{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Forecasting with aeon\n",
"\n",
"In forecasting, past data is used to make predictions about future values of a time\n",
"series. This is distinct from learning tasks such classification, regression and\n",
"clustering, where it may not be future values being predicted.\n",
"\n",
"\n",
"\n",
"This notebook gives a tutorial on forecasting. More specific notebooks in aeon on\n",
"forecasting are:\n",
"\n",
"- [Probabilistic Forecasting](forecasting_proba.ipynb)\n",
"- [Hierarchical, Global, and Panel Forecasting](forecasting_hierarchical_global.ipynb)\n",
"- [Forecasting with skearn](forecasting_sklearn.ipynb).\n",
"\n",
"`aeon` provides a common, `scikit-learn`-like interface to a variety of classical and ML-style forecasting algorithms, together with tools for building pipelines and composite machine learning models, including temporal tuning schemes, or reductions such as walk-forward application of `scikit-learn` regressors.\n",
"\n",
"**Section 1** provides an overview of common forecasting workflows supported by `aeon`.\n",
"\n",
"**Section 2** discusses the families of forecasters available in `aeon`.\n",
"\n",
"**Section 3** discusses advanced composition patterns, including pipeline building, reduction, tuning, ensembling, and autoML.\n",
"\n",
"**Section 4** gives an introduction to how to write custom estimators compliant with the `aeon` interface.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": []
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table of Contents\n",
"\n",
"* [1. Basic forecasting workflows](#1.-basic-forecasting-workflows)\n",
" * 1.1 Data container format\n",
" * 1.2 Basic deployment workflow - batch fitting and forecasting\n",
" * 1.2.1 Basic deployment workflow in a nutshell\n",
" * 1.2.2 Forecasters that require the horizon when fitting\n",
" * 1.2.3 Forecasters that can make use of exogeneous data\n",
" * 1.2.4 Multivariate Forecasters\n",
" * 1.2.5 Prediction intervals and quantile forecasts\n",
" * 1.2.6 Panel forecasts and hierarchical forecasts\n",
" * 1.3 Basic evaluation workflow - evaluating a batch of forecasts against ground truth observations\n",
" * 1.3.1 The basic batch forecast evaluation workflow in a nutshell - function metric interface\n",
" * 1.3.2 The basic batch forecast evaluation workflow in a nutshell - metric class interface)\n",
" * 1.4 Advanced deployment workflow: rolling updates & forecasts\n",
" * 1.4.1 Updating a forecaster with the update method\n",
" * 1.4.2 Moving the \"now\" state without updating the model\n",
" * 1.4.3 Walk-forward predictions on a batch of data\n",
" * 1.5 Advanced evaluation worfklow: rolling re-sampling and aggregate errors, rolling back-testing\n",
"* [2. Forecasters in aeon - searching, tags, common families](#2.-forecasters-in-aeon---searching,-tags,-common-families)\n",
" * 2.1 Forecaster lookup - the registry\n",
" * 2.2 Forecaster tags\n",
" * 2.2.1 Capability tags: multivariate, probabilistic, hierarchical\n",
" * 2.2.2 Finding and listing forecasters by tag)\n",
" * 2.2.3 Listing all forecaster tags)\n",
" * 2.3 Common forecaster types\n",
" * 2.3.1 Exponential smoothing, theta forecaster, autoETS from statsmodels\n",
" * 2.3.2 ARIMA and autoARIMA\n",
" * 2.3.3 BATS and TBATS\n",
" * 2.3.4 Facebook prophet\n",
" * 2.3.5 State Space Model (Structural Time Series)\n",
" * 2.3.6 AutoArima from StatsForecast\n",
"* [3. Advanced composition patterns - pipelines, reduction, autoML, and more](#3.-advanced-composition-patterns---pipelines,-reduction,-automl,-and-more)\n",
" * 3.1 Reduction: from forecasting to regression\n",
" * 3.2 Pipelining, detrending and deseasonalization\n",
" * 3.2.1 The basic forecasting pipeline\n",
" * 3.2.2 The Detrender as pipeline component\n",
" * 3.2.3 Complex pipeline composites and parameter inspection\n",
" * 3.3 Parameter tuning\n",
" * 3.3.1 Basic tuning using ForecastingGridSearchCV\n",
" * 3.3.2 Tuning of complex composites\n",
" * 3.3.3 Selecting the metric and retrieving scores\n",
" * 3.4 autoML aka automated model selection, ensembling and hedging\n",
" * 3.4.1 autoML aka automatic model selection, using tuning plus multiplexer\n",
" * 3.4.2 autoML: selecting transformer combinations via OptimalPassthrough\n",
" * 3.4.3 Simple ensembling strategies\n",
" * 3.4.4 Prediction weighted ensembles and hedge ensembles\n",
"* [4. Extension guide - implementing your own forecaster](#4.-extension-guide---implementing-your-own-forecaster)\n",
"* [5. Summary](#5.-summary)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Package imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:10.687691700Z",
"start_time": "2023-08-25T09:25:07.453121300Z"
}
},
"outputs": [],
"source": [
"import warnings\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# hide warnings\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Basic forecasting workflows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This section explains the basic forecasting workflows, and key interface points for it.\n",
"\n",
"We cover the following four workflows:\n",
"\n",
"* Basic deployment workflow: batch fitting and forecasting;\n",
"* Basic evaluation workflow: evaluating a batch of forecasts against ground truth\n",
"observations;\n",
"* Advanced deployment workflow: fitting and rolling updates/forecasts; and\n",
"* Advanced evaluation worfklow: using rolling forecast splits and computing\n",
"split-wise and aggregate errors, including common back-testing schemes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1 Data container format\n",
"\n",
"All forecasting workflows make common assumptions on the input data format.\n",
"`aeon` uses `pandas` for representing time series for forecasting:\n",
"\n",
"* `pd.DataFrame` for time series and sequences. Rows represent time indices, columns\n",
"represent variables;\n",
"* `pd.Series` can be used for univariate time series;\n",
"* `numpy` arrays (1D and 2D) can also used.\n",
"\n",
"The `Series.index` and `DataFrame.index` are used for representing the time series index.\n",
"`aeon` supports pandas integer, period and timestamp indices for simple time series.\n",
"\n",
"`aeon` supports additional data structures for panel and hierarchical\n",
"time series, see the [data structure notebooks](../datasets/datasets.ipynb) for more\n",
"details.\n",
"\n",
"**Example:** as the running example in this tutorial, we use a textbook data set, the Box-Jenkins airline data set, which consists of the number of monthly totals of international airline passengers, from 1949 - 1960. Values are in thousands. See \"Makridakis, Wheelwright and Hyndman (1998) Forecasting: methods and applications\", exercises sections 2 and 3."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:14.040404400Z",
"start_time": "2023-08-25T09:25:07.512960700Z"
}
},
"outputs": [],
"source": [
"from aeon.datasets import load_airline\n",
"from aeon.visualisation import plot_series"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:14.367530200Z",
"start_time": "2023-08-25T09:25:08.130310300Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"y = load_airline()\n",
"\n",
"# plotting for visualization\n",
"plot_series(y)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:14.454298900Z",
"start_time": "2023-08-25T09:25:08.431538600Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"PeriodIndex(['1949-01', '1949-02', '1949-03', '1949-04', '1949-05', '1949-06',\n",
" '1949-07', '1949-08', '1949-09', '1949-10',\n",
" ...\n",
" '1960-03', '1960-04', '1960-05', '1960-06', '1960-07', '1960-08',\n",
" '1960-09', '1960-10', '1960-11', '1960-12'],\n",
" dtype='period[M]', name='Period', length=144)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.index"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"### 1.2 Basic deployment workflow - batch fitting and forecasting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The simplest use case is batch fitting and forecasting, i.e., fitting a forecasting model to one batch of past data, then asking for forecasts at time point in the future.\n",
"\n",
"The steps in this workflow are as follows:\n",
"\n",
"1. Preparation of the data\n",
"2. Specification of the time points for which forecasts are requested. This uses a `numpy.array` or the `ForecastingHorizon` object.\n",
"3. Specification and instantiation of the forecaster. This follows a `scikit-learn`-like syntax; forecaster objects follow the familiar `scikit-learn` `BaseEstimator` interface.\n",
"4. Fitting the forecaster to the data, using the forecaster's `fit` method\n",
"5. Making a forecast, using the forecaster's `predict` method\n",
"\n",
"The below first outlines the vanilla variant of the basic deployment workflow, step-by-step.\n",
"\n",
"At the end, one-cell workflows are provided, with common deviations from the pattern (Sections 1.2.1 and following)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 1 - Preparation of the data\n",
"\n",
"We assume here the data is assumed to be in `pd.Series` or `pd.DataFrame` format."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:14.454298900Z",
"start_time": "2023-08-25T09:25:08.438519Z"
}
},
"outputs": [],
"source": [
"from aeon.datasets import load_airline\n",
"from aeon.visualisation import plot_series"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:14.983883Z",
"start_time": "2023-08-25T09:25:08.442508200Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# in the example, we use the airline data set.\n",
"y = load_airline()\n",
"plot_series(y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 2 - Specifying the forecasting horizon"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we need to specify the forecasting horizon and pass that to our forecasting algorithm.\n",
"\n",
"There are two main ways of doing this:\n",
"\n",
"* Using a `numpy.array` of integers. This assumes either integer index or periodic index (`PeriodIndex`) in the time series; the integer indicates the number of time points or periods ahead we want to make a forecast for. E.g., `1` means forecast the next period, `2` the second next period, and so on.\n",
"* Using a `ForecastingHorizon` object. This can be used to define forecast horizons, using any supported index type as an argument. No periodic index is assumed.\n",
"\n",
"Forecasting horizons can be absolute, i.e., referencing specific time points in the future, or relative, i.e., referencing time differences to the present. As a default, the present is that latest time point seen in any `y` passed to the forecaster.\n",
"\n",
"`numpy.array` based forecasting horizons are always relative; `ForecastingHorizon` objects can be both relative and absolute. In particular, absolute forecasting horizons can only be specified using `ForecastingHorizon`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Using a `numpy` forecasting horizon"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:14.983883Z",
"start_time": "2023-08-25T09:25:08.668903800Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
" 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
" 35, 36])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fh = np.arange(1, 37)\n",
"fh"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This will ask for monthly predictions for the next three years, since the original\n",
"series period is 1 month. To predict only the second and fifth month ahead, one would\n",
" pass a list as follows:\n",
"\n",
"```python\n",
"import numpy as np\n",
"fh = np.array([2, 5]) # 2nd and 5th step ahead\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Using a `ForecastingHorizon` based forecasting horizon\n",
"\n",
"The `ForecastingHorizon` object considers the input absolute or relative depending on the `is_relative` flag.\n",
"\n",
"`ForecastingHorizon` will automatically assume a relative horizon if temporal difference types from `pandas` are passed; if value types from `pandas` are passed, it will assume an absolute horizon.\n",
"\n",
"To define an absolute `ForecastingHorizon` in our example:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:15.193322900Z",
"start_time": "2023-08-25T09:25:08.676882200Z"
}
},
"outputs": [],
"source": [
"from aeon.forecasting.base import ForecastingHorizon"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:15.196315600Z",
"start_time": "2023-08-25T09:25:08.686855700Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"ForecastingHorizon(['1961-01', '1961-02', '1961-03', '1961-04', '1961-05', '1961-06',\n",
" '1961-07', '1961-08', '1961-09', '1961-10', '1961-11', '1961-12',\n",
" '1962-01', '1962-02', '1962-03', '1962-04', '1962-05', '1962-06',\n",
" '1962-07', '1962-08', '1962-09', '1962-10', '1962-11', '1962-12',\n",
" '1963-01', '1963-02', '1963-03', '1963-04', '1963-05', '1963-06',\n",
" '1963-07', '1963-08', '1963-09', '1963-10', '1963-11', '1963-12'],\n",
" dtype='period[M]', is_relative=False)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fh = ForecastingHorizon(\n",
" pd.PeriodIndex(pd.date_range(\"1961-01\", periods=36, freq=\"M\")), is_relative=False\n",
")\n",
"fh"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `ForecastingHorizon` can be converted from relative to absolute and back via the\n",
"`to_relative` and `to_absolute` methods. Both of these conversions require a compatible `cutoff` to be passed:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:15.196315600Z",
"start_time": "2023-08-25T09:25:08.694833900Z"
}
},
"outputs": [],
"source": [
"cutoff = pd.Period(\"1960-12\", freq=\"M\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:15.197313900Z",
"start_time": "2023-08-25T09:25:08.701814900Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"ForecastingHorizon([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
" 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36],\n",
" dtype='int32', is_relative=True)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fh.to_relative(cutoff)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:15.197313900Z",
"start_time": "2023-08-25T09:25:08.709794Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"ForecastingHorizon(['1961-01', '1961-02', '1961-03', '1961-04', '1961-05', '1961-06',\n",
" '1961-07', '1961-08', '1961-09', '1961-10', '1961-11', '1961-12',\n",
" '1962-01', '1962-02', '1962-03', '1962-04', '1962-05', '1962-06',\n",
" '1962-07', '1962-08', '1962-09', '1962-10', '1962-11', '1962-12',\n",
" '1963-01', '1963-02', '1963-03', '1963-04', '1963-05', '1963-06',\n",
" '1963-07', '1963-08', '1963-09', '1963-10', '1963-11', '1963-12'],\n",
" dtype='period[M]', is_relative=False)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fh.to_absolute(cutoff)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 3 - Specifying the forecasting algorithm\n",
"\n",
"To make forecasts, a forecasting algorithm needs to be specified. All `aeon`\n",
"forecasters follow the same interface, so the steps are the same, no matter which forecaster is chosen.\n",
"\n",
"For this example, we choose the naive forecasting method of predicting the last seen value. More complex specifications are possible, using pipeline and reduction construction syntax; this will be covered later in Section 2."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:15.197313900Z",
"start_time": "2023-08-25T09:25:08.715778500Z"
}
},
"outputs": [],
"source": [
"from aeon.forecasting.naive import NaiveForecaster"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:15.197313900Z",
"start_time": "2023-08-25T09:25:08.725751Z"
}
},
"outputs": [],
"source": [
"forecaster = NaiveForecaster(strategy=\"last\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 4 - Fitting the forecaster to the seen data\n",
"\n",
"Now the forecaster needs to be fitted to the seen data:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:15.198310Z",
"start_time": "2023-08-25T09:25:08.733730900Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
NaiveForecaster()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# optional: plotting predictions and past data\n",
"plot_series(y, y_pred, labels=[\"y\", \"y_pred\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.2.2 Forecasters that require the horizon already in `fit`\n",
"\n",
"Some forecasters need the forecasting horizon to be provided in `fit`. Such\n",
"forecasters will produce informative error messages when it is not passed in `fit`.\n",
"All forecaster will remember the horizon when passed in `fit` for prediction. An\n",
"example:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:16.969608700Z",
"start_time": "2023-08-25T09:25:09.352077300Z"
}
},
"outputs": [],
"source": [
"# step 1: data specification\n",
"y = load_airline()\n",
"\n",
"# step 2: specifying forecasting horizon\n",
"fh = np.arange(1, 37)\n",
"\n",
"# step 3: specifying the forecasting algorithm\n",
"forecaster = NaiveForecaster(strategy=\"last\", sp=12)\n",
"\n",
"# step 4: fitting the forecaster\n",
"forecaster.fit(y, fh=fh)\n",
"\n",
"# step 5: querying predictions\n",
"y_pred = forecaster.predict()"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-10T16:07:06.475031Z",
"iopub.status.busy": "2021-04-10T16:07:06.473831Z",
"iopub.status.idle": "2021-04-10T16:07:06.613175Z",
"shell.execute_reply": "2021-04-10T16:07:06.613700Z"
}
},
"source": [
"#### 1.2.3 Forecasters that can make use of exogeneous data\n",
"\n",
"Many forecasters can make use of exogeneous time series, i.e., other time series that\n",
" are not to be forecast, but are useful for forecasting `y`. Exogeneous time series are\n",
" always passed as an `X` argument, in `fit`, `predict`, and other methods (see below). Exogeneous time series should always be passed as `pandas.DataFrames`. Most forecasters that can deal with exogeneous time series will assume that the time indices of `X` passed to `fit` are a super-set of the time indices in `y` passed to `fit`; and that the time indices of `X` passed to `predict` are a super-set of time indices in `fh`, although this is not a general interface restriction. Forecasters that do not make use of exogeneous time series still accept the argument (and do not use it internally).\n",
"\n",
"The general workflow for passing exogeneous data is as follows:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:16.969608700Z",
"start_time": "2023-08-25T09:25:09.369034400Z"
}
},
"outputs": [],
"source": [
"# step 1: data specification\n",
"y = load_airline()\n",
"# we create some dummy exogeneous data\n",
"X = pd.DataFrame(index=y.index)\n",
"\n",
"# step 2: specifying forecasting horizon\n",
"fh = np.arange(1, 37)\n",
"\n",
"# step 3: specifying the forecasting algorithm\n",
"forecaster = NaiveForecaster(strategy=\"last\", sp=12)\n",
"\n",
"# step 4: fitting the forecaster\n",
"forecaster.fit(y, X=X, fh=fh)\n",
"\n",
"# step 5: querying predictions\n",
"y_pred = forecaster.predict(X=X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NOTE: as in workflows 1.2.1 and 1.2.2, some forecasters that use exogeneous variables may also require the forecasting horizon only in `predict`. Such forecasters may also be called with steps 4 and 5 being\n",
"```python\n",
"forecaster.fit(y, X=X)\n",
"y_pred = forecaster.predict(fh=fh, X=X)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.2.4. Multivariate forecasting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All forecasters in `aeon` support multivariate forecasts - some forecasters are \"genuine\" multivariate, all others \"apply by column\".\n",
"\n",
"Below is an example of the general multivariate forecasting workflow, using the `VAR` (vector auto-regression) forecaster on the Longley dataset from `aeon.datasets`. The workflow is the same as in the univariate forecasters, but the input has more than one variables (columns)."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:18.206302600Z",
"start_time": "2023-08-25T09:25:09.391972100Z"
}
},
"outputs": [],
"source": [
"from aeon.datasets import load_longley\n",
"from aeon.forecasting.var import VAR\n",
"\n",
"_, y = load_longley()\n",
"\n",
"y = y.drop(columns=[\"UNEMP\", \"ARMED\", \"POP\"])\n",
"\n",
"forecaster = VAR()\n",
"forecaster.fit(y, fh=[1, 2, 3])\n",
"\n",
"y_pred = forecaster.predict()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The input to the multivariate forecaster `y` is a `pandas.DataFrame` where each column is a variable."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:18.428707800Z",
"start_time": "2023-08-25T09:25:09.578471700Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
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1947
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83.0
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234289.0
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1948
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88.5
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259426.0
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1949
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88.2
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258054.0
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1950
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89.5
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284599.0
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1951
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96.2
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328975.0
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1952
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98.1
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346999.0
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1953
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99.0
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365385.0
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1954
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100.0
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363112.0
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1955
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101.2
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397469.0
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1956
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104.6
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419180.0
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1957
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108.4
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442769.0
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1958
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110.8
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444546.0
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1959
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112.6
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482704.0
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1960
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114.2
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502601.0
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1961
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115.7
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518173.0
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1962
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116.9
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554894.0
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],
"text/plain": [
" GNPDEFL GNP\n",
"Period \n",
"1947 83.0 234289.0\n",
"1948 88.5 259426.0\n",
"1949 88.2 258054.0\n",
"1950 89.5 284599.0\n",
"1951 96.2 328975.0\n",
"1952 98.1 346999.0\n",
"1953 99.0 365385.0\n",
"1954 100.0 363112.0\n",
"1955 101.2 397469.0\n",
"1956 104.6 419180.0\n",
"1957 108.4 442769.0\n",
"1958 110.8 444546.0\n",
"1959 112.6 482704.0\n",
"1960 114.2 502601.0\n",
"1961 115.7 518173.0\n",
"1962 116.9 554894.0"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The result of the multivariate forecaster `y_pred` is a `pandas.DataFrame` where columns are the predicted values for each variable. The variables in `y_pred` are the same as in `y`, the input to the multivariate forecaster."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:18.493534500Z",
"start_time": "2023-08-25T09:25:09.588444900Z"
}
},
"outputs": [
{
"data": {
"text/html": [
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1963
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121.688295
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578514.398653
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"
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1964
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124.353664
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601873.015890
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1965
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126.847886
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625411.588754
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],
"text/plain": [
" GNPDEFL GNP\n",
"Period \n",
"1963 121.688295 578514.398653\n",
"1964 124.353664 601873.015890\n",
"1965 126.847886 625411.588754"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are two categories of multivariate forecasters:\n",
"\n",
"* forecasters that are genuinely multivariate, such as `VAR`. Forecasts for one\n",
"endogeneous (`y`) variable will depend on values of other variables (`X`).\n",
"* forecasters that are univariate, such as `ARIMA`. Forecasts will be made by\n",
"endogeneous (`y`) variable, and not be affected by other variables (`X`).\n",
"\n",
"To display complete list of multivariate forecasters, search for forecasters with `'multivariate'` or `'both'` tag value for the tag `'y_input_type'`, as follows:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:21.474069400Z",
"start_time": "2023-08-25T09:25:09.599415500Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ColumnEnsembleForecaster\n",
"DynamicFactor\n",
"EnsembleForecaster\n",
"ForecastByLevel\n",
"ForecastingGridSearchCV\n",
"ForecastingPipeline\n",
"ForecastingRandomizedSearchCV\n",
"HierarchyEnsembleForecaster\n",
"MockForecaster\n",
"MultiplexForecaster\n",
"Permute\n",
"TransformedTargetForecaster\n",
"VAR\n",
"VARMAX\n",
"VECM\n",
"YtoX\n"
]
}
],
"source": [
"import warnings\n",
"\n",
"from aeon.registry import all_estimators\n",
"\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"for forecaster in all_estimators(\n",
" filter_tags={\"y_input_type\": [\"multivariate\", \"both\"]}\n",
"):\n",
" print(forecaster[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Univariate forecasters have tag value `'univariate'`, and will fit one model per column.\n",
"To access the column-wise models, access the `forecasters_` parameter, which stores the fitted forecasters in a `pandas.DataFrame`, fitted forecasters being in the column with the variable for which the forecast is being made:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:21.474069400Z",
"start_time": "2023-08-25T09:25:11.026785800Z"
}
},
"outputs": [
{
"data": {
"text/html": [
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forecasters
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ARIMA()
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],
"text/plain": [
" GNPDEFL GNP\n",
"forecasters ARIMA() ARIMA()"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from aeon.datasets import load_longley\n",
"from aeon.forecasting.arima import ARIMA\n",
"\n",
"_, y = load_longley()\n",
"\n",
"y = y.drop(columns=[\"UNEMP\", \"ARMED\", \"POP\"])\n",
"\n",
"forecaster = ARIMA()\n",
"forecaster.fit(y, fh=[1, 2, 3])\n",
"\n",
"forecaster.forecasters_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.2.5 Probabilistic forecasting: prediction intervals, quantile, variance, and distributional forecasts\n",
"\n",
"`aeon` provides a unified interface to make probabilistic forecasts.\n",
"The following methods are possibly available for probabilistic forecasts:\n",
"\n",
"* `predict_interval` produces interval forecasts. Additionally to any `predict` arguments, an argument `coverage` (nominal interval coverage) must be provided.\n",
"* `predict_quantiles` produces quantile forecasts. Additionally to any `predict` arguments, an argument `alpha` (quantile values) must be provided.\n",
"* `predict_var` produces variance forecasts. This has same arguments as `predict`.\n",
"* `predict_proba` produces full distributional forecasts. This has same arguments as `predict`.\n",
"\n",
"Not all forecasters are capable of returning probabilistic forecast, but if a forecasters provides one kind of probabilistic forecast, it is also capable of returning the others. The list of forecasters with such capability can be queried by `registry.all_estimators`, searching for those where the `capability:pred_int` tag has value`True`.\n",
"\n",
"The basic worfklow for probabilistic forecasts is similar to the basic forecasting workflow, with the difference that instead of `predict`, one of the probabilistic forecasting methods is used:\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:21.475025500Z",
"start_time": "2023-08-25T09:25:11.246198200Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
NaiveForecaster(sp=12)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
NaiveForecaster(sp=12)
"
],
"text/plain": [
"NaiveForecaster(sp=12)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"\n",
"from aeon.datasets import load_airline\n",
"from aeon.forecasting.naive import NaiveForecaster\n",
"\n",
"# until fit, identical with the simple workflow\n",
"y = load_airline()\n",
"\n",
"fh = np.arange(1, 13)\n",
"\n",
"forecaster = NaiveForecaster(sp=12)\n",
"forecaster.fit(y, fh=fh)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we present the different probabilistic forecasting methods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### `predict_interval` - interval predictions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`predict_interval` takes an argument `coverage`, which is a float (or list of floats), the nominal coverage of the prediction interval(s) queried. `predict_interval` produces symmetric prediction intervals, for example, a coverage of `0.9` returns a \"lower\" forecast at quantile `0.5 - coverage/2 = 0.05`, and an \"upper\" forecast at quantile `0.5 + coverage/2 = 0.95`."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:21.475025500Z",
"start_time": "2023-08-25T09:25:11.260187300Z"
}
},
"outputs": [
{
"data": {
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357.265915
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1961-02
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331.265915
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450.734085
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1961-03
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359.265915
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478.734085
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1961-04
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401.265915
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520.734085
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1961-05
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412.265915
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531.734085
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1961-06
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475.265915
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594.734085
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1961-07
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562.265915
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681.734085
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1961-08
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546.265915
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665.734085
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1961-09
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448.265915
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567.734085
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401.265915
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520.734085
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1961-11
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330.265915
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449.734085
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1961-12
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372.265915
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491.734085
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" \n",
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"text/plain": [
" Coverage \n",
" 0.9 \n",
" lower upper\n",
"1961-01 357.265915 476.734085\n",
"1961-02 331.265915 450.734085\n",
"1961-03 359.265915 478.734085\n",
"1961-04 401.265915 520.734085\n",
"1961-05 412.265915 531.734085\n",
"1961-06 475.265915 594.734085\n",
"1961-07 562.265915 681.734085\n",
"1961-08 546.265915 665.734085\n",
"1961-09 448.265915 567.734085\n",
"1961-10 401.265915 520.734085\n",
"1961-11 330.265915 449.734085\n",
"1961-12 372.265915 491.734085"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coverage = 0.9\n",
"y_pred_ints = forecaster.predict_interval(coverage=coverage)\n",
"y_pred_ints"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The return `y_pred_ints` is a `pandas.DataFrame` with a column multi-index: The first level is variable name from `y` in fit (or `Coverage` if no variable names were present), second level coverage fractions for which intervals were computed, in the same order as in input `coverage`; third level columns `lower` and `upper`. Rows are the indices for which forecasts were made (same as in `y_pred` or `fh`). Entries are lower/upper (as column name) bound of the nominal coverage predictive interval for the index in the same row."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pretty-plotting the predictive interval forecasts:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:21.475025500Z",
"start_time": "2023-08-25T09:25:11.275121700Z"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from aeon.visualisation import plot_series\n",
"\n",
"# also requires predictions\n",
"y_pred = forecaster.predict()\n",
"\n",
"fig, ax = plot_series(y, y_pred, labels=[\"y\", \"y_pred\"], pred_interval=y_pred_ints)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### `predict_quantiles` - quantile forecasts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"aeon offers `predict_quantiles` as a unified interface to return quantile values of predictions. Similar to `predict_interval`.\n",
"\n",
"`predict_quantiles` has an argument `alpha`, containing the quantile values being queried. Similar to the case of the `predict_interval`, `alpha` can be a `float`, or a `list of floats`."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:21.476023Z",
"start_time": "2023-08-25T09:25:11.521492400Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
Quantiles
\n",
"
\n",
"
\n",
"
\n",
"
0.275
\n",
"
0.975
\n",
"
\n",
" \n",
" \n",
"
\n",
"
1961-01
\n",
"
395.291896
\n",
"
488.177552
\n",
"
\n",
"
\n",
"
1961-02
\n",
"
369.291896
\n",
"
462.177552
\n",
"
\n",
"
\n",
"
1961-03
\n",
"
397.291896
\n",
"
490.177552
\n",
"
\n",
"
\n",
"
1961-04
\n",
"
439.291896
\n",
"
532.177552
\n",
"
\n",
"
\n",
"
1961-05
\n",
"
450.291896
\n",
"
543.177552
\n",
"
\n",
"
\n",
"
1961-06
\n",
"
513.291896
\n",
"
606.177552
\n",
"
\n",
"
\n",
"
1961-07
\n",
"
600.291896
\n",
"
693.177552
\n",
"
\n",
"
\n",
"
1961-08
\n",
"
584.291896
\n",
"
677.177552
\n",
"
\n",
"
\n",
"
1961-09
\n",
"
486.291896
\n",
"
579.177552
\n",
"
\n",
"
\n",
"
1961-10
\n",
"
439.291896
\n",
"
532.177552
\n",
"
\n",
"
\n",
"
1961-11
\n",
"
368.291896
\n",
"
461.177552
\n",
"
\n",
"
\n",
"
1961-12
\n",
"
410.291896
\n",
"
503.177552
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Quantiles \n",
" 0.275 0.975\n",
"1961-01 395.291896 488.177552\n",
"1961-02 369.291896 462.177552\n",
"1961-03 397.291896 490.177552\n",
"1961-04 439.291896 532.177552\n",
"1961-05 450.291896 543.177552\n",
"1961-06 513.291896 606.177552\n",
"1961-07 600.291896 693.177552\n",
"1961-08 584.291896 677.177552\n",
"1961-09 486.291896 579.177552\n",
"1961-10 439.291896 532.177552\n",
"1961-11 368.291896 461.177552\n",
"1961-12 410.291896 503.177552"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred_quantiles = forecaster.predict_quantiles(alpha=[0.275, 0.975])\n",
"y_pred_quantiles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`y_pred_quantiles`, the output of predict_quantiles, is a `pandas.DataFrame` with a two-level column multiindex. The first level is variable name from `y` in fit (or `Quantiles` if no variable names were present), second level are the quantile values (from `alpha`) for which quantile predictions were queried. Rows are the indices for which forecasts were made (same as in `y_pred` or `fh`). Entries are the quantile predictions for that variable, that quantile value, for the time index in the same row."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remark: for clarity: quantile and (symmetric) interval forecasts can be translated into each other as follows.\n",
"\n",
"**alpha < 0.5:** The alpha-quantile prediction is equal to the lower bound of a predictive interval with coverage = (0.5 - alpha) * 2\n",
"\n",
"**alpha > 0.5:** The alpha-quantile prediction is equal to the upper bound of a predictive interval with coverage = (alpha - 0.5) * 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### `predict_var` - variance predictions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`predict_var` produces variance predictions:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:21.476023Z",
"start_time": "2023-08-25T09:25:11.533431200Z"
}
},
"outputs": [
{
"data": {
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"1961-10 1318.833333\n",
"1961-11 1318.833333\n",
"1961-12 1318.833333"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred_var = forecaster.predict_var()\n",
"y_pred_var"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The format of the output `y_pred_var` is the same as for `predict`, except that this is always coerced to a `pandas.DataFrame`, and entries are not point predictions but variance predictions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### `predict_proba` - distribution predictions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To predict full predictive distributions, `predict_proba` can be used.\n",
"As this returns `tensorflow` `Distribution` objects, the deep learning dependency set `dl` of `aeon` (which includes `tensorflow` and `tensorflow-probability` dependencies) must be installed."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:21.480012400Z",
"start_time": "2023-08-25T09:25:11.541409800Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred_proba = forecaster.predict_proba()\n",
"y_pred_proba"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Distributions returned by `predict_proba` are by default marginal at time points, not joint over time points.\n",
"More precisely, the returned `Distribution` object is formatted and to be interpreted as follows:\n",
"* Batch shape is 1D and same length as fh\n",
"* Event shape is 1D, with length equal to number of variables being forecast\n",
"* i-th (batch) distribution is forecast for i-th entry of fh\n",
"* j-th (event) component is j-th variable, same order as y in `fit`/`update`\n",
"\n",
"To return joint forecast distributions, the `marginal` parameter can be set to `False` (currently work in progress). In this case, a `Distribution` with 2D event shape `(len(fh), len(y))` is returned."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.2.6 Panel forecasts and hierarchical forecasts\n",
"\n",
"`aeon` provides a unified interface to make panel and hierarchical forecasts.\n",
"\n",
"All `aeon` forecasters can be applied to panel and hierarchical data, which needs to be presented in specific input formats.\n",
"Forecasters that are not genuinely panel or hierarchical forecasters will be applied by instance.\n",
"\n",
"The recommended (not the only) format to pass panel and hierarchical data is a `pandas.DataFrame` with `MultiIndex` row. In this `MultiIndex`, the last level must be in an `aeon` compatible time index format, the remaining levels are panel or hierarchy nodes.\n",
"\n",
"Example data:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:21.481010Z",
"start_time": "2023-08-25T09:25:13.797057Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
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"
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],
"text/plain": [
" passengers\n",
"l2_agg l1_agg timepoints \n",
"l2_node01 l1_node02 1949-01 52.943320\n",
" 1949-02 54.874511\n",
" 1949-03 59.379565\n",
" 1949-04 58.414313\n",
" 1949-05 55.840000\n",
"... ...\n",
"l2_node03 l1_node05 1960-08 2221.886565\n",
" 1960-09 1801.134311\n",
" 1960-10 1606.045386\n",
" 1960-11 1320.202973\n",
" 1960-12 1487.967083\n",
"\n",
"[864 rows x 1 columns]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from aeon.testing.utils.data_gen import _bottom_hier_datagen\n",
"\n",
"y = _bottom_hier_datagen(no_levels=2)\n",
"y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As stated, all forecasters, genuinely hierarchical or not, can be applied, with all workflows described in this section, to produce hierarchical forecasts.\n",
"\n",
"The syntax is exactly the same as for plain time series, except for the hierarchy levels in input and output data:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:22.535217900Z",
"start_time": "2023-08-25T09:25:13.823983800Z"
}
},
"outputs": [
{
"data": {
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],
"text/plain": [
" passengers\n",
"l2_agg l1_agg timepoints \n",
"l2_node01 l1_node02 1961-01 154.000381\n",
" 1961-02 152.311358\n",
" 1961-03 150.682121\n",
" l1_node03 1961-01 2805.041069\n",
" 1961-02 2770.577861\n",
" 1961-03 2737.335536\n",
"l2_node02 l1_node01 1961-01 426.544850\n",
" 1961-02 421.282983\n",
" 1961-03 416.207550\n",
" l1_node04 1961-01 892.307468\n",
" 1961-02 879.432170\n",
" 1961-03 867.062017\n",
" l1_node06 1961-01 3237.664605\n",
" 1961-02 3192.489057\n",
" 1961-03 3149.040796\n",
"l2_node03 l1_node05 1961-01 1465.123534\n",
" 1961-02 1443.194107\n",
" 1961-03 1422.142219"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from aeon.forecasting.arima import ARIMA\n",
"\n",
"fh = [1, 2, 3]\n",
"\n",
"forecaster = ARIMA()\n",
"forecaster.fit(y, fh=fh)\n",
"forecaster.predict()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similar to multivariate forecasting, forecasters that are not genuinely hierarchical fit by instance.\n",
"The forecasters fitted by instance can be accessed in the `forecasters_` parameter, which is a `pandas.DataFrame` where forecasters for a given instance are placed in the row with the index of the instance for which they make forecasts:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:22.536189100Z",
"start_time": "2023-08-25T09:25:14.201973100Z"
}
},
"outputs": [
{
"data": {
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"text/plain": [
" forecasters\n",
"l2_agg l1_agg \n",
"l2_node01 l1_node02 ARIMA()\n",
" l1_node03 ARIMA()\n",
"l2_node02 l1_node01 ARIMA()\n",
" l1_node04 ARIMA()\n",
" l1_node06 ARIMA()\n",
"l2_node03 l1_node05 ARIMA()"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"forecaster.forecasters_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the data is both hierarchical and multivariate, and the forecaster cannot genuinely deal with either, the `forecasters_` attribute will have both column indices, for variables, and row indices, for instances, with forecasters fitted per instance and variable:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:22.651879800Z",
"start_time": "2023-08-25T09:25:14.218928600Z"
},
"pycharm": {
"is_executing": true
}
},
"outputs": [],
"source": [
"from aeon.forecasting.arima import ARIMA\n",
"from aeon.testing.utils.data_gen import _make_hierarchical\n",
"\n",
"y = _make_hierarchical(n_columns=2)\n",
"\n",
"fh = [1, 2, 3]\n",
"\n",
"forecaster = ARIMA()\n",
"forecaster.fit(y, fh=fh)\n",
"\n",
"forecaster.forecasters_"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Further details on hierarchical forecasting, including reduction, aggregation, reconciliation, are presented in the \"hierarchical forecasting\" tutorial."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"### 1.3 Basic evaluation workflow - evaluating a batch of forecasts against ground truth observations\n",
"\n",
"It is good practice to evaluate statistical performance of a forecaster before deploying it, and regularly re-evaluate performance if in continuous deployment. The evaluation workflow for the basic batch forecasting task, as solved by the workflow in Section 1.2, consists of comparing batch forecasts with actuals. This is sometimes called (batch-wise) backtesting.\n",
"\n",
"The basic evaluation workflow is as follows:\n",
"\n",
"1. Splitting a representatively chosen historical series into a temporal training and test set. The test set should be temporally in the future of the training set.\n",
"2. Obtaining batch forecasts, as in Section 1.2, by fitting a forecaster to the training set, and querying predictions for the test set\n",
"3. Specifying a quantitative performance metric to compare the actual test set against predictions\n",
"4. Computing the quantitative performance on the test set\n",
"5. Testing whether this performance is statistically better than a chosen baseline performance\n",
"\n",
"NOTE: Step 5 (testing) is currently not supported in `aeon`, but is on the development roadmap. For the time being, it is advised to use custom implementations of appropriate methods (e.g., Diebold-Mariano test; stationary confidence intervals).\n",
"\n",
"NOTE: Note that this evaluation set-up determines how well a given algorithm would have performed on past data. Results are only insofar representative as future performance can be assumed to mirror past performance. This can be argued under certain assumptions (e.g., stationarity), but will in general be false. Monitoring of forecasting performance is hence advised in case an algorithm is applied multiple times."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"**Example:** In the example, we will us the same airline data as in Section 1.2. But, instead of predicting the next 3 years, we hold out the last 3 years of the airline data (below: `y_test`), and see how the forecaster would have performed three years ago, when asked to forecast the most recent 3 years (below: `y_pred`), from the years before (below: `y_train`). \"how\" is measured by a quantitative performance metric (below: `mean_absolute_percentage_error`). This is then considered as an indication of how well the forecaster would perform in the coming 3 years (what was done in Section 1.2). This may or may not be a stretch depending on statistical assumptions and data properties (caution: it often is a stretch - past performance is in general not indicative of future performance)."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"#### Step 1 - Splitting a historical data set in to a temporal train and test batch"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from aeon.forecasting.model_selection import temporal_train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = load_airline()\n",
"y_train, y_test = temporal_train_test_split(y, test_size=36)\n",
"# we will try to forecast y_test from y_train"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"108 36\n"
]
},
{
"data": {
"image/png": 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RERERUdtmO+mqjOzp9TGuidpcph08QZ2m3VbIdgsqtsxHZe5CyFYTRJ0RsTlTYRg4HaKaA1mIiIiIiIiIiHzhqCmDXF0KANC06+71cZrayki7KQ+K7IAgqgISH9WPlZE+UhQFsmT2+sNhO4WKzS/AtPEZyFYTAEC2mmDa+DQqNs+Dw1bp9bm8HXz+4YcfIj4+Hlartc7t1157LW699dZ6j1uyZAmefPJJ7NixA4IgQBAELFmyBABgMplw1113ISEhAbGxsRg1ahR27NjhPnbHjh0YOXIkYmJiEBsbi/79+2PLli1YvXo1Jk6ciIqKCvc558yZ49NjTkRERERERER0JtcSbXVsJkRNlNfHqWPSAZUWisMK+6mCQIVHDWBlpI8UezUOL4zzal9R3x5pd+5HZe5Cj9src1+HYcBDKHw/G3JNWaPny5hSDsGLF9iECRMwbdo0fP3115gwYQIAoLS0FN999x2WLl1a73E33ngjfvvtN/z4449Yvnw5AMBgMLjPqdfr8cMPP8BgMODtt9/G6NGjsW/fPrRr1w633HIL+vbtizfffBMqlQq5ubnQaDQYNmwYXnnlFcyePRt79+4FAERHRzd6H4iIiIiIiIiI6iO5+0V6P7wGAARRBY0hC9LJPZDK90NjyAxEeNQAVkYGkCoyGY7qUndF5NlkqwmOmuNQRSb79bp6vR4333wzFi9e7L7tX//6F9LT03HxxRc3eFx0dDTUajWSk5ORnJwMvV6PNWvWYNOmTfjss88wYMAAZGdnY/78+TAajfj8888BAAUFBRgzZgy6d++O7OxsTJgwAX369IFWq4XBYIAgCO5zMhlJRERERERERM3hqozUtvMtGQmcXqrNvpHBwcpIHwnqSGRMKfd+f1EDUWf0mJAUdUaoozqgw59/9fra3rr77rsxcOBAHD16FB07dsSSJUtwxx13QBAEr8/hsmPHDlRVVSE+Pr7O7TU1NcjLywMAPPjgg7jrrrvwz3/+E2PGjMGECROQlZXl87WIiIiIiIiIiBojNWGStotriI2dE7WDgslIHwmC4NVSaRdZMiM2ZypMG58+Z1tszlQosuRTbwNv9e3bF3369MGHH36IcePG4ffff8d3333XpHNVVVUhJSUFq1evPmeba0r2nDlzcPPNN+O7777DDz/8gCeeeAKffPIJrrvuumbcCyIiIiIiIiKiczVlkraLJq4rAEBiMjIomIwMMFETBcPA6QCcPSJbcpr2XXfdhVdeeQVHjx7FmDFjkJaW1ugxWq0WDoejzm39+vVDcXEx1Go1OnXqVO+xXbt2RdeuXfHAAw/gpptuwuLFi3Hdddd5PCcRERERERERUVM4qktrZ28IPk3SduEy7eBiz8gWIKojYBjwENLvOYL0e44i/Z4jMAx4KKCJSAC4+eabceTIEbzzzju48847vTqmU6dOyM/PR25uLsrKymC1WjFmzBgMHToU1157LZYuXYpDhw5h3bp1eOyxx7BlyxbU1NRg6tSpWL16NQ4fPoy1a9di8+bNOO+889znrKqqwooVK1BWVobq6upA3m0iIiIiIiKikCRLZigOGxzVpVAcNsiSOdghhSXbiT0AALUhE6LG+5Z2Lu5l2pWHIdstfo2NGsdkZAsRNVEQVFqoIhMgqLQBWZp9NoPBgPHjxyM6OhrXXnutV8eMHz8el156KUaOHImEhAT8+9//hiAI+P7773HhhRdi4sSJ6Nq1K/785z/j8OHDSEpKgkqlwokTJ3Dbbbeha9euuOGGG3DZZZfhySefBAAMGzYM//d//4cbb7wRCQkJmDdvXgDvNREREREREVHoke0WVGyZj4JFqe6Pii0vMRnWBJJ7ibbvw2sAQIxMhKg1AFBgr8jzY2TkDS7TbuWOHj2KW265BTqdzqv9dTqde0L2mWJiYrBgwQIsWLDA43H//ve/Gzzvm2++iTfffNOrGIiIiIiIiIhaE1kyo2LLfJg2PnP6NqvJPV/CMOChFilaai1ck7Q17XwfXgM454Go47JhK9kCqXx/k/pOUtOxMrKVKi8vx1dffYXVq1djypQpwQ6HiIiIiIiIqM0SRA0qcxd63FaZ+zoEUdPCEYU31yRtbRMmabu4+0ZyiE2LY2VkK9W3b1+Ul5fjhRdeQLdu3dy39+zZE4cPH/Z4zNtvv41bbrmlpUIkIiIiIiIiahNkqwmy1dTAtgqoIhNaNqgwpSjK6crI5iQjja5k5D6/xEXeYzKylTp06JDH27///ntIkuRxW1JSUgAjIiIiIiIiImqbRK0Bos7oMSEp6owQdYaWDypMydWlkC0nAEFs0iRtF07UDh4mI9uYjIyMYIdARERERERE1CrJkhmCqIFsNUHUGaHIEqr2fgqVPgGxfSbDtOnZc46JzZkKRZYgqLRBiDj82GqH16gNnSGq9U0+jyauKwAu0w4GJiO9pChKsENos/jYExERERERUahzTcuuzF3oTkbG9pmM2JwpOL70LiRc+gEgiKjMff309pypMAycDlEdEezww4Zriba2XdMmabtojF0AAHLNcTgs5VBFxDU7NvIOk5GNUKlUAACbzQa9vukZd2o6m80G4PRzQURERERERBRK6p2WXVsJ2e6iFyHqDDAMeAjGgY/AXn0MKn0iAIWJSB9J7n6RzUtGitoYqKJS4DAfg910AKrkgf4Ij7zQ7GSkw+HArl27kJGRgbi41pdFVqvViIyMxPHjx6HRaCCKHEDekmRZxvHjxxEZGQm1mrlzIiIiIiIiCj0NTsve8QaMgx+FIAgQNFGwVxWh5H/Xw2EpQ/qdeS0cafiz+WGStovGmA2H+Rik8n3QMRnZYnzO7tx///3o1asXJk2aBIfDgYsuugjr1q1DZGQkvv32W1x88cUBCDN4BEFASkoK8vPz651CTYEliiLS09MhCEKwQyEiIiIiIiI6hy/TslVRKbBX5kORzJAqDkLbrlsLRhreFEU5XRnZzg/JyLhsWI7+wiE2LcznZOTnn3+Ov/zlLwCAb775Bvn5+fjjjz/wz3/+E4899hjWrl3r9yCDTavVIjs7271cmFqWVqtlRSoRERERERGFLOdEbO+mZQuCAI0xG7bjuZDK9zEZ6QNHdTFka3mzJ2m7cIhNcPicjCwrK0NycjIA4Pvvv8eECRPQtWtX3HnnnXj11Vf9HmCoEEURERHs40BEREREREREdSmyhNicqTBtfPqcbZ6mZWviTicjyXtS+QFo4s+HoNb7pdemJi679rxMRrYkn8vNkpKSsHv3bjgcDvz4448YO3YsAKC6upoDRoiIiIiIiIiozRE1UTAMnA7j4Mcg6ozO23RGGAfPck7L1kTV2f90RR6Tkd6SJTN0yQORdPUXSPnTMsiSudnnVBtrk5Gm/VAUpdnnI+/4XBk5ceJE3HDDDUhJSYEgCBgzZgwAYOPGjejevfklskRERERERERE4UZURyAy80oYBvwdstUElT4Biix5rOBzJyPZq9Arst2Cii3zUZm7ELLVBFFnRGzOVGeitxkVkhpDJiCooEhmOMxFUEd39GPUVB+fk5Fz5sxBr169UFBQgAkTJkCn0wEAVCoVZsyY4fcAiYiIiIiIiIjCQdmqe2GvPISESxYjstMldZZmn0kT5+wTycrIxsmSGRVb5sO08ZnTt1lN7iXxhgEPnVN56i1BpYXakAm76QCk8v1MRrYQn5ZpS5KE0aNHo3fv3njggQeQmprq3nb77bfjmmuu8XuAREREREREREShTpHtkMp+g1xTBo2hc4P7unoVytWlcFhMLRBd+BJEDSpzF3rcVpn7OgRR06zza4zsG9nSfEpGajQa7Ny5M1CxEBERERERERGFJenkH1AcFgjaGKiNWQ3uK2pjoIrq4DyO1ZENkq0mj1PKT2+raNb53UNsuGS+xfg8wOYvf/kL3nvvvUDEQkREREREREQUlqzHcwEA2va9IQiNp1tOD7HZG8iwwp6oM7qHAnneZmjW+U9XRjIp3FJ87hlpt9vx/vvvY/ny5ejfvz+iouquy3/55Zf9FhwRERERERERUTiwleYCAHSJOV7tr4nrCsuR1UyCNUKRJcTmTHX3iDxTbM5UKLJUb29Ob7gqI+2sjGwxPicjf/vtN/Tr1w8AsG9f3ReMIAj+iYqIiIiIiIiIKIy4kpHahByv9j9dGclkZENETRQMA6cDiozKHW/4dZo2cMYy7Yp8KA4Jgqp5PSipcT4nI1etWhWIOIiIiIiIiIiIwpKiKLAd3wEA0Cb08eoYJiO9pzhs0Cb1Q9pd+VAkM0SdEYosNTsRCQCq6I4Q1JFQ7NWwVx5yJycpcHzuGely4MAB/PTTT6ipqQHgfOEREREREREREbU19sp8yLYKQKWFNr6HV8ecXh58AIrsCGR4Yc9atA6l3/wJxz4fB1VkIgSVFqImqvEDvSAIIjTGLgA4xKal+JyMPHHiBEaPHo2uXbvi8ssvx7FjxwAAkyZNwkMPPeT3AImIiIiIiIiIQpl7iXZ8T6/7F6pjOwEqLRSHFfZThYELrhWwHFsHANC26x6Q87uXapczGdkSfE5GPvDAA9BoNCgoKEBkZKT79htvvBE//vijX4MjIiIiIiIiIgp17knaXvaLBABBVEFjyALApdqNsRatBwBEdBwekPOrmYxsUT4nI5cuXYoXXngBqampdW7Pzs7G4cOH/RYYEREREREREVE4sJU6+0V6O0nbhX0jG6c4JFiLNwEAdB2GBuQaGqMrGcnnoSX4nIw0m811KiJdTp48CZ1O55egiIiIiIiIiIjCha0JlZEAk5HesB3PhWKvgRjRDpq4bgG5hvt5YM/IFuFzMvKCCy7Ahx9+6P5cEATIsox58+Zh5MiRfg2OiIiIiIiIiCiU2c3FcJiPARCgbd/Lp2NdyTUmI+tnKXL2i9SlDIEgNHkOc4NcPSMVhxWyVB2Qa9Bpal8PmDdvHkaPHo0tW7bAZrNh+vTp+P3333Hy5EmsXbs2EDESEREREREREYUk1/AaTVw2RG20T8eyMrJxrmRkRIfA9IsEAFVEOyRd8z9EpF4I2VYJQVRDkSW/TeymunxOKZ9//vnYt28fRowYgWuuuQZmsxnXX389tm/fjqysrEDESEREREREREQUkpq6RBsANO2cyUhH1RHIktmPUbUOiqLA6k5GDgvYdWS7BdZjG1H4biYK30lHwaJUVGx5CbLdErBrtmU+V0YCgMFgwGOPPebvWIiIiIiIiIioBciSGYKogWw1QdQZWQXWDO5J2ol9fT5WFdEOor495JoySOX7oGvCOVoze8VBOKpLAJUW2qT+AbmGLJlRsWU+TJuePX2b1QTTxqcBAIYBD/G14Wc+JyN37tzp8XZBEBAREYH09HQOsiEiIiIiIiIKUbLdgoot81GZu9CdjIzNmQrDwOkQ1RHBDi/sNHWStovGmA0rk5EeuftFJvYL2NemIGpQmbvQ47bK3NdhHDQjINdty3xepp2Tk4O+ffuib9++yMnJcX+ek5OD7t27w2Aw4Pbbb4fF0ngp65w5cyAIQp2P7t27u7dbLBZMmTIF8fHxiI6Oxvjx41FSUlLnHAUFBbjiiisQGRmJxMREPPzww7Db7b7eLSIiIiIiIqJWT5bMqNj8Akwbn4FsNTlvq60Cq9g8j0uFfSRbK2CvyAPQtGXawJl9IznJ+WzWovUAArxE22pyvxY8b6sI2LXbKp+TkV999RWys7OxaNEi7NixAzt27MCiRYvQrVs3fPzxx3jvvfewcuVKzJo1y6vz9ezZE8eOHXN/rFmzxr3tgQcewDfffIPPPvsMP//8M4qKinD99de7tzscDlxxxRWw2WxYt24dPvjgAyxZsgSzZ8/29W4RERERERERtXqNVYEJoqaFIwpv1uPO1aOqmDSo9PFNOgeH2NTPUuQclKwLYDJS1Bkh6owNbDME7Nptlc/LtJ955hm8+uqruOSSS9y39erVC6mpqXj88cexadMmREVF4aGHHsL8+fMbD0CtRnJy8jm3V1RU4L333sPHH3+MUaNGAQAWL16M8847Dxs2bMCQIUOwdOlS7N69G8uXL0dSUhJycnLw1FNP4ZFHHsGcOXOg1Wp9vXtERERERERErZZsKW+0CkwVmdCyQYUx1/AaXUKfJp+DyUjPHJaTkE7uAQBEpAwN2HUUWUJszlR3j8gzxeZMhSJLEFTML/mTz5WRu3btQkZGxjm3Z2RkYNeuXQCcS7mPHTvm1fn279+PDh06oHPnzrjllltQUFAAANi6dSskScKYMWPc+3bv3h3p6elYv95Zprt+/Xr06tULSUlJ7n0uueQSVFZW4vfff6/3mlarFZWVlXU+iIiIiIiIiFozqfIQBG00q8D8yFa6HUDTl2gDpydqS+X7oCiKP8JqFazHNgAANHHZAU2Qi5ooGAZOh3HwLPdrQ9QZYRw8y9lHlcNr/M7nZGT37t3x/PPPw2azuW+TJAnPP/+8u9/j0aNH6yQI6zN48GAsWbIEP/74I958803k5+fjggsuwKlTp1BcXAytVguj0VjnmKSkJBQXFwMAiouLz7mO63PXPp4899xzMBgM7o+0tDSv7jsRERERERFROLKd/APHPr0YNQUrEdtnssd9XFVg5L3Tk7RzmnwOjSELEFRQpCo4zN4VdrUF7uE1HYYH/FqiOgKGAQ8h/Z4jSL1zH9LuykdMn//jQKcA8XmZ9sKFC3H11VcjNTUVvXv3BuCslnQ4HPj2228BAAcPHsTkyZ6/uZ3psssuc/+/d+/eGDx4MDIyMvCf//wHer3e19C8NnPmTDz44IPuzysrK5mQJCIiIiIiolZDlswQRE3ttGwD7BX5ELWxOPXb+0i8/GNAEFGZ+zqnaTeDbLdAOuFcRqxrRmWkoNJCbciE3XQAUvk+qKM7+CnC8OZKRgZyeM2ZXBWQFZvnw3zgSxj63Q/jwOktcu22xudk5LBhw5Cfn4+PPvoI+/Y5+xlMmDABN998M2JiYgAAt956a5OCMRqN6Nq1Kw4cOICxY8fCZrPBZDLVqY4sKSlx95hMTk7Gpk2b6pzDNW3bUx9KF51OB51O16QYiYiIiIiIiEKZbLegYst8VOYuPJ1s7DMZKTesBgQBokYPw4CHYBjwdzhqSqGOTIaiOJiI9JF04ndAcUCMiIcqpnkFTpq4ru5kpD7tYv8EGMYUhw224i0AAF0A+0V6ok3ojVO7FqE672smIwPE52XaABATE4P/+7//w8svv4yXX34Zf/3rX92JyOaoqqpCXl4eUlJS0L9/f2g0GqxYscK9fe/evSgoKMDQoc4vxKFDh2LXrl0oLS1177Ns2TLExsaiR48ezY6HiIiIiIiIKJzIkhkVm1+AaeMz7kE1stUE06ZnndOyawdxiJoonPx1Jkr+dz0qd77FvnhNYC3NBeBcoi0IQrPOxSE2dVlLt0NxWCDq27sfm5YS2flKZwzFm2DnsvmA8LkyEnAOnVm1ahVKS0shy3KdbbNnz/b6PH//+99x1VVXISMjA0VFRXjiiSegUqlw0003wWAwYNKkSXjwwQfRrl07xMbG4t5778XQoUMxZMgQAMC4cePQo0cP3HrrrZg3bx6Ki4sxa9YsTJkyhZWPRERERERE1OYIogaVuQs9bqvMfR3GQTPcn6uikiCd+A2247taKrxWxR+TtF00xmwATEa6WF1LtFOGNjvR6yt1dAfokgbCWrIZ1Qe/Q2yvu1r0+m2Bz8nId955B3/729/Qvn17JCcn1/miEATBp2TkkSNHcNNNN+HEiRNISEjAiBEjsGHDBiQkOKck/eMf/4Aoihg/fjysVisuueQSvPHGG+7jVSoVvv32W/ztb3/D0KFDERUVhdtvvx1z58719W4RERERERERhT3ZanJXRHreVuGeTKwxdgEASBV5LRVeq2JzVUY2o1+ky+nKyP3NPldrYClaCwDQtVC/yLNFZl3lTEbmfc1kZAD4nIx8+umn8cwzz+CRRx5p9sU/+eSTBrdHRERg4cKFWLjQ87s6AJCRkYHvv/++2bEQERERERERhTtRZ4SoM3pMSDq3Gdyfa+Jqq/FMB1oqvFZDkR2wle0E0LxJ2i6ads5kpL0yH4rdCkHddld7KooCS9F6AC03vOZskVlXo3zdbNQUroRsOwVR2/zWhHSazz0jy8vLMWHChEDEQkRERERERETNoMgSYnOmetwWmzMViiy5P3dVRsrVpZCtlS0SX2shle+FYq+BoI50L7FuDlVkMgRtDKDIbb5S1W46ALnmOASVDrrEfkGJQdPuPKiNXQCHDTWHfgpKDK2Zz8nICRMmYOnSpYGIhYiIiIiIiIiaQdREwdD/QRgHPQpRZ3TepjPCOHgWDAOn1xlUI2pjoIpMAsDqSF+5+kVqE3pDEFXNPp8gCBxiU8tVFalNGhC0ClFBEBDZ+SoAgPngN0GJoTXzeZl2ly5d8Pjjj2PDhg3o1asXNBpNne3Tpk3zW3BERERERERE5JvKHW9Bm9QPaXcfhmKrgqgzQJEliOqIc/ZVG7vAUV0CybQfuqTgVKGFI2vpDgD+6RfpoonrClvJViYja/tFRnQYGtQ4orKuRuW2f6Am/wcoDgmCStP4QeQVn5ORixYtQnR0NH7++Wf8/PPPdbYJgsBkJBEREREREVEQmfd9CtvxHUi44lNEZ18HABBUWo/7aoxdYC1ay8pIH8lWE0R9e+gS+/rtnKyMdHJUH4eobx+0fpEuupQhEPUJkGuOw3L0V+jTRwU1ntbE52Rkfn5+IOIgIiIiIiIiomZy1JTBdtxZtaf3Ipnj6htpZzLSK7JkhiBqYBw0E/EXvwzZdspv527ryUjXYxt/8UtQRSZCcUiNHxRAgqhCZOcrUPX7ElQf/IbJSD/yuWeki81mw969e2G32/0ZDxERERERERE1kaVwNQBAE98TqqikRvfXxDmTkVI5k5GNke0WVGyZj4JFqTiyuCsK383EqZ2LINstfjl/W05G1n1su6Hw3UxUbl/gt8e2qdx9I/O+hqIoQY2lNfE5GVldXY1JkyYhMjISPXv2REFBAQDg3nvvxfPPP+/3AImIiIiIiIjIOzWFqwEA+rSRXu2vrq2M5DLthsmSGRWbX4Bp4zOQrSbnbVYTTBufRsXmeZAlc7Ov4ZrKLVtOwlFT1uzzhYuWeGybSp8xBoI6Eo5The6hRdR8PicjZ86ciR07dmD16tWIiDjd/HbMmDH49NNP/RocERERERERtV5mmx02u4zSKitsdhlmG1feNVdN4UoAQISXyUjXMm3ZcgIOS3nA4gp3gqhBZe5Cj9sqc1+HIDZ/uImoiYQqJg0AIJXvb/b5wkVLPLZNJar10GeMAQBU530dtDhaG5+Tkf/973/x+uuvY8SIERAEwX17z549kZeX59fgiIiIiIiIKHw1lGy0SA7MW5WH5CeXInnOUiQ/uRQvrsqDRXIEMeLwZj9V6Oz9KIjQp17o1TGiJgqqqA7O41kdWS/ZanJX7XneVuGX62jiukLUt4ejutQv5wsHLfXYNlVk56sBANV53wQ1jtbE5wE2x48fR2Ji4jm3m83mOslJIiIiIiIiartcycbX1uTDVCPBqNdg2ohMzBjVBQ5FwbxVeXhq2eneeKYaCXNrP394ZBaitD7/udrm1RSuAgDoEvtD1Bm8Pk5j7AKHuQiSaT90yQMDFV5YE3VGiDqjx6SZc5v3j3dD2g1/Fpp2XSFbTFAcNiiyBFET5ZdzhypRa2iRx7apIjtfDggibGU7IVUehiY2I6jxtAY+V0YOGDAA3333nftzVwLy3XffxdChQ/0XGREREREREYUls82O51YewFPL9sFU45yI60o2vvrrQahFAa+tyfd47II1+dCITZ612qZZapOREeneLdF2UXOITaMUWUJszlSP22JzpkKRmz/5WbZbUJ33NQrfzUThe5koWJSKii0vBX2Ii7/IkhmKwwZHdSkUhw2yZEblzndQfXgpYvtM9niMvx7b5lDp2yO6201IvOoLqCKT6sRPTePzW03PPvssLrvsMuzevRt2ux2vvvoqdu/ejXXr1uHnn38ORIxEREREREQURjSiWG+y8evdJZg4MN2dpDybqUZChUVCQrQukCG2OoqioKbAmYzUp43y6VgNh9g0StREwTBwOqAoqNyxELLVBFFnRGzOVBgGToeojmj8JA2QJTMqtsyHadMzp2+rHeICAIYBD4V1haRrWnZl7hmPXZ/JiM2ZguNL70LCpR8AgojK3Nf9/tj6Q/zIV1Gx9WWULZ0UkvGFG5+TkSNGjEBubi6ef/559OrVC0uXLkW/fv2wfv169OrVKxAxEhERERERURgxWaR6k437j5th0Gtg1Gs87mPUa2CICN7AinAlle+Dw1wEQaWDroNvqxaZjPSOqI6AruMwpA38O2TbKagi2jmXUfshGdXYEBfjoBnNvkawuBOtG89KtG56FgAQf9F8iDoDDAMegnHQDMjWCog6g98e2+aSJTMqtv3DHS/QuhLFwdCkJhxZWVl45513/B0LERERERERtQLGiPqTjXZZgc0hY9qITHePyDNNG5EJSZah9b2rWJvmWqKtSxkKUa336VhXMtJuOgBFUTgPoh6SKQ8lX10BMSoZabfvhqDSQlBp/XJub4a4qCIT/HKtltZgonXHGzAOfhSCIECoTei57qe/Htvmas2J4mDx+bv7tm3bsGvXLvfn//vf/3Dttdfi0Ucfhc1m82twREREREREFH4k2Zls9GTaiEyoBGDGqC6YPbYrjHpnFaRRr8HssV0xY1QXDq9pgprClQAAvY/9IgFAbcwCUJv0spzwa1ytSc3hZQAArTEbojbar+d2Dcipf1twh7g0R6hPy25MuMcfinxORv71r3/Fvn3Od68OHjyIG2+8EZGRkfjss88wffp0vwdIRERERERE4SVKq8b0kVmYNSbbY7IxUqtGhEaFh0dm4dgT43Dw0dEofHwM7r8wExEaVZCjDz+KIsNS6JzhEJHqezJSVOuhikkDwCE2Dak5vBQAoM8Y5/dzt8SAnGAJ90RruMcfinxORu7btw85OTkAgM8++wwXXXQRPv74YyxZsgRffPGFv+MjIiIiIiKiMLR4cyH6pRpx5PGxKJkzDsVPjMPDI7PqJBujtGro1CKmfbULmc+swJr88iBGHL5sx3dAtpZD0MZAlzygSec43Tdyvz9DazUUhw01tUvh9Z38n4x0DcgxDp7lTnyJOiOMg2c5h6SEcU/CcE+0hnv8ocjn2ndFUSDLMgBg+fLluPLKKwEAaWlpKCsr8290REREREREFHYURcEbaw9hT2kV/v2XfrgxpyMA1NsHMiFGhzKzDWsPncSVPZJaMtRWoabAuUQ7ouMFEMSmLXHXGLvAUrgKdg6x8chStA6KZIYYmQhtQp+AXENUR7iHuNjNx6DSt3dObg6BIS7N4Uy0PgwoMip3vBF206jdk9SBkJ32HW58/i41YMAAPP300xgzZgx+/vlnvPnmmwCA/Px8JCXxhwYREREREVFb91vxKewprYJWJeLSbomN7j+8Uzss3lSIdfknWyC61sdSuBoAoE/zfYm2CydqN6zm0E8AgMiMsRCEwA1XclVAVua+gao9/0Rsn8mIGzIrYNdrKTVHfoY2qR/S7sqHIlWH1LRsb7gSxYYBf4ejphSqyGRAcYRN/KHG51fQK6+8gm3btmHq1Kl47LHH0KWL8xvW559/jmHDhvk9QCIiIiIiIgovn+YWAQAu654AQ23PyIYM79QOALCp0ASr3RHQ2FobxWGD5eivAJqXjFQzGdmgQPaL9ERj7AK5psw9JT3cVe16F6Xf/AkV216FKjIBgkobdkvPRU0UTu16ByX/ux4nVj8QdvGHEp8rI3v37l1nmrbLiy++CJWKjYaJiIiIiIjaMkVR8J8dzmTkDbXLsxvTNSEK7aO0KDPbsO1IBYbWJiepcdbiTVDs1RD1CdC0P7/J59HEnU5GKooCQRD8FWLYs1cVwVa2C4AAffqYFrmmayq65dgGyJI5rBNfDosJ1Yd+BABEZV0V5GiaRxPfA9KJ36DYa4IdSljzuTKysLAQR44ccX++adMm3H///fjwww+h0TT+jhcRERERERG1XrlFlThQZkaEWsRVXvZ/FAQBwzvFAQDWHuIQG1+4h6qkXtSs5cOa2M4ABCi2U5CrS/0UXetQc3g5AECb1B+qyIQWuabakAVVTDogS7AUrWuRawZKdd7/AIcNmvge0LbvFexwmkWX1B8AYK/Ig8PC71VN5fN3qptvvhmrVjm/2RUXF2Ps2LHYtGkTHnvsMcydO9fvARIREREREVH4cC3RvuK8JETrvF+MN6y2GnLdIfaN9IUrGRmRPqpZ5xHUOqhj0gFwqfbZag6f7hfZUgRBgD7tYgCApXZAUbiq2vspACC6241BjqT5VBHtoDZ0BgDYSrYGOZrw5XMy8rfffsOgQYMAAP/5z39w/vnnY926dfjoo4+wZMkSf8dHREREREREYUJRFPwn9ygA4IacDj4dOzzTmYxcm38SiqL4PbbWSJbMkG1VEPXtm9Uv0kUdx76RZ1Nkh7syUt/pkha9tus5rQnjvpEOcwkshc5kalTXG4IcjX+4qiOtTEY2mc/JSEmSoNPpAADLly/H1VdfDQDo3r07jh075t/oiIiIiIiIKGxsLjThUHkNorQqXHFe41O0z9Q/1QCdWsRxsw37y8wBirD1kCUzBEGFpCs/Rdqd+6GKSm72OU9P1N7f7HO1FtaSLZCt5RC1BuiSB7XotSNqk5G20u1huyTYvP8LQJGhSxoIjTEr2OH4hS5pAADn1wY1jc/JyJ49e+Ktt97Cr7/+imXLluHSSy8FABQVFSE+Pt7vARIREREREVF4cC3RvqpHEiK1vs1L1alVGJhmBOCsjqT6yXYLKrbMR8E76TiyuBsK381ExZaXINstzTqvhhO1z+Gaoh2RMRqC6PMM4GZRR3eAJq4bAAWWIz+36LX9xbVEO6oVLNF20bIystl8Tka+8MILePvtt3HxxRfjpptuQp8+fQAAX3/9tXv5NhEREREREbUtsqzg852uKdq+LdF2cfWNXMO+kfWSJTMqNr8A08ZnIFtNztusJpg2Po2KzfMgS02vKnUlI+3lTEa61BxyJiMjM1p2ibaLqxdoOC7VlioPw3psPQABUV3/FOxw/EaX2BeAAEfVEdjNxV4dI0tmKA4bHNWlUBy2Zr1OWwOfk5EXX3wxysrKUFZWhvfff999+z333IO33nrLr8ERERERERFReNhQUI5CkwUxOjUu7ebbEm2XEbV9I9exMrJegqhBZe5Cj9sqc1+HIGqafG61qzKyIo99OwE4ak7AWrIZAKBvweE1Z3L1jbSEYTLSvO8zAEBE6oVQRzftDYpQJGpjoGnXDYB3Q2zclcyLUt0f/qhkDmc+JyMBQKVSIS4urs5tnTp1QmJi037gEBERERERUXhzLdG+pmcSIjSqJp1jWCfn35l7j5tRZrb6LbbWRLaa3BWRnrdVNPncGkMmIIhQJDMcZs6EqClYASgyNPE9oI5JDUoMEakXAhAgnfwD9qqioMTQVOZWuETb5XTfyIaTkYGsZA5nTUpGfv7557jhhhswZMgQ9OvXr84HERERERERtS2OOku0Ozb5PO0itTgvMRoAsO5QeA7sCDRRZ4SoMzawzdDkcwsqLdSxnQAAkimvyedpLVz9IvVBWqINAKqIdtAm9gUQXtWRtpN/wHZ8ByCqEdXlumCH43feJiMDWckcznxORi5YsAATJ05EUlIStm/fjkGDBiE+Ph4HDx7EZZddFogYiYiIiIiIKIRtP2qC5FBg1GswrmtCs841rHap9hou1fZIkSXE5kz1uC02ZyoUWWrW+d