{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Project Tasks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the first few assignments, we have learned how to infer part based components (known as mutational signatures) generated by particular mutational processes using Non-negative Matrix Factorization (NMF). By doing this, we are trying to reconstruct the mutation catalog in a given sample with mutational signatures and their contributions.\n",
"\n",
"In this group project, you will use similar mutational profiles and signature activities to predict cancer types but with much larger sample size. \n",
"You should:\n",
"* Separate the data into training and test groups within each cancer type.\n",
"* Find out which features are informative for the prediction of the cancer type (label). You should combine the profiles and activities and use each data type independently.\n",
"* Implement different models for classification of the samples given the input data and evaluate the model performance using test data to avoid overfitting. Explain briefly how does each model that you have used work.\n",
"* Report model performance, using standard machine learning metrics such as confusion matrices etc. \n",
"* Compare model performance across methods and across cancer types, are some types easier top predict than others.\n",
"* Submit a single Jupyter notebook as the final report and present that during the last assignment session "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data include both mutational catalogs from multiple cancers and the predicted activities in the paper [\"Alexandrov LB, et al. (2020) The repertoire of mutational signatures in human cancer\"](https://www.nature.com/articles/s41586-020-1943-3). The data either are generated from whole human genome (WGS) or only exomes regions (WES). Since the exome region only constitutes about 1% of human genome, the total mutation numbers in these samples are, of course, much smaller. So if you plan to use WGS together with WES data, remember to normalize the profile for each sample to sum up to 1.\n",
"\n",
"Note that, the data is generated from different platforms by different research groups, some of them (e.g. labeled with PCAWG, TCGA) are processed with the same bioinformatics pipeline. Thus, these samples will have less variability related to data processing pipelines."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cancer types might be labeled under the same tissue, e.g. 'Bone-Benign','Bone-Epith', which can also be combined together or take the one has more samples."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is a link to background reading [\"Pan-Cancer Analysis of Whole Genomes\"](https://www.nature.com/collections/afdejfafdb). Have a look especially the paper [\"A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns\"](https://www.nature.com/articles/s41467-019-13825-8)."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import re"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mutational catalogs and activities - WGS data"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
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" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Mutation type</th>\n",
" <th>Trinucleotide</th>\n",
" <th>Biliary-AdenoCA::SP117655</th>\n",
" <th>Biliary-AdenoCA::SP117556</th>\n",
" <th>Biliary-AdenoCA::SP117627</th>\n",
" <th>Biliary-AdenoCA::SP117775</th>\n",
" <th>Biliary-AdenoCA::SP117332</th>\n",
" <th>Biliary-AdenoCA::SP117712</th>\n",
" <th>Biliary-AdenoCA::SP117017</th>\n",
" <th>Biliary-AdenoCA::SP117031</th>\n",
" <th>...</th>\n",
" <th>Uterus-AdenoCA::SP94540</th>\n",
" <th>Uterus-AdenoCA::SP95222</th>\n",
" <th>Uterus-AdenoCA::SP89389</th>\n",
" <th>Uterus-AdenoCA::SP90503</th>\n",
" <th>Uterus-AdenoCA::SP92460</th>\n",
" <th>Uterus-AdenoCA::SP92931</th>\n",
" <th>Uterus-AdenoCA::SP91265</th>\n",
" <th>Uterus-AdenoCA::SP89909</th>\n",
" <th>Uterus-AdenoCA::SP90629</th>\n",
" <th>Uterus-AdenoCA::SP95550</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>C>A</td>\n",
" <td>ACA</td>\n",
" <td>269</td>\n",
" <td>114</td>\n",
" <td>105</td>\n",
" <td>217</td>\n",
" <td>52</td>\n",
" <td>192</td>\n",
" <td>54</td>\n",
" <td>196</td>\n",
" <td>...</td>\n",
" <td>117</td>\n",
" <td>233</td>\n",
" <td>94</td>\n",
" <td>114</td>\n",
" <td>257</td>\n",
" <td>139</td>\n",
" <td>404</td>\n",
" <td>97</td>\n",
" <td>250</td>\n",
" <td>170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>C>A</td>\n",
" <td>ACC</td>\n",
" <td>148</td>\n",
" <td>56</td>\n",
" <td>71</td>\n",
" <td>123</td>\n",
" <td>36</td>\n",
" <td>139</td>\n",
" <td>54</td>\n",
" <td>102</td>\n",
" <td>...</td>\n",
" <td>90</td>\n",
" <td>167</td>\n",
" <td>59</td>\n",
" <td>64</td>\n",
" <td>268</td>\n",
" <td>75</td>\n",
" <td>255</td>\n",
" <td>78</td>\n",
" <td>188</td>\n",
" <td>137</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 2782 columns</p>\n",
"</div>"
],
"text/plain": [
" Mutation type Trinucleotide Biliary-AdenoCA::SP117655 \\\n",
"0 C>A ACA 269 \n",
"1 C>A ACC 148 \n",
"\n",
" Biliary-AdenoCA::SP117556 Biliary-AdenoCA::SP117627 \\\n",
"0 114 105 \n",
"1 56 71 \n",
"\n",
" Biliary-AdenoCA::SP117775 Biliary-AdenoCA::SP117332 \\\n",
"0 217 52 \n",
"1 123 36 \n",
"\n",
" Biliary-AdenoCA::SP117712 Biliary-AdenoCA::SP117017 \\\n",
"0 192 54 \n",
"1 139 54 \n",
"\n",
" Biliary-AdenoCA::SP117031 ... Uterus-AdenoCA::SP94540 \\\n",
"0 196 ... 117 \n",
"1 102 ... 90 \n",
"\n",
" Uterus-AdenoCA::SP95222 Uterus-AdenoCA::SP89389 Uterus-AdenoCA::SP90503 \\\n",
"0 233 94 114 \n",
"1 167 59 64 \n",
"\n",
" Uterus-AdenoCA::SP92460 Uterus-AdenoCA::SP92931 Uterus-AdenoCA::SP91265 \\\n",
"0 257 139 404 \n",
"1 268 75 255 \n",
"\n",
" Uterus-AdenoCA::SP89909 Uterus-AdenoCA::SP90629 Uterus-AdenoCA::SP95550 \n",
"0 97 250 170 \n",
"1 78 188 137 \n",
"\n",
"[2 rows x 2782 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## PCAWG data is performed by the same pipeline\n",
"PCAWG_wgs_mut = pd.read_csv (\"./project_data/catalogs/WGS/WGS_PCAWG.96.csv\")\n",
"PCAWG_wgs_mut.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Accuracy is the cosine similarity of reconstruct catalog to the observed catalog "
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" 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>Cancer Types</th>\n",
" <th>Sample Names</th>\n",
" <th>Accuracy</th>\n",
" <th>SBS1</th>\n",
" <th>SBS2</th>\n",
" <th>SBS3</th>\n",
" <th>SBS4</th>\n",
" <th>SBS5</th>\n",
" <th>SBS6</th>\n",
" <th>SBS7a</th>\n",
" <th>...</th>\n",
" <th>SBS51</th>\n",
" <th>SBS52</th>\n",
" <th>SBS53</th>\n",
" <th>SBS54</th>\n",
" <th>SBS55</th>\n",
" <th>SBS56</th>\n",
" <th>SBS57</th>\n",
" <th>SBS58</th>\n",
" <th>SBS59</th>\n",
" <th>SBS60</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Biliary-AdenoCA</td>\n",
" <td>SP117655</td>\n",
" <td>0.968</td>\n",
" <td>1496</td>\n",
" <td>1296</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1825</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Biliary-AdenoCA</td>\n",
" <td>SP117556</td>\n",
" <td>0.963</td>\n",
" <td>985</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 68 columns</p>\n",
"</div>"
],
"text/plain": [
" Cancer Types Sample Names Accuracy SBS1 SBS2 SBS3 SBS4 SBS5 SBS6 \\\n",
"0 Biliary-AdenoCA SP117655 0.968 1496 1296 0 0 1825 0 \n",
"1 Biliary-AdenoCA SP117556 0.963 985 0 0 0 922 0 \n",
"\n",
" SBS7a ... SBS51 SBS52 SBS53 SBS54 SBS55 SBS56 SBS57 SBS58 SBS59 \\\n",
"0 0 ... 0 0 0 0 0 0 0 0 0 \n",
"1 0 ... 0 0 0 0 0 0 0 0 0 \n",
"\n",
" SBS60 \n",
"0 0 \n",
"1 0 \n",
"\n",
"[2 rows x 68 columns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Activities:\n",
"PCAWG_wgs_act = pd.read_csv (\"./project_data/activities/WGS/WGS_PCAWG.activities.csv\")\n",
"PCAWG_wgs_act.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Mutation type</th>\n",
" <th>Trinucleotide</th>\n",
" <th>ALL::PD4020a</th>\n",
" <th>ALL::SJBALL011_D</th>\n",
" <th>ALL::SJBALL012_D</th>\n",
" <th>ALL::SJBALL020013_D1</th>\n",
" <th>ALL::SJBALL020422_D1</th>\n",
" <th>ALL::SJBALL020579_D1</th>\n",
" <th>ALL::SJBALL020589_D1</th>\n",
" <th>ALL::SJBALL020625_D1</th>\n",
" <th>...</th>\n",
" <th>Stomach-AdenoCa::pfg316T</th>\n",
" <th>Stomach-AdenoCa::pfg317T</th>\n",
" <th>Stomach-AdenoCa::pfg344T</th>\n",
" <th>Stomach-AdenoCa::pfg373T</th>\n",
" <th>Stomach-AdenoCa::pfg375T</th>\n",
" <th>Stomach-AdenoCa::pfg378T</th>\n",
" <th>Stomach-AdenoCa::pfg398T</th>\n",
" <th>Stomach-AdenoCa::pfg413T</th>\n",
" <th>Stomach-AdenoCa::pfg416T</th>\n",
" <th>Stomach-AdenoCa::pfg424T</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>C>A</td>\n",
" <td>ACA</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>133</td>\n",
" <td>185</td>\n",
" <td>202</td>\n",
" <td>185</td>\n",
" <td>96</td>\n",
" <td>134</td>\n",
" <td>12</td>\n",
" <td>279</td>\n",
" <td>75</td>\n",
" <td>135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>C>A</td>\n",
" <td>ACC</td>\n",
" <td>16</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>48</td>\n",
" <td>70</td>\n",
" <td>126</td>\n",
" <td>88</td>\n",
" <td>35</td>\n",
" <td>54</td>\n",
" <td>16</td>\n",
" <td>112</td>\n",
" <td>31</td>\n",
" <td>91</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 1867 columns</p>\n",
"</div>"
],
"text/plain": [
" Mutation type Trinucleotide ALL::PD4020a ALL::SJBALL011_D \\\n",
"0 C>A ACA 35 9 \n",
"1 C>A ACC 16 2 \n",
"\n",
" ALL::SJBALL012_D ALL::SJBALL020013_D1 ALL::SJBALL020422_D1 \\\n",
"0 2 7 5 \n",
"1 4 10 5 \n",
"\n",
" ALL::SJBALL020579_D1 ALL::SJBALL020589_D1 ALL::SJBALL020625_D1 ... \\\n",
"0 7 3 5 ... \n",
"1 9 1 2 ... \n",
"\n",
" Stomach-AdenoCa::pfg316T Stomach-AdenoCa::pfg317T \\\n",
"0 133 185 \n",
"1 48 70 \n",
"\n",
" Stomach-AdenoCa::pfg344T Stomach-AdenoCa::pfg373T \\\n",
"0 202 185 \n",
"1 126 88 \n",
"\n",
" Stomach-AdenoCa::pfg375T Stomach-AdenoCa::pfg378T \\\n",
"0 96 134 \n",
"1 35 54 \n",
"\n",
" Stomach-AdenoCa::pfg398T Stomach-AdenoCa::pfg413T \\\n",
"0 12 279 \n",
"1 16 112 \n",
"\n",
" Stomach-AdenoCa::pfg416T Stomach-AdenoCa::pfg424T \n",
"0 75 135 \n",
"1 31 91 \n",
"\n",
"[2 rows x 1867 columns]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nonPCAWG_wgs_mut = pd.read_csv (\"./project_data/catalogs/WGS/WGS_Other.96.csv\")\n",
"nonPCAWG_wgs_mut.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Cancer Types</th>\n",
" <th>Sample Names</th>\n",
" <th>Accuracy</th>\n",
" <th>SBS1</th>\n",
" <th>SBS2</th>\n",
" <th>SBS3</th>\n",
" <th>SBS4</th>\n",
" <th>SBS5</th>\n",
" <th>SBS6</th>\n",
" <th>SBS7a</th>\n",
" <th>...</th>\n",
" <th>SBS51</th>\n",
" <th>SBS52</th>\n",
" <th>SBS53</th>\n",
" <th>SBS54</th>\n",
" <th>SBS55</th>\n",
" <th>SBS56</th>\n",
" <th>SBS57</th>\n",
" <th>SBS58</th>\n",
" <th>SBS59</th>\n",
" <th>SBS60</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ALL</td>\n",
" <td>PD4020a</td>\n",
" <td>0.995</td>\n",
" <td>208</td>\n",
" <td>3006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>365</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ALL</td>\n",
" <td>SJBALL011_D</td>\n",
" <td>0.905</td>\n",
" <td>66</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 68 columns</p>\n",
"</div>"
],
"text/plain": [
" Cancer Types Sample Names Accuracy SBS1 SBS2 SBS3 SBS4 SBS5 SBS6 \\\n",
"0 ALL PD4020a 0.995 208 3006 0 0 365 0 \n",
"1 ALL SJBALL011_D 0.905 66 0 0 0 144 0 \n",
"\n",
" SBS7a ... SBS51 SBS52 SBS53 SBS54 SBS55 SBS56 SBS57 SBS58 SBS59 \\\n",
"0 0 ... 0 0 0 0 0 0 0 0 0 \n",
"1 0 ... 0 0 0 0 0 0 0 0 0 \n",
"\n",
" SBS60 \n",
"0 0 \n",
"1 0 \n",
"\n",
"[2 rows x 68 columns]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nonPCAWG_wgs_act = pd.read_csv (\"./project_data/activities/WGS/WGS_Other.activities.csv\")\n",
"nonPCAWG_wgs_act.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mutational catalogs - WES data"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Mutation type</th>\n",
" <th>Trinucleotide</th>\n",
" <th>AML::TCGA-AB-2802-03B-01W-0728-08</th>\n",
" <th>AML::TCGA-AB-2803-03B-01W-0728-08</th>\n",
" <th>AML::TCGA-AB-2804-03B-01W-0728-08</th>\n",
" <th>AML::TCGA-AB-2805-03B-01W-0728-08</th>\n",
" <th>AML::TCGA-AB-2806-03B-01W-0728-08</th>\n",
" <th>AML::TCGA-AB-2807-03B-01W-0728-08</th>\n",
" <th>AML::TCGA-AB-2808-03B-01W-0728-08</th>\n",
" <th>AML::TCGA-AB-2809-03D-01W-0755-09</th>\n",
" <th>...</th>\n",
" <th>Eye-Melanoma::TCGA-WC-A885-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-WC-A888-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-WC-A88A-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-WC-AA9A-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-WC-AA9E-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-YZ-A980-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-YZ-A982-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-YZ-A983-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-YZ-A984-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-YZ-A985-01A-11D-A39W-08</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>C>A</td>\n",
" <td>ACA</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>C>A</td>\n",
" <td>ACC</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 9495 columns</p>\n",
"</div>"
],
"text/plain": [
" Mutation type Trinucleotide AML::TCGA-AB-2802-03B-01W-0728-08 \\\n",
"0 C>A ACA 0 \n",
"1 C>A ACC 0 \n",
"\n",
" AML::TCGA-AB-2803-03B-01W-0728-08 AML::TCGA-AB-2804-03B-01W-0728-08 \\\n",
"0 0 0 \n",
"1 2 0 \n",
"\n",
" AML::TCGA-AB-2805-03B-01W-0728-08 AML::TCGA-AB-2806-03B-01W-0728-08 \\\n",
"0 0 4 \n",
"1 0 0 \n",
"\n",
" AML::TCGA-AB-2807-03B-01W-0728-08 AML::TCGA-AB-2808-03B-01W-0728-08 \\\n",
"0 0 2 \n",
"1 1 3 \n",
"\n",
" AML::TCGA-AB-2809-03D-01W-0755-09 ... \\\n",
"0 0 ... \n",
"1 0 ... \n",
"\n",
" Eye-Melanoma::TCGA-WC-A885-01A-11D-A39W-08 \\\n",
"0 1 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-WC-A888-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-WC-A88A-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-WC-AA9A-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-WC-AA9E-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-YZ-A980-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-YZ-A982-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-YZ-A983-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 1 \n",
"\n",
" Eye-Melanoma::TCGA-YZ-A984-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-YZ-A985-01A-11D-A39W-08 \n",
"0 0 \n",
"1 0 \n",
"\n",
"[2 rows x 9495 columns]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Performed by TCGA pipeline\n",
"TCGA_wes_mut = pd.read_csv (\"./project_data/catalogs/WES/WES_TCGA.96.csv\")\n",
"TCGA_wes_mut.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"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>Cancer Types</th>\n",
" <th>Sample Names</th>\n",
" <th>Accuracy</th>\n",
" <th>SBS1</th>\n",
" <th>SBS2</th>\n",
" <th>SBS3</th>\n",
" <th>SBS4</th>\n",
" <th>SBS5</th>\n",
" <th>SBS6</th>\n",
" <th>SBS7a</th>\n",
" <th>...</th>\n",
" <th>SBS51</th>\n",
" <th>SBS52</th>\n",
" <th>SBS53</th>\n",
" <th>SBS54</th>\n",
" <th>SBS55</th>\n",
" <th>SBS56</th>\n",
" <th>SBS57</th>\n",
" <th>SBS58</th>\n",
" <th>SBS59</th>\n",
" <th>SBS60</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AML</td>\n",
" <td>TCGA-AB-2802-03B-01W-0728-08</td>\n",
" <td>0.811</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AML</td>\n",
" <td>TCGA-AB-2803-03B-01W-0728-08</td>\n",
" <td>0.608</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 68 columns</p>\n",
"</div>"
],
"text/plain": [
" Cancer Types Sample Names Accuracy SBS1 SBS2 SBS3 \\\n",
"0 AML TCGA-AB-2802-03B-01W-0728-08 0.811 3 0 0 \n",
"1 AML TCGA-AB-2803-03B-01W-0728-08 0.608 4 0 0 \n",
"\n",
" SBS4 SBS5 SBS6 SBS7a ... SBS51 SBS52 SBS53 SBS54 SBS55 SBS56 \\\n",
"0 0 0 0 0 ... 0 0 0 0 0 0 \n",
"1 0 7 0 0 ... 0 0 0 0 0 0 \n",
"\n",
" SBS57 SBS58 SBS59 SBS60 \n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"\n",
"[2 rows x 68 columns]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"##Activities\n",
"TCGA_wes_act = pd.read_csv(\"./project_data/activities/WES/WES_TCGA.activities.csv\")\n",
"TCGA_wes_act.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\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>Mutation type</th>\n",
" <th>Trinucleotide</th>\n",
" <th>ALL::TARGET-10-PAIXPH-03A-01D</th>\n",
" <th>ALL::TARGET-10-PAKHZT-03A-01R</th>\n",
" <th>ALL::TARGET-10-PAKMVD-09A-01D</th>\n",
" <th>ALL::TARGET-10-PAKSWW-03A-01D</th>\n",
" <th>ALL::TARGET-10-PALETF-03A-01D</th>\n",
" <th>ALL::TARGET-10-PALLSD-09A-01D</th>\n",
" <th>ALL::TARGET-10-PAMDKS-03A-01D</th>\n",
" <th>ALL::TARGET-10-PAPJIB-04A-01D</th>\n",
" <th>...</th>\n",
" <th>Head-SCC::V-109</th>\n",
" <th>Head-SCC::V-112</th>\n",
" <th>Head-SCC::V-116</th>\n",
" <th>Head-SCC::V-119</th>\n",
" <th>Head-SCC::V-123</th>\n",
" <th>Head-SCC::V-124</th>\n",
" <th>Head-SCC::V-125</th>\n",
" <th>Head-SCC::V-14</th>\n",
" <th>Head-SCC::V-29</th>\n",
" <th>Head-SCC::V-98</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>C>A</td>\n",
" <td>ACA</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>C>A</td>\n",
" <td>ACC</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 9693 columns</p>\n",
"</div>"
],
"text/plain": [
" Mutation type Trinucleotide ALL::TARGET-10-PAIXPH-03A-01D \\\n",
"0 C>A ACA 0 \n",
"1 C>A ACC 0 \n",
"\n",
" ALL::TARGET-10-PAKHZT-03A-01R ALL::TARGET-10-PAKMVD-09A-01D \\\n",
"0 0 0 \n",
"1 0 0 \n",
"\n",
" ALL::TARGET-10-PAKSWW-03A-01D ALL::TARGET-10-PALETF-03A-01D \\\n",
"0 1 0 \n",
"1 1 0 \n",
"\n",
" ALL::TARGET-10-PALLSD-09A-01D ALL::TARGET-10-PAMDKS-03A-01D \\\n",
"0 0 0 \n",
"1 0 0 \n",
"\n",
" ALL::TARGET-10-PAPJIB-04A-01D ... Head-SCC::V-109 Head-SCC::V-112 \\\n",
"0 2 ... 0 0 \n",
"1 0 ... 1 0 \n",
"\n",
" Head-SCC::V-116 Head-SCC::V-119 Head-SCC::V-123 Head-SCC::V-124 \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"\n",
" Head-SCC::V-125 Head-SCC::V-14 Head-SCC::V-29 Head-SCC::V-98 \n",
"0 0 0 0 1 \n",
"1 0 1 0 0 \n",
"\n",
"[2 rows x 9693 columns]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"other_wes_mut = pd.read_csv(\"./project_data/catalogs/WES/WES_Other.96.csv\")\n",
"other_wes_mut.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Cancer Types</th>\n",
" <th>Sample Names</th>\n",
" <th>Accuracy</th>\n",
" <th>SBS1</th>\n",
" <th>SBS2</th>\n",
" <th>SBS3</th>\n",
" <th>SBS4</th>\n",
" <th>SBS5</th>\n",
" <th>SBS6</th>\n",
" <th>SBS7a</th>\n",
" <th>...</th>\n",
" <th>SBS51</th>\n",
" <th>SBS52</th>\n",
" <th>SBS53</th>\n",
" <th>SBS54</th>\n",
" <th>SBS55</th>\n",
" <th>SBS56</th>\n",
" <th>SBS57</th>\n",
" <th>SBS58</th>\n",
" <th>SBS59</th>\n",
" <th>SBS60</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ALL</td>\n",
" <td>TARGET-10-PAIXPH-03A-01D</td>\n",
" <td>0.529</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ALL</td>\n",
" <td>TARGET-10-PAKHZT-03A-01R</td>\n",
" <td>0.696</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 68 columns</p>\n",
"</div>"
],
"text/plain": [
" Cancer Types Sample Names Accuracy SBS1 SBS2 SBS3 SBS4 \\\n",
"0 ALL TARGET-10-PAIXPH-03A-01D 0.529 0 0 0 0 \n",
"1 ALL TARGET-10-PAKHZT-03A-01R 0.696 0 0 0 0 \n",
"\n",
" SBS5 SBS6 SBS7a ... SBS51 SBS52 SBS53 SBS54 SBS55 SBS56 SBS57 \\\n",
"0 0 0 0 ... 0 0 0 1 0 0 0 \n",
"1 0 0 0 ... 0 0 0 1 0 0 0 \n",
"\n",
" SBS58 SBS59 SBS60 \n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"\n",
"[2 rows x 68 columns]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"other_wes_act = pd.read_csv(\"./project_data/activities/WES/WES_Other.activities.csv\")\n",
"other_wes_act.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Imports and helpers"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"import numpy as np\n",
"import pandas as pd\n",
"import sklearn\n",
"from sklearn.decomposition import PCA\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"#import torch \n",
"\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.metrics import roc_auc_score\n",
"from sklearn.metrics import roc_curve\n",
"from sklearn.metrics import classification_report\n",
"\n",
"from sklearn.model_selection import cross_val_score, train_test_split, KFold\n",
"from sklearn.model_selection import StratifiedShuffleSplit\n",
"from sklearn.model_selection import StratifiedKFold, GridSearchCV\n",
"from sklearn.model_selection import learning_curve\n",
"\n",
"from sklearn.preprocessing import LabelEncoder\n",
"\n",
"# These ones are work in progress\n",
"def plot_roc_auc(X_tst, y_test, model, is_multi_class=False):\n",
" probs = model.predict_proba(X_tst)\n",
" probs = probs[:, 1]\n",
" if is_multi_class:\n",
" auc = roc_auc_score(y_test, probs, multi_class='ovo')\n",
" else:\n",
" auc = roc_auc_score(y_test, probs, multi_class='ovo')\n",
" \n",
" fp_rate, tp_rate, thresholds = roc_curve(y_test, probs)\n",
" \n",
" plt.figure(figsize=(7,6))\n",
" plt.axis('scaled')\n",
" plt.xlim([0,1])\n",
" plt.ylim([0,1])\n",
" plt.title(\"AUC & ROC\")\n",
" plt.plot(fp_rate, tp_rate, 'g')\n",
" plt.fill_between(fp_rate, tp_rate, facecolor = \"green\", alpha = 0.7)\n",
" plt.text(0.95, 0.05, f'AUC = {auc}', ha='right', fontsize=12, weight='bold', color='blue')\n",
" plt.xlabel(\"False Positive Rate\")\n",
" plt.ylabel(\"True Positive Rate\")\n",
"\n",
"def plot_confusion_mat(y_test, y_pred, labs=None, size=None):\n",
" cm = sklearn.metrics.confusion_matrix(y_test, y_pred)\n",
" if size is None:\n",
" plt.figure(figsize=(12,10))\n",
" else:\n",
" plt.figure(figsize=size)\n",
" if labs is None:\n",
" sns.heatmap(cm, square=False, annot=True, fmt='d', cmap='viridis', cbar=True)\n",
" else:\n",
" sns.heatmap(cm, square=False, annot=True, fmt='d', cmap='viridis', xticklabels=labs, yticklabels=labs, cbar=True)\n",
" plt.xlabel('Predicted label')\n",
" plt.ylabel('True label')\n",
" #plt.ylim(0, 2)\n",
"\n",
"def plot_learning_curve(model, X, y):\n",
" N, train_lc, val_lc = learning_curve(model, X, y, cv=7, train_sizes=np.linspace(0.3, 1, 25))\n",
" plt.figure(figsize=(7,6))\n",
" plt.title(\"Learning curve\")\n",
" plt.plot(N, np.mean(train_lc, 1), color='blue', label='training score')\n",
" plt.plot(N, np.mean(val_lc, 1), color='red', label='validation score')\n",
" #plt.hlines(N, np.mean([train_lc[-1], val_lc[-1]]), N[0], N[-1], color='gray', label='mean', linestyle='dashed')\n",
"\n",
"def plot_trn_tst_dist(y_all, y_train, y_test, y_pred, in_cols=False):\n",
" #fig = None\n",
" #ax = None\n",
" if in_cols:\n",
" fig, ax = plt.subplots(2,2)\n",
" else:\n",
" fig, ax = plt.subplots(4,1)\n",
"\n",
" fig.set_size_inches(15,8)\n",
"\n",
" plt_sets = [y_all, y_train, y_test, y_pred]\n",
" plt_labels = [\"All\", \"Train\", \"Test\", \"Pred\"]\n",
" plt_set_df = pd.DataFrame()\n",
" for i in range(len(plt_sets)):\n",
" s = pd.Series(plt_sets[i]).value_counts().sort_index()\n",
" plt_set_df[plt_labels[i]] = s\n",
" \n",
" pd.DataFrame({plt_labels[i]: s}).plot(ax=ax.flat[i], kind=\"bar\")\n",
" #sns.countplot(x=s, \n",
" # palette=sns.hls_palette(2),\n",
" # ax=ax[i])\n",
" ax.flat[i].tick_params(axis=\"x\", rotation=90)\n",
"\n",
" fig.tight_layout()\n",
" with pd.option_context('display.max_rows', None,\n",
" 'display.max_columns', None,\n",
" 'display.precision', 2,\n",
" ):\n",
" print(plt_set_df)\n",
"\n",
"\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dataset preprocess, combine profile data to a single data frame\n",
"\n",
"From all profile sets, a combined data frame is made, which has samples in the rows and features in the columns."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Profile data:\n",
"\n",
"---Data set diagnostics print---\n",
"\n",
"Missing entries in mutations: 0\n",
"The shape of the mutations data frame (23829, 97)\n",
"Checking normalization: sum of some rows:\n",
" Thymoma::TCGA-4V-A9QI-01A-11D-A423-09 1.0\n",
"CNS::TCGA-06-0216-01B-01D-1492-08 1.0\n",
"Prost-AdenoCA::SP114926 1.0\n",
"CNS::TCGA-06-1802-01A-01W-0643-08 1.0\n",
"Sarcoma-bone::IC086T_WGS 1.0\n",
"dtype: float64\n",
"\n",
"\n",
"Some tumor counts:\n",
" Breast 1858\n",
"Lung 1668\n",
"CNS 1595\n",
"Liver 1358\n",
"Kidney 1269\n",
"Name: tumor_types, dtype: int64\n",
"\n",
"\n",
"Tumor types with smallish counts: 0\n",
"Series([], Name: tumor_types, dtype: int64)\n",
"\n",
"\n",
"Unique tumor types: 51\n",
"['ALL', 'AML', 'Adrenal-neoplasm', 'Biliary-AdenoCA', 'Bladder-TCC', 'Blood-CMDI', 'Bone', 'Breast', 'CNS', 'CNS-NOS', 'Cervix', 'ColoRect-AdenoCA', 'ColoRect-Adenoma', 'DLBC', 'Eso-AdenoCA', 'Eso-SCC', 'Ewings', 'Eye', 'Head-SCC', 'Kidney', 'Liver', 'Lung', 'Lymph', 'Meninges-Meningioma', 'Mesothelium-Mesothelioma', 'Myeloid', 'Neuroblastoma', 'Oral-SCC', 'Ovary-AdenoCA', 'Panc', 'Para-AdenoCA', 'Para-Adenoma', 'Pheochromocytoma', 'Pit-All', 'Prost-AdenoCA', 'Prost-Adenoma', 'Sarcoma', 'Sarcoma-bone', 'Skin-BCC', 'Skin-Melanoma', 'Skin-SCC', 'Small-Intestine-carcinoid', 'SoftTissue-Leiomyo', 'SoftTissue-Liposarc', 'Stomach-AdenoCA', 'Testis-CA', 'Thy-AdenoCA', 'Thymoma', 'Transitional-cell-carcinoma', 'UCS', 'Uterus-AdenoCA']\n"
