pymaceuticals_starter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pymaceuticals Inc.\n",
    "---\n",
    "\n",
    "### Analysis\n",
    "\n",
    "- Add your analysis here.\n",
    " "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "•\tThe gender of the mice in the study was nearly identical between male and female (51% vs 49% respectively). Future studies should explore potential correlation between gender and drug efficacy.\n",
    "•\tBoth Capomulin and Ramicane appear to be equally effective. Further analysis to compare percent of change (between starting and ending tumor volume) by drug regimen would provide a more in-depth study of the effectiveness of each. \n",
    "•\tIn the data comparing tumor volume by drug regimen, Infubinol has one outlier, the others all have data points within the expected upper and lower bounds.\n",
    "•\tThe tumor volume over time for Mouse i738 will using Capomulin shows a steady decrease from start through timepoint 25, but begins to increase from timepoints 25-40. Further variables should be reviewed to determine influence on drug efficiacy. \n",
    "•\tThe correlation factor between mouse weight and tumor volume shows a strong positive correlation of 0.84. Based on this correlation, mouse weight should be one of the variable included in analysis of other factors because it could skew the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "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>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>38.825898</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>35.014271</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>34.223992</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>32.997729</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mouse ID Drug Regimen   Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "0     k403     Ramicane  Male          21          16          0   \n",
       "1     k403     Ramicane  Male          21          16          5   \n",
       "2     k403     Ramicane  Male          21          16         10   \n",
       "3     k403     Ramicane  Male          21          16         15   \n",
       "4     k403     Ramicane  Male          21          16         20   \n",
       "\n",
       "   Tumor Volume (mm3)  Metastatic Sites  \n",
       "0           45.000000                 0  \n",
       "1           38.825898                 0  \n",
       "2           35.014271                 1  \n",
       "3           34.223992                 1  \n",
       "4           32.997729                 1  "
      ]
     },
     "execution_count": 360,
     "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",
    "\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 DataFrame\n",
    "combined_data = pd.merge(mouse_metadata, study_results,on=\"Mouse ID\",how=\"outer\")\n",
    "\n",
    "# Display the data table for preview\n",
    "combined_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice.\n",
    "number_of_mice=len(mouse_metadata[\"Mouse ID\"])\n",
    "number_of_mice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
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       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
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       "      <td>g989</td>\n",
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       "      <td>g989</td>\n",
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       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>53.442020</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>54.657650</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "909     g989     Propriva  Female          21          26          0   \n",
       "911     g989     Propriva  Female          21          26          5   \n",
       "913     g989     Propriva  Female          21          26         10   \n",
       "915     g989     Propriva  Female          21          26         15   \n",
       "917     g989     Propriva  Female          21          26         20   \n",
       "\n",
       "     Tumor Volume (mm3)  Metastatic Sites  \n",
       "909           45.000000                 0  \n",
       "911           47.570392                 0  \n",
       "913           49.880528                 0  \n",
       "915           53.442020                 0  \n",
       "917           54.657650                 1  "
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our data should be uniquely identified by Mouse ID and Timepoint\n",