19I9v4EBtFUVBzeBkAILLTuKDG4lqqXVO4Oqhx+MJVFanPGAeVvvUNOz49xGZLgy0NAlnJHM58Tka+8cYbWLRoEV577TVotVpMnz4dy5Ytw7Rp01BR0TYfRCIiIiIiorbIbLPDZpeRFBOB/MdG48e7B0OrbtICPLcRtUNs1nGIjUeiJgqGgdNhHPSou0JS1BlhHDwLhoHTIWqimnX+0xO19zc31LAmndgNMSIequg06DoMD2osEe5k5Kqw6OWpKIp7inZ0txuCHE1gaBN6A6Iacs1xOE4V1rtfICuZw5nPc+kLCgowbNgwAIBer8epU6cAALfeeiuGDBmC119/3b8REhERERERUcixSA7MW5WH19bkw1QjwajX4N4RmeidEtvknpEAMLy2MnJLYQUskqNZ52qtZGsFtEn9kHZXPhSpGqLOAEWWIKojmn1u9xCbNlwZKUtmqI1ZSLr6C6gikwDFEdR4IjoOB0QNHKcKYK84CI0xK6jxNEY6uQey1QRBrUdk56uDHU5AiGo9tPE9YTu+A9aSLVDHpnvcz1XJbNr49DnbXJXMgkob6HBDjs9vWSUnJ+PkSec7VOnp6diwYQMAID8/Pywy9ERERERERNQ8Zpsdz608gKeW7YOpxrks2FQj4all+/D8ygMw2+xNPndWfCQSo7WwOWRsPcLVd55Yizei9Js/oeS/V0MVmQBBpW12RaSLpo0nI12TjwvfSceRxd1Q+G6noE8+FjVRiEgZAsBZHRmqZMkMxWGDqI1F2p37kTz+J4ja6GCHFTDe9I10VjI/HLBK5nDlczJy1KhR+PrrrwEAEydOxAMPPICxY8fixhtvxHXXtb6mpERERERERFSXRhTx2pp8j9sWrMmHRmz6Um1BEDC8E/tGNsRWm/xQB6BCThPn6hmZB0WR/X7+UBbKk48j0i4GAFgKVgb0Oq6EoqO6FIrD5vV9diVxCxalovC9zih8NxM1h34KahI30LReDrGpyf+htpL5ENLvOYr0e47AMOAhv1Qyhyufl2kvWrQIsuz8hjRlyhTEx8dj3bp1uPrqq/HXv/7V7wESERERERFRaDFZJHdF5DnbaiRUWCQkROuafP7hme3w1W/F7BtZD2vJNgCALrGf38+tju0EiGoo9ho4qoqgjkn1+zVCVWOTj42DZrRwRKfp00bBtOEp1Bz5GYoiQxCa15vVE1dCsTJ3IWSrCaLOiNicqc4KvgYSZ7JkRsWW+TBtfOb0bVZT7eeCM/HWCisAdUnO15+tZCsURYEgCB73q9y5CJbClWh34XwY+k0DgDa5NPtMPn/1iqIItfp0DvPPf/4zFixYgHvvvRdabdt+MImIiIiIKPS4hqyUVllhs8vNWkJMTsYIDYx6jedteg0MEZ63eWv4GUNs2A6sLkVRYC11VmLpaif6+pMgqp0JSbS9pdqhPPlYlzwQgiYKcs1xSGW/+f38zakKbSyJK4jN+34QqrTx50NQ6SDbKuqdPm83F8NyZDUAIDLryhaMLrQ1KZVeXl6O+fPnY9KkSZg0aRJeeukldx9JIiIiIiKiUOEaspL85FIkz1mK5CeX4sVVebBIwR1IEe4kWca0EZket00bkQlJbt7y3r4dDYhQizhRLWHv8apmnau1sZ8qgFxTBohqaNr3Csg1NMZsAG0vGRnKk48FlRYRHUYACEzfyOYkFEM5iRtIgkoDbUIfAIC1ZIvHfcz7vwAUGbrkQdAYOrdkeCHN52TkL7/8gszMTCxYsADl5eUoLy/HggULkJmZiV9++SUQMRIREREREfmsviErc/0wZKWti9KqMWNUFzw+NttdIWnUazB7bFfMGNUFUVqfO4LVoVWLGJRuBMC+kWezlTqXaGvjzw9YzznXEJv6qr1aK9luQWyfyR63uSYfB5M+fSQAwNJIj8KmaE5CMZSTuIF2eojNNo/bzXs/BQBEdbuxxWIKBz7/hJgyZQpuuOEGvPnmm1CpVAAAh8OByZMnY8qUKdi1a5ffgyQiIiIiIvJVY0NWHh2d3cIRtS4RGhVGdWmP6SO7oNJiR3ykFpIsI0Kj8sv5h2e2wy8HT2JdfjnuGpzhl3O2Bq5hGYFYou3iGmIjle8P2DVC0amdbyM2ZwoAoHLHGz71TWwJ+k6XItGYDX36KDiqSyHqjFBkqdn9GB2WCgiaaIg6o8eEZGMJRUWWEJszFaaNT5+zzZXEba09ErW1r0NPlZFSxSFYj20ABBFRXf/U0qGFNJ+TkQcOHMDnn3/uTkQCgEqlwoMPPogPP/zQr8ERERERERE1VaCHrLR1kkPGZe9sRLROjY3TLoBWLULbtE5gHrn6Ru4uOeW3c7YGrgosbQCTkWpjF4j69gjXbp2yZIYgatzJRG8SdrbjO1G+bjaqdv8TSdd9C+PgRyFbKyDqDM7jQ2DysdrQGea9/0HZ0kl+S5TaTu5F6TfXI27E84jtMxmmTc+es09jCUVRE4XYvvcCihySSdxAcr0pYCvdDkV2QBBP58rM+/4DAIhIvQjqqJSgxBeqfE5G9uvXD3v27EG3bt3q3L5nzx706dPHb4ERERERERE1h2vIiqeEpFGvQZRWjT9KTyHNqIdGFGGySDBGaCDJcrOXGbcFO49VwmKXodcoyIjT+/38wzvF4as7BmJM1/YoOWVFnJ7PjaIosLkqIwMwSdtFl9gXaXfuh6P6OBSHzS/Vdy2lKROhFUVG2cp7AcUBTfx50MSmAwBUkQkAQmPysXti9RnJQteAGQBeT6yuk6jVxsJuygOgQsX215B8zVeAIKIy9/XTj12fyY0mFK3Hd+D497cibsTTSL+nELK1MqSSuIGkiesGQRMNRaqCdHIPtO3Pd29zLdGO5hLtc/j8XXzatGm47777cODAAQwZMgQAsGHDBixcuBDPP/88du7c6d63d+/e/ouUiIiIiIjIB64hK3OX7Ttn29ThnbDu8En0SYnFCysP4PW1h2CqkWDUazBtRCZmjOrit+XGrdXGwyYAwKB0I0RR8Pv5dWoVth4xYeKnuXxuatkr8yFbywGVFtr4ngG5hmy3oDJ3oU/JvFDhTthtfOb0bV4k7Kp2/xPWY+shaKIQf9H8FovXF40NmDEOmtHoOTwmavtMRsqNqwFZhqiJgmHAQzAOmgHZWg5BE42aw8thK9uFiOSB9Z7XtH4OpPI/YN77KaKyrg6pJG6gCaIKusS+sBz9FdaSre5kpO3EbtjKdgGiBpFdrgtylKHH52TkTTfdBACYPn26x22CIEBRFAiCAIeDE+qIiIiIiCg4XENWZEU5J9k4fWQX7DtehdfW5OPp5af74rkG3ADAwyOz2nQVXmM2FpQDAAanx/n93GabHfNW5fG5OYvNtUS7fS8Iav+3GGhqMi9UNCVh57CcxMk1MwEAxsGzoI5JC2iMTeXNgBlXEtDjPvU9t5ueBQQRhgEPAYD7+VVFJuHErzNRufUlaBP7osNN6yEI57ZhsBzbiOqD3wGCCnFDn2jGPQxf2qQB7mRkTM/bAZyuiozsNA6qCP9/jwx3Pjf0yM/Pb/Dj4MGD7n998fzzz0MQBNx///3u2ywWC6ZMmYL4+HhER0dj/PjxKCkpqXNcQUEBrrjiCkRGRiIxMREPP/ww7HZOxSMiIiIiImBvaRX6pRpR+PgYFM8Zh+InxuHhkVmI1KrQIykGr6895PG4BWvyoRH91/+wNdpw2JmMHJLh/z+0Gxs+1FafG/fwmsTA9ItsLJkniJqAXNdfmjIR2rTpBcg1ZdC0Ow+GvtMCHGHTNXdidVOeW2P/ByBoY2Er3Y6qPR95PLZ83WwAQHSPW6GJ69pgDK2V7qwhNoqioGqvs19kVNcbghZXKPP5raSMDP9PMdu8eTPefvvtc5Z1P/DAA/juu+/w2WefwWAwYOrUqbj++uuxdu1aAM4p3ldccQWSk5Oxbt06HDt2DLfddhs0Gg2effbcpqtERERERNS2vLXhMN5efxh/G5qBheOdf2+4hqxwwE3TnTDbsL/MDMC5TNvf+Nx4Zi11VkYGapJ2c6vvgs2VsGtsIrSrb6LDchJxQ2cjouNwqKJSIKhCN9na3InVTXluVZGJMA58BOVrH0P5utmIyh4PURPp3l5TuAqWwlWAqIFx8GNNu2OtgHuITdlOKA4bbMd3wl6RB0GtR2Tnq4IcXWgK+ttJVVVVuOWWW/DOO+8gLu70O2oVFRV477338PLLL2PUqFHo378/Fi9ejHXr1mHDhg0AgKVLl2L37t3417/+hZycHFx22WV46qmnsHDhQthstnqvabVaUVlZWeeDiIiIiIhaF4vkwKe5RQCA63ufO8nUNeDGE6NeA0NE6CYmgm1T7RLtrglRaBfp/75wfG7OpSjy6WXaSYEZXtPc6rtgcyXsPIntMxmWYxsh26pQsWU+ChalovCddBS+mwlbyTZo2/dq4Wh9I2qiYBg4HcbBs9zPkagzwjh4lrOfZyPL55v63Mb2vRfqmAw4qo6iYts/3LcrioLytc6qyNje90AT6//CtXChNnSGqIsDHDbYyn5DlWuJducrIWqjgxxdaAp6MnLKlCm44oorMGbMmDq3b926FZIk1bm9e/fuSE9Px/r16wEA69evR69evZCUlOTe55JLLkFlZSV+//33eq/53HPPwWAwuD/S0kKzJwQRERERETXd17+XwFQjIc0YgZFZ7c/Z7hpw48m0EZmQZDnQIYatDQUmAMCQAPSLBPjceGI35UG2VUBQ6aBt1yMg12gwmVdbfRfKnANY/g7joEfPStg9hti+9wIOm7tvoqtK0NU3sWLzPMiSOXjBe0FUR8Aw4CGk33MEaXceQNpd+YjucatXg4Wcz+0Uj9saem5FdQTiRjj7TFZsmQ+72dk6r+bwMliLN0JQ62EY+EgT71HrIAgCtK6l2sWbYN73GQAgilO06xXUjr+ffPIJtm3bhs2bN5+zrbi4GFqtFkajsc7tSUlJKC4udu9zZiLStd21rT4zZ87Egw8+6P68srKSCUkiIiIiolbmgy2FAIBb+6d5nPbsGnADOPsQugbcTB3RqU1PbPbGxtp+kYMD0C8SqP+5acvTtF39IrUJfQK2nNhVfQc4+wieOXE5HKZpA0DNkdXQJvVD2l2HoEhmiDqDM9EmqKDrOAyl39/k8ThvJ1IHm6sCsmrf56jYOh/69DFIvOxDr46L7XsvoCio3PGGT5PSo7pOQHXe14jqdiNEXSwc1aWI6DgCiVd9DsmUB3VUst/uX7jSJfWH7Xguqg8thSLbIOqMiMy4JNhhhaygJSMLCwtx3333YdmyZYiIaNlvaDqdDjpd2+svQkRERETUVhRVWPDT3lIAwO0DUuvdL0KjwsMjs/Do6GycrLYhJkKNX/JOQKcO+iKykCXLCjYVmgAAgwPQL9LF9dzMHJ2NY5UWJERrYXcobTIRCQDW0u0A4K7AChRX9Z1x0Aw4LOUQtdGoKVztcZJyKLIcXobK3IUwDJyOdsOd/RVdvRQd1aVh3RPzTBEdhqJ8TRmq87+HYrc2Ol3dbi5G8ReXIG74U0i/pxCytdKdqG0sySwIAtqPfRsVm19E2dJJdZPUg9p2VaRLzPmTnK+Z6lKoIhNhO7E7IBPvW4smfTcxmUx49913MXPmTJw8eRIAsG3bNhw9etTrc2zduhWlpaXo168f1Go11Go1fv75ZyxYsABqtRpJSUmw2WwwmUx1jispKUFysjPrnpycfM50bdfnrn2IiIiIiKjt+de2I5AVYHinOGQnNNyzK0qrhlYtIk6vQY95q3DFe5uw8xj7ytdnX1kVTDUS9BoRvVJiA3qtKK0aOrWIaV/tQuYzK/DzwRMBvV4oswV4kvaZRE0UBJUWqsgEFH0yAqVfX4eagpUBv64/WIqcbd207Xufsy3ce2KeSZcyGKqoDlBslagpXNHo/tUHvoJ0cg8qNs+DoNJBFZkAQaVttNck4Bz4U7FlPkybnvWwvP3FkF/eHmiy3YKq3R+g8N1MHFncDYXvZqIm/3vIdkuwQwtZPicjd+7cia5du+KFF17A/Pnz3cnCL7/8EjNnzvT6PKNHj8auXbuQm5vr/hgwYABuueUW9/81Gg1WrDj9otq7dy8KCgowdOhQAMDQoUOxa9culJaWuvdZtmwZYmNj0aNHYHpoEBERERFRaFMUBR/WLtG+bYD37Zh0GhX6pDiTEd/tKW1k77Zrw2ETAGBAqhEaVctUy3Uw6FFmtmHVgbIWuV6oURT5jEnagRle44kgiIjoeAEAwHzgqxa7blPJtirYju8A4KwcPFu498Q8kyCIiOxyDQDAvL/x56bK1cew6wTfryVqUJm70OO2ytzXIYhtb6CUiyyZUbH5hXP7kG58Jiz6kAaLzz85HnzwQdxxxx3Yv39/neXVl19+OX755RevzxMTE4Pzzz+/zkdUVBTi4+Nx/vnnw2AwYNKkSXjwwQexatUqbN26FRMnTsTQoUMxZMgQAMC4cePQo0cP3HrrrdixYwd++uknzJo1C1OmTOEybCIiIiKiNmpLYQV2l1QhQi3ihj4dfDr28vMSAQA/7ClpZM+2a2NBYPtFejKqi3MA0aoDbbMyUirfB0WqgqCOhKZd9xa9dlT2dQCA6ryvocj2Fr22r6wlWwDFAVV0KtQx574R0dyJ1KEmqkvtc3PwGyiO+hOp9qqjsB5d6zwm+3qfr+Ncwm5qYFuFz+dsLZiobRqfe0Zu3rwZb7/99jm3d+zYscGhMU3xj3/8A6IoYvz48bBarbjkkkvwxhtvuLerVCp8++23+Nvf/oahQ4ciKioKt99+O+bOnevXOIiIiIiIKHwsqa2KvL5XCgx63/4QdCUj1x8uxwmzDfFRWr/HF+7cw2sC2C/ybBdnxQMAdh6rRJnZivZRbav4xLVEW5uYA0Fs2dEPER0vgBgRD9lyApajv0KfNrJFr+8L6zHnEm1PVZEuZ/bElK0VXvdNDEURHUdA1LeHXFPmfG7SR3ncz7zvCwAKdB2GeUzSNsa1vN1TQjLclrf7mzeJ2nDpQ9qSfK6M1Ol0qKw8t3/Kvn37kJDQvAd49erVeOWVV9yfR0REYOHChTh58iTMZjO+/PLLc3pBZmRk4Pvvv0d1dTWOHz+O+fPnQ60O6pBwIiIiIiIKEqvdgU+2O3vZ3z7Q9z+60+MicX5yDGQF7gE4dJrZanf30xzSgpWRiTE6nJ8cAwBY3QarI60ltUu0E1tuibaLIKoRmXU1AO+WAweTq1+kLqX+ZCRQtyemt30TQ1Gd56aBZfTmZizRBlrX8nZ/a019SFuSz8nIq6++GnPnzoUkOb/YBEFAQUEBHnnkEYwfP97vARIREREREXnrm99LUF4joaMhwr2011eXn5cEAPjhDyYjz7bliAmyAqQaItDRoG/Ra1/sWqqd1waTkbX9IrUt2C/yTO7lwHn/g6LIQYmhMYoiw3psA4CGKyNbG/dzc8DzcyNVHoa1eCMAoUlLtIHWt7zdn5iobRqfk5EvvfQSqqqqkJiYiJqaGlx00UXo0qULYmJi8MwzzwQiRiIiIiIiIq+szitD+ygtbu2fCpUoNOkcV9Qu1f7xj1I4ZMWf4YW9jQUmAMDg9JarinQZWbtUu60NsVFkB2yl2wEAuqTAT9L2RJ8+CqLWAIf5mDvhF2qkk39AtpogqCM9TtJurfRpI53PTXUxrLWVoWeq3v8FACAi9UKoo1KafB3X8vb0e44g/Z6jSL/nCAwDHgrL5e3+xERt0/i8ntlgMGDZsmVYs2YNdu7ciaqqKvTr1w9jxowJRHxERERERESNMtvsUIsiHrq4C164sgeqbY4mn2toRhyMeg1OVEvYWFCOYZ3a+THS8ObuF9mCS7RdLsqKhyAAf5RW4VilBSmxbSMJIpX/AcVeDUETDY2xa1BiEFRa6DtfAfMfH8O8/ytEdBgWlDga4l6inTwQgqrtDA0RVFpEZl2Jqj0fwXzgK0R0HF5nu3uKdvafmn0tV2LN1QNRULGnLtC6+pC2FJ8rI11GjBiByZMnY/r06UxEEhERERFR0FgkB+atykPKk0uR9ewKpD21HAvXHoJFalpCUq0ScUk35x/b3+/hUm0XRVGwoXaS9pAMY4tfv12kFjkdYgG0repIa+3wGl1iXwiiKmhxnF4O/F8oSuhVDLuG1+ja0BJtl8ja58Z81nMjmfKcw48E0T0VnQKjtfQhbSlNmvSyYsUKrFixAqWlpZDluj0J3n//fb8ERkRERERE1BizzY55q/Lw1LJ97ttMNRLm1n7+8MgsRGl9/7Pn8vMS8WluEb7fU4KnL+vut3jD2ZEKC45VWqEWBfTrGJyhDCO7tMf2o5VYlXcCN/dLDUoMLc1W4uoXGZwl2i76TuMgaKJgP3UYttJtQVsyXh/3JO1Ghte0RvqMsRA0UXCcKoCtZCt0yQMAAOZ9nwMAItJGQhWZGMwQierwuTLyySefxLhx47BixQqUlZWhvLy8zgcREREREVFL0YgiXluT73HbgjX50IhNWwx2abdECAKQW1SJoxU1zQmx1dhQu0S7d0osIpuQ4PWHkVm1Q2zaaGVkMIlqPSI7XQoAMO//MqixnM1RfRxS+X4AgC5lcJCjaXnO5+YyAID5wOnnxjVFO7qJU7SJAsXnnyBvvfUWlixZgltvvTUQ8RAREREREXnNZJFgqvE8rdRUI6HCIiEhWufzeROidRiUZsTGAhN++KMUdw3OaG6oYW9DEPtFulzQuR1UooCDJ6px+GQ1MtpFBi2WlqDIdtiO7wAQvOE1Z4rsch3M+7+A+cBXiBv+NAShaUOi/M1SO1RH0+48qCLaZo/XyOzrYN7/OcwH/ou44c9AKt8HW9lOQFQjMuuaYIdHVIfPbxPabDYMGxZ6zWqJiIiIiKjtMUZoYNR7HlZh1GtgiGj6IIvLz0sCwL6RLhuD2C/SJTZCgwGpziXiq/JOBC2OliKd3Au1sQtUsZlQG7sEOxxEZl4GQaWD3XQA0onfgh2OW1vuF+kS2ekyCKoI53NTtstdFalPGw2VPj7I0RHV5XMy8q677sLHH38ciFiIiIiIiFo1s80Om11GaZUVNrsMs80e7JDCniTLmDYi0+O2aSMyIZ3V494Xl5/n7LG2bN9xWO1Nn87dGtjsMrYdqQAADEkPXmUk4OwbCQCrW/lSbVkyQ23MQtLVXyD11u1Q7MFvFyBqY6DPGAsAMO//KsjRnOaapN0W+0W6iNpo93NTfegnWI/nQtS3RxSXaFMI8nmZtsViwaJFi7B8+XL07t0bGk3ddxpffvllvwVHRERERNRauCY+v7YmH6YaCUa9BtNGZGLGqC6I0ARvQm64i9Kq8feLsyArCl5fe8ivj23fDgYkx+hQfMqKXw+exJiuCX6MPLzsPFYJi11Gu0gNurQP7pTYkV3a4/mVB7DyQBkURQmZpcJnkyUzBFED2WqCqDNCkSWvJ+zKdgsqtsxHZe5C9/GxOVNhGDgdojoiwJE3LLLLtag++C1qClcjbujsoMYCAIrDBlvJFgBARBuujASA6J63I7rnHdCnj0JU9Z+gikyEIvNNLwo9Picjd+7ciZycHADAb7/VLcsO1R8CRERERETBFKiJz+T02pp89Es14ujssaiy2mGI0ECS5WYneUVRwGXnJWLxpkJ8t6ekTScj3f0i0+OC/nff8E5x0KgEHKmwIO9EddCTo540J5koS2ZUbJkP08ZnTt9mNcG08WkAgGHAQ14nNQMhMusaJF4VB336KNjNJVBFxPmUaPWGL4lca2kuFIcVYkQ81MZsv8UQjvQZY1Gx6QWULZ0UcklsojP5/BvPqlWrAhEHEREREVGr1djE50dHt+0/oJvrk9yj2HXsFL66fSCu6ZUMAND63pHKo8u7O5OR3+8pxT/a8AyITQWnk5HBFqlVY0h6HH7NP4mVB8pCLhnZ3GSiIGpQmbvQ47bK3NdhHDTDvwH7SFDpYCvZGrCEl6+JXOuxdQCAiA5Dgp4oDyb3192mZ0/fFkJJbKIz+ecnNBERERER1cubic/UNKWnrNh17BQAYFim/xNlY7smQC0KKK+RcPhktd/P3xL80av0ZI2E9lFaDA7i8JozhXLfyMaSiYLY8FAl2WqCbDU1sK2iuSE2mSyZUbH5BZg2PeuO0ZXwqtg8D7Jk9s/5Nz7j9fld/SJ1KW170G5zv+6IWpJXlZHXX389lixZgtjYWFx//fUN7vvll1/6JTAiIiIiotbCNfHZU0KyuROf27pVec5kVO+UWCRE6/x+/tgIDVb831D0SzXAVCPBZpchyXLYLKtvbq9Ss80OtSjitet6ITFaC7tDaYGoGzeySzzmLnNO1A61vpHeJBNVkfUv+Rd1Rog6o8dzOLcZ/BSp7wJdtenr+RVFgdU1vKaN94ts7tcdUUvy6ieowWBwf3M3GIL3jY+IiIiIKBy5Jj7PPaNnpMvU4Z0gybLflhW3NSv2O5ORo7LbB+T8FsmB5fuP45rFm8Nu8FBze5WG8tClIRlxiFCLKDllxZ6SKvRIjglqPGdqTjJRcUiwlmxFbJ/JdZbbusTmTIUiSxBUWn+G7LVAJ7x8Pb+98hAc1cWAqIE2qX+Tr9sahHISm+hsXiUjFy9e7PH/RERERETUuPomPk8d3gn3jsjEB5uPYPLwTsEOMyytrF2mO7qL/5ORp5N5+923hdPgoeb0Kg31oUs6tQrDM9thxf4yrDxQFlLJSEWWEJsz1d2r70yxfSZDMuVBG3+eh+PsOP7j7bCV/YaUCSsAQQi5adqBTnj5en7rsdol2ol9Iar1zbp2uGvw6y7ISWyis4XuT04iIiIiolbk+ZX7MSAtrs7E5/yT1bjojXX4o7QKBr0at/RLDXaYYeXQyWocPFENlSjggs7t/H7+cB881Fiv0iqrHTpZhEYUYbJIMNZOIY/SqsPivo/s0h4r9pdha6Ep2KHUIWqiYBjwd0CRUbnjjTOSiVMQ22cyjn02GjE5kxHT49Y6E6Otpbmwlf0GqSIPtpN7YBjwdxgHzYRsrYCoMzgnSgd5InKgE16yw9pAVeiUc85vKdoAANCltO0l2kDt193A6QCcS9pDKYlNdDavkpF9+/b1ugfHtm3bmhUQEREREVFrI8sK3ttUiGdXHMCqvw3FRVnOKr5uidG4skcS/iitwrxVebg4Kx4JUbpzEkPkmasqclCaEbEB6LvpzeChQPSp9JeGepUOSjdCr1Xh+ZUHzlmGPX1kFk5Uh/59v7x7AnomxWBM1/YorbKG1Gvm1O+LoU3qh7S7DkGRzO5kYtWejwAA0dnjUbH5xbrJyj6TkTJhBazHd0KfeqH7XK5lyaFQ1VZ/wmuKXxJep3YuQmzOFOf5z3psYvtMhq3sN0SkDHbvby06PUmbAFEdAcOAh2AcNCOkkthEZ/Pqu/S1114b4DCIiIiIiFqvrUcqUHLKihidGkMz6lbwPX/5ebDZZTw2Jhuvrcmvs4w7VPrzhaqVAe4XGe6DhxrqVbpoQh88v3K/xyXoEWoR91/YOeTve7fEGHy5qxgTP80NudfMqZ1vQzr5BxIu/wjRXScAcCYTY3vfg4iOF6Iyd2Gd6j/ZanJ+LgjOqsoQdmbCy1FzHKLOCFvZ781OeFlLt6N87SxU/f4Bkq79BsbBj7oTatbS7Tj22Wg4ao6jw03roDFkQrZWwnbiNwCsjDyTqIkCEFpJbKKzeZWMfOKJJwAADocDa9euRe/evWE0GgMZFxERERFRq/HN7mIAwCXdEqBV1x1UI4oCnrq0G15cnYenl4dnb8JgUBTFXRk5KgD9IoGGk3nTRmSG/OChKK0aD17U+ZxepTNGdkH3hGi8tuaQx+Pmrc7DlOGdQvq+u3pahuJrxnZiN6STfwAqLSIzxp2zXWPsjModb3g8tjJ3IYyDZgY6xGZzJbzsVUUo+WggAAHpdxdAEJuWBFZkB8qW/w1QHNC2Px8aQwaA0wk1bUJvCNooyOV/oPSbPyHlxl9gPZ4LTbseEFQaqKM7+OV+EVHL8Omnh0qlwrhx41BeXh6oeIiIiIiIWp3v9pQAAK44L8njdp1ahdfXHvK4bcGafGjE0E14BcuekioUn7IiQi1iaEZcQK4RpVVjxqgumD22K4x6ZyWgUa/B42OzMWNUl7BIED/y7W70SzXi6OyxKJkzDsVPjMPUCzo1ugRdcige7/vssV1D4r431tMymK8Z8/4vAAD69DEeB7p4MzE6XOgScgCHBLnmOKwlW5p8nsrc12Er3QZRa0C7i18+Z7uo1iPpys+gikyC4pBgK/sNuuSBSLr6C6RMWA1ZMjfjXhBRS/P5J8j555+PgwcPIjMzMxDxEBERERG1KkcrarD9aCUEAbise6LHfcK9N2EwrKitihyR2S6gS3IjNCo8PDILj47OxolqG2Ij1NhwuDzoy4C9ITlk/GvbUby9oQA7HroIvVJiAQBaiFALYoPLsKN1amjVovu+V1gkGGp7MobCfW+J14wsmesMmFFkyV0R2BBXMjIqe7zH7YGeSN2SBJUG+k7jYN73Garzv6vTz9FbUmUBytc5V2PGXfA81FHJHvdTx6Qi6dqvoY5ORWXuQpT89yoOaSEKUz6/XfT000/j73//O7799lscO3YMlZWVdT6IiIiIiOi07/aUAgAGp8chMcZzcsTVm9DjthDpzxdqVtUmI0cGaIn2maK0zsScViWi87MrMPbtDSiqsAT8us21saAcZpsD7aO06JkUU2ebawm6J65l2MDp+54QrYNWLQa9ItIl0K8Z2W5BxZb5KFiU6v6o2PISZHvDz7vt5B5IJ3YDogaRna/yuI9rIrUnronU4SQy83IAQM3B770+RpbMUBw2OKpLodK3R8JlHyK6+y2IOX9ig8dp4rqicscbMG161p3Mla0mmDY+jYrN81ghSRQmfE5GXn755dixYweuvvpqpKamIi4uDnFxcTAajYiLC8zyCCIiIiKicPXdbucS7St7eF6iDXifGCInh6xgdd4JAMDoAA2v8SQ+SovO7SIBnF56H8qW73MmbEdnt4coCnW21bcEPVSWYTfGX68Zs80Om11GaZUVNrsMs80OWTKjYvMLMG18xueEl3lf7RLtjLFQRRg97uOaSG0cPAuizrmPqDPCOHiWs7rPi+rLUKLvdAkgiLCV7YS9sqDR/c9O9Ba+2wm2km2IH70QgtBwikIQNajMXehxW2Xu6xBEvnFDFA58/gmzatWqQMRBRERERNTq1EgOLN9/HABwZT39IoHTiSHA2e/ONWjk3hGdQmIycKjZdqQCphoJhgg1+nVs2SWtV/RIwsYCE77bU4K7h2S06LV9taL2a6++hO2ZS9BDbRl2Y+p7zfgyTdsiOTBvVR5eO+P4GSO74OGL0htMeBkHzaj3nO4l2l2ub/DaZ06kdk2MVmQpLJcZq/TtoUsZAmvROlTn/4DYPn+td19ZMqNiy3yYNj5z+jb3JHERhgEPNZiM9abfpmvoDRGFLp+TkRdddFEg4iAiIiIianVW7i9DjSQjzRiBXikxDe57ZmKozGyDQa/G+kPl0Kk5vOZsrinaF2XFQ61q2cfnyvOSMPvHvVi+rww1kgP6EE3cnbLYsbHABAAYk11/csZVAenqrxjK08HPduZrpviUFfFRGhSU13iViHRN437qjGnhphoJ/9p2BPcOjGlSwst28g9IJ353LtHO8rxE+0yupJvrXIJK2+gxoSoy8/LaZOT3DSYjG6tsbCjRC7SufptEbZlXP2l27twJubbMfefOnQ1+EBERERGR07dnTNEWBKGRvU/354vSqnDeC6swbtEGrD9cHugww87KA86Kv1Et0C/ybH06xKKjIQLVkgOra5OioeiXgydglxVkxUeiU+3S8tbI9Zr5Oa8Mmc+swF3/2eHVcfVN4y4+ZYVKZ3Avnz5bQwkv8/4vAQD69NFQRbStFmaRna8AAFgKVza4jL25k8RbW79NorbKq2RkTk4OysrK3P/v27cvcnJyzvno27dvQIMlIiIiIgoXiqK4+0Ve1UC/SE8Meg3GdHVWS72x9pC/QwtrVrsDa/JPAgBGN1DxFyiCIOCK2iX339YOJwpFy91LtNvGktXR2Qk4UW3D+sPlOHSyutH965vGXWa2YfX+kgYSXlPqTXhVNzJFuzXTtOsBdWwnKA4rLIX1t3ZzVTbWv63hysbW1m+TqK3yKhmZn5+PhIQE9/8PHjyI/Pz8cz4OHjwY0GCJiIiIiMLFzmOVOFJhQaRG1aSJz1OGdwIAfLazCKWnrH6OLnxtOFyOGklGUowOPZKigxKDK7n87e5iKIoSlBgas2L/6eE1bUEHQwQu7hwPAPgk92ij+zc0jXv2igLPCa9BjyK2z2TYq4rOOUYq3wdb2S5AVHu1RLu1EQTBPVW7uoGp2s7Kxiket3lb2ejqt5l+zxGk33MU6fcccfaaDMN+m0RtlVfJyIyMDPeykoyMjAY/iIiIiIgI+Ka2KnJM1/ZNGgjSP9WIwelGSA4F725qfEJtW+FKso3q0t6rpe+BMCq7PfQaEYUmC3YdOxWUGBpSXGnBb8WnIAjAyC7xwQ6nxfy5b0cAwCfbz00Wns3mkDG1NuF/tku7JcIiq89JeOkzxuLYZ6Nx/Idbocj2Ose4Btfo00ZDFdGueXckTOldycj87+tN0gtqPWJz7oVx0KPNqmwUNVEQVFqoIhMgqLSsiCQKMz4PsHHZvXs3CgoKYLPZ6tx+9dVXNzsoIiIiIqJw51qifUUDU7Qb87dhnbCxIBdvrz+ER0Z2gUoMTvItlLiG1wSjX6SLXqPC6C4J+HZPCb7dU4LeHWKDFosnK2ofo74dDGgfpQtyNC3n+l4pmPLlLuw8VondxafQI7n+oVErD5Th3hGZAIDX1x6qZxq3889l14AZtbEzHNXFkK0mVGx9GcaB093nc/WLjMpueIp2axaReiEETRQc5iLYjudCl3huGzfzvs9h2vA04i54DumDCyFbK8N6kjgRNY3PyciDBw/iuuuuw65duyAIgvsdD9e7kg6Hw78REhERERGFmZJTVmwqNAFoXjLyhj4d8PdvdqPQZMG3u0twzfnJfoowPJ2y2LGpdkJ0sJcfX9EjEd/uKcF3u0vw6OjsoMZythX72tYSbZf4KC0u6ZaA7/aU4pPco5h7aXeP+8mygse+/wMORcF/buuPWWO6osIiwRChgSTL9VYyq6NS0O6il1C2dBLKN8xFZOcroY3vAcl0ALbjOwBBhcistlucI6ojoE8fg+q8/6H64HfnJCMVhw3l62bDXnEQtuO5iOp8RauYJE5EvvNqmfaZ7rvvPmRmZqK0tBSRkZH4/fff8csvv2DAgAFYvXp1AEIkIiIiIgovP/xRCkUB+qca0MHQ9GqfCI0Kdw5KBwC8se7cyb9tzcaCk+ieGI3+qYagT4i+srZv5IaCchyvCp2enoqiYEXttHHXEKS2xL1UO7eo3qXC3+0pwe8lp3C0woJUgx5atYiEaF3tJPuG63Wiz/uLczmyw4aypXdDke2oKfwZor499GmjoNK3nWXxnrimalfnn9s38tSud2GvOAhVZBIMfe9r6dCIKIT4nIxcv3495s6di/bt20MURYiiiBEjRuC5557DtGnTAhEjEREREVFY2VVUifZR2mZVRbr8dUgGBAFYtq8M+45X+SG64DDb7LDZZZRWWWGzyzDb7I0fdNbxwzPj8b87B+GXKcN9Pt7fOhr06NsxFooCfB9CU7X3l5lRaLJApxYxIrPt9S68pmcy9BoRB8rM2Hqk4pztiqLg+ZUHADjbINQ3xKY+giCg/eiF0CYNgGHQDCiyHZEZY5B2537Ej3zFH3chrOk7XQoAsJVsgd1c7L5dtp1C+cZnAADGIY9D1AZn+BQRhQafk5EOhwMxMc7eG+3bt0dRkbM5cEZGBvbu3evf6IiIiIiIwogr4Tb1gkzkPzYafx3a/AGPmfGRuKK7M6n55rpDzT5fMFgkB+atykPyk0uRPGcpkp9cihdX5cEiedfiyXV8x7nLkPXsCnScu8yn4wPFlWz+bk9JQK/jSyJ3ee0S7eGd2kHfhMFJ4S5ap8ZVPZztDP69/dyp2r8ePIn1h8uhU4u474LMJl1DHd0RKeN/gq1kKwrfSUfh+9kofDcTVX/8G7Ld0qz4w506KhnapAEAgJr8H9y3V2x9GXLNcaiNXRDTc2KwwiOiEOFzMvL888/Hjh07AACDBw/GvHnzsHbtWsydOxedO3f2e4BEREREROHgzIRb1rMrkPbUcry9/rBfEmaTa6f+LtlcCLM1uBWBvjLb7Hhu5QE8tWwfTDUSAMBUI2Husn14fuUBd2KtvoSbt8cHg2up9k97j8NmlwNyDV8TuSv2O5dot7V+kWf6c98OAID/7CiCLNddqv38yv0AgNsHpCEltmktFGTJjIqtL8O06VnIVpPzNqsJpo1Po2LzPMiSuenBtwKR7qna3wEA7OZiVGx7BQDQbvhTEFS+VaMSUevjczJy1qxZkGXnD9q5c+ciPz8fF1xwAb7//nssWLDA7wESEREREYW6QCfMxnVNwLiu7bHkz32hEoUmL3UOBo0o4rU1nvtdLliTD40o1ptwq5EcUAlCo8cHy4BUI5JidDhltePX/BN+P7+vX1cOWXFPGx+T3fb6Rbpc1j0Rhgg1jlZY6jwvuUcr8OPe4xAF4OGLs5p8fkHUoDJ3ocdtlbmvQxDbdrLN1TfSWpoL2W5FxbYFUCQzdEkDEdml7U4bJ6LTfP7Jfckll+D6653fQLp06YI//vgDZWVlKC0txahRo/weIBERERFRqPMm4dYcoijgP7cNwNYjJnSYu6xJS52DxWSR3Im0c7bVSDhlteO5lfs9Jtze21iA8pqGj6+weN7WEkRRwOXnJQIAvt3t/6Xavn5dbT1iQoXFDqNeg36pBr/HEy50ahWu65UCwDnIxuWFVc5ekTf06YCs9lFNPr9sNbkrIj1vO7dXZVuiTchB0rXfIPW2HZAtJxA35DEkXvU52o18BYIgBDs8IgoBfnkbsV27dvymQkRERERtVmMJt+YmzMw2O176+SCeXr4/5JYqN8YYoal3SEhWfCRidGq8tuaQx+2v/noQ8ZHaeo836jUwRAS3Cu3K2r6R6/JP1ju9ual8TcQu3++sihyZFQ+V2Lb/Prupdqr25zuKIDlkHCgz47MdzsTkI6O6NOvcos4IUWdsYFvbTQQDgOKwwlq0HoXvZqLw3U4ofDcTtpJt0LbvFezQiChEBG9NAxERERFRK9FQws0fCbNAV14GkiTLmDbC86CQ+y/sjJM1tnoTbnknqmGW7PUeP21EJiQ5ML0avTWuawL+N3EgVk4ehpJmTAo/u1/mj3+UIFqn8unrasU+V7/ItrtE22VkVjwSo7U4US3hl7wyfLTtCNpFanFZ90T06dC8ZKEiS4jNmepxW2zOVChy8Kp1g02WzKjY/MK5/TQ3Pct+mkTkpg52AERERERE4c6VcJu7bN8521wJM20z6gC8qbxMiNY1+fyBFKVV46GLsiArCl5fewimGglGvQbTRmRi0qB0iIIAo17j8f4Z9RpEadSYUVvJtmBNfp3jZ4zqgoggT4xWiQI2F5pw+ye5TYrN1S/ztTPu29ThnXDviEysP1SOqcM74enl+885burwTrA5ZGjVzq+rapsdaw+VAwDGdG27w2tc1CoRU4ZnondKLIZ2ikfn9tH4+8VZ9b6OfCFqomAYOB2As0ekbDVB1BkRmzMVhoHTIaqbNhinNWisn6Zx0IwWjoiIQhGTkURERERtjNlmh0YUYbJIMEZoIMkyorT8tbA5orRqTB/pOeHmj4SZq/KyvoRdsJcqN+ber3bh2l4pODp7LKqsdhhqv+4iNCqYbfZGE7lRWjUeHpmFR0dno8Ii1Tk+mMw2O+atyquTLHQtnweAh0dmNfjach3/1Bn33VQjuc93a/9UzBydDVEQ6iRiXcnKR7/fgxev6gGdWoVNhSZ0TYiCSgSym9EPsTV58KLOeGHlAUz8tGmJ4oaI6ggYBjwE46AZkK0VEHUGKLLUphORgHf9NFWRrNwlausExYvGJv369cOKFSsQFxeHuXPn4u9//zsiIyNbIr4WUVlZCYPBgIqKCsTGxgY7HCIiIqKAsUgOPLfyQJ0qrFCpMAt3b68/hKSYCIzrmgCz7XTCzR+JXrPNjhdX5XlM2M0e27XRpFcwFZRXo9MzK5z/nzUaqcZz/46wSA48v/JASFY+NsRml5H85NJ6k8TFT4xzVy4253jXGwiuRGxRpQXXLt6MnccqcffgNMy/qifUKhHFp6xIjtFBVpSQ/XpoKZ4SvS6h/poJZ4rDhoJFqR4TkqLOiPR7jkBQaVs+MCJqEd7m17xaK7Jnzx6Yzc7eDk8++SSqqqr8EuSbb76J3r17IzY2FrGxsRg6dCh++OEH93aLxYIpU6YgPj4e0dHRGD9+PEpK6k6pKygowBVXXIHIyEgkJibi4Ycfht0eug28iYiIiILFbLPjuZUHPE4tDvUhKOHgnQ0FuH7JZnyxswgJ0Tpo1aLfkh1RWudS5dlju7p7CBr1Gswe2xUzRnUJ6aTKR9uOAgAuzor3mIgEgAiNCg+PzELxE+NQMmccip8Yh4dHZoV0IhJo/uAib4+P0qqhVYvur6tO7SIx/6oeyOkQi6cvOw8vrs5Dx7nLkPXsCnScuywspqwHWjj3WQ1n7KdJRN7w6reWnJwcTJw4ESNGjICiKJg/fz6io6M97jt79myvL56amornn38e2dnZUBQFH3zwAa655hps374dPXv2xAMPPIDvvvsOn332GQwGA6ZOnYrrr78ea9euBQA4HA5cccUVSE5Oxrp163Ds2DHcdttt0Gg0ePbZZ72Og4iIiKgtaOyP80dHZ7dwRK3HoZPV2Ha0AqIAXNo9MSDXcCXsHh2djZIqK9pFarC3tCqkE3aKouCfW48AAP7SP7XBfV0JVVfvy+b02GwpzV0+35zjx3RNwDeTBuG1NflNXibemoVzn9Vwxn6aROQNr34yLVmyBE888QS+/fZbCIKAH374AWr1uYcKguBTMvKqq66q8/kzzzyDN998Exs2bEBqairee+89fPzxxxg1ahQAYPHixTjvvPOwYcMGDBkyBEuXLsXu3buxfPlyJCUlIScnB0899RQeeeQRzJkzB1qt5/Jvq9UKq9Xq/ryystLrmImIiIjCFf84D5wvdx0DAFzYOT6gj6ErsXToZDX6/2MLFEXB0dkNLwUOpq1HKvBHaRUi1CL+1Dsl2OH4XXMHF0myjHtHZHpcSuzN8QlROry+9pDHbW39DYZw77MazthPk4ga49VvLd26dcMnn3yCzZs3Q1EUrFixAtu3bz/nY9u2bU0OxOFw4JNPPoHZbMbQoUOxdetWSJKEMWPGuPfp3r070tPTsX79egDA+vXr0atXLyQlJbn3ueSSS1BZWYnff/+93ms999xzMBgM7o+0tLQmx01EREQULlx/nHvcptcgRqeGLCsw2+yw2WWUVllhs8tcvu0FVzLy+l4tk3AbmhEHrUrEiWoJX/9e3CLXbApXVeS15ycjthUmf+pbPj9rTLZXy+ejtGrcf2FnzBqT3aTl981dJt6auRLFnrgSvRQ4oiYKgkoLVWQCBJUWooZDlYjoNJ9r9mU/f9PetWsXhg4dCovFgujoaHz11Vfo0aMHcnNzodVqYTQa6+yflJSE4mLnL1zFxcV1EpGu7a5t9Zk5cyYefPBB9+eVlZVMSBIREVGr11AV19ThnbDm0En062jAq78exGtr/D8RurUqqrBg3aFyAMB1vZJb5JpqlYjbB6biuRUHsHhzIf7Up0OLXNcXkkPGJ9ud/SIbW6Idzs5cPl9eIyFap8KyvcdxstqGDgZ9g8eWnLJi3KL1ePKS7jj2xFhUWuw+TQpn9V/9XIliAGE3GImIqLVrUgORvLw8vPLKK9izZw8AoEePHrjvvvuQlZXl87m6deuG3NxcVFRU4PPPP8ftt9+On3/+uSlheU2n00Gn4xIkIiIialuitGo8MqoLZEXB62vrJhsfvKgzthSa8MovB9l/zkdf/easihyaEYeOjSSf/GniwHQ8t+IAftpbiiOmGqQaW+7a3vhp73EcN9uQGK3FuK4JwQ4noFyvi6QYHcYv2YyvfivGzNFd8Mxl5zV43DsbD2PXsVOYt+oArj0/GQnRzgSZt/0ym7tMvLU7M1HsmkTubaKXiIgCx+efTD/99BN69OiBTZs2oXfv3ujduzc2btyInj17YtmyZT4HoNVq0aVLF/Tv3x/PPfcc+vTpg1dffRXJycmw2WwwmUx19i8pKUFysvMd5+Tk5HOma7s+d+1DRERERKf9e/tR9Es14sjjY+tMLY6N0GB4ZnyD/ec4fdazL3e27BJtly7to3Bh53aQFeDD2uXQoeRftTHd1Lcj1Kq287Vzc7+OAIB3NxTAaq9/orXdIWPR+sMAgL8Ny2jStcJ5ynpLOXsSOR8TIqLg8/m3ghkzZuCBBx7Axo0b8fLLL+Pll1/Gxo0bcf/99+ORRx5pdkCyLMNqtaJ///7QaDRYsWKFe9vevXtRUFCAoUOHAgCGDh2KXbt2obS01L3PsmXLEBsbix49ejQ7FiIiIqLWRFEUvLQ6D9cv2YzPdxad88d5BfvP+ex4lRU/HzwBoOWTkYCzOhIAFm8qgCwrLX79+phqJPyvtpflra14ibYn1/RMRkdDBI6bbfiiNlHtyTe7S3CkwoL2UVpM6N30Zfau6r/iJ8bVeYOB1X9ERBSqfE5G7tmzB5MmTTrn9jvvvBO7d+/26VwzZ87EL7/8gkOHDmHXrl2YOXMmVq9ejVtuuQUGgwGTJk3Cgw8+iFWrVmHr1q2YOHEihg4diiFDhgAAxo0bhx49euDWW2/Fjh078NNPP2HWrFmYMmUKl2ETERERnWVjgQl7Squg14i4pue5q0gaG3DTlvvP1ed/vxdDVoB+HQ3IjI9s8ev/qXcKYnRq5J2oxq/5JwJ6LV8GG32+swhWu4yeSTHo29EQ0LhCjVol4p4hzkrHN9Ydqnc/17ZJg9ObnThk9R8REYUTn5ORCQkJyM3NPef23NxcJCYm+nSu0tJS3HbbbejWrRtGjx6NzZs346effsLYsWMBAP/4xz9w5ZVXYvz48bjwwguRnJyML7/80n28SqXCt99+C5VKhaFDh+Ivf/kLbrvtNsydO9fXu0VERETU6r2/qQAAMKF3Bxg8JB05fdZ37iXavVu+KhIAonRq3JjjrKpbvKkwYNexSA7MW5WH5CeXInnOUiQ/uRQvrsqDRfK8DNm1RPsv/VMhCELA4gpVdw1Oh1oUsO5QOXKPVpyz/Y/SU1ixvwyCAPzfkKYt0SYiIgpXPr9ldvfdd+Oee+7BwYMHMWzYMADA2rVr8cILL9SZUO2N9957r8HtERERWLhwIRYuXFjvPhkZGfj+++99ui4RERFRW2O22vFpbhEAYOKgNI/71Dd9durwTpw+64GpRsKKA2UAgPFBWKLtcuegdLy7sQCf7SzCguvOR6yfK1jNNjvmrcrDU2cMSWlosNGhk9X45eBJCAJwS23/xLYmJTYC43un4NPcIryx7hAWTehTZ/ub65y9Iq88LwkZ7Vq+opaIiCiYfE5GPv7444iJicFLL72EmTNnAgA6dOiAOXPmYNq0aX4PkIiIiIia7/Odx3DKakdWfCQu7Bxf735nTp81WSREaVVYuvc4Dp4wo0dybAtGHPq+2V0MyaGgZ1IMuiVGBy2OwelGnJcYjT2lVfgkt8i9RNhXZpsdGlGEySLBWDt1OEqrhkYU8dqafI/HLFiTj0dHZ9e57ae9pWgfpUWfDrEhN+G7JU0e1gmf5hbh421HMe/KHu4WCFVWOz7Y4qxinTy8UxAjJCIiCg6fl2kLgoAHHngAR44cQUVFBSoqKnDkyBHcd999bXIJBhEREVE4WLzZuUT7joFpjf7O5uo/lxitw0Nf78b4D7Zg/uqDLRFmWAn2Em0XQRAwcdDpQTZNUd8y7GrJjtIqa4ODjcprt7l6Sl7aLRH5j43GG9f3atodaiVGZLbD+ckxqJYcWLL59BL6j7cfRaXFji7tozA2OyGIERIREQWHz8nIM8XExCAmJsZfsRARERFRAOw/XoVfDp6EKAC3D/C8RLs+dwx07v/x9qMoPWUNRHhhqcpqx097jwMI7hJtl1v7p+L85BjMGJUNq93h1ZAZF7PNjudWHsBTy/a5k46uZdgLfslH+yhtg4ONYnQqmGokzFt1AMlPLkXmsyuQ9tRyfLTtaL09JdsCQRAweVgnAMCb6w5BlhUoioI31h4CAPxtWAZEkcUcRETU9jQrGUlEREQUSL5M76X6La6tyrqkW6LPy2YHpxsxMM0Im0PGOxsPByK8sPT9nlJY7DKy4iPRKyX4b84nxejw8+Rh2HrEhJQnl3k1ZMaloWXY81bnQXLIuLeewUZTh3dCaZUNL/+ch6eW7T8nmfn8ygNt+nX7l/6piNGpsb/MjF/yT2D70QoUVVqg14i4w8c3BoiIiFoLJiOJiIgoJPk6vZc8sztkfLjFOdl44kDfkx+CILgTUW+uOwzJwYnaALC5sBzto7S4vldKSLQqMtvseOXXg3h6ue8JQZNFanAZtuRQMHNUF8we29VdIWnUazB7bFc8OjobKbE6vF5b7Xe2BWvyoRHb7p8c0To1Hh6Zha/uGIhBaXFIiNYh/7HRWD15OOIitcEOj4iIKCh8HmBDREREFGi+Tu+l+i3ddxxFlRbER2pwVc+kJp1jQp8UPPztbhRVWvDVrmLckNPBz1GGjvqGuJy9ffLwTMy5pBsqa0Kj6s9Z3XjI4zZPQ2bOZIzQwKjXeExIGvUaROucPURdg40qLBIMtY9NhEbVaE/JCouEhGhdk+5Xa/DghZ3x/MoDmPhprntC/b0jMtErOYYT6omIqE3y6W1KSZIwevRo7N+/P1DxEBERETU6vbctV1r56v3agSZ/6Z8KnbppiQ+dWuWe0PzamvAeZNPQ0v/GqnHP3J5V2xfx7Q2HQ6Jat7HqxgqL520AIMkyptWzDHvaiExIsrMa1jXYKCFaB61adCdpXclMT4x6DQwRnre1BWabHS+syjunYvUpLmEnIqI2zKff5DUaDXbu3BmoWIiIiIgAAOU1TU+s0Gknq21Yd6gcAHBn7bTlpvq/oRlQiwLWHirH1iMmP0TX8hpKNjY0xOX5lQdw0mzDcyv317s92Eml5iQEo7RqPDKqC2aNyT5nGfaMUV0arUL2NpnZFvGNFSIionP5/NPvL3/5C957771AxEJERERtzNlVapUWCYs3FSBap2KlVTO4HtcayYH9M0dh+V+HoldKbLPOmRIbgRv6OJdnv15PciWUNZRsfPXXfKhFod6k0UfbjiBap25wGXSwk0rNTQj+evAE+qUaUfj4GJTMGYfiJ8bh4ZFZXi0jjtKqMaOenpLeJDNbs+ZUrBIREbVWPv9mYLfb8f7772P58uXo378/oqKi6mx/+eWX/RYcERERtV6uKrXX1uS7+6hNHd4J947IxPpD5Zg6vBOeXn5uaxhXYkXLOXweeXpc7x3RCcM6xTW7P929IzLx8faj+Pf2Isy7skdY9QFsqELt693FmDgwrd6kkV6j8qpaN5iPhyshCDiTo67nftqITMwY1aXR5/7t9Yfx1W/FmDOuK2aP6wYAPr3GIjSqentKtmWN9ePkGytERNQW+ZyM/O2339CvXz8AwL59++psC4VJgkRERBT66htQ40o+3jU4HSMy20EUhCYlVtqq+h7Xp5bthwCh2YN/BmfEYULvFNzcLxXROjVKq6weh7yEooYq1PYfN8Ogrz9pVCM5ENfA9lBJKp2ZECyqtCAhWosys63R10t5tQ3f7SkFAFx7fkqTr+/6GnAlZfmGwemK1bnL9p2zjW+sEBFRW+Xzb42rVq0KRBxERETUhjRUpfb62kOYNaZrnem9x05Z0D5Ki6MVFiYiG9BYf7qGJip7690bc/DiqrqTgcMhSdxQhZpdVmBz1J80uqVfKix2R1gklVwJwU9yj+Llnw/iws7t8PntAxs85otdx2BzyDg/OQa9OzRvOT/V1dyKVSIiotaoyW9hHzhwAHl5ebjwwguh1+uhKAorI4mIiMgr3vRRS4jWuRMrqw6cwPRvdyPVEIGtD1zI3znq4e3j2lRmmx3zV+fVWT7v6rsIoNmVl4HUWIWaSkCjSaNwSipdeV4SHv3+D3z9ewlKTlmRFFP/8/7xtqMAgJv7dWyp8NoULmEnIiKqy+ffFk+cOIEbbrgBq1atgiAI2L9/Pzp37oxJkyYhLi4OL730UiDiJCIiolbE1z5qV/dMwuQvdiK3qBIbDpdjaKd2LRVqWAl0f7qWqLwMlCitGg+PzIKsKHh97aF6k4kNJY3CKal0fkoshmTEYcPhcizZXIhHahOpZztiqsHPB08AAG7KYTIyULiEnYiI6DSffwo+8MAD0Gg0KCgoQGRkpPv2G2+8ET/++KNfgyMiIqLW6cAJM6YO7+Rxm6fJv+0itfhzX2ei5I11hwIcXfhq7kTlxoT7ZOCnlu1Dv1Qjjs4eW+/E6CitGlq1iIRoHbRq8ZxKz8a2h5K7BqcDAN7bVABFUTzu8+/tR6EowAWZ7ZDRLtLjPkRERET+5HMycunSpXjhhReQmppa5/bs7GwcPnzYb4ERERFR67SzqBI3/Wsb7h2RicfHZsOod1brGfUazB7bFTNGdfGY4Jk8rBMA4LMdx1B6ytqSIYeNKK0a91/YGbPGeP+4+sJVeelxW4gMcalPcaUF//jlIK5fshl/lFSFRTKxuW7s0wExOjUOlJmxOu+Ex30+3s4l2kRERNSyfP7ty2w216mIdDl58iR0uqb3ICIiIqLWz2p34LZ/b8euY5WY89NezLuqBx4b3dWrJa8D0owYlGbEpkIT3ttUgJkhvCQ4WI5XWXHpog14fFw3FD0xFqcsdr8uJQ7nycCLNhRAcigYkhGHvqmGYIfTIqJ0atzUtyMWbTiMdzcWYGSX9nW2/158CjuKKqFRCZjQp0OQoiQiIqK2xuffFi+44AJ8+OGH7s8FQYAsy5g3bx5Gjhzp1+CIiIiodZn9417sPFaJhCgtZo/r5vOS18m1S7vfXn8YDtnzstO27B+/HMT2oko8s3wfdCr/LyV2TQaePbZrncrLWWOy/VJ5GSg2u4y31h8CANxbzzL21uruIc6l2l/sPIYTZludbR9tOwIAuKx7ItpFals8NiIiImqbfP6Ncd68eRg9ejS2bNkCm82G6dOn4/fff8fJkyexdu3aQMRIRERErcD6/JOY/3MeAGDRhD4NTvetzw19OuChr39HgakG3+0pwdU9k/0dZtgqr7Zh4dpDAIDHxmQHbOL4mUNcymskROtUWLr3OApMNeiaEB2QazbXF7uOofiUFSmxOozvlRLscFpU/1Qj+naMxfajlfjn1iO4/8LOAABZVvBv1xLtvqkNnYKIiIjIr3yujDz//POxb98+jBgxAtdccw3MZjOuv/56bN++HVlZWYGIkYiIiMKU2WaHzS6j9JQVvTvG4ovbB+LR0V1wzflNSyJGaFS4c5Cz0uuN2sQbOb225hBOWe3olRKDq3sENknrqmhNitHhvq9+w/gPtmDOT3sDes3mcE0A/7+hnaBVh+Yy8kCaNCgDAPDuxsPuQTbrDp/E4fIaxOjUuKpnUjDDIyIiojamSWtpDAYDHnvsMX/HQkREFJLMNjs0ogiTRYKxtv9eqC5HDSUWyYF5q/Lw2pp8mGokGPUaTB3eCY+N6dqs8/7f0E6Y/3Melu47jn3Hq0K2Gq8lVVokvPrrQQDAo6OzIYqBqYr05N4LMvH+5kJ8uqMIT4zrhm6JgX8+fHlNbioox4bD5dCqRNwzJCPgsYWiW/p1xMPf/o7dJVXYcLgcQzu1w0fbnFWR1/dKht4P/USJiIiIvNWkt4bLy8sxf/58TJo0CZMmTcJLL72EkydP+js2IiKioHMl1JKfXIrkOUuR/ORSvLgqDxbJEezQQprZZsdzKw/gqWX7YKqRAACmGglPL9+PF1YegNlmb/K5M+MjcXn3RADAP7ce8Uu84e7NdYdRXiOhW0IU/tS7ZQeR9OlgwNU9k6AowPMr9wf8er6+Jl+vrYq8MadDk1oDtAYGvQY31A6o+ST3KCS7jJX7jwMAbu7HJdpERETUsgTFtVbDS7/88guuuuoqGAwGDBgwAACwdetWmEwmfPPNN7jwwgsDEmggVVZWwmAwoKKiArGxscEOh4iIQoTZZse8VXl4ysPk4Nlju+LhkVmskKyHzS4j+cml7kTkmYx6DYqfGNes5bK/5JWhvMaOMV3bw2x1wKhvuxWrZqsdnZ9dgeNmG5b8OQe3DUhr8Rg2F5gweMGvUIkC9j0yCpnxkQG5jq+vyeJKCzKeWQ7JoWDjtAswMN0YkLjCwbYjJhSaLBjTtT0qauww6NX4Je8ExnVLhKoFK2mJiIio9fI2v+bzXwFTpkzBjTfeiPz8fHz55Zf48ssvcfDgQfz5z3/GlClTmhU0ERFRKNGIorvX3NkWrMmHRgz/3nPuno5VVtjs8jkVi41tr4/JInlMRALOCskKi+dt3hqUHoetR0xIe2q5s0IuDCtWm/rYnu3LXcegAMhsF4mb+nb0b5BeGphuxCXdEuCQFTy/KnDVkb6+JhdtKIDkUDA0I65NJyIBoEdSjPs1k/rUMqQ9tRwbCsohOeRgh0ZERERtjM/lAwcOHMDnn38Olep0bxmVSoUHH3wQH374oV+DIyIiCiZvEmoJ0eG77NNTT8dpIzIxY1QXRGhUjW5viCFCDaNeU29lpCFC0+S4XdVxTy8/nfQy1UiYW1stFw4Vq815bF1cfRMv6ByP/MdG42iFBRpV8BLkj43Jxk97j2PJ5kI8PqYrUo16v1+jvMb716Rkl/GfHc6+iPeOyPR7LOGkvtfMU8v2Q4AQFq8ZIiIiaj18/o21X79+2LNnzzm379mzB3369PFLUERERMFWcsqCaK0zoeZJcxNqwVZfT8e5y/bh+ZUHUGmRGtzeUBXfxsPlWL6/DFOHd/K4fdqITEhy06uxwr1itbHH3psKyTP7JnZ+dgXSnlqOj7cdDWpl6IjMeFycFQ/JoWDe6jy/nz+vrArROlWDr8kYnRqnLBJsdhnHzTZsvO8CfHfXIIzvneL3eMJJuL9miIiIqHXx6jePnTt3uj+mTZuG++67D/Pnz8eaNWuwZs0azJ8/Hw888AAeeOCBQMdLRETkd2cvly2utOCmf23Dsn3HA5ZQC7aGkhMfbTuCCLWqScmLX/JOYOyi9Xj4m924/8LOmD22qzt5ZNRrMHtsV8wY1aVZVViBXgIeaM1NDPkjmRkoj43JBgC8u+EwSk5Z/HbejYfLMfS1tVi+r/4k95xxXWGXFby42pmkdS9FPlwOh+xTi/RWJ9xfM0RERNS6ePWXQE5ODgRBwJmzbqZPn37OfjfffDNuvPFG/0VHREQUYJ6Wy04d3gmf3tofj/+4By9ffT5EQcCCs7b7spw2FDWUnNBrVF4vh3UtFTZZJMRGqFFhkZBq0KNDbAR0ahEPj8zCo6OzUWGRYIhwDplp7uNmjNAEbAl4S2ju8v/GkpmPjs72S5xNMapLe/w5pwNuzOmI2AgNSqusMEb4PlyozteVTo3SKivaR2nx0dZCfHBzv3Nek9NGZOKOgWmYt+oAlyJ7EO6vGSIiImpdvPqNLD/f8y+8RERE4czTZF5TjeROZrxwRU9EatXuhJrJIiFKq8LSvcexo6gSgzPighV6szWUnKiRHIjTN5y8iNKqcMoiYf7PB89J5K6ZOhyRGlWdpKMruab1vUPMOSRZxrQRme4ekWdyVaz64zqB0lhiKLY2MXRmQs6V0Cssr0GkVh2yvUwFQcCiCX0wb9UBTPw0t0n9MOt7g2DN1OHQqUXoNSqPSW6NKOL1tYc8njPYSdpgC/fXDBEREbUuXiUjMzIyAh0HERFRi2uowuz1tYcwa0xXAHBXUyVG6/DIt7vx4uo8XNUjCf+7c1CLxepvDSUnbumXCovdUe/2qcM7obTKhvc3FZxThfb08v0QBWcVWqBEadWYMaoLAJxTsfrg/7d353FRle3/wD8zDDPsIMqayiLgnkuWIaSpiJqV/rLvU2Zlz9dHW4SyxYXclzJtsdSs76ue0pbHVssyM1BwQcktNHdZcwVcYNiE2c7vD5t5GJgNZgU+79eLP5xz5pz7HC/nMJfXfV/Dol2++s3UvU9JiER20XXc0TkAq/c0TfQ+nxgFT6mby1a51ShUeHNXy5sLmfoPgoZxpT1GwyR3WXW9yyZpnc3Yv5nmNk0iIiIisgWR0HDutYUuX76M7OxslJWVQdNovaznn3/eZoNzlMrKSvj7+0Mul8PPz8/ZwyEiIgcpq65H6OJ0o9tLFyc3SV6cu1qNHiuzIBIBZ2YPR2yQj72HaTfV9SqsysrHun3FRrtpv5GZ3yR5MWdEDEQAwpdmGE2IlSxKhlRi30orbeWgvE4JX5kEv54pw9u7CrBt2mBddaGrMnbvX7gnCkcuyrGn8LpeQk9rflIsnh0Sif/L+ctgMnPhqDinTkdWqDQIXZLe4riw5v3Wnrs9aPhvxr8F0+eJiIiITLE0v9bs3z42bNiAp59+GlKpFB07doRIJNJtE4lErTIZSURE7ZO/h6TZFWZxQT64v2cItp4uxXt7i7Duob6OGKpdvJGZh0FdOuDSwlGorlc1WdPRw8h0WE93N5eoQmtYHadUa/DqttM4e7UGKzLzseK+nnY9t7Xe2VOIgZ0Dmtx7b6kEidGB+MfnRwy+T1ux66pVbtauh2nN+zkV2TxDFaVEREREjtbs30AWLFiAhQsXQi6Xo7i4GEVFRbqfwsJCe4yRiIjI5j76/S/8drZl3bJnDo0GAGw4dAHltQp7DdGubirVWJtdjIc2HELuxQoE+cgglYibVEl5SyWQSsRNtmvXPTTEGVOF3d3EWHV/LwDAu3sKUXyj1qHnbw5BEPDx73/hoQ2HkF14vcm9raxTmU3IaRPFJYuSUbo4GSWLkjFreDenT7e1Ni6seb92KrI9OrgTERERke00OxlZW1uLRx99FGIx/yeViIhahxqFCgqVBmXV9VCoNDhysQKr9xRi7i+n8eKwbs1OXgyP6Yjbw/xQq1TjowPnHXkpNrPlRAmq6lWI6OCJuyMCm/1+bRWaIaYSufZ0f68QDO/WEfUqDeb9esbh57fUH5fkuCivg7fUDfdEd2yy3dKEnLFEsTNZGxc1ClWL/oNAy1WTtERERET0X83OKE6dOhXffvutPcZCRERkc9rOvKFL0hG6OB2hS9Kx5UQJdj83BM8nRiHAQ9Ls5IVIJNJVR67NLoJS7fjEm7W+OHIRwK1mNWKxyMzeTbliFZpIJMJbD/aGSARsyr2E3/8qd/gYLPHjiRIAwJjuwQbjzBUTvZYyFhfzk2Lxyr3m17J8Z3chUhOjMD8ptsVx5YpJWiIiIiL6r2Y3sFGr1bj//vtx8+ZN9O3bF+7u+v9z/84779h0gI7ABjZERG2Toc68WgtGxWG2FY0+6lVqRL62E6VV9fhy8kBMGnCbtcN1mNKqenRelgG1RsDp2cPRPbjlTXhcsSHG/359FBsOXUB8RAdkpyTorW/tCvq9vQvHr1Rh46T+eOKOLgb3MdY8yNlrQlqqYVz4yCTYfqYMnx2+iO+mDIKbkeT3sctyDFy9B92DfLDj6bvRyVvmUnFFRERERKbZrYHNihUr8Ntvv6F79+4A0KSBDRERkatwF4uxNrvI4La12UWYNzK2xceWSdzwbHwkFqefxbt7CvFo//BW8xzclHsJao2Au7oEWJWIBFyzIcbyMT1w7JIcs4fHoF6lQWW9CgEuktAqvF6D41eq4CYWYVzPEKP7GWse1BoSkYB+XJTXKjD1m2OouKnEhkMXMHVw1yb7C4KAV34+BUEA+oX7IdzfU/d+wDXiioiIiIhso9m/kb/99tv45JNP8NRTT9lhOERERLZjbWdfc54dEoEVmXk4dKECB89XYHBEhxYfy5G0U7Qfv6Ozk0diH+H+Hsh8bgje3lWAf3591KUqC7ecvDVFe2hUIAK9pCb3dcVEb0t08JJiwahYvPzTKSzYfgaP9A+Hj0z/V9Btp8uwM+8apG5ivD7WtTuhExEREZF1mv1brUwmQ0JCgj3GQkREZFP27vgc5CPDK/d2ww9P3Ym+YX66Bjk1CpVVx7WnkyVV+OOSHBKxCI8OCHf2cOyiRqHCO7sLsXxHni4ZXXFTiaUZ5/BGZr5T/362/L1e5Pg+oU4bgzPMGBKFbh29UFJVj1VZ+XrblGoNZm09BQB44Z4oRHX0csYQiYiIiMhBmp2MfOGFF7B27Vp7jIWIiMimHNEIJG1EDI5crEDnZRm6BjlvZhWgTqm2+tj28PnfVZH39QxGJ++WV4W6MlPT89dkF8Fd7JwKw2s19cguugEAGN+7fSUjpRIxVt7fCwDw9u4CXKy4qdv20e/ncaasGp28pXjViqUTiIiIiKh1aPY07YMHDyIzMxNbt25F7969mzSw2bx5s80GR0REZA1vqQQvDo2GRhCwbl+xzafrahvkLN+Rp3tNW4EHALOsaJBjDxqNgP/88fcU7YFtc4o2YP/p+VraJi0VdUqL1qTceqoMGgHoH+6HiMD2V/33//qE4p6oQOwtuoH5v57BhkkDUFWnxNrsQgDA4uTu8DdSyUxEREREbUezvyEFBATgoYcessdYiIiIbEqh0uD+fx/Ay/fG4PLCUaiqV9m0EYi5CjxXq/LaVXAdF+V1CPB0x/29jDdPae200/MNJSRtMT0fuNXtelVWAdY2o9v1T3+vF/lgO6uK1BKJRHjrgd6Y8lUu/l/fMNSr1JDXqXD4xaHYX1yO4d06OnuIREREROQAzU5Gfvrpp/YYBxERkc19f/wK9hWXo2jzcRTNG2nzRiCOqsCzFW3jmv/pF9ZqujK3hHZ6vrZCtSHt9HxLYsBY5aO2InZZg+Obq4itVajw29kyAMCEdrZeZEN3dg3AvtRErN6t31woNTEK90QFQtJ2w5KIiIiI/ubUtowrVqzAnXfeCV9fXwQHB2PChAk4e/as3j51dXWYMWMGOnbsCB8fH0ycOBGlpaV6+5w/fx7jxo2Dl5cXgoODMWvWLKhUrts8gIjImBqFCgqVxmAjFFPbyLD1+25VLU67uyvc3Wz/yLN3gxxbqlWo8N3xywCAJ9poF20tb6kEc0fEYOGoON3fT4CnO+YnxWLOiBiLps5rKx9Dl6Q3WQtU0oI1KXfkXcNNpQYRHTzRL9zPugtsxWoUKry7p2lzoWUu0FyIiIiIiByj2ZWRUVFREIlERrcXFhZafKzdu3djxowZuPPOO6FSqfDqq68iOTkZp06dgre3NwDgxRdfxC+//IJvv/0W/v7+SElJwUMPPYR9+/YBANRqNcaNG4fQ0FDs378fV65cwZNPPgl3d3e8/vrrzb08IiKnMTbtM21EDASg2VNC27tjl+XYV1wOiViEaYMj7HIOUxV4KYmRFlfgOUJW/jV4SNwQ5C1DQmSgs4djdx7ubpg1vBteHRkLeZ0SXlI3pJ+9ih9PlGDSgNtMvtdU5WMnbykm3h7W7IrYLQ2maJv6Paqta21LGxARERGR7TU7GTlz5ky9PyuVSuTm5mL79u2YNWtWs461fft2vT9v2LABwcHBOHLkCIYOHQq5XI5///vf+M9//oMRI0YAuDVNvGfPnvj9999x9913Iz09HadOncKOHTsQEhKC/v37Y9myZZgzZw4WL14MqVTa5Lz19fWor6/X/bmysrJZ4yYisjVTyY+Jt4fhuz+vNGtKKAHr9xcDuNU0I9zfwy7n0FbgAbcSKdpEcUpCJFITovD9n1fw5KAudjm3lrkmKtrtfcP8UTRvJAqu17abZJj2PgT5yPDR73/h6e/+RFSgF/7n9jBITFTKmkqYvbe3ENPvjmjWmpRqjYCtJ2/N6mhvXbQba21LGxARERGR7TX72+sLL7xg8PX3338fhw8ftmowcrkcABAYeKti48iRI1AqlUhKStLt06NHD3Tt2hU5OTm4++67kZOTg759+yIk5L8L8Y8ePRrPPvssTp48iQEDBjQ5z4oVK7BkyRKrxkpEZEvGkh+dvKWI7ujVLiqJmtuZ2BT5TSW+PHIJAPBcQqQNR9lU4wo8fw935F+rwbD1+5F3rQa3+XtgZGyQVecwdm/MNVExtj2uk3e7q6idPPA2zPv1DIpu1OKbY5fxmIlu4qYSZgXXa1GjVBmtiE01UBGb89cNXK1RIMDTHfdEt/2qVFMc0VyIiIiIiFybzeaOjR07Ft9//32L36/RaDBz5kwkJCSgT58+AICSkhJIpVIEBATo7RsSEoKSkhLdPg0Tkdrt2m2GpKWlQS6X634uXLjQ4nETEdmCseRHqK8MZdUKs5VErZ2p9flaYuPhC6hVqtE7xBdDo+3foddbKoFUIkaQjwxSiRg9Q3xwV5cAqDUC5v96Btdq6lu83qexe1NVp8SKzHwsyzint/be0oxzWJGZh9KqOryemWdwe3tcm89LKsHz90QBAFZm5UMQBKP7mlsL1Nvd+JqUKQlR+OWU/trWP5649fvI/T2D7bJ2aWuiXdrAEG1zISIiIiJq22w2r++7777TVTS2xIwZM3DixAlkZ2fbakhGyWQyyGScAkRErsNYtVBJVT2CfaRtupKoJZ2JTREEAR/8PUX72SGRTpmSLBKJ8OHDt+OmUo11D/XFmr1FWLevuNnrfRq7N+v3F2POiBijFbP/+eMS5o2Mw7rsYoPb21JFbXPMGBKJVVn5OH6lCttOl2FcrxCD+9UoVEhJiMTyHXlNtmkTZt5SSZOK2NNlVRi2fj/OXa2GxE2Mh/qGQRAEnC6tQidvKR5s51O0AeNLG3ANXCIiIqL2o9nJyAEDBuh9sRMEASUlJbh69SrWr1/fokGkpKRg69at2LNnDzp3/u+0qdDQUCgUClRUVOhVR5aWliI0NFS3z8GDB/WOp+22rd2HiMjVnSytMpj8uFajQOH1WuNNUhIicb7iJmI6eTtqqDZn64YWmfnXcPZqDXxkbk7tGu3h7oYPHr5d1zlYqzmJVmP3JtRXhtKqeqMVs57ubii/ybX5GuvgJcXTd0fi7d0FWJmVbzAZKQi3qlkXj+4OACaTyA3XpASA28P8kBgViDNl1ViSfg53dQlAkI8U6x66HcE+UqjUxqsx2xNDSxsoNRomIomIiIjaiWYnIydMmKD3Z7FYjKCgINx7773o0aNHs44lCAJSU1Pxww8/YNeuXYiK0p+2c8cdd8Dd3R07d+7ExIkTAQBnz57F+fPnER8fDwCIj4/Ha6+9hrKyMgQHBwMAMjIy4Ofnh169ejX38oiIHC4r/xpmbD6O3c8NgUgkMri+n6FKotTESKQkRGH4B/uxKLk7/qdfuJOvpGVs3dBi/b5iAMATd3SBr4dzG/v4SCVY9/d4GrMk0Wrs3pRU1SPIRMXsTaUaHTy5Np8hLw6NxtrsImQX3UB20XUkRulP4//3wfP4IOcvZBeXI336YMxPirM4YSYSibD+ob6QScRYOCoOa7NbVhHbHjRO5LpK13kiIiIisr9mf0tbtGiRzU4+Y8YM/Oc//8GWLVvg6+urW+PR398fnp6e8Pf3x9SpU/HSSy8hMDAQfn5+SE1NRXx8PO6++24AQHJyMnr16oUnnngCq1atQklJCebPn48ZM2ZwKjYRuTz5TSWe+ioXFyrqsG5fEWYPj8G8RtVCsr8TF40riRRqDd7IzMOp0mosyziHO7sEINzPwyYNYBxFEAT4yiQ2S5qVVNZhX/ENAMBzQyJtNcwWszbR6m9k+v61GgV2FVw3WjE7eWBn1KnURrdrpxq3xwRQuL8HnhzUGR8fOI+VmflInPrfZGTBtRq8uOUkAOCJOzojxPdWF/bmJMwkbmK8PrYH3txV0OKKWCIiIiKitsyp30I++OADyOVy3HvvvQgLC9P9fP3117p9Vq9ejfvvvx8TJ07E0KFDERoais2bN+u2u7m5YevWrXBzc0N8fDwef/xxPPnkk1i6dKkzLomIqFme//EELlTUoVtHL8y6N6ZJI5SGCYvG23xkEiwZ3QOz7+2Gnc/E45OD523WAMYRVGoNnv7uT6SfvYoUIx2vUxIiUX5TYfZYNQoVFCoNVBoBBa+ORNaz8egd6mvjETefuUYo5hKtxy7Ljd6bouu1mGOgicrCUXGYOyIGfh7uBpusaLe352TYrHu7QSwCfjldhpMlVQAAtUbAlK9yUaNQY1h0R7w4NLrFx5dJ3ExWxLqL218SmIiIiIhISySYaifZgFgsNtsEQCQSQaVqfd05Kysr4e/vD7lcDj8/P2cPh4jaiS0nSvD/NhyCWATsnZGA+MiWNQGrrldhVVa+wWYbC0fFuUwVVo1CBXexWFe5eehCBaZ9ewxiEbA/NRHv7C7Um4aekhCJ1MQoPPv9n3jrgd5Gqz7rlGqsyMzXm96emhiJtBGxTp8OW6NQ4c2sAoPVieb+bn4+WYI5v5zG7ueGYN2+4ibT97XTfbX3tWE1bcNjmtveXr205SSGdeuIUXFBqFao4CuT4LezV/HajnP47slBiAj0avGxy6rrEbo43ej20sXJ7W69TiIiIiJq+yzNr1mcjNyyZYvRbTk5OVizZg00Gg3q6uqaP1onYzKSiLQaJ8xsnbjRHr/8phI+MjdknLuGv27U4gUrqrAUKg1Cl6QbneZcsigZUolzK7EMJQy1ycZjVyqRFBvUJGlWq7yVyJv59xp/htbeUwtCk27TWq6SiK1TqvFGZn6TROtLw7oZrZosr1Wgz1u7cKWyHqvu74lnh0QyoWhjNQoVVmbm68VVSkIkZt0bY/Vao63h3yQRERERka1Zml+z+Lft8ePHN3nt7NmzmDt3Ln7++WdMnjyZU6OJqFWrU6qxKqvAaAWaPY6fkhCJtGZ2i27M1g1gbK1GoWqSMKy4qcTyHXkQiUSYPbwbAAMNLSRSpI2MbVL1qV17z0Mi1iUqDWlJJ257aNw52Fcmwa9nyjBkbTaWju6Ohw00Hnrpp5O4UlmP7kHemJEQBc+/44/NPmxDG5ON42r5jjyIRSKrk9hKjYbrdRIRERERGdGi34QvX76MadOmoW/fvlCpVDh69Cg2btyIiIgIW4+PiMghahQqrMjMx7KMc7rEnjbp9UZmPmoU1i1BYez4y3fkYaWVx7d2XUJ7cxeLjSYM15pZP0/qJja69t7Pp0tRWacym4h1BQ3X+/Rwd8Pvf5XjTFk1pn5zDOeuVuvtu+10KTYevgiRCPjkkf66RCTZjqmYtMWajt5SCdfrJCIiIiIyolm/bcvlcsyZMwcxMTE4efIkdu7ciZ9//hl9+vSx1/iIiBzC3skJex5fW4VliLYKy5nKb5qv3DTGVNVn3tUa+Hu6diLWmNfG9sDQ6EBU1avw8MbDqP07GV1Zp0TattMAgJn3RLd4HVEyzZJqYmtpK2JLFiWjdHEyShYlY9bwbk5fx5SIiIiIyNks/va7atUqREdHY+vWrdi0aRP279+Pe+65x55jIyJyGFslJ7Rdncuq66FQaVCjUOFCeS3KquvtlvwwVoU1PykWqYlRkLo5Zjpo42uvrldhU+5F+MjcWpwwNFX1qdIIUKhdOxFrjMRNjE2P34EQXxlUGgEnSqqgUGkgr1Nhf2oitk8bjOVjujt7mG2Wo6qJG1bESiViVkQSEREREaEZa0bOnTsXnp6eiImJwcaNG7Fx40aD+23evNlmgyMi5zDXxMXeTV6cQZucMNZwwpLkhLE1IZ9PjIKn1M3q45vSeF1CPw93ZJwrwz3v78PkgbdhXlKcVcc3x9i1pyZGIae4HCkJkQa7fZtbP8/c2ntuImDuiBgA0GsQY8u1Pu0lzM8DW566E1EdvbA2uwhjPjrQoBt4FIZGd3T2ENssrulIREREROQ8FmcPnnzySYhEInuOhYhcgLkmLvZu8uIsSo0GqYmRWJbRNGGWmhhpNjlhqkkLADwTH4HUxCiDXZ9tlfxo3ABGXqfCmbJqLM04h/G9Q9EnzHg3M2uYu/b/vasLEqMCIRaJmp0w1FZ9AqaTjQ0Tsdpu060hHnuH+Rps0LMs4xxEgEt0A2+LLI0rIiIiIiKyPZEgCIKzB+FslrYeJ2rrDCWVtBaMisWMIVF4f3+RwYTdwlFxrTpxolTfmiK7Zm8h1u0rblLd9/GB85g7Isbof8ooVBqELkk3WvlYsigZGkHAG5n5Dkt+CIKACZ8ews+nSjGosz/2pyZCYsWUbWMVsZZcu1Qi1r2/YcLQ0nix5r2uzNJ7R/bRVuOKiIiIiMgZLM2v8TduItIx1WTlP39cwryRcVibXWxw+5rsIrw6MtaOo7OvTw6ex3t7i/DWA70wPylOl5woLq/FsPX7caasGlI3EZ4ZEtkkIVdVp4JCLZhdEzLIR+bQCj6RSIQPJt6OvUW7cPiiHG/tLsDcES37OzJUEZuaGIWXh0Vb1KAmyEfWpHKzOZWg1rzXlVmyVqn2msn22mpcERERERG5Mv7WTUQ6phIjnu5uVnVFdmU3lWos35GHM2XVyL9Wo9dwIi7IB6mJUegR7IMnB3XByqx8hC5JR+jidIQuSceqrHxIxCJ09LasIYajG1qE+3vg3fG90SPYB71DfFGvUus117FEjUKFFZn5WJZxTvf3r51KvH5fMUJ8ZK2yo7UrcFQjFSIiIiIiIlfBZCQR6ZhKjNxUqtHBs20mTj7YX4xL8jp0CfDA0/ERTbY/OyQS308ZhLXZRViekdcoIZd3a9p1rdJluzo/cUdnZKck4NCFCoQtydAlUt/MKkCdUm32/aYqZlftKoCylXa0dgXaRiqG8N4REREREVFbxGQkEemcL69FSkKkwW2TB3ZGnUptNHGSkhCJPYXXcaGiFjUKFRQqTbMr8Jyhqk6FNzLzAQALR3WHTGJ4ynS3jt5Yt6/Y4LZ1+4oR5CPD3BExWDgqTpewDfB0x8JRcZg7Isap69DVKtV4b28Rlu/QT6QuzTiHNzLzzf79mJtKrFQLLnvtrk7bSIX3joiIiIiI2gt+yyEiAEDxjVpM+vIPbPvXYIhEwNrsYoNNVgx1oE1NjEJqYhSe2pSLDZP6Y1VWvtH3u5r3sgtxrUaB2E7emDKos9H9LF3bzxW7OpuqbLRkrU9txayxJis+sltTz13x2lsDD3c33jsiIiIiImo3mIwkIqg1AqZsysWRi3LM3HIC//dwP8wbGWcwMWIscVJdr8LL93bDmr8r8LS0FXgAXK7b9o1aBd7aVQAAWDy6u8lO0+YScg3XhARcqyGGtU1Szlfcqpht+PeqpZ1KLIXYJa+9teC9IyIiIiKi9oLfdogI7+wuwN6iG/CRuWHZmB66SjdjTVYMNWEJ8fVAQmSg0anMa7KL4C52rY+c9fuLUVmnQt8wXzzSL9zkvq15bT9rmqTkXa3Go58fQWpiFBaMiuVUYiIiIiIiIrIKv0EStXMnS6qwYPtZAMA7D/ZGdEfvFh/L2go8R6hRqOAuFqP8phIvDo1Gn1A/+MncIBaLTL5Pu7YfoD9F3ZWnoGtpE6naCtWGUhIiUatUQyppmiiuV6kx6Ys/8MelSsz6+RTWPdTXaMUsERERERERkSWYjCSXpU0aVdQpEfB34qNhBZa57WRcw3sXFeiJr564AxlnyzD1rq5WHdfSqczOUqdUY1VWAdY2SCamJESaXTNRq7Wu7WcskZqSEInUxCg8890x/Psf/eEt0//38+q2M/jjkhyBXu547b5bFbMApxITERERERFRyzFzQy7JUNKoYQWaue1knLGE3FsP9oZIZLo60BxTFXgN1xZ0hhqFCquyCrCswdgqbiqxfEcexCKRxetZtta1/QwlUstvKvHgJwdx4HwFKutU2PK/d8H973Uzd54rw+o9hQCATx7pj9v8PZ05fCIiIiIiImojWse3aGpXahQqrMjMx7KMc7oKO20TlDcy81FZpzS5vUahcubwXZqxe7t8Rx5W2uDeaSvwFo6K01tbcH5SLOY4aG3BGoUKCpUGZdX1UKg0umsy11Ha1daztIfGa32G+MrwzoO94eXuhuLym8i9JIdCpUFpVT3ujgzE5qfuxLIxPfBg71BnD52IiIiIiIjaCJEgCIKzB+FslZWV8Pf3h1wuh5+fn7OH0+4pVBqELkk3ONW3W0cvnJw13Oj2AE93lCxKNrj+HZm+t7a8dw2ngXtL3ZB+9ipu1CowdXCE1cc2pU6pxorM/CYVs3NGxKC8VonOyzKMvrd0cbLT17N0lj0F19AzxBdrs4uwbl+xXsVs2shYeLLamIiIiIiIiMywNL/Gadrkckw1QfF0d0P5TddvkmIte62H6agGM9qxBvvIsCn3EiZ/+Qd8ZG64r2cIwvw8rD6+IcamYS/NOAcPiRgzh0a79HqWznRHlwCsysrH8h15utdaMoWdiIiIiIiIyByWj5HL0TZBMeSmUo0Onsa3t4WkknZNx9Al6QhdnI7QJel4M6sAdUq11cc2dW/tde8e6ReOu7oEoLpejVe3nbb6eC2Zhr1qVwGUag1SEyMNbteuZ9le3bp3xQa3tZcp7EREREREROQY/IZJLkUQBJy5Wo2UhEiD2ycP7Iw6lRrPJ0YZ3J6SEIlL8jo7jtC+zK2Xae2ajkqNxui9tVdCTiwW4b0JfQAAGw9fxIG/ylt8LGOJ2pp6FUqr601WfSrVAtJGxDZZz3LhqDjMddB6lq7KkopZIiIiIiIiIltov9++yeUo1Ro88/2fyCkux+7nhkAkAtZmFxvslj13RAyAW1VbDde3S02MwrD1+5GSEIUpd3a2y1RnezLXZOXVkbFWHX9X/nWk/p3Ibbg2oL07kQ+O6IApgzpj4+GLeOHHE9ifmgixuHmdu62dhu0ju9W8pXFHaaVG0+47sGsrZjmFnYiIiIiIiOzNtTMz1KY1Xhfx4Ply5BSX49zVamTlX8Ps4TGYNzLOYNLIw92tSVJJodboEnn/0y8MKzPzHZpwswVzFWrlN5UI8ZW1aE1J+U0lpn17DAGe7vjmiTswP8nwvbWX1+/rie+PX8HBCxX4/MhFTLmzS7Peb24a9oyESDyfGIWlDZKVWtqqTynEuvukXRtTygJxKDUai+4dERERERERkbWYjCST7NVIRTvddm2jysbdzw3B8SuVGBEbpNvXWNKoSVJJIkbayFg8fHsY1mYXNWnGoU20uHIzDnMVar4yN5TXKvDu3kKjVaPGLEo/i5KqevjKJIgN8oZUInZoQi7MzwPzk+Kw4dAFBPvKoFBpmhVX5hK1SrVgsGK2NSShnc1bKuG9IyIiIiIiIocQCYIgOHsQzmZp6/H2pk6pxorMfL2EoS2SE4am22otGBWH2VYmCxUqDUKXpBtN6JUsSoZU4ppVXhfKa/HRgfN6iVSt+Umx+N+7uuKTg4a3LxwVZzTRevSSHIPe3QONAPw2/W6Migtqso8jKFRqVNWr8d7ewmZXrVr696pNoDes+nTV5LOr4b0jIiIiIiKilrI0v+aaGRlyOksbqRjrbGyKqem2a23Qube1NuPYlX8ND35yCKmJUZifFNukycqrI2MR5ueBdfuKDb7fWNdjjUbAjM3HoRGAf/QLd1oiEgCUGgFrsguxfEdesxv0KDWWdcP2lkp0VZ9SiZjJtGbgvSMiIiIiIiJ74zdNMsiSRiqGplpbUuFmSbJQO324JVpDM47G09+v1tTjlZ9P4tiVSizcfgarHuhtcE3HMjMdow3du025l5DzVzl8ZG54+8Fejrg8o27FVbHBbeYa9IgApCZGQxAc23yHiIiIiIiIiGyHyUgyyFzCsKpehff2FrZoXUZ/D4ldk4WmmnGkJEY6vRmHsfUyf512N17ddhqrx/fRJdYar+loLtHqLXVD3tUahPvL4C4Wo/ymEhP6hmKz7E5U3FTgNn9Px12oAdYkot/cVYCvj17G6vGGE7VERERERERE5Po4TZsM0iYMDenW0Qu+MkmzpwsDwN7C69hx7hpSEiINbm843baltM04Fo6K05vqPD8pFqkJUfjyyEWrjm8NY9Pfl+/Iw9rsIrz9YG+TiTVtotWQlIRI7P+rHAGeEqzKykfoknSELUlHl2U78MfFCjzS/za7XFNzaJOpBreZSERfltfhzawCnCmrRmWdilOJiYiIiIiIiFopfounJvYWXkdlnQopCZEGG6W8ODQa5Tctq3BrOB3Zz0OCiptK/F/OX/jssQEQi0R269zr4e6GWcO74dWRsboKuoLrNRi2fj/OlFUjwFOKf/QPt/o8zWVq+vu6fcWYnxRn8v2muh7PGRGD06VVBjuJL9+RB7FI5PRO4qaqVrWJaENVq/O3n0GtUo0hkR3w8O1hjhgqEREREREREdkBk5GkZ8e5qxj/6UFEdPBCdkqCwYTh/97VFWKRyOR0YR+pRNc1u/F05M8eGwBvA8lCW0+31SbdtNN+e4b4YnT3IJwpq8brO/OQGNUBnbxlunUbHdE52BbrZRpKtCo1Gni6u6FPqB+S/u93g+8ztyajIxhLpqYkRCI1MQoiA+85ekmOjYcvAADefqA3RCJDexERERERERFRa8BkZDunV7kok6BWqUZEBy9EdvCEl4mEYY1CZXxdxoRIlFbX45OD5y2q0Gu8LqI9vfVAbyjVGixK7o612UVWNUJp3ITGkmSmrZrrGLt39m4OZAuNk6l+Hu7YmXcV97y/D3d2CcCGR/vrEo6CIOCVn09BEIBJA27D4IgOTh07EREREREREVmHych2zFgjlX0pCfCWukEq+W9SrnHSy+R04eExgAgm15R0VoWem1iE1+/ribd2FbSo+Y6WJZ3EGycrb9QqkH+9xuj0d1PTlC3VGjqJA02Tqd5SN+Rdq8GZsmokRAVi+t0RAID0s2XIzL8GmUSM18f2cNp4iYiIiIiIiMg2mIxsp7RTqJc1qGxsXLkoNXMMo9OFpW4oq6532Qo9D4mbVYlSY/dOm8ycPbwbxCKRwUTvzHui0SvEFyKRyGQis6Vauiajsw3r1gmvje2BDYcuIMxXhnqVGvI6FRKjO2LzU3ei8HoNIgK9nD1MIiIiIiIiIrKSSBAEwdmDcLbKykr4+/tDLpfDz8/P2cOxKWNTiRUqDUKXpButoCtZlAyppOVJK3sf3xpl1fUIXZxudHvp4mSTiVJz1/bXvJF4a3cBlmU0rX6cnxSLV+7tBjexCO5isV4S11brVdYp1XgjM99uzYHsRRAEVNxUYvWeQr3p8ykJkUgbGQtPFx47ERERERERUXtnaX6NlZFtmKGpxKmJUXh5mOXdsFvKlSv0rJ3KbGpdRolYBKnEDWuziw1u13bM1iZi7bFeprGKVVdORAJArVKNd/e6bidwIiIiIiIiIrKe683XJJuoUaiwIjMfyzLO6RJnFTeVWJZxDuv3FSPER4YAT8NJN1usLahdU3LhqDjdeQI83bFwVBzmjohxalJJmyg1JCUhEgq1xuh7VWoNfGUSo/cuNsgbcgsSvfbmLZVAKhEjyEcGqUTcKpJ47mIx1mYXGdy2JrsI7mJ+XBERERERERG1dvx230aZSuys2lVgMiGnrVy0lrZCr2RRMkoXJ6NkUTJmDe/m9Ao9Y4nS+UmxSE2MwtL0c1Brmq5eoFBp8NiXfyD97FWkJEQaPPb4XqEI8HS3a6K3rbKkEzgRERERERERtW6uXy5FLWIusaNUCUa7YdtybcHGXZNdpXmKoanMlyvrMPLDHJwoqYK7mwivjoyFu9t/19s8dKECJ0qqsPC3s9iXkgCxSNTk3j1/T5RLT1F3Za2lEzgRERERERERtRyTkW2UucSOj+zWNN7WuLagrTROlEYGemFRchwWp5/DzKHRWJmV36SRyu7nhuDs1Wr4yCQm750jEr1tDZO4RERERERERG0fu2mjbXbTrlGo8GZWgcHEzsJRcWwGYkLB9RpsPHRBr5GK1oJRcZht4b3TdjK3R8fstqq1dgInIiIiIiIiau8sza85tcxoz549eOCBBxAeHg6RSIQff/xRb7sgCFi4cCHCwsLg6emJpKQk5OXpJ4hu3LiByZMnw8/PDwEBAZg6dSqqq6sdeBWuyZUbyLi6Lv6eWLev2OC2tc1opNIam8g4m6uuM0pEREREREREtuHUZGRNTQ369euH999/3+D2VatWYc2aNfjwww9x4MABeHt7Y/To0airq9PtM3nyZJw8eRIZGRnYunUr9uzZg+nTpzvqElwaEzstw0YqzsUkLhEREREREVHb5TLTtEUiEX744QdMmDABwK2qyPDwcLz88st45ZVXAAByuRwhISHYsGEDHn30UZw+fRq9evXCoUOHMGjQIADA9u3bcd999+HixYsIDw83eK76+nrU19fr/lxZWYkuXbq0qWna1HIKlQahS9KNrrdZsigZUgnXLiQiIiIiIiIi0moV07RNKSoqQklJCZKSknSv+fv7Y/DgwcjJyQEA5OTkICAgQJeIBICkpCSIxWIcOHDA6LFXrFgBf39/3U+XLl3sdyHU6mgbqRiibaRCRERERERERETN57LJyJKSEgBASEiI3ushISG6bSUlJQgODtbbLpFIEBgYqNvHkLS0NMjlct3PhQsXbDx6as243iYRERERERERkX20y6yKTCaDTCZz9jDIhWnX23x1ZKxeN2yut0lERERERERE1HIuWxkZGhoKACgtLdV7vbS0VLctNDQUZWVlettVKhVu3Lih24eopdhIhYiIiIiIiIjItlw2GRkVFYXQ0FDs3LlT91plZSUOHDiA+Ph4AEB8fDwqKipw5MgR3T6ZmZnQaDQYPHiww8dMRERERERERERExjm11Ku6uhr5+fm6PxcVFeHo0aMIDAxE165dMXPmTCxfvhyxsbGIiorCggULEB4eruu43bNnT4wZMwbTpk3Dhx9+CKVSiZSUFDz66KNGO2kTERERERERERGRczg1GXn48GEMHz5c9+eXXnoJADBlyhRs2LABs2fPRk1NDaZPn46KigokJiZi+/bt8PDw0L3nyy+/REpKCkaOHAmxWIyJEydizZo1Dr8WIiIiIiIiIiIiMk0kCILg7EE4W2VlJfz9/SGXy+Hn5+fs4RAREREREREREbUqlubXXHbNSCIiIiIiIiIiImpbmIwkIiIiIiIiIiIih2AykoiIiIiIiIiIiBzCqQ1sXIV22czKykonj4SIiIiIiIiIiKj10ebVzLWnYTISQFVVFQCgS5cuTh4JERERERERERFR61VVVQV/f3+j29lNG4BGo8Hly5fh6+sLkUjk7OHYXGVlJbp06YILFy6wWzg5DOOOHI0xR87AuCNnYNyRozHmyBkYd+QMjDvrCIKAqqoqhIeHQyw2vjIkKyMBiMVidO7c2dnDsDs/Pz/+YyKHY9yRozHmyBkYd+QMjDtyNMYcOQPjjpyBcddypioitdjAhoiIiIiIiIiIiByCyUgiIiIiIiIiIiJyCCYj2wGZTIZFixZBJpM5eyjUjjDuyNEYc+QMjDtyBsYdORpjjpyBcUfOwLhzDDawISIiIiIiIiIiIodgZSQRERERERERERE5BJORRERERERERERE5BBMRhIREREREREREZFDMBlJREREREREREREDsFkJBERERERERERETkEk5FOtGfPHjzwwAMIDw+HSCTCjz/+qLe9tLQUTz31FMLDw+Hl5YUxY8YgLy/P4LEEQcDYsWMNHmfnzp0YMmQIfH19ERoaijlz5kClUpkd365duzBw4EDIZDLExMRgw4YNzRo/uR5bxNy9994LkUik9/PMM8/o7XP+/HmMGzcOXl5eCA4OxqxZsyyKuW+//RY9evSAh4cH+vbti23btjV7fOR6HBF3x44dw6RJk9ClSxd4enqiZ8+eeO+99ywan7m4e+qpp5qce8yYMS27GeQwjvq807p+/To6d+4MkUiEiooKs+MzF3ebN29GcnIyOnbsCJFIhKNHjzbn8skJHBVzjbeLRCJ89dVXZsfHZ2zb5Ii427Bhg8G4E4lEKCsrMzk+PmPbJkd93vF7LGnZKneSk5ODESNGwNvbG35+fhg6dChu3ryp237jxg1MnjwZfn5+CAgIwNSpU1FdXW12fOZirqqqCjNnzkRERAQ8PT0xZMgQHDp0qEX3oq1gMtKJampq0K9fP7z//vtNtgmCgAkTJqCwsBBbtmxBbm4uIiIikJSUhJqamib7v/vuuxCJRE1eP3bsGO677z6MGTMGubm5+Prrr/HTTz9h7ty5JsdWVFSEcePGYfjw4Th69ChmzpyJf/3rX/jtt98sGj+5JlvF3LRp03DlyhXdz6pVq3Tb1Go1xo0bB4VCgf3792Pjxo3YsGEDFi5caHJs+/fvx6RJkzB16lTk5uZiwoQJmDBhAk6cONHs8ZFrcUTcHTlyBMHBwfjiiy9w8uRJzJs3D2lpaVi3bp3JsZmLO60xY8bonXvTpk1W3BFyBEfEXUNTp07F7bffbtHYLIm7mpoaJCYmYuXKlc24anImR8bcp59+qrfPhAkTTI6Nz9i2yxFx98gjj+htu3LlCkaPHo1hw4YhODjY6Nj4jG27HBF3/B5LDdki5nJycjBmzBgkJyfj4MGDOHToEFJSUiAW/zctNnnyZJw8eRIZGRnYunUr9uzZg+nTp5scmyUx969//QsZGRn4/PPPcfz4cSQnJyMpKQmXLl2ywd1ppQRyCQCEH374Qffns2fPCgCEEydO6F5Tq9VCUFCQ8NFHH+m9Nzc3V7jtttuEK1euNDlOWlqaMGjQIL39f/rpJ8HDw0OorKw0Op7Zs2cLvXv31nvtkUceEUaPHm3R+Mn1tTTmhg0bJrzwwgtGj7tt2zZBLBYLJSUlutc++OADwc/PT6ivrzf6vn/84x/CuHHj9F4bPHiw8PTTTzdrfOTa7BV3hjz33HPC8OHDTe5jLu4EQRCmTJkijB8/vlnnJtdi77hbv369MGzYMGHnzp0CAKG8vNzk/pbEnVZRUZEAQMjNzTU7DnId9oy5lvzOxWds++CoZ2xZWZng7u4ufPbZZyb34zO2fbBX3PF7LBnT0pgbPHiwMH/+fKPHPXXqlABAOHTokO61X3/9VRCJRMKlS5eMvs9czNXW1gpubm7C1q1b9fYZOHCgMG/ePNMX24axMtJF1dfXAwA8PDx0r4nFYshkMmRnZ+teq62txWOPPYb3338foaGhBo/T8BgA4Onpibq6Ohw5csTo+XNycpCUlKT32ujRo5GTk9Oi6yHXZ2nMAcCXX36JTp06oU+fPkhLS0Ntba1uW05ODvr27YuQkBDda6NHj0ZlZSVOnjxp9PzmYq4546PWw1ZxZ4hcLkdgYKDJfSz9rNu1axeCg4PRvXt3PPvss7h+/brZayPXZcu4O3XqFJYuXYrPPvtM73/WTeEztv2x9WfdjBkz0KlTJ9x111345JNPIAiCyfPzGds+2esZ+9lnn8HLywsPP/ywyfPzGds+2Sru+D2WLGVJzJWVleHAgQMIDg7GkCFDEBISgmHDhunFZE5ODgICAjBo0CDda0lJSRCLxThw4IDR85uLOZVKBbVabTCe2/MzlslIF9WjRw907doVaWlpKC8vh0KhwMqVK3Hx4kVcuXJFt9+LL76IIUOGYPz48QaPM3r0aOzfvx+bNm2CWq3GpUuXsHTpUgDQO05jJSUleskkAAgJCUFlZaXemgrUdlgac4899hi++OILZGVlIS0tDZ9//jkef/xx3XZjsaPdZoyx92nfY+n4qHWxVdw1tn//fnz99ddmp1WYizvg1vSxzz77DDt37sTKlSuxe/dujB07Fmq1uoVXTc5mq7irr6/HpEmT8Oabb6Jr164Wn9+SuKO2xZafdUuXLsU333yDjIwMTJw4Ec899xzWrl1r8vx8xrZP9nrG/vvf/8Zjjz0GT09Pk+fnM7Z9slXc8XssWcqSmCssLAQALF68GNOmTcP27dsxcOBAjBw5Ure2ZElJSZOlJyQSCQIDA1v0PVYbc76+voiPj8eyZctw+fJlqNVqfPHFF8jJyWnXz1gmI12Uu7s7Nm/ejHPnziEwMBBeXl7IysrC2LFjdZUXP/30EzIzM/Huu+8aPU5ycjLefPNNPPPMM5DJZIiLi8N9990HALrj+Pj46H6MLcxPbZ8lMQcA06dPx+jRo9G3b19MnjwZn332GX744QcUFBRYdJ7z58/rxdzrr79u0/FR62KPuDtx4gTGjx+PRYsWITk5GUDL4w4AHn30UTz44IPo27cvJkyYgK1bt+LQoUPYtWuX1ddPzmGruEtLS0PPnj2Nfmm3Ju6obbHlZ92CBQuQkJCAAQMGYM6cOZg9ezbefPNNAHzGkj57PGNzcnJw+vRpTJ06Vfcan7HUkK3ijt9jyVKWxJxGowEAPP300/jnP/+JAQMGYPXq1ejevTs++eQTi8/V0pj7/PPPIQgCbrvtNshkMqxZswaTJk1q189YibMHQMbdcccdOHr0KORyORQKBYKCgjB48GBd2XBmZiYKCgoQEBCg976JEyfinnvu0T3EX3rpJbz44ou4cuUKOnTogOLiYqSlpSE6OhoA9Dp0+vn5AQBCQ0NRWlqqd9zS0lL4+fmZ/V9Qar3MxZwhgwcPBgDk5+ejW7duCA0NxcGDB/X20cZSaGgowsPD9WJOO43WWMw1XH6gJeMj12eLuNM6deoURo4cienTp2P+/Pm6162Ju8aio6PRqVMn5OfnY+TIkc26VnIdtoi7zMxMHD9+HN999x0A6KbKdurUCfPmzcOCBQtsFnfU+tnys67xPsuWLUN9fT2fsdSErePu448/Rv/+/XHHHXfoXuMzlhqzVdzxeyxZylzMhYWFAQB69eql976ePXvi/PnzAG7FTllZmd52lUqFGzdu6D63Whpz3bp1w+7du1FTU4PKykqEhYXhkUce0cVye9R+07CtiL+/P4KCgpCXl4fDhw/rpmTPnTsXf/75J44ePar7AYDVq1fj008/1TuGSCRCeHg4PD09sWnTJnTp0gUDBw4EAMTExOh+tGXJ8fHx2Llzp94xMjIyEB8fb+erJVdgLOYM0cad9gM+Pj4ex48f1/sgz8jIgJ+fH3r16gWJRKIXc9pfWJsTc80ZH7Ue1sQdAJw8eRLDhw/HlClT8Nprr+ntb4u407p48SKuX7+ud25qvayJu++//x7Hjh3TPYM//vhjAMDevXsxY8YMm8YdtR3WftYZ2qdDhw6QyWR8xpJRtoi76upqfPPNN3pVkQCfsWScLeKO32OpOYzFXGRkJMLDw3H27Fm9/c+dO4eIiAgAt2KnoqJCb03SzMxMaDQaXbLc2pjz9vZGWFgYysvL8dtvv7XvZ6yTG+i0a1VVVUJubq6Qm5srABDeeecdITc3V/jrr78EQRCEb775RsjKyhIKCgqEH3/8UYiIiBAeeughk8eEgW5gq1atEv7880/hxIkTwtKlSwV3d3ezHcMKCwsFLy8vYdasWcLp06eF999/X3BzcxO2b99u8fjJ9Vgbc/n5+cLSpUuFw4cPC0VFRcKWLVuE6OhoYejQobp9VCqV0KdPHyE5OVk4evSosH37diEoKEhIS0szObZ9+/YJEolEeOutt4TTp08LixYtEtzd3YXjx4/r9mnJvwlyPkfE3fHjx4WgoCDh8ccfF65cuaL7KSsrMzk2c3FXVVUlvPLKK0JOTo5QVFQk7NixQxg4cKAQGxsr1NXV2eFuka04Iu4ay8rKsqibtiWfd9evXxdyc3OFX375RQAgfPXVV0Jubq5w5coV624M2Y0jYu6nn34SPvroI+H48eNCXl6esH79esHLy0tYuHChybHxGdt2OfKz7uOPPxY8PDzMfsZp8Rnbdjkq7vg9lrRskTtZvXq14OfnJ3z77bdCXl6eMH/+fMHDw0PIz8/X7TNmzBhhwIABwoEDB4Ts7GwhNjZWmDRpksmxWRJz27dvF3799VehsLBQSE9PF/r16ycMHjxYUCgUNrxLrQuTkU6k/dLS+GfKlCmCIAjCe++9J3Tu3Flwd3cXunbtKsyfP1+or683eUxDycjhw4cL/v7+goeHhzB48GBh27ZtFo+vf//+glQqFaKjo4VPP/20WeMn12NtzJ0/f14YOnSoEBgYKMhkMiEmJkaYNWuWIJfL9c5TXFwsjB07VvD09BQ6deokvPzyy4JSqTQ7vm+++UaIi4sTpFKp0Lt3b+GXX37R296SfxPkfI6Iu0WLFhk8R0REhNnxmYq72tpaITk5WQgKChLc3d2FiIgIYdq0aUJJSYnN7g/Zh6M+7wyd05Iv6uY+7z799FOD41+0aFFLbgc5gCNi7tdffxX69+8v+Pj4CN7e3kK/fv2EDz/8UFCr1WbHx2ds2+TIz7r4+Hjhsccea9b4+IxtmxwVd/weS1q2yp2sWLFC6Ny5s+Dl5SXEx8cLe/fu1dt+/fp1YdKkSYKPj4/g5+cn/POf/xSqqqosGp+pmPv666+F6OhoQSqVCqGhocKMGTOEioqKFt+PtkAkCH8vcERERERERERERERkR1wzkoiIiIiIiIiIiByCyUgiIiIiIiIiIiJyCCYjiYiIiIiIiIiIyCGYjCQiIiIiIiIiIiKHYDKSiIiIiIiIiIiIHILJSCIiIiIiIiIiInIIJiOJiIiIiIiIiIjIIZiMJCIiIiIiIiIiIodgMpKIiIiIiIiIiIgcgslIIiIiIiIiIiIicggmI4mIiIiIiIiIiMgh/j+oI4ebBBVwQwAAAABJRU5ErkJggg==",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plotting for illustration\n",
"plot_series(y_train, y_test, labels=[\"y_train\", \"y_test\"])\n",
"print(y_train.shape[0], y_test.shape[0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"#### Step 2 - Making forecasts for y_test from y_train\n",
"\n",
"This is almost verbatim the workflow in Section 1.2, using `y_train` to predict the indices of `y_test`."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:22.710722400Z",
"start_time": "2023-08-25T09:25:15.034748600Z"
}
},
"outputs": [],
"source": [
"# we can simply take the indices from `y_test` where they already are stored\n",
"fh = ForecastingHorizon(y_test.index, is_relative=False)\n",
"\n",
"forecaster = NaiveForecaster(strategy=\"last\", sp=12)\n",
"\n",
"forecaster.fit(y_train)\n",
"\n",
"# y_pred will contain the predictions\n",
"y_pred = forecaster.predict(fh)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.032864800Z",
"start_time": "2023-08-25T09:25:15.044719900Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plotting for illustration\n",
"plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Steps 3 and 4 - Specifying a forecasting metric, evaluating on the test set\n",
"\n",
"The next step is to specify a forecasting metric. These are functions that return a\n",
"number when input with prediction and actual series. They are different from\n",
"`sklearn` metrics in that they can accept series with indices rather than `np\n",
".ndarray`. Forecasting metrics can be invoked using the functions in `aeon.benchmarking\n",
".forecasting` e.g.,`mean_absolute_percentage_error` or `mean_squared_error`.\n"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.032864800Z",
"start_time": "2023-08-25T09:25:15.374838400Z"
}
},
"outputs": [],
"source": [
"from aeon.performance_metrics.forecasting import mean_absolute_percentage_error"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.032864800Z",
"start_time": "2023-08-25T09:25:15.379824700Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.13189432350948402"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# option 1: using the lean function interface\n",
"mean_absolute_percentage_error(y_test, y_pred, symmetric=False)\n",
"# note: the FIRST argument is the ground truth, the SECOND argument are the forecasts\n",
"# the order matters for most metrics in general"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 5 - Testing performance against benchmarks\n",
"\n",
"In general, forecast performances should be quantitatively tested against benchmark performances.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.3.1 The basic batch forecast evaluation workflow in a nutshell - function metric interface\n",
"\n",
"For convenience, we present the basic batch forecast evaluation workflow in one cell.\n",
"This cell is using the lean function metric interface."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.033861900Z",
"start_time": "2023-08-25T09:25:15.410741500Z"
}
},
"outputs": [],
"source": [
"from aeon.datasets import load_airline\n",
"from aeon.forecasting.base import ForecastingHorizon\n",
"from aeon.forecasting.model_selection import temporal_train_test_split\n",
"from aeon.forecasting.naive import NaiveForecaster\n",
"from aeon.performance_metrics.forecasting import mean_absolute_percentage_error"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.034859300Z",
"start_time": "2023-08-25T09:25:15.414733100Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.13189432350948402"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# step 1: splitting historical data\n",
"y = load_airline()\n",
"y_train, y_test = temporal_train_test_split(y, test_size=36)\n",
"\n",
"# step 2: running the basic forecasting workflow\n",
"fh = ForecastingHorizon(y_test.index, is_relative=False)\n",
"forecaster = NaiveForecaster(strategy=\"last\", sp=12)\n",
"forecaster.fit(y_train)\n",
"y_pred = forecaster.predict(fh)\n",
"\n",
"# step 3: specifying the evaluation metric and\n",
"# step 4: computing the forecast performance\n",
"mean_absolute_percentage_error(y_test, y_pred, symmetric=False)\n",
"\n",
"# step 5: testing forecast performance against baseline\n",
"# under development"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.3.2 The basic batch forecast evaluation workflow in a nutshell - metric class interface\n",
"\n",
"For convenience, we present the basic batch forecast evaluation workflow in one cell.\n",
"This cell is using the advanced class specification interface for metrics."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.035264300Z",
"start_time": "2023-08-25T09:25:15.430689Z"
}
},
"outputs": [],
"source": [
"from aeon.datasets import load_airline\n",
"from aeon.forecasting.base import ForecastingHorizon\n",
"from aeon.forecasting.model_selection import temporal_train_test_split\n",
"from aeon.forecasting.naive import NaiveForecaster\n",
"from aeon.performance_metrics.forecasting import mean_absolute_percentage_error"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.044833Z",
"start_time": "2023-08-25T09:25:15.437670100Z"
}
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'load_airline' is not defined",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn[1], line 2\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;66;03m# step 1: splitting historical data\u001B[39;00m\n\u001B[1;32m----> 2\u001B[0m y \u001B[38;5;241m=\u001B[39m \u001B[43mload_airline\u001B[49m()\n\u001B[0;32m 3\u001B[0m y_train, y_test \u001B[38;5;241m=\u001B[39m temporal_train_test_split(y, test_size\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m36\u001B[39m)\n\u001B[0;32m 5\u001B[0m \u001B[38;5;66;03m# step 2: running the basic forecasting workflow\u001B[39;00m\n",
"\u001B[1;31mNameError\u001B[0m: name 'load_airline' is not defined"
]
}
],
"source": [
"# step 1: splitting historical data\n",
"y = load_airline()\n",
"y_train, y_test = temporal_train_test_split(y, test_size=36)\n",
"\n",
"# step 2: running the basic forecasting workflow\n",
"fh = ForecastingHorizon(y_test.index, is_relative=False)\n",
"forecaster = NaiveForecaster(strategy=\"last\", sp=12)\n",
"forecaster.fit(y_train)\n",
"y_pred = forecaster.predict(fh)\n",
"\n",
"# step 3: computing the forecast performance for a given metric\n",
"mean_absolute_percentage_error(y_test, y_pred, symmetric=False)\n",
"\n",
"# step 5: testing forecast performance against baseline\n",
"# under development"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4 Advanced deployment workflow: rolling updates & forecasts\n",
"\n",
"A common use case requires the forecaster to regularly update with new data and make forecasts on a rolling basis. This is especially useful if the same kind of forecast has to be made at regular time points, e.g., daily or weekly. `aeon` forecasters support this type of deployment workflow via the `update` and `update_predict` methods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4.1 Updating a forecaster with the `update` method\n",
"\n",
"The `update` method can be called when a forecaster is already fitted, to ingest new data and make updated forecasts - this is referred to as an \"update step\".\n",
"\n",
"After the update, the forecaster's internal \"now\" state (the `cutoff`) is set to the latest time stamp seen in the update batch (assumed to be later than previously seen data).\n",
"\n",
"The general pattern is as follows:\n",
"\n",
"1. Specify a forecasting strategy\n",
"2. Specify a relative forecasting horizon\n",
"3. Fit the forecaster to an initial batch of data using `fit`\n",
"4. Make forecasts for the relative forecasting horizon, using `predict`\n",
"5. Obtain new data; use `update` to ingest new data\n",
"6. Make forecasts using `predict` for the updated data\n",
"7. Repeat 5 and 6 as often as required\n",
"\n",
"**Example**: suppose that, in the airline example, we want to make forecasts a year ahead, but every month, starting December 1957. The first few months, forecasts would be made as follows:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.044833Z",
"start_time": "2023-08-25T09:25:15.457616900Z"
}
},
"outputs": [],
"source": [
"from aeon.datasets import load_airline\n",
"from aeon.forecasting.ets import AutoETS\n",
"from aeon.visualisation import plot_series"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.044833Z",
"start_time": "2023-08-25T09:25:15.461605300Z"
}
},
"outputs": [],
"source": [
"# we prepare the full data set for convenience\n",
"# note that in the scenario we will \"know\" only part of this at certain time points\n",
"y = load_airline()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.310476100Z",
"start_time": "2023-08-25T09:25:15.469584600Z"
}
},
"outputs": [],
"source": [
"# December 1957\n",
"\n",
"# this is the data known in December 1957\n",
"y_1957Dec = y[:-36]\n",
"\n",
"# step 1: specifying the forecasting strategy\n",
"forecaster = AutoETS(auto=True, sp=12, n_jobs=-1)\n",
"\n",
"# step 2: specifying the forecasting horizon: one year ahead, all months\n",
"fh = np.arange(1, 13)\n",
"\n",
"# step 3: this is the first time we use the model, so we fit it\n",
"forecaster.fit(y_1957Dec)\n",
"\n",
"# step 4: obtaining the first batch of forecasts for Jan 1958 - Dec 1958\n",
"y_pred_1957Dec = forecaster.predict(fh)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.311473700Z",
"start_time": "2023-08-25T09:25:18.975247700Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plotting predictions and past data\n",
"plot_series(\n",
" y[:-35],\n",
" y_pred_1957Dec,\n",
" y_pred_1958Jan,\n",
" y_pred_1958Feb,\n",
" labels=[\"y_1957Dec\", \"y_pred_1957Dec\", \"y_pred_1958Jan\", \"y_pred_1958Feb\"],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"... and so on.\n",
"\n",
"A shorthand for running first `update` and then `predict` is `update_predict_single` - for some algorithms, this may be more efficient than the separate calls to `update` and `predict`:"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.900898200Z",
"start_time": "2023-08-25T09:25:21.576753800Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"1958-04 349.166352\n",
"1958-05 346.925002\n",
"1958-06 394.064152\n",
"1958-07 435.855204\n",
"1958-08 433.331082\n",
"1958-09 384.856436\n",
"1958-10 335.547256\n",
"1958-11 293.184412\n",
"1958-12 333.288639\n",
"1959-01 338.608873\n",
"1959-02 336.996809\n",
"1959-03 388.137360\n",
"Freq: M, dtype: float64"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# March 1958\n",
"\n",
"# new data is observed:\n",
"y_1958Mar = y[[-34]]\n",
"\n",
"# step 5&6: update/predict in one step\n",
"forecaster.update_predict_single(y_1958Mar, fh=fh)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4.2 Moving the \"now\" state without updating the model\n",
"\n",
"In the rolling deployment mode, may be useful to move the estimator's \"now\" state (the `cutoff`) to later, for example if no new data was observed, but time has progressed; or, if computations take too long, and forecasts have to be queried.\n",
"\n",
"The `update` interface provides an option for this, via the `update_params` argument of `update` and other update funtions.\n",
"\n",
"If `update_params` is set to `False`, no model update computations are performed; only data is stored, and the internal \"now\" state (the `cutoff`) is set to the most recent date."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.901895500Z",
"start_time": "2023-08-25T09:25:22.199123900Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
AutoETS(auto=True, n_jobs=-1, sp=12)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
AutoETS(auto=True, n_jobs=-1, sp=12)
"
],
"text/plain": [
"AutoETS(auto=True, n_jobs=-1, sp=12)"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# April 1958\n",
"\n",
"# new data is observed:\n",
"y_1958Apr = y[[-33]]\n",
"\n",
"# step 5: perform an update without re-computing the model parameters\n",
"forecaster.update(y_1958Apr, update_params=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4.3 Walk-forward predictions on a batch of data\n",
"\n",
"`aeon` can also simulate the update/predict deployment mode with a full batch of data.\n",
"\n",
"This is not useful in deployment, as it requires all data to be available in advance; however, it is useful in playback, such as for simulations or model evaluation.\n",
"\n",
"The update/predict playback mode can be called using `update_predict` and a re-sampling constructor which encodes the precise walk-forward scheme."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.901895500Z",
"start_time": "2023-08-25T09:25:22.204079700Z"
}
},
"outputs": [],
"source": [
"# from aeon.datasets import load_airline\n",
"# from aeon.forecasting.ets import AutoETS\n",
"# from aeon.forecasting.model_selection import ExpandingWindowSplitter\n",
"# from aeon.visualisation import plot_series"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NOTE: commented out - this part of the interface is currently undergoing a re-work. Contributions and PR are appreciated."