]
}
],
"source": [
"\n",
"def prepare_mut_df(raw_mutation_dfs, is_profile, small_sample_limit=None):\n",
"\n",
" mutations_all = pd.DataFrame()\n",
"\n",
" for df in raw_mutation_dfs:\n",
" # Make a copy of the original data frame and start processing from there\n",
" mutations = df.copy()\n",
" \n",
" if is_profile:\n",
" mutations['mut_tri'] = mutations.apply(lambda a: '{}_{}'.format(a['Mutation type'], a['Trinucleotide']), axis=1)\n",
" mutations = mutations.set_index('mut_tri').drop(['Mutation type', 'Trinucleotide'], axis=1)\n",
" mutations = mutations.T\n",
" else:\n",
" mutations['mut_tri'] = mutations.apply(lambda a: '{}::{}'.format(a['Cancer Types'], a['Sample Names']), axis=1)\n",
" mutations = mutations.set_index('mut_tri').drop(['Cancer Types', 'Sample Names', 'Accuracy'], axis=1)\n",
" \n",
" # Rename some index names\n",
" renamed_items = list(mutations.index)\n",
" index_items = list(mutations.index)\n",
"\n",
" # Combine rows for low count labels\n",
" for i in range(len(index_items)):\n",
" result = index_items[i]\n",
" for to_sub in ['Bone', 'Breast', 'Cervix', 'CNS', 'Eye', 'Liver', 'Lymph', 'Lung', 'Kidney', 'Myeloid', 'Panc' ]:\n",
" result = re.sub( to_sub + r'(-\\w*)', to_sub, result)\n",
" \n",
" renamed_items[i] = result.replace('Ca', 'CA')\n",
" \n",
" mutations.rename(index=dict(zip(index_items, renamed_items)), inplace = True)\n",
" \n",
" # Normalize \n",
" row_sums = mutations.sum(axis=1)\n",
" mutations = mutations.divide(row_sums, axis = 0)\n",
"\n",
" mutations_all = pd.concat([mutations_all, mutations])\n",
"\n",
" mutations_all.sort_index(inplace=True)\n",
"\n",
" # Do we need to renormalize after obtaining the full dataframe?\n",
" \n",
" # Figure out tumor types based on the first part of the index\n",
" tumor_types = [a.split(':')[0] for a in mutations_all.index]\n",
" # Prepare a list with all the types appearing only once\n",
" unique_tumor_types = sorted(list(set(tumor_types)))\n",
" # Attach this back to the frame\n",
" mutations_all[\"tumor_types\"] = tumor_types\n",
"\n",
" # Get rid of types with very few samples if the limit is specified\n",
" if small_sample_limit is not None:\n",
" counts = mutations_all[\"tumor_types\"].value_counts()\n",
" small_counts = list(counts[counts < small_sample_limit].index)\n",
" mutations_all = mutations_all.loc[~mutations_all[\"tumor_types\"].isin(small_counts)]\n",
"\n",
" \n",
" return (mutations_all, unique_tumor_types)\n",
"\n",
"\n",
"def print_dset_diag(mut_df, unique_tumor_types, small_sample_limit):\n",
" # Check if the data frame is ok\n",
" print(\"\\n---Data set diagnostics print---\\n\")\n",
" print(\"Missing entries in mutations:\", mut_df.isnull().sum().sum())\n",
" print(\"The shape of the mutations data frame\", mut_df.shape)\n",
"\n",
" # Check to see if the rows are normalized to one, take a sample from the data frame\n",
" norm_df = mut_df.sample(n=5, random_state=5)\n",
" print(\"Checking normalization: sum of some rows:\\n\", norm_df.iloc[:,0:-1].sum(axis=1))\n",
" print(\"\\n\")\n",
"\n",
" # Check some counts of tumor types\n",
" tumor_counts = mut_df[\"tumor_types\"].value_counts() #.sort_values(ascending=True)\n",
" print(\"Some tumor counts:\\n\", tumor_counts.head(5))\n",
" print(\"\\n\")\n",
"\n",
" small_counts = tumor_counts < 1.5*small_sample_limit\n",
" print(\"Tumor types with smallish counts:\", sum(small_counts))\n",
"\n",
" print(tumor_counts[small_counts])\n",
" print(\"\\n\")\n",
"\n",
" # Tumor types\n",
" print(\"Unique tumor types: \", len(unique_tumor_types))\n",
" print(unique_tumor_types)\n",
"\n",
"\n",
"small_sample_limit = 0\n",
"\n",
"profile_raw_data_sets = [PCAWG_wgs_mut, TCGA_wes_mut, nonPCAWG_wgs_mut, other_wes_mut]\n",
"profile_mut_all, prf_unique_tumor_types = prepare_mut_df(profile_raw_data_sets, True, small_sample_limit)\n",
"\n",
"# Print some diagnostics from the prepared data set\n",
"print(\"Profile data:\")\n",
"print_dset_diag(profile_mut_all, prf_unique_tumor_types, small_sample_limit)\n",
"\n",
"# Data matrix X for fitting, omit the tumor labeling from there, use that information in constructing true y\n",
"# Note: this contains profile data only\n",
"X_prf = profile_mut_all.drop(\"tumor_types\", axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dataset preprocess for activites data"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Activities data:\n",
"\n",
"---Data set diagnostics print---\n",
"\n",
"Missing entries in mutations: 0\n",
"The shape of the mutations data frame (23829, 66)\n",
"Checking normalization: sum of some rows:\n",
" mut_tri\n",
"Thymoma::TCGA-4V-A9QI-01A-11D-A423-09 1.0\n",
"CNS::TCGA-06-0216-01B-01D-1492-08 1.0\n",
"Prost-AdenoCA::SP114926 1.0\n",
"CNS::TCGA-06-1802-01A-01W-0643-08 1.0\n",
"Sarcoma-bone::IC086T_WGS 1.0\n",
"dtype: float64\n",
"\n",
"\n",
"Some tumor counts:\n",
" Breast 1858\n",
"Lung 1668\n",
"CNS 1595\n",
"Liver 1358\n",
"Kidney 1269\n",
"Name: tumor_types, dtype: int64\n",
"\n",
"\n",
"Tumor types with smallish counts: 0\n",
"Series([], Name: tumor_types, dtype: int64)\n",
"\n",
"\n",
"Unique tumor types: 51\n",
"['ALL', 'AML', 'Adrenal-neoplasm', 'Biliary-AdenoCA', 'Bladder-TCC', 'Blood-CMDI', 'Bone', 'Breast', 'CNS', 'CNS-NOS', 'Cervix', 'ColoRect-AdenoCA', 'ColoRect-Adenoma', 'DLBC', 'Eso-AdenoCA', 'Eso-SCC', 'Ewings', 'Eye', 'Head-SCC', 'Kidney', 'Liver', 'Lung', 'Lymph', 'Meninges-Meningioma', 'Mesothelium-Mesothelioma', 'Myeloid', 'Neuroblastoma', 'Oral-SCC', 'Ovary-AdenoCA', 'Panc', 'Para-AdenoCA', 'Para-Adenoma', 'Pheochromocytoma', 'Pit-All', 'Prost-AdenoCA', 'Prost-Adenoma', 'Sarcoma', 'Sarcoma-bone', 'Skin-BCC', 'Skin-Melanoma', 'Skin-SCC', 'Small-Intestine-carcinoid', 'SoftTissue-Leiomyo', 'SoftTissue-Liposarc', 'Stomach-AdenoCA', 'Testis-CA', 'Thy-AdenoCA', 'Thymoma', 'Transitional-cell-carcinoma', 'UCS', 'Uterus-AdenoCA']\n"
]
}
],
"source": [
"act_raw_data_sets = [PCAWG_wgs_act, TCGA_wes_act, nonPCAWG_wgs_act, other_wes_act]\n",
"act_mut_all, act_unique_tumor_types = prepare_mut_df(act_raw_data_sets, is_profile=False, small_sample_limit=small_sample_limit)\n",
"\n",
"# Print some diagnostics from the prepared data set\n",
"print(\"Activities data:\")\n",
"print_dset_diag(act_mut_all, act_unique_tumor_types, small_sample_limit)\n",
"\n",
"# Data matrix X for fitting, omit the tumor labeling from there, use that information in constructing true y\n",
"# Note: this contains profile data only\n",
"X_act = act_mut_all.drop(\"tumor_types\", axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check profile data content"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Some content from the full profile set:\n"
]
},
{
"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>mut_tri</th>\n",
" <th>C>A_ACA</th>\n",
" <th>C>A_ACC</th>\n",
" <th>C>A_ACG</th>\n",
" <th>C>A_ACT</th>\n",
" <th>C>A_CCA</th>\n",
" <th>C>A_CCC</th>\n",
" <th>C>A_CCG</th>\n",
" <th>C>A_CCT</th>\n",
" <th>C>A_GCA</th>\n",
" <th>C>A_GCC</th>\n",
" <th>...</th>\n",
" <th>T>G_CTT</th>\n",
" <th>T>G_GTA</th>\n",
" <th>T>G_GTC</th>\n",
" <th>T>G_GTG</th>\n",
" <th>T>G_GTT</th>\n",
" <th>T>G_TTA</th>\n",
" <th>T>G_TTC</th>\n",
" <th>T>G_TTG</th>\n",
" <th>T>G_TTT</th>\n",
" <th>tumor_types</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ALL::11</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.133333</td>\n",
" <td>0.066667</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.066667</td>\n",
" <td>0.0</td>\n",
" <td>0.066667</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>ALL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALL::2211636</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>ALL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALL::2211638</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.333333</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>ALL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALL::2211640</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>ALL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALL::2211642</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>ALL</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 97 columns</p>\n",
"</div>"
],
"text/plain": [
"mut_tri C>A_ACA C>A_ACC C>A_ACG C>A_ACT C>A_CCA C>A_CCC C>A_CCG \\\n",
"ALL::11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"ALL::2211636 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"ALL::2211638 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"ALL::2211640 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"ALL::2211642 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
"mut_tri C>A_CCT C>A_GCA C>A_GCC ... T>G_CTT T>G_GTA T>G_GTC \\\n",
"ALL::11 0.133333 0.066667 0.0 ... 0.066667 0.0 0.066667 \n",
"ALL::2211636 0.000000 0.000000 0.0 ... 0.000000 0.0 0.000000 \n",
"ALL::2211638 0.000000 0.000000 0.0 ... 0.000000 0.0 0.000000 \n",
"ALL::2211640 0.000000 0.000000 0.0 ... 0.000000 0.0 0.000000 \n",
"ALL::2211642 0.000000 0.000000 0.0 ... 0.000000 0.0 0.000000 \n",
"\n",
"mut_tri T>G_GTG T>G_GTT T>G_TTA T>G_TTC T>G_TTG T>G_TTT \\\n",
"ALL::11 0.0 0.0 0.000000 0.0 0.0 0.0 \n",
"ALL::2211636 0.0 0.0 0.000000 0.0 0.0 0.0 \n",
"ALL::2211638 0.0 0.0 0.333333 0.0 0.0 0.0 \n",
"ALL::2211640 0.0 0.0 0.000000 0.0 0.0 0.0 \n",
"ALL::2211642 0.0 0.0 0.000000 0.0 0.0 0.0 \n",
"\n",
"mut_tri tumor_types \n",
"ALL::11 ALL \n",
"ALL::2211636 ALL \n",
"ALL::2211638 ALL \n",
"ALL::2211640 ALL \n",
"ALL::2211642 ALL \n",
"\n",
"[5 rows x 97 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"Some content from the full profile set:\")\n",
"profile_mut_all.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1800x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(25, 5))\n",
"sns.set_theme()\n",
"profile_mut_all[\"tumor_types\"].value_counts().sort_index().plot(kind=\"bar\")\n",
"#sns.countplot(x=profile_mut_all[\"tumor_types\"], palette=sns.hls_palette(2))\n",
"plt.xticks(rotation=90);\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check activites data content"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Some content from the full act set:\n"
]
},
{
"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>SBS1</th>\n",
" <th>SBS2</th>\n",
" <th>SBS3</th>\n",
" <th>SBS4</th>\n",
" <th>SBS5</th>\n",
" <th>SBS6</th>\n",
" <th>SBS7a</th>\n",
" <th>SBS7b</th>\n",
" <th>SBS7c</th>\n",
" <th>SBS7d</th>\n",
" <th>...</th>\n",
" <th>SBS52</th>\n",
" <th>SBS53</th>\n",
" <th>SBS54</th>\n",
" <th>SBS55</th>\n",
" <th>SBS56</th>\n",
" <th>SBS57</th>\n",
" <th>SBS58</th>\n",
" <th>SBS59</th>\n",
" <th>SBS60</th>\n",
" <th>tumor_types</th>\n",
" </tr>\n",
" <tr>\n",
" <th>mut_tri</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ALL::11</th>\n",
" <td>0.066667</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.066667</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>ALL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALL::2211636</th>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>ALL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALL::2211638</th>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.333333</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.666667</td>\n",
" <td>0.0</td>\n",
" <td>ALL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALL::2211640</th>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>ALL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALL::2211642</th>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.250000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>ALL</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 66 columns</p>\n",
"</div>"
],
"text/plain": [
" SBS1 SBS2 SBS3 SBS4 SBS5 SBS6 SBS7a SBS7b SBS7c \\\n",
"mut_tri \n",
"ALL::11 0.066667 0.0 0.0 0.0 0.066667 0.0 0.0 0.0 0.0 \n",
"ALL::2211636 0.000000 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 \n",
"ALL::2211638 0.000000 0.0 0.0 0.0 0.333333 0.0 0.0 0.0 0.0 \n",
"ALL::2211640 0.000000 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 \n",
"ALL::2211642 0.000000 0.0 0.0 0.0 0.250000 0.0 0.0 0.0 0.0 \n",
"\n",
" SBS7d ... SBS52 SBS53 SBS54 SBS55 SBS56 SBS57 SBS58 \\\n",
"mut_tri ... \n",
"ALL::11 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"ALL::2211636 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"ALL::2211638 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"ALL::2211640 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"ALL::2211642 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" SBS59 SBS60 tumor_types \n",
"mut_tri \n",
"ALL::11 0.000000 0.0 ALL \n",
"ALL::2211636 0.000000 0.0 ALL \n",
"ALL::2211638 0.666667 0.0 ALL \n",
"ALL::2211640 0.000000 0.0 ALL \n",
"ALL::2211642 0.000000 0.0 ALL \n",
"\n",
"[5 rows x 66 columns]"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"Some content from the full act set:\")\n",
"act_mut_all.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1800x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(25, 5))\n",
"sns.set_theme()\n",
"act_mut_all[\"tumor_types\"].value_counts().sort_index().plot(kind=\"bar\")\n",
"#sns.countplot(x=profile_mut_all[\"tumor_types\"], palette=sns.hls_palette(2))\n",
"plt.xticks(rotation=90);\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testing with a single RandomForest binary classifier"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dimension of the training data (16680, 96) and test data (7149, 96)\n",
" All Train Test Pred\n",
"0 23342 16351 6991 7149.0\n",
"1 487 329 158 NaN\n",
"Accuracy: 0.9778990068541055\n",
" precision recall f1-score support\n",
"\n",
" 0 0.98 1.00 0.99 6991\n",
" 1 0.00 0.00 0.00 158\n",
"\n",
" accuracy 0.98 7149\n",
" macro avg 0.49 0.50 0.49 7149\n",
"weighted avg 0.96 0.98 0.97 7149\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jr/miniconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/home/jr/miniconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/home/jr/miniconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1080x576 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 864x720 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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aOUVRiC2IZ13SRgp1xfQP7I2ni4rtSRvQGrW2jifsnJSEEM1YTmUePyT9xLmiC7TzCGBy95HsS91FbkWuraOJVkJKQohmSGvUsSVlJ3syD+KicmZCt1Ek5p9mzVm5Wlo0LSkJIZoRs2LmRE4MUcmbKddfvlpaqeQnuVpa2IiUhBDNRHpZJmsubCClLI1ObdoztEMf9lzaLldLC5uSkhDCxsr1FWy8tJXD2SfwdPZgSvfR/Jx9lHXxh2wdTQgpCSFsxWQ2cSDrKNEp26k2VTOy4yBKdbn8EP8dJsVk63hCAFISQtjEheJk1l7YQHZlDrf5dSXEy5fdKdspry63dTQhapGSEKIJFetKWH8xmlN5sfi5+jClx2gOp+3nRFa6raMJUScpCSGagMFkYGf6fral7QYUxnYeRlZ5stxbWjR7UhJCNCJFUYgrSGBd0kYKdEX0C+yFt7Oa7Rd/kqulRYsgJSFEI8mtzOOHpI0kFJ0nyCOAKd1HsS91FzkVObaOJoTVpCSEaGA6o44tqbvYk3EQJ5WaCd1Gcj4/ltVnv7F1NCFumJSEEA1EURSO55wiKnkzZfpywoP7gYOOjYk/oDfpbR1PiJsiJSFEA0gvz2TthQ1cKk2jo3cIwzr0YfelHRRpi2wdTYhbIiUhxC0oqS5l06UdHNGcwMPJnUndR3M6+xg/xB+2dTQhGoSUhBA3QWvUsTNtL7syDmBWzIzoEEaZPp/1crW0sDNSEkLcAKPZyMGsY2xJ3UmFoZKBQX3wcHbkQOr2X91bWgj7ISUhhBUUReFUXiw/XdpKgbaQ7r6dGebTl/2puymoKrB1PCEaTZOVREpKCvPnz6ekpAQfHx+WLVtGly5dai1TWFjIyy+/jEajwWAwMHz4cF555RXUaukyYTsXipOJuriZtPIMgj0Dmdx9FIfT93Mqe5+townR6BybakWLFi1izpw5bNu2jTlz5rBw4cKrlvnss8/o3r07GzduZOPGjcTHx7N9+/amiihELdkVOXx6Zjnvx/yHUn0pk7qNAqWUNWe/IbMsw9bxhGgSTVIShYWFJCQkEBkZCUBkZCQJCQkUFdU+PdDBwYHKykrMZjN6vR6DwUBQUFBTRBTColhXwqpza3nz+Hskl6Yyrstw2no4sy7hOxILzts6nhBNqkn242g0GoKCglCpVACoVCoCAwPRaDT4+flZlvvDH/7AH//4R0aPHo1Wq+Whhx5i8ODBTRFRCLRGLdvT9rIn4wCKojCi4yCqDIVEX1gntw4VrVaz2tm/detWevXqxZdffkllZSVz585l69atTJkyxerX8Pf3bMSELUNAgJetIzQL1o6DwWRgR/IB1sVvplxfyaDg2/F0deRg+k4qDZU4qMG5hR4Xc3ZpmbkbUmseAyfnW3/vTTJ6wcHB5ObmYjKZUKlUmEwm8vLyCA4OrrXcqlWrePPNN3F0dMTLy4tx48Zx7NixGyqJwsIKzObWO/VyQIAX+fly4xprxsGsmGvOWEreSqGuiJ5+XRni7c/+tD0UVhU2UdLG4+yiRl9ttHUMm2rtY2BwuvX33iTHJPz9/QkNDSU6OhqA6OhoQkNDa+1qAujQoQP79+8HQK/Xc+TIEXr27NkUEUUrc77oIv88+SEr4r/FRe3E5O4jya1I4sdzP9hFQQjRUBwURWmSj93JycnMnz+fsrIyvL29WbZsGd26dWPu3LnMmzePfv36kZ6ezqJFiygoKMBkMjFs2DAWLFhwQ6fAypaEbElA/eOQVaEhKnkzCYXn8XVtw9D2/TitOc6Fwgs2SNm4WvunaJAxCPIM5KuZX97SazRZSTQVKQkpCbh6HIp1JURf2s6xnJ9xVbswsmMYqSUXOK2JwayYbZi08bT2P5AgY9AQJdF6j+iIVqHKoGV72h72Zh5EAUZ2HESFvoCN53+QM5aEsIKUhLBLBpOBXen72Za6myqjloHt+uCqVtibsoUqQ5Wt4wnRYkhJCLtiVsyczD3N5mM7yK8s5Db/bnTw8mVf6h65t4MQN0FKQtiNxKIkoi5uIqMim47ewUzuPoLD6fs5kZll62hCtFhSEqLFyyzPJip5M+eKLuDn6sPk7qM4W/Aza84esXU0IVo8KQnRYhXpiom+tJ3jOadwU7syoetILhUl8EP8d6idm2zuSiHsmpSEaHGqDFVsTdvNvsyaW4SO7jSYEl0uPyWuxWC+csaSlIQQDUFKQrQYBpOBfVmH2Za6G61Rx6DgvjipTOy+tFnOWBKikUhJiGbPrJg5kRPDxkvbKK4uobd/d4K92rA/dQ9F2mJbxxPCrklJiGbtXOEFopI3k1mRTQevYIZ0COVQ2j6OZWbbOpoQrYKUhGiWMsqziLq4mcTiJPzdfJncfTSnso+yVs5YEqJJSUmIZqVQW8TGS9s4kRuDh5M7E7qOJLkonrVnv0Gh9c7JJYStSEmIZqHSUMXW1F3szzyMg4MDEZ2GUKzV/OaMJSFEU5OSEDalNxnYl3mIbWl70Bl1DA6+HbWjgd2XNskZS0I0A1ISwmZi8+P5MuF7dKZqerftQbBnG/al7qZYzlgSotmQkhA2kVR8if/Ff4OfaxtGBw3kSMZ+jmVobB1LCPEbUhKiyWWWZ/NZ7Er83Xzo1MaXdQmrbR1JCFEPmbtANKn8qkI+OvMFrmpnevu3Z2fyDltHEkJcg5SEaDKl1WV8dPq/mBQTwzr0IfrCRltHEkJch5SEaBJVBi0fn/kf5YYKxnYezPqEH+z23tJC2BMpCdHo9CYDn8WuJKcyj4ndR7D+3FqM5tZ7c3ohWhIpCdGoTGYTy+O/4VJpKpN7jGbDuXVUG6ttHUsIYSUpCdFoFEXh28R1xBUkMLH7aLYm/USFvsLWsYQQN0BKQjSaqOTNHM05ydjOw9ifuk0ukhOiBZKSEI1iR9pedqbvY1j7gcRojpBbkWfrSEKImyAlIRrcEc1JopI3MyAolNTieNJL020dSQhxk6QkRIOKzY/n28QfuM2vG2W6bC4UJtk6khDiFkhJiAaTVHyJ5fHf0MGrHWpHHadzztg6khDiFklJiAZxZT4mX1cfgjw9OJJx2NaRhBANQEpC3LJfz8cU2lbmYxLCnkhJiFtSWl0u8zEJYcekJMRNq5mP6QvK9OUyH5MQdkpKQtyUX8/HNKnHSJmPSQg7JSUhbpjMxyRE6yElIW6Ioih8e/7KfEyjZD4mIeyclIS4IVHJmzmqOckdnYeyP3WHzMckhJ2TkhBW+/V8TKc1R8mtyLV1JCFEI5OSEFa5Mh9T/6DeMh+TEK2IlIS4rivzMfX060q5TiPzMQnRikhJiGv69XxMTo7VMh+TEK2MlISol8zHJIRQW7vgoUOH2LRpE0VFRXz22WfExcVRUVHBiBEjGjOfsJFa8zEFdOCnxChbRxJC2IBVWxJff/01ixcvpkuXLpw4cQIAV1dX3n///UYNJ2yj9nxMoUSf/8nWkYQQNmJVSXz55ZesWLGCp556CkfHmqd069aNlJSURg0nmt7V8zGtk/mYhGjFrCqJyspKgoODAXBwcADAaDTi5OTUeMlEk6s9H9MomY9JCGFdSQwZMoTPP/+81mNfffUVw4YNa5RQouldPR/TDzIfkxDCugPXr7zyCk8//TRr166lsrKSyZMn4+npyWeffdbY+UQT+PV8TJO6R8h8TEIIC6tKIjAwkHXr1hEXF0dWVhbBwcH079/fcnxCtGy/zMc0jP2p22U+JiGEhVV/5Z955hkcHBzo378/U6dOZeDAgTg6OvLcc881dj7RyH6Zj2kApzVHZD4mIUQtVpXEsWPH6nz8+PHjDRpGNK1f5mMKJbU4QeZjEkJc5Zq7m65cB2EwGK66JiIjI4OQkJDGSyYaVe35mLJlPiYhRJ2uWRI5OTlAzYHNK19fERwczB//+MfGSyYaTVpZRq35mE5myXxMQoi6XbMk3nrrLQDCwsJ44IEHmiSQaFx6k56VCd/h4eROsJcX2y9us3UkIUQzZtXZTVcKoqKiguLi2me+dOzYseFTiUYTlbyFvKoC7uoRweqz39g6jhCimbOqJJKTk3nxxRdJTEzEwcEBRVEsV16fO3euUQOKhpNYlMS+zEOM6BDGtoubZLoNIcR1WXV20+LFixk2bBjHjx/H09OTEydOMHv2bN5+++3GzicaSJVBy9fn1hDo0ZbCqkyKtEW2jiSEaAGsKonExEReeuklvL29URQFLy8v/vrXv8ossC3ImgsbKNOXM6jdbZzIOmHrOEKIFsKqknBxccForJnozdfXl+zsbMxmMyUlJVavKCUlhdmzZzN58mRmz55Nampqnctt3ryZadOmERkZybRp0ygoKLB6HaJup/JiOZF7iohOQ9ictNHWcYQQLYhVxyQGDx7Mli1buO+++5g8eTJz587F2dmZ4cOHW72iRYsWMWfOHKZPn86GDRtYuHAhX331Va1l4uLi+Oijj/jyyy8JCAigvLwcZ2fnG3tHopbS6nK+P7+ejt4hJBedpby63NaRhBAtiFUl8evdSn/+85/p0aMHVVVV3HvvvVatpLCwkISEBFasWAFAZGQkS5cupaioCD8/P8tyK1eu5IknniAgIAAALy8vq9+IuJqiKHybuBa9Sc9t/iGsT5DbjwohbswNz9Dn6OjIjBkzmDlzJuvXr7fqORqNhqCgIFQqFQAqlYrAwEA0Gk2t5ZKTk8nIyOChhx7i3nvv5ZNPPkFRlBuNKC47rDnO2cJExnYZxpYLm2wdRwjRAl13S+LIkSOcO3eOTp06MWHCBIxGI99++y3//e9/8fHx4aGHHmqwMCaTifPnz7NixQr0ej1PPvkkISEhzJgxw+rX8Pf3bLA8LVVAgBe5FfmsvxjNbf5dOVtwApPKgLPK6lua2wVnl9b1fusiY9C6x8DJ+dbf+zVf4fPPP+fTTz+lR48eXLx4kd/97nccP34cZ2dnli5dytixY61aSXBwMLm5uZhMJlQqFSaTiby8PMvd7q4ICQlhypQpODs74+zszPjx44mNjb2hkigsrMBsbr1bHwEBXuTmlfLvU8sBCPTw5MT5CzZO1fScXdToq1v3XfVkDGQMDE63/t6vubtp9erVfP3116xdu5YVK1awYsUKIiMj+fbbb60uCAB/f39CQ0OJjo4GIDo6mtDQ0FrHI6DmWMXBgwdRFAWDwcDRo0fp3bv3jb+rVm5X+n6SS1O5s8tQtl/caus4QogW7JolUVxczO233w7AwIEDcXZ25tFHH72pFS1evJhVq1YxefJkVq1axZIlSwCYO3cucXFxANx99934+/tz1113MWPGDHr06MHMmTNvan2tVXpJFtGXtnF7wG0cTNuLwWSwdSQhRAvmoFzjyPCgQYP4+eefURQFRVEYMWIEx44dq3Uwubndna41724ymo386/QnFFUV07ttCDuTd9g6ks209t0MIGMAMgZBnoF8NfPLW3qNax6TqKqqok+fPpbvFUWxfH9l/iaZu6n52JSyg7SSTO7qMYY1MnmfEKIBXLMkdu3a1VQ5xC26VJrKjrS9DG3fn90pWzEpJltHEkLYgWuWRPv27Zsqh7gFOmM1Xyasxte1DXqlhNyKPFtHEkLYieZ1QEHclB+TN1GoLWJYh34czTxi6zhCCDsiJdHCxRcmcjDrKCM7DmL7xWgUWudBeyFE45CSaMEqDVV8c24t7TwCyKlIpUhbfP0nCSHEDbihktBoNJw+fbqRoogbtfr8j5QbKhnQrgensn+2dRwhhB2yqiSys7N58MEHmTp1Ko8//jgAW7duZcGCBY0aTtTvZO5pfs47wx2dh7HlgtwjQgjROKwqiYULFzJ27FhOnTqFWl1zQtSoUaM4fFimnraFkupSVp//kc5t2nOh4Azl+gpbRxJC2CmrSiIuLo6nnnoKR0dHHBwcgJp7PZSXyw1smpqiKKw6txaj2Ug333Yk5CfYOpIQwo5ZVRL+/v6kpaXVeuzixYtXzeIqGt+BrKOcK7rA2K7D2Jok94gQQjQuq0riiSee4Omnn2bdunUYjUaio6N54YUXmDt3bmPnE7+SV5XPjxejuc2/GyezDqEz6mwdSQhh56y6I8XMmTPx8fFh9erVBAcHExUVxfPPP8+ECRMaO5+4zGQ28VXCGlSOKgLc3TiRmWLrSEKIVsCqkjCZTEyYMEFKwYZ2pO8jpSyNqT0iWBf/va3jCCFaCat2N40aNYrFixfz889yLr4tZJRnsSllO/2DenMgdTcGs9wjQgjRNKwqieXLl+Pu7s6LL77IuHHjePfddzl//nxjZxOAwWTgq4TVeDp7oHYwkFWeZetIQohWxKqS6NOnD3/961/Zu3cvb7/9NqWlpTz22GNMmzatsfO1ehtTtpFdmcPoTmHsS91j6zhCiFbmhudu6tq1K927dyc4OJisLPlU25iSii+xO/0AQ0MGsDN5C2bFbOtIQohWxqoD12VlZWzbto3o6GjOnDnDqFGjePLJJxk/fnxj52u1dEYdX59bjb+bL+X6PPIr820dSQjRCllVEhEREYSFhREZGclHH32El5dXY+dq9dYlbaRIV8KUHiP5Pk5uRSqEsA2rSmLHjh0EBgY2dhZxWWx+PIc1J4joNIQtSdG2jiOEaMXqLYkTJ04wZMgQAJKTk0lOTq5zuREjRjROslaqWFfCqnNrae/VjqyyJEp1pbaOJIRoxeotiSVLlhAdXfMptr4pwR0cHNi1a1fjJGuFTGYTy+O/wagY6dW2Az8m/GDrSEKIVq7ekrhSEAC7d+9ukjCt3cZL27hUmsZdPcewPn61reMIIYR1p8A+88wzdT7+3HPPNWiY1iy+MJEd6XsZGjKAPZe2U22qtnUkIYSwriSOHTtW5+PHjx9v0DCtVUl1KV8lrCbYM5ASXTY5FTm2jiSEEMB1zm56//33ATAYDJavr8jIyCAkJKTxkrUSJrOJ5We/xWA20DegM+sS1to6khBCWFyzJHJyaj7RKopi+fqK4OBg/vjHPzZeslZic+pOkktTmNpjDD+ek+MQQojm5Zol8dZbbwEQFhbGAw880CSBWpPEoiS2pe5mcHA/9qfupNooxyGEEM1LvSWRmZlJhw4dgJprITIyMupcrmPHjo2TzM6VVpexMv47gjzaUmnII7s829aRhBDiKvWWxLRp04iJiQFg4sSJODg4oChKrWUcHBw4d+5c4ya0Q2bFzMr479CZqhke1Icf4tfYOpIQQtSp3pK4UhAAiYmJTRKmtdiSuosLJclM6T6GDYlSEEKI5uuGpwqHmjObZJrwm3Oh+CJbUnYS1q4vB9N2oTPqbB1JCCHqZVVJ/PnPf+bUqVMArFu3jrvvvpu7776btWvldM0bUaYvZ0X8dwR6+FNtLJa7zAkhmj2rSuLIkSPcfvvtAKxcuZIVK1awdu1a/vvf/zZqOHtiVsx8Gf89WqOWAe16cCTjsK0jCSHEdVk1VbjBYMDZ2Znc3FxKSkoYPHgwAAUFBY0azp5sT9tDYnESk7tHsDFRJu4TQrQMVpVEaGgo//nPf8jKymLs2LEA5Obm4unp2ZjZ7EZS8SWiL21nYFAfDqfvRWvU2jqSEEJYxardTW+88QYXLlygurqa559/Hqg5+2natGmNGs4elOsrWBH/LW3d/TCay8gsq/t6EyGEaI4clN9e/NDCFRZWYDY3j7dkVsx8emYFF0qSmdB1CKvPftfo63R2UaOvNjb6epo7GQcZA5AxCPIM5KuZX97Sa1i1uwlqzmrasGEDubm5BAUFMX36dO6///5bWrm925m+j4Si8zXHIc6vs3UcIYS4YVaVxKeffkpUVBRPPPEEISEhZGdn88UXX5CXl1fvvSZau+SSVDZe2kb/wN4czdhPlaHK1pGEEOKGWVUSa9eu5euvv6Z9+/aWx0aPHs3DDz8sJVGH0uoylsd/g69rGxwctKSXptk6khBC3BSrDlxrtVr8/PxqPebj44NOJ1cL/5beZOA/sV+iNWoZEhLK/tR9to4khBA3zaqSiIiI4KWXXuLSpUvodDqSk5OZP38+o0ePbux8LYpZMfP1udWkl2cyodtIohLlOIQQomWzqiQWLlyIh4cH06dPJywsjBkzZuDm5sarr77a2PlalM0pOzmVF8u4riPYmrRB7g8hhGjxrnsKbFlZGRkZGXTu3Bl3d3eKi4vx9fXF0fGm5gZsdLY6BfZkTgwrEr5jcHA/LhaeIb00vckzgJzyd4WMg4wByBg0xCmw1/xLv3fvXsaMGcP999/PHXfcwfHjx/H392+2BWErKaXpfJ24lm4+nSiqSrdZQQghREO75l/7999/n5deeomYmBjmzZvHv//97yaK1XIU6Yr5T9xK2rh40dbdldM5Z2wdSQghGsw1SyIjI4OHH34YNzc3HnroIdLS5FTOX9MZq/ksdiUGs4HwkNvYkbzd1pGEEKJBXbMkzGaz5Wu1Wo3JZGr0QC2FWTGzMuE7sitymNBtOD+eW2/rSEII0eCueTGdTqfjoYcesnxfWVlZ63uAb775pnGSNXMbkrcQV5DApG6j2Zj4IwaTwdaRhBCiwV2zJN54441a38+cObNRw7QUR7JPsDN9H8PaD+RQ+h7KqstsHUkIIRrFNUvi3nvvbaocLUZScTLfnV9PT7+uZJUlkS23IBVC2DE5l/UG5FcV8t+4r/F388XL2YH4vHhbRxJCiEYlJWGlKoOWT2NXgINCv6Au7EnZbetIQgjR6KQkrKAoCivivyVfW8DYLkP4KTHK1pGEEKJJNFlJpKSkMHv2bCZPnszs2bNJTU2td9lLly4xYMAAli1b1lTxrimjIouEovOM7TyMjYnrMZpb72X+QojWxaqS0Ov1vPfee4wfP57BgwcDcPDgQVatWmX1ihYtWsScOXPYtm0bc+bMYeHChXUuZzKZWLRoERMmTLD6tRtbTF4cjg6OaCpSKddX2DqOEEI0GatK4s033+TChQu88847ODg4ANCzZ0+++866ezYXFhaSkJBAZGQkAJGRkSQkJFBUVHTVsp9//jljx46lS5cuVr6FxqUoCqdyz9DDtzOJ+edsHUcIIZqUVSWxc+dO3n33XcLCwiyT+wUFBZGbm2vVSjQaDUFBQahUKgBUKhWBgYFoNJpayyUmJnLw4EEee+yxG3gLjSujIosCXREh3gGU6EpsHUcIIZqUVbcvdXJyumpKjqKiInx8fBosiMFg4NVXX+Wtt96ylMnN8Pf3bLBMANuzE3F0cKRQl4Wzi1XDZXMtJWdjk3GQMYDWPQZOzrf+3q16hSlTpvC3v/2Nl19+GYC8vDzefPNN7r77bqtWEhwcTG5uLiaTCZVKhclkIi8vj+DgYMsy+fn5pKen89RTTwE197FQFIWKigqWLl1q9RtqyPtJKIrCodST9PDrTJzmVIuYl761z59/hYyDjAHIGBicbv29W7W76YUXXqB9+/bcc889lJWVMXnyZAIDA3n22WetWom/vz+hoaFER0cDEB0dTWhoaK37ZoeEhHDs2DF2797N7t27efTRR3nggQduqCAaWkb55V1NXgGU6kptlkMIIWzFqi0JZ2dnFixYwIIFCygqKsLX19dyANtaixcvZv78+XzyySd4e3tbTm+dO3cu8+bNo1+/fjeevpGdyovF0cGRnAq5iZAQonW67u1Loea+EvXp2LFjgwa6VQ21u0lRFBYdWUZbDx/OF/zcYrYkWvvm9RUyDjIGIGPQELcvtWpLYuLEiTg4OPDrPrmyJXHunH2eFppenkmhroj+7XpwPLNlFIQQQjQ0q0oiMTGx1vf5+fl89NFHhIeHN0qo5uCXC+hkV5MQovW6qWk5AgICWLBgAf/6178aOk+zoCgKp/LO0NOvi1xAJ4Ro1W567qZLly6h1WobMkuzUbOrqZhgL/8WcyxCCCEag1W7m+bMmVPrbCatVsvFixetPgW2pTmVF4vKQUVOef0H7IUQojWwqiRmzZpV63s3Nzd69+7dbOZXakg1u5piL+9qOmnrOEIIYVPXLQmTycTRo0dZunQpzs7OTZHJptLLMynSFdM/qCfH5KwmIUQrd91jEiqVikOHDt3wxXMt1c95Z1A5qNBUpNk6ihBC2JxVB64fffRRPvzwQwwGQ2PnsSlFUYjJi6Onv5zVJIQQcJ3dTdHR0URGRrJq1SoKCgpYsWIFfn5+tbYq9u7d29gZm0xaeUbNrqZ2PTmWUWbrOEIIYXPXLImFCxcSGRnJP//5z6bKY1OncmvOatKUy64mIYSA65TElWk4hg4d2iRhbMmsmGvOavLvwrm8E7aOI4QQzcI1S8JsNnP06FGuNQfgiBEjGjyULZzJj6e4uoSwkNtkV5MQQlx2zZLQ6/UsWLCg3pJwcHBg165djRKsqe3JOICfmw+JebG2jiKEEM3GNUvCzc3NbkrgWtLKMkguTWVc1xFEJXxv6zhCCNFs3PTcTfZkd8YBXFUuZJeloNAwtz4VQgh7cM2SsOJ+RC1eSXUpp/JiGRTcl9M5MbaOI4QQzco1SyImxv7/aO7LPIyiKBjNVVQbq20dRwghmpVWvbup2qTnYNZR+gb05GT2MVvHEUKIZqdVl8TxnJ+pMmoJ8PCiWFts6zhCCNHstNqSMCtm9mQcpKN3MKeyZUpwIYSoS6stiYTC8+RW5dPLvwvppTINhxBC1KXVlsSejIO0cfEiqfCsraMIIUSz1SpLIqtCQ2JxEoOC+3I2L97WcYQQotlqlSWxOWUnrioXCqqyMCtmW8cRQohmq9WVRFpZBqfz4xjeYSAnso7bOo4QQjRrra4kNiRvwdPJg7zKDLl4TgghrqNVlURiURLniy8yvMMA2YoQQggrtJqSUBSFDcmb8XVtw6WiBIxmo60jCSFEs9dqSiImP4708iyGtO/HmVy5Z4QQQlijVZSEyWxi46WtBHkEcDbnpJzRJIQQVmoVJXE05yR5VQUMbHcb5woSbR1HCCFajFZREkeyTxLiFcSJzEO2jiKEEC1KqyiJ4uoSAj38SClJtXUUIYRoUey+JMyKmTJ9OU6O17ydtxBCiDrYfUlUGqowK2YcHWydRAghWh67L4mS6jIAjGa9jZMIIUTLY/clUVpdCkCVsdLGSYQQouWx+5Io05cDUG2osnESIYRoeey+JEov727SSUkIIcQNs/uSKNGX4eHkTvnlLQohhBDWs/uSKK0uw9vFk/LqCltHEUKIFsfuS6KsuhwvFw/ZkhBCiJtg9yVRqi/D3clNJvUTQoibYNclceVqa2dHla2jCCFEi2TXJVFhqKzZgnBQbB1FCCFaJLsuiVK52loIIW5JqygJrUGuthZCiJvRKkpCZ9DaOIkQQrRM9l0S+stbEjJvkxBC3BS7LolTebG4O7lRoZcL6YQQ4mbYdUlUm/S4qJwp1hbbOooQQrRIdl0SFfoKuvl2pEom9xNCiJtityVRbdKjNxvkjnRCCHEL7LYkyi3HIWQ6DiGEuFl2XxIGU7WNkwghRMulbqoVpaSkMH/+fEpKSvDx8WHZsmV06dKl1jIff/wxmzdvRqVSoVareeGFF4iIiLip9VUYakpCa5RrJIQQ4mY1WUksWrSIOXPmMH36dDZs2MDChQv56quvai3Tv39/nnjiCdzc3EhMTOThhx/m4MGDuLq63vD6yvU110bojboGyS+EEK1Rk+xuKiwsJCEhgcjISAAiIyNJSEigqKio1nIRERG4ubkB0KtXLxRFoaSk5KbWeeXaCJ1JtiSEEOJmNUlJaDQagoKCUKlqpuxWqVQEBgai0WjqfU5UVBSdOnWiXbt2N7XOckMFziontHL6qxBC3LQm2910I44fP87777/P8uXLb/i5/v6eAOiTq/Fy9kBrLsPZpVm+zUbT2t5vfWQcZAygdY+Bk/Otv/cmGb3g4GByc3MxmUyoVCpMJhN5eXkEBwdftWxMTAx/+ctf+OSTT+jWrdsNr6uwsAKzWaGgrBgPJ3c0hWXojcaGeBstgrOLGn1163m/9ZFxkDEAGQOD062/9ybZ3eTv709oaCjR0dEAREdHExoaip+fX63lYmNjeeGFF/jggw/o27fvLa2zwlCJh7O7nN0khBC3oMmuk1i8eDGrVq1i8uTJrFq1iiVLlgAwd+5c4uLiAFiyZAk6nY6FCxcyffp0pk+fzvnz529qfeWGClzVzg2WXwghWqMm21nXvXt31q5de9Xj//3vfy1fr1u3rkHWpSgK5foK1HJvayGEuCV2ecW11qjFrJhxQO5tLYQQt8IuS+LKlBxmxWTjJEII0bLZZ0lcvqd1tVxIJ4QQt8Q+S+LylkS1nNkkhBC3xC5L4srkftUyA6wQQtwSuywJraFmUj+9bEkIIcQtscuSqDJqUTuq0BllS0IIIW6FXZaE1qjFVe2KTqYJF0KIW2KnJaHDTe0iJSGEELfILkuiSrYkhBCiQdhlSWiNOtycXKiWYxJCCHFL7LQktDirnFFkWg4hhLgldlcSiqJQaaiSyf2EEKIB2F1JFOiKqDBU4uXsZusoQgjR4tldSVwovghAiS7fxkmEEKLls7+SKEqmnUcAKcXJto4ihBAtnt2VRFp5Bt38OpJVlmXrKEII0eLZXUmYFTMGk1wfIYQQDcHuSgKgQl9q6whCCGEX7LIk5D4SQgjRMOyyJHSGKltHEEIIu2B3JeGmdrXcdEgIIcStsbuS8HT2oKy63NYxhBDCLthdSbg5uVGpr7R1DCGEsAt2VxIuKjVGs9HWMYQQwi7YXUmoVTKxnxBCNBS7KwmVg5SEEEI0FLsrCSGEEA3HDktCbjQkhBANxf5KQpGSEEKIhmJ/JeFg6wBCCGE/7K8khBBCNBgpCSGEEPWSkhBCCFEvOywJOXAthBANRW3rAA1PjlzbI0WBA39Zia4wCICRbzyFZ0iG5efbH98CwPAlz+Ld6RIAMR+8Sn7MSLpNX0WPGd/UvI7ZkfQd95B9eAKVOe1xVJnwCM6g86QfaTdsf4Nm1hYEkvjN0xSdG4iDoxn/23+m90Of4dKmuM7lL0Y9xKUND9f5s4h/PgrAgb98WefPr7zHwvgwUjbPpCKzC0atJ66++bQfs40ud63F4fL/GoUJA0iOeoSKzM4oZhXuQVl0jVxNuyEHgV/G8rdCRu3g9if/RVlqdy5GPUJ5encM5d44tymm3dD99Lj3axydDACceHsZxef713q+R/tURr3+DAA5xyNI3vAwusIAFMUBt7a5dBy/kU7jNlmWrytHx3E/EfrIpwDoitpyYc0TFJ0bgFHnTptuifSe8x+8OqYCUJTYjwvfP01lbjsUkwpX3wKCR+ym2/RvLWMBkLlvCmk7plOV2x6Vi442XZIY/NICAAwVnpz//ikK4wehr/DGxbuYwEGHue2B5Zb3Wp7elcTvfk9pcm9UztUEDj5Erwf/i9qt5r42adtnkHVgEhVZnUBR1frvEUBz9A4ydkdSqemISe+CW0AOXSb9SPsx2+v8PTQ1OywJ2ZKwR8Xn+1kKAkBzeBw9Z9b9B7M+itmB0x++Sv7p4TiojLTtdwInz3JKL/VGc+yOBi0JxezAqfdeozK7M/59f8ZsdCL3xBh0RQEMe+XPdT7Hp3sinSZGWb6v1HSg8Gw4ardKnLxKUQxOtX6OAhm7p9X8oQ/MBqA4qQ/l6d3xCz2DA2pyTg4n6YcnULno6DRhI8ZqF2LeX4xZ74pPj3jUblUUxA0h9tP5eHeei3ugpvY6gOyDEzBqPS3rKM/sSnFif/xCT6N21ZJzIoLULbNQzI70evCLWs/99Wu5tCmyfK0tDMTVPxffXnHoitpScGYYiV8/h2dwBn6hsb88x7eAoPCDlu99bzt7eXwdOfXea1RkdsW3VxyuvvnkHL+Dk/98i4hlT6B201Jd4oeTRznBwxIxVHmS9/Mokjc8gqt/Pu0jdgCQsmkWST88gdqtknZD9+HgaKbkYqhlfee/f4rsQxNxblNEu/AD5J8ZSvrOGajdK+lx7yqMWjdOvvMmhnIfAsMPos0PImv/VEzVbvR/ehkAZak9cPIox9WvoNZ/w1cUnh2MNr8dbW//GX25D4Xxg4hf8QJO3qUEDjxW538rTcnuSkJvrrZ1BNEINEfuBMCr00XK03ugOXonPe7/stYnwuvJOTGG/NPDAQh7fhFt+52y/KxS06HO5xgqPEn+6aF6XzN4+B7adLtw1eP5p4dTmd0Zzw4pDHrxFVAcOfDX5ZQmh1KU2A+/3nFXPadtv59p2+9ny/enP6r5NNs+Yhtql2pwqab3nP9Yfp53ehjpO2fU/AEbegCAoMGH6DJ5PWo3Lc4uak59PI/sQxMpjB9Epwkb0Zf6Yda7AjDoxVdQu+rY88fvMVS0QVcYgHugptY6yjO6kL5jBo5qPe3H1nyy9+mRQMQ/HsfZu+Y2wW5B2Vza8DCF8YOuek+/fq1f6zp1HV2nrrN8f/jVT6jI7Io2vx38qiTcA7PrfI1KTQcqMrvioDYw+KWXcVSbMFa7kh8zksx9U+kyZT3Bw/fR+Y5D6KtrJvyMeX8R+aeHoy1oB4BR607yT3NwUBsY9urzeARnXbWeqrxgALpFfk+nCRs5//2TpG27H21BzR/7rAOTMZT70HbAMQY++wZGnSt7531PzvEIetz3Je6BOfR76p2a9X/wap0l0XH8Rvo89gGO6pqcV7bCiuLDpCQag94oJWFvzAYnck+OBqDXg//l9MevoCsMovh83X9s65N/ehgAbbqfq1UQAB7BmXU+x6hzJ33HjHpf06tTcp0lUZbWHQDvLkk1ReZgxrtzMrrCoJpP+tfJrS0IJD9mBA6OJjpO+KnOZdK31+TqOC7a8gfmyq6WK8zGmv/FXXwLAHAP1BA46BB5p0Zx6t3XUblVYahog1+fGHx6xl+9jsvvPXjEHlwul4JHu+xayyiX1+F6eR2/tvvZNTXj0DmZnjNX1Bqr0ku3oTlyJ1X5wVRkdsUjJJ2AQUdqPb/0Ui92/v5H1G5V+PeJ4bYH/oeLT7FlV49iUlGe3h33wGyq8kIAKM/oanl+ZU4wyVvuorrYn4LYIbj4FhAycicAJcm9MetdcfEpIH7FnyhL64Fb21y6z1hl2fXWaeIGSi/14lL0g5Qm9yb/zFCcPEvpNH4j8MvvuU2XmveldtXhEZxJeXp3KjK74h6Yc9WY/Fabrkm1vv/t78zW7K4kZGeT/ck/MxRjlRfO3sX49oojYMAxNIcnoDly5w2VhL6sDQBu/nlWP8etbR6TVky94cz6Mh8AVC46y2Mq55qvq0t9r/v89J33oJhVBA4+iHtA7lU/L8/oQtG5gTg6VdNx7OY6XyP72Ehyjt+Bk2cpXe9eY3k8eORuii/cTsnFvjW5XKsIHHwIR7XpN++hDZqjYwGu2gV1RXFSH9K234ujUzU97vtl95/KVUvbAcdw9S2gJDmUonMD+flfrzPqjd9bjslUZHcifeeMmic4mGh7+0nUrr/cn97FpxDfXnGoXLTknxmG5sh4qvKCGfbKi7gHamg3dB85x+/g2NL3a+cu9bN8rS3yr1Xyfr3P4ORVU3aGcm8Aqkva4uJTROCgI+QcG0PcZ/NxD/gT3l0u0qbbeXx6nKP4fH80R8cBEBR+ALdAzeUx8rn8fn/1e3ax/vf8W6nb7qU0uQ/ugVl0vLPu32tTs7uScJCasDuaIzX/cwYMOIaDo0LgoCNoDk8g90QEoQ9/iqOTAUenaswGF8unWgCz0QkAlXPN1uWV3SPawkCr132zu5ucvUsAMFW7Wh4zXv66vgPXluV0rmTtnwxA50lRdS5j+YQ/fK/lff1a5v5JnPtyHk4e5Qz+y99x888HoCKrE2c+XoDKRceoN+eidqvk2Ovvkfj1c7j55xEw4ITlNTL23oXZ4IJf6OmrtlAA8s8M4cwnfwcHhYF/XIp3l2TLz8KeX2zZFWg2qjk4/wt0hUEUnetP8PB9ALQfvZOQkbvQ5rfjzKcvk7b9Ppw8y+k27XsAxvzrYctrVGrac+jvX1Ca3IfqEl9cfIrp9/TbtBu2r2afv2c5uqK2pG2731ICAG37nGXi8qlUF7clYeU8NEfG4+ikp+/jH9Qat0EvLMTZuxRjlQcFsUPJPzMU7y4XOfPJ3ym71JueM5fTaeIGLqx+kozd08BBYcAf3vrl96z75fd85evr/Z5/KzlqDskbHsEtQMPgv76M2q3qhp7fWOyuJMxm0/UXEi2GodKT/NghAGQdmELWgSmWnxm1nuSdHka7IQdxC9RQmdWFkouhtOl2AVO1C+XpNbsCrhxwDRhwjJyjd1KaHErB2UG0vf1XxyRyQq7ajQI3v7vJ6/IZVqUpt9VMJ6Y4Up7Wo+ZnHWt+Vl3ii1Hrgdqj3LIrByD74CSMWk+8u1zA97ardwFd7xP+lYOxbgG5DHphQa197RXZnUBxxMmj3LKLza1tHrrCICo1HS0lYTaqyNxzNwCdJ/141TqyD48jfvkLqF2rCPvTInx6JFp+Zqp2wVDlgatv0VXPc3A0A2DUuqF20+LgqOAepKFNlyTK03pSmdMeAF2xH07ulahc6th9fPk1FJOawEFHCBx0BJPemaOLPwTAv0+MZR3OLgYcHMDVrwCf285SEDeEqtya408eIWk4OJpQzL+6vYBS00qqy1s0lVmdAWjT7TwqZz3eXWp2DVVmdwTAu1MymsMTKE3pZVlnpaYjOJjx7JB6dfY6KGYHzq36A5l7IvHqdJFBf154wwXTmOyuJExISdiTnOMRKEYn1G6V+PY+Y3m8MrsTVbkd0BweR7shB+kwZivnv3uaC6vnknsiAl1RIPpSP1z9cy0Hg9sN3Y/myDgKYocS8+8ltO13AmevUsrSeuLqn0vYvKVXrf9mdzcFhh3BIzidyqwunHr3DcxGJ3RFgXh3S7ScvZP0w+NkH5pIp4lRloOzigLpO6cB9e/isXzC7xNz1Sf8zH1TSPrhCQB8ul8gY08kAE4e5XSf/i1tul7AUa1HVxjEz/96DbVrFcXn+4GDGZ8eCb8a9zuoLvHHPSiTtgOO11pHwdlBnP3iRVAc8e52npzjd5Bz/A6g5kC1vrwNB1/+Ar/Q07j551GSHIquMAhn7yL8Qmt+h0eXfIBb21zcAnOoLvYn/0zNB4G2t9f8rgrjB3Hh+7n4hZ5B7V5B/pma40l+fWIshXr2f3/GqHXHxaeI4nP9qcprj1eniwSP2APA6Q8XgqLGPSgdQ5Un+TE1Jy34X16Hq28R7YbvQXN4Aqfeew33dlkUnB1Us/tt0OGaMeyRQGH8YOJX/An/PjHkxYyoefy2mrFqP2Ybl6IfpCB2KKc/WoA2vx1mozNBQ/bhfnmXVOa+yZQk9bV8SMg/NQJdQZCl4C7++P/I3BMJDia8OyeTsukBoObDTacJG+v8b6Ap2V1JCPuiOVpzVlOHsZu57YHllseLEvtxctk/KIgLR1/hVfM/k4NC1r4plKV3x8m9ksBBh+g5a4Xl06iDo8LAeUtI3zEDzeFxFMYPwkFlwiMkvcGvkXBwVAh7YSGJ3zxDcWJ/cFAIDD9I7zmfXvN5BWeGUpXbARefQtoNvTpTrU/4dZSIrtjf8rXmaITla1f/XLpP/xa3tnkMnPcal376HaUXQ1EUR7w6XaTLlHW1tgbSd0wHoNOEn646g6y6xA+UmutwC+OGUBg3xPKz3nP+g5NHOSEjd1J0biDFif1Ru1USOOgwPe77EmevMgD8+pymIHYIRef7o3LR4d01iY53biJ4xF6g5oC/b6+zlFzsU3N9QpsiOk3YQPfpqyzr8uqQQvrOeyg8Owgnj3I6jN1Ez5krLQfxfXvFoTkynpLknjiqjbgHZxIyametLaPQ//cRKic9uT+PojKnA763JdBz1nLLcaDbn3yXC2ufoCghjKxDE3HxLqbjuJ/oOWsFAGo3LYNfWsD5756iIHYIjs562kdso9fvPresoySpL9mHJlq+L8/oTnlGd1zb5hI46AjVV35nioqsA5Mty/n2im0WJeGgKPY1t/ab+5axL2WvrWPYjLOL2nLKX2sm4yBjADIGQZ6BfHWD1xP9lt1Ny6GY7arzhBDCpuyuJMxydpMQQjQYuysJRTHbOoIQQtgNOywJ2ZIQQoiGYnclAbIlIYQQDcXuSsIkWxJCCNFg7K4kzIpcTCeEEA3F7kpCjkkIIUTDsbuSKKsus3UEIYSwG3ZXEmY5cC2EEA3G7kpCCCFEw2mykkhJSWH27NlMnjyZ2bNnk5qaetUyJpOJJUuWMGHCBCZOnMjatWubKp4QQog6NFlJLFq0iDlz5rBt2zbmzJnDwoULr1pm48aNpKens337dlavXs2HH35IZmbdt5UUQgjR+JqkJAoLC0lISCAysmZu+8jISBISEigqqn1Tks2bNzNr1iwcHR3x8/NjwoQJbN26tSkiCiGEqEOT3E9Co9EQFBSESlVzByiVSkVgYCAajQY/P79ay4WEhFi+Dw4OJifn+jcS/zVvF2+CPK2/PaW9cXJWY3BqvVMjXyHjIGMAMgZt3dve8mvY3U2H3pr0hq0jCCGE3WiS3U3BwcHk5uZiMtVcDW0ymcjLyyM4OPiq5bKzf7nPsEajoV27dk0RUQghRB2apCT8/f0JDQ0lOjoagOjoaEJDQ2vtagKYMmUKa9euxWw2U1RUxM6dO5k8eXJdLymEEKIJNNntS5OTk5k/fz5lZWV4e3uzbNkyunXrxty5c5k3bx79+vXDZDLx2muvcejQIQDmzp3L7NmzmyKeEEKIOtjdPa6FEEI0HLniWgghRL2kJIQQQtRLSkIIIUS9pCSEEELUS0pCCCFEvVpcSchsstaNwccff8zdd9/NPffcw3333ceBAweaPmgjs2Ycrrh06RIDBgxg2bJlTRewCVg7Bps3b2batGlERkYybdo0CgoKmjZoI7JmDAoLC3nqqaeYNm0aU6ZMYfHixRiN9jNdx7Jlyxg3bhy9evXiwoULdS5z038XlRbmkUceUaKiohRFUZSoqCjlkUceuWqZH3/8UXniiScUk8mkFBYWKhEREUpGRkZTR2001ozB/v37laqqKkVRFOXcuXPK4MGDFa1W26Q5G5s146AoimI0GpWHH35Y+fOf/6y8/fbbTRmx0VkzBrGxscrUqVOVvLw8RVEUpaysTNHpdE2aszFZMwavv/665Xev1+uVmTNnKps2bWrSnI3pxIkTSnZ2tnLnnXcq58+fr3OZm/272KK2JGQ2WevHICIiAjc3NwB69eqFoiiUlJQ0ddxGY+04AHz++eeMHTuWLl26NHHKxmXtGKxcuZInnniCgIAAALy8vHBxcWnyvI3B2jFwcHCgsrISs9mMXq/HYDAQFBRki8iNIjw8/Kppjn7rZv8utqiSuNZssr9d7lZnk22urB2DX4uKiqJTp052NQ+WteOQmJjIwYMHeeyxx2yQsnFZOwbJyclkZGTw0EMPce+99/LJJ5+g2Mk1tNaOwR/+8AdSUlIYPXq05Z/BgwfbIrLN3OzfxRZVEuLGHT9+nPfff593333X1lGanMFg4NVXX2XJkiWWPyKtkclk4vz586xYsYKvv/6a/fv3s2HDBlvHalJbt26lV69eHDx4kP3793Py5Em72bvQ2FpUSchsstaPAUBMTAx/+ctf+Pjjj+nWrVtTR21U1oxDfn4+6enpPPXUU4wbN44vv/ySNWvW8Oqrr9oqdoOy9r+FkJAQpkyZgrOzM56enowfP57Y2FhbRG5w1o7BqlWruOeee3B0dMTLy4tx48Zx7NgxW0S2mZv9u9iiSkJmk7V+DGJjY3nhhRf44IMP6Nu3ry2iNiprxiEkJIRjx46xe/dudu/ezaOPPsoDDzzA0qVLbRW7QVn730JkZCQHDx5EURQMBgNHjx6ld+/etojc4Kwdgw4dOrB//34A9Ho9R44coWfPnk2e15Zu+u9igx5ibwIXL15UZs6cqUyaNEmZOXOmkpycrCiKojz55JNKbGysoig1Z7MsXLhQGT9+vDJ+/Hjl+++/t2XkBmfNGNx3333KsGHDlHvuucfyT2Jioi1jNzhrxuHXPvjgA7s7u8maMTCZTMqbb76pTJkyRbnrrruUN998UzGZTLaM3aCsGYO0tDTlscceUyIjI5WpU6cqixcvVgwGgy1jN6ilS5cqERERSmhoqDJy5EjlrrvuUhSlYf4uyiywQggh6tWidjcJIYRoWlISQggh6iUlIYQQol5SEkIIIeolJSGEEKJeUhKiRXnkkUea/ay+P/30E0888US9Pz958qTdXLcj7J+UhLCZcePG0b9/f8LCwiz/5ObmNnmORx55hH79+hEWFsawYcN47rnnyMvLu+nXu+eee1i+fLnl+169epGWlmb5Pjw8nG3btt1S5rp8+OGH9O3bl7CwMMLDw3nwwQeJiYmx+vm/zSkESEkIG/vss8+IiYmx/GOrmTkXLlxITEwM27Zto6ysjLfeessmOW7V1KlTiYmJ4ejRowwbNoznn3/e1pFECyclIZqV0tJSfv/73zN8+HCGDBnC73//+3pnqkxLS+Phhx9m8ODBDBs2jD/96U+WnyUnJ/P4448zdOhQJk+ezObNm61av4+PD5MnTyYpKQmAU6dOcf/99zN48GDuv/9+Tp06ZVl2/fr1jB8/nrCwMMaNG8dPP/1kefx3v/sdAA899BAA06dPJywsjM2bN3Ps2DHGjBkD1ExjPm/evFoZXn/9dV5//XUAysvL+fvf/87o0aOJiIjgvffes8xTdC1qtZpp06aRm5trmTY7NjaW2bNnEx4ezujRo3nttdfQ6/X15gTYs2cP06dPt2yZJCYmWjWOwo400lXiQlzXnXfeqRw6dKjWY0VFRcrWrVuVqqoqpby8XPnjH/+oPPPMM5afP/zww8qaNWsURVGUF154Qfnkk08Uk8mk6HQ65cSJE4qiKEplZaUyZswY5YcfflAMBoNy9uxZZejQocqFCxfqzPHr1ywsLFQeeeQR5aWXXlKKi4uV8PBw5ccff1QMBoOyceNGJTw8XCkqKlIqKyuVsLAwyxQQubm5ltdft26d8uCDD1pe/7bbblNSU1Mt3x89elSJiIhQFEVRMjMzlf79+yvl5eWKotRMnTBq1CglJiZGURRFeeaZZ5RXX31VqaysVAoKCpT7779f+e677+p8Hx988IHy4osvKoqiKNXV1co///lPZejQoZbpJ+Li4pSYmBjFYDAoGRkZypQpU5QVK1bUm/Ps2bPK8OHDldOnTytGo1FZv369cueddyrV1dV1rl/YJ9mSEDb17LPPEh4eTnh4OH/4wx/w9fVl8uTJuLm54enpyTPPPMOJEyfqfK5arSY7O5u8vDxcXFwIDw8HYO/evbRv3577778ftVpN3759mTx58jWPA7z++uuEh4czffp0AgICePnll9m7dy+dO3dmxowZqNVqIiMj6datG3v27AHA0dGRpKQkdDodgYGBNzVhXPv27enTpw87d+4E4OjRo7i6ujJw4EAKCgrYv38/f//733F3d8ff35/HHnuMTZs21ft6W7duJTw8nAEDBrB27Vo++OAD1Go1ALfffjsDBw5ErVbToUMHZs+eXe/YAqxZs4bZs2czYMAAVCoV9957L05OTpw+ffqG36doudS2DiBat48//piRI0davtdqtbz11lscOHCA0tJSACorKzGZTFfdE+Ivf/kL77//PjNnzqRNmzY8/vjjzJw5k6ysLGJjYy2lATVTSN9zzz315njllVeYNWtWrcfy8vJq3aQFamaWzc3Nxd3dnffee4/ly5ezYMECBg0axN/+9je6d+9+w2MQGRlJdHQ0M2bMIDo62nKXtezsbIxGI6NHj7Ysazabr3kHsilTpvDOO+9QVFTEvHnziI+PZ9iwYUDNvaDffvttzp49i1arxWQyXXOG4OzsbKKioli1apXlMYPBcEsH9UXLIyUhmpXly5eTkpLCmjVrCAgI4Ny5c8yYMaPOO6kFBARY9t2fPHmSxx9/nCFDhhAcHMyQIUNYsWLFLWUJDAysNf8+1MzBHxERAdTcIjYiIgKdTse///1vXn31Vb799tsbXs/UqVNZtmwZOTk57Nixg9WrVwPQrl07nJ2dOXr0qGVrwFp+fn4sWbKEmTNnEhkZSWBgIIsXL6ZPnz68++67eHp6snLlymtuXQUHB/P000/zzDPP3PB7EvZDdjeJZqWyshIXFxe8vb0pKSnho48+qnfZLVu2WA5qt2nTBgcHBxwdHRk7diypqalERUVhMBgwGAzExsaSnJx8Q1nuuOMOUlNT2bhxI0ajkc2bN3Px4kXGjh1LQUEBu3btoqqqCmdnZ9zd3eu9+13btm3JyMiodz1+fn4MHTqUl19+mQ4dOli2RgIDAxk1ahRvv/02FRUVmM1m0tPTOX78uFX5u3fvTkREBF988QVQM7YeHh54eHiQnJzMd999d82cs2bN4vvvv+fMmTMoikJVVRV79+6loqLCqvUL+yAlIZqVRx99lOrqaoYPH87s2bMtn9rrEhcXx6xZswgLC+OZZ55hwYIFdOzYEU9PT/73v/+xefNmIiIiGD16NO+8847lTB5r+fr68tlnn7FixQqGDRvGF198wWeffYafnx9ms5kVK1YQERHB0KFDOXHiBIsWLarzdZ577jnmz59PeHh4vWdZRUZGcvjwYcuupiv+8Y9/YDAYuOuuuxgyZAjz5s0jPz/f6vfwf//3f6xZs4bCwkL+9re/ER0dzaBBg3j11Ve56667rpmzX79+LF26lNdee40hQ4YwadIk1q9fb/W6hX2Q+0kIIYSol2xJCCGEqJeUhBBCiHpJSQghhKiXlIQQQoh6SUkIIYSol5SEEEKIeklJCCGEqJeUhBBCiHpJSQghhKjX/wfLqMqlMgxhuwAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 504x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# For binary classification, construct y vector for a sigle selected tumor type\n",
"\n",
"target_type = \"Biliary-AdenoCA\"\n",
"\n",
"y_prf = profile_mut_all[\"tumor_types\"].values\n",
"\n",
"# Encode for classificaiton to two classes: if the type is the desired type, set to 1 otherwise 0\n",
"y_prf_bin = [1 if tumor_type == target_type else 0 for tumor_type in y_prf]\n",
"\n",
"# Split the data for fitting and prediction, use simple splitting here\n",
"X_prf_train, X_prf_test, y_prf_train, y_prf_test = train_test_split(X_prf, y_prf_bin, test_size = 0.3, random_state=898)\n",
"\n",
"print(f\"Dimension of the training data {X_prf_train.shape} and test data {X_prf_test.shape}\")\n",
"\n",
"# Make a model\n",
"model_rfs = RandomForestClassifier()\n",
"\n",
"# Fit the model \n",
"clf= model_rfs.fit(X_prf_train, y_prf_train)\n",
"\n",
"# Predict with unused (test) data \n",
"y_prf_pred = model_rfs.predict(X_prf_test)\n",
"\n",
"# What we got \n",
"plot_trn_tst_dist(y_prf_bin, y_prf_train, y_prf_test, y_prf_pred, in_cols=True)\n",
"print(f\"Accuracy:\", accuracy_score(y_prf_test, y_prf_pred))\n",
"print(classification_report(y_prf_test, y_prf_pred))\n",
"\n",
"# Plot some results\n",
"plot_confusion_mat(y_prf_test, y_prf_pred, labs=[\"0\", \"1\"])\n",
"plot_roc_auc(X_prf_test, y_prf_test, model_rfs)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We notice the training material is very skewed and results in classifier, which does not predict class 1 in any case. This is not good. Let's try oversampling, how it effects the situation. You need to install imbalanced-learn for this to work."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: imbalanced-learn in /home/jr/miniconda3/lib/python3.9/site-packages (0.9.0)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /home/jr/miniconda3/lib/python3.9/site-packages (from imbalanced-learn) (2.2.0)\n",
"Requirement already satisfied: numpy>=1.14.6 in /home/jr/miniconda3/lib/python3.9/site-packages (from imbalanced-learn) (1.19.2)\n",
"Requirement already satisfied: joblib>=0.11 in /home/jr/miniconda3/lib/python3.9/site-packages (from imbalanced-learn) (1.1.0)\n",
"Requirement already satisfied: scipy>=1.1.0 in /home/jr/miniconda3/lib/python3.9/site-packages (from imbalanced-learn) (1.6.2)\n",
"Requirement already satisfied: scikit-learn>=1.0.1 in /home/jr/miniconda3/lib/python3.9/site-packages (from imbalanced-learn) (1.0.1)\n"
]
}
],
"source": [
"# You need to install imbalanced-learn for the following cells to work.\n",
"# Install a pip package in the current Jupyter kernel\n",
"import sys\n",
"!{sys.executable} -m pip install imbalanced-learn"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Counter({0: 16351, 1: 16351})\n",
" All Train Test Pred\n",
"0 23342 16351 6991 7138\n",
"1 487 16351 158 11\n",
" precision recall f1-score support\n",
"\n",
" 0 0.98 1.00 0.99 6991\n",
" 1 0.00 0.00 0.00 158\n",
"\n",
" accuracy 0.98 7149\n",
" macro avg 0.49 0.50 0.49 7149\n",
"weighted avg 0.96 0.98 0.97 7149\n",
"\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1080x576 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 864x720 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 504x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from collections import Counter\n",
"from imblearn.over_sampling import RandomOverSampler\n",
"\n",
"oversample = RandomOverSampler(sampling_strategy='minority')\n",
"X_over, y_over = oversample.fit_resample(X_prf_train, y_prf_train)\n",
"print(Counter(y_over))\n",
"\n",
"# Make a model\n",
"model_rfs = RandomForestClassifier()\n",
"\n",
"# Fit the model \n",
"clf = model_rfs.fit(X_over, y_over)\n",
"\n",
"# Predict with unused (test) data \n",
"y_prf_pred = model_rfs.predict(X_prf_test)\n",
"\n",
"# What we got \n",
"plot_trn_tst_dist(y_prf_bin, y_over, y_prf_test, y_prf_pred, in_cols=True)\n",
"#print(f\"Accuracy:\", accuracy_score(y_prf_test, y_prf_pred))\n",
"print(classification_report(y_prf_test, y_prf_pred))\n",
"# Plot some results\n",
"plot_confusion_mat(y_prf_test, y_prf_pred, labs=[\"0\", \"1\"])\n",
"plot_roc_auc(X_prf_test, y_prf_test, model_rfs)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Area under curve has improved, but correctly predicted 1:s missing."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Counter({0: 16351, 1: 329})\n",
" All Train Test Pred\n",
"0 23342 329 6991 5136\n",
"1 487 329 158 2013\n",
" precision recall f1-score support\n",
"\n",
" 0 0.99 0.73 0.84 6991\n",
" 1 0.05 0.68 0.10 158\n",
"\n",
" accuracy 0.73 7149\n",
" macro avg 0.52 0.70 0.47 7149\n",
"weighted avg 0.97 0.73 0.82 7149\n",
"\n"
]
},
{
"data": {
"image/png": 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CACi02267Leeee27e9KY3ZWJiYtk/z2NaWU16e9c3uwVgARy7TxNYAACFVavV8p3vfCc7d+5Me3t7yuVypqamUq/XUyqVUq/XMz09nXK5nEaj8YK10zEzczRzc41l2qJiMSBe/Q4ePNLsFlgGjt3Vr1WO3fb2tpOeKHAPCwCgkD7wgQ/kwQcfzF133ZWOjo4kSU9PTyqVSsbGxpIkY2NjqVQq6e7uPmkNAFh5XGEBABTOww8/nJ07d2bjxo255pprkiQvfelLc9ddd2Xbtm0ZHh7Ojh070tnZmVqtNr/eyWoAwMoisAAACuflL395vvGNbzxvbWBgILt37z7tGgCwspgSAgAAABSOwAIAAAAoHIEFAAAAUDgCCwAAAKBwBBYAAABA4QgsAAAAgMIRWAAAAACFI7AAAAAACkdgAQAAABSOwAIAAAAoHIEFAAAAUDgCCwAAAKBwBBYAAABA4QgsAAAAgMIRWAAAAACFI7AAAAAACkdgAQAAABSOwAIAAAAoHIEFAAAAUDgCCwAAAKBwBBYAAABA4QgsAAAAgMIRWAAAAACFI7AAAAAACkdgAQAAABSOwAIAAAAoHIEFAAAAUDgCCwAAAKBw1jS7AQCAn1Sr1TI+Pp4f/OAH2bdvX17xilckSTZt2pSOjo6sXbs2SbJ169ZceeWVSZL9+/dneHg4hw8fTldXV2q1WjZu3NisTQAAFklgAQAUzlVXXZXf+q3fyr/6V//qObU777xzPsD4caOjo6lWq9myZUv27t2bkZGRfOITnzgT7QIAy8CUEACgcC6//PKUy+VTfv3MzEwmJiYyODiYJBkcHMzExERmZ2eXq0UAYJm5wgIAWFG2bt2aRqORyy67LDfddFM6OzszOTmZ/v7+lEqlJEmpVEpfX18mJyfT3d19Wu/f07NuOdqGpujtXd/sFoAFcOw+TWABAKwYu3btSrlczvHjx7N9+/bceuutueOOO5b0M2ZmjmZurrGk71lUBsSr38GDR5rdAsvAsbv6tcqx297edtITBaaEAAArxjPTRDo6OlKtVvPAAw/ML5+amkq9Xk+S1Ov1TE9Pn9a0EgCgWF40sKjVatm0aVMuueSSPPTQQ/PL9+/fn6GhoWzevDlDQ0M5cODAstYAgNb2xBNP5MiRp884NRqN3HvvvalUKkmSnp6eVCqVjI2NJUnGxsZSqVROezoIAFAcLxpYXHXVVdm1a1cuvvjiZy1/5k7c4+PjqVarGRkZWdYaANA6br/99vzKr/xKfvjDH+a6667L61//+szMzOQ3f/M3c/XVV2dwcDD79+/P6Ojo/Drbtm3LJz/5yWzevDmf/OQn8973vreJWwAALNaL3sPi8ssvf86yZ+7E/bGPfSzJ03fivu222zI7O5tGo7HkNWdHAKC13Hzzzbn55pufs3zPnj0vuM7AwEB27969jF0BAGfSgm66ebI7cTcajSWvCSwAAACgtazKp4R4HBmribtAs1rYlwEAOB0LCix+/E7cpVLpWXfibjQaS147XR5HxmrSKo80YnXr7V1vX16lXuxxZAAAC7Wgx5qe7E7cy1EDAAAAWsuLXmFx++2357777sujjz6a6667Ll1dXbnnnnuybdu2DA8PZ8eOHens7EytVptfZzlqAAAAQOt40cDihe7SfbI7cS9HDQAAAGgdC5oSAgAAALCcBBYAAABA4QgsAAAAgMIRWAAAAACFI7AAAAAACkdgAQAAABSOwAIAAAAoHIEFAAAAUDgCCwAAAKBwBBYAAABA4QgsAAAAgMIRWAAAAACFI7AAAAAACkdgAQAAABSOwAIAAAAoHIEFAAAAUDgCCwAAAKBwBBYAAABA4QgsAAAAgMIRWAAAAACFI7AAAAAACkdgAQAUTq1Wy6ZNm3LJJZfkoYceml++f//+DA0NZfPmzRkaGsqBAwdOqQYArDwCCwCgcK666qrs2rUrF1988bOWj46OplqtZnx8PNVqNSMjI6dUAwBWHoEFAFA4l19+ecrl8rOWzczMZGJiIoODg0mSwcHBTExMZHZ29qQ1AGBlWtPsBgAATsXk5GT6+/tTKpWSJKVSKX19fZmcnEyj0XjBWnd3dzPbBgAWSGABAPBjenrWNbsFWDK9veub3QKwAI7dpwksAIAVoVwuZ2pqKvV6PaVSKfV6PdPT0ymXy2k0Gi9YO10zM0czN9dYhi0oHgPi1e/gwSPNboFl4Nhd/Vrl2G1vbzvpiQL3sAAAVoSenp5UKpWMjY0lScbGxlKpVNLd3X3SGgCwMrnCAgAonNtvvz333XdfHn300Vx33XXp6urKPffck23btmV4eDg7duxIZ2dnarXa/DonqwEAK4/AAgAonJtvvjk333zzc5YPDAxk9+7dz7vOyWoAwMpjSggAAABQOAILAAAAoHAEFgAAAEDhCCwAAACAwhFYAAAAAIUjsAAAAAAKR2ABAAAAFI7AAgAAACgcgQUAAABQOAILAAAAoHAEFgAAAEDhCCwAAACAwhFYAAAAAIWzZrFvsGnTpnR0dGTt2rVJkq1bt+bKK6/M/v37Mzw8nMOHD6erqyu1Wi0bN25MkgXXAAAAgNawJFdY3Hnnndm7d2/27t2bK6+8MkkyOjqaarWa8fHxVKvVjIyMzL9+oTUAAACgNSzLlJCZmZlMTExkcHAwSTI4OJiJiYnMzs4uuAYAAAC0jkVPCUmengbSaDRy2WWX5aabbsrk5GT6+/tTKpWSJKVSKX19fZmcnEyj0VhQrbu7+5T76elZtxSbBYXQ27u+2S3AkrAvAwBwOhYdWOzatSvlcjnHjx/P9u3bc+utt+baa69dgtYWbmbmaObmGk3t4UzxBWD1O3jwSLNbgEXr7V1vX16l2tvbnCgAAJbFoqeElMvlJElHR0eq1WoeeOCBlMvlTE1NpV6vJ0nq9Xqmp6dTLpcXXAMAAABax6ICiyeeeCJHjjx9xqzRaOTee+9NpVJJT09PKpVKxsbGkiRjY2OpVCrp7u5ecA0AAABoHYuaEjIzM5Mbbrgh9Xo9c3NzGRgYyOjoaJJk27ZtGR4ezo4dO9LZ2ZlarTa/3kJrAAAAQGtYVGCxYcOG7Nmz53lrAwMD2b1795LWAAAAgNawLI81BQAAAFgMgQUAAABQOAILAAAAoHAEFgAAAEDhCCwAAACAwhFYAAAAAIWzqMeaAgA0w6ZNm9LR0ZG1a9cmSbZu3Zorr7wy+/fvz/DwcA4fPpyurq7UarVs3Lixuc0CAAsisAAAVqQ777wzr3jFK561bHR0NNVqNVu2bMnevXszMjKST3ziE03qEABYDFNCAIBVYWZmJhMTExkcHEySDA4OZmJiIrOzs03uDABYCFdYAAAr0tatW9NoNHLZZZflpptuyuTkZPr7+1MqlZIkpVIpfX19mZycTHd39ym/b0/PuuVqGc643t71zW4BWADH7tMEFgDAirNr166Uy+UcP34827dvz6233pprr712Sd57ZuZo5uYaS/JeRWdAvPodPHik2S2wDBy7q1+rHLvt7W0nPVFgSggAsOKUy+UkSUdHR6rVah544IGUy+VMTU2lXq8nSer1eqanp+dfCwCsLAILAGBFeeKJJ3LkyNNnnhqNRu69995UKpX09PSkUqlkbGwsSTI2NpZKpXJa00EAgOIwJQQAWFFmZmZyww03pF6vZ25uLgMDAxkdHU2SbNu2LcPDw9mxY0c6OztTq9Wa3C0AsFACCwBgRdmwYUP27NnzvLWBgYHs3r37zDYEACwLU0IAAACAwhFYAAAAAIUjsAAAAAAKR2ABAAAAFI7AAgAAACgcgQUAAABQOB5rCgAALMqxY4/n6NHDqddPNLuVpiiV1mTduq6cc855zW4FVhWBBQAAsGDHjj2eI0cOpaurN2ed1ZG2trZmt3RGNRqNPPXU8Rw+fDBJhBawhEwJAQAAFuzo0cPp6upNR8falgsrkqStrS0dHWvT1dWbo0cPN7sdWFUEFgAAwILV6ydy1lkdzW6j6c46q6Nlp8TAchFYAAAAi9KKV1b8JP8PYOm5hwUAALCk1neek7PXLv1XjR89eSJHHjt20te89a2/naeeeionTjyV733vu/lH/2ggSfKKV1ySd7979EU/Y8+eP82TTz6ZoaF/tSQ9AwsnsAAAAJbU2WvX5Orf37vk77vv/Vty5EVe80d/9PEkyeTkI7n++t/MH//xp55VP3HiRNaseeGvQW98428stk1giQgsAACAVe03fuPqDA5uyX/5L1/KS15ycd72tndk27b35PHHH8/x48fzy7/8z/KOd9yYJPnoR/99jh07lne+8/dy77378vnPfy7r13fm29/+VtavX5fbb39fenoubPIWQWsQWAAAAKveo48+mn/37/59kuTJJ59MrfaBnHvuuTlx4kRuuumd+c//+W/zS7/0y89Z7+tfn8jHP353+vsvSq12e/70Tz+df/2vf/dMtw8tSWABAACseq997evn/zw3N5cdOz6Yr371vyZpZGZmJg8//NDzBhb/+B//fPr7L0qS/MzP/Gy+9KUvnqmWoeUJLAAAgFXv3HPPmf/zpz+9K0eOPJb/8B/+OGvXrk2ttj3Hjz/5vOt1dPyPR7a2t5dSr9eXvVfgaR5rCgAAtJQjR46kp+fCrF27NgcPTudv/uavmt0S8DxcYQEAALSU/+V/uSa33PKHue66avr6+nPZZf+k2S0Bz0NgAQAALKkfPXki+96/ZVne91SVyy/JPff830mSP/3Tfc+qXXRROX/0R5943vXe8pZ/Pf/n173u6rzudVe/4M/A8hJYAAAAS+rIY8dypNlNACuee1gAAAAAhSOwAAAAAApHYAEAACxCWxqNuWY30XRP/z9oa3YbsKoILAAAgAXr6Dg7hw8/mhMnnkqj0Wh2O2dco9HIiRNP5fDhR9PRcXaz24FVxU03AQCABbvggt4cPfrfMzs7lbm5erPbaYr29lLOOWdd1q07v9mtwKoisABogvWd5+Tsta31K7i3d32zWzhjfvTkiRx57Fiz2wA4I9ra2rJ+fVfWr+9qdivAKlPI0fL+/fszPDycw4cPp6urK7VaLRs3bmx2WwBL5uy1a3L17+9tdhssk33v3+Jxfk1kHAEAq0Mh72ExOjqaarWa8fHxVKvVjIyMNLslAGCFMI4AgNWhcIHFzMxMJiYmMjg4mCQZHBzMxMREZmdnm9wZAFB0xhEAsHoUbkrI5ORk+vv7UyqVkiSlUil9fX2ZnJxMd3f3Kb1He3trPU6o74Jzmt0Cy6jV9udW4thd3Vrl2C3adhpHnD6/i1a3VtufW4ljd3VrlWP3xbazcIHFUrjggvOa3cIZ9dGbX9PsFlhGPT3rmt0Cy8Sxu7o5dlcu4whWE7+LVi/H7urm2H1a4aaElMvlTE1NpV5/+pFI9Xo909PTKZfLTe4MACg64wgAWD0KF1j09PSkUqlkbGwsSTI2NpZKpXLKl3ECAK3LOAIAVo+2RqPRaHYTP+lb3/pWhoeH89hjj6WzszO1Wi0ve9nLmt0WALACGEcAwOpQyMACAAAAaG2FmxICAAAAILAAAAAACkdgAQAAABSOwAIAAAAoHIEFAAAAUDgCCwAAAKBw1jS7ATiZQ4cO5Yc//GGS5KKLLsoFF1zQ5I4AgJXCOAJgZRNYUEjf/e53c8stt2RiYiJ9fX1Jkunp6bzqVa/Ke9/73mzcuLG5DQIAhWUcAbA6tDUajUazm4CfdM0116RarWZwcDDt7U/PXJqbm8u+ffvyqU99Kp/+9Keb3CGwUFdffXX27dvX7DaAVcw4AlYv44jW4goLCunw4cN5wxve8Kxl7e3t2bJlSz784Q83qSvgVH3zm998wdqhQ4fOYCdAKzKOgJXNOIJnCCwopK6uroyNjeX1r3992trakiSNRiP79u1LZ2dnk7sDXszg4GAuvvjiPN9FfIcPHz7zDQEtxTgCVjbjCJ5hSgiFdODAgYyOjubrX/96+vv7kyRTU1N55StfmW3btuVlL3tZkzsETuaqq67Kpz71qfnj98e9+tWvzl/91V81oSugVRhHwMpmHMEzXGFBIW3cuDEf//jHMzs7m8nJySRJuVxOd3d3kzsDTsVrXvOa/OAHP3jegcav/dqvNaEjoJUYR8DKZhzBM1xhAQAAABROe7MbAAAAAPhJAgsAAACgcAQWAAAAQOEILAAAAIDCEVgAAAAAhSOwAAAAAApHYAEAAAAUjsACAAAAKByBBQAAAFA4AgsAAACgcAQWAAAAQOGsaXYDy+HQocczN9dodhssg56edZmZOdrsNoDT5Nhdvdrb23LBBec1u40lZRyxevldBCuTY3f1erFxxKoMLObmGgYaq5i/W1iZHLusFMYRq5u/W1iZHLutyZQQAAAAoHAEFgAAAEDhCCwAAACAwlmV97AAgOVWr5/IoUMHc+LE8Wa3csasWdORCy7oTalk+AAAC9GK44dnLGQcYcQBAAtw6NDBnH32uTnvvIvS1tbW7HaWXaPRyOOPP5ZDhw7mwgvLzW4HAFakVhs/PGOh44hFBRbf//7387u/+7vzPx85ciRHjx7N3//932f//v0ZHh7O4cOH09XVlVqtlo0bNybJgmsAUBQnThxvqcFGW1tbzjuvM0ePHm52KwCwYrXa+OEZCx1HLOoeFi996Uuzd+/e+f+uuuqqDA4OJklGR0dTrVYzPj6earWakZGR+fUWWgOAImnFwQYAsDit+u/pQrZ7yaaEHD9+PPv27ctHP/rRzMzMZGJiIh/72MeSJIODg7ntttsyOzubRqOxoFp3d/dStQoAy2J95zk5e+3Sz7b80ZMncuSxY0v+vgBAcxk7nNyS/Z+5//7709/fn5/5mZ/Jgw8+mP7+/pRKpSRJqVRKX19fJicn02g0FlQTWABQdGevXZOrf3/vkr/vvvdvyZEXec1b3/rbeeqpp3LixFP53ve+m3/0jwaSJK94xSV597tHT+lzHnjg/8mJEyfyi7/4S4vsGAA4Fc0cOyTJb/zG1eno6MhZZ3Vkbq6e3/7tt+Rf/IvNC/7ce+/dl7/927/O7be/b8Hv8eOWLLD4sz/7s/zLf/kvl+rtFqWnZ12zWzhjjj9VT8dZpWa3cUb19q5vdgtnTCv+/bJ6rbZjd3q6PWvWnLmng7/YZ33sY3+SJHnkkUdy3XVvyic/+f877c/4ylceyLFjx/LLv/zLL/ia9vb2Vfd32cqW68xekbXS/rtazrACy+v222t52cv+pzz00H/L7/zOW3L55Vekq6srSXLixImsWdO8fyeW5JOnpqbypS99Ke9739MpSrlcztTUVOr1ekqlUur1eqanp1Mul9NoNBZUOx0zM0czN9dYik0rvN7e9cuSyFEM+96/JQcPnko2CsXW27t+1e3Lc3NzOXFi7ox93ql+Vr0+l6SREyfm8nd/9zf5xCf+Y5588njOOuus3HDDTfnZn/25fPe7B7J9+3vzox/9KHNz9fz6r1+dK674p/mLv/izzM3N5e///ou56qrX5Dd/89rnvP/c3Nyz/i7b29ta6kTBarNcZ/YohlM9wwqQJK94xStz7rnnZvv20bzkJRfne9/7Xg4fPpT/+B8/mb/8y7H8+Z/vTr1ez7p167J163B+6qc25qmnnsoHPvC+fPnL/yW9vX35qZ/auKQ9LUlg8Rd/8Rd59atfnQsuuCBJ0tPTk0qlkrGxsWzZsiVjY2OpVCrz0zoWWgMATs0PfvD9/PEffzT/5//573Leeevy7W9/K1u3/q/58z+/J3/+53+af/pP/1muvfb6JMljjz2Wzs7ObNnyP+fYsWN55zt/r7nNAwBn3AMP/D85fvx41qxZkwcf/Go+9KH/kHPOOSdf+cqXc//9n89dd/1ROjo68nd/94X87//7rfnwh/9j9u79s0xOPpI/+ZPP5MSJE/nd333raV9wcDJLFli85z3vedaybdu2ZXh4ODt27EhnZ2dqtdqiawDAqfniF/8uP/jB9/O7v/u2+WX1ej2zszO59NL/T+6664N56qmn8gu/cHl+4Rcub1qfmzZtSkdHR9auXZsk2bp1a6688kqPRweAM+Tmm/8wHR1rc95552X79lruu+9zedWrfi7nnHNOkuQLX/hP+eY3H87b3nZtkqTRaOTIkceSJA888F/y678+mDVr1mTNmjXZvPnX81//6z8sWW9LEliMj48/Z9nAwEB27979vK9faA0AODWNRiNXXPFPc8sttz6n9s//+VX52Z/9x/n7v//P+eQn/zj33PPZjIzc1oQun3bnnXfmFa94xbOWPfOY8y1btmTv3r0ZGRnJJz7xiUXVAIDneuYeFs+4777P5dxzz5n/udFIXv/6N+T663/nOes2Gst7K4Yzd7cwAOCM+cVf/KV88Yt/l29/+1vzy77+9a8lSb7//e+lu7snr3vd1bnuurdmYuLp5eedd14ef/xoU/r9cc88Hn1wcDDJ0485n5iYyOzs7IJrAMDC/LN/dmU+97l7Mj09leTpKzb/23/7epLk8sv/ST73uXtz4sSJPPnkj/L5z39uST+7tW4LDQDL6EdPnsi+929Zlvc9XRs2/FRGRm7L//F/3JYnn3wyJ048lZ/7uZ9PpfIzuf/+z+e++z6Xs85ak7a2ttx44+8nSX7lV34173nP/5Zrr62+4E03l8PWrVvTaDRy2WWX5aabbsrk5KTHowPQEoo0dnghl176C3nb296R4eGbUq/P5cSJp/Krv/ov8spXVvKGN/zP+eY3v5nf/M3/b/r6+nPppZdlcvIHS/bZAgsAWCJHHjvW9Dvyl8svyT33/N9Jnr7K4hd/8Zee85rf+q0357d+683PWf6Sl1ycj33sU8ve44/btWtXyuVyjh8/nu3bt+fWW2/Ntddee0Z7+EmeesJq0kqPcWV1Wy378k8+Fv3YE0/m2BNPLstnncrj1/fsuec5y0ZHnzud9HWve31e97rXP89nrM173nPLKfd0uo9HF1gAAE3zzJ3EOzo6Uq1W8/a3vz3vete7PB79DFktXwB4YavtkdK0ptX0ePQz/Vj0ojndx6O7hwUA0BRPPPFEjhx5etDSaDRy7733plKpPOvx6Eme9ZjzhdYAgJXHFRYAsECNRiNtbW3NbuOMWeo7gc/MzOSGG25IvV7P3NxcBgYGMjo6msTj0QFYvVpt/PCMhYwjBBYAsADt7aXU6yeyZs1ZzW7ljKnXT6S9vbRk77dhw4bs2bPneWsejw7AatSK44dnLGQcYUoIACzAOeesy5Ejh9NotMY81EZjLkeOHMo557ghJQAsVKuNH56x0HGEKywAYAHWrTs/hw4dzNTU95O0wg0a29LRcXbWrTu/2Y0AwIrVeuOHZyxsHCGwAIAFaGtrS3d3X7PbAABWEOOH02NKCAAAAFA4AgsAAACgcAQWAAAAQOEILAAAAIDCEVgAAAAAhSOwAAAAAApHYAEAAAAUjsACAAAAKByBBQAAAFA4AgsAAACgcAQWAAAAQOEILAAAAIDCEVgAAAAAhSOwAAAAAApHYAEAAAAUjsACAAAAKJxFBxZPPvlkRkdH85rXvCZXX311brnlliTJ/v37MzQ0lM2bN2doaCgHDhyYX2ehNQAAAKA1LDqw+Df/5t9k7dq1GR8fz759+3LjjTcmSUZHR1OtVjM+Pp5qtZqRkZH5dRZaAwAAAFrDogKLxx9/PHv27MmNN96Ytra2JMmFF16YmZmZTExMZHBwMEkyODiYiYmJzM7OLrgGAAAAtI41i1n5e9/7Xrq6uvKhD30oX/ziF3PeeeflxhtvzNlnn53+/v6USqUkSalUSl9fXyYnJ9NoNBZU6+7uPuW+enrWLWazoFB6e9c3uwVYEvZlAABOx6ICixMnTuR73/teXvWqV+UP//AP85WvfCW/8zu/kw9+8INL1d+CzMwczdxco6k9nCm+AKx+Bw8eaXYLsGi9vevty6tUe3ubEwUAwLJYVGDxkpe8JGvWrJmfwvHzP//zueCCC3L22Wdnamoq9Xo9pVIp9Xo909PTKZfLaTQaC6oBAAAArWNR97Do7u7OFVdckS984QtJnn7Cx8zMTDZu3JhKpZKxsbEkydjYWCqVSrq7u9PT07OgGgAAANA62hqNxqLmTnzve9/Lu9/97hw+fDhr1qzJ7/3e7+XVr351vvWtb2V4eDiPPfZYOjs7U6vV8rKXvSxJFlw7Va02JeTq39/b7DZYJvvev8Vl9KwKpoSsXqtxSohxBKuFcQSrhXHE6vVi44hFTQlJkg0bNuRP/uRPnrN8YGAgu3fvft51FloDAAAAWsOipoQAAAAALAeBBQAAAFA4AgsAAACgcAQWAAAAQOEILAAAAIDCEVgAAAAAhSOwAAAAAApHYAEAAAAUjsACAAAAKByBBQAAAFA4AgsAoOk+9KEP5ZJLLslDDz2UJNm/f3+GhoayefPmDA0N5cCBA/OvXWgNAFhZBBYAQFN97Wtfyz/8wz/kJS95yfyy0dHRVKvVjI+Pp1qtZmRkZNE1AGBlEVgAAE1z/Pjx3HrrrRkdHU1bW1uSZGZmJhMTExkcHEySDA4OZmJiIrOzswuuAQArz5pmNwAAtK4PfvCDecMb3pANGzbML5ucnEx/f39KpVKSpFQqpa+vL5OTk2k0GguqdXd3n3JPPT3rlnALobl6e9c3uwVYEvbl1iSwAACa4stf/nK++tWvZuvWrc1u5VlmZo5mbq7R7DbOCF8AVr+DB480uwVYtN7e9fblVaq9ve2kJwoEFgBAU3zpS1/Kt7/97Vx11VVJkh/+8Id5y1vekne9612ZmppKvV5PqVRKvV7P9PR0yuVyGo3GgmoAwMrjHhYAQFO87W1vy9/8zd/k/vvvz/3335+LLrooH/3oR/O6170ulUolY2NjSZKxsbFUKpV0d3enp6dnQTUAYOVxhQUAUDjbtm3L8PBwduzYkc7OztRqtUXXAICVRWABABTC/fffP//ngYGB7N69+3lft9AaALCymBICAAAAFI7AAgAAACgcgQUAAABQOAILAAAAoHAEFgAAAEDhCCwAAACAwln0Y003bdqUjo6OrF27NkmydevWXHnlldm/f3+Gh4dz+PDhdHV1pVarZePGjUmy4BoAAADQGpbkCos777wze/fuzd69e3PllVcmSUZHR1OtVjM+Pp5qtZqRkZH51y+0BgAAALSGZZkSMjMzk4mJiQwODiZJBgcHMzExkdnZ2QXXAAAAgNax6CkhydPTQBqNRi677LLcdNNNmZycTH9/f0qlUpKkVCqlr68vk5OTaTQaC6p1d3cvRasAAADACrDowGLXrl0pl8s5fvx4tm/fnltvvTXXXnvtErS2cD0965r6+bCUenvXN7sFWBL2ZQAATseiA4tyuZwk6ejoSLVazdvf/va8613vytTUVOr1ekqlUur1eqanp1Mul9NoNBZUOx0zM0czN9dY7KatCL4ArH4HDx5pdguwaL296+3Lq1R7e5sTBQDAsljUPSyeeOKJHDny9AC00Wjk3nvvTaVSSU9PTyqVSsbGxpIkY2NjqVQq6e7uXnANAAAAaB2LusJiZmYmN9xwQ+r1eubm5jIwMJDR0dEkybZt2zI8PJwdO3aks7MztVptfr2F1gAAAIDWsKjAYsOGDdmzZ8/z1gYGBrJ79+4lrQEAAACtYVkeawoAAACwGAILAAAAoHAEFgAAAEDhCCwAAACAwhFYAAAAAIUjsAAAAAAKR2ABAAAAFI7AAgAAACgcgQUAAABQOAILAAAAoHAEFgAAAEDhCCwAAACAwhFYAAAAAIUjsAAAAAAKR2ABAAAAFI7AAgAAACgcgQUAAABQOAILAAAAoHAEFgAAAEDhCCwAAACAwhFYAAAAAIUjsAAAAAAKR2ABAAAAFM6aZjcAALSud7zjHfn+97+f9vb2nHvuubnllltSqVSyf//+DA8P5/Dhw+nq6kqtVsvGjRuTZME1AGBlcYUFANA0tVotn/3sZ7Nnz568+c1vzrvf/e4kyejoaKrVasbHx1OtVjMyMjK/zkJrAMDKsmSBxYc+9KFccskleeihh5I8fYZjaGgomzdvztDQUA4cODD/2oXWAIDVZf369fN/Pnr0aNra2jIzM5OJiYkMDg4mSQYHBzMxMZHZ2dkF1wCAlWdJpoR87Wtfyz/8wz/kJS95yfyyZ85wbNmyJXv37s3IyEg+8YlPLKoGAKw+73nPe/KFL3whjUYjH/nIRzI5OZn+/v6USqUkSalUSl9fXyYnJ9NoNBZU6+7uPuV+enrWLf1GQpP09q5/8RfBCmBfbk2LDiyOHz+eW2+9NXfccUd++7d/O0nmz3B87GMfS/L0GY7bbrsts7OzaTQaC6qdzkADAFg5tm/fniTZs2dP3ve+9+XGG29saj8zM0czN9doag9nii8Aq9/Bg0ea3QIsWm/vevvyKtXe3nbSEwWLnhLywQ9+MG94wxuyYcOG+WUnOzOy0BoAsLq98Y1vzBe/+MVcdNFFmZqaSr1eT5LU6/VMT0+nXC6nXC4vqAYArDyLusLiy1/+cr761a9m69atS9XPknApJ6uJs1+sFvZlftLjjz+exx57bD5QuP/++3P++eenp6cnlUolY2Nj2bJlS8bGxlKpVOavtlxoDQBYWRYVWHzpS1/Kt7/97Vx11VVJkh/+8Id5y1vekne9613zZzhKpdKzznA0Go0F1U6HSzlZTVz+xmrgUs7V68Uu5TyZY8eO5cYbb8yxY8fS3t6e888/Pzt37kxbW1u2bduW4eHh7NixI52dnanVavPrLbQGAKwsiwos3va2t+Vtb3vb/M+bNm3Kzp0784pXvCJ33323MyMAwAu68MIL85nPfOZ5awMDA9m9e/eS1gCAlWVJnhLyfJwZAQAAABZqSQOL+++/f/7PzowAAAAAC7Xop4QAAAAALDWBBQAAAFA4AgsAAACgcAQWAAAAQOEILAAAAIDCEVgAAAAAhSOwAAAAAApHYAEAAAAUjsACAAAAKByBBQAAAFA4AgsAAACgcAQWAAAAQOEILAAAAIDCEVgAAAAAhSOwAAAAAApHYAEAAAAUjsACAAAAKByBBQAAAFA4AgsAAACgcAQWAAAAQOEILAAAAIDCEVgAAAAAhSOwAAAAAApHYAEAAAAUjsACAAAAKByBBQAAAFA4axb7Bu94xzvy/e9/P+3t7Tn33HNzyy23pFKpZP/+/RkeHs7hw4fT1dWVWq2WjRs3JsmCawAAAEBrWPQVFrVaLZ/97GezZ8+evPnNb8673/3uJMno6Giq1WrGx8dTrVYzMjIyv85CawAAAEBrWHRgsX79+vk/Hz16NG1tbZmZmcnExEQGBweTJIODg5mYmMjs7OyCawAAAEDrWPSUkCR5z3veky984QtpNBr5yEc+ksnJyfT396dUKiVJSqVS+vr6Mjk5mUajsaBad3f3KffT07NuKTYLCqG3d/2LvwhWAPsyAACnY0kCi+3btydJ9uzZk/e973258cYbl+JtF2xm5mjm5hpN7eFM8QVg9Tt48EizW4BF6+1db19epdrb25woAACWxZI+JeSNb3xjvvjFL+aiiy7K1NRU6vV6kqRer2d6ejrlcjnlcnlBNQAAAKB1LCqwePzxxzM5OTn/8/3335/zzz8/PT09qVQqGRsbS5KMjY2lUqmku7t7wTUAAACgdSxqSsixY8dy44035tixY2lvb8/555+fnTt3pq2tLdu2bcvw8HB27NiRzs7O1Gq1+fUWWgMAAABaw6ICiwsvvDCf+cxnnrc2MDCQ3bt3L2kNAAAAaA1Leg8LAIBTdejQobz1rW/N5s2bc/XVV+ed73zn/KPM9+/fn6GhoWzevDlDQ0M5cODA/HoLrQEAK4vAAgBoira2tlx//fUZHx/Pvn37smHDhtxxxx1JktHR0VSr1YyPj6darWZkZGR+vYXWAICVRWABADRFV1dXrrjiivmfL7300jzyyCOZmZnJxMREBgcHkySDg4OZmJjI7OzsgmsAwMqzqHtYAAAshbm5udx9993ZtGlTJicn09/fn1KplCQplUrp6+vL5ORkGo3GgmqeOAasJus7z8nZa1vrq1xv7/pmt3DG/OjJEzny2LFmt1EIrbWXAwCFdNttt+Xcc8/Nm970pkxMTDS1l56edU39fFhKrfQlr9Vc/ft7m90Cy2Tf+7fkbMduEoEFANBktVot3/nOd7Jz5860t7enXC5namoq9Xo9pVIp9Xo909PTKZfLaTQaC6qdjpmZo5mbayzT1haLL7Or38GDR5rdAsvAsbv6tcqx297edtITBe5hAQA0zQc+8IE8+OCDueuuu9LR0ZEk6enpSaVSydjYWJJkbGwslUol3d3dC64BACuPKywAgKZ4+OGHs3PnzmzcuDHXXHNNkuSlL31p7rrrrmzbti3Dw8PZsWNHOjs7U6vV5tdbaA0AWFkEFgBAU7z85S/PN77xjeetDQwMZPfu3UtaAwBWFlNCAAAAgMIRWAAAAACFI7AAAAAACkdgAQAAABSOwAIAAAAoHIEFAAAAUDgCCwAAAKBwBBYAAABA4QgsAAAAgMIRWAAAAACFI7AAAAAACkdgAQAAABSOwAIAAAAoHIEFAAAAUDgCCwAAAKBwBBYAAABA4SwqsDh06FDe+ta3ZvPmzbn66qvzzne+M7Ozs0mS/fv3Z2hoKJs3b87Q0FAOHDgwv95CawAAAEBrWFRg0dbWluuvvz7j4+PZt29fNmzYkDvuuCNJMjo6mmq1mvHx8VSr1YyMjMyvt9AaAAAA0BoWFVh0dXXliiuumP/50ksvzSOPPJKZmZlMTExkcHAwSTI4OJiJiYnMzs4uuAYAAAC0jjVL9UZzc3O5++67s2nTpkxOTqa/vz+lUilJUiqV0tfXl8nJyTQajQXVuru7l6pVAAAAoOCWLLC47bbbcu655+ZNb3pTJiYmluptF6SnZ11TPx+WUm/v+ma3AEvCvgwAwOlYksCiVqvlO9/5Tnbu3Jn29vaUy+VMTU2lXq+nVCqlXq9neno65XI5jUZjQbXTMTNzNHNzjaXYtMLzBWD1O3jwSLNbgEXr7V1vX16l2tvbnCgAAJbFoh9r+oEPfCAPPvhg7rrrrnR0dCRJenp6UqlUMjY2liQZGxtLpVJJd3f3gmsAAABA61jUFRYPP/xwdu7cmY0bN+aaa65Jkrz0pS/NXXfdlW3btmV4eDg7duxIZ2dnarXa/HoLrQEAAACtYVGBxctf/vJ84xvfeN7awMBAdu/evaQ1AAAAoDUsekoIAAAAwFITWAAAAACFI7AAAAAACkdgAQAAABSOwAIAAAAoHIEFAAAAUDgCCwAAAKBwBBYAAABA4QgsAAAAgMIRWAAAAACFI7AAAAAACkdgAQAAABSOwAIAAAAoHIEFANAUtVotmzZtyiWXXJKHHnpofvn+/fszNDSUzZs3Z2hoKAcOHFh0DQBYeQQWAEBTXHXVVdm1a1cuvvjiZy0fHR1NtVrN+Ph4qtVqRkZGFl0DAFYegQUA0BSXX355yuXys5bNzMxkYmIig4ODSZLBwcFMTExkdnZ2wTUAYGVa0+wGAACeMTk5mf7+/pRKpSRJqVRKX19fJicn02g0FlTr7u5u2vYAAAsnsAAA+DE9Peua3QIsmd7e9c1uAVgAx+7TBBYAQGGUy+VMTU2lXq+nVCqlXq9neno65XI5jUZjQbXTNTNzNHNzjWXYuuIxIF79Dh480uwWWAaO3dWvVY7d9va2k54ocA8LAKAwenp6UqlUMjY2liQZGxtLpVJJd3f3gmsAwMrkCgsAoCluv/323HfffXn00Udz3XXXpaurK/fcc0+2bduW4eHh7NixI52dnanVavPrLLQGAKw8AgsAoCluvvnm3Hzzzc9ZPjAwkN27dz/vOgutAQArjykhAAAAQOEILAAAAIDCEVgAAAAAhbOowKJWq2XTpk255JJL8tBDD80v379/f4aGhrJ58+YMDQ3lwIEDi64BAAAArWNRgcVVV12VXbt25eKLL37W8tHR0VSr1YyPj6darWZkZGTRNQAAAKB1LCqwuPzyy1Mul5+1bGZmJhMTExkcHEySDA4OZmJiIrOzswuuAQAAAK1lyR9rOjk5mf7+/pRKpSRJqVRKX19fJicn02g0FlTr7u5e6jYBAACAAlvywKIIenrWNbsFWDK9veub3QIsCfsyAACnY8kDi3K5nKmpqdTr9ZRKpdTr9UxPT6dcLqfRaCyodrpmZo5mbq6x1JtWSL4ArH4HDx5pdguwaL296+3Lq1R7e5sTBQDAsljyx5r29PSkUqlkbGwsSTI2NpZKpZLu7u4F1wAAAIDWsqgrLG6//fbcd999efTRR3Pdddelq6sr99xzT7Zt25bh4eHs2LEjnZ2dqdVq8+sstAYAAAC0jkUFFjfffHNuvvnm5ywfGBjI7t27n3edhdYAAACA1rHkU0IAAAAAFktgAQAAABSOwAIAAAAoHIEFAAAAUDgCCwAAAKBwBBYAAABA4QgsAAAAgMIRWAAAAACFI7AAAAAACkdgAQAAABSOwAIAAAAoHIEFAAAAUDgCCwAAAKBwBBYAAABA4QgsAAAAgMIRWAAAAACFI7AAAAAACkdgAQAAABSOwAIAAAAoHIEFAAAAUDgCCwAAAKBwBBYAAABA4QgsAAAAgMIRWAAAAACFs6bZDQC0ovWd5+Tsta31K7i3d32zWzhjfvTkiRx57Fiz2wAAWNFaa7QMUBBnr12Tq39/b7PbYJnse/+WHGl2EwAAK1whp4Ts378/Q0ND2bx5c4aGhnLgwIFmtwQArBDGEQCwOhQysBgdHU21Ws34+Hiq1WpGRkaa3RIAsEIYRwDA6lC4KSEzMzOZmJjIxz72sSTJ4OBgbrvttszOzqa7u/uU3qO9vW05WyycvgvOaXYLLKNW259biWN3dWuVY7do22kccfr8LlrdWm1/biWO3dWtVY7dF9vOwgUWk5OT6e/vT6lUSpKUSqX09fVlcnLylAcaF1xw3nK2WDgfvfk1zW6BZdTTs67ZLbBMHLurm2O3OYwjTp/fRaub30Wrl2N3dXPsPq2QU0IAAACA1la4wKJcLmdqair1ej1JUq/XMz09nXK53OTOAICiM44AgNWjcIFFT09PKpVKxsbGkiRjY2OpVCqnfBknANC6jCMAYPVoazQajWY38ZO+9a1vZXh4OI899lg6OztTq9Xyspe9rNltAQArgHEEAKwOhQwsAAAAgNZWuCkhAAAAAAILAAAAoHAEFgAAAEDhCCwAAACAwhFYAAAAAIWzptkNwMkcOnQoP/zhD5MkF110US644IImdwQArBTGEQArm8CCQvrud7+bW265JRMTE+nr60uSTE9P51WvelXe+973ZuPGjc1tEAAoLOMIgNWhrdFoNJrdBPyka665JtVqNYODg2lvf3rm0tzcXPbt25dPfepT+fSnP93kDoGFuvrqq7Nv375mtwGsYsYRsHoZR7QWV1hQSIcPH84b3vCGZy1rb2/Pli1b8uEPf7hJXQGn6pvf/OYL1g4dOnQGOwFakXEErGzGETxDYEEhdXV1ZWxsLK9//evT1taWJGk0Gtm3b186Ozub3B3wYgYHB3PxxRfn+S7iO3z48JlvCGgpxhGwshlH8AxTQiikAwcOZHR0NF//+tfT39+fJJmamsorX/nKbNu2LS972cua3CFwMldddVU+9alPzR+/P+7Vr351/uqv/qoJXQGtwjgCVjbjCJ7hCgsKaePGjfn4xz+e2dnZTE5OJknK5XK6u7ub3BlwKl7zmtfkBz/4wfMONH7t136tCR0BrcQ4AlY24wie4QoLAAAAoHDam90AAAAAwE8SWAAAAACFI7AAAAAACkdgAQAAABSOwAIAAAAonP8XftEYtAfLmswAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 1080x576 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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lyWtfnZx6wor9Wv84Nzl52opEdsiQpOU1K2Zld9i+3z4qdbLz+cf1dwn0oePG7Z7jxu2x0rHPz/pVrvjZHfnqpCOy1YhhWfz80lzzm3sz4/pfpvuf/rk7qXXvHL77iv/X+v1f37PSPqwbrDskU971tuzT8uo8veT5fO/Wu/OF2bfW50PRL+666KT+LiFJcuStH6rbua7c7f+t27leDn3esP7dokWL0tHRkSRpbm7O8OHD1+h9NKxAb2lYgd4opWH9z18fU7dzXbX7F+t2rpdD3W4cMHz48DVuUgEAWHu50xUAQAEG4ob+9eLKAABQNAkrAEABBuL+qPUiYQUAoGgSVgCAAgzE/VHrRcIKAEDRJKwAAAUww1pNwgoAQNEkrAAABZCwVpOwAgBQNA0rAABFMxIAAFAAIwHVJKwAABRNwgoAUAAJazUJKwAARZOwAgAUwK1Zq0lYAQAomoQVAKAAZlirSVgBACiahBUAoAAS1moSVgAAiiZhBQAogIS1moQVAICiSVgBAAogYa0mYQUAoGgSVgCAAtQkrJUkrAAAFE3DCgBA0YwEAAAUoDtGAqpIWAEAKJqEFQCgALa1qiZhBQCgaBJWAIAC2NaqmoQVAICiSVgBAApghrWahBUAgKJJWAEACmCGtZqEFQCAoklYAQAKYIa1moQVAICiSVgBAApQq/V3BeWSsAIAUDQJKwBAAbpjhrWKhBUAgKJpWAEAKJqRAACAArhxQDUJKwAARZOwAgAUwI0DqklYAQAomoQVAKAAbhxQTcIKAEDRJKwAAAWwS0A1CSsAAEWTsAIAFEDCWk3CCgBA0SSsAAAFsA9rNQkrAABFk7ACABTAPqzVJKwAABRNwgoAUAC7BFSTsAIAUDQNKwAARTMSAABQACMB1SSsAAAUTcIKAFAAu1pVk7ACAFA0CSsAQAHMsFaTsAIAUDQJKwBACQyxVpKwAgBQNAkrAEABzLBWk7ACAFA0CSsAQAFqZlgrSVgBACiahBUAoABmWKtJWAEAKJqEFQCgBBLWShJWAACKpmEFAKBoRgIAAApQ6rZWxx9/fB555JEMGjQoQ4cOzVlnnZWWlpbMnTs3kydPzpNPPplhw4Zl+vTpGT16dJKs8VoVCSsAAJWmT5+ea6+9Ntdcc02OPvronH766UmSqVOnpq2tLbNmzUpbW1umTJnS85o1XauiYQUAKEGtjo+XYKONNur5+ZlnnklDQ0MWLlyY9vb2tLa2JklaW1vT3t6eRYsWrfHa6hgJAABYy3R2dqazs3OV401NTWlqalrl+BlnnJFbbrkltVotX/rSl9LR0ZFRo0alsbExSdLY2JiRI0emo6MjtVptjdaGDx9eWa+GFQCgAPW8ccDll1+eGTNmrHJ80qRJOfHEE1c5/slPfjJJcs011+T888/PRz7ykT6v8Z9pWAEA1jITJ07M+PHjVzn+QunqP/uP//iPTJkyJZtttlnmz5+frq6uNDY2pqurKwsWLEhzc3Nqtdoara2OGVYAgBLUcYa1qakpW2655SqPf21YFy9enI6Ojp7fb7rppmy88cYZMWJEWlpaMnPmzCTJzJkz09LSkuHDh6/x2uo01GqlbqLwwrof27a/SwAGiJ3PP66/SwAGgLsuOqm/S0iSvPqKT9XtXHPfc1qvnvf444/n+OOPz5IlSzJo0KBsvPHGOfXUU/P6178+c+bMyeTJk9PZ2ZmmpqZMnz49Y8aMSZI1XquiYQVesTSsQG+U0rCO/sZ/1+1c8947uW7nejkYCQAAoGi+dAUAUIIB9Tfv+pKwAgBQNAkrAEAR6rcP60AjYQUAoGgSVgCAEphhrSRhBQCgaBpWAACKZiQAAKAERgIqSVgBACiahBUAoAQ121pVkbACAFA0CSsAQAFqZlgrSVgBAChaZcJ6yimnpKHhxWcpzj///Je1IACAtZKEtVJlw7r11lvXsw4AAHhBlQ3rpEmT6lkHAMDazS4BlXr9patbbrkl1113XRYtWpTLLrssd999d5555pnssccefVkfAABruV596eob3/hGzj777IwePTq/+c1vkiTrrbdePvOZz/RpcQAAa4uGWv0eA02vGtbLL788X/3qV3PMMcdk0KAVLxkzZkzmzp3bp8UBAECvRgIWL16c5ubmJOnZOWD58uUZPHhw31UGALA2GYDJZ730KmHddddd88UvfnGlY1//+tez22679UlRAADwd71KWM8888x8+MMfzne+850sXrw448aNy4YbbpjLLrusr+sDAFg72CWgUq8a1pEjR+Z73/te7r777vzlL39Jc3Nzdtppp555VgAA6Cu97ji7u7uzbNmyJElXV1dqbngLAEAd9Cphvf/++3PCCSdk6dKlGTVqVB577LGsu+66ufTSS7P99tv3dY0AAK98ssBKvWpYTz/99Bx55JF5//vfn4aGhtRqtXzta1/L6aefnu9///t9XSMAAGuxXo0EzJs3LxMnTuzZ0qqhoSFHHXVU5s2b15e1AQCsPWp1fAwwvWpY991339x0000rHfvJT36S/fbbry9qAgCAHpUjAaecckpPotrV1ZWTTjopO+ywQzbbbLM89thjueeee3LAAQfUrVAAgFe0AZh81ktlw7r11luv9Pu2227b8/NrXvOa7LXXXn1XFQAA/E1lwzpp0qR61gEAsHZz44BKvdolIEmWLl2auXPn5oknnlhpD9Y99tijTwoDAICklw3r7bffno9+9KNZunRpnnnmmWy44YZZvHhxNttss/z4xz/u6xoBAF7xGsywVurVLgGf+tSn8sEPfjC33XZbNthgg9x222057rjj0tbW1tf1AQCwluv1PqxHHXXUSseOOeaYfO1rX+uLmgAA1j72Ya3Uq4Z1o402yjPPPJMk2XTTTfPggw+ms7Mzzz77bJ8WBwAAvZphPfDAA/Ozn/0sb3/72/POd74zRx11VNZZZ50cfPDBfV0fAABruV41rGeccUbPz0cffXR22mmnLF68OHvvvXefFQYAAMlL2Nbqn40dO/blrgMAYK1ml4BqlQ1rW1tbz61ZV+fKK698WQsCAIB/VtmwHnHEEfWso9fGbf6G/i4BGCA2a7y1v0sABoKL+ruAv3Gnq0qVDev48ePrWQcAALygXm1rBQAA/WWNvnQFAMDLzJeuKklYAQAomoQVAKAEEtZKvUpYly5dmosvvjgHHHBA3vSmNyVJfvGLX+SKK67o0+IAAKBXDet5552XBx54IBdeeGHP3qyvfe1rc9VVV/VpcQAAa4uGWv0eA02vRgJuvPHGzJ49O0OHDs2gQSt63FGjRmX+/Pl9WhwAAPSqYR08eHC6urpWOrZo0aIMGzasL2oCAFj7DMDks156NRJw8MEH59RTT82f//znJMmCBQsybdq0/Pu//3ufFgcAAL1qWE866aRsscUWOfTQQ9PZ2Zlx48Zl5MiROeGEE/q6PgCAtUOtjo8BplcjAUOGDMkZZ5yRM844I4sWLcomm2zS8+UrAADoS71qWP8+CvB3ixcv7vl5q622enkrAgBYCw3Eb+/XS68a1gMPPDANDQ2p1f5xJf+esN533319UxkAAKSXDev999+/0u9//etfM2PGjIwdO7ZPigIAWOvUjFtW6dWXrv7VpptumjPOOCMXXXTRy10PAACspFcJ6wt56KGHsmTJkpezFgCAtZcZ1kq9aljb2tpW2hVgyZIlefDBB21rBQBAn+tVw3rEEUes9Pv666+f7bffPqNHj+6LmgAAoMeLNqxdXV359a9/nXPOOSdDhgypR00AAGsd21pVe9EvXTU2NuaWW25xowAAAPpFr3YJmDhxYi655JIsW7asr+sBAFg7uTVrpdWOBMycOTOtra254oor8vjjj+erX/1qhg8fvlLa+tOf/rSvawQAYC222oZ1ypQpaW1tzQUXXFCvegAA1kpmWKuttmH9+61Y3/zmN9elGAAA+FerbVi7u7vz61//uqdxfSF77LHHy14UAMBaR8JaabUN69KlS3PGGWdUNqwNDQ358Y9/3CeFAQBA8iIN6/rrr68hBQCoBwlrpV5tawUAAP2lV1+6AgCgb9kloNpqE9Y77rijXnUAAMALMhIAAEDRNKwAABRttTOsAADUiRnWShJWAACKpmEFAKBoRgIAAApgW6tqElYAAIomYQUAKIGEtZKEFQCAoklYAQBKIGGtJGEFAKBoElYAgALYJaCahBUAgKJJWAEASiBhrSRhBQCgaBJWAIACmGGtJmEFAKBoElYAgBJIWCtJWAEAKJqEFQCgBBLWShJWAACKpmEFAKBoRgIAAApgW6tqElYAAIomYQUAKIGEtZKEFQCAomlYAQBKUKvjo5eeeOKJfOhDH8q4cePy9re/PZMmTcqiRYuSJHPnzs2ECRMybty4TJgwIfPmzet53ZquVdGwAgDwghoaGvLBD34ws2bNyg9/+MNstdVWufDCC5MkU6dOTVtbW2bNmpW2trZMmTKl53VrulZFwwoAUICGWv0evTVs2LDstttuPb/vvPPOefTRR7Nw4cK0t7entbU1SdLa2pr29vYsWrRojddWx5euAADWMp2dnens7FzleFNTU5qaml7wNd3d3bnqqquy//77p6OjI6NGjUpjY2OSpLGxMSNHjkxHR0dqtdoarQ0fPryyXg0rAEAJ6rhLwOWXX54ZM2ascnzSpEk58cQTX/A155xzToYOHZr3vOc9aW9v7+sSV6JhBQBYy0ycODHjx49f5XhVujp9+vQ8/PDDueyyyzJo0KA0Nzdn/vz56erqSmNjY7q6urJgwYI0NzenVqut0drqmGEFAChAPWdYm5qasuWWW67yeKGG9eKLL84999yTSy+9NEOGDEmSjBgxIi0tLZk5c2aSZObMmWlpacnw4cPXeG3116ZWG1Db1B446Ij+LgEYIBr+NiMFsDqzl32rv0tIkux48sV1O9fdF57Uq+f98Y9/TGtra0aPHp311lsvSbLlllvm0ksvzZw5czJ58uR0dnamqakp06dPz5gxY5JkjdeqaFiBVywNK9AbxTSsH6tjw/rp3jWspTASAABA0XzpCgCgBAPqb971JWEFAKBoGlYAAIpmJAAAoAAN/V1AwSSsAAAUTcIKAFACX7qqJGEFAKBoElYAgAI0SFgrSVgBACiahBUAoAQS1koSVgAAiiZhBQAogYS1koQVAICiSVgBAApgl4BqElYAAIomYQUAKIGEtZKEFQCAoklYAQAKYIa1moQVAICiaVgBACiakQAAgBIYCagkYQUAoGgSVgCAAvjSVTUJKwAARZOwAgCUQMJaScIKAEDRJKwAACWQsFaSsAIAUDQJKwBAAewSUE3CCgBA0SSsAAAlkLBWkrACAFA0CSsAQAEaaiLWKhJWAACKJmEFACiBgLWShBUAgKJpWAEAKJqRAACAArhxQDUJKwAARZOwAgCUQMJaScIKAEDRJKwAAAUww1pNwgoAQNEkrAAAJZCwVpKwAgBQNAkrAEABzLBWk7ACAFA0CSsAQAkkrJUkrAAAFE3CCgBQADOs1SSsAAAUTcIKAFCCmoi1ioQVAICiaVgBACiakQAAgAL40lU1CSsAAEWTsAIAlEDCWknCCgBA0SSsAAAFaOju7wrKJWEFAKBoElYAgBKYYa0kYQUAoGgSVgCAAtiHtZqGlQHlwpvOTsvur03X8hWT6Y//ZVGObvlI1hm8Tk678iPZduyYbDZ6ZD721qm562ft/VwtUC+HHj8uBx21T0bv8G/56f/8Mhd+4PM9azu/dYec+Nn3Z9N/e1Xuv+3BXPiBz2fBnx5Pknzyh5Ozw17b9zx3nSHr5JEHHs2xu3y87p8BqKZhZcCZceKXc8OXb1rl+D233Jfvf+a6nPXt/+qHqoD+tPDRRfnmeVfnTQe9IeuuP6TneNOIjTL1O/+Vi479Qn4983d53yfelTO++ZF8ZK+zkiRnvP2/V3qfC26ckjt/ck9da4ceNRFrFTOsvCIsX7Y8V3/m+tx7y/3p7rIvCKxtbrnmN/nltbfn6YVPr3R8r/Fvzrz2R3Lz927NsueX5RvTvpsxO22drbbbfJX3GLX1ptlhr+3z4ytvrlfZQC9pWBlwjj7vyHx3wZfzf28+Jzvt+7r+Lgco2Nav2zIP3fVwz+/PPft8OubMz9av23KV577tPXvnnl/cn8fm/bWeJUKPhlr9HgONkQAGlC9NviIPtz+S5UuXZ793vyXnXDs5H97llHQ8NL+/SwMKtP6G6+XJv3audGxx57NZf6P1V3nu296zT775qavrVRrwEvRrwvr2t7+9P0/PAHT/bQ9myTPPZdnS5fnR13+We2/5Q958yC79XRZQqCXPPJcNmlZuToc2rZ8lTy9Z6djr37Jdhm82LDd/79f1LA9WVqvjY4Dp84T1wQcfrFx74okn+vr0vMLVarU0NDT0dxlAoR5ufyQHvnefnt/XG7pumseMysPtj6z0vAPfu09+cc1teW7x8/UuEeiFPm9YW1tbs8UWW6T2At98e/LJJ/v69LyCbLDx0LTs9tr8/mft6Vrelf0m7Jkd92nJ50/6WpJk8JB1kr81r4OHrJPB6w7OsueX9WPFQL0MahyUxnUaM6hxUAY1DsrgdQena3lXbrnmtnzov4/MXuPfnFuvvyNHnvmOzL37T/nzHx7tee2Q9QZnn3fsnmlHXNSPnwBYnT5vWLfYYot885vfzKhRo1ZZ23ffffv69LyCrDN4nbzvnHdnq+23SHdXd/58/19y9vgL8sgDK/7h+cr9n8lmo0cmSf571oota97z6uMz/2FfoIBXuiNPPzzvnfLOnt/fduTe+ca07+Yb53w30951USZ95v059fJJuf+2B3PekZ9d6bV7HrZrFj/1bO786b31LhtWMhC/DFUvDbUXij5fRtOnT8+BBx6YN77xjausnXvuuTnzzDNf0vsdOOiIl6s04BWuobGxv0sABoDZy77V3yUkSfYef2HdznXz1SfX7Vwvhz5PWE899dTKtZfarAIAvGK5cUAl+7ACAFA0+7ACABTADGs1CSsAAEWTsAIAlEDCWknCCgBA0SSsAAAFMMNaTcIKAEDRJKwAACXoFrFWkbACAFA0CSsAQAkErJUkrAAAFE3CCgBQALsEVJOwAgBQNA0rAABFMxIAAFCCmpmAKhJWAACKJmEFACiAL11Vk7ACAFA0CSsAQAkkrJUkrAAAFE3CCgBQgAa7BFSSsAIAUDQJKwBACbr7u4BySVgBACiahhUAoAANtVrdHi/F9OnTs//++2e77bbLAw880HN87ty5mTBhQsaNG5cJEyZk3rx5/+u1KhpWAAAqHXDAAbnyyiuzxRZbrHR86tSpaWtry6xZs9LW1pYpU6b8r9eqaFgBAEpQq+PjJRg7dmyam5tXOrZw4cK0t7entbU1SdLa2pr29vYsWrRojddWx5euAADWMp2dnens7FzleFNTU5qaml709R0dHRk1alQaGxuTJI2NjRk5cmQ6OjpSq9XWaG348OGV59OwAgCUoI77sF5++eWZMWPGKscnTZqUE088sW519JaGFQBgLTNx4sSMHz9+leO9SVeTpLm5OfPnz09XV1caGxvT1dWVBQsWpLm5ObVabY3WVscMKwBAARpq9Xs0NTVlyy23XOXR24Z1xIgRaWlpycyZM5MkM2fOTEtLS4YPH77Ga6u/NrWBdR+wAwcd0d8lAANEw99mpABWZ/ayb/V3CUmSt+17Xt3OdePPTu/1c88999zMnj07jz/+eDbZZJMMGzYs1113XebMmZPJkyens7MzTU1NmT59esaMGZMka7xWRcMKvGJpWIHe0LCWzwwrAEAJBlaGWFdmWAEAKJqEFQCgAA3d/V1BuSSsAAAUTcIKAFACM6yVJKwAABRNwgoAUAIBayUJKwAARZOwAgAUoMEMayUJKwAARZOwAgCUQMJaScIKAEDRJKwAACVwp6tKElYAAIomYQUAKIBdAqpJWAEAKJqGFQCAohkJAAAogZGAShJWAACKJmEFACiBhLWShBUAgKJJWAEASuDGAZUkrAAAFE3CCgBQADcOqCZhBQCgaBJWAIASSFgrSVgBACiahBUAoAQS1koSVgAAiiZhBQAogYS1koQVAICiSVgBAErgTleVJKwAABRNwwoAQNGMBAAAFMCtWatJWAEAKJqEFQCgBBLWShJWAACKJmEFAChBt4S1ioQVAICiSVgBAEpghrWShBUAgKJJWAEASiBhrSRhBQCgaBJWAIASSFgrSVgBACiahBUAoAT2Ya0kYQUAoGgSVgCAEtS6+7uCYklYAQAomoYVAICiGQkAACiBba0qSVgBACiahBUAoAS2taokYQUAoGgSVgCAEphhrSRhBQCgaBJWAIASSFgrSVgBACiahBUAoAQS1koSVgAAiiZhBQAoQXd3f1dQLAkrAABFk7ACAJTADGslCSsAAEWTsAIAlEDCWknCCgBA0TSsAAAUzUgAAEAJuo0EVJGwAgBQNAkrAEABajU3DqgiYQUAoGgSVgCAEphhrSRhBQCgaBJWAIASuHFAJQkrAABFk7ACAJSg2y4BVSSsAAAUTcIKAFACM6yVJKwAABRNwgoAUICaGdZKElYAAIomYQUAKIEZ1koSVgAAiqZhBQCgaEYCAABK0G0koIqEFQCAoklYAQBKULOtVRUJKwAARZOwAgAUoGaGtZKEFQCAoklYAQBKYIa1koQVAICiSVgBAApghrWahBUAgKJJWAEASmCGtZKEFQCAojXUajUDEwAAFEvCCgBA0TSsAAAUTcMKAEDRNKwAABRNwwoAQNE0rAAAFE3DCgBA0TSsAAAUTcMKAEDRNKwMaHPnzs2ECRMybty4TJgwIfPmzevvkoACTZ8+Pfvvv3+22267PPDAA/1dDvASaVgZ0KZOnZq2trbMmjUrbW1tmTJlSn+XBBTogAMOyJVXXpktttiiv0sB1oCGlQFr4cKFaW9vT2tra5KktbU17e3tWbRoUT9XBpRm7NixaW5u7u8ygDWkYWXA6ujoyKhRo9LY2JgkaWxszMiRI9PR0dHPlQEALycNKwAARdOwMmA1Nzdn/vz56erqSpJ0dXVlwYIF/uwHAK8wGlYGrBEjRqSlpSUzZ85MksycOTMtLS0ZPnx4P1cGALycGmq1Wq2/i4A1NWfOnEyePDmdnZ1pamrK9OnTM2bMmP4uCyjMueeem9mzZ+fxxx/PJptskmHDhuW6667r77KAXtKwAgBQNCMBAAAUTcMKAEDRNKwAABRNwwoAQNE0rAAAFE3DChRh8uTJufjii5Mkt99+e8aNG1eX82633XZ5+OGHX3Dtve99b77zne/06n3233///PKXv1yjGv43rwVYG2hYgV7bf//9s9NOO2WXXXbJnnvumdNOOy2LFy9+2c8zduzYzJo160Wf9/3vfz//+Z//+bKfH4CyaFiBl+Syyy7LHXfckauvvjp33313Pv/5z6/ynOXLl/dDZQC8UmlYgTUyatSo7L333vnjH/+YZMWf1q+88socdNBBOeigg5IkP/nJT3LYYYdl7Nixefe7353777+/5/Xt7e0ZP358dtlll3z0ox/N888/37N26623Zp999un5vaOjI5MmTcruu++e3XbbLdOmTcucOXMyderU3Hnnndlll10yduzYJMnSpUszffr07Lffftlzzz0zZcqUPPfccz3v9aUvfSl77bVX9tprr3z3u9/t9ef905/+lKOOOiq77bZbdtttt3zsYx9LZ2fnSs+5++67c8ghh2TXXXfNaaedttJnWt21AGD1NKzAGuno6MjPf/7ztLS09By78cYb8+1vfzvXX3997r333px++umZNm1abr311kyYMCHHH398li5dmqVLl+aEE07IYYcdlttuuy0HH3xwZs+e/YLn6erqyrHHHpvNN988N910U37+85/nkEMOyTbbbJNPfOIT2XnnnXPHHXfk9ttvT5JccMEFmTt3bq655prMnj07CxYsyKWXXpok+fnPf56vfOUr+cpXvpLZs2fnV7/6Va8/b61Wy7HHHpubb745N9xwQx577LFccsklKz3nhz/8Yb785S/nRz/6UebOnZvPfe5zSbLaawHAi9OwAi/JCSeckLFjx6atrS277rprPvzhD/esHXPMMRk2bFjWW2+9fPvb386ECRPyhje8IY2NjRk/fnwGDx6cO++8M7///e+zbNmyTJw4MYMHD87BBx+cHXfc8QXPd9ddd2XBggX5+Mc/nqFDh2bdddftSVP/Va1Wy3e+852cfvrpGTZsWDbccMMce+yxPfeMv+GGG3L44Ydn2223zdChQzNp0qRef+6tt946b3nLWzJkyJAMHz4873//+/Ob3/xmpecceeSRaW5uzrBhw3Lcccf1nHd11wKAF7dOfxcADCyXXnpp9txzzxdca25u7vn50UcfzTXXXJMrrrii59iyZcuyYMGCNDQ0ZNSoUWloaOhZ23zzzV/wPTs6OrL55ptnnXVe/D9XixYtypIlS3L44Yf3HKvVaunu7k6SLFiwIDvssEPP2hZbbPGi7/l3CxcuzLnnnpvbb789ixcvTq1WS1NT00rP+efPv/nmm2fBggVJVn8tAHhxGlbgZfPPDWhzc3M+/OEP57jjjlvlebfddlvmz5+fWq3W85pHH300W2211SrPbW5uTkdHR5YvX75K0/rP50uSTTbZJOutt16uu+66jBo1apX3GjlyZDo6Onp+f/TRR3v92T796U+noaEh1157bTbZZJPceOONmTZt2krP+df3HjlyZM9nqLoWALw4IwFAnzjiiCPyrW99K7///e9Tq9Xy7LPP5qc//WmeeeaZ7LzzzllnnXXy9a9/PcuXL8/s2bNz9913v+D77LTTTtl0003z6U9/Os8++2yef/75/Pa3v02SjBgxIvPnz++ZBR00aFCOOOKInHfeeVm4cGGSZP78+bn55puTJAcffHCuvvrqPPjgg1myZElmzJjR68+zePHiDB06NE1NTZk/f36+9KUvrfKcb37zm3nsscfy5JNP5gtf+EIOOeSQF70WALw4DSvQJ3bcccecc845mTZtWnbdddccdNBB+f73v58kGTJkSC655JJcffXV2XXXXXP99dfnwAMPfMH3aWxszGWXXZaHH344b33rW7PPPvvkhhtuSJLsvvvuec1rXpO99toru+22W5LklFNOydZbb513vetdeeMb35j3ve99mTt3bpJk3333zcSJEzNx4sQceOCB2X333Xv9eSZNmpT29vaMHTs2xxxzTM9OCP+stbU1Rx99dN72trdlq6226klUV3ctAHhxDbVardbfRQAAQBUJKwAARdOwAgBQNA0rAABF07ACAFA0DSsAAEXTsAIAUDQNKwAARdOwAgBQNA0rAABF+/8BIAF2qBpgqPYAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 864x720 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 504x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from imblearn.under_sampling import RandomUnderSampler\n",
"\n",
"print(Counter(y_prf_train))\n",
"undersample = RandomUnderSampler(sampling_strategy='majority')\n",
"X_under, y_under = undersample.fit_resample(X_prf_train, y_prf_train)\n",
"\n",
"# Make a model\n",
"model_rfs = RandomForestClassifier()\n",
"\n",
"# Fit the model \n",
"clf = model_rfs.fit(X_under, y_under)\n",
"\n",
"# Predict with unused (test) data \n",
"y_prf_pred = model_rfs.predict(X_prf_test)\n",
"\n",
"# What we got \n",
"plot_trn_tst_dist(y_prf_bin, y_under, y_prf_test, y_prf_pred, in_cols=True)\n",
"#print(f\"Accuracy:\", accuracy_score(y_prf_test, y_prf_pred))\n",
"print(classification_report(y_prf_test, y_prf_pred))\n",
"# Plot some results\n",
"plot_confusion_mat(y_prf_test, y_prf_pred, labs=[\"0\", \"1\"])\n",
"plot_roc_auc(X_prf_test, y_prf_test, model_rfs)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This seems to make things worse. Let's try over and under sampling together."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" All Train Test Pred\n",
"0 23342 3270 6991 7131\n",
"1 487 1635 158 18\n",
" precision recall f1-score support\n",
"\n",
" 0 0.98 1.00 0.99 6991\n",
" 1 0.39 0.04 0.08 158\n",
"\n",
" accuracy 0.98 7149\n",
" macro avg 0.68 0.52 0.53 7149\n",
"weighted avg 0.97 0.98 0.97 7149\n",
"\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1080x576 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 864x720 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 504x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"over = RandomOverSampler(sampling_strategy=0.1)\n",
"X_over_under, y_over_under = over.fit_resample(X_prf_train, y_prf_train)\n",
"under = RandomUnderSampler(sampling_strategy=0.5)\n",
"X_over_under, y_over_under = under.fit_resample(X_over_under, y_over_under)\n",
"\n",
"# Make a model\n",
"model_rfs = RandomForestClassifier()\n",
"\n",
"# Fit the model \n",
"clf = model_rfs.fit(X_over_under, y_over_under)\n",
"\n",
"# Predict with unused (test) data \n",
"y_prf_pred = model_rfs.predict(X_prf_test)\n",
"\n",
"# What we got \n",
"plot_trn_tst_dist(y_prf_bin, y_over_under, y_prf_test, y_prf_pred, in_cols=True)\n",
"#print(f\"Accuracy:\", accuracy_score(y_prf_test, y_prf_pred))\n",
"print(classification_report(y_prf_test, y_prf_pred))\n",
"# Plot some results\n",
"plot_confusion_mat(y_prf_test, y_prf_pred, labs=[\"0\", \"1\"])\n",
"plot_roc_auc(X_prf_test, y_prf_test, model_rfs)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that the area under curve value improves a little."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Multiple RandomForest models experiment, but still binary classification\n",
"\n",
"Note: this takes quite a while to compute"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\nfor tgt_tumor_type in prf_unique_tumor_types:\\n y_prf = [1 if tumor_type == tgt_tumor_type else 0 for tumor_type in profile_mut_all[\"tumor_types\"]]\\n X_prf_train, X_prf_test, y_prf_train, y_prf_test = train_test_split(X_prf, y_prf, test_size = 0.3, random_state=1)\\n model = RandomForestClassifier(n_estimators=100, random_state=0)\\n model.fit(X_prf_train, y_prf_train)\\n y_prf_pred = model.predict(X_prf_test)\\n print(f\"Accuracy score for {tgt_tumor_type}: {accuracy_score(y_prf_test, y_prf_pred)}\")\\n'"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"for tgt_tumor_type in prf_unique_tumor_types:\n",
" y_prf = [1 if tumor_type == tgt_tumor_type else 0 for tumor_type in profile_mut_all[\"tumor_types\"]]\n",
" X_prf_train, X_prf_test, y_prf_train, y_prf_test = train_test_split(X_prf, y_prf, test_size = 0.3, random_state=1)\n",
" model = RandomForestClassifier(n_estimators=100, random_state=0)\n",
" model.fit(X_prf_train, y_prf_train)\n",
" y_prf_pred = model.predict(X_prf_test)\n",
" print(f\"Accuracy score for {tgt_tumor_type}: {accuracy_score(y_prf_test, y_prf_pred)}\")\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multiple class RandomForest classification (slow to run)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"label_encoder = LabelEncoder()\n",
"\n",
"y_prf = profile_mut_all[\"tumor_types\"].values\n",
"y_prfm = label_encoder.fit_transform(y_prf)\n",
"\n",
"X_prfm_train, X_prfm_test, y_prfm_train, y_prfm_test = train_test_split(X_prf, y_prfm, test_size = 0.2)\n",
"\n",
"model_rfm = RandomForestClassifier(n_estimators=1000)\n",
"model_rfm.fit(X_prfm_train, y_prfm_train)\n",
"\n",
"y_prfm_pred = model_rfm.predict(X_prfm_test)\n",
"\n",
"#print(f\"Test accuracy:\", accuracy_score(y_prfm_test, y_prfm_pred))\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test accuracy is poor with the current data and these parameters."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jr/miniconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/home/jr/miniconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/home/jr/miniconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.28 0.12 0.17 59\n",
" 1 0.50 0.40 0.44 100\n",
" 2 0.25 0.05 0.08 41\n",
" 3 0.50 0.01 0.02 111\n",
" 4 1.00 0.23 0.37 31\n",
" 5 0.54 0.51 0.52 69\n",
" 6 1.00 0.08 0.14 13\n",
" 7 0.31 0.69 0.43 361\n",
" 8 0.34 0.55 0.42 310\n",
" 9 0.00 0.00 0.00 15\n",
" 10 0.50 0.16 0.24 63\n",
" 11 0.39 0.69 0.50 238\n",
" 12 0.00 0.00 0.00 10\n",
" 13 0.45 0.34 0.39 117\n",
" 14 0.46 0.27 0.34 98\n",
" 15 0.35 0.13 0.19 130\n",
" 16 0.12 0.04 0.06 50\n",
" 17 0.71 0.33 0.45 30\n",
" 18 0.49 0.21 0.29 159\n",
" 19 0.39 0.65 0.49 262\n",
" 20 0.62 0.75 0.68 276\n",
" 21 0.63 0.66 0.64 319\n",
" 22 0.38 0.54 0.44 229\n",
" 23 0.00 0.00 0.00 15\n",
" 24 0.00 0.00 0.00 24\n",
" 25 0.80 0.73 0.76 11\n",
" 26 0.38 0.48 0.42 75\n",
" 27 0.82 0.38 0.51 24\n",
" 28 0.65 0.16 0.25 127\n",
" 29 0.56 0.38 0.46 242\n",
" 30 0.00 0.00 0.00 3\n",
" 31 0.00 0.00 0.00 4\n",
" 32 1.00 0.03 0.05 38\n",
" 33 0.00 0.00 0.00 4\n",
" 34 0.55 0.46 0.50 233\n",
" 35 0.00 0.00 0.00 9\n",
" 36 0.00 0.00 0.00 65\n",
" 37 0.00 0.00 0.00 38\n",
" 38 0.94 0.59 0.73 27\n",
" 39 0.80 0.85 0.83 205\n",
" 40 0.25 0.11 0.15 9\n",
" 41 0.00 0.00 0.00 7\n",
" 42 0.00 0.00 0.00 3\n",
" 43 0.00 0.00 0.00 4\n",
" 44 0.47 0.23 0.31 146\n",
" 45 0.00 0.00 0.00 39\n",
" 46 0.50 0.49 0.50 108\n",
" 47 0.00 0.00 0.00 28\n",
" 48 0.38 0.53 0.44 74\n",
" 49 0.00 0.00 0.00 9\n",
" 50 0.75 0.41 0.53 104\n",
"\n",
" accuracy 0.45 4766\n",
" macro avg 0.37 0.26 0.27 4766\n",
"weighted avg 0.47 0.45 0.42 4766\n",
"\n",
" All Train Test Pred\n",
"0 308 59 59 25.0\n",
"1 556 100 100 80.0\n",
"2 247 41 41 8.0\n",
"3 487 111 111 2.0\n",
"4 168 31 31 7.0\n",
"5 357 69 69 65.0\n",
"6 67 13 13 1.0\n",
"7 1858 361 361 803.0\n",
"8 1595 310 310 496.0\n",
"9 128 15 15 3.0\n",
"10 291 63 63 20.0\n",
"11 1185 238 238 423.0\n",
"12 29 10 10 NaN\n",
"13 512 117 117 89.0\n",
"14 486 98 98 57.0\n",
"15 599 130 130 49.0\n",
"16 275 50 50 16.0\n",
"17 165 30 30 14.0\n",
"18 798 159 159 67.0\n",
"19 1269 262 262 434.0\n",
"20 1358 276 276 333.0\n",
"21 1668 319 319 335.0\n",
"22 1192 229 229 328.0\n",
"23 65 15 15 NaN\n",
"24 112 24 24 NaN\n",
"25 71 11 11 10.0\n",
"26 379 75 75 96.0\n",
"27 126 24 24 11.0\n",
"28 549 127 127 31.0\n",
"29 1157 242 242 166.0\n",
"30 9 3 3 NaN\n",
"31 15 4 4 NaN\n",
"32 182 38 38 1.0\n",
"33 15 4 4 NaN\n",
"34 1091 233 233 194.0\n",
"35 63 9 9 NaN\n",
"36 346 65 65 NaN\n",
"37 203 38 38 18.0\n",
"38 129 27 27 17.0\n",
"39 1070 205 205 218.0\n",
"40 46 9 9 4.0\n",
"41 34 7 7 2.0\n",
"42 15 3 3 NaN\n",
"43 19 4 4 1.0\n",
"44 667 146 146 73.0\n",
"45 191 39 39 1.0\n",
"46 560 108 108 106.0\n",
"47 123 28 28 1.0\n",
"48 389 74 74 104.0\n",
"49 57 9 9 NaN\n",
"50 548 104 104 57.0\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1080x576 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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rwzSvSbrPS1cohpbWDRH2LFxoLr9Hn05px1c5s5hj+FnMMfwspeUAytuvKi1Hziyl5Cx3OYA9+6+kGRYWFoVZv/rg2fMIGBmpYGv9FZym2WDwiE1CMpWy7JSaQ/Q54xNguVRSUhKcnJxgbW0NGxsbBAcHAwCGDBmC/fv363/v77//xg8//JDhOF6/fo3Tp09j6dKluHjxIkJDQ9P8XadOndCrVy+4uLik+bvLly+jf//+sLe3h729PY4cOQIgpVDXpEkT/P7777C1tUXHjh1x7tz/Gth9fHxgbW0Na2trjBo1CmFhYQAANzc3NGnSBLa2tvrfLVGiRJr/e3h4QKPRwNbWFp6enp+0zDIjr1ketOzWFJuddiI2OhbXTwbhtN85tO/fJtdmMcfws6TMObXvMk7/dQURrz/8BGd7h29w0ONstvNSe3jjCRLiEwEAWq0WWq0WpauUEJqhhHX0rhPeZ3DK9xwiwqKEj1tHzvmJfRuHrXM98PxhKLRaLQL3XsCzBy9Q7atKQnOUuC0ocZ7k2L4BoFSFYjjmfRYJcYl4/SIC5w9eRYWaZYTnyLV9S7ncTu29hNP7Lr/3ODFiQS9smuuNxPgkYZn8HmWPko6vcmYxx/CzmGP4WUrL0VHSflVpOXJmKS3nXVHRcXj2PAIAoAKQnJyMMqULCxm30pad0nIok7TJ/CfinwFiASyXunv3Lnr16gV/f3907txZX6Tq378/3Nzc9L/n5uaGPn36ZDgOX19ftG3bFsWKFUOHDh3g4+MDAAgLC8PMmTPh4uKCnTt3wsTkf3cDR0REYNasWVi6dCm8vLywdu1aODk5ISIi5SAaHh6OBg0awMfHB6NGjcKSJUsAALdv38aSJUuwceNG+Pv7o1q1apg7dy4A4MaNG6hXr9575zUhIQH+/v6wt7eHnZ0d9uzZg/j4+E9feB9QpnppJCcl48mdp/phwVceoELtsrk2izmGnyXnPGWkRJnCqNukKg55nBE+7jErvodf+Cb8cW0pXj0Lx5l9l4SO/3NZR6Ll5PwUKmGBstVKC7+jTYnbghLnSS4+aw6gTbdvkCefKYqWLoTGHb7EuYNXJc+VavvOKS2tv0JiQiLOHroudLz8HmWfUo6vcmYxx/CzmGP4WUrLSU0p+1Wl5ciZpaScIYPawHfXGKxc2hcN6qV9zUGAxzj87T8ZY0d0gNuu00LylLTslJhD9LljASyXqlSpEmrXrg0AaNCgAUJCUrota9WqFV6+fIng4GAEBwcjJCQEbdu2zXAcXl5esLOzAwDY2dnpn6y6dOkSateujcqVKwMAevbsqf+bixcv4vHjxxgyZAg0Gg2GDBkClUqFhw8fAgDMzMz0eamnKzAwEG3atEGJEil3UfXq1QunT6ccaLVa7Qfn9Z9//kGlSpVQvnx5lC5dGrVr18aBAweyuMQyJ1/+vIh+8zbNsOg3b2FWIF+uzWKO4WfJOU8ZaefwDa4HBuN5SJjwca8cuwm2RX7AhG9n46RPytMYIn0u60i0nJoftbEaU7eMxoGtxxBy6z+h41bitqDEeZLL1RO3UKFmGXg9cYHbrd9x5+IDnAq4IGmmlNt3TshrlgeDpmuwbqa78HHze5R9Sjm+ypnFHMPPYo7hZyktJzWl7FeVliNnllJy1m08gt7fr0P3fi4I2HcZ83/phi9KF9J/3rX7cnSxX4blLgdwJ/i5kEylLDul5hB97lgAy6VMTf/3/gcjIyMkJqacnKlUKvTt2xfbt2/H9u3b0bNnT6jVanh6ekKj0UCj0cDPzw9Xr15FcHAwZsyYAUtLSzg6OuLx48e4cOHCBwtSWq0WNWrUgK+vr/7f0aNH8eWXX35wurRaLVQqVYbjrFOnDq5eff9d2Z6enrh79y4sLS1haWmJmzdvStYNYkxULMwKpj3QmBfMh7eRMbk2izmGnyXnPGWkXfdvcNA9ULLxJydrcf3ULRQrUwTWw8S+a/BzWUei5cT8qFQqTNk8ConxiVg1Vkw/86kpcVtQ4jzJQaVS4VfvSTjpfx62pYbDoeJo5C9kjsFzHCTNlHL7zgn9fuqKw+6BeP5I/M0R/B6JoYTjq5xZzDH8LOYYfpbSct6lhP2q0nLkzFJKzs1bTxETE4+EhCT8dfAart14giZfV07zO7FxCfDbcxHTJndFIQuzbGcqZdkpNYfoc8cCmALZ2tri4MGD2Lt3LxwcUhp7unXrpi9Y2djYwNPTEz/++CMOHz6s/zdmzBh4enqiYcOGuHHjBh48eAAAcHf/352/DRs2xMOHD/Hvv//qh125cuWjT3E1a9YMR48e1b9nbPfu3WjevDkAoE+fPjh9+jT8/f31v//8+XPs3r0bL168wNmzZ3Ho0CH9dB49ehTXrl3Df/+Jv7v6ye2nUBurUaZqKf2wyvUqStKVkVxZzDH8LDnn6V21G1dC0ZIWOLHnkuRZamM1SlcuKXScn8M6kkJOzM9E12EoVNICc3r8hqREce8S0lHitqDEeZJDgcLmKFGuKPzWH0JCfCIiX0Xj723H8fV37+9uObuk3r5zQoNWNWDzY1u4XV0It6sLUaxMYUxf/yMcRn+X7XHzeyRWbj6+ypnFHMPPYo7hZykt531y835VaTlyZiktR0erRYY3pBupVMibxxjFiuXPdobSlp3Scog+dyyAKVD+/PnRqlUrtGjRAkWKFEn3eVxcHPbu3QsbG5s0w7t27Yr9+/cjX758mDt3LoYPH45evXpBrVbrf8fCwgIuLi5YvXo1bGxs0LlzZ6xateqjBbBq1aph0qRJ+OGHH2BtbY2goCDMmDEDAFCyZEls3boVe/fuRbt27WBtbY2xY8eiSJEi8PHxQevWrZE///8OyHny5EG7du3g5eWVncWUodi3cTjhFYiBs3sir1ke1GleA801X+Pg1qO5Nos5hp8lZY6R2ggmeYxhZGQEI7Uq5Wf1/3b97R2a4MTey4iJjst2VmqFihfEtz2aIa95HhgZqdCoQz207dkMl46IfY+MEtbRu1LWmQmM1EZpfhZJzvkBgLGrB6N8zTJwsl2M+NgESTKUuC0ocZ7k2L4jXkXh6f0X6Dq4LYzURjC3yIf2fVrg3rUQoTk6cmzfUi43/XFCrUr1sxGmdV+OEW3mYrTlfIy2nI9Xz8KxwnE7/P/I/nbB79GnU9rxVc4s5hh+FnMMP0tpOYDy9qtKy5EzSwk5+c3z4OtGlWBqoobaSIX2bWuj3pdlcfb8fTRqWBFVq5SAkZEKZmamGDnMEpFRsXgk4Gl/JSw7JecQfe5U2o9VLijXSUxMhI2NDRYuXIh69aS72zk36WCU+W6PChTOj0kbR+CrDvUQGRaFDdPc8M+OE5JMl1xZzDH8rOzkGJcu9d7P+k7sjH6TrNIM27Z0L9x+2weTPMbYfvFX/Dp0Iy6duP3RnKTQzJ8YWxQrgJk7x6NyvfJQGanw4tFL+Kz6C/v++CdTf69NzHwjcm5YR1BlvqG6v1N3DJiVdp+1ZbY7ts7x+Pgfa5MznZPd5aZKdXPEh5QoXwzbglchPjYeSYn/m77lI11xeMfJj/69NinzT9Pkim3BQLNyw/atNs989yyVvyyH4Qv7oFLdckhOTsaVY0FYNWkr3ryMzNTfJ8dkrtuRbG/fyZk7Dc/WfgGAcbH0N0Tp9J3cBf0cu6YZts05AG5L9qQZtvnsPCybtA2XjgW9d1yJoS8zNT0Av0fvUhmbZOr3lHh8lTOLOYafxRzDz8oNOZndpwLK3K8qLUfOrNyQk/Bd4/d+ZmGRD4vmOqB82SJITtbiUcgrbNxyHOcvPkCbVjUweEArFC9WAHFxiQi6/RTrNx3FvfuhGY7L5O9zss0Tcz4950Cy+Pf1fq46V5qY05OgCPvu/5bTk5AOC2AKc+jQIcybNw/t27fXP2FFWSuAEeU2HyqAiZSVAlh2ZeVCMlfIQoEgW7JQAMuuzBbAsisrBTDKITJt31kpgGVXZgtg2ZXZAlh2fagAJlJWCmCUVlYaa7NDccdXIqIMyLVPBbhfJfl9qAAmUlYLYJQzWAATp3PFCTk9CYqw78HvOT0J6Rjn9ASQWO3atUO7du1yejKIiIiIiIiIiIiIiIhyDN8BRkRERERERERERERERIrCAhgREREREREREREREREpCgtgREREREREREREREREpCgsgBEREREREREREREREZGiGOf0BBAREREREREREREREeWIZG1OTwFJhE+AERERERERERERERERkaKwAEZERERERERERERERESKwgIYERERERERERERERERKQoLYERERERERERERERERKQoLIARERERERERERERERGRohjn9AQQEVH2JIe9kidImyxPjhIpcNlpk7U5PQlkKOTavlUqeXIAaJOSZMuSQ3JUdE5PAn2E0rY5OanUallyuI4MH7cF0tEmJuT0JBBJxuTAhZyeBCJl0rKNQ6n4BBgREREREREREREREREpCgtgREREREREREREREREpCgsgBEREREREREREREREZGisABGREREREREREREREREisICGBERERERERERERERESkKC2BERERERERERERERESkKMY5PQFEREREREREREREREQ5QqvN6SkgifAJMCIiIiIiIiIiIiIiIlIUFsCIiIiIiIiIiIiIiIhIUVgAIyIiIiIiIiIiIiIiIkVhAewz4eDgAI1GAysrK9SuXRsajQYajQaWlpawt7fP6ckzKAUK58csT0f4RW7FtvsuaNu7Za7PYo7hZ8k5TwDwRZWS8A/fhJ82jZBk/Camxpi4fhi23l0Jn1eb4HJ2Ab7u2ECSLKWtI6XlaEZ2xOrA+djzdhsc/5Bme9NR2rKTM0tpOeWql8ZCf0d4PlqFPy4uQPOuX0mSAyhv2QFAm+5N4Hp+IXxfuGLTVWfUbV5deAa/R59Orv2q0tYRz01yRxa3BcPPYo7hZyktR84speXwWow5RAQY5/QEkDzc3d0BAI8fP0a3bt3g6+sLAAgMDMSiRYtyctIMzphVg5EYn4gepYagSoOK+DVgGu5dfoCHNx7n2izmGH6WnPMEAKOXf4/b5+9JMm4AUBurERoShsnt5uDFo5f4pnNDzNgxDsMa/oTnD0OFZiltHSktJ+zpa7jN90Lj7+ojTz5ToeN+l9KWnZxZSsoxUhth1o4x2PvHEUzXLMGXLWtg9s5xGNXqFzwJfi4sR0dJyw4AvrKsg8Fze2L+gNW4de4eipQqJHT8OvwefTq59qtKW0c8N8kdWdwWDD+LOYafpbQcObOUlsNrMeYQEZ8AIwBJSUlwcnKCtbU1bGxsEBwcDAAYMmQI9u/fr/+9v//+Gz/88AMAoH///li4cCH69OmDNm3aYOPGjQgICECvXr1gaWmJffv26f/u2LFjsLW1hbW1NQYOHIiHDx8CSCm+2djY4Oeff4a1tTXs7Oxw584djBs3DlZWVhg8eDDevn0LADh9+jR69uypH8+ePXskWRZ5zfKgZbem2Oy0E7HRsbh+Mgin/c6hff82uTaLOYafJec8AUAbh6aIDo/GxX+uSzJ+AIh9G4etcz3w/GEotFotAvdewLMHL1Dtq0pCc5S2jpSWAwAnvM/glO85RIRFCR93akpcdkqbJ7lyylUvjaKlCsFr9d9ITtbi8rEgXA+8g3a9mgvNAZS37ACg/wx7uC3wQdDZYGi1WoQ9fY2wp6+FZvB7lD1y7FeVuI54bmL4WdwWDD+LOYafpbQcObOUlgPwWow5lCVaLf+J+GeAWAAj3L17F7169YK/vz86d+4MFxcXAClFLjc3N/3vubm5oU+fPvr/P3v2DNu2bcPu3buxYsUK3LlzBzt37sSyZcuwYMECAEBYWBh++uknLFmyBP7+/ujatSsmT56sH0dwcDD69u0Lf39/NGjQAIMHD8a0adOwd+9eGBkZ6QtdtWvXxvbt2+Hj44NNmzZh0aJFePPmjfBlUaZ6aSQnJePJnaf/m8YrD1Chdtlcm8Ucw8+Sc57MCuTDAKfuWD/V7eO/LFChEhYoW6208LuYlLaOlJYjJyUuO6XNk1w5KlVGw1SoUKuM0BxAecvOyEiFal9VgkXxgth0xRnbbi/DqKX9YZrXRGgOv0eGT4nr6F08NzG8LG4Lhp/FHMPPUlqOnFlKy5GT0pad0nKIPncsgBEqVaqE2rVrAwAaNGiAkJAQAECrVq3w8uVLBAcHIzg4GCEhIWjbtq3+7zp16gQjIyOULFkShQoVQvv27QEAderUwfPnzxEXF4fLly+jZs2aqFq1KgCgW7duuHnzJqKiovTZtWrVApBS5KpVqxZKlSqlH4/uabFXr15h7Nix6Nq1KwYPHow3b97g/v37wpdFvvx5Ef3mbZph0W/ewqxAvlybxRzDz5JzngbM6o6/Nh9B6ONXwsf9PmpjNaZuGY0DW48h5NZ/QsettHWktBw5KXHZKW2e5MoJuf0M4S8j0X1cJ6iN1fjKsg6+bFEDec3Ed/uitGVXqIQFTEyN0cr2a0z6bh5GNvsZVepXQJ8pGqE5/B4ZPiWuo9R4bmKYWdwWDD+LOYafpbQcObOUliMnpS07peUQfe5YACOYmv6vQcjIyAiJiYkAUu6W7tu3L7Zv347t27ejZ8+eUKvV+t/NkyeP/me1Wq3/v+53EhMTodVqocroVuwMslOPQ/f/pKQkAMAvv/yCb775Bv7+/vD19UWpUqUQFxeXndnOUExULMwKpj3QmBfMh7eRMbk2izmGnyVXTuV6FfCVZV14rdj38V8WRKVSYcrmUUiMT8SqsZuEj19p60hpOXJS4rJT2jzJlZOUmIQ5fVbim+/qY8ed39FtdEcc9z6Ll/+J7cYPUN6yi4+NBwD4rj2AV8/eICIsCl4r9+PrjvWF5vB7ZPiUuI50eG5iuFncFgw/izmGn6W0HDmzlJYjJ6UtO6XlEH3uWACjD7K1tcXBgwexd+9eODg4ZPnvGzZsiJs3b+rfK+bt7Y3atWsjf/78WRpPZGQkypQpA5VKhZMnT+qfDBPtye2nUBurUaZqKf2wyvUqSvLySbmymGP4WXLl1G9dCyUrFMPWOyuw48FqdB/fBS1tv8Gq0/OE5qQ20XUYCpW0wJwevyEpMUn4+JW2jpSWIyclLjulzZOcy+7+9cf4qcsi9Kg0FjPsf0OpisVx6/w94TlKW3ZR4W8R+jhM8r7b+T0yfEpcRzo8NzHcLG4Lhp/FHMPPUlqOnFlKy5GT0pad0nKIPncsgNEH5c+fH61atUKLFi1QpEiRLP99kSJFsHjxYkyePBnW1tbw8/ODs7NzlsczadIkLF68GD179sRff/2FGjVqZHkcmRH7Ng4nvAIxcHZP5DXLgzrNa6C55msc3Ho012Yxx/Cz5MrZu/EwBtWeiJFNpmNkk+nY43oIZ/ZdwgzrRUJzdMauHozyNcvAyXYx4mMTJMlQ2jpSWg4AGKmNYJLHBEZqozQ/i6bEZae0eZJz2VWqUxYmeYyRJ58puo3piCKlLHDA7aTwHCUuu7+3HofN8A6wKF4A+QuZwW5URwTuuyQ0g9+j7JFjv6rEdQTw3MTQs7gtGH4Wcww/S2k5cmYpLQfgtRhziAgAVFqtxLd4Uq6WmJgIGxsbLFy4EPXq1cvpyflkHYwy//RagcL5MWnjCHzVoR4iw6KwYZob/tlxQpLpkiuLOYaflZ0cI9NPe69Nv5/t8UWVklj8/ZpM/b42KfN3xpYoXwzbglchPjYeSYnJ+uHLR7ri8I6PN0JnJSs3rCNF5qgyd+HU36k7BsxKuw/eMtsdW+d4ZC5Hm/zx3/l/uWbZGWBWbshRFyiQ6Zwf5zqg44DWMDZW49rpO3D5yQ1P773I9N8nRUZm+ndzw7Izypf59wiojdUY4dwPbXs0RXxcAo55nsGGn3chIe7jDcTJMZnvroXfo3dkcp8KZHO/aoD71OxmqVJ10f4hPDfJHVncFj5dblhHn3OOnFlKy5EzK1fkGOA5A5BLlp0Ccw4kuwufps9V59KjcnoSFGHf09U5PQnpsABG73Xo0CHMmzcP7du3x4wZM3J6crIlKwUwotzmUwtgWZWVi/3clEWfKAsXXtmSxQsvUq6sFMCyKysFsNwgKwWw7MhKAYzewX3qJ8ts0SO7eG5i+LgtENFngecMlAoLYOKwACaGIRbAjHN6AshwtWvXDu3atcvpySAiIiIiIiIiIiIiIsoSvgOMiIiIiIiIiIiIiIiIFIUFMCIiIiIiIiIiIiIiIlIUFsCIiIiIiIiIiIiIiIhIUVgAIyIiIiIiIiIiIiIiIkUxzukJICIiIiIiIiIiIiIiyhFabU5PAUmET4ARERERERERERERERGRorAARkRERERERERERERERIrCAhgREREREREREREREREpCgtgREREREREREREREREpCgsgBEREREREREREREREZGiGOf0BBARUfYYFbKQJSfxRagsOXJSqdWy5GiTtbLkQJssT47cWTKQbVtISpIlR05GefLKkpMUGSlLDgBAJc89YmpzM1lykqKiZMmhbFDYPlVOStyv0qfhtkB6Mh3HAXD/TfJT4Dan+uZLWXK0Z67KkkO5lFamdhuSHZ8AIyIiIiIiIiIiIiIiIkVhAYyIiIiIiIiIiIiIiIgUhQUwIiIiIiIiIiIiIiIiUhQWwIiIiIiIiIiIiIiIiEhRWAAjIiIiIiIiIiIiIiIiRTHO6QkgIiIiIiIiIiIiIiLKEcnanJ4CkgifACMiIiIiIiIiIiIiIiJFYQGMiIiIiIiIiIiIiIiIFIUFMCIiIiIiIiIiIiIiIlIUFsCIiIiIiIiIiIiIiIhIUWQvgDk4OECj0cDKygq1a9eGRqOBRqPBtGnTJMucMWMGzp07BwA4ePAgrly5ov/s6tWrmDRpkmTZU6dOxbZt24SNb+XKlVi0aBEAwMvLC2PHjhU27qwYMmQIHj16lCPZUitQOD9meTrCL3Irtt13QdveLXN9FnMMP0uqHOsf2mD5X1Pg93A5Ji7vrx9eolwR7HvmAq/g3/T/ek/oLCQTUN46MjE1xsT1w7D17kr4vNoEl7ML8HXHBsJzAEAzsiNWB87Hnrfb4PjHCEkyAOWtI7ly5NwWAGUtOwBY/Nd0+L/eCJ9QV/iEumLD5cWS5Mg1P3J9XwGgZPmimOMxAe4PV2H7nWUYuaQfjNTiT+WVts3JmcUcw89ijuFnMcfws5SWI+exXGnLjts3c+TImvKLLXYETID3oSn4Y/codLJpqP+sk01DbHIfDd/DU/Hr731QpFh+IZmAMtcR0efKWO5Ad3d3AMDjx4/RrVs3+Pr6pvk8MTERxsZiJ+vXX3/V/3zw4EHUrVsX9erVAwB8+eWXWLp0qdA8pUhKSoJarc7wM1dXV5mnRj5jVg1GYnwiepQagioNKuLXgGm4d/kBHt54nGuzmGP4WVLlhD17g52/70ejtrVgmtck3efdq09GclJytjIyorR1pDZWIzQkDJPbzcGLRy/xTeeGmLFjHIY1/AnPH4YKywGAsKev4TbfC42/q488+UyFjjs1pa0juXLk3BYAZS07ndUTtmD/5qPCx5uaXPMj1/cVAEb9NgBvQiPQp/p45Lcww3xfR1gPsYTv2oNCc5S4zSltnpSWI2cWcww/izmGn6W0HDmP5Upbdty+mSNH1s4/T+K3X/2RkJCEchWKwtllIIJvPUNeM1P8MNwSjqO24ElIGEZM7ITpc7ph8sg/DXp+ciqH6HNmEF0gWlpaYvXq1ejfvz+cnJwQGhqK/v37w97eHl26dMHixf+7O3jlypWYOHEihgwZgk6dOmHo0KGIiYkBkFLcsra2hkajQdeuXREYGAgA6N+/P/755x8cP34chw8fxvr166HRaODj44PAwEDY29vrx+/j4wNra2tYW1tj1KhRCAsLA5DytNUPP/yA8ePHo0uXLujVqxdCQ1Maum7duoU+ffrAzs4OVlZW2Lx5c6bmOz4+HosWLULXrl1hY2ODUaNG6T9zdXVF9+7dYWdnh+HDh+uzssLDwwM2NjawsbFBt27d8PLlSyQmJmLw4MH6ZTtt2jTEx8fr53Hw4MFwdHSEvb09bt++jYsXL6J379768Zw4cUK/zm7fvq1fvosWLULv3r3Rrl07LFmyRD8NDx8+xMCBA2FtbQ07OzscO3ZM/1mNGjWwZs0adOvWDe3atcPp06exdOlS2NraomvXrggODgaAD24PouU1y4OW3Zpis9NOxEbH4vrJIJz2O4f2/dvk2izmGH6WlDmn9l7C6f2XEfEqWsCUZo4S11Hs2zhsneuB5w9DodVqEbj3Ap49eIFqX1USmgMAJ7zP4JTvOUSERQkft44S15EStwWlLTu5yDk/cnxfdUpVKIZj3meREJeI1y8icP7gVVSoWUZohhK3OaXNk9Jy5MxijuFnMcfws5SWA8h3LFfasuP2zRy5sh7eD0VCQhIAQKsFtFotSpctjKYtq+PY4Rt4eD8UiYnJcPvjGOp9VQGlyxTOdqYS1xF9nFabzH8C/hkigyiAASlFjq1bt2L+/PkoWLAg1q5dCy8vL/j4+ODatWtpCifXrl3D0qVLsW/fPiQmJsLf3x8AsGLFCsyaNQu+vr7w9fVFnTp10mS0atUKlpaWGDp0KHx9fWFra5vm89u3b2PJkiXYuHEj/P39Ua1aNcydO1f/+dWrVzFlyhTs2bMHVatW1XdtWKZMGWzevBne3t5wd3fH7t279cWbD1m/fj1CQkLg5eUFPz8/fZavry8ePXqE3bt3w9vbG61bt8bChQuztDwDAwOxbt06bNy4EX5+ftiyZQsKFCgAtVqNJUuWwMvLCwEBAUhKSoKnp6f+7y5cuIAxY8bAy8sLpUuXxujRo+Ho6Ag/Pz94e3vjyy+/zDDv6dOncHNzg4+PD9zd3fHgwQMAwOTJk9G1a1f4+/vD2dkZjo6OePXqlf7vChYsCE9PT0yePBkjR45Eo0aN4OPjA41GgzVr1uh/50Pbg0hlqpdGclIyntx5qh8WfOUBKtQum2uzmGP4WXLO07v+PDcPWy/8ignL+qNgEXMh41TiOnpXoRIWKFutdK69K0uJ60iJ24JSl933c3pgd4gLfjs8E/Va1RQ+/pzcp0rJZ80BtOn2DfLkM0XR0oXQuMOXOHfwqtAMJW5zSpsnpeXImcUcw89ijuFnKS1HTkpbdty+mSNn1hjHzvA7Mg1/7B6FV2FROHPqDlQqQKX63++o/v8/FSsXz3aeEtcR0edM9i4Q3yd1MSopKQmLFy/GxYsXodVq8fLlSwQFBaF169YAgJYtW6JgwYIAgHr16unfRdW0aVMsXLgQnTp1QuvWrVG9evUsTUNgYCDatGmDEiVKAAB69eoFjUaj//yrr75C6dKlAQD169fHqVOnAACxsbH45ZdfcOvWLahUKrx48QJBQUGoUqXKB/P++ecfTJ06FaamKY/ZFylSBABw+PBhXLt2DXZ2dvrlkT9/1vqxPXLkCDQaDYoXT9nxm5ub68f1xx9/4NixY0hOTsabN2+QN2/eNPNYvnx5AMClS5dQpUoVfPXVVwAAtVoNCwuLDPM6deoEIyMjFChQAFWqVMGjR49QrFgx3Lx5E926dQMAVK1aFbVq1cKlS5dgaWkJAOjcOeWdQ7pi5bfffgsAqFu3Lg4cOKCf5g9tDyLly58X0W/ephkW/eYtzArky7VZzDH8LDnnSSciLBpjOy5E8LXHKFjYHKMW9sRPq7/Hz71XZXvcSlxHqamN1Zi6ZTQObD2GkFv/SZYjJSWuIyVuC0pcdht/3oVHN58gMT4RbRyaYrbnRIxs8jOe3n8hLCMntgU5XD1xC50HtoHXExeojdU44HYCpwIuCM1Q4jantHlSWo6cWcwx/CzmGH6W0nLkpLRlx+2bOXJmrXTeh9VL96PWl2VR/6uKSIhPwplTdzFjXjcEeJ/Hk5BX6PdDayQna5Eng1c/ZJUS1xHR58xgngAzMzPT/7xp0yZERETA3d0d/v7+aN++PeLi4vSf58mTR/+zWq1GUlLKo7DTp0/Hr7/+ChMTE4wbNw67d+/O0jRotVr9HQMZeV/ub7/9huLFi8Pb2xt+fn6oV69emunVcXBwgEajQZ8+ffR575uOESNG6J9kCwgIwM6dOz847bNnz4ZGo4FGo8G9e/fe+3v+/v44f/483Nzc4O/vjz59+ui7QAT+Vyj70PRl5H3LJiOpl7Hu74yMjPSFQN3/ExMTAXx8exApJioWZgXTHmjMC+bD28iYXJvFHMPPknOedGLfxuHO5UdITkpG+MtIuEzbjUZta8Msf96P//FHKHEd6ahUKkzZPAqJ8YlYNXaTJBlyUOI6UuK2oMRld+tsMGKiYpEQn4iDbidw4/QdfN2pvtCMnNinSk2lUuFX70k46X8etqWGw6HiaOQvZI7BcxyE5ihxm1PaPCktR84s5hh+FnMMP0tpOXJS2rLj9s0cubOSk7W4fjkExYoXgHW3xrh07j62uB6B0wIHbPMeh+dPwxHzNg4vX0RmO0uJ64joc2YwBbDUIiMjUbx4ceTJkwfPnz/HoUOHMvV39+7dQ40aNTBw4EDY2Njg6tX03cLkz58fkZEZ7wybNWuGo0eP6t+3tXv3bjRv3jxT01uqVCkYGxvj9u3bOHfuXIa/5+7uDl9fX2zfvh1Aynu0/vzzT30BStc1oKWlJbZv3443b94ASHlXWFBQ0AenIXXXj5UrV0bbtm3h6+uLly9fAgCio6MRHx+PyMhIFC5cWL8cAgIC3jvOhg0bIjg4GBcvXgSQ8iSWbpoyI3/+/KhVqxa8vb0BAMHBwQgKCkL9+llr6PrU7eFTPLn9FGpjNcpULaUfVrleRUm6tpIrizmGnyXnPL2PvuD9/nsAMk2J60hnouswFCppgTk9fkNS4vsL/YZOietIiduCUpddah+7+ehTGMI+VbQChc1RolxR+K0/hIT4RES+isbf247j6+/qCc1R4jantHlSWo6cWcwx/CzmGH6W0nLkpLRlx+2bOTmVpTY20r/ny9/zHL53WI0eVktx/J+bUKuN8OBe9nuWUOI6IvqcGWQBrH///rhw4QJsbW0xa9YsNGvWLFN/t3TpUnTt2hUajQanTp3CkCFD0v2OjY0NAgICoNFo4OPjk+azatWqYdKkSfjhhx9gbW2NoKAgzJgx46O5I0aMgLu7O7p164Y1a9bg66+/ztT0Dh06FGXKlIGtrS00Gg1++eUXACndQdrY2KBfv36wtraGvb09zp8/n6lx6nzzzTcYOnQovv/+e9jY2GDgwIGIiIiAra0toqOj0aVLF4wbNw6NGjV67zgKFSqElStXYuHChfrpuH79epamY8mSJfDz84O1tTUmT56MxYsX67t6zKxP3R4+RezbOJzwCsTA2T2R1ywP6jSvgeaar3Fw69Fcm8Ucw8+SMsdIbQSTPMYwUhvByOh/P9doWBFlqpSASqVCgcLmGP5rD1w+eRtvI2MNen5yMmvs6sEoX7MMnGwXIz42Qfj4dVLWmUnKOkv1s0hKXEdK3BaUtuzMLczQqP2X+m26ba/m+LJlTZw/cEVojpzbghzfVwCIeBWFp/dfoOvgtjBSG8HcIh/a92mBe9dChOYobZuTM4s5hp/FHMPPYo7hZyktB5DvWK60ZcftmzlyZBUqbIZv29dB3nwmMDJSoVGTKmjboS4unbsPE1O1/n1fxUsWxPhpXeG96wyiclGbhpzriOhzptJmpZ87olyqg1HmuwgqUDg/Jm0cga861ENkWBQ2THPDPztOSDJdcmUxx/CzspNjXOL9L3ntO7kL+k3ukmbYtiV78PjucwyaboNCxQrgbWQsLhwLwh9zvPE6NOK940p8EZq5mUHuWUcqtTpTv1eifDFsC16F+Nh4JCUm64cvH+mKwztOfvTvtcmZP9T2d+qOAbPS7rO2zHbH1jkeH/9jbfLHf+f/5ZZ1JFeObNvCB7oIzkhuWHZGeTLXdapFsQKY6zMZ5f7/Zc8ht59iy2xPXDh8LVN/nxyX+YvZbC83VeYavrL1fQWgNjf7+C/9v8pflsPwhX1QqW45JCcn48qxIKyatBVvXn68m5ekqKhM5+SGbc5Qs5hj+FnMMfws5hh+Vq7IyeRxHMj+sdwQz7+VliNnFnNyJkv1zZcZDrcoZIaZ8x1QuVpJqIxUePE0HD7uZ7DP9yLM8+fB0rWD8EWZwnj7Nh5/B1zC5nX/IPkD197aM+l7CpNifrIiuzkHkt2FT9PnqlOR9A/SUNbtf+Wa05OQDgtg9FnISgGMKLf5UAFMpKwUwHKLzBY9sisrBbBsBsmTo0CybQtZLIDlBpktgGVXVgpg2ZaFhrPsyEoBLDuyUgAjIiLK9WQ6jgPg+TeRAO8rgImWlQJYbsECmDgsgIlhiAUwg+wCkYiIiIiIiIiIiIiIiOhTsQBGREREREREREREREREisICGBERERERERERERERESkKC2BERERERERERERERESkKCyAERERERERERERERERkaIY5/QEEBERERERERERERER5QitNqengCTCJ8CIiIiIiIiIiIiIiIhIUVgAIyIiIiIiIiIiIiIiIkVhAYyIiIiIiIiIiIiIiIgUhQUwIiIiIiIiIiIiIiIiUhQWwIiIiIiIiIiIiIiIiEhRjHN6AoiIKHuSw9/IE6SS8Z4JbbI8MUlJsuSQ4eO28OmS42LlCVLgPigpKkqWHCJFk2vfINN+gbKB2wLpcB1Ratw3GDztmas5PQlEQDK/w0rFJ8CIiIiIiIiIiIiIiIhIUVgAIyIiIiIiIiIiIiIiIkVhAYyIiIiIiIiIiIiIiIgUhQUwIiIiIiIiIiIiIiIiUhQWwIiIiIiIiIiIiIiIiEhRjHN6AoiIiIiIiIiIiIiIiHKEVpvTU0AS4RNgREREREREREREREREpCgsgBEREREREREREREREZGisABGREREREREREREREREisICGBERERERERERERERESkKC2Akmxo1aiA6OjrNsCZNmuDx48cAAK1Wiz///BNdunSBlZUVOnXqhJ9//hkRERHQarVYsWIFunTpAhsbG1hZWWHTpk2STGeBwvkxy9MRfpFbse2+C9r2bilJjpxZzDH8LDnnCQC+qFIS/uGb8NOmEZKMXzOyI1YHzseet9vg+Ic0GTpKW0dKy5EzS2k5cmYpLYf7IObkRBZzDDuL+4XckaW04wTXEXNyIktpOXJl8TiRO7KYQ0RZZZzTE0Cks2zZMpw9exZ//vknihUrhuTkZBw8eBBv3rzByZMncfr0aXh5eSFPnjyIj4/Ho0ePJJmOMasGIzE+ET1KDUGVBhXxa8A03Lv8AA9vPM61Wcwx/Cw55wkARi//HrfP35Nk3AAQ9vQ13OZ7ofF39ZEnn6lkOYDy1pHScuTMUlqOnFlKy+E+iDk5kcUcw87ifiF3ZCntOMF1xJycyFJajlxZPE7kjizmEFFW8QkwMgjR0dHYtGkT5s2bh2LFigEAjIyM8N1336FcuXJ4/vw5ChcuDFPTlJMQU1NTVK1aVfh05DXLg5bdmmKz007ERsfi+skgnPY7h/b92+TaLOYYfpac8wQAbRyaIjo8Ghf/uS7J+AHghPcZnPI9h4iwKMkyAOWtI6XlyJmltBw5s5SWA3AfxBz5s5hj+FncLxh+ltKOE1xHzMmJLKXlyJnF44ThZzGHpKRNTuY/Af8MEQtgZBCCg4NhamqKypUrZ/i5lZUVgoOD8d1332HatGnw9fVFYmKi8OkoU700kpOS8eTO0/9N25UHqFC7bK7NYo7hZ8k5T2YF8mGAU3esn+omfNw5QWnrSGk5cmYpLUfOLKXlyElpy05pOXJmMSd3ZMmB68jwc+TCdcScnMhSWo7cWXLgOmKO3DlEnzsWwCjHqVQqaLXaD/5OiRIlsGfPHixYsAAVK1bE2rVrMWzYMOHTki9/XkS/eZtmWPSbtzArkC/XZjHH8LPknKcBs7rjr81HEPr4lfBx5wSlrSOl5ciZpbQcObOUliMnpS07peXImcWc3JElB64jw8+RC9cRc3IiS2k5cmfJgeuIOXLnEH3u+A4wkk2RIkUQHh4Oc3NzAEBiYiKioqJQpEgRFCpUCHFxcbh//z4qVaqU4d8bGxujcePGaNy4Mbp164YWLVogPDwchQoVEjaNMVGxMCuY9kBjXjAf3kbGCMuQO4s5hp8lV07lehXwlWVdjGwyXeh4c5LS1pHScuTMUlqOnFlKy5GT0pad0nLkzGJO7siSA9eR4efIheuIOTmRpbQcubPkwHXEHLlziD53fAKMZNO8eXPs2rVL//9du3ahfv36yJcvH8zNzTFo0CA4OTkhLCwMAKDVauHn54dHjx7h2rVrePz4fy+AvH79OiwsLFCwYEGh0/jk9lOojdUoU7WUfljlehUlefmkXFnMMfwsuXLqt66FkhWKYeudFdjxYDW6j++ClrbfYNXpeUJz5KS0daS0HDmzlJYjZ5bScuSktGWntBw5s5iTO7LkwHVk+Dly4TpiTk5kKS1H7iw5cB0xR+4cos8dC2AkmxkzZuDJkyewtraGRqPB8ePHsXjxYv3nEydOhKWlJfr37w8rKytYWVnh7NmzKFSoEF6/fo0JEyagc+fO0Gg0WLNmDVavXg0jI7GbcOzbOJzwCsTA2T2R1ywP6jSvgeaar3Fw61GhOXJmMcfws+TK2bvxMAbVnoiRTaZjZJPp2ON6CGf2XcIM60VCcwDASG0EkzwmMFIbpflZNKWtI6XlyJmltBw5s5SWA3AfxBz5s5hj+FncLxh+ltKOE1xHzMmJLKXlyJnF44ThZzGHiD6FSvuxly8RKUAHI4dM/26BwvkxaeMIfNWhHiLDorBhmhv+2XFCkumSK4s5hp+VnRwjU9NPyuz3sz2+qFISi79fk6nfT05IzPS4+zt1x4BZab93W2a7Y+scj8yNQJuc6azcsI4+5xw5s5SWI2dWrshRZb4Bgvsg5uREFnNyKCuT+wbuF3JHVq44ThjgtiBnFnMMP0tpOdnO4nHC8NcRcz4550Cyu/Bp+lx1NB+Q05OgCH9Fb8npSUiHBTD6LGSlAEaU23xqASyrslIAy7YsXDwQ0WciCw2b2cZ9EFHuIde+gfsFw8dtgYgywn0DKRgLYOKwACaGIRbA2AUiERERERERERERERERKQoLYERERERERERERERERKQoLIARERERERERERERERGRorAARkRERERERERERERERIpinNMTQERERERERERERERElCOStTk9BSQRPgFGREREREREREREREREisICGBERERERERERERERESkKC2BERERERERERERERESkKCyAERERERERERERERERkaKwAEZERERERERERERERESKwgIYERERERERERERERERKYpxTk8AERFlj5FFQXmC3kTIkwMgOT5etiw5GJctI0tO4uMnsuQAAFQy3UOjTZYnR2nzIyOVWp3TkyABeeZJm6yVJUeJ2x0ZPrn2DXLlKO3cRImM8uWVJSf57VtZcohIEJ4HGT6lXYs1byBPDonFfYVi8QkwIiIiIiIiIiIiIiIiUhQWwIiIiIiIiIiIiIiIiEhRWAAjIiIiIiIiIiIiIiIiRWEBjIiIiIiIiIiIiIiIiBSFBTAiIiIiIiIiIiIiIiJSFOOcngAiIiIiIiIiIiIiIqKcoE3W5vQkkET4BBgREREREREREREREREpCgtgREREREREREREREREpCgsgBEREREREREREREREZGisABGREREREREREREREREisICmIRq1KiB6OjoNMOaNGmCx48fAwA2b96MsLCwnJi0NMaNG4dmzZohISHhvb/j5eWFsWPHSjodfn5+sLW1RadOndC5c2dMmDAB//33n/7z4OBg1KhRA5s3b5Z0OgoUzo9Zno7wi9yKbfdd0LZ3y1yfxRzDz5Iqx/qHNlj+11T4PVqBicsHZPg7fSZ1wb7na9CgdU0hmal9UaUk/MM34adNI4SPWye3ryMTUzXGL+6Fzaec4HljIVbtnYzG39bSf96gRTWsPzwN3rcWY+HOUShRprCQXLmWm2ZkR6wOnI89b7fB8Q/ptgOA82ToOSamxpi4fhi23l0Jn1eb4HJ2Ab7u2CDX5siZxW0ud2Qx59PI+Z3Vkfr8RGnrSM4sOXJ8nq1L82/vm00YuaSf8ByA64g5OZOltBw5s5hj2FlKOicuU7Yw9hyagikzbQAA5SsWw+oNP8Br30R47ZuIRcv6oHzFYkIziT5Xxjk9AZ+zLVu2oHnz5ihatGiW/i4xMRHGxmJWXXh4OE6fPo1KlSrhn3/+wXfffSdkvFnl7u6OTZs2wcXFBRUrVgQABAYG4uXLl/jiiy8AAB4eHmjatCk8PT0xaNAgyaZlzKrBSIxPRI9SQ1ClQUX8GjAN9y4/wMMbj3NtFnMMP0uqnLBnb7Bz2T40+rY2TPOapPu8dIViaGndEGHPwrOV8z6jl3+P2+fvSTJundy+jozUaoQ+DcdPPVYh9MlrfG1ZC9NcBmLEd4sRGx2Hn9f9gGVTdiLw4HUMmGSFaasHYoLtMoOdn3eFPX0Nt/leaPxdfeTJZyp03O/iPBl2jtpYjdCQMExuNwcvHr3EN50bYsaOcRjW8Cc8fxia63LkzOI2lzuymPNp5PzO6kh9fqK0dSRnlhw5tqWG6X/OY2aKXfdW4pj3WWHjT43riDk5kaW0HDmzmGPYWUo6Jx4zsRNuBf3vpv+wl5GY87Mnnj97AyMjFWzsG2HGL7YYNmiDkDyizxmfAMsha9aswYsXLzB27FhoNBrcvXsX8fHxWLRoEbp37w6NRgNHR0f9E2RTp07F3LlzMXjwYPTo0QOPHz9GkyZN9ONL/f+wsDAMGjQI1tbWsLa2xvz58987HX5+fmjTpg369OkDT09P/fD4+Hg4OTmhY8eOGDBgAK5cuZLm71xdXdG9e3fY2dlh+PDhCA1NuThduXIlJk6ciCFDhqBTp04YOnQoYmJiAADR0dGYNm0aunbtiq5du2L9+vX68a1atQrTpk3TF7+AlKfl6tWrByCl6Ofv7485c+YgLi4OV69e/ZTF/lF5zfKgZbem2Oy0E7HRsbh+Mgin/c6hff82uTaLOYafJWXOqb2XcHrfZUS8js7w8xELemHTXG8kxidlO+tdbRyaIjo8Ghf/uS583DpKWEdxMfFw+30/Xjx+Ba1WizOHbuB5yCtU+7IsWnSuh4e3n+HEnstIiEvEtt/3o1LtL1C2SgmDnZ93nfA+g1O+5xARFiV83Klxngw/J/ZtHLbO9cDzh6HQarUI3HsBzx68QLWvKuXKHDmzuM0ZfhZzPp2c31lA+vMTJa4jJc6TTivbrxEeGoFrJ28JHzfXEXNyIktpOXJmMcfws5RyTvxtu9qIiorFxfMP9MOio+Lw/NmblP+ogORkLb4oW0RIHmWSNpn/RPwzQCyA5ZARI0agRIkSWLFiBXx9fVG1alVs2LABBQoUgIeHB3x9fVGiRIk0RaKLFy9i5cqV8PLy+uC4/f398cUXX8Df3x/+/v4YNWrUe3/Xy8sL9vb26NixIy5duoTnz58DAHbt2oXHjx8jICAA69atS1MA8/X1xaNHj7B79254e3ujdevWWLhwof7za9euYenSpdi3b5++cAUALi4uSE5Ohr+/P3bu3AlfX18cPXoUYWFhePbsGerXr//e6Txy5AgqVKiAChUqwM7OLk2xTqQy1UsjOSkZT+481Q8LvvIAFWqXzbVZzDH8LDnnKbWW1l8hMSERZw+JbwAyK5APA5y6Y/1UN+HjTk2J66hQsfwoU6k4Ht5+hvLVS+H+zf/dFRYXE4+nD8NQoXqpbGXk1DYnJc6T4ee8q1AJC5StVlqSO1BzIkfuLCkocZtT2jwpLScjUn6P5Dg/UeI6UuI86XTo2xIHd5yUZNxcR8zJiSyl5ciZxZzckSUHKefHzMwUA39sjXWrDmb4ufe+Sdh7aCpGje+IHVulOT4RfW7YBWIOUKlUGQ4/fPgwoqKi8NdffwFIeQqrZs3/vZOnU6dOMDMz++j469evj02bNmHRokX45ptv0LJlxv3U3rhxAxEREWjatClUKhU6dOgAX19fDB06FIGBgbC1tYWJiQlMTExgY2ODCxcu6Kfz2rVrsLOzAwAkJSUhf/78+vG2bNkSBQsWBADUq1cPjx49AgCcPn0a06dPh0qlQv78+dGlSxecPn0aderU+eg8eXh46PNsbW1ha2uLadOmIU+ePB/926zIlz8vot+8TTMs+s1bmBXIJzRHzizmGH6WnPOkk9csDwZN12BGzxWSjH/ArO74a/MRhD5+Jcn4dZS2jtTGRvhpRX8c9DyLx8EvkM8sD968Snt3W3RkDPKZ581WTk5sc1LjPBl+TmpqYzWmbhmNA1uPIeTWfx//AwPPkTtLKkrc5pQ2T0rLeZfU3yM5zk+UuI6UOE8AULxsEXzZsiZ+G7lRkvFzHTEnJ7KUliNnFnNyR5YcpJyfQUPaYH/AZYS+iMzwc7vOS5E3rwk6dK73vyfCiChbWACTUJEiRRAeHg5zc3MAKd34RUVFoUiRjB9h1Wq1mDVrFpo1a5bh56mLX8bGxtBqtfr/x8XF6X9u2LAhfHx8cOrUKfj6+mL9+vXYsWMHRo0ahcePU+6kdHNzg4eHByIiItCuXTsAKQU3c3NzDB06NM24M5rOESNGoHv37hl+nroopVar9dOm1WrTFf9UKhWKFSuGkiVL4sqVKxkW616+fImTJ08iKCgILi4uAICYmBj8/fffsLa2fu90foqYqFiYFUx7QDMvmA9vI2OE5siZxRzDz5JznnT6/dQVh90D8fxRmPBxV65XAV9Z1sXIJtOFj/tdSlpHKpUKjsv6ITE+CS4zPVJy38bBLH/aYpdZ/ryIiY7NVlZObHNS4zwZfo6OSqXClM2jkBifiFVjN0mSIWeO3FlSUuI2p7R5UlpOalJ/j+Q6P1HiOlLiPAFA+z4tcf30bTx/+FKS8XMdMScnspSWI2cWc3JHlhykmp8qVUuiYeNKGPH9h9/rFRubgACf8/AImIDBfdchPPztB3+fiD6MXSBKqHnz5ti1a5f+/7t27UL9+vWRL1/KTtTc3ByRkf+r+FtaWmLz5s2IjU1p2IyKikJwcHCG4y5WrBgSEhLw8OFDAEBAQID+s5CQEP0TVtOmTcP169eRnJyM1atXw9fXF76+vjA1NcWePXvg4eGBw4cP4/Dhwzhx4gRUKhXOnTuHZs2awdfXF4mJiYiNjU0zfktLS2zfvh1v3qTciRAfH4+goKBMLQ8PDw9otVpERUVh7969+mLfyJEjsXDhQv3TYgBw/PhxXL58Gd7e3ujYsSOOHDmin9b58+dL0g3ik9tPoTZWo0zV/3UvVrleRUm6YJErizmGnyXnPOk0aFUDNj+2hdvVhXC7uhDFyhTG9PU/wmH0d9ked/3WtVCyQjFsvbMCOx6sRvfxXdDS9husOj1PwJSnpaR1NN65FwoVK4B5wzYhKTGl3+RHt5+hUu0v9L+TJ58pSlcohoe3n2UrKye2Oalxngw/R2ei6zAUKmmBOT1+Q1Ki+PcPyp0jd5aUlLjNKW2elJaTmtTfI7nOT5S4jpQ4TwDQvncLHHA7Icm4Aa4j5uRMltJy5MxiTu7IkoNU81OvYXmULGUBN88x2OU7Dg69mqLVtzXhsnFwut9VGamQJ68JihYvkK1MImIBTFIzZszAkydPYG1tDY1Gg+PHj2Px4sX6zwcMGIDp06dDo9Hg7t27GDp0KGrWrInu3bvD2toaffr0eW8BzNjYGDNmzMD333+P/v37Q61W6z87c+YM7OzsoNFo8OOPP2L27NkwMkq7qg8ePIjy5cujYsWKaYZ37doVnp6e6NGjB7744gt06dIFw4YNw9dff63/HVtbW9jY2KBfv36wtraGvb09zp8//9HlMXLkSGi1WlhbW6NXr16wsbFB69atAQC9evXCjz/+iLFjx6JTp06wsrKCt7c3SpQoAW9v73RPerVr1w5Xr17VP9EmSuzbOJzwCsTA2T2R1ywP6jSvgeaar3Fw61GhOXJmMcfws6TMMVIbwSSPMYzUqlQ/G2Fa9+UY0WYuRlvOx2jL+Xj1LBwrHLfD/4/sZ+7deBiDak/EyCbTMbLJdOxxPYQz+y5hhvWibI/7XUpYRwAwer4DylctiV9+cEV8XIJ++Kn9V1Cxemm06FwPJnmM0Xd8Rzy4+R8eB7/IVp6c36OU7c4ERmqjND+Lxnky/BwAGLt6MMrXLAMn28WIj034+B8YeI5cWdzmDD+LOdkjx/dIrvMTJa4jJc5T7SZVUeyLwjjufVb4uHW4jpiTE1lKy5EzizmGn5Xbz4n3+l3EwJ4uGP79Bgz/fgMCfC8g8NRdTJu0A181roQq1UrCyEgFMzNTDB/dAVGRsXgk0VPKRJ8TlfZDfd0RKUQHI4dM/26BwvkxaeMIfNWhHiLDorBhmhv+2SHNnYFyZTHH8LOyk2NcvNh7P+s7uQv6OXZNM2ybcwDcluxJM2zz2XlYNmkbLh17/9OcyW8iMjU97+r3sz2+qFISi79fk+m/SY6Pz/Tv5op1VLbMez8rUaYw/jw9C/GxCUhKStYPXzltN/7xOY8GLatj5JxuKFG2MG5dfISlk7bjxXveXZL4+Iks8wMAUGXuQqO/U3cMmJV2H7xltju2zvHIXI42+eO/8/+yNU+ZnB8gm/OUhfkBcsf2rUp1E86HlChfDNuCVyE+Nl7/lCMALB/pisM7xL3gWa4cEVna5Mydhuea71EW8Fiu/By59g2ZzXlXVs9PlHZuYqhZ2ckxysT7snXGrhiEPPlM4TxkfZanMflt5rui4jpiTk5kKS1Hzizm5FCW0q4tmzfI1K/1/6EVvihTGIvm+qF125oY+GMbFC9eEHFxCbgd9BQb1v6D+x+5+fXAiRmZmyb6qA7qnjk9CYpwIGnXx39JZiyA0WchKwUwotzmQwUwkT61APZJWVloZMoNPlQAEykrBbBsy0LBKFuyWDD6ZEqbHxl9auMzZb4AJiBInhyiVOTaN8iVo7RzEyXKSgEsO7JSACMiokxQ2rVYJgtgIrAAJg4LYGIYYgGMXSASERERERERERERERGRorAARkRERERERERERERERIrCAhgREREREREREREREREpCgtgREREREREREREREREpCjGOT0BREREREREREREREREOUKbnNNTQBLhE2BERERERERERERERESkKCyAERERERERERERERERkaKwAEZERERERERERERERESyuH//Pnr27ImOHTuiZ8+eePDggSQ5LIARERERERERERERERGRLGbNmoU+ffrgr7/+Qp8+feDk5CRJDgtgREREREREREREREREJLmwsDDcuHEDXbt2BQB07doVN27cwKtXr4RnGQsfIxEREREREREREREREX02IiIiEBERkW54wYIFUbBgQf3/nz59ipIlS0KtVgMA1Go1SpQogadPn6JIkSJCp4kFMPosHEh2z+lJICIiIiIiIiIiIiIDw7ZjMVauXIlVq1alGz569GiMGTMmB6aIBTAiIiIiIiIiIiIiIiLKhoEDB8LOzi7d8NRPfwFA6dKl8fz5cyQlJUGtViMpKQkvXrxA6dKlhU8TC2BERERERERERERERET0yd7t6vB9ihYtilq1aiEgIAAajQYBAQGoVauW8O4PAUCl1Wq1wsdKRERERERERERERERE9I7g4GBMnToVERERKFiwIBYtWoTKlSsLz2EBjIiIiIiIiIiIiIiIiBTFKKcngIiIiIiIiIiIiIiIiEgkFsCIiIiIiIiIiIiIiIhIUVgAIyIiIiIiIiIiIiIiIkVhAYyIiIiIiIiIiIiIiIgUhQUwIiIiIiIiIiIiIiIiUhQWwIiIiIiIiIiIiIiIiEhRWAAj+ohp06bJkhMZGSlLDpFcEhMTcfPmTbx69UryrIiICMkzcsq5c+dyehKIiIhkw+Me0ecrKSkJMTEx6YbHxMQgKSkpB6Yo+6KiovD48eN0w0NCQhAVFZUDU0RZIfUx6cKFC5gxY4akGQDw7NkzuLi4SJ6jRHK3Nfz333+y5hF9DoxzegKIDN3p06dlybG2tsaRI0eEj/f06dN49OgREhMT9cP69u0rPCcjsbGxyJs3ryxZ9HGnT59Gs2bNPjrsUy1evBi2traoXr06YmNj0atXLzx58gSJiYlwdnZG+/btheRs3rwZrVq1QpUqVZCUlIRhw4bhxIkTsLCwgIuLCxo1aiQkJ7Vdu3ahZ8+e+v8nJydj2bJlmDhxovAsAHjx4gV8fHzg6ekJrVaLv//+W+j4nz59CmdnZwQFBSEuLk4//NChQ0Jzctq5c+fQuHHjnJ4MIcLCwhASEoIGDRrk9KQYvEuXLsmynJKTk3H79m3UrFlT8iwiubi7u0Or1aJHjx5phm/ZsgXm5ubo1q2bJLlSH/eIPua///7DF198kdOTkS0nTpzAzZs305zbjR49WnjO6dOnERwcjH79+uHly5eIjIxEpUqVhIx7yZIlqFy5MhwcHNIMd3d3x7Nnz/DTTz8JydEJCgrCrFmzEBQUhPj4eP3wmzdvCstYvHgxWrdujbJly6YZfunSJZw9exZz5swRktOtWzeoVKr3fu7h4SEkJ7Xjx4/j1KlTUKlUaN68OVq2bCk8Y8yYMZg7dy4KFSoEAHj9+jV++eUXLF++XHiWjtTHpNDQUPj4+MDLywsqlQpdu3YVOn6dhIQEHDhwAJ6enrh27Zqw6/GMREZG4v79+2n2P19//bXwHKn3cznR1gAA8fHxOHDgADw8PHDt2jWcPXtWkhyizxULYEQfodVqc22Oo6Mjbt26hZo1a0KtVgsf//tcunQJHh4e+Ouvv4QeuNevXw9zc/N0BTxXV1cAwJAhQ4RlyU3KC0mdxYsXw9vbO80wZ2dneHl5CRn/kSNH4OjoCADw8/ODiYkJTp06hXv37mH69OnCTrg9PDz028CePXvw9OlTnDx5EteuXcOSJUuwY8cOITmp7du3D//++y/mzZuHqKgoTJgwAeXKlROakZiYiMOHD8PDwwOXL19GYmIiNm7cKElD/vTp02FlZYWgoCD9MitfvrzQjJz6vkp1wXrnzh2EhoaiefPmaYYfP34cpUuXRtWqVYXkpNanTx+sW7cOWq0Wtra2KFiwIFq3bo0pU6YIzYmJicHatWvx+PFjLF26FMHBwbh//76w7+yhQ4cQFRUFjUaTZri7uzuKFi0KS0tLITk6s2bNglqtRp8+fWBtbY08efIIHb+OkZERZsyYAU9PT0nGn1pGjVoFChRAgwYN8OOPP8Lc3FxYllw3zsjR6Aik7Fs9PT3TNZYsWLBAyPibNm36wQZHKW6kunfvXrrlZmtrK2TcO3bswJYtW9INt7W1xaBBg4QWwOQ87gHA7du3UbZsWZiZmQEAoqOj8d9//6FatWqS5AEpDVr79++Hl5cXNm/eLEmGlNtDalI2bh49evSDn7dp00ZIzrvkaHCMjIyEq6trun1QRt+z7FiyZAmuXr2Ku3fvol27djh06JCwm9xSW79+PY4ePYrQ0FD069cPiYmJmD59urDz72PHjmHy5Mnphvft2xcajUZ4AeyXX37B+PHjsWDBAmzYsAFubm5Cj6lAyg1ZGRW5rK2tsXbtWmE5uvPDI0eO4N69e+jevTsAwMvLC7Vr1xaWo+Pq6gpfX1906dIFALBo0SLcunULgwcPFpoTEhKiL34BQOHChfHo0SOhGYD0x6SkpCT8888/8PDwwKVLl9ChQwdERUXh+PHjQsaf2s2bN+Hh4YG9e/eiVq1aCAoKwvHjx2Fqaio8CwD27t2LRYsWISIiAiVKlMCjR49Qs2bNdG0P2SXHfk7utobr16/r11VcXBx+/fVXrFmzRmgGEbEARvRRH2rUMPScq1evYs+ePbIUv8LCwuDj4wMPDw88fvwYY8aMga+vr9AMf3//DBsaBwwYAAcHB2EN6u9rzNJqtVCpVMIbs6S+kHz48CEePHiAqKioNA0MkZGRGXYx8qlMTU31yy0wMBBdunSBiYkJatSoIbTLErVaDRMTEwApDYsajQZFixZFmzZt8PvvvwvLSW3Tpk1wcXGBvb09EhISMGbMGNjZ2Qkb/4IFC7Bnzx5Ur14ddnZ2WLFiBaysrCRrBHz9+jUcHBywZcsWNGzYEPXr18egQYOEZsj1fQXkaURdunQpxo8fn264hYUFlixZIrQBQ+ft27coUKAAfH19YW1tjcmTJ0Oj0QgvgP3yyy8oXrw4goKCAAClSpXCpEmThBXAXF1dsWrVqnTD27Rpg7FjxwovgPn6+uLcuXPYvn07li1bBmtra/Tp00d40RoAqlSpgsePH6e7q1u0Zs2a4eHDh/pGbV9fX1SoUAHPnz/HL7/8AmdnZyE5U6dOxbVr11C7dm3Jzx3kaHQEACcnJyQlJSEwMBC9e/dGQECA0CdDdfs5Dw8PhIeHo2fPntBqtfD09ETJkiWF5ehs2bIFu3btQmhoKL788kucO3cOX3/9tbCCR0JCAvLnz59ueMGCBdMURbNL7uMekLJ979q1S/9/ExMTTJkyRdiNQKldvXoVHh4e2L9/P7788ktJClKA9NuDjtSNmxs2bHjvZyqVSngBTM4Gx+nTp6NKlSp48OABxo0bB09PT9SpU0d4ztGjR+Ht7Q17e3vMmTMHo0aNwuzZs4XnBAQEwNPTU/+EVqlSpYR242dkZJTh8UetVktyvRwfH49mzZpBq9WiRIkSmDBhAvr374+hQ4cKyzAyev+bR0TO0zfffAMAWLlyJbZs2aIfd9u2bTFo0CDhTwP6+flh586d+mNG//790bt3b+EFsKSkJCQlJem3i4SEhDQFfxHkOCa1atUK5cuXR9++fbF8+XLkyZMH7dq1EzZ+HTs7O7x9+xZ2dnbw9vZGqVKlYGlpKVnxCwDWrl0LLy8vDB48GD4+Pjh58qQkT3LLsZ+Tq61hy5Yt8PT0RGxsLOzt7eHj44O+ffvqC8pEJBYLYER4f3cBWq0WYWFhwnLu3r373s9ENiroVKhQAbGxsZI0KOkcOnQInp6eOH/+PDp27Ih58+bB0dFR6EVDahmduIm+u1+Ou/lTk/pC8sKFC/Dy8sLLly/TNDDkz59faEN6UlISoqKikC9fPpw7dw7ff/+9/jORFylJSUlISEiAiYkJLly4oL+7EUi5IJJCcnIyoqKiYGxsjPj4eOHb3I4dO9CwYUMMHToUTZs2BSBt8V13Um9mZob//vsPxYoVk6SvcTm+r3I1oj5+/DjDru7q1auHJ0+eCM3S0X1vAgMDYWVl9d5Goey6ffs2Fi1ahBMnTgAAzM3NkZycLGz80dHRKFasWLrhJUqUwNu3b4XlpNa4cWM0btwYN2/exIgRI/Dnn3+idevWcHR0RJUqVYTlvHr1CjY2Nmjy1p/IAAC3AUlEQVTUqJH+qRIAwrvlOXv2bJqGe11j1p9//gkrKythORcvXkRAQIB+HyElORodgZRChL+/P6ytrTFs2DD06dMnw2L2pypTpgyAlHW0bds2/fCff/4Z/fr1E/606+7du+Hu7o7evXtj48aNuH37NtatWyds/B+6MSY6OlpYjtzHPSDl/CH1tm1qair0Bp3Xr1/D19cXnp6eSEhIgK2tLfLly/fB4k52Sb096EjduLl161Zh4/qQnGhwfPjwIVauXIlDhw6ha9eu+O677yS5TjI1NYWxsTFUKhUSEhJQsmRJPHv2THhO3rx50x0jRH534+PjERMTg3z58qUZHh0dLbzoAUB/XmVhYYGgoCCULFlSkvO6V69eoUiRIumGSdELzPPnzxEXF6d/FUF8fDxevHghPAdAmhsmMrp5QoSWLVtiwoQJGDBgAICU73GrVq2EZshxTPr666/x77//4sSJEyhZsqS+YCmaWq3WFwl1bUxSH1+NjY1RtGhR/TG1RYsWWLlypfAcOfZzcrU1zJ8/H82aNcOcOXP0N+nJdfM90eeIBTAiQPjd9O/zoYsdKbpo+umnn9CvXz80atQoTUO0yK4jRo0ahWbNmmHfvn36k3qpDtxxcXFITEyEsXHaXVdCQoLQJ5l0jVlAyoUJgHQXLCJJfSFpZ2cHOzs7eHl5wd7eXth439WrVy9069YNBQoUQKlSpVC3bl0AKV3HiVx+3333HQYNGoTChQtDpVKhYcOGAIAnT55IVuzt3bs3qlevDi8vL7x48QITJ07Ev//+K6zP/hMnTsDf3x+LFy/GmzdvYGtrK+mLvhs3bozw8HD07t0b9vb2MDU1RceOHYVmyPV9lasR9UMXPFIVXr/55ht07NgRWq0Wv/zyCyIiIj54F/Gnenf/ExcXJ7RBJjY29r2fidwWUjt37hzc3Nxw+fJldO/eHQ4ODvj3338xcuRI/PXXX8JyunTpIsudmq9fv0ZcXJz+XCE+Ph7Pnz+HSqUS+q7NUqVKCRvXx8jV6KhbZmq1GjExMShQoIAkDYEvXrxI08D56tUrhIaGCs8xNTWFmZkZkpOTodVqUb16daFdQTVq1Aiurq7pCnd//PEHvvrqK2E5ch/3gJQGupCQEH1D06NHj4TeVNCqVSs0btwYs2fP1i8rd3d3YePPiNTbg47UjZsfukkQgLBuhnOiwVF3DWZiYoLw8HBYWFhIUpgyNzdHTEwMGjZsiKlTp6J48eKS3DRTqlQpnDt3DiqVCsnJyVi7dq3QbkStrKwwZcoUzJ8/X19QiYyMhJOTEzp16iQsJ3Xe69evMXToUPTu3RvJyckYO3as0AwHBweMHTsWv/76KypUqAAgpTA6c+bMdO86E6Fz587o2bOn/gaZffv2oXPnzsJz6tati2nTpsHBwQEqlQru7u766z+RJk6ciHXr1mHhwoXQarVo27at8CKyHMek5cuX482bN/Dz88OCBQvw5s0bREVFpTkuieDh4YG7d+/Cw8MDPXv2ROXKlfH27dsMC8uimJqaQqvVokKFCti6dSvKlCmD169fC8+RYz8nV1vDnj174OHhgd69e6NSpUqws7OT7fUrRJ8jFsCIgA/efbNixQphd+ccPnxYyHgya968eShZsiQKFCggWVdG69atg6enJzp37gxLS0vJungBgNatW2PhwoWYNm2afn6Sk5Ph7Ows/C6wzZs3w9XVVV8AK1q0KIYMGYKBAwcKzQGkv5DUKVeuHKKjo2Fubg53d3dcvXoVQ4YMEXbC3bdvX3z55Zd48eIFWrRooR+uVqsxffp0IRkAMHbsWOzfvx/Pnj3DrFmz9AWB8PBw4ResOrr3DgApy9HNzQ1LliwRNv6CBQuib9++6Nu3L4KCguDh4YHY2Fj07dsX1tbW6NWrl7As4H9Ff1tbW3zzzTeIiopC9erVhWbI9X2VqxG1SJEiuHHjRrp3KNy4cSPNewlE0r0fqVy5cjA1NUVUVBTmzZsnPKdx48ZYu3Yt4uPjERgYiE2bNgntlrBGjRr6J3BS27NnjyT7Omtra5ibm6Nfv35wdnbWF2E1Gg38/PyEZonsCvVDdI1ZnTt3hkqlwv79+9GxY0dER0enuWnjU7m5uQEAKlasiEGDBqF9+/ZpbpyR4h1gcjQ6AikFtjdv3qBVq1YYMmQIChcunOETidk1cOBA2Nra4ttvvwWQ0k3PsGHDhOfky5cPCQkJqFmzJpydnVG6dOkPFpmzytHREf369cOhQ4dQv359AMCVK1fw6tWrNE+4ZZfcxz0AGD16NHr37q3vTu/o0aNC96kDBgyAv78/fvvtN3Tr1k34jSUZkXp70JG6cfNDjdkqlQqHDh0SkpMTDY4VK1ZEeHg4rK2t0bNnTxQoUAC1atUSnvPbb79BrVZjypQp2LRpEyIjI4U/jQwAM2fOxJQpU3Dnzh3Ur18fjRs3FnpOPGrUKEydOhWtWrVCxYoVAQAPHjyApaUlxowZIyxHR9djRevWrXHmzBnExcUJf5Jp4MCB+ifGdTdlxMXFYeDAgcK7IAeACRMmoH79+jhz5gy0Wi3Gjx+vPzaJNHPmTKxevVq/H23evDlGjhwpNCMpKQmzZ8/GvHnzhHfhmJpcxyQLCwv0798f/fv3x40bN+Dh4YHu3bujYsWKaZ70z66qVati6tSpmDx5Mv755x94enqiZcuWaNOmDX777TdhOTrjxo1DVFQUJk+ejF9++QWRkZGYNWuW8Bw59nNytTVUqVIFU6ZMSbOOXr58iZ9++gnW1tbC27eIPncqLUvMRB/07bff4siRIzk9GZ+kc+fO2LdvnyxZ4eHh8PX1hZeXF4KDgzFkyBBYW1ujcuXKwjKio6MxdOhQPH36VN8IfePGDZQqVQqurq7C7sjx9fXFmjVrMGPGDNSvXx9arRZXrlzBggULMHz4cNjY2AjJ0QkNDcWUKVNw5swZqFQq/YVk0aJFheZYW1vDz88Pd+/exbhx42BjY4NTp04Jfwm3nN6+fQuVSiXZ3Ww5KSEhAQcPHoSXlxdcXV2Fjz8mJgbPnj1LUywSdYc1kPJ9HTZsGP777z9Jv6+p6S5YAwICUKVKFaEXrMePH8fMmTMxatQofPnllwBSulZzcXHB7Nmz0bp1ayE5qWV0R3yBAgWEv1coISEBGzZswOHDh6HVamFpaYmhQ4eme3rvU92/fx/9+/dHkyZN9A3qly9fRmBgILZu3YpKlSoJydG5evWqfh1J7cGDB5g2bRqeP3+Ow4cP4/r16zh8+LAkjXSHDx/WN2Y1adJEaJFy2rRpH/x8wYIFwrIykpCQIEmjIwD9e0OSk5Ph5+eHqKgo2NraSpIVFBSEs2fP6tdRjRo1hGfcvn0bZcuWRUxMDH777TdERkZixIgRQhvUY2Nj4e/vjxs3bkCr1aJOnTro2rWr5MdaqY97QMr+6NSpUwBSutXSPY0hSlJSEo4ePQpPT0+cOXMGSUlJcHFx0T+hLJoc2wOQ8h6UunXrIiwsTN+4OWnSJDRv3lxojlySkpL0DY4nT55Ep06dZGlwPHfuHCIjI9G6dWtZ3tEspZiYGCQnJ0vW+8LDhw/T7INEf1dTe/ToER49epTmnFj0e+eAlOsW3bld1apV03SdnBulfjJdSgMGDMiR61U5jklAylP9Bw8eFNqldUZCQ0Ph4+MjvGtmpYmKikJ4eHi69/uGhISgcOHCknX1CQBhYWHw9vaGj48PAgICJMsh+hyxAEb0EW3atMHRo0eFjKtZs2b6n7Varb7LDd3Pp0+fFpKjM378eEyfPh0lSpQQOt6PuXbtGjw9PbF3714EBgYKH//p06dx/fp1aLVa1K1bN81yFaFv376YOXNmuvf9BAUFYe7cufo75UWT+kJS9yLcjRs3wtTUFP3794etrS18fHyEjP9979IDoO8WQxR/f3+sWLECjx8/BgCULVsWY8aMEV6cdHZ2hqOjI8aOHZvhvIm64+zOnTsIDQ1N15B07NgxfPHFF0ILU0BK3/m///47ChUqpJ8vkXdYp3bq1Cl9A4YU39eMSHXBeuLECbi4uODGjRsAgDp16mD48OGSNZhZWlri6dOnKFCgAICULoCKFi0KU1NT/Pbbb8LecyZHA0ZoaCjc3NzSNGb16dNHkuOTVquFh4cHHjx4AEdHRzx+/BgvXrwQ2n2bzqBBg/DDDz9g6dKl8PX1RXJyMqytrbFnzx7hWUokV6MjZZ5cDT9yH/dywqtXr+Dt7Q1vb29ERETg2LFjOT1Jucrz58/h7e0NLy8voe8be5dcDY7x8fFp9nWiC8r37t3D2rVr8ejRozTvlvbw8BCaA0i7737fvuH48eMoXbq08H3D4sWL4ePjg0qVKumf9FCpVEILLufOncPr16/RoUOHNMP37t2LUqVKCTs/keu6RadFixawsbFB7969Ub58eaHjTs3FxQUvX76Era1tmqKhyG1BjmOSu7s7tFotevTokWb4li1bYG5ujm7dumU7A0h5R3tUVJS+95LU+UWLFhV6A5VObGwsAgIC0u1/RL56A5BnP+fk5ITWrVujffv2aYb7+/vj7Nmzwl6B8OzZM4SGhqa7ae/KlSsoUaKErN2SE30O2AUi0UeI7BfezMwMhQoVgr29PVq3bi3Ju1xSi4yMhLW1NRo2bJimcVPkyW9GDSV169aFhYUFRo0aJSwH+N9JQrNmzdI0ol+5cgUlS5YU9lTEy5cv0xW/AKBmzZp4+fKlkIx3ydEImJiYiPPnz+Ovv/7Cr7/+CgBCu4rL6F16jx49wpo1a5CcnCwsx93dHX/++SdmzpyJBg0aQKVS4cKFC1iyZAliY2PTXVRkR6NGjQAAbdu2FTbOjCxduhTjx49PN7xQoUJYsmQJ1q5dKzTvzz//xP79+4U/SZSR5s2bo3nz5njx4gUuXryIW7duCX0qIqMLVhMTE5ibmwt/v2PLli3RsmVLoeP8kHbt2qFJkyb6C7CDBw/iypUraNGiBX799VdhRWVLS0tJGzCSkpKQP3/+dNt4TEyM/ukckRYsWICwsDBcv34djo6OMDc3x/z58yVpBNTdya/rTsbIyCjdO9VEuHfvHtasWYOQkBBJGzYTExOxa9cu/c0rTZs2RY8ePYQ9DZja+xodRR/7zp8/j6VLl6Y7xoq66cjR0RHOzs7vvQlE9DoKCwvDtm3b0jX+iDq3W7x4MVq3bp2uAHbp0iWhDT9yH/cA4MKFC3B2dkZISAiSkpIkuwFNp0iRIhg8eDAGDx6MK1euSJIh9fagI1fjZkJCAg4dOgQPDw+cOXMGdnZ2mD9/vrDxZ9TgWLRoUXzzzTfo2rWrsJzU9u/fjwULFujfPajb7m7evCk0Z9y4cdBoNLCzs5P06bKlS5fC3d0dVapUkWTf/b59g4WFhST7hoMHD+LQoUOSPuG6cuXKDLtbrVWrFmbPno3NmzcLyZHrukXHz88Pu3btwsCBA1GlShX07dsX3377rfD36umOo6l75RF9454cx6QdO3ZkWFi1tbXFoEGDhBXAXF1dsWrVqnTD27Rpg7Fjx0pSABs9ejSMjIxQp06dNF1oiybHfu7cuXMZnutYW1sL3f84Oztn2FNJREQE/vzzTyxdulRYFhGxAEYEAO+9S0qr1eLNmzfCcg4dOoTAwEB4e3tjy5YtaNeuHezt7SW7y7Vr166SXczpyNVQAsh3kvChJ7Ck6KpC6gtJnXHjxmHOnDlo2rQpqlWrhvv37wvtTiT1u/JevnyJ1atX4+DBg/jhhx+Evj/mzz//xMaNG9MUb9q0aYPq1avjxx9/FFoA010gNGnSBF988YWw8b7r8ePHGRZd69WrhydPngjPK1WqlOTFr71792LGjBkoWLAgZsyYgdmzZ6NMmTJ4+PAhxo0bhz59+gjJkasRVa67NlM7c+YMZsyYof9/+/bt4erqiokTJwp9x4vUDRhLlixB5cqV073o3d3dHc+ePRPegBoYGAgfHx/9+7kKFy6MuLg4oRk6arUaCQkJ+mX1/PlzSW5umThxIjp16gR7e3tJGzbnzJmDJ0+ewNbWFlqtFn5+fggKChJ6LNeRo9ERAKZPn47x48ejbt26kqwb3btBRRfc32fkyJGoXbs2mjVrJsm2IFfDj9zHPQCYMWMGRo4ciQYNGkiyLaxfvx7m5ubpznl0TyLXq1dPeKbU24OO1I2bQUFB8PT0REBAAGrXrg1bW1sEBwdj9uzZQnNyosHR2dkZK1eulGwfpGNsbIwff/xRsvHr7N+/HwcPHpSsGzC59w2lS5eW5MaV1F69epXhO5crVaqkf9+0CLrrFrneT1q0aFGMHDkSw4YNw6FDhzB79mzMnTsX/fr1Q9++fYX1LiDHe9Tl2O4SEhIy/N4ULFgwzY0F2RUdHZ3hu05LlCiBt2/fCstJ7enTp7L0fiDHfu5D+2mRxd2goCB8/fXX6Ya3bNkSCxcuFJZDRClYACPCh++SyuhEKDuaNGmCJk2a4O3bt9izZw/69++PMWPGCGsMTk2Ok1+5GkoA+U4SwsLC3tvNocgXfutIfSGp0759+zSP8leqVCnDu8OyIyoqCuvXr4enpyd69OiBffv2CZ8vrVabYfGmdOnSQnNS69mzJ6pUqQJ7e3t07NhReHdxCQkJn/TZpxozZgxmzJiBNm3apJkXkUXXtWvXwsPDAxEREfj+++/h6emJKlWq4Pnz5xg8eLCwfZ5cDSVy3bWZWnJyMi5cuKDvGufixYuIiYkB8OGLs6ySugHj2LFjmDx5crrhffv2hUajEV4Ay5MnT5oLVJFPoL6rT58+GD16NF6/fo2VK1fCx8cHEyZMEJ6TnJyM4cOHCx/vu86ePYs9e/boty8rKytYW1tLkiVHoyOQ0rDUuXNnycZft25dAP+7CUTXuCTVu11iYmIkebG8jlwNP3If9wAgb968km3PQEr3SJ6enumGDxgwAA4ODpK8d0Xq7UFH6sZNW1tbNGvWDF5eXvrzuWXLlgnPyYkGx+LFi0tS/HxXq1atcOzYMUneSZpa8eLFJb1mkXvfMHXqVAwfPhwtWrRIU9wVefPeh25aEnlDk86rV68wd+5cnD59GiqVCs2bN8eMGTNQpEgR4VkxMTHw9fXF9u3bUb58eTg4OCAwMBBDhgwR/t6usLCwNDc0ibw5UY7tTncOn5Ho6GghGcCHt6kPTUN2VKtWDS9evJD81Rty7edevXqV7vvy6tUriHyD0IfGJeW1C9HnigUwIqQvFL148QI+Pj7w9PSEVqvFyJEjheYFBwfD29sbBw4cQOvWrdGkSROh49dZuHAhRo0ahXz58mHAgAG4ceMGZs+ena4/6OyQq6EEkO8koXnz5rh27VqGn0nx/iKpLyRTO3HiBG7evJnm4mH06NFCxr1hwwZs3rwZnTp1gr+/vyQXWUBKF12RkZH69yLpRERECL17LrUjR47g2LFj8Pb2xvz589GhQwfY29ujYcOGQsZfpEgR3LhxA7Vr104z/MaNGyhUqJCQjNT++ecf/PPPP3jw4IFkTx0aGRmhSpUqAFIaunU/lyxZUmh3anI1lMh112Zqs2bNwoQJE5A3b16oVCrExMRg6dKliI6OxqBBg4RmSdmAYWRklOGTCWq1WvhxAgCqV68OPz8/aLVaPH78GOvXr9d3CySara0typYti3/++QcxMTFYtGgRGjduLDynQYMGCAoKEn5TzrtKlSqF+Ph45M2bF0DK/laqmwvkaHQEUp6G37FjBzp37pymmCv6ybOQkBBMmjQJN2/ehEqlQu3ateHs7JzhXf/ZUb9+feFdyb5LjoYfuY97ANC6dWscPXpU0vfMZfR0lJTvWJRjewCkb9ycOXMmvLy80LdvX9jb2wu9VkktJxoc+/fvj2XLlqFDhw5ptgXRPYA0a9YMI0eOhJGREUxNTSXr4rNBgwb6p5KluIlK7n3D+vXrERoaips3b0r2FGW5cuVw6tSpdO+XOn36tCQ9TMyaNQtVq1bF1KlTodVqsXv3bjg5OQm/8XHu3Ln466+/YGlpiaVLl6JatWoAUm6E7dSpk7Cc06dPY+rUqQgLC4ORkRESEhJQqFAhodu2HNtdo0aN4Orqmu5miD/++EPoe2pr1KgBf3//dDd87NmzR7+ORBs9ejR69OiBmjVrSvbqDUCe/ZyDgwPGjh2LX3/9Vd9bzsOHDzFz5sx0vVlkR548eRASEpLuPDEkJETydzMTfY5YACP6f4mJiTh8+DA8PT1x6dIlJCYmYuPGjWjQoIGwjO3bt8PX1xempqaws7ODj4+PpF3/nDp1ClOnTsWRI0dQsmRJ/P777xg6dKjwi0o5GkoA+U4SFixYIGxcmSH1haTOkiVLcPXqVdy9exft2rXDoUOHhBb0lixZguLFi+PixYsYOnRous9FvQfFzs4OI0eOxNy5c1GxYkUAwP379zFr1izJnnpUq9Vo27Yt2rZti/DwcPz222/o06ePsPc3jBw5EiNHjsSoUaP076W4evUqXFxchHf/AwAHDhzA4cOH9Y3cUkhd2Hg3R2TRQ66GErnu2kytcePGOHDgAO7fvw+tVovKlSvrG1dFbutSN2DEx8cjJiYm3fEuOjoa8fHx2R7/u6ZOnYqFCxciNDQUPXr0gKWlpWTd0507dw6NGzeWpOiV2pUrV+Dl5YVKlSqlOU6Ifr9U2bJl0bNnT1hZWQFIeUK5cePG+qeiRRan5Gh0BFKecJw5c6b+aXWp3r/j5OSEHj166J8G9fLygpOTEzZt2iQ0p1evXujXrx9KlSolybYgV8OP3Mc9ANi1axfWrVsHc3NzSRrO4uLikJiYmO4mj4SEBMnuupd6e9CRunGzb9++6Nu3L27dugVPT0/07NkT0dHR8PT0RMeOHYXdKJYTDY7Pnz/H5s2b4ePjk+amI5HvLwJS9kELFixAnTp1JO1q8erVqwCArVu36oeJvIlK7n3D9evX8ddff0lyQ47O+PHjMXz4cDg4OOifBrxy5Qrc3d0led/ho0ePsHLlSv3/x44dK0lRuXTp0ti7dy8KFiyY7jORT385Oztj8+bNmDBhAry9veHu7o7//vtP2PgBebY7R0dH9OvXD4cOHUL9+vUBAJcvX8br16+xbds2IRkAMGnSJPTv3x9HjhxJkxMYGJjmeyvSTz/9BEtLS9SuXVvSczo59nMDBw7Eq1evYGNjoz8uxMXFYdCgQUJvQPz+++8xYsQITJ8+Pc1+YcGCBbJ0Z0v0uVFpRbdQE+VCCxYswJ49e1C9enXY2dmhQ4cOsLKyEt7fdM2aNVG7dm2ULVs2w5Ns0XfI2NjYwM/PD87OzqhTpw6srKxga2sLHx8fYRl//vknDhw4kGFDiaWlpdCTBD8/P6xfv/69JwmiTuzv3r37wc9F37HZv3//dMNUKpXwbiOsra3h7e0Ne3t7+Pn54fnz55g9ezZcXFyEjP/UqVOIj49P1+3T27dvYWpqmu6ux0+l1Wrh4uICV1dXfTEgISEBP/74I0aOHCnZBWx4eDgCAgLg7e2NqKgo2NraYsSIEcLGf+LECbi4uODGjRsAgNq1a2PEiBFo1aqVsAydgQMHYuPGjUKfxHpXgwYN9N+Vu3fv6n/WarW4d+8eLl68KCTn+PHjmDlz5nsvWEV1kTFlyhRUrVo1w7s2b968CWdnZyE574qJicGzZ8+QlJSkHyZqH/Tff//hiy++wIYNG9CzZ880T1Vev34dderUEXLH//LlyxEcHIz58+frGzEjIyPh5OSE8uXLS9JloFzs7e0RFRUFOzs72NnZoVSpUpLknDlzJsPhqd+9KMK0adM++LnIG0S+++47yRsdgZR3oixfvlzyRmGNRgNfX9+PDssuKysrdOvWLV0jk8ht4ffff8fmzZvTNPwMHDgQEydOFJYByHvcA/DebnHLlCkjZPzz5s0DkPI90q2b5ORkLFy4EImJiXBychKSk5oc2wOQsq9r0KBBuhypbjxKSEjQ35h49uxZYecMcl1LpNa2bVvs2rVL8q7BunfvLrzwmVPk3DcMHToUv//++wffAS3CrVu3sGHDBty4cQNarRZ16tTB4MGDJXmy29raGps3b0bRokUBpHQdOGjQIPj7+wvPAlJudEp9nir6Bl97e3t4eXmha9euCAgIAJBSNH/fKws+lRzbXWxsLPz9/dNsB127dhW+zEJDQ+Hm5pYmp0+fPpLth6ytrSXbvlKTcz/39u1bfbtQ1apVJena2t3dHatXr8bz58+h1WpRqlQpjBgxAj179hSeRfS5YwGMCCnvimnYsCFGjBiBpk2bAoD+CRmRvL293/vZ06dPhXe1+P3336NMmTI4efIkfHx8YG5uDjs7O+EnJxk1lAwaNEiSRs3UJwlASndqok8SatasiVKlSkGtVqd7ik2KOzbl0q1bN3h6ekKj0cDDwwMmJib6CwoRFi1ahMqVK6e7Q3zLli149uyZ8Pf8xMfH4+HDh9BqtahYsaIkL2XXGT16NM6fP4927drB1tZW8qc9pObk5ITg4GC0b99esq7H3tdgryOycU6OC9aXL1+iX79+KFSoUIZ3bRYvXlxYlo6bmxuWLFmCQoUK6YsEIvdBdnZ2+uPSuxeUqT/LrsTEREydOhWHDh3SP7X54MEDWFpaYtGiRZIUYk+fPo1Hjx6l6Z5SdNd6Ordv34a3tzcCAgJQrVo12Nvbo2vXrpJkKYlcjY69evXCzp07Jc0AUr4zS5cuReXKlQGkPJk8adIkYcfY1DmivpsfIkfDj9JER0dj2LBh+O+///RPJd+4cQOlSpWCq6urJNu6XNuDXI2bqZ05cwbffPON8K4X5W5wlKKhPiNr166FhYWF5N29Aik3H506dQoqlQotWrRAixYthGfIZeLEibh+/TpatWqV5pxY9HWLnHx8fLB06VJ8++23UKlUOHr0KCZOnCi8wPvXX39h/vz5ePHiBQDpnrDu0aMHdu/ejcGDB6Nv374oU6YMRo4cmWuvyVN78OCB/tw4N5s5cyb69esneXe8cuzn3n26UKVSoUiRIpJ2S6jrPUlXtCYi8VgAI0LKu4N0L65+8+YNbG1t4enpiSNHjkia++67xv7++2+h43/16hX8/PzQoEEDNGjQAI8fP8aZM2dgb28vNAeQp6Hkxx9/xIYNGwBIe5IwZcoUXLx4Ee3bt4e9vb3wJ74yIseF5IABA7Bu3TosWrQIkZGRKF68OM6fPw93d3ch4+/SpQv8/PzSdXuQlJQEjUajv2Mvuz7WjZAUF/q+vr7o2LGjpF0GAikXjrt27dJvC82bN0ePHj2EPyHxvqc85Or+c8WKFRg7dqwsWSLJddemTrt27bBlyxZhTye8K/UTwe8+HSz6aWEg5eng1MtO99SwaFOnTsW1a9fSPakg9fYdHx+PefPmwd3dXXjjT7du3TLcD0hxF6xcxUO5Gh2XLVuGhIQEWFlZSfr+nWPHjmHKlCmoVauWvgFw8eLFaNmypdCc3377DY0bN5b8BfA6jx49wuHDh1GuXDm0a9dO+PjlOu4BKTebOTs7IygoKM27UEU3op46dUq/r6tbt64k74/VkWt7kKtxMzWpi3tyNTguWrQIz58/l7yr84yeJJKiGOHq6gpfX1906dIFALB3717Y2tpi8ODBQsYfFRWFnTt3wsLCAra2tliyZAlOnTqFihUrYvr06cLfS/m+92KJekcyAAQGBqJChQr6p8RdXV0REBCAsmXLwsnJCSVLlhSWpXP79m2cOXMGWq0WzZo1k+R6tl27dvj9999Rt25dSZ+wDggIQKtWrfDw4UNMmjQJkZGRmD59OmxsbIRnyXlMAqTdz7169Qrz5s1LMy8zZsyQ5D3dGo0GwcHBknfVLcd+rmnTplCpVGluhI6KikKDBg2wePFiSd7bp6SbCogMFQtgRO8ICgqCh4cHAgICUKVKFVhbW6NXr17Cxi/Hu8Zyyt27dxEYGAgg5cShSpUqQscvRYPs+7x9+xb79++Ht7c34uLiYGtrC2tr6zRdhIki9YWkzsuXL1GwYEEkJSVh06ZNiIyMRP/+/YWdxH3o7mCRdw7XrFkz3Ulp6qdjdE8BiaLVaqHRaODn5yd0vBlZtGgRbt68qS9S+/j4oGbNmrn6LtSMfPvtt8JvMJD7ghVIuXkio/ceiCL1kyupL7rfvQCX8oI8IiICZ86cQdmyZSXp+qdjx44ICAiAiYmJ8HFnJPUTYFWrVoWdnZ3wRpnUT1TGxcVhz549KFGihPAu6SZPnozbt2+jZs2akhcP5Wh0BFK6QHyXVE9zh4WF4cqVK9BqtWjQoIEkjUxNmzZFeHi4ZO+xGjRoEKZOnYqaNWvi2bNnsLa21t9EpdFoMHz4cCE5OnIe977//ntYWVnhjz/+wPz587Fjxw6UL19e+DYHpJzv379/HyqVChUrVpSsy2GptwcduRo3U5P6vF+uBke5ujqXi7W1NXbs2KHv0jgqKgq9e/cWdp4/duxYqNVqxMTEIDw8HNWqVUOHDh0QGBiIO3fuYP369UJy5GRnZ4fNmzfDwsIC//77L8aPHw8nJyfcvHkTDx48SPO+rtxEries5ST3tZiU+7kxY8agatWq6NWrF7RaLXbv3o3bt2+/9/wrO+TqqjunJCUlYefOnThx4gTWrFkjdNxytQURfe6ke/kHUS5Vs2ZN/Pzzz5gyZQoOHDgAb29vYQWwd981tnz5clhZWUlW/JLrTlcg5eRwyZIl+PbbbwEA69atw+TJk4U2Amq1WsTGxqbrllBH5FMYZmZmsLe3h729PY4ePYopU6YgOjo63ft/RPDz88POnTv1F5L9+/dH7969hZ/0FCtWDEDK3WCiu9sEUp5+iImJSbceoqOjER8fLywnKCgozf+1Wi18fX2xatUqfZdDIqlUKpQrVw5v3ryBhYWF8PGnduLECXh7e+sbyjp37gx7e3vhF13vFotatGgBBwcHyd/FkzpftMWLF6e7YH348KGwZbd582a0atUKVapUQVJSEoYPH47jx4/DwsICLi4uaNSokZCc1Jo3b47FixejS5cukjy5EhcXh+DgYGi12jQ/6z4TZfLkyfjxxx9Rs2ZNhIeHQ6PRIH/+/Hj9+jUmTJiQrtvU7JLqPVwZsbOzw9u3b6HRaLB7927hd6brvNuA0LJlS/zwww/Cc65du4Y9e/ZI+gJzHSmKDhkR/T7XDylatCjatm0raYanp6ek43/x4oW+MO3n54dmzZphxYoViIiIQN++fYUXwOQ67gHA69ev4eDggC1btqBhw4aoX7++0HfV6ly9ehVjx47VF6QSExOxcuVK1KlTR3iW1NuDzowZM2TJSa1Tp06SjfvdBseFCxdK1uC4detW4eN8n9evX+Py5ctQqVSoX78+ChUqJEmO7prl3Z9FCA4Oxp49e5CQkICWLVtix44dUKlUaN26tSTdC8fExMDFxSXNOfHw4cOFXlcmJibqryEOHz6Mbt26wcrKCp07d5bkKaYLFy7A2dkZISEhSEpKkqww3r9/fyxbtgwdOnSQ9AnrvXv3onXr1sifPz+WL1+OK1euYOLEiZLsU+U8JgHAV199Jcl4gZQnuFMXV8eOHSvJew6B/52nvn37FgAk7TZZrv1camq1Gn379pXkpg+52oKIPncsgBG9h4mJCaysrGBlZSVsnDt27EDDhg0xdOhQ/bvGpGxwnj59OqysrHDz5k0sWbJEf6erFP744w94e3vr34MTGhqKwYMHCz2pv3XrFho2bJjuyR8p+huPjIyEv78/fH19YWpqCkdHR3Tu3FnY+N8l5YWkzuXLlzF+/HgkJyfj6NGjuHr1Knbv3o25c+cKGb+VlRWmTJmC+fPn6+chMjISTk5OkjViHD58GL///juKFy+OZcuWoW7dupLkmJmZwc7ODq1bt05zQi/FxVDqfYJU+4eMikUPHjyQ7UkzKeZL6gtWDw8PfTdwe/bswZMnT3Dy5Elcu3ZNv38VTXdH6P79+/XDRD65Ehsbm6aon/pnkevoxo0b+gZ1X19fVKlSBX/88QeePXuGYcOGCS+AVaxYEYMGDZL0HXc6M2bMyJF3AkZFRSEkJET4eCtUqIDY2FjJ38sFyNPoqCPlE+q6rnLeJVWDY5kyZSR9uih1I+aFCxfQvn17AEDBggUlK4zKcdwDoH8q1MzMDP/99x+KFSuW7l0fIvz666+YP3++vuvDf//9F3PnzpXkSQmptwcduRo3XV1d9cciXbE19TBR5G5wlONps+PHj8PR0RG1atUCkHLd5OzsLDyrbt26mDZtmv7GKXd3d6Hn37rjtomJCUqXLp1mnyDFk91z585FUlISpk+fDiDlfG/OnDmSdZt86dIljBgxAoB0+7sZM2Zg5MiRaNCggaRdEz5//hybN2+Gj4+PPkeKJ6zXrFkDKysrXLlyBSdOnMCAAQMk26cC8h2TgJRuu0NDQyV5l3BycjLCwsL03byGhYUhOTlZeA4AhISEYNKkSbh58yZUKhVq164NZ2dnlCtXTmiOXPu590lKSpJkvHK0BRF97lgAI5LRiRMn4O/vj8WLF+vfNSbVQRSQ705XndQnblKcxNWsWVOWLhDHjx+P27dvo3Pnzli6dCnKli0rad67F5K7d++WpJCzYMECuLq6YvLkyQCAL7/8ElOnThU2/lGjRmHq1Klo1aqV/mW+Dx48gKWlJcaMGSMsBwDOnTuHJUuWIDk5GdOnT5f0/RpASqOwVO8rSq1ly5YYMmQI7OzsoFKp4O3tLfz9MYA8dzeOHTv2vY3Cb968EZaTmpQXrGq1Wt/wcvr0adja2qJo0aJo06YNfv/9d6FZOlI/uSLXkzGpG9TPnz+vb1AvVaqUJA0L8fHxKF++PG7fvi183DohISEoV64cChUqpH//ZWqi735O/Q6w5ORkPH78GN9//73QDCClqN+vXz80atRI0vdyAfI1Okr9hLpcT+DoSP10kYmJCe7cuYOiRYvi7Nmz+Pnnn/WfiXwyVEeu4154eDjy58+PN2/eoE+fPrC3t4epqakkN+jExMSkOS9p2rTpR99f+qnketpMrsbNvXv3pit2ZTRMBLkaHOV62uz333+Hm5ubvsAfHBwMR0dH4Q3DM2fOxOrVqzFv3jwAKU+ri+xZIjIyEkePHgWQ0ouE7mcg5eYP0a5evZqm+8avvvpK+FNZ1atXh7OzM0qUKIEHDx6gSZMmAKSZHwDImzcvrK2tJRl3alu3bsXff/+NEiVKSJqju145efIkHBwcYG1tjT/++EOSLDmOSRMmTMCcOXNgYmICjUaD169fY9iwYcL3CYMHD4atrS2+/fZbqFQqHD16VHjX2TpOTk7o0aMHunXrBgDw8vKCk5MTNm3aJDRHjv1cRsfr8PBw7Ny5E9WqVROWoyP1TQVElIIFMCIZFSxYEH379kXfvn317xqLjY1F3759hb9rDJDvTlcAKF++PFasWIGePXvqiziiL4rl6p5t//79sLCwgJubG7Zv364fLtXd3O9eSLZo0UJ/Z6BICQkJ6RplRd5JaWxsjCVLluDhw4f6l7/XqVNHeOFo6NChuHfvHkaPHo2OHTsCSHuiKsXTA3J11eXo6IidO3fiwIED0Gq1aN++PXr27ClJltR3N6buBkw3ft3Tm1J0ESb1BWtSUhISEhJgYmKCCxcuoHv37vrPEhIShOUAKQUcU1PT9zaYSrGNS+358+ewsLDAmTNnMHbsWP1wKRrUpbprO7V58+Zh3bp1GDp0aLrPIiMjcfbsWaF5U6ZM0f+sVqtRtmxZ9OzZU/ixYt68eShZsiQKFCggeTeIcjQ6AtI/oV6mTBn9z1FRUXj48KEk3TLpSP100cSJE9GvXz/ExMTAwcFBfxPQyZMnUalSJSEZqclx3Nu7dy+mTZsGc3NztGvXDitXroSXlxeioqJQvXp1oVlAyj7633//1ff2cObMGcn223I9bSZ14+bJkydx4sQJvHjxAosXL9YPl6pAIGeDo1xPmyUmJqZ5urVKlSpITEwUmgGkXFc6OjoKH69O6dKlsWHDBgApN8roftb9Xwpv377VP9UoRbF61qxZWLZsGQIDA7F8+XJ91pUrV/S9MYjUunVrHD16FG3atBE+7tS++OILyYtfQMp1hJ+fH/bs2aN//5Loc28dOY5J9+/fR4ECBbB//340adIE06ZNQ48ePYTvE2xtbVG7dm2cOXMGWq0WAwYMEH6Dls6rV6/SXBt169ZNkvccyrGfa9iwYZr3jatUKhQpUgTNmzeXpDvgmTNnwsXFRbKbCogoBQtgRDlEyneN6TRu3Bjh4eHo3bu3pHe6AsDs2bMxb948fYNSixYtMGfOHKEZqRuZdP744w/h70GR4h1pGUlKSsLff/8NCwsLODo64s8//8SpU6eQkJAgyZOBpqamiI6O1hcj7t69m+bJDFGkflrq2LFjAFK6jJg2bRoApDlBFdkVpk5YWBgWLFiAp0+fws3NDUFBQbh48SJ69+4tNMfIyAh9+vRBnz59hI73XXLc3WhnZ4crV65g48aNCA4OBgBUq1YN33//PerVqyc0C5D+gvW7777DoEGDULhwYahUKjRs2BAA8OTJE+HdxfXs2RPe3t7pLsAA6bZxKQ0dOhS2trYwMTFBo0aN9Bffly5dwhdffCEsx83N7YOfi+wCcd26dQAyfopOiganjF4iLsW79J49e4Z9+/YJH+/7SN3oqCP1E+oAcPToUTg5OUGtVuPw4cO4evUqVq9ejbVr1wrNkfrpoqZNm+LUqVOIjo5GwYIF9cMbNmwoyTtr5TjurVmzBjt37kStWrXw77//YvXq1ZK+l2n69OkYN26c/inKhIQErFixQpIsuZ42k7px08TEBObm5lCpVGm6VyxRokSGNxpkl9wNjnI8bVakSBF4eXnpCyre3t4oUqSI8JyoqCi4uLjg33//hUqlQtOmTTFixAhh8yXnO9MAwNraGj179kSXLl2gUqmwZ88e4e9JKliwIJycnNINb968OZo3by40CwB27dqFdevWwdzcXP90qBQ3cdarVw8TJ05Ep06d0lxTij4PmjlzJlxdXeHg4IBy5cqleYpONDmOSbqCzdmzZ9GmTRvky5dPsq4qq1evLsmNHu8yMjLCvXv3ULlyZQApRT4pbqSSYz/37vvGpWZmZqbvoYeIpKPSSnH1TEQG57///pPsTtecZGdnB29vb6HjjIqKQnh4eLquD0NCQlC4cGFhF3hOTk64ffs24uPjUbZsWcTHx6NNmzY4e/YstFqt8G7Vjh49ijVr1iAkJAStWrXC8ePH4ezsLMmFl9KMGDECrVu3xvbt2+Hv74/4+Hh069YtzdMLIoSFhWHr1q0ICQlJczfb8uXLheYkJydj586d+Pfff6HVatG8eXP07NlT6MXXxYsXMXToUPTq1Qv169eHVqvF1atXsXPnTri6uqJ+/frCsuSyf/9+PHv2DF26dNE3ol+/fh2vX7+WpMsuJQkNDcXLly9Rs2ZNfRH++fPnSEpKElYE0xXEX79+jTNnzugbhU+fPo2WLVtK1gD9rm+//RZHjhzJlTnjx4/H9OnTZbmje/369fD390/T6GhjY4Mff/xRaM7o0aNRvXr1NE+o37x5E6tXrxaa061bN6xduxZDhgzRd9dsZWWFvXv3Cs3p1asXxo8fn+bpot9++02yd6EA0txspCPHcU+j0cDX11f/f1tbW8m71E5ISMD9+/eh1WpRuXJlSd5dBMi3PdjZ2WHp0qVpGjcnTZoELy8voTm3b99W3LWK7tiU+mkzrVYr/GnlR48eYfLkyfrG21q1asHZ2Vn4+5/Hjh2L/Pnzo3v37tBqtfD29kZERISkx9h+/fph27Ztko3/6NGjac6JW7duLVmWjpTz9OTJkwyH/197dx5XY97/D/x12lCRZbLNZF/HTkZZYhoaIW1IEmasqWGiyDKhLEOYexjEZBkzWdukbDPcE+7JknuMtbuRKDOEREo6Lef3R79zfTuEGa7rOs6Z1/PxmMdd13Ff78/h1HXO9f683+/KNpO+CW9v7+eOKRQKSSp/5CLHNWn69Ol4/Pgxrl+/jgMHDsDAwAAeHh4a1ykx/Pe//0VYWBiysrJQWloqWSIUKN+gOnv2bGE2V2pqKlasWCH65yP17zl1O16pfs/JqaioCPHx8c+95uSazU30T8EKMCI9VNnOz1q1aqFWrVooLCyUrA1LcnIyMjMzNS7cYu64r4wUOfwVK1bAzs7uuQTY+fPncfbsWdEq21JSUpCYmIjCwkL07t0bp06dgomJCTw8PCRpA9W1a1eEhYXhxIkTUKlU8PHxkWWulRzOnDlTaYWEWLKzs+Hp6Yndu3cDKK+mk2Kn3meffYbmzZvD1tZW0vZjcuxujIiIwNKlSzFgwADh2IABA9CxY0ds3LgR69evFzWeHB9Yn62gvXHjhqStzvbu3QtbW1vJ5xDKwdLSUqPyJiEhAUOGDBE1hvpmoq+vL/bt2ye04c3KysLq1atFjSWXyuaLqUnR2urx48dwcnJCly5dNHZzi52EB8orA1u3bi3cdAwICJDkpmPFCnWFQoGePXuKXqGu9mx1WcU5amKprLpIin+fivbv3y9ZAkyO615xcTHS09OF94xKpVLjeylaQpWUlMDY2BilpaW4efOmZHHkqjbz9/eHl5cX2rZtK1QiV2xV+KYOHjwIR0dHnD17ttIWsmJ/npDzhqNc1WaNGjXCnj17UFBQAJVKJVml2fXr15GQkCB8361bN9Gv58+SqhWmWt++fSVvF/isgoICyc4tdqLrReSq1nvw4AEWL16MX375RbiOz5s3T5IKRzmuScuXL8fJkyfRunVrmJqaIjs7GzNnzhQ9zrx58zB16lR07txZsgozNTs7OyQkJODChQtQqVTo3LmzJP8+cv2ek9P06dNRXFyMjh07SvK+kYjKMQFGpIcqa5ulJlX7rKCgIFy6dAnvv/++pDfuS0tL4evrK7QUkqJcPCUlpdKbY05OTqK2MjIxMRFavTRq1Eh4w2NgYCD6TmGVSgUvLy/Ex8dL3l5PG5YtWyZ6JWBF6uHLanl5eZIkX/Py8hAaGir6eZ8lR7Lo2rVrGskvtf79+yMsLEy0OGpyJQ8r8vf3l/R1d+XKFWzevBnFxcWwsbGBra0tbGxs8M4770gWUy6bN2+W7IbZH3/8oTGD0srKChkZGaLGkCsx9bLWX1K0sB0yZIjkNzIrkuOmY506dUSvqK6MmZkZ7t+/L1Q4nj59GtWrVxc9TseOHXHkyBFZqovUpGwYIsd17+nTp5g4caLGMfX3CoVC9NbXkZGRWLlyJWrWrCm8HqSIA8j3erCzs0NiYiJ+++03SW5u/v7777C1tcXx48dhbGwsyc9ORXLecJSrvVVcXBw+/PBDWFhYAAAePnyI48ePi76prmHDhnjw4IHw75+bmyv5Rh0p/42uX78udMioeP2OioqSLCYg7hxmtcDAQISFhcHd3b3S+b5SPKcTJ04IialevXqhV69eosdYsGABWrRoIcxD3b17N4KDg/HNN9+IHkuOa1LVqlXRv39/4ft69eqhXr16ksRxcnIS/bwvUqdOHfTq1UsY5SDVxuvMzExkZmZqjIyQO4Etpps3b8rafpzon4oJMCI9JHffYqC83VlCQoLkN2EMDQ3x9OlTlJWVwcDAQJK2Yy/bIVXZh4nXVXEH8rO7kYuKikSLA5Sv28rKCo8ePRI+GOsTqbv5Ojg4IDg4GAUFBYiJicGOHTskGVrdsmVLZGdnS/IhqCI5kkVVq1Z9rcdel1zJw4qkft0tWLAAQPlcpp9//hmrV6/G7du3dW4GWGWk/LurWbMm1q1bh+HDhwMAoqOjUbNmTVFjyJWYqmzGmJR69uwp+e+fsLAwBAYGYtq0aZVeU8VKxJ87dw7dunVDUlJSpY+LfbNk5syZmDhxIm7dugVvb2/cuHEDGzZsEDUGUH7j/uuvv9ZoE6c+JhUpb97Lcd2T++doy5YtSEhIkK0KQ45qsyVLlmDevHn48MMPnzsmhhYtWqBv374wMzODUqnE2rVrNWabiU3OG45yVZtt2bIFLi4uwvc1a9bEli1bREuAqSv+TE1N4ezsLLwWfv75Z0n/rQBgz549kp1bPcPKzc1Ntg1UgDTPaezYsQAgJIqk9u2332Lfvn0YPHgwAODLL7+Ei4sLxo8fL2qczMxMrF27Vvh+2rRpos9pU5PymjR27Fh89913sLGx0Xj/I1VrQjs7OyQlJcmSHDp8+DCWLl2Ku3fvAvi/5yT2Z5ZVq1Zh7969aN68uXDPRqFQ6HQCzMrKCvn5+XpRzUb0NmMCjEjPPXjwAL/99hsUCgU6deqEWrVqSRKnfv36kpy3Mp06dYKfnx+GDBkCMzMz4biYb3wq7myseEzMm7bP7kiu+LWYiTY1U1NTuLq6ws7OTmPAuD70l362NZ1YSktLoVQqMWHCBMTHxyMvLw9JSUkYM2aM8GFPTHl5eRg6dKjk7cfkSBY923Lq2cfEJlfysKKuXbtKev5Lly4hOTkZycnJuHfvHnr37i35TSa5jBkzRrJzL1++HEuWLBF2vdrY2GD58uWixpD7hrpc3N3d0aVLF3h5eQkzhcTWrVs3ANC4kS6F2NhYdOvWDREREc89JsXNkk6dOmH79u3473//C6C8Gr9GjRqixgDKbwQ+6/r166Kdv7I22t26dROOi72bW67rnpwsLS1lS37JVW2WkpLy3LHKWhW+rg0bNmDXrl1o27YtTp06hXXr1kl6vZPzhqM221tVrJB4U+rPDi1atNBIsI4YMUK0GBXJlTgsKyvDlClTRD3ni0j9nNq3bw8AkraFryg+Ph67du0Sfo68vb3h6ekpegKsrKwMOTk5qFOnDoDyThZlZWWixlCT8pqk7oARHR39xuf6K3bv3o2NGzfCzMwMJiYmks4AW7FiBdauXYv27dtL2m7x0KFD+Omnn/QqWVS9enW4u7ujT58+GtcIfbhHQ/Q2YQKMSI8dOXIEX3zxBdq1aweVSoXU1FSEhoZqlNyLpUmTJhg3bhz69++vceGWYgaY+ubSzp07hWNi3swaPnw4pk2bhiVLlggzsm7evIkvvvhCqCgQg9w3UBs3bqw3M78qKikpQd++fStNWr6plStXolmzZhg+fDiGDh0q7KLdvn07Vq1aJfobU7naj8mRLKqs5ZSaFAlebdxEDQ4OluzcADBs2DB06dIFM2fOhLW1taSxpFTZDXVHR0fJbqjXq1dPkhk4/wTHjh3DgQMH8NVXX+Hx48fw8vKCs7OzqDca7O3tAQCurq6inbMy6nk7cs0o2bRpE4YPHy7ZLuQ9e/Zg9+7duHHjBoYNGyYcf/z4MZo2bSpanMraaKu/l2I3t9xtN+XQs2dPrFixAoMHD9a4HkkxA0zqarODBw/i4MGD+OOPPzB9+nTheH5+vqjV3AYGBmjbti2A8k0LX375pWjnroycNxzlqjaztLTEkSNH4ODgAKC8IkOdMBCDn5+faOf6K+RKHHbu3Bmpqalo06aNZDHU5HpOnp6eCA8P12iH6evri8jISNFjVXx/IFVSYvz48XBxcUG/fv2gUCiQlJSEGTNmSBJLymtS3bp1AQAHDhx47jPSt99++8LPTa9LrkQbUP77p2PHjrLE0afkFwA0bdpU1PdxRFQ5hUrq/j1EpDWOjo5Yv369cEG9ceMGfHx8JPkQNmfOnEqPL1u2TPRYcvjqq6+wbds24cZFUVERxo0bB39/fy2v7O8rLS3FkSNHYGFhgZ49e+K7777DL7/8gqZNm8LX11fyOQtiW7FiBVxcXNCqVSs8ffoUI0eOxB9//IGSkhKEhYWJmuAdPHgw4uPjn2uJUlZWhqFDh2oMAdcl48ePx6VLl/Rqx/2LZnGJfZO9sLAQ69ev1xjE7ePjI0mP+wsXLuDUqVNITk5GTk4Ounbtip49ewo3t3RFmzZtXpr0FOuGutwt7/Tdf//7X8yYMQN5eXlwdXXF1KlTRb2ZWlkLxOrVq6Nz585wc3N74x3EL5vTBoifjFi8eDEOHDiA3r17w8vLC506dRL1/H/88Qdu3bqF0NBQjeS7ubk5WrduLWvrLno5dZK3IqlmgI0cORK7du0S/bxqqampuHLlCtauXYtp06YJx83NzWFrayvajchBgwZh7dq1QuJ12rRpGt+L/fP6otlBUiR5Jk2ahNWrV0t+0zY9PR1Tp04VKmMMDQ2xfv16NGvWTLKYo0ePxg8//CDJuR0dHWVJHLq4uODatWto2rSpxntiKeZlyfWcnJ2dsW/fvlcee1Pqz//Dhw+HQqHA3r17oVKpJPn8n5aWhjNnzkClUsHW1laSDQVycXV1fe5zS2XHxJCfn4+bN2+iXbt2op+7osTERPz+++8YMGCApBs/VqxYgTt37mDgwIEacfgen4hehQkwIj1W2YdiT09PjcopXaRSqRAVFYWbN28iICAAt27dwt27d0VvR/bkyRPhBlqLFi002gbqkuDgYKSlpUGpVOK9995DUVER+vXrJ7SuWb16tZZX+PcMGjQIiYmJUCgU2LNnD/bu3YsdO3bg+vXrmDt3rqi73ZycnLB///6//djrunHjBubMmYPs7GwcO3YMly9fxrFjx/DZZ5+JGkeuZJE+mjt3LkpLS4WWP+obJFIm+7Ozs/Hzzz9j48aNOj0DbMOGDTA2NoaHhwdUKhX27t0LY2Nj0dohzp07F0uXLoW3t/dzj927dw+HDh0SJY6+++OPP7Br1y4kJCTAxsYGw4cPx6lTp3DkyBHExcWJFic0NBSXLl0S2skeOHAA7du3R0ZGBpo2bYr58+e/0fkrS0KoSZWMKCwsRFxcHHbu3AkjIyN4eXlhyJAhos6Fq0ipVOLRo0ewtLSU5PxykOu6p6/WrFmDp0+fSl5tdvv2bTRo0EDUc1akjZ9XucycOROXLl2StNqsrKwMJ06cQO/evZGRkQGVSoVmzZpJnhh3cXER9bpQkVyJwzNnzlR6XIo2gnI9JycnJ+zevVv47FpQUAAPDw/RN+49efIE69atE1rq9ezZE1OnTpXkM3NxcTEyMjKgUCjQtGlTGBlJ08xKymvSf/7zH5w8eRLx8fEaM8zy8/Nx8eJF0RNgSUlJCA4OhqGhIY4dO4aLFy9i3bp1CA8PFzUOUF6NvGbNGtSsWVNjNpfYv7sre4+vUCiwfft2UePIKScnB8uWLcPt27cRGRmJ1NRU/Prrr/D09NT20oj0ClsgEumx3r17Y8OGDRg2bBhUKhViYmIwYMAASdpOFRYWYuPGjcjKysKqVauQnp6OjIwMSdotLlu2DDk5Obh8+TICAgJgZmaGpUuXir5Tz9TUVCjl37JlCz799FNRzy+XlJQUJCYmorCwEL1798apU6dgYmICDw8P0QZjy8nExESoGjh9+jQGDx4MY2NjtG7dWtRZB0D5zcXCwsLnflYKCgqgVCpFjQUACxcuhI+PD1atWgUAaNu2LWbNmiX6jUB9THTJdRP14sWLGonPrl27SvZzFBISguTkZBQVFcHGxgaff/65ZHOZ5HD8+HGNDRjjx4+Hp6en6PPAnm15d+/evUo/MNPzpkyZgrS0NIwcORIxMTGoVasW+vXrh59//hkHDhwQNVZqaiq+//574Yawh4cHfHx8EB4eDhcXlzc+vzbmtFWrVg0eHh6oU6cOli5dik2bNuHrr79GUFAQBg0aJEoMf39/hISEwNjYGM7OzsjNzcXkyZNFn7mSmpqKBQsWIDU1VeN6J3YCXq7rntySk5ORnp6O0aNHIycnB3l5eZK0OFInHyom+KW46Th8+HBJ5wPK/fMq5w1HOdpbGRgYYMOGDejbt6+slTFStvGTq02lXPOyAPme05AhQ/Dpp58Kr+edO3dK8l7V1NQUgYGBop/3WSkpKZg5cyaqVq0KlUoFpVKJ1atXSzKLV8prkrGxMczMzKBQKDSShHXr1sWkSZPe+PzPWrNmDaKiooTWih06dKh0jqgYvv/+exw5ckRo8ygVudpay2n+/Pmws7PDjh07AADNmjVDYGAgE2BEImMCjEiPqdt7PNvWbMWKFaLPcVi4cCEsLS2RmpoKAKhfvz5mzpwpSQLs9OnTiIuLE27i16pVC0VFRaLHqWj//v06mwBTJ4xMTU3RqFEj4QOXgYEBjI2Ntby6v6+0tBT5+fmoVq0aUlJS8MknnwiPiZ2UGjRoEGbPno2lS5cKuzUfP36M4OBgDBw4UNRY6nPb2dkJVXlS/Rvp4457OW+iPnnyRPjgWtl8qzf1559/omHDhmjdujU++eQTWFlZCY9dvnxZ8g+XUnn48CFu3rwpzCLMzMzEw4cPRTt/dnY2li1bptGS9/79+xgzZoxeJn2l4OzsDAcHB42qAXWzCLF3jt+/f1/j95uRkRFu374NExMT0W+qypGMuH//Pnbt2oWYmBh06NABYWFh6N69O7KysuDt7S1aAiwjIwPVq1fHoUOH0KNHD8yZMwcjRowQPQG2cOFCfP7551i2bBkiIiIQGRkJMzMzUWMA8l335LRp0yYkJSXh3r17GD16NIqLizF37lxJOjDIlTiSYz6gnOS84SjX7Kz27dvjwoULsszhUduzZ49k55ZrLs7jx4/x7bff4urVqxqfJ6WoKJHrOU2ePBl169bFsWPHoFKpMHLkSFE2lqgdPHgQjo6OL5wpJvYM8JCQEKxcuRLdu3cHUJ4QW7hwIeLj40WNA0h7Tfrggw/wwQcfwMHBAa1atRLlnK/ybIW4VEnrhg0bSvr5JCsrC1ZWVi9sb63LLTGzs7Ph6emJ3bt3Ayj/N3rTNuBE9DwmwIj0mDoZJYe0tDQsX74cJ0+eBACYmZkJPejFVqVKFY25IVLFqUiXu8UqlUqkp6cLO+bUXwOQPHEohZEjR8Ld3R3Vq1dH/fr10b59ewDA77//jtq1a4say9fXF0FBQejTpw+aNGkCoDx5ZG9vL0lixdDQEMXFxcLrOzs7W5I3wPq4416um6hOTk7w8PDA4MGDoVAokJiYqNHGRAy+vr6IjY2Fh4cHhg0bplHdOn/+fElmBMjB398fI0aMEH5mr1y5gtDQUNHO/80332D8+PH45ptv4OfnJ1R+ubq6YvLkyaLF0WeOjo7PHXvZ/LY38cEHH2DSpElwdnaGQqFAfHw8unXrhoKCAlFv0MiVjHBxcYGbmxt27NiB+vXrC8etrKzg5uYmWpySkhIAwNmzZ9G3b19Uq1ZNkuuEUqmEra0tVCoV6tatC39/f3h7e4u+S12u656cEhISEB0djeHDhwMo3xSWn58vWTw5ErwmJiZwcXGBi4uLMB9w1apVkswHlIOcNxzlqjZLSUnBzp070bhxY43qEilmWZ08efK5hJHYiT65Eodz585F8+bNcePGDUyfPh3R0dGSzUuS4zmVlpbC19cX4eHhkm3++f333+Ho6IhLly5Jcv5nValSRUh+AYC1tTWqVq0qSSw5rkn/+c9/0KBBA1SvXh2BgYG4ePEi5s+fj969e4sax8zMDPfv39fonCLV7O+OHTtixowZks3mWrx4MTZu3FjpexBdb5P7bDvPvLw8nb73RPS2YgKMSM/l5ubit99+g0KhQKdOnVCzZk1J4jx7o7moqEiyC3erVq0QHx8PlUqFW7duYdOmTejWrZsksdQCAgIkPb+Unj59KrQ+AKDxtVQ3NqXk5eWFDh064O7du+jVq5dw3NDQEHPnzhU1lpGREVauXImbN2/iypUrUKlUaNeunVDBIrZRo0bBz88Pubm5WLt2LeLi4uDv7y96HH3ccS/XTdRJkyahTZs2SE5OhkqlQkBAAOzs7ESNUfF3p/pmd2WP6RoHBwd069YNv/32G1QqFbp06SJq0rpatWrYuHEjxowZA5VKhQMHDsDNzU2StjL65kU7aoHnX4NiCQ4Oxq5du3D48GGoVCr07t0bI0eOhLGxsahVBVInI9Q74KdMmVLpTRgvLy9MmzZNtHjNmzfHp59+iuvXr2PmzJl4+vSpaOeuSP3708LCAqmpqahXrx7++OMP0ePIdd2TU9WqVZ+7pkr1fkvOarOK8wFtbW2F+YDjx4+XbA6UVOS84ShXtZnY74FfZOXKlbh48SKuXbuGjz76CEePHoWtra1o55e7uujmzZtYu3Ytjh49iiFDhsDBwUH09w1yPidDQ0M8ffoUKpVKst876mualPNvK7K2tkZ8fLzQxnH//v2iv/dWk+OaFBMTg08++QSnTp3CgwcPsHTpUixevFj0BFhAQAAmTpyIW7duwdvbGzdu3MCGDRtEjaGmToZWbFGoUChES4Bt3LgRgHbaW0vNwcEBwcHBKCgoQExMDHbs2AF3d3dtL4tI7zABRqTHTpw4gcDAQLRt2xYA8L///Q9hYWEaSQOxWFtbIzw8HEqlEqdPn8bWrVtfOtD6TQQFBeHLL7/EvXv3MGLECNjb22P27Nminb+yG4H169cXjutaib0+vlGsrL1Ls2bNJIvXuHFjyZJeFbm4uOC9997Dv//9bxQWFmL58uWwtrYWPY4+7riX8yaqnZ0d7OzsoFQq8ejRI9HPX/GGxbM3L3QxaV1RnTp1JLs2qH9Hz549G59//jn69esHe3t7nf3dLaeX3eyruJNXTMbGxvD29pZ8PpvUyYjQ0FC0b98eLVu2FO2cL6Outm/dujVMTU2RnZ2NmTNnih5n8ODByM3NxaRJk+Dp6YmysjJRE3lqcl335FS/fn2kpKRAoVCgrKwM4eHhkr0+5Ko2q2w+IFA+B1Ps+YBykPOGo1zVZnLNskpKSkJsbCzc3NwQEhICX19fLFq0SLTzy11dpK44NjY2xsOHD2FhYYE7d+6IGkPu59SpUyf4+vpiyJAhGq1rxUpGqMk1Azw2NhZbt27F/PnzAZRXKNesWRM//PADFAoFkpOTRYslxzVJ3Wb69OnTcHJyQteuXSVJwHfs2BHbt2/Hf//7XwBAly5dUKNGDdHjAPLN5vrll1/QoUMHoZItLy8Ply9fFjUJL7cJEyYgPj4eeXl5SEpKgre3t+jdRYiICTAivfbVV18hMjISzZs3BwCkp6cjMDBQkgSYv78/IiIiYGZmhrCwMNjb20u2697c3ByLFy+W5NxA+Y1AhUIBlUqF27dvC/MN8vPz0aBBA71MKOkSd3f3F968VCgU2Lt3r8wrEpe1tbXkN//0cce9XDdR/f39ERISAmNjYzg7OyM3NxeTJ08Wdf5OUVGR0Kq04tfqx3RVamoqFixYgNTUVI15fWLNo6x4zTE1NcWZM2dw5swZALrfHkVq2riu5eTk4Pvvv0dWVpZGldmzc0vflNTJiCVLliAuLg7Xrl2Di4sLhgwZAgsLC9HO/6yqVatq3FysV68e6tWrJ2qMsrIy2NraolatWrCzs8OZM2dQVFQk2bwnOa57cvriiy8we/Zs/P777+jUqROsra2xcuVKSWLJVW1W2XxANbHnA8pBzhuOclWbyTXLysTEBEZGRlAoFCguLka9evVETRipE+2LFi16rh1uXl6eaHHUmjRpgocPHwotrqtXry5sHhWL3M9JnfCoWAkqZjWOmlwzwKOjo0U936tIfU2qWrUqNmzYgP3792Pnzp1QqVQoLi4W7fwV5xMbGRlpJMcLCwtRrVo10WKpjR8/HqNHj0a/fv0k3ay3YsUKjVbw5ubmzx3TRUOHDhUqHIlIGkyAEemxkpISIfkFlLfNkaqVkbGxMXx8fODj4yPJ+QG8sG2EmljtI9Q3AhcvXoxu3boJM1EOHTqEK1euiBKDXl9l1X6ZmZnYsGGDLPPgpPCypB4g/vwGfdxxD8hzEzUjIwPVq1fHoUOH0KNHD8yZMwcjRowQNQGmb21L1RYuXIjPP/8cy5YtQ0REBCIjIzV2Jr8pbk7QLZ999hmaN28OW1vbSm+qi0XqZIS7uzvc3d1x69YtxMbGwtPTE61atYKPjw9at24tWhw1qRPJQHn7w3nz5gk3HY2NjUVvkyv3dU9OlpaW2LJlCwoLC1FWVibq77lnyVVtZmFhgSdPnujVrnu5bjjKVW0m1ywrMzMzFBYWokuXLggKCoKlpaUkv8PnzJkjzKoFyjciTpgwQdQWuQCE68Enn3yCDh06CG3CpSDXc5KrGkeuGeDvvvuu6Od8lpzXpGXLlmHHjh2YNWsWLC0tkZmZCScnJ9HO36VLl5c+FzHfL6h5eHjgu+++w+LFi+Hh4YHhw4cLlcJiera1p4GBAUpLS0WPI4dp06a99N9J7A1hRP90TIAR6bHatWsjJiZGGL4eGxsr6rwVoHwXzsvMmjVLtFjqthG5ubk4c+aM8KE7OTkZvXv3Fr0n/IULF4RWCwAwcOBAbNmyRdQY9PdV3MV2//59rFu3Dj/99BM+/fRT0V8DclEn9X7++Wdcv34dw4YNA1DeI/7999+XJKa+7LiX+yaqehPB2bNn0bdvX1SrVk30Vkb6mshRKpWwtbWFSqVC3bp14e/vD29vb87o+ofKy8tDaGio5HHkSka89957GDduHN555x2sWbMGvXr1kiQBJnUiWa158+a4desW3nvvPdHPDWjnuie1pKSklz4udvUFIF+1mb7sutfGDUe5qs3kmGUFAKtXr4ahoSFmz56NrVu34vHjx5L8vb3zzjtYsWIFZs2ahcLCQkyZMkXUJEFlpH5fLOdzevz4MTIyMjSqAbt37y5qDKlngAcGBiIsLOyF7/XFfI8v5zWpadOmmDdvHh48eAAAaNSoESZPniza+dUVeRs2bICxsTE8PDygUqmwd+9eyWY+Ozg4wMHBAdevX8eOHTswZMgQ9OrVC2PGjEH79u1Fi2NmZobffvsNnTp1AgD89ttvMDU1Fe38cvrwww8BlN9zunDhgrAhIyEhQbaWtkT/JEyAEemxkJAQBAQEYMGCBVAoFGjbti3CwsJEjaF+w5GZmYmzZ89iwIABAICffvpJ9EGu6kG7vr6+2LdvH6ysrAAAWVlZWL16taixgPIWASkpKcKHoZSUFI2WAqQ9+fn52LRpE6KjozFixAgcPHhQstZMclC/yV27di22b98ufMj78MMPMW7cOPj5+YkSRx933Mt9E7V58+b45JNPkJGRgZkzZ+Lp06eix9BX6h3iFhYWSE1NRb169fDHH39oeVWkLS1btkR2drbo7fsqk5mZiczMTI1dwmIlI1QqFU6cOIGYmBikpaXB0dERe/bsEd6jiE2uRPKDBw8wdOhQdOvWTePmklg3uuW67skpIiICQPm/0cWLF9GqVSsA5VUSnTt3liQBJleCV1923WvrhqMc1WZyzLICypM4alOnThX9/GpBQUGYPn06tm7dihMnTqBv376izoy0sbGp9D2x+rUu5lwpNamfk9qBAwewfPly5OXloW7dusjMzESbNm1ET1hLPQN87NixACrv/iE2Oa9Jv/32Gz7//HOUlZUhKSkJFy9exJ49e0TfFHT8+HGNNpjjx4+Hp6cnxowZI2qcyhgbG6NKlSqYPXs2+vTpg6CgIFHOGxgYCF9fX2Gu77Vr1/DNN9+Icm65ubq6AgD27duHyMhIVK1aFUB5NZ2UXZWI/qmYACPSY40aNcKePXtQUFAAlUolSYJA/WZw4sSJGkOxfXx8RHuj86w//vhD48aSlZUVMjIyRI+zYMECzJgxQ+iTXVRUpNG2grQjIiIC27Ztw8CBA7F//37Rqxq1KTs7G0VFRcIbYKVSibt374p2fn3ccS/XB9Y///wTDRs2FFq9tG7dGqampsjOzsbAgQNFiaHvBg0ahNzcXEyaNAmenp4oKysT5mLQP09eXh6GDh2KLl26oEqVKsJxsSsJVq1ahb1796J58+ZCtaaYs1Ds7OxgaWkJNzc3+Pr6QqFQoKioCNeuXQMA4SaNWORKJA8ePBiDBw8WZqJKRerrnpzUbccCAwMxd+5cYYf6hQsXRN9gIne1mb7supfzhqPc1WZyzLICgOvXryM8PByZmZkarfXFeo1X3Gy4aNEiTJw4ET169MDo0aNFnV8k51wpuZ6TWnh4OGJiYjB+/HjExcXhP//5D44cOSJqDKDyGeBiVjKpK4cqS07PmTNHkqS1HNekZcuW4dtvv0VAQAAAoEOHDpLcN3n48CFu3ryJxo0bAyjfDPTw4UPR4wDAkSNH8MMPPyAnJwejRo1CYmIizMzMUFJSAgcHB1GeX1lZGapVq4bExEScP38eKpUKXbp0kXTuqhzu3LmjMRvQ2NgYt2/f1uKKiPQTE2BEekylUiEqKgo3b95EQEAAbt26hbt376Jr166ix7p9+7ZGn+datWpJtrO/Zs2aWLduHYYPHw6g/ANMzZo1RY9jbW2Nn376CRkZGVCpVGjWrNlzg4tJfitXroSlpSV+/fXXSne862IVk5qjoyM8PDwwaNAgAMDBgwdFTa7o4457Nak/sPr6+iI2NhZVq1ZFeHi48DqrV68ejhw5wp16f8Enn3wCoDxhcObMGRQVFel05Sa9mSFDhmDIkCGSxzl06BB++uknyV5r6mqLLVu2YOvWrRrJIoVCgaNHj4oaT45EckpKCvbt24fU1FQoFAq0bt0afn5+krQHk/q6pw3p6elCoggAOnbsiIULF4oaQ+5qs4q77lUqFdLT03V21z0gzw1HuavN1K0v3d3dUVBQAENDQ1GTEWrTp0+Hs7MzXF1dJZn9pZ5fpK7EUqlUuHTpEjZv3gyFQiHa/KKKc6Xy8/Nx8+ZNSWamAfI9JzUjIyPUqVNHqNLs1asX1q5dK2oMoPIZ4HPmzBG6tkhJigo9QJ5rUnFx8XObY6RoTejv748RI0agffv2UKlUuHr1KkJCQkSPA5R//p44cSL69OmjcdzIyEhjpMSbqDifVIqKam354IMPMHHiRI0NGmyBSCQ+JsCI9NiyZcuQk5ODy5cvIyAgAGZmZli6dKkkCYJmzZph3rx5GhUlzZo1Ez0OACxfvhxLliwReqbb2Nhg+fLlksTKzMxESkoKgPI3ps2bN5ckDv11W7ZsgVKpfG7n8ZMnT3Q+Qenv749OnTrhzJkzUKlU+Pzzz9GvXz/R4+jTjns1qT+wVrypXXHH87OP0YupN2XcuHEDgYGByM7ORlpamiSbMujtp/6gLzVLS0tJE61yz+yTOpH8008/ITQ0FFOmTBGqhn/99VcEBARg/vz56N+/v2ixAPmue3IyMjLCvn37hDlP8fHxMDIS92O3nNVmQPkNfH3adS/HDUe5qs0CAgIwYcIEtGnTBg8fPoSzszPMzc2Rm5uLd955R9gwKBYjIyNMmDBB1HNWpJ5fpPbo0SOcOXMGVlZWaNOmjejxkpKSEBwcDENDQxw7dgwXL17EunXrEB4eLloMuZ+TiYkJVCoVGjdujO+//x7vvvsucnNzRY9TGakSU8+S6r23lNekuXPnYunSpTAxMUFBQYGwEfHatWsalfBicXBwgLW1tcbvbakqdzdt2vTCx8Rsiyn1fFJt+OKLL7B7924cPnwYKpUK/fr1g4eHh7aXRaR3mAAj0mOnT59GXFyc8AGsVq1aGoNwxbR06VKsW7cOoaGhUKlUsLGxkaxfd7169bBmzRpJzl1RXFwcVq5cKbzp3bhxIwICAiTv4U8vd+LECTRr1uy5D/Tbt2/HnTt30LNnTy2tTBz29vawt7eHUqnEoUOHMG7cOGzbtk3UGPq4417qm6gV2xg929LoZS2O6P9U3JQRGBgo6aYMent99913GDt2LFasWFHp47NmzRIljrpFXOfOnTFjxgwMHDhQ4waTru0eVrdUfBGxWi2uX78eERERaNmypXCsbdu2sLa2xuzZs0VPgAHyXPfktGzZMgQGBuKLL76AQqFAy5YtJduoJUe1mVpZWZlQwVJWViZJDLnIecNR6mqzK1euCEmUffv2oXnz5tiyZQvu3LmDyZMni54A69OnD44fPw47OztRz6v2soSev7+/6M9nzZo1QvUKUN6OLjMzU9QYcj+n6dOnIz8/HwEBAVi4cCEeP36MBQsWiBrjReTaFCble2+prknqSr8pU6Zg/PjxyM7ORlBQEE6cOPHC90Rvqnbt2rC3t8f58+exevVqHD58GGfPnhU9zvXr17FhwwZkZWVJ0hpVTer5pNpgbGyM0aNHY/To0QCAc+fOYcGCBViyZImWV0akX5gAI9JjVapU0XhzKOWHVXNzc42E1+3bt7F9+3ZRhyNHRka+9HEvLy/RYgHllUaxsbGwtLQEANy7dw/jx49nAkzLjh8/LvRMr8jLywvOzs6i3TzVlosXLyI6OhoHDx5Ehw4d4OLiInoMfdxxD0h7E7WoqAjp6elQqVQaX6sfo1eTc1MGvb3USSip5wepW8SpqStmAHFngMmlspa/amK2Wnz69KlG8kutVatWkv28ynHdk1Pz5s0RExOD/Px8AOXvkdXdBMQmR7UZUL75KDAwEO+//z5UKhX+97//ISwsDL169RI9lhzkvOEodbVZxcT+uXPnhCR1/fr1JUkS2NraYurUqTAwMBAqjRQKhWiVP5cvX5Y1oQdA+KynJnZHCbmfk62tLQCgevXqsm8mEPM15+7uXun5VCoVcnJyRIvzLKmvSX379kWzZs1w4sQJAMDUqVPRqFEjUWMAQE5ODuLi4hAVFYVbt27hs88+w759+0SPA0DYaOTm5iZJa1Q19XxSfXPv3j3ExcUhJiYGCoVClhbhRP80TIAR6bFWrVohPj4eKpUKt27dwqZNm9CtWzfJ4hUXF+PHH39EdHQ0Ll26JPou4UuXLgEAcnNzcebMGeHNfXJyMnr37i16AgzQ/ED07Icj0g4DA4NK31gbGhrqbCVObm4u9u3bh+joaBQXF8PFxQXVqlV77gaumPRtxz0g7QfWp0+fCruDAWh8rauvO7nJuSmD3l4jR44EAMlnDlZMeOkDuVotFhcXo7i4+Ll5JEqlEkqlUrQ42rjuye3JkyeIi4tDdHQ0VCoVjhw5InoMuarNvvrqK0RGRgqtwNPT0xEYGKizCTBAvhuOclSbZWdnw8LCAmfOnNGYCShF0jo4OBjLli1Du3btYGBgIPr51a0iAXkSemZmZrh//75w7tOnT6N69eqixpD7OS1evBh+fn7CjOzc3FysX78e8+bNE+X8ciWmpOomUxk5rklpaWnC/Qvg/6rl1PPZxEoiHz16FNHR0Th37hw+/vhjLF68GIGBgS/dSPOmysrKMGXKFMnOryZX+2w5lJaW4t///jeioqJw/vx5DBgwAPn5+UJilIjExQQYkR4LCgrCl19+iXv37mHEiBGwt7eX5I3k1atXERUVhQMHDqBt27ZITU3FiRMnRN89px6o6+vri3379sHKygoAkJWVhdWrV4saCwAaNWqENWvWwMPDAwqFAnv27BFikvYolUoUFhaiWrVqGscLCgpEvTknpz59+sDa2hqLFi0S5iHt3btX0pj6suNerpuocs/50Udyb8qgt1tRURHi4+Ofa5cjdhWvp6cndu7c+cpjuiQtLQ1nzpyBQqFAjx49RGt/CAAfffQRZs+ejUWLFgk3gfPy8rBw4UJ89NFHosXRxnVPDiUlJTh27BiioqLw22+/oaSkBJs3b0bnzp0liSdXtVlJSYnGHNzmzZs/Nw9TF2jjhqPU1WaTJk2Ci4sLjI2N0a1bN+H3wfnz59GwYUNRYlRkYWEhedtsORN6AQEBmDhxIm7dugVvb2/cuHEDGzZsED2OnM8pJSVFSH4B5RX3Yra9kysxVVml5JMnTySpIJfjmtSkSZOXzsoSi6+vL2xtbXHw4EHUrl0bgPSb9Tp37ozU1FRJZtpVVFJSgujoaFy9elXjZ0d9n0iX9OnTB40aNYKXlxe+/vprVKlSRdT3WUSkiQkwIj1mbm6OxYsXSxrD1dUVT548gaurK2JjY1G/fn3Y29uLnvyq6I8//tBIRFlZWSEjI0P0OIsWLcLixYuFloe9evVCSEiI6HHo7xk0aBBmz56NpUuXwtzcHADw+PFjBAcH6+wcqzFjxmD//v1YvXo13N3d8fHHH0sSRx933OvrTVR9VNmmjKCgIG0vi7Rk+vTpKC4uRseOHSV9z/D06VON70tLS/Ho0SPJ4kktMjIS4eHh6NevH1QqFTZu3IgpU6Zg1KhRopx/xowZWLhwIfr27YvGjRsDAG7evImBAwdi5syZosQA5LvuyWnZsmVITExEq1at4OrqijVr1mDQoEGSJb8qkrrarHbt2oiJiYGbmxsAIDY2Vrixqku0dcNRymozR0dHWFtb4/79+xo3nxs0aIDQ0FDR4qj1798fO3fuhKOjo0b7xWc3pr0uuRN6HTt2xPbt2/Hf//4XANClSxfUqFFD1BhyP6fS0tLnjomZsJYzMfUsLy8vxMbGin5eOa5JJiYmePfdd0U/77M2btyI6OhoODo6wt7eXtJNjsOGDQNQ/vqKiYlB06ZNNX4viD0DLDg4GKWlpTh9+jQ8PT2RkJAAa2trUWPIpXv37jh16hROnjyJevXqidoal4iep1DJNaWSiLQiOTkZmZmZGm96xWwVOGzYMDx48AAuLi5wc3PDe++9h48++ki0WRSVGTduHLp37y70S4+Ojsbp06d1vn0b/TUlJSUICgrC0aNH0aRJEwDAjRs3YG9vj+XLl0sy+0IOpaWlSEpKQnR0NM6cOYPS0lKsX78eNjY2osVo3749rK2tMW3aNCFZJPXPq9RWrFiB/fv3o3HjxsIHVicnJ51+Tvrm2V3H6ree6t2o3bt3l31NpH2Ojo44ePCgZOePiIhAREQE8vPzNdpZPX36FE5OTjq7oeXjjz/Gjh07UKdOHQDlA+E9PT1x+PBhUeP8+eefSEtLg0qlQqtWrSS5aSfHdU9OHTt2RJcuXeDj4yM8BymvsXJWm2VmZiIgIABXr16FQqFA27ZtERYWJsncGilNnz4dp06dQr9+/eDu7o4PPvhAsn+jyqrNfv75Z51vb1VZhYdCocDVq1dFi3Hv3j0hoad+r5CdnY3S0lJJEkZykPM5zZ07F6amppg4cSJUKhUiIiJQUFAgaZWMejOs1FxcXBAXFyfJuaW+Jo0YMQJ79uwR5Vx/xcOHD7Fv3z7ExMQgPT0dEydOhJOTE5o1ayZajI8++uilryuxkzpOTk7Yv3+/8L+PHz/G559/js2bN4saRy6PHj1CfHw8YmJi8OjRI+Tn5yM6Oppdh4gkwAQYkR4LCgrCpUuX8P7772vMTBL7ze+1a9cQFRWF/fv3o1mzZrh27RqOHTsm2k7AZ2VnZ2PJkiU4ffo0AMDGxgZz585FvXr1RI2jUqmwe/du/PLLL1AoFOjVqxeGDx/OeT9viZs3b+LKlStQqVRo166dsFNdHzx48ACxsbGIjY1FXl4ejh8/Lsp59TVZpG83UfWNu7u78PX169eFNloqlQoKhUL03aGkGyZNmoTVq1cLlbxie/z4MR49eoTQ0FAEBwcLx83NzWFhYSFJTDmMHj0aP/zwg8YxLy8vREZGamlF4pDquienvLw87N+/H9HR0Xj06BFcXFwQHR2Nn3/+WfRYz1abDRgwAIMGDZK8XW9BQQFUKpVkP7dykOuGY8+ePYVqMwcHB6HaTNffc+mLirOYKlK/NxFrHpM25OfnY8mSJcLvng8//BBz5swRfbZZRVImpiry8/PDN998I3kcfbgmVXTp0iVER0fjwIEDwj0UMcj17642bNgwREVFwcXFBTt37kS1atWEZJiuu3LlCqKiopCYmIgmTZpg9+7d2l4SkV5hAoxIj3388cdISEh4bpC5VEpKSoSdjufOnYOdnZ0ks7nksnz5cly9elVo9xIXF4c2bdqIPp+EqDI3btxAkyZNcOHCBXTs2FG08+p7skjfPrDqG7k/KNPba+bMmbh06RL69Omj0QJRimtsSUkJMjIyoFAo0KRJE52sFL527RoAID4+Hjk5OULbodjYWDRo0AA+Pj7aXJ4opLruaUNqaiqioqKQkJCA5s2bw8nJCSNHjhTt/HJXmwHlVWCZmZka7dX69u0rWTw5SHnDUc5qM7nl5ubit99+g0KhQKdOnTTmTemKjz76CDVr1oSbmxvs7OxgYGCg8bgcrer0iVyJKTnp0zUpISEBQ4YMgVKpFLXttFyVf2rjx4/H6tWrERERgV9//RW1atVCfn4+tm7dKtsapPTkyRMYGRnhp59+wqBBg7S9HCK9wgQYkR4bO3YsIiIiZEuAVXT37l3s27cPEydOlOT8169fR2pqKpRKpXBM7P7WTk5OiI2NFW6UFRcXw83NTS92GNHbT44PFPqaLNKnD6z6Ru4PyvT2etGNMj8/P1HjXLp0CZ999hlMTEygUqlQUlKCtWvXol27dqLGkZq9vf0LH1MoFHpxU10ffz8UFxfjxx9/RGxsLL799lvRzitntRlQXkEeFxeHpk2bCokChUKB7du3SxJPTlLecNTH9lYnTpxAYGAg2rZtCwD43//+h7CwMPTq1UvLK/v7Tp8+jdjYWPz666/46KOP4ObmJszn0hczZszQ6Q2pQPn7+jlz5iA7OxvHjh3D5cuXcezYMXz22WeSxtWna5JUz6Vdu3aVzsyTqpKytLQUhoaGKCsrE1oguri46HRVckX69Jojetvo3vZHIvrLmjRpgnHjxqF///4aO33EnAFWGXV7HqmSX9u3b8fu3btx7949dOjQASkpKejevbskA14rtjtk60OSkxz7U2rXro2PPvoI48ePx4ULFySPJxd/f3/ExsYy+UX0FhM70fUiixcvxtKlS4V2V6dOnUJoaCh27dolS3yxSN3a7m2gj/syV61ahaCgINETKzVq1ICXlxe8vLyEarOnT5/Cy8tL9GozAPjpp59w9OhRydqba5OXlxdiY2Ml2W1vYWEBb29veHt7C9Vmw4YN0+n2Vl999RUiIyOFdsbp6ekIDAzUyQRYjx490KNHDzx58gSJiYnw9vbGZ599hlGjRml7aaLJyMiQ7NxyJaYWLlwIHx8frFq1CgDQtm1bzJo1S/IEmD5dk6R6Lk2aNMGmTZskOXdlHj16BHNzc5iYmMDZ2RlKpRL5+fmyxZeaPr3miN42Bq/+I0Skq5RKJRo1aoS0tDRcunRJ+E9qUr8J2bNnD/bu3YsGDRpg8+bN2Lt3ryTzPHr37o2JEydi//79SEhIwOTJk9G7d2/R4xBVpmvXrrLE8ff3BwC9Shbxw8Pb5dq1a8J/RUVFSE9P1zhG/0w5OTkICAgQNuWkpqZi586doscpLCzUmPViY2ODwsJC0ePI6dq1a4iMjERkZCTS09O1vRzRyHXdk5OYs1ZepE2bNpg/fz5OnDgBLy8vSaoBGzRooJWOEnKQ6z3D+++/j4CAAJw4cQJjx46VJaYUSkpKhOQXADRv3hwlJSVaXNGbSU9Px/r16xEREQE7Ozv06NFD20sSlZSvb3ViSj1brG3btjh06JDocR4/fgw7OzthM6qBgYEsv4/06Zo0ZswYSc5rYmKCd99994X/iW3y5MkabXhLSkowZcoU0eNoy3vvvaftJRDpLVaAEemxZcuWaSWumH2lX3R+U1NTlJWVQaVSoVWrVsjMzBQ9TmBgIHbt2oUff/wRKpUK/fv3h4eHh+hxiNT27dsHZ2dnAEBwcPBzx6Sgj8kiffrAqg8mTZqk8X3F6mB9ad1Gf9/8+fNhZ2eHHTt2AACaNWuGwMBAeHp6ihqnWrVqOHXqlDAn6cyZMzpdxRIXF4eVK1eiX79+AICNGzciICAAQ4cO1e7CXtOmTZswfPhw1KpVS7ju6RM5r7FSVZsBQFBQEKZMmYJevXrJ2lVCDnLecJSy2kwutWvXRkxMjDAjOTY2FrVr19byqv6+HTt2YN++fTAxMYGrqyvi4uJ0+trwItu2bZPs3OrElLrFolSJKUNDQxQXFwsJsOzs7OdmtklB169J48ePx+jRo9GvXz/h51Vscm+MUCqVGj+npqamKCoqknUNUtK3OXpEbxMmwIj+IaTu/33+/Hl07twZQHmFlpSqVauG4uJitGnTBmFhYWjQoAGePn0qehwDAwOMGjVKr9pg0Ntt27ZtzyW7KjsmJn1MFun6B1Z9809o3UZ/X3Z2Njw9PYU2YCYmJpLc0Jo7dy6mT58u3LQvLi7GmjVrRI8jly1btiA2NhaWlpYAgHv37mH8+PE6mwC7e/cuBg8ejN69e2P06NF6VY0MlM/OkouU1WabNm3CvXv3cPXqVRgaGkoWRy7JyclCZaj6hmPFY1LRh01HISEhCAgIwMKFCwGUV/2EhYVpd1GvISQkBO+//z7q1auHpKQkJCUlaTz+9ddfa2llb66wsBDh4eG4desWVq1ahfT0dGRkZKB///6ixpErMTVq1Cj4+fkhNzcXa9euRVxcnNDBQmzXr1/Hhg0bkJWVpVHZGBUVJUk8KXl4eOC7777D4sWL4eHhIWw2EZPU930q8+DBAyHpnpOTg7KyMtnXIKYvv/wSvr6+qFatGsaMGYMrV65g0aJFkn7+J/onYgKM6B9Cyv7fALBgwQIYGhpi1KhRcHJyQpUqVSSNVVxcjKCgIKxevRq3bt0S9QbDq841a9Ys0WIRAcDFixdx4cIF5ObmIjIyUjien5+P4uJi0eNpo9JMaoWFhVi/fj1++eUXKBQK9OzZEz4+Pnq5m5dIHxgZaX4MycvLk+TmcMeOHXHkyBFkZGRApVKhWbNmOt/KTZ38evZrXTR//nzMnDkTcXFxmD9/PoyMjODl5YUhQ4ZI+l5Sao8fP0ZGRgaKiopw9uxZAED37t0ljSllcuXy5cs4fPiw3szDXbFiBWJjYzWOhYWFISYmRtK4+tDeqlGjRtizZw8KCgqgUqlgbm6u7SW9Fm11SpHDwoULYWlpidTUVABA/fr1MXPmTNETYHIlplxcXPDee+/h3//+NwoLC7F8+XJYW1uLHgco3zQ8cOBAuLm56Xyy38HBAQ4ODrh+/Tp27NiBIUOGoFevXhgzZgzat2+v7eW9Fm9vb3h6egqfWfft2/dcpwld88svvyAoKAg///wz6tWrh6+++gqTJk3S6c/lRG8jJsCI/iGk3nG4b98+pKSkYMeOHfjXv/4FJycnjBo1ClZWVqLHatWqFYDykvclS5aIfn5TU1PRz0n0MtnZ2bh06RIKCws15vSZmZlJ8gFdG5VmUgsNDUVpaSnmzp0LoHynZkhIiF7f4CDSZQ4ODggODkZBQQFiYmKwY8cOuLu7ix4nIyMDDRs2RKtWrXDixAkkJSXBw8NDktmhcmjUqBHWrFkDDw8PKBQK7NmzR5L3WnKqVq0aPDw8UKdOHSxduhSbNm3C119/LVk7P6kdOHAAy5cvR15eHurWrYvMzEy0adPmuYSL2KSsNmvSpAmePHkCMzMzyWLI4ebNm7hx4wby8/M1Kn4eP34s2WxAbVWbSSUuLg4ffvih8Dv04cOHOH78uM5Vobq6ump7CZJJS0vD8uXLcfLkSQDlnyekqJKRMzFlbW0t2bkrKisr06uZUhUZGxujSpUqmD17Nvr06YOgoCBtL+lvGzZsGKysrJCUlASVSoXFixdLvrlELmfPnsWAAQNQr149vdlsQvQ2Uaj0oQ6fiF7p4cOHqFmzpiyxrl69Ch8fH9y9exd2dnYIDAzUGJb8uuSqzCopKcGPP/4ICwsL9OzZE9999x1++eUXNG3aFL6+vsKgXyKxnTx5Er1795bs/OpKs2+//VZjDlN+fj7279+PhIQEyWJLzcnJCfv37xe+V6lUGDp0qMYxInq7xMfH49ixY1CpVLC3t5ckCe/s7IyoqCjcuXMHY8eORa9evXDv3j2Eh4eLHksOOTk5WLx4sUa167x581CnTh1tL+213L9/H7t27UJMTAw6dOiA0aNHo3v37sjKyoK3tzd+/vlnbS/xbxs6dCi2bt2K8ePHIy4uDv/5z39w5MgRLFq0SJJ4FavN1MS+IThjxgxcvnwZffr00ZgBpmtdEWJjYxETE4NLly5pVECYm5vDw8NDmK0nJldX1+eSn25ubpJXm0ll6NChiI+P1zjm4uKCuLg47SxIRKNHj8YPP/yg7WW8sREjRmDPnj3Cv0tRURGGDRums++J3d3dK00ISNGWMDg4GKNGjUKbNm1EP7fcjhw5gh9++AE5OTkYNWoUXFxcYGZmhpKSEjg4OOh8i/IbN26gSZMm2l7GG/vkk0/w7rvv4j//+Q/i4uJgZmYGV1dXnf15JXpbsQKMSA8928P8WX379pUkbkpKCiIjI/Hbb79h2LBhGD58OE6dOoWpU6fi8OHDb3x+uSqzQkJCkJaWBqVSiffeew9FRUXo168fzp49iwULFkg6S43+2a5cuYL27dsLyerc3FxER0djwoQJopxf7kozuT158kT4PSHVTm4iEs/QoUMlrxowMDCAsbExkpKS4OnpiYkTJ+pstWtpaSkiIyPx1VdfaXsponFxcYGbmxt27NiB+vXrC8etrKzg5uamxZW9PiMjI9SpUwelpaUAgF69emHt2rWSxJKr2qxZs2Zo1qyZqOfUBldXV7i6uiImJkby15c2qs20Rf1a13X5+fnaXoIorK2tER4eDqVSidOnT2Pr1q2wt7cXPY5cianZs2cLXxcVFSExMRF169YVNYb6uZSUlCAmJgZNmzbVaMOrizPAoqKiMHHiRPTp00fjuJGREebPn6+lVYnH399f8spqOaxatQrx8fEYNmwYLCwscOvWLXzyySfaXhaR3mECjEgPRUREvPAxhUIhSQLMyckJZmZmGD16NMLCwoTZHs7Ozs/tEnxdU6ZMeWllllhSUlKQmJiIwsJC9O7dG6dOnYKJiQk8PDx0rr0H6ZbExESNPua1atVCQkKCaAmw/v37o3///pJXmmmDk5MTPDw8MHjwYCgUCiQmJursTW4ifSb3nM2ioiJkZ2fj2LFjwmwSXW2AYWhoKMyT0gelpaXw9fWFp6dnpY9PmzZN5hWJw8TEBCqVCo0bN8b333+Pd999F7m5uZLECg8PR0xMzHPVZmLz8/MT/ZzaZGVlhYKCApiZmWHv3r24ePEiJk6cKGo70f/+97+IiYnB/fv3NT6bmZuba9zQ1zWWlpY4cuQIHBwcAACHDx/W2QrUZ1WsbtRl/v7+iIiIgJmZGcLCwmBvby/JnCQ5ElMA8MEHH2h837t3b3z66aeixtDln8kX2bRp0wsfkyIhKjddfS/3rNq1a2PcuHHC9++9955ezIsketswAUakh77//ntZ45WVlWHp0qXo0KFDpY9v3rxZlDhyVWaZmJhAoVDA1NQUjRo1Ej4MqXeRE0mlsjfyUuyqlbrSTBsmTZqENm3aIDk5GSqVCgEBAbCzs9P2sojoGVu2bEH79u3Rp08fWQbMjx07FoMHD4atrS06dOiArKwsnW5l3K9fP2zevBkuLi4alfHVqlXT4qpej6GhIRISEl6YANNV06dPR35+PgICArBw4UI8fvwYCxYskCSWXNVmhYWFWL9+vdB6s1evXpgyZYpOvu6A8s8U8fHx+P3337F161YMHToU8+bNw/bt20WLIWe1mZzmzp2LqVOnIiwsDED5z/H69eu1vCpx7NmzR9tLEIWxsTF8fHzg4+MjaRw5ElOVyc/PR1ZWlqjnfPa56IPr169jw4YNyMrKQklJiXBcF6vZKtO1a1dtL0EUNjY2lVZSJicna2E1RPqLCTAiPSfHXAADAwMsXLgQ0dHRop73WXJVZimVSqSnp0OlUml8DUDj75FIbE2aNMHWrVsxbtw4qFQqbNu2DY0aNRI9jtSVZtpiZ2cHOzs7KJVKPHr0SNvLIaJKbNu2DXFxcUhMTET//v3h5uaGFi1aSBbPw8MDHh4ewvcNGzbE1q1bJYsnNfVN57CwMCgUCqhUKigUCly9elXLK3s9tra2OHToEAYOHKjtpYiitLQUmZmZsLW1RfXq1bFt2zZJ48lVbRYaGorS0lLMnTsXQPkN1JCQEJ1tn2xkZASFQoHjx4/D09MT3t7eOHTokCSx5Kg2k0tZWRlu3bqFAwcOICMjAyqVCs2aNZNlM4NUioqKEB8f/1ySQNfm21WkreckRWIK0Gy1qH4NStUiztPTE+Hh4bCwsABQPkfd19cXkZGRksST0owZMzBw4EC4ubnp9M/oiwQHB2t7CaKoeA+tqKgI+/fvF7opEZF4+FNFpMfkmgsAAM2bN8etW7ckLdeWqzLr6dOnmDhxovB9xa8r251DJJZ58+YhMDAQq1evhkKhQJcuXV7ZLux1yFVpJid/f3+EhITA2NgYzs7OyM3NxeTJkzF+/HhtL42IKrCxsYGNjQ2ePHmCQ4cOISQkBEVFRQgMDIS1tbXo8QoLC7Fx40ZkZWVh1apVuHHjBjIyMtC/f3/RY8khNTVV20sQ1Q8//ICHDx+iatWqqFatmpDQ09Wdz4aGhti3b59G0lVKclWbXbx4Efv37xe+79q1q063BS8pKcG5c+dw+PBhLFmyBIB074PkqDaTi4GBATZs2IC+fftKunFBTtOnT0dxcTE6duyoNy0Q5XpOciWmKrYnNDQ0xHvvvYd69eqJHgconyesTn4BQM2aNXV2NlxZWRmmTJmi7WWIYtq0aS+9D/P111/LuBpxvfvuuxrfT58+HWPGjBF1xAcRMQFGpNfkmgsAAA8ePMDQoUPRrVs3jZY8Yr4Zkasy69ixY6Kdi+jvqFevHrZv344nT54AgMbPkpjkqjSTU0ZGBqpXr45Dhw6hR48emDNnDkaMGMEEGNFbytTUFJ07d0ZGRgYSEhJw7949SeIsXLgQlpaWQuKofv36mDlzps4mwAoLCys9rqut6KTuHqANPXv2lKWqTe5qsydPngjvS170OtQV06dPR0hICGxsbNCyZUtkZGSgcePGksSSs9pMDu3bt8eFCxfQsWNHbS9FFDdv3sTBgwe1vQxRyfWc5EpMVWxPmJOTg6ysLMkSYGVlZRq/6woKCnR2k2Dnzp2RmpqKNm3aaHspb+zDDz/U9hJkk5WVhT/++EPbyyDSO0yAEekxueYCAMDgwYMxePBgSc6txsos0ncqlQpRUVG4efMmAgICcOvWLdy9e1f0HudyVZrJSd3i5ezZs+jbty+qVasGAwMDLa+KiJ6Vl5eHxMRE7Nu3D8bGxnB1dUViYqJkCf+0tDQsX74cJ0+eBACYmZmhrKxMklhy6NKli0brQzVdbYH47M5nfSBXVZuc1WZOTk7w8PDA4MGDoVAokJiYCGdnZ8njSqV///4aSfCmTZvim2++kSSWnNVmckhJScHOnTvRuHFjjd/bujpXyMrKCvn5+TA3N9f2UkQj13OSKzE1atQobNy4ESqVCi4uLqhRowbs7Ow0EnBiGTJkCD799FNhNuXOnTt1rtp12LBhAMp/98TExKBp06aoUqWK8Lgu/qy6urpqewmSqTgDrKysDCUlJZg3b56WV0Wkf5gAI9Jjcs0FAOR5U8LKLNJ3y5YtQ05ODi5fvoyAgACYmZlh6dKlon9QkavSTE7NmzfHJ598goyMDMycORNPnz7V9pKIqBK9e/dGixYt4ObmJszAOXv2rPB43759RY33bIvkoqKiStvA6oqKLRDVsyKkem8nh9u3byMsLAypqaka1fxHjx7V4qrejJxVbXJVm02aNAlt2rRBcnIyVCoVAgICYGdnJ2lMKRUWFmL9+vX45ZdfoFAo0LNnT/j4+EhSSSlntZkc1HPg9EX16tXh7u6OPn36aLQL1OUZYHI9J7kSU0+ePEH16tWxb98+ODk5ISAgAM7OzpIkwCZPnoy6devi2LFjUKlUGDlyJFxcXESPI6Xc3Fydnc/4Iq/aqKnLP68V3zMYGRnhnXfe0cuZbUTaxgQYkR6Tay4AUL7DKDo6GlevXtW4gaFvb76IpHT69GnExcUJCeVatWqJ2t5TTa5KMzn8+eefaNiwoVDh0bp1a5iamiI7O1vyG4JE9Pd16tQJAHD48OHnHlMoFKInwKytrREeHg6lUonTp09j69atsLe3FzWGtlSpUgXDhg3D6NGjNaridcncuXMxaNAgXL16FStXrsTOnTt1viWvnFVtclSblZaWYsSIEYiOjtbppFdFoaGhKC0tFZI5UVFRCAkJkeRzi5zVZnKoWPWjD5o2bYqmTZtqexmikus5yZWYUiqVAMo/Jw0aNAgGBgaSJghcXV11uuKoevXqevdzqg+bNStTWlqKadOm6WU7aKK3DRNgRHrM1tYWAGSZCxAcHIzS0lKcPn0anp6eSEhIkGSYPZE+q1KlikZLK6nadMlVaSYHX19fxMbGomrVqggPDxeeQ7169XDkyBH4+PhoeYVEVNH3338vazx/f39ERETAzMwMYWFhsLe3x6RJk2Rdg5gqzl4qKyvDxYsXcffuXS2u6M3k5uZi+PDh2L59O7p06YJOnTph3Lhx2l7WG5Gzqk2Om2aGhobChpyKbbR02cWLF7F//37h+65du0rW5kzOajM5PH78GN9+++1zmx63b9+uxVW9Pj8/P20vQXRyPSe5ElMffPABPv74Y6hUKixcuBB5eXmStTnXh029+jgaQh9/TgH9vL4Sva2YACPSY0VFRYiPj0dWVpYwHweQpkRc/UHSyckJkydPxqhRo/D555+LHodIn7Vq1Qrx8fFQqVS4desWNm3ahG7duokeR65KMzlUbGVW8ffcs48R0dtry5Yt+PTTTyU5t7GxMXx8fPQmGV5xBpihoSEaNWqk07Mi1C0qTU1N8eeff+Kdd97Bn3/+qeVVvRk5q9rkqjZr0qQJvLy88PHHH2vsxPfy8pIlvhSePHkiPJeKiWWxyVltJoe5c+eiefPmuHHjBqZPn47o6Gi0a9dO28v62w4ePAhHR0dERkZW+rguvrblfk5yJaYWLFiA1NRUWFlZwcTEBAUFBVi8eLHocQD92NSblpYmbISuSKp5lHLKycnBsmXLcPv2bURGRiI1NRW//vqrMLNNF+nj9ZXobcQEGJEemz59OoqLi9GxY0eN/t9SUO9YMTQ0RGFhIapXr67TO5KJtCEoKAhffvkl7t27hxEjRuDDDz9EUFCQ6HHkqjSTQ8Xn8eyOR33cAUmkj/bv3y96AuxFNwDVdPXGQsUZYPrA2toaDx8+hKenJ9zc3GBiYqLz7WvlrGqTq9qsoKAALVu2xPXr10U9r7Y4OTnBw8MDgwcPhkKhQGJiIpydnSWJJWe1mRxu3ryJtWvX4ujRoxgyZAgcHBx0sqr2999/h6OjIy5duqTtpYhG7uckV2Lqxo0baNasGapUqYITJ07g6tWr8PDwED0OoB+beps0aYJNmzZpexmSmD9/Puzs7LBjxw4AQLNmzRAYGKjTCTB9u74Sva2YACPSYzdv3sTBgwdliWVhYYFHjx6hT58+mDhxImrVqoV33nlHlthE+sLc3Py5D47Hjh0TfV6NXJVmcigqKkJ6ejpUKpXG1+rHiOjtJ0W1ZmhoKNq3b4+WLVuKfm5tS05ORnp6OkaPHo2cnBzk5eXp7Awb9awYFxcXfPDBB8jPz0erVq20vKo3I2dVm1zVZrparfQikyZNQps2bZCcnAyVSoWAgABJ55vJVW0mB/WmSmNjYzx8+BAWFha4c+eOllf1902bNg0AsGjRouc2iubl5WljSW9M/ZzmzZsHc3Nzjcfy8/NFjydXYurzzz9HVFQUsrKysGDBAvTq1QuzZ89GeHi46LH0YVOviYmJrLMo5ZSdnQ1PT0/s3r0bQPlzlaodplz07fpK9LZiAoxIj1lZWSE/P/+5N8BS2LRpEwwNDeHv74/4+Hjk5+fDxcVF8rhE+uLgwYO4ffs2PvzwQzRt2hTHjx/Hv/71LxQWFoqeAJOr0kwOT58+xcSJE4XvK37NCjAi3RAQECD6OZcsWYK4uDhcu3YNLi4uGDJkCCwsLESPI7dNmzYhKSkJ9+7dw+jRo1FcXIy5c+di586d2l7aa5k7dy7c3d3RrVs3NGzYUNvLeWMPHz6Eubk5Hj16hFGjRkle1SZntVlycjIyMzM12g3rYiVlaWkpRowYgejoaEmTXmpyVpvJoUmTJnj48KHwvKpXr462bdtqe1mvbc6cOVi1apXwfX5+PiZMmIA9e/ZocVVvxtvbG7Gxsa889qbkSkwZGBjA2NgYSUlJ8PT0xMSJEyX7GdKHTb3qTRj6yMhI8xZ2Xl6ezre8LywsxMaNG5GVlYVVq1YhPT0dGRkZ6N+/v7aXRqRXmAAj0mPVq1eHu7s7+vTpo7GzTYoZYOqBtwYGBkx8Ef1NixcvxvHjx9GuXTtER0ejX79+iIqKwrRp0zBy5EjR48lVaSaHY8eOaXsJRPQaMjIy0LBhQ1SpUgUqlQqbNm2Ch4eHaEkqd3d3uLu749atW4iNjYWnpydatWoFHx8ftG7dWpQY2pCQkIDo6GgMHz4cAFC/fn1JdvbL5f3338eSJUtQUFAAFxcXuLq6on79+tpe1ms5cOAA5syZAzMzM3z00UdYu3YtYmJiJK1qk6vabPbs2bh8+TLef/994T2/rjI0NBRmn6qrPaQkd7WZ1FauXAmg/HdsQUEBDA0NMXnyZC2v6vW98847WLFiBWbNmoXCwkJMmTIFTk5O2l7WaykpKUFxcTHKysrw9OlTITHw+PFjSSoP5UpMFRUVITs7G8eOHYO/vz8A6eb86sOmXl1O3r6Kg4MDgoODUVBQgJiYGOzYsQPu7u7aXtYbWbhwISwtLYUW1/Xr18fMmTOZACMSGRNgRHqsadOmkrfECQwMRFhYGNzd3SuttoiKipI0PpE+OHnyJGJjY2FmZoacnBz069cP8fHxkvz8yllpRkT0InLtHH/vvfcwbtw4vPPOO1izZg169eql0wmwqlWrPre7W5erXUePHo3Ro0cjLS0NsbGxGDFiBFq2bInNmzdre2l/24YNG7Br1y60bdsWp06dwrp16/D9999LFk/OarPz588jISFBbyoLmjRpAi8vL3z88cdCa0JA/Io2uavNpBQQEIAJEyagTZs2ePjwIZydnWFubo7c3Fy88847QlJe1wQFBWH69OnYunUrTpw4gb59+8Lb21vby3ot4eHh+Oabb6BQKNC5c2fhuLm5OT755BPR48mVmBo7diwGDx4MW1tbdOjQAVlZWahevbrocQBu6n3bTZgwAfHx8cjLy0NSUhK8vb11uqIWANLS0rB8+XKcPHkSAGBmZqbT87mJ3lZMgBHpMT8/P8ljjB07FsD/zXAgor+vWrVqMDMzAwDUqVMHTZo0kST5JXelGRHRi1TcOT5y5EhMmjRJ1JsYKpUKJ06cQExMDNLS0uDo6Ig9e/bAyspKtBjaUL9+faSkpEChUKC0tBQbN27UizlnLVq0wAcffICbN2/izJkz2l7OazEwMBBawdnY2ODLL7+ULJbc1Wa6WpVXmYcPHyIrKwv169fH9evXJY0ld7WZlK5cuYI2bdoAAPbt24fmzZtjy5YtuHPnDiZPnqxzCbCKFVGLFi3CxIkT0aNHD4wePRqFhYWoVq2aFlf3evz8/ODn54eQkBAEBwdLHk+uxJSHh4fGbLGGDRti69atoscBgHPnzmHVqlXIzMxEaWmpcDw5OVmSePT3DR06FEOHDtX2MkTz7MaSoqIinW/rSPQ2YgKMSI/l5ORg2bJluH37NiIjI5Gamopff/0Vnp6eosVo3749AOCDDz4Q7ZxE/zQPHjxAZGSk8P3jx481vhdrR7KclWZERC8j9c5xOzs7WFpaws3NDb6+vlAoFCgqKsK1a9cAlCdcdNEXX3yB2bNn4/fff0fnzp1hbW0ttCTTRf/73/8QGxuLhIQEtGzZEq6urli9erW2l/VaiouLkZ6eLryOlUqlxvdivubkqjZTvxdp0qQJxo0bh/79+2u0Vde1GWAVE4dKpRJr166Fra2tpDHlqjaTWsUE3rlz54T2XPXr19fJKtQuXbpAoVBApVIJ/3vp0iVs3rwZCoUCV69e1fYSX9uMGTNQVlYGAwMDpKWl4ffff8eAAQM0fnbFIHVi6ty5c+jWrRuSkpIqfbxv376ixVKbO3cuPv/8c7Rv3x4GBgain5/ezGeffYbQ0FDUrFkTQPkczIULF+Lrr7/W7sLegLW1NcLDw6FUKnH69Gls3bqVXVmIJMAEGJEemz9/Puzs7LBjxw4AQLNmzRAYGChqAmzatGkv/dCjy29GiOTSs2dPXLp0Sfje1tZW43uxyFVpRkT0KlLvHDc2NsbDhw+xZcsWbN26VSO5plAocPToUdFiyUGduAPKb9CVlJTAwMAABgYGyM3NRZ06dbS4utc3bdo0uLi4ICoqSuerjJ4+fYqJEydqHFN/L/ZrTq5qs4rvRRo1aoS0tDRJ4silssShlAkwOavN5JCdnQ0LCwucOXMG06ZNE44XFRVpcVWvRz1vR+3Ro0c4c+YMrKyshEo3XTVmzBj88MMPKCgowPjx49GqVSucOHFCtN8TciWm4uLi0K1bN0RERDz3mEKhkCQBVqNGDTg6Oop+XhJHVlaWkPwCgFq1aiEzM1N7CxKBv78/IiIiYGZmhrCwMNjb2+v0XEWitxUTYER6LDs7G56enti9ezcAwMTERPSdTB9++KGo5yP6J1q2bJksceSqNCMiehWpd44fO3ZMtHO9DSZNmvTcMYVCgYKCAjx69EgnqxVSUlLQoEEDfPfdd9i+fTtat24NPz8/WFtba3tpr0XO15xc1WZyvT+Ri7baVMpVbSalSZMmwcXFBcbGxujWrZvwGjt//jwaNmyo5dX9fS+baebv769zLR0rUqlUMDU1RWJiIkaMGIHPPvsMTk5Oop1frsSUemOHlLMUnzVkyBDs3LkTjo6OGlWPutgSUx+VlpaitLRUmNVWXFwMpVKp5VW9mczMTPj4+MDHx0c4lp6ejubNm2txVUT6hwkwIj1mZKT5I56Xlyd6P2FXV1dRz0f0T3fixAn88ssvUCgU6NWrF3r16iXaueWqNCMiepXCwkJs3LgRWVlZWLVqFW7cuIGMjAyhrZYUEhISMGTIEMnOL6VnkytPnjzB1q1bsWPHDowbN047i3oDP/30E0JDQ+Hj4yPMkf31118xc+ZMfPHFF5K+DvSBnNVmALBp0yaMGDFCo+1UdHQ0JkyYIGocqWm7TaUuJ8AcHR1hbW2N+/fva1RINWjQAKGhoVpc2eu5fPmyXs00q6ioqAhKpRInTpzAmDFjAEDUTbByJaaSkpLw+eefSxrjWXXq1MEXX3yBkJAQABBaZOriJhN91Lt3b/j7+wuv6+3bt6NPnz5aXtWbCQgIQGxs7CuPEdGbYQKMSI85ODggODgYBQUFiImJwY4dO+Du7i5qjO+++w5jx47FihUrKn181qxZosYj0mfffvst9u3bh8GDBwMAvvzyS7i4uGD8+PGinF/fdnITke5auHAhLC0thTZU9evXx8yZMyVNfGzevFlnE2BqJSUl2LlzJ7799lv07dsXMTExqFevnraX9betX78eERERaNmypXCsbdu2sLa2xuzZs5kAewW5KxwTExM1qhBr1aqFhIQEnUuA6WObSjlZWlrC0tJS45gu/v4BgKpVqwpf68NMs4oGDRoEGxsbNGvWDF27dsW9e/c0qpnelDYSU3JZvXo1tm/fjnbt2nEG2FtoxowZ2LhxI7788kuoVCp8+OGHlVbI64IHDx7gwYMHKCoq0tiI8fjxYzx58kTLqyPSP0yAEemxCRMmID4+Hnl5eUhKSoK3tzecnZ1FjaF+M11xqDMRvZ74+Hjs2rUL5ubmAABvb294enqKlgCrSMpKMyKiV0lLS8Py5ctx8uRJAICZmRnKysokjSl2Fbzc4uLisHbtWnTo0AHfffedTs9wfPr0qUbyS61Vq1Y6OU9I31X2s1NaWqqFlbwZfWxTSa9Pn2aaVeTn54cxY8bA3NwcBgYGMDU1xdq1a7W9rL8tIyMDw4YNe+HjUVFRosesW7cuOnToIPp5SRzGxsbw8/ODn5+ftpfyxvbv34/vvvsOd+/e1diYUb16dZ3bXEKkC5gAI9JTpaWl8PX1RXh4OIYOHSpZnGHDhuHgwYPo2rUrevbsie+++w6//PILmjZtCl9fX8niEukrdfLr2a/FJHWlGRHRqxgbG2t8X1RUJHmCSt0yRxc5OTnhyZMn+Oyzz9C+fXuUlpbi2rVrwuO6djO9uLgYxcXFz70OlEqlzs/z0EdNmjTB1q1bMW7cOKhUKmzbtg2NGjXS9rLeanK3qaS/R99mmlWkUqlw+PBh3LhxA4GBgcjNzcXdu3dFq9aTKzFVt25d2bvJ2NjYICwsDIMGDdKomtO1a+w/wejRo/HDDz9oexmvbezYsRg7dizCw8MxZcoUbS+HSO8pVLq+FZKIXmjcuHHYunWrpG0cgoODkZaWBqVSiffeew9FRUXo168fzp49C6C8jQAR/TVz5swBAAwfPhwKhQJ79+6FSqUSvXWhk5MTdu7cKSTY8vPz4enpif3794sah4joRVasWIEaNWogPj4eCxYswNatW9G6dWv4+/tLEk+pVGpUrOjaQHt7e3vha4VCoZEs1MWb6V9++SXu3r2LRYsWoXr16gDKZ9WqW2Oqr4f0dsjOzkZgYCB+/fVXKBQKdOnSBWFhYahbt662l0b02u7duyfMNFN/Xs7OzkZpaalOJ8GWLl2KnJwcXL58GYcOHUJubi4mTpwoWmLKwcEBixcvfuHjH3zwgShxXF1dZZ+DVPFaq6aL19h/AhcXF8TFxWl7Ga/tzz//1PheoVCgdu3aorYrJaL/wwowIj3WqVMn+Pr6YsiQITAzMxOO9+3bV7QYKSkpSExMRGFhIXr37o1Tp07BxMQEHh4eklaeEemjL774AuvWrRM+VPbs2RNTp06VJJYclWZERC/i7++PiIgImJmZISwsDPb29pLMcTh8+DCWLl2Ku3fvAtDdgfZyz3yS2owZM7Bw4UL07dsXjRs3BgDcvHkTAwcOxMyZM7W8OnpWvXr1sH37dmEuCVufkz7Qp5lmFZ0+fRpxcXFwdXUFUD6zT8y2jmZmZqIluV5GG3v19e1aq89MTEy0vYQ34ubm9tyGpvz8fHTu3BkrVqzQ6SQ80duICTAiPfbf//4XALBz507hmEKhEDUBZmJiAoVCAVNTUzRq1Eh4I2JgYPBcWxsiejlTU1MEBgZKHqd9+/aYM2eORqVZ+/btJY9LRKRmbGwMHx8f+Pj4SBpnxYoVWLt2Ldq3b8+B9m8RExMTLF26FH5+fkhLS4NKpUKrVq3w7rvvantp9AKcHUqkG6pUqaLRAUbs+ZpyJaa2bdsGoLx1+7PtRCs7JpZr167h9OnTAMpbIjZv3lySOPRm9uzZo+0lvJFTp049d6y0tBS7du1CaGgoNmzYoIVVEekvJsCI9Nj3338veYyKA52fHe6s6wOEieQSGRn50se9vLxEjSdnpRkRUWVWrFjx3LHq1aujc+fOsLW1FS2OpaUlOnbsKNr5SFwNGzbkLmcdwNmhRLqjVatWiI+Ph0qlwq1bt7Bp0yZ069ZNtPPLlZiqWbMmAODAgQPPnbOyY2KIi4vDypUr0a9fPwDAxo0bERAQwM42Wib3Z2VtMTQ0hJeXl2jtSono/zABRqSHkpKSXvq4mBVgzw54rvi1lLPHiPTJpUuXZI0nV6UZEdGL5OTkICUlBf379wcAHD16FN26dcPBgwfh6OgoWmWYt7c3/vWvf2HAgAEcaE/0muLj47Fr1y6hZbK3tzc8PT2ZACN6CwUFBeHLL7/EvXv3MGLECNjb22P27NminV+uxNR//vMfnDx5Enfv3tXYNJOfny/K+SuzZcsWxMbGCq0x7927h/HjxzMBpmVyf1bWtooza4lIHEyAEemhiIgIAOXVWRcvXkSrVq0AAGlpaejcubOoCTD2ySZ6c8uWLZMlzj9l9xwRvf3u3r2LmJgYWFhYAACmTp2K2bNnY8eOHRgxYoRoCbDs7Gxs27YNcXFxQgtEDrQn+vs4O5RId6i7PKiJmTSSKzFlYmICMzMzYdyCWt26dSWZGapWcS7cszPiSDvk+qwsp8LCwueOPXz4ELt27ULLli21sCIi/cYEGJEeUrc+DAwMxNy5c9GpUycAwIULF1hOTfQWU6lU2L17t8aMDfWcLjH803bPEdHbKzs7W0h+AYCFhQX++OMPmJubizrY/Pvvv8eRI0dQt25d0c5J9E/D2aFEusPb2xuxsbGvPPa65EpMff311/jhhx+QmpoKPz8/0c77Mo0aNcKaNWvg4eEBhUKBPXv2wMrKSpbY9Ndcv34dqampUCqVwjEXFxftLeg1denSBQqFQhgfolAoULt2bfTs2RPz5s3T8uqI9A8TYER6LD09XUh+AUDHjh2xcOFC7S2IiF5qxYoVuHr1Ktzc3ACU96G/ceMGZs2aJcr59XH3HBHpphYtWuCLL76Am5sbFAoFYmJi0KRJEyiVSqFSSwwNGzZk8ovoDXF2KNHbr6SkBMXFxSgrK8PTp0+FG+uPHz+utNrkdcmVmMrJyUFubi6ysrI0no9atWrVRI+5aNEiLF68GEOHDoVCoUDPnj0REhIiehx6Pdu3b8fu3btx7949dOjQASkpKejevbtOJsBSU1O1vQSifxQmwIj0mJGREfbt2wdnZ2cA5f37jYz4Y0/0tjp58iRiY2OFn1NHR0e4ubmJlgBTk7rSjIjoVZYuXYp169YhNDQUKpUKPXr0QGBgIAwMDIRWzmLo2LEjZsyYgYEDB2rMABOzHTSRvuPsUKK3X3h4OL755hsoFAp07txZOG5ubo5PPvlEtDhyJaYcHBzQr18/KJVK4fmoK2YUCgWuXr0qSpyKqlSpgq+++krjmJQzx+jv2bNnD/bu3QtPT09s3rwZaWlp2Lhxo7aXRUQ6gHfCifTY0qVLMWvWLMyfPx8GBgZo2bIlgoKCtL0sInqJikkoqRJSUleaERG9irm5OWbPnl3pY7Vr1xYtjrr1q7o9NFD+u5UJMKK/LicnB8uWLcPt27cRGRmJ1NRU/Prrr/D09NT20ojo//Pz84Ofnx9CQkIQHBwsWRy5ElP+/v7w9/eHl5fXK+cYi0Xq9pH0ZkxMTGBqaoqysjKoVCq0atUKmZmZ2l4WEekAherZ7RpEpHf+/PNPxMbGIj4+HiqVCkeOHNH2koioEsuXL8f//vc/uLq6QqFQIDY2Fq1atXrhTeLX5eTkpFFpVlxcDDc3N+zfv1/UOEREL1JUVIT4+HhkZWWhpKREOM5EPNHbx8fHB3Z2dtixYwf2798PpVIJd3d3vm8geosplUqUlpYK34vdMlDOxBQAPHnyBAA05o6JRd0+cuTIkdi9e7dQ1ZaXl4exY8fi0KFDosekv8/Lywvbtm3D3LlzYWlpiQYNGmDPnj28FhHRK7ECjEhPlZSU4NixY4iOjsb58+dRUlKCLVu2aMwEI6K3Q2lpKZRKJQIDA7F79278+OOPUKlUsLe3x4gRIySJKUelGRHRi0yfPh3FxcXo2LEjTExMJIuTlJRU6XFWgBH9ddnZ2fD09MTu3bsBlO/CF3NWHxGJ58cff0RoaCju3r0ractAdfJLysQUAGRlZWHmzJm4evUqFAoF3n//fYSFhcHKykq0GJW1j1QoFDAzMxO1fSS9mQULFqC4uBhBQUFYvXo1bt26hRUrVmh7WUSkA1gBRqSHli1bhsTERLRq1Qqurq4YMGAABg0ahGPHjml7aURUieXLl6NZs2YYPny4xvHt27fjzp07oldEyFVpRkT0Io6Ojjh48KDkcby9vYWvlUolrl69ivfffx+7du2SPDaRvhgxYgT27NkDFxcXxMXFIS8vD6NHj0Z8fLy2l0ZEzxgwYACWL1+Ozp07S5qoliMxBQCffPIJBg8eDHd3dwBATEwMEhISsHXrVlHjABDaRz569AhnzpyBlZUV2rRpI3ocIiKSFyvAiPTQzp070aVLF0yaNAk2NjYAWOFB9DY7fvw4AgICnjs+evRoDB06VLQEmDYqzYiIKmNlZYX8/HyYm5tLGqfi7C8AuHbtmiQ3zYj0mYODA4KDg1FQUICYmBjs2LFDuBlNRG8XCwsLdO3aVfI4wcHBGDFihEZiKjg4WPRr7IMHDzBs2DDhe3d3d2zfvl3UGAEBAZgwYQKCg4Px8OFDODs7w9zcHLm5ufD3939ukyJpR05ODr7//vvn2md//fXXWlwVEekCJsCI9NDJkyexf/9+rFixAo8ePYKLi4tG/28iersYGBjA0NCw0uNiJq9XrlwpVJp5enoKw+u3b9+Or776irN3iEg21atXh7u7O/r06aPRAlHq30MtWrTA//73P0ljEOmbCRMmID4+Hnl5eUhKSoK3tzecnZ21vSwiqsSAAQOwY8cODBo0CFWqVBGOiz0DTI7EFFD+eej69eto1qwZACAjI6PSz01v4vLly0Kl1759+9C8eXNs2bIFd+7cweTJk5kAe0t89tlnaN68OWxtbUV/DRCRfmMCjEgP1ahRA15eXvDy8kJqaiqioqLw9OlTeHl5wcnJCSNHjtT2EomoAqVSicLCwuc+mBYUFECpVIoWR65KMyKiV2natCmaNm0qeZyKM8DKyspw8eJFlJWVSR6XSN8MHToUQ4cO1fYyiOgVvvrqKwDl7fyknAEmR2IKAPz9/eHl5YW2bdsCAFJTU0Wf+1S1alXh63PnzqF///4AgPr167OTzlskLy8PoaGh2l4GEekgJsCI9FybNm0wf/58zJ49Gz/++CNiY2OZACN6ywwaNAizZ8/G0qVLhXZgjx8/RnBwMAYOHChaHLkqzYiIXsXPz0+WOBEREcLXRkZGsLKyYqscor+JbaeIdEdqaqosceRITAGAnZ0dEhMTcfz4cSgUCsyYMQPt27cXPU52djYsLCxw5swZTJs2TTheVFQkeix6PS1btkR2djbq1aun7aUQkY5hAozoH8LY2BiDBg3CoEGDtL0UInqGr68vgoKC0KdPHzRp0gQAcOPGDdjb2+Ozzz4TLY5clWZERC9y8OBBODo6IjIystLHvby8RI337AwwIvr72HaKiJ4ldWJKPZerTZs2MDAwwOrVq1G9enVJ5nJNmjQJLi4uMDY2Rrdu3dCiRQsAwPnz59GwYUPR4tCbycvLw9ChQ9GlSxeN9p7cjEFEr8IEGBERkZYZGRlh5cqVuHnzJq5cuQKVSoV27dqhcePGosaRq9KMiOhFfv/9dzg6OuLSpUuSxjl37hy6deum0QKxor59+0oan0ifsO0U0dvPxsam0o4O6haIycnJosSRKzF15coVjblcLVq0kGwul6OjI6ytrXH//n0hJgA0aNCAv/veIkOGDMGQIUO0vQwi0kFMgBEREb0lGjduLHrSqyK5Ks2IiF5E3VZo2bJlksaJjY1Ft27dNFogqikUCibAiP4Gtp0ievtFR0fLEkeuxFTFCh855nJZWlrC0tJS4xh/571dXF1dtb0EItJRTIARERH9Q8hVaUZE9FckJycjMzNTY6aQWC0QFy9eDIAtEInexLRp06BQKJCfn8+2U0RvuXffffe5Yzdu3BA2vYlFzsQU53JRRQ8ePEBoaCiSk5OhUCjQs2dPzJs3D7Vr19b20ojoLccEGBER0T+M1JVmRESvMnv2bFy+fBnvv/++JDOFrl279tLH1fM9iOjFPvzwQzx8+BAqlQp2dnaoUaOGtpdERH+Dv78/YmNjRT+vHIkpzuWiZy1YsAAtWrRAUFAQVCoV9uzZg+DgYHzzzTfaXhoRveWYACMiIiIiIlmdP38eCQkJMDY2luT8kyZNeuFjCoUCR48elSQukT6pUqUK/vWvf8HMzAxKpRJr166Fra2ttpdFRH+RSqUS/ZxyJaY4l4uelZmZibVr1wrfT5s2Dc7OzlpcERHpCibAiIiIiIhIVvXr15f0/MeOHZP0/ET/BBs2bMCuXbvQtm1bnDp1CuvWrWMCjEiHdO3aVfRzypmY4lwuqqisrAw5OTmoU6cOACAnJwdlZWVaXhUR6QImwIiIiIiISFZNmjTBuHHj0L9/f5iYmAjHxZoBVlFycjLS09MxevRo5OTkIC8vD02bNhU9DpG+MTAwQNu2bQEANjY2+PLLL7W8IiL6O4KDgyU5LxNTpA3jx4+Hi4sL+vXrB4VCgaSkJMyYMUPbyyIiHcAEGBERERERyUqpVKJRo0ZIS0uTNM6mTZuQlJSEe/fuYfTo0SguLsbcuXOxc+dOSeMS6YPi4mKkp6cLbdSUSqXG95ylR/T2mDZtGhQKxQsf//rrr2VcDZH4XFxc8P777+PMmTNQqVQYM2YMr0NE9JcoVFI0BSYiIiIiItKyoUOHIjo6GsOHD0dcXBwAwMnJCfv379fuwoh0gL29/Qsf4yw9ordLbGzsSx93dXWVaSVERERvF1aAERERERGRrAoLC7Fx40ZkZWVh1apVSE9PR0ZGBvr37y9qnKpVq8LY2Fjj2Mt2yBPR/+EsPSLdwQQX6avAwECEhYXB3d290vdwUVFRWlgVEekSJsCIiIiIiEhWCxcuhKWlJVJTUwEA9evXx8yZM0VPgNWvXx8pKSlQKBQoKytDeHg4WrZsKWoMIiIibVuxYsVLH581a5ZMKyES19ixYwEAs2fP1vJKiEhXMQFGRERERESySktLw/Lly3Hy5EkAgJmZGcrKykSP88UXX2D27Nn4/fff0alTJ1hbW2PlypWixyEiItImU1NTbS+BSBLt27cHANy+fRvOzs4aj+3bt08bSyIiHcMEGBERERERyerZtoRFRUWQYjSxpaUltmzZgsLCQpSVlcHMzEz0GERERNrm5+en7SUQSWrbtm3PJcAqO0ZE9CwmwIiIiIiISFbW1tYIDw+HUqnE6dOnsXXrVtjb20sSKzMzE5mZmSgtLRWO9e3bV5JYRERE2nby5ElcvXoVRUVFwjEmyEhXXbx4ERcuXEBubi4iIyOF4/n5+SguLtbiyohIVzABRkREREREsvL390dERATMzMywcuVK2NvbY9KkSaLHWbVqFfbu3YvmzZvDwMAAAKBQKJgAIyIivbRy5UpcvHgR165dw0cffYSjR4/C1tZW28siem3Z2dm4dOkSCgsLcenSJeG4mZkZli1bpsWVEZGuUKik6DVCRERERET0jIo7dyvj5eUlarwBAwYgNjYW5ubmop6XiIjobeTk5ITY2Fi4ubkhPj4e2dnZWLRoEdavX6/tpRG9kZMnT6J3797aXgYR6SBWgBERERERkSxCQ0PRvn17tGzZUpZ4lpaWTH4REdE/homJCYyMjKBQKFBcXIx69erhzp072l4W0Ws7d+4cunXrhtLSUiQlJT33OKv6iehVmAAjIiIiIiJZLFmyBHFxcbh27RpcXFwwZMgQWFhYiB5HfYOkc+fOmDFjBgYOHIgqVaoIj/NmCRER6SMzMzMUFhaiS5cuCAoKgqWlJQwNDbW9LKLXFhsbi27duiEiIuK5x9jWmoj+CrZAJCIiIiIiWd26dQuxsbE4ePAgWrVqBR8fH7Ru3Vq083t7e7/wMYVCge3bt4sWi4iI6G1x//591KhRA6Wlpdi6dSseP34Mb29vNGzYUNtLIyIi0gomwIiIiIiISHaPHz9GQkIC1qxZgxkzZmD48OHaXhIRERERvYXOnj2L999/H2ZmZti7dy8uXryIiRMnwsrKSttLI6K3HBNgREREREQkC5VKhRMnTiAmJgZpaWlwdHSEi4uLZDcvPD09sXPnzlceIyIi0gfXr19HeHg4MjMzUVJSIhyPiorS4qqI3pyTkxPi4+Nx7do1TJ8+HUOHDsUvv/zCqn4ieiXOACMiIiIiIlnY2dnB0tISbm5u8PX1hUKhQFFREa5duwYAaNGihajxnj59qvF9aWkpHj16JGoMIiKit8X06dPh7OwMV1dXzv4ivWJkZASFQoHjx4/D09MT3t7eOHTokLaXRUQ6gAkwIiIiIiKShbGxMR4+fIgtW7Zg69atqNiMQqFQ4OjRo6LEiYiIQEREBPLz82Frayscf/r0KZycnESJQURE9LYxMjLChAkTtL0MItGVlJTg3LlzOHz4MJYsWQKgfGMTEdGrMAFGRERERESyOHbsmCxxPDw8MHDgQISGhiI4OFg4bm5uDgsLC1nWQEREJLc+ffrg+PHjsLOz0/ZSiEQ1ffp0hISEwMbGBi1btkRGRgYaN26s7WURkQ7gDDAiIiIiItJbJSUlyMjIgEKhQJMmTWBkxD2ARESkn5KTkzF16lQYGBjAxMQEKpUKCoUCycnJ2l4aERGRVjABRkREREREeunSpUv47LPPhJuAJSUlWLt2Ldq1a6ftpREREYluwIABmDlzJtq1awcDAwPh+LvvvqvFVRG9uaKiIsTHxyMrKwslJSXC8VmzZmlxVUSkC7j9kYiIiIiI9NLixYuxdOlSYQ7YqVOnEBoail27dml5ZUREROKzsLDAwIEDtb0MItFNnz4dxcXF6NixI0xMTLS9HCLSIUyAERERERGRXiosLBSSXwBgY2ODwsJCLa6IiIhIOv3798fOnTvh6OiIKlWqCMerVaumxVURvbmbN2/i4MGD2l4GEekgJsCIiIiIiEgvVatWDadOnYKNjQ0A4MyZM7wJSEREeutf//oXAGDRokVQKBTCDLCrV69qd2FEb8jKygr5+fkwNzfX9lKISMdwBhgREREREemlCxcuYPr06UKrnOLiYqxZswbt27fX8sqIiIik8+jRI5w5cwZWVlZo06aNtpdD9MZmzpyJS5cuoU+fPhotEDkDjIhehRVgRERERESklzp27IgjR44gIyMDKpUKzZo1g7GxsbaXRUREJKqAgABMmDABbdq0wcOHD+Hs7Axzc3Pk5ubC398fw4cP1/YSid5I06ZN0bRpU20vg4h0ECvAiIiIiIhIL2VkZKBhw4aoUqUKTpw4gatXr8LDwwMWFhbaXhoREZFoBg0ahAMHDgAAvvvuOyQlJWHLli24c+cOJk+ejH379ml5hURERNrBCjAiIiIiItJLn3/+OaKiopCVlYUFCxagV69emD17NsLDw7W9NCIiItFUqVJF+PrcuXPo378/AKB+/fpQKBTaWhaRqE6ePImrV6+iqKhIOObn56fFFRGRLjDQ9gKIiIiIiIikYGBgAGNjYyQlJcHT0xOhoaG4ffu2tpdFREQkuuzsbDx9+hRnzpzBBx98IByvmCwg0lUrV67Et99+i23btuHu3bvYuXMnbty4oe1lEZEOYAKMiIiIiIj0UlFREbKzs3Hs2DHY2NgAANgBnoiI9M2kSZPg4uICBwcHdOvWDS1atAAAnD9/Hg0bNtTy6ojeXFJSEjZv3ow6deogJCQEMTExePLkibaXRUQ6gC0QiYiIiIhIL40dOxaDBw+Gra0tOnTogKysLFSvXl3byyIiIhKVo6MjrK2tcf/+fbRp00Y43qBBA4SGhmpxZUTiMDExgZGRERQKBYqLi1GvXj3cuXNH28siIh2gUHELJBERERER/QOUlpaitLQUJiYm2l4KEREREf1FY8aMwcaNG7F8+XI8fvwYlpaWOHfuHPbu3avtpRHRW44JMCIiIiIi0kuFhYXYuHEjsrKysGrVKqSnpyMjIwP9+/fX9tKIiIiI6C+6f/8+atSogdLSUmzduhWPHz+Gt7c3W3wS0StxBhgREREREemlhQsXoqSkBKmpqQCA+vXr45tvvtHyqoiIiIjoryotLcW//vUvmJiYoFq1apg6dSpmz57N5BcR/SVMgBERERERkV5KS0tDQEAAjI2NAQBmZmYoKyvT8qqIiIiI6K8yNDREZmamtpdBRDqKCTAiIiIiItJL6sSXWlFREdgBnoiIiEg3zJ07FwBgY2ODkJAQXLhwAdeuXRP+IyJ6FSNtL4CIiIiIiEgK1tbWCA8Ph1KpxOnTp7F161bY29tre1lERERE9BdcvXoVABAVFQUA+Pnnn4XHFAoFjh49qo1lEZEOUai4BZKIiIiIiPRQcXExIiIicOzYMQCAvb09Jk2aBENDQy2vjIiIiIhexdXVFbGxsdpeBhHpMCbAiIiIiIhIr0RGRr70cS8vL5lWQkRERESvq127dqhRo8YLH09OTpZxNUSki9gCkYiIiIiI9EpoaCjat2+Pli1banspRERERPSamjRpgk2bNml7GUSkw5gAIyIiIiIivbJkyRLExcXh2rVrcHFxwZAhQ2BhYaHtZRERERHR32BiYoJ3331X28sgIh3GFohERERERKSXbt26hdjYWBw8eBCtWrWCj48PWrdure1lEREREdFfMGLECOzZs0fbyyAiHcYEGBERERER6a3Hjx8jISEBa9aswYwZMzB8+HBtL4mIiIiIiIhkwAQYERERERHpFZVKhRMnTiAmJgZpaWlwdHSEi4sLrKystL00IiIiIiIikgkTYEREREREpFf69OkDS0tLuLm5oUePHlAoFBqPt2jRQksrIyIiIiIiIrkwAUZERERERHrF3t5e+FqhUKDiRx6FQoGjR49qY1lEREREREQkIybAiIiIiIiIiIiIiIiISK8YaHsBRERERERERERERERERGJiAoyIiIiIiIiIiIiIiIj0ChNgRERERET0jxQUFISvvvoKAJCSkoKPP/5YlritW7fGzZs3K33M29sbe/fu/Uvnsbe3xy+//PJaa3iT/y8REREREZEuYAKMiIiIiIjeWvb29ujYsSO6dOmCnj17Ys6cOSgoKBA9jrW1NQ4fPvzKPxcTEwNPT0/R4xMREREREZG4mAAjIiIiIqK3Wnh4OH799VfExsbi4sWL2LBhw3N/pqSkRAsrIyIiIiIiorcVE2BERERERKQT6tWrhz59+uD3338HUN5KMDIyEg4ODnBwcAAA/Pvf/4azszOsra0xcuRIpKamCv//K1euwNXVFV26dMHnn3+OoqIi4bHTp0/Dzs5O+P727dvw8/ODjY0NevTogZCQEKSnp2PBggU4f/48unTpAmtrawCAUqnE8uXL0a9fP/Ts2RPBwcF4+vSpcK6IiAj07t0bvXv3RlRU1F9+vpmZmRgzZgx69OiBHj16YObMmcjLy9P4MxcvXsSgQYPQvXt3zJkzR+M5vezvgoiIiIiISN8xAUZERERERDrh9u3bOH78ONq2bSsc++mnn7Bnzx4cOHAAly9fxty5cxESEoLTp0/Dw8MDU6dOhVKphFKphK+vL5ydnXHmzBkMHDgQR44cqTROaWkpJk+ejIYNG+LYsWM4fvw4Bg0ahObNm2PRokXo3Lkzfv31V6SkpAAAwsLCkJGRgbi4OBw5cgR3797FunXrAADHjx/Hli1bsGXLFhw5cgTJycl/+fmqVCpMnjwZJ06cwMGDB3Hnzh2sXbtW48/s378fmzdvxo8//oiMjAysX78eAF76d0FERERERPRPwAQYERERERG91Xx9fWFtbY1Ro0ahe/fumDJlivDYpEmTULNmTVStWhV79uyBh4cHOnXqBENDQ7i6usLY2Bjnz5/Hb7/9huLiYowdOxbGxsYYOHAgOnToUGm8Cxcu4O7du5g1axZMTU1RpUoVodrrWSqVCnv37sXcuXNRs2ZNmJubY/LkyUhMTAQAHDx4EG5ubmjVqhVMTU3h5+f3l59348aN0atXL5iYmKB27dr45JNPcPbsWY0/4+XlhQYNGqBmzZrw8fER4r7s74KIiIiIiOifwEjbCyAiIiIiInqZdevWoWfPnpU+1qBBA+HrP//8E3Fxcfjhhx+EY8XFxbh79y4UCgXq1asHhUIhPNawYcNKz3n79m00bNgQRkav/rj04MEDFBYWws3NTTimUqlQVlYGALh79y7at28vPPbuu+++8pxqOTk5WLx4MVJSUlBQUACVSoUaNWpo/JmKz79hw4a4e/cugJf/XRAREREREf0TMAFGREREREQ6q2JCq0GDBpgyZQp8fHye+3NnzpxBdnY2VCqV8P/5888/YWVl9dyfbdCgAW7fvo2SkpLnkmAV4wFArVq1ULVqVSQmJqJevXrPnatu3bq4ffu28P2ff/75l5/bqlWroFAoEB8fj1q1auGnn35CSEiIxp959tx169YVnsOL/i6IiIiIiIj+CdgCkYiIiIiI9MLw4cOxa9cu/Pbbb1CpVHjy5Al+/vln5Ofno3PnzjAyMsL27dtRUlKCI0eO4OLFi5Wep2PHjrC0tMSqVavw5MkTFBUV4dy5cwCAOnXqIDs7W5ilZWBggOHDh2Pp0qXIyckBAGRnZ+PEiRMAgIEDByI2NhbXrl1DYWEhvvnmm7/8fAoKCmBqaooaNWogOzsbERERz/2ZHTt24M6dO3j48CE2btyIQYMGvfLvgoiIiIiI6J+ACTAiIiIiItILHTp0QGhoKEJCQtC9e3c4ODggJiYGAGBiYoK1a9ciNjYW3bt3x4EDBzBgwIBKz2NoaIjw8HDcvHkTH374Iezs7HDw4EEAgI2NDVq0aIHevXujR48eAIDAwEA0btwYI0aMQNeuXTFu3DhkZGQAAPr27YuxY8di7NixGDBgAGxsbP7y8/Hz88OVK1dgbW2NSZMmwcHB4bk/M2TIEHz66afo378/rKyshIqvl/1dEBERERER/RMoVCqVStuLICIiIiIiIiIiIiIiIhILK8CIiIiIiIiIiIiIiIhIrzABRkRERERERERERERERHqFCTAiIiIiIiIiIiIiIiLSK0yAERERERERERERERERkV5hAoyIiIiIiIiIiIiIiIj0ChNgREREREREREREREREpFeYACMiIiIiIiIiIiIiIiK9wgQYERERERERERERERER6RUmwIiIiIiIiIiIiIiIiEiv/D80aLhlOVWtNAAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 2160x2160 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"integer_mapping = {l: i for i, l in enumerate(label_encoder.classes_)}\n",
"labs = list(integer_mapping.keys())\n",
"\n",
"print(classification_report(y_prfm_test, y_prfm_pred))\n",
"\n",
"plot_trn_tst_dist(y_prfm, y_prfm_test, y_prfm_test, y_prfm_pred, in_cols=False)\n",
"plot_confusion_mat(y_prfm_test, y_prfm_pred, labs=labs, size=(30, 30))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see from the reports that some classes are not predicted at all (Nans produced in the report above). From the confusion matrix above, with this grouping Breast cancer group has quite a few miss classifications."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Multiple class RandomForest with over sampling (SMOTE), takes a long time to run!"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\nfrom imblearn.over_sampling import SMOTE\\n\\nover_m = SMOTE()\\nX_sm, y_sm = over_m.fit_resample(X_prfm_train, y_prfm_train)\\n\\n# Make a model\\nmodel_rfsm = RandomForestClassifier(n_estimators=1000)\\n\\n# Fit the model \\nclf = model_rfsm.fit(X_sm, y_sm)\\n\\n# Predict with unused (test) data \\ny_prf_pred_m = model_rfsm.predict(X_prfm_test)\\n\\n# What we got \\n\\nplot_trn_tst_dist(y_prfm, y_sm, y_prfm_test, y_prf_pred_m, in_cols=True)\\n#print(f\"Accuracy:\", accuracy_score(y_prfm_test, y_prf_pred_m))\\nprint(classification_report(y_prfm_test, y_prf_pred_m))\\n# Plot some results\\n#plot_confusion_mat(y_prfm_test, y_prfm_pred, labs=labs, size=(30, 30))\\nplot_confusion_mat(y_prfm_test, y_prf_pred_m, labs=labs, size=(30, 30))\\n'"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"from imblearn.over_sampling import SMOTE\n",
"\n",
"over_m = SMOTE()\n",
"X_sm, y_sm = over_m.fit_resample(X_prfm_train, y_prfm_train)\n",
"\n",
"# Make a model\n",
"model_rfsm = RandomForestClassifier(n_estimators=1000)\n",
"\n",
"# Fit the model \n",
"clf = model_rfsm.fit(X_sm, y_sm)\n",
"\n",
"# Predict with unused (test) data \n",
"y_prf_pred_m = model_rfsm.predict(X_prfm_test)\n",
"\n",
"# What we got \n",
"\n",
"plot_trn_tst_dist(y_prfm, y_sm, y_prfm_test, y_prf_pred_m, in_cols=True)\n",
"#print(f\"Accuracy:\", accuracy_score(y_prfm_test, y_prf_pred_m))\n",
"print(classification_report(y_prfm_test, y_prf_pred_m))\n",
"# Plot some results\n",
"#plot_confusion_mat(y_prfm_test, y_prfm_pred, labs=labs, size=(30, 30))\n",
"plot_confusion_mat(y_prfm_test, y_prf_pred_m, labs=labs, size=(30, 30))\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multiple class RandomForest classification with hyper parameter search, very slow one!"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\ndef print_res(res):\\n print(f\"Best parameters are: {res.best_params_}\")\\n print(\"\\n\")\\n mean_score = res.cv_results_[\\'mean_test_score\\']\\n std_score = res.cv_results_[\\'std_test_score\\']\\n params = res.cv_results_[\\'params\\']\\n for mean,std,params in zip(mean_score,std_score,params):\\n print(f\\'{round(mean,3)} + or -{round(std,3)} for the {params}\\')\\n\\nparam_grid_rf = {\\n \\'n_estimators\\': [100, 200, 500, 1000],\\n \\'max_depth\\': [2, 4, 8, 16, 32, None]\\n}\\n\\ncv = GridSearchCV(model_rfsm, param_grid_rf, cv=5, n_jobs=-1)\\n\\ncv.fit(X_sm, y_sm)\\n#print_res(cv)\\n'"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"def print_res(res):\n",
" print(f\"Best parameters are: {res.best_params_}\")\n",
" print(\"\\n\")\n",
" mean_score = res.cv_results_['mean_test_score']\n",
" std_score = res.cv_results_['std_test_score']\n",
" params = res.cv_results_['params']\n",
" for mean,std,params in zip(mean_score,std_score,params):\n",
" print(f'{round(mean,3)} + or -{round(std,3)} for the {params}')\n",
"\n",
"param_grid_rf = {\n",
" 'n_estimators': [100, 200, 500, 1000],\n",
" 'max_depth': [2, 4, 8, 16, 32, None]\n",
"}\n",
"\n",
"cv = GridSearchCV(model_rfsm, param_grid_rf, cv=5, n_jobs=-1)\n",
"\n",
"cv.fit(X_sm, y_sm)\n",
"#print_res(cv)\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's use values obtained from above to run the classifier."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\nmodel_mrfh = RandomForestClassifier(n_estimators=1000, max_depth=None)\\nmodel_mrfh.fit(X_sm, y_sm)\\n\\ny_prfmh_pred = model_mrfh.predict(X_prf_test)\\nprint(classification_report(y_prfm_test, y_prf_pred_m))\\n\\n#print(f\"Test accuracy:\", accuracy_score(y_prfm_test, y_prfmh_pred))\\nplot_confusion_mat(y_prfm_test, y_prfmh_pred, labs=labs)\\n'"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"model_mrfh = RandomForestClassifier(n_estimators=1000, max_depth=None)\n",
"model_mrfh.fit(X_sm, y_sm)\n",
"\n",
"y_prfmh_pred = model_mrfh.predict(X_prf_test)\n",
"print(classification_report(y_prfm_test, y_prf_pred_m))\n",
"\n",
"#print(f\"Test accuracy:\", accuracy_score(y_prfm_test, y_prfmh_pred))\n",
"plot_confusion_mat(y_prfm_test, y_prfmh_pred, labs=labs)\n",
"\"\"\""
]
}
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