From 6c0cf4165398d2aa4beec05d3cc7f0453c9b9127 Mon Sep 17 00:00:00 2001 From: Axel Fahy Date: Wed, 21 Aug 2019 15:08:03 +0200 Subject: [PATCH 01/10] feat(plot): separation of plots in plot module Closes #20. --- Makefile | 4 + README.md | 4 +- bff/__init__.py | 24 ++- bff/fancy.py | 435 +----------------------------------------- bff/plot/__init__.py | 16 ++ bff/plot/plot.py | 441 +++++++++++++++++++++++++++++++++++++++++++ setup.py | 1 + tests/test_fancy.py | 1 - 8 files changed, 482 insertions(+), 444 deletions(-) create mode 100644 bff/plot/__init__.py create mode 100644 bff/plot/plot.py diff --git a/Makefile b/Makefile index 4207bd0..643a01d 100644 --- a/Makefile +++ b/Makefile @@ -4,10 +4,14 @@ all: test +build-python: + python setup.py sdist bdist_wheel + clean: rm -rf coverage_html_report .coverage rm -rf bff.egg-info rm -rf venv-dev + rm -rf dist/ test: lint style coverage diff --git a/README.md b/README.md index d3e9e9b..11d1697 100644 --- a/README.md +++ b/README.md @@ -34,7 +34,7 @@ git clone https://github.com/axelfahy/bff.git cd bff python -m venv venv-dev source venv-dev/bin/activate -pip install requirements_dev.txt +pip install -r requirements_dev.txt pip install -e . ``` @@ -46,6 +46,8 @@ make all ## Release History +* 0.2.0 + * ADD: Separation of plots in submodule ``plot``. This breaks the previous API. * 0.1.9 * ADD: Option ``loc`` in ``plot_series`` function. * ADD: Function ``cast_to_category_pd`` to cast columns to category ``dtype`` automatically. diff --git a/bff/__init__.py b/bff/__init__.py index 70d3720..dfd839e 100644 --- a/bff/__init__.py +++ b/bff/__init__.py @@ -3,10 +3,19 @@ from ._version import get_versions +# Import submodules. +from . import plot + from .fancy import ( - cast_to_category_pd, concat_with_categories, get_peaks, idict, - mem_usage_pd, parse_date, plot_history, plot_predictions, plot_series, - plot_true_vs_pred, read_sql_by_chunks, sliding_window, value_2_list + cast_to_category_pd, + concat_with_categories, + get_peaks, + idict, + mem_usage_pd, + parse_date, + read_sql_by_chunks, + sliding_window, + value_2_list, ) from .config import FancyConfig @@ -19,10 +28,7 @@ 'idict', 'mem_usage_pd', 'parse_date', - 'plot_history', - 'plot_predictions', - 'plot_series', - 'plot_true_vs_pred', + 'plot', 'read_sql_by_chunks', 'sliding_window', 'value_2_list', @@ -30,8 +36,8 @@ ] # Logging configuration. -FORMAT = '%(asctime)-15s %(message)s' -logging.basicConfig(format=FORMAT) +FORMAT = '%(asctime)s [%(levelname)-7s] %(name)s: %(message)s' +logging.basicConfig(format=FORMAT, level=logging.INFO) __version__ = get_versions()['version'] del get_versions diff --git a/bff/fancy.py b/bff/fancy.py index 9a106b6..adbff0b 100644 --- a/bff/fancy.py +++ b/bff/fancy.py @@ -8,22 +8,12 @@ import math import sys from functools import wraps -from typing import Any, Callable, Dict, Hashable, List, Sequence, Set, Tuple, Union +from typing import Any, Callable, Dict, Hashable, List, Sequence, Set, Union from dateutil import parser from scipy import signal -from scipy.stats import sem -import matplotlib as mpl -import matplotlib.lines as mlines -import matplotlib.patches as mpatches -import matplotlib.pyplot as plt -import numpy as np import pandas as pd -TNum = Union[int, float] - -FORMAT = '%(asctime)-15s %(message)s' -logging.basicConfig(format=FORMAT) -LOGGER = logging.getLogger(name='bff') +LOGGER = logging.getLogger(__name__) def cast_to_category_pd(df: pd.DataFrame, deep: bool = True) -> pd.DataFrame: @@ -403,427 +393,6 @@ def wrapper(*args, **kwargs): return _parse_date(func) if func else _parse_date -def plot_history(history, metric: Union[str, None] = None, title: str = 'Model history', - axes: plt.axes = None, figsize: Tuple[int, int] = (12, 4), - grid: bool = False, style: str = 'default', - **kwargs) -> Union[plt.axes, Sequence[plt.axes]]: - """ - Plot the history of the model trained using Keras. - - Parameters - ---------- - history : tensorflow.keras.callbask.History - History of the training. - metric : str, default None - Metric to plot. - If no metric is provided, will only print the loss. - title : str, default 'Model history' - Main title for the plot (figure level). - axes : plt.axes, default None - Axes from matplotlib, if None, new figure and axes will be created. - If metric is provided, need to have at least 2 axes. - figsize : Tuple[int, int], default (12, 4) - Size of the figure to plot. - grid : bool, default False - Turn the axes grids on or off. - style : str, default 'default' - Style to use for matplotlib.pyplot. - The style is use only in this context and not applied globally. - **kwargs - Additional keyword arguments to be passed to the - `plt.plot` function from matplotlib. - - Returns - ------- - plt.axes - Axes object or array of Axes objects returned by the `plt.subplots` - function. - - Examples - -------- - >>> history = model.fit(...) - >>> plot_history(history, metric='acc', title='MyTitle', linestyle=':') - """ - if metric: - assert metric in history.history.keys(), ( - f'Metric {metric} does not exist in history.\n' - f'Possible metrics: {history.history.keys()}') - - with plt.style.context(style): - # Given axes are not check for now. - # If metric is given, must have at least 2 axes. - if axes is None: - fig, axes = plt.subplots(1, 2 if metric else 1, figsize=figsize) - else: - fig = plt.gcf() - - if metric: - # Summarize history for metric, if any. - axes[0].plot(history.history[metric], - label=f"Train ({history.history[metric][-1]:.4f})", - **kwargs) - axes[0].plot(history.history[f'val_{metric}'], - label=f"Validation ({history.history[f'val_{metric}'][-1]:.4f})", - **kwargs) - axes[0].set_title(f'Model {metric}') - axes[0].set_xlabel('Epochs') - axes[0].set_ylabel(metric.capitalize()) - axes[0].legend(loc='upper left') - - # Summarize history for loss. - ax_loss = axes[1] if metric else axes - ax_loss.plot(history.history['loss'], - label=f"Train ({history.history['loss'][-1]:.4f})", - **kwargs) - ax_loss.plot(history.history['val_loss'], - label=f"Validation ({history.history['val_loss'][-1]:.4f})", - **kwargs) - ax_loss.set_title('Model loss') - ax_loss.set_xlabel('Epochs') - ax_loss.set_ylabel('Loss') - ax_loss.legend(loc='upper left') - - # Put the grid on axes. - if metric: - for ax in axes.flatten(): - ax.grid(grid) - else: - axes.grid(grid) - - fig.suptitle(title) - return axes - - -def plot_predictions(y_true: Union[np.array, pd.DataFrame], - y_pred: Union[np.array, pd.DataFrame], - label_true: str = 'Actual', label_pred: str = 'Predicted', - label_x: str = 'x', label_y: str = 'y', - title: str = 'Model predictions', - ax: plt.axes = None, - figsize: Tuple[int, int] = (12, 4), grid: bool = False, - style: str = 'default', **kwargs) -> plt.axes: - """ - Plot the predictions of the model. - - If a DataFrame is provided, it must only contain one column. - - Parameters - ---------- - y_true : np.array or pd.DataFrame - Actual values. - y_pred : np.array or pd.DataFrame - Predicted values by the model. - label_true : str, default 'Actual' - Label for the actual values. - label_pred : str, default 'Predicted' - Label for the predicted values. - label_x : str, default 'x' - Label for x axis. - label_y : str, default 'y' - Label for y axis. - title : str, default 'Model predictions' - Title for the plot (axis level). - ax : plt.axes, default None - Axes from matplotlib, if None, new figure and axes will be created. - figsize : Tuple[int, int], default (12, 4) - Size of the figure to plot. - grid : bool, default False - Turn the axes grids on or off. - style : str, default 'default' - Style to use for matplotlib.pyplot. - The style is use only in this context and not applied globally. - **kwargs - Additional keyword arguments to be passed to the - `plt.plot` function from matplotlib. - - Returns - ------- - plt.axes - Axes returned by the `plt.subplots` function. - - Examples - -------- - >>> y_pred = model.predict(x_test, ...) - >>> plot_predictions(y_true, y_pred, title='MyTitle', linestyle=':') - """ - with plt.style.context(style): - if ax is None: - __, ax = plt.subplots(1, 1, figsize=figsize) - - # Plot predictions. - ax.plot(np.array(y_pred).flatten(), color='r', - label=label_pred, **kwargs) - # Plot actual values on top of the predictions. - ax.plot(np.array(y_true).flatten(), color='b', - label=label_true, **kwargs) - ax.set_xlabel(label_x) - ax.set_ylabel(label_y) - ax.set_title(title) - ax.grid(grid) - - # Sort labels and handles by labels. - handles, labels = ax.get_legend_handles_labels() - labels, handles = zip(*sorted(zip(labels, handles), - key=lambda t: t[0])) - ax.legend(handles, labels, loc='upper left') - - return ax - - -def plot_series(df: pd.DataFrame, column: str, groupby: str = '1S', - with_sem: bool = True, with_peaks: bool = False, - with_missing_datetimes: bool = False, - distance_scale: float = 0.04, label_x: str = 'Datetime', - label_y: Union[str, None] = None, title: str = 'Plot of series', - ax: plt.axes = None, color: str = '#3F5D7D', - loc: Union[str, int] = 'best', - figsize: Tuple[int, int] = (14, 6), dpi: int = 80, - style: str = 'default', **kwargs) -> plt.axes: - """ - Plot time series with datetime with the given resample (`groupby`). - - Parameters - ---------- - df : pd.DataFrame - DataFrame to plot, with datetime as index. - column : str - Column of the DataFrame to display. - groupby : str, default '1S' - Grouping for the resampling by mean of the data. - For example, can resample from seconds ('S') to minutes ('T'). - with_sem : bool, default True - Display the standard error of the mean (SEM) if set to true. - with_peaks : bool, default False - Display the peaks of the serie if set to true. - with_missing_datetimes : bool, default False - Display the missing datetimes with vertical red lines. - distance_scale: float, default 0.04 - Scaling for the minimal distance between peaks. - Only used if `with_peaks` is set to true. - label_x : str, default 'Datetime' - Label for x axis. - label_y : str, default None - Label for y axis. If None, will take the column name as label. - title : str, default 'Plot of series' - Title for the plot (axis level). - ax : plt.axes, default None - Axes from matplotlib, if None, new figure and ax will be created. - color : str, default '#3F5D7D' - Default color for the plot. - loc : str or int, default 'best' - Location of the legend on the plot. - Either the legend string or legend code are possible. - figsize : Tuple[int, int], default (14, 6) - Size of the figure to plot. - dpi : int, default 80 - Resolution of the figure. - style : str, default 'default' - Style to use for matplotlib.pyplot. - The style is use only in this context and not applied globally. - **kwargs - Additional keyword arguments to be passed to the - `plt.plot` function from matplotlib. - - Returns - ------- - plt.axes - Axes object or array of Axes objects returned by the `plt.subplots` - function. - - Examples - -------- - >>> df_acceleration = fake.get_data_with_datetime_index(...) - >>> _, axes = plt.subplots(nrows=3, ncols=1, figsize=(14, 20), dpi=80) - >>> colors = {'x': 'steelblue', 'y': 'darkorange', 'z': 'darkgreen'} - >>> for i, acc in enumerate(['x', 'y', 'z']): - ... plot_series(df_acceleration, acc, groupby='T', - ... ax=axes[i], color=colors[acc]) - """ - assert 'datetime' in df.index.names, ( - 'DataFrame must have a datetime index.') - assert column in df.columns, ( - f'DataFrame does not contain column: {column}') - - with plt.style.context(style): - if ax is None: - __, ax = plt.subplots(figsize=figsize, dpi=dpi) - - # By default, the y label if the column name. - if label_y is None: - label_y = column.capitalize() - - # Get the values to plot. - df_plot = (df[column].groupby('datetime').mean() - .resample(groupby).mean()) - x = df_plot.index - - ax.plot(x, df_plot, label=column, color=color, lw=2, **kwargs) - - # With sem (standard error of the mean). - if with_sem: - df_sem = (df[column] - .groupby('datetime') - .mean() - .resample(groupby) - .apply(sem) - if groupby not in ('S', '1S') else - df[column].groupby('datetime').apply(sem)) - - ax.fill_between(x, df_plot - df_sem, df_plot + df_sem, - color='grey', alpha=0.2) - - # Plot the peak as circle. - if with_peaks: - peak_dates, peak_values = get_peaks(df_plot, distance_scale) - ax.plot(peak_dates, peak_values, linestyle='', marker='o', - color='plum') - - # Plot vertical line where there is missing datetimes. - if with_missing_datetimes: - df_date_missing = pd.date_range(start=df.index.get_level_values(0).min(), - end=df.index.get_level_values(0).max(), - freq=groupby).difference(df.index.get_level_values(0)) - for date in df_date_missing.tolist(): - ax.axvline(date, color='crimson') - - ax.set_xlabel(label_x, fontsize=12) - ax.set_ylabel(label_y, fontsize=12) - ax.set_title(f'{title} (mean by {groupby})', fontsize=14) - - # Style. - # Remove border on the top and right. - ax.spines['top'].set_visible(False) - ax.spines['right'].set_visible(False) - - # Remove ticks on y axis. - ax.yaxis.set_ticks_position('none') - ax.xaxis.set_ticks_position('bottom') - - # Draw Horizontal Tick lines. - ax.xaxis.grid(False) - ax.yaxis.grid(which='major', color='black', alpha=0.5, - linestyle='--', lw=0.5) - - # Set thousand separator for y axis. - ax.yaxis.set_major_formatter( - mpl.ticker.FuncFormatter(lambda x, p: f'{x:,.1f}') - ) - - handles, labels = ax.get_legend_handles_labels() - labels_cap = [label.capitalize() for label in labels] - # Add the sem on the legend. - if with_sem: - handles.append(mpatches.Patch(color='grey', alpha=0.2)) - labels_cap.append('Standard error of the mean (SEM)') - - # Add the peak symbol on the legend. - if with_peaks: - handles.append(mlines.Line2D([], [], linestyle='', marker='o', - color='plum')) - labels_cap.append('Peaks') - - # Add the missing date line on the legend. - if with_missing_datetimes: - handles.append(mlines.Line2D([], [], linestyle='-', color='crimson')) - labels_cap.append('Missing datetimes') - - ax.legend(handles, labels_cap, loc=loc) - plt.tight_layout() - - return ax - - -def plot_true_vs_pred(y_true: Union[np.array, pd.DataFrame], - y_pred: Union[np.array, pd.DataFrame], - marker: Union[str, int] = 'k.', corr: bool = True, - label_x: str = 'Ground truth', - label_y: str = 'Prediction', - title: str = 'Predicted vs Actual', - lim_x: Union[Tuple[TNum, TNum], None] = None, - lim_y: Union[Tuple[TNum, TNum], None] = None, - ax: plt.axes = None, figsize: Tuple[int, int] = (12, 5), - grid: bool = False, style: str = 'default', - **kwargs) -> plt.axes: - """ - Plot the ground truth against the predictions of the model. - - If a DataFrame is provided, it must only contain one column. - - Parameters - ---------- - y_true : np.array or pd.DataFrame - Actual values. - y_pred : np.array or pd.DataFrame - Predicted values by the model. - label_x : str, default 'Ground truth' - Label for x axis. - label_y : str, default 'Prediction' - Label for y axis. - title : str, default 'Predicted vs Actual' - Title for the plot (axis level). - lim_x : Tuple[TNum, TNum], default None - Limit for the x axis. If None, automatically calculated according - to the limits of the data, with an extra 5% for readability. - lim_y : Tuple[TNum, TNum], default None - Limit for the y axis. If None, automatically calculated according - to the limits of the data, with an extra 5% for readability. - ax : plt.axes, default None - Axes from matplotlib, if None, new figure and axes will be created. - figsize : Tuple[int, int], default (12, 5) - Size of the figure to plot. - grid : bool, default False - Turn the axes grids on or off. - style : str, default 'default' - Style to use for matplotlib.pyplot. - The style is use only in this context and not applied globally. - **kwargs - Additional keyword arguments to be passed to the - `plt.plot` function from matplotlib. - - Returns - ------- - plt.axes - Axes returned by the `plt.subplots` function. - - Examples - -------- - >>> y_pred = model.predict(x_test, ...) - >>> plot_true_vs_pred(y_true, y_pred, title='MyTitle', linestyle=':') - """ - with plt.style.context(style): - if ax is None: - __, ax = plt.subplots(1, 1, figsize=figsize) - - y_true = np.array(y_true).flatten() - y_pred = np.array(y_pred).flatten() - ax.plot(y_true, y_pred, marker, **kwargs) - ax.set_xlabel(label_x) - ax.set_ylabel(label_y) - ax.set_title(title) - ax.grid(grid) - - # Calculate the limit of the plot if not provided, - # add and extra 5% for readability. - def get_limit(limit, data, percent=5): - if not limit or not isinstance(limit, tuple): - lim_max = data.max() - lim_min = data.min() - margin = (lim_max - lim_min) * percent / 100 - limit = (lim_min - margin, lim_max + margin) - return limit - - ax.set_xlim(get_limit(lim_x, y_true)) - ax.set_ylim(get_limit(lim_y, y_pred)) - - # Add correlation in upper left position. - if corr: - ax.text(0.025, 0.925, - f'R={np.round(np.corrcoef(y_true, y_pred)[0][1], 3)}', - transform=ax.transAxes) - - return ax - - def read_sql_by_chunks(sql: str, cnxn, params: Union[List, Dict, None] = None, chunksize: int = 8_000_000, column_types: Union[Dict, None] = None, **kwargs) -> pd.DataFrame: diff --git a/bff/plot/__init__.py b/bff/plot/__init__.py new file mode 100644 index 0000000..8c9499a --- /dev/null +++ b/bff/plot/__init__.py @@ -0,0 +1,16 @@ +"""Plot module of bff.""" + +from .plot import ( + plot_history, + plot_predictions, + plot_series, + plot_true_vs_pred, +) + +# Public object of the module. +__all__ = [ + 'plot_history', + 'plot_predictions', + 'plot_series', + 'plot_true_vs_pred', +] diff --git a/bff/plot/plot.py b/bff/plot/plot.py new file mode 100644 index 0000000..3103456 --- /dev/null +++ b/bff/plot/plot.py @@ -0,0 +1,441 @@ +# -*- coding: utf-8 -*- +"""Plot functions of the bff library. + +This module contains fancy plot functions. +""" +import logging +from typing import Sequence, Tuple, Union +from scipy.stats import sem +import matplotlib as mpl +import matplotlib.lines as mlines +import matplotlib.patches as mpatches +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +import bff.fancy + +TNum = Union[int, float] + +LOGGER = logging.getLogger(__name__) + + +def plot_history(history, metric: Union[str, None] = None, title: str = 'Model history', + axes: plt.axes = None, figsize: Tuple[int, int] = (12, 4), + grid: bool = False, style: str = 'default', + **kwargs) -> Union[plt.axes, Sequence[plt.axes]]: + """ + Plot the history of the model trained using Keras. + + Parameters + ---------- + history : tensorflow.keras.callback.History + History of the training. + metric : str, default None + Metric to plot. + If no metric is provided, will only print the loss. + title : str, default 'Model history' + Main title for the plot (figure level). + axes : plt.axes, default None + Axes from matplotlib, if None, new figure and axes will be created. + If metric is provided, need to have at least 2 axes. + figsize : Tuple[int, int], default (12, 4) + Size of the figure to plot. + grid : bool, default False + Turn the axes grids on or off. + style : str, default 'default' + Style to use for matplotlib.pyplot. + The style is use only in this context and not applied globally. + **kwargs + Additional keyword arguments to be passed to the + `plt.plot` function from matplotlib. + + Returns + ------- + plt.axes + Axes object or array of Axes objects returned by the `plt.subplots` + function. + + Examples + -------- + >>> history = model.fit(...) + >>> plot_history(history, metric='acc', title='MyTitle', linestyle=':') + """ + if metric: + assert metric in history.history.keys(), ( + f'Metric {metric} does not exist in history.\n' + f'Possible metrics: {history.history.keys()}') + + with plt.style.context(style): + # Given axes are not check for now. + # If metric is given, must have at least 2 axes. + if axes is None: + fig, axes = plt.subplots(1, 2 if metric else 1, figsize=figsize) + else: + fig = plt.gcf() + + if metric: + # Summarize history for metric, if any. + axes[0].plot(history.history[metric], + label=f"Train ({history.history[metric][-1]:.4f})", + **kwargs) + axes[0].plot(history.history[f'val_{metric}'], + label=f"Validation ({history.history[f'val_{metric}'][-1]:.4f})", + **kwargs) + axes[0].set_title(f'Model {metric}') + axes[0].set_xlabel('Epochs') + axes[0].set_ylabel(metric.capitalize()) + axes[0].legend(loc='upper left') + + # Summarize history for loss. + ax_loss = axes[1] if metric else axes + ax_loss.plot(history.history['loss'], + label=f"Train ({history.history['loss'][-1]:.4f})", + **kwargs) + ax_loss.plot(history.history['val_loss'], + label=f"Validation ({history.history['val_loss'][-1]:.4f})", + **kwargs) + ax_loss.set_title('Model loss') + ax_loss.set_xlabel('Epochs') + ax_loss.set_ylabel('Loss') + ax_loss.legend(loc='upper left') + + # Put the grid on axes. + if metric: + for ax in axes.flatten(): + ax.grid(grid) + else: + axes.grid(grid) + + fig.suptitle(title) + return axes + + +def plot_predictions(y_true: Union[np.array, pd.DataFrame], + y_pred: Union[np.array, pd.DataFrame], + label_true: str = 'Actual', label_pred: str = 'Predicted', + label_x: str = 'x', label_y: str = 'y', + title: str = 'Model predictions', + ax: plt.axes = None, + figsize: Tuple[int, int] = (12, 4), grid: bool = False, + style: str = 'default', **kwargs) -> plt.axes: + """ + Plot the predictions of the model. + + If a DataFrame is provided, it must only contain one column. + + Parameters + ---------- + y_true : np.array or pd.DataFrame + Actual values. + y_pred : np.array or pd.DataFrame + Predicted values by the model. + label_true : str, default 'Actual' + Label for the actual values. + label_pred : str, default 'Predicted' + Label for the predicted values. + label_x : str, default 'x' + Label for x axis. + label_y : str, default 'y' + Label for y axis. + title : str, default 'Model predictions' + Title for the plot (axis level). + ax : plt.axes, default None + Axes from matplotlib, if None, new figure and axes will be created. + figsize : Tuple[int, int], default (12, 4) + Size of the figure to plot. + grid : bool, default False + Turn the axes grids on or off. + style : str, default 'default' + Style to use for matplotlib.pyplot. + The style is use only in this context and not applied globally. + **kwargs + Additional keyword arguments to be passed to the + `plt.plot` function from matplotlib. + + Returns + ------- + plt.axes + Axes returned by the `plt.subplots` function. + + Examples + -------- + >>> y_pred = model.predict(x_test, ...) + >>> plot_predictions(y_true, y_pred, title='MyTitle', linestyle=':') + """ + with plt.style.context(style): + if ax is None: + __, ax = plt.subplots(1, 1, figsize=figsize) + + # Plot predictions. + ax.plot(np.array(y_pred).flatten(), color='r', + label=label_pred, **kwargs) + # Plot actual values on top of the predictions. + ax.plot(np.array(y_true).flatten(), color='b', + label=label_true, **kwargs) + ax.set_xlabel(label_x) + ax.set_ylabel(label_y) + ax.set_title(title) + ax.grid(grid) + + # Sort labels and handles by labels. + handles, labels = ax.get_legend_handles_labels() + labels, handles = zip(*sorted(zip(labels, handles), + key=lambda t: t[0])) + ax.legend(handles, labels, loc='upper left') + + return ax + + +def plot_series(df: pd.DataFrame, column: str, groupby: str = '1S', + with_sem: bool = True, with_peaks: bool = False, + with_missing_datetimes: bool = False, + distance_scale: float = 0.04, label_x: str = 'Datetime', + label_y: Union[str, None] = None, title: str = 'Plot of series', + ax: plt.axes = None, color: str = '#3F5D7D', + loc: Union[str, int] = 