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.902892Z",
"start_time": "2023-08-25T09:25:22.208066800Z"
}
},
"outputs": [],
"source": [
"# for playback, the full data needs to be loaded in advance\n",
"# y = load_airline()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.902892Z",
"start_time": "2023-08-25T09:25:22.212055800Z"
}
},
"outputs": [],
"source": [
"# step 1: specifying the forecasting strategy\n",
"# forecaster = AutoETS(auto=True, sp=12, n_jobs=-1)\n",
"\n",
"# step 2: specifying the forecasting horizon\n",
"# fh - np.arange(1, 13)\n",
"\n",
"# step 3: specifying the cross-validation scheme\n",
"# cv = ExpandingWindowSplitter()\n",
"\n",
"# step 4: fitting the forecaster - fh should be passed here\n",
"# forecaster.fit(y[:-36], fh=fh)\n",
"\n",
"# step 5: rollback\n",
"# y_preds = forecaster.update_predict(y, cv)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.5 Advanced evaluation worfklow: rolling re-sampling and aggregate errors, rolling back-testing\n",
"\n",
"To evaluate forecasters with respect to their performance in rolling forecasting, the forecaster needs to be tested in a set-up mimicking rolling forecasting, usually on past data. Note that the batch back-testing as in Section 1.3 would not be an appropriate evaluation set-up for rolling deployment, as that tests only a single forecast batch.\n",
"\n",
"The advanced evaluation workflow can be carried out using the `evaluate` benchmarking function.\n",
"`evalute` takes as arguments:\n",
"- a `forecaster` to be evaluated\n",
"- a `scikit-learn` re-sampling strategy for temporal splitting (`cv` below), e.g., `ExpandingWindowSplitter` or `SlidingWindowSplitter`\n",
"- a `strategy` (string): whether the forecaster should be always be refitted or just fitted once and then updated"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:23.948769Z",
"start_time": "2023-08-25T09:25:22.215047700Z"
}
},
"outputs": [],
"source": [
"from aeon.forecasting.model_evaluation import evaluate\n",
"from aeon.forecasting.model_selection import ExpandingWindowSplitter\n",
"from aeon.forecasting.theta import ThetaForecaster"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:24.097372900Z",
"start_time": "2023-08-25T09:25:22.219037Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# visualization of a forecaster evaluation\n",
"fig, ax = plot_series(\n",
" y,\n",
" df[\"y_pred\"].iloc[0],\n",
" df[\"y_pred\"].iloc[1],\n",
" df[\"y_pred\"].iloc[2],\n",
" df[\"y_pred\"].iloc[3],\n",
" df[\"y_pred\"].iloc[4],\n",
" df[\"y_pred\"].iloc[5],\n",
" markers=[\"o\", \"\", \"\", \"\", \"\", \"\", \"\"],\n",
" labels=[\"y_true\"] + [\"y_pred (Backtest \" + str(x) + \")\" for x in range(6)],\n",
")\n",
"ax.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Todo: performance metrics, averages, and testing - contributions to `aeon` and the tutorial are welcome."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Forecasters in aeon - searching, tags, common families\n",
"\n",
"This section summarizes how to:\n",
"\n",
"* search for forecasters in aeon\n",
"* properties of forecasters, corresponding search options and tags\n",
"* commonly used types of forecasters in `aeon`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1 Listing all forecasters in `aeon`\n",
"\n",
"Generally, all forecasters available in `aeon` can be listed with the `all_estimators` command.\n",
"\n",
"This will list all forecasters in `aeon`, even those whose soft dependencies are not installed."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:25.471698500Z",
"start_time": "2023-08-25T09:25:22.651879800Z"
}
},
"outputs": [
{
"data": {
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\n",
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0
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ARDL
\n",
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<class 'aeon.forecasting.ardl.ARDL'>
\n",
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\n",
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1
\n",
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ARIMA
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\n",
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BATS
\n",
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<class 'aeon.forecasting.bats.BATS'>
\n",
"
\n",
"
\n",
"
6
\n",
"
BaggingForecaster
\n",
"
<class 'aeon.forecasting.compose._bagging.Bagg...
\n",
"
\n",
"
\n",
"
7
\n",
"
ColumnEnsembleForecaster
\n",
"
<class 'aeon.forecasting.compose._column_ensem...
\n",
"
\n",
"
\n",
"
8
\n",
"
ConformalIntervals
\n",
"
<class 'aeon.forecasting.conformal.ConformalIn...
\n",
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"
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"
9
\n",
"
Croston
\n",
"
<class 'aeon.forecasting.croston.Croston'>
\n",
"
\n",
"
\n",
"
10
\n",
"
DirRecTabularRegressionForecaster
\n",
"
<class 'aeon.forecasting.compose._reduce.DirRe...
\n",
"
\n",
"
\n",
"
11
\n",
"
DirRecTimeSeriesRegressionForecaster
\n",
"
<class 'aeon.forecasting.compose._reduce.DirRe...
\n",
"
\n",
"
\n",
"
12
\n",
"
DirectTabularRegressionForecaster
\n",
"
<class 'aeon.forecasting.compose._reduce.Direc...
\n",
"
\n",
"
\n",
"
13
\n",
"
DirectTimeSeriesRegressionForecaster
\n",
"
<class 'aeon.forecasting.compose._reduce.Direc...
\n",
"
\n",
"
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"
14
\n",
"
DontUpdate
\n",
"
<class 'aeon.forecasting.stream._update.DontUp...
\n",
"
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"
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"
15
\n",
"
DynamicFactor
\n",
"
<class 'aeon.forecasting.dynamic_factor.Dynami...
\n",
"
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"
\n",
"
16
\n",
"
EnsembleForecaster
\n",
"
<class 'aeon.forecasting.compose._ensemble.Ens...
\n",
"
\n",
"
\n",
"
17
\n",
"
ExponentialSmoothing
\n",
"
<class 'aeon.forecasting.exp_smoothing.Exponen...
\n",
"
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"
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18
\n",
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ForecastByLevel
\n",
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<class 'aeon.forecasting.compose._grouped.Fore...
\n",
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19
\n",
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ForecastX
\n",
"
<class 'aeon.forecasting.compose._pipeline.For...
\n",
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20
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"
ForecastingGridSearchCV
\n",
"
<class 'aeon.forecasting.model_selection._tune...
\n",
"
\n",
"
\n",
"
21
\n",
"
ForecastingPipeline
\n",
"
<class 'aeon.forecasting.compose._pipeline.For...
\n",
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\n",
"
\n",
"
22
\n",
"
ForecastingRandomizedSearchCV
\n",
"
<class 'aeon.forecasting.model_selection._tune...
\n",
"
\n",
"
\n",
"
23
\n",
"
HierarchyEnsembleForecaster
\n",
"
<class 'aeon.forecasting.compose._hierarchy_en...
\n",
"
\n",
"
\n",
"
24
\n",
"
MockForecaster
\n",
"
<class 'aeon.testing.mock_estimators._mock_for...
\n",
"
\n",
"
\n",
"
25
\n",
"
MockUnivariateForecasterLogger
\n",
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<class 'aeon.testing.mock_estimators._mock_for...
\n",
"
\n",
"
\n",
"
26
\n",
"
MultioutputTabularRegressionForecaster
\n",
"
<class 'aeon.forecasting.compose._reduce.Multi...
\n",
"
\n",
"
\n",
"
27
\n",
"
MultioutputTimeSeriesRegressionForecaster
\n",
"
<class 'aeon.forecasting.compose._reduce.Multi...
\n",
"
\n",
"
\n",
"
28
\n",
"
MultiplexForecaster
\n",
"
<class 'aeon.forecasting.compose._multiplexer....
\n",
"
\n",
"
\n",
"
29
\n",
"
NaiveForecaster
\n",
"
<class 'aeon.forecasting.naive.NaiveForecaster'>
\n",
"
\n",
"
\n",
"
30
\n",
"
NaiveVariance
\n",
"
<class 'aeon.forecasting.naive.NaiveVariance'>
\n",
"
\n",
"
\n",
"
31
\n",
"
OnlineEnsembleForecaster
\n",
"
<class 'aeon.forecasting.online_learning._onli...
\n",
"
\n",
"
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"
32
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"
Permute
\n",
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<class 'aeon.forecasting.compose._pipeline.Per...
\n",
"
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"
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"
33
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"
PolynomialTrendForecaster
\n",
"
<class 'aeon.forecasting.trend.PolynomialTrend...
\n",
"
\n",
"
\n",
"
34
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"
Prophet
\n",
"
<class 'aeon.forecasting.fbprophet.Prophet'>
\n",
"
\n",
"
\n",
"
35
\n",
"
ReconcilerForecaster
\n",
"
<class 'aeon.forecasting.reconcile.ReconcilerF...
\n",
"
\n",
"
\n",
"
36
\n",
"
RecursiveTabularRegressionForecaster
\n",
"
<class 'aeon.forecasting.compose._reduce.Recur...
\n",
"
\n",
"
\n",
"
37
\n",
"
RecursiveTimeSeriesRegressionForecaster
\n",
"
<class 'aeon.forecasting.compose._reduce.Recur...
\n",
"
\n",
"
\n",
"
38
\n",
"
SARIMAX
\n",
"
<class 'aeon.forecasting.sarimax.SARIMAX'>
\n",
"
\n",
"
\n",
"
39
\n",
"
STLForecaster
\n",
"
<class 'aeon.forecasting.trend.STLForecaster'>
\n",
"
\n",
"
\n",
"
40
\n",
"
SquaringResiduals
\n",
"
<class 'aeon.forecasting.squaring_residuals.Sq...
\n",
"
\n",
"
\n",
"
41
\n",
"
StackingForecaster
\n",
"
<class 'aeon.forecasting.compose._stack.Stacki...
\n",
"
\n",
"
\n",
"
42
\n",
"
StatsForecastAutoARIMA
\n",
"
<class 'aeon.forecasting.statsforecast.StatsFo...
\n",
"
\n",
"
\n",
"
43
\n",
"
TBATS
\n",
"
<class 'aeon.forecasting.tbats.TBATS'>
\n",
"
\n",
"
\n",
"
44
\n",
"
ThetaForecaster
\n",
"
<class 'aeon.forecasting.theta.ThetaForecaster'>
\n",
"
\n",
"
\n",
"
45
\n",
"
ThetaModularForecaster
\n",
"
<class 'aeon.forecasting.theta.ThetaModularFor...
\n",
"
\n",
"
\n",
"
46
\n",
"
TransformedTargetForecaster
\n",
"
<class 'aeon.forecasting.compose._pipeline.Tra...
\n",
"
\n",
"
\n",
"
47
\n",
"
TrendForecaster
\n",
"
<class 'aeon.forecasting.trend.TrendForecaster'>
\n",
"
\n",
"
\n",
"
48
\n",
"
UnobservedComponents
\n",
"
<class 'aeon.forecasting.structural.Unobserved...
\n",
"
\n",
"
\n",
"
49
\n",
"
UpdateEvery
\n",
"
<class 'aeon.forecasting.stream._update.Update...
\n",
"
\n",
"
\n",
"
50
\n",
"
UpdateRefitsEvery
\n",
"
<class 'aeon.forecasting.stream._update.Update...
\n",
"
\n",
"
\n",
"
51
\n",
"
VAR
\n",
"
<class 'aeon.forecasting.var.VAR'>
\n",
"
\n",
"
\n",
"
52
\n",
"
VARMAX
\n",
"
<class 'aeon.forecasting.varmax.VARMAX'>
\n",
"
\n",
"
\n",
"
53
\n",
"
VECM
\n",
"
<class 'aeon.forecasting.vecm.VECM'>
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" name \\\n",
"0 ARDL \n",
"1 ARIMA \n",
"2 AutoARIMA \n",
"3 AutoETS \n",
"4 AutoEnsembleForecaster \n",
"5 BATS \n",
"6 BaggingForecaster \n",
"7 ColumnEnsembleForecaster \n",
"8 ConformalIntervals \n",
"9 Croston \n",
"10 DirRecTabularRegressionForecaster \n",
"11 DirRecTimeSeriesRegressionForecaster \n",
"12 DirectTabularRegressionForecaster \n",
"13 DirectTimeSeriesRegressionForecaster \n",
"14 DontUpdate \n",
"15 DynamicFactor \n",
"16 EnsembleForecaster \n",
"17 ExponentialSmoothing \n",
"18 ForecastByLevel \n",
"19 ForecastX \n",
"20 ForecastingGridSearchCV \n",
"21 ForecastingPipeline \n",
"22 ForecastingRandomizedSearchCV \n",
"23 HierarchyEnsembleForecaster \n",
"24 MockForecaster \n",
"25 MockUnivariateForecasterLogger \n",
"26 MultioutputTabularRegressionForecaster \n",
"27 MultioutputTimeSeriesRegressionForecaster \n",
"28 MultiplexForecaster \n",
"29 NaiveForecaster \n",
"30 NaiveVariance \n",
"31 OnlineEnsembleForecaster \n",
"32 Permute \n",
"33 PolynomialTrendForecaster \n",
"34 Prophet \n",
"35 ReconcilerForecaster \n",
"36 RecursiveTabularRegressionForecaster \n",
"37 RecursiveTimeSeriesRegressionForecaster \n",
"38 SARIMAX \n",
"39 STLForecaster \n",
"40 SquaringResiduals \n",
"41 StackingForecaster \n",
"42 StatsForecastAutoARIMA \n",
"43 TBATS \n",
"44 ThetaForecaster \n",
"45 ThetaModularForecaster \n",
"46 TransformedTargetForecaster \n",
"47 TrendForecaster \n",
"48 UnobservedComponents \n",
"49 UpdateEvery \n",
"50 UpdateRefitsEvery \n",
"51 VAR \n",
"52 VARMAX \n",
"53 VECM \n",
"\n",
" estimator \n",
"0 \n",
"1 \n",
"2 \n",
"3 \n",
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"52 \n",
"53 "
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from aeon.registry import all_estimators\n",
"\n",
"all_estimators(\"forecaster\", as_dataframe=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The entries of the last column of the resulting dataframe are classes which could be directly used for construction, or simply inspected for the correct import path."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2 Forecaster tags\n",
"\n",
"All forecasters `aeon` have so-called tags which describe properties of the estimator, e.g., whether it is multivariate, probabilistic, or not. Use of tags, inspection, and retrieval will be described in this section."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2.1 Capability tags: multivariate, probabilistic, hierarchical"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Every forecaster has tags, which are key-value pairs that can describe capabilities or internal implementation details.\n",
"\n",
"The most important \"capability\" style tags are the following:\n",
"\n",
"`requires-fh-in-fit` - a boolean. Whether the forecaster requires the forecasting horizon `fh` already in `fit` (`True`), or whether it can be passed late in `predict` (`False`).\n",
"\n",
"`y_input_type` - a string. Whether the forecaster is univariate (`\"univariate\"`), strictly multivariate (`\"multivariate\"`), or can deal with any number of variables (`\"both\"`).\n",
"\n",
"`capability:pred_int` - a boolean. Whether the forecaster can return probabilistic predictions via `predict_interval` etc, see Section 1.5.\n",
"\n",
"`ignores-exogeneous-X` - a boolean. Whether the forecaster makes use of exogeneous variables `X` (`False`) or not (`True`). If the forecaster does not use `X`, it can still be passed for interface uniformity, and will be ignored.\n",
"\n",
"`capability:missing_values` - a boolean. Whether the forecaster can deal with missing data in the inputs `X` or `y`.\n",
"\n",
"Tags of a forecaster instance can be inspected via the `get_tags` (lists all tags) and `get_tag` (gets value for one tag) methods.\n",
"\n",
"Tag values may depend on hyper-parameter choices."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:25.592375200Z",
"start_time": "2023-08-25T09:25:22.764579Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"{'y_input_type': 'univariate',\n",
" 'ignores-exogeneous-X': False,\n",
" 'capability:pred_int': True,\n",
" 'capability:missing_values': True,\n",
" 'y_inner_type': 'pd.Series',\n",
" 'X_inner_type': 'pd.DataFrame',\n",
" 'requires-fh-in-fit': False,\n",
" 'X-y-must-have-same-index': True,\n",
" 'enforce_index_type': None,\n",
" 'fit_is_empty': False,\n",
" 'python_version': None,\n",
" 'python_dependencies': 'pmdarima'}"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from aeon.forecasting.arima import ARIMA\n",
"\n",
"ARIMA().get_tags()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `y_inner_type` and `X_inner_type` indicate whether the forecaster can deal with panel or hierarchical data natively - if an panel or hierarchical mtype occurs here, it does (see data types tutorial).\n",
"\n",
"An explanation for all tags can be obtained using the `all_tags` utility, see Section 2.2.3."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2.2 Finding and listing forecasters by tag"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To list forecasters with their tags, the `all_estimators` utility can be used with its `return_tags` argument.\n",
"\n",
"The resulting data frame can then be used for table queries or sub-setting."
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:25.593373300Z",
"start_time": "2023-08-25T09:25:22.769565800Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
" name \\\n",
"0 ColumnEnsembleForecaster \n",
"1 DynamicFactor \n",
"2 EnsembleForecaster \n",
"3 ForecastByLevel \n",
"4 ForecastingGridSearchCV \n",
"5 ForecastingPipeline \n",
"6 ForecastingRandomizedSearchCV \n",
"7 HierarchyEnsembleForecaster \n",
"8 MockForecaster \n",
"9 MultiplexForecaster \n",
"10 Permute \n",
"11 TransformedTargetForecaster \n",
"12 VAR \n",
"13 VARMAX \n",
"14 VECM \n",
"\n",
" estimator \n",
"0 \n",
"13 \n",
"14 "
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# this lists all forecasters that can deal with multivariate data\n",
"all_estimators(\n",
" \"forecaster\",\n",
" as_dataframe=True,\n",
" filter_tags={\"y_input_type\": [\"multivariate\", \"both\"]},\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Important note: as said above, tag values can depend on hyper-parameter settings, e.g., a `ForecastingPipeline` can handle multivariate data only if the forecaster in it can handle multivariate data.\n",
"\n",
"In retrieval as above, the tags for a class are usually set to indicate the most general potential value, e.g., if for some parameter choice the estimator can handle multivariate, it will appear on the list."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2.3 Listing all forecaster tags"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To list all forecaster tags with an explanation of the tag, the `all_tags` utility can be used:"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:25.595368Z",
"start_time": "2023-08-25T09:25:22.892239600Z"
}
},
"outputs": [
{
"data": {
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]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"from aeon.registry import all_tags\n",
"\n",
"# wrapping this in a pandas DataFrame for pretty display\n",
"pd.DataFrame(all_tags(estimator_types=\"forecaster\"))[[0, 3]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3 Common forecasters in `aeon`\n",
"\n",
"`aeon` supports a number of commonly used forecasters, many of them interfaced from state-of-art forecasting packages. All forecasters are available under the unified `aeon` interface.\n",
"\n",
"Some classes that are currently stably supported are:\n",
"\n",
"* `ExponentialSmoothing`, `ThetaForecaster`, and `autoETS` from `statsmodels`\n",
"* `ARIMA` and `AutoARIMA` from `pmdarima`\n",
"* `AutoARIMA` from `statsforecast`\n",
"* `BATS` and `TBATS` from `tbats`\n",
"* `PolynomialTrend` for forecasting polynomial trends\n",
"* `Prophet` which interfaces Facebook `prophet`\n",
"\n",
"This is not the full list, use `all_estimators` as demonstrated in Sections 2.1 and 2.2 for that.\n",
"\n",
"For illustration, all estimators below will be presented on the basic forecasting workflow - though they also support the advanced forecasting and evaluation workflows under the unified `aeon` interface (see Section 1).\n",
"\n",
"For use in the other workflows, simply replace the \"forecaster specification block\" (\"`forecaster=`\") by the forecaster specification block in the examples presented below."
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:25.595368Z",
"start_time": "2023-08-25T09:25:22.902211100Z"
}
},
"outputs": [],
"source": [
"# imports necessary for this chapter\n",
"from aeon.datasets import load_airline\n",
"from aeon.forecasting.base import ForecastingHorizon\n",
"from aeon.forecasting.model_selection import temporal_train_test_split\n",
"from aeon.performance_metrics.forecasting import mean_absolute_percentage_error\n",
"from aeon.visualisation import plot_series\n",
"\n",
"# data loading for illustration (see section 1 for explanation)\n",
"y = load_airline()\n",
"y_train, y_test = temporal_train_test_split(y, test_size=36)\n",
"fh = ForecastingHorizon(y_test.index, is_relative=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3.1 Exponential smoothing, theta forecaster, autoETS from `statsmodels`\n",
"\n",
"`aeon` interfaces a number of statistical forecasting algorithms from `statsmodels`: exponential smoothing, theta, and auto-ETS.\n",
"\n",
"For example, to use exponential smoothing with an additive trend component and multiplicative seasonality on the airline data set, we can write the following. Note that since this is monthly data, a good choic for seasonal periodicity (sp) is 12 (= hypothesized periodicity of a year)."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:25.595368Z",
"start_time": "2023-08-25T09:25:22.913181400Z"
}
},
"outputs": [],
"source": [
"from aeon.forecasting.exp_smoothing import ExponentialSmoothing"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:26.242673200Z",
"start_time": "2023-08-25T09:25:22.918169Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.0511419028267833"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
},
{
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"forecaster = ExponentialSmoothing(trend=\"add\", seasonal=\"additive\", sp=12)\n",
"\n",
"forecaster.fit(y_train)\n",
"y_pred = forecaster.predict(fh)\n",
"plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"])\n",
"mean_absolute_percentage_error(y_test, y_pred, symmetric=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The exponential smoothing of state space model can also be automated similar\n",
" to the [ets](https://www.rdocumentation.org/packages/forecast/versions/8.13/topics/ets) function in R. This is implemented in the `AutoETS` forecaster."
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:26.242673200Z",
"start_time": "2023-08-25T09:25:23.524903Z"
}
},
"outputs": [],
"source": [
"from aeon.forecasting.ets import AutoETS"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:26.984654200Z",
"start_time": "2023-08-25T09:25:23.530901400Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.06186354612548242"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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FEEIIIYQQQgghRKfTrMrIYqmMbC+SjGwjmurA53XjrSjE53WjqY72DimkBQsWMGzYsBa959KlS7HZbC16TyGEEEIIIYQQQpzZ3McDlZGDG3xNYKK2bNNuP+06TftMoXmclGx5jtLMJWguO4rZRvSwOVhHz0MxyEAWIYQQQgghhBBCiMbwVhahVRQCYOwysMHXGasqIz32/fg0LzpF3yrxidpJZWQj+Xw+NNXR4C+vu4ySzU9j37gIzWUHQHPZsW98gpLNz+B1lzb4Xg0dfP7OO+8QGxuLy+WqdvzSSy/luuuuq/W6pUuX8thjj7F161Z0Oh06nY6lS5cCYLfbueWWW4iLiyM6OprJkyezdevW4LVbt25l0qRJREVFER0dzciRI9myZQtr167lxhtvpKSkJHjPBQsWNOo1F0IIIYQQQgghhDhZYIu2IToVxRjR4OsMUSmgN+HzuvCUZbdWeKIOUhnZSD5PBYeWxDToXCWsK8k37aU0c0nI9dLMl7GOuoecv/ZDqyyq9349Zxeja8A32MyZM7njjjv4+OOPmTlzJgCFhYV89tlnLF++vNbrrrzySnbs2MGXX37JypUrAbBarcF7hoWF8cUXX2C1Wnn99deZMmUKe/bsoUuXLlx77bUMHz6cV199Fb1eT2ZmJkajkfHjx/Piiy/yyCOPsHv3bgAiIyPrfQ5CCCGEEEIIIYQQtVGD/SIbPrwGQKfoMVr7oB7fhVq8F6M1tTXCE3WQyshWpA9PxFtRGKyIPJXmsuOtPIo+PLFFHzcsLIxrrrmGt99+O3js73//OykpKUycOLHO6yIjIzEYDCQmJpKYmEhYWBjr1q1j06ZNvP/++4waNYp+/frx3HPPYbPZ+Pe//w1AdnY2U6dOZeDAgfTr14+ZM2eSnp6OyWTCarWi0+mC95RkpBBCCCGEEEIIIZojUBlp6tK4ZCSc2KotfSPbh1RGNpLOEE7P2cUNP18xophtIROSitmGIaIb3a76tsGP3VC/+93vGD16NEeOHKF79+4sXbqUG264AZ1O1+B7BGzdupXy8nJiY2OrHa+srGT//v0A3H333dxyyy38v//3/5g6dSozZ86kT58+jX4sIYQQQgghhBBCiPqoTZikHRAYYuORidrtQpKRjaTT6Rq0VTpAUx1ED5uDfeMTNdaih83Bp6mN6m3QUMOHDyc9PZ133nmH6dOn8/PPP/PZZ5816V7l5eUkJSWxdu3aGmuBKdkLFizgmmuu4bPPPuOLL77g0UcfZdmyZVx22WXNeBZCCCGEEEIIIYQQNTVlknaAMaY/AKokI9uFJCNbmWKMwDp6HuDvEdmW07RvueUWXnzxRY4cOcLUqVNJTk6u9xqTyYTX6612bMSIEeTn52MwGOjVq1et1/bv35/+/ftz1113cfXVV/P2229z2WWXhbynEEIIIYQQQgghRFN4KwqrZm/oGjVJO0C2abcv6RnZBhSDBeuoe0i59TAptx4h5dbDWEfd06qJSIBrrrmGw4cP8+abb3LTTTc16JpevXqRlZVFZmYmRUVFuFwupk6dyrhx47j00ktZvnw5Bw8eZMOGDTz44INs2bKFyspK5syZw9q1azl06BDr169n8+bNpKWlBe9ZXl7OqlWrKCoqoqKiojWfthBCCCGEEEII0SFpqgOf1423ohCf142mOto7pE7JfWwXAAZrKoqx4S3tAoLbtEsPoXmcLRqbqJ8kI9uIYoxApzehD49Dpze1ytbsU1mtVq644goiIyO59NJLG3TNFVdcwfnnn8+kSZOIi4vjX//6Fzqdjs8//5xzzz2XG2+8kf79+3PVVVdx6NAhEhIS0Ov1HDt2jN/+9rf079+fWbNmccEFF/DYY48BMH78eP73f/+XK6+8kri4OJ555plWfNZCCCGEEEIIIUTHo3mclGx5juw3egS/SrY8L8mwJlCDW7QbP7wGQAmPRzFZAR+ekv0tGJloCNmmfZo7cuQI1157LWazuUHnm83m4ITsk0VFRfHSSy/x0ksvhbzuX//6V533ffXVV3n11VcbFIMQQgghhBBCCHE60VQHJVuew75x0YljLntwvoR11D1tUrR0ughM0jZ2afzwGvDPAzHE9MNdsAW1eG+T+k6KppPKyNNUcXExH374IWvXrmX27NntHY4QQgghhBBCCHHG0ilGSjOXhFwrzXwZnWJs44g6t8AkbVMTJmkHBPtGyhCbNieVkaep4cOHU1xczNNPP82AAQOCxwcPHsyhQ4dCXvP6669z7bXXtlWIQgghhBBCCCHEGUFz2dFc9jrWStCHx7VtUJ2Uz+c7URnZnGSkLZCM3NMicYmGk2TkaergwYMhj3/++eeoqhpyLSEhoRUjEkIIIYQQQgghzkyKyYpitoVMSCpmG4rZ2vZBdVJaRSGa8xjolCZN0g6QidrtR5KRZ5iePXu2dwhCCCGEEEIIIcRpSVMd6BQjmsuOYrbh01TKd7+LPiyO6PTbsW96ssY10cPm4NNUdHpTO0Tc+birhtcYrL1RDGFNvo8xpj8g27TbgyQjhRBCCCGEEEIIIZopMC27NHNJMBkZnX470cNmc3T5LcSd/zfQKZRmvnxifdgcrKPnoRgs7R1+pxHYom3q0rRJ2gFGW18AtMqjeJ3F6C0xzY5NNIwkI4UQQgghhBBCCCGaodZp2VWVkF3OexbFbMU66h5so+/DU5GHPiwe8EkispHUYL/I5iUjFVMU+ogkvI48PPZ96BNHt0R4ogGaPU3b6/WSmZlJcXFxS8QjhBBCCCGEEEII0anUOS176ysYo3uh0+lQjBF4ncco+O/l5CwdiE6RrdmN5W6BSdoBMsSmfTQ6GXnnnXfyl7/8BfAnIs877zxGjBhBcnIya9euben4hBBCCCGEEEIIITq0hkzLDtBHJOEpzUJz5KOWHGijCE8PPp/vRGVklxZIRsoQm3bR6GTkv//9b9LT0wH45JNPyMrK4pdffuGuu+7iwQcfbPEAhRBCCCGEEEIIIToy/0RsWx1rJ6Zl63Q6qchrIm9FPpqruNmTtANkiE37aHQysqioiMTERAA+//xzZs6cSf/+/bnpppvYvn17iwcoTm833HADl156aXuHIYQQQgghhBBCNJlPU4keNifkWmBa9smCFXmSjGwUtXgfxtizMMWPbJFemyfeB0lGtqVGJyMTEhLYuXMnXq+XL7/8kmnTpgFQUVGBXq9v8QBPFw7VjdvrobCyHLfXg0N1t3dIQgghhBBCCCGEaAGKMQLr6HnYxj4YrJBUzDZsYx/yT8s2RlQ7/0RFniQjG0pTHZgTR5NwyQck/WYFmupo9j0NthPbtH0+X7PvJxqm0dO0b7zxRmbNmkVSUhI6nY6pU6cCsHHjRgYObH6J7OnI6VF5dvsaFu9ah91dic0Uxty0DOYPnYzFYGzv8JrN7XZjMknTXSGEEEIIIYQQZy7FYCE89VdYR/0BzWVHHxaHT1NDVvAFk5HSq7BBNI+Tki3PUZq5BM1lRzHbiB42x5/orXp9Haobo6JgdzuxmSyomkaEse5chdGaCjo9PtWB15GLIbJ7WzydM16jKyMXLFjAX/7yF2699VbWr1+P2WwGQK/XM3/+/BYPsKPx+Xw4VFeDv0rdTp7atpqFW1dgd1cCYHdXsnDrCp7atppSt7PB92polv6dd94hNjYWl8tV7fill17KddddV+e1CxYsYNiwYbz++uskJycTHh7OrFmzKCk50Ww3sLV60aJFdOvWjQEDBgCQk5PDrFmzsNlsdOnShV//+tccPHgweJ3X6+Xuu+/GZrMRGxvLvHnz5DcPQgghhBBCCCFOG0Vr5pLz1364i3ag05tqVEQGGGP8/46Wysj6aaqDks1PY9+4KDgkSHPZsW98gpLNz6CpFcEisKRlj5G0bAFJyx7j2e1rcHrUOu+t05swWFMB2ardlhpVGamqKueffz6vvfYaV1xxRbW166+/vkUD66gqPG6i/96wQT1dzREcmPkgi3etC7m+eNc67h0yid7vL6LIVX95cen/LCLCaK73vJkzZ3LHHXfw8ccfM3PmTAAKCwv57LPPWL58eb3X79u3j/fee49PPvmE0tJSbr75Zm6//Xb+8Y9/BM9ZtWoV0dHRrFixAvB/NmbMmMG4ceP49ttvMRgMPPHEE5x//vls27YNk8nE888/z9KlS/nrX/9KWloazz//PB9++CGTJ0+uNyYhhBBCCCGEEKIj82ke1KId+LxOjNbedZ4b6FWoVRTiddrRW2xtEGHnpFOMlGYuCblWmvkyplHzeLaqCCwgUAQGcO+QSUQYTbVWThpt/fDY96EW7yUseWJbPKUzXqMqI41GI9u2bWutWE47ieFRFDrLgxWRp7K7KznqLCcxPKpFHzcsLIxrrrmGt99+O3js73//OykpKUycOLHe651OJ++88w7Dhg3j3HPPZfHixSxbtoz8/PzgOREREbz11lsMHjyYwYMH8+6776JpGm+99RZDhgwhLS2Nt99+m+zsbNauXQvAiy++yP3338/ll19OWloar732GlartZYohBBCCCGEEEKIzkM9/gs+rxOdKQqDrU+d5yqmKPQR3fzXSXVknTSXPVgRWYNiwKgY6iwCMypKnZWTlu4TANky35Ya3TPyf/7nf/jLX/7CU0891RrxdHjhBhOl/7OowecbFT02U1jIhKTNFEa38Gg2XDS3wY/dUL/73e8YPXo0R44coXv37ixdupQbbrgBnU5X77UpKSl0736iT8K4cePQNI3du3cHJ6kPGTKkWp/IrVu3sm/fPqKiqidWnU4n+/fvp6SkhLy8PMaOHRtcMxgMjBo1SrZqCyGEEEIIIYTo9FxHMwEwdR2KTld/7Zcxpj9eRy5q8W4sSWNaObrOSzHbUMy2kAlJo60fdndlnUVg5aqLP+9cxxO1VE7e1fc3sP4hSQq3oUYnIz0eD3/9619ZuXIlI0eOJCKiev+DF154ocWC64h0Ol2DtkoHOFQ3c9MyqpULB8xNy6gqC274/Rpq+PDhpKen88477zB9+nR+/vlnPvvssxa7/6nve3l5OSNHjqy2lTsgLi6uxR5XCCGEEEIIIYToiNyFmQCY44c16HxjTH+ch9dKEqwePk0letgc7BufqLEW3vtiwk1htRaB9YmKJdJo5uU6KicfSJ+MEtYVj1RGtplGJyN37NjBiBEjANizp/o3TEOq7s40EUYT84f6eyLWmKadPhmLvvWmad9yyy28+OKLHDlyhKlTp5KcnNyg67Kzs8nNzaVbN3/J+Pfff4+iKMFBNaGMGDGCd999l/j4eKKjo0Oek5SUxMaNGzn33HMBf2L7hx9+CH6ehBBCCCGEEEKIziqQjDTFDWvQ+cGJ2pKMrJNijMA6eh74NEq3vlJtmnb08DlUalqtRWB3DjqX4/VUTpa4nejDE1GLf8HnVdG1Yp5G+DU6GblmzZrWiOO0ZjEYuXfIJB5In0KJ6sRq9DdKbc1EJMA111zDH/7wB958803eeeedBl9nsVi4/vrree655ygtLeWOO+5g1qxZwS3aoVx77bU8++yz/PrXv+bxxx+nR48eHDp0iP/85z/MmzePHj168H//93889dRT9OvXj4EDB/LCCy9gt9tb4JkKIYQQQgghhBDtx+fz4T66FQBTXHqDrpFkZMP5vG5MCSNIviULn+pAMdvwaSqKwUIE/iE1Gj6W7FpfrQjspgFjUNDV2T7Pagoj31UCmgdP6cHgcCHRehqdjAzYt28f+/fv59xzzyUsLAyfzyeVkXWIMPr7K8bpIwEw6Vv/Ma1WK1dccQWfffYZl156aYOv69u3L5dffjkXXnghx48f51e/+hWvvPJKndeEh4fzzTffcN9993H55ZdTVlZG9+7dmTJlSrBS8p577iEvL4/rr78eRVG46aabuOyyyygpKWnO0xRCCCGEEEIIIdqVpzQLzV0CehOm2EENuiaQ9PLY9+HTvOiUNkgUdFKu3A0UfvIbTAmj6X71egB0+hNzLN7eu4mRsT3IufJhHB53tSKwhrTP01ti8JbnoNr3SjKyDTQ6GXns2DFmzZrFmjVr0Ol07N27l969e3PzzTcTExPD888/3xpxiiY6cuQI1157LWZz4/pS3nbbbdx2220h15YuXRryeGJiIn/7299qvafBYODFF1/kxRdfbFQsQgghhBBCCCFERxbcoh07uFqSrC6G6F6gN+HzuvCU5WC09mq1+Do7Z94GAExdBoZcf2ffFn44dpi/n3ctV/ce7j+3KrfbkPZ5jph+uIu2oRbvhdTWfz5nuvrHO53irrvuwmg0kp2dTXh4ePD4lVdeyZdfftmiwYmmKy4u5sMPP2Tt2rXMnj27vcMRQgghhBBCCCFOW8FJ2g3sFwmgU/QYrX0A2apdH1fudwBYuk+osZZfUcoPxw4DMCmxT8jrA+3zjlz1CPt/8wA5Vz7MvUMmBdvnGaqqIdViGWLTFhpdGbl8+XK++uorevToUe14v379OHToUIsFJppn+PDhFBcX8/TTT1cbPDN48OBa36fXX3+9rcITQgghhBBCCCFOG+5Cf7/Ihk7SDjDG9Ec9vsufjOw1vRUi6/x8XhVX/iYAzN3G1Vj/8shuAEZ17UFieOiBuuCvkDzmdPDrVX8lv6KMny+fF2ypZ7QFkpGSFG4LjU5GOhyOahWRAcePH2/0VmDReg4ePBjy+Oeff46qqiHXEhISiIqKYsGCBa0XmBBCCCGEEEIIcZpxN6EyEmSITUO4j2bi81SiWLpgjBlQY/3zw7sAuKBHWr33irVE4PP5KHI5+LbgAJf1HAKc9D7YpTKyLTQ6GXnOOefwzjvvsHDhQgB0Oh2apvHMM88wadKkFg9QtKyePXu2dwhCCCGEEEIIIcRpw+PIx+vIA3SYug5p1LWB5JokI2vnzPX3izQnnY1OV73boKp5WXHE/9pd2IBkJMA5ib352V7At/knJyP9lZE+rwtNrUAx1izCEy2n0cnIZ555hilTprBlyxbcbjfz5s3j559/5vjx46xfv741YuwQfD5fe4dwxpLXXgghhBBCCCFERxUYXmOM6YdiimzUtVIZWb9AMtLSrWa/yPUFWZSqTuIsEYzq2qPGeijnJPTmtV++49uCA8FjeksXEn79Xyw9zkVzl6JTDPg0FcUY0TJPQlTT6AE2Z511Fnv27CEjI4Nf//rXOBwOLr/8cn766Sf69AndKLQz0+v945fcbnc7R3LmCrz2gfdCCCGEEEIIIYToKJq6RRvA2MWfjPSWH0ZTHS0Y1enB5/PhCiYjx9dY/+LwLwDM6D4QRdewFNe5ib0ByDyeS4m7EgDN48SVt5Gct1LJeTOF7Dd6ULLleTSPsyWehjhFoysjAaxWKw8++GBLx9IhGQwGwsPDOXr0KEajEUVpdP5WNIOmaRw9epTw8HAMhiZ9XIUQQgghhBBCnEJTHegUI5rLjmK2SRVYMwQmaVtSpuBQ3RgVBbvbic1kQdW04JCUUPSWLihhXdEqi1CL92COH95GUXcOnpIDeCsKQG/ClDCyxvoXVf0iG7pFG6BbuJW+UV3ZV1bE+oKDnJ+YQsmW57BvejJ4juayY9/4BADWUffI90YLa3R2Z9u2bSGP63Q6LBYLKSkpp9UgG51OR1JSEllZWTItvJ0oikJKSgo6na69QxFCCCGEEEKITk/zOCnZ8hylmUuCycjoYXOwjp6HYrC0d3idjrtwK8aYgRgHXM3T29eweNc67O5KbKYw5qZlMH/oZCwGY63XG239cEkyMqRgv8j4ETU+m4fKj/OzvQBFp2N69/6Nuu85ib3ZV1bEtwUHuKBbH0ozl4Q8rzTzZWxj5jcteFGrRicjhw0bFkwKBXr5nZwkMhqNXHnllbz++utYLHX/EFuwYAGPPfZYtWMDBgzgl1/8ZbZOp5N77rmHZcuW4XK5mDFjBq+88goJCQnB87Ozs7nttttYs2YNkZGRXH/99fzxj39s0So6k8lEv379ZKt2OzGZTFKRKoQQQgghhBAtQFMd/iqwjYtOHJMqsCbTXCV4SvYTdfF/eXr71yzcuiK4ZndXBv9875BJRBhNISsnLT3OxZX3HWqxTHI+lSv3OyD0Fu3Pq7Zoj4/vRYy5cQNnzklI5e29m/g2/wCay47msoc8z79Wgj48rnGBizo1OmP34Ycfct9993HvvfcyZswYADZt2sTzzz/Po48+isfjYf78+Tz00EM899xz9d5v8ODBrFy58kRAJyUR77rrLj777DPef/99rFYrc+bM4fLLLw8OyvF6vVx00UUkJiayYcMG8vLy+O1vf4vRaOTJJ5+s8VjNoShKvclVIYQQQgghhBCiI9MpRqkCa0Guo9tQwroSlTKJxe8tCnnO4l3reCB9Ck6PyrMhKifvGz2fin3/lSE2IThz/fkfc6hkZE7jt2gHnJfon3myuSiHSiUcxWwLmZBUzDYUs7XR9xd1a3QyctGiRfz5z39mxowZwWNDhgyhR48ePPzww2zatImIiAjuueeeBiUjDQYDiYmJNY6XlJTwl7/8hX/+859MnjwZgLfffpu0tDS+//57zj77bJYvX87OnTtZuXIlCQkJDBs2jIULF3LfffexYMECTKba+zIIIYQQQgghhBBnGs1ZLFVgLch9NBN9eCJ2Vzn2qmEop7K7KylTXby081ue2Lqy2vFA5eScjKdwbXy8TWLuLLzO46jH/QlHS9K4amuVHpU1efsAuKDHwEbfu2dkDMkRNnIcdr4vzGLksDnB6uCTRQ+bg09T0eklv9SSGr33dfv27fTs2bPG8Z49e7J9+3bAv5U7Ly+vQffbu3cv3bp1o3fv3lx77bVkZ2cD8MMPP6CqKlOnTg2eO3DgQFJSUvjuO3+Z7nfffceQIUOqbdueMWMGpaWl/Pzzz7U+psvlorS0tNqXEEIIIYQQQghxOlNLD6IzRaKYbSHXpQqs8dyFP+GtyCfGEo3NFBbynD5RsUQZzby8a33I9cW71hGVMglv5bFgOzwBrrzvATDG9KuRIP86fz+VXpUe4VaGxCQ1+t46nY5zEvxTtdcdy8U6eh62sQ8FvzcUsw3b2If8fVSlbUGLa3QycuDAgTz11FPV+ieqqspTTz3FwIH+bPSRI0eqJQhrM3bsWJYuXcqXX37Jq6++SlZWFueccw5lZWXk5+djMpmw2WzVrklISCA/Px+A/Pz8Go8T+HPgnFD++Mc/YrVag1/JyckNeu5CCCGEEEIIIURn5D7+C3nvTqQyezXR6beHPCdQBSYaznU0E62yCGfFUeamZYQ8585B51LsrqyzcrLEWYpiisLraFhh15kgOLym24Qaa59XTdG+oEdak4fdnpPoT0Z+m38AxWDBOuoeUm49TI+b9pB8SxZR6f8rA51aSaO3aS9ZsoRLLrmEHj16MHToUMBfLen1evn0008BOHDgALffHvqH28kuuOCC4H8PHTqUsWPH0rNnT9577z3CwkL/RqEl3H///dx9993BP5eWlkpCUgghhBBCCCHEaUNTHegUY9W0bCuekiwUUzRlO/5K/IX/BJ1CaebLMk27GTSPE/WYPykWruiYP3QyGj6W7FpfrSfkTQPGoKDDZgoLmZC0mcKwWqzkV+SjFu/BENmtrZ9KhxRIRp46vMbn8/FF1fCapmzRDjivKhn5/dFDuLwezFUVkCWbn8Ox7z9YR9yJbfS8Jt9f1K7Rycjx48eTlZXFP/7xD/bs8TdXnTlzJtdccw1RUVEAXHfddU0Kxmaz0b9/f/bt28e0adNwu93Y7fZq1ZEFBQXBHpOJiYls2rSp2j0KCgqCa7Uxm82YzeYmxSiEEEIIIYQQQnRkmsdJyZbnKM1cciLZmH47SbPWgk6HYgzDOuoerKP+gLeyEEN4Ij6fVxKRjaQe+xl8XhRLLPqIJPaUFDIytgfZsx6mwuPGGpiWrTfiUN3MTcuoNm07YG5aBhVHt6FVFqEW7yEseWLbP5kOxud1487fAoD5lH6Ru0uOcqDsGCZFz5Ru/Zr8GP2j44i3RFLoLGdzUQ4ZCakAmOKGUrb9DSr2fyzJyFbS6G3aAFFRUfzv//4vL7zwAi+88AK///3vg4nI5igvL2f//v0kJSUxcuRIjEYjq1atCq7v3r2b7Oxsxo3zfxDHjRvH9u3bKSwsDJ6zYsUKoqOjGTRoULPjEUIIIYQQQgghOhNNdVCy+WnsGxcFB9VoLjv2TU9SmvlycBCHYozg+Lf3U/Dfyynd9pr0xWsCV2EmAKb4Yeh0Ot7PyuTy1UuZ890HxIVFYtIbiDD6X+8Io4n5QyfzcPq0YG9JmymMh9OnMT99Mroj3wDIRO0qrsKf8HmdKGFdMcb0r7b2RdUW7XMT+xBpbHqhmU6nq7ZVOyC896/8MeRvwiPb5ltFoysjwT90Zs2aNRQWFqJpWrW1Rx55pMH3+cMf/sDFF19Mz549yc3N5dFHH0Wv13P11VdjtVq5+eabufvuu+nSpQvR0dHMnTuXcePGcfbZZwMwffp0Bg0axHXXXcczzzxDfn4+Dz30ELNnz5bKRyGEEEIIIYQQZxydYqQ0c0nItdLMl7GNmR/8sz4iAfXYDtxHt7dVeKcV99FMAMxx6QB8kr0TgPOS+oY832Iwcu+QSTyQPoX8yjJizRHkVZZi0RtxVw0OkmSknyuwRTtpXI2ekLvshXQ1R3BhM7ZoB5yb0JsPDm7jm4ID3M8UAAyR3TAnjMZVsJmKA58RPeSWZj+OqK7Rycg333yT2267ja5du5KYmFjtQ6HT6RqVjDx8+DBXX301x44dIy4ujoyMDL7//nvi4vxTkv70pz+hKApXXHEFLpeLGTNm8MorrwSv1+v1fPrpp9x2222MGzeOiIgIrr/+eh5//PHGPi0hhBBCCCGEEKLT01z2YEVk6LWS4GRio82fNFNL9rdVeKcVd6AyMm4Y2eXF/HT8CIpOx0XJabVeE6iU/M+h7fxx6yp+lTKIv2RcGaz+U4v3tnrcnYEz1z953HxSv0iH6saoKNyfPoU/jf01FR53bZc32LlVlZEbCg7i0bwYFD0A4X0u9icj938sychW0Ohk5BNPPMGiRYu47777mv3gy5Ytq3PdYrGwZMkSliwJ/VsdgJ49e/L55583OxYhhBBCCCGEEKKzU8w2FLMtZELSv2YN/tkY4++3p9r3tVV4pw2f5sVdtA3wb9P+NMdfFTk+vhdxlsh6rx9kS6DI5WB17l58Ph/GLv5kpKc0C5/Hhc5w5u729Pl8OHO/A04Mr3F6VJ7dvobFu9ZVGw40f+hkLAZjkx/rrJjE4GChn44dYXRcCgDhfS6heMMjVOasRnOXoZia35pQnNDonpHFxcXMnDmzNWIRQgghhBBCCCFEM/g0lehhc0KuRQ+bg09Tg38OVEZqFYVortI2ie90oRbvxuepRGcIx2jrx3+zfwbgkuTBDbp+QnwvjIqebIedrPLj6MMT0ZmiwKed8ZWqHvs+tMqj6PRmzPEjcKhuntq2moVbVwSnkdvdlSzcuoKntq3GoTa9QlLRKcHBNd8WnOgbaeyShsHWF7xuKg9+1bwnJGpodDJy5syZLF++vDViEUIIIYQQQgghRDMoxgisI+/GNuYBFLPNf8xswzb2Iayj51UbVKOYotCHJwBSHdlYgX6RprihlHrcfJ3vTyBenNKwZGSE0czZVVV4q/P2odPpTtqqfWb3jQxURZoSRqEzmDEqCot3rQt57uJd6zAqTZrNHHRe1Vbtb/Kzgsd0Oh3hvS8GwHHgk2bdX9TU6G3affv25eGHH+b7779nyJAhGI3Vy2HvuOOOFgtOCCGEEEIIIYQQjVO69TVMCSNI/t0hfGoliikSn6aiGCw1zjXY+uKtKEC178WcMKIdou2cXIVbAX+/yC8P70bVvAy0xtPfGtfge0xK6su3BVmsydvHLf3HYozpj7vgB0lGVvWLtHQbB4Dd7QxWRJ7K7q6kRHUSp69/a3xtzknwJyPXFRxA82koOn9yM6LPJZT++Ccqs77A51XR6Zu+HVxU1+hk5BtvvEFkZCRff/01X3/9dbU1nU4nyUghhBBCCCGEEKIdufI34ukyGKPOgB0TNhRUjESEONdo64srd71URjaS5rKjhHXFHD+cT3L8W7QvTh7UqHtMSurL45krWJO3z983UiojAfBWHEUJ6xrsF2kzWYJ9HU9lM4VhNdZMsjfG8NjuRBhMFLsr2VGcz9Au3QAwJ52NEhaHVnkU55FvCUuZ3KzHESc0OhmZlZVV/0lCCCGEEEIIIYRoc15nMVFTX+eZnRtZvOzxeod9mJPOpiLrMzySjGwQTXWgU4zYxtxP7MQXcDntfLH1NQAuSTmrUfcaG9eTML2RgsoydpUUkHKGJyMDr23sxOfRh8fj8/r7m6qaxty0DBZuXVHjmrlpGaiahknf9Mc1KHrGx/diRe4evs0/EExG6hQ94b0vovznpVQc+ESSkS2oyRvr3W43u3fvxuPxtGQ8QgghhBBCCCGEaCKHq5xndm5k4bZVdQ77cKhu3F4Pzj5XkHjTfvSDbmnPsDsFzeOkZMtzZL/Rg8Nv9yfnrVSWb3wdu7uSOEsEY6t6QDaUWW9gQtXwlDW5+87oysjqr+0Act5KpfSnl9A8TiKMJuYPncyD6VOxmcIAf0Xkw+nTmJ8+mQijqdmPf15iHwB+On6k2vFg38j9H+Pz+Zr9OMKv0ZWRFRUVzJ07l7/97W8A7Nmzh969ezN37ly6d+/O/PnzWzxIIYQQQgghhBBC1M8cEc/iXzaEXFu8ax0PpE/B6VF5dvsaFu9ad6JycuB45iepNSonhZ+mOijZ8hz2jYtOHHPZ+e++DWAexkXd+6NvwiCVyUl9WZm7h1V5+7it3yz/fZ3H8VYWoQ/r2mLxd2S1vbb2jU8AYB11DwfKyxgZ24PsWQ9T4XVjNVpQNQ1LC/VxnN59AINjEpjSrT+FleXYTP77h/Wchs4QjrcsB/fRTMzxw1vk8c50jf5Ouf/++9m6dStr167FYjmxL3/q1Km8++67LRqcEEIIIYQQQojTl8Ptwe3RKCx34fZoONyy86657JUldQ77KFNd/HHbKhZuXVG9cnLbqmqVk6I6nWKkNHNJtWM+YJXRX9l4SXLDpmifalJSXwC+zt+PT29BH5UMgFq8t+nBdjKhXtuA0syX0SlGvjj8C5evXsqt698nzhKJSW9okYrIgEG2BLYUHSbl3YUkLVtA0rLHeHb7GtzoiRx8PQAV+z9uscc70zU6GfnRRx/x8ssvk5GRgU6nCx4fPHgw+/fvb9HghBBCCCGEEEJ0XnUlG52ql2fW7CfxseUkLlhO4mPLeXbNfpyqtx0j7tw85UewWaKCW1lP1ScqliijmZd3rQ+5vnjXOoxNqO47E2guO5rLXu3YbiWWw4oVs8/D5Nj4Jt13RGx3oo0W7O5KMo/nYozpjxLWFW9FYQtE3TmEem2rr5WwJt/f03RU1x4t/vgO1c3T21azaOvKkK0NjMPvAqBi/yct/thnqkb/lDl69Cjx8TW/yRwOR7XkpBBCCCGEEEKIM1ddyUaH28MfV+9j4Yo92Cv9QyrslSqPr9jDU6v3SYVkE1Vmr6Isey1z08aHXL9z0LkUuyvrrJwsUZ2tGWKnpZhtKGZbtWOrjL0ByNDyiIxo2pZqg6Ln3ET/fdbk7aXLhCdJvmkv5oRR+LxuNNXRrLg7A8VkrfHaBtfMNrzGSL7N9w9TDlSStiSjorB417qQa4t3rSMsqgdKeDzuom2opYda/PHPRI1ORo4aNYrPPvss+OdAAvKtt95i3LhxLReZEEIIIYQQQohOqa5k45+/PYBB0bF4XVbIa19alyXVeU3kzFmDc9193DnonJDDPm4aMIYYU1itlZM2UxhWoyXk2pnOp6lED5tT7dhKgz+JeHH3fvg0tcn3nlyVYFudu4eK/R+T81YqOX9JJfuNHpRseR7Nc3okiDXVgc/rxltRGEy0lm57k4pDy4lOvz3kNdHD5rD56CHKPS66mMMZ2iWpxeOyu531JuijBt1I/MUfoA9PqBa/aJpGD7B58sknueCCC9i5cycej4c///nP7Ny5kw0bNvD111+3RoxCCCGEEEIIIToRo6LUmmz8eGcBN45OCSYpT2WvVClxqsRFmlszxNOOz+ejMnsNqiOX3678C/83ZApHrnqEMtVVbdiHQ3UzNy2DhVtX1LjH3LTxqJqGSd8OT6CDU4wRWEfPA5+P0q1LyHWrbDckoAN+k3EnijGiyfcOVPuty9tL4a6XMaEBNYe4NOcx2ltgWnZp5hI0lx3FbCM6/Xaih83m6PJbiDv/b6BTKM18+cT6sDlYR8/jm5/9bQXOS+yDomv5X1TYTBZsprCQCclAgt4weh4lPzxP0fKba8SnGCSB31iNfhczMjLIzMzE4/EwZMgQli9fTnx8PN999x0jR45sjRiFEEIIIYQQQnQidqdaa7Jx71EH1jAjtrDQU3BtYUasFpno3Fhq8R68jly2G5P5+mgOv/3mX+CjxrCPCKOJ+UMn83D6tOqVk0MnM2/Q+BYdCnK6UQwWzN3Hk3xLFofO/4Su5gjGdk0mKap5U6/Pikmkqzkchw+26xNqrAeGuHRWmuqgZPPT2DcuCvaG1Fx27JuepDRzCbHnPYditmIddQ8ptx4m5dYjpNx62J+ANVhYk+fvFzm5FbZoA6iaxty0jJBrc9MyUL0qJT++gH3Tk9Xj3/gEJZufkQrJJmh0ZSRAnz59ePPNN1s6FiGEEEIIIYQQpwGbxZ9sDJWQ9Gg+3F6NOzJSeXzFnhrrd2Sk+qvzGl87c0Zz5qwBYIV1AqhwYXIaFkPoBJbFYOTeIZO4P30KeRWlxFkiqTi0guIPLyTsqnUyD6IWamk2FR4PevSM7z6YA73HcLD8eLPvq+gUzotP5oOc3Xxn6MFIb1619cAQF314XLMfqz3UOS176yvYxj6ATqdDV1X5GXieOr0Jl9fD+kJ/lfXEpD6tEl8gQQ/+HpF2dyU2Uxhz0zKYnz4ZMz6K6pj2bRszv1XiOp01Ohn5448/YjQaGTJkCAD//e9/efvttxk0aBALFizAZJLfogghhBBCCCHEmUzV6k426nUwf7K/yumldVnYK1VsYUbuyEhl/uS+WIyyT7ixKnNW4wO+JBHQuKLXkDrPjzD6Ez1Xrf1/ZJUd5+2iN0nTitCcx9CHNa/S73TlsXTlZTss/n5RtYRVn6jYWhO/DTW528CqZGQyc1ybq635h+dYm3X/9tSQadm1JVq/P3oIp9dDQlgUadaaVaMtJZCgn58+mfyKMuLCIsEHFr0Rb0Vhk+MXoTX6V02///3v2bPH/xfKgQMHuPLKKwkPD+f9999n3rx5LR6gEEIIIYQQQojOJcJkYN6kPjw0tV9wO7YtzMgj0/ozf3Jfwk0GLEY9907qQ96j0znwwBRyHp7KneemSiKyCXw+DWfO1+xSunJQ1QjTG7mg+8B6rzPrDcRZIilyOVgfMRQAtXhfa4fbKTlUN0/v+JqF21YFewva3ZUs3LqCp7atxqG6m3X/iQkpAPyoT8JJ9e+B6GFzmjUgp72FmkRefa32RGtgi/akpL6tXrEbYTRRobr59aq/0vu9RVR43SfFaAt5TWdPFLeXRicj9+zZw7BhwwB4//33Oe+88/jnP//J0qVL+eCDD1o6PiGEEEIIIYQQndDbm3MY0cPG4YenUbBgOvmPTufeSX2qJRsjTAbMBoU7PtxO6qJVrMsqbseIOy/30a1ormK+tAwGYEb3AUQYGzYAaHr3/gCsM/YCQLXvbZUYOzujorB414aQa4t3rWv2BPgBXVLoHh6NqtPzU1g/wJ/oso19yD8kpRMPrwk1iTygvkRrIBk5MbF1tmifqoslAp/PR5HLwboC//bw5sQvQmv0d4vP50PT/JOdVq5cyYUXXghAcnIyRUVFLRudEEIIIYQQQohOx+fz8cr6g1y+dDOf7MwnLtKMyaAQYQrdKSwuykyRw836g83vv3cmqsxeDcBycxoAl/ca2uBrp3cfAMAmbwSVGPDYpTIyFLvLEXLaMvgrJEtUZ7Pur9PpmJTkT0L+PPJJety0l+RbsogacnOnn9bsn0R+L7YxDwQrDBuSaHWoLjYezQZab3hNKOck9gYIJiMDk9RtYx9qVPyido3uGTlq1CieeOIJpk6dytdff82rr74KQFZWFgkJrbd/XwghhBBCCCFE57Ajv4xdheWY9ArnD4iv9/wJvbrw9qYcNmRJMrIpnDlr2at0YZ9mwqjo+VVyWoOv7R8dR0qEjWyHnU2G7lwoyciQrKYwbKawkAlJmykMq7H5CcNJSX35+/4fWJufRWnFt5Tv+n9Ep99OzNkPNfvebcGhujEqCna3E5vJgqppwenslYe/xpQwguRbsvCpFShmKz5NrTPRur7wIKrmJSXCRu+o2LZ6GmQk9Oa1X77j2/wDwWOKwYJ11D1YR/0Bb2Uh+vBE8Hk7faK4vTS6MvLFF1/kxx9/ZM6cOTz44IP07evPTv/73/9m/PjxLR6gEEIIIYQQQojO5d3MXAAuGBiHNaz+wR4TenUBYFOOHZfH26qxnW58XjfOI9/yldG/jXVat/5YTWENvl6n0zGtqjryW0MKqiQjQ3LkbWTuwHEh1+amZaBW7SBtjklV06Kzyo6jxo0ETkxJ7+icHpVnt68hadljJC1bQNKyx3h2+xqcHv8W5vLtb1H4yW8o+fHP6MPj0OlN9VYUBrdot0G/yJOdk5AKwE/Hj1B2UsWrYoygbPubFPz3co6tvUsqIpuh0ZWRQ4cOZfv27TWOP/vss+j10mhYCCGEEEIIIc5kPp+P97b6k5GzhnVv0DX94yLoGmGiyOHmx8MljKtKTor6ufI34fNU8FWYf2DN5fVM0Q5lWrf+/GXPRtYZUlDtH+Pz+do0+dPRecpzqVg9m3mz1uLTKby8a321adrz0ydj0TdvmjZAz8gufDn9d4yPT6XUXUniTfspy1mLplagGMOb/0RaiUN18+z2NSzcuiJ4LDDcB+APgzOoOPglABF9Lm7wfdeeNLymLfWIsJEa2YWs8uN8V3go2MoAwBg7CPXYDnye0Fv2RcM0ujIyJyeHw4cPB/+8adMm7rzzTt555x2MxuZ/8wkhhBBCCCGE6Lwyc0vZV+TAYlC4eFDDWnnpdDom9IoBYP1BGWLTGJU5azioWNmlxKDXKVySPLjR95jSrR+KTsc+fSy5qg+torAVIu28Kg+tRC3+hWOr5jCqazLZsx4m96pHybvqUe4dMqlFEpHgry5cV5BFynsL6fHeQrq99yQvF/twNT5106b8w33WhVxbvGsdJr0RxRSNMXYQpq4NS5aXuCvZcsyfe5qU2LbJSICMqurIk7dqA5gT/BWrnpL9eJ3ys6qpGv2Jvuaaa1izxl8mnJ+fz7Rp09i0aRMPPvggjz/+eIsHKIQQQgghhBCi8whs0b4oLYFIc8M3442vqobcIENsGqUyZw1fGfzJmolJfYi1NH7raBdzOKO7JgOw3pAsW7VPUXnoKwB+NPfm0lVvM+7Tl4i3RGLSG4I9EZvLobp5attqnti6MtiX0u6uZOG21Ty9fS0O1d0ij9Ma7G5nncN97M5S9OGJRA64ssH3/Cb/AJrPR7/oriRH2loo0obLOGWITYDe0gWD1b/mLvihzeM6XTQ6Gbljxw7GjBkDwHvvvcdZZ53Fhg0b+Mc//sHSpUtbOj4hhBBCCCGEEJ2Ez+fjvcwjAMwa1q1R105I9Scj12cdx+fztXhspyNNdaC5y/nKXLVFu2fjt2gHTOvWH4BvDT0lGXkSn+al8tBKANYbUwAYHtu9xbex111duB6j0nGrI20mC7Za+pTaTGFYzRF4K/KJ6D+rwfdcm78fgIntUBUJcG6CP+G4sSgbl9dTbS1QHemSZGSTNfrTrKoqZrMZgJUrV3LJJZcAMHDgQPLy8lo2OiGEEEIIIYQQncbmHDsHiyuJMOm5KK3+KdonG9nDitmgcNThZm+Ro5UiPH1oqgOdTo9r8ltsU2LRoeOy5iQjq/rirTck4yze01Jhdnqugi1ormIUk5U1Jf7P5bTu/Vv8ceqrLiypZa0jUDWNuWkZIddmp02gOPtrjNGpGG19GnzPNe3ULzKgX3RX4i2RuLweNhflVFszJ4wC/J8N0TSNTkYOHjyY1157jW+//ZYVK1Zw/vnnA5Cbm0tsbNuNWhdCCCGEEEII0bEEtmhfPCiBcFPj5qWaDXpGJ9sAf3WkqJ3mcVKy5Tmy30zhbx/eAsDZYQpxzZjjMDYuhShFh10J46ei7JYKtdOrPLQcAEfyNH467v98T03q1+KPU291ocnS4o/ZUiKMJuYPncxD6dOCz8FmCuPB9KnMHZTBnT+uQN+v4VWRRU4HW6te64lJDU9gtiSdThfsG7muoHrfSJNURjZbo5ORTz/9NK+//joTJ07k6quvJj09HYCPP/44uH1bCCGEEEIIIcSZRdN8/HtbYIp247ZoBwT6Rq6TvpG10lQHJZufxr5xESgG9sVPpKs5gqnHV1Oy+Rk0tWlVpUZFz3kx/gKjQAWggMqD/mTkpugx+PAxNCaJxPDoFn+cuqoL5w4cR8WxXS3+mC3JYjAyIaEX2bMe5siVj5B31aPc3n8UF372Iu+X67ijOAzNpzXoXl9XbdEebEsgISyqNcOu07lVfSO/za/eN9IcPxzQ4S0/jMeR36B7aaoDn9eNt6IQn9fd5O/T00XjflUFTJw4kaKiIkpLS4mJiQkev/XWWwkP77ij5oUQQgghhBBCtJ7vs4vJsTuJMhs4f0DjtmgHZKR24Zk1sEEqI2ulU4xUHFxO1MX/JSplIoucFbxpicKePZ6KTY9jGzO/yfee1n0Anx4rYq0ahs/na/G+iJ2Nt/IYroLNAHyrWYHDTO3W8lu04UR1IfgnUNvdldhMYcxNy2Be2mhKl98Iv/5vqzx2S8gpt3PB8jdJsESx5zfzMekNWH5Zyl25f+WmyEvZUWZn2/E8BtkSsLud2EwWVE0LOQDoxBbtlq9AbYyMqr6RGwoP4tU09FV9OxVTFMYuA1CP/4K74AcMvS+q8z6BSubSzCVoLjuK2Ub0sDlYR89DMXTcitfW1OhkJIBer6+WiATo1atXS8QjhBBCCCGEEKITCmzR/vXgBCxGfZPuMb6X/9+Zu486KHK46BphbrH4Theau4yYSz/hmZ0bWfz9kyeSVgPHMe/ST9Dc5ejDujTp3uf3ORu2redHJR67/RAxMb1aNvhOpjJ7Ffg0DF0GseqofzDT1FboFxlgMRi5d8gkHkifQn5lGbHmCPIqijn+3iTU4l14ynMxRDat6ri1BQbO9Iy0EWn0f986dr/LOO9h/t/gIUwcOpPFO9cx5cvXqiVa5w+djMVQvb3AYYedruaIdusXGTA0Joloo4VS1cm24jyGx3YPrpkTRqEe/wVXwQ+E15GM1FQHJVue81cyB4657Ng3PgGAddQ9KMaI1nsSHVSTxjH9+9//ZtasWZx99tmMGDGi2pcQQgghhBBCiDOLt9oW7e71nF27LuEm0uIjAdhwsLhFYjvdOA0RPLNzIwu3rQoOPLG7K1m4bTXP7NyI09D0HYt9bUn0xIFHp2f1wc0tFXKnFegXeaTbdHIcdsx6A+dU9RFsLRFGEya9gWUHfqL3+4t44ed16Iz+PozOnDWt+tjNEdhafV6iv8ej+/gvuI9uBcXABemzeHnnehZtXVn9M7t1BU9tW41DdQPgUN24vB7+NPZSDsx8kEnt1C8yQK8ojI/vBcC3+dX7Rp4YYlN330idYqQ0c0nItdLMl9EpTe/z2pk1Ohn50ksvceONN5KQkMBPP/3EmDFjiI2N5cCBA1xwwQWtEaMQQgghhBBCiA7spyN2VK8PW5iR6f3jmnWv8alVfSNlq3ZIRsXA4l82hFxb/MsGjEqTNkAGnWfyJ4ZW5O5t1n06O5/PR+WhFQBsCPNPGs+ITyXcUHNbcWsYYI2nyOVgbf4+wpInAVCZs7ZNHrspgsnIqgSiY/e7AIT3+w0mg5mXd60Led3iXeswKgpOj8qz29fQbdlj9P33k6S8t5A/7fgGp0dtmydQi3MS/cnnb2sdYrMFn89X6/Way47mstexVtIygXYyjU5GvvLKK7zxxhssXrwYk8nEvHnzWLFiBXfccQclJWfmiyiEEEIIIYQQZyKH24Pbo5EQZSHrwSl8+buxmAxN2oAXlFE1xGaDDLEJye52BqvLaq5VUqI6m3X/yVb/cJZVxWf2668e24liiUUfmcxah3/wyrRW3KJ9qnMTe6NDx+6SoxTHjwOgMmdNnYmv9pJdXsyBsmPodQoZCan4fD7Kq5KRUQOvrPczW6a6+OO2VSzcuqLOysn2cE5V38h1BVnVXntT3FBQDGiVR/GW5dR6vWK2oZhtdaxZWzTezqLRf0tkZ2czfvx4AMLCwigrKwPguuuu41//+lfLRieEEEIIIYQQokNyql6eWbOfxMeW0/OJlSQvXMlnuwpxqt5m3XdCVWXklpySZt/rdGQzWbCZwmpZC8NqbN5AjImJqeh9GgdUyCo71qx7tReH6sbt9VBYWY7b62l0MktTHRhsfUi45APi/+cnvi7wT1NureE1ocSYwxke6+8P+b0uDhQj3rJsPCUH6rmy7QX6RY7q2oMoowX1+C40lx2dIQxL9/Pq/Mz2iYolymjm5V3rQ64HKifby6iuyZj1Bgqd5ewpPRo8rhjCMMUOBvzVkbXxaSrRw+aEXIseNgef1r6Vn+2l0e9oYmIix4/7f0OSkpLC999/D0BWVlaHzNALIYQQQgghhGhZDreHP67ex8IVe7BX+v8xba9UWbhiD0+t3ofD7WnyvfvEhhMfacLt1fjhsOy+O5WqacxNywi5NjctA1XTmnX/2Nj+DPPm09UcwZaiw826V3sIbPdNWvYYScsWkLTsMZ7dvqbB230Dk49z3kzh8NsD+GTpeMo9buLMEaR3SWrl6KubmOgf4PL10cNYks4G/NWRHU1gi/a58Sn4vG4UUzTJN+0l8YqvUEyRdX5m7xx0LsXuylat9m0Os97A2K4pAHxblZQOrjWgb6RijMA6+l5sYx4IVkgqZhu2sQ/5p2mfgcNroAnJyMmTJ/Pxxx8DcOONN3LXXXcxbdo0rrzySi677LIWD1AIIYQQQgghRMdiVBQWr8sKufbSuqxmVTLpdDom9JK+kbWJMJr4w+AJPJg+NVhtZjOF8XD6NOanTybC2LyehsbYNF6fOpsDMx/k7LiUJlUWtheH6uapbaubvN1XUx2UbH4a+8ZFwT5/3/r8n8VzzC7whE6YtZaJVf0X1+btw5I8EQBn9upWfUxNdeDzuvFWFOLzutFUR73XfJ1XVRlZ8gPZb/Qg5y+9yXkrlcqDX6F5nEQYTcwfOpmH06fV+MzeNGAMMaawVq32ba5zEqu2auef2jeyYUNsKrO+wJQwguRbDpJy6xFSbj3sn6JtaN/n1Z4a3dn2jTfeQKv6Tcvs2bOJjY1lw4YNXHLJJfz+979v8QCFEEIIIYQQQnQsdqcarIissVapUuJUiYs0N/n+E1K78OGOfOkbWYvFK55k5JBryLnyYRweN1ajBVXTsOibP5nXG96Nj/bu5OUNC7G7K7GZwpiblsH8oZOxGDr25F+jorC4jkEpD6RPqfP6UJOP1xn8VXFjj32NTnm8ZQJtoHMSeqPXKewvO8axuBnogcrDX+Pzaeh0Lb91OVAVWpq5BM1lRzHbiB42x1/BV0vi7FD5cbLKj6MHBv78Ahr+nwuay4594yJAh3XUPViMEdw7ZBIPpE+hRHVW+8w6VDdz0zJYuHVFjfsHqn1N+hZ/ug2WkRB6iI05YQQA7oIf8Pl86HS6kNeXbnsDZ85qupz7HNYRdwCg07fNIKSOqtGfXkVRMBhO5DCvuuoqXnrpJebOnYvJdGa/mEIIIYQQQoiOJzBkpbDchdujNWsLsfCzWYzYwkInpmxhRqyW5iWtJpw0xEbagVXn8/n4V0E+l69eykc/f0GcJRKT3tDsikg4UVn4xLZVHW6QSEM0d7jPqZOPSzCzXR8PwPjK3W0++TjaZGFkbHcANnij0Bkj0CqPohbtaPHHClUV6k8oPkHJ5mdqrZBcW1UVOVQ7SgQ1f0FRmvkyOsX/8yDCaMKkN9T4zNZVOdkS1b7NNS6+J4pOx8HyYg477MHjptiz0OnNaO4SPPZ9Ia/1OPJxHl4LQHifX7VBtJ1Dk1LpxcXFPPfcc9x8883cfPPNPP/888E+kkIIIYQQQgjRUZw8ZCVxwXISH1vOs2v2y2CUZlI1jTsyUkOu3ZGR2uy+hcO7W7EYFI5VqOw+Wt6se51ucgp38bPiT9ZO7j2hRe9dX2Vhew4SaYjmDvc5dfLxd4YeaDqFPt7jdDcZ2mXy8cQkf9/ItQUHsXTz911sjb6RoapCA05OKJ4q0C9yjPtQyHV/grf+JK7FYOTeIZPIu+pR8q9eQN5Vj3LvkEktUu3bXFFGCyOqksIn943U6Y2Y4tKB2ofYOPZ+AD4Nc+IYjNberR9sJ9HonyTffPMNqampvPTSSxQXF1NcXMxLL71Eamoq33zzTWvEKIQQQgghhBCNVtuQlcdbYMjKmS7CZGD+5L48PK1fsELSFmbkkWn9mT+5LxGmRncEq8ZkUBiTYgOkb+Spvtjr/3f3UF0ZiVFdW/Teza0sbG91DUqZnTaBSm/dQ2w0j5Po9NuDfw5s0T7Hc6jdJh8Hk5H5+wlLmQSAs54ehU1xalVozbXQCcVAMnKcEvpaf4K3YUnc2ionO4KMBH8i8adThjqdGGLzY8jrHLvfBSBiwJWtGF3n0+i/IWbPns2sWbN49dVX0ev9m/a9Xi+33347s2fPZvv27S0epBBCCCGEEEI0Vn1DVh6Y0q+NIzq9WIx6JvftyrxJfSl1eogNN/l7wBlbprnbhNQufHPgOBuyirllbM8Wuefp4Ms8f9+6aVFN78lZm0BlYaiEZEcYJFKfCKOJe4ZMRMPHkl3rgz0vZ6dNYO6gDOZ89x9enzCTcEPoJFfZtteJHjYbgJKtr7DO6E9GTu8zvs6+ia1pQnwvDDqFQ+XFFMRNpfvFHxCWMhlvRSGK2YZPU5s9kdnrLEFnjEQx20ImJGtLKB4sO87B8mIMOoWJZ12GZ/POGucEkridvUfiBd0Hcl5ib6Z2609hZTk2U1XPy+RJlG59JWRlpFpyEFfe96BTiOj/m3aIuuNqdGXkvn37uOeee4KJSAC9Xs/dd9/Nvn2h98gLIYQQQgghRFtryJAV0XSqV+OCNzeSumgVTlXDZFCaXRF5skDfyJ0FZS12z87Oo3lZW+7/3M7o1vLJ9LoqCwODRDq6JzNXMjK2B4evfIT8qx4l76pHuX3A2Vy84i/888BP3Lnxv5SrLtxeD4WV5cFp4e7juyne8Ah5708h8qybUK/aymHFilHRc8GEu9pt8nGk0cyYOH9S9NsKD+6CH8h5K5XsN3qQ/UYPSrY8j+ZpesWq+/hu8t4dT2X2qmpVoSerrSp0bVVV5OiuyXQbeQe2MQ8Et7krZhu2sQ/5k7jNTJZ2BBMSUtlSdJjkdxeStGwBScse49nta1B6no8xZiDuwp/wadXbfzj2vAeApcd5GCKS2iPsDqvRf1OMGDGCXbt2MWDAgGrHd+3aRXp6eosFJoQQQgghhBDNERiyEiohaQszEmEy8EthGcm2MIyKgt2pYrMYUTWtRZNqp6tteaU4PRphRh89Y0L36WuOCb1i+PCG0Uzt35WCMhcxYfLebCg8SCl6YrRKxqWOa/H7BwaJgL9HZKCycE5aBvPTJ3eI/n118fl8/H3/Fp7ZsYb3lM2MVcoo8FQSOfBqnht5FXdu/pRFIy/g2e1rePmkysm5aRnMG3Q2Rls/jLFpGKNTWLd3E2fFJNI7qgvR4V3a9XlNTOzDhsKDrPj5EybnvBA8HhgwA2AddU+Dkn6a6kCnGP3Tsk3ReOz7AT0lPy0m8dcfgk6hNPPlE9O002+vtSo0sEV7gjWKvHfPIybjCVJuzUFzlaKYrf6qzXZK4rYkh+rm2e1rWLR1ZfBYYLATwJxznqXs44tRj+/C1PWsE9dVbdGOHnorDtXt/3vG7QxWVXakbehtrdE/xe+44w7+7//+j3379nH22WcD8P3337NkyRKeeuoptm3bFjx36NChLRepEEIIIYQQQjRCYMjK4yv21FibM6EXGw4dJz0pmqdX7+Pl9QexV6rYwozckZHK/Ml9W2y78elq4yE7AGNSbCiKrsXvbzbo+eGwnRvfzZT3pspn+78H4BxvDmFdh7TKY5jQuCslhfuHPMgxZxlWi5V1eXsw+Tp+VeTOY4fIrSzD7PMwuHgTKv5KNfvGJxgMfDh5Not3rgudVPJ5mXPOs9gShuJQ3VyZOoyJiX1JDIvCobrbNXE0MakvT25bxbclJfiAU7/bSjNfxjZmfr330TxOSrY8R2nmkmrJxqQr14KmoRgjsI66B9uY+WiuYnTGSCoPrcRdtB1L4uga9wskI0cULEct/gXH7neJ6HMJ+vA4gE6/NTugvsFO91/5II6wrrgKfggmI93HduIu2o4x9iz0qRfz1LY11RL8c9MymD90MhZDx07wt5ZGJyOvvvpqAObNmxdyTafT4fP50Ol0eL0yoU4IIYQQQgjRPgJDVjSfr0aycd6kvuw5Ws7idVk8sXJv8JrAgBuAeyf1OaOr8OqzMbsYgLEpMS1+b4fbwzNr9st7c4qvjuwGYEo46Awt3zNSUx2UbHkO+8ZFKGFdcYUnM0o/nQJXJZl9Ihk87t4OveV2df5BAEZ5cjFTPR9R/su/SBjzAEt2rQ957eJfvuOBKx/Gg45nt63uUImj8fG9MCl68rUIDilWemnVh8kEBswEkoChnPzennydfdOToFOwjroHIPj+6sMTOPbt/ZT+8Dym+OF0u/o7dLoTnf6yyo5xqLwYg07HWYc/Ap2emHGPtuCz7jjqHezkqkAfnoir4AeiBl8PnDS4ZurrPLVtTbCKMnBN4M/3Dpl0RlZINrpnZFZWVp1fBw4cCP5vYzz11FPodDruvPPO4DGn08ns2bOJjY0lMjKSK664goKCgmrXZWdnc9FFFxEeHk58fDz33nsvHo9MxRNCCCGEEELA7sJyRvSwkfPwVPIXTCf/0encO6kP4SY9gxKieHn9wZDXvbQuC6PS6H8unVG+P+RPRp7ds+WTkfUNHzoT35sjjhK2OyrQ+XxMi09plcfQKUZKM5cAoFUWYTz2E90c/n/bf/HLcnRKx67iWnnkFwDGe3JqrOkM/sE8dSWVyjwqf9y2ioVbVwTPCySOntq2Gofqbr3g6xBmMDI2LhmA7/U9aqw3ZGL1ye/tqUozXw753tpG3oXOFI278CfKd/2j2lqgX+QwpZxwPEQOug5jTP8GPZ/OJjDYKfRaGDZLFN6K/OAQG5/PR/nu91DCuhIRP6zOqsoz8WcZNCEZ2bNnzwZ/NdTmzZt5/fXXa2zrvuuuu/jkk094//33+frrr8nNzeXyyy8Prnu9Xi666CLcbjcbNmzgb3/7G0uXLuWRRx5p7NMSQgghhBBCnIZe+/4Qly/dzLxPdhIfaa42ZEUG3DTdMYebvUUOwL9Nu6WdDu+NQ3XXGJLSHF9UJdqGegvo1m1kS4RYg7/Czl7t2ARPNgDf+rqiuUpCXNUxeDQvXx/1JyEDMZ/M5/FXOdaWVOoTFUuU0czLtVVOtnPiaGJ8LwC+N9RMRtY2YOZkod7b6ms131t9eDy20fcBULzhETS1Irj2dV7V8BrHTlCM2MY+2JCn0SnVN9jJ7XagVRbhLtqGz+vGXfADnpL9GKJ71l9VqTZ9+FBn1u4p2PLycq699lrefPNNYmJO/EatpKSEv/zlL7zwwgtMnjyZkSNH8vbbb7Nhwwa+/97fJ2P58uXs3LmTv//97wwbNowLLriAhQsXsmTJEtzu2n/Qu1wuSktLq30JIYQQQgghTi9O1cu7mbkAXD605iTTwICbUGxhRqyWjl0F1p42VW3R7h8XQZfwlt9i2NnfG6dH5dnta0ha9li1ybtOT9OTqF8e3gXARM9BTAkjWirUavwVdrZqxzKqEnvfGVPAFNUqj9sSNhflUKa6iFF0pGlFNdYjB1yJq/JYrUmlOwedS3E9lZPtmTia1CMNgE1hA9E1YWJ1qPe2+lroysro4XMxRPXEW36Ekh//BPgr/wKVkWO9h4keeivG6IYXpHU2gcFOD6dPCyazbaYwHk6fxvz0yURYrCjmGPC6cRftoLxqi7YpLr3OBLjNFIbV2PkH/DRFuycjZ8+ezUUXXcTUqVOrHf/hhx9QVbXa8YEDB5KSksJ3330HwHfffceQIUNISEgInjNjxgxKS0v5+eefa33MP/7xj1it1uBXcnJyCz8rIYQQQgghRHv7+OcC7JUqyTYLk/p0rbEeGHATyh0Zqahaxx/Y0V6+z7YDcHYr9IuEzv3eOFQ3T21b3aJbfd1eDyur+kVO1HIxdRnUojEH+DSV6GFzqh1L9xYQ4XNTrLPwY1HN7c8dxapcf3/RyT0GETvm/mDizZ+we5Do4XMxlhzgvrMyeHjo5OpJpaGTubHvMGI6cOLo7LieWPQGCr0+Kmb9SPJN+0i+JYvIQdc1aGK1/72dHXKtrspKxWAhJsPfZ7Jky3N4HAVklR8nx2HH6PMyEjvWqurJ05nFYOTeIZPIvepR9v/mAbJnPcwfhkzEojei0+kwJfirlV35m3DseR+A8NQL662q7Mg/y1pTuyYjly1bxo8//sgf//jHGmv5+fmYTCZsNlu14wkJCeTn5wfPOTkRGVgPrNXm/vvvp6SkJPiVk9Nxf6AKIYQQQgghmuZvW/z/P/+6kckhpz0HBtw8Mq1/sArPFmbkoWn9mD+57xk5IKWhNlb1ixzbCv0iofb35pFp/Tv8e1Pf5N2mbPXdUHiQMo9KF62CEV26odO3TmWoYozAOnoetrEPBZN5ZnM050T5E3SrCzvuv50DychzLB5MCSNIvuUgKbceIeXWw1hH/QGdMQJTwjCOfzCdOTbInfUAeb+5l9xZDzDH6uP4B9NRNU+HTRyZ9QbGV23V/rrwEOV7/k3OX/tRvKFhQ2MUYwTRw+diG/