    "# Get the duplicate mice by ID number that shows up for Mouse ID and Timepoint. \n",
    "duplicate_mouse = combined_data[combined_data.duplicated([\"Mouse ID\",\"Timepoint\"])]\n",
    "duplicate_mouse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
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       "      <th>909</th>\n",
       "      <td>g989</td>\n",
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       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>48.786801</td>\n",
       "      <td>0</td>\n",
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       "      <th>911</th>\n",
       "      <td>g989</td>\n",
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       "      <th>912</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>51.745156</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>49.880528</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>51.325852</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>53.442020</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>55.326122</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>54.657650</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "      <td>56.045564</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>59.082294</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>35</td>\n",
       "      <td>62.570880</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mouse ID Drug Regimen     Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "908     g989     Propriva  Female          21          26          0   \n",
       "909     g989     Propriva  Female          21          26          0   \n",
       "910     g989     Propriva  Female          21          26          5   \n",
       "911     g989     Propriva  Female          21          26          5   \n",
       "912     g989     Propriva  Female          21          26         10   \n",
       "913     g989     Propriva  Female          21          26         10   \n",
       "914     g989     Propriva  Female          21          26         15   \n",
       "915     g989     Propriva  Female          21          26         15   \n",
       "916     g989     Propriva  Female          21          26         20   \n",
       "917     g989     Propriva  Female          21          26         20   \n",
       "918     g989     Propriva  Female          21          26         25   \n",
       "919     g989     Propriva  Female          21          26         30   \n",
       "920     g989     Propriva  Female          21          26         35   \n",
       "\n",
       "     Tumor Volume (mm3)  Metastatic Sites  \n",
       "908           45.000000                 0  \n",
       "909           45.000000                 0  \n",
       "910           48.786801                 0  \n",
       "911           47.570392                 0  \n",
       "912           51.745156                 0  \n",
       "913           49.880528                 0  \n",
       "914           51.325852                 1  \n",
       "915           53.442020                 0  \n",
       "916           55.326122                 1  \n",
       "917           54.657650                 1  \n",
       "918           56.045564                 1  \n",
       "919           59.082294                 1  \n",
       "920           62.570880                 2  "
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optional: Get all the data for the duplicate mouse ID. \n",
    "all_rows_dup_data = combined_data.loc[combined_data[\"Mouse ID\"] == \"g989\"]\n",
    "all_rows_dup_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
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       "      <th>1</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>35.014271</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>34.223992</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>k403</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>32.997729</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>z969</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>63.145652</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>z969</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>65.841013</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>z969</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>69.176246</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>z969</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>70.314904</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>z969</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>73.867845</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1880 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse ID Drug Regimen   Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "0        k403     Ramicane  Male          21          16          0   \n",
