diff --git a/Machine Learning/Feature Importance/README.md b/Machine Learning/Feature Importance/README.md
new file mode 100644
index 00000000..e7226aa0
--- /dev/null
+++ b/Machine Learning/Feature Importance/README.md
@@ -0,0 +1,30 @@
+# Feature importance for data frame analytics
+
+In the notebook we assume that you have [Elasticsearch 7.6 or later](https://www.elastic.co/downloads/elasticsearch) and you run it locally on the port 9200. If you don't, learn how to [get Elasticsearch up and running](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/getting-started-install.html).
+
+Set up a local instance of Jupyter using the following instructions
+
+1. Set up a virtual environment called `env`
+
+```
+python3 -m venv env
+```
+
+2. Activate it
+
+```
+source env/bin/activate
+```
+
+3. Install the required dependencies for your chosen Jupyter notebook
+
+```
+pip install -r requirements.txt
+```
+
+4. Launch Jupyter
+
+```
+jupyter notebook
+```
+
diff --git a/Machine Learning/Feature Importance/feature_importance_in_elasticsearch.ipynb b/Machine Learning/Feature Importance/feature_importance_in_elasticsearch.ipynb
new file mode 100644
index 00000000..dfa076c5
--- /dev/null
+++ b/Machine Learning/Feature Importance/feature_importance_in_elasticsearch.ipynb
@@ -0,0 +1,4669 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Better Understand Your Data with Feature Importance and Data Frame Analytics\n",
+ "\n",
+ "This is a companion notebook to the blog post."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "from itertools import groupby\n",
+ "from operator import itemgetter\n",
+ "import pprint\n",
+ "\n",
+ "import eland\n",
+ "from elasticsearch import Elasticsearch, helpers\n",
+ "import ipywidgets as widgets\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as pl\n",
+ "import pandas as pd\n",
+ "import plotly.graph_objects as go\n",
+ "import plotly.io as pio\n",
+ "from plotly.subplots import make_subplots\n",
+ "import requests\n",
+ "import seaborn as sns\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This notebook assumes that you have [Elasticsearch 7.6](https://www.elastic.co/downloads/elasticsearch) running locally on the port 9200."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Some general notebook setting\n",
+ "sns.set_style(\"whitegrid\")\n",
+ "sns.set_color_codes(\"muted\")\n",
+ "sns.set_context(\"talk\")\n",
+ "host = 'http://localhost:9200'\n",
+ "es = Elasticsearch(host)\n",
+ "analysis_job_id = \"world-happiness-report\"\n",
+ "dataset_index = \"world-happiness-report\"\n",
+ "result_index = \"whr-regression-results\"\n",
+ "write_images = False # this variable controle writing images for the blog post"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get the dataset direclty from the official source\n",
+ "df = pd.read_excel('https://s3.amazonaws.com/happiness-report/2019/Chapter2OnlineData.xls', 'Table2.1')\n",
+ "dataset = df.loc[:, :'Negative affect']\n",
+ "dataset = dataset.dropna()\n",
+ "dataset.columns = [col.lower().replace(' ', '_') for col in dataset.columns]\n",
+ "input_features = list(dataset.columns)\n",
+ "input_features.remove('country_name')\n",
+ "input_features.remove('year')\n",
+ "input_features.remove('life_ladder')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " country_name | \n",
+ " year | \n",
+ " life_ladder | \n",
+ " log_gdp_per_capita | \n",
+ " social_support | \n",
+ " healthy_life_expectancy_at_birth | \n",
+ " freedom_to_make_life_choices | \n",
+ " generosity | \n",
+ " perceptions_of_corruption | \n",
+ " positive_affect | \n",
+ " negative_affect | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " Afghanistan | \n",
+ " 2008 | \n",
+ " 3.723590 | \n",
+ " 7.168690 | \n",
+ " 0.450662 | \n",
+ " 50.799999 | \n",
+ " 0.718114 | \n",
+ " 0.177889 | \n",
+ " 0.881686 | \n",
+ " 0.517637 | \n",
+ " 0.258195 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " Afghanistan | \n",
+ " 2009 | \n",
+ " 4.401778 | \n",
+ " 7.333790 | \n",
+ " 0.552308 | \n",
+ " 51.200001 | \n",
+ " 0.678896 | \n",
+ " 0.200178 | \n",
+ " 0.850035 | \n",
+ " 0.583926 | \n",
+ " 0.237092 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " Afghanistan | \n",
+ " 2010 | \n",
+ " 4.758381 | \n",
+ " 7.386629 | \n",
+ " 0.539075 | \n",
+ " 51.599998 | \n",
+ " 0.600127 | \n",
+ " 0.134353 | \n",
+ " 0.706766 | \n",
+ " 0.618265 | \n",
+ " 0.275324 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " Afghanistan | \n",
+ " 2011 | \n",
+ " 3.831719 | \n",
+ " 7.415019 | \n",
+ " 0.521104 | \n",
+ " 51.919998 | \n",
+ " 0.495901 | \n",
+ " 0.172137 | \n",
+ " 0.731109 | \n",
+ " 0.611387 | \n",
+ " 0.267175 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " Afghanistan | \n",
+ " 2012 | \n",
+ " 3.782938 | \n",
+ " 7.517126 | \n",
+ " 0.520637 | \n",
+ " 52.240002 | \n",
+ " 0.530935 | \n",
+ " 0.244273 | \n",
+ " 0.775620 | \n",
+ " 0.710385 | \n",
+ " 0.267919 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " country_name year life_ladder log_gdp_per_capita social_support \\\n",
+ "0 Afghanistan 2008 3.723590 7.168690 0.450662 \n",
+ "1 Afghanistan 2009 4.401778 7.333790 0.552308 \n",
+ "2 Afghanistan 2010 4.758381 7.386629 0.539075 \n",
+ "3 Afghanistan 2011 3.831719 7.415019 0.521104 \n",
+ "4 Afghanistan 2012 3.782938 7.517126 0.520637 \n",
+ "\n",
+ " healthy_life_expectancy_at_birth freedom_to_make_life_choices generosity \\\n",
+ "0 50.799999 0.718114 0.177889 \n",
+ "1 51.200001 0.678896 0.200178 \n",
+ "2 51.599998 0.600127 0.134353 \n",
+ "3 51.919998 0.495901 0.172137 \n",
+ "4 52.240002 0.530935 0.244273 \n",
+ "\n",
+ " perceptions_of_corruption positive_affect negative_affect \n",
+ "0 0.881686 0.517637 0.258195 \n",
+ "1 0.850035 0.583926 0.237092 \n",
+ "2 0.706766 0.618265 0.275324 \n",
+ "3 0.731109 0.611387 0.267175 \n",
+ "4 0.775620 0.710385 0.267919 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dataset.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We use `eland` to upload the dataset to Elasticsearch."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " country_name | \n",
+ " freedom_to_make_life_choices | \n",
+ " generosity | \n",
+ " healthy_life_expectancy_at_birth | \n",
+ " life_ladder | \n",
+ " log_gdp_per_capita | \n",
+ " negative_affect | \n",
+ " perceptions_of_corruption | \n",
+ " positive_affect | \n",
+ " social_support | \n",
+ " year | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " Afghanistan | \n",
+ " 0.718114 | \n",
+ " 0.177889 | \n",
+ " 50.799999 | \n",
+ " 3.723590 | \n",
+ " 7.168690 | \n",
+ " 0.258195 | \n",
+ " 0.881686 | \n",
+ " 0.517637 | \n",
+ " 0.450662 | \n",
+ " 2008 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " Afghanistan | \n",
+ " 0.678896 | \n",
+ " 0.200178 | \n",
+ " 51.200001 | \n",
+ " 4.401778 | \n",
+ " 7.333790 | \n",
+ " 0.237092 | \n",
+ " 0.850035 | \n",
+ " 0.583926 | \n",
+ " 0.552308 | \n",
+ " 2009 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " Afghanistan | \n",
+ " 0.600127 | \n",
+ " 0.134353 | \n",
+ " 51.599998 | \n",
+ " 4.758381 | \n",
+ " 7.386629 | \n",
+ " 0.275324 | \n",
+ " 0.706766 | \n",
+ " 0.618265 | \n",
+ " 0.539075 | \n",
+ " 2010 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " Afghanistan | \n",
+ " 0.495901 | \n",
+ " 0.172137 | \n",
+ " 51.919998 | \n",
+ " 3.831719 | \n",
+ " 7.415019 | \n",
+ " 0.267175 | \n",
+ " 0.731109 | \n",
+ " 0.611387 | \n",
+ " 0.521104 | \n",
+ " 2011 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " Afghanistan | \n",
+ " 0.530935 | \n",
+ " 0.244273 | \n",
+ " 52.240002 | \n",
+ " 3.782938 | \n",
+ " 7.517126 | \n",
+ " 0.267919 | \n",
+ " 0.775620 | \n",
+ " 0.710385 | \n",
+ " 0.520637 | \n",
+ " 2012 | \n",
+ "
\n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " 1699 | \n",
+ " Zimbabwe | \n",
+ " 0.642034 | \n",
+ " -0.048634 | \n",
+ " 52.380001 | \n",
+ " 4.184451 | \n",
+ " 7.562753 | \n",
+ " 0.239111 | \n",
+ " 0.820217 | \n",
+ " 0.725214 | \n",
+ " 0.765839 | \n",
+ " 2014 | \n",
+ "
\n",
+ " \n",
+ " 1700 | \n",
+ " Zimbabwe | \n",
+ " 0.667193 | \n",
+ " -0.097354 | \n",
+ " 53.799999 | \n",
+ " 3.703191 | \n",
+ " 7.556052 | \n",
+ " 0.178861 | \n",
+ " 0.810457 | \n",
+ " 0.715079 | \n",
+ " 0.735800 | \n",
+ " 2015 | \n",
+ "
\n",
+ " \n",
+ " 1701 | \n",
+ " Zimbabwe | \n",
+ " 0.732971 | \n",
+ " -0.068105 | \n",
+ " 54.400002 | \n",
+ " 3.735400 | \n",
+ " 7.538829 | \n",
+ " 0.208555 | \n",
+ " 0.723612 | \n",
+ " 0.737636 | \n",
+ " 0.768425 | \n",
+ " 2016 | \n",
+ "
\n",
+ " \n",
+ " 1702 | \n",
+ " Zimbabwe | \n",
+ " 0.752826 | \n",
+ " -0.069670 | \n",
+ " 55.000000 | \n",
+ " 3.638300 | \n",
+ " 7.549491 | \n",
+ " 0.224051 | \n",
+ " 0.751208 | \n",
+ " 0.806428 | \n",
+ " 0.754147 | \n",
+ " 2017 | \n",
+ "
\n",
+ " \n",
+ " 1703 | \n",
+ " Zimbabwe | \n",
+ " 0.762675 | \n",
+ " -0.038384 | \n",
+ " 55.599998 | \n",
+ " 3.616480 | \n",
+ " 7.553395 | \n",
+ " 0.211726 | \n",
+ " 0.844209 | \n",
+ " 0.710119 | \n",
+ " 0.775388 | \n",
+ " 2018 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "1512 rows × 11 columns
"
+ ],
+ "text/plain": [
+ " country_name freedom_to_make_life_choices generosity \\\n",
+ "0 Afghanistan 0.718114 0.177889 \n",
+ "1 Afghanistan 0.678896 0.200178 \n",
+ "2 Afghanistan 0.600127 0.134353 \n",
+ "3 Afghanistan 0.495901 0.172137 \n",
+ "4 Afghanistan 0.530935 0.244273 \n",
+ "... ... ... ... \n",
+ "1699 Zimbabwe 0.642034 -0.048634 \n",
+ "1700 Zimbabwe 0.667193 -0.097354 \n",
+ "1701 Zimbabwe 0.732971 -0.068105 \n",
+ "1702 Zimbabwe 0.752826 -0.069670 \n",
+ "1703 Zimbabwe 0.762675 -0.038384 \n",
+ "\n",
+ " healthy_life_expectancy_at_birth life_ladder log_gdp_per_capita \\\n",
+ "0 50.799999 3.723590 7.168690 \n",
+ "1 51.200001 4.401778 7.333790 \n",
+ "2 51.599998 4.758381 7.386629 \n",
+ "3 51.919998 3.831719 7.415019 \n",
+ "4 52.240002 3.782938 7.517126 \n",
+ "... ... ... ... \n",
+ "1699 52.380001 4.184451 7.562753 \n",
+ "1700 53.799999 3.703191 7.556052 \n",
+ "1701 54.400002 3.735400 7.538829 \n",
+ "1702 55.000000 3.638300 7.549491 \n",
+ "1703 55.599998 3.616480 7.553395 \n",
+ "\n",
+ " negative_affect perceptions_of_corruption positive_affect \\\n",
