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versae/auto_analysis.ipynb

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auto_analysis.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
},
"colab": {
"name": "auto_analysis.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"include_colab_link": true
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/versae/25d3da73ce4ee3fd430a377f4df6e877/auto_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "U1GECwy-c896",
"colab_type": "text"
},
"source": [
"## Analysis and interactions between variables"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ZhHG0b-Ft7Ws",
"colab_type": "code",
"colab": {}
},
"source": [
"!pip install -qU statsmodels"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "fohUqKAWAegG",
"colab_type": "code",
"colab": {}
},
"source": [
"!pip install -q markdown python-markdown-math"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "K3-A4SdzAbus",
"colab_type": "code",
"colab": {}
},
"source": [
"import IPython\n",
"\n",
"try:\n",
" import google.colab\n",
" def load_mathjax_in_cell_output():\n",
" display(IPython.display.HTML(\n",
" \"<script src='https://www.gstatic.com/external_hosted/\"\n",
" \"mathjax/latest/MathJax.js?config=default'></script>\"))\n",
" get_ipython().events.register('pre_run_cell', load_mathjax_in_cell_output)\n",
"except ImportError:\n",
" pass"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "HyuOdAauAjof",
"colab_type": "code",
"outputId": "e354fd6b-2ec3-4c54-d515-403e6620450c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 72
}
},
"source": [
"try:\n",
" import google.colab\n",
" import IPython\n",
" import markdown as md\n",
" import mdx_math\n",
"\n",
" Markdown = lambda string: IPython.display.HTML(\n",
" md.markdown(string, extensions=[mdx_math.MathExtension(enable_dollar_delimiter=True)])\n",
" )\n",
"except ImportError:\n",
" pass"
],
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: DeprecationWarning: 'etree' is deprecated. Use 'xml.etree.ElementTree' instead.\n",
" \"\"\"\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "sxoveHwXc89-",
"colab_type": "code",
"outputId": "0a62fccd-67d5-4f02-b824-1f0a426e49c8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
}
},
"source": [
"from itertools import combinations, product\n",
"import os\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import statsmodels.api as sm\n",
"\n",
"from scipy import stats\n",
"from scipy.linalg import norm\n",
"from scipy.stats import pearsonr, spearmanr, ttest_ind, ttest_1samp, ranksums, f_oneway, f as F\n",
"from scipy.signal import correlate2d\n",
"from statsmodels.formula.api import ols\n",
"from statsmodels.graphics.api import interaction_plot, abline_plot\n",
"from statsmodels.graphics.gofplots import qqplot\n",
"from statsmodels.stats.libqsturng import psturng\n",
"from statsmodels.stats.multicomp import pairwise_tukeyhsd, MultiComparison\n",
"from statsmodels.stats.multitest import multipletests\n",
"import warnings\n",
"\n",
"from IPython.display import display, HTML # Markdown does not really work\n",
"%matplotlib inline"
],
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9A0-qt0_iRD_",
"colab_type": "code",
"outputId": "e0fffc96-f323-4772-c238-5ad9225ed459",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
}
},
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"execution_count": 6,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "2tIH3TdGjqE1",
"colab_type": "code",
"outputId": "2219fb3e-3eb2-48c8-c648-b2902dd5d2c4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
}
},
"source": [
"participants = pd.read_csv(\n",
" \"/content/drive/My Drive/Notebooks/espe_stats_thesis/participants.csv\"\n",
")\n",
"control = pd.read_csv(\n",
" \"/content/drive/My Drive/Notebooks/espe_stats_thesis/control.csv\"\n",
")"
],
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Abbppynnj_WA",
"colab_type": "code",
"outputId": "82f6e317-8c62-476f-ced0-4aa9b1ee1946",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 826
}
},
"source": [
"assibilated = participants[participants.rhotic==\"ř\"]\n",
"assibilated['stress'] = assibilated['stress'].astype(str)\n",
"assibilated['round'] = assibilated['round'].astype(str)\n",
"assibilated"
],
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" \n",
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" This is separate from the ipykernel package so we can avoid doing imports until\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>participant</th>\n",
" <th>gender</th>\n",
" <th>class</th>\n",
" <th>age</th>\n",
" <th>task</th>\n",
" <th>word</th>\n",
" <th>rhotic</th>\n",
" <th>stress</th>\n",
" <th>stress_change</th>\n",
" <th>position</th>\n",
" <th>round</th>\n",
" <th>duration</th>\n",
" <th>intensity_r</th>\n",
" <th>intensity_vowel</th>\n",
" <th>relative_intensity</th>\n",
" <th>F2</th>\n",
" <th>F3</th>\n",
" <th>COG</th>\n",
" <th>age_range</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>P1</td>\n",
" <td>F</td>\n",
" <td>culto</td>\n",
" <td>[45, 65)</td>\n",
" <td>T1</td>\n",
" <td>rubí</td>\n",
" <td>ř</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>onset</td>\n",
" <td>1</td>\n",
" <td>125.5</td>\n",
" <td>68.1900</td>\n",
" <td>80.6100</td>\n",
" <td>12.4200</td>\n",
" <td>1823.4763</td>\n",
" <td>n</td>\n",
" <td>858.5834</td>\n",
" <td>45-65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>P1</td>\n",
" <td>F</td>\n",
" <td>culto</td>\n",
" <td>[45, 65)</td>\n",
" <td>T3</td>\n",
" <td>riga</td>\n",
" <td>ř</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>onset</td>\n",
" <td>1</td>\n",
" <td>148.5</td>\n",
" <td>81.3700</td>\n",
" <td>85.5800</td>\n",
" <td>4.2100</td>\n",
" <td>1743.2603</td>\n",
" <td>s</td>\n",
" <td>345.9892</td>\n",
" <td>45-65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202</th>\n",
" <td>P1</td>\n",
" <td>F</td>\n",
" <td>culto</td>\n",
" <td>[45, 65)</td>\n",
" <td>T4</td>\n",
" <td>poder</td>\n",
" <td>ř</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>final coda</td>\n",
" <td>2</td>\n",
" <td>211.3</td>\n",
" <td>81.0700</td>\n",
" <td>85.8700</td>\n",
" <td>4.8000</td>\n",
" <td>2010.0564</td>\n",
" <td>n</td>\n",
" <td>322.9013</td>\n",
" <td>45-65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203</th>\n",
" <td>P1</td>\n",
" <td>F</td>\n",
" <td>culto</td>\n",
" <td>[45, 65)</td>\n",
" <td>T4</td>\n",
" <td>pulir</td>\n",
" <td>ř</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>final coda</td>\n",
" <td>2</td>\n",
" <td>236.7</td>\n",
" <td>82.9300</td>\n",
" <td>86.4100</td>\n",
" <td>3.4800</td>\n",
" <td>2000.1542</td>\n",
" <td>s</td>\n",
" <td>210.0935</td>\n",
" <td>45-65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>232</th>\n",
" <td>P1</td>\n",
" <td>F</td>\n",
" <td>culto</td>\n",
" <td>[45, 65)</td>\n",
" <td>T5</td>\n",
" <td>dafer</td>\n",
" <td>ř</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>final coda</td>\n",
" <td>2</td>\n",
" <td>233.4</td>\n",
" <td>84.3600</td>\n",
" <td>86.4600</td>\n",
" <td>2.1000</td>\n",
" <td>2021.2593</td>\n",
" <td>s</td>\n",
" <td>134.2027</td>\n",
" <td>45-65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9258</th>\n",
" <td>P31</td>\n",
" <td>M</td>\n",
" <td>culto</td>\n",
" <td>[45, 65)</td>\n",
" <td>T5</td>\n",
" <td>nerró</td>\n",
" <td>ř</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>intervocalic</td>\n",
" <td>1</td>\n",
" <td>158.2</td>\n",
" <td>53.6228</td>\n",
" <td>62.2294</td>\n",
" <td>-8.6066</td>\n",
" <td>1953.7549</td>\n",
" <td>N</td>\n",
" <td>1647.4220</td>\n",
" <td>45-64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9273</th>\n",
" <td>P31</td>\n",
" <td>M</td>\n",
" <td>culto</td>\n",
" <td>[45, 65)</td>\n",
" <td>T5</td>\n",
" <td>létar</td>\n",
" <td>ř</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>onset</td>\n",
" <td>2</td>\n",
" <td>194.8</td>\n",
" <td>48.0000</td>\n",
" <td>58.0000</td>\n",
" <td>-9.9440</td>\n",
" <td>2241.8905</td>\n",
" <td>N</td>\n",
" <td>2145.0493</td>\n",
" <td>45-64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9275</th>\n",
" <td>P31</td>\n",
" <td>M</td>\n",
" <td>culto</td>\n",
" <td>[45, 65)</td>\n",
" <td>T5</td>\n",
" <td>lirra</td>\n",
" <td>ř</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>final coda</td>\n",
" <td>2</td>\n",
" <td>168.2</td>\n",
" <td>53.0000</td>\n",
" <td>52.0000</td>\n",
" <td>0.7064</td>\n",
" <td>2052.2117</td>\n",
" <td>N</td>\n",
" <td>783.2786</td>\n",
" <td>45-64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9288</th>\n",
" <td>P31</td>\n",
" <td>M</td>\n",
" <td>culto</td>\n",
" <td>[45, 65)</td>\n",
" <td>T5</td>\n",
" <td>nerró</td>\n",
" <td>ř</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>intervocalic</td>\n",
" <td>2</td>\n",
" <td>137.8</td>\n",
" <td>52.0000</td>\n",
" <td>63.0000</td>\n",
" <td>-11.0785</td>\n",
" <td>2032.2105</td>\n",
" <td>N</td>\n",
" <td>2299.7797</td>\n",
" <td>45-64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9291</th>\n",
" <td>P31</td>\n",
" <td>M</td>\n",
" <td>culto</td>\n",
" <td>[45, 65)</td>\n",
" <td>T5</td>\n",
" <td>firrá</td>\n",
" <td>ř</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>onset</td>\n",
" <td>2</td>\n",
" <td>147.6</td>\n",
" <td>54.0000</td>\n",
" <td>62.0000</td>\n",
" <td>-8.6910</td>\n",
" <td>2114.6360</td>\n",
" <td>N</td>\n",
" <td>2101.6276</td>\n",
" <td>45-64</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1043 rows × 19 columns</p>\n",
"</div>"
],
"text/plain": [
" participant gender class age ... F2 F3 COG age_range\n",
"0 P1 F culto [45, 65) ... 1823.4763 n 858.5834 45-65\n",
"112 P1 F culto [45, 65) ... 1743.2603 s 345.9892 45-65\n",
"202 P1 F culto [45, 65) ... 2010.0564 n 322.9013 45-65\n",
"203 P1 F culto [45, 65) ... 2000.1542 s 210.0935 45-65\n",
"232 P1 F culto [45, 65) ... 2021.2593 s 134.2027 45-65\n",
"... ... ... ... ... ... ... .. ... ...\n",
"9258 P31 M culto [45, 65) ... 1953.7549 N 1647.4220 45-64\n",
"9273 P31 M culto [45, 65) ... 2241.8905 N 2145.0493 45-64\n",
"9275 P31 M culto [45, 65) ... 2052.2117 N 783.2786 45-64\n",
"9288 P31 M culto [45, 65) ... 2032.2105 N 2299.7797 45-64\n",
"9291 P31 M culto [45, 65) ... 2114.6360 N 2101.6276 45-64\n",
"\n",
"[1043 rows x 19 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jh-Ct4MgZphU",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 444
},
"outputId": "742ebd15-c0d1-472e-d43a-ed0c121f1fa3"
},
"source": [
"control"
],
"execution_count": 9,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>word</th>\n",
" <th>participant</th>\n",
" <th>tipo</th>\n",
" <th>stress</th>\n",
" <th>position</th>\n",
" <th>duration</th>\n",
" <th>intensity_r</th>\n",
" <th>intensity_vowel</th>\n",
" <th>relative_intensity</th>\n",
" <th>F2</th>\n",
" <th>F3</th>\n",
" <th>COG</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>rubí</td>\n",
" <td>Diego</td>\n",
" <td>real</td>\n",
" <td>0</td>\n",
" <td>onset</td>\n",
" <td>181.4</td>\n",
" <td>88.20</td>\n",
" <td>89.51</td>\n",
" <td>1.31</td>\n",
" <td>2103.0874</td>\n",
" <td>n</td>\n",
" <td>395.8655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ramón</td>\n",
" <td>Diego</td>\n",
" <td>real</td>\n",
" <td>0</td>\n",
" <td>onset</td>\n",
" <td>195.9</td>\n",
" <td>84.77</td>\n",
" <td>86.01</td>\n",
" <td>1.24</td>\n",
" <td>1586.4926</td>\n",
" <td>s</td>\n",
" <td>355.3838</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>rapé</td>\n",
" <td>Diego</td>\n",
" <td>real</td>\n",
" <td>0</td>\n",
" <td>onset</td>\n",
" <td>184.0</td>\n",
" <td>84.55</td>\n",
" <td>86.69</td>\n",
" <td>2.14</td>\n",
" <td>1579.4586</td>\n",
" <td>n</td>\n",
" <td>434.1337</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>robé</td>\n",
" <td>Diego</td>\n",
" <td>real</td>\n",
" <td>0</td>\n",
" <td>onset</td>\n",
" <td>147.9</td>\n",
" <td>83.13</td>\n",
" <td>87.84</td>\n",
" <td>4.71</td>\n",
" <td>2066.6991</td>\n",
" <td>s</td>\n",
" <td>513.4429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>rosé</td>\n",
" <td>Diego</td>\n",
" <td>real</td>\n",
" <td>0</td>\n",
" <td>onset</td>\n",
" <td>167.3</td>\n",
" <td>81.68</td>\n",
" <td>86.14</td>\n",
" <td>4.46</td>\n",
" <td>2068.9170</td>\n",
" <td>s</td>\n",
" <td>476.7154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>raíz</td>\n",
" <td>Diego</td>\n",
" <td>real</td>\n",
" <td>0</td>\n",
" <td>onset</td>\n",
" <td>153.2</td>\n",
" <td>83.77</td>\n",
" <td>87.23</td>\n",
" <td>3.46</td>\n",
" <td>1588.1257</td>\n",
" <td>n</td>\n",
" <td>192.5060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>hablar</td>\n",
" <td>Diego</td>\n",
" <td>real</td>\n",
" <td>1</td>\n",
" <td>final coda</td>\n",
" <td>229.6</td>\n",
" <td>77.71</td>\n",
" <td>86.04</td>\n",
" <td>8.33</td>\n",
" <td>2068.1156</td>\n",
" <td>n</td>\n",
" <td>300.3890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>raíl</td>\n",
" <td>Diego</td>\n",
" <td>real</td>\n",
" <td>0</td>\n",
" <td>onset</td>\n",
" <td>150.0</td>\n",
" <td>84.02</td>\n",
" <td>86.95</td>\n",
" <td>2.93</td>\n",
" <td>1411.6369</td>\n",
" <td>n</td>\n",
" <td>222.2709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>rayón</td>\n",
" <td>Diego</td>\n",
" <td>real</td>\n",
" <td>0</td>\n",
" <td>onset</td>\n",
" <td>150.3</td>\n",
" <td>83.94</td>\n",
" <td>87.25</td>\n",
" <td>3.31</td>\n",
" <td>2000.3198</td>\n",
" <td>n</td>\n",
" <td>216.8329</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>beber</td>\n",
" <td>Diego</td>\n",
" <td>real</td>\n",
" <td>1</td>\n",
" <td>final coda</td>\n",
" <td>265.6</td>\n",
" <td>79.11</td>\n",
" <td>87.36</td>\n",
" <td>8.25</td>\n",
" <td>2241.5551</td>\n",
" <td>s</td>\n",
" <td>292.3172</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>82 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" word participant tipo ... F2 F3 COG\n",
"0 rubí Diego real ... 2103.0874 n 395.8655\n",
"1 ramón Diego real ... 1586.4926 s 355.3838\n",
"2 rapé Diego real ... 1579.4586 n 434.1337\n",
"3 robé Diego real ... 2066.6991 s 513.4429\n",
"4 rosé Diego real ... 2068.9170 s 476.7154\n",
".. ... ... ... ... ... .. ...\n",
"77 raíz Diego real ... 1588.1257 n 192.5060\n",
"78 hablar Diego real ... 2068.1156 n 300.3890\n",
"79 raíl Diego real ... 1411.6369 n 222.2709\n",
"80 rayón Diego real ... 2000.3198 n 216.8329\n",
"81 beber Diego real ... 2241.5551 s 292.3172\n",
"\n",
"[82 rows x 12 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "hhVlc0VsZhSL",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"outputId": "ecbd4bb7-3dc0-46ae-ff74-7f9310c2ce1d"
},
"source": [
"props_to_diff = [\"intensity_r\", \"duration\", \"relative_intensity\", \"F2\", \"COG\"]\n",
"control_dict = control.set_index(\"word\")[props_to_diff].to_dict()"
],
"execution_count": 10,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
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}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "noXKg4C8ciA3",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 521
},
"outputId": "f52e948b-7a01-4760-c55a-224cf74252fc"
},
"source": [
"diffed_props = []\n",
"for prop in props_to_diff:\n",
" assibilated.loc[:, f\"diff_{prop}\"] = assibilated[[\"word\", prop]].apply(\n",
" lambda row: abs(control_dict[prop][row[\"word\"]] - row[prop]),\n",
" axis=1\n",
" )\n",
" diffed_props.append(f\"diff_{prop}\")\n",
"assibilated.head()"
],
"execution_count": 11,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
],
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"<IPython.core.display.HTML object>"
]
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"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:845: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" self.obj[key] = _infer_fill_value(value)\n",
"/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:966: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" self.obj[item] = s\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>participant</th>\n",
" <th>gender</th>\n",
" <th>class</th>\n",
" <th>age</th>\n",
" <th>task</th>\n",
" <th>word</th>\n",
" <th>rhotic</th>\n",
" <th>stress</th>\n",
" <th>stress_change</th>\n",
" <th>position</th>\n",
" <th>round</th>\n",
" <th>duration</th>\n",
" <th>intensity_r</th>\n",
" <th>intensity_vowel</th>\n",
" <th>relative_intensity</th>\n",
" <th>F2</th>\n",
" <th>F3</th>\n",
" <th>COG</th>\n",
" <th>age_range</th>\n",
" <th>diff_intensity_r</th>\n",
" <th>diff_duration</th>\n",
" <th>diff_relative_intensity</th>\n",
" <th>diff_F2</th>\n",
" <th>diff_COG</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>P1</td>\n",
" <td>F</td>\n",
" <td>culto</td>\n",
" <td>[45, 65)</td>\n",
" <td>T1</td>\n",
" <td>rubí</td>\n",
" <td>ř</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>onset</td>\n",
" <td>1</td>\n",
" <td>125.5</td>\n",
" <td>68.19</td>\n",
" <td>80.61</td>\n",
" <td>12.42</td>\n",
" <td>1823.4763</td>\n",
" <td>n</td>\n",
" <td>858.5834</td>\n",
" <td>45-65</td>\n",
" <td>20.01</td>\n",
" <td>55.9</td>\n",
" <td>11.11</td>\n",
" <td>279.6111</td>\n",
" <td>462.7179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>P1</td>\n",
" <td>F</td>\n",
" <td>culto</td>\n",
" <td>[45, 65)</td>\n",
" <td>T3</td>\n",
" <td>riga</td>\n",
" <td>ř</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>onset</td>\n",
" <td>1</td>\n",
" <td>148.5</td>\n",
" <td>81.37</td>\n",
" <td>85.58</td>\n",
" <td>4.21</td>\n",
" <td>1743.2603</td>\n",
" <td>s</td>\n",
" <td>345.9892</td>\n",
" <td>45-65</td>\n",
" <td>3.95</td>\n",
" <td>33.0</td>\n",
" <td>1.67</td>\n",
" <td>408.5168</td>\n",
" <td>62.4619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202</th>\n",
" <td>P1</td>\n",
" <td>F</td>\n",
" <td>culto</td>\n",
" <td>[45, 65)</td>\n",
" <td>T4</td>\n",
" <td>poder</td>\n",
" <td>ř</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>final coda</td>\n",
" <td>2</td>\n",
" <td>211.3</td>\n",
" <td>81.07</td>\n",
" <td>85.87</td>\n",
" <td>4.80</td>\n",
" <td>2010.0564</td>\n",
" <td>n</td>\n",
" <td>322.9013</td>\n",
" <td>45-65</td>\n",
" <td>6.29</td>\n",
" <td>14.8</td>\n",
" <td>7.96</td>\n",
" <td>426.8812</td>\n",
" <td>984.9650</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203</th>\n",
" <td>P1</td>\n",
" <td>F</td>\n",
" <td>culto</td>\n",
" <td>[45, 65)</td>\n",
" <td>T4</td>\n",
" <td>pulir</td>\n",
" <td>ř</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>final coda</td>\n",
" <td>2</td>\n",
" <td>236.7</td>\n",
" <td>82.93</td>\n",
" <td>86.41</td>\n",
" <td>3.48</td>\n",
" <td>2000.1542</td>\n",
" <td>s</td>\n",
" <td>210.0935</td>\n",
" <td>45-65</td>\n",
" <td>7.69</td>\n",
" <td>48.4</td>\n",
" <td>9.06</td>\n",
" <td>555.6025</td>\n",
" <td>969.2970</td>\n",
" </tr>\n",
" <tr>\n",
" <th>232</th>\n",
" <td>P1</td>\n",
" <td>F</td>\n",
" <td>culto</td>\n",
" <td>[45, 65)</td>\n",
" <td>T5</td>\n",
" <td>dafer</td>\n",
" <td>ř</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>final coda</td>\n",
" <td>2</td>\n",
" <td>233.4</td>\n",
" <td>84.36</td>\n",
" <td>86.46</td>\n",
" <td>2.10</td>\n",
" <td>2021.2593</td>\n",
" <td>s</td>\n",
" <td>134.2027</td>\n",
" <td>45-65</td>\n",
" <td>7.16</td>\n",
" <td>8.1</td>\n",
" <td>8.32</td>\n",
" <td>402.3354</td>\n",
" <td>870.6699</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" participant gender class ... diff_relative_intensity diff_F2 diff_COG\n",
"0 P1 F culto ... 11.11 279.6111 462.7179\n",
"112 P1 F culto ... 1.67 408.5168 62.4619\n",
"202 P1 F culto ... 7.96 426.8812 984.9650\n",
"203 P1 F culto ... 9.06 555.6025 969.2970\n",
"232 P1 F culto ... 8.32 402.3354 870.6699\n",
"\n",
"[5 rows x 24 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hEhVyzbYc8_W",
"colab_type": "text"
},
"source": [
"### Analysis (auto)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "3dpF5kdic8_Y",
"colab_type": "code",
"outputId": "9b5c0b21-fbc3-437d-9d07-4a1bbb1309ed",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
}
},
"source": [
"def info(text):\n",
" display(HTML(\"\"\"<div class=\"bg-info\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(10, 148, 170);\">{}</div>\"\"\".format(text)))\n",
"\n",
"def success(text):\n",
" display(HTML(\"\"\"<div class=\"bg-success\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(25, 142, 51);\">{}</div>\"\"\".format(text)))\n",
"\n",
"def danger(text):\n",
" display(HTML(\"\"\"<div class=\"bg-danger\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(149, 15, 28);\">{}</div>\"\"\".format(text)))\n",
"\n",
"def report(text, result=True):\n",
" if result:\n",
" success(text)\n",
" else:\n",
" danger(text)"
],
"execution_count": 12,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
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"<IPython.core.display.HTML object>"
]
},
"metadata": {
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},
{
"cell_type": "code",
"metadata": {
"id": "tjwSJAydc8_f",
"colab_type": "code",
"outputId": "38e20fea-338d-4ded-c3b0-a956b2700b15",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
}
},
"source": [
"def tukey_scheffe_summary_pvalues(model, summary, alpha):\n",
" pvalues = psturng(np.abs(summary.meandiffs / summary.std_pairs), len(summary.groupsunique), summary.df_total)\n",
" groups = np.array(list(combinations(summary.groupsunique, 2)))\n",
" t_stats = summary.meandiffs / summary.std_pairs / np.sqrt(2)\n",
" k = len(summary.groupsunique) # model.df_model\n",
" v = model.df_resid\n",
" scheffe_pvalues = 1 - F.cdf((t_stats ** 2) / (k - 1), k - 1, v)\n",
" counts = pd.Series(summary.groups).value_counts()\n",
" critical_values = [np.sqrt((k - 1) * model.fvalue * model.mse_total * (1.0 / counts[g1]) + (1.0 / counts[g2]))\n",
" for (g1, g2) in groups]\n",
" return (\n",
" pd.DataFrame({\n",
" \"group1\": groups[:,0],\n",
" \"group2\": groups[:,1],\n",
" \"meandiff\": summary.meandiffs,\n",
" \"lower\": summary.confint[:,0],\n",
" \"upper\": summary.confint[:,1],\n",
" \"critical\": (summary.confint[:,1] - summary.confint[:,0]) / 2.0,\n",
" \"reject\": summary.reject,\n",
" \"pval\": pvalues,\n",
" \"scheffe_lower\": summary.meandiffs - critical_values,\n",
" \"scheffe_upper\": summary.meandiffs + critical_values,\n",
" \"scheffe_critical\": critical_values,\n",
" \"scheffe_reject\": scheffe_pvalues < alpha,\n",
" \"scheffe_pval\": scheffe_pvalues,\n",
" })[[\"group1\", \"group2\", \"meandiff\", \"lower\", \"upper\", \"critical\", \"reject\", \"pval\",\n",
" \"scheffe_lower\", \"scheffe_upper\", \"scheffe_critical\", \"scheffe_reject\", \"scheffe_pval\"]],\n",
" pvalues,\n",
" scheffe_pvalues)"
],
"execution_count": 13,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
],
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"<IPython.core.display.HTML object>"
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},
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]
},
{
"cell_type": "code",
"metadata": {
"id": "6fzuA1b1c8_n",
"colab_type": "code",
"outputId": "684fc5d9-1360-4b76-988f-55655febd5b4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
}
},
"source": [
"# From https://gist.github.com/alimuldal/21b4442b7e7f36f6a17b\n",
"\n",
"def kw_nemenyi(groups, to_compare=None, alpha=0.05, method='tukey'):\n",
" \"\"\"\n",
" Kruskal-Wallis 1-way ANOVA with Nemenyi's multiple comparison test\n",
"\n",
" Arguments:\n",
" ---------------\n",
" groups: sequence\n",
" arrays corresponding to k mutually independent samples from\n",
" continuous populations\n",
"\n",
" to_compare: sequence\n",
" tuples specifying the indices of pairs of groups to compare, e.g.\n",
" [(0, 1), (0, 2)] would compare group 0 with 1 & 2. by default, all\n",
" possible pairwise comparisons between groups are performed.\n",
"\n",
" alpha: float\n",
" family-wise error rate used for correcting for multiple comparisons\n",
" (see statsmodels.stats.multitest.multipletests for details)\n",
"\n",
" method: string\n",
" the null distribution of the test statistic used to determine the\n",
" corrected p-values for each pair of groups, can be either \"tukey\"\n",
" (studentized range) or \"chisq\" (Chi-squared). the \"chisq\" method will\n",
" correct for tied ranks.\n",
"\n",
" Returns:\n",
" ---------------\n",
" H: float\n",
" Kruskal-Wallis H-statistic\n",
"\n",
" p_omnibus: float\n",
" p-value corresponding to the global null hypothesis that the medians of\n",
" the groups are all equal\n",
"\n",
" p_corrected: float array\n",
" corrected p-values for each pairwise comparison, corresponding to the\n",
" null hypothesis that the pair of groups has equal medians. note that\n",
" these are only meaningful if the global null hypothesis is rejected.\n",
"\n",
" reject: bool array\n",
" True for pairs where the null hypothesis can be rejected for the given\n",
" alpha\n",
"\n",
" Reference:\n",
" ---------------\n",
"\n",
" \"\"\"\n",
" # omnibus test (K-W ANOVA)\n",
" # -------------------------------------------------------------------------\n",
" if method is None:\n",
" method = 'chisq'\n",
" elif method not in ('tukey', 'chisq'):\n",
" raise ValueError('method must be either \"tukey\" or \"chisq\"')\n",
" groups = [np.array(gg) for gg in groups]\n",
" k = len(groups)\n",
" n = np.array([len(gg) for gg in groups])\n",
" if np.any(n < 5):\n",
" warnings.warn(\"Sample sizes < 5 are not recommended (K-W test assumes \"\n",
" \"a chi square distribution)\")\n",
" allgroups = np.concatenate(groups)\n",
" N = len(allgroups)\n",
" ranked = stats.rankdata(allgroups)\n",
" # correction factor for ties\n",
" T = stats.tiecorrect(ranked)\n",
" if T == 0:\n",
" raise ValueError('All numbers are identical in kruskal')\n",
" # sum of ranks for each group\n",
" j = np.insert(np.cumsum(n), 0, 0)\n",
" R = np.empty(k, dtype=np.float)\n",
" for ii in range(k):\n",
" R[ii] = ranked[j[ii]:j[ii + 1]].sum()\n",
" # the Kruskal-Wallis H-statistic\n",
" H = (12. / (N * (N + 1.))) * ((R ** 2.) / n).sum() - 3 * (N + 1)\n",
" # apply correction factor for ties\n",
" H /= T\n",
" df_omnibus = k - 1\n",
" p_omnibus = stats.chi2.sf(H, df_omnibus)\n",
" # multiple comparisons\n",
" # -------------------------------------------------------------------------\n",
" # by default we compare every possible pair of groups\n",
" if to_compare is None:\n",
" to_compare = tuple(combinations(range(k), 2))\n",
" ncomp = len(to_compare)\n",
" dif = np.empty(ncomp, dtype=np.float)\n",
" B = np.empty(ncomp, dtype=np.float)\n",
" Rmean = R / n\n",
" A = N * (N + 1) / 12.\n",
" for pp, (ii, jj) in enumerate(to_compare):\n",
" # absolute difference of mean ranks\n",
" dif[pp] = np.abs(Rmean[ii] - Rmean[jj])\n",
" B[pp] = (1. / n[ii]) + (1. / n[jj])\n",
" if method == 'tukey':\n",