       "1        k403     Ramicane  Male          21          16          5   \n",
       "2        k403     Ramicane  Male          21          16         10   \n",
       "3        k403     Ramicane  Male          21          16         15   \n",
       "4        k403     Ramicane  Male          21          16         20   \n",
       "...       ...          ...   ...         ...         ...        ...   \n",
       "1888     z969     Naftisol  Male           9          30         25   \n",
       "1889     z969     Naftisol  Male           9          30         30   \n",
       "1890     z969     Naftisol  Male           9          30         35   \n",
       "1891     z969     Naftisol  Male           9          30         40   \n",
       "1892     z969     Naftisol  Male           9          30         45   \n",
       "\n",
       "      Tumor Volume (mm3)  Metastatic Sites  \n",
       "0              45.000000                 0  \n",
       "1              38.825898                 0  \n",
       "2              35.014271                 1  \n",
       "3              34.223992                 1  \n",
       "4              32.997729                 1  \n",
       "...                  ...               ...  \n",
       "1888           63.145652                 2  \n",
       "1889           65.841013                 3  \n",
       "1890           69.176246                 4  \n",
       "1891           70.314904                 4  \n",
       "1892           73.867845                 4  \n",
       "\n",
       "[1880 rows x 8 columns]"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a clean DataFrame by dropping the duplicate mouse by its ID.\n",
    "clean_combined_data = combined_data[combined_data[\"Mouse ID\"] != \"g989\"]\n",
    "clean_combined_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice in the clean DataFrame.\n",
    "\n",
    "mice = clean_combined_data[\"Mouse ID\"].unique()\n",
    "len(mice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Mean Volume</th>\n",
       "      <th>Std. Dev. Volume</th>\n",
       "      <th>Median Volume</th>\n",
       "      <th>Variance Volume</th>\n",
       "      <th>SEM Volume</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>40.68</td>\n",
       "      <td>4.99</td>\n",
       "      <td>41.56</td>\n",
       "      <td>24.95</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceftamin</th>\n",
       "      <td>52.59</td>\n",
       "      <td>6.27</td>\n",
       "      <td>51.78</td>\n",
       "      <td>39.29</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infubinol</th>\n",
       "      <td>52.88</td>\n",
       "      <td>6.57</td>\n",
       "      <td>51.82</td>\n",
       "      <td>43.13</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ketapril</th>\n",
       "      <td>55.24</td>\n",
       "      <td>8.28</td>\n",
       "      <td>53.70</td>\n",
       "      <td>68.55</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naftisol</th>\n",
       "      <td>54.33</td>\n",
       "      <td>8.13</td>\n",
       "      <td>52.51</td>\n",
       "      <td>66.17</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placebo</th>\n",
       "      <td>54.03</td>\n",
       "      <td>7.82</td>\n",
       "      <td>52.29</td>\n",
       "      <td>61.17</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propriva</th>\n",
       "      <td>52.32</td>\n",
       "      <td>6.62</td>\n",
       "      <td>50.45</td>\n",
       "      <td>43.85</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramicane</th>\n",
       "      <td>40.22</td>\n",
       "      <td>4.85</td>\n",
       "      <td>40.67</td>\n",
       "      <td>23.49</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stelasyn</th>\n",
       "      <td>54.23</td>\n",
       "      <td>7.71</td>\n",
       "      <td>52.43</td>\n",
       "      <td>59.45</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zoniferol</th>\n",
       "      <td>53.24</td>\n",
       "      <td>6.97</td>\n",
       "      <td>51.82</td>\n",
       "      <td>48.53</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Mean Volume Std. Dev. Volume Median Volume Variance Volume  \\\n",