+ "0 0.258195 0.881686 0.517637 \n",
+ "1 0.237092 0.850035 0.583926 \n",
+ "2 0.275324 0.706766 0.618265 \n",
+ "3 0.267175 0.731109 0.611387 \n",
+ "4 0.267919 0.775620 0.710385 \n",
+ "... ... ... ... \n",
+ "1699 0.239111 0.820217 0.725214 \n",
+ "1700 0.178861 0.810457 0.715079 \n",
+ "1701 0.208555 0.723612 0.737636 \n",
+ "1702 0.224051 0.751208 0.806428 \n",
+ "1703 0.211726 0.844209 0.710119 \n",
+ "\n",
+ " social_support year \n",
+ "0 0.450662 2008 \n",
+ "1 0.552308 2009 \n",
+ "2 0.539075 2010 \n",
+ "3 0.521104 2011 \n",
+ "4 0.520637 2012 \n",
+ "... ... ... \n",
+ "1699 0.765839 2014 \n",
+ "1700 0.735800 2015 \n",
+ "1701 0.768425 2016 \n",
+ "1702 0.754147 2017 \n",
+ "1703 0.775388 2018 \n",
+ "\n",
+ "[1512 rows x 11 columns]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "eland.pandas_to_eland(dataset, es, dataset_index, es_if_exists='replace', es_refresh=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Create Regression Job"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, we create a data frame analytics regression job to analyze the data. We exclude fields `year`, `country_name` and `country_name.keyword` from analysis, since we don't want those to influence our model. \n",
+ "\n",
+ "Note that we set the parameter `num_top_feature_importance_values` to 8, meaning we want to get feature importance for all input feature."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'allow_lazy_start': False,\n",
+ " 'analysis': {'regression': {'dependent_variable': 'life_ladder',\n",
+ " 'num_top_feature_importance_values': 8,\n",
+ " 'prediction_field_name': 'life_ladder_prediction',\n",
+ " 'randomize_seed': -2725393660543450313,\n",
+ " 'training_percent': 100.0}},\n",
+ " 'analyzed_fields': {'excludes': ['year', 'country_name'], 'includes': []},\n",
+ " 'create_time': 1582205185677,\n",
+ " 'description': '',\n",
+ " 'dest': {'index': 'whr-regression-results', 'results_field': 'ml'},\n",
+ " 'id': 'world-happiness-report',\n",
+ " 'model_memory_limit': '1gb',\n",
+ " 'source': {'index': ['world-happiness-report'], 'query': {'match_all': {}}},\n",
+ " 'version': '7.6.0'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "api = '/_ml/data_frame/analytics/{}'.format(analysis_job_id)\n",
+ "config = {\n",
+ " \"id\": analysis_job_id,\n",
+ " \"description\": \"\",\n",
+ " \"source\": {\n",
+ " \"index\": [\n",
+ " dataset_index\n",
+ " ],\n",
+ " \"query\": {\n",
+ " \"match_all\": {}\n",
+ " }\n",
+ " },\n",
+ " \"dest\": {\n",
+ " \"index\": result_index,\n",
+ " \"results_field\": \"ml\"\n",
+ " },\n",
+ " \"analysis\": {\n",
+ " \"regression\": {\n",
+ " \"dependent_variable\": \"life_ladder\",\n",
+ " \"num_top_feature_importance_values\": 8, \n",
+ " }\n",
+ " },\n",
+ " \"analyzed_fields\": {\n",
+ " \"includes\": [],\n",
+ " \"excludes\": [\n",
+ " \"year\",\n",
+ " \"country_name\"\n",
+ " ]\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "pprint.pprint(requests.put(host+api, json=config).json())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Start Analysis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's start the data frame analytics job. If everything goes smoothly, we receive `{'acknowledged': True}` as a result."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'acknowledged': True}\n"
+ ]
+ }
+ ],
+ "source": [
+ "api = \"/_ml/data_frame/analytics/{}/_start\".format(analysis_job_id)\n",
+ "print(requests.post(host+api).json())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The analysis can take a couple of minutes. We can query the `_stats` API for progress. Once, all phases are at 100% and the `state` is \"stopped\", the job is done."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'count': 1,\n",
+ " 'data_frame_analytics': [{'id': 'world-happiness-report',\n",
+ " 'progress': [{'phase': 'reindexing',\n",
+ " 'progress_percent': 100},\n",
+ " {'phase': 'loading_data',\n",
+ " 'progress_percent': 100},\n",
+ " {'phase': 'analyzing',\n",
+ " 'progress_percent': 100},\n",
+ " {'phase': 'writing_results',\n",
+ " 'progress_percent': 100}],\n",
+ " 'state': 'stopped'}]}\n"
+ ]
+ }
+ ],
+ "source": [
+ "api = \"/_ml/data_frame/analytics/{}/_stats\".format(analysis_job_id)\n",
+ "pprint.pprint(requests.get(host+api).json())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
+ "source": [
+ "We use `eland` to read out the results as a data frame."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " freedom_to_make_life_choices | \n",
+ " generosity | \n",
+ " healthy_life_expectancy_at_birth | \n",
+ " life_ladder | \n",
+ " log_gdp_per_capita | \n",
+ " ml.feature_importance.freedom_to_make_life_choices | \n",
+ " ml.feature_importance.generosity | \n",
+ " ml.feature_importance.healthy_life_expectancy_at_birth | \n",
+ " ml.feature_importance.log_gdp_per_capita | \n",
+ " ml.feature_importance.negative_affect | \n",
+ " ml.feature_importance.perceptions_of_corruption | \n",
+ " ml.feature_importance.positive_affect | \n",
+ " ml.feature_importance.social_support | \n",
+ " ml.life_ladder_prediction | \n",
+ " negative_affect | \n",
+ " perceptions_of_corruption | \n",
+ " positive_affect | \n",
+ " social_support | \n",
+ " year | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " count | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ " 1512.000000 | \n",
+ "
\n",
+ " \n",
+ " mean | \n",
+ " 0.729690 | \n",
+ " 0.001999 | \n",
+ " 62.959863 | \n",
+ " 5.409960 | \n",
+ " 9.171052 | \n",
+ " -0.004103 | \n",
+ " -0.002247 | \n",
+ " 0.019618 | \n",
+ " 0.007107 | \n",
+ " 0.000812 | \n",
+ " -0.011679 | \n",
+ " -0.003905 | \n",
+ " -0.005602 | \n",
+ " 5.410009 | \n",
+ " 0.265886 | \n",
+ " 0.754519 | \n",
+ " 0.707982 | \n",
+ " 0.807877 | \n",
+ " 2012.413360 | \n",
+ "
\n",
+ " \n",
+ " std | \n",
+ " 0.145450 | \n",
+ " 0.163199 | \n",
+ " 7.757576 | \n",
+ " 1.135926 | \n",
+ " 1.185309 | \n",
+ " 0.079271 | \n",
+ " 0.062254 | \n",
+ " 0.544845 | \n",
+ " 0.283028 | \n",
+ " 0.049033 | \n",
+ " 0.070746 | \n",
+ " 0.176999 | \n",
+ " 0.248083 | \n",
+ " 1.129786 | \n",
+ " 0.082820 | \n",
+ " 0.186170 | \n",
+ " 0.108593 | \n",
+ " 0.121685 | \n",
+ " 3.587590 | \n",
+ "
\n",
+ " \n",
+ " min | \n",
+ " 0.257534 | \n",
+ " -0.336385 | \n",
+ " 32.299999 | \n",
+ " 2.661718 | \n",
+ " 6.457201 | \n",
+ " -0.391376 | \n",
+ " -0.282606 | \n",
+ " -1.156878 | \n",
+ " -0.649802 | \n",
+ " -0.292035 | \n",
+ " -0.348986 | \n",
+ " -0.612048 | \n",
+ " -0.803056 | \n",
+ " 2.625077 | \n",
+ " 0.094316 | \n",
+ " 0.035198 | \n",
+ " 0.362498 | \n",
+ " 0.290184 | \n",
+ " 2005.000000 | \n",
+ "
\n",
+ " \n",
+ " 25% | \n",
+ " 0.634053 | \n",
+ " -0.109475 | \n",
+ " 57.680834 | \n",
+ " 4.555756 | \n",
+ " 8.222128 | \n",
+ " -0.046729 | \n",
+ " -0.040778 | \n",
+ " -0.518731 | \n",
+ " -0.194008 | \n",
+ " -0.029062 | \n",
+ " -0.052148 | \n",
+ " -0.120019 | \n",
+ " -0.194140 | \n",
+ " 4.553412 | \n",
+ " 0.206123 | \n",
+ " 0.700817 | \n",
+ " 0.619016 | \n",
+ " 0.740086 | \n",
+ " 2009.076923 | \n",
+ "
\n",
+ " \n",
+ " 50% | \n",
+ " 0.747040 | \n",
+ " -0.020337 | \n",
+ " 64.900002 | \n",
+ " 5.306705 | \n",
+ " 9.363767 | \n",
+ " 0.002419 | \n",
+ " -0.001344 | \n",
+ " 0.034621 | \n",
+ " 0.011989 | \n",
+ " 0.008435 | \n",
+ " -0.010465 | \n",
+ " -0.009805 | \n",
+ " -0.006797 | \n",
+ " 5.299802 | \n",
+ " 0.253639 | \n",
+ " 0.808815 | \n",
+ " 0.716696 | \n",
+ " 0.831893 | \n",
+ " 2013.000000 | \n",
+ "
\n",
+ " \n",
+ " 75% | \n",
+ " 0.846806 | \n",
+ " 0.095960 | \n",
+ " 68.393416 | \n",
+ " 6.217162 | \n",
+ " 10.130584 | \n",
+ " 0.047567 | \n",
+ " 0.036186 | \n",
+ " 0.486662 | \n",
+ " 0.224262 | \n",
+ " 0.034174 | \n",
+ " 0.030711 | \n",
+ " 0.118931 | \n",
+ " 0.222982 | \n",
+ " 6.209506 | \n",
+ " 0.313533 | \n",
+ " 0.879489 | \n",
+ " 0.800659 | \n",
+ " 0.904775 | \n",
+ " 2015.428571 | \n",
+ "
\n",
+ " \n",
+ " max | \n",
+ " 0.985178 | \n",
+ " 0.669101 | \n",
+ " 76.500000 | \n",
+ " 7.970892 | \n",
+ " 11.670484 | \n",
+ " 0.227002 | \n",
+ " 0.253046 | \n",
+ " 0.988484 | \n",
+ " 0.737218 | \n",
+ " 0.158346 | \n",
+ " 0.243821 | \n",
+ " 0.537137 | \n",
+ " 0.458581 | \n",
+ " 7.924716 | \n",
+ " 0.704590 | \n",
+ " 0.983276 | \n",
+ " 0.943621 | \n",
+ " 0.987343 | \n",
+ " 2018.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " freedom_to_make_life_choices generosity \\\n",
+ "count 1512.000000 1512.000000 \n",
+ "mean 0.729690 0.001999 \n",
+ "std 0.145450 0.163199 \n",
+ "min 0.257534 -0.336385 \n",
+ "25% 0.634053 -0.109475 \n",
+ "50% 0.747040 -0.020337 \n",
+ "75% 0.846806 0.095960 \n",
+ "max 0.985178 0.669101 \n",
+ "\n",
+ " healthy_life_expectancy_at_birth life_ladder log_gdp_per_capita \\\n",
+ "count 1512.000000 1512.000000 1512.000000 \n",
+ "mean 62.959863 5.409960 9.171052 \n",
+ "std 7.757576 1.135926 1.185309 \n",
+ "min 32.299999 2.661718 6.457201 \n",
+ "25% 57.680834 4.555756 8.222128 \n",
+ "50% 64.900002 5.306705 9.363767 \n",
+ "75% 68.393416 6.217162 10.130584 \n",
+ "max 76.500000 7.970892 11.670484 \n",
+ "\n",
+ " ml.feature_importance.freedom_to_make_life_choices \\\n",
+ "count 1512.000000 \n",
+ "mean -0.004103 \n",
+ "std 0.079271 \n",
+ "min -0.391376 \n",
+ "25% -0.046729 \n",
+ "50% 0.002419 \n",
+ "75% 0.047567 \n",
+ "max 0.227002 \n",
+ "\n",
+ " ml.feature_importance.generosity \\\n",
+ "count 1512.000000 \n",
+ "mean -0.002247 \n",
+ "std 0.062254 \n",
+ "min -0.282606 \n",
+ "25% -0.040778 \n",
+ "50% -0.001344 \n",
+ "75% 0.036186 \n",
+ "max 0.253046 \n",
+ "\n",
+ " ml.feature_importance.healthy_life_expectancy_at_birth \\\n",
+ "count 1512.000000 \n",
+ "mean 0.019618 \n",
+ "std 0.544845 \n",
+ "min -1.156878 \n",
+ "25% -0.518731 \n",
+ "50% 0.034621 \n",
+ "75% 0.486662 \n",
+ "max 0.988484 \n",
+ "\n",
+ " ml.feature_importance.log_gdp_per_capita \\\n",
+ "count 1512.000000 \n",
+ "mean 0.007107 \n",
+ "std 0.283028 \n",
+ "min -0.649802 \n",
+ "25% -0.194008 \n",
+ "50% 0.011989 \n",
+ "75% 0.224262 \n",
+ "max 0.737218 \n",
+ "\n",
+ " ml.feature_importance.negative_affect \\\n",
+ "count 1512.000000 \n",
+ "mean 0.000812 \n",
+ "std 0.049033 \n",
+ "min -0.292035 \n",
+ "25% -0.029062 \n",