PAKYna+isrI/rPJKL/LOLOfwfFHE1m/h66miMYaY2h27iHMEQkNvfpdQoRRhMGncJvv/knvd9fxNbjucE1c8JIlLCuVBxcjk9zo5hthPecEayqfDB9auiqyjNweA00YZp2S8nJyeH//u//WLFiBRZL2/52wWw2Yza3/OQxIYQQQgghRMeQW+Lkq92FAFw/qmaPtQCLUc+9k/rwwJR+HK9wE2Ux8M3+Y5gN7b6JrMPSNB+bcuwAjG2FfpEBgffm/in9yCt1EhdpwuP1YTHqW+0xW0JDesTF6SMbdc8vDvv7RZ7rOYQlsXX6RQYoBgvWUfdgGzMfr7MYxRTJlO//zpf79rIydw/3DZ3cqo/fFOWqi++OHgJgXMVOCj+ZjXX0PLpMeAIAnd6f8PFWFOIu2Iz7k0txhHVFH55IfkU+WqV/W7fFU8H8qudXY5p2+mQsrZQEbqiJiX1ZnbePNfn7uXHAOIrXFVGR9Tk+j6ve6eoeRz75H8wgZsJCUm7NQXOVopit+DS1WmWlQ3VjVBTsbic2kwVV04gwmug67Q3KSg6h+nSMSuzHgZkj2Xt0L9HxfVr7aXcoekWhR4SN9YUH+erIbiYk+Cu4o8662f89U1GIPjwe97Gdwfdkd2khI2N7kD3rYSo8bqxVr2t7f57aU5P+hrXb7bz11lvcf//9HD9+HIAff/yRI0eONPgeP/zwA4WFhYwYMQKDwYDBYODrr7/mpZdewmAwkJCQgNvtxm63V7uuoKCAxER/1j0xMbHGdO3AnwPnCCGEEEIIIc48f//xMJoPJvSKoV9c3YmfCJMBk0EhJszIoGfWcNFfNrEtT/rK12ZPUTn2SpUwo8KQpOhWfawIkwGzQeGOD7eTumgVXx841qqP1xLqm7zblK2+X1YlIyd6DmKOb91kJPir6HR6E/rwOHKXZZD+08MArCvIosLTPhOl6/JtwQFUzUuvyBgSC/1t3Uxdh9Y47+S+iVplEeqxHcFEpGK2oZgig9tx8656lPyrF5B31aPcO2RSh0gcTUzyJ/52HM/DmDgaY+xZ+NylVOasqvfain0foh7fRcnmZ9DpzejD49DpTdUqImvrderyqLh88KdDB+n23iJS//0UKe8t5KO8bFyat9pgmzPBjO4DAPiqaqiU5nFSvvNv5LyVyuG3B5DzViqVWZ+jefw9Rt/P2sblq5dy58aPiAuLxKQ3nLEVkQGNTkZu27aN/v378/TTT/Pcc88Fk4X/+c9/uP/++xt8nylTprB9+3YyMzODX6NGjeLaa68N/rfRaGTVqhPfVLt37yY7O5tx48YBMG7cOLZv305hYWHwnBUrVhAdHc2gQa3TQ0MIIYQQQgjRsfl8Pt6p2qL921EN7w9vNupJT/IPcfhsV2E9Z5+5vj9kB2BUDxtGfdtUkHazhlHkcLNmX83BJB1NXT3iZqdNwNHIZF52eTE77PkoPo0MTzbmVhpeE4pOp2Dpfg6pmp3ueg235uWb/ANt9vgNFewXmZCKWrQVAEu3cTXOC9UTM+DkvokRRhMmvYE4S8dKHI3umsx/p97E5kvu4qirkrirvyPq4o9w5n5X77XlVX0MI/rPDLleV6/TPaVFPL3jWxZuW1V9bdsqnt7+DZXtP46kTU2vSkb+UHSEgrJCSjY/jX3jouC0cs1lx75xESWbn0FTHXyS7Z9pMjGpb3uF3OE0+hNz9913c8MNN7B3795q26svvPBCvvnmmwbfJyoqirPOOqvaV0REBLGxsZx11llYrVZuvvlm7r77btasWcMPP/zAjTfeyLhx4zj77LMBmD59OoMGDeK6665j69atfPXVVzz00EPMnj1btmELIYQQQghxhtqSU8LOgnIsBoVZ6d0ade2FafEAfLGroJ4zz1wbs1u3X2Qok/v6BxCt2dfxKyNr6xH3YPpU5g7K4MZvl1FYWdbg+wW2aA/z5hOj12PsMrBV4q5NRL/L0AHj3f4k5MrcPW36+A0R7BcZ5gOfF31kDwxRNX8REaonZmMmUrc3zedj89FsUt5bSPdlj9Ht3Sd42Q6WEXfj89Y+qd1TfgTXkfUARPS7POQ5tfU67WqOoHdUbO19UH/ZgFHpuD1cW0NSeDTDunTDh49V+QcpzVwS8rzSzJc56Chjhz0fvU7hgh5t+73bkTX6E7N582Zef/31Gse7d+9e59CYpvjTn/6EoihcccUVuFwuZsyYwSuvvBJc1+v1fPrpp9x2222MGzeOiIgIrr/+eh5//PEWjUMIIYQQQgjReSytqoq8fEgS1rDGba0MJCO/O1TMMYeb2IiOURHVkQSH17Riv8hTTewTC8C2vFKKHC66RnTs4pNCZ3mwR5zD48ZmsmB3O5m5+h2+LTjA79a/x9JzribCYKrRm+9UXx45sUXbFD8MXRsnfizdz0GxxDLBtZf3w/uysirx11EUVJaxrTgPgHHqQSB0VWTAyT0xNVdJyL6JHZFDdfPs9jU8sXVl8Ji/OnE1APf0H0FUVOh2dY49HwA+IgZchcuSgOb1VPvc6aruFarXaWJ4FIXO8rr7oLqdxIU1rg9qZze9+wAyj+fyZc7PjKuqiDyV5rLzycGfAMhI6EUXc3gbRtixNboy0mw2U1pas3/Knj17iIuLa1Ywa9eu5cUXXwz+2WKxsGTJEo4fP47D4eA///lPjV6QPXv25PPPP6eiooKjR4/y3HPPYTCcWVl5IYQQQgghhJ/L42XZT/5e9tePbvgW7YCUmHDOSoxC8xEcgCNOcLg8wX6aZ7dhZWR8lJmzEqMAWNsJqiM/zdnJ5auXct03/yC+qkdcfFgkb0yYybkJvXkr40r+9PM3NXrzOT3Vq9tcXg95FaV0NUdwrnoIc3zbbdEO0CkGwvtcwnhPDjp8bC/OI7+i4/RUXZ23D4D0Lt2IKtwIgDmp9mQkVO+JeWrfxI6qzintv3yHKaz270fHnvcxxgwkasprNXpCPrN9DRVeN1ZzWMhep/kVZcRbIuvug2rq2Inc1hDoG7mi4CBUVdmeSjHb+DTvIAAXJw9um8A6iUYnIy+55BIef/xxVNX/Q1Kn05Gdnc19993HFVdc0eIBCiGEEEIIIURDffJzAcWVKt2tluDW3sa6MC0BgC9+kWTkqbYctqP5oIfVQndr6OREa5kY2Kq9v+MnIwPVjGfH9ax2vL81jvcm/ZbFO9exaOvKGr35ntq2Gofq7ynpUN34gH9NvI4DMx9g3EVvYkme2JZPIyii72V08TkZ7LMDsDKv41RHBvtFJvXFlfc9UHdlZGdV75T2yhJ8Pq3Gmlp6CFf+RiwZT/v7Pp7SE/KJrSt4aec6jjsdzAnR67TI5eBA2bFa+6DOTctA1Wo+7ulufHwvIg1mCp0ODg28LfRJQ2bzTUEWAL9KlrkmJ2t0MvL555+nvLyc+Ph4KisrOe+88+jbty9RUVEsWrSoNWIUQgghhBBCiAZZu7+IrhEmrhvZA72ia9I9Lqraqv3lL4V4NV9Lhtfpbcy2AzA2pe2qIgMmVW3V7uhDbFxeD2uqqvVmhOgRZzVZWLJrfchrF+9ah1FRglONuy97jL7/fpKU9/y9AZWeM1oz9FqFpUxGMVkZ79oPwMojHaNvpM/nY1VVD8uJURY0lx2dITzkJO3Ort4p7ZYoXPmba6xV7P0AJawrUT0n11pZuWTXehLCorh/6GQeTp9Wrdfpw+nT6G/tyvxa1uanT+4wA37akklvYHI3/0CaDbZxIfuQfpd4IR6fxkBrPP2szdtJfLpp9H5mq9XKihUrWLduHdu2baO8vJwRI0YwderU1ohPCCGEEEIIIerlcHswKAr3TOzL078aRIXb2+R7jesZgy3MyLEKlY3ZxYzv1aUFI+3cgv0i23CLdsB5fWLR6eCXwnLySp0kRXfMraHrCrKo8KgkhkUxNCapxnp9FW5lqouXdn4bujegTs+9Qya1efJHpzcR1vsiMvZ+wxuMYmXuXnw+Hzpd0xL+LWV/2TGyHXaMip6RrkM4AXPiaHT6xvWK7QwCU9oXbl1RY21u2njKstegO7waS9LYamvle95HH55Iiaui7spK1UmcJZJ7h0zigfQplKhOrEZ/T0lz1esZas1yGr7WDTW9+wA+zv6ZFXkHmD+tZh/STzd8BMDFUhVZQ5Pnr2dkZHD77bczb948SUQKIYQQQggh2o1T9fLMmv0kPbacPk+uInnhSpasP4hTbVpC0qBXmDHAX8Xy+S7Zqh3g8/n4vmqS9tk9bW3++F3CTQzrFg107OrIr6q2aM/oPiBksq6uCrc+UbFEGc28XE/lZHuI6HsZI715mH1e8ipL+dnesgNsmyIw2Xt8fE8MgX6Rp+EWbTgxpT1UdeJ9QybiXDcfx76P8PlOVHOr9v24C37AW1mIzRJVd2Wl0RJ8HJPeQJzF3+v05MR3XWtnokDfyA2FBynzKdX6kHr1Fr447P9ZcHGK9Is8VZMmvaxatYpVq1ZRWFiIdkpvgL/+9a8tEpgQQgghhBBC1Mfh9vDMmv0sXHFi26i9UuXxqj/fO6kPEabG/7PnwrR43s3M5fNdBTxxQc2ttmeiwyVO8kpdGBQdI7pb2yWGSX278tORUtbsP8Y1I3q0Swz1+erwbgBmdA/9uamrwu3OQefWOtUYTqpg07f95OKwXtOxGC2M8RzmW2NPVubu5awQlZ9tKdAvckpSf1xblwBgqWd4TWdmMRiD1YkFleV0MYeT7SjGrNPhKc/BpzpwF/yAOXEUAI49/wbA1HVIPZWV/r6PJn2bPp1Or3dULP2j49hTepTVefu4rOeQ4Nq6gizs7kq6miNq9I4VTaiMfOyxx5g+fTqrVq2iqKiI4uLial9CCCGEEEII0VaMisLidVkh115al9XkKrLzB8Sj00FmbilHSkInhs4031dt0R6aFE14ExK8LWFSn6ohNh20MjKn3M4Oez6KTse07v1DnlNXhdtNA8ZgM4Weahw4L1DB1tYUQxjhvc5ngicbgBXt3DfSq2nkV5bR1RzBpC5dUYv9iUnzKduUTzeB6sQ1eXvp/f4i7tr4XxSDhfBeFwDg2Pef4LmOPe8DENl/JhFGE3cOPpcH06dK38cWFKiODPwSIuDTnJ0AXJichr6dqpk7skb/DfLaa6+xdOlSrrvuutaIRwghhBBCCCEazO5UsVeqodcqVUqcKnGR5kbfNy7SzJhkGxuz7XzxSyG3jJXKlu/bsV9kwDm9u6BXdBw4VsGh4xX07BLebrGE8lWuPyExtmsKXcy1x3Zyhdup/fccqrvDVrCF972MjP3/B8A3BftxeT2Y9S2fmHaoboyKgt3txGbyvzYnJ8scqhuDovDOudcQb4nEU1lIWcxA0OnQW86MHq9j43tS5HLwdf5+ylQn4f0uw7H33zj2fUTMhEWoxXtwF20DxUB4n1+jal4uWP4m84dOJveqRymVvo8tYnr3ASzetY7lubuDfVR9Ph+fZP8MwMXJskU7lEanZ91uN+PHj2+NWIQQQgghhBCiUWwWI7aw0P+QtoUZsVqa/o/sC9MSAOkbGbCxHftFBkRbjIzq4d8ivmb/sXaLozZfVfWIm9FjQL3n1tZ/r67KyfauYAtPvYCBunK6ag7C9Sa2Hs9t8ccITBJPWvYYScsWkLTsMZ7dvganR6223i04aXwhf9q3gy6z1hLe97IWj6ej6h8dR9+orrg1Lytz9xLe6wJ0egse+z7Uou3Bqsiw5Cnow2JZm7efTUXZ/O+Gf2PQKdL3sYWcl9gbs97AofJidpccBWBXSQH7y45hUvRMr6VC+kzX6GTkLbfcwj//+c/WiEUIIYQQQojTmsPtwe3RKCx34fZoONye9g6p01M1jTsyUkOu3ZGRinpKj/vGuDAtHoAVe47i8jR9OvfpwO3R+PFwCQBnp7RfZST4+0YCrO1gW7XVqqQQ1N4vsqEsBiP3nnUueVc9Sv5Vj5J31aP84axz272CTTFFETX4Bj6cciMHZj5It/Bo3F4PDtXdIvd3qG6e2raahVtXBPtm2t2VLNy6gqe2rabU7axlfSXP7Pwe/aAbWiSOzkCn03FRchoAn+XsQjFFEtZzGgAVB7/CdTQTJawrEf1nAvDhoe0AXNrzLNk23IIijGbOSegNnBhe9Um2f4v25KR+RBobX5l/Jmh0PbXT6eSNN95g5cqVDB06FKOx+g/DF154ocWCE0IIIYQQ4nQRmPi8eF0W9koVW5iROzJSmT+5LxajTA1oqgiTgT9M7IPm8/Hy+oMt+toO72YlMcpMfpmLbw8cZ2r/uBaMvHPZlleK06PRJdxI364R7RrLpL5deWr1PlbvKwpui+wINh7NplR1EmsOZ2RsDzTVgU4xornsKGYbPk1FMTbstdM8Ttw/PMPxX5ahM4RR4KkkcuDVaKPnoRjap2dkQETG03y1/WsuXr8Qu7sSmymMuWkZzB86GYuhYcnS2rZhGxWFxbvWhbzmnwd+5IH0KbWuL/7lOx4YNr3Jz6szuih5EH/e+S2fH96F5tOIHHw9kYNvICxlMhEVv0EfHo9P8+DVND7K3gFQbciKaBnndx/Aytw9fHlkN/83+Nxgv8hfpQxq58g6rkYnI7dt28awYcMA2LFjR7W1jvKXgBBCCCGEEB1Ja018Fn6L12UxooeNI49Mo9zlwWox+vugNTPJqyg6LkiL5+1NOXy2q+CMTkYG+0WmxLT7v/sm9IrBqNdxuMTJ/mMV7Z4cDfiyaov2tO4D0GluSrY8R2nmkmAyMnrYHKwNSCZqqoOSLc9h37io2nH7xicAsI66p8FJzZbmUN08u+Nbnti26kRcVZWLAPcOmVTvtt/ANuvFu9bVSGYW1zFJPMxgrHO9PSeNt5dzElKJMpopqCzjh6LDjOw5jZJNT1O0/OZqn7sdPa+koLIMmymMiYl92jvs08707gNg8yd8k7+fQ+XH+a7wEAAXJ0sysjaN/n88a9asaY04hBBCCCGEOG3VN/H5gSn92jii08uyzCNszyvjw+tH8+shiQCYGt+RKqQLB/qTkZ/vKuRPv26RW3ZKm7JPJCPbW7jJwNkpMXybdZzV+4o6TDIysEVzemIvSjY/XS2ZqLnsDU4m6hQjpZlLQq6VZr6Mbcz8Foy6ceqqXFy8ax0PpE+p83qH6ubZ7WuqDecJJDMtBgN3Dj4XmyksZMKx0qMSUzVpPNR6e04aby8mvYHp3QfwwcFtfHpoK333/Yh905PB9cDn7t0CJwC/Sh6EqRUGDp3pBtkS6BFuxen1sGTXBmLN4aRE2ugRYWvv0DosaRQghBBCCCFEK2vIxGfRNIVlLrbnlQEwPrXlE2XT+sdhUHQUV6ocOl7R4vdvCy3Rq/R4pUrXCBNj23F4zck6Wt/Igsoyfjx2BPD3i6wrmahT6t7KrLnsaC57HWslzYq1Oez1VSa6nXVeX1cy89nta3F7vcxJmxBy/Zrew3F6VeamZYRcn5s2oVk9Yjuri3r4+0Z+fmRvyM+dD/j4+HFAtmi3Fp1OxwdTbuDAzAe5beB4Dsx8kL9mXNXeYXVoDUqJX3755SxdupTo6Gguv/zyOs/9z3/+0yKBCSGEEEIIcboITHwOlZBs7sTnM92a/f5k1NCkaOIiW35QQLTFyKr/HceIHlbslSpuj+bvb9dJttU3t1epw+3BoCgsvmwI8ZEmPF5fG0Rdv0l9Y3l8hX+idkfoG7n8iL/lwojY7sQpXrLrSSbqw2vf8q+YbShmW8iEpH/N2hIhN4mtvspEU92ViXa3s85kpkfzct+gceh8Got/+e7ENu6B45iXNpYwvZ75QycDVN/mPXAc9w2ZSNgZOBn6gh4D0aHjx+O55LpVEk9Z/1mJ44guknC9USY7txKnR+WT7J3M2PVGtdYD/aK7NriP6pmmQX+DWq3W4A93q7X9fvAJIYQQQgjRGQUmPj9+Us/IgDkTeqFqWottKz7TrNrrT0ZO7te1Ve7vVL2s3HuUX7+9udMNHmpur9KOPHTp7J4xWAwKBWUudhWUMygxql3jCWzRntF9YLOSiT6viqvgB6LTb6+23TYgetgcfJqKTt8+STe3x8ncgeNYuG11jbW5A8fh9jgx1dGz0Way1JnMjDSaOfrvC5gzaj73z3qAEmcpVks0ZYdWc/y980iauQpLeBz3DpnEA+lTKHFXEm0wUJa9BvMZOsIiPiyKsXEpfH/0EN+EDWJWxeZq68uN/h6R53fvT7jhzEvWtrZA64EnQrQegIb1UT0TNSgZ+fbbb4f8byGEEEIIIUT9apv4PGdCL+ZmpPK3zYe5fUKv9g6zU1pdtU13St+WT0aeSObtDR7rTIOHmtOrtKMPXTIb9ExI7cKqvUWs3lfUrslIr6ax/MhuAGZ0H4BPU4keNifYI/Jk0em3o9r3Y4pNq7Hm0zwc/fJ63EU7SJq5CnS6Jg3AaU0RBhPzBo0DqFa5OGfgeOYNOpuwepJdqqYxNy2jWs/IgLlpGaiaB8/xX3B/cimOsK7owxPJr8hHqyyqlsgNJHci8r8hf/nvMFp7o/S5sIWfbedxUXIa3x89xNcx59RIRn5l7AvApckD2yO0015z+6ieqeTXr0IIIYQQQrSBp1bvDU58LlgwnfxHp3PtiB6c98oG5ny4nX/8eLi9Q+x0Dh6v4MCxCvSKjnN6d2nx+9eXzDMqHfufU/X1Ki13eWrtJ9kZnnugb+QPOfZ2jeOHY4c55qog2mjh7PieKMYIrKP+gG3MAyhmG+CviLSNfZDoYbMp/PRKSra+hqY68HndeCsK8XnduAp+xF20A7VkP+7ju7CO+gMptx4m5dYjpNx62D/4ph0TkQA+TUXd/S/m2CB31gMc/s08smc9zJjYJFy//BOfVnf/2wijifuGTubB9KnYTGGAvyLy4fRpzE+fjMXnJTr9dgC0yiLUYzvQKv2/cIgeNrvG/SuyvkKrLMKcNK7ln2wnclHV1OZvKnWYRz8Y/NwdsPRkv74LRkXPr3oNa78AT2P1tR4oUevuo3qmatCvsoYPH97gHhw//vhjswISQgghhBDidKNpPv6yKYcnV+1jzW3jOK+PP4kyID6SXw1K4JfCcp5Zs5+JfWKJizBjd6rYLMZO1ZuwPQSqIsck24huhb6bDRk81Bp9KltKXb1Kx6TYCDPpeWr1vhrbsOdN6sOxio7/3C8cGMfghCim9u9KYbmr3b5nfjp2mK7mCM5J7I1R8W9fL/v5bUwJI0i+5SA+1YFituLTVMp3/QOAyH5XULL5WUq3vnKi8jH9dpJmrsJ1dBthPc4N3j/QX7K9tmafTDFGEHXWTZRsfobCb+7BYwgnQz+Dgy43X8+4lYw6poQHLD+ym5GxPci+8mEqPG6sRguqpmHRG7H/9BLRw2YD1HhtotNvx120A0vS2OC9XLkbALB0O7t1nnAnMTQmieQIGzkOOz91n8WFZ9+P5irhX3t+gp+WMyWpH9aq5K9oWfW1HjjTJrw3VIN+Sl966aWtHIYQQgghhBCnrx8Ol1BQ5iLKbGBcz+oVfE9dmIbbo/Hg1H4sXpdVbRt3R+nP11GtbuV+kZ198FBdvUrfmJnOU6v3htyCbjEo3Hlu7w7/3AfER/Gf7fnc+G5mq3zPOFQ3RkXB7nZiM/kTZif3fgusn98jjf/pM4ojFSemXJdtex31+C/EXfgPIvvPBPzJxOiht2Lpfi6lmUuq9YTUXHb/n3U6rKP+0OzYW5NisGAddQ+2MfPxVh7l7M0rOXhwOx8f2U1Gt/oHpLy1ZyOfH97F06Mu4g9DJgFg0oOr8CeK1z9E+c9/I+HST7CNfQDNVYJituIq/Im896fgrTxKt6s3YLSmorlKcR/bAXDGV0bqdDouSk7jtV++4/O8A1zUazj68Dg+yt4JwGW9ZIp2a6m/9YCGSf4Kr6FBychHH30UAK/Xy/r16xk6dCg2m6014xJCCCGEEOK08cnOfABmDIjDZKi+vVVRdCw8fwDPrt3PEys7Z2/C9uDz+YKVkZNboV8k1J3MuyMjtcMPHoowGbj7vN41epXOn9SXgXGRLF53MOR1z6zdz+wJvTr0cw/0tGyt7xmnR+XZ7WuqT2xOy2D+0MlYDMY615XyQ6jHfwG9ifCe02vc22jrTenWV0I+bmnmEmxj7m9y3G1FqaqA9JTnkrH3FZYZz+GjQzt4etSv6txVWeyqYEWu/z0KbC0G8GleilbeBj4vpq5nYbT2BE5UhZrihqIzRaAV/0LhJ78h6cpvcB3NxNhlEDq9EUNkt9Z6qp3GRT0G8dov3/FZzi5ePttHtqOYH44dRtHpuOSk11q0rAijKfSE97QMf+sBffv/4qYjatRPZ71ez/Tp09m1a5ckI4UQQgghhGigz3YVAHBRWkLIdbNBz8vrD4Zcq2/QyJlqV0E5+WUuLAaFcT1jWuUxIkwG5k/2D3946aStzHMzenWaitX7Pt3JjIEJHHlkGuUuD1aLEY9Pq3cLuur1hXzuHaVatznDeeoTmI67sJbpuHefdR4v7Pi61vW7UvyJtLCUqSEnZ2sue8hJ2yfWSoJJuI7OHDeM8zzZmAwe9pcdY0dxPkO6JNV6/oeHdqBqXobEJJFmO/HzsDTzZdyFP6KYrHSZ+EKN6xRDGAm/ep/cf43D51VxF+3AnDiahEs+QB+eiKY6ggnSM9WkpL6E6Y3kOOxsL85jTd4+AM5JSCU+rH2nzZ/uLAbjiQnvqrNa6wERWqN/VXTWWWdx4MABUlNTWyMeIYQQQgghTitHSir56UgpOh1cMDA+5DmdvTdhe1hVVRWZkdqlVRNjFqOeeyf14YEp/ThW4SbaYuD7Q8XtnoxrCNWr8fcfj/D699lsvec8hiRFA2BCwaBT6tyGHWk2YDIowede4lSxVvVk7AjPvTW/Z+qajvvPAz/yQPqUOqfn3j/kPJSwrkT0uyLkOf6p0LaQCcmTJ0Z3Bjq9ka69JpNxOJvVxt58lL29zmTke1mZAMxKTQ8eU0uzKd7g340Zc85TGCISQ15riOpBwqUfY4jsQWnmEgo+urhDTRpvb2EGI1O69ePTnJ2szt3H1uIjdDVHcFlP2aLdFgItHOL0kQCyNbseja6rf+KJJ/jDH/7Ap59+Sl5eHqWlpdW+hBBCCCGEECd8tqsQgLEpMcRHhU6OBHoThlzrIP35Opo1VcnISa20RftkESZ/Ys6kV+j95Cqmvf49uSUdf0LqxuxiHG4vXSNMDE6oXhkV2IIeSmAbNpx47nGRZkwGpcO0C2jN75m6puOGGYwUuyvrnp7rLEEf2YPw3heHPMenqUQPmxNyLXrYnHonUnc04akXMk09AMBHh3bUel6R08Hqqmq93yT3D04S14d1Je6Cd4gceC1RZ91Y52MZY/pTuvUV7JueDCZzNZcd+8YnKNn8DJrqaJkn1Uld22cEH065gd8NPJtHhs3gwMwHuab3iPYOS4gaGp2MvPDCC9m6dSuXXHIJPXr0ICYmhpiYGGw2GzExrbM9QgghhBBCiM7qs53+Ldq/GhR6izY0PDEk/Lyaj7X7jwEwpZWG14QSG2Gid5dw4MTW+45s5R5/wnZKv64oSvU+foEt6I9M6x9M6tnCjDwyrT/zJ/ftMEnH2rTU94zD7cHt0Sgsd+H2aDjcnuB03FAqPSoxprBa122mMKyWaIxdBqK32EKeoxgjsI6eh23sQyhm/zmK2YZt7EP+6r5Ott04rNcMJnsPovg0Mo/nklV2LOR5/zm0Ha9PY3iXbsTtfpvsN3qQ/UYPct7qhbvgR2KnLEGnqztFoVOMlGYuCblWmvkyOuXM/sXNr5IHsaXoMCnvLqTvv58k5b2FLN65DqencyW4xemv0X/DrFmzpjXiEEIIIYQQ4rRTqXpZufcoAL+qpV8knB69CdvSj4dLsFeqWC0GRnRv2y2tFw1KYGO2nc92FfC7s3u26WM31qqqz15tCduTt6B3tG3Y9ante6YxPS2dqpdn1uxn8UnXz5/Ul9nn9GDuwPEs3LaqxjXXpA7D6VXrmJ47nrLsNYSnTK3zsU+eSB2YGO3T1E65zVgf1pVuicMYVZLLJkMP/pv9M3cOPrfGee9XbdG+WF+EfeNzweMnJokrWEfdU2cy9nTqt9nSAr1OF21dGTx2ci/Te4dMqjYNXoj21Ohk5HnnndcacQghhBBCCHHaWb23iEpVI9lmYUhS3QMETk4MFTncWMMMfHewGLOh405rbi+BKdrn9YnFoG/b1+dXaQk88uVuVu4polL1EtZBE3dlTg8bs+0ATO1Xe3ImUAEZ6K/YkaeDn+rk75n8MhexEUayiysblIgMTONeeNK0cHulyt9/PMwdY23MGzQW8LH4l+9OTMcdOI55g8Zi1ly1Ts+dlzaa4/+eSszMmonMUwWSboHkmU7feRNF4akXMn3z+/5k5KEdNZKRBZVlrM3fD8CUw8tC3qM082VsY+bX+TinU7/NllZXr9PFu9bxQPqUNo5IiNo1KBm5bds2zjrrLBRFYdu2bXWeO3To0BYJTAghhBBCiM7u05OmaOt0unrOPpEYijDpSXt6DYdLnKybM4Hxvbq0apydzep9/oq/yW3QL/JU6d2i6W61cKTEydp9RVxQR8Vre/rmwDE8mo8+seH0qtpafjoKfM98vb+Iez7ZSb+uEayfm1HvdbVN484vc6EYIzn2wRTmjHmQ+2c9QImzFKslmrJDqyn+6GK6zVqDRV9zem5F8T6OvzcRg7UXesuZ1cIsvPdFTNvwNE+Ence6wiyOOsuJs0QG1z84uA3N52N0lyS6Zx0OeY+GVDYG+m3aNz5RYy3Qb7MzJ3Wbo65ep3Z3JSWqMzhcRYj21qBk5LBhw8jPzyc+Pp5hw4ah0+nw+Xw1ztPpdHi93hYPUgghhBBCiM7G5/MF+0VeXEe/yFCsYUam9o9j6eYcXll/UJKRJ3F5vKzLOg7AlDoq/lqLTqfjorQE3vj+EJ/uKuywyciVwS3aZ8aW1Sn94jhW4abokJuDxyvqTcDWNo27yOFm7d4CRveagf2TS3GEdUUfnkh+RT5aZRG2sQ8GE16nTs89+uW1qMW/YB11T8s/wQ7O2GUQPaNiGewt5Gd9PB9n/8zN/ccG19/P2grArN7DUXKbXtkY6LcJ/kpKmaZ9QqDXaaiEpM0UhtV45r42ouNpUA1+VlYWcXFxwf8+cOAAWVlZNb4OHDjQqsEKIYQQQgjRWWzLK+VwiZNwo75JE59nT+gFwPvbciksc7VwdJ3X94eKqVQ1EqLMDEponyqfQHL50535IYs0OoJVe08MrzkTdLNamNg7FoBlmUfqPb+uadyPrMoODphB86Ae2wGaB9uYB4hOvx1PeW6Na9TiPbiLtoNiILxP6CnapzOdTlc1Vdu/Ffvkqdq5FSV8W+CvQr0ieSDRw2aHvEdDJ4kH+m2m3HqYlFuPkHLrYX+vyTM4EQn+oU5z00JXBc9Ny5BBaKJDaVAysmfPnsFtJT179qzzSwghhBBCCAGfVFVFTu3ftUkDQUb2sDE2xYbq9fHWpuyWDq/TCiTZJvft2qCt761hcr+uhBkVcuxOtueVtUsMdckvdbIjvwydDib1jW3vcNrMVcO78//bu+/wKMusDeD39PSEkkoghST0DkJIQIEQUCx8sKsi1mXVVQKKKwLSQUURK6i7a0Ndu+KqqEAgoQRCEUNvqYSWhJKeTKa93x9hxoTMTGYyNcn9u65cl8zzzjvPvD6TmTk5zzkA8FVW02DhjVRaHVKuB/xvNLFHEJQ6aZOAl2fEeFz6dhwu//YABJ2m0X2qs78HAHh2HQeJR/vMZPaMug3J14ORWy+eQaVaCQD4Lv8IBAhICIpEN/8Q+A2chYCbnrepk7hY5g2RRA6JVyBEEnmr60DuCN4yOeb3H4vFA8Ybur0HyD2xeMB4zB8wls1ryK1Y3cBG78SJEygsLIRKpWp0+5133mnzpIiIiIiIWjv9Fu1JNmzjfWJkJPYVHsK/Mwswb0wMJGLXBN/cib55jSvqRep5yiQYFxOIjSeLsfFkMfqH+blsLsZsu36NBoX5o7O3wsWzcZ4p/UIxc8NRHLlUgRNFlegdYrppVFrOFcxKjAIArNtdYKIbd/3XZX0NQ2lANLQ1RdDVlaH84OsIuL5dGACqszcAALxjpzjo2bk/j/DRiJPUIUJbhrMIwG/nT+HuqIH45noX7b9GDUD1me9QtvcFdBi1Ct2Gn4OurqJVdxJ3Nx7SprVM1TodPCTGs4CJXMXqYGReXh7+7//+D0ePHm1UO1L/V0nWjCQiIiKi9q64sg77z5UBsC0YefeAMDz78wmcK1Ni44li3NU3xE4zbJ0qlRrsv94h2tXbjyf1DsLGk8X45UQxnh8X69K53Gjbmfa1RVuvk7ccE3oE4peTJfjq0AWsmNjT6HE6nYCFv56CVhDwzYNDsCgpDuVKNfw9ZPWBGxOZzFLvUHS8+TVc2TIDpXtXwCv6dsg79Ya6LAeqy4cBkQRe3dtvco5Y6gGvbklIvpCL9yVD8L+zxzAiMAKZl89CBBGmdO2F0u8ehaY8D6rLh+AdPalNdBJ3NzfWMpVbn5hP5HAWbdNu6KmnnkJUVBRKSkrg5eWF48ePY+fOnRg6dCi2b9/ugCkSEREREbUuv50qgSAAQ8L9Eebf8mwfD5kEf7upGwDg3T1NO/+2N/sKr6FnkA+GhPu7vEP07dfrRu4tLMXlKvep6SkIArZd7zaeFNc+mtc0ZNiqfeiiyXqev5wsxvHiSlwoVyLc3xNyqRiBPgrIpWJDd25TfHrdD8+o2wCtCle2PApBp0HtuR0Qe3aGZ9exkHi2n23xxnhFTzLUjdx3+SxSL55GZ4U3RodEwTf3G2jK8yDxCob/oKdcPFMiciWrg5GZmZlYsWIFOnfuDLFYDLFYjMTERKxatQqzZ892xByJiIiIiFqVoxcr0NlbblNWpN7jIyIgEgGpZ67gzOUqO8zONapVGqg0OpRU1UGl0aFapWn+TjfcPyGqE378203YOTPB6vvbWxd/Twzq4gdBAH49WeLSuTSUfaUa58qUUEjFSIxqf7UL7+oTAk+ZGDlXqnHwfHmTcUEQ8HJaDoD6MgimmtiYIhKJ0HncO5AHD4X/TfMh6DTwikhC179lo9OYN+3xFFo1z8iJGOYXgI1jH8CRyXMxLjQOeX9diHfjp6Dy9NcAgIARiyGWu6b5FBG5B6uDkVqtFr6+9bU3OnfujIsX64sDR0RE4PTp0/adHRERERFRK6IPuKWMikL+wnF4PN72Bo9RnbwwqWd9UPO9PQU2n88VlGotVqfnImT5FoQs24KQ5VvwanoulGrLSjzp799lRSq6v7QNXVakWnV/R9EHm385WezQx7EmkLv1+hbthMiO8GxB46TWzkchxR2968sZfJnVtKv2rrxryDxbCoVUjKdGRbXoMaQ+XRA6dTNUxQdx7v1uOPdRLM59EIWqU19Cp1HaNP/WTuodgs737MLeKxfR7ZuV6P7dS+j2zUp8lXcIHe78AR4RyfDt84irp0lELmZ1MLJv3744fPgwAGD48OFYvXo1du/ejRUrViA6OtruEyQiIiIiag0aBty6v7QNXVduxb8zz9olYPbk9a6/6w+cQ3WdazMCrVWt0mBVWg5Wpp5BWa0aAFBWq8aK1DN4OS3HEFgzFXCz9P6uoN+qvfn0Zag0Ooc8hrWB3G3Z9Vu021u9yIbuHRQGAPjm8EXodI23ar+clg0AeGhoV4T6tayEgk5djfKDr6Ns/0vQ1ZXV31ZXhrJ9L6D8wGro1NUtn3wrV61WYfWJTLxwZBvKVLUAgDJVLVYe3orVJ/bCe+x7ELGZClG7Z3UwctGiRdDp6t9oV6xYgfz8fIwaNQq//vor3n77bbtPkIiIiIjI3Tk6YJYcF4jkuM5Yf+8gSMSiFm91dgWZWIy1GcbrXb6dkQ+ZWGwy4Far1kIiEjV7f1cZGh6AYF8FKus02JV/1e7nt3ZdaXWCodt4Umz7qxepd2vPIPh7SHGhXNno/8uhC+XYdPoyxCJg7i3dW3x+kViGikPvGB2rOLQOInH7DbbJxGKsPbnb6NjaU5nw8Al18oyIyB1Z/c49YcIETJkyBQAQExODU6dO4cqVKygpKcHYsWPtPkEiIiIiIndnScDNFmKxCN88OBQHz5chbEVqi7Y6u0qZUm0IpDUZq1Wjsk6DVWnZRgNuH+4rRGmt+fuXK42POYNYLMJtvYIAABtP2H+rtrXr6uD5MpQrNQjwlGFwuL/d59NaKKQS/F+/+qDXV4cuGm5/Jb2+VuTdA8LQvbN3i8+vqyszZEQaH2taq7K9KFMpDRmRTcdqUa5u39vYiaieXf6M2LFjR4hEInucioiIiIio1Wku4GZrwKxapcFrO/LwwtZst9uq3JwAD5nJJiHdO3nBVyHF2owCo+Nv7cpDJy+5yfsHeMrg7+HaLLTbr9eN3JN/zWT35payNhC7Nbs+K3JM906QiNv397Np17tqf3f4ItRaHXKuVOPbw/WByXljY2w6t1gRALEiwMxY+w0EB8g9ECD3NDHmCX9Zy7bGE1Hb4ro9DUREREREbYS5gJs9AmaOzrx0JLVOh9mJxhuFPD06GtdqVSYDbrlXa1Ct1pi8/+zEKKh1jqnVaKnkuED8+MgwpD05EsU2dAq/sV7mplPF8FFIrFpX287o60W23y3aemO6d0KQjxxXa9TYmXsFn/9xHh295Li1ZxAGhNkWLBR0avgNTDE65jcwBYLOddm6rqbWqjGrV4LRsVm9EqDWtt9rQ0R/krp6AkRERERErZ0+4LYi9UyTMX3ATG5DHoAlmZeBPooWn9+RvOVS/PPm7tAJAtbtLkBZrRoBnjLMTozCjJu6QSwSIcBTZvT5BXjK4C2TYv71TLa3M/Ib3X/+2Bh4uLhjtEQswoFzZXjoq0Mtmpu+XubaBs8tJSESsxKjkFlQipSESLywNbvJ/VISIqHS6iCX1q+rGpUGuwtKAQBJce23eY2eVCLGzIQo9A/1Q3xkJ0R39sGzt3Q3+TqyhljmDf9hzwGorxGpqyuDWBEAv4Ep8B/2HMTS9pv95yWR4LlewwFBh7WnMlGmqkWA3BOzesbjuV7D4Slpfx3eiagpkWDvvQStUEVFBfz9/VFeXg4/Pz9XT4eIiIjIoapVGsjEYpQp1QjwkEGt08Fbzr9R26pGpcHLaTlNAm72CJipNDqELN9iMmBXtDTZEJRyRw9/mYXJ/UIxoUcgquo08G+w7qpVGryanms0kLtkfBzmjuluOE4mFqNcqW50f1eqVmmwOj0XK5uZe0vuvygpFg8MCUd4gCdeSctpFIjVBytXpp7Bq3f0hkIqwfbcK5j9wzFIxMAfc25mGS3UX99XHPSaBOq7aovEMujqyiFW+EPQqSGWtbwWZVugrSnBpW+T4JH4Mny7jUG5sgL+Hn6oPJsG5e4FCP3rNki8mLlL1FZZGl+zKBg5ePBgbNu2DR06dMCKFSvw7LPPwsvLy64TdiUGI4mIiKi9UKq1WJWW0ygLy10yzFq7f2cWINjXA8lxgahWaewaMLM0YOeOCktrEPnitvr/XjQO4QFNv0co1Vq8fEPArTWsS1uDxJbe/8ZA7MUKJSZ/fABHLlXg0eFdseaOPpBKxCiqrEOIrwI6QXDb9eAstgaKqWUErQqF/wmvzxb17AyJVwi0NUXQ1V6BWBGAbo+dh0gid/U0ichBLI2vWfTn05MnT6K6uhoAsHz5clRVVdllku+99x769+8PPz8/+Pn5IT4+Hr/99pthXKlUYubMmejUqRN8fHwwdepUFBc37lJXWFiISZMmwcvLC0FBQZg7dy40Gvct4E1ERETkKtUqDVal5RjtWuzuTVBag/f3FmLK+gP4/shFBPooIJeK7Rbs8JbXb1VeMj7OUEMwwFOGJePjMH9sjFsHVT7/4wIA4JbunYwGIgHAQybB3DHdUbQ0GcXLklG0NBlzx3R360AkYHvjIkvv7y2XQi4VG9ZVZEcvrLmjNwaG+eGFW3vh1e256LIiFd1f2oYuK1JbRZd1R2vNdVZbs4b1NHW1V6C+egy62vrGSu29niYR/cmiTy0DBw7EI488gsTERAiCgDVr1sDHx8fosUuWLLH4wcPDw/Hyyy8jNjYWgiDgk08+wV133YWsrCz06dMHc+bMwS+//IJvv/0W/v7+SElJwZQpU7B7924AgFarxaRJkxASEoI9e/bg0qVLePDBByGTyfDSSy9ZPA8iIiKi9qC5L+fPj4t18ozajoJrNfjjQjnEImBizyCHPIY+YPf8uFgUV9Who5cMp0uq3DpgJwgCPjt4HgBw/5Bws8fqA6r62pe21Nh0Fn3jIlOZjc01LrLl/klxgfh5xk1Ym5HfqKak/g8MANp19l9rrrPamrGeJhFZwqJ3pvXr12Pp0qXYuHEjRCIRfvvtN0ilTe8qEomsCkbecccdjf794osv4r333sPevXsRHh6ODz/8EF988QXGjh0LAPj444/Rq1cv7N27FyNGjMCWLVtw4sQJbN26FcHBwRg4cCBWrlyJefPmYdmyZZDLjad/19XVoa6uzvDviooKi+dMRERE1Frxy7njbDh6CQAwOrqTQ6+hPrBUcK0GQ974HYIg4MIS960XefB8OU6VVMFDKsZf+oe6ejp2Z2vjIrVOZ6j92JL7B3orsG53gdGx9v4HBlsDxdRyYqkH/If+EwE3zW9cT5OBSCK6zqJPLT169MBXX32FAwcOQBAEbNu2DVlZWU1+/vjjjxZPRKvV4quvvkJ1dTXi4+Nx8OBBqNVqJCUlGY7p2bMnunXrhszMTABAZmYm+vXrh+DgYMMxEyZMQEVFBY4fP27ysVatWgV/f3/DT9euXVs8byIiIqLWQv/l3OiYpwy+Cil0OgHVKg1UGh1Kquqg0ui4fdsC+mDklH7OCbjFR3SAXCLG1Ro1fjpe5JTHbAl9VuTkviHwa4PBH1Pb5xclxVq0fd5bLsXTo6OxKCm2Rdvvbd0m3pbpA8XG6AO95DhimTdEEjkkXoEQSeTtvrEPETVmdc6+zs6/tI8ePYr4+HgolUr4+Pjghx9+QO/evXHo0CHI5XIEBAQ0Oj44OBhFRfUfuIqKihoFIvXj+jFTFixYgGeeecbw74qKCgYkiYiIqM0zl8WVkhCJjIJrGNzFH2/tysPaDMd0n22LLpYrsaegFADwf/1CnPKYUokYDw0Lx6ptOfj4wDn8ZUCYUx7XGmqtDl9l1deLbG6LdmvWcPt8aa0aPgoJUk9fxrUaFcL8Pc3et7iyDsn/ycTyCT1xael4VCj/bHxkyeuN2X+m6QPFAFpdYyQiorauRQVEcnNz8eabb+LkyZMAgN69e+Opp55C9+7drT5Xjx49cOjQIZSXl+O7777DQw89hB07drRkWhZTKBRQKLgFiYiIiNoXb7kU88bGQCcIWLe7cbDxmZuj8fu5Mry5M4/156z0w7H6rMj4iA7o0kzwyZ4eGdYNq7blYPPpEpwvq0V4gPMe2xKbT1/G5WoVgnzkSI4LdPV0HEr/ugj2VWDq+gP44VgRFoyLwYu39jJ7v/f3ncXRS5VYnZ6DyX1DEOhTHyCztF6mrdvE27qGgWJ9J3JLA71EROQ4Vr8zbd68Gb1798b+/fvRv39/9O/fH/v27UOfPn2Qmppq9QTkcjliYmIwZMgQrFq1CgMGDMBbb72FkJAQqFQqlJWVNTq+uLgYISH1f3EOCQlp0l1b/2/9MURERET0py+zLmBweADOLx7fqGuxn4cMCVGdzNafY/dZ4zYcce4Wbb2Yzt4YHd0ROgH49Pp2aHfy3+tzmjaoC6SS9rN27hvcBQDwwd5C1GlMd7TWaHX4T+ZZAMATIyNa9Fitucu6s9zYiZzXhIjI9az+VDB//nzMmTMH+/btw+uvv47XX38d+/btw9NPP4158+bZPCGdToe6ujoMGTIEMpkM27ZtM4ydPn0ahYWFiI+PBwDEx8fj6NGjKCkpMRyTmpoKPz8/9O7d2+a5EBEREbUlgiDgte25mLL+AL47crHJl/Ny1p+z2uWqOuzIuwrA+cFIoD47EgA+3l8InU5w+uObUlarxo/Xa1k+0Ia3aBtzV58QdPH3wOVqFb6/Hqg25ucTxThfrkRnbzn+2r/l2+z12X9FS5Mb/YGB2X9EROSurA5Gnjx5EjNmzGhy+9/+9jecOHHCqnMtWLAAO3fuREFBAY4ePYoFCxZg+/btmD59Ovz9/TFjxgw888wzSE9Px8GDB/HII48gPj4eI0aMAAAkJyejd+/eeOCBB3D48GFs3rwZixYtwsyZM7kNm4iIiOgG+wrLcLKkCp4yMe7q03QXSXMNbtpz/TlTfjxeBJ0ADO7ij6hOXk5//L/0D4WvQorcqzXYlX/VoY9lTWOj745cRJ1Ghz7BvhjUxd+h83I3UokYj42oz3R8d0+ByeP0YzOGd7M5cMjsPyIiak2sDkYGBgbi0KFDTW4/dOgQgoKCrDpXSUkJHnzwQfTo0QPjxo3DgQMHsHnzZowfPx4A8MYbb+D222/H1KlTMXr0aISEhGDDhg2G+0skEmzcuBESiQTx8fG4//778eCDD2LFihXWPi0iIiKiNu+j/YUAgL/2D4O/kaAju89az7BFu7/zsyIBwFshxT0D67PqPt5/zmGPo1RrsTo9FyHLtyBk2RaELN+CV9NzoVQb34as36J9/5BwiEQih83LXf19eDdIxSLsKSjFoQvlTcZPlVRiW/YViETAP0a0bIs2ERFRa2X1n8weffRRPPbYY8jLy8PIkSMBALt378Yrr7zSqEO1JT788EOz4x4eHnjnnXfwzjvvmDwmIiICv/76q1WPS0RERNTeVNdp8PWhiwCAR27qavQYU91nUxIi2X3WiLJaNbblXAEATHXBFm29v93UDR/sK8S3Ry7i7f/rCz87Z7BWqzRYnZ6LlQ2apJhrbFRwrQY7865BJAKmX6+f2N6E+nlgav9QfH3oIt7dU4D//HVAo/H39tTXiry9VzAiOjo/o5aIiMiVrA5GLl68GL6+vnjttdewYMECAEBYWBiWLVuG2bNn232CRERERGS7745cQmWdBt07eWF0dCeTxzXsPlumVMNbLsGW05eRd7UavUP8nDhj9/fziSKotQL6BPuiR5CPy+YxvFsAegX54GRJFb46dNGwRdha1SoNZGIxypRqBFzvOuwtl0ImFmNtRr7R+7ydkY/nx8U2um3z6RJ09pZjQJif23X4dqYnR0bi60MX8cUfF7D69t6GEghVdRp88nt9FuuTCZEunCEREZFrWL1NWyQSYc6cOTh//jzKy8tRXl6O8+fP46mnnmqXWzCIiIiIWoOPD9Rv0X54WNdmP7Pp688F+Sjwz59OYOonv2PN9jxnTLNVcfUWbT2RSIRHbvqzkU1LmNqGXaPWoKSqzmxjo9LrY/qakhN7BCF/4Ti8O6Vfy55QG5EY1RF9Q3xRo9Zi/YE/t9B/kXUBFUoNYjp7Y3xsoAtnSERE5BpWByMb8vX1ha+vr73mQkREREQOkH25CjvzrkEsAh4aanyLtikPD6s//ousCyiprHPE9FqlqjoNNp++DMC1W7T1HhgSjr4hvpg/NhZ1Gq1FTWb0qlUarErLwcrUM4ago34b9ts789HZW262sZGvQoKyWjVWp+cgZPkWRL20DV1XbsXnf1wwWVOyPRCJRHhyZCQA4L09BdDpBAiCgHd3FwAAnhgZAbGYyRxERNT+2BSMJCIiInIka7r3kmkfX8/KmtAjyOpts8O7BWBY1wCotDq8v++sI6bXKv16sgRKjQ7dO3mhX6jr/zgf7KvAjidH4uD5MoQuT7WoyYyeuW3Yq7fnQq3VYZaJxkYpCZEoqVLh9R25WJma3SSY+XJaTrt+3d4/JBy+Cimyr1RjZ/5VZF0ox8UKJTxlYjxs5R8GiIiI2goGI4mIiMgtWdu9l4zTaHX49Pf6zsaPDLM++CESiQyBqPf2nIVay47aAHDgXCk6e8sxpV+oW5QqqlZp8OauPLyw1fqAYJlSbXYbtlorYMHYGCwZH2fIkAzwlGHJ+Dg8Py4WoX4KrLue7XejtzPyIRO3368cPgop5o7pjh8eHoabunZAoI8C+QvHYfuTCejgJXf19IiIiFzC6gY2RERERI5mbfdeMm3Lmcu4WKFEJy8Z7ugT3KJz/HVAKOZuPIGLFUr8cLQIdw8Ms/Ms3YepJi43jj+ZEIVlE3qgotY9sv7qsxsLjI4ZazLTUICHDAGeMqMByQBPGXwU9TVE9Y2NypVq+F+/Nh4ySbM1JcuVagT6KFr0vNqCZ0ZH4+W0HDzy9SFDh/pZiVHoF+LLDvVERNQuWfVnSrVajXHjxiE7O9tR8yEiIiJqtntve860stZH1xua3D8kHAppywIfCqnE0KF5bUbrbmRjbut/c9m4Dce7X6+L+O+9Z90iW7e57MZypfExAFDrdJhtYhv27MQoqHX12bD6xkaBPgrIpWJDkFYfzDQmwFMGfw/jY+1BtUqDV9Jzm2SsruQWdiIiases+iQvk8lw5MgRR82FiIiICABQWtvywAr96VqNCnsKSgEAf7vebbml/hEfAalYhN0FpTh4vswOs3M+c8FGc01cXk7LwbVqFValZZscd3VQyZaAoLdcinljY7AoKbbJNuz5Y2OazUK2NJjZHvEPK0RERE1Z/e53//3348MPP3TEXIiIiKiduTFLrUKpxsf7C+GjkDDTygb661qr1iJ7wVhsfTwe/UL9bDpnqJ8H7h5Qvz17nYngijszF2x8a1c+pGKRyaDR53+ch49CanYbtKuDSrYGBHflXcXg8ACcW5yE4mXJKFqajLljulu0jdhbLsV8EzUlLQlmtmW2ZKwSERG1VVZ/MtBoNPjoo4+wdetWDBkyBN7e3o3GX3/9dbtNjoiIiNoufZba2ox8Qx21lIRIzEqMQmZBKVISIvHC1qalYfSBFTn78Bll7LrOSozEyMgONtenm5UYhS+yLuDLrItYfXvvVlUH0FyG2k8nivDIsK4mg0aeMolF2bquvB76gCBQHxzV/7+fnRiF+WNjmv1//+/Ms/jhWBGWJcdhSXIPALDqNeYhk5isKdmeNVePk39YISKi9sjqYOSxY8cwePBgAMCZM2cajblDJ0EiIiJyf6Ya1OiDj38f3g2JUR0hFolaFFhpr0xd15Wp2RBBZHPjn+ERHfDX/qG4b3A4fBRSlFTVGW3y4o7MZahlX66Gv6fpoFGtWosOZsbdJajUMCB4sUKJQB85rlSrmn29lNao8MvJEgDA5L6hLX58/RrQB2X5B4M/M1ZXpJ5pMsY/rBARUXtl9afG9PR0R8yDiIiI2hFzWWrrdhdgUVJco+69lyqV6Owtx4VyJQORZjRXn85cR2VLfXDPQLya3rgzcGsIEpvLUNPoBKi0poNG0weHQ6nRtoqgkj4g+NWhC3h9Rx5GR3fEdw8NM3uf749egkqrQ98QX/QPs207PzVma8YqERFRW9TiP2Hn5OQgNzcXo0ePhqenJwRBYGYkERERWcSSOmqBPgpDYCU95yqe23gC4f4eODhnND9zmGDpdW2papUGa7bnNto+r6+7CMDmzEtHai5DTSJCs0Gj1hRUur1XMJ7/9RR+Ol6M4so6BPua/v/+xR8XAAD3De7irOm1K9zCTkRE1JjVnxavXr2Ku+++G+np6RCJRMjOzkZ0dDRmzJiBDh064LXXXnPEPImIiKgNsbaO2p19gvHk90dw6GIF9p4tRXxkR2dNtVVxdH06Z2ReOoq3XIq5Y7pDJwhYt7vAZDDRXNCoNQWV+ob6YUREB+w9W4r1B85h3vVA6o3Ol9ViR95VAMC0gQxGOgq3sBMREf3J6nfBOXPmQCaTobCwEF5eXobb77nnHmzatMmukyMiIqK2KedqNVISIo2OGev829FLjnsH1QdK3t1T4ODZtV62dlRuTmvvDLwy9QwGhwfgwpLxJjtGe8ulkEvFCPRRQC4VN8n0bG7cnfx9eDcAwIf7CyEIgtFjvsy6AEEARkV1RERHL6PHEBEREdmT1cHILVu24JVXXkF4eHij22NjY3H27Fm7TYyIiIjapiMXKzDtv39gVmIUFo+PRYBnfbZegKcMS8bHYf7YGKMBnidHRgIAvj18CSWVdc6ccqvhLZfi6dHRWJRk+XW1hj7z0uiYmzRxMaWoQok3duZhyvoDOFVc1SqCiba6Z0AYfBVS5Fypxvbcq0aP+SKLW7SJiIjIuaz+9FVdXd0oI1Lv2rVrUChaXoOIiIiI2r46jRYPfpmFo5cqsGzzaay+ozcWjouzaMvr0K4BuKlrAPafK8OH+wuxwI23BLvK5ao6TPzPXixO7oGLS8ejUqmx61bi1twZ+D97C6HWChgR0QGDwv1dPR2n8FZIMW1QF/xn71l8sK8QY2I6Nxo/XlSJwxcrIJOI8NcBYS6aJREREbU3Vn9aHDVqFD799FPDv0UiEXQ6HVavXo0xY8bYdXJERETUtizZdBpHLlUg0FuOJck9rN7y+uT1rd3/zjwLrc74ttP27I2deci6WIEXt56BQmL/rcT6zsBLxsc1yrxclBRrl8xLR1FpdPhXZgEAYJaJbext1aMj6rdqf3/kEq5WqxqNff7HeQDArT2D0NFL7vS5ERERUftk9SfG1atXY9y4cfj999+hUqnw3HPP4fjx47h27Rp2797tiDkSERFRG5CZfw1rduQCAP7z1wFmu/uacveAMPzzp+MoLKvFLyeLcWefEHtPs9UqrVHhnd0FAICFSbEO6zjesIlLaa0aPgoJtpy+jMKyWsQF+jjkMW31/dFLKKqsQ6ifAlP7hbp6Ok41JDwAg7r4IetCBT47eB5Pj44GAOh0Ar7Ub9EeFG7uFERERER2ZXVmZN++fXHmzBkkJibirrvuQnV1NaZMmYKsrCx0797dEXMkIiKiVqpapYFKo0NJZR36d/HD9w8Nw/PjYnBX35YFET1kEvztpvpMr3evB96o3tqMAlTWadAv1Bd39nZskFaf0Rrsq8BTPxzD1E9+x7LNpx36mLbQdwD/R3wk5FL33EbuSDNuigAAfLDvrKGRzZ6z13C2tBa+Cinu6BPsyukRERFRO9OivTT+/v5YuHChvedCRETklqpVGsjEYpQp1Qi4Xn/PXbejuhOlWovV6blYm5GPslo1AjxlSEmIxMKkOJvO+4/4SKzZkYstZy7jzOUqt83Gc6YKpRpv7coDADw/LhZisWOyIo2ZNSoKHx04h68PX8TS5B7oEeT4/x/WvCb3F5Zi79lSyCViPDYiwuFzc0fTB3fB3I3HcaK4CnvPliI+siM+/6M+K3JKvxB42qGeKBEREZGlWvSn4dLSUqxZswYzZszAjBkz8Nprr+HatWv2nhsREZHL6QNqIcu3IGTZFoQs34JX03OhVGtdPTW3Vq3SYFVaDlamnkFZrRoAUFarxgtbs/FKWg6qVZoWnzuqkxdu6xkEAPjs4Hm7zLe1e2/PWZTWqtEj0Bt/6e/cRiQDwvxxZ59gCALwclq2wx/P2tfkuutZkfcMDGtRaYC2wN9ThruvN6j56tAFqDU6pGVfBgDcN5hbtImIiMi5RIJ+r4aFdu7ciTvuuAP+/v4YOnQoAODgwYMoKyvDzz//jNGjRztkoo5UUVEBf39/lJeXw8/Pz9XTISIiN1Gt0mB1ei5WGukcvGR8HOaO6c4MSRNUGh1Clm8xBCIbCvCUoWhpsk3bZXfmXkFprQZJcZ1RXadFgGf7zVitrtMg+qVtuFytwvp7B+LBoV2dPocDhWUY/vYuSMQinJk3FlGdvBzyONa+JosqlIh4cSvUWgH7Zo/CsG4BDplXa/DH+TKcK1MiKa4zyms18PeUYmfuVST3CILEiZm0RERE1HZZGl+z+lvAzJkzcc899yA/Px8bNmzAhg0bkJeXh3vvvRczZ860adJERETuRCYWG2rN3ejtjHzIxK2/9pyhpmNVHVQaXZOMxebGTSlTqo0GIoH6DMlypfExS93UrQMOni9D15Vb6zPkWmHGakuv7Y02HL0EAUBURy9MG9TFvpO00LBuAZjQIxBanYCX0x2XHWnta/I/ewuh1gqIj+jQrgORANA72NfwmglfmYquK7dib2Ep1Fqdq6dGRERE7YzV6QM5OTn47rvvIJH8WVtGIpHgmWeewaeffmrXyREREbmSJQG1QJ/Wu+3TWE3H2YlRmD82Bh4ySbPj5vh7SBHgKTOZGenvIWvxvPXZcS9s/TPoVVarxorr2XKtIWPVlmurp6+bOCq6E/IXjsOFciVkEtcFyBcmxWLz6ctYf+AcFifFITzA0+6PUVpr+WtSrdHhm8P1dRFnJUbZfS6tianXzMrUbIggahWvGSIiImo7rP7EOnjwYJw8ebLJ7SdPnsSAAQPsMikiIiJXK65UwkdeH1AzxtaAmquZqum4IvUMXk7LQYVSbXbcXBbfvrOl2Jp9BSkJkUbHZydGQa1reTZWa89Ybe7aW5Ih2bBuYvRL29B15VZ88ccFl2aGJkZ1wi3dO0GtFbB6e67dz597pQo+ConZ16SvQopKpRoqjQ6Xq1XY99Qo/PL3mzC1f6jd59OatPbXDBEREbUtFn3yOHLkiOFn9uzZeOqpp7BmzRpkZGQgIyMDa9aswZw5czBnzhxHz5eIiMjubtwuW1ShxLT//oHUM5cdFlBzNXPBic//OA8PqaRFwYuduVcx/j+ZmPvzCTw9OhpLxscZgkcBnjIsGR+H+WNjbMrCcvQWcEezNTBkj2CmoyxMigUAfLD3LIorlXY7776zpYhfuxtbz5gOci9LjoNGJ+DV7fVBWsNW5LOl0OqsKpHe5rT21wwRERG1LRZ9Exg4cCBEIhEa9rp57rnnmhx333334Z577rHf7IiIiBzM2HbZlIRIfP3AECzedBKv39kXYpEIb98wbs12WndkLjjhKZNYvB1Wv1W4TKmGn4cU5Uo1wv09EebnAYVUjLljuuP5cbEoV6rh71HfZMbW6xbgIXPYFnBnsHX7f3PBzOfHxdplni0xNqYz7h0YhnsGdoGfhwwlVXUI8LC+uVCjdaWQoqSqDp295fj84Dl8ct/gJq/J2YlReHhYV6xOz+FWZCNa+2uGiIiI2haLPpHl5xv/wEtERNSaGevMW1arNgQzXpnUB15yqSGgVqZUw1suwZbTl3H4YgWGR3Rw1dRtZi44UavWooOn+eCFt1yCSqUaa3bkNQnkZqQkwEsmaRR01AfX5NZXiGlCrdNhdmKUoUZkQ/qMVXs8jqM0Fxjyux4YahiQ0wf0zpXWwksuddtapiKRCP/56wCsTs/BI18falE9TFN/IMhISYBCKoanTGI0yC0Ti7Fud4HRc7o6SOtqrf01Q0RERG2LRcHIiIgIR8+DiIjI6cxlmK3bXYBFSXEAYMimCvJRYN7GE3h1ey7u6B2MH/92k9Pmam/mghPTB4dDqdGaHE9JiERJlQof7S9skoX2wtZsiEX1WWiO4i2XYv7YGABokrH6zM3Rbp/9Zu7apyREIiP/KoaEB+CNnU0DvbMTo+Apl7htllu1SoNXt7e8uZC5PxA0XFf6czQMcpdU1bltkNbVTL1mrG2aRERERGQPIqHh3msLXbx4ERkZGSgpKYHuhnpZs2fPttvknKWiogL+/v4oLy+Hn5+fq6dDREROUlJVh5BlW0yOFy9LbhK8OHO5Cj1fSYdIBJx6bgxiA30cPU2HqarTYHV6DtbtLjDZTfvltJwmwYt5Y2MgAhC2ItVkQKxoaTLkUsdmWukzB8uVavgqpPjtVAle256LXx8dbsgudFemrv1To6Jw8Hw5duZdbRTQ01uUFIsnRkbi35lnjQYzl4yPc+l2ZJVGh5DlW1q8Lmy5v62P3R40fM34t2D7PBEREZE5lsbXrP70sX79ejz++OOQy+Xo1KkTRCKRYUwkErXKYCQREbVP/h5SqzPM4gJ9cHuvYGw8WYy3duVj3ZR+zpiqQ7yclo2hXTvgwpLxqKrTNKnp6GFiO6ynTOIWWWgNs+PUWh2e//UkTl+uxqq0HKy6rZdDH9tWr+/Mw+DwgCbX3lsuRWJ0R9z92UGj99Nn7Lprlput9TBtuT+3IjfPWEYpERERkbNZ/Qlk8eLFWLJkCcrLy1FQUID8/HzDT15eniPmSEREZHfv7z2Lzadb1i376dHRAID1B86htEblqCk6VK1ai7UZBZiy/gCyzpch0EcBuVTcJEvKWy6FXCpuMq6ve2iMK7YKyyRirL69NwDgzZ15KLhW49THt4YgCPhg71lMWX8AGXlXm1zbCqWm2YCcPlBctDQZxcuSUbQ0GXPHdHf5dltb14Ut99dvRXZEB3ciIiIish+rg5E1NTW49957IRbzL6lERNQ6VKs0UGl0KKmqg0qjw8HzZXhjZx7m/3ISc27ubnXwYkxMJ/QP9UONWov39xU686nYzY/HilBZp0FEB0+MiOho9f31WWjGmAvkOtLtvYMxpnsn1Gl0WPjbKac/vqX+uFCO8+VKeMslGBXdqcm4pQE5U4FiV7J1XVSrNC36A4GeuwZpiYiIiOhPVkcUZ8yYgW+//dYRcyEiIrI7fWfekOVbELJsC0KWb8GPx4qw48mRmJ0YhQAPqdXBC5FIZMiOXJuRD7XW+YE3W/334HkA9c1qxGJRM0c35Y5ZaCKRCGvu7AORCPgy6wL2ni11+hws8b9jRQCAiT2CjK4zdwz0WsrUuliUFItnb2m+luXrO/IwKzEKi5JiW7yu3DFIS0RERER/srqBjVarxe23347a2lr069cPMlnjv9y//vrrdp2gM7CBDRFR22SsM6/e4vFxeM6GRh91Gi0iX9yG4so6fD59MKYN6mLrdJ2muLIO4StTodUJOPncGPQIankTHndsiPG3rw9h/YFziI/ogIyUhEb1rd3BgNe24+ilSnwybSAeGNLV6DGmmge5uiakpRquCx+FFJtOleDT38/ju4eGQmIi+H34YjkGv7ETPQJ9sPXxEejsrXCrdUVERERE5jmsgc2qVauwefNm9OjRAwCaNLAhIiJyFzKxGGsz8o2Orc3Ix8JxsS0+t0IqwRPxkVi25TTe3JmHeweGtZr3wS+zLkCrE3BT1wCbApGAezbEeGFiTxy+UI7nxsSgTqNDRZ0GAW4S0Mq7Wo2jlyohEYswqVewyeNMNQ9qDYFIoPG6KK1RYcY3h1FWq8b6A+cwY3i3JscLgoBnfz4BQQAGhPkhzN/TcH/APdYVEREREdmH1Z/IX3vtNXz00Ud4+OGHHTAdIiIi+7G1s29znhgZgVVp2Thwrgz7C8swPKJDi8/lTPot2vcPCXfxTBwjzN8DaU+OxGvbc/HI14fcKrPwx+P1W7RHR3VERy+52WPdMdDbEh285Fg8Phb//OkEFm86hXsGhsFH0fgj6K8nS7At+wrkEjFeutW9O6ETERERkW2s/lSrUCiQkJDgiLkQERHZlaM7Pgf6KPDsLd3xw8PD0C/Uz9Agp1qlsem8jnS8qBJ/XCiHVCzCvYPCXD0dh6hWafD6jjy8sDXbEIwuq1VjReoZvJyW49L/Pz9erxd5V98Ql83BFWaOjEL3Tl4oqqzD6vScRmNqrQ5zN54AADw1KgpRnbxcMUUiIiIichKrg5FPPfUU1q5d64i5EBER2ZUzGoEsGBuDg+fLEL4y1dAg59X0XCjVWpvP7QifXc+KvK1XEDp7tzwr1J2Z257/dkY+ZGLXZBheqa5DRv41AMBdfdpXMFIuFeOV23sDAF7bkYvzZbWGsff3FuJUSRU6e8vxvA2lE4iIiIiodbB6m/b+/fuRlpaGjRs3ok+fPk0a2GzYsMFukyMiIrKFt1yKOaOjoRMErNtdYPftuvoGOS9szTbcps/AA4C5NjTIcQSdTsAXf1zfoj24bW7RBhy/PV9P36SlTKm2qCblxhMl0AnAwDA/RHRsf9l//9c3BKOiOmJX/jUs+u0U1k8bhEqlGmsz8gAAy5J7wN9EJjMRERERtR1Wf0MKCAjAlClTHDEXIiIiu1JpdLj9w3345y0xuLhkPCrrNHZtBNJcBp67ZXltz72K8+VKBHjKcHtv081TWjv99nxjAUl7bM8H6rtdr07PxVorul3/dL1e5J3tLCtSTyQSYc0dffDQV1n4v36hqNNoUa7U4Pc5o7GnoBRjundy9RSJiIiIyAmsDkZ+/PHHjpgHERGR3X1/9BJ2F5Qif8NR5C8cZ/dGIM7KwLMXfeOavw4IbTVdmVtCvz1fn6HakH57viVrwFTmoz4jdmWD8zeXEVuj0mDz6RIAwOR2Vi+yoWHdArB7ViLe2NG4udCsxCiMiuoIadtdlkRERER0nUvbMq5atQrDhg2Dr68vgoKCMHnyZJw+fbrRMUqlEjNnzkSnTp3g4+ODqVOnori4uNExhYWFmDRpEry8vBAUFIS5c+dCo3Hf5gFERKZUqzRQaXRGG6GYGyPj3t1dn7X46IhukEns/5bn6AY59lSj0uC7oxcBAA+00S7aet5yKeaPjcGS8XGG/z8BnjIsSorFvLExFm2d12c+hizf0qQWqLQFNSm3Zl9BrVqHiA6eGBDmZ9sTbMWqVRq8ubNpc6GVbtBciIiIiIicw+rMyKioKIhEIpPjeXl5Fp9rx44dmDlzJoYNGwaNRoPnn38eycnJOHHiBLy9vQEAc+bMwS+//IJvv/0W/v7+SElJwZQpU7B7924AgFarxaRJkxASEoI9e/bg0qVLePDBByGTyfDSSy9Z+/SIiFzG1LbPBWNjIABWbwlt7w5fLMfuglJIxSI8OjzCIY9hLgMvJTHS4gw8Z0jPuQIPqQSB3gokRHZ09XQczkMmwdwx3fH8uFiUK9Xwkkuw5fRl/O9YEaYN6mL2vuYyHzt7yzG1f6jVGbE/Ntiibe5zVFvX2kobEBEREZH9WR2MfPrppxv9W61WIysrC5s2bcLcuXOtOtemTZsa/Xv9+vUICgrCwYMHMXr0aJSXl+PDDz/EF198gbFjxwKo3ybeq1cv7N27FyNGjMCWLVtw4sQJbN26FcHBwRg4cCBWrlyJefPmYdmyZZDL5U0et66uDnV1dYZ/V1RUWDVvIiJ7Mxf8mNo/FN8duWTVllAC3t1TAKC+aUaYv4dDHkOfgQfUB1L0geKUhEjMSojC90cu4cGhXR3y2HrNNVHRj/cL9Uf+wnHIvVrTboJh+usQ6KPA+3vP4vHvjiCqoxf+2j8UUjOZsuYCZm/tysNjIyKsqkmp1QnYeLx+V0d766J9o9ZW2oCIiIiI7M/qb69PPfWU0dvfeecd/P777zZNpry8HADQsWN9xsbBgwehVquRlJRkOKZnz57o1q0bMjMzMWLECGRmZqJfv34IDv6zEP+ECRPwxBNP4Pjx4xg0aFCTx1m1ahWWL19u01yJiOzJVPCjs7cc0Z282kUmkbWdic0pr1Xj84MXAABPJkTacZZN3ZiB5+8hQ86Vatz87h5kX6lGF38PjIsNtOkxTF2b5pqomBqP6+zd7jJqpw/ugoW/nUL+tRp8c/gi7jPTTdxcwCz3ag2q1RqTGbGzjGTEZp69hsvVKgR4yjAquu1npZrjjOZCREREROTe7LZ37NZbb8X333/f4vvrdDo8/fTTSEhIQN++fQEARUVFkMvlCAgIaHRscHAwioqKDMc0DETqx/VjxixYsADl5eWGn3PnzrV43kRE9mAq+BHiq0BJlarZTKLWzlx9vpb45PdzqFFr0SfYF6OjHd+h11suhVwqRqCPAnKpGL2CfXBT1wBodQIW/XYKV6rrWlzv09S1qVSqsSotBytTzzSqvbci9QxWpWWjuFKJl9KyjY63x9p8XnIpZo+KAgC8kp4DQRBMHttcLVBvmemalCkJUfjlROPa1v87Vv955PZeQQ6pXdqa6EsbGKNvLkREREREbZvd9vV99913hozGlpg5cyaOHTuGjIwMe03JJIVCAYWCW4CIyH2YyhYqqqxDkI+8TWcStaQzsTmCIOC961u0nxgZ6ZItySKRCP/6S3/UqrVYN6Uf3t6Vj3W7C6yu92nq2ry7pwDzxsaYzJj94o8LWDguDusyCoyOt6WMWmvMHBmJ1ek5OHqpEr+eLMGk3sFGj6tWaZCSEIkXtmY3GdMHzLzl0iYZsSdLKnHzu3tw5nIVpBIxpvQLhSAIOFlcic7ectzZzrdoA6ZLG7AGLhEREVH7YXUwctCgQY2+2AmCgKKiIly+fBnvvvtuiyaRkpKCjRs3YufOnQgP/3PbVEhICFQqFcrKyhplRxYXFyMkJMRwzP79+xudT99tW38MEZG7O15caTT4caVahbyrNaabpCREorCsFjGdvZ01Vbuzd0OLtJwrOH25Gj4KiUu7RnvIJHjvL/0NnYP1rAm0mro2Ib4KFFfWmcyY9ZRJUFrL2nw36uAlx+MjIvHajly8kp5jNBgpCPXZrMsm9AAAs0HkhjUpAaB/qB8SozriVEkVlm85g5u6BiDQR451U/ojyEcOjdZ0NmZ7Yqy0gVqnYyCSiIiIqJ2wOhg5efLkRv8Wi8UIDAzELbfcgp49e1p1LkEQMGvWLPzwww/Yvn07oqIab9sZMmQIZDIZtm3bhqlTpwIATp8+jcLCQsTHxwMA4uPj8eKLL6KkpARBQUEAgNTUVPj5+aF3797WPj0iIqdLz7mCmRuOYseTIyESiYzW9zOWSTQrMRIpCVEY894eLE3ugb8OCHPxM2kZeze0eHd3AQDggSFd4evh2sY+PnIp1l2fz40sCbSaujZFlXUINJMxW6vWooMna/MZM2d0NNZm5CMj/xoy8q8iMarxNv4P9xfivcyzyCgoxZbHhmNRUpzFATORSIR3p/SDQirGkvFxWJvRsozY9uDGQK67dJ0nIiIiIsez+lva0qVL7fbgM2fOxBdffIEff/wRvr6+hhqP/v7+8PT0hL+/P2bMmIFnnnkGHTt2hJ+fH2bNmoX4+HiMGDECAJCcnIzevXvjgQcewOrVq1FUVIRFixZh5syZ3IpNRG6vvFaNh7/KwrkyJdbtzsdzY2Kw8IZsIcX1wMWNmUQqrQ4vp2XjRHEVVqaewbCuAQjz87BLAxhnEQQBvgqp3YJmRRVK7C64BgB4cmSkvabZYrYGWv1NbN+/Uq3C9tyrJjNmpw8Oh1KjNTmu32rcHgNAYf4eeHBoOD7YV4hX0nKQOOPPYGTulWrM+fE4AOCBIeEI9q3vwm5NwEwqEeOlW3vi1e25Lc6IJSIiIiJqy1z6LeS9995DeXk5brnlFoSGhhp+vv76a8Mxb7zxBm6//XZMnToVo0ePRkhICDZs2GAYl0gk2LhxIyQSCeLj43H//ffjwQcfxIoVK1zxlIiIrDL7f8dwrkyJ7p28MPeWmCaNUBoGLG4c81FIsXxCTzx3S3ds+0c8PtpfaLcGMM6g0erw+HdHsOX0ZaSY6HidkhCJ0lpVs+eqVmmg0uig0QnIfX4c0p+IR58QXzvP2HrNNUJpLtB6+GK5yWuTf7UG84w0UVkyPg7zx8bAz0NmtMmKfrw9B8Pm3tIdYhHwy8kSHC+qBABodQIe+ioL1Sotbo7uhDmjo1t8foVUYjYjViZuf0FgIiIiIiI9kWCunWQDYrG42SYAIpEIGk3r685ZUVEBf39/lJeXw8/Pz9XTIaJ24sdjRfi/9QcgFgG7ZiYgPrJlTcCq6jRYnZ5jtNnGkvFxbpOFVa3SQCYWGzI3D5wrw6PfHoZYBOyZlYjXd+Q12oaekhCJWYlReOL7I1hzRx+TWZ9KtRar0nIabW+flRiJBWNjXb4dtlqlwavpuUazE5v7f/Pz8SLM++Ukdjw5Eut2FzTZvq/f7qu/rg2zaRues7nx9uqZH4/j5u6dMD4uEFUqDXwVUmw+fRkvbj2D7x4cioiOXi0+d0lVHUKWbTE5Xrwsud3V6yQiIiKits/S+JrFwcgff/zR5FhmZibefvtt6HQ6KJVK62frYgxGEpHejQEzewdu9OcvrVXDRyFB6pkrOHutBk/ZkIWl0ugQsnyLyW3ORUuTIZe6NhPLWMBQH2w8fKkCSbGBTYJmNer6QN7T12v8Gau9pxWEJt2m9dwlEKtUa/FyWk6TQOszN3c3mTVZWqNC3zXbcamiDqtv74UnRkYyoGhn1SoNXknLabSuUhIiMfeWGJtrjbaG1yQRERERkb1ZGl+z+NP2XXfd1eS206dPY/78+fj5558xffp0bo0molZNqdZidXquyQw0R5w/JSESC6zsFn0jezeAsbdqlaZJwLCsVo0XtmZDJBLhuTHdARhpaCGVY8G42CZZn/raex5SsSFQaUxLOnE7wo2dg30VUvx2qgQj12ZgxYQe+IuRxkPP/HQclyrq0CPQGzMTouB5ff2x2Yd96Nfkjevqha3ZEItENgex1Tod63USEREREZnQok/CFy9exKOPPop+/fpBo9Hg0KFD+OSTTxAREWHv+REROUW1SoNVaTlYmXrGENjTB71eTstBtcq2EhSmzv/C1my8YuP5ba1L6GgysdhkwHBtM/Xz5BKxydp7P58sRoVS02wg1h00rPfpIZNg79lSnCqpwoxvDuPM5apGx/56shif/H4eIhHw0T0DDYFIsh9za9IeNR295VLW6yQiIiIiMsGqT9vl5eWYN28eYmJicPz4cWzbtg0///wz+vbt66j5ERE5haODE448vz4Lyxh9FpYrldY2n7lpirmsz+zL1fD3dO9ArCkv3toTo6M7orJOg7988jtqrgejK5RqLPj1JADg6VHRLa4jSuZZkk1sK31GbNHSZBQvS0bR0mTMHdPd5XVMiYiIiIhczeJvv6tXr0Z0dDQ2btyIL7/8Env27MGoUaMcOTciIqexV3BC39W5pKoOKo0O1SoNzpXWoKSqzmHBD1NZWIuSYjErMQpyiXO2g9743KvqNPgy6zx8FJIWBwzNZX1qdAJUWvcOxJoilYjx5f1DEOyrgEYn4FhRJVQaHcqVGuyZlYhNjw7HCxN7uHqabZazsokbZsTKpWJmRBIRERERwYqakfPnz4enpydiYmLwySef4JNPPjF63IYNG+w2OSJyjeaauDi6yYsr6IMTphpOWBKcMFUTcnZiFDzlEpvPb86NdQn9PGRIPVOCUe/sxvTBXbAwKc6m8zfH1HOflRiFzIJSpCREGu323Vz9vOZq70lEwPyxMQDQqEGMPWt9Okqonwd+fHgYojp5YW1GPia+v69BN/AojI7u5Ooptlms6UhERERE5DoWRw8efPBBiEQiR86FiNxAc01cHN3kxVXUOh1mJUZiZWrTgNmsxMhmgxPmmrQAwD/iIzArMcpo12d7BT9ubABTrtTgVEkVVqSewV19QtA31HQ3M1s099z/dlNXJEZ1hFgksjpgqM/6BMwHGxsGYvXdplvDeuwT6mu0Qc/K1DMQAW7RDbwtsnRdERERERGR/YkEQRBcPQlXs7T1OFFbZyyopLd4fCxmjozCO3vyjQbsloyPa9WBE7W2fovs27vysG53QZPsvg/2FWL+2BiTf5RRaXQIWb7FZOZj0dJk6AQBL6flOC34IQgCJn98AD+fKMbQcH/smZUIqQ1btk1lxFry3OVSseH+DQOGlq4XW+7rziy9duQYbXVdERERERG5gqXxNX7iJiIDc01WvvjjAhaOi8PajAKj429n5OP5cbEOnJ1jfbS/EG/tyseaO3pjUVKcIThRUFqDm9/dg1MlVZBLRPjHyMgmAblKpQYqrdBsTchAH4VTM/hEIhHem9ofu/K34/fz5VizIxfzx7bs/5GxjNhZiVH4583RFjWoCfRRNMnctCYT1Jb7ujNLapXqnzPZX1tdV0RERERE7oyfuonIwFxgxFMmsakrsjurVWvxwtZsnCqpQs6V6kYNJ+ICfTArMQo9g3zw4NCueCU9ByHLtyBk2RaELN+C1ek5kIpF6ORtWUMMZze0CPP3wJt39UHPIB/0CfZFnUbbqLmOJapVGqxKy8HK1DOG///6rcTv7i5AsI+iVXa0dgfOaqRCRERERETkLhiMJCIDc4GRWrUWHTzbZuDkvT0FuFCuRNcADzweH9Fk/ImRkfj+oaFYm5GPF1KzbwjIZddvu65Ru21X5weGhCMjJQEHzpUhdHmqIZD6anoulGpts/c3lzG7ensu1K20o7U70DdSMYbXjoiIiIiI2iIGI4nIoLC0BikJkUbHpg8Oh1KjNRk4SUmIxM68qzhXVoNqlQYqjc7qDDxXqFRq8HJaDgBgyfgeUEiNb5nu3skb63YXGB1bt7sAgT4KzB8bgyXj4wwB2wBPGZaMj8P8sTEurUNXo9birV35eGFr40DqitQzeDktp9n/P81tJVZrBbd97u5O30iF146IiIiIiNoLfsshIgBAwbUaTPv8D/z69+EQiYC1GQVGm6wY60A7KzEKsxKj8PCXWVg/bSBWp+eYvL+7eSsjD1eqVYjt7I2HhoabPM7S2n7u2NXZXGajJbU+9Rmzppqs+Cjqt56743NvDTxkEl47IiIiIiJqNxiMJCJodQIe+jILB8+X4+kfj+HffxmAhePijAZGTAVOquo0+Oct3fH29Qw8PX0GHgC367Z9rUaFNdtzAQDLJvQw22m6uYBcw5qQgHs1xLC1SUphWX3GbMP/r3r6rcRyiN3yubcWvHZERERERNRe8NsOEeH1HbnYlX8NPgoJVk7sach0M9VkxVgTlmBfDyREdjS5lfntjHzIxO71K+fdPQWoUGrQL9QX9wwIM3tsa67tZ0uTlOzLVbj3s4OYlRiFxeNjuZWYiIiIiIiIbMJvkETt3PGiSizedBoA8PqdfRDdybvF57I1A88ZqlUayMRilNaqMWd0NPqG+MFPIYFYLDJ7P31tP6DxFnV33oKupw+k6jNUG0pJiESNWgu5tGmguE6jxbT//oE/LlRg7s8nsG5KP5MZs0RERERERESWYDCS3JY+aFSmVCPgeuCjYQZWc+NkWsNrF9XRE189MASpp0sw46ZuNp3X0q3MrqJUa7E6PRdrGwQTUxIim62ZqNdaa/uZCqSmJERiVmIU/vHdYXx490B4Kxq/fp7/9RT+uFCOjl4yvHhbfcYswK3ERERERERE1HKM3JBbMhY0apiB1tw4mWYqILfmzj4QicxnBzbHXAZew9qCrlCt0mB1ei5WNphbWa0aL2zNhlgksrieZWut7WcskFpaq8adH+3HvsIyVCg1+PFvN0F2vW7mtjMleGNnHgDgo3sGoou/pyunT0RERERERG1E6/gWTe1KtUqDVWk5WJl6xpBhp2+C8nJaDiqUarPj1SqNK6fv1kxd2xe2ZuMVO1w7fQbekvFxjWoLLkqKxTwn1RasVmmg0uhQUlUHlUZneE7NdZR2t3qWjnBjrc9gXwVev7MPvGQSFJTWIutCOVQaHYor6zAisiM2PDwMKyf2xJ19Qlw9dSIiIiIiImojRIIgCK6ehKtVVFTA398f5eXl8PPzc/V02j2VRoeQ5VuMbvXt3skLx+eOMTke4ClD0dJko/XvyPy1tee1a7gN3FsuwZbTl3GtRoUZwyNsPrc5SrUWq9JymmTMzhsbg9IaNcJXppq8b/GyZJfXs3SVnblX0CvYF2sz8rFud0GjjNkF42LhyWxjIiIiIiIiaoal8TVu0ya3Y64JiqdMgtJa92+SYitH1cN0VoMZ/VyDfBT4MusCpn/+B3wUEtzWKxihfh42n98YU9uwV6SegYdUjKdHR7t1PUtXGtI1AKvTc/DC1mzDbS3Zwk5ERERERETUHKaPkdvRN0ExplatRQdP0+NtIaikr+kYsnwLQpZtQcjyLXg1PRdKtdbmc5u7to66dvcMCMNNXQNQVafF87+etPl8LdmGvXp7LtRaHWYlRhod19ezbK/qr12B0bH2soWdiIiIiIiInIPfMMmtCIKAU5erkJIQaXR8+uBwKDVazE6MMjqekhCJC+VKB87QsZqrl2lrTUe1Tmfy2joqICcWi/DW5L4AgE9+P499Z0tbfC5TgdrqOg2Kq+rMZn2qtQIWjI1tUs9yyfg4zHdSPUt3ZUnGLBEREREREZE9tN9v3+R21Fod/vH9EWQWlGLHkyMhEgFrMwqMdsuePzYGQH3WVsP6drMSo3Dzu3uQkhCFh4aFO2SrsyM112Tl+XGxNp1/e85VzLoeyG1YG9DRnciHR3TAQ0PD8cnv5/HU/45hz6xEiMXWde62dRu2j6K+ecuNHaXVOl2778Cuz5jlFnYiIiIiIiJyNPeOzFCbdmNdxP2FpcgsKMWZy1VIz7mC58bEYOG4OKNBIw+ZpElQSaXVGQJ5fx0QilfScpwacLOH5jLUSmvVCPZVtKimZHmtGo9+exgBnjJ888AQLEoyfm0d5aXbeuH7o5ew/1wZPjt4Hg8N62rV/Zvbhj0zIRKzE6OwokGwUk+f9SmH2HCd9LUx5UwQh1qns+jaEREREREREdmKwUgyy1GNVPTbbdfekNm448mROHqpAmNjAw3HmgoaNQkqScVYMC4Wf+kfirUZ+U2acegDLe7cjKO5DDVfhQSlNSq8uSvPZNaoKUu3nEZRZR18FVLEBnpDLhU7NSAX6ueBRUlxWH/gHIJ8FVBpdFatq+YCtWqtYDRjtjUEoV3NWy7ltSMiIiIiIiKnEAmCILh6Eq5maevx9kap1mJVWk6jgKE9ghPGttvqLR4fh+dsDBaqNDqELN9iMqBXtDQZcql7ZnmdK63B+/sKGwVS9RYlxeJvN3XDR/uNjy8ZH2cy0HroQjmGvrkTOgHY/NgIjI8LbHKMM6g0WlTWafHWrjyrs1Yt/f+qD6A3zPp01+Czu+G1IyIiIiIiopayNL7mnhEZcjlLG6mY6mxsjrnttmvt0Lm3tTbj2J5zBXd+dACzEqOwKCm2SZOV58fFItTPA+t2Fxi9v6muxzqdgJkbjkInAHcPCHNZIBIA1DoBb2fk4YWt2VY36FHrLOuG7S2XGrI+5VIxg2lW4LUjIiIiIiIiR+M3TTLKkkYqxrZaW5LhZkmwUL99uCVaQzOOG7e/X66uw7M/H8fhSxVYsukUVt/Rx2hNx5JmOkYbu3ZfZl1A5tlS+CgkeO3O3s54eibVr6sCo2PNNegRAZiVGA1BcG7zHSIiIiIiIiKyHwYjyajmAoaVdRq8tSuvRXUZ/T2kDg0WmmvGkZIY6fJmHKbqZf726Ag8/+tJvHFXX0Ng7caajs0FWr3lEmRfrkaYvwIysRiltWpM7heCDYphKKtVoYu/p/OeqBG2BKJf3Z6Lrw9dxBt3GQ/UEhEREREREZH74zZtMkofMDSmeycv+CqkVm8XBoBdeVex9cwVpCREGh1vuN22pfTNOJaMj2u01XlRUixmJUTh84PnbTq/LUxtf39hazbWZuTjtTv7mA2s6QOtxqQkRGLP2VIEeEqxOj0HIcu3IHT5FnRduRV/nC/DPQO7OOQ5WUMfTDU6ZiYQfbFciVfTc3GqpAoVSg23EhMRERERERG1UvwWT03syruKCqUGKQmRRhulzBkdjdJayzLcGm5H9vOQoqxWjX9nnsWn9w2CWCRyWOdeD5kEc8d0x/PjYg0ZdLlXq3Hzu3twqqQKAZ5y3D0wzObHsZa57e/rdhdgUVKc2fub63o8b2wMThZXGu0k/sLWbIhFIpd3EjeXtaoPRBvLWl206RRq1FqMjOyAv/QPdcZUiYiIiIiIiMgBGIykRraeuYy7Pt6PiA5eyEhJMBow/NtN3SAWicxuF/aRSw1ds2/cjvzpfYPgbSRYaO/ttvqgm37bb69gX0zoEYhTJVV4aVs2EqM6oLO3wlC30Rmdg+1RL9NYoFWt08FTJkHfED8k/Xuv0fs1V5PRGUwFU1MSIjErMQoiI/c5dKEcn/x+DgDw2h19IBIZO4qIiIiIiIiIWgMGI9u5RpmLCilq1FpEdPBCZAdPeJkJGFarNKbrMiZEoriqDh/tL7QoQ+/GuoiOtOaOPlBrdVia3ANrM/JtaoRyYxMaS4KZ9mquY+raObo5kD3cGEz185BhW/ZljHpnN4Z1DcD6ewcaAo6CIODZn09AEIBpg7pgeEQHl86diIiIiIiIiGzDYGQ7ZqqRyu6UBHjLJZBL/wzK3Rj0MrtdeEwMIILZmpKuytCTiEV46bZeWLM9t0XNd/Qs6SR+Y7DyWo0KOVerTW5/N7dN2VKtoZM40DSY6i2XIPtKNU6VVCEhqiMeGxEBANhyugRpOVegkIrx0q09XTZfIiIiIiIiIrIPBiPbKf0W6pUNMhtvzFyUN3MOk9uF5RKUVNW5bYaeh1RiU6DU1LXTBzOfG9MdYpHIaKD36VHR6B3sC5FIZDaQ2VItrcnoajd374wXb+2J9QfOIdRXgTqNFuVKDRKjO2HDw8OQd7UaER29XD1NIiIiIiIiIrKRSBAEwdWTcLWKigr4+/ujvLwcfn5+rp6OXZnaSqzS6BCyfIvJDLqipcmQS1setHL0+W1RUlWHkGVbTI4XL0s2Gyht7rmdXTgOa3bkYmVq0+zHRUmxePaW7pCIRZCJxY2CuPaqV6lUa/FyWo7DmgM5iiAIKKtV442deY22z6ckRGLBuFh4uvHciYiIiIiIiNo7S+NrzIxsw4xtJZ6VGIV/3mx5N+yWcucMPVu3MpuryygViyCXSrA2o8DouL5jtj4Q64h6maYyVt05EAkANWot3tzlvp3AiYiIiIiIiMh27rdfk+yiWqXBqrQcrEw9YwicldWqsTL1DN7dXYBgHwUCPI0H3exRW1BfU3LJ+DjD4wR4yrBkfBzmj41xaVBJHyg1JiUhEiqtzuR9NVodfBVSk9cuNtAb5RYEeh3NWy6FXCpGoI8Ccqm4VQTxZGIx1mbkGx17OyMfMjF/XRERERERERG1dvx230aZC+ys3p5rNiCnz1y0lT5Dr2hpMoqXJaNoaTLmjunu8gw9U4HSRUmxmJUYhRVbzkCra1q9QKXR4b7P/8CW05eRkhBp9Nx39Q5BgKfMoYHetsqSTuBERERERERE1Lq5f7oUtUhzgR21RjDZDduetQVv7JrsLs1TjG1lvlihxLh/ZeJYUSVkEhGeHxcLmeTPepsHzpXhWFEllmw+jd0pCRCLRE2u3exRUW69Rd2dtZZO4ERERERERETUcgxGtlHNBXZ8FPXbeFtjbUF7uTFQGtnRC0uT47Bsyxk8PToar6TnNGmksuPJkTh9uQo+CqnZa+eMQG9bwyAuERERERERUdvHbtpom920q1UavJqeazSws2R8HJuBmJF7tRqfHDjXqJGK3uLxcXjOwmun72TuiI7ZbVVr7QRORERERERE1N5ZGl9zaZrRzp07cccddyAsLAwikQj/+9//Go0LgoAlS5YgNDQUnp6eSEpKQnZ24wDRtWvXMH36dPj5+SEgIAAzZsxAVVWVE5+Fe3LnBjLurqu/J9btLjA6ttaKRiqtsYmMq7lrnVEiIiIiIiIisg+XBiOrq6sxYMAAvPPOO0bHV69ejbfffhv/+te/sG/fPnh7e2PChAlQKpWGY6ZPn47jx48jNTUVGzduxM6dO/HYY4856ym4NQZ2WoaNVFyLQVwiIiIiIiKitstttmmLRCL88MMPmDx5MoD6rMiwsDD885//xLPPPgsAKC8vR3BwMNavX497770XJ0+eRO/evXHgwAEMHToUALBp0ybcdtttOH/+PMLCwow+Vl1dHerq6gz/rqioQNeuXdvUNm1qOZVGh5DlW0zW2yxamgy5lLULiYiIiIiIiIj0WsU2bXPy8/NRVFSEpKQkw23+/v4YPnw4MjMzAQCZmZkICAgwBCIBICkpCWKxGPv27TN57lWrVsHf39/w07VrV8c9EWp19I1UjNE3UiEiIiIiIiIiIuu5bTCyqKgIABAcHNzo9uDgYMNYUVERgoKCGo1LpVJ07NjRcIwxCxYsQHl5ueHn3Llzdp49tWast0lERERERERE5BjtMqqiUCigUChcPQ1yY/p6m8+Pi23UDZv1NomIiIiIiIiIWs5tMyNDQkIAAMXFxY1uLy4uNoyFhISgpKSk0bhGo8G1a9cMxxC1FBupEBERERERERHZl9sGI6OiohASEoJt27YZbquoqMC+ffsQHx8PAIiPj0dZWRkOHjxoOCYtLQ06nQ7Dhw93+pyJiIiIiIiIiIjINJemelVVVSEnJ8fw7/z8fBw6dAgdO3ZEt27d8PTTT+OFF15AbGwsoqKisHjxYoSFhRk6bvfq1QsTJ07Eo48+in/9619Qq9VISUnBvffea7KTNhEREREREREREbmGS4ORv//+O8aMGWP49zPPPAMAeOihh7B+/Xo899xzqK6uxmOPPYaysjIkJiZi06ZN8PDwMNzn888/R0pKCsaNGwexWIypU6fi7bffdvpzISIiIiIiIiIiIvNEgiAIrp6Eq1VUVMDf3x/l5eXw8/Nz9XSIiIiIiIiIiIhaFUvja25bM5KIiIiIiIiIiIjaFgYjiYiIiIiIiIiIyCkYjCQiIiIiIiIiIiKncGkDG3ehL5tZUVHh4pkQERERERERERG1Pvq4WnPtaRiMBFBZWQkA6Nq1q4tnQkRERERERERE1HpVVlbC39/f5Di7aQPQ6XS4ePEifH19IRKJXD0du6uoqEDXrl1x7tw5dgsnp+G6I2fjmiNX4LojV+C6I2fjmiNX4LojV+C6s40gCKisrERYWBjEYtOVIZkZCUAsFiM8PNzV03A4Pz8/vpjI6bjuyNm45sgVuO7IFbjuyNm45sgVuO7IFbjuWs5cRqQeG9gQERERERERERGRUzAYSURERERERERERE7BYGQ7oFAosHTpUigUCldPhdoRrjtyNq45cgWuO3IFrjtyNq45cgWuO3IFrjvnYAMbIiIiIiIiIiIicgpmRhIREREREREREZFTMBhJRERERERERERETsFgJBERERERERERETkFg5FERERERERERETkFAxGEhERERERERERkVMwGOlCO3fuxB133IGwsDCIRCL873//azReXFyMhx9+GGFhYfDy8sLEiRORnZ1t9FyCIODWW281ep5t27Zh5MiR8PX1RUhICObNmweNRtPs/LZv347BgwdDoVAgJiYG69evt2r+5H7sseZuueUWiESiRj//+Mc/Gh1TWFiISZMmwcvLC0FBQZg7d65Fa+7bb79Fz5494eHhgX79+uHXX3+1en7kfpyx7g4fPoxp06aha9eu8PT0RK9evfDWW29ZNL/m1t3DDz/c5LEnTpzYsotBTuOs33d6V69eRXh4OEQiEcrKypqdX3PrbsOGDUhOTkanTp0gEolw6NAha54+uYCz1tyN4yKRCF999VWz8+N7bNvkjHW3fv16o+tOJBKhpKTE7Pz4Hts2Oev3Hb/Hkp69YieZmZkYO3YsvL294efnh9GjR6O2ttYwfu3aNUyfPh1+fn4ICAjAjBkzUFVV1ez8mltzlZWVePrppxEREQFPT0+MHDkSBw4caNG1aCsYjHSh6upqDBgwAO+8806TMUEQMHnyZOTl5eHHH39EVlYWIiIikJSUhOrq6ibHv/nmmxCJRE1uP3z4MG677TZMnDgRWVlZ+Prrr/HTTz9h/vz5ZueWn5+PSZMmYcyYMTh06BCefvpp/P3vf8fmzZstmj+5J3utuUcffRSXLl0y/KxevdowptVqMWnSJKhUKuzZsweffPIJ1q9fjyVLlpid2549ezBt2jTMmDEDWVlZmDx5MiZPnoxjx45ZPT9yL85YdwcPHkRQUBD++9//4vjx41i4cCEWLFiAdevWmZ1bc+tOb+LEiY0e+8svv7ThipAzOGPdNTRjxgz079/forlZsu6qq6uRmJiIV155xYpnTa7kzDX38ccfNzpm8uTJZufG99i2yxnr7p577mk0dunSJUyYMAE333wzgoKCTM6N77FtlzPWHb/HUkP2WHOZmZmYOHEikpOTsX//fhw4cAApKSkQi/8Mi02fPh3Hjx9HamoqNm7ciJ07d+Kxxx4zOzdL1tzf//53pKam4rPPPsPRo0eRnJyMpKQkXLhwwQ5Xp5USyC0AEH744QfDv0+fPi0AEI4dO2a4TavVCoGBgcL777/f6L5ZWVlCly5dhEuXLjU5z4IFC4ShQ4c2Ov6nn34SPDw8hIqKCpPzee6554Q+ffo0uu2ee+4RJkyYYNH8yf21dM3dfPPNwlNPPWXyvL/++qsgFouFoqIiw23vvfee4OfnJ9TV1Zm839133y1MmjSp0W3Dhw8XHn/8cavmR+7NUevOmCeffFIYM2aM2WOaW3eCIAgPPfSQcNddd1n12OReHL3u3n33XeHmm28Wtm3bJgAQSktLzR5vybrTy8/PFwAIWVlZzc6D3Icj11xLPnPxPbZ9cNZ7bElJiSCTyYRPP/3U7HF8j20fHLXu+D2WTGnpmhs+fLiwaNEik+c9ceKEAEA4cOCA4bbffvtNEIlEwoULF0zer7k1V1NTI0gkEmHjxo2Njhk8eLCwcOFC80+2DWNmpJuqq6sDAHh4eBhuE4vFUCgUyMjIMNxWU1OD++67D++88w5CQkKMnqfhOQDA09MTSqUSBw8eNPn4mZmZSEpKanTbhAkTkJmZ2aLnQ+7P0jUHAJ9//jk6d+6Mvn37YsGCBaipqTGMZWZmol+/fggODjbcNmHCBFRUVOD48eMmH7+5NWfN/Kj1sNe6M6a8vBwdO3Y0e4ylv+u2b9+OoKAg9OjRA0888QSuXr3a7HMj92XPdXfixAmsWLECn376aaO/rJvD99j2x96/62bOnInOnTvjpptuwkcffQRBEMw+Pt9j2ydHvcd++umn8PLywl/+8hezj8/32PbJXuuO32PJUpasuZKSEuzbtw9BQUEYOXIkgoODcfPNNzdak5mZmQgICMDQoUMNtyUlJUEsFmPfvn0mH7+5NafRaKDVao2u5/b8HstgpJvq2bMnunXrhgULFqC0tBQqlQqvvPIKzp8/j0uXLhmOmzNnDkaOHIm77rrL6HkmTJiAPXv24Msvv4RWq8WFCxewYsUKAGh0nhsVFRU1CiYBQHBwMCoqKhrVVKC2w9I1d9999+G///0v0tPTsWDBAnz22We4//77DeOm1o5+zBRT99Pfx9L5Uetir3V3oz179uDrr79udltFc+sOqN8+9umnn2Lbtm145ZVXsGPHDtx6663QarUtfNbkavZad3V1dZg2bRpeffVVdOvWzeLHt2TdUdtiz991K1aswDfffIPU1FRMnToVTz75JNauXWv28fke2z456j32ww8/xH333QdPT0+zj8/32PbJXuuO32PJUpasuby8PADAsmXL8Oijj2LTpk0YPHgwxo0bZ6gtWVRU1KT0hFQqRceOHVv0PVa/5nx9fREfH4+VK1fi4sWL0Gq1+O9//4vMzMx2/R7LYKSbkslk2LBhA86cOYOOHTvCy8sL6enpuPXWWw2ZFz/99BPS0tLw5ptvmjxPcnIyXn31VfzjH/+AQqFAXFwcbrvtNgAwnMfHx8fwY6owP7V9lqw5AHjssccwYcIE9OvXD9OnT8enn36KH374Abm5uRY9TmFhYaM199JLL9l1ftS6OGLdHTt2DHfddReWLl2K5ORkAC1fdwBw77334s4770S/fv0wefJkbNy4EQcOHMD27dttfv7kGvZadwsWLECvXr1Mfmm3Zd1R22LP33WLFy9GQkICBg0ahHnz5uG5557Dq6++CoDvsdSYI95jMzMzcfLkScyYMcNwG99jqSF7rTt+jyVLWbLmdDodAODxxx/HI488gkGDBuGNN95Ajx498NFHH1n8WC1dc5999hkEQUCXLl2gUCjw9ttvY9q0ae36PVbq6gmQaUOGDMGhQ4dQXl4OlUqFwMBADB8+3JA2nJaWhtzcXAQEBDS639SpUzFq1CjDm/gzzzyDOXPm4NKlS+jQoQMKCgqwYMECREdHA0CjDp1+fn4AgJCQEBQXFzc6b3FxMfz8/Jr9Kyi1Xs2tOWOGDx8OAMjJyUH37t0REhKC/fv3NzpGv5ZCQkIQFhbWaM3pt9GaWnMNyw+0ZH7k/uyx7vROnDiBcePG4bHHHsOiRYsMt9uy7m4UHR2Nzp07IycnB+PGjbPquZL7sMe6S0tLw9GjR/Hdd98BgGGrbOfOnbFw4UIsXrzYbuuOWj97/q678ZiVK1eirq6O77HUhL3X3QcffICBAwdiyJAhhtv4Hks3ste64/dYslRzay40NBQA0Lt370b369WrFwoLCwHUr52SkpJG4xqNBteuXTP83mrpmuvevTt27NiB6upqVFRUIDQ0FPfcc49hLbdH7TcM24r4+/sjMDAQ2dnZ+P333w1bsufPn48jR47g0KFDhh8AeOONN/Dxxx83OodIJEJYWBg8PT3x5ZdfomvXrhg8eDAAICYmxvCjT0uOj4/Htm3bGp0jNTUV8fHxDn625A5MrTlj9OtO/ws+Pj4eR48ebfSLPDU1FX5+fujduzekUmmjNaf/wGrNmrNmftR62LLuAOD48eMYM2YMHnroIbz44ouNjrfHutM7f/48rl692uixqfWyZd19//33OHz4sOE9+IMPPgAA7Nq1CzNnzrTruqO2w9bfdcaO6dChAxQKBd9jySR7rLuqqip88803jbIiAb7Hkmn2WHf8HkvWMLXmIiMjERYWhtOnTzc6/syZM4iIiABQv3bKysoa1SRNS0uDTqczBMttXXPe3t4IDQ1FaWkpNm/e3L7fY13cQKddq6ysFLKysoSsrCwBgPD6668LWVlZwtmzZwVBEIRvvvlGSE9PF3Jzc4X//e9/QkREhDBlyhSz54SRbmCrV68Wjhw5Ihw7dkxYsWKFIJPJmu0YlpeXJ3h5eQlz584VTp48KbzzzjuCRCIRNm3aZPH8yf3YuuZycnKEFStWCL///ruQn58v/Pjjj0J0dLQwevRowzEajUbo27evkJycLBw6dEjYtGmTEBgYKCxYsMDs3Hbv3i1IpVJhzZo1wsmTJ4WlS5cKMplMOHr0qOGYlrwmyPWcse6OHj0qBAYGCvfff79w6dIlw09JSYnZuTW37iorK4Vnn31WyMzMFPLz84WtW7cKgwcPFmJjYwWlUumAq0X24ox1d6P09HSLumlb8vvu6tWrQlZWlvDLL78IAISvvvpKyMrKEi5dumTbhSGHccaa++mnn4T3339fOHr0qJCdnS28++67gpeXl7BkyRKzc+N7bNvlzN91H3zwgeDh4dHs7zg9vse2Xc5ad/weS3r2iJ288cYbgp+fn/Dtt98K2dnZwqJFiwQPDw8hJyfHcMzEiROFQYMGCfv27RMyMjKE2NhYYdq0aWbnZsma27Rpk/Dbb78JeXl5wpYtW4QBAwYIw4cPF1QqlR2vUuvCYKQL6b+03Pjz0EMPCYIgCG+99ZYQHh4uyGQyoVu3bsKiRYuEuro6s+c0FowcM2aM4O/vL3h4eAjDhw8Xfv31V4vnN3DgQEEulwvR0dHCxx9/bNX8yf3YuuYKCwuF0aNHCx07dhQUCoUQExMjzJ07VygvL2/0OAUFBcKtt94qeHp6Cp07dxb++c9/Cmq1utn5ffPNN0JcXJwgl8uFPn36CL/88kuj8Za8Jsj1nLHuli5davQxIiIimp2fuXVXU1MjJCcnC4GBgYJMJhMiIiKERx99VCgqKrLb9SHHcNbvO2OPackX9eZ+33388cdG57906dKWXA5yAmesud9++00YOHCg4OPjI3h7ewsDBgwQ/vWvfwlarbbZ+fE9tm1y5u+6+Ph44b777rNqfnyPbZucte74PZb07BU7WbVqlRAeHi54eXkJ8fHxwq5duxqNX716VZg2bZrg4+Mj+Pn5CY888ohQWVlp0fzMrbmvv/5aiI6OFuRyuRASEiLMnDlTKCsra/H1aAtEgnC9wBERERERERERERGRA7FmJBERERERERERETkFg5FERERERERERETkFAxGEhERERERERERkVMwGElEREREREREREROwWAkEREREREREREROQWDkUREREREREREROQUDEYSERERERERERGRUzAYSURERERERERERE7BYCQRERERERERERE5BYORRERERERERERE5BQMRhIREREREREREZFT/D9iAIFtr2VACgAAAABJRU5ErkJggg==",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"forecaster = Prophet(\n",
" seasonality_mode=\"multiplicative\",\n",
" n_changepoints=int(len(y_train) / 12),\n",
" add_country_holidays={\"country_name\": \"Germany\"},\n",
" yearly_seasonality=True,\n",
" weekly_seasonality=False,\n",
" daily_seasonality=False,\n",
")\n",
"\n",
"forecaster.fit(z_train)\n",
"y_pred = forecaster.predict(fh.to_relative(cutoff=y_train.index[-1]))\n",
"y_pred.index = y_test.index\n",
"\n",
"plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"])\n",
"mean_absolute_percentage_error(y_test, y_pred, symmetric=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3.5 State Space Model (Structural Time Series)\n",
"\n",
"We can also use the [UnobservedComponents](https://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.structural.UnobservedComponents.html) class from [statsmodels](https://www.statsmodels.org/stable/index.html) to generate predictions using a state space model."