       "Drug Regimen                                                              \n",
       "Capomulin          40.68             4.99         41.56           24.95   \n",
       "Ceftamin           52.59             6.27         51.78           39.29   \n",
       "Infubinol          52.88             6.57         51.82           43.13   \n",
       "Ketapril           55.24             8.28         53.70           68.55   \n",
       "Naftisol           54.33             8.13         52.51           66.17   \n",
       "Placebo            54.03             7.82         52.29           61.17   \n",
       "Propriva           52.32             6.62         50.45           43.85   \n",
       "Ramicane           40.22             4.85         40.67           23.49   \n",
       "Stelasyn           54.23             7.71         52.43           59.45   \n",
       "Zoniferol          53.24             6.97         51.82           48.53   \n",
       "\n",
       "             SEM Volume  \n",
       "Drug Regimen             \n",
       "Capomulin          0.33  \n",
       "Ceftamin           0.47  \n",
       "Infubinol          0.49  \n",
       "Ketapril           0.60  \n",
       "Naftisol           0.60  \n",
       "Placebo            0.58  \n",
       "Propriva           0.54  \n",
       "Ramicane           0.32  \n",
       "Stelasyn           0.57  \n",
       "Zoniferol          0.52  "
      ]
     },
     "execution_count": 366,
     "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",
    "\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",
    "# Assemble the resulting series into a single summary DataFrame.\n",
    "\n",
    "tumor_volume_stats = clean_combined_data[['Drug Regimen', 'Tumor Volume (mm3)']]\n",
    "\n",
    "tumor_df = tumor_volume_stats.groupby([\"Drug Regimen\"]).mean()\n",
    "tumor_df = tumor_df.rename(columns={\"Tumor Volume (mm3)\": \"Mean Volume\"})\n",
    "tumor_df['Std. Dev. Volume'] = tumor_volume_stats.groupby([\"Drug Regimen\"]).std()\n",
    "tumor_df['Median Volume'] = tumor_volume_stats.groupby([\"Drug Regimen\"]).median()\n",
    "tumor_df['Variance Volume'] = tumor_volume_stats.groupby([\"Drug Regimen\"]).var()\n",
    "tumor_df['SEM Volume'] = tumor_volume_stats.groupby([\"Drug Regimen\"]).sem()\n",
    "\n",
    "tumor_df[\"Mean Volume\"] = tumor_df[\"Mean Volume\"].map(\"{:,.2f}\".format)\n",
    "tumor_df[\"Std. Dev. Volume\"] = tumor_df[\"Std. Dev. Volume\"].map(\"{:,.2f}\".format)\n",
    "tumor_df[\"Median Volume\"] = tumor_df[\"Median Volume\"].map(\"{:,.2f}\".format)\n",
    "tumor_df[\"Variance Volume\"] = tumor_df[\"Variance Volume\"].map(\"{:,.2f}\".format)\n",
    "tumor_df[\"SEM Volume\"] = tumor_df[\"SEM Volume\"].map(\"{:,.2f}\".format)\n",
    "\n",
    "tumor_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "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>Mean</th>\n",
       "      <th>StdDev</th>\n",
       "      <th>Median</th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</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>40.68</td>\n",
       "      <td>4.99</td>\n",
       "      <td>41.56</td>\n",
       "      <td>24.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceftamin</th>\n",
       "      <td>52.59</td>\n",
       "      <td>6.27</td>\n",
       "      <td>51.78</td>\n",
       "      <td>39.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infubinol</th>\n",
       "      <td>52.88</td>\n",
       "      <td>6.57</td>\n",
       "      <td>51.82</td>\n",
       "      <td>43.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ketapril</th>\n",
       "      <td>55.24</td>\n",
       "      <td>8.28</td>\n",
       "      <td>53.70</td>\n",
       "      <td>68.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naftisol</th>\n",
       "      <td>54.33</td>\n",
       "      <td>8.13</td>\n",
       "      <td>52.51</td>\n",
       "      <td>66.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placebo</th>\n",
       "      <td>54.03</td>\n",
       "      <td>7.82</td>\n",
       "      <td>52.29</td>\n",
       "      <td>61.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propriva</th>\n",
       "      <td>52.32</td>\n",
       "      <td>6.62</td>\n",
       "      <td>50.45</td>\n",
       "      <td>43.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramicane</th>\n",
       "      <td>40.22</td>\n",
       "      <td>4.85</td>\n",
       "      <td>40.67</td>\n",
       "      <td>23.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stelasyn</th>\n",