+ "50% 0.008435 \n",
+ "75% 0.034174 \n",
+ "max 0.158346 \n",
+ "\n",
+ " ml.feature_importance.perceptions_of_corruption \\\n",
+ "count 1512.000000 \n",
+ "mean -0.011679 \n",
+ "std 0.070746 \n",
+ "min -0.348986 \n",
+ "25% -0.052148 \n",
+ "50% -0.010465 \n",
+ "75% 0.030711 \n",
+ "max 0.243821 \n",
+ "\n",
+ " ml.feature_importance.positive_affect \\\n",
+ "count 1512.000000 \n",
+ "mean -0.003905 \n",
+ "std 0.176999 \n",
+ "min -0.612048 \n",
+ "25% -0.120019 \n",
+ "50% -0.009805 \n",
+ "75% 0.118931 \n",
+ "max 0.537137 \n",
+ "\n",
+ " ml.feature_importance.social_support ml.life_ladder_prediction \\\n",
+ "count 1512.000000 1512.000000 \n",
+ "mean -0.005602 5.410009 \n",
+ "std 0.248083 1.129786 \n",
+ "min -0.803056 2.625077 \n",
+ "25% -0.194140 4.553412 \n",
+ "50% -0.006797 5.299802 \n",
+ "75% 0.222982 6.209506 \n",
+ "max 0.458581 7.924716 \n",
+ "\n",
+ " negative_affect perceptions_of_corruption positive_affect \\\n",
+ "count 1512.000000 1512.000000 1512.000000 \n",
+ "mean 0.265886 0.754519 0.707982 \n",
+ "std 0.082820 0.186170 0.108593 \n",
+ "min 0.094316 0.035198 0.362498 \n",
+ "25% 0.206123 0.700817 0.619016 \n",
+ "50% 0.253639 0.808815 0.716696 \n",
+ "75% 0.313533 0.879489 0.800659 \n",
+ "max 0.704590 0.983276 0.943621 \n",
+ "\n",
+ " social_support year \n",
+ "count 1512.000000 1512.000000 \n",
+ "mean 0.807877 2012.413360 \n",
+ "std 0.121685 3.587590 \n",
+ "min 0.290184 2005.000000 \n",
+ "25% 0.740086 2009.076923 \n",
+ "50% 0.831893 2013.000000 \n",
+ "75% 0.904775 2015.428571 \n",
+ "max 0.987343 2018.000000 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "result = eland.DataFrame(host, result_index)\n",
+ "display(result.describe())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data Analysis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For our analysis we focus only on the data from the latest survey in 2018."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "dataset = eland.eland_to_pandas(result[result.year==2018])\n",
+ "dataset.index = dataset.country_name\n",
+ "# Note that data frame analytics reports only the feature importance values that are different from zero.\n",
+ "# Hence, we need to fill the missing values in the data frame with zeros.\n",
+ "dataset = dataset.fillna(0) \n",
+ "# feature improtance baseline is the prediction average for the training set\n",
+ "base_line = result[result['ml.is_training'] == True]['ml.life_ladder_prediction'].mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For convenience, we create a data frame consisting solely of the feature importance values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " log_gdp_per_capita | \n",
+ " social_support | \n",
+ " healthy_life_expectancy_at_birth | \n",
+ " freedom_to_make_life_choices | \n",
+ " generosity | \n",
+ " perceptions_of_corruption | \n",
+ " positive_affect | \n",
+ " negative_affect | \n",
+ "
\n",
+ " \n",
+ " country_name | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Afghanistan | \n",
+ " -0.593632 | \n",
+ " -0.480017 | \n",
+ " -0.782510 | \n",
+ " -0.126955 | \n",
+ " -0.068387 | \n",
+ " -0.102996 | \n",
+ " -0.429456 | \n",
+ " -0.136437 | \n",
+ "
\n",
+ " \n",
+ " Moldova | \n",
+ " -0.090517 | \n",
+ " 0.183065 | \n",
+ " 0.080556 | \n",
+ " 0.143671 | \n",
+ " 0.038303 | \n",
+ " -0.028691 | \n",
+ " -0.144087 | \n",
+ " 0.051797 | \n",
+ "
\n",
+ " \n",
+ " Mongolia | \n",
+ " -0.075222 | \n",
+ " 0.253702 | \n",
+ " -0.052248 | \n",
+ " 0.040877 | \n",
+ " 0.003333 | \n",
+ " 0.023333 | \n",
+ " -0.171364 | \n",
+ " 0.017292 | \n",
+ "
\n",
+ " \n",
+ " Montenegro | \n",
+ " 0.018458 | \n",
+ " -0.086684 | \n",
+ " 0.572892 | \n",
+ " -0.021426 | \n",
+ " -0.027756 | \n",
+ " 0.049633 | \n",
+ " -0.230666 | \n",
+ " -0.050989 | \n",
+ "
\n",
+ " \n",
+ " Morocco | \n",
+ " -0.042927 | \n",
+ " -0.353456 | \n",
+ " -0.042791 | \n",
+ " 0.043565 | \n",
+ " 0.015816 | \n",
+ " 0.012439 | \n",
+ " -0.074167 | \n",
+ " -0.075258 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " log_gdp_per_capita social_support \\\n",
+ "country_name \n",
+ "Afghanistan -0.593632 -0.480017 \n",
+ "Moldova -0.090517 0.183065 \n",
+ "Mongolia -0.075222 0.253702 \n",
+ "Montenegro 0.018458 -0.086684 \n",
+ "Morocco -0.042927 -0.353456 \n",
+ "\n",
+ " healthy_life_expectancy_at_birth freedom_to_make_life_choices \\\n",
+ "country_name \n",
+ "Afghanistan -0.782510 -0.126955 \n",
+ "Moldova 0.080556 0.143671 \n",
+ "Mongolia -0.052248 0.040877 \n",
+ "Montenegro 0.572892 -0.021426 \n",
+ "Morocco -0.042791 0.043565 \n",
+ "\n",
+ " generosity perceptions_of_corruption positive_affect \\\n",
+ "country_name \n",
+ "Afghanistan -0.068387 -0.102996 -0.429456 \n",
+ "Moldova 0.038303 -0.028691 -0.144087 \n",
+ "Mongolia 0.003333 0.023333 -0.171364 \n",
+ "Montenegro -0.027756 0.049633 -0.230666 \n",
+ "Morocco 0.015816 0.012439 -0.074167 \n",
+ "\n",
+ " negative_affect \n",
+ "country_name \n",
+ "Afghanistan -0.136437 \n",
+ "Moldova 0.051797 \n",
+ "Mongolia 0.017292 \n",
+ "Montenegro -0.050989 \n",
+ "Morocco -0.075258 "
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "feature_importance_df = dataset[['ml.feature_importance.{}'.format(feature) for feature in input_features]]\n",
+ "feature_importance_df.columns = input_features\n",
+ "feature_importance_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Feature Importance"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The figure above shows the decision path our model takes when predicting the happiness score of Argentina. The path\n",
+ "starts at the baseline of 5.41 and then incrementally adds the feature importance values until it finally results in the\n",
+ "predicted happiness score of 5.83. If the decision path goes left, the feature has a negative effect on the model\n",
+ "prediction (e .g., “generosity”). If the decision path goes right, the feature has a positive effect (e.g.,\n",
+ "“healthy_life_expectancy_at_birth”)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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ARo8ejb6+vhQkWFlZcfv2bQ4fPswnn3wiBWOjR4/Gy8uLe/fuFeuax48f59q1a8ybN4/Ro0cD4Obmxrhx43KVXbZsGSYmJuzcuRM9PT0AOnfuzCeffELDhg1zlY+NjSUsLAwTExMA7OzscHd3Z+vWrUydOrVY7UxOTiY0NFT69m9hYcGcOXMIDQ3FxcWF7OxsFixYgIODA4GBgdJ5w4cPZ8CAAQQEBLB8+XLpeFpaGh999FGePZEFWbduHffv32fNmjVKK4qnTp2KYgdCS0tLwsLCUFX9Z7aFs7MzAwYMYO/evUyZMqVYr+1ttWvXpmfPnixfvhxra2ucnZ2Vnp89e7ZSj93IkSMxMzPjhx9+4PHjxyX+YJXJZFIeq7dlZWUhk8moXbs2RkZGSq89L4pevsLKVXXV8T5kZ2eTkJDA8+fPSUxMJCMjAyDf91ZZycrK4uOPPwZg06ZNqKuX3cfri9QsPIOiyZD9swupZ9BN6qskYqxTtOukpqYC5X8fKrOqeA9kMlmBX3irz2++wJkzZwByBViKP0xnz54F4Ny5cwCMGjVKqZy7u3uxr3nu3Dk0NTUZPny4dExVVVUK+BSePn3K3bt3cXFxkQI8AHt7e6Uesjf169dPCvAAOnXqhIWFhfQ6imPIkCFKwzuDBg3C0NBQquvevXtER0fj5OTEixcvpP8yMzOxtbUlIiIiV52urq7FbsexY8do3bp1niljVFRUgJz5c4oPbZlMxsuXL9HR0aFp06bcuXOn2K+tuN4M8FJTU3nx4gU2NjbI5fI8r18WZDIZ2tra1KxZs1oFLELxqaqqUrNmTbS1td/p7gbPnz/n8ePHPH78mOfPn5dp3XHJmai/9bZXV1UhLjmzTK8jVD2iJ68aefToEerq6piamiodNzMzQ11dnUePHgHw+PFj1NXVc/WemZmZFfuajx8/pm7dumhpaSkdf7sNjx8/BqBJkya56sgveMmrPWZmZly7dq3Y7Xy7LsXrV9yTqKgoIKcXKy9vBx6ampq5hk2LIiYmhoEDBxZYJjs7my1btrBjxw5iY2OVPsiMjIxylS/stRXX48ePWbVqFSdPniQxMVHpueTk5BLVCTm7XuSX01Exp6xGjRpFqksxF6285whWdtX5PmhpaaGlpSXdg/LOF6qjoyP929zcnMaNG5dZ3TUbpJMd9gT4pycvG7C3tiry3LyquNtDcVXFe1DYjhciyBOEIlAMlXp5eWFhYVFo+beD2rIUGBjIypUrGTZsGDNmzMDQ0BBVVVW8vb2ldpYXmUzGuHHjSExMZMKECZibm6Ojo0N8fDyenp4FLoYQBKFkTPS18R7allk/3wBAQ00Fb5e2YvGFUCgR5FUjDRs2JCsri4cPHyr1mD18+JCsrCyp565BgwZkZWXx6NEjpW+jJVmh16BBAyIiInj9+rVS4PPw4cNc5eCfHrM3PXjwIM+682pPdHR0ieaEvV2X4vV369YNQLoPBgYG0rHyYGpqSmRkZIFlwsLC6NKlC97e3krHX716Rc2aNXOVL+y15UUxNPy2yMhIoqKi8PHxYciQIdLxCxcuFNhmQRBKZ2iHRngfusvz5Ay+cmopFl0IRSImt1QjPXv2BGDz5s1Kx7ds2aL0vIODAwA7duxQKrdt27ZiX9PBwYGMjAz27t0rHcvOzmb79u1K5UxMTGjZsiX79u1TGvK7cOEC9+/fz7PuY8eO8fTpU+nx5cuXiYyMpEePHsVu5/79+0lJSZEeh4aGkpiYKNXVunVrGjduzIYNG/LMeffixYtiXzMvffv25fbt25w8eTLXc4peOjU1tVw9docPHyY+Pj7POgt7bXlRDIu+evVK6bhiWPrN68vlcuk9JAhC+dHXzlm1q64mPrqFohE9edWIlZUVLi4u7Nixg1evXtGhQweuXr1KaGgow4cPl+YptGnThv79+7NhwwZevnwppVBR9LLl18uTl759+9KuXTsWL17MgwcPaNq0qdJcrjfrmjVrFpMmTcLNzY1hw4aRkJDAtm3baNGihbQq6k2NGjVi9OjRuLq6kpaWxqZNm6hTp06J9mDV09PD3d2dIUOGSGlGLCwsGDx4MJATWC1atIiJEycyePBghgwZgomJCXFxcVy4cAFTU1O+//77Yl/3bRMmTODIkSNMmzZNSqHy4sULjh07hp+fH40aNaJXr14EBATg5eWFjY0NkZGRhISE5DsHqLDXlpeGDRtiZGTErl270NXVRUdHh3bt2mFubo6pqSk+Pj7Ex8ejp6dHWFhYrmBQKB4/Pz/8/f25fPlykdPtvAuKnJRv5rP09PQkIiIizy8iQvnS0cxZRZmW8e4WlAjvNxHkVTPffvstjRo1Ijg4WEo/Mn36dCZPnqxUzsfHh9q1a3Pw4EHCwsLo1q0by5cv58MPPyzWzghqamqsXbuWxYsXExwcjKqqKv369WPKlCm4ubkpDeH26NGDlStXsmLFCnx9fTE1NWXJkiWcOHEiz9Wrw4YNk5Ihv3z5EhsbG+bNm5fn4oPCTJkyhVu3bhEYGEhaWhq9evXi66+/Vsp31bVrV3bt2kVAQABbt24lNTUVExMTbGxsSrSSNi96enrs2LGDVatWcfz4cYKCgqhTpw729vbSUOzkyZNJS0sjJCSEQ4cO0apVK9auXYuvr2+JX9vb1NXV8fHxYdmyZSxYsICsrCzpgz4wMJBvv/2WtWvXoqWlRb9+/Rg9enSuVCuCIJQtRZCX8loEeUIRyQWhiO7cuSO3sLCQHzhwoNR1HTt2TG5hYSH/9ddfi31uTEyM3MLCQr5x48ZSt+PSpUtyCwsL+bFjx0pdV2XzPr22a9euya9du5bv81FRUfKoqKgi15eWliZPS0sri6a9E6tWrZJbWFjIExMTy7Te0t4Hxe9aUFCQdCwjI0P++vXrsmheuVK8Z+7duye/d+9euV/v4cOH8gYNGsgbNGggf/jwYblcw2N9uNxsTqjc+9CdYp/7ru5DZVYV70FhfzvFwL6QJ0XagTdt3rwZVVVVOnXqVKq6ZDIZW7duRU9Pj9atW5eqnYIgvFsaGhpin+MKoqMhhmuF4hHDtUKe1q5dy7179+jSpQuqqqqcO3eOs2fPMnLkSOrXr49MJit0sYGOjg66urp88803ZGZmYm1tTUZGBkePHuXatWvlutdpRkZGrhxub3uXWz6lp6eTlJRUYBlDQ0Px4VnN/f3333z11VecO3cObW1tXFxcmDlzpjS0HhQUxIEDB/jjjz9ISkrC1NQUd3f3XInLb926xYoVK/jtt99IS0ujdu3adOnShSVLlkhl0tPTWbNmDaGhocTHx1OnTh2GDBnClClTCtyt4e05eW/uJa2lpcX69euJi4vD0tKS+fPn065dO6Xz//jjD1auXElERATp6elYWVkxY8YM7O3ty+o2Vlk6WmK4VigeEeQJebKxseF///sfq1evJjU1lfr16zNt2jRp7t6TJ0/o06dPgXVMnTqVadOmYWdnx6ZNmzh16hSvX7/GzMxMaZuz8nDt2jXGjBlTYJklS5bkuV1aeTh06BBeXl4FltmyZQtdunR5J+0RKqfp06djamrK7NmzuXLlCuvXryc1NZUFCxYAsHPnTlq0aIGjoyPq6uqcOnWKb775BrlcLv0+/f3334wfP55GjRrx6aefUqNGDeLj4zl27Jh0nezsbCZPnsyNGzdwdXWlSZMm3L59m8DAQOLi4pSCwaI6cOAAqampjBw5EhUVFdatW8e0adM4fvy4FKT+/vvvjBo1igYNGjBx4kS0tLQICQnh008/Zf369XTt2rX0N7EKkxZeZGZVcEuE94UI8oQ89ejRo8AUG3Xq1GHjxo0F1qFY7ens7Fymk/IbNWpU6N6DVlZWhbavefPmmJiYvJN9DB0cHAptj5WVVZles0uXLlVqj8b8ZGZmEhcXp3Ts9evXQPkmpX5bvXr1Sr0xfZMmTfDz8wNy9ovW0tJi165djB8/nsaNG7Nt2zal3m93d3fGjx/Pxo0bpSDv2rVrJCYmcuTIEWkXBm1tbWbNmiWd98svvxAREcHOnTtp3769dLxRo0b4+voyYcIEmjVrVqy2x8XFcfToUamHvGnTpnz++eecP3