'best', + figsize: Tuple[int, int] = (14, 6), dpi: int = 80, + style: str = 'default', **kwargs) -> plt.axes: + """ + Plot time series with datetime with the given resample (`groupby`). + + Parameters + ---------- + df : pd.DataFrame + DataFrame to plot, with datetime as index. + column : str + Column of the DataFrame to display. + groupby : str, default '1S' + Grouping for the resampling by mean of the data. + For example, can resample from seconds ('S') to minutes ('T'). + with_sem : bool, default True + Display the standard error of the mean (SEM) if set to true. + with_peaks : bool, default False + Display the peaks of the serie if set to true. + with_missing_datetimes : bool, default False + Display the missing datetimes with vertical red lines. + distance_scale: float, default 0.04 + Scaling for the minimal distance between peaks. + Only used if `with_peaks` is set to true. + label_x : str, default 'Datetime' + Label for x axis. + label_y : str, default None + Label for y axis. If None, will take the column name as label. + title : str, default 'Plot of series' + Title for the plot (axis level). + ax : plt.axes, default None + Axes from matplotlib, if None, new figure and ax will be created. + color : str, default '#3F5D7D' + Default color for the plot. + loc : str or int, default 'best' + Location of the legend on the plot. + Either the legend string or legend code are possible. + figsize : Tuple[int, int], default (14, 6) + Size of the figure to plot. + dpi : int, default 80 + Resolution of the figure. + style : str, default 'default' + Style to use for matplotlib.pyplot. + The style is use only in this context and not applied globally. + **kwargs + Additional keyword arguments to be passed to the + `plt.plot` function from matplotlib. + + Returns + ------- + plt.axes + Axes object or array of Axes objects returned by the `plt.subplots` + function. + + Examples + -------- + >>> df_acceleration = fake.get_data_with_datetime_index(...) + >>> _, axes = plt.subplots(nrows=3, ncols=1, figsize=(14, 20), dpi=80) + >>> colors = {'x': 'steelblue', 'y': 'darkorange', 'z': 'darkgreen'} + >>> for i, acc in enumerate(['x', 'y', 'z']): + ... plot_series(df_acceleration, acc, groupby='T', + ... ax=axes[i], color=colors[acc]) + """ + assert 'datetime' in df.index.names, ( + 'DataFrame must have a datetime index.') + assert column in df.columns, ( + f'DataFrame does not contain column: {column}') + + with plt.style.context(style): + if ax is None: + __, ax = plt.subplots(figsize=figsize, dpi=dpi) + + # By default, the y label if the column name. + if label_y is None: + label_y = column.capitalize() + + # Get the values to plot. + df_plot = (df[column].groupby('datetime').mean() + .resample(groupby).mean()) + x = df_plot.index + + ax.plot(x, df_plot, label=column, color=color, lw=2, **kwargs) + + # With sem (standard error of the mean). + if with_sem: + df_sem = (df[column] + .groupby('datetime') + .mean() + .resample(groupby) + .apply(sem) + if groupby not in ('S', '1S') else + df[column].groupby('datetime').apply(sem)) + + ax.fill_between(x, df_plot - df_sem, df_plot + df_sem, + color='grey', alpha=0.2) + + # Plot the peak as circle. + if with_peaks: + peak_dates, peak_values = bff.fancy.get_peaks(df_plot, distance_scale) + ax.plot(peak_dates, peak_values, linestyle='', marker='o', + color='plum') + + # Plot vertical line where there is missing datetimes. + if with_missing_datetimes: + df_date_missing = pd.date_range(start=df.index.get_level_values(0).min(), + end=df.index.get_level_values(0).max(), + freq=groupby).difference(df.index.get_level_values(0)) + for date in df_date_missing.tolist(): + ax.axvline(date, color='crimson') + + ax.set_xlabel(label_x, fontsize=12) + ax.set_ylabel(label_y, fontsize=12) + ax.set_title(f'{title} (mean by {groupby})', fontsize=14) + + # Style. + # Remove border on the top and right. + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + + # Remove ticks on y axis. + ax.yaxis.set_ticks_position('none') + ax.xaxis.set_ticks_position('bottom') + + # Draw Horizontal Tick lines. + ax.xaxis.grid(False) + ax.yaxis.grid(which='major', color='black', alpha=0.5, + linestyle='--', lw=0.5) + + # Set thousand separator for y axis. + ax.yaxis.set_major_formatter( + mpl.ticker.FuncFormatter(lambda x, p: f'{x:,.1f}') + ) + + handles, labels = ax.get_legend_handles_labels() + labels_cap = [label.capitalize() for label in labels] + # Add the sem on the legend. + if with_sem: + handles.append(mpatches.Patch(color='grey', alpha=0.2)) + labels_cap.append('Standard error of the mean (SEM)') + + # Add the peak symbol on the legend. + if with_peaks: + handles.append(mlines.Line2D([], [], linestyle='', marker='o', + color='plum')) + labels_cap.append('Peaks') + + # Add the missing date line on the legend. + if with_missing_datetimes: + handles.append(mlines.Line2D([], [], linestyle='-', color='crimson')) + labels_cap.append('Missing datetimes') + + ax.legend(handles, labels_cap, loc=loc) + plt.tight_layout() + + return ax + + +def plot_true_vs_pred(y_true: Union[np.array, pd.DataFrame], + y_pred: Union[np.array, pd.DataFrame], + marker: Union[str, int] = 'k.', corr: bool = True, + label_x: str = 'Ground truth', + label_y: str = 'Prediction', + title: str = 'Predicted vs Actual', + lim_x: Union[Tuple[TNum, TNum], None] = None, + lim_y: Union[Tuple[TNum, TNum], None] = None, + ax: plt.axes = None, figsize: Tuple[int, int] = (12, 5), + grid: bool = False, style: str = 'default', + **kwargs) -> plt.axes: + """ + Plot the ground truth against the predictions of the model. + + If a DataFrame is provided, it must only contain one column. + + Parameters + ---------- + y_true : np.array or pd.DataFrame + Actual values. + y_pred : np.array or pd.DataFrame + Predicted values by the model. + label_x : str, default 'Ground truth' + Label for x axis. + label_y : str, default 'Prediction' + Label for y axis. + title : str, default 'Predicted vs Actual' + Title for the plot (axis level). + lim_x : Tuple[TNum, TNum], default None + Limit for the x axis. If None, automatically calculated according + to the limits of the data, with an extra 5% for readability. + lim_y : Tuple[TNum, TNum], default None + Limit for the y axis. If None, automatically calculated according + to the limits of the data, with an extra 5% for readability. + ax : plt.axes, default None + Axes from matplotlib, if None, new figure and axes will be created. + figsize : Tuple[int, int], default (12, 5) + Size of the figure to plot. + grid : bool, default False + Turn the axes grids on or off. + style : str, default 'default' + Style to use for matplotlib.pyplot. + The style is use only in this context and not applied globally. + **kwargs + Additional keyword arguments to be passed to the + `plt.plot` function from matplotlib. + + Returns + ------- + plt.axes + Axes returned by the `plt.subplots` function. + + Examples + -------- + >>> y_pred = model.predict(x_test, ...) + >>> plot_true_vs_pred(y_true, y_pred, title='MyTitle', linestyle=':') + """ + with plt.style.context(style): + if ax is None: + __, ax = plt.subplots(1, 1, figsize=figsize) + + y_true = np.array(y_true).flatten() + y_pred = np.array(y_pred).flatten() + ax.plot(y_true, y_pred, marker, **kwargs) + ax.set_xlabel(label_x) + ax.set_ylabel(label_y) + ax.set_title(title) + ax.grid(grid) + + # Calculate the limit of the plot if not provided, + # add and extra 5% for readability. + def get_limit(limit, data, percent=5): + if not limit or not isinstance(limit, tuple): + lim_max = data.max() + lim_min = data.min() + margin = (lim_max - lim_min) * percent / 100 + limit = (lim_min - margin, lim_max + margin) + return limit + + ax.set_xlim(get_limit(lim_x, y_true)) + ax.set_ylim(get_limit(lim_y, y_pred)) + + # Add correlation in upper left position. + if corr: + ax.text(0.025, 0.925, + f'R={np.round(np.corrcoef(y_true, y_pred)[0][1], 3)}', + transform=ax.transAxes) + + return ax diff --git a/setup.py b/setup.py index 8b3f35f..de6a2e2 100644 --- a/setup.py +++ b/setup.py @@ -26,6 +26,7 @@ 'matplotlib==3.1.1', 'numpy==1.16.4', 'pandas==0.25.0', + 'python-dateutil==2.8.0', 'pyyaml==5.1.1', 'scipy==1.3.0', 'typing==3.7.4' diff --git a/tests/test_fancy.py b/tests/test_fancy.py index 8226171..1304ad5 100644 --- a/tests/test_fancy.py +++ b/tests/test_fancy.py @@ -43,7 +43,6 @@ def test_cast_to_category_pd(self): country_type = CategoricalDtype(categories=['China', 'Switzerland'], ordered=False) optimized_types = {'name': np.dtype('O'), 'age': np.dtype('int64'), 'country': country_type} - print(df_optimized.dtypes.to_dict()) self.assertDictEqual(df_optimized.dtypes.to_dict(), optimized_types) def test_concat_with_categories(self): From c7b80948043d23870e2041ab1a0bccd2e6aae4f6 Mon Sep 17 00:00:00 2001 From: Axel Fahy Date: Wed, 21 Aug 2019 15:09:30 +0200 Subject: [PATCH 02/10] fix(plot-series): correction of resampling Removed the useless groupby and allowed to plot series without resampling the data. --- README.md | 1 + bff/plot/plot.py | 49 +++++++++++++++++++++++++----------------------- 2 files changed, 27 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index 11d1697..5567139 100644 --- a/README.md +++ b/README.md @@ -48,6 +48,7 @@ make all * 0.2.0 * ADD: Separation of plots in submodule ``plot``. This breaks the previous API. + * FIX: Correction of resampling in the ``plot_series`` function. * 0.1.9 * ADD: Option ``loc`` in ``plot_series`` function. * ADD: Function ``cast_to_category_pd`` to cast columns to category ``dtype`` automatically. diff --git a/bff/plot/plot.py b/bff/plot/plot.py index 3103456..21e7202 100644 --- a/bff/plot/plot.py +++ b/bff/plot/plot.py @@ -187,8 +187,8 @@ def plot_predictions(y_true: Union[np.array, pd.DataFrame], return ax -def plot_series(df: pd.DataFrame, column: str, groupby: str = '1S', - with_sem: bool = True, with_peaks: bool = False, +def plot_series(df: pd.DataFrame, column: str, groupby: Union[str, None] = None, + with_sem: bool = False, with_peaks: bool = False, with_missing_datetimes: bool = False, distance_scale: float = 0.04, label_x: str = 'Datetime', label_y: Union[str, None] = None, title: str = 'Plot of series', @@ -205,15 +205,18 @@ def plot_series(df: pd.DataFrame, column: str, groupby: str = '1S', DataFrame to plot, with datetime as index. column : str Column of the DataFrame to display. - groupby : str, default '1S' + groupby : str or None, default None Grouping for the resampling by mean of the data. For example, can resample from seconds ('S') to minutes ('T'). - with_sem : bool, default True + By default, no resampling is applied. + with_sem : bool, default False Display the standard error of the mean (SEM) if set to true. + Only possible if a resampling has been done. with_peaks : bool, default False Display the peaks of the serie if set to true. with_missing_datetimes : bool, default False Display the missing datetimes with vertical red lines. + Only possible if a resampling has been done. distance_scale: float, default 0.04 Scaling for the minimal distance between peaks. Only used if `with_peaks` is set to true. @@ -270,24 +273,21 @@ def plot_series(df: pd.DataFrame, column: str, groupby: str = '1S', label_y = column.capitalize() # Get the values to plot. - df_plot = (df[column].groupby('datetime').mean() - .resample(groupby).mean()) + df_plot = df[column] + if groupby: + df_plot = df_plot.resample(groupby).mean() + x = df_plot.index ax.plot(x, df_plot, label=column, color=color, lw=2, **kwargs) # With sem (standard error of the mean). - if with_sem: - df_sem = (df[column] - .groupby('datetime') - .mean() - .resample(groupby) - .apply(sem) - if groupby not in ('S', '1S') else - df[column].groupby('datetime').apply(sem)) + sem_alpha = 0.3 + if groupby and with_sem: + df_sem = df_plot.resample(groupby).sem() ax.fill_between(x, df_plot - df_sem, df_plot + df_sem, - color='grey', alpha=0.2) + color='grey', alpha=sem_alpha) # Plot the peak as circle. if with_peaks: @@ -296,16 +296,19 @@ def plot_series(df: pd.DataFrame, column: str, groupby: str = '1S', color='plum') # Plot vertical line where there is missing datetimes. - if with_missing_datetimes: - df_date_missing = pd.date_range(start=df.index.get_level_values(0).min(), - end=df.index.get_level_values(0).max(), - freq=groupby).difference(df.index.get_level_values(0)) + if groupby and with_missing_datetimes: + df_date_missing = pd.date_range(start=df_plot.index.get_level_values(0).min(), + end=df_plot.index.get_level_values(0).max(), + freq=groupby).difference(df_plot.index) for date in df_date_missing.tolist(): ax.axvline(date, color='crimson') ax.set_xlabel(label_x, fontsize=12) ax.set_ylabel(label_y, fontsize=12) - ax.set_title(f'{title} (mean by {groupby})', fontsize=14) + # Add the groupby if any + if groupby: + title += f' (mean by {groupby})' + ax.set_title(title, fontsize=14) # Style. # Remove border on the top and right. @@ -329,8 +332,8 @@ def plot_series(df: pd.DataFrame, column: str, groupby: str = '1S', handles, labels = ax.get_legend_handles_labels() labels_cap = [label.capitalize() for label in labels] # Add the sem on the legend. - if with_sem: - handles.append(mpatches.Patch(color='grey', alpha=0.2)) + if groupby and with_sem: + handles.append(mpatches.Patch(color='grey', alpha=sem_alpha)) labels_cap.append('Standard error of the mean (SEM)') # Add the peak symbol on the legend. @@ -340,7 +343,7 @@ def plot_series(df: pd.DataFrame, column: str, groupby: str = '1S', labels_cap.append('Peaks') # Add the missing date line on the legend. - if with_missing_datetimes: + if groupby and with_missing_datetimes: handles.append(mlines.Line2D([], [], linestyle='-', color='crimson')) labels_cap.append('Missing datetimes') From 0bce61cf78f91e39ac402130c73847227a3e3f78 Mon Sep 17 00:00:00 2001 From: Axel Fahy Date: Thu, 22 Aug 2019 15:05:31 +0200 Subject: [PATCH 03/10] feat(plot): update styles of plots and add parameters Parameter `rotation_xticks` added to rotate the xticks. Closes #15. Modification of style according to Data-Ink Ratio. Update of dependencies. --- bff/plot/plot.py | 239 +++++++++++++++++++++++++++++++++---------- requirements_dev.txt | 1 + setup.py | 6 +- 3 files changed, 188 insertions(+), 58 deletions(-) diff --git a/bff/plot/plot.py b/bff/plot/plot.py index 21e7202..71d97cc 100644 --- a/bff/plot/plot.py +++ b/bff/plot/plot.py @@ -5,7 +5,6 @@ """ import logging from typing import Sequence, Tuple, Union -from scipy.stats import sem import matplotlib as mpl import matplotlib.lines as mlines import matplotlib.patches as mpatches @@ -21,8 +20,11 @@ def plot_history(history, metric: Union[str, None] = None, title: str = 'Model history', - axes: plt.axes = None, figsize: Tuple[int, int] = (12, 4), - grid: bool = False, style: str = 'default', + axes: plt.axes = None, + loc: Union[str, int] = 'best', + grid: Union[str, None] = None, + figsize: Tuple[int, int] = (16, 5), dpi: int = 80, + style: str = 'default', **kwargs) -> Union[plt.axes, Sequence[plt.axes]]: """ Plot the history of the model trained using Keras. @@ -39,10 +41,16 @@ def plot_history(history, metric: Union[str, None] = None, title: str = 'Model h axes : plt.axes, default None Axes from matplotlib, if None, new figure and axes will be created. If metric is provided, need to have at least 2 axes. - figsize : Tuple[int, int], default (12, 4) + loc : str or int, default 'best' + Location of the legend on the plot. + Either the legend string or legend code are possible. + grid : str or None, default None + Axis where to activate the grid ('both', 'x', 'y'). + To turn off, set to None. + figsize : Tuple[int, int], default (16, 5) Size of the figure to plot. - grid : bool, default False - Turn the axes grids on or off. + dpi : int, default 80 + Resolution of the figure. style : str, default 'default' Style to use for matplotlib.pyplot. The style is use only in this context and not applied globally. @@ -70,7 +78,8 @@ def plot_history(history, metric: Union[str, None] = None, title: str = 'Model h # Given axes are not check for now. # If metric is given, must have at least 2 axes. if axes is None: - fig, axes = plt.subplots(1, 2 if metric else 1, figsize=figsize) + fig, axes = plt.subplots(1, 2 if metric else 1, + figsize=figsize, dpi=dpi) else: fig = plt.gcf() @@ -82,10 +91,10 @@ def plot_history(history, metric: Union[str, None] = None, title: str = 'Model h axes[0].plot(history.history[f'val_{metric}'], label=f"Validation ({history.history[f'val_{metric}'][-1]:.4f})", **kwargs) - axes[0].set_title(f'Model {metric}') - axes[0].set_xlabel('Epochs') - axes[0].set_ylabel(metric.capitalize()) - axes[0].legend(loc='upper left') + axes[0].set_title(f'Model {metric}', fontsize=14) + axes[0].set_xlabel('Epochs', fontsize=12) + axes[0].set_ylabel(metric.capitalize(), fontsize=12) + axes[0].legend(loc=loc) # Summarize history for loss. ax_loss = axes[1] if metric else axes @@ -95,19 +104,46 @@ def plot_history(history, metric: Union[str, None] = None, title: str = 'Model h ax_loss.plot(history.history['val_loss'], label=f"Validation ({history.history['val_loss'][-1]:.4f})", **kwargs) - ax_loss.set_title('Model loss') - ax_loss.set_xlabel('Epochs') - ax_loss.set_ylabel('Loss') - ax_loss.legend(loc='upper left') - - # Put the grid on axes. - if metric: - for ax in axes.flatten(): - ax.grid(grid) - else: - axes.grid(grid) + ax_loss.set_xlabel('Epochs', fontsize=12) + ax_loss.set_ylabel('Loss', fontsize=12) + ax_loss.set_title('Model loss', fontsize=14) + ax_loss.legend(loc=loc) + + # Global title of the plot. + fig.suptitle(title, fontsize=16) + + # Style, applied on all axis. + axes_to_style = axes.flatten() if metric else [axes] + for ax in axes_to_style: + # Remove border on the top and right. + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + # Set alpha on remaining borders. + ax.spines['left'].set_alpha(0.4) + ax.spines['bottom'].set_alpha(0.4) + + # Remove ticks on y axis. + ax.xaxis.set_ticks_position('bottom') + ax.yaxis.set_ticks_position('none') + + # Draw tick lines on wanted axes. + if grid: + ax.axes.grid(True, which='major', axis=grid, color='black', + alpha=0.3, linestyle='--', lw=0.5) + + # Set a thousand separator axis. + ax.xaxis.set_major_formatter( + mpl.ticker.FuncFormatter(lambda x, p: f'{x:,.1f}') + ) + ax.yaxis.set_major_formatter( + mpl.ticker.FuncFormatter(lambda x, p: f'{x:,.1f}') + ) + + if not metric: + # Style of ticks. + plt.xticks(fontsize=10, alpha=0.7) + plt.yticks(fontsize=10, alpha=0.7) - fig.suptitle(title) return axes @@ -117,7 +153,10 @@ def plot_predictions(y_true: Union[np.array, pd.DataFrame], label_x: str = 'x', label_y: str = 'y', title: str = 'Model predictions', ax: plt.axes = None, - figsize: Tuple[int, int] = (12, 4), grid: bool = False, + loc: Union[str, int] = 'best', + rotation_xticks: Union[float, None] = None, + grid: Union[str, None] = 'y', + figsize: Tuple[int, int] = (14, 5), dpi: int = 80, style: str = 'default', **kwargs) -> plt.axes: """ Plot the predictions of the model. @@ -142,10 +181,19 @@ def plot_predictions(y_true: Union[np.array, pd.DataFrame], Title for the plot (axis level). ax : plt.axes, default None Axes from matplotlib, if None, new figure and axes will be created. - figsize : Tuple[int, int], default (12, 4) + loc : str or int, default 'best' + Location of the legend on the plot. + Either the legend string or legend code are possible. + rotation_xticks : float or None, default None + Rotation of x ticks if any. + Set to 90 to put them vertically. + grid : str or None, default 'y' + Axis where to activate the grid ('both', 'x', 'y'). + To turn off, set to None. + figsize : Tuple[int, int], default (14, 5) Size of the figure to plot. - grid : bool, default False - Turn the axes grids on or off. + dpi : int, default 80 + Resolution of the figure. style : str, default 'default' Style to use for matplotlib.pyplot. The style is use only in this context and not applied globally. @@ -165,7 +213,7 @@ def plot_predictions(y_true: Union[np.array, pd.DataFrame], """ with plt.style.context(style): if ax is None: - __, ax = plt.subplots(1, 1, figsize=figsize) + __, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) # Plot predictions. ax.plot(np.array(y_pred).flatten(), color='r', @@ -173,16 +221,43 @@ def plot_predictions(y_true: Union[np.array, pd.DataFrame], # Plot actual values on top of the predictions. ax.plot(np.array(y_true).flatten(), color='b', label=label_true, **kwargs) - ax.set_xlabel(label_x) - ax.set_ylabel(label_y) - ax.set_title(title) - ax.grid(grid) + ax.set_xlabel(label_x, fontsize=12) + ax.set_ylabel(label_y, fontsize=12) + ax.set_title(title, fontsize=14) + + # Style. + # Remove borders on the top and right. + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + # Set alpha on remaining borders. + ax.spines['left'].set_alpha(0.4) + ax.spines['bottom'].set_alpha(0.4) + + # Remove ticks on y axis. + ax.xaxis.set_ticks_position('bottom') + ax.yaxis.set_ticks_position('none') + # Style of ticks. + plt.xticks(fontsize=10, alpha=0.7, rotation=rotation_xticks) + plt.yticks(fontsize=10, alpha=0.7) + + # Draw tick lines on wanted axes. + if grid: + ax.axes.grid(True, which='major', axis=grid, color='black', + alpha=0.3, linestyle='--', lw=0.5) + + # Set a thousand separator axis. + ax.xaxis.set_major_formatter( + mpl.ticker.FuncFormatter(lambda x, p: f'{x:,.1f}') + ) + ax.yaxis.set_major_formatter( + mpl.ticker.FuncFormatter(lambda x, p: f'{x:,.1f}') + ) # Sort labels and handles by labels. handles, labels = ax.get_legend_handles_labels() labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0])) - ax.legend(handles, labels, loc='upper left') + ax.legend(handles, labels, loc=loc) return ax @@ -194,6 +269,8 @@ def plot_series(df: pd.DataFrame, column: str, groupby: Union[str, None] = None, label_y: Union[str, None] = None, title: str = 'Plot of series', ax: plt.axes = None, color: str = '#3F5D7D', loc: Union[str, int] = 'best', + rotation_xticks: Union[float, None] = None, + grid: Union[str, None] = 'y', figsize: Tuple[int, int] = (14, 6), dpi: int = 80, style: str = 'default', **kwargs) -> plt.axes: """ @@ -233,6 +310,12 @@ def plot_series(df: pd.DataFrame, column: str, groupby: Union[str, None] = None, loc : str or int, default 'best' Location of the legend on the plot. Either the legend string or legend code are possible. + rotation_xticks : float or None, default None + Rotation of x ticks if any. + Set to 90 to put them vertically. + grid : str or None, default 'y' + Axis where to activate the grid ('both', 'x', 'y'). + To turn off, set to None. figsize : Tuple[int, int], default (14, 6) Size of the figure to plot. dpi : int, default 80 @@ -268,7 +351,7 @@ def plot_series(df: pd.DataFrame, column: str, groupby: Union[str, None] = None, if ax is None: __, ax = plt.subplots(figsize=figsize, dpi=dpi) - # By default, the y label if the column name. + # By default, the y label is the column name. if label_y is None: label_y = column.capitalize() @@ -314,17 +397,23 @@ def plot_series(df: pd.DataFrame, column: str, groupby: Union[str, None] = None, # Remove border on the top and right. ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) + # Set alpha on remaining borders. + ax.spines['left'].set_alpha(0.4) + ax.spines['bottom'].set_alpha(0.4) # Remove ticks on y axis. - ax.yaxis.set_ticks_position('none') ax.xaxis.set_ticks_position('bottom') + ax.yaxis.set_ticks_position('none') + # Style of ticks. + plt.xticks(fontsize=10, alpha=0.7, rotation=rotation_xticks) + plt.yticks(fontsize=10, alpha=0.7) - # Draw Horizontal Tick lines. - ax.xaxis.grid(False) - ax.yaxis.grid(which='major', color='black', alpha=0.5, - linestyle='--', lw=0.5) + # Draw tick lines on wanted axes. + if grid: + ax.axes.grid(True, which='major', axis=grid, color='black', + alpha=0.3, linestyle='--', lw=0.5) - # Set thousand separator for y axis. + # Set a thousand separator for y axis. ax.yaxis.set_major_formatter( mpl.ticker.FuncFormatter(lambda x, p: f'{x:,.1f}') ) @@ -355,14 +444,17 @@ def plot_series(df: pd.DataFrame, column: str, groupby: Union[str, None] = None, def plot_true_vs_pred(y_true: Union[np.array, pd.DataFrame], y_pred: Union[np.array, pd.DataFrame], - marker: Union[str, int] = 'k.', corr: bool = True, + with_correlation: bool = True, + marker: Union[str, int] = 'k.', label_x: str = 'Ground truth', label_y: str = 'Prediction', title: str = 'Predicted vs Actual', + ax: plt.axes = None, lim_x: Union[Tuple[TNum, TNum], None] = None, lim_y: Union[Tuple[TNum, TNum], None] = None, - ax: plt.axes = None, figsize: Tuple[int, int] = (12, 5), - grid: bool = False, style: str = 'default', + grid: Union[str, None] = 'both', + figsize: Tuple[int, int] = (14, 7), dpi: int = 80, + style: str = 'default', **kwargs) -> plt.axes: """ Plot the ground truth against the predictions of the model. @@ -375,24 +467,31 @@ def plot_true_vs_pred(y_true: Union[np.array, pd.DataFrame], Actual values. y_pred : np.array or pd.DataFrame Predicted values by the model. + with_correlation : bool, default True + If true, print correlation coefficients in the top left corner. + marker : str or int, default 'k.', + Style of the markers to plot, by default black points. label_x : str, default 'Ground truth' Label for x axis. label_y : str, default 'Prediction' Label for y axis. title : str, default 'Predicted vs Actual' Title for the plot (axis level). + ax : plt.axes, default None + Axes from matplotlib, if None, new figure and axes will be created. lim_x : Tuple[TNum, TNum], default None Limit for the x axis. If None, automatically calculated according to the limits of the data, with an extra 5% for readability. lim_y : Tuple[TNum, TNum], default None Limit for the y axis. If None, automatically calculated according to the limits of the data, with an extra 5% for readability. - ax : plt.axes, default None - Axes from matplotlib, if None, new figure and axes will be created. - figsize : Tuple[int, int], default (12, 5) + grid : str or None, default 'both' + Axis where to activate the grid ('both', 'x', 'y'). + To turn off, set to None. + figsize : Tuple[int, int], default (14, 7) Size of the figure to plot. - grid : bool, default False - Turn the axes grids on or off. + dpi : int, default 80 + Resolution of the figure. style : str, default 'default' Style to use for matplotlib.pyplot. The style is use only in this context and not applied globally. @@ -412,15 +511,16 @@ def plot_true_vs_pred(y_true: Union[np.array, pd.DataFrame], """ with plt.style.context(style): if ax is None: - __, ax = plt.subplots(1, 1, figsize=figsize) + __, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) y_true = np.array(y_true).flatten() y_pred = np.array(y_pred).flatten() + ax.plot(y_true, y_pred, marker, **kwargs) - ax.set_xlabel(label_x) - ax.set_ylabel(label_y) - ax.set_title(title) - ax.grid(grid) + + ax.set_xlabel(label_x, fontsize=12) + ax.set_ylabel(label_y, fontsize=12) + ax.set_title(title, fontsize=14) # Calculate the limit of the plot if not provided, # add and extra 5% for readability. @@ -432,13 +532,42 @@ def get_limit(limit, data, percent=5): limit = (lim_min - margin, lim_max + margin) return limit + # Set x and y limits. ax.set_xlim(get_limit(lim_x, y_true)) ax.set_ylim(get_limit(lim_y, y_pred)) # Add correlation in upper left position. - if corr: + if with_correlation: ax.text(0.025, 0.925, f'R={np.round(np.corrcoef(y_true, y_pred)[0][1], 3)}', - transform=ax.transAxes) + fontsize=12, transform=ax.transAxes) + + # Style. + # Remove borders on the top and right. + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + # Set alpha on remaining borders. + ax.spines['left'].set_alpha(0.4) + ax.spines['bottom'].set_alpha(0.4) + + # Remove ticks on axis. + ax.xaxis.set_ticks_position('none') + ax.yaxis.set_ticks_position('none') + # Style of ticks. + plt.xticks(fontsize=10, alpha=0.7) + plt.yticks(fontsize=10, alpha=0.7) + + # Draw tick lines on wanted axes. + if grid: + ax.axes.grid(True, which='major', axis=grid, color='black', + alpha=0.3, linestyle='--', lw=0.5) + + # Set a thousand separator axis. + ax.xaxis.set_major_formatter( + mpl.ticker.FuncFormatter(lambda x, p: f'{x:,.1f}') + ) + ax.yaxis.set_major_formatter( + mpl.ticker.FuncFormatter(lambda x, p: f'{x:,.1f}') + ) return ax diff --git a/requirements_dev.txt b/requirements_dev.txt index 34c459b..3771af6 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -12,3 +12,4 @@ recommonmark Sphinx sphinx-autodoc-typehints sphinx-rtd-theme +wheel diff --git a/setup.py b/setup.py index de6a2e2..10252c2 100644 --- a/setup.py +++ b/setup.py @@ -24,11 +24,11 @@ } REQUIRES = [ 'matplotlib==3.1.1', - 'numpy==1.16.4', + 'numpy==1.17.0', 'pandas==0.25.0', 'python-dateutil==2.8.0', - 'pyyaml==5.1.1', - 'scipy==1.3.0', + 'pyyaml==5.1.2', + 'scipy==1.3.1', 'typing==3.7.4' ] CLASSIFIERS = [ From 5f45db17ea3f9e60239bf4f05a1b4b91c4cf98c9 Mon Sep 17 00:00:00 2001 From: Axel Fahy Date: Thu, 22 Aug 2019 17:04:38 +0200 Subject: [PATCH 04/10] feat(plot-true-vs-pred): add histograms on the side Closes #17. --- bff/plot/plot.py | 87 +++++++++++++++++++++++++++++++++++------------- 1 file changed, 63 insertions(+), 24 deletions(-) diff --git a/bff/plot/plot.py b/bff/plot/plot.py index 71d97cc..852ce42 100644 --- a/bff/plot/plot.py +++ b/bff/plot/plot.py @@ -445,7 +445,7 @@ def plot_series(df: pd.DataFrame, column: str, groupby: Union[str, None] = None, def plot_true_vs_pred(y_true: Union[np.array, pd.DataFrame], y_pred: Union[np.array, pd.DataFrame], with_correlation: bool = True, - marker: Union[str, int] = 'k.', + with_histograms: bool = False, label_x: str = 'Ground truth', label_y: str = 'Prediction', title: str = 'Predicted vs Actual', @@ -469,8 +469,9 @@ def plot_true_vs_pred(y_true: Union[np.array, pd.DataFrame], Predicted values by the model. with_correlation : bool, default True If true, print correlation coefficients in the top left corner. - marker : str or int, default 'k.', - Style of the markers to plot, by default black points. + with_histograms : bool, default False + If true, plot histograms of `y_true` and `y_pred` on the sides. + Not possible if the `ax` is provided. label_x : str, default 'Ground truth' Label for x axis. label_y : str, default 'Prediction' @@ -503,6 +504,7 @@ def plot_true_vs_pred(y_true: Union[np.array, pd.DataFrame], ------- plt.axes Axes returned by the `plt.subplots` function. + If `with_histograms`, return the three axes. Examples -------- @@ -511,16 +513,32 @@ def plot_true_vs_pred(y_true: Union[np.array, pd.DataFrame], """ with plt.style.context(style): if ax is None: - __, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) + if with_histograms: + fig = plt.figure(figsize=figsize, dpi=dpi) + grid_plt = plt.GridSpec(4, 4, hspace=0.5, wspace=0.2) + # Define the axes + ax_main = fig.add_subplot(grid_plt[:-1, :-1]) + ax_right = fig.add_subplot(grid_plt[:-1, -1], xticklabels=[], yticklabels=[]) + ax_bottom = fig.add_subplot(grid_plt[-1, 0:-1], xticklabels=[], yticklabels=[]) + else: + __, ax_main = plt.subplots(1, 1, figsize=figsize, dpi=dpi) + else: + assert not with_histograms, ( + 'Option `with_histograms is not possible when providing an axis.') + ax_main = ax y_true = np.array(y_true).flatten() y_pred = np.array(y_pred).flatten() - ax.plot(y_true, y_pred, marker, **kwargs) + # Default marker is black if not provided. + if 'c' not in kwargs: + kwargs['c'] = 'black' - ax.set_xlabel(label_x, fontsize=12) - ax.set_ylabel(label_y, fontsize=12) - ax.set_title(title, fontsize=14) + ax_main.scatter(y_true, y_pred, **kwargs) + + ax_main.set_xlabel(label_x, fontsize=12) + ax_main.set_ylabel(label_y, fontsize=12) + ax_main.set_title(title, fontsize=14) # Calculate the limit of the plot if not provided, # add and extra 5% for readability. @@ -533,41 +551,62 @@ def get_limit(limit, data, percent=5): return limit # Set x and y limits. - ax.set_xlim(get_limit(lim_x, y_true)) - ax.set_ylim(get_limit(lim_y, y_pred)) + ax_main.set_xlim(get_limit(lim_x, y_true)) + ax_main.set_ylim(get_limit(lim_y, y_pred)) # Add correlation in upper left position. if with_correlation: - ax.text(0.025, 0.925, - f'R={np.round(np.corrcoef(y_true, y_pred)[0][1], 3)}', - fontsize=12, transform=ax.transAxes) + ax_main.text(0.025, 0.925, + f'R={np.round(np.corrcoef(y_true, y_pred)[0][1], 3)}', + fontsize=12, transform=ax_main.transAxes) + + if with_histograms: + # Histogram on the right. + ax_bottom.hist(y_pred, histtype='stepfilled', orientation='vertical', + color='cadetblue') + ax_bottom.invert_yaxis() + + # Histogram in the bottom. + ax_right.hist(y_true, histtype='stepfilled', orientation='horizontal', + color='cadetblue') + + # Style of histograms. + # Remove borders. + for spine in ['top', 'right', 'left', 'bottom']: + ax_bottom.spines[spine].set_alpha(0.3) + ax_right.spines[spine].set_alpha(0.3) + # Remove ticks on axis. + ax_bottom.xaxis.set_ticks_position('none') + ax_bottom.yaxis.set_ticks_position('none') + ax_right.xaxis.set_ticks_position('none') + ax_right.yaxis.set_ticks_position('none') # Style. # Remove borders on the top and right. - ax.spines['top'].set_visible(False) - ax.spines['right'].set_visible(False) + ax_main.spines['top'].set_visible(False) + ax_main.spines['right'].set_visible(False) # Set alpha on remaining borders. - ax.spines['left'].set_alpha(0.4) - ax.spines['bottom'].set_alpha(0.4) + ax_main.spines['left'].set_alpha(0.4) + ax_main.spines['bottom'].set_alpha(0.4) # Remove ticks on axis. - ax.xaxis.set_ticks_position('none') - ax.yaxis.set_ticks_position('none') + ax_main.xaxis.set_ticks_position('none') + ax_main.yaxis.set_ticks_position('none') # Style of ticks. plt.xticks(fontsize=10, alpha=0.7) plt.yticks(fontsize=10, alpha=0.7) # Draw tick lines on wanted axes. if grid: - ax.axes.grid(True, which='major', axis=grid, color='black', - alpha=0.3, linestyle='--', lw=0.5) + ax_main.axes.grid(True, which='major', axis=grid, color='black', + alpha=0.3, linestyle='--', lw=0.5) # Set a thousand separator axis. - ax.xaxis.set_major_formatter( + ax_main.xaxis.set_major_formatter( mpl.ticker.FuncFormatter(lambda x, p: f'{x:,.1f}') ) - ax.yaxis.set_major_formatter( + ax_main.yaxis.set_major_formatter( mpl.ticker.FuncFormatter(lambda x, p: f'{x:,.1f}') ) - return ax + return ax_main if not with_histograms else (ax_main, ax_right, ax_bottom) From b535719f01d9a8f1de4b858e31e81eefdbd8349b Mon Sep 17 00:00:00 2001 From: Axel Fahy Date: Fri, 23 Aug 2019 13:39:48 +0200 Subject: [PATCH 05/10] test(bff): add unittests for assertions in functions --- tests/test_fancy.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/tests/test_fancy.py b/tests/test_fancy.py index 1304ad5..c477582 100644 --- a/tests/test_fancy.py +++ b/tests/test_fancy.py @@ -74,6 +74,11 @@ def test_concat_with_categories(self): self.assertTrue(pd.api.types.is_categorical_dtype( df_concat['country'])) + # Check assertion if columns don't match. + df_left_wrong = pd.DataFrame([['John', 'XXL', 'China']], + columns=['name', 'size', 'country']) + self.assertRaises(AssertionError, concat_with_categories, df_left_wrong, df_right) + def test_get_peaks(self): """ Test of the `get_peaks` function. @@ -93,6 +98,9 @@ def test_get_peaks(self): assert_array_equal(peak_dates, peak_dates_res) assert_array_equal(peak_values, peak_values_res) + # Check assertion if index is not of type `datetime`. + self.assertRaises(AssertionError, get_peaks, pd.Series(values, index=range(len(values)))) + def test_idict(self): """ Test of the `idict` function. From 829948c65b1648c845ea6d1e08783a3537e229e1 Mon Sep 17 00:00:00 2001 From: Axel Fahy Date: Fri, 23 Aug 2019 14:11:24 +0200 Subject: [PATCH 06/10] test(config): add test for missing functions Coverage of config file is now 100%. --- tests/test_config.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/tests/test_config.py b/tests/test_config.py index b6c6aad..e2157d2 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -6,6 +6,7 @@ import filecmp from pathlib import Path import unittest +import unittest.mock from bff.config import FancyConfig @@ -40,6 +41,26 @@ def test_create_config(self): self.assertFalse(filecmp.cmp(dest, path_conf_b)) # Check that the configurations are the same. self.assertEqual(config_a, config_b) + # Check the size. + self.assertEqual(len(config_b), 3) + + # Check the representation of the config. + print_pretty = ( + "{ 'database': { 'host': '127.0.0.1',\n" + " 'name': 'porgs',\n" + " 'port': 3306,\n" + " 'pwd': 'bacca',\n" + " 'user': 'Chew'},\n" + " 'env': 'prod',\n" + " 'imports': {'star_wars': ['ewok', 'bantha']}}""") + + self.assertEqual(str(config_b), print_pretty) + + print_repr = ("{'database': {'user': 'Chew', 'pwd': 'bacca', 'host': '127.0.0.1', " + "'port': 3306, 'name': 'porgs'}, " + "'imports': {'star_wars': ['ewok', 'bantha']}, 'env': 'prod'}") + + self.assertEqual(repr(config_b).split('\n')[1], print_repr) # Removes the configs. dest.unlink() From 2699e3ada5f1385be24b8a3dcb4198d57b318c41 Mon Sep 17 00:00:00 2001 From: Axel Fahy Date: Mon, 26 Aug 2019 10:46:15 +0200 Subject: [PATCH 07/10] feat(plot-history): replace input from `history` to dictionary The input is now a dictionary object. Instead of giving the `history` object from Keras, the dictionary must be provided (`history.history`). --- bff/plot/plot.py | 33 ++++++++++++++++----------------- 1 file changed, 16 insertions(+), 17 deletions(-) diff --git a/bff/plot/plot.py b/bff/plot/plot.py index 852ce42..06816b9 100644 --- a/bff/plot/plot.py +++ b/bff/plot/plot.py @@ -19,10 +19,9 @@ LOGGER = logging.getLogger(__name__) -def plot_history(history, metric: Union[str, None] = None, title: str = 'Model history', - axes: plt.axes = None, - loc: Union[str, int] = 'best', - grid: Union[str, None] = None, +def plot_history(history: dict, metric: Union[str, None] = None, + title: str = 'Model history', axes: plt.axes = None, + loc: Union[str, int] = 'best', grid: Union[str, None] = None, figsize: Tuple[int, int] = (16, 5), dpi: int = 80, style: str = 'default', **kwargs) -> Union[plt.axes, Sequence[plt.axes]]: @@ -31,8 +30,8 @@ def plot_history(history, metric: Union[str, None] = None, title: str = 'Model h Parameters ---------- - history : tensorflow.keras.callback.History - History of the training. + history : dict + Dictionary from the history object of the training. metric : str, default None Metric to plot. If no metric is provided, will only print the loss. @@ -67,12 +66,12 @@ def plot_history(history, metric: Union[str, None] = None, title: str = 'Model h Examples -------- >>> history = model.fit(...) - >>> plot_history(history, metric='acc', title='MyTitle', linestyle=':') + >>> plot_history(history.history, metric='acc', title='MyTitle', linestyle=':') """ if metric: - assert metric in history.history.keys(), ( + assert metric in history.keys(), ( f'Metric {metric} does not exist in history.\n' - f'Possible metrics: {history.history.keys()}') + f'Possible metrics: {history.keys()}') with plt.style.context(style): # Given axes are not check for now. @@ -85,11 +84,11 @@ def plot_history(history, metric: Union[str, None] = None, title: str = 'Model h if metric: # Summarize history for metric, if any. - axes[0].plot(history.history[metric], - label=f"Train ({history.history[metric][-1]:.4f})", + axes[0].plot(history[metric], + label=f"Train ({history[metric][-1]:.4f})", **kwargs) - axes[0].plot(history.history[f'val_{metric}'], - label=f"Validation ({history.history[f'val_{metric}'][-1]:.4f})", + axes[0].plot(history[f'val_{metric}'], + label=f"Validation ({history[f'val_{metric}'][-1]:.4f})", **kwargs) axes[0].set_title(f'Model {metric}', fontsize=14) axes[0].set_xlabel('Epochs', fontsize=12) @@ -98,11 +97,11 @@ def plot_history(history, metric: Union[str, None] = None, title: str = 'Model h # Summarize history for loss. ax_loss = axes[1] if metric else axes - ax_loss.plot(history.history['loss'], - label=f"Train ({history.history['loss'][-1]:.4f})", + ax_loss.plot(history['loss'], + label=f"Train ({history['loss'][-1]:.4f})", **kwargs) - ax_loss.plot(history.history['val_loss'], - label=f"Validation ({history.history['val_loss'][-1]:.4f})", + ax_loss.plot(history['val_loss'], + label=f"Validation ({history['val_loss'][-1]:.4f})", **kwargs) ax_loss.set_xlabel('Epochs', fontsize=12) ax_loss.set_ylabel('Loss', fontsize=12) From e6b8bc0f5ccf99da9667c7ff549b548c2246b12f Mon Sep 17 00:00:00 2001 From: Axel Fahy Date: Thu, 29 Aug 2019 10:21:02 +0200 Subject: [PATCH 08/10] fix(plot-series): correction of the missing datetimes option --- bff/plot/plot.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/bff/plot/plot.py b/bff/plot/plot.py index 06816b9..7976dec 100644 --- a/bff/plot/plot.py +++ b/bff/plot/plot.py @@ -11,6 +11,7 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd +from pandas.plotting import register_matplotlib_converters import bff.fancy @@ -18,6 +19,8 @@ LOGGER = logging.getLogger(__name__) +register_matplotlib_converters() + def plot_history(history: dict, metric: Union[str, None] = None, title: str = 'Model history', axes: plt.axes = None, @@ -366,7 +369,7 @@ def plot_series(df: pd.DataFrame, column: str, groupby: Union[str, None] = None, # With sem (standard error of the mean). sem_alpha = 0.3 if groupby and with_sem: - df_sem = df_plot.resample(groupby).sem() + df_sem = df_plot.sem() ax.fill_between(x, df_plot - df_sem, df_plot + df_sem, color='grey', alpha=sem_alpha) @@ -379,9 +382,9 @@ def plot_series(df: pd.DataFrame, column: str, groupby: Union[str, None] = None, # Plot vertical line where there is missing datetimes. if groupby and with_missing_datetimes: - df_date_missing = pd.date_range(start=df_plot.index.get_level_values(0).min(), - end=df_plot.index.get_level_values(0).max(), - freq=groupby).difference(df_plot.index) + df_date_missing = pd.date_range(start=df.index.min(), + end=df.index.max(), + freq=groupby).difference(df_plot.dropna().index) for date in df_date_missing.tolist(): ax.axvline(date, color='crimson') From fcbf75e345badce867fe9389728ed4525a29abc3 Mon Sep 17 00:00:00 2001 From: Axel Fahy Date: Thu, 29 Aug 2019 10:26:02 +0200 Subject: [PATCH 09/10] test(plot): add testing of plots output and assertions --- .gitignore | 3 + .travis.yml | 6 +- Makefile | 19 +++- README.md | 11 +- requirements_dev.txt | 1 + setup.cfg | 1 + tests/test_plot.py | 248 +++++++++++++++++++++++++++++++++++++++++++ 7 files changed, 281 insertions(+), 8 deletions(-) create mode 100644 tests/test_plot.py diff --git a/.gitignore b/.gitignore index 92f1c15..5eba16e 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ +# Baseline images for tests +tests/baseline + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/.travis.yml b/.travis.yml index 2f042d3..0847313 100644 --- a/.travis.yml +++ b/.travis.yml @@ -6,8 +6,10 @@ install: - pip install -r requirements_dev.txt - pip install -e . script: - - flake8 bff - - pytest --codestyle --docstyle --pylint --pylint-rcfile=.pylintrc --pylint-error-types=CWEF + - flake8 + - mypy bff tests + - pytest --codestyle --docstyle + - pytest --pylint --pylint-rcfile=.pylintrc --pylint-error-types=CWEF - pytest --cov=bff tests after_success: - coveralls diff --git a/Makefile b/Makefile index 643a01d..e96b1bd 100644 --- a/Makefile +++ b/Makefile @@ -1,28 +1,41 @@ # Makefile to simplify test and build. -.PHONY: test clean test-env lint style coverage - +.PHONY: all all: test +.PHONY: all build-python: python setup.py sdist bdist_wheel +.PHONY: clean clean: rm -rf coverage_html_report .coverage rm -rf bff.egg-info rm -rf venv-dev rm -rf dist/ -test: lint style coverage +.PHONY: test +test: code lint style coverage + +.PHONY: baseline-plot +baseline-plot: + pytest --mpl-generate-path=tests/baseline tests + +.PHONY: code +code: + pytest --mpl tests +.PHONY: lint lint: pytest --pylint --pylint-rcfile=.pylintrc --pylint-error-types=CWEF +.PHONY: style style: flake8 mypy bff tests pytest --codestyle --docstyle +.PHONY: coverage coverage: rm -rf coverage_html_report .coverage pytest --cov=bff tests --cov-report=html:coverage_html_report diff --git a/README.md b/README.md index 5567139..e31f99a 100644 --- a/README.md +++ b/README.md @@ -44,10 +44,15 @@ pip install -e . make all ``` +To test plots, images with baseline should be placed in `tests/baseline` and can be generated using `make build-baseline`. + +As of *v0.2*, plots are not yet tested in the travis build. + ## Release History * 0.2.0 * ADD: Separation of plots in submodule ``plot``. This breaks the previous API. + * ADD: Tests for the plot module using ``pytest-mlp``. * FIX: Correction of resampling in the ``plot_series`` function. * 0.1.9 * ADD: Option ``loc`` in ``plot_series`` function. @@ -68,16 +73,16 @@ make all * ADD: Add Makefile for testing code and style. * ADD: Add python-versioneer to handle version of package. * 0.1.3 - * CHANGE: Restructuration of repo. * ADD: Travis, flake8, coveralls and PyUp configurations. * ADD: Function `get_peaks` to get the peaks of a time series. * ADD: Function `plot_series` to plot a time series. + * CHANGE: Restructuration of repo. * 0.1.2 - * CHANGE: Add axes in plot functions. * ADD: Function `plot_predictions` function to plot the actual values and the predictions of a model. + * CHANGE: Add axes in plot functions. * 0.1.1 - * CHANGE: Improvement of `plot_history` function. * ADD: Readme with instructions. + * CHANGE: Improvement of `plot_history` function. * FIX: Fix the imports in the test. * 0.1.0 * Initial release. diff --git a/requirements_dev.txt b/requirements_dev.txt index 3771af6..39b8ffd 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -6,6 +6,7 @@ pytest pytest-codestyle pytest-cov pytest-docstyle +pytest-mpl pytest-pylint python-coveralls recommonmark diff --git a/setup.cfg b/setup.cfg index 2489110..9ef7e95 100644 --- a/setup.cfg +++ b/setup.cfg @@ -26,6 +26,7 @@ testpaths = bff docstyle_convention = numpy docstyle_add_ignore = D401 codestyle_max_line_length = 100 +markers = mpl_image_compare: mark a test for image comparison. [mypy] ignore_missing_imports=True diff --git a/tests/test_plot.py b/tests/test_plot.py new file mode 100644 index 0000000..f0075b0 --- /dev/null +++ b/tests/test_plot.py @@ -0,0 +1,248 @@ +# -*- coding: utf-8 -*- +"""Test of plot module. + +This module test the various functions present in the plot module. + +Assertion and resulting images are tested. +""" +import unittest +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import pytest + +import bff.plot as bplt + + +class TestPlot(unittest.TestCase): + """ + Unittest of Fancy module. + """ + # Set the set to have the same result each time. + np.random.seed(42) + + history = { + 'loss': [2.3517270615352457, 2.3737808328178063, 2.342552079627262, + 2.310529179309481, 2.3773420348239305, 2.3290258640020935, + 2.3345777257603015, 2.336566770496081, 2.34276949460782, + 2.321525989465378, 2.3300879552735756, 2.3224288386915197, + 2.324129374183003, 2.3158747431021838, 2.3194296072475873, + 2.2962934024369894, 2.296843618603807, 2.298411148876401, + 2.302087271033819, 2.2869889256942213], + 'acc': [0.085427135, 0.09045226, 0.110552765, 0.110552765, 0.06030151, + 0.14070351, 0.110552765, 0.10552764, 0.09045226, 0.10050251, + 0.11557789, 0.12060302, 0.110552765, 0.10552764, 0.1356784, + 0.15075377, 0.11557789, 0.12060302, 0.10552764, 