" # p-values obtained from the upper quantiles of the studentized range\n",
" # distribution\n",
" qval = dif / np.sqrt(A * B)\n",
" p_corrected = psturng(qval * np.sqrt(2), k, 1E6)\n",
" elif method == 'chisq':\n",
" # p-values obtained from the upper quantiles of the chi-squared\n",
" # distribution\n",
" chi2 = (dif ** 2.) / (A * B)\n",
" p_corrected = stats.chi2.sf(chi2 * T, k - 1)\n",
" reject = p_corrected <= alpha\n",
" return H, p_omnibus, p_corrected, reject\n",
"\n",
"\n",
"def kw_dunn(groups, to_compare=None, alpha=0.05, method='bonf'):\n",
" \"\"\"\n",
" Kruskal-Wallis 1-way ANOVA with Dunn's multiple comparison test\n",
"\n",
" Arguments:\n",
" ---------------\n",
" groups: sequence\n",
" arrays corresponding to k mutually independent samples from\n",
" continuous populations\n",
"\n",
" to_compare: sequence\n",
" tuples specifying the indices of pairs of groups to compare, e.g.\n",
" [(0, 1), (0, 2)] would compare group 0 with 1 & 2. by default, all\n",
" possible pairwise comparisons between groups are performed.\n",
"\n",
" alpha: float\n",
" family-wise error rate used for correcting for multiple comparisons\n",
" (see statsmodels.stats.multitest.multipletests for details)\n",
"\n",
" method: string\n",
" method used to adjust p-values to account for multiple corrections (see\n",
" statsmodels.stats.multitest.multipletests for options)\n",
"\n",
" Returns:\n",
" ---------------\n",
" H: float\n",
" Kruskal-Wallis H-statistic\n",
"\n",
" p_omnibus: float\n",
" p-value corresponding to the global null hypothesis that the medians of\n",
" the groups are all equal\n",
"\n",
" Z_pairs: float array\n",
" Z-scores computed for the absolute difference in mean ranks for each\n",
" pairwise comparison\n",
"\n",
" p_corrected: float array\n",
" corrected p-values for each pairwise comparison, corresponding to the\n",
" null hypothesis that the pair of groups has equal medians. note that\n",
" these are only meaningful if the global null hypothesis is rejected.\n",
"\n",
" reject: bool array\n",
" True for pairs where the null hypothesis can be rejected for the given\n",
" alpha\n",
"\n",
" Reference:\n",
" ---------------\n",
" Gibbons, J. D., & Chakraborti, S. (2011). Nonparametric Statistical\n",
" Inference (5th ed., pp. 353-357). Boca Raton, FL: Chapman & Hall.\n",
"\n",
" \"\"\"\n",
" # omnibus test (K-W ANOVA)\n",
" # -------------------------------------------------------------------------\n",
" groups = [np.array(gg) for gg in groups]\n",
" k = len(groups)\n",
" n = np.array([len(gg) for gg in groups])\n",
" if np.any(n < 5):\n",
" warnings.warn(\"Sample sizes < 5 are not recommended (K-W test assumes \"\n",
" \"a chi square distribution)\")\n",
" allgroups = np.concatenate(groups)\n",
" N = len(allgroups)\n",
" ranked = stats.rankdata(allgroups)\n",
" # correction factor for ties\n",
" T = stats.tiecorrect(ranked)\n",
" if T == 0:\n",
" raise ValueError('All numbers are identical in kruskal')\n",
" # sum of ranks for each group\n",
" j = np.insert(np.cumsum(n), 0, 0)\n",
" R = np.empty(k, dtype=np.float)\n",
" for ii in range(k):\n",
" R[ii] = ranked[j[ii]:j[ii + 1]].sum()\n",
" # the Kruskal-Wallis H-statistic\n",
" H = (12. / (N * (N + 1.))) * ((R ** 2.) / n).sum() - 3 * (N + 1)\n",
" # apply correction factor for ties\n",
" H /= T\n",
" df_omnibus = k - 1\n",
" p_omnibus = stats.chi2.sf(H, df_omnibus)\n",
" # multiple comparisons\n",
" # -------------------------------------------------------------------------\n",
" # by default we compare every possible pair of groups\n",
" if to_compare is None:\n",
" to_compare = tuple(combinations(range(k), 2))\n",
" ncomp = len(to_compare)\n",
" Z_pairs = np.empty(ncomp, dtype=np.float)\n",
" p_uncorrected = np.empty(ncomp, dtype=np.float)\n",
" Rmean = R / n\n",
" for pp, (ii, jj) in enumerate(to_compare):\n",
" # standardized score\n",
" Zij = (np.abs(Rmean[ii] - Rmean[jj]) /\n",
" np.sqrt((1. / 12.) * N * (N + 1) * (1. / n[ii] + 1. / n[jj])))\n",
" Z_pairs[pp] = Zij\n",
" # corresponding p-values obtained from upper quantiles of the standard\n",
" # normal distribution\n",
" p_uncorrected = stats.norm.sf(Z_pairs) * 2.\n",
" # correction for multiple comparisons\n",
" reject, p_corrected, alphac_sidak, alphac_bonf = multipletests(\n",
" p_uncorrected, method=method\n",
" )\n",
" return H, p_omnibus, Z_pairs, p_corrected, reject"
],
"execution_count": 14,
"outputs": [
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"id": "7NRTfU-Zeesu",
"colab_type": "code",
"outputId": "11ed6693-85c1-4a00-9308-66731ec7fad6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
}
},
"source": [
"import warnings; warnings.filterwarnings(\"ignore\")\n",
"from itertools import combinations\n",
"from statsmodels.stats.diagnostic import normal_ad\n",
"from statsmodels.stats.diagnostic import lilliefors\n",
"from statsmodels.stats.libqsturng import psturng\n",
"from statsmodels.stats.weightstats import ttest_ind as stats_ttest_ind\n",
"from statsmodels.graphics.gofplots import qqplot\n",
"from scipy.spatial.distance import chebyshev\n",
"from scipy.stats import kruskal\n",
"from scipy.stats import ks_2samp\n",
"from scipy.stats import levene\n",
"from scipy.stats import mannwhitneyu\n",
"from scipy.stats import norm\n",
"from scipy.stats import obrientransform\n",
"from scipy.stats import shapiro\n",
"\n",
"def outliers(y, threshold=3.5, c=norm().ppf(3/4.)):\n",
" # warning: this function does not check for NAs\n",
" # nor does it address issues when \n",
" # more than 50% of your data have identical values\n",
" m = np.median(y)\n",
" abs_dev = np.abs(y - m)\n",
" left_mad = np.median(abs_dev[y <= m])\n",
" right_mad = np.median(abs_dev[y >= m])\n",
" y_mad = left_mad * np.ones(len(y))\n",
" y_mad[y > m] = right_mad\n",
" modified_z_score = c * abs_dev / y_mad\n",
" modified_z_score[y == m] = 0\n",
" return modified_z_score > threshold"
],
"execution_count": 15,
"outputs": [
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"source": [
"def auto_h0(df, independent_variables, dependent_variables, alpha=0.05):\n",
" for indepen in independent_variables:\n",
" display(Markdown(\"# {}\".format(indepen)))\n",
" for depen in dependent_variables:\n",
" msgs = []\n",
" display(Markdown(\"- ### {}\".format(depen)))\n",
" series = []\n",
" # Outliers\n",
" outs = {}\n",
" series_length = 0\n",
" for k in df[indepen].unique():\n",
" serie = df[df[indepen] == k][depen]\n",
" serie_outliers = outliers(serie)\n",
" if np.any(serie_outliers):\n",
" outs[k] = serie[serie_outliers]\n",
" serie = serie[~serie_outliers] # keep only not outliers\n",
" df[df[indepen] == k][depen] = df[df[indepen] == k][depen][~serie_outliers]\n",
" # normalize\n",
" # serie = serie / np.linalg.norm(serie)\n",
" # serie = (serie - serie.mean()) / serie.std()\n",
" series.append((k, serie))\n",
" series_length += len(serie)\n",
" series_keys = [k for (k, _) in series]\n",
" series_values = [v for (_, v) in series]\n",
" display(Markdown(\"*Outliers*\"))\n",
" if outs:\n",
" display(pd.DataFrame({\"outliers\": pd.DataFrame(outs).count()}))\n",
" display(pd.DataFrame(outs).fillna(\"\"))\n",
" outlier_ratio = 100 * pd.DataFrame(outs).count().sum() / series_length\n",
" else:\n",
" display(Markdown(\"**No outliers detected**\"))\n",
" outlier_ratio = 0\n",
" series_count = len(series)\n",
" # Normality\n",
" norms = {}\n",
" is_normal = True\n",
" qq_fig, qq_axes = plt.subplots(nrows=1, ncols=series_count, figsize=(18, 6))\n",
" h_fig, h_ax = plt.subplots(nrows=1, ncols=1, figsize=(18, 6))\n",
" ch_fig, ch_axes = plt.subplots(nrows=1, ncols=series_count, figsize=(18, 6))\n",
" for i, (var, serie) in enumerate(series):\n",
" l_stat, l_pval = lilliefors(serie)\n",
" s_stat, s_pval = shapiro(serie)\n",
" ad_stat, ad_pval = normal_ad(serie)\n",
" norms[var] = {\n",
" (\"\", \"N\"): len(serie),\n",
" (\"Kolmogorov-Smirnov*\", \"Stat.\"): l_stat,\n",
" (\"Kolmogorov-Smirnov*\", \"Sig.\"): l_pval,\n",
" (\"Kolmogorov-Smirnov*\", \"Normal\"): l_pval > alpha,\n",
" (\"Shapiro-Wilk\", \"Stat.\"): s_stat,\n",
" (\"Shapiro-Wilk\", \"Sig.\"): s_pval,\n",
" (\"Shapiro-Wilk\", \"Normal\"): s_pval > alpha,\n",
" (\"Anderson-Darling\", \"Stat.\"): ad_stat,\n",
" (\"Anderson-Darling\", \"Sig.\"): ad_pval,\n",
" (\"Anderson-Darling\", \"Normal\"): ad_pval > alpha,\n",
" }\n",
" # assesing normality by Shapiro-Wilkinson only\n",
" is_normal = is_normal and s_pval > alpha\n",
" qq_ax = qq_axes.flat[i]\n",
" qq_ax.set_title(\"{} = {}\".format(indepen, var))\n",
" qqplot(serie, line='s', ax=qq_ax)\n",
" ch_ax = ch_axes.flat[i]\n",
" sns.distplot(serie, rug=True, ax=h_ax, label=var)\n",
" sns.distplot(serie, rug=True, ax=ch_ax, label=var)\n",
" ch_ax.set_title(var)\n",
" for dot in qq_fig.findobj(lambda x: hasattr(x, 'get_color') and x.get_color() == 'b'):\n",
" dot.set_alpha(0.33)\n",
" qq_fig.tight_layout()\n",
" h_fig.tight_layout()\n",
" ch_fig.tight_layout()\n",
" display(Markdown(\"*Normality (&ast;Lilliefors correction)*\"))\n",
" norms_df = pd.DataFrame.from_dict(norms)[series_keys].T\n",
" display(norms_df)\n",
" display(qq_fig)\n",
" plt.close()\n",
" # Sizes\n",
" is_same_size = len(set(norms_df[(\"\", \"N\")].values)) == 1\n",
" # Shapes\n",
" chebydist_dict = {}\n",
" ksdists_dict = {}\n",
" distances_dict = {}\n",
" is_same_shape = True\n",
" is_good_ratio = True\n",
" distances_dict = {}\n",
" ratios_dict = {}\n",
" for (s1, s2) in combinations(series, 2):\n",
" hist_1, bin_edges_1 = np.histogram(s1[1], bins=10, density=True)\n",
" cdf_1 = np.cumsum(hist_1 * np.diff(bin_edges_1))\n",
" hist_2, bin_edges_2 = np.histogram(s2[1], bins=10, density=True)\n",
" cdf_2 = np.cumsum(hist_2 * np.diff(bin_edges_2))\n",
" key = \"{}, {}\".format(s1[0], s2[0])\n",
" chebyshev_dist = chebyshev(cdf_1, cdf_2)\n",
" ks2s_stat, ks2s_pval = tuple(ks_2samp(s1[1], s2[1]))\n",
" distances_dict.update({key: {\n",
" (\"Chebyshev\", \"\"): chebyshev_dist,\n",
" (\"Kolmogorov-Smirnov 2-sample\", \"Sig.\"): ks2s_pval,\n",
" (\"Kolmogorov-Smirnov 2-sample\", \"Stat.\"): ks2s_stat,\n",
" (\"Kolmogorov-Smirnov 2-sample\", \"Eq.\"): ks2s_pval > alpha,\n",
" }})\n",
" is_same_shape = is_same_shape and ks2s_pval > alpha\n",
" if 1.0 * len(s1[1]) / len(s2[1]) > 1.5:\n",
" # display(Markdown(\"*Ratio N({}) / N({}) > 1.5*\".format(s1[0], s2[0])))\n",
" is_same_shape = is_same_shape and False\n",
" ratios_dict.update({\"N({}) / N({})\".format(s1[0], s2[0]): \"> 1.5\"})\n",
" elif 1.0 * len(s2[1]) / len(s1[1]) > 1.5:\n",
" # display(Markdown(\"*Ratio N({}) / N({}) > 1.5*\".format(s2[0], s1[0])))\n",
" is_same_shape = is_same_shape and False\n",
" ratios_dict.update({\"N({}) / N({})\".format(s2[0], s1[0]): \"> 1.5\"})\n",
" else:\n",
" # display(Markdown(\"*Ratio N({}) / N({}) <= 1.5*\".format(s1[0], s2[0])))\n",
" is_same_shape = is_same_shape and True\n",
" ratios_dict.update({\"N({}) / N({})\".format(s1[0], s2[0]): \"<= 1.5\"})\n",
" display(Markdown(\"*Ratios*\"))\n",
" display(pd.DataFrame.from_dict({\"Ratio\": ratios_dict}))\n",
" # Shapes\n",
" display(Markdown(\"*Shapes*\"))\n",
" display(pd.DataFrame.from_dict(distances_dict).T)\n",
" display(h_fig)\n",
" plt.close()\n",
" display(ch_fig)\n",
" plt.close()\n",
" # is_heavy_tailed = ? # how can we know this?\n",
" is_heavy_tailed = False # we assume data is not heavy-tailed\n",
" # Homogeneity of variance\n",
" is_variance_homogeneous = True\n",
" ob_stat, ob_pval = f_oneway(*obrientransform(*series_values))\n",
" variances_dict = {indepen: {\n",
" (\"1-way ANOVA of O'Brien transform\", \"Stat.\"): ob_stat,\n",
" (\"1-way ANOVA of O'Brien transform\", \"Sig.\"): ob_pval,\n",
" (\"1-way ANOVA of O'Brien transform\", \"Eq.\"): ob_pval > alpha,\n",
" }}\n",
" is_variance_homogeneous = ob_pval > alpha\n",
" if is_normal:\n",
" lev_center = \"mean\"\n",
" lev_stat, lev_pval = levene(*series_values, center=lev_center)\n",
" variances_dict[indepen].update({\n",
" (\"Levene's Test for symmetric distributions\", \"Stat.\"): lev_stat,\n",
" (\"Levene's Test for symmetric distributions\", \"Sig.\"): lev_pval,\n",
" (\"Levene's Test for symmetric distributions\", \"Eq.\"): lev_pval > alpha,\n",
" })\n",
" is_variance_homogeneous = is_variance_homogeneous and levs_pval > alpha\n",
" elif is_heavy_tailed:\n",
" lev_center = \"trimmed\"\n",
" levt_stat, levt_pval = levene(*series_values, center=lev_center)\n",
" variances_dict[indepen].update({\n",
" (\"Levene's Test for heavy-tailed distributions\", \"Stat.\"): levt_stat,\n",
" (\"Levene's Test for heavy-tailed distributions\", \"Sig.\"): levt_pval,\n",
" (\"Levene's Test for heavy-tailed distributions\", \"Eq.\"): levt_pval > alpha,\n",
" })\n",
" lev_stat, lev_pval = levt_stat, levt_pval\n",
" is_variance_homogeneous = is_variance_homogeneous and levt_pval > alpha\n",
" else:\n",
" lev_center = \"median\"\n",
" levsk_stat, levsk_pval = levene(*series_values, center=lev_center) # same as Brown-Forsythe test\n",
" variances_dict[indepen].update({\n",
" (\"Levene's Test for skewed (non-normal) distributions\", \"Stat.\"): levsk_stat,\n",
" (\"Levene's Test for skewed (non-normal) distributions\", \"Sig.\"): levsk_pval,\n",
" (\"Levene's Test for skewed (non-normal) distributions\", \"Eq.\"): levsk_pval > alpha,\n",
" })\n",
" lev_stat, lev_pval = levsk_stat, levsk_pval\n",
" is_variance_homogeneous = is_variance_homogeneous and levsk_pval > alpha\n",
" display(Markdown(\"*Equality of Variances*\"))\n",
" display(pd.DataFrame.from_dict(variances_dict).T)\n",
" display(Markdown(\"*Difference of distributions*\"))\n",
" tests_dict = {indepen: {}}\n",
" msgs.append((\n",
" \"Inspection of Q-Q Plots and a Shapiro-Wilkinson test (p = {norm_pval:0.3f}) \"\n",
" \"revealed that {depen} {norm} normally distributed for all groups ({keys}), \"\n",
" \"and there {homog} homogeneity of variance as assessed by \"\n",
" \"Levene's test for equality of variances for {lev_for}s (p = {lev_pval:0.03f}). \".format(\n",
" norm_pval=s_pval,\n",
" depen=depen,\n",
" lev_pval=lev_pval,\n",
" norm=\"was\" if is_normal else \"was not\",\n",
" keys=\", \".join(series_keys),\n",
" homog=\"was\" if is_variance_homogeneous else \"was not\",\n",
" lev_for=lev_center,\n",
" ), True))\n",
" if not is_normal:\n",
" display(pd.DataFrame.from_dict(tests_dict).T)\n",
" msgs.append((\n",
" \"Inspection of histograms / CDF plots and Kolmogorov-Smirnov test reported \"\n",
" \"that groups {shape} a similar shape (p = {ks:0.03f}). \".format(\n",
" shape=\"had\" if is_same_shape else \"did not have\",\n",
" ks=ks2s_pval,\n",
" ), True))\n",
" if series_count == 2:\n",
" # is_normal\n",
" if is_variance_homogeneous:\n",
" # ttest_stat, ttest_pval = ttest_ind(*series_values, equal_var=True)\n",
" ttest_stat, ttest_pval, ttest_df = stats_ttest_ind(*series_values, usevar=\"pooled\")\n",
" tests_dict[indepen].update({\n",
" (\"t-Test (eq. var.)\", \"Stat.\"): ttest_stat,\n",
" (\"t-Test (eq. var.)\", \"Sig.\"): ttest_pval,\n",
" (\"t-Test (eq. var.)\", \"Diff.\"): ttest_pval < alpha,\n",
" })\n",
" else:\n",
" # ttest_stat, ttest_pval = ttest_ind(*series_values, equal_var=False)\n",
" ttest_stat, ttest_pval, ttest_df = stats_ttest_ind(*series_values, usevar=\"unequal\")\n",
" tests_dict[indepen].update({\n",
" (\"t-Test (not eq. var.)\", \"Stat.\"): ttest_stat,\n",
" (\"t-Test (not eq. var.)\", \"Sig.\"): ttest_pval,\n",
" (\"t-Test (not eq. var.)\", \"Diff.\"): ttest_pval < alpha,\n",
" })\n",
" if is_normal:\n",
" display(pd.DataFrame.from_dict(tests_dict).T)\n",
" if ttest_pval >= alpha:\n",
" compare = \"different\"\n",
" else:\n",
" compare = \"higher\" if series_values[0].mean() > series_values[1].mean() else \"lower\"\n",
" msgs.append((\n",
" \"{normality}, an independent t-test{assum} was run on the data with \"\n",
" \"a {ci}% confidence interval (CI) for the mean difference. \"\n",
" \"It was found that {depen} in the {s1_k} group \"\n",
" \"({s1_m:0.3f} ± {s1_d:0.3f}{magnitude}) {sig} significantly {compare} \"\n",
" \"than the {s2_k} group ({s2_m:0.3f} ± {s2_d:0.3f}{magnitude}) \"\n",
" \"(t({df}) = {val:0.3f}, p = {pval:0.3f}) with a difference \"\n",
" \"of {diff:0.3f} ({ci}% CI){magnitude}.\".format(\n",
" normality=\"Therefore\" if is_normal else \"Although normality assumption seemed violated\",\n",
" norm_pval=s_pval,\n",
" depen=depen,\n",
" lev_pval=lev_pval,\n",
" norm=\"was\" if is_normal else \"was not\",\n",
" keys=\", \".join(series_keys),\n",
" homog=\"was\" if is_variance_homogeneous else \"was not\",\n",
" lev_for=lev_center,\n",
" assum=\" not assumming equality of variance\" if not is_variance_homogeneous else \"\",\n",
" ci=int(100 - alpha * 100),\n",
" magnitude=\"\",\n",
" sig=\"was\" if ttest_pval < alpha else \"was not\",\n",
" compare=compare,\n",
" s1_k=series_keys[0],\n",
" s1_m=series_values[0].mean(),\n",
" s1_d=series_values[0].std(),\n",
" s2_k=series_keys[1],\n",
" s2_m=series_values[1].mean(),\n",
" s2_d=series_values[0].std(),\n",
" diff=abs(series_values[0].mean() - series_values[1].mean()),\n",
" val=ttest_stat,\n",
" pval=ttest_pval,\n",
" df=ttest_df,\n",
" ), is_normal and ttest_pval < alpha))\n",
" # not is_normal\n",
" ranksum_stat, ranksum_pval = ranksums(*series_values)\n",
" mann_stat, mann_pval = mannwhitneyu(*series_values, alternative=\"less\")\n",
" tests_dict[indepen].update({\n",
" (\"Mann-Whitney-Wilcoxon RankSum\", \"Stat.\"): ranksum_stat,\n",
" (\"Mann-Whitney-Wilcoxon RankSum\", \"Sig.\"): ranksum_pval,\n",
" (\"Mann-Whitney-Wilcoxon RankSum\", \"Diff.\"): ranksum_pval < alpha,\n",
" (\"Mann-Whitney U-Test\", \"Stat.\"): mann_stat,\n",
" (\"Mann-Whitney U-Test\", \"Sig.\"): mann_pval,\n",
" (\"Mann-Whitney U-Test\", \"Diff.\"): mann_pval < alpha,\n",
" })\n",
" if is_same_shape:\n",
" s1_metric = series_values[0].median()\n",
" s2_metric = series_values[1].median()\n",
" else:\n",
" s1_metric = series_values[0].rank().mean()\n",
" s2_metric = series_values[1].rank().mean()\n",
" compare = \"different\" if mann_pval >= alpha else (\"higher\" if s1_metric > s2_metric else \"lower\")\n",
" msgs.append((\n",
" \"{normality}, a Mann-Whitney U test was run on the data with \"\n",
" \"a {ci}% confidence interval (CI) for the {mannfor} difference. \"\n",
" \"It was found that {depen} in the {s1_k} group \"\n",
" \"({s1_d} = {s1_m:0.3f}{magnitude}) {sig} statistically significantly {compare} \"\n",
" \"than the {s2_k} group ({s2_d} = {s2_m:0.3f}{magnitude}) \"\n",
" \"(U = {val:0.3f}, p = {pval:0.3f}) with a difference \"\n",
" \"of {diff:0.3f} ({ci}% CI){magnitude}.\".format(\n",
" normality=\"Therefore\" if not is_normal else \"Although normality assumption was hold\",\n",
" norm_pval=s_pval,\n",
" depen=depen,\n",
" shape=\"had\" if is_same_shape else \"did not have\",\n",
" ks=ks2s_pval,\n",
" mannfor=\"median\" if is_same_shape else \"mean ranks\",\n",
" lev_pval=lev_pval,\n",
" norm=\"was\" if is_normal else \"was not\",\n",
" keys=\", \".join(series_keys),\n",
" homog=\"was\" if is_variance_homogeneous else \"was not\",\n",
" lev_for=lev_center,\n",
" ci=int(100 - alpha * 100),\n",
" magnitude=\"\",\n",
" sig=\"was\" if mann_pval < alpha else \"was not\",\n",
" compare=compare,\n",
" s1_k=series_keys[0],\n",
" s1_m=s1_metric,\n",
" s1_d=\"Mdn\" if is_same_shape else \"mean rank\", # series_values[0].std(),\n",
" s2_k=series_keys[1],\n",
" s2_m=s2_metric,\n",
" s2_d=\"Mdn\" if is_same_shape else \"mean rank\", # series_values[1].std(),\n",
" diff=abs(s1_metric - s2_metric),\n",
" val=mann_stat,\n",
" pval=mann_pval,\n",
" ), not is_normal and mann_pval < alpha))\n",
" else: # series_count != 2\n",
" anova_stat, anova_pval = f_oneway(*series_values)\n",
" krusk_stat, krusk_pval = kruskal(*series_values)\n",
" krusk_to_compare = tuple(combinations(range(len(series_keys)), 2))\n",
" krusk_stat_, krusk_pval_, krusk_z, krusk_pvals, krusk_reject = kw_dunn(\n",
" series_values, alpha=alpha, to_compare=krusk_to_compare\n",
" )\n",
" krusk_groups = [(series_keys[g1], series_keys[g2]) for g1, g2 in krusk_to_compare]\n",
" if is_normal:\n",
" tests_dict[indepen].update({\n",
" (\"1-way ANOVA\", \"Stat.\"): anova_stat,\n",
" (\"1-way ANOVA\", \"Sig.\"): anova_pval,\n",
" (\"1-way ANOVA\", \"Diff.\"): anova_pval < alpha,\n",
" })\n",
" tests_dict[indepen].update({\n",
" (\"Kruskall-Wallis\", \"Stat.\"): krusk_stat,\n",
" (\"Kruskall-Wallis\", \"Sig.\"): krusk_pval,\n",
" (\"Kruskall-Wallis\", \"Diff.\"): krusk_pval < alpha,\n",
" })\n",
" model = ols(\"{} ~ C({})\".format(depen, indepen), df).fit()\n",
" anova_table = sm.stats.anova_lm(model, typ=2)\n",
" anova_dfs = anova_table[\"df\"]\n",
" anova_f = anova_table[\"F\"][0]\n",
" anova_eta = anova_table['sum_sq'][0] / (anova_table['sum_sq'][0] + anova_table['sum_sq'][1])\n",
" display(pd.DataFrame.from_dict(tests_dict).T)\n",
" violations = \"\"\n",
" if is_normal and not is_variance_homogeneous:\n",
" violations = \"assumption of heteroscedasticity\"\n",
" elif not is_normal and not is_variance_homogeneous:\n",
" violations = \"assumptions of normality and heteroscedasticity\"\n",
" elif not is_normal and is_variance_homogeneous:\n",
" violations = \"assumptions of normality\"\n",
" if is_variance_homogeneous:\n",
" display(anova_table)\n",
" # is_normal and is_variance_homogeneous\n",
" if anova_pval < alpha:\n",
" # Tukey does not work with different series sizes, Scheffé test should used instead\n",
" tukey = pairwise_tukeyhsd(\n",
" endog=df[depen], # Data\n",
" groups=df[indepen], # Groups\n",
" alpha=alpha) # Significance level\n",
" # plt.vlines(x=49.57,ymin=-0.5,ymax=4.5, color=\"red\")\n",
" display(Markdown(\"*Multiple Comparison of Means - Tukey HSD and Scheffe*\"))\n",
" pairwise, tukey_pvals, scheffe_pvals = tukey_scheffe_summary_pvalues(model, tukey, alpha)\n",
" display(pairwise)\n",
" display(tukey.plot_simultaneous())\n",
" plt.close()\n",
" pairwise_pos = []\n",
" pairwise_neg = []\n",
" if len(set(pd.Series(tukey.groups).values)) == 1: # same sizes\n",
" posthoc_test = \"Tukey HSD\"\n",
" posthoc_reject = \"reject\"\n",
" else:\n",
" posthoc_test = \"Scheffe's\"\n",
" posthoc_reject = \"scheffe_reject\"\n",
" pairwise_cols = [\"group1\", \"group2\", \"meandiff\", posthoc_reject, \"pval\"]\n",
" for pair in pairwise[pairwise_cols].sort_values(posthoc_reject, ascending=False).to_dict(\"records\"):\n",
" if pair[posthoc_reject]:\n",
" g1_mean = tukey.data[tukey.groups == pair[\"group1\"]].mean()\n",
" g2_mean = tukey.data[tukey.groups == pair[\"group2\"]].mean()\n",
" g1_std = tukey.data[tukey.groups == pair[\"group1\"]].std()\n",
" g2_std = tukey.data[tukey.groups == pair[\"group2\"]].std()\n",
" pairwise_pos.append(\"statistically significantly {rel} for \"\n",
" \"the {g1} group ({g1_m:0.3f} ± {g1_c:0.3f}{magnitude}) \"\n",
" \"than for \"\n",
" \"the {g2} group ({g2_m:0.3f} ± {g2_c:0.3f}{magnitude}, p={pval:0.3f})\".format(\n",
" rel=\"higher\" if g1_mean > g2_mean else \"lower\",\n",
" g1=pair[\"group1\"], \n",
" g1_m=g1_mean,\n",
" g1_c=g1_std, # pair[\"meandiff\"],\n",
" g2=pair[\"group2\"],\n",
" g2_m=g2_mean,\n",
" g2_c=g2_std, # pair[\"meandiff\"],\n",
" pval=pair[\"pval\"],\n",
" magnitude=\"\",\n",
" ))\n",
" else:\n",
" pairwise_neg.append(\"no statistically significant difference between the \"\n",
" \"{g1} and {g2} groups (p={pval:0.3f})\".format(\n",
" g1=pair[\"group1\"], \n",
" g2=pair[\"group2\"],\n",
" pval=pair[\"pval\"],\n",
" ))\n",
" if pairwise_pos and pairwise_neg:\n",
" pairwise_report = (\"{explain} {test} post hoc test \"\n",
" \"revealed that {depen} was {pos}. There was {neg}.\".format(\n",
" explain=\"A\" if posthoc_reject==\"reject\" else \"Given the different sizes, a\",\n",
" test=posthoc_test,\n",
" depen=depen,\n",
" pos=\", and \".join(pairwise_pos),\n",
" neg=\", and \".join(pairwise_neg)\n",
" ))\n",
" elif pairwise_pos and not pairwise_neg:\n",
" pairwise_report = (\"{explain} post hoc test \"\n",
" \"revealed that {depen} was {pos}.\".format(\n",
" explain=\"A\" if posthoc_reject==\"reject\" else \"Given the different sizes, a\",\n",
" test=posthoc_test,\n",
" depen=depen,\n",
" pos=\", and \".join(pairwise_pos),\n",
" ))\n",
" else:\n",
" pairwise_report = (\"{explain} post hoc test \"\n",
" \"revealed that {depen} there was {neg}.\".format(\n",
" explain=\"A\" if posthoc_reject==\"reject\" else \"Given the different sizes, a\",\n",
" test=posthoc_test,\n",
" depen=depen,\n",
" neg=\", and \".join(pairwise_neg)\n",
" ))\n",
" else:\n",
" pairwise_report = \"\"\n",
" msgs.append((\"{normal} {was} statistically significant difference between groups as determined \"\n",
" \"by one-way ANOVA (F({df}) = {f:0.03f}, p = {pval:0.03f}, η² = {eta:0.03f}). {pairwise}\"\n",
" .format(\n",
" normal=\"Despite violating the {}, there\".format(violations) if violations else \"There\",\n",
" was=\"was a\" if anova_pval < alpha else \"was no\",\n",
" df=\", \".join([str(int(d)) for d in anova_dfs]),\n",
" f=anova_f,\n",
" eta=anova_eta,\n",
" pval=anova_pval,\n",
" depen=depen,\n",
" pairwise=pairwise_report,\n",
" ), is_variance_homogeneous and anova_pval < alpha))\n",
" if not is_variance_homogeneous or not is_normal:\n",
" normality_txt = (\"Since both normality and homogeneity of variance assumptions were\" if not is_normal\n",
" else \"Although normality assumption was hold, since the assumption of homogeneity of variance was\")\n",
" if is_same_shape:\n",
" krusk_vals = [(g, s.median()) for g, s in series]\n",
" else:\n",
" krusk_vals = [(g, s.rank().mean()) for g, s in series]\n",
" krusk_vals_texts = [\"{:0.3f} for {} group\".format(v, g) for (g, v) in krusk_vals]\n",
" if krusk_pval < alpha:\n",
" # Run Dunn's test, Conover-Iman test (more powerfule than Dunn's),\n",
" # Dwass-Steel-Citchlow-Fligner test, or Schaich and Hamerle as post-hoc\n",
" display(Markdown(\"*Multiple Comparison of Means - Dunn with Bonferroni correction*\"))\n",
" display(pd.DataFrame.from_dict({\n",
" \"Group\": [\"{} - {}\".format(g1, g2) for (g1, g2) in krusk_groups],\n",
" \"Z-pairs\": krusk_z,\n",
" \"Sig. (Bonf. corr.)\": krusk_pvals,\n",
" \"Diff.\": krusk_reject, \n",
" }))\n",
" pairwise_pos = []\n",
" for gi, (g1, g2) in enumerate(krusk_to_compare):\n",
" if krusk_reject[gi]:\n",
" g1_mean = series_values[g1].mean()\n",
" g2_mean = series_values[g2].mean()\n",
" g1_std = series_values[g1].std()\n",
" g2_std = series_values[g2].std()\n",
" pairwise_pos.append(\"statistically significantly {rel} for \"\n",
" \"the {g1} group ({g1_m:0.3f} ± {g1_c:0.3f}{magnitude}) \"\n",
" \"than for \"\n",
" \"the {g2} group ({g2_m:0.3f} ± {g2_c:0.3f}{magnitude}, p = {pval:0.3f})\".format(\n",
" rel=\"higher\" if g1_mean > g2_mean else \"lower\",\n",