+e3r17A+Dt7Y2ZmRm7d++W7pWbmxsuLi4sX75cBHmF0NHM+chOFcO1QhGJIE8oES0trXJNClxahoaGlap9JiYmSvvsCmUjMzOTXr165ZlE+11r0qQJp0+fLlWg9/aw6+jRowkODub8+fO4ubkpBXhJSUlkZmbSuXNnzp8/T1JSEvr6+lKQdezYMQYPHpznXr9hYWG0aNGCxo0bK027UARZERERxQ7ynJyclKZAdOzYEcjZqg8gISGB8PBwZs2alWvqgoODA5s2bSItLa3IW9dVR4qePBHkCUUlgjxBEIRK4u29mxWPFfsMX7lyBT8/P65fv54rKbciyOvcuTP9+/dn3rx5+Pr60rlzZ/r27cvAgQOlOZ/R0dH8+eef+faclSS599s7zRgaGgL/JNR++PAhcrkcX1/ffNP9JCQkiCCvAP8EeWK4VigaEeQJgvDe0tDQ4PTp01VmuLYgDx8+5OOPP8bc3BxPT0/q16+PhoYGZ86cYdOmTdK+wSoqKqxatYobN25w7NgxLl68yJw5c9iwYQM7d+5EV1eX7OxsWrVqxRdffJHntfJLrF2QvHoM4Z/dURTt+/TTT/PtZTc2Ni72dauTGmK4VigmEeQJgvBe09DQyBWUKNL2lNfq7fISFRVF/fr1lR5DTi/ZyZMnycjIYM2aNUq9ZuHh4XnW1b59eywtLZk6dSonT55k5syZHDp0iBEjRmBqasr9+/ff6ZQGxc+osk/1qMx0xY4XQjGJPHmCIAiVxNv7RW/fvh0VFRW6d++OmlrOB7z8jX2Dk5KSCAoKUjonMTEx197GLVu2BHJSCwH079+fR48esW/fvlxtSElJkXpCy1KtWrXo1KkTO3fuzHM4uKz2f67K/tnxQgzXCkUjevIEQRAqiaioKKZMmUK3bt24cuUKBw8eZOTIkTRu3Bh7e3s0NDSYPHkyrq6upKSksGfPHmrVqsWzZ8+kOvbt28fOnTvp06cP9evXJy0tjf3796OnpyetmB8yZIiU1ufChQvY2NiQmZnJ/fv3OXz4MMHBwZiZmZX561OkTho0aBAjRoygUaNGPH36lCtXrvD69Wu2b99e5tesShTDtWmZoidPKBoR5AnVjqOjI507d+a7774r1nnh4eGMGTNG5LMTys2qVav44YcfWLZsGVpaWnzyySdS6hNzc3NWrVrFihUrpL2l3dzcMDY2Zu7cuVIdnTt35tatWxw+fJjnz5+jp6dH+/btWbp0qTRkqqamxpo1a9iwYQO//PILR44cQVdXF1NTUyZOnEjdunXL5fVZWFiwd+9e/Pz82LNnD69evaJ27dq0bt260LyWwj89eZkyOZmybDTUxGCcUDAR5AmCUCp//vknhw4dwsXFhUaNGlV0c95L06ZNY9q0aQBSnry8ODo64ujomOv4sGHDpH+3atVKWr1a0NxETU1NJk+eLCU4z0teOSnf/nJUUN7KvI6bmZmxbNmyfK8p5E8R5EHO4gvDGiLIEwomgjyh2jly5AgqKioV3Ywq48GDB/j7+9O5c2cR5AlCOVIkQ4acNCqGNcpvNbdQNYivAUK1o6mpWa6pLqqL1NTUim6CIFQrb/bkxb5MK6CkIOQQQZ5QKSQnJ7N48WIcHR1p06YNXbt2Zdy4cdy+fVsqs23bNgYMGECbNm3o0aMH3333Xa6EsACnT59m9OjR2NjYYGtri6urK8ePH5eed3R0xNPTU3qckJCAj48PgwcPxsbGhg4dOjBhwgTu3btX6teVmZmJv78/H3zwAW3btqVLly64ublx4cKFfNuj4OHhgYeHh/Q4PDwcS0tLjhw5wrJly+jWrRs2NjZMmzZNaeK94lxnZ2du3rzJyJEjadeuHf369SM4ODjXdf7++2+8vLyws7Ojbdu2uLi4cOTIEaUyimsfPnwYX19fHBwc6NChA8HBwUyZMgWAMWPGYGlpiaWlZb5pPQRBKLnjd+Olf4/+KZzgq7EV2BrhfSCGa4VKYf78+Zw+fRp3d3dpq6UrV65w//59WrdujZ+fH/7+/jg4OOXY3FYAACAASURBVDB69GgiIyPZtGkTkZGRrF+/Xhp+3bNnD1999RVWVlZMnjwZXV1dbt++zYULF+jbt2+e146JieH48eN8+OGHNGrUiOfPn7N7927c3d05ePBgqSah+/v7s379ekaNGkWLFi1ISkri1q1b3L59G3t7+xLVGRAQgLq6OpMmTSI+Pp4tW7bw8OFD9u7dq9RDmZCQwKRJkxg0aBBOTk4cPHgQLy8vtLW1GThwIJAzZ8vDw4OYmBjc3d2pX78+Bw8eZMaMGSxdujTXnsP+/v5oa2vz6aefkpKSQqdOnRg7diybN29m8uTJmJubAxR7SywAmUyW79yu9PR09PX1pTlmhVEk3i1q+aqqOt8HmUxGUlKSdA/Kex/nNxNy//XXX2Xe0/0iNYtvfomWHmfIsvEMukl9lUSMdQr/KFe0pzrsZ52fqngPZDKZlF4pLyLIEyqFM2fO8NlnnzFhwoRcz7148YK1a9fSs2dP1q5dKwV0is3UT506haOjI0lJSXh7e2NjY8OWLVukLZyAXHnD3mRpaUlYWJhSxn5nZ2cGDBjA3r17pZ6qkjh9+jQjRoxQWv1YWsnJyYSGhqKrqwvkrFicM2cOoaGhuLi4SOXi4uL46quvpN7AkSNH4uLigq+vLwMGDEBFRYXdu3fz559/snz5cinwc3V1ZcSIESxdupSBAwcqBY5ZWVns2LFDaSeJzp07s3nzZrp16yZWHQtCOYlLzkRdFd7Mg6yuqkJccmaRgjyhehLvDKFSMDAwICIigmHDhlGzZk2l5y5evEhmZiZjx45VWjAxevRoVq1axenTp3F0dOT8+fOkpqYyadIkpQAPKHChxZtlZTIZr169QkdHh6ZNm3Lnzp1Sv64bN24QFxdHvXr1SlWXwpAhQ6QAD2DQoEF4e3tz9uxZpSBPU1OTESNGSI+1tLQYPnw4Pj4+/PXXXzRr1oyzZ89St25dBgwYoHSem5sb8+fP5/bt21hbW0vPubi4lNtWYWpqalhaWub5XHR0Tg9GUXeweF93vChr1fk+qKmpYWRkJN2D/N5bZUVHR0f6t7m5eYm2hitIzQbpZB55rHQsG7C3tsJEv/Cfr6L3qrzvQ2VWFe/B9evXC3xezMkTKoXZs2dz6dIlHBwccHV1Zc2aNdKm7I8f5/xha9q0qdI5urq6mJiYSM/HxMQA0KJFi2JdOzs7m02bNknz5uzs7OjatSu///47SUlJpXpd06dPJyYmhl69ejF06FCWL1/O/fv3S1Xn20lq1dXVadiwoXS/FOrWrZvrw11xrqLso0ePaNKkSa4gWDHsqri3CmL1rCBUDBN9bWwa53wBVlNVQVtDFW+XtkUK8ITqSwR5QqUwcOBAjh8/zn//+19q1arFjz/+iJOTE+fOnSv3awcGBrJkyRI6duzI999/z/r169m4cSMtWrQocJi3KDp16sSxY8fw9vamadOm7Ny5E2dn51xbUeVFJqt8We2rY4+QIFQGcrmcqL9TAPCwM+Xsl70Z2kF86RIKJoI8odIwMTFh1KhRBAQEcOLECYyMjJQ2Y3/w4IFS+dTUVJ4+fSo9b2pqCsAff/xRrOuGhYXRpUsXvL29cXJywsHBgW7duvHq1asyeFVgZGTE0KFD8fX15fTp01hZWbFq1SrpeUNDwzyv9XYvmoJi6FIhKyuLR48eKW1aDxAfH59rwr3i3IYNG0r/j46OzhXMKu7123UKglAx7jx5xdOknD2F3e2aiB48oUhEkCdUOMUquDcZGxtTr149Xr9+Tbdu3dDQ0GDr1q1KwciOHTvIzMykV69eANjb26Ojo8PatWuljdgVCuqRU1NTy/X84cOHiY+Pz+eMonv58qXSYx0dHZo0aaK0AXzjxo25ceOGUptPnTrFkydP8qxz//79pKSkSI9DQ0NJTEyU9iVVyMjIYM+ePUqP9+7dS8OGDaXh2B49ehAXF6eUMiUjI4OdO3dK200VRjEXqbRD24Ig5O/07zlpkkyNdWhWR7eQ0oKQQyy8ECpcSkoKPXv25IMPPsDKygpdXV0uXbrEtWvX8PT0xNjYmEmTJuHv78/EiRPp1asXkZGR/Pzzz9jb29O7d28A9PX18fT0ZN68eYwYMQInJyd0dXW5c+cOmpqazJ8/P8/r9+rVi4CAALy8vLCxsSEyMpKQkJAymTjt5OREp06daNOmDUZGRvz2228cOnSI0aNHS2VGjBhBWFgYEyZMYMCAATx8+JCQkBCpZ/Jtenp6uLu7M2TIECmFioWFBYMHD1YqV7duXVavXs3Dhw8xNTUlNDSUP//8E19fX2kO3siRI9m9ezdffvklt27dklKo3L17l6VLlxYpabSVlRXq6ur89NNPJCUloampiZ2dHbVq1SrFnRME4U2n7j0FoLdlHbFjj1BkIsgTKpy2traUIPjYsWPI5XJMTU2ZP38+o0aNAnL29jQyMmL79u0sWbKEmjVrMmbMGGbMmKH0B2/kyJHUqlWLn376iYCAADQ0NGjevDmffvppvtefPHkyaWlphISEcOjQIVq1asXatWul/T9Lw8PDg5MnT3Lx4kUyMjJo0KABM2bMYPz48VKZ7t274+npycaNG/H29qZNmzYEBgbi4+OTZ51Tpkzh1q1bBAYGkpaWRq9evfj6669zBWQ1a9Zk0aJFfPvtt+zatYu6deuyePFiBg0aJJXR1tZmy5Yt+Pr6EhQUREpKCs2bN2fFihVKK24LYmxszMKFC1mzZg3//e9/kclkbNmyRQR5gpS+Z+vWrRXckvdbQmoGVx/mjAr0sjKp4NYI7xMVeWlnlguC8E6Eh4czZswYAgIC8k3srODh4cGrV684cODAO2pd6SjSALyZruVNirmEb68szs/7mjpk3bp1fP/999jZ2bF58+ZS1/cu7sOzZ8/YtWsXffv2pWXLlkrPVWSQp3jPvKsUKjExMdjZ2QFw6dKlMk2hcuD6I2bsuo6Wuio35n+Atkb+yW/zUxXThxRXVbwHhf3tFD15giAIlURISAgNGzYkIiKCp0+fYmJS+Xttnj9/jr+/Pw0bNswV5K1fv76CWlW1KObjdWtWq0QBnlB9iYUXglAC6enpPHv2rMD/3l78IQgFuX//Pvfu3WPevHnUqFGDQ4cOFXpOWW+dVdY0NTVzJSYXikeWLedMZE6Q11sM1QrFJII8QSiBQ4cO4eDgUOB/165dq+hmCu+RkJAQateuTffu3enTpw+//PKL0vPh4eFYWlpy+PBhfH19cXBwoEOHDtLz9+7dw93dnXbt2tGjRw9Wr17N/v37ad++PbGxyhvZnzp1CldXV6ytrbG1tWXKlCm5UvN4eHjg7OxMZGQkHh4etG/fnu7du/PTTz8ptWnIkCEAeHl5YWlpiaWlJcHBwVIdiiHbN1/D0aNHCQgIoHv37rRt25axY8fmuv6vv/7K9OnT6dWrF23atKFnz554e3tXu314b8Ym8CIl5wtjb0sR5AnFI4ZrBaEEHBwc2LhxY4FlrKysyvSaXbp0KfLG2mKie9E9TUon5kUajY1rVGjusdDQUPr374+amhpOTk5MmjSJBw8e5Nrpxd/fH21tbT799FMplU58fLy07d/EiRPR0dFhz549ea6ODg4OZu7cufTq1YsvvviClJQUtm7dyqhRozhw4AC1a9eWyiYkJDBhwgQ+/PBDBgwYwJEjR1i2bBkWFhb07NmTZs2aMXPmTJYvX87IkSOxtbUFUAo+87JmzRpUVVWZMGECr169Yv369cyePVsp5c+RI0dIT0/Hzc0NIyMjbt68ybZt24iLi1PKM1nVnfr/odrmJno0NtYppLQgKBNBniCUgImJyXsxX6o6yJRlE5eo3LujyEOopZVd4LlHfotj2dHfUVdVIStbzuwPLPmwTcn2GK5nqI2GWskGR65evUpsbCwDBw4EcnI+GhoaEhISwvTp05XKZmVlsWPHDqU9hH/66ScSExM5cOCANKl86NChfPDBB0rnpqSk4O3tzejRo/n666+l4wMGDGDQoEFs2rSJ2bNnS8fj4uLw9fWVVmQPHz4cR0dHgoKC6NmzJ7Vr16Znz54sX74ca2trnJ2di/R6s7KyCA4OloJQQ0NDFi9eTGRkJBYWFkDOVodvLhgZOXIkZmZm/PDDDzx+/LjaJOo+/fs/qVMEobhEkCcIwnsrU5ZN3x/OEP136eamKVJTLz50l8WH7paoDrNaOhyf1bNEgV5oaCh169aVesI0NDTo168foaGhuYI8FxcXpQAP4Ny5c3Ts2FFp1aCRkREDBw5k586d0rGLFy+SlJTEgAEDePHihXRcV1cXKysrIiIilOrV19fHyclJeqypqUnbtm2lfaJLatiwYUq9jB07dgRyVqgqgrw3A7zU1FTS09OxsbFBLpdz586dahHkPU1K52ZsIiCGaoWSEUGeIAhCBcrKyuLw4cPY29vz8OFD6bi1tTV79+7l5s2btGvXTjreqFHu/UofPXokBYhvejuNR1RUFIBSMu6CytevXz9X4l1DQ8MiTxvIT/369ZUeGxgYACht7/f48WNWrVrFyZMnSUxMVCqfnJxcquu/L878/1CtnpY6HZsYV3BrhPeRCPIEQXhvaaipcnxWzwKGa7XyOg2Av5NfM/LHS7zO+mdIV0tdld0T7aill/95+SnpcO2FCxd48eIFISEhhISE5Ho+JCREKcgrTc47RVpUX19fjI1zBw1v3y9V1fJZm6emlncaEEX7ZDIZ48aNIzExkQkTJmBubo6Ojg7x8fF4enqSnV3wMHxVoUid4tC8NprqYp2kUHwiyBME4b2moaaaa0J6enrOB2JBAVFjYx2WDG3L3H230FBVJTM7G2+Xtlib1izX9r4tJCSEevXq4eXlleu5AwcOcPjwYTw9PQuso2HDhkq9gApvD6sqeurq1KlDly5dStHqf5THFluRkZFERUXh4+Mjrd6FnIC4usiUZXP2D0XqFDEfTygZEeQJglBtDe3QCIcWtStsdW1aWhonTpzA2dmZDz/8MNfz2tranDx5kkuXLqGunv+fawcHB7Zv387vv/8uzctLSEjIlWvPwcEBPT091q5di62tba46X7x4kWcPX0Fq1KgBKA+1lpaiB/HNDZnkcjlbtmwps2tUdlejX5KUngVALzEfTyihIvX/+vn5YWlpWaa/xEXxdo6lwsoWdWVXWYiNjVXKBwXg6emJo6OjUrnk5GS8vLzo1q0blpaW+Pn5vbM2CtWDpaUlixcvLrSc4ve4tDw9PaWJ8lWBib42tmY1KyR9yokTJ0hNTaV37955Pm9nZ4e2tnaew7hvmjBhAvr6+nz88ccEBASwYcMG3NzcqFcvZ6WwordNX1+fr7/+mosXLzJs2DDWrl3L7t27+eGHHxg0aFCJtlJr2LAhRkZG7Nq1iz179nDw4MFSL8wwNzfH1NQUHx8fAgMD2bZtG2PGjCEuLq5U9b5PFKlTWtU3oK7B+7U9n1B5vFeD/M+ePcPPz4+7d0u2+q0iBAYGEhISgru7O0uXLqVfv34V3aRK5338uZa1nTt3Kn1hqGivX7/Gz8+P8PDwim5KlRYaGkqNGjWkPU/fpq2tTdeuXTl27Jg0zzAv9evXZ8uWLZibmxMYGMjmzZtxcXGRhjrfnGs3ZMgQNm7ciLGxMT/++CPe3t6EhYVhY2OjtJK2qNTV1fHx8UFdXZ0FCxYwa9YsLl++XOx63qShoUFgYCAtW7Zk7dq1+Pv706RJE3x8fEpV7/vk1L3/T50ihmqFUnivhmsL2iOxMli0aJHS8AJAREQENjY2fP755xXUqsqvsv9c34Vdu3ZhYGDA0KFDy+0an332GRMnTixS2devX+Pv78/UqVPLbO6WkFtgYGCxyhS0qrVly5Zs375d6djChQvR0tKiZk3leYZdu3ala9euBV43v4Ta3333Xa5jvXr1olevXoXWkV9C70aNGuU63qxZszwTjpd2Ze/74FFCGr/HJwEidYpQOu9VT15lp6GhkWufxr///ht9ff0KapEg/ENdXb3A1aaQs6pR7Ln7fnp7u6+XL18SGhqKjY1NvqtZhcop5MYjAAxqqGPzjhcCCVVLsYK8xMREvvzyS2xtbbG1tcXLy4u0tDSlMkFBQbi4uNCuXTu6dOnCnDlzeP78uVKZ48ePM3HiRBwcHGjTpg19+/YlICAAmUyW77UL2yNRoaB9FmNiYrC0tGTTpk256r9w4QKWlpacOXOmOLdEyZtz8hR7NMbGxnLixAmpvYo9JNPT01m+fDl9+vShTZs29O7dm5UrV5KVlVXs6169epVx48bRoUMHrK2t+fjjj/ntt9+k5//++2/s7OwYP3680nmRkZG0adOGefPmScccHR35/PPPOXPmDB999BFt27Zl8ODBed6XhIQEFi1aRI8ePWjTpg39+/fPc2J0eno6K1eu5IMPPqBNmzY4ODgwc+ZM4uPjC/25FnX/SsU8sSdPnjB58mRsbGyws7PDx8cn1/sqOzubjRs3MmjQINq2bUvXrl2ZPHkyf/zxBykpKVhbW/Ptt9/meh1RUVFYWlrm6i0pSFBQEGPGjKFr1660adOGgQMHsmPHDqUyjo6O3Lt3j4iICOn1F3Uu6pv2799P//79adu2LSNGjODGjRtKz+c1J08xn2///v18+OGHtG3bltDQUDp16gTkbKGlaNPb80mLcq+Fd2fEiBF4e3uze/du/P39GTp0KCkpKUXuvRUqh+CrsSw9ktNbmZyexYHrjyq4RcL7rFjDtdOnT6dx48b85z//4c6dO+zZswdjY2O++OILIOcDISAgACcnJ/71r3/x7NkztmzZwq1btwgODpbSGezbtw8dHR3GjRuHjo4Oly5dYtWqVSQnJzNnzpw8r12UPRIL22excePGdOjQgZCQED7++GOl+kNCQqhVqxb29vbFuSX5atasGUuXLmXJkiU0aNCAsWPHAmBsbEx2djaTJ0/mxo0buLq60qRJE27fvk1gYCBxcXEsWbKkyNe5ePEiEydOpH379kyfPh25XM7u3btxd3dn7969NG/enFq1ajFv3jxmzpzJ7t27GTlyJFlZWXh6emJiYsKXX36pVOdff/3FF198gZubGy4uLuzZs4fPP/+crVu3Svc7NTUVDw8Pnj9/jqurK3Xr1iU8PJzFixfz6tUrpk6dCuT0DH366adEREQwePBgxo4dS3JyMqdPnyY6OrrQn2tx9q/Mysrik08+oUOHDnz55ZdcvHiRDRs20LhxY0aNGiWV8/T05MCBA/Tu3ZuRI0fy+vVrwsPDuX37Ni1atKBv374cOXIELy8vpR6QkJAQNDQ0pK2nimLnzp20aNECR0dH1NXVOXXqFN988w1yuVxKSDt37lwWL16MtrY2kydPBlDaP7QoLl26xMGDB3F3d0ddXZ3t27czbtw49u/fj6mpaYHnXrhwgcOHDzNq1CgMDAxo1aoVCxcuZN68efTr10+aR/pmgFjUe10cMpks36G49PR09PX1i7w5vSKPWnXazL5bt26cPHmS3bt3A9CqVSvmzZuHjY1NtboPCjKZjKSkJOm9UN7DvG8uCvnrr79ITS3+LiwvUrOYszea7P+f9ZMtB8+gm9RXScRYp3SzqxTtqQ7D3fmpivdAJpMV2FNfrHdN27ZtWbhwofQ4ISGBvXv38sUXXxAbG8vq1av54osv+OSTT6QyPXr0wNXVlX379uHm5gbkJOJ8M3+Vm5sb8+bNY+fOncycOTPXkCdQpD0SC9tnEcDZ2Zn58+cTFRVFkyZNgJwPgqNHjzJ06NAC0xQUR+3atXF2dmblypXUq1dPqb379+8nIiKCnTt30r59e+l4o0aN8PX1ZcKECTRr1qzQa2RnZ7NgwQIcHByU5u0MHz6cAQMGEBAQwPLlywEYOHAgYWFh+Pj4YG9vz/79+7lz5w4bN25ET09Pqd4HDx6wZs0aqVdSsQfm8uXLpTk2Gzdu5NGjRxw4cEDKveXq6oqBgQE//vgjHh4eGBoaEhwcTEREBF999ZVS79SkSZOQy+WoqKgU+HMtzv6VaWlpDBkyhEmTJgFIQerevXulwON///sfBw4cYNy4cUq5xyZMmCDNp3R2diYkJIRLly4pBf0hISE4ODjkmt9UkG3btim1393dnfHjx7Nx40YpyOvbty9+fn4YGBiUeIX4H3/8wf79+7GysgKQvuisWbOm0C8NUVFRHDx4kKZNm0rHGjRowLx587C0tMyzTUW518K7NXPmTGbOnKl0rLokDa4qDv2eSGa28rxudVUV4pIzSx3kCdVTsd41rq6uSo87duzIsWPHSE5O5vjx48jlcvr166e0J6KpqSl16tQhIiJCCvLe/NBLTk4mIyODjh07snv3bv766y/pg6q4irLP4sCBA1m8eDEhISFMmzYNgJMnT5KSksJHH31UousWV1hYGC1atKBx48ZK90oxEToiIqJIQd69e/eIjo5m2rRpSvUA2Nra5tqHcv78+QwaNIipU6dy//593Nzc8px83aBBA6VUMIaGhgwaNIgdO3aQmpqKjo4OYWFhdO7cGV1dXaVrOzg4sGvXLm7cuEGPHj04duwYtWvXzvODvyhJVIu7f+XIkSNz3YdffvlFenz06FHU1NSYMmVKvu3p1q0bderUISQkRArybt68SXR0NP/+978LbXN+7U9KSiIzM5POnTtz/vx5kpKSymy+pq2trdLvjampKd27d+fs2bOFnmtnZ6cU4BVVYfe6uNTU1PJN8RIdHQ0UfbcHRc9VaXaHqAqq831QU1PDyMhIugdlkT6oIDo6/yTkNjc3z7VFXEFeZ8lYfPAuW6+/zPVcNmBvbVXqFD+K3qvyvg+VWVW8B9evXy/w+WIFefntN5iYmEhUVBTZ2dn07ds3z3PfDAT++OMPVqxYwaVLl3LtQZiUlFScJuVqX2H7LBoYGNC7d29CQ0OlIC8kJIQmTZoobR1UnqKjo/nzzz/zXd32dsCWH8U+lLNnz87z+be3JDI2NsbT05MvvviCBg0aSMPsb8treM/MzIzs7GyePHlCs2bNiI6O5vfffy/0NcTExGBubl7iid/F2b9SR0cHIyMjpWOGhoZK58XExFCvXr0Cgys1NTUGDx7Mzz//zDfffIOWlha//PILenp6ufIgFubKlSv4+flx/fr1XPNXyzLIMzMzy/PYqVOneP36dYELLvLaC7UwRbnXgiAULvZlKlO2X+VGbM7vjmVdPaL+TkVT7Z9dWCoih6NQNRQryCtov8Hs7GzU1NT46aef8uyheXMDand3d/T09Jg+fTqmpqZoaWlx+/Ztli1bVqrhhaLus+js7Mznn3/OrVu3aNy4MefOnZPmQr0L2dnZtGrVKt8gq6jfABXDi15eXlhYWBTpnHPnzgE5K++eP39e6Hyt/GRnZ9O9e3elofk3NW/evET1vqm4+1eW5QpCZ2dnNmzYwKlTp+jXrx+HDx/mgw8+KFaPyMOHD/n4448xNzfH09OT+vXro6GhwZkzZ9i0aVOlGUorbMVtXt71ak0VFRWxqEMoFsVnUmV26t5T/r37OolpmaiowMy+Fkzt3ZznKa8rbBcWoWops0F+U1NTZDIZZmZmBfYMREREkJCQgL+/v7SCD5BWnRakrPZI7NGjBzVr1iQkJARzc3MyMzMZPHhwmdRdFKampty/f59u3bqVqh5FMGhgYFCkuk6cOMEvv/zCZ599xu7du5k7dy5bt27NdV/z2gMzOjoaVVVVqTfX1NSU169fF3pdU1NTfvvtN7KysvKd75jfz7U89q80NTXl4sWLvHr1SvrikRcrKyssLS0JCQlBV1eX58+fF3s4/+TJk2RkZLBmzRqlYeW8EgyX9r2tGM58+1itWrVKFMSVx36kpaGlpcXLly8LnWQsCJDzBTE9PZ1atWrl6kGvDGTZcpYfi8T/1H0AjHU1WelqTfcWOYmPTfS1RXAnlIkyy5PXr18/VFVVCQgIyPVcdnY2CQkJORfMY0/CjIyMXGkl8lJWeyRqaGjg5OTEoUOHOHDgAO3bt89zuKu89O/fn0ePHrFv375cz6WkpBSY2f5NrVu3pnHjxmzYsCHPP2RvDvsmJCQwf/58OnbsyIwZM1iwYAGXL19m27Ztuc57/PgxJ0+elB4nJiYSGhpKx44dpXkn/fv35/Lly3kGLC9evJB+vn379uX58+fs3LkzVzlFmfx+ruWxf2W/fv2QyWSsXr063/YoDBkyhLNnz7Jjxw5MTEyKnRRYEYy8WW9SUhJBQUG5ytaoUaNU7+srV65w79496fHDhw85f/48PXr0KFF9WlpaqKiovPOtDPNjYGCAXC7nyZMnokdPKJBMJuPJkycAlTJH6fPk13isD5cCvA6mRhyc7iAFeIJQlsqsJ8/MzIzp06ezYsUKYmJi6N27NzVq1CAmJoawsDA+++wzRowYgY2NDYaGhnh6euLh4YGKigoHDhzI9QGblzf3SNTV1UVHR4d27doVa4KrgrOzM9u2bePZs2d8/fXXJXnJJTZkyBAOHTqEl5cXFy5cwMbGhszMTO7fv8/hw4cJDg4uUtCppqbGokWLmDhxIoMHD2bIkCGYmJgQFxfHhQsXMDU15fvvvwdyMt8nJyezZMkSVFRU6N+/P05OTvj6+tKrVy+le9i0aVM8PT1xc3OjZs2a/PzzzyQnJzNjxgypzIQJEzhx4gTjx49n2LBhtGzZkuTkZO7du8fRo0e5evUq6urquLi4sH//fr799ltu3ryJjY0NKSkpnD17lmnTptG5c+d8f65v7l8ZHx+Pnp4eYWFhpQo8unbtyqBBg9i4cSNRUVHY29uTlZVFeHg4H374oVKP4aBBg1i2bBknT57kk08+KfJ0AAV7e3s0NDSYPHkyrq6upKSksGfPHmrVqsWzZ8+UyrZu3Zpt27axevVqzMzMMDY2LnRHgje1aNGCTz75BA8PD9TU1Ni+fbt07ZLQ1NTEwsKCw4cP06RJE4yMjGjRokWRpwWUtRo1jqsYZQAAIABJREFUalC3bl3i4+NJTk5GS0urwJ+HIhCs7r1+1e0+ZGdn8/r1a+RyOXXr1pW+QFYWl6NeMHXHVeJf5XyRH+/QFM8BVmioiX0JhPJRpmuyP/vsM8zMzNiyZQt+fn6oqKjQoEED+vbtKw3r1axZk8DAQHx8fFixYgUGBgZ89NFHdO3aNVey3lyN/f89EpctW8aCBQvIyspiyZIlJQry2rVrR9OmTYmJiSlW3rOyoKamxpo1a9iwYQO//PILR44cQVdXF1NTUyZOnEjdunWLXFfXrl3ZtWsXAQEBbN26ldTUVExMTLCxsZFWQx89epSDBw/y1VdfKc3BmzdvHhEREcydO5ctW7ZIQ3Tm5uZ4eXmxbNkyoqKiMDMzw9/fX2lTeh0dHbZv386aNWsICwsjKCgIAwMDzM3NmT17tvShoq6uzrp161i9ejUHDx7k8OHD1KxZk06dOkmBbH4/16FDhxIYGMi3337L2rVr0dLSol+/fowePbrEqUYAli5diqWlJUFBQZw/fx4DAwPatWtHmzZtlMqZmJjQtWtXzp8/X6KV1+bm5qxatYoVK1bg4+ND7dq1cXNzw9jYmLlz5yqV/eyzz4iNjWXdunWkpKTQuXPnYgV5dnZ2tG7dmtWrV/PkyRMsLS1ZsWKFlCaoJBYtWsTChQv57rvvyMjIYOrUqRUW5EHOwqEaNWqQlJREenp6gV8MFQu43l4cUt1Ut/ugpqaGsbEx+vr6lSrAk8vlrDv3gO+O3EOWLUdPS53vh7djQNv6hZ8sCKWgIi9KF1oVNXjwYBo0aMDatWsruimVhqOjI1ZWVnkOZ1ZHkydPJjY2ltDQ0IpuSpWmSANgbW1dJvVVxVQJJSHuw7u7BzExMdjZ2QE5yckVnQ+JaZl8secGR+/EA2BVT5/VoztgXkcv37rKg3gvVM17UNjfzmrbR3z9+nUiIyNL1SMkVG1xcXGcO3funeVPFASharn9OJGP/M9LAd5w20bs+9z+nQd4QvVV7VJoR0ZG8ttvv7FhwwYaNGggbdmkIJPJCs1Tp6Ojg66ubnk2k4SEBDIzM/N9XjEsIZS9mJgYrl69yu7du9HU1GT48OG5yrw9p+5t2trapZ70/S6uIQhC+dh9+SFfH7hNRlY2WuqqLHJuw786FX9qkSCURrUL8sLCwggICMDc3Jzvv/8eDQ0NpeefPHlCnz59Cqxj6tSpUiLl8jJt2rRcO1a8qWHDhkorYIWyc/nyZby8vGjYsCFLly7NM5h2cHAosA4XFxe+++67UrXjXVxDEIQypqbJkhMxHLqXs3uFWS0dVo/uQOsGhhXcMKE6qnZB3rRp0woM0OrUqcPGjRsLrKMkCz2Ka86cOQWuIi1J7rOiEIFjzl69Q4cOLbBMYe8RExOTUrfjXVxDEIQypG+Ceo9JUoD3Qau6fD+iPYY1NAo5URDKR7UL8gqjpaVV6iTFZeHtlZ5C5fIu3iOV4X0oCELRnP4zEfWB/0VFswZqKuA5oCUTujetdInFhepFBHmCIAiCUEKZsmy+O3yP9eejUdGsgTw1gVWjbXHqYl7RTROE6ru6VhAEQRBK40liGq4/XmL9+QcAZD+5S9bBRbRrUL4L8wShqESQJwiCIAjFdO6PZzitOs+V6Jz5d2M7miA7sQLSkyq4ZYLwDzFcKwiCIAhFlJ0tx+/kfVaciEQuB8MaGqwYaU1z3desq757CwiVVIX05N24cYN//etftG/fHktLS2JjYyuiGUr8/PyqVBbsyio4OBhLS0vu3r37Tq4XHh6OpaUl4eHh0jEPDw88PDyUyj19+pQpU6bQuXNnLC0tCQ4Ofifte5OjoyOff/55mdWnuNeV4fdLEKqCFykZfLzpMsuP5wR47RsZcnC6A72txEp3oXJ65z15mZmZzJgxAz09Pf773/+ipaUlkvoW4vr165w7d46xY8diYGBQ0c35P/buPS7n83/g+Ovu7qySVEiKHMqSiooSkXxbmVFEIUwxZsK+TOb7/Y2Z2YyxyuY45JBjbcPSNuZs9XWczdgcUo4roRKd7n5/tPsztw4qh7tyPR+PPdb9+Vz39Xnf132r932dPvXSvHnzSE5OZsKECZiYmNCpUyd1hyQIQi1yIu0Ob284wfV7DwEY4W7NzL7t0dGUqzkyQajYC0/y0tLSuHHjBh9//DEBAQEv+vJ10qlTp4iJiSEgIEAkec/AqlWryhxLSUmhT58+vPHGG2qI6Pno378/ffv2RVtbW92hCEKdVVJSwtojqcz97ncKi0vQ15YzL9CB/k7N1R2aIDzRC0/ylLcMe9LtmB48eICent6LCEl4yZSX9Ny+fRsDg/p1P0m5XI5cLnoZBKGmcvOLmL79F3b9cgOANuYGLB3eiTbm4naCQt3wQufkRUZGMnz4cAAmTJiAra0toaGhREZG4uLiQmpqKmFhYTg7OzN79mzpeSdOnOCNN96gU6dOODk5MWrUKH799dcy9f/555+8/fbbuLm50bFjRwYPHszhw4fLlDt27BgDBw7EwcEBHx8fNm3aVG68RUVFxMTE0Lt3bzp06ICPjw9LliyhuLhYpZytrS1z585lx44dvPrqqzg6OjJs2DBSU1MBWLlyJT179qRjx46MGzeOu3fvVrnNoqOjmTdvHgC9e/fG1tZWZZ5VVWN8EuV7kJ6eTnh4OE5OTvTq1Uuam3b69GmCg4Pp2LEjvr6+Zdr12rVrzJo1C19fXzp27EiXLl2IiIio0nywrKwsXn/9dXx8fLh27Zp0vKrve3U9OidPOW+tpKSE2NhYqX2V7t69y5w5c+jRowcdOnTA19eX2NjYGl133759DBs2DGdnZzp37kxwcDA//vhjmXIpKSnS57N37958/fXXZcqkpaURERGBq6srjo6OhISEqMw7fPS1Pf4eVCWOn376ieDgYJycnOjcuTMTJkzgypUrKmVSU1OZOHEi3bp1w8HBgR49ejBlyhRycsTqQqFu+yvnIQknruEfdUBK8Po7WfDNhG4iwRPqlBfakzdkyBCaNGnC0qVLGTlyJPb29piamrJjxw6KiooICwuja9euREZGSsOSR44cYezYsTg6OhIREUFJSQmbN29m+PDhbNu2jTZt2gBw/vx5hg4dioWFBWPHjkVHR4cdO3YwZswYVq1ahbu7u1QuLCyMxo0bM3HiRIqKioiOjqZx48Zl4v3Pf/5DQkICffv2pXPnzhw7doyoqChu3LjBhx9+qFI2OTmZPXv2EBISQlFREcuWLePtt9/mtdde44cffmD06NFcvXqV2NhY5s+fz0cffVSlNuvTpw9paWl8++23zJgxg0aNGgFI8xirE+OTFBUVMWbMGNzd3enVqxfbt2/nvffeQ0tLi08++YSgoCD8/PxYvXo1kyZNYv/+/TRoULof1JkzZzh58iR9+/aladOmXLt2jbi4OEaMGMGuXbsq7JXNyMhg1KhRFBUVsX79epo2bQpU/X1/Wq6ursyfP593330XT09PXn/9delcXl4eoaGhZGZmEhwcTJMmTUhOTmbu3LlkZ2fz9ttvV/k6W7du5T//+Q92dnaMGzeOBg0a8Ntvv3H48GF8fHykcqmpqUyePJmgoCACAwPZtm0bkZGR2Nvb07ZtWwAyMzMJCQmhoKCA0NBQDAwM2LZtG2FhYaxatYouXbo8VRzx8fG899579OzZk2nTpnH//n3WrVvH0KFD+eabbzA1NaWgoICwsDDkcjmjRo3C2NiYmzdv8tNPP5Gdnf3EnnpBqK3iT1xl+vZfKCwuXSkrl8Gs/h0Y3sVK3L1CqHNeaJLn7OxMQUEBS5cuxc3NTfqjsmPHDh48eMDrr7/OpEmTpPIKhYJZs2bh6enJ0qVLpeODBg3Cz8+PJUuWsGjRIgA++ugjrK2t2bx5M1papfcJDAkJISAggEWLFklJXlRUFDKZjLi4OJo0aQKAr68v/fr1U4n13LlzJCQkEBwcLPUqDhs2DENDQynZsLOzk8qnpqaSlJREs2bNANDU1GTBggUkJCSwY8cOaYjw9u3b7Nixg9mzZ0txVsbOzg57e3u+/fZbfHx8sLS0rHGMT/LgwQMGDRpEeHg4AD4+PvTq1Ytp06axevVqqQ1bt25NWFgYP/zwAwMGDACgZ8+evPrqqyr19erViyFDhpCUlCSVe9StW7cYOXIkMpmMdevWSfdirc77/rRatGhBixYtePfdd7GxsaF///7SudWrV3Pt2jW++eYb6X7FwcHBGBkZsXz5ckJDQ2nY8Mk3Hc/JyeGjjz7C2dmZ2NhYleHikse2XLh48SJxcXHSwg8/Pz+8vLyIj49n+vTpACxfvpzMzEw2b96Mk5MTUNo2/v7+fPLJJxWuDK5KHPfv3+ejjz5i2LBh/Pe//5XO+/n58dprr7FmzRqmTp3KxYsXuXr1Klu3bqVjx45SucruC/0kxcXFnD9/vsbPf1ReXh7AM6uvrhLtUL02yMorYvq2VAoV/xzTkMloo5PLH3/8Uelzb968Kf186dIl6bq1hfgs1M82KC4urnRaTq3aDDk4OFjl8blz57hy5Qp9+/YlKytL+q+wsJDOnTuTkpIClA6pJScn8+qrr5KTkyOVy87OxtPTkzNnzvDgwQOKi4s5dOgQffr0kRI8KE1aPD09Va69f/9+gDIT8UeNGgXAgQMHVI5369ZNSvAAHB0dAXjttddU/ph27NiRgoICMjIyatJETxVjVQQFBUk/N2nShKZNm2JhYSElePDPa3t0GFBXV1f6ubCwkDt37mBlZYWRkRFnz54tc53r168zfPhwNDU1VRI8qPr7/rwlJSXh5uZGgwYNVOLw9PQkPz+f06dPV6meQ4cOkZeXx5tvvllmPuDjPQO2trYqK3tNTExo1aoV6enp0rH9+/fj7OwsJXgARkZGBAQE8Ntvv1X42apKHEeOHCEnJwc/Pz+V19ygQQPs7OyktlfOX/zpp58oKCioUjsIQm13M7ewzL9JLbmMm7mFaopIEJ5OrdkMWVtbWyXxAqQ5bVOnTi33ORoapTlqWloaJSUlLFy4kIULF5Zb9u7du2hqavLw4UOsra3LnG/VqpWUNEHpHDNNTU2srKxUyllbW6OpqakydwzAwsJC5bFyuEo5/Pj48ezs7DLPqa7qxvgk+vr6ZXqmDA0NpSHZR49B6WtQevjwIcuWLSM+Pp5bt26p9FCVN0dr6tSp6OjosHnz5jJb6FT1fX/erly5wvnz51US3EcpFxE9iTJBUw63Vqa8z0TDhg25d++e9Pj69evlbvFiY2MjnTczM6tRHMq2HzZsWLnnlT2aLVq04I033uCLL75gzZo1uLm50atXL1577bUaL2CRy+XPbK9K5Tf1l33vS9EO1WuDRhYPKdl9XeWYAujmZIe5oW75T/qbvr6+9LONjY30b6W2EJ+F+tkGp06dqvR8rUnydHR0yhxTJgozZsygXbt2FT5XoSjtWx8zZgweHh7lljExMVFJSp61ihKPirpRHx+mqw0qirUqr2HOnDnEx8czcuRInJycMDQ0RCaTMWXKlHJfq6+vLwkJCWzcuLHM3Laqvu/Pm0KhoHv37owePbrc889qXuCjXlQCWxFl2y9cuLDc/Ssf/XcaGRlJYGAge/bs4dChQ8yePZulS5eyefPmMl/YBKEuMDfUJaizJRtT0pEBOloafBTg8MQETxBqq1qT5JVH+U3IyMiowuTt0XI6OjqVljMxMUFXV7fMKkGAy5cvqzxu3rw5RUVFpKWl0bJlS+l4WloaRUVFNG/+4vZIqmiyb22KUTnvLjIyUjqWn59f4UrLkSNH0qxZM6Kjo2nYsKHKHSiq+r4/b1ZWVuTn5z91DMqe1j///FNlTmVNWVhYlPm8wj+f4Yp6iKsSh7LtzczMKl3AodSuXTvatWvH+PHjpTvZxMXFMXny5Cq9FkGobbrYNGZjSjrG+lokTekhEjyhTqtVc/IeZ29vT4sWLfjqq6948OBBmfPK4bLGjRvj6upKXFxcuUNoymNyuRxPT09++OEHbt26JZ2/ePEihw4dUnmOl5cXAGvXrlU5rtw+Q3n+RVAOAzyeMNWmGMvr7Vu3bl2lW7lMmjSJ0NBQ5s6dq7JNSFXf9+fN19eX//3vf2W2JlHGUNXe2G7duqGvr8+yZcvKzF+rSY+ul5cXJ0+e5JdffpGO5eTkEB8fj729fblDtVWNw9PTEwMDA5YtW0ZRUVGZOpRtn5ubW+Z827Zt0dTUJD8/v9qvSRBqC+WXarmGhkjwhDqvVvfkyeVy5syZw9ixY+nXrx8DBgzA3NycmzdvcvjwYaysrPj0008B+L//+z+GDRvGa6+9RlBQEJaWlvz1118cP36c/Px8NmzYAJSu/jt48CAhISEEBwdTXFzM+vXradOmjcqKGzs7OwICAti4cSPZ2dl06tSJEydOsHPnTgYNGvRCx/Tt7e0BWLRoEf7+/mhpadGrV69aFWPPnj355ptvMDAwoE2bNpw6dYojR45gbGxc6fNmzpzJ/fv3ee+99zAwMMDHx6da7/vzFB4ezp49ewgLC2PgwIG0b9+e3Nxczp07x/fff8+JEyfQ1HzyPyFDQ0MiIyP5v//7P4KCgujbty8NGjTg7NmzaGtr8/7771crrrFjx7Jr1y7Cw8NVtlC5c+dOhXNSqxqHoaEh//3vf4mMjGTgwIH4+/tjbGzMtWvX2Lt3L71792bKlCn8/PPPfPDBB/j6+tKqVSsUCgXffvstMpkMX1/far0eQahNNP4eOKmNU2oEobpqdZIH4O7uzqZNm1iyZAnr1q0jLy8Pc3NznJ2dVVbjtmvXjm3bthEdHc3WrVvJzs7G1NQUe3t7RowYIZWzs7Nj1apVzJs3j6ioKJo2bcrEiRPJyMgos6z6ww8/xNLSkvj4eJKSkjA3NyciIoJx48a9sNcP8Morr/DOO++wYcMGDh48iEKhYM+ePejr69eaGGfOnImGhgY7duwgPz+fTp06sXr1amk7lorIZDI+/PBDcnNzmTJlCsuXL8fd3b3K7/vzpK+vz4YNG/jyyy9JSkpi+/btGBkZYWNjw9SpU6t1N4khQ4bQuHFjVqxYwZIlS9DS0qJNmzaMGTOm2nGZmpoSFxfHp59+ytq1aykoKMDe3v6Je+RVNY4BAwbQpEkTli9fzvLlyykqKqJp06a4ubnRt29foHTisqenJ/v27WPz5s3o6elha2vLihUrVFb9CkJdo/F3T55CJHlCPSArEV9XBEFQM+UKsWeVINbHVXQ1Idqh+m2w+9cbjFt/goZ6Wpx+/19Vvk56ejpdu3YF4Oeffxara2uh+tgGT/rdWavn5AmCIAjCiyQTPXlCPVLrh2vrs/v37z9xV3QTE5Onusl8Tk4ODx8+rLRMRRP1a7OCggKVvePKY2hoqLJJ8/OQlZVV6eISLS2tJ85LFASh