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:48.927992700Z",
"start_time": "2023-08-25T09:25:48.917022200Z"
}
},
"outputs": [],
"source": [
"from aeon.forecasting.structural import UnobservedComponents"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:49.629118900Z",
"start_time": "2023-08-25T09:25:48.923006100Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.049737049410244434"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# We can model seasonality using Fourier modes as in the Prophet model.\n",
"forecaster = UnobservedComponents(\n",
" level=\"local linear trend\", freq_seasonal=[{\"period\": 12, \"harmonics\": 10}]\n",
")\n",
"\n",
"forecaster.fit(y_train)\n",
"y_pred = forecaster.predict(fh)\n",
"plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"])\n",
"mean_absolute_percentage_error(y_test, y_pred, symmetric=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Advanced composition patterns - pipelines, reduction, autoML, and more\n",
"\n",
"`aeon` supports a number of advanced composition patterns to create forecasters out of simpler components:\n",
"\n",
"* Reduction - building a forecaster from estimators of \"simpler\" scientific types, like `scikit-learn` regressors. A common example is feature/label tabulation by rolling window, aka the \"direct reduction strategy\".\n",
"* Tuning - determining values for hyper-parameters of a forecaster in a data-driven manner. A common example is grid search on temporally rolling re-sampling of train/test splits.\n",
"* Pipelining - concatenating transformers with a forecaster to obtain one forecaster. A common example is detrending and deseasonalizing then forecasting, an instance of this is the common \"STL forecaster\".\n",
"* AutoML, also known as automated model selection - using automated tuning strategies to select not only hyper-parameters but entire forecasting strategies. A common example is on-line multiplexer tuning.\n",
"\n",
"For illustration, all estimators below will be presented on the basic forecasting workflow - though they also support the advanced forecasting and evaluation workflows under the unified `aeon` interface (see Section 1).\n",
"\n",
"For use in the other workflows, simply replace the \"forecaster specification block\" (\"`forecaster=`\") by the forecaster specification block in the examples presented below."
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:49.694942500Z",
"start_time": "2023-08-25T09:25:49.620142400Z"
}
},
"outputs": [],
"source": [
"# imports necessary for this chapter\n",
"from aeon.datasets import load_airline\n",
"from aeon.forecasting.base import ForecastingHorizon\n",
"from aeon.forecasting.model_selection import temporal_train_test_split\n",
"from aeon.performance_metrics.forecasting import mean_absolute_percentage_error\n",
"from aeon.visualisation import plot_series\n",
"\n",
"# data loading for illustration (see section 1 for explanation)\n",
"y = load_airline()\n",
"y_train, y_test = temporal_train_test_split(y, test_size=36)\n",
"fh = ForecastingHorizon(y_test.index, is_relative=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1 Reduction: from forecasting to regression\n",
"\n",
"`aeon` provides a meta-estimator that allows the use of any `scikit-learn` estimator for forecasting.\n",
"\n",
"* **modular** and **compatible with scikit-learn**, so that we can easily apply any scikit-learn regressor to solve our forecasting problem,\n",
"* **parametric** and **tuneable**, allowing us to tune hyper-parameters such as the window length or strategy to generate forecasts\n",
"* **adaptive**, in the sense that it adapts the scikit-learn's estimator interface to that of a forecaster, making sure that we can tune and properly evaluate our model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Example**: we will define a tabulation reduction strategy to convert a k-nearest neighbors regressor (`sklearn` `KNeighborsRegressor`) into a forecaster. The composite algorithm is an object compliant with the `aeon` forecaster interface (picture: big robot), and contains the regressor as a parameter accessible component (picture: little robot). In `fit`, the composite algorithm uses a sliding window strategy to tabulate the data, and fit the regressor to the tabulated data (picture: left half). In `predict`, the composite algorithm presents the regressor with the last observed window to obtain predictions (picture: right half).\n",
"\n",
"\n",
"\n",
"Below, the composite is constructed using the shorthand function `make_reduction` which produces a `aeon` estimator of forecaster scitype. It is called with a constructed `scikit-learn` regressor, `regressor`, and additional parameter which can be later tuned as hyper-parameters"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:49.706910400Z",
"start_time": "2023-08-25T09:25:49.630115700Z"
}
},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsRegressor\n",
"\n",
"from aeon.forecasting.compose import make_reduction"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:49.863492800Z",
"start_time": "2023-08-25T09:25:49.636100100Z"
}
},
"outputs": [],
"source": [
"regressor = KNeighborsRegressor(n_neighbors=1)\n",
"forecaster = make_reduction(regressor, window_length=15, strategy=\"recursive\")"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:50.134767900Z",
"start_time": "2023-08-25T09:25:49.644080500Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.12887507224382988"
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"forecaster.fit(y_train)\n",
"y_pred = forecaster.predict(fh)\n",
"plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"])\n",
"mean_absolute_percentage_error(y_test, y_pred, symmetric=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the above example we use the \"recursive\" reduction strategy. Other implemented strategies are:\n",
"* \"direct\",\n",
"* \"dirrec\",\n",
"* \"multioutput\"."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parameters can be inspected using `scikit-learn` compatible `get_params` functionality (and set using `set_params`). This provides tunable and nested access to parameters of the `KNeighborsRegressor` (as `estimator_etc`), and the `window_length` of the reduction strategy. Note that the `strategy` is not accessible, as underneath the utility function this is mapped on separate algorithm classes. For tuning over algorithms, see the \"autoML\" section below."
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:50.135764300Z",
"start_time": "2023-08-25T09:25:50.012095500Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"{'estimator__algorithm': 'auto',\n",
" 'estimator__leaf_size': 30,\n",
" 'estimator__metric': 'minkowski',\n",
" 'estimator__metric_params': None,\n",
" 'estimator__n_jobs': None,\n",
" 'estimator__n_neighbors': 1,\n",
" 'estimator__p': 2,\n",
" 'estimator__weights': 'uniform',\n",
" 'estimator': KNeighborsRegressor(n_neighbors=1),\n",
" 'pooling': 'local',\n",
" 'transformers': None,\n",
" 'window_length': 15}"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"forecaster.get_params()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2 Pipelining, detrending and deseasonalization\n",
"\n",
"A common composition motif is pipelining: for example, first deseasonalizing or detrending the data, then forecasting the detrended/deseasonalized series. When forecasting, one needs to add the trend and seasonal component back to the data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3.2.1 The basic forecasting pipeline\n",
"\n",
"`aeon` provides a generic pipeline object for this kind of composite modelling, the `TransforemdTargetForecaster`. It chains an arbitrary number of transformations with a forecaster. The transformations can either be pre-processing transformations or a post-processing transformations. An example of a forecaster with pre-processing\n",
"transformations can be seen below."
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:50.135764300Z",
"start_time": "2023-08-25T09:25:50.019076800Z"
}
},
"outputs": [],
"source": [
"from aeon.forecasting.arima import ARIMA\n",
"from aeon.forecasting.compose import TransformedTargetForecaster\n",
"from aeon.transformations.detrend import Deseasonalizer"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:50.460895Z",
"start_time": "2023-08-25T09:25:50.028053Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.1396997352629755"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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JSCGEEEIIIYQQQgjR6jSqMrJEKiObiyQjm4jmceD3ufFVFOH3udE8juYOKawnnniCQYMGRfWas2fPxmazRfWaQgghhBBCCCGEOLm5DwcrI/vX+5zgRG1Zpt18mnWa9slC8zopXf0iZbmz0Fx2VJONpEGTsQ6biqqXgSxCCCGEEEIIIYQQkfBVFqNVFAFgaN+n3ucZqiojvfYd+DUfiqqLSXyiZlIZGSG/34/mcdT7w+c+Qumq57CvmI7msgOguezYVzxN6arn8bnL6n2t+g4+f//990lOTsblclXbftlll3H99dfXeN7s2bN58sknWbduHYqioCgKs2fPBsBut3PLLbeQkpJCUlISY8aMYd26daFz161bx+jRo0lMTCQpKYlTTz2V1atXs3jxYm666SZKS0tD13ziiScieuZCCCGEEEIIIYQQxwou0dYnZaMa4ut9nj4xC3RG/D4X3iP5sQpP1EIqIyPk91awe1a7eh2rWjqQ+fttlOXOCru/LPdVrEPvpeDdnmiVxXVer8udJSj1+AabOHEiU6ZM4YsvvmDixIkAFBUVMWfOHObOnVvjeVdddRW//PIL3377LfPnzwfAarWGrmmxWPjmm2+wWq28+eabjB07lq1bt9K+fXuuu+46Bg8ezOuvv45OpyM3NxeDwcDpp5/Oyy+/zGOPPcaWLVsASEhIqPM1CCGEEEIIIYQQQtTEE+oXWf/hNQCKqsNg7Y7n8CY8JdswWLNjEZ6ohVRGxpAuLg1fRVGoIvJ4msuOr/Iguri0qN7XYrFw7bXX8t5774W2/fOf/yQrK4tzzjmn1vMSEhLQ6/WkpaWRlpaGxWJhyZIlrFy5kk8++YShQ4fSs2dPXnzxRWw2G59++ikA+fn5jBs3jj59+tCzZ08mTpxITk4ORqMRq9WKoiiha0oyUgghhBBCCCGEEI0RrIw0to8sGQlHl2pL38jmIZWREVL0cXS5s6T+x6sGVJMtbEJSNdnQx3ei09U/1vve9XXrrbcybNgw9u7dS+fOnZk9ezY33ngjiqLU+xpB69ato7y8nOTk5GrbKysr2bFjBwD33HMPt9xyC//v//0/xo0bx8SJE+nevXvE9xJCCCGEEEIIIYSoi6cBk7SDgkNsvDJRu1lIMjJCiqLUa6l0kOZxkDRoMvYVT5+wL2nQZPyaJ6LeBvU1ePBgcnJyeP/995kwYQK//vorc+bMadC1ysvLSU9PZ/HixSfsC07JfuKJJ7j22muZM2cO33zzDY8//jgffvghl19+eSNehRBCCCGEEEIIIcSJGjJJO8jQrhcAHklGNgtJRsaYaojHOmwqEOgR2ZTTtG+55RZefvll9u7dy7hx48jMzKzzHKPRiM/nq7ZtyJAh7N+/H71eT9euXWs8t1evXvTq1Ys//elPXHPNNbz33ntcfvnlYa8phBBCCCGEEEII0RC+iqKq2RtKRJO0g2SZdvOSnpFNQNWbsQ69l6zb9pB1216ybtuDdei9MU1EAlx77bXs2bOHt99+m9///vf1Oqdr167k5eWRm5tLcXExLpeLcePGMXLkSC677DLmzp3Lrl27WLp0KQ8//DCrV6+msrKSyZMns3jxYnbv3s1PP/3EqlWr6Nu3b+ia5eXlLFiwgOLiYioqKmL5soUQQgghhBBCiBZJ8zjw+9z4Korw+9xoHkdzh9QquQ9tAkBvzUY11L+lXVBomXbZbjSvM6qxibpJMrKJqIZ4FJ0RXVwKis4Yk6XZx7NarVx55ZUkJCRw2WWX1eucK6+8kvPOO4/Ro0eTkpLCv//9bxRF4euvv+ass87ipptuolevXlx99dXs3r2b1NRUdDodhw4d4ne/+x29evVi0qRJnH/++Tz55JMAnH766fzf//0fV111FSkpKTz//PMxfNVCCCGEEEIIIUTLo3mdlK5+kfy3MkIfpatfkmRYA3hCS7QjH14DoMZ1RDVaAT/e0h1RjEzUhyzTbuP27t3Lddddh8lkqtfxJpMpNCH7WImJibzyyiu88sorYc/797//Xet1X3/9dV5//fV6xSCEEEIIIYQQQrQlmsdB6eoXsa+YfnSbyx6aL2Edem+TFC21FcFJ2ob2kQ+vgcA8EH27nrgPrMZTsq1BfSdFw0llZBtVUlLCZ599xuLFi7nzzjubOxwhhBBCCCGEEOKkpagGynJnhd1Xlvsqimpo4ohat+AkbWMDJmkHhfpGyhCbJieVkW3U4MGDKSkp4bnnnqN3796h7f3792f37t1hz3nzzTe57rrrmipEIYQQQgghhBDipKC57Gguey37StHFpTRtUK2U3+8/WhnZmGSkLZiM3BqVuET9STKyjdq1a1fY7V9//TUejyfsvtTU1BhGJIQQQgghhBBCnJxUoxXVZAubkFRNNlSTtemDaqW0iiI05yFQ1AZN0g6SidrNR5KRJ5kuXbo0dwhCCCGEEEIIIUSbpHkcKKoBzWVHNdnwax7Kt3yEzpJCUs4d2Fc+c8I5SYMm49c8KDpjM0Tc+rirhtford1Q9ZYGX8fQrhcgy7SbgyQjhRBCCCGEEEIIIRopOC27LHdWKBmZlHMHSYPu5ODcW0g57x+gqJTlvnp0/6DJWIdNRdWbmzv8ViO4RNvYvmGTtIMMth4AaJUH8TlL0JnbNTo2UT+SjBRCCCGEEEIIIYRohBqnZVdVQrY/+wVUkxXr0HuxDbsfb8U+dJaOgF8SkRHyhPpFNi4ZqRoT0cWn43Psw2vfji5tWDTCE/XQ6GnaPp+P3NxcSkpKohGPEEIIIYQQQgghRKtS67Tsda9hSOqKoiiohnh8zkMc+N8VFMzug6LK0uxIuaMwSTtIhtg0j4iTkXfffTd///vfgUAi8uyzz2bIkCFkZmayePHiaMcnhBBCCCGEEEII0aLVZ1p2kC4+HW9ZHppjP57SnU0UYdvg9/uPVka2j0IyUobYNIuIk5GffvopOTk5AHz55Zfk5eWxefNm/vSnP/Hwww9HPUAhhBBCCCGEEEKIliwwEdtWy76j07IVRZGKvAbyVexHc5U0epJ2kAyxaR4RJyOLi4tJS0sD4Ouvv2bixIn06tWL3//+92zYsCHqAYq27cYbb+Syyy5r7jCEEEIIIYQQQogG82sekgZNDrsvOC37WKGKPElGRsRTsh1D8ikYO54alV6bR98HSUY2pYiTkampqWzcuBGfz8e3337L+PHjAaioqECn00U9wLbC4XHj9nkpqizH7fPi8LibOyQhhBBCCCGEEEJEgWqIxzpsKrYRD4cqJFWTDduIRwLTsg3x1Y4/WpEnycj60jwOTGnDSL3kP6T/Zh6ax9Hoa+ptR5dp+/3+Rl9P1E/E07RvuukmJk2aRHp6OoqiMG7cOABWrFhBnz6NL5Fti5xeDy9sWMTMTUuwuyuxGS3c1XcUDwwcg1lvaO7wGs3tdmM0StNdIYQQQgghhBAnL1VvJi77IqxD/4zmsqOzpODXPGEr+ELJSOlVWC+a10np6hcpy52F5rKjmmwkDZocSPQ2okLSYM0GRYff48DnKESf0DmKUYuaRFwZ+cQTT/D3v/+d2267jZ9++gmTyQSATqfjgQceiHqALY3f78fhcdX7o8ztZMb6hUxbNw+7uxIAu7uSaevmMWP9Qsrcznpfq75Z+vfff5/k5GRcLle17ZdddhnXX399rec+8cQTDBo0iDfffJPMzEzi4uKYNGkSpaVHm+0Gl1ZPnz6dTp060bt3bwAKCgqYNGkSNpuN9u3bc+mll7Jr167QeT6fj3vuuQebzUZycjJTp06VvzwIIYQQQgghhGgzihfdRcG7PXEX/4KiM55QERlkaBf4PVoqI+umeRyUrnoO+4rpoSFBmsuOfcXTlK56vlEVkorOiN6aDchS7aYUUWWkx+PhvPPO44033uDKK6+stu+GG26IamAtVYXXTdI/6zeop4Mpnp0TH2bmpiVh98/ctIT7Boym2yfTKXbV/c1T9tvpxBtMdR43ceJEpkyZwhdffMHEiRMBKCoqYs6cOcydO7fO87dv387HH3/Ml19+SVlZGTfffDN33HEHH3zwQeiYBQsWkJSUxLx584DA18a5557LyJEj+fHHH9Hr9Tz99NOcd955rF+/HqPRyEsvvcTs2bN599136du3Ly+99BKfffYZY8aMqTMmIYQQQgghhBCiJfNrXjzFv+D3OTFYu9V6bLBXoVZRhM9pR2e2NUGErZOiGijLnRV2X1nuq9iGN64wzmDride+HU/JNiyZ5zTqWqJ+IqqMNBgMrF+/PlaxtDlpcYkUOctDFZHHs7srOegsJy0uMar3tVgsXHvttbz33nuhbf/85z/JysrinHPOqfN8p9PJ+++/z6BBgzjrrLOYOXMmH374Ifv37w8dEx8fzzvvvEP//v3p378/H330EZqm8c477zBgwAD69u3Le++9R35+PosXLwbg5Zdf5sEHH+SKK66gb9++vPHGG1it1hqiEEIIIYQQQgghWg/P4c34fU4UYyJ6W/daj1WNiejiOwXOk+rIWmkue6giMvy+0rD76is0xEaWzDeZiHtG/va3v+Xvf/87M2bMiEU8LV6c3kjZb6fX+3iDqsNmtIRNSNqMFjrFJbH0wrvqfe/6uvXWWxk2bBh79+6lc+fOzJ49mxtvvBFFUeo8Nysri86dj/ZJGDlyJJqmsWXLltAk9QEDBlTrE7lu3Tq2b99OYmL1xKrT6WTHjh2Ulpayb98+RowYEdqn1+sZOnSoLNUWQgghhBBCCNHquQ7mAmDsMBBFqbv2y9CuFz5HIZ6SLZjTh8c4utZLNdlQTbawCcnAvsYVORlsMtm8qUWcjPR6vbz77rvMnz+fU089lfj46v0P/vKXv0QtuJZIUZR6LZUOcnjc3NV3FNPWzTth3119R+HRtIiuV1+DBw8mJyeH999/nwkTJvDrr78yZ86cqF3/+Pe9vLycU089tdpS7qCUlJSo3VcIIYQQQgghhGiJ3EW5AJg6DqrX8YZ2vXDuWSxJsDr4NQ9JgyZjX/H0CfuSBk3Gr3lQdA0fqhusjPRKZWSTiTgZ+csvvzBkyBAAtm6t/g1Tn6q7k028wcgDAwM9EU+Ypp0zBrMudtO0b7nlFl5++WX27t3LuHHjyMzMrNd5+fn5FBYW0qlToGR8+fLlqKoaGlQTzpAhQ/joo4/o2LEjSUlJYY9JT09nxYoVnHXWWUAgsb1mzZrQ15MQQgghhBBCCNFaBZORxpRB9To+NFFbkpG1Ug3xWIdNBb9G2brXojpNG45Zpl2ah9/nQYlhnkYERJyMXLRoUSziaNPMegP3DRjNQzljKfU4sRrMeDQtpolIgGuvvZY///nPvP3227z//vv1Ps9sNnPDDTfw4osvUlZWxpQpU5g0aVJoiXY41113HS+88AKXXnopTz31FBkZGezevZv//ve/TJ06lYyMDP74xz8yY8YMevbsSZ8+ffjLX/6C3W6PwisVQgghhBBCCCGaj9/vx31wHQDGlJx6nSPJyPrz+9wYU4eQeUsefo8D1WTDr3kanYgE0CV0RtHH4fdW4C3bFUpOitiJaIDNsbZv3853331HZWWgF6L0/atdvMGIUacnxZyAUacn3tDwEuL6slqtXHnllSQkJHDZZZfV+7wePXpwxRVXcMEFFzBhwgQGDhzIa6+9Vus5cXFx/PDDD2RlZYUG1Nx88804nc5QpeS9997L9ddfzw033MDIkSNJTEzk8ssvb8xLFEIIIYQQQgghmp23LA/NXQo6I8bkfvU65+jy4O34NV8sw2v1XIVLKfryN+z7dAK6uI4oOiOqIb7uE+tBUVQMth6ADLFpKhFXRh46dIhJkyaxaNEiFEVh27ZtdOvWjZtvvpl27drx0ksvxSJO0UB79+7luuuuw2SKrC/l7bffzu233x523+zZs8NuT0tL4x//+EeN19Tr9bz88su8/PLLEcUihBBCCCGEEEK0ZKEl2sn9692/UJ/UFXRG/D4X3iMFGKxdYxZfa+fctxQAY/s+Mbm+oV1P3MXr8ZRsg+yY3EIcI+LKyD/96U8YDAby8/OJi4sLbb/qqqv49ttvoxqcaLiSkhI+++wzFi9ezJ133tnc4QghhBBCCCGEEG1WaJJ2PftFAiiqDoO1OyBLteviKlwGgLnzGTG5vj7YN7JEKiObQsSVkXPnzuW7774jIyOj2vaePXuye/fuqAUmGmfw4MGUlJTw3HPPVRs8079//xrfpzfffLOpwhNCCCGEEEIIIdoMd1GgX2R9J2kHGdr1wnN4UyAZ2XVCDCJr/fw+D679KwEwdRoZk3sYbMFkpCSFm0LEyUiHw1GtIjLo8OHDES8FFrGza9eusNu//vprPB5P2H2pqakkJibyxBNPxC4wIYQQQgghhBCijXE3oDISZIhNfbgP5uL3VqKa22No17vuExog9D5Iz8gmEXEy8swzz+T9999n2rRpACiKgqZpPP/884wePTrqAYro6tKlS3OHIIQQQgghhBBCtBlex358jn2AgrHDgIjODSbXJBlZM2dhoF+kKf00FKXBc5hrFRwm5Pe50DwVqIYTi/BE9EScjHz++ecZO3Ysq1evxu12M3XqVH799VcOHz7MTz/9FIsYWwSZFt585NkLIYQQQgghhGipgsNrDO16ohoTIjpXKiPrFkxGmjvFpl8kgM7cntRL/4c54yw0dxmKqseveaI2sVtUF3FK+ZRTTmHr1q2MGjWKSy+9FIfDwRVXXMHPP/9M9+7dYxFjs9LpdAC43e5mjuTkFXz2wfdCCCGEEEIIIYRoKRq6RBvA0D6QjPSV70HzOKIYVdvg9/txhZKRp8fsPprXiWvfCgreyabg7Szy38qgdPVLaF5nzO55Mou4MhLAarXy8MMPRzuWFkmv1xMXF8fBgwcxGAyoamxKgkV4mqZx8OBB4uLi0Osb9OUqhBBCCCGEEOI4mseBohrQXHZUk02qwBohNEm74+CIz9WZ26NaOqBVFuMp2YqpAddoy7ylO/FVHACdEWPqqTG5h+ZxULr6Rewrnzm6zWXHvuJpAKxD75XvjSiLOLuzfv36sNsVRcFsNpOVldWmBtkoikJ6ejp5eXkyLbyZqKpKVlYWiqI0dyhCCCGEEEII0eppXielq1+kLHdWKBmZNGgy1mFTUfXm5g6v1WnoJO0gg60nLklGhhXqF9lxSMy+NhXVQFnurLD7ynJfxTb8gZjc92QWcTJy0KBBoaRQsJffsUkig8HAVVddxZtvvonZXPsXyhNPPMGTTz5ZbVvv3r3ZvHkzAE6nk3vvvZcPP/wQl8vFueeey2uvvUZqamro+Pz8fG6//XYWLVpEQkICN9xwA88++2xUq+iMRiM9e/aUpdrNxGg0SkWqEEIIIYQQQkRBqApsxfSj26QKrME0Vyne0h1Aw5ZpQ6BvpGvfMjwlMsn5eK7CZUCMl2i77Gguey37StHFpcTs/iejiDN2n332Gffffz/33Xcfw4cPB2DlypW89NJLPP7443i9Xh544AEeeeQRXnzxxTqv179/f+bPn380oGOSiH/605+YM2cOn3zyCVarlcmTJ3PFFVeEBuX4fD4uvPBC0tLSWLp0Kfv27eN3v/sdBoOBZ5555oR7NYaqqnUmV4UQQgghhBBCiJZMqsCiy3UwsHpUl5iJzpLcoGvIEJuaOQsD+R9TDJORqsmGarKFTUgG9lljdu+TVcTJyOnTp/O3v/2Nc889N7RtwIABZGRk8Oijj7Jy5Uri4+O5995765WM1Ov1pKWlnbC9tLSUv//97/zrX/9izJgxALz33nv07duX5cuXc9pppzF37lw2btzI/PnzSU1NZdCgQUybNo3777+fJ554AqPRGOnLE0IIIYQQQggh2izNWSJVYFEUHF5jSslp8DUkGRmez3kYz+FNAJjTR8bsPn7NQ9KgyaHq4GMlDZqMX/Og6CS/FE0Rr33dsGEDXbp0OWF7ly5d2LBhAxBYyr1v3756XW/btm106tSJbt26cd1115Gfnw/AmjVr8Hg8jBs3LnRsnz59yMrKYtmyQJnusmXLGDBgQLVl2+eeey5lZWX8+uuvNd7T5XJRVlZW7UMIIYQQQgghhGjLPGW7UIwJqCZb2P1SBRY5d9HPQMOXaMPRidqekq2hdngCXPuWA2Bo1zOmCXLVEI912FRsIx4JfW+oJhu2EY8E+qhK24KoizgZ2adPH2bMmFGtf6LH42HGjBn06dMHgL1791ZLENZkxIgRzJ49m2+//ZbXX3+dvLw8zjzzTI4cOcL+/fsxGo3YbLZq56SmprJ//34A9u/ff8J9gp8Hjwnn2WefxWq1hj4yMzPr9dqFEEIIIYQQQojWyH14M/s+OofK/IUk5dwR9phgFZiov6OTtAc1+BoGa3dQdPg95fgc9SvsOhmEhtd0OiPm91L1ZqxD7yXrtj1k/H4rmbfkkZjzfzLQKUYiXqY9a9YsLrnkEjIyMhg4cCAQqJb0+Xx89dVXAOzcuZM77gj/w+1Y559/fui/Bw4cyIgRI+jSpQsff/wxFosl0tDq7cEHH+See+4JfV5WViYJSSGEEEIIIYQQbYbmcaCohqpp2Va8pXmoxiSO/PIuHS/4FygqZbmvyjTtRtC8TjyHAsuITY2ojFR0RvTWbLz27XhKtqJP6BSlCFu3YDIylsNrjhWsgCxd9SKO7f/FOuRubMOmNsm9TzYRJyNPP/108vLy+OCDD9i6NdDPYOLEiVx77bUkJiYCcP311zcoGJvNRq9evdi+fTvjx4/H7XZjt9urVUceOHAg1GMyLS2NlStXVrvGgQMHQvtqYjKZMJlMDYpRCCGEEEIIIYRoyTSvk9LVL1KWO+tosjHnDtInLQZFQTVYsA69F+vQP+OrLEIfl4bf75NEZIQ8h34Fvw/VnIwusXEFToZ2vULJSEvmOdEJsBXz+9y4968GwBTDfpHhGFMGcmTDW1Ts+EKSkTES8TJtgMTERP7v//6Pv/zlL/zlL3/hD3/4QygR2Rjl5eXs2LGD9PR0Tj31VAwGAwsWLAjt37JlC/n5+YwcGfhCHDlyJBs2bKCoqCh0zLx580hKSqJfv36NjkcIIYQQQgghhGhNNI+D0lXPYV8xPTSoRnPZsa98hrLcV0ODOFRDPId/fJAD/7uCsvVvSF+8BnAV5QKBJdqKojTqWjLEpjpX0c/4fU5US4fQs2kqcd0uCsSwfyVeWTYfExFXRkJg6MyiRYsoKipC07Rq+x577LF6X+fPf/4zF198MV26dKGwsJDHH38cnU7HNddcg9Vq5eabb+aee+6hffv2JCUlcddddzFy5EhOO+00ACZMmEC/fv24/vrref7559m/fz+PPPIId955p1Q+CiGEEEIIIYQ46SiqgbLcWWH3leW+im34A6HPdfGpeA79gvvghqYKr02JxiTtIIOtJyDJyCBXcIl2+shGJ3ojpU/ohCl1GK4Dq6jYOYekAbc06f1PBhEnI99++21uv/12OnToQFpaWrUvCkVRIkpG7tmzh2uuuYZDhw6RkpLCqFGjWL58OSkpgSlJf/3rX1FVlSuvvBKXy8W5557La6+9Fjpfp9Px1VdfcfvttzNy5Eji4+O54YYbeOqppyJ9WUIIIYQQQgghRKunueyhisjw+0pDk4kNth4AeEp3NFV4bYo7WBnZiH6RQUcrI7c1+lptgbPwJwBMTdQv8nhx3S8OJCN3fCHJyBiIOBn59NNPM336dO6///5G3/zDDz+sdb/ZbGbWrFnMmhX+rzoAXbp04euvv250LEIIIYQQQgghRGunmmyoJlvYhGRgnzX0uaFdVTWefXtThddm+DUf7uL1QOMmaQcZ2geSkd6yPPxeF4r+5F3t6ff7cRYuA5pueM3x4rpfQsnSx6gsWIjmPoJqbHxrQnFUxD0jS0pKmDhxYixiEUIIIYQQQgghRCP4NQ9JgyaH3Zc0aDJ+zRP6PFgZqVUUobnKmiS+tsJTsgW/txJFHxdaYt0Yurg0FGMi+LWTvlLVa9+OVnkQRWfC1HFIs8RgaN8Xva0H+NxU7vquWWJoyyJORk6cOJG5c+fGIhYhhBBCCCGEEEI0gmqIx3rqPdiGP4RqsgW2mWzYRjyCddjUaoNqVGMiurhUQKojIxXsF2lMGYii6hp9PUVRZIhNlWBVpDF1aLNViCqKQly3iwFw7PyyWWJoyyJept2jRw8effRRli9fzoABAzAYDNX2T5kyJWrBCSGEEEIIIYQQIjJl697AmDqEzFt343eXo5qs+DUPqt58wrF6Ww98FQfw2LdhSm2eKrTWyFW0DohOv8ggQ7teuA+skWRkVb9Ic6eRzRpHfPdLKFv7VyrzvsHv86DoDHWfJOol4mTkW2+9RUJCAt9//z3ff/99tX2KokgyUgghhBBCCCGEaEaOrR/hPriOlAs/IqHn5QAoOmPYYw22HrgKf5LKyAhpLjuqpQOmjoOjdk2pjAzwVRxEtXRotn6RQab001AtKWiVB3Hu/RFL1phmjactiTgZmZeXF4s4hBBCCCGEEEII0Ui+ymLcBwNVe5Z6JHOCfSO9koysF83jQFEN2IY/SPI5f0FzH4natU/2ZGTw2Saf8xK6uI74fZ66T4ohRdUR1+1Cyn+dTcXOLyUZGUUR94wMcrvdbNmyBa/XG814hBBCCCGEEEII0UDOgsUAGJL7o4tPrfN4Q7tAMtJTIsnIumheJ6WrXyT/rQz2vNeLgneyObL+LTSvMyrXP5mTkdWfbW8K3smm7OdXovZsGyrUN3LHF/j9/maNpS2JOBlZUVHBzTffTFxcHP379yc/Px+Au+66ixkzZkQ9QCGEEEIIIYQQQtRPZcFiACyZo+t1vL6qMlKWaddO8zgoXfUc9hXT0Vz2wDaXHfuKpyld9Tyax9HoewSncmvOw/gqixt9vdaiKZ5tQ1m6jEPRx+E7UhAaWiQaL+Jk5IMPPsi6detYvHgxZvPR5rfjxo3jo48+impwQgghhBBCCCHaLofbi9urUVTuwu3VcLhl5V1jVRYsBMBcz2RkcJm25jyEz1kSs7haO0U1UJY7K+y+stxXUdTGDzdRDXHoEjMB8JRsa/T1WoumeLYNpeotWLqMA6BixxfNFkdbE3Ey8vPPP+fVV19l1KhRKIoS2t6/f3927NgR1eCEEEIIIYQQQrRetSUbnR4fzy/aQdqTc0l7Yi5pT87lhUU7cHp8zRhx6+Y9UhDo/aioWDLOqtc5qiEeXXynwPlSHVkjzWUPVe2F31calfsY2vVCtXTAV1EUleu1Bk31bBsqrtslAFTs+LJZ42hLIh5gc/DgQTp27HjCdofDUS05KYQQQgghhBDi5BVMNs5ckoe90oPNYmDKqGweGNMDn9/P84t2MG3e0d549koPT1V9ft/o7sQbI/519aRXWbAIAFPHU1FN1nqfZ7D1wOcoxGPfhiltWKzCa9VUkw3VZAubNAvsq//zrk37M57B0L4XmtOO3+fGr3lQDfFRuXZLpRqtTfJsGyqu2wWgqLiL1+Mp240hqUuzxtMWRFwZOXToUObMmRP6PJiAfOeddxg5cmT0IhNCCCGEEEII0So53F6eXbidafO2Yq8MTMQNJhv/9uNO9KrCzCV5Yc99ZUkeBrXBs1ZPas6qZKQ5q35LtIP0MsSmTn7NQ9KgyWH3JQ2ajF9r/ORnzeukYscXFLyTTcHfs8l/K4PS1S81+xCXaNE8Dvw+N76KIvw+N5rHQdn6t6nYPZeknDvCnhOtZ9sYOksHEnpfQ8eL/4MuLrVa/KJhIv5T0zPPPMP555/Pxo0b8Xq9/O1vf2Pjxo0sXbqU77//PhYxCiGEEEIIIYRoRQyqWmOy8YuNB7hpWFYoSXk8e6WHUqeHlARTLENsc/x+P5X5gWSkJXNMROcaZIhNnVRDPNZhU8Hvp2zdLDSXHdVkI2nQZKzDpqLqzXVfpBaax0Hp6hexr5x+dFvVEBcA69B7W3WFZHBadlnuMc8u5w6SBt3Jwbm3kHLeP0BRKct9NerPNhqSR/+N0jV/oXjuzS0yvtYm4mTkqFGjyM3NZcaMGQwYMIC5c+cyZMgQli1bxoABA2IRoxBCCCGEEEKIVsTu9NSYbNx20IHVYsBmMYQ9xmYxYDU338CK1spTshWfoxBFZ8LUKbJVi5KMrB9Vb8bU+XQyh/0ZzX0Enbl9YBl1FJJRdQ1xsQ1/oNH3aC6hROuK4xKtK58BIPnsF1FNVqxD78U2/AE0VymqyRq1Z9tYmsdB6dq/huKFtpUobg4NasLRvXt33n777WjHIoQQQgghhBCiDbCZa042ejU/bp/GlFHZoR6Rx5oyKhuPpmGMvKvYSS24RNuUPhJVb4no3GAy0mvfjt/vl3kQNfDYd3DgswtR49PIvGEjis6IojNG5dr1GeKii0uJyr2aWq2J1nWvYRvxEIqioFQl9IKvM1rPtrHacqK4uUT8033t2rVs2LAh9Pn//vc/LrvsMh566CHcbndUgxNCCCGEEEII0fp4tECyMZwpo7LRKfDAmB48Nr4XNkugCtJmMfDY+F48MKaHDK9pgMqChQBYIuwXCaC3dQeqkl7OQ1GNqy2p3D0PAKOtJ6oxIarXDg7IqXlf8w5xaYyWPi27Lq09/pYo4mTkH/7wB7ZuDfz1aufOnVx11VXExcXxySefMHXq1KgHKIQQQgghhBCidYk36pk6ujuPjOsZNtkYZ9RjNui4b3R39j0+gZ0PjaXg0XHcfVY2ZoOumaNvffx+DWdBYIaDOSPyZKSqt6BLzARkiE1tKnfPBcDSZULUr90UA3KaS2tPtLb2+FuiiJORW7duZdCgQQB88sknnH322fzrX/9i9uzZ/Oc//4l2fEIIIYQQQgghWqH3VhUwJMPGnkfHc+CJCex/fAL3je5eLdkYb9Rj0qtM+WwD2dMXsCSvpBkjbr3cB9ehuUpQjImY0oY26BpH+0Zui2ZobYbf56ayaim8pWv0k5HBATm2EY+EEl+qyYZtxCOBISmtuCdha0+0tvb4W6KIa9/9fj+apgEwf/58LrroIgAyMzMpLi6ObnRCCCGEEEIIIVodv9/Paz/tYlNROf/+7RCuGtQZoMY+kCmJJoodbn7adZiL+qU2ZahtQmV+YIm2ufOZKGrDlrgbbD1wFizCK0NswnIWLsXvcaDGdcSYkhOTe6h6c2iIi9exD52lQ2BycwsY4tIYgUTrfeDXKFv3WqubRh2apA4tdtp3axPxT6mhQ4fy9NNPM27cOL7//ntef/11APLy8khNlX80hBBCCCGEEOJk98v+I2wqKseoUzmvd8c6jz+ja3veW1nA0rzDTRBd2+MsWAyAJfPoEm2Hx41BVbG7ndiMZjyaRryh5oEgMlG7dpW7vgMgrst4FCV2w5WCFZBlua9Rvun/kZRzB+1OeyRm92sqlXu+x5g6hMxb8vB7KlrUtOz6CCaKrUP/jK+yCF1cGvh9rSb+libi76CXX36ZtWvXMnnyZB5++GF69Aj8wPr00085/fTTox6gEEIIIYQQQojW5aPcQgDO75OCtapnZG3O6NoegJUFdlxeX0xja2v8PjfOvT8CR5ORTq+HFzYsIv3DJ0n/8AnSP3ySFzYswumteTmpXpKRtYplv8hwDLYeaJXFoSnprV35hnco+vI3lK79G7q4FBSdsdUtPVcN8RzZ8DYH/ncFhxb/qdXF35JEXBk5cODAatO0g1544QV0Omk0LIQQQgghhBAnM7/fz8frAsnISVXLs+vSKyWeDvFGih1u1u4pZWRVclLUzbV/JX5vBaolBUOHU3B43LywYRHT1s0LHWN3V4Y+v2/A6LAVkoZ2R5ORfr8fRVGa5gW0At7yQtzFGwAFS9a4Wo+NtCK1JsGp6M59y9E8jlad+PI57VTs+haA+O4XN3M0jWNI7ofn0C/4vZXNHUqrFnFlZEFBAXv27Al9vnLlSu6++27ef/99DIa6/+IlhBBCCCGEEKLtyi0sY3uxA7Ne5eJ69n9UFIUzurYD4KddMsQmEqGhKhlnoygqBlVl5qYlYY+duWkJBjWQBnB43Lh9Xooqy3H7vHgSsjG064vffQStoqjJ4m8NKnfPB8CYeiq6uJQaj2tIRWpN9Nbu6BKzQPPgLFza4Nhbgood/wOfG0NyP4wdBjR3OI1iSj0VAG/pDnxO+VnVUBEnI6+99loWLQr8sNu/fz/jx49n5cqVPPzwwzz11FNRD1AIIYQQQgghROsRXKJ9Yd9UEkz1X4x3elU15NJd0jcyEsFkpDlrDAB2txO7O3zVlt1dSZnHFT5p9sv3tJ/0PYZ2fWSp9nEqdx/tF1kTh8fNjPULmbZuXuj5BytSZ6xfiMPjjuieiqJgyTwHAGfVgKLWqnzLRwAk9L4qpvc5PsEe6TOvD525PXprNwDcB9ZE/foni4iTkb/88gvDhw8H4OOPP+aUU05h6dKlfPDBB8yePTva8QkhhBBCCCGEaCX8fj8f5+4FYNKgThGde0Z2IBn5U95h/H5/1GNrizSPA81djmrpEOoXaTOasRktYY+3GS0YVR3Prl8QNmn2/KZlmEfNkGTkMfyaL1QZael6bo3HNbQitbaEWfA9rWzFfSN9jgM4CwLJ1Phek2J2n2hWpdYlWB3pkmRkg0WcjPR4PJhMJgDmz5/PJZdcAkCfPn3Yt29fdKMTQgghhBBCCNFqrCqws6ukknijjgv71j1F+1inZlgx6VUOOtxsK3bEKMK2Q/M4UBQdqRd9RObvt6GLTwPAo2nc1XdU2HOmDjgHo07Hq5t+Crt/5qalJGaNxlu+N2ZxtzauA6vRXCWoRiumtOE1HldXRWqp20llhAkzc1Uy0l30c6tdEuzY9h/wa5hSh2GwdY/NPaJclVoXU+pQIPC1IRom4mRk//79eeONN/jxxx+ZN28e5513HgCFhYUkJydHPUAhhBBCCCGEEK1DcIn2xf1SiTNGNi/VpNcxLNMGBKojRc00r5PS1S+S/3YWe97rTcE72ZSufgnN6yTeYGRK/zN5OGdcqELSZrTwaM547u5/FqV1Jc2cZWiu1pn4ioXgFG1zl7Eoas1f03VVpJp0+horUmtKmOkTOmFo1xvw49zzfeNfTDMILtGOj+ES7VhUpdbGKJWRjRZxMvK5557jzTff5JxzzuGaa64hJycHgC+++CK0fFsIIYQQQgghxMlF0/x8uj44RTuyJdpBwb6RS6RvZI00j4PSVc9hXzEdzWUPbHPZsa94mtJVz/Nr8S7OnPMqpyZnUHj14+y/5gn2Xf049w0YjUlnoJ3RUmvSzGpOwn3g5yZ8RS1b5a5AMjKuS81LtKGuitTRGHV6ZtVYkXo0YXa8YC/Q1rhU21O2G9e+ZYBCfK/fxOw+dVal1tQntYHLuE0dBwMKvvI9eB3763WO5nHg97nxVRTh97nRPCd39XfEychzzjmH4uJiiouLeffdd0Pbb7vtNt54442oBieEEEIIIYQQonVYnl9Cgd1JoknPeb0jW6IdNKqqb+RSqYyskaIaKMudFXZfWe6rvLsjl82lRby3bRUmnZ4UcwJGnZ54gxGoPWl2V99RHMlfhKtojfTtBHyVh3AdWAWApZbhNQDxBiNTB46uoSL1TMrqTJg5w+4L9o10tsJkpGPrJwCYM85Cn9CwP1DUR51VqaqOZyKsSq2NakzE0L43UL8hNqFK5rcyQh/BSuaTVWR181V0Oh3t2rWrtq1r167RiEcIIYQQQgghRCsUXKJ9af9UzAZdg65xetfA75lbDjoodrjoEG+KWnxtheayhyoij1fpOsI/dwSSI7f0HhH2mHiDkQcGBqrtZm5agt1dic1o4a6+o3hg4BiK/zUUv8eBz7Evpgmk1qAyfwH4NQzJ/dAnZtR5/N9++YFTkzMouOpRHF43VoMZj6Zh0hmwGRVsRkvYhKTNaMFqMIe9pjnjLEDBc3gz3vLCVvWeOJpgiTYcTbBPWzfvhH2BqlRdrVWpD+WMjfieptSheA5vxnVgDXHdLqzxOM3joHT1i9hXTD+6raqSGcA69F5UQ3zE92/tGpSM/PTTT/n444/Jz8/H7a6eQV67dm1UAhNCCCGEEEII0Tr4qi3R7tzg67SPM9K3YwKbispZuquES/qnRSvENkM12VBNtrAJyUWWUyh2VdIpLonzOveu8RpmvYH7BozmgZyx7K8oI8WSgN/vx6w34Pe5APDYd7SqxFcsBPtFWupYog2Bac4v/fo9Je5K5p77B8Z26gmAsSovX1vC7K6+o/BoWujYY+nM7TF2HIy7aC3OgkUk9L2u4S+oCbkPb8Z9cB2oeuJ7XB7Te8UbjNx9yllo+Jm16adqCfa7+59Zd59Uj5MUXUJE9zSlDqV80z/r7BtZVyWzbfgDEd23rYh4mfYrr7zCTTfdRGpqKj///DPDhw8nOTmZnTt3cv7558ciRiGEEEIIIYQQLdjPe+14fH5sFgMTeqU06lqnVy3VXiJLtcPyax6SBk0Ou+9TW6Di8cYew9CrtVenxhuMKMDVi/8f3T6eztbSgwAYbD0A8Nq3Ry/oVsjv91O5O5A4jOs6oc7j/7N7AyXuSrLibZyTduLU6GBF6qM5409Yxv1AzpjQMvpwgku1KwsWR/5CmkmwKtLSZQI6S2yHHZd7XIz/5s0a+6Ta6uqTWkNVam2ODrFZXWtLg9oqmQP7SiO+d1sQcTLytdde46233mLmzJkYjUamTp3KvHnzmDJlCqWlJ+dDFEIIIYQQQoiTkcPtxe3VSE00k/fwWL69dQRGfcS/ZlYzqmqIzVIZYhOWaojHOmwqtuEPoZpsgW0mG6WDH2JxhQ+A3/eq33BZk05PelwSxS4HX+/ZBBxNRnrs26IffCviObQR1ZyMLiETU6cz6jz+71uXA4Fnr6tpGE1VReq+qx9n96RHyJ/0KJd3HYBZZ6j12uZQMnJRq+jl6ff7Q1O0E3pPivn9PszLZe3hPdy/+iuMqi7iPqkeTYv4nsaUgaDq0SoP4jtSUONxwUrmmvdZI753WxDxvxL5+fmcfvrpAFgsFo4cOQLA9ddfz7///e/oRieEEEIIIYQQokVyenw8v2gHaU/OpcvT88mcNp85m4pwenyNuu4ZVZWRqwtKG32ttkpzlWJMHULmLXlk3baXrNv28J+4IQCM7dST7MT6V6JdkNEXgG/2bAZAH0pGnryVkZrHgd7WndRL/kPGDRvAX/vX4ZbSIr7fvxNVUbipZ+2J4HiDEaNOz9pDe+n2yXRu/vGjOuMxdz4DVAO+I/l4S3dG9Fqag+fwJjSXHUVvIa7bJTG/39tblgFwa6/TUBTlhP2NqUqtiaq3YEzuDwSqI2tSWyVz0qDJ+LXIp3m3BRH3jExLS+Pw4cN06dKFrKwsli9fTk5ODnl5ea0iQy+EEEIIIYQQonEcbi/PL9rBtHlbQ9vslR6mzduKAtw3ujvxxgaNKKB7chwdE4wUlbtZs6c0lJwUR7n2r6Doy99g7nwm6RMX4NV8/GPnOgBu6RV+cE1Nzs/oA8CKg/kUOx3EneTJyODk47LcWWguO6rJRtKgyViHTUXVh1/O+/etK4DAs8yIt9XrPiM7duGQq4Jil4PCilI6xdVcIaca4jGnn4Zz749UFizCYDtxGXhLoHkcKKoB1ZhE5u+34S7egGqMrBdjpNYW72F18R4Mqo7f9Rxa43HBqtSHcsZSWFFGijmBCp+7zqrU2phSh+I+uA7XgTXE97wi7DGBSub7wK9Rtu61en9NtXURV0aOGTOGL774AoCbbrqJP/3pT4wfP56rrrqKyy+PbVNSIYQQQgghhBDNz6CqzFySF3bfK0vyMNSwTLU+FEXhjK7SN7I27qqhGfqqpNR3e7ewt6KUZFMcl2adEtG1MuJtDGyXjh8/3+7djKFdsGfkDvz+yJevtmaax0Hpquewr5ge6vMXnHxcuup5NI/jhHPcPi/vbw9Uxt3S67R63yvFnMDwlEwAvq6qSq2NOfMcAJz5C+t9j4bQPA78Pje+iiL8PnfY1xz2vKokbv5bGRT8vRsF72RTues7NK8zpvG+U5UIvrzLAFLMtSc+g1Wp03Pn0e2T6fx7x8+NurcxNZD8rGuITWXeN1WVzLtClczWofeetIlIaEAy8q233uLhhx8G4M477+Tdd9+lb9++PPXUU7z++utRD1AIIYQQQgghRMtid3qwV4ZfXmiv9FDqbNzSw2A1pPSNDM91YC0Apo6BpdnBhMzvegzFpIu8IvWCzKNLtfVJXUHV4/dW4isvjE7ArURdk48V9cQquv/l/8pBp4NOcUlcUFVlWl/BJfJfF2yq81hLZmA4UeWe72OWJD42oRj8KF39Up0JxZqTuNNrTOJGQ7nHxb92Br4Xbo2gIrhfuzSKXQ7m7Kn7udfGlBr4/nMfWFPrSuGy9W9R9OVvOLLhHXRxKSg6I6ohvlH3bu0iTkaqqopef/SH29VXX80rr7zCXXfdhdEY+Tp7IYQQQgghhIil4JCVonIXbq+Gw+1t7pBaPZvZgM0SfnmjzWLAam740kcgVBm5dNdhaQd2HL/fj6soUIllSj2VfRVlzKlKZtV3cM3xzq9Kin23ZzMaaiAhycm3VLshk4/frhpcU58J5scLJiPnF27F5av955IpbRiKIR6t8iCe4l8iuk99NKQqNKghSdxo+CgvlyMeFz0SOzA6vUe9zwsm37/fv4MjnoZXbhqTT0HRmdDcpTVOn/c69uPcsxiAuO4XNfhebU2DaudLSkp48cUXufnmm7n55pt56aWXOHxY/mIlhBBCCCGEaFmOHbKS9sRc0p6cywuLdshglEbyaBpTRmWH3TdlVHaDptMea3BnK2a9yqEKD1sOljfqWm2N90g+WmUxqHoMHQbw9Z6NtDNaOL1jV/rZ0hp0zdNSsmhntFDirmT5wd0YbD2Bky8ZGenk451HDrGgcBsKSoMSwYOTO5NuScLhdfPD/toH0yg6I+ZOgYnQlQWLIr5XXRqTUGxIEjca3tkSSATf2ntE2ME1NemVlEKPxA54NB/z9jZ8aryiM2BMyQFqHmLj2PYf8GuY0oZjsHZr8L3amoiTkT/88APZ2dm88sorlJSUUFJSwiuvvEJ2djY//PBDLGIUQgghhBBCiIg53F6eXbidafO2hpYU2ys9PDVvKzMWbpcKyUaIN+p5YEwPHh3fM1QhabMYeGx8Lx4Y06PBw2uCjHqV4Vk2QPpGHs9dtBZDuz5YL5+LV9EzrlMvdk58mHdHXdXga+pVHRM69wYCS7UNtmDfyJMrGal5nSTl3BF2X7jJx+9uXQnAuAgnmAcpihIaIPR1PZYMW7JGA+Cso0dhQzQmoRhpEjcacg/tZWVxQWBwTY+aB9eEoygKF1ZVR9bnudfGFOobuTbsfseWwLT0+N4N//5siyJORt55551MmjSJvLw8/vvf//Lf//6XnTt3cvXVV3PnnXfGIkYhhBBCCCGEiFgsh6wIMBt0jOnRgYJHx7H3sfHsf3wC943ujtkQ2VLVmoT6RuaVROV6bYWnLJ/2kxbzysEjpH/4JN0+eYasj6fxwY61OL0N79UZXKr9zZ5NoSE2npKGV421RkfWv0nSoDuxDX8olFxTTTZsIx4JTD4+ps+fV/PxRf6vANzSO7IJ5scKLhmuV9/IrufR8eL/kDLu9YgHzNTG5yxFMSQ0OKHo1zwkDZocdl+4JG40BPukXpZ1Ch0tiRGff8ExyUitET04jamnAuErIz2lu3DtWw6KSnyv3zT4Hm1RxP/6bt++nXvvvRed7ug/MDqdjnvuuYft20+uv5oIIYQQQgghWq5YD1k52Xl8Gue/vYLs6QtwejSMerXRFZHHCvaN3HjgSNSu2Rb4007j+Y0rmLZuAXZ3JQB2dyXT1s1jxvqFODzuBl33vIzeKCjkHi6kyJKBaulAa+3W2ZCJ0O6D6ylZ+hj7PhlLwim/J+u2PWEnHzs8btw+LwedDpZdNIUvx93MJZn9GxzruE49Mag6th8pZmvpwVqP1Vu74T6whoJ3siMaMFMb9+Et7PvodCrzF0RUFXos1RBP0uC76pXEjQaHx8UHO6oG1/Su/wTzY52V2o0EvYkDlUdYe2hvg2MxVSUj3UU/49eqt/9wbP0YAHPG2ejj0xt8j7Yo4mTkkCFD2LTpxIz9pk2byMnJiUpQQgghhBBCCNFYdQ1ZiTfq2Vx0RAbcNND6fWU4vRo+zU+XdpaoX/+Mru347MZhLLh9JAeOyHsDgeE1cSk5zNy8NOz+mZuWNLjiN8WcwPCUTAAWuePI/P02ks9+KWrVd02lIROh/X6N4oV3gd+HIbkvhqQsFJ3xhMnHTq+HFzYsIv3DJ8n46CmyPp7GioP5aI0YspRoMHNWWqCXYG1LhkMDZlY+E/GAmeOvE0rUep147TsAHaU/z8Q67D5sIx6pnlAc/lCdCUXXwXXs++hsjKlDyLqtIGwSN5rmFW7FqOronpjM6PTuDbqGUadnfOdeAMwp2NjgWAzteqMYEvB7K/Acrv7+BZdoJ8gS7RNE/GerKVOm8Mc//pHt27dz2mmBDPTy5cuZNWsWM2bMYP369aFjBw4cGL1IhRBCCCGEECICwSErT83besK+yWd0Zenuw+SkJ/Hcwu28+tMu7JUebBYDU0Zl88CYHlFbbtxWrdhtB2B4lg1Vrf/wiPoy6XWs2WPnpo9y5b2p4i3fg91vDFVEHs/urqTU4yRFl9Cg65+f0YcVB/P5ctM8Jvx4DZrLjmqykTRociAhFYPEUjRpHgelq1/EvmL60W1VCTsgkBwLk1Qr3/j/cO1bhmKIJ/nsF8Ne2+Fx88KGRUxbNy+0ze6u5Ol181CA+waMJt5gbFDcF2b0ZUHhNr4u2MTd/c8Ke0xdA2Zswx+o8z7BRG1Z7qyj723OHaRftRg0DdUQj3XovdiGP4DmKkExJFC5ez7u4g2Y04bVeF37sifwlGzGseUj4rtfgi4uJRCzrmHPoyYOjxuDqjI4OYOdEx9mV/lhVKXh7TYuzOzLZ7s3MKdgE48PPrdB11BUHaaOg3Hu/RHXgTUYO5wCgPvQRtzFG0A1ENfj8gbH2FZFnIy85pprAJg6dWrYfYqi4Pf7URQFn08m1AkhhBBCCCGaR3DIiub3n5BsnDq6B1sPljNzSR5Pzz/aFy844AbgvtHdo7rsuK1ZkR/o5Tgiq13Ur+1we3l+0Q55b47jLvoZW9Z4bEZL2ISkzWjBamh4wvC8tGyeABYfqaDSdQQT9UvmtRQNSdj5nIc5vORBAGwjHkGfmBn2fIOqMnPTkrD7Zm5awkM5YxsYNVyQ0Zd7Vn7BDwd2csTjJDHMe1ifATPBJGDYY2pK1K58BhQV69B7AULvry4ulUM/PkjZmpcwdhxMp2uWoYRJ/Dn3raBi5xxQdLQb+XgkLzsiwarUmZuWYHdXYjNauKvvKLonJmPW1zzpuzYXVPVJXXNoD/sqykiPS2rQdYypQ0PJyMT+NwBHqyLjuk5AZ47+z8jWLuIUcl5eXq0fO3fuDP1vJGbMmIGiKNx9992hbU6nkzvvvJPk5GQSEhK48sorOXDgQLXz8vPzufDCC4mLi6Njx47cd999eL0nd+m+EEIIIYQQImBLUTlDMmwUPDqO/U9MCA1ZiTPq6JeayKs/7Qp7ngy4qdvy3YFk5Gldov+LtgwfCs9VuIwj+Yu5q+/pYfff1XcUHq3hwziGdMikg78Sh2Jkja56j7uy3FdR1IYlfZpKQyZC21c+h1ZZjKF9X6yDp9R4bbvbWWdFakP1tKbQM6kDHs3HvL3hhwY1dmJ1XYnacO+t7dQ/oRiTcBf9TPmmD8KeW7L0MQAS+l2PoV2vWmNoKIfHzYz1C5m2bl5U+6SmWhIZ1iGQfG7MVG3TcUNs/H4/5VsC/SLje01q8HXbsoh/gnfp0qXeH/W1atUq3nzzzROWdf/pT3/iyy+/5JNPPuH777+nsLCQK664IrTf5/Nx4YUX4na7Wbp0Kf/4xz+YPXs2jz32WKQvSwghhBBCCNEGvbF8N1fMXsXULzfSMcFUbciKDLhpuEMON9uKAz3qhmfZon59eW/CcxWtxbnkfu7tfzYP54zDZgz06rQZLTyaM54HcsY0eKkwAO4yzvYEksCLDV2r7aopmdeS1DdhF+yb6HXsp93Ix+h48ad0mPAOiq7mZKvNaA497xP3Na4iFY5W6dWUFGvsxOqGJGp1cR2xDbsfCCQdNU9Ftf2VBYtwFiwC1YBtxMO13r8x6qpKbcwfJy6MYJp5TUJDbIrX4/e5cR9Yg7d0B4reQly3ixt83bas2f+cVF5eznXXXcfbb79Nu3ZH/6JWWlrK3//+d/7yl78wZswYTj31VN577z2WLl3K8uXLAZg7dy4bN27kn//8J4MGDeL8889n2rRpzJo1C7e75sy4y+WirKys2ocQQgghhBCibXF6fHyUWwjAFQNPnGRa14Abq7llV4E1p5VVS7R7pcTTPi66feFA3ptw/H4N94G1eEo2M33N/zg1OYM9Vz3G/mueYN/Vj3PfgNGYa0mm1YdqsjGaIgAW67uesK+u6rvmVmvCLucOnPtWoLnLQwNuCt7OouCdbNwH1mLsMKDWa3s0jbv6jgq7r7EVqQAXVCXFvtmzCc1/4rVUQzzWYVNPHDBTz4nVDa2sTBp8F/rELvjK91K69q+h7X6/n5KfAoVgSQNvw5BU/4K0SMWyKvWCjH5AYCiOy9ewVbZ6azdUUzvwuXEX/0J5cIl2t4tQjQ3r39rWNXsy8s477+TCCy9k3Lhx1bavWbMGj8dTbXufPn3Iyspi2bJlACxbtowBAwaQmpoaOubcc8+lrKyMX3/9tcZ7Pvvss1it1tBHZmb4nhBCCCGEEEKI1uuLXw9gr/SQaTMzunuHE/YHB9yEM2VUdqOTC23Z8nw7AKfFoF8kyHsTjte+A81dik9n5u95G7li4WxyD+0hxZyAUadvXEVkFb/m4YL+F6Pza5TFZVKcchqqJfC9U5/qu+YWGMDyZ2zDHzouYfcwSYPvAp871Dex2kTqlc/UOZE63mDkgYFjeCQWFanAWandSNCb2F95hJ8PFYZ/fXoz1qH3knXbHjJ/v53MW/JI6Hd9vQYLBRK1d4bdV9t7q+rNtBsV6DNZuvpFvI5A67zK3fNw7V+BordgraqejJVYVqUOTu5EuiUJh9fN9/t3NOgaiqJgDC7V3r8Sx9ZPAIiXKdo1ataOvx9++CFr165l1apVJ+zbv38/RqMRm81WbXtqair79+8PHXNsIjK4P7ivJg8++CD33HNP6POysjJJSAohhBBCCNHG/GN1AQDXn5oZdtpzcMANBPoQBgfcTB7V9aSe2FwfK6r6RY6IQb9IqPm9OZmnabsOrAFgXftRHHZX0sEUz7CUrKjeQzXEk3naA8zrup2hqT2xOyfTwZyIszSPRFs3VL0pqveLhco9izGmDiHzll34PQ5UkzWQaFN0mDqfTtHX14Q9rz4TqY94XAxJziB/0qM4vC5sRgseTWt0RSqAUadnXKeefJ7/C1/v2cipHTLCHhesgCzf+imla17EkjWOjue/X+f1VUN8ICHr91O27rWIJqXH95pIxY4viO99FaopCV9FEebOo+h48ad47DvQx6c17EXXU7Aq9dhJ5kHBqlRjA38kqIrKBZl9+fvWFXxdsIkJnXs36Dqm1FNxH8ylYtdc/Job1WQjrkvDJnSfDJotGVlQUMAf//hH5s2bh9ncuN4KkTKZTJhMLf+HqBBCCCGEEKJhCkudfLclsNz0hqHhf6kHMBt03De6Ow+N7cnhCjeJZj0/7DiESd/si8haLE3zs7LADsCIGPSLDAq+Nw+O7cm+MicpCUa8Pv9JmYgEcBX9DMA8U29ww0VZ/dCr0X8WblQWFu3hiu8/rDa1+AFbD5r2N/eGce6eR1nuLKzDptL+jMAUcEUXqFr0VRQ1aiL1f3dv4I5l/2FCp158c+5tAA1OgoVzQWZfPs//haUHdtV5rLnTSEqWFFOR9zV+rwuljkSx17Gf/f85l3ZnTCPrtgI0V1koUVtXZaWiKHQY/yalq16geO7NRxOZOXdgHR7bqkgIVKX+ecA5aPiZtemn6l+XOWManQy+ICOQjJyzZxN/9V+Kopz4x6u6JJ5yM7bhD+CrKEIX1xH3oY11vicnswYlI+12O59++ik7duzgvvvuo3379qxdu5bU1FQ6d+5cr2usWbOGoqIihgwZEtrm8/n44YcfePXVV/nuu+9wu93Y7fZq1ZEHDhwgLS2QdU9LS2PlypXVrhucth08RgghhBBCCHHy+efaPWh+OKNrO3qm1N6zKzjQpp3FQK/nFlJgd/LzPWeR06ll98drLluLy7FXerAYVAakJ8X0XsH3ZspnG1ieb+edSTlc0v/k/F3PfWANfuBblxnwcElm/6jfw+Fx88KGRTx9TAVacGoxwH0DRkdlOXgsOQsDbd2MHQaesC/YNzFcQrI+PTH/l/8LAOek92h8oGFcktmflLHxjE3vxYHKI7SrqrwM98xN6SPQxXfC5yiksmABcdkX1Hrtiu2f4Tm8idJVzxPf/ZJQ0jWYqK2N5nEElrevfObotqrl7Sgq1qH31tmzsrFm/vpjqE9qudeF1WCOWlXquE49Mao6dh45xJbSg/SxdYzofM3rpHzjPyjLnXVMxemdGDsMqNcS+pNRxH/uW79+Pb169eK5557jxRdfxG63A/Df//6XBx98sN7XGTt2LBs2bCA3Nzf0MXToUK677rrQfxsMBhYsWBA6Z8uWLeTn5zNy5EgARo4cyYYNGygqKgodM2/ePJKSkujXr1+kL00IIYQQQgjRBvj9ft6vWqL9u6H1b8dkMujISQ8kI+ZsKqrj6JPX8t12AIZm2DDomqaCtJPVQrHDzaLtxU1yv5bG79dwFa1lk9qBArcHi87A+M69on6fWE4tbgqauxz3wXVAoHLweI2ZSF3qrmThvu0AXJp1ShSiPVGiwcTq4j1kfTyNTh8+SfqHT/LChkU4vSfGpSgqcT0uBcCx7bM6r10e7GPYa2LEcSmqgbLcWWH3leW+iqLGdqCU3+/nra3LuWLhbBbv2x7VPqkACQYTZ6d1B2Bh4baIztU8DkpXPXdiH9IV0+vsQ3oyi/gnyT333MONN97Itm3bqi2vvuCCC/jhhx/qfZ3ExEROOeWUah/x8fEkJydzyimnYLVaufnmm7nnnntYtGgRa9as4aabbmLkyJGcdtppAEyYMIF+/fpx/fXXs27dOr777jseeeQR7rzzTlmGLYQQQgghxElqdUEpGw+UY9arTMrpFNG5F/QNVMR8s+lALEJrE1bkx7ZfZDhjegSGqCzafqjJ7tmSeEq24veUs8AU6Gc3oXNv4vTRr1CM5dTipuA6sBr8PnQJGegTT/xDRGMmUn+zZzMezUcfa8eIK+fqw+FxM2P9Qqavmx96D4JVqTPWL8ThcZ9wTnyPywGo2Pklfl/NiVRv+V5ce38KnNPziohjCyxht9eyrzTia0Zi7aG95DvsxOkNMatKvb77qXw29kZu7DWMospy3D5v2Gd+vOZO1LZWES/TXrVqFW+++eYJ2zt37lzr0JiG+Otf/4qqqlx55ZW4XC7OPfdcXnvttdB+nU7HV199xe23387IkSOJj4/nhhtu4KmnnopqHEIIIYQQQojWY3ZVVeQVA9KxWiL7RTCYjFy2u4RDDjfJ8S17SWpzCA2viWG/yOOd0z0ZgPX7yih2uOgQ3/KLTxweNwZVxe52YjOaa1xuWx/uquE18819QYNLs6K/RBuOTi0Ol5AMTC1u2c/dtS+wRDtcVWRQcCK1bfgDaK7SevdN/Hx3YIl2rKoi66pKfShn7AnbzZ1HoVo6oFUW49z7I5asMWHPd2z9D+DH1On0sEnaujR2eXtjfb57AwDndu6DRR+b5N7lXQcwY/1Cbvrxo+o9KQeOwVzLPeuTqK2tD+nJKuLKSJPJRFlZ2Qnbt27dSkpK4x7w4sWLefnll0Ofm81mZs2axeHDh3E4HPz3v/89oRdkly5d+Prrr6moqODgwYO8+OKL6PXNOiRcCCGEEEII0UxcXh8f/rwXgBuGRf5Ld1a7OE5JS0TzExqAI45yuLys3xf4ffC0JqyM7Jho4pS0RAAWt4LqSKfXwwsbFpH+4ZOkf/hErctt68N1YC17lER+1eJQFYWLMmPTliw4tTicu/qMpKJ4U0zuGy3BfpGm9JqTkRCokFR0RnRxKSg6Y539Dl0+L9/s2QzAZV1ik4xsSFWqouqJ634JAI7tNS/VdjRiiTY0bnl7NHxe1aszVs/e4XHz/PpFEVWlBgUTtTXvk97D4UScjLzkkkt46qmn8HgCX2yKopCfn8/999/PlVdeGfUAhRBCCCGEEKK+vvz1ACWVHjpbzaGlvZG6oG8qAN9slmTk8VbvsaP5IcNqprPV0qT3Pie4VHtHy05GBpfbTls3L+LERk1cRWuZb+gGwJmp2SSbYzMsJN5g5IGBY3g0Zzw2Y+D9tRktPJIzjqn9TqPihz/h92sxuXdj+f0arn3LMbTrg9rratw+b0TLbWuzoHAb5V4XneKSGNohI0oRVxesSg2/z4LVEL5yM7RUe/v/wr43nrLduPavAJQGLdGGxi1vb6ytpQfZaD+AXlG5MKNvTO7RmF6pzZ2oba0iTka+9NJLlJeX07FjRyorKzn77LPp0aMHiYmJTJ8+PRYxCiGEEEIIIUS9LN5RTId4I9efmoFOVRp0jQurlmp/u7kIn+aPZnit3op8OwAjspquKjJodNVS7ZY+xCbaQ2D8mg930c/MMwQGbMRqmXCQWW/gvgGj2Xf14xRc9Sj5kx5lXKdelHx+Ea69P+Latzym928oz+HN6OLSaD/pe17asiZqValwdIr2pVmnoCqxGeJTa1Vq31F4tPBJYEvmaFSjFV/FflxVlaHHqtj2HwDMGWehj09vcHzB5e1Zt+0h67a9ZN22JzBFO8bTooNLtEen96CdKS4m92hMr9TmTNS2ZhGvZ7ZarcybN48lS5awfv16ysvLGTJkCOPGjYtFfEIIIYQQQghRJ4fbi15VufecHjx3UT8q3L4GX2tkl3bYLAYOVXhYkV/C6V3bRzHS1i3UL7IJl2gHnd09GUWBzUXl7Ctzkp4U2yRIQ9UnsZGiS6j39Twlmzns01itCwxjuiRG/SKPFext6fC4Gfz5Xyj3utiY0gsOrMax7TPMnU6PeQyRchYuwzzqOZ7ftJxp6xaEtgerUgHuGzA64r6dPk3ji/xfgdgmgoNVqRBIWlfrW5gzBrMufN9CRWckrvtFlG/6AMf2zzB3PqPa/tAU7Z6/aXSMwcRasAeioqv+LKPZJzUo1ku0oT69Umv/WdPQPqQnswan9EeNGsUdd9zB1KlTJREphBBCCCGEaDZOj4/nF+0g/cm5dH9mAZnT5jPrp104PQ1LSOp1Kuf2Dvyy/fUmWaod5Pf7WV41Sfu0LrYmv3/7OCODOiUBLbs6sqHLbWviOrCGxfqu+BSVnPadyE5MjkaY9dIjqQNmnR6nz8vPyWcDULH9c/z+llcx7C7eQGLWOczctDTs/oZUpQIsP7ibImc5VqOZc9K7NzbMWh1blZo38WHyJz3Ktd2H1JiIDIqrWqrtOO698dh3BIYfKSrxPS+PaezR7pMKsNdRyoqD+SgoMU0EN7Qq9ViR9iE92TUoGblgwQIeeughbrnlFn7/+99X+xBCCCGEEEKIpuJwe3l24XamzduKvTLwS6+90sNT87YyY+F2HG5vg64bnKr99aYDUYu1tdtT6mRfmQu9qjCkc/MMZRjdCvpG1pbYmFzPxMax3AfWMq+qX2SspmjXRFEUzs3oA8AiXyKKIR7vkd24i9Y2aRz14S3bhb2yrMHLbWsSXKJ9YUY/DKquUTHWR7zBiFGn5+uCTXT7ZDoPr/m6znMsXcajGOLxHckPTV4HcGz9FABz5mh0cR1jFnMs+qQCfFH17E9LySI9Lilq8R6vpl6pj+aM54GcMY2u7hQnijgZ+eSTTzJhwgQWLFhAcXExJSUl1T6EEEIIIYQQoqkYVJWZS/LC7ntlSV6DKqEAzuvdEUWB3MIy9paGT26cbJZXLdEemJ5EnDHijl9RMbp7VTKyBVdGBhMbD+eMq5bYeDhnHJP7ncGPB3ZGdD37/rX8qO8CwCUx7hcZznmdA8nIuYU7iOt6HgCObf9t8jhq46s4iGv/SmyWpKhWpfr9fj7fHftlwuEM6ZBBscvB/MJteLTaq7xVvYW4rucD4Nh+9L0JTtFOaOAU7fqKdp/UoM+a8NkHq1ILr36cHb95iPxJj/LH/mfWWZUqGibir4g33niD2bNns2LFCj7//HM+++yzah9CCCGEEEII0VTsTk+oIvKEfZUeSp0NWyKYkmBieKYNkKnaQcubsV9k0Jnd2qNTFXYeqmD34Ypmi6Mu28oOcmpyBvmTHmX/1U+w7+rHuTSrP+d8/RpXLJzNTwfCJ9CP59e8LC45TKViINOSwKD2nWIc+YnGduqBXlHZWnaQAxnBhNdnLWqptnPfcrTKYhz7VjV6ue2xfrXvZ8eRQ5h0es7t3Dsaodbb0A4ZdDDFU+ZxsrRoV53Hx/WsvlTbfXgL7uL1oOqJ635pTGNtzACYmhx2VbB4/w4ALusyoFHx1Ve8wYhJp+f+VV/R7ZPpfFWwqUnuezKKOBnpdrs5/fSW16xWCCGEEEIIcfKxmQ3YLOErV2wWA1Zzw6taLuibCkjfyKAVzdgvMijJbGBoRmCJeEteqv3prvVcsXA2U1Z8RoolAaNOz6D2nemR1AGXz8vUVV9ysLIct89LUdX/hlvK6jm8hflxAwG4rGsOitKwCfGNYTVaOL1jVwB+0GWg6Ex47dvxHPqlyWOpiWtf1RTpPQu5d8DZYatS7+x3BtuPHIzousGqyPGdepFgMEU15rqoisqEqgTot3s213l8XNfzUXTmwHtTvCFUFWnJHIvOEts+o9HukwrwVcFGfH6NU2xp9Ejq0NgQI9LTmkKxy8E3eyQZGSsRJyNvueUW/vWvf8UiFiGEEEIIIdo0h9uL26tRVO7C7dUa3M9QHOXRNKaMyg67b8qo7IgroY4V7Bs5b+tBXN6GT+duC9xejbV7SgE4Lav5KiPhaN/IxS14qfZXBRsBODvt6MATnaryr7OvY2LXgXw+7vfM3LSk1mEfmseBkpTNvpSRdDDFc0nnnk3+OoLOzQgkxb7bn4ely3gAHNtazspIZ2EgGWmwZnP/qq84NTmDPVc9xv5rAlWp4zv14pyvX2PCt2+xvaz+XzfBfpGxHJ5Sm/Or+nXWJxmpGhNC703Fru9wHcxFtXQgPsZLtKH2Pql39j2DYpcj4ms21/J4OPrc5+7dgq8R/4aImkXc6MPpdPLWW28xf/58Bg4ciMFQ/S+Nf/nLX6IWnBBCCCGEEG1FcOLzzCV52Cs92CwGpozK5oExPTAbYj8Uoa2KN+r58znd0fx+Xv1pV1Sf7eBOVtISTew/4uLHnYcZ1yslipG3Luv3leH0arSPM9CjQ/NOiR3dowMzFm5n4fZi/H5/s1QL1ia/vITcw4WoisL56d3xVRShmmz4NQ/xhnheP/03/PXXH5i+bn7onOCwD4D7BozGovg5UlqAydqV9866jo7meNylO9G8LlR901boQaBv5MNrvmHRvu3oBl8CO7+ismAx7UY+1uSxHM/vc+M+sBoAreMw/rHmfd7cspwNl91Hv3aB6ubByZ2J0xsodjm4a9l/+dc51xOvN2B3O7EZzXg07YQhJQXlJbg1Hx3NCVyc1a/JXxfAhM69UVBYX7KPvY5SOsfXPjgqof8NJPS/EUvWGOIrfoMuriN+LfZ/9Ar2SdXwM2vTT9jdldiMFib3PYPJ/UZx2fx3ee/Ma+hlrd/P0Aqvm7l7twBNt0T7WCNSsmhntFDirmTFwXxOT+3a5DG0dREnI9evX8+gQYMA+OWX6mXZLe0fASGEEEIIIVoCh9vL84t2MG3e1tC24MRngPtGdye+mQaCtAUzl+QxJMPG3sfGU+7yYjUb8Ghao5O8qqpwft+OvLeygDmbDpzUychQv8isds3+e98ZXdth0CnsKXWy41BFsydHjzenqipymFml4v3elLvsqCYbSYMmYx02lXi9kVmbfgp77sxNS3goZywun4u/7s5j5uYPQomdu/qM5P6kLEweH6ohrilfEjntO5FmSWR/5RHWJuZwxsX/wZI1Bq/jADpzO/yaB9UQvfdB8zhQVANa1bOr7fquolz8PheqOZnvKzScPi9dE9rR13Z0enSCwcSX427mdz/8i/fPvpa//vp9taTZXX1H8cDAMZj1gWIrh8dNijmBz8f+nlRLAs3VHrODOZ7hKZmsOJjPt3s3c3OvEbUeb+kyntKVz1E89+bQswt+3an6yJdKR+LYPqkVXjdWoxmnz8ttP33M8oP5XDD3bZZcOJm0ekzF/m7vFip9HromtGuWPql6Vcf4zr35OC+Xb/ZskmRkDET8/3gWLVoUiziEEEIIIYRos+qa+PzQ2OZbftkWfJi7lw37jvDZDcO4dEAaAMbIO1KFdUGfQDLy601F/DW2MyBatJX5R5ORzS3OqOe0rHb8mHeYhduLW1wy8sv8DQCcffh7NLcdAM1lx77iadS4jrh6XVfrsI8jHhd/2/gj09cvqLZ92vqFgMJ9A0fT1K9YURTO7dybf2xfzdz9uxjmWxezhJfmdVK6+kXKcmfV6/qufUsBMHc6jS+rEsEXZ/Y/IWmeFpfEB2f/lpkbl9RYlTp1wGhUReGFDYuYuWlJjcnKpnRe5z6BZOSe2pORmsdB6eoXsa985ui2qq87AOvQe6OaMD7eJ7vWM33dfG7qOYx3Rl0FgFGn55XTLmftob0YVB2bSg/Q3hRXa0UqwOqDBXQwxXNZlwHN9seP8zP6VCUjNzPt1PObJYa2LDr/QgshhBBCCCFqFKuJzwKKjrjYsO8IAKdnRz9RNr5XCnpVoaTS06KnN9cmGr1KD1d66BBvZEQzDq85VkvtG1nucbFof+APD2O8J/4Bomzt37AZLTUO++iemEyiwVRz5eTmpRjU5qmiPrdzoI/e19uXYV/5DJrLDhxNeJWueh7NE3lvwGNpHgelq57DvmJ6va8f6heZNjLUq/PirP5hr281mmutSvWhMWP9QqatmxdKGAeTlTPWLww7ZCjWzqvqXzi/cBserebetYpqoCx3Vth9ZbmvoqixTaR+kf8rAOek96i2vaMlkXnn/R/fX3Ani/btqLVPqsPjxu3zcmvv09g58WHu6X92TGOuzXlVw4N+PryXfRVlzRZHW1Wvn2JXXHEFs2fPJikpiSuuuKLWY//73/9GJTAhhBBCCCHaiuDE53AJycZOfD7ZLdoRSEYNTE8iJSH6vfSSzAYW/N9IhmRYsVd6cHu1QDVPK1lW39hepQ63F72qMvPyAXRMMOL1NdN61eOM7pHMU/MCE7VbUt/IeYVbcWs+snx2umslJ+z3lu7A7XVyV99RoWq8Y93d7yzs7spaKydL3U5SLAlRj70u4zv3QlUUtrg1CpUEOvnLq+0vy30V2/AHGnWPuhJqx1/f7/fjqkpG/hLfhyLnDyQZzJyZGn6old3trPHZ6hUVo6pn5qYlYfcHl9A3taEdMuhgiqfY5WBZ0S7OOmYo0rE0lz2UwA2/rxRdXGxaTeQdOcSGkn3oFJULMvqesL+DKZ4XNixqVRWpHS2JDOuQyariAr7du5mbeg5v8hjasnpVRlqt1tAPd6vVWuuHEEIIIYQQorraJj5PPqNroyY+n+wWbAskI8f07BCT6zs9PuZvO0jmtPlkTptP2pNzeWHRDpyelj9d2+H28uzC7UybtzWUCA/2Kp2xcHudFZLBRGb6k3Pp/swCMqfN568/7GwRr/20Lu0w61UOHHGx6UB53Sc0kWBl3lh/IeHSo6rJRrw+MOzj0ZzxoQpJm9HCoznj+X2v4diM5horJ21GC1ZjbHv/1aS9KY7h7dMB+EHf5YT9wYRXY9QnoXYsb9kufBX7QTXwbVXh8nkZfTDqwv+xoLZn29OaQmldiWCPs34vJIpURWVCVZXeN7VM1VZNNlSTrZZ9scvXfJkf+Lo/MzWb9qYT+5kaVLXWJK/Pr/Hs+gUtqiIVjlal1vbcRcPU68957733Xtj/FkIIIYQQQtStponPk8/oyl2jsvnHqj3ccUbX5g6zVVpYtUx3bI/oJyOPDh7aFtrWmgYPNaZXaUsfumTS6zgjuz0LthWzcHsx/dISmy2WIJ+mhYbXXJw9FH6Zf8IxSTl34LHvwJzcl/sGjOahnLGUepxYDWY8mg/HTw+iZF9SY+XkXX1H4dE0jI2bzdRg52X2Y/mhQn4wdOFqz6/V9kUj4RVMqIVLSIa7vmtfoCrS1HEwX+0JTF++OLPmydceTavx2V6S2S+0hD5cQtJmtGA1NE8i+LyMPvxr51q+3bOZZ4deGPYYv+YhadDkUI/IYyUNmoxf86DoTuzPGA1fFgS+Fi6pYXl8nRWpOj2v1jHUqTmcn9GHabnzmLd3Kx7Nh0Ftpm+8Nkh6RgohhBBCCNEEZizcFpr4fOCJCex/fALXDcng7NeWMvmzDXywdk9zh9jq7Dpcwc5DFehUhTO7tY/69etK5hnUlv3rVF29Sstd3hr7SbaG1x7sG7mmwN68gVRZWZzPQacDq9HMeWfeh234Q6FKNdVkwzbiYZIG3UnRV1dRuu4NLHgwoNHe78KAhq54HZW75lLx/d3cP+DssJWTD+SMCTvwo6lMSOsGwE/6LNzHpROCCa/G0HwuknLuCLsvadCdJ1zfWbgcgH0dTuNX+wF0isr5VdVs4cQbaq5KndL/zFCyMpxgIrg5nNu5NwoK60v2sdcRvvpUNcRjHTYV24hHjvu6eyQw/CdGw2tKXBV8v38nUHOvztZYkQowNDmTDqZ4yjxOlhXtapYY2qp6/Slr8ODB9e7BsXbt2kYFJIQQQgghRFujaX7+vrKAZxZsZ9HtIzm7eyCJ0rtjAhf1S2VzUTnPL9rBOd2TSYk3YXd6sJkNrao3YXMIVkUOz7SRFIO+m/UZPBSLPpXRUluv0uFZNixGHTMWbj+hn+TU0d05VNHyX/sFfVLon5rIuF4dKCp3Nfv3THCJ9nmd++Dc9D7G1CFk3rILv8eBarLi1zyUb/oAgISeV1K66gXK1r12dGJ0zh2kT1yA6+B6LAZzmMpJDbOuefvLDkvrQQdTHMUuWGfuyTDnlqpp13dGZZr2kfVvkTToToATnk1Szh24i3/BnH50orSrMDBJe6G+K7CLs1KzaRdmmfCxzHpDzc9WBw8MHANwYu/CnDHN9vw7mOMZnpIZmKq9t+ap2qrejHXovdiGP4DmKg193UVjynlNvtmzGZ9f4xRbGt0Sk8Me01orUnVqYIn8v3au5Zs9m2vs1ykiV6+f0pdddlmMwxBCCCGEEKLtWrOnlANHXCSa9IzsUr2Cb8YFfXF7NR4e15OZS/KqLeOOZNDIyWhhjPtFtvbBQ8FepU8ds9Q66K2JOcxYuC3sEnSzXuXus7q1+Nfeu2Mi/92wn5s+ym0R3zNfVfXNuyizH0d+uhHP4c2kXPABCb0mAqDojCQNvA1z57Moy52FfeUzoXM1lz3wuaJgHfpngFAFZIouMKymuZZmHyvQvzCwZHjtkOlc0e9UVJMNd