzAHQ4O1jWsow2extBR1CGi4s7qlApcDF5kRPRE4ylxihWi7TYWvBZfA2TzChUCrwy+Qp7p/dSVasArA+uZ0v3FlxmV42je+dK1RKRXIRCpUDAHmCtfy1LfUvxWr21Dk00qRtdV2/5st+2tjba2q4uV7zvvvtu9dsJIWosni0xGstxYDTGWFSrEBgKuup6ZgbA2UianxwLE04VAGh1mHlydRsr2t0NswkCLTETSRdJ5kv0tzp4cImfwYATvbSaEuKOaKh9jcWlfakqFFNaS6qZE2Bxg68X/MvAFQJnG5gkKSpuo2IGYhdgbI/2/6HFAYHl0CD9rlFVmD4Cp3+kDTAHcLVryYxAg12kVSlAclyrLunYAL0PaK3qhBBv0lBr/m3iMrtwmV1aa6r8LOOj4xj1RjxmD72eXrqcXYQcIVqsLeh1jdO+VzSOaD7KucQ5js8dZzY/i1FvJGALNFSbqapS5cDMAXZN7CJX0S5M7Pf083jv403R3i1XzhHJRVBVlQ5nB+sC61jiW4LN2FgXjormJb1MhFikVFVlNlNkIp7n7EyaiXiedKGM1WhgMOCs+7kZM6kCzx8PMzyTBsBmMvCu5UE2DrRg1Nd37G+UypeZSubxO828Z00Ha7s8WE0NckJICFE7Oh1YPdrX5QTH7BmYOQ56M1jd4OnWKjguJznMjfOLoqhTlSLER7Qh2jOnIB8FvQlaBxpnADhA/CKc/CftvYCWFFz2bui+T0sMNIpqRavMqBQhsBR6H4LWJdBgeyEhRG2YDWY6nZ2AdjV2qpTiyOwRDs4cxG6y02ptZcAzQJujjZAjhMPUYK34RF3IV/Iki0kSxQTJYpLp7DQT6QlSxRRui5tedy/GBppZpaoqw7Fhfjb2M2KFGAB+m5/Heh9jyDvUUBdXXku2nCWcCWM2mFniXcJq/2r6PH3Spk7Uncb5V0MI8Y4pispMusB4LM+ZcJrpZIFMqYzFYMDnMBNyW+u+MiNdKPOzUxH2j8RQAb0ONg20snV5sOFmThQrVSbieYwGHfcPtHL/QCstjgY6ISSEqB+vT3CAdoKzmNIqOKYOaUOBLR7wdGpDgn394G6vbcyicVQrWhVA9DzMHNMGgauKNny6dUljDc/OReHUj2D6sHbbYIaBrTD4LjA2UMsVVYVsRHs/vj5t9kdoNRga6LMQQtQVs8GM3+bHb/Ojqiq5So5YIcZ4ehy9To/L7KLd0U6Pu4eQPUTQHsTcSMlscUcUq0VmsjMkiglihRjhbJh4MU6+nKdYLQLa/2tui5sl9saZl3HZRHqC7aPbGU+PA+AwOdjctZn1wfUYGqVS9TpK1RJTmSn0Oj1rAmtYG1hLl7Or4T4jsXjIrleIJqYoKrFcidl0kUiqwPnZLJF0gXypis1sxGc30eG1NsQiVaxUefnsHK+cnaNUVQBY2e7midVt+J0NdBICrdVUOFkgU6wwFHTywBI//X656kkIsYCMFjAGwHFpxka1BIUUzJ7WTuaandrJ6Pa12n9NUkYu3kBVtUHT0fMQPqb9uVLUEme+3saqygBtCPi5n8HILlCqgE6rylj6JNi8tY7u5pQykBgDWwus+P+g8x6pwhJCLCidTofD5JivzKgqVdKlNBeSFzgdO41Jb8JtdtPj7qHT2UnIEaLV2trwJ3XFrcuWs5yNn+Vw5DCz+VkqSgW9To/VaMVmtM0nwRrh3MO1xPIxfj72c07FTgFg1BvZ1L6JBzofaPg5NIqqMJOdIVfJ0efu4772++h39zfsZyUWD0lqNDBVVXnxdASTQYffaaXVacZnN2M2Srn5YlUoV5nLFJnLlJhO5BmL50jmyuRK2rAqh9mI32HB3tI4f/WrisqB0Tg/PzVD+tIQ8G6fjSdXt9PXgImARK7ETKpAyG3lXSuCrO7wyN9ZIcTtZzBfGt7s124XkhA5qZ2sdgah/S4ILAN3R+MMdxa3Ry52pSIjMa6dQDc7tVkTjZj8qpZh5GU4t/3KEHD/Um1uhruztrHdLKWifSZqFbrugf7N4Gr8nt1CiPpn0BvwWr3zg4Evt6o6OneUg5GD2I12PBYP3a5u2hxt+G1+Wq2tmAzSrqbZJQoJTsdOc2z2GLOFWRwmB53Ozqb57HPlHC9PvMy+mX0oqnZx5V2Bu9jSvQW35foDjBtFLB9jLj9Hm6ONLd1bWNGyomk+O9H8GufMpniTiqJycirFdKqA2aDHatLjtGhX3rd7bPidZlqdFrw2kwwbbmLxbImxWI7zsxkm43nSxQqlShWDXo/DbMRrN9PhNdR9W6k3UlWV4XCa50+EiaS1MtUWh5lfWtXG6o7GGgIOWsJpIp7DajLw8JCfe/tb8dhksyCEqJHLraqqFcjOwpmfaCd+WwYuVW8MyZXfi8nlgd+R0xA7C/mkVu3jCIC3p9bR3RpVgcn9cPrHUEhox1ztWmVDYEXjJe+ys1q7KW8fDGyB4EqZmyGEqJk3tqoqVAqkSikOzBxAURUsBgsus4sOZwedzk5aba34bX4ZMNxEIrkIJ6InOBU9RaKYwGPxMOgdbJrB8qVqib3Te9k9tXu+bdagd5DHeh4j5AjVOLp3LlvOMpWZwm12s6V7C+sC63CanbUOS4ibIkmNJtDuttLiMFMoK2SKFU5Ppzk6kdBKRs1GXDYj3T47bR4rAacFv9OCzSxloY0smSszGstydibDSDRLulDBaNDhtppod1uxNPiQ6Yl4jp8cD3NxLgs09hDwiqIwlShQqlRZGnLx4BI/3S1yolAIUScMRm22hrsdimmInNLmcNhboGVQ69Xv7gBnSE6gNptqWRuSPXsGIie02Qw6vZbICLQ11rDs11NVrc3aqR9Beko7ZvXCsieh697Ge1+lHCRGweqGpe+Gno1gbrxKVSFE89LpdNhMNmyvq+YrVApkyhlORk9ydPYoRr0Rp9lJ0Bakw9lBi7WFFmsLXqtXhg83kHK1TDgX5sTcCc7Ez5ApZ2i1trLE23izMa6nqlQ5PHuYneM7yZQzALTZ23is9zEGvAM1ju6dy5VzzORmMOgM3BW8i3tC9xC0B2sdlhC3RJIaTUKn02EzG7CZDQT+/+z9eZxbZ333/7+079Jo9hnP5vEytuN4iR07zuKsOCxpaGlp+aX4pi1NgEIDoYUb2vv+EqA0hQct3C2UUsqj9G6h0BLSG7oAodkIsRPbseM4tidex8vsm6TRLp3z++OMx5l4d8bWSPN+Ph4nko6ORtc1l+JLcz7n+nxCVj4/wzRJ5YpMZAtsPzqGYZq4HDYCHicNIS9tNX5qgx5qJ9NWaTXH7BZL5zk+mpoKZMTSOZx2O9VlUuD7Ygwnsvx0bz97euMAOO02blxQw62L68suEGeaJkOJLKOpHK3VfjZ01rCkMYTTUWYnU0Rk7vCErM0oWCmITm6H489bJ1D9NVbKnkiLFeQotxoEclpyBIa7rQLy8ZNWfQlfdfkV/D6b2HErmDH8qvXY6YWFb4L5t5RfDZBiDmInwchD00prdUakzNJlicic5XV68Tq91PqstJf5Yp6J/AQ9iR5eHXsVGza8Ti8BV4DGQCONgUaqvdVEvVEi7ohqc8wCpmkSz8UZyYwwmh6lL9lHX7KPRC5Bvpin1l9LU7Cp1M2cMaZpsm90H08ce4LRzCgAVZ4qbm+9neW1y8s6aGOaJrFcjOHUMB6Hh0VVi1hZv1J1M6TslflfLnI+dpuNoMdJ0OOEyVR/uYK1mqNnNEX3QAJsEJwsGL2wPsi8qJ/mKi9+tz4aV1O+aJDOF8nkiqRyRdL54tTjiWyBsVSOvvEM4+k8ToeNqN/NwvpQRQQyAOLpPP+9f5AdPaMYJtiAVa1V3LWsgai/zE5CYPWnN5amOuDmLcsbWdUaLbugjIjMYXanVWcjWG9d9Z5PWel7Dv239bw3bNUiqF5g5fMP1luprGT2KhZg7AgM7LFqqaTGrABWpM1KM1XuJobg1f+0AjUAdgd03GIFNMptVcOpYEYxC9H50L4BGpZbfRIRKVMuh4uoI0rUGwWYSlmVLCTpHutmz/Ae7DY7PqcPv8tPg7+Ben89EU+EiDtCxBPB79Jq9yvJMA0GU4MMp4cZTA1yInGCeC5OMp+kaBZxO9wEXAHq/fV4nd5SN3dGHYkd4b97/pvepLXC0+/0s7FlI2sa1pR1gM0wDUbSI4xnxwm7w6xtWMvSmqU0B5srJk2YzG06cz3HuJ12qp1uqgPWieJTqznGUnme7B7EYbMT9jtprw7QXuNnXpWP2qBHqzhmgDn5u46l88QzeWKpPKNJq2h0IlMgVzTIFw3yRZOiYZ56FXabHbfDRsjrYkFdEEcFjUU6V+SZA0M8d2iYfNHq85LGEJuWNdIYKb8vSqfqZriddtbPr2F9ZzW1wQo4WSQic5fNZp0UPnVi2ChCNm7VXxjYa6Xy8Yat9FTVndZtqBF80fKrWVCJUqPWqoWTOyF+3Bq/QB3ULy2/NExnkx6HAz+xVhRNFu9k3hroequ1uqicFHPWypnCZDCj7Qarboaz/C7uEBG5kNemrDq1msMwDdKFNKl8ilfHXmXP8B5s2HA5XPicPsKeMI3+Rmp8NYRcIYLuICF3CL/Tr6vNL5NpmgynhzmWOEb3aDcDqQEyhQx2mx2/y0/AFaDWV1vWJ/bPpy/ZxxM9T3AodggAl93FhuYNbGjegMdRvn/H54t5BtODpPNparw13NZyG13VXdT4yuy7kcgFKKgxx01bzYGPQtEgls6z52SMXcfG8Hmc1AXdLKwP0lylVRwXYpom6by1umIiUyCZLZLI5BmayDKYyJLMFEjni2QLBmDisNvxuhx4nVZRb5fDjttpr6jAxdnkiwZbDo3w9KtDpPNFANqq/bz5mkY6asvsikqs/vTHMmQm62Zs6Kwpy36IiFyQ3WEFLHzWlZYYBasWx/hxqzaDDXCHrBPK1fMh1GClNvLXgEfFB98w04RMDNKjkEtav/9iwbo18tZtIWdd5Z9LWjUz0mPgDkKk1UrHVAlySTj4Mzj6rNVvsAIAXW+1UqSVk2nBjI7JYMY1CmaIyJxjt9kJuAIEXNP/jsoVc6QKKUYzo5xMnMSYDGJ7HB48Ds9UYKTGW0PIbQU7gq4gAVcAn9OnK9LPYjwzbgUyxrrpnegllU/hc/qo8dZMq41SqYZSQzx1/Cn2je4DrM/emoY13DLvlrIulp0v5ulN9mIYBk3BJq5tuZZF0UVn/D8lUil0dlqmcTrs1AQ91AQ9UysLRpJ5evYP4nzdKo7mKh91c3QVRyZfZDyVZzydYyJTIJ7OM5LMMZrKkc4VyeaLZAsmpmlgAG6HA6/Ljs/lIOJz4Xba5+TVJEXDZEfPGE/sHyCeKQBQH/Jw9zWNLGkMld3vJJMv0h/LkDcMmiM+1ndWs6wprLoZIjJ32J3TgxymYQU5UsMwftQ6Ce90gytgBTeqWq2VAv7JQIc3ohUd52IUrZUIqRFrmxi0akZkY5MBjeL0483J/9js1mZ3WL/fuiWVsSoDrBP/R56GQ09AIWPtq+6ErrdBzYLStu1S5TMw0adghoic07HEMQpGYc4X8XU73Lgdbqo8VVP7TNMkV8yRLWZJF9IcGDvAXmMvJiY204bH6cFtt14XcUeo8lYR9oQJOAP4XX78Tv/USgRnudeTugiGaRDLxuhL9nFw7CDHEsdI5BK4HW6qvdU0BZrK7m/xyzGWGePpE0/z8tDLmNYXJ5bXLue21tuo9laXuHWXr2gU6U/1kylkaAu1saZhDZ2RTlwOV6mbJnJFVf6/3nLZbDarqHjgPKs4agPuyVocPpoivsljK4dhmMQzecZSecZTOUaSVm2LsVSWZK5IrmBMHet22PFMrroIeVxzYsXFxSoaJi8dH+e/9w8wlrKuqKzyubhraQOr2qrKrjZIPJ1nMJHBbrPRUetnVWuUhfVBvK7KXJYrInLRbHbrRPpra2wUJlcNJAesug6mYZ1wdwetug7+GvDXWmmsPMHJdFch636lXS2Yz8DEgHV1fjFvrTIoTq6yOPW4kLNWYyR6rQBRLmm91ma3fjeuAPjrYC79oVrMw7Hn4MDjkJuw9oWboeueyVRaZfI9wihMBqnGrPGLtEDbegUzROQMBaPAz3p+xlh2jD0je7i99XYaA42lbtasYbNZgQuP00P4VAHRSYZpkCvmrM3I0Zfq42jiKEWjaJ24N60aH26HG4/DQ8QdocZbQ8QbmVopEnAFCLqCZVk7omgUieVijGfGGcuOMZQaojfZSzKXJJlP4rA7iHqj1Pvr50QgAyCejfPzkz9n5+DOqZU+XdEubmu9jYZAQ4lbd/kM02A4PUwsG6M50Mya1jUsqV6iYIbMGZV1BlquqLOt4jhVi8NusxH2uQh7nTSEvVQHPIR9TsJeF2Gfi5DHOatXdJimSTJXZDyVI5a26l0MJDIMxLNMZAukc0UMw8RmA6/Lgd/tpCHkwjNHV1xcLMM0eflkjP/eN8jwRBaAoMfJbV11XN9RjauMVjQYpsloMsfIRJaAx8mKliqubYnQURNQ8EpE5Hycnsli1K+5Aq5YgHwSshOQHIbiy4BprTZwOMAx+Rr35AoPXzV4Q+DyW8EQdwDck/dn8x9uRtFaYRHvtVJBjR6yapIUJ1MmmVgpu04xJ1da2J1WH71VEGqeu0WiiwU4vtVKNZUZt/b5a600U82rymMFyqmUYakh6/Pgr4HO26BuMVS1g0N/jonImdKFNM3BZsaz4xwYO8CBsQMsq1nGrS23UuevK3XzZjW7zY7X6T1nQMI0TfJGfmqlR3+qn55ED4Zhney22+247W48Tg+13lrawm3U+mqp9dUSdodnxd//RaNo1R8ppEjlU6QLaSbyEwwkBxhIDTCRnyBdSGOaJk67E7/LT9AdpD5QP6fScSXzSX5x8hds799OwbQyRXRGOrm97XbmBeeVuHWXzzRNxjJjjKRHqPXVsql9E8tqluF3+UvdNJGrqmy+RY+NjfHggw/ywx/+EIB7772Xv/qrv6Kqquqcr/nBD37A17/+dXbs2MHIyAg7d+5k1apV047JZrP84R/+If/8z/9MOp3mzjvv5K//+q9paTmdj/dy3rvSnW0VRyJTYDSZp3c8Q2Gy0LXLYbOCAJOrOupCHgIeJz6XA7/bCg743Nb9yznBbRgmhmlimNZJZ3Py1jBNDAOKplV0+9S+onF6fyZvFe0eSmTpj2eYyFjBi1yxONl2q10hj5P6kAenfe5M/m+UaZrs64vzs32D9Met9BB+t4ONi+q4obMGt7N8fpcFw2AwniWRyRMNuNm4qI5l88I0RSrs6mERkavJ4QTH61Z0nGIUrNUdhQxkEpAcslYvmObpAIDDY13Z7nBbqzp8EfBEwBMAp89a4eHyWTUkXH5wecHuOp2W6UqekEiNWkGM2AkYOWBdmZ9LTaaDqoJwixWImUMnFS6ZUYTjL8DBn1o1QcD6rCzaBK03lEeQJ5+2Alr5pPXZbLgWGq6x0mS5ldtaRM4v5A7x5o43c23ttbw8/DKvjLzC3pG97BvZx7V113Jry61EvdFSN7Ms2Wy2qZRWQc6sn1A0iuSKOTLFDCcmTnAodggbNgKuANXeatrCbdT56qjz11HlqZrRIIFpmuSMHJlChnQhTbqQJlPIkClmSOaSxHNxxrPjTOQnyBVzU8EZAwObacPpcOJ3+ol4IjQGGudUAOO1UvkUW/q2sK1vGzkjB0BrqJU72u6gPdxe4ta9MfFsnMHUICF3iI2tG1lRt4KwO3zhF4pUoLIJatx3332cOHGCH//4xwA88MADbN68mR/96EfnfE0ymeSmm27ine98J/fff/9Zj/nIRz7Cj370I7773e9SU1PDH/zBH3DPPfewY8cOHA7HZb/3XON02IkG3Lz+a1WuYJDJF0nnihxMTrC3Nw4267yE3WbD5bThdthxO+wEPA5CXhcep4OiaVA0TAqGiWGYFA0omsZr7lsBCkwwsIIZ5qnbyfuGaWW4sJ5/bcCDqfsATrttMrDiJOp3l9UJ99nGNE1eHZjgZ/sGODmeBsDjtHPLolpuXFBbVumZcgWDgXiGdL5IQ8TLLYtqWdIYJuKfxVcEi4hUArsT3M5zn/g1DSt906mC2KlhSPRNFsouTg9YONxWAMHuApsD7DZgMrBhd04GF5ynV4e4A1baK6d3coWJd/p9bJBPWQGXfMpKJ5VPWWmi0uPWKozMuPUY22R6rVqI+MsnRVIpGUU4sQ0O/NQqhg7gCcPCN1k1J2bzqpzXSvRbqzOiHdC0EmoWQlBXVovIpavyVvGri3+Vm5M389Txp+ge62b30G72DO9hdf1qbpl3C2GPTmjOJIfdgc/uw+fyTQWOikaRZD7JUHqIY/FjYAO/01r9cKpOh8/pw+/0TwVMTm127BTNIgWjcMbtqfvJfJJ4Nk4inyBbzFIoFqyAhZGbqhMC4HQ4p2qF+Jw+Io6I9R5zNHjxesl8ki29W9jWv428Ya2KbQo0cXvr7SyoWjArVtlcDtM0Gc+OM5Iewe/ys7ZhLasbVlPrqy1100RKqiyCGvv27ePHP/4xW7duZf369QB84xvfYMOGDXR3d9PV1XXW123evBmAo0ePnvX5WCzGN7/5Tf7xH/+Ru+66C4B/+qd/orW1lZ/97Gfcfffdl/3eYnE77biddsK+M/8INUyTXMEgVzTIFwzGUnkG4lkM0zrnYMO6isJmAxuTt7bJfVj37ZP77TabdYz9dc/bbGe9tWGz3qNMJ7XZxjRN9vcneGL/4FQww+2wc+PCGm5ZWIfPXT7BjHSuSF88TdEwaY36ua49SldDqKz6ICJS0Wz208GG8zGN19StmAx4mCaYeSsoYhqTm3n6/qnACJNfREysLxQOlxUEsdmsn1XMTU8dZXNYxzgmi6KHmrQS41IYRTi5wwpmpIatfZ4QLLgL2jdYv9dyYBpWmjGHC675ZWi5vjxWlYjIrNcQaOA3lvwGJydO8tSxpzgUO8SOgR3sGtzF6vrV3DTvJiKes6x+lBnhsDsIe8JTASTTNEnmk6QKKRK5xFSQwjAMbDabVYR68mJLu82OYRpMnrHAtJmnv0OYYNpMnDYnLocLl92F2+7G5/Hhsrtw2p0KWFyEZD7Jc73Psb1/+7RgxsaWjSyOLi7b8z5Fo8hweph4Lk7EHWF903qWVi+lKdhU6qaJzAplEdTYsmULkUhkKqgAcMMNNxCJRHjuuecuO7CwY8cO8vk8mzZtmtrX3NzM8uXLee6557j77rsv+72z2SzZbHbqcTwev6w2VjK7zUpNVU5X78t0hmmytzfOk92D9MWsNFMuh43182vYuLiOYBkVjp/IFuiPpbHbbcyvDbB6svi3Vu6IyIVozp+lbPbJWh6eN/ZzjKKVEssoWAEQh0vpo2aKUYAT2+HQf1tpxsCqk7LgTui4qXyCGWAFu0YPQbABltxj1cwQkYpUynl/XnAev7nsN+mJ9/DksSc5ljjG9oHtvDj4IqvqVnHTvJuUluoqsNlsBN1Bgu4z01e9lmmaU4ENmXkTuQme632OHQM7pgUzbm29lUVVi8o2mJEr5hhMDZIpZKjz1XF92/Usji7W/9sir1MWZxz7+/upr68/Y399fT39/f1v6Oe63W6i0en/MDQ0NEz93Mt970ceeYRPf/rTl902kdnMME32nIzxZPcgA3HrC73bYeeGzmpuXlRewYxYOs9APIPXZeeaeRFWtlQxv1bFv0Xk4mnOr3B2x+TV9m8wOCKnFXNw/Hk49MTpmhmuACy4AzpungxGlZFc0lqhUb8ElvwShBpK3SIRuYJmw7zfHm7nt5b/FkdjR3nmxDMcjR/lxcEX2TW0ixW1K7i55WaqvdUlbaOcyiKhvytnWjwbZ2vfVrYPbKdgWAXAmwPNbGzdWNbBjFQ+xWBqENM0aQ42s6JuBQurFqoAuMg5lPTM48MPP3zBLwPbtm0Dzp4myDTNK/KP1et/7uW89yc/+Uk++tGPTj2Ox+O0trbObENFrrKiYfLyyXGe3D/E0IQVzPA47WxYUMNNC2onC8eXh2S2QO94Gp/bwfUd1axoidAS9ZXtFyARKR3N+SIXqZCFnl/A4Scn645gpZnqvB3ab7xwSrHZKDUCE0PQtgEW323VZBGRijab5v2OSAcdkQ6OxY/xzIlnOBw7zK6hXbw09BLX1l3LLfNuocZXU5K2icy0kfQIz/U+x+6h3RTNIgDNwWZubbmVhVULy/ZvedM06U/2ky1mWVi1kOW1y+mIdOCyl0ktMZESKekZyA996EO8613vOu8xHR0d7N69m4GBgTOeGxoaoqHh8q+EamxsJJfLMTY2Nm21xuDgIDfeeOPUMZfz3h6PB4+nzK4yEzmHfNFgR88Yzx4cZjSZA8DrsnPTAqsAeDnVm8jmi5wcT2OzwbUtEa7vqKa1Wlc+iMjl05wvcgG5FBx9Bo48YxVWB/BFrZUZrevLK83UKaYJsRNWHZbFd8P8jeAon4s7ROTyzcZ5vy3cxruXvZvjieP8/MTPOTh+kN1Du3l56GWW1SzjxuYblYdfylbfRB+/6P0Fe0f2Tu1rC7Vx87yby7oAOEC+mOdY/BhV3iruar+LJdVLlK5M5CKV9Jt3bW0ttbW1Fzxuw4YNxGIxXnjhBdatWwfA888/TywWmwo+XI41a9bgcrl4/PHH+fVf/3UA+vr62LNnD1/4wheu6HuLlINUrsDWw6NsOTRMMmddCeF3O7h5YS03dNaUVT2UfNGgbzxNrmiwoD7I+vnVdNYGsSvNlIiUC9OA+Emo7bIKVovMdulxK5jR8wtrlQZAoA4W3gXz1ljF18uRUYSRg+Crgq63Q9PKUrdIRASA1lAr9y29j5MTJ/n5iZ/z6tirvDLyCq+MvML8yHxuar6J+ZH5ZX0SWOYG0zQ5Gj/KL07+gsOxw1P7F0cXc2PzjbSF20rYupmRyCXoS/axMLKQ29tup95/Zup7ETm3svhLYunSpbz5zW/m/vvv5+tf/zoADzzwAPfcc8+0Qt1LlizhkUce4Vd+5VcAGB0d5dixY/T29gLQ3d0NWKsvGhsbiUQivPe97+UP/uAPqKmpobq6mj/8wz/k2muv5a677rqk9xapJOOpHL84OMy2o2PkigYAVX4XNy+sZW17dVkVzy4aJgPxDBPZPK3VftbPr2FJYwino3z6ICICQM8W2PcjOLkDOu+A5tWTtR5EZpl4r5Vi6uQOKxgHEGqChW+C5lXlW2TdKEJyEFKjUN0JS++BqvI/qSIilWdecB7vWvIu+pP9PNf7HK8Mv8KR2BGOxI7QFGjixuYbWVqzVFeEy6xjmAbdo9081/scJydOAmDDxvLa5dzYfCMNgfKvW3Uq3VSumGND0wY2NG/A5/SVulkiZacsghoA3/72t3nwwQfZtGkTAPfeey9f+cpXph3T3d1NLBabevzDH/6Q3/7t3556fCrV1ac+9SkefvhhAL70pS/hdDr59V//ddLpNHfeeSff+ta3cDhOnyS4mPcWqQT9sQw/PzDESyfGMUxrX1PEyy2L6rh2XqSsimcbpsnwRJbRZI7GsJfbl9RxTXOkrFaXiIhMkxyyrmyP98Kuf4L9P4L5t1q5/F36Q0hKzDRhuBsOPWndnlK9ABbcDvXLyjiYUYBEP2TjEKi3ghnNq8EbKXXLRETOqzHQyDsWvYM7Wu9ga99WXhx8kb5kH48eeJTosSg3NN/AqrpVuBzK3S+llS1k2Tm4kxf6X2A8Ow6A0+Zkdf1qbmi+gag3ev4fUCbyxTzHE8cJe8Lc2X4ny6qXaeWUyGWymaZplroRc0E8HicSiRCLxQiHwzPyM/NFg7956hAANcHZldNTyodhmrw6kGDLoREODE5M7e+sC7BxUR2L6oNlNcmapslIMsfIRJZowM2atiir2qoIefVFXaSSXIl5daZcsbb17oQd/2AFN448Y51gBXB6oO1GK6e/rzL+4JMyYhTg5IvWyoxE3+ROm5WSqfN2iLaXtHlvSDFv9Sk3AeFmq/5Hw3Lwzq5/c0Qq3Wye8+HKte9be75FspCc0ZQ0qXyKbf3beKH/BdKFNAB+p581DWtY27iWkDs0Y+8lcjHGMmO80P8COwd3kita9Tt9Th9rGtawrnEdQXewxC2cOa9NN3Vb620VsepE5Eq42Hm1bFZqiMjMyuSL7OgZY8vhkani3zbgmnkRNi6qpSVaXsWzTdNkLJVnaCJDlc/N7V31rGqrospfhsVHRUTOxemx6hHMvw16d0yeSO63bo88bV093rGxvE8kS3nIJuDYFjj6C8hOrpR2uKHtBmsFkb+mtO17IwpZa0VUMQuRVuh6KzRcA+7y+m4kIvJ6fpefW1tvZUPzBnYN7mJL3xZi2Rg/P/lzftH7C5bVLGNd4zpaQi2lbqpUMNM0OZY4xvN9z9M92o2Jda11ra+W9U3rWVG7oqJWDxWNIv3JfvJGXummRGaQghoic8xQIsuWwyO8eGyMXMHKc+112bm+vZr1nTVUB8ovCDCeyjEQzxD2ubhlYS2r2qLUavWSiFQyh9O6arxlHQzts1L+jBywahic3GGdiO242QpyOMrv33WZpUwTxo7C0WehbxeYRWu/J2ytFGq7sbxP/GcnYKLfqp1RPR9a10HdUnB5S90yEZEZ5Xa4Wde0jrWNa9k/up/n+57neOI4e4b3sGd4D83BZtY3rmdZzTIcqt8lMyRfzPPKyCts699GX7Jvav+CyALWN69nQWRBWWWJuJCiUWQwNchEfoKmQBPrm9aztHppRfVRpJQU1BCZAwzT5MDABFsOD/PqwOkUU/UhDxsW1LC6NVpWxb9Piafz9MXThDwuNiyo4bq2KPVhnXgQkTnEZrNqFdQvg9hxKy1V707r/kv/DHt/CG3rof2m8r5yXkqrmLOCZUefhfjJ0/ur2k8Hz+xl+meFaUBqBJLDVm2aui5ovs66raCrREVEzsZus7OsZhnLapbRN9HHC/0vsGd4D70TvTx28DEe73mcNQ1rWNOwpqLSAMnVNZQaYsfADl4aeolsMQtY9TJW1K1gfdN66vx1JW7hzDJMwwpm5CZo8DewsWUjXdVdWp0hMsPK9K8PEbkYsXSe7T2j7OgZYzyVB6wUU0saQ2xYUMuCukBZXiWQyOTpi2XwuR1c31HNmvYoTRF9QRCROS7SCqt+E5a9HY5thZ5fQHoMDj1hreSoX2adgK7rKt+CzXJ1JYesQMaJFyBv5V7H7oJ5q6H9FqhqLW373ohiDiYGIBO3An6dt1kppqrarGChiMgc0xRs4u0L385d7Xfx4sCLbO/fTiKf4OkTT/Pzkz+nK9rF6obVdEY6set7hFxAwSiwb2QfOwZ2cCxxbGp/laeK6xqu47r66/C7ynh151kYpsFQaoh4Nk59oJ6bmm9iSfWSiuunyGyhoIZIhSkaJt39cbYdHePVgcRkdkorxdSatigbFtSWZYopgIlsgb5YGo/TzqrWKta0R2mt1hcEEZFp3EGr7saCO2BgL/T8HIa6YfAVa/PXWml1WtaBr6rUrZXZppCF/pfg+AswcvD0fn+NteKndT24A6Vr3xuVm7Dq0BhFiMyz/l+pWwL+6lK3TERkVgi4AtzScgs3Nt/IvtF9vND3AicmTrBvdB/7RvcR8URYVbeKVfWriHgipW6uzDKj6VF2DO7gpcGXSBVSANiw0VXdxZqGNXRGOsvywsrzMUyD4fQwsWyMOl8dmzo2saxmmYIZIleYghpl7vkjozjtNjYsqCHk1RL5uWxkIsv2njFe7BkjkS1M7Z9fG+D6jijXNEdwOcrzippUrkDveBqnw8618yKsba+mtdpXcV+GRERmlM0OjcutbWIQep61TlSnhqH7P6H7v6xVG63roOFapdqZy0wTxo/Cseehb6cV2ADABvVLof1mqF9S3it88ikYP27VmDmVYqp2EThVg0tE5GwcdgfLa5ezvHY5A8kBdg7uZPfQbmLZGE+feJpnTjzDgqoFXFd/HYuii1R7Yw7LFDLsHdnLS0MvcTxxfGp/2B1mdf1qVtevJuwJl7CFV85YZozh1DC1vlruaruLZTXLlKpN5CpRUKOMZfJFXjg6Sq5g8MyBIRbVh1jdVsXSpnDZnryWS5POFdnTG2PX8XGODCen9gc8Tta0VbG2vZraUPn+sZ7OFemLpcEGS5vCrO2opqPGr2CGiMilCtbDNe+ArrdB30tw/HkYPQRD+63N5bdO8rauh0iL0u/MFZkYnNgOJ563Al+n+GsmC9FfD75o6do3E4o5iJ2wVmbUL7VSsFV36jMuInIJGgINvHn+m7mz7U72j+7nxcEX6Yn3cHD8IAfHDxJwBVhRu4Lldctp9Dfq77U5oGgUORQ7xO6h3XSPdlM0i4C1KmNB1QLWNKxhUXRRxaYqS+VT9E70EnQFuWneTVq5JFICCmqUMdOEWxbWsrcvTl8sQ/dAgu6BBB6ndTX76rYo7TV+7PpCUVHyRYP9/QleOj5O90CComElmLIBixqCrG2vZklTCKe9fL88FAyDE2NpDMNkYX2QtR3VdNYGsNv1WRYReUOcHmtlRus6qzDyiRes1RuZcWslR8+zEGqyTmY3ry7/E9pypnwaBl6G3l1WQMs0rP0ONzSttIIZ1Z3lvSoDrCBGog+yCas/HTdbQQ1dSSwictlcDhfX1l3LtXXXMpIeYefgTl4aeolkPsmWvi1s6dtCra/WWuFRs5xqn1L7VRLTNOlP9rN7aDcvD788lV4KoN5Xz4q6FSyvXV6xqzIA8sU8vRO9mJhcU3sN6xrX0RhoLHWzROYkBTXKmM/tYFVrFataqzBM2Hl8jF3HxhlP59neM8b2njGifhcrWqpY3hyhucqrKybKVNEwOTw0wUsnxnmlN062YEw91xj2srIlwsrWKqr85Vkr47Xi6Ty9sTQdNQFuWljLwvogDgUzRERmXqAWut4Ki98Mwwfg+Fbof9k6Ebzvh9ZW1W4FN5pWqf5GOStkYOAV6N0JQ/usE/6nROdbgYzmVeD0lqyJM8Y0rQLnqSEINcPiu6FxBbgqoG8iIrNIja+Gu9rv4vbW2zkwfoCXh17m1bFXGU4P89Txp3jq+FM0B5u5tvZaltUsI+QOlbrJchlM02QgNcC+EaumynB6eOq5gCvA8trlrKxbSYO/oaLPNxmmwUBqgFQuRXu4nesbr6ezqrNiV6KIlAMFNSpEXcjDpmWN3LW0gaMjSXYeG2fPyRhjqTxPvzrE068OEfW7uKY5wjXNYVqrtYJjtisYBoeHkuzri/NKb5yJ19TJqPK5WNlaxcqWKhojlfFHumGanBhLUzQMblxQwy2L6gh49E+UiMgVZ7NbNQbquiCXsmoqnHwRRg/DeI+17f0362r3plXW1fxeLa+f9QpZGNxrBTIG94GRP/1csNEKYjRfZ6UmqxTpcYiftNJnLX4rtKwFb+VeLSoiMhs47A6WVC9hSfUSsoUs+0f38/LwyxyJHaF3opfeiV5+evSndEQ6WFq9lMXRxRV9JX8lME2TkxMn2Te6j/0j+xnLjk0957BZ472ibgULqhZU/El9wzQYz4wzkhmh3lfPxvkbWVazDLej/C8oFSl3OmNYYew2G521QTprg/zSimb29cfZczLGqwMJxlJ5nj04zLMHhwl5nSxrCnNNc4T5tQFdCT9LpHNFugcS7OuL8+pAYtqKDL/bwbXzIqxqraq4oFQqW+D4WIrGKh+3La5jSWOooq/yEBGZtdx+aL/J2jIxq/5G3y4rwHFqe+UxK8DReC3UL6usk+LlLhO3AhmDe63UUsXc6ecCdZOrblZDuKl0bbwSckmrbobLB/M3QtsGCNaVulUiIm+Iy+EinoxT5akqmxOoHqeHlfUrWVm/koncBHtH9rJneA8nJk5wJHaEI7Ej/OeR/6Qp0ERXdReLo4sr/gr/cmGYBscTx9k3so/9o/uJ5+JTzzltThZGF7K0eimLoovwVsLKzvNIF9LEs3GS+SQmJmFXmFvm3cKq+lVacSQyiyioUcHcTjsrW6yr+XMFgwODCV7pjbOvL04iU+D5I6M8f2QUv9vBovogixpCLKoPEvK6St30OWU8lWNvnzUuR4aTTJbIACDkcbKkKcyypnBFpmEyTZO+WIZUrsDqtii3ddVVRAotEZGK4I1YJ4jnb7SugO/bZV31P95jFRkfPWSt4PDXQsMyqL8GqheAQ18vrxrTsE7mD7xiBTJix6c/76+xVtc0r4bwvMorjl3MwfhxwLSCbB03Q7S91K0SEZkRG1s28tzJ5zgSO4Lf5ach0FBWV8UH3UHWNa1jXdM6xjJj7B3ZS/doNycmTtCX7KMv2cdTx58i4onQFbUCHO3hdhyqfXTVxLNxDsUOcWj8EIfHD5MpZqaec9vdLIouYmnNUhZWLSybwNrlKBgF4tk48VycvJHH5/RR5animtpraA400xhoVBFwkVnIZpqmeeHD5I2Kx+NEIhFisRjh8MwstcwXDf7mqUMA1AQ9F/26gmFwaDDJK70x9vbFSeWK055vinhZVB9kYX2Ijho/Tkf5fHEqB6lcgcNDSQ4NTXBoKMnwRHba8/UhD0snAxnzor6KWpHxWtl8kaOjSaoDbm5dXM+18yIVF7QRkSvnSsyrM+WKta13J7z0XahbMnM/83KkRqF/t3USfeQQmK/5HuFwQ+1iK8BRv1R1OK6EXBJGDp5ekZFNTH8+0mqtoGm4xrpfid8jjCLEe63fRc1C6LjJ+v/Cru+sIpVoNs/5cGXbly/m2Tu6l2192xhMDVLrryXqjc7oe1xtE7kJDowdoHusm8OxwxSM02mW3Q437eF25ofnMz8yn3p/vVZxzKCCUaAn3sOhcSuQMZQemva81+Glq7qLJdVLWFC1AKe9si9UmchN0J/sx2F3EHaHaQm10BpqpcHfQK2vtuL7LzJbXey8qv9D5yCn3U5XY4iuxhBvN0x6RpMcHJjgwOAEJ8fT9MUy9MUyPHNgGJfDSme1sD5IR02AxohXJ54vUa5gcHTECmIcHkrSO57mtZFEG9Be458KZFxKgKocGabJUCJLLJ3nmuYIt3XVUR+q7OWrIiIVxV8NnbdZWyEDQ6++5gR7HAb2WBtYqzhqFlgrOGoWgK+6Mk+yX0nZhBU8Gj1kBTMSfdOfd3iseij1y6ytkmtImCYkByE1Yq08WfJWqwi4s3KvHhWRuc3lcLGybiWdkU52DuzkpaGXODR2iOZgMz6Xr9TNuyxBd5DVDatZ3bCafDHP4dhhXh17lVfHXiWZT3Jg7AAHxg4A4Hf66Yh00BHuYH5kPtXeagU5LkG+mKc32cvxxHF64j30xHumBZEA5gXnsaBqAQuqFjAvOK+sVgNdrlwxR+9EL3abnVX1q1havZSGQAM+Z3n+PyUyVymoMcc57KdrcGy6BiayBQ4OTnBwMMGBgQkS2QLdAwm6B6yrAF0OGy1RP+3Vftpq/LRF/fhVzHmKaZqMp/IcH0txYixt3Y6mKb5uQVRdyMOCuiAL6wLMrw3ic8+NJbZjyRwDiQw1QTdvXt7IdW1R3M7K/9IkIlKxnF5oWmFtpmkVaR7ca6VDGj8GqWFrO/68dby3anqQI1CvIMdrmaZVy+RUeq+RQzAxcOZxwQZrZUL9Muv3OBeuJEyNwkS/lVJrydtg3hrwKK+1iMwNIXeIja0bWVy9mBf6X6B7tBu7zU5zsLmsryZ3OVx0VXfRVd2FYRr0J/s5GjvKkfgRjsWPkSqk2Duyl70jewHr99Aeaqc52Exz0EoLVMlpkS5VIpfgeOL41Naf7McwjWnHhFyhqSBGZ6SzbINjl6NoFBlIDZAupOmMdHJ94/V0hDsUKBMpU+U7+8kVEfQ4WdVaxarWKkzTpD+e4eDgBIeGJjg2miKTNzgynOTIcHLqNXVBD23VflqqfTSGvTSEvXhdc+MkfSpb4MT46eDFibEUydel8wKo8rlYUBdkQX2AztogYd/cqluSyOTpi6UJelxsXFTLmvZqogF9+RQRqSg2G0RarG3RJsinYfTI6RP048cgMw4nd1gbgMtvXXEfaTl9G6yHOXCVIKYJ6VGrJkbshFUPI3YCchNnHhtqstIsnQoGzZWT+aZprcqYGDxd46VtAwRqS90yEZGSaAw0ck/nPSypXsILfS9wJHaEoDtIva++7GtRnArSNAebuXHejRSNIicnTnI0fpQjsSOcSJwgkUuwZ2QPe0asFaE2bNT562gONE+9tsHfUPa/iwsxTZNELsFAaoDB1CADyQFOTJxgPDt+xrFBV5DWUCut4VY6I53U+erm3El80zQZzYwykhmhyd/EnW130lXdhcs+t87LiFQaBTXknGw2G00RH00RH7csqsMwTYYTWY6NpugZTXFsJMXQRHZq23FsbOq1VT4XDZMBjsaIh4awl7qgpyzrc5imyUS2wFDC6udgIstwwrqNpfNnHO+w2WiMeGmJ+miN+mmv8VMdcM+5Lw4AmXyRk2MpnA47q9uirJtfTVNk7lwJIiIyp7l8VgHxhmXW40LWKjJ+KpXSWA/kUzBywNpOsbsg3AyReRBusVYlBGrBEy7PVR2maQUqkkPWlui3ghfxE1bg5ww2q+/VCyYDGZ3gDlz1ZpeUaVjBjOSQVZdlwe0w7zoINZa6ZSIiJWe32aeKau8d2cuOgR0cHj9M2BOmzl9XMemDHHYHbeE22sJtbGzZSL6Y5/jEcU4kTtA70UvvRC8T+QkGU4MMpgbZNbTLep3NQa2vlhpfjXXrrZl6XI6rOjKFDMPp4WkBjMHU4LSi3q/V4G+wghiTW8QTmZPnIk45VTcj7A5ze8vtrKxfScA1x75XiVQoBTXkotltNurDXurDXtZ2VAPWSoVjYyl6RlL0xdIMxK0T/eOT26m0VdbrIeJzUeV3E/W7iPis2yq/myqfi4jfhasEQQ/DNEnlisTTeeKZPPF0gXgmz3gqz1Aiw9BElkzeOOfra4NuWqL+qSBGY8Rbkn7MJvmiQe94moJhsqghxPrOGjpq/HP6y5SIyJzn9FhFxGsXW4+LBZjog9jJyZP8J62tmLOCH+M9019vd0GgxqrTEai1bv2Tjz0h6+eXYp4xTStgk0tYqaOSkym3kkPW/eQwFLNnf63dAaHm0ytcwvOsgE4ZnnSZEaYx+XsbsWq3LLwLmldbq3dERGQaj8PD6vrVdEW72Duyl52DOzk4fpCoJ0qNr6ZighunuBwuOiOddEY6p/bFs3F6k71TQY7eiV4yxQwDqQEGUmembwy7w9T4aqjx1hByhwi5QwTdQUIu677P6buqf7Oapkm6kCaWjTGeHWc8Oz51/9Rt9hzfIWzYqPXVUu+vp95fT3OwmZZgCx5nZdfovFjJfJKB5AAuu4vV9atZ27iWWp9WeopUEgU15A3xe5wsaQyzpPF0Ucp0rkh/PMPA5HbqfiZvMJbKM5bKc+QcPy/gceJ3O/C5rM3rsuNzn7pv3bqdduw2GzabNZHbbEzdt9usFSYFwyBXsLbs5JYrGOSKRbJ56/FEtkA8nSeRKZxR8+L1bEA04KYu6KE+5KFucqsPeedMPYwLMU2TRLbAyESOfNGgvcbP+vk1LG4IluUKHRERucIcToi0WtsppmEFAeInrGBH/KR1kjs9BkbeWuWQ6D/7z7M7rRUN7uDpzTP52OW3np/aHK+7dVrBCaNgvY9RsIIuRn7ydvJ+bgKyE5O3CWvLTVjPn5cNfFErGBNsOB3ACDXOjXoYF2IUrXFOjVi/o8VvhuZVVhBLRETOy+/ys7ZxLUuql/DKyCvsHNzJofFDRL1Rarw1FX1hWdgTJuwJs6R6CWD9TTqWHWM4PcxIemTabaqQIp6LE8/FORI7+xkJu81OyGUFOgKuAC67C7fDjcvuwuVw4ba7T9/aXdhtdgzTwMTEMI0z7humQaaQIVPMkC6kSRfSZArT75uc/1wEWHUwTgUvGgIN1PvrqfXVlnU9lSslno0zlBrC7XCzpHoJK+tW0h5ur+j/D0TmKv0LKDPO53YwvzbA/NrTS/pM0ySeKTCWzDGezjE+GdyIpXOMpfKMp3LkiybJbIFk9kInBmaeDSugEvY5CXtd1uZzUhey0mbVBN1zfvXFuaSyBUaSOZK5AiGPi866AMuawnQ1huZMbRUREZkhNrt1VX6wHpqvO73fKFr1J5LD1onv1PDpFRGpUWt1h1GwVkpkYqVpu8NjrRgJ1IC/zjo5H5i89dVYQRyZLp+2in/nM9bvauk90LjCWqUhIiKXJOgOsr5pvRXcGH6Fl4Ze4uDYQWr8NVR758a/qzabjWpvtdXf6PTn0vk0wxkryDGaGWUiN0Eil2Aib92mCikM0yCWixHLXd3vEkFXkIgnQpWnaur2tfddDtV+OB/TNBnPjjOcHibgCrCyfiXLa5fTEmxRMEOkgumvK7kqbDYbEZ+LiM8FnJm/0JxMARVL50nni6RzRTL5Iun8a28N0rki2YIBmBim9ToT6+JKE9O6NcFht+F22vE47biddtyOU/cdU/uCHidhn4uw10nI68Jh12R3sTL5IqPJHIlsHp/LQXPEx9LmMO01fuqCHn1xEBGRmWV3TAYI6s7+fDF3egXFqS2bnLyfsE6aGwUrOGIUznK/YC37tLus4IPdZa2gmHbfZa348IQmV4GEXnM/OHdTRl0q07BW3iSHrN9tVTvMW22lJfOGL/x6ERE5r4gnwo3zbmRZzTJ2D+3mpeGXODx+mHnBeXM6NZHP5aPVZdWZOJuiUZwKcEzkJ0jlU+SNPLlijryRJ1/MkzNyU49zxRwmJnabHTt2bDabdd9mn8wiYd33ODz4nD68Ti8+p++M+z6nTysuLpNpmoxkRhjLjBF2h7mh6QaW1SyjMaAaXCJzgf7llFnBZrMR8DgJePSRnM2S2QInx1O4HHbqw142LKimozZIU9iLXUEhEREpFYfburpfV/jPXsUcTAxYKbt8UWi7ERqXQ7TDClqJiMiMqvJWsbF1I4uii3iu9zkOjB8g4ArQ4G/QRWhn4bA7iHgiRDyRUjdFLuC1wYyoN8rGlo0srV5KjU9pK0XmEp1BFpELKhomJ8fTFAyD1W1RVrVWMa/Kp1oZIiJXkycM3ggM7ofIPGuVgMhsl89A7Lh1P9xsFf+uW6IAlIjIVdIUbOLehfeyd2QvW3u3cnD8IM3BZgKuMzMoiMx28WycgdQAUU+UO1rvYFntMsJurfQUmYsU1BCR84ql8/TF0syL+rh5YS1LG8NalSEiUgo1C+C6/wHHtkL/yxDvtYpsu/2lbpnImYyiVeg9n4b6pdC6DqoXgFNpukRErjaX3cXKupW0hdrY2reVPcN7GLWP0hxoxqHVclIG0oU0fRN9eBwe1jWu47qG6+ZMrRgROTsFNUTkrPJFg+NjKZx2GzcvrGXDghpCXhUoExEpqUgLLP9VaF4NPc/B0H7AZu2fw3myZZZJjUCi31pRtPQeaFypQukiIrNA1Bvl7o676Yx0srVvK4fHD1MXqKPKU1XqpomcVcEo0DvRi2EadEW7WNu4lpZQS6mbJSKzgP66EJEzDE9kGZnI0lkX5JZFtXTWBUvdJBEROcVms1ZtROdbQY2eX8DIISuoEW7RyWMpnXwaxnvAHbDSTLVvsFKmiYjIrGG32emq7qIl1ML2/u3sGtrFSHqEen89IbdSW8rsYJgGg6lBJnITtIZaub7xehZWLdTKIhGZor96RWRKJl/k+GiKoNfJXUsbuH5+NV6XvjSIiMxKdjs0LIPaRTCwB47+AkZeBXcQgvXg9Ja6hTJXvDbVVMNymL8Rou2lbpWIiJxHwBXg1tZb6azqZPfQbg6OH6Q/2U+dr46IJ6Ji4lISBaPASHqEeC5Ona+OW+bfwtKapXgcWpEsItOVTZXfsbExNm/eTCQSIRKJsHnzZsbHx8/7mh/84Afcfffd1NbWYrPZ2LVr17TnR0dH+f3f/326urrw+/20tbXx4IMPEovFph3X0dGBzWabtn3iE5+Y4R6KlEbRMBmeyPLqQILe8TRdjSHeta6NWxbXKaAhIlIOHC4rHdX174XlvwaBeoidsFZxJIfANErdQqlUpmF9xob2WysyVvw6rPz/KaAhIlJGWkOtvK3zbfxG12+wrnEd2WKWA2MHGE4PY5pmqZsnc0Qqn6In3kNPvAef08edbXfyG0t+g1X1qxTQEJGzKpuVGvfddx8nTpzgxz/+MQAPPPAAmzdv5kc/+tE5X5NMJrnpppt45zvfyf3333/G8729vfT29vLFL36RZcuW0dPTw/vf/356e3v5/ve/P+3Yz3zmM9N+RjCodDxS3hKZPMMTOfJFg2jAxYYFNSyqD9JW7cfpKJt4p4iInOLyQdt6mLcGxo7C4F5rG9oPTp+1esMdKHUrpRIU8zAxANkY+Gpg0SZouwG84VK3TERELlNjoJHGQCOr6lexf3Q/Lw+/zMHxg4TdYWp9tUr7IzPONE3Gs+OMpEfwODy0h9tZXrOc+ZH5eLXiWEQuoCyCGvv27ePHP/4xW7duZf369QB84xvfYMOGDXR3d9PV1XXW123evBmAo0ePnvX55cuX8+ijj049XrBgAZ/73Od497vfTaFQwOk8/esJhUI0NjbOUI9ESiNXMBieyJLI5gm4nSyoC7CsOcz82oCKgIuIVAqHE2oXWtv8jTByEPp2wdgxKGTAF4VAHejkhFyqXNIqAG7kIdwMC+6A+qXgry51y0REZIbU+mq5ed7NrKhbQfdINy8NvcTh2GF8Th91/jpdNS9vWK6YYyQ9QiKfIOqOcn3j9XRVdzEvOA+7TRdYisjFKYugxpYtW4hEIlMBDYAbbriBSCTCc889d86gxuWIxWKEw+FpAQ2Az3/+83z2s5+ltbWVd77znXzsYx/D7XbP2PuKXCmFosFoKkcslcdht1Ef9nDjghoW1AepD3mUK1VEpJL5qqBlLTRfB7Hj1qqNvt3WbbDeCm6InI9pQnoMkoPgcENNp7UaqLYLXLqKUkSkUoXdYa5vup5raq/hwPgBXh56mb6JPgzToNZXS9ij1XlyYYZpkMqnSOaTpPIpimYRl91Fra+Wm+bdxMKqhUQ8kVI3U0TKUFkENfr7+6mvrz9jf319Pf39/TP2PiMjI3z2s5/lfe9737T9H/7wh7nuuuuIRqO88MILfPKTn+TIkSP83d/93Tl/VjabJZvNTj2Ox+Mz1k6RCykaJuOpHKPJHDYbRANuNiyoobPOSi/ldurqBxGRmVIWc77dbtU5iLZbaYKOb4Pjz1vpqSKt4AmVuoUy2xhFmBi0Ahq+iPW5aVwB0fnW50lEZI4qi3l/BvldflbWreSamms4ljjGvpF9HBo/xEBqgIgnQo23RqmpZErRKBLLxkgWkmQLWWw2G36nn7A7zJLqJdT766nx1VDvq8flULYIEbl8JQ1qPPzww3z6058+7zHbtm0DOOvV5KZpzthV5vF4nLe97W0sW7aMT33qU9Oee+ihh6bur1ixgmg0yq/92q/x+c9/npqamrP+vEceeeSCfROZSUXDJJbOM5bKYRgmVQEX18+vZsFkIMPn1hdNEZEroezmfG8EFt0FDddAz3PQ95KVUqiqFZS/WApZSPRBPgXBBljyNuuzEtSqHhERKMN5f4Y47U46I510RjoZSg1xYOwAr4y8wpHYEdwON/X+etVBmMNyxRyDqUGyhSxV3io6wh20BFuo8dVQ7a0m4okotZSIzCibaZpmqd58eHiY4eHh8x7T0dHBd77zHT760Y8yPj4+7bmqqiq+9KUv8du//dvn/RlHjx5l/vz57Ny5k1WrVp3xfCKR4O6778bv9/Pv//7veL3nn4hPnjxJS0vLtBofr3e2qzdaW1un0lvNBMMw+b9beugZTWIDqvxuqvwunLp6bk7JFooMxDJkCkWqfG466wIsrA/RXuMn4CmLxVgiIpcsHo8TiURmdF69XFdjzr9iTBNGDsHRZ2DoVSuoEZkHds0fc05uwgpmmCZUtcG8tVa9DE+w1C0TkTluNs35UObz/gxL5VMciR3h5eGXOTlxklwxR9QbJeqN6gT2HJEpZOhP9mOaJs3BZlbWrWRhdCE+p6/UTRORMnWx835J/2Ktra2ltrb2gsdt2LCBWCzGCy+8wLp16wB4/vnnicVi3HjjjW+oDfF4nLvvvhuPx8MPf/jDCwY0AHbu3AlAU1PTOY/xeDx4PFe2gJbdbuPXr2/h2GiKw0NJDg4mODKsAMdckcwWGIhnME1orfaxqi1KZ12AsAp+i4hcVVdjzr9ibDaroHi0HQb2wJGfw1A3+GtUTHwumFYvwwN1SyfrZSwCpYQQETmrsp73Z5jf5eea2mtYUr2EkxMnOTR+iP1j+zkyfgSXw6qb4Hf5S91MuQKS+SQDyQHsNjsd4Q5W1K2gM9KplFIictWUxWV4S5cu5c1vfjP3338/X//61wF44IEHuOeee6YVCV+yZAmPPPIIv/IrvwLA6Ogox44do7e3F4Du7m4AGhsbaWxsJJFIsGnTJlKpFP/0T/9EPB6fyodZV1eHw+Fgy5YtbN26ldtvv51IJMK2bdt46KGHuPfee2lra7uav4az8rudLGkMs6QxTDJbx/ExBTgqXSydZzCewe20s6ghyKpWK5jhcmh8RUTkMjlc0LwaahfDyR1wbCsMd4PLD8FGFYSuNKYBySFIjVjpyNpuhOaVEGlTvQwREblkDruDtnAbbeE2rm+8np54D3tH93IicYLeiV7CnjDV3mqcWgla1kzTJJ6LM5waxu1ws6R6CdfWXUt7qF11VUTkqiubGeXb3/42Dz74IJs2bQLg3nvv5Stf+cq0Y7q7u4nFYlOPf/jDH05LTfWud70LgE996lM8/PDD7Nixg+effx6AhQsXTvtZR44coaOjA4/Hw/e+9z0+/elPk81maW9v5/777+fjH//4FennGxHwnD/AYQeiAQ8RnwuHfWZqkcjVYZgmoxM5RpJZQl4X17VXsaKlitaoH7vGUkREZoo7APM3QtMqa8VG74swfgzMIgTqwVtlre6Q8mQaVvHv1AgEamHR3dC0UvUyRERkxgTdQa6pvYZlNcvoT/ZzKHaI/SP76Yn3YLfZiXqihD1hpacqEwWjQDwXJ5aNUTSKBN1BVtWvYnntcuYF581YnVsRkUtV0poac0kp84AmswWOjaY4ODjBoaEJxlN5HHYb1X43Eb8LuyahWSmTLxJP50lkCxQNkyq/ixUtVVzTHKYhrCtmRWRum235tV9rNrftkhULMHYE+nbD0D7IxKzARqAeHGVzbYwYRZgYsFJNBRug9XormOGLlrplIiIXNNvn1dnevtkgU8jQE+/h4PhBjsaOEs/FcdqdVHurCblDOjE+y6QLaWLZGBO5Cew2O2FPmLZQG+3hdpoCTdT4akrdRBGpYGVRU0OujoDHydKmMEubwiQyeXpGrADHkWFrFYfTbqcm6CHsderLRAnlCgbxTJ54Ok/eMPA6HYR9Lq5rqKIl6qejJkDEr/yUIiJyFTmcVn2F2kUwMQSDe63VG6OHwGa36m54I1q9MVsZBUj0QzZupRFb9nZoWmGNmYiIyFXidXrpqu6iq7qLWDbGicQJDo4f5HjiOIOpQdwON9XeagKugM5JlEi2kGUwPUimkMHr8FLtrWZV3SrmBefRGGhUbRQRmXUU1JhjQl4Xy+dFWD4vQiyd59hIiu6BOEdHUvTF0gTcTmqDHnxu5UO80vJFg0SmQDydJ1ss4nLYiXhdXDsvQku1n8aIl7qgB7dTy3JFRGQWCNZB8FZoXQfDB6D/ZRg9Aoleq/ZGoM5KXyWlU8xDIQP5NORT1hZuhoV3QeNy8IRK3UIREZnjIp4IEU+Ea2qvYSwzxvHEcQ6MHeDkxEn6kn14HB4ingghd0gpqq6CRC7BUGoIu81OS7CFruoumgJN1PnrVANFRGY1/Qs1h0V8Lq5tiXBtS4SRiSxHhpPsORmjL5Yhky8S8bupCbhVgHqGFA2TRCZPPFMgnS/gtNsJeZ0sagjSXhOgMeylPuzB61JASUREZjGXz7rav2mFtXpj9LCVnip+wqq/4QlZAQ6np9QtrVyFDGQnoJCGfMZKL2UD7E5weq3C7lVt0LDcCmYo2CQiIrNQ1Bsl6o1ybe21jGRGOJE4weHxw/Ql+xhOD2PDRsAVIOKJ4HUqBfNMMUyD0cwoY5kxAq4Ay2uXs6xmGa2hVgUyRKRs6F8rAaAm6KEm6OG6tignx9McHprgld44PSNJ6/mAR/U3LlHRMElmC8QzeVK5AjabjbDXRWvUx/w6K4jREPYS8Oh/QxERKVPBOmtruR7iJ2HkEPTvhtgx60S7twr81eBwl7ql5S+fgdQwZBPW79MbtlJKBevBX2M99oSt1FKesGqeiIhI2bDZbNT6aqn11bKqfhXxXJz+ZD8nJ05yNHaUodQQmWJmahVH0BXEYdfFgJcqX8wzlB4imU8S9Ua5uflmuqq7aAg0lLppIiKXTH/tyDR2u43Waj+t1X7Wd9bQM5Kiuz/OoaEkBwYm8Djt1ATdhLyq7fB6RcNkIlsg8ZogRtDjpD7kobOumqaIj/qwl4hPvzsREakwdjtUtVpb+43Wio3hV63i4uM9VsFxbxh8NdYqArk4rw1kON1Wke/2m6B6vpVWSqthRESkAoXdYcLuMIujiynMKzCUGqIv2cfR2FH6k/30pHswTAOP00PIFSLoDmqFwTkUjSKxXIxYJoaJSWOgkY0tG1lYtZCQW2kpRaR86V99OSevy0FXY4iuxhCxVJ4jI0n29sY5MZaidzxN0OuiJuCes+mSCoZVEyORKZDJF7HZIOx10RjxMr82QEPYS33IQ8TnUrEzERGZO5xuqF1obQvugNgJGDtqFRlP9EIha6VD8leDO1jq1s4uxQLkk1YQI5uwghbBBui4GaIdEGnVCgwREZlTnHYnTcEmmoJNXNdwnVUDIj3EcGqYY4ljDKWGOJ44TtEo4na4CbqDBF1B3HN4lWi+mCeWixHPxjExiXgiXFt3LZ2RTuZH5s/p342IVA79VSQXJeJ3scpfxcqWCANxq/7GK70x+mJp8kWDKp+b6oAbZwXX3zBMK51ULJ0nmStgt9mIeF20VfvpqPFTH/ZQF9JKDBERkSku7+kAR+dtp+tuDOyFiQHrvt0J7hB4glbB8bmQTsIoThbzTlq3hQxgWr8Llx98UQUyREREziLkDhFyh+iMdLKuaR2pfIqh9BBDqSFOJE7Ql+qjb6KPvJEHwOVw4XP68Dq8+Jw+XI7K/Hs9W8wynhknkUvgsDuIeqKsbVxLW6iNecF5+F3+UjdRRGRG6S8kuSQ2m43GiJfGiJfrO6KcGEtzcDDB/v4ER0aSGKaJ3+Uk5HUS9DjLPsiRyhWIp62UUiZMpZNaUF9DU8SqiaFUXCIiIhfB4bRO0kc7oP1mmOi3VnHEe63gRmbcum8a1goFd9DaXD6ohBWPuQlIDltBDJsd3H6rb9ULIDLPCmT4qqxbT8RK6SUiIiLn5Xf5aXe10x5uZ23jWtKFNKOZUeLZOPFcnKHU0FQdibHMGAWjgImJ2+HG67QCHT6nr6zSVxmmQSqfIplPkswnMUwDt8NNtbealXUraQm10BxsxuNQmkoRqVzl86+2zDpOh52O2gAdtQFuWljH8bEUA/EMx0ZSDCWy9CRTFA0Tl8NOyOsk5HHimeWpqtK5IvFMnolsgaJh4HU5iAbcXDsvTHPUR1PER9SvdFIiIiJviN1u1YQIN1uPDQPSY5AcsrbYCWtVR3LQqisBVlDE5bc2t788io/nkpOBjKSVcivcAg3LrOLevqhVSF2rMERERGaMz+ljXnAe84LzpvYZpkEynySeixPLxqYFO1L5FCPpEQpGARs23E731MoOv8uP3VbaiwxM0yRbzE4FMHLFHHabHb/TT8gdYkl0CXWBOmq8NTQEGnDZddGliMwN+itKZoTP7WBxQ4jFDSFYBBPZAkOJLEOJLCfGUpwcS9MXz5AvFAHwupz43Q58bgdelwN7iYIEmXyReDpPPFOwCo25HES8LhY3VjGvyk9j2EttsLLTaomIiJSc3Q6BGmtjibWvkIPUiLWlx2BiEBJ9kI1b+4w8mFg1PJxea3WHwzN5W8I/6HNJq325CSsAE26GhuXWCpVQk1ZgiIiIXGV2m30qbdXrgx2JXIJ4Lk48awU8BtODDKeHSeQSDKQGME0Tr9NLyB0i4ApcsRUdhmmQKWSsrWjdmqYJgMfpIeAKsKhqEU3BJqq91VR7q4l4IiUPuoiIlIqCGnJFBD1W+qn5tQHWza8mky8yPJFlZCLHWDJHXyxjPU7mSOeLYILDbsPvduC6nACCeeZDwzQpGubUbdE0MQxrP5iYWMXQw14Xa9qrmBf10xD2UBv0XF4bREREZOY43RBusrZTTNMqoJ0es9JVpcetQEdy2Aom5MesQuRG4fRr7C7rZ9mcVkDB9vrNYaW3sjmseh52h7X/fIyiVQfj1JbPTAZZTCulVKgJGu+cDGQ0K5AhIiIyC9ltdiKeCBFPBEKn9xeNIvFcnJH0CEOpIY4ljjGSHmE0M0rBKOBxeAi6gwRcARw2B3abHRu282Z0KBgF8sU8OSNH3siTK1q3hWIBbIAJPpe1QqTR30idv46oN0rIFSLsCRP1RCu2HoiIyOVQUEOuCq/LQUvUT0v0dHGqXMFgPJ0jlsozlsozlMjQG8uQzRuX/ga2Mx86HXY8Tjtupx2Py4Fn8rHH5cDpsOF3W/Ux6kIKYoiIiJQFmw28YWujffpz+YwV2MhNTG6T91NjkB61gg/FnFWzwzDAzFv3zaIVjDCK1n2jCJjWFRKnvl+YgO21O2xW8MLptdJI1dZBoA48YSu1VHieAhkiIiJlymF3EPVGiXqjLIwuZAMbmMhNMJweZig9xMnESfpT/QwmBymaRUxMDMOYCk7YsGHaTOt28juFw+7AZXfhdrhx291U+6sJe8JE3BEC7gAhd4iw23qs4IWIyIUpqCEl43baqQ95qQ95p/aZpolhnudFky4mWZXdrroXIiIic4bLa22BmvMfVyxMBi8Kk9tkIMPIQzFv7SvmTz9+7X2bHTwhK3jhDVv3Xb6r0z8REREpmaA7SNAdpCPSwfWN15MupIllYxQMK5V1wSxQNIoYpkHRLFI0ihTNIjabbaoYuc/pw+/043V6lTZKROQNUlBDZhWbzYZDsQgRERG5UhxOrK/AnlK3RERERMrUqSCFiIiUhkLDIiIiIiIiIiIiIiJSFhTUEBERERERERERERGRsqCghoiIiIiIiIiIiIiIlAUFNUREREREREREREREpCwoqCEiIiIiIiIiIiIiImVBQQ0RERERERERERERESkLzlI3YK4wTROAeDxe4paIiIiUv1Pz6an5dTbRnC8iIjJzZvOcD5r3RUREZtLFzvsKalwliUQCgNbW1hK3REREpHIkEgkikUipmzGN5nwREZGZNxvnfNC8LyIiciVcaN63mbP1cocKYxgGvb29hEIhbDbbjPzMeDxOa2srx48fJxwOz8jPnA0qsV/qU/moxH5VYp+gMvtViX2CK9Mv0zRJJBI0Nzdjt8+ubJpXYs6Hyvx8VGKfoDL7pT6Vj0rsVyX2CSqzX3Ntzgf9rX8pKrFfldgnqMx+qU/loxL7VYl9gtLO+1qpcZXY7XZaWlquyM8Oh8MV9T/EKZXYL/WpfFRivyqxT1CZ/arEPsHM92s2Xq0JV3bOh8r8fFRin6Ay+6U+lY9K7Fcl9gkqs19zZc4H/a1/OSqxX5XYJ6jMfqlP5aMS+1WJfYLSzPuz7zIHERERERERERERERGRs1BQQ0REREREREREREREyoKCGmXM4/HwqU99Co/HU+qmzKhK7Jf6VD4qsV+V2CeozH5VYp+gcvt1tVXi77ES+wSV2S/1qXxUYr8qsU9Qmf2qxD6VQqX+HiuxX5XYJ6jMfqlP5aMS+1WJfYLS9kuFwkVEREREREREREREpCxopYaIiIiIiIiIiIiIiJQFBTVERERERERERERERKQsKKghIiIiIiIiIiIiIiJlQUENEREREREREREREREpCwpqzHJ//dd/zfz58/F6vaxZs4af//zn5z3+6aefZs2aNXi9Xjo7O/mbv/mbq9TSi3cpfXrqqaew2WxnbPv377+KLT6/Z555hl/6pV+iubkZm83Gv/3bv13wNeUwTpfar3IYq0ceeYTrr7+eUChEfX09v/zLv0x3d/cFXzebx+ty+lQOY/W1r32NFStWEA6HCYfDbNiwgf/6r/8672tm8zjBpfepHMbp9R555BFsNhsf+chHznvcbB+rUtK8P/s/95U472vOP222j1UlzvuVOOeD5v3XKofxKgXN+eXxmde8Xx5jVYnzfiXO+VCZ877m/NOu5lgpqDGLfe973+MjH/kIf/zHf8zOnTu55ZZbeMtb3sKxY8fOevyRI0d461vfyi233MLOnTv5oz/6Ix588EEeffTRq9zyc7vUPp3S3d1NX1/f1LZo0aKr1OILSyaTrFy5kq985SsXdXw5jBNcer9Omc1j9fTTT/PBD36QrVu38vjjj1MoFNi0aRPJZPKcr5nt43U5fTplNo9VS0sLf/Znf8b27dvZvn07d9xxB29/+9t55ZVXznr8bB8nuPQ+nTKbx+m1tm3bxt/+7d+yYsWK8x5XDmNVKpr3T5vNn/tKnPc151vKYawqcd6vxDkfNO+fUi7jdbVpzj9ttn/mNe+fNpvHqhLn/Uqc86Ey533N+ZarPlamzFrr1q0z3//+90/bt2TJEvMTn/jEWY//+Mc/bi5ZsmTavve9733mDTfccMXaeKkutU9PPvmkCZhjY2NXoXVvHGA+9thj5z2mHMbp9S6mX+U2VqZpmoODgyZgPv300+c8ptzG62L6VI5jZZqmGY1Gzb/7u78763PlNk6nnK9P5TROiUTCXLRokfn444+bt956q/nhD3/4nMeW61hdDZr3y+tzb5qVOe9rzi+fsTLNyp33K3HON03N+6eUy3hdSZrzy+szf4rm/bGr0qaZUInzfqXO+aZZmfO+5nzLlRwrrdSYpXK5HDt27GDTpk3T9m/atInnnnvurK/ZsmXLGcfffffdbN++nXw+f8XaerEup0+nrF69mqamJu68806efPLJK9nMK262j9MbVU5jFYvFAKiurj7nMeU2XhfTp1PKZayKxSLf/e53SSaTbNiw4azHlNs4XUyfTimHcfrgBz/I2972Nu66664LHltuY3W1aN6frhw+9xdrto/TG1FO41SJcz5U3rxfiXM+aN4vt/G60jTnT1cOn/lLMdvH6o0op7GqxHm/0uZ8qMx5X3P+1RsrBTVmqeHhYYrFIg0NDdP2NzQ00N/ff9bX9Pf3n/X4QqHA8PDwFWvrxbqcPjU1NfG3f/u3PProo/zgBz+gq6uLO++8k2eeeeZqNPmKmO3jdLnKbaxM0+SjH/0oN998M8uXLz/nceU0Xhfbp3IZq5dffplgMIjH4+H9738/jz32GMuWLTvrseUyTpfSp3IZp+9+97u8+OKLPPLIIxd1fLmM1dWmed9SLp/7SzHbx+lylNs4VeKcD5U171finA+a96G8xutq0ZxvKZfP/KWa7WN1OcptrCpx3q+kOR8qc97XnH/1x8o54z9RZpTNZpv22DTNM/Zd6Piz7S+lS+lTV1cXXV1dU483bNjA8ePH+eIXv8jGjRuvaDuvpHIYp0tVbmP1oQ99iN27d/Pss89e8NhyGa+L7VO5jFVXVxe7du1ifHycRx99lPe85z08/fTT5/xiUA7jdCl9KodxOn78OB/+8If56U9/itfrvejXlcNYlYrm/dn/ub8c5TBOl6LcxqkS53yorHm/Eud80Lx/SrmM19WmOX/2f+YvVzmM1aUot7GqxHm/kuZ8qMx5X3O+5WqOlVZqzFK1tbU4HI4zrmoYHBw8I+p1SmNj41mPdzqd1NTUXLG2XqzL6dPZ3HDDDRw4cGCmm3fVzPZxmkmzdax+//d/nx/+8Ic8+eSTtLS0nPfYchmvS+nT2czGsXK73SxcuJC1a9fyyCOPsHLlSv7P//k/Zz22XMbpUvp0NrNtnHbs2MHg4CBr1qzB6XTidDp5+umn+cu//EucTifFYvGM15TLWF1tmvfPbbZ97i/VbB+nmTJbx6kS53yovHm/Eud80LwP5TVeV4vm/HObbZ/5yzHbx2qmzNaxqsR5v9LmfKjMeV9z/tUfKwU1Zim3282aNWt4/PHHp+1//PHHufHGG8/6mg0bNpxx/E9/+lPWrl2Ly+W6Ym29WJfTp7PZuXMnTU1NM928q2a2j9NMmm1jZZomH/rQh/jBD37AE088wfz58y/4mtk+XpfTp7OZbWN1NqZpks1mz/rcbB+nczlfn85mto3TnXfeycsvv8yuXbumtrVr1/Kbv/mb7Nq1C4fDccZrynWsrjTN++c22z73l2q2j9NMmW3jVIlzPsydeb8S53zQvH9KuYzXlaI5/9xm22f+csz2sZops22sKnHenytzPlTmvK8533JFx+qKlB+XGfHd737XdLlc5je/+U1z79695kc+8hEzEAiYR48eNU3TND/xiU+Ymzdvnjr+8OHDpt/vNx966CFz79695je/+U3T5XKZ3//+90vVhTNcap++9KUvmY899pj56quvmnv27DE/8YlPmID56KOPlqoLZ0gkEubOnTvNnTt3moD5F3/xF+bOnTvNnp4e0zTLc5xM89L7VQ5j9YEPfMCMRCLmU089Zfb19U1tqVRq6phyG6/L6VM5jNUnP/lJ85lnnjGPHDli7t692/yjP/oj0263mz/96U9N0yy/cTLNS+9TOYzT2dx6663mhz/84anH5ThWpaJ5vzw+95U472vOt5TDWFXivF+Jc75pat4/pVzG62rTnF8+n3nN++UxVpU471finG+alTnva863XO2xUlBjlvvqV79qtre3m26327zuuuvMp59+euq597znPeatt9467finnnrKXL16tel2u82Ojg7za1/72lVu8YVdSp8+//nPmwsWLDC9Xq8ZjUbNm2++2fyP//iPErT63J588kkTOGN7z3veY5pm+Y7TpfarHMbqbP0BzL//+7+fOqbcxuty+lQOY/U7v/M7U/9O1NXVmXfeeefUFwLTLL9xMs1L71M5jNPZvP6LTjmOVSlp3p/9n/tKnPc1558228eqEuf9SpzzTVPz/muVw3iVgub88vjMa94vj7GqxHm/Eud806zMeV9z/mlXc6xspjlZsUNERERERERERERERGQWU00NEREREREREREREREpCwpqiIiIiIiIiIiIiIhIWVBQQ0REREREREREREREyoKCGiIiIiIiIiIiIiIiUhYU1BARERERERERERERkbKgoIaIiIiIiIiIiIiIiJQFBTVERERERERERERERKQsKKghIiIiIiIiIiIiIiJlQUENEakIt912Gx/5yEcA6Ojo4Mtf/nJJ2yMiIiJXhuZ8ERGRuUPzvoicjYIaIlJxtm3bxgMPPHBRx57tS1Emk+G3fuu3uPbaa3E6nfzyL//yWV/79NNPs2bNGrxeL52dnfzN3/zNGcc8+uijLFu2DI/Hw7Jly3jssccutTsiIiJyDprzRURE5g7N+yJyioIaIlJx6urq8Pv9l/36YrGIz+fjwQcf5K677jrrMUeOHOGtb30rt9xyCzt37uSP/uiPePDBB3n00UenjtmyZQu/8Ru/webNm3nppZfYvHkzv/7rv87zzz9/2W0TERGR0zTni4iIzB2a90XkFAU1RKTsJJNJ/sf/+B8Eg0Gampr48z//82nPv/6KjIcffpi2tjY8Hg/Nzc08+OCDgLWMtaenh4ceegibzYbNZgMgEAjwta99jfvvv5/GxsaztuFv/uZvaGtr48tf/jJLly7ld3/3d/md3/kdvvjFL04d8+Uvf5k3velNfPKTn2TJkiV88pOf5M4779RyWRERkYukOV9ERGTu0LwvIhdLQQ0RKTsf+9jHePLJJ3nsscf46U9/ylNPPcWOHTvOeuz3v/99vvSlL/H1r3+dAwcO8G//9m9ce+21APzgBz+gpaWFz3zmM/T19dHX13fRbdiyZQubNm2atu/uu+9m+/bt5PP58x7z3HPPXUp3RURE5izN+SIiInOH5n0RuVjOUjdARORSTExM8M1vfpP/+3//L29605sA+Id/+AdaWlrOevyxY8dobGzkrrvuwuVy0dbWxrp16wCorq7G4XAQCoXOeZXGufT399PQ0DBtX0NDA4VCgeHhYZqams55TH9//yW9l4iIyFykOV9ERGTu0LwvIpdCKzVEpKwcOnSIXC7Hhg0bpvZVV1fT1dV11uPf+c53kk6n6ezs5P777+exxx6jUCjMSFtOLWE9xTTNM/af7ZjX7xMREZEzac4XERGZOzTvi8ilUFBDRMrKqS8TF6u1tZXu7m6++tWv4vP5+L3f+z02btw4tWz0cjU2Np5xFcbg4CBOp5OamprzHvP6KzpERETkTJrzRURE5g7N+yJyKRTUEJGysnDhQlwuF1u3bp3aNzY2xquvvnrO1/h8Pu69917+8i//kqeeeootW7bw8ssvA+B2uykWi5fcjg0bNvD4449P2/fTn/6UtWvX4nK5znvMjTfeeMnvJyIiMtdozhcREZk7NO+LyKVQTQ0RKSvBYJD3vve9fOxjH6OmpoaGhgb++I//GLv97DHab33rWxSLRdavX4/f7+cf//Ef8fl8tLe3A9DR0cEzzzzDu971LjweD7W1tQDs3buXXC7H6OgoiUSCXbt2AbBq1SoA3v/+9/OVr3yFj370o9x///1s2bKFb37zm/zzP//z1Ht/+MMfZuPGjXz+85/n7W9/O//v//0/fvazn/Hss89euV+QiIhIhdCcLyIiMndo3heRS2KKiJSZRCJhvvvd7zb9fr/Z0NBgfuELXzBvvfVW88Mf/rBpmqbZ3t5ufulLXzJN0zQfe+wxc/369WY4HDYDgYB5ww03mD/72c+mftaWLVvMFStWmB6Px3ztP4nt7e0mcMb2Wk899ZS5evVq0+12mx0dHebXvva1M9r6r//6r2ZXV5fpcrnMJUuWmI8++ujM/0JEREQqlOZ8ERGRuUPzvohcLJtpXmLSOhERERERERERERERkRJQTQ0RERERERERERERESkLCmqIiIiIiIiIiIiIiEhZUFBDRERERERERERERETKgoIaIiIiIiIiIiIiIiJSFhTUEBERERERERERERGRsqCghoiIiIiIiIiIiIiIlAUFNUREREREREREREREpCwoqCEiIiIiIiIiIiIiImVBQQ0RERERERERERERESkLCmqIiIiIiIiIiIiIiEhZUFBDRERERERERERERETKgoIaIiIiIiIiIiIiIiJSFhTUEBERERERERERERGRsqCghoiIiIiIiIiIiIiIlAUFNUREREREREREREREpCwoqCEiIiIiIiIiIiIiImVBQQ0RERERERERERERESkLCmqISNl46qmnsNlsPPXUU1fl/QYGBqipqcFms/H973//qrzna/3u7/4uy5cvp6qqCp/Px+LFi/nYxz7G8PDwVW+LiIjI1TTX5vzZ2hYRERERkdnIWeoGiIjMVh/84Afxer0le/9kMskDDzzAwoUL8Xq9bN++nc997nP853/+Jzt37sTtdpesbSIiIpWk1HP+a82mtoiIiIiIzEYKaoiInMWjjz7KT37yE7761a/ynve8pyRt+Od//udpj++44w5CoRC/93u/x7PPPssdd9xRknaJiIhUktkw58/GtoiIiIiIzFZKPyUiV9WBAwe47777qK+vx+PxsHTpUr761a+ecdz+/ft585vfjN/vp7a2lve///0kEokzjuvo6OC3fuu3zth/2223cdttt03bNz4+zh/8wR/Q2dmJx+Ohvr6et771rezfv3/acaOjo3zwgx/kc5/7HG1tbW+ovzOtrq4OAKdTMWkREZndNOdfmtnUFhERERGR2UxnxUTkqtm7dy833ngjbW1t/Pmf/zmNjY385Cc/4cEHH2R4eJhPfepTgJVL+tZbb8XlcvHXf/3XNDQ08O1vf5sPfehDl/3eiUSCm2++maNHj/I//+f/ZP369UxMTPDMM8/Q19fHkiVLpo598MEHmT9/Ph/60Id45plnLul9CoXCRR3ncDiw2WwX/TOz2Sy7du3if//v/83NN9/MTTfddEntEhERuZo05592sXP+G2mLiIiIiMhcoqCGiFw1H/3oRwmFQjz77LOEw2EA3vSmN5HNZvmzP/szHnzwQaLRKF/60pcYGhpi586drFy5EoC3vOUtbNq0iWPHjl3We3/5y1/mlVde4fHHH+euu+6a2v+Od7xj2nH/8R//wb/8y7/w4osvYrdf+mI2l8t1Ucf9/d///VmvNn29rVu3smHDhqnHb33rW/nud7+Lw+G45LaJiIhcLZrzT7uYOf+NtkVEREREZC5RUENEropMJsN///d/84EPfAC/3z/t6sa3vvWtfOUrX2Hr1q285S1v4cknn+Saa66ZOrlxyn333cfjjz9+We//X//1XyxevHjayY3Xi8VivO997+N//s//yfLlyy/rfbZt23ZRx82fP/+ijrv22mvZtm0bqVSKXbt28Wd/9me86U1v4oknnsDv919WG0VERK4kzfnTXWjOn4m2iIiIiIjMJQpqiMhVMTIyQqFQ4K/+6q/4q7/6q7MeMzw8PHXs2U4ANDY2Xvb7Dw0NXTA/9R//8R/jcrn40Ic+xPj4OAATExMApFIpxsfHiUQi500hsWrVqotqz8WutAgEAqxduxaAjRs3sn79em644Qa+/vWv89BDD13UzxAREbmaNOdPd6E5fybaIiIiIiIylyioISJXRTQaxeFwsHnzZj74wQ+e9ZhTJzVqamro7+8/4/mz7fN6vWSz2TP2Dw8PU1tbO/W4rq6OEydOnLeNe/bs4ejRo2c9kfKe97wHgLGxMaqqqs75M2Y6/dTrrV27FrvdzquvvnrJrxUREbkaNOdPd6E5fybaIiIiIiIylyioISJXhd/v5/bbb2fnzp2sWLECt9t9zmNvv/12vvCFL/DSSy9NS0fxne9854xjOzo62L1797R9r776Kt3d3dNOcLzlLW/h//v//j+eeOIJ7rjjjrO+75e//OWpKyRP2bVrFw899BAPP/wwt956K8Fg8Lz9nOn0U6/39NNPYxgGCxcuvKzXi4iIXGma86e70Jw/E20REREREZlLbKZpmqVuhIjMDXv37uXmm29m0aJFfOADH6Cjo4NEIsHBgwf50Y9+xBNPPAFYV2eeOgnyJ3/yJzQ0NPDtb3+bZ555huPHj/Pkk09y2223AfDtb3+bd7/73XzgAx/gV3/1V+np6eELX/gC8XicxYsX89RTTwGQSCTYsGEDPT09fOITn2DdunWk02mefvpp7rnnHm6//faztvmpp57i9ttv51//9V/5tV/7tavxawLg3//93/nGN77BvffeS3t7O/l8nu3bt/PlL3+Z6upqtm/fTiQSuWrtERERuRSa89+Y2dQWEREREZHZxl7qBojI3LFs2TJefPFFli9fzv/6X/+LTZs28d73vpfvf//73HnnnVPHNTY28vTTT7Ns2TI+8IEP8O53vxuv18tXvvKVM37mfffdxxe+8AV+8pOfcM899/C1r32Nr33tayxevHjacaFQiGeffZb3vve9/O3f/i1ve9vbuP/+++nu7qa5ufmK9/1SLVy4ELfbzWc/+1l+6Zd+iXe84x185zvf4b3vfS/PP/+8AhoiIjKrac4XEREREZErRSs1RERERERERERERESkLGilhoiIiIiIiIiIiIiIlAUFNUREREREREREREREpCwoqCEiIiIiIiIiIiIiImVBQQ0RERERERERERERESkLCmqIiIiIiIiIiIiIiEhZUFBDRERERERERERERETKgrPUDZgrDMOgt7eXUCiEzWYrdXNERETKmmmaJBIJmpubsdtn1zUamvNFRERmzmye80VERKQ0FNS4Snp7e2ltbS11M0RERCrK8ePHaWlpKXUzptGcLyIiMvNm45wvIiIipaGgxlUSCoUA64tYOBwucWtERETKWzwep7W1dWp+nU0054uIiMyc2Tzni4iISGkoqHGVnEo/EQ6HdYJDRERkhszG9E6a80VERGbebJzzRUREpDSUkFJERERERERERERERMqCghoiIiIiIiIiIiIiIlIWFNQQEREREREREREREZGyoKCGiIiIiIiIiIiIiIiUBQU1RERERERERERERESkLCioISIiIiIiIiIiIiIiZUFBDRERERERERERERERKQsKaoiIiIiIiIiIiIiISFlQUENERERERERERERERMqCghoiIiIiIiIiIiIiIlIWFNQQEREREREREREREZGyoKCGiIiIiIiIiIiIiIiUBQU1RERERERERERERESkLCioISIiIiIiIiIiIiIiZUFBDRERERERERERERERKQsKaoiIiMgVY+bz5AcGKCYSpW6KiIiIiIiIiFQAZ6kbICIiIpXDyGYpDA1THB4i19tH/vhxjEQC3/VrCd16a6mbJyIiIiIiIiJlTkENERERuWxGKkVhcJDC0BC5Eycp9PdRTCQw83mwO3CEQhRTKSgUS91UEREREREREakACmqIiIjIRTPzeQpDQ+T7B8gd6yF/shcjEccsFLG53diDQZyNTdg9nqnXKPWUiIjI3JDPFknFc4RrvNjstlI3R0RERCqUghoiIiJyTqZhUBwdJT8wQP7ESXI9PRRjMcxsBpvLjT0cxtXSis3lKnVTRUREpARymQLx4QzjA0nG+lMALLupmUCV5wKvFBEREbk8CmqIiIjIFNMwKI6NWasxBgbIH+2hMDqKkUyCw44jGMJZX4/d6y11U0VERKREsqk88eEMYwNJYgNp0sk8dpsNl9dOIW9immapmygiIiIVTEENERGROWwqiDE4SH5ggNzRoxTHxjFSSbDZsPsD2MNhnI2N2Oz2UjdXRERESqSYNxjtSzLal2R8MEUuVQAb+IIuoo1+7HYbxYJBYjRb6qaKiIhIhVNQQ0REZI4xcjkrldSxY+QOH6Y4Po6RTgNgDyiIISIiIqfls0VGTk7QfyhGYiyDzWbDG3RRNRnIEBEREbnaFNQQERGZA4xslvxJqyZG9sBBiqMjmIUi9mAQeySCs6lJQQwRERGZkk3lGT4xQf/hGMnxHC6vg0idD4dT3xdERESktBTUEBERqVBTgYyjR61AxtgoZtHAEQ7jmteCze0udRNFRERklknFcwwdSzB4NE4qkcMbcBJt0qoMERERmT0U1BAREakgZi5H7uRJckeOkD1wgOLoKKZh4AhHcLW0YnO5St1EERERmWWKRYOJ0QwjJycYOjZBNpXHF3JT0xzApmCGiIiIzDJls250bGyMzZs3E4lEiEQibN68mfHx8fO+xjRNHn74YZqbm/H5fNx222288sorU8+Pjo7y+7//+3R1deH3+2lra+PBBx8kFotN+zkdHR3YbLZp2yc+8Ykr0U0REZFLZhaL5E6cZOLZXzD6f/+R8e/9C8ktWzHzBVytbXgWLsJZX6+AhoiIiEwp5IuM9Sc5+vIwL/3sOHueOcnJ7nEcLhvVzQH8YbcCGiIiIjIrlc1Kjfvuu48TJ07w4x//GIAHHniAzZs386Mf/eicr/nCF77AX/zFX/Ctb32LxYsX8yd/8ie86U1voru7m1AoRG9vL729vXzxi19k2bJl9PT08P73v5/e3l6+//3vT/tZn/nMZ7j//vunHgeDwSvTURERkYtgmiaFwSHyx3rI7NtHfmAQM5vBHgrjbG7G7vGUuokiIiIyy+QyBRIjGcaHUoz1JclMFDANA7fPRTDqxekqm+seRUREZA4ri6DGvn37+PGPf8zWrVtZv349AN/4xjfYsGED3d3ddHV1nfEa0zT58pe/zB//8R/zjne8A4B/+Id/oKGhge985zu8733vY/ny5Tz66KNTr1mwYAGf+9znePe7302hUMDpPP3rCYVCNDY2XuGeioiInJ9ZKJA9eJD07t3kT5zESCaxBwI46+qw+3ylbp6IiIjMMtl0gfhQmrGBJLGBNJlUHgCP30m41qvC3yIiIlJ2yiKosWXLFiKRyFRAA+CGG24gEonw3HPPnTWoceTIEfr7+9m0adPUPo/Hw6233spzzz3H+973vrO+VywWIxwOTwtoAHz+85/ns5/9LK2trbzzne/kYx/7GO7zFFjNZrNks9mpx/F4/KL7KyIi8npmPk/24EFSL+4k19ODzeHAUVuLa968UjdtztOcLyIis00uXSA2nGasP8l4f5psKg828AVdVNX7sTuUVkpERETKV1kENfr7+6mvrz9jf319Pf39/ed8DUBDQ8O0/Q0NDfT09Jz1NSMjI3z2s589I+Dx4Q9/mOuuu45oNMoLL7zAJz/5SY4cOcLf/d3fnbPNjzzyCJ/+9KfP2y8REZELMXO508GMY8ewOZ24WlqUXmoW0ZwvIiKzwbRAxkCabPI1gYxGP3bVxxAREZEKUdKgxsMPP3zBkwDbtm0DwGY78wuYaZpn3f9ar3/+XK+Jx+O87W1vY9myZXzqU5+a9txDDz00dX/FihVEo1F+7dd+jc9//vPU1NSc9X0/+clP8tGPfnTaz29tbT1vW0VERE4xczkyBw6Q3jkZzHC5FcyYpTTni4hIqWTTBeIKZIiIiMgcU9Kgxoc+9CHe9a53nfeYjo4Odu/ezcDAwBnPDQ0NnbES45RT9S/6+/tpamqa2j84OHjGaxKJBG9+85sJBoM89thjuFyu87bphhtuAODgwYPnDGp4PB48OvEkIiKXqDg+Tu74cdI7d5E7cQKby4WrtQ37eVIeSmlpzhcRkaspm8oTH86cDmSkFMgQERGRuaWkQY3a2lpqa2sveNyGDRuIxWK88MILrFu3DoDnn3+eWCzGjTfeeNbXzJ8/n8bGRh5//HFWr14NQC6X4+mnn+bzn//81HHxeJy7774bj8fDD3/4Q7xe7wXbs3PnToBpwRIREZHLYRoGhaEh8id7yR4+RL63FyORwOb24G5txaZghoiIyJyXSeYnV2SkGB9MkU0VsNvBq0CGiIiIzEFlUVNj6dKlvPnNb+b+++/n61//OgAPPPAA99xzz7Qi4UuWLOGRRx7hV37lV7DZbHzkIx/hT//0T1m0aBGLFi3iT//0T/H7/dx3332AtUJj06ZNpFIp/umf/ol4PD5V3LOurg6Hw8GWLVvYunUrt99+O5FIhG3btvHQQw9x77330tbWdvV/GSIiUvbMXI58Xx+5kyfJHThIYXgII53G5vHiiERw1tVjs9tL3UwREREpIaNoEBtKM9STYGwwRTZZwO6w4Q26iCqQISIiInNYWQQ1AL797W/z4IMPsmnTJgDuvfdevvKVr0w7pru7m1gsNvX44x//OOl0mt/7vd9jbGyM9evX89Of/pRQKATAjh07eP755wFYuHDhtJ915MgROjo68Hg8fO973+PTn/402WyW9vZ27r//fj7+8Y9fye6KiEiFMQsF8r29ZA8eJHvwEMXRUYxCHkcgiCNajaslUOomioiIyCyQyxQY7UsyeDROfDiDaZr4w24CTX5sCmSIiIiIYDNN0yx1I+aCeDxOJBIhFosRDodL3RwREblKCiMj5I4cIfPKXvL9/ZiFAo5wGHskMmeKfmcPHyawbh2hO++YsZ85m+fV2dw2ERGZvZLjWYZPTDB0LE4qnsfpsROIuHG6HKVu2kUrFgwSo1lW3tFCMHrh1M4XQ/OqiIiIvF7ZrNQQEREpF0YqRa6nh0x3N7mjRzESCeyBIM7GRuwXUbtJRERE5oZiwUoxNXwswWhfklymiDfoJNqk9FIiIiIi56KghoiIyAwwczkrvdThw2S7X6UwOgoOO86aWpyNTdhsOjEhIiIiUMgXSYxkiA2lGe1NkopnwbThj7gJ1ejiBxEREZELUVBDRETkMpmFglXwu6eH7P5uCsPDmMUijkgEd0cHNqemWREREbHqZFiBjBSjfSnSEzkwTNx+F6EaH06XvdRNFBERESkbOtsiIiJyCcxikUJ/P9ljx8ju209heAgzl8ceCuFsbp4zdTJERETk/Ip5g7GBJGP9Kcb7U2RSeQA8fieRWh8OpwIZIiIiIpdDQQ0REZGLYGSzZHbvJrNvH/mBQcxsBnswhLNBdTJERETktEK+yMiJJH2HxkmMZrDZbHgCTqrq/dgdSkcpIiIi8kYpqCEiInIBhbExEv/9BNn9+7D7Azjr6rD7fKVu1qxmmib5kyfJ7N6NPRwudXNERESuuEKuyPDJCfoPxoiPZnC57UTqtCJDREREZKYpqCEiInIeuePHSTz+M3K9J3G3tSu91AUUhodJ795N5uWXKY6OAuDq6Chto0RERK6gfK7IyImJqZUZLo+DqgYfDoeCGSIiIiJXgoIaIiIiZ2GaJpk9r5B48kmMVBJP5wJsDkepmzUrFRMJMnv2kN69m0Jv79R+m8uFZ8kSnI2NJWydiIjIlZHPFhmeDGZMjGZxeR1EG/zYFcwQERERuaIU1BAREXkdM5djYutWUlu2YvN48MzvLHWTZh0zlyOzfz/pXbvIHT4Mpmk9YbfjXrAA34oVeLq6sHs8ZA8fLm1jRUREZpBhmIycmOBE9xiJkQxun4Noo+pliIiIiFwtCmqIiIi8RnFigoknniC9+2WcdXU4qqpK3aRZwzRN8seOkd61i8wrr2Bms1PPuVpa8K5Ygfeaa3AEgyVspYiIyJWTGM1wYv8oIyeTOJw2BTNERERESkBBDRERkUn5/n4Sjz9O7uhRXK1tKgY+qTg+TnrXLtIvvTRVJwPAEY3iXbkS38qVOKurS9hCERGRKyuXKdB3MEbfoXFymSLhGi8uj9JSioiIiJSCghoiIjLnmcUi2VdfZeKppyiMjeHuXIDNObenSDOXI7NvH+mdO8kdOTK13+Z2473mGnyrVuFqa8NmV95wERGpXFOppvaPEh/JEIi4CVV7S90sERERkTltbp+xERGROc00TfI9PSS37yB38CC4XFZAwzZ300jke3tJvfgimd27T6eXstlwz5+Pb9UqPEuXYne7S9tIERGRq2Aq1dSJJA6XjermAHb73P2OcDHMUzW2RERERK4gBTVERGROyvf2ktqxg8z+bsxCHldT85xNN2Wk02RefpnUiy9S6Oub2u+IRvGtXo1v5UrVFhERkTkjGcsyeDTOwNG4lWqq1ovLrVRT55OK5+g7OM5oX4qFa+pL3RwRERGpcApqiIjInFIYHrZWIux5BSOVwtnUNCcLW59apZJ68UUyr7wChYL1hMOBd+lSfGvW4O7oUHopERGZE0zTJDGaYbAnwfDxBLl0AX9YqabOp1g0GD42Qe/BcWKD6an98ZFMCVslIiIic4GCGiIiMicUYzFSL71EetdLFGMxXA0NuObNK3WzrjojnSa9axep7dspDg9P7XfW1+NbswbfihXY/f4StlBEROTqMQ2T2FCagaNxRnonKOQM1c24gInxLH0Hxxk4EqeQM6ydNqhuCtDYGcbl0WkGERERubL0bUNERCqakU6T3v0y6Rd3kB8ewVlTg2fRojlVN8M0TfInT5Leto30nj1TqzJsbjfea6/Fd911uObNm1O/ExERmduMosH4QJr+IzHG+lMYRYNAlQdPrf5EPptiwWDoWILeA+PEh0+vxPD4nTQtjNDYGcEbcFEsGCRGsyVsqYiIiMwF+sYmIiIVySwUyOzvJvXCC+RPnsQeieBZuHBOpVMyslmrVsb27dNqZTgbGvBffz3eFSuwezwlbKGIiMjVZRgmY33J16RMMglGvbg8qplxNqlEjt4D4/Qfik1blVHbEqRpQYTqpgA2FU8XERGRq0xBDRERqSimaZI7cpTUthfIHjqEzePF3dmJzTl3przC4CCpF14gvXs3ZnbyakmnE+811+C//npcLS1alSEiInOKaZrEBtP0HhxntDeJzQahGg9Ol4IZr2cYJqMnJzh5YJyxvtTUfo/fSfOiKhoXRPD45s73KhEREZl99E1EREQqRn5gkNT2bWT27sUsFnG1tM6ZlQimYZDt7ib1/PPkjhyZ2u+oqcG/di2+VatUK0NEROakxGiGvkPjDB+fwCiYBGs8uNwKZrxeNl2g72CMvoPjZFOFqf3VzQGaF1VR06xVGSIiIjI7KKghIiJlrxiPk3pxJ+mXXsJIJHA2NeEIBkvdrKvCSKVI7dhBats2jFjM2mmz4VmyBP+6dbjnz9eqDBERmZNS8Rz9h8cZ7EmQyxQIRb24tcLgDPHhNCe6xxg6lsCczDDl9DhoWhCheWEEX8hd2gaKiIiIvI6+0YmISNkykkkye/eS2rGD/NAwztpaPIsWlbpZV0W+r4/U88+Tfvnl04W//X78112H//rrcVRVlbaBIiIiJZJNFxg4EqP/UJxMMkegykOo2lvqZs0qRtFk6FiCE91jJEZOF/4O13ppXlRFXXsIh2Pu1CETERGR8qKghoiIlB0jkyGzd59VAHtgAHs4PCeKgJuGQXb/fpJbtpA/dmxqv7OpCf/69fiWL8fmcpWwhSIiIqWTzxUZOpag98A4qVgWX8hNdXNAKxZfI5su0HdgnN4D4+QyRQBsdhv1HSFaFkcJ1Sj4IyIiIrOfghoiIlI2jFyO7P79pLbvIN97EnsgiHvBAmyOys6LbWQypHfuJPX88xTHxqyddjveZcvwr1+Pq7VVJ2xERGTOKhYMRk5O0PvqOPGRDB6/g+om1X94rfhIhpP7xxg8Fp9KMeX2OWheFKV5UQS3V6cGREREpHzom4uIiMx6Zj5P5tVXSe/YQe74cew+P+75ndiclT2NFcbGrBRTL76Imc0Ckymm1q61UkyFwyVuoYiISOmYhsloX5Leg+OMD6RwuuxEG33YlTYJsH4/wycnOLFvjNhQemp/uNbLvK4oda0h7A4FfkRERKT8VPbZIBERKWtmsUj24EFS23eQ6+nB5nbjbu+o6BRLpmmSP36c5JYtZPftA9MEwFFbS+CGG/CtXInNrYKdIiIyd5mmSXw4Te+BcUZ6k9iAcK0Pp0vBDLBWrvQfinGie4x0Ig+AzQb17WHmLakiXOMrcQtFRERE3hgFNUREZFbKHT9O8vkXyB08AHYHrtZW7BV8Mn+qXsYvfkH+xImp/e7OTgI33mil2arwmiEiIiIXkhzP0ntgnKHjCYpFg1DUi8tT2WkoL1Y2VeBk9xi9B8cp5KwcU063neZFVcxbXIXHX7kXhYiIiMjcUjZnR8bGxti8eTORSIRIJMLmzZsZHx8/72tM0+Thhx+mubkZn8/HbbfdxiuvvDLtmNtuuw2bzTZte9e73vWG31tERC5PYWSE+E9+wvi//Au5A6/ibGzC3d5esQENM58ntW0bw1/5CuPf+54V0HA48K1eTc3v/R7V73kPnkWLFNAQEZE5LZvKc/TlYfY8c4K+QzG8QRfVjQEFNICJsQz7nutj6/87xLG9oxRyBr6Qi0Vr69nwKwvoXFWngIaIiIhUlLJZqXHfffdx4sQJfvzjHwPwwAMPsHnzZn70ox+d8zVf+MIX+Iu/+Au+9a1vsXjxYv7kT/6EN73pTXR3dxMKhaaOu//++/nMZz4z9djnm74c93LeW0RELo2RTJLavZv0jh0UxsZwNTZVdM0II5UitW0bqeefx/j/s3fnQXLe52Hnv+/Zd/d0z33P4D6JgxfAm6IESo6Lu3JFsVZZ5dqibKe8cuQ42lVl15ZsR4q8W4m2omxFtpK1K5Y3WR/ZlRyXROrgIRIEQQDERRA3BhjMPX3f77V/NDAkCJAEgZnp6/lUDRrzTs/M03O9v/497/M8hQIAit9P8P77CT74INq7zlNCCCFEu7Ith/mJHFfPpihkqoSiJp2D/nqHVXee55GeLXL5rSSp6eLS8VhPgOFNcToHwzIoXQghhBAtqymSGqdOneKHP/whr732Gg8++CAAf/RHf8TevXs5ffo0GzduvOl9PM/jW9/6Fv/8n/9zfumXfgmAP/mTP6G3t5c/+7M/41d+5VeW7hsMBunr61u2zy2EEOL2eZZF+e23KR54HWt6Ci2ewLd+A4rSmk/E7VSK4v79lI4cwatWAVBjMUJ79xLYvRvV56tzhEIIIUT9ua5HcqrA1TMpMnMlfEGNzv5Q22/Ue67H/GSeKyeT5JLl2kEFekYiDG1OEO2UhI8QQgghWl9TJDX2799PLBZbSioA7Nmzh1gsxquvvnrLxMLFixeZmZlh3759S8d8Ph+PP/44r7766g1Jje9973v86Z/+Kb29vXzqU5/id37nd5YqOe7kcwNUKhUqlcrS69ls9s6/AEII0YI8z6N68SLFA69TuXABNRDAXLsORWvNNhLW3ByFl1+mfOIEuNf6XPf1EXr4Yfxbt7bs424Hcs4XQojllZkvcfVMiuRUAVWFjr4AmtbebRgdx2X2QpYrp5JLw79VTaFvbYzhzXEC4dZs0ymEEEIIcStNkdSYmZmhp6fnpuM9PT3MzMy87/sA9Pb23nC8t7eXiYmJpdf/7t/9u4yPj9PX18eJEyf4yle+wtGjR3n++efv+HMDfOMb3+BrX/vahz84IYRoQ3YqRfH11ykdO47n2LUh4C1aoVCdnKTw8stU3n576Zi5Zg2hRx7BXLOmZStS2omc84UQYnkUMhWmz2eYm8jiWC6RTj+G2d5Jf6vqMHUmzeTpFFbZAWrDvwc3xhnc0IHpb4qn9EIIIYQQy6quK6CvfvWrH7oJcPDgQYBbbvp4nvehm0Hvfft73+fZZ59d+v+2bdtYv3499913H4cPH2b37t13/Lm/8pWv8Ju/+ZtLr2ezWYaHhz8wViGEaHWeZVE6cYLiawewF+bR+wdacnbE9SqUwssvU71woXZQUfBt3kz40UcxBgbqG6BYVnLOF0KIu5NLlpmbyDJ/OUe1ZBOO+9p+sHW1bDN5KsXVM2kcu1bh6QvqDG9O0L8uhqa3d+WKEEIIIdpbXZMav/7rv85nP/vZD7zP2NgYx44dY3Z29qa3zc/P31SJcd31GRkzMzP09/cvHZ+bm3vf9wHYvXs3hmFw9uxZdu/eTV9f30f+3FBrdeVr0auOhRDiTlSvXKGwfz+Vs+dQQyHMdetR1NZ6Qu65LpUzZyi8/DLW5GTtoKoSuOceQo88gt7dXd8AxYqQc74QQnx0nueRWywzczFD8mqBatkhFDOJJNp7JkS5YHHlVJLpcxlcxwMgFDMZ3pqgZzSK2uYzRYQQQgghoM5Jja6uLrq6uj70fnv37iWTyfD666/zwAMPAHDgwAEymQwPPfTQLd/nekup559/nl27dgFQrVZ58cUX+eY3v/m+n+vkyZNYlrWUCLmTzy2EEOIdTj5P8Y03KB0+glsutWSrKc91KZ88SeGll7Dn5moHdZ3g7t2EHn4YraOjrvEJIYQQjcJzPTLzJWYvZVm8msex3Voyo80HXBezVS6/lWT2YgavVphBpNPP6LZOOgdD0q5SCCGEEOJdmqIB5+bNm/nkJz/Js88+y3e+8x0AvvCFL/CLv/iLNwzq3rRpE9/4xjf49Kc/jaIo/JN/8k/4+te/zvr161m/fj1f//rXCQaDfO5znwPg/PnzfO973+MXfuEX6Orq4q233uKf/tN/yq5du3j44Yc/0ucWQghxI8+2KZ8+TXH/a1SnpzB6ejEGB+sd1rLyHIfyiRPkX3oJZ2EBAMXnI3j//QT37kULh+scYX15rouTTuOVSyBXlgohRFvzPI/0bJGZCxmS00VcxyXc4cMMNMVT0hWTT1W4fHKRucs5qBVm0NEbYGRrJ/G+oCQzhBBCCCFuoWlWkN/73vf44he/yL59+wB45pln+Pa3v33DfU6fPk0mk1l6/ctf/jKlUol//I//MalUigcffJDnnnuOyLX+7aZp8pOf/IT/4//4P8jn8wwPD/O3/tbf4nd+53fQNO0jfW4hhBDg5HJY09NYk5NUzp/Hnl9A8fnwrV2HorXOoE/PcSgdO0bhpZdwkkkAlECA0J49BB98EDUQqHOE9eWWy9gLC3jFIlpHjNBDD+HfsqXeYQkhhKij6XMZLh1fwHU8Qh1m2w+4ziXLTBxfZGEyv3QsMRBidFsnse72XkcIIYQQQnwYxfM8r95BtINsNkssFiOTyRCNRusdjhBCLAvPcbDn57GmpqlcvIh19SpuLgeAGomgdXS0VKspz7YpHT1K4eWXcVIpAJRgkNDevQQfeADV376tM65XZTjJJIquYfQP4N+2Dd+a8RVpv9XI59VGjk0IIeph+nyGC2/OY/o1glGz3uHUVW6xzKUTiyy+K5nRPRJhZGuiJeaJOLZLLllhx8eGCMeX5/HIeVUIIYQQ79Xel8cIIYS4I3YqRen4carnzmEnk3ilMorPhxaLoXd1tVRVBlxLxfY6sQAAgbBJREFUZhw5Qv7ll3GvVQSqoRChhx4icP/9LZW4+ajcchl7fh6vVEKLdxC87z78GzdgDA2h6LLMEEKIdjd7McvFo/MYPrWtExrZhRKXji+SnCosHesZjTC6vZNQrH3XEUIIIYQQd0J2G4QQQtw2z7JqA7H378eaX7iWxOhu2XZLnuPUkhkvvfROMiMcJvTwwwTvuw/FbN/NGSebxV6YR1FUjKFB/Fu34lu7Fk2uoBRCCHHN/OUcF47Oo+lK227cZxZKTBxbJDl9LZmhQO9olJFtibb9mgghhBBC3C1JagghhLgt1tWr5Pfvp3L6DGo4jG/9ehRVrXdYK2JpZsaLLy61mVLDYUKPPkrw3ntRDKPOEdaH57o4ySR2MokWChHYuhX/tm2YIyNSlSGEEOIGC5N5zh+ZQ1FYtjZEzSS7UOLisQVS08XaAQV6x6OMbu1s64oVIYQQQojlIDsQQgghPpBbLFI8fJjiwTdwS0WM4eGWbbfkOQ7l48fJv/ji0gBwNRwm9MgjtcqMdk1mWBb2/DxuPo+WiBN+9FH8mzZi9PXVOzQhhBANKDlV4PzhOTzPI9rZmtWc7ye3WObisYV32kwp0LcmxsjWBMGIJDOEEEIIIZaDJDWEEELckud5VM6epfDqfqzJK2idXfgGBuod1orwXJfyiRPkX3gBZ3ERuDYz4+GHCd5/f9u2mXLyeez5eXAdjL5+wo89im/9emkxJYQQ4n2lZgqcOzSHY7vEutsnoZFLlrl0/F0DwBXoG48yuq2TgCQzhBBCCCGWlSQ1hBBC3MROpSi89hrlY8dBUTDH17RkeyHP86icOkX+pz+tbd4DSjBI6KGHCD7wQMtWpHwQz3FqLabSKbRAAN+6tQS2bcNcswa1TZM7Qgghbk96rsi5Q3NYVZuOnmC9w1kV+XSFS8cWWLjyTjKjdzTK6HZpMyWEEEIIsVJab4dKCCHEXalcvEjuxz/GnplFHxhAC4frHdKy8zyP6rlz5H76U+ypKQCUQKCWzHjwwbZMZrjFIvb8PJ5VRU8kCD/yKP4N69H7+1EUpd7hCSGEaHDZhRLnDs1SKdl09LR+hUYhU2Hi+CJzE7mlYz2jEUa3d8oAcCGEEEKIFSZJDSGEEEBto798/Di5F17AK5Ux161ryUHg1YkJcj/5CdbEBACKaRLcu5fQ3r2ogdbfhHk3z3VxUimcVArFNDGHh/Fv24ZvzThqKFTv8IQQQjQB13GZm8hx5a1kLaHRG2jpZHg5b3Hp+AIzF7Pg1Y51j4QZ3d5FuEOSGUIIIYQQq0GSGkIIIfBsm8Jrr1F45VUUvx9zbKzeIS07a2qK3E9+QvXcudoBXSf4wAOEH3mk7TbwPcepDf7OZtEScUJ79uDbsB5jcLAlE1lCCCFWRiFT4cpbSeav5DD9WksnNColm8snFpk6l8Zza8c6h8KM39NJOO6vb3ANxLbceocghBBCiDZwV0mNarXK3NwcrnvjwmVkZOSughJCCLF63GKR3AsvUDpyBL2rG62jo94hLSt7fp7cT35C5dSp2gFVJbB7N+HHH2+7gdeebWPPzeEU8hg9vYQffgjfhg1t93V4P7KuEUKI2/Pu6oxS3iLa7ccwtXqHtSKsisPlt5JcPZ3CdWqlGR19Qdbs6CLa1V4Vnh/EqjrkkxUUFRL9Ifxho94hCSGEEKKF3VFS4+zZs/yjf/SPePXVV2847nkeiqLgOM6yBCeEEGJl2akUueeeo3zmDObQMGqwdYZ6OpkM+Z/9jNKbb4LngaLgv+cewk88gZ5I1Du8VeVZFtbsLF65hNHfT/jxx/Fv2thS3++7IesaIYS4fe+tzkgMBFuyOsO2XCbfTnHlVBLnWvVBtMvP+I4u4n3tVeH5QWzLJZcsgwfx/hAD62J09ARR1Nb7mRBCCCFE47ijpMY/+Af/AF3X+eu//mv6ZYCoEEI0perkJLnnnseamsI3No5imvUOaVm4xSL5l16iePAg2DYAvs2biXzsY+g9PXWObnW51Sr2zAyeVcUYGCC4eze+DRtQ/dIm491kXSOEEB+uXaozXMdl6myGiROLWJVaUjvUYTK+o5vOwZCcI65x7Foyw3U8OnqDDKzrIN4fQpVkhhBCCCFWwR0lNd58800OHTrEpk2bljseIYQQq6B86hS5n/wUJ5/DXLMGRWv+TQm3UqH42msUXnkFr1IBwBwbI/yJT2AODdU5utXlViq1ZIZtY44ME9i5C9+G9agtkrhabrKuEUKID1bIVLh8cpH5K3l8gdaszvBcj9lLWS4eW6BSqF0UEYgYjN3TRc9opOUe751yHJd8qoJjucS6Awys7yDRH0LVZCaXEEIIIVbPHSU1tmzZwsLCwnLHIoQQYoV51SqFQ4covPIKiqbjG19T75DummfbFA8dovDSS7j5PAB6Xx+RT3wCc+3attqEuCGZMTpKcPcufOvWoRjS1/qDyLpGCCFuzao4zFzIMHUuTaVgt2R1hud5LF4tcPHNeQqZKgBmQGdseyd9a2NSefAuhUyFct4m2uVnYH0HnYNhNF2SGUIIIYRYfbed1Mhms0v//+Y3v8mXv/xlvv71r7N9+3aM92yWRGXgqBBCNBw7mST/wguUT51CS3Q2/VwJz3UpnzxJ/ic/wUmlANASCcIf+xj+rVtR1PZ5kn1TMuPe3fjWrpVkxgeQdY0QQrw/1/VYvJrn6tspsoslAhGzJasz0nNFLry5QHa+BIBuqoxsSTC4MS6b9e9iVx2yCyXMoMGaXV30rYmhG62V3BJCCCFEc7ntpEZHR8cNi1jP83jqqaduuI8M1BRCiMbjeR6VM2fIv/gS9twcxvBw089UqFy4QO7557GnpgBQw2HCjz9OYPduFP2OihCbklRm3DlZ1wghxK3lkmUm306yeLWAqinE+0KoWmslM/KpChePzrN4tQCAqikMbowzsiWB4ZPN+us8zyOfqmBVHLqGIwxv7iQc99U7LCGEEEKI209q/OxnP1vJOIQQQqwAt1KhsH9/bWi2otbaMTVxBYM1O0vu+eepnj0LgGKahB5+mOBDD7XVvAjPsrCmpiSZcRdkXSOEEDeqlmymzqWZuZChWnaIdvpbboO/XLC4dGyBmQvXqvUU6F8bY2x7J76gnEPfrVq2yS6WCUXN2lyRkYjMzRBCCCFEw7jtpMbjjz++knEIIYRYZtbsHPkXfkbl9Bn0vj60WKzeId0xJ5Mh/9OfUjp6FDwPVJXgffcRevxxtHC43uGtGs91sefncbNZzLFRgvfeK8mMO9SM6xrHdqkUbQIRo+VawAgh6sd1XOYv55k8naSQrhCMmUQSzV3R+V5W1eHyySRXT6dwHQ+AruEwa3Z2E4y2z0URt8N1PXKLZTzXo39tjOHNCQJh+RoJIYQQorHcUY+O/+v/+r8Ih8N85jOfueH4n//5n1MsFvn7f//vL0twQgghPrqlWRMvvYyTTmOOj6M0aRWDWypR+PnPKbz2Gtg2AP6tWwk/9RR6Z2edo1tdTjaLPTuDlugk+qlP4t+2DdUnLSCWQ7Osa5LTBS4dXSAYM+kajtDRG8QXaJ92a0KI5Vcp2UwcW2B2IotuasT7Qy01GNt1XK6eSTNxYhG76gIQ6wmwdlc30a5AnaNrLJ7nUc5bFLJVop1+hjd30jkQQmmhnwchhBBCtI47eib8L//lv+Tf/bt/d9Pxnp4evvCFLzTMk38hhGg3Tr5A4ZVXKB05guL319pNNeEV3Z5tUzx4kPyLL+KVasM7jdFRIvv2YQ4N1Tm61eVWKlhTV1F1g+D9DxB84H70eLzeYbWUZlnXeK5HMV+lWrFZvFrAHzboHAiRGAgR7QrIUFshxEeSmS9x8eg82YUS0a5AS7Wa8jyPuUs5Lhydp1KoXRQRjJms2dlN52CoKddGK8VzPYq5KqWchT9s1Aalr49jStJcCCGEEA3sjlYqExMTjI+P33R8dHSUy5cv33VQQgghPhrP86ieP0/+5ZepTk5iDAw2ZVsmz/OovPUWuR//GCeZBEDv7ib8iU/g27ChrTYhPNfFnpnBLRbxrVtLaM8ezNHReofVkpppXaOqCh09QVzXo1KwmDqXZvp8mlCHj66hCPG+EKEOs61+V4QQH43nesxczHL55AJWxW256ozkdIELR+bJpyoAmAGd8Xs66V0Ta6nHebdc16OYqdTaGoZNxu/ponskQiDSnNW9QgghhGgvd5TU6Onp4dixY4yNjd1w/OjRo3S2WTsQIYSoN7dQoPDaAYqHD4Pn4VuzFkVvvqvrqpcvk3vuOawrVwBQw2HCTz5JYNcuFK11rh79MJ7n4aTTOPPz6L09RD72JP4tW2RuxgpqxnWNqioEIiaBiIljuxSzVS4enWfydIqOngC9YzE6egMy1FUIcYNq2ebyyUWmz2fwBXTifcF6h7Rs8qkKF47Mk5wuAKAZKiNbEgxtiksl27s4jkshXcUqO4Q6TAY3xukejsigdCGEEEI0lTva9frsZz/LF7/4RSKRCI899hgAL774Ir/xG7/BZz/72WUNUAghxPurnD9P/uWfU71yGaO3OYeB24uL5H78YypvvQWAYhgEH36Y0EMPtdXMCM+2sRcWcLNZ1GiU0MMPEbzvPrRotN6htbxmX9douro01LdaqrWmWrxaINrlp3c8RmIghGG2T2JQCHFruWSZi8fmSc8UiXT6Mf3NdwHErVSKNhePLTBzIQMeKAoMbOhgdFtnyzzG5WBbLoV0BcdyCSf8jN/TRWIgJF8jIYQQQjSlO1rB/P7v/z4TExM89dRT6NeuBnZdl7/39/4e/+Jf/ItlDVAIIcTN3EKBwoEDFA8dBs9tyuoMt1gk/+KLFA8eBMcBRSGwaxfhJ59sq418t1DAnp/Hc2z0rm6CD9yPf/169K6ueofWNlppXWMGdMyAjm255FMV0nMzhGImfWtidA6G8YfkSlwh2o3necxfznHp+CKVokVHXxCtBaq4bMvlyqkkV95K4joeAN0jYcZ3dhOUFkpLrIpDPlUGFKJdfvrW1JLduiHJbiGEEEI0L8XzPO9O3/ns2bO8+eabBAIBtm/fzqj0+n5f2WyWWCxGJpMh2kabdUKI5Ve5cIH8Sy9jXbmM3oTVGZ5tUzxwgPxLL+GVywCY69YR2bcPo7e3ztGtDs91cVIpnGQSxefDHB0lsG0r5po1qH5/vcNrCitxXl2udc1KnfPnJrKcPjBDoj902+/jOh7FbK1nuj9s0j0aoXsoQjjePlVQQrSrStGimK2SmikwdS6DbqiE476mn7njuh4z5zNcPLaAVXYAiHb5Wbu7h1h3oM7RNY5y0aKYqaJpKh29QXrHo02b0JLn0kIIIYR4rzu6rPd3f/d3+a3f+i3Wr1/P+vXrl46XSiX+t//tf+O3f/u3ly1AIYQQtass7dlZyiffonjkMDguZpNVZywNAX/+eZxUCgC9t5fI00/jW7u2ztGtDs+ysOfncfN5tHic0J49+DZtxBgYQFGbb5OhVbTyukbVFMJxP6EOj1LO4srJRWYvZOhf28Hghg50aUslREvwXI9ywaKQqVLIVMjMFSnlLaySg+u5hDv8+ILNs2a4Fc/zSE4VOH94nmK2CkAgYrBmZzddw+GmT9YsB8/zKOUtStkqZkCndzxK71iUaGcARYakCyGEEKKF3FGlhqZpTE9P09PTc8PxxcVFenp6cBxn2QJsFXJ1iRDiTji5HNWLFym//TbWlSu4xWJTVmdUJyfJ/ehHWJcvA9eGgD/1FIGdO9tiM9+tVrFnZ/EqFYz+fgI77sG3bl3TfR8byXKeV5d7XdNIlRq3UspXKWaqxPtDjG7tJNolVzYL0Yw8zyM5XSA7XyIzV6JctKiWHRQ8dJ+OGdAwfTqq1vyb2blkmfOH50nPFgHQfRpj2zoZWN/REo/vbrmuRzFTpVK08IdMukfCdI9EW6YqT55LCyGEEOK97uhyHc/zbnklzNGjR0kkEncd1K2kUim++MUv8v3vfx+AZ555hn/zb/4NHR0dHxjn1772Nf7wD/+QVCrFgw8+yL/9t/+WrVu3AnDp0iXGx8dv+b7/z//z//CZz3wGgLGxMSYmJm54+//0P/1P/Mt/+S+X4ZEJIcSNvGqV6uQklbPnqJw9i5NOoZg+tM5OjKHheof3kTjpNLkf/5jy8eO1A4ZB6KGHCD38cFsMAXfLZeyZGTzHwRwaIrBrF74N69visTeTeqxr6ikQNjH9OunZIsVMhcGNcfrXdqDprZ9gFKJVVEs2V95OMnM+g+t6mH4NX0An3OFrqSvyK0WLi0cXmLmQBUBRFYY2xRnZmsCQSjMcuzb826q4hDpMBjd00zUckflJQgghhGh5HympEY/HURQFRVHYsGHDDRsAjuOQz+f51V/91WUPEuBzn/sck5OT/PCHPwTgC1/4Ap///Of5wQ9+8L7v8wd/8Af8q3/1r/jjP/5jNmzYwO///u/ziU98gtOnTxOJRBgeHmZ6evqG9/nDP/xD/uAP/oBPfepTNxz/3d/9XZ599tml18Ph8DI+OiGEAHthgfK5c1Teegt7bg7PddHiCcy165qumsEtlyn8/OcU9u8H2wZFwb9jB5GPfawtqhPcQgFrbhYFBXNslMDOnfjWrEExZXBpI6nnuqbeNF0l0R+imK1y4c15svNlRrd1EuqQhJsQjS41U2DixCKZhRKRhB9foLnbSt3KrYaA94xGGN/ZTSAsG/ZWxSGfruC5HpFOP+NrYnQOhDF8kugRQgghRHv4SCvgb33rW3iexz/6R/+Ir33ta8TetTFlmiZjY2Ps3bt32YM8deoUP/zhD3nttdd48MEHAfijP/oj9u7dy+nTp9m4ceNN7+N5Ht/61rf45//8n/NLv/RLAPzJn/wJvb29/Nmf/Rm/8iu/gqZp9PX13fB+/+W//Bd++Zd/+aakRSQSuem+jcBz3abb7BRC3MgtFCgeeZPi4cO4mQxqJII+OITahBvgnutSOnyY/E9/ilsoAGCOjRF5+mmMgYE6R7eyPM/DzWaxF+ZRDBP/+vUEduzAHB9H0WSToRHVa13TSIJREzOgs3g1RyFdYXhLgp6xKGoLXektRKuwqw6TZ1JMnUnjuR6J/lDL/a56rsfMhdoQ8GrpnSHg6+7tkVZ51CpXCpkqqqYS7wvROxYl3heUSjshhBBCtJ2PlNT4+3//7wMwPj7OQw89hGGszlUy+/fvJxaLLSU0APbs2UMsFuPVV1+9ZVLj4sWLzMzMsG/fvqVjPp+Pxx9/nFdffZVf+ZVfuel9Dh06xJtvvsm//bf/9qa3ffOb3+T3fu/3GB4e5jOf+Qz/7J/9M8w6bzh6rkv6z/8cLZEguHs3emdnXeMRQnw0nm1Tfvs0xdcPYF29itbZhbl+fdMOuqycP0/uRz/Cnp0FQOvsJLJvH76NG5v2Md0Oz7axFxZws1nUSJjA9u0E7rkHY3hYks4Nrl7rmkajGyrx/hCFdIWzb8ySmS8xsjVBINx8iVUhWlV2ocTEiUWS0wXCHT78LVitkJyuDQEvpCsA+MMGa3Z1093mQ8BvGP7trw3/7hmNEuuS4d9CCCGEaF+3ndTIZrNLQ7l27dpFqVSiVCrd8r7LPbxrZmbmpuGdAD09PczMzLzv+wD09vbecLy3t/em+RjX/ft//+/ZvHkzDz300A3Hf+M3foPdu3cTj8d5/fXX+cpXvsLFixf57ne/+74xVyoVKpXK0uvZbPZ973vHHAcnmaR84gTlU28T3LWTwI4daDI8TYiGV71yhcKBA1TOnEXx+Wotppr0an57YYHcj35E5cwZABS/n/ATTxC8/34UvfVaYlxXazE1B46D3t1N8IH78a9bh97dXe/QxG1YznXNqpzzV5iiKITjfqyKw+zFDLmFEr1ronQPR1ty81SIZuHYLtPn00y+ncKuuC15VX4hU+H84XmSU7UKT91UGd3WyeCGDlSttR7rR+F5HuW8RTFr4QvpDG/tpGc4Im0ChRBCCCH4CEmNeDzO9PQ0PT09dHR03PJqmeuDNh3Hua2P+dWvfpWvfe1rH3ifgwcPAnzg5/sg7337+71PqVTiz/7sz/hf/9f/9aa3felLX1r6/z333EM8Hudv/+2/zTe/+U0636c64hvf+MaHPrblovfXWrrkXniR8okTBHbfS2DbVtRQaFU+vxDi9jnpNIWDBykfP45XqWAMDqL6/fUO6464xSL5F16gePAguC6oKsH77yf8xBOowWC9w1sRnuPgpFI4qRSq349v3VoCW7dijo837fexXS3numY1z/krzfBpJAauzdo4usD0uSzdYxF6RiKEYrKRJsRqKmQqTJxYZGEyRyBsEkm01nmmWra5dHyRqbNp8EBRYGBDnLHtnW0/G6KUtyhkKviDBsOb4/SOxwhGpXpOCCGEEOK6205q/PSnPyWRSCz9fzlKgH/913+dz372sx94n7GxMY4dO8bstXYm7zY/P39TJcZ11+dfzMzM0N/fv3R8bm7ulu/zF3/xFxSLRf7e3/t7Hxr3nj17ADh37tz7JjW+8pWv8Ju/+ZtLr2ezWYaHhz/0Y98RRUFPJNDicez5eXI/+hHl48cI3ncfvs2bUX2yCSFEvbmVCuUTJyi+/jr2wgJ6bx/a4FC9w7ojnuNQfP118i++iHftynbfhg1Enn4avaurztGtHCeTwZ6ZQevsJPTIw/g3bEDv72/rlhjNbDnXNat6zl8FiqIQivkIRk1KOYsrJxeZvZChezhCz1i05TZWhWg0nusxfyXH5ROLFPNVYt1BdKN1KhZcx2XydJqJE4s4lgtA11CYNbu6237jvlyoJTNMv87wployQxLKQgghhBA3u+2kxuOPP770/yeeeGJZPnlXVxddt7EBtnfvXjKZDK+//joPPPAAAAcOHCCTydzUKuq68fFx+vr6eP7559m1axcA1WqVF198kW9+85s33f/f//t/zzPPPEP3bbQNOXLkCMANyZL38vl8+FY5maCoKkZvL15XF/bsLJn/+l8xjx4jcN+9+Navb8qhw0I0O891qZw7R/HAAaoTl1GjUcx165ty1oLneVTOnCH3ox/hLC4CoPf2Enn6aXxr19Y5upVlzc3hFQqEHnuM4H33ooXD9Q5J3KXlXNfU45y/GhRFIRg1CUQMKgWbq2fSzE3k6BwI0TMu/dyFWAnVss3lt5LMnM+gmyqJ/lDLJM89z2P+cp4Lb85TzlsAhOM+1t7bQ7y3NSs8b1e5aFFIVTEDGoMb4vSNx6TNlBBCCCHEB7ijZucPP/wwjz/+OE888QQPP/wwoRVuc7R582Y++clP8uyzz/Kd73wHgC984Qv84i/+4g1Dwjdt2sQ3vvENPv3pT6MoCv/kn/wTvv71r7N+/XrWr1/P17/+dYLBIJ/73Odu+Pjnzp3jpZde4m/+5m9u+tz79+/ntdde48knnyQWi3Hw4EG+9KUv8cwzzzAyMrKij/tOKZqGMTCAZ1lYMzNU/7//D6Ovj8COHfjWr5eZG0KsEmtqisLBg1ROvQ2qijk2htKkg4itmRlyP/oR1QsXAFBDIcIf+xiB3bubMkFzuzzXxZqcRNF1op98Gv+OHS2zuSTesdrrmmajKAr+sIE/bFAp2cxOZFmYzNPRG6R7JEJHXxDDbO9WMUIsh8x8iUvHF8jMFYkk/JiB1plLlV0oce7QHNmFMgBmQGfNzi56x6Nte151XY9Srko5b2H6dQbWx+hb00E4LskMIYQQQogPc0cr5V/8xV/kxRdf5Nvf/jblcpl77713aTPgkUceIbwCV7B+73vf44tf/CL79u0D4JlnnuHb3/72Dfc5ffo0mUxm6fUvf/nLlEol/vE//sekUikefPBBnnvuOSKRyA3v9x/+w39gcHBw6WO/m8/n4z//5//M1772NSqVCqOjozz77LN8+ctfXvbHuNwUw8AcHsazLOz5ebJ/8zdoiQT+rVvxb9qEca1FlxBieTmZDMVDhygdPYZbLGAMDDbtjAknnyf/059SOnwYPA90ndCePYQefbTlZ0h4tk114hJ6ZxeRjz/V8tUo7awe65pm5Qvo+AI61bJNaqbA4tU8gahJ93CExECIcNzXthuUQtwp13GZOndtGHjVJd4XQtVa4/eoXLC48OY8c5dyAKiawsjWBMObEy038Px22VWHQqaKXXUJRAxGtnXSNRgmHG/tdZUQQgghxHJSPM/z7vSdHcfh4MGDvPDCC7zwwgtLPakrlcpyxtgSstkssViMTCZDdJkqJTzLYvG738WjNlPjQ+/vujjJJHYyiRYK4Vu3Fv+2bZgjIyh661wJJkS9uJUK5ZMna3Mz5hfQurvR4/F6h3VHPMui8NprFF5+Ge/a33T/1q2EP/GJpn1MH4VbLmNduYw5Mkrk6X0Y7zO/SdTPSpxXl2tdsxKxAcxNZDl9YIZEf+NUkriORylfu9LY8OnEegJ0D0v1hhC3q5SvMnFikfmJHP6w0TIzJWzL5fLJRSbfTuE6taebfWuijO/oxhdsv+cdnudRLtiUclVUTSHS6ad3NEa8P4jpb7+vx0e1UudVIYQQQjSvu1pBnT17lqNHj3L06FGOHTtGNBrl0UcfXa7YxDJTVBW9qwu9qwsnm6V08iTlt05hDA8T2L4Nc3wc7T1VLEKID+c5DpXz5ym+/jrViQnUcARz3bqmbMvkeR6Vt94i99xzOOk0APrAANFPfhJzdLS+wa0SJ5fDnprCv20rkY9/XFr2tRFZ13x0qlYbKh6K+aiWbZLTBRYn8wRjteqNruFIy2zSCrGcPM8jOVXg0vEFCukK0e5ASyQCPddj+kKGi0cXsMoOAB29Adbu7iGSaL9KBNfxKGQqVEs2vpBB/9oYXUMRot0BVJlJJIQQQghxx+4oqfHLv/zLvPTSS7iuy2OPPcZjjz3GV77yFe65557ljk+sEC0aRYtGa1cjT01RvXgBrSOOb/16fOvXYw4PNW3vfyFWi1soUDl/ntLxE1hXrtTmZow28dyMq1fJ/vCHWJcvA6BGo0Q+/nH827c3ZYLmTtiLizjpFMG9ewg/9hhqCw5/FjdrpnXNXRTYrjjTr2P69Vr1Rq7KpeOLTJ/P0DMaoXc8JskNIQDHcknNFpmbyJKaKaAoSm0YeAtscCenC5w/PEchXQUgEDFYu7uHzsHWGXZ+u1zXo5ipUCnahBN+RrYkiPeHCITl76AQQgghxHK4o6TGn//5n9PV1cU/+Af/gCeffJJHH31U+k03KdXvxxwZwXMcnHSa4qE3KB05gt7Tg3/rVnxrxtG7u+sdphANxZqbo3LmDOWTJ7EXFlBMH3p/f9POmHAyGXI/+Qnlo0drBwyD0MMPE3r4YVSzPZ58e46DNT2N4nlEnvo4wfvva5tEjmiedU0hU+HcG3Mo9ykkBhqnBdV7qZpCqMNHMGZSzltcOZVk7nKOnrEovaNRSW6ItlQuWCSnCsxeypJPlVFUhVDMbInWQ4VMhfOH50lOFQDQTZWx7V0MrO9omdkgt8vzPIrZWku+YMzHuvs66R6JtEQVjhBCCCFEI7mjVXQymeSll17ihRde4H/5X/4XTp48yY4dO3jiiSd44okn+NSnPrXccYoVpmgaemcnemcnbqWCs7hI7rnnKITDmKMj+Ddtwly7Vq5aFm3Ls22qly9TPnmSyrnzuIU8WqwDc3wNitacT1TdapXCz39O4dVXwbIA8O/Y0VYtlzzPw0kmcRYX0Hv7CD/2KP5Nm+odllhlzbKuOfXKNKWcxbGfTZIYCLHu3p6GThAoikIgYuIPG7Xkxskkc5ey9I5F6R2LEog0buxCLAfP88gtllmYzLFwpUC5YGEGNGLdgZYYkl0t21w6tsDUuQx4oCgwuDHO6LZODF9zro3ulOd5lPMWhUyVYMRk/J4uesZj+ALNn7QSQgghhGhEdzUo/Lrz58/z+7//+/zpn/4pruviOM5yxNZSGmFQ+Ef++J6Hm8thLy6C62AODxPauxdz7dq2KyEX7cuzLMqnTlE6dgxr8ip4LlpnF1osVu/Q7pjnupSPHSP34x/j5nIAGCMjRD/5SYzBwTpHt3qcXA5rehotFiO4ezeBnTvQGvDqfHFrKzk09G7XNSsV2+TpJK/8xTkWJ/N4795A3N7ZFFcBe55HKW9RylbxhQx6x68lN6Qdi2gxnuuRnK5VZaRni9hVl2DUwB82WmIN7Tguk2+nuHwiiWO7AHQNhVmzq7uhE60rpVy0KKQq+IIGPeNR+sbl79pyk0HhQgghhHivO67UePHFF3nhhRd44YUXOHnyJIlEgv/mv/lvePLJJ5c7RlEniqIszd7wLAtr6irpv/or/Nu2EXrwQfTOznqHKMSKsmZmyP/8FSpnTjd9i6nrqpcukf3Rj7CnpgDQ4nEin/gEvi1bWmKj5XbUZgldRTEMgvfdS+i++6TNXptrlnWN6dcZWN/Bmp3dnDs0R3KqwOTbKWYvZhnf0UX/2lhD9+VXFIVgxCQQNijlLSaOLzJ7MUvXYJjOwTDRLj+q1vxXr4v2VspVl1qu4UGoozVaTEEtMTk3kePCm/NUCjYA4YSPdbt76OgN1jm61WdXHTILZQyfxsCGOP1rY4RiUtUuhBBCCLEa7miF3d3dTVdXF48++ijPPvssTzzxBNu2bVvu2EQDUQwDc3QMJ5+nePgw1YsXCT3wAP577pGWVKLluNUqxSNHKB54HTeXwxgaavpkhr24SO7556mcOgWA4vMReuwxQg8+2LSDzT8qz7axpqfxqlV869YRevABjJGRtknmiPfXbOuaYNTknieHWJwqcP7QHMVslTOvz3L1bJp19/YQb/DNxXcnN8p5i6tn08xcyBBO+OkZjRLvC+IPtcffJdE6XMdlbiLH5KkkxXyVSMLfMskMgMx8ifOH58gulAHwBXXGd3TROx5tu/Oo53nkUxWsikPXUJihjXGiXYF6hyWEEEII0VbuaKV95MgR1qxZszREc2Jigm9961ts2bKFffv2LWuAorFo4TDquvU48/Nkf/QjyqfPENq7B3PNmrZ7QiNaU3VyksLPf07l3Dm0jji+devqHdJdcUsl8i++SPH118FxQFEI3Hsv4SefbJtWS57rYs/P42YzGAODhPY8iG/DBhS9dTabxN1p1nVN50CIeN8YU2fSXDq+QCFV4eiPr9A1HGbtru6Gn1lxfeZGIGJiWw7FTIWzB2fwhQyp3hBNJZ8qc/mtJAuTeXwBjUR/qGXWxaVclQtvLjB/udauUtUVRrZ0Mrw53hJzQT6qatkmt1gmGDMZu6eLnpGI/I0SQgghhKiDO9rR+a3f+i1+6Zd+iV/91V8lnU7z4IMPYhgGCwsL/Kt/9a/4tV/7teWOUzQQRVHQe3rQ4nGsq+9pSbUCsz2EWA1uqUTx0CGKB9/ALZcwR8dQzMbeEPwgnuNQPHiQ/Asv4JVKAJjr1hHZtw+jt7fO0a0Oz3Vxkkns5CJGVzehfU8T2L4NNSBXU4obNfO6RlUVhjbF6RmLcOnYIlPn0ixcybN4tcDQpjij2xLoRuPP29ANjWhXYGnY7tWzaWbOZwh3+ukeiRDrChCMmg3dXku0H9tymLmQ4eqZNJWiTazb3xS/b7fDqjhMnFjk6pkUngso0LcmxviOrrYcfu06HrnFEp4HA+s6GNqUwB+WijIhhBBCiHq5oxXp4cOH+df/+l8D8Bd/8Rf09vZy5MgR/vIv/5Lf/u3fbugn/2L5KIaBOXatJdUbh6ieP4+5bh2+8XGMwSG0cKjeIQpxWyoXL1J45RWqFy6idXfjGxiod0h3zPM8KqdPk3vuOZzFRQD0nh4i+/bhW7++ztGtHjuVwpmfq80MefJjBO7ZjiaDJcX7aIV1jenX2fBALwMbOjh/aI7UTJErbyWZuZCpzdtY09jzNq67VfXGuTeKGD6dYMysVW90+gnHfXJ1tKirzHyRy28lSU0X8Yd1OgdaY93rOi5Xz6SZOLGIXa0NAU/0h1izq5twvD1bzpbyFsVMhWhngOEtCRIDrVOJI4QQQgjRrO4oqVEsFolEIgA899xz/NIv/RKqqrJnzx4mJiaWNUDR+GotqdbhpFKUjhyhdOgwWkcMc2wc35pxjKEhtGs/L0I0EntxkdKRNym9+SaubdfaqDXxfAlreprcj35E9eJFANRQiPCTTxLYvRtFa40rRz+Mk81iz86iRsKEHn6YwK5d6PF4vcMSDa6V1jXhDh/3fGyI5FSBc4fmKOUszhyY5erpNOvu7Sbe1zwbr9erN6DW8qWYrZKZn0fTVYIRg8RAiGhXgEhn61wdLxqbbTlkF8qkZgrMT+SwLZeO3kBLtGHyPI/5y3kuvDlPOW8BtSHna3f3kOhvnr8by8m2XHKLZXRDZWRrJwPrO1pqTooQQgghRDO7o1XZunXr+H//3/+XT3/60/zoRz/iS1/6EgBzc3NE5UrYtqSoKnpnJ3pnJ57j4GQylI4dpfTmEbRYDHNkFHPtGszhYblaWtSdk81SOnaM0pEjOOk0em8fRixW77DumJPJkPvJTygfOwaeB7pOaM8eQo8+2vQDzm+Xk89jz0yjBoIE77uXwK7dGL099Q5LNIlWW9coikLnYJh4f+ideRvpCkd/MknnUG3eRjDaXO31TL++tJloWw7lvMXEySSKCoGwSaI/SLyvluRohQ1m0Tgcp7axnZ4tsng1Tyln4XkewYhJpLM1zrHvHQJuBjTGd3TTNx5tigqv5WZVHQqpCq7r0dEbZGRLJ7FuaV0phBBCCNFI7iip8du//dt87nOf40tf+hJPPfUUe/fuBWpXN+7atWtZAxTNR9E09EQCPZGoJTiyWUpvnaR0/BhaLIZvwwb8GzdiDA21zdXjojG4xSLlkycpvnEIa2EePdGJuX5D07YQcMtlCj//OYX9+8G2AfBv3074qafapjrByeexZ2dRDIPA9nsI7N6NOTRY77BEk2nVdc31eRu941EuHV/g6pk0i5N5klN5BjfEGd3WieFrvvOwbmiE4xrhODi2S7lgMXk6zdTZNMGYj66hMB29QSJxf1tuyIq757oe+WSZ9FwtkVHMVHFsD39IJ9rlb5nEWTFb5cKb8yxcyQOgagojWxIMbU6gG63xGD+KatmmkK6iALHeIH1rYsT7gi3z/RZCCCGEaCWK53nenbzjzMwM09PT7NixA1WtLfRef/11otEomzZtWtYgW0E2myUWi5HJZJbtqk/Pspj71rdQgiGMrq5l+ZgryXNdnHQaJ5lE0XWMoSEC27Zirl2LFg7XOzzRwtxqlcqpUxQPvoE1PY0ai6F3d6Oozfkk1XMcSocOkX/hBdxCAQBjdJTo009jDLbHhn4tmTGDYpj4NqwnuHMnxshI0yaoxEe33OfV5VzXrMQ5H2BuIsvpAzN33AqmkKlw/vA8yana3w3dVBnd3sng+jiq1vy/O47tUspVqRRtdFMjHPfRNRyhoyfYdJUpoj6qZZvFqwXmJrIU0hXsqoMZ0AlEzJba5K+WbC4dX2T6XBrPo+2HgFeKFvl0Fd1QiffVkhmxniCqJEUbxkqdV4UQQgjRvO44qSE+mpVYiLmVCuc/sQ/Pton+wi9grlvXNBt6bqmEvbCAWy6hxxP4N2/Ct2EjxkB/0240i8bjWRaVc+coHnyD6uXLqMEgem8vit6cT9iXhoA//zzOwgIAWmdnbQj4xo1N8/t/N5xcrjYzw+fDt2E9gZ07MYaH2+Kxixs18gZHoyY1rktOFzh/eI5CugpAIGKwZmc3XcPhlvldsioOpVwVq1LblI51B4j3h4gk/ASjZss8TrE8SvkqC1dyzF7KUsxYGD6VQMRsykqmD+LYLldOJbnyVhLHrj0F7BwMsWZnN6GO9hoC7nke5YJFMVPF9OskBkP0jsWIdvnl70MDauRzvhBCCCHqozl39gQA1uXLONksXrlM6k//FHPNGiJPP43R11fv0D6UGghgDg/X2lOlUhRe3U/x0GHMkWF8GzZg9PTUrqQ35cpK8dE56TSV8+cpHT+ONTWNYhiYY2NNPQS8OjlJ7vnnsS5dAkAJBgk/8QTB++5rizZuSwPAA34CO+6pJTMGB2XjQYg7kOgPEf/UGNMXMlw8ukApZ3Hy5Sli3QHW3ttNtLP5e8cbPg3DF8DzPKolh+RUgfnLOUy/TjBm0jkQJtLpJxT3oWlyMUW7yiXLzF/OMn8lT7lgEQgZxPtb7wp91/WYuZDh0rEFqiUHgEjCz5rd3cR7g3WObvWV8hbFTAVf0GBoU5ye0SjheGvMRxFCCCGEaBdSqbFKVqr91Py3v03x4BuUjh4FxwFFIbBrF+Enn2y6gdxusYg9P49XraD4fGjRKMbQMObgAHpPD3pXlyQ5xPvyXBdrcpLy6dNU3n4bJ5NBDYbQurtRm/jnxl5cJPfjH1N5663agTYbAr5UmREI4Nu4kcCOHRiDA5LMEA191WajV2q8m225XHkryZVTSVyntiTsGYuwZmc3/lDzJoJvxfM8rEptyLhVcdEMlUDYID4QItYZINLpb7kr88XNPNcjM19i7lKWxekCVtkmGDXxh42WO7d4nsfi1QIXjsxTzNYqs/xhgzU7uugejbTc4/0w5YJFIVPB9Ov0jkXpHY9Ja7om0cjnfCGEEELUhyQ1VslKJTUWv/tdPGpPSPI//jHlkycBUAyD0MMPE3z44abc0HXLZdx8HieXA9tG8Zk3JDm0rm70rs622NQVH8wtFKhcuEj5xHGqk5N4VQstkUDr6GjqVmZOPk/hxRcpvvEGuC4oCv4dO4g8+SRaR0e9w1txbqmENT2Fohv4t2wmuGsXxsBAvcMSDaSRNziaKalxXblocfHNBWYvZgFQVIWhjR2MbG3OYeK3w7YcygWbStFGUcAfNIh2B4h1BwjH/QRjZstdsd/OqiWb9FyR+cs50nNFXMcjFPPhC7Zm4Xp6rsiFNxfIzpcA0H0aY9s6GVjf0RIzdD6KStEin6piBjS6RyL0jcfart1Ws2vkc74QQggh6kOSGqtkpZMaeiIBQPXKFXI//CHW5CQAaiRC+GMfI7BzZ1Nv8LrlMm4uh5PP15IcpoEaCqP39WIODqF3daJ1dqLFYk39OMWH8ywLO5XCSaWwZmepnHwLO7mIYvrQu7tRA83dNsWtVCju30/hlVfwqrWrKs3164l8/ONN0VrubnnVKtb0NLgO5rp1hO6/XwaAi1tq5A2OZkxqXJdbLHP+yBzp2WsboabK6LZOBjd0oLZwmybX8agULSpFG8f20M3aTIV4X5BIwk844W/LAcrNznVcsgtlktN5FicLlAsWqq4QivlaNlmXT1W48OY8yakCAKqmMLQpzsiWBLrZmo/5/VRKNoVUGd2n0z0Upm9tTNpMNalGPucLIYQQoj4kqbFKViupAdcG3508Sf7HP8ZJpQDQe3oIf/zj+DZsaInNQbdSwS0UcPN5vEoFVAU1FEKLxTCHhtF7e9G7OtE7O6VlVRPzbBsnncZOJnGSKazpaezZmdr3vlwGFLRYDC2RaPq5Ep7jUDp8mPwLL+Dm8wDoAwO1IeDj43WObuV5joM9O4tbLGCOjhK8/35869Y1/fdVrJxG3uBo5qQG1NYRyakC54/MU8xca1kTMhjf0UXPWHu0rLEqTi3JUXJQFDADOpGEn47eWpIjFDNbOsnT7IrZKunZInOXsxRSFVzXwx8y8IeNlq2+KeWrXDq2uFRthQL9a2OMbe9q2WqU91Mp2hQyFTRdpWs4TN+aWEvMCmpnjXzOF0IIIUR9tNcKt00oikJg2zb8mzZRPHCA/MsvY8/Nkf6zP8MYHSWybx/m0FC9w7wrqs+H6vPBtWSO5zi4xSJOOkNhehoctzaXIxLGGBjAGBhA7+pC6+pCC4frHL14P261ij03jz03h3X1KtbUVC2BUSoCoBhmLXmV6ET3+1uiKsdzXcpvvUX+pz/FWVwEQIvHCX/84/i3bGmJx/hBPNfFWVjASacx+vuIfOxJfJs3N2XbPCFahaIodA6GSfSHmLmY5eLRBcoFi1OvTnPlVJK1u7uJ961sYqXeaoPGNcLx2pDlaskmPVNg4Ur+WhWHQaIvRKQzQDjhw/TLkrqeXMe9Nvy5SnK6QGqmQLVkY/h1wgkfutG6CfJq2WbixCJTZ9N4bu1Y90iE8R1dbTUvwvM8ynmLYrZam5kxGqVvbYxIp78tErFCCCGEEO1GKjVWyWpWaryXWypRePllCgcOgG0D4NuyhchTT6F3dS1LLI3o+lwON5/HsywUXattiHd1YQ6PoHcmUCMRtHAYNRxG0WVDYrW5lQr2/MI7SYzJKzi5XK3tkm7UvjfBIEog0HKb+57nUT13jtxPfoI9PQ2AEgwSfvxxgvfd1xY/j046jT0/hxZPELjvXoLbtqGGWnujVCyfRr5qs9krNd7LsV0m305x+WQSx67tmsb7g6zZ2U0k0X6tXKyqQ6VgUS07wDuzOK5XcQQirTdwutFUSzbFXJVStkouVSa3UKZadrCqtcqaYMzEF9Bb+vtgVx2uvJ1i8lQSx649nYv3BRnf2U20s31+L13Xo5StUipY+EMGPSMRukci0maqxTTyOV8IIYQQ9dH6u2YCNRAgsm8fwQcfJP+zn1F6800qb71F5e23Cdx7L+EnnmjJ6gXV768NEr+WuPEsC7dQwJqapnr+Aiig6DqK31+r/OjowOjqRuuIoYbDqOEIaiiIGgrJVePLyE6lsK5coXppAuvq5FISQzFM1HAYva+/VoXTwqqXL5P78Y+xJiYAUHw+Qg89RHDv3pZ/7ABusYg1NYUaDBLa+xCB3bvQ4/F6hyWEeB+aXpur0b8utnRFeGq6yKHpCXpGI4zd015XhBumhnFtNsH1WRzzl3PMXspg+nT8EYNYT5Bwh49QzEcgbKC0aMuj5WRXHRzbxXU8HNvDddyl12vHXCpFi+ximWK2ilW28VxQNAXTrxGIGERMX0snMuCdJOOVU0nsai3JGE74WLOze9UTnvXkOC6FdO3nIBgzWbOjm67hMIFw+/wtEkIIIYRoZ5LUaCNaLEbsv/1vCe7dS/7HP6Zy5gylgwcpHz1KcM8eQg8/XEsCtCjFMNA6OtA6OpaOeZaFWy7jVSrYU9NUL1yEa8VLiq6jmCaKaaIGArXZDfEO1FCtekANBVEDgVoCJBRquUqC5eRkMlQvX6Fy7izVK1dwsznQdbRotC2SGNdZMzPkf/ITKmfO1A7oOsEHHiD8yCNtUaHgWRbW1BS4Lv4tWwg9cD/G4GC9wxJC3CbTr7P+vl6GNsa5eHSBuYlc7eVy7lrv/k58QaPeYa4qVVMIREwCERPP87DKDuW8RW4xieeB6dPwhQxi3X7CcX8tyRExZB7HNa7jkpkvsTiZJzVbxHU8PNfDdT08x8P1PHhXTbkHGD4V068TiATQ2ujr6DguU2fTXD6ZxLpWJRSMmozt6KJ7ONzyyZzrHNslnyxj2x7RhJ+x7V10DoakBZwQQgghRJuR1V8bMnp7if/dv0v10iVyzz2HdfUqhZdeonjwIKFHHiH0wANtM1xbMQw0w4BI5Ka3edUqbrWKV63i5PPYySTe6SpLDYtRakkPnw/V70dLxNG7utGiUdRwCC0SQY1Eau2T2jDh4eTzWJcvUzl3nsqlS7jZLOg6eiKB3t3TVl8Te3GR/M9+RvnEiVrSTFUJ7NxZq5KKxeod3orzXBd7bg43n8McGSH4wIP41q9rq58BIVpJIGKy5ZEBhreUuXh0geRUgelzGWYvZhnc0MHI1k4MX+vOMHg/iqJgBnTMQG157XkeVsWhWrK4eqaM54LuU/H5dTRdBRVUVUFRlNrwahUUVUFVFDRDxR82MP06voCO4dcwA/pShUizq5RsUtMFZi9lyS+WcT0Pf9jA8Gu1r4mqoF77erTLZv37cV2PmfMZLh1fpFqqtZH1hw3G7umkdzTaNlVA15MZjuMR6w7Qv7aDeH+wpeelCCGEEEKI9ydJjTZmjo2RePZZKqdOkfvpT3Hm58k//zzF/fsJP/44gd2726Kv//tRTBPtA5I7nuviWRZepYJXLlO9NEHl9Jkb3l/x+VDN2nBrNRyq3YZCqH4/iulD9ftq7a/8fpRAEDXgR9Ea/8mZZ1m4lcrSY3crVbxK+VrVSxV7cZHqxYs4mTRoGno8gb52bdttYtvJJIWXXqJ09Ci4tWSYf+tWwh/7WEvPs3k3O5nEXljA6Okm/Oij+LduaZvKHCFaXSTh554nh0jPFbn45gKZ+RJXTqWYOpdheHOcoU0JdKO9/u6/m6IomH596Qpyz/Owqy7Vso1tuYB3vTi0dut5taIEj6VqBTxAAd3Q0A0Vw6cRiJoEIya6qaJqKqp2LQmg1ZIjqqYsHdMNFc1Q0XS17skBz/XIJcssXs2zcCVPqVDFMDXCna09yPtOua7H3KUsl44vUs5bAPiCOqPbO+lbE6slwtrArZIZicFQW1XpCCGEEEKIm7XvjrUAak+4/Vu24Nu0ifKxY+R/9jOcdJrsf/2vFF55hfCTT+K/556224y+HYqqovh84PPBewbWeZ6HV63WNv0rFezFRbzZWTzLWtrcvt5PQdF0FEMHw6wlQCJhtFgMNRZDCwZrra4CARS/f6kqRPH5VvV74pbLWNPTWFNTVM9fwC3kcS0bbLuW2LFt3ukPoYCqonV0YI6vaYokzXJz0mnyL71E6ciRpe+3b8MGwk8+iTEwUOfoVs71uTVuoYBbLoHnoYYjhB99lODuXWgy2FGIu6LptQ3szHyJcNxXu9q/AXT0BNn5iWGSUwUuHF2gkKpw6dgiV0+nGdmaYGB9R8PEWk+KomD4tI9cxeK6Ho7lYlsulZJFIVvBsT0UAIWl068HKAqoSi2hUUt0gKqqqIaKYWqYAQ3Tr2H4dXRdRTe1WvusqLGsm8Se52FbLtWSjVV2qBRt5q/kyC6UsKsugYhBoi/UNlUGH8X1ZMbEiUVKuVoyw/BrjG7tpH99rG028x3bJZ+q4NjuO8mMgZD8LRFCCCGEEAAonud5H363+kulUnzxi1/k+9//PgDPPPMM/+bf/Bs63jUf4b3+6q/+iu985zscOnSIxcVFjhw5ws6dO2+4T6VS4bd+67f4v//v/5tSqcRTTz3F//l//p8MDQ3d1ed+r2w2SywWI5PJEF2mjT3Pslj87nfxUNATieX5mLZN6fBh8i++iJvPA6B1dxP52Mfwbd5c96v8WpF3PTFQrd54a1tcv2RTUVQwjNqcj2u3aii4NM9DC4drFR+hMFr0WturUOiOv1+e5+Gk07UkxpUrWBcv4mQyeLZT+7g+31Iciq6DrkviC3Cy2Vort8OHwan1uzbXriX85JOYw8N1jm55eZ6Hm8/XXopF8NxrP5chtHgcY2gIvbMLo7cHvbu73uGKFrQS59XlslKxuY7L4tUCU+fSZOdLaKZaS2400Can53nMTeS4dGzhhg3Zka0JBtZJcmOlua5Xu7DCfafaw3XeGbZde3Fx3Wv5EA90U8UX0Il2B4gk/ASjJsGY+YHVE55bS1pYFWfppVq2qZZsSjmLcr6KVXVryRjbwXM9NF0lFPO1ZWuy23GrZIbu0xjZHGdwY7xtfnfencyIdgUYWCfJDNHY53whhBBC1EfTJDU+9alPMTk5yR/+4R8C8IUvfIGxsTF+8IMfvO/7/Mf/+B+5ePEiAwMDPPvss7dMavzar/0aP/jBD/jjP/5jOjs7+af/9J+STCY5dOgQ2rUrzO/kc79XsyQ1lj52tUrh9dcp/PzneKUSAHpfH+EnnsC3aZMkN1aZ5zhLyQ9s+51EyPVj76r+UEwfis+HFgqidXahd3XW5nxEIqiBQG0Hw3Wvtbpwa/933aXjTipF5dx5rLnZWmJLVdGiMbRYDMVorwGwt8vJ5Sj8/OcU33gD7Fq/a3N8vJbMGB2tc3TLz8lksOZm0UJhtHgHxuAgRk8PWiKBnki0xdBzUX+NvMGx0rE5tsvi1TxTZ9PkFsvoPpVwh6+hhk+7rsfsxSwTxxcpF2obtGZAY2RrJ/3r2udq80b37pZY1ZKN69YqgsyARjjuJ9YVwAxo2NVaAqNcsKgULSpFG8fycBwX13Jx3GuVI1Brd2Wo6Lq69P92aZV0J5Z+V06802ZK92kMb44zuCHeNi3cqmWbQroCnkKkyy/JDHGDRj7nCyGEEKI+miKpcerUKbZs2cJrr73Ggw8+CMBrr73G3r17efvtt9m4ceMHvv+lS5cYHx+/KamRyWTo7u7mP/7H/8gv//IvAzA1NcXw8DB/8zd/w9NPP33Xn/u6ZktqXOeWShRefZXia6/hVauAJDca2VLbq/L1+RYVvGoFuDZo0zAAD1yPdxp5u3ie9073KM9D8ftrLbDCYanA+ABOJkPhlVcoHjq0lMwwRkcJP/kkvvHxOke3/Jx8HntmGjUQxL91C4Gdu9B7uuXvgKiLRt7gWK3YbMthcbLA9Lk02cUypl8j1OFD1Rrnd9J1PWYuZJg4vkileC3pG9AZ3Zqgf12soRIxouZ626hq2caxXFCUpS5Xqqag6Sqafv229n/5Pn50t0pmGNeSGQNtkszwPI9y3qKYtdBNlY7eIL1jUTp6g5LMEDdo5HO+EEIIIeqjKWZq7N+/n1gstpRUANizZw+xWIxXX331thML73Xo0CEsy2Lfvn1LxwYGBti2bRuvvvoqTz/99Ip97mahBgJEnnqK0N69FPbvp/jaa9gzM6T/0396J7mxcaNsfDcIRVGW5nxosdgNb7te7XF9cwJFuelFNqdvj51MUvj5zym9+eZSmyljeLhWmbFmTct9Hd1SCWt6CkXXCWzfTvDeezEGB+sdlhBtTzc0esejdA6GWJjMM3UuTWqmiC9YS240wt8iVVUYWNdB33isltw4UUtunH1jjstvJRnZmqBvrVRuNBLdUNENk2DUrHcoLcmxXWYuZLjyVmqpisnwaQxvqc2faYdkhut4FLMVKkUbX8hgcFMH3UMRIp3+hvi7JYQQQgghGl9TJDVmZmbo6em56XhPTw8zMzN39XFN0yQej99wvLe3d+nj3unnrlQqVCqVpdez2ewdx9kI1GDwxuTGgQOS3Ggyiqa15dDu5WQvLJB/6SXKx48vtfwyxsYIP/445vh4yz0Rd6tV7Kkp8Dx869cTvPdezLGxlnucQtytep/zdVOjb02MzqEwC5dzXD2dIjlVIJzw4ws0xlJP1RQG1nfQtybK9PkMEyeSteTGwTkmTiwyvDlB/7r22NAV7cmuOlw9m2by7RRW+doFEX6NkWvJjHaoTHBdj9xiGdtyCcVMBjfG6RwMEwhLAk0IIYQQQnw0dX2m+9WvfpWvfe1rH3ifgwcPAtxyE83zvBXZXHvvx72Tz/2Nb3zjQx9bM/rA5EZvL6FHH8W/ZYtsnouWYs3MUHj5ZconTy617TLXrSP82GMtOTPDc12sqSm8ShlzdIzgfffhW7dWfq+FeB+Ncs43TI3+dR3E+0JMnkkxdzFLMVsl2ulvmA1TVVMZ3BCnb22M6XMZrrxVS26cPzzPxIlFhjbV5gjIMGnRKqplm8m3U1w9k6618wJ8QZ3hLQn618Ya5ndzpbmOS2qmRLTLz8D6DuL9IQxTfs+FEEIIIcSdqWtS49d//df57Gc/+4H3GRsb49ixY8zOzt70tvn5eXp7e+/48/f19VGtVkmlUjdUa8zNzfHQQw8t3edOPvdXvvIVfvM3f3Pp9Ww2y/Dw8B3H2mhumdyYnSXzF39BPh4n9PDDBHbulMHSoml5nod1+TKFV16hcvr00nHfxo2EHnsMc2iojtGtHLdcpnrlMkZPL6F9+/Bv3IBiyhWUQnyQRjvn+8MGa3d10zkQ4sqpJOnZEv6wTjBqNkyllaapDG2MM7Cug9lLWS6fXKSUs7h0bJErbyUZ2BBneFMcs0EqTYT4qMp5i8unksycz+A6tQsiglGTka0JesaibTU83bFd0rNF4n0h1t3XI5UZQgghhBDirtX1mWJXVxddXV0fer+9e/eSyWR4/fXXeeCBBwA4cOAAmUxmKflwJ+69914Mw+D555/n7/ydvwPA9PQ0J06c4A/+4A/u6nP7fD58Pt8dx9Ys3p3cKL7+OoUDB3BSKbJ//dfkX3iB4N69BO+7D9Xvr3eoQtwWz3WpvP02hVdewZqcrB1UFPxbthB67DGMvr76BriC7MVFnFSKwNZthJ94HP09rfmEELfWiOd8RVGI94WIdPqZvZjl6uk0yakCkU4/pr9xEgWqptC/NkbfeJT5yzkmTiYppCtceSvJ1dMp+tbGGN6cIBCWiyREc8gly0yeSjE3kb1e3Emk08/I1gRdQ+GGSSyuFttyycwV6RwMs+7eHnxB+V0WQgghhBB3T/G868vtxvapT32KqakpvvOd7wDwhS98gdHRUX7wgx8s3WfTpk184xvf4NOf/jQAyWSSy5cvMzU1xd/6W3+L//Sf/hMbN26kr6+Pvmsbk7/2a7/GX//1X/PHf/zHJBIJfuu3fovFxUUOHTqEdq3Vyu187g+TzWaJxWJkMhmi0eiyfE08y2Lxu9/FQ0FPJJblY94tt1qldPgwhVdfxc1kAFD8foIPPEBozx7UUKjOEQpxa55lUXrzTQr79+MsLtYOahqBnTsJPfQQ+m0kYJuV5zhUr1xGNX0EH9pLaPduqbISDW8lzqvLpRFjK2arTL6dZH4iBypEuwINeaW453ksXi1w+eQi2YVy7aAC3cMRhjfHiXYF6hugELdw/ef2yqkkmbnS0vF4X5CRrQk6eoNtl8yA2hyRzHyJ7pEIa3f3NFRCVTSXRjyvCiGEEKK+mmZl+b3vfY8vfvGL7Nu3D4BnnnmGb3/72zfc5/Tp02SubaQDfP/73+cf/sN/uPT69VZXv/M7v8NXv/pVAP71v/7X6LrO3/k7f4dSqcRTTz3FH//xHy8lNG73c9fVtYHFjUA1TUJ79hC87z5Kx49T+PnPcRYWKLz0EoX9+wnu3Elwz56W3iAWzcUtFim+/jrF11/HLRSAa4m4++8n+OCDaJFInSNcWW6hgHV1EmNoiPATT+AbH693SEKIFRCMmqy/r5fOwTBXTiVJTReI9QQbbjC3oih0DYXpHAyRni1x+eQiqZki85dzzF/OEe0OMLwpXrvivQGTMqK9OLbLzPkMk6dTlHIWAIoC3aMRhjYliHa2b6WyVXHILpToHY+xZle3zM8QQgghhBDLqmkqNZrdilRqeB7Z//pfKR07hhqJond3N9xVYJ7rUjl9msLLL2Ndvbp03LdhA8G9ezHHxxsuZtEe7Lk5CgcOUD56FM+qbUSosRihvXsJ7N6N2mCtZFaCNTuLm88T2LGD8GOPtnwCR7SWRr5qs5FjAygXLC4cmWf+So5oV2O1o7qVfKrMlbdTzF3K4l27jsMfNhjaFKdvTazhEjOi9ZWLFldPp5k+l8au1n4odVOlf10Hgxs78Ld5i6Vq2Sa3WKF/bYzxnV3ohiQ0xN1p9POqEEIIIVafJDVWyUotxDzbpnTsGIVXXsHJ5TCGR1AbcKiv53lUL12iuH8/lTNnuN5kWO/tJbhnD4Ht26XdjVhxnutSOXOG4muvUb14cem43tdH6OGH8W/diqK1/hNvz7KoXp5ADUcIP/IIgR33tMXjFq2lkTc4Gjm26+yqw6XjC0yfyxCIGk0xuLdSsrl6OsXU2VtsJG/owB+SdYRYOZ7nkZ4tMXU2zcKV3NK8jEDEYHCjJNiuqxRt8ukKgxs6GNvehabL10TcvWY4rwohhBBidUlSY5Ws9ELMmpkh/+KLlM+cRe/sRO/sXPbPsVzsxUWKr71G6c038apVANRQiMB99xF84AG0cLjOEYpW4xaLlA4fpnjwIE46XTuoKPg2bSL44IOYY2NtUzFkLy7iLCxgrl1L5InHMQYH6x2SEHekkTc4Gjm2d3Mdl8nTKa6cSqEbCuF4c7TKcWyXmQsZJt9+p+UPCnQOhhlc30G8vz3nF4iVYVUdZi9kmTqbppitLh2P9dRaoXUOSiu068p5i2KuytDGOKPbOlE1SWiI5dEs51UhhBBCrB5JaqyS1ViIudUqxYNv1GYDVMqYwyMoeuO2lHBLpdpQ8QMHloaKo2n4t2wheN99GKOjsikh7oo1M0PxwAFKx4/DtRZTSiBA8N57Cd5/P1pHR30DXEVuqYR1dRI1HCF4330Ed+9CDcjAXdG8GnmDo5Fjey/P85i/nOPi0QXsqkOsJ9A0517P9Vi8mmfydJr0bHHpuD9sMLC+g/61MQyfVKGJO5NLlpk6k2b2UhbXqT1dUnWF3rEogxs6miYJuBqsqkM+WUHVFIY2xhnanECVRI9YRs10XhVCCCHE6pCkxipZzYVYdWKC/EsvUb10Cb23Dy0WW9HPd7c8x6Hy9tsU9u/HunJl6bjW3U3w3nsJ7NiBGgzWMULRTNxKhfKJE5QOH8aanFw6rvf1EXzwwbZrdeY5DvbMDG65jH/TRkIPPYTR11fvsIS4a428wdHIsb2f9GyR80fmKGSqxHsDTXeFdSFTYepsmpkLWRyr1ppKURV6xiIMru8g0ulvmmSNqB/Hdpm/nOPqmTS5xfLS8WDMZHB9B71rojIf4l0c2yWXLOO5kOgPMbC+o6kSo6J5NON5VQghhBArS5Iaq2S1F2JusUjhtdcoHjoMnocxONjQVRvXWVNTFN94g/Lx40utqdB1/Fu31qo3hofliZK4ied5WJOTlA4fpnzixDs/O6qKf/Nmgg8+iDEy0nY/O04mgz07i97bS2jvXvybNzXF3wEhbkcjb3A0cmwfpJCpcP7IHKnpIh29gabcvHVsl9lLWabOpMmnKkvHw3EffWtj9IxGGn4wulhdnueRmSsxcyHD/OUcjl17aqSo0D0cYWBDB7Fu2ah/N9fxyKcr2JVaddfA+jiJgZBUZ4gV06znVSGEEEKsHElqrJJ6LMQ8z6N67hz5l3+ONXUVLdHYszbezS2XKR8/TvGNN7BnZpaO6729BO69l8C2baihUB0jFI3ALRQoHTtG6fBh7Lm5peNaZyfB3bvx79zZljNavGqV6tVJVN0gsHMnwQfuR5MngKLFNPIGRyPH9mGqJZsLb84zN5HDH9YJRs2m3Mz1PI/cYpmrZ9LMTeTw3Hc2qjsHw/StickmbJsr5S1mL2aYuZClnLeWjvvDBv1rY/SvjWEGJAH2bp7nUchUqRQswgk/g+vjdA2HZRi4WHHNfF4VQgghxMqQpMYqqedCzC0UKB55k+KhQ7i5HMbQUNP00vc8D+vqVUpvvEHpxImluQioKr716wns2IFvw4a2aifU7jzbpnL2LKVjx6icPg2OU3uDYdTmsdx7b1tWZcC1VlNzc7j5PL4144QeeghzbKzeYQmxIhp5g6ORY7sdjuUyfSHN9NkMpXyVcNyPL9i8m7vVss3cRI6ZCxnyyXeqNwyfRu94lL41UZmP0CZsy2XhSo6ZC9kb5rBoukr3aIS+NVGpyrgFz/Uo5S2K2SqhqEn/+g56RqMys0asmmY/rwohhBBi+UlSY5U0wkLMmpmhsP81KqffBt3AGBhA0ZrnyYhbKlE6epTSm29iT08vHVf8fvxbtxLYsaNtN7Nbnee6VC9donz8OOW33sIrv9PnWu/vr1VlbN/eNMm65eY5Dvb8PG42i97bS/C+e/Fv24ZqmvUOTYgV0wjn1ffTyLF9FKVclatn08xdG5Qc6fSjG819RXY+VWHmYobZi1mssrN0PBz30TsepXs4gj8sF0q0Esd2WbxaYP5ylsWrhaWh3wAdfUH61tS+71JtcDPHcSlmqlRLDoGwQfdYhL7xGP6Q/I6I1dUq51UhhBBCLB9JaqySRlmIeY5D5cwZCvtfw7o6idbVjZ5I1C2eO2XNzVE+epTSsWO42ezSca2jA/899xC45x707u46Rijulud52FNTlI4fp3ziBG4ut/Q2NRLBv307gXvuwejvr2OU9eW5bi2ZkUmj9/QSvHc3/s2bpTWbaAuNcl69lUaO7U6k54pMvp0iNVNAN1XCcX/Tt21yXY/UdIGZCxkWJgtL7akAIp1+ukcidI9ECEiCoyk5tsviVIH5iRyLV/M3JDICEYPe8Rh941FJYL0Pq+pQSFdwbI9I3EfveIzOwRC+oHy9RH202nlVCCGEEHdPkhqrpNEWYrWWVEdqLanyeYzB5mlJ9W6e61KdmKD85pu1K/ivD4gGtO5u/Js349+8Gb2/Xyo4moDnulhXr1I5fZryyZM4yeTS25RAAP+WLfi3b8ccHUVR2/eKSs91sRcWcNMp9O5uArt249+6pS3nh4j21Wjn1Xdr5NjulOO4LE7mmTydIp8sE4iaBCOtUQ1mVRzmJrLMTeTIzJVueFsk4ad7JFxLcLTI421Vju2SnCowd4tEhj9s0D0SoWckQjjhkzXh+ygXLYqZKqqmEOsO0jsWJd4fRDeap7JbtKZWPK8KIYQQ4u5IUmOVNOpCzJqeprB/P5UzZ0FVay2pmnQ+hVetUj59mvLRo1QuXHhn1gKgdnTg37QJ/5YtGMPDbb0h3mg8y6Jy8SKVt9+mcvo0bj7/zhsNA//Gjfi3b8e3bh2K3rw93ZeDW63iptPY6RRGVxf+XbsIbN2KFonUOzQhVl2jnlehsWO7W9Wyzcz5DNPnM1SKNuG4r6nnbbxXpWSzcCXH/OU86bkivGuVHE746BoKkxgIEUn4ZWO8ARRzVZJXCyxO5UnPlm6ouPGHDLpHJZFxO8p5i0KmghnQSQyE6BmJEu0ONH1FlmgdrXxeFUIIIcSdkaTGKmnkhZjnOFTOnad48HWqE5dRg0H03t6mmrfxXm65TOXMGcqnTlE9exbv+oBxQA2F8G3ahG/DBsyxMVS/DAddbW6hQOXsWcpvv0313Lkbvj+Kz4dv3bra92jjRlSfr46R1pdnWTi5HG4uh1etohgGWiyKf/s9BLZvQ2uwvyVCrKZGPq82cmzLpZCuMH0uzdyVHI7lEo77MP2tk9wAqJZs5q/kmb+cuynBYfg04v1BOgfCxPuDLffYG5XruKTnSiSnCixezVPKWTe83R826B4O0z0aJSKJjA9lWy65hTKaqdI3HqVnNEqoo33XXaJxtcN5VQghhBAfjSQ1VkkzLMTcapXKW29ReP0g9uwMWqITrbOz6Z8QepZF5fx5ym+9ReX06RuGTKOqGEND+NauxVy7tumGpzcLr1qleuUK1QsXqFy4UBv0/q4/PWo0in/jRnybNmGOjbVtRYZn2+8kMSoVFF1HjUQwBgYwR4bRe3rQu7slEScEjX1ebeTYllt2ocTUuTSLV2tzKSIJH7rZeufRatlmYTJPcqpAarqIY7s3vD3S6ScxEFqq4pAr3JeH53rk0xUycyVSs0VSMwVc+531g6JArCdI52Dtax+Mmk2/bl0NnudRSFeolhzi/SGGNyeIdTdfG1rRPtrpvCqEEEKI2yNJjVXSTAsxJ5+ndPQYpSOHsVMpjN4+tFis3mEtC89xqF66VGt1dP48zuLiDW9X/H7M8fGlJIcWj8uT4zvgOQ7W1BTVCxdqL1eu3NAODEDv68O3cSP+TZvafuaJWy5jz87i2RZaLIbe14/vehKjp6cp590IsdIa+bzayLGtBM/1SM8VmTqXITVdQFFqm/ya3pqtHl3XIztfYnGqQHKqQCFdueHtqq4Q7QoQ6w7Q0RMg0hlAN1rza7HcXNcjnyyTniuRni2SmS/hWDcmkEy/RmIwTOdASOY93IFKySafLBOMmgxtStA9EmnZ31XROtrtvCqEEEKIDydJjVXSjAsxe3GR4huHKJ88gVsu14aJt9gV4nYqRfX8eSrnz1O9eBGvdOOAUDUcxhgawhgexhwebuqZIyvJLRaxrl6tvUxOUr18Ga/ynk2eaBRzzRp8a9Zgjo9L6yRqbbisuVkUFIzhYQI7d+AbG0MNheodmhANr5HPq40c20pyXY/UdIGrZ1Nk5kpohkok7kPVWnvDtFK0SE4VSE4XSM0Usas3bsKjQCTuJ9ZTS3REuwP4Au1Zkfhe1zfYc8kymbkSmYXSDZUYAJquEusOEOsJkBgIEY5LW6k74Ti1VlMoCr1jEYY2JvCHZU0rmkO7nleFEEII8f4kqbFKmnkhVp28SvH1A5TfOoXW1YUej9c7pBXhuW6tuuBaksOanLypugBVRe/rw7yW6DAGB2vVHG00eNyzLKyZGazJyaVEhpNM3nQ/JRCoVb2Mj2OuWdMSrcyWi5NOYy8soPhMfGvWENixA3N0tG3bbglxJxr5vNrIsa0Gx3ZZvJpn6mya7EKJYNQkEDHrHdaq8DyPQqZa26CfL5KZK1Ep2jfdz/BrhDt8hOM+wnE/obiPYNRs2bZVnudRLljkkxVyyTL5VO3WKjs33Vc3VWI9QTp6AsR6goTjvpb9uqwEz/NwXQ/P9XAdD88Fu+pQLljEeoIMb04Q7wvKmkw0lXY/rwohhBDiZpLUWCXNvhDzLIvCgQMU9u9H0XT0gYGWfzLkWRbW1FSt8uDKFazJSdxc7uY7GgZ6V9dSqyDjesugWKypv0aeZWEvLmIvLGDPz+PMzy/9H9e96f5aZyfG4CDG4CDmyAh6X19bJXs+jOe6OMkkTjKJGgnj27SJwLZtGIODTf1zIkS9NPJ5tZFjW01W1WH6bJqpc2msikO0qz3bMJUL1rUkR4nMXJFCpnrL+ymqQqjDJNxRS3D4IyaBkIE/bGD4mqPFkm05lHIWpWyVUt6ilKtSzFkUM5WbK1gAFAhGTSJxP5EuPx09QUIdMhfjdlkVh3yqgufWns551OaMqKqCoiq1W01B01T61kTpWxNryZk3ovXJeVUIIYQQ7yVJjVXSKgux8qlT5H72Ak46hTnaXgOdPc/DzWRqCY5rSQ5rdhbsm6/ABFB8PvTubrREAi0WQ4tGUTs60KJRtFgMxe+v65N2z3Vx83mcbBY3l8PJZnHSaZzrSYx0+oZh3u+mhkK1tlzXkhjGwABqMLi6D6BJeI6DvbCAm8mgJeL4t27Dv2UzRk9PvUMToqk18nm1kWOrh+xCiStvJ1m8WsAX1AnF2nvT2rFdCukK+XSFfLJ2W0hVbho+/m6aoRII1xIc128Nv47h0254WamKBs/1sKoO1ZJDtWxjlWu31bJDtWRTylUp5Sysys2VF9ddT9pE4n7CCR/hhJ9wh0/mOdwB23LJLZZQVIXOgTAdvUE0XUXVlBtv9VpCQ9NVtDZMKIrWIedVIYQQQryXJDVWSSstxKyZGXI//gnVixcxhobaejP7+tX39vw89uxs7XZuDntx8ebWVe+hmCZaLIYajaIGgyg+H6rfj+L333jr86GYZu3SO0VBgdr/33PrOQ5epYJXreJVKriVyg2ve5UKTj6Pm83i5HK1qpMP+fVXAoFaFUp39zu3LVCFsho828aen8fN5dC7u/Dv3Elg82a0WKzeoQnREhr5vNrIsdWLY7vMXcoy+XaKcsEi0ulvmuqD1eB5HuW8RT5VS3KUclXKeYtS3rpli6b3oxnqUoJDN9WlK/aXrtpXFVSVpWN44DoujlNrVeQ6Hq7tLv3fcVysilNLVtzmMwbDrxGImAQiBoGwSTBiEIiahGI+VE3WDnfDsV3y1xJg8b4QA+s7iPcGa99LIVqYnFeFEEII8V6S1FglrbYQc/IF8i++SOnNN9HicfTOznqH1FA8x8FZXKxVPKRSOJlM7SWbxclk8IrFeodYo6qo4XCtiiQSQYtG0bu60Lq70bu7UUMhSV58RJ5lYc3O4pVKGH29BHbtwrdxE1pYhn8LsZwa+bzayLHVWzFb5cqpJPOXc2i6Qjjhl3kJH8Kx3aUEx9JtwVpKNtjXkw6rwPBpGH4N069j+jWMa7eBiLGUyNANSVYtN9f1KKQqVCsOse4AA+s66BwMoWpSfSHag5xXhRBCCPFe7dM7SCwrLRwi+vQ+tM4ExVdeoXqlWJsNIDMUAFA0bWnGxq141epSgsPJZvFKpVplRbmMVy7jlsu1aotrr3uWBZ7HUg7yVreahnqtquN6dcdNr19LYFxPYqjhsHzPlolbqWDPzuJVqxiDAwR37cK3YQNqIFDv0IQQomEEoyYb7u8l0R/iyqkkqekCwZiPQNiod2gNS9NVQh0+Qh2+972P53rY1jtVFVbFwa46tWHRbu3tnndtcPT1W9dDURRU7drLtbZF6rUZDNf/X0tk6Cva3krc2vXB8+V8rbpp7J4uuobDkjgSQgghhBBtT5Ia4o4puk54zx6Mzk5yP/0Z1fPna+2oZBP3QymmWWvn1NVV71DEXfBcFzebxU4uoigKxtAwgV078a1fj2qa9Q5PCCEakqIqdI9EiHb5mTqbZvZSlsWrVSIJH2ZAlqZ3QlGVpbZTojWU8xb5dIVQ1GTt7m56RqOYfvn9EEIIIYQQAiSpIZaBb/16tI4Oci++SPXceVAU9P5+VN/7X1EoRDNzi0XsxUW8Ugk1FiNwzw78G9Zjjo2hGHK1sRBC3A5f0GB8RzfdIxGmzqVZuJKnkK4S7vRhmLI5L9qTVXHIJcsYpsbIlgT96zrwh2RtIYQQQgghxLtJUkMsC727m45Pf5rK+fOUDh2icvESiq5j9PfXhlwL0eQ828ZOJnEyaVSfH2NgAP+WzZhjY+jxeL3DE0KIphWO+1l/Xy89o1GmzqRJThdQVIgk/Gi6tEgU7cF1XHLJCq7j0TUUZnBjnGinVD8LIYQQQghxK5LUEMtG0TT8GzbgW7OGytmzFA8donr5Morpw+jrkyvYRVNySyWsmWlwXfTOLgKPPYZvzVqMgX6ZRyKEEMtEURQ6eoJEuwKkpgtcPZMiM1dC92mE4z6Z5SBalud5FDNVygWLaFeAoU1xEgNh+ZkXQgghhBDiA0hSQyw7Rdfxb96Mb+1aymfOUnzjDaoTE6iBAHpvL4ouP3ai8bnVKvbUFHgevnXrCGzfjjk6iur31zs0IYRoWaqq0DkYpqM3yMKVPFfPpkhNFzF8tWHZUrkhWkmlaJFPVQiETdbs6qZ3PCat14QQQgghhLgNsrssVoximgS2bcW3fh2Vt9+meOgQlYsX0CJR9J4eucpdNCTPtrFmZvAqZczRMYL33Ydv3VoUTTYZhBBitWi6Su94lMRAiMWreWYuZsgulAGPUIdPBiaLpuQ6HpWSTaVo4Vguhl9nYEOcwfUdBCLSrlUIIYQQQojbJc8IxYpTfT4CO3bg27CB8smTFA++QeXsWfTubvREot7hCQGA5zjYc3O4+RzGwCDB++/Hv3GDzIQRQog6MnwafWtidI9GSM8UmZvIkpotkk9W8EcMAmEDRZE2PaIxeZ6HVXGoFG2qZRtFUfAFDBL9ITp6Q0QSPsJxqQAVQgghhBDio2qaS+VTqRSf//znicVixGIxPv/5z5NOpz/wff7qr/6Kp59+mq6uLhRF4c0337zh7clkkv/xf/wf2bhxI8FgkJGREb74xS+SyWRuuN/Y2BiKotzw8j//z//zMj/C1qcGAgTvu4/45/47wo89hletUjl7FieXq3dooo15ros9P0/1/DnUYJDoL/wC8c/9dwS2b5OEhhBCNAhNU+kcDLNpbz/bHx9iaFMcz4HkVIFcsozjuPUOUYglVsUhPVskOVWgnLfxhwxGt3ax9ZFBdn58mM0PDdC/NiYJDSGEEEIIIe5Q01RqfO5zn2NycpIf/vCHAHzhC1/g85//PD/4wQ/e930KhQIPP/wwn/nMZ3j22WdvevvU1BRTU1P87//7/86WLVuYmJjgV3/1V5mamuIv/uIvbrjv7/7u797wMcLh8DI9svajxWJEnngc/+bNFN94g/Jbb2HPz2EMDMq8ArFqPNfFWVjASafREnHCH/84ge3b0eR3WwghGpaiKEQSfiIJP/3rOkhO5Zm5mCUzW0IzanM3dKNprtkRLaZatimkqygKxPtCdA6GCcd9BKMmigz+FkIIIYQQYtk0RVLj1KlT/PCHP+S1117jwQcfBOCP/uiP2Lt3L6dPn2bjxo23fL/Pf/7zAFy6dOmWb9+2bRt/+Zd/ufT62rVr+Rf/4l/w3//3/z22baO/a6B1JBKhr69vmR6RADB6e4j+wqcIbN1C4fWDVM6fQ9ENjP5+FMOod3iiRXm2jT0/j5vLoXd1En7ySfxbt6DH4/UOTQghxEfgDxkMrI/TMxYlOVVg5sL1uRsQjvswfDILSayOatkmn6qgaiqdgyH61sTo6AlKIkMIIYQQQogV0hRJjf379xOLxZYSGgB79uwhFovx6quvvm9S405kMhmi0egNCQ2Ab37zm/ze7/0ew8PDfOYzn+Gf/bN/hvkBrWkqlQqVSmXp9Ww2u2wxthJFUTDHxjCGhqicPUvhwOtUL0+AqqF3d8tV82LZeNUq1twcXqmE3tND+JGH8W3ciBaJ1Ds0IUSTk3N+femGRs9olM6hMOmZIrMXa3M3XMcl1OHDF2iK5a5oQpWSTSFdQdNVukci9I3HiHUHJJkhhBBCCCHECmuKZ3kzMzP09PTcdLynp4eZmZll+zyLi4v83u/9Hr/yK79yw/Hf+I3fYPfu3cTjcV5//XW+8pWvcPHiRb773e++78f6xje+wde+9rVli63VKbqOf/NmzDVrqF68SOnkW1gTl7CnptA6OtASCRS9KX5cRYNxy2Xs2Vk826oNAN+1E9+GDaiBQL1DE0K0CDnnN4brczcS/SEy8yVmL2VJThXIJysEYyb+kC5DxcVdsy0Xq2xTylXRTZ3e0Si9a2JEu/zy8yWEEEIIIcQqUTzP8+r1yb/61a9+6CbAwYMHee655/iTP/kTTp8+fcPb1q9fz//wP/wPHzq0+9KlS4yPj3PkyBF27tx5y/tks1n27dtHPB7n+9//PsYHtD/6y7/8S/723/7bLCws0NnZecv73OqqzeHh4aVKEPHBPM/Dnp2lcvYs5ZMnsRcXUQwTvasLNRSqd3iiwXmui5NO46SSKKqGOTJMYOdOzHXrUGX4txAtIZvNEovFGuK8Kuf8xuR5HvlUhblLWRYm85SLFr6ATjBqoukyd0N8OMdxscpO7aXq4Hkemq5h+jTifUF6xqJEOiWZIcRKa6RzvhBCCCEaQ10vff/1X/91PvvZz37gfcbGxjh27Bizs7M3vW1+fp7e3t67jiOXy/HJT36ScDjMf/kv/+UDExpQa30FcO7cufdNavh8Pnw+313H1q4URcHo68Po6yN4771ULlyk/NZJqpcv401NocViaPG4zN4QN3ByOZxkEq9aReuIEdi9G/+6dZhjY1LpI4RYMXLOb0w3DRWfzjM3kSMzX0JRIBiT1lTiRq7rUS5YVAoWrguqqmD4NQJRk97uAKGoSSBiEggb6KbMbBFCCCGEEKJe6vpMrquri66urg+93969e8lkMrz++us88MADABw4cIBMJsNDDz10VzFks1mefvppfD4f3//+9/H7/R/6PkeOHAGgv7//rj63uD1qMEhg21b8WzZjTU1TOXeWyukzVC9fBjy0jjhaRweKJk8u25FbLuMkk7j5PGoohLlmHP/GjZijo2hyJZcQQgggGDUJRhP0rYmRni0xfzlLZq5EPlnBF9IJRkxUTa62b1fVsk0xW8WxPAJhg77xGOGEn0DEIBA2MSX5JYQQQgghRENpihX65s2b+eQnP8mzzz7Ld77zHQC+8IUv8Iu/+Is3DAnftGkT3/jGN/j0pz8NQDKZ5PLly0xNTQEsta/q6+ujr6+PXC7Hvn37KBaL/Omf/inZbHZpuGd3dzeaprF//35ee+01nnzySWKxGAcPHuRLX/oSzzzzDCMjI6v5ZWh7iqpiDg1iDg0S2rMHa3KSyvkLVM6do3rpIqhqLcERi6Go0laiFXm2jVsq4ZXLtdtKBcUw0Ht7CD38EOboGHpPt7SBEEIIcUu6odE1FKZrKEw+VSE5nWf+co70bBFVUwhETMyAJueRNuDYLqVclUrRQfepdPQE6RqOEO8NShJDCCGEEEKIBtc0K/bvfe97fPGLX2Tfvn0APPPMM3z729++4T6nT58mk8ksvf7973+ff/gP/+HS69dbXf3O7/wOX/3qVzl06BAHDhwAYN26dTd8rIsXLzI2NobP5+M//+f/zNe+9jUqlQqjo6M8++yzfPnLX16Rxyluj+r341u3Dt+6dbiPPEz1yhUq5y9QvXiB6oXzoOnonZ2okYhsTDQhz3XxSiXcYrGWvKhWAQ9F01ECftRAAN/gAHpPD+bQEMbgoLSXEkII8ZGE4z7CcR/96zpIzxSZv5Iju1Ainyqj+zQCYQPTL+eWVuK6HpWiTTlnARCMmfSv6yDRHyLU4ZM1oxBCCCGEEE2iroPC24kMN1sdTjZL9fIVKmdOU524jJPPo4XDaJ2dqNLvvKG5pRJONotbyIProgaCqKEQencXek8PWqwDLRpBi8VqySppNyZEW2vk82ojxyY+WDFbJTNXZGEyTy5Vxq44mAFdZig0Kdf1qJZtqkUbu+qCAmZAJ94XpHMwTEd3EM2Q6l4hGp2cV4UQQgjxXnL5mWgpWjRKYNtWAtu2Ys/PU7l4kfKJk9hTU3iOgxaX+RuNwq1UcHM5nGwWHAfF70OLxfBv3oQxMIDR21sbBi/fKyGEEKukNnvDpG9NjEKmQmauxPyVHPl0Fcd28AUMAhEDTZeN8Ea0lMQo2VhlF0WtJTHCCT8dPQFCHX5CMRNf0Kh3qEIIIYQQQoi7IEkN0bL07m707m6Cu3ZRnZykcu4clTNnqF64gGKaaIkEaigk8zdWkec4OMkkdjqFapi1JNQ92zGHhtB7e9G7uqSNlBBCiLpTVIVw3E847qd/XYxcskJ6tsji1TzZhRKuC4GwgT9kyIDxOnBdD7vqYFfd2q3l4nmgKGD6dcJxP7HuAOEOP6EOSWIIIYQQQgjRamT3ULQ8xTDwjY/jGx/H2buX6sWLlE+dwpqawp6dBUANBFAjkVqSQyoDlp1bLGIvLOBVK+jxOOFHHsW3dg16T4+0BRNCCNHQVE0l1h0g1h1gaFOc7EKJ9GyRhat50nNFFBT8YR1fyEBVJcFxpzzPw3U8XNfDc975v+u4125rL3iAAoapoZsa0a4AoQ4f/pCBL6gTiJj4Q5LEEEIIIYQQopVJUkO0FS0cJrB9O/5t23DSaey5eey5WaqXJrBTSez5OXA9lEAALRJBDQZRDHlifCc818VJpXBSKRTTxBwaxL9tG741a1BDoXqHJ4QQQnxkmq4S7wsR7wsxtDlBdr5EcrpAarpIaqaAqqkEwga+gI4iCY7bUinZFNNVXM9D0xQUVUHVFFRNRdUUfD4Dw6dh+DR8AR1f2MAfrCUwzKCOpknFrRBCCCGEEO1GkhqiLSmKgh6Po8fjsHED3iOP4GYy2AsLWHNzVCcmsOcXcJJJPMcGFBSfr1bREQig+P1S0fEenufhVSp45TJOLodbKqEn4oT27MG3YT3G4KC0+hJCCNEyDFOjczBM52CYSskmO19i4Vp7qmKmiKIq+II6vpBsvL+X53mUchalfBXDp9M9GqFrKIzh09AMFU1Xl26l+kUIIYQQQgjxXpLUEIJakkPr6EDr6MC3bh089BBOPl+rNEhncDJprNlZnPkFnEwGb2YGz/NAAcXnR/X72ybZ4bkuXqWCWy7jlcu4pRK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"text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -1039,9 +1045,12 @@ " well_model,\n", " well_idata,\n", " wrt=\"arsenic\",\n", - " conditional=[\"dist100\", \"educ4\"],\n", + " conditional={\n", + " \"dist100\": np.linspace(0, 4, 50),\n", + " \"educ4\": np.arange(0, 5, 1)\n", + " },\n", " subplot_kwargs={\"main\": \"dist100\", \"group\": \"educ4\", \"panel\": \"educ4\"},\n", - " fig_kwargs={\"figsize\": (16, 4), \"sharey\": True},\n", + " fig_kwargs={\"figsize\": (16, 6), \"sharey\": True, \"tight_layout\": True},\n", " legend=False\n", ")" ] @@ -1064,7 +1073,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1110,143 +1119,143 @@ " 0\n", " arsenic\n", " dydx\n", - " (2.36, 2.3601)\n", - " 0.16826\n", - " 0.00\n", - " -0.084280\n", - " -0.105566\n", - " -0.063403\n", + " (2.19, 2.1901)\n", + " 0.44629\n", + " 2.50\n", + " -0.129122\n", + " -0.160397\n", + " -0.095716\n", " \n", " \n", " 1\n", " arsenic\n", " dydx\n", - " (0.71, 0.7101)\n", - " 0.47322\n", + " (1.51, 1.5101)\n", + " 0.32249\n", " 0.00\n", - " -0.097837\n", - " -0.125057\n", - " -0.070959\n", + " -0.122982\n", + " -0.164771\n", + " -0.083558\n", " \n", " \n", " 2\n", " arsenic\n", " dydx\n", - " (2.07, 2.0701)\n", - " 0.20967\n", - " 2.50\n", - " -0.118093\n", - " -0.139848\n", - " -0.093442\n", + " (0.63, 0.6301)\n", + " 0.19921\n", + " 1.00\n", + " -0.143040\n", + " -0.173520\n", + " -0.111401\n", " \n", " \n", " 3\n", " arsenic\n", " dydx\n", - " (1.15, 1.1501)\n", - " 0.21486\n", - " 3.00\n", - " -0.150638\n", - " -0.194765\n", - " -0.108946\n", + " (0.75, 0.7501)\n", + " 0.53544\n", + " 2.50\n", + " -0.166454\n", + " -0.216468\n", + " -0.114219\n", " \n", " \n", " 4\n", " arsenic\n", " dydx\n", - " (1.1, 1.1001)\n", - " 0.40874\n", - " 3.50\n", - " -0.161272\n", - " -0.214761\n", - " -0.108663\n", + " (1.01, 1.0101)\n", + " 0.05358\n", + " 0.00\n", + " -0.118757\n", + " -0.157194\n", + " -0.074935\n", " \n", " \n", " 5\n", " arsenic\n", " dydx\n", - " (3.9, 3.9001)\n", - " 0.69518\n", - " 2.25\n", - " -0.073908\n", - " -0.080525\n", - " -0.067493\n", + " (0.74, 0.7401)\n", + " 0.32587\n", + " 1.25\n", + " -0.147418\n", + " -0.176669\n", + " -0.114935\n", " \n", " \n", " 6\n", " arsenic\n", " dydx\n", - " (2.97, 2.9701000000000004)\n", - " 0.80711\n", - " 1.00\n", - " -0.108482\n", - " -0.123517\n", - " -0.093042\n", + " (4.35, 4.350099999999999)\n", + " 0.36181\n", + " 2.00\n", + " -0.049172\n", + " -0.057212\n", + " -0.040185\n", " \n", " \n", " 7\n", " arsenic\n", " dydx\n", - " (3.24, 3.2401000000000004)\n", - " 0.55146\n", - " 2.50\n", - " -0.088049\n", - " -0.097939\n", - " -0.078020\n", + " (4.54, 4.5401)\n", + " 0.30681\n", + " 1.25\n", + " -0.047353\n", + " -0.053571\n", + " -0.040729\n", " \n", " \n", " 8\n", " arsenic\n", " dydx\n", - " (3.28, 3.2801)\n", - " 0.52647\n", - " 0.00\n", - " -0.087388\n", - " -0.107331\n", - " -0.068076\n", + " (2.47, 2.4701000000000004)\n", + " 0.32265\n", + " 1.25\n", + " -0.110712\n", + " -0.128014\n", + " -0.093747\n", " \n", " \n", " 9\n", " arsenic\n", " dydx\n", - " (2.52, 2.5201000000000002)\n", - " 0.75072\n", - " 0.00\n", - " -0.099035\n", - " -0.129517\n", - " -0.073222\n", + " (3.57, 3.5701)\n", + " 0.04755\n", + " 1.25\n", + " -0.060246\n", + " -0.068420\n", + " -0.053363\n", " \n", " \n", "\n", "" ], "text/plain": [ - " term estimate_type value dist100 educ4 \n", - "0 arsenic dydx (2.36, 2.3601) 0.16826 0.00 \\\n", - "1 arsenic dydx (0.71, 0.7101) 0.47322 0.00 \n", - "2 arsenic dydx (2.07, 2.0701) 0.20967 2.50 \n", - "3 arsenic dydx (1.15, 1.1501) 0.21486 3.00 \n", - "4 arsenic dydx (1.1, 1.1001) 0.40874 3.50 \n", - "5 arsenic dydx (3.9, 3.9001) 0.69518 2.25 \n", - "6 arsenic dydx (2.97, 2.9701000000000004) 0.80711 1.00 \n", - "7 arsenic dydx (3.24, 3.2401000000000004) 0.55146 2.50 \n", - "8 arsenic dydx (3.28, 3.2801) 0.52647 0.00 \n", - "9 arsenic dydx (2.52, 2.5201000000000002) 0.75072 0.00 \n", + " term estimate_type value dist100 educ4 \\\n", + "0 arsenic dydx (2.19, 2.1901) 0.44629 2.50 \n", + "1 arsenic dydx (1.51, 1.5101) 0.32249 0.00 \n", + "2 arsenic dydx (0.63, 0.6301) 0.19921 1.00 \n", + "3 arsenic dydx (0.75, 0.7501) 0.53544 2.50 \n", + "4 arsenic dydx (1.01, 1.0101) 0.05358 0.00 \n", + "5 arsenic dydx (0.74, 0.7401) 0.32587 1.25 \n", + "6 arsenic dydx (4.35, 4.350099999999999) 0.36181 2.00 \n", + "7 arsenic dydx (4.54, 4.5401) 0.30681 1.25 \n", + "8 arsenic dydx (2.47, 2.4701000000000004) 0.32265 1.25 \n", + "9 arsenic dydx (3.57, 3.5701) 0.04755 1.25 \n", "\n", " estimate lower_3.0% upper_97.0% \n", - "0 -0.084280 -0.105566 -0.063403 \n", - "1 -0.097837 -0.125057 -0.070959 \n", - "2 -0.118093 -0.139848 -0.093442 \n", - "3 -0.150638 -0.194765 -0.108946 \n", - "4 -0.161272 -0.214761 -0.108663 \n", - "5 -0.073908 -0.080525 -0.067493 \n", - "6 -0.108482 -0.123517 -0.093042 \n", - "7 -0.088049 -0.097939 -0.078020 \n", - "8 -0.087388 -0.107331 -0.068076 \n", - "9 -0.099035 -0.129517 -0.073222 " + "0 -0.129122 -0.160397 -0.095716 \n", + "1 -0.122982 -0.164771 -0.083558 \n", + "2 -0.143040 -0.173520 -0.111401 \n", + "3 -0.166454 -0.216468 -0.114219 \n", + "4 -0.118757 -0.157194 -0.074935 \n", + "5 -0.147418 -0.176669 -0.114935 \n", + "6 -0.049172 -0.057212 -0.040185 \n", + "7 -0.047353 -0.053571 -0.040729 \n", + "8 -0.110712 -0.128014 -0.093747 \n", + "9 -0.060246 -0.068420 -0.053363 " ] }, - "execution_count": 14, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1266,7 +1275,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1301,124 +1310,124 @@ " \n", " \n", " \n", - " 1\n", + " 2659\n", " 1\n", - " 2.36\n", - " 16.826000\n", + " 2.19\n", + " 44.629002\n", " 0\n", - " 0\n", - " 0.16826\n", - " 0.00\n", + " 10\n", + " 0.44629\n", + " 2.50\n", " \n", " \n", - " 2\n", + " 1204\n", + " 1\n", + " 1.51\n", + " 32.249001\n", " 1\n", - " 0.71\n", - " 47.321999\n", - " 0\n", " 0\n", - " 0.47322\n", + " 0.32249\n", " 0.00\n", " \n", " \n", - " 3\n", + " 2308\n", " 0\n", - " 2.07\n", - " 20.966999\n", + " 0.63\n", + " 19.921000\n", " 0\n", - " 10\n", - " 0.20967\n", - " 2.50\n", + " 4\n", + " 0.19921\n", + " 1.00\n", " \n", " \n", - " 4\n", - " 1\n", - " 1.15\n", - " 21.486000\n", + " 2428\n", " 0\n", - " 12\n", - " 0.21486\n", - " 3.00\n", + " 0.75\n", + " 53.543999\n", + " 1\n", + " 10\n", + " 0.53544\n", + " 2.50\n", " \n", " \n", - " 5\n", - " 1\n", - " 1.10\n", - " 40.874001\n", + " 526\n", + " 0\n", + " 1.01\n", + " 5.358000\n", " 1\n", - " 14\n", - " 0.40874\n", - " 3.50\n", + " 0\n", + " 0.05358\n", + " 0.00\n", " \n", " \n", - " 6\n", + " 242\n", " 1\n", - " 3.90\n", - " 69.517998\n", + " 0.74\n", + " 32.587002\n", " 1\n", - " 9\n", - " 0.69518\n", - " 2.25\n", + " 5\n", + " 0.32587\n", + " 1.25\n", " \n", " \n", - " 7\n", - " 1\n", - " 2.97\n", - " 80.710999\n", + " 2606\n", " 1\n", - " 4\n", - " 0.80711\n", - " 1.00\n", + " 4.35\n", + " 36.181000\n", + " 0\n", + " 8\n", + " 0.36181\n", + " 2.00\n", " \n", " \n", - " 8\n", + " 643\n", " 1\n", - " 3.24\n", - " 55.146000\n", + " 4.54\n", + " 30.681000\n", " 0\n", - " 10\n", - " 0.55146\n", - " 2.50\n", + " 5\n", + " 0.30681\n", + " 1.25\n", " \n", " \n", - " 9\n", - " 1\n", - " 3.28\n", - " 52.646999\n", - " 1\n", + " 1549\n", " 0\n", - " 0.52647\n", - " 0.00\n", + " 2.47\n", + " 32.264999\n", + " 0\n", + " 5\n", + " 0.32265\n", + " 1.25\n", " \n", " \n", - " 10\n", + " 2740\n", " 1\n", - " 2.52\n", - " 75.071999\n", + " 3.57\n", + " 4.755000\n", " 1\n", - " 0\n", - " 0.75072\n", - " 0.00\n", + " 5\n", + " 0.04755\n", + " 1.25\n", " \n", " \n", "\n", "" ], "text/plain": [ - " switch arsenic dist assoc educ dist100 educ4\n", - "1 1 2.36 16.826000 0 0 0.16826 0.00\n", - "2 1 0.71 47.321999 0 0 0.47322 0.00\n", - "3 0 2.07 20.966999 0 10 0.20967 2.50\n", - "4 1 1.15 21.486000 0 12 0.21486 3.00\n", - "5 1 1.10 40.874001 1 14 0.40874 3.50\n", - "6 1 3.90 69.517998 1 9 0.69518 2.25\n", - "7 1 2.97 80.710999 1 4 0.80711 1.00\n", - "8 1 3.24 55.146000 0 10 0.55146 2.50\n", - "9 1 3.28 52.646999 1 0 0.52647 0.00\n", - "10 1 2.52 75.071999 1 0 0.75072 0.00" + " switch arsenic dist assoc educ dist100 educ4\n", + "2659 1 2.19 44.629002 0 10 0.44629 2.50\n", + "1204 1 1.51 32.249001 1 0 0.32249 0.00\n", + "2308 0 0.63 19.921000 0 4 0.19921 1.00\n", + "2428 0 0.75 53.543999 1 10 0.53544 2.50\n", + "526 0 1.01 5.358000 1 0 0.05358 0.00\n", + "242 1 0.74 32.587002 1 5 0.32587 1.25\n", + "2606 1 4.35 36.181000 0 8 0.36181 2.00\n", + "643 1 4.54 30.681000 0 5 0.30681 1.25\n", + "1549 0 2.47 32.264999 0 5 0.32265 1.25\n", + "2740 1 3.57 4.755000 1 5 0.04755 1.25" ] }, - "execution_count": 12, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1445,7 +1454,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1481,9 +1490,9 @@ " 0\n", " arsenic\n", " dydx\n", - " -0.111342\n", - " -0.134846\n", - " -0.088171\n", + " -0.127551\n", + " -0.160444\n", + " -0.093784\n", " \n", " \n", "\n", @@ -1491,10 +1500,10 @@ ], "text/plain": [ " term estimate_type estimate lower_3.0% upper_97.0%\n", - "0 arsenic dydx -0.111342 -0.134846 -0.088171" + "0 arsenic dydx -0.127551 -0.160444 -0.093784" ] }, - "execution_count": 15, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1518,19 +1527,19 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "estimate -0.111342\n", - "lower_3.0% -0.134846\n", - "upper_97.0% -0.088171\n", + "estimate -0.127551\n", + "lower_3.0% -0.160444\n", + "upper_97.0% -0.093784\n", "dtype: float64" ] }, - "execution_count": 14, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1550,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1588,162 +1597,162 @@ " arsenic\n", " dydx\n", " 0.00\n", - " -0.092389\n", - " -0.119320\n", - " -0.068167\n", + " -0.114469\n", + " -0.150676\n", + " -0.078639\n", " \n", " \n", " 1\n", " arsenic\n", " dydx\n", " 0.25\n", - " -0.101704\n", - " -0.126096\n", - " -0.076910\n", + " -0.130916\n", + " -0.169176\n", + " -0.091776\n", " \n", " \n", " 2\n", " arsenic\n", " dydx\n", " 0.50\n", - " -0.102112\n", - " -0.122443\n", - " -0.082142\n", + " -0.124030\n", + " -0.153207\n", + " -0.094285\n", " \n", " \n", " 3\n", " arsenic\n", " dydx\n", " 0.75\n", - " -0.106004\n", - " -0.124247\n", - " -0.088132\n", + " -0.122760\n", + " -0.149194\n", + " -0.098292\n", " \n", " \n", " 4\n", " arsenic\n", " dydx\n", " 1.00\n", - " -0.110580\n", - " -0.127803\n", - " -0.093221\n", + " -0.129400\n", + " -0.155829\n", + " -0.102841\n", " \n", " \n", " 5\n", " arsenic\n", " dydx\n", " 1.25\n", - " -0.112334\n", - " -0.128771\n", - " -0.094870\n", + " -0.127170\n", + " -0.151142\n", + " -0.101722\n", " \n", " \n", " 6\n", " arsenic\n", " dydx\n", " 1.50\n", - " -0.114875\n", - " -0.132652\n", - " -0.096790\n", + " -0.132069\n", + " -0.157954\n", + " -0.104550\n", " \n", " \n", " 7\n", " arsenic\n", " dydx\n", " 1.75\n", - " -0.122557\n", - " -0.142921\n", - " -0.101423\n", + " -0.137053\n", + " -0.165037\n", + " -0.105561\n", " \n", " \n", " 8\n", " arsenic\n", " dydx\n", " 2.00\n", - " -0.125187\n", - " -0.148096\n", - " -0.101350\n", + " -0.140081\n", + " -0.172672\n", + " -0.103777\n", " \n", " \n", " 9\n", " arsenic\n", " dydx\n", " 2.25\n", - " -0.125367\n", - " -0.150676\n", - " -0.099852\n", + " -0.136278\n", + " -0.173375\n", + " -0.101215\n", " \n", " \n", " 10\n", " arsenic\n", " dydx\n", " 2.50\n", - " -0.130748\n", - " -0.159912\n", - " -0.101058\n", + " -0.139632\n", + " -0.180047\n", + " -0.098404\n", " \n", " \n", " 11\n", " arsenic\n", " dydx\n", " 2.75\n", - " -0.137422\n", - " -0.170662\n", - " -0.102995\n", + " -0.153269\n", + " -0.202273\n", + " -0.104104\n", " \n", " \n", " 12\n", " arsenic\n", " dydx\n", " 3.00\n", - " -0.136103\n", - " -0.172119\n", - " -0.099548\n", + " -0.146033\n", + " -0.195693\n", + " -0.093097\n", " \n", " \n", " 13\n", " arsenic\n", " dydx\n", " 3.25\n", - " -0.156941\n", - " -0.202215\n", - " -0.107625\n", + " -0.162131\n", + " -0.224284\n", + " -0.100161\n", " \n", " \n", " 14\n", " arsenic\n", " dydx\n", " 3.50\n", - " -0.142571\n", - " -0.186079\n", - " -0.098362\n", + " -0.152834\n", + " -0.212448\n", + " -0.090461\n", " \n", " \n", " 15\n", " arsenic\n", " dydx\n", " 3.75\n", - " -0.138336\n", - " -0.181042\n", - " -0.093120\n", + " -0.144096\n", + " -0.200710\n", + " -0.082893\n", " \n", " \n", " 16\n", " arsenic\n", " dydx\n", " 4.00\n", - " -0.138152\n", - " -0.185974\n", - " -0.089611\n", + " -0.137381\n", + " -0.203622\n", + " -0.070478\n", " \n", " \n", " 17\n", " arsenic\n", " dydx\n", " 4.25\n", - " -0.176623\n", - " -0.244273\n", - " -0.107141\n", + " -0.187189\n", + " -0.278350\n", + " -0.083767\n", " \n", " \n", "\n", @@ -1751,27 +1760,27 @@ ], "text/plain": [ " term estimate_type educ4 estimate lower_3.0% upper_97.0%\n", - "0 arsenic dydx 0.00 -0.092389 -0.119320 -0.068167\n", - "1 arsenic dydx 0.25 -0.101704 -0.126096 -0.076910\n", - "2 arsenic dydx 0.50 -0.102112 -0.122443 -0.082142\n", - "3 arsenic dydx 0.75 -0.106004 -0.124247 -0.088132\n", - "4 arsenic dydx 1.00 -0.110580 -0.127803 -0.093221\n", - "5 arsenic dydx 1.25 -0.112334 -0.128771 -0.094870\n", - "6 arsenic dydx 1.50 -0.114875 -0.132652 -0.096790\n", - "7 arsenic dydx 1.75 -0.122557 -0.142921 -0.101423\n", - "8 arsenic dydx 2.00 -0.125187 -0.148096 -0.101350\n", - "9 arsenic dydx 2.25 -0.125367 -0.150676 -0.099852\n", - "10 arsenic dydx 2.50 -0.130748 -0.159912 -0.101058\n", - "11 arsenic dydx 2.75 -0.137422 -0.170662 -0.102995\n", - "12 arsenic dydx 3.00 -0.136103 -0.172119 -0.099548\n", - "13 arsenic dydx 3.25 -0.156941 -0.202215 -0.107625\n", - "14 arsenic dydx 3.50 -0.142571 -0.186079 -0.098362\n", - "15 arsenic dydx 3.75 -0.138336 -0.181042 -0.093120\n", - "16 arsenic dydx 4.00 -0.138152 -0.185974 -0.089611\n", - "17 arsenic dydx 4.25 -0.176623 -0.244273 -0.107141" + "0 arsenic dydx 0.00 -0.114469 -0.150676 -0.078639\n", + "1 arsenic dydx 0.25 -0.130916 -0.169176 -0.091776\n", + "2 arsenic dydx 0.50 -0.124030 -0.153207 -0.094285\n", + "3 arsenic dydx 0.75 -0.122760 -0.149194 -0.098292\n", + "4 arsenic dydx 1.00 -0.129400 -0.155829 -0.102841\n", + "5 arsenic dydx 1.25 -0.127170 -0.151142 -0.101722\n", + "6 arsenic dydx 1.50 -0.132069 -0.157954 -0.104550\n", + "7 arsenic dydx 1.75 -0.137053 -0.165037 -0.105561\n", + "8 arsenic dydx 2.00 -0.140081 -0.172672 -0.103777\n", + "9 arsenic dydx 2.25 -0.136278 -0.173375 -0.101215\n", + "10 arsenic dydx 2.50 -0.139632 -0.180047 -0.098404\n", + "11 arsenic dydx 2.75 -0.153269 -0.202273 -0.104104\n", + "12 arsenic dydx 3.00 -0.146033 -0.195693 -0.093097\n", + "13 arsenic dydx 3.25 -0.162131 -0.224284 -0.100161\n", + "14 arsenic dydx 3.50 -0.152834 -0.212448 -0.090461\n", + "15 arsenic dydx 3.75 -0.144096 -0.200710 -0.082893\n", + "16 arsenic dydx 4.00 -0.137381 -0.203622 -0.070478\n", + "17 arsenic dydx 4.25 -0.187189 -0.278350 -0.083767" ] }, - "execution_count": 16, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1789,7 +1798,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1828,50 +1837,50 @@ " arsenic\n", " dydx\n", " 0.00\n", - " 0.00591\n", - " -0.085861\n", - " -0.109133\n", - " -0.061614\n", + " 0.02409\n", + " -0.121559\n", + " -0.164432\n", + " -0.079044\n", " \n", " \n", " 1\n", " arsenic\n", " dydx\n", " 0.00\n", - " 0.02409\n", - " -0.096272\n", - " -0.127518\n", - " -0.069670\n", + " 0.02454\n", + " -0.052945\n", + " -0.063185\n", + " -0.041371\n", " \n", " \n", " 2\n", " arsenic\n", " dydx\n", " 0.00\n", - " 0.02454\n", - " -0.056617\n", - " -0.065433\n", - " -0.046970\n", + " 0.02791\n", + " -0.124374\n", + " -0.168024\n", + " -0.081240\n", " \n", " \n", " 3\n", " arsenic\n", " dydx\n", " 0.00\n", - " 0.02791\n", - " -0.097646\n", - " -0.128131\n", - " -0.069660\n", + " 0.03380\n", + " -0.122091\n", + " -0.164796\n", + " -0.079337\n", " \n", " \n", " 4\n", " arsenic\n", " dydx\n", " 0.00\n", - " 0.03252\n", - " -0.076300\n", - " -0.095832\n", - " -0.057900\n", + " 0.03612\n", + " -0.115350\n", + " -0.153084\n", + " -0.073624\n", " \n", " \n", " ...\n", @@ -1884,78 +1893,78 @@ " ...\n", " \n", " \n", - " 2992\n", + " 1491\n", " arsenic\n", " dydx\n", " 4.00\n", " 1.13727\n", - " -0.070078\n", - " -0.094698\n", - " -0.046623\n", + " -0.070647\n", + " -0.105976\n", + " -0.037434\n", " \n", " \n", - " 2993\n", + " 1492\n", " arsenic\n", " dydx\n", " 4.00\n", " 1.14418\n", - " -0.125547\n", - " -0.172943\n", - " -0.075368\n", + " -0.132963\n", + " -0.200970\n", + " -0.061558\n", " \n", " \n", - " 2994\n", + " 1493\n", " arsenic\n", " dydx\n", " 4.00\n", " 1.25308\n", - " -0.156780\n", - " -0.218836\n", - " -0.088258\n", + " -0.167529\n", + " -0.252454\n", + " -0.073173\n", " \n", " \n", - " 2995\n", + " 1494\n", " arsenic\n", " dydx\n", " 4.00\n", " 1.67025\n", - " -0.161465\n", - " -0.227211\n", - " -0.085394\n", + " -0.170250\n", + " -0.268071\n", + " -0.076301\n", " \n", " \n", - " 2996\n", + " 1495\n", " arsenic\n", " dydx\n", " 4.25\n", " 0.29633\n", - " -0.176623\n", - " -0.244273\n", - " -0.107141\n", + " -0.187189\n", + " -0.278350\n", + " -0.083767\n", " \n", " \n", "\n", - "

2997 rows × 7 columns

\n", + "

1496 rows × 7 columns

\n", "" ], "text/plain": [ " term estimate_type educ4 dist100 estimate lower_3.0% upper_97.0%\n", - "0 arsenic dydx 0.00 0.00591 -0.085861 -0.109133 -0.061614\n", - "1 arsenic dydx 0.00 0.02409 -0.096272 -0.127518 -0.069670\n", - "2 arsenic dydx 0.00 0.02454 -0.056617 -0.065433 -0.046970\n", - "3 arsenic dydx 0.00 0.02791 -0.097646 -0.128131 -0.069660\n", - "4 arsenic dydx 0.00 0.03252 -0.076300 -0.095832 -0.057900\n", + "0 arsenic dydx 0.00 0.02409 -0.121559 -0.164432 -0.079044\n", + "1 arsenic dydx 0.00 0.02454 -0.052945 -0.063185 -0.041371\n", + "2 arsenic dydx 0.00 0.02791 -0.124374 -0.168024 -0.081240\n", + "3 arsenic dydx 0.00 0.03380 -0.122091 -0.164796 -0.079337\n", + "4 arsenic dydx 0.00 0.03612 -0.115350 -0.153084 -0.073624\n", "... ... ... ... ... ... ... ...\n", - "2992 arsenic dydx 4.00 1.13727 -0.070078 -0.094698 -0.046623\n", - "2993 arsenic dydx 4.00 1.14418 -0.125547 -0.172943 -0.075368\n", - "2994 arsenic dydx 4.00 1.25308 -0.156780 -0.218836 -0.088258\n", - "2995 arsenic dydx 4.00 1.67025 -0.161465 -0.227211 -0.085394\n", - "2996 arsenic dydx 4.25 0.29633 -0.176623 -0.244273 -0.107141\n", + "1491 arsenic dydx 4.00 1.13727 -0.070647 -0.105976 -0.037434\n", + "1492 arsenic dydx 4.00 1.14418 -0.132963 -0.200970 -0.061558\n", + "1493 arsenic dydx 4.00 1.25308 -0.167529 -0.252454 -0.073173\n", + "1494 arsenic dydx 4.00 1.67025 -0.170250 -0.268071 -0.076301\n", + "1495 arsenic dydx 4.25 0.29633 -0.187189 -0.278350 -0.083767\n", "\n", - "[2997 rows x 7 columns]" + "[1496 rows x 7 columns]" ] }, - "execution_count": 17, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1980,12 +1989,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2030,7 +2039,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -2066,9 +2075,9 @@ " 0\n", " arsenic\n", " eyex\n", - " -0.525124\n", - " -0.652708\n", - " -0.396082\n", + " -0.664831\n", + " -0.867586\n", + " -0.461033\n", " \n", " \n", "\n", @@ -2076,10 +2085,10 @@ ], "text/plain": [ " term estimate_type estimate lower_3.0% upper_97.0%\n", - "0 arsenic eyex -0.525124 -0.652708 -0.396082" + "0 arsenic eyex -0.664831 -0.867586 -0.461033" ] }, - "execution_count": 19, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -2097,7 +2106,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -2133,9 +2142,9 @@ " 0\n", " arsenic\n", " eydx\n", - " -0.286753\n", - " -0.351592\n", - " -0.220459\n", + " -0.352009\n", + " -0.452965\n", + " -0.251448\n", " \n", " \n", "\n", @@ -2143,10 +2152,10 @@ ], "text/plain": [ " term estimate_type estimate lower_3.0% upper_97.0%\n", - "0 arsenic eydx -0.286753 -0.351592 -0.220459" + "0 arsenic eydx -0.352009 -0.452965 -0.251448" ] }, - "execution_count": 20, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -2164,7 +2173,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -2200,9 +2209,9 @@ " 0\n", " arsenic\n", " dyex\n", - " -0.167616\n", - " -0.201147\n", - " -0.134605\n", + " -0.190065\n", + " -0.236576\n", + " -0.142506\n", " \n", " \n", "\n", @@ -2210,10 +2219,10 @@ ], "text/plain": [ " term estimate_type estimate lower_3.0% upper_97.0%\n", - "0 arsenic dyex -0.167616 -0.201147 -0.134605" + "0 arsenic dyex -0.190065 -0.236576 -0.142506" ] }, - "execution_count": 21, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -2238,14 +2247,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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QQgghRN2TYKUQQgghhBBCCCGEEKImSLBSCCGEEEIIIYQQQghREyRYKYQQQgghhBBCCCGEqAkSrBRCCCGEEEIIIYQQQtQECVYKIYQQQgghhBBCCCFqQt0FK//+7/+erq4u3G43mzZt4qc//ellH//jH/+YTZs24Xa7WbJkCZ///OfnaKVCCCGEEEIIIYQQQohrUVfByq997Wt88IMf5KMf/Sh79uzhjW98Iw899BB9fX2XfPzp06d5+OGHeeMb38iePXv4kz/5Ex5//HH+/d//fY5XLoQQQgghhBBCCCGEuJK6Clb+9V//Ne95z3v4rd/6LXp6evjMZz5De3s7//N//s9LPv7zn/88HR0dfOYzn6Gnp4ff+q3f4t3vfjf/7b/9tzleuRBCCCGEEEIIIYQQ4krs872Aq1Uul9m1axd//Md/POP4/fffzwsvvHDJ57z44ovcf//9M4498MAD/OM//iOVSgWHw3HRc0qlEqVSafrndDpdhdULIUT9kf1QCCFkLxRCCJC9UAgxt+oms3JiYgJd12lqappxvKmpiZGRkUs+Z2Rk5JKP1zSNiYmJSz7nySefJBQKTd/a29ur8waEEPPOMBTFik62pJHMlxnPlBhLFxlJFRlKFhhMFuifynN2Msep8SwnxjL0jmQ4PJQmW9Lme/lzTvZDIYSQvVAIIUD2QiHE3KqbzMrzLBbLjJ+VUhcdu9LjL3X8vI985CN86EMfmv45nU7LRizEHDIMRcUwqOgKTTfQDIVuKCq6gW6o6Z81/dyfhvmYimZQ1g0KZZ2iplPWFLpuUDYMNN18nqYrDGWgG6ArhWEoDKVQgFLm/nD+74ZS5/YLCzarhV9Y38qG9vD8/uPMMdkPhRBC9kIhhADZC4UQc6tugpWxWAybzXZRFuXY2NhF2ZPnNTc3X/LxdrudhoaGSz7H5XLhcrmqs2ghbnIlTadYNihq+nQQsVgxKGsGFd2gWDGPFyo6xYpOUTMoVwz01wQULwQWzWCmOvf6FsxgIjB9zGa1YLOYAUaLBawWC1aL+XfzuBWHw4LVYj7WAuiGOrcGc62likFJ0ylpBoWKTsBln/6i42Yi+6EQQsheKIQQIHuhEGJu1U2w0ul0smnTJp599lne9ra3TR9/9tln+cVf/MVLPue2227jO9/5zoxjzzzzDLfeeusl+1UKIS7POBfUK1TMQJ6mm0HHsmZmOBbKOpmSRiJXJlmoUChrlHUzK9LMhDSwYAGLQqlzAUSrBfu5IKLNajl3MwOKNqsZVDwfcDz/8+tlRiulKOsGuZJOrqSRLWnkSjr58oU/82X93E2bDpRqxuUDkXcub5yNf04hhBBCCCGEEEK8Rt0EKwE+9KEP8dhjj3Hrrbdy22238YUvfIG+vj5+53d+BzBT0wcHB/mnf/onAH7nd36Hz33uc3zoQx/ive99Ly+++CL/+I//yP/9v/93Pt+GEDWppOnTAT0z49HMKsyXNFLFCql8hUxRM4OT54OPyizFBjO/UQEWC7hsVpx2G067lYDLgsNmxWEzg5HXQ9MNMkWNdNFcQ6akkT3392zpwi1X0qjo15cBabWAx2HD47Thcdhwn7t5HDb8rrraKoUQQgghhBBCiLpVV5/Af/VXf5XJyUk+8YlPMDw8zJo1a3jqqadYvHgxAMPDw/T19U0/vquri6eeeoo/+IM/4O/+7u9obW3lf/yP/8F/+A//Yb7eghDzxjAUufK5wN65gF+upDGZLTGVK5MtaZQqirKuU9GVWWJtAaXMYKPTZsVht+C0W/G77dhflQl5I0qaTjJfIVWokC5USJ77M1WoTAco82X9ml7TYbPgc9rxuez4XDa8Tjs+pw2P047XaTt3u/B3j8MMrL5exuax0cwNvUchhBBCCCGEEEJcnboKVgL83u/9Hr/3e793yfu+/OUvX3TszjvvZPfu3bO8KiFqg1KKdFEjXahMZxumCxXGsyUS+QrFc30jSxUDMHs+2m02XHYrboeNkNeK0+bCYbNcdnDVtShpOolchalcmUS+zFS+TDJfIXnuz0Ll6gKRdquFgNtOwO3A77Kf+7sdv8v82e+2m3+67Djt1uter6HMsvWKriifG9qjGcZ1v54QQgghhBBCCCGuXt0FK4UQJqUU2ZLGVK7MZK7MWLrIYKJAslChWDmXHXku3uiyvyog6XHgtFuxVikYCVAo60xkS0zmykzmSkxmy9MZm7mryIp0O6yEPI7pW9DjIOQ2/wy6HQQ9djwO2w0HUM/3tCydG6RT1sxhP7phgMWCMpNJcdjNTFKn3YLfZWdR2E3MLw3FhRBCCCGEEEKI2SbBSiHqQEnTSRc0MxuxUGEsU2Q4WZzu2agZBjaLxSx3dtlp8LluKLvwUgylSOYrjKWLjGVKTGRLjGdLTGRKVwxIehw2oj4nEa+DiM9J2Osk4nEQ9joJex24HbaqrbOsnQtEVgxKmkFJ19H1cxPEp/tpWnHbbTRGXIS9ZkD01X0qp8vDnTZc9uqtTQgh6oZSUM5BKQ3FNJQyUEiA1QZL3zzfqxNCCCGEEAuYBCuFqDHFis5gskAiV2YqV2Y0XSKRL5tDb86VTFstlum+i1GfE4eteoHJ86XkI6kCI+kSo+kiY5ki45nSZYfXBN12GvwuGnxO8+Z3EfU5ifqcVQ1Gvjo7sqQZlDSdYsVAYaAMCw67xQw62m00h1xEvGZw1OuyEXCZwVy/247Pab/hfptCCFHXdO1cMDJpBiNLWTMwmRuHwhRUCqAVzRsKtBKEOqD7Hqhidr4QQgghhBCvJsFKIWpAplhhIFHgzESOE2NZkoUKuqGwWiy4HVYzM9HrxO20VbV8WzMMxtIlhlMFhpJFhlNFRtIFipVL92i0WS00+l00Bs7d/C5iARcxv7OqGYiGMvtFFis6Rc2gVDFLti+VHRkLe4gFzKDk+T6W5/taSjBSCCEAvQKFpJkZef6WGYb8BFSKoBVA180+GFjA7jp3c4M7ZP7dYoXs2Dy/ESGEEEIIcTOQYKUQc6yiG2bWZL7MVLbMYKrA4FSBVKGCxQIhj5OOqLeq2ZIAuqEYTRcZSBQYTOYZTBQYzZTQjYuzJa0WiPldNIfcNAfdxANu4kEzIFjNAKBuqOmM0cK54T9KKbBYcNmteOw2/C47nQ1eGnxOAh6HZEcKIcTrUepcufYU5KcgNwmZQTNTspw3MyWVYQYe7W5weM4FI5vBJpeEQgghhBCiNsiVqRBzIFfSODNpZk32TxXIlTUKZQ0wg3IBt4Mljf6qBd6UUqQKFfqm8vRN5emfyjOcKqJdIjDpdlhpCXloDblpCXvOBSdd2KscLC1VdHJlnVxJo1jRUSgsFgseh9kbsj3iMQOiPue5oToOAu7qDNYRQogFxzDM8u38FOQnITsKyT6zrLucM7MpLZZzQUkveKNg95g9J4UQQgghhKhhEqwUYpYUKzoDiTwnxrIcG82QyFewWiwE3Q4afE7cYU/VSrp1QzGcKnBmMs/ZyRz9U3nSRe2ix7kdVtrCHhZFvLSFPbSGPUS8jqoHAyu6Qa5kDv8plDUUFpx2Kz6XjUVRD60hD2HvqyZ/exxVzyQVQogF5XwvydwEZEYgedY8Vs6a2ZJWGzh94PCBtwFszvlesRBCCCGEENdFgpVCVEmhrDOeKTGWMUut+xN5krkKhlJEvE6WxKqXOakZBv1TBU5PZDkzaWZPlrWZfSatFmgOuemIemmPmLeo31nVnpcAmm5QqOjkz2VNaobCZrXgd9nN80e8Zk9Jn5Oo14nHKVk9QghxWUqZGZOZYUgPw9QpKEyaJd5KB4sNnH5w+SHQBFa5nBNCCCGEEAuHXN0KcQMmsyX6EwVOjWcZSJjZjBVd4bBZCLoctEe9OO03njGoG4qhZIGT41lOTeQ4O5m7aDK322FlcdRHZ4OXjgYfbWFPVc79aiVNJ1d6dSm3OXTH47Thc9npbvTRGvYQ87to8DsJuB1VPb8QQixIhmEOu8mOQnoEJk+YP5ez5v1OHzgD4I2BbQ73VaXMsvL0IKSHzKngHbfP3fmFEEIIIcRNSYKVQlwDw1CMZ0v0T+U5NpplKFkgW9KwWy2EPA4WhasTnASYypU5PpbhxFiWk+PZiyZ0+1x2lsR8dMbMAGVT0F31SeG5kk62pJEvaSgUTpsNn/tCKXfE5yTscRD2Ogi6HVhl2I0QQlyZrpmByeyIGQScOm1O6C7nAWVmTLpCEGw1h+HMBWWYA3nS/ZAaMG/poQsBUwBPVIKVQgghhBBi1kmwUogr0A3FSLpI/1Seo8NpRjMl8mUNt91GxOukOVSdIGFFNzg9kaN3JMOx0QyTufKM+z0OG10xH92NPpY0+okHXFXtNVko66SLlXOl3AY2q9ljssHn5JaOMI3nsiUbfC4p5RZCiGuha5AbM3tNpgZg6iQUkuZ0bqvVzJr0RCG0aG6Ck0qZmZvJfkj1m4N50gOglS7xYAv44xBsA3fYfK4QQgghhBCzSIKVQlxCtqQxkioynCrQO5JhPFuiVNHxOu1EvU7aI96qnCdVqHB0JE3vSIaT49kZpd1WC3REvSyNB1je5Ke1igN5lFLkyzqZokamVEEps4w84HbQ3RiiJeyhweekwe8i6LbLNG4hhLgW58u600OQGoSpExcyJ61WcAXBFwdndX6XXFEpA4mz5lCe5FkzQFkpXPw4q93M5gwtgmA7hNog0HxhWE92zJwwLoQQNylDGSRLSZKlJC2+Fjx2z3wvSQghFiQJVgqBGbwbSZuDcc5M5hhMFEgXKhjKLLeO+91VyyYcSxc5PJzm8HCagcTMD4tBt53lTQFWNgdY0ujH7aheBmNJ00kVKmSKGrph4HHaCXsdrGoN0Br2EA+4afA7ZSq3EEJcK8OA/OSrBuKcNIOVpawZ3HMFzX6TIe/sB/sMzQyQJs5A8oz5ZyFx8eOsdjNbMtQO4XbzT3+TOVVcCCEEAAWtwER+gsniJKP5UYayQ2TKGXSl82Dng/Q09Mz3EoUQYkGSYKW4qRmG4sxkjn39KY6PZciVdVw2KyGPg86YD7v1xgN3SimGU0UODKY4NJRmIjuzzK494mFFc5CVzQFaQu6qZTFWdINMUSNVqFDRDRw2C2GPk1vaw7RHvTSH3MT8rqpNKBdCiJtKKWuWTyf7zIE4hSko5cCCOanbHTaDgbNd1l3OQeK0OTE8ccYs7TYqr3mQxQxERjogvBjCHRBokSniQgjxKhW9QkkvkSgmGCuMMZAZYCg3RLacRTM0bFYbPoePoCvIcHYYhbTFEEKI2SJXqeKmVNYMTo5n2dOX5PREFt1QNAXdLKpSeff5AOXBwRQHBlMz+k/aLBa64z56WoL0tAQJVnFidrakkciVKVQ0bFYrAbed5c1+Oht8NAfdxIMuXHbJmhFCiOtSTJnl1BMnYPL4uYxFBa7A3AUnC0kzc3PqpBmgzIxc/BiHFyKdF26hDnC4Z3ddQghRB3RDJ1FKMJ4fZyw/RrKUJK/lKWgFKnoFzdDIa3k0Q8NpcxJwBmjzt+GwzbxetyBf9gshxGySYKW4aeiGYjhV4MxEjiMjGYaTBaxWCy1BT9VKvCezJfYNJNnbn5qRQWm3WljeFGBtW4gVzYGqlXcrpciVdaZyZQplDZ/LTkvIzbKmAE1BF80hN16n/N9cCCGuWyFpZiyOH7swGMdqA08YGrpnPzuxkDADo5MnzVt+4uLH+OIQXQLRLjM46YtLb0khhDgnW87Sn+lnODfM2fRZ0uU0hXN9e512J3aLHZvVht1qx213E3FHLgpOCiGEmFsSxRAL3limyKnxHIeH0oxmihQrOgGXg46oD6f9xjNgsiWN/QNJ9vUn6X9VD8rpAOWiECubAriqFKA0lCJdqJDMVyhpOj6XnUURNyuag3REvVWfEi6EEDedYtoMTL46QGmzmxO7Y8tmt69jKQMTx80A5cTxSwQnLeYAnIZuM0AZWQIu/+ytRwgh6lBBKzCQGeBU6hSnkqdIlVJYLBZ8Dh9hV5gWX4tcLwshRA2TYKVYsMYzJfb0Jdg/mCJTqFR1UI5uKI6NZth1NsHRkTTGuZY1FmBp3M/69jCrW4JVC1BWdINUvkKqUMZQEPDY6Y77WBr30x7x0igBSiGEuDF6xSzxHj8Co4cgnzCDkt4GaFwxe+XdWsnMmJzoNW+vLeu2WM8FJ5dCdKkZoJyvkm5Dkz6XQoiala/kGcoO0Z/p50TiBFOlKQDC7jBd4S6ss92mQwghRNXIFadYcKZyZfb2J9nbnyCVrxAPumkNeary2qPpIrvOJtjTnyRX0qaPt4U9bGgPs25RiECVelAWKzrJfIV0sYLNYiHsdbBxcYTOmI9FYS8hr5SnCCHEDTEMc4J34gwM74X0kBmQ8zbMXgalMszzjB+B8V6YOg1Kn/mYYJt5/oZlEO2en+CkUlDJQyltZnsaOtjdEGyd+7UIIcQl6IZOupxmJDfC2fRZzqTPkC6lUSiCriAdwQ7s8gWLEELUJdm9xYKQylcYSRcZTOTZP5hiKlem0e9ieVPghjMOK7rBgcEUr5ye4uxUfvq432XnlvYwtyyO0ByszgdJQykSuTIT2RIuu40Gv5MNHWE6ol7awtXrrSmEEDctrQypfnNq9tgRyI6YQTlnwMxgtM9CYLCcg/GjF26lzMz7PRGIrTAzOGPLzGnic03XoJwx11bJgwKcHnCHoGmNGUANNJlTxSWTXwgxxwxlMFGYYKo4RaqUYiw/xnh+nFwlR66Sw2qxEnKF6Ah2YJvNVh1CCCHmhAQrRV0qaTqDiQIjKbMf5WimSLakoRREvE6WNwWw3uCHqdF0kZdPT7GnP0GxYgBgtcDK5iC3Lo6wrCmAzVqdD2y6oZjIlkjky0S8Tt64LMbypiAtYTcOm5SsCCHEDSkkITVwbpJ3L+QnzbJvlx98jeD0Vfd8SpkZm6OHYOywmbmJunC/zQmx5eeCkyvBF5v7AKBWupA1qZXMLFJXAAIt5pCeQJM5qMfXaPbrFEKIOaaUYqIwQX+mn96pXkbzoxS0c4NxbE48dg9+p5+4Ly4l3kIIscDI1aeoG8WKzkCiwJnJHMdGMkzmymi6gddpJ+hxEA+4bzh4qBuKoyNpXjw1yanx3PTxiNfB5s4oGxdHCFapzBugUNaZzJXIlzVifhcPrG5iVUuIiM9ZtXMIIcRNRynIjplBwoljZiZlMWUGBF0hCLWD3VXdc+oaTB6D0YMwehiKyZn3B1og3gONPebU7rkuTdRK5uCgYtIsdbc5wR001xRebGZM+hvNTEohhJgnSikmi5MMZAY4ljjGcG6YfCU/PaW71d4qfdqFEOImIMFKUfNGUkUODaY4OpJhKl9GNxQhj4P2iLcq07zBnOi988wUO05PkSpUAHNYTk9LkK1dUbrj/hvO1DyvWNGZypXJFCu4HTaagm7WLYqzsiWI3yX/lxRCiOtWSMDUKRg5CMmzZtag3W2WWTfEq9+DspwzMydHDprl3Xrpwn1Wh5k92bQK4qvMNcwlQzMDtIUk6OULwcnWDWZw8nxJt6M6PZ2FEOJ6nc+gHMgOcDxxnOHcMLlKzgxQuiIyuVsIIW5CEhkRNckwFAOJAnv7E/SOZMmWKoS9Tjqi3qqWRY+mi/z8xAR7+5No50Z6e502tnRG2dIVJeytXoZjMl9mLFPEYbMSD7jZtiTK4gYfrWFP1crJhRDipnN+mvbYIZg4bgbnbE6ztDq4qPrl1YUkjB6A4f0wddIcmHOeKwTNqyG+2uw9aZvDLHmloJw111fOgtUK7rAZLI10QbAF/M3zN0lcCCFeRSnFeGGcgcwAJ5InJEAphBBiBglWipqiG4pT41n29ic5MZ6lXDGIB920RaqX+aGU4sRYlp+dmOD4WHb6eFvYw+3dDaxpC1U1IJrMlxlNF/G7HGzubGBNW5C2sAe79KIUQojrlxk1sxmH90J62AxKehvMPpDV7l2Wn4ThfeYteXbmfYEWaF5rDqEJLar+uS9HL5vByWLSnNbt8pul3LE7zLUEW81jQghRIyYKE5xNn+V44jij+VHylTwuu0sClEIIIWaQYKWoCZpucGI8y66zCU5P5EBBU9CNr4pl0bqhODCY5CfHJhhJFwGz1HtVa5A3dMdY3OCt2gWSoRSpQoXxTAm/y87WJQ3c0hGmJSTldkIIcd1KWbPMe+yI2YuylAZX0OwBWe0sxvwkDO01g6Gp/pn3RTqheZ1588Wqe97Lmc6eTJgl6DaH2WOybTNEO80ApTdmZlUKIUQNSZfT7B3dy/6J/aTLaTx2j9mD0t8630sTQghRgyRYKeZVRTc4Pppld98Up8fzWK3QGvLgdlSvr1hZM9h1doqfnpggmTf7UTptVjZ1Rrh9SQMN/uoNWSiUdSay5sCcoMfBlq6oBCmFEOJGVArmoJzxY2YmZSFhZlH64hBsq26ZdyFhBiiHdr8mQGmBhqXQci5AOZdDaAzdzJzMT5l9KJ0+CDRD48pz2ZNtUtothKhZJb3EkckjvDLyCuP5cWLeGMsiy+Z7WUIIIWqcBCvFvFBKcXI8y4snJzk9kcNmtbIo4sFVxSBlsaLz4qlJfn5ignxZB8DntHH70hjbuhrwOKtzLkMpJrNlpvIlXDYbrWE3a9riLGn0E5Wp3kIIcX1KGRjaA/0vQ27CTIX3RM2gYTUH5ZSyZvbk0G4za3PauQBl6wYzQOkKVO+cV2JokE9AYQpQZu/JlnXmekLt4GuU7EkhRE3LlrMMZAfYNbqLvnQffqef7kg31rlslSGEEKJuSbBSzLmxdJEXT01yaDCNQtEe9eKyV++DZ76s8cLJSV44OUGxYg4+iHgdvHFZI5sWR6rWj1I3FGOZIqlChajPyRuXxljeFKQtIgNzhBDiulUK5vCasz+HzBC4oxBdYpY8V4tehpEDMLjLzNZ89ZCc6BJo3Qgt6+c2QKmVzMzOYhKwmIHZjq1mgDK82JzkLYQQNUopxVRxiuHcMKdTp+nP9JMup7Fb7SwOLsZRzT1cCCHEgifBSjFnciWNXWcT7Dw7RSpfoS3ixV/FnpS5ksbPTkzw0qlJSpr5wbMx4OLuFY2sbQtXLYBY0Q3G0iWypQpNQTe3dzewqjVEyCMXYUIIcd0qBTNwePYFs+zbFYTYyuplUSrDnBY+uNMMhuqlC/eF2s0AZesG8ESqc74rrucS/Se9Uei8A6LdEFlslnwLIUSN0gyNsfwYQ9khTqVOMZobJVvJYrfaCblCdAY7sVUzE14IIcRNQ4KVYtblShqHh9O8cnqKkXSRmN/F8qZA1YbZ5MsaPzs+wQsnJynrZpCyOejm7pVxVrcGsVbpPGXNYCRVoKgZtIbd3NPTyMrmYFWHAAkhxE2lnIPEWZg6CeO9kBsHuxsallUvkzI7apaSD+6EYurCcW8DtG0yb/6m6pzrSpQyhwLlJ0ErgtNv9p+M91zoP2mvXh9lIYSotopeoT/bz0B6gJOpkySKCUp6CbfdTcgVosnXtKAnehcqBU6nT2NDgrBCCDGb6ibKkkgkePzxx/n2t78NwKOPPspnP/tZwuHwJR9fqVT40z/9U5566ilOnTpFKBTi3nvv5dOf/jStrTJ1bi5kSxqHh9LsPDPFaLpIwO1gWTxQtQzHQlnn5ycn+PmJielMytawm3tWNLGyJVC1IGWxojOcKqIbBu1RLxs7IqxoDlR1CJAQQtxUEmfNfpTjxy70ZfREzIzCagQpK3kY3A0DL0Oy78Jxh9fMnmzbbE70nosP1Mow+2/mJ81Sb5ffLDVvWm2Wd/vjc7MOIYS4AeP5cU6nTnN48jDjhXF0Q8fv9NPobcRtX7hDvjRDoz/Tz6nUKU4nTzOUGwLgrkV3ze/ChBBigaubYOU73/lOBgYGePrppwF43/vex2OPPcZ3vvOdSz4+n8+ze/du/uzP/oz169eTSCT44Ac/yKOPPsrOnTvncuk3nWS+zNGRDLvPJhhJFwl5HCytYpCyrBm8cHKCnxwfn+5J2Rx0c29PEz0t1cvYzJY0RtNFLEBXzMctHRGWxv047dIYXAghrksxBWdfhP4dUMmBJwYN3WCtwuWIMmDyBPTtgJH9YFTM4xYrNPbAos3QtAZsc3Dpowwonsug1MtmSXvDUoivMoOkvpgEKIUQNa+iVziZOsnRyaOczZwlV84RcAVo9bfitC3MIZJKKSaLk5xKnuJk8iRn0meonP99ck6jpxHj1b2OhRBCVF1dBCuPHDnC008/zUsvvcTWrVsB+OIXv8htt91Gb28vK1asuOg5oVCIZ599dsaxz372s2zZsoW+vj46OjrmZO03C6UUA4kCR4bTHB5Kk8iXCXqqm0mpG4pXzkyx/egYmZIGQDzg4s09TVUr9zaUIpmvMJ4p4nXa6GkJsKE9TFfML0NzhBDieumaGUA8/WNIDZqlz+Eq/R4upszgZ/8OMzh4XqAF2rdA261zMyhHGeZa8lMXApSNy81AaaQT/I2zvwYhhKiColbkeOI4+8b3MZgdxGqx0uBpoNW/MKvTynqZ06nTnEie4GTyJMlScsb9PoePJaElLAkvYUloCQFngJOJk/OzWCGEuEnURbDyxRdfJBQKTQcqAbZt20YoFOKFF164ZLDyUlKpFBaL5XVLxwFKpRKl0oWm++l0+rrXfTMoVnROT+Q4MJji1HiWQkUn5nOxrKl6ZdiGUhwYSPHskVGmcmXAnO59b08T69vDVQtSjmdKJPJlIl4nty+Nsbo1SFvYs6D77ghxObIfihumlWHqlBlIHDsCDg80VmFojjJg7Cj0vQBjhy9M87a7oW0jtG8zh+bM9v6tDHNATn4KDA3cIYivNN/j+QxKUfdkLxQ3i1wlR+9UL/vG9zGaG8Vld9EeaF9wk7yVUkwUJjiePM6JxAn6Mn0zMiVtFhvtgXa6w910h7tp8i7sPpxXS/ZCIcRcqotg5cjICPF4/KLj8XickZGRq3qNYrHIH//xH/POd76TYDD4uo978skneeKJJ657rTcDpRQj6SLHR7McGkoxlilhs1iIB91Vne4NcGIsy9MHhxlKFQHwu+zcvTLO5s4Idmt1yrHzJY3+RJ6o38mDq5tZ2RIk6luYpS1CXAvZD8V1y02YA3OG9kB6CFBmf0bHDfY1K6ag70XoewmKyQvHo93QsQ1a1sNclCaW85AbNSeYe6LQsgFiS80ApTc6++cXc0r2QrHQFbQCRyaPsHt0N2OFMfwOP4tDi7FXo0VHjajoFc6kz3A8cZwTyRMXZU9GXBGWhpfSHemmM9i5YMvcb4TshUKIuTSvv4E+/vGPX3HDe+WVVwAu+W2WUuqqvuWqVCq84x3vwDAM/v7v//6yj/3IRz7Chz70oemf0+k07e3tVzzHzaCk6RwfzXJwMMXZqTz5skbI7aSzwYfDVt0+jiOpIk8fGubYaBYAl93Km5Y3cnt3Ay57dQbb6IZiKFmgrBvc0hHhjmUxYn6ZwirEebIfimtiGJA4DUN7YfwoFBPgDJrl3jcy4VopmDwOZ34OowcuZFE6vGaZd/s2s6x8thk65CfMLEq7G8Lt54KUy8ATnv3zi3kje6FYqMp6md6pXnaN7mI4N4zf6ac73I3VsjD6s2fKGY4ljnEscYzTqdNohjZ9n81iY3FwMcsiy1gaXkrUHZXsySuQvVAIMZfmNVj5gQ98gHe84x2XfUxnZyf79+9ndHT0ovvGx8dpamq67PMrlQq/8iu/wunTp3nuuecum1UJ4HK5cLkkYPVquZLG0ZE0u88mGUoWsNksNPpdtEe8VT9XulDh2SOj7D6bQAFWC2xd0sA9K+L4qpi1mSpUGEkVaA57eOPSGKtaglilJ6UQM8h+KK6KYcDUSeh/2cym1EvgbzJ7Rt7IB79K3nzNsz+H3PiF49El0HH7uSzKOShNLGUgO2YO7PE2wJK7zVLv8GKoUoa/qG2yF4qFJlPO0JfpY8/oHgYyA7gdbrpCXdhutEXHPFNKMZIf4diUGaAczg3PuD/oDE4HJ7tCXZI9eY1kLxRCzKV5DVbGYjFisSv3c7rttttIpVK8/PLLbNmyBYAdO3aQSqW4/fbbX/d55wOVx48fZ/v27TQ0NFRt7TeDZL7M4aE0e/uTjKZL+Fw2OmPVz6IEc8L3z06M8+Nj41R0BcCa1iAPrG6moYrZjrmSxlCygNth5bbuBm7rjhHyLKw+PEIIMSd0bWaQ0tAg1AZO/429bnoYzvwUBneag2rAzMxs2wyL3wDBlhtf+5XoFTNAWkiCy29mT7asMyd6z8WwHiGEqLJUKcVgdpBTqVP0pftIl9I47U46gh113ZNSN3TOpM9wbOoYvYle0uWZfRTb/G0sjyxnWWSZ9J4UQog6UheNSHp6enjwwQd573vfyz/8wz8A8L73vY9HHnlkxnCdlStX8uSTT/K2t70NTdP45V/+ZXbv3s13v/tddF2f7m8ZjUZxOuWbtEup6AZnJ/P0jqQ5NpolkS8T9jpZGp+dadhKKfYNpPjBoRFShQoAHVEvD69ppqPBV7XzFMo6Q8k8NpuVtYtCbO6M0h6tfmaoEEIsaHoFUgOQOAOjByEzYpZpB1vBeQN7tjJg5KAZpJw8fuF4oAU674C2TWbp9Wwr5yAzbJZ8B5rM4Ghsmfn+5AOuEKLOGMqgP9PPwYmDnEmdIV1OY7faCbvCdIW76rbcu6yXOZE8wdHJoxxPHqekXxj64rA6WBJaworoCpaGl+K/0S/QhBBCzIu6CFYCfPWrX+Xxxx/n/vvvB+DRRx/lc5/73IzH9Pb2kkqlABgYGODb3/42ABs2bJjxuO3bt3PXXXfN+prryUS2xMmxLAcGUwynihhK0eBzsbyKU71fq38qz3f3D9GfKAAQ9jh4YE0z69pCVfvWUzMMBhIFDKVY1hRgS1eUzgaflHwLIcS1SA2a07zHDpsl0XrZzDAMLrqxoTmVPPTtMIOUhSnzmMUKTWvNIGXD0rmb6J0bB5vLDE62boTY8hsfCCSEEPPgfLbh/vH9nEqdQjM0op4oS7xL6jZAma/k6Z3q5ejUUU6lTqErffo+n8PH8shyVkRX0BXsqutMUSGEEKa6CVZGo1G+8pWvXPYxSqnpv3d2ds74WVwsU6xwZiLP0ZE0ZyfzpIsVAi4HiyKeqg2xeb3z/uDQKLv7EgA4bVbuXNHIHUtjVS0xTxUqDKUKdDX4eMPS2KxlhwohxIJ0fmDO4C4zUFnKgidklnrfaJZjdhzO/AT6d1wo9Xb4YPFtZjajJ3Lj678cpaCUNqeW6yXzfIvfAM1rpRelEKJuFbXidJDybPosAHFvHK+jPquJMuUMR6eOcmTyCGfTZ1Fc+GwXdUdZEV3ByuhKFvkXSXm3EEIsMHUTrBQ3TilFqlBhNF3i5HiWY6MZkvkKdquFBr+LlpB7Vn/Ra4bBiycnee7oGCXNnOa6sSPM/auaCVaxb6RuKAYSeRTwpqUxbl8aq+pwHiGEWNB0DSaOwcArMHnCDCYGWsyp3jdCKZg6Bae2w+ghOP+hM9AMXXeapd6zPeyglDEnelcKZmZobBk0rYFoF3ijs3tuIYSYBUopRvOjnEqe4vDUYSbyE9isNpp9zbjnon1GlaVKKQ5PHubI5BEGsgMz7mv2NbMyupKV0ZU0eholQCmEEAuYRHAWuGS+zHCqyGiqyJnJHJO5MtmSBkDU66S7cW6yDY+NZvju/iEmsmYGzaKIh19Y11r1vpGZYoXBZIFFEQ93rYizLO6XCxkhhLgalSKMH4H+V8yelBaLGaS8kV6UYPZ/HN5nBilT/ReOx1eZQcrY8tkt9S5lzwUo8+bwn3CHWWYe7QJ/fPbOK4QQs6igFTibPsuRySP0ZfrIlrOEXCEWhxZjt9bXR7xkMcnhqcMcnjzMUHZoxn2LAovoifawMrqSiHuWs+6FEELUjPr6TSauilKKwWSBg4MpjgxnSBcrKAU+p52A20484J6zcuhkvsz3DgxzaMiczOdz2XlwdRO3dESq1gtTKUUyX2E8W8LlsLK1q4E3Lo8RdEu/GiGEuKJyzsx07H/ZDCbaXGZAz+66sdfVStD3Epx+3uwJCWB1wKJbYcld4G+60ZVf5txFs7dmOWsGKIOLoHkNRDrNTE75EksIUYfOZ1GeTJ7kyOQRJooT2Cw2Yp4Yrf7W+V7eNTmfQXlo4hBDuZkBysXBxfREe+hp6CHgDMzTCoUQQswnCVYuIJpucHoix76BJCfGshQqOo1+N92N/lkbknO5tfzsxATbe8eo6AqrBW5b0sCbe5pwO6rTD1M3FBPZEsl8maDHwZauKGvbQiyKeCSbUgghriQ/ZU70HthpTsB2+iHaDTc6mKCUgdM/gbM/N7MZwXztzjtg8R3gmqXJrIZuDunJTZjvIdgGS+89l0HZLH0ohRB1K1POMJAZoHeql7OZs+QqOTOLMlhfWZSZcsYMUE4eYiBzocTbgoWOYAerGlbRE+2RCd5CCCEkWLkQGIbi+FiWHacmOTtlfjCMB1x0zFNm4fGxDN/eO8Rkziz57mzw8eiGVpqD1embYyjFeMYMUjYGXNzb00RPa5CY/wazgIQQ4maQGoTh/TC8FwqT4I5AbAVYb/CLpNwEnHrOzNA0zHYj+BrNLMpFm2evH2WlCNlhsw+ltwG63giNPWYWpU0uc4QQ9SlVSjGYHeRU6hR96T7SpTR2q50GT0NdZVHmK3mOTh3l4MRBzqTPzLivI9DB6thqCVAKIYS4iFzF17mzkzlePj1F72gGgEVhD64qZS5eq3ShwvcODHNgMAVAwGXnobXNrF8UrlqmYyJXZjRTJOZ38uCaZtYtCsvwHCGEuBKtBFOnYWS/Odm7nANfzAzqWW4w4zA9CCd+CEN7mR6aE+6A7jeb07Vv9PVfTzEFmREzYzLcaQ7oiS0Fd2h2zieEELPIUAYThQmGc8OcTZ2lP9NPppzBZrURdoXpCndhna39tMrKepneqV4OThzkZOokhjKm71vkXzQdoAy6gvO4SiGEELVMojx1aiRVZOfZKQ4OpihrBq1hD17n/Pzn1A3FS6cm+eGRUUqagQW4rbuBe6tY8p0raQwmC/hddt60LMatnVHC3lmeGiuEEPXMMCA9AOPHYXS/2cMRzF6RNzrZG8zJ3id+CGOHLxxr7IGlbzbLyWejHYeumcNyClNmaXnrLeYtukSyKIUQdcdQBoOZQQYyA5xInWCyMElBK+CwOQi7wsS8sboJUOqGzqnUKQ5MHKB3qpeKUZm+r8nbxJrYGlY3rCbsDs/fIoUQQtQNubKvI7qhODOZ4+BgiuOjWTIljZagm6Bn/gbJ9E/l+ebeQYZTRQDaIx5+cUMbrWFPVV7fUIrhZJGiprN+UZitS6JVe20hhFiwxnvNvpHJfnPYjDt0riz6Br/kUQomeuH4szB18txBC7RuMDMpQ4tucOGvc85iEnLj5t99MVh6PzSvhmD9lEIKIcR5SikGsgPsGd3D8eRxynoZn8NHxB2hzdE238u7akopBrODHBg/wKHJQ+S1/PR9EVeENbE1rImtodHbOI+rFEIIUY8kWFkH8mWN46NZ9vUn6ZvKYyhFY8A9r0G7YkXnB4dGePn0FApwO6w8uLqFWzurN+W7WNE5O5Uj5nfx4JpmVrcGsc7RFHMhhKhLWhnO/AzO/MT8e7AVnL4bf11lwOhhOP4MpPrMY1YbLNoC3feYvSmrTRmQGTWzKD1haNkATauhoRsc8qWVEKI+DWeH2TO+h96pXsp6mWZfM16Hd76XdU2milMcGD/AgYkDTBWnpo/7HD5WN6xmTWwNbf42GXgphBDiukmwskZVdIP+qTynxrMcHckwkS3jsltpDXuqVlp9PZRSHBxK8919Q2RK5gCFW9rDPLS2BX8Ve0dOZEpM5cusbg1x94o4jQEZniOEEJeVHYdj34eRA+CLQ3jxjb+mMsxhPMefgcyQeczqgMW3wZJ7zCDibCgkzAnlvkZY/VZoXGlmVAohRJ2aKk6xa2QXh6cOk6/kafY119VQmYJW4PDkYfaN75sxydthdbAyupK1sbUsCS+pm7J1IYQQtU2ClTUkX9aYzJbpm8pxaCjNWLqEZijCXgdLGn3YrfP7y38qV+bb+wY5NpoFoMHn5K23tNHdWL0LLd1QnJ3M4XbYuH9VE7d2RnHa5aJHCCFel1JmgPL4M5AdNfs32t03+JoGDO0xy72zI+YxuwsW32FO93YFbnjZl1TOQWrAzJzsussMinqjs3MuIYSYA0WtyIGJA+wc3UmimKDJ21Q307x1Q+dk6iT7x/fTO9WLrnQALFjoCnWxrnEdK6Mrcd5oixEhhBDiNSRYOU+UUoyki4xnSkxkSgwkC0zlyuRLOiVdJ+By0Bbx4LLPXxblebqh+PmJCX50dJSKrrBZLdy5vJE7lzfisFUvkJgva/RN5Vjc4OPNK5vojFWhdFEIIRaiSgHSQ2ZPyvFeszTb6jAzEG8kq8XQzwUpn4HcuYE8djd03Qldb6pOSfmlzllIQH7SLC1vXgtdb6zOECAhhJgnhjI4kTzBy8Mv05/pJ+QKsTS8tC5Ko0dzo+wb38eBiQPkKrnp43FPnHXxdayNrSXgnKUvreqAbugo1HwvQwghFjQJVs6xYkXn5HiWAwMpzk7mKFR0LFjwOG34nHZaw86ayiQcSOT5xp4LA3S6Yj7euqGt6mXZE5kSiUKZjR1R7umJE3TP39AgIYSoWVOnYXAPTJ0wh87omhlADDSb07GvlzJgcDcc/4E5yAbA4TWzKDvfWP0ekUpBKW2eS9fAG4H2LRBfBbHlMM+VBEIIcb0KWoGz6bMcnTzK8eRxrBYrnaFO7Nba/tiVr+Q5OHGQfeP7GM4NTx/32r2sbVzL+sb1NHmb6iLYWm1KKfJannQ5Ta6cw2qxEnVHcd9oFYMQQojXVdu/NReQ8UyJ46MZ9g2mGEsXsVksxINu2qvY57GaShWdZ4+M8uLJSRTgcdh4eG0zGzsiVb1I0Q1FfyKP3Wrh/lVNbO6MYq9itqYQQiwIpQyc+Tn074By3iyNDi+uwnTv80HKV2VSOryw5O5zQcpZ+CBWyUPiLLj8EO8xA5QN3ebEciGEqEO6oTOUG+JU8hTHpo4xWZrEZrER98ZreniOoQxOJU+xd3zvjDJvq8XK8shy1jeuZ2l4KTbr/Fd6zTVDGWTKGZKlJBW9gtfhJeKKsDG+kRZfC03eprrqOSqEEPWmNiNlC0SxonN6IsfR4QwnJ7JkChWCHgedDb6qlk9X29HhNN/aN0SqUAFgQ3uYh6s8QAegrBmcnsjSGvFwb09TVXtfCiHEgmAYMHYITj1vBvgCzdUpj1YGDO2F409D9lVByu5zQcrZyBZRBqQHzWBry3ozazPUVv3zCCHEHEmVUpxOnebw5GGGc8OU9BJhV5jOYGdNB/imilPsHdvLvvF9ZMqZ6ePN3mbWx9ezNra2poOs1aYbOmWjTEWvUDbKZMoZdEMn4AywLLyMrlAXrf5Wou6oDBASQog5IsHKWTCWKXJ0OMPBwRTj2RJWi4WY30VL0F3TpROZYoXv7h/mwGAKgIjXwS9uaGN5U/V70uRKGv1TeVa1BnlgdTMRnzTmFkKIaYYBqX7ofxmG95q9HBtXmn/eCKVgZD8ce9qctg2zn0kJZmZoqh/8TbDiLWaw0iaXIEKI+qMZGv2Zfo4njnMicYJkOYnH7iHujdd0WXBFr3B06ih7xvZwJn1m+rjb5mZd4zrWx9fT4muZvwXOEUMZZMtZ0uU0Ra2IxWLBZrHhsDrMm83BmoY1dIY6afO3EXJJ1r8QQswH+aQwC354eJQjI2kiXlfNZ1GC2Ydl59kE3z84TLFiYAHuWBrjzT1Ns9I/M5EvM54usWVJlDevbMLjrN1vnoUQYk6VczBxzMx6TJwxB+mE22+sHyWYQcqxw9D7fUgPmMfsbjNI2XXn7AUpi2lzQrnVCh3bzPPJdG8hRB3KV/KcSJ5g//h+hnPDGMog4o7U/NCckdwIe8b2cGD8AEW9OH28O9TNhvgGVkRX1Hw/zRulGzqJUoJ0KY1SCr/TT5u/ja5QF2FXGLfdjcfuwW1z47a7F/y/hxBC1APZiWdBWTcIuZ00B2v329XzJrIlvrFnkNMT5qS/1rCbt92yiLZwlYcpnDOSLpIva9y9spE3LI1Jf0ohhABID8PoITOLMjsONgcEmm48SAlm8PPo9yB51vzZ5oIld0LXXeCchTI/Q4f8BOQmzb6UzWugdaMMzhFC1KVUKUXvVC/7x/czXhjHbXfT4m/BZavusMlqKuklDk0cYvfoboZyQ9PHQ84QG+Ib2BDfsOAzBpVSZCtZJguT6EqnwdXApqZNLAososXXsuDfvxBC1DsJVt6kdEPx0+PjPHd0DM1QOGwW7u1p4vbuGDZr9b8dNpSifyqPw2bl4bUt3NIerulvoYUQYtYZhhlAHNoDowfNUmlPFGJLoRpZHVOnofcpmDxu/mx1QNcbofue6gRBX8vQzKBrOQO+Rlh2LzStgWAryH4vhKgzk4VJDkwc4PDkYZKlJEFnkK5QV832olRKMZQbYvfobg5NHKJslAFzWM7K6Epuid9CV6hrwfdcLFQKJEtJsuUsfqef5ZHlrIiuYHFwMR777CRjCCGEqD4JVt6E+qfyfGPPICNpsxRkadzPWze0EZ2lvpEV3eD0RI54wMX9q5tYGq9+D0whhKgbugaTJ2BwJ4wfA70E/mYItVfn9VMDZpBy7LD5s9UGHbfD0vvAHazOOV7N0M3+l6U0hBbB8gfMKd+zcS4hhJhlU8UpDo4fZP/EftLlNA3uhpou9S7pJQ5OHGTX6C5GciPTxxvcDdzSdAvrG9fjc/jmcYWzSylFXsuTKCYoVAq47W4avY3c1nobXaEuYp7YfC9RCCHEdZBg5U2kpOn88PAoL5ycRAFep423rG1hwyxmOeZLGn2JPMvifh5Y3Uy8DkrjhRBiVhRTMN4Lg7sg2Q8oM+uwWlmO2XE49pSZqQlgscKiLWbw0BOpzjlezdDNfpTFJARaYNl90LxudkrLhRBiliWKCQ5OHOTA+AESpQQxb6ymg5TD2WF2je7iwMQBKkYFAJvFRk9DDxvjG1kcXFyza79euqGT1/IUtSIFrUBZL4MFvHYvzd5mlkWW0eZvI+6N12wGrBBCiKsjwcqbRO9Ihm/tHSRZMC9mNrSHeXhtC37X7P1PYDJbYipf5tbFEd7c04RvFs8lhBA1SSlzCvbIIRjZB/kpc/p2uN0ccFMNhSQc/wH07wBlmMdaN8LyB8Efr845zlMGFBKQnzTfmy8Gq95qTvd2zUJpuRBCzKKKUZkx2TtVShH1RFkWWVaTgb6KXuHg5EF2jeya0YuywW32Y1zXuA6vY2F9YVTWyyRLSTKlDBaLBZ/Dh9fupT3QTswTI+QKEffGaXA31OR/MyGEENdHokcLXLak8b39Q+wbSAEQ9jp464Y2ljfNXim2UoqBRAEDxb09cbZ2NcggHSHEzaVSgInjZpbj1CnzZ28DNK4wMx6roZyDEz+EMz81+0UCxFfByrdAsK0653j1uTLDoFfMLM22jdC4EiJdEqQUQtSdRDHB6dRpDk4cZDQ/iqEMou4oSyO1mUk5nh9n1+gu9o3vo6SXALMXZU+0h01NmxZcFuX5vpO5Sg6H1UHUHWVN6xraA+00eBoIOAMLvvemEELc7CRYuUAppdjTn+R7+4cpVHQswO3dDdy7qgmXffbKIiq6wZmJHFG/kzevbGJVq/QsE0LcRDKjMH7EDFJmRsxBOf7m6gb0tBKc/jGcfA40s/cw0W4zSBldUr3zAJTzkB40A6yNK8yBOdEl4AlX9zxCCDHLClqBvnQfx5PHOZM6Q6qcwu/w0+pvxWmbnb7tN0I3dI5OHWXn6E7Ops9OH4+4Imxs2siG+IYF04vy1X0ni1oRl81Fo7eRzc2bWeRfRLOvGYfNMd/LFEIIMYckWLkATWZLfHPvICfHcwC0hNy87ZY2FkVmtywkW9IYSORZ2ujngTXNNEl/SiHEzcAwIHEahvfC6BGzN6UnDNGlYKvir1lDg74X4fgz5uRwMDMoVz5iZjlWM6umUoT0AKDMIOXi282AqFUyWYQQ9aOiVxjODXMqdYrjU8eZLE1ixUrUEyXujddkNmK6lGbX6C72jO0hW8kCYMHC8shyNjVvojvUXZPrvh65So6p4hRFrYjP4ZO+k0IIIaZJsHIB0Q3Fz46P86OjY2iGwm618OaVce5Y1ojNOrsXNWOZIqlCha1dDdy1olH6UwohFj6tDBPHzIE5kydAL4O/yRyaU80PksowMzV7nzJ7RYJZUr7iYWi9pXpl5WBmbaYGQOkQW2ZOEY8tMyeKCyFEHSjpJYayQwxkBjieOM5kcRLN0Ai5QnQGO2syAKaU4nT6NDtHdtI71YtCAeB3+LklfgsbmzYScoXmeZXVUdAKTBWmyGt5vHYvbb42lkeXs8i/iJgntmACsUIIIW6MRJQWiIFEnm/sGWQ4ZZYEdjf6eOuGNhr8rlk9r24o+hN5HFYrD61pYdPiyKwHRoUQYl6VczB2BAZegcRZM5AXaAFnlcvxlIKJXjjy3XNZjoArAMsegI5tZol5tWgls9xbr0BDN3TcZmZrVjMzVAghZolu6AxlhziROsGJxAkSpQS6oRNwBmq2zBugpJXYN76PnaM7mShMTB9fHFzM5ubNrIisqMng6vUo62WGskNYsNDqb2V5ZDkdwQ4aPY0SoBRCCHER+RRS54oVnWcPj/LSqUkU4HHYeMu6Fm5pD8/6L/5CWefsVI5FEQ9v7mmiu1GGLAghFrD8FIwcMDMpMyPg8ECkE+yz8KVQsg+OftfM3ATzHN1vhq47q3s+vQypQfPP6BIzCBrvAekNJoSoA8likjPpMxyaPMRIboSyXibkCrHIv6imexyO58d5ZeQV9o/vp2yUAXBanaxrXMetzbcS98bneYXVoxna9H+bzmAnm5s30xnqlAE5QgghLkuClXXs8FCKb+8bIl00p8BuaA/z8NoW/HNQgn2+7HtjR4S7V8QJeWv3glAIIW5IehiG9po9KfOT5jTs2PLZKY3OjZvl3kN7zJ+tNlh8Byy7D5xV/EJIr5iZlFrRnOjdsc2cJG6vzewjIYQ4L1fJMZAZ4FTqFCeTJ0mX03jsHuLeOG577fZLN5TBscQxXh5+mTPpM9PHY54Ym5s3sy62DtdsfPk1D84PzEmX0mQrWdr8bdzafCvLI8txWOUzgxBCiCuTYGUdShUqfGffEIeH0wBEfU5+cUMry+KBWT+3Zhj0TeZxO208tKaFjR1h7Db5ZlQIsQClBs0syuF9UEyDv9HMOpyNbJBSBo79APpeMHtUYoG2TbDiIbM/ZbUYGqSHzFL2yGKz3LtpjQQphRA17dUByjPpM6RKKaxYibgjLA0vreky4nwlz56xPewc2UmqnALMgTkroivMLMNgZ02v/2qcD05myhlyZXPAp8fhIewK84a2N7CqYRUeu2eeVymEEKKeSLCyjuiG4qVTkzx7ZJSyZmC1wJuWNXL3yjiOOQgYFis6ZydzLG7wcW9PEx0NsztdXAgh5kVqAAZ2wsh+KGUh0AyhRbNzLq0Ep7bDye2gl8xjjT3Q84g56bta9LJZul7OQqgdVr7FDFI6ajcLSQhxc8uWswxkBzidOj0jQBl2h+kKddV8GfFIboSXh1/m4MRBNGVWQXnsHjY2beTWplvremCObuhkK1mylSyFSgGLxYLHbgYn18TW0OxtptHbSNQdrfn/TkIIIWpT3QQrE4kEjz/+ON/+9rcBePTRR/nsZz9LOBy+quf/9m//Nl/4whf4m7/5Gz74wQ/O3kJnSf9Unm/tHWTo3ACdjqiXt97SRnNwbj5oposVhpNF1reHuW9VEwG3lHAIIRaQYgqmTsHoIZg8aWYeBlrMwN5sMHTofwmOPW1mVYJ5rp5Hzenb1VLOQWbYPF9oESx/EJrXglO+bBJC1J6yXuZU6hSnkmYGZbqUxmqxEnLX7iTvVzOUQe9ULzuGd9CX6Zs+3uxrZkvzFtbE1mCv5nC0OaQZGslSknQpjQULPoePZm8z7YF2Yp4YMU+MiDsiwUkhhBBVUTe/Ld/5zncyMDDA008/DcD73vc+HnvsMb7zne9c8bnf/OY32bFjB62trbO9zKorVnSeOTzCjlNTKMDtsPLQ6hY2dUawzlHJyES2RDJf5g1LG7hrRRynXS5ChBALgFYyA5TjR2G8F/IJc/q1rxHCHbNzTqXMIT1Hvwu5MfOYN2ZmOrZsgGrt6+WcmSFqc5jBz7ZNEFsh5d5CiJpU0AocTxxn3/g+c2K0xULYFaYrXPsZlHCh1PuVkVdIl802TVaLlZ5oD5ubN9MeaK/LUm9DGSSKCVKlFBYshFwhNjVtojPYSbOvGX81eykLIYQQr1IXwcojR47w9NNP89JLL7F161YAvvjFL3LbbbfR29vLihUrXve5g4ODfOADH+AHP/gBb3nLW+ZqyTdMKcW+gSRPHRghW5r7ATrnDSYKaMrgvlVNbO1qwGqtvwstIYSYITcBY0dgaLc5PAfMvpCxZbMzNOe8qdNw5NuQOG3+7PTBsgdg8e1QzUyb/CRkx6BtIyzabA7Qsdb+h30hxM0nW87Sm+hl39g+xvJjuO1u2gPtNT3J+9XG8mPsGN7BgfED06XeXruXTU2b2NS0iaArOM8rvD5Frch4YZyiViTijrApvonOUCdtgTbpPSmEEGJO1EWw8sUXXyQUCk0HKgG2bdtGKBTihRdeeN1gpWEYPPbYY/zRH/0Rq1evvqpzlUolSqXS9M/pdPrGFn8dxtJFvrVviNMTZoPqmN/Jo+vbWBqfu28vdUNxdjKHz2Xn4dWtrG6t3746QojrUwv7YdXoGiTOmH0ox45AIQHuEES7wDbL2YbZUTj6PfPcAFYHLLkLut9c/Z6RqX5z0vfyB6HrTWamqBDihiyovbAGFLUiAxmzF+WJ1AkShQQBV4DOUO2XeYOZbXg8cZwdwztmTPVu9jazpaV+S72VUiRKCaYKUzhtTtp8bayOraYr1CUZlAKQvVAIMbfq4jfpyMgI8Xj8ouPxeJyRkZHXfd5f/uVfYrfbefzxx6/6XE8++SRPPPHEda3zRpU1g+29Y/z0+DiGArvVwj0r49yxNDanE7fPD9JZFPFy/+omFjf45uzcQojaMZ/7YdWUsmaJ9+BuSJ4xp2H74hBfVb2S69dTTMPxp6HvpQsTvju2mYFEd5W/ADJ0s6TdFTD7XrZuqO7rC3ETWxB74TxQSlHUixQ185ar5OjP9nMicYJEMYFCEXaF6Y5010Wpd0krsXd8Ly8Pv0yilADMqd4royvZ0rKFjkBHXZZ6K6WYLE6SKCQIuUJsbt7MisgKWv2tdRE8FnNH9kIhxFya12Dlxz/+8StueK+88grAJX/5K6Ve96Jg165d/O3f/i27d+++pguHj3zkI3zoQx+a/jmdTtPePksDFs5RSnFwKM1TB4ZJFSoArGwO8Mi6VqK+ue0vNpUrM5Etsb49zD0r44S90t9MiJvVfOyHVZMZgZFDMLzHLIl2uCG4aG6mX2slOPmcOeVbL5vHmtbAykfMyeLVVspAsh8ii6HnF8w/hRBVU9d74Ryr6BVOp09zdPIo44VxynqZslGmolfQlIZSipArRHuwvW6yDxPFBC+PvMyesT2Uz+3pbpubW5puYXPzZsKu8Pwu8DoZymCiMEG6lCbsDnNn+52salhFxB2Z76WJGiV7oRDi1bREgsrgECiFZ+2aqr/+vF4lfOADH+Ad73jHZR/T2dnJ/v37GR0dvei+8fFxmpqaLvm8n/70p4yNjdHRcWFIgq7r/OEf/iGf+cxnOHPmzCWf53K5cLlcV/8mbtBYush39g9xctws+Q57HfzCulZ6Wua2x42hFANTebDAPSvj3NbdgGMOszmFELVnrvfDG2boZl/I4b0wdhiKGfBGIbZ8dntRvvr8r53wHe6Anl+Ehu7qnksrQW4cSmlw+qHtFjNj0xut7nmEEPW3F86DTDnDyeRJ9o/vZyQ3gsViTot22Bx4HV4cVgd2q71uMg+VUpxNn2XH8A56E73Tx2OeGFuat7CucR3O2W4hMguKWpFMOUOmnEEpRdQd5Z6Oe1gZXUnIJS2fxOXJXijEzU0ZBtrICJWhIUonTlAZHUVPJnG2ty+8YGUsFiMWi13xcbfddhupVIqXX36ZLVu2ALBjxw5SqRS33377JZ/z2GOPce+998449sADD/DYY4/xrne968YXf4NKFZ3njo7x85MT0yXfb1reyJuWNc75tO2yZnB6MktTwM29q5pY3hSY0/MLIcQNqRTMid6Du8xgpdLB1wShOfq2/3UnfD8CLeurW25eykB6CCxWM0uz641mMDbQMvtl7UII8RpFrcjOkZ0cnDzIVHEKn8PHosCiuhmQ81qaoXFo8hA7hncwkrvQaqo73M3Wlq10h7rrJuB6XqFSmM5yddlchFwhlseX0xpopSPQIf0ohRBCXJYqlymdPElh/37Kff2oYhGLx4MtFDI/kyg1K+eti/qLnp4eHnzwQd773vfyD//wDwC8733v45FHHpkxXGflypU8+eSTvO1tb6OhoYGGhoYZr+NwOGhubr7s9PDZZijFnr4kzxwaIXNuyndPc4C3zEPJN0CupNGfyLOiOcADq5uJ+eXbMiFEnchNwOihc1O9R8DuhGArOLxzt4aLJnz7z034vq26E76VgvQgVPKw6FZoXgeRTvM9CyHEPBjLj/Hj/h9zPHmciDtCd7g+ek9eSq6SY9foLnaO7CRbyQJgt9pZ37ieLc1baPQ2zvMKr41SilQpxWRhEofNQUegg2WRZTR5m4h5Yzis9RlMFkIIMXf0bI7SsWMU9u6lMjKMxWbHHo9j9V74rGUUi7N2/roIVgJ89atf5fHHH+f+++8H4NFHH+Vzn/vcjMf09vaSSqXmY3lXpW8qz3f3DzGQKADQ4HPyyLoWVjTPbcn3eZPZElO5Mlu7GrhnZRyPU5poCyFqnGGYgcGRA2agspAATxgals7t5OvsmJlJORcTvrWS+Z49UVj5MLTcAtb6DAgIIeqfUoojU0f46cBPSRQTdAY76zaTciw/xo7hHewf34+udAACzgCbmzezMb4R71x++VUFFb3CZHGSTDlD0BlkU9MmVkZX0hZoq9tAshBCiNmjNA1VqWAUixjpNHo6g5FJo42NUxkepjIxgc3nw9negcU5t0kSdROsjEajfOUrX7nsY9QV0k9fr0/lbEsXKvzg0Ah7+pMAuOxW7l4R5/buhjmd8n2eUoqBRAEDxX2rmti6pAGbtb5KWoQQN5n8lDntemS/+adeAV/j3Ez1frVSxuxJ2ffihQnf7VthxUPVn/ANZvZobswc0LP8gdkZ0COEEFepqBV5cfhFdo7sxGlzsiS8pO7KopVSnEieYMfwDk6lTk0fb/W1srVlK6saVtXVFOzzWZRTxSksWKb7ai6LLCPqlj7GQgghQFUqVEZGqAwOUh4YQBWKGKUiVDSUrqMqFVSpiNLNL+4sDidWnw9XdzcW2/z8TqybYGU9KmsGPz0+zk+Oj1PRzUDqpo4I969uIuCen2+gK7rB2ckcYa+T+1Y1zfkgHyGEuGrFtBmYHDti/llMgd0F/hZwznG2i1aEk9tnTviOrzanb89GALGcM8u+7W5Y/hB0vsF870IIMceUUkwWJ+lL93Fk6ghn02dp8jYRdNXXNWRFr7B/Yj87hncwUZgAwIKFFdEVbGvZRnugva4CrwWtwERhgpJWIugMsr5xPcsiy2gPtNfl8B8hhBDVpTSNcl8f5b5+SsePoU8lUJUyFrcHi91uBiFtNiwOBxaPB6s7jsVeOyHC2lnJAmIoxZHhNK+cmSJdNPtSdkS9PLKuhUWR+SsnmcqVGc8UWRr38+aeJlrDnnlbixBCvK70MAzthZF9Zpm3xQa+mBkUnOsyNkM3syiPPQ1ls4/ZrE34BnNYUGrAfJ9Na8zel9El1T+PEEJcQaqUoi/dx7HEMQayA+QreTx2T92VfWfLWV4ZeYVdo7vIa3kAnDYnt8RvYUvzFiLuyDyv8OoZyiBRTJAoJnDanLT52uiJ9dAZ7JRp3kIIcZNQSoFSWC7TFkrPZMhuf57i4UMo3cAaCGBvacHqqp/kBwlWVlmqUOGLPznNcMpsNBrxOnhwTQtrWoPz9m2tphv0TeVx2q3cszLO1iUNuB31U94ihLhJJPthcLdZ6l1Mg78RGpbBfJTjKQXD+6D3e5AbN4/N1oRvMPtSpvrN8zYuh47bzT6c0ptSCDFHynqZ0fwoI7kRzqTOMJofJVPO4LA5iLqjtPha6irzcCQ3wo7hHRycODjdjzLsCrOleQu3xG/BVSfZ6kopMuUMyVKSsl4m7A6zrWUbSyNLafNLL0ohhLhZKKWo9PdT2LcPbXwC762bcK9ceVEvycrQEJkf/ojy2TM42juweuozSU2ClVUWdNvxOG04bWZg8LbuBhzz0JfyvFShwnCqQFfMx90r4nTGfPO2FiGEmKYUlNJm5mQhAZMnzYE55ZyZQRlaNH9rmzxhTvhO9pk/z9aEbzAzN9NDUMmZwcnFb4DGFfMToBVC3FRKeonJwiTjhXFGciP0pftIlVJUjAouu4uQM0Sjt7GugmHn+1G+NPwSp1Onp48vCixiW8s2VkZX1sX70QyNdClNqpRCVzoBZ4DOYCfLI8vpCnXhd/rne4lCCCHmiDIMymfPUti7j9KJE6hKBYvLReo736V48CDeLVtwLlkCFgvFQ4fJPv88eiaNc0l3TZV1X6v6XXmNslgs/ML6FrIFne74/F5IjKSK5Csab1oW4/alMbxO+c8thJhH5TxMHDODgZlhM1hZzpvDcqxW8DebJdbzJT1kTvgeO2z+bHPCkruh+26zd2S15SYgOwrBVnPKd/M6sEufMSHE7CjpJcbyY4zlxxjMDDKcGyZbyVLWy1itVoLOIC3+lrrsd/h6/ShXNaxia8tWFgXm8Quwq6CUIlfJkS6nyVfyWC1WQq4QaxvXsji4mFZfK2F3eL6XKYQQYg7pqRTlgQGKR45SPnkSZeg4mpqx+swENKNcptTfT7m/H9fKldiCQfIvv4LF6cTZVX8D8F5LolezIOJ1op8fwDAPDKXom8zjdFh5ZF0r6xeF6v5/qEKIOqWUGZgcPQLDeyA7bgYmnX5weM3S6vn+YJyfgmPfh4GdgDL7RXbcZmZTumdhgEQ5C8kBcAdg+YPQvmV2ziOEuKlU9AolvYRmaGhKQzd0KkaFyeIkg5lB+jP9ZMoZKkYFh82B3+GnydeEy1Yf5dCXcr4f5c7RnRS0AgAum8vsR9myhbArPL8LvIJ8Jc9YfgzN0PA6vDS4G9jStIUmXxPNvma8jvnrdS+EEGLuaYkElYEBSidOUu7vx8ikwWbH0dSE1Tvzd4LV6cTV2YWRy1E8eBCl6Tiam7GFw/Oz+CqTYOUCoxkGp8dzNAZcPLCmme5GKRMRQsyT/JQ5mGbiuBmg80QgtrT6pdTXq5yDE8/CmZ+a5dgALRtgxcPgj1f/fHrZ7MsJsGgTdN5hZlUKIcQNSJfTHJs6xv6J/eQreZRS6ErHUAa60tEMDZvVVteZk681mhvlpeGXLu5H2XKuH2WNB2BzlRyjuVHsVjvLwstYHl1Ok7eJqDsqCQZCCHETqoyNUdizl2LvUYx0Bux27JEI9sbuyw7SAbD6fLiWLkMZxhUfW09q5BOjqIZSRef0ZI6umI+H17bQFJyFskUhhLhamREY2gPBtvkt734trQSnfwwnnwPNHIZGwzLo+YXZWacyzOzSYtrsS9n1Jogtl+E5QogbMlGY4OjUUQ5MHCBRTBBwBvA5fFgt1umbzWLDXitfEN2gy/WjvK3lNlZEV9R8P8psOctobhSnzUlPQw/rG9fTHmiv+XULIYSYHVoiQWHfPgr796On0zga49i749cVdFxIgUqQYOWCUSjrnJ3KsbYtxP2rmwl5HPO9JCGEAKzgDs33IkyGBn0vwvFnoJQxjwVbYeUvQOPK6k/4BnN4UHoYAk2w5j5o3QB1MoFWCFFbynqZycIkE4UJBrODnEieIFVKEfVE6Q53L9iA1/l+lC8NvcRkcRIw+1H2NPSwrWVbzfejBChqRYayQzhtTtbF17E2tpZF/kWSRSmEEDcpI58nv2cvhb170KemsMUacS1dJr8XXkWClQvA+UDlxo4ID65pxu2QKbJCCDFNGWaGZ+9TkDc/6OJtMMu9W28xe1RWW6UAqT6we6D7Hli8zSyDF0KIa5AupzmVPEV/pp/hrDkQp6SXsFlsRNwRlkUW7gebeu9HCeZU7+HcMJqusTS8lC3NW2gPts/3soQQQsyjyvAwmee2Uz51ElskinPpsgWXFVkNEqyscxKoFEKI16GUOdn76PcgM2QecwXMwTkd22and6ahQ2rALC9vWgVdd0JkcfXPI4RYsJRSjOZH6Z3q5fDUYRLFBE6bE7/DT9wbx7XAs7NHciPsGN4xox9lyBVia8tWbmm8pS7ef0U3Bxtly1kWBRaxuXkzS8NLF0xJvhBCiIsZpRLFgwepDA/jWr4cV1cXFseFildlGBQPHiT7k5+gpzPmxG6HVMS+HvmNWccKZZ0+CVQKIcTFJk+YQcrEub5mdreZ4dh15+yVYecmIDcKoXazL2XTWrDJr1khxJUVtSLpcppEMUHvVC+nUqcoaAUi7siCLvE+TynF8eRxdgzvuKgf5baWbayMrqz5fwPd0EmUEqSKKWxWGw3uBm5vvZ3VDatx26WPvBBCLFRK1ykdP07+5Zcp9/WBxUrxwAEc7e141q/HtXQpANmf/YzCrl1YPF6cS5Ys2MqIapFPUXVKMiqFEOISkv3Q+z0YP2r+bHVA1xuh+83g9M3OOSsFSPaZr7/sQejYamZwCiHEJeQreSYLk0wWJ5kqTjGSGyFVTlHUihS1IjaLjUZvI22Btvle6qyr6BX2je9jx/COGf0oVzWsYmvL1prvR6kbOqlSimQpiUIRcUXY0rKFzlAni/yLFsTkdSGEuNmochk9l8MWCl2xPLs8MED+5ZcpHTsGNjvOTjOb0iiVqIyMUj77beyNcaxeL+XTp7C3tGILyOeEqyHByjqUKVYYShYkUCmEEOdlhqH3+zCy3/zZYjVLvZc9MHsDfgwd0oNmsLJpNSy5C8LSi0wIcbGBzACD2UH6M/2MF8bJlrPoho7FYsFtd+Oxe4h5Yrhsrpsi0yJdSrNzdCe7RnfVXT9KzdDMAGUxCRYIu8JsiG+gK9RFe6Adj90z30sUQghxjZSmURkaonz2LMWjRzFyOWzhMM6uLhzNzdjjcWw+H1oigTYxgTYxQaWvH218DKNYxNHahtVzYf+3ulw429tRmoY2MYGeTErZ9zWSYGWdmciUSBbK3N7dwJ0r4hKoFELc3HITcOxpGNwFKMACbRth+YPga5y98xaSkB6CYAv0/AI0r5OSbyHE63q+/3nOpM/gdXjxO/y0+dtw2G6+DyxD2SF2DO/g0OQhDGUAZrBva8tWNsQ34LLVbj/Kil5hND9KSSsRcUe4telWFocW0+Zvw+vwzvfyhKgLqlymMjpqZqvZbFhsNrDbsbrdMwI9QtyIytgYxQMHqYyM4F65AueSJdgjFw+6PP+/x/LAAKUjR9HGx1GVCtZgEKvPj55MkfvZzwGw+n1YnS6MXA6jWASLBavXizUYwtH2+lUAFrsdR3PzrL3XhUw+WdUJpRQDiQKguH91M1s6o1itC/+bdyGEuKRCAo4/A/07zGnfYAYMVzwEgZbZO69ehsRZsDnMTMrON4AnPHvnE0IsCJqhEXFHiHvj872UOWcog2OJY7w09BJ9mb7p4+2Bdra1bGNFdEVN96Os6BVG8iOU9TKL/Iu4JX4LXaEuCVAKcR1Kp06Rfur7qEoFrFYsVgtYbVicTpydi3Eu7sTR1nrJwNJcUOUyRj6PLRyel/OLG1MZHaWwfz/Fw4cx0hksHg/l06ewBoO4upfiXrEcW0MD2ugo5f5+yqfPoCcSqEoZqz+AvbkZq/tCj2FbMAiYg3GMXA5VLmOLxbC73TdFFcR8k2BlHdANxemJLCGvg/tXNdPTEpzvJQkhxPwopuDED6HvBbMMG6BxJax4GMIds3depSA7CoUpiC03h/U0dM/e+YQQos6VtBJ7xvfw8vDLJEtJAKwWK6sbVrO1ZSut/tb5XeAVVPQKw7lhNEOjzd/GxqaNLAsvuykzYoWoGsPAKORxLu5EGQacuxnFIoUDByjs2Ys14MfR0oKzsxNbKIwtFMQWCGD1zVLv8XMqg4Nkf/Yz9GSKwP334erquuxjy319WH0+rH4/Vp8fm9+HxeMBXUfp+vSfFrsdq1e+3HgtpWlY7FcXjlK6jioWMYpFjEIBVSqhNB0M3fxT16iMjlI8fAQ9m8Xe2IhzWTMWi8UMNKbTFPbvo7h/HxavFyObAwvYAkHsLS1YXZfP6rdYrdJnch5IsLLGlTSdMxM5OqJeHljTzKKIbHRCiJtQKQMnfwRnfg5GxTzWsMzMpIwumd1zV/JmNqUnAqt+EdpuBYdMdhVCiEtJFBO8PPIye8b2UNbLAHjsHjY2bWRz02aCrtr+0l03dEbyIxS1Iu2Bdm6J3yJBSiGqyWLB4nDw6rw0q8+HvaHBDCxls5TP9lE6ftx8uNOF1ePG6vdjb2jA6vVh9bixuFxYnE6sXi/2WAxb6Pp6lBvlMvndu8nv2IGRy4PVSvp73yP4wAO4li276PGFQ4fIbt+ONjk1HWyzulxY3C4sdgcoA2UYKN0ApbDY7Ti7l+BasgRne/tNX+5uFArkdrxM6egRswWA12eWU/u8WN1uVKmMUSmjSmVUpYIqFTGyOVSljKpoqHIZpWuvekUL51tB2WMxHK0zvwizWK3YwmFs4bCZOVsqYY83XXFwjph/EqysYelzg3TWtIW4f1UTYa9MFBRC3GRK2XNByp9dCFJGuswgZWz57J8/M2Jmc7ZuhO67ICA9Z4QQ4rWUUvRl+tgxvIPeqV4UCoCYJ8bWlq2si62r+WCfoQzG8mNky1lafC1sat/EiugKmegtxByyWK3YgsEL5bdKmVl0xSJ6Ko02No7StHMtgM6FOy0WrH4f9lgM5+LFOOJxrMGgmYlXKGDkCxiFPErXsQUCZpZmIIgtGECbnCL3859ROn4cWySKq9usmin395N66vsE79dw9/SYa9E0ci/tIPfiC1jsDtwrV5rHDcMMoBWL5tqsVix2BxaHBaxWVLlMYfduCrv3YI9GcC1fjnPxYuyxGNZg8KYqJy739ZH9yU8pnzmNLRRGVTSMTBalaea/nWGcaw9ghXM3i81mBrddbqx+hzmgxm6/rn83i9OJzSl7er2QYGWNGk0XyZY07lga403LG2WQjhDi5lLOwsnnzCDlucwcQh1mkLJxJcz2hZ1egcRpcPrNbMpFm2WAjhBCvIZmaByaPMSO4R2M5Eamjy8JLWFbyza6w901/0G8oleYKEyQrWRp9DTyhs430NPQI1O9hagBFosFi9sNbjev92lY6TpGNos2Mkr51GmwWrA4nVCpmKXm00FNzLY+WMxMSI8bVa5gFAo4FndifVUQy9neTmVoiPT3n0ZVKri6u8n8+McU9uzB1hCb0VPTYrVOr/H12BsaUJqGnkiQe/Elci/tMAOs4TCOjg7sjY3Yo1Gs58rda33fvByl6+bgpFcxSiXyr+wk/8orGKUizs4umYotrkg+edUYQynOTuZx2a08vLaZW9ojMkhHCHHzKGXh1HY489NXBSnbzene8VWzH6QEc9J3ZsjM3Fz+wOz2whRCiDqULWfZObqTXaO7yFVyANitdtY3rmdL8xYavY3zvMIry1fyjOfH0ZVOk7eJO9ruYHlkOX6nf76XJoS4BhabDVsoNF0GrnQdVamY2Xi2i0OcyjBQpRJGsYjV57yobPg8R2srlZER0j94BkdzM6Uzp3Euar/u/pMWu90MSjY2ojQNI59Hm5yiPDAAhjLL2l0urF4P9oYGbA0N2CMRHIsWzdvAoWuhZ7PkX3mFUu8xLE4nFq8Xm9+H1edHGxmheOIE9lgM1+v8ewvxWhKsrCH5skb/VJ7mkJv7VzfT3SgXS0KIm0QpY2ZSnv35q4KUi84FKVfPTZBSL0OyHyxWWHIPLLkTnNInWAghzhvKDvHy8MscnDyIoQwAAs4Am5s3szG+seYnZBvKIFlMMlWcwmVz0RXqYk1sDV2hLin3FmKBsNhslwxSTt9vtWLxeK6qd6SjuRltYoLyQD+uriVVywa02O0Xl7ufKyU3CkVKJ06ijhwBQ2ENBnB2dOBaugxnR/v0c26UUgp9chJVKmFvabnuHo7KMCgdPUruxRcpDw1hD0cwymVIJqmcK++22Gy4OjvNjFchrpIEK2uAbigGkwUqmsH69jBvWt5IzH/5iVRCCLEgFFPngpQvXOhJGWo3MxrnKkhp6GYmZSlrDu3peiM0rpibcwshRI3TDZ3eRC87hnfQn+mfPt7mb2Nry1Z6oj3YrLXdrqikl5jIT5DX8kRcEba0bGFFZAWt/lasFhmyIIR4ffZYDHssNqvnsFgsWFwucLlmlLsrw8DIZCge7aV46BC2YBBnZyfOzk4cLS3YGhquqWTcKJXQhocpDw1RPnkKbXwcpeu4upfg2bABZ2fnJYOWStcxCoULgWC7HYvVijY+Tu7FFykePgwOJ67upZcNFAtxLSRYOc+mcmXGMkUWRTy8YWmMnuaglH0LIRa+/KQZpOx/yQwWglluvexBiPfMXaAwNw7ZMQi2wvKHoGUd2OXLIiGEyFfy7B7bzc6RnaTLaQCsFiurGlaxtXkrbYG2eV7h5SmlSJfTTBYmsVqstPhaWBNbw5LwEoLO2p5ILoQQcG7g0LkSd2UY6KkUhYOHyO/bh83nxx5vxNW9FEdLM/bGxkuWqBvlMpWBAUonT1I+cQI9lcLQNGxeH9ZQCIvFQrG3l9Lx4zi7u/GuX49j0SL0RAJtYoLK8AiVgX70XN7MSrVawGoGLI18HiOdxtHWdt3l8UK8HglWzhNNNzgzmcPrtHP3ijibu6L4XfKfQwixwGXH4OQPYWDnuUmOmNO9l90/N4NzzivnINkH7pBZat6+Bdzy4VUIIUZyI2ap98RBNKUB4LV72dS0iVubbyXgDMzzCi9PMzQmChNkShmCriDrG9ezIrqC9kA7dqtcawsh6pPFasUeiWCPRFBKYeRyaKNjlM+cwWKzYw34sTc341zUjr2xEYvDTvnMGYpHj5rl3oaBLRTGsaj9onJ215JujEKB0okTlE+cxBoIYOTzqFIJbFasPj9WtxuUMocWvaovqHPp0roeCCRql/zGngfFis6ZyRzdjX7uWRmnPSrfQgghFrjUAJz4IQzvA5R5LLbcDFJGu+cuSGnokOoDXYPWjWZfymDL3JxbCCFq1OuVerf4WtjSsoXVDatrPtCXq+SmB+bEPXG2NG9haXgpDZ6G+V6aEEJUlcViweb3Y/ObMy5UpYKezVI+dZrS0V4zeGi3YZTL2IIhHG2Lrtgv0urx4OpaglEsYuTz2ONxM0ApxDyp7auOBShVqDCSLrChPcx9q5oIuKvTpFcIIWpSZhhO/xjGj1w4Fl8Ny+6DSOfcriU3AdlRiCyGrjuhaQ1cZzNxIYRYCHKVHLtHd7NzdCeZcgYwS717oj1sbt5Me6C9pjNmDGWQKCZIFBO47W6WhJawOraarlAXLpu09BBC3BwsDoc5Mfzc1HClaShdx+q69n3Q6nZLkFLUBAlWzqHRdJFcSeNNyxp547JGnHb5kCyEWMB2/xMc+da5HyzQegssvdfsDzmXKkVIngWnzyz57tgGLv/crkEIIWrIQGaAV0Ze4fDkYXRl9g32OXxsjG+si1Lvil5hLD9GQSsQdofZ1rqNFZEVtPhaajq4KoQQc8Fit2OxS6hH1Df5X/AcMJTi7GQel93KQ2tb2NgRlgspIcTCt+QueOWLsGgLdN8DvtmdpHgRZUB6CMpZiK8y1xBun9s1CCFEjajoFQ5PHuaVkVcYyg1NH2/zt7G5eTOrGlbVfKl3USsymh9FN3Ra/a2sja2lO9xd88FVIYQQQlyb2r4iWQBKms6ZiRzNITf3rWpmaVyyeYQQN4lwB2x4DFrXz/25SxlI9YMvbmZTtt4CNvmVJ4S4+SSKCXaO7mTv2F4KWgEAm8XGmtgabm2+lTZ/bU/1BsiWs4zlx7BarHQGO1nXuI4loSU4bNJOSQghhFiIbuiTW7lcZmxsDMMwZhzv6Oi4oUUtFKlChZFUgZ6WIPetaqLBL71zhBBza973afsc73uGZk75Vgo6bjOzO73RuV2DEKLmzPteOMcMZXAieYKdIzs5kTwxfTzkCrGpaRMb4xvxOmp7wKNSilQ5xUR+Ao/dw6qGVayJraEj0IHNapvv5QlRt262/VAIUZ+uK1h5/Phx3v3ud/PCCy/MOK6UwmKxoOt6VRZXzyZzZYqazhuXxbhjWSNuh1xUCSHmzk25T+cmIDcKkSVmkDLeM3dTxoUQNelm2wtzlRx7xvawe3Q3yVJy+viS0BI2N29mWWQZVktt90w3lMFUcYqpwhQhV4jNTZtZHVtNs69Z2igJcQNutv1QCFHfritY+Zu/+ZvY7Xa++93v0tIyN42sE4kEjz/+ON/+9rcBePTRR/nsZz9LOBy+7POOHDnChz/8YX784x9jGAarV6/mX//1X2f1myMrFpoCLu7pibO2LSQXVkKIOTcf+/S80YqQODdAZ9lDsHib+XchxE3vZtgLlVL0Z/rZObqTI5NHpgfmuG1u1sfXc2vTrTR4GuZ5lVcnWUwyXhgn4o5wZ/udrIyuJOaZ437HQixQN8N+KIRYOK4rWLl371527drFypUrq72e1/XOd76TgYEBnn76aQDe97738dhjj/Gd73zndZ9z8uRJ7rjjDt7znvfwxBNPEAqFOHLkCG63e1bXelt3Az6XndawZ1bPI4QQr2c+9uk5Nz1AJ2dmUXbfbfbJFEKIcxbyXljUihyYOMCukV2MFcamj7f6Wrm1+VZWN6yum56OJb3EYGYQt93NHa13sKFpA0FncL6XJcSCspD3QyHEwnNdwcpVq1YxMTFR7bW8riNHjvD000/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ul1Nra+uoZaLRqKLR6D4/CwDGw08m5XZ0yNu9W7mdO5V79TV53Qn5Pb2SjKxwRHZ5ueyqatkzYszCAA4S+n8AmBxuzuubfZFTqienno60eruyymU8uVlPkpEdshWOOopW9AUYNgEGAADAdFUSd8IOP/xwveMd79CaNWv07W9/W5J06aWX6qyzztJhhx2WL7d48WKtXbtW55xzjlzX1Xve8x49+eST+u///m95npfff6O+vl6RSEQvvfSS7r33Xp155plqbGzU888/r2uvvVZLly7VCSecUJC2AphevJ4eeV1d8jo75e7uUO6N1+V1dsrv6ZXJ5STbll1RIbuiUqEZTbIcp9BVBgAA2Cs356l7d1qJ3Wn5ri/fGMmXfN/IGEnGBK99yXN9pbr7AoycL0kKhYMAo6wqrFCEGRgAAAAYqiRCDUm69957deWVV+r000+XJJ199tm64447hpTZsmWL4vG4JOm1117TL37xC0nSW9/61iHl1q9fr1WrVikSiegPf/iDvvrVr6qnp0ezZ8/WO9/5Tn3uc5+Tw41DAGPktrcru+1VZbb8TX4yKau8XHZZuezyMtnlFbKjEVmRvkc4LL+nJ1hG6vXX5SXi8pNJmZwr2ZbsWJms8nKFZs6UHYkUumkAAABj4uV8JXan1LUzpY43epTqzsn3fdn9MyosS/lowlLw2rJk2VI44qiiJiInbBNgAAAAYJ9KJtSor6/XPffcs9cyxpj863nz5g15P5LZs2dr48aNE1I/ANOL19Wl7LZtSr/wonKvviq/t0dWrExWNColumVcN9jrwvcky5KMgn+99/+1ZFnBElLl5QrX1skiwAAAACUmH2TsSqnj9R6lurMyvlG0IqzqxpickF3oKgIAAGAKKplQAwAKzevpUW7bNmX+/ndlXtkqLxGXHY7Iqa9XqLWVbxYCAIApzc15SnXnlOrOqrcro47tvX1BhhQtD6m6sYwgAwAAAJOOUAMA9uBns/LjcXnd3fITCbnxuNydO+Xu3Cmvq0tyHIXq6hV600JZNv9wBwAAU4/vG6V7ggAj1Z1VYndavV0ZZdOePNeTZVmKlBFkAAAA4OAj1AAwrZlcTrm2tiC06OiQu3OX/HiX/HRGfjotGROs+xyNyS4vV2T+AjbrBgAARSuTzMkJ2wqF92+8kk27SnVnlUzk1NOZVvfutLIpV9mMJ0uSE7YViTmqrIsqFCbEAAAAQOEQagCYdvzeXuW2b1f21VeV+fvf5XV0yuRykm3LLiuTFYvJqatTKBZjJgYAACh6xjeKt6e0c2tCnW1J2XYwi6K8KqKyqrAiZSFFYqHgucyRbVtK9eSUTGSVTGSVaE8plcgpm3bl+yY4P+YoWhFSZX2UJTYBAABQVAg1AEwLbmencq+/oezWV5R9Zau8eFwyRnZVlUKtrbKj0UJXEQAAYL/4nq/OHUnteDkIM3zPV3lVRMYYpbqz6ulIy/OMLEmypFDEUShsy7It5dKecllPkhSO2opEQ6qeEZPj8IUOAAAAFDdCDQBTlp9OK/PSS0o//7xyb7whv7tHCjlyamoVmTtXVoi/AgEAwMFhjJGb8+VmPbnZgedc1pOb9ZRJugqFbZVVR1RWGVasMqJIzBlxloSX89WxvVdt/4grvislyaiiNqpIbPSxje8beX2/3/hGZVVhVUWYhQEAAIDSwx09AFOO29mpzAsvKv3M08rt2CkrFJJTX69QUzPLSQEAgAnlub66d6fl5nx5bvDwXV9uzlcuE8yGcDOecllffs6X5/nyXCPf8wc+xEi2Y8n4km+C5Z/CMUfRspCq6mMqr4mqrDJYRirRnlLbS3ElOtJyQpaqGqJj2j/Dti3ZUUfhKHuDAQAAoLQRagCYEozvK/fGG0o//7zSf9siPx6XXVUVzMgIhwtdPQAAMMX4nq+O7Um98WKnEu0pGV+SJcn0PSsIEizHCgIFx5Lj2ApFQ3JCtmzHGnWWhOf5cjOeMslg0+7+zw5HHWXTnsJRR7VNZXJCfFkDAAAA0w+hBoCSZYyRyWSUfeUVpZ59VtlXXpHJZOXU1yuycCGzMgAAwIQzvlHXzqTe+HuXOrcnZdlSdePEBgyOY8sptxUtH/hihu8buVlPFbVR2TZLRgEAAGD6ItQAUJT8TEZuW5u87h6ZbFYmm5GfzshPJmVSSfnJlPxUSiaXCzb9dmyFZzTJrqgodNUBAMAUlWhP6Y2/d2n3670yvlFVfVShyMFZzsm2rb3umQEAAABMF4yKARQF4/vyOjqU296m7KuvKrf1FXnxhIznSrIkYyTLCjb3DoVkOU7w2nEUmTOHJaYAAMCk6e3KaPtLXdq1rUduzlNl3d435QYAAAAweRiJAygYP5VSbnubctvfUPYfL8ttb5ff2ys5jpzqaoUPOURWJFLoagIAgGkqmchqx8tx7XgloVw6WPqpujxW6GoBAAAA0xqhBoCDymSzyr76qjJ//7syf39JfiIu4/myy8tl19Qo1No66qaZAAAAB0OqJ6sdLye045WEMr05lddEVFVPmAEAAAAUA0INAJPO+L7cHTuUefllZZ7/q9z2XTK+L6e2TuHZLB0FAACKQ7o3F4QZLyeU7s2qrDqi+pkVfOECAAAAKCKEGgAmjdfVpewrryj9178p9/rr8tMp2VXVCh8yi2WlAABA0Uj35rRza0I7/pFQqiersirCDAAAAKBYEWoAmBDGGPk9PfI6OuR2dCj3xnZlXgqWl7KiMYUaGhSuqCh0NQFMEj+dlsnlCl0NANgv6Z6cdr3arbZ/xJXqzipWGSbMAAAAAIocoQaAcfF7e+V2dgYhxq525V5/TW5XXCbZK+P5km3LqatT5E0LZdl2oasLYJxMLievu1t+d3fwnEjk3+ePdXfLZLMqW3a0qk49rdBVBoARGd8o1ZNTMpFVb1dG8V1JpbpzyiRzQZjRWiHLJswAAAAAih2hBoARmVxOfm+v/GQyeO577XZ1ydvdIS/eJb83GXwz27Zkl1cEm33X17NHBlACjDEyyaS8REJeIjEQVgwKLbxEQiaVGvtnpjOTWGMA2D+e5ysZzyqZyKqnK63EzpQySVe5jCcjKRx1FClzVFFDmAEAAACUEkINYJrxMxmZVCp4TqeDJWPSafnpTBBgdCfkxePye3rkZ7MymUzfkjLBP/atUEhWJCK7vFzhmbXsjQEUIeN5+VDC7+6WF48PvB4UYMjzxvaBoZCcqirZ1dXBc1XVsPd2VZVy27ZObsMAYB88z1dPR1rxnSntfr1HqZ6c3Jwny7IUiYUUrQipsj7K8lIAAABACSPUAKYgP53uWxamR35Pt/yeHrm7dsnd3SE/lZJxczLZnOTmZHwzcKJl5UMLKxKRXVklu75BCof5xz9QJIzrDsyo6A8rBs+2SCTk9/RIxuz7wyxLdkVFEFJUV4/8XFUlq6yMvwMAFC3f89XdkVF8Z1K73+hRMpGV5/qKloVUXhNROOIUuooAAAAAJhChBlCijOsGG3MP+ja2u3u3vPbd8nq6ZTIZ+em01Hdf0wqHZUWjsqJR2bFYEF6Ew7Ic/qEPFIvBMyy8eFx+X2iRf98fWIyF4wzMpqiuzs+qcGpqBkKLykpZIYYCxcYYo1R3TqGwrXDUYVkcYAS5jKdkIqv4rqR2v96rZCKTDzIq66IKhRnfAAAAAFMVdzKAAjPZbLAElOdJnhdssm384LXvS74fHPNceT098uIJuTt3yuvokJ9OyaQzMp4rqW+WRTQqKxaTU1ehUDRKaAEUifweFvF48OjqCgLJ/uAiHpff3T22GRah0EBQMTi0qKnJv7bLy2XZ9uQ3DBMumcjqrw9vl+f6CoVsxSrDKqsKK1oeViQWUjjmBMvolIfkhLjGmLp8z1c25SmTygXPyZyS3Vklu7LKZjzlMq4811ckRpABAAAATCeEGsAkMb4fbK7d3R3sVZFKy6RT8tJp+d09wQa8vT3y0xkZ1+1b294EQYYxkm/6nn0ZYySZYNaFZcmKxWTHYrKrqmU3RtmYGygCJpfLhxNeV9fATItBD7nuvj+of4ZFTc2QkKL/tVNTI6u8nOWgpjDfM8okXUXKHHmer0R7Sp1tvfJ9I0uWLFtywo6iZSHNmFOl+pkVqqiJFrraKGLZtKtc2lO0IjRpN/5zGU89nWn1dmXk5nw5YVuOY8l2bDkhW7ZjyXas/GvfN/JyvjzXyHP94JHz5WY95bKesilP6d6c3KwnN+vL+EZGkuNYCkVshSKOouUxhcIEewAAAMB0Q6gBHCCTzQ5ZAspLJPr2r9gtP5mSSadkcoNvZBpZoXAQRPQtAWVXVATfqLZtybKC4GLQa9k2NzCBAjLGyKRSA4FFX2gxOLgY67JQ+aWf+kOLvkd/cJH/+wDTXrRs5JkY/TeDM8mcXn5ql15/oVO1zeWaMbtKtU3lcg7gJq8xRrmMp1zaUybpBjfDM56csK1IzFE4GlI46igScxQ6yPsUZNOu0j05hSKOYhUh2c7+tdMYo2zKVaonJ981wQyYyvCEL+/l+0bJeEaJ3Wkl2lPycr5kJN8YGV+SjHw/+KJC/3cWLEeybUuWbQ08O8Fr27EUigSzc8JRJ3jE+p4jQ5cn8z1f6V5XmWRO6Z6ckj1Z9XRklOl15eY8RcpCqmspV82MclU1xBQtG/8/BTzXV29XRr1dGXXtSqlnd1rpVLBXl2VZMiYYwmjw5DNLsi1LlmPJGCPfM/nJaf1FLUv5ACTUF95V1Nj7fb0BAAAATF0lE2p0dnbqyiuv1C9+8QtJ0tlnn63bb79dtbW1o57zgQ98QHffffeQY8uXL9ejjz6af5/JZHTdddfpRz/6kVKplFavXq1vfOMbmjVr1qS0o1SZXC6/ifR042cywUyL3l75vX3PyV55nZ3yurqCmRjpjEw63TejQsFG27GY7LIyWXV1wd4VhBJA0TLGBHvUDAot/P7gou+9yWb3+TlWOCy7tnZgVkXf68GzLqbj36OYWLZtye67uV1ZJ2WSrtpf7dGubd2qrI2qaW616lorVF4dGXau8Y1c189/+93NesqmPWVTrnrjGaW6c8qlPbk5T57rD0wSlGRkZNu2QuHgm/ehqKNYRbA0Vv8N9/5v0Odfj3NWgO8bpXtySnVn1RvPKNGeVjKeVS7rynGC311RE1FlXUyxirCiFSHFKoJ69LczkwpCkFRPTsl4Rt2708qkXOUyroyRwhFHkbKQqhtiqqiLqawqrPKqiCLjuNHv5jz1dGSU2J1Sxxu9SiaycrNBGGTbfV9WUN8fpPpu9svKv5cZuLlv+l+b4LWM8iGI+k5zQnYwEyJkK1YRVqw8pGzGUzKezc90MMbItq389YiURZRNu3rjxS69/kKXouUhVTeWqb6lQlUNQftHGqt4OV9uzpOb8+VmgzAtsTut+I6k0klXXs6XE7YULQupZkaZnL2ED8YEgY7xB8ILxkcAAAAA9lfJ3Fm56KKL9Nprr+m3v/2tJOnSSy/VxRdfrF/+8pd7Pe8d73iHvve97+XfRyJD/4F/1VVX6Ze//KXuu+8+NTQ06Nprr9VZZ52lJ554Qs403YvA+H5wI6+jQ+7u3XLf2K5c23bJdhRdsEDhWYco3NIiZy+BUqEYYyTXDfan8P2RCgx562ezMqlUXygx8Ox198jv6ZYXT8hPpWSyWZls3zJRfaxwJAgvolE5dXXBXhbT9L8ZoNgZ3w9Ci/5ZFns+xrg0lF1REQQU/cHF4Ne1tbLKyrhBh4MuWh7sr+G5vpKJrP6+aaeifwupvqVCFbVRZTPBXgTZlKds2pXvGfmuL88z8j0/3zU6oeCb8aGIrWh5RE54+CxB3/Pl9i0ZlE25SnVn1f6qnw8+ZKlvuaFgmaFQ2FakLAgcwjFn6GwExxo2OyGbctXTlVF3eyqYKZLxJEnhiK1wLKSqypg811cu46mzLan2V7tljCUnbCsccfLhRjKe6QswvGAmhK2+WQ4hlVcHN+9zfYFO28tx+S/FZduWwjFHZZVh1TSWKVIezs+UGProDyqkVHdW8Z0pde7oVbonJ+NL4Zij8uqIwtHJGRP4/tDlmro70ura6Qf1jwZ/BhV10aCOewhHHVXUROX7wRJnHa/3aNe2hCLR4JzapnJ5rq9s2lU25SqbCsIt3wuuue8FoYRlBf/dVdZF9iu4sixLjmNJDJcAAAAAHICSCDX++te/6re//a0effRRLV++XJL03e9+VytWrNCWLVt02GGHjXpuNBpVS0vLiD+Lx+O688479YMf/ECnnnqqJOmee+7R7Nmz9fvf/15vf/vbJ74xRcjr6ZXX2SFv927lduxU7vXX5SXi8pMpyZj88kgm56r3L3+R/mxkV1Uq3Nys6IIFCrW2KtzUJCsy/BuhE8UfvLluPCG/OxEEEtmsTCYjk8vJZHMyuWywB0X/vhQjGXTcuJ6MG5w78F1USY4TbLodDsuKRII17CMR9q4AilR+pkV/SNE3k2rwI9i3Zi8sa2DvikFBxeAAYzL/ngMOlBOyVVUfU6UJZins3NYt80oi+Ga/Y8nu28vACdkKR0P5vQ32J4izHVuRfXwT33P7QxNf2UywL0JnW1K+5w/MWNDAUkNDZ4MEKzGGYyFFK0KqrI8Oq58TsvMzMvq5fXsxpLqz6ulMB8s1lYVUUTvyzX1JisRCfZ8T7Efieb5yaU+98aziu1IDlRzEsiXLDv7MLMuSm/VkWZYi5Y6qG8sOysbttm3JjgRLTx3IZ5T1Lb9ljFE27amnI62utt7g78L+vTD6gpxwLKRYf6DjENwCAAAAKKySCDUeeeQR1dTU5AMNSTruuONUU1Ojhx9+eK+hxoYNG9TU1KTa2lqtXLlSN998s5qamiRJTzzxhHK5nE4//fR8+ZkzZ2rJkiV6+OGHRw01MpmMMplM/n0ikTjQJh40fiYTzMDo6JDb3q7ca6/J6+yU39Mr47mS7cguL5ddWaVQU/PwmQdNTcE3nru7lX31NWVe/LusSFhObZ0i8+fJqagINnJ03fyMCeMNfu0FG1xXVMoui8mKRGVFIrKjA7MeZNnyu4PNdt323XLb2uR1dwczJjKZ4K6H7QzsQWHbQT1te+CYExrTmvR2RUgKh4MAgzXsgaJljJFJpwfCis7O/Gu3P7TI5fb+IZY1MMtipEd1NbOtsFel0v9blqVYeVix8oMfxFuWpVDYkg7y5s2hcDArJFYx/jY7ji2nYvTP6F8WyvgmeO1LTnj00KRUWFawdNSB7K8BAAAAAAdTSfzrpa2tLR9EDNbU1KS2trZRzzvjjDN03nnnae7cuXr55Zf12c9+Vm9729v0xBNPKBqNqq2tTZFIRHV1dUPOa25u3uvnrl27VjfddNP4GzRGfjYrv7e374a9I8vpu3nvOMNuvBnPC2YruO6gWQs5yc0FQUZnp7Kvvy5vxw55vckgHLAt2bEy2RUVCs+qG/MsBMu2899alvr2nIjHlXz88UFLPln5Ta8tyxqyAbY8Lwg9fH/IDpJB20KyLEt+/00j25ZdVia7rEyh6uog+GBpF2BKMrlcMBurL7Bw+4OLvvDCpNN7/wDLCjbhrqsbCCoGvya0wAE6WP0/ilMwO0NSiYcYAAAAAFDqChpq3Hjjjfu8OfDYY49J0og3so0xe73BfcEFF+RfL1myRMccc4zmzp2rX/3qVzr33HNHPW9fn3v99dfrmmuuyb9PJBKaPXv2XtsxHr0PPaTUM88OhAOOPSQgsEIhWaGw/L7won8mhDw/mB3h+UGAYIKNPq1YTHZ5uUJNTbJjsQmrpx2Nym5qUmiE4Gl/mEFhRygaZeYEMMXkl4gaFFYMDi78MXzr3a6sHB5Y1NUFDzbhxiQ7WP0/AAAAAAAYXUHv/lxxxRW68MIL91pm3rx5evrpp7Vjx45hP9u1a5eam5vH/PtaW1s1d+5cvfjii5KklpYWZbNZdXZ2DpmtsXPnTh1//PGjfk40GlU0Gh3z7x0vP5ORn0wq1Nwc7APRv1eEbyTfk5/JBsf7ll+yQuFgOafBszkcp2TCAWuEGSgASotx3WB5qL5l7gYHGF5nZzCDbC+sSGQgpBgUWIT63rOnBQrpYPX/AAAAAABgdAUNNRobG9XY2LjPcitWrFA8Htdf/vIXHXvssZKkP//5z4rH43sNH/a0e/duvfrqq2ptbZUkLVu2TOFwWOvWrdP5558vSdq+fbueffZZ3XLLLeNo0cSzQiE5VVWFrgYA5PmpVBBadHbK6+gYeN0/28KY0U/u24w7VF8/EF4MCi6s8nKWmAMAAAAAAMCoSmKdjsMPP1zveMc7tGbNGn3729+WJF166aU666yzhmwSvnjxYq1du1bnnHOOenp6dOONN+rd7363Wltb9corr+hTn/qUGhsbdc4550iSampqdMkll+jaa69VQ0OD6uvrdd111+nII4/UqaeeWpC2AkCh5ZeJ6p9t0f/o7JTb0SGTSu31/CGzLerrg1kW/SFGTQ1LRAEAAAAAAGDcSubO0r333qsrr7xSp59+uiTp7LPP1h133DGkzJYtWxSPxyVJjuPomWee0fe//311dXWptbVVp5xyin784x+ratDMh1tvvVWhUEjnn3++UqmUVq9erbvuuksOyyABmMKM78tPJIaEFoNf72uZKLuyciC06A8s+p7tigpmWwAAAAAAAGBSlEyoUV9fr3vuuWevZcygJU/Kysr0u9/9bp+fG4vFdPvtt+v2228/4DoCQDExvi8vHg+Cit2786GFu3u3vM5OyfNGP9myBva06J9t0dCQn4Fhs68AAAAAAAAACqBkQg0AwHD54GL37oHAYtAm3XsNLhwn2Mti0EyLUH9wUVvLMlEAAAAAAAAoOtyxAoAil18qavfugRkX/c/7Ci5CoYE9LfpDi77XTk2NLNs+eA0BAAAAAAAADtABhRrZbFY7d+6U7/tDjs+ZM+eAKgUA001+c+7duwfCi0GzLuS6o588OLhoaAhmXvQ929XVBBfAHhi/AAAAAABQusYVarz44ov64Ac/qIcffnjIcWOMLMuSt7dvDQPANOanUkNDi0HPJpsd/cTBS0UNDi4aGggugDFi/AIAAAAAQOkbV6jxgQ98QKFQSP/93/+t1tZWWZY10fUCgJJlcrmBJaJ275bX3i6377VJJkc/sX9z7r6wIr9cVENDsFSU4xy8RgBTEOMXAAAAAABK37hCjc2bN+uJJ57Q4sWLJ7o+AFASBm/Q7ba35wMMd/du+fG4ZMyo59pVVfmwYshzXR2bcwOTiPELAAAAAAClb1x3z4444gi1t7dPdF0AoOj4yeTAMlF9My689vZ97nNhxWJDA4vGxvzsCzsaPYgtANCP8QsAAAAAAKVvzKFGIpHIv/7yl7+sj3/84/riF7+oI488UuFweEjZ6urqiashAEwy47ryOjuD0GLwrIv29r0vF+U4wd4W/YFFQ4NCfa+t8nKWtgGKAOMXAAAAAACmljGHGrW1tUNu0BljtHr16iFl2GgTQLEyxsjv6RmYcdEfXrS3y+vqknx/1HPt6uohsy1CjY3BPhe1tWzQDRQ5xi8AAAAAAEwtYw411q9fP5n1AIAJMWST7l27hmzUbdLpUc+zIpGB0KI/wGhsZLkooMQxfgEAAAAAYGoZc6ixcuXKyawHAIyZMUZ+b28QVvQFF257u7z+WRejbdJtWXJqa4csE+XMmKFQQ4PsqiqWiwKmIMYvAAAAAABMLePaKPx73/ueKisrdd555w05/pOf/ETJZFLvf//7J6RyAKY347ryOjqC0GLXriFLRu111kU0GsyyGLxcVGOjQvX1svZYQx/A9MH4BQAAAACA0jeuUONLX/qSvvWtbw073tTUpEsvvZSbAgD2i59MBvtc7BFceJ2do+910T/rom+ZqFBDg0IzZshpaJBdWcmsCwDDMH4BAAAAAKD0jSvU2Lp1q+bPnz/s+Ny5c7Vt27YDrhSAqcd4nryurvwyUe6gh0kmRz0vv9fFoAezLgCMB+MXAAAAAABK37hCjaamJj399NOaN2/ekONPPfWUGhoaJqJeAEqUn07nZ1rkZ160t8vt6JA8b9Tz7Jqa4cFFYyN7XQCYMIxfAAAAAAAofeMKNS688EJdeeWVqqqq0sknnyxJ2rhxoz760Y/qwgsvnNAKAig+xhj5iUQ+uBg888Lv7h79xHB4YI+LvuWi+l/bkcjBawCAaYnxCwAAAAAApW9cocYXvvAFbd26VatXr1YoFHyE7/t63/vep5tvvnlCKwigcEwuJ7ejIwgt+mZd9O95YbLZUc+zKyuHLhnVF17Y1dWybPsgtgAABjB+AQAAAACg9I0r1IhEIvrxj3+sL3zhC9q8ebPKysp05JFHau7cuRNdPwCTzBgjv7c3WDJq8HJR7e3yurokY0Y+0bbl1NcPCS6cvg277bKyg9oGABgLxi8AAAAAAJS+cYUan//853Xddddp0aJFWrRoUf54KpXS//7f/1s33HDDhFUQwMQwnievs3NgtsXgjbpTqVHPs2KxoftczJihUEODnPp6WY5zEFsAAAeG8QsAAAAAAKXPMma0r2GPznEcbd++XU1NTUOO7969W01NTfL2shnwVJRIJFRTU6N4PK7q6uoJ+9z4r36l9LPPKbLHhqbA3vip1IjBhdfRIfn+yCdZlpza2mCfiz1mXtgVFWzUDWBMMn9/UZUrV6nyxBMm7DMnso+d6PHLZPX/3R1pPb3+NVXVR+WEWLIPAFDcsmlXmaSrt66eo1hleEI+c7L6WAAAMDWMa6aGMWbEm5xPPfWU6uvrD7hSAPbO+L68rq4hoUX/Xhd+T8+o51nh8JC9LvKvGxpkhSfmHyAAUKwYvwAAAAAAUPr2K9Soq6uTZVmyLEuHHnrokBsDnuepp6dHl1122YRXEpiu/ExmeHDR3i63o0Ny3VHPs6ur82GFM3ij7qoqNuoGMO0wfgEAAAAAYOrYr1DjtttukzFGH/zgB3XTTTeppqYm/7NIJKJ58+ZpxYoVE15JYCozvi8vHh+6VNTu3XLb2+V3d49+YigUhBaDl4zqm31hR6MHrwEAUOQYvwAAAAAAMHXsV6jx/ve/X5I0f/58HX/88QofxOVqOjs7deWVV+oXv/iFJOnss8/W7bffrtra2lHPGW0fgFtuuUUf+9jHJEmrVq3Sxo0bh/z8ggsu0H333TcxFQf6+Ol0PqwYEl7s3r33WReVlcFsi/7wYsYMOQ0NcmprmXUBAGNQyPELAAAAAACYWGMONRKJRH6DrqVLlyqVSimVSo1YdjI28rrooov02muv6be//a0k6dJLL9XFF1+sX/7yl6Oes3379iHvf/Ob3+iSSy7Ru9/97iHH16xZo89//vP592VlZRNYc0wnxvPkdXXJ3b17YOZF3+u97XUhxxk266I/yLD57xEAxq3Q4xcAAAAAADCxxhxq1NXVafv27WpqalJtbe2IsyD6N+D0PG9CK/nXv/5Vv/3tb/Xoo49q+fLlkqTvfve7WrFihbZs2aLDDjtsxPNaWlqGvP+v//ovnXLKKVqwYMGQ4+Xl5cPKAqMxxsjv7c3PuuifbeG2t8vr7JT28t9/ftbFoJkXTmMjsy4AYJIUcvwCAAAAAAAm3phDjT/+8Y+qr6/Pvx5taafJ8Mgjj6impiYfaEjScccdp5qaGj388MOjhhqD7dixQ7/61a909913D/vZvffeq3vuuUfNzc0644wz9LnPfU5VVVUT2gaUHj+bzQcWewYYJp0e/cRwWKH6+iCwGDzzoqFBdix28BoAACjo+AUAAAAAAEy8MYcaK1euzL9etWrVZNRlVG1tbWpqahp2vKmpSW1tbWP6jLvvvltVVVU699xzhxx/73vfq/nz56ulpUXPPvusrr/+ej311FNat27dqJ+VyWSUyWTy7xOJxBhbgmIzbLmo/gBj9275e7uuliWnpiYfWuTDi4YG2dXVzLoAgCIxkeMX+n8AAAAAAApvvzYK73fCCSdo5cqVWrVqlU444QRVVFSM65ffeOONuummm/Za5rHHHpM08qbf/ctFjMX//b//V+9973sV2+Ob8mvWrMm/XrJkiRYtWqRjjjlGTz75pI4++ugRP2vt2rX7rDeKh/F9+YnEkMDC6+gInjs7Jd8f9VyrrGwgtBgcYNTXy2KjWQAoKQc6fqH/BwAAAACg8MYVapx11lnauHGj7rjjDqXTaS1btix/k+DEE09UZWXlmD7niiuu0IUXXrjXMvPmzdPTTz+tHTt2DPvZrl271NzcvM/f8z//8z/asmWLfvzjH++z7NFHH61wOKwXX3xx1FDj+uuv1zXXXJN/n0gkNHv27H1+NiaPMUZ+T8/w0GL3brkdHZLrjn5yOBwEFv0bdQ96tsvLD14jAACT6kDHL/T/AAAAAAAU3rhCjeuvv17XX3+9PM/TY489pg0bNmjDhg36yle+IsuyhizNsDeNjY1qbGzcZ7kVK1YoHo/rL3/5i4499lhJ0p///GfF43Edf/zx+zz/zjvv1LJly3TUUUfts+xzzz2nXC6n1tbWUctEo1FFo9F9fhYm1pANujs6hgQYXkeHTDY7+sm2Lae+XqH6+uHBRVUVy0UBwDRwoOMX+n8AAAAAAApvXKFGvxdffFFPPfWUnnrqKT399NOqrq7WSSedNFF1yzv88MP1jne8Q2vWrNG3v/1tSdKll16qs846a8gm4YsXL9batWt1zjnn5I8lEgn95Cc/0X/+538O+9yXXnpJ9957r84880w1Njbq+eef17XXXqulS5fqhBNOmPB2YN/yMy46OvLBRX7Wxb6CC8uSU1sbhBd7zLpwampkOc7BawgAoGgdrPELAAAAAACYeOMKNS644AI99NBD8n1fJ598sk4++WRdf/31estb3jLR9cu79957deWVV+r000+XJJ199tm64447hpTZsmWL4vH4kGP33XefjDH6//6//2/YZ0YiEf3hD3/QV7/6VfX09Gj27Nl65zvfqc997nNyuAE+afJ7XPTNsMgHGB0d8jo79xlc2DU1AzMuBs+8qK2VFTqgnA4AMIUVYvwCAEAxMcbI94w815fnGvmu3/c6eO+5ft8xM+j4nmUHlfOMjG+0ZOUhhW4aAACYRsZ1B/gnP/mJGhsb9YEPfECnnHKKTjrppDHvozFe9fX1uueee/Zaxhgz7Nill16qSy+9dMTys2fP1saNGyekfhjKuK68rq6BwKKzM3jd2Rlszr23PS4sS05NTTDDYvCSUfX1curqCC4AAONSiPELAADjMSR8yA0NEwaODQ4hRg4i8j/vL+/50vB/Nk9IfQEAAA6Wcd0d7ujo0EMPPaQNGzboM5/5jJ577jkdddRRWrVqlVatWqUzzjhjouuJIuSnUkFY0dk5LLjw43FpbwNbxxlYKqq+Xk7fI1RXR3ABAJgUjF8AAJPB94fOYnBz/h5hRP/7PUKH3AizIAYdm2y2Y8kJ2bJDwfPAw5I96HX/8Xw5x5YTHijne/6k1xUAAGAwy0zAVypeeuklfeELX9A999wj3/fled5E1K1kJBIJ1dTUKB6Pq7q6esI+N/6rXyn97HOKzJs3YZ+5P4znyUsk8sHFnrMtTCq11/OtcDgfVjh1dUPCC6e6mj0uAGCKyfz9RVWuXKXKEyduX6rJ6mOlAx+/TFbdujvSenr9a6qqj8oJ2RP2uQAAyfjBbIX8zIWcL3fPAGJIKLHnbIk9jru+fG9yA4iRQgcn1B8sDLy391FmyM8dS5ZtTUj9smlXmaSrt66eo1hleEI+czL7fwAAUPrGPVNj48aN2rBhgzZs2KDnnntO9fX1ete73qVTTjllouuISWKMkUkm5XV15YOKIY94XPL3/q0bu7JSTt/siiHBRV2d7MpKWdbEDJQBADhQjF8AoPT4fhA85MOH/lChL2QYNitiUFjhuXsEFrnJDSAsS4NChOGhw5Cfhaw93g89NnimBP+mAgAAGGpcocaMGTPU2Niok046SWvWrNGqVau0ZMmSia4bJoCfyQR7W/QHFf2vu7rkdXXJZDJ7/wDHCQKL2tpgaai+wMKpr5dTWys7Gj04DQEA4AAxfgGAyTemEGKPIGJ4MGHyYYTxJymEsCQnZCsUHhxAjBA+DHltKTQsnBh4bzuEDwAAAAfDuEKNTZs2acGCBfnNNbdu3arbbrtNRxxxhE4//fQJrSD2zs9m5Xd1yd0jrMiHFsnkPj/DrqrKBxf52RZ9r+2qKlk2S18AAEof4xcAGM6YgeWVhoQLg5dlyo2wV8Seyzb1nT9ZIUT//g9DZj+E9wwlhr4eFlgMKmM7zIAAAAAoVeMKNa677jqde+65uuyyy9TV1aXly5crHA6rvb1dX/nKV3T55ZdPdD2nLZPNKtfWJi8ezy8J1R9Y+PG4/N7efX6GVVaWDynyMy76Q4zaWlnhiVn3FAAwsYwxkjFjDpeN78vkcjK5nOS6wWvXlXFdaV9baFmWrFBIViQiKxwOHpHIlNr/iPELgKnC94YGEHvOfhgcRuw5C2L4TInJCSEs2wpChUHBQn6Ww6BgYs8y+TBij5DCnqD9HwAAAFD6xhVqPPnkk7r11lslST/96U/V3NysTZs26f7779cNN9zATYEJ0vnDHyn1xBP7LGfFYvmAYnB40f+wY7GDUFsAwGiM50m+P/TZ82Q8Lwgc+oKHoeFD37Nl9R2zBh23Bt4OKWpJ4XAQTvQ9O7U1sisq9hmMGN+Xn+iWn0rKT6WkeFzGzcl4/sAvUF9d8r+7rx791fPNhG06OhkYvwAolGA2hBk1WBgWUAwJJbyDsyTT4OWYRgoXRggl9vZzQggAAABMlnGFGslkUlVVVZKkBx98UOeee65s29Zxxx2nrVu3TmgFpzO7skLSoJkWIz1qamSXlRW4pgBw8AwJAgbNRDCuK3me5DjBTf2+hwa9tkKhIZ8j35fx/SBgGPzs+8HN+76HGfQ6f6yv3EB5X8OCh74wwLJsybEl2wlmPjiOLMeW5TiyystlV1bIKS+XXVkpu7xcViQqKxoJ9i1ynIEgxJi+MMSX/L5n4wefU1YmKxKRXVYmKxqVHYvJikb3ewlB47ry02n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" ] }, "metadata": {}, @@ -2257,10 +2266,13 @@ " well_model_interact,\n", " well_idata_interact,\n", " wrt=\"arsenic\",\n", - " conditional=[\"dist100\", \"educ4\"],\n", + " conditional={\n", + " \"dist100\": np.linspace(0, 4, 50),\n", + " \"educ4\": np.arange(0, 5, 1)\n", + " },\n", " slope=\"eyex\",\n", " subplot_kwargs={\"main\": \"dist100\", \"group\": \"educ4\", \"panel\": \"educ4\"},\n", - " fig_kwargs={\"figsize\": (16, 4), \"sharey\": True},\n", + " fig_kwargs={\"figsize\": (16, 6), \"sharey\": True, \"tight_layout\": True},\n", " legend=False\n", ")" ] @@ -2276,7 +2288,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -2288,72 +2300,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Modeling the probability that switch==0\n", - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [Intercept, dist100, arsenic, educ4, dist100:educ4, arsenic:educ4]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " 100.00% [8000/8000 05:18<00:00 Sampling 4 chains, 0 divergences]\n", - "
\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 319 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "well_model_interact = bmb.Model(\n", " \"switch ~ dist100 + arsenic + educ4 + dist100:educ4 + arsenic:educ4\",\n", @@ -2466,19 +2415,24 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Mon Nov 06 2023\n", + "Last updated: Mon Dec 04 2023\n", "\n", "Python implementation: CPython\n", "Python version : 3.11.0\n", "IPython version : 8.13.2\n", "\n", + "pandas: 2.1.0\n", + "numpy : 1.24.2\n", + "bambi : 0.13.0.dev0\n", + "arviz : 0.16.1\n", + "\n", "Watermark: 2.3.1\n", "\n" ] diff --git a/tests/test_interpret.py b/tests/test_interpret.py new file mode 100644 index 000000000..f9be28957 --- /dev/null +++ b/tests/test_interpret.py @@ -0,0 +1,192 @@ +""" +This module contains tests for the helper functions of the 'interpret' sub-package. +Tests here do not test any of the plotting functionality. +""" +import numpy as np +import pandas as pd +import pytest + +import bambi as bmb +from bambi.interpret.helpers import data_grid, select_draws + + +CHAINS = 4 +TUNE = 500 +DRAWS = 500 + + +@pytest.fixture(scope="module") +def mtcars(): + "Model with common level effects only" + data = bmb.load_data("mtcars") + data["am"] = pd.Categorical(data["am"], categories=[0, 1], ordered=True) + model = bmb.Model("mpg ~ hp * drat * am", data) + idata = model.fit(tune=TUNE, draws=DRAWS, chains=CHAINS, random_seed=1234) + return model, idata + + +# ------------------------------------------------------------------- +# Tests for `data_grid` +# +# `data_grid` serves several functions: (1) the ability to create a pairwise +# grid of data passing an argument to 'conditional' with no regard to the effect +# type, and (2) the ability to create a grid of data with respect to an effect +# type by passing an argument to 'conditional', 'variable', and 'effect_type'. +# The tests below test these two functionalities. +# ------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "conditional", + [ + # default values for 'hp', 'drat', and 'am' + (["hp", "drat"]), + # default values computed for 'am' and user-passed for 'hp' and 'drat' + ({"hp": np.linspace(50, 350, 7), "drat": [2.5, 3.5]}), + # user-passed for 'hp', 'drat', and 'am' + ({"hp": np.linspace(50, 350, 7), "drat": [2.5, 3.5], "am": [0, 1]}), + ], + ids=["defaults", "defaults_and_user_passed", "user_passed"], +) +def test_data_grid_no_effect(request, mtcars, conditional): + model, idata = mtcars + grid = data_grid(model, conditional) + + id = request.node.name + if id == "defaults": + assert grid.shape == (48, 3) + assert grid.columns.tolist() == ["hp", "drat", "am"] + elif id == "defaults_and_user_passed": + assert grid.shape == (14, 3) + assert grid.columns.tolist() == ["hp", "drat", "am"] + elif id == "user_passed": + assert grid.shape == (28, 3) + assert grid.columns.tolist() == ["hp", "drat", "am"] + + +def test_data_grid_no_effect_kwargs(request, mtcars): + model, idata = mtcars + grid = data_grid(model, ["hp", "drat"], num=10) + + assert grid.shape == (100, 3) + assert grid.columns.tolist() == ["hp", "drat", "am"] + + +@pytest.mark.parametrize( + "conditional, variable", + [ + # default values for 'conditional' and 'variable' + (["drat", "am"], "hp"), + # user-passed for 'conditional' and 'variable' + ({"drat": np.arange(1, 5, 1), "am": np.array([0, 1])}, {"hp": np.array([150, 300])}), + # user-passed for 'conditional' and default value for 'variable' + ({"drat": np.arange(1, 5, 1), "am": np.array([0, 1])}, "hp"), + ], + ids=["defaults", "user_passed", "defaults_and_user_passed"], +) +def test_data_grid_comparisons(request, mtcars, conditional, variable): + model, idata = mtcars + grid = data_grid(model, conditional, variable=variable, effect_type="comparisons") + + id = request.node.name + if id == "defaults": + assert grid.shape == (200, 3) + assert grid.columns.tolist() == ["drat", "am", "hp"] + elif id == "user_passed" or id == "defaults_and_user_passed": + assert grid.shape == (16, 3) + assert grid.columns.tolist() == ["drat", "am", "hp"] + + with pytest.raises( + ValueError, + match="'If passing an argument to 'variable', the parameter 'effect_type' must be either " + f"'comparisons' or 'slopes'. Received: {None}", + ): + data_grid(model, conditional, variable=variable, effect_type=None) + + +@pytest.mark.parametrize( + "conditional, variable", + [ + # default values for 'conditional' and 'variable' + (["drat", "am"], "hp"), + # user-passed for 'conditional' and 'variable' + ({"drat": np.arange(1, 5, 1), "am": np.array([0, 1])}, {"hp": np.array([150])}), + # user-passed for 'conditional' and default value for 'variable' + ({"drat": np.arange(1, 5, 1), "am": np.array([0, 1])}, "hp"), + ], + ids=["defaults", "user_passed", "defaults_and_user_passed"], +) +def test_data_grid_slopes(request, mtcars, conditional, variable): + model, idata = mtcars + grid = data_grid(model, conditional, variable=variable, effect_type="slopes") + + id = request.node.name + if id == "defaults": + assert grid.shape == (200, 3) + assert grid.columns.tolist() == ["drat", "am", "hp"] + elif id == "user_passed" or id == "defaults_and_user_passed": + assert grid.shape == (16, 3) + assert grid.columns.tolist() == ["drat", "am", "hp"] + + with pytest.raises( + ValueError, + match="'If passing an argument to 'variable', the parameter 'effect_type' must be either " + f"'comparisons' or 'slopes'. Received: {None}", + ): + data_grid(model, conditional, variable=variable, effect_type=None) + + +@pytest.mark.parametrize( + "effect_type, eps", [("comparisons", 1), ("slopes", 1e-2)], ids=["comparisons", "slopes"] +) +def test_data_grid_eps(request, mtcars, effect_type, eps): + model, idata = mtcars + grid = data_grid(model, ["drat", "am"], "hp", effect_type, eps=eps) + unit_difference = np.unique(np.abs(np.diff(grid["hp"]))) + + id = request.node.name + if id == "comparisons": + # centered difference 'eps' of 1 adds and subtracts 1 to the default "hp" value + assert unit_difference == np.array([2]) + elif id == "slopes": + # finite difference 'eps' of 1e-2 adds 0.01 to the default "hp" value + assert unit_difference == np.array([0.01]) + + +# ------------------------------------------------------------------- +# Tests for `select_draws` +# +# Select posterior or posterior predictive draws conditioned on the +# observation that produced that draw by passing a `condition` dictionary. +# `data_grid` is used to create the grid of data that is passed to `model.predict`. +# Then, different 'condition' dictionaries are passed to `select_draws` to +# ensure the output shape of the selected draws is correct. +# ------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "condition", + [ + ({"hp": 250}), + ({"drat": 2.5}), + ({"hp": 250, "drat": 2.5}), + ], + ids=["1", "2", "3"], +) +def test_select_draws_no_effect(request, mtcars, condition): + model, idata = mtcars + + conditional = {"hp": np.linspace(50, 350, 7), "drat": [2.5, 3.5], "am": [0, 1]} + grid = data_grid(model, conditional) + + idata = model.predict(idata, data=grid, inplace=False) + draws = select_draws(idata, grid, condition=condition, data_var="mpg_mean") + + # (CHAINS, DRAWS, n) where n is the number of observations that satisfy the condition + id = request.node.name + if id == "1": + assert draws.shape == (CHAINS, DRAWS, 4) + elif id == "2": + assert draws.shape == (CHAINS, DRAWS, 14) + elif id == "3": + assert draws.shape == (CHAINS, DRAWS, 2) diff --git a/tests/test_interpret_messages.py b/tests/test_interpret_messages.py index a1a875471..b44e8aa8c 100644 --- a/tests/test_interpret_messages.py +++ b/tests/test_interpret_messages.py @@ -24,32 +24,28 @@ def test_predictions_list(mtcars, caplog): conditional = ["hp", "drat", "am"] plot_predictions(model, idata, conditional) - main_msg = "Default computed for main variable: hp" - group_panel_msg = "Default computed for group/panel variable: drat, am" + conditional_msg = "Default computed for conditional variable: hp, drat, am" interpret_log_msgs = [r.message for r in caplog.records] - assert main_msg in interpret_log_msgs - assert group_panel_msg in interpret_log_msgs - assert len(caplog.records) == 2 + assert conditional_msg in interpret_log_msgs + assert len(caplog.records) == 1 def test_predictions_list_unspecified(mtcars, caplog): model, idata = mtcars caplog.set_level("INFO", logger="__bambi_interpret__") - # List of values with unspecified covariates + # List of values with unspecified covariate "am" conditional = ["hp", "drat"] plot_predictions(model, idata, conditional) - main_msg = "Default computed for main variable: hp" - group_msg = "Default computed for group/panel variable: drat" + conditional_msg = "Default computed for conditional variable: hp, drat" unspecified_msg = "Default computed for unspecified variable: am" interpret_log_msgs = [r.message for r in caplog.records] - assert main_msg in interpret_log_msgs - assert group_msg in interpret_log_msgs + assert conditional_msg in interpret_log_msgs assert unspecified_msg in interpret_log_msgs - assert len(caplog.records) == 3 + assert len(caplog.records) == 2 def test_predictions_dict_unspecified(mtcars, caplog):