9lAO14ocT6gPRJKnRl999RUxMTGVltmzZ89Tbbsxd+5cEhISKi1TF2/xcvLkSZW5luWZN28egYGBzzWOQYMGVbrptJubG+vWrXuuMQiC8OwoF16InjyhPhBJnhoNGDCAzp07V1rmaXvZwsPDef3115+qjtrIzs6O1atXV1rmeWxW/LhPP/200i1DjIyMnnsMgiA8O2LhhVCfiCRPjVq0aPHcJ+e2adPmhSQ7L1rDhg3VulGy0pOSdEEQ6haZ1JOn3jgE4VkQCy8EQRAE4W//zMkTWZ5Q94kkTxAEQRD+JnryhPpEJHmCIAiC8DcxJ0+oT0SSJwiCIAh/k0m3NRNDtkLdJ5I8QRAEQfibsicPxF55Qt0nkjxBEAAIDQ0lNDRU3WEIglo9muSJIVuhrhNJniAI5crIyCA6Oprff/9d3aEIwguj8U+OJxZfCHWe2CdPEAQAVq1apfI4MzOTmJgYmjdvTvv27dUUlSC8WDLRkyfUI6InTxDqoCfd87gmtLW10dbWfub1CkJd8mhPnsjxhLpOJHmCACQnJxMYGIiDgwM+Pj5s2rSJ6OhobG1tVcpt376dgIAAOnbsSJcuXZg+fTqZmZkqZby9vXnrrbdISUlh4MCBODg40Lt3b77++usy17179y5z5syhR48edOjQAV9fX2JjY8vEZmtrS2JiIgsXLsTT05NOnTpJ59PS0oiIiMDV1RVHR0dCQkJITk5WqaOwsJCYmBj+9a9/4eDgQJcuXQgJCeHw4cNSmUfn5CUnJzNgwAAAZsyYga2tLba2tsTHxzN9+nS6du1KUVFRmdcTHBzMoEGDqtLkglArPTon71b2QzVGIghPTwzXCi+9s2fPEh4eTpMmTZg4cSIKhYIlS5ZgYmKiUi4mJoYlS5bQt29fBg8eTEZGBrGxsZw5c4b4+Hh0dXWlsqmpqUyePJmgoCACAwPZtm0bkZGR2Nvb07ZtW6C0Ny40NJTMzEyCg4Np0qQJycnJzJ07l+zsbN5+++0y19fV1WXMmDHcv38fKB1SDQkJoaCggNDQUAwMDNi2bRthYWGsWrWKLl26SM9dtWoVQ4cOpW3btuTk5HDmzBl+++03unXrVqZNWrduzZQpU1i0aBFDhgyRbt/WqVMnmjZtytdff82hQ4fo2bOn9Jz09HROnjzJzJkzn/5NEQQ12Xf+L+ln38UHmBfoQGAnSzVGJAg1J5I84aUXFRWFpqYmcXFxmJmZAeDn54e/v79U5urVq3zxxRdMmzaN0aNHS8d79OhBcHAwCQkJhISESMcvXrxIXFyc1OPm5+eHl5eX1BMGsHr1aq5du8Y333wj3cM4ODgYIyMjli9fTmhoKA0bNpTqLCoqYuPGjejo6EjHli9fTmZmJps3b8bJyQmAQYMG4e/vzyeffEJ8fDwA+/btIygoiPfee69KbWJqaoqXlxeLFi3CycmJ/v37S+eaN29OkyZN2LFjh0qSt3PnTjQ1Nenbt2+VrvG44uJizp8/X6PnPk45nP2s6qurRDtUrw2y8oqI2XtFepxfpCBy+y80k93DRL/yP5c3b96Ufr506dJzmVLxNMRnoX62QXFxMXK5vMLzYrhWeKkVFxdz9OhR/vWvf0kJHoC1tTXdu3eXHv/444+UlJTQp08fsrKypP+srKwwMzMjJSVFpV5bW1uVIVUTExNatWpFenq6dCwpKQk3NzcaNGigUqenpyf5+fmcPn1apc6AgACVBA9g//79ODs7SwkegJGREQEBAfz2229kZGRIx06fPq3yh6imNDQ06NevH3v37lX5Q7Zjxw48PDxo3LjxU19DENThZm4hWo/9vdTUkHEzt1A9AQnCUxI9ecJL7fbt2zx8+BArK6sy56ytraWfU1NTUSgU+Pj4lFtPVlaWymMLC4syZRo2bMi9e/ekx1euXOH8+fO4u7tXqU5Ly7JDRtevX1dJJpVsbGyk82ZmZkRERPDWW2/Rs2dPXnnlFbp3706/fv1o06ZNudd+kv79+7Ny5Ur27NlDv379OHv2LBcvXmTcuHE1qg9ALpeXmQNZU8pv6s+qvrpKtEP12qCRxUMU398A/llxoSiBbk52mBvqVvxEQF9fX/rZxsZG6p2vLcRnoX62walTpyo9L5I8QagChUKBXC5nxYoVKlssKBkZGak81tB4cie5QqGge/fuKsO/j3o8AXt0zl91ubq68sMPP7B3714OHz5MXFwcK1eu5IMPPmDgwIHVrq9du3a88sor7Ny5k379+rFjxw709fUrTIIFoS4wN9TlowAHZsSfIb9IAcAAZ4snJniCUFuJJE94qTVu3BgdHR3S0tLKnLty5Z+5OVZWVhQXF2NtbV1uj1pNWFlZkZ+fj4eHR43rsLCw4PLly2WOK4892qNobGxMYGAggYGB0qKPqKioCpO88pLZR/Xv358FCxZw+/Ztdu3ahY+Pj0pvhiDURYGdLPFsa8qUzac5fCGT32/kqDskQagxMSdPeKnJ5XI8PDz4/vvvpflrUJrgHTx4UHrcp08fNDQ0WLJkSZk6FAoFd+/erfa1fX19+d///ldmuxMoHaqtys3Rvby8OHnyJL/88ot0LCcnh/j4eOzt7aV5hnfu3FF5nr6+Pi1btiQ/P7/CuvX09ADIzs4u9/xrr72GQqHggw8+4NatW/Tr1++J8QpCXWBuqMtkn9JV8Kev3uN0evX/fQtCbSB68oSX3ttvv82hQ4cICQlhyJAhKBQK1q9fT9u2baVbellbWxMREcHixYtJT0+nV69e6OnpkZ6eTlJSEuPHjycoKKha1w0PD2fPnj2EhYUxcOBA2rdvT25uLufOneP777/nxIkTaGpW/k907Nix7Nq1i/DwcJUtVO7cucPChQulcn379sXV1ZUOHTpgbGzMr7/+ynfffcewYcMqrLt58+YYGxuzadMmGjRogL6+Ph07dpTmGpmamtKtWzd2794t/SwI9YWLdSPsmhpy7mYO636+gmMLY3WHJAjVJpI84aXXoUMHVqxYwfz58/n8889p1qwZERERXLp0iUuXLknlxo8fj7W1NbGxsURHRyOTybCwsMDHx6dGQ676+vps2LCBL7/8kqSkJLZv346RkRE2NjZMnTq10mXxSqampsTFxfHpp5+ydu1aCgoKsLe3V9kjD0o3Ot67dy9HjhyhoKAACwsLJk2aRFhYWIV1a2pq8sknn7BgwQJmzZpFUVER8+bNU5lQPmDAAA4cOIC/v3+V4hWEukImkxHqbs3MhF/Zcfo6M/3b06iBuCOMULfISqoyJiQIL6G33nqLCxcu8P3336s7lForKSmJiIgItm3bhoODQ43rUa4Qe3QrmKdRH1fR1YRoh6drg/v5RXT9aA85+UXM8LPjTa/WFZZNT0+na9euAPz8889idW0tVB/b4Em/O8WcPEGAMnPTUlNTOXDgAG5ubmqKqG7YsmULrVu3fqoETxBqqwY6mgzsXLrQan3yFRQK0Sci1C1iuFZ46RUVFdG7d28CAgJo0aIF165dY9OmTWhpaREeHq7u8GqlXbt28fvvv3Po0CFmz56t7nAE4bm2vlpBAAAgAElEQVQZ3tWaNUdSSc96wP4/MuhlZ67ukAShykSSJ7z05HI5np6e7Nq1i4yMDLS1tXF2dmbKlCm0bNlS3eHVSu+88w76+voMHjy42gtOBKEuaWNuQLc2jTl84TaxR1NFkifUKSLJE156MpmMjz/+WN1h1Cn16d6PgvAkoV2tOXzhNvv+yCDtdh5WjcV+kELdIObkCYIgCEIlfNo3oamRLiUlsCH5ypOfIAi1hEjyBEEQBKESmnINhnYpvb/15mPpPCwsVnNEglA1IskTBEEQhCcIdmuBllzG3bxCdv5yQ93hCEKViCRPEARBEJ7A3FCXVzs0A2Dd0VS1xiIIVSWSPEEQBEGogtCu1oC4n61Qd4gkTxAEQRCqwLVl6f1sAdb9LBZgCLWfSPJect7e3kRGRqo7jGdq2bJleHt70759e0JDQ9UdTq0UHx+Pra0tV69eVXcoglBnKO9nC7Dj9HXu3C9Qc0SCUDmR5L0ELl68SHR09EvxB/3AgQN89tlndOnShXnz5jFu3Dh1h6RWcXFxxMfHqzsMQag3Bjg1x1BHk/wiBVuPp6s7HEGolNgM+SVw+fJlYmJicHNzw9LSUuXc7t27kclkaors2UtJSUFTU5M5c+agqSk+3ps2bcLIyIjAwECV4/3796dv375oa2urKTJBqJuU97NdcySV9T+nEe5pg4ZG/fkdKtQvoifvBSgpKeHhw4fqDqNc2traaGlpqTuMZ+b27dvo6enVugTvwYMHNTr3vMjlcnR0dOpVgi8IL8rwrqV75qVl5bHq0GX+yqmdv98FQSR5QHR0NLa2tly+fJmIiAicnZ1xd3dn/vz5FBYWqpTdvn07AQEBdOzYkS5dujB9+nQyMzNVynh7e/PWW2+xf/9+AgICcHBw4LvvvpPOJyQkEBgYiKOjI25ubowcOZJjx4491XVef/11HBwc6NevH/v375fKxMfHM2HCBABGjBiBra0ttra2JCcnS3U8PicvLS2NiIgIXF1dcXR0JCQkRCr/aL22tracOnWKuXPn0rVrV5ycnJgwYQJZWVkqZc+cOUNYWBhdunShY8eOeHt7M2PGjCe+L49bv349fn5+dOjQgR49evDxxx+rJEi2trbEx8eTk5Mjvc7qDFVeuHCBiIgIKU5/f3+WLl2qUubo0aMEBwfj6OiIq6srERERpKerDtlERkbi4uJCamoqYWFhODs7M3v2bABCQ0Pp378/v/zyCyEhIXTs2JGVK1dK8UdHR5eJ6/H3SNn2x48f5z//+Q+urq64uLgwc+ZMcnNzVZ537tw5UlJSpPZQzlGsaE7ek9pYWe9bb71FSkoKAwcOxMHBgd69e/P1119Xua0FoS5rY25IG7MGAHy8+xw95v/E7nNZT3iWILx4tau7Q80iIiKwsrJi6tSpHD9+nFWrVpGXl8esWbMAiImJYcmSJfTt25fBgweTkZFBbGwsZ86cIT4+Hl1dXamuixcv8u677xIcHMzgwYOxsbEBYPHixXz55Ze4uLgwefJkZDIZJ0+e5NixY7i4uFT7OpcuXWLatGmEhIQQEBDA1q1beeutt1i3bh2dOnXC1dWVkSNHsnbtWsaNGyfF0bp163LbIDMzk5CQEAoKCggNDcXAwIBt27YRFhbGqlWr6NKli0r52bNnY2xszMSJE7l69Spr167lgw8+YPHixUBpz1pYWBiWlpaMHz8efX19rl69yg8//FCt9yY6OpqYmBg8PT0ZNmwYf/zxB2vWrOGPP/5g1apVyGQy5s+fz5YtW/jtt9+kpKpTp05Vqv/3339n2LBh6OjoEBwcTLNmzUhNTWXfvn3SvL4jR44wZswYWrZsyaRJk8jNzSU2NpaQkBC+/fZbTExMpPqKiooICwuja9euREZGYmRkJJ3LysrizTffpF+/fgwYMIBmzZpVqy2UZs2ahbGxMZMmTeLPP/9k8+bNZGRksHz5cgDee+895s6di66urvQaTE1NK6yvKm2slJqayuTJkwkKCiIwMJBt27YRGRmJvb09bdu2rdHrKS4ufmb3xM3LywPEPXZFOzyfNsjKKyL19n0AihUlFCtKmP/TVdA1gofZXLp0SbpubSE+C/WzDYqLi5HL5RWeF0neI1q2bCn1pCj/4G/atImwsDBkMhlffPEF06ZNY/To0dJzevToQXBwMAkJCYSEhEjHU1NTWbNmDe7u7irHli1bxquvvsqiRYvQ0CjtSB01ahQlJSUAXL16tVrXuXz5Ml9++SXe3t4ABAYG8q9//YtFixaxbt06WrRogZubG2vXrsXDw6NMkva45cuXk5mZyebNm3FycgJg0KBB+Pv788knn5TpGTMxMWHlypVSAqBQKFi3bh05OTkYGhpy8uRJ7t27x+7du1WSoHfeeedJb4ckKyuLZcuW4eXlxbJly6RrWVpasnDhQn766Se8vb3p378/R48e5fz58/Tv37/K9QPMmTMHDQ0NEhISaNq0qXRc+b4AzJ8/n0aNGhEXFyclbT169GDIkCEsX75cpbftwYMHvP7660yaNKnMtf766y/mzp3LoEGDqhXj43R1dVm9erU0NG1mZkZ0dDQpKSm4ubnh4+NDdHQ0RkZGT2yPqrax0sWLF4mLi5OSaD8/P7y8vIiPj2f69OlP9boEoba7mVuItlxGkeKf3w+aGiAzMKXkYbYaIxMEVSLJe8TQoUNVHg8bNoz4+HgOHTpEfn4+JSUl9OnTR2U40srKCjMzM1JSUlSSr5YtW6okeAA//vgjCoWCCRMmSAmekvKP6o8//lit61hYWKj88W3YsCGvvfYaGzduJC8vD319/Wq1wf79+3F2dpYSPAAjIyMCAgJYvnw5GRkZmJmZSeeCg4NVenhcXFxYs2YN165dw87ODkPD0j2lfvjhB4KCgsq87qo4cuQIhYWFjBw5UuVaw4YNIyoqin379qm0QXVlZWVx/PhxRo8erZLgwT/vy19//cXvv//OuHHjVHrlnJyccHJyYt++fWWGvYODg8u9np6eXrWT0PIMGTJEZe7hsGHDiI6O5sCBA7i5uVWrruq2sa2trUovqYmJCa1atSozdF0dcrkcW1vbGj//Ucpv6s+qvrpKtMPzaYNGFg9RfH8D+CfJU5TIKMktnVJjY2NDixYtntn1ngXxWaifbXDq1KlKz4sk7xEtW7Ys9/G1a9fIzc1FoVDg4+NT7nMfn4f2+CpWgPT0dORyuTRkWp7U1NRqXcfKyqpMGWtraxQKBTdu3KhwWLYi169fL3eIUxnz9evXVZK8x4calQlQdnbpt1k3Nzd8fX35v//7P2lrE29vb/z9/au8svP69esAtGrVSuV4gwYNMDc3l87XlDIxqWyYsaIYoLRtdu3apXJMW1ubJk2alFtXkyZNnslil8c/r40aNaJhw4Zcu3at2nVVt40tLCzK1NGwYUPu3btX7WsLQl1jbqjLRwEOTN/+C4XFpYnelB4WzFkjevGE2kUkeVWkUCiQy+WsWLGi3BWJj/buAOjo6LyQ66hbRXMBlMOcMpmMqKgoTp8+zd69ezl06BDTp0/nq6++Ii4ujgYNGrzIcF+Yyt7/R+dUVkVxcfHThvPM1aRHVhDqk8BOltg1NcQ/6hAALYyr9+9aEF4EkeQ9IjU1VaVnKjU1FSjttTA2Nqa4uBhra+tye+mqwsrKiuLiYi5dukS7du0qLVPV66SlpZU5duXKFTQ0NGo0od/CwoLLly+XOa48Vl4PTlU4Ojri6OjIlClT+O6776T/BwUFVSkmZQyPXj8vL4+//voLT0/PGsWkpBxW+fPPP6sUw+Mej6umGjZsKPWAKhUUFJCRkVFu+dTUVGmxDsCdO3e4d++eSixV3SLlebexINRHr1g0pK25AX/+lcvxq7lPfoIgvGDi6/gjNm7cqPJ4w4YNyGQyunfvTp8+fdDQ0GDJkiVlnqdQKLh798k3q+7duzcaGhrExMSgUChUzil7vqp7nevXr7N3717p8b1799i5cycuLi7SfDzl/3Nycp4Yo5eXFydPnuSXX36RjuXk5BAfH4+9vb3KUG1V3Lt3T2XxAkD79u2B0gSmKjw8PNDS0mLdunUqdW3cuJHCwkJ69uxZrZgeZ2JiQufOndm6dSs3b95UOae8nrm5Oe3bt2f79u0q7fjLL79w8uTJp44BSpPNx7fS2bJlS4U9eZs3b6aoqEh6vGHDBqB0MYiSnp5emcSxPM+7jQWhvurWpnTFukjyhNpI9OQ9IjU1lQkTJuDh4cHx48fZtWsXQ4YMkXp6IiIiWLx4Menp6fTq1Qs9PT3S09NJSkpi/PjxT+yVatmyJWPGjGHZsmWEhobi4+ODXC7n1KlTtGvXjnHjxmFtbV2t67Rq1YrIyEhCQkJo1KgRW7ZsITc3V2VVp52dHZqamqxYsYKcnBy0tbXp2rUrjRs3LhPj2LFj2bVrF+Hh4SpbqNy5c4eFCxdWu00TEhKIi4ujd+/eWFlZ8eDBA7Zu3YqBgYFKMlIZExMT3nzzTWJiYhg7diw9e/bkjz/+YMuWLXTr1o1evXpVO67HzZw5k+HDhxMQEMDgwYNp3rw5V65c4fjx42zatAmAd999l/DwcEJCQhg4cKC0hYqZmRljx4596hiCgoJ4//33mThxIh4eHpw7d45Dhw7RqFGjcss/fPiQN954A19fX2kLFU9PT5UV1Pb29qxfv54vvvgCa2trTExMyiwIghfTxoJQH3m0bsyaI6n8disPNHWgKF/dIQmCRCR5j4iKiuKzzz5jwYIF6OjoMHr0aJWtPsaPH4+1tTWxsbFER0cjk8mwsLDAx8cHDw+PKl3jnXfewdLSkg0bNvDZZ5+hr69P+/btcXV1rdF1bGxsmDFjBgsWLCA1NRVra2tiYmJUhvFMTEz44IMP+PLLL5k5cybFxcXExsaWm+SZmpoSFxfHp59+ytq1aykoKMDe3r7cPfKqws3NjTNnzpCYmEhmZiaGhoZ07NiR+fPnV2v12cSJEzE2NmbDhg3MmzePRo0aMWLECCZNmvRM7tpgb29PXFwcn3/+ORs3bqSgoABLS0uVVbAeHh6sXLmSqKgoFi1ahLa2Nh4eHkybNk1le5iaGjx4MFevXmXbtm0cPHiQzp07s3r1akaNGlVu+ffff5+EhAQ+//xzFAoFAQEBzJw5U6XM+PHjuXr1KitXruT+/fu4ubmVm+TB829jQaiPutg0RkMGRYoSZOZtKbn+q7pDEgSJrOTxsbSXkHIT2P/973+1bmFDZby9vbGzs+OLL75QdyjCCxQfH8+MGTP4+uuvpaHvuk65DcCjW/c8jfq4VUJNiHZ4MW3Qf8lhTqffpfi371Gc2MbPP/8stlCphepjGzzpd6eYkycIgiAIT8GzTemoiEYzOzVHIgiqxHCtoFZZWVmVbhGipaWFsbFxjevPycnh4cPKbx5e3cUkgiAIj+rW2pQlP11EZmIFOgbqDkcQJCLJE9Rq0KBBlW7e6+bmxrp162pc/9y5c0lISKi0TH26j6EgCC9eJ+tGaMtlFBSXIGtSf4YChbpPJHmUTjifOHGiusOotke3TqmrPv30U/LzK16N9rRzJMPDw3n99defqo7aJjAwkMDAQHWHIQjC33S15Dg0a8Dxq7nIxJCtUIuIJE9Qq86dOz/X+tu0aUObNm2e6zUEQRA6Wxpw/GouGk3rx2IooX4QCy8EQRAE4Sm5WJbOxZMZmXMzu2obvQvC8yaSPEEQBEF4Su3M9CjJvw+Iu18ItYdI8gRBEAThKck1ZJTc+gMQSZ5Qe4gkTxAEQRCegZKb54DSJE/cZ0CoDUSSJwiCIAjPgOLG7wBkPSjij1uiN09QP5HkCS+V+Ph4bG1tuXr16jMtWxucPn2awYMH4+joqBL3vn376NevHx06dKhXt/MRhFon+yYleXcBOHwhU83BCILYQkUQiIuLQ0dHp07vPVdYWMikSZMwMDBg5syZ6OjoYGJiQlZWFlOmTKF9+/bMmjULLS2tZ37tAwcOcPr06Tq516QgPGslN35H1tqdIxczGe3ZSt3hCC85keQJL5X+/fvTt29ftLW1pWObNm3CyMioTJJXXtnaKi0tjRs3bvDxxx8TEBAgHT927Bh5eXlMnjwZNze353LtgwcPEhsbK5I8QQAUN8+h0dqd5EtZFBUr0JSLATNBfcSnT3ipyOVydHR0kMlkz7SsumVlZQFgaGhYpeOCIDwfJTdL5+Xl5Bex9fhV/sqp/N7ZgvA8iSRPUKvo6GhsbW25fPkyERERODs74+7uzvz58yksLJTKFRUVERMTQ+/evenQoQM+Pj4sWbKE4uJilfoOHz5MSEgILi4uODs74+vry2effSadf3yenbe3N+fOnSMlJQVbW1tsbW0JDQ0tt+zYsWPx9fUt93X4+/szatQo6bFCoWDVqlX4+fnRoUMHPD09mTNnDvfv369W+5w7d47IyEh69+6Ng4MD3bp1Y8aMGdy5c0cqExkZyfDhwwGYMGGC9BpCQ0OZPn06AAMGDMDW1pbIyEjpeSdOnOCNN96gU6dOODk5MWrUKH799dcyMVy4cIGIiAi6dOlCx44d8ff3Z+nSpdK1Y2NjAaT2E/P+hJda3l1M9OQAvP/Nb/SY/xPxJ+rGvF6h/hHDtUKtEBERgZWVFVOnTuX48eOsWrWKvLw8