0.14572865], + 'val_loss': [2.3074077556791077, 2.306745302662272, 2.3061152659403104, + 2.3056061252970226, 2.30513324273213, 2.3046621198808954, + 2.304321059871107, 2.304280655512054, 2.3042611346560324, + 2.3042683235268466, 2.3044002410326705, 2.304716517416279, + 2.3049982415602894, 2.305085456921962, 2.3051163034046187, + 2.3052417696192022, 2.3052861982219377, 2.305426104982545, + 2.305481707112173, 2.3055578968795793], + 'val_acc': [0.11610487, 0.11111111, 0.11485643, 0.11360799, 0.11360799, + 0.11985019, 0.11111111, 0.10861423, 0.10861423, 0.10486891, + 0.10362048, 0.096129835, 0.09238452, 0.09113608, 0.09113608, + 0.08739076, 0.08988764, 0.09113608, 0.096129835, 0.09363296] + } + y_true = [1.87032178, 1.22725664, 9.38496685, 7.91451104, 7.60794146, + 9.65912261, 2.54053964, 7.31815866, 5.91692937, 2.78676838, + 7.92586481, 2.31337877, 1.78432016, 9.55596989, 6.64471696, + 3.33907423, 7.49321025, 7.14822795, 4.11686499, 2.40202043] + + y_pred = [1.85161709, 1.33317135, 9.45246137, 7.91986758, 7.54877922, + 9.71532022, 3.56777447, 7.88673475, 5.56090322, 2.78851836, + 6.70636033, 2.67531555, 1.13061356, 8.29287223, 6.27275223, + 2.49572863, 7.14305019, 8.53578604, 3.99890533, 2.35510298] + + # Timeseries for testing. + AXIS = {'x': 'darkorange', 'y': 'green', 'z': 'steelblue'} + + data = (pd.DataFrame(np.random.randint(0, 100, size=(60 * 60, 3)), columns=AXIS.keys()) + .set_index(pd.date_range('2018-01-01', periods=60 * 60, freq='S')) + .rename_axis('datetime')) + + # Data with missing values. + data_miss = (data + .drop(pd.date_range('2018-01-01 00:05', '2018-01-01 00:07', freq='S')) + .drop(pd.date_range('2018-01-01 00:40', '2018-01-01 00:41', freq='S')) + .drop(pd.date_range('2018-01-01 00:57', '2018-01-01 00:59', freq='S')) + ) + + def test_plot_history(self): + """ + Test of the `plot_history` function. + + Only checks the assertions. + """ + self.assertRaises(AssertionError, bplt.plot_history, self.history, 'f1_score') + + @pytest.mark.mpl_image_compare + def test_plot_history_default(self): + """ + Test of the `plot_history` function. + """ + ax = bplt.plot_history(self.history, title='Model history with random data', + grid='both', figsize=(10, 5)) + return ax.figure + + @pytest.mark.mpl_image_compare + def test_plot_history_with_ax(self): + """ + Test of the `plot_history` function. + + An ax is provided for the plot. + """ + fig, ax = plt.subplots(1, 1, figsize=(10, 5), dpi=80) + # When creating the ax separately, style seems to be 'classic'. + bplt.plot_history(self.history, axes=ax) + return fig + + @pytest.mark.mpl_image_compare + def test_plot_history_with_metric(self): + """ + Test of the `plot_history` function. + + Metrics is plotted as well as the loss. + """ + axes = bplt.plot_history(self.history, metric='acc') + return axes[0].figure + + @pytest.mark.mpl_image_compare + def test_plot_predictions(self): + """ + Test of the `plot_predictions` function. + """ + ax = bplt.plot_predictions(self.y_true, self.y_pred) + return ax.figure + + def test_plot_series(self): + """ + Test of the `plot_series` function. + + Only checks the assertions. + """ + # Check assertion when no datetime index. + columns = ['name', 'age', 'country'] + df_no_date = pd.DataFrame([['John', 24, 'China'], + ['Mary', 20, 'China'], + ['Jane', 25, 'Switzerland'], + ['James', 28, 'China']], + columns=columns) + self.assertRaises(AssertionError, bplt.plot_series, df_no_date, 'age') + + # Check assertion when column doesn't exist. + df_date = pd.DataFrame({'datetime': pd.date_range('2019-02-01', periods=50, freq='S'), + 'values': np.random.randn(50)}).set_index('datetime') + self.assertRaises(AssertionError, bplt.plot_series, df_date, 'col') + + @pytest.mark.mpl_image_compare + def test_plot_series_all_same_axis(self): + """ + Test of the `plot_series` function. + + Check the behaviour with all plots on the same ax. + """ + ax = bplt.plot_series(self.data, 'x', groupby='3T', title=f'Plot of all axis', + color=self.AXIS['x']) + for k in list(self.AXIS.keys())[1:]: + bplt.plot_series(self.data, k, groupby='3T', ax=ax, color=self.AXIS[k]) + return ax.figure + + @pytest.mark.mpl_image_compare + def test_plot_series_mult_axis(self): + """ + Test of the `plot_series` function. + + Check the behaviour with multiple axis on the figure. + """ + fig, axes = plt.subplots(nrows=len(self.AXIS), ncols=1, + figsize=(14, len(self.AXIS) * 3), dpi=80) + for i, k in enumerate(self.AXIS.keys()): + bplt.plot_series(self.data, k, ax=axes[i], title=f'Plot of axis - {k}', + color=self.AXIS[k]) + return fig + + @pytest.mark.mpl_image_compare + def test_plot_series_with_peaks(self): + """ + Test of the `plot_series` function. + + Check the behaviour with peaks and resampling. + """ + ax = bplt.plot_series(self.data, 'x', groupby='2T', with_peaks=True, + title=f'Plot of x with peaks') + return ax.figure + + @pytest.mark.mpl_image_compare + def test_plot_series_with_missing_datetimes(self): + """ + Test of the `plot_series` function. + + Check the behaviour with missing datetimes. + """ + ax = bplt.plot_series(self.data_miss, 'x', groupby='S', with_missing_datetimes=True, + title=f'Plot of x with missing datetimes') + return ax.figure + + @pytest.mark.mpl_image_compare + def test_plot_series_with_missing_datetimes_groupby(self): + """ + Test of the `plot_series` function. + + Check the behaviour with missing datetimes and groupby. + """ + ax = bplt.plot_series(self.data_miss, 'x', groupby='T', with_missing_datetimes=True, + title=f'Plot of x with missing datetimes') + return ax.figure + + @pytest.mark.mpl_image_compare + def test_plot_series_with_sem(self): + """ + Test of the `plot_series` function. + + Check the behaviour with sem and resampling. + """ + ax = bplt.plot_series(self.data, 'x', groupby='3T', with_sem=True, + title=f'Plot of x with standard error of the mean (sem)') + return ax.figure + + def test_plot_true_vs_pred(self): + """ + Test of the `plot_true_vs_pred` function. + + Only checks the assertions. + """ + __, ax = plt.subplots(1, 1, figsize=(14, 7), dpi=80) + self.assertRaises(AssertionError, bplt.plot_true_vs_pred, self.y_true, self.y_pred, + ax=ax, with_histograms=True) + + @pytest.mark.mpl_image_compare + def test_plot_true_vs_pred_default(self): + """ + Test of the `plot_predictions` function. + """ + ax = bplt.plot_true_vs_pred(self.y_true, self.y_pred) + return ax.figure + + @pytest.mark.mpl_image_compare + def test_plot_true_vs_pred_with_ax(self): + """ + Test of the `plot_predictions` function. + + An ax is provided for the plot. + """ + __, ax = plt.subplots(1, 1, figsize=(14, 7), dpi=80) + ax = bplt.plot_true_vs_pred(self.y_true, self.y_pred, ax=ax) + return ax.figure + + @pytest.mark.mpl_image_compare + def test_plot_true_vs_pred_with_histograms(self): + """ + Test of the `plot_predictions` function. + + The option `with_histograms=True` is used. + """ + axes = bplt.plot_true_vs_pred(self.y_true, self.y_pred, + with_histograms=True, marker='.', c='r') + return axes[0].figure From bad16c25731675f359ac22061402a9cabd70f533 Mon Sep 17 00:00:00 2001 From: Axel Fahy Date: Thu, 29 Aug 2019 14:19:53 +0200 Subject: [PATCH 10/10] docs(bff): add plots and examples in documentation Closes #18. --- .gitignore | 1 - Makefile | 11 + README.md | 1 + doc/README.md | 8 + doc/source/_static/style.css | 3 + doc/source/_templates/layout.html | 4 + doc/source/conf.py | 6 +- doc/source/example_plots.rst | 1 + doc/source/fancy.rst | 8 +- doc/source/index.rst | 21 +- doc/source/quickstart.rst | 33 +- notebooks/notebook-bff-plot.ipynb | 604 ++++++++++++++++++ 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notebooks/notebook-bff-plot.ipynb create mode 100644 notebooks/notebook-bff-plot.rst create mode 100644 notebooks/notebook-bff-plot_files/notebook-bff-plot_11_1.png create mode 100644 notebooks/notebook-bff-plot_files/notebook-bff-plot_15_0.png create mode 100644 notebooks/notebook-bff-plot_files/notebook-bff-plot_17_0.png create mode 100644 notebooks/notebook-bff-plot_files/notebook-bff-plot_19_1.png create mode 100644 notebooks/notebook-bff-plot_files/notebook-bff-plot_21_1.png create mode 100644 notebooks/notebook-bff-plot_files/notebook-bff-plot_23_1.png create mode 100644 notebooks/notebook-bff-plot_files/notebook-bff-plot_25_1.png create mode 100644 notebooks/notebook-bff-plot_files/notebook-bff-plot_27_1.png create mode 100644 notebooks/notebook-bff-plot_files/notebook-bff-plot_29_0.png create mode 100644 notebooks/notebook-bff-plot_files/notebook-bff-plot_31_1.png create mode 100644 notebooks/notebook-bff-plot_files/notebook-bff-plot_5_1.png create mode 100644 notebooks/notebook-bff-plot_files/notebook-bff-plot_7_1.png create mode 100644 notebooks/notebook-bff-plot_files/notebook-bff-plot_9_1.png diff --git a/.gitignore b/.gitignore index 5eba16e..18ee398 100644 --- a/.gitignore +++ b/.gitignore @@ -73,7 +73,6 @@ instance/ doc/_build doc/build doc/source/generated -doc/source/_static # PyBuilder target/ diff --git a/Makefile b/Makefile index e96b1bd..9ec494c 100644 --- a/Makefile +++ b/Makefile @@ -7,12 +7,23 @@ all: test build-python: python setup.py sdist bdist_wheel +.PHONY: build-notebook +build-notebook: + # Convert .ipynb to rst file. Needs pandoc installed. + jupyter nbconvert --to rst notebooks/notebook-bff-plot.ipynb + .PHONY: clean clean: rm -rf coverage_html_report .coverage rm -rf bff.egg-info rm -rf venv-dev rm -rf dist/ + rm -rf notebooks/notebook-bff-plot_files/ + +.PHONY: clean-notebooks +clean-notebooks: + # Corrections of path to images in notebook export. + sed -i 's/\.\. image:: /\.\. image:: \.\.\/\.\.\/notebooks\//g' notebooks/notebook-bff-plot.rst .PHONY: test test: code lint style coverage diff --git a/README.md b/README.md index e31f99a..81adc60 100644 --- a/README.md +++ b/README.md @@ -53,6 +53,7 @@ As of *v0.2*, plots are not yet tested in the travis build. * 0.2.0 * ADD: Separation of plots in submodule ``plot``. This breaks the previous API. * ADD: Tests for the plot module using ``pytest-mlp``. + * ADD: Images from plot in the documentation and notebook with examples. * FIX: Correction of resampling in the ``plot_series`` function. * 0.1.9 * ADD: Option ``loc`` in ``plot_series`` function. diff --git a/doc/README.md b/doc/README.md index 892eb9d..fac897a 100644 --- a/doc/README.md +++ b/doc/README.md @@ -6,3 +6,11 @@ make html ``` +## Convert the notebooks to rst files + +From main makefile of project: + +```sh +make build-notebook +``` + diff --git a/doc/source/_static/style.css b/doc/source/_static/style.css new file mode 100644 index 0000000..b07bdb1 --- /dev/null +++ b/doc/source/_static/style.css @@ -0,0 +1,3 @@ +.wy-nav-content { + max-width: none; +} diff --git a/doc/source/_templates/layout.html b/doc/source/_templates/layout.html new file mode 100644 index 0000000..3e44f4a --- /dev/null +++ b/doc/source/_templates/layout.html @@ -0,0 +1,4 @@ +{% extends "!layout.html" %} +{% block extrahead %} + +{% endblock %} diff --git a/doc/source/conf.py b/doc/source/conf.py index a211af1..b697fa9 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -38,8 +38,10 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ + 'nbsphinx', # Needed to import notebooks. 'recommonmark', 'sphinx.ext.autodoc', + 'sphinx.ext.mathjax', 'sphinx.ext.napoleon', # Needed to separate type from variable name in docstrings. 'sphinx_autodoc_typehints', # Need to be imported after `sphinx.ext.napoleon`. 'numpydoc', @@ -53,7 +55,7 @@ # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. -exclude_patterns = [] +exclude_patterns = ['**.ipynb_checkpoints'] # -- Options for HTML output ------------------------------------------------- @@ -66,7 +68,7 @@ # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = [] +html_static_path = ['_static'] # Theme from Read the Docs. html_theme_options = { diff --git a/doc/source/example_plots.rst b/doc/source/example_plots.rst new file mode 100644 index 0000000..cfcbeb5 --- /dev/null +++ b/doc/source/example_plots.rst @@ -0,0 +1 @@ +.. include:: ../../notebooks/notebook-bff-plot.rst diff --git a/doc/source/fancy.rst b/doc/source/fancy.rst index a76932b..fcbe861 100644 --- a/doc/source/fancy.rst +++ b/doc/source/fancy.rst @@ -13,10 +13,10 @@ All of bff's functions. bff.idict bff.mem_usage_pd bff.parse_date - bff.plot_history - bff.plot_predictions - bff.plot_series - bff.plot_true_vs_pred + bff.plot.plot_history + bff.plot.plot_predictions + bff.plot.plot_series + bff.plot.plot_true_vs_pred bff.read_sql_by_chunks bff.sliding_window bff.value_2_list diff --git a/doc/source/index.rst b/doc/source/index.rst index 9c30571..69a445b 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -12,10 +12,25 @@ The bff package contains some utility functions from plots to data manipulations The goal of this package is to have easy access of the functions I am using frequently on projects. -This is still a work in progress, contributions are welcome. -Contents --------- +.. |history_with_metric| image:: ../../tests/baseline/test_plot_history_with_metric.png + :width: 30% + :align: middle + :alt: History with metric + +.. |series_peaks| image:: ../../tests/baseline/test_plot_series_with_peaks.png + :width: 30% + :align: middle + :alt: Series with peaks + +.. |true_vs_pred| image:: ../../tests/baseline/test_plot_true_vs_pred_default.png + :width: 30% + :align: middle + :alt: True vs prediction + +|history_with_metric| |series_peaks| |true_vs_pred| + +This is still a work in progress, contributions are welcome. .. toctree:: :maxdepth: 2 diff --git a/doc/source/quickstart.rst b/doc/source/quickstart.rst index 996227e..218969b 100644 --- a/doc/source/quickstart.rst +++ b/doc/source/quickstart.rst @@ -10,15 +10,46 @@ If you use ``pip``, you can install it with:: pip install bff +Examples +-------- + +Here are some examples of possible plots from the `plot` module. + +.. toctree:: + :maxdepth: 2 + + example_plots + Development ----------- +Setup +~~~~~ + +The developement environment can be installed as follow: + +.. code-block:: bash + + git clone https://github.com/axelfahy/bff.git + cd bff + python -m venv venv-dev + source venv-dev/bin/activate + pip install -r requirements_dev.txt + pip install -e . + +Unittest +~~~~~~~~ + You can run the test using:: - make all + make all This will run unittests for code and code style checks. +To test plots, images with baseline should be placed in `tests/baseline` and can be generated using :code:`make build-baseline`. + +As of *v0.2*, plots are not yet tested in the travis build. + Contributing ------------ diff --git a/notebooks/notebook-bff-plot.ipynb b/notebooks/notebook-bff-plot.ipynb new file mode 100644 index 0000000..5bb0a62 --- /dev/null +++ b/notebooks/notebook-bff-plot.ipynb @@ -0,0 +1,604 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Examples of plots\n", + "\n", + "This notebook presents examples of the `bff.plot` module.\n", + "\n", + "For each function, the `ax` can be provided and is returned by the function.
\n", + "This allow to plot multiple things on the same axis and modify it if needed." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import bff.plot as bplt\n", + "\n", + "np.random.seed(42)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Variables with fake data to display.\n", + "history = {\n", + " 'loss': [2.3517270615352457, 2.3737808328178063, 2.342552079627262,\n", + " 2.310529179309481, 2.3773420348239305, 2.3290258640020935,\n", + " 2.3345777257603015, 2.336566770496081, 2.34276949460782,\n", + " 2.321525989465378, 2.3300879552735756, 2.3224288386915197,\n", + " 2.324129374183003, 2.3158747431021838, 2.3194296072475873,\n", + " 2.2962934024369894, 2.296843618603807, 2.298411148876401,\n", + " 2.302087271033819, 2.2869889256942213],\n", + " 'acc': [0.085427135, 0.09045226, 0.110552765, 0.110552765, 0.06030151,\n", + " 0.14070351, 0.110552765, 0.10552764, 0.09045226, 0.10050251,\n", + " 0.11557789, 0.12060302, 0.110552765, 0.10552764, 0.1356784,\n", + " 0.15075377, 0.11557789, 0.12060302, 0.10552764, 0.14572865],\n", + " 'val_loss': [2.3074077556791077, 2.306745302662272, 2.3061152659403104,\n", + " 2.3056061252970226, 2.30513324273213, 2.3046621198808954,\n", + " 2.304321059871107, 2.304280655512054, 2.3042611346560324,\n", + " 2.3042683235268466, 2.3044002410326705, 2.304716517416279,\n", + " 2.3049982415602894, 2.305085456921962, 2.3051163034046187,\n", + " 2.3052417696192022, 2.3052861982219377, 2.305426104982545,\n", + " 2.305481707112173, 2.3055578968795793],\n", + " 'val_acc': [0.11610487, 0.11111111, 0.11485643, 0.11360799, 0.11360799,\n", + " 0.11985019, 0.11111111, 0.10861423, 0.10861423, 0.10486891,\n", + " 0.10362048, 0.096129835, 0.09238452, 0.09113608, 0.09113608,\n", + " 0.08739076, 0.08988764, 0.09113608, 0.096129835, 0.09363296]\n", + "}\n", + "\n", + "y_true = [1.87032178, 1.2272566 , 9.38496685, 7.91451104, 7.60794146,\n", + " 9.65912261, 2.5405396 , 7.31815866, 5.91692937, 2.78676838,\n", + " 7.9258648 , 2.31337877, 1.78432016, 9.5559698 , 6.64471696,\n", + " 3.33907423, 7.49321025, 7.14822795, 4.11686499, 2.40202043]\n", + "\n", + "y_pred = [1.85161709, 1.33317135, 9.45246137, 7.9198675 , 7.54877922,\n", + " 9.7153202 , 3.56777447, 7.88673475, 5.56090322, 2.78851836,\n", + " 6.70636033, 2.67531555, 1.13061356, 8.29287223, 6.27275223,\n", + " 2.4957286 , 7.14305019, 8.53578604, 3.99890533, 2.35510298]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot of history\n", + "\n", + "The `plot_history` function can plot the loss and a metric from the `history.history` dictionary usually returned by a Keras model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot of only loss with grid and a different style for matplotlib. " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bplt.plot_history(history, title='Model history with random data', grid='both', figsize=(10, 5), style='seaborn')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot of history using a previously created axis." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "__, ax = plt.subplots(1, 1, figsize=(10, 5), dpi=80)\n", + "bplt.plot_history(history, axes=ax, style='seaborn')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot of history with loss and acc." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([,\n", + " ],\n", + " dtype=object)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bplt.plot_history(history, metric='acc')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot of predictions\n", + "\n", + "Plot of actual and predicted values on the same axis." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bplt.plot_predictions(y_true, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot series\n", + "\n", + "Series can either be plot on the same axis or in different ones in the same figure.\n", + "\n", + "Some fake data are created in a `DataFrame`. The index must be named `datetime`. A different color is assigned to each of the acceleration." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "AXIS = {'x': 'darkorange', 'y': 'green', 'z': 'steelblue'}\n", + "data = (pd.DataFrame(np.random.randint(0, 100, size=(60 * 60, 3)), columns=AXIS.keys())\n", + " .set_index(pd.date_range('2018-01-01', periods=60 * 60, freq='S'))\n", + " .rename_axis('datetime'))\n", + "\n", + "data_miss = (data\n", + " .drop(pd.date_range('2018-01-01 00:05', '2018-01-01 00:07', freq='S'))\n", + " .drop(pd.date_range('2018-01-01 00:40', '2018-01-01 00:41', freq='S'))\n", + " .drop(pd.date_range('2018-01-01 00:57', '2018-01-01 00:59', freq='S'))\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot of x, y and z acceleration on the same axis. The function is returning the axis so it can be used in the next plot." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = bplt.plot_series(data, 'x', groupby='3T', title=f'Plot of all axis', color=AXIS['x'])\n", + "for k in list(AXIS.keys())[1:]:\n", + " bplt.plot_series(data, k, groupby='3T', ax=ax, color=AXIS[k])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time, accelerations are plot on a figure containing a different axis for each acceleration." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "_, axes = plt.subplots(nrows=len(AXIS), ncols=1, figsize=(14, len(AXIS) * 3), dpi=80)\n", + "for i, k in enumerate(AXIS.keys()):\n", + " bplt.plot_series(data, k, ax=axes[i], title=f'Plot of axis - {k}', color=AXIS[k])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A resampling of the data is done by averaging each 2 minutes (`2T`). A peak detection is done as well." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bplt.plot_series(data, 'x', groupby='2T', with_peaks=True, title=f'Plot of x with peaks')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A resampling of the data is done by averaging each 3 minutes (`2T`). The standard error of the mean (SEM) is plotted as well. This is usefull to see if the data are close to the mean or not since there was a resampling." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bplt.plot_series(data, 'x', groupby='3T', with_sem=True, title=f'Plot of x with standard error of the mean (sem)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot of a serie with missing data. By specifying the resampling, we can easily see if some of the datetime are missing." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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hhnPyXatWLZfC0c/PD48//jgA4MaNG7JyCqGl3JTKMmPGDHz++ef466+/0KpVK9SpUwe//fabpOtlZxxc+yta5C0oKIDdbnfJe+7cOZ6sfn5++Oqrr2C3211Khk8++QSLFy/GwIEDsWPHDhw7dgw///wzTy4pOXJzc0XTxcW9rjjriVOOgIAAfPDBB1iwYAEAYO3atcjIyMCnn34qG3a5cuXwf//3f/j999+xf/9+XLp0CXXq1BH01FWxYkVkZ2crkpkgCIIwF/IiRBAEQXgFm83GW02X44EHHkBBQQESExN5E5nbt2+77qvh/vvvh81m8zAGyRhDQkKC6vCchIaGYt68eWjdujUmTpyI999/H02bNpV8p0OHDnA4HAgODsahQ4cwdOhQPPTQQ2jZsiWCg4MREhKC119/XZM8VqFp06ZYsWIF8vPzceLECYwePRrvvPMOLl26hKZNm6JmzZqYM2cOZs+ejbNnz2LGjBno27cvHn30UVlDxVLUrl0biYmJHtedygS5d4Wei4+P59XBtWvXonfv3hg+fLjrmtKdJw8++CCAwnpTsWJFj/uNGjVSFI4RKJXlvvvuw4QJEzBhwgRcuXIFixcvxogRI1C/fn18/PHHkmGnpqaifv36hsnbokULLF68WPB+jRo1kJOTg82bN2PcuHEYPHiw697mzZsNkYFLQkICGjRo4PrtbFu4u8f69OmDWbNm4cCBA5g/fz46deqEunXrqo6rSZMm6N69O6ZMmYL4+Hie0i85ORmPPvqojpQQBEEQRkE7WAiCIAhL0r59ewDw8BCyZs0aVK9e3TWhqFChAux2u+RqOlB4DOHJJ5/EunXreNc3bdqE7OxsV3xqSEtLw0cffYROnTrh0KFDaNKkCT788EOetxIh6tevj0aNGmHy5Mmw2+0ub0EdOnTA6tWrERERgQ4dOkiGUaFCBUU7UnxN+fLl0bZtW/z222/Iz8/H5cuXefdtNhueeOIJTJ48GQBw4cIFXfG1a9cOmZmZ2LNnj+ua3W5XNMFu164dgoODeV6Jzp8/73GEKSsry7UbxMmyZcs8wvPz8/MoI6fHqPj4eDz11FMe/x566CEA6nYdaUWpLFweeeQRjB49Gg888IBkWTm/T6HjYVp55ZVXcO3aNdSsWVNQXufuk/z8fF75OBwOrFq1yjA5nKxfv97jd8OGDXnKuMcffxzPPPMMfvzxRxw8eBCff/65bLhOJbI7ly9fRsWKFXk7fQoKCnDjxg3X8S+CIAjCt9AOFoIgCMKSPPHEE+jWrRu+//575Obm4vHHH0dgYCA2btyImTNnonz58gCA5s2bo6CgAFOnTkX79u1RpUoV0dXckSNHomvXrujevTs++eQT3LhxA0OHDkW7du3w2muvqZZx4MCByMjIwIIFC+Dv748VK1bgqaeewujRozFixAjJdzt06IDFixejffv2qFSpEoBC98szZ85EuXLlZBU+zZs3x6JFi7Bp0ybUqVPH9c8KnDlzBoMHD0aPHj3QpEkT5OTkYPr06bj//vvRpk0bpKam4uWXX8YHH3zgsm+yZMkSVKhQQVaxJEenTp3Qpk0bfPDBBxg7dixq1KiBmTNngjEm66Z58ODBmDVrFjp27Ihhw4YhJycHw4cP9zhW1rFjR8yfPx/NmzdHvXr1sGTJEly/ft0jvObNm2PTpk343//+h8qVK+PRRx/FI488gm+++QaffPIJzp49i2effRaMMURFRSEoKAibNm1yuSQHgD///BPdunVDQECA6+iOUSiV5dlnn0XXrl3RsmVLVKxYEZs2bUJKSgpefvll0bAbNmyIunXr4vjx44btxvrwww+xYMECPP/88/j222/RvHlzpKen49y5c4iOjsbcuXNRtWpVtGvXDuPGjUP16tVx7733Yvr06bJKTy2sXr0aAQEBeOaZZxAUFITNmze7jgNx6du3L3r16oVatWopyouOHTuiUaNGeOONN9CkSROkpqZi3bp12Lp1K7755hvesasLFy4gMzNTk4KYIAiCMB7awUIQBEFYluXLl+PLL7/EH3/8gddffx0hISGYO3cuz3ZL586d0b9/f0yYMAHt2rVDnz59RMPr0qULAgMDERkZia5du2LYsGHo1q0btm/f7lLYKCUwMBBLlizB3LlzXRPwli1bYuzYsfjtt99w9OhRyfedigSuQuG5555z7ehwt0fhzg8//ID27dujV69eaNOmDebOnatKfjOpVasW6tatiwkTJuD1119Hr169UL58eezZswfVq1dHxYoV8eSTT+Kvv/7Cm2++ie7du+PmzZvYunUrWrRooTv+jRs34umnn8aXX36JTz75BP/+97/x3nvvoWrVqpLv1axZE7t374bNZkOPHj0wfPhwjBo1Ck888QTvuRkzZqBjx4747rvv8OGHH+L+++/HtGnTPMKbNWsWbDYbXn31VbRp0wYnTpwAAEyaNAmTJk1CUFAQunTpgh49emDhwoV45pln4OfnBwBo1aoVRo4ciVWrVuHZZ59VZKRXC0pk+b//+z+sWLEC7777Lrp27YpDhw5h9erVkgoWAHjnnXewbds2w2StUKECdu/ejXfeeQeTJk1Cx44d0bt3b+zbt4/3Ha1cuRItW7ZE79698cUXX6BNmzb48ccfDZPDyapVqxAaGoo33ngDGzduxMSJEwXtq3Tt2hU2mw29evXCPffIr23++OOPKF++PH799Ve88soreO+993DhwgXMmDEDEydO5D27bds21KtXD88884xh6SIIgiC0Y2NKTMETBEEQBEGUYJ5++mlUqlQJ+/fv97UoZYYLFy6gZcuWiIiIwCOPPOJrcXzG6tWr0bNnT1y+fFnWPpNaWrVqhS5dumDUqFGGhksQBEFogxQsBEEQBEGUKubPn4+8vDz861//Qnp6OpYtW4a///7btUuD8B49evRA1apVLbXDyltERUXhypUr6N+/Px5//HEEBQUZGv7evXvx9ttv4+rVq5qNdBMEQRDGQjZYCIIgCIIoVVSqVAlTp07FtWvXkJ+fj8ceewyrV68m5YoPGDduHFavXo2CggKUK1e2Tqb/9ttvWLFiBdq2bYuZM2caHn56ejqWLl1KyhWCIAgLQTtYCIIgCIIgCIIgCIIgdFK2lhIIgiAIgiAIgiAIgiBMgBQsBEEQBEEQBEEQBEEQOiEFC0EQBEEQBEEQBEEQhE7IyC2AtWvXonv37r4Wg/AiUTXaAz62PtQkMdS3AhAEQRAEYRmi6r4A5Nn1BVLBD01u7TNGIIIgLIvP5zI2oEkCzWWEoB0sAOx2nZ0ZQRAEQRAEQRAEQRBlGlKwEARBEARBEARBEARB6IQULARBEARBEARBEARBEDohBQtBEARBEARBEARBEIROSMFCEARBEARBEARBEAShE1KwEARBEARBEARBEARB6IQULARBEARBEARBEARBEDohBQtBEARBEARBEARBEIROSMFCEARBEARBEARBEAShk3u8GdncuXNx5MgRJCQkYNq0aWjcuDEAIDY2FlOmTEFaWhoqV66Mr7/+Gg8//LDsPXd2796NdevWgTGGJ554Av369cM993g1iQRBEARBEARBEARBlEG8uoPl2Wefxfjx41GjRg3e9VmzZqFTp06YM2cOunXrhqlTpyq6xyU+Ph7Lly/H+PHjMXfuXKSkpGDnzp2mpocgCIIgCIIgCIIgCALwsoKlZcuWqFatGu9aamoqrly5gueffx5AoRImMTERcXFxkvfcOXjwINq2bYsHHngANpsNr776KkJCQsxOEkEQBEEQBEEQBEEQhO9tsCQmJuLBBx9E+fLlAQA2mw3Vq1dHYmKi5D2hcLg7Y2rWrCn4HEEQBEEQBEEQBEEQhNGQgRIAaWlpOH/+PB566CEAQFJSEmrUqAG73Y7k5GTUqlULWVlZSEtLQ506dZCWloaMjAzUr18fd+7cQXZ2Nho0aIDbt28jNzcXjRo1ws2bN2G329G0aVNcvXoVBQUFaNasGS5fvgwAgn+XK1cOjRs3RmRkJPz8/FCvXj1cu3YN/v7+qFWrFq5fv45KlSqhWrVqiImJwb333osqVaogNjYWVapUQUBAAG7fvo0HHngAfn5+SEhIoDSJpAkMsAFgRXXA6L9tRb+l/o6NjaVyojRRmihNlCZKE6WJ0lSYJsZUjSME/2YMiYmJ1klTaSwnShOlyQJpqsg0thEa//aY9zAgNze3TJZTixYtIIWNMcYknzCBzz77DD///DMaN26M1NRU9O7dG6tWrUL58uXBGMNHH32ECRMmICAgQPRe7dq1eWEGBgYiLi4OX375JQDg+PHjWLt2LSZMmCArz4oVK/D++++bklbCmkTVaF/cUviIJomhvhWAIAiCIAjLEFX3BSDPri+QCn5ocmufMQIRBGFZfD6XsQFNEmguI4TPjwhVrVoVTZo0QXBwMADg0KFDqFatGmrXri15z51nn30WR48eRXJyMhhj2L59O5577jkvpoQgyg5pGVlQq5vNzbMjV+/A0QswxpCeme1rMQwjJzfPI9+zc/OQlJKOgoICr8iQnpnttbgAIDM7Bw5Hvqp3jJKRMYa0jCzBa9zrGVk5yNcZX1Z2riuduXl2ZOfm6QqvtOPtemgkBQUFlm6XuHWRKCS/oAAZWTm+FsNruLd7YteshJaxTGkmKzsXdofDO3Hl5CLPbnxcQmMeq+PIz0dmdnFbITSOIEoWXlWwzJo1Cx9//DHu3LmDESNG4IsvvgAAfPXVV9i+fTv69OmD9evXY9CgQa53pO5Nnz4dR44cAQDUqlULPXv2xA8//IAvvvgCVatWRadOnbyZPIIoE4Qev4Au/cdh5ortit/JszvQbeBEvDVgguU7vl9mrEHnfmNxISrG16LoJic3D28OmIB3Bk1yDZqyc/Pwau/f0W3gRAyZvNx0GaJvJaBzv7H4dvwS0+MCgKSUdPy/PmPw+fA/Fb9zMeomOvcbi5Ez1+qOf8bybejSfxxCj19wXZu2dCu69B+HLv3HYc+hM4hNuIvX+47BgN/ma47nbmoGXuszGp/8PAsORz7e/eYPvPnleFKyiHDp6i107jcWw6at8rUomvh56kp07jcWl67e8rUoHiSnFdXFn2b6WhRLMWj0Arzedwxuxif5WhTTWb8zDF36j8PGPUdd19ZuP4gu/cdh/c4wH0omzuHTl9Gl/zj8sXizr0WxBCnpmfh/fceg19CZpiudMrJy0KXfOLz//VRDld65eXa8NWAib8xTEug9/C/8vz5jcCc5DQAwcUEQuvQfh2NnI30sGaEVr9pgcR7fcadu3bqYNGmS6nsDBw7k/e7YsSM6duyoT0iCICQZOatwEvr3rsMY8MFrit65GhPvWsmLuHYLTzza0CzxdPNP0cR4zupdmPbzZz6WRh/nrtxAdk4esgHExCWhcf2aCL94zXXfG5338k3/AABOceI1kx2hpwAA0beUGzn/c9UOAMVlr4fA3YVK/9/+Wo9dC0YAADbuLZ50/D57Pbq+2BYAcCHqpuZ4dh0IBwDExN3Brfi7SE7LBACcuRSNdv9upjnc0srctbsAAIdORfhYEm2EhRee+16wfg8m/tDLx9Lw2XXwNAAg5nbpVySo4dyVQiX92u2H8M3HnX0sjbk4F1ymLt2Cri8Vtm9/rtrpuvd2x2d8JpsYY+cGAgA27z+Obz/p4mNpfM++sLNgjCE24S7y8wtwzz3lTYsrLDwCjvx8JN5NQ0p6Fh6seq8h4V6IjEFWTi4A4HrsHTR9uJYh4ZrNtZsJAIDtoafwYZcO2PbPSQDApIVBWDPlW1+KRmjE50eECIIgrEhp3TRMu6E9ceSXzGMjRNmDPl+CMAbqCwmrQcfVSg+kYCFkOR8Zg20hJ7HzQDjveEdunh07D4Qj8W6aIfGcu3IDR89cAVC4VXFH6CnemUTCGthcdsWLycnNw84D4bgaE48doaeQlZ3rA8mkiUtMxu5Dp71mJyDiWiwOnrzk+p2Vk4sdoadwNzWDJ9PKLaHYFnLS9W0xxhB89Dyu3oz3CDM3z45dB8ORcDeVd/3gyUuyRweKO27tHfiV63EIPXFR1TtiA4as7ML8SE7LELyvFS0DlHwTFCxOKZyraVz2Hj5reHxOdh08jdw8O9Izs+Nx2p8AACAASURBVLEj9JRX7Hbcir+LPYfOwJFvZRscnu1WaSIpJR07D4QjR+ERsehbCdh/5BwYY0i4m4pdB8MFj286HPnYfeg04hKTFYVrxAQhLjEZuw6eht3hgCM/H3sOncGt+Lsez2Vm52BH6CmkpGdKhnfq4jVTdtCdvXxd0S5AuTw5dCrCkke/Sj0y5XIzPqkEtGvKuHozHsFHz/l0An/qwlXB73DngVOq7I2EHr+A4+ciC8eanP41z+7A7kNneM8eOHERl6NjtQtNEBohN82EJLl5dnz56zzX7wuRMRhctNV1zppdCNx9BFXuDcCmP4fqiicrJxdfFdkjWDJuAEbP/huXo2MRfPQ8xn37ga6wCWOxCcxTZq7Yji3BJ1y/D568hN8GvedFqeR579spAICEpFS839l8A9h9fpkNAJgx7DM83qwB/li0GXvCzqBuzQexYuLXPJmAwglP/56dEHr8IkbOXAMACF76Ky/Meev2YP3OMFSu5I+tc34GUGhb4uepKwEA+xaPRLly5unNew//CwDwx9CP0fqxxrrCmrgwCPuPnMPDtath6fiB8i+YiF5js1JMWrjJ45qZSo+9h8+i2gNVcOnqTZyOuI4nmzfE1B8/NS0+AHj/+6kACm1xvNPpWVPj0opQu1Wa6D38L9xNzcC5KzcUHXf4+MdCeykF7B1MXBCEnNw8RN2IR7/3+MesV287gPnr9wLwbI/MwtkuxiXeRSX/Cq6jJu7xj527AQdOXEST+jWxYLTwEfQ7yWkYPHYRAODv6d/jofvvM0TGzOwcDPh9AQBg2fiBqF+7mqZwLkTF4KcpKwAUtt8lHVsJ+tDkVA0ffD8NAJCakYVurzxtvkAm8ulPswAAvw54F8+1eczr8d9JTsPgcYsBFH6HXD3PnDW7cehUBGYM+1w2nCNnrmD49NWu38fPRWFYv7cBAEuDgl1HawDg9KVozFi+DYD32i7d0AaWUgPtYCEkyXTbibAlpHgS7ZxQG2HpmrvjIfLGbZfG+fDpy7rDJsyHq1wB4LHLwUrbHvcfOafoOaNkPl90Dn9PWOHKitBKLFBsO+T4+SjRsLYXDR643+XVmOKdLu7fKxfnwNeIZJ2JuK74WbEBt7McbsTd0S+QgvikMGMHi1OKfWbtVpFI5s4D4ThdVEbhF6PNiV8AI2zYENpw7ozbvP+4qveOnY107XrZceCUx321u62MbOv3hJ1F8NHzovcPFPUzUTGeu/2ccHfexCelGCZbWkaxkjT6VoLmcC5EFtthKrBQP6mVe8qXvmlF6InS065JjS+4MINn+rEJ0t/h2cs3FIUT7rYDxjmuAoDdRbagisNUPk4hCKMpfS0hYSjeWozgGtMiV4+EmZT8ISyf++6t5PqbO+h3x1dKLm/Hq+mIkAk7WExPtUQERg+OibJLOQWDANOGCaVA4SBHSdrxoYQyUGSEJoorhg22Ur+jkCBIwUJIImRvQ4r8ggJMWLAR63YcUvVeec6xBrtOBcuZiGgMn7bKY2X8QlQMhk1bxVvxL4k4HPkYN28DAncd9o0ABvaMUTduY/i0VaLnzxPupmLEjNWadzKdungNw6et0uQm8+zlGzh9KVpTvDxMHkjcV7mi62/PoyfWGsVMWhhkqEtGLURci8X3E5fik59mYltI4Y4gvTtYIq7FYvi0VYi8cdsIEVXjrlBJTefvKkxIKvyOjhTZuDJNDlbc/sYYvDNJLyVlInvp6i0Mn7YKUV6qSzbe3wJ5pDLf9M6vlabbTPtsavtYPUoFXu6arJxgjGHqki34ZcYaDJ+2yvK2Kf5cuQPz1+9R/PzBk5fwy4w1SEpJBwDsDTuDUbPWih7JtNLOWncys3Pw65/rXB66jMKoNDPGMGXxZqzY/I/oM3aHA2PnBmLDniO8b0SqLWaMYdaK7VhQdCxRn4y6g1DFpn3HMGbO38iza3cPXZIWRxgK89iIsiqNkA0WQhqVY9I9h864Ji1az+Ln6zQoNnD0QgDA5ehYnnuz/qMKbckcPxeFWbpi8C3bQ09hR9G/t0rkueDiStV31FzY7Q6EnrgoeEZ23NxAnLxwDf8cu6DpDK3z7H10LMdlr4ped9CYhfrP7prcX/qVL9795XSFLRW5LweVW4JP4D8tmuB/7VqaFofcRNppGwcAJizYiNc6tNa9g8UZptOVrldQ0Tb/Pns9zkRc1/wdqcHZ/kbF3MbKSYNNjas00nfkHADA0bOR2Dl/uFfjtoIOimvzTaqpWvj3PtNk8Gofy8l0s48IHT0byXMZL9bv6sGoOnTp6i2sLVqoe+251qhT40HZd5y2yBz5+Rj9dU/89td6AMB9lSsJuslWmtu+6DKXbgzBvsNnse/wWbzy3397J1IVhRcWHoGgfccAAG++3A4BFf09ntkSfAI7D4Rj54FwTP3pE949sTy9dPUW1u0MAwC8/vx/ULPa/Ypl8jV/LN4MAGjycC30ePW/PpbGe9xKED72XtahHSyEoThXDtTCnfTp3cHiJD4pVfC6Ui8LVuVOsjFem7Ri5BjcLqPpv3ZT+9l2Lt5eTTdLiSEUrNqYjJBMT/qSU431GuSONi9CxrQ5vvI2IbfT0Be79rhn7q2ABXQHqhDy6OML1Oab3rYvh5PuAsZEFaa3NOxKVEriXeGxgxhGKRXMVn6nynhbMgKjksBdLMjKUTdmu8FdUAFw87a1dtMpwUh7QVyU7uSTK8ekFE4/LvKsmIdRKRnSOLuNcnL1tYG+2g1ilGdVANbQehOaIAUL4QFjzGUHRe0RIfdwtFAaXOKVZozaaq+oflioc9FsG0hhEvILCkw4PiMQuQmDeG6bYQZm22Uyw8itN5EbSOotccYYcvPssvXTKEUVUYhQvRfqH838Pow+WmWcrMJylTQbbhbq4lx5Z4U8LFeuOGP0Kp5E23eBcIX6Mm+WkcORD8YY/P38FD+vBrG8VBsOtx1ylpXUOEBTEXo5362Ie5/qrB+E9SEFC+HB0MnL8c7gyUjPzFbcsQjZyFCz3ZX7qFUbOqtQUmwJcBGaAI6dG+gDSbRNNvcePotX+4zG9lBPLxuCcWjoADOzczFw9ALTOs+JC4MMC8u9Do6atRZvDZigeQebFCHHzuPVPqM9PFUZSYlXsJg83vp67CJ0/Pw3fPrzn5L186KILSVLUALbzVf7jPawTxV+MRpXouNcvxPvpuGtARM0x8H7lgXyyMhsO3Y2Eq/1GY012w8qfENdxR43bwPe+Go8z3OQFtR+T0LPr952QEO8vps43bydhDe+HIce3/yB1/qMVm1Hz4lR9YVbL3UrWAqE3xcK9vuJS9F98GSB47bmk56ZjXcGT8aQyctQoUKxBQex9AfuOoxX+4xGWHiErninL9uKzv3H8rxhyeU5t890ltUPk5ahe9HcwQNOeDabRD3RUdZaX523bjde7ztG1BagN2EM+OdYsdeqO8nprrqYkpaJboMm8txUE9aFFCyEB0fOXEFyagaCOGd15Rg6ebnHNSbSqcnhKOGTHUIZSoy3mTEl0jJY++3PdbDbHRg/b4MJEhVz7kqMh4FSdYinzcjBg3seBh89j7TMbEmDd+4oVRT+MmMN7HYHJhmoIHLHDC9CpQmnsefoWwk+mXiUVex2B0bP+dvj+rh5xcrppUHBvG31xqOyFZZoXr+fuBR5dgf+WrVTWVASYQk1HztCTyEzKwdz1uxSFL6ZzF6tTAbuLmFfrkvPXLENmdm5iL+Tgjy7A7NW7vChNPzy1at3UrMz9Pi5KNxNzcDWkGKFvrf0Xpv2HUNyagaOnolEBb9iBYvYru7py7fBbnfgxz9W6Io3cPcRZOfkYVORTRUlcPtM53jg2NlI3E3NcLmKF+vile6M99Zi4orNocjJs2P07PVeiU8KxhhGzOArULYU5efaHYeQmp7lck9vFWhHjTCkYCFMQ+v5R9rBYm28uQ5cEnfrADoHyiYn2cy+UI2NGO4WcF9T0neweJOS+k2WULEFJ4eMd1/nB20T/LP4WgnMN7ONxbqjJ494igS9ZakDb+eZHDzFk+4dLMLtu5U9tvhXKD4ilJun3SuNWSjpM7nF5p7TFqtuPkWufjvrqa89MBLqIAVLGeRKdBzPnWhWdi4OnYrApau3EHGt2G1fWPhlxQZn/Yq07dyGQmzgxxjDyQtXEX+n2IgXt6NTY+Q2MzsHh05FWMYgoDfw+YCXI8DRM1dUuxH0ufxepqROSLWgJqnlyvG7n8vRsYa7plU6MHcfgGdk5SAsPMLD3eKFqBhcv8U3oCgRubLnUOjmWcoQbUzcHZy7coN3TVW98uFo1uHIx+HTl0VdpXI5fSkasV7wSBB+6RriEpORlZNreP9xMeqmYca5AeWDbyPIysnzyA+5epZnd/COCDvlsTscCAuPEC33I2euIDlN2uC1N1dGb99JwakLVwvjVZmncmIyxnDKbczjgnsUxmIT/oS7qTh+LopXDvF3UnDifJTpZaPniJD741qUkEamT2m7xo2Tu4PFyPbJqPEId1cNA1zfjjIhxG8ZWqs4gXH72DMR0bgV71ke7mMSrTiT5+xngMIyPHQqAlk5uarDc1YLqyqlLkbd9LUIloTcNJcxktMy0HvEXwCAoFlDUfW+APw8dSVOXbzm8ez5yBhMW7ZVUbj+fp5VSayDOnz6smtLo5CLQDVGbn/8YwXORFzH68//B999+obi90oyegwPG80Pk5aZGr4ZugmrdlJaUT349PIgXqwIy5crB+ew8U5yGr4YUejqeMvsn3BvQEWvyObEfTXu2/GLEXEtFt1eeRoDPngNAHAj7o7L1fvuhSPgd48x3eed5DSXm2ehtDPG8OGQ6QCAeb/1wyMNanuE4c0yVVvfFgbuw8otoWhYtzoWjx0g+ty5KzcwaEyhi2ezXUl/PabQfXublk1w7FwUXnr2CQzr+7bucGMT7qLfqLkAgJ3zh/NWobUim90GFn1Obh5+mrICr7ZvhSG93wQgv6Fu1ortOHnBc/ywYP1erN52EI3q1cCiMV953B8yaRnuDaiILbN/Eg1bKmmSE0WRF6Xy8t1v/gAATPjuQ8PP6hw9E+myISVVt63WN3X/ejIA4NcB7+K5No8BAHoU5dOYwT3xbKt/mRY3t3j17q4RW/l3D9YMpdHZy9c1tWv+HBsser3pcFGaRjU2WI6difQ41iIXntjny33OyOGfs4+d8N2HrnGre3kYuav29KVoVz8TvPRXjJu3AfuPnMPT/26Gcd9+wHu2pB+xuX0nBTfjk1Cv5kO+FsVS0A6WMsaN2GJ3dU7NqpByxQnX2JIUTm07b9VB5NkQoTC5WwlVNDZnIq4DgKkGMAlfYh1lkhp81WH6up8WmvSIiVSeM5jhrvgb6uJQIe4yOnfy/b3rsOvaec4OEkUDXoXaQe7OFbm0h0u01Wahty6v3lpo7DNaZufPkdNXdMUjh5Bi+ti5KADAnkNnDImDa+fIKFs1Qvlvk7mvF54xb5l6HORmt8EpzupthYZspXbzyOYRY17f8Xjg5CXDw0yQcPtsdlkawf4j5zyuCY7jDMSmwwiLe50RtbHlhfw+eiZS8bPcNFeuVKxoz8y2nt0rbp6GHDsv+IxYESrdRWPG7t+tISdF75UzMD53xx/Ob0jIIUhpIMICBoKtBilYyhhmrXQ6V+q4AwRfnicmzENLH2TRcSPhQ8pztuPyVq0MHFP5dMKiIW6rHREAhFYeS6bS09tiG1X3BENRsJBhFL4sba1ZKJb3ZtUBfTZYSub3ZORkVAgli3VKEfUipDNcJZQvr3yaxa23fpxd4WkZZhqxFpFF5r56u2X8nSli37bZXbZUvTXqiJDROOuFFccHTmiM74k1axOhm/TMbExZvBkn3c5FatEiKyEuMdnDXoEVGoMjpy/jj8WbkZahxzOL7ygoKMCcNbuwg7eiWPynVVe8jMKcMZwX8szCxWJ0lZGzoyBWhNzBDFckrUfgwsIjMHXpFt7ukhVbQhW5G1US486D4Zrkko1bRSWftXKHsB0HGbSWeVxiMiYt2qTtZSdemkCeiYjG5EWbkJKW6ZX4LkfHYtLCINwuKg/GGOav3+O6P23pVk3n7T2QKLwVm//h9w0cNu45ikWB+3T3EWrHCULxue9yEWPJxv38sCQa0kOnxF3TSqX40tVbmLQwCPF3UrB+ZximLN6MyW513GgbLO4s3RiMwKLdcdzc1W2w2CSE8oOh8HjY3sNnTYnTZuA4x/2IUFxiMiYtDDLFdt+FqBhMXrTJtRuRe+yE214IIbYTW4n9Km+jVsFi1tzDyZbgEx47xYTqzf6jnruxnPjK8L41v3pCL2SDpZQyY/k27Dp4GkH7jvHPGepcKZaa/KzbcYjXcKoZLJiljBlS5D46K9uAga4P2H/kHFYVbbHv1L4VAE/jb95eAbOSDZiSgC9zS7hqGPutTZi/UfK+WGy8wQxv9KVNDiFXlQvW7wUAPN+2Jao/WEVbwEWEX4zW9b5W3AeJQyYv87RlYtII7YeJSxFzO0lSHqswcHShnYO7qRkY/XVP1e+rPUPutBl0PjIGi8Z8hQMnLiI2Idl1P/TERdTesB/93+ukWhYuYtkdeeM25q3bI3gvPTMbU5duAQD8p0VjPPFoQ10y6GXK4s145b//lnzm3JUbWBTopmARSfuV63Ga5GAM6DtqDgDg8OkruJPs/eOIF6NuYmHgPgDA8+1a8uUrQVMtrmLvxacfNzz8cjbhHY5a8LSxtUTQ6KwRbZvTTlf0rQTMGPY5b6fmluATrvbCndw8O1/5wpEl06DjhmqQtcHCddOsNnCDB0V2hwOTFgbpDscKO1gk893CzUNJaru8he9rE2EKlxSch9MyUeZ+RO6f043YO/wjQhYaiEdcK5nnA512cqyEXn2OGoVQSVXmmNXZCK4kSvXHXjDid0GjBXnu1mneDhYTFIalycuY05aJN74Md+VKSUDco4F0jmVkapvEOG2MCK1MX5PwDKUUoW/eBiBFYudYJmdBIVlmR49cG2vU92i3S7uavZOcrjis5FT93ofElCtmf1dJKcXpzM7Jc9uqYXLkJRS93Zb7Yp83PJWdvVxos6u826RdzCaR1I4QK1YLhwpvn4ByI7e81Cr8GI3a+WX2sTcxrDRXIoyDFCylFLFBkZGfsdyAiTE1FsuNkMh34RuBoDFDt3J0f0YsWUZZircSHmnXKHsJSrJq5PKkgDHD069aCVZUp8VWi7QMceTSreYcfEmhpFRjX5uY8GUbJxSzlDxC97SIz5383FO+vPoAOBhVftqKQb/tDP7RBPXv8K8bW5fclWe0ClwM3wZLkQ0Khd+Oh5tmptZeCAxrYBUfO3F7jJsGn7RhMlGKGg5WEJwNNkXtgbcX2ITKykpjZCvJIkZJkNFblL5RJ6Ecg48IAfxOMfTERbw1YAJCj5trbV4JN+OtvRJ76sJVvDVgAt97gxuhxy/grQETcIjj5UDwjOmRc3hrwASEhYufUQeA3YdO460BE3D8nHIr997k56kreWdq3xowAQdOXAQATFiwEe9/PxWp6SXTto5W1A44PLwpqDZMpw5Fq91FdVbMyK1a7A4Heg//S/IZv3tkJpk+1ALwPNgYbNSzOFjjBj1WN8rJTWlGVg4++GEaxs4NNLWIlR41OX4uEm8NmIDdh0573Nu45yi6DZzocV1M8S5V5I58joJFpu57qzjl66AxyiVuUNv/OYmBoxdoDIRPemY2PvhhGsbP26ArHPfvh+eO2Ko2WHwgFnc3AStguBwdi24DJwra1Ao+WjjmEYPb74UWjSGEMCOZ5RUqOD0W0yR2i1sBtWOJIUWukQHpNkdLXRPr/9QG5b7ok52bh15DZ+DXP9epF0ojJVU/wVjhzrDugydjvsjR1bIGKVjKGHxvHcaPrLjhT1oYhOS0TAyfvlplGEZLZX0Gj1uM5LRMj8Ebt4yGT1+N5LRMXOQe/xLIq1Gz1iI5LVPQJgWX0bP/RnJaJr6bsFSdsF4YkWdm5+Cgm7vM5LRMDJu2CowxbAs5idiEZATtO6o6bG9o2M2KQu9EuaCgwNT0q6kZvAE0d5VZpaG5Q6ciEHnjtnRccmerfdjoyBnPFZPMV2oOtfXHl8f8Ancfxq34u9h5QN5AsZ7+cH6RrR85vpuwFMlpmRg9+2+Pe1OXbsFdgaMvwgoW6XjsvB0s+oZ5arfNG9m+FIalvVzGu9mHUrRqbrMJpmHdjkO4FX9XchFECyX1GKzZcPuBAsYwYvpq3E3NwKyVOzyeHTlzreRROK4XoeHTVikUQLmsUrgfETI5OsOQG2twlYGiXrtEUiVV541vP5RT3m3ssTX4BG7E3cE+lYacxdoQUWQeLfYiZG2mL9uKxLtpWL75H1+LYglIwVLGMMCWpCnQtjJtlNYtxVLVITs3z/W33u3v3sBKK/5qt/UqwZjkcRS/Klsmh4KVtJLSvpQUOUsKdnuxosHM79Ddg56RaKkR3NVl2d1bchh2RsgrrxS9Z/x3ZEoZM2O95ejBaooerjSM8ft9tbh7EVIEtyh0lIvSI0Kex8GNid+XiH6HNmPX6Yxq293DUWtnxgik2i5rjw8YckqRrTsjIAULYSiitl8Yw6Wrt5Cjo5Msi3hzbn43NQPXiwxocnGWnS6XowZ2DOkZxS4Lq1SupPr9G3F3dBs9jYm743LDKAS3I0zPzJbdYaEP5VuJ8/MLkKrRZblhnXtRpeZPLLQHZ4hhOpkwtKTd0mMhAbJycnHp6i1DB3FZObm84ypmwXVdbXSTyRjDxaibkn1XQUGBamPPcmFyBFAVLlB4bM6J0iMKapDqE0Ttgsm0ToLJ1GxnS2RFXUHluHI9Dhkmem2xu0/aRHbyeRurLdbwx5LqbIelpGfyjMmasbDgxO5w4HxkjGgcSj3TJCTxXQyXuA5EJcp2k/F/xyUm43aiuJtrz/flP3iuIwk1XoRi4u4oPhp6PjJG8r7ct0eKi5IJuWkuc5jbaIsNbDbsPoLpy7fh0UZ10KheTVNlKE3IrSoZ2Qc7zzAvHvsVGtat4bq+7Z+TmLggCA3rVsfisQO8ss4l1S+mZRYrWO7VoGABgB//WI4/hn6i6d34Oyn4cMh0AMDuhSPgd490M7o0KARLg0I0xWU0BQUM05ZuNTZQTmEJDWg82gTnb5FCVqsv8YYS0lsrR1pX4mStWygQv9/Iubgem4ghvd/Eq0Uu4fXS55c5hoQjRXZuHnp884frN2/hV8XOKLGsd/Zd/2pcF7NH9hF8JvjoecHrUvWm36i5aNawDub+2hdXosXdDgsHIZ0WNUZu5eqc0O3t/5zChAUb0aBOdcl31aBuS7153+P5yBjBCZFRUY6atZb3202NYEwkBqPEyKzRcOulWts0GVk5+OSnma7fSu2FiKZP4hv5/a+/EXLsPLp3ehb9e3q6ZHc/diJETm6ea0zhkoUnl2wQhiMbp4KuSvSIkMJ+jvtcRlYO3vt2iuBzWuule5hKygooVMo4y2vPol9k29gvf52nST4nyzf9g8/ffklXGIT3oR0sZQy+RX19M5OWj9RX/OySoGAAQMS1WF1xljXk7FGYMeBxt32ycksoAI57WB8feeEOlir4aVudPXnhmub4T0dEu/7OzNKxq8cHmLmSpwdrb3313tlnYS8y3on9emzh971m2wHDwoyJu2NYWGIk3EmVf0gHizfsBwBc4tq+MojL0YX9YcgxYQUNIDzpttmk6wV3l4RiLyYiCE2SnH2Cs85wMdaGgrHv6RHNKOWHp7tavjFXK1PgVQVL8d9WzhXnt7tWwPguoMyDndBuWJ5XJCvmABP5WwFSLZJYUFr6Erm26OZtvvML9x0sYvl+8sJV1985uQbvLrFgUSvB4kM4n0AKllKK2ByY1+DonCcLeScQPyKkPFxLdiY+wlqnoo2hNKWppNVVPV6EFE2cBApXrE3gn7HXbnzbGwo/qyuAjKWEfaEGiSu22urNSaUQWibdvGNZOsUX+ry0tHuavIOA+dzNtzfxZU1TYoNFky0TjXDl8UX7y6vjOuJXYuTW122MEKYaw9fgRkiqzdE6BnB/T80RISNRmtVlaxxS8iEFSykhaN8x7Ag9hYhrsViwfi8ys4VX1rmfp16jZkLfupIGQO8q7c4D4di075jH9dOXohWHYWXmrduNu6kZuJOchnky3im83eBuCzmpuNZs3n9c9F5mtnln3I2mRLmClqkPQjtYFqzfK2tzIL+gAMs3CVuG59aHtdsPYd663Ty36B51VGYwFHk9DvPX70E65ygYlxPno7Bk437XMQgl7ZjcdyI7PvPSZ2aF4ZO3jlyt2hrqsVvOVDSmy9eDWi2xOxxGToQVHPvj3RS5LOtKXOgdyVd4HDlzxfX3mYjrgs9orduO/Hys3c7fobAt5KS2wDgwxlcgXbp6E/n5BT5pB5QozbzlRjojKwcLA/e5fnvtG+REM2+dMs9gciiatAuNpbl/K0z+letxmL9uD9I02llzZ/eh09i4R95TozcWmqQUzVrrh7v9NvfdfkaMLQhhzkfGYOycQPwp4BWstEA2WEoBV6LjMGXxZtXvlcSVoaSUdIydGwgAeLxZA969QWMWInjpr74Qy1BWbA7FhcibyM7Ng91E7xRamLBgo/xDRUxetAlPNGuABnU9z+nPXr1L8l0reTSYtDBI9TtW6XTdV2iEFCzLNoUgMTkNQ3u/KRrOztBw3oBXihWbQxG09xi2zP5JsZzc7HK6dU9ISsVPfbp5PPvt+CUAgICK/nin07PeUQj4UvUhahZAxQqvReqjk9DjFzFnzW4A0N1maznPrwZvrC4b7TmCu4NFb9316ThBRdqHTFrm+lvMILvWogzay1/QuRV/F8s2GW9X63ZiCmqxAsuufHqrX5uxbBvv2Jwvmi+jdusoOaIn1MZo2bnWe/hfAIAbt+/g1wHvqn6fS1pGtsud/GNN66FZwzr8B/S0CxreFcsNXTtY3V4tb1PqUlsmTpUiACi+2AAAIABJREFUWWW86E3iEpKx82A4ala7X9B2UWnAqu04oYJrt+KVP2zyh6yksRM2hKks/DSOB5nEu+aevfclpy5eU3Tm3xvNsh5lx52UwrPF7nKeOH/V82GFeLszOnY20u2KNbw+KJFBaV6duiBdHpevi9tOEvqeJXfEFMnEUxAI1OSTMjK57Dl55YiQ6VGoxqzvwBtHrqJvJcg/ZARqskjsWK2OVXsjikgoCG/awdLTX6tCaJIp9qgJ0Utx2c12XFJKOu+3PiUWx5irFRsaDt6S78SFKN5vvW2dL3NVyREh2fqjMv1Hz7iPWdTD7cO5Xtpc6MhUpWNKJYsIuvQ8bm2bnM1DbyCtbPeiICrR+o36PsfNgxQspQCu0kEOI48ICQ6IrNwClEKMym8qN2OxbG5qNP5otvFFPdVPSSumt3rT92FdzB6g5Xuh7CX7YoH4bVDexsiJL+tFSOCa1ERbbIIge0SoRH1ixgnLd1fvu0xQZoPFW/LxZSnJ7a+SI0JC/Sv3O1KbeiOM2fPtNRrbymowwSLePkgGJi2H3BEhX+xcLcFVXRUlzX6hFuiIUClAzFaBEEYauVWFAa3GwZOX8PPUlQYIU4owoY0yYzAjsAZqeBxGIGTN3x25sca6HYewca/8uWUtyCpC3H4btdKtZeVy0OgFeFjIjaugTNrrgy+8Wpk14FcTrlnpVhsq1/aFaZFIBeWeDyJ9nNYo9SgXI2/cxsc/zsT7ndvj5Wf/LR6HwauWhtZOQSu36mPQlA7GeOX78Y8z8Fm3F+HnJz90FTo2bVZT4VROHD8XhZkrtqH/e8q2vDPwFRu+VCRITniKbnnPXb1g9KZjxqRPiRchQftDOtodbxojNhpuGYgZwuei65tWcUTI2RZdjo6VPSp/+lK0Yo+p6ZnZGDh6gaJnrYzW2uprr6RmQjtYSgHZuXmKnzXSTbPWD0rQyK2C0NyVK9aconsXs1xHGoWzjjH+RQUvmiKOLLNWbjcgjB24FX9XewAmVmyt5Sz1nlhxno64js37j7uM0Uq9J2wwW5lsSqqT3u/EshOf4of03NbFtKVbDQnHlHGWiv5O7L6eYxFpGVmIvpXgsmWgBcH4bcr3n+qtu2r1K0Z+Ku5BRd9KdNlokiNIwBC+ZtkkdHhA8Tf63YQliL6ViB849mDUYPkjQl7aweJeu63uvloK910SQsilT+03rMdboFCcsjbgTSoexvvb+Eg8ysbjp2fKf5go/20rVa4AhS7vr91UdmS2NO36sHhTZwikYCnDlF69IaEHI9s9sTqmp3E1s5NR6zHI2xNvXyn79QysxScNRiXGCzZY3H9bbHBg6DehspLFJuhQJnIwLE+1uuwUue7z4wmibku9g9AkQ5ObZpl3BO/7Ou9FMPJ741VXaybXRQFTMHE3oZOyuuJJCkWeNQUK3tcp5u0k8eLAQ+0RIT2mDtzfVZLOlPRMN7n0lZSaEwili8J8K8UbWEjBUvYwfjVLLYLGaTWIVYq/S8UYNe7gBsPdXppfUIDYxGRjIuHGJ+s2V3vpxiUmw+5wIKEUGkEW3Omh4uO5pXEyrGeAq2S7slB9UFoFjGiT5AZp7vIpyXNHfj7Pm4ta4hKTRXf/mMndlHRRLyxGYeiAXSQou8OB2xzjjFrtEnDLXtDYo0LyJDzCSdU/I5p4Z11yOPIR59aeJyQpbydvxifhZnySpMJVtG2X22WlQr+i2aCiFQcNHKHMUiTE30lBbp5dWgwdNliysjnthTkWkE0IUyAWH2k1hPKVW8d9oeTlRhmbcNcQuy4KY+bIwBFCwxEh2TGgBdoD929esqx9rXWTwqjtgaUIssFSGlBRr/UeEeK3d9o+qJMXrslEolAWTbGXLszoeBcF7kevrv8DAIyatbZEnec9fi4S301YiooV/JAjM6BUjJdH5XrKVO7doZOXawtXcgeLdP6IDcqNMu6oqB3T+Zlole+HiUs1vXfgxEUMm7YK/360ASYN6SX7vJFuzZPTMvHmVxOwc/5ww8I0E4+0F5XVoNELcSHqpuvywZOXpMNRUI96fPMHZgz7DI83a6Bazj6/zBa9p9ZNs5rSDj1xASs2h6JV80aw2Qr7X64L18OnL0u+z82XD76fpiJmPlq+IaN3K24NPmlIOO5J0beKzg3X+P78fGQMvvx1HurXeggfvtFBUxhOucTk6zZoomb5hBA0q2RA3sjVdSthpZ1zs1buwPnIGIz8qoe6MAwcOokqHyXa7SvX4yTDlDu+paT90W1qQcXuYJ/XCQMpRUkRhRQsZQyfGbmVoTQ1HCURsfz/59gFc+IzJVRgycZgADBOuSKAkZNZISRVGT76ZvOVbA0XQeuqrGIbLJpCV4eHLAplk1Ume0RU+N/89XsAFNqxUZIPsscvVJaB3Gp3SYCrXDGStdsPaVKwKD1n745QyakZ1K/YHAoAOHWxuC7OXbtb8fuW3PWhka0hJ4wJSMOONuFgVKxea2T9jjAAQMztJGlZFKRBrC3PzuHYATShwhiVL4sC9xkSjtEoOnqlA23KTT7BR89LPKsufG0Gr4Wv61kAVOOW2d3gNve6Hkry8TcujKkrV2e+lab+xR06IlQaUFFBufXf7ImiWLyEBTGrgEwZbBkeZHHYFq+p5hmTk5mgS66yaJvccwcrutKlxIigzoyzugLY4uKpRk1+e7tsvJ7VRs5GLI3QMQlLrQO5MDJ3bSYfETKyT1Nki8uINLi16YwxQ8YSVvVYIuim2cAjQpoULKpmy6qD14BwJHqyxsMGi8x9M7D62MJsvDkP9TakYCkDrNl+sNj9LOdjzsrJxdrtB1WFJef5QytRMbeNC6wMYVbjHLjrMK5ES2+vVEJ2Th7W7TjEWz2z2WD4BODE+ShsDz2lKdgjZ65g96HThsrjDTbsOeK1c9Gqzgm7v6vEBoseN82a39SO1RVx3kKPTRInllF+KaxIZrS5R89Eit4TnXTrOUqoov6Wk3Bdqi5OLS/5/jsTKm+zvOn4crKlZKLD8yxjorLCPWSjjgjJySyW/3piVvKu0Dfu65qfnJop/5AJcLMiaO9RnI+M8bju/oLWvPKVvu3s5esAgKSUdOw6qHzs6es6oZXktAys2X4QSSnprmslNS1qoCNCpQGZmvrXqp3YuOcoVk0ezLs+dckWV+OlOCqTvoqfpqyUf4jwwDgjt/yApi/fZki4s1Zu1+eyWAGO/Hx8O34JAPWDvuzcPAwpcqnZoE51Re9YZRFs2tKtKFeuHB6uXc30uPQM/MUmiHzbAwL3FRu5NWJlU/p+WV9lEqPPL3M0vedud8K9DMW2YwthVMkoXUkzui5k5eRK2grQ820Ygsq4RCepMvkmbMBb+bNm4lkXjROAG7QZihsjVoidUnHls0EkF0zYaWKUQtsiXbcHwgaeOTtY9Iav4Z0RM5S5QwegMWPl24nV2w5i9baDCF76q+g4Qt/mV9/UiAG/L0Dw0l9dY08uUu2klcchDEz00x86eTkirsVia/AJLB0/kHfPKuNpM6AdLGUEp+cA7vepVrniiXU/9rKC1VfSxZQrcv2Emo4kIytH03sA3/vBjdg7VlgwVZWG05ei3d7l3zeq75Laui4nrrJJg/DxACV4wcathyzePKplxKDKrGrt7rJSD1oHWl4/ImRwfDz7FcIRClyUW4nXEKQFseqEwlixrDHDUDKW4O1gEbNfYcYxJ4PC1DqhNruEBNPHZO5bCZPFKygo0Ky4lUJ2R5Oib0Jz9Ii8Ubp27ovlRcS1WADAjbg78g+XIkjBUsbQOyEvKYPgMoNRO1i8WDyMGacYYgxIz8jW8T53y7MREhmAiqyRs4JvFHpWVvWuysql0RurUFZXZJZknDmrtQ3yvoLFq9GZVPOUh6r2+zIyf6z61RlpK8XsHSxGwj2SWq6cedMH9yqnO18sblBTULFuYO03u41UG35h2pQXRlZOnilp8LC5IlFB5GKnOY46XNll1Y/SAEjBUhpQWD9Lkrtds7FKWxh+6RoGjV7gazHwyme/ejU+Xfnv9nJaph4FS/Hf2tyWm7Bap2ZgZVDfJLvirUvBItLuyBi5TUpJh93h4D13m2vzw+YMRj4TBo1ZiBPnoxTJK4jHDhaLNCBFmFIPTUwj38BxYTz7jpwtvqYiLMNMsCj+lry/Y8bpVcoXGDX8FatPs1Zsx69/rhNVWpyOuG6QBMbx25/rPK5x7Qsoxr1dMaBuMRQem+3zy2zMX6e83qi1wSKq+C66LtV+XIy6iUGjF/A8W0nG6/Y7KzsXP0xaqtqGoNbarKdU9oWd9bg2Y/k2/P7XelceyfavJjQ5iwL34eepKwv7WJMwyoBpWkaWZDuvta+Se8tMA6xi9vPEkjJpYRA27z9umjy+ovSqV0jBUjpQ2LbExCXpHoxy35c7N6o9Du8MYK0wRfp6zCLBAaRy+wNWSIU6CpNm1JZfIDun+JiP2h0dmoz2lWKNuxhSbiTlvldlNliEn9n+zylemY6dG8h5STJaHvF3Ulx2erRgxe/Mqh4xtKLVVpO3y8bo7klJf7d80z+83zabvhZUTRpU72BR6e1j3c4w7Dt8FkdPX1H+khcRKh/usVQn05Zt1RQ+dxKnR5HNhTGGiGuxWL75H+Qb6AKY25aL1oqiZ85IKMb6jZqL0xHXMXjsIpEn3EJ3K4Plm//B0TOR+HPVThmJ3UL1QZO582C4x7W/dx3GnrAzOH6uUOkvVOqGVn2BsJZsDMbBk5ewM9RTPuOilUqESDshcC0rJ0/0+ZKKw5Gv6vktwQa5mDcJxtR9X1YcUxkNKVjKEI58dR+0FkqLT3dfo7SdKqnZbZ4NC5XPux8REhDMUvNYN/nK2Wxu18zJWLOPCIk9kZ6Zzct/Ia81XjkiZMHvzGq7aDSjV1Fv1A4Wha2uFfo4vXVeVf56qf3LzPFUWoiL6d0yUJIFiUmp2sLmHhHyYd0yesKTxVn40It7vtxNzdAUjreO1CrFmUdCfSTfyK2+spF6X+sOYG6YhvbBIt+AL47Pqcl3tf2xXaWCxfIwpmqc5Hy2tC0UcSEFSxmisIHS10jJfgsags/OlTHyVxZRuoNFZaOem2dHjgXyW3YgqTBZundk8X4py/PUtGLDnnriz82zC17PsyvfsmvmFlYuegb+io4mSoUv8i0480lN/5yudTDppYlPRlYO8uwOZGUb/I1qkF8szXl2h+ltSOLdNKRlZCl61tOFuHFyCNYXxgytD2YY3pSbGKiRXnUbIxK4rEwCt73lht5XMKhc9vURrKjO8+ydyRwR0oN7EIZ9bhrdNN9JTjNIANGYpe9qSL/ScYQR1U+LcwG1ChPJpw2rHxJRFDDB/kCr8ktsB0tZ2NnBpQQ0f5ohN82lAaU2WFRqGOUQalTVTsSycnLx5lcTtAqg7b0SgBmNjiM/H+9+OwXZOXnYMPMH4yNQg46yk9x0qtbYmpIz5W4Y4cI6N8+ObgMnCt5zbhsWxF1GL3VOprhp5qRF7Jl56/aggp9wN/XP8QvIsztUTQA79xsrfEPH0TI5bDab4ue/GbdYlRxa2BF6Cp3at9L8/jtfT0bFCn4InPG9IfIIDSjf+3YKAODrj15H15faSr9vUj+w/Z+TGD9/o2d8AGat3GFKnGqQbgeNi8ewvkhOJgGhS80uLYWYskqvMEi5dnTG8m0I3H2k+HkT+x53WRgzZtqpVebYhGQs2xSCD7t0MEAKT+R2sGjh/e+nYvUf36B8uXLS6xcaBxG8o20qw/5ixGzRcEVl9XFb8PPUlThyRuAYYxFqpSt1O1jUUgbadtrBUhpQWE+lXJ0ZJorK8E+cixJdyS/LmLEzITYhGcmpGcjJzUO4QuNyZmGsok/Pu25HhLzEpau3BM/xy+KjTkmPgWyxSQM3v6VsD3AVX+6r/Ukp6V4pN1969xBrU7XufBg3b4MecVBQUICsnFycj4zRHAbfyK34c1OXbpEPzKTVSyHlClBYHut3hhkUqbaJlO4qr0ZB6CUtrlWH22bLxbNFZUJsRo35uMqVwnANCdar6KnLC9bvNVCSQpzyCGUl7+CvhsxOvJuGFM5uW17YJazwmMGLw9xwpeDWFynlihZETTaUrKLhoW1IUnq3sJCCpQxRYNAqgBM1YalRSpfg9sUwlDZUJa2jdGKVbZC87LPZvCaVUWft3XfdeASruMeTlsfsI0KSofMULPxbhdtsve+muYR+dqooKUl0LxutCjfFhsVLRMYYuYVFbczCcRvZV5WMMlCPUUZuSzqeR4SMyRe5T9xXuS+UPmO/F/f+i/NbY3vp7TGcFWxfSaG2vNQcBS8JMKhrl61dmsZACpYyROT1OIQev+BrMYyjNB/eU4g6o1LWaNKSktORmi5tX0Fp5603TTxDbbpCAq7ejEfw0fPK4tUqt1udd58U7j96jh+2zvxx5Odjz6EzuHlbm4cXQGr3h/wRIUD6M7fn53unGdCRjd4Q77aA8V8uZnz6Rhmn0/ItJNxNxa6D4cjNs3vUL7ObOUu0o17s+wwrZ5n7anaJXbkep08YlcjlgVFVwoxJJDfEnQfM8xjjEa+Ru1TdfsclaO2PjP1ujJokC7YpKvNvR+gpj2ti9dYCLZgo4mM/cam1Knt2HzrN++1xNE1JuBozU60XodKGs86X5mkc2WApDSisoFOWKNhu7WV0acEFOqVLV2/pkMY6mO2m2ZeWu4dPX63rfUMnOJygypVTnyfc/P/0p1mK39N85EZopYvz955DZ/DMk4/ixacf1xa+Gxt2H5G1NyE7cVJSXhLPSK3qOhz5XvIi5DaJV/PdeXh6Mp6RM9eYGr7R6C2yj4fORFZOLiKv38YLBtV1pRi9cmtG/ZW1Ia6m+hoctxEs3rDf/Ei8gHtemaG844Z54vxVjYF4XjK12ZUJ/LSEC2ghij2WaBVImCUb9dVDpzxGlLvQ0U+x9PKPRhtgg8ULH70ZUSz8ex8/Di+qnsRssFhCga8BrXKTFyHC2vhuX6OPIhbn4tWbvhbBEBQ3OtYrAq9jnAFW5cZI9WKUTQ+henL09BXuA7rCDzmmbEeOFEo8gUgpYdwOgPB+2R35hqxJmuAczTCstG3dKjjdm27756RxxwcUPmeFbCnU2Ul8MzJC+mK7vXw5WSBjBVBSv/Q0s3xbG9rDsRp68sTjVcOOCGkXSsjYulE7goQ3sDDOfZ07dZlbWPpPCJmGYFKZtfsjAFZtvryKms+rLGQXKVgI7yBmqNFyzTtRluB5EdKwg0VrL2FU5yLUofFWYSwwKBHbgaLUyK1UqTgc+R6ZoNQblBo8BncqN7AQ4hh5zM8rGPxN+WbioDxOm8p2UTQ9Bu6qKa2YUpd9lK96ytPoVW1ncPJHvcSFFlKwmHk0jLsIozUasfG1EfVMKgx9xSccrrRCWU98xbjnl5nzE8srjDSgKknOI0LmiGIJ6IgQ4VOsYuzUaihtdN77bgoa16uJd1/7L1585gnBZzbsOYKIa7F4p+MzxgloEXTXHgUrOWZ0slqPCOWY5HFLbV/Pe17jajnPk4zE+1ITPLsjH35+5d0DttRszaxBmppJiBntrJ5UrdxywDA5jDIMqvxYpu+Rq1Oye0VUHRFSX9JCtt7k6mBJHgtobW6WbQrBGc5xFzOM3JqVr9k5eYaEM2L6avTv2QnnI2Nw4MRFfPNxZ49n3G1lcFEyUVVyRCj46DlBOyZOhBQsuik+I6T4lVkrtmPdzjD88FlXBeEXBc+L0saLzhBllsdRt8L/l20KURXMiOmrUbmSv3AUFm8e1H5nJbm9E0JzakrxChQpWAjNCO7ks3orWFJQ2Ojk5xfgyvU4/PbXelEFy7SlWwEAlfwrFAevX0JT8VY14nVyXmzojfOKICOzBTovJUeEtOaHkKtDM5Is6YXBR1hBBi1k5eQiPTPb9VtvKrydC0a77NYywZF9RaZumF13hOxsGWkXxusoKSIN5bjv8Fneb6t7SjGDf45fQGJyGi5GFR7vrlK5kscz567E4D6B6wBw9rJyeyxSysKRM9dKvut3T3nJ+3qQK3fn93rzdhLWFbmIn7BA2I08F5cbaLcjQkZ8/3KKVzlHBkL8I+GEwxcKCSVxGi1XWWkBykJTR0eEiFJFaTlyZMYk8U5ymvGB+gAdpzU84A5sbAJhm4WRNlg8J/+GBC0Tr/JnFblpVii0e7x2R75HerW0Ad7yEqIFnylSTIo2120XlpH2BbxDyR8Zqskzo/oi2YmIVUfcDLJFblgemWzk1qpcjo51/R2bcFf46KtIOtIysgWvC6GrnAReNipvhYIRGiNkZucaEp83MNoNcQmoxqoobekBtH1fpWPGJgwpWAjNqOlc1DQm3FXp0mxhWorSoigyBwN7Jk5QWmywCO2gUBmtLsyvJSJnuFUkwH3bu81W5P2Hc03STbNEKnNyjdmmLteWuU8O1ZSfWU2YqiNCFhrMORwaPWgJwCCkcNWYWKV2xQ3OS3Mm1fruA9rdiGq2waIpNmtgVBGa8Z0qDVNKEW727oH8fE7cKhtMdf22uT2m2m/GKY1sG1B0W+14w7V71OMID2dhSWMH5c0jf/+fvTuPr6Oq+wf+nXuzNEvTJE3SNE33jdIFoUCK7LKW6sPi4wayiFqKUKSioIgiUlAeWaugVFFQQRbxQQER3JBFkbU8P0Kl+5qmSdM0aZqmvcm9vz/S3N5lljMz58w5Z+bz5vXS25t7Z86dOXPmnO+cJUX2vW54LXns5VAMJUule6wsruqFWpf4bBBgAanMLsg//uPt9Gu35d2z/3jLX4IUEdG4EpNbfvLkwX9wffrt/qCf/9W76QcP/N719zwv08zAy43LTyPP6au5wZNnXnyL5l92C63MWFLddpJbm9Nyy0+ezHr6KYzmdQEh+c1jIZXXSPB5bPmtIsQ4B4sSNWnD9rrj0fiZf9kt9Ie/veE2YZbaHXpQqnFcLQR0PxZ5X7CT6O+nN95bK2XfuQaLlfwD3tPbZ/F59pMjul511mW3ePqeWd7PWkWIUvTUX16nK777U1fbPW/x/9BtJss3587J4pdpeRJgIPqcK2/ju7MDhD7otPg9bsrBux58Wp3AjsuEHJwXKbyNHQRYgCvZF/vqjdvkJoAXESuhcN+iHNs7urhtK7uikfsOGy9BPW6NQi8rHwUst6vz2k2tlMjpPuznacbQHEN+uF1dwtX506AC4VTB5yn3aaOf/Rgm3/daKWZNhwrlqN8sxZJ9E4l+uvPBp/3tKMOvfv8it22FlZhJzJ1P9tvvrxewX29ihuEqf7tZNS5m+GjymJaR2bwOizE9Qzlv3v3LZzxt+7mX38nvgcl5DpYggqMqlLtmDs5T7DLAwGHfvz8QAFfh2HjNAhpUjzxDgAU841OmBjS4GbIpflxd3az89L7IaPwHGUnnNgcLpycsIn8700RxHGsIIn6K1MknA9h1kL+vf4DvU3puQ4S87tAnEdeeYxIFnm+rTQ84nHfZD2espCgV2JDdzMmfg6RW7yHD3TXh6rPuUyOS3e/M6sHiu5df3sYPpsHfpge3EUTdSUIeDbruEikROHAIsGgs0d9Pr727yrLrpEq8VnoVjwMI4+apDHiXmS+DDLCwVmi3bt/JsC2/qfE5RMhpOALDppknuZVVQ/ZxjHN766jIy/KwXi+XvB4sfvMv54rayrVbaOPWduvdcd2boMat45xC7FraOv2l5QDnIJ66Fe6g5gtwM2ErK9n3B7fczoUWdF0plUrRW81raVt7J3V29Qjcj7BNc8nPmdv45zsf+N6e7b5SwTwEULEOGFYZNW+JqRALyzRr7IHf/pUe/eOrspMhlF2DKszFU3iLHL54PtkJsp7GWlm44Gt304u//C4Rmd+QTSujqvUGYfittscjIxEs6RHSXhXQxVolZhVuUT8xMcAv4JQivhXvLa0ddPlNy20/w3uZZhnc5N/3Vm9yt22LAihs14wI3T3ul7Z1wvYUXp1zY7gMo7sJyPgJxgwdopfefJ9u/OFjnrfDvr+U6Wu/2xr8t6/Nedqn/+1x3Rw3Kl07Ogrzs2T0YNGYlOBK1tUgfty+DnNMgL6yZtO3mDhSxA0gqJtyEHM1OG6DaT9yKylOh0nFOhTPbBlk0CB3FSH/jQdfX8/YENH7azezfVCywSet1unQsa+IyoGroHrO8R4+x0ql8s1tnY+l18FQvZRHD4W/vfae723IlpL1ZMkPCZlU5HVvudiaShejC27bfrr+TjcQYAF3Ao5Kx2MRzaJCxuWHv0BzK3u5wuD2q3JjgjeWfMczb3paatHx78E/AbTat+4GPC5tbmbwVAd7fFQoRp0Dgg5DhESWP5YNB09fky7Q8y1hyW7VDC4i5GZlIH3nYEkzm0CX44mzm//Ia70n6OG6qmdj9+dL9V8k1tDxCvMqQhgiBK4kMsbPB3Hjjts8zQjvZcmvsX/fI39Kv3717f8c3D6fzSvBTzZUfQ4Wp++YpXnoc7t276G7HmRbeSBFRL986kUqLx1G550+L2P7rpNpmR77z/jfj1ddu7O75T/01N/zPxTyupDZMBun8/aTR1/wtC+eT+lTxDfv8JwvSDwfwx0kZGipE0X7FNStQd4R8j+PltttWnHbg4Vl2M9QMMBPUKC7p5fufPBpWvGf9Z63kcsu6ZnH3G+Zs78/Z9U+j9t75JmXqSAeo0/OP5ZhKXh+brr3cWppc56PjrdnXnS/QmRU5Wap+x75E02f2ECnHDPH9PP/WjE4b88+ISunqUGZAMubb75Jv/rVryiVStHAwACdd955dMopp1BXVxfdeeedtG3bNiosLKTLL7+cZs2aZbqNN954gx544AFKJpM0YcIEuvrqq6m0tDTgXxJ+/f0DVFAQD2RfMZseLPpW15zxejrw+J/+yWU7qvLbUMhdRSioxhOvxoZd5fLOXzxNG1usJ+vM1NnVQz//3d+IiOjkebOoqqKcOQ2OT6Z9NlqzfiFLd3CXh/buh57J2scvfpcfYMkbw57K8hXcAAAgAElEQVSR7z5Y3+Juhwryku//s26rp331D+ROcst3fgHR1Lnv+BgiJLIDi3Xfd6cv8k8MJ0ElTcaEx4wfCYzbOVjcDRHymCgaXEL7DweWxeXN7PjzDILaTSzu5mgvf/zPRER0/JGHckkXKxnBlUR/P23Z3uH4uaFr1nX/FZ+nV51Av7mhtodVgGVoafj1W9oCS1PQlBh/kUql6I477qAlS5bQsmXL6MYbb6R7772X9u7dSw8++CBNnz6dli9fTldffTXdfvvt1N+fP0leX18f3XPPPXTDDTfQ8uXLqbq6mh599FEJvyb8bBuHnK/5qA4RCnGvOaVkZtcgDzm3LvoGkdVFxzafRL6+fXyfKLBUFHkOmXJ77axct8XxM3ap29UtbiUJIj0nJHSzLz+7Nnhvj2kSZbUrtkwk/AanoHIIjqpvquYtb/URb3dUw3DXm9TNJLfSVqHzyW+2SCRyAiw+rzZZy4kHSdYwbkWLAPBAmdarYRjU0zNYUe3t7aXhw4dTYWEhvfLKKzR//nwiIpo6dSpVV1fTe+/lTzL15ptv0uTJk6mxsZGIiBYsWEAvvfRScD8gQtIRWxclgdcJndwu2RcaHiMsqlbQVCVrsjc3PVi8n1N/v4dXXmLqwaLc0kfZ8o5F6uD7dr3sdJBKpbSt1KXIvGHOawien8+J5JgChzTKGK7j3IFF/nE1k0qlghsiJKIDC9N+FRoi5PJgu/q0ctVJuzFCmS/9ZYyEzbxXouZgUfV6FsXt77VcbU3XUHPEzjcLJYYIGYZB1157Ld166600bNgw6unpoeuvv5727t1L/f39VFVVlf7sqFGjqL09v9t7e3s71dXVpf9dV1dHO3fupIGBAYrHgxnOAvzp3njxyss97z/rttI3736Ebfsh6iLj50ae+V0/Szi65ebpyAVfu5vuu3EhDS8ryfubWZr93uZSqRR950eP0Yat7VRUaH6LcFUJYIuwWMrMq0xnyGV+aN2xi2Gb+W/93wcb6Ts/eowOnzHR1f546Ni1m8t21m3ZTtf+4Jd02ocP47I9FvmxKvvz9cNf/5EWf/Ysm+1lf7+7p5fOu+oHXNLm9TOyOaVxf4LfUtl5+/bwFyI9jqsdLivaC2hcMc2BxX2v3hmG4arR72bOFlXrPWbnnWeAIpF7vWd13fX4ME+pXMNHZv5gD7h725fu5R04UyLAMjAwQI899hhdf/31NGvWLFq9ejXdfPPNtGzZskD2393dTc3NzTRy5EgiIuro6KC6ujpKJBLU2dlJ9fX11NvbS93d3dTQ0EDd3d3U09NDY8eOpR07dtDevXtp/Pjx1NraSvv27aOJEyfSli1bKJFI0JQpU2jdunWUTCZp2rRptGrVKiIi09exWIwmTZpEa9asocLCQmpsbKT169dTcXEx1dfX08aNG6mkpIRqampo82Zv3f952L9/P61bu4b6+vqy3k+mktS+w3zOh2TSfGLDLVucuuenqLm5OSvINmTjxo1M6bXf+iCD8+uhYtrudUuL9dwNVsfLznW3/5K6eti6bvI4drx1d3enr6fqmjrnLxDR5s2bqdDjfEDNzc20dXtX+t+tra2mww9ZgiEdHc5jdTPt27eP+bMtbZ30q6depJM+NC7vb/39/bRhQ/a57Onpoe7ubupPeBvq07KtlV58vdn2M7s6d1FzczONHz+ekg6rwrBUxBImx31I5qozLMeN95OzVatW0drN27PeSyQS9JXv/4L6B5L019f+H9f95dqxY0fee/c9/Cw11uWXiaxWrVpFiUSC7nzkRdrRuZt+8+wreZ95//33PW/fSnNzM+3c2ZX1XltbG23d1GvxDaInX3iN/uv4mVn33CHJgQHauHFT3nc6u9wP29qwYQPt3++8wtHAAN/gxO7d7tPa09NDmzbl/+4h/f3yJg4cqtfk2ru3z/T9IX19ag472LRpE3V2dtp+pqWlhfbu9Z/+zNVeWOsRZq8z9fTssd3nunXraLNNXhrcBnseNWjwOm9pyS4ze3p60nVYO93dXbTfxb1rw/oNjp9JpVLU29tLPbv5BKZZOdXTN23aRFMbq03zV2b9el8fe33BzOo1a9KvOzs76YOMa7Rt+3azr9jq2tVFu3ZZP5jYs2cPbfewXdl27txJLS0tVFhYSFu2ss2t1tXVRb27O2nHTvsyIteGDRss08Ai95r0U164eZ3Z1snUun079e7JL2uam7PrkitXrqRJkyblfcaqnVteXk4VFRXU0tJCFRUVVFpaSq2trVRVVUWFhYXU1tYmre0+c+bMvN+bSYkAy7p162jnzp3pyWunTp1KNTU1tGHDBorH49TZ2ZluYG/fvp1qa2vztlFbW0srVqxI/7utrY2qq6uZeq9UVFRkHaj6+vr064aGBiKi9AkkoqzGfkVFRfr1lClT0q+nTZuWfj1jxoz068z9+H0d5KScmYqKimjGjBlUPOzvWe/HjBjVjKwx/Y5VT5ShIV1WCgsKLDPx+PHjiehl5wTbMCS+HspbZrxMIuxmdY7BY6eWioqK9PXU3WPd4MrU2DiWiou8FWMzZ86kRHx9+t+jR4+meEH+tliGqWWWDywKCwtdfX4gmaJDZ84koifytjNhQva5HLohFRYVEpH7Sr9Z+ZqrqqoqfV069TJjKaLsyunMvxUXFxORfSXZiMWIOK5UM23aNEoVlhPRn9PvFRQUcF0Nx45Z3jLiBTRixAjP2xy6P+3d92fLzxx66KFE9KTnfZiZOXMmdfatzHqvrraOKivKbL+XWV5l3nNj8TiNHTuWS9omTJjANB9PLMa3R+zw4ewTSg8pKyujsWPzA65DzMqxoEydOtX0/aKiItvvDRs2TERyfBs3bpzpA55MDQ2jqaRkve1nWFj11nP7OlNZmf21NWnSJNrauY+IXrX8THm5i0nPafA679qffZ2Ul5c7NkiIiCorK6m7lz2IOXnyZMosm80YhkGlpaVUMaLC9nO8OZVN48ePo9raWqqszM9fRcXF6dfFGa+9mDjxYC/LqqqqrGt01KhRrrdXMWIEVVZWEpH5g7qysjJP25Wturo6XS+vGFFJLPe/ihEVVFc9gmrr6h0/m2mCRR28urqa6fu513VQ7RercqZ+1Cgq3dxBRNkP2XOv+cz2sNlnrF5nlsGZdaLM+qqMtrsdJcZf1NbW0s6dO9PR3m3bttG2bdtozJgxdNxxx9Fzzz1HRESrV6+mjo4O01WE5s6dS2vXrk33iHj22WfphBNOCO5HSKBmZ0cLFq0sp7ZXVCe59XJ2Ve3+ymrl2i2BL9nGFJ8UcFy9zIFgtUxz3pCLA294ntAv6DlTyL7XSdZNXlIez19FSDJuCbAZ0CEoeJ+7VT95yWySW6/69ido5VrnlZF0WG44yCSyXpNOadrYkt9TSx98yiUx1xyPcW/BZaiYyyFCbob2qjjJbaK/n1autQ/s+l4t0aKOQOT9nmp3LDWvihIR0ZpNrXJ2rP7txZRVsre0uuvdHSZK9GCprKykxYsX02233ZbulbFo0SKqra2lSy65hO644w5auHAhFRQU0DXXXEMFB57OPPzww1RdXU3z58+nkpISWrx4MS1dupSSySSNGzeOlixZIvmXCWYYUgbypQtkDvt++u/2y96JnOQ2BPeALLoHWC6/aTkdNn083fPNz7v6np9sKGsitk3b+DQmRJxyHRqQdkRcBUpO2Mfh5Kv4s9ywmuTWi6u/9wva02s/jGVwp5ofNM4yu43bcWokBh1cdyOoe6usnMUzS/s9UgYZ7o4308pfBz6qYBXp+8v/13SYaeY9R8Uix+56VjG9brz53lr66v88xPbh1ND/uZ3kNlys6kifvfaegFOiDiUCLEREJ5xwgmmPk8rKSrr55ptNv3PBBRdk/bupqYmampqEpA8O4tnYWLFyg+3fIzvJrYeKgJtKiYoVDSKidz8Y7HIa1A1aVsP53++az1PAhc/KJEtFIfPplWoVBTHPgHN6sOheg5TJw7LKA8mkdW9GTueCKbjCb3e+OHc4UCCROXDNMBBwjII+7H5352bSWtfbVvCxmtUcXnzPm/X9S9QR0fl6f/bFt4TvQ+fjA2yi2XoNCRUbybzLjHg8mlnUS0UgHsklrf12nc3rO+tre6xc9xKxeEwspMKo2BChTLLKPNXqQkFUzkTtwa67upX+fuvJZ4PucaXD6hkyU2h1Pt2snBZVYpZpdt6oSnlaaBGvWBXJ7qFY5jnxW97bVnM83FSZVqZSJ0u5JzGfMK9eJDgd4F80W68hITsa7+YC91oYRDNo4LEhqWLETXGZ97Ignyi43dX7a8zHaD/38jscUpNtwMMKVkK5zNdCroL8tYVDQZenaP0ZK0nlzlfwzN/fDDQtqhwyZc5dzvX5wXq21Td0wXqc33eYR4OFiGChUy/hdVu208+e+IvtZ9wky/cQIcNlzdZF2tzM18KDr3spx6yQHawRH1DTtSqansjVxQ8YKh/cXrqqFN8gjjJDhADM2A0RUqaCKYKHO1TQlQd1eM8HsiYvdZt3V20wb7R07NpNW7dbLOvnMT/IuK5Uv5RVS55q6XHDS+U+kdGD5fKblqdf7+ntozfeW8slXazUuO/Yp0FmGm+693HT91U4aqL8v1Xmq6m4JuG8XXr9vVy353uIkMB6TNDz1D33kvcHIDyv4bxNZfxbxCFRooj0QWZNWtdDp8Z9US3owQJKi2rQwFsHFjffiuZxzSXrlsDzSeXOruxli4casF7PsNuu/DxurCp1UTeTH4hTO72s7H6FsFWEPGzWbohQ0FSpSNoOL1AkjZlSIR4itKml3flDDMJ7hNgZBonrAhFwtWfNpm22f2dNju/rOWvVoNw5WFAX9GPoSLo/R7jaww4BFo3Jjj2YFSi8K3Z2PVh0XzXHjpff5m44ldqFO2sD1n+9Q+3jkMnqmFj9hLBcH9k/Q9YyzVJ2e2Df5jvncnoVyP8s12BCpQCL7ATQgW7+Cpw7IvZ8GJagpJn9nPKnKufUj6CHCLnJV0EHE/wEFXnmhMxtDQ4REk/n6z2IulOU6q5RhQCLzjRqQHktDEROchu64kmj/MDL48+9Stff9Yjn77PkS9bVRUTr7N5j+n5uRebF15t97SeZkjAHC8eLUUzlKKcHi+TCg1flSs7vyN7p52+4j5584TXbb6gUYJF+8jWla4Pg3/+3mp52mOdnv8JLTPPAo7F86TfvpU3bdjh+zjBcLtPsQtBVJD9HTeQyzZnb7sjpAcuDrlXRdL5zkf7/+2AjXfKNH9I/3/mASxpYy8krvvtTLvsDcRBgAde8TurkhV1BrWuFjYWXG5S74VSa3gFzrFy31df3sya5DTLk5iHvOk1EmMvzMs2KdeVXoQuzckVNMI8gxWw2Z7tdu3vph7/+o+13BgbUmXhZlbwgY3iXHwomicmNP3zM8TP7Ev0BpERv6zZvp6U//q3j59wOC3c1AW/ALX/H6zCg9Nil44Hf/lXA/rhvUlm33v8kbdjaTt9b/jtX3/NbRodtMvEwQoBFY/KbHey83thUaFzpIixDQoIlpybg5d7a3bOXf0JMBL3sLRF7ZUOVLK5z92fwh/8S5fLT4AfrPVrFoA8v+/cjwMKia7d5L8wshoBy/kDeUy7Awvhdv9d7/jLNAVyLGl/uaHe4F+by3SsEWCAQXi8+lSqSqnPz5EeVhqpsuRO6BnWT8JKvrdLGu3Lv9hjwOGKs2+jsYqigh4zILCmjfPVyjal0H1ClImlXhCs1pOoAVY6bCIl+BFhymZ3uPoahVCIbt0FXe/w8rOB5uezs6hG27TAKon6MUxB+CLBoTFaPhZTJK5DPcDXJreICylq5uwmq4sFzP4//6Z8Wf/GWH2T0YLGTWcztYnkCKkBeAz/AQ/SL3/0t7z1eAQe7Uy0qqKFW7nKP9+XheSifzd9+6nI4oR+s6VetXOFpX8h7sPA6dV27ex0/E4sZ5ObexRS4O5BJA68z+zhuWeWvz+N/6/1PWm/bA6djjgd4DEJcHtp5q3mt7CQEBgEWjUWhDENXPXZRXdLaF4ETyckWljlY3Arbk/Kn/vq66fsoG6NLxyyuY5pZJZPqzBGkCqWqI0OZL+j4ip9Mnxlf4T7LLd/N5W1e82td5nB7XesvrKnmNRmwDhBgAc+CGcapZ2EjA+Zg8UDWMVN4mITrvXAoCHSrVKiQXj75QcLv8DbpiDJ4n3tPm1Mg/7mmY5pZhfze6+XneT3dMcMQdjiDDko79dqyS43Ie0wQ9y8V7pGuDS0iFMQyzcL3ALIhwKIzSTf1373wGq3euM30b1aFKgoT8WJhGiKkmebVm1x93kt3+aDqK7nz0phS+ILmXTnaZbE8tkwvvt6MVQQkUSHrv/vBRtrbt192MgYxXm9hHiIUdkGeujAt08w0qa+FlMVrHvxub3NrB73w6rtC9yETatLupVIpevc/G2QnQykIsGhM1kOTnz7xF/rit34sZ+dgyU2lBL1dDuBUc7zi5p9x2Y4tl2n1+rROuSdPkvPqt3/4aP4qDHKSkuVPL7/jext2p1rUqlWeOrColic58pq973vkT3wTIliYzyHk81xsCyzug673vPbuas/fVbkHS+6cLrlQvXRm+TBa02Jyw9Z22UlQDgIs4BkqTOJ4ObRhmoMFQ8PyBXVEmJ40Z2Q1HsWAymXJ/32wMe89ldPLS8+ePiHbxbXNh6wJnyFfiG693HgtIt0+GNC5LLYN+GAOFmkCGSKk+THKtaV1h+wkKAcBFo2pOMlh2AoNnaBXigdGbqRA0XlOyH0ly/Mktyz74V3f47g9XAXs7M51YWE8wJTYU+q2osIcLAphvd50/512VKyLQT6d6kgiA9EIcptLX8cSs4lGWRQcIMCiMa0uRI+1qxUrN9BXvv8LatvZlfe3b9z5sN9USfXtZY9a/s3LucUcLB7IqvUHMQeLxwJCtSeCSuZqtQ6RZ3Y/Q1RjRLHsBQHBHCwaC/DciWz8K3kvYfDkC69x3Z7o02kY6tUjVPP2++tkJ4Gr91Zvlp0E5SDAAp4FVXy+/f56uvMXfwhob8FIEdFLb75v/XcPB9fVHCzuNw8ceat7BNS7RkLFSPknamGtLNr8LnH5QO9jGdasIBoaXCCCm3uHaj1YbEcICbxcArnfany5B9Ej7bfP/0v4PkAuBFg0FqX6yvaO/B4skC1Mc7DIEqVrysoAyypCvGl23JUPCHGg0rWgUuOc97nXvdhmbbSGuQeL7ucwKlQLsNgReo/BHCy2NMomoDAEWDQmvcJisv8oNDxUpVPlwUlQWTtzP6rnXLfHxGtuSLkMsIi+5sOUr3UiKqgh+7YFkuDEa6unV8yE16ZS7sqegYEkyyaJSLOGs8j4Cq5FU1rlD1AeAiw6QyEJGTAHi39BVTyCCI56DUzICJLqVuHTLLmW7H6HqDzq6VyH5HibCUteciKlZ1xQQt4yW7luq+wkWFq89AHmz6oXrLdOj9BlmoVteZByh5nR0CFXL5+AjhBg0ViIqyvgAW4JGvEyyW2I52BRXViPiF2eQj4AFuyrCIU3P+HeC7yJvFqCuBbRmx2iDgEWjckeIuRm936TioiyM8NNDxYcTyLSqxLgduiO11OcdDtEiMMh1OcsDApzY3GIsCFCQrYanAiceiHCfM2E95eFi071SKHXC+ZgMTWUPXTKJ7oJ830gFwIsOlMxoyqYJNWIOkQxIzyXc9gLYZV/nZRVhGz26bau07c/4TM10WE7REjUkA6Fe28x4Xx9oC6vP5xCflKplLCqrWrXmm16RBW/B/4DALHC0yKLoJC3QSPNSyO3qLBAQEqiI8jAgpd9BZU69coVxWrFYaLeyY4c7U+Baq1WCfDEG3gTGQQRXeYYht7lGq5m4AEBFo1JHyKEKLhSpoyrl50EYOTlynEflPFWTUimnFdliJqw96gyIyofeJrjNnqHHwA406kcETpCSKcDIQECpsADAiw6QyEZWp4KeEzB4lruJRTYFRXIKkLevidsaEiIRKGC6nbOH+btah6Y1zv1IALup/m8lpG4vgbpXE7qentM17txPQMHCLBoTHYZZlaIWt1U/d4solbeiW7A6XoDFE7hAxNU0mQcgSgELFRkd9RVOiMq5Y/de/Zy3d4DT/6V6/aChuAC6EKlciTMlj/+Z3roqb/LToZnhsQWxzMvviVt30F4OuS/LxMCLBrDzQKyIDv4EuTl5G1fblcR8lZJcN1zgcOBs9uECg24/J5O4bjY7O4honoy4baV7U8vvyM7Cb7IbIyoA8dgyNB9R6VhFqrWlW2PkeZDhF58vVn4PkRRKOuGTiLRLzsJgUGABSCCcAMZIqfi5aWBHlQdUfbcThAc2x4sovIB8hdAaA2VG6oGNXQh8j4clgcEoqgUHAR9IcACPqCQhjBROz8HVWFl2U/UK2hRaDtE/RwDgDf9/QPUP+BtkuzB+w/vpdAHG8z9/QNct+vXgMdj5HefMvYLEDVY1xW4srotRqFBohN06873zbt/Q3XVFYHsy9NKKi4/7/UMu31yxuPSxtNO9QjrwCJmsyAJHvbiGGTan+inUy+9yfLvv/z9i8El5oBUKkVP/Omf9MTz/wp833a+9oNfWv5N1D3x+VdW0POvrBCybQA4CD1YAEICT5zdy6zD8J68knnHor7jtdbPsJ8gA3RqBANzJ2EJybVm8ztUCnqplBaAXGqUUXr4+ZN/k7Lfex/5k5T9eoYiTxoMEQIeEGABz1zVef1WkFHgRYqsBlVQe9W97oRgXjjYXWbiJrlF3oFwQXkIvCFPQVhFpQ6AAAuAgqJR/Jjb0dlNbzWvE76fTdt25L2n0jwnfnkNSbIkLZUieqt5Le3o7ObyW+y2oWJsNRrXp6AAi5CtgjQqXqCgtfdWb5adBOmi0ghVSXoFLMnpCLuBZDTmAMIcLOCZ2Q1A1E1ha2uHkO2GiZtDr3Kd+L+/fHsg+7noumV0/WUfD2RfPATXu8Z5Ty++/h498szLRERUVOj/NoKqpBx251pUDxYv8DQXVBZT+YaqmVUbWmQnQQmIr8iDIUJiqVS3EAk9WCAQfi+nvv0JLunQhejiHTfvQfs0yldug5ciKwktbZ3Ctq2i3EMflqeLdj8DyzQDQND+36pNspOgCJSTEE5R6cGCAAsEIiwNkqDgaMmhcjZVOW1+oXxQj6gzEsT8zhAcPOsF4A9lnkQo1ISKyjLhGCIEnrXu2CU7CZDBTSMVPSAH5R8GdWs17nuweN2Pt+/5YrdPBTOr7pXfp/7yOnX17LH9zL/e+SCg1ABoTsEyCvT27/9bLTsJkbN64za64xd/oJ7ePtlJCTUEWAAAQk7zdrKDYCIsk8fV08q1W7zta2iXNmcCTRf+7v7lM46f+f3f3hCyb8ynAgAAqnn3Pxvo3f9skJ2M0EumohFgwRAhgEhCs9WMyj0TVB1Gc8jEMb63YTsXiO+t+5d/7FVIlZ48ZWNF8z5gQkgAAGAXlR4sCLAAQGTlNg1UDWIQhbtJr1uvBoWzidJUvr4AvEKICQCADQIsAAAhl9vc27Rth5R0iOB5DhbXn+fQaLbZBBov4ZFKpTxFpxCWAQAA0B9WEQLwAE8oAQQJ6NqScQ3b7VP2EASz/evW40YlOHIQNrLLKAAAXaAHCwDIg0AV5HCbI1Dp58MwDAQFOPF6HBG4VxdKGSwiBADAKpmMxv0cARaAkMAyzeHmeplmQemIGrNrBe19b1KplKdgyaZtO6hj124BKQK/9vTtk50EAADQxM7uHtlJCASWaQZQ0PaOLtlJAMUE1ah33QAWOwWLdAZCVfykvPVG+cEDvycior8/dBPvFIFPUenuDQAA/i353i9kJyEQ6MECXOHJLgC4ZT8HS4AJYd0/CjopMFQIAAAAVIcAC0BIoO3hnl4NNpdplR2Z4ET6KTIMBRIRDn4nB8ZZADWFo6wFAAA+EGABiKDfvfBv2UlQgk4NNlWHCIkOUskOgsUMg1Zv3Jb13iPPvCwpNXpDnArCKCSxbAAA4AQBFoAI+vvr78lOAgjmtc4vpQ2scMPbIKKf/favWe+98d5aOYkJAT8BM9nBNgAAAAAnCLAAP4b/LuAAgdKoweZ6FSGNHqsqXW5odBxVl0r5PNMKZxMAAAAAIgRYgCdUfqVSupGqKI3iK4GdXddDhASlQxUxBFiUgTIOVISVxgAAIBMCLAAADlRoZKeS0Wxcyv7VvX37JKcgPN5qXks//s3znr+/q7uXY2oA+NiyvUN2EgAAQCEIsABAZLE+ETdi8gMsbikQE2KmU08i8O76ux7x9f2f/fYvnFICAAAAIAYCLMAXGkry4Ni7xtqwV6ELuPvhER7TLCEf2f42RF/ggK7d6MECAAAAakOABQDAgQo9WFSNM/BY2WVbWyeHlEDYYRUhAAAAUB0CLAAQXYwNNvnhFfe8DhGSMZHo6o3bAt8n6CeJAAsAAAAoDgEWgJDA0133WI+YCpPcAkRdMpmUnQQAAAAAWwiwAFdo5MuDIy9OLCa/qAzq2lLtElYtPSBPMqIraQEAAIA+5LcaAAAkYQ1aqNCBxW2AxfCYaNWCpDKGLIGaMEQIAAAAVIcACwBEFnMwQYEIS0BrCAEoS7XgHwAAAEAuBFiAK1R/5UHjQxwl5mBR9Pwi30FQMEQIAAAgHG675rOykyAMAiwAAA68DreRKixDhNRKDkikWt4EAAAAbxrra2QnQRgEWABCAo0PcVToweLm9PrJC8hFoCqsIgQAAACqK5CdgCGJRIIeeOABevvtt6moqIgmTpxI11xzDbW0tNBdd91F3d3dVFZWRldffTWNGzfOdBt//vOf6YknnqBUKkVz5syhyy+/nAoKlPmJAEIhvuKeVpPcugh9+AqwuPyu6HxXUV5COzq7xe4EtDCAIUIAAAChoELdWhRlog8PPfQQGYZB999/PxmGQZ2dnUREdO+999KZZ55Jp5xyCr366qt0991305133pn3/e3bt9Ovf/1ruvvuuwhNH2EAACAASURBVKmyspKWLl1Kzz//PC1YsCDonxJp6EUhD1ZbcY/1iOk2RChFPm5cimWjgnhcdhJAEbi/AAAAhINudWs3lBgi1NfXRy+88AJdeOGF6YNdVVVFXV1dtHr1ajrppJOIiOjDH/4wtbe307Zt2/K28eqrr9LRRx9NVVVVZBgGzZ8/n/7xj38E+TMAIKRiMf1uAobHdYQQqANVYZlmAAAAUJ0SPVhaW1tp+PDh9MQTT9CKFSuoqKiIzj//fCorK6Pq6mqKH3iCaRgG1dbWUnt7O40ePTprG+3t7VRXV5f+96hRo6i9vT3Q3wEgFdoerrEPEdIrwBKuJ/1h+i3gR7jyNQAAAISREgGWgYEBamtro7Fjx9LFF19M69atoxtuuIFuvPHGQPbf3d1Nzc3NNHLkSCIi6ujooLq6OkokEtTZ2Un19fXU29tL3d3d1NDQQN3d3dTT00Njx46lHTt20N69e2n8+PHU2tpK+/bto4kTJ9KWLVsokUjQlClTaN26dZRMJmnatGm0atUqIiLT17FYjCZNmkRr1qyhwsJCamxspPXr11NxcTHV19fTxo0bqaSkhGpqamjz5s2BHBs3kqlkemiXLoaq6wbn10PNcavXInTs7BC49XDq6NjJ9DkVJtdM7E8wf7a7u5v27NnjaT/d3btdfb6ru8vTfljt3dsndPugj0SiX3YSAALBUo8Isn4BAOryU164eZ3Z1uGhr6+PmpubLdu55eXlVFFRQS0tLVRRUUGlpaXU2tpKVVVVVFhYSG1tbdLa7jNnzrT9bUoEWGpra8kwjPRQoEmTJlF9fT21t7fTzp07aWBggOLxOKVSKWpvb6fa2lrTbWQOHdq+fbvp58xUVFRkHaj6+vr064aGBiKi9AkkGhy+lPndIVOmTEm/njZtWvr1jBkz0q8z98PrtSpisVjWsdGBIem1CFVV1US0VvBewqW6mi2/qjBZdkFhIRHtZfrs8OHDqby8zNN+ysvLXX0+swwUYdiwYUK3DxrRrCcZgFeq1S8AQF1BtV94lzOlJSU0caxzOzezbZnZHs9s58tou9tRYg6WiooKOuyww+jtt98mosHgSGtrK82YMYMmT55ML774IhER/fOf/6Sampq84UFEg/OzvP7669TZ2UmpVIqee+45OuGEE4L8GYDe26AZ1hEHKizT7MZ/1m2lt99fLzsZAFylsIoQAABAKOg2/N4N+Y9lD7jiiito2bJl9OCDD5JhGHTllVfSyJEj6corr6S77rqLHn/8cSotLaUvf/nL6e8sW7aMmpqaqKmpierr6+n888+na6+9loiIZs+eTWeeeaasnwMQOMxPII4a9wD287t46QMC05ED2Q4CkkzJH6oHAAAAYEeZAEt9fT3deuutee+PGTOGbr/9dtPvXHXVVVn/PuOMM+iMM84Qkj4ACB/2oJQSEZZAqBaoUy09IE8SPVgAAADCIcRVayWGCEF4oDEkD4498KBaNlIsOSARyjgAAIBwMEIcYUGABSAknnnxLdlJCDH5Dbsdne5W9/Eq5fK3otELQUkirwEAAISCGsPvxUCABQAiC8EBEzgkoCgMEQIAAADVIcAC/IQ4EgkAkiAIBgegBwsAAEA4hHkVIQRYgJ8U2kKgF2TXfG6HCImmVmpAJizTDAAAAKpDgAUAIgtDhPzDEYSgJJNYphkAACAM0IMFAAAiATEnUBWGCAEAAIDqEGABgOhibK9FqV2nWq8e1dID8mCSWwAAgHCIxdCDBYDJu//ZIDsJAMxUm29EBWs2tbr6vOgAyIat7UK3D/pADxYAAIBwiIV4iFCB7ARAeLy9ch217+yWnQwA7qLUi+KN/7dGdhKyROnYgz3MwQIAABAOmIMFgAGCK6AbtN0B9IFgGwAAQDhgiBAAQAixNtjCHGX3DW1eCAiGCAEAAIRDLBbeMER4fxkAACd4cg4gHya5BQAACIcwz8GCAAsAgAM06wDkQ6ATAAAgHMLcOxwBFgCILOYhQoLTAcBb6bBi2UkAAAAAMIUeLAAAIcT6QBzPza1hqWs1TR43SnYSAAAAAEwZmOQWAAAAdBHmrrcAAACgN/RgAQAIIfS+gLBCgAUAAABUFeZ6CgIsABBdrPEVTK5pCYdGTWF+MgQAAACgKgRYNIXVFAAAwAriKwAAAADBQ4BFUwiwAPjHPEQIrVXQDvIsAAAAQNAQYNEUwisA/jHHKRHQtIR5bNSEmCAAAABA8BBg0RR6sAAAgBXMwQIQbfMOmyo7CQAAkYQAi64QXwHwDYFKCCsjhgALQLShDAAAkAEBFk2hWz5AcBCHAd0YaFwBRBo6sQEAyIEACwAAeIfgk5Ji6MECEGkGIiwAAFIgwKIpPFEH8I91iBDqqaCbmIHbO0CUIcACACAHamCawtwRAP49+sdXmT6Hyw10gx4sANGGAAsAgBwIsAAAOMCcR9YQ7FUTJrkFiDaUAAAAciDAoqmCgjhdfdFHZScDIBIwYSjoJo4hQgCRhiArAIAcjjWwFStW2P795z//ObfEALt4LEZNh02VnQyASEAPFtANGlcA0YYHAwAAcjgGWI499li655578t7v6uqiT3ziE/T1r39dSMLAGcbXAoBsCD2pCXOwAEQbqogAAHI4BlieffZZuv3222nBggXU3t5OREQvvfQSzZ49m3bt2uXYwwUAAACChSFCAAAAAMFzrIGddNJJtGLFCorH4zRnzhy67LLL6Mwzz6QrrriCXnjhBWpoaAginQAA0mAeV9ANhghBmHzlko/JTgIAAAATpkdcI0eOpHvuuYeSyST97Gc/oyVLltB1112HISqS4egDAICZGO7PAJGGOVgAAORgCrA89thjdMQRR9CZZ55JDz74IN133330hS98gXp7e0WnDwAAFIZlmtWEOVggTPBADwAAdOEYYPnc5z5HixYtonvvvZceeughuvDCC2nFihW0cuVKOuKII+idd94JIp0AAADAKBbDHCwAAAAAQStw+sAHH3xA77zzDk2YMCH93vjx4+nll1+m73znO3TcccfRnj17RKYRLOCJDgAAmMEQIQAAAIDgOQZYXn75ZYrH43nvx2Ix+u53v0unnXaakIQBAKgDw2As4dAoCQF4AAAAgOA59iE2C65kOv7447klBlxC/RkAAExgDhYIE+Rm91KIfgMASIFB2iDEcXNn0GcWHCc7GQAAkRTHHCwAAHmuunCB7CQAQMihBgZC1FQOp9nTxslOBgAIhqekasIQIQCAfONG19Do2irZyQCAEEOARWOGwp1msXQrAIA8GCIEAJAPsWcAEA0BFhAIdzEIB8QLQTfowQKhgvwMnBiGgewEAEIhwAIAAJ6pHnw64tBJspMgBXqwAESb6mUzAEBYIcCiMUTgAQDsxePRvM1hklsAgHyoOgOAaKiBgRApQgAIAECW3Xv2yk4CAEiEOpgFwyCEWQBAJARYAAAAQmZnV4/sJABwg2CBexgiZA55CQBEQ4BFY6pPYqh6+gBYYVUsa1imWU0F8bjsJAAAKEflFTgBIBwQYAEAgNCKapwXc7AAAOQzjOjeFwAgGKiBgRCpVArPCAAAJIkX4PYO4YFeB2xwlFjgKAGAWKiBAQCAdxghpCT0YAEAyIfeKwAgGmpgIA7uYgAgWVSffCPAAgCQzzCMyN4XACAYqIFpDJPIAgQDnTRAN7EY7g8AALlQMgKAaAiwgDC4iUFoYBUhS1hhSU0IwEOYsGbnwgKsngUOUDYCgGAIsAAAOEAMAXRyw6L/RoAFIumFB74tOwnKSKHvJQCAFAiwaEz1+jMq+AAAwTNimGEAogn1DnCCZZoBQDQEWAAAILRQkQYAgCEIPwOAaAiwgBCpVAoNGwgNdLW2hiOjJpwXCItFnz4dkVLgBr2cAEA0BFg0hig8QDAwBwsAgBwnHz1LdhIgbBBkAQCBEGABAIAQi2ZFGqs7QVigx4E3eAhnDvkJAERDgAUEwk0MQgKNVUtoyAMAqAdDW80hvgJhguysJgRYdKbwVYU2l36+fNEC2UkAAB5QAEPIKFzdUReKAVMG+vYAgGAIsIAweEqgjw8fPp1GjhguOxnKQj1VXyiHADSHa9gbHDdzOC4AIBgCLACAYR4Odu/ZKzsJ6kLWAQAATWAOFgAQTbkAy1/+8hf62Mc+Rq+99hoREXV1ddGNN95ICxcupCuuuILee+89y+++8cYbtGjRIlq4cCHdeuut1NvbG1SypVC9kyNuYgAgW1TLIQRNISxUr+soC0WAKYOie18AuU446lDZSYCAKBVgaWtro+eff56mT5+efu/BBx+k6dOn0/Lly+nqq6+m22+/nfr7+/O+29fXR/fccw/dcMMNtHz5cqqurqZHH300yORDBkyuphdUNgAAQFW4RwGAzr7+xXNpzrTxspMBAVEmwJJKpWjZsmV02WWXUWFhYfr9V155hebPn09ERFOnTqXq6mrTXixvvvkmTZ48mRobG4mIaMGCBfTSSy8Fk3hJVK9vKJ48AAAAAIgQBOtAhpoqzHMYJcoEWJ566imaMWMGTZkyJf3e7t27qb+/n6qqqtLvjRo1itrb2/O+397eTnV1del/19XV0c6dO2lgYEBswgFCAr2OwAvV801UhxdghBCEBdrDwJWBPAUyINNFSYHsBBARbdy4kV599VX6/ve/L2X/3d3d1NzcTCNHjiQioo6ODqqrq6NEIkGdnZ1UX19Pvb291N3dTQ0NDdTd3U09PT00duxY2rFjB+3du5fGjx9Pra2ttG/fPpo4cSJt2bKFEokETZkyhdatW0fJZJKmTZtGq1atIiIyfR2LxWjSpEm0Zs0aKiwspMbGRlq/fj0VFxdTfX09bdy4kUpKSqimpoY2b95MFCu0/E2yJZNJ2rBxo+xkOBpqgxicXw8Vo1avVbN7dw8lEvlD7wCc7NmzR3YSbO3bv092EgK3ZcuWSP5uCKfVq1dTIpFk+mxzc7Pg1ASDpR5h9jozrrp3LyZnN5PYn6B9+1A+QrA2btxA/VTEfbuZ7Y+hf4t6nbsvv3p7e23bueXl5VRRUUEtLS1UUVFBpaWl1NraSlVVVVRYWEhtbW3S2u4zZ860/W1KBFiam5upra2NLrvsMiIi6uzspB/96Ed0/vnnUzwep87OznQvlu3bt1NtbW3eNmpra2nFihXpf7e1tVF1dTXF43HH/VdUVGQdqPr6+vTrhoYGIqL0CSSirB41FRUV6deZvW+mTZuWfj1jxoz068z9+H2t8somMSNGEyZMkJ0MR4ak16oZPrycCguDLQ5mTR1L763eHOg+VVdRXkrdPXpNzl1WViY7CbaKi4plJyFwY8Y0UufeTbKTob0jZ02mN99bKzsZkTd16lTa/f46ps86VXp14bV+kfleSUkJ72SFQnFxERUXR+++AHJNmDCB1m9p477d3LaFyPYL73ZMaWkpU5s3s92d2R7PjAfIaLvbUSLActZZZ9FZZ52V/vc3vvENOvvss2nevHm0atUqeu655+j888+n1atXU0dHB82aNStvG3PnzqWf/OQntGXLFmpsbKRnn32WTjjhhCB/BuRAF0ywE4spM0JRGTENLxqsVgNhpd/VGE6YMwN4Ghw2ijwFAOIoEWCxc8kll9Add9xBCxcupIKCArrmmmuooGAw2Q8//DBVV1fT/PnzqaSkhBYvXkxLly6lZDJJ48aNoyVLlkhOPQBYQaU5Hw6JAFE9poh7+YcLEjQ2shKTaprCZQ0SGIaBB1IRomSA5Xvf+176dWVlJd18882mn7vggguy/t3U1ERNTU1C0wbsojq5pK6CLvfjMeSPXAg6AagDlyPorKGuir7+xXPp/sdeoM5utefKChLuswAgGvrogxCI0oITDBHKp2PFD5e6mlAGQ7joVzaq4MzjD6fTPnyY7GQoxSAETwFALLRwAIAMMgKvcOgYTBBNz0OidkMe+Qy8Qi9MgPDBPQFkQK6LFgRYNKbyTcIwgm+wg3cpSqEnggJUvqYBogaXoxpQLgJXyE4AIBgCLCAEuqcDuIcn5sALSmAecD2qAjEW4AlBOwgc8lykIMACAqEw0cVgwx5NMtl0vP8ilqqeFK5lLnS8HsMI5wF4woMMABANARaNodIBEC46PlVTvTGv3xEFVeh4PQKAPVzWIAPyXbQgwALCoDDRDU6YbGjQqaOhrkp2EnzBME0AgHyGgT4sACAWAiwaU/kWgaq9XgZ7IQR31mqqKhCAM6HjMVG9He81aLXgxLmcUwK6UfkeGyXGgf8AWAwrLpKdBIA8ZmXYx04+kmI6VvzAEQIsIAyexoMVw1C/YS6DlteM4icSPTnAMw0vR4BcKAOzaXmfhVDCtRleCLAAAE0ZVy87CUB4Yi4CKtPgFfKOInAawAWny9YgorWbtweSFoAhZvlyfENt8AmBQCDAojPFKx2KJw8OOH7uDLrgYyfITgaQpkOEZCfAicdjqvOTJY2TrhQNL0cAcIDAKajg9GMPo3NPbZKdDBAEARYQQufGSdR86fwzqbioMPD9oo6TT8eKn/LXusfkKf6rHCl/XjSg4/UYVjgVwA3yEihg0afPoIKCuOxkgCAIsIA4qBFpJcj2mEEGnrKbiMVwzXAXwR4smOMIwgRDJ4En5CeQIzvfoYkUbgiwaAxP10BXKe37B0DoaZxFEVzhBLdYgNBB1RkAREOABQCkQCUnn45BUzTmIazwpFsNGhaLStHxvuJH5nV78TknyUuIBOee2kT3XH+p7GSAidzLEPeXcEOABcRIoVIE1nBjMRfT8qJRO8LiNa/p3stK9/SrQMvLMaxwMjzTebijF5llX1FhQd7fwxxwqhtZQTOnjJWdDIDIQ4BFY+G9RUCQZFU2IlbnYxPiip9ukD8BgWAAvSUjVpBH7OdqxTCM7GAnbi+hhgALCIPKKYA7Ol4xYazQLbn4o7KT4F8Iz0vgdLwgQyjMPQ6Av8y6ZyqZXxCGOT9FrbdSGOCMhRMCLABARMHfmENcx/FMx1WEVB+K4jafHT5jIp19ytGaV1T1Tft3rvyU7CSk2TXEzjrhiABTAgBemN2fQl/3CPvv01TuacFD6HBDgEVjKkfhU5RSOn0gn9btV2FwzcimY5ArTOIKHX+7lMTjqD4FSZ1cAboLc8NW7wcDAOGBGgJAxIW3qqEfHWOSqM+pCRVt//CQACCEQnxZo9TXB24v4YYACwA4EtHQwM0ln5arCCnekPe8ipDivyu0NLkGEHwBUJ9ZMR72HiwomxSF8xIpCLAAgCPcsAOi4XEO6yoNuv8szZOvBpvLUb8rVV+4/4Abkc4uKPi1gXIt3BBgASFSKRQeYSJiXgTdG7Ai6NiDpXnNFtlJAAgc7m/BwvEGcKb6pPNRhiIsWhBg0RgqHMADUz7inNcMg6hr9x6u2wwDHa/pZDIpOwlCoKIKtvS7VAEiJ2pDPVMpFE0qi1ZujDYEWEAYDduKYEFEz4qmOdO4b1N3uGYE8HpMNa8JRa1hIYLdXA1hnsdBNTzLxekTG/htDEAxKPfVhXtGtCDAAgBEZN+eFHFbqCgvEbBVvenYgyWstO/BonnyVYdLVU/xGKq9oYeLEzSA+l644U6jMZWvzRSlEK0NE5UzW4jgKKtD5weBOqddF6gcB8cgg9stCE/4IcywipC6cF6iBQEWgKgLfgqWA9vEzSbXwk+eJjsJoYN8phk0gEGgsK56BkCE4lMnqJqEGwIs4NtVnz1LdhJAMBG9kXRcMUe0CY11spMAB+j8pNswQjDESQEoosJH5+varaHgcnR+MaDcV5tZ+ROlMilKEGDRmCpDcMrLhpn/QY3kAQdCerAIWPoZwK8w1HXC8BtUh55RATLS/+NbMhmdiwMNtwhKoWzShSptOBADARbwzbQwx31dP3aVMQE3bNxc8uGYAOgBV6qeEHQIv8xr0+x8x+PhbfqgB4t+EBALp/CWMhAYq6EeKDT0wNKoF3EmkT1AZTo3xFKpFHqxcGC7TDMKsMDwPNZRuiyimkftzvG5pzZRealFr+sQwCpZ6sq7Hg/8U+e6BljDlagzRe6dUb2Jq+LYww/hsyGb8yjiHIvON9UjyoVuH8INdR6wvcfitqclDBGKti9+8lTZSRAqzL1zAHSCKxF8i1nMpYH6ZzC4dQm1qYyJ6cEiNodoGfjTMMmgHsMw0FVcMAznCxaWaQZW2UOEpCVDioJ4XHYSwEJeBxYd66jADAEW8M2skEAlJmSEzMEiFvIgEHnPBzoHKFKplLZjIcaOrpGdBCZWDxaAP55H+ri5nHp8amCoboacelDYG7XowQKgBlyJGlPlRoE5WOTyG0gw0uNAnT/DjWEIX0VIx67guGbUoWP+CYMJY+rogo8d7/n7iz59Ore02F2Pw8tKuO2Ht4vOPpE+etJc2clQ0kVnnyQ7CYEZqhv4Kcm+csnH+CQGAoEeLN5cdeEC4fvI7fWI2l64IcACvqFRGH4iusOL7mKfTCaFbh/CDT2g5Dl+7qGev1tfU8kxJdYqFA6wfPTkI2lS4yjZyeCGZx2juKiQ27ai4L8+cpTsJHAV9toqAizenHdak+wkQMggwKIxVW4Ull2lVUlgyPFqB9rVYUXE0ETH5QY0DLDgkhHA4/WhY/7JpHOASIegfZHCDfUwzg8Txt8kmg7XkWg6D/X0AkOE9IHrM9xwJYJvpnOwSEhHVH3izGO4bMe2PabhKkIDGOIBPmCIENguIqR43Ri5F/w6dHKj7CR4Yle3CHujtgABFnUZhII5QgpkJwD0ZzkHC544BWLKuNF0xrEfoudfXeHp+ywVDh1XEdK9BwLIpXMPEN35KRpw2gD8u/+mRTR1fL3sZIBL6MECoAYEWDSmSiRelXREWXVludDtizjHwgMs/QNCty8CriX+3HcRH/w8AnTyIDjvD4oR8Gv6xAbZSeAjYgHXOOZgURbqd9GCUCf4ZjUHC8qSYARxnMUEWLhvMksSj7KBvDfWdR8ipHfqveNZrqBCrAbDMFCf8KBkWJHsJEDAykqKZSfBEcrVQTgO4YYAC/hmOgcLGrehImSSWzylBkFuvOKTPr49mC9TLgIspcPUq9RqXQZrMERI9fJL6/MfoBOOOpQOO2SC7GRwFTNidMShE+n0Yw+TnRTlhLlRaxgGHTV7iuxkOArvGbAX1d8dVQiwaEyVG4XVHCygl6Bn2zesVp8C8Onkplnp117z9UCKfYjQ1Rd/1NM+eGkcNTLr32hah9N1XzhHdhJCp6iwgO65/lJa9KnTZSeFm3g8Rnd+/XNUWIBZAKK0itD3vnIBxWMaNOvQZiAiHIaw0+BKBNVZNZRVf8IXFkEE2ngH0QwB2wwDVYKmQDQw4GYOFrmV+LBlG2WuA0WS4ZYyx4+TkP0ckCjMeUmXnxbVul/YymWwhwAL+BbVwjJSNJyDBcCbwWCJ9kMsNE8+mIhyoSnqtw9dJyE+tNMmjJadhECF+FSGQpSLsUx4CB1uCLCAb5ZRWZQdoSHmVCKDgLrcTHIbRCzGdvLCkNVYw/VrQAdhfrp86jFzfA2B+vjp8zimRq5QN2pd5GGpD0ZDfK05idKQtahDgAV8Qw8W8AL5Jh8OiTpUW4Xq6NlTLf9mlm1QkfPPrjGm+rWqfQ+sDEE0ihU/nb7EYjH69ILjPH9/6nh9e8CE6DJw5CYPYw48CXIPOU5BqCHAAr6ZFtSpcD8RCpOh82RXEbFaitvffrlvEoCbZJJ9DpYgGrN2AZO8slbzVoWfe0eYAgu5UGQC+ISLiIgGg27S9o3KH0QAAizgW8xANpJNfDBLxDrNYtM877BpQrcvQqi7L0tSaje0xoabAEsQ5kyfYPk31Fch1Azx7WI8EDqoaY51bzlQl5s8LDPIEdUrLbd+hzIn3NAyBt+sCgmzdyeNHSU0LbOmjhO6fRFmT3OX5mMPP4Q+Mm92+t9BFNIidiHyBn/83Bl0zOHThW0f9BGPxehT8491/T0VhgiNrBxOTy77Gv3mjiVUUzmc/YuGIaUTy8XnnMRnQw5Fw1cvPZvPfpySgfqvb6ceM0d2Emy57fFUVHhw6eNf3HolPXv/9byTJN0tS86XnQR/Mi7c3PMb5katm58m9TiE+BwADEGABXxzM3ykcniZwJQMGls/Uvg+eKqtHuHuCwbRtIDHRIvoWSHyHjtp7Cjth0kAP1UV7ssdV5Pcut46m5GV5TSycjiNrq1ymFMlf4iQjDlYCgvigexHiVVRFG4kqNSInDKuXnYSmLAes8zPTWyso7KSYaKSJE1BPPs6Vik/gR328xSXOAdLVLNT7u+O6GGIDARYwDeznggpSpnelKNasKqM5ZyIOW/IDHlwSITwEmxQbYiQnbCVq04B3agOpdOxocsj0Kfj7wY1IScNknlNYQ4WiAIEWMA3N7ORiy7UdSy3PSU56N8p4MCKmDgXgBdXQ4Qk95ZSpQGqSjp4CdvvkSHRP5B+XVxU6Hk74usOONeqqa+plJ0ErbjJwlimOXiYgyVaEGAB3ywLapO3RRcnOo4K8VvIBlFG896FYYh9Bq3rjUv1dN+qyNj8w6aPF74PN0OEZDPNNfokP4/jZWDzd41/Njc8i5G66hH04i+/a/uZo2ZPsfzb/v396dff/8oFntIQRKnIug8Rabn3218UsFV+ZN2WHr3zK9y3qfo91g9Xvw09WPKMGVUtfB8sbZSLzj5ReDpAPARYwLcw37BUFfQxF7E/kb/BMPQMtgEbN73mvNJpiJAqonQviM4v9advfyL9urjYew8W4VjzrpB7IfdNRhqOpzOsIiSf1f0SdddwQIAFfHNVUOPOJ8TY0TWev3uwL4l1qS7itEWpMcYKR4SN28qhlwrL9IljhG6fK7N5sKQnyjvHOVhszn+YryE32T7Q82+zr4rykvTrqopyz7tQ5XahSjrAmzDXO9z8MqlDtEN8Dmwx/mwZE9QDfwiwaO6GRf8dyH5qqobTZ//rBNO/mT1NTqXMK8lRLVdt+TwmBhl0+rGH8UmLzV64b1FktNsWTQAAIABJREFUD5ZQN7MgCJ//+Cmyk8AsN7drHFvxLcI/XUkTx9TRok+dTl++aAGNGulyxbwDRN4rhvIL6ibhpHOg2TUXmVhmoAnXGkQBAiyaO/XDcwLZz1cvPZs+Mm+26d/cPE1Gw5eP3KMYj4m9lMX0YOG/TQBeSkuKmT8r+4kTS2X5S585g444dKLQdHDrdu6wmaCKDl2fdqt2n/30guPo3FOblD6erMdMxLFV7XzlY0vf+IZawekAXqQGWJTP72KoXP4BfwiwABO7YsGq0JBTluj3tMLLzSYMc7AIHQNsyG/0eoEbMBu3x8nNU0yVHnh6TYth6FgSqsfLfS+K7PJaZh72esyE9mBxeZHhtFvDsZFLl1WEkE8c4OYdCgiwADOrQICbyg8KVgH8HlOG7ws5bUInuUVGCzPVzq/soEzu4TBLj2rHzI5TWnX6LXxF9XcTif7tzFnK5IOHTm488Cf1AkjBUu93yC6bWRTE41y24+ZhXRATxVvvXL18EgSD7FdcG6Ljw0HIhwBLRBx2yAS6+JyThGzbrKhMUcqi0iC6YI1mwS2amFWEuG9Se+GpaIsVlaPEnh38H5HrLzuPnlz2NX8b8ZmMyWNH+dsADAq4B1Mo5rnwUfbefu3FdPu1F9EX/lufeZvckLjAUiQ8fvc1XLajTQ8WaXs+6MhZk6Xsd+r40fTjGxfSb+5YImX/EBwEWCIiHjNo7kwxBYpaPVj0q+ipUimxqyPrNkTIID2eXIFHqlw0ijA7HG4bvbOnjaeRlcN9psPfeRlZNbh/x61E5PQfOrmRhhUXyU6GL7o8jfWTpUpLiunIWVOooIBPTwRdqfKAQLc5PqpHeF9dyyu5k9zKPz+lw9jnWONl6HfPmNxIo2urAt8/BAsBlgjxW6ZZft/FdlUoWMNGx2NqGEQxgRPz6nhMgJ3Q6Xu8bFtQNI91s6rkdyVSwfFcyD6uhmFkBaJlJEeXAElQ7E5BKHry+KDE9R9p7Gcg8nOwqJAGCxEvRkJDiQDL/v37aenSpXTZZZfR4sWL6Vvf+hZt27aNiIi6urroxhtvpIULF9IVV1xB7733nuV23njjDVq0aBEtXLiQbr31Vurt7Q3qJ0h1zef+S/xObEpEN08Lol4B4YVnxZ/l/PG+IaZSRIdMGkMlw/R+OquLKglPyEQS+YRSpSLK13Xu8nfwKFOKiwp9b4OIYQ4WybXjaz9/TmCNBC/7McgI9F5rt6vcvx3zoWme9uHmOHgZDs2a/2UH3fy46rNnefoe8/Wm4LHJDBA2zZkqMSX2vveVC3z3VnNz+GXOwaLENaTQfT4X2knhoESAhYjozDPPpJ/85Cf0wx/+kJqammjZsmVERPTggw/S9OnTafny5XT11VfT7bffTv39/Xnf7+vro3vuuYduuOEGWr58OVVXV9Ojjz4a9M+Q4mMnHyl1/6ZlZcq8EFWiYFWM7MYCCxHnrSAep59+93LTv6ncLb5yeBl9cv6HTf926cc/EnBqnP3uh9fScUccIjsZXIlcRcgLUVuPMVaCcz+VOvCfGzyO0fCyEl/f16Fe+avbrqKzTjwisP2F7Z5565ILaOmXP8P8eZZJIXN97jz35TCXo+x51a9gzvF5p8/jNt+HGZm9Ilh87ysXyE6CpWM+NJ2e+ck3AtufzDOlQ51XiIj+7KhSIsBSVFRERx55ZPomM336dGprayMioldeeYXmz59PRERTp06l6upq014sb775Jk2ePJkaGwdnc1+wYAG99NJLAf2CaOBRKEa2YOVM92WahzZn1YCMx/0VTYZhCG2txS2GN6nYGLJKq84UPMxCMDdYFDkgFeX+AixpDj8nsJ9rsh+/ZZNbqjda3TIMgzlwOPh5gYkhct/TS0wqAjOMUy8zM6pnVRXvz5n8ribk5ve5uQYBwD0la95PP/00NTU10e7du6m/v5+qqg5OBjRq1Chqb2/P+057ezvV1dWl/11XV0c7d+6kgYGBQNKsupRFjxIerLZr+m4AZfrEiK1EweuQBtktceIY+3Ok+q3fKs+HvWtnRXmp7CRESmYl2HYS6rx/u+89yOP+UFTIaYiQKiWACstdZ87BospxiTKb8+91vpogs5TYiXjVyJ9ZxzOV+f7BP4RxxTI3R1/kHHhOpC4RLRHK72gpkJ2AXI8//ji1tLTQLbfcQvv37w9kn93d3dTc3EwjR44kIqKOjg6qq6ujRCJBnZ2dVF9fT729vdTd3U0NDQ3U3d1NPT09NHbsWNqxYwft3buXxo8fT62trbRv3z6aOHEibdmyhRKJBE2ZMoXWrVtHyWSSpk2bRqtWrSIiMn0di8Vo0qRJtGbNGiosLKTGxkZav349FRcXU319PW3cuJFKSkqopqaGNm/eTOXl5VRRUUEtLS2Ov3HPnj20q3OX52O0b98+2rBxg+nfPvjgg7z3kskkrVq9Kv99wQGvffv201lNU4mSSWpes5k6uvbYfn7o3mtwfj1UjFq9zrSry9152b17N+3r60v/u2fPHtqyeZOrbWT64IP/0PCyEtOhd0P69u71vP1Mw4oK6chZE+nTZ8yl5uZmMorENNjb2rZza+zlSqWStGPHDvP9bm9zvb0508fTiNJCqh9ZQYlEwm/y8uzq2kU7d+70vZ35x84m2tft+Ll5sybQa+9t8L0/O2Z5de6MCVReNowmjq6k1tbW9Pt79+6l7t3slcmBgQFas2YN7du3j/k7W1u2Mn/WjUQiQe3t7dTW1ka7dlmXE/sT2ffKrVu35uWlHTt2UM8e6/KwP5Gg5uZmX+ndtq2FvvTp0+i+R//s6fv79u0bTEPBMNvPrV2z1vJviYR1OVZUWED7bf6ea2dn/nXT1dVFO9taqKOjg3k7Xu3t20vJ5MF75patW5i+tz+xn7a3bueShkQikXU9menZ02P5t76+venraahutGkT+/1q9+4eWrlyJfX3J5k+39zcTOXl1nNO5T6g6+ruovb2durq6mLafmYdprm5Oau+Z5f37GzcuDHvPZZ6hOXrVIra29tN67C1ddmBhc+ceTT95k+vZ73X29ubVYdtYSzfMuslPHgtj/r7D56jzoxyc9WqVenfdMlHj6Zv/fhp32nkoaWlJX2e/NiwYSPR/t00bZrzPEcyHwb1C6jnuJU7R2dHR4fwNufq1auppvJDpm3CTLkdAy7+aBM99My/bbftq7xw8TqzrcNDb28vczu3oqKCSktLqbW1laqqqqiwsJDa2tqktd1nzpxp+9uUCrD87//+L/3rX/+ipUuXUnFxMRUXF1M8HqfOzs50L5bt27dTbW1t3ndra2tpxYoV6X+3tbVRdXU1xRm63FVUVGQdqMxCrqGhgYgofQKJKKtHTUVFRfr1lCkHxwpnFnAzZsxIv87cD6/XmemxUlZWRpVVlY6fs1JcXEwNjeaF/yGHHEJEf8h6zzAMmj5tOhE9k/V+vEBslisqKqJ5R8+leUfPpbsfeoae+uvrtp83JL3ONHj+NlgnMsfw4cNp2LCDDZDysjKqqfa+5NshhxxCFeWltKljheVnSktLich/I/3DRxxC3/7SJ9L/3rrdfJt+u8WPGjWKinMCLJ9ZcBz95tlXfG2XaPDJT11tLRGtzPvbyJoa19sriMfp5iUX+U6XlarKSqquriaidb62c91lQ+ftcdvPff/aS+mki76d9d7JTbPo7/+2nqDcraKi/Dl6KkcMp29l5K0hJSUlNHw4+/LD8Xg8qyxn0TC6gYjecvUdFsOGDaPa2lqqra2lHb3WjY3iouwlJxvGNNC61uwGY21tLZVvNg8MEhEVFBY6VhicNDQ00BnTxnsOsBQXF9PMmTOppc2+rLE7PwU295ibFn+KvnHnw8zpGbxuso0YMYJG106g9j3+glEsSktKqLBgLxENBvsaxzQyfa+4qIhGjTrYkK6trqD2nc7BUTOFhYWODb+y0jLrtBQPy6sb7djDFiwhIiovL6cZM2bQa+/mN0TMOOXh3PrjiIoRVFtby1w/Gsxf+7L2NVTfW7HOW9Bt/Pjxee/5qmsYRvp35tZhMxvW55xyNB3fdFhegKW0tDSrDjtmzBiWn0ElpZyGCB7gtTzKrPdXVR48r5m/6fhjjqKv7Y/RDx74vfcEctLQ0JA+T35MmDiBZk4Zy/RZmUOHB+/f9g8/RRus0x40cuRI03oFT9OmTaXi4mLLNuGQ3N5Fn/vkAmrZ2Ud//ue7ltsOqv3Cuw9OaWmp63ZuZns8szyX0Xa3o8wQoaeeeor+8Y9/0M0330xlZQdv1scddxw999xzRDQY/evo6KBZs2blfX/u3Lm0du1a2rJl8AnPs88+SyeccEIwiY8Ag2yGArmZuZxPciADry7rdk80RO3Deulvf/szyMjrrM31iY1F8rzsI4ju4W6e2utAtQm0hU1yy7yyiaAEqMrj73WdRxQY8ccjX+vcNV103h4a1sPjGHm9xwR5fjLzU6Kfb49iVXJZZp4J+7DdTG6uFZlzsKhwv1I5XyicNHBBiQDLjh076IEHHqA9e/bQ9ddfT1dddRVdc83gTOeXXHIJrVy5khYuXEh33XUXXXPNNeknVA8//HA6+FJSUkKLFy+mpUuX0sKFC2nHjh30qU99StpvUpGfMi1FdnOt5L9vVT4E2QhSuQD1zeQ4VgtcipfXaTv28IBWszFJL7fsYBiWFeKkonnu+VeseycFIZDr3mIXR8+Zqm1ZkHnc7H9C9o8/ZOIYKZW0hrr8Hh+uHEi0n/yiQuWdp8yGEPucx+ocBLNrb2Jj+Oa/IFIiHudKP+uQbc0m2wZnpx4zR+Le5ecTszLyjOM+FPg+o+zEo/z1mFWdEkOEampq6OmnzcdCVlZW0s0332z6twsuyF5yrampiZqamrinLwy8Tr6Wybq3Qf5bAwMD5mUoypc8vA7JT2++nD5+1Q/c75+p0PeXyovOPpGmjBtNx83NDrAE2QjkcQ0MsTpkXhryoo9BGG/qLD07Hvr+Ytq0bQcdd8QhtH4Ln/kogsa8THPGx26/9iKaPC5/SIfoXLDsm5fSyEr2oVh+2OVpN5MB+0sDx43Z78n/FhQrAkbXuh/SKvon8IgheA7kSjo//f0DXHvPKJbNHGkad7fk5lyed/o8uu83zwtMjTXVyqMh5y84nn7+5N9kJ4NrXVVl133xHNlJEEqJHiygPrshQmb6B9jHWAMfIhs3fm+IDXXVdMJRhzLPXO93DhbRQQWr7SeT0bgxusW9BwnD6R3fUEvHz50RTIBJUE2dPcBy8HNHzhoaTxxsXpwzfUKg+4sKr2VhkJV0L/uaYhIEtCP8OuYQYdGtp1yC86ID2gXzNTtfTliP/1GzJvteEtoPFZaeN7tWCwriNGMS23xD4F/psGLnD2kMARZg5maI0MBAUsq478xCU5dbp5dKSfArhfrbofWQMXfvszLI5AbKKUPYJW0g6T6wqEBdQzumyxBr9/zUWWZFFPnkIM+HQsODyGNJUxXnJ3KbJuFBcw7b0K293s84BwvrsVHx8tLslESDihlFJci0oYAAS4T4vWbNisTPnXeyaVlp1dAMYyNIBhHH0S5/XHnBfJ8bN9+6ZYVUwA2Y5/woVpX9FHqwmOLdOKqtrsh7z3YXLk6Ll6fxohpWrHOwmP30hZ88PXdjXNJ01olHcNmOmfQSkIpUwOX3SDDoGwvPJSKisz9ylOdz6Od4shyCKz4znyY21nneBwtRWcLtKbYtZrxOchtwfh/qTfqFT5zKed9qXLesmeXEo8M9B4SqVMglqtxjcp13WpNpOXLxOScFnxifLjn3ZNlJkAoBFmCW+SRt3mFT6Y/3f5MuPudk04JqYCBpeo8T38tXzUJTd9Mm+FtC0G1wI+7zqa1ZPnBT+f3VbVdRUaH1FFVW2UzVSW5l491QjcUMZbvyThrLbwJP1iF1Zhly0thR9Js7luR8xH/5+LVLz/a9Db+CKuflX80p+tAhE+mP93+Trr74o7ITY+oj82bT1AmjrSvT/GYX57Qdi60zr9ilfx3jpis/Rc/8+Bs0ifNkw06HRuawFDPDy0qEBwaDxJw3Jedhu3RedPaJgaRBRvCc5fxcdeEC0/cb60fSs/dfzztJQl1y7sn0x/u/SUfNnuL84RBCgCVC/BYomb0mkqkUlZZYj58b4Dy2l1XWECFdGrv619ccue3ZEWdtWNrt08f5LxlWZLMsufUqQipOchtGBhkUd1FZd9MrxUvvsMztlxQXuf6+Fb/LNJcMy00Lj8nOxRdYovbAY7tDvz/I3pilJcXKrqzkNb+7TZMGU7B4noMr6CqAYRi29Tc/27UzrLiQ+z6dON2ThxUFnyZRmK8RyZUOu3SylHPFHM6ZjtUunnWLoJSWFId+rhUrCLAAs8xyL7N8Nu3BkkyG4kmPqrLPhfq3CrcjgZif3AvimHc16sGiwnXIOw1mARNeDV6/k4OOqqnkkg6i7F6DtpVSi9+u25BMHmUZz8ld5Zeths2/bL5lGDltKL3ygRkVyjEnOq7+wTJvGHPvHoe/qzC5KajA55x+HMrl8tJh5n8QmEeZ41/CUgBBQoAlIlIpf0FrwzCyb7IZGzOdgwWrCImlWUXFKvBglSf99mDxO0TIy/aJvD3B1OxUqkvycfz6F8+lo+dM8T9fUYbMBgmf7Ot8kG776oU8duSLU4PO67XMuipT2LCWMXNnTrL829e/eC411FXnvR9UUEHUmTuYfubwlfW2NJmDJVPmRLelw4rp3m9/0fvGHB9MuP+d31386fTrxZ89iy0ZLrYfpsYsc0D9wHm4/DNnCEyNNbtyOKhr4bBDJgSyHy+syhEdgsxwEAIskeL9VpJKpbInXMz4m+kqQhZPRaQ/EFSQ7KfM6fMq8uS43LbKDSHDsK7AyX/iDTKkUkRnHn84/c9XL6LqEeXctsvak4ul3jVYzjjnz6Y5U+lDMyYw7VcWr1dZ7vH84idO9Z8YBtMn+pvDKgiNo0bSHdddYvn3M48/nP7na9bBN6uij98ULKLnYPG/jdzfeuwRh/jfqGCJjADLPd+8lGZOGet5W049VLwc4ynjDy7nPbKSrWyN7F3Y5fH91PxjxaTDgQq1O4OIxozKDxgL3afPQgYBFr0gwBIhfis62U9T7TfW348eLMKIWX1YKLd5z2+AxbwHi7/vs/zdyzLNomMyYbwnuw1K6hr3Yr8OnD/nJh9IP16C8izPwK3K15VhZN+jeQXxTbfjkFese7i4S5Po4SWsx8guGbn1ItZlkGXKnC+vsMB8Xitec6d6yYesK6lBdPAKNMh+uAnhhgALMDFyHtunsruw5LFcpllweTbvQ9Ms/yZ7Xg+epow7+FSnMGOyzzKrcaUsBJ4cqyFCVrv0PUTI5D03vUu8HgpVl2nmOS+ICkznYJHZ4s3JW1UVZVw263eSW5NPek5LppMkL2/qeYiQ4a5cMdvP0BEcO7rGUxr88JrHeV0aZtvxPixG7OfNBL1iTD/rZP8Si67MHiwFFgEWdvx7sPhNxexp42w/6zb/qty7T5seDjnpdNPDr6ykmFtPYV0OF+gpPC1O4OK0Dx9m+bfMaG9mwWS1TLPZPCwin0D89xnH0GcWHGf591/ddpW4nfvgupA3iGZOGUvf+tIn6K6vX5JVKVp+0yLvCRF4cqxuiFa7lB0Ms3+yYVhWZLxMchvETf7bX/qE+J1IZncYM/PftZ8/R3ha7r9pke0y35kaR420/Jv/LsW571jnTzd7+uqlZzON368oL6VrP3+O63kdHANLHosqv8u/Z5owRqPlXRny0dBHfrb0S/SVSz5m8RmTnoG+EuaG93P3tc+fTYs/exbdfu3F1ltn7aVhk47MObjuuO5iLXqwZAaBCn0uo+x4DD2UZ37LwBOPmknXfv4cuu9GH3PLHFBbXUFLv3w+/c9XL6TrL/u47+3x5uVI/fS7l3NPh5Pc8v30Yz/E/N1blpxv+v5tX72QrrpwgatyOfCAFOPuojDU/Mc3LpSdBOEQYImKVIppMrqGuirTieyIsrtXZxZMZmVG/8AA+9MbTs7+yFFUWGDdqGmoqwowNey8FvKnzJtNhx+aPSlh3cgKD/v3tHtX3N4wfHfllzReX8Ubo0EGVQ7n06OCeZ8KPxo668QjhO+jbuQI+vjp85g+W2yzdCl7Dxb/x9tNzi0vHUYfP43t95114hHM8zoMXT6Ok9zapNbuEswN3DpPpmv7Z+FkXEZTxtXTf33kKObPB1Xm+TkWI8pL6eOnz6ORlcPz/5ie45bDEKGMfHnolLHUzzjZv8yhCpnDua17sARXDvGQVT81DDrrxCPo0MlsZZDdkrLnndZE5aXD6Og5U+mEI2f4TidvzKs9ZXxs6oTR9JF5swWlyHn/px4zJ+d82X+3cniZ6e8cU1dN553WRCUulgJ3m19rqtzXr72Qfd8JQmO99YOlsECAJUJEXbRWyzQnAn56k5sOFRu7oqk6ptR9gMV/0ZS7T1e9SzyOJfeyilAQ4vFwFfVu87noir+fs26XMuZJbpk+ZLB+UjpRgQ/ZPeOCknt9+DvrmSsGut8Sr9uw6GuYfZSdbYQla3tB14G8SDDMwcLKsQOLz22y1iNE1f1UrV/px/xhLds3nYLvLrZlsm/b+7HfHqXIP5ESjdoGuHg6Y/25rB4sDtv58oULaHRtVV6F9tRjxEXKIxNQ0fBnuj03frvy+62L290I7dqpXoYIBZFt/fYIOvfUJlefl1GNsCu7ZJQNPBqEzOfNw74Omz4+exOZ/2A5Xi53OdbFEyuniqxdDxa7r7q9DoJagpiV1yzFb1JIEx4PUW6anHrN+PkFQfWsyJpY2DCYV6kqLPQ794l38w6bSkREw4oK/c3hRkTnOvRq83IfUqVXTC4l0+W1fOCbCnf7NlyWaxwTK2MeqCj69FlyVquSDQGWqEilfLfkrBqdZjeaj8ybTUWFBfSH+76e9X59jbxhOkreEHlT9Ce6zXl+nzSb5VWejWyrvKTkJLeG/0mDz//o8S73KbrHiMlxlpj3TSdD5ZAeL12+Wb9/1zc+5/68+vDALVfYzoExaPA4Ov5um8vM7jJ3fR2YZTMN7yP+ksz6tNnqwLOViUsu/qhDMkQP+2S81mz+lswJsBw1ewo9fvc1jtusKCth2rcIVRXl9NSPrqP/vfc63/eJiY119MTdX6Vf/+DLFp/wcA4zvsJ6d3VzjYbpuZwuPSSy5nA88J//jWb9H0MarOfSs/uOH6xfVy2w79eMyY3023u+mhVw1vE+6hbbLHwQMRaNBSP3DWflOU9EwlZwSCGoXBJZ0XAbePBb0TNNA8dVhKz+PJByv0xzEPcZEccT7LFWIOxyJddlhfO2HaNpEw6u3mCXjsrhZbRr9x5f+y8qLKDqynKmzzrGV7yuIpRzPEXtRzquZUrOuJe8v9ofI9ZDaHW9DDW+RC3TPHSO2ZciZvvgUHrrqkc4ftZvzxG/Kh1WPXNz6Gurreep8HIKdQkaADsjO8KS/TfHlag43hMDDrAw0/S2Y6emqiJ0Q9WdROvXRtiI4WW+r9nMCk72KkI+NxxxMm8YQRlWXGT6vlVy/TYsDSP/HuVqChbDsGxcGTarCCnZg4X8L7+pWldat5XushLriQtzjSjnMyEwj4bB8FK2J9syGiFeyhrWdIoqx6IyB0uuDVvbnT/EstKQac9ALylijwGlWHs1+eZ/+1n3DBebC0sA3OkceSmnsjYpJODJb262/9/eecdHVaX//3OnpfdCgBBCCjX0EggtIXSQLr0jGDqsKEVEFGUVS0CwoKviCotiQ/anfldRUEDR/a66Kl+X6gpKiwRCJ2Xm90eYyZ2Z28vMncnz9sXLyb3nnvPcc88959znPOd5/I1Bp39euK/VMrLk1vIWufISasPqLVgC5AH5gNpQFcHRqxOYNboP5/F5EwagSaN6mD9xoOoyxFaYjEagLj6KInBfSp6E1vVUv048JtzhvvVgQI+2sspW/SHkp4HQiD5YGIZBZHgo+nblD8EuhhHfJc9HJNQPDS3shNZNGmL0gDzeNFar5XZfOUC2LGq2CAklEwo7L7cs8Y8f4yCmYHUAWD1vjOp8pThMHN2fv81I4dFF3GFFpeD9zCTbmPsMskqtgd0NyLW46dgyS2NpfI+YxZciCxbWRUYch/jgW1TSE6H+LCstxYeSiMBerPX4W3GWt+9dThPhGmeE2rBeVnREcEIKliBh/ODunKFYR/Xrgs0PFSEpPlqGB3bxNG4duSzts3YdVKCuRnrutgo2Nj9UhGGFNU5R7+zXBSE26aHzAPVObrmQ8yEg9oHH97Fqt8vfIuQr7pk2xGdlaRvFxBsHHF7PQGjuE2KzYsP9MzBnXH/eNKP6dsbmh4o0C8WoxWpVuGTLm+DoUVxhmsUUHw4H8ju1kJ2/p6WAlD5hzvj+mDue1W5kVnXXdk3lXcBCzRYlPbY3CQbQ0bC4nh1rnq2zLah5n6QpINXn5enkVg6Th/aUld6XGHURzci0b5HhbxFcZDSog788MsffYnDCyJzraWsFIrdsDYsWIGC3psqgNvQpgfmFSihC7TvrFkXIbYuQ9BdFy9UuPT7C/YGmw4UBNexKZDKb1IeL9BqkZDQ9s8nEb7HF8N+TkoHRgI/MC7Uy6jFdCIBq8xlatyEpz0tSkQoHHdFJt9IwzTIrqsY/R4C1Np3E5awHkWeh1ceCcbYd8qdTo2AJBkTvWWWdSJ0/yikmqL5lFVavr9uq2xYhzzdPTBSe885bkHMnXLet5xYhqQRTk6zNkIKlVqFTFCFVuSpHzIIlaE2XBSpczQCgVX15hd1TIJKWzj2VYDLx+2ABwz8Vb5GdpptMTuR+JPqjJsVEbNdc3eoeA89GZjz83YY9MZY0wqgJ0yyEp+LG3z5hdCtXw6GvQ47w1pWmGfWViSL4IcO+/raSS6cW7JRPalV3zMkUzUuRHAaervhzLmUkRRVbFtEtl36QW2mZvraYcK9Hedd6Bs7QSg4p+Gr+eW0rAAAgAElEQVRMrw0WLLUBiiIUROg7CDIek1PpA41eGGfYDT4WTByIZ7Z+qOha7+0hCixYVHob52qTcgYt0e1nPG1+zICuAID0+sl4YMN2yeXJwWw2wV5ZpUvefGgx3r/y6Fxs+/sXiI2OwKQhPXH2j0soWr1ZcX5ePlj8OBHn9MHiwx5K73uXu9IneKEGCLdH/pNKwzQH+lgzaUhPvL7rc1nXvLRmNg58+x+M6tfFdYzrmQ8r7KRaPk843x2DPIS7x/RFfGwkurRp4nWutn0YTRmWD5vVgpfe2i0pvUEeoSBadVn+aAvSbbD0fxLNMurj5xO/c5fvYQEvdfxaMn0opysENnJqXf64qa7ejKQs9Du1oCpIwRJEaBXBhD96CjdyOg1NO/Yg6axkd7oaj9tcz7tP19aKFSxeYfcUWbCoVLBwHJPl/ExEaL7zNqsFk4bou5febDKhAr5VsMiGo34yGtTBA3PudP0tFhpUCAccxt/Dq7l4/C2Y17pQ9OXjzlOzjwONwil7Z6tRvpK/SAze1jxh3J9sw3pJUi5xI7thXWQ3rOuexiPRmAFdFUcok/sxqPvHicT8w8NCMGVYAec5Ne+NkZsYX/8ybUQv7P3mkI+l0ZdapiMDoM+71TyrAa+Cxat8iXn26pyjXCDPMhn5Q7TRrFIDjlr2btEWoSBCLIKJ1IGDL5kasz5dqI0jYYAgx5SWC028tXu6YJHRXoRWVRj4t/3Lte7RYvJkiPfdA6OvBvlUPilFqdk+6OOuVl3d8V9rYuS9O1xWoYZX7AFefV+ITZu1NH/eu7oxQfxaLe6stk9J9G4dRqpfcXczgbNFyPfUyCnnvRa6P4dMa0OHgzs/IXHUh2lWdXlQEThtVTmkYAkixMYetauSUj+ae3Rszi9DbVNhBihcz3dk384AgAHd27oiY0wf2UvStXKxKlwVZQmBPI/oHZ57d+NiIgWzEHxf/Dg4KLXu8eWApndJDBhDTVa4Wors7Sii8N/w8N7V2zTEzKfl5Cl6pQ4PwOVvQyTvBnUTBHPhg2sFMjk+RlQutbc6dqC0cNt6keVhiaIYJfWgZN7BKscu98tJJlpu5QjWLULC8zZ5CxdyMVI/b3gCpK68gmSoXJADlH1bcPrsFpr2yS5BGex+pEubxj4qVVv6dWvjbxH8Dm0RCiK02iKkhuT4GKyafad4QokwjLCzUUH8Xx3S8O02UMXMGd8ffbu2QWZaHTgcDvzy23lkpaXglXc+ExRPyYBptarrmhgGSKubiK3rFuLq9ZtITojBGx/ud0uzdd0CnLtQhukrnpWfvyQZBNquCoTKnjIsH6/t3Kt5mWpvQ48m6x2mWV0pUp9Vi6wGOHTslGg6G6sNv7D6bvx84jds+OsHiuUTokNOFl5dOw91EsSVBe7wbRFy/1tx1Sq8UGhVc8f6exARpszRIZeCZctj83C25BLuWvmclxWo1LDRYuR3aoG0unOw+6sfsP2D/eIXaAC7PUeGh2JH8T0AgNGLnwIApCTGYvW8MbL8IAnVg9y+Tuh9dXv+zkhOrLJXzxuN1Zt2yCpPjTxSCVoFi8TbEnf8qloStRl45yjHslXz0gOPnh1b4PN/qtsW5rlYK7ddcCaX2TSq9TryCla7RUjJOPLwgrGqyvQHmx8qQmZaHcE0teFdIgVLEKGndYiXQ0me1yM7va7gnmy5HUywTljYyO50daoSsao2m0xo0qie6+/G6fUEUtfgzxWo1BTWSrfH/UWEhSIjVf6HmlSnbIx3kZzIbuICZceLWOX4Cs/60brJVvtg8ShT4zL4CA8L8TrG1U+F2Kyu37HREaiXFKeyZOFabJSarCp3XSyclIZpFpBFisUJH1zWX+GhIchowD0ZrLGoUVzk7esZZKalYP+3P8u7Tl2xbuUneyjfTCZGNAKQdz7yy1ZkwMIqyKn0YpdtMau0cOQsU30ewTtb8WMUIUVvgRrLPMWXuiE2d9VjAUZ6VDTlZYTarAixWXGrvEJxHuzitapvZz+h1smtnluElGC1BN5nOvs7oTZDW4SCCDELlmANIegkAESsFXhZFvhBV81VppiPokBBaIzXa1eTJitMWmNw23G2BYtUZZvWsCfwogZ/Ao2HS6mkB3q/op6WMaITZrkxfEUwUhekVvEhuRwFLd/G8hnj4LBgMVA1uqHmg9lIbcMTu2bW0WqtDDUSQ4MyxOY1RvYxobYetZxLMQwjXTF0+/9cpct998xms3wnt6o17dKSGbkvIKRDCpYgQvSdlPDWOhwOaW+3cccOwmj4qK3IWcHv372tqrLqSrFGkDgYy1de+KZCJw/ljoY0sm9nhLKsM/wBA0YbR8g6wnYs6oCWHynKUNNuHpo/FnUSYjB/4kB1Mvj5kYVo0G7V3IO/LDK5fQ04PNL4/30aPSAP9evE465RvV3HOH2wyN6OpIFwEghWi1tBDyw8Jx+YPQqJcdEIC7W5jil6Djo/O72emD/eJsXtT64RtdqxTKAhKK43Ga6amjSqhx4dmoGRueVHSh+5YNIg1daktcFXpRHGG70hBUsQoYVWmTdEs+cWIb6XQ0QEz45jZdEorzR1EmOFM+GRyausAJnsGLGf0cIk3vVb5pDZowO/k2Q+Nq6cgX5da5xqiX1QLJs5XHYZ7LwbpAg52eSXQQsELVi4op4oFKRhfe5JwvyJA/H6uoXCF3tZMWmP5D5JItL9DHBd7H2IbcFSUVHJnUgW+nYUQvWXlZaCN4vvcTm6Voo/u+SiMX1lX1NjPRF8KBkfBaN4yM7L+9iccf2x7YlFblsdnR9z7LI1bUeuvLjvTY4/MDVyGXEe4EK6WYeLwi6t8PaGJWjXPENDMfTvQLTaIuKPrq7KbpeUzus2ZAqr9lvD08mtFhaycmTa/FARrBaL7LmplOc/ok8unlo6VVa+tYXaoDhiQwqWIEKLLUJSDViMiJHnJ0LI/jA0+I16Ox+Vm4EmQmiQiUD2Kp2dqSpb6CNHoA/Q0vJFbvXq4YPFC4O9F2wFy62KSg361QDtmH0MXz2rmdxptdrmU6U/qyiud9//47y0OnV+OOltscabvYyKCtYPCMkfr1zdMqtiKyqqZJdt2LDoKhf49LgrPitJrcuyS1Tk8MF+pgygyXxNWRQhrnIFrGskysm7UC15j5C0ZISxIQVLECHWwUiZ3EmdAPLORURk0HOwrDV9ko9uVKoTW0/UWhZIbSNe5v4aN63WTRrynpMy2derrQeCaaVQ3WmF1vUgxSqJD65+L4nljDU81GZ4izp3+fSRVfSR+biOpLYgvnRabDkSLlijNs5l0adRWFMx1D5SV7tkla2HIoOvP5Elv7FfccVo9VqeLy1TJ4fEdLLaqU59jj/GaT7Fh+gdyhGVAdo0S5dxgTfZ6TVh45UsVrXkml/cvskmjaQ77m6czhW+XmCRykePNL1+km8KInSFFCxBhBbjRDCtwATPnfiHRxaNw7DCTnjp4dmyr1UzEIkpL6YOL8Cwwk54YLb39jJX+RzH5H7gLr97JIb17oTnHpzplbukKEI6DMYj+uQKnue6Q3/oY5bNGo5hvTvVyKBBnmwFCCPxGUilaUZ9DOjZTlJaqYqzhvWSMHd8f8wd3x/1kuMN7+Q22FGi8KzZPcJ97YsPF7n9ve2JRRjRJxdx0RHc+cltBDoqnJSE4vaHNYFzVd6z7MfumYg7+3XBApV+gcSQMyeSaumRmZaiVBw/IX+LkMAhWYi1y788MgfDCjsJJ/IDvnhTHpw72u1vX/n5emD2nW7ju1ymjejl+i3dxW0Ny2eN8Crf+e7NGt0HPTq6bzMfnN8eua2yvfKZMrwAo/p1wbolkySVyzfnkLqNUOoYPLxPLkYPyMMajhDNzz04U7UPQa1w3s6ahePkX1sLJiSkYAkitFghtdsdkiYUSl+OYFLgBDvJ8TFYNGWw22qDFDyHS623QDXNqI9FUwYjPpa1T9/h4feFo0y5r0d8TCQWTR6M5pkNvM6Z/LRFqHv7ZiJRhARuUkORxZ5pXHR13WlJrMdHq1c7U3GDG1ZMh5kjhK9a7uyfhzv751X/4W8LFpG97rVhwqMUT58BThrWq1lpzMlOQ/068VgwaRD3Civ8N/5xP1sFsvihibj84LAtWBxA59aNMXfCAIzo25lXoeVE2HeMsJ8dOa+t1DlYer3AWqEW+nCXEo5YT7LSUrBoirZjTaBQkJvj9rdUHyxqSYiNUjy+Wy1mhIW4Oz4Wm7t54pybsXG2w/CwEMybMMB1fESfXCyZPpRz7AsLsWHehAHo5KZ8kb9FaOyArqIyy8FqsWDOuP7ozuGPsHlmAyyaPEjT8tTAMNXzUsIbUrAEEWIDnZSxX7KSRscxU7rZtucHFqEKrT4AvbYIybxcpwmZlh83kmTU6T7oQ/j2s1TZztioVZhJeXWMECZcsisF/4tqCLhCBPMhqf1p4QlWIzQP08zr/Eaa43w+7C4FC/sCHRoor+N+GT5YpG6x9qMPLyWoqm0tb9VAHZPYOCy+LUf7NmB3cCtYjNTaPMdBzeYzPBWu5XxJrR8orWShOWBgQAqWWoUUHyzcx7V6ocXC23Zp01jxYO55ndF9Hjjxd2cZGuLtR0CONcCQXh3dr2WUGH3WYGLkX80w7pMIPauUYaT6YJGG3GYqVDtcbd6wTgJl4r2lQbv7UptXx5wsjSTxLU0zUl2/Y6LCNc/fSD1w+5xMwfPj7+gOgHv7hlDzcIahHZzfQVQGX1mw2GzuJuvStkxq16ex4bPmkQpXn6bl0O7sH7N4tu0424UUpK9PBVif7Me5VHhoiOt3Tnaa5vkP8dheNGagttYIvOhQp/WTuf2IGakf5rptLYZy8QUM9YVwyXlHQQfV/fq4Qd0AKPd7GIjUBh2R9PhzRMCjxoLF8zjfBEFMqREVEYbHl0zC0idf5zz/4NzRmLJ8k7ighCpSEmPx0PyxiAgLgdWirhuYN2EAfjv7B779v184zyvZIqSLqwItJ+WSfLD4fgQRMhHmkic8NARP3DsJi/78Kioq5Ud48BcMGC8ll5r6lrMyxU66/anFuHj5qqRtdP74RhHzs5GSGIuXHp6NkBCr/g5bfQTXGBRisyK7ofczYreZqcML0KZpOppmpGJw0drbmbkS8pb3tycX4b+/nUebZo1UyS2Xu0YV4tjJs9j7zSG341ufWAiL2Szu9F5BmXLfsRdW3+22jYqPt9Yv4T1n5wzTrN3L5KynhNgorLh7JNZufsd17ol7J6Nts0bYuusLWXmJoXdEJK2Ran3HNS/kmyvmtW2CL787zJvXrNF9AAAWixlb1y3E1es30aBuoiQ55HBHfnsUb/m76+/xg7ujeWYqSkov4/G/7FScr6+fcHr9JBlKcj+2P50sWNz8jXGrlFWXwSXr/IkD8df396rKd8bIQrRrnoHmWaniiXnkCAQCZM1bM8iChXDD4XDo/hJwTXSdhIbYAm1tJyBhGAZNGtVDqorIKU5sVgs6sFbwPZ+f1lGEGJ507uVw+WDRrmFL2VIieaubXP2TQNlK7rFFdhrqJMTKvs7v+NCnDB91k+I4ffRw4/vZBfuDjwH3BCc7vS7SPD5ctHpX5NaqXjWU3VDcqajFbEaHnCxEhod6nRO6j7joSLRtnuHuR4B3AUJUDMl0yMni9BuUWse7T+f0SeUDh5hNM/gjerBlSoqP5k1Xs01LfzIb1HH7u2PLLFgsZsnXS67TAJvkCLVbpa0oOlJYGRDBeg9TUxIE25IaTCYTOrKs21z9QESY4HWqH6HGH8mNUuuIJzIAnsq6amWjtLrwhWJBqAguxajNapFskcbny8hiMaNjyyxEhHmPPXJlDBQCzopPAaRgIdzgW6nw7NjUbFcW6xwsZukTGkECRFvqj25G3y00jKqbkrpFSGi1kOv+TBo5MZUcwUYvHywC5zgHcAli1Hbn076YuPkqwoORkG+Jps8qo1LrLKXvhdnM3ddovW1VTW7KwjTzvyd6vUMuHywsxbJsVzYC57R8JnzP3ZOyK9c1K9MnSLVg4aho/rmifv2h3I83Lkm4cjCy5YDgu+lDOeRiNpskT5WE2oxYezKbpH1XCL3DfHNIqX2pdgsXRn6ihBNSsNQipLzckp206fiCPzDnTkSEhWDy0J74858m8MtAfYwkJtzR3W0fMx+qJusC7Ua2ObROz3XikB6IjYpA7y6tBNMN9fApw4UkHyx+aJ++iiKgB6E2K+aM6yc5vT/M7KvDPyor1wjqFV9XmT/8YDkcDi+LiEqlCpbb4sv9sJo1ug+iI8IwOL+9rOvCQm1u4ci95ampTzErOrGqV+bklv9c13ZNUb9OPIDqd/n+opHyC+BAjqNhqfTr1kZW+jULxiLUZsXSmcMF0909pi+iI8JwR4GwP56D/z4iq3x/w154mzaiAKE2Kx6e7x1CVg5aKysemj8GoTYrlok8I6kYWZkiFyOMPZ50b98M8TGRGNm3syb5NW4k7L9kzvj+iIoIEw3pvXjyHYgMD8Wofl28znk2iekje3mlAQRcLWj1JIKhaQbDPYhAPlhqEZLcVDikplQhh0j2TRrVw/vPLXNZsux+9UH0nvaQrjIFM00b1ceu55f5rA7VTkykXi804eY6lxAbhbefWSJqIXXXnb3Fy/bj5Evow0rog1ZIYs66FMpL4/uPjgjDu5vuw7k/yvDc9n/wpBL2A+WLZzK6f55kfwxe+MUJS81Pf7RZLRQs7Zo3wu/nL+LcH5ckX3PPtCFY9tRW19+VVcoUj64QvjKrLiUxFu9uus+rrxGrD6vFgi2PzUfh1NWc5dpZClQ5YcU5n72CZyPU54bYrPjr4wtgYhhU2e2aWaLW+GBhHfSQXe6tLJ81Av/Y/73Xcb5sundojv+3uYnoPdVNiuN87oEOu90O6NEOE+7oIfkefbXa3rNjC3Rt1xQWsxmvvPOZrGu5rVWkX9+rc0t8dvBHWWUGKlqNIw8vGAu7wwGzySTYRhZMHIhntn4oWja7P+RKVr9OPN6T8G42rJ+Enc8uhcVsxtv/+MrjbE3G0ZHhmDw0//ZRdVuE5GJk5Z9WCrNggBQshBvqwzTL60D4+gl2J8jbIRq4k5GDLzpL7zrkckanHWqjCAlnzu3AzM0HgoR2xZ+9iA8YRqoPFr2eK3++XAO4kQdjNhazWd4rHRi35aK2OXgDtLtns8ywtp6pK6vUOnCW39iUfGQzjLDihG2hZjabVCmwvGIISblFkTRO2SX1sxKKA1gWLG5ObiVerCFSn2ewKVcA9/pmwLjfo4E6Nm3rnmOO5DbHULuQpOpy7/xUlC1nrqLZNheGgfm2IGx5POtV6rY7oXKcqH2H2fM+uWOSlgTY1KfWQluEahMabhGqnxyvVprb5Wl3MVe4YYI7/CrngqZO5SuJIqQE9seH3hFxpNyTFMeaysrmP6fpFiE/KGaE6jWtrns0El8ojmxW7zUIoS0cQsRGR6iSJSZK/vVWa81EMU5l+Uqwa9AepYwRXnvjPdpGer1khYV7H1LT7uSOdw1S3J0PV7EUqGI+pdyianAasLgLo6SO6iTEyL5GLlx+4TxN7bUyvY8IE99KWxth16/Q4gLnu8GT3EgfiVzRiQJkXcJFPYE5ueeteL5Std0Hm1TqJsW5fmc0kO9UWCsFYKAsmgkRDPcgBilYCDfEwvGtuHsk+nVtgwlDeiguQ8l7tXjKYAzOb4/cVtmc56eP6IXkePfJXqAPGm2apeOlNbNV5TG6fx5aNm7odVx0xULmMxLcmiJbv6Kk42XQjBVl4MatcgV5SEeK/4/7Z49Cr84tdZXDE+HnIPuhCuQlLyu19M5rhaKxfV1/O+DwjlalMO8/Tb2D99wLq++G1SOKyNQRBeiT1xoPzLlTVjntW2RgZN/OrvCjcmnTNB2j++dhpscWtuJlU3mviQgLxd1j+mBk387o2DKLN50nWvWcnuOJ1lZdL6y+GwWdcrB++TS34+z3s1FqMv40jf8ZS4Hd3tVZjUi79qH5Y9A7r5VXW/HaIiTZ4NS73p2Xrl08Hr27tMLCyYPE8/F48e/oJexrRCQzScmU+sFRUlbdpDiMH9wdAET9rdQm2NGR9PowCgu1aZaXXBFnjCpEn7zWeHDuaFYevtnqrJYVd4+onpPf0Z03jZYzYV3uK0A+tkf27YzEuGr/XstnjeBNxzVETB/RS0YYbWGMrJwwsmy+hrYI1SKkzAv5wjQ7X5m+XVujb9fWmsrRplk6vv/5v4LXDL3tmGr50zX76tmd8uRh+apk8is8/dGDc0cjLjpScbYDurfFnPH9JafXy9xc7keVySQxSo+HBEksBZtSp5aAhLGekSZfcnwMVs25U/O92Z7KHavVgoqKSgA8W4Qk5Gn0MXFl0SivY96RzeTfRHbDuhgi4NQ4o0EdPDh3NFZu2F5dBqqVFkoceDIMg/kTBwIAXtzxiaLrne/zS2/tdh1v2zxD8Lpxg/gn3nrj2aeIKRjkdkFNM+rjwXmjvaKysJvCyqJRSIiNkpexSx6NHayK3J+znJ4dW6BnxxZe56uq3LcIqZOlWpi8tk2R17appEvYtTB3fH9YLfpPIbmsoDzbiZaKu1mj+yhWggYr7OqWvWAi8YLnVs3CtBWb5GWuEVx9uvg0gL1lzX+LeX27tkHfrvKcNqvZIhQI6PWRb7Na8PaGJbKueWDOnSj08UKbP5EeKCX4IQsWwg27w6HuQzuwjUb8gl6Dm9Cj8OkHtU5leQ6i7O0c6n0uyCvbp3iWzXrpNA0F7Mt7vF2W1MhADBhN2pXRFUuBjhbjgVxLRKdKxP1vbiQ71NbKgkVlhbC3AErxA+WCI6mYtSpnNn50lOzeN2i3RYjmLOK4bzfTpw0Yri/mEIhXRCURuWrFJ6Y09KwJLfMm6wx1+FMR6Q9IwVKLkDIJcfB8oNVX6HdADIYB8jvmyEjPcjImkjZQ3uV2LYRXoFMSY7UvlGvyoGLwaN0kXaAomQ4qGUZwO4MzN8/Omr2/VU7UkBCbu+8eo098+glYkAn5vBB6Dn1lhi3VvI44HFmK4aswzcE0qeKyimBjt9tRVVUFi4mBzWJ2/auqquL8x5fGecxsYtyOw+5wu8bzn8Vs8srDYjbBYjYJyuJw2F3nAQcY1JRjt/PLzACC92W6LT8YsPKz89aH2D8TKx+uf1aLSfB6B6v+GAAmk3d+zrQMqyx2HklxUbBZzBjaq4Ns+e12Oyy3LWdaZDdQ1RalvlYuRZCbkktV0YaE7UPDaD2OW3hwjwcn9iyk3osSH1VNWduCfUHzLHVtno0vt7AbrT15IhSgoHlmTZ2rttrTibbNG/GeM3rda430RYvgrxnaIlSLkLRFyOPvjStnoMpu9/JvoqVMd/TqgNjocDROF45j73WtLhL5jlfXzsPp86W8fmWcPL96Fv710wk88sLbusqjRrvcqklDPHbPRCTd3p8qRxHmiYlhkNe2CdYsHIe0lERMWb5R0nVsXxlytgi9vm4B/rz5XXz38y+S0vt7WBg7sBv+8vanNQdYdc21Ki1lIBs3sBvqJ8fj4efeqjko0B50m+hIrNxqHyxeds6ECIPy2yM6Msyrry0vL8fJkydRUVEBABjTuzUqKmsU30eOHOHMb/m0vpxp+I5XVla5nfPEZjG70jvT2awW2O12l9KUSxa73e5KHxNphtl+w/X3zSsXceTIZU7ZoiPCOPNzpgm1WXHkyBHEhzpcx86f/R0Xzitr/+0b10HTBvz3bzIxvHUNAGmJoTVynPkNvTtkoFtL948+5/Ut0uJcaY8fO+bqBx6cNRAVFZUIuX1vcnn6T6NQWWVHlgInj0rgGpe8tp6pmgwYYyaREBuFZ+6fDsvufYC90t/iuKGVBcvU4QXY8t4eznwiw0Lx9LKp+NNjWyTnt27JJMWyiMFWJK24ewRioiJ8rtB5fMkkLH3ydZ+WaTSy0+ti3ZJJiI2OMGyErnbNM7B28QTXYmig+38ktIEULLUK+RYsXA5ShUsQL8NzfDabTMjvJM2KJZhMzBqlJqNRqnjkhrjoSPTOayVbwSLo8FTkWiWTqM6tG2uTF1N9Tff2zYSTeeRrYStYZGwRSo6PQUFujmQFi7+xWMzIyW6An46e8jqndIuQxWJGr84t3RUsAmiuYFEyaffHPvIAX3Xh62tPnjyJqKgoJCQkgGEYnDrzB26W1ziKzm7IrfxmQk5zpuE7Xl5RCevp87zyhdlsSL0d0cOZR1iIDZVVdlRUVvLKUllVBfNv5wAAdRLiYLWYEXLuDwDV0afYVmps2RLjYjijKznTRISFol5yPK5cu4mzf5QCABrVT4ZFoe+R86WXUXblKu95s9mMjFR+xcXFsqv441K1sigjNQXnLpTh2o0bbmmc9XPh0hWUll0BAGSl1dVsxdDhcODChQs4efIksrKkO05WirNPU/N+B8pb26pJOo4bsI9x98Ei3yLViVgks2aZqbLyjo7kcRqqQR2ys8hskILMNG2jAkppzzkqrcScGH3WLPa4OoksQvoKobaf17aJ7GuI4IYULIQbDh/oXrXSkVC3FZwonUhbWB/9qsI0B0TD4hZSi7C4NUXwV4RZJESs4iIlVj4DJY6QuXMSTREQ7UE5drsdFRUVSEhIcCkOGJMJDFPzjM08K4d8afiOm6rsbue88jOZXOmd6RiTCYy95m8xWUwmE0ws+dl5espm8jjHlZfZbIbZbGIdM/PKIIbJo165yhXKm/1czBYzZ37O69nnzGazphP9hIQElJaWwm63i4aLVgunBYvH38H+jvoboS1CorAd3gtdq6VBoiaTTGl+nGobOnng0SVXQFsFB/Uz2lEbFE/G3NBG+A0lju/UoJdHegKCswKueizIzUGIzYqG9ZIQ6uGXRA1CzywlKRYpibGICA91HfN03tiOZ39rZlodxMdEIi46Ak0zUt0m+mqiCIkhtw3OmzBAJ0m8UbpFiBOBvoBtLaQlbFGzG9YFAN6IPZqoVwzQnVGquWUAACAASURBVCy+HSa6n4xIEM4Q0dNH9FJdvoPD/w17S6iW20NtVgssZqdiQLjyoyLCNC/fEz+4bNUsJ382XeezU2NRKlWZane1T/40RjdsXRbgIZ/dnrOnj3WRNs1+zmLKmVCbFen1qy3OCnKl++bTAzljg9G3hIhbLHsfG9SzPWdave9UzgLb3NtR9e6dMVQvcdyI4bOYIggOyIKlFiHJB4uWUUj8jPjAX3vhGlCjIsLw/rNLYbFou9oplJfZZMLr6xbAbneg311rONM8ed8U/Hzid8x9+CW3/KwWC954+k9wOBxuEYQAeU5u5cirhFH9umDTto80zZNPRE2jCLmV516gbhYsrHLGDOiKLm0bIyIs1CudAw4wHoo4XyhL9CijRVYDfLB5BcJDQ/CPA99Luqawc0t0bp3NWTdaEBZqQ1bDuoBD2+1gDMOgUYM6gMOBE7+dQ1WVZzSYai5duoQuHdti29/+hiaNqv0ebP3rq/j4fz7EwS8PiLyjDvfOXcUrYbTR0P1DN7BHMKnic85JjK5R8aB/97Y4f6EMr7z7mb9FUQS7uoWUJGJjp1ufzZGUYRj85ZE5qKysQmiITa6Y7IyUX8uBmCJRrDm2a94I3/6f/C3Iem179XxOnvI7x6MPPv+XLuV7y6Psujv752Fgz3bC46BGVThuUDfp1nqB1T1pSmCPStpCCpZahJTVJkd1Qt1lcZVXizsi/8LdDaqa1MgqqQarhz8Dz8HfZDIhLJRbLk/FihM9LViMjKZbhFio9X2kVGklNHHynHwGsnWbEkWJXsoVJ3op0UwMIzqjjo2NxebNmzFj+nR8//33+OWXE3h+4wa8+e4u3z5nrm97jfOTdTm/IYEggfxu2DksrAKRkBCWHyCGCSh/cmqc3DJuOhXxay1msyGcmbrdp1ikJJHbCufoq/3cjQkiPO4aC73HQSdKFxoCvNuSTeD0avpDCpZaRGS4eEek1oLFl3OGQJ9wKSUxLkpSOsOYrcp8TpwrZDJvRUnIR6nUTdI2bHaShlsgNLVg0fD9kvoxwX6nhdqvb0Np155+prKqCqVl/E5YPbnISnu+tEz0OADEx0RK+oDq378/evbsiSVLluCrr7/B/MVLkNogTYJU0luHUcN+SqG2jH/s8MVO5M4zjFBXbOV3bVKwsPG0OnQ7Z4BnxMbdCE74WYVYObZUi9xOakoCfvmN3+G3L+Gr+piocJRdua5/+RqPsXq0JTkyWq0141t4aIjmshCBASlYahEdcjLRt2trfHzg37xpfP1R7usxdcn0ofjifw+B+Z89Aadq/dPUO3Dg2/9gxqhCSemF5m9617tbmGa5ZamQbcGkQfjmh6OYcEd35ZmIcM80+ft975sxDHv/+RO++eGY2/GWjdNQNJY/bGvbZo0wOL891jzvHkGKLwy2pj6UZOQ1d3x/fPPDUfTtJt2PCBdui4Yi5Xv66lFWnngeZg3KCRRKy65i9KKndC1jx/p7JPtVeeqpp5CRkYGsxk0wdvxEiSV4thv+duT08xI48N9LeGgI4mIifSiLvqxZOA47d3+NP00dAsD9XfXq51T0e77Sc7CV3yaGgT62hvrAriLPBRCx+uP1wWLw+RdbGSR2jylJsRjZtzNu3CrHh59/Kyn/NQvGofi1v2NEn84CQngfatMsHSaGweRh+ZLK4cmGl5l39nb9Ll42DZu2fYjxg7tjybq/eqV9etlUbN31OWaP6489X/+E8xfKsPurH2SUdls+loBaK721Ut7I+TaKi47E2IFdUVp2FR1b6h9pzUjUntmSOKRgqUWYTCasuHukoIJF9QeakVZlOEQZnN8eg/Pb4/i6l3wvj0w8q3JIr44Y0qujf4RRgdwBTs2AOKJPLkb0yZV9ndRW2zEnE0nx0bLzH9izHQb2bIf8yasAVG9t+vjlVaLXFS+fhqP/PeN1nM/FhF5bhMQUEfExkXjivinqy4G0Sa0vFcHsCZ9vLWeIffv2ISQkBKdO/oqrV64gMorPes/zuYhHANHa15QWiEnj/U7UHGhwO7x14CB8t93bN0P39s1qUqt4VEawFqlS4RfM37hZsAhaoQjnw37fqnQaqwCNHKC7jUXi7Wf+xIEAwKlg4ZInNSUBTy2dKluuxun1MGdcf9nXSWXCHT1cvzMa1MHTy6bxpm3XPAPtmmcAqHFKr0TBwsZzy3igUjS2n79FIPxMcLRkQjMcdoehdCSEPvjyw0JuUVyWCe4mymolEsYXNaNX/evl5FZsglklUq7k+5VhweKZp5I6lXKF0pC8gUh8TCR2rL9HcvoTJ8+6fmekpYged5YhhdLSUhQVFeHdd9/Fxmefx+NrH8aaPz8hWbZgRM7bHdTjeADeHFuhYJjtuxJxG3/lXsy6IJAsWOSg13jOma+CejN8VbPuU68IhQThawJ3AzLhhVOTLJWYKO+QY77oiNkOS9PqJcm6Vs4Kclue8L6BgtoxW2gSl9e2ibrMRXCT3WCrxGIIvQNq3w+nF/rCzi1V5sSN3Dav1ZaihiLvsdQV5DCWk+VGqXV40zE+tCXRy9mrGL27tPJ5mRazGcnxMZL/xcVEuv5JOZ4cH+PyvxIVLrw9Z+7cuZg4cSI6deqEpSsewMGvvsT+fZ9zpmV3MTYNQ8yHhxlr/3yYDk7IAwW2Q/OMNPe+IV8kpG+0AcKrNstMdf3u3dn377Ya2Ip7rXyweI49WvbnPTo0V52HnO2qnOfdAn4puzuufI1gjaU17NqxaqBgcXOsrFHDIutV9TgjAtYWyIIliHhw7mh88uW/RUPCvvzIHPx07BTKrlzDK++4hw30RZjmsBAbnrxvCq5cu+EyK5QKW2kg1t31794Wz277H1y/eUuBlP5Hz3F00pCe+mXugWwXLAGmkJHD1nULcPD7I+jfo626jHh8sAzKbw+GYbDjowM4dfaCaDZSzbTFnkmTRvUk5SNGiM2Kp5ZOwaUr10Xz9FU7YW8R0tOsHQC2rluIXXv+idioCAwr7KRrWf4mKT4al65c4zz39ttv46effsJrr70GAAgPj8Cjjz+JZfcswqihgxAT4+7DxWQyITUlAVVVdoSF2HDjVnnNSQX9aMN6Sbh5q8K1CKFVS1PbpUdFhMLuiOV2qhlgyH19rRYLnl42FRfLrqJ5ZgO3c7PH9UN6/WR0asXt70CoL/GVNUluq2xMGZaPk2f+wMLJgySHZDca3v2udEtDtgWLnoqCGaMKUTc5Du1bZKrIRZrDdQmXK0arOgqkGZXWFiwhGircCXV0yMnEirtHICVR20ARRoUULEFETFQ4RvXrIqpgyUxLQWZaCrZ/sM/rnOP2f3rTIUfNwCcNs8mEwQXtseOjL3Uvy5AIPEZfDjpyP4Q5owj5EKHS1U546iXHY0RfAad2EuGrIrPJhDsKOuDYr2ckKVi0CGfdnLUyy4ecNiB1Uuy9RUhyEbIuYluw6O1HITUlQdf99UbCJGAZNGrUKIwaNcrtWKfcLvhs/9deyhUn7HCdanuQ0BCbe8h6N6dH/ltBZhgGsVH6RUgzOnxWumEhNl7fW2azyRBKe4ZhMG1EL3+LoQjJUeA43jx3Y1bf+GAJsVkxvLd8X2xs3C1YxNIG1pZfz+fkb+sMdv1ZNd6Sy7Z8U0OgbeszKn27qguEEEjQFqFaDNegobZDN1oX5O+Bg1C3Z9tnBJnZrVSTeKkTZ6F0UibK4v5UJIkheI1e7zp7Ra2ySr1CighExJ3mEsaE5gDq0Worqa8sWLSAkaNhEc1M2WW+qiMjKQ/IgoUIFkjBQgCo9kwOABPu6I6FkwYBALqxPPgbBbmTpUH57QFAUeSXQCW9frU/jFH9urgdn3I7rJ+eIYyduM0LJH49O51f9u/uvX1Gv93a0nDun580tGZrlVbbYtRyz7TqMKYFLD8EA3u2AwAvU0wxxctdt0OAjx3YVbTcts2q/b3MGCktbLjWeK4acrUbLfClBQuhDexVy5AQ7gm2nLGE7ftETZSLuOjaa31iNJpmVPsDmMiKmkLwIPD93allNoBq/02REaFe50fenoc0SElw+QgzMQwSYt0jgkm1AnE+r6nDCySlV0omyzm3p6NuT7gUIeksv2R3jertdV4KEeGhXhYYSlQhd90Ovezckj9ucDdF8ugF+9FbLWZMHFL9jCez5luy8mP17VpZsJCilpALbREiAADPrZqJi5evuRQRLbIaIFGBUsJoqxJpdRPxzjP3IjLce+APVl5aMxtlV64jMc79+U0dXoD+3dv6fP+jVOuE7U8txtXrN70mXj5BRMiN989A6eWrSI6v2Z7w7APV78ydi57UWzov2IN9v25t0LppOpJZ72vdpDi8u/E+N4fSALCj+E+4frMcI+av48x3wh09UNilFVISY/HZwR9ryuOonyfum4zSS1eRnMC9ZcNNXp1N9N955l7d2o0vfbAEGlaLBRWVlf4WwwuTyYSstOqPCS2cFJvNJmSlpQAMwxnlTCohNisyG6SAYRg4HA4cP3VW/KIgxVfbdviK2bTyLrc5D8GPkIVDbHQEdj67FDarxeXAmk1WWgre3rAE0ZHhsFkteP/ZZbBYTHj9fW6H1WLMGFWIwQUdUEfCuKOGsBAb/v78ctdvuYTYrPh/L6yAw+FAVEQY3tt0H5Y9tRWHfzktOQ+zyYSdzy5FeUUlhs19XLYMQLU/qS5tqoMaPP/gLM4273/lgXsUoRkjCzGoZ3vFc1V2eyULFsJfkIKFAFDdqbE73ToGdULkcHfNLukav3yw+xGrxeKlXAGqJ7R1k+J8IoN7WEdpzynEZvXfYCiiGLRYzG7KFecxo0zOuSYiXOFwvXxLeCCnjVjMZknKFb1gf6ApfcclhWlmfaBXkgWLGwZwbcELWzGmTX7amK5TGFJjYKT+2+iIbR0X8wvEno84HUcrXYtjGMZni0RREcKRzpzwKQvZC3tx0ZFeCx5SCA8NQXio8mhm7IUXLdq8PorRmsZgtZhVz1XLy2uU/iE2+sz1JUbwd2UUaItQEJMY53vFghHCIRL+x03BokF/yw7dZ7QBM45DkaElVquZ9bv63n3h6JKtiFGzag+IW7ZxKYPEiOIwR9cDi1n7LULBMgnhWrEmlFOblC+12VlvoOELC2CjWT/LQarsctq8UxGltCwnUubkUZHSFEmA9LYQEaZMMaRmC6YTts+gyHDp9yZERJhyJVdtIpDfY60x1pcKD6dPn0ZxcTEuX76MiIgILFq0CGlpaV7pPvnkE7z11ltwOBxo1aoVZs+eDYsGL2ug8cDsUfjbB/uxYtYIn5U5e1w/fHbwR8ybMEDXctjWEEHyjRL8aPCg0uomorBzS9yqqEROtve770/WLZmEx196D+MH6+PbpmG9JPTq3BIVFZVokVUdnnTuhP44XVKKws4tdSkTADq3aYzcVtlIiI1CXLQ+SqRlM4fjnY8PYmXRKPHEHtzZPw///PEYWjVJV1y+FEWHHluENq2cgae3/B3T/eS/RivqJMbi9PlS+ljWgBCbVfaqbSBPZWeP64ffzl1w8x2lCwadKDxz/3Rs+OsHmDW6j79FEWVE38746vsjaNlYu7GX/VhG9MkVjCoWLMybMABnSi6id5dWvGlWzxuN13d9gWV3DVdVVtHYvtj7zSHMGS8elW7KsHz837FTyG2dzZtm2ogCfP3vo25+6LiYOKQHvv/5F4wd2A15bZviiVfex/GTNVshN6yY7nVN66bpaJ6ZirBQGzIb1BGVV4y6SXHondcKN26Uo91tvz9KWD5rBP784ruIjgzHsN6dVMulJdNGFODg90eq36NAHgiCmIDQPjz77LPo378/CgsLceDAAaxfvx5PP/20W5pz585h69atWL9+PWJjY/HII4/gH//4BwYNGuQnqf1HYZdWKBTowPVgzICuGDNA3DGmlpCi1Liwn40W01uGYfDAnDs1yElaWXLIblgXf3lkjk7SVMuzyuPeE+OisfmhIt3KBKq3xjy+ZJImefHVaf/ubRU7pw0PDcGmB2aqEUsSbgoWjaIItchOw8uPztUkL39is1qQXj/Z32IEBYrqMYDHwITYKLyw+m5/i+E3WjVJD5g+ICzEhk0P3KVpnuw5woJJgT1PlzpnkDJu53fKQX4n9UrHsQO7YexAac5soyLC8NyDswTTTBlWgCnDxB0Lsx36Ns2oj5cfmYMJ967H7+dKAVQrUzyJCAsVLV8uShZtPOnXrQ36dTNmWGHn8zj+3FZ/i+JGsFjnaoHhVcZlZWU4evQo8vPzAQB5eXkoKSnBmTNn3NIdOHAAnTp1QlxcHBiGwYABA/D558qcaBEEoQ73LULU4RKBC3sbDEUR8g3p6elITk5GRUWF69jBrw6gaUYqFi1aBADYtWsXFi9erCj/lcuW4Ksv92siKx8dOnTA3r17RdO99spfUFJyXlKeO3fuxMGDB11///v777B4/mylItYKaPQhCIIgfI3hFSwlJSWIj493OZhjGAZJSUkoKSnxSpecXLMCVKdOHa80hDu+Wnns1CpLs7yaZ6W6fue24jdnDFTYoX9DA9j7OTs0amqdeD9KIo0GdRNdv7WIOqKGZrfDhypxiCcVp6O7hqxQknriDImqFZkiYTOlwrWa5gk7zGO9ZOO3ZSPgfIfEnFZ7Kl/ZbT4tLQ27du0CAISHheCdHW8gp2Vr1/khQ4aguLhYkXyPPPYkCgv9vz3LYjHjtVf/gkulpZLSeypYOnfORfHG5znTUvSMajq3buxvEQgOshpq04cbgUap3nNpvebXWbdDLQcS7Vtk+lsEwkcE03utloDYIqQ3ly9fxqFDh5CQkAAAuHDhgmv17OLFi0hJScH169dx+fJl1KtXD5cvX8bVq1fRoEED/PHHH7hx4wYaNmyIs2fP4tatW2jUqBF+++03VFRUICsrCydOnIDdbkfjxo1x5MgRAOD8bTKZkJGRgWPHjsFqtSI1NRW//PILQkJCkJKSgl9//RVhYWFITEzEqVOnEBkZiejoaJw+fRrR0dEIDw/H2bNnERcXB6vVivPnzwveU2pCGEb2aoMmmQ3x66+/6nZPS6begVff/hidW2Xg+vXrqu4pp2EsxvTrhKYZ9RBpYxAbGYJ2TdNw6NAhWc8JjuqVLaedhda/nZ8NQr9Pnz7tek5rF4zCdz//is45DfCfk6UIC7HilxPHArrtLZrYD5ev3gDKr6CkpMTQ71OI4wYmDuqM1JQkHD78H0Xvk1b3NGlAe+z7LhZjBufj0KFDujynlTPvwBffHkHbrJTq90Gne1o4rgBXbgGN60bi0KFDqp/TvNE9UVJ2He2bNsChQ4cUP6clEwvxe+l1FLTLwKFDh0TvaeawrrCGRcDmuMF7H/7sy9U8p+PHj6OyshJ2ux3l5eUAgJCQENy6dYv3N8MwsFqtKC8vd/ttMplgsVhQJyEK129WICYqAjdu3IDZbIbJZEJFRQXM5uooEZWVlUitE48r127CYmZQZXcgJiIUN27cAABMmjQJL730EoYOHQpU3cKP//4eY8aMwbVrV3Hjxg1s374d7733Hnbs2IFff/0V06ZNw7Vr1+BwODBw4ECsXr0aH3/8MVauXAmGYWC327Fq1Sr06z8AAwcOxPx58zB06BAUFRXBYrHgxIkT+P3339GsWTO8/vrriIiIwIULFzB37lz8+OOPSEhIQLNmzVBeXo7Nmzd73dPBgwcxb948VFVVoUOHDigvL0dlZSXKy8tRXFyMt956CxUVFbBYLFi/fj06deqEVzZvQsn5c/jTgtlYERqKV199FY0bN8ZDDz2Ezz//HOXl5cjKysKLL76IL774Art27cInn3yCV155BUVFRWjStCkWLliI/Qe+xIkTJ9CuXTsUFRXh448/xuXLl7F+w0Z89NEH2PfFF6iqqsLf/vY3NG7cGCaTCW+88QY2bdqEyspKREZG4sknn0Tbtm3xzTffYP78+bDb7aisrMSsWbMwZ071FsjKykpYLBZUVVWhoqICpaWluHnzpqH6cuf7tH7pJHy8/1sMKWiPCxcu+KUvV3RPDoeseQTnb4fD8GNuSrQZY/p1RJNGqar6cn/e04NzRuKHn48hNT4Uly9fxqlTp7B4Yh9cvHoLMbZKnDp1SrN7umdSXxw7eRZdW2fh2LFjhpvvCd3T4G7NUVV+AwWdW+n6rREoY66W9xTqUNhHKPzt9d3jAG7duoX5Y/Jx6lwpenZo7jVvDdbn1KJFCwjBOAzu8resrAwzZ87E9u3bYTab4XA4MHnyZKxbtw5169Zoct99912cOXMGc+dW72n93//9X+zYsQPr1q0TLWPbtm2YMGGCbvdAGI/jyd39vn89s2SffwUgCKLWU1VVhSNHjqBx48Ywm804M3EZKv77uy5lWdPro+7Wx0TTpaenY+fOnZgwYQI++eQT7Nq1C7/++itCQkJw6dIlrF+/Hlu2bMHOnTuxc+dOLFy4ECkpKVi+fDkAoLS0FPHx8WjdujVeeOEFdOnSBXa7HZcvX0ZsbCzy8/OxaNEiDBs2DFOnTsXPP/+MPXv2ICQkBD169MC8efMwbtw43HvvvSgpKcGrr76Kq1evIi8vD+3bt8eWLVvc5C0vL0dmZiZeffVV9O7dGx9//DH69euHPXv2ID8/HyUlJUhKqrYWO3jwIKZOnYr//Oc/bvfapk31Xv+1a9eiqqoKDzzwAABgzZo1OHv2LJ599llMnToVbdq0cW2T2rt3LxYtWoTvv/8e//3vf9GoUSO89957GDZsGF5++WUsXLgQf//731FQUIAnnngC33zzDd566y0cOHAAjz76KN577z2EhIRg3759KCoqwqFDhzB06FCMHTsW48aNAwBcvHgRcXHuznc92wyhHcfr9wLKK8QTCmGzIvP3z7QRiCAIw+L3bxkGyDxP3zJcGN6CJSYmBpmZmdi7dy8KCwvx5ZdfIjEx0U25AlT7Zlm6dCnGjx+P2NhYfPTRR+jRo4efpCYIgiAIQg2TJk1yKVK2bduGbdu2cabr0aMH7r33Xly9ehU9e/ZE797VjhYLCwuxcOFCjBo1Cn379nUpMTwZPnw4wsOrw5l26tQJx48fBwB8+umnKC4uBsMwiIqKwpgxY3Ds2DGv6//zn//AYrG4yu3bty8yMjJc57/77js8+uijuHDhAiwWCw4fPowbN24gLMw7hOjOnTtRVlaGd955B0C18iY9PV1SfYWGhmLYsGEAqn3AREZGoqCgwHVfzvp7//338e9//xu5ubmua0tLS3Hjxg0UFBRgzZo1OHr0KHr16oVu3aQ5yiQIgiAIohrDK1gAYN68eSguLsaOHTsQHh6OhQsXAgCeeeYZ5ObmIjc3FykpKRg/fjzuu+8+AEDLli3Rv794eDKCIAiCIKqRYmHiKyZPnox27dqhcePGyM7m97k1cuRI5OXl4ZNPPsGmTZuwfv16fPjhh3j66adx6NAh7NmzB1OmTMGECRNccwQ2oaGhrt9msxmVlZWc5chx2O1MW15ejhEjRmDPnj3o2LEjLl++jJiYGNy6dYtTweJwOLBx40b07dtXcllOQkJCXL/NZjPvfTkcDkyZMgVr1671ymPRokUYOnQodu/ejRUrViAnJwfPPfecbFkIgiAIorYSEAqW+vXr48knn/Q6vmDBAre/+/Xrh379+vlKLIIgCIIgdKJevXr485//jKZNmwqmO3r0KDIzMzF58mR06tQJeXl5AKotS1q0aIEWLVrAYrHg448/llV+r1698Nprr6FHjx64du0aduzYgbZtvcOKN23aFJWVldizZw8KCgqwe/dulxXMzZs3UV5ejrS0NADAxo0b3a6Njo5GWVmZ6+9hw4ahuLgY3bp1Q3h4uMtvWYsWLbzSKmXIkCGYMGECioqKkJaWBrvdjm+//RYdOnTA4cOH0aRJE8ycORMNGjTAihUrVJdHEARBELWJgFCwEARBEARR+5g2bZpomrfffhtbt26FzWaD3W7HCy+8AABYsWIFDh8+DJvNhvDwcDz/PHfEHT5WrVqFGTNmoFmzZkhMTETr1q0RGxvrlc5ms+HNN9/EnDlzUFVVhY4dO6J16+qIR9HR0XjkkUfQqVMnJCYmYuzYsW7XLliwADNnzkR4eDi2bNmCpUuX4tatW8jNzXVZwSxduhQtWrTApEmTMHXqVOzcuRNz585FVpayCH3du3fHunXrMHz4cJcj3kGDBqFDhw7YtGkTPvvsM9hsNpjNZjz11FOKyiAIgiCI2orhndz6AnJyW/vwu2MokJNbgiD8Dzks5aeiogJVVVUIDQ3FtWvX0K9fP8yfPx9jxozxt2h+hdqMfpCTW4IgpOL3bxlycssLWbAQBEEQBEF4cPHiRQwYMABVVVW4efMmhg4ditGjR/tbLIIgCIIgDAwpWAiCIAiCIDxITk7Gv/71L3+LQRAEQRBEAGHytwAEQRAEQRAEQRAEQRCBDilYCIIgCKKW4nSkSu7YCKk424qcsNUEQRAEUVugLUIEQRAEUUsxmUywWq24cOECEhIS6KOZEMThcODChQuwWq0wmWiNjiAIgiA8IQULQRAEQdRi0tLScPLkSZSWlvpbFCIAsFqtSEtL87cYBEEQBGFISMFCEARBELUYm82GrKws2O122ipECMIwDFmuEARBEIQApGAhCIIgCII+nAmCIAiCIFRCsymCIAiCIAiCIAiCIAiVkIKFIAiCIAiCIAiCIAhCJaRgIQiCIAiCIAiCIAiCUAnjII92WLx4MTp06OBvMRTx4YcfYuDAgf4Wg6hlULsjfA21OcIfULsjfA21OcIfULsjfE0gtzmr1YrRo0fznicFS4CTmpqK3377zd9iELUManeEr6E2R/gDaneEr6E2R/gDaneErwnmNkdbhAiCIAiCIAiCIAiCIFRCChaCIAiCIAiCIAiCIAiVmFevXr3a30IQ6ujSpYu/RSBqIdTuCF9DbY7wB9TuCF9DbY7wB9TuCF8TrG2OfLAQBEEQBEEQBEEQNIRd0wAADJBJREFUBEGohLYIEQRBEARBEARBEARBqIQULARBEARBEARBEARBECqx+FuA2sDp06dRXFyMy5cvIyIiAosWLUJaWhoA4MUXX8TXX3+N8+fPY8OGDcjIyODN580338Tu3bsBAD169MCkSZMknWNTVlaGp59+GmfOnIHVasXs2bORk5Mjeo4ILIzU5nbs2IHPPvsMp0+fxooVK9C5c2fXOWpzwYOR2tz69evx888/w2azISwsDDNnzkR2djYA4NatW3jmmWdw9OhRMAyDyZMno2vXrprUAeF7jNbujh8/DoZhYLFYMGXKFLRu3RoA9XXBhJHanJMffvgBK1euxIwZMzB06FAA1NcFG0Zqd8uXL8f58+cREREBACgsLHS1O+rrgge929zRo0fx4osv4sSJE2jbti1WrlzJm0fAfb86CN1ZsWKFY/fu3Q6Hw+HYv3+/Y/Hixa5zP/74o6OkpMQxffp0x/Hjx3nz+PHHHx2zZ8923Lhxw1FeXu5YtGiR45tvvhE958n69esd27ZtczgcDseRI0ccU6ZMcVRUVIieIwILI7W5w4cPO86cOeNYtmyZ46uvvnI7R20ueDBSmzt48KCjsrLS4XA4HN98841j+vTprnPbt293FBcXOxwOh+Ps2bOOCRMmOC5fvqzu5gm/YaR2d/XqVdfv48ePO8aOHeuw2+0Oh4P6umDCSG3O4ahud4sXL3Y89NBDjp07d7qOU18XXBip3XHN55xQXxc86N3mSkpKHIcPH3Z89NFHjjVr1gjKEmjfr7RFSGfKyspw9OhR5OfnAwDy8vJQUlKCM2fOAABycnKQmJgoms++ffvQq1cvhIaGwmq1ok+fPvjiiy9Ez3myf/9+DBgwAACQnZ2N+Ph4/PTTT6LniMDBaG2ucePGSElJ4TxHbS44MFqby83NhdlsBgA0adIEFy5cQFVVFQDgiy++cLW5OnXqoGXLlvjqq69U3T/hH4zW7pyruQBw7do1t3PU1wUHRmtzALB582aMGTMG0dHRbseprwsejNju+KC+LjjwRZtLTExE48aNYbVaRfMJtO9XUrDoTElJCeLj412TfYZhkJSUhJKSEtn5JCcnu/5OTk525SF07ujRo3BG4r5y5QoqKysRFxfnSlunTh2UlJQIniMCCyO1OSGozQUPRm5zu3btQocOHVyylZSUICkpiTMfIrAwYrt77bXXMHPmTKxduxbLly8HwzDU1wURRmtzBw4cAMMwyM3N5SyD+rrgwGjtDgC2bNmCefPm4fHHH8fZs2cB0LwumPBFmxMi0L9fScES5GRnZ0v62CUIraA2R/gavja3Z88e7N+/H/PmzfO9UETQw9XupkyZgpdeeglLly7Fli1bUFlZ6R/hiKCE3eYuXryIN998E7NmzfKvUETQ49nX3XPPPXjhhRewceNGtGjRAg8//LD/hCOCkkD/liAFi84kJSWhtLTUZZ7ucDi8VhWk5nP+/HnX3+fPn3flIXSOTVRUFMxmMy5evOg6du7cOSQlJQmeIwILI7U5IajNBQ9GbHP79u3D9u3bsWbNGsTGxrqVwV7ZUNJ2CWNgxHbnpE2bNrh+/Tr++9//Ul8XRBipzR07dgylpaVYsGABZsyYgQMHDuCNN97A66+/7sqH+rrgwEjtDoBrawjDMBg8eDDOnj2LK1euUF8XRPiizUklEL9fScGiMzExMcjMzMTevXsBAF9++SUSExNRt25dWfl069YNn332GW7evImKigp88skn6NGjh+g5rnw++ugjANXmVxcuXHB5WhY6RwQORmtzYmVQmwt8jNbm9u/fj9dffx2PPPKI1yDLbnPnzp3Djz/+6BbZiggcjNTuKisrXXvTAeDIkSMoKytz+Z+ivi44MFKb69ixI7Zu3YqXX34ZL7/8Mrp27YqxY8e6InRQXxc8GKndVVVV4dKlS66/v/zyS8TGxiIqKsqVD/V1gY8v2pzcfALp+5VxOBwOv0pQC/j9999RXFyMK1euIDw8HAsXLkR6ejoA4Nlnn8U///lPXLx4EVFRUQgPD8eLL77Imc8bb7zhCnPVvXt3TJkyRfTc0aNHsW3bNpeZ1aVLl/DUU0/h3LlzsFgsKCoqQqtWrUTPEYGFkdrcm2++iY8++ghlZWUICwuDzWbDhg0bEBMTQ20uiDBSmxs2bBji4uJcEz4AePTRRxEVFYWbN29iw4YNOHbsGEwmEyZNmoRu3bppXR2EjzBKu7t16xYeeOABXLt2DWazGaGhoZg4cSKNr0GIUdqcJ+vXr0ejRo1c4XKprwsujNLubt68ieXLl6OiogIMwyA6Ohp33XUXGjVqBID6umBC7zb3+++/4/7778etW7dQXl6OqKgojB49GgMHDgz471dSsBAEQRAEQRAEQRAEQaiEtggRBEEQBEEQBEEQBEGohBQsBEEQBEEQBEEQBEEQKiEFC0EQBEEQBEEQBEEQhEpIwUIQBEEQBEEQBEEQBKESUrAQBEEQBEEQBEEQBEGohBQsBEEQBEEQEtm3bx8iIyNRVVXlb1EIgiAIgjAYpGAhCIIgCMKw5Ofnw2azISoqCjExMWjQoAGGDRuGDz74QHIee/fuBcMwqKyslFX21KlTMXHiRLdj3bt3x9WrV2E2m2XlRRAEQRBE8EMKFoIgCIIgDM19992HK1euoKysDP/617/Qt29fjB07Fvfff7+/RSMIgiAIgnBBChaCIAiCIAKG5ORkzJkzB+vXr8djjz2GY8eOYe/evcjLy0NCQgLi4uLQq1cvfP/99wCAkydPYsCAAQCA2NhYREZGYu3atQCAS5cuYfbs2WjYsCESEhIwcOBAnDhxAgCwdu1abNu2DW+++SYiIyMRGRmJkydPelnDrF69Gt26dcOqVatQt25dREdH47777sPFixcxZswYxMTEID09He+//77bfXz44YfIzc1FXFwcsrOz8cwzz/iqCgmCIAiC0AlSsBAEQRAEEXCMHz8eAPDpp5/CarXiySefxJkzZ3Dy5ElkZWVh6NChKC8vR1paGj766CMA1QqVq1evYsWKFXA4HBg+fDguX76M7777DqdPn0bLli0xePBgVFRUYMWKFZgwYQLGjBmDq1ev4urVq0hLS+OU5euvv0ZCQgJOnjyJTz/9FMXFxejTpw/mzZuHixcvYsGCBZg2bRquX78OANizZw/Gjx+PtWvX4sKFC3jvvffwxBNPYNu2bb6pPIIgCIIgdIEULARBEARBBBxhYWFITEzEhQsX0LVrV+Tl5bl8tTz++OM4efIkDh8+zHv9d999hwMHDmDz5s2Ij49HSEgI1q5di19++QVff/21LFnS0tKwcOFCWK1WdOzYETk5OWjfvj26d+8Ok8mEKVOm4OLFizh69CgAoLi4GLNnz0ZhYSFMJhNycnJQVFSEV199VVWdEARBEAThXyz+FoAgCIIgCEIuN27cQElJCRISEvDDDz/g/vvvx7fffosrV67AZKpePzp//jzv9UePHkVlZSVSU1O9zp06dUqWLHXr1nX7OyIiwu1YREQEAODKlSuusnfv3o3nn3/elaaqqorXQoYgCIIgiMCAFCwEQRAEQQQc27dvB8Mw6NWrFwYPHowBAwbgr3/9K+Li4nDx4kXEx8fD4XAAgEvhwiYlJQU2mw0lJSWwWq2cZXBdpwUpKSkYN24cVq1apUv+BEEQBEH4B9oiRBAEQRBEwFBSUoLNmzdj0aJFuPfee5GdnY2ysjJER0cjJiYGpaWluOeee9yuSUlJAQC3LUPdunVDTk4OZs+e7bJ0uXjxIt555x2Xr5SUlBQcP34cVVVVmt7DwoULsXHjRnz66aeorKxEZWUlfvrpJ3zxxRealkMQBEEQhG8hBQtBEARBEIZm3bp1iIyMRHR0NNq2bYsPP/wQW7duxWOPPQYAeOWVV/DWW28hKioKnTt3dkUNctK4cWPMnz8fBQUFiI2NxWOPPQaz2YxPPvkE4eHhyM3NRVRUFFq3bo333nsPDMMAAGbNmgUASExMRGxsLE6ePKnJ/QwbNgyvv/46Vq1aheTkZCQnJ+Ouu+7CH3/8oUn+BEEQBEH4B8bhtJ8lCIIgCIIgCIIgCIIgFEEWLARBEARBEARBEARBECohBQtBEARBEARBEARBEIRKSMFCEARBEARBEARBEAShElKwEARBEARBEARBEARBqIQULARBEARBEARBEARBECohBQtBEARBEARBEARBEIRKSMFCEARBEARBEARBEAShElKwEARBEARBEARBEARBqIQULARBEARBEARBEARBECohBQtBEARBEARBEARBEIRK/j9sCAiwAPxzKAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bplt.plot_series(data_miss, 'x', groupby='S', with_missing_datetimes=True,\n", + " title=f'Plot of x with missing datetimes')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Same as the previous plot, but with a group by minute (`T`). Since this is regroup by minute, there are less data missing. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bplt.plot_series(data_miss, 'x', groupby='T', with_missing_datetimes=True,\n", + " title=f'Plot of x with missing datetimes')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot true vs pred\n", + "\n", + "Plot the real data against the predictions. The correlation (`R`) can be calculated or not using the `with_correlation` option." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bplt.plot_true_vs_pred(y_true, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the `with_histograms` option, the function will plot histograms on the side, showing the distribution of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = bplt.plot_true_vs_pred(y_true, y_pred, with_histograms=True, marker='.', c='r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot using a previously created axis." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "__, ax = plt.subplots(1, 1, figsize=(14, 7), dpi=80)\n", + "bplt.plot_true_vs_pred(y_true, y_pred, ax=ax)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/notebook-bff-plot.rst b/notebooks/notebook-bff-plot.rst new file mode 100644 index 0000000..5c4dc0c --- /dev/null +++ b/notebooks/notebook-bff-plot.rst @@ -0,0 +1,333 @@ +Examples of plots +================= + +This notebook presents examples of the ``bff.plot`` module. + +For each function, the ``ax`` can be provided and is returned by the +function. This allow to plot multiple things on the same axis and modify +it if needed. + +.. code:: ipython3 + + import matplotlib.pyplot as plt + import numpy as np + import pandas as pd + + import bff.plot as bplt + + np.random.seed(42) + +.. code:: ipython3 + + # Variables with fake data to display. + history = { + 'loss': [2.3517270615352457, 2.3737808328178063, 2.342552079627262, + 2.310529179309481, 2.3773420348239305, 2.3290258640020935, + 2.3345777257603015, 2.336566770496081, 2.34276949460782, + 2.321525989465378, 2.3300879552735756, 2.3224288386915197, + 2.324129374183003, 2.3158747431021838, 2.3194296072475873, + 2.2962934024369894, 2.296843618603807, 2.298411148876401, + 2.302087271033819, 2.2869889256942213], + 'acc': [0.085427135, 0.09045226, 0.110552765, 0.110552765, 0.06030151, + 0.14070351, 0.110552765, 0.10552764, 0.09045226, 0.10050251, + 0.11557789, 0.12060302, 0.110552765, 0.10552764, 0.1356784, + 0.15075377, 0.11557789, 0.12060302, 0.10552764, 0.14572865], + 'val_loss': [2.3074077556791077, 2.306745302662272, 2.3061152659403104, + 2.3056061252970226, 2.30513324273213, 2.3046621198808954, + 2.304321059871107, 2.304280655512054, 2.3042611346560324, + 2.3042683235268466, 2.3044002410326705, 2.304716517416279, + 2.3049982415602894, 2.305085456921962, 2.3051163034046187, + 2.3052417696192022, 2.3052861982219377, 2.305426104982545, + 2.305481707112173, 2.3055578968795793], + 'val_acc': [0.11610487, 0.11111111, 0.11485643, 0.11360799, 0.11360799, + 0.11985019, 0.11111111, 0.10861423, 0.10861423, 0.10486891, + 0.10362048, 0.096129835, 0.09238452, 0.09113608, 0.09113608, + 0.08739076, 0.08988764, 0.09113608, 0.096129835, 0.09363296] + } + + y_true = [1.87032178, 1.2272566 , 9.38496685, 7.91451104, 7.60794146, + 9.65912261, 2.5405396 , 7.31815866, 5.91692937, 2.78676838, + 7.9258648 , 2.31337877, 1.78432016, 9.5559698 , 6.64471696, + 3.33907423, 7.49321025, 7.14822795, 4.11686499, 2.40202043] + + y_pred = [1.85161709, 1.33317135, 9.45246137, 7.9198675 , 7.54877922, + 9.7153202 , 3.56777447, 7.88673475, 5.56090322, 2.78851836, + 6.70636033, 2.67531555, 1.13061356, 8.29287223, 6.27275223, + 2.4957286 , 7.14305019, 8.53578604, 3.99890533, 2.35510298] + +Plot of history +--------------- + +The ``plot_history`` function can plot the loss and a metric from the +``history.history`` dictionary usually returned by a Keras model. + +Plot of only loss with grid and a different style for matplotlib. + +.. code:: ipython3 + + bplt.plot_history(history, title='Model history with random data', grid='both', figsize=(10, 5), style='seaborn') + + + + +.. parsed-literal:: + + + + + + +.. image:: ../../notebooks/notebook-bff-plot_files/notebook-bff-plot_5_1.png + + +Plot of history using a previously created axis. + +.. code:: ipython3 + + __, ax = plt.subplots(1, 1, figsize=(10, 5), dpi=80) + bplt.plot_history(history, axes=ax, style='seaborn') + + + + +.. parsed-literal:: + + + + + + +.. image:: ../../notebooks/notebook-bff-plot_files/notebook-bff-plot_7_1.png + + +Plot of history with loss and acc. + +.. code:: ipython3 + + bplt.plot_history(history, metric='acc') + + + + +.. parsed-literal:: + + array([, + ], + dtype=object) + + + + +.. image:: ../../notebooks/notebook-bff-plot_files/notebook-bff-plot_9_1.png + + +Plot of predictions +------------------- + +Plot of actual and predicted values on the same axis. + +.. code:: ipython3 + + bplt.plot_predictions(y_true, y_pred) + + + + +.. parsed-literal:: + + + + + + +.. image:: ../../notebooks/notebook-bff-plot_files/notebook-bff-plot_11_1.png + + +Plot series +----------- + +Series can either be plot on the same axis or in different ones in the +same figure. + +Some fake data are created in a ``DataFrame``. The index must be named +``datetime``. A different color is assigned to each of the acceleration. + +.. code:: ipython3 + + AXIS = {'x': 'darkorange', 'y': 'green', 'z': 'steelblue'} + data = (pd.DataFrame(np.random.randint(0, 100, size=(60 * 60, 3)), columns=AXIS.keys()) + .set_index(pd.date_range('2018-01-01', periods=60 * 60, freq='S')) + .rename_axis('datetime')) + + data_miss = (data + .drop(pd.date_range('2018-01-01 00:05', '2018-01-01 00:07', freq='S')) + .drop(pd.date_range('2018-01-01 00:40', '2018-01-01 00:41', freq='S')) + .drop(pd.date_range('2018-01-01 00:57', '2018-01-01 00:59', freq='S')) + ) + +Plot of x, y and z acceleration on the same axis. The function is +returning the axis so it can be used in the next plot. + +.. code:: ipython3 + + ax = bplt.plot_series(data, 'x', groupby='3T', title=f'Plot of all axis', color=AXIS['x']) + for k in list(AXIS.keys())[1:]: + bplt.plot_series(data, k, groupby='3T', ax=ax, color=AXIS[k]) + + + +.. image:: ../../notebooks/notebook-bff-plot_files/notebook-bff-plot_15_0.png + + +This time, accelerations are plot on a figure containing a different +axis for each acceleration. + +.. code:: ipython3 + + _, axes = plt.subplots(nrows=len(AXIS), ncols=1, figsize=(14, len(AXIS) * 3), dpi=80) + for i, k in enumerate(AXIS.keys()): + bplt.plot_series(data, k, ax=axes[i], title=f'Plot of axis - {k}', color=AXIS[k]) + + + +.. image:: ../../notebooks/notebook-bff-plot_files/notebook-bff-plot_17_0.png + + +A resampling of the data is done by averaging each 2 minutes (``2T``). A +peak detection is done as well. + +.. code:: ipython3 + + bplt.plot_series(data, 'x', groupby='2T', with_peaks=True, title=f'Plot of x with peaks') + + + + +.. parsed-literal:: + + + + + + +.. image:: ../../notebooks/notebook-bff-plot_files/notebook-bff-plot_19_1.png + + +A resampling of the data is done by averaging each 3 minutes (``2T``). +The standard error of the mean (SEM) is plotted as well. This is usefull +to see if the data are close to the mean or not since there was a +resampling. + +.. code:: ipython3 + + bplt.plot_series(data, 'x', groupby='3T', with_sem=True, title=f'Plot of x with standard error of the mean (sem)') + + + + +.. parsed-literal:: + + + + + + +.. image:: ../../notebooks/notebook-bff-plot_files/notebook-bff-plot_21_1.png + + +Plot of a serie with missing data. By specifying the resampling, we can +easily see if some of the datetime are missing. + +.. code:: ipython3 + + bplt.plot_series(data_miss, 'x', groupby='S', with_missing_datetimes=True, + title=f'Plot of x with missing datetimes') + + + + +.. parsed-literal:: + + + + + + +.. image:: ../../notebooks/notebook-bff-plot_files/notebook-bff-plot_23_1.png + + +Same as the previous plot, but with a group by minute (``T``). Since +this is regroup by minute, there are less data missing. + +.. code:: ipython3 + + bplt.plot_series(data_miss, 'x', groupby='T', with_missing_datetimes=True, + title=f'Plot of x with missing datetimes') + + + + +.. parsed-literal:: + + + + + + +.. image:: ../../notebooks/notebook-bff-plot_files/notebook-bff-plot_25_1.png + + +Plot true vs pred +----------------- + +Plot the real data against the predictions. The correlation (``R``) can +be calculated or not using the ``with_correlation`` option. + +.. code:: ipython3 + + bplt.plot_true_vs_pred(y_true, y_pred) + + + + +.. parsed-literal:: + + + + + + +.. image:: ../../notebooks/notebook-bff-plot_files/notebook-bff-plot_27_1.png + + +Using the ``with_histograms`` option, the function will plot histograms +on the side, showing the distribution of the data. + +.. code:: ipython3 + + ax = bplt.plot_true_vs_pred(y_true, y_pred, with_histograms=True, marker='.', c='r') + + + +.. image:: ../../notebooks/notebook-bff-plot_files/notebook-bff-plot_29_0.png + + +Plot using a previously created axis. + +.. code:: ipython3 + + __, ax = plt.subplots(1, 1, figsize=(14, 7), dpi=80) + bplt.plot_true_vs_pred(y_true, y_pred, ax=ax) + + + + +.. parsed-literal:: + + + + + + +.. image:: ../../notebooks/notebook-bff-plot_files/notebook-bff-plot_31_1.png + diff --git a/notebooks/notebook-bff-plot_files/notebook-bff-plot_11_1.png 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