/GvjU54uYp+puSnRyj/9R+kXvYlthEPhRJqrqKf2ffJWHyVB+l0zVIM1mw0VxnuQ78A8K0j0Me0psq849X2bGtNVjaj8zr3CSQj99ScjARCFZDBYTWxWpodFJyiXduzD1akwolJ3mMrUltia4Lzq5bIf1PLEnkRuXolIx9//HEAfD4fP/30EwMHDsRms8UyLiGEEEIIIdqMLzfuB+Dc3ikY9dWXNqqqwrTzevPC4h08Pb919iZsDn6/P1QZOSYG/SKh9mTelFHZgV+QW3Dnq3ijnnvO7nZCr9IHRvegT0oCM5fsCnve84t3cOcZXVv0aw/2tGwp3zN5Rw7xi30/OkVlbJxCxeHNoDMS12XCCccabN0oW/da2OuU5c7CNvzBWIfbKMH+hd8VbuOJrHQOfDAMUMi6NR+lgT31/JqP4vm3g9+HscMpGKyBATnBhJoxZSCKMR6tZDNFX/6G9Kt+wHUwF0P7fpSqFn48uBcIJIKjoSUmgs/L6MOTuXOZX7itxfQvdPm8fLs3MNylpn6RQa2xIhWOLpHfULKPgnI7mQm2ZoulLYnop7NOp2PChAls2rRJkpFCCCGEEELU05xNBwC4sG9q2P0mvY5Xf9oVdl9dg0ZOVpsOlLP/iAuzXmVkl3YxuUe8Uc8DY3oAgfchmMy7a1TXVlOxev9XGzm3Typ7HxtPucuL1WzA69fqXILu8fnDvvaWUq3bmOE8sRBcoj0qtSvG3V9RAViyxoUd6FKfidHBJFxLNKFzLxQUCivKKE3siWpIwlu2E9eB1dWWUEeiLPdV3EVrUY1W2p/zlxP2q3oLqRd9QuG/R+L3eXAX/4IpbRipl/yHHw4U4fvpE/rZUumeFJs/TLQEQztk0MEUT7HLwbKiXS1iyfD3+3dwxOMi3ZLE0A4ZdR7fGitSk83xnJaSxbKDu/l272Zu7X1as8bTVkT8p6JTTjmFnTt3kp2dHYt4hBBCCCGEaFP2llby894yFAXO79Mx7DGtvTdhc1hQVRU5Krt9TBNjZoOO+0Z356GxPTlU4SbJrGf57pJmT8bVh8en8c+1e3lzeT7r7j2bAelJABhR0StqrcuwE0x6jHo19NpLnR6sVT0ZW8Jrb4rvGc3jQFENaO4jqMbEQO+9GoaAfBmcop3ZH8fPfwQgvueVYY+NdGJ0S5NiTmD+eX9gWIcsSj2VdPzdOo7sXoBz/4oGJSM9ZfmULA2sxmx35gz08Wlhj9MnZpB62RfoEzIoy53Fgc8vRnPZ+SjhYtBlc1Hn3o16XS1dYIl876qp2ltaRDIyuET7osx+qErjK6VbYkUqBKpSlx3czTd7JBkZLRF/tTz99NP8+c9/5quvvmLfvn2UlZVV+xBCCCGEEEIcNWdTEQAjstrRMTF8ciTYmzDsvhbSn6+lWVSVjBwdoyXax4o3BhJzRp1Kt2cWMP7N5RSWNs9010isyC/B4fbRId5I/9TEavuCS9DDCS7DhqOvPSXBhFGvtph2AbH+ntG8Lo6UFuDWNIo9XtyaxpHSAjSv64Rjy9xOfqiaJnxukgnPoY2gGojrdnHYa/s1D0mDJofdF42J1LHm9HpYtG8HWR9PI/OjaXT66GletYOx7w31vobmceD3ufFVFKGzdCDl/PdJ6HMdiafcVOt5hna9KFv3GvaVz6C57LhR+V7tBMDo8tVoHkdjXlqLd17VpPDlLWCys9/v58tQv8joLI9vqc7P6AvAgsJtuHzeZo6mbYj4X5ILLrgAgEsuuaTahG2/34+iKPh8vuhFJ4QQQgghRCs3Z2NgifZF/cIv0YbW35uwqfk0P4t3HAJgbIyG14STHG+kW/s4Dpa7mbPpALee1qXJ7t0Q87cGErZje3ZAVZVq+2pagt5SlmHXJVrfMw63F4OqVptgb1HcuPx+/ro7j5mbPzjav67PSO5PysTk8aEajk5sXla0G6vBTLI5jvTC+dgBS5fx6My2sPdUDfFYh00FAsuTQxOjB02OykTqWHJ43LywYRFPHzNoxO6uZNr6hQD8uf8ZJJhrr+zUvE5KV79I+eYPUfQW/N5KEnpfRfLYWShV1XUOjzvwvrid2IyB5brxBiOKaqAsd1boWqt1nTiimEjWKuix5f+hnP1IDF51y3FBRh8+G3sjY9N7caDyCO2MltCzaWo/H9rLnopS4vQGxqa37VYig5M7kWpJ5EDlEVYczOestG7NHVKrF3EyctGiRbGIQwghhBBCiDan0uNj/raDAFxUQ79IaBu9CZvS2j2l2Cs9WM16hnRu2iWtF/ZLZUW+vVUkIxdUfe3VlLA9dgl6S1uGXZdoJFOdHh/PL9rBzGPOf2B0D+48M4MXNixi2voFoWOPJtwU7hs4mniOJsz6tUtl58SH2VZ2ENeSuwLx9bii1nurejPWofdiG/5AaGK0X/O06EQkVPXq3LQk7L6Zm5fxUM64Ws/XPA6O/DobX8fhdBw6FXtlGTZLEkfyF3Hk19kk9r8Rt2LghQ2LThxkMnAMBs+RasvbFxoC1b2jvXkorpIW32+zsSw6A6uL93DTjx+d8GzM+qatoP+yIFAVOaFT7ya/d1NTFZWbegxjRMcshnXIpKiyvFqSXEQu4mTk2WefHYs4hBBCCCGEaHMWbium0qORaTMzID2x1mOPTQwVO9xYLXqW7SrBpJeKyOMFp2if3T0Zva5pn89FfVN57NstzN9aTKXHh6WFJu6OOL2syLcDMK5nzcmZ4LLrYH/F1lSBe+z3zP4jLpLjDeSXVNYrERmcxj3tmMpKe6WHOZsOcPfZXZm5eWnY82ZuXspDg8bj9HrCJsymTngH76fjiOsefon2sYL9J4PJM0XX8pMadrcTu7uyhn2V2J1ldEyouVpZUQ0Yel/LXzcuZ+byZ6pVnU7tdw2VqLywfiHTjq+8rPr8vgFnh/ptKpYO7Ek5kw5OP2Mdea2i32ZjBKtSp6+bH9pW/dmMjkpirKaq1OMF+0XWNUW7rXggZywvbFjUIhLBbUG9kpHr16/nlFNOQVVV1q9fX+uxAwcOjEpgQgghhBBCtHZfHTNF+9gWRzUJJobijTr6PreIPaVOlkw+g9O7to9pnK3Nwu2Bir8xTdAv8ng5nZLobDWzt9TJ4u3FnF9LxWtz+mHnIbyan+7JcXRtH1f3Ca1U8Hvm+x3F3PvlRnp2iOenu0bVeV5t07jt7spaE25HPC5e2bjkxKXK6+aB38cfx76OzhybCe/NzWY0YzNawj4fm9GC1RSP5nHUOOjH4XXz4sblYatO4/Qm/njK2TVXXm5awkM5Y2k3agbuuHQSskbzhrOCjuZ4yvNPw3wkD7/maRVJ3YaotSq16tnUR23JxpqS7Mcn3PZVlLLHUYqqKFyY2bb7RULgmb20YXHME8Enk3r92WvQoEEUFxeH/nvw4MEMGjTohI/BgwfHNFghhBBCCCFaC7/fH+oXeXEt/SLDsVoMjOsVqJZ67add0Q6tVXN5fSzJOwzA2Foq/mJFURQurEpAflU1nKglmh9aot12l6wea2zPFA5VuFm2u4RdhyvqPL6madybispJMliwGS1hz+uemEyiwcSrtSxVju/Ydn8v9mgad/UNn+y9q8/pHMlfhLOg5tZuRr25xqrTL/ZsorSORHCp24mh7/W8WqrQ+ePp9Pj0GbI+fprXSxVMp9xaYxK0LairKrXUU/dQrWCyMf3DJ0n/8AnSP3ySFzYswun14PC4mVFVlRq8TzDhNmP9Qio8bhweN26fF80POyc+zPzz/o8O5rb7zIPqSgQb1Hr0p616dkWV5bh9Xhwed7TDbFXqlYzMy8sjJSUl9N87d+4kLy/vhI+dO3fGNFghhBBCCCFai/X7ythT6iTOoGvQxOc7z+gKwCfrCyk6cuIE35PV8t0lVHo0UhNN9EtNaJYYgsnlrzbux+/3N0sMdVmw7ejwmpNBJ6uZc7olA/Bh7t46j69pGnexw83y/EM1Jtzu7ncWJXUlzDxt9/s13mDkgYFjeDRnfChhazNaeCRnPPefcibOJQ9QsfPrGs+vrep0W+lBrMaaE8E2owWTTs+z6xcwbd384xJm85mxfmGbTvAEq1LD77NgNdTeb7S2ZOPfNv6ArpaE23d7t6AoSiiRmfXxNLI+nsbCwu04vS17+ns0NDYRXFsS+GRVr2Rkly5dQstKunTpUuuHEEIIIYQQAr6sqooc16tDgwaCnJphY0SWDY/Pzzsr86MdXqsVTLKN6dGhXkvfY2FMzw5YDCoFdicb9h1plhhqs7/MyS/7j6AoMLpHcnOH02SuHtwZgA9/LqzzWLdPY3JVwv94vxRWcH+YhNujOeO5sdsp2AzG2pNCNexrK8x6A/cNGM2+qx9nz1WPkT/pUc5I7Yr/YC6eks1U5H1dY5LeVkuy0evX8Gi+GhPBUweMxqjTM2vTT2H317dCrbWqtSq17yg8mlbr+bVV931ZsImyWhLFD+aM4bkwicynq6om23ISGBqXCK6r4rStP7uaNPg7dePGjXz77bd88cUX1T6EEEIIIYQQhJZoX9iInoK3n94VgDeX7cKntcwKvKYWHF7THP0igywGHWN7BFaOBfuCtiQLqp7R4E5WOsSbmjmapnPFgHT0qsL6fWVs3F97knjh9mLuGpXNI+N6hiokbRYDj43vxS0jsrAck3Dbf80T7Lv6cf7c73RKPruAI7sXclffM8Je966+Z9SZFGoL4g1GjDo9iQYTff4zg/Pnvs12UyaKIR6foxD3wdyw5znL93FX39PD7rur7yhUlLCVl4/mjOfu/mdSFoWlyq1VTVWpj+aM54GcMXX2LKytuq+2qtQOpnjGpvdq9DLl1qwxieBoLPFuiyKepr1z504uv/xyNmzYgKIoob94BP8q6fP5ohuhEEIIIYQQrcyBIy5WFtiBxiUjJ+V04s9fbqTA7uSrjQe49JS0KEXYOh1xellZNSG6uZcfX9ivI19tOsCcjQd4aGzPZo3leAu2nlxLtIOS442c2zuFOZuK+DB3L0+d1yfscZrm5+GvN+Pz+/n4d6fyyLhelDo9WM0GPJoWqmQOJndSdIF2AEZdIv6c/6N0yf1MnfQ9oFQf9NFnZNWgj5NnkEWCwcTgDp0pLCjj8z1b+EPWOCp2/I+KnXMwHdc70+9z41jwB/58/gdoKMza9FP1ISk5YzDrAonh+waM5qGcsZR6nFgNgSErJp0Bm1GpfYBOHUuVW7tgVepDOWMpcpbTzhjHRvv+0HOrTW3Dh46tSj12kjlAWlwidndFnUng4PdJWxRMBAMnDvc55us2nPos8W7Lz64mEadg//jHP5KdnU1RURFxcXH8+uuv/PDDDwwdOpTFixfHIEQhhBBCCCFal282F+H3w6kZVjpZG/7Lsdmg4/fDswB4bWn4yb8nkxX5h+nTMYFTM6zNPiH6oqq+kcvzSzhY3nJ6BPr9fhZUTRsPDkE6mYSWaucW1rhUeM6mA/x64Ah7S51kWC0Y9SopCSaMejU0nbsmCX1/i97WjcMfn82UlHj2Xf04+656hMJJDzMlrf1JlYgMuqLLQAD+s2s9cd0uBKAi78S+kUc2vIMzfz7PzXmIU5MzKLjq0VDV6X0DRldL6AQrL1PMCRh1+lBiuLFLlduC4LPZ4yil2yfTuXDeO3i1uovCPJrG5FqeXU1Vqdd1G0KKOaFR/SrbgmAiuPDqx9nxm4fIn/Qod/c/q85EcGN7fbZVEScjly1bxlNPPUWHDh1QVRVVVRk1ahTPPvssU6ZMiUWMQgghhBBCtCobCsvoEG9sVFVk0B9O64KiwLytxWw9WB6F6JqHw+3F7dUoKnfh9mo43N6Izz8jO5n//X44P9x5RsTnR1tnq4XBnZPw++HrFjRVe1uxgwK7E5NeZVR2++YOp8ld2j8Ni0Fle7GDNXtKT9jv9/uZsXA7EGiDEG6ITW0URaHD2FkoxgRM7hL0fi/ttQoM+EiMO3n6cx7rkqz+6BWVX+z7KUgeDoD7wGq8jv2hYzT3EUpWTMeNypuVSVyxcDZLD+w6IdlYl8YuVW5LhnbIAOCQq4Lv99c9TDjeYOSP/c/k4ZxxYZ9dnMFYrR9oMFF8Z1WS92RPAkPgGZp0eqau+opun0znP7vX13mOPLvwIk5G+nw+EhMTAejQoQOFhYHmwF26dGHLli3RjU4IIYQQQohWJJhwm3xmNnkPj+UPIxs/4DE7OY4L+wSSmq8v3dXo6zUHp8fH84t2kPbkXNKemEvak3N5YdEOnJ76tXgKnt/5qXl0f2YBnZ+aF9H5sRJMNs+Jcd/ISBK586uWaJ/RtT2WBgxOau0STHou7hdoZ/Dvn0+cqv3jzsMs212CSa/yxzOzG3QPfUJn0q/8DveBNRS8nUXBO10peCeb8s3/RvO23Z6FNWlvimN0eg8AvijahzF1KACVed+Ejild8xe0yoP8aB3JIa+PNEsi56R3b9D9wiXMjq+sPBnoVR2XdTkFCFSl1uXXkv2cOedVhiZnUFjLswtXlSpJ4Opy2nei2OWo13OPNxi5f+CYGpPAJ9uzC4o4GXnKKaewbt06AEaMGMHzzz/PTz/9xFNPPUW3bt2iHqAQQgghhBCtwbEJt+7PLCBz2nzeXLY7KgmzO6qm/s5eVYDD1bwVgZFyuL08u3A70+ZtxV7pAcBe6eGpeVuZsXB7KLFWU8Ktvuc3h+BS7e+2HMTtjU11S6SJ3AXbAku0T7Z+kce6enAnAD5eV4h23OCnGQu3AXDD0EzSkxq2PFLzOChd8xfsK59Bc9kD21x27CuepnTV82geR8ODb6Wu7HrMUu3sCwCoyJsDgNexn9K1LwPwRftxAPy2+6no1YYny2taxn2yCT73z3ZvwFdHhd3sbavYXFrEe9tXYWrAs5Mk8FG/yQ489wWF2zjsqqjz+P/l/1Jna4KTTcTJyEceeQSt6ov8qaeeIi8vjzPPPJOvv/6aV155JeoBCiGEEEII0dLFOmE2oVcKE3p1YPbVg9GpSoOXOjcHg6oyc0n4fpevLMnDoKo1JtwqPT50ilLn+c1laIaN1EQTR1xefsw7FPXrR/p15dP8oWnj43qefP0ig87v0xGrWc/eUme19yV3bynfbjmIqsB95zSsKg9AUQ2U5c4Ku68s91UU9eRLMFzW5RRURWHtob0UpZ0NgKsoF83ronTtK/g9Do50HMl3JYGl8zf0HNqc4bYZo9N70M5oochZzpKimvsKezQf/9yxBoAbewxr8P0kCRzQ29qRAe3S8fo1/pf/S53Hv/zrD1yxcDb/b/uak/7ZBUX8L/e5557LFVdcAUCPHj3YvHkzxcXFFBUVMWbMmKgHKIQQQgghREtXn4RbY6iqwse/G8qaPXY6PTWvQUudm4vd6Qkl0k7YV+nhiMvLswu3hU24/X1FPiWVtZ9f6gy/rymoqsIFfTsC8NXG6C/VjvTras0eO6VOLzaLgSEZ1qjH01qY9DouH5AOBAbZBD23KNArclJOJ7p3iG/w9TWXPVQRGX7fib0q27oUcwJnpQZWSn51xEfqZV+S8bt1aM5DtDvtYTpe/CnfZd+K168xvEMm/WxpzRxx22BQdVxaj6Xa3+zZTJGznI7mBC7I7NtU4bVpv6mqSv00r/al2hsO72NVcQF6ReWKrgOaIrRWISp/Rmzfvj2KokTjUkIIIYQQQrQ6dSXcGpswc7i9vPT9Tp6ev63FLVWui81sqHFISPfkOBJNemYu2RV2/99+3ElynLHG820WA1Zz81ahXVTVN3Jp3uEapzc3VKSJ2PnbAlWRo7sno1NP7t/Prqmaqv3pukI8Po3txQ4+WRdITN4/pkejrq2abKgmWy37Ts5E8NGl2utwFS6j4J3sUD9N1/41/HP/PgBu6Nnwyjxxoiurppn/d9cGNH/4pdrvbVsJBJbHGxqxPF4c9ZuuOQAs2LeNklqWas/evgqAi7P6k2JOaJLYWoPmW9MghBBCCCFEG1Fbwi0aCbNYV17GkkfTmDIq/KCQu8/qxuFKd40Jtx2HKnB4vDWeP2VUdrNPIp3QK4X/3TSMhXeczoFGTAo/vl/mt5sPkGDSRfR1tWBrsF/kybtEO2h092Q6Jhg5VOHhhx3FfLB2D+3jjJzfpyM5nRqXLPRrnv/f3n3HNXWvfwD/JGSwwcEQUYaAe0+EahVEO/Wnba2183rb21Zstfe692it1k5te+/tsnvb29a2CgoOFDduq0xxAQ72yjq/PzCpkSQkkAV83q8Xf5Bzcs43h29I8uR5vg+8+yUa3ObdLxGCxnHZuo6kbaay/9oFnDn4jt56mnsOf4iTJYWQiyWYHNbPcYNsgeKCIuEjc8WV6jKkF52vt72ouhy/XzgDAHiSgWCr6ebrj16+gVBq1Pgl/5TBfRRqFb7IqiuPf4rXXo/zvmshIiIiImomTAXcrBEws3XmpS15yCT458guWBQfqQus+bpJsWRMFKYN6Yy2bqYzHz2kEswbHYElY6Lq3X/e6Ah4yCR2eyyGuIhFOHihBJ1WbkPQ8uRGdwq/db3MNSlZGBjsi/S8YiTebF50u8SYUCjUf82rKoUKe/KKAQDxUa23eY2WxEWM6TFh+OnJwYgObYfHB3VC7sI4/PfBPk0+tljqAZ/Bc+A7dJEuQ1Is94Xv0EXwGTwHYmnjS8CbsyB3Hwz3CwEAJEn11+TcJKsrDR7fuTvayN3tPraWTO4iwX2degIAfjBQqv1l9hGoBA0Gt++Enm1YHm9N2mxgQ9cdADZfOINrtZXo4OaNsR272nNoTs+xr9xEREREZHeVChWkYjFKapTwdZVCqdE4PKDT3HnIJJgzqgs0goANe/JQUq2Er5sUL8SGYd7oCLhKm1YWp828NBSQdIZS5YbM+OkEJvTugEtLxqCiVgWfm/POVeqCSkVd5uOK5HP17qcN5HrIJJg9qgsWxEWitEapd39HqlSosDY1G6u2Zepu05bPA8DsUV1MPre09195y2MvqVbqjvfYwGDMj4uEWCTCO2m5unmVGBOKGbFhWPD7Gbx2Xw/IJS44cKEEUX4ecBEDkU1YD7EleWlkONakZOGpb49a/TkplrjCZ9A/4TtkHjS1pRDLfSBolBBLGtehu6X4v47h2Hv1PLZII/CE4hgAoBYu+EVaF4h5vHM3Rw6vxZoU2gdfZB/GprzjeH3IfRCL6vLOBEHQlWizPN76Hgjrg+VHk5B8+RxKaqvhK3fT26699o9HDGpS9/iWyKzMyAEDBqC4uO5bthUrVqCqquHW5URERETkfIx1LXb2JijNweeHL2JAsC8uLh6DwmUJKFiagNmjulglYGbrzEtbyi+uwmeHL2LixoO4XlkLP085ZBKxLkjnITMv89FDJoFMIq53f0dqavm8qftv2JOHkDbucJO6YPaoLihYmqCbV38b0hlj/rMPG/bk4YWfTqC8Romhndvg578Nwd4Zd6CKz2e9QLGt1lkVSz0gcpHBxd0PIhdZq82IvNXE8Lou2YdcgnBNVBeYSZWEokTshkChEmM693Xk8FqshKAoeEnluFhVigNXL+huP3TtAk6VFMLVRYIpYf0dOMKWqYdvIHr4BtSVal/QL9W+XFWKLZf+BMDyeEPMCkaeOXMGlZWVAIDly5ejoqLCKid///330adPH3h7e8Pb2xvR0dH4448/dNtramowffp0tGvXDp6enpg0aRIKC/W71OXn5+Oee+6Bu7s7/P39MXv2bKhUzruANxEREZGjVCpUWJ2SZbBrsbM3QWkOPtiXj4kbD+LH45etHjAzN2DnjL48cgkAcGeXdgj2NVye6Wog4GatQK4tNbV83tz73x6IDW3rjnX39UC/IG+suqs7XtuRjY4rktHlle3ouCKZXzCgea+z2px1dvdEf1cxBJFIV6r9o6wHAODh9m0gRuuel7biKpHi3k511/nWrtra5ikTQnrXy9oj63hA17hJv1T786zD0AgCYgPCEOXDdXxvZ9a7ln79+uGpp55CbGwsBEHAunXr4OlpuAvQkiVLzD55cHAwXn31VURGRkIQBHz66acYP348MjIy0LNnT8yaNQu//fYbvv/+e/j4+CAxMRETJ07Enj17AABqtRr33HMPAgMDsXfvXly5cgWPP/44pFIpXnnlFbPHQURERNQaNPThfEFcpJ1H1HLk3ajCkUulEIuAcd38bXIObcBuQVwkCitq0dZdirNFFU4dsBMEAZ8fvggAeHRgsMl9tQFVP085AEDWDJa3b2r5fFPuHx/lh1+nDcH6tNxGl4m3ZOYEerVzjaxHLPXAg93jkZGRhH3eg3E/XHFa3AOorcbfY55j9qgNTQrtg69zMvDj+eNYO/he1KpV+CbnKADgyQhm5tnKA6F9seJoMpIunUWpoho+Mje98nhmRRpm1ivTxo0bsXTpUmzevBkikQh//PEHJJL6dxWJRBYFI++77z69319++WW8//772LdvH4KDg/HRRx/hq6++wujRowEAn3zyCbp37459+/Zh2LBhSEpKwunTp7Ft2zYEBASgX79+WLlyJebOnYtly5ZBJpMZPG9tbS1qa2t1v5eVlZk9ZiIiIqLmih/ObWfTiSsAgBHh7Wx6DbWBpbwbVRj45iEIgoBLSxIgkzhn4O7wxVL8WVQBV4kYD/Tp4OjhWJ22fN7UepemgqpKjQYzYsP01oy05P5+HnJs2JNncFtr/4Khua+z2pw9HDEY3dsGIa5DFIoVVciUuePgtXx0b9fJ0UNr0cZ17AYPiQznK4px+PpFZJVdQ4miGp08fDG6Q4Sjh9di9WwTiO4+/jhTWoRfL5zGo10GYk9RHjLLrsFDIsODoVyawBCz3rV07doV33zzDQ4ePAhBELB9+3ZkZGTU+zly5EijB6JWq/HNN9+gsrIS0dHROHz4MJRKJeLj43X7dOvWDZ07d0Z6ejoAID09Hb1790ZAQIBun7Fjx6KsrAynThlurQ4Aq1evho+Pj+6nUyf+UyQiIqKWT/vh3OA2Nym85BJoNAIqFSooVBoUVdRCodKwfNsM2mDkxN72CbhFh7SBzEWM61VK/HKqwC7nbAxtVuSEXoHwboHBH2Pl84viI80qn/eQSTBzRLjBTuPm3L85d1m3tea8zmpzF+DqiUPXLqLzdysR8t0qdP5uJVKvZKNG1Xrnoz24SaS4O7iua/mWi38i9UoW2ss98HjEILhwWQKb0nXVzq0r1dZmRT4U1g+eUn7Ja4jFOfsaK//TPnHiBKKjo1FTUwNPT0/89NNP6NGjB44ePQqZTAZfX1+9/QMCAlBQUPeGq6CgQC8Qqd2u3WbM/Pnz8dJLL+l+LysrY0CSiIiIWjxTWVyJMaFIy7uBAR198PbuHKxPs35H6JbqcmkN9ubVNXv8v96BdjmnxEWMJwYHY/X2LHxy8AIe6Btkl/NaQqnW4JuMuvUiGyrRbs5uLZ8vrlbCU+6C5LNXcaNKgSAf02u0FZbXIuG/6Vg+thuuLB2DshqVRZ3Cmf1nnDZQDECvEzn/n9lWpVKB106k4uVj23S3lSiqsepYMkQAZvceBQ+p4QpGaronIwfjkS79ERcUhaLqCrwxZDyq1QpHD6vFeyC0L1Yd24Yj1y+iqLocKZezAABPsUTbqEYtIJKdnY233noLZ86cAQD06NEDL774Irp06WLxsbp27YqjR4+itLQUP/zwA5544gns3LmzMcMym1wuh1zO6DQRERG1Lh4yCeaOjoBGELBhj36w8aWR4Th0oQRv7crh+nMW+ulkXVZkdEgbdGwg+GRNTw3ujNXbs7D1bBEullQj2Ne5mhNsPXsVVysV8PeUISGqZS/er31eBHjJMWnjQfx0sgDz4yLw8l3dTd7vg/3nceJKOdamZmFCr0D4edYFyMxdL7OpZeIt3a2B4tIapUWBXmocqViM9WfSDG5bfyYNC/rG2XlErcvIwC5YfXw7ntr9LUoU1fCVuWFG91jM6zMarpLW++WErfVqE4itY59BtF8ortdW4uT/zcbeolwM9w919NCclsWvTFu3bkWPHj1w4MAB9OnTB3369MH+/fvRs2dPJCcnWzwAmUyGiIgIDBw4EKtXr0bfvn3x9ttvIzAwEAqFAiUlJXr7FxYWIjCw7hvnwMDAet21tb9r9yEiIiKiv3ydcQkDgn1xcfEYva7F3q5SxIS1M7n+HLvPGrbpuH1LtLUi2ntgRHhbaATgs5vl0M7ki5tjmtK/IyQurWfuPDKgIwDgw335qFUZ7xysUmvw3/TzAIDnhoc06lzNucu6vdzeiZzXxLZKFDUoUVQb2VaNUmWNnUfUelQqFVhzPAUvH9um+xuUKKqx8lgyXj2egkolMyRtpVatwu6CXHT+biXCvn8Znb9bibTCPNSqucyNMRa/K5g3bx5mzZqF/fv344033sAbb7yB/fv3Y+bMmZg7d26TB6TRaFBbW4uBAwdCKpVi+/btum1nz55Ffn4+oqOjAQDR0dE4ceIEioqKdPskJyfD29sbPXr0aPJYiIiIiFoSQRDw+o5sTNx4ED8cv1zvw3kp15+z2NWKWuzMuQ7A/sFIoC47EgA+OZAPjUaw+/mNKalW4ueba1k+1oJLtA0Z3zMQHX1ccbVSgR9vBqoN+fV0IS6W1qC9hwwP9ml8mb02+69gaYLeFwzM/iNH8JW5wldmOEvbV+YGH6mrnUfUejSUlcovFG2jUqnAq8dTsOpYsl4QeBWDwCZZPBvPnDmDadOm1bv9b3/7G06fPm3RsebPn49du3YhLy8PJ06cwPz587Fjxw5MnToVPj4+mDZtGl566SWkpqbi8OHDeOqppxAdHY1hw4YBABISEtCjRw889thjOHbsGLZu3YpFixZh+vTpLMMmIiIius3+/BKcKaqAm1SM8T3rV5E01OCmNa8/Z8zPpwqgEYABHX0Q1s7d7ud/oE8HeMklyL5ehd251216LksaG/1w/DJqVRr0DPBC/44+Nh2Xs5G4iPHMsLpMx/f25hndT7tt2tDOTQ4cMvuPnIVSo8GM7rEGt83oHsvGQTbErFTHYBC4cSy+Kn5+fjh69Gi9248ePQp/f3+LjlVUVITHH38cXbt2RVxcHA4ePIitW7dizJgxAIA333wT9957LyZNmoQRI0YgMDAQmzZt0t3fxcUFmzdvhouLC6Kjo/Hoo4/i8ccfx4oVKyx9WEREREQt3scH8gEAD/YJgo+BoCO7z1pOV6Ldx/5ZkQDgIZdgcr+6rLpPDlyw2XlqlGqsTc1G4PIkBC5LQuDyJLyWmo0apeEyZG2J9qMDgyESiWw2Lmf196GdIRGLsDevGEcvldbb/mdRObZnXoNIBDw7rHEl2kTOyEMqw7w+o7G47xhdhqSvzA2L+47BvL6j2bzGhpiV6hgMAjeOxV+ZPf3003jmmWeQk5OD4cOHAwD27NmDNWvW6HWoNsdHH31kcrurqyveffddvPvuu0b3CQkJwe+//27ReYmIiIham8paFb49ehkA8NSQTgb3MdZ9NjEmlN1nDSipVmJ71jUAwCQHlGhr/W1IZ3y4Px/fH7+Md/6vF7ytnMFaqVBhbWo2Vt7SJMVUY6O8G1XYlXMDIhEw9eb6ia1NB29XTOrTAd8evYz39ubhvw/21dv+/t66tSLv7R6AkLb2z6glsiVXiRSze4/Cgr5xKFXWwEfqWtc4yIXZ9bakzUpdeax+Lw9tVqqML+NWpw0CGwpIMghsnMWZkYsXL8aSJUuwfv16jBw5EiNHjsSGDRuwbNkyLFq0yBZjJCIiIqIm+uH4FZTXqtClnTtGhLczut+t688VLEvAhcXx6N/RBznXK+042ubh19MFUKoF9AzwQld/T4eNY2hnX3T390S1UoNvbgacG8NYGbZULMb6tFyD9zHU2Gjr2SK095BhdER7p+vwbU/PDw8FAHx15JLeWqwVtSp8eqgui/X5mFAHjIzI9jykMshcJPBz9YTMRcKMSDtgVqpjcGmCxrE4M1IkEmHWrFmYNWsWysvLAQBeXl5WHxgRERERWc8nB+tKtJ8c3KnBslltlpu/pxzP/nAc/913Hk8O6oSPH+5n62E2K44u0dYSiUR4akhnzNl8Gp8cyNetV2gJbRn2+lsyYl+IDcOc0V1wo9J0Y6PiaiUCvOSoVKggFYsxrqs/Hh0YjMulrbs0LTasLXoFeuFkQTk2HryAmSPCAQBfZVxCWY0KEe09MCbSz8GjJKKWhFmp9qcNAgN1a0SWKKrhK3PDjO6xmNd3NK+9EU1aSdPLy4uBSCIiIiInl3m1ArtybkAsAp4YZLhE25gnB9ft/1XGJRSV19pieM1SRa0KW89eBeDYEm2txwYGo1egF+aNjkStSm1WkxmtSoUKq1OysDL5nC7oqC3DfmdXLtp7yEw2NvKSu6CkWom1qVkIXJ6EsFe2o9PKbfjyyCWja0q2BiKRSJcd+f7ePGg0AgRBwHt78gAAzw0PgVjc+tbTJCLbYlaq/WmDwFceXoqCKctw5eGlmN17FAORJrCtDxERETktS7r3knGfHKwrCR3b1d/istmhnX0xuJMvFGoNPth/3hbDa5Z+P1OEGpUGXdq5o3cHx385H+Alx87nh+PwxRJ0WJ5sVpMZLVNl2Gt3ZEOp1mCGkcZGiTGhKKpQ4I2d2ViZnFkvmPlqSlarft4+OjAYXnIJMq9VYlfudWRcKsXlshq4ScV40sIvBoiIyHkxCGwZBiOJiIjIKVnavZcMU6k1+OxQXWfjpwZbHvwQiUS6QNT7e89DqebaRwBw8EIx2nvIMLF3B6foFl2pUOGt3TlYtc3ygGBJjekybKVawPzREVgyJkqXIenrJsWSMVFYEBeJDt5ybLiZ7Xc7Q2tKtiaecglmj+qCn54cjCGd2sDPU47chXHY8XwM2rjzgyoREbVOFq8ZSURERGRrlnbvJeOSzl3F5bIatHOX4r6eAY06xoN9O2D25tO4XFaDn04U4KF+QVYepfPQrntYUqOEr6sUSo1Gb65ptz8fE4ZlY7uirNo5sv7qshvzDG57Jy0XC+Iijd7X11UKXzepwYCkr5sUnnIJZBIxZo/qggVxkSitUcLn5rVxlbqgqKLWZDCztEYJP095ox5XS/DSiHC8mpKFp749qluPc0ZsGHoHerFDPRERtUoWfU2pVCoRFxeHzMxMW42HiIiIyOLuvWTcxwfqGtc8OjAYcknjAh9yiYuuKcr6tByrjc0RTJX+N5SNe+v2LjfXRfzPvvNOka3bUHZjaY3hbUBdJ9AXjJRhvxAbpusE6iGrC0r6ecohk4h1QVptMNMQXzcpfFxb75pZlQoV1qRm18tYXckSdiIiasUseicvlUpx/PhxW42FiIiICABQXN34wAr95UaVAnvzigEAfxvSuUnHejY6BBKxCHvyinH4YokVRmd/poKNppq4vJqShRuVCqxOyTS63dFBpaYEBD1kEswdHYFF8ZH1yrDnjY5oMAvZ3GBma8QvVoiIiOqz+NXv0UcfxUcffWSLsRAREVErc3uWWlmNEp8cyIen3IWZVk2gva7VSjUy54/Gtn9Eo3cH7yYds4O3Kx7qW1eevcFIcMWZmQo2vr07FxKxyGjQ6MsjF+Epl5gsg3Z0UKmpAcHdOdcxINgXFxbHo3BZAgqWJmD2qC5mlRF7yCSYZ2RNSXOCmS1ZUzJWiYiIWiqL3xmoVCp8/PHH2LZtGwYOHAgPDw+97W+88YbVBkdEREQtlzZLbX1arm4dtcSYUMyIDUN6XjESY0Kxalv9pWG0gRUZ+/AZZOi6zogNxfDQNk1en25GbBi+yriErzMuY+29PZrVOoCmMtR+OV2ApwZ3Mho0cpO6mJWt68jroQ0IAnXBUe3f/oXYMMwbHdHg3/4/6efx08kCLEuIwpKErgBg0XPMVepidE3J1qyh9Tj5xQoREbVGFgcjT548iQEDBgAAzp07p7fNGToJEhERkfMz1qBGG3z8+9DOiA1rC7FI1KjASmtl7LquTM6ECKImN/4ZGtIGD/bpgEcGBMNTLkFRRa3BJi/OyFSGWubVSvi4GQ8aVSvVaGNiu7MElW4NCF4uq4GfpwzXKhUNPl+KqxT47UwRAGBCrw6NPr92DmiDsvzC4K+M1RXJ5+pt4xcrRETUWln8rjE1NdUW4yAiIqJWxFSW2oY9eVgUH6XXvfdKeQ3ae8hwqbSGgUgTGlqfzlRHZXN9OLkfXkvV7wzcHILEpjLUVBoBCrXxoNHUAcGoUambRVBJGxD85uglvLEzByPC2+KHJwabvM+PJ65AodagV6AX+gQ1rZyf9DU1Y5WIiKglavRX2FlZWcjOzsaIESPg5uYGQRCYGUlERERmMWcdNT9PuS6wkpp1HXM2n0awjysOzxrB9xxGmHtdG6tSocK6Hdl65fPadRcBNDnz0pYaylBzEaHBoFFzCird2z0AC37/E7+cKkRheS0CvIz/3b86cgkA8MiAjvYaXqvCEnYiIiJ9Fr9bvH79Oh566CGkpqZCJBIhMzMT4eHhmDZtGtq0aYPXX3/dFuMkIiKiFsTSddTu7xmA5388jqOXy7DvfDGiQ9vaa6jNiq3Xp7NH5qWteMgkmD2qCzSCgA178owGE00FjZpTUKlXB28MC2mDfeeLsfHgBcy9GUi93cWSauzMuQ4AmNKPwUhbYQk7ERHRXyx+FZw1axakUiny8/Ph7u6uu33y5MnYsmWLVQdHRERELVPW9UokxoQa3Gao829bdxke7l8XKHlvb56NR9d8NbWjckOae2fglcnnMCDYF5eWjDHaMdpDJoFMIoafpxwyibhepmdD253J34d2BgB8dCAfgiAY3OfrjEsQBOCOsLYIaetucB8iIiIia7I4GJmUlIQ1a9YgODhY7/bIyEicP3/eagMjIiKilun45TJM+eIIZsSGYfGYSPi61WXr+bpJsWRMFOaNjjAY4Hl+eCgA4PtjV1BUXmvPITcbHjIJZo4Ix6J486+rJbSZlwa3OUkTF2MKymrw5q4cTNx4EH8WVjSLYGJTTe4bBC+5BFnXKrEj+7rBfb7KYIk2ERER2ZfF774qKyv1MiK1bty4Abm88WsQERERUctXq1Lj8a8zcOJKGZZtPYu19/XAwrgos0peB3XyxZBOvjhwoQQfHcjHfCcuCXaUqxW1GPfffVic0BWXl45BeY3KqqXEzbkz8H/35UOpFjAspA36B/s4ejh24SGXYEr/jvjvvvP4cH8+RkW019t+qqAcxy6XQeoiwoN9gxw0SiIiImptLH63eMcdd+Czzz7T/S4SiaDRaLB27VqMGjXKqoMjIiKilmXJlrM4fqUMfh4yLEnoanHJ6/M3S7v/k34eao3hstPW7M1dOci4XIaXt52D3MX6pcTazsBLxkTpZV4uio+0SualrShUGvw7PQ8AMMNIGXtL9fSwulLtH49fwfVKhd62L49cBADc1c0fbd1ldh8bERERtU4Wv2Ncu3Yt4uLicOjQISgUCsyZMwenTp3CjRs3sGfPHluMkYiIiFqA9NwbWLczGwDw3wf7muzua8xDfYPwz19OIb+kGr+dKcT9PQOtPcxmq7hKgXf35AEAFsZH2qzj+K1NXIqrlfCUuyDp7FXkl1Qjys/TJudsqh9PXEFBeS06eMsxqXcHRw/HrgYG+6J/R29kXCrD54cvYuaIcACARiPga22Jdv9gU4cgIiIisiqLMyN79eqFc+fOITY2FuPHj0dlZSUmTpyIjIwMdOnSxRZjJCIiomaqUqGCQqVBUXkt+nT0xo9PDMaCuAiM79W4IKKr1AV/G1KX6fXezcAb1VmflofyWhV6d/DC/T1sG6TVZrQGeMnx4k8nMenTQ1i29axNz9kU2g7gz0aHQiZxzjJyW5o2JAQA8OH+87pGNnvP38D54mp4ySW4r2eAI4dHRERErUyjaml8fHywcOFCa4+FiIjIKVUqVJCKxSipUcL35vp7zlqO6kxqlGqsTc3G+rRclFQr4esmRWJMKBbGRzXpuM9Gh2LdzmwknbuKc1crnDYbz57KapR4e3cOAGBBXCTEYttkRRoy444wfHzwAr49dhlLE7qiq7/t/x6WPCcP5Bdj3/liyFzEeGZYiM3H5oymDuiI2ZtP4XRhBfadL0Z0aFt8eaQuK3Ji70C4WWE9USIiIiJzNeqr4eLiYqxbtw7Tpk3DtGnT8Prrr+PGjRvWHhsREZHDaQNqgcuTELgsCYHLk/BaajZqlGpHD82pVSpUWJ2ShZXJ51BSrQQAlFQrsWpbJtakZKFSoWr0scPauePubv4AgM8PX7TKeJu79/eeR3G1El39PPBAH/s2Iukb5IP7ewZAEIBXUzJtfj5Ln5MbbmZFTu4X1KilAVoCHzcpHrrZoOabo5egVGmQknkVAPDIAJZoExERkX2JBG2thpl27dqF++67Dz4+Phg0aBAA4PDhwygpKcGvv/6KESNG2GSgtlRWVgYfHx+UlpbC29vb0cMhIiInUalQYW1qNlYa6By8ZEwUZo/qwgxJIxQqDQKXJ+kCkbfydZOiYGlCk8pld2VfQ3G1CvFR7VFZq4avW+vNWK2sVSH8le24WqnAxof74fFBnew+hoP5JRj6zm64iEU4N3c0wtq52+Q8lj4nC8pqEPLyNijVAva/cAcGd/a1ybiagyMXS3ChpAbxUe1RWq2Cj5sEu7KvI6GrP1zsmElLRERELZe58TWLPwVMnz4dkydPRm5uLjZt2oRNmzYhJycHDz/8MKZPn96kQRMRETkTqVisW2vudu+k5UIqbv5rz+nWdKyohUKlqZex2NB2Y0pqlAYDkUBdhmRpjeFt5hrSuQ0OXyxBp5Xb6jLkmmHGamOv7e02nbgCAUBYW3dM6d/RuoM00+DOvhjb1Q9qjYBXU22XHWnpc/K/+/KhVAuIDmnTqgORANAjwEv3nAlemYxOK7dhX34xlGqNo4dGRERErYzF6QNZWVn44Ycf4OLy19oyLi4ueOmll/DZZ59ZdXBERESOZE5Azc+z+ZZ9GlrT8YXYMMwbHQFXqUuD203xcZXA101qNDPSx1Xa6HFrs+NWbfsr6FVSrcSKm9lyzSFjtSnXVku7buId4e2QuzAOl0prIHVxXIB8YXwktp69io0HL2BxfBSCfd2sfo7iavOfk0qVBt8dq1sXcUZsmNXH0pwYe86sTM6ECKJm8ZwhIiKilsPid6wDBgzAmTNn6t1+5swZ9O3b1yqDIiIicrTC8hp4yuoCaoY0NaDmaMbWdFyRfA6vpmShrEZpcrupLL7954uxLfMaEmNCDW5/ITYMSk3js7Gae8ZqQ9fenAzJW9dNDH9lOzqt3IavjlxyaGZobFg73NmlHZRqAWt3ZFv9+NnXKuApdzH5nPSSS1Beo4RCpcHVSgX2v3gHfvv7EEzq08Hq42lOmvtzhoiIiFoWs955HD9+XPfzwgsv4MUXX8S6deuQlpaGtLQ0rFu3DrNmzcKsWbNsPV4iIiKru71ctqCsBlO+OILkc1dtFlBzNFPBiS+PXISrxKVRwYtd2dcx5r/pmP3racwcEY4lY6J0wSNfNymWjInCvNERTcrCsnUJuK01NTBkjWCmrSyMjwQAfLjvPArLa6x23P3nixG9fg+2nTMe5F6WEAWVRsBrO+qCtLpS5PPFUGssWiK9xWnuzxkiIiJqWcz6JNCvXz+IRCLc2utmzpw59fZ75JFHMHnyZOuNjoiIyMYMlcsmxoTi28cGYvGWM3jj/l4Qi0R457btlpTTOiNTwQk3qYvZ5bDaUuGSGiW8XSUorVEi2McNQd6ukEvEmD2qCxbERaK0Rgkf17omM029br6uUpuVgNtDU8v/GwpmLoiLtMo4G2N0RHs83C8Ik/t1hLerFEUVtfB1tby5kN68kktQVFGL9h4yfHn4Aj59ZEC95+QLsWF4cnAnrE3NYimyAc39OUNEREQti1nvyHJzDb/hJSIias4MdeYtqVbqghlr7ukJd5lEF1ArqVHCQ+aCpLNXcexyGYaGtHHU0JvMVHCiWqlGGzfTwQsPmQvKa5RYtzOnXiA3LTEG7lIXvaCjNrgms3yFmHqUGg1eiA3TrRF5K23GqjXOYysNBYa8bwaGbg3IaQN6F4qr4S6TOO1apiKRCP99sC/WpmbhqW+PNmo9TGNfEKQlxkAuEcNN6mIwyC0Vi7FhT57BYzo6SOtozf05Q0RERC2LWcHIkJAQW4+DiIjI7kxlmG3Yk4dF8VEAoMum8veUY+7m03htRzbu6xGAn/82xG5jtTZTwYmpA4JRo1Ib3Z4YE4qiCgU+PpBfLwtt1bZMiEV1WWi24iGTYN7oCACol7H60shwp89+M3XtE2NCkZZ7HQODffHmrvqB3hdiw+Amc3HaLLdKhQqv7Wh8cyFTXxDcOq+0x7g1yF1UUeu0QVpHM/acsbRpEhEREZE1iIRba6/NdPnyZaSlpaGoqAia29bLeuGFF6w2OHspKyuDj48PSktL4e3t7ejhEBGRnRRV1CJwWZLR7YXLEuoFL85drUC3NakQiYA/54xCpJ+nrYdpMxW1KqxNzcKGPXlGu2m/mpJVL3gxd3QERACCViQbDYgVLE2ATGLbTCtt5mBpjRJecgn++LMIr+/Ixu9PD9VlFzorY9f+xTvCcPhiKXblXNcL6Gktio/Ec8ND8Z/08waDmUvGRDm0HFmh0iBweVKj50VT7t/Uc7cGtz5nfBpRPk9ERERkirnxNYvffWzcuBH/+Mc/IJPJ0K5dO4hEIt02kUjULIORRETUOvm4SizOMIvy88S93QOw+Uwh3t6diw0Te9tjqDbxakomBnVqg0tLxqCiVlVvTUdXI+WwblIXp8hCuzU7TqnWYMHvZ3D2aiVWp2Rh9d3dbXrupnpjVw4GBPvWu/YeMgliw9vioc8PG7yfNmPXWbPcmroeZlPuz1LkhhnKKCUiIiKyN4vfgSxevBhLlixBaWkp8vLykJubq/vJycmxxRiJiIis7oN957H1bOO6Zc8cEQ4A2HjwAoqrFLYaok1VK9VYn5aHiRsPIuNiCfw85ZBJxPWypDxkEsgk4nrbteseGuKIUmGpixhr7+0BAHhrVw7yblTZ9fyWEAQBH+47j4kbDyIt53q9a1tWo2owIKcNFBcsTUDhsgQULE3A7FFdHF5u29R50ZT7a0uRbdHBnYiIiIisx+JgZFVVFR5++GGIxfwmlYiImodKhQoKlQZFFbVQqDQ4fLEEb+7KwbzfzmDWyC4WBy9GRbRDnw7eqFKq8cH+fHs+FKv5+WQBymtVCGnjhmEhbS2+vzYLzRBTgVxburdHAEZ1aYdalQYL//jT7uc315FLpbhYWgMPmQvuCG9Xb7u5ATljgWJHauq8qFSoGvUFgZazBmmJiIiI6C8WRxSnTZuG77//3hZjISIisjptZ97A5UkIXJaEwOVJ+PlkAXY+PxwvxIbB11VicfBCJBLpsiPXp+VCqbZ/4K2pvjh8EUBdsxqxWNTA3vU5YxaaSCTCuvt7QiQCvs64hH3ni+0+BnP872QBAGBcV3+D88wZA73mMjYvFsVH4l93NryW5Rs7czAjNgyL4iMbPa+cMUhLRERERH+xuIGNWq3Gvffei+rqavTu3RtSqf4392+88YZVB2gPbGBDRNQyGerMq7V4TBTmNKHRR61KjdCXt6OwvBZfTh2AKf07NnW4dlNYXovglclQawScmTMKXf0b34THGRti/O3bo9h48AKiQ9ogLTFGb31rZ9D39R04caUcn07ph8cGdjK4j7HmQY5eE9Jct84LT7kEW/4swmeHLuKHJwbBxUjw+9jlUgx4cxe6+nli2z+Gob2H3KnmFRERERGZZrMGNqtXr8bWrVvRtWtXAKjXwIaIiMhZSMVirE/LNbhtfVouFsZFNvrYcokLnosOxbKks3hrVw4e7hfUbF4Hv864BLVGwJBOvk0KRALO2RBj1bhuOHapFHNGRaBWpUFZrQq+ThLQyrleiRNXyuEiFuGe7gFG9zPWPKg5BCIB/XlRXKXAtO+OoaRaiY0HL2Da0M719hcEAf/69TQEAegb5I0gHzfd/QHnmFdEREREZB0WvyN//fXX8fHHH+PJJ5+0wXCIiIisp6mdfRvy3PAQrE7JxMELJTiQX4KhIW0afSx70pZoPzow2MEjsY0gH1ekPD8cr+/IxlPfHnWqzMKfT9WVaI8Ia4u27jKT+zpjoLcx2rjLsHhMJP75y2ks3vInJvcLgqdc/y3o72eKsD3zGmQuYrxyl3N3QiciIiKiprH4Xa1cLkdMTIwtxkJERGRVtu747Ocpx7/u7IKfnhyM3h28dQ1yKhWqJh3Xlk4VlOPIpVJIxCI83D/I0cOxiUqFCm/szMGqbZm6YHRJtRIrks/h1ZQsh/59fr65XuT4XoEOG4MjTB8ehi7t3FFQXou1qVl625RqDWZvPg0AePGOMIS1c3fEEImIiIjITiwORr744otYv369LcZCRERkVfZoBDJ/dAQOXyxB8MpkXYOc11KzUaNUN/nYtvD5zazIu7v7o71H47NCnZmp8vx30nIhFTsmw/BaZS3Scm8AAMb3bF3BSJlEjDX39gAAvL4zGxdLqnXbPtiXjz+LKtDeQ4YFTVg6gYiIiIiaB4vLtA8cOICUlBRs3rwZPXv2rNfAZtOmTVYbHBERUVN4yCSYNSIcGkHAhj15Vi/X1TbIWbUtU3ebNgMPAGY3oUGOLWg0Ar46crNEe0DLLNEGbF+er6Vt0lJSozRrTcrNp4ugEYB+Qd4Iadv6sv/+r1cg7ghri925N7Dojz+xcUp/lNcosT4tBwCwLKErfIxkMhMRERFRy2HxJyRfX19MnDjRFmMhIiKyKoVKg3s/2o9/3hmBy0vGoLxWZdVGIA1l4DlblteO7Ou4WFoDXzcp7u1hvHlKc6ctzzcUkLRGeT5Q1+16bWo21lvQ7fqXm+tF3t/KsiK1RCIR1t3XE098k4H/690BtSo1SmtUODRrBPbmFWNUl3aOHiIRERER2YHFwchPPvnEFuMgIiKyuh9PXMGevGLkbjqB3IVxVm8EYq8MPGvRNq55sG+HZtOVuTG05fnaDNVbacvzzZkDxjIftRmxK285fkMZsVUKFbaeLQIATGhl60XeanBnX+yZEYs3d+o3F5oRG4Y7wtpC0nKnJRERERHd5NC2jKtXr8bgwYPh5eUFf39/TJgwAWfPntXbp6amBtOnT0e7du3g6emJSZMmobCwUG+f/Px83HPPPXB3d4e/vz9mz54Nlcp5mwcQERlTqVBBodIYbIRiahsZ9t6euqzFp4d1htTF+i95tm6QY01VChV+OHEZAPBYC+2ireUhk2De6AgsGROl+/v4ukmxKD4Sc0dHmFU6r818DFyeVG8tUEkj1qTclnkN1UoNQtq4oW+Qd9MeYDNWqVDhrV31mwutdILmQkRERERkHxZnRoaFhUEkEhndnpOTY/axdu7cienTp2Pw4MFQqVRYsGABEhIScPr0aXh4eAAAZs2ahd9++w3ff/89fHx8kJiYiIkTJ2LPnj0AALVajXvuuQeBgYHYu3cvrly5gscffxxSqRSvvPKKpQ+PiMhhjJV9zh8dAQGwuCS0tTt2uRR78oohEYvw9NAQm5zDVAZeYmyo2Rl49pCadQ2uEhf4ecgRE9rW0cOxOVepC2aP6oIFcZEorVHCXeaCpLNX8b+TBZjSv6PJ+5rKfGzvIcOkPh0szoj9+ZYSbVPvo1q65ra0ARERERFZn8XByJkzZ+r9rlQqkZGRgS1btmD27NkWHWvLli16v2/cuBH+/v44fPgwRowYgdLSUnz00Uf46quvMHr0aAB1ZeLdu3fHvn37MGzYMCQlJeH06dPYtm0bAgIC0K9fP6xcuRJz587FsmXLIJPJ6p23trYWtbW1ut/LysosGjcRkbWZCn5M6tMBPxy/YlFJKAHv7c0DUNc0I8jH1Sbn0GbgAXWBFG2gODEmFDNiwvDj8St4fFAnm5xbq6EmKtrtvTv4IHdhHLKvV7WaYJj2Ovh5yvHBvvP4xw/HEdbWHQ/26QCJiUxZUwGzt3fn4JlhIRatSanWCNh8qq6qo7V10b5dc1vagIiIiIisz+JPry+++KLB2999910cOnSoSYMpLS0FALRtW5excfjwYSiVSsTHx+v26datGzp37oz09HQMGzYM6enp6N27NwIC/lqIf+zYsXjuuedw6tQp9O/fv955Vq9ejeXLlzdprERE1mQs+NHeQ4bwdu6tIpPI0s7EppRWK/Hl4UsAgOdjQq04yvpuz8DzcZUi61olRr63F5nXKtHRxxVxkX5NOoexa9NQExVj26Pae7S6jNqpAzpi4R9/IvdGFb47dhmPmOgmbipgln29CpVKldGM2BkGMmLTz9/A1UoFfN2kuCO85WelmmKP5kJERERE5NysVjt211134ccff2z0/TUaDWbOnImYmBj06tULAFBQUACZTAZfX1+9fQMCAlBQUKDb59ZApHa7dpsh8+fPR2lpqe7nwoULjR43EZE1GAt+BHrJUVShaDCTqLkztT5fY3x66AKqlGr0DPDCiHDbd+j1kEkgk4jh5ymHTCJG9wBPDOnkC7VGwKI//sS1ytpGr/dp7NqU1yixOiULK5PP6a29tyL5HFanZKKwvAavpGQa3N4a1+Zzl0nwwh1hAIA1qVkQBMHovg2tBeohNb4mZWJMGH47rb+29f9O1r0fube7v03WLm1OtEsbGKJtLkRERERELZvV6vp++OEHXUZjY0yfPh0nT55EWlqatYZklFwuh1zOEiAich7GsoUKymvh7ylr0ZlEjelMbIogCHj/Zon2c8NDHVKSLBKJ8O8H+qBaqcaGib3xzu5cbNiTZ/F6n8auzXt78zB3dITRjNmvjlzCwrgobEjLM7i9JWXUWmL68FCsTc3CiSvl+P1MEe7pEWBwv0qFCokxoVi1LbPeNm3AzEMmqZcRe6aoHCPf24tzVysgcRFjYu8OEAQBZwrL0d5DhvtbeYk2YHxpA66BS0RERNR6WByM7N+/v94HO0EQUFBQgKtXr+K9995r1CASExOxefNm7Nq1C8HBf5VNBQYGQqFQoKSkRC87srCwEIGBgbp9Dhw4oHc8bbdt7T5ERM7uVGG5weDHtUoFcq5XGW+SEhOK/JJqRLT3sNdQrc7aDS1Ssq7h7NVKeMpdHNo12lXqgvcf6KPrHKxlSaDV2LUJ9JKjsLzWaMasm9QFxdVcm+92bdxl+MewULy+MxtrUrMMBiMFoS6bddnYrgBgMoh865qUANCngzdiw9riz6IKLE86hyGdfOHnKcOGiX3g7ymDSm08G7M1MbS0gVKjYSCSiIiIqJWwOBg5YcIEvd/FYjH8/Pxw5513olu3bhYdSxAEzJgxAz/99BN27NiBsDD9sp2BAwdCKpVi+/btmDRpEgDg7NmzyM/PR3R0NAAgOjoaL7/8MoqKiuDv7w8ASE5Ohre3N3r06GHpwyMisrvUrGuYvukEdj4/HCKRyOD6foYyiWbEhiIxJgyj3t+LpQld8WDfIAc/ksaxdkOL9/bkAQAeG9gJXq6ObezjKZNgw83x3M6cQKuxa1NQXgs/Exmz1Uo12rhxbT5DZo0Ix/q0XKTl3kBa7nXEhumX8X90IB/vp59HWl4xkp4ZikXxUWYHzEQiEd6b2BtyiRhLxkRhfVrjMmJbg9sDuc7SdZ6IiIiIbM/iT2lLly612smnT5+Or776Cj///DO8vLx0azz6+PjAzc0NPj4+mDZtGl566SW0bdsW3t7emDFjBqKjozFs2DAAQEJCAnr06IHHHnsMa9euRUFBARYtWoTp06ezFJuInF5ptRJPfpOBCyU12LAnF3NGRWDhbdlC8puBi9sziRRqDV5NycTpwgqsTD6HwZ18EeTtapUGMPYiCAK85BKrBc0KymqwJ+8GAOD54aHWGmajNTXQ6mOkfP9apQI7sq8bzZidOiAYNSq10e3aUuPWGAAK8nHF44OC8eH+fKxJyULstL+CkdnXKjHr51MAgMcGBiPAq64LuyUBM4mLGK/c1Q2v7chudEYsEREREVFL5tBPIe+//z5KS0tx5513okOHDrqfb7/9VrfPm2++iXvvvReTJk3CiBEjEBgYiE2bNum2u7i4YPPmzXBxcUF0dDQeffRRPP7441ixYoUjHhIRkUVe+N9JXCipQZd27ph9Z0S9Rii3Bixu3+Ypl2D52G6Yc2cXbH82Gh8fyLdaAxh7UKk1+McPx5F09ioSjXS8TowJRXG1osFjVSpUUKg0UGkEZC+IQ+pz0egZ6GXlEVuuoUYoDQVaj10uNXptcq9XYa6BJipLxkRh3ugIeLtKDTZZ0W5vzcGw2Xd2gVgE/HamCKcKygEAao2AJ77JQKVCjZHh7TBrRHijjy+XuJjMiJWKW18QmIiIiIhISySYaid5C7FY3GATAJFIBJWq+XXnLCsrg4+PD0pLS+Ht7e3o4RBRK/HzyQL838aDEIuA3dNjEB3auCZgFbUqrE3NMthsY8mYKKfJwqpUqCAVi3WZmwcvlODp749BLAL2zojFGztz9MrQE2NCMSM2DM/9eBzr7utpNOuzRqnG6pQsvfL2GbGhmD860uHlsJUKFV5LzTaYndjQ3+bXUwWY+9sZ7Hx+ODbsyatXvq8t99Ve11uzaW89ZkPbW6uXfj6FkV3aYUyUHyoUKnjJJdh69ipe3nYOPzw+CCFt3Rt97KKKWgQuSzK6vXBZQqtbr5OIiIiIWj5z42tmByN//vlno9vS09PxzjvvQKPRoKamxvLROhiDkUSkdXvAzNqBG+3xi6uV8JS7IPncNZy/UYUXm5CFpVBpELg8yWiZc8HSBMgkjs3EMhQw1AYbj10pQ3ykX72gWZWyLpA38+Yaf4bW3lMLQr1u01rOEoitUarxakpWvUDrSyO7GM2aLK5SoNe6HbhSVou193bHc8NDGVC0skqFCmtSsvTmVWJMKGbfGdHktUabw3OSiIiIiMjazI2vmf1ue/z48fVuO3v2LObNm4dff/0VU6dOZWk0ETVrNUo11qZmG81As8XxE2NCMd/CbtG3s3YDGGurVKjqBQxLqpVYtS0TIpEIc0Z1AWCgoYVEhvlxkfWyPrVr77lKxLpApSGN6cRtC7d3DvaSS/DHn0UYvj4NK8Z2xQMGGg+99MspXCmrRVc/D0yPCYPbzfnHZh/WoZ2Tt8+rVdsyIRaJmhzEVmo0XK+TiIiIiMiIRr0Tvnz5Mp5++mn07t0bKpUKR48exaeffoqQkBBrj4+IyC4qFSqsTsnCyuRzusCeNuj1akoWKhVNW4LC2PFXbcvEmiYev6nrEtqaVCw2GjBc38D6eTIXsdG19349U4iyGlWDgVhncOt6n65SF+w7X4w/iyow7btjOHe1Qm/f388U4tNDFyESAR9P7qcLRJL1mJqT1ljT0UMm4XqdRERERERGWPRuu7S0FHPnzkVERAROnTqF7du349dff0WvXr1sNT4iIruwdXDClsfXZmEZos3CcqTi6oYzN40xlfWZebUSPm7OHYg15uW7umFEeFuU16rwwKeHUHUzGF1Wo8T8388AAGbeEd7odUTJNHOyiZtKmxFbsDQBhcsSULA0AbNHdXH4OqZERERERI5m9qfftWvXIjw8HJs3b8bXX3+NvXv34o477rDl2IiI7MZawQltV+eiilooVBpUKlS4UFyFoopamwU/jGVhLYqPxIzYMMhc7FMOevtjr6hV4euMi/CUuzQ6YGgq61OlEaBQO3cg1hiJixhfPzoQAV5yqDQCThaUQ6HSoLRGhb0zYrHl6aFYNa6ro4fZYtkrm/jWjFiZRMyMSCIiIiIiWLBm5Lx58+Dm5oaIiAh8+umn+PTTTw3ut2nTJqsNjogco6EmLrZu8uII2uCEsYYT5gQnjK0J+UJsGNxkLk0+vim3r0vo7SpF8rki3PHuHkwd0BEL46OadPyGGHvsM2LDkJ5XjMSYUIPdvhtaP6+htfdcRMC80REAoNcgxpprfdpKB29X/PzkYIS1c8f6tFyM+2D/Ld3AwzAivJ2jh9hicU1HIiIiIiLHMTt68Pjjj0MkEtlyLETkBBpq4mLrJi+OotRoMCM2FCuT6wfMZsSGNhicMNWkBQCejQ7BjNgwg12frRX8uL0BTGmNCn8WVWBF8jmM7xmIXh2MdzNrioYe+9+GdEJsWFuIRSKLA4barE/AdLDx1kCsttt0c5iPPTt4GWzQszL5HESAU3QDb4nMnVdERERERGR9IkEQBEcPwtHMbT1O1NIZCippLR4TienDw/Du3lyDAbslY6KadeBEqa4rkX1ndw427Mmrl9334f58zBsdYfRLGYVKg8DlSUYzHwuWJkAjCHg1JctuwQ9BEDDhk4P49XQhBgX7YO+MWEiaULJtLCPWnMcuk4h19781YGjufGnKfZ2ZudeObKOlzisiIiIiIkcwN77Gd9xEpGOqycpXRy5hYVwU1qflGdz+TlouFsRF2nB0tvXxgXy8vTsX6+7rgUXxUbrgRF5xFUa+txd/FlVA5iLCs8ND6wXkymtUUKiFBteE9POU2zWDTyQS4f1JfbA7dwcOXSzFup3ZmDe6cX8jQxmxM2LD8M+R4WY1qPHzlNfL3LQkE7Qp93Vm5qxVqn3MZH0tdV4RERERETkzvusmIh1TgRE3qUuTuiI7s2qlGqu2ZeLPogpkXavUazgR5eeJGbFh6ObviccHdcKa1CwELk9C4LIkBC5PwtrULEjEIrTzMK8hhr0bWgT5uOKt8T3Rzd8TPQO8UKtS6zXXMUelQoXVKVlYmXxO9/fXlhK/tycPAZ7yZtnR2hnYq5EKERERERGRs2Awkoh0TAVGqpVqtHFrmYGT9/fm4VJpDTr5uuIf0SH1tj83PBQ/PjEI69NysSo587aAXGZd2XWV0mm7Oj82MBhpiTE4eKEEHZYn6wKpr6Vmo0apbvD+pjJm1+7IhrKZdrR2BtpGKobw2hERERERUUvEYCQR6eQXVyExJtTgtqkDglGjUhsNnCTGhGJXznVcKKlCpUIFhUpjcQaeI5TXqPBqShYAYMmYrpBLDJdMd2nngQ178gxu27AnD36ecswbHYElY6J0AVtfNymWjInCvNERDl2Hrkqpxtu7c7Fqm34gdUXyObyaktXg36ehUmKlWnDax+7stI1UeO2IiIiIiKi14KccIgIA5N2owpQvj+D3vw+FSASsT8sz2GTFUAfaGbFhmBEbhie/zsDGKf2wNjXL6P2dzdtpObhWqUBkew88MSjY6H7mru3njF2dTWU2mrPWpzZj1liTFU95Xem5Mz725sBV6sJrR0RERERErQaDkUQEtUbAE19n4PDFUsz8+ST+80BfLIyLMhgYMRY4qahV4Z93dsE7NzPwtLQZeACcrtv2jSoF1u3IBgAsG9vVZKfphgJyt64JCThXQ4ymNknJL6nLmL3176qlLSWWQeyUj7254LUjIiIiIqLWgp92iAhv7MzG7twb8JS7YOW4brpMN2NNVgw1YQnwckVMaFujpczvpOVCKnaufznv7c1DWY0KvTt4YXLfIJP7Nue1/ZrSJCXzagUe/vwwZsSGYfGYSJYSExERERERUZPwEyRRK3eqoByLt5wFALxxf0+Et/No9LGamoFnD5UKFaRiMYqrlZg1Ihy9Ar3hLXeBWCwyeT/t2n6Afom6M5ega2kDqdoM1VslxoSiSqmGTFI/UFyrUmPKF0dw5FIZZv96Ghsm9jaaMUtERERERERkDgYjyWlpg0YlNUr43gx83JqB1dB2Mu7WaxfW1g3fPDYQyWeLMG1I5yYd19xSZkepUaqxNjUb628JJibGhDa4ZqJWc13bz1ggNTEmFDNiw/DsD8fw0UP94CHXf/4s+P1PHLlUirbuUrx8d13GLMBSYiIiIiIiImo8Rm7IKRkKGt2agdbQdjLOWEBu3f09IRKZzg5siKkMvFvXFnSESoUKa1OzsfKWsZVUK7FqWybEIpHZ61k217X9DAVSi6uVuP/jA9ifX4KyGhV+/tsQSG+um7n9XBHe3JUDAPh4cj909HFz5PCJiIiIiIiohWgen6KpValUqLA6JQsrk8/pMuy0TVBeTclCWY3S5PZKhcqRw3dqxq7tqm2ZWGOFa6fNwFsyJkpvbcFF8ZGYa6e1BSsVKihUGhRV1EKh0ugeU0MdpZ1tPUtbuH2tzwAvOd64vyfcpS7IK65GxqVSKFQaFJbXYlhoW2x6cjBWjuuG+3sGOnroRERERERE1EKIBEEQHD0IRysrK4OPjw9KS0vh7e3t6OG0egqVBoHLkwyW+nZp545Ts0cZ3e7rJkXB0gSD69+R6WtrzWt3axm4h8wFSWev4kaVAtOGhjT52KbUKNVYnZJVL2N27ugIFFcpEbwy2eh9C5clOHw9S0fZlX0N3QO8sD4tFxv25OllzM6Pi4Qbs42JiIiIiIioAebG11imTU7HVBMUN6kLiqudv0lKU9lqPUx7NZjRjtXfU46vMy5h6pdH4Cl3wd3dA9DB27XJxzfEWBn2iuRzcJWIMXNEuFOvZ+lIAzv5Ym1qFlZty9Td1pgSdiIiIiIiIqKGMH2MnI62CYoh1Uo12rgZ394SgkraNR0DlychcFkSApcn4bXUbNQo1U0+tqlra6trN7lvEIZ08kVFrRoLfj/T5OM1pgx77Y5sKNUazIgNNbhdu55la1V37fIMbmstJexERERERERkH/yESU5FEAT8ebUCiTGhBrdPHRCMGpUaL8SGGdyeGBOKS6U1NhyhbTW0XmZT13RUajRGr62tAnJisQhvT+gFAPj00EXsP1/c6GMZC9RW1qpQWFFrMutTqRYwf3RkvfUsl4yJwjw7rWfprMzJmCUiIiIiIiKyhtb76ZucjlKtwbM/Hkd6XjF2Pj8cIhGwPi3PYLfseaMjANRlbd26vt2M2DCMfG8vEmPC8MTgYJuUOttSQ01WFsRFNun4O7KuY8bNQO6tawPauhP50JA2eGJQMD49dBEv/u8k9s6IhVhsWefuppZhe8rrmrfc3lFaqdG0+g7s2oxZlrATERERERGRrTl3ZIZatNvXRTyQX4z0vGKcu1qB1KxrmDMqAgvjogwGjVylLvWCSgq1RhfIe7BvB6xJybJrwM0aGspQK65WIsBL3qg1JUurlXj6+2PwdZPiu8cGYlG84WtrK6/c3R0/nriCAxdK8Pnhi3hicCeL7t9QGfb0mFC8EBuGFbcEK7W0WZ8yiHXXSbs2powJ4lBqNGZdOyIiIiIiIqKmYjCSTLJVIxVtue362zIbdz4/HCeulGF0pJ9uX2NBo3pBJYkY8+Mi8UCfDliflluvGYc20OLMzTgaylDzkruguEqBt3bnGM0aNWZp0lkUlNfCSy5BpJ8HZBKxXQNyHbxdsSg+ChsPXoC/lxwKlcaiedVQoFapFgxmzDaHILSjecgkvHZERERERERkFyJBEARHD8LRzG093trUKNVYnZKlFzC0RnDCULmt1uIxUZjTxGChQqVB4PIkowG9gqUJkEmcM8vrQnEVPtifrxdI1VoUH4m/DemMjw8Y3r5kTJTRQOvRS6UY9NYuaARg6zPDMCbKr94+9qBQqVFeq8bbu3Mszlo19++qDaDfmvXprMFnZ8NrR0RERERERI1lbnzNOSMy5HDmNlIx1tnYFFPltuut0Lm3uTbj2JF1Dfd/fBAzYsOwKD6yXpOVBXGR6ODtig178gze31jXY41GwPRNJ6ARgIf6BjksEAkASo2Ad9JysGpbpsUNepQa87phe8gkuqxPmUTMYJoFeO2IiIiIiIjI1vhJkwwyp5GKoVJrczLczAkWasuHG6M5NOO4vfz9amUt/vXrKRy7UoYlW/7E2vt6GlzTsaiBjtGGrt3XGZeQfr4YnnIXvH5/D3s8PKPq5lWewW0NNegRAZgRGw5BsG/zHSIiIiIiIiKyHgYjyaCGAobltSq8vTunUesy+rhKbBosNNWMIzE21OHNOIytl/nH08Ow4PczeHN8L11g7fY1HRsKtHrIXJB5tRJBPnJIxWIUVysxoXcgNskHo6RagY4+bvZ7oAY0JRD92o5sfHv0Mt4cbzhQS0RERERERETOj2XaZJA2YGhIl3bu8JJLLC4XBoDdOdex7dw1JMaEGtx+a7ltY2mbcSwZE6VX6rwoPhIzYsLw5eGLTTp+Uxgrf1+1LRPr03Lx+v09TQbWtIFWQxJjQrH3fDF83SRYm5qFwOVJ6LA8CZ1WbsORiyWY3K+jTR6TJbTBVIPbTASiL5fW4LXUbPxZVIGyGhVLiYmIiIiIiIiaKX6Kp3p251xHWY0KiTGhBhulzBoRjuJq8zLcbi1H9naVoKRaif+kn8dnj/SHWCSyWedeV6kLZo/qggVxkboMuuzrlRj53l78WVQBXzcZHuoX1OTzWMpU+fuGPXlYFB9l8v6muh7PHR2BM4XlBjuJr9qWCbFI5PBO4qayVrWBaENZq4u2/IkqpRrDQ9vggT4d7DFUIiIiIiIiIrIBBiNJz7ZzVzH+kwMIaeOOtMQYgwHDvw3pDLFIZLJc2FMm0XXNvr0c+bNH+sPDQLDQ2uW22qCbtuy3e4AXxnb1w59FFXhleyZiw9qgvYdct26jPToHW2O9TEOBVqVGAzepC3oFeiP+P/sM3q+hNRntwVgwNTEmFDNiwyAycJ+jl0rx6aELAIDX7+sJkcjQXkRERERERETUHDAY2crpZS7KJahSqhHSxh2hbdzgbiJgWKlQGV+XMSYUhRW1+PhAvlkZerevi2hL6+7rCaVag6UJXbE+LbdJjVBub0JjTjDTWs11jF07WzcHsobbg6nerlJsz7yKO97dg8GdfLHx4X66gKMgCPjXr6chCMCU/h0xNKSNQ8dORERERERERE3DYGQrZqyRyp7EGHjIXCCT/BWUuz3oZbJceFQEIILJNSUdlaHnIhbhlbu7Y92O7EY139Eyp5P47cHKG1UKZF2vNFr+bqpM2VzNoZM4UD+Y6iFzQea1SvxZVIGYsLZ4ZlgIACDpbBFSsq5BLhHjlbu6OWy8RERERERERGQdDEa2UtoS6pW3ZDbenrkoa+AYRsuFZS4oqqh12gw9V4lLkwKlxq6dNpg5Z1QXiEUig4HemXeEo0eAF0QikclAZmM1dk1GRxvZpT1evqsbNh68gA5ectSq1CitUSE2vB02PTkYOdcrEdLW3dHDJCIiIiIiIqImEgmCIDh6EI5WVlYGHx8flJaWwtvb29HDsSpjpcQKlQaBy5OMZtAVLE2ATNL4oJWtj98URRW1CFyWZHR74bIEk4HShh7b+YVxWLczGyuT62c/LoqPxL/u7AIXsQhSsVgviGut9SprlGq8mpJls+ZAtiIIAkqqlXhzV45e+XxiTCjmx0XCzYnHTkRERERERNTamRtfY2ZkC2aolHhGbBj+OdL8btiN5cwZek0tZTa1LqNELIJM4oL1aXkGt2s7ZmsDsbZYL9NYxqozByIBoEqpxlu7nbcTOBERERERERE1nfPVa5JVVCpUWJ2ShZXJ53SBs5JqJVYmn8N7e/IQ4CmHr5vhoJs11hbUrim5ZEyU7jy+blIsGROFeaMjHBpU0gZKDUmMCYVCrTF6X5VaAy+5xOi1i/TzQKkZgV5b85BJIJOI4ecph0wibhZBPKlYjPVpuQa3vZOWC6mY/66IiIiIiIiImjt+um+hTAV21u7INhmQ02YuNpU2Q69gaQIKlyWgYGkCZo/q4vAMPWOB0kXxkZgRG4YVSeeg1tRfvUCh0uCRL48g6exVJMaEGjz2+B6B8HWT2jTQ21KZ0wmciIiIiIiIiJo350+XokZpKLCjVAlGu2Fbc23B27smO0vzFEOlzJfLahD373ScLCiH1EWEBXGRkLr8td7mwQslOFlQjiVbz2JPYgzEIlG9a/fCHWFOXaLuzJpLJ3AiIiIiIiIiajwGI1uohgI7nvK6Mt7muLagtdweKA1t646lCVFYlnQOM0eEY01qVr1GKjufH46zVyvgKZeYvHb2CPS2NAziEhEREREREbV87KaNltlNu1Khwmup2QYDO0vGRLEZiAnZ1yvx6cELeo1UtBaPicIcM6+dtpO5LTpmt1TNtRM4ERERERERUWtnbnzNoWlGu3btwn333YegoCCIRCL873//09suCAKWLFmCDh06wM3NDfHx8cjM1A8Q3bhxA1OnToW3tzd8fX0xbdo0VFRU2PFROCdnbiDj7Dr5uGHDnjyD29Zb0EilOTaRcTRnXWeUiIiIiIiIiKzDocHIyspK9O3bF++++67B7WvXrsU777yDf//739i/fz88PDwwduxY1NTU6PaZOnUqTp06heTkZGzevBm7du3CM888Y6+H4NQY2GkcNlJxLAZxiYiIiIiIiFoupynTFolE+OmnnzBhwgQAdVmRQUFB+Oc//4l//etfAIDS0lIEBARg48aNePjhh3HmzBn06NEDBw8exKBBgwAAW7Zswd13342LFy8iKCjI4Llqa2tRW1ur+72srAydOnVqUWXa1HgKlQaBy5OMrrdZsDQBMgnXLiQiIiIiIiIi0moWZdqm5ObmoqCgAPHx8brbfHx8MHToUKSnpwMA0tPT4evrqwtEAkB8fDzEYjH2799v9NirV6+Gj4+P7qdTp062eyDU7GgbqRiibaRCRERERERERESWc9pgZEFBAQAgICBA7/aAgADdtoKCAvj7++ttl0gkaNu2rW4fQ+bPn4/S0lLdz4ULF6w8emrOuN4mEREREREREZFttMqoilwuh1wud/QwyIlp19tcEBep1w2b620SERERERERETWe02ZGBgYGAgAKCwv1bi8sLNRtCwwMRFFRkd52lUqFGzdu6PYhaiw2UiEiIiIiIiIisi6nDUaGhYUhMDAQ27dv191WVlaG/fv3Izo6GgAQHR2NkpISHD58WLdPSkoKNBoNhg4davcxExERERERERERkXEOTfWqqKhAVlaW7vfc3FwcPXoUbdu2RefOnTFz5kysWrUKkZGRCAsLw+LFixEUFKTruN29e3eMGzcOTz/9NP79739DqVQiMTERDz/8sNFO2kREREREREREROQYDg1GHjp0CKNGjdL9/tJLLwEAnnjiCWzcuBFz5sxBZWUlnnnmGZSUlCA2NhZbtmyBq6ur7j5ffvklEhMTERcXB7FYjEmTJuGdd96x+2MhIiIiIiIiIiIi00SCIAiOHoSjlZWVwcfHB6WlpfD29nb0cIiIiIiIiIiIiJoVc+NrTrtmJBEREREREREREbUsDEYSERERERERERGRXTAYSURERERERERERHbh0AY2zkK7bGZZWZmDR0JERERERERERNT8aONqDbWnYTASQHl5OQCgU6dODh4JERERERERERFR81VeXg4fHx+j29lNG4BGo8Hly5fh5eUFkUjk6OFYXVlZGTp16oQLFy6wWzjZDecd2RvnHDkC5x05Aucd2RvnHDkC5x05Audd0wiCgPLycgQFBUEsNr4yJDMjAYjFYgQHBzt6GDbn7e3NJxPZHecd2RvnHDkC5x05Aucd2RvnHDkC5x05Audd45nKiNRiAxsiIiIiIiIiIiKyCwYjiYiIiIiIiIiIyC4YjGwF5HI5li5dCrlc7uihUCvCeUf2xjlHjsB5R47AeUf2xjlHjsB5R47AeWcfbGBDREREREREREREdsHMSCIiIiIiIiIiIrILBiOJiIiIiIiIiIjILhiMJCIiIiIiIiIiIrtgMJKIiIiIiIiIiIjsgsFIIiIiIiIiIiIisgsGIx1o165duO+++xAUFASRSIT//e9/etsLCwvx5JNPIigoCO7u7hg3bhwyMzMNHksQBNx1110Gj7N9+3YMHz4cXl5eCAwMxNy5c6FSqRoc344dOzBgwADI5XJERERg48aNFo2fnI815tydd94JkUik9/Pss8/q7ZOfn4977rkH7u7u8Pf3x+zZs82ac99//z26desGV1dX9O7dG7///rvF4yPnY495d+zYMUyZMgWdOnWCm5sbunfvjrffftus8TU075588sl65x43blzjLgbZjb3+32ldv34dwcHBEIlEKCkpaXB8Dc27TZs2ISEhAe3atYNIJMLRo0ctefjkAPaac7dvF4lE+OabbxocH19jWyZ7zLuNGzcanHcikQhFRUUmx8fX2JbJXv/v+DmWtKwVO0lPT8fo0aPh4eEBb29vjBgxAtXV1brtN27cwNSpU+Ht7Q1fX19MmzYNFRUVDY6voTlXXl6OmTNnIiQkBG5ubhg+fDgOHjzYqGvRUjAY6UCVlZXo27cv3n333XrbBEHAhAkTkJOTg59//hkZGRkICQlBfHw8Kisr6+3/1ltvQSQS1bv92LFjuPvuuzFu3DhkZGTg22+/xS+//IJ58+aZHFtubi7uuecejBo1CkePHsXMmTPx97//HVu3bjVr/OScrDXnnn76aVy5ckX3s3btWt02tVqNe+65BwqFAnv37sWnn36KjRs3YsmSJSbHtnfvXkyZMgXTpk1DRkYGJkyYgAkTJuDkyZMWj4+ciz3m3eHDh+Hv748vvvgCp06dwsKFCzF//nxs2LDB5Ngamnda48aN0zv3119/3YQrQvZgj3l3q2nTpqFPnz5mjc2ceVdZWYnY2FisWbPGgkdNjmTPOffJJ5/o7TNhwgSTY+NrbMtlj3k3efJkvW1XrlzB2LFjMXLkSPj7+xsdG19jWy57zDt+jqVbWWPOpaenY9y4cUhISMCBAwdw8OBBJCYmQiz+Kyw2depUnDp1CsnJydi8eTN27dqFZ555xuTYzJlzf//735GcnIzPP/8cJ06cQEJCAuLj43Hp0iUrXJ1mSiCnAED46aefdL+fPXtWACCcPHlSd5tarRb8/PyEDz74QO++GRkZQseOHYUrV67UO878+fOFQYMG6e3/yy+/CK6urkJZWZnR8cyZM0fo2bOn3m2TJ08Wxo4da9b4yfk1ds6NHDlSePHFF40e9/fffxfEYrFQUFCgu+39998XvL29hdraWqP3e+ihh4R77rlH77ahQ4cK//jHPywaHzk3W807Q55//nlh1KhRJvdpaN4JgiA88cQTwvjx4y06NzkXW8+79957Txg5cqSwfft2AYBQXFxscn9z5p1Wbm6uAEDIyMhocBzkPGw55xrznouvsa2DvV5ji4qKBKlUKnz22Wcm9+NrbOtgq3nHz7FkTGPn3NChQ4VFixYZPe7p06cFAMLBgwd1t/3xxx+CSCQSLl26ZPR+Dc25qqoqwcXFRdi8ebPePgMGDBAWLlxo+sG2YMyMdFK1tbUAAFdXV91tYrEYcrkcaWlputuqqqrwyCOP4N1330VgYKDB49x6DABwc3NDTU0NDh8+bPT86enpiI+P17tt7NixSE9Pb9TjIedn7pwDgC+//BLt27dHr169MH/+fFRVVem2paeno3fv3ggICNDdNnbsWJSVleHUqVNGz9/QnLNkfNR8WGveGVJaWoq2bdua3Mfc/3U7duyAv78/unbtiueeew7Xr19v8LGR87LmvDt9+jRWrFiBzz77TO+bdVP4Gtv6WPt/3fTp09G+fXsMGTIEH3/8MQRBMHl+vsa2TrZ6jf3ss8/g7u6OBx54wOT5+RrbOllr3vFzLJnLnDlXVFSE/fv3w9/fH8OHD0dAQABGjhypNyfT09Ph6+uLQYMG6W6Lj4+HWCzG/v37jZ6/oTmnUqmgVqsNzufW/BrLYKST6tatGzp37oz58+ejuLgYCoUCa9aswcWLF3HlyhXdfrNmzcLw4cMxfvx4g8cZO3Ys9u7di6+//hpqtRqXLl3CihUrAEDvOLcrKCjQCyYBQEBAAMrKyvTWVKCWw9w598gjj+CLL75Aamoq5s+fj88//xyPPvqobruxuaPdZoyx+2nvY+74qHmx1ry73d69e/Htt982WFbR0LwD6srHPvvsM2zfvh1r1qzBzp07cdddd0GtVjfyUZOjWWve1dbWYsqUKXjttdfQuXNns89vzryjlsWa/+tWrFiB7777DsnJyZg0aRKef/55rF+/3uT5+RrbOtnqNfajjz7CI488Ajc3N5Pn52ts62StecfPsWQuc+ZcTk4OAGDZsmV4+umnsWXLFgwYMABxcXG6tSULCgrqLT0hkUjQtm3bRn2O1c45Ly8vREdHY+XKlbh8+TLUajW++OILpKent+rXWAYjnZRUKsWmTZtw7tw5tG3bFu7u7khNTcVdd92ly7z45ZdfkJKSgrfeesvocRISEvDaa6/h2WefhVwuR1RUFO6++24A0B3H09NT92NsYX5q+cyZcwDwzDPPYOzYsejduzemTp2Kzz77DD/99BOys7PNOk9+fr7enHvllVesOj5qXmwx706ePInx48dj6dKlSEhIAND4eQcADz/8MO6//3707t0bEyZMwObNm3Hw4EHs2LGjyY+fHMNa827+/Pno3r270Q/tTZl31LJY83/d4sWLERMTg/79+2Pu3LmYM2cOXnvtNQB8jSV9tniNTU9Px5kzZzBt2jTdbXyNpVtZa97xcyyZy5w5p9FoAAD/+Mc/8NRTT6F///5488030bVrV3z88cdmn6uxc+7zzz+HIAjo2LEj5HI53nnnHUyZMqVVv8ZKHD0AMm7gwIE4evQoSktLoVAo4Ofnh6FDh+rShlNSUpCdnQ1fX1+9+02aNAl33HGH7kX8pZdewqxZs3DlyhW0adMGeXl5mD9/PsLDwwFAr0Ont7c3ACAwMBCFhYV6xy0sLIS3t3eD34JS89XQnDNk6NChAICsrCx06dIFgYGBOHDggN4+2rkUGBiIoKAgvTmnLaM1NuduXX6gMeMj52eNead1+vRpxMXF4ZlnnsGiRYt0tzdl3t0uPDwc7du3R1ZWFuLi4ix6rOQ8rDHvUlJScOLECfzwww8AoCuVbd++PRYuXIjFixdbbd5R82fN/3W377Ny5UrU1tbyNZbqsfa8+/DDD9GvXz8MHDhQdxtfY+l21pp3/BxL5mpoznXo0AEA0KNHD737de/eHfn5+QDq5k5RUZHedpVKhRs3buj+bzV2znXp0gU7d+5EZWUlysrK0KFDB0yePFk3l1uj1huGbUZ8fHzg5+eHzMxMHDp0SFeSPW/ePBw/fhxHjx7V/QDAm2++iU8++UTvGCKRCEFBQXBzc8PXX3+NTp06YcCAAQCAiIgI3Y82LTk6Ohrbt2/XO0ZycjKio6Nt/GjJGRibc4Zo5532H3x0dDROnDih9488OTkZ3t7e6NGjByQSid6c075htWTOWTI+aj6aMu8A4NSpUxg1ahSeeOIJvPzyy3r7W2PeaV28eBHXr1/XOzc1X02Zdz/++COOHTumew3+8MMPAQC7d+/G9OnTrTrvqOVo6v86Q/u0adMGcrmcr7FklDXmXUVFBb777ju9rEiAr7FknDXmHT/HkiWMzbnQ0FAEBQXh7NmzevufO3cOISEhAOrmTklJid6apCkpKdBoNLpgeVPnnIeHBzp06IDi4mJs3bq1db/GOriBTqtWXl4uZGRkCBkZGQIA4Y033hAyMjKE8+fPC4IgCN99952QmpoqZGdnC//73/+EkJAQYeLEiSaPCQPdwNauXSscP35cOHnypLBixQpBKpU22DEsJydHcHd3F2bPni2cOXNGePfddwUXFxdhy5YtZo+fnE9T51xWVpawYsUK4dChQ0Jubq7w888/C+Hh4cKIESN0+6hUKqFXr15CQkKCcPToUWHLli2Cn5+fMH/+fJNj27NnjyCRSIR169YJZ86cEZYuXSpIpVLhxIkTun0a85wgx7PHvDtx4oTg5+cnPProo8KVK1d0P0VFRSbH1tC8Ky8vF/71r38J6enpQm5urrBt2zZhwIABQmRkpFBTU2ODq0XWYo95d7vU1FSzummb8//u+vXrQkZGhvDbb78JAIRvvvlGyMjIEK5cudK0C0M2Y48598svvwgffPCBcOLECSEzM1N47733BHd3d2HJkiUmx8bX2JbLnv/rPvzwQ8HV1bXB/3FafI1tuew17/g5lrSsETt58803BW9vb+H7778XMjMzhUWLFgmurq5CVlaWbp9x48YJ/fv3F/bv3y+kpaUJkZGRwpQpU0yOzZw5t2XLFuGPP/4QcnJyhKSkJKFv377C0KFDBYVCYcWr1LwwGOlA2g8tt/888cQTgiAIwttvvy0EBwcLUqlU6Ny5s7Bo0SKhtrbW5DENBSNHjRol+Pj4CK6ursLQoUOF33//3ezx9evXT5DJZEJ4eLjwySefWDR+cj5NnXP5+fnCiBEjhLZt2wpyuVyIiIgQZs+eLZSWluqdJy8vT7jrrrsENzc3oX379sI///lPQalUNji+7777ToiKihJkMpnQs2dP4bffftPb3pjnBDmePebd0qVLDZ4jJCSkwfGZmndVVVVCQkKC4OfnJ0ilUiEkJER4+umnhYKCAqtdH7INe/2/M3ROcz6oN/T/7pNPPjE4/qVLlzbmcpAd2GPO/fHHH0K/fv0ET09PwcPDQ+jbt6/w73//W1Cr1Q2Oj6+xLZM9/9dFR0cLjzzyiEXj42tsy2SvecfPsaRlrdjJ6tWrheDgYMHd3V2Ijo4Wdu/erbf9+vXrwpQpUwRPT0/B29tbeOqpp4Ty8nKzxmdqzn377bdCeHi4IJPJhMDAQGH69OlCSUlJo69HSyAShJsLHBERERERERERERHZENeMJCIiIiIiIiIiIrtgMJKIiIiIiIiIiIjsgsFIIiIiIiIiIiIisgsGI4mIiIiIiIiIiMguGIwkIiIiIiIiIiIiu2AwkoiIiIiIiIiIiOyCwUgiIiIiIiIiIiKyCwYjiYiIiIiIiIiIyC4YjCQiIiIiIiIiIiK7YDCSiIiIiIiIiIiI7ILBSCIiIiIiIiIiIrKL/wdQ5x7gdVIOOgAAAABJRU5ErkJggg==",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"forecaster = TransformedTargetForecaster(\n",
" [\n",
" (\"deseasonalize\", Deseasonalizer(model=\"multiplicative\", sp=12)),\n",
" (\"forecast\", ARIMA()),\n",
" ]\n",
")\n",
"\n",
"forecaster.fit(y_train)\n",
"y_pred = forecaster.predict(fh)\n",
"plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"])\n",
"mean_absolute_percentage_error(y_test, y_pred, symmetric=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the above example, the `TransformedTargetForecaster` is constructed with a list of steps, each a pair of name and estimator, where the last estimator is a forecaster scitype. The pre-processing transformers should be series-to-series transformers which possess both a `transform` and an `inverse_transform` method. The resulting estimator is of forecaster scitype and has all interface defining methods. In `fit`, all transformers apply `fit_transforms` to the data, then the forecaster's `fit`; in `predict`, first the forecaster's `predict` is applied, then the transformers' `inverse_transform` in reverse order."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The same pipeline, as above, can also be constructed with the multiplication dunder method `*`.\n",
"\n",
"This creates a `TransformedTargetForecaster` as above, with components given default names."
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:50.461893300Z",
"start_time": "2023-08-25T09:25:50.387092500Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
"
],
"text/plain": [
"TransformedTargetForecaster(steps=[Deseasonalizer(model='multiplicative',\n",
" sp=12),\n",
" ARIMA()])"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"forecaster = Deseasonalizer(model=\"multiplicative\", sp=12) * ARIMA()\n",
"forecaster"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The names in a dunder constructed pipeline are made unique in case, e.g., two deseasonalizers are used.\n",
"\n",
"Example of a multiple seasonality model:"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:50.464883900Z",
"start_time": "2023-08-25T09:25:50.400058300Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"{'steps': [Deseasonalizer(model='multiplicative', sp=12),\n",
" Deseasonalizer(model='multiplicative', sp=3),\n",
" ARIMA()],\n",
" 'Deseasonalizer_1': Deseasonalizer(model='multiplicative', sp=12),\n",
" 'Deseasonalizer_2': Deseasonalizer(model='multiplicative', sp=3),\n",
" 'ARIMA': ARIMA(),\n",
" 'Deseasonalizer_1__model': 'multiplicative',\n",
" 'Deseasonalizer_1__sp': 12,\n",
" 'Deseasonalizer_2__model': 'multiplicative',\n",
" 'Deseasonalizer_2__sp': 3,\n",
" 'ARIMA__concentrate_scale': False,\n",
" 'ARIMA__enforce_invertibility': True,\n",
" 'ARIMA__enforce_stationarity': True,\n",
" 'ARIMA__hamilton_representation': False,\n",
" 'ARIMA__maxiter': 50,\n",
" 'ARIMA__measurement_error': False,\n",
" 'ARIMA__method': 'lbfgs',\n",
" 'ARIMA__mle_regression': True,\n",
" 'ARIMA__order': (1, 0, 0),\n",
" 'ARIMA__out_of_sample_size': 0,\n",
" 'ARIMA__scoring': 'mse',\n",
" 'ARIMA__scoring_args': None,\n",
" 'ARIMA__seasonal_order': (0, 0, 0, 0),\n",
" 'ARIMA__simple_differencing': False,\n",
" 'ARIMA__start_params': None,\n",
" 'ARIMA__suppress_warnings': False,\n",
" 'ARIMA__time_varying_regression': False,\n",
" 'ARIMA__trend': None,\n",
" 'ARIMA__with_intercept': True}"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"forecaster = (\n",
" Deseasonalizer(model=\"multiplicative\", sp=12)\n",
" * Deseasonalizer(model=\"multiplicative\", sp=3)\n",
" * ARIMA()\n",
")\n",
"\n",
"forecaster.get_params()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also create a pipeline with post-processing transformations, these are transformations *after* the forecaster, in a dunder pipeline or a `TransformedTargetForecaster`.\n",
"\n",
"Below is an example of a multiple seasonality model, with integer rounding post-processing of the predictions:"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:50.768074200Z",
"start_time": "2023-08-25T09:25:50.412027700Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"1958-01 334.0\n",
"1958-02 338.0\n",
"1958-03 317.0\n",
"Freq: M, dtype: float64"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from aeon.transformations.func_transform import FunctionTransformer\n",
"\n",
"forecaster = ARIMA() * FunctionTransformer(lambda y: y.round())\n",
"forecaster.fit_predict(y, fh=fh).head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Both pre- and post-processing transformers can be present, in this case the post-processing transformations will be applied after the `inverse-transform` of the pre-processing ones."
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:50.828910700Z",
"start_time": "2023-08-25T09:25:50.462890Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Period\n",
"1958-01 339.0\n",
"1958-02 334.0\n",
"1958-03 381.0\n",
"Freq: M, dtype: float64"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"forecaster = (\n",
" Deseasonalizer(model=\"multiplicative\", sp=12)\n",
" * Deseasonalizer(model=\"multiplicative\", sp=3)\n",
" * ARIMA()\n",
" * FunctionTransformer(lambda y: y.round())\n",
")\n",
"\n",
"forecaster.fit_predict(y_train, fh=fh).head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3.2.2 The `Detrender` as pipeline component\n",
"\n",
"For detrending, we can use the `Detrender`. This is an estimator of series-to-transformer scitype that wraps an arbitrary forecaster. For example, for linear detrending, we can use `PolynomialTrendForecaster` to fit a linear trend, and then subtract/add it using the `Detrender` transformer inside `TransformedTargetForecaster`.\n",
"\n",
"To understand better what happens, we first examine the detrender separately:"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:50.829907900Z",
"start_time": "2023-08-25T09:25:50.560628400Z"
}
},
"outputs": [],
"source": [
"from aeon.forecasting.trend import PolynomialTrendForecaster\n",
"from aeon.transformations.detrend import Detrender"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:51.049321400Z",
"start_time": "2023-08-25T09:25:50.566611900Z"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# linear detrending\n",
"forecaster = PolynomialTrendForecaster(degree=1)\n",
"transformer = Detrender(forecaster=forecaster)\n",
"yt = transformer.fit_transform(y_train)\n",
"\n",
"# internally, the Detrender uses the in-sample predictions\n",
"# of the PolynomialTrendForecaster\n",
"forecaster = PolynomialTrendForecaster(degree=1)\n",
"fh_ins = -np.arange(len(y_train)) # in-sample forecasting horizon\n",
"y_pred = forecaster.fit(y_train).predict(fh=fh_ins)\n",
"\n",
"plot_series(y_train, y_pred, yt, labels=[\"y_train\", \"fitted linear trend\", \"residuals\"]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the `Detrender` is of scitype series-to-series-transformer, it can be used in the `TransformedTargetForecaster` for detrending any forecaster:"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:51.270758300Z",
"start_time": "2023-08-25T09:25:50.844869600Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.0561016822006403"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"forecaster = TransformedTargetForecaster(\n",
" [\n",
" (\"deseasonalize\", Deseasonalizer(model=\"multiplicative\", sp=12)),\n",
" (\"detrend\", Detrender(forecaster=PolynomialTrendForecaster(degree=1))),\n",
" (\"forecast\", ARIMA()),\n",
" ]\n",
")\n",
"\n",
"forecaster.fit(y_train)\n",
"y_pred = forecaster.predict(fh)\n",
"plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"])\n",
"mean_absolute_percentage_error(y_test, y_pred, symmetric=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3.2.3 Complex pipeline composites and parameter inspection\n",
"\n",
"`aeon` follows the `scikit-learn` philosophy of composability and nested parameter inspection. As long as an estimator has the right scitype, it can be used as part of any composition principle requiring that scitype. Above, we have already seen the example of a forecaster inside a `Detrender`, which is an estimator of scitype series-to-series-transformer, with one component of forecaster scitype. Similarly, in a `TransformedTargetForecaster`, we can use the reduction composite from Section 3.1 as the last forecaster element in the pipeline, which inside has an estimator of tabular regressor scitype, the `KNeighborsRegressor`:"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:51.270758300Z",
"start_time": "2023-08-25T09:25:51.126117400Z"
}
},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsRegressor\n",
"\n",
"from aeon.forecasting.compose import make_reduction"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:51.534025900Z",
"start_time": "2023-08-25T09:25:51.130106100Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.05870838788931646"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"forecaster = TransformedTargetForecaster(\n",
" [\n",
" (\"deseasonalize\", Deseasonalizer(model=\"multiplicative\", sp=12)),\n",
" (\"detrend\", Detrender(forecaster=PolynomialTrendForecaster(degree=1))),\n",
" (\n",
" \"forecast\",\n",
" make_reduction(\n",
" KNeighborsRegressor(),\n",
" window_length=15,\n",
" strategy=\"recursive\",\n",
" ),\n",
" ),\n",
" ]\n",
")\n",
"\n",
"forecaster.fit(y_train)\n",
"y_pred = forecaster.predict(fh)\n",
"plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"])\n",
"mean_absolute_percentage_error(y_test, y_pred, symmetric=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As with `scikit-learn` models, we can inspect and access parameters of any component via `get_params` and `set_params`:"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:51.535023300Z",
"start_time": "2023-08-25T09:25:51.408363Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"{'steps': [('deseasonalize', Deseasonalizer(model='multiplicative', sp=12)),\n",
" ('detrend', Detrender(forecaster=PolynomialTrendForecaster())),\n",
" ('forecast',\n",
" RecursiveTabularRegressionForecaster(estimator=KNeighborsRegressor(),\n",
" window_length=15))],\n",
" 'deseasonalize': Deseasonalizer(model='multiplicative', sp=12),\n",
" 'detrend': Detrender(forecaster=PolynomialTrendForecaster()),\n",
" 'forecast': RecursiveTabularRegressionForecaster(estimator=KNeighborsRegressor(),\n",
" window_length=15),\n",
" 'deseasonalize__model': 'multiplicative',\n",
" 'deseasonalize__sp': 12,\n",
" 'detrend__forecaster__degree': 1,\n",
" 'detrend__forecaster__regressor': None,\n",
" 'detrend__forecaster__with_intercept': True,\n",
" 'detrend__forecaster': PolynomialTrendForecaster(),\n",
" 'detrend__model': 'additive',\n",
" 'forecast__estimator__algorithm': 'auto',\n",
" 'forecast__estimator__leaf_size': 30,\n",
" 'forecast__estimator__metric': 'minkowski',\n",
" 'forecast__estimator__metric_params': None,\n",
" 'forecast__estimator__n_jobs': None,\n",
" 'forecast__estimator__n_neighbors': 5,\n",
" 'forecast__estimator__p': 2,\n",
" 'forecast__estimator__weights': 'uniform',\n",
" 'forecast__estimator': KNeighborsRegressor(),\n",
" 'forecast__pooling': 'local',\n",
" 'forecast__transformers': None,\n",
" 'forecast__window_length': 15}"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"forecaster.get_params()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.3 Parameter tuning\n",
"\n",
"`aeon` provides parameter tuning strategies as compositors of forecaster scitype, similar to `scikit-learn`'s `GridSearchCV`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.3.1 Basic tuning using `ForecastingGridSearchCV`\n",
"\n",
"The compositor `ForecastingGridSearchCV` (and other tuners) are constructed with a forecaster to tune, a cross-validation constructor, a `scikit-learn` parameter grid, and parameters specific to the tuning strategy. Cross-validation constructors follow the `scikit-learn` interface for re-samplers, and can be slotted in exchangeably.\n",
"\n",
"As an example, we show tuning of the window length in the reduction compositor from Section 3.1, using temporal sliding window tuning:"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:51.535023300Z",
"start_time": "2023-08-25T09:25:51.414376400Z"
}
},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsRegressor\n",
"\n",
"from aeon.forecasting.compose import make_reduction\n",
"from aeon.forecasting.model_selection import (\n",
" ForecastingGridSearchCV,\n",
" SlidingWindowSplitter,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:51.535023300Z",
"start_time": "2023-08-25T09:25:51.420329800Z"
}
},
"outputs": [],
"source": [
"regressor = KNeighborsRegressor()\n",
"forecaster = make_reduction(regressor, window_length=15, strategy=\"recursive\")\n",
"param_grid = {\"window_length\": [7, 12, 15]}\n",
"\n",
"# We fit the forecaster on an initial window which is 80% of the historical data\n",
"# then use temporal sliding window cross-validation to find the optimal hyper-parameters\n",
"cv = SlidingWindowSplitter(initial_window=int(len(y_train) * 0.8), window_length=20)\n",
"gscv = ForecastingGridSearchCV(\n",
" forecaster, strategy=\"refit\", cv=cv, param_grid=param_grid\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As with other composites, the resulting forecaster provides the unified interface of `aeon` forecasters - window splitting, tuning, etc requires no manual effort and is done behind the unified interface:"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:52.403701600Z",
"start_time": "2023-08-25T09:25:51.424343900Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.16607972017556033"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
"
],
"text/plain": [
"RecursiveTabularRegressionForecaster(estimator=KNeighborsRegressor(),\n",
" window_length=7)"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gscv.best_forecaster_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.3.2 Tuning of complex composites\n",
"\n",
"As in `scikit-learn`, parameters of nested components can be tuned by accessing their `get_params` key - by default this is `[estimatorname]__[parametername]` if `[estimatorname]` is the name of the component, and `[parametername]` the name of a parameter within the estimator `[estimatorname]`.\n",
"\n",
"For example, below we tune the `KNeighborsRegressor` component's `n_neighbors`, in addition to tuning `window_length`. The tuneable parameters can easily be queried using `forecaster.get_params()`."
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:52.465536100Z",
"start_time": "2023-08-25T09:25:52.408687400Z"
}
},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsRegressor\n",
"\n",
"from aeon.forecasting.compose import make_reduction\n",
"from aeon.forecasting.model_selection import (\n",
" ForecastingGridSearchCV,\n",
" SlidingWindowSplitter,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:52.643061400Z",
"start_time": "2023-08-25T09:25:52.414672100Z"
}
},
"outputs": [],
"source": [
"param_grid = {\"window_length\": [7, 12, 15], \"estimator__n_neighbors\": np.arange(1, 10)}\n",
"\n",
"regressor = KNeighborsRegressor()\n",
"forecaster = make_reduction(regressor, strategy=\"recursive\")\n",
"\n",
"cv = SlidingWindowSplitter(initial_window=int(len(y_train) * 0.8), window_length=30)\n",
"gscv = ForecastingGridSearchCV(forecaster, cv=cv, param_grid=param_grid)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:58.959177Z",
"start_time": "2023-08-25T09:25:52.417694300Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.13988948769413537"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gscv.fit(y_train)\n",
"y_pred = gscv.predict(fh)\n",
"plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"])\n",
"mean_absolute_percentage_error(y_test, y_pred, symmetric=False)"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:58.977129Z",
"start_time": "2023-08-25T09:25:58.951198500Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"{'estimator__n_neighbors': 2, 'window_length': 12}"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gscv.best_params_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An alternative to the above is tuning the regressor separately, using `scikit-learn`'s `GridSearchCV` and a separate parameter grid. As this does not use the \"overall\" performance metric to tune the inner regressor, performance of the composite forecaster may vary."
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:58.998074Z",
"start_time": "2023-08-25T09:25:58.959177Z"
}
},
"outputs": [],
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"# tuning the 'n_estimator' hyperparameter of RandomForestRegressor from scikit-learn\n",
"regressor_param_grid = {\"n_neighbors\": np.arange(1, 10)}\n",
"forecaster_param_grid = {\"window_length\": [7, 12, 15]}\n",
"\n",
"# create a tunnable regressor with GridSearchCV\n",
"regressor = GridSearchCV(KNeighborsRegressor(), param_grid=regressor_param_grid)\n",
"forecaster = make_reduction(regressor, strategy=\"recursive\")\n",
"\n",
"cv = SlidingWindowSplitter(initial_window=int(len(y_train) * 0.8), window_length=30)\n",
"gscv = ForecastingGridSearchCV(forecaster, cv=cv, param_grid=forecaster_param_grid)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:26:06.047228700Z",
"start_time": "2023-08-25T09:25:58.966157500Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.14493362646957736"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gscv.fit(y_train)\n",
"y_pred = gscv.predict(fh)\n",
"plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"])\n",
"mean_absolute_percentage_error(y_test, y_pred, symmetric=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NOTE: a smart implementation of this would use caching to save partial results from the inner tuning and reduce runtime substantially - currently `aeon` does not support this. Consider helping to improve `aeon`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.3.3 Selecting the metric and retrieving scores\n",
"\n",
"All tuning algorithms in `aeon` allow the user to set a score; for forecasting the default is mean absolute percentage error. The score can be set using the `score` argument, to any scorer function or class, as in Section 1.3.\n",
"\n",
"Re-sampling tuners retain performances on individual forecast re-sample folds, which can be retrieved from the `cv_results_` argument after the forecaster has been fit via a call to `fit`.\n",
"\n",
"In the above example, using the mean squared error instead of the mean absolute percentage error for tuning would be done by defining the forecaster as follows:"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:26:06.051246700Z",
"start_time": "2023-08-25T09:26:06.041246700Z"
}
},
"outputs": [],
"source": [
"from aeon.performance_metrics.forecasting import mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:26:06.079142400Z",
"start_time": "2023-08-25T09:26:06.046260400Z"
}
},
"outputs": [],
"source": [
"mse = mean_squared_error\n",
"\n",
"param_grid = {\"window_length\": [7, 12, 15]}\n",
"\n",
"regressor = KNeighborsRegressor()\n",
"cv = SlidingWindowSplitter(initial_window=int(len(y_train) * 0.8), window_length=30)\n",
"\n",
"gscv = ForecastingGridSearchCV(forecaster, cv=cv, param_grid=param_grid, scoring=mse)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The performances on individual folds can be accessed as follows, after fitting:"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:26:12.524912Z",
"start_time": "2023-08-25T09:26:06.057201900Z"
}
},
"outputs": [
{
"data": {
"text/html": [
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" \n",
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mean_test_mean_squared_error
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mean_fit_time
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mean_pred_time
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rank_test_mean_squared_error
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0
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3.0
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"text/plain": [
" mean_test_mean_squared_error mean_fit_time mean_pred_time \\\n",
"0 2600.750255 0.072074 0.002209 \n",
"1 1134.999053 0.097644 0.002344 \n",
"2 1285.133614 0.117036 0.003169 \n",
"\n",
" params rank_test_mean_squared_error \n",
"0 {'window_length': 7} 3.0 \n",
"1 {'window_length': 12} 1.0 \n",
"2 {'window_length': 15} 2.0 "
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gscv.fit(y_train)\n",
"gscv.cv_results_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.4 autoML aka automated model selection, ensembling and hedging\n",
"\n",
"`aeon` provides a number of compositors for ensembling and automated model selection. In contrast to tuning, which uses data-driven strategies to find optimal hyper-parameters for a fixed forecaster, the strategies in this section combine or select on the level of estimators, using a collection of forecasters to combine or select from.\n",
"\n",
"The strategies discussed in this section are:\n",
"* autoML aka automated model selection\n",
"* simple ensembling\n",
"* prediction weighted ensembles with weight updates, and hedging strategies"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.4.1 autoML aka automatic model selection, using tuning plus multiplexer\n",
"\n",
"The most flexible way to perform model selection over forecasters is by using the `MultiplexForecaster`, which exposes the choice of a forecaster from a list as a hyper-parameter that is tunable by generic hyper-parameter tuning strategies such as in Section 3.3.\n",
"\n",
"In isolation, `MultiplexForecaster` is constructed with a named list `forecasters`, of forecasters. It has a single hyper-parameter, `selected_forecaster`, which can be set to the name of any forecaster in `forecasters`, and behaves exactly like the forecaster keyed in `forecasters` by `selected_forecaster`."
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:26:12.575774800Z",
"start_time": "2023-08-25T09:26:12.521950300Z"
}
},
"outputs": [],
"source": [
"from aeon.forecasting.compose import MultiplexForecaster\n",
"from aeon.forecasting.exp_smoothing import ExponentialSmoothing\n",
"from aeon.forecasting.naive import NaiveForecaster"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:26:12.580790200Z",
"start_time": "2023-08-25T09:26:12.525935100Z"
}
},
"outputs": [],
"source": [
"forecaster = MultiplexForecaster(\n",
" forecasters=[\n",
" (\"naive\", NaiveForecaster(strategy=\"last\")),\n",
" (\"ets\", ExponentialSmoothing(trend=\"add\", sp=12)),\n",
" ],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:26:12.581787800Z",
"start_time": "2023-08-25T09:26:12.530896Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
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],
"text/plain": [
"MultiplexForecaster(forecasters=[('naive', NaiveForecaster()),\n",
" ('ets',\n",
" ExponentialSmoothing(sp=12, trend='add'))],\n",
" selected_forecaster='naive')"
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gscv.best_forecaster_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.4.2 autoML: selecting transformer combinations via `OptimalPassthrough`\n",
"\n",
"`aeon` also provides capabilities for automated selection of pipeline components *inside* a pipeline, i.e., pipeline structure. This is achieved with the `OptionalPassthrough` transformer.\n",
"\n",
"The `OptionalPassthrough` transformer allows to tune whether a transformer inside a pipeline is applied to the data or not. For example, if we want to tune whether `sklearn.StandardScaler` is bringing an advantage to the forecast or not, we wrap it in `OptionalPassthrough`. Internally, `OptionalPassthrough` has a hyperparameter `passthrough: bool` that is tuneable; when `False` the composite behaves like the wrapped transformer, when `True`, it ignores the transformer within.\n",
"\n",
"To make effective use of `OptionalPasstrhough`, define a suitable parameter set using the `__` (double underscore) notation familiar from `scikit-learn`. This allows to access and tune attributes of nested objects like TabularToSeriesAdaptor(StandardScaler()). We can use `__` multiple times if we have more than two levels of nesting.\n",
"\n",
"In the following example, we take a deseasonalize/scale pipeline and tune over the four possible combinations of deseasonalizer and scaler being included in the pipeline yes/no (2 times 2 = 4); as well as over the forecaster's and the scaler's parameters.\n",
"\n",
"Note: this could be arbitrarily combined with `MultiplexForecaster`, as in Section 3.4.1, to select over pipeline architecture as well as over pipeline structure.\n",
"\n",
"Note: `scikit-learn` and `aeon` do not support conditional parameter sets at current (unlike, e.g., the `mlr3` package). This means that the grid search will optimize over the `scaler`'s parameters even when it is skipped. Designing/implementing this capability would be an interesting area for contributions or research."
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:26:15.272566Z",
"start_time": "2023-08-25T09:26:15.107009400Z"
}
},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"from aeon.forecasting.compose import TransformedTargetForecaster\n",
"from aeon.forecasting.model_selection import (\n",
" ForecastingGridSearchCV,\n",
" SlidingWindowSplitter,\n",
")\n",
"from aeon.forecasting.naive import NaiveForecaster\n",
"from aeon.transformations.adapt import TabularToSeriesAdaptor\n",
"from aeon.transformations.compose import OptionalPassthrough\n",
"from aeon.transformations.detrend import Deseasonalizer"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create pipeline\n",
"pipe = TransformedTargetForecaster(\n",
" steps=[\n",
" (\"deseasonalizer\", OptionalPassthrough(Deseasonalizer())),\n",
" (\"scaler\", OptionalPassthrough(TabularToSeriesAdaptor(StandardScaler()))),\n",
" (\"forecaster\", NaiveForecaster()),\n",
" ]\n",
")\n",
"\n",
"# putting it all together in a grid search\n",
"cv = SlidingWindowSplitter(\n",
" initial_window=60, window_length=24, start_with_window=True, step_length=24\n",
")\n",
"param_grid = {\n",
" \"deseasonalizer__passthrough\": [True, False],\n",
" \"scaler__transformer__transformer__with_mean\": [True, False],\n",
" \"scaler__passthrough\": [True, False],\n",
" \"forecaster__strategy\": [\"drift\", \"mean\", \"last\"],\n",
"}\n",
"gscv = ForecastingGridSearchCV(forecaster=pipe, param_grid=param_grid, cv=cv, n_jobs=-1)"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.1299046419013891"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gscv.fit(y_train)\n",
"y_pred = gscv.predict(fh)\n",
"plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"])\n",
"mean_absolute_percentage_error(y_test, y_pred, symmetric=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"### 3.4.3 Simple ensembling strategies\n",
"\n",
"TODO - contributions in this section are appreciated"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from aeon.forecasting.compose import EnsembleForecaster"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.1661796803565588"
]
},
"execution_count": 135,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ERERE1LJZz7kqI3t6fY5roja3aYdOSKdptxSS3YzSHYtQlr0UksUIUWNATMY06AfNhKjkQBYiIiIiIiIiIl84KoshVRQBAFStunl9nqqqMtJuzIEsOSCIiqDER7VjZaSPZFmGZDN5/cdhPY/S7S/DuHU+JIsRACBZjDBufQGl2xfCYS3z+lreDj7/+OOPERcXB4vFUu3xm2++GXfeeWet5y1fvhzPP/889uzZA0EQIAgCli9fDgAwGo247777EB8fj5iYGIwcORJ79uxxn7tnzx6MGDEC0dHRiImJwYABA7Bjxw6sX78ekyZNQmlpqfuac+bM8ek1JyIiIiIiIiK6mGuLtjImDaIq0uvzlNGpgEIN2WGB/XxesMKjOrAy0keyvQLHl8Z6dayoa42Uew+jLHupx/Wy7DehH/go8j9Kh1RZXO/12k8tgeDFF9iECRMwffp0fPPNN5gwYQIAoKioCCtWrMDKlStrPe/WW2/Fb7/9hh9//BGrV68GAOj1evc1dTodfvjhB+j1erz77rsYNWoUDh06hFatWuGOO+5Av3798Pbbb0OhUCA7OxsqlQrDhg3D66+/jmeffRYHDx4EAERFRdX7HIiIiIiIiIiIamNz94v0fngNAAiiAip9J9jOHYCt5DBU+rRghEd1YGVkECkikuCoKHJXRF5KshjhqDwDRURSQO+r0+lw++23Y9myZe7H/vnPfyI1NRVXXnllnedFRUVBqVQiKSkJSUlJ0Ol02LBhA7Zt24YvvvgCAwcORHp6OhYtWgSDwYAvv/wSAJCXl4fRo0ejW7duSE9Px4QJE9C3b1+o1Wro9XoIguC+JpORREREREREROQPV2WkupVvyUjgwlZt9o0MDVZG+khQRqD91BLvjxdVEDUGjwlJUWOAMrIt2v75V6/v7a37778fgwYNwsmTJ9GuXTssX74c99xzDwRB8PoaLnv27EF5eTni4uKqPV5ZWYmcnBwAwIwZM3Dffffh//2//4fRo0djwoQJ6NSpk8/3IiIiIiIiIiKqj60Bk7RdXENs7JyoHRJMRvpIEASvtkq7SDYTYjKmwbj1hRprMRnTIEs2n3obeKtfv37o27cvPv74Y4wdOxa///47VqxY0aBrlZeXo02bNli/fn2NNdeU7Dlz5uD222/HihUr8MMPP+C5557Dp59+iltuucWPZ0FEREREREREVFNDJmm7qGK7AABsTEaGBJORQSaqIqEfNBOAs0dkY07Tvu+++/D666/j5MmTGD16NFJSUuo9R61Ww+FwVHusf//+KCgogFKpRIcOHWo9t0uXLujSpQseeeQR3HbbbVi2bBluueUWj9ckIiIiIiIiImoIR0VR1ewNwadJ2i7cph1a7BnZCESlFvqBjyL1gRNIfeAkUh84Af3AR4OaiASA22+/HSdOnMD777+Pe++916tzOnTogNzcXGRnZ6O4uBgWiwWjR49GZmYmbr75ZqxcuRLHjh3Dpk2b8NRTT2HHjh2orKzEtGnTsH79ehw/fhwbN27E9u3b0b17d/c1y8vLsWbNGhQXF6OioiKYT5uIiIiIiIioSZJsJsgOKxwVRZAdVkg2U6hDCkvWswcAAEp9GkSV9y3tXNzbtMuOQ7KbAxob1Y/JyEYiqiIhKNRQRMRDUKiDsjX7Unq9HuPHj0dUVBRuvvlmr84ZP348rr76aowYMQLx8fH497//DUEQ8P333+Pyyy/HpEmT0KVLF/z5z3/G8ePHkZiYCIVCgbNnz+Kuu+5Cly5dMHHiRFxzzTV4/vnnAQDDhg3D//3f/+HWW29FfHw8Fi5cGMRnTURERERERNT0SHYzSncsQt57ye4/pTteZTKsAWzuLdq+D68BADEiAaJaD0CGvTQngJGRN7hNu5k7efIk7rjjDmg0Gq+O12g07gnZF4uOjsbixYuxePFij+f9+9//rvO6b7/9Nt5++22vYiAiIiIiIiJqTiSbCaU7FsG4df6FxyxG93wJ/cBHG6VoqblwTdJWtfJ9eA3gnAeijE2HtXAHbCWHG9R3khqOlZHNVElJCb7++musX78eU6dODXU4RERERERERC2WIKpQlr3U41pZ9psQRFUjRxTeXJO01Q2YpO3i7hvJITaNjpWRzVS/fv1QUlKCl19+GV27dnU/3rNnTxw/ftzjOe+++y7uuOOOxgqRiIiIiIiIqEWQLEZIFmMda6VQRMQ3blBhSpblC5WR/iQjDa5k5KGAxEXeYzKymTp27JjHx7///nvYbDaPa4mJiUGMiIiIiIiIiKhlEtV6iBqDx4SkqDFA1OgbP6gwJVUUQTKfBQSxQZO0XThRO3SYjGxh2rdvH+oQiIiIiIiIiJolyWaCIKogWYwQNQbIkg3lBz+DQhePmL5TYNz2Yo1zYjKmQZZsEBTqEEQcfqxVw2uU+o4QlboGX0cV2wUAt2mHApORRERERERERER+ck3LLste6k5GxvSdgpiMqTiz8j7EX/0PQBBRlv3mhfWMadAPmglRqQ11+GHDtUVb3aphk7RdVIbOAACp8gwc5hIotLF+x0beYTKSiIiIiIiIiMgPtU7LrqqEbHXFKxA1eugHPgrDoCdgrzgNhS4BgMxEpI9s7n6R/iUjRXU0FJFt4DCdht14BIqkQYEIj7zg9zRth8OB7OxslJSUBCIeIiIiIiIiIqKwUue07D1vQRXTAYIgQFRFwmE+i8L/jUP+8m4QRG7N9pU1AJO0XTjEJjR8TkY+/PDD+PDDDwE4E5FXXHEF+vfvj5SUFKxfvz7Q8RERERERERERNWneTMt2UUS2gb0sF5KpALbSo40UYfMgy/KFyshWAUhGcohNSPicjPzyyy/Rt29fAMC3336L3Nxc/PHHH3jkkUfw1FNPBTxAIiIiIiIiIqKmzDkR21DH2oVp2YIgsCKvgRwVBZAsJX5P0nbhEJvQ8DkZWVxcjKSkJADA999/jwkTJqBLly649957sW/fvoAHSM3bPffcg5tvvjnUYRARERERERE1mCzZEJMxzeOaa1r2xdwVeUxG+sRWcgSquF5QJwwISK/NC+8Dk5GNyedkZGJiIvbv3w+Hw4Eff/wRY8aMAQBUVFRAoVAEPMDmwmSzwuqwo6iyHFaHHSabNdQhEREREREREVEAiKpI6AfNhGHIU+4KSVFjgGHI085p2arIasdfqMhjMtJbks0ETdIgJN74Fdr8aRUkm8nvayoNF7Zpy7Ls9/XIOz5P0540aRImTpyINm3aQBAEjB49GgCwdetWdOvmf4lsc2S22/DKvnVYcmADjNZKGNQ6PNQ9C7P6jIRWqQp1eH6zWq1Qq9l0l4iIiIiIiFouUalFRNr10A98DJLFCIUuHrJk81jB505GslehVyS7GaU7FqEseykkixGixoCYjGnORK8fFZIqfRogKCDbTHCYTkEZ1S6AUVNtfK6MnDNnDj788EM88MAD2LhxIzQaDQBAoVBg1qxZAQ+wqZFlGSabxes/ZVYzFuxdi3l7VsForQQAGK2VmLdnFRbsXYsyq9nra3mbpf/4448RFxcHi8VS7fGbb74Zd955Z53nzpkzBxkZGXj33XeRkpKCiIgITJw4EaWlF5rturZWz58/H23btkXXrl0BAPn5+Zg4cSIMBgNatWqFm266CceOHXOf53A4MGPGDBgMBsTFxWHmzJn8zQMRERERERE1G8XrHkL+R+mwFv8GQaGuURHpoop1/hzNysj6STYTSre/DOPW+e4hQZLFCOPWF1C6faFfFZKCQg2lPg0At2o3Jp8qI202G66++mq88847GD9+fLW1u+++O6CBNVUVditi/undoJ7WmkgcnfAUlhzY4HF9yYENeLz3CHT8Yj6KLfV/8ZT9ZT4iVZp6j5swYQKmT5+Ob775BhMmTAAAFBUVYcWKFVi5cmW95x85cgSff/45vv32W5SVlWHy5MmYMmUK/vWvf7mPWbNmDWJiYrBq1SoAzs+Nq666CpmZmfj111+hVCrxwgsv4Oqrr8bevXuhVqvx6quvYvny5fjoo4/QvXt3vPrqq/j6668xcuTIemMiIiIiIiIiaspkyQ5b8W+QHWao9B3rPNbVq1CqKILDbIRCa2iECMOTIKpQlr3U41pZ9pswDPavME5lSIfdeAS2ksPQpVzp17XIOz5VRqpUKuzduzdYsTQ7SRHRKDKXuysiL2W0VuKMuRxJEdEBva9Op8Ptt9+OZcuWuR/75z//idTUVFx55ZX1nm82m/Hxxx8jIyMDl19+OZYsWYJPP/0UBQUF7mMiIyPxwQcfoGfPnujZsyc+++wzSJKEDz74AL1790b37t2xbNky5OXlYf369QCA119/HbNnz8a4cePQvXt3vPPOO9Dr9bVEQURERERERBQ+bOf+gOwwQ1BHQ2noVOexojoaisi2zvNYHVknyWJ0V0R6Xiv1uOYt9xAbbplvND73jPzLX/6CDz/8EAsWLAhGPE1ehFKNsr/M9/p4laiAQa3zmJA0qHVoGxGDTdc95PW9vXX//fdj0KBBOHnyJNq1a4fly5fjnnvugSAI9Z6bmpqKdu0u9EnIzMyEJEk4ePCge5J67969q/WJ3LNnD44cOYLo6OqJVbPZjJycHJSWluL06dMYMmSIe02pVGLgwIHcqk1ERERERERhz3ImGwCgbt0HglB/7ZcqtgscplOwlRyEts3gIEcXvkSNAaLG4DEh6Vzzr8hJZeBk88bmczLSbrfjo48+wurVqzFgwABERlbvf/Daa68FLLimSBAEr7ZKu5hsVjzUPQvz9qyqsfZQ9yzYJMmn63mrX79+6Nu3Lz7++GOMHTsWv//+O1asWBGw61/6vpeXl2PAgAHVtnK7xMfHB+y+RERERERERE2RtSgbAKBJyPDqeFVsF5hPrGcSrB6yZENMxjQYt75QYy0mYxpkyQZB0fChuq7KSDsrIxuNz8nI3377Df379wcAHDpU/QvGm6q7liZSpcasPs6eiDWmafcdCa0ieNO077vvPrz++us4efIkRo8ejZSUFK/Oy8vLw6lTp9C2rbNkfMuWLRBF0T2oxpP+/fvjs88+Q0JCAmJiYjwe06ZNG2zduhWXX345AGdie+fOne7PJyIiIiIiIqJw5UpGquMzvDrePVGbycg6iapI6AfNBGQJZXveCug0beCibdqluZAdNghBzNOQk8/JyHXr1gUjjmZNq1Th8d4j8GTfUSi1maFXaWGTpKAmIgHg9ttvx2OPPYb3338fH3/8sdfnabVa3H333Vi0aBHKysowffp0TJw40b1F25M77rgDr7zyCm666SbMnTsXycnJOH78OP7zn/9g5syZSE5Oxt/+9jcsWLAA6enp6NatG1577TUYjcYAPFMiIiIiIiKi0JFlGdYzewAA6vi+Xp3DZKT3ZIcV6sT+SLkvF7LNBFFjgCzZ/E5EAoAiqh0EZQRkewXsZcfcyUkKHp8G2FzsyJEj+Omnn1BZ6eyFyL5/dYtUqaFWKBGvjYJaoUSkquElxN7S6/UYP348oqKicPPNN3t9XufOnTFu3Dhce+21GDt2LPr06YO33nqrznMiIiLwyy+/IDU11T2gZvLkyTCbze5KyUcffRR33nkn7r77bmRmZiI6Ohq33HKLP0+RiIiIiIiIKOTsZbmQrKWAQg11XA+vzrmwPfgIZMkRzPDCnuXUJhR9+yec/nIsFBEJEBRqiKrI+k/0giCIUBk6A+AQm8bic2Xk2bNnMXHiRKxbtw6CIODw4cPo2LEjJk+ejNjYWLz66qvBiJMa6OTJk7jjjjug0fjWl/LBBx/Egw8+6HFt+fLlHh9PSkrCP/7xj1qvqVQq8frrr+P111/3KRYiIiIiIiKipsy9RTuup9f9C5UxHQCFGrLDAvv5fKj0HYIWX7gzn94EAFC36haU66ti02Et3gtbyWEgLSi3oIv4XBn5yCOPQKVSIS8vDxEREe7Hb731Vvz4448BDY4arqSkBF9//TXWr1+PqVOnhjocIiIiIiIiombLPUnby36RACCICqj0nQBwq3Z9LKc2AwC07YYH5fpKV9/IElZGNgafKyNXrlyJn376CcnJydUeT09Px/HjxwMWGPmnX79+KCkpwcsvv1xt8EzPnj1rfZ/efffdxgqPiIiIiIiIqNmwFjn7RXo7SdtFFdsFtnMHnMnIDmODEFn4kx02WAq2AQA0bTODcg+VwZWMZFK4MficjDSZTNUqIl3OnTvn81ZgCp5jx455fPz777+HzWbzuJaYmIjo6GjMmTMneIERERERERERNTPWBlRGAhxi4w3rmWzI9kqI2lZQxXat/4QGcL8P7BnZKHxORl522WX4+OOPMW/ePACAIAiQJAkLFy7EiBEjAh4gBVb79u1DHQIRERERERFRs2E3FcBhOg1AgLp1b5/OdSXXmIysnfmUs1+kps1QCEKD5zDXyTVMSHZYINkqIKpqFuFR4PicjFy4cCFGjRqFHTt2wGq1YubMmfj9999x7tw5bNy4MRgxNgmcFh46fO2JiIiIiIioqXINr1HFpkNUR/l0Lisj6+dKRmrbBqdfJAAotK2QeNP/oE2+HJK1DIKohCzZAjaxm6rzOaXcq1cvHDp0CFlZWbjppptgMpkwbtw47N69G506dQpGjCGlUCgAAFarNcSRtFyu1971XhARERERERE1FQ3dog0AqlbOZKSj/AQkmymAUTUPsizD4k5GDgvafSS7GZbTW5H/QRry309F3nvJKN3xKiS7OWj3bMl8rowEAL1ej6eeeirQsTRJSqUSEREROHPmDFQqFUQxOCXB5JkkSThz5gwiIiKgVDbo05WIiIiIiIguIdlMEEQVJIsRosbAKjA/uCdpJ/Tz+VyFthVEXWtIlcWwlRyCpgHXaM7spUfhqCgEFGqoEwcE5R6SzYTSHYtg3PbihccsRhi3vgAA0A98lF8bAeZzdmfv3r0eHxcEAVqtFqmpqc1qkI0gCGjTpg1yc3M5LTxERFFEamoqBEEIdShERERERERhT7KbUbpjEcqyl7qTkTEZ06AfNBOiUhvq8MJOQydpu6gM6bAwGemRu19kQv+gfW4Kogpl2Us9rpVlvwnD4FlBuW9L5nMyMiMjw50UcvXyuzhJpFKpcOutt+Ldd9+FVlv3J8qcOXPw/PPPV3usa9eu+OOPPwAAZrMZjz76KD799FNYLBZcddVVeOutt5CYmOg+Pi8vDw8++CDWrVuHqKgo3H333XjppZcCWkWnVquRnp7OrdoholarWZFKREREREQUAO4qsK3zLzzGKrAGkyylsJfmAGjYNm3A2TfScnozbCWc5Hwpy6nNAIK8RdtihGQx1rFWCkVEfNDu3xL5nLH7+uuv8cQTT+Dxxx/H4MGDAQDbtm3Dq6++iueeew52ux2zZs3C008/jUWLFtV7vZ49e2L16tUXArooifjII49gxYoV+OKLL6DX6zFt2jSMGzfOPSjH4XDguuuuQ1JSEjZt2oTTp0/jrrvugkqlwosvvljjXv4QRbHe5CoRERERERFRU8YqsMCynHHuHlVEp0Chi2vQNTjEpnbmU878jyaIyUhRY4CoMXhMSDrX9EG7d0vlczJy/vz5eOONN3DVVVe5H+vduzeSk5PxzDPPYNu2bYiMjMSjjz7qVTJSqVQiKSmpxuOlpaX48MMP8cknn2DkyJEAgGXLlqF79+7YsmULhg4dipUrV2L//v1YvXo1EhMTkZGRgXnz5uGJJ57AnDlzoFarfX16RERERERERM2WZC5hFVgAuYbXaOL7NvgaTEZ65jCfg+3cAQCAtk1m0O4jSzbEZExzVwdfLCZjGmTJBkHB/FIg+bz3dd++fWjfvn2Nx9u3b499+/YBcG7lPn36tFfXO3z4MNq2bYuOHTvijjvuQF5eHgBg586dsNlsGD16tPvYbt26ITU1FZs3O8t0N2/ejN69e1fbtn3VVVehrKwMv//+e633tFgsKCsrq/aHiIiIiIiIqDmzlR2DoI6CqDF4XGcVmO+sRbsBNHyLNnBhorat5JC7HR4BltNbAACq2PSgJshFVST0g2bCMORp99eGqDHAMORpZx9Vti0IOJ+Tkd26dcOCBQuq9U+02WxYsGABunXrBgA4efJktQRhbYYMGYLly5fjxx9/xNtvv43c3FxcdtllOH/+PAoKCqBWq2EwGKqdk5iYiIKCAgBAQUFBjfu4PnYd48lLL70EvV7v/pOSkuLVcyciIiIiIiIKR9Zzf+D0Z1eiMm8tYvpO8XiMqwqMvHdhknZGg6+h0ncCBAVkWzkcJu8Ku1oC9/CatsODfi9RqYV+4KNIfeAEku89hJT7chHd9/840ClIfN6mvXTpUtx4441ITk5Gnz59ADirJR0OB7777jsAwNGjRzFliudvbhe75ppr3P/fp08fDBkyBO3bt8fnn38OnU7na2hemz17NmbMmOH+uKysjAlJIiIiIiIiajYkmwmCqKqalq2HvTQXojoG53/7CAnXfgIIIsqy3+Q0bT9IdjNsZ53biDV+VEYKCjWU+jTYjUdgKzkEZVTbAEUY3lzJyGAOr7mYqwKydPsimI78B/r+D8MwaGaj3Lul8TkZOWzYMOTm5uJf//oXDh1y9jOYMGECbr/9dkRHRwMA7rzzzgYFYzAY0KVLFxw5cgRjxoyB1WqF0WisVh1ZWFjo7jGZlJSEbdu2VbtGYWGhe602Go0GGo2mQTESERERERERNWWS3YzSHYtQlr30QrKx7xS0mbgeEASIKh30Ax+FfuBjcFQWQRmRBFl2MBHpI9vZ3wHZAVEbB0W0fwVOqtgu7mSkLuXKwAQYxmSHFdaCHQAATRD7RXqiju+D8/veQ0XON0xGBonP27QBIDo6Gv/3f/+H1157Da+99hr++te/uhOR/igvL0dOTg7atGmDAQMGQKVSYc2aNe71gwcPIi8vD5mZzk/EzMxM7Nu3D0VFRe5jVq1ahZiYGPTo0cPveIiIiIiIiIjCiWQzoXT7yzBune8eVCNZjDBuexFl2W+6B3GIqkic+3U2Cv83DmV732FfvAawFGUDcG7RFgTBr2txiE11lqLdkB1miLrW7temsUR0vN4ZQ8E22LltPih8rowEnENn1q1bh6KiIkiSVG3t2Wef9fo6jz32GG644Qa0b98ep06dwnPPPQeFQoHbbrsNer0ekydPxowZM9CqVSvExMTgoYceQmZmJoYOHQoAGDt2LHr06IE777wTCxcuREFBAZ5++mlMnTqVlY9ERERERETU4giiCmXZSz2ulWW/CcPgWe6PFZGJsJ39DdYz+xorvGYlEJO0XVSGdABMRrpYXFu022T6nej1lTKqLTSJg2Ap3I6KoysQ0/u+Rr1/S+BzMvL999/Hgw8+iNatWyMpKanaJ4UgCD4lI0+cOIHbbrsNZ8+eRXx8PLKysrBlyxbExzunJP3973+HKIoYP348LBYLrrrqKrz11lvu8xUKBb777js8+OCDyMzMRGRkJO6++27MnTvX16dFREREREREFPYki9FdEel5rdQ9mVhl6AwAsJXmNFZ4zYrVVRnpR79IlwuVkYf9vlZzYD61EQCgaaR+kZeK6HSDMxmZ8w2TkUHgczLyhRdewPz58/HEE0/4ffNPP/20znWtVoulS5di6VLPv9UBgPbt2+P777/3OxYiIiIiIiKicCdqDBA1Bo8JSeea3v2xKraqGs94pLHCazZkyQFr8V4A/k3SdlG1ciYj7WW5kO0WCMqWu9tTlmWYT20G0HjDay4V0elGlGx6FpX5ayFZz0NU+9+akC7wuWdkSUkJJkyYEIxYiIiIiIiIiMgPsmRDTMY0j2sxGdMgSzb3x67KSKmiCJKlrFHiay5sJQch2yshKCPcW6z9oYhIgqCOBmSpxVeq2o1HIFWegaDQQJPQPyQxqFp1h9LQGXBYUXnsp5DE0Jz5nIycMGECVq5cGYxYiIiIiIiIiMgPoioS+gEzYBj8JESNwfmYxgDDkKehHzSz2qAaUR0NRUQiAFZH+srVL1Id3weCqPD7eoIgcIhNFVdVpDpxYMgqRAVBQETHGwAApqPfhiSG5sznbdqdO3fGM888gy1btqB3795QqVTV1qdPnx6w4IiIiIiIiIjIN2V73oE6sT9S7j8O2VoOUaOHLNkgKrU1jlUaOsNRUQib8TA0iaGpQgtHlqI9AALTL9JFFdsF1sKdTEZW9YvUts0MaRyRnW5E2a6/ozL3B8gOGwSFqv6TyCs+JyPfe+89REVF4eeff8bPP/9cbU0QBCYjiYiIiIiIiELIdOgzWM/sQfx1nyEq/RYAgKBQezxWZegMy6mNrIz0kWQxQtS1hiahX8CuycpIJ0fFGYi61iHrF+miaTMUoi4eUuUZmE/+Cl3qyJDG05z4nIzMzc0NRhxERERERERE5CdHZTGsZ5xVezovkjmuvpF2JiO9ItlMEEQVDINnI+7K1yBZzwfs2i09Gel6beOufBWKiATIDlv9JwWRICoQ0fE6lP++HBVHv2UyMoB87hnpYrVacfDgQdjt9kDGQ0REREREREQNZM5fDwBQxfWEIjKx3uNVsc5kpK2Eycj6SHYzSncsQt57yTixrAvyP0jD+b3vQbKbA3L9lpyMrP7adkX+B2ko2704YK9tQ7n7RuZ8A1mWQxpLc+JzMrKiogKTJ09GREQEevbsiby8PADAQw89hAULFgQ8QCIiIiIiIiLyTmX+egCALmWEV8crqyojuU27bpLNhNLtL8O4dT4ki9H5mMUI49YXULp9ISSbye97uKZyS+ZzcFQW+329cNEYr21D6dqPhqCMgON8vntoEfnP52Tk7NmzsWfPHqxfvx5a7YXmt6NHj8Znn30W0OCIiIiIiIio+TJZ7bDaJRSVW2C1SzBZufPOX5X5awEAWi+Tka5t2pL5LBzmkqDFFe4EUYWy7KUe18qy34Qg+j/cRFRFQBGdAgCwlRz2+3rhojFe24YSlTro2o8GAFTkfBOyOJobn5OR//3vf/Hmm28iKysLgiC4H+/ZsydycnICGhwRERERERGFr7qSjWabAwvX5SDp+ZVImrMSSc+vxCvrcmC2OUIYcXizn8939n4UROiSL/fqHFEVCUVkW+f5rI6slWQxuqv2PK+VBuQ+qtguEHWt4agoCsj1wkFjvbYNFdHxRgBARc63IY2jOfF5gM2ZM2eQkJBQ43GTyVQtOUlEREREREQtlyvZuGRDLoyVNhh0KkzPSsOskZ3hkGUsXJeDeasu9MYzVtowt+rjx0d0QqTa5x9XW7zK/HUAAE3CAIgavdfnqQyd4TCdgs14GJqkQcEKL6yJGgNEjcFj0sy55v3rXZdWw1+EqlUXSGYjZIcVsmSDqIoMyLWbKlGtb5TXtqEiOl4LCCKsxXthKzsOVUz7kMbTHPhcGTlw4ECsWLHC/bErAfnBBx8gMzMzcJERERERERFRWDJZ7Xhp7RHMW3UIxkrnRFxXsvGNX49CKQpYsiHX47mLN+RCJTZ41mqLZq5KRmpTvdui7aLkEJt6yZINMRnTPK7FZEyDLPk/+Vmym1GR8w3yP0hD/odpyHsvGaU7Xg35EJdAkWwmyA4rHBVFkB1WSDYTyva+j4rjKxHTd4rHcwL12vpDoWuNqK63IeGGr6CISKwWPzWMz79qevHFF3HNNddg//79sNvteOONN7B//35s2rQJP//8czBiJCIiIiIiojCiEsVak43f7C/EpEGp7iTlpYyVNpSabYiP0gQzxGZHlmVU5jmTkbqUkT6dq+IQm3qJqkjoB80EZBlle5ZCshghagyIyZgG/aCZEJXa+i9SB8lmQumORTBum3/hsaohLgCgH/hoWFdIuqZll2Vf9Nr1nYKYjKk4s/I+xF/9D0AQUZb9ZsBf20CIG/EGSne+huKVk5tkfOHG52RkVlYWsrOzsWDBAvTu3RsrV65E//79sXnzZvTu3TsYMRIREREREVEYMZpttSYbD58xQa9TwaBTeTzGoFNBrw3dwIpwZSs5BIfpFASFBpq2vu1aZDLSO6JSC027YUgZ9Bgk63kotK2c26gDkIyqb4iLYfAsv+8RKu5E69ZLEq3bXgQAxF2xCKJGD/3AR2EYPAuSpRSiRh+w19Zfks2E0l1/d8cLNK9EcSg0qAlHp06d8P777wc6FiIiIiIiImoGDNrak412SYbVIWF6Vpq7R+TFpmelwSZJUPveVaxFc23R1rTJhKjU+XSuKxlpNx6BLMucB1ELmzEHhV9fBzEyCSl374egUENQqANybW+GuCgi4gNyr8ZWZ6J1z1swDHkSgiBAqErouZ5noF5bfzXnRHGo+PzdfdeuXdi3b5/74//973+4+eab8eSTT8JqtQY0OCIiIiIiIgo/NsmZbPRkelYaFAIwa2RnPDumCww6ZxWkQafCs2O6YNbIzhxe0wCV+WsBADof+0UCgNLQCUBV0st8NqBxNSeVx1cBANSGdIjqqIBe2zUgp/a10A5x8UdTn5Zdn3CPvynyORn517/+FYcOOX97dfToUdx6662IiIjAF198gZkzZwY8QCIiIiIiIgovkWolZo7ohKdHp3tMNkaoldCqFHh8RCecfm4sjj45CvnPjMbDl6dBq1KEOPrwI8sSzPnOGQ7aZN+TkaJSB0V0CgAOsalL5fGVAABd+7EBv3ZjDMgJlXBPtIZ7/E2Rz8nIQ4cOISMjAwDwxRdf4IorrsAnn3yC5cuX46uvvgp0fERERERERBSGlm3PR/9kA048MwaFc8ai4LmxeHxEp2rJxki1EhqliOlf70Pa/DXYkFsSwojDl/XMHkiWEgjqaGiSBjboGhf6Rh4OZGjNhuyworJqK7yuQ+CTka4BOYYhT7sTX6LGAMOQp51DUsK4J2G4J1rDPf6myOfad1mWIUkSAGD16tW4/vrrAQApKSkoLi4ObHREREREREQUdmRZxlsbj+FAUTn+/Zf+uDWjHQDU2gcyPlqDYpMVG4+dw/U9Ehsz1GahMs+5RVvb7jIIYsO2uKsMnWHOXwc7h9h4ZD61CbLNBDEiAer4vkG5h6jUuoe42E2nodC1dk5ubgJDXPzhTLQ+DsgSyva8FXbTqN2T1IEmO+073Pj8XWrgwIF44YUXMHr0aPz88894++23AQC5ublITORfGkRERERERC3dbwXncaCoHGqFiKu7JtR7/PAOrbBsWz425Z5rhOiaH3P+egCALsX3LdounKhdt8pjPwEAItqPgSAEb7iSqwKyLPstlB/4f4jpOwWxQ58O2v0aS+WJn6FO7I+U+3Ih2yqa1LRsb7gSxfqBj8FRWQRFRBIgO8Im/qbG56+g119/Hbt27cK0adPw1FNPoXNn5zesL7/8EsOGDQt4gERERERERBRePss+BQC4pls89FU9I+syvEMrAMC2fCMsdkdQY2tuZIcV5pO/AvAvGalkMrJOwewX6YnK0BlSZbF7Snq4K9/3AYq+/RNKd70BRUQ8BIU67Laei6pInN/3Pgr/Nw5n1z8SdvE3JT5XRvbp06faNG2XV155BQoFGw0TERERERG1ZLIs4/M9zmTkxKrt2fXpEh+J1pFqFJus2HWiFJlVyUmqn6VgG2R7BURdPFStezX4OqrYC8lIWZYhCEKgQgx79vJTsBbvAyBAlzq6Ue7pmopuPr0Fks0U1okvh9mIimM/AgAiO90Q4mj8o4rrAdvZ3yDbK0MdSljzuTIyPz8fJ06ccH+8bds2PPzww/j444+hUtX/Gy8iIiIiIiJqvrJPleFIsQlapYgbvOz/KAgChneIBQBsPMYhNr5wD1VJvsKv7cOqmI4ABMjW85AqigIUXfNQeXw1AECdOACKiPhGuadS3wmK6FRAssF8alOj3DNYKnL+BzisUMX1gLp171CH4xdN4gAAgL00Bw4zv1c1lM/fqW6//XasW+f8ZldQUIAxY8Zg27ZteOqppzB37tyAB0hEREREREThw7VF+7ruiYjSeL8Zb1hVNeSmY+wb6QtXMlKbOtKv6whKDZTRqQC4VftSlccv9ItsLIIgQJdyJQDAXDWgKFyVH/wMABDV9dYQR+I/hbYVlPqOAABr4c4QRxO+fE5G/vbbbxg8eDAA4PPPP0evXr2wadMm/Otf/8Ly5csDHR8RERERERGFCVmW8Xn2SQDAxIy2Pp07PM2ZjNyYew6yLAc8tuZIspkgWcsh6lr71S/SRRnLvpGXkiWHuzJS1+GqRr236z2tDOO+kQ5TIcz5zmRqZJeJIY4mMFzVkRYmIxvM52SkzWaDRqMBAKxevRo33ngjAKBbt244ffp0YKMjIiIiIiKisLE934hjJZWIVCtwXff6p2hfbECyHhqliDMmKw4Xm4IUYfMh2UwQBAUSr/8MKfcehiIyye9rXpiofdjvazUXlsIdkCwlENV6aJIGN+q9tVXJSGvR7rDdEmw6/BUgS9AkDoLK0CnU4QSEJnEgAOfnBjWMz8nInj174p133sGvv/6KVatW4eqrrwYAnDp1CnFxcQEPkIiIiIiIiMKDa4v2DT0SEaH2bV6qRqnAoBQDAGd1JNVOsptRumMR8t5PxYllXZH/QRpKd7wKyW7267oqTtSuwTVFW9t+FATR5xnAflFGtYUqtisAGeYTPzfqvQPFtUU7shls0XZRszLSbz4nI19++WW8++67uPLKK3Hbbbehb9++AIBvvvnGvX2biIiIiIiIWhZJkvHlXtcUbd+2aLu4+kZuYN/IWkk2E0q3vwzj1vmQLEbnYxYjjFtfQOn2hZBsDa8qdSUj7SVMRrpUHnMmIyPaN+4WbRdXL9Bw3KptKzsOy+nNAAREdvlTqMMJGE1CPwACHOUnYDcVeHWOZDNBdljhqCiC7LD69XXaHPicjLzyyitRXFyM4uJifPTRR+7HH3jgAbzzzjsBDY6IiIiIiIjCw5a8EuQbzYjWKHF1V9+2aLtkVfWN3MTKyFoJogpl2Us9rpVlvwlBVDX42kpXZWRpDvt2AnBUnoWlcDsAQNeIw2su5uobaQ7DZKTp0BcAAG3y5VBGNewXFE2RqI6GqlVXAN4NsXFXMr+X7P4TiErmcOZzMhIAFAoFYmNjqz3WoUMHJCQ07C8cIiIiIiIiCm+uLdo39UyEVqVo0DWGdXD+nHnwjAnFJkvAYmtOJIvRXRHpea20wddW6dMAQYRsM8Fh4kyIyrw1gCxBFdcDyujkkMSgTb4cgADbuT9gLz8VkhgaytQMt2i7XOgbWXcyMpiVzOGsQcnIL7/8EhMnTsTQoUPRv3//an+IiIiIiIioZXFU26LdrsHXaRWhRveEKADApmPhObAj2ESNAaLGUMeavsHXFhRqKGM6AABsxpwGX6e5cPWL1IVoizYAKLStoE7oByC8qiOt5/6A9cweQFQisvMtoQ4n4LxNRgazkjmc+ZyMXLx4MSZNmoTExETs3r0bgwcPRlxcHI4ePYprrrkmGDESERERERFRE7b7pBE2hwyDToWxXeL9utawqq3aG7hV2yNZsiEmY5rHtZiMaZAlm1/Xd/eNbOFDbGRZRuXxVQCAiA5jQxqLa6t2Zf76kMbhC1dVpK79WCh0zW/Y8YUhNjvqbGkQzErmcOZzMvKtt97Ce++9hyVLlkCtVmPmzJlYtWoVpk+fjtLSlvkiEhERERERtUQmqx1Wu4TEaC1ynxqFH+8fArWyQRvw3LKqhths4hAbj0RVJPSDZsIw+El3haSoMcAw5GnoB82EqIr06/oXJmof9jfUsGY7ux+iNg6KqBRo2g4PaSxadzJyXVj08pRl2T1FO6rrxBBHExzq+D6AqIRUeQaO8/m1HhfMSuZw5vNc+ry8PAwbNgwAoNPpcP78eQDAnXfeiaFDh+LNN98MbIRERERERETU5JhtDixcl4MlG3JhrLTBoFPhoaw09GkT0+CekQAwvKoyckd+Kcw2h1/Xaq4kSynUif2Rcl8uZFsFRI0esmSDqNT6fW33EJsWXBkp2UxQGjoh8cavoIhIBGRHSOPRthsOiCo4zufBXnoUKkOnkMZTH9u5A5AsRghKHSI63hjqcIJCVOqgjusJ65k9sBTugDIm1eNxrkpm49YXaqy5KpkFhTrY4TY5Pv/KKikpCefOOX9DlZqaii1btgAAcnNzwyJDT0RERERERP4xWe14ae0RzFt1CMZK57ZgY6UN81YdwoK1R2Cy2ht87U5xEUiIUsPqkLDzBHffeWIp2Iqib/+Ewv/eCEVEPASF2u+KSBdVC09GuiYf57+fihPLuiL/gw4hn3wsqiKhbTMUgLM6sqmSbCbIDitEdQxS7j2MpPE/QVRHhTqsoPGmb6SzkvnxoFUyhyufk5EjR47EN998AwCYNGkSHnnkEYwZMwa33norbrml+TUlJSIiIiIioupUooglG3I9ri3ekAuV2PCt2oIgYHgH9o2si7Uq+aEMQoWcKtbVMzIHsiwF/PpNWVOefKxNuRIAYM5bG9T7uBKKjooiyA6r18/ZlcTNey8Z+R92RP4Haag89lNIk7jBpvZyiE1l7g9VlczHkPrASaQ+cAL6gY8GpJI5XPm8Tfu9996DJDm/IU2dOhVxcXHYtGkTbrzxRvz1r38NeIBERERERETUtBjNNndFZI21ShtKzTbER2kafP3haa3w9W8F7BtZC0vhLgCAJqF/wK+tjOkAiErI9ko4yk9BGZ0c8Hs0VfVNPjYMntXIEV2gSxkJ45Z5qDzxM2RZgiD415vVE1dCsSx7KSSLEaLGgJiMac4KvjoSZ5LNhNIdi2DcOv/CYxZj1ceCM/HWDCsANYnOrz9r4U7IsgxBEDweV7b3PZjz16LV5Yug7z8dAFrk1uyL+fzZK4oilMoLOcw///nPWLx4MR566CGo1S37xSQiIiIioqbHNWSlqNwCq13yawsxORm0Khh0Ks9rOhX0Ws9r3hp+0RAbtgOrTpZlWIqclViaqom+gSSISmdCEi1vq3ZTnnysSRoEQRUJqfIMbMW/Bfz6/lSF1pfEFUT/vh80Veq4XhAUGkjW0lqnz9tNBTCfWA8AiOh0fSNG17Q1KJVeUlKCRYsWYfLkyZg8eTJeffVVdx9JIiIiIiKipsI1ZCXp+ZVImrMSSc+vxCvrcmC2hXYgRbizSRKmZ6V5XJuelQab5N/23n7t9NAqRZytsOHgmXK/rtXc2M/nQaosBkQlVK17B+UeKkM6gJaXjGzKk48FhRratlkAgtM30p+EYlNO4gaToFBBHd8XAGAp3OHxGNPhrwBZgiZpMFT6jo0ZXpPmczLyl19+QVpaGhYvXoySkhKUlJRg8eLFSEtLwy+//BKMGImIiIiIiHxW25CVuQEYstLSRaqVmDWyM54Zk+6ukDToVHh2TBfMGtkZkWqfO4JVo1aKGJxqAMC+kZeyFjm3aKvjegWt55xriE1t1V7NlWQ3I6bvFI9rrsnHoaRLHQEAMNfTo7Ah/EkoNuUkbrBdGGKzy+O66eBnAIDIrrc2WkzhwOe/IaZOnYqJEyfi7bffhkKhAAA4HA5MmTIFU6dOxb59+wIeJBERERERka/qG7Ly5Kj0Ro6oedGqFBjZuTVmjuiMMrMdcRFq2CQJWpUiINcfntYKvxw9h025JbhvSPuAXLM5cA3LCMYWbRfXEBtbyeGg3aMpOr/3XcRkTAUAlO15y6e+iY1B1+FqJBjSoUsdCUdFEUSNAbJk87sfo8NcCkEVBVFj8JiQrC+hKEs2xGRMg3HrCzXWXEnc5tojUV31deipMtJWegyW01sAQURklz81dmhNms/JyCNHjuDLL790JyIBQKFQYMaMGfj4448DGhwREREREVFDBXvISktnc0i45v2tiNIosXX6ZVArRagb1gnMI1ffyP2F5wN2zebAVYGlDmIyUmnoDFHXGuHarVOymSCIKncy0ZuEnfXMXpRsehbl+/8fEm/5DoYhT0KylELU6J3nN4HJx0p9R5gOfo7ilZMDlii1njuIom/HITZrAWL6ToFx24s1jqkvoSiqIhHT7yFAlppkEjeYXL8UsBbthiw5IIgXcmWmQ58DALTJV0AZ2SYk8TVVPicj+/fvjwMHDqBr167VHj9w4AD69u0bsMCIiIiIiIj84Rqy4ikhadCpEKlW4o+i80gx6KASRRjNNhi0Ktgkye9txi3B3tNlMNsl6FQy2sfqAn794R1i8fU9gzC6S2sUnrcgVsf3RpZlWF2VkUGYpO2iSeiHlHsPw1FxBrLDGpDqu8bSkInQsiyheO1DgOyAKq47VDGpAABFRDyApjH52D2x+qJkoWvADACvJ1ZXS9SqY2A35gBQoHT3EiTd9DUgiCjLfvPCa9d3Sr0JRcuZPTjz/Z2IzXoBqQ/kQ7KUNakkbjCpYrtCUEVBtpXDdu4A1K17uddcW7SjuEW7Bp+/i0+fPh1/+9vfcOTIEQwdOhQAsGXLFixduhQLFizA3r173cf26dMncJESERERERH5wDVkZe6qQzXWpg3vgE3Hz6Fvmxi8vPYI3tx4DMZKGww6FaZnpWHWyM4B227cXG09bgQADE41QBSFgF9fo1Rg5wkjJn2Wzfemir0sF5KlBFCooY7rGZR7SHYzyrKX+pTMayrcCbut8y885kXCrnz//4Pl9GYIqkjEXbGo0eL1RX0DZgyDZ9V7DY+J2r5T0ObW9YAkQVRFQj/wURgGz4JkKYGgikLl8dWwFu+DNmlQrdc1bp4DW8kfMB38DJGdbmxSSdxgE0QFNAn9YD75KyyFO93JSOvZ/bAW7wNEFSI63xLiKJsen5ORt912GwBg5syZHtcEQYAsyxAEAQ4HJ9QREREREVFouIasSLJcI9k4c0RnHDpTjiUbcvHC6gt98VwDbgDg8RGdWnQVXn225pUAAIakxgb82iarHQvX5fC9uYTVtUW7dW8IysC3GGhoMq+paEjCzmE+h3MbZgMADEOehjI6JagxNpQ3A2ZcSUCPx9T23m57ERBE6Ac+CgDu91cRkYizv85G2c5XoU7oh7a3bYYg1GzDYD69FRVHVwCCArGZz/nxDMOXOnGgOxkZ3fNuABeqIiM6jIVCG/jvkeHO54Yeubm5df45evSo+7++WLBgAQRBwMMPP+x+zGw2Y+rUqYiLi0NUVBTGjx+PwsLCaufl5eXhuuuuQ0REBBISEvD444/DbudUPCIiIiIiAg4WlaN/sgH5z4xGwZyxKHhuLB4f0QkRagV6JEbjzY3HPJ63eEMuVGLg+h82R1uOO5ORQ9sH/gft+oYPtdT3xj28JiE4/SLrS+YJoioo9w2UhkyENm57GVJlMVStukPfb3qQI2w4fydWN+S9NQx4BII6Btai3Sg/8C+P55ZsehYAENXjTqhiu9QZQ3OluWSIjSzLKD/o7BcZ2WViyOJqynz+VVL79oGfYrZ9+3a8++67NbZ1P/LII1ixYgW++OIL6PV6TJs2DePGjcPGjRsBOKd4X3fddUhKSsKmTZtw+vRp3HXXXVCpVHjxxZpNV4mIiIiIqGV5Z8txvLv5OB7MbI+l450/b7iGrHDATcOdNVlxuNgEwLlNO9D43nhmKXJWRgZrkra/1Xeh5krY1TcR2tU30WE+h9jMZ6FtNxyKyDYQFE032ervxOqGvLeKiAQYBj2Bko1PoWTTs4hMHw9RFeFer8xfB3P+OkBUwTDkqYY9sWbAPcSmeC9khxXWM3thL82BoNQhouMNIY6uaQr5r5PKy8txxx134P3330ds7IXfqJWWluLDDz/Ea6+9hpEjR2LAgAFYtmwZNm3ahC1btgAAVq5cif379+Of//wnMjIycM0112DevHlYunQprFZrrfe0WCwoKyur9oeIiIiIiJoXs82Bz7JPAQDG9ak5ydQ14MYTg04FvbbpJiZCbVvVFu0u8ZFoFRH4vnB8b2qSZenCNu3E4Ayv8bf6LtRcCTtPYvpOgfn0VkjWcpTuWIS895KR/34q8j9Ig7VwF9StezdytL4RVZHQD5oJw5Cn3e+RqDHAMORpZz/PerbPN/S9jen3EJTR7eEoP4nSXX93Py7LMko2OqsiY/o8AFVM4AvXwoVS3xGiJhZwWGEt/g3lri3aHa+HqI4KcXRNU8iTkVOnTsV1112H0aNHV3t8586dsNls1R7v1q0bUlNTsXnzZgDA5s2b0bt3byQmJrqPueqqq1BWVobff/+91nu+9NJL0Ov17j8pKU2zJwQRERERETXcN78XwlhpQ4pBixGdWtdYdw248WR6VhpskhTsEMPWljwjAGBoEPpFAnxvPLEbcyBZSyEoNFC36hGUe9SZzKuqvmvKnANYHoNh8JOXJOyeQky/hwCH1d030VUl6OqbWLp9ISSbKXTBe0FUaqEf+ChSHziBlHuPIOW+XET1uNOrwULO93aqx7W63ltRqUVslrPPZOmORbCbnK3zKo+vgqVgKwSlDvpBTzTwGTUPgiBA7dqqXbANpkNfAAAiOUW7ViHt+Pvpp59i165d2L59e421goICqNVqGAyGao8nJiaioKDAfczFiUjXumutNrNnz8aMGTPcH5eVlTEhSURERETUzPxjRz4A4M4BKR6nPbsG3ADOPoSuATfTsjq06InN3tha1S9ySBD6RQK1vzcteZq2q1+kOr5v0LYTu6rvAGcfwYsnLofDNG0AqDyxHurE/ki57xhkmwmiRu9MtAkKaNoNQ9H3t3k8z9uJ1KHmqoAsP/QlSncugi51NBKu+dir82L6PQTIMsr2vOXTpPTILhNQkfMNIrveClETA0dFEbTtspBww5ewGXOgjEwK2PMLV5rEAbCeyUbFsZWQJStEjQER7a8KdVhNVsiSkfn5+fjb3/6GVatWQatt3G9oGo0GGk3L6y9CRERERNRSnCo146eDRQCAuwcm13qcVqXA4yM64clR6ThXYUW0Volfcs5Cowz5JrImS5JkbMs3AgCGBKFfpIvrvZk9Kh2ny8yIj1LD7pBbZCISACxFuwHAXYEVLK7qO8PgWXCYSyCqo1CZv97jJOWmyHx8Fcqyl0I/aCZaDXf2V3T1UnRUFIV1T8yLadtmomRDMSpyv4dst9Q7Xd1uKkDBV1chdvg8pD6QD8lS5k7U1pdkFgQBrce8i9Ltr6B45eTqSerBLbsq0iW612Tn10xFERQRCbCe3R+UiffNRYO+mxiNRnzwwQeYPXs2zp07BwDYtWsXTp486fU1du7ciaKiIvTv3x9KpRJKpRI///wzFi9eDKVSicTERFitVhiNxmrnFRYWIinJmXVPSkqqMV3b9bHrGCIiIiIiann+uesEJBkY3iEW6fF19+yKVCuhVoqI1anQY+E6XPfhNuw9zb7ytTlUXA5jpQ06lYjebWKCeq9ItRIapYjpX+9D2vw1+Pno2aDerymzBnmS9sVEVSQEhRqKiHic+jQLRd/cgsq8tUG/byCYTznbuqlb96mxFu49MS+maTMEisi2kK1lqMxfU+/xFUe+hu3cAZRuXwhBoYEiIh6CQl1vr0nAOfCndMciGLe96GF7+ytNfnt7sEl2M8r3/wP5H6ThxLKuyP8gDZW530Oym0MdWpPlczJy79696NKlC15++WUsWrTInSz8z3/+g9mzZ3t9nVGjRmHfvn3Izs52/xk4cCDuuOMO9/+rVCqsWXPhi+rgwYPIy8tDZmYmACAzMxP79u1DUVGR+5hVq1YhJiYGPXoEp4cGERERERE1bbIs4+OqLdp3DfS+HZNGpUDfNs5kxIoDRfUc3XJtOW4EAAxMNkClaJxqubZ6HYpNVqw7Utwo92tqZFm6aJJ2cIbXeCIIIrTtLgMAmI583Wj3bSjJWg7rmT0AnJWDlwr3npgXEwQREZ1vAgCYDtf/3pS7+hh2meD7vUQVyrKXelwry34TgtjyBkq5SDYTSre/XLMP6db5YdGHNFR8/ptjxowZuOeee3D48OFq26uvvfZa/PLLL15fJzo6Gr169ar2JzIyEnFxcejVqxf0ej0mT56MGTNmYN26ddi5cycmTZqEzMxMDB06FAAwduxY9OjRA3feeSf27NmDn376CU8//TSmTp3KbdhERERERC3UjvxS7C8sh1YpYmLftj6de233BADADwcK6zmy5dqaF9x+kZ6M7OwcQLTuSMusjLSVHIJsK4egjICqVbdGvXdk+i0AgIqcbyBL9ka9t68shTsA2QFFVDKU0TV/EeHvROqmJrJz1Xtz9FvIjtoTqfbyk7Cc3Og8J32cz/dxbmE31rFW6vM1mwsmahvG556R27dvx7vvvlvj8Xbt2tU5NKYh/v73v0MURYwfPx4WiwVXXXUV3nrrLfe6QqHAd999hwcffBCZmZmIjIzE3Xffjblz5wY0DiIiIiIiCh/Lq6oix/VuA73Otx8EXcnIzcdLcNZkRVykOuDxhTv38Jog9ou81JWd4gAAe0+XodhkQevIllV84tqirU7IgCA27ugHbbvLIGrjIJnPwnzyV+hSRjTq/X1hOe3cou2pKtLl4p6YkqXU676JTZG2XRZEXWtIlcXO9yZ1pMfjTIe+AiBD03aYxyRtfVzb2z0lJMNte3ugeZOoDZc+pI3J58pIjUaDsrKa/VMOHTqE+Hj/XuD169fj9ddfd3+s1WqxdOlSnDt3DiaTCf/5z39q9IJs3749vv/+e1RUVODMmTNYtGgRlMqQDgknIiIiIqIQsdgd+HS3s5f93YN8/6E7NTYCvZKiIclwD8ChC0wWu7uf5tBGrIxMiNagV1I0AGB9C6yOtBRWbdFOaLwt2i6CqEREpxsBeLcdOJRc/SI1bWpPRgLVe2J62zexKar23tSxjd7kxxZtoHltbw+05tSHtDH5nIy88cYbMXfuXNhszk82QRCQl5eHJ554AuPHjw94gERERERERN769vdClFTa0E6vdW/t9dW13RMBAD/8wWTkpXacMEKSgWS9Fu30uka995Wurdo5LTAZWdUvUt2I/SIv5t4OnPM/yLIUkhjqI8sSLKe3AKi7MrK5cb83Rzy/N7ay47AUbAUgNGiLNtD8trcHEhO1DeNzMvLVV19FeXk5EhISUFlZiSuuuAKdO3dGdHQ05s+fH4wYiYiIiIiIvLI+pxitI9W4c0AyFKLQoGtcV7VV+8c/iuCQ5ECGF/a25hkBAENSG68q0mVE1VbtljbERpYcsBbtBgBoEoM/SdsTXepIiGo9HKbT7oRfU2M79wckixGCMsLjJO3mSpcywvneVBTAUlUZerGKw18BALTJl0MZ2abB93Ftb0994ARSHziJ1AdOQD/w0bDc3h5ITNQ2jM/7mfV6PVatWoUNGzZg7969KC8vR//+/TF69OhgxEdERERERFQvk9UOpSji0Ss74+Xre6DC6mjwtTLbx8KgU+FshQ1b80owrEOrAEYa3tz9Ihtxi7bLFZ3iIAjAH0XlOF1mRpuYlpEEsZX8AdleAUEVBZWhS0hiEBRq6DpeB9Mfn8B0+Gto2w4LSRx1cW/RThoEQdFyhoYICjUiOl2P8gP/gunI19C2G15t3T1FO/1Pft/LlVhz9UAUFOypCzSvPqSNxefKSJesrCxMmTIFM2fOZCKSiIiIiIhCxmxzYOG6HLR5fiU6vbgGKfNWY+nGYzDbGpaQVCpEXNXV+cP29we4VdtFlmVsqZqkPbS9odHv3ypCjYy2MQBaVnWkpWp4jSahHwRREbI4LmwH/i9kuelVDLuG12ha0BZtl4iq98Z0yXtjM+Y4hx8JonsqOgVHc+lD2lgaNOllzZo1WLNmDYqKiiBJ1XsSfPTRRwEJjIiIiIiIqD4mqx0L1+Vg3qpD7seMlTbMrfr48RGdEKn2/ceea7sn4LPsU/j+QCFeuKZbwOINZydKzThdZoFSFNC/XWiGMozo3Bq7T5ZhXc5Z3N4/OSQxNDZroatfZGi2aLvoOoyFoIqE/fxxWIt2hWzLeG3ck7TrGV7THOnaj4GgioTjfB6shTuhSRoIADAd+hIAoE0ZAUVEQihDJKrG58rI559/HmPHjsWaNWtQXFyMkpKSan+IiIiIiIgai0oUsWRDrse1xRtyoRIbthns6q4JEAQg+1QZTpZW+hNis7Glaot2nzYxiGhAgjcQRnSqGmLTQisjQ0lU6hDR4WoAgOnwf0Iay6UcFWdgKzkMANC0GRLiaBqf8725BgBgOnLhvXFN0Y5q4BRtomDx+W+Qd955B8uXL8edd94ZjHiIiIiIiIi8ZjTbYKz0PK3UWGlDqdmG+CiNz9eNj9JgcIoBW/OM+OGPItw3pL2/oYa9LSHsF+lyWcdWUIgCjp6twPFzFWjfKiJksTQGWbLDemYPgNANr7lYROdbYDr8FUxHvkbs8BcgCA0bEhVo5qqhOqpW3aHQtswerxHpt8B0+EuYjvwXscPnw1ZyCNbivYCoRESnm0IdXrNnslmhEkUYrWYY1FrYJAmRKrXX6y2Nz78mtFqtGDas6TWrJSIiIiKilsegVcGg8zyswqBTQa9t+CCLa7snAmDfSJetIewX6RKjVWFgsnOL+LqcsyGLo7HYzh2E0tAZipg0KA2dQx0OItKugaDQwG48AtvZ30IdjltL7hfpEtHhGggKrfO9Kd7nrorUpYyCQhcX4ujCg8lmhdVhR1FlOawOO0w2q1frZrsNr+xbhzafPo82n85Bm0+fxyv71sFst3m13hL5nIy877778MknnwQjFiIiIiKiZs1ktcNql1BUboHVLsFktYc6pLBnkyRMz0rzuDY9Kw22S3rc++La7s4ea6sOnYHF3vDp3M2B1S5h14lSAMDQ1NBVRgLOvpEAsL6Zb9WWbCYoDZ2QeONXSL5zN2R76NsFiOpo6NqPAQCYDn8d4mgucE3Sbon9Il1EdZT7vak49hMsZ7Ih6lojsgVt0W5oMhFoeELxvNWMBXvXYt6eVTBanV+jRmsl5u1ZhQV716LUWlnn+qUxthQ+b9M2m8147733sHr1avTp0wcqVfXfNL722msBC46IiIiIqLlwTXxesiEXxkobDDoVpmelYdbIztCqQjchN9xFqpV47MpOkGQZb248FtDXtl9bPZKiNSg4b8GvR89hdJf4AEYeXvaeLoPZLqFVhAqdW4d2SuyIzq2xYO0RrD1SDFmWm8xW4UtJNhMEUQXJYoSoMUCWbF5P2JXsZpTuWISy7KXu82MypkE/aCZEpTbIkdctovPNqDj6HSrz1yM289mQxgIAssMKa+EOAIC2BVdGAkBUz7sR1fMe6FJHIrLiT1BEJECWms4vvfzdylzXuitZuOTABhitlTCodXioexZm9RkJrVJV57pDlvHKvnWYt2eV+16uhCEA/K3nZXj991/xwiXrb/+xCU/0GYklBzZ4fL6fHN2FJ/uOqnV9yYENeLLvqIa/oGHM52Tk3r17kZGRAQD47bfqZdlN9S8BIiIiIqJQCtbEZ3JasiEX/ZMNOPnsGJRb7NBrVbBJkt9JXlEUcE33BCzblo8VBwpbdDLS3S8yNTbkP/cN7xALlULAiVIzcs5WhDw56ok/yUTJZkLpjkUwbp1/4TGLEcatLwAA9AMf9TqpGQwRnW5Cwg2x0KWOhN1UCIU21qdEqzd8SeRairIhOywQtXFQGtIDFkM40rUfg9JtL6N45eSgJLFDlSz0N5k4vUcW3ti/oUYycd6eVdAplXi45xX1JhTf9LCeFBGNwsrz7orHS+mUKpRYK2tdN1orUWozI14RVc8r3/z4/C+edevWBSMOIiIiIqJmq76Jz0+Oatk/QPvr0+yT2Hf6PL6+exBu6p0EAFD73pHKo2u7OZOR3x8owt9b8AyIbXkXkpGhFqFWYmhqLH7NPYe1R4qbXDLS32SiIKpQlr3U41pZ9pswDJ4V2IB9JCg0sBbuDFrCy9dEruX0JgCAtu3QkCfKQ8n9ebftRYi61lDF9YKjoqDG511DE4rBTBbO6HUFXvvt51rXH+qRhcVBSCYCwLf5BzApfXCDEooFFecRr42CQa3zuF5ptyFWrat13aDWQa8KbaVzqATmb2giIiIiIqqVNxOfqWGKzluw7/R5AMCwtMAnysZ0iYdSFFBSacPxcxUBv35jCESv0nOVNrSOVGNICIfXXKwp942sL5koiHUPVZIsRkgWYx1rpf6G2GCSzYTS7S/DuO1Fd4yuRGvp9oWQbKbAXH/rfK+v7+oXqWnTMgbt1tb3UBBVqDi2EtE3/A9J9+ZAddMPSLo3B9E3/BcVx36CIKqC1hexrI71xft/hVIU60wWahXKOtejVZo6k4mldVQf1ledeLj0DPRVCUNPLk4oXqrYYsLPBTl4qHuWx3Nv79gfZoe91vWHumf51dc4nHlVGTlu3DgsX74cMTExGDduXJ3H/uc//wlIYEREREREzYVr4rOnhKS/E59bunU5zmRUnzYxiI/SBPz6MVoV1vxfJvon62GstMFql5yVQmGyrd7fXqUmq92ZSLilNxKi1LA75EaIun4jOsdh7irnRO2m1jfSm2SiIqL2Lf+ixgBRY/B4DeeaPkCR+i7YVZu+Xl+WZVhcw2vCpF9ksLY6q+wViL35WyzcvxVLtrx4Yb1bJmbe/C1MdhsW/b7B5+pEf/sifpO/H/ekD2pwstCXZGJDqhPtsgSb5MBD3bOqPXeXixOKntaPnj+LJ/qMBICa70vfkdAqVJhVz3pL5NXfoHq93v3NXa8P3Tc+IiIiIqJw5Jr4PPeinpEu04Z3gE2SAratuKVZc9iZjByZ3joo1zfbHFh9+AxuWrY97AYP+durtCkPXRraPhZapYjC8xYcKCxHj6TokMZzMX+SibLDBkvhTsT0nQLjthdrrMdkTIMs2SAo1B7ODj5/E62Bvr697BgcFQWAqII6cUCD7+uLpjpkZUavy/HavvWYt3dN9fW9a6FTavBwr/q3Mnta97cvor/JwmAnEx/qngURQr0Jw9rW7+s6BFqFCo/3HoEn+45Cqc0Mvcr5vrsSjVpl3estkVfJyGXLlnn8fyIiIiIiql9tE5+nDe+Ah7LS8I/tJzBleIdQhxmW1lZt0x3VOfDJyAvJvMPux8Jp8JA/vUqb+tAljVKB4WmtsOZwMdYeKW5SyUhZsiEmY5q7V9/FYvpOgc2YA3Vcdw/n2XHmx7thLf4NbSasAQShyU3TDnbVpq/Xt53b7+yPqO8IUel5m60nTbFv4iO9Lsfff/vF47pOqcLDPS+vP5n4xyaP69+eOIBJXULTF9HfZGGwk4kXVyf6k1B0ff64htGoL/l9TX3rLU3T/ZuTiIiIiKgZWbD2MAamxFab+Jx7rgJXvLUJfxSVQ69T4o7+yaEOM6wcO1eBo2croBAFXNaxVcCvH+6Dh+rrVVpusUMjic6ki9kGQ9UU8ki1Miye+4jOrbHmcDF25htDHUo1oioS+oGPAbKEsj1vXZRMnIqYvlNw+otRiM6Yguged1abGG0pyoa1+DfYSnNgPXcA+oGPwTB4NiRLKUSN3jlROoSJSKCeRGsAqjYlh8VdFSrqWkMRkQRHRQGkymLEZEx1X9+VLDwfPxRJ9+agsiSn2nWCUZ34aK8r8GodQ1Ye7nk5Xv/dz2RirX0R92OSH1ud/alOvLgvYiiThcFOJgJMKDYmr5KR/fr187oHx65du/wKiIiIiIiouZEkGR9uy8eLa45g3YOZuKKTs4qva0IUru+RiD+KyrFwXQ6u7BSH+EhNjcQQeeaqihycYkBMEPpuejN4KBh9KgOlrl6lg1MN0KkVWLD2SI1t2DNHdMLZiqb/3K/tFo+eidEY3aU1isotTepr5vzvy6BO7I+U+45BtpncycTyA/8CAESlj0fp9leqJyv7TkGbCWtgObMXuuTL3ddybUsO1dbsi4mqSOgHzQQAlP/xbwhKHWR7JaK6/dmnqs3akoXn976HmH7TIXa5FVp9GoyVpYjX6WEuzUVUZCJsxiOQY3t4Tia26up3deL0npfhjd9/rTG12d++iaFMJgJNpy+iP8lCJhObF6++S998881BDoOIiIiIqPnaeaIUhectiNYokdm+egXfgmu7w2qX8NTodCzZkFttG3dT6c/XVK0Ncr/IcB88VFev0vcm9MWCtYc9bkHXKkU8fHnHJv/cuyZE4z/7CjDps+wm9zVzfu+7sJ37A/HX/gtRXSYAcCYTY/o8AG27y1GWvbRaT0jJYnR+LAjOqsomTFRqoR7wOBIHP+lOOlnMJdUSkQ2pTHyi95Uwn9wAda/J+HteHpYc+KTa+swebWC2mLC4amqzS7VkYo8svLF/Q41korM6UYmHe9bfN9HT1OZQ901sLn0R/U0WMpnYfHiVjHzuuecAAA6HAxs3bkSfPn1gMBiCGRcRERERUbPx7f4CAMBVXeOhVlYfVCOKAuZd3RWvrM/BC6vDszdhKMiy7K6MHBmEfpFA3cm86VlpTX7wUKRaiRlXdKzRq3TWiM7oFh+FJRuOeTxv4focTB3eoUk/d1dPy6b4NWM9ux+2c38ACjUi2o+tsa4ydETZnrc8nluWvRSGwbMDEoc/g1bqTSb+9kv1hFW3YZjVd7SflYkSpo54Gy/v34wX9qypse5MJl6OJT/P9fh860omAsC3+QcwKT08+yayLyI1Nz59d1YoFBg7diwOHDjAZCQRERERkZdWHCgEAFzXPdHjukapwJsbj3lcayr9+ZqaA4XlKDhvgVYpIrN9bFDuEalWYtbIzgCc74MrmfdQVocmUX3njSe+24+ruiVW61Vql6V6t6DbHLLH595UKg+bck9L0+GvAAC61NEeB7pcPDH60r6IF0+MDsXU5tl9RkIGfE8m7l0DCCIe6pGFxQ2uTMzGk33H4M0DtQxh8SOZCDSPvokA+yJS8+Dzr4p69eqFo0ePIi0tLRjxEBERERE1KydLK7H7ZBkEAbimW4LHY8K9N2EorKmqisxKaxXUxJhWpcDjIzrhyVHpOFthRYxWiS3HS0KejPOGzSHhn7tO4t0tedjz6BXo3SYGAKCGCKUg1rkNO0qjhFopup97qdkGfVVPxqbw3Bvja0aymZwDZqznIaqjnQNkVJH1nmc6/BVUsd0QccUbsDrsNZKFosYAdeIgaAY/jejUK2GsLEO8Lgbn89bBsm0+RI0+qFObZ/S6Aq/VMohlfIc++OrY3lqGsDR8mzMQ2mQi0Dz6JgJMJlLz4HMy8oUXXsBjjz2GefPmYcCAAYiMrP7NOCYmJmDBERERERGFuxUHigAAQ1JjkRDtOTkS7r0JQ2FdVTJyRJC2aF/Mtd1XrRDR8cU1OFNuxYlnxqCtPrSTjeuzNa8EJqsDrSPV6JkYXW3N2y3orufuSuw1lW3pwf6akewWnC/Nh1bfAUabHQalBHNpPqINaRCVztfCU2WiypQPSBJaTfwZCw9sw5IDH9RIFqplB1qNX4mX9/2CJVtevGircyaeGL8SFQ4HXvnN81RmoO5kYoRShb81cGpza00kOkbH1TGEpakkE4dj3p7VNdZv79gPZoet2fdNJGoOfE5GXnvttQCAG2+8sdqEbVmWIQgCHA5H4KIjIiIiIgpzK/Y7t2hf38PzFm0g/HsTNjaHJGN9zlkAwKggDa/xJC5SjY6tInCm3IoVBwpx/9D2jXbvhlh9yJmwHZXeGqIoVFurbQt6U9mGXZ9Afc2YrHZnQvGiCfY6wQqLLOPvx3Ox5I9/VU8WxqRAY5NgFZSeh7D0ykLEiDex8MAWzPPQ9xBwJRM3OLc2X7y+dy0iVNoGJxMB4Jv8/bingVObkyKiUWQub7KVic5kIjCz+xBAlrDkj83V3puZ3YdAp1CwbyJRGPA5Gblu3bpgxEFERERE1OxU2hxYffgMAOD6WvpFAs2jN2Fj2nWiFMZKG/RaJfq3q9mTL5iu65GIrXnGsEhGrqn63KstYXvxFvSmtg27PoFIppptDixcl4MlF50/a0RnTL0s2bnN2UOyEBAwo/eVeG2f54nO7iErv9Y9ZCUYyUTAv4RhQcV5JNQxpKXeZGJaX1RaTXUnE2UZT/TKAmRHjWTiE72yoBHkOpOJKmsZTn8+AtOyFmD2xCdRai6DXhuD88fX4tznV6DNhDXQRsSzOpGoifM5GXnFFVcEIw4iIiIiomZn7eFiVNokpBi06N0mus5jL04MFZus0OuU2HysBBolKyIv5ZqifUWnOCgVjfv6XN89Ec/+eBCrDxWj0uaArokm7s6b7diaZwQAjE6Pr/W4proN2xsXf80UnLcgLlKFvJJKrxKRrmnc8y6qrDRW2rDiQCEevqIDlvzheYjKJ7nZeDJjTBhsZfa9+rDYYsLR82cbuM15OGZ2Hwzz7jcwq2oauKdkogYyTn0+CtMGP1UzmfjVWLSduK7OrdCyOhqOigKc//ZmmKqG/xRUDf8RNQb3wCAmE4maNq+SkXv37kWvXr0giiL27t1b57F9+vQJSGBEREREROHuu4umaF/c4qg2rsRQpFqB7i+vw4lSMzZMG45hHVoFNc5ws/aIs+JvZCP0i7xU37YxaKfX4mSpGeuPFOOaOipeQ+mXo2dhl2R0iotAh1YRoQ4naFxfMz/nFOPRb/cjvXUkNj6UVe95dU3jNtaRLGzKyUTA/76IXfStG7TN2Wopw7nPr4S9LBeGQY/Vmkx0VBTBWrgdVg/JRADuSeK1JRNlyYaYjGkwbn3BOX286jwAiMmYBlmyQVBcmDhORE2TV8nIjIwMFBQUICEhARkZGRAEAbIs1ziOPSOJiIiIiJxkWXb3i7yhjn6Rnuh1KozuEo/l2/Px1sZjTEZexGJ3YEPuOQDAqDoq/oJFEARc1z0R7205ju8OFDXZZORq9xbtxn+NQmFUejzOVlhRfNyKY+cq6k3A1jaN+0BROWJUfiYT60gWBjuZ6G9fRI0X5wI1Kw9VuliUOMyQHRaY89cisuP11dZdyURn9aIBksVYI5l4cWVjbURVJPSDZgIAyrLfhGQxQtQYEJMxDfpBMyEqm/ZQKSJy8ioZmZubi/j4ePf/ExERERFR3faeLsOJUjMiVIoGTXyeOrwDlm/Pxxd7T+G1G3vWOom7pdlyvASVNgmJ0Rr0SIwKSQw39KhKRu4vwJu39PKq6rWxrTl8YXhNS9BWr8WVHeOwLucsPs0+iVkj0+s8vrZp3MUmK7bk1b5V+faO/WG2W2pPJnbLhAApZMnEQPVF9HWbsyAIiEi7FmV73kLF0e8RUZWMvJSzsnEqjFvn11jztrJRVGqhH/goDINnQbKUQtToIUs2JiKJwohXycj27dt7/H8iIiIiIvLs26qqyNFdWjdoIMiAZAOGpBqwNc+ID7bl4clRdSdXWgpXkm1k59YhSwKOTG8NnUpEvtGMfafPo0/bmJDEUZuCMjN+KzgPQQBGdI4LdTiN5s/92jmTkbtP1ZuMtDokTBveAS+sPlxj7bdTFXhiqOdk4cweQ1Cx+RnMynq55nq3TDzRMws6tbMqM5TJxFDQuZKRud9DlmWPX5+CUoeYjIcAWUbZnrcaXNkoqiIBAIoIZ9EUt2YThRefB9i47N+/H3l5ebBardUev/HGG/0OioiIiIgo3Lm2aF/nxzbeB4d1wNa8bLy7+RieGNEZCrHpVeA1NtfwmlD0i3TRqRQY1Tke3x0oxHcHCptcMnJN1WvUr60erSNbTkXtuN5tMPU/+7D3dBn2F5xHj6Tah0atPVKMh7LSAABvbjxWbRr3fUNSoVUqavZFtFWi5OurYS3cDnVcDzze+y73eoxCifPH10DKXw2kjwMQfslEf2mTL4egioTDdArWM9nQJPSrcYzp0JcwbnkBsZe9hNQh+ZAsZaxsJGqBfE5GHj16FLfccgv27dtXrXek67ce7BlJRERERC1d4XkLtuUbAfiXjJzYty0e+3Y/8o1mfLe/EDf1SgpQhOHpvNmObVUTokO9/fi6Hgn47kAhVuwvbHJVq2sOtawt2i5xkWpc1TUeKw4U4dPsk5h7dTePx0mSjKe+/wMOWcbndw3A06O7oNRsg16rclYnVlUy10wWRkPu+38oXrkdZ9c/jHZth0Ed1wMG0zmc+vdQSOYSxD5wonGebBMkKrXQpY5GRc7/UHF0RY1kpOywomTTs7CXHoX1TDYiO17HykaiFkr09YS//e1vSEtLQ1FRESIiIvD777/jl19+wcCBA7F+/foghEhEREREFF5++KMIsgwMSNajrb7h1T5alQL3Dk4FALy1ib3bt+adQ7eEKAxI1od8QvT1VUOJtuSV4Ey5JaSxXEyWZaypmjY+ukvLGF5zsT/3awcA+DT7lMehqwCw4kAhfi88j5OlZiTrdVArRcRHaaBWiu7p3LWJ6v4X6NKuBRxWFK+8H7JkR+VxZ+9IXcpIKHQtZ1u8JxEdrwMAVOR+X2Pt/L4PYC89CkVEIvT9/tbYoRFRE+JzMnLz5s2YO3cuWrduDVEUIYoisrKy8NJLL2H69OnBiJGIiIiIKKzsO1WG1pFqv6oiXf46tD0EAVh1qBiHzpQHILrQMFntsNolFJVbYLVLMFntPp8/PC0O/7t3MH6ZOtzn8wOtnV6Hfu1iIMvA9weKQhrLxQ4Xm5BvNEOjFJGV1vKmsN/UMwk6lYgjxSbsPFFaY12WZSxYewSAsw2CQaeqcUxdBEFA61FLoU4cCP3gWZAlOyLaj0bKvYcRN+L1QDyFsKbrcDUAwFq4A3ZTgftxyXoeJVVDawxDn4GoDs3wKSJqGnxORjocDkRHO3tvtG7dGqdOnQLgHGxz8ODBwEZHRERERBRGXAm3aZelIfepUfhrpv/DH9PiInBdN2dS8+1Nx/y+XiiYbQ4sXJeDpOdXImnOSiQ9vxKvrMuB2eZdiyfX+e3mrkKnF9eg3dxVPp0fLK5k84oDhUG9jy+J3NVVW7SHd2gFXQMGJ4W7KI0SN/RwtjP49+6TNdZ/PXoOm4+XQKMU8bfL0hp0D2VUO7QZ/xOshTuR/34q8j9KR/4HaSj/49+Q7Ga/4g93ysgkqBMHAgAqc39wP1668zVIlWegNHRGdM9JoQqPiJoIn5ORvXr1wp49ewAAQ4YMwcKFC7Fx40bMnTsXHTt2DHiARERERETh4OKEW6cX1yBl3mq8u/l4QBJmU4Z3AAAs354PkyW0FYG+MlnteGntEcxbdQjGShsAwFhpw9xVh7Bg7RF3Yq22hJu354eCa6v2TwfPwGqXgnIPXxO5aw47t2i3tH6RF/tzv7YAgM/3nIIkVd+qvWCtc3r23QNT0CamYS0UJJsJpTtfg3Hbi5AsRudjFiOMW19A6faFkGymhgffDESkXQsAqMhdAQCwmwpQuut1AECr4fMgKHyrRiWi5sfnZOTTTz8NSXL+RTt37lzk5ubisssuw/fff4/FixcHPEAiIiIioqYu2AmzsV3iMbZLayz/cz8oRKHBW51DQSWKWLLBc7/LxRtyoRLFWhNulTYHFIJQ7/mhMjDZgMRoDc5b7Pg192zAr+/r55VDkt3Txkent7x+kS7XdEuAXqvEyVJztfcl+2Qpfjx4BqIAPH5lpwZfXxBVKMte6nGtLPtNCGLLTra5+kZairIh2S0o3bUYss0ETeIgRHQeF+LoiKgp8Plv7quuugrjxjm/gXTu3Bl//PEHiouLUVRUhJEjRwY8QCIiIiKips6bhJs/RFHA53cNxM4TRrSdu6pBW51DxWi2uRNpNdYqbThvseOltYc9Jtw+3JqHksq6zy81e15rDKIo4NruCQCA7/YHfqu2r59XO08YUWq2w6BToX+yPuDxhAuNUoFbercB4Bxk4/LyOmevyIl926JT68gGX1+yGN0VkZ7XavaqbEnU8RlIvPlbJN+1B5L5LGKHPoWEG75EqxGvQxCEUIdHRE1AQH6N2KpVK35TISIiIqIWq76Em78JM5PVjld/PooXVh9ucluV62PQqmodEtIpLgLRGiWWbDjmcf2NX48iLkJd6/kGnQp6bWir0K6v6hu5KfdcrdObG8rXROzqw86qyBGd4qAQW/bPZ7dVTdX+cs8p2BwSjhSb8MUeZ2LyiZGd/bq2qDFA1BjqWGu5iWAAkB0WWE5tRv4Hacj/oAPyP0iDtXAX1K17hzo0ImoiQrengYiIiIiomagr4RaIhFmwKy+DySZJmJ7leVDIw5d3xLlKa60Jt5yzFTDZ7LWePz0rDTYpOL0avTW2Szz+N2kQ1k4ZhkI/JoVf2i/zxz8KEaVR+PR5teaQq19ky92i7TKiUxwSotQ4W2HDLznF+NeuE2gVocY13RLQt61/yUJZsiEmY5rHtZiMaZCl0FXrhppkM6F0+8s1+2lue5H9NInITRnqAIiIiIiIwp0r4TZ31aEaa66EmdqPOgBvKi/jozQNvn4wRaqVePSKTpBkGW9uPAZjpQ0GnQrTs9IweXAqREGAQafy+PwMOhUiVUrMqqpkW7wht9r5s0Z2hjbEE6MVooDt+Ubc/Wl2g2Jz9ctcctFzmza8Ax7KSsPmYyWYNrwDXlh9uMZ504Z3gNUhQa10fl5VWO3YeKwEADC6S8sdXuOiVIiYOjwNfdrEILNDHDq2jsJjV3aq9evIF6IqEvpBMwE4e0RKFiNEjQExGdOgHzQTorJhg3Gag/r6aRoGz2rkiIioKWIykoiIiKiFMVntUIkijGYbDFoVbJKESDX/WeiPSLUSM0d4TrgFImHmqrysLWEX6q3K9Xno6324uXcbnHx2DMotduirPu+0KgVMVnu9idxItRKPj+iEJ0elo9Rsq3Z+KJmsdixcl1MtWejaPg8Aj4/oVOfXluv8eRc9d2OlzX29OwckY/aodIiCUC0R60pWPvn9AbxyQw9olApsyzeiS3wkFCKQ7kc/xOZkxhUd8fLaI5j0WcMSxXURlVroBz4Kw+BZkCylEDV6yJKtRSciAe/6aSoiWLlL1NIJsheNTfr37481a9YgNjYWc+fOxWOPPYaIiIjGiK9RlJWVQa/Xo7S0FDExMaEOh4iIiChozDYHXlp7pFoVVlOpMAt3724+hsRoLcZ2iYfJeiHhFohEr8lqxyvrcjwm7J4d06XepFco5ZVUoMP8Nc7/f3oUkg01f44w2xxYsPZIk6x8rIvVLiHp+ZW1JokLnhvrrlz053zXLxBcidhTZWbcvGw79p4uw/1DUrDohp5QKkQUnLcgKVoDSZab7OdDY/GU6HVp6l8z4Ux2WJH3XrLHhKSoMSD1gRMQFOrGD4yIGoW3+TWv9oocOHAAJpOzt8Pzzz+P8vLygAT59ttvo0+fPoiJiUFMTAwyMzPxww8/uNfNZjOmTp2KuLg4REVFYfz48SgsrD6lLi8vD9dddx0iIiKQkJCAxx9/HHZ7023gTURERBQqJqsdL6094nFqcVMfghIO3t+Sh3HLt+OrvacQH6WBWikGLNkRqXZuVX52TBd3D0GDToVnx3TBrJGdm3RS5V+7TgIAruwU5zERCQBalQKPj+iEgufGonDOWBQ8NxaPj+jUpBORgP+Di7w9P1KthFopuj+vOrSKwKIbeiCjbQxeuKY7Xlmfg3ZzV6HTi2vQbu6qsJiyHmzh3Gc1nLGfJhF5w6t/tWRkZGDSpEnIysqCLMtYtGgRoqKiPB777LPPen3z5ORkLFiwAOnp6ZBlGf/4xz9w0003Yffu3ejZsyceeeQRrFixAl988QX0ej2mTZuGcePGYePGjQAAh8OB6667DklJSdi0aRNOnz6Nu+66CyqVCi+++KLXcRARERG1BPX9cP7kqPRGjqj5OHauArtOlkIUgKu7JQTlHq6E3ZOj0lFYbkGrCBUOFpU36YSdLMv4fztPAAD+MiC5zmNdCVVX70t/emw2Fn+3z/tz/ugu8fh28mAs2ZDb4G3izVk491kNZ+ynSUTe8OpvpuXLl+O5557Dd999B0EQ8MMPP0CprHmqIAg+JSNvuOGGah/Pnz8fb7/9NrZs2YLk5GR8+OGH+OSTTzBy5EgAwLJly9C9e3ds2bIFQ4cOxcqVK7F//36sXr0aiYmJyMjIwLx58/DEE09gzpw5UKs9l39bLBZYLBb3x2VlZV7HTERERBSu+MN58Pxn32kAwOUd44L6GroSS8fOVWDA33dAlmWcfLburcChtPNEKf4oKodWKeJPfdqEOpyA83dwkU2S8FBWmsetxN6cHx+pwZsbj3lca+m/YAj3PqvhjP00iag+Xv2rpWvXrvj000+xfft2yLKMNWvWYPfu3TX+7Nq1q8GBOBwOfPrppzCZTMjMzMTOnTths9kwevRo9zHdunVDamoqNm/eDADYvHkzevfujcTERPcxV111FcrKyvD777/Xeq+XXnoJer3e/SclJaXBcRMRERGFC9cP5x7XdCpEa5SQJBkmqx1Wu4Sicgusdonbt73gSkaO6904CbfM9rFQK0ScrbDhm98LGuWeDeGqiry5VxJimmHyp7bt80+PTvdq+3ykWomHL++Ip0enN2j7vb/bxJszV6LYE1eil4JHVEVCUKihiIiHoFBDVHGoEhFd4HPNvhTgb9r79u1DZmYmzGYzoqKi8PXXX6NHjx7Izs6GWq2GwWCodnxiYiIKCpz/4CooKKiWiHStu9ZqM3v2bMyYMcP9cVlZGROSRERE1OzVVcU1bXgHbDh2Dv3b6fHGr0exZEPgJ0I3V6dKzdh0rAQAcEvvpEa5p1Ih4u5ByXhpzREs256PP/Vt2yj39YXNIeHT3c5+kfVt0Q5nF2+fL6m0IUqjwKqDZ3Cuwoq2el2d5xaet2Dse5vx/FXdcPq5MSgz232aFM7qv9q5EsUAwm4wEhFRc9egBiI5OTl4/fXXceDAAQBAjx498Le//Q2dOnXy+Vpdu3ZFdnY2SktL8eWXX+Luu+/Gzz//3JCwvKbRaKDRcAsSERERtSyRaiWeGNkZkizjzY3Vk40zruiIHflGvP7LUfaf89HXvzmrIjPbx6JdPcmnQJo0KBUvrTmCnw4W4YSxEsmGxru3N346eAZnTFYkRKkxtkt8qMMJKtfXRWK0BuOXb8fXvxVg9qjOmH9N9zrPe3/rcew7fR4L1x3Bzb2SEB/lTJB52y/T323izd3FiWLXJHJvE71ERBQ8Pv/N9NNPP6FHjx7Ytm0b+vTpgz59+mDr1q3o2bMnVq1a5XMAarUanTt3xoABA/DSSy+hb9++eOONN5CUlASr1Qqj0Vjt+MLCQiQlOX/jnJSUVGO6tutj1zFEREREdMG/d59E/2QDTjwzptrU4hitCsPT4ursP8fps579Z2/jbtF26dw6Epd3bAVJBj6u2g7dlPyzKqbb+rWDUtFyPndu798OAPDBljxY7LVPtLY7JLy3+TgA4MFh7Rt0r3Cest5YLp1EzteEiCj0fP5XwaxZs/DII49g69ateO211/Daa69h69atePjhh/HEE0/4HZAkSbBYLBgwYABUKhXWrFnjXjt48CDy8vKQmZkJAMjMzMS+fftQVFTkPmbVqlWIiYlBjx49/I6FiIiIqDmRZRmvrs/BuOXb8eXeUzV+OC9l/zmfnSm34OejZwE0fjIScFZHAsCybXmQJLnR718bY6UN/6vqZXlnM96i7clNPZPQTq/FGZMVX1Ulqj35dn8hTpSa0TpSjQl9Gr7N3lX9V/Dc2Gq/YGD1HxERNVU+JyMPHDiAyZMn13j83nvvxf79+3261uzZs/HLL7/g2LFj2LdvH2bPno3169fjjjvugF6vx+TJkzFjxgysW7cOO3fuxKRJk5CZmYmhQ4cCAMaOHYsePXrgzjvvxJ49e/DTTz/h6aefxtSpU7kNm4iIiOgSW/OMOFBUDp1KxE09a+4iqW/ATUvuP1eb//1eAEkG+rfTIy0uotHv/6c+bRCtUSLnbAV+zT0b1Hv5Mtjoy72nYLFL6JkYjX7t9EGNq6lRKkQ8MNRZ6fjWpmO1Hudamzwk1e/EIav/iIgonPicjIyPj0d2dnaNx7Ozs5GQkODTtYqKinDXXXeha9euGDVqFLZv346ffvoJY8aMAQD8/e9/x/XXX4/x48fj8ssvR1JSEv7zn/+4z1coFPjuu++gUCiQmZmJv/zlL7jrrrswd+5cX58WERERUbP30bY8AMCEPm2h95B05PRZ37m3aPdp/KpIAIjUKHFrhrOqbtm2/KDdx2xzYOG6HCQ9vxJJc1Yi6fmVeGVdDsw2z9uQXVu0/zIgGYIgBC2upuq+IalQigI2HStB9snSGut/FJ3HmsPFEATg/4Y2bIs2ERFRuPL5V2b3338/HnjgARw9ehTDhg0DAGzcuBEvv/xytQnV3vjwww/rXNdqtVi6dCmWLl1a6zHt27fH999/79N9iYiIiFoak8WOz7JPAQAmDU7xeExt02enDe/A6bMeGCttWHOkGAAwPgRbtF3uHZyKD7bm4Yu9p7D4ll6ICXAFq8lqx8J1OZh30ZCUugYbHTtXgV+OnoMgAHdU9U9sadrEaDG+Txt8ln0Kb206hvcm9K22/vYmZ6/I67snon2rxq+oJSIiCiWfk5HPPPMMoqOj8eqrr2L27NkAgLZt22LOnDmYPn16wAMkIiIiIv99ufc0zlvs6BQXgcs7xtV63MXTZ41mGyLVCqw8eAZHz5rQIymmESNu+r7dXwCbQ0bPxGh0TYgKWRxDUg3onhCFA0Xl+DT7lHuLsK9MVjtUogij2QZD1dThSLUSKlHEkg25Hs9ZvCEXT45Kr/bYTweL0DpSjb5tY5rchO/GNGVYB3yWfQqf7DqJhdf3cLdAKLfY8Y8dzirWKcM7hDBCIiKi0PB5m7YgCHjkkUdw4sQJlJaWorS0FCdOnMDf/va3FrkFg4iIiCgcLNvu3KJ9z6CUev/N5uo/lxClwaPf7Mf4f+zAovVHGyPMsBLqLdougiBg0uALg2waorZt2BU2O4rKLXUONiqpWnP1lLy6awJynxqFt8b1btgTaiay0lqhV1I0KmwOLN9+YQv9J7tPosxsR+fWkRiTHh/CCImIiELD52TkxaKjoxEdHR2oWIiIiIgoCA6fKccvR89BFIC7B3reol2bewY5j/9k90kUnbcEI7ywVG6x46eDZwCEdou2y50DktErKRqzRqbDYnd4NWTGxWS146W1RzBv1SF30tG1DXvxL7loHamuc7BRtEYBY6UNC9cdQdLzK5H24hqkzFuNf+06WWtPyZZAEARMGdYBAPD2pmOQJBmyLOOtjccAAA8Oaw9RZDEHERG1PH4lI4mIiIiCyZfpvVS7ZVVVWVd1TfB52+yQVAMGpRhgdUh4f+vxYIQXlr4/UASzXUKnuAj0bhP6X84nRmvw85Rh2HnCiDbPr/JqyIxLXduwF67Pgc0h4aFaBhtNG94BReVWvPZzDuatOlwjmblg7ZEW/XX7lwHJiNYocbjYhF9yz2L3yVKcKjNDpxJxj4+/GCAiImoumIwkIiKiJsnX6b3kmd0h4eMdzsnGkwb5nvwQBMGdiHp703HYHJyoDQDb80vQOlKNcb3bNIlWRSarHa//ehQvrPY9IWg02+rchm1zyJg9sjOeHdPFXSFp0Knw7JgueHJUOtrEaPBmVbXfpRZvyIVKbLk/ckRplHh8RCd8fc8gDE6JRXyUBrlPjcL6KcMRG6EOdXhEREQh4fMAGyIiIqJg83V6L9Vu5aEzOFVmRlyECjf0TGzQNSb0bYPHv9uPU2VmfL2vABMz2gY4yqajtiEul65PGZ6GOVd1RVll06j6c1Y3HvO45mnIzMUMWhUMOpXHhKRBp0KUxtlD1DXYqNRsg77qtdGqFPX2lCw12xAfpWnQ82oOZlzeEQvWHsGkz7LdE+ofykpD76RoTqgnIqIWyadfU9psNowaNQqHDx8OVjxERERE9U7vbcmVVr76qGqgyV8GJEOjbFjiQ6NUuCc0L9kQ3oNs6tr6X1817sXrnar6Ir675XiTqNatr7qx1Ox5DQBskoTptWzDnp6VBpvkrIZ1DTaKj9JArRTdSVpXMtMTg04FvdbzWktgstrx8rqcGhWr87iFnYiIWjCf/iWvUqmwd+/eYMVCREREBAAoqWx4YoUuOFdhxaZjJQCAe6umLTfU/2W2h1IUsPFYCXaeMAYgusZXV7KxriEuC9YewTmTFS+tPVzreqiTSv4kBCPVSjwxsjOeHp1eYxv2rJGd661C9jaZ2RLxFytEREQ1+fy331/+8hd8+OGHwYiFiIiIWphLq9TKzDYs25aHKI2ClVZ+cL2ulTYHDs8eidV/zUTvNjF+XbNNjBYT+zq3Z79ZS3KlKasr2fjGr7lQikKtSaN/7TqBKI2yzm3QoU4q+ZsQ/PXoWfRPNiD/mdEonDMWBc+NxeMjOnm1jThSrcSsWnpKepPMbM78qVglIiJqrnz+l4HdbsdHH32E1atXY8CAAYiMjKy2/tprrwUsOCIiImq+XFVqSzbkuvuoTRveAQ9lpWHzsRJMG94BL6yu2RrGlVhRcw6fR55e14eyOmBYh1i/+9M9lJWGT3afxL93n8LC63uEVR/AuirUvtlfgEmDUmpNGulUCq+qdUP5ergSgoAzOep676dnpWHWyM71vvfvbj6Or38rwJyxXfDs2K4A4NPXmFalqLWnZEtWXz9O/mKFiIhaIp+Tkb/99hv69+8PADh06FC1taYwSZCIiIiavtoG1LiSj/cNSUVWWiuIgtCgxEpLVdvrOm/VYQgQ/B78M6R9LCb0aYPb+ycjSqNEUbnF45CXpqiuCrXDZ0zQ62pPGlXaHIitY72pJJUuTgieKjMjPkqNYpO13q+XkgorVhwoAgDc3KtNg+/v+hxwJWX5C4MLFatzVx2qscZfrBARUUvl878a161bF4w4iIiIqAWpq0rtzY3H8PToLtWm954+b0brSDVOlpqZiKxDff3p6pqo7K0Pbs3AK+uqTwYOhyRxXRVqdkmG1VF70uiO/skw2x1hkVRyJQQ/zT6J134+iss7tsKXdw+q85yv9p2G1SGhV1I0+rT1bzs/VedvxSoREVFz1OBfYR85cgQ5OTm4/PLLodPpIMsyKyOJiIjIK970UYuP0rgTK+uOnMXM7/YjWa/Fzkcu5785auHt69pQJqsdi9bnVNs+7+q7CMDvystgqq9CTSGg3qRROCWVru+eiCe//wPf/F6IwvMWJEbX/r5/suskAOD2/u0aK7wWhVvYiYiIqvP5X4tnz57FxIkTsW7dOgiCgMOHD6Njx46YPHkyYmNj8eqrrwYjTiIiImpGfO2jdmPPREz5ai+yT5Vhy/ESZHZo1VihhpVg96drjMrLYIlUK/H4iE6QZBlvbjxWazKxrqRROCWVerWJwdD2sdhyvATLt+fjiapE6qVOGCvx89GzAIDbMpiMDBZuYSciIrrA578FH3nkEahUKuTl5SEiIsL9+K233ooff/wxoMERERFR83TkrAnThnfwuOZp8m+rCDX+3M+ZKHlr07EgRxe+/J2oXJ9wnww8b9Uh9E824OSzY2qdGB2pVkKtFBEfpYFaKdao9KxvvSm5b0gqAODDbXmQZdnjMf/efRKyDFyW1grtW0V4PIaIiIgokHxORq5cuRIvv/wykpOTqz2enp6O48ePBywwIiIiap72nirDbf/chYey0vDMmHQYdM5qPYNOhWfHdMGskZ09JnimDOsAAPhiz2kUnbc0ZshhI1KtxMOXd8TTo71/XX3hqrz0uNZEhrjUpqDMjL//chTjlm/HH4XlYZFM9NetfdsiWqPEkWIT1uec9XjMJ7u5RZuIiIgal8//+jKZTNUqIl3OnTsHjabhPYiIiIio+bPYHbjr37ux73QZ5vx0EAtv6IGnRnXxasvrwBQDBqcYsC3fiA+35WF2E94SHCpnyi24+r0teGZsV5x6bgzOm+0B3UoczpOB39uSB5tDxtD2seiXrA91OI0iUqPEbf3a4b0tx/HB1jyM6Ny62vrvBeex51QZVAoBE/q2DVGURERE1NL4/K/Fyy67DB9//LH7Y0EQIEkSFi5ciBEjRgQ0OCIiImpenv3xIPaeLkN8pBrPju3q85bXKVVbu9/dfBwOyfO205bs778cxe5TZZi/+hA0isBvJXZNBn52TJdqlZdPj04PSOVlsFjtEt7ZfAwA8FAt29ibq/uHOrdqf7X3NM6arNXW/rXrBADgmm4JaBWhbvTYiIiIqGXy+V+MCxcuxKhRo7Bjxw5YrVbM/P/t3Xd4VNW6BvB3JlPSEwIpRCCFJHQpohgSQCAErHDBcxBR8RwONoKCSpNeFCk2QPRePQqWgxVFUTGBhBKINAPSSaUnoaS3afv+ATMnk0xLpiZ5f8+TP5g9s2fNZiWTefOt9c2ahZMnT+LmzZvYt2+fPcZIRERELUBG3k2s2Z0DAPi/v/U22d3XmL/3DsUrP53EhZJq/HK6EI/0CLH1MJut4ioF3t+XDwCYlxBtt47jdZu4FFcr4S13Q/LZa7hQUo2YQG+7PKe1vj9+FQXltWjvK8e4Xu2dPRyHuquDP/re4YvMy2X4/MglTB8cCQDQaARs1i7R7tvB1CmIiIiIbKrRlZE9e/bEuXPnEB8fj9GjR6OyshJjx45FZmYmOnfubI8xEhERUTNVqVBBodKgqLwWd97hi+8n3Y3XhkdhdM+mhYjuUjf8855blV4bbgdvdMu69HyU16rQq70PHulu35BWW9Ea7CPHSz+cwLhNh7H497N2fU5raDuAPxcbDpnENZeR29Pke8IAAB8fOK9rZLP//E2cL66Gj1yCh3sEO3N4RERE1Mo0aS2Nn58f5s2bZ+uxEBERuaRKhQpSsRglNUr4395/z1WXo7qSGqUaq9JysC49DyXVSvh7SJEUF455CTFWnfe52HCs2Z2D5HPXcO5ahctW4zlSWY0S7+3NBQC8NjwaYrF9qiINmTYoAp8cuoivj13BosQu6BJk//+PxnxPHrxQjD/OF0PmJsYz94bZfWyuaGK/OzBz20mcKqzAH+eLERsegC//vFUVObZXCDxssJ8oERERkaWa9Kfh4uJirFmzBpMnT8bkyZPx1ltv4ebNm7YeGxERkdNpA7WQJckIWZyMkCXJWJ2Wgxql2tlDc2mVChVWpGZjWco5lFQrAQAl1Uos35GFlanZqFSomnzuiLaeeKBrEADg8yOXbDLe5u6D/edRXK1El0AvPHqnYxuR9A71wyM9giEIwJupWXZ/vsZ+T66/XRU5vk9ok7YGaAn8PKT4++0GNV8dvQylSoPUrGsAgMf7cYk2EREROZZI0K7VsNCePXvw8MMPw8/PD/379wcAHDlyBCUlJfj5558xePBguwzUnsrKyuDn54fS0lL4+vo6ezhEROQiKhUqrErLwTIDnYMXjojBzKGdWSFphEKlQciSZF0QWZe/hxQFixKtWi67J+c6iqtVSIhph8paNfw9Wm/FamWtCpFv7MS1SgU2PtYHT/Xv6PAxHLpQggFr98JNLMK52cMQ0dbTLs/T2O/JgrIahL2+A0q1gAMvDsLdnfztMq7m4M9LJbhYUoOEmHYorVbBz0OCPTk3kNglCG4OrKQlIiKilsvSfK3RnwKmTp2K8ePHIy8vD1u2bMGWLVuQm5uLxx57DFOnTrVq0ERERK5EKhbr9pqrb216HqTi5r/3nG5Px4paKFSaBhWL5o4bU1KjNBhEArcqJEtrDB+z1D2d2uDIpRJ0XLbjVoVcM6xYbeq1rW/L8asQAEQEeGJC3ztsO0gL3d3JHyO7BEKtEfBmmv2qIxv7Pfl/f1yAUi0gNqxNqw4iAaB7sI/ue6bDshR0XLYDf1wohlKtcfbQiIiIqJVpdPlAdnY2vvvuO7i5/XdvGTc3N7z88sv47LPPbDo4IiIiZ7IkUAv0br7LPg3t6fhifATmDIuCu9TN7HFT/Nwl8PeQGq2M9HOXNnnc2uq45Tv+G3qVVCux9Ha1XHOoWLXm2mpp900cFNkWefOG43JpDaRuzgvI5yVE4/ez17Dx0EUsSIhBB38Pmz9HcbXl35NKlQbfHLu1L+K0+Aibj6U5MfY9sywlCyKImsX3DBEREbUcjf6NtV+/fjh9+nSD20+fPo3evXvbZFBERETOVlheA2/ZrUDNEGsDNWcztqfj0pRzeDM1G2U1SpPHTVXxHThfjB1Z15EUF27w+IvxEVBqml6N1dwrVs1de0sqJOvumxj5xk50XLYD//nzslMrQ+Mj2uK+zm2hVAtYtSvH5ufPuV4Bb7mbye9JH7kE5TVKKFQaXKtU4MBLg/DLv+7BuDvb23w8zUlz/54hIiKilsWi3zz++usv3deLL76Il156CWvWrEF6ejrS09OxZs0azJgxAzNmzLD3eImIiGyu/nLZgrIaTPjiT6Scu2a3QM3ZTIUTX/55Ce4StyaFF3tybmDE/2Vg5s+nMH1wJBaOiNGFR/4eUiwcEYM5w6KsqsKy9xJwe7M2GLJFmGkv8xKiAQAf/3EeheU1NjvvgfPFiF23DzvOGQ+5FyfGQKURsHrXrZBWtxT5fDHUmkZtkd7iNPfvGSIiImpZLPok0KdPH4hEItTtdTNr1qwG93v88ccxfvx4242OiIjIzgwtl02KC8fXT96FBdtP4+1HekIsEmFtveONWU7rikyFEx5SN4uXw2qXCpfUKOHrLkFpjRId/DwQ6usOuUSMmUM747Xh0SitUcLP/VaTGWuvm7+71G5LwB3B2uX/5sLM14ZH22ScTTEsqh0e6xOK8X3ugK+7FEUVtfB3b3xzIb15JZegqKIW7bxk+PLIRWx6vF+D78kX4yPw9N0dsSotm0uRDWju3zNERETUslj0G1lenuFfeImIiJozQ515S6qVujBj5YM94CmT6AK1kholvGRuSD57DceulGFAWBtnDd1qpsKJaqUabTxMhxdeMjeU1yixZndugyA3PSkOnlI3vdBRG67JGr9DTANKjQYvxkfo9oisS1uxaovnsRdzwZDv7WCobiCnDfQuFlfDUyZx2b1MRSIR/u9vvbEqLRv/+Ppok/bDNPYHgvSkOMglYnhI3QyG3FKxGOv35Rs8p7NDWmdr7t8zRERE1LJYFEaGhYXZexxEREQOZ6rCbP2+fMxPiAEAXTVVkLccs7edwupdOXi4ezC2/vMeh43V1kyFExP7dUCNSm30eFJcOIoqFPjk4IUGVWjLd2RBLLpVhWYvXjIJ5gyLAoAGFasvD4l0+eo3U9c+KS4c6Xk3cFcHf7yzp2HQ+2J8BDxkbi5b5VapUGH1rqY3FzL1B4K680p7jrohd1FFrcuGtM5m7HumsU2TiIiIiGxBJNRde22hK1euID09HUVFRdDU2y/rxRdftNngHKWsrAx+fn4oLS2Fr6+vs4dDREQOUlRRi5DFyUaPFy5ObBBenLtWga4r0yASAWdmDUV0oLe9h2k3FbUqrErLxvp9+Ua7ab+Zmt0gvJg9LAoiAKFLU4wGYgWLEiGT2LfSSls5WFqjhI9cgt/OFOGtXTn4dcoAXXWhqzJ27V8aFIEjl0qxJ/eGXqCnNT8hGs8PDMf/Zpw3GGYuHBHj1OXICpUGIUuSmzwvrHm8tc/dGtT9nvFrwvJ5IiIiIlMszdca/dvHxo0b8eyzz0Imk6Ft27YQiUS6YyKRqFmGkURE1Dr5uUsaXWEWE+iNh7oFY9vpQry3Nw/rx/ZyxFDt4s3ULPTv2AaXF45ARa2qwZ6O7kaWw3pI3VyiCq1udZxSrcFrv57G2WuVWJGajRUPdLPrc1vr7T256NfBv8G195JJEB8ZgL9/fsTg47QVu65a5WbtfpjWPJ5Lkc0zVFFKRERE5GiN/g1kwYIFWLhwIUpLS5Gfn4+8vDzdV25urj3GSEREZHMf/XEev59tWrfs6YMjAQAbD11EcZXCXkO0q2qlGuvS8zF24yFkXipBoLccMom4QZWUl0wCmUTc4Lh230NDnLFUWOomxqqHugMA3t2Ti/ybVQ59/sYQBAEf/3EeYzceQnrujQbXtqxGZTaQ0wbFBYsSUbg4EQWLEjFzaGenL7e1dl5Y83jtUmR7dHAnIiIiIttpdBhZVVWFxx57DGIx/5JKRETNQ6VCBYVKg6KKWihUGhy5VIJ39uRizi+nMWNI50aHF0Oj2uLO9r6oUqrx0YELjnwpNrP1RAHKa1UIa+OBe8MCGv14bRWaIaaCXHt6qHswhnZui1qVBvN+O+Pw57fUn5dLcam0Bl4yNwyKbNvguKWBnLGg2JmsnReVClWT/kCg5aohLRERERH9V6MTxcmTJ+Pbb7+1x1iIiIhsTtuZN2RJMkIWJyNkSTK2nijA7hcG4sX4CPi7SxodXohEIl115Lr0PCjVjg/erPXFkUsAbjWrEYtFZu7dkCtWoYlEIqx5pAdEImBz5mX8cb7Y4WOwxI8nCgAAo7oEGZxnrhj0WsrYvJifEI1X7zO/l+Xbu3MxLT4C8xOimzyvXDGkJSIiIqL/anQDG7VajYceegjV1dXo1asXpFL9v9y//fbbNh2gI7CBDRFRy2SoM6/WghExmGVFo49alRrhr+9EYXktvpzYDxP63mHtcB2msLwWHZalQK0RcHrWUHQJanoTHldsiPHPr49i46GLiA1rg/SkOL39rV1B77d24fjVcmya0AdP3tXR4H2MNQ9y9p6Qlqo7L7zlEmw/U4TPDl/Cd5P6w81I+H3sSin6vbMHXQK9sePZe9HOS+5S84qIiIiITLNbA5sVK1bg999/R5cuXQCgQQMbIiIiVyEVi7EuPc/gsXXpeZg3PLrJ55ZL3PB8bDgWJ5/Fu3ty8Vif0GbzPrg58zLUGgH3dPS3KogEXLMhxvJRXXHscilmDY1CrUqDsloV/F0k0Mq9UYnjV8vhJhbhwW7BRu9nrHlQcwgiAf15UVylwORvjqGkWomNhy5i8oBODe4vCAJe/fkUBAHoHeqLUD8P3eMB15hXRERERGQbjf6N/K233sInn3yCp59+2g7DISIish1rO/ua8/zAMKxIzcKhiyU4eKEEA8LaNPlcjqRdov3EXR2cPBL7CPVzR+oLA/HWrhz84+ujLlVZuPXkrSXagyMCEOApM3lfVwx6m6KNpwwLRkTjlZ9OYcH2MxjfJxTecv1fQX89XYSdWdchcxPjjftduxM6EREREVmn0b/VyuVyxMXF2WMsRERENmXvjs+B3nK8el9n/PD03ejV3lfXIKdSobLqvPZ0sqAcf14uhUQswmN9Q509HLuoVKjw9u5cLN+RpQujS6qVWJpyDm+mZjv1/2fr7f0iR/cMcdoYnGHqwAh0buuJgvJarErL1jumVGswc9spAMBLgyIQ0dbTGUMkIiIiIgdpdBj50ksvYd26dfYYCxERkU05ohHI3GFROHKpBB2Wpega5KxOy0GNUm31ue3h89tVkQ90C0I7r6ZXhboyU8vz16bnQSp2ToXh9cpapOfdBACM7tG6wkiZRIyVD3UHALy1OweXSqp1xz764wLOFFWgnZcMr1mxdQIRERERNQ+NXqZ98OBBpKamYtu2bejRo0eDBjZbtmyx2eCIiIis4SWTYMbgSGgEAev35dt8ua62Qc7yHVm627QVeAAw04oGOfag0Qj4z5+3l2j3a5lLtAH7L8/X0jZpKalRWrQn5bZTRdAIQJ9QX4QFtL7qv//pGYJBEQHYm3cT8387g40T+qK8Rol16bkAgMWJXeBnpJKZiIiIiFqORn9C8vf3x9ixY+0xFiIiIptSqDR46N8H8Mp9UbiycATKa1U2bQRirgLP1aq8duXcwKXSGvh7SPFQd+PNU5o77fJ8Q4GkLZbnA7e6Xa9Ky8G6RnS7/un2fpGPtLKqSC2RSIQ1D/fApK8y8T+92qNWpUZpjQqHZwzG/vxiDO3c1tlDJCIiIiIHaHQY+emnn9pjHERERDb3/fGr2JdfjLwtx5E3b7jNG4E4qgLPVrSNa/7Wu32z6crcFNrl+doK1bq0y/MtmQPGKh+1FbHL6pzfXEVslUKF388WAQDGtLL9Iuu6u5M/9k2Lxzu79ZsLTYuPwKCIAEha7rQkIiIiotuc2pZxxYoVuPvuu+Hj44OgoCCMGTMGZ8+e1btPTU0Npk6dirZt28Lb2xvjxo1DYWGh3n0uXLiABx98EJ6enggKCsLMmTOhUrlu8wAiImMqFSooVBqDjVBMHSPDNuy7VbU45d5OkLrZ/i3P3g1ybKlKocJ3x68AAJ5soV20tbxkEswZFoWFI2J0/z/+HlLMT4jG7GFRFi2d11Y+hixJbrAXqKQJe1LuyLqOaqUGYW080DvU17oX2IxVKlR4d0/D5kLLXKC5EBERERE5RqMrIyMiIiASiYwez83Ntfhcu3fvxtSpU3H33XdDpVLhtddeQ2JiIk6dOgUvLy8AwIwZM/DLL7/g22+/hZ+fH5KSkjB27Fjs27cPAKBWq/Hggw8iJCQE+/fvx9WrV/HUU09BKpXijTfeaOzLIyJyGmPLPucOi4IANHpJaGt37Eop9uUXQyIWYcqAMLs8h6kKvKT4cIsr8BwhLfs63CVuCPSSIy48wNnDsTt3qRtmDu2M14ZHo7RGCU+ZG5LPXsOPJwowoe8dJh9rqvKxnZcM4+5s3+iK2K11lmib+j2qpWtuWxsQERERke01OoycPn263r+VSiUyMzOxfft2zJw5s1Hn2r59u96/N27ciKCgIBw5cgSDBw9GaWkp/v3vf+M///kPhg0bBuDWMvFu3brhjz/+wL333ovk5GScOnUKO3bsQHBwMPr06YNly5Zh9uzZWLx4MWQyWYPnra2tRW1tre7fZWVljRo3EZGtmQo/xt3ZHt/9dbVRS0IJ2LA/H8Ctphmhfu52eQ5tBR5wK0jRBsVJceGYFheB7/+6iqf6d7TLc2uZa6KiPd6rvR/y5g1Hzo2qVhOGaa9DoLccH/1xHs9+9xciAjzxtzvbQ2KiUtZUYPbe3lw8c29Yo/akVGsEbDt5a1VHa+uiXV9z29qAiIiIiGyv0Z9eX3rpJYO3v//++zh8+LBVgyktLQUABATcqtg4cuQIlEolEhISdPfp2rUrOnXqhIyMDNx7773IyMhAr169EBz83434R44cieeffx4nT55E3759GzzPihUrsGTJEqvGSkRkS8bCj3ZeMkS29WwVlUSN7UxsSmm1El8euQwAeCEu3IajbKh+BZ6fuxTZ1ysxZMN+ZF2vxB1+7hgeHWjVcxi7NuaaqBg7HtPOq9VV1E7sdwfm/XYGeTer8M2xK3jcRDdxU4FZzo0qVCpVRitipxmoiM04fxPXKhXw95BiUGTLr0o1xRHNhYiIiIjItdls7dj999+P77//vsmP12g0mD59OuLi4tCzZ08AQEFBAWQyGfz9/fXuGxwcjIKCAt196gaR2uPaY4bMnTsXpaWluq+LFy82edxERLZgLPwI8ZGjqEJhtpKouTO1P19TbDp8EVVKNXoE+2BwpP079HrJJJBJxAj0lkMmEaNbsDfu6egPtUbA/N/O4HplbZP3+zR2bcprlFiRmo1lKef09t5bmnIOK1KzUFhegzdSswweb41783nKJHhxUAQAYGVaNgRBMHpfc3uBekmN70mZFBeBX07p723944lbv4881C3ILnuXNifarQ0M0TYXIiIiIqKWzWbr+r777jtdRWNTTJ06FSdOnEB6erqthmSUXC6HXM4lQETkOoxVCxWU1yLIW9aiK4ma0pnYFEEQ8MHtJdrPDwx3ypJkkUiEDx+9E9VKNdaP7YW1e/Owfl9+o/f7NHZtNuzPx+xhUUYrZv/z52XMGx6D9en5Bo+3pIraxpg6MByr0rJx/Go5fj1dhAe7Bxu8X6VChaS4cCzfkdXgmDYw85JJGlTEni4qx5AN+3HuWgUkbmKM7dUegiDgdGE52nnJ8EgrX6INGN/agHvgEhEREbUejQ4j+/btq/fBThAEFBQU4Nq1a9iwYUOTBpGUlIRt27Zhz5496NDhv8umQkJCoFAoUFJSolcdWVhYiJCQEN19Dh48qHc+bbdt7X2IiFzdycJyg+HH9UoFcm9UGW+SEheOCyXViGrn5aih2pytG1qkZl/H2WuV8Ja7ObVrtLvUDR88eqeuc7BWY4JWY9cmxEeOwvJaoxWzHlI3FFdzb7762njK8Oy94Xhrdw5WpmUbDCMF4VY16+KRXQDAZIhcd09KALizvS/iIwJwpqgCS5LP4Z6O/gj0lmH92DsR5C2DSm28GrM1MbS1gVKjYRBJRERE1Eo0OowcM2aM3r/FYjECAwNx3333oWvXro06lyAImDZtGn744Qfs2rULERH6y3buuusuSKVS7Ny5E+PGjQMAnD17FhcuXEBsbCwAIDY2Fq+//jqKiooQFBQEAEhJSYGvry+6d+/e2JdHRORwadnXMXXLcex+YSBEIpHB/f0MVRJNiw9HUlwEhn6wH4sSu+BvvUOd/EqaxtYNLTbsywcAPHlXR/i4O7exj7dMgvW3x1OfJUGrsWtTUF6LQBMVs9VKNdp4cG8+Q2YMjsS69Dyk591Eet4NxEfoL+P/98EL+CDjPNLzi5H8zADMT4ixODATiUTYMLYX5BIxFo6Iwbr0plXEtgb1g1xX6TpPRERERPbX6E9pixYtstmTT506Ff/5z3+wdetW+Pj46PZ49PPzg4eHB/z8/DB58mS8/PLLCAgIgK+vL6ZNm4bY2Fjce++9AIDExER0794dTz75JFatWoWCggLMnz8fU6dO5VJsInJ5pdVKPP1VJi6W1GD9vjzMGhqFefWqheS3g4v6lUQKtQZvpmbhVGEFlqWcw90d/RHq626TBjCOIggCfOQSm4VmBWU12Jd/EwDwwsBwWw2zyawNWv2MLN+/XqnArpwbRitmJ/brgBqV2uhx7VLj1hgAhfq546n+HfDxgQtYmZqN+Mn/DSNzrldixtaTAIAn7+qAYJ9bXdgbE5hJ3MR44/6uWL0rp8kVsURERERELZlTP4V88MEHKC0txX333Yf27dvrvr7++mvdfd555x089NBDGDduHAYPHoyQkBBs2bJFd9zNzQ3btm2Dm5sbYmNj8cQTT+Cpp57C0qVLnfGSiIga5cUfT+BiSQ06t/XEzPuiGjRCqRtY1D/mLZdgyciumHVfZ+x8LhafHLxgswYwjqBSa/Dsd38h+ew1JBnpeJ0UF47iaoXZc1UqVFCoNFBpBOS8Nhxpz8eiR4iPjUfceOYaoZgLWo9dKTV6bfJuVGG2gSYqC0fEYM6wKPi6Sw02WdEeb81h2Mz7OkMsAn45XYSTBeUAALVGwKSvMlGpUGNIZFvMGBzZ5PPLJW4mK2Kl4tYXAhMRERERaYkEU+0k6xCLxWabAIhEIqhUza87Z1lZGfz8/FBaWgpfX19nD4eIWomtJwrwPxsPQSwC9k6NQ2x405qAVdSqsCot22CzjYUjYlymCqtSoYJULNZVbh66WIIp3x6DWATsnxaPt3fn6i1DT4oLx7T4CDz//V9Y83APo1WfNUo1VqRm6y1vnxYfjrnDop2+HLZSocLqtByD1Ynm/m9+PlmA2b+cxu4XBmL9vvwGy/e1y32117VuNW3dc5o73lq9vPUkhnRuixExgahQqOAjl+D3s9fw+o5z+O6p/ggL8GzyuYsqahGyONno8cLFia1uv04iIiIiavkszdcsDiO3bt1q9FhGRgbWrl0LjUaDmpqaxo/WyRhGEpFW/cDM1sGN9vzF1Up4y92Qcu46zt+swktWVGEpVBqELEk2usy5YFEiZBLnVmIZCgy1YeOxq2VIiA5sEJpVKW8FedNv7/FnaO89tSA06Dat5SpBbI1SjTdTsxsErS8P6Wy0arK4SoGea3bhalktVj3UDc8PDGegaGOVChVWpmbrzaukuHDMvC/K6r1Gm8P3JBERERGRrVmar1n82/bo0aMb3Hb27FnMmTMHP//8MyZOnMil0UTUrNUo1ViVlmO0As0e50+KC8fcRnaLrs/WDWBsrVKhahAYllQrsXxHFkQiEWYN7QzAQEMLiQxzh0c3qPrU7r3nLhHrgkpDmtKJ2x7qdw72kUvw25kiDFyXjqUju+BRA42HXv7pJK6W1aJLoBemxkXA4/b8Y7MP29DOyfrzavmOLIhFIqtDbKVGw/06iYiIiIiMaNJvwleuXMGUKVPQq1cvqFQqHD16FJs2bUJYWJitx0dE5BCVChVWpGZjWco5XbCnDb3eTM1GpcK6LSiMnX/5jiystPL81u5LaG9SsdhoYLjOzP55Mjex0b33fj5diLIaldkg1hXU3e/TXeqGP84X40xRBSZ/cwznrlXo3ffX04XYdPgSRCLgk/F9dEEk2Y6pOWmLPR29ZBLu10lEREREZESjftsuLS3F7NmzERUVhZMnT2Lnzp34+eef0bNnT3uNj4jIIewdTtjz/NoqLEO0VVjOVFxtvnLTGFNVn1nXKuHn4dpBrDGv398VgyMDUF6rwqObDqPqdhhdVqPE3F9PAwCmD4ps8j6iZJol1cTW0lbEFixKROHiRBQsSsTMoZ2dvo8pEREREZGzWfzpd9WqVYiMjMS2bduwefNm7N+/H4MGDbLn2IiIHMZW4YS2q3NRRS0UKg0qFSpcLK5CUUWt3cIPY1VY8xOiMS0+AjI3xywHrf/aK2pV2Jx5Cd5ytyYHhqaqPlUaAQq1awexxkjcxNj8xF0I9pFDpRFwoqAcCpUGpTUq7J8Wj+1TBmD5qC7OHmaL5ahq4roVsTKJmBWRRERERERoxJ6Rc+bMgYeHB6KiorBp0yZs2rTJ4P22bNlis8ERkXOYa+Ji7yYvzqANJ4w1nLAknDC2J+SL8RHwkLlZfX5T6u9L6OsuRcq5Igx6fx8m9rsD8xJirDq/OcZe+7T4CGTkFyMpLtxgt29z++eZ23vPTQTMGRYFAHoNYmy516e9tPd1x9an70ZEW0+sS8/DqI8O1OkGHoHBkW2dPcQWi3s6EhERERE5j8XpwVNPPQWRSGTPsRCRCzDXxMXeTV6cRanRYFp8OJalNAzMpsWHmw0nTDVpAYDnYsMwLT7CYNdnW4Uf9RvAlNaocKaoAktTzmF0jxD0bG+8m5k1zL32f97TEfERARCLRI0ODLVVn4DpsLFuEKvtNt0c5mOP9j4GG/QsSzkHEeAS3cBbIkvnFRERERER2Z5IEATB2YNwNktbjxO1dIZCJa0FI6IxdWAE3t+fZzCwWzgiplkHJ0r1rSWya/fmYv2+/AbVfR8fuIA5w6KM/lFGodIgZEmy0crHgkWJ0AgC3kzNdlj4IQgCxnx6CD+fKkT/Dn7YPy0eEiuWbBuriLXktcskYt3j6waGls4Xax7ryiy9dmQfLXVeERERERE5g6X5Gn/jJiIdU01W/vPnZcwbHoN16fkGj69Nz8Nrw6PtODr7+uTgBby3Nw9rHu6O+QkxunAiv7gKQzbsx5miCsjcRHhuYHiDQK68RgWFWjC7J2Sgt9yhFXwikQgfjLsTe/N24fClUqzZnYM5w5r2f2SoInZafAReGRJpUYOaQG95g8rNxlSCWvNYV2bJXqXa10y211LnFRERERGRK+Nv3USkYyoY8ZC6WdUV2ZVVK9VYviMLZ4oqkH29Uq/hREygN6bFR6BrkDee6t8RK9OyEbIkGSGLkxGyJBmr0rIhEYvQ1suyhhiObmgR6ueOd0f3QNcgb/QI9kGtSq3XXMcSlQoVVqRmY1nKOd3/v3Yp8YZ9+Qj2ljfLjtauwFGNVIiIiIiIiFwFw0gi0jEVjFQr1Wjj0TKDkw/25+NyaQ06+rvj2diwBsefHxiO7yf1x7r0PCxPyaoXyGXdWnZdpXTZrs5P3tUB6UlxOHSxBO2XpOiC1NVpOahRqs0+3lTF7KpdOVA2047WrkDbSMUQXjsiIiIiImqJGEYSkc6F4iokxYUbPDaxXwfUqNRGg5OkuHDsyb2BiyVVqFSooFBpGl2B5wzlNSq8mZoNAFg4ogvkEsNLpju39cL6ffkGj63fl49AbznmDIvCwhExusDW30OKhSNiMGdYlFP3oatSqvHe3jws36EfpC5NOYc3U7PN/v+YW0qsVAsu+9pdnbaRCq8dERERERG1FvyUQ0QAgPybVZjw5Z/49V8DIBIB69LzDTZZMdSBdlp8BKbFR+DpzZnYOKEPVqVlG328q3kvPRfXKxWIbueFSf07GL2fpXv7uWJXZ1OVjZbs9amtmDXWZMVbfmvpuSu+9ubAXerGa0dERERERK0Gw0giglojYNLmTBy5VIrpW0/gfx/tjXnDYwwGI8aCk4paFV65rzPW3q7A09JW4AFwuW7bN6sUWLMrBwCweGQXk52mzQVydfeEBFyrIYa1TVIulNyqmK37/6qlXUosg9glX3tzwWtHREREREStBT/tEBHe3p2DvXk34S13w7JRXXWVbsaarBhqwhLs44648ACjS5nXpudBKnatHzkb9uejrEaFXu19ML53qMn7Nue9/axpkpJ1rQKPfX4E0+IjsGBENJcSExERERERkVX4CZKolTtZUI4F288CAN5+pAci23o1+VzWVuA5QqVCBalYjOJqJWYMjkTPEF/4yt0gFotMPk67tx+gv0TdlZega2mDVG2Fal1JceGoUqohkzQMimtVakz44k/8ebkMM38+hfVjexmtmCUiIiIiIiKyBMNIclna0KikRgn/28FH3Qosc8fJuLrXLiLAA189eRdSzhZh8j2drDqvpUuZnaVGqcaqtBysqxMmJsWFm90zUau57u1nLEhNigvHtPgIPPfdMfz7733gJdf//nnt1zP483IpAjyleP2BWxWzAJcSExERERERUdMxuSGXZCg0qluBZu44GWcskFvzSA+IRKarA80xVYFXd29BZ6hUqLAqLQfL6oytpFqJ5TuyIBaJLN7Psrnu7WcoSC2uVuKRTw7iwIUSlNWosPWf90B6e9/MneeK8M6eXADAJ+P74A4/D2cOn4iIiIiIiFqI5vEpmlqVSoUKK1KzsSzlnK7CTtsE5c3UbJTVKE0er1SonDl8l2bs2i7fkYWVNrh22gq8hSNi9PYWnJ8QjdkO2luwUqGCQqVBUUUtFCqN7jWZ6yjtavtZ2kP9vT6DfeR4+5Ee8JS6Ib+4GpmXS6FQaVBYXot7wwOw5em7sWxUVzzSI8TZQyciIiIiIqIWQiQIguDsQThbWVkZ/Pz8UFpaCl9fX2cPp9VTqDQIWZJscKlv57aeODlzqNHj/h5SFCxKNLj/HZm+tra8dnWXgXvJ3JB89hpuVikweUCY1ec2pUapxorU7AYVs7OHRaG4SokOy1KMPrZwcaLT97N0lj0519Et2Afr0vOwfl++XsXs3OHR8GC1MREREREREZlhab7GZdrkckw1QfGQuqG42vWbpFjLXvthOqrBjHasQd5ybM68jIlf/glvuRse6BaM9r7uVp/fEGPLsJemnIO7RIzpgyNdej9LZ7qroz9WpWVj+Y4s3W1NWcJOREREREREZA7Lx8jlaJugGFKtVKONh/HjLSFU0u7pGLIkGSGLkxGyJBmr03JQo1RbfW5T19Ze125871Dc09EfFbVqvPbraavP15Rl2Kt25UCp1mBafLjB49r9LFurW9cu3+Cx1rKEnYiIiIiIiByDnzDJpQiCgDPXKpAUF27w+MR+HVCjUuPF+AiDx5PiwnG5tMaOI7Qvc/tlWruno1KjMXpt7RXIicUivDemJwBg0+FLOHC+uMnnMhbUVtaqUFhRa7LqU6kWMHdYdIP9LBeOiMEcB+1n6aosqZglIiIiIiIisoXW++mbXI5SrcFz3/+FjPxi7H5hIEQiYF16vsFu2XOGRQG4VbVVd3+7afERGLJhP5LiIjDp7g52WepsT+aarLw2PNqq8+/KvoFpt4PcunsD2rsT+YCwNpjUvwM2Hb6El348gf3T4iEWN65zt7XLsL3lt5q31O8ordRoWn0Hdm3FLJewExERERERkb25djJDLVr9fREPXihGRn4xzl2rQFr2dcwaGoV5w2MMhkbuUrcGoZJCrdEFeX/r3R4rU7MdGrjZgrkKteJqJYJ95E3aU7K0Wokp3x6Dv4cU3zx5F+YnGL629vLGA93w/fGrOHixBJ8fuYRJd3ds1OPNLcOeGheOF+MjsLROWKmlrfqUQay7Ttq9MWUsEIdSo7Ho2hERERERERFZi2EkmWSvRira5bbr6lU27n5hII5fLcOw6EDdfY2FRg1CJYkYc4dH49E722Ndel6DZhzaoMWVm3GYq1DzkbuhuEqBd/fmGq0aNWZR8lkUlNfCRy5BdKAXZBKxQwO59r7umJ8Qg42HLiLIRw6FStOoeWUuqFWqBYMVs80hhHY2L5mE146IiIiIiIgcQiQIguDsQTibpa3HW5sapRorUrP1AkNbhBOGlttqLRgRg1lWhoUKlQYhS5KNBnoFixIhk7hmldfF4ip8dOCCXpCqNT8hGv+8pxM+OWj4+MIRMUaD1qOXS9H/3T3QCMDvz9yLETGBDe7jCAqVGuW1ary3N7fRVauW/r9qA/S6VZ+uGj67Gl47IiIiIiIiaipL8zXXTGTI6SxtpGKss7EpppbbrrNB597m2oxjV/Z1PPLJIUyLj8D8hOgGTVZeGx6N9r7uWL8v3+DjjXU91mgETN1yHBoB+HvvUKcFkQCg1AhYm56L5TuyGt2gR6mxrBu2l0yiq/qUScQM0xqB146IiIiIiIjsjZ80ySBLGqkYWmptSYWbJWGhdvlwUzSHZhz1l79fq6zFqz+fxLGrZVi4/QxWPdzD4J6ORWY6Rhu6dpszLyPjfDG85W5465Hujnh5Rt2aV/kGj5lr0CMCMC0+EoLg2OY7RERERERERGQ7DCPJIHOBYXmtCu/tzW3Svox+7hK7hoWmmnEkxYc7vRmHsf0yf5tyL1779TTeGd1TF6zV39PRXNDqJXND1rVKhPrJIRWLUVytxJheIdgivxsl1Qrc4efhuBdqgDVB9OpdOfj66BW8M9pwUEtEREREREREro/LtMkgbWBoSOe2nvCRSxq9XBgA9ubewI5z15EUF27weN3ltk2lbcaxcESM3lLn+QnRmBYXgS+PXLLq/NYwtvx9+Y4srEvPw1uP9DAZrGmDVkOS4sKx/3wx/D0kWJWWjZAlyWi/JBkdl+3An5dKML7PHXZ5TY2hDVMNHjMRRF8prcHqtBycKapAWY2KS4mJiIiIiIiImil+iqcG9ubeQFmNCklx4QYbpcwYHIniassq3OouR/Z1l6CkWon/zTiPzx7vC7FIZLfOve5SN8wc2hmvDY/WVdDl3KjEkA37caaoAv4eMvy9T6jVz9NYppa/r9+Xj/kJMSYfb6rr8exhUThdWG6wk/jyHVkQi0RO7yRuqmpVG0Qbqlqdv/0MqpRqDAxvg0fvbO+IoRIRERERERGRHTCMJD07zl3D6E8PIqyNJ9KT4gwGhv+8pxPEIpHJ5cLeMomua3b95cifPd4XXgbCQlsvt9WGbtplv92CfTCySyDOFFXgjZ1ZiI9og3Zect2+jY7oHGyL/TINBa1KjQYeUjf0DPFFwv/+YfBx5vZkdARjYWpSXDimxUdAZOAxRy+XYtPhiwCAtx7uAZHI0L2IiIiIiIiIqDlgGNnK6VUuyiWoUqoR1sYT4W084GkiMKxUqIzvyxgXjsKKWnxy8IJFFXr190W0pzUP94BSrcGixC5Yl55nVSOU+k1oLAkzbdVcx9i1s3dzIFuoH6b6ukuxM+saBr2/D3d39MfGx/roAkdBEPDqz6cgCMCEvndgQFgbp46diIiIiIiIiKzDMLIVM9ZIZV9SHLxkbpBJ/hvK1Q+9TC4XHhoFiGByT0lnVei5iUV444FuWLMrp0nNd7Qs6SReP6y8WaVA9o1Ko8vfTS1TtlRz6CQONAxTvWRuyLpeiTNFFYiLCMAz94YBAJLPFiE1+zrkEjHeuL+r08ZLRERERERERLbBMLKV0i6hXlansrF+5aLMzDmMLheWuaGootZlK/TcJW5WBaXGrp02zJw1tDPEIpHBoHf6oEh0D/aBSCQyGWQ2VVP3ZHS2IZ3b4fX7u2LjoYto7yNHrUqN0hoV4iPbYsvTdyP3RiXCAjydPUwiIiIiIiIispJIEATB2YNwtrKyMvj5+aG0tBS+vr7OHo5NGVtKrFBpELIk2WgFXcGiRMgkTQ+t7H1+axRV1CJkcbLR44WLE00GpeZe2/l5w7Fmdw6WpTSsfpyfEI1X7+sMN7EIUrFYL8S11X6VNUo13kzNtltzIHsRBAEl1Uq8sydXb/l8Ulw45g6PhocLj52IiIiIiIiotbM0X2NlZAtmaCnxtPgIvDLE8m7YTeXKFXrWLmU2tS+jRCyCTOKGden5Bo9rO2Zrg1h77JdprGLVlYNIAKhSqvHuXtftBE5ERERERERE1nO99ZpkE5UKFVakZmNZyjldcFZSrcSylHPYsC8fwd5y+HsYDt1ssbegdk/JhSNidM/j7yHFwhExmDMsyqmhkjYoNSQpLhwKtcboY1VqDXzkEqPXLjrQC6UWBL325iWTQCYRI9BbDplE3CxCPKlYjHXpeQaPrU3Pg1TMH1dEREREREREzR0/3bdQpoKdVbtyTAZy2spFa2kr9AoWJaJwcSIKFiVi5tDOTq/QMxaUzk+IxrT4CCxNPge1puHuBQqVBo9/+SeSz15DUly4wXOP7h4Cfw+pXYPelsqSTuBERERERERE1Ly5frkUNYm5YEepEox2w7bl3oL1uya7SvMUQ0uZr5TVYPiHGThRUA6pmwivDY+G1O2/+20euliCEwXlWPj7WexLioNYJGpw7V4cFOHSS9RdWXPpBE5ERERERERETccwsoUyF+x4y28t422OewvaSv2gNDzAE4sSY7A4+RymD47EyrTsBo1Udr8wEGevVcBbLjF57RwR9LY0DHGJiIiIiIiIWj5200bL7KZdqVBhdVqOwWBn4YgYNgMxIedGJTYduqjXSEVrwYgYzLLw2mk7mdujY3ZL1Vw7gRMRERERERG1dpbma04tM9qzZw8efvhhhIaGQiQS4ccff9Q7LggCFi5ciPbt28PDwwMJCQnIytIPiG7evImJEyfC19cX/v7+mDx5MioqKhz4KlyTKzeQcXUd/Tywfl++wWPrGtFIpTk2kXE2V91nlIiIiIiIiIhsw6lhZGVlJXr37o3333/f4PFVq1Zh7dq1+PDDD3HgwAF4eXlh5MiRqKmp0d1n4sSJOHnyJFJSUrBt2zbs2bMHzzzzjKNegktjsNM0bKTiXAxxiYiIiIiIiFoul1mmLRKJ8MMPP2DMmDEAblVFhoaG4pVXXsGrr74KACgtLUVwcDA2btyIxx57DKdPn0b37t1x6NAh9O/fHwCwfft2PPDAA7h06RJCQ0MNPldtbS1qa2t1/y4rK0PHjh1b1DJtajqFSoOQJclG99ssWJQImYR7FxIRERERERERaTWLZdqm5OXloaCgAAkJCbrb/Pz8MGDAAGRkZAAAMjIy4O/vrwsiASAhIQFisRgHDhwweu4VK1bAz89P99WxY0f7vRBqdrSNVAzRNlIhIiIiIiIiIqLGc9kwsqCgAAAQHBysd3twcLDuWEFBAYKCgvSOSyQSBAQE6O5jyNy5c1FaWqr7unjxoo1HT80Z99skIiIiIiIiIrKPVpmqyOVyyOVyZw+DXJh2v83XhkfrdcPmfptERERERERERE3nspWRISEhAIDCwkK92wsLC3XHQkJCUFRUpHdcpVLh5s2buvsQNRUbqRARERERERER2ZbLhpEREREICQnBzp07dbeVlZXhwIEDiI2NBQDExsaipKQER44c0d0nNTUVGo0GAwYMcPiYiYiIiIiIiIiIyDinlnpVVFQgOztb9++8vDwcPXoUAQEB6NSpE6ZPn47ly5cjOjoaERERWLBgAUJDQ3Udt7t164ZRo0ZhypQp+PDDD6FUKpGUlITHHnvMaCdtIiIiIiIiIiIicg6nhpGHDx/G0KFDdf9++eWXAQCTJk3Cxo0bMWvWLFRWVuKZZ55BSUkJ4uPjsX37dri7u+se8+WXXyIpKQnDhw+HWCzGuHHjsHbtWoe/FiIiIiIiIiIiIjJNJAiC4OxBOFtZWRn8/PxQWloKX19fZw+HiIiIiIiIiIioWbE0X3PZPSOJiIiIiIiIiIioZWEYSURERERERERERA7BMJKIiIiIiIiIiIgcwqkNbFyFdtvMsrIyJ4+EiIiIiIiIiIio+dHmauba0zCMBFBeXg4A6Nixo5NHQkRERERERERE1HyVl5fDz8/P6HF20wag0Whw5coV+Pj4QCQSOXs4NldWVoaOHTvi4sWL7BZODsN5R47GOUfOwHlHzsB5R47GOUfOwHlHzsB5Zx1BEFBeXo7Q0FCIxcZ3hmRlJACxWIwOHTo4exh25+vry28mcjjOO3I0zjlyBs47cgbOO3I0zjlyBs47cgbOu6YzVRGpxQY2RERERERERERE5BAMI4mIiIiIiIiIiMghGEa2AnK5HIsWLYJcLnf2UKgV4bwjR+OcI2fgvCNn4LwjR+OcI2fgvCNn4LxzDDawISIiIiIiIiIiIodgZSQRERERERERERE5BMNIIiIiIiIiIiIicgiGkUREREREREREROQQDCOJiIiIiIiIiIjIIRhGEhERERERERERkUMwjHSiPXv24OGHH0ZoaChEIhF+/PFHveOFhYV4+umnERoaCk9PT4waNQpZWVkGzyUIAu6//36D59m5cycGDhwIHx8fhISEYPbs2VCpVGbHt2vXLvTr1w9yuRxRUVHYuHFjo8ZPrscWc+6+++6DSCTS+3ruuef07nPhwgU8+OCD8PT0RFBQEGbOnGnRnPv222/RtWtXuLu7o1evXvj1118bPT5yPY6Yd8eOHcOECRPQsWNHeHh4oFu3bnjvvfcsGp+5eff00083eO5Ro0Y17WKQwzjq553WjRs30KFDB4hEIpSUlJgdn7l5t2XLFiQmJqJt27YQiUQ4evRoY14+OYGj5lz94yKRCF999ZXZ8fE9tmVyxLzbuHGjwXknEolQVFRkcnx8j22ZHPXzjp9jSctW2UlGRgaGDRsGLy8v+Pr6YvDgwaiurtYdv3nzJiZOnAhfX1/4+/tj8uTJqKioMDs+c3OuvLwc06dPR1hYGDw8PDBw4EAcOnSoSdeipWAY6USVlZXo3bs33n///QbHBEHAmDFjkJubi61btyIzMxNhYWFISEhAZWVlg/u/++67EIlEDW4/duwYHnjgAYwaNQqZmZn4+uuv8dNPP2HOnDkmx5aXl4cHH3wQQ4cOxdGjRzF9+nT861//wu+//27R+Mk12WrOTZkyBVevXtV9rVq1SndMrVbjwQcfhEKhwP79+7Fp0yZs3LgRCxcuNDm2/fv3Y8KECZg8eTIyMzMxZswYjBkzBidOnGj0+Mi1OGLeHTlyBEFBQfjiiy9w8uRJzJs3D3PnzsX69etNjs3cvNMaNWqU3nNv3rzZiitCjuCIeVfX5MmTceedd1o0NkvmXWVlJeLj47Fy5cpGvGpyJkfOuU8//VTvPmPGjDE5Nr7HtlyOmHfjx4/XO3b16lWMHDkSQ4YMQVBQkNGx8T225XLEvOPnWKrLFnMuIyMDo0aNQmJiIg4ePIhDhw4hKSkJYvF/Y7GJEyfi5MmTSElJwbZt27Bnzx4888wzJsdmyZz717/+hZSUFHz++ec4fvw4EhMTkZCQgMuXL9vg6jRTArkEAMIPP/yg+/fZs2cFAMKJEyd0t6nVaiEwMFD46KOP9B6bmZkp3HHHHcLVq1cbnGfu3LlC//799e7/008/Ce7u7kJZWZnR8cyaNUvo0aOH3m3jx48XRo4cadH4yfU1dc4NGTJEeOmll4ye99dffxXEYrFQUFCgu+2DDz4QfH19hdraWqOP+/vf/y48+OCDercNGDBAePbZZxs1PnJt9pp3hrzwwgvC0KFDTd7H3LwTBEGYNGmSMHr06EY9N7kWe8+7DRs2CEOGDBF27twpABCKi4tN3t+SeaeVl5cnABAyMzPNjoNchz3nXFN+5+J7bOvgqPfYoqIiQSqVCp999pnJ+/E9tnWw17zj51gypqlzbsCAAcL8+fONnvfUqVMCAOHQoUO623777TdBJBIJly9fNvo4c3OuqqpKcHNzE7Zt26Z3n379+gnz5s0z/WJbMFZGuqja2loAgLu7u+42sVgMuVyO9PR03W1VVVV4/PHH8f777yMkJMTgeeqeAwA8PDxQU1ODI0eOGH3+jIwMJCQk6N02cuRIZGRkNOn1kOuzdM4BwJdffol27dqhZ8+emDt3LqqqqnTHMjIy0KtXLwQHB+tuGzlyJMrKynDy5Emjz29uzjVmfNR82GreGVJaWoqAgACT97H0Z92uXbsQFBSELl264Pnnn8eNGzfMvjZyXbacd6dOncLSpUvx2Wef6f1l3RS+x7Y+tv5ZN3XqVLRr1w733HMPPvnkEwiCYPL5+R7bOtnrPfazzz6Dp6cnHn30UZPPz/fY1slW846fY8lSlsy5oqIiHDhwAEFBQRg4cCCCg4MxZMgQvTmZkZEBf39/9O/fX3dbQkICxGIxDhw4YPT5zc05lUoFtVptcD635vdYhpEuqmvXrujUqRPmzp2L4uJiKBQKrFy5EpcuXcLVq1d195sxYwYGDhyI0aNHGzzPyJEjsX//fmzevBlqtRqXL1/G0qVLAUDvPPUVFBTohUkAEBwcjLKyMr09FajlsHTOPf744/jiiy+QlpaGuXPn4vPPP8cTTzyhO25s7miPGWPscdrHWDo+al5sNe/q279/P77++muzyyrMzTvg1vKxzz77DDt37sTKlSuxe/du3H///VCr1U181eRstpp3tbW1mDBhAlavXo1OnTpZ/PyWzDtqWWz5s27p0qX45ptvkJKSgnHjxuGFF17AunXrTD4/32NbJ3u9x/773//G448/Dg8PD5PPz/fY1slW846fY8lSlsy53NxcAMDixYsxZcoUbN++Hf369cPw4cN1e0sWFBQ02HpCIpEgICCgSZ9jtXPOx8cHsbGxWLZsGa5cuQK1Wo0vvvgCGRkZrfo9lmGki5JKpdiyZQvOnTuHgIAAeHp6Ii0tDffff7+u8uKnn35Camoq3n33XaPnSUxMxOrVq/Hcc89BLpcjJiYGDzzwAADozuPt7a37MrYxP7V8lsw5AHjmmWcwcuRI9OrVCxMnTsRnn32GH374ATk5ORY9z4ULF/Tm3BtvvGHT8VHzYo95d+LECYwePRqLFi1CYmIigKbPOwB47LHH8Mgjj6BXr14YM2YMtm3bhkOHDmHXrl1Wv35yDlvNu7lz56Jbt25GP7RbM++oZbHlz7oFCxYgLi4Offv2xezZszFr1iysXr0aAN9jSZ893mMzMjJw+vRpTJ48WXcb32OpLlvNO36OJUtZMuc0Gg0A4Nlnn8U//vEP9O3bF++88w66dOmCTz75xOLnauqc+/zzzyEIAu644w7I5XKsXbsWEyZMaNXvsRJnD4CMu+uuu3D06FGUlpZCoVAgMDAQAwYM0JUNp6amIicnB/7+/nqPGzduHAYNGqR7E3/55ZcxY8YMXL16FW3atEF+fj7mzp2LyMhIANDr0Onr6wsACAkJQWFhod55CwsL4evra/avoNR8mZtzhgwYMAAAkJ2djc6dOyMkJAQHDx7Uu492LoWEhCA0NFRvzmmX0Rqbc3W3H2jK+Mj12WLeaZ06dQrDhw/HM888g/nz5+tut2be1RcZGYl27dohOzsbw4cPb9RrJddhi3mXmpqK48eP47vvvgMA3VLZdu3aYd68eViwYIHN5h01f7b8WVf/PsuWLUNtbS3fY6kBW8+7jz/+GH369MFdd92lu43vsVSfreYdP8eSpczNufbt2wMAunfvrve4bt264cKFCwBuzZ2ioiK94yqVCjdv3tT93GrqnOvcuTN2796NyspKlJWVoX379hg/frxuLrdGrTeGbUb8/PwQGBiIrKwsHD58WLcke86cOfjrr79w9OhR3RcAvPPOO/j000/1ziESiRAaGgoPDw9s3rwZHTt2RL9+/QAAUVFRui9tWXJsbCx27typd46UlBTExsba+dWSKzA25wzRzjvtD/jY2FgcP35c7wd5SkoKfH190b17d0gkEr05p/2FtTFzrjHjo+bDmnkHACdPnsTQoUMxadIkvP7663r3t8W807p06RJu3Lih99zUfFkz777//nscO3ZM9x788ccfAwD27t2LqVOn2nTeUcth7c86Q/dp06YN5HI532PJKFvMu4qKCnzzzTd6VZEA32PJOFvMO36OpcYwNufCw8MRGhqKs2fP6t3/3LlzCAsLA3Br7pSUlOjtSZqamgqNRqMLy62dc15eXmjfvj2Ki4vx+++/t+73WCc30GnVysvLhczMTCEzM1MAILz99ttCZmamcP78eUEQBOGbb74R0tLShJycHOHHH38UwsLChLFjx5o8Jwx0A1u1apXw119/CSdOnBCWLl0qSKVSsx3DcnNzBU9PT2HmzJnC6dOnhffff19wc3MTtm/fbvH4yfVYO+eys7OFpUuXCocPHxby8vKErVu3CpGRkcLgwYN191GpVELPnj2FxMRE4ejRo8L27duFwMBAYe7cuSbHtm/fPkEikQhr1qwRTp8+LSxatEiQSqXC8ePHdfdpyvcEOZ8j5t3x48eFwMBA4YknnhCuXr2q+yoqKjI5NnPzrry8XHj11VeFjIwMIS8vT9ixY4fQr18/ITo6WqipqbHD1SJbccS8qy8tLc2ibtqW/Ly7ceOGkJmZKfzyyy8CAOGrr74SMjMzhatXr1p3YchuHDHnfvrpJ+Gjjz4Sjh8/LmRlZQkbNmwQPD09hYULF5ocG99jWy5H/qz7+OOPBXd3d7M/47T4HttyOWre8XMsadkiO3nnnXcEX19f4dtvvxWysrKE+fPnC+7u7kJ2drbuPqNGjRL69u0rHDhwQEhPTxeio6OFCRMmmBybJXNu+/btwm+//Sbk5uYKycnJQu/evYUBAwYICoXChlepeWEY6UTaDy31vyZNmiQIgiC89957QocOHQSpVCp06tRJmD9/vlBbW2vynIbCyKFDhwp+fn6Cu7u7MGDAAOHXX3+1eHx9+vQRZDKZEBkZKXz66aeNGj+5Hmvn3IULF4TBgwcLAQEBglwuF6KiooSZM2cKpaWles+Tn58v3H///YKHh4fQrl074ZVXXhGUSqXZ8X3zzTdCTEyMIJPJhB49egi//PKL3vGmfE+Q8zli3i1atMjgc4SFhZkdn6l5V1VVJSQmJgqBgYGCVCoVwsLChClTpggFBQU2uz5kH476eWfoOS35oG7u592nn35qcPyLFi1qyuUgB3DEnPvtt9+EPn36CN7e3oKXl5fQu3dv4cMPPxTUarXZ8fE9tmVy5M+62NhY4fHHH2/U+Pge2zI5at7xcyxp2So7WbFihdChQwfB09NTiI2NFfbu3at3/MaNG8KECRMEb29vwdfXV/jHP/4hlJeXWzQ+U3Pu66+/FiIjIwWZTCaEhIQIU6dOFUpKSpp8PVoCkSDc3uCIiIiIiIiIiIiIyI64ZyQRERERERERERE5BMNIIiIiIiIiIiIicgiGkUREREREREREROQQDCOJiIiIiIiIiIjIIRhGEhERERERERERkUMwjCQiIiIiIiIiIiKHYBhJREREREREREREDsEwkoiIiIiIiIiIiByCYSQRERERERERERE5BMNIIiIiIiIiIiIicgiGkUREREREREREROQQ/w+RZJQJwl4e/wAAAABJRU5ErkJggg==",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ses = ExponentialSmoothing(sp=12)\n",
"holt = ExponentialSmoothing(trend=\"add\", damped_trend=False, sp=12)\n",
"damped = ExponentialSmoothing(trend=\"add\", damped_trend=True, sp=12)\n",
"\n",
"forecaster = EnsembleForecaster(\n",
" [\n",
" (\"ses\", ses),\n",
" (\"holt\", holt),\n",
" (\"damped\", damped),\n",
" ]\n",
")\n",
"forecaster.fit(y_train)\n",
"y_pred = forecaster.predict(fh)\n",
"plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"])\n",
"mean_absolute_percentage_error(y_test, y_pred, symmetric=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"### 3.4.4 Prediction weighted ensembles and hedge ensembles\n",
"\n",
"For model evaluation, we sometimes want to evaluate multiple forecasts, using temporal cross-validation with a sliding window over the test data. For this purpose, we can leverage the forecasters from the `online_forecasting` module which use a composite forecaster, `PredictionWeightedEnsemble`, to keep track of the loss accumulated by each forecaster and create a prediction weighted by the predictions of the most \"accurate\" forecasters.\n",
"\n",
"Note that the forecasting task is changed: we make 35 predictions since we need the first prediction to help update the weights, we do not predict 36 steps ahead."
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from aeon.forecasting.online_learning import (\n",
" NormalHedgeEnsemble,\n",
" OnlineEnsembleForecaster,\n",
")\n",
"from aeon.performance_metrics.forecasting import mean_squared_error"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"First we need to initialize a `PredictionWeightedEnsembler` that will keep track of the loss accumulated by each forecaster and define which loss function we would like to use."
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hedge_expert = NormalHedgeEnsemble(n_estimators=3, loss_func=mean_squared_error)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"We can then create the forecaster by defining the individual forecasters and specifying the `PredictionWeightedEnsembler` we are using. Then by fitting our forecasters and performing updates and prediction with the `update_predict` function, we get:"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0978975689038194"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"forecaster = OnlineEnsembleForecaster(\n",
" [\n",
" (\"ses\", ses),\n",
" (\"holt\", holt),\n",
" (\"damped\", damped),\n",
" ],\n",
" ensemble_algorithm=hedge_expert,\n",
")\n",
"\n",
"forecaster.fit(y=y_train, fh=fh)\n",
"y_pred = forecaster.update_predict_single(y_test)\n",
"plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"])\n",
"mean_absolute_percentage_error(y_test, y_pred, symmetric=False)"
]
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"source": [
"## 4. Extension guide - implementing your own forecaster\n",
"\n",
"`aeon` is meant to be easily extensible, for direct contribution to `aeon` as well as for local/private extension with custom methods.\n",
"\n",
"To get started:\n",
"\n",
"* Follow the [\"implementing estimator\" developer guide](https://www.aeon-toolkit.org/en/stable/developer_guide/add_estimators.html)\n",
"* Use the [simple forecasting extension template](https://github.com/aeon-toolkit/aeon/blob/main/extension_templates/forecasting_simple.py) for forecasters without stream, probabilistic, or hierarchical functionality\n",
"* Use the [advanced forecasting extension template](https://github.com/aeon-toolkit/aeon/blob/main/extension_templates/forecasting.py) for forecasters with stream, probabilistic or hierarchical functionality\n",
"* For probabilistic and hierarchical forecasters, it is recommended to familiarize yourself with the interfaces via the tutorials\n",
"\n",
"1. Read through the [forecasting extension template](https://github.com/aeon-toolkit/aeon/blob/main/extension_templates/forecasting.py) - this is a `python` file with `todo` blocks that mark the places in which changes need to be added.\n",
"2. Optionally, if you are planning any major surgeries to the interface: look at the [base class architecture](https://github.com/aeon-toolkit/aeon/blob/main/aeon/forecasting/base/_base.py) - note that \"ordinary\" extension (e.g., new algorithm) should be easily doable without this.\n",
"3. Copy the forecasting extension template to a local folder in your own repository (local/private extension), or to a suitable location in your clone of the `aeon` or affiliated repository (if contributed extension), inside `aeon.forecasting`; rename the file and update the file docstring appropriately.\n",
"4. Address the \"todo\" parts. Usually, this means: changing the name of the class, setting the tag values, specifying hyper-parameters, filling in `__init__`, `_fit`, `_predict`, and optional methods such as `_update` (for details see the extension template). You can add private methods as long as they do not override the default public interface. For more details, see the extension template.\n",
"5. To test your estimator manually: import your estimator and run it in the worfklows in Section 1; then use it in the compositors in Section 3.\n",
"6. To test your estimator automatically: call `aeon.testing.estimator_checks.check_estimator` on your estimator. You can call this on a class or object instance. Ensure you have specified test parameters in the `get_test_params` method, according to the extension template.\n",
"\n",
"In case of direct contribution to `aeon` or one of its affiliated packages, additionally:\n",
"* Add yourself as an author to the code, and to the `CODEOWNERS` for the new estimator file(s).\n",
"* Create a pull request that contains only the new estimators (and their inheritance tree, if it's not just one class), as well as the automated tests as described above.\n",
"* In the pull request, describe the estimator and optimally provide a publication or other technical reference for the strategy it implements.\n",
"* Before making the pull request, ensure that you have all necessary permissions to contribute the code to a permissive license (BSD-3) open source project."
]
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"## 5. Summary\n",
"\n",
"* `aeon` comes with several forecasting algorithms (or forecasters), all of which share a common interface. The interface is fully interoperable with the `scikit-learn` interface, and provides dedicated interface points for forecasting in batch and rolling mode.\n",
"\n",
"* `aeon` comes with rich composition functionality that allows to build complex pipelines easily, and connect easily with other parts of the open source ecosystem, such as `scikit-learn` and individual algorithm libraries.\n",
"\n",
"* `aeon` is easy to extend, and comes with user friendly tools to facilitate implementing and testing your own forecasters and composition principles.\n",
"\n",
"\n",
"## Useful resources\n",
"* For a good introduction to forecasting, see [Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice. OTexts, 2018](https://otexts.com/fpp2/).\n",
"* For comparative benchmarking studies/forecasting competitions, see the [M4 competition](https://www.sciencedirect.com/science/article/pii/S0169207019301128) and the [M5 competition](https://www.kaggle.com/c/m5-forecasting-accuracy/overview)."
]
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![](\"img/forecasting.png\")
\n",
"\n",
"This notebook gives a tutorial on forecasting. More specific notebooks in aeon on\n",
"forecasting are:\n",
"\n",
"- [Probabilistic Forecasting](forecasting_proba.ipynb)\n",
"- [Hierarchical, Global, and Panel Forecasting](forecasting_hierarchical_global.ipynb)\n",
"- [Forecasting with skearn](forecasting_sklearn.ipynb).\n",
"\n",
"`aeon` provides a common, `scikit-learn`-like interface to a variety of classical and ML-style forecasting algorithms, together with tools for building pipelines and composite machine learning models, including temporal tuning schemes, or reductions such as walk-forward application of `scikit-learn` regressors.\n",
"\n",
"**Section 1** provides an overview of common forecasting workflows supported by `aeon`.\n",
"\n",
"**Section 2** discusses the families of forecasters available in `aeon`.\n",
"\n",
"**Section 3** discusses advanced composition patterns, including pipeline building, reduction, tuning, ensembling, and autoML.\n",
"\n",
"**Section 4** gives an introduction to how to write custom estimators compliant with the `aeon` interface.\n",
"\n"
]
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"source": [
"## Table of Contents\n",
"\n",
"* [1. Basic forecasting workflows](#1.-basic-forecasting-workflows)\n",
" * 1.1 Data container format\n",
" * 1.2 Basic deployment workflow - batch fitting and forecasting\n",
" * 1.2.1 Basic deployment workflow in a nutshell\n",
" * 1.2.2 Forecasters that require the horizon when fitting\n",
" * 1.2.3 Forecasters that can make use of exogeneous data\n",
" * 1.2.4 Multivariate Forecasters\n",
" * 1.2.5 Prediction intervals and quantile forecasts\n",
" * 1.2.6 Panel forecasts and hierarchical forecasts\n",
" * 1.3 Basic evaluation workflow - evaluating a batch of forecasts against ground truth observations\n",
" * 1.3.1 The basic batch forecast evaluation workflow in a nutshell - function metric interface\n",
" * 1.3.2 The basic batch forecast evaluation workflow in a nutshell - metric class interface)\n",
" * 1.4 Advanced deployment workflow: rolling updates & forecasts\n",
" * 1.4.1 Updating a forecaster with the update method\n",
" * 1.4.2 Moving the \"now\" state without updating the model\n",
" * 1.4.3 Walk-forward predictions on a batch of data\n",
" * 1.5 Advanced evaluation worfklow: rolling re-sampling and aggregate errors, rolling back-testing\n",
"* [2. Forecasters in aeon - searching, tags, common families](#2.-forecasters-in-aeon---searching,-tags,-common-families)\n",
" * 2.1 Forecaster lookup - the registry\n",
" * 2.2 Forecaster tags\n",
" * 2.2.1 Capability tags: multivariate, probabilistic, hierarchical\n",
" * 2.2.2 Finding and listing forecasters by tag)\n",
" * 2.2.3 Listing all forecaster tags)\n",
" * 2.3 Common forecaster types\n",
" * 2.3.1 Exponential smoothing, theta forecaster, autoETS from statsmodels\n",
" * 2.3.2 ARIMA and autoARIMA\n",
" * 2.3.3 BATS and TBATS\n",
" * 2.3.4 Facebook prophet\n",
" * 2.3.5 State Space Model (Structural Time Series)\n",
" * 2.3.6 AutoArima from StatsForecast\n",
"* [3. Advanced composition patterns - pipelines, reduction, autoML, and more](#3.-advanced-composition-patterns---pipelines,-reduction,-automl,-and-more)\n",
" * 3.1 Reduction: from forecasting to regression\n",
" * 3.2 Pipelining, detrending and deseasonalization\n",
" * 3.2.1 The basic forecasting pipeline\n",
" * 3.2.2 The Detrender as pipeline component\n",
" * 3.2.3 Complex pipeline composites and parameter inspection\n",
" * 3.3 Parameter tuning\n",
" * 3.3.1 Basic tuning using ForecastingGridSearchCV\n",
" * 3.3.2 Tuning of complex composites\n",
" * 3.3.3 Selecting the metric and retrieving scores\n",
" * 3.4 autoML aka automated model selection, ensembling and hedging\n",
" * 3.4.1 autoML aka automatic model selection, using tuning plus multiplexer\n",
" * 3.4.2 autoML: selecting transformer combinations via OptimalPassthrough\n",
" * 3.4.3 Simple ensembling strategies\n",
" * 3.4.4 Prediction weighted ensembles and hedge ensembles\n",
"* [4. Extension guide - implementing your own forecaster](#4.-extension-guide---implementing-your-own-forecaster)\n",
"* [5. Summary](#5.-summary)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Package imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:10.687691700Z",
"start_time": "2023-08-25T09:25:07.453121300Z"
}
},
"outputs": [],
"source": [
"import warnings\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# hide warnings\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Basic forecasting workflows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This section explains the basic forecasting workflows, and key interface points for it.\n",
"\n",
"We cover the following four workflows:\n",
"\n",
"* Basic deployment workflow: batch fitting and forecasting;\n",
"* Basic evaluation workflow: evaluating a batch of forecasts against ground truth\n",
"observations;\n",
"* Advanced deployment workflow: fitting and rolling updates/forecasts; and\n",
"* Advanced evaluation worfklow: using rolling forecast splits and computing\n",
"split-wise and aggregate errors, including common back-testing schemes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1 Data container format\n",
"\n",
"All forecasting workflows make common assumptions on the input data format.\n",
"`aeon` uses `pandas` for representing time series for forecasting:\n",
"\n",
"* `pd.DataFrame` for time series and sequences. Rows represent time indices, columns\n",
"represent variables;\n",
"* `pd.Series` can be used for univariate time series;\n",
"* `numpy` arrays (1D and 2D) can also used.\n",
"\n",
"The `Series.index` and `DataFrame.index` are used for representing the time series index.\n",
"`aeon` supports pandas integer, period and timestamp indices for simple time series.\n",
"\n",
"`aeon` supports additional data structures for panel and hierarchical\n",
"time series, see the [data structure notebooks](../datasets/datasets.ipynb) for more\n",
"details.\n",
"\n",
"**Example:** as the running example in this tutorial, we use a textbook data set, the Box-Jenkins airline data set, which consists of the number of monthly totals of international airline passengers, from 1949 - 1960. Values are in thousands. See \"Makridakis, Wheelwright and Hyndman (1998) Forecasting: methods and applications\", exercises sections 2 and 3."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:14.040404400Z",
"start_time": "2023-08-25T09:25:07.512960700Z"
}
},
"outputs": [],
"source": [
"from aeon.datasets import load_airline\n",
"from aeon.visualisation import plot_series"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:14.367530200Z",
"start_time": "2023-08-25T09:25:08.130310300Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(![](\"img/forecasting-to-regression-reduction.png\")
\n",
"\n",
"Below, the composite is constructed using the shorthand function `make_reduction` which produces a `aeon` estimator of forecaster scitype. It is called with a constructed `scikit-learn` regressor, `regressor`, and additional parameter which can be later tuned as hyper-parameters"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:49.706910400Z",
"start_time": "2023-08-25T09:25:49.630115700Z"
}
},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsRegressor\n",
"\n",
"from aeon.forecasting.compose import make_reduction"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:49.863492800Z",
"start_time": "2023-08-25T09:25:49.636100100Z"
}
},
"outputs": [],
"source": [
"regressor = KNeighborsRegressor(n_neighbors=1)\n",
"forecaster = make_reduction(regressor, window_length=15, strategy=\"recursive\")"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-25T09:25:50.134767900Z",
"start_time": "2023-08-25T09:25:49.644080500Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.12887507224382988"
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
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