" g1=series_keys[g1], \n",
" g1_m=g1_mean,\n",
" g1_c=g1_std, # pair[\"meandiff\"],\n",
" g2=series_keys[g2],\n",
" g2_m=g2_mean,\n",
" g2_c=g2_std, # pair[\"meandiff\"],\n",
" pval=krusk_pvals[gi],\n",
" magnitude=\"\",\n",
" ))\n",
" else:\n",
" pairwise_neg.append(\"no statistically significant difference between the \"\n",
" \"{g1} and {g2} groups (p = {pval:0.3f})\".format(\n",
" g1=series_keys[g1], \n",
" g2=series_keys[g2],\n",
" pval=krusk_pvals[gi],\n",
" ))\n",
" pairwise_report = (\"A follow-up Dunn's post-hoc test with Bonferroni correction \"\n",
" \"revealed that {depen}{therep}{pos}.{theren}{neg}.\".format(\n",
" explain=\"A\" if posthoc_reject==\"reject\" else \"Given the different sizes, a\",\n",
" test=posthoc_test,\n",
" depen=depen,\n",
" therep=\" was \" if pairwise_pos else \"\",\n",
" pos=\", and \".join(pairwise_pos),\n",
" theren=\" There was \" if pairwise_neg else \"\",\n",
" neg=\", and \".join(pairwise_neg),\n",
" ))\n",
" else:\n",
" pairwise_report = \"\"\n",
" msgs.append((\n",
" \"{normality} violated, a Kruskal-Wallis H test was run and showed that there \"\n",
" \"{was} a statistically significant difference in {depen} between the different {indepen} groups \"\n",
" \"(H = χ²({df}) = {kw_stat:0.3f}, p = {kw_pval:0.3f}) with a {kw_for} {depen} of {kw_vals}. \"\n",
" \"{pairwise}\".format(\n",
" normality=normality_txt,\n",
" was=\"was\" if krusk_pval < alpha else \"was not\", \n",
" depen=depen,\n",
" indepen=indepen,\n",
" df=series_count - 1,\n",
" kw_stat=krusk_stat,\n",
" kw_pval=krusk_pval,\n",
" kw_for=\"median\" if is_same_shape else \"mean ranks\",\n",
" kw_vals=\" and \".join([\", \".join(krusk_vals_texts[:-1]), krusk_vals_texts[-1]]),\n",
" pairwise=pairwise_report,\n",
" ), krusk_pval < alpha))\n",
" display(Markdown(\"*Results for {} as independent variable, and {} as the dependent one*\".format(indepen, depen)))\n",
" info(\"Distributions {} normally distributed (with {} outliers), \"\n",
" \"distribution shapes are {}, \"\n",
" \"distribution sizes are {} by a factor {} 1.5, \"\n",
" \"and their variances {} homogeneous\".format(\n",
" \"are\" if is_normal else \"are not\",\n",
" \"no\" if not outlier_ratio else \"{:0.03f}%\".format(outlier_ratio),\n",
" \"similar\" if is_same_shape else \"different\",\n",
" *((\"balanced\", \"lower than or equal to\" if is_good_ratio else (\"unbalanced\", \"greater than\"))),\n",
" \"are\" if is_variance_homogeneous else \"are not\",\n",
" ))\n",
" for msg in msgs:\n",
" report(*msg)"
],
"execution_count": 16,
"outputs": [
{
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"cell_type": "markdown",
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"id": "wusAYNhXlCLS",
"colab_type": "text"
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"source": [
"#### Report"
]
},
{
"cell_type": "code",
"metadata": {
"id": "hmTbIiaZg1Tf",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "e141508c-f4f5-4c19-8cbf-ade475c2c4bc"
},
"source": [
"alpha = 0.05\n",
"dependent_variables = ['duration', 'intensity_r', 'relative_intensity', 'F2', 'COG']\n",
"independent_variables = ['gender', 'age', 'stress', 'position']\n",
"df = assibilated.copy()\n",
"auto_h0(df, independent_variables, dependent_variables, alpha)"
],
"execution_count": 17,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
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"data": {
"text/html": [
"<h1>gender</h1>"
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"<IPython.core.display.HTML object>"
]
},
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"output_type": "display_data",
"data": {
"text/html": [
"<ul>\n",
"<li>\n",
"<h3>duration</h3>\n",
"</li>\n",
"</ul>"
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"<IPython.core.display.HTML object>"
]
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"metadata": {
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"output_type": "display_data",
"data": {
"text/html": [
"<p><em>Outliers</em></p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
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"tags": []
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},
{
"output_type": "display_data",
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"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe tbody tr th {\n",
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"\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>outliers</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>F</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M</th>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" outliers\n",
"F 1\n",
"M 5"
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
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" <th></th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1589</th>\n",
" <td></td>\n",
" <td>390.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1590</th>\n",
" <td></td>\n",
" <td>349.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3406</th>\n",
" <td></td>\n",
" <td>361.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6399</th>\n",
" <td>367.8</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>7419</th>\n",
" <td></td>\n",
" <td>404</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8348</th>\n",
" <td></td>\n",
" <td>977</td>\n",
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"text/plain": [
" F M\n",
"1589 390.1\n",
"1590 349.9\n",
"3406 361.1\n",
"6399 367.8 \n",
"7419 404\n",
"8348 977"
]
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"<p><em>Normality (&ast;Lilliefors correction)</em></p>"
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" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">Kolmogorov-Smirnov*</th>\n",
" <th colspan=\"3\" halign=\"left\">Shapiro-Wilk</th>\n",
" <th colspan=\"3\" halign=\"left\">Anderson-Darling</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>N</th>\n",
" <th>Stat.</th>\n",
" <th>Sig.</th>\n",
" <th>Normal</th>\n",
" <th>Stat.</th>\n",
" <th>Sig.</th>\n",
" <th>Normal</th>\n",
" <th>Stat.</th>\n",
" <th>Sig.</th>\n",
" <th>Normal</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>F</th>\n",
" <td>479</td>\n",
" <td>0.0598532</td>\n",
" <td>0.00031045</td>\n",
" <td>False</td>\n",
" <td>0.97246</td>\n",
" <td>7.67706e-08</td>\n",
" <td>False</td>\n",
" <td>2.65709</td>\n",
" <td>1.0489e-06</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M</th>\n",
" <td>558</td>\n",
" <td>0.0719043</td>\n",
" <td>2.97521e-07</td>\n",
" <td>False</td>\n",
" <td>0.962487</td>\n",
" <td>9.86735e-11</td>\n",
" <td>False</td>\n",
" <td>5.29718</td>\n",
" <td>4.34123e-13</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Kolmogorov-Smirnov* ... Anderson-Darling \n",
" N Stat. Sig. ... Stat. Sig. Normal\n",
"F 479 0.0598532 0.00031045 ... 2.65709 1.0489e-06 False\n",
"M 558 0.0719043 2.97521e-07 ... 5.29718 4.34123e-13 False\n",
"\n",
"[2 rows x 10 columns]"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1296x432 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<p><em>Ratios</em></p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>N(F) / N(M)</th>\n",
" <td>&lt;= 1.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Ratio\n",
"N(F) / N(M) <= 1.5"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<p><em>Shapes</em></p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>Chebyshev</th>\n",
" <th colspan=\"3\" halign=\"left\">Kolmogorov-Smirnov 2-sample</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>Eq.</th>\n",
" <th>Sig.</th>\n",
" <th>Stat.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>F, M</th>\n",
" <td>0.0731961</td>\n",
" <td>True</td>\n",
" <td>0.567435</td>\n",
" <td>0.0481065</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Chebyshev Kolmogorov-Smirnov 2-sample \n",
" Eq. Sig. Stat.\n",
"F, M 0.0731961 True 0.567435 0.0481065"
]
},
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}
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 1296x432 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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"<p><em>Results for gender as independent variable, and duration as the dependent one</em></p>"
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"<div class=\"bg-info\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(10, 148, 170);\">Distributions are not normally distributed (with 0.579% outliers), distribution shapes are similar, distribution sizes are balanced by a factor lower than or equal to 1.5, and their variances are homogeneous</div>"
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"<div class=\"bg-success\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(25, 142, 51);\">Inspection of Q-Q Plots and a Shapiro-Wilkinson test (p = 0.000) revealed that duration was not normally distributed for all groups (F, M), and there was homogeneity of variance as assessed by Levene's test for equality of variances for medians (p = 0.682). </div>"
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"<div class=\"bg-success\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(25, 142, 51);\">Inspection of histograms / CDF plots and Kolmogorov-Smirnov test reported that groups had a similar shape (p = 0.567). </div>"
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"<div class=\"bg-danger\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(149, 15, 28);\">Although normality assumption seemed violated, an independent t-test was run on the data with a 95% confidence interval (CI) for the mean difference. It was found that duration in the F group (173.162 ± 48.376) was not significantly different than the M group (174.703 ± 48.376) (t(1035.0) = -0.514, p = 0.607) with a difference of 1.541 (95% CI).</div>"
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"<div class=\"bg-danger\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(149, 15, 28);\">Therefore, a Mann-Whitney U test was run on the data with a 95% confidence interval (CI) for the median difference. It was found that duration in the F group (Mdn = 167.800) was not statistically significantly different than the M group (Mdn = 168.300) (U = 131867.000, p = 0.356) with a difference of 0.500 (95% CI).</div>"
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" <td>1.03438e-13</td>\n",
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" <td>9.45411e-30</td>\n",
" <td>False</td>\n",
" <td>0.899786</td>\n",
" <td>9.76597e-19</td>\n",
" <td>False</td>\n",
" <td>19.6828</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Kolmogorov-Smirnov* ... Anderson-Darling \n",
" N Stat. Sig. ... Stat. Sig. Normal\n",
"F 480 0.113565 2.80664e-16 ... 11.063 1.20495e-26 False\n",
"M 563 0.13991 9.45411e-30 ... 19.6828 0 False\n",
"\n",
"[2 rows x 10 columns]"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1296x432 with 2 Axes>"
]
},
"metadata": {
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}
},
{
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"data": {
"text/html": [
"<p><em>Ratios</em></p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>N(F) / N(M)</th>\n",
" <td>&lt;= 1.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Ratio\n",
"N(F) / N(M) <= 1.5"
]
},
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"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<p><em>Shapes</em></p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
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{
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"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>Chebyshev</th>\n",
" <th colspan=\"3\" halign=\"left\">Kolmogorov-Smirnov 2-sample</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>Eq.</th>\n",
" <th>Sig.</th>\n",
" <th>Stat.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>F, M</th>\n",
" <td>0.0834628</td>\n",
" <td>False</td>\n",
" <td>0.0151467</td>\n",
" <td>0.096196</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Chebyshev Kolmogorov-Smirnov 2-sample \n",
" Eq. Sig. Stat.\n",
"F, M 0.0834628 False 0.0151467 0.096196"
]
},
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}
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 1296x432 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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"<p><em>Results for gender as independent variable, and intensity_r as the dependent one</em></p>"
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"<div class=\"bg-info\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(10, 148, 170);\">Distributions are not normally distributed (with no outliers), distribution shapes are different, distribution sizes are balanced by a factor lower than or equal to 1.5, and their variances are not homogeneous</div>"
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"<div class=\"bg-success\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(25, 142, 51);\">Inspection of Q-Q Plots and a Shapiro-Wilkinson test (p = 0.000) revealed that intensity_r was not normally distributed for all groups (F, M), and there was not homogeneity of variance as assessed by Levene's test for equality of variances for medians (p = 0.010). </div>"
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"<div class=\"bg-success\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(25, 142, 51);\">Inspection of histograms / CDF plots and Kolmogorov-Smirnov test reported that groups did not have a similar shape (p = 0.015). </div>"
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"<div class=\"bg-danger\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(149, 15, 28);\">Although normality assumption seemed violated, an independent t-test not assumming equality of variance was run on the data with a 95% confidence interval (CI) for the mean difference. It was found that intensity_r in the F group (74.253 ± 8.710) was not significantly different than the M group (73.918 ± 8.710) (t(1039.5047440441053) = 0.559, p = 0.576) with a difference of 0.335 (95% CI).</div>"
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"<div class=\"bg-danger\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(149, 15, 28);\">Therefore, a Mann-Whitney U test was run on the data with a 95% confidence interval (CI) for the mean ranks difference. It was found that intensity_r in the F group (mean rank = 240.500) was not statistically significantly different than the M group (mean rank = 282.000) (U = 133001.500, p = 0.331) with a difference of 41.500 (95% CI).</div>"
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" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9171</th>\n",
" <td></td>\n",
" <td>-12.4682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9180</th>\n",
" <td></td>\n",
" <td>-12.9026</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9191</th>\n",
" <td></td>\n",
" <td>-11.2082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9209</th>\n",
" <td></td>\n",
" <td>-11.7611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9220</th>\n",
" <td></td>\n",
" <td>-11.7928</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>66 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" F M\n",
"7274 -11.3189\n",
"7296 -16.4163\n",
"8144 -11.3974\n",
"8153 -12.1681\n",
"8154 -14.1809\n",
"... .. ...\n",
"9171 -12.4682\n",
"9180 -12.9026\n",
"9191 -11.2082\n",
"9209 -11.7611\n",
"9220 -11.7928\n",
"\n",
"[66 rows x 2 columns]"
]
},
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"<p><em>Normality (&ast;Lilliefors correction)</em></p>"
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" <th colspan=\"3\" halign=\"left\">Kolmogorov-Smirnov*</th>\n",
" <th colspan=\"3\" halign=\"left\">Shapiro-Wilk</th>\n",
" <th colspan=\"3\" halign=\"left\">Anderson-Darling</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>N</th>\n",
" <th>Stat.</th>\n",
" <th>Sig.</th>\n",
" <th>Normal</th>\n",
" <th>Stat.</th>\n",
" <th>Sig.</th>\n",
" <th>Normal</th>\n",
" <th>Stat.</th>\n",
" <th>Sig.</th>\n",
" <th>Normal</th>\n",
" </tr>\n",
" </thead>\n",
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" <th>F</th>\n",
" <td>459</td>\n",
" <td>0.0772357</td>\n",
" <td>6.80382e-07</td>\n",
" <td>False</td>\n",
" <td>0.986394</td>\n",
" <td>0.000272272</td>\n",
" <td>False</td>\n",
" <td>2.24445</td>\n",
" <td>1.06824e-05</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M</th>\n",
" <td>518</td>\n",
" <td>0.104417</td>\n",
" <td>9.41423e-15</td>\n",
" <td>False</td>\n",
" <td>0.96673</td>\n",
" <td>1.91183e-09</td>\n",
" <td>False</td>\n",
" <td>6.30921</td>\n",
" <td>1.6542e-15</td>\n",
" <td>False</td>\n",
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"text/plain": [
" Kolmogorov-Smirnov* ... Anderson-Darling \n",
" N Stat. Sig. ... Stat. Sig. Normal\n",
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"M 518 0.104417 9.41423e-15 ... 6.30921 1.6542e-15 False\n",
"\n",
"[2 rows x 10 columns]"
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57gZ1uIaEqnRFRESk1vlTRZnfD489BsOGuQEkN94I48ZBgwbVunYRERERCR+7vem8+hPo2xe+/RaOP961pDnhhN/+JjPT1aHJye558DEzUwGhiIiIyEG3p12C+12UffYZ3H47fPed6xszYwZ06lRt30NEREREws+uN53tyl8oumsILJwDhxzi+gxeeqk7WlxJdrZ7f2VJSa61TbjSkBIRERGpkYIFW0GBK8AKCtxzn88VZUlJO78/Kcld38nate44SLdusGkTPP88fPSRwkERERER+e2mc/2YPDo+P5SLRraj9dJ5LPrnaFi2zNWRnt9Ha+np7gRLZXl57nq40g5CERERqZH2tkswWJQFr8EuRVlxMdxzD0yY4I4WjxgBQ4ZAYmK1fgcRERERCV/Z6wN0XT6bDv8dSlxuNqtPuoKFl97FytKmdNhL2di1q7txDe4mdV4e5OS4oXjhSjsIRUREpEba2y7Brl1dEZab6/pH5+a65127WHjtNWjf3vUa/OtfYckSGDtW4aCIiIiI7PDxx1w5ozPHPnQ9BQ1b8sHEz/mq99NsjGn6hzsBMzJc7+vERHesODExvAeUgHYQioiISA21t12CwaIsM9MVZenpcE6LJTS5oQ+8/z4ccYR7POOM0H0BEREREQk/v/wCgwfDSy9Rt3FT3rz8OTZ0v4Skuoa83H3fCZiREd6B4K4UEIqIiEjY290wkj86uvFbUZaTA2PGwP33Q506MH063HwzREeH7PuIiIiISGjtWl+e2GE7zZ65y7Wh8XphzBi8AwfSYXMCeZVuOp95Zs0K/vaVAkIREREJa7tOj8vLc8979vz9LsGdCja/H554AoYOhc2b4YYbYPx4SEsL6fcRERERkdCqXF82SffT6J3ZpPYaBnnZcOWVMHEiNG0K1LydgH+WAkIREREJK7vezd28ec/DSC65ZA8FW2Ym9O4N33wDJ54I77wDf/lLtX0HEREREQlfwWF3rdd+xFFP9iPll+/Ibt2FzwbP5azhx4V6eSGhISUiIiISNoJ3cwsK3G7BggLXKrC4eOf3BYeR/M66dXDFFS4U3LABnnsOPvlE4aCIiIiI/KZo8UrOeuQiuo/uTuz2zXze9798NOEzliRFZjgI2kEoIiIiYSR4N7fybsFGjWDhwp2bQQeHkfympMT1i5kwAcrK3ITiO+5wPQdFRERERAC2b4eJE7lq6jQCHi8/XDKOn87rjz82gbxc/nA6cW2mgFBERETCRna22zlYWceObhdhbu5uhpFYC/PmQb9+8PPP8Pe/w9Sp0KpVSNYvIiIiImEo2Jt62DDYuJGif17Fs0dMxNv8EJKi2a/pxLWVAkIREREJG+npLgAM7iAEiIuDM86AxMRdhpEULoVz+sK770K7dvDee5Fd1YmIiIjIb4J9rb2ffsjp8/qRsvp76NoV5s2jzrHHco5vL8PuIpACQhEREQkbXbu6HoSw827Bnj0rFWy5uTB2LMyY4VLDadPg1lshOjpk6xYRERGR8OHzwXsP/szp7w2m5bevkJ+awbwrnqfj+IvJaG6AyJlOvK8UEIqIiEjYyMhwYeBu7+YGAjB7Ntx5J2zaBNdf73oONmwY6mWLiIiISLjYvp2C2yZw7dv3YqNcn8Hl5w1gW3E8mVmQ0TzUCwxPCghFREQkrOz2bm5WFvTuDV9/DV26wFtvwTHHhGR9IiIiIhKG/H54/HEYPpx2GzfyyylXs/iyiRSnNgEgKdrdgJbdU0AoIiIi4Wv9ejeN+KmnoEkTeOYZuOwyMCbUKxMRERGRcLFgAfTtCwsXwokn8m7vN1nbqPNOfa3z8iJ7SvEfUUAoIiIiIRNsHp2d7Qq2rl0rdg+WlMC998L48VBa6o4VDx0KdeqEeskiIiIiEi5+/hkGDYJXX4XmzeGFF6BnT9qtMfywm77Wmme3Z55QL0BEREQik8/nBpIUFEDjxu5xzhzY+MSbcOSRbufgaafB4sUwcaLCQRERERFxcnNh8GBo1w7ee8/dVF6yBC6+GIz5ra91YqI7kJKYuMvQO/kd7SAUERGRkMjMhHr1+O3oxyH5yzhrdj8a/vA2HHYYvPMOnH12aBcpIiIiIuHD74fHHoPhw93QumuucUPrmjT53Vs1pXj/KCAUERGRkMjOdjsHvYXbaffSONq8dS/lMQl8fMFUTn7xNoiJCfUSRURERCRcfPAB9Ovn+gx26wZvv62hdQeRAkIREREJifS0AI3eeYrjXrmD2O0bWdX9WjLPm0hUk3RQNigiIiIiACtWuD6Dr73m+gy++CJcdBEYs+d+1rLf1INQREREqt8XX3Dh5C6c/MS1bK/fkv+N/4IPLn+MDTadrl1DvTgRERERCbncXBcMHnEEvP++60m9dKlrJlgRDu6un7XPF+qF10zaQSgiIiLVZ8MGN3zkySeJbdyYzdOe4oOGl5O9yUN6opssp7u+IiIiIhHM74dHH4URI2Dz5h19Bhs33ultu/azDj5mZqqe/DMUEIqIiEjVKy2F6dNh3DgoLoYhQ2DYMBokJXFJqNcmIiIiIuFh/nzXZ3DRIjjpJJg2DY45xh0lfn7no8TBftaVJSW5qcWy/3TEWERERKrWW29Bhw4weDCccgosXgyTJrkKTkRERETkp5/gggvgjDMgL8+dFf7oo9/Cwd0dJY6Kcm+tLC/PBYiy/7SDUERERKrGTz+5O8Bvvglt28Jbb+Frf45rJP2WGkmLiIiIRLo1i3IouHM8bd6ZQSA6loIhd7H9ur5kfhtH9gxXL27evPujxEVFkJPjfk9KcuFgTo5rWSP7L6Q7CI0xjxtjNhpjfqh0bbQxZp0x5vuKnx6hXKOIiIjsp7w8d4S4fXv4+GOYMgUWLcLX/hw1khYRERERKC9n68SHqN+lDYe9dQ+rT76SOeOXM8V7B7OeitupXnz/fdehprKkJNeqsGdPSEx0x4oTE91z3Xz+c0K9g3A2cD/w1C7Xp1lrp1T/ckRERORPCwTgmWdcOLhhA1x7LWtvmcinKxqR/SD8/DM0bapG0iIiIiIRbf586NuX1B9+YH3bk8m8fho5rf5CDLBpuXtLp07uMTkZGjWChQt33hkYPEqckaE68mAJaUBorf3YGNMilGsQERGRA+PzwdKnv6LDI7fTePUXbGhxPB/2e53NrY5jw2vQsqW7A5yVBdu2uTu+aWnub9VIWkRERCRC/PQTDBwIc+dCixa8ee1LFJz9DzxR5re3lJT8/s86dnS7CHNzdZS4KoXrkJLbjDELK44gp+zuDcaYXsaYr40xX2/atKm61yciIiLA2m+y2X7RdZw1/DiSt65i5rGz6dM5ky2tj2PRIli+3A0w9njc3d+oKHctSI2kRURERGq5nBwYMADatycw/wO+v3QS99+6hHfr/JNVq81Ob42NdT+VxcW52SU6Sly1Qn3EeHceBMYBtuJxKnDdrm+y1s4CZgF07tzZVucCRUREIpnPB59/XEqjl+7j2LfH0thfxLLzB/F4k+Fspy6pwIoVLhhMTXWBYFoaHHaYO068YYM7jay7vyIiIiK1WHk5PPIIjBwJW7aQf/F1PH3YeGIyGtEwCQoD8Omn7q0tWrjaMHjKZNfdggoEq17YBYTW2uzg78aYR4B5IVyOiIiI4ELBzExYsgQSP32H6xf1pf6mZXzbuAf/PXYarf7alo1fQkrFvv9t21zPmOLiHdPl0tKgQwdYu9bd/U1Pd+Ggir3IY4x5HDgP2GitPbLi2mjgBiB4NGSotfat0KxQREREDsj770O/frB4MZxyCkybxrxlRxNTsKMPdatW7nHtWrdrMD0devVy1zIzVS9Wt7ALCI0xja21wW5EFwI/7O39IiIiUrV8PjdtuFnJCno+3Z8jf3mDDUltePPmebwfcy45OVC+3BV7RUXub+rVg7ZtYcECFxoGdwxGRbnWMyryIt5sNKhORESk9lm+3BV7b7wBLVuy6aGXmV/3QrI/Nnz7LRx33I6AENzOwdhY6NNn549RrVj9QhoQGmP+C3QHGhhj1gKjgO7GmE64I8argBtDtkAREZEIFtw1uGBuHhf+OIEzF0+jlBjeOOlu3j28D9GxMRzWdsex4W7d4KOP3N+ecgrExLiQsFEj3QGWnWlQnYiISC2zbRuMGwf33UcgLp6Fl07ijZZ9WP5pHB06uCAwLs7ViqeeuuMosfpRh49QTzG+dDeXH6v2hYiIiES4YBiYne12+W3dCt9+Yzk/71nuyhpMavF6Pml1Na8cexfFKY2Jj3d1YOVjw+XlcPLJ7vPKy10D6V69FAjKfrnNGHMV8DUwwFq7LdQLEhERkb0oL4dZs1yfwa1byf/X9TzddhwxGY3YssjVlT/84PoJdurkTpd8/z2cfrr6UYebsDtiLCIiItUreIS4Xj3wet2d3YQfv+bu7N603ZzFj3WOZfrZr7Cx5QmUlkJ5oTtKXK+eayCtY8NykOzToDoAY0wvoBdAhv6HJyIiEhrvvef6DP74ozs+cu+9zFva6bc+g7m5bmBdUZE7eXziie5tX36p0yXhSAGhiIhIhMvMdGFfcjL88sVG+iwaSpelj5Mbk8bzZz3Omw2uZvUaD0dY8PuhfXtYuNC9PzFRhZ0cHPszqM5aOwuYBdC5c2db9asTERGR4ImTkoXLOPmNgbT8YR459VuR+e9XWNnx7/g/2rnPYLA/dfDkCbhjxuecA5dcEtrvIr+ngFBERCTCZWdDk7Qy2sy7nx7PjSbWX8hrLfvzUNoIOhyZTJqF4lKw1v1kZLiiTqGgHEwaVCciIhK+fD6Y++Q2un88lnYf3E+JJ56nO97Nmr/35vslsfCJ2x1Yuc/gYYfB55/vfPJER4rDlwJCERGRCHdU9nt0mtSHehuWsqT5X3nhhGms8B5OsQ8KC10omJrqBo707KlgUA6cBtWJiIiEt8r9qb2UU/e/D3P1dyNJLN3Gx21v4K0TxpKXkM7qr6B5c/c3K1b8vs+gTp7UHAoIRUREIkyw4Cte/DMnv96f7ovmsj6xNc+c/Qbbup7Ljz8YKHUF3Nq1bkLxmWfC+eeroJODQ4PqREREwkewNlyyxB0FNsYNrOvQAQ5f/S5HP9OfZtt/ZGmj7rzU7V5eX3UUR0VD3XjYvBkOP9x9TnCAXeU+gzp5UnMoIBQREYkQPh/MnQufvpPP1esmctGiqZQTzbMdJrH6wr6s3hDLhm/gmGPcjkG/3x0P6dpVRZ2IiIhIbRQcVuf3w8qVbvjczz/D0fFLOWvBADqte4uNdVszquOrZKZdQMcMQ70cWL0aDj0UGjRwR4jBHSMG9RmsqRQQioiI1CKVj4NERblrmza5Iu7XdZbuvz7HA78MJrXoV95Ju5J53SbhT29C3Ha3SzA31x3/UEEnIiIiUrv5fDBliqsVgxOHmyZspcfPY/nHhpmURSfwYKvJrDz3drYVxpLzf679TEYGfPstpKRAly6waJH7vFNOUZ/BmkwBoYiISC2RlQUPPADl5W5aXHbFTNjYWEhd9S2Tf+rNUfmfsbTOMTz8t5eYt6UL9UrgsEqT5ZKS3HEQEREREak9dr2JvHWrC/k2boR27WDdqjLOWv4w/147iviSHN5Iv4Fv/z6Wj5Y05IgyiI6Gjh1dXbltGxx1FHTu7HYennyy+zfKy9VnsCZTQCgiIlIL+HwwcyZ4va73y6JFkJ8P9QObuH7dMM5Z9yi50Q0Y3ewxPmp5DYkBD/XquTu8wclyAHl5kJ4e2u8iIiIiIgemcl/B1avh11+hZUto2tTViT4ftG7tAr26me/wlK8/LQqX8H/1T+X50+/ls7yOtCp279myxX1mcEpxs2YaXFcbKSAUERGp4YLHQ378ERo3dnd4bWkZV259gCtWjCLBFvB6i7482XwkG4rrEYu789uihSv4tmzRkRARERGR2mLXvoI+H5SUQEEBvPeemzocFQV11i7ljpX96bD2bdbGtWboEa8xP/F82qYaTuwIa9a4gDAtzX2udgjWbgoIRUREapjd3RHOz4e6dV3hV/eL93n05760KPyRTxLOYvZR9+Jp346Ni90R4oYN3WTi/Hzo0cP1m1HBJyIiIlLz7a6voMfjasCcHAgEoCx7K4N/HcO5q2ZSFpPIi8dNYVbMbcTXiyWlDFq1cseObxVJM1kAACAASURBVL5ZdWEkUUAoIiJSQwSnEP/vf5CQAMXFbvdfSYk7Wlxn40oGbhhA922vsS6uFQMOfZ35CX+jVSND3TJ3dDg+3gWDF14I55+vok9ERESktgjuHNy40Z0q8flc3eetSH6K88q4MuchLvtyFHX8ubzR6AY+O3ss2+Ma0tpC27Y6OhzJFBCKiIiEsWAouGCB2y0YG+uOBm/c6Ao+gLSEAv618i56+qbgN15mt53IQ/H9aNQijn/8xR0v2bbNHSdp1w66dlXhJyIiIlLbZGa6vtKNGrkbyfXqudMlAB3Wvk1fX38yCpfyVd3TeeH4e0g+qSO5a2HDeneKRDePI5sCQhERkTDl88GsWbB8uTsiUqcOrFrljgN7PFAn0dJ5xfP02zCYhiVr+bz15Uxu8B+iWxzCcQ1h4EAVeSIiIiKRYskSVzOuW+emFaemQrP8Jdy8YgBdc99mQ51DmXDc6/h7/I0W9Q1+P5x6qm4ei6OAUEREJMwEdw2++KLrFdiwodsFWL++6x+Tlwcd/d8xbGlvOuR8ytLEvzCxw/MUH3MiDXQ8RERERCSiBGvHN990PaXbtIH6bOGMz8Zw4YYHKIqqwztnTmX7Vbdx5ckxqhFltxQQioiIhJHKuwb9fjDGHScOBNx04tbJm7l44XAuyZtFrrc+k9vM4vMjriM2IYoCHQ8RERERiRiV+1Pn5cEhh8DW7DKO+N+D3Lh+NPFluSw4tBeHvTCWvx6dFurlSphTQCgiIhICu04izstz1zdvdr0FU1Ndv8GYGBcO2rJyzlr2IH22jiS+PI9XmvXhmVajSGlZjyMz1FtQREREJFLsGgw2auSmFh+T/RZ9fP05JG8ZX9c7g/nn3kNh6w6cfnSoVyw1gQJCERGRapaVBQ884HrErF/vphB7PO5n82b3noQEKC111/6ybT7DNvWhbelivqx7BrPaT6fTZUcwQzsFRURERCJKcFLx8uU7gkHP0h+Ztqo/x219l3WJbXj0/Ll8lnIe3U4yJCaGesVSUyggFBERqUY+H8ycCV6vO0JcULBj6EhMDCQnw9atLjw8KnkVN/wygJM3vcIab0uGtnuVJjdfwMgLjIJBERERkQgUnFRcWgqHxG3hjnWj6bH6QYq8dbi/1T3MaXgr7RrHEGMhJ8e1nxHZFwoIRUREqtHcubBypQsDN2504WBcnAsF69Z1d4JLthVyvW8SN+VNJoCH/x45nmXnDuCmW+IUDIqIiIhEoGB7mieegGjKOG3ZA9ywbjSJ/u283vgmHmk6hpQ2DfBucCdU1Jda9pcCQhERkSoU7BGzYAGsWeN+kpIgPR2iolwwGBXl+gx6oyynb36Rf28aRHrpGt6rfykPt76bU69synUq8EREREQiSjAUzMqCb76BuFhL62VvcefmAbQoWUZW0pnc3/Ie8lscSUm+62N94YUKBuXPUUAoIiJSBYLB4Ny5bqegxwPFxe5YcX6++71RI7d7cNMmODbm/xi/qjfH5H/MyuROPHfds6w/9CSm9VSBJyIiIhJpKvesXr4cOngW02d1f7oVvsfK6LYMaPsG3zY+l4A1FKyDa65RMCgHRgGhiIjIQRYs6H7+GbZvd2Ggx+NeS0lx12JioKwMWiVv4ZrNI7h8y8MUxKQwu8vDrDr9eg5vH0VPTSUWERERiRiVT5589x2kpUEj72aGrR/NpdsfosCTxJRDpvFm81vIKYwhuhC6dnU9rG+7LdSrl5pOAaGIiMgBCh7/WLIEFi+GL75wAaDHs2MQSUqKm1aclAR16kCct5zLtj7ELdkjiS/bjqf3bdQdPZprUlJC/XVEREREpJpUDgWXL3eD7BISINaUcv7KB7ht6xgSA3m8mHITkxNHU5jQgDYpUGbg2GPhyCPRpGI5KBQQioiIHIDKxz/Wr4fsbCgsdIVabq4LCD0edy021u0mPLF0AaPW96ZlwQ+sPvQ0Yh6YTuMzjwz1VxERERGRauTzwaxZLhgM1o2FBZaj173JiNwBtCxdzsexZ/KfRtNYGd+ewkJI8OzoYd24sSYVy8GjgFBERORP8vlg5kx3pze4U7CoyAWBRUXuuMeWLTteaxOzmjs2DeTM3JfYENeCt65/mSNHXEjj5ibUX0VEREREqtncue5mc3ExbNsGLQoWM3xrP7rmv88v0W25qek83o3qgSfKkBwP1kKTJu5USufOrhVNV7WkkYNEAaGIiMifNHcurFzpjhNv3Ajl5RAf73oLFhe7gDA5GTzFhdy0/W5uWfkfjMfw+bljOeSegfRoGx/qryAiIiIiIeDzwfvvu5oxI2Ezty8dRc+tD5Hvqcv4BvfydNIt1EuLJrHA1ZoJCXDKKXD88QoFpWooIBQREfkTgkVdQgIY4455bN3qjhYXF7uegzZgOSf/JYblDKRRiQ/+9S+4+25OUEUnIiIiEpGCPQdffBG2rC/l8tyZ3L51DAmBfJ5OuoVJsaMxDerTINWdQmnSxE0n1oRiqWoKCEVERP6EuXMhP9/1fSksdMNH4uJc/5g6deD4+IXcvqIPxxZ8SEGbo+DRp+Hkk0O9bBERERGpJpUHkGza5G4qb9sG/nJLt5x5DN06gOYlP/Fh7Nk81v4efok/grxfINXr+gt2765gUKqPAkIREZH9lJUFs2e7PjDB4SPbt7tgMKl0C3fZkfRY+BCl8fXYOuFBUofc4LYYioiIiEhEyMqCu++GZctc+5nERFi7Fg4rW8R/SvvTrfh/rIw+jL5t3uRtziGqyFAvFs46C8aMUSgo1U8BoYiIyH4IDiZJTNzRD2bzZkhOLOey/FncmDuCuOIcuPUW4saMIS41NdRLFhEREZFqlJUFgwfDr79Caak7ZeLfsIm7C0ZxdfHD5HmSuavRdJ6uczNRsdE0SnVHiTt3hp49FQ5KaCggFBER2Q+ZmW4YSZs28NNPbihJj8SPuGVZb1rnL6S4y6nw8HTo0CHUSxURERGRahQ8Ujx7tmtD4/GAKSvliu330zdvLIk2n0djbmVGyihIrU+jdFi3zu0wTEtTOCihpYBQRERkLyr3jlmzBrKzwVvRF+aoFB//+mYQJ69/kQ0xGbx93RzOefSfrsGMiIiIiEQMnw/mzIHly137mfg4y9Fr32BM/gBa+lcwP/qvDE+4hxXR7fBYaOhxQ0hSUuC006BXL4WDEloKCEVERPagcu+YwkIIBNyE4kRPEX9fOJnb8ifh8VhePWo089oNYtSoBFA2KCIiIhJRfD4YNcrVjBs3QvvAIqZt6cdx2+ezPOpwLk95i7c5B48H6qe6ANHrhYICN4TkmmsUDkroKSAUERHZjV17x5SVgcFykXmFOzcNoFlgNe/U7cmsQyeT0K45t96qwk5EREQkkvh87jjxnDnuqHDblE2M2zKSi3NnkedJZnKzGTwWfROFZdF4i6FdO2jVytWM7dpB166qHyV8KCAUERHZRXAQSUmJu8NbWAgt8xcxuawP3UoXsDSmA1c3WsCS9O60awfjxqm4ExEREYkUlYPBDRsg1pTSu+w++v8ylgRbwFN1b+P+1FGUJKaSWhc6t4Jbb4UuXUK9cpE9U0AoIiKyi+AgkrQ0iM7byqD8UVxZ8CB5pi5Dk2fydrNeNGjk5ehWcOqpCgdFREREIoXPB7Nmwf/+B/5yS4+yuYzcPpDWdgXvx/RgQr0pbKzfjvJyiDVw2WXuGLHqRQl3CghFRCTi7TqIZO1asOV+ril7hEHbh5Mc2MaTcTcx2jOW4pj6NI12OwvT0tzREBERERGJDHPnulY0KWsWMmVLP7qVfMAyTzt6Jr5NVvJfAYgphpYt4cIL4bbbQrxgkX2kgFBERCJW5eMhW7e64cN+P3TY9jFTynrTMfB/ZMacwuiUGSwyHSkpgYwmrndM9+66GywiIiISKYJ14ysPbaTfthFcUfwouaYedyTex5NxN1JQGk18GaSmQnKyO058/vmhXrXIvlNAKCIiEaPyTsEVK9yUuZISiI524WBK/hrGlQzmovLnWWua0Sv5Bd6u05PYOMOhjd1EY/WOEREREYkcwWDwtRdK+Nuq+/i4aBzxtpBHYm9nUsxI8mNSiYqC+HgIBFxd2aOHJhNLzRPSgNAY8zhwHrDRWntkxbVU4AWgBbAKuNhauy1UaxQRkZotGArOmweLF7tpxF4vFBW53/1+8JQU0bt8KgNK78JDgMnxI3kgaQhl0QnExcEhh7gdgwoHRURERGq/nerHHyyn5LzOy8UDaen/mfeiz2VY7BR+iT2cqCjwGnfDuVkzuOgiBYNSc4V6B+Fs4H7gqUrX7gDmW2snGWPuqHg+JARrExGRGi4ry+36W7bMHSEOBKC42L1mLRgsFwReY2JJf1rYVbwW9U/ujJ7C+pgWJMdBajKkp7sjxe3ahfa7iIiIiEjVq1w/pmcv5Lm8vpxUtoClph3/SHiHzKSz8XjcAJLycoiLg3POgTFjFAxKzRbSgNBa+7ExpsUuly8Aulf8/iTwIQoIRURkP/l8MHOmCwajoty1khL36PfDEXYxd5f24VT/fBabI+kRO58PPacB4Cl3AaLXq2EkIiIiIpEiKwsGD4aSNRsZunkElxa4PoMD4+7nEc+NmGgvnlIXCtapA40auVBQ4aDUBqHeQbg76dba9RW/bwDSd/cmY0wvoBdAhv6fKCIiu5g7F378Edatc7sGgyFhcmAbd5aOplf5TLZTl4Fx9/Fc0k34jZfYioKvXj2oW9cdFdEwEpGDT21mREQk3Ph88PCMEnqumsF1v44jNlDEw7G9+U/MSHI9KfjLwRNwNaWpOFacmgq33KI6UWqHcAwIf2OttcYYu4fXZgGzADp37rzb94iISOQJNpJ+8EHIzwePx10vL/FzVdljjCobRn228Lj3RiYljqMosQEGSK+vvjEi1Wg2ajMjIiJhwrfa8vq1rzHmk4E0L1/J+7HnMS55CsvNYRQVQcAPiYnuvYEA1FfdKLVQOAaE2caYxtba9caYxsDGUC9IRERqhmDPmO++c7sGY2Lc4wnlnzK1vDedAt/xqeckRibPYElsJ9LTodOh2iUoUt3UZkZERMLF/z35PZ6B/bh984cs9bbnyobvMq/0LALFrtVMVJRrO+P1KhiU2i0cA8K5wNXApIrH10O7HBERqQmCPWN+/dUd+TAGGpauZXz5YP5Z+l/WmqZcHfs8XzS/mNtuNwoERcLPPrWZAbWaERGRgyA7m1+uHEGH9x9lm0llUMJM5qT0oqjMS3Kyu8lcVuaCwWOOgXPP1Q1lqd1CGhAaY/6Lu1PcwBizFhiFCwZfNMZcD6wGLg7dCkVEpCZ49VW44w7YuNEFg3W8xdxSOJX+JROJws89iSN4qvEQjjg2kdHnwyWXhHrFIrI3e2szU/G6Ws2IiMifU1IC06dTNno8TYuKeCi2L5PjRpDnTaEk1/WiBteXumlTuO8+6NIltEsWqQ6hnmJ86R5eOr1aFyIiIjWOz+cGkcyZA19/7a7FxljOLnqdiSX9aWl/Ya73H4xLnkJ+WkuSk6FVK00jFgljajMjIiJVx1p49VXK+g0i2reSD2LOY3DcVNbEt8XvB4/fhYLFxZCUBC1bwoUXKhyUyBGOR4xFRET2KCsLpk6FTz91BVx5ubve1r+Eydv7cEbgfZZ4juCChPf5NPYMPAYaR0OPHuoXIxLm1GZGRESqxnffQb9+8NFHbE1rz4TD3+O5TWdiLZSWuMEjUVE7hts1bOiCwfPPD+2yRaqTAkIREakRfD6491547rmdpxPHFuUwyozhZv/95FOHIXHTeTz2ZgrLoklPdqGggkGR8KI2MyIiUi2ys2H4cHjsMfz1Unn2hAe4a/MNbN7sJRBw9WR0tNtcaIw7fRwfrxvLEpkUEIqISNgLTif+9FMoLXUFnL/Uz1X+JxgTGEoDNjPbewPjYseTG5OGMdA0HWbP1rEQkXCkNjMiIlKlioth+nQC4ydgC4t4qUk/huSPYMN39YiJcUeJAwH3toQE3BFjD6SlwT33uKPFIpFGAaGIiIQ1nw9mzoStW91zjwc6l3zGlNLeHG2/JdOcyLnmHX6M+QtxsRDlgTp1YNIkhYMiIiIiEcVaeOUVGDQIfvmFrxudz9CUyXyxrS3G7NglWFYGiYluQrG17nhxgwZw110KByVyKSAUEZGwNns2fPklbNkCDUrWMbZkCP8qf5Z1HMKVUc8xJ+oSjMcQHeWKvIwMGDpUxZ2IiIhIRPnuO4pv7kvcFx+zMuFI+sa/z/ztZ/x2fNhad5y4tNS9vbzc7SSMioLjjoPBg3VzWSKbAkIREQk7lScUf/UVxHtK6F1+DwNKJhBNGf/xDuOemDsoMHWgHFJS4Kij4NxzXTNp9YsRERERiQxrv97A9tuHcfjnT5Bv6jPI+yCPlf0bvF5swO0WhB29BuPi3GNZmRtGcuKJMGaM6kcRBYQiIhI2fD63Y/DZZ11P6aJCy3n2De7296e1/Zk3ov7O8PipLCtrRRRQPxUuugj69lVRJyIiIhIpfD548+ViPDPu5bJVE2hICdM9/ZkcO5zN5fUIBMCUudMlUNG/2r9jF2FMjGtJc/HF0KuX6kgRUEAoIiJhIjiI5MsvIS8P2viXMrGsL2fzLktMO86Pe4+PY88EINYDAwZoupyIiIhIpMnKtHzS92Uu/W4QzcpXMc97AYOZzHLbBiqG2QV//P4dx4vBHScOBNy1889XOChSmQJCERGpdllZ8PDD7nHbNle8FRW5Yi2hLJexgbHc6p9BAYkMjJrGrOhbCURFUyfONZQ+6SQYOTLU30JEREREqtP6N7+l7tV9GbzlExZ7juSc6P/xYdTplJe7OhJcAOjx7HiekOCmFYMLA084wQWD6jcosjMFhCIiUi0q9xX8/nvXGNrvd6+VloKxAa41s5lg7ySNTTxhrmeUdwLZtiFeINYLdetCUhLceGNIv4qIiIiIVKf168nvM4z0ObOJNvXpE/sQj9rrsVFe/OX8NogEdgSE1rrH+Hho2hTGjdMQO5G9UUAoIiJVKisLpk6Fjz6CggJ+u8MbCLjXjYEuZHEvvTnWfk0mXfib5y2+NcdgKvrEBAKuyEtOdhOKdcdXREREpPbzLS9m3cBpHPXWRGL8JUwzA/hP9HCKY5IpKwbjdyFgWUW/QWNc7RjsPdikiTt5oh2DIn9MAaGIiFSZV1+FESNg7VooKXEhX3n5jru6je2v3MUdXGmfZh1NuMrzDM/ay/B6DR7r3hMbC+npcNll6jkoIiIiEgl8qy1fDH6JLq8Mokv5al43f+fO6MksDxzqasQi10/QXxEQer0uGASFgiJ/lgJCERE56Hw+uPdeePTRHcFg8NiHMeD1l9DX3MswO54YSpnInfzHM5TSmDrE4Ao9jwdat4Z//9s1kVYwKCIiIlK7+Xzw7sRvOPqpvvQs+pSFpiPXeuazwJyGqRg0Yq0LBD2eHc8bNoSzzlIoKHIgFBCKiMhB5fPB5Mnw+uvurq7X6xpDWwveKMt5vMkU+tHGruB1zmcgU1nBoURHQRRQrx40agTdusGgQQoGRURERGo7nw9eum89zWcN4/rts9lMA24yD/O4uR4/UVARDlbuLQjQsiUcfjhMn66aUeRA/WFAaIxpDay11pYYY7oDHYGnrLU5Vb04ERGpeebOhQ8+cP0Go6J2HCluXb6MaeX9OIe3WcZhnBv1Du+Zs/F6ITXBTSdOTYVDD4Xu3bVrUCTUVAOKiEh1WLO8iGU3TuOmjyfiDZRyb9RAJjCMHJsMuEAwyFpXXwaPFB9+OAwerJpR5GDYlx2ELwOdjTGHArOA14HngB5VuTAREalZfD6YPRsefBC2bnWhoDEQV7qdcXYcvbmXQhIY5JnKE4m30aBJDCPUV1AknKkGFBGRqmMtzJlD8s2DOXPrauZ6L+RO7938ZA91r/vdW4I7BoMta6KiICEBLr0U+vZVHSlysOxLQBiw1pYbYy4E7rPW3meM+a6qFyYiIjVDMBh89llYs8YdKw4EgECAq3iKcf47aMhGnjTXMip6Iq26pDP6Iu0QFKkBVAOKiEiV2DDvawJ9+tJk5WesMkfR3/MEH5tT8RgI+HfsGjRmRx/r6GhIToZTToH+/dVrUORg25eAsMwYcylwNfC3imvRVbckERGpKYL9BufOhS1boKzMFXHHBr5gWqA3x/MlX5gT6BnzBtsOPZbp4+DCC0O9ahHZR6oBRUTk4Pr1VzbeMJRGbz3JJtOQG6Me4Ql7LQEThfW71jRer7vhHAwHY2KgWTO4TCdPRKrUvgSE1wI3AROstb8YY1oCT1ftskREJNz5fDBgAHz4IWzf7o5+NGY948vv5Cr7JL/SmGujnmJOzOWkN/bwzCO60ytSw6gGFBGRg6OoiJyR95Aw4y6SS8uY4hnMRDOMPFOXgOW3ISTG7DhS7PVC8+YKBkWqyx8GhNbaH40xQ4CMiue/AP+p6oWJiEj4Cu4cXLDATSiOtqXcWjqd4YGxxFLC5KghTE8cRllcEke2hCuuUDgoUtOoBhQRkQNmLbz4IuUDBlNvnY+34/9B/+i7WWFbu5Y0lYLBYP9qgCZN3NC6MWMUDIpUF88fvcEY8zfge+CdiuedjDFzq3phIiISnrKyoGdPePxxyM2FM0rf4tvyDvwnMJiP6E5Hz2KGmknk+JOIj4djj3X9BkWkZlENKCIiB+Srr6BbN7jkElZuS+GvsQs4v+xlfgq0xhjXszrYa9Bat2MwJcUdJ778coWDItXtDwNCYDRwHJADYK39HmhVhWsSEZEwlZUFI0bAihXQ1vzE64HzeK3sXKyFv0W9xd+j3uBnTxtiYqBpU7juOhg0SMWdSA01GtWAIiKyv379lY09robjjmPT5yu4MeoR2hd9w3x/d6x1/QX9fvfW4JTi4IRiY9yN5V69VD+KVLd9GlJirc01wb2+TqCK1iMiImHsnnvAtziPO3PG0zswjWLiGBI1hfu4nYA3higL6enw73+rV4xILaAaUERE9l1REUydStm4u0guLWeKdwjj/UPJw/UZNP4d/QWDOwbLy93zhAQ4+2xNJxYJpX0JCBcbYy4DoowxbYDeQGbVLktERMKJzwfTpwVIfu1pPiq/g8ZsYLa5luGeieTENSJQDvHxcOihMGOGCjuRWkI1oIiI/DFrWT7uBVImDSataA2v80/uMHfjM63wVxwlrnjbb0NIgj0HU1JcO5rRo1U/ioTavhwxvh1oD5QA/wW2A32rclEiIhI+srJg8sVfccmMrjxafg0+05wTvV9wY/TjbIpqRGkpxMYqHBSphVQDiojIXi167EsWp3aj7ahLWVdUn9M8H3KxeYmfbSvKynaEg0FRUe4xJsadOunbFx55RPWjSDjYlynGhcCwih8REYkQWVkwY1g253x8J/f5n2A9jfi3dzaz/VeC9eA1rsgrL3dF3ahRKu5EahPVgCIiskfr1rHp33fS4Z2n2WjSuTn6UZ6w11BSHrXHP/F6Xe0YEwMnnaTaUSTc7DEgNMa8wW9Dx3/PWquZlCIitdRrL5ay9Nb7eGjzWOIpYooZxFg7nEJbl9h4KCtzzaW9XujUCWbNUr9BkdpCNaCIiOxRYSFMnQqTJlGvuJz76tzBmJI7yQnU/e3Y8K67BoOiotypk4svdkPvVDuKhJe97SCcUm2rEBGRkPL5YO5cmDcPUr54h1E5ffk7y3jL9GBQ1DSW2bau2Au4YDAhAaKjXWF3770q8ERqGdWAIiKyM2vh+edhyBBYs4Ytp17EOYvuZrWnJaUGbIGrEYNzrYK9BsEFg02bwqmnuunE2jUoEp72GBBaaz+qzoWIiMj/s3ef4VGXWR/Hv/dMKiSU0BFEEGQVQRBcFRH77iouK1bUR8WGriJdepdeRUVdXAVRUUFkRWzYG5EVRQVEQVCH3kuAhLT/8+IwTmAhDJDJJOH3ua5caZOZe97o4dynFL5AwBJ806dD+a2/MDq7C39nLsupRyvm8rbXCvZvlwu2E2dnWwVhhQrQr5+CPJGSRjGgiIgcYMEC6NIFUlPJbNCEuZ1epPPrLVm/w5KAfn8oMRhMCvp8oRnVgwZBmzZRO72IhCm/FuMZnufd6JxbzCHaTDzPaxTRk4mISEQFAjBmDLw7M40e24fxYPYEMonjYUYzkU5kEXfA4z3PWooTEqB5c22bEympFAOKiAgAa9ZA797w4otkVqjKE2c+x+Bfb2f3435ycy0J6HkHVgrm5FhisE4daNsW2rVTp4lIcZFfi3Gn/Z+vLoyDiIhI4Zrzhkep11/is009qOatZ5q7g17eCNZT7X8em5trA6XLlIHbbrONcwr2REosxYAiIieyvXth7FgYNQpycvj52t60/b43y1YkHzBfMFg9GBQTA02bwsyZihNFiiPf4X7hed76/V8+4Hne73k/gAcK53giIhIJPzy3kBa9LmDUuttYQw0u8H/FnW4qG5wlB53jj0HTzlly8PLLbU7h+PEK+kRKMsWAIiInKM+zuTP169uK4Vat+PalZVz13XCWrUn+Y0ldMEkYrB6Mi4OUFChbVrOpRYqzwyYI87jiED+7sqAPIiIihWDTJja2vocz7/4z1feu5B7/czR3X/GVdy65uaEWEc+zJSSlSkG9ejaT+q231FIscoJRDCgicqJYsMBmyNx6K7tLV6b/RZ9R7fMZnNu2NqtWQWamJQaDycG8Mwezs616sEULxYoixVl+Mwj/id0S13HO/ZDnV8nAl5E+mIiIFIxAAObOzsL/1BPcvHwQKd5extOVkTH92e0vS06mPc7ns6AvWDFYoYLFiV27KtgTOZEoBhQROYGsXm1zBl96CapW5ZsOU7jl3dtZ942PzEyrGITQJXKQ51nsGBsLlSvDaadBt26Ff3wRKTj5zSCcDrwDjAB65fl5mud52yJ6KhEROW7BDcVrp8xj8I5O/ImfeJe/0YUJ/MSfcDkQ42zpSFaWBXopKdCpkwZKi5zgFAOKiJR0e/fatrpRo+yGnxgxywAAIABJREFUuE8f3mzQi7s7J7Nt24FdJYfj80FSEpx7ri6URUqCwyYIPc/bCewEbnbO+YEq+x+f5JxL8jwvUEhnFBGRoxQIwOOdV3L5m125KnsOv3AqV/Mmb9EKsJ4Q5ywejImB5GRITITu3aFDh+ieXUSiSzGgiEgJlpsLL78MvXrZluIbb2Rtx1EMnHIKL42HjAx7WLCz5OCvwWLIatXgvvt0qSxSkuRXQQiAc64DMAjYCAT/s+ABjSJ3LBEROWa7dxP4v+EM/XwcWcTSxz+SCbmdyfDiD3hY8EZ43z6rIqxfH1q3jsJ5RaRIUgwoIlLCfPUVdO5s8wabNmVJ35cZ9EELPrwadu48sFowb0IwN9eShHFxdqncpAk884wSgyIlzREThEBnoL7neVsjfRgRETl2gd89FnabzgVv9KBF9jqmcRt93Eg2+aqT42H/rM/D77fPcXEW6A0apEBPRA6gGFBEpCRYvdoqBqdPh2rV+KXfVO759Da+6mxzBsMRG2tbis86SzGjSEkVToJwNdZmIiIiRdQPU7+FTh25dteXLKQpbXiNVM63pGDW/z4+JsYShMnJcNttdpmsQE9EDqIYUESkONuzB0aPtlmDubns7NCXPrt68fyEJPbssarA4MiZ/JQrB+edB61aWbeJYkaRkimcBOEq4BPn3FvAvuAPPc8bH7FTAc6534A0IAfI9jyvWSRfT0SkWNq8mY339OXMOf9mKxW51/8sz9OOrBzfAQ9zLtQ2Eh+vDcUiEpaoxIAiInKccnOtWrBXL1i7Fm66ibUdR9HnX7WYNcvmDOaXGAwmDhMT4brrYMgQJQVFTgThJAgD+z/i9n8Upks8z9tSyK8pIlL0ZWXBpElk9x9EhT17mBTTmcHeALZ75QCrEMzNDQV+zkH58nDHHaoWFJGwRTMGFBGRY5GaasHef/8LzZqxYeKrTF56AU9fDxs25L+VOK+4OGjbFvr3V9wocqI4YoLQ87zBhXEQEREJ0/vvQ6dOsGwZiyv/hW5Jj/Jt+umkp0Pu/jkywZvf2FirGKxeHaZOVbWgiIQvWjGgukhERI7B6tXQs6dtKK5WDZ5/ntml/49efXysWgXZ2Ud+CuegdGmoXRsGD4Y2bSJ/bBEpOsLZYlwJ6AE0ABKCP/c879IIngtsctY855wH/MvzvMkRfj0RkaJt1Sro1g3+8x+oU4e57d/goff+zvYdjoyMA9tEsrOtitDns+TgyJFKDorI0YliDAjqIhERCU/eOYOeB/36Qc+ezH4/iYcegs2bw6sajI+3pXW33qo5gyInqnBajF8CXgWuBu4H7gA2R/JQ+7XwPG+tc64y8L5z7ifP8z4L/tI51x5oD3Cy/uslIiXZnj0wYgSMHWtZv+HD+er8LnS/P4F16w+cL+j3Q06OfZ2YCBdeaHGikoMicgyiFQOKiMiR5ObCSy/ZnMF166wfeORIAq4Wjw6AyZNh794jJwdjYixeHDZM8aLIic535IdQwfO8Z4Esz/M+9TzvLiDiN8ee563d/3kTMBv480G/n+x5XjPP85pVqlQp0scRESl8nmdtIvXrW9R2/fWs/ehnhmT1ps3NCfzyi1UKBhOCzlmQl5QE118PS5bAW28p2BORYxaVGJBQF8k3+y+E/4dzrr1zbqFzbuHmzcpZisgJZv58Wyt8++1w0knwxRfw8svM/rYWV1wBjz1m98v5JQf9fqhUCWbMgI8+UrwoIuElCLP2f17vnGvlnGsCpETwTDjnSjvnkoNfA38BlkTyNUVEipRFi6BlS7jlFqhSBb74gsDwFxn5wkk8+yxs2xYK+oLLSJyzgdKNG8O4cWoNEZHjVugx4H4tPM87G7gSeNA51/LgB+iiWEROSIEA3HwzXHCBbSeeNg2++opAzQu45x4LG5cvD10eH4pz1k586qnwr39pzqCIhITTYjzUOVcW6AY8DpQBukT0VFAFmO2cAzvjdM/z3o3wa4qIRN/mzdYT/MwzUKECPPMMqX+6k3ET/MybB7t3W2LQ57Ob32Bi0Oezj5QUuOkmJQdFpEBEIwY8oIvEORfsIvks/78SESnBdu+GUaNs3AzYauEePSApidRU202yYEF4i0hKl7Y7aI2gEZGDhbPFeO7+L3cCl0T2OH+85irgrMJ4LRGRIiE7G556CgYMgLQ0dt3ViacqDWTK2HKsWgVZ++t47N4kVDUYXEQSH28bi5s0scHSIiLHKxox4P7OEZ/neWl5ukiGFMZri4gUObm58OKL0Lu3zRm8+WYYOZLUtSczrh18+ilsCWOdU7DL5LLLLNzURbKIHEo4W4ynYLNgDrB/Do2IiByvDz+ETp1g6VK4/HLW9ZxI7xfOYN5UayXO2yZy8CyZnBz7WW6ujaDp0UNBn4gUjCjFgOoiEREB+PJL6NwZFi5kX+M/89KVM5n4dXOW14eMjPCfxu+3+dTXXANDhihOFJHDC6fFeG6erxOANsC6yBxHROQE8ttv0K0bvP461K4Ns2fDP/7B2G6O2bNh3778Z8iA3QiXKQPnnAODBqlVREQKVKHHgOoiEZET3u+/W8/wq6/CSSexYsAL3PPRLSx82UdGhl0Kh8s5m1hz662Wa1RyUETyE06L8ay83zvnXga+iNiJRERKuj17bI7M6NF2rTt0KIEbujFnXgIvnAfffGOJQb8/VB14KDExlhzs1AnatVPQJyIFSzGgiEghyjtn0DkYOJA59R+mQ8/SrF179InBhARLDPbvrxhRRMITTgXhweoBlQv6ICIiJZ7nwYwZ0L07rFljc2RGj2b21zUYeD2sXAnp6aGHH6560O+3oO+kk2DkSG2fE5FCoxhQRKSg5ebCCy/YnMH1620V8ciRTH6nJr0ehJ07/3fETH78fqheHSZOVIwoIkcnnBmEadj8Gbf/8wagZ4TPJSJSsnz/PXTsCJ99Bo0bw/TpcOGFzJ4NDz0E27fbnhLnDrwhdu7AoDAmxjYVt2wJXbuqpVhEIkcxoIhIhH3xhfX+fvMNnHsuGybNYvLi83mmud0lHw2/HxIT4eyz7QJZMaKIHK1wWoyTC+MgIiIl0pYt1tsxeTKULw//+hfcfTf4/QQCMHy43Qzn5lrF4MHtI8HkYOnS0L695seISOFRDCgiEiG//WZzBmfMsDmDA1+k01c388mtvgO6ScKVnAx16sC112rsjIgcu3wThM65ROBW4Iz9P1oIvOZ5XmakDyYiUqxlZ8PTT8OAAbBrF3ToYFtEypcHIDUV/vlPWLLEEoM+X+hPfT5LFMbGQny8tRNPnqw2EREpPIoBRUQiYPduGDECxo0jBx+vnT6Iruu6s25w6aN6Gp8PKlZUR4mIFCzf4X7hnGsI/AhcCPy2/+OvwJfOuXLOuaGFcUARkWLn44+hSRPrHT77bPjuO5g4kdSfytOuHdSoARdeaF3HwTmDubn2EUwO+nzWKlK2LAwbpuSgiBQexYAiIgUsN5df+k1lW8V6MHw4L+67nlr7ltN22UDW7Qw/ORgXZ0nBL76AjRth5kwlB0Wk4ORXQfgY0N7zvPfz/tA5dzmwBFgayYOJiBQ7v/9uC0heew1OOQVmzYI2bQisdgy5x4K49HQrLjzcsOlgi3H58nDJJboVFpGoUAwoIlJQPv+czf/XmbqBb/mKc+nMbBZw3lE/jd8Pt9+urcQiEjn5JQirHRwYAnie94FzLgtQPYuICMDevTB6NIwaZVtFhgyB7t0JbE7k0W4wbRps3Wq/OnjpSF7O2RKSK66Ap55S8CciUaMYUETkOK394ld23N+TBktnkkENbuElXqEt3uGb+A7J57NZ1L1724eISKTklyD0OefiPc/bl/eHzrkEIMvzvL2RPZqISBHneVYt2L07BAJw002WKDz5ZAIBu+F94w0bNxN8+KGSgzEx9vPSpaFePSUHRSTqFAOKiByrtDR+bjeCU2aPp7znYyCDGMPDpFPqqJ8qLg7OOMNGWmvcjIhEWn7XF9OAWc65WsEfOOdOAWYAL0T2WCIiRdwPP1gP8I03Wj/wp5/CK68Q4GSGDIEWLeCllyAt7fAVg2BVg2BtI1WrQt++Sg6KSNQpBhQROUqB33J5o80UNpU/jfqvj2CGdwOnsZwhDDyq5KDfD3XrwuDBsGIFLFqk5KCIFI7DVhB6njfUOdcB+Nw5F/wv2h5grOd5jxfK6UREipqtW+0a9+mnoVw5K/e7915S/+tnyJWWJ0xPt4fm104c5PfbR/PmtoxE8wZFJNoUA4qIhC8QgDndP+PC1zvzj5xFpHIef+c//Jdzw34O56BCBbj4Ys2fFpHoya/FGM/zngCecM4l7/8+rVBOJSJS1GRnw+TJ1je8Ywc88IBd7aakMHs29OxpAWJWVmgTcX7JQZ8Pata0IsT27RUIikjRohhQROTQUlNh3Dj48ktI3vIrw7J70IHXCFCTm5nOK7QF3BGfJyYGUlJsK7GSgiJSFOSbIAxSUCgiJ7RPP4WOHUNtxRMnQsOGgAWJ3bvD2rWWHAxuIT6cmBhrGxk+XO0iIlL0KQYUETGpqdCrl32Oz0qjD8PpwgRy8NOfIYyj2xFbif1+qFTJ7pjbty+kg4uIhCmsBKGIyAkpEICHH4YZM2ww4MyZcN11pH7lGHe93Rxv2WLFhUdqJ46JgTJl4I47oHNnzRkUERERKS5GjLCPPWk53MHzDKcPVdnI89xOH4azjpOO+BxJSVYt2K+fqgVFpGhSglBE5GDp6TBmDIwcaVm/QYPg4YdJ/b4UvS62m+O8rcRw+ORgTAwkJtqMwYEDFRCKiIiIFAfBVuJ582zpXEs+ZQJdOJtFzOd8WjOHr/nzEZ8nORluusmm1OiCWESKsiMmCPcPp+4GnOx53r3OuXpAfc/z5kb8dCIihcnzYNYs6xn+/Xe44QYYM4aAq8WQjvDKK7Bnjz3UuSO3E/v9Nlvm1ltVNSgixY9iQBEp6QIBmDrVwr/ffoOMDAsHs7NDl7+1WcVz9OB6ZhGgJm15mVe5iSPNGYyLgwsu0BI6ESk+wqkgnAJ8AwT/s7YWmAkoOBSRkmPxYujUCT7+2OYLfvwxXHwxs2fb0uKff4acnFArcX7txM5BtWpw333Qrp0SgyJSbCkGFJESJzUVhgyBzz8PXfweSjK7/pgzmE0M/XiEcXQjg8TD/o1zULq0WolFpHgKJ0F4qud5NznnbgbwPG+vc+7Ia5lERIqDbdus9/epp2xI4KRJBP7WnqkvxjD1LiskzM21gA8Onxj0+eymODkZmjSBZ55RYlBEij3FgCJSrOXdOLx9O2Rm5n/JC+Ajh3ZMZRh9w5ozGBtrScFateDaa3U5LCLFVzgJwkznXCLgATjnTgX2RfRUIiKRlpNjWbx+/fC2b2dh0/vpkz2E7wZWYGdnmzEIR04MBsXGQtmycNZZNrJQgaGIlACKAUWk2Am2DU+dam3DR4rh8rqIT5hAF5rwHV/SnL/zJgs5538e5xycey6MH68qQREpOcJJEA4E3gVqOudeAi4A2kXyUCIiEfXZZ9CxI3z/PTubXESnWo/x+k+NyMy0xGDe2YJHCiqdg8qVrWqwVSto3VrJQREpMRQDikixEKwU/PRT2LLl6P++NqsYw8Ncx+v8zsncxCvM4EYOnjPo99s24p49oXfvgjm7iEhRccQEoed57zvnvgXOw/4L2cnzvGP4z66ISJStXg0PPwyvvkpa+ZqMO/1VRv14Axn7HM6Ft3gE7HFxcbaZWIOnRaSkUgwoIkXZ8VQKBiWzi74MozOPkkUsfRnKeLr+z5xB56BCBbj4YujaVbGfiJRMh00QOufOPuhH6/d/Ptk5d7Lned9G7lgiIgUoPZ0d/cZS+vER5GR7jPUPZMT2HuzdXuqPhxxp8QhYcBgTA1dcocHTIlJyKQYUkaIo7zzBrVtD42COhY8c7mQKw+hLFTYxlTvow3DWUx2/H2J9Nj6mXDm7EFZSUEROBPlVEI7L53cecGkBn0VEpMAEAjB1isf2Kf+hc6ArtbzfeI3r6M5YArmncLSXzH4/VKoEgwdD+/YRObKISFGhGFBEoi5YITh3LqxYATt3HluV4MEu4hMepTON+Z4vuIA2sW+xo24z7m+rBSMicmI7bILQ87xLCvMgIiIFZfZs+HeXpXQNdOIy70MWcyaX8iEfB/9NexTBpc8HpUpBy5aqGhSRE4NiQBGJlmBScPp0WLXKqgSdK5jEYB1WMpaHacNsVvtrMb7Zq5w//gbmN9dydhERCGMGoXMuAXgAaIH9s/pz4GnP8zIifDYRkaOSmgrj+2/noo8H8UbuJHZRhofc4zzl3U9OWDuZTEwMpKRArVpw9dW6TRaRE5NiQBEpLIEAPPooTJtm7cN5HU9y0DmoWmoXj1UZyrWrJ+KLi4U+w6jZpQtdExOP/AQiIieQcP7FPA1IAx7f//0twAvADZE6lIhIOPK2nqxcnsMNu57lSa8vKWzjGdrTj0fY6lUM67ni4iAhARo3hpEjVSkoIoJiQBGJgGD8NmuW7Y/LyIDMTMjJKbjXSE6GurVzGFzrOa5K7Yd/1Sa78R0+HKpVK7gXEhEpQcJJEJ7ped4Zeb7/2Dn3Y6QOJCKSn9RU+Ne/4NNPYd06az25wPuC9+nI2SziMy6kI4/xPY3Der5gC/Gpp8K116paUEQkD8WAIlJgDlUlWFDtw85B6dJ5RsJkfAydO8ObP0CLFvDo29C06fG/kIhICRZOgvBb59x5nud9BeCcOxdYGNljiciJLhCAOXOsOnDpUkhLsxvmvLNoquWuYTQ9uIWXWU0NbuIVZnAjkP8sGd/+zXR16kBbDaQWETkcxYAiUiBmz4YBA2D5csjODsVyx5McjI21pGCtWnkueTN/gYcfhv/8x34xYwZcf729oIiI5CucBGFTYL5zLrD/+5OBn51ziwHP87xGETudiJyQUlNh0CD47jvYvt1aTvIGkfFk0I1x9GE4fnIYQn9G0ZO9lD7sc8bG2hbiRo2gVSto3VpJQRGRI1AMKCLHbfZs6NrVqgaDsdyxJAbj46FGDbjwQmjf/qBxMDt3wtChMHGizY0ZPhy6dLH5MSIiEpZwEoR/i/gpROSEl5oK48bBl1/Cli2Qm3tg24l99vgHbzCertThV2ZxLd0Zy2/UPuC5fL7Q11WqWJVg585KCIqIHCXFgCJyTPKOhFm9+sB47miSg/HxR+j4yMmBZ5+1vuItW+DOOy1RqDmDIiJH7YgJQs/zfnfOlQdq5n2853nfRvJgIlLyBYdUT58Oq1ZZjOfzhSoG8zqdH3mUzvyF91lCAy7jAz7iskM+b2ysBZQ33gj9+ysxKCJyLBQDisixmDwZhgyBbdtg3z679A2Xz2cfKSlw661HuOD96COrEvzhBysrnDBBcwZFRI7DEROEzrlHgHbASiD4T3YPuDRyxxKRkiyYGJwyBdavt1k0wRvl7OwDH1uWHQxkMA/xOGkk05GJPMU/ySb2j8f4fBZ85tt6IiIiR0UxoIiEK1gx+MEHFtt53oEdHfnx+21hXHIynHQSXH31EeZDr1hhcwbfeANOOQVmzoTrrtOcQRGR4xROi/GNwKme52VG+jAiUvIEk4Fz51qLyd69tmgkK8uSej6fBZEH3y77yOEunmMYfanIFp7hXvoxlC1UwucD//4YsHx5qF07jGBSRESOlmJAETnAkZbI5eaGukBycv7374OXuj4f1K9/lMviduyw9uHHHrNb4REjrMRQcwZFRApEOAnCJUA5YFOEzyIiJUTe1uFff7WqQL/fAsesrAPbhw81i6Y5X/IYHWnKt3zBBfyNd/nedzYJCVAh0ZaNnHeeqgRFRCJMMaCI/GH2bBg4EFautKQgHN1MQb/fPhIS4O67Yfz4MF84Ozs0Z3DrVrjrLksUVq16TO9DREQOLZwE4QhgkXNuCbAv+EPP81pH7FQiUmwFNxAvXAi7d4cqA4M3y8Huj0NtsavOWkbRk//jJdZwErfHTGdeSlvOauz4YpCSgSIihUwxoMgJLu+l78qVoeo/OLrZgs6F2oivuMIK/8Ly4Yc2Z3DxYmjZ0uYMnn320b4NEREJQzgJwueBUcBi4Cj+NyAiJ4rg3JnUVFizxi56g0Fj3uAxeMt88IiYRJdBJ28CfRlGDNmM8PXlucq9qNs4iX6toHVrtQ6LiESBYkCRE1DeDcTr14dahYOXugfPiz5YsI3YudDfVKhwlCNhVqyA7t2tn7l2bXjtNbj2Ws0ZFBGJoHAShHs9z3ss4icRkWLh4JmCaWmQmWnBoHMWRAYDx+AsmkPFcs6Bw+Nq703GeV2py0reib+Gb24Zx+2D6tBbCUERkWhTDChyAklNte3Dn38euuwNfkD4i0eCsV/p0lC2LAwYYGNhwrJjBzzyCDz+uM0ZHDkSOnXSnEERkUIQToLwc+fcCGAOB7aXfBuxU4lIkRMIwKOPWovJ9u2hW+HM/aPrg8tGgrMGD64czMs5ONO/jDHZnfkL8/gl7nRebDuPlo9cwZVKDIqIFBVRiQGdc38DJgJ+4N+e542M5OuJnKjydoBs2ADp6XbRGxNjsVt2dmg8zKE6Qw4WvCz2+aB6dbjkkqOYF52dDc88Y9nErVttSOEjj2jOoIhIIQonQdhk/+fz8vzMAy4t+OOEKDgUKRpSU2HcOPjkE9i1KxQoBpeNBBODQTk5odvlvPMGgz+rVmon/XIGc3f64+yLKc3nrSdQa/SD/N+psYX6vkRE5IgKPQZ0zvmBScAVwBrga+fcHM/zfozUa4qciGbPhv79YeNG2LPHLnyDG4jzzo3OO3MwyO8PtR3HxtpH4vEskXv/fejaFZYsgYsushvpxo0L7L2KiEh4jpgg9DzvksI4SF4KDkWiK+/smQ0b7GfB2YFZWQcGinlnzAQTgjExoeDROShTBi6/NJfh9adS99+9YfNmuOduYocN48LKlQv/DYqIyBFFIwYE/gz84nneKgDn3CvAPwDFgCLHKBCwUX5z58Ly5dYJkpYWqvYLxm95ZwYevIzE5wvNFoyJgcqVoW1bWzZyzHOily+3OYNvvmlzBmfNgjZtNGdQRCRKwqkgxDnXCmgA/DH8wfO8IZE6FAoORQrdwUlBv9+6PTwvdEscDCIPnisYDCbzVgympFhXSIsW0PfSVKoO7wizFkLz5vD229C0aeG+QREROWpRiAFPAlbn+X4NcG4EX0+kRJs9G4YNg19/tRZisGrBYGyXNzkYlLczJJgk9Pvt63r1LDEY1qKRw9mxw4YdPv64lR6OGmVzBuPjj/EJRUSkIBwxQeicexooBVwC/Bu4HvhvhM+l4FAkAoI3yB9/bAtGdu2CvXvtFjk93W6Eg0nB4Ofg7XJOTuj7YAsK2PcxMaFb5fh4qFkTzjgDWjVZx43f9qL0tS/YMJoXX4RbbtHNsIhIMRClGDAszrn2QHuAk7XmXuSQUlNh6FCL8yA0OzpYDeh5odgu+LXPZ8nA4HKShAQL4S688Bhahw8WnDPYvz9s22ZzBocOhSpVCuT9iojI8QmngrC553mNnHM/eJ432Dk3Dngn0gc7EgWGIuELbh5+6SWbNeN5FvTt22eBYFaWPS7vzBkIJQGDyb9gQJk3qCxVCpKT4aST4Oqr998oV9ln82OGDrVItHdv6NMHkpKi8fZFROTYRCMGXAvUzPN9jf0/O4DneZOByQDNmjXzDv69yIkuEIBeveC33ywvF4z58naC5N1ODPb72P0joStWLIAW4rzefx+6dIGlS+Hii2HCBM0ZFBEpYsJJEO4vRmevc646sBWoFrkjAWEEhwoMRY4smBicORPWrAlto8vKsq/hwNmBwe/hwOUjwcrAYDBZtqzNkO7a9aCbZM+Dt96yAPCXX6B1a9twUrduYbxdEREpWNGIAb8G6jnnamOxX1vglgi/pkiJEmwrXrLEqgHzVgRCKK47+OcVK0KtWnkufAsiMfjzzzZncO5cqFMHXn8drrlG3SQiIkVQOAnCuc65csAY4Ftse90zET2VgkOR45aaCqNHw6JFoYRgsK0EQsm/4AwaOPA2OSfnwKAyLg7KlYPrrz/MbfLPP1ti8J13oH59ePdd+OtfI/4+RUQkYgo9BvQ8L9s51wF4D/ADz3metzSSrylSksyebRe4e/bYCJjcXMjICHWM5J0j7fdbbFepEjzyiO0HKTDbt9ucwSeesDmDo0dDx46aMygiUoSFs8X4kf1fznLOzQUSPM/bGclDKTgUOTqHmi24YUNoVmDe7cN5A8NgkjDv7MG8ScHYWJs907ChJQZbtz5EYnDXLgsAJ060fuNx46BDB8soiohIsRWNGHD/674NvB3p1xEpSYJdI088EUoOxsXZrOmY/f/ii4mxC+CkJPtdhQpw3nkFMFswr+xs23o3cKDNGbz3XosTNWdQRKTIO2yC0Dl3DrDa87wN+7+/HbgO+N05N8jzvG2RPJiCQ5Ejy9tCvG2bJf5ycmw5XHZ2aNYMHJgYzNtiEkwIxsba3/j9YSQFg3/8/PM2X3DTJrjrLutnUQAoIlKsRTsGFJHw5Z0zvW6dzRqMibHP8fFQunSok6RuXbjpJhgwIEKHmTfPukl+/BEuucTmDJ51VoReTEREClp+FYT/Ai4HcM61BEYCDwGNsdl/10f8dCJyWIEAjBljHb27dlnwF2wXDm6hg9BmumD1YFycfY6JsY+EhNCykdNOg1at8kkKBi1YYG0i//2vXT2/+Sacc07E37OIiBQKxYAixUAgAJMn2/jn7dtDF8HBOdPBZGFCApQpYy3E7dpF4CA//WRzBt96C0491fqc//EPzRkUESlm8ksQ+vPcEN8ETPY8bxbWZvJd5I8mIocTCEC3bvDJJ6G5Mp4X2kZ88OKRuLhQ8jAlBRo0CDMReLD1661i8PnnoVo1mDYNbr01lI0UEZGSQDGgSDEwZ47NnA4uoouNtRgwI8M6QsCShHFxFvO1b19Ai0eCtm2z9uFJk+ymecwYeOghzRkUESmm8k1pw65sAAAgAElEQVQQOudiPM/LBi4D2of5dyISQcHb4q+/trxccL4MhFqMnQv9DqxiMDHRuj3GjTuG4DAz02YMDhlikWbPntC3LyQnF+h7ExGRIkExoEgRFgjAo4/afW1GRmhudHDBXFxc6LK4Rg0YMaKAF5AE5wwOGGBzbYJzBitXLsAXERGRwpZfkPcy8KlzbguQDnwO4JyrC0R8QLWIHNr8+bB5c6hV2O+3ysHMTPs+2D4cnC2YmGjb6Vq0gIcfPobk4Ntv29riFSvg6qth/HioVy8i701ERIoExYAiRVRwxMwbb4QSgbt3WyyYmGjfx8XZRXGdOvDYYwW4gATgvfdsTfKPP8Kll9qcwUaNCvAFREQkWg6bIPQ8b5hz7kOgGjDP84L7TvFhc2hEJAqWLbNRL7m5kJ5ut8OlS4dajEuXtu7fuDibOVOzJlx88TG0Ey9fboOm337bhhO+/TZceWWk3paIiBQRigFFiq758y03FxdnjRxbt9rXmZkWB/r9UK4cnHkmDBpUgMnBn36y+TZvv21zBv/zHwsuNWdQRKTEyLdNxPO8rw7xs+WRO46I5CcQgJUr7euqVa3DY/du+75UKUsAHvdN8a5dMHSo9a4kJMDYsTZPJtivLCIiJZ5iQJGiaeNGSEuz5GBODlSsaJ0j27fb7xs0gNtvP4aL4cPZtg0GD4Ynn7Rgc+xY6NBBcwZFREogzZERKQYCARtEPW0abNpkYwATEqx1ODHRKgmbNIEePY4jOZibCy+8AL16wYYNcOedMHy4ZSJFREREJOqqVLHk4O7dliiMibEFdKVK2QjA6dMLKDGYlQVPPw0DB8LOnbbhZPBgzRkUESnBlCAUKeJSU2H0aPj5Z5s9WLas/Twry26Rq1WDM86wETDHHBB+/bVVCS5YAOeea4Nt/vznAnsPIiIiInL8atWypOCaNXZZnJNjF8VlylgHcIEkB9991+YMLlsGl11mQWbDhgXwxCIiUpQpQShShAUCMGmSdXf4/dbNsWePzZaJjbVb5Dp1bDvxMQWEGzZAnz4wZYpVCk6dCrfdZpOtRURERKRICAQsTJszx5o+Kla0wr7MTDjvPMvnHfe8wWXLLMv4zjtQt65dGP/975ozKCJyglCCUKQImz/f5gw6ZzfEZctasnDPHkhKss8bN0Lz5kf5xJmZ8Pjj1iqSkWHrjfv1s+tnEREREYm64IiZmTNh8eJQpWCtWlY9WLeuNX6cfPJxJge3bg3NGUxKgnHjbM6g5k+LiJxQlCAUKcI2brTCvq1brYLQOahQwZKEOTk2f/Dyy4+yevCdd2w78c8/w1VXWdvIaadF7D2IiIiIyNEJBGDyZPjyS1i61C6MPQ/27oVVq2yR8K5dsG7dcewLycqCp56ydcc7d8J991misFKlgnwrIiJSTKiPUKQIq1IFqle32+LYWCv2y8y0QdT16tltcevWYT7ZL79Ym8hVV1lvyty58NZbSg6KiIiIFCGBgO0GmTEDFi2y5XQ+n8WCnmcfmzaF5lFXqXIML/LOO9CoEXTqBE2bwvffWwWhkoMiIicsJQhFirDmza1y8NxzoXFjKF3abpDr1YO//c0Wyh2xejAtzTYTN2gAn3xiG0+WLIFWrQrjLYiIiIhImIKVg4sWWedIbq51jQSThLm59rF7tyUKY2KOctTMjz/ClVfahXFODrz5JsybB2eeGbH3JCIixYNajEWKuPh4WzIMcPfdVjEYVktxbi689BL07Anr18Mdd8CIEbb2WERERESKnPnzYfNmK+TbsMGqBsE6SLKy7PvcXEsOpqTAAw+EGRdu3WqtxE89ZXMGx4+HBx/UnEEREfmDEoQiRVRqqnV6ZGeHWo337QvzjxcuhI4d7UnOOQdmz7YyRBEREREpsjZutHivVi2bMZiQYMlB3/6+L+dsBvXNN0PnzmEkB7OyLKAcPNjmDN5/v31dsWLE34uIiBQvajEWKYICAZg0ydqLq1WzQHHJEusEmT8/nz/cuNHKDP/8Z1i5Ep57Dr76SslBERERkWLA74fVq2HZMhstc/LJoVnUFStCmzbw+utWAJhvctDz4O23oWFDyyQ2a2ZzBidNUnJQREQOSRWEIkXQnDm2oS4uDrZssSRh6dL5bKrLyoInnrDWkb17oWtX6N8fypYt7KOLiIiIyDFITYUPP7TJMFlZUKGCVQw2aWKzqMOaPQ229rhbN3jvPVtG9+abNnvauYi/BxERKb5UQShSxKSmwtSplhjcutXaS5Yvz2dT3bx5toWua1ebUr14MYwdq+SgiIiISDER7B4pW9bCuSpVbBbhvn02jzCs5OCWLdChA5x1FixYABMmWFx49dVKDoqIyBGpglCkiAgELDE4daptpktKsvmD+/ZZkLhihQWGf2yqW7nSkoJz5sCpp+p2WERERKSYmj/f4r7KlS2UO/dcawqJj4fatY+QHMzMDM0ZTEuzOYODBqmVWEREjooqCEWKgEAAJk+2UTE+H5Qvb/FdVpZ9v3Ej7Nmzf1Ndym7o0wfOOMP6UEaOtFYS3Q6LiIiIFEsbN0LVqpCeHvpZYuJhukeCPA/eesvmDHbpYjOov//exs4oOSgiIkdJFYQiRcD8+dZG4vdDcrItI6lQwW6OfT5ISYFbbvY4f9V0uL6HDSO87TZLDlavHu3ji4iIiMhxqFLFukaWLrXvExNh2zaIicnTPZLX0qXWSTJvns0ZnDsXrrpKl8UiInLMlCAUiaJAwDqEJ0+GDRsspitd2hKFiYmWHExOhsvKfcO9z3eEb+ZD06bw2mtw/vnRPr6IiIiIFIDmzWHtWmjQwO6B162z5OCDDx7UXrxlCwwcCE8/beuNH33UWkxiY6N2dhERKRmUIBSJgmBicM4c2LTJFpH4/TZCZseOUJKwTMYm+m3ry9VfP4urWBGefRbatbPMoYiIiIiUGPHx8PXX9nWLFtC6dZ7kYGambTEZPNiGVT/wgM0ZrFAhWscVEZESRglCkUIWCMDMmbaZODfX4j2woDA317725WRxy6ZJdEsbRKK3B9e5MwwYAOXKRe/gIiIiIlLggrFhuXK2by4tzS6MgdCcwW7dLHj8619h/HibRS0iIlKAlCAUKWRz5lh8t2iRXQBnZ1uHSG4ulCoFDda/z4g9naibtYz0ln/B9/SjcPrp0T62iIiIiBSgYEfJtGmwfbsVAzZoAM2aWbJw6atLOPn9rvD++/CnP9k2uyuvjPaxRUSkhFKCUKQQBQIW41WrBpUqWYIw2F58cvYqBuzsxgU7/sPm5Dp81vENWo75u4ZNi4iIiJQwgQCMGQOff25fJyTYpXFWFuRu3EzXXQNp8MW/oFxZmDgR/vlPzRkUEZGIUoJQpBDNmWNJwcWLraU4NhbKxuzh3sAIHkwfS46L4fn6w/n1mi7c9UACKDcoIiIiUuLMmWPzBjMzISnJOklyMzK5YfUTdPhhCIk5u/nlLw9y2ksDNWdQREQKhRKEIoUkWD1YtSqsWQO5OR5/3/0KD216mCpZa3mnwq3MPncUja48ibtaH7SxTkRERERKjE8+gW3bYOdOiI3xuHTvXAaldaNOzgrml7uS6WePo8fk00G5QRERKSRKEIoUkmD14JYt0CBzER1WdKTRzi9Ynnw2n3d4levHX4CmyoiIiIiUbIEA/PorxMRAI7eY/lu70nLfB6yI+RNty7zN5rOvpE0bXRaLiEjhUoJQJEICAZg/H5Ytg6VL4csvoVrMZh7e1Y+bdj3DrpgKPPPnZ3in6p082tkf7eOKiIiISCGYPx+a1drM5Z8P4Notk9nlytI3+TGmxN9PlRqxtDofWreO9ilFROREowShSAQEAjBzJuTkwA8/wI/fZ3Fn2lP0TB9I6dw0ni/biedPGYg/qRxtrtANsYiIiMgJITOT6q88zmPvDSFm3x5er9aBkfEDWZuegnPQpg20a6fYUERECp8ShCIFLBCAgQPh559hxw44J+1D3tzYidOylvJZ/OWMrTGRXxPPoEpFG0qtG2IRERGREs7z4M03oVs3Wv7yC6sbXsUX14xlwa7TabQTmsVBw4bQoUO0DyoiIicqJQhFClAgAJMnw6JF0DDpV/5vdXf+uvt1fvfVpsNJs3kv4R8kJTvS02DvXrjmGt0Qi4iIiJRoP/wAXbvChx/C6aez6fl3mLH5b5QrB82TIS3NLpV1aSwiItGkBKHIcQoEbAHJ11/bvMF92/Zw99ZR3LdzNDn4GVVmKE8mdCMzJ4FKpaBiRShTxhKDCgRFRERESqhNm2DAAHjmGShXDh5/HO67j8qxsdywf1b1+vVQpQpcoZEzIiISZUoQihyHYMXg8uUQG+Px519n0Htbd07KXcOc0jczvNxo1vtrsHs3JCRAcjJs3gy1a8MDDygQFBERESlx9u2zZOAjj1jLyEMPWaIwJeWAi2WAc86B5s0VE4qISPQpQShyHObMgdRUqLn1Ox76pRNN93zGktjGPJQyneVVLsQ58O2GlBT7qFvXAsHWrRUIioiIiJQonmfBYbdusHIltGoFY8fCn/4EWMw4ejT89htUqGCVg599Bhs2QPv2ig1FRCS6lCAUOUapqTD731u4c1V/bk6bzE5fefpW/Bcvl7qbtL1+UrIs0NuyxeJDBX4iIiIiJdQPP0CXLvDRR3DGGfDuu/DXv/7x60AAJk2Cbdts3IxzsGYN1Kxp3SXz5ytOFBGR6FKCUOQYBFZls7zj08xeOoBS2buYntKBEXGDyEoqT6wPUhLA74c9e6BxYyUHRUREjsQ5Nwi4F9i8/0d9PM97O3onEgnDpk3Qvz/8+982Z/CJJ+C++yDG/pkV2D9rcOZMWLwYMjNDnSXx8bB9uz1048Yovw8RETnhKUEocpQ2vvwRMQ914o6tS/im3GUMr/woi70zyc2F3WkQFwfNmsF551mS8IYblBwUEREJ0wTP88ZG+xAiR7RvHzz2GAwdanMGO3a0OYPly//xkNRUePJJ2LkTliyxqsGMDNtanJEB1arZn9aoYe3GIiIi0aQEoUgYAgH47o3fqflYd5r88hprYk+ha61ZzK/SBg9HBZ8Ff36/LSBp2NCSgho6LSIiIlKCeB785z/QvTusWgVXX21zBuvXP+BhwZbimBjIybFldXv3Qmws7N5tecR166BsWahUyWJGERGRaCpyCUK1l0hRs/rnvax9aBRXfjyanFzH5JpDmJTQHVcqkV2boXJlaxOpX98CwGHDlBQUERE5Rh2cc7cDC4FunudtP9SDnHPtgfYAJ+t/ulJYvv/e5gx+/DE0aADvvQd/+cshHzp/PmRnW5z4++9QtarNHHTO4sXsbEhPh5tvhnbtFDuKiEj0FbkE4X5qL5Ho8zx47TVS/tmNmltX8+1pNzE0eTSZVU+mzE5bPlKpkrWIrF9vN8EPPqgAT0RE5HCccx8AVQ/xq77AU8AjgLf/8zjgrkM9j+d5k4HJAM2aNfMicliRoI0bQ3MGU1KsNLB9+z/mDB7KsmXWXbJ6tVUOJidbK/Hatfa5fn3rOOnQoRDfh4iISD6KaoJQJLp++MFmyXz6KXurn8WCgS/ywu8t2bcJsjKtHSQrCypUsORgixbWaaLkoIiIyOF5nnd5OI9zzj0DzI3wcUTyt28fTJxocwbT06FzZ0sU5pkzeLBAAKZOhRdesCrBuDhISrLqwfLlraLwqqtsLE3r1oX3VkRERI6kqCYIj9heotYSiYitW23A9NNP2ya6p55iRua9/LDUz6pV1hKSlQWJiZYcrFTJLpKVHBQRETk+zrlqnuet3/9tG2BJNM8jJ7CD5wz+/e82Z/C00/L9s0AAJk+GDz6wGHH79tBSkrg42LMHWrbUnGoRESmafNF4UefcB865JYf4+AfWXnIq0BhYj7WX/A/P8yZ7ntfM87xmlSpVKsTTS4mUnW1r5k47zZKDDzwAK1YQuOp+1m/ys327tYPk5NgF8p494PPZnz3wgAI8ERGRAjDaObfYOfcDcAnQJdoHkhPQd9/BpZfCtdfajfC8eTBnzhGTg2APS021juSsLEsSli1rnci1a8NNN8GECdC2rWJHEREpeqJSQaj2EilSPvkEOnWytuJLLoGJEwmUbcic6TBjhiUBa9SA+Hj7vG2btYhcf71uf0VERAqK53m3RfsMcgLbuBH69YNnn7U2kaeegnvuyXfOYF6pqdZavHWrFSCmpVn1YLVqdsFcrhycfnpk34KIiMjxKHItxmovkUITCFjryMyZluWbOZPAOdcx503HnDmwa5fd/qak2NyYatXg8stDcwfbto32GxARERGR45KRYXMGhw2zNpEuXWzOYLlyYT9Fair06GEtxdnZNl9w1y4oUwY2bbJYMibGLpZFRESKqiKXIMTaSxpjG+x+A+6L7nGkxElPh9GjYdQou+IdNAgefpjU70vxZD9YudKCuqQk2zpXurR9vWsXLF8OZ54JVapE+02IiIiIyDHzPHj9dXj4Yfj1V9sYMmZMWK3EeQUCttR43z67b/71V9i922LH9HSLJevW1UgaEREp+opcglDtJRIxngezZlnV4O+/ww03WCBYq9YfwV1MjA2RzsqCnTshORk2b7bW4sxM2LDBvr7iimi/GRERERE5JosWWaXgp5/aze/771ubyDGYP9+qBitVsvjx1FOt82T3boiNtek1gwcrOSgiIkVfVJaUiBS6xYvhssssKVimDHz8sQ0YrFULCAV3KSlWMRgXF1pCUrasPUVw2PQNNyjIExERESl2NmywuYJNm8LSpbaYbtGiY04Ogo0urFrV5lPv22ftxXXr2oXyhRcqOSgiIsWHEoRSsm3bBg89BI0b21a6SZPg22/h4osPeFgwuEtPh+rVoVSpUBVhUpIFfZdeasWHCvJEREREipGMDBg5EurVg2nToGtXWLEC7rsv7CUkh1OlisWOfj/UrGlVg5s322Wz2opFRKQ4UYJQSqacHLsVPu00ePJJuP9+CwQfeOCQgWAwuNu7137dsKElBT3PZlS3bAnt2yvIExERESk2guNlzjgDeve2bpKlS2Hs2KNaQpKfWrWsUWX7dttg7PNZ+Dl6NJx/foG8hIiISKEocjMIRY7bZ59Bx47w/fdw0UXw2GPQqFG+f9K8OaxdCw0awLp1FuTVqwePPqrgTkRERKTY+fZbmzP42Wd28/vBB5YgLECBgI2padjQ4scNG+yi+cEHFT+KiEjxowShlByrV9smuldftR6PV1+1gYHOHfLhwaBu2TJLCDpnF83ly8N551nSUBWDIiIiIsXIhg3Qty9MmQIVKlhHyT33WA9wAZs/3woRy5aFOnXsZzt32i48JQhFRKS4UYJQir/0dGsVGTHCMnwDB0KPHjZI8DACAZg50zqRV62ymDEnx26A/X4lB0VERESKlYwMmDABhg+3bSHdukG/fqFtcxGwcSNUq3bgz5KTYf36iL2kiIhIxGgGoRRfngevv25zZQYMgFatrBxw0KB8k4MQuvFdt86WkFSoYJ/Xr7efz59fOG9BRERERI6D59mt7+mnQ58+tpH4xx9hzJiIJgfBZlinpR34s7Q0+7mIiEhxowpCKZ6WLoVOneDDD+HMM+Gjj+CSS8L+82XLrAVk/nyoVMkWlCQnW6uxbn5FREREioFvvrE5g59/bvOmP/wQLr20UF46EIAtW2y0YZUq9vIJCbBjB1xxRaEcQUREpECpglCKl+3bLTF41lk2fPrxx2HRoqNKDgYCsHKlBXCVKsGePbB8OWzaZNWDuvkVERERKcLWr4e77oJzzoGffoLJky0uLMTk4MyZkJgYSgbOm2dTb264QWNqRESkeFIFoRQPOTnw7LPWOrJ9O7RvD488AhUrHvVTzZ9vt7xLl9pCkt27ISvLBko3aqSbXxEREZEiKT09NGcwK8uW0/XpE/FW4oPlXU5StqzFjTt3QunSSg6KiEjxpQShFH1ffAEdO1ql4IUXwmOPQePGx/RUgQC8845tLI6JsYUklStbkjA314I6LSgRERERKUKCcwZ79LAb3TZtbMbgqadG5ThaTiIiIiWREoRSdK1ZY4Hgyy9DjRrwyitw442W3TsGwXaQhAR7CuesvfjSSyEuzm5927Yt4PcgIiIiIsfum2+gc2e7MD7rLJgy5ahGy0RCcDlJ3sJFjagREZHiTjMIpejJyIBhw6B+fdtS3L+/zZe56abjSg6OHQsLF0J2NmzebD8vVQq++87aips3L8D3ICIiIiLHbt06uPNOmzO4fDk884wlC6OcHASLGXfssLbi3Fz7rFhSRESKO1UQStHhefDGG9C1K/z6K1x7rWX1atc+rqcNVg5u2mTtIBkZ9lL79llQ53kaKC0iIiJSJKSnw/jxMGKEzRns0cPmDJYpE+2THSA+Hj77zL4+5xzFkiIiUvwpQShFw48/2nbiDz6ABg3s82WXHffTBisHN2+GXbts7mCVKvaRkABnnqmB0iIiIiJR53kwY4YlBAMBuygePTpqcwYPJzUVnnzSOlKqVIHq1e3SWUREpLhTi7FE144d0KWLrQ9euBAmTrRlJAWUHAxWDlatCikpsGyZDZZOSIANG9QOIiIiIhJ1CxfaIrq2baF8efj4Y5g1q0gmB3v0sI7nXbtgyxZYsgRycmyzsYiISHGmCkKJjpwceO456NvXoqt774WhQ6FSpeN62kDAArRly2DBAls+kp0dqhwE2LbNfla5stpBRERERKJm3TprH37+eQvM/v1vaNcO/P5on+x/BAIwaZJVC1aqZN3Pq1dDzZr2NuLjo31CERGR46MEoRS+L7+Ejh3h22+hRQt47z1o0uS4nzZYMZiTA6tWWSIwPh4qVLCEIVhAl5MDzZopOSgiIiISFenpMG6czRnMzoaePYvknMG85s+3owaTg8GE4PbtFlued150zyciInK8lCCUwrN2rQWAL70EJ50E06dbK8kxbCbOWym4fbs9xc8/hyoGU1IsgNuzx74//XRVDoqIiIhElefBq69aPBgIwHXX2ZzBOnWifbIj2rjRRtbExFjlIEBsrDXCpKRoZI2IiBR/ShBK5GVkwIQJMGyYZej69oVevSAp6aifKhCAOXNsh0mpUvbUPp8tPc7Otovn9HTYvduGRu/ebQnEevVUOSgiIiISNV9/DZ072w1v48YwbRpcdFG0TxU2v9/mWq9YYdWDWVk2yzo+Hh54QLGliIgUf0oQSuR4Hrz5pi0hWbUKrrnG2kmO8pY4b7XgqlWW6KtaFVautARgYiIkJ8POnRa85eRY0nD3bpsLs20brF+vykERERGRQrd2rbUPT5tmA6GffRbuuKNIzhk8lODl9Jw5tpikenXrUNmyBWrXtqUl558f7VOKiIgcPyUIJTKWLbNb4nnzrL933jy44oqjfprgXMFy5UIJwJ9+grPOsorBpCSLO2vXtmpCz7PkYG4ubN5si/Auvtj+TslBERERkUKyd69dDI8caUFbr16WKExOjvbJ8pX3YnrpUhths2+fXTRXrQpbt9rnU0+Fhg2VHBQRkZJDCUIpWDt3wuDB8PjjULo0PPqo9V3Exob158GgbONGS+p9840l/qpWtURgzZqWLPz9d2sxzsy0v9uzBypWtN9t22Z/m5VlxYonn2xzYZQcFBEREYkwz4NXXrE5g6tXw/XX25zB2rWjfbLDCsafqakWeyYkWEi7caPNHARrL87Ohrp1bc71+edbh4qIiEhJoQShFIzcXJgyxW6GN2+Gu++2mYOVK4f9FHmrBWNi4NNPYflyaNrUkoQbNticl1q14PvvoX59azNOToa0NEsQOqeKQREREZGo+O9/rYMkNRWaNIEXX4SWLaN9qkM6VFJwzRq7396yxZKB6elQoYJVEMbGWlHk9u32dVqadUyLiIiUFEoQyvFLTYWOHWHhQivVe/tty+odpfnzLTlYtiwsXmwBWaVKFsA1amSJwUDAqgIbNrTgLCkJatSw5SSeZy3FqhgUERERKURr10Lv3vDCC9b28dxzcPvtRW7O4JGSghkZdtmclWVJwcRESwrGx9tdeHa2LSapUcM+H8P0HBERkSJLCUI5duvW2TyZF16wic0vvgi33GKR1THYuBGqVbOvd+60ZF+wWnDvXksW7tplwdmpp9pow759lQgUERERiYq9e2HsWBg1yrbE9eljsWERnDOYmgpPPmkx5vLlh04Kgs2yzsmxj9KlrWKwXDm7jN661RKHDRtC69aKQUVEpGRRglCO3r59NlvwkUcsmurd2wLCpKTjetoqVaxdo2xZ+0hPtyrBRo3s5nb9ejjlFOjeXQGZiIiISNR4Hrz8ss0ZXLPG5rqMGlVk5gwGNw9//TXs3m1Jv59+gpQU+zo723KbBycFnbNKQc+zWdf79tnn5GR7nj/9SVuLRUSk5FKCUMLnefDWW9ClC/zyi12djh9v5XwFoHlzm0EIUK+ezSAEuOgiawGpWVNzBUVERESiasECmzP41Vdw9tkwfTpceGG0T/WHQAAmT7YqwdhYa3jZtMmSfeXKWTd0fLw99uCkYGKifXgenHSSVRtWrmxdLOeco6pBEREp2ZQglPD8/LMFg+++a9tB3n0X/vrXAnnqvJuL4+OtcjAnJzTTOjvbWjyuuEJBmYiIiEhUrFljXSMvvmhzBqdMsTmDPl+0T/ZHLLlsmeUvN2+2asGcHKv+27YNMjMt4ZeYaF/7fP+bFKxe3RKJF10E556rmdYiInJiUYJQ8rdrFwwZAhMnWo/FuHHQoQPExRXI0+fdXFytmrUY79ihSkERERGRImHvXhgzxlqIc3NtAHTPnkVizmCwlfiDDyxMzciwZOD27fb9+vU2z7pUKUsK7tlj8eb69TbORklBERGRECUI5dByc+H55+2meNMmuOsuGDbMBgUWoLybiyH0ef58BWciIiIiUZOba3MGe/Wy6sEbb7Qk4SmnRPtkQOiSeflyK2hcudLmBCYl2edgteDmzRZf7tlj7cIJCfZ9qVJKCoqIiOSlBKH8r1jvY8gAABv0SURBVAUL4KGHbLLz+efD3LnQrFmBv0wgAO+8Y7NfypeH006zGS/JyXazKyIiIiJR8NVXNlpmwQJo2tQShS1aRPtUfwgEbHny5s0WM9atayNpkpJCbcUbN0KNGpbbjI+3/SkVKlii8JprNE9QRETkYEoQSsj69VYx+Pzz1n8x7f/bu/cwK8ty8ePfG9REQlRkowIeA8UjISqiaaa208pT8gtju7OTe3cCNbGQtqWJkbrTyr132mVi5iHJTbrVUiyPiYkoGYhgnEIFFXVgQOI0z++PZ00MwzAzwMy8i1nfz3XNxVrvad2Ll8N93c/z3O8vYNiwFustU7fXYMeOsGhRHsWNyEtCJk3K9cjttmvxiYqSJElqyoIFORe8/facC44bB+eeWxZ9BmvVzhx8880c4uLFMH16ni0Iedlwnz75/cqVedbgQQflYmC/fs4WlCRpYywQKjdl+dGPcq/BlStzX5nRo1u0t0z9XoO//33uD3PYYXlpyA475J+pU/NMwpNPbrGPliRJUmOWL899Bq++OlfYvv3tnA++//1FR7aeujMHly6FbbbJK56nT8/p7Nq1eaA5Ak49NQ9I29dakqTmsUBY6R58MC8heeUV+MQn4Ic/zMOuLax+r8FVq/LT5RYvhkGD8kOSq6pyTmoiJ0mS1AZqauCOO3Kfwddeg09/OvcZ3GuvoiNbT92Hkbz5Zp4J2LFjfmpxv375Z8aMXM/s1SvnkXvu6WxBSZI2hQXCSjVrFlx4YS4Q9u2bfz3llBb/mNplxb/6Vc41998/9xns2jUvK66qyu+7d8/NpDt3NpGTJKk9i4ghwHeBfsCRKaXn6uwbBXwBWAsMTyk9VEiQlaBun8GBA3OydswxRUe1nrqFwerq/DCSzp3hpZfg4INzYfCdd2DHHfNXuPhi80hJkjaXBcJKs3QpXHklXH99bgB47bX5gSTbbbfZl6zbW7BHj1wInD8/j+TOmQOHHJK3VVWt6zO4//7w6KP54SQ1NTnpq6pyabEkSRVgGnAWcGPdjRFxIDAUOAjYA3gkIvqmlNa2fYjt2IIFecbgHXfkvi+33gr/8i9t2mewNnecMSO3nNl55zxYDHn58Lvv5mXC77yTlw3vtlvevmAB7LFHXhE9b14uEq5dm4uDrkCRJGnLWCCsFDU1cNttOSFctAg+9zm46qqccW2B+r0F583L+eaxx+YZgR07wrRpuSD47rs595w5MxcN+/bNH79wYS4snnyyiZ0kSe1dSmkGQETU33U6cFdKaSUwNyL+ChwJTGrbCNup5ctzj8Frrimkz2DdouCcOeueMNyxYy78rVqVj3vf+/KMwNmzc344f37uWb3TTvkrLFsGBx6Yu+MsXJgfQmJxUJKkLWeBsBI8+ywMH56XkBx1FNx7Lxx5ZItcun5vwddfz70FFy7MBcJddoEVK9bvNTh/fn59/vkmc5Ik6R96As/Uef9qadsGIuJ84HyAPU0mGldTk59K/K1v5URt6FAYO7ZN+gzWLwoecsi6AeSnnsohdOuW2x/WWrUK9tknFwBXrMh55vz5+WEks2blAedeveCAA/Jgs8VBSZJahgXC9mzRIrj0UrjlljxVb9w4OPfcLV5CUndJ8fPP51pjbYFwyZK8TKSqKm9bsQI6dcrJXPfueSXzoEE5N5UkSe1TRDwCNLRMYXRK6d4tvX5K6SbgJoCBAwemLb1eu/X007nP4OTJcMQRednH4MEt/jF1c8OOHfO2t97asCg4bVrODXv3zgXAd9/NswRXr97wmjvtlPPJfv3gz3/OTyzu1SuntwsX5pUnp51mcVCSpJZSSIHQ5tStbNUq+PGP4Yor8pNARo7My0h23HGLL11/SfH228Pjj8MJJ6x7+EhtL5m+fXP/69rR3yVL7DMoSVIlSCmdtBmnvQb0rvO+V2mbNtXf/pZnDN55Z27a94tfwLBhLdZnsH5BcNGiPOtvm21yXgh55XL9omDtqpLa3LCqKh+77bYbfsbOO+ce1WvW5CJjTU1eXnzmmRYGJUlqDUXNILQ5dWv57W/zSPGsWXDqqXDddblStwXqJoGzZ+fR29oZg/3754eNTJ0KJ56Yc9A5c3JvmG7d4KCD4MUX8/GdO9tnUJIkbdR9wB0R8UNyHtgHeLbYkLYyy5at6zMIcNllcMklOQnbAhsrCO6+O/z+93lwuGfPPCuwW7d8zowZMGDA+kXBTp1yKO+9l18vXQpvv52LiXV7EL79dn5IyTHH5P6E++2XZxIOHmweKUlSaymkQGhz6lbw17/ChRfC/fdDnz75149/fIsvW3/G4KRJOQns0iXPGOzeHY4/Prc5XLgwJ22jRuVeMbXvhw41mZMkSVlEnAn8BOgOPBARU1NK/5xSmh4RdwMvAWuArzpI3Ew1NfDLX+Yk7PXX4TOfge9/f4MErG6hr0ePdauNG9vWWEGwQ4dc2Ntllzw2XdtqBvJzUOoXBVesyOftsUceQB4wIB+3114bPsV4551zUfDLXzaPlCSpLZRbD0KbU2+q6moYMybPFNxuuzxqPGJEfr0Z6ieOixev/xCS3XbLy0FmzVqXyG2/PZxyyvp9BY8+egu/lyRJapdSShOACRvZNwYY07YRbeXq9hk88kj49a/5W8+jcz43Yf2iX91B3+pquOmmvL22+NfQtsYKgrXtZf7+9/X7TwN84AMNFwW7dnUAWZKkctRqBUKbU7ey2ifSffObeareZz+bR4p3332zL1l/tmB1NUycCB/96LoC4f775zx00aIcQnW1fQUlSZI2RUMz+RoqljV63Pz5LB/+LTrfdxfVXXsy7Su30XPkZ6BDhw3yufHj89LduoO+Xbvm2XqQW8ZsbFtjBUHIueGjj+YZf336rOtBePzxsHy5RUFJkrYWrVYgtDl1K5o8GYYPz08AOeIImDABjjpqiy/79NMbJo677ZYTu9oCYPfuuVH0q6/mumSPHvYVlCRJ5a25Bbm2uG5DA7Ljx8OQIeufu7Hj/t+py+h9xw+oueZatqsJnv/kd5h79kiqVnfm6XsaLgQCPPHEht1nVq7cML762xorCNbU5EUrffvmnHHNGjjuuHzcmjUWBSVJ2pqU2xJjm1M35o034NJL4ZZb4J/+CX7+8zxzsEOHRhPUje2rv33GjPxQkboOPTTPIlyyJPcdrK7OvWguvthkT5Iklb/mFuTa6roNDcjWbq973gbHdanhA3+8jV2vHAVVC/nbMcN45vTvs80+eWy9dFiDhcAuXfKv1dXrrge5mFhf/W2NFQRrB4vPP9+8UJKkrV0hBUKbU2+i1avhhhvgu9/NzVy+8Q34j/+AHXcEGk9QoeF9gwevSzxrt8+enZtI77vvuo/efns46aS83RmDkiRpa9PcglxbXfeNNzbsCNOlS86zNnZctxlPcditF7LL7OdYuNdRdPrt/3LvnwY1eB3YsBBYXZ0XndTOAqwd9K3tJ113ILj+NguCkiRVhqKeYmxz6uZ66CFWf/UCtp39MvMO+BjTv3gdhww5gD13XHdIYwkqNLzvzjvhsMPW337oofCXv0C3buuSxKqqLR9hlyRJKkpzC3Jtdd0ePRou4PXoseFxad58jvrNJfSedDfv7dKTx774SxadcA5DB3Wgx7zmFwJr8znI+WHdQl9zt5kLSpLUvpXbEuNWVU79Z5o8b/ZsuOgiuO8+lnXbj8lf/z+qjvk41cuCl+stX2kqQW1o34IFcOyx62/fe+/cTNrZgpIkqb1obkGura47eHBezQHrF/DWe+DbsmWc8sexdP7ptdChA9PO/i5TTriYxSs6M+TYxq/TUCGwbj7XUF7X3G2SJKn9qpgCYbn1n2m08fRtV8F//idsuy1TzxnLC8ddQJdd30cHGl6+0lSC2tC+3r0b3t6vX24mLUmS1B40qyDXhtfdc8+cJzZYwKupgV/8AkaNouuiRSw/YxiPnDSWeWt60WMXGDJ4/ULfRq+DBT5JkrRpKqZAWG79ZzY4b8fEfs/cQbcxl8C7r8O558LYsTw+fg9232X9c+svX2kqQW1o3znnrFuC3JLJsiRJUjlpqpBWxHX33LOB4556Ci64AKZMgaOOggkT6DxoEKdv6nUkSZI2Q8UUCMut/0zd83aaPYX+twxn15lP80bvw9nhgV/D0UcDzVu+0lSCurF9PXu2fLIsSZJUblqrkNYi1503D775Tbj7bujVC26/PY/kRrREiJIkSc1SMQXCcus/06MHrH7tTY5+YDT7/OFmVnbZlSfOu5nXTz6PoUd3+MdxzV2+0liCurF9jjpLkiQVpLoaxo7NbWU6doTLL4eLL4Yddig6MkmSVIE6NH1I+zB4cC6sLVmS27ssWZLfDx5cwHVXr+afZ1zPpy7ty96PjWPWqRcw/spZTD748ww+dv1bUjs7sPbBIZ07+1RhSZKkrVZNDdxyC/TtC1ddlRO7mTPhssssDkqSpMJUzAzCsuk/M3EijBjBzjNmsOK4j/K7067nlW360aP7+o2n63+GBUFJkqSt3JNP5j6Dzz8PgwbBb36T+w1KkiQVrGIKhFBw/5k5c+Ab38iJ4L77wr330umTn+RU+8tIkiS1b3Pn5j6D48dD795wxx0wdKh9BiVJUtmomCXGhVm+HL79bTjwwDx78KqrYPp0OO00k0JJkqT2rLoaLr0U+vWDBx6AK66Al1/2ISSSJKnsVNQMwjaVEtx1F4wcCa+9BsOGwQ9+kB8dLEmSpPZr7Vq49dZcHHzjDfjXf82DxOaBkiSpTFkgbA0vvADDh8NTT8GAAfCrX8ExxxQdlSRJklrbE0/kPoMvvJCfWnfffXDkkUVHJUmS1CiXGLekt96Cf/s3OPzwvHzkZz+DZ5+1OChJktTezZ2bn1x3/PGweDHceWceLLY4KEmStgIWCFvC6tXw4x9D375w880wYgS88gp88YvQsWPR0UmSJKm1LF0Ko0bBAQfAgw/C974HM2f6EBJJkrRVcYnxlnrkkVwQfOklOOkk+NGP8gNJJEmS1H6tXQvjxsHo0bnP4Gc/C2PG2GdQkiRtlZxBuLnmzoWzzoKTT4YVK2DCBHj4YYuDkiRJ7d3jj8PAgXm1yH775ZYy48ZZHJQkSVstC4SbavlyuOwy6NcPHnoIrrwyzx484wyXkUiSJLVnc+bApz4FH/4wvPMO3HVX7jN4xBFFRyZJkrRFXGLcXCnlpxGPHAmvvgrnnANXXw29ehUdmSRJklrT0qVw1VVw3XWw7bZ5gPiii6BTp6IjkyRJahEWCJtj6lQYPhyefBL694c77oAPfajoqCRJktSa1q6FW27JfQbffBPOOy/3Gdxjj6IjkyRJalEuMW7M4sXw5S/D4YfnZcQ33gjPPWdxUJIkqb177LGcA37pS9CnD0yenIuFFgclSVI7ZIGwIWvWwA035GTwZz+Dr30NXnkFzj8fOnYsOjpJkiS1ltmz84PoTjgBqqpyi5knn8wPJZEkSWqnXGJc3x/+ACNGwLRpcOKJcP31cPDBRUclSZKk1rR0aV4+fP31uc/gmDFw4YX2GZQkSRXBGYS15s2Ds8/ORcFly+Cee2DiRIuDkiRJ7dnatXnFSJ8+cM01MGxYXjly6aUWByVJUsVwBuF778EPfpCfSBwBV1wBF19sQihJktTePfooXHABvPgiHHssPPhg7jsoSZJUYSq3QJgSjB+fi4ELFsDQoblI2Lt30ZFJkiSpNc2eDSNHwoQJsNdecPfdeSVJRNGRSZIkFaIylxi/+GJuPP3pT8Muu8Djj8Odd1oclCRJas+WLIFLLoF+/XIrmauugpdfhiFDLA5KkqSKVlkFwrffhq9+FT74QfjLX+B//gemTIHjjis6MkmSJLWWtWvhpptyn8Frr4Vzz4VZs2DUKNh++6KjkyRJKlxlLTGeMQNuvBG+8hW4/PI8e1CSJEnt28qVcOWVcMAB+SnFAwYUHZEkSVJZqawC4bHHwty5LiWWJEmqJDvsAJMmwR57uJRYkiSpAZVVIASLg5IkSZWoZ8+iI5AkSSpbldWDUJIkSZIkSdJ6LBBKkiRJkiRJFcwCoSRJkiRJklTBLBBKkiRJkiRJFcwCoSRJktpERAyJiOkRURMRA+ts3zsiVkTE1NLPT4uMU5IkqdJU3lOMJUmSVJRpwFnAjQ3sm51S6t/G8UiSJAkLhJIkSWojKaUZABFRdCiSJEmqwyXGkiRJKgf7RMQLEfF4RHyosQMj4vyIeC4innvrrbfaKj5JkqR2yxmEkiRJajER8QiwWwO7RqeU7t3IaQuBPVNKb0fE4cBvIuKglNLShg5OKd0E3AQwcODA1BJxS5IkVTILhJIkSWoxKaWTNuOclcDK0uspETEb6As818LhSZIkqQEuMZYkSVKhIqJ7RHQsvd4X6APMKTYqSZKkymGBUJIkSW0iIs6MiFeBo4EHIuKh0q7jgBcjYirwa+DfU0rvFBWnJElSpXGJsSRJktpESmkCMKGB7fcA97R9RJIkSQJnEEqSJEmSJEkVLVLa+h/8FhFvAfOLjqOC7QosLjoIAd6LcuK9KC/ej/LhvSjWXiml7kUH0ZIiohqYWXQc8u92mfA+lAfvQ3nwPhTPe1AedgU6N5UDtoslxu0t0d3aRMRzKaWBRcch70U58V6UF+9H+fBeqBXM9M9U8fy7XR68D+XB+1AevA/F8x6Uh9J92Lup41xiLEmSJEmSJFUwC4SSJEmSJElSBbNAqJZwU9EB6B+8F+XDe1FevB/lw3uhluafqfLgfSgP3ofy4H0oD96H4nkPykOz7kO7eEiJJEmSJEmSpM3jDEJJkiRJkiSpglkglCRJkiRJkiqYBUK1iIi4JiJejogXI2JCROxUdEyVKiKGRMT0iKiJCB8pX4CI+FhEzIyIv0bEt4qOp5JFxM8j4s2ImFZ0LJUsInpHxKMR8VLp36cRRcek9iUivlfKQaZGxMMRsUfRMVUi88HyYC5YHHPA8mD+Vzxzv/IQEdtHxLMR8efSfbi8seMtEKqlTAQOTikdCswCRhUcTyWbBpwFPFF0IJUoIjoC/wWcAhwInBMRBxYbVUUbB3ys6CDEGuAbKaUDgUHAV/17oRZ2TUrp0JRSf+B+4LKiA6pQ5oPlwVywAOaAZWUc5n9FM/crDyuBj6SUDgP6Ax+LiEEbO9gCoVpESunhlNKa0ttngF5FxlPJUkozUkozi46jgh0J/DWlNCeltAq4Czi94JgqVkrpCeCdouOodCmlhSml50uvq4EZQM9io1J7klJaWudtZ8Cn8BXAfLA8mAsWxhywTJj/Fc/crzykbFnp7baln43mSBYI1Ro+D/y26CCkgvQEFtR5/yr+Zyj9Q0TsDXwQ+FOxkai9iYgxEbEAGIYzCMuB+aAqjTmg1ABzv2JFRMeImAq8CUxMKW30PmzTdmFpaxcRjwC7NbBrdErp3tIxo8nTiW9vy9gqTXPuhSSVm4h4P3APcEG9GV9Sk5r6vy+lNBoYHRGjgK8B32nTACuE+WB5MBeUtDUw9yteSmkt0L/UF3hCRBycUmqwP6cFQjVbSumkxvZHxHnAJ4ATU0ou7WlFTd0LFeo1oHed971K26SKFhHbkhPE21NK/1t0PNr6bML/fbcDD2KBsFWYD5YHc8GyZA4o1WHuV15SSlUR8Si5P2eDBUKXGKtFRMTHgEuA01JK7xUdj1SgyUCfiNgnIrYDhgL3FRyTVKiICOBmYEZK6YdFx6P2JyL61Hl7OvByUbFUMvNBVThzQKnE3K88RET30sxBIqITcDKN5EgWCNVSbgC6ABMjYmpE/LTogCpVRJwZEa8CRwMPRMRDRcdUSUrN2b8GPERuxnt3Sml6sVFVroi4E5gE7B8Rr0bEF4qOqUIdA5wLfKT0f8TUiDi16KDUroyNiGkR8SLwUWBE0QFVKPPBMmAuWAxzwPJh/lcWzP3Kw+7Ao6X8aDK5B+H9Gzs4nPkvSZIkSZIkVS5nEEqSJEmSJEkVzAKhJEmSJEmSVMEsEEqSJEmSJEkVzAKhJEmSJEmSVMEsEEqSJEmSJEkVzAKhpMJFRLeImFr6WRQRr5VeV0XES20cyxkRcWCd91dExEmbcZ29I2LaRvYdFBF/iIiZETE7Ii6PiBb/97ix7xIRj0XEwJb+TEmSpOYyBzQHlFQ+LBBKKlxK6e2UUv+UUn/gp8B1pdf9gZqW/ryI2KaR3WcA/0ioUkqXpZQeacHP7gTcB4xNKe0PHAIcCYxoqc+oo1W/iyRJ0pYwBzQHlFQ+LBBKKncdI+JnETE9Ih4uJVdExH4R8buImBIRT0bEAaXte5dGZl+MiN9HxJ6l7eMi4qcR8Sfg6obOj4jBwGnANaXR6/1K551dusYREfF0RPw5Ip6NiC6lz3syIp4v/Qxu4vt8BvhjSulhgJTSe8DXgJGlz/huRFxce3BETIuIvUuvf1OKd3pEnF/nmGURMaYU1zMR0aOp71JXRHw0IiaV4h8fEe8vbR8bES+Vfi+v3eQ7J0mStPnMAc0BJbUhC4SSyl0f4L9SSgcBVcCnSttvAr6eUjocuBj479L2nwC3ppQOBW4HflznWr2AwSmlixo6P6X0NHlkd2RpNHt27YkRsR3wK2BESukw4CRgBfAmcHJKaQDw6Xqf15CDgCl1N5Q+p1NE7NTEuZ8vxTsQGB4R3UrbOwPPlOJ6AvhSY9+lrojYFfg2cFLpOzwHXFS69pnAQaXfyyubiE2SJKklmQOuYw4oqdU1NsVaksrB3JTS1NLrKcDepdHNwcD4iKg97n2lX48Gziq9vg24us61xqeU1jZx/sbsDyxMKU0GSCktBYiIzsANEdEfWAv03fSv2GzDI+LM0uve5MT5bWAVcH9p+xTg5E245iDyEpQ/ln4vtgMmAUuAvwM3R8T9da4vSZLUFswB1zEHlNTqLBBKKncr67xeC3Qiz36uKvWo2RTLS79u7vkNuRB4AzisdN2/N3H8S8BxdTdExL7A2ymlqohYw/qzu7cvHfNh8oj10Sml9yLisdp9wOqUUiq9Xsum/dsewMSU0jkb7Ig4EjgROJu8BOYjm3BdSZKkLWEOiDmgpLbjEmNJW53SyO3ciBgCENlhpd1PA0NLr4cBT27i+dVAlwY+diawe0QcUTqnS+RG113Jo8o1wLlAxybCvx04NtY9Sa4TeUnKd0r75wEDSvsGAPuUtncF3i0lhgeQR32bsrHvUtczwDER8YHSZ3aOiL6lEfauKaUHyQnwYY1dRJIkqbWZA5oDSmo9Fgglba2GAV+IiD8D04HTS9u/DnwuIl4kJ2sbezLcxs6/CxgZES9ExH61B6eUVpH7y/ykdM5E8ujtfwOfLW07gHUj1A1KKa0gN44eHRGzgMXkhtW3lw65B9glIqaTR2xnlbb/DtgmImYAY8lJXVMa/C714nkLOA+4s/R7Nqn0PboA95e2PQVc1IzPkyRJam3mgE0zB5S0yWLdjGRJUluLiDOAHwInpJTmFx2PJEmSWp85oKRyY4FQkiRJkiRJqmAuMZYkSZIkSZIqmAVCSZIkSZIkqYJZIJQkSZIkSZIqmAVCSZIkSZIkqYJZIJQkSZIkSZIqmAVCSZIkSZIkqYL9f4/FbPUyZZerAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1296x432 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<p><em>Ratios</em></p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
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"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>N(F) / N(M)</th>\n",
" <td>&lt;= 1.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Ratio\n",
"N(F) / N(M) <= 1.5"
]
},
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"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<p><em>Shapes</em></p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
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},
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>Chebyshev</th>\n",
" <th colspan=\"3\" halign=\"left\">Kolmogorov-Smirnov 2-sample</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>Eq.</th>\n",
" <th>Sig.</th>\n",
" <th>Stat.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>F, M</th>\n",
" <td>0.0802189</td>\n",
" <td>True</td>\n",
" <td>0.212052</td>\n",
" <td>0.0669787</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Chebyshev Kolmogorov-Smirnov 2-sample \n",
" Eq. Sig. Stat.\n",
"F, M 0.0802189 True 0.212052 0.0669787"
]
},
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}
},
{
"output_type": "display_data",
"data": {
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"text/plain": [
"<Figure size 1296x432 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 1296x432 with 2 Axes>"
]
},
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}
},
{
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"data": {
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"<p><em>Equality of Variances</em></p>"
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">1-way ANOVA of O'Brien transform</th>\n",
" <th colspan=\"3\" halign=\"left\">Levene's Test for skewed (non-normal) distributions</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>Eq.</th>\n",
" <th>Sig.</th>\n",
" <th>Stat.</th>\n",
" <th>Eq.</th>\n",
" <th>Sig.</th>\n",
" <th>Stat.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>gender</th>\n",
" <td>True</td>\n",
" <td>0.842614</td>\n",
" <td>0.0394435</td>\n",
" <td>True</td>\n",
" <td>0.431623</td>\n",
" <td>0.618967</td>\n",
" </tr>\n",
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" 1-way ANOVA of O'Brien transform ... Levene's Test for skewed (non-normal) distributions \n",
" Eq. Sig. ... Sig. Stat.\n",
"gender True 0.842614 ... 0.431623 0.618967\n",
"\n",
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"<p><em>Difference of distributions</em></p>"
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"<p><em>Results for gender as independent variable, and relative_intensity as the dependent one</em></p>"
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"<div class=\"bg-info\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(10, 148, 170);\">Distributions are not normally distributed (with 6.755% outliers), distribution shapes are similar, distribution sizes are balanced by a factor lower than or equal to 1.5, and their variances are homogeneous</div>"
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"<div class=\"bg-success\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(25, 142, 51);\">Inspection of Q-Q Plots and a Shapiro-Wilkinson test (p = 0.000) revealed that relative_intensity was not normally distributed for all groups (F, M), and there was homogeneity of variance as assessed by Levene's test for equality of variances for medians (p = 0.432). </div>"
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"<div class=\"bg-success\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(25, 142, 51);\">Inspection of histograms / CDF plots and Kolmogorov-Smirnov test reported that groups had a similar shape (p = 0.212). </div>"
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"<div class=\"bg-danger\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(149, 15, 28);\">Although normality assumption seemed violated, an independent t-test was run on the data with a 95% confidence interval (CI) for the mean difference. It was found that relative_intensity in the F group (3.624 ± 5.931) was not significantly different than the M group (3.299 ± 5.931) (t(975.0) = 0.851, p = 0.395) with a difference of 0.325 (95% CI).</div>"
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"<div class=\"bg-danger\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(149, 15, 28);\">Therefore, a Mann-Whitney U test was run on the data with a 95% confidence interval (CI) for the median difference. It was found that relative_intensity in the F group (Mdn = 3.020) was not statistically significantly different than the M group (Mdn = 3.025) (U = 119272.500, p = 0.535) with a difference of 0.005 (95% CI).</div>"
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"<p><strong>No outliers detected</strong></p>"
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"<p><em>Normality (&ast;Lilliefors correction)</em></p>"
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" <th colspan=\"3\" halign=\"left\">Anderson-Darling</th>\n",
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" <th></th>\n",
" <th>N</th>\n",
" <th>Stat.</th>\n",
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" <th>Stat.</th>\n",
" <th>Sig.</th>\n",
" <th>Normal</th>\n",
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" <th>Normal</th>\n",
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" <tr>\n",
" <th>F</th>\n",
" <td>480</td>\n",
" <td>0.0264125</td>\n",
" <td>0.638843</td>\n",
" <td>True</td>\n",
" <td>0.993757</td>\n",
" <td>0.0453013</td>\n",
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" <td>0.511284</td>\n",
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" <td>563</td>\n",
" <td>0.0423764</td>\n",
" <td>0.017499</td>\n",
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" <td>0.993611</td>\n",
" <td>0.0175294</td>\n",
" <td>False</td>\n",
" <td>0.985961</td>\n",
" <td>0.0132394</td>\n",
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" Kolmogorov-Smirnov* ... Anderson-Darling \n",
" N Stat. Sig. ... Stat. Sig. Normal\n",
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"M 563 0.0423764 0.017499 ... 0.985961 0.0132394 False\n",
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\n",
"text/plain": [
"<Figure size 1296x432 with 2 Axes>"
]
},
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}
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"data": {
"text/html": [
"<p><em>Ratios</em></p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
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"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>N(F) / N(M)</th>\n",
" <td>&lt;= 1.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Ratio\n",
"N(F) / N(M) <= 1.5"
]
},
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"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<p><em>Shapes</em></p>"
],
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"<IPython.core.display.HTML object>"
]
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{
"output_type": "display_data",
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>Chebyshev</th>\n",
" <th colspan=\"3\" halign=\"left\">Kolmogorov-Smirnov 2-sample</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>Eq.</th>\n",
" <th>Sig.</th>\n",
" <th>Stat.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>F, M</th>\n",
" <td>0.219631</td>\n",
" <td>False</td>\n",
" <td>0.00498567</td>\n",
" <td>0.106657</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Chebyshev Kolmogorov-Smirnov 2-sample \n",
" Eq. Sig. Stat.\n",
"F, M 0.219631 False 0.00498567 0.106657"
]
},
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},
{
"output_type": "display_data",
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\n",
"text/plain": [
"<Figure size 1296x432 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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N2rr7zn8BAABAhKlv7daBmjNaOjWTGSsjZHxqgm5bkKefb69UYzvL3wCRKJifrtsklZhZkZnFqX/TkQ2D+myQdE/g+A5JmwKFvQ2SVgd2OS6SVCJp67keZmbLJf2jpJXOuY7gXwXAaGnt6tUz2yqVnRyvlfMnse7gJYgy060L8tTT59ev3z/pdRwAAICQ8255vaKjTEunZnkdZUz7q+uL1NXr1483H/c6CgAPnLdAGFhT8H5JL0s6IGm9c26fmX3FzFYGuj0uKcvMyiR9XtK6wLX7JK2XtF/SS5Luc875JMnMfiZps6QZZlZlZmsD9/qOpBRJr5rZLjP73jC9K4Bh8EfrDi6ZrPiY6PNfhHPKTU3QDTNytLuqRa8fqvU6DgAAQMjo7vNp54lmzc1LU0pCrNdxxrTi8SlaNnO8frT5mLp6fV7HATDKYoLp5JzbKGnjoLYvDTjuknTnWa59UNKDQ7SvOUv/4mAyAfDG64f61x28fUEe6w4Ooxun52hPVYv++fm9evXz1yspLqgfzwAAAGPa+5Ut6u7za3ERaw+Ohr+6fqpWP/aefrGjSncvmeJ1HACjiN9AAQTteEO7Nh2o1fyCdF0xhXUHh1NMdJRuW5Cnx94q1zdfOax//shsryMBAAB4yjmnLRUNmpCaoMmZSV7HuWBPbTnhdYSgDMzpnFN+RqK++cphOaeL3jH6riWThysegFHCCq8AgtLV69P60kqlJ8Vq5TzWHRwJhdnjdNeSyXrinQrtqWrxOg4AAICnqpo6dbKlS4uLMvnuOUrMTNcWZ6uhvUcHa854HQfAKKJACCAoL+4+qeaOXv35ogIlxLLu4Ej5X8tnKjs5Xuuee199Pr/XcQAAADyztaJRcdFRml+Q7nWUiDJnUpoykmL15pF6r6MAGEUUCAGc1/tVzdpZ2aybZo7XlKxxXscZ09ISY/XllXO07+QZPf52hddxAAAAPNHZ49P71c2aX5DOH6dHWXSU6epp2TrR2KGTzZ1exwEwSigQAjin5o4evbCrWgUZibppxniv40SE5ZdN0M2zcvXwbw/rREOH13EAAABG3Y4TTer1OTYn8cjCyRmKjTZtqWjwOgqAUUKBEMBZ+Z3Tz7dXyfbbpp0AACAASURBVO+kP19UoOgo1n4ZDWamf7t1jmKiovRPL+yRc87rSAAAAKPGOaetFY0qyEjUpPREr+NEpMS4aM3LT9euymZ19fq8jgNgFFAgBHBWW8obVFHfro/Mnais5Hiv40SUiWmJ+sflM/TWkXq9sKva6zgAAACjpqK+XXVt3VpSlOV1lIi2ZGqWen1OO040eR0FwCigQAhgSI3tPXp532mVjE/WFVMyvI4Tke5eMkULJqfr3351QI3tPV7HAQAAGBVbKhqVEBuluflpXkeJaHnpiSrISNSW8kZmtAARgAIhgD/hd06/2FElM+m2BXkyY2qxF6KjTF+7/XK1dvXqq7/e73UcAACAEdfZ49OBmjOaX5Ch2Gh+XfXakqlZqmvrVnl9u9dRAIwwfuIC+BNbKxpVUd+uD8+dqPSkOK/jRLQZE1J07/VT9dyOam0pZ5FoAAAwtu092aI+v9OCgnSvo0DS3Lw0JcZG8z0UiAAUCAH8kab2Hr2075SKxydrEVOLQ8L9N5UoLz1R//uXe9Xr83sdBwAAYMTsPNGs7OQ45WewOUkoiI2O0qIpGdpfc0ZnOnu9jgNgBFEgBPAHzjk9v7N/QwymFoeOxLho/evKOTp8uk3//U6F13EAAABGRFN7j441tGt+QQbfQ0PI4qJM+Z207Xij11EAjCAKhAD+YGdls8rq2rTisgnKYGpxSLlldq5unjVe3/rtEdW0dHodBwAAYNjtrmqWJKYXh5is5HhNz03WtopG+fxsVgKMVRQIAUjqXxD6N3tPqSAjUVcWZnodB0P4l4/Okd85/duv2LAEAACMLc457TzRrMKsJGWM4w/VoWZxYZbOdPXp8OlWr6MAGCEUCAFIkl7Zf0od3X1aNT9PUUzpCEkFmUm6/6ZibdxzSr87XOd1HAAAgGFT3dypurZuLShgDexQNGNCilLiY1R6jGnGwFhFgRCAqpo6tLWiUUunZWlSOgtCh7K/un6qpmaP07/8cq+6+3xexwEAABgWOyubFR1luiwvzesoGEJ0lGnB5AwdOt2qM11sVgKMRRQIgQjnd04bdp9UcnyMbpmV63UcnEd8TP+GJccaOvTE28e8jgMAAHDJfH6n9yubNXNCihLjor2Og7NYNCVDfte/0zSAsYcCIRDhth1rVFVTp1bMnaiEWL6QhYPrp+fo5lm5+s6mIzp9psvrOAAAAJfkSG2r2nt8TC8Ocdkp8SrMStL2441yjs1KgLGGAiEQwdq6+/TKvtOamj1O8/KZzhFO/vdHZqnX5/TQbw56HQUAAOCS7KpsVlJctKZPSPY6Cs7jiimZqm/r0bGGDq+jABhmFAiBCPbbA6fV3efTR+dNkrExSViZkjVOn7muSM/trNaOE01exwEwxpnZcjM7ZGZlZrZuiPPxZvZM4PwWMysccO6BQPshM/vQgPYnzKzWzPYOulemmb1qZkcC/2ZIETCGdff5dKDmjObmpSkmil9PQ93cvDTFx0Rp+3G+fwJjDT+BgQhV29ql0mONWlyUqdzUBK/j4CLcd1OxclPj9eUN++T3M80DwMgws2hJj0haIWm2pDVmNntQt7WSmpxzxZIelvRQ4NrZklZLmiNpuaRHA/eTpB8G2gZbJ+k151yJpNcCnwGMUYdPt6nX5zSX2SxhIS4mSpfnp2lPdbO6etkwDxhLKBACEerlvacUGx2lD8xkY5JwNS4+RutWzNTuqhY9u6PK6zgAxq7Fksqcc+XOuR5JT0taNajPKklPBo6flbTM+oemr5L0tHOu2zlXIakscD85596U1DjE8wbe60lJtw7nywAILftOtmhcXLQKs8Z5HQVBWjQlU70+pz1VLV5HATCMKBACEaiivl0HTrXqhuk5So6P8ToOLsGt8/O0cHK6vv7SQbV29XodB8DYlCepcsDnqkDbkH2cc32SWiRlBXntYLnOuZrA8SlJQ/4ly8zuNbNSMyutq6sL5j0AhJhen18HT7Vq9qRURbHcTdjIz0jU+JR4lR4f6m88AMIVBUIgwvid02/21ig1IUZXT8v2Og4ukZnpX1fOUX1bj777xlGv4wDAsHL922QOuYaCc+4x59wi59yinJycUU4GYDiU1bapp8+vOZOYXhxOzEyLCjNV2dSpU2e6vI4DYJgwdAiIMHuqW1TV1KmPLcxXXAx/Iwg1T205cVHXzS9I12Nvlis5PkbpSXHn7X/XkskX9RwAEalaUsGAz/mBtqH6VJlZjKQ0SQ1BXjvYaTOb6JyrMbOJkmovJTyA0LXv5BklxEZpag7Ti8PN/IJ0vbS3RjtPNGnFZRO9jgNgGFAdACJId59Pr+w7pQmpCVowOd3rOBhGH5zdPwPv1f2nPU4CYAzaJqnEzIrMLE79m45sGNRng6R7Asd3SNoUGP23QdLqwC7HRZJKJG09z/MG3useSb8chncAEGJ8fqcDNWc0a0IquxeHoeT4GM3ITdGuymb5HZvlAWMBP4mBCPLjzcfV1NGrFXMnsM7LGJOeFKdrirO1s7JZ1c2dXscBMIYE1hS8X9LLkg5IWu+c22dmXzGzlYFuj0vKMrMySZ9XYOdh59w+Sesl7Zf0kqT7nHM+STKzn0naLGmGmVWZ2drAvb4m6RYzOyLp5sBnAGNMeX2bOnt9mjMp1esouEgLJmeotatPR2vbvI4CYBgwxRiIEB09ffre745qWs44lYxP8ToORsAN03O07VijNu6p0WeuLZJRBAYwTJxzGyVtHNT2pQHHXZLuPMu1D0p6cIj2NWfp3yBp2aXkBRD69p08o7joKJXk8r00XM2ckKKE2CjtrGzmf0dgDGAEIRAhfvreCdW39WjZzCE3g8QYkBAbrWWzclVR366Dp1q9jgMAADAkv3Paf/KMpk9IUWw0v5KGq5joKF2el659J1vU3evzOg6AS8RPYyACdPT06ftvHtW1xdkqzGYR6LFscWGmspPj9Ju9p+Tzsx4MAAAIPccbOtTW3cf04jFgweR09fqc9p0843UUAJeIAiEQAX4/evDvbi7xOgpGWHSUafmciapv61bp8Uav4wAAAPyJ/SdbFBNlmsm01LA3OTNJmePitLOyyesoAC4RBUJgjBs4evDKwkyv42AUzJqYoilZSdp0sFY9fX6v4wAAAPyBc/2jzYrHJys+NtrrOLhEZqYFBekqr2tXc0eP13EAXAIKhMAYx+jByGNm+uDsCWrt6tPm8gav4wAAAPzByeYuNXf2as6kNK+jYJgsmJwhJ2l3ZbPXUQBcAgqEwBjG6MHIVZQ9TjNyU/Tm4Tp19rBoNAAACA0HT5+RSZoxgenFY0XmuDhNyUzSzspmOcca2EC4okAIjGGMHoxst8zOVWevT28dqfM6CgAAgCTp8KlW5WckKjk+xusoGEYLJmeotrVbJ5u7vI4C4CIFVSA0s+VmdsjMysxs3RDn483smcD5LWZWOODcA4H2Q2b2oQHtT5hZrZntHXSvTDN71cyOBP6dcfGvB0Surl6fHnurXNcUZzF6MEJNSk/U5flpeudovVq7er2OAwAAIlxbd5+qmjo1ndGDY87cvDTFRJl2sFkJELbOWyA0s2hJj0haIWm2pDVmNntQt7WSmpxzxZIelvRQ4NrZklZLmiNpuaRHA/eTpB8G2gZbJ+k151yJpNcCnwFcoBd2VquutVt/e2Ox11HgoVtm5crnd3r9UK3XUQAAQIQ7crpVTtLM3FSvo2CYJcZFa+aEFL1f2Syfn2nGQDgKZgThYkllzrly51yPpKclrRrUZ5WkJwPHz0paZmYWaH/aOdftnKuQVBa4n5xzb0pqHOJ5A+/1pKRbL+B9AEjy+50ee6tccyal6uppWV7HgYeykuO1aEqmtlU0qbGdneUAAIB3Dp1uVXJ8jCamJ3gdBSNgweQMtff4dOR0q9dRAFyEYAqEeZIqB3yuCrQN2cc51yepRVJWkNcOluucqwkcn5KUO1QnM7vXzErNrLSujvW1gIFeO1ir8rp2/fUN09Rfq0cku2nmeJlJrx047XUUAAAQoXx+p8OnWzU9N0VRfD8dk6bnpigpLlo72c0YCEshvUmJ698Cacjxyc65x5xzi5xzi3JyckY5GRDavv+7o8rPSNSHL5vgdRSEgLTEWC2dmqVdlc2qb+32Og4AAIhAlY0d6ur1s3vxGBYdZZqXn64DNWfU0sn610C4CaZAWC2pYMDn/EDbkH3MLEZSmqSGIK8d7LSZTQzca6IkFs4CLsD2400qPd6ktdcWKSY6pP8GgFF0XUm2YqJNm1iLEAAAeODQ6VZFmVQyPtnrKBhBCyanq8/vtHFPzfk7AwgpwVQPtkkqMbMiM4tT/6YjGwb12SDpnsDxHZI2BUb/bZC0OrDLcZGkEklbz/O8gfe6R9Ivg8gIIOCxN48qPSlWH7+y4PydETFSEmK1tChLuyubVccoQgAAMMoOnWrVlKxxSoiNPn9nhK289ETlJMfr+R3nGxcEINSct0AYWFPwfkkvSzogab1zbp+ZfcXMVga6PS4py8zKJH1egZ2HnXP7JK2XtF/SS5Luc875JMnMfiZps6QZZlZlZmsD9/qapFvM7IikmwOfAQShvK5Nr+w/rU8unaKkuBiv4yDEXDc9p38U4UHWIgQAAKOnpbNXp850aUYu04vHOjPTgsnp2nqsUScaOryOA+ACBFVBcM5tlLRxUNuXBhx3SbrzLNc+KOnBIdrXnKV/g6RlweQC8Mf+660KxUZH6S+uKvQ6CkJQcnyMrpqapbeO1KustlXF4/mSDgAARt6hU/272rL+YGSYX5CuVw+c1vM7q/V3N5d4HQdAkBhiBISIp7acuKTr27r79PPSSi2YnKFX9zNCDEO7tiRH75U36tuvlenbaxZ4HQcAAESAQ6dblZ4Uq/Ep8V5HwShIT4rT0qIsPb+zSp9bVixj12ogLLCDATBGlB5rVJ/f6ZppWV5HQQhLjo/R0qlZevH9kzpyutXrOAAAYIzr8/l1tLZNM3JTKBRFkNsX5ulYQ4d2nGj2OgqAIFEgBMYAn99pS0WjinOSNT41wes4CHHXlWQrKTZa//HaEa+jAACAMa6ioV09Pj/rD0aYFXMnKiE2Ss/tqPI6CoAgUSAExoD9NWfU0tmrqxg9iCCMi4/RX1xdqF/vqVFZbZvXcQAAwBhWdrpN0VGmqTnJXkfBKEqOj9GH5kzQr96vUXefz+s4AIJAgRAYAzYfbVBGUiwLPyNoa68tUnxMlL77xlGvowAAgDGsrK5NUzKTFBfDr56R5rYFeWrp7NXrB+u8jgIgCPyUBsJcTUunjjW0a+nULEWxrguClJ0crzWLJ+uFXdWqbOzwOg4AABiD6tu6VdPSpeLxjB6MRNcWZys7OV7P72SaMRAOKBACYW7z0QbFRpsWTcn0OgrCzL3XT1W0mb73O0YRAgCA4ffu0QZJ0jSmF0ekmOgorZw3SZsO1qq5o8frOADOgwIhEMY6uvu0u6pZ8wsylBgX7XUchJmJaYn62BX5+nlplU61dHkdBwAAjDHvHKlXQmyU8jISvY4Cj9y+ME+9PqdfvV/jdRQA50GBEAhjpceb1Otzumoqm5Pg4nz2hmnyOaf/eqvc6ygAAGAMcc7p7bJ6TctJZhmcCDZnUqpKxifr+Z3VXkcBcB4UCIEw5XdO71U0qCh7nCakJXgdB2FqclaSVs2fpKe2nFBDW7fXcQAAwBhxrKFD1c2dTC+OcGam2xbmafvxJp1oYN1rIJRRIATC1KFTrWru6GX0IC7Z395YrK4+n554p8LrKAAAYIx4u6xektigBLp1fp7MxChCIMRRIATC1LZjjUqJj9GsialeR0GYKx6frA9fNlE/eve4znT1eh0HAACMAe8cqVdeeqKyxsV5HQUem5SeqKVFWXp+Z5Wcc17HAXAWFAiBMNTS2atDp1q1cEqGoqNY0wWX7rM3TlNrd59++t4Jr6MAAIAw5/M7vXu0XtcWZ8tYfxCSbluYp2MNHdpZ2ex1FABnQYEQCEPbjzfJSVo0JcPrKBgjLstL03Ul2XrinQp19/m8jgMAAMLY3uoWnenq0zUl2V5HQYhYcdkExcdE6fkdTDMGQhUFQiDM+J3T9uONmpYzTlnJ8V7HwRjy19dPU11rt15gfRgAAHAJfr/+4NXTWCsb/VISYvXBORP04vsn1dPn9zoOgCFQIATCzNHaNjV19OrKwkyvo2CMuaY4S3Mmper7b5bL72d9GAAAcHHePlKvWRNTlc0fszHAbQsmqbmjV28cqvU6CoAhUCAEwsy2Y41KiovWbDYnwTAzM/3NDdNUXteuVw+c9joOAAAIQ509Pm0/3qRrixk9iD92XUmOssbF6YVdzFYBQhEFQiCMtHX36UBNqxZOzlBMNP/5YvituGyCCjIT9b3fHWWXOQAAcMG2HWtUj8+va4pZfxB/LDY6Sh+dN0m/PVCrls5er+MAGIQKAxBGdhxvks85NifBiImJjtK9103VzhPN2nasyes4AAAgzLxTVq+46CgtLmI5HPyp2xfmqafPr417aryOAmAQCoRAmHDOqfR4o6ZkJWl8aoLXcTCG3XFFgTLHxen7vzvqdRQAABBmNpc3aP7kdCXFxXgdBSFobl6apuWMYzdjIATxUxsIExUN7apv69GNM8Z7HQVjwFNbTpzz/ILJ6XrtQK2++ephTbiEgvRdSyZf9LUAACC8tHb1am91i+6/qdjrKAhRZqbbF+brGy8fUmVjhwoyk7yOBCCAEYRAmCg91qSE2ChdNinN6yiIAFcVZSk22vT2kTqvowAAgDBRerxJfictmcoGJTi7VfMnSZJe2MkoQiCUUCAEwkBHT5/2VrdoXn664mL4zxYjLyk+RosKM7WrslnNHT1exwHgMTNbbmaHzKzMzNYNcT7ezJ4JnN9iZoUDzj0QaD9kZh863z3NbJmZ7TCzXWb2tpkxFAkIE1vKGxUbbVo4mfWycXb5GUlaUpSp53dWsykeEEKoNABhYFdls/r8TlcWstgzRs+10/p3H3z3aIPHSQB4ycyiJT0iaYWk2ZLWmNnsQd3WSmpyzhVLeljSQ4FrZ0taLWmOpOWSHjWz6PPc87uS7nbOzZf0lKR/Hsn3AzB8tlQ06PL8dCXGRXsdBSHutgV5Kq9v1+6qFq+jAAigQAiEOOecth1rVF56oialJ3odBxEkY1ycLs9P19Zjjers8XkdB4B3Fksqc86VO+d6JD0tadWgPqskPRk4flbSMjOzQPvTzrlu51yFpLLA/c51TycpNXCcJunkCL0XgGHU0dOnPVUtWsLuxQjCirkTFRcTxTRjIIRQIARCXFVTp06f6Wb0IDxxXUm2evr82lLBKEIgguVJqhzwuSrQNmQf51yfpBZJWee49lz3/IykjWZWJemTkr42LG8BYERtP96kPr9j/UEEJS0xVrfMytWLu0+q1+f3Og4AUSAEQt62Y42Ki47SvHw2J8Hom5iWqJLxyXrnaANf3gCMlr+X9GHnXL6k/5b0zaE6mdm9ZlZqZqV1dWyoBHhtS3mjoqNMV0xh/UEE57YFeWpo79Ebh/gZDoQCCoRACOvu9en9qhZdnp+m+FjWcoE3rp+eo/buPu040eR1FADeqJZUMOBzfqBtyD5mFqP+qcEN57h2yHYzy5E0zzm3JdD+jKSrhwrlnHvMObfIObcoJyfnYt4LwDDaUtGgy/LSlBwf43UUhIkbZuQoOzlOv9he5XUUAKJACIS03VUt6vH5mV4MT03NHqe89ES9faRefnaaAyLRNkklZlZkZnHq33Rkw6A+GyTdEzi+Q9Im17815QZJqwO7HBdJKpG09Rz3bJKUZmbTA/e6RdKBEXw3AMOgq9en3ZUtWsr6g7gAsdFRunV+nl47eFqN7T1exwEiHgVCIIRtO9aoCakJys9gcxJ4x8x0/fQcNbT3aN/JM17HATDKAmsK3i/pZfUX69Y75/aZ2VfMbGWg2+OSssysTNLnJa0LXLtP0npJ+yW9JOk+55zvbPcMtP+VpF+Y2W71r0H4D6P1rgAuzo4TTerx+bVkKgVCXJiPXZGvXp/Thl1sVgJ4jfHfQIg62dyp6uZOfeTyierfCBLwzpxJqcocF6e3jtTpskmp/H8SiDDOuY2SNg5q+9KA4y5Jd57l2gclPRjMPQPtz0t6/hIjAxhFW8obFWXSIma94ALNmpiqy/JS9eyOKn3qmiKv4wARjRGEQIjadqxRMVGmBQUs9AzvRZnp2uJsVTV16lhDh9dxAABACNlS0aDZk1KVmhDrdRSEoTsW5mtv9RkdqGGmCuAlCoRACOrp82t3VbMuy0tTYhybkyA0XDElQ0lx0XrrCDvNAQCAft19Pu080awlRVleR0GYWjk/T7HRxmYlgMcoEAIhaG91i7p6/VpUyOhBhI7Y6ChdNTVLB0+16vSZLq/jAACAELC7skXdfX4tYYMSXKTMcXFaNjNXL+yqVq/P73UcIGJRIARC0LZjjcpOjlNR1jivowB/ZOnULMVGm94+Uu91FAAAEAK2lDfITFpMgRCX4I4r8lXf1qPfHWKmCuCVoAqEZrbczA6ZWZmZrRvifLyZPRM4v8XMCgeceyDQfsjMPnS+e5rZMjPbYWa7zOxtMyu+tFcEwsvpM1063tihRVMy2QgCIWdcfIyumJKhXZXNOtPZ63UcAADgsS0VjZqRm6L0pDivoyCM3TAjR9nJcXqWacaAZ85bIDSzaEmPSFohabakNWY2e1C3tZKanHPFkh6W9FDg2tmSVkuaI2m5pEfNLPo89/yupLudc/MlPSXpny/tFYHwUnqsUdFmWjiF6cUITdcW58jvnN492uB1FAAA4KFen1/bjzdp6VTWH8SliY2O0q3z8/TawdNqbO/xOg4QkYIZQbhYUplzrtw51yPpaUmrBvVZJenJwPGzkpZZ/9CnVZKeds51O+cqJJUF7neuezpJqYHjNEknL+7VgPDT5/NrZ2WzZk1MUXJ8jNdxgCFl/v/s3XlgVNXdPvDnzEz2fZns+x4ICCTsgoioUBXcUMS22mrta7WttW9b219fa7W2tZvVunXR1rohoigVBAWUTQxJANlC9p2Qfd9n5vz+yAwdYzYgyZnl+fzD5M695z53yCQn37nnHC9XTI/0w6HyJvQNGFXHISIiIkWOVbehZ8DI+QdpQtyUGYUBo8R7R2tURyFySuMpEEYCqLL6utq8bdh9pJQGAG0AgkY5drQ27wawTQhRDeBrAH47XCghxD1CiFwhRG5DA+cpIMdwsrYd3f1GzI1jJ4ts29LkYPQOmJBT3qw6ChERESmSXTY4moDzD9JESA/3xYxIP7yZUwUppeo4RE7HFhcp+QGAr0gpowD8E8CfhttJSvk3KWWWlDJLr9dPaUCiyZJb3owATxckhnirjkI0qqgAT8QHe+FASROMJnbgiIiInFF2aTOSQ7wR5O2mOgo5iFvnRuP02Q4cq25THYXI6YynQFgDINrq6yjztmH3EULoMDg0uGmUY4fdLoTQA7hESplt3v4mgEXjuhIiO9fU2YeShi5kxgZCw8VJyA4sTQ5GW88AjlW3qo5CREREU8xgNCG3vBnzE3j3IE2c1bMi4O6iwZu5VWPvTEQTajwFwhwAyUKIeCGEKwYXHdkyZJ8tAO4wP74ZwG45eE/wFgDrzKscxwNIBnBolDZbAPgJIVLMbV0JIP/CL4/IfuRWtEAAyOTiJGQnUkJ9EOLjhn1FjRwGQkRE5GROnGlHV78R8+O5QAlNHF93F1wzIwJbjp5Bd79BdRwipzJmgdA8p+D9AHZgsFi3UUp5UgjxqBBitXm3FwEECSGKATwI4CHzsScBbARwCsB2APdJKY0jtWne/i0AbwshPsfgHIQ/mrjLJbJNA0YTDle0IDXMB34eLqrjEI2LEAJLkvU4296LovpO1XGIiIhoCmWXDs4/yDsIaaKtmxeNzj4Dth6rVR2FyKmMa5lUKeU2ANuGbHvY6nEvgLUjHPs4gMfH06Z5+2YAm8eTi8hR7D5dj44+AxcnIbtzSbQfPjp1FvuKGpAS6qM6DhEREU2R7LJmJAR7IcTHXXUUcjBZsQFI0HvhzZwqrM2KHvsAIpoQtrhICZHT2XCoEr7uOhZYyO7oNBosSgxGSUMXalp7VMchIiKiKWA0SeSUcf5BmhxCCKybG43cihYU13eojkPkNFggJFLsTGsP9hQ2YE5sALQaLk5C9mdefCDcdBrsK2pQHYWIiIimQH5tOzr6DJx/kCbNjXOioNMIbMytVh2FyGmwQEik2MbcKpgkkBXLT2DJPrm7aDEvLhAnatrQ0tWvOg4RERFNss84/yBNsmBvN6xID8XbedXoN5hUxyFyCuOag5CIJofRJLExpwpLkoMR6OWqOg7RBVuUFIwDJY3YX9KI62ZGqI5DREREkyi7rBkxgZ4I9/NQHYVs1OvZlRfdRqivO5q6+vHIlpPIiPTD+vkxE5CMiEbCOwiJFNpb1IAzbb1YN5e/7Mi++Xm44JIof+SWN6O736A6DhEREU0Sk0kip7wZ8+N59yBNruRQb/h5uCCnvFl1FCKnwAIhkUIbDlUiyMsVV04LVR2F6KItSdZjwCiRXcZOHBERkaMqqOtAa/cA5idw/kGaXBohkBUXgKL6TjR19qmOQ+TwWCAkUqS+oxe78utxU2YUXHV8K5L9C/NzR0qoNz4tacKAkXPFEBEROaJsy/yDvIOQpkBWbCA0AryLkGgKsCpBpMimvGoYTBK3zo1WHYVowixJ1qOrz4Ajla2qoxAREdEkyC5rRqS/B6IDPVVHISfg5+GC9HBf5Fa0oM9gVB2HyKGxQEikgJQSb+ZUYV58IBL13qrjEE2YhGAvRPp7YH9xA0xSqo5DREREE0hKiUNlnH+Qptb8+CB09xvxwfGzqqMQOTQWCIkUOFjShIqmbqzj3YPkYIQQWJIcjMbOfpyubVcdh4iIiCZQcX0nmrr6MT+BBUKaOgl6LwR5ueK17ArVUYgcGguERAq8Fdx1AgAAIABJREFUll0Jf08XfGVGuOooRBNueoQfAjxdsLeoUXUUIiIimkCfmRcimx/PBUpo6miEwLz4QOSUt+D0WX4ATTRZWCAkmmL1Hb3YcfIsbp4TBXcXreo4RBNOqxFYnBSMyuZu5HJCaSIiIoeRXdqEUF83xAZx/kGaWpkxAXDVafDaZ5WqoxA5LBYIiabYW7mDi5Osnx+jOgrRpMmKDYSHixZ/3VuqOgoRERFNACklssuaMT8+CEII1XHIyXi66XDtjHBsPlKDrj6D6jhEDokFQqIpZDRJvJ5dicVJQUjg4iTkwFx1GixICMLO/DoU13eqjkNEREQXqayxCw0dfZx/kJS5fUEsOvsMeO/oGdVRiBwSC4REU2hvYQNqWntw+/xY1VGIJt3CxCC46TT4654S1VGIiIjoImVz/kFSbE6MP9LDffHKZxWQUqqOQ+RwWCAkmkKvZVdA7+OGK6eFqo5CNOm83XRYNzcGm4/UoKa1R3UcIiIiugjZpU0I9nZDot5LdRRyUkIIfG1BLPJr25FT3qI6DpHDYYGQaIrUtPZg9+l63JoVDRct33rkHO5ZmgAA+DvnIiQiIrJb/51/MJDzD5JSN8yOhJ+HC/71aZnqKEQOh1UKoiny5qFKSADr5kWrjkI0ZSL8PXDD7EhsyKlEY2ef6jhERER0Aaqae1Db1sv5B0k5D1ct1s2Lxo6TdRyhQjTBWCAkmgIDRhM25FTh8tQQRAV4qo5DNKX+Z1ki+gwm/PMAP+klIiKyR5+VNQHg/INkG76+MA5SSrxysEJ1FCKHwgIh0RTYeaoO9R19uH1+jOooRFMuUe+Nr2SE49+fVqC9d0B1HCIiIjpP2aXNCPB0QXKIt+ooRIj098DV08PwxqFK9PQbVcchchgsEBJNgX99Wo5Ifw8sSw1RHYVIiXuXJaKjz4BXP+MnvURERPYmu6wJ8+IDodFw/kGyDd9YHI+2ngG8e7RGdRQih8ECIdEky69tR3ZZM76+MBZadqrISWVE+uGyFD1e3FfGT3qJiIjsSE1rD6pbeji8mGzK3LgATAv3xT8PlEFKqToOkUPQqQ5A5Ohe/rQc7i4a3DqXi5OQc7vv8iTc8teDeDOnEncujlcdh4iIiIZ4PbvyS9uOVLYAAJq7+od9nkgFIQTuXByHH286hoMlTViUFKw6EpHd4x2ERJOopasf7x6twQ2zI+Hv6ao6DpFS8+IDMS8uEC/sKUWfgXcREhER2YOyxi64u2gQ5ueuOgrRF6y+JAKBXq546UC56ihEDoEFQqJJ9GZuFXoHTLhjUZzqKEQ24XtXJONsey825VWrjkJERETjUNbYhbggL2gEp8oh2+LuosX6eTHYdboO5Y1dquMQ2T0WCIkmicFowisHK7AgIRBpYb6q4xDZhMVJQZgd44/nPi5Bv8GkOg4RERGNor1nAE1d/YgP9lIdhWhYX18YCxeNBi/uL1MdhcjucQ5CokmyM78eNa09+L9r01VHIbIZQgh874pkfOOfOdh8pBq3zo1RHYmIxkEIsRLAUwC0AP4hpfztkOfdAPwbQCaAJgC3SinLzc/9FMBdAIwAviel3DFam0IIAeBXANaaj3leSvn0ZF8jEX1ZmfmuLBYIyRaMNAfmjCg/bMipREygJ7zczq/EsX4++6JEFryDkGiSvPxpOSL9PbAiPVR1FCKbsixFj5lRfnj24xIYjLyLkMjWCSG0AJ4FsArANAC3CSGmDdntLgAtUsokAE8CeMJ87DQA6wBMB7ASwHNCCO0Ybd4JIBpAmpQyHcCGSbw8IhpFaWMn3HQahPt5qI5CNKJLk4IxYJT4rKxJdRQiu8YCIdEkOH22HQdLm/DVBbHQafk2I7ImhMD9lyehsrkb7x09ozoOEY1tHoBiKWWplLIfgwW7NUP2WQPgZfPjTQCuMN8JuAbABilln5SyDECxub3R2rwXwKNSShMASCnrJ/HaiGgUpQ1diA/2glbD+QfJdoX6uiM11AeflTRhgB8+E10wVi6IJsG/DpTDTafBurnRqqMQ2aQrp4UiPdwXz35cDKNJqo5DRKOLBFBl9XW1eduw+0gpDQDaAASNcuxobSYCuFUIkSuE+EAIkTxcKCHEPeZ9chsaGi7owohoZG3m+QcTOLyY7MCSlGB09RtxuLJFdRQiu8UCIdEEq+/oxTuHa3BTZhQCvFxVxyGySUIIfHd5Ekobu/D+Md5FSERf4AagV0qZBeDvAF4abicp5d+klFlSyiy9Xj+lAYmcQVljJwAgQe+tOAnR2OKDvBAV4IH9RY0wSX74THQhWCAkmmAvf1qOAZMJ31qSoDoKkU1bOT0MKaHeeHpXEe8iJLJtNRicE9Aiyrxt2H2EEDoAfhhcrGSkY0drsxrAO+bHmwHMvOgrIKLzVtrQBXcXDcL83FVHIRqTEAKXJgWjqasf+bXtquMQ2SUWCIkmUGefAa8crMDV08K42hvRGDQage9fkYKSBt5FSGTjcgAkCyHihRCuGFx0ZMuQfbYAuMP8+GYAu6WU0rx9nRDCTQgRDyAZwKEx2nwXwOXmx5cBKJyk6yKiUZQ2diE+2BsawfkHyT5Mj/BDgKcL9hU1qo5CZJdYICSaQG/mVKG914B7LuPdg0TjsSojDGlhPnhqZxFXNCayUeY5Be8HsANAPoCNUsqTQohHhRCrzbu9CCBICFEM4EEAD5mPPQlgI4BTALYDuE9KaRypTXNbvwVwkxDiOIDfALh7Kq6TiP6rtbsfzZx/kOyMVjN4F2FlczfKG7tUxyGyOzrVAYgcxYDRhJf2l2FuXADmxASojkNkFzQagQdWpOB/Xs3De0fP4KbMKNWRiGgYUsptALYN2faw1eNeAGtHOPZxAI+Pp03z9lYA11xkZCK6CKXm4kqCngVCsi+ZsYHYdboeewobEMcCN9F54R2ERBNk67Fa1LT24NtLE1VHIbIrV08PxfQIXzy9uwgDvIuQiIhIudKGLni6ahHqy/kHyb646jRYnBSMgroOnGntUR2HyK6Mq0AohFgphCgQQhQLIR4a5nk3IcSb5uezhRBxVs/91Ly9QAhx9VhtikGPCyEKhRD5QojvXdwlEk0+KSX+urcUiXovLE8LUR2HyK4IIfCDFSmoaOrG5sND1z0gIiKiqVbW2In4YC/OP0h2aUF8ENx0GnxSUK86CpFdGbNAKITQAngWwCoA0wDcJoSYNmS3uwC0SCmTADwJ4AnzsdMwOOn0dAArATwnhNCO0eadGFzVLk1KmQ5gw0VdIdEU2F/ciPzadtyzNAEaDTtSROfrivQQzIzyw9O7i9Bv4F2EREREqrR09aOle4AL7pHd8nDVYmFCEE6eaUd9e6/qOER2Yzx3EM4DUCylLJVS9mOwYLdmyD5rALxsfrwJwBVCCGHevkFK2SelLANQbG5vtDbvBfColNIEAFJKlv3J5v11Tyn0Pm64fnak6ihEdslyF2F1Sw825VWrjkNEROS0/jv/oLfiJEQXblFSMHRagT2FDaqjENmN8RQIIwFUWX1dbd427D7mVenaAASNcuxobSYCuFUIkSuE+EAIkTxcKCHEPeZ9chsa+KYndfIqWrC/uBF3XxoPN51WdRwiu7UsVY9Z0f54ZncRegeMquMQERE5pdKGzsH5B33cVEchumDebjrMiwvE59WtaO7qVx2HyC7Y4iIlbgB6pZRZAP4O4KXhdpJS/k1KmSWlzNLr9VMakMjaU7uKEOjliq8tjFUdhciuCSHwv1el4kxbL17PrlQdh4iIyOlIKVHa2IWEYC8Izj9Idm5Jsh5CCOzlXYRE46Ibxz41GJwT0CLKvG24faqFEDoAfgCaxjh2pO3VAN4xP94M4J/jyEikxOHKFuwtbMBPVqbB03U8byci53Ihhb4EvRf++GEBBAA3l/Hdlbt+fsx5n4eIiIi+qKV7AG09A7gshTdgkP3z9XBBZkwA8ipbcHlaCPw8XFRHIrJp47mDMAdAshAiXgjhisFFR7YM2WcLgDvMj28GsFtKKc3b15lXOY4HkAzg0BhtvgvgcvPjywAUXtilEU2+p3YWIcDTBV/n3YNEE+bqaWHo6jfiQEmT6ihEREROpbShEwCQwAVKyEEsTdFDSon9RbyLkGgsYxYIzXMK3g9gB4B8ABullCeFEI8KIVabd3sRQJAQohjAgwAeMh97EsBGAKcAbAdwn5TSOFKb5rZ+C+AmIcRxAL8BcPfEXCrRxDpa1Yo9hQ341tIEeLnx7kGiiRId6In0cF/sK2pAd59BdRwiIiKnUdzQCW83HfScf5AcRKCXK2ZFByC7rBntvQOq4xDZtHFVNaSU2wBsG7LtYavHvQDWjnDs4wAeH0+b5u2tAK4ZTy4ilZ7aWQh/Txd8fWGc6ihEDufKaaH4y6527C1qwMqMcNVxiIiIHJ7JJFFS34nkUB/OP0gO5fJUPY5WtWBPYQOumxmhOg6RzbLFRUqIbN7nVa34uKAB31qSAG/ePUg04cJ83XFJtD8+LWlCew8/7SUiIpps+Wfb0dVvRFKIt+ooRBMqyNsNc2ICkFPWjDb2K4lGxAIh0QV4eleR+e5Bzj1INFlWpIfCJCU+LqhXHYWIiMjh7S9qBAAk6VkgJMdzeWoITFJiD1c0JhoRC4RE5ymvogW7Ttfj7kvj4ePOlbCIJkuglyvmxgUip7wZjZ19quMQERE5tP3FjQjxcYMvV3olBxTg5YrM2ADklDejtbtfdRwim8QCIdF5kFLitx/kI9jbDd9YHK86DpHDW54WAp1Ggx0nz6qOQkRE5LB6B4w4VNaMZA4vJge2LDUEkOBdhEQjYIGQ6Dx8dKoOOeUt+MGVyVy5mGgK+Li7YElKME6eaUdFU5fqOERERA4pt7wFfQYT5x8khxbg6YrMuADklrfwLkKiYbBASDROBqMJT2w/jQS9F27NilYdh8hpLEnSw8ddhw9OnIWUUnUcIiIih7OvqAEuWoH4YBYIybEtS9EDAvi4gHcREg3FAiHROL2VV42Shi78+Oo06LR86xBNFVedBivSQ1HZ3I2TZ9pVxyEiInI4+4oaMScmAK469nHJsfl7umJuXADyKprRxDmuib6AvwGIxqG734A/fVSIzNgAXD09VHUcIqeTGRuAEB837Dh5FgaTSXUcIiIih9HY2YdTte1YmqJXHYVoSixLDYFWI7DrdL3qKEQ2hQVConF4cV8ZGjr68LOvpEEIoToOkdPRCIFVGWFo6urHobJm1XGIiIgcxoHiRgDApUnBipMQTQ1fdxcsTAjG51WtOH2Wo1OILFggJBpDY2cf/rq3FFdNC0VmbKDqOEROKyXUB4l6L+w+XY+efqPqOERERA5hf1Ej/DxckBHppzoK0ZRZmhIMNxcN/rCjUHUUIpvBAiHRGH63/TR6B4z48co01VGInJoQAqsywtHTb8SeQk4sTUREdLGklNhf3IjFSUHQajhKhpyHp6sOS5L12Jlfh7yKFtVxiGyCTnUAIlt2pLIFG3OrsSQpGIfKmjm0kUixCH8PzIr2x6cljViQEAh/T1fVkYiIiOxWSUMXatt68d0kzj9IzmdRYhCOVLbg9ztO441vLeBUUuT0eAch0QiMJomH3zsJX3cdlqeFqI5DRGZXThtcKOijU3WKkxAREdm3/UWDd+QvSeb8g+R83HRa3Hd5Ej4rbcZ+81ycRM6MBUKiEWzIqcTxmjasygiHm4tWdRwiMvP3dMWixGAcqWpFTWuP6jhERER2a39xI2KDPBEd6Kk6CpES6+fHINLfA7/fUQAppeo4REqxQEg0jJaufvx+RwHmxwdiZhQnbCayNctS9fB01eKDE7XszBEREV2A3gEjDhQ34bIUDi8m5+Wm0+KBFck4Vt2GrcdrVcchUooFQqJh/G5HATp6DXh0TQbnoiCyQe4uWixPC0FpQxcK6zpUxyEiIrI7B0ub0DNgxBXpoaqjECl145wopIX54HfbC9BnMKqOQ6QMC4REQxyrbsWGnErcsTAOqWE+quMQ0QjmxQciyMsVH5w4C4PRpDoOERGRXdmdXw8PFy3mxweqjkKklFYj8NCqNFQ2d+O1zypVxyFShgVCIisDRhN+8vZxBHu74YErk1XHIaJR6DQaXD09DPUdfXgrr1p1HCIiIrshpcTu0/W4NDkY7pxrmwiXpeixOCkIT+8uQlvPgOo4REqwQEhk5YVPSpBf245fXZ8BX3cX1XGIaAzTI3wRG+SJP+woYGeOiIhonArqOlDT2oMr0kJURyGyCUII/HRVOlq7B/D8JyWq4xApwQIhkVlRXQf+srsY184Mx9XTw1THIaJxEELgupkRaO7ux9O7ilTHISIisgu7T9cDAC5ngZDonIxIP9wwOxIvHShDTWuP6jhEU44FQiIARpPEjzYdg5ebFr9cPV11HCI6DxH+Hlg3Nxovf1qO4nouWEJERDSW3fn1mBHph1Bfd9VRiGzKD69KAQD88cMCxUmIph4LhEQA/nmgDEerWvHI6ukI8nZTHYeIztP/XpUKD1ctHn0/H1JK1XGIiIhsVnNXPw5XtmA57x4k+pKoAE98Y1EcNh+pwckzbarjEE0pFgjJ6ZU3duEPHxZgRXoIVl8SoToOEV2AIG83PLAiBXsLG7Arv151HCIiIpu1p7AeJglckc4CIdFwvnN5Evw8XPDbD06rjkI0pVggJKdmNEn8eNMxuGg0+NX1MyCEUB2JiC7Q1xfGIinEG49tPYU+g1F1HCIiIpu0K78eeh83ZET4qY5CZJP8PFxw/+VJ2FfUiD2FDarjEE0ZFgjJqb2wpwSHypvxyOrpCPPjHCxE9sxFq8EvrpuGiqZuvLS/XHUcIiIimzNgNGFPYQOWp4ZAo+EH40Qj+drCWEQHeuA32/JhNHH6GnIOLBCS0zpW3YonPyrENTPDceOcSNVxiGgCLEnW48ppoXhmdxHq2ntVxyEiIrIpueUt6Og1YDmHFxONyk2nxY+uTsPpsx1453C16jhEU4IFQnJK3f0GPLDhKPQ+bvg1hxYTOZSfX5OOAaPEE5w3hoiI6At2n66Dq1aDS5OCVUchsnnXzgjHJVF++OOHhegd4PQ15PhYICSn9Nj7+Shr6sKfbpkFP08X1XGIaALFBnnh7iXxeOdIDQ5XtqiOQ0REZDN2na7HgsQgeLnpVEchsnkajcBPv5KOs+29eHF/meo4RJOOBUJyOh+ePIs3DlXinqUJWJgYpDoOEU2C+y5PQqivG3655SRMnDeGiIgIhXUdKG3owgoOLyYatwUJQViRHoLnPylBU2ef6jhEk4oFQnIqtW09+Mnbx5AR6YsfXpmqOg4RTRIvNx0eWpWGz6vbsInzxhBdNCHESiFEgRCiWAjx0DDPuwkh3jQ/ny2EiLN67qfm7QVCiKvPo82nhRCdk3VNRM5m67FaCAGszAhTHYXIrvxkZRq6+w14aleR6ihEk4oFQnIaA0YTvvv6EfQbTHhq3Wy46vjtT+TIrp8ViTkx/vjd9gJ09A6ojkNkt4QQWgDPAlgFYBqA24QQ04bsdheAFillEoAnATxhPnYagHUApgNYCeA5IYR2rDaFEFkAAib1woiczLbjtZgXF4gQH3fVUYjsSnKoD9bPj8Fr2ZUoqutQHYdo0nDyCXIaf/iwALkVLXhq3Swk6r1VxyGiSSaEwCOrp2PNswfwl93F+NlX0lVHIrJX8wAUSylLAUAIsQHAGgCnrPZZA+AR8+NNAJ4RgyuArQGwQUrZB6BMCFFsbg8jtWkuHv4ewHoAN0zmhRE5i8K6DhTVd+KxNdNVRyGyKa9nV45rv5hAL7hoBe57/TDuXBQ/6r7r58dMRDSiKcdbqMgp7D5dh7/uKcX6+TFYMytSdRwimiIzo/xxS2Y0Xtpfxk98iS5cJIAqq6+rzduG3UdKaQDQBiBolGNHa/N+AFuklLWjhRJC3COEyBVC5DY0NJzXBRE5G8vw4qs5vJjogni76XB5aggK6zpRcJZ9SnJMLBCSw6tp7cGDGz/HtHBfPHzt0BFRROTofrwyFV5uOvzfeycgJRcsIbJlQogIAGsB/GWsfaWUf5NSZkkps/R6/eSHI7JjW4/XYn48hxcTXYyFiUEI8nLFthO1MHIRPHJALBCSQxucd/AwDEaJZ2+fA3cXrepIRDTFgrzd8NCqNHxW2ozNR2pUxyGyRzUAoq2+jjJvG3YfIYQOgB+AplGOHWn7bABJAIqFEOUAPM3DkonoAhXWdaC4vhPXzAhXHYXIruk0GqzKCENDRx8OlTerjkM04cZVIOTKdWSvfvX+KRyubMVvb5qB+GAv1XGISJFbs6IxO8Yfj2/NR1s3FywhOk85AJKFEPFCCFcMLjqyZcg+WwDcYX58M4DdcvCW3S0A1pn7ivEAkgEcGqlNKeVWKWWYlDJOShkHoNu88AkRXaD3j9VCw+HFRBMiPdwXCcFe2HmqDj39RtVxiCbUmAVCrlxH9urtvGq8fLACd18aj2tnRqiOQ0QKaTQCv7o+Ay3d/fjdjtOq4xDZFfOcgvcD2AEgH8BGKeVJIcSjQojV5t1eBBBkvtvvQQAPmY89CWAjBhc02Q7gPimlcaQ2p/K6iJyBlHJw9WIOLyaaEEIIXDMzHL0DRuw+Xac6DtGEGs8qxly5juzOiZo2/GzzcSxICMRDq9JUxyGiSTbeFegWJgTh9exK+Lq7IDrQ87zPw1XpyFlJKbcB2DZk28NWj3sxOHfgcMc+DuDx8bQ5zD7eF5KXiAYV1nWiuL4Tdyzk6sVEEyXczwOZsQE4WNqE+fFBCPZxUx2JaEKMZ4gxV64ju9LS1Y//eTUPgV6ueGb9HOi0nGqTiAatSA+Fj7sO7x2t4eTSRETk8LYe5/Bioslw5bRQuGg1+ODEqGULIrtiU5UTrlxHF8tokvjehiOob+/D81/NRLA3P80hov9yc9HimpkRONPWi+yyJtVxiIiIJo2UEluPneHwYqJJ4OPugmUpeuSfHVwEiMgRjKdAyJXryG784cMC7CtqxGPXT8esaH/VcYjIBmVE+CI5xBsfnapDew8XLCEiIsd0+mwHShq6uHox0SRZlBSMAE8XbDteC5PkyBSyf+MpEHLlOrILHxyvxfOflOC2eTG4dS7nCSOi4QkhsPqSCBhNEluPc1gIERE5prdyq+GiFbiGi/URTQoXrQYrM8Jxtr0XueUtquMQXbQxFymRUhqEEJZV5rQAXrKsXAcgV0q5BYMr171ivtuvGYMFP5j3s6xcZ4B55ToAGK7Nib88mmrjXShgIlgvFlBU14H/fetzzIr2xyOrhy6yTUT0RUHebrgsVY9d+fXIqu9AcoiP6khEREQTpt9gwuYj1bhyWigCvVxVxyFyWBkRvogL8sRHp85iZpQf3F20qiMRXbBxzUEopdwmpUyRUiaaV6KDlPJhc3EQUspeKeVaKWWSlHKeZXVi83OPm49LlVJ+MFqbw5yXK9fRmNp7B/DtV/Lg4arF81+dAzcdfygT0diWJusR5OWKLUfPYMBoUh2HiIhowuzKr0NL9wDWZkWPvTMRXTAhBK6ZEYHufiM+KahXHYfootjUIiVE58tkkvjhxs9R0dyNZ9fPQbifh+pIRGQnXLQarJ4VgaaufuwtalAdh4iIaMJszK1CmK87liZzMUeiyRYZ4IHZMf44UNyExs4+1XGILhgLhGTXnvukGB+dqsP/+0o65icEqY5DRHYmOcQHMyL9sKegAU3s0BERkQM429aLPYUNuDkzClqNUB2HyClcNT0MWq3ANs5vTXaMBUKyW3sKG/DHjwqxZlYEvrE4TnUcIrJT18wIh1YjsPloDSRXoCMiIjv39uFqmCRwc2aU6ihETsPX3QXLU0Nw+mwHPj7NocZkn8ZcpITIFjV39eOx908h1Mcds6MD8MahKtWRiMhO+Xq4YGVGGN47egZ5FS3IigtUHYmIiOiCSCnxVm4V5scHIi7YS3UcIqeyKCkIuRXNePT9U1icFAxXHe/HIvvC71iyOwNGE17LroCExO3zY/iDl4gu2ty4QMQFeWHbiVq09w6ojkNERHRBDpU1o7ypG7dwcRKiKafTaHDNjAiUNXbhnwfKVMchOm+srJBdkVLivaM1qG3rxS1Z0QjydlMdiYgcgEYI3Dg7EgajxH8+P6M6DhER0QXZmFsNbzcdVs0IUx2FyCmlhvngirQQPL2rCPXtvarjEJ0XFgjJrmSXNeNwZSuWp4UgLcxXdRwiciDBPm64Ii0EJ8+04+SZNtVxiIiIzktH7wC2Ha/FdZeEw9OVM0kRqfJ/107DgFHit9tPq45CdF5YICS7UdnUha3HapEa6oPlaSGq4xCRA7o0WY9wP3ds+fwMevqNquMQERGN238+r0XPgJHDi4kUiwv2wl1L4vHO4RrkVbSojkM0biwQkl3o6B3A64cq4efpgluyoqERQnUkInJAWo3AjbOj0NlrwLYTtarjEBERjYuUEi8dKMP0CF/MivZXHYfI6d1/eRJCfd3wyJaTMJmk6jhE48ICIdk8o0nijUNV6Bkw4vb5MfBw1aqOREQOLDLAA0tT9MiraEHB2Q7VcYiIiMa0p7ABxfWduHtJPAQ/SCdSzstNh5+uSsfxmja8lVelOg7RuHByCrJ520/UorypC7dkRSHcz0N1HCJyAlekhSC/th2bj1Tj+1ek8IMJIiKyOa9nV557/NKBMvi669DRa/jCdiJSZ82sCLzyWQV+t70AKzPC4efhojoS0ah4ByHZtM+rW3GgpAkLE4IwKzpAdRwichI6rQZrM6PR2WfA1uNc1ZiIiGzX2bZeFNd3YmFCEHQa/nlHZCuEEPjl6ulo7u7HUzuLVMchGhN/g5DNqm/vxTuHqxEb5IlVM8JUxyEiJxMZ4IHLUkJwuLIV+bXtquMQEREN60BxI1y0AnPjA1VHIaIhMiL9sG5uDP59sBxFdZy6hmwbC4Rkk/oNJryRUwlXrQa3zY3hp6FEpMTlaXqE+brj3SM16O4zqI5DRESSvQnlAAAgAElEQVT0BR29Azha3YrM2AB4unL2KCJb9L9XpcDTVYtf/ucUpOSCJWS7WHUhm/T+sTOob+/DLVnR8OVcDUSkiE6jwc2ZUejqN2DLMQ41JiIi2/JZaTNMJolFicGqoxDRCIK83fDglSnYX9yIHSfPqo5DNCIWCMnmHK1qRW5FCy5L0SM51Ed1HCJychH+HlieFopj1W1472iN6jhEREQAgAGjCdllTUgL90Wwt5vqOEQ0iq8uiEVamA8e/c8pdPdzVArZJhYIyaY0dvTh3aM1iA3yxBXpoarjEBEBAC5L0SM20BM/33wCVc3dquMQERHhSGUruvuNuDSJdw8S2TqdVoPHrs/AmbZePL2rWHUcomGxQEg2Y8A4OO+gTiOwbm4MtBqhOhIREQBAqxG4JSsaAPCDN4/CYDQpTkRERM5swGjCnsJ6RPp7IC7IU3UcIhqHuXGBuDkzCv/YV4riei5YQraHBUKyGVuP16K2rRdrM6Pgx3kHicjGBHi54rHrM5Bb0YLnPylRHYeIiJzYxtwqtHQPYEV6CITgh+pE9uKhVWnwdNXi/949yQVLyOawQEg24Vh1Kw6VNWNJcjBSw3xVxyEiGtb1syOxZlYE/ryrCEcqW1THISIiJ9Q7YMRfdhUjJtATKZyvm8iuBHu74Ucr03CwtAlbPucCeGRbWCAk5Zo6+7D5SA2iAzxw1bQw1XGIiEb16JoMhPm644E3j6Kjd0B1HCIicjKvZVfibHsvrpwWyrsHiezQ+nkxmBnlh19tzUc7+5JkQ1ggJKUM5nkHNUJg3TzOO0hEts/PwwV/XjcL1S09+Mnbxzg8hIiIpkxXnwHPf1KMRYlBSNR7q45DRBdAqxF4bE0GGjv78KcPC1XHITqHBUJS6oMTZ3GmtRc3zYlCgKer6jhEROMyNy4QP746FduOn8U/D5SrjkNERE7i5YPlaOzsxw+vSlUdhYguwiXR/vjq/Fi8fLAcn1e1qo5DBIAFQlLo5Jk2HCxtwuLEIEyL4LyDRGRf7lmagCunheLX2/KRV8H5CImIaHK19Qzgr3tKsTwtBJmxAarjENFF+tHKVOi93fDTd47DYDSpjkMEneoA5JzaegbwzuEaRPp74OoMzjtIRPZHCIE/rL0E1/5lH+5//TC2fm8JAr14JzQREU2OF/eXoa1nAA9emaI6ChGN4vXsynHvuyI9FK8fqsR33ziCJcn6EfdbPz9mIqIRjYp3ENKUM0mJt/KqYDCZcGtWNHQafhsSkX3y83DB87dnoqmrH9/fcARGE+cjJCKiiVfb1oN/7CvFV2aEISPST3UcIpog0yN8kRbmg535dWjp6lcdh5wcKzM05fYXNaK0oQvXzYxAsI+b6jhERBclI9IPv1w9HfuKGvGHDwtUxyEiIgf0+NZ8GEwSP12VrjoKEU0gIQRWXxIBAYEtn5/h4nekFAuENKVqWnrw0ak6TI/w5dwpROQw1s2Nxm3zYvD8JyV453C16jhERORAPi1pxPvHanHvZYmIDvRUHYeIJpi/pytWTAtFQV0Hjte0qY5DTowFQpoy/QYT3sythJebFjfMjoQQQnUkIqIJIYTAo2umY0FCIB56+zjyKppVRyIiIgcwYDThF++dRHSgB+5dlqg6DhFNkoUJQYj098D7x2rR3WdQHYecFAuENGW2Hj+Dps5+rM2Khqcr18chIsfiotXg+dszEeHvjnv+nYfqlm7VkYiIyM69/Gk5iuo78fC10+HuolUdh4gmiVYjcOOcSHT3G/D+8VrVcchJsUBIU+LkmTbklLdgSbIeiXpv1XGIiCZFgJcr/nHHXPQbTbj75Vx08hNgIiK6QPXtvfjzziIsS9VjRXqI6jhENMnC/TywLDUER6tacbq2XXUcckIsENKka+sZwDuHaxDp74EV09i5ISLHlhTijedun4Oi+k5857XD6DeYVEciIiI79JsPTqPfYMIj103n1DxETmJZqh5hvu5492gNevqNquOQk2GBkCaVSUpsyquCwWTCrVnR0Gn4LUdEjm9Jsh6/uWEG9hY24Acbj8Jo4op0ZN+EECuFEAVCiGIhxEPDPO8mhHjT/Hy2ECLO6rmfmrcXCCGuHqtNIcRr5u0nhBAvCSFcJvv6iGzNvqIGbD5Sg3uWJiAu2Et1HCKaIjqNBjfNiUJnnwHbONSYphirNTSp9hc1oqShC9fOjECwj5vqOEREU+aWudH4f19Jx9Zjtfj5uycgJYuEZJ+EEFoAzwJYBWAagNuEENOG7HYXgBYpZRKAJwE8YT52GoB1AKYDWAngOSGEdow2XwOQBmAGAA8Ad0/i5RHZnPbeAfx40zEk6r1w//Ik1XGIaIpFBnhgSbIeeZUtKKzrUB2HnAgLhDRpalp78NGpOkyP8EVWbIDqOEREU+5bSxNw3+WJeONQJX63o0B1HKILNQ9AsZSyVErZD2ADgDVD9lkD4GXz400ArhCDYyLXANggpeyTUpYBKDa3N2KbUspt0gzAIQBRk3x9RDbl0f+cQl17L/54yywuTELkpJanhUDv44bNRzjUmKbOuAqEHFZC56vfYMKbOVXwctPihtmRnDeFiJzW/16VivXzY/D8JyV47pNi1XGILkQkgCqrr6vN24bdR0ppANAGIGiUY8ds09wH/BqA7cOFEkLcI4TIFULkNjQ0nOclEdmmnafqsCmvGt9ZloRZ0f6q4xCRIi5aDW6eE4WO3gG893mN6jjkJMYsEHJYCV2I94+dQVNnH9ZmRcPTVac6DhGRMkIIPLYmA6svicDvthfgzzsLOdyYaHyeA7BXSrlvuCellH+TUmZJKbP0ev0URyOaeC1d/XjoneNIC/PB965IVh2HiBSLDvTE8rQQHKtuw3tHWSSkyTeeys25ISAAIISwDAE5ZbXPGgCPmB9vAvDM0GElAMqEEJZhJRipTSnlNkujQggOK7FDx2vakFvRgstS9EjUe6uOQ0SknFYj8OSts+Cq0+DPO4vQ1WfAz76SzruryV7UAIi2+jrKvG24faqFEDoAfgCaxjh2xDaFEL8AoAfw7QnIT6Tc69mVY+6zIacSzV19WDc3GpvyqqcgFRHZustSQlBY14mfbz6BzNgARAV4qo5EDmw8Q4w5rITGrbW7H5uPVCM6wAMr0kNVxyEishlajcDvbpqJOxbG4u/7yvCzzSe4ujHZixwAyUKIeCGEKwZHh2wZss8WAHeYH98MYLd5DsEtANaZp6OJB5CMwXkFR2xTCHE3gKsB3CalNE3ytRHZhKNVrThW3YblaSGI8PdQHYeIbIRWI3BLVjQkgAff/Jx9R5pUtrxICYeV2BmD0YQ3c6sgJXDr3BhoNbwzhojImkYj8Mjq6fjOssGFSx7ceBR9Bk48TbbN/OHv/QB2AMgHsFFKeVII8agQYrV5txcBBJlHizwI4CHzsScBbMTgyJPtAO6TUhpHatPc1gsAQgEcFEIcFUI8PCUXSqRIQ0cf3j1ag5hAT1yWEqI6DhHZmEAvV/xy9XQcKm/GC3tKVMchBzaeIcYcVkLj8szHxaho6sbazCgEermqjkNEZJOEEPjxyjR4uenw+x0FqG7pwQtfzYTex011NKIRmaeA2TZk28NWj3sBrB3h2McBPD6eNs3bOXkxOY1+gwlvHKqETiNw2zx+wE5Ew7txTiR2F9TjyY8KsTgpmIsY0aQYzx2EHFZCY8opb8bTu4owK9ofs2MCVMchIrJ5912ehGfXz8HJM21Y88x+nKhpUx2JiIim2PvHzuBsey/WZkbDz8NFdRwislFCCPz6+hkI83PHfa8dRnNXv+pI5IDGLBByWAmNpa17AA9sOIqoAE+sviRCdRwiIrtxzcxwbPqfRZAA1r5wEB8cr1UdiYiIpsiRypZzC/ulhvmojkNENs7P0wXP3T4HDR19eODNo5yPkCbcuIZwcFgJjURKiZ+9exx17b3YdO8inDrTrjoSEZFdyYj0w3v3L8a3X8nDva8dxp2L4vDQqjS4u2hVRyMioklS396Ld4/WIC7Iiwv7EdG4zYzyxyOrp+Nnm4/jL7uL8MCKFNWRyIHY8iIlZAfeyq3G1mO1ePCqFM6DQER0gUJ83PHGtxbgzkVx+Nen5VjzzAHk1/IDFyIiR9TTb8Qrn1XAVavBurnRnHeQiM7LbfOiceOcSDy1qwh7ChtUxyEHwgIhXbCShk78YstJLEwIwreXJqqOQ0Rk19xdtHhk9XT86xtz0dTVjzXPHMA/9pXCxOEjREQOw2iSeCOnEq3dA7h9fix8Oe8gEZ0nIQQev34GUkN98MCGI6hp7VEdiRwEC4R0QfoMRnzvjSNwd9HgyVtn8ZNPIqIJsiw1BDseWIKlKXr8ams+bn7hU5w8wwVMiIgcwQcnalFc34k1syIQF+ylOg4R2SkPVy2e/2omDEaJu/6Vg47eAdWRyAGwQEgX5DfbTuPkmXY8cdNMhPm5q45DRORQgrzd8PevZ+IPay9BRVM3rvvLfjyy5STaetj5IyKyV7nlzfi0pAmLEoOQFReoOg4R2bn4YC88e/scFNV34v7Xj8BgNKmORHaOBUI6b+8fO4N/fVqOby6Ox1XTw1THISJySEII3JwZhd0/XIavLojFvw+W44o/foINhyrZASQisjPljV147+gZJIV4Y1VGuOo4ROQglqbo8fj1GdhT2ICHt5yElJyahi4cC4R0XkoaOvGTTccwJ8YfD61KUx2HiMjh+Xm64NE1Gdhy/6WICfTEQ+8cx5VP7sV7R2tg5PyEREQ2r6ShE69mV8Df0wW3zY3h1DxENKHWzYvBvcsS8Xp2Jf62t1R1HLJjLBDSuHX3G3Dvq3lwc9HimfVz4Krjtw8R0VTJiPTD2/cuwt+/ngU3nQbf33AUq57ai23Ha1koJCKyUfXtvbjjpUMQAO5cFAcPV63qSETkgH50VSqunRmO33xwGu8fO6M6DtkpneoAZB+klPj55hMoqu/Ey9+Yhwh/D9WRiIicjhACV04LxRVpIdh2ohZ/+qgQ33ntMGKDPHHXpfG4OTMKnq781U5EZAs6egdw5z9z0NzVjzsXxSHI2011JCJyUBqNwB/WXoK69l48sOEodBoNVmZwOjA6P7wFjMbljUNVeOdIDb5/RTKWpuhVxyEicmoajcC1MyPw0Q8uw/O3z0Gglysefu8kFv12N/6wowA1rT2qIxIRObV+gwn3vnoYBXUdePb2OYgK8FQdiYgcnLuLFi/eORczovxw/+uHsf3EWdWRyM7wNgMa06GyZvxiywksTdHju8uTVcchIlLm9exK1RGGtTYzGvPiArGvqBHPflyMZz8uRkqoD+bFByIl1GfE+a7Wz4+Z4qRERI7PZJL48abPsb+4Eb+/eSYuTw2x2d8fRORYfN1d8PI35+GOlw7h/tcP45n1c3gnIY0bC4Q0quqWbtz7ah6iAzzxl9tmc1JlIiIbFRvkhdggL7R09yO3vAV5Fc145bMK+LrrkBkbiKy4AAR4uqqOSUTk0KSU+H/vnsC7R8/gR1enYm1WtOpIRORkWCSkC8UCIY2oq8+Au1/ORb/RhL/fkQU/DxfVkYiIaAwBnq64cloolqeFoOBsB3LKm/FJQT0+KahHcqg35sUFITVs5LsKiYjowkgp8cv/nMIbhyrxnWWJ+M6yRNWRiMhBXMhdyNfNjEBjRx/ufTUP18wMx8KEIAjx3/4fR5LQUCwQ0rBMJokfbvwchXUd+Oc35iFR7606EhERnQetRmBahC+mRfh+4a7CV7Mr4OOuQ2ZMAC5NCkZMEOfFIiK6WFJK/Hb7afzr03LcdWk8fnR16hf+ECcimmruLlp889J4bMytxvvHalHf3ofrLongh8Q0IhYIaVh/3lWE7SfP4ufXpOMyLkpCRGTXrO8qLKwbvKtwT2EDlv7+YyxOCsK6uTG4anoo3HRa1VGJiOzSn3cW4a97SvHVBTH4+TXpLA4SkU1w02lx+/wYfHSqDnsKG9DY1Yf182Lg6cpSEH0ZvyvoSzYcqsTTu4qwNjMKd10arzoOERFNEK1GID3cF+nhvmjrGUBeRTNyK1rw3TeOwNNVi9nR/pgbF4gQX/cJPS+HsBCRo5JS4k8fFeIvu4uxNjMKj67OYHGQiGyKRghcPT0MIT5ueOdIDZ7/pAS3cH5UGgYLhPQFH548i59tPo5lqXr8+sYZ7OAQETkoPw8XLE8LxbLUEJTUdyKnvBmflTbjQEkTYoM8sTAhCNMj/DgMhYhoBCaTxKPvn8K/Pi3HurnRePyGGdDwZyYR2ajZMQEI9HLFhpwqvLCnBBLAAyuS4e7CESQ0iAVCOudQWTO++8YRzIzyx3O3z4GLVqM6EhERTTKNEEgO9UFyqA86+ww4XNGCQ+XN2JBTBV/3WixICEJWXCC83dhlICKyMJokHnr7GN7Kq8Zdl8ZzWDER2YXYIC98/4pkbDteixf2lGBnfh1+f/NMzI4JmJD2L2QxlbFwJMrUYQWIAACnz7bj7pdzEBnggZfunMs5CYiInJC3mw5LU/R48MoUfH1BLEJ83fHhqTr8bvtpvJ1XjTOtPaojEhEp128w4XtvHMFbedV4YEUyi4NEZFfcXbS4cU4U/v3NeejuM+Cm5z/Fjzd9jqrmbtXRSDFWgQhljV2446VD8HDV4t/fnIdAL1fVkYiISCGNEEgL90VauC/q23txsLQJRypbkVfZgtggTyxKDMa0cF8OPyYip9PWPYD/eTUPB0ub8PNr0nH3kgTVkYiILsjSFD12/GApnvyoCK9mV+CdwzVYmxWN+5cnIdLfQ3U8UoAFQidXXN+B9X/PhsEk8ca3FiAqwFN1JCIisiEhvu5YMysSV00LQ15lCw6WNOKNQ5UI8HTBpUnByIwNhKuOAxKIyPGVN3bhmy/noLq5B3+65RLcOCdKdSQioovi4+6Ch6+bhnuWJuC5T4rxxqFKbMqrwnUzI3BzZhQWJARxblUnwgKhEys424Hb//EZAIEN9yxASqiP6khERGSjPFy1uDQpGIsSg3C6tgN7ixrwn2O12HW6HgsSgrAgIYjzFBKRwzpU1ox7XsmFAPDq3fMxLz5QdSQiogkT5ueOR9dk4NuXJeKFT0rw7pEavHOkBpH+HrhhdiRWz4pAcog3p1NwcOzJO6mTZ9rw1X9kw1WnwevfWoBEvfeEtLszvw4AsCI9FADwxPZ8ZMYGnvt6Z37duccjeWJ7Pn6yMh1PbM9HgKcrEvTeWJEeOuaxlnNbn9+6vb/tLUGC+TpXpId+6Wvrdqzz5lU04ycr0889/7e9JbhnaeKXzmc5z878OpQ2dJ7LbX1cS3c/MmMDRzynpW3LtgPFDVicpEdpQyeqmrvh7a4795pYzmH5FwDyKprPtV/a0AkAX7jGX/7nBH5xXca57HkVzQCAth4Dfn3DDPzfu8cRHTh4F2ltWw/cXbTIjA1EaUMnWrr7AeDc+S3HW67Zcj2//M8JADh3rPW1Wr4fAOBAccO5FbMs/9/Wr19tWw/C/TzQ0t1/7px5Fc3nHh8obkC/QcLXQ4feASMAnHutWrr70d5jgK+HDp29BlyWGnLuNUmw+l4/UNyAcD+Pc6+jheWclgwVTd3w9Rh87Vu6+8+1uaegHpelhpw71vJ/97e9Jedew3A/D9yzNPEL12d53S3XbrlmAKhq7sZj1884973wy/+cOHddlmyW/wfLYwDn/p+svzetv7b+3siraEZnrwGPXT8DT2zPBwD0DhgR7udx7jqsv88t7ViuK2HIzwvL+9P6e9K6Hct7wfJ4uNdr6HvR+j1hfbx1m9aGvues35+W/BZD3+OW81q//6zPZf2eHC6jNett1scONdLPs7HaHLrdOs9oLG0MbWvoazGeTBohcKatB0kh3liVEYa9hQ3Yfboe+4oakBkbgEuT9F+aquLJjwrxgytTxsxJRLbDGd63473Gu1/OwZ7CBkQHeOKlO+ciLtjrvM8T6uv+hd+pFkO3DfdzeazfJ9aG/hy3/O62/E4f+jvK+vf3UEN/dw/XRm3b4Ny0lr6KpY+2OEl/7l9gsM8FAAajRHSgJ2rbes49tpzb0v+z7vcAOLevt/t/+3wAEO7n8YW+1s78Ouw+XY/laYN9NEvb1m1ZWPpQbT0G+HnozvWFLddk6aNa+pNtPQa46cS567K+lqrmbui04ly/NDM28FxfCwC83XVf+FvE0p8FcO71svTBatt60G+QuDxtsH9Z3tSNX98wAzvz67CnoB46rTjXdz1Q3IA+g4SfuS9seT0s3F206Ow1wNtd94U+oIX1c3sK6uHtPnxpwJK1pbv/C3+jWV5D6769JY/1MZY+alVz9xf65NZ9S8trYf29Yvl/Bb78fW7dB7f0/yx/51j3Ey3936F905H6qJa+koV1H9dyvOX5A8UN5/6uGu49bP3eOZ+/Z63/9rTu6w3ty43Ut7Nua6xzDbf4R6S/Bx67PgO+7jqkhvvi7bxqPPdJMZ75uBhhvu5YnBSMS5ODsDgxGCG+7uf1u2K4n4HjyesMv49sBQuETiivohnf/FcuvFy1eP1bC867kzOa3afrAfy3c9LWY8Du0/XnvrZ+PJK2HsO5f9t6DChv6saK9NAxj7Wc2/r81u2VN3WjvKn73PNDv7ZuxzrvUJZjhp7Pch7LdkvukY4b7pyWfaz3sz6P9Wti3eZwxw0974r0UPQZ5IjXBQBG+cW2+gyGL+1rff6h+QePkV86duj3g3X71u0OzWY5j/U5h57fctxw1zVam9ZfD91mfaz1eYc712jfI0OPH2n/0droM8hhn7fOMrSNofuPltW6neFyD/faDX29LO/P4Z6zvL5D33PD7WNpy3Je62Os3x+jtTPae9diuPf40PffaO+t0X6eWW8bLuNw+421fbR9rfOMxtLG0LbG8/N5pEyWc39toRfq23uxr7gROWUtyC5tRkakH5Ym6xEZMPgHz1O7itixI7IzzvC+/f/t3Xl8XNV99/HPmRlptI42a7O1WDY23o2NsR0CJA8Fx0AIEGhwoCwPtLz69KFP86LP05DSV0KSJ2lI2qRJkwKhpA0uqSGQgNnD4hgCtQ0Y76u8W5ZkW7Iky9pnTv+4V/JYaGQbSxrN3O/79bqvuXPunatzzr1zzk9n7nK6MrZ19fD15zfzxtbDXDyxgH+5dS65GWd/r+4fv7mT794w84xigIHa5dP1J9H6t9f9+7H+fVSs/rt3W/373Fj9XP/tnE2cNFBcOZD+sU//vrr/34wVLwy23ZPx18dj1IFist71w2782z/Wjf58dH76x5mnq4Pe9+Ee+7Fy9d9Wr94yDJSn6M/2Lutfv/23H/3+dPF3rOWx6m+g/A/2/010Wm/81/s/yEDH7EDbHShGjY5pgY+9H2h7sb7D0fn4JP/PRr+P/mys14G2dTZ/q7+f/X4Xe793DV+YPZb6lg7e2naYP+w8ypvb6nl27UEASkJp1LV04PcZZo7LYWppCGttzLMMzzQ+788L/dFooQFCj/ntRwf56jMbGZubxtK7F/SdLSYiIvJJFYXSuHFuGVdOLea9XUdZvaeRjTXNTCzM5LJJhfHOnojIWdtZf5y/eHIt1e6ZQ0/cNZ+AX/dbFRHvKQ6l8eX5FXx5fgWRiGVLbQurdjewqaaZ59Yd4kdv7MA647Okp/gpyUmjJOROOWkUhYIEA/74FkLOiAYIPSISsfzw9R38dEU1Cyfk8/CtF5KnpxWLiMgQCqWnsHhGKZ89v4g1exp5d9dR/u29vQA8v66Ga2aW6h9sERnVrLX8+oODfGP5ZjKDfpbetYA/eXy12i4REcDnM8wYl8OMcTkAPLfuEBsf/Byba5rZXn+cF9bXUtfczof7j9HVEwHAwCljD3uOnmBcbroecjcKaYDQA9q7wvz1r9fx8sY6llxUzreum6Evo4iIDJu0FD+XTS7k4okFrD/YxLNra/irZev4/qvb+bNLq/jSReVkpCoEEZHR5UBjGw88t4m3dxzhUxMK+PGSCygKpcU7WyIio1pWMMCCCQUsmFBAwOeMM0Sspamtm7rmDupa2qlr7qDxhHPfysfe2Y3POA9GmTgmi4lFQ/M8BDl3is6T3KaaZr7y1Dp2HWnl766Zyt2XVOnJQyIiMiICfh8XVubz7NoaHrt9Ho+u3MWDL2zhn97cye2fGs8dn6qkICsY72yKiMeFI5ZfvreXf/jddgzwreum8ycLKvH5FDOLiHwSPmPIz0wlPzOVaWNDAPztbzcCcPvCSg4cc+7R+N7uBt6pPgo4Dz6aUhJiSmk2hVlBjVvEgQYIk1Q4Yvn527v54evbyc9M5Ym75nOp7gMlIiJxcuW0Yq6cVswHext59O3d/OTNnTy6chdfmlfOn15aRWXB0D0wS0TkTK3df4xvvbCFdQea+Oz5hXznhpmMy02Pd7ZERJLWlNIQU0qdQcOungj7Gk/wb+/upbMnwqub63h1cx0FmalMLQ31XcosI0MDhEno4LE2/vrp9aze08hVM0r47g0zdb9BEREZFeaNz2fe+HyqDx/nsbf3sOz9/Ty5eh9XzSzl7kuqmFOeq1+MRWTY7XYfPvLFf3mPMVlBfnTzbK6/YJzaHxGREZQa8DGpKBuAv7x8Ek1tXWyrO87W2hb+a1cDf3DPLvz7V7Zy7ayxTB8bUjs9jDRAmETau8I8+vYuHlm5C78x/MMfz+bGuQp0RERk9DmvKJuHbprFfYsm84t39/CrVft5aUMtU0tD3LqgguvnjCMrqDBFRIbWoaZ2Hv79Lv5zzX4AvnLFJP7s0glkqr0REYm73IxUFk4oYOGEAjq6w2w51MIzaw/y+Dt7eHTlbiaMyeTzs8dy7axSJhVnxzu7SUc9YRKw1rJ8/SG+98o2aps7uGZWKV+7agpleRnxzpqIiMigikNpfO2qqfzl5ZN4fl0N/7FqP3/33Cb+/uWtXDt7LF+cW8a8yjzdC0xEzsmmmmYee2c3L22oxQK3zK9g6ap9fOWKyR9b91er959RmohIIoATQ7QAABIySURBVDtdu3am7d5wtY9pKX7mVubxzNqDrHngCl7dVMeLGw7xz2/t5Cdv7mRKSTbXzh7L52eV6lY1Q0QDhAksHLG8vqWOh1fuZv2BJmaMC/HjJXOYX5Uf76yJiIiclaxggFsXVHLL/ArWHWjiydX7Wb7+EMveP0BZXjo3zBnH9XPGMbFQT7oTkTPT0R3mza2HWbpqL6t2N5IVDHDnxeO589PjKcvLYOmqffHOooiInIH8zFRuWVDBLQsqONzSwcsba3lhQy0/eG07P3htO7PKcrh21liumVXKWN1H9hPTAGECau8K88yHB/jXP+xhX0Mb5fnpfP/GWdx0YZnOsBARkYRmjGFORR5zKvL45hem87stdfxmbQ0/W1HNP79VzeTiLBZPL2HR9BLdh0ZEPiYSsaze08hzH9Xw8sZajnf2MDYnjQeunsrN88sJpaXEO4siInIOikJp3PnpKu78dBU1Te28tOEQL6yv5Tsvb+U7L29lXmUe184ey1UzSyjKTot3dhOKBggTRCRiWbO3kRfWH+KljbU0tXUzuzyXry6ewueml+DXwKCIiCSZzGCAG+aUccOcMupbOnhpQy2vba7jpyuq+clb1ZTlpXPZ5EIuPW8MF08cQ06G/vEX8aKmti7e2XmUlTuOsHLHEY4c7yQz1c/iGaV8ce44Fk4oUKwsIpKExuWmc89lE7nnsonsPXqCF93Bwm8s38w3X9jMgqoCrphWzGcmFzKxMFM/LJ+GBghHsY7uMGv3H2PFtsO8uKGW2uYO0lP8XDGtmNsWVnLR+Dwd4CIi4gnFoTTuuqSKuy6poqG1kze21vP6lnqe/6iGX63ej8/AzLJcFlTlM7cil7kVeRSF9KuxyLkY6vtK3bKg4py3Ya1lX0Mb6w828dH+Jt7YWk/NsXYskJ7i57yiLC4/v4ippSFSAz72NbSxr6Et5vZ0b0ERkeQwfkwm914+iXsvn8SO+uO86J5c9e0Xt/BtoCwvnc+eX8jFE8dw0fh8CrOD8c7yqKMBwlGkub2brbUtfLjvGO/tOsoHe4/R2RMhxW/4zORC7r9qCldOKyYjVbtNRES8qyAryM0XVXDzRRV0hyOsO9DEOzuP8m71Uf793b38/O0I4PyqPKsshyklIaaUZjO1JERZXrpuxyEyyvWEIzSc6OJwSyc1Te3sOXqC3Uda2X30BNWHW2lu7wacAcHiUJD/MaWIycXZzvdbP56LiHje5OJs7lt0PvctOp8DjW38fscRVm4/wm/WOg/EA6gak8m8Sue2NtPGhji/OJv0VH+ccx5fZzTSZIxZDPwY8AP/aq39Xr/lQeAJ4EKgAbjZWrvXXfY14G4gDPwfa+1rg23TGFMFLAMKgA+B26y1XedWzNEjErHUtXSwv7GN/Y1tHGhsY3vdcbbUtnDwWHvfelNKsrl1QSUXTyxg/oR83S9FRERkACl+HxeNz+ei8fncd+VkOnvCbD7Uwtp9x/hofxObDzXz6uY6rHXWT0vxUZGfQWVBJpX5GVQWZFCSk05xKEhJKI2CrKAuRRyAYsHRKRKxnOjqobWzhxOdPRzviDHf2UNrhzPf6k7t3RF6whF6wpbuiPPaE47QHXFeAb794hZ8PoPPgM9Evxp8Pmfe7zN9r/7e9z6D3+C+nkzbUX+ciLV0hy1h92+2d4f78nSis4djbd00tHYSsaeWtTA7SNWYTK6eWcqsshxml+UyuTiLpz84GIeaFxGRRFGen8FtCyu5bWElXT0RNh1q5oO9jazZc4zXt9bz6w+dfsRnnEHDKSUhxo9xYsXxBZlU5GdQmO2N+PC0A4TGGD/wM+BK4CDwvjFmubV2S9RqdwPHrLXnGWOWAA8BNxtjpgFLgOnAWOANY8xk9zOxtvkQ8CNr7TJjzCPuth8eisKeq9rmdjq7I3T2ROjqidDZE3Zfe6cwHd1hjnf00NLRw/GObprbu2lo7eJoaydHWztpaO2iJyri8RkYX5DJBeW53LKggmmlIWaOy6EgS6e7ioiInK1gwM/cijzmVuT1pbV19bCjvpVttS1UH25lX2Mb+xpO8PaOI3T2RE75vN9nyElPISc9hVDva1qgLy0nPYWstADBgJ9gwOdMKVHzAT+pAR9+n8HgDKAYA8acnPcZZ5mJeu83ZtTeQ1Gx4KmstUQsRKzF9nuNWIsFbOTk+3DEOrFj2Ikfu/rNf2xZT5gTXeGTg3kdPacMoEUP9p3oCp9RnoMBH1nBAFlpAbKCATKDAXLTU0jxGwI+HwG/IcXvHLe9aUtX7WN2ea5TjsjJMobd8oYj9pQyhiPQFYkQ7nbfu5/re41YttW2OAOJPh8pfmdgMSPVT2bQyVdxdho56SkUh4IUhtIoynYG7qsKM/VjuYiInLPUgK8vTrznMqdPP3isnc2HWthS28LW2hY2uT8uh/uN24zJClIUClLk9lXZbp+alRYgu6+PTSErGCAj1U/Ab0j1+wj4fQR8htSA8xrwn+wDo398c+LF+A5CnskZhPOBamvtbgBjzDLgOiA6KLwOeNCdfwb4qXFKdh2wzFrbCewxxlS722OgbRpjtgKXA7e46/zS3e6oCAoveWjFKQfJ6WQHA4TSUyjISqU4lMb0sSHGZAUpy8ugIt+ZSnPTSPH7hjHXIiIi3paRGuCC8lwuKM89JT0SsRxt7aSupYO65g7qWjqob+mgqc35ga+5vZvmti4ONLb1vT+bOOBs5KSnsP4bi4Zl20NAsSDw9PsH+JtnN4zY3wv4TN+AXu+Ul5lKWX6G84+IO9CXnRboG2Drv37vOqmBs481l67axxdmjx3SMg3FPQhFRESGijGG8vwMyvMzWDyjpC+9OxzhUFM7+xqcKz8Pt3RQ39LJ4eNOzLjz8HFaO5yz9XuGKDb8889M5P6rpgzJtj4pY+3ghTHG3AQsttb+qfv+NmCBtfbeqHU2uescdN/vAhbgBHSrrLX/4aY/Drzifuxj24xa/zw3vRx4xVo7Y4B83QPc4749H9h+toVPIGOAo/HOxCikeolNdROb6iY21U1sqpuBqV5iG811U2mtLTzTlRULCqP7eJYzo32YHLQfE5/2YeJLhn04YCyYsE+7sNb+HPh5vPMxEowxH1hr58U7H6ON6iU21U1sqpvYVDexqW4GpnqJTXUz/LwUC8abjufEp32YHLQfE5/2YeJL5n14Jtcb1ADlUe/L3LQB1zHGBIAcnBtUx/psrPQGINfdRqy/JSIiIiIjR7GgiIiISJI7kwHC94FJxpgqY0wqzo2ml/dbZzlwhzt/E/CWda5dXg4sMcYE3SfSTQLWxNqm+5kV7jZwt/n8Jy+eiIiIiJwjxYIiIiIiSe60lxhba3uMMfcCrwF+4BfW2s3GmG8BH1hrlwOPA0vdG0834gR5uOs9jXMT6x7gf1trwwADbdP9k18Flhlj/j/wkbttr9PlMwNTvcSmuolNdROb6iY21c3AVC+xJU3dKBYUkuh49jDtw+Sg/Zj4tA8TX9Luw9M+pERERERERERERESS15lcYiwiIiIiIiIiIiJJSgOEIiIiIiIiIiIiHqYBwjgwxvzCGHPYGLMpKu0HxphtxpgNxpjfGmNy3fTxxph2Y8w6d3ok6jMXGmM2GmOqjTE/McaYeJRnKMWomweNMTVRdXB11LKvueXfboz5XFT6Yjet2hhz/0iXYzjEqJunouplrzFmnZvumePGGFNujFlhjNlijNlsjPkrNz3fGPO6MWan+5rnphu33NXu921u1LbucNffaYy5I9bfTBSD1I3n25tB6sbT7c0g9aK2xpg0Y8waY8x6t26+6aZXGWNWu+V8yjgP3MA4D+V4yk1fbYwZH7WtAY8lkZEUI67wfN+ZSGLsQ0/3Y4lmkH5X38UEoZgyOSjOc1lrNY3wBFwGzAU2RaUtAgLu/EPAQ+78+Oj1+m1nDbAQMMArwFXxLtsw1c2DwP8dYN1pwHogCFQBu3BudO535ycAqe460+JdtuGom37L/xH4uteOG6AUmOvOZwM73GPj+8D9bvr9Ud+pq91yG7ceVrvp+cBu9zXPnc+Ld/mGqW48394MUjeebm9i1Uu/dbza1hggy51PAVa75XsaWOKmPwL8L3f+L4BH3PklwFODHUvxLp8m700MHHN5vu9MpCnGPvR0P5Zo0yDxiL6LCTINsg/1XUygSXGeM+kMwjiw1r6N84S/6LTfWWt73LergLLBtmGMKQVC1tpV1jkSnwCuH478jqSB6mYQ1wHLrLWd1to9QDUw352qrbW7rbVdwDJ33YQ2WN24Z+Z8CfjPwbaRjMeNtbbWWrvWnT8ObAXG4ezzX7qr/ZKT5bwOeMI6VgG5br18DnjdWttorT0GvA4sHsGiDLlYdaP2ZtDjJhZPtDenqxePtzXWWtvqvk1xJwtcDjzjpvdva3rboGeAP3LrL9axJDKiYsQVnu87E4ni5sSnODbxKaZMDorzHBogHJ3uwvllqFeVMeYjY8xKY8ylbto44GDUOgcZvCFKdPe6p9H/ovcUe5zyHohap7cOYqUns0uBemvtzqg0zx037qndc3B+8Sm21ta6i+qAYnfek8dNv7qJ5vn2ZoC6UXtDzGPG022NMcZvnMurD+P887ULaIoacI8uZ9+x4S5vBgpI4mNGkoL6zuSgfiwBKY5NfIopE5viPA0QjjrGmAeAHuBJN6kWqLDWzgHuA35ljAnFK39x8jAwEbgApz7+Mb7ZGZW+zKln9HjuuDHGZAHPAl+x1rZEL3PPYLJxydgoEKtu1N4MWDdqbxj0++TptsZaG7bWXoBz1u18YEqcsyQybLzedyYw9WMJSHFs4lNMmfgU52mAcFQxxtwJfB641e0IcE9NbXDnP8QZxZ4M1HDqZYFlblrSsdbWu1/WCPAYJ0/RrQHKo1btrYNY6UnJGBMAvgg81ZvmtePGGJOC0yE/aa39jZtc715y0Xu542E33VPHTYy6UXvDwHWj9mbQY8bzbU0va20TsAL4FM7lXQF3UXQ5+44Nd3kO0EASHjOSVNR3Jjj1Y4lHcWziU0yZXLwc52mAcJQwxiwG/gb4grW2LSq90Bjjd+cnAJOA3e4p5y3GmIXute63A8/HIevDrrdzdN0A9D6pbTmwxH2CUBVO3awB3gcmuU8cSsW5aejykczzCLsC2Gat7bucz0vHjVuOx4Gt1tofRi1aDvQ+we0OTpZzOXC7cSwEmt16eQ1YZIzJcy8BWOSmJaxYdaP2ZtC68XR7M8j3CdTWFJqTT/xOB67Euc/QCuAmd7X+bU1vG3QT8JY7GB/rWBIZDTzfdyY6r/djiUZxbOJTTJkcFOe57Ch4UorXJpzLs2qBbpxr0u/GuXnlAWCdO/U+EedGYLObtha4Nmo783Aaml3ATwET77INU90sBTYCG3C+cKVR6z/gln87UU/IxHnC1w532QPxLtdw1Y2b/u/An/db1zPHDXAJzmUXG6K+P1fj3APiTWAn8AaQ765vgJ+55d8IzIva1l3ud7Ea+J/xLtsw1o3n25tB6sbT7U2senGXeb2tmQV85NbNJk4+yXkCTuBXDfwaCLrpae77anf5hNMdS5o0jeTEwDGX5/vORJpi7ENP92OJNg0Sj+i7mCDTIPtQ38UEmhTnOZNxCyAiIiIiIiIiIiIepEuMRUREREREREREPEwDhCIiIiIiIiIiIh6mAUIREREREREREREP0wChiIiIiIiIiIiIh2mAUERERERERERExMMC8c6AiEgyMMaEgY1RSdcDk4DvAalAF/D/rLVvxSF7IiIiIjKMFAuKSKIz1tp450FEJOEZY1qttVn90uYA9dbaQ8aYGcBr1tpx8cmhiIiIiAwXxYIikug0QCgiMgQGCgr7LTdAA1Bqre0cuZyJiIiIyHBTLCgiiU6XGIuIDI10Y8w6d36PtfaGfstvBNYqIBQRERFJSooFRSSh6QxCEZEhMNivxsaY6cByYJG1dtfI5kxEREREhptiQRFJdHqKsYjIMDLGlAG/BW5XQCgiIiLiLYoFRSRRaIBQRGSYGGNygZeA+62178Y7PyIiIiIychQLikgi0QChiMjwuRc4D/i6MWadOxXFO1MiIiIiMiIUC4pIwtA9CEVERERERERERDxMZxCKiIiIiIiIiIh4mAYIRUREREREREREPEwDhCIiIiIiIiIiIh6mAUIREREREREREREP0wChiIiIiIiIiIiIh2mAUERERERERERExMM0QCgiIiIiIiIiIuJh/w1gdclCCblrxAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1296x432 with 2 Axes>"
]
},
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}
},
{
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"data": {
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"<p><em>Equality of Variances</em></p>"
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">1-way ANOVA of O'Brien transform</th>\n",
" <th colspan=\"3\" halign=\"left\">Levene's Test for skewed (non-normal) distributions</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>Eq.</th>\n",
" <th>Sig.</th>\n",
" <th>Stat.</th>\n",
" <th>Eq.</th>\n",
" <th>Sig.</th>\n",
" <th>Stat.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>gender</th>\n",
" <td>True</td>\n",
" <td>0.475426</td>\n",
" <td>0.509706</td>\n",
" <td>True</td>\n",
" <td>0.45302</td>\n",
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" 1-way ANOVA of O'Brien transform ... Levene's Test for skewed (non-normal) distributions \n",
" Eq. Sig. ... Sig. Stat.\n",
"gender True 0.475426 ... 0.45302 0.563508\n",
"\n",
"[1 rows x 6 columns]"
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"<p><em>Difference of distributions</em></p>"
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"<p><em>Results for gender as independent variable, and F2 as the dependent one</em></p>"
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"<div class=\"bg-info\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(10, 148, 170);\">Distributions are not normally distributed (with no outliers), distribution shapes are different, distribution sizes are balanced by a factor lower than or equal to 1.5, and their variances are homogeneous</div>"
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"<div class=\"bg-success\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(25, 142, 51);\">Inspection of Q-Q Plots and a Shapiro-Wilkinson test (p = 0.018) revealed that F2 was not normally distributed for all groups (F, M), and there was homogeneity of variance as assessed by Levene's test for equality of variances for medians (p = 0.453). </div>"
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"<div class=\"bg-success\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(25, 142, 51);\">Inspection of histograms / CDF plots and Kolmogorov-Smirnov test reported that groups did not have a similar shape (p = 0.005). </div>"
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"<div class=\"bg-danger\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(149, 15, 28);\">Although normality assumption seemed violated, an independent t-test was run on the data with a 95% confidence interval (CI) for the mean difference. It was found that F2 in the F group (2004.098 ± 277.869) was significantly higher than the M group (1961.409 ± 277.869) (t(1041.0) = 2.431, p = 0.015) with a difference of 42.689 (95% CI).</div>"
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"<div class=\"bg-danger\" style=\"padding: 1rem; margin-bottom: .5rem; background-color: rgb(149, 15, 28);\">Therefore, a Mann-Whitney U test was run on the data with a 95% confidence interval (CI) for the mean ranks difference. It was found that F2 in the F group (mean rank = 240.500) was not statistically significantly different than the M group (mean rank = 282.000) (U = 147452.000, p = 0.995) with a difference of 41.500 (95% CI).</div>"
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"<p><strong>No outliers detected</strong></p>"
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"<p><em>Normality (&ast;Lilliefors correction)</em></p>"
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" <th colspan=\"3\" halign=\"left\">Shapiro-Wilk</th>\n",
" <th colspan=\"3\" halign=\"left\">Anderson-Darling</th>\n",
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" <tr>\n",
" <th></th>\n",
" <th>N</th>\n",
" <th>Stat.</th>\n",
" <th>Sig.</th>\n",
" <th>Normal</th>\n",
" <th>Stat.</th>\n",
" <th>Sig.</th>\n",
" <th>Normal</th>\n",
" <th>Stat.</th>\n",
" <th>Sig.</th>\n",
" <th>Normal</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>F</th>\n",
" <td>480</td>\n",
" <td>0.196342</td>\n",
" <td>2.81748e-51</td>\n",
" <td>False</td>\n",
" <td>0.80631</td>\n",
" <td>1.4859e-23</td>\n",
" <td>False</td>\n",
" <td>34.8501</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
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" <tr>\n",
" <th>M</th>\n",
" <td>563</td>\n",
" <td>0.109099</td>\n",
" <td>1.22827e-17</td>\n",
" <td>False</td>\n",
" <td>0.915081</td>\n",
" <td>3.14242e-17</td>\n",
" <td>False</td>\n",
" <td>13.8537</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
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" N Stat. Sig. ... Stat. Sig. Normal\n",
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"M 563 0.109099 1.22827e-17 ... 13.8537 0 False\n",
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\n",
"text/plain": [
"<Figure size 1296x432 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<p><em>Ratios</em></p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>N(F) / N(M)</th>\n",
" <td>&lt;= 1.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Ratio\n",
"N(F) / N(M) <= 1.5"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<p><em>Shapes</em></p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
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}
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>Chebyshev</th>\n",
" <th colspan=\"3\" halign=\"left\">Kolmogorov-Smirnov 2-sample</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>Eq.</th>\n",
" <th>Sig.</th>\n",
" <th>Stat.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>F, M</th>\n",
" <td>0.13091</td>\n",
" <td>False</td>\n",
" <td>1.06193e-12</td>\n",
" <td>0.231753</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Chebyshev Kolmogorov-Smirnov 2-sample \n",
" Eq. Sig. Stat.\n",
"F, M 0.13091 False 1.06193e-12 0.231753"
]
},
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}
},
{
"output_type": "display_data",
"data": {
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JeSVJkiRJSoIFoaR+66mqXTy+ZhdzJ4/gTTNH9em5L5o1in3N7Ty2uoai/GzOm1rap+eXJEmSJKmvWBBK6peWbt7Lr1/axpxxJVx52jhCCH16/hAC804fx/6Wdn69bBvF+TmcMn5on2aQJEmSJKkv+A5CSf1O1c4G7ltSzeTSQt5dWU5WH5eDf5IVAtecVc7EEQUsWLyZdTUNieSQJEmSJKk3WRBK6lda2ju4b8lmRhbl8t65k8hJJfufqZxUFu87dxIjC3P50bMb2VbXlGgeSZIkSZJ6mgWhpH7lsVU17Gtu5+ozxjMkN5V0HAAKcrP5wHkV5Oek+P7TG6je05h0JEmSJEmSeowFoaR+Y1d9C0+u2cWZE4czcWRh0nH+zLCCXD5wXgWt7Z3cfO9SOjtj0pEkSZIkSeoRFoSS+oUYI79ctpXsVODSOaOTjnNIo0vyufLUcSzaUMt3n1qfdBxJkiRJknqEBaGkfuHlbfWs2dnAJSeNpjg/J+k4h3XGxGFcctJovvjgKqp21icdR5IkSZKkE2ZBKClxzW0d/PqlrYwuyeOcKSOTjnNEIQT+/eqTKcxNcfOCpbR3dCYdSZIkSZKkE2JBKClx33hsLXsa27jy1HGkskLScY5qVHE+n/uLU1hWXcc3HlubdBxJkiRJkk6IBaGkRG3a3cg3/rCWUycMZUpZUdJxuu2tp4zlqtPG8V8Pr2H5lrqk40iSJEmSdNwsCCUl6t9+vZLsrMDlJ49NOsoxu23eHIYX5vKJBUtpae9IOo4kSZIkScfFglBSYh5dtZPfr9zB3108naFD+u/CJIczrCCXL77jVFbtqOfLD61JOo4kSZIkScfFglBSIlraO7jtlyuZUlrIh14/Oek4x+1Ns0Yx/6xy/vsPa1mycU/ScSRJkiRJOmbdKghDCJeFEFaFEKpCCLcc4vu8EMK96e+fCyFUHPDdrenxVSGES492zBDCxSGEF0IIL4YQngwhTDuxS5TUH33nyfWs37Wff71qDrnZA/vvKv73FScxdugQPvmTpTS2ticdR5IkSZKkY3LUP5WHEFLAHcDlwGzg2hDC7IOm3QDsiTFOA24HvpDedzYwH5gDXAZ8PYSQOsoxvwFcF2M8HbgL+KcTu0RJ/c22uia++nAVl84ZzYUzypKOc8KK83P40rtOZf2u/XzxgVVJx5EkSZIk6Zh057ads4GqGOO6GGMrcA8w76A584AfpLfvAy4OIYT0+D0xxpYY43qgKn28Ix0zAiXp7aHA1uO7NEn91TceW0tHZ+Sfrjj47xoGrvOmlvKB8yr4/tMbWLKxNuk4kiRJkiR1W3cKwvHA5gM+V6fHDjknxtgO1AEjj7DvkY75YeA3IYRq4H3A5w8VKoRwYwhhcQhhcU1NTTcuQ1J/ULu/lQWLN/P2M8ZRPqIg6Tg96h8uncmYknz+deFKOjtj0nEkSZIkSeqW/vjir48Db40xTgC+B/znoSbFGO+MMVbGGCvLygb+I4pSpvjhMxtobuvkxgunJB2lxxXmZXPrW2fx0pY6frJk89F3kCRJkiSpH+hOQbgFKD/g84T02CHnhBCy6Xo0ePcR9j3keAihDDgtxvhcevxe4LxuXYmkfq+ptYMfPrORS04axbRRxUnH6RVXnTaOyknD+eIDq6hraks6jiRJkiRJR9WdgvB5YHoIYXIIIZeuRUcWHjRnIXB9evudwCMxxpgen59e5XgyMB1YdIRj7gGGhhBmpI/1ZuDl4788Sf3JfUs2U7u/lRsvnJp0lF4TQuBfr5pDbWMrX3l4TdJxJEmSJEk6quyjTYgxtocQbgIeBFLAd2OMK0IItwGLY4wLge8APwohVAG1dBV+pOctAFYC7cBHY4wdAIc6Znr8L4H7QwiddBWGH+rRK5aUiI7OyLeeWM8ZE4dxVsXwpOP0qpPHD2X+WRP5wdMbuPbs8kF7t6QkSZIkaXAIXTf6DWyVlZVx8eLFSceQdAS/XraNj971At9875lcdvLY13x/13ObEkh1fN4zd+JR5+xuaOGN//cxTi8fxg8/dDZdC7tLkiRJkpScEMKSGGPlweP9cZESSYNMjJE7H1/L5NJC3jx7TNJx+sTIojxufvMMnlizi4de3pl0HEmSJEmSDsuCUFKve3ZdLUur6/jwBZNJZWXOnXTvPWcS00cV8W+/WklzW0fScSRJkiRJOiQLQkm97s7H11JalMs7zpyQdJQ+lZPK4tNXzmFTbSPfeXJ90nEkSZIkSTokC0JJvWrV9noeXVXD9edWkJ+TSjpOnzt/eimXzhnNHY9Wsb2uOek4kiRJkiS9hgWhpF515+PrGJKT4r3nTEo6SmL+6YrZtHdGPv/bl5OOIkmSJEnSa1gQSuo12+qa+MWLW7jmrHKGF+YmHScx5SMK+KsLp/DzF7eyeENt0nEkSZIkSfozFoSSes33ntpABG44f3LSURL3kTdOZezQfD7zy5V0dsak40iSJEmS9CoLQkm9Yl9zG3c9t4krThlL+YiCpOMkriA3m09dNpOXttTxy2Vbk44jSZIkSdKrLAgl9Yq7nttEQ0s7N144Jeko/ca808Zz0tgS/u/vVtHa3pl0HEmSJEmSAAtCSb2gtb2T7z65ntdPG8nJ44cmHaffyMoK3HL5LDbXNvHj5zYmHUeSJEmSJMCCUFIv+P3KHeysb+HD53v34MEunF7K66eN5KuPVFHf3JZ0HEmSJEmSLAgl9bx7nt/EuKH5XDijLOko/U4IgVsuO4na/a3c+fi6pONIkiRJkmRBKKlnba5t5MmqXbyrspxUVkg6Tr90yoShXHnaOL79xHp27mtOOo4kSZIkKcNZEErqUT9ZUg3AuyonJJykf/vkW2bQ3tnJlx9ek3QUSZIkSVKGsyCU1GM6OiP3Ld7MBdPLmDC8IOk4/dqkkYVcN3cS9z6/mbU1DUnHkSRJkiRlMAtCST3miTU1bK1rZv5Z5UlHGRBuumga+dlZfOmBVUlHkSRJkiRlMAtCST3m3uc3M6Iwl0tOGp10lAGhtCiPGy+cygMrtvPCpj1Jx5EkSZIkZSgLQkk9YldDC79fuYN3nDme3Gz/09JdH75gMqVFeXz+N68QY0w6jiRJkiQpA/mneEk94qcvVNPeGbnGx4uPSWFeNh+7ZDqLNtTyyCs7k44jSZIkScpAFoSSTliMkXue30zlpOFMG1WcdJwBZ/5Z5UwuLeQLD7xCR6d3EUqSJEmS+pYFoaQTtnjjHtbV7PfuweOUk8riHy6dyeodDdy/pDrpOJIkSZKkDGNBKOmE3bNoM0V52Vxx6tikowxYl588htPKh/Hlh1bT3NaRdBxJkiRJUgaxIJR0QvY1t/Hrl7Zy1enjKMjNTjrOgBVC4B8vncnWumb+59mNSceRJEmSJGUQC0JJJ2Thi1tpbutkvo8Xn7DzppVy/rRSvv7YWuqb25KOI0mSJEnKEBaEkk7Ivc9v5qSxJZwyfmjSUQaFf7h0JrX7W/n2E+uTjiJJkiRJyhAWhJKO2/Itdby0pY75Z5UTQkg6zqBwWvkwLj95DN9+Yh27G1qSjiNJkiRJygAWhJKO24LFm8nNzuLtp49POsqg8om3zKCprYM7Hl2bdBRJkiRJUgawIJR0XJrbOvjZH7fw1pPHMLQgJ+k4g8q0UcW883UT+J9nN7Jlb1PScSRJkiRJg5wFoaTj8tvl26hvbueasyYmHWVQ+tglMwD48u9XJ5xEkiRJkjTYWRBKOi73Pr+ZipEFnDNlRNJRBqXxw4bwvnMncf8L1VTtrE86jiRJkiRpELMglHTMtu5t4tl1tVx95gQXJ+lFf/PGqQzJSfEfv/MuQkmSJElS77EglHTMfrl0KwBXnTYu4SSD28iiPP7ywin8dvl2lm7em3QcSZIkSdIgZUEo6ZgtXLqV08qHUVFamHSUQe/DF0xhRGEuX3pwVdJRJEmSJEmDlAWhpGNStbOeFVv3Mc+7B/tEUV42f/PGqTxZtYunqnYlHUeSJEmSNAhZEEo6Jgtf3EpWgLedOjbpKBnjvedMYtzQfL74wCvEGJOOI0mSJEkaZCwIJXVbjJGFS7dy7tSRjCrJTzpOxsjPSfH3l8xgaXUdDyzfnnQcSZIkSdIgY0EoqduWVdexYXeji5Mk4OozxzNtVBFfenAV7R2dSceRJEmSJA0iFoSSum3h0q3kprK4bI6PF/e17FQWn7p0Jut27WfB4uqk40iSJEmSBpHspANIGhg6OiO/XLqVN84sY2hBTtJxEnXXc5sSOW+MkYkjCvg/v32Z1vZOcrOP/nc875k7sQ+SSZIkSZIGMu8glNQtz63bzc76Fq463ceLkxJC4LI5Y6hvbufpta5oLEmSJEnqGRaEkrpl4dKtFOamuHjW6KSjZLSK0kJOGlPMH1bX0NjSnnQcSZIkSdIgYEEo6aha2jv4zUvbuHTOGIbkppKOk/HeMmcMre2dPLa6JukokiRJkqRBwIJQ0lH9YVUN+5rbudLHi/uF0SX5nDlpOM+s282extak40iSJEmSBjgLQklHtXDpVkYU5nL+tNKkoyjt4lmjCMBDK3ckHUWSJEmSNMB1qyAMIVwWQlgVQqgKIdxyiO/zQgj3pr9/LoRQccB3t6bHV4UQLj3aMUOXz4UQVocQXg4h/N2JXaKkE9HQ0s5DL+/gilPGkpPy7xT6i2EFuZw3dSQvbt7LtrqmpONIkiRJkgawo/5pP4SQAu4ALgdmA9eGEGYfNO0GYE+McRpwO/CF9L6zgfnAHOAy4OshhNRRjvkBoByYFWM8CbjnhK5Q0gn5/crtNLd1unpxP/SGGaPIy8nidyu8i1CSJEmSdPyyuzHnbKAqxrgOIIRwDzAPWHnAnHnAv6a37wO+FkII6fF7YowtwPoQQlX6eBzhmB8B3hNj7ASIMe48/suTMtddz23qkeP84OkNDBuSw6rt9azZ0dAjx1TPGJKb4o0zRvHAiu2sq2lgSllR0pEkSZIkSQNQdwrC8cDmAz5XA3MPNyfG2B5CqANGpsefPWjf8entwx1zKnBNCOEvgBrg72KMa7qRU1IPa2hpZ83Oes6fVkZWCEnHGbSmbvrJce9bnht4PmcKj72wkktmbeQ1v02pEScW7nhUfrDvzylJkiRJOm798YVieUBzjLES+Bbw3UNNCiHcGEJYHEJYXFNT06cBpUyxfEsdnRFOKx+adBQdRm5W5F3jdlHVOIRFe4uTjiNJkiRJGoC6UxBuoeudgH8yIT12yDkhhGxgKLD7CPse6ZjVwE/T2z8DTj1UqBjjnTHGyhhjZVlZWTcuQ9KxWla9l1HFeYwpyU86io7gwpF1TMhv4e4tpbTHpNNIkiRJkgaa7hSEzwPTQwiTQwi5dC06svCgOQuB69Pb7wQeiTHG9Pj89CrHk4HpwKKjHPPnwJvS228AVh/fpUk6EXsbW9mwu5FTJwwj+Hhxv5YK8J7xNWxryeP3NcOTjiNJkiRJGmCO+g7C9DsFbwIeBFLAd2OMK0IItwGLY4wLge8AP0ovQlJLV+FHet4CuhYfaQc+GmPsADjUMdOn/Dzw4xDCx4EG4MM9d7mSumtZdR0Ap03w8eKB4MyhDZxcvJ/7tpZywYg6irI7k44kSZIkSRogurNICTHG3wC/OWjsXw7YbgbedZh9Pwd8rjvHTI/vBa7oTi5JvWdp9V7Khw9hZFFe0lFe40QW9RisQoD3TdjJLS9XcP+2Uq4vdwF4SZIkSVL39MdFSiQlbMe+ZrbVNXPqhGFJR9ExqCho4U2ldTy4czhbm3OSjiNJkiRJGiAsCCW9xrLqvQTgFB8vHnCuGVdDTlYnP64elXQUSZIkSdIAYUEo6c/EGFlaXcfUsiJK8r0LbaAZltPB28fsZnFdMcvrC5KOI0mSJEkaACwIJf2Z6j1N1O5v5VTvHhywrhi9h7LcNn64eRQdMek0kiRJkqT+zoJQ0p9ZWr2XVFZgzjgLwoEqNyvynvE72diUz30b8pOOI0mSJEnq57q1irGkzNAZIy9V1zFzdDFDclNJx+nfYiQrtpPqaCK7o5lUZ3PXz46unyG205GVR0cqr+vnAdudWbl0pPKIoff+GZ87vJ7f7mzkS8sLuWJCC0U53kooSZIkSTo0C0JJr1pXs5/6lnZOK3f14lfFSPFc7JoAACAASURBVG5bHYXN2yhq2kJh0zaGtNSQ3dFIVuw8oUO3pYbQnDuS5ryRNOWOpDmvtOtz7nBi1on95zkEeH/5Tv7plQq+uaqAT568/4SOJ0mSJEkavCwIJb1qWfVecrOzmDWmOOkoiclpq6ewaSuFzVspatpKYdM2cjoaAegMWTTmjWZv0TTaswtoz8qnIzWE9lQ+Hal82rPyX92OIZuszlZSnS2kOlq6fh643dFCbns9Q1p2MbRhLWXtS1/NEAm05A6jKXckjfljqS8sp2HIBDpSx/a48PTCZuaVN/Ot1QVcO6WJ8QUnVmhKkiRJkgYnC0JJALR3dLJ8ax1zxpaQk8qs15OmOpoYUbeS0rpllDRuBrpKusa8UewpnsH+IePYP2QsjXmjj+nOvo5UHm10r2xNdbSQ37qb/Jbd5LfuYkhL1/awhrWEXZEINOaPpmFIOfWFE6kvKKc15+jvifzUKQ08sCWPL7xUxFfm7ut2dkmSJElS5rAglATA6h31NLd1ZszjxaGzg6ENVZTWLWN4/WqyYgdNuaVsHvUm9hVW0Jg/hs6snD7L05HKSxeR4/5sPKujlaKmaoobN1PcuJnSumWM3rMYgJacEuoLJlJXOIW9xdNpzy58zXHHF3Ry44xGvvpKIR+Y1siZI9v75HokSZIkSQOHBaGUAaZu+slR5/xy3ThKsgt4c/MDZG/qg1BJiJHCpq2U1i1jZN1ycjqaaEsVsHP469g17FT254/tenlfP9KZymVf0RT2FU3pGoidFDTvSBeGmyjZv4HSuuVEoGHIePYWz2BP8Qya8ka9ei1/PauRezfk85kXi/nZRXvI6l+XKEmSJElKmAWhJJo6sli8t4iLSuvIHozlUYwMr3+FCTsfo6Clhs6QYk/xTHYNO5W6oqm9uppwjwtZNA4ZS+OQsewYeTbESEHzdobVr2F4w2rKdz5K+c5HackZyt6i6VB4JoUjp3PrKQ18/Pmh3LM+n/dMaU76KiRJkiRJ/YgFoSSe31tEW8zivBGD7x11xfs3MnHHQxQ1baEpr5R1495GbcnsY17wo98K4dXCcOuoC8lpa2BYwxqG1a+mdO9SWLQYUrm8ffQprBp2Af/x0ulcOr6FkXkx6eSSJEmSpH7CglAST9WWUJbbxozCpqSj9JghzTsp3/EwwxvW0JpdzLpxV1Iz7DQIg3sBlracImqGn0HN8DMIne2cXbwbti8jbFvKLW1LuDGriFVPVnLuGafC8IpB/89DkiRJknR0FoRShtvXlmLZvkKuHF07KN5Nl9tWx4Sdj1G6dykdWXlsGnURO0bO7dMFR/qLmJUNo07q+nXyO6BmFdtWLOX0/U/D04/BkOEw7kwY/zoo7n/vX5QkSZIk9Q0LQinDPbOnmE4Crx/gjxenOpoYV/MkY2oXAbB95DlsLT2f9uyChJP1E1nZMHoOFSNP5m0P/CVvyV7MPxQ9Qda6R2Htw10FYflcmHAW5L52NWRJkiRJ0uBlQShluKf3lDAhv4WJQ1qSjnLchtZXMXXLL8ju2M+uYadRXfYGWnOHJR2rXyrMjvzDGe389TNvoHTqmdxweg1sexGqF8HKn8Mrv4Qxp8HEc2HkNO8qlCRJkqQMYEEoZbCalmxeaSjgmnE1A7IHCp3tlO98hLG7n6Uxr4xXJl1H45AxScfq9y4d18Ibx7Rw+4pC3jahhdEV50PF+bBvK2x6BrYshq0vQEEpTDyn687CvOKkY0uSJEmSeokFoZTBnt5TAjAgHy/Ob9nFtOqfUti8nR3DK9k45s3EDHzP4PEIAT5zegNv/t0IPrusiK/OTf/+l4zrelfhSVfCtqVdZeErv4JVv4HRJ8Ok86B0hgubSJIkSdIgY0EoZbCnakuYXtjE6Ly2pKN0X4yU7X2RSdseoDMrm9Xl17CnZGbSqQacSUUd/M2s/Xx5ZRHzK5p4/egD/h1I5Xa9i3DCWdCwAzY92/UI8vZlUFgGFRfAhLMhJz+5C5AkSZIk9RhvA5Ey1OamXDY25Q+ouwdTHc1Mq76fKVt/SUPBeF6a+leWgyfgr2c2MqmwnX9+sZiWjsNMKhoNs+fBxZ+B098LOQWw4qfw8Kdh+f3QsLNPM0uSJEmSep53EEoZ6qnaEgKRc4cPjIKwqHEz06p/Sm7bPjaPuoitpef5qOsJyk/BZ85o4ANPDuPbqwv46EmNh5+cyoYJlV2/9myEDU/Axqe7fpbNgooLYdQsf08kSZIkaQCyIJQyUIzwdG0JpxQ3MizncLeO9RMxMmb3M0zc8TAtOUNZOfmDNBRMSDrVoPHGMa1cPr6Zr7xcyFUTmykv7Dz6TsMndf066SrY9HRXUfj8nV2Lmky+AE55F+QV9X54SZIkSVKP8FYPKQNVNeazozWX8/r748UxMnH775i04yFqS2axfOqNloO94J9PayAVIv/rhWJiPIYd80tgxmVw8b/AGe+H3EJY8TO4fQ48fBvU7+i1zJIkSZKknmNBKGWgp2pLyAmdzB1en3SUwwqd7Uyt/ilja59j24i5VE14Jx0pF8XoDeMKOrn11P08sSOPBRuO459xVjaMPxPO/zic9zGoOB+e+E/48smw8G+hZnXPh5YkSZIk9RgLQinDdKYfLz5j6H4KUt14nDQBWR0tzNx0N6X7VrBp9MVsGvMWCCHpWIPadVOaOKeslc8uLWJr4wn8X8OIyTD/x/C3S+CM98KyBXDHWXDX/K5HkY/pFkVJkiRJUl+wIJQyzPL6Auras/vt6sXZ7Q3M3vBDSvZvYO34eWwrfb3lYB/ICvClyn10RLhlyTE+anwoI6fC226Hv18Ob7gFNj8H37scvn0JrPg5dPbzd19KkiRJUgaxIJQyzFO1JQzJ6uDMoQ1JR3mNvNZa5qz/Hvktu1g1cT67hp2WdKSMUl7YyS2n7OfxHXn85HgeNT6UojJ4063w8RVwxX9A4274yfXw1dfBom9B6xFWTpYkSZIk9QkLQimDtHYGFu0t5qxhDeRm9a9HPQuatjFn3fdIdTTzcsX7qCuennSkjPTeqU3MLW3l35YWse1EHjU+WG4BnPXhrkeP3/1DKBgJv/lk14Imj/477N/Vc+eSJEmSJB0TC0Ipg7xYV0hjR6rfPV5c0rCO2Rt+QGdWNisnf5D9rlScmKwAX6yspz0Gbj3WVY27dYIUzJ4HH34IPvgATDwH/vCFrqLwl38Pu9f28AklSZIkSUdjQShlkKf2lFCS3c4pJfuTjvKq4fteYeamu2jJGcaKyR+kOa806UgZb1JRB/94SgOPbc/jvo29tHJ0CDDpXLj2brhpMZx6Dbx4V9ejx/dcB5sX9c55JUmSJEmvYUEoZYjGjiyW7C3i3OH1pPrJmh9DG9Yyrfo+9uePZeXkD9CWU5J0JKW9f2oTZ5e2ctvSIrY39fL/VZROh6u+Ah9fDhd+EjY8Cd95M3z3clj1AHT2z9W2JUmSJGmwsCCUMsTze4toi1n95vHiosbNTN+0gKa8MlZNuo6OVC/dqabj8qdHjds6A7f2xKrG3VE0Ci76p64FTS77PNRthruvgW+c13V3YXtrH4SQJEmSpMxjQShliKdrSyjLbWVGYVPSUSho3s7MjXfTmlPMK5aD/VZFUQefOrmBR7fncX9vPWp8KHlFcM5H4O/+CFd/C0IW/Pwj8JXT4emvQUt932WRJEmSpAxgQShlgLq2FMv2FXLeiHpCwo8X57XsZtaGH9ORlcsrk95He3ZRsoF0RB+Y1vWo8Wd6elXj7kjlwKnvho88BdfdDyOmwO/+d9eCJg/fBg07+zaPJEmSJA1SFoRSBnh2TzGdBM5P+PHi3LY6Ttr4P0DklYr30po7NNE8Oro/PWrcGeFji0poT+J1gCHA9EvgA7+CDz8Ck98AT/wn3H6yKx9LkiRJUg+wIJQywFO1JZTnNzNxSEtiGbLb9zNrw/+Q6mjmlUnXuVrxAFJR1MFnz6hn0a5cvvJyYbJhJrwOrvkR/O0SOP3a/7/y8YL3w5YlyWaTJEmSpAHKglAa5Pbsb2XV/gJePyK597alOpqZtfHH5LbVsWritTQOGZtYFh2fv5jUwjsmNfHVlwt4emdO0nFg5FS48r/g71+C8z8Oax+Db10E338brHmIvllVRZIkSZIGBwtCaZBbVr0XILHVi7M625i56W6GtOxkTfm7aSicmEgOnbjbzmhgcnEHf7+ohN0tCb/M8k+KR8Mln4abV8BbPtv1uPGP3wHfPB+W3uvKx5IkSZLUDRaE0iC3tLqOGYWNjMpr6/Nzh84Opm9eQFFjNWvHX01d8bQ+z6CeU5gd+drcfextzeITz5fQ2Z9u0ssrhvP+Fj62FOZ9HTrb4Wc3wn+d2vW+wsbapBNKkiRJUr9lQSgNYtv3NbN9XzPnJfF4cYxUbPs1wxrWsn7c26gdOrvvM6jHzR7Wzj+f1sBj2/P4zpohScd5rexcOOM6+MgzcN19UDYTHv5M18rHv7oZdlUlnVCSJEmS+h0LQmkQW7Z5LwE4d3jfP148ZvezjNr7ItVlF1Az/Iw+P796z3unNHHpuGa+8FIRS2uzk45zaFlZMP3N8P5fwEeehpOvhj/+CL72Ovjxu2HdH3xPoSRJkiSlWRBKg1SMkaXVe5k6qohhOR19eu6h9WuYuOMhaktOYkvZG/v03Op9IcAXK+sZPaSTm54byr62fvI+wsMZPQfm3QEfXwFvuKVrteMfXtX1nsI//hjak1vdW5IkSZL6g35664ekE7VxdyN7Gtu4eNboPj3vkOYaplffT2P+aNaOn9fVJmnQGZob+crZdbz7D8O5dUkxX5u77///Vi/+XqLZjqh4DLzhH7tKwvWPwS/+Bh74R5j0eph0PuQV/fn8yg8mElOSJEmS+pIFoTRIvbBpD7mpLOaML4GtfXPO7PZGZmy6h46sHFZPvIbOrNy+ObES8brSdj4xZz9fXF7E+aNauXZKc9KRuieVAxPPgfK5sGs1rHsMVj8AVQ/B+EqY8gYoHpt0SkmSJEnqM916xDiEcFkIYVUIoSqEcMshvs8LIdyb/v65EELFAd/dmh5fFUK49BiO+ZUQQsPxXZaU2ZrbOnhpSx1zxpWQl53qk3N2rVj8E3Lb97G6/Bpac4b2yXmVrL+e2cgFo1r59IvF/fd9hIcTQtciJnP/quvR4wlndd1Z+IcvwHPfhJ0v+55CSZIkSRnhqAVhCCEF3AFcDswGrg0hHLwc6Q3AnhjjNOB24AvpfWcD84E5wGXA10MIqaMdM4RQCQw/wWuTMtaDK7bT0t7JmZP66H9GMVKx/beUNG5k3bir2F8woW/Oq8RlBfivuXWMyu/kxqeHsqNpgL7atngMnHoNXPJpmPlW2LcVFv03fP0cWPxdaPHvqyRJkiQNXt253eNsoCrGuA4ghHAPMA9YecCcecC/prfvA74WQgjp8XtijC3A+hBCVfp4HO6Y6fLwS8B7gL84gWuTMtb9L2xh2JAcJpcW9sn5RtcuYtSeF9hSej67h53SJ+dU9zy3vrZPzvOxigb++ZWJXPdoIZ+euYncrOO7827u5BE9nOwY5RbB9LfAlItg2wuw/SX41cfh95+G066Fsz4MZTOSzShJkiRJPaw7t3qMBzYf8Lk6PXbIOTHGdqAOGHmEfY90zJuAhTHGbUcKFUK4MYSwOISwuKamphuXIWWGHfuaeXJNDadPHEZWHywQMrShiknbf0dt8SyqR72p18+n/mnikBZumryNqsYh3LlxzMB/MjeVDRPOhr96Aj70O5hxadedhHecBT+4ClYuhI72pFNKkiRJUo/oV8+ChRDGAe8Cvnq0uTHGO2OMlTHGyrKyst4PJw0QP//jFjojnFne+48X57fsYtrm+2nMH8Xa8W93xeIMd9awBq4ZV8MTtUNZuCPhOwF7SggwcS6849tw88tw0T/D7rWw4H3wX6fCH74E9TuSTilJkiRJJ6Q7BeEWoPyAzxPSY4ecE0LIBoYCu4+w7+HGzwCmAVUhhA1AQfqxZEndEGPk/heqOXPiMEqL83r1XKmOJmZuuocYslldPp/OlCsWC/5izG7OHb6Pu7eU8UJd3zzi3meKyuDCT8LHlsL8u6B0Bjz6Wbh9Dtz3Idj4tIuaSJIkSRqQulMQPg9MDyFMDiHk0rXoyMKD5iwErk9vvxN4JMYY0+Pz06scTwamA4sOd8wY469jjGNijBUxxgqgMb3wiaRuWL5lH6t3NPCO1/XyIiExMnXLL8ht28vqie+mNdcVi9UlBPhIxTYmFzTzlXXjqG4ahMVxKhtmXQHv/znctATO/ktY8xB873L4xuvh+e+4qIkkSZKkAeWoBWH6nYI3AQ8CLwMLYowrQgi3hRCuSk/7DjAyfbffzcAt6X1XAAvoWtDkAeCjMcaOwx2zZy9Nyjz3v1BNbnYWbzt1XK+eZ+yupxhev5pNo99CQ0H50XdQRsnLinxy6hZysyJfXDuBhvZ+9TaLnlU6DS77P/CJl+HKr0BWFvz6ZvjPk+A3n4KaVUknlCRJkqSjCnEQPA5VWVkZFy9enHQMKVGt7Z3M/feHOG9aKXe850zuem7Tq99N3fSTHjtPyf71zNrwP+wumc3aCVf73kEd1uqGfD6zeiKzipq4dfpmsrvxr0riqxgfrPKDxzY/Rqh+HhZ9C1b+HDpaYfKFXasfz3wrpHJ6J6ckSZIkdUMIYUmMsfLg8UF8W4eUWR5dtZM9jW2888zee7w4p62eaZt/SnPuSNaPu9JyUEc0o6iZGydtZ3l9Id/bNDozXs8XApSfDe/4Fnx8JVz8L1C7Hha8H24/GR75HOzdnHRKSZIkSfozFoTSIHH/kmpKi/K4YHpprxw/xA6mVd9HVmxlTfm7XJRE3fKGkft4+5hdPLRrOAu29s6/m/1WURlc8In0oiZ3w9hT4fEvda1+fNd8WP076OxIOqUkSZIkkZ10AEknrnZ/K4+u2sn151aQneqd3r98x8OUNG6masLVNOWX9co5NDjNH7eL+vZsfrq9lKLsDq4YvSfpSH0rKwWz3tr1a89GeOEH8MKPYPVvYWg5vO56OON9UDwm6aSSJEmSMpR3EEqDwC+XbqWtI/ba6sXD973M2N3Psn3EWeweenKvnEODVwjw4YnbmTtsHz+sHs0fdpckHSk5wyd1PXZ880p41w9gxBR45LNw+xy4932w9lHo7Ew6pSRJkqQM4x2E0iBw/wvVzB5bwklje754yW/ZzdQtv6BhyHg2jX5zjx9fmSErwN9O3kZTVYpvbhhLYaqTymENScdKTioH5ry969futbDke/DHH8PLC7tKw9d9EE6/DgpHJp1UkiRJUgbwDkJpgFuzo55l1XW9cvdgVmcb0zf/hM6QYs2EdxKz/h979x0eR3Uufvw725tWu+rdluXejQ22qYaAgYReQokDgdxL7r3JTcJNuclNg/Qe+EFIQg+EEkwLxXTjgnsHd1uWrd7LrrR9Z35/zGotyTKWbdkr2e/nec4zszNnzr4zO1pLr8+cI/+nII6d2aDxrbJqRjlC3LevgG1+R6pDGhoyy2D+z+F/dsB1j4ArF977EfxxPLz0b3BgJafHDC9CCCGEEEKIVJEEoRDD3IsbqzEZFK6eXjC4DWsaI2vfxB5upLzoOiKW9MFtX5yWbEaN742pItca5Xd7C9nXZU11SEOH2QZTPw93vg3/tVrvRbj7HXjicnhoDqz5GwTbUx2lEEIIIYQQ4hQkCUIhhrG4qvHqphrmjcsmyzW4iZbsto1kd3xMTfYFdLjKBrVtcXpLM6n8YEwVLpPKL/cWUxuSGbEPkTMBPvtb+NZOuOpBMDvgre/CH8bDq1+F6g3Sq1AIIYQQQggxaCRBKMQw9tHeZhp8Ya4/Y3AfL3YE6xlZ/zbtrjJqss8f1LaFAMiwxPjBmEoMwM93F9MUlsfX+2VxwhlfhLs+hLuWwrSbYNsr8OhF8LfzYf3jEPanOkohhBBCCCHEMCcJQiGGsZc2VJNuN3PRhJxBa9MQDzO6+kViRgflhdfoU9AKcQLk26L835gqQqqBn+waQV3InOqQhraC6XDl/Xqvws/9ATQV3rhb71X4xt1Q/0mqIxRCCCGEEEIMU9JlQ4hhqqUzzNtb67l1dglWk3FwGtU0SuvexBZpY8fI24iZnIPTrhCHMdIR5kdjK/nFnmLu2TWCiQUdjE2Ppzqsg9Y/keoI+qcYYdaXof0AHFgBG5/WexN6RsCIs6FgBhj7eXR71h0nP1YhhBBCCCHEkCc9CIUYpl5YX00krrJgTsmgtZndvpmsjq1U51yA3zli0NoV4tOUOsLcM7YSRdG4aamXrW3yf1cDoijgHQnTvwAX3wMTr4FoELY8B+//BLa9DP76FAcphBBCCCGEGA4kQSjEMKSqGs+uPcCcURmMzkkblDbtoUZG1L1Fh7OU2qxzB6VNIQaqyB7hnnGVOEwatyz1sKFZkoRHxeKEUfNg3vdh7tcgezzsXwFLfw0rH4CaDRCPpTpKIYQQQgghxBAlf4EJMQwt29NEVWuQ7146flDaM6hRRle/hGqwUl54LSjyfwfi5MuzRlk4r40vLPOwYLmXR89u55zcaKrDGl4UBTJH6yXsh6o1ULkKNj0Nlpdh1yIYMRec2amOdHDII9NCCCGEEEIMCskCCDEM/WN1JVkuK5dOyhuU9kbUvY093MTeomuJml2D0qYQx6LAofLPee2UOOPcscLD4rp+xtETA2NNg9EXw4U/gNn/ARllULEEPvwFrPkL1H8M6hAa71EIIYQQQgiRMtKDUIhhpqY9yOKdDfznvDIspuPP8We2f0JO+yZqss7F5xo1CBEKcXxybCrPX9DGbR95uGtlOvfP9vG5onCqwxq+FIP+yHH2eAi2Q9VqvVfh+sfB5tEnNSmeAzZ3qiMVQgghhBBCpIj0IBRimPnn2ko04OYzj39yElu4hdK6N/E5iqnOmXfc7QkxWLxWjWfOb2d6RpT/Xu3m0d12NC3VUZ0C7B4Yexlc9GOYeSe4cvTHjj+4Bzb+HVr2IhdaCCGEEEKI04/0IBRiGInGVZ5fV8WF43IoznAcV1uKGtPHHVSMlBddL+MOiiHHbdZ46rx2/medm59/nEa538RPZ/gxy616/AxGyJ+ql85GOLACqtdC7SZw5cHIc6DwTDDbUh2pEEIIIYQQ4iSQP7OEGEbe295Aoz/MgjnH33uwpOE9nKF69hVeTcQsjxaKoclhgofm+Pjq+C6eq7Bz23IP7REl1WGdWlw5MOlauPhemHozGM2w9SV4/8fwyQvgq011hEIIIYQQQogTTHoQCjGM/GP1AQo9di4Ym3Nc7Xh9O8hrXUdd5hza08YOUnRCnBgGBb4zuYuytDjf25DGtYu9PHZOB6PSZIKNQWW0QMkcvbRXwv6PoGodHFgJ3lIYeS7kTQOj/OoghBBCCCHEqUZ6EAoxTJQ3dbKyvIVbZ5dgNBx7DyprpI1RNa/RaS+gKuczgxihECfWdSNCPHt+O76ogWsWe1nRYE51SKcuTwlMvxUuvgcmXA1hP2x6Wh+rcOcbEGhNcYBCCCGEEEKIwSQJQiGGiWfXVGI2Knx+VvExt6GocUZXvwTA3qLr0QzGwQpPiJNiVlaUVy9qJc+ucttHHp7ZJ2PknVAWJ5RdCBf+H8z+D/COhL0fwOKfwdpHoHE7aGqqoxRCCCGEEEIcJ3lOSIhhIBSN8+KGai6dlEd2mvWY2ylu/ABXsJbdxTcStngHMUIhTp5ip8pLF7bx9TVufrDRza4OEz+Y2olV8t0njmKA7PF6CbZB5Sq9rN0GjkwYcQ4UnwUWV6ojFUIIIYQQQhwDSRAKMQy8vqWWjmCUL8weccxtePy7yW9ZTX3GmbS5JwxidEKcfGlmjUfP6eBXH7t4dI+DDS1mHpzto1TGJTzx7F4Y91kYMx/qP4b9K2DHa7BrEeRP18cq9IwARSaTEUIIIYQQYriQBKEQw8A/1lQyOsfFnFEZx3S8JepjVM2/6LLlUZl7ySBHJ0RqGBX44bROZmdH+M56N1d84OXnM/xcOyKc6tBODwYTFJyhF18dHFgBNeugZj24i2DkOfo+07H3ehZCCCGEEEKcHDIGoRBD3NaaDrZUtfOF2SUox9AjR1FjlFW/hEGLs6foejSD/L+AOLVcUhBh0cWtTPLEuHtdOt9al0ZXTHqvnVTufJhyA1x8L0y5EbQ4fPxPeP8n8MmL0FGd6giFEEIIIYQQn0IyBUIMcc+sOYDNbOC6M4qO6fgpex/CHahib9F1hK2ZgxydEENDgUPl2fPb+X87nDyww8GmVjMPzPYxyRNLdWinF5NNH4+w5Gxoq4ADK6FqDRz4CNKLoWQOFMwEs0wuI4QQQgghxFAiPQiFGMJ8oSivbqrlqmkFpNvNR318XvNKJpU/SqNnBi3pk09AhEIMHSYD/M+kLp45v53OqMK1i708tdeOpqU6stOQokDGKJixQO9VOOk6UGPwyUJ4/8ew5Tlo2498OEIIIYQQQgwN0oNQiCHspQ3VBKNxFsw5+slJbOFm5m75Ph2uMg7kX3YCohNiaDo7J8pbl7Ty7XVufrw5jQ/qLPziDD9FTjXVoZ2eLA4oPR9Gngftlfrsx7Ub9Z6FaflQPAcKZ4JVZkAWQgghhBAiVaQHoRBDVDSu8ujyCs4c6WVqkefoDlbjnL3le5hjAT6a/ntUw9H3PhRiOMu0ajx2Tgf3TPezrtnM/HczeGKPnbh0WEsdRQHvCJh2M1zyU5hyExjMsP0VvVfhukegbgvE5bFwIYQQQgghTjbpQSjEEPX6llpq2oP87JpJR3/w8j+Q17KG1VN+ii+tjOy2jYMfoBBDnEGBL40OcnF+mB9sTOPeLWm8VmXjNzN9jE2Ppzq805vJBiPm6sVfB9XroHoDNGwDswMKpkPRWeAZoScWhRBCCCGEECeUJAiFGIJUVeOvS8sZl5vGheNyju7g/R/Bkl9RUXAF+wqvOTEBCjGMFDlVnjy3pgJdNQAAIABJREFUg39VWbl3cxqfez+D/xof4L/Gd2E1pjo6QVo+TLgKxl8Bzbv1ZGHVOn2CE2c2FJ2pP4LskEmWhBBCCCGEOFEkQSjEELR4ZyO7Gzq576bpKEfTe8bfAC/eCRmjWDfph9LzRogERYFrSsKclxvhZ1vSuH+Hk0U1Vn4908fMTHmkdUhQDJA9Xi+TQ1C/BarWwq5Fekkv0XsW5k+TZKEQQgghhBCDTBKEQgwxmqbx0JK9FHntXDE1f+AHxmN6cjDshy++Smy/88QFKcQwlWnVuO8sH1cXh/jBxjRu+NDL9SNCfHtyF3l2mcRkyDDboHi2XgItULsZ6jbDjtf00p0sHP0Z8JSkOlohhBBCCCGGPUkQCjHErNvfxsbKdn529SRMxqOYR2jxT+HAR3Dtw5A7EfZXnrgghRjmLsyP8O78Vh7Y4eCJvQ7erLbxlXFd3DU2gEP+ZRxaHJl6InD0Z6CrWZ/IpGeysHAmTLwGJlwBGaNSHa0QQgghhBDDkvwZJMQQ85cle8l0WrhxVvHAD9r5Jqy4H2bdCdNuOnHBCXEKcZk1vj+1iwVlQX79iYv7trt4bp+d70zu4roRIQzyhP7Q48zqnSw0mmDbq/Dej/SSORrGXApjLoERZ4PJmuqIhRBCCCGEGBaOonuSEOJE217r48NdTdx5bik28wBnT2jdB6/8JxTMgMt+fWIDFOIUVOxU+fMcHy/OayPPrvLt9W6u+sDLqkZzqkMTn8aZBefeDV9ZCl/fDJf/Vp/1eN2j8PQ18NtR8PwXYMOT4KtNdbRCCCGEEEIMadKDUIgh5K9Ly3FZTSyYM2JgB0SD8M/bwGCAG/8uvWWEOA6zsqK8clEbr1dZ+c0nLm5Z5uXi/DDfnNjFZK9MZDKkZZTC7K/oJdIFFctgz7uw+13Y+YZeJ3cKjLpA71lYMhccGamNWQghhBBCiCFEEoRCDBGVLQHe+LiWfz9vFOn2AfZcevPb0LAVvrAQvANMKgohDsugwNUlYS4tDPPYbgd/3e3gig8yuDg/zDcmdjFFEoVDn8UJ4y7Xi6ZB4w49Wbj3fVj7CKx6EFAgdxKMOEdPGI44B1zZqY5cCCGEEEKIlJEEoRBDxMPLyzEZDNx5bunADtj4FGz+B5z/XX28LSHEoLEZ4asTAiwoC/LkXjuP7XFw5QcZfCY/zNcndDEtQxKFw4Ki6JM25U6Ec78J0RDUbIADK/VJnTY9DWv/ptfNGgcj5kLBGfoMydkTwGRJbfxCCCGEEEKcJJIgFGIIaPKHeWF9NdfPLCTXbTvyAXVb9N6Doy6Eed878QEKcZpKt2h8Y2KAO8YE+fteO4/udnD14gzm5YX5xoQuZmRKonBYMdtg5Dl64TsQj0LtZj1ZeGAlbH1FH7MQwGjRexnmT4f8aXrSMGfiiR3KYf0TJ67tVJh1R6ojEEIIIYQQAyQJQiGGgCdWVBCNq9x1ftmRKwfb4IXb9AH6r38UDAOczEQIcczcZo3/nhDgS6ODPFWuJwqv/TCDc3IifHlMgHl5EZn1eDgymqH4TL2cezeoKrRVQN1mPXFYtxm2vQwbEok7gxlyxuu9DbPHQdYYfT2zTMaAFUIIIYQQw5okCIVIMV8oytOrDvDZyfmUZjk/vbKq6jMWd9TAHW/pSUIhxEmTZtb46vgAt5cF+eV6jbcbvdy5wkOBNczluW2cn9GBzailOsxDzC6VCTkGxGDQk32ZZTD5en2bpiWShlv0pGHDVqhaC1tfPHicYtBnUO5OGmaOBk+Jvi29WB5VFkIIIYQQQ54kCIVIsWfXVOIPx/jPeQPoPbj8D7D7Lbj8t3qPFyFESrjMGlfntfK53FZWt7l5s8HLY5V5PF+TzcVZ7Vya00amRR4/PiUoCmSM0sukaw9ujwSgZS807z5YmnZD+YcQD/dsANwFiYRhImnoKYH0QnAXQlo+2Nwn/bSEEEIIIYToaUAJQkVRLgPuB4zAo5qm/brPfivwFDATaAFu0jRtf2Lf94EvA3Hg65qmvfNpbSqK8gwwC4gCa4GvaJoWPb7TFGJoCkXjPPZRBeeNyWJyYfqnV97xOnz4c5h6E5x118kJUAjxqUwKnJvh4xyvj11ddhY1eHmtIYPXGzKY4/VzaU4b45xBFHn8+NRjcUD+VL30pMbBVwvtldB+QF+2JZYHVsEnC0FT+7Tl0hOFALb0RPGAPbG0pYM1Te+pKIQQQgghxAlwxAShoihG4M/AJUA1sE5RlNc0Tdveo9qXgTZN00YrinIz8BvgJkVRJgI3A5OAAuB9RVHGJo45XJvPAAsSdZ4F/g34y3GepxBD0pMr99PkD/PALTM+vWLdx/DyXVA4C678f0i2QYihRVFgvCvIeFeQxrCZtxu9LG5OZ2Wbm0JbmM9ktXN+ZgdpJvXIjYnhzWAET7FeOOfQ/fEodFTrSUR/Xe9l3WZoLYdQx6FJRMWgJwltPZKGh6yny1iIQgghhBDimAykB+FZwF5N0/YBKIryPHA10DNBeDVwT2L9ReBBRVGUxPbnNU0LAxWKouxNtMfh2tQ0bVF3o4qirAWKjvHchBjSWjrD/HnxXi6ekMOcUZmHr9jZCM/dAnYv3PysPgunEGLIyrFGua24kc8XNLGqzc37zR6eqs7l2ZpsZnv8XJTdwSRXQPL8pyujGTJK9dJX9yzGmgqRLgi168nCUAcEe6x3NuqPNMdCh7ZhsvVOGto9YM8AR4a+tHtlcishhBBCCHGIgSQIC4GqHq+rgdmHq6NpWkxRlA4gM7F9dZ9jCxPrn9qmoihm4IvAN/oLSlGUu4C7AEpKSgZwGkIMLfd/sIdANM73Lp9w+EqxMPxzAQRa4M63IS335AUohDguNqPGhVkdXJjVQWXQygdN6SxvTWdFWzr51ggXZrVzXoaPDBmrcFh7dk3loLVVVtnaZ4szUQrAil56jEZhUCNYon4sMR+WqB9z1Icl5te3dfmwtNdijnWi0HPiHOXQpKEzE5w5erE4Bu18hBBCCCHE8DGUJyl5CFimadry/nZqmvYw8DDArFmzht6UkUJ8ivKmTp5ZU8mtZ5UwOsfVfyVNgzfuhqo1cOOTUDD9pMYohBg8JfYwd5Q08oWiJla3pfFBs4dna3J4riabSWkBzs/s4CxPJ3ajPIIsBk41WAhZMwlZD98LXdHiWKI+rNF2JrijEGyFQKu+bN6j90jsmUC0uMCZDa5EwtCVo792ZkvPQyGEEEKIU9hAEoQ1QHGP10WJbf3VqVYUxYT+/9stRzj2sG0qivITIBv4ygDiE2LY+dWindjNRr558ZjDV1r1IGx+BuZ9v/fMmUKIYcti0Dg/08f5mT7qQmaWt6bzUYubh/YX8KiicqbHz3mZPqa6uzDKI8hiEGiKkbDFS9jihZKMQyvEYxBsga4m/dHlzkZ9vXE7hNccrKcY9WShuwDSCsCdry9t6TIurhBCCCHEKWAgCcJ1wBhFUUrRk3g3A7f2qfMacDuwCrgBWKxpmqYoymvAs4qi/BF9kpIx6DMTK4drU1GUfwMuBT6jaX1H6BZi+FtV3sL7Oxr47mXjyHQdZjD53e/Cuz+CiVfD+d89uQEKIU6KfFuUzxc0c2N+M7u77CxvdbOq1c2KtnTSTTHmen3M8foZ5wpikPyLOFGMJnDl6qXvKBbRYI/EYb0+kUpLOdRsOFjH7NBnYHYXgLsQPCV6W9LbUAghhBBiWDligjAxpuDXgHcAI/C4pmnbFEX5KbBe07TXgMeApxOTkLSiJ/xI1HsBfUKTGPBVTdPiAP21mXjLvwIHgFX6PCe8rGnaTwftjIVIIVXV+MWi7RR67Nx5Tj8D1AM07oQX74S8KXDNX8BgOLlBCiFOKkWBca4g41xBvlTUwCafi+Utbj5o9vB2UwZec5SzPJ3M8foYL8lCcTKZ7XrCz9NnrOdIQJ952V8LvsSyai3Ew/p+gxnSi6BpJxTM0EvmaEkaCiGEEEIMYQMagzAxs/CiPtt+3GM9BNx4mGN/AfxiIG0mtg/lcRGFOC6vbq5ha42P+2+ejs3czx9KgVZ47ib9j7JbngOL8+QHKUQKranoO0nD6cVkgDM9nZzp6SQYN7Cxw8mqNjeLm9N5p8mLxxRjttcvyUKRWhYHZJbppZumQlczdFRBe6W+3PgUrPlr4hgX5E+HollQPFsvzsOPnSiEEEIIIU4uScYJcZIEI3F+984uphalc+XUgkMrxKPwwm16b4wvvan3vhBCnLbsRpVzMvyck+EnFFfY0OFidY9kodsU44z0TmZ5OpmS1oXNKPN1iRRSDPoYha4cKJypbzvjNmjeDbWb9FKzEVb9GVbcp+/PHAMliWRh8RzIGiPjGQohhBBCpIgkCIU4SR77aB91HSHuv3kGhr7dflQVXv1P2L8crv0bFJ+ZmiCFEEOSzaj1ShZu6nCxrsPF2vY0lrR4MCsqU9wBZqX7menpxGOOpzpkIfRHinMm6GV6YvjqaFBPFlau1h9L3rkINv1D32fPSCQLz4KSOfqjyWZ76uIXQgghhDiNSIJQiJOg0R/iL0vKuXRSLmeV9plFUtPg7e/BJwvhMz+GaTenJkghxLBgM2rMzfAzN8NPTIOdfgfrO1ysb3exsSMfKmG0M8gMdyfT07sY5QilOmQhDjLbYcTZegH938DmPVC1GqrWQOUa2P2Wvs9ghvxperKweLa+dOWkLnYhhBBCiFOYJAiFOAnue38P4ZjK/142/tCdy34Ha/8Gc78G5/7PyQ9OCDFsmRSY7A4w2R3g9qJGqkJW1re72NDu4sW6LBbWZZNmjHFhU4x5eRHOyw2TbZNHkcUQoiiQPVYvZ9ymb+tqgeq1iV6Ga2DtI7DqQX2fd6T+OPKIuVAyF7LGymPJQgghhBCDQBKEQpxguxv8PL+2ktvmjmRUtqv3znWPwoe/gGm3wCU/kz9yhBDHTFGgxB6mxB7muvwWfDEjn/gcbO5wsbLRxWtVNgAme6Kcnxvh7JwIMzOj2IfzbwLrn0h1BJRVnt4T65wQzkwYd7leAGJhqPtY72VYuRrKP4CPn9f32TP0ROGIuVByNuRPBaM5dbELIYQQQgxTw/nPAiGGPE3T+PmbO3BaTXzjM2N679z6Erz5bRh7OVz1ABgMqQlSCHFKcpviyXELzxyZwfZ2E0sbLCytt/DwbgcP7XJiMWhMz4gyNzvK2TkRpmdEsfYzwboQKWWy6mPzFp8JZ/+3/lhySzlUrtLLgZWw6029rtmhz5RckuhhWHQmWF2f3r4QQgghhJAEoRAn0sL11Szb3cRPrpyI12k5uGPvB/DyV/Q/Xm58Qno7CCFOKIMCk70xJntjfHV8gK6YwrpmM6sazaxqsvDADgf373BiM2rMyowyO1vvXTgtI4bTJI8kn2yqBr6YkfaoifaoiY6YkVDcQEg1EFYNiXVFfx03ENMUjIqGQQGjomGkx7oCDmOcNFOcD5vjuE0x3OY4bpNebAb1hHVeL49XHvOxt84uOfxORYGs0Xo544v6Nn99ImG4Wk8YLvsdaCooRr1XYcnZ+hiGJXPBlX3McQkhhBBCnKokQSjECVLVGuDe17cxd1Qmt88d2WPHOvjnAsgeD7c+LzM0CiFOOqdJY15ehHl5EaCLjojC2mYzKxstrGoy88dtTjQUDGhM8MSYmRllZmaUMzKjFDlOXELpdBGKK9SHLTSEzdSHLTSGzbRGzbRHjbRFTXRETagc/iLbDHFsBg2bUcVmUDEoGqqmoAJxTUHVFOKavh7XFLriBqJa/73ULYpKjjVKvi1CnjVCvjVCni1KvjWC1xwbPp91Wh5MulYvACGfPo7hgUTScP1jsPrP+r7M0YkehnOg6Cz9tfTiF0IIIcRpThKEQpwAqqrx7YVbUBSF3904FYMh8RdW40549kZw5cKCl8CWntpAhRACSLdoXFIQ4ZKCCAAdEYXNrWY2tOjlpQM2nip3AJBjizPVG2OSJ8Zkb5TJnhh5dkka9qRp0Bk30BC2HEwEhg4mBDtivX/9SjPFyDTH8JhjlNjDeBPrXnMcjzlGuimWTAZaDHrvwKONJ6wq+GImfDGjXqKmZC/FhrCZ2pCFTR1OYj0SiVaDSr41QqkjxChniFJHiBH2MBbDwHqVllUuPLpAezJmHPuxPaUXwZQbYOI14KuCln3Qtk8f5mPT03odswM8I/QJULwj9XWz7dC2Zt0xODEJIYQQQgxBkiAU4gR4fEUFaypa+d0NUyny6n9U014JT18LRgt88RVIy01tkEIIcRjpFo0L8iJckKcnDGMq7PKZ2JhIGG5tM/FBnQUt0cstw6IyyRtlkkdPHI5xxxjpimM7hccz1DRoDBnY7rfT0KM3YEPYTEPYQle898lnmqPkWiOckd5Jni1CrjVKnlVfOozqCY1VUcBm1LAZo+RYo4etp2rQHDFTHzZTF9KTm9VBfWbsD1s8ABjQKLKHKXWEKXWEGO0MUmoPYRrqHfCMJvCW6oXP6I8fdzZC2369tO+H3TsBDVD0HondyUJPif4fe0IIIYQQpzBJEAoxyPY0+PntO7u4ZGIuN8ws0jf6auGpayDaBV9aBBmlqQ1SCCGOgslAMvn3xbIgAIEY7Ogwsa3NzNZ2E9vaTTy220FU05OGChpFTpWytBhlafHkstQVJ8umHnUvuFTwRxVqAwZqA0aqA0YqO43s79KXB7qMhOIKkAXoibPsRNJvtNOXTP7lWSPkWKMD7nWXSgYFcqx6EnGqO5DcrmnQEjWxr8tGRcDGvoCNzR1OlrboveAtispoZ4hxrgDjXUHGuoInPOl53BSDngRMy9MfNQaIBqH9wMGkYe0mfVxD0P9z75OFUHgGFJyhLzNGIV1nhRBCCHGqkAShEIMoGle5+4XNuKwmfnXdFBRFgdYKeOpqCLTqjxXnTU51mEIIcdwcJpiZGWNmZiy5LaLCXp+Jcr8xUfT11U2WRDJNZ1Y0cu0qBY44+XaVfEecgsQy06qSbtFIN+tL8yD3TItr0B5RaA0baA0baIsYaAnrr+uDejJQLwb8sd5vbjVolLjijHDGOTc3wkhXnGBnG7nWKFmWKKZTNFekKJBliZFl6eQsbyegJw1boyb2dNnZ2WlnV6edf9Vn8goKChrF9jDjXUEmuAJMTAvgMcdTfBYDYLbr4wNnj9dfayp0NetPAHRUQiwM65+A2EP6fls6FMyAvKmQPw3ypiTGMzyFu84KIYQQ4pQlCUIhBtGDi/eytcbHXxfMJMtl1cccfOpqiIfh9tf0HgdCCHGKshhgoifGRE+s13ZVg9qAgXK/icouPflWF9SXG1vN1Fdbkz0P+3IYEwlDi4bbrGIx6LPzmgxgVMCkaBgNYFL0XothVSEUVwjHFUJxfUKQ7m2dUYX2iJJ8NLovr0VPWpa44szJjlDg0F8XOOIUOlRy+un5uKYi0G9bpzpFgUxLjEyLnzleP6Bf6+6E4e5OB8ta3Lzb5AWgwBZmUiJZOGwShooBXDl6KZqlj0EYj0HTDqjZCLUb9eXqv4CaeHTbZIfcSfrMyXlTIG8a5EwAiyO15yKEEEIIcQSSIBRikGypaufBD/dy3RmFXDY5T3806enrwGjWHyvOnZjqEIUQIiUMChQ5VYqckX73qxo0hw3UBfRefR1RhY6IgY6Igi968LUvqif9oqqBuAax7pl6VYhpCqoGVqOGzahhNejj7qWZVf21EVwmlQyrRoZVxWtRybSqeK0amVYVj0U9pcdMPBlsRo0p7gBT3AGghbgGFQEb2/wOdvgdfNTq5r3mRMLQGk4mCyemBfAOh4Qh6GMZ5k3Ry8zb9W2xCDTvgvpP9FL3MXzyEqx/XN+vGPSxD7PHQ854yJ6gLzPH9D8ZihBCCCFECkiCUIhBEIrGufuFzeSkWfnJlZPgwEp49iaweeC2VyGzLNUhCiHEkGVQIMem99ATpw6jAqOdIUY7Q1yd15pMGG73O9jR6WBFq5v3eyQMJ6QFmJQWYIIrmOLIj5LJcjBp2E3T9PEMu5OGTTv1pwp2vw1aIhnanTjMmZBIHiaWmaMlcSiEEEKIk04ShEIMgt+8vZN9TV3848uzSa9ZCs8vgPQiuO1fkF6Y6vCEEEKIlOuZMLwKPWG4P2Bje6eD7X4HK1vdfJBIGI6qiHFWdpSZmXopdcWH13wgiqLPguwdCROuPLg9FoGWvfpjyk27oHGHnjzc9VbvxGHGqN5Jw+zxkDUGTNZUnI0QQgghTgOSIBTiOK3c28wTK/bzpbNHcm50Jbx4p/7o0IJXwJWd6vCEEII1Fa2pDuGozC7NSHUI4iQwKlDmDFHmDHFlbiuqBvsDVrZ3OqiNe3ir2srzFXYAMiwqZySShbOyokzxRofnI+Emiz7kSN9hR2JhPXHYmEgcNu3Qexz2TRx6RuiJwqyxek/DrLH6a2f2gGdUfnZN5SCf1Ilz6+ySVIcghBBCnDYkQSjEcdjX1MlXn91IWbaT/yvcDAu/BoWz4AsLwe5JdXhCCCHEsGFQYJQzzChnmNml+piS5X4jG1rMrG82s7HFzPt1eg86s6Ix3hNjsifGZG+UyZ4Y49JjwzNpCHrPwNxJeukpFobmPXovw+bdibIXKpZBLHSwni1dH9MwayxkJRKHmWP0nogmy8k9FyGEEEIMS5IgFOIYNXeG+dIT6zAoCgtnfIzl9R9B6QVw87NgdaU6PCGEEGJYMygwxh1njDvOzaV6MqwlrLAxkTDc2m5mUbWV5xK9DE2Kxhh3jMlePXE4xh1jdFqcbJs6OI8nr39iEBo5Dmn5eim9ADQVgu3Q2QBdjdCZKDvfhHDHwWMUA9gzwJWbnJF5us9G0JrFrtLbB9zrUAghhBCnPkkQCnEMgpE4//b39bT5/Xw4YREZy56DcZ+DGx6XgcWFEEKIEyTTqnFJQYRLCvQZsTUNqgMGtraZ2dpuYmubicV1VhbutyePSTOpjEqLU+aOUZYWpywtxihXnAKHisuspepUjo9iAEeGXpjQe180dDBpmEweNui9D9Uo3Q83T973CD5nKT7nSHzOUvwufdnpKEY1mE/2GQkhhBAixSRBKMRRiqsa33h+E/XV+1ie/wie3VvgvG/BhT8Aw3B9tkkIIYQYfhQFip0qxc4wlxeFAT1p2BAyUO4zUu43Ue7XlysbLbx8oPe/026zSoFDpcAeJ98Rp8CuUuCIk2NT8Vg1vBYVr0XFPpx+YzbbwFOil540FYJt7Nxbjj3STMTswd1VQX7zSspq/pWspipGOh3FycShzzkSv3MkPlcpYYv3JJ+MEEIIIU6W4fTrjhApp2kaP3tjO207lvJh2p+xd4bh80/DxKtSHZoQQggh0JOGeXaVPLvKObnRXvs6owr7/EYqOo3UBY3UBQzUBIzUBQ1sajXTFjH026bNqCcLPRYVr0XDadZwGDXsJg27UcORWHa/NhnApIBR0deNioZZAaMBDICiHOy52Pch356vP+0J4E+rpxxm3WjIZbfRhsWhUVt4GSajAZNBwa51kRGsxBPYj7urAnfXftydevLQqEaSx4fN6Qd7HboO9j7sdBShSa9DIYQQYliTBKEQR+Gx5fuIr3mY521PY3SN1McbzBmf6rCEEEIIMQAus8bUjBhTM2L97g/GoDZopDlkoC2i0BY20BYx0B5RaIsYEq8VWrsMBGMKwbhCMKYQiCvEteEynl9ilvDtuw/ZYzSUYjWVYTUZsJqM2K0axaYWRlHHSK2GEq2GolA1eZ3LKKt5NXmcqpjwO4r0nobOUr24Sml3jSZmlnGZhRBCiOFAEoRCDNDbmyvwvPdN/s28DG3MZXDdw/qsgUIIIYQ4JdhNJMYpjB/VcZoGUY1k0jCmQlxTiGkQVyGqKcRViGmg9kgk9h0BUeuxoee+gdbrL66eoprC1rpOYqpCRFOIqQpRTSGqGohoCmHVQDCul5BqIBgzUBM2slctIRgfiT9mJKbpvSzddFGq1FGm1DLKUMdoXy2jO/cwWvkICwcTsD6Dh3ZLLkFrNpoji6g9m6A1a2A9Do0ZR67TbdYdA68rhBBCiENIglCIAfh42ycUvnIbU4z7iJ73v5gv/B4Y+n8MSQghhBCnF0UBiwIWi0b6p6bsUs8e7DzmYzUNQqoBf8yYKFb8sdE0xcaxN2akLWqmI2zAEmsnO1ZPiVbHWEMV42LVlAVXY+3QE4cqCg1KFo3GfDosuYRt2SjODByudIwynrMQQgiREpIgFOIIKte9QfGb/4FZieO/9h+kTbsy1SEJIYQQQpx0igJ2o4rdqJJjjR6hdhph1U1bdBJrIibeChvQQh04wo14Ig3kxesoidcyOfoJxoAGrRDRjFSRR60xnzZzHks6inF5c8jL9FLgBMNweYpbCCGEGIYkQSjE4QTbqX/xO5SUv8A+pQjLrc9RNGZqqqMSQohT3pqK1lSHIMRRkXu2f1aDRp41Sp41Cmmg/+lRkCgzaNKgPKwRDnRgCDRjCzXijTYwWt1Pfng9VAPVENCsbNUKqTUV4rMVEnflY/fmU+BxMjItTrZVPWSyFyGEEEIcHUkQCtGfHW8QfPWbZIVa+Kftes77999TkHUU4+AIIYQQQohPZVDAY1PA5oEMDzCaGFAJVMUjlLkitLU0EOuoI62rlrMjm3EHlkMAaIRmzc1utYh3lSLal+4jmjUBe8EkSvJzGJXtZGSmE5tZHlkWQgghBkIShEL01NmItug7KNtfZZ86gufy7+F/77iJNNsABtIWQgghhBCDQjNa2Bu0gMMFjjIAGgFTrAtbsAEt0IIp0MSISCMzY0uwdr4LncB+qFSz2aWV8L5WTJV5JC3O0YTdpWS4nWS5rGS7rKTZTCjK4PY7vHV2yaC2J4QQQpxMxnvuuSfVMRy3hx9++J677ror1WGI4UzTYMtzaM/eRKx+G3+I3sjaaT/n3gWXYLcMzzz6n97fzahsFwBT9jzE4x2zmB9cRHV9IzM63qO8NYK3bjmxnMnJYzrK12LLKOSeXSXc0Pwga7OeAAAgAElEQVQQq+pgctv7fFijMNuwg7wDr2No2Yuj+RP+HrkIS9UKzm98msK2dXj9O+mq3clX6q9mbMtiHmidzRfUf+Hbt5avNVzFN1t+SmnTh3gbVtHYUIOvs4tvVZ/PD5q+w+v1HirrmyhuWoLSuI1V9RpjWxYzufE13mlIJ79xKdOb/kV9Qx1r6lXObVnI56oWcEPLX5jYtIjGhhqKm5ZQ1LiUr7bdgrP2Iwobl2Jq2kq8YTu+zi4+qW6lKNHO8mYX41oWE6zbyZiWxVxVvYCftX2Xr7TeirduOUVNSyhr/hB7w3piTfvwtG5Ba9iOp3ULWxrj7KtrprmxAXvnAcJtNVTVNzKuZTFbGuMUNS5lVb1Gc2MDf20/kxkd71HUuIQtjXHGtSzmjtprub3tQUb4N/JmpZHp7e8ztmUxuzoMVNU38vXKCyhqWsK5LQspa17MTp8l+TlN33UfhY1LWNbk5DOt/yTWXM7ohreJt+7n75GLWFibxZKWdJoiZq6p+g12LUjm/jcIN+3D3LSVXJopalxCe80u2vwBihqXUNcRYkbHe1ij7VirVrClMc6FjU/jbt5ISesqvP6drKrTmKrtwt21n6LGJXTV7iSWMxn7tueJ5UxmQsWTlLdGOKP2Wdoaq7E2baXFH2BO08LkeZl8VclrtTg6hRuaH8IabWfjgTbGtSymriNEWWwPGw+0YfJVMaX+ZdY0WTD5qpjTtBCvfyfWaDtFjUsob41QFttDdX0jtoxCpu+6jwciVzA/uCh5n3cvixqXkN2+OXl+gSx9mIDCxiWUVb+KUQ3h7tqfLNX1jZTF9iT3fbTPh73zAGWxPTzeMYsLah6hriOU/Dn5Zsu97OowYMsoTLbr7trPxgNtPNA6m6aImUlpAabvuo/m+gOM61qfPG+Tr4qy2J7ke3e/Z3fMBbXv8mDsai6oeYS/Ry7ihuaHaPZOJ2vnMwSypjKh4kmavdPZs30Tmdn5AMl9AAtrs9judySvtbtrP37nSCZUPIk12p5cL2pcknzfvwQvZn5wUTIWv3Nk8nthYW1WMoY92zcxVduF3zkSgDHbH6A1ezYd5Wv5ddPc5HlPqHgSGrb1us+676PuGAobl1DUuAQatuFVW5Ntdl/P7hhmdLzHX4IXc0HNIwSyprKwNov5wUXJ+hMqnuQvwYuZlBbgnl0lfEH9V3JfdzvdSyB53Xpu67ne3Wazd/qAvnO74+nv/LrPofseXxydwucqf0t7zlnJ61vYuISNB9qSn+XC2iwuqHmEEf6NyevWfX/2jLG7blHjEqrqG8nMzueeXSU0RczJ73xbRmGv61PYuITHO2ZR1Lgk+d7WqhW9Pqfu+92rtpJ34HVas2ezsDYreX1ndLyXvO973nt9z7P7mnRfl57XszuOSWmBQz4nd9d+aNmrl6wxvS/2ygcg0Aote/lTw3Tm5vQzFt6ut/R9ba/1Pn7XW4e216du1XsPkV52Jn/a5ux1/J+2OQ95r+5t9213Jr97us+n+zMMZE1Nnmv39ej7Gfa997p/pvrbf8+uEuZldSTfo+/16/kz3l/bfdvsXnZ/Z/R3j3TfY+G2muQ9032ufWPovud6fjf11PdePFKcPeMCUA0WnmkZw5h8L2HvGHxZ06nPPocuRxFd9kL25c5HMZoYHd3Fucpm5rOKqyKLuML3AmVN72OoXkNN+VY27a1kfXWAbc0qDf4wneEYmgZWswGjQeH9HQ3sa+5K/i71/o4GRmW7ksv+TClK11c+/BWUnnfoek/d2z/8FX/am8vcskz+9N5u5pZl9tt2f/qtf7j3G+h+4Ka/raK6LXhUsRz2vfZ/lDy/vnrFP4C4jtaR2h/o9f7Te7uZW/kw7P9oQDEe7eeYEifgeg8pp/r5CXGc7r333rp77rnn4b7bh2fmQ4jB1HYA3vgmlC9mp3kSXw3dwfXzL+Q788oG/X+WT6bFOxu5eEIuAEVNy3gx9B/83raMosT++VQCsKbHMfNDb7OGs9jR6aDMVk6ZoRzi8HVTOTTpdTLibQC8WJfF722v6xtjIawxH1OB5qiZBcbX+WHnLRTFllFkgOaQmQyjflyaAnOUnRDayV3R27DaYnzN+Eqv2M8y7NRXFLhLeTW5fbayk9mJfTEMyXpzlJ3JOjs6Hbxl69GeAoR2Mr/HE0YLtNchDmUGQIOgasSkRRLHJt5PgwwFimiBeOLAOJRRDt1thXoE3b0PmGrcDcAvOz/PfNvbQGJfXL8+ZcZyCMDXTZXJtufH9Ho/5Ba+bnolGcP80NvJz8ka8+nx8zrEoAz9dVm8nBfrsnpdg9/b2shoWgZALomxsZr0uGYqQGhX8lgAd6DyYJyAXQtBTH/PBVQmP3+AqYl4prKbNYlj51MJCsxEb3dqaHev8+p5rX5Ydwu/t1XiDlSyoPvaxcshhP46UXcBryfXrTFfMsb5VEIIioA1nIU15kvcjwfv8+5lt+5jmxOvixLXpnvZrajH+xc1LeMuQ+J1iESb5ZTFy5M/J1abj/kx/eemZ3sLgB923sKOTgc3FjRjjfmYqfggwMHzDvW+Lv3Fo59XeWJZmfzMmnuc0wLtddYwo9e+7mOB5LUGqMmZhzugv+5e7/m+L7ZmJa9bUdMyanLmJb8XesawQHsdmvT2ADI0/ed7fuht7grdljxvd6ASN4feZ93rNTnzku/tBmgqT7bZN4aD8enn2P2Zd9d3Byp5sTWLGwua2dHpoCh2cF93O93Lntet57ae633jPZLuePo7v+Q5JO5x/Weglf0c/N4talrGAkh+lt2fffc9Awfvz54xHqzb/V0xgx2djsT3wLLkz0nP63Pw34SD702fz6n7fqepvNf7dF/f+baD9333sc2J8+l9nr3v6Z6647ixoDn5OnlP9PjOYdzlvQ9sLdcLcH/odu6e1HVo43ve0ffZ3ul9/J53Dm2vT93isP79df8OZ6/j79/hPOS9urf1/A7qPp/uz7CZ3v8Ow6GfYd97r/tnqr/9Ozodye3dn0nPOj1/xvtru2+b3cvu74z+7pHe99hZvT67vjF033M9v5t66nsvHinOnnH1d94AKArjDjwLwIrLPwHg1remALDonIV4/HtI9++mbN8zFFhDXBtdoR8XBl/Ywa7GInapxezVCtmkFdJkLWFn0A0ouKwmMpwWFu9sZN7Y7F6/Xx3W0l/Dhd8/dL2/Okt/zf2hqdx9yVju/2APd18y9tPb7qHf+od7v4HuRx9Tc01F61HFctj3guT59dUr/gHEdbSO1P5Ar/f9H+zhbpt+LgOJ8Wg/x5Q4Add7SDnVz0+IE0QShOL0Fe6ENX+F5X9EBR6wfoWHOi/gtzdN5+rphUc8XAghhBAnXt8JQGYfYX/fOj33zz5Ce2sqWnvUyelzfE4/75XTq64Yetrd42l3jwdgUsWTvHzxB9z61hTem/2knjjs3EuJbzdT/Guxxf36QRp0Wm2UawXs3V5AuVrIpYYC/v56DSZyeWT5PrwOCxlOMxlOCxkOC16nBU3ThvV/LgshhDi9SYJQnH4iAVj3KKy4DwIt1OVdxJcbb6QmlsVTX57J7FFD/JEAIYQQQgjRr7LKhQN67e7ch6oYaUsbR1vaONA0zLEubJFm7OFmllTFGa3U8Fnrx9hjHyWPj2hG6jpz2OsvZHu8iHK1gI+0Asq1Au57dwfFzjglkW9R/OhCip1xCuJnkvvO0+TZVbJtKiZDj2DWP9FnPefgtll3DOZlEUIIIY5IEoTi9BENwYYnYPkfoauR0IgL+W34Oh7fn8m0onRevmk6ZYcZU0YIIYQQQpzCFIWo2UXU7MLvHMm9FXqvw39O3YkxHuanW1z8adR63jmgcbN3F2eHK7gosgEFLdlEuzGDWjWfcqOL7QfqWRHPY7/2eao+TCOCGQWNLJtKnl0lN/I/5G10kRe7GoDlDfqEeL6oQppJQ/ohCiGEONkkQShOfbEwbHwKlv8B/HVoI8/nzQm/4btr9TF8fnTFOL509kiMBvlVTAghhBBC9BY3WvlYK6PZE+W35eOZWaKPfayocWyR1kSvwyaKTX48XU1MjG3jStYkxyzWUOgye2kx5lKr5HJAy6FcMbGt2sMbsc8CGl9c7gVg6r+ycRhVcpcuITvNSqbTQobTklx6nRYynVYynBYAIjEVS69uiUIIIcSxkQShOHXFwrDleVj2O+iogpK5VM67n/9e7WLLznbmjcvg59dMpsjrOHJbQgghhBBC9KAZjARt2QRt2bQxgeLSDH3HG9+E+b+AriZYcR/KmPm4As24upoZ0bWWudEAmBON2MCv2TE6vKzozCc/M50asjlgn0hFxMNev4e1ATutQX2W5b7G/vAt0qwmMlwWvA4LmZFvk7FwCxkuC+l2M+l2M25bYmk3k67mkd4VwW0zYTJKYlEIIcRBkiAUp56GbbDxafj4eQi2QeEsIp+9j/srCvnbSxWk2wPcf/N0rppWIANJCyGEEEKIwWdx6gUOnTk7GoR3vg+zvgzrH+PF+Pnc4a6mqKuRCf6tTI6Hob1HfaMFLTefmKuAkD0PvzWXNlMO96/1c/FZ06iO2amMuGgJqtRpGWzb00xrV4RIXO0nsD/Cz97TQzQZsJuN2M1GbGYjdosRu/lg0vCrz2xM7DNgS9SxJepbzQYMR/g9+tYe68+u6X9W+O7tt35KnVtnl3zq+wghhBgckiAUp4awH7a+pD9KXLMBjBYY/zkCUxbwTEMpj7+8n7qOfVx/RhE//NwEvInHMoQQQgghhDipzHZ9mTcFgHtjt3PHmY1c/mIO+69sgGgXlJ4PHTXgq4GOahRfDeaOGsyNG0jz11KgxnjYAmzu0a4jC6zNUHIxmiuPuCOHoMVDp9GDz5BOB25a3v4lTZc8yNIKP6FInGA0TjCqEozEaeuKUBuNJ5t785O6Tz0Nq8nQK2FoTyYR9e3+2BW46QJgT4P/kLpCCCGGFkkQiuFL06BqLWx6Cra+ov8ylT0BLv0V9SOv4vFNfp57rhJ/eCdzRmXwhxuncfborFRHLYQQQgghRP8UBSwuyJ+ml/6ocehq4opfLuSNL42BznrwJ8qGJyDQgtKwDVNnI2lanDQgv/tYI7B4NrcYHYQtXsIWLyGLl7AjsW728uy2AK1aGlfPnUKHwUMbLnyqjWAMQomEYiga71FUgtE4vmCUBl+IUGL/kh59CJ9Yub/fU/nloh3YzEZeCN9L14qKHj0VDYlkopHNVW36NpMRmyXR49FkwGIyHPFpoCP1UDxcr8UjtSeEEKciSRCK4SUe1ZOCe96FXYugeTeYnTDlejjjdrYpo3n0o/28/vrHaMBnp+Tz7+eVMrXIk+rIhRBCCCGEOH4GI6TlsVUbBeMu671vwxNw1xJ9XVUh1A6BFr10NcM/vwAX/YjyffuxRtqwRtqwh5vx+PdgjbRhUsPM6B4fcUPvpiMmF1FTGhFzGhFTGlFzGhGzm6i9zzaTm4jJxdlr/ws/di4J/45bzp1EMH4wwRiOxnl3ewMT8tMIRVXSAkFaonHaAtFk4jGm9jPoYs/LoIDVdDCZ2J1cdFiMOK36n7kbDrThsBjZoI6h2R/GYdXrHOnxaCGEOB1JglAMff562Pu+nhQs/xDCPjCYoGQunP3f+Mqu4MOKIAvfruajvStwWIzcNnckd5wzkuIMmYBECCGEEEKceGsqWgGYfZj1nrq3z+51bE6ybnl8YD3V+vZo67+nnBUoAAr0/dbPw4T+2zPGAjzwxmoy8HH3OZnYIm1YIh1YYn7MUT+WmC+x7MQRrMfj34Ml6sMc60ShT0JPAQcRPrbdBesharQTMzmJGp3ETA7utEBxPIeoxcFIy1vsyLs9uS9qchJS7ASw04Udv2bDr9rwazY64lb8cRPBmJrswagnHuM0d4YJRuIEIvqj0i9trAbgae6F93d3h4Xdoj/i/Nel5TgtRhwWEw6rEafFhNNqwplYd1nlz2UhxOlDvvHE0BPpgtrNUL5YTwrWf6xvT8uHSdfAmPk0ZM7h3X0B3t1Uz+qXVhONa+S5bfzvZeO59awS0h3mT38PIYQQQgghhqiyyoUDqDW+33pHOvZI+6u18VSTjSO48/+3d/dBdtX1Hcffn3vu3rt7d7OEhIeACUgNaQxUEB2wI2MVRfChYDtUsB2kLR1mOjjVmWqVOg5K7Yz4R33C6jACIq0GBkvNUC2iweGPqjwjBIIkgBIekpAN2ee9T9/+cX673Gx2Nwkku9m9n9fMnXvO9/zOuedsvnvzO9895/wAqGed1LNOKB85/UoRZM0xssYoxeYoWWOUNc98D4Av1C7m0mN/R9askjWrFBpjZM0qImPxwBNkzSoAq575PlnUZty3iY9DNAolmoUSjUKZRlaiUSjR6CzTrOTT/719Ge88apih6KRnx2948vB30N/sYlezk53RxS92HsnyapNtwxVeaFTYXu+iFlM/G3H16A0spZ/yHffQW2ygw5bTU24pJpaLE8VFM7P5yt9gNrfqY7D1UXj+QXjuQXj+Adi+EaIJymDFGfDuK2muPJtNOp47H9/GT9dv5eFn7wHghCO6+dszT+C9a5bx5hWLKRR8u4CZmZmZ2aySaGSdNLJOqpMW3dB4H+cetXGPVS68fzU3/1EeP2PDVdy35gqI5iuFxOYYWaOaFx6niRWaLcsbY3TUh1OsykezR+jqS3tTgFW7frDb519RBuppJoPIoFEoUVeZqkqMqcwoZTaP9nK0+hiICts5hv5qF33be3m5XmIgygxHJ89RZphOhqPMSnXyrs/dRGdlEV09h9HbXWFJT5ml3SWW9pRZ0l2amF7aXWJJd4lKKdvr8xTNzA42FwhtdjSbMPA87NgMfZvhxUfzYuDWDdBI/3FXlsKxpxGrP8jOw0/mgXgjD2xr8vATL/Obnz/PwGh+u8QpKxbzqXP+kHNOOpo3HNnj/0zNzMzMzBYCFSYKja/Vhfev5ubTHiNr1njrxqt5aOXlLYXGGl/bdCSffP3TE/OvFByrFKJGsVmlt1ll8dgAbyw8C0BNvydTlUI09n4mPZa/6juyieLhUJQZopNhOumLMltSvFYoQ0cFlSpk5QodnRVKnd2UuxbR1d1Dd/ciurt76O3tpbe3l65KD+qo5CNiF7ugUHjNPy8zMxcI7cCpDsHgVnj5Weh7Ki8E9j2dFwV3Pg310VfalhYRx57K8GmX8WL3ap4sruLx4cVseGGAh3/5MtsHxoAnKRbE6mMW8aenHMupyxfzjlVHsuyw195hMDMzMzOzBU4FGlkZgLHy0t0W/aS5mr8+/LC9buLC+1fzTGc+IvMDqz8FwFPLP0SxMUKxPkxHY3hiutgY4dZfbuSaC1ZBdRiqgxSrQ/RWh1hUHaIxNkhtZIDG6CDNsUGo7qJQH6JQH6WjOUrHSBVG9v8waypRK3TSzDqJji7o6EKlCsVSN8XOvOiojgpkJSjmPw/W/ysUS5CV89j4sqwExc4pYuVX2k6OuUBptiDsU4FQ0rnA14AM+E5EfGnS8jLwPeAtwA7gwoh4Ji27ArgUaAD/EBF3zLRNSScAa4Gl5GNnXRwRk69Ut4MtIi/4je7a/TXSlxcBB7bm74Nb80FEBrdBdWD3TWQlxhYdx2D38fQddzrbOl7H081lPDJyBA/3V/jd5lFGNzZT65eQXuKEI7o5c+URnLL8MN60YjFrjumls2PqZ4GYmZmZmZnNtih0UCt0UOvo3aOed3vzcK457QN7rCPyk+8ZT8CbzfyiitoI1IYZGR5gV38/A4MDDA4OMDw4wMjwIKMjQ1RHBqmNDtEcG6ZZHSZqI3SMjdGlMbqoUqZKl7bSRZUuValojDJ1SqpTiozS3V8+cD+QQnFS8bADCh0t78WW+eI08QPRLs0D/P5Xu7ctFPMRwAtZmi62xNK0xqdd8LT2tNcCoaQM+CZwNrAFuFfSuoh4rKXZpcDOiFgp6SLgauBCSWuAi4CTyIfO+pmkVWmd6bZ5NfCViFgr6dtp2986EAc7Lw1uh9owNOvQqEGzlqbr+fR4bHx+pmX1UcZGhqiNDhHVYaI2DNURqA+j2giqDZFVB8iq/RRrA/ml89MYK1ToLy5hV7aEPi3npeLJbM0W80Kjl82jvWysHsULLKU5tPuXa1dHxnFLKhx3RIUzVx2dTy+tcNySCssP76JcdDHQzMzMzMwOXTMP9LIa7rvhgHxOV3otA+hJLyrpteegMcN12DFWoG+swI6xAs+k6V1VMVgXA7UCAzUx8OJmBnpXMloLxup1qrUGhahTUo0ytbyQSI0SeWx8ukyNLtXoLlSpFGpUCnUqhTzWSY2uRo3OZo2y6pRo0KEGReoUqZExSjEaZKRXNChMvNcpRJNCNChEA7W8i+Yex7lPrj/n1a1HPgjOVAXE0CsFRhWKKGtpoxkKj+NFx5mWay/Fy4np7JW2Ksz82q2NWqanWLcw3XayPdef/NmkR35Jk6aZJj5Te7366fH3iPyV/jUnYvsznZXz4nKb2ZcjPh3YFBFPAUhaC5wPtBYIzwc+n6ZvBa5R/mC484G1ETEGPC1pU9oeU21T0uPAWcBfpjY3pu22b4Fw7Udgy70HbHMdZIxFiVHKjESJEcqMUmIkygxTpp8V9EeFASr0R4V+und730U322MxKnXTnRXpyV4ZvaunXKSnXOT47jKn9ZQmHro78QDenhKLykU/M9DMzMzMzOwAqxShUmyyonsvRbXbvwhnf3ViNgLGmtBfyxisFfMiYk0M1vOC4mhDjDZgtCFG6mKkIbY38vhIS3x0PFbP2480RK0pak1oxKs7BxRNijQpUqeDBsX06qBOUePTjd2XKy1Pywo0KdIgo0lR4/NNstQ+a1me0STTnvHieGGToKgGJy3rZvVRXfkFOs1Geh+fbuSDcTaHdl8ek9vV91yvmS7usbn14ZtgzXlzvRezTjFRWZ2mgXQBcG5E/F2avxg4IyI+1tLm0dRmS5rfDJxBXtz7VUT8R4pfB/wkrbbHNlvar0zxFcBPIuLkKfbrMuCyNHsy8Oj+HrzZAnEE8NJc74TZHHDuW7ty7lu7cu5bO3P+W7ty7h94x0fEHpchz9trJiPiWuBaAEn3RcRb53iXzOaE89/alXPf2pVz39qVc9/amfPf2pVzf/bsy9M3nwNWtMwvT7Ep20gqAoeRD1Yy3brTxXcAi9M2pvssMzMzMzMzMzMzO0D2pUB4L3CipBMklcgHHVk3qc064JI0fQGwPvJ7l9cBF0kqp9GJTwTumW6baZ270jZI2/zRqz88MzMzMzMzMzMzm8lebzGOiLqkjwF3ABlwfURskHQVcF9ErAOuA25Kg5D0kRf8SO1uIR/QpA5cHpEPjTvVNtNHfhpYK+mLwINp23tz7T4fsdnC4/y3duXct3bl3Ld25dy3dub8t3bl3J8lex2kxMzMzMzMzMzMzBaufbnF2MzMzMzMzMzMzBYoFwjNzMzMzMzMzMza2LwoEEr6C0kbJDUlvXXSsiskbZL0hKRzWuLnptgmSZ9piZ8g6dcpfnMaJMVs3pkux83mK0nXS9om6dGW2BJJd0p6Mr0fnuKS9PWU/7+RdFrLOpek9k9KumSqzzI7lEhaIekuSY+l/s7HU9z5bwuepE5J90h6OOX/F1J8yj57Gvzw5hT/taTXt2xryvMCs0OZpEzSg5JuT/POfVvwJD0j6RFJD0m6L8Xc75lj86JACDwK/Dlwd2tQ0hryAVFOAs4F/j19wWbAN4H3AWuAj6S2AFcDX4mIlcBO4NLZOQSzA2cvOW42X32X/Lu81WeAn0fEicDP0zzkuX9iel0GfAvyjgVwJXAGcDpw5XjnwuwQVgf+MSLWAG8DLk/f6c5/awdjwFkRcQpwKnCupLcxfZ/9UmBnin8ltZv2vGBWj8Ts1fk48HjLvHPf2sW7IuLUiBi/CMz9njk2LwqEEfF4RDwxxaLzgbURMRYRTwObyBPjdGBTRDwVEVVgLXC+JAFnAbem9W8EPnTwj8DsgJsyx+d4n8xek4i4G+ibFD6f/Lsadv/OPh/4XuR+BSyWdAxwDnBnRPRFxE7gTvYsOpodUiLihYh4IE0PkJ8ovg7nv7WBlMeDabYjvYLp++ytvxe3Au9OffzpzgvMDlmSlgMfAL6T5mc6X3Xu20Lnfs8cmxcFwhm8Dni2ZX5Lik0XXwq8HBH1SXGz+Wa6HDdbaI6OiBfS9IvA0Wl6f7//zeaFdMvYm4Ff4/y3NpHuAHoI2EZ+greZ6fvsE3melu8i7+M7/20++irwT0Azzc90vurct4UkgJ9Kul/SZSnmfs8cK871DoyT9DNg2RSLPhsRP5rt/TEzs0NLRISkmOv9MDtYJPUAPwQ+ERH9+YUhOee/LWQR0QBOlbQYuA1YPce7ZHbQSfogsC0i7pf0zrneH7NZdmZEPCfpKOBOSRtbF7rfMzcOmQJhRLznVaz2HLCiZX55ijFNfAf55ajF9FeX1vZm88lMuW+2kGyVdExEvJBuJdiW4tP9DjwHvHNS/BezsJ9mr4mkDvLi4H9GxH+lsPPf2kpEvCzpLuCPmb7PPp7/WyQVgcPI+/juG9l883bgPEnvBzqBXuBrOPetDUTEc+l9m6TbyG+Ld79njs33W4zXARelEZ1OIH9o5T3AvcCJaQSoEvlDW9dFRAB3ARek9S8BfHWizUdT5vgc75PZwbCO/Lsadv/OXgd8NI1q9jZgV7ol4Q7gvZIOTw8pfm+KmR2y0jOkrgMej4h/a1nk/LcFT9KR6cpBJHUBZ5M/h3O6Pnvr78UFwPrUx5/uvMDskBQRV0TE8oh4PXlffn1E/BXOfVvgJHVLWjQ+Td5feRT3e+bcIXMF4Uwk/RnwDeBI4H8kPRQR50TEBkm3AI+RjwB4ebpFAUkfI0+ODLg+IjakzX0aWCvpi8CD5B1ys3klIuoz5LjZvCTpB+R/BTxC0hbyUcm+BNwi6VLgd8CHU/MfA+8nfxD3MPA3ABHRJ+lfyImVlZ0AAAKgSURBVIvoAFdFxOSBT8wONW8HLgYeSc9hA/hnnP/WHo4BbkyjrhaAWyLidkmPMXWf/TrgJkmbyAe2ughgpvMCs3lmuvNV574tFEcDt6VHqRSB70fE/0q6F/d75pTyPzqYmZmZmZmZmZlZO5rvtxibmZmZmZmZmZnZa+ACoZmZmZmZmZmZWRtzgdDMzMzMzMzMzKyNuUBoZmZmZmZmZmbWxlwgNDMzMzMzMzMza2MuEJqZmZnZfpG0TNJaSZsl3S/px5JWSTpJ0npJT0h6UtLnJKllvXMl3SNpo6SHJN0s6bi5PBYzMzMzA0XEXO+DmZmZmc0TqeD3f8CNEfHtFDsF6AW+C/x9RPxUUgX4IXB7RHxT0snAbcB5EfF4Wu884OWIuHsODsXMzMzMEhcIzczMzGyfSToL+HxEvGNS/FLgTyLioy2xNwC/iIgVkm4C1kfEDbO7x2ZmZma2N77F2MzMzMz2x8nA/VPET5ocj4jNQI+k3rT8gYO/e2ZmZma2v1wgNDMzM7NZJWlpegbhbyV9cq73x8zMzKzduUBoZmZmZvtjA/CWKeKPTY5L+gNgMCL603qnAUTEjog4FbgW6Dm4u2tmZmZme+MCoZmZmZntj/VAWdJl4wFJbwKeAM6U9J4U6wK+Dnw5Nfsy8FlJb2zZVmV2dtnMzMzMZuJBSszMzMxsv0g6Fvgq+RWDo8AzwCeATuAbwDFABtwEXBWpwynpA8DnyUc8fgn4PXBlRPx2do/AzMzMzFq5QGhmZmZmZmZmZtbGfIuxmZmZmZmZmZlZG3OB0MzMzMzMzMzMrI25QGhmZmZmZmZmZtbGXCA0MzMzMzMzMzNrYy4QmpmZmZmZmZmZtTEXCM3MzMzMzMzMzNqYC4RmZmZmZmZmZmZt7P8BnSUsDKTj18sAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1296x432 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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