Zs2aBcB//vMfEhIS6Nu3L507d+bYsWNERUVx48YNPvzwQwD+/PNP3nzzTTp16sSUKVPQ0NDgypUrHD9+vMLrvvfee8ydOxddXV3GjRsHgKmpabll/fz8iIyM5OzZs7zyyivS8fPnz3Px4kWVJG/mzJns2LGDgQMHMnLkSK5cucL69eu5cOECa9asqXLv4JEjR0hPTycwMBAzMzP+/PNPtmzZwoULF9iyZQsymYwhQ4bQpEkTli5dysiRI7G3t5deQ6tWrdi8eTNTpkyhWbNmWFlZSfWOHTsWR0dHIiIiKCkpYfPmzQwfPpxt27ZJt4L7/fffGTZsGDo6OgQHB9OsWTNSU1PZt28f48aNY8iQIWRkZHDo0CHmz59fpddUkeLiYs6fP/9UdSjl5eUBPLP66irRDi+uDW7evFn6g64Rdx+WfvksKFZAMURu/4VmsnuY6KvvT674LNTPNiguLkYul1d4XiR5Qq3QsmVLoqOjAaSkYtOmTYSFhXH//n0SEhIIDg5m9uzZUhlDQ0MpMbGzs+Pw4cPo6OiwevXqSj/0j/Lx8SE6OhojIyP69+//xLJaWlokJiaqJHmJiYloamrSp08foHQeXHx8PFFRUSo9fw4ODkyZMoWDBw/So0ePKsU3dOhQRo8erXLMycmJd955h+PHj0s9lgUFBSxduhQ3Nzd8fHyksrdu3WLz5s14eXnRvn3pPTUVCgWzZs3C09NT6pEDGDRoEH5+fixZsoRFixYBMGfOHDQ0NEhISKBp06ZSWeUeYM7OztjY2HDo0KEntp8gvAxkBqY8/hVOU0PGzdxCtSZ5wstJfOKEWmHo0KEqj4cNG0Z8fDyHDh0iOzsbgDfeeEOlzKhRo9i8eTMHDhzAzs4OIyMjHjx4wMGDB+nZs+czj9HQ0BBPT092797Nv//9b+n47t27cXd3p1GjRtJjY2NjXF1dpTlxAC4uLsjlclJSUqqc5Onq6ko/5+fnc//+fRwdHQH47bffcHFxqfbrOHfuHFeuXGHixIkq8QF07tyZlJQUoHQ+3/Hjxxk9erRKggc8l3mKcrn8mQ31Kr+pv+xDx6IdXlwb6OvrA1CSm4nisX2QFUA3JzvMDXXLPvEFEZ+F+tkGp06dqvS8SPKEWqFly5blPr527RrZ2dloampKQ41K1tbWaGpqcu3aNaB0XtzWrVt58803MTMzw8PDgz59+uDj4/PMkhJ/f3+mTZvGmTNncHBw4Ny5c1y+fJkxY8ZIZa5cucLdu3dxd3cvt47HE6vK3L17l5iYGL777jtu376tci4nJ6dGryE1NRWAqVOnlnteQ6N0qm56ejoAbdu2rdF1BOHlVIIyx9PXlqMoKeGjAAe1JnjCy0skeUK9oaury4YNG0hOTmb//v0cPHiQb775hm7durFixYoqD+FWxtvbGx0dHRITE3FwcCAxMREtLS1pqBZKh0PNzMwqnKNmbm5e5etNnjyZkydPEhYWRvv27dHX10ehUBAeHl7j2yYpnzdjxgzatWtXozoEQSifzLz0S5G2XMZXo1ywMTMQCZ6gNiLJE2qF1NRUmjVrpvIYwMLCAkNDQ4qKikhLS1Pp8UtLS6OoqIjmzZtLxzQ0NHB3d8fd3Z3IyEhWrFjBggULSElJqbBnrTq9fAYGBnTv3p3du3fz7rvvsnv3bjw9PTEyMpLKWFlZkZycjIuLy1PtsXfv3j2OHj3KxIkTefvtt6XjyrapqRYtWgBgZGSEh4fHE8v9+eefldZXF7aYEYQXRdakdCjQpaUJXW3KX8QlCC+K2EJFqBU2btyo8njDhg3IZDK6d++Ol5cXAGvXrlUpo9y6Q3n+0W1FlJSLDfLz8yu8tp6enjTvryr8/f25du0amzZtIjU1FT8/P5Xzvr6+FBYWsnz58jLPLSgoIDe3ave0rKjn8fF2qC57e3tatGjBV199xYMHD8qcVw4nm5iY0LlzZ7Zu3frPysG/PdqLqKenB1CtNhSE+kqjaWnveFebxmqORBBET55QS6SmpjJhwgQ8PDw4fvw4u3btYsiQIVJvUkBAABs3biQ7O5tOnTpx4sQJdu7cyaBBg6RJtF988QXHjh2jR48eWFpakpWVxcaNG2natCmdO3eu8Nr29vasX7+eL774Amtra0xMTCrs9QPo1asXurq6zJ8/Hx0dHXr37q1yvmvXrgQFBREdHc2vv/6Ku7s7GhoapKamkpiYyIIFCyrtQVMyMDDA1dWVlStXUlhYSJMmTTh8+PBT30tXLpczZ84cxo4dS79+/RgwYADm5ubcvHmTw4cPY2VlxaeffgqUbgUzfPhwAgICGDx4MM2bN5e2pdm0aRMAHTp0AODDDz/E09MTuVxO3759nypGQaiTdA2RGZeOLIgkT6gNRJIn1ApRUVF89tlnLFiwAB0dHUaPHs0777wjnf/www+xtLQkPj6epKQkzM3NiYiIkPa2g9L5cteuXSM+Pp47d+7QqFEj3NzcmDhxYqV3fBg/fjxXr15l5cqV3L9/Hzc3t0qTPH19fby8vEhKSsLHxwcDA4MyZebMmYO9vT1btmxh4cKFaGtrY2lpSVBQEHZ2dlVul4ULFzJnzhw2btxISUmJNL+we/fuVa6jPO7u7mzatIklS5awbt068vLyMDc3x9nZmeDgYKmcvb09cXFxfP7552zcuJGCggIsLS1Vtkvp3bs3I0aMYOfOnXz77beUlJSIJE94KcnMS3vxtOUyHFs0VHM0ggCykprO3haEZyA6OpqYmBj+97//qcxrE14uym0AnJycnkl99XGrhJoQ7fDi2iA9PR2PiM+R2/aiU3MD4id6PdfrVZf4LNTPNnjS704xJ08QBEEQngGNvxddODdvoOZIBKGUGK4VBDXIycnh4cPKb1xuZmb2gqIRBOFp3ckrQmZsAYBz87JTOARBHUSSJwhqMHfuXBISEiotU5/urygI9d2p66Wr5kuKCmjfRE/N0QhCKZHkCWo1ceJEJk6cqO4wXrjw8HBef/11dYchCMIzcuLafQBKMi6iLa/+7QYF4XkQSZ4gqEGbNm1o06aNusMQBOEZOXnt7568W6IHXqg9xMILQRAEQXgKGTn5XLlTuuF6yc0/1ByNIPxDJHmCIAiC8BSSL98GSufjldxOVW8wgvAIkeQJgiAIwlP4+dLfSV7GRVAUqTkaQfiHSPIEQRAE4Skcvfh3kndTzMcTaheR5AlCBUJDQwkNDVV3GFW2bNkyvL29ad++vRR3YWEhH3/8MT169MDW1pbIyEg1RykI9ctfOQ+5mPH3ylqx6EKoZcTqWuGllpGRwaZNm/Dx8aF9+/bqDqfGDhw4wGeffUZgYCBdunSRNlLetm0bq1evZvTo0djZ2WFlZfXMr718+XJsbGzw8fF55nULQm2XfCkLAB1NGYViPp5Qy4gkT3ipZWZmEhMTQ/PmzcskeatWrVJTVNWXkpKCpqYmc+bMQVNTU+V48+bNmT59+nO79vLly/Hx8RFJnvBSUs7Hc2jagKOKYjVHIwiqxHCtIFRAW1sbbW1tdYdRJbdv30ZPT08lwVMeNzQ0VFNUglD/KZM8cb9aoTYSSZ7wXEVHR2Nra0t6ejrvvvsunTt3pnPnzsyYMYMHDx6olN2+fTsBAQF07NiRLl26MH36dDIzM1XKKBQKoqOj8fT0xNHRkdDQUC5cuIC3t7fKfLO7d+/yySef0K9fP5ydnenUqRPh4eGcO3dOKpOcnMyAAQMAmDFjBra2ttja2hIfHw+ozsnLzMykffv2fPnll2Ve4+nTp7G1teWbb76Rjt24cYN3330Xd3d3OnToQL9+/di5c2e122/79u2MGDFCqsff35+NGzeqlFHGnJOTo/IabG1tSU5O5ty5c9Lx5ORkqR1XrVqFn58fHTp0wNPTkzlz5nD//v0yMSQkJBAYGIijoyNubm6MHDmSY8eOSdfOyckhISFBuoaY9ye8LB6dj9eqsa6aoxGEssRwrfBCRERE0KJFC/79739z9uxZtm7diomJCdOmTQMgJiaGJUuW0LdvXwYPHkxGRgaxsbGcOXOG+Ph4dHVLf4EuXLiQlStX4u3tjaenJ+fOnSMsLIz8/HyV66Wnp/Pjjz/y6quvYmlpSWZmJps3b2b48OHs2rWLJk2a0Lp1a6ZMmcKiRYsYMmQInTt3BqBTp05l4jc1NcXFxYXExETGjx+vci4xMREdHR169+4NwF9//cXgwYPR0tJixIgRNGzYkD179vDvf/+bgoICAgMDq9xucXFxtG3bFm9vbzQ1Nfnpp5+YPXs2JSUlDBs2DID58+ezZcsWfvvtN2bPng3AK6+8wqaK+GIAAB4aSURBVPz581m6dCkPHz5k8uTJALRu3RqAmTNnsmPHDgYOHMjIkSO5cuUK69ev58KFC6xZswaZTAbA4sWL+fLLL3FxcWHy5MnIZDJOnjzJsWPHcHFxYf78+bz//vvY29szePBggOcy708QaqOoH/+Ufp6dlIasVRdKLierMSJBUCWSPOGFcHBw4IMPPpAe3717l23btjFt2jSuXr3KF198wbRp0xg9erRUpkePHgQHB5OQkEBISAiZmZmsWbMGX19foqKipHIxMTFER0erXM/W1pakpCQ0NP7prO7fvz9+fn5s27aNCRMmYGpqipeXF4sWLcLJyYn+/ftX+hr8/f2ZNWsWly5dwsbGBoCSkhKSkpLo0aMHBgYGQGlipKGhwddff42RkREAQ4cOJTw8nM8++4wBAwaoxFWZ9evXSwkuwPDhwwkLC2P16tVSkte/f3+OHj3K+fPnVV5D27Zt2bZtG9nZ2SrHjx07Rnx8PFFRUfj6+krHHRwcmDJlCgcPHqRHjx6kpqaybNkyXn31VRYtWiTFPGrUKEpKSqRrz5kzhxYtWjyx/Z6kuLiY8+efzerEvLw8gGdWX10l2uH5tUFWXhEbU9Kkx/nFJci7hlJ043cuXbokXbe2EJ+F+tkGxcXFyOXyCs+L4VrhhQgODlZ57OLiwt27d8nNzeXHH3+kpKSEPn36kJWVJf1nZWWFmZkZKSkpABw9epSioiKGDh2qUtfw4cPLXE9bW1tKSoqLi7lz5w76+vq0atWKs2fP1ug1+Pr6IpfLSUxMlI6dOnWK69ev4+/vD5QmfT/88APe3t4UFRWpvJ7u3buTkZHB5cuXq3zNRxO8nJwcsrKycHNzIz09nZycnBq9jt27d2NsbIyrq6tKfC4uLsjlcqm9f/zxRxQKBRMmTCiTlCp7+gThZXUztxD54/8MFEXIDEzVEo8glEf05AkvRLNmzVQeK3u47t27R2pqKgqFosLVmVlZpVsUXL9+HQBra2uV88bGxjRs2FDlmEKhIDY2lo0bN3L16lWKi4tVyteEiYkJXbp0ITExkQkTJgClQ7V6enr07NlTijU7O5uNGzeWmTundOfOnSpf8/jx40RHR3Pq1KkycxhzcnJqtKjiypUr3L17F3d393LPK9s7PT0duVwu9Vo+b3K5HFtb22dSl/Kb+rOqr64S7fD82qCRxUPYfR0o+eeghiYluZnY2NjQokWLZ3q9pyU+C/WzDU6dOlXpeZHkCS9ERd3JJSUlKBQK5HI5K1asKLeHSJkQVsfSpUv5/PPPGThwIJMmTaJhw4ZoaGjw0UcfSUONNeHn58d///tfLly4QOvWrUlKSsLLywt9fX2gNLkECAwMpF+/fuXW0bZt2ypdKy0tjVGjRmFjY0NkZCTNmjVDS0uL/fv3s2bNGula1aVQKDAzM2P+/Pnlnjc3N69RvYLwMjE31OXtXm1Y9Pe8PG25jLxD6+BhtpojE4R/iCRPUDsrKyuKi4uxtrbG0tKywnIWFhZAaU/Uoz2Dd+7c4d69eyplk5KS6NKlCx999JHK8ezsbBo1aiQ9ru6wY58+fZg9ezaJiYl069aNmzdvSkO1UNrb16BBA0pKSvDw8KhW3Y/bu3cvBQUFfPnll9JrB6QVsjVlZWVFcnIyLi4ulW4Ro3xfLl26RLt27SosJ4ZuhZdVWHcbKcmb9S8rpq0Riy6E2kXMyRPUrk+fPmhoaLBkyZIy5xQKBXfv3gXA3d0dTU3NMsOgGzZsKPM8uVxepscuMTGRW7duqRzT09MDSpO/qmjUqBFdu3YlMTGRxMRE9PX18fLyUrlunz59+O6777h06VKZ5yuHQqtC2fv56OvIyclh+/btVa6jPL6+vhQWFrJ8+fIy5woKCsjNzQWgd+/eaGhoEBMTU6bX8NGY9PT0qtx+glCfGOho0sRIB4B7D8VGyELtI3ryBLWztrYmIiKCxYsXk56eTq9evdDT0yM9PZ2kpCTGjx9PUFAQpqamjBgxgq+++oq33nqLbt26cf78eQ4cOECjRo1UepR69uzJkiVLmDFjBs7Ozvzxxx/s2LGjzDyZ5s2bY2xszKZNm2jQoAH6+vp07Nix0vk0fn5+zJw5k5s3b+Lt7a2yOALg3//+N8nJyQwcOJAhQ4ZgY2PDnTt3OHPmDGfPnmXv3r1Vapdu3bqhpaXFuHHjCA4O5v79+2zdupXGjRuTkZFRjRZW1bVrV4KCgoiOjubXX3/F3d0dDQ0NUlNTSUxMZMGCBXh4eNCyZUvGjBnDsmXLCA0NxcfHB7lczqlTp2jXrh3jxo0DwN7enqNHj7J69WrMzc2xtLTE0dGxxvEJQl1iY2rArex80u7mP7mwILxgIskTaoXx48djbW1NbGws0dHRyGQyLCws8PHxURn2nDp1Krq6umzdupXDhw/j5OTEqlWrGDp0qMrQ47hx43jw4AE7duzgu+++45VXXmHZsmUsXLhQ5bqampp88sknLFiwgFmzZlFUVMS8efMqTfL69OnDrFmzuH//Pn5+fmXOm5ubs3XrVmJiYkhMTOT27dsYGxtja2vLpEmTqtwmNjY2REVFsXjxYj755BNMTU0JCQnBxMSE9957r8r1lGfOnDnY29uzZcsWFi5ciLa2NpaWlgQFBWFnZyeVe+edd7C0tGTDhg189tln6Ovr0759e1xdXaUy06dP57///S+LFy/m4cOHBAQEiCRPeGnYmDXg6KXbpN8RSZ5Q+8hKnmYWuiDUAtnZ2bi6ujJ58uQyGxULdYNyhZiTk9Mzqa8+rqKrCdEOz78NVh26zJydZ7FupMOFqJEA/Pzzz2J1bS1UH9vgSb87xZw8oU55+PBhmWNr164FwM3N7UWHIwjCS87GrPSetdfuFYBM/EkVahcxXCvUKTt27ODbb7/Fy8sLPT09jh8/zq5du/D09JRuS1YXPGlOna6ubo32wBME4cVqbVp6p5siRQkYmELOX2qOSBD+IZI8oU6xs7Nj586drFixgvv372NqasrIkSOle7PWFZ6enpWeDwgI4OOPP35B0QiCUFPNG+mhralBQZECmVETSkSSJ9QiIskT6hQHBwdpeLYuW716daXnxYbEglA3yDVktGyszx+3cpEZNaXk2hl1hyQIEpHkCYIaPO1GyYIg1B42pgalSV7DJuoORRBUiFmigiAIgvAUlIsvMGqq3kAE4TEiyRMEQRCEp2BjVrr4QmYkevKE2kUkeYIgCILwFJQ9eTK9hqClp+ZoBOEfIskTBEEQhKeg3EYFRG+eULuIJO//27v3oKjO+4/jb4LLLRIFb9EAgo5BDREJKRmU6gRovRIv1IJg1aKxpsZorE7QplZjRseMqBUT00ER89OuBsQoxnt1bIkGJv7iZWKqokHFCTW6lYuAgOzvj/zYhoCJGtyjy+c14yT7nHOe891ndtyPzznnWRERkZ+gjYeJtu7O377QfXnyEFHIE2kmqampBAYGUlpaanQpDRQVFREYGEh2dratLTk5mcjISAOrEnEsfm1dAfSErTxUFPJERER+It/6kKeZPHmIaJ08kRZo0aJFWK1Wo8sQcRi2mTzdkycPEc3kibRAJpMJFxcXo8sQcRh+Xt+GPDw78c3NamOLEfl/Cnkizez69eu89tprhISEEB4ezjvvvENNTY1t+9atWxk/fjzh4eEEBQUxdOhQ/va3vzXq59SpU0yaNIkXXniBPn36EBkZydy5cxvsU1VVxYoVK4iKiiIoKIgXX3yRv/zlL9TW1v5gjd+/J6/+vr2MjAzMZjPR0dEEBQURGxvLyZMnGx1/7tw5Xn31VcLCwujTpw+//vWv+eSTT+51qEQcxkVLFQBOrUzE/88Zsv+3yOCKRHS5VqTZvfbaa/j5+TF79myOHTvGunXrqKioYMGCBQCYzWZ69OhBZGQkrVq14tChQyxcuBCr1UpiYiLwbVCcNGkSPj4+vPLKK3h4eFBUVMT+/ftt56mrq2Pq1KmcOHGC+Ph4/P39+eKLL3j//fcpLi5myZIl91z79u3bqaioIC4uDicnJ9auXcv06dM5cOAAJpMJgDNnzpCQkECXLl2YMmUKrq6u5OTk8PLLL7Nu3TrCw8Pva9xu377NmTNn7uvY76uoqLDV2pJpHOwzBpaKWtbl/9v2uvq2leStJ+nsVIK3x8PxNavPgmOOwe3bt3F2dr7j9ofj0yfiQPz9/UlNTQUgMTERV1dXNm/ezKRJk/D19WXjxo24ubnZ9h83bhyTJk1i/fr1tpD3+eefU1JSwp49e/D29rbtO2vWLNv/79ixg/z8fMxmM8HBwbZ2Hx8fUlJSmDx5Mt27d7+n2ouLi9m3bx+enp4ABAQE8Pvf/57c3FxefPFFABYvXkzXrl3ZsmWLLfiNHTuWUaNGsWLFivsOeSKPquLyGkzOTtTU/fc+11aPOVFcXvPQhDxpmfTpE2lmCQkJDV4nJiaSnZ1Nbm4uY8eObRDwysrKqKmpISwsjNzcXMrKyvD09LSFrP379zNmzBgee6zxnRV79+6lR48e+Pr6YrFYbO31ISs/P/+eQ96wYcNs5wZ4/vnnAbh8+TIAN27cIC8vj1mzZlFWVtbg2IiICDIyMqisrMTd/d5X/Xd2diYwMPCej2tK/b/Um6u/R5XGwT5j4NWlirp9XwP/DXl1QP++Peno6XbH4+xJnwXHHIPjx4//4HaFPJFm5u/v3+TrK1euAHDs2DFSU1M5fvw4lZWVDfatD3lhYWEMGjSI+fPns3z5cl544QUiIyMZOnSo7YGJixcvcv78+TvOnH03+N2tLl26NHjdpk0bANvaf5cuXcJqtZKSkkJKSkqTfdy4ceO+Qp7Io6qjpxuLRz3LvG2nMD32GDV1dSwe9exDE/Ck5VLIE7GjS5cuMXHiRLp160ZycjKdO3fGZDJx+PBhMjIyqKurA8DJyYlVq1Zx4sQJDh48SG5uLm+88Qbp6emYzWYef/xx6urq6N27N3PmzGnyXL6+vvdcX1MzhoBtuZX6+l5++WX69evX5L7fvbws0lKMfs6HiB7tuWypxNfbXQFPHgoKeSLNrLCwkM6dOzd4Dd/Okh08eJDq6mrWrFnTYNYsLy+vyb6Cg4MJDg7m9ddfZ9euXbb/jhkzBj8/PwoKCu4Yth6E+uDo6upq1/OKPAo6erop3MlDRUuoiDSz7y+HsmnTJpycnPj5z39uewrquwsRl5WVsXXr1gbHlJSUNFqsuFevXgBUV3+7BtegQYO4cuUK27Zta1TDzZs3uXXr1k9/M9/Trl07fvazn2E2m5u8HHw/l4hFROTB0EyeSDMrLCxk2rRp9OvXj2PHjvHxxx8TFxeHr68v/fv3x2QyMXXqVOLj47l58yaZmZm0a9eOb775xtbHtm3bMJvNREVF4efnR2VlJZmZmbRu3ZoBAwYAMHLkSHbt2sXcuXP55JNPCAkJoaamhoKCAnbv3k12djZdu3Zt9vc3f/58EhMTGT58OGPGjMHHx4erV69y7Ngxbt26xaZNm5r9nCIicu8U8kSa2apVq1i+fDnLli3D1dWVpKQk29In3bp1Y9WqVaxcuZKlS5fSvn17xo4di7e3N/PmzbP1ERYWxqlTp9i9ezfXrl3D09OTPn368M4779gumTo7O7NmzRrS09PZsWMHe/bs4fHHH8fPz48pU6bQqdOD+Xmlp59+mqysLFJTU8nMzKS0tJT27dvzzDPPMH78+AdyThERuXdOVv2ApYgYrH4ZgL59+zZLf464VML90DhoDOppHBxzDH7s706FPBEx3LFjxwB+cOX2e3H79u1m7e9RpXHQGNTTODjmGNS/p9DQ0Ca363KtiDgcR/pL/KfQOGgM6mkcWuYYaCZPRERExAFpCRURERERB6SQJyIiIuKAFPJEREREHJBCnoiIiIgDUsgTERERcUAKeSIiIiIOSCFPRERExAEp5ImIiIg4IIU8EREREQekkCciIiLigBTyRERERByQQp6IiIiIA2pldAEiIs3h1KlTvP/++5w+fZrr16/j6elJz549mTZtGs8995zR5RkmLS2NZcuW0bNnT7Zv3250OXaRl5fH+PHjm9y2a9cuunfvbueKjHXy5ElWr17N559/Tm1tLb6+vkycOJHRo0cbXZpdJCcns23btjtu/8c//kGnTp3sWJH9KOSJiEO4fPkyt2/fZsyYMXTo0IGysjJycnIYN24caWlp9O/f3+gS7e6bb75hzZo1eHh4GF2KISZMmMAzzzzToM1Rv8zv5PDhw0ybNo2wsDBmzJhBq1atKCws5Ouvvza6NLuJi4sjPDy8QZvVamXBggU89dRTDv2ZUMgTEYcwdOhQhg4d2qBt7NixREdH88EHH7TIkJeSkkJQUBBWq5XS0lKjy7G7sLAwoqOjjS7DMGVlZcydO5f4+HjefPNNo8sxTEhICCEhIQ3aPvvsMyorK4mJiTGoKvvQPXki4rDc3d3x9vZukQHn5MmT7Nixg7lz5xpdiqHKy8upra01ugxD5OTkUFpayowZM4Bvx8JqtRpc1cNh586dODk5MXz4cKNLeaAU8kTEoZSXl2OxWLhw4QLLly/n7NmzjS7VODqr1cqiRYsYOXIkvXr1Mrocw8yZM4fQ0FCCg4NJSkrizJkzRpdkV0ePHqVbt24cPnyYgQMHEhoaSlhYGMuWLeP27dtGl2eYmpoadu/eTUhICD4+PkaX80Dpcq2IOJR58+axd+9eAEwmE/Hx8UydOtXgquzro48+oqCggHfffdfoUgxhMpkYNGgQAwYMwMvLizNnzpCenk5CQgJZWVkEBAQYXaJdXLx4keLiYpKTk5k8eTK9e/fm0KFDpKWlcevWLf74xz8aXaIhcnNzuXHjhsNfqgWFPBFxMNOmTSMuLo7i4mK2b99OdXU1NTU1uLi4GF2aXZSXl5OSksKUKVPo2LGj0eUY4rnnnmvwRHVUVBSRkZHExsayevVqUlJSDKzOfioqKigpKeEPf/gDU6ZMAeCXv/wlFRUVmM1mXnnlFby9vQ2u0v527tyJyWRiyJAhRpfywOlyrYg4lMDAQPr3709sbCzr1q3jiy++aFH3pa1ZswaTycRvf/tbo0t5qPTs2ZPw8HA+/fRTo0uxGzc3N4BG953FxMRQU1PDqVOnjCjLUDdv3uTvf/87EREReHl5GV3OA6eQJyIOy2QyERUVxb59+6iqqjK6nAfu6tWrbNiwgYSEBK5du0ZRURFFRUXcunWLmpoaioqKKCkpMbpMw3Tu3LlFvf8OHToA0L59+wbt9a9b0ljUO3DgQIt4qraeQp6IOLSqqiqsVis3b940upQH7vr169TU1LBs2TKioqJsf06cOMH58+eJiooiLS3N6DINc/ny5RYxe1Ovfo3Af//73w3ai4uLAVrkpdqcnBw8PDyIjIw0uhS70D15IuIQLBZLoy+t8vJy9u7dS+fOnWnXrp1BldmPj49Pkw9brFy5koqKCubNm4e/v7/9C7Ozpj4Ln332GXl5eYwcOdKgquxv8ODBpKWlkZWVxeuvvw58++R1ZmYmHh4e9O3b1+AK7ctisXD06FGGDRuGu7u70eXYhUKeiDiEmTNn4urqSkhICB06dODrr78mOzub4uJili9fbnR5duHp6dnk4r8bNmzA2dm5xSwMPHPmTNzd3QkJCcHLy4tz586xZcsWvLy8mD59utHl2U1QUBAjR47kr3/9K9evX6d3794cPnyY3Nxc5syZQ+vWrY0u0a527dpFbW1ti7lUC+Bk1cqIIuIAsrKy2L59OwUFBZSWluLp6Unfvn1JSkoiLCzM6PIM9Zvf/IbS0tIW89u1H3zwATk5OVy6dIny8nK8vb2JiIhg+vTpdOnSxejy7Kq6upr33nuPjz76iGvXruHj48PEiROJj483ujS7i4uL4/Lly/zzn//E2dnZ6HLsQiFPRERExAHpwQsRERERB6SQJyIiIuKAFPJEREREHJBCnoiIiIgDUsgTERERcUAKeSIiIiIOSCFPRERExAEp5ImISIuWnZ1NYGAgeXl5P9j2oM4l8qDoZ81ERMTu8vLyGD9+fIM2Dw8PAgICGDFiBOPGjXtkf5UgLy+P/Px8JkyYwBNPPGF0OdKCKeSJiIhhhg8fzoABA7BarVy9epVt27axePFiCgoKWLRokWF1jRgxgmHDhmEyme752Pz8fFavXs2oUaMahbyf0q/IvVLIExERw/Tu3ZsRI0bYXickJDBkyBAyMzOZMWMG7du3b3RMTU0NdXV1uLq6PrC6nJ2dH8hM4oPqV6QpuidPREQeGq1btyYkJASr1crly5dJTU0lMDCQc+fOsWTJEgYMGECfPn04fvy47ZgjR46QlJTE888/z7PPPktMTAxms7nJ/j/88EMGDx5MUFAQv/jFL8jIyKCpn3C/071z1dXVpKWlMWLECIKDgwkNDWX06NFs3LgRgOTkZFavXg1AVFQUgYGBBAYGkpqa+oP9WiwWFi5cyMCBAwkKCmLgwIEsXLiQ//znP03WdfToUdatW0d0dDRBQUEMGjSIbdu23eNoi6PTTJ6IiDw0rFYrFy9eBMDLy8vWPnv2bNzc3EhKSgKgQ4cOAGzZsoU///nP9O3bl6lTp+Lu7s6RI0dYsGABly5d4o033rD1kZGRwZIlS+jZsyezZs2isrKS9PR02rVrd1e1VVdXM2nSJPLz84mIiOCll17C1dWVs2fPsm/fPsaNG0dcXBzl5eXs37+fuXPn2t5DYGDgHfstKytj7NixXLx4kdjYWHr37s2XX36J2Wzm008/JTMzk9atWzc4ZsWKFVRVVREXF4eLiwtms5nk5GT8/PwIDQ29q/cjjk8hT0REDFNZWYnFYgHg6tWrbNy4kX/961/07dsXf39/235PPPEE69evp1Wr/35tXb16lbfffpthw4aRkpJia09MTOTtt98mIyODhIQEfH19KS0tZeXKlXTv3p3Nmzfj7u4OQGxsLEOGDLmrWjds2EB+fj6/+93vmDVrVoNtdXV1AISEhBAYGMj+/fuJjo7Gx8fnR/tdu3YthYWFzJ8/n8TERFt7r169eOutt1i7di0zZ85scEx1dTVZWVm4uLgAMHjwYKKioti0aZNCntjocq2IiBgmNTWV8PBwwsPDGTFiBFu3biUyMpJ33323wX4TJkxoEPAA9u7dS3V1Nb/61a+wWCwN/kRGRlJXV8eRI0cAyM3NpbKyksTERFvAA3jyySeJiYm5q1pzcnJo06YN06ZNa7Ttscfu/+t0//79eHt7ExcX16A9Li4Ob29vDhw40OiYhIQEW8AD6NSpEwEBARQWFt53HeJ4NJMnIiKGiYuLY/DgwTg5OeHu7o6/vz9t27ZttN93Z/XqnT9/HoCJEyfesf9r164BUFRUBEC3bt0a7dO9e/e7qvXixYv06tWr2R/4KCoqIigoqFGIbdWqFf7+/pw+fbrRMb6+vo3a2rZty5UrV5q1Nnm0KeSJiIhhunbtSr9+/X50Pzc3t0Zt9Q9MLF26lI4dOzZ5XFNhyBH8lJlDaTkU8kRE5JFUP7vn5eX1o0Gx/t64CxcuEB4e3mBb/Yzg3ZzvwoULVFdXN7hU+n1OTk531V89X19fvvrqK2praxvM5tXW1lJYWOiwQVUePP1TQEREHklDhgzBxcWF1NRUqqqqGm0vKyujuroagP79++Pm5samTZuorKy07VNcXExOTs5dnS8mJoaSkhLee++9Rtu+uwyLh4cHACUlJXfVb3R0NBaLhczMzAbtH374IRaLhejo6LvqR+T7NJMnIiKPpCeffJIFCxbw5ptvMnToUF566SWeeuopLBYLZ8+e5cCBA3z88cf4+PjQpk0bZsyYwdKlS4mPj2fkyJFUVlayefPmO9739n3jx4/n0KFDrFmzhlOnThEREYGLiwsFBQV89dVXZGRkABAcHAzAsmXLiImJwdXVlR49evD000832e/kyZPZs2cPb731FqdPn6ZXr158+eWXZGVlERAQwOTJk5ttzKRlUcgTEZFHVmxsLP7+/qSnp7NlyxbKyspo27YtAQEBzJgxw7aeHkBSUhIeHh6sX7+elJQUOnfuTFJSEp6ensybN+9Hz+Xi4kJ6ejrp6ens3LmT5cuX4+rqSteuXRk9erRtv9DQUGbPns3mzZv505/+RG1tLa+++uodQ56npydms5lVq1Zx8OBBsrOzadeuHfHx8UyfPr3RGnkid8vJ2tRS3yIiIiLySNM9eSIiIiIOSCFPRERExAEp5ImIiIg4IIU8EREREQekkCciIiLigBTyRERERByQQp6IiIiIA1LIExEREXFACnkiIiIiDkghT0RERMQB/R9lWCtGW5R0QQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "def plot_decision(countries, annotate=False):\n",
+ " y = np.arange(0, len(input_features)+1)\n",
+ " x = {c:[base_line] for c in countries}\n",
+ "\n",
+ " pl.plot([base_line]*y.size, y, c='k', label='baseline')\n",
+ " \n",
+ " for feature in input_features[::-1]:\n",
+ " feature_importance_column = 'ml.feature_importance.'+feature\n",
+ " for country in countries:\n",
+ " x[country].append(x[country][-1] + dataset.loc[country, feature_importance_column])\n",
+ " for country in countries:\n",
+ " pl.plot(x[country], y, '.-', label=country)\n",
+ " if annotate:\n",
+ " pl.annotate(\"{:.2f}\".format(x[country][-1]), \n",
+ " xy=(x[country][-1], y[-1]),\n",
+ " xytext=(0,4),\n",
+ " textcoords=\"offset points\", ha='center')\n",
+ " \n",
+ " pl.yticks(ticks=np.arange(len(input_features)+1), labels=['baseline'] + input_features[::-1])\n",
+ " pl.xlim(dataset['ml.life_ladder_prediction'].min(), dataset['ml.life_ladder_prediction'].max())\n",
+ " pl.xlabel(\"Prediction\")\n",
+ " pl.title(\"Decision path\")\n",
+ " pl.legend()\n",
+ " pl.grid(True)\n",
+ " \n",
+ " \n",
+ "pl.Figure(figsize=(400,300))\n",
+ "plot_decision([\"Argentina\"])\n",
+ "if write_images:\n",
+ " pl.savefig(\"images/decision.png\", bbox_inches = \"tight\")\n",
+ "pl.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Feature Importance Summary Barplot"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Since feature importance is computed for individual data points, we, of course, can aggregate those to average\n",
+ "magnitudes over the complete dataset and get a quick summary of which features are, in general, more important than\n",
+ "others."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " feature | \n",
+ " value | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " healthy_life_expectancy_at_birth | \n",
+ " 0.484172 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " log_gdp_per_capita | \n",
+ " 0.223408 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " social_support | \n",
+ " 0.213599 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " positive_affect | \n",
+ " 0.142607 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " freedom_to_make_life_choices | \n",
+ " 0.056362 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " feature value\n",
+ "0 healthy_life_expectancy_at_birth 0.484172\n",
+ "1 log_gdp_per_capita 0.223408\n",
+ "2 social_support 0.213599\n",
+ "3 positive_affect 0.142607\n",
+ "4 freedom_to_make_life_choices 0.056362"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# aggregate the feature importance values\n",
+ "total_feature_importance = feature_importance_df.abs().mean().sort_values(ascending=False).to_frame().reset_index()\n",
+ "total_feature_importance.columns = ['feature', 'value']\n",
+ "total_feature_importance.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "barplot = sns.barplot(data=total_feature_importance, x='value', y='feature', color='b')\n",
+ "barplot.set_xlabel(\"Feature importance avg. magnitude\", fontsize='small')\n",
+ "barplot.set_ylabel(\"\")\n",
+ "barplot.set_title(\"Feature importance summary\")\n",
+ "if write_images:\n",
+ " fig = barplot.get_figure()\n",
+ " fig.savefig(\"images/total_feature_importance.png\", bbox_inches = \"tight\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Feature Importance Distribution Plot"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here, we see a combination of a scatter plot and a violin plot showing the importance values\n",
+ "for individual features. Features with higher overall feature importance tend to have a more extensive spread from\n",
+ "minimum to maximum."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " country_name | \n",
+ " feature | \n",
+ " importance | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " Afghanistan | \n",
+ " log_gdp_per_capita | \n",
+ " -0.593632 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " Afghanistan | \n",
+ " social_support | \n",
+ " -0.480017 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " Afghanistan | \n",
+ " healthy_life_expectancy_at_birth | \n",
+ " -0.782510 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " Afghanistan | \n",
+ " freedom_to_make_life_choices | \n",
+ " -0.126955 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " Afghanistan | \n",
+ " generosity | \n",
+ " -0.068387 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " country_name feature importance\n",
+ "0 Afghanistan log_gdp_per_capita -0.593632\n",
+ "1 Afghanistan social_support -0.480017\n",
+ "2 Afghanistan healthy_life_expectancy_at_birth -0.782510\n",
+ "3 Afghanistan freedom_to_make_life_choices -0.126955\n",
+ "4 Afghanistan generosity -0.068387"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Prepare data in the right format\n",
+ "fi = feature_importance_df.stack().reset_index()\n",
+ "fi.columns = ['country_name', 'feature', 'importance']\n",
+ "fi.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Estimate optimal kernel density bandwidth for the violin plot\n",
+ "from sklearn.model_selection import GridSearchCV, LeaveOneOut\n",
+ "from sklearn.neighbors import KernelDensity\n",
+ "\n",
+ "# We can have only one bandwidth, we choose the best for the most \n",
+ "# important feature\n",
+ "most_important_feature = total_feature_importance['feature'][0]\n",
+ "x = fi[fi.feature==most_important_feature]['importance'].to_numpy()\n",
+ "bandwidths = 10 ** np.linspace(-1, 1, 100)\n",
+ "grid = GridSearchCV(KernelDensity(kernel='gaussian'),\n",
+ " {'bandwidth': bandwidths},\n",
+ " cv=LeaveOneOut())\n",
+ "grid.fit(x.reshape(-1,1));\n",
+ "bw = grid.best_params_['bandwidth']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "