pymaceuticals_starter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pymaceuticals Inc.\n",
    "---\n",
    "\n",
    "### Analysis\n",
    "\n",
    "- Capolmulin and Ramicane appear to be the most popularly tested drugs when compared to all others tested. \n",
    "\n",
    "- When looking at tumor volume vs. time point for the mouse treated with Capomulin on the line plot, there is a noticeable decrease in tumor size (volume) as time goes on (timepoint). \n",
    "\n",
    "- Additionally, when looking at the scatter plot of the average tumor volume vs. mouse weight, we see that tumor volume remains higher in heavier mice. \n",
    "\n",
    "- Based on our correlation calculation of 0.84, we can pretty confidently say that average tumor volume and mouse weight are strongly correlated, as revealed through the linear (uphill) relationship on the scatter plot.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Mouse ID</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b128</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f932</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>g107</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a457</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Female</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c819</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "0     b128          0                45.0                 0    Capomulin   \n",
       "1     f932          0                45.0                 0     Ketapril   \n",
       "2     g107          0                45.0                 0     Ketapril   \n",
       "3     a457          0                45.0                 0     Ketapril   \n",
       "4     c819          0                45.0                 0     Ketapril   \n",
       "\n",
       "      Sex  Age_months  Weight (g)  \n",
       "0  Female           9          22  \n",
       "1    Male          15          29  \n",
       "2  Female           2          29  \n",
       "3  Female          11          30  \n",
       "4    Male          21          25  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "import numpy as np\n",
    "from scipy.stats import linregress\n",
    "\n",
    "# Study data files\n",
    "mouse_metadata_path = \"data/Mouse_metadata.csv\"\n",
    "study_results_path = \"data/Study_results.csv\"\n",
    "\n",
    "# Read the mouse data and the study results\n",
    "mouse_metadata = pd.read_csv(mouse_metadata_path)\n",
    "study_results = pd.read_csv(study_results_path)\n",
    "\n",
    "# Combine the data into a single dataset\n",
    "combinedDF = pd.merge(study_results, mouse_metadata, how='left', on='Mouse ID')\n",
    "\n",
    "# Display the data table for preview\n",
    "combinedDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice.\n",
    "miceCount = combinedDF[\"Mouse ID\"].unique()\n",
    "numMice = len(miceCount)\n",
    "numMice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['g989'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the duplicate mice by ID number that shows up for Mouse ID and Timepoint. \n",
    "duplicateMouseID = combinedDF.loc[combinedDF.duplicated(subset = [\"Mouse ID\", \"Timepoint\"]),\"Mouse ID\"].unique()\n",
    "duplicateMouseID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>g989</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>g989</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>g989</td>\n",
       "      <td>5</td>\n",
       "      <td>48.786801</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>g989</td>\n",
       "      <td>5</td>\n",
       "      <td>47.570392</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>g989</td>\n",
       "      <td>10</td>\n",
       "      <td>51.745156</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>g989</td>\n",
       "      <td>10</td>\n",
       "      <td>49.880528</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>g989</td>\n",
       "      <td>15</td>\n",
       "      <td>51.325852</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>g989</td>\n",
       "      <td>15</td>\n",
       "      <td>53.442020</td>\n",
       "      <td>0</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>g989</td>\n",
       "      <td>20</td>\n",
       "      <td>55.326122</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>g989</td>\n",
       "      <td>20</td>\n",
       "      <td>54.657650</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>g989</td>\n",
       "      <td>25</td>\n",
       "      <td>56.045564</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>g989</td>\n",
       "      <td>30</td>\n",
       "      <td>59.082294</td>\n",
       "      <td>1</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1592</th>\n",
       "      <td>g989</td>\n",
       "      <td>35</td>\n",
       "      <td>62.570880</td>\n",
       "      <td>2</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "107      g989          0           45.000000                 0     Propriva   \n",
       "137      g989          0           45.000000                 0     Propriva   \n",
       "329      g989          5           48.786801                 0     Propriva   \n",
       "360      g989          5           47.570392                 0     Propriva   \n",
       "620      g989         10           51.745156                 0     Propriva   \n",
       "681      g989         10           49.880528                 0     Propriva   \n",
       "815      g989         15           51.325852                 1     Propriva   \n",
       "869      g989         15           53.442020                 0     Propriva   \n",
       "950      g989         20           55.326122                 1     Propriva   \n",
       "1111     g989         20           54.657650                 1     Propriva   \n",
       "1195     g989         25           56.045564                 1     Propriva   \n",
       "1380     g989         30           59.082294                 1     Propriva   \n",
       "1592     g989         35           62.570880                 2     Propriva   \n",
       "\n",
       "         Sex  Age_months  Weight (g)  \n",
       "107   Female          21          26  \n",
       "137   Female          21          26  \n",
       "329   Female          21          26  \n",
       "360   Female          21          26  \n",
       "620   Female          21          26  \n",
       "681   Female          21          26  \n",
       "815   Female          21          26  \n",
       "869   Female          21          26  \n",
       "950   Female          21          26  \n",
       "1111  Female          21          26  \n",
       "1195  Female          21          26  \n",
       "1380  Female          21          26  \n",
       "1592  Female          21          26  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optional: Get all the data for the duplicate mouse ID. \n",
    "allData = combinedDF.loc[combinedDF[\"Mouse ID\"] == \"g989\", :]\n",
    "allData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b128</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Female</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f932</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>g107</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a457</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Female</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c819</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ketapril</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "0     b128          0                45.0                 0    Capomulin   \n",
       "1     f932          0                45.0                 0     Ketapril   \n",
       "2     g107          0                45.0                 0     Ketapril   \n",
       "3     a457          0                45.0                 0     Ketapril   \n",
       "4     c819          0                45.0                 0     Ketapril   \n",
       "\n",
       "      Sex  Age_months  Weight (g)  \n",
       "0  Female           9          22  \n",
       "1    Male          15          29  \n",
       "2  Female           2          29  \n",
       "3  Female          11          30  \n",
       "4    Male          21          25  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a clean DataFrame by dropping the duplicate mouse by its ID.\n",
    "cleanDF = combinedDF[combinedDF[\"Mouse ID\"].isin(duplicateMouseID) == False]\n",
    "cleanDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice in the clean DataFrame.\n",
    "cleanDFMice = cleanDF[\"Mouse ID\"].unique()\n",
    "numMiceClean = len(cleanDFMice)\n",
    "numMiceClean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Mean Tumor Volume</th>\n",
       "      <th>Median Tumor Voume</th>\n",
       "      <th>Tumor Volume Variance</th>\n",
       "      <th>Tumor Volume Std. Dev.</th>\n",
       "      <th>Tumor Volume Std. Err.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</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>Capomulin</th>\n",
       "      <td>40.675741</td>\n",
       "      <td>41.557809</td>\n",
       "      <td>24.947764</td>\n",
       "      <td>4.994774</td>\n",
       "      <td>0.329346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceftamin</th>\n",
       "      <td>52.591172</td>\n",
       "      <td>51.776157</td>\n",
       "      <td>39.290177</td>\n",
       "      <td>6.268188</td>\n",
       "      <td>0.469821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infubinol</th>\n",
       "      <td>52.884795</td>\n",
       "      <td>51.820584</td>\n",
       "      <td>43.128684</td>\n",
       "      <td>6.567243</td>\n",
       "      <td>0.492236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ketapril</th>\n",
       "      <td>55.235638</td>\n",
       "      <td>53.698743</td>\n",
       "      <td>68.553577</td>\n",
       "      <td>8.279709</td>\n",
       "      <td>0.603860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naftisol</th>\n",
       "      <td>54.331565</td>\n",
       "      <td>52.509285</td>\n",
       "      <td>66.173479</td>\n",
       "      <td>8.134708</td>\n",
       "      <td>0.596466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placebo</th>\n",
       "      <td>54.033581</td>\n",
       "      <td>52.288934</td>\n",
       "      <td>61.168083</td>\n",
       "      <td>7.821003</td>\n",
       "      <td>0.581331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propriva</th>\n",
       "      <td>52.320930</td>\n",
       "      <td>50.446266</td>\n",
       "      <td>43.852013</td>\n",
       "      <td>6.622085</td>\n",
       "      <td>0.544332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramicane</th>\n",
       "      <td>40.216745</td>\n",
       "      <td>40.673236</td>\n",
       "      <td>23.486704</td>\n",
       "      <td>4.846308</td>\n",
       "      <td>0.320955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stelasyn</th>\n",
       "      <td>54.233149</td>\n",
       "      <td>52.431737</td>\n",
       "      <td>59.450562</td>\n",
       "      <td>7.710419</td>\n",
       "      <td>0.573111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zoniferol</th>\n",
       "      <td>53.236507</td>\n",
       "      <td>51.818479</td>\n",
       "      <td>48.533355</td>\n",
       "      <td>6.966589</td>\n",
       "      <td>0.516398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Mean Tumor Volume  Median Tumor Voume  Tumor Volume Variance  \\\n",
       "Drug Regimen                                                                 \n",
       "Capomulin             40.675741           41.557809              24.947764   \n",
       "Ceftamin              52.591172           51.776157              39.290177   \n",
       "Infubinol             52.884795           51.820584              43.128684   \n",
       "Ketapril              55.235638           53.698743              68.553577   \n",
       "Naftisol              54.331565           52.509285              66.173479   \n",
       "Placebo               54.033581           52.288934              61.168083   \n",
       "Propriva              52.320930           50.446266              43.852013   \n",
       "Ramicane              40.216745           40.673236              23.486704   \n",
       "Stelasyn              54.233149           52.431737              59.450562   \n",
       "Zoniferol             53.236507           51.818479              48.533355   \n",
       "\n",
       "              Tumor Volume Std. Dev.  Tumor Volume Std. Err.  \n",
       "Drug Regimen                                                  \n",
       "Capomulin                   4.994774                0.329346  \n",
       "Ceftamin                    6.268188                0.469821  \n",
       "Infubinol                   6.567243                0.492236  \n",
       "Ketapril                    8.279709                0.603860  \n",
       "Naftisol                    8.134708                0.596466  \n",
       "Placebo                     7.821003                0.581331  \n",
       "Propriva                    6.622085                0.544332  \n",
       "Ramicane                    4.846308                0.320955  \n",
       "Stelasyn                    7.710419                0.573111  \n",
       "Zoniferol                   6.966589                0.516398  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen\n",
    "\n",
    "# Use groupby and summary statistical methods to calculate the following properties of each drug regimen: \n",
    "# mean, median, variance, standard deviation, and SEM of the tumor volume. \n",
    "mean = cleanDF[\"Tumor Volume (mm3)\"].groupby(cleanDF[\"Drug Regimen\"]).mean()\n",
    "median = cleanDF[\"Tumor Volume (mm3)\"].groupby(cleanDF[\"Drug Regimen\"]).median()\n",
    "variance = cleanDF[\"Tumor Volume (mm3)\"].groupby(cleanDF[\"Drug Regimen\"]).var()\n",
    "stdDev = cleanDF[\"Tumor Volume (mm3)\"].groupby(cleanDF[\"Drug Regimen\"]).std()\n",
    "stdErr = cleanDF[\"Tumor Volume (mm3)\"].groupby(cleanDF[\"Drug Regimen\"]).sem()\n",
    "\n",
    "# Assemble the resulting series into a single summary DataFrame.\n",
    "summaryStats = pd.DataFrame ({'Mean Tumor Volume': mean,\n",
    "                             'Median Tumor Voume': median, \n",
    "                             'Tumor Volume Variance': variance,\n",
    "                             'Tumor Volume Std. Dev.': stdDev,\n",
    "                             'Tumor Volume Std. Err.': stdErr})\n",
    "summaryStats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">Tumor Volume (mm3)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>var</th>\n",
       "      <th>std</th>\n",
       "      <th>sem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</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>Capomulin</th>\n",
       "      <td>40.675741</td>\n",
       "      <td>41.557809</td>\n",
       "      <td>24.947764</td>\n",
       "      <td>4.994774</td>\n",
       "      <td>0.329346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceftamin</th>\n",
       "      <td>52.591172</td>\n",
       "      <td>51.776157</td>\n",
       "      <td>39.290177</td>\n",
       "      <td>6.268188</td>\n",
       "      <td>0.469821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infubinol</th>\n",
       "      <td>52.884795</td>\n",
       "      <td>51.820584</td>\n",
       "      <td>43.128684</td>\n",
       "      <td>6.567243</td>\n",
       "      <td>0.492236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ketapril</th>\n",
       "      <td>55.235638</td>\n",
       "      <td>53.698743</td>\n",
       "      <td>68.553577</td>\n",
       "      <td>8.279709</td>\n",
       "      <td>0.603860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naftisol</th>\n",
       "      <td>54.331565</td>\n",
       "      <td>52.509285</td>\n",
       "      <td>66.173479</td>\n",
       "      <td>8.134708</td>\n",
       "      <td>0.596466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placebo</th>\n",
       "      <td>54.033581</td>\n",
       "      <td>52.288934</td>\n",
       "      <td>61.168083</td>\n",
       "      <td>7.821003</td>\n",
       "      <td>0.581331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propriva</th>\n",
       "      <td>52.320930</td>\n",
       "      <td>50.446266</td>\n",
       "      <td>43.852013</td>\n",
       "      <td>6.622085</td>\n",
       "      <td>0.544332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramicane</th>\n",
       "      <td>40.216745</td>\n",
       "      <td>40.673236</td>\n",
       "      <td>23.486704</td>\n",
       "      <td>4.846308</td>\n",
       "      <td>0.320955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stelasyn</th>\n",
       "      <td>54.233149</td>\n",
       "      <td>52.431737</td>\n",
       "      <td>59.450562</td>\n",
       "      <td>7.710419</td>\n",
       "      <td>0.573111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zoniferol</th>\n",
       "      <td>53.236507</td>\n",
       "      <td>51.818479</td>\n",
       "      <td>48.533355</td>\n",
       "      <td>6.966589</td>\n",
       "      <td>0.516398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Tumor Volume (mm3)                                          \n",
       "                           mean     median        var       std       sem\n",
       "Drug Regimen                                                             \n",
       "Capomulin             40.675741  41.557809  24.947764  4.994774  0.329346\n",
       "Ceftamin              52.591172  51.776157  39.290177  6.268188  0.469821\n",
       "Infubinol             52.884795  51.820584  43.128684  6.567243  0.492236\n",
       "Ketapril              55.235638  53.698743  68.553577  8.279709  0.603860\n",
       "Naftisol              54.331565  52.509285  66.173479  8.134708  0.596466\n",
       "Placebo               54.033581  52.288934  61.168083  7.821003  0.581331\n",
       "Propriva              52.320930  50.446266  43.852013  6.622085  0.544332\n",
       "Ramicane              40.216745  40.673236  23.486704  4.846308  0.320955\n",
       "Stelasyn              54.233149  52.431737  59.450562  7.710419  0.573111\n",
       "Zoniferol             53.236507  51.818479  48.533355  6.966589  0.516398"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a summary statistics table of mean, median, variance, standard deviation, \n",
    "# and SEM of the tumor volume for each regimen\n",
    "# Using the aggregation method, produce the same summary statistics in a single line.\n",
    "summaryTable = cleanDF.groupby([\"Drug Regimen\"])[[\"Tumor Volume (mm3)\"]].agg([\"mean\", \"median\", \"var\", \"std\", \"sem\"])\n",
    "summaryTable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar and Pie Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of timepoints for all mice tested for each drug regimen using Pandas.\n",
    "barPandas = cleanDF[\"Drug Regimen\"].value_counts().plot.bar(figsize = (5, 3), fontsize = 8)\n",
    "plt.title(\"Number of Timepoints per Drug Regimen\", fontsize = 9, fontweight = \"bold\")\n",
    "plt.xlabel(\"Drug Regimen\", fontsize = 8)\n",
    "plt.ylabel(\"Number of Mice\", fontsize = 8)\n",
    "barPandas\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate # of data points for each drug regimen\n",
    "drugCount = cleanDF[\"Drug Regimen\"].value_counts()\n",
    "\n",
    "# Set x-axis to length of drug count series\n",
    "xAxis = np.arange(len(drugCount))\n",
    "\n",
    "# Generate a bar plot showing the total number of timepoints for all mice tested for each drug regimen using pyplot.\n",
    "barPyplot = plt.bar(xAxis, drugCount, width = 0.7)\n",
    "plt.title(\"Number of Timepoints per Drug Regimen\", fontsize = 9, fontweight = \"bold\")\n",
    "plt.xlabel(\"Drug Regimen\", fontsize = 8)\n",
    "plt.ylabel(\"Number of Mice\", fontsize = 8)\n",
    "\n",
    "# Label tick marks\n",
    "ticks = [value for value in xAxis]\n",
    "plt.xticks(ticks, drugCount.index.values, rotation = \"vertical\", fontsize = 8)\n",
    "\n",
    "# Display plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get count of \"sex\" column\n",
    "genderCount = cleanDF[\"Sex\"].value_counts()\n",
    "plt.title (\"Female vs. Male Mice\", fontsize = 8)\n",
    "\n",
    "# Generate a pie plot showing the distribution of female versus male mice using Pandas\n",
    "genderCount.plot.pie(autopct=\"%1.1f%%\", figsize = (3, 3), fontsize = 8)\n",
    "\n",
    "# Make sure axis is equal\n",
    "plt.axis(\"equal\")\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create labels\n",
    "labels = [\"Female\", \"Male\"]\n",
    "\n",
    "# Generate a pie plot showing the distribution of female versus male mice using pyplot\n",
    "plt.pie(genderCount, labels=labels, autopct=\"%1.1f%%\", radius=0.7)\n",
    "plt.title (\"Female vs. Male Mice\", fontsize = 8)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quartiles, Outliers and Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Mouse ID</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a203</td>\n",
       "      <td>45</td>\n",
       "      <td>67.973419</td>\n",
       "      <td>2</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a251</td>\n",
       "      <td>45</td>\n",
       "      <td>65.525743</td>\n",
       "      <td>1</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a262</td>\n",
       "      <td>45</td>\n",
       "      <td>70.717621</td>\n",
       "      <td>4</td>\n",
       "      <td>Placebo</td>\n",
       "      <td>Female</td>\n",
       "      <td>17</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a275</td>\n",
       "      <td>45</td>\n",
       "      <td>62.999356</td>\n",
       "      <td>3</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a366</td>\n",
       "      <td>30</td>\n",
       "      <td>63.440686</td>\n",
       "      <td>1</td>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID  Timepoint  Tumor Volume (mm3)  Metastatic Sites Drug Regimen  \\\n",
       "0     a203         45           67.973419                 2    Infubinol   \n",
       "1     a251         45           65.525743                 1    Infubinol   \n",
       "2     a262         45           70.717621                 4      Placebo   \n",
       "3     a275         45           62.999356                 3     Ceftamin   \n",
       "4     a366         30           63.440686                 1     Stelasyn   \n",
       "\n",
       "      Sex  Age_months  Weight (g)  \n",
       "0  Female          20          23  \n",
       "1  Female          21          25  \n",
       "2  Female          17          29  \n",
       "3  Female          20          28  \n",
       "4  Female          16          29  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the final tumor volume of each mouse across four of the treatment regimens:  \n",
    "# Capomulin, Ramicane, Infubinol, and Ceftamin\n",
    "\n",
    "# Start by getting the last (greatest(max)) timepoint for each mouse\n",
    "maxDF = cleanDF.groupby('Mouse ID').max()['Timepoint'].reset_index()\n",
    "\n",
    "\n",
    "# Merge this group df with the original DataFrame to get the tumor volume at the last timepoint\n",
    "mergedDF = pd.merge(maxDF, cleanDF, on=('Mouse ID', 'Timepoint'), how='left')\n",
    "mergedDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Capomulin Regimen\n",
      "Values below 20.7 could be outliers.\n",
      "Values above 51.83 could be outliers.\n",
      "The interquartile range of Capomulin is: 7.78\n",
      "Number of outliers for Capomulin is: 0\n",
      "----------------------------------------------------------\n",
      "Ramicane Regimen\n",
      "Values below 17.91 could be outliers.\n",
      "Values above 54.31 could be outliers.\n",
      "The interquartile range of Ramicane is: 9.1\n",
      "Number of outliers for Ramicane is: 0\n",
      "----------------------------------------------------------\n",
      "Infubinol Regimen\n",
      "Values below 36.83 could be outliers.\n",
      "Values above 82.74 could be outliers.\n",
      "The interquartile range of Infubinol is: 11.48\n",
      "Number of outliers for Infubinol is: 1\n",
      "Outlier final tumor volume(mm3) is 36.32\n",
      "----------------------------------------------------------\n",
      "Ceftamin Regimen\n",
      "Values below 25.36 could be outliers.\n",
      "Values above 87.67 could be outliers.\n",
      "The interquartile range of Ceftamin is: 15.58\n",
      "Number of outliers for Ceftamin is: 0\n",
      "----------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Put treatments into a list for for loop (and later for plot labels)\n",
    "treatments = [\"Capomulin\", \"Ramicane\", \"Infubinol\", \"Ceftamin\"]\n",
    "\n",
    "# Create empty list to fill with tumor vol data (for plotting)\n",
    "tumorVol = []\n",
    "\n",
    "# Use loop to locate each treatment \n",
    "for treatment in treatments:\n",
    "    \n",
    "    # Locate the rows which contain mice on each treatment (drug) and get tumor volumes\n",
    "    vol = mergedDF.loc[mergedDF[\"Drug Regimen\"] == treatment, 'Tumor Volume (mm3)']\n",
    "    \n",
    "    # add subset\n",
    "    tumorVol.append(vol)\n",
    "\n",
    "    # Calculate the IQR and quantitatively determine if there are any potential outliers. \n",
    "    quartiles = vol.quantile([.25,.5,.75])\n",
    "    lowerq = quartiles[0.25]\n",
    "    upperq = quartiles[0.75]\n",
    "    iqr = upperq-lowerq\n",
    "    lower_bound = lowerq - (1.5*iqr)\n",
    "    upper_bound = upperq + (1.5*iqr)\n",
    "    \n",
    "    # Determine outliers using upper and lower bounds\n",
    "    outliers = vol.loc[(vol < lower_bound) | (vol > upper_bound)]\n",
    "        \n",
    "    # Print results    \n",
    "    print(f\"{treatment} Regimen\")\n",
    "    print(f\"Values below {round(lower_bound, 2)} could be outliers.\")\n",
    "    print(f\"Values above {round(upper_bound, 2)} could be outliers.\")\n",
    "    print(f\"The interquartile range of {treatment} is: {round(iqr, 2)}\")\n",
    "    print(f\"Number of outliers for {treatment} is: {outliers.count()}\")\n",
    "    if outliers.count() > 0:\n",
    "        for i in outliers.index:\n",
    "            print(f'Outlier final tumor volume(mm3) is {round(outliers[i], 2)}')\n",
    "    print('----------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a box plot that shows the distrubution of the tumor volume for each treatment group.\n",
    "\n",
    "# First, set fliers to improve readability, then create box plot\n",
    "fliers = dict(marker='o', markerfacecolor='r', markersize=8)\n",
    "plt.boxplot(tumorVol, flierprops=fliers)\n",
    "\n",
    "# Add title and labels\n",
    "plt.title(\"Distribution of Tumor Volume for Each Treatment\", fontsize =10, fontweight = 'bold')\n",
    "plt.ylabel(\"Final Tumor Volume (mm3)\")\n",
    "plt.xticks([1, 2, 3, 4],['Capomulin', 'Ramicane', 'Infubinol', 'Ceftamin'])\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line and Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JEn744QcMHjwYSqUSGo0GX331la4vxaV88skn6NOnD6qrq+Ht7Y33338f06ZNA9DyF+ru3bsxZ84cODo6Ij09HZ6ennjkkUeQlJQEa2vr63pNFhYW+OabbzBgwADU1dWhrKzsor+ATV1oaCh27dqF22+/HTY2Njh69Ci0Wi1uvvlmvPLKKwBa+lKsWbMGAQEBaGxshJubm65l4kKSJGH16tUIDg6GWq3G3r17kZub2+71GeLJJ5/E2LFjYWNjgwMHDlyxf0lMTAz+/PNPjBs3Ds3NzcjJyUFYWBjeeustow1WJ0kS3NzcYGtre9lt2vo5vummm/Dqq6/C29sbOTk5CA8Px6uvvqq3L6VSiYcffhgBAQEoKChARkYG/P398eSTT+KFF14AAIwePRrbtm3DhAkTIEkSjh07BktLS8yYMQNPPvmkQa9v8uTJuOeee+Du7o5Tp05BpVIhKioK8fHxuuMBwD//+U98+eWX6N+/PwoLCyFJEqZPn47k5GR4e3vr7c/CwgLp6emwtbXF9OnTsWvXLl2/H+p8JCGMdGGdyITk5OToOnImJCQgJiZG3oKIiKhdsEWGiIiIzBaDDBEREZktXloiIiIis8UWGSIiIjJbDDJERERkthhkiIiIyGx16ikKtFotCgsLYW9v325DiBMREZFxCSFQVVUFb29vKBRXbnPp1EGmsLDwkhOsERERkenLz8+/5HxeF+rUQcbe3h5Ayxtx4UR0REREZLrUajV8fX113+NX0qmDTOvlJAcHBwYZIiIiM9OWbiHs7EtERERmi0GGiIiIzBaDDBEREZktBhkiIiIyWwwyREREZLYYZIiIiMhsMcgQERGR2WKQISIiIrPFIENERERmi0GGiIiIzBaDDBEREZktBhkiIiIyWwwyRGR0Gq2ARivkLoOIugCTCDJxcXGQJAmLFy/WLauursaCBQvg4+ODbt26oXfv3vj444/lK5KIrqqithHvbElH/5e2YNKHO9Cs0cpdEhF1chZyF5CSkoJVq1YhIiJCb/ljjz2GhIQEfPnll/D398eWLVswb948eHt7Y+rUqTJVS0SXUl7TiP/uyMIXybmobmgGABw/o8ah0xUY5Ocic3VE1JnJ2iJTXV2N2bNnY/Xq1XB2dtZbt2vXLtx7772IiYmBv78//vnPfyIyMhL79u2TqVoi+ruymka8+esJjHzzT6xIyER1QzPCvOwR6eMIAEhML5a5QiLq7GQNMvPnz8fEiRMxduzYi9aNHDkSmzZtQkFBAYQQSEhIwMmTJzF+/PjL7q+hoQFqtVrvQUTGV1rdgLhfjmPkm3/i48RM1DRq0Ke7Az65exB+XjgK90b5AwAS0ovkLZSIOj3ZLi3Fx8cjNTUVKSkpl1z/wQcf4MEHH4SPjw8sLCygUCjw3//+FyNHjrzsPuPi4vDSSy+1V8lEXV5JdQNWbcvC/+3KRV2TBgAQ3sMBi8b0wtjeHpAkCQAwupc7JAk4UqBGkboeHg7WcpZNRJ2YLEEmPz8fixYtwpYtW2BtfelfcB988AF2796NTZs2wc/PD9u2bcO8efPQvXv3S7bgAMDSpUvx+OOP656r1Wr4+vq2y2sg6kqKquqxKikLX+7JRX1TSwfeCB9HLBoTghvD/gowrdzsVIjwccKh/AoknizGHYP5/5CI2ockhOjweyQ3bNiAW2+9FUqlUrdMo9FAkiQoFApUVlbC2dkZ69evx8SJE3Xb/OMf/8Dp06fx66+/tuk4arUajo6OqKyshIODg9FfB1FnV6Sux8qkLHy1JxcNzS0BJtLHEYvH9kJMqPtFAeZC/9l6Eu//cQq39PPCR7MHdVTJRNQJGPL9LUuLzJgxY5CWlqa3bO7cuQgLC8OSJUug0WjQ1NQEhUK/C49SqYRWy9s5idrbOXU9Pk7MxNq9eboA09/XCYvGhiCm15UDTKvYMA+8/8cpbD9ZgiaNFpZKkxjtgYg6GVmCjL29PcLDw/WW2drawtXVVbc8OjoaTz31FLp16wY/Pz8kJSXhf//7H9599105SibqEs5W1mNlUia+3puHxvMBZmBPJywa2wujQ9zaFGBaRfRwhKutFUprGrE/txzDAl3bq2wi6sJkH0fmcuLj47F06VLMnj0bZWVl8PPzw2uvvYaHH35Y7tKIOp0zlXX4ODET8Xvz0Xh+ELvBfs5YNDYEI4MNCzCtFAoJ0b3cse5AARLTixlkiKhdyNJHpqOwjwzRlRVU1OHjxAx8m3JaF2Bu8HfBorEhiApyvaYAc6FNhwqxcO0BhHnZ49fFo41RMhF1ASbfR4aI5HW6vBYfJWbiu335aNK0/C0zNKAlwAwPvP4A02p0iBsUEnDibBUKK+rg7dTNKPslImrFIEPUheSX1eKjxAx8v/+0LsAMD3TForEh7XLpx8nGCgN7OmNfbjkS04tx19CeRj8GEXVtDDJEXUBeaS1WJGTgh9TTaD4/K/WIYFcsGtMLNwS071xIMaHu2JdbjoT0IgYZIjI6BhmiTiy3tAbL/8zAugMF0JwPMKNC3LBoTAgG+3fMZI4xoR7495aT2JlRgoZmDVQWyqv/EBFRGzHIEHVCOSU1+PDPDGw4+FeAGd3LHYvGhGCQn/NVftq4+no7wMNehaKqBqRkl2NkiFuHHp+IOjcGGaJOJKu4GsvPB5jz+QUxoe5YOCYEA3t2bIBpJUkSYkLd8e2+00hML2KQISKjYpAh6gQyiqqx/M9T2HSoUBdgbgzzwMIxIejv6yRrbQAQG+qBb/edRkJ6EZ6b1EfucoioE2GQITJjGUVV+OCPDPx4uBCtI0KNOR9gIk0gwLQaEeIGC4WEzOIa5JXWoqerjdwlEVEnwSBDZIZOnqvCB3+cwk9pZ3QBZmxvTywaE4J+Po7yFncJDtaWGOzvjN1ZZUg8WYR7hvvLXRIRdRIMMkRmJP1sS4D5+chfAeamPp5YOCYE4T1ML8BcKCbUA7uzypBwgkGGiIyHQYbIDJw4q24JMGlndctu7uuFhWNC0MfbPKbfiA31wBu/nEByZinqmzSwtuRt2ER0/RhkiEzYscKWAPPr0b8CzC39vPDojSHo3d08AkyrXp528Ha0RmFlPXZllSI21EPukoioE2CQITJBRwoq8cEfp7Dl2DkAgCQBt/TrjoU3hiDUy17m6q6NJEmICfPA13vykJRezCBDREbBIENkQo4UVOK930/h9+N/BZhJEd5YeGMwQjzNM8BcKDa0Jcj8eaIIL07uY7TJKYmo62KQITIBlXVNeOvXE/h6bx6EABQSMDnSG4/eGIxgD/MPMK2iglxhpVQgr6wW2SU1CHS3k7skIjJzDDJEMhJCYNOhQryy+ThKqhsAAFMivbFobAiCOuGXvK3KAjcEuGBHRgkS0osZZIjoujHIEMkkt7QGz204gu2nSgAAge62eG1aPwwPcpW5svYVE+qOHRklSEwvwgMjA+Quh4jMHIMMUQdrbNZi9fYsfPDHKTQ0a2FlocCC2GA8FB3YJWaGjg3zwKs/HceerDLUNDTDVsVfQ0R07fgbhKgD7c0uw7Pr03CqqBoAMDLYDa9MC0eAm63MlXWcQDdb9HSxQV5ZLZIzSzGuj6fcJRGRGWOQIeoA5TWNeOOXE/hmXz4AwM3OCs9N7IOp/b273J07kiQhNtQdX+zKRWJ6EYMMEV0XBhmidiSEwLrUArz283GU1TQCAGbd0BP/ujkMjjaWMlcnn5gwj/NBphhCiC4X5ojIeBhkiNpJZnE1nlt/BLuySgEAoZ72eO3WcAz2d5G5MvkND3SFykKBgoo6nCqqRq9OMEYOEcmDQYbIyOqbNPg4MRMfJ2aiUaOFtaUCi8b0wj9GBcBSqZC7PJNgbanE8CBXJKYXI+FEEYMMEV0z/lYlMqLkjBJMeH873v/jFBo1WsSEumPrY9F4JCaIIeZvWqcoSEgvkrkSIjJnbJEhMoLS6ga89tNxrDtQAABwt1dh2eS+uKWfF/t/XEZsqAdexFHsyymHur4JDtZdt88QEV07Bhmi66DVCny7Lx9xv5xAZV0TJAm4e5gfnhwfyi/mq+jpaoNAd1tkFddg56kSTOjXXe6SiMgMMcgQXaOT56rw7Po0pOSUAwD6dHfA69P7ob+vk7yFmZHYUA9kFWcjMb2YQYaIrgmDDJGB6ho1+PDPU1i1LQvNWgEbKyUeH9cL90X5w4L9YAwSG+qBT3dkIyG9iLdhE9E1YZAhMkBiehGe33gE+WV1AIBxfTyxbEpf9HDqJnNl5mlIgDNsrJQoqmrAsTNq9PV2lLskIjIzDDJEbVCkrsfLm49h8+EzAIDujtZYNqUvxvf1krky86ayUCIqyA2/Hz+HxPRiBhkiMhjbwYmuQKsV+L/duRjzThI2Hz4DhQQ8MDIAWx+PZogxktgwdwBAwgnehk1EhjOJIBMXFwdJkrB48WLdMkmSLvl4++235SuUupRjhWpM/zgZz284gqqGZkT4OGLTgpF4flIf2HHGZqOJOT+eTGpeOSpqG2WuhojMjey/jVNSUrBq1SpEREToLT9z5oze819++QUPPPAAZsyY0ZHlURdU09CM934/iTU7c6DRCtipLPDU+FDMGeYHpYKdUY2th1M3hHraI/1cFbafKsHkSG+5SyIiMyJri0x1dTVmz56N1atXw9nZWW+dl5eX3mPjxo2IjY1FYGCgTNVSV/D7sXO46T/bsHp7NjRagYn9uuOPJ6Jxb5Q/Q0w7imm9vMRRfonIQLIGmfnz52PixIkYO3bsFbc7d+4cfvrpJzzwwAMdVBl1NWcq6/DQ/+3DP/63DwUVdejh1A2f3TcEK2YPhKeDtdzldXqt0xUkpRdDqxUyV0NE5kS2S0vx8fFITU1FSkrKVbf94osvYG9vj+nTp19xu4aGBjQ0NOieq9Xq666TOjeNVuCL5By8syUdNY0aWCgkPDAqAIvGhMDGSvYrr13GID9n2KssUFrTiLSCSkRyUEEiaiNZflPn5+dj0aJF2LJlC6ytr/7X7po1azB79uyrbhsXF4eXXnrJWGVSJ3f4dAWeWZ+GIwUtgXdgTye8Pr0fwrwcZK6s67FUKjAyxA2/HDmLhPQiBhkiajNJCNHh7bgbNmzArbfeCqVSqVum0WggSRIUCgUaGhp067Zv347Ro0fj4MGDiIyMvOJ+L9Ui4+vri8rKSjg48MuJWlTVN+GdLSfxv1050ArAwdoCSyaEYdaQnlCwH4xsvk3Jx9M/HEakrxM2zh8hdzlEJCO1Wg1HR8c2fX/L0iIzZswYpKWl6S2bO3cuwsLCsGTJEr2A8+mnn2LQoEFXDTEAoFKpoFKpjF4vdQ5CCPx29Cxe3HQU59QtgXdqf288N7EP3O35uZFbdGhLh9/DpytQWt0AVzueEyK6OlmCjL29PcLDw/WW2drawtXVVW+5Wq3Gd999h3feeaejS6RO5nR5LV7ceBR/nB90zc/VBq9OC8eoEHeZK6NWng7W6OvtgKOFamw7VYxbB/jIXRIRmQGT7s0YHx8PIQRmzZoldylkppo0WqzZkY33fj+FuiYNLJUSHo4OwvzYYFhbKq++A+pQsaEeOFqoRsIJBhkiahtZ+sh0FEOusVHnk5pXjmfWpeHE2SoAwA0BLnj91nAEe9jLXBldzv7cMsz4eBccu1ki9flxHLuHqIsy+T4yRO2psq4Jb/16Al/vzYMQgJONJZ65pTduH+QDSeIXoynr7+sMx26WqKxrwsH8cgzyc5G7JCIycQwy1GkIIbDpUCFe2XwcJdUtnXlnDPTBM7eEseOomVAqJIzu5Y4fDxUi4UQxgwwRXZVJTBpJdL1yS2twz5q9WBR/ECXVDQh0t8XaB4fhnTsiGWLMTGwopysgorZjiwyZve/25eP5jUdQ36SFlYUCC2KD8VB0IFQW7Mxrjkb3cockAUcL1ShS18ODU0QQ0RWwRYbMVn2TBkvXHcZT3x9GfZMWwwNd8dvi0Vg4JoQhxoy52akQ4eMEAEg8WSxvMURk8hhkyCzll9Xi9pW7sHZvPiQJeHxcL3z1j6EIcLOVuzQygtbLS4m8vEREV8EgQ2YnIb0Ikz7cgbSCSjjbWOKLuTdg4ZgQTi/QibTOhr39ZAmaNFqZqyEiU8Y+MmQ2NFqB9/84hQ//PAUhgEgfR6yYPRA+zjZyl0ZG1q+HI1xtrVBa04j9ueUYFugqd0lEZKLYIkNmoaymEXM/T8EHf7SEmDnDeuLbh4czxHRSCoWE6F68e4mIro5BhkzeofwKTP5wB7adLIa1pQLv3hGJV6f1Y4feTi4mrOXyUuIJdvglosvjpSUyWUIIfLUnDy//eAyNGi38XW2w8u5BCPPidBNdwegQNygkIP1cFQor6uDt1E3ukojIBLFFhkxSXaMGT3x3CM9tOIJGjRbj+3pi06MjGWK6ECcbKwzs6QwASExnqwwRXRqDDJmc7JIa3PrRTqxLLYBCApZOCMPKOYPgYG0pd2nUwWLPX15iPxkiuhwGGTIpvx09iykf7sCJs1Vws1Phq38Mw0PRQZzssYuKOT+ezM6MEjQ0a2SuhohMEfvIkElo1mjx9pZ0fJKUBQAY7OeMFbMHwpPD03dpfbo7wMNehaKqBqRkl2NkiJvcJRGRiWGLDMmuuKoBcz7dowsxD4wMwNp/DmOIIUiSpGuV4eUlIroUBhmS1b6cMkz8YDt2Z5XB1kqJFXcNxPOT+sBSyY8mtWgd5ZdBhoguhZeWSBZCCKzZmYO4n4+jWSsQ7GGHlXMGIdjDTu7SyMSMCHGDhUJCVnEN8kpr0dOVgyAS0V/4Zy91uOqGZixYewCvbD6GZq3A5EhvbJw/giGGLsnB2hKD/c/fhn2SrTJEpI9BhjrUqXNVmLp8B346fAYWCgnLJvfBB3f2h62KjYN0ebrLSycYZIhIH4MMdZhNhwoxdcVOZBbXwMvBGt88NBz3jQjgrdV0VTHng0xyZinqm3gbNhH9hUGG2l1jsxbLNh3FwrUHUNuoQVSQKzYvHIlBfs5yl0ZmopenHbwdrdHQrMWurFK5yyEiE8IgQ+3qbGU9Zq3ejc+TcwAA82KC8H8PDIWbnUrewsisSJJ0wSSSvLxERH9hkKF2k5xRgokfbMf+3HLYW1tg9T2D8fTNYVAqeCmJDPfXbdjFEELIXA0RmQr2sCSj02oFVm7LxL9/S4dWAL27O2DlnIHwc7WVuzQyY1FBrrBSKpBXVouskhoEufMuNyJiiwwZWWVdE/75f/vx1q8tIea2QT5YPy+KIYaum63KAkMDXQBwNmwi+guDDBnNsUI1pizfgd+Pn4OVUoG46f3w9m0RsLZUyl0adRKtdy8lcpRfIjqPQYaM4vv9p3HrRzuRW1qLHk7d8P0jwzHrhp68tZqMqnXepT1ZZahpaJa5GiIyBQwydF3qmzRYui4NT353CA3NWkT3csfmR0ciwsdJ7tKoEwp0s0VPFxs0arRIzuRt2ETEIEPXIb+sFrev3IW1e/MgScBjY3vhs/uGwNnWSu7SqJOSJAmxnA2biC7AIEPXJDG9CJOX70BaQSWcbCzx+dwbsGhsCBS8tZra2YXjyfA2bCLi7ddkEK1W4P0/TuGDP09BCCDSxxErZg+EjzNnJKaOMTzQFSoLBQor63GqqBq9PO3lLomIZGQSLTJxcXGQJAmLFy/WW378+HFMmTIFjo6OsLe3x7Bhw5CXlydPkYTymkbM/TwF7//REmLmDOuJbx8ezhBDHcraUomoIFcAnESSiEwgyKSkpGDVqlWIiIjQW56ZmYmRI0ciLCwMiYmJOHToEJ5//nlYW1vLVGnXdii/ApM+3IGkk8WwtlTg3Tsi8eq0flBZ8NZq6nixYa2j/DLIEHV1sl5aqq6uxuzZs7F69Wq8+uqreuueffZZ3HLLLXjrrbd0ywIDAzu6xC5PCIGv9+bhpU3H0KjRwt/VBh/PGYTe3R3kLo26sJheHgCOYl9OOdT1TXCwtpS7JCKSiawtMvPnz8fEiRMxduxYveVarRY//fQTevXqhfHjx8PDwwNDhw7Fhg0brri/hoYGqNVqvQddu7pGDZ747hCeXX8EjRotburjiU2PjmSIIdn1dLVBoLstmrUCO0+VyF0OEclItiATHx+P1NRUxMXFXbSuqKgI1dXVeOONN3DzzTdjy5YtuPXWWzF9+nQkJSVddp9xcXFwdHTUPXx9fdvzJXRq2SU1uPWjnViXWgCFBPxrQhg+uXsQ//Ilk/HXJJK8vETUlckSZPLz87Fo0SJ8+eWXl+zzotVqAQBTp07FY489hv79++Nf//oXJk2ahJUrV152v0uXLkVlZaXukZ+f326voTPbcvQspny4AyfOVsHNzgpf/WMYHo4O4ii9ZFJiddMVcDZsoq5Mlj4y+/fvR1FREQYNGqRbptFosG3bNixfvhw1NTWwsLBAnz599H6ud+/e2LFjx2X3q1KpoFKp2q3uzq5Zo8W/t5zEyqRMAMBgP2esmD0Qng7sYE2mZ0iAM2yslCiqasCxM2r09XaUuyQikoEsQWbMmDFIS0vTWzZ37lyEhYVhyZIlUKlUGDJkCNLT0/W2OXnyJPz8/Dqy1C6juKoBC9cewK6slmHf7x8RgKW3hMFSKfuNbUSXpLJQYkSwG7YeO4fE9GIGGaIuSpYgY29vj/DwcL1ltra2cHV11S1/6qmnMHPmTIwePRqxsbH49ddf8eOPPyIxMVGGiju3fTllmP91Ks6pG2BrpcSbt0VgUoS33GURXVVsqAe2HjuHhBNFmB8bLHc5RCQDkx3Z99Zbb8XKlSsRFxeHhQsXIjQ0FD/88ANGjhwpd2mdhhACn+3Mwes/H0ezViDYww4r5wxEsAdHSiXz0DobdmpeOSpqG+Fkw3m+iLoaSXTiXnJqtRqOjo6orKyEgwNvGb5QeU0jlvxwGFuOnQMATIrojjdnRMBWZbLZluiSxv9nG9LPVeGDWQMwJZItiUSdgSHf3+wA0QXtzirFhPe3Y8uxc7BUSnhxch98OGsAQwyZpZiwllaZRE5XQNQlMch0Ic0aLd7dko5Zq3fjrLoegW62WD9vBOaOCOCt1WS2Wm/DTjpZDK220zYwE9Fl8E/wLuJ0eS0WxR/E/txyAMDtg3ywbEpftsKQ2Rvk5wx7lQVKaxqRVlCJSF8nuUsiog7EFpku4KfDZzDh/e3Yn1sOe5UF3r+zP96+PZIhhjoFS6UCo3q5AeAov0RdEYNMJ1bb2Ix//XAY879ORVV9Mwb0dMLPi0Zhav8ecpdGZFQtk0gCCenFMldCRB2Nf5J3UscK1Xh0bSoyi2sgScC8mCAsHtuLA9xRpxR9/jbsw6crUFLdADc7jvBN1FXwW62TEULg853ZmLZiJzKLa+Bhr8JXDwzFU+M5Si91Xp4O1ujr7QAhgG0n2SpD1JXwm60TKatpxIP/24dlPx5Do0aLMWEe+HXxaEQFu8ldGlG7+2s2bAYZoq6kzZeWmpqa8P3332PTpk04ePAgysvL4ezsjP79+2PSpEm4/fbbYWXFUTXlkpxRgsXfHERRVQOslAo8c0sY7o3y523V1GXEhrljeUIGtp0sRrNGCwu2QBJ1CW36n/7pp5/C398fq1evRt++ffHaa6/hyy+/xGuvvYa+ffvi008/RUBAANasWdPe9dLfNGm0eOvXE5j96R4UVTUgyN0WG+aPwH0cG4a6mP6+znCysURlXRMOna6Quxwi6iBtapHZs2cPkpOTLznz9PTp0/Hcc88hNzcXr7/+Ou6//36jF0mXll9Wi0fXHsDB/AoAwKwbfPH8pD6wsWIfbup6lAoJo0PcselQIRJOFGOQn4vcJRFRB+BcS2Zq48ECPLf+CKoammFvbYE3pkdgYkR3ucsiktW61NN4/NtD6OvtgJ8WjpK7HCK6RoZ8f1/3n+4ajQZKpfJ6d0NtVNPQjGWbjuK7/acBtIxq+v6d/eHjbCNzZUTyG93LHZIEHC1U45y6Hp4O1nKXRETtrM294SorK/Hggw8iNjYWv/zyC44fP46QkBB069YNEyZMQGlpaXvWSQCOFFRi8oc78N3+05AkYOGNwfjmn8MYYojOc7NTIcLHCQCQxLuXiLqENgeZxx57DLW1tfDx8cEdd9yBb7/9FmvXrkViYiJqa2vx/PPPt2edXZoQAv/dnoVbP9qJrJIaeDlY4+t/DMPjN4Xyzgyiv4k9Pzgepysg6hra3EfG29sbJ0+ehFarhZOTE/Ly8uDj4wMAyMrKQmxsLHJzc9u1WEN1hj4yJdUNePK7Q0g8/9flTX088eaMCDjb8lZ3oks5lF+BqSt2wl5lgdQXxnEgSCIz1C59ZGpqamBnZwcAsLe314UYAAgMDERZWdk1lkuXs/1UMR775hBKqhtgZaHA85P6YM7QnrytmugK+vVwhKutFUprGrE/txzDAl3lLomI2lGb/1Rxc3NDVVUVAGDlypV664qLi2Fjw34axtLYrEXcL8dx96d7UVLdgF6edti0YATuHubHEEN0FQqFpJt7iZeXiDq/NgeZBx98EEVFLb8UZs2apbdu3bp1iI6ONm5lXVRuaQ1uX5mMT5KyAACzh/bExvkjEeZlnpfGiOQQc366gsQT7PBL1NlxHBkTsv7AaTy3/ghqGjVw7GaJN2f0w83hHBuGyFAVtY0Y+MpWaAWw8183oodTN7lLIiIDdMg4Ms3NzaitrdVbZg5hwRRVNzTjhQ1HsO5AAQDgBn8XvHdnf3jzly/RNXGyscLAns7Yl1uOxPQizB568ajkRNQ5GNydf/fu3YiMjIS1tTWcnZ3h7OwMJycnODs7t0d9nd7h0xWY9MF2rDtQAIUEPDa2F9b+cxhDDNF1ig07Pxs2Ly8RdWoGt8jce++9mDlzJuLj49nB9zpotQKrt2fh7d/S0awV8Ha0xvuzBmCIP+eHITKGmFB3vP1bOpIzS9DQrIHKgiOQE3VGBgeZoqIivPTSS7x75joUVdXjiW8PYfupEgDAhHAvvDE9Ao42ljJXRtR59OnuAA97FYqqGpCSXY6RIW5yl0RE7cDgS0t33XUXNmzY0A6ldA0J6UW45f3t2H6qBNaWCrx+az98NHsgQwyRkUmShBjehk3U6Rl811J5eTluuOEGuLu7w8vLS2/dunXrjFrc9TKlu5YamjV4+9d0/HdHNgAgzMseH84agBBPe1nrIurMfkk7g0e+SkWguy3+fCJG7nKIqI3a9a6lOXPmQKVSYdSoUewj00ZZxdVYGH8ARwrUAIB7h/th6S29YW3Ja/ZE7WlEiBssFBKyimuQW1oDP1dbuUsiIiMzOMgkJSWhsLBQ9hYOcyCEwPf7T+PFTUdR26iBk40l3poRgZv6el39h4noujlYW2KwvzN2Z5UhMb0Y90YxyBB1Ngb3kenTp49uqgK6vKr6JiyKP4invj+M2kYNhgW64NdFoxliiDpY7PlRftlPhqhzMrhFZvr06Zg4cSLmzZt3UR+ZKVOmGK0wc3YgrxwL4w8gv6wOSoWEx8aG4JGYYCgVvNOLqKPFhnkg7pcT2JVZivomDS/pEnUyBnf2DQgIuPSOJAlZWVlGKcpYOrqzr1YrsHJbJt7dchLNWoEeTt3wwawBGOTHwQKJ5CKEwMg3E1BQUYfP5g7RtdAQkelq186+2dnZ11xYZ3ZOXY/Hvz2InRmlAICJEd3x+q394NiNt1UTyUmSWmbD/npPHhJPFDHIEHUyBveRaQ9xcXGQJAmLFy/WLbvvvvsgSZLeY9iwYfIVeQV/HD+HCe9vx86MUnSzVOKtGRFYPmsAQwyRifirn0wxOvE8uURdksEtMidPnsTTTz+N1NRUVFdX660rKyszuICUlBSsWrUKERERF627+eab8dlnn+meW1lZGbz/9tTQrEHczyfweXIOAKB3dwd8OGsAgj3s5C2MiPREBbnCSqlAXlktskpqEOTO/6NEnYXBQWbWrFno168fPvnkk+seR6a6uhqzZ8/G6tWr8eqrr160XqVSXdSh2FRkFFXj0bUHcPxMy9gwc0f4Y8nNYexISGSCbFUWGBrogu2nSpBwoohBhqgTMTjIZGRkICUlBQrF9V+Vmj9/PiZOnIixY8deMsgkJibCw8MDTk5OiI6OxmuvvQYPj8tf325oaEBDQ4PuuVqtvu4aL+W7ffl4YeNR1DVp4GJrhX/fHoEbwzzb5VhEZBwxoR7YfqoEienF+MeoQLnLISIjMTiNTJgwAbt3777uA8fHxyM1NRVxcXGXPc5XX32FP//8E++88w5SUlJw44036gWVv4uLi4Ojo6Pu4evre911XkplXRPqmjQYEeyKXxeNYoghMgOx5+dd2ptdhpqGZpmrISJjuaa5lqKjoxEeHn7RZZ933323TfvIz8/H4MGDsWXLFkRGRgIAYmJi0L9/f7z33nuX/JkzZ87Az88P8fHxmD59+iW3uVSLjK+vr9Fvv9ZqBTanncGkft2h4NgwRGZBCIGYfycit7QWq+8ZjHF9+AcIkalq19uvFy9ejKKiImg0GpSXl19Tgfv370dRUREGDRqkW6bRaLBt2zYsX74cDQ0NUCr1+5p0794dfn5+OHXq1GX3q1KpoFKprqkmQygUEqZEerf7cYjIeCRJQkwvd3yxKxcJ6UUMMkSdhMFBZt26dTh58iS6d+9+zQcdM2YM0tLS9JbNnTsXYWFhWLJkyUUhBgBKS0uRn59/Xccloq4tJswDX+zKReKJIgghIElsUSUydwYHmZ49e6Jbt27XdVB7e3uEh4frLbO1tYWrqyvCw8NRXV2NZcuWYcaMGejevTtycnLwzDPPwM3NDbfeeut1HZuIuq7hga5QWShQWFmPk+eqEeplL3dJRHSdDA4y8+fPx+23346nn34anp76TbOXGgvmWiiVSqSlpeF///sfKioq0L17d8TGxuKbb76BvT1/8RDRtbG2VCIqyBUJ6cVISC9ikCHqBAzu7Hu5264lSYJGozFKUcbS0XMtEZHp+9+uHLyw8SiGBrjgm4eGy10OEV2CId/fBt9+rdVqL/kwtRBDRHQpMb1axqLan1sOdX2TzNUQ0fUyibmWiIg6Sk9XGwS526JZK7DzVInc5RDRdWpTkJk1axaOHTt2xW2OHTuGWbNmGaUoIqL2FKObRLJI5kqI6Hq1qbPv1KlTMXnyZLi6uuLGG29EWFgYHBwcoFarceLECfz5558oLS3F66+/3t71EhFdt9hQD3y6I1s3GzZvwyYyX23u7CuEwM8//4xNmzbh4MGDKC8vh7OzM/r374/JkyfjlltuMcr8S8bEzr5EdCkNzRoMeHkrahs12PzoSIT3cJS7pHZ1rFCNirpGRAW5yV0KUZu0y8i+kiRh4sSJmDhx4nUXSEQkJ5WFEiOC3bD12Dkkphd12iBTWt2AN389gW/3nQYAbFowAhE+TvIWRWRkptWEQkTUQWJ1/WSKZa7E+DRagf/blYPYfyfqQgwAJHbC10rEIENEXVLM+dmwD+SVo6K2UeZqjGd/bjmmLN+B5zcehbq+GX26O2D20J4AgJ0ZvEuLOh8GGSLqkryduiHU0x5aAWzrBLdhl1Q34MnvDmHGx8k4WqiGg7UFXpnaFz8+OhL/GBUIADiQV4G6Ro75RZ0LgwwRdVkxYS2tMoknzPc27GaNFl8kt1xG+n5/y2WkOwb74M8nY3D3cH8oFRL8XW3g7WiNRo0WKTllMldMZFzXFGSam5uxc+dOfPPNNwCAmpoa1NTUGLUwIqL21tpPJvFkMbRag2ZrMQn7csoweflOvLjpKKrqmxHewwHr5kXhrdsi4Wan0m0nSRKiglvuWErOLJWrXKJ2YXCQOXHiBHr37o2ZM2figQceAAD88ccf+Mc//mH04oiI2tMgP2fYqyxQVtOIwwWVcpfTZsVVDXj824O4beUuHD+jhmM3S7wyLRwb54/EwJ7Ol/yZqCBXAEBypvlfRiO6kMFBZt68eVi0aBFOnz4NS0tLAEBMTAx27Nhh9OKIiNqTpVKBUb1aWioSzODyUrNGi892ZuPGfydiXWoBAODOIb7484lo3D3MD0rF5Qf2G3G+RSatoBKVtZxjijoPg4PMwYMHMW/ePADQjYbp4OCAqqoq41ZGRNQBWqcrSDTx6Qr2Zpdh0oc78NKPx1DV0Ix+PRyxfl4U3pgRAdcLLiNdjqeDNYLcbSEEsDubl5eo82jzgHitPD09kZOTg8DAQN2ykydPwsfHx6iFERF1hJheLR1+DxdUoqS6Qa9viSkoqqrHGz+fwLoDLS0wTjaWeGp8KO4c0vOKLTCXMiLYDZnFNUjOKMH4vl7tUS5RhzO4ReaRRx7BjBkz8OOPP0Kj0WDLli24++67sWDBgvaoj4ioXXk4WKOvtwOEALadNJ0B45o1Wny6Ixtj/p2EdQcKIEnArBt88ecTMZg99MqXkS6ntZ/MTnb4pU7E4BaZhQsXwsLCAkuXLoVGo8HixYsxb948PPzww+1RHxFRu4sN9cDRQjUS0osxfaD8rct7skrxwsajSD/Xcsk+0scRL08NR6Sv03Xtd1igKyQJyCiqRpG6Hh4O1kaolkheBgcZoKXDb2s/GSIicxcb5o7lCRnYdrIYzRotLJTyDLFVpK7H6z8fx4aDhQBaLiMtuTkMMwf7QnENLTB/52RjhXBvR6QVVCI5sxTTBvS47n0Sye2agoxGo0Fubi6qq6v1lkdERBilKCKijtTf1xlONpaoqG3CwfwKDPZ36dDjN50f1O6930+huqH5/GWknnjqplA421oZ9VhRQa5IK6jEzowSBhnqFAwOMhs2bMCDDz6I0lL9a6ySJEGj4dDXRGR+lAoJo0PcselQIRLSizo0yOzKLMWLm47g5LmWPwwjfZ3wytS+7TZLdVSwGz7ZloXkzFIIIXR3nxKZK4PbTxcuXIi4uDjU1NRAq9XqHgwxRGTOYs9PV5BwomM6/J5T12Ph2gOYtXo3Tp6rhrONJd6c0Q/rH4lqtxADAEP8nWGplFBQUYe8stp2Ow5RRzG4Raa+vh73338/FApO00REncfoEHdIEnDsjBrn1PXwbKeOsE3nB7V7//dTqGnUQJKA2UN74smbQuFkY9zLSJdiY2WBAb7O2JtThp0ZpfBztW33YxK1J4PTyPz58/Hhhx+2Ry1ERLJxtVPpWkKS0tunVSY5owQT3t+O138+gZpGDQb0dMKPC0bi1Wn9OiTEtIoK5nQF1HkY3CLz4IMPYtSoUXjrrbfg6empty41NdVohRERdbTYUHccyq9AQnoR7hjia7T9nq2sx6s/HcPmw2cAAC62VvjXhDDcNtDHKHcjGWpEsBve+/0UdmWWQqsVstRAZCwGB5nbbrsNAQEBmDFjBmxsbNqjJiIiWcSGeuC9309h+6kSNGm0sLzO27Abm7VYszMbH/xxCrWNGigkYM4wPzwxLhSONpZGqtpwkT5O6GapRGlNI9LPVaF3dwfZaiG6XgYHmcOHD6OsrAxWVh3XDEpE1BH69XCEq60VSmsasS+nHMPPj4R7LXZmlOCFjUeQWVwDABjY0wkvTw1HeA9HY5V7zawsFLghwAVJJ4uxM6OEQYbMmsF/bgwbNgyZmZntUQsRkawUCgnRoS13L13rJJKFFXWY/1UqZv93DzKLa+Bqa4W3b4vA9w9HmUSIaTXifD+ZXZyugMycwS0ygwcPxvjx4zFnzhx4eelPOrZw4UKjFUZEJIfYUA+sSy1AQnoRlt7Su80/19jcMjfSB3+cQl1Ty2Wke4b747FxveDYTb7LSJcTFeQGANiTXSbraMZE18vgILNnzx4EBQVh165desslSWKQISKzNyrEDQoJOHmuGgUVdejh1O2qP7P9VDFe3HQUWecvIw32c8ZLU/uir7fptMD8XZ/uDrrRjA+drsQgP2e5SyK6JgYHmYSEhPaog4jIJDjZWGFgT2fsyy1HYnoRZg/1u+y2BRV1eHXzMfxy5CwAwM3OCksn9Mb0gT1MfsRchULC8EBX/HLkLHZlljDIkNliWyIR0d/EhnkAuPwovw3NGqxIyMDYd5Lwy5GzUEjAfVH++OOJGMwY5GPyIaZVVHDL5aWdGewnQ+bL4BYZhUJx2f+knKaAiDqDmFB3vP1bOnZmlKChWQOVhVK3LulkMZZtOorskpbLSEP8nfHy1HCzvPMn6vxdWfvzylHfpIG1pfIqP0FkegxukTlw4ABSU1N1j82bNyM6Ohoff/zxNRcRFxcHSZKwePHiS65/6KGHIEkS3nvvvWs+BhFRW/Xp7gAPexXqmjTYm10GADhdXouH/m8f7l2zF9klNXCzU+E/MyPx7UPDzTLEAECgmy28HKzR2KzF/txyucshuiYGt8hERkZe9HzQoEEYO3Ys/vnPfxpcQEpKClatWoWIiIhLrt+wYQP27NkDb29vg/dNRHQtJElCbKgHvtmXj9+OnsWh/AosT8hAfZMWSoWEe4f7Y/G4EDhYm97dSIaQJAlRwa5Yl1qAnRklGHH+UhOROTFKHxlra2vk5uYa/HPV1dWYPXs2Vq9eDWfnizuaFRQUYMGCBfjqq69gaWnevzCIyLy0zob95e48/HvLSdQ3aXFDgAt+WjgSL0zuY/YhplXrbdg7OZ4MmSmDW2Q++OADvee1tbVYt24dYmJiDD74/PnzMXHiRIwdOxavvvqq3jqtVou7774bTz31FPr27dum/TU0NKChoUH3XK1WG1wTERHQMh+RpVJCk0bA3V6F5yb2xpRIb7PpyNtWrQPjpZ2ugLq+qdMENOo6DA4y69ev13tuZ2eHCRMm4LHHHjNoP/Hx8UhNTUVKSsol17/55puwsLAwaGyauLg4vPTSSwbVQUR0KfbWllhx10Bkl9TgrqE9Yd9Jv+C7O3ZDoJstskpqsCerDOP6eF79h4hMiCzjyOTn52PRokXYsmULrK2tL1q/f/9+vP/++0hNTTXor5+lS5fi8ccf1z1Xq9Xw9TXeDLZE1LXc1Nfr6ht1AsODXJFVUoOdGSUMMmR2JCGEuNpGbb1E4+DQtp77GzZswK233gql8q9b/TQaDSRJgkKhwJtvvomnnnoKCoVCb71CoYCvry9ycnLadBy1Wg1HR0dUVla2uTYioq7m57QzmPdVKnp52mHLY9Fyl0Nk0Pd3m1pknJycrtgyIoSAJEltHkdmzJgxSEtL01s2d+5chIWFYcmSJejevTvGjx+vt378+PG4++67MXfu3DYdg4iI2mZ4YEs/mZPnqlFc1QB3e5XMFRG1XZuCTHZ2tlEPam9vj/DwcL1ltra2cHV11S13dXXVW29paQkvLy+EhoYatRYioq7O2dYKfbo74NgZNZIzSzC1fw+5SyJqszYFGT+/S881UlJSAjc3jjtARGTuRgS7tgSZjFIGGTIrBo8jU1tbi4ceegg2Njbw9PSEjY0NHn74YdTU1FxXIYmJiVccuTcnJ+eyI/8SEdH1aZ13KTmrROZKiAxjcJB54okncPLkSfzxxx8oLCzEH3/8gVOnTuHJJ59sj/qIiKgD3ODvAguFhPyyOuSX1cpdDlGbGRxkNm3ahB9++AHDhw+Hp6cnhg8fjm+//RYbN25sj/qIiKgD2Kos0N/XCQCwM4OtMmQ+DA4yQgi926KBlhmx23AXNxERmTDd5SVOV0BmxOAgM2nSJNx2223Yt28fiouLkZKSgpkzZ2Ly5MntUR8REXWQEUEtd4smZ5byj1MyGwYHmXfffRc9e/bEqFGj4OXlhdGjR8PHxwfvvPNOe9RHREQdpH9PJ1hbKlBS3YCT56rlLoeoTdocZAICAvDaa6+hsrISa9asQW1tLc6cOYPa2lqsWbMG9vb27VknERG1M5WFEkP8XQAAyZnsJ0Pmoc1B5plnnsFPP/0Ef39/3HLLLVi/fj1cXFw63UywRERd2Yjz/WR2ZrCfDJmHNgeZBx98EMnJyUhLS0N4eDgWLFgAb29vPPHEEzh27Fh71khERB0k6nw/mT1ZpWjWaGWuhujqDO4jExYWhrfeegv5+fn49NNPkZ2djYEDByIqKqo96iMiog7U19sRDtYWqGpoxpHCtk0YTCQng4NMK6VSiQEDBmDAgAFwd3dnqwwRUSegVEgYfr5VhuPJkDkwOMg0Njbim2++wfjx4xEQEICtW7filVdeQWFhYXvUR0REHSwqqHU8GQYZMn1tmjQSAFJTU7FmzRqsXbsWVlZWuOeee/Dhhx+iV69e7VkfERF1sBHBLS0y+3LKUd+kgbWlUuaKiC6vzUFm6NChuOWWW7BmzRpMmjQJSiU/2EREnVGQux087FUoqmpAal65roWGyBS1Ocjk5eWhe/fu7VkLERGZAEmSEBXkig0HC5GcUcogQyatzX1kGGKIiLqO1nmXdrKfDJm4a75riYiIOq/WgfEOn65EVX2TzNUQXR6DDBERXaSHUzf4u9pAoxXYm10mdzlEl2VQkNFoNLj99ttRX1/fXvUQEZGJGB7E6QrI9BkUZJRKJXbs2AELizb3ESYiIjPVehs2x5MhU2bwpaWHHnoIb731VnvUQkREJmR4YEuQOXG2CiXVDTJXQ3RpBjetbNy4EUeOHMF//vMf9OjRAwrFX1koNTXVqMUREZF8XO1UCPOyx4mzVdiVWYrJkd5yl0R0EYODzOLFi9uhDCIiMkUjgt1w4mwVkhlkyEQZHGTuvffe9qiDiIhM0IhgV3y6I5v9ZMhkXdPt1/Hx8bj55psRHh6Om2++GfHx8caui4iITMAQfxcoFRJyS2txurxW7nKILmJwkHn//ffxxBNPIDo6Gs8//zyio6Px5JNP4r333muH8oiISE721paI9HEEACRn8jZsMj0GX1pavnw5fv31V/Tr10+3bPLkyZg2bRr7zxARdUIjgt2QmleB5IwS3DHYV+5yiPQY3CJTVlaGsLAwvWW9evVCWRlHfiQi6oyGB7Xchr0zsxRCCJmrIdJncJAZMmQIXnjhBTQ3NwNoGe33pZdewpAhQ4xeHBERyW9gT2eoLBQormpARlG13OUQ6TE4yKxYsQIbNmyAu7s7+vbtCzc3N/zwww9YsWJFe9RHREQys7ZUYoi/CwD2kyHTY3AfmaCgIBw5cgS7d+9GQUEBfHx8cMMNN3DaAiKiTmx4kCt2ZJRgZ0YJ7o3yl7scIp1rSh9KpRIjRowwdi1ERGSiRgS74e3f0rE7qxQarYBSIcldEhGAa7i0VFRUhMWLF2P06NEYOHCg3uNaxcXFQZIkvbueli1bhrCwMNja2sLZ2Rljx47Fnj17rvkYRER07fr1cIS9tQXU9c04WlgpdzlEOga3yMycORNKpRKzZs2CjY3NdReQkpKCVatWISIiQm95r169sHz5cgQGBqKurg7/+c9/cNNNNyEjIwPu7u7XfVwiImo7pULCsEBXbD12DjszShHh4yR3SUQAriHIpKamori4GFZWVtd98OrqasyePRurV6/Gq6++qrfurrvu0nv+7rvv4tNPP8Xhw4cxZsyY6z42EREZJiqoJcgkZ5bgkZggucshAnANl5aGDh2KjIwMoxx8/vz5mDhxIsaOHXvF7RobG7Fq1So4OjoiMjLSKMcmIiLDjAh2AwCk5JShoVkjczVELQxukfnf//6H2267DaNHj4aXl5feuoULF7Z5P/Hx8UhNTUVKSsplt9m8eTPuvPNO1NbWonv37ti6dSvc3Nwuu31DQwMaGhp0z9VqdZvrISKiKwvxsIObnQol1Q04kFeBYYGucpdEZHiQ+c9//oN9+/ahqalJr4+MJEltDjL5+flYtGgRtmzZAmtr68tuFxsbi4MHD6KkpASrV6/GHXfcgT179sDDw+OS28fFxeGll14y7AUREVGbSJKEqCBXbDpUiOSMEgYZMgmSMHC8aQcHB+zevRt9+vS55oNu2LABt956K5RKpW6ZRqOBJElQKBRoaGjQW9cqJCQE999/P5YuXXrJ/V6qRcbX1xeVlZVwcHC45nqJiKjFNyl5WPJDGgb7OeP7R6LkLoc6KbVaDUdHxzZ9fxvcIuPu7o7AwMBrLg4AxowZg7S0NL1lc+fORVhYGJYsWXLJEAMAQgi9oPJ3KpUKKpXqumojIqLLiwpqubx/ML8CNQ3NsFVxMFSSl8GfwOeffx4PPvggXnjhBXh6euqta2urh729PcLDw/WW2drawtXVFeHh4aipqcFrr72GKVOmoHv37igtLcVHH32E06dP4/bbbze0ZCIiMhJfFxv4unRDflkd9maXITbs0pf6iTqKwUHm/vvvBwB89dVXkKSWkR2FEJAkCRqNcXqxK5VKnDhxAl988QVKSkrg6uqKIUOGYPv27ejbt69RjkFERNdmRJAb4svykZxZwiBDsjM4yGRnZ7dHHUhMTNT929raGuvWrWuX4xAR0fWJCnZDfEo+dmZwAkmSn8FBxs/Prz3qICIiMzH8/N1Kx86oUVbTCBfb6x8glehaXfOlpUtZs2bNdRVDRESmz91ehVBPe6Sfq8KuzFJMjOgud0nUhRk8sq+jo6Peo7a2Ft9//z0sLS3boz4iIjJBUcEtrTLJmSUyV0Jd3TUNiPd3CQkJWL16tVEKIiIi0xcV5IbPduYgOZP9ZEheBrfIXEpMTAx++uknY+yKiIjMwNBAFygkILukBoUVdXKXQ12YwUFGrVbrPc6ePYu33nrronmXiIio83KwtkSEjxMAsFWGZNXmIDNx4kQAgJOTE5ydnXUPb29vrFy5EitXrmy3IomIyPSMaO0nk8F+MiSfNveR2b59OwAgKytLNxAeANjZ2cHVlROHERF1NVFBbliRkImdmSW6gVGJOprBnX39/f3boQwiIjI3g/ycYWWhwDl1A7JKahDkbid3SdQFtTnINDY24sMPP8SVJsteuHChUYoiIiLTZ22pxGA/ZyRnliI5o4RBhmTR5iDT3Nx8xWkDJElikCEi6mKiglyRnFmKnRmluHu4v9zlUBfU5iBjY2ODhISE9qyFiIjMTFSwG7DlJHZllUKrFVAo2E+GOpZRxpEhIqKuKaKHI+xUFqisa8KxM2q5y6EuqM1B5kp9Y4iIqGuyUCowNMAFALCTt2GTDNocZKqqqtqzDiIiMlNRwW4AODAeyYOXloiI6Lq0Doy3N7sMjc1amauhroZBhoiIrksvD3u42lqhrkmDg/kVcpdDXQyDDBERXReFQsLwoJZWGfaToY7GIENERNdtxPl+MrvYT4Y6GIMMERFdtxFBLUHmQH45ahubZa6GuhIGGSIium6+Lt3Qw6kbmjQCe7PL5C6HuhAGGSIium6SJOnuXuLlJepIDDJERGQUrf1kdmaywy91HAYZIiIyiuGBLS0yRwvVqKhtlLka6ioYZIiIyCg8HKwR4mEHIYDdWby8RB2DQYaIiIxGd3kpg0GGOgaDDBERGY1uYDz2k6EOwiBDRERGMyzQFQoJyCquwdnKernLoS6AQYaIiIzGsZsl+vVwBAAks1WGOgCDDBERGdXwIPaToY7DIENEREbVOjBecmYJhBAyV0OdHYMMEREZ1WA/F1gpFThTWY+c0lq5y6FOziSCTFxcHCRJwuLFiwEATU1NWLJkCfr16wdbW1t4e3vjnnvuQWFhobyFEhHRVXWzUmJATycAwM4M9pOh9iV7kElJScGqVasQERGhW1ZbW4vU1FQ8//zzSE1Nxbp163Dy5ElMmTJFxkqJiKitWseTYYdfam+yBpnq6mrMnj0bq1evhrOzs265o6Mjtm7dijvuuAOhoaEYNmwYPvzwQ+zfvx95eXkyVkxERG1x4QSSWi37yVD7kTXIzJ8/HxMnTsTYsWOvum1lZSUkSYKTk9Nlt2loaIBardZ7EBFRx4vwcYKtlRLltU04fpa/i6n9yBZk4uPjkZqairi4uKtuW19fj3/961+466674ODgcNnt4uLi4OjoqHv4+voas2QiImojS6UCNwS4AACSeRs2tSNZgkx+fj4WLVqEL7/8EtbW1lfctqmpCXfeeSe0Wi0++uijK267dOlSVFZW6h75+fnGLJuIiAzAfjLUESzkOOj+/ftRVFSEQYMG6ZZpNBps27YNy5cvR0NDA5RKJZqamnDHHXcgOzsbf/755xVbYwBApVJBpVK1d/lERNQGUecHxtubXYYmjRaWStnvL6FOSJYgM2bMGKSlpektmzt3LsLCwrBkyRK9EHPq1CkkJCTA1dVVjlKJiOgahXnZw8XWCmU1jTiUX4HB/i5yl0SdkCxBxt7eHuHh4XrLbG1t4erqivDwcDQ3N+O2225DamoqNm/eDI1Gg7NnzwIAXFxcYGVlJUfZRERkAIVCwvBAV/yUdgbJmaUMMtQuTLKd7/Tp09i0aRNOnz6N/v37o3v37rpHcnKy3OUREVEbRZ2/DZsD41F7kaVF5lISExN1//b39+f8HEREnUBrP5kDeRWoa9Sgm5VS5oqoszHJFhkiIuoc/F1t4O1ojUaNFvtyy+QuhzohBhkiImo3kiQh6vxt2Ds5ngy1AwYZIiJqV1FBLf1kOJ4MtQcGGSIialetA+OlFVSisrZJ5mqos2GQISKiduXpYI0gd1sIAezO5uUlMi4GGSIianetdy8l8zZsMjIGGSIiancjWseTyWSLDBkXgwwREbW7YYGukCQgo6gaRep6ucuhToRBhoiI2p2TjRXCvR0BAMlslSEjYpAhIqIO0XobNqcrIGNikCEiog7ROjBecmYpp6Eho2GQISKiDjHE3xmWSgkFFXXIK6uVuxzqJBhkiIioQ9hYWWCArzMATldAxsMgQ0REHSYqmNMVdCamcImQQYaIiDpM63QFuzJLodXK/yVI1y41rxzTVuzEyXNVstbBIENERB0m0scJ3SyVKK1pRLrMX4B0bcpqGrHk+8OY/lEyDp2uxFu/pstaD4MMERF1GCsLBW4IcAHA8WTMjVYrEL83Dze+k4hv9uUDAO4Y7IM3Z/STtS4LWY9ORERdzohgVySdLEZyRgkeGBkgdznUBkcKKvH8xiM4kFcBAAjzsser08Ix2N9F3sLAIENERB2sdQLJPdllaNZoYaHkxQFTpa5vwrtbTuJ/u3KgFYCdygKPjeuFe4f7mcx5Y5AhIqIO1ae7A5xsLFFR24RDpysxyM9Z7pLob4QQ2HiwEK/+dBwl1Q0AgMmR3nhuYm94OljLXJ0+BhkiIupQCoWE4YGu+OXIWezKLGGQMTGnzlXh+Y1HsDurDAAQ6G6LV6aG6+44MzWm0S5ERERdSut0BRwYz3TUNDQj7pfjmPD+duzOKoO1pQJPjQ/FL4tGmWyIAdgiQ0REMmidQHJ/XjnqmzSwtlTKXFHXJYTAb0fP4uUfj6Gwsh4AMLa3J16c3Ae+LjYyV3d1DDJERNThAt1s4eVgjbPqeuzPLTfpv/g7s9zSGry46SgS04sBAD7O3bBscl+M7eMpc2VtxyBDREQdTpIkRAW7Yl1qAXZmlDDIdLD6Jg1WJmXio8RMNDZrYaVU4KHoQMyLCUY3K/NqHWOQISIiWUQFubUEGQ6M16ES0ouwbNNR5Ja2zEA+KsQNL03pi0B3O5kruzYMMkREJIsR5yeQTDtdAXV9ExysLWWuqHMrrKjDyz8ew69HzwIAPB1UeGFSX9zSzwuSJMlc3bVjkCEiIll0d+yGQDdbZJXUYE9WGcaZUb8Mc9LYrMWandn44I9TqG3UQKmQMDfKH4vH9YKdyvxjgPm/AiIiMlvDg1yRVVKDnRklDDLtYHdWKZ7fcASniqoBAEP8nfHKtHCEeTnIXJnxMMgQEZFsRgS74as9edjFfjJGVVRVj7ifT2D9gQIAgKutFZbe0hszBvYw68tIl8IgQ0REshke2NJPJv1cFYqrGuBur5K5IvOm0Qp8uTsX//4tHVUNzZAkYPbQnnjqpjA42nTOPkgMMkREJBtnWyv06e6AY2fUSM4swdT+PeQuyWwdyCvHcxuO4GihGgAQ4eOIV6aGI9LXSd7C2plJTFEQFxcHSZKwePFi3bJ169Zh/PjxcHNzgyRJOHjwoGz1ERFR+2m9e4mXl65NeU0jlq47jOkfJ+NooRoO1hZ4ZVo41s8b0elDDGACQSYlJQWrVq1CRESE3vKamhqMGDECb7zxhkyVERFRR9DNu5RZInMl5kWrFfgmJQ83vpOItXvzIQQwY6AP/nwyBncP84NS0bn6wlyOrJeWqqurMXv2bKxevRqvvvqq3rq7774bAJCTkyNDZURE1FFu8HeBhUJCflkd8stqzWJ+H7kdLazE8xuOIDWvAgAQ6mmPV6aF44YAF3kLk4GsLTLz58/HxIkTMXbsWKPsr6GhAWq1Wu9BRESmzVZlgf7nL4HszGCrzJVU1TfhpR+PYvKHO5CaVwFbKyWevaU3Ni8c2SVDDCBji0x8fDxSU1ORkpJitH3GxcXhpZdeMtr+iIioY0QFu2FfbjmSM0tx5w095S7H5AghsOlQIV796TiKqxoAABMjuuP5iX3g5Wgtc3XykqVFJj8/H4sWLcKXX34Ja2vjnYClS5eisrJS98jPzzfavomIqP2MCGrp8JucWQohhMzVmJaMoirctXoPFsUfRHFVAwLcbPG/+2/AirsGdvkQA8jUIrN//34UFRVh0KBBumUajQbbtm3D8uXL0dDQAKXS8Nk3VSoVVCqOQUBEZG7693SCtaUCJdUNOHmuGqFe9nKXJLvaxmZ8+GcG/rs9C00aAZWFAgtig/HP6ECoLMxrhur2JEuQGTNmDNLS0vSWzZ07F2FhYViyZMk1hRgiIjJfKgslhvi7YPupEiRnlnTpICOEwJZj5/Dyj8dQUFEHABgT5oFlU/qyI/QlyBJk7O3tER4errfM1tYWrq6uuuVlZWXIy8tDYWEhACA9PR0A4OXlBS8vr44tmIiI2t2IYDdsP1WCnRmlmDsiQO5yZJFXWotlPx7FnyeKAAA9nLph2ZS+nIfqCkx2ZN9NmzZh7ty5uud33nknAODFF1/EsmXLZKqKiIjaS9T5fjJ7skrRrNHCQin7UGcdpr5Jg1XbsrAiIQMNzVpYKiX8c3QgFsSGoJsVr1JciSQ6ca8qtVoNR0dHVFZWwsGh88z0SUTUGWm0AgNe3gJ1fTM2zB+huyW7s0s6WYwXNx5BTmktgJZA9/LUcAR72MlcmXwM+f422RYZIiLqWpQKCcODXPHb0XPYmVHS6YPMmco6vLL5GH5OOwsA8LBX4blJfTA5onunm6G6PXWddjsiIjJ5UUEt0xUkd+LpCpo0Wqzalokx7yTh57SzUCok3D8iAH88EY0pkd4MMQZiiwwREZmM1gkk9+WUo75JA2vLztM/pL5Jg+/25WNlUpbubqRBfs54ZWo4+niz+8O1YpAhIiKTEeRuBw97FYqqGpCaV65roTFnNQ3N+GpPLlZvz9aNyutmp8LT40Nx2yAfKLrI5I7thUGGiIhMhiRJiApyxYaDhUjOKDXrIFNZ24TPk3PwWXI2KmqbAADejtZ4KDoIM4f4dqrWJjkxyBARkUmJCnbDhoOF2JlZgicRKnc5BiuuasCnO7Lx5e5cVDc0AwAC3GzxSHQQpg3oASsLdk81JgYZIiIyKa3jyRw+XYmq+ibYW1vKXFHbFFbUYdW2LKzdm4eGZi0AIMzLHvNigzGxX3coeQmpXTDIEBGRSfFxtoGfqw1yS2uxN7sMY3qb9qi2OSU1+DgxE+sOnEaTpmVotkhfJyyIDcaYMA/2gWlnDDJERGRyooLckFuah50ZpSYbZNLPVmFFQgY2Hy6E9vzQssMCXbAgNgQjgl15G3UHYZAhIiKTMyLYFWv35pnkeDKH8iuwPCEDW4+d0y2LDXXHghuDMcjPRcbKuiYGGSIiMjnDA1v6yZw4W4WS6ga42alkrqhlDqjlCRnYfqolXEkSMCHcC/NighHew1Hm6rouBhkiIjI5rnYqhHnZ48TZKuzKLMXkSG9Z6hBCIPFkMT5KyEBKTjmAlqkUpvb3xryYIAR72MtSF/2FQYaIiEzSiGA3nDhbhWQZgoxWK7Dl2FksT8jAkQI1AMBKqcDtg33wcHQQfF1sOrQeujwGGSIiMkkjgl3x6Y7sDu0n06zR4sfDhfgoIROniqoBAN0slZg9tCceHB0ITwfrDquF2oZBhoiITNIQfxcoFRJyS2txurwWPs7t1wrS0KzBD/sLsDIpE3lltQAAe2sL3Bflj7kjAuBia9Vux6brwyBDREQmyd7aEpE+jkjNq0ByZinuGGz8IFPb2Iy1e/OxelsWzqrrAQAutlZ4YGQA7h7uBwczGYyvK2OQISIikzUi2K0lyGSU4I7Bvkbbr7q+Cf+3Kxef7shGWU0jAMDLwRr/HB2IWTf0RDcrzoNkLhhkiIjIZA0PcsWHf2ZgZ2YphBDXPchcWU0j1uzIxhe7clBV3zIPUk8XGzwSE4TpA3tAZcEAY24YZIiIyGQN7OkMlYUCxVUNyCyuvubbnc+p67FqWxa+3pOHuiYNACDEww7zY4MxKaI7LJScyNFcMcgQEZHJsrZUYoi/C3ZklGBnRqnBQSa/rBYfJ2Xi+32n0ahpmcgxvIcDFsQG46Y+XpwHqRNgkCEiIpM2PMj1fJApwb1R/m36mYyiKnyUkImNhwqhOT8R0hB/Z8yPDUZ0L3fOg9SJMMgQEZFJGxHshrd/S8furFJotALKK7SiHCmoxIqEDPx69CzE+YkcR4W4YUFsMIaen/aAOhcGGSIiMmn9ejjC3toC6vpmHC2sRISP00Xb7Mspw/KEDCSmF+uW3dTHE/NjgxHpe/H21HkwyBARkUlTKiQMC3TF1mPnsDOjVBdkhBDYkVGC5X9mYE92GQBAIQGTI70xLyYYoV6cB6krYJAhIiKTFxXUEmSSM0vw0OhA/H78HFYkZuJQfgUAwFIpYcbAlnmQ/N1s5S2WOhSDDBERmbwRwW4AgL3ZZbjlg+04cbYKAGBtqcCdQ3rin6MD4e3UTc4SSSYMMkREZPJCPOzgZqdCSXUDTpytgp3KAncP98MDIwPgZqeSuzySEYMMERGZPEmS8HB0IL7em4dp/Xvg3uH+cLThPEgESEK03qDW+ajVajg6OqKyshIODg5yl0NERERtYMj3N8dkJiIiIrPFIENERERmyySCTFxcHCRJwuLFi3XLhBBYtmwZvL290a1bN8TExODo0aPyFUlEREQmR/Ygk5KSglWrViEiIkJv+VtvvYV3330Xy5cvR0pKCry8vDBu3DhUVVXJVCkRERGZGlmDTHV1NWbPno3Vq1fD2dlZt1wIgffeew/PPvsspk+fjvDwcHzxxReora3F119/LWPFREREZEpkDTLz58/HxIkTMXbsWL3l2dnZOHv2LG666SbdMpVKhejoaCQnJ3d0mURERGSiZBtHJj4+HqmpqUhJSblo3dmzZwEAnp6eess9PT2Rm5t72X02NDSgoaFB91ytVhupWiIiIjJFsrTI5OfnY9GiRfjyyy9hbW192e0kSX+qdiHERcsuFBcXB0dHR93D19fXaDUTERGR6ZElyOzfvx9FRUUYNGgQLCwsYGFhgaSkJHzwwQewsLDQtcS0tsy0KioquqiV5kJLly5FZWWl7pGfn9+ur4OIiIjkJculpTFjxiAtLU1v2dy5cxEWFoYlS5YgMDAQXl5e2Lp1KwYMGAAAaGxsRFJSEt58883L7lelUkGl4pwbREREXYUsQcbe3h7h4eF6y2xtbeHq6qpbvnjxYrz++usICQlBSEgIXn/9ddjY2OCuu+6So2QiIiIyQSY7aeTTTz+Nuro6zJs3D+Xl5Rg6dCi2bNkCe3t7uUsjIiIiE8FJI4mIiMikcNJIIiIi6hJM9tKSMbQ2NnE8GSIiIvPR+r3dlotGnTrItM7LxPFkiIiIzE9VVRUcHR2vuE2n7iOj1WpRWFgIe3v7Kw6kdy3UajV8fX2Rn5/P/jcmgOfDtPB8mBaeD9PC83F1QghUVVXB29sbCsWVe8F06hYZhUIBHx+fdj2Gg4MDP4gmhOfDtPB8mBaeD9PC83FlV2uJacXOvkRERGS2GGSIiIjIbDHIXCOVSoUXX3yRUyKYCJ4P08LzYVp4PkwLz4dxderOvkRERNS5sUWGiIiIzBaDDBEREZktBhkiIiIyWwwyREREZLYYZK7BRx99hICAAFhbW2PQoEHYvn273CV1Cdu2bcPkyZPh7e0NSZKwYcMGvfVCCCxbtgze3t7o1q0bYmJicPToUXmK7QLi4uIwZMgQ2Nvbw8PDA9OmTUN6erreNjwnHefjjz9GRESEbpC14cOH45dfftGt57mQV1xcHCRJwuLFi3XLeE6Mg0HGQN988w0WL16MZ599FgcOHMCoUaMwYcIE5OXlyV1ap1dTU4PIyEgsX778kuvfeustvPvuu1i+fDlSUlLg5eWFcePG6ebcIuNKSkrC/PnzsXv3bmzduhXNzc246aabUFNTo9uG56Tj+Pj44I033sC+ffuwb98+3HjjjZg6darui5HnQj4pKSlYtWoVIiIi9JbznBiJIIPccMMN4uGHH9ZbFhYWJv71r3/JVFHXBECsX79e91yr1QovLy/xxhtv6JbV19cLR0dHsXLlShkq7HqKiooEAJGUlCSE4DkxBc7OzuK///0vz4WMqqqqREhIiNi6dauIjo4WixYtEkLw/4cxsUXGAI2Njdi/fz9uuukmveU33XQTkpOTZaqKACA7Oxtnz57VOzcqlQrR0dE8Nx2ksrISAODi4gKA50ROGo0G8fHxqKmpwfDhw3kuZDR//nxMnDgRY8eO1VvOc2I8nXrSSGMrKSmBRqOBp6en3nJPT0+cPXtWpqoIgO79v9S5yc3NlaOkLkUIgccffxwjR45EeHg4AJ4TOaSlpWH48OGor6+HnZ0d1q9fjz59+ui+GHkuOlZ8fDxSU1ORkpJy0Tr+/zAeBplrIEmS3nMhxEXLSB48N/JYsGABDh8+jB07dly0juek44SGhuLgwYOoqKjADz/8gHvvvRdJSUm69TwXHSc/Px+LFi3Cli1bYG1tfdnteE6uHy8tGcDNzQ1KpfKi1peioqKLUjV1LC8vLwDguZHBo48+ik2bNiEhIQE+Pj665TwnHc/KygrBwcEYPHgw4uLiEBkZiffff5/nQgb79+9HUVERBg0aBAsLC1hYWCApKQkffPABLCwsdO87z8n1Y5AxgJWVFQYNGoStW7fqLd+6dSuioqJkqooAICAgAF5eXnrnprGxEUlJSTw37UQIgQULFmDdunX4888/ERAQoLee50R+Qgg0NDTwXMhgzJgxSEtLw8GDB3WPwYMHY/bs2Th48CACAwN5ToyEl5YM9Pjjj+Puu+/G4MGDMXz4cKxatQp5eXl4+OGH5S6t06uurkZGRobueXZ2Ng4ePAgXFxf07NkTixcvxuuvv46QkBCEhITg9ddfh42NDe666y4Zq+685s+fj6+//hobN26Evb297i9LR0dHdOvWTTdmBs9Jx3jmmWcwYcIE+Pr6oqqqCvHx8UhMTMSvv/7KcyEDe3t7XX+xVra2tnB1ddUt5zkxEvlumDJfK1asEH5+fsLKykoMHDhQd7spta+EhAQB4KLHvffeK4RouZ3xxRdfFF5eXkKlUonRo0eLtLQ0eYvuxC51LgCIzz77TLcNz0nHuf/++3W/l9zd3cWYMWPEli1bdOt5LuR34e3XQvCcGIskhBAyZSgiIiKi68I+MkRERGS2GGSIiIjIbDHIEBERkdlikCEiIiKzxSBDREREZotBhoiIiMwWgwwRERGZLQYZIgIAvP7665g1a5bcZVzSww8/jCVLlhh1nxqNBhEREThy5Mhlt3FyckJiYqJRj/t348aNw++//96uxyDqzDhFAVEXYWdnp/t3XV0dLCwsYGlpCQAYNWoUfvnlF7lKu6qVK1e2edvExERMmzYNFRUVV9zuf//7H0JCQi4aRr6jPffcc1i8eDEOHDggax1E5ootMkRdRHV1te4xatQovPnmm7rnphxi2suKFSswd+5cucvA6NGjUVFRgZ07d8pdCpFZYpAhIgDAsmXLMG3aNN1zSZKwYsUK9OnTB7a2trj77rtRVlaGmTNnwsHBAQMGDMCJEyd021dXV2PBggXo2bMnPDw8cM8996CyshIAkJOTA0mSsHr1avj7+8PV1RXz5s1DY2Oj7ue3bNmCAQMGwNHREQMHDtS73HLfffdh8eLFevv6v//7PwQHB8PJyQn33XcfmpqaUFpaigkTJqCyshJ2dnaws7PD9u3bL3qtZ86cwYEDBxAdHa1bptVq8fzzz8PT0xPe3t5YsWKF3s8cOHAAI0eOhIuLC9zd3TFr1iyUlpYCADZu3IjAwEBcOOPLrl274OzsjPr6emRnZ2Ps2LFwdHSEi4sLRowYgdraWt37fOONN2LTpk2GnjIiAoMMEV3BunXrsH37dpw8eRJbtmzB6NGjsWDBApSVlSEiIgJPP/20btv7778fZWVlOHz4MLKzs9HU1IQFCxbo7W/9+vU4ePAg0tLSkJycjLi4OABAZmYmpk6diueffx6lpaV45plnMGXKFGRnZ1+2tp9++gmpqak4duwYfv/9d3z11VdwdXXFL7/8AkdHR73Wp787cOAAevToAXt7e92yzz//HJ9//jmSkpKQkZGBffv2oaqqSrdeoVDgjTfewLlz53DkyBEUFBTgX//6FwBg4sSJqKurQ1JSkt7+7rrrLlhbW+PZZ59FcHAwSkpKcO7cObz99tuwsPjryn6fPn1w8ODBNp4VIroQgwwRXdZTTz0FV1dX9OjRA9HR0QgPD8eoUaNgYWGBmTNnIjU1FQBQXFyMH374AcuXL4eTkxNsbW3x8ssv45tvvoFGo9Htb9myZXBycoK3tzeWLl2K//u//wMAxMfHIyYmBtOnT4eFhQVuu+02jBw5EmvXrr1sbcuWLYODgwO8vb0xYcIE7N+/v82vq7y8HA4ODnrLvvrqKzz66KMICwuDjY0N3njjDWi1Wt36yMhIjBw5EpaWlvD09MTjjz+u6whsYWGBe+65B59//jkAoL6+Ht9++63u0pWlpSXOnDmDnJwcWFpaIioqClZWVrp9Ozg4oLy8vM31E9FfGGSI6LK8vLx0/7axsbnoeXV1NYCWyz1arRaBgYFwcnKCk5MThgwZAoVCgbNnz+p+xs/PT+/fBQUFAIDTp0/D399f79iBgYE4ffp0m2qztbXVaz25GmdnZ6jVar1lhYWFevV5enpCpVLpnmdkZGDq1Knw9vaGg4MD5syZg5KSEt36+++/Hz/88AOqq6uxfv16+Pj4YPDgwQCAt99+Gz169MDYsWPh7++PZcuW6YUktVoNZ2fnNtdPRH9hkCGi6+br6wuFQoHCwkJUVFToHvX19ejRo4duu9zcXN2/8/LydOt8fHyQk5Ojt8/s7Gz4+PgYXItCcfVfa/3790dBQYEuiAGAt7e3Xn1FRUVoaGjQPX/44YfRo0cPHDt2DGq1Gl9++aVen5jQ0FBERkbi+++/x+eff477779ft87DwwMfffQRcnNzsXnzZqxcuRLr16/XrT927Bj69+9v8GslIgYZIjICLy8vTJs2DQsWLNC1Upw9e1bvyxoAXn75ZVRUVKCwsBBxcXGYPXs2AGDmzJlITEzExo0bodFodH1z7rzzToNr8fT0RFVVFYqLiy+7jbe3N/r376/Xp2XWrFlYsWIF0tPTUVdXh6VLl+qFIrVaDXt7ezg4OCA/Px9vv/32Rft94IEH8M4772Dbtm2YM2eObvm3336LvLw8CCHg6OgIpVKp10cmISEBkyZNMvi1EhGDDBEZyeeff667pOTg4IBRo0Zd1G9l6tSp6N+/P8LDwzF06FA888wzAIDg4GCsW7cOL774IpydnfHyyy9j/fr1CAwMNLiO0NBQPPDAA+jduzecnJywY8eOS243f/58fPbZZ7rn999/P+bMmYNRo0YhMDAQAwYM0OsM/O6772Lz5s1wcHDA1KlTMWPGjIv2eccddyA3Nxc333wz3N3ddcv379+PqKgo2NnZYfjw4XjggQcwZcoUAMD27dthb29/yU7JRHR1kriwbZSIqB3k5OQgICAA5eXlcHJykrscAC0j+w4YMABr165F3759jbbfoKAg/Oc//9EFlasZP348nnzySYwbN85oNRB1JRzZl4i6JKVSicOHDxt1n/Hx8WhubsbEiRPb/DO//fabUWsg6moYZIiIjKB3794oKyvDF198AaVSKXc5RF0GLy0RERGR2WJnXyIiIjJbDDJERERkthhkiIiIyGwxyBAREZHZYpAhIiIis8UgQ0RERGaLQYaIiIjMFoMMERERmS0GGSIiIjJb/w+vE/R+/+2EIQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a line plot of tumor volume vs. time point for a mouse treated with Capomulin\n",
    "\n",
    "# Identify mouse treated with Capomulin\n",
    "capMouse = cleanDF.loc[cleanDF['Mouse ID'] == 'l509']\n",
    "\n",
    "# Create line plot \n",
    "plt.plot(capMouse[\"Timepoint\"], capMouse[\"Tumor Volume (mm3)\"])\n",
    "\n",
    "# Add title and labels \n",
    "plt.title(\"Capomulin Treatment of Mouse l509\", fontweight=\"bold\", fontsize=10)\n",
    "plt.xlabel(\"Timepoint (days)\", fontsize = 9)\n",
    "plt.ylabel(\"Tumor Volume (mm3)\", fontsize = 9)\n",
    "\n",
    "# Display plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a scatter plot of average tumor volume vs. mouse weight for the Capomulin regimen\n",
    "fig1, ax1 = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "# First, isolate Capomulin regimen\n",
    "cap = cleanDF.loc[cleanDF['Drug Regimen'] == 'Capomulin']\n",
    "\n",
    "# Get mean tumor volume for each mouse ID \n",
    "meanVol = cap.groupby(['Mouse ID']).mean()\n",
    "plt.scatter(meanVol['Weight (g)'], meanVol['Tumor Volume (mm3)'])\n",
    "\n",
    "# Add title and labels\n",
    "plt.title(\"Average Tumor Volume vs. Mouse Weight\", fontsize=10, fontweight=\"bold\")\n",
    "plt.xlabel(\"Weight (g)\", fontsize = 9)\n",
    "plt.ylabel(\"Average Tumor Volume (mm3)\", fontsize = 9)\n",
    "\n",
    "# Display plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between mouse weight and average tumor volume is 0.84.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the correlation coefficient and linear regression model \n",
    "# for mouse weight and average tumor volume for the Capomulin regimen\n",
    "weight = meanVol[\"Weight (g)\"]\n",
    "avgVol = meanVol[\"Tumor Volume (mm3)\"]\n",
    "correlation = st.pearsonr(weight, avgVol)\n",
    "print(f\"The correlation between mouse weight and average tumor volume is {round(correlation[0], 2)}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d4LrbtGkjf89eeeUVvdsYetz1vfd++eUX/Pvvv1r7O3HiBKpXr44PPvgAW7dulX+2bt68iXPnzsnHEgCmTZsmH8t9+/Zh1qxZ+M9//vPQr0nT85KUlIS0tDQ5rNSsWRNhYWH49ddf5R4QzWMRERHy1xgTE4MDBw7Ir7t48WK88847el/L29sbISEhAIAtW7agqKgId+/e1ZrmbSxN6NX0OJP1MchQmfzwww/yD/KJEycgpPFWEELIU0aTkpLkS0ajR4+GQqHAjRs3AAD9+/eHr6+vvL+5c+cCkE5eQUFBaNGiBQICAtCwYUN8//33j6wnMjISgPQLOiwsDLVq1dLpbvbw8JBPwGvWrEG9evXQoEED+XKTRp06dfDCCy8AACZNmoQ6deogMjISPj4+6NixI5KTk406VsW1bt1anoLarVs3dOjQAfPmzSvz/koaNmwYmjRpAkAKHk2aNEH79u2hVqsRFBSk99KEoapUqSJfrtAE1sGDB8vhxtPTE9OnTwcAbNy4EWFhYQgNDZV7MObMmVPqvrt06QIHBwfcv38fUVFRePzxx7FmzZoy11rShAkTULNmTdy5cwcNGzZE8+bNUbduXfj7+8s9Kw+j2Ubz/i3eC5WRkYG2bdvC398fkZGRaNSoEXbv3g3gwfsyKysL586dw7lz51BYWGhU7YmJibh161apYcvQ4+7u7o62bdsCAN5880107doVAwYM0OmRW79+PYKDg1GrVi089thjiIqKAiD9/NStWxedO3dGr169AAADBw5Eo0aN0KRJE/j4+KB3796PXEdKE06Kioq0Ptf8X9Ne/DEPDw+8++67AIBPPvkEwcHBaN68Ofz9/REVFYWdO3eW+npTpkwBIIXKOnXqICwsDNeuXXtojQ/TqFEjANJl2qZNm6JNmzbyZTWyDgYZKhPNWjH169fX6e4eNGgQFAqF1poytWvXlv8SA6Bz8hg+fDi2bt2K6Oho3Lt3D+fOnYOXlxdGjx6N559//pH1TJ8+HaNHj4aPjw+ys7MxdOhQObQUN3fuXLz00kvw8vJCVlYWJkyYgN69ewOA/BcfACxevBiffPIJmjZtirS0NFy5cgW1a9fG5MmTy7UQlp+fH9auXYuGDRsiMzMTQgiDgpqh3NzcsHfvXowfPx4BAQE4f/48vL29MXLkSBw4cKDUHhFDlVz0ruRlpRkzZuC7775DixYt5N6Vdu3aYfPmzRg5cmSp+23UqBG++eYb1K5dG2lpaahatSq+/PLLctVaXLVq1XDw4EGMHTsW/v7+8riSjh074pNPPnnk8wcPHiwHUBcXF7k3EZAua4wZMwYBAQFISUnBtWvX0KhRI8ydO9eg9+6jeHp6omrVqg/tPTP0uC9fvhwdO3aEEAKpqan48ssv5R4LjU6dOiEmJgZqtRqnTp2CWq1G165dsWPHDvmyS3x8PGbOnIn69evj0qVLuHHjBho3bowZM2YgIiLioV9P+/bt4ejoqPV6+v4fFhaGmjVryp9PmzYNK1asQMuWLXHnzh38/fffqF69Ol5++WU8+eSTpb7exIkTMWPGDPj5+SErKwuDBg2Se400PbTGeO655/DUU09BqVTi1KlT+OOPP/Suv0SWoxC8+EeVyM2bN+Hu7i5fY8/MzER4eDhu3ryJoUOHYu3atVaukIhMKSsrCwUFBfKltfz8fLRs2RKnTp1CmzZtcODAAStXSOXFwb5UqRw4cACjRo1Cq1at4O7ujgMHDiAzM1Ore56IKo6UlBS0adMGrVq1go+PD44ePYq0tDQ4Ojpi9uzZ1i6PTICXlqhSCQsLQ4sWLXDs2DH8/PPPcHZ2xpAhQ3Dw4MFHzgghIvtTrVo1dOrUCWfPnsWOHTtQUFCAPn36IDExET179rR2eWQCvLREREREdos9MkRERGS3GGSIiIjIbjHIEBERkd2q0LOW1Go10tLS4OXlZfSS6URERGQdQgjk5OQgKCjokbdRqdBBJi0tTWexJyIiIrIP165dQ3Bw8EO3qdBBRnMTsmvXruncZIyIiIhsU3Z2NkJCQuTz+MNU6CCjuZzk7e3NIENERGRnDBkWwsG+REREZLcYZIiIiMhuMcgQERGR3WKQISIiIrtlE0EmLi4OCoUCkyZN0mo/e/Ys+vfvD6VSCS8vL7Rp0wZXr161TpFERERkc6weZA4fPoxvvvkGkZGRWu0XL15Ehw4d0KhRIyQmJuLPP//Eu+++Czc3NytVSkRERLbGqtOvc3NzMWLECCxZsgRz5szReuydd95Bnz598OGHH8ptderUsXSJREREZMOs2iMzYcIE9O3bF927d9dqV6vV2LZtGxo0aIBevXqhevXqaN26NeLj4x+6v4KCAmRnZ2t9EBERUcVltSCzbt06JCcnIy4uTuexjIwM5ObmYt68eYiJicHOnTsxaNAgPPnkk0hKSip1n3FxcVAqlfIHb09ARERUsVnl0tK1a9fw2muvYefOnXrHvKjVagDAgAED8PrrrwMAmjdvjv379+Orr75CdHS03v1OmzYNkydPlj/XLHFMREREpqNSCxxKyURGTj6qe7mhVZgfHB2sc3NmqwSZo0ePIiMjA4899pjcplKpsHfvXixatAh5eXlwcnJCeHi41vMaN26Mffv2lbpfV1dXuLq6mq1uIiKiyi7hVDpmbzmD9Kx8uS1Q6YbYfuGIiQi0eD1WCTLdunXDyZMntdrGjh2LRo0aYerUqXB1dUXLli1x7tw5rW3Onz+P0NBQS5ZKRERE/y/hVDrGrU6GKNF+Iysf41YnY/HIKIuHGasEGS8vL0RERGi1eXp6wt/fX26fMmUKnnnmGXTq1AldunRBQkICtmzZgsTERCtUTEREVLmp1AKzt5zRCTEAIAAoAMzecgY9wgMsepnJ6uvIlGbQoEH46quv8OGHH6Jp06b49ttvsWHDBnTo0MHapREREVU6h1IytS4nlSQApGfl41BKpuWKgpXXkSlOX0/Lc889h+eee87yxRAREZGWjJzSQ0xZtjMVm+2RISIiIttR3cuwlfUN3c5UGGSIiIjokVqF+SFQ6YbSRr8oIM1eahXmZ8myGGSIiIjo0RwdFIjtJy2LUjLMaD6P7Rdu8fVkGGSIiIjIIDERgVg8MgoBSu3LRwFKN6tMvQZsaLAvERER2b6YiED0CA+o3Cv7EhERkf1ydFCgbV1/a5cBgJeWiIiIyI4xyBAREZHdYpAhIiIiu8UgQ0RERMZ74glAoQD27LFqGRzsS0RERIb7+2+gfv0Hn3ftCgh9t5K0DAYZIiIiMoxCzxTr06ctX0cxvLRERERED7d5s/4QIwQQHm75eophjwwRERHpp1YDjo667YcOAS1bWr4ePdgjQ0RERLqmTNENMWFhUi+MjYQYgD0yREREVFxmJuCvZ9XeW7eAqlUtX88jsEeGiIiIJI0b64aYV1+VemFsMMQA7JEhIiKi5GTgscd024uK9I+RsSHskSEiIqrMFArdELNhg9QLY+MhBmCQISIiqpy+/bb0KdVPPmn5esqIl5aIiIgqk/v3AVdX3fZz54AGDSxfTzmxR4aIiKiyGD5cN8RobjFghyEGYI8MERFRxXftGlCrlm57bi7g6Wn5ekyIPTJEREQVmaurboiZP1/qhbHzEAOwR4aIiMiqVGqBQymZyMjJR3UvN7QK84Ojg55BuMb65RegRw/ddrVa/yBfO8UgQ0REZCUJp9Ixe8sZpGfly22BSjfE9gtHTERg2XYqBOCg54JLYiIQHV22fdowXloiIiKygoRT6Ri3OlkrxADAjax8jFudjIRT6cbvdM4c3RCjVErhpgKGGIA9MkRERBanUgvM3nIGQs9jAoACwOwtZ9AjPMCwy0w5OYC3t2779etAUFA5q7Vt7JEhIiKysEMpmTo9McUJAOlZ+TiUkvnonXXooBtixoyRemEqeIgB2CNDRESlMNsgVEJGTukhxuDtzpwBmjTRbb9/H3B2LmNl9odBhoiIdJhlECrJqnu5lW87fbOOVqwARo8uR1X2iZeWiIhIi1kGoZKWVmF+CFS6obT+LQWk4NgqzE/7gf/9r/T7I1XCEAMwyBARUTGPGoQKSINQVWp9W5ChHB0UiO0XDgA6YUbzeWy/8AeX8oqKpAAzdKj2xsePSyGmEmOQISIimUkHodJDxUQEYvHIKAQotS8fBSjdsHhk1INLeOPH6455ad5cCjDNmlmmWBvGMTJERCQzySBUK7K3AcoxEYHoER6gv+aMDKBGDd0n3bkD+PhYvFZbxSBDRGQB9nKCLfcgVCuy1wHKjg4KtK3rr91YsyaQlqbd9s470oJ3pIVBhojIzOzpBKsZhHojK1/vOBkFpEsfOoNQrUwzQLlkzZoBylqXamzZgQNAu3a67SqV/tsOEMfIEBGZk73NADJ6EKoNqDADlBUK3RCzbVvp904iAAwyRERmY68nWIMHodoIux+gPHJk6VOq+/SxfD12hpeWiIjMxJgTrM4YCSt76CBUG2O3A5RLuz/SpUtAWJjl67FTDDJERGZityfY/6d3EKoNsssByvp6YIBKvyZMWfDSEhGRmdjlCdYOlXmVXGtITtYfYnJyGGLKiEGGiMhM7OoEa8fsZoCyQgE89ph226hRUoCpUsU6NVUADDJERGZiNyfYCsCmByh/8knpg3lXrrR8PRWMQoiK25eVnZ0NpVKJrKwseOsbUEVEZAH2tI6MvbOphQdVKsBJz1DUH34ABg+2fD12xJjzN4MMEZEF2NQJlsyvdWvg0CHd9op7yjUpY87fnLVERGQB9jIDiMopPR0ICtJtT0kBate2eDmVAYMMERGRKegbB1OnDnDxouVrqURsYrBvXFwcFAoFJk2apPfxl156CQqFAgsXLrRoXURERI+0Y4f+EHP/PkOMBVg9yBw+fBjffPMNIiMj9T4eHx+PP/74A0H6uuqIiIisSaHQvY3AzJnSWBhnZ+vUVMlYNcjk5uZixIgRWLJkCXx9fXUev379OiZOnIjvv/8eznxDEBGRrXjttdKnVM+ebfl6KjGrBpkJEyagb9++6N69u85jarUao0aNwpQpU9CkSRMrVEdERFRCfr4UYD77TLt9717OSLISqw32XbduHZKTk3H48GG9j8+fPx9OTk549dVXDd5nQUEBCgoK5M+zs7PLXScREREAafXdvDzddgYYq7JKj8y1a9fw2muvYfXq1XBz073HyNGjR/Hpp59i+fLlUJR2Yy094uLioFQq5Y+QkBBTlk1ERJXR2bNSL0zJEHP7NkOMDbDKgnjx8fEYNGgQHB0d5TaVSgWFQgEHBwfMnz8fU6ZMgYODg9bjDg4OCAkJweXLl/XuV1+PTEhICBfEIyKistH3x3Tv3sD27ZavpRKx+QXxunXrhpMnT2q1jR07Fo0aNcLUqVMRGBiIXr16aT3eq1cvjBo1CmPHji11v66urnB1dTVLzUREVIksXQr85z+67Wq1/nBDVmOVIOPl5YWIiAitNk9PT/j7+8vt/v7aK2A6OzsjICAADRs2tFidRERUyQgBOOgZdfHtt/qDDVkdV/YlIiICgCeeALZt023nOBibZjNBJjEx8aGPlzYuhoiIqFwyMwF/PffBOnMGaNzY8vWQUWwmyBAREVmcvvEu7u7A3buWr4XKxOq3KCAiIrK4ffv0h5h79xhi7AyDDBERVS4KBdCxo3bbxInSWBg9a5uRbWOQISKiymH27NLvj/T555avh0yCY2SIiKhiKyrSfyfq7dulxe3IrjHIEBFRxVW/PvD337rtnFJdYfDSEhERVTxXrkiXkUqGmLQ0hpgKhj0yRERUsegbB/P448Dhw5avhcyOPTJERFQxjBqlP8SoVAwxJqZSCxy4eBubj1/HgYu3oVJbr5eLPTJERGT/9AWY6dOBDz6wfC0VXMKpdMzecgbpWflyW6DSDbH9whETEWjxehhkiIjIfpV2J2qOgzGLhFPpGLc6GSWP7o2sfIxbnYzFI6MsHmZ4aYmIiOzPzZv6Q8zixQwxZqJSC8zeckYnxACQ22ZvOWPxy0zskSEisgCVWuBQSiYycvJR3csNrcL84OhQSm8CPRx7YaziUEqm1uWkkgSA9Kx8HErJRNu6em7CaSYMMkREZmZrYwrs1vLlwNixuu3XrwNBQRYvp7LJyCk9xJRlO1MxOsicO3cOx48fx507d+Dr64vmzZujYcOG5qiNiMju2eKYArvEXhirq+5l2H2oDN3OVAwKMoWFhVi8eDEWLVqE1NRU1KtXD97e3sjOzsbff/+N4OBgTJgwAePHj4ezvmWgiYgqoUeNKVBAGlPQIzyAl5lK06ABcOGCbjsDjMW1CvNDoNINN7Ly9b6nFQAClNJlU0syaLBvs2bN8Oeff2LJkiXIzs7GiRMnsG/fPpw4cQLZ2dn49ttvcfLkSTRr1szc9RIR2Q1jxhRQCSqV1AtTMsQMGsQQYyWODgrE9gsHIIWW4jSfx/YLt3goN6hHZseOHQgNDdW/AycndOrUCZ06dcLVq1dNWhwRkT2z1TEFNo+XkWxWTEQgFo+M0hnzFWDr68iUFmJKqlWrVrmKISKqSGx1TIHNOnkSiIzUbf/lF6BbN8vXQ3rFRASiR3iAzczCM2qw75UrV3D58mV07NgRDg4OWLNmDS5cuICePXuibdu25qqRiMgu2eqYApvEXhi74uigsOgU64cxeEG8+Ph4NGnSBL1790avXr2wYMECrFmzBvv370fnzp0RHx9vxjKJiOyPrY4psClTp+oPMXl5DDFkEIUQhr1ToqKi8MUXX0ChUKBdu3bYunUr+vTpAwBYvnw5vv76axw4cMCsxRorOzsbSqUSWVlZ8Pb2tnY5RFRJcR2ZUrAXhkphzPnb4CDj6+uLO3fuAABcXV2Rl5cHJyfpylRhYSECAgJw+/btcpZuWgwyRGQruLJvMQww9AjGnL8NvrTk6Ogo/79Ro0ZyiNEoKioyskwiospDM6ZgQPOaaFvXv3KGmOxs/SFm1iyGGCozgwf7NmzYEH///Tfq1auHP//8U+uxo0ePonbt2qaujYiIKgoL9cKw56vyMTjIrF+/Hr6+vnofu3//PubNm2eyooiIqILYtg144gnd9r/+Akx8exuORaqcDB4jY484RoaIyIosOBamtHtaaSrgPa3sizHn7zLd/To+Ph7JycnIzc3Val+wYEFZdkdERBVJz57Arl267SoV4GDw0EyD8Z5WlZvRQeb555/H1q1bER0dDQ8PD3PURERE9kgI/UElKgo4etRsL2vMPa1sZRE3Mh2jg8yGDRtw+vRpBAUFmaMeIiKyR1acUs17WlVuRvfxBQcHsyeGiIgkly/rDzHr1llsSjXvaVW5Gd0j8/XXX+OFF17Aiy++iBo1amg9FqnvZl9ERFQx2cjCdrynVeVmdJBJTU3Frl27sGHDBq12hUIBlUplssKIiMhGLVwIvP66bvs//wD+lh+Dormn1bjVyVAAWmGG97Sq+Iy+tDRp0iR8/PHHyMvLg1qtlj8YYojsk0otcODibWw+fh0HLt6GSl1hV2QgU1Ao9IcYIawSYjRiIgKxeGQUApTal48ClG6cel3BGb2OTLVq1XDz5k04mGEKnalxHRmih+MCYmSwKlWkO1KXZGNLkXFl34rBLPda0pgwYQIWLVpU5uKIyDZoFhArOW31RlY+xq1ORsKpdCtVRjaloEDqhSkZYl580eZCDMB7WlVGRvfItGjRAqdPn0a1atV0BvsmJyebtLjyYo8MkX4qtUCH+btLXXtDMzhy39SuPBFUZjYymJcqH7Ou7Dtp0qSy1kVENoILiNFD7d0LREfrtv/xB9CqleXrIXoIo4PMs88+a446iMiCuIAYlYq9MGRnynSvpePHj+u919Krr75qkqKIyLy4gBjpGDIE+PFH3faCAsDFxfL1EBnI6CAzY8YMfPzxx2jWrJnWCr8KhYJBhshOcAEx0sJeGLJjRgeZxYsXIzk5GeHh4eaoh4gsgAuIEQAGGKoQjJ5+7efnhzp16pijFiKyIC4gVollZOgPMfPnM8SQ3TF6+nV8fDw2b96M6dOn60y/trUpzpx+TfRoXECskmEvDNkBs06/9vHxwa5du7By5Uq5TQjBey0R2SnNAmJUwS1dCvznP7rtKSlA7doWL4fIVIwOMv/5z38wduxYDB8+XGuwLxER2Sj2wlAFZnSQuX37Nt577z0oSvvBICIi2xAcDFy/rtuuVpcebojsjNGDfYcNG4b4+HgzlEJERCahUklBpWSIaddO6oVhiKEKxOgemevXr2PYsGGIiopCQECA1mMbN240WWFERFQGvIxElYzRPTKPPfYYpk2bhl69eqFZs2ZaH2UVFxcHhUIh38epsLAQU6dORdOmTeHp6YmgoCCMHj0aaWlpZX4NItJPpRY4cPE2Nh+/jgMXb0Ol5gnPLp08qT/EbN7MEEMVmtE9MrGxsSYt4PDhw/jmm28QGRkpt929exfJycl499130axZM9y5cweTJk1C//79ceTIEZO+PlFllnAqHbO3nNG6gWSg0g2x/cK5jow9MVMvDKfmkz0wKMicOXPGoJV8z549i8aNGxv84rm5uRgxYgSWLFmCOXPmyO1KpRK7du3S2vbzzz9Hq1atcPXqVdSqVcvg1yAi/RJOpWPc6mSdWxTcyMrHuNXJXBTPHrzxBrBggW57djbg5VWuXTPkkr0w6NLSsGHDMGTIEGzfvh15eXlaj929exc7duzAkCFDMGzYMKNefMKECejbty+6d+/+yG2zsrKgUCjg4+NT6jYFBQXIzs7W+iAiXSq1wOwtZ/TeZ0nTNnvLGV5msmUKhf4QI4RJQsy41claIQZ4EHITTqWXa/9EpmRQkDl27Bj69u2L2NhY+Pj4oHbt2oiMjETt2rWhVCoxc+ZM9OnTB8nJyQa/8Lp165CcnIy4uLhHbpufn4+3334bw4cPf+gKf3FxcVAqlfJHSEiIwfUQVSaHUjJ1TlLFCQDpWfk4lJJpuaLIMAqF/ktJQphkLAxDLtkbgy4tOTg4YMyYMRgzZgxu3LiBY8eO4c6dO/D19UXz5s0RGGhcN+O1a9fw2muvYefOnXBzc3votoWFhRg6dCjUajW+/PLLh247bdo0TJ48Wf48OzubYYZIj4yc0kNMWbYjC8jJAfT9Iffqq8Cnn5rsZYwJuVwRmmyB0YN9AwIC0Lt373K96NGjR5GRkYHHHntMblOpVNi7dy8WLVqEgoICODo6orCwEE8//TRSUlKwe/fuR95vwdXVFa6uruWqjagyqO718D8gjN2OzMyCU6oZcsneGD392hS6deuGkydP4vjx4/LH448/jhEjRuD48eNaIebChQv45Zdf4O/P5E9kKq3C/BCodENp808UkAZ2tgrzs2RZVNLWrfpDzPHjZptSzZBL9sboHhlT8PLyQkREhFabp6cn/P39ERERgaKiIgwePBjJycnYunUrVCoVbty4AQDw8/ODi4uLNcomqjAcHRSI7ReOcauToQC0xkNoTpux/cI51daarLSwnSbk3sjK1ztORgEggCGXbIhVemQeJTU1FT/99BNSU1PlMTiaj/3791u7PKIKISYiEItHRiFAqf2XdYDSjVOvraljR/0hpqjIIgvbaUIuAJ0eO4ZcskUKISruko/Z2dlQKpXIysp65PgaosqKi57ZCCEABz1/WwYEAOmWn+7MdWTImow5f5cpyCxbtgzff/89MjIycOLECSQlJeHmzZt4+umny1y0OTDIEJFdsNH7IzHkkrUYc/42+tLSBx98gE8++QRDhw7F1atXAQCBgYH46KOPylYtEVEloPeeVleu6A8xS5ZYPcQA0mWmtnX9MaB5TbSt688QQzbJ6B6ZsLAw/PbbbwgODoavry/u3LkDtVqNqlWrIjPTthbPYo8MEdkCfZdpLs9/Qv/GNhBgiKzNrD0yeXl58gJ4iv//S6KwsJDrtxAR6VFyuf8X/tioP8TcuMEQQ1QGRgeZNm3a4IsvvtBqW7p0Kdq3b2+yooiIKoKSy/1fnv8E3klcqrudSg3UqGHZ4ogqCKPXkVm4cCG6deuGFStWIDc3F+3atcPNmzfxyy+/mKM+IiK7pVnuv7TLSLWnbgUArOVy/0RlZnSQqVOnDs6ePYutW7fi8uXLCAkJwRNPPAFPT09z1EdEZLduZWbrDTE7GrTDuEHT5c+53D9R2ZVpZV83NzcMHjzY1LUQEVUcCgX662nW9MIUx+X+TYdTxisfo4PMvXv38PXXXyM5ORm5ublaj23cuNFkhRER2aX9+wE9YwafGRaHP2o11Wrjcv+mxUX8Kiejg8yIESNw/vx59O3bFx4eHuaoiYjIPpWysJ2+XhhAuscVl/s3Dc3ssJLzvm5k5WPc6mTedqMCMzrI7N69G1evXuW6LEREGqNGAatX67bfu4eEv+8Aq5MtX1MlUnJ2WHECUs/X7C1n0CM8gKGxAjJ6+nWjRo2QnZ1tjlqIiOyPQqE/xAgBlYsrZm85U/pTIZ1gVWquH1MemtlhpREA0rPycSjFthZtJdMwukdm+fLleOmllzBgwAAEBARoPda/v76hbUREFZAB90cy5gTL6ddlZ+isL84Oq5iMDjI///wzdu7ciSNHjmiNkVEoFAwyRFTx/fMPUK2abvucOcA772g18QRrGYbO+uLssIrJ6CDz3nvvYceOHejevbs56iEisl1G3qWaJ1jLaBXmh0ClG25k5esdJ8PZYRWb0WNk3N3d0blzZzOUQkRko1at0h9i/v77ofdH0pxgSxteqoA0PZgn2PJxdFAgtl84AOgca83nnB1WcRkdZGbMmIF33nkHBQUF5qiHiMi2KBTA6NG67UIAdes+9Kk8wVpOTEQgFo+MQoBSu3crQOnGqdcVnEII42636uvri5ycHCgUCnh5eWk9lplpWyPCjbkNOBGRlvr1pR6XktTq0i8xlYILtVkOV/atGIw5fxs9RiY+Pr6sdRER2T61GnB01G1v0QJILtt6MDERgegRHsATrAU4Oig4A6ySMTrIREdHm6MOIiLrM3IwrzF4giUyjzLNWirNzJkzy1UMEZFVnDkDNGmi2/7jj8BTT1m+HiIymNFB5tixY1qfp6en49ixY4iJiTFZUUREFmPGXhgiMj+jg8ymTZt02tatW4f9+/ebpCAiIot4+21g/nzd9n//BZRKi5dDRGVj9KwlfdRqNapWrcpZS0RkH9gLQ2TTzDprSZ///e9/qFKliil2RURkPgwwRBWO0UHG19cXimK/DO7duwdnZ2csWbLEpIUREZlMXh6g74+tl18GFi+2fD1EZDLlXkemSpUqaNCggc7ieERENoG9MEQVGteRIaKKKSEB6N1bt/3oUSAqyvL1EJFZGBRkJk+ebNDOFixYUK5iiIhMgr0wRJWGQUHmzp075q6DiKj8evQAfvlFt72wEHAyydwGIrIxBv1kL1u2zNx1EBGVj75eGKVSWheGiCqsMv2JcuXKFaxduxapqakIDg7GsGHDEBoaauraiIgejZeRiCo1B2Of8OuvvyI8PBx79uyBSqVCYmIimjRpgl/0decSEZlLaqr+EPPllwwxRJWI0T0yU6dOxcqVK/FUsRupbdy4EW+99RaSy3iLeyIio7AXhoj+n9E9MhcvXsSgQYO02gYOHIhLly6ZrCgiIr1WrdIfYq5fZ4ghqqSM7pEJCwvDli1bMGDAALlt27ZtCAsLM2lhRERa2AtDRHoYHWTmzZuHgQMHokuXLqhduzYuX76MxMREbNy40Rz1EVFl16wZcOKEbjsDDBHBiEtLK1aswL1799CzZ0+cOnUK7du3hxAC7du3x4kTJ9CrVy9z1klElU1RkdQLUzLEvPgiQwwRyRRCGPYbITQ0FP/++y+GDh2K559/Hi1btjR3beVmzG3AiciG8DISUaVmzPnb4B6Zy5cvY/369cjOzkanTp3QtGlTfPrpp7h9+3a5CyYiAiD1vugLMb//zhBDRHoZ3CNT3L///ovVq1dj+fLlOHXqFPr374/nn38ePXv2NEeNZcYeGSI7wl4YIvp/ZumRKc7HxwcTJ07EkSNHsGfPHhw5cgS99d1llojoUaZP1x9i7t1jiCGiRyrzXdQOHDiAZcuWYf369fDz88OsWbNMWBYRVQr6Akz16sDNm5avhYjsklFB5ubNm1i5ciWWLVuGlJQUDBo0CBs2bEC3bt3MVR8RVUS8jEREJmLwpaV+/fohJCQEa9aswfjx45GWloY1a9YwxBCR4bKy9IeYzz5jiCGiMjG4RyYkJAQHDx5EVFSUOeshooqKvTBEZAYGB5kvv/zSnHUQUUW1bRvwxBO67SkpQO3aFi/HWlRqgUMpmcjIyUd1Lze0CvODo0Mp4Y6IDFbmwb5ERI/EXhgAQMKpdMzecgbpWflyW6DSDbH9whETEWjFyojsX5mmXxMRPdQTT+gPMWp1pQwx41Yna4UYALiRlY9xq5ORcCrdSpURVQxGBRm1Wo3Tp0+jqKjIpEXExcVBoVBg0qRJcpsQArNmzUJQUBDc3d3RuXNnnD592qSvS0QmJoQUYLZt025/4okHj1UiKrXA7C1noC+6adpmbzkDlbpyhTsiUzIqyDg4OKBVq1ZwdHQ0WQGHDx/GN998g8jISK32Dz/8EAsWLMCiRYtw+PBhBAQEoEePHsjJyTHZaxORCSkUgIOeXylCAFu2WL4eG3AoJVOnJ6Y4ASA9Kx+HUjItVxRRBWP0paXIyEhcvHjRJC+em5uLESNGYMmSJfD19ZXbhRBYuHAh3nnnHTz55JOIiIjAihUrcPfuXaxZs8Ykr01EJpKSor+nZdu2SncZqaSMnNJDTFm2IyJdRg/2HTBgAPr164fx48cjJCQEDsX+Auvfv79R+5owYQL69u2L7t27Y86cOXJ7SkoKbty4oXXvJldXV0RHR2P//v146aWX9O6voKAABQUF8ufZ2dlG1UO2hbM87AAH8z5UdS83k25HRLqMDjJff/01AGDBggVa7QqFwqggs27dOiQnJ+Pw4cM6j924cQMAUKNGDa32GjVq4MqVK6XuMy4uDrNnzza4BrJdnOVh4z77DHjtNd32f/8FlEqLl2OrWoX5IVDphhtZ+XrHySgABCilkE5EZWN0kElJSSn3i167dg2vvfYadu7cCTe30v8SUZT4a08IodNW3LRp0zB58mT58+zsbISEhJS7XrIszSyPkr/4NbM8Fo+MYpixJvbCGMzRQYHYfuEYtzoZCkDrPa05irH9wtnTSFQOZZp+rVarcfDgQfz444/4448/oFarjXr+0aNHkZGRgcceewxOTk5wcnJCUlISPvvsMzg5Ock9MZqeGY2MjAydXpriXF1d4e3trfVB9oWzPGxY9er6Q4wQDDEPERMRiMUjoxCg1P6jLUDpxlBOZAJl6pF54oknkJqaiqCgIKSlpSE4OBhbtmxBnTp1DNpHt27dcPLkSa22sWPHolGjRpg6dSrq1KmDgIAA7Nq1Cy1atAAA3L9/H0lJSZg/f76xJZMdMWaWR9u6/pYrrDLLzwfc3XXb33kHKDa2jUoXExGIHuEBHPNFZAZGB5kJEyagd+/emDt3LlxcXHD//n3MmDEDEyZMwI4dOwzah5eXFyIiIrTaPD094e/vL7dPmjQJc+fORf369VG/fn3MnTsXHh4eGD58uLElkx3hLA8bw8tIJuPooGD4JjIDo4PMoUOHEB8fDxcXFwCAi4sL3n//fQQFBZm0sLfeegv37t3D+PHjcefOHbRu3Ro7d+6El5eXSV+HbAtnediIAweAdu1020+cAJo2tXw9RESlMDrI+Pj44O+//0Z4eLjcdunSJfj4+JSrkMTERK3PFQoFZs2ahVmzZpVrv2RfOMvDBrAXhojsiNGDfceNG4devXrho48+wg8//ICPPvoIMTExGDdunDnqo0pGM8sDeDCrQ4OzPMzsxRf1h5jCQoYYIrJZCiGM/w21cuVKrF69GqmpqQgODsbIkSMxevRoc9RXLtnZ2VAqlcjKyuIMJjvDdWQsTF+AiYwE/vzT8rUQUaVnzPm7TEHGXjDI2Deu7GsBvIxERDbImPO30WNkAODWrVs4efIkcnNztdqNvUUB0cNwlocZ3bolrQtT0sqVwKhRlq+HiKiMjA4yixcvxuuvvw6lUgkPDw+53dhbFBCRlbAXhogqEKODzKxZs7B161Z0797dHPUQkbmsWQOMGKHbnp4OBARYvh4iIhMwOsi4uLigc+fOZiiFiMyGvTBEVEEZPf16xowZmDZtGgoKCsxRDxGZUsuWvD8SEVVoRgeZDh06YOPGjahSpQr8/Py0PohMSaUWOHDxNjYfv44DF2/zRpHGUKmkAHPkiHb72LEMMERUoRh9aWno0KHo2LEjnnnmGa3BvkSmxHVkyoGXkYioEjF6HRkvLy9kZWXBwcHozhyL4zoy9inhVDrGrU7WuUWB5vS8eGQUw4w+Z84ATZrotu/dC3TsaPl6iIjKyJjzt9FpJCYmBgcPHixzcUQPo1ILzN5yRu99ljRts7ec4WWmkhQK/SFGCIYYIqrQynTTyD59+qBPnz4IKDFlc8GCBSYrjCqnQymZWpeTShIA0rPycSglk4vlAcCsWcDs2brtd+8C7u4WL4eIyNKMDjJFRUUYNGgQAODOnTsmL4gqt4yc0kNMWbar0PSNhfH2BrKyLF8LEZGVGB1kli1bZo46iAAA1b3cTLpdhcTBvLwPFxHJjA4yV69eLfWxWrVqlasYolZhfghUuuFGVr7ecTIKAAFK6cRV6eTkSD0uJX38MfDGG5avx0o4o42IijN61pKDgwMUCgU0T1MU++tQpVKZtrpy4qwl+6SZtQRAK8xU6llL7IUBwBltRJWFWWct3blzB5mZmbhz5w7u3LmDU6dO4emnn8bKlSvLXDBRcTERgVg8MgoBSu3LRwFKt8p3otq5U3+IuXDBZCHGXhYe5Iw2ItLH6B4ZfXJychAVFYULFy6YoiaTYY+Mfav04yAs0AtjT5dpDly8jWFLHr30w9oX2nBGG5GdM2uPjD55eXm4deuWKXZFJHN0UKBtXX8MaF4Tbev6V54Q89RT+kOMSmXyEDNudbLOdPcbWfkYtzoZCafSTfZapsAZbUSkj8GDfX///Xe0b98ekydP1mq/e/cufv75ZwwcONDUtRFVLkIA+lbM7t4d2LXLpC/1qMs0CkiXaXqEB9hMgOSMNiLSx+Ag07t3b2RnZ+usHVOlShXMnDkTI0eONHlxRJWGhQfz2uPCg5zRRkT6GBxkNENpuI4MkQldvQqEhuq2x8cDAwaY7WXt8TKNo4MCsf3CMW51MhTQP6Mttl+4zfQgEZFlGDxGRlHaX4xEVDYKhf4QI4RZQwxgv5dpOKONiEoyuEcmNzcXfn4P77LNzMwsd0FEFd7ixcD48brtmZmAr69FSrDnyzQxEYHoER5QuWe0EZHM4CDj5uaGTZs2mbMWoorPRha2s/fLNJoZbUREBq8j4+3tjezsbHPXY1JcR4ZsRkgIkJqq227llXntaR0ZIqo8jDl/Gz3Yl4iMUFAAuOkZZ/Lmm8BHH1m+nhJ4mYaI7J3BQWb69OnmrIOo4rGRy0iPwss0RGTPDJ61NG3aNHPWQVRxHDqkP8QcO2ZzIYaIyN4Z3CNDRAawk14YIqKKwiT3WiKq9F55RX+IuX+fIYaIyIzYI0NUXvoCTOPGwJkzlq+FiKiSKVOPzLJly9C9e3dERkYCAJKSkrB+/XqTFkakUgscuHgbm49fx4GLt6FS21jPhkKhP8QIwRBDRGQhRgeZDz74AJ988gmGDh2Kq1evAgACAwPxkQ1MJaWKI+FUOjrM341hSw7itXXHMWzJQXSYvxsJp9KtXRpw+7b+ALN0KS8jERFZmMEL4mmEhYXht99+Q3BwMHx9fXHnzh2o1WpUrVrV5m5RwAXx7FPCqXSMW52ss3S+JjpY9Z46HMxLRGR2xpy/je6RycvLQ2CgdBLR3EiysLAQrq6uZSiVSJtKLTB7yxm99//RtM3ecsbyl5l+/FF/iLl+nSGGiMiKjA4ybdq0wRdffKHVtnTpUrRv395kRVHldSglU2u5/JIEgPSsfBxKsWDvn0IBDBmipxgBBAVZrg7ST60GFi4EmjYF3N2BatWk71dZxykdPCjdfbxqVWlV5gYNgBkzgLt39W8/a9aD8VL6Pt5+u6xfmW24cgX4/HOgTx+gTh3A1VU6NjExwE8/lf68pCRg9mygb1/pe6JQAI0alb2Ohx1jhQLI1/N7o3Pnhz8nIaHs9ZDNMHrW0sKFC9GtWzesWLECubm5aNeuHW7evIlffvnFHPVRJZORU3qIKct25dKhA/D777rtanXpl5jIsoQAnnlG6jHz8ZFOmv/8A2zYAGzbBuzZA7Rubfj+vv8eePZZQKUCHnsMqFULOHIE+OADYOtW4LffAC8v/c9t3x6oV0+3/bHHyvSl2YwRI6SfA3d36Vi2bg1cvAj8/LP08frrwIIFus977TXgzz9NW4unJzB4sP7HHB1Lf95TTwFVqui216xpmrrIqowOMnXq1MHZs2exdetWXL58GSEhIXjiiSfg6elpjvqokqnupee+ROXYrkxUKsBJz4/GiBHA6tXme10y3rJlUoipX18KGTVqSO0bNkgnvBEjgL/+0v/9LCk1FXj+een7v3QpMHas1F5QAIwaBfzwA/DWW8Dixfqf//zzwJgxJvmybEqtWtLXP3KkFCQ0tm0DBg4EPvlE6p3p2VP7eT17Ak8/DbRsKfXgREWVv5aqVYHly41/3scfA7Vrl//1ySaVaR0ZNzc3DC4tFROVQ6swPwQq3XAjK1/vOBkFgACldGNDs+BgXvvy3/9K/3744YMQA0h/gffvL1362LxZ+vxRli+XLk/06PEgxADSpZQvvpBO3N99B8yZA/jbwb2pxowBVqwAUlLKdxJfs0Z/e9++wHPPAd98A6xdqxtkPvzwwf8vXy776xM9gtFjZMaOHYvnnntO52PcuHGYO3cu/vrrL3PUSZWEo4MCsf3CATyYpaSh+Ty2X7jp78587pz+ELNnT+UJMYcPS8fgYePdZs+Wtpkzx3J1lSYlRRoH4+4unVRL0vyxtWWLYfs7elT6t3Nn3ceqVQPCw4HCQmD79jKVKzt+XApH/v7SYPGSxo6VjvGECeV7HUto1kz6Ny3NunVQpWZ0kHF0dMTatWuRkZEBNzc3ZGRkYN26dbh37x6SkpLQrFkz/Pjjj+aolSqJmIhALB4ZhQCl9uWjAKWbeaZelzYIUQj9J7WKqmVLaTzH/v3A6dO6j6vV0qUcR0ftHgtr0Yy/iIgAnJ11H9dcyjB0nEZenvSvr6/+x/38Hr6/3buBSZOAl1+Wgp4mGJXUvLk05iYzUxqPUzwo//ij1DPUqJF0OcTWXbok/RsQYJnXy8uTjt1LL0ljc1auBHJzH/28774Dxo8HJk4EPvsM+P810KiCEEZ66qmnxPbt27XaduzYIZ566ikhhBBr164VERERxu7WLLKysgQAkZWVZe1SqAyKVGqx/+9/RPyxVLH/739EkUpt2hf44AMhpNOI9kdurmlfx5588410DCZN0n1sxw7psX79DN9fdLT+Y/ywjz17DNv3p59K2w8apP/xf/+VHvfzM2x/w4dL20+dqv/xhg2lx///d50sNrb0r+Wpp4TIydHdl1otRNeu0jb//a/Ulpoq1ersLERysmE1P8yzz0r7T0kp/770uXNHiGrVpNfYsOHh26akSNs1bFj21yvtGPv7C7F1q/7nlPb+c3YW4r33yl4LmZ0x52+jx8js2rVL53YEPXr0wNNPPw0AGDJkCF544QVTZCyq5BwdFGhb10xjEfRdRnJzA+7dM8/r2Yvhw4E33wRWrQLmzZMugWh8+630rzE/3zExxo/PMPSve81f4h4e+h/XDEw15C92AIiOlsaDrF0LvPce4OLy4LGDB6XLjwCQk6P9vHr1pN6T3r2B0FDgzh1g715pYPCGDdLg4U2btJ+jUEjjVyIjgenTgW7dgDfekHpp5s0DWrQwrGZrGjcOuHULaNMGGDTI/K83erT0/mzaFFAqgQsXpNlSq1YBTz4pDfZu1Ur7OZ06SYOw27UDAgOBa9ekXq85c4CZMwFvb2l2Fdk3Y1NS06ZNxfLly7XaVqxYIffC/PPPP6JatWrG7tYs2CNDOpyc9P+FRg+MGycdk7VrH7TdvCn9FRsUJERRkfVqK27OHKnOkSP1P15YKD3u4mLY/nJzhahVS3pOTIwQp04JkZ0t9UQFBz9478TEGLa/tDSptwAQ4vff9W+zfr30uLe39G90tBAqlWH71zh7Vup9KflRt+6DXqGSj8XFGfcaJcXFPejtunjx0dubokemNNOmSfvu0cPw5/z8s/QcpVKIu3dNXxOVmzHnb6N/gyclJQkfHx/RtGlT0adPH9G0aVPh4+MjEhMThRBC7Ny5U8SV94fERBhkSJabqz/A2Mh71ab8+ad0bLp2fdA2f77UNmOG9eoqydSXloQQ4sSJB2Gm+EetWkK89Zb0/2HDDN/fm29Kz5k5s/RtBg2StvH0FOLKFcP3rbFnj/GX76KjjX8djeXLhVAopHoPHDDsOeYMMv/+K4SjoxS0CwoMf97jj0s17d5t+pqo3Mx6aalTp05ISUnB1q1bkZaWhqCgIPTt2xe+/z9ArkePHujRo4dJe42IyoVTqo0TGSldLtizR1r4rG5dabCkQgH85z/G7WvePGkdF2O8/bZhK8DWqiX9m5qq/3FNu2Y7QzRtKtX7ww/SQnhFRdLMnOHDH8zUatLE8P3Vry/9m17KzU7T06VLIoC0cvBffxlXLyANSNf3XjbV9OviNm+W3gPOzsDGjdL7xNqUSqB6delY/vOP4att168vfY9L+96Q3SjTOjI+Pj4YOXKkqWshMq3du6WxByWdOyctO0+le/llaVzId98BvXoB589L64QYe0JMSJCWqjfGmDGGBRnN1N9Tp6Rp0SVnLiUnS/9GRhr3+u7u0niM0aO12zWrlxszk+3OHelffavKCiF9rf/8IwWl//1P+vzECWnhN1uTmCitogxIKyCXXDfGWtRqIDtb+r++41yah31vyK4YPf0aAA4cOIC4uDi88cYbmDx5svxhjMWLFyMyMhLe3t7w9vZG27ZtsWPHDvnx3NxcTJw4EcHBwXB3d0fjxo2xuLQVNYlKUij0hxghGGIM8fTT0jTk5csfrGRblkH8iYnGXvQwPCiEhQGNG0sDtLdt031cswzEE08YX3dJSUlSMGrS5OHr7BQnxINBvvpuU/DZZ8DOnUDHjtKA1alTpd4BW5wscfSotMDg/fvSoG9bWhA1IUGall2vnjR41xC3bj3oCTPFisNkXcZet1q0aJFwd3cXAwcOFK6urmLgwIHCw8NDDDPmurEQ4qeffhLbtm0T586dE+fOnRPTp08Xzs7O4tSpU0IIIZ5//nlRt25dsWfPHpGSkiK+/vpr4ejoKOLj4w1+DY6RqYSGDdN/ejR2ACUJ8dprD45ftWpC3L9v7Yp0LVki1Ve/vjQgWWPDBqk9LEy37o0bpbEao0bp7u/YMWmQcHFHj0qDnBUK3fEUt24JsWKFEPn52u05OUK89JJUQ0CAEHl52o+fPCmEm5s0yPfyZant/n0hHntMes6SJQYfglKZavr1X389mGb92Wdl24ehY2Teflva5vPPtdt/+EGIc+d0t09MlL43gBALFmg/duCA9P1Sl1i2ISVFiPbtpef072/sV0IWYtbBvvXq1ZMH9vr4+AghhNi2bZsYM2aMsbvS4evrK7799lshhBBNmjQR75WY5x8VFSVmGDHYkEGmElGrTT+osbI7c+bBcZwyxdrV6KdSPRgs6+srxODBQnTuLIUONzf9s4WWLSv9vREdLZ20e/SQQnHbtkI4OEgzlr75Rnd7zQna21uI1q2FGDJEeq5mtpKPjxD79mk/Jz9fiMhI6fGVK7UfO3tWCHd3aSDthQtlOyYapgoyzZs/CLP6ZkeVNgtqyRLpmLRu/WAfbm4P2lq3lkKivppjY/W3168vRM+e0nHW7BMQYuhQ3T9WNN/nwEDp+/rMM1KAcXOT2ps00Q6/ZFPMGmS8vLzk//v6+gq1Wi3UarXwM2ZmQAlFRUVi7dq1wsXFRZw+fVoIIcRLL70kHn/8cZGamirUarXYvXu3qFKlivjtt99K3U9+fr7IysqSP65du8YgUxmUdpGCyk/z166+v4ZtRVGRtKhckybSScrfX4gnn5SmT+vzsCCzZMmDMKOZbj58uNRTo092trSAXnS0EDVrCuHqKoSHh1TLG29Ii9yV9Prr0usPGaJ/n198IT3eurVu75AxTBVkQkPLNgvqYQsFaj5KLn5YWpDZvl2IESOEaNRICodOTkJUry5E795Sb40+Z85ISwlERUnfTycnabp1mzbS+4XTrm2aWYNM/fr1xfXr14UQUg/J7t27xcmTJ0XVqlWNLvTEiRPC09NTODo6CqVSKbZt2yY/VlBQIEaPHi0ACCcnJ+Hi4iJWlvzrpYTY2FgBQOeDQaaCSk3V/8vxxx+tXVnF8Pvv7NUiIqswJsgYPdh33Lhx+OOPPwAAkydPRs+ePdGiRQtMKMMNzho2bIjjx4/j4MGDGDduHJ599lmcOXMGAPDZZ5/h4MGD+Omnn3D06FH897//xfjx4/GLZuaAHtOmTUNWVpb8ce3aNaNrIjuhUADBwbrtQhh2p2N6tLlzpX8nTrRuHURED6EQwrjFNPLy8uDh4QHF/6/Nce3aNeTm5qJx48blLqZ79+6oW7cuFi5cCKVSiU2bNqFvsbvaPv/880hNTUVCQoJB+8vOzoZSqURWVha8DR3NTrbtu++kJcdL+ucf6W7CVD7790vH+NQp4NAhabbNoUOAQ5kmOBIRlYkx52+j1pFRq9WoWrUqsrOz4fz/azaEhISUvdIShBAoKChAYWEhCgsL4VDil6ejoyPUarXJXo/sDBe2M7/z54GlSwEvL6BfP2DRIoYYIrJpRgUZBwcH1KtXD5mZmahRo0a5Xnj69Ono3bs3QkJCkJOTg3Xr1iExMREJCQnw9vZGdHQ0pkyZAnd3d4SGhiIpKQkrV67EggULyvW6ZIcaNpROsCUxwJjemDHSBxGRnTB6Zd+XX34ZTz31FKZOnYqQkBCtXpNII1bQvHnzJkaNGoX09HQolUpERkYiISFBvr3BunXrMG3aNIwYMQKZmZkIDQ3FBx98gJdfftnYksleCaG/N+C114CFCy1eDhER2R6jx8iUvNwj70ihgEqlMklRpsIxMnZs505pafyS2AtDRFThmW2MDACOUSHzuncPqFnzwX1QNPbvB9q2tU5NRERks8o8iu/69es4ePCgKWuhym7RIsDDQzvEJCRIvTAMMUREpIfRQSY9PR3R0dEIDQ1F9+7dAQDr16/HC7Z4ozOyD2lp0oykV1550Na9O6BS6b+8RERE9P/KtCBe69atkZubK0/B7tatG3799VeTF0eVwIsvSpeSijt7Fti1i9N+iYjokYweI/P777/jxx9/hJOTk7wonr+/P27fvm3y4qgCS06WFlsrbupUYN4869RDRER2yeggo1QqkZmZierVq8ttqamp5V5XhioJlQpo2RI4dky7nSvzEhFRGRjddz98+HCMGDECJ0+ehBAC58+fx8svv4wxXESLHmXDBsDJSTvErFwpDeZliCEiojIwOsjMnDkTLVq0QLt27ZCVlYXHHnsMjRs3xltvvWWO+qgiyMqSBvMOHvygrV49oKAAGDXKenUREZHdM3pBvOL++ecf+Pv7y2NlbA0XxLMB778PzJyp3fb770C7dtaph4iIbJ4x52+je2TGjx8vrx9TtWpVmw0xZGWXLkm9MMVDzNChgFrNEENERCZjdJARQuCJJ55AgwYN8P777yMlJcUcdZG9EgJ48kmgbl3t9suXgbVrS7+DNRERURkYHWQWL16M9PR0zJ8/H8eOHUN4eDg6deqE7777zhz1kT3Zu1da+2XTpgdt8+ZJ4SY01Hp1ERFRhVWuMTKAdBfrMWPGYOfOnbxpZGVVUADUrw9cu/agzdkZuH0b8PKyXl1ERGSXzDpGRuPQoUOYOHEiIiIi8PfffyM2NrasuyJ7tnQp4OamHWI2bwbu32eIISIiszN6Qbw5c+Zg1apVuH37NoYMGYLNmzejHQdvVj4ZGUDJRRDbtgV++w1wdLROTUREVOkY3SNz+PBhzJ07F2lpaVi8eLEcYhITE01dG9mqSZN0Q8zx48D+/QwxRERkUUb3yGzevFn+f3p6OpYvX47vvvsO6enpyMvLM2lxZGNOngQiI7XbJk4EPv/cOvUQEVGlZ3SPjEqlQnx8PPr164datWphxowZGDFiBK5evWqO+sgWqNVAx466IebGDYYYIiKyKoODzLlz5/DWW28hKCgIo0aNgq+vL3bs2IFq1aphwoQJ8Oe9ciqmbduky0X79j1o+/praUo1bxRKRERWZvClpcaNG8Pf3x/z58/HM888A09PTwDgyr4VVW4uULWqNLVaIzBQWrHXzc16dRERERVjcI/MmDFjkJ+fj3feeQezZs3CqVOnzFkXWdN//ytNnS4eYnbvBtLSGGKIiMimGBxkli5dihs3buD999/H77//jmbNmiEqKgo5OTnIysoyZ41kKVevSrcQePPNB239+kljZLp0sV5dREREpTBqsK+npyeef/557N+/HydPnkSXLl3g6emJZs2a4fnnnzdXjWQJo0bp3kbgwgXgp594fyQiIrJZZV7ZNzw8HP/9739x/fp1rFq1Cunp6aasiyzl4EEpqKxe/aBt5kxpMG+9etari4iIyADlvteSLeO9lh6isFCaTv3XX9rtd+4APj5WKYmIiAiw0L2WyI6tWQO4uGiHmHXrpF4YhhgiIrIjRq/sS3bszh3Az0+7rWlTIDkZcOJbgYiI7A97ZCqLd97RDTGHDgEnTjDEEBGR3eIZrKI7dw5o1Ei7bexYYOlS69RDRERkQgwyFZUQQO/ewM8/a7enpgI1a1qnJiIiIhPjpaWK6JdfAAcH7RCzcKEUbhhiiIioAmGPTEVy7x4QEgLcvv2gzctLuku1h4f16iIiIjIT9shUFF9+KYWV4iFm+3YgO5shhoiIKiz2yNi79HQgKEi7rWtXYNcu6fKSHbtfpMaqA5dxJfMuQv08MKptbbg42ffXREREpsUgY89efhn4+mvtttOngfBw69RjQnHbz2DJbylQF1t3+oPtZ/FCxzBM62P/Xx8REZkG/7y1R8eOSfdHKh5i3nxTGsxbQULM13u1QwwAqAXw9d4UxG0/Y53CiIjI5jDI2BOVCnj8cSAqSrv91i3go4+sU5OJ3S9SY8lvKQ/dZslvKbhfpLZQRUREZMsYZOzFxo3SCrxHjz5oW75c6oWpWtVqZZnaqgOXdXpiSlILaTsiIiKOkbF12dmAUqndFhYm3fDRxcU6NZnRlcy7Jt2OiIgqNvbI2LIPPtANMfv2AZcuVcgQAwChfoZNFTd0OyIiqtgYZGxRSoo0mHfGjAdtTz8NqNVA+/bWq8sCRrWtDQfFw7dxUEjbERERMcjYEiGAwYOBOnW02y9dAv73PyncVHAuTg54oWPYQ7d5oWMY15MhIiIADDJlolILHLh4G5uPX8eBi7ehetToVEP89pu0gN2GDQ/a5s6Vwk3Yw0/sFc20PuF4qVOYTs+MgwJ4qRPXkSEiogcUQggTnIVtU3Z2NpRKJbKysuDt7W2SfSacSsfsLWeQnpUvtwUq3RDbLxwxEYHG7/D+faBBA+DKlQdtDg7Av/9K90mqxLiyLxFR5WTM+ZtBxggJp9IxbnUySh4wTcfB4pFRxoWZZcuA557Tbtu0CRg4sBxVEhER2Tdjzt+cfm0glVpg9pYzOiEGAASkMDN7yxn0CA+A46NGq/7zD1CtmnZb69bA778Djo4mqpiIiKjiYz+9gQ6lZGpdTipJAEjPysehlMyH7+iNN3RDzLFjwMGDDDFERERGYo+MgTJySg8xBm13+jQQEaHdNn488MUX5ayMiIio8mKQMVB1L7eybadWA127AklJ2u3p6UBAgImqIyIiqpysdmlp8eLFiIyMhLe3N7y9vdG2bVvs2LFDa5uzZ8+if//+UCqV8PLyQps2bXD16lWr1NsqzA+BSjeUNvpFAWn2UqswvweN27dLl4uKh5jFi6Up1QwxRERE5Wa1IBMcHIx58+bhyJEjOHLkCLp27YoBAwbg9OnTAICLFy+iQ4cOaNSoERITE/Hnn3/i3XffhZubYT0jpubooEBsP2n9kpJhRvN5bL9waaBvXh5QpQrQt++DjapXB+7dA15+2SL1EhERVQY2Nf3az88PH330Ef7zn/9g6NChcHZ2xqpVq8q8P6usI/PJJ8DkydpP+vVX6fISERERPZLdTb9WqVT44YcfkJeXh7Zt20KtVmPbtm1466230KtXLxw7dgxhYWGYNm0aBj5kjZWCggIUFBTIn2dnZ5u81piIQPQID8ChlExk5OSjupd0Ockx7bruLQT69gW2bKkUtxYgIiKyBqtOvz558iSqVKkCV1dXvPzyy9i0aRPCw8ORkZGB3NxczJs3DzExMdi5cycGDRqEJ598EkklB80WExcXB6VSKX+EhISYpW5HBwXa1vXHgOY10bauPxyfGwuUfK3z54GtWxliiIiIzMiql5bu37+Pq1ev4t9//8WGDRvw7bffIikpCT4+PqhZsyaGDRuGNWvWyNv3798fnp6eWLt2rd796euRCQkJMemlJS2HDkkL2RU3Ywbw/vumfy0iIqJKwm4uLbm4uKBevXoAgMcffxyHDx/Gp59+is8//xxOTk4ID9e+OWDjxo2xb9++Uvfn6uoKV1dXs9YMQJp1FBkJnDql3Z6ZCfj6mv/1iYiICICNrewrhEBBQQFcXFzQsmVLnDt3Tuvx8+fPIzQ01ErVFbNxo3aIWbtWCjcMMURERBZltR6Z6dOno3fv3ggJCUFOTg7WrVuHxMREJCQkAACmTJmCZ555Bp06dUKXLl2QkJCALVu2IDEx0VolPxAZCbRtC9y9Cxw5AjjZxJhpIiKiSsdqZ+CbN29i1KhRSE9Ph1KpRGRkJBISEtCjRw8AwKBBg/DVV18hLi4Or776Kho2bIgNGzagQ4cO1ir5gfr1gf37rV0FERFRpWdT68iYmjnWkSEiIiLzMub8bVNjZIiIiIiMwSBDREREdotBhoiIiOwWgwwRERHZLQYZIiIislsMMkRERGS3GGSIiIjIbjHIEBERkd1ikCEiIiK7xSBDREREdotBhoiIiOwWgwwRERHZLQYZIiIislsMMkRERGS3GGSIiIjIbjHIEBERkd1ikCEiIiK7xSBDREREdotBhoiIiOwWgwwRERHZLQYZIiIislsMMkRERGS3nKxdAFFpVGqBQymZyMjJR3UvN7QK84Ojg8LaZRERkQ1hkCGblHAqHbO3nEF6Vr7cFqh0Q2y/cMREBFqxMiIisiW8tEQ2J+FUOsatTtYKMQBwIysf41YnI+FUupUqIyIiW8MgQzZFpRaYveUMhJ7HNG2zt5yBSq1vCyIiqmwYZMimHErJ1OmJKU4ASM/Kx6GUTMsVRURENotBhmxKRk7pIaYs2xERUcXGIEM2pbqXm0m3IyKiio1BhmxKqzA/BCrdUNokawWk2UutwvwsWRYREdkoBhmyKY4OCsT2CwcAnTCj+Ty2XzjXkyEiIgAMMmSDYiICsXhkFGp4u2q11/B2xeKRUVxHhoiIZAwyZMNK65MhIiKSMMiQzdEsiHcjW3tm0s1sLohHRETaGGTIpnBBPCIiMgaDDNkULohHRETGYJAhm8IF8YiIyBgMMmRTuCAeEREZg0GGbAoXxCMiImMwyJBN4YJ4RERkDAYZsjmaBfEClNqXjwKUblwQj4iItDhZuwAifWIiAtEjPACHUjKRkZOP6l7S5ST2xBARUXEMMmSzHB0UaFvX39plEBGRDeOlJSIiIrJbDDJERERktxhkiIiIyG4xyBAREZHdYpAhIiIiu2W1ILN48WJERkbC29sb3t7eaNu2LXbs2KF325deegkKhQILFy60bJFERERk06wWZIKDgzFv3jwcOXIER44cQdeuXTFgwACcPn1aa7v4+Hj88ccfCAoKslKlREREZKusFmT69euHPn36oEGDBmjQoAE++OADVKlSBQcPHpS3uX79OiZOnIjvv/8ezs7O1iqViIiIbJRNLIinUqnwww8/IC8vD23btgUAqNVqjBo1ClOmTEGTJk2sXCERERHZIqsGmZMnT6Jt27bIz89HlSpVsGnTJoSHSzcMnD9/PpycnPDqq68avL+CggIUFBTIn2dlZQEAsrOzTVs4ERERmY3mvC2EeOS2Vg0yDRs2xPHjx/Hvv/9iw4YNePbZZ5GUlIR79+7h008/RXJyMhQKw++tExcXh9mzZ+u0h4SEmLJsIiIisoCcnBwolcqHbqMQhsQdC+nevTvq1q2Lxo0bY/LkyXBweDCER6VSwcHBASEhIbh8+bLe55fskVGr1cjMzIS/v79Rgaiiys7ORkhICK5duwZvb29rl1Nh8ThbBo+zZfA4WwaPszYhBHJychAUFKSVBfSxiTEyGkIIFBQUYNSoUejevbvWY7169cKoUaMwduzYUp/v6uoKV1dXrTYfHx9zlGrXNFPeybx4nC2Dx9kyeJwtg8f5gUf1xGhYLchMnz4dvXv3RkhICHJycrBu3TokJiYiISEB/v7+8PfXvuuxs7MzAgIC0LBhQytVTERERLbGakHm5s2bGDVqFNLT06FUKhEZGYmEhAT06NHDWiURERGRnbFakPnuu++M2r60cTFkOFdXV8TGxupcfiPT4nG2DB5ny+Bxtgwe57KzqcG+RERERMbgTSOJiIjIbjHIEBERkd1ikCEiIiK7xSBDREREdotBpoLZu3cv+vXrh6CgICgUCsTHx+tsc/bsWfTv3x9KpRJeXl5o06YNrl69avli7dyjjnVubi4mTpyI4OBguLu7o3Hjxli8eLF1irVTcXFxaNmyJby8vFC9enUMHDgQ586d09pGCIFZs2YhKCgI7u7u6Ny5M06fPm2liu3To45zYWEhpk6diqZNm8LT0xNBQUEYPXo00tLSrFi1fTLkPV3cSy+9BIVCgYULF1quSDvDIFPB5OXloVmzZli0aJHexy9evIgOHTqgUaNGSExMxJ9//ol3330Xbm5uFq7U/j3qWL/++utISEjA6tWrcfbsWbz++ut45ZVXsHnzZgtXar+SkpIwYcIEHDx4ELt27UJRURF69uyJvLw8eZsPP/wQCxYswKJFi3D48GEEBASgR48eyMnJsWLl9uVRx/nu3btITk7Gu+++i+TkZGzcuBHnz59H//79rVy5/THkPa0RHx+PP/74A0FBQVao1I4IqrAAiE2bNmm1PfPMM2LkyJHWKagC03esmzRpIt577z2ttqioKDFjxgwLVlaxZGRkCAAiKSlJCCGEWq0WAQEBYt68efI2+fn5QqlUiq+++spaZdq9ksdZn0OHDgkA4sqVKxasrOIp7VinpqaKmjVrilOnTonQ0FDxySefWKdAO8AemUpErVZj27ZtaNCgAXr16oXq1aujdevWei8/Ufl16NABP/30E65fvw4hBPbs2YPz58+jV69e1i7NbmVlZQEA/Pz8AAApKSm4ceMGevbsKW/j6uqK6Oho7N+/3yo1VgQlj3Np2ygUCt7Prpz0HWu1Wo1Ro0ZhypQpaNKkibVKsxsMMpVIRkYGcnNzMW/ePMTExGDnzp0YNGgQnnzySSQlJVm7vArns88+Q3h4OIKDg+Hi4oKYmBh8+eWX6NChg7VLs0tCCEyePBkdOnRAREQEAODGjRsAgBo1amhtW6NGDfkxMo6+41xSfn4+3n77bQwfPpw3OCyH0o71/Pnz4eTkhFdffdWK1dkPm7r7NZmXWq0GAAwYMACvv/46AKB58+bYv38/vvrqK0RHR1uzvArns88+w8GDB/HTTz8hNDQUe/fuxfjx4xEYGKhzd3d6tIkTJ+LEiRPYt2+fzmMKhULrcyGEThsZ5mHHGZAG/g4dOhRqtRpffvmlhaurWPQd66NHj+LTTz9FcnIy38MGYo9MJVK1alU4OTkhPDxcq71x48actWRi9+7dw/Tp07FgwQL069cPkZGRmDhxIp555hl8/PHH1i7P7rzyyiv46aefsGfPHgQHB8vtAQEBAKDT+5KRkaHTS0OPVtpx1igsLMTTTz+NlJQU7Nq1i70x5VDasf7tt9+QkZGBWrVqwcnJCU5OTrhy5QreeOMN1K5d23oF2zAGmUrExcUFLVu21Jnqd/78eYSGhlqpqoqpsLAQhYWFcHDQ/hFzdHSUe8bo0YQQmDhxIjZu3Ijdu3cjLCxM6/GwsDAEBARg165dctv9+/eRlJSEdu3aWbpcu/Wo4ww8CDEXLlzAL7/8An9/fytUav8edaxHjRqFEydO4Pjx4/JHUFAQpkyZgp9//tlKVds2XlqqYHJzc/H333/Ln6ekpOD48ePw8/NDrVq1MGXKFDzzzDPo1KkTunTpgoSEBGzZsgWJiYnWK9pOPepYR0dHY8qUKXB3d0doaCiSkpKwcuVKLFiwwIpV25cJEyZgzZo12Lx5M7y8vOSeF6VSCXd3dygUCkyaNAlz585F/fr1Ub9+fcydOxceHh4YPny4lau3H486zkVFRRg8eDCSk5OxdetWqFQqeRs/Pz+4uLhYs3y78qhj7e/vrxMSnZ2dERAQgIYNG1qjZNtnvQlTZA579uwRAHQ+nn32WXmb7777TtSrV0+4ubmJZs2aifj4eOsVbMcedazT09PFmDFjRFBQkHBzcxMNGzYU//3vf4VarbZu4XZE3/EFIJYtWyZvo1arRWxsrAgICBCurq6iU6dO4uTJk9Yr2g496jinpKSUus2ePXusWru9MeQ9XRKnXz+cQgghzB2WiIiIiMyBY2SIiIjIbjHIEBERkd1ikCEiIiK7xSBDREREdotBhoiIiOwWgwwRERHZLQYZIiIislsMMkRkE+bOnYthw4YZvL1CocDx48fL/boxMTHYsWOHQdv+/vvv6NixY7lfk4hMh0GGiMrk448/Rtu2bbXaRowYAXd3d+Tn58ttn3/+OSIjIx+5v+nTp2Pt2rUmqe3y5ctQKBT4999/H7rdnj17cOvWLfTu3dug/bZv3x5OTk7YvHmzCaokIlNgkCGiMunSpQuOHDmCnJwcuS0pKQlhYWE4ePCg3JaYmIguXbpYo8RH+uKLLzB27FijnvPss89i0aJFZqqIiIzFIENEZdKiRQtUqVIF+/btAwBcuHABbm5uGDZsGPbs2QNAutPv3r175SCTnJyMLl26wM/PD/Xq1cOSJUvk/c2aNQsDBw6UPz99+jTatGkDLy8vdOnSBW+99RY6d+6sVcPBgwcREREBb29v9O/fH1lZWQCAVq1aAQCCg4NRpUoVfP/99zr1FxYWIiEhAV27dtVq//zzzxESEgJ/f3/MmDEDzZs3x/Lly+XHu3XrhsTERK0AR0TWwyBDRGXi4OCATp06yXdOT0xMROfOnREdHS23nTp1CpmZmYiOjsaNGzfQo0cPjBs3Drdu3UJ8fDxiY2Px66+/6uy7sLAQ/fv3R+/evXH79m3MmzcPS5cu1dnuf//7H3799VdcvXoVqamp+OSTTwAAhw4dAgCkpqYiNzcXI0aM0HnuhQsXcPfuXa07Cv/666+YOXMmNmzYgPT0dDg4OOD06dNazwsJCYGbmxtOnTpVpuNGRKbFIENEZdalSxe59yUxMRHR0dFo3bo1jh8/jnv37iExMRHNmzeHr68vVq1ahU6dOuHpp5+Go6MjIiIiMHbsWKxZs0ZnvwcPHsTt27fxzjvvwMXFBa1bt8Yzzzyjs93UqVNRo0YN+Pj44KmnnsLRo0cNrv3OnTvw8PCAo6Oj3LZmzRqMGDECrVq1gouLC9599114enrqPNfb2xt37twx+LWIyHwYZIiozLp06YLk5GRkZ2cjKSkJnTt3hqurK1q0aIH9+/drjY+5fPkytm/fDh8fH/njs88+Q3p6us5+09LSEBgYCCcnJ7mtVq1aOtsFBATI//f09DTqco+vry/u3r0LlUql9bohISHy587OzggMDNR5bnZ2Nnx9fQ1+LSIyHwYZIiqzyMhI+Pj4YOnSpXBxcZFDQHR0NPbs2YO9e/fKY1BCQkIwaNAg/Pvvv/JHTk4Otm/frrPfoKAg3LhxA0VFRXLb1atXDa7LweHRv9rq168PDw8PnDt3Tut1r127Jn9eVFSkE7SuXbuGe/fuISIiwuB6iMh8GGSIqMwUCgWio6Mxf/58rYG40dHR+O6773Dnzh153ZVRo0Zh9+7d2LBhAwoLC1FYWIjjx4/j8OHDOvtt06YNfH19ERcXh8LCQhw+fBjr1683uK5q1arBwcEBFy9eLHUbZ2dn9OrVS740BgDDhg3DmjVrcOTIERQWFmLOnDnIy8vTet7u3bsRHR0NLy8vg+shIvNhkCGicunSpQtu3LiB6Ohoua1t27bIzMzE448/Lp/wa9asiZ9//hlff/01AgMDUaNGDUyYMAHZ2dk6+3R2dkZ8fDy2bt0KX19fvPXWWxg5ciRcXV0Nqsnd3R2xsbHo3bs3fHx89I7DAYAJEyZozUjq3r07YmNjMXDgQAQEBKCoqAgNGjTQet2VK1di4sSJBtVBROanEEIIaxdBRPQoL774ItRqNb799luT7jcmJgavvfaa3kXx7t+/D39/f+zYsQMdOnTA/v378dZbb8lTzonI+hhkiMgm/fbbb6hduzZq1qyJPXv2YMCAAdiwYQN69epl1tfduHEjevfuDbVajRkzZmDjxo24cOECXFxczPq6RFQ2vLRERDbp0qVLaNOmDapUqYKXX34Zc+fONXuIAYBVq1YhMDAQQUFBOHr0KDZv3swQQ2TD2CNDREREdos9MkRERGS3GGSIiIjIbjHIEBERkd1ikCEiIiK7xSBDREREdotBhoiIiOwWgwwRERHZLQYZIiIislsMMkRERGS3/g/JpQ46o62MgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot weight and tumor size again\n",
    "plt.scatter(weight, avgVol)\n",
    "plt.title(\"Average Tumor Volume vs. Mouse Weight\", fontsize=10, fontweight=\"bold\")\n",
    "plt.xlabel(\"Weight (g)\", fontsize = 9)\n",
    "plt.ylabel(\"Average Tumor Volume (mm3)\", fontsize = 9)\n",
    "\n",
    "# Add linear regression calculation and line\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(weight, avgVol)\n",
    "regress_values = weight * slope + intercept\n",
    "plt.plot(weight, regress_values, \"r-\")\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "plt.annotate(line_eq,(19,38),fontsize=15,color=\"red\")\n",
    "             \n",
    "# Display plot \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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