       "      <td>54.23</td>\n",
       "      <td>7.71</td>\n",
       "      <td>52.43</td>\n",
       "      <td>59.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zoniferol</th>\n",
       "      <td>53.24</td>\n",
       "      <td>6.97</td>\n",
       "      <td>51.82</td>\n",
       "      <td>48.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Mean StdDev Median Variance\n",
       "Drug Regimen                              \n",
       "Capomulin     40.68   4.99  41.56    24.95\n",
       "Ceftamin      52.59   6.27  51.78    39.29\n",
       "Infubinol     52.88   6.57  51.82    43.13\n",
       "Ketapril      55.24   8.28  53.70    68.55\n",
       "Naftisol      54.33   8.13  52.51    66.17\n",
       "Placebo       54.03   7.82  52.29    61.17\n",
       "Propriva      52.32   6.62  50.45    43.85\n",
       "Ramicane      40.22   4.85  40.67    23.49\n",
       "Stelasyn      54.23   7.71  52.43    59.45\n",
       "Zoniferol     53.24   6.97  51.82    48.53"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A more advanced method to generate a summary statistics table of mean, median, variance, standard deviation,\n",
    "# and SEM of the tumor volume for each regimen (only one method is required in the solution)\n",
    "\n",
    "# Using the aggregation method, produce the same summary statistics in a single line\n",
    "\n",
    "regimen_stats= clean_combined_data.groupby(\"Drug Regimen\")\n",
    "\n",
    "tumor_by_regimen = regimen_stats[\"Tumor Volume (mm3)\"].agg([\"mean\", \"std\", \"median\", \"var\"])\n",
    "\n",
    "tumor_by_regimen[\"mean\"] = tumor_by_regimen[\"mean\"].map(\"{:,.2f}\".format)\n",
    "tumor_by_regimen[\"median\"] = tumor_by_regimen[\"median\"].map(\"{:,.2f}\".format)\n",
    "tumor_by_regimen[\"var\"] = tumor_by_regimen[\"var\"].map(\"{:,.2f}\".format)\n",
    "tumor_by_regimen[\"std\"] = tumor_by_regimen[\"std\"].map(\"{:,.2f}\".format)\n",
    "\n",
    "#tumor_by_regimen.rename(columns={\"mean\": \"Mean Volume\", \"median\": \"Median\", \"var\": \"Variance\", \n",
    "#                                \"std\": \"Standard Devation\"})\n",
    "\n",
    "tumor_by_regimen_renamed = tumor_by_regimen.rename(columns={\"mean\": \"Mean\", \"std\": \"StdDev\", \n",
    "                                                            \"median\": \"Median\", \"var\": \"Variance\"})\n",
    "\n",
    "tumor_by_regimen_renamed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar and Pie Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nJ53vbyDnOwQArl27hsuXL2stU5KfEUy8TNB///0HAPDw8ECTJk10luvXrx+AnBdpSX4ZN23aFOXLl9e6z8nJST0w4NGjRyUWg+oxsbCwQM+ePXWWUz0muY8xFapEQ1eH3wcPHmDatGlo3rw5nJycNGbVlyRJnVzfunWryLGcO3cOH3zwAV5//XU4OjrCzMxMfR5/f38AOa+re/fuFek8Lw9icHZ2xtChQ9UdhD///HMMGTIEAHD16lV1p9m3335b5326u7urP7gL+xyX9Gu6fPny8PT0BABMnTq1xN6f3bt3h4WFhdZ9FStWROvWrQHkzCWXm7m5ufoL9vfff0dGRobG/rt376of29xfMiVN9R4BdL9PAODNN98s0TgSEhLUHbB79eqls1ytWrXg6+tbLOeMiIiAvb29+la5cmW0bdsWe/fuhb+/P0JCQrTGcvDgQQghIEkSWrVqpZG8vXyrW7cugJz3v7Hq2qlTJ537fHx8AOT8iFYNtNBVBkCe0dAHDhwAALRu3RqZmZk6HwcnJydUrFgRgOZjkVtJfkYw8SomFSpUUP9tyIgLfVTLCKm+AHXJvb9IIyzy4ebmpne/jY0NABR760huqsfEx8cHVlZWOsuplmYCSvYxKYzExEQAmq8Vlb///ht+fn6YPXu2emJXXaPdVPdTWFOnTkXjxo3x888/4+rVq0hKSkJ2dnaJnOtlCoUCHh4eGDhwIP777z/MmzdPvS/38lmGvvYL+xzL8ZpesGABJElCSEgImjdvDk9PT7z77rtYvnw57ty5U+j7zS33aDdtateuDUD74zRq1CgAOa2QL49YVLV22dvb45133imGSA2T+/Wm7X2i4u3tXaJx5H4t5pdsFFfipc/169cxZcoUraNUVT/EhBCoVKmSRvL28m3x4sUAoHGFRO666nvvqa7uGFIGAFJSUjT2qR6Lbdu26X0c7O3tERcXBwA6rxaV5GcEE69iUq1aNfXf+oaqGkI1i7qdnZ3ecvb29nmOKQmqyzr5yf1rtbgZ+pjkLlOSj0lBZWRkICwsDEDeN3R4eDj69++PZ8+ewcvLC0uWLMG5c+cQHR2NxMRE9UzWqktWRZl+4I8//lDP3h8YGIgNGzbg+vXriI2NRVJSEpKTk3H16lV1+aJOdZB75vpnz54hMzMTERER2LBhA1q2bKlRNvfzZehrv7DPsRyv6bfeeguHDh3CG2+8AYVCgcjISGzYsAEfffQRatasiZYtWxb58nx+j5Nqf0pKSp4v7Ro1aqhbxHKvqpCdnY1169YByGlBVv2QkUPu1lx9X3yqL72S8uzZM/Xf+dXfkM8kQ1StWhUip981srOz8ejRI2zduhU1a9YEAGzZskU911luhflxlJqaqv5b7rpqu3xYmDJA3vdnUR+L3EryM8Kwe6Z8NW3aVD1J5vHjx4t0X6ovldxvCG1y78+dhAH6m+lzK+oXq1wMfUxyl3n5MTGmc+fOqfvtNW/eXGPfb7/9htTUVDg4OODUqVM6+2gkJSUVOY4ff/xRHcPRo0ehUOT97fXyZaeiUCqVBn9Y536+DH3tm9JzrE3btm3Rtm1bPH36FCdPnkRwcDD27t2L8+fPIygoCIGBgTh+/LjeLgX65Ld8jOpxsra21vplNmrUKBw7dgz//vsvoqOjUblyZezfv189H56clxkBzclBX36fFERRP/9yv2YNfYyLkyRJcHV1xVtvvYVWrVqhQYMGiIqKwpw5c/Duu+9qXG5Txero6Fioqy3GrmtxsrOzw9OnTzFmzBgsWrTI2OHoxBavYmJnZ4eOHTsCyJnoryiXElST+V2/fl1vuWvXruU5RkV1Oe7lptjcMjIySrRTfnFS1e/evXs6f6EAOa1Hqg8PfZMiyu3XX39V/616nahcunQJANCuXTudSVdERESxJF6qc73zzjtaky4AGi1ecsr9fBn62jel51ifcuXKoUuXLpg1axbOnTuHQ4cOwcrKCunp6eqOvoWRX+u6ar+ux+mtt95C+fLlkZWVpZ6EV9X65efnh2bNmhU6toISQqg7+ltZWaFVq1aFvq/c3RH0fQZGRUVp3V61alX13/n1qSyOPpf6uLi4YNmyZQByPrOnTZumsV+VhCUmJhZqzV9TqmtRqR4LU1/vk4lXMZo0aRKAnA+Q0aNHG9wEGRsbq5EABQYGAsiZzVvfpYg//vgDQE6z7Mu/DitXrgwACAkJ0Xn8oUOHkJ6eblCMhaXq+Kutb0JBqB6TjIwMvTNoqx6T3McYW1BQkPrSzWuvvYYuXbpo7Fe1hOl7jNavX6/3HIY+zsVxrpLy2muvqUe1/fnnnzrLRUVFqVtGjPEcF8drul27dmjXrh2AonVN2LVrl85Wm/j4ePUs+y9f1lWxsrLCoEGDAOQkXE+ePMHff/8NAOpR03KZP38+7t69CyCnpU1fH6/8qD7/AN2fgZmZmTh48KDWfeXLl1f3j1M9HtrcuXOnyF1LDNGrVy/1Z/wff/yhkQDl/iGna4SqPqZW16JQPRbBwcEmnSQy8SpGrVu3xvvvvw8gZ6TJu+++q/fXlqpcw4YNNX55vfvuu1AqlQCAsWPHqr8sczt9+rR6iHjv3r3VIzRUmjZtCgC4cuWK1uz/2bNn6kSxJKnievjwYZHup1u3burWoC+++AIJCQl5yoSGhqo7azds2BD169cv0jmLw969e9GjRw9kZmbCwsICS5cuzXMZRNVROCgoKM8yKUBO607uTujaGPo4q861c+dOrT8M1q5dq/PLqKSZmZlh+PDhAHIeN20JdnZ2Nj7++GN1svHee+/JGiNg2GMdFxen9blUycrKUnd41zY1iqGioqLUS5W9bMKECerPDn2XDFWP4e3bt/HRRx8hLS0NFhYWGDx4cKHjKojs7GzMnz8fU6dOBZAz/cZXX31VpPusX7++eiqN3P3XcpszZ47WJcZUVKM+z58/j99//11r3J999lmR4iwI1fqV2dnZ+Oabb9Tba9asiW7dugEAvv/+e/WySbokJSXlGQ1oanUtrI8++ghWVlbIysrC4MGD871KoK9hokQVeOYv0islJUVjorkqVaqIb775Rpw5c0ZER0eL2NhYceXKFfHTTz9pTNT28mSAuWeub9q0qdi3b5+IjY0V4eHhYvHixcLR0VE9y29YWFieOJ48eaKe3d7Dw0Ns27ZNxMbGiocPH4qtW7cKf39/4eTkpJ5wNL8lg/TRNwnr999/r57wcPv27SIxMVFkZGSIjIwMkZWVZfgDK4TYvHmz+jHx9fUV27ZtE48ePRJRUVFizZo1Omeuz011fHFNoLpnzx71rOWJiYkiMjJSnDlzRixbtkw9c7mq/mvXrtV6f7knhmzYsKHYv3+/ePz4sQgNDRWLFi0S5cuXF5UqVRIVKlTQ+Tj/+eef6vv4+eefxZMnT9SPc+5JCHMve9O/f39x7tw5ERcXJy5fvizGjRsnzMzMRO3atQs1Qay2+hT0cc49c72VlZX4+uuv88ygrbrvosxcX9Kv6SNHjghra2sxYMAAsXHjRnHz5k0RHx8vHjx4IA4dOqTxGbFkyZICPUa56+jl5SUkSRKffPKJuHr1qoiPjxdnz54V/fr10zkRqTYBAQEak7L27t27wDG9LPcEqrln909ISBBhYWHi8OHDYtasWerZ0IGcpYl0TYya+3Wl7TPvZaoJZCVJElOnThX37t0T8fHx4vTp02LYsGECgHq29vxmrre0tBQzZswQd+7cEfHx8SIoKEj9HKpWDyjJCVRVWrVqpZ5g9N69e+rtkZGR6gmmLSwsxMcffyxOnDghHj9+LOLj48WtW7fE1q1bxfDhw4W9vb3YunVrsdfV0AlU9X2mGDIRdH73tXLlSo33x7Jly8TNmzdFQkKCePTokThz5oxYunSpaNOmjbCzs8tzfHF8RuSHiVcJyMzMFNOmTRM2NjYaH2babmZmZuL999/PMxt5UddqFEKITZs26VzXsFy5cuL48eMGr9Woj777iImJEc7OzlpjKMxs9gsXLizwWo25FXfiZcitZcuW4tKlS3rvc/To0TqPL1++vPjvv//0Ps6pqanqJTdevuX+EHv+/HmeL9ncN39/f3H69GmjJV5C5Kw5J8dajfoU9TVtyPqgQM7yLdpm586P6viff/5ZtG3bVuf9a1urUZuff/5Z47jdu3cXOKaX5U688rtZWFiId999V+/ySQVNvB49eqSR1L18Gz9+vEFrNVaqVEnnfUyfPl0MGTJEABBvvPFGoR6ngiReR48eVZ971KhRGvtu374t6tSpY9DjvXPnzmKvq6kkXkIIsWrVKvUya/puFSpUyHOsHIkXLzWWADMzM3z99dcIDQ3Fd999hw4dOsDDwwPW1tZQKpVwc3NDx44dMXfuXISHh+Pnn3/WOirxu+++Q3BwMAYNGgRPT08olUo4ODigfv36mDZtGm7fvq135E///v1x7NgxdO/eHU5OTrC0tISXlxc++OADXLp0SZb+Mc7Ozjh58iSGDx+OatWqqS+hFtbYsWNx5coVvPfee/Dx8YG1tTVsbW1Ru3ZtjBs3Drdv30bv3r2LKfqCsbS0RMWKFVGjRg306tULs2fPxvXr1/Hff/+pJy7U5ccff8SaNWvQtGlT2NrawtraGtWrV8cnn3yCixcv6uyjo6JUKnHs2DF88sknqFWrls65zmxsbHD06FFMnz4dvr6+UCqVcHR0RP369TFnzhycOXMGLi4uhX4MioO/vz9u3LiB+fPno2nTpihXrhwsLCzg5uaGvn374t9//8W6det0Thxa0gx5TTdv3hwHDx7ElClTEBgYCC8vL/X738vLC/369cO///6L9evXGzx0XhtLS0vs378fCxcuRMOGDWFvbw9bW1s0bNgQS5YswaFDhwyaDmLAgAHqcm5ubujcuXOhY8qPtbU1XF1d8dprr+Hdd9/F0qVL8eDBA6xfvz5Pl4micHV1xenTpzFu3Dj4+PjA0tISTk5O6NixI3bt2qV3ZnMVPz8/XLt2DZ999hl8fHygVCrh7OyMTp06Yffu3ZgxY4Z6pJ+Dg0Oxxa5L69at1X0D165di4iICPW+GjVq4OLFi9iwYQN69+6NKlWqQKlUwtLSEm5ubnjjjTfw7bff4u7du+jevbvJ17UoRowYgbCwMMyYMQPNmjWDk5MTzMzMYGtri1q1amHgwIH4448/NOYwk5MkRAlOvkRERCYvNTUVlSpVQmJiIqZMmaLRh4j0q1u3Lq5cuYJPPvkES5YsMXY4JepVqmtJYosXEdErbvv27UhMTIQkSbLP3VWahYWFqadfadiwoZGjKVmvUl1LGhMvIqJX3MKFCwEAb7zxBqpXr27kaEyHvoW6MzMz8fHHH0MIASsrK71ryJYGr1JdjY2JFxHRK0YIgczMTMTGxmLSpEk4e/YsAMgyxUxp8ttvv6Fp06ZYuXIlbt68iadPnyIqKgrbt29Hq1atsGfPHgA5U3eo5qArrV6luhoblwwiInrFrF27Vj1nmsrbb7+NDh06GCki03X69GmcPn1a5/6BAwcWed4xU/Eq1dWYmHgREb2izMzMULVqVQwcOBBffPGFscMxOe+88w6ysrJw8OBB3Lt3DzExMcjMzISLiwuaNGmCESNG5FmJorR6lepqbBzVSERERCQTtnjJJDs7Gw8fPoS9vX2eJWOIiIjINAkhkJycDDc3NygURe8az8RLJg8fPoSHh4exwyAiIqJCiIyMRJUqVYp8P0y8ZKKamT4yMtLkZ/0lIiKiHElJSfDw8MizwkxhMfGSieryooODAxMvIiKiUqa4uglxHi8iIiIimTDxIiIiIpIJEy8iIiIimTDxIiIiIpIJEy8iIiIimTDxIiIiIpIJEy8iIiIimXAeLyKiYiCEQEZGBrKzs40dChFpoVAoYGFhYfRl+5h4EREVQVZWFuLi4pCcnIyMjAxjh0NEelhYWMDe3h4VK1aEmZmZUWJg4kVEVEhZWVmIjIxEWloaHB0dYWdnBzMzM6P/oiYiTUIIZGVl4dmzZ3j69ClSUlLg4eFhlOSLiRcRUSHFxcUhLS0Nnp6esLa2NnY4RJQPOzs7ODo6IiIiAnFxcXB1dZU9BnauJyIqBCEEkpOT4ejoyKSLqBSxtraGg4MDkpOTIYSQ/fxMvIiICiEjIwMZGRmws7MzdihEVED29vbq97DcmHgRERWCavSisTroElHhqd63xhiFzMSLiKgI2JGeqPQx5vuWnetNmRwvDCNc3yYiInpVscWLiIiISCZMvIiIiF5RR48ehSRJaNOmjbFDeWXwUiMREZUoLy8v3L9/X/2/JEnq+ZR8fX3RpEkTDBw4ELVr1zZOgOfOFftdHj1/Hm0/+EBjm6re1atXR7du3TB+/HiUK1eu2M9Npo2JFxERyaJGjRpwcXEBAKSmpiIuLg4HDx7EwYMHMWfOHPTt2xc///wznJycjBxp8WpRty5gZwchBB48eIBLly7h4sWLWL9+PYKCguDm5ma02GxsbFCrVi14enoaLYZXDRMvIqKSVBpHPZbQoJsvvvgCw4YN09gWFxeHDRs2YPbs2fjrr79w/fp1nDp1Co6OjiUSgzGc+PVXoFEj9f9nzpxBr169EB4ejokTJ2LDhg1Gi61x48YICQkx2vlfRezjRURERlOxYkWMGTMG586dQ+XKlRESEoKxY8caO6wS1bhxY8yaNQsAsHPnTmRlZRk5IpITEy8iIjK6qlWrYvny5QCA33//HZGRkRr7vby8IEkSwsPDceTIEXTp0gUVK1aEJEk4evQoAKBNmzYa/79s2LBhkCQJa9asybMvPSMD36xejVp9+8KqRQu4d+2KD+bORWxCAmb88gukgADM+OWXYqtvQEAAAODZs2eIi4vLs//FixeYP38+GjVqBAcHB9jY2KBevXr47rvvkJaWpvU+hRBYsWIF6tatC2tra7i6umLgwIEIDQ3FmjVrIElSnhZHXZ3rw8PDIUkSvLy8AAC//vor6tevDxsbG7i7u+PTTz9FcnIygJzF4hcsWAB/f39YW1ujSpUqmDx5MtLT03XWPyQkBCNGjICXlxeUSiWcnJzw5ptv4vDhw1rL537+T506hS5duqB8+fKwtbVFYGCgzuNMERMvIiIyCT169ICbmxsyMzOxf/9+rWU2bdqE9u3b4/Tp0/D29kaVKlWKfN7MzEz0GD8eXy5fjtsREfCqXBku5ctj1d9/o/GwYXiSlFTkc7zsxYsX6r9tbGw09kVFRSEgIACTJ0/G5cuX4erqCi8vL1y/fh2TJk1C+/btkZKSkuc+R40ahQ8//BBXrlxB5cqV4eHhge3bt6NRo0a4d+9eoWP97LPP8N577yE5ORk+Pj6IiYnB0qVL0atXL2RnZ+Ott97ChAkTIIRA1apV8fDhQ8yfPx/vvfee1vvbsmUL6tati9WrV+PJkyeoXbs2LC0tsWfPHrRv3x5Lly7VGcvu3bvRqlUrnD17Fj4+PrCwsMCJEyfQqVMnnQm3qWHiRUREJkGhUKBZs2YAgLNnz2otM23aNEyfPh0xMTE4c+YMIiIi1McU1qJNm7Dv1ClUcHRE0K+/IuTPP3FxwwaE7tiB8vb2WPHXX0W6f2327t0LAPD29oa9vb16e3Z2Nt555x3cuHED/fv3x4MHD3Dnzh3cuHEDYWFhCAwMxIkTJ/DVV19p3N+ff/6J3377DUqlEjt27EBoaCjOnTunTuLmz59fqDijoqKwatUqHDx4EHfv3sXVq1dx8eJFODk54fDhw+jbty/OnTuHixcv4saNGwgJCcHhw4dhaWmJdevW4caNGxr3d+XKFQwZMgQKhQK//PILnj59iosXLyI6Oho7d+6Evb09xo0bh8uXL2uNZ/z48fj666/x+PFjnDt3DrGxsRg0aBAyMzMxefLkQtVRbky8iIjIZHh4eAAAYmJitO7v2rUrvvrqK5ib54wNkyQJSqWy0OfLzs7Gos2bAQBLPvsMzevW/f+xVKqELXPnIruYBhuoRjX+8MMP6kRoypQpGmX++ecfBAcHIyAgAOvXr4erq6t6X5UqVfDHH3/Azs4OK1as0Gj1WrBgAYCcxLRnz57q7RUqVMDmzZtha2tbqJgzMzMxY8YMvPHGG+ptr732Gt5//30AwI4dO7B06VLUq1dPvb9Nmzbo06cPAGDfvn0a9zdz5kykpaWpW8QUiv+fhnTv3h1z5sxBVlYWlixZojWezp07Y/Lkyeq1Fi0sLLBo0SIolUqcPn0aCQkJhaqnnJh4ERGRyVAlCKr+Qy8bMmRIsZ7vxo0biIqJga21Nd5u3z7P/uoeHgjMlVQUhhQQAEmSoFAo4OHhgc8++wwODg5YunQpRo0apVF227ZtAHL6o6mSy9wqV66MgIAAPHv2DOfPnweQ81idPn0aADB8+PA8x5QvXx69evUqdPwjRozIs02VaFWoUEHrfdevXx8AEBoaqt6Wnp6OPXv2wMzMLE9fM5UePXoAAI4dO6Z1/8uPF5AzQEPVFy33+UwVp5MgIiKT8ezZMwCAg4OD1v1+fn7Fer47d+4AAHy9vGBpYaG1zOvVq+Po/yU5haGaxys9PR13795FQkICHB0dERgYmKfs1atXAQA//fQTNm7cqPX+bt++DSDnMiAA3L17F0IIODk56ZwT7PXXXy9U7M7OzlqfC2dnZwCAj4+PzuOA//98quJOTU2FpaUlunbtqvU48X+ti6q6vUzX+VxcXHDr1i2N85kqJl5ERGQyIiIiAEA90erLCnvJTJfnz58DAOxf6uCem30Rz5l7Hq/09HR88803mDlzJjp37oyrV6+iYsWK6rKJiYkAgGvXruV7v6pLjeo65Oor9jJ9+/R5ueO/ivR/89Plt1/kukyrqlt6ejqCgoL0njc1NVXrdl3Pv+qSpSihOeiKEy81EhGRScjOzsbJkycB5Mx1VVDavuxzUyUouam+yJ/lGmX4smQtxxWWpaUlZsyYgZ49e+LRo0d5OoTb2dkBAA4cOAAhhN6b6nKdug56Wnt0XbqVk6pu7u7u+datNCRQhcXEi4iITMKOHTvw6NEjWFhYoGPHjgU+XpWAxMbGat1/9+7dPNtq1qwJAAi5fx8ZmZlaj7tahKkYdJk7dy4UCgXWrFmjEZdqvUpDWrxUqlevDoVCgbi4OERHR2sto7qEaUw1atSAhYUFoqOj8eTJE2OHYzRMvIiIyOju37+Pjz/+GEBOB3p3d/cC34e3tzcA7VNRnDt3TusUBX5+fnB3ccGzFy/w56FDefaHPniA/y5eLHAs+fHz80OPHj2QlZWlMdWDajTgzz//rPNy28vs7e3RpEkTANA6OWxiYiJ27NhR5JiLysbGBp06dUJ2drbOUYuvAiZeRERkNHFxcViyZAkaNWqE6Oho1K5dGz/88EOh7qtLly4AgJUrV+LMmTPq7Xfu3MHQoUO1jhJUKBQY278/AODT77/HqVwtQw8eP8Y7X3yhvoRZ3D7//HMAwLp16/DgwQMAQO/evdG0aVOEhISge/fueVrp0tLS8M8//+QZaTh+/HgAwKxZs7Br1y719oSEBPTv399kOp3PmjULSqUSs2fPxrx58/JMBBsdHY3FixdjxYoVRoqw5DHxIiIiWXzzzTdo2bIlWrZsiYCAAFSrVg3Ozs4YM2YM4uLi8Pbbb+O///7TOaIxP507d0b79u2RmJiIZs2awdfXF3Xq1IGvry8qVqyIt99+W+txYwcMQMemTRH39CmajRgBv7ffRoN330W1nj0Rn5iID/r2BQCYKYr3K7Np06YIDAxEeno6vv/+ewA5ieC2bdtQv359HDx4EDVq1ECNGjXQtGlT+Pv7w8HBAd26dcOePXs07uutt97CiBEjkJKSgh49esDHxwcBAQFwd3fH6dOnMWnSpJw6/N/8V8ZSr149bNq0CUqlElOmTEGFChVQv359NGnSBJ6ennBzc8PYsWMRHh5u1DhLEhMvIiKSxZ07dxAUFISgoCCEhIQgMzMT7du3x5dffokbN25gy5YtqFChQqHvX5IkbN++HePHj4ebmxvCwsLw/PlzTJkyBfv374eFjukizM3NseuHHzD7ww9Rw9MToVFReBQfj6HduuH06tVQ/t9x+kY+Fpaq1WvlypXqvmmVK1fGyZMnsXz5crRq1Qrx8fG4ePEikpOT0bhxY8ycORNHjhzJc18rV67E8uXLUadOHURFReH+/fvo3r07zpw5g8qVK+fUoZCjG4tT7969cePGDYwZMwZeXl64desWbty4ARsbG/Tu3Rtr164tNbPQF4YkyvLQAROSlJQER0dHJCYmGv5rroSatzXw6ScqlNTUVISFhaFatWqwsrIydjhUFOfO6d3dfdw47D5xAtu/+w69XlpM2mD/N52EsXzyySdYtmwZFi5ciLFjxxo1FlNQkPdvob6/9WCLFxERkQ4PHj/GgTNnYGZmhqavvWbscArl2bNn+PPPPwEALVq0MHI0xMSLiIheebNXrcKd/5u8VeVWeDh6TpiAtPR09GzVCpVyTXRqihYtWoRLly5pbIuKikLfvn3x6NEjNGjQAAEBAcYJjtQ4cz0REb3yfv37b0xbsQIVy5WDV+XKSHz+XJ2Iebu7Y8mECUaOMH87duzAuHHj4OjoCG9vb6SlpSEkJATZ2dmoWLEi1q5da+wQCUy8iIiIMG3kSGw9eBDXQkNxLTQUCkmCv7c3erVpg88GDUL5YujbU9LGjBmD8uXL48KFCwgJCYEQAj4+PujSpQsmTZpUqLnRqPgx8SIiolfeyJ49MbJnT2OHUSS9e/dG7969jR0G5YN9vIiIiIhkwsSLiIiISCZMvIiIiIhkwsSLiIiISCZMvIiIioCLfxCVPsZ83zLxIiIqBHPznEHhaWlpRo6EiApK9b5VvY/lxMSLiKgQzM3NYWtriydPniArK8vY4RCRgbKysvDkyRPY2toaJfHiPF5ERIVUsWJFREZGIiwsDI6OjrC2toaZmRkkORa4p9IlNdXYEbzShBDIyspCSkoKEhMTkZ2djcqVKxslFiZeRESFZGNjg2rVqiEmJgYJCQmIi4szdkhUGHI8b2FhJX8OypeZmRlsbGzg4uICS0tLo8TAxIuIqAgsLS1RpUoVCCGQkZGB7OxsY4dEBdWlS8mfIySk5M9BeikUClhYWBi9RZqJFxFRMZAkyWi/oKmI7t8v+XNYWZX8OahUYOd6IiIiIpmYfOIlhMCJEycwceJENG3aFOXKlYOlpSXc3NzQt29fHDlyRO/xJ0+eRM+ePeHs7Axra2vUrl0bs2bNQmo+HR1v3ryJQYMGoXLlyrCysoKPjw8mTJiAp0+fFmPtiIiI6FUiCROf/e/QoUNo3749gJzrs9WrV4etrS3u3LmDZ8+eAQCmTp2KWbNm5Tl2w4YNGDp0KLKysuDu7g4XFxdcu3YNGRkZCAgIwNGjR2FjY5PnuCNHjuDNN99ESkoKnJ2d4eHhgZCQELx48QLe3t4IDg6Gq6trgeqRlJQER0dHJCYmwsHBwbCD5LgObdpPPxFRyeNnLelRqO9vPUpFi1f16tWxfPlyxMXF4datW7hw4QLi4+MxZcoUAMDs2bOxe/dujePCw8MxcuRIZGVl4dtvv0VkZCQuXLiAO3fuoFatWjh79iwmTZqU53zJycno168fUlJS8OmnnyIqKgrnz59HREQEWrRogdDQUIwcOVKWuhMREVHZYvItXklJSbCxsdE5yVnXrl2xd+9e9OjRA3///bd6+0cffYTly5ejY8eO2Ldvn8YxwcHBaNGiBSwsLBAZGanRevXdd99h0qRJ8PPzw9WrV2FmZqbeFxERAR8fH2RmZuL8+fNo0KBBgerBFi8iIhPEz1rS45Vr8XJwcNA7s2yHDh0AALdv31ZvE0Jg+/btAKC1dap58+bw9fVFRkaGRrIGANu2bQMADBs2TCPpAgBPT0/1Zc8///yzELUhIiKiV5nJJ175UXWSt7a2Vm+LiIhAdHQ0AKBFixZaj1NtP336tHqbqiWroMcRERERGaJUJ15CCGzduhWAZqJ0584dAIBSqYSbm5vWY729vTXKAjn9wjIyMjT2G3IcERERkSFK9QSqK1euxMWLF2FpaYmxY8eqtyckJAAAypUrp3OG2vLly2uUfflv1X5DjtMmLS1Nvfo5kHONmIiIiF5tpbbF68KFCxgzZgyAnFGNPj4+6n2qy4/6ZpFWKpUAgJSUlDzH6TtW23HazJ07F46Ojuqbh4eH3vJERERU9pXKxCssLAzdunVDamoqBg4ciAkTJmjst/q/pRnS09N13oeqNSp33zCrXEs66DpW23HaTJkyBYmJiepbZGSk3vJERERU9pW6S42PHj1Chw4dEB0djTfffBNr1qzJczlRdTnw6dOnEEJovdyoulSY+5Ji7r8TEhJQuXJlg47TRqlUqlvHiIiIiIBS1uL15MkTdOjQAffu3UPr1q2xdetWWFhY5ClXo0YNADmtUw8fPtR6X6GhoRplAcDLy0t9f6r9hhxHREREZIhSk3g9e/YMXbt2xbVr1xAQEIBdu3bpvNzn6emJSpUqAQCCgoK0llFtb9KkiXqbubm5elLUghxHREREZIhSkXilpaWhZ8+eOH36NPz9/fHvv//C3t5eZ3lJktC7d28AwKpVq/LsDw4ORkhICCwsLNCjRw+NfX369AEArFmzBllZWRr7IiIicPDgQQBA3759i1QnIiIievWYfOKVlZWF/v374/Dhw/Dx8cGBAwdQoUKFfI+bOHEiLC0tsX//fnz33XdQrYx0//59jBgxAgAwatQodcuYygcffICKFSvi5s2bGD9+vHper/j4eAwcOBCZmZno0qULGjZsWMw1JSIiorLO5Ndq3LRpEwYOHAggp1+Vi4uL1nKVK1dWT6aqsm7dOgwfPhzZ2dlwd3eHi4sLrl27hoyMDDRs2BDHjh2Dra1tnvs6dOiQetSks7MzPD09cfPmTbx48QJeXl44efJknoQtP1yrkYjIRPGzlvQo7rUaTX5UY+5JSO/cuaNzxviqVavm2TZkyBBUr14dc+fORXBwMG7cuAFvb28MGDAAn3/+ucb0Ebm98cYbOHfuHGbPno3Dhw/j6tWrcHd3R+/evTF16tR8RzQSERERaWPyLV5lBVu8iIhMFD9rSY/ibvEy+T5eRERERGUFEy8iIiIimTDxIiIiIpIJEy8iIiIimTDxIiIiIpIJEy8iIiIimTDxIiIiIpIJEy8iIiIimZj8zPVEVIw4USQRkVGxxYuIiIhIJky8iIiIiGTCS41EVLrwcikRlWJs8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCUc1EhERkWl4BUYts8WLiIiISCZs8SIiMoZX4Jc9yYyvqVKBLV5EREREMmHiRURERCQTJl5EREREMmEfLyp5Jd3vQI4+B+w7QURExYAtXkREREQyYeJFREREJBMmXkREREQyYeJFREREJBMmXkREREQyYeJFREREJBMmXkREREQyYeJFREREJBMmXkREREQyYeJFREREJBMmXkREREQyYeJFREREJBMmXkREREQyYeJFREREJBMmXkREREQyYeJFREREJBMmXkREREQyYeJFREREJBMmXkREREQyYeJFREREJBMmXkREREQyYeJFREREJBMmXkREREQyYeJFREREJBMmXkREREQyYeJFREREJBMmXkREREQyYeJFREREJBMmXkREREQyYeJFREREJBMmXkREREQyYeJFREREJBMmXkREREQyYeJFREREJJNSkXiFhYVh5cqVeO+991C3bl2Ym5tDkiTMnj1b5zEzZsyAJEl6byEhITqPv3nzJgYNGoTKlSvDysoKPj4+mDBhAp4+fVoCNSQiIqJXgbmxAzDE4sWLsXjx4kId6+HhAU9PT637bGxstG4/cuQI3nzzTaSkpMDZ2Rn+/v4ICQnBggULsH37dgQHB8PV1bVQ8RAREdGrq1QkXhUrVkS3bt3QuHFjBAQE4Ndff8Vff/1l0LEjRozAjBkzDD5XcnIy+vXrh5SUFHz66af4/vvvYWFhgfj4ePTs2RNBQUEYOXIkdu/eXcjaEBER0auqVCReU6dO1fh/8+bNJXauFStWIDY2Fn5+fvjhhx9gZmYGAHBycsLGjRvh4+ODf/75BxcuXECDBg1KLA4iIiIqe0pFHy85bdu2DQAwbNgwddKl4unpifbt2wMA/vzzT9ljIyIiotKtVLR4FcWRI0dw/fp1xMfHo0KFCmjcuDGGDBmCSpUq5SmbmZmJ8+fPAwBatGih9f5atGiBf//9F6dPny7RuImIiKjsKfOJ1/HjxzX+/+uvvzBjxgwsX74cw4YN09gXHh6OjIwMAIC3t7fW+1Ntv3PnTvEHS0RERGVamb3UWLlyZXzxxRc4e/Ys4uPj8eLFCwQFBaFLly5ISUnBiBEjsGvXLo1jEhIS1H+XL19e6/2qtucuq01aWhqSkpI0bkRERPRqK7OJ1//+9z/MmTMHjRo1QoUKFWBtbY3mzZvjn3/+Qe/evSGEwLhx4yCEUB+Tmpqq/tvS0lLr/SqVSgBASkqK3vPPnTsXjo6O6puHh0cx1IqIiIhKszKbeOkiSRLmzZsHALh37x6uXLmi3mdlZaX+Oz09XevxaWlpAABra2u955kyZQoSExPVt8jIyKKGTkRERKXcK5d4AUDNmjVRoUIFAMDdu3fV23NfXtR1KVG1XdelSBWlUgkHBweNGxEREb3aXsnECwAsLCwA5IxkVPHy8lJvDw0N1XqcanuNGjVKOEIiolJAkkr2RlTGvJKJV1xcHGJiYgAAVapUUW83NzdXT4oaFBSk9VjV9iZNmpRwlERERFTWvJKJ1w8//AAhBBwdHREQEKCxr0+fPgCANWvWICsrS2NfREQEDh48CADo27evPMESERFRmVEmE6/r169j9OjRuH79usb21NRUfPPNN5g/fz4A4PPPP88zevGDDz5AxYoVcfPmTYwfP149r1d8fDwGDhyIzMxMdOnSBQ0bNpSnMkRERFRmSCL3fAomKigoCD179lT//+zZM6SlpcHGxkZjdOHFixfh4eGBS5cuoX79+gAAZ2dneHp6AgBu3ryJFy9eAABGjhyJlStXQtLSh+DQoUPo1q0bUlNT1cerjvXy8sLJkye1znyvT1JSEhwdHZGYmGh4R3s5+jfI8fSXdD3KQh0A1sNQZaEOAOthqLJQB4D1MJQJ1qFQ3996lIoWr4yMDMTHx6tvqikdXrx4obFddWnQy8sLs2bNQpcuXWBnZ4dbt27h6tWrqFChAt566y38+++/+PXXX7UmXQDwxhtv4Ny5c+jfvz8kScLVq1fh6uqK8ePH48KFCwVOuoiIiIiAUtLiVRawxasElYU6AKyHocpCHQDWw1BloQ4A62EoE6zDK9niRURERFQWMPEiIiIikgkTLyIiIiKZMPEiIiIikgkTLyIiIiKZFCnxWrduHfbt22dQ2f3792PdunVFOR0RERFRqVak6SQUCgUCAwNx7NixfMu2bdsWx48fz7MMz6uC00mUoLJQB4D1MFRZqAPAehiqLNQBYD0MZYJ1MLnpJAzN2zhdGBEREb3qZOvj9ejRI9ja2sp1OiIiIiKTY16QwhEREQgPD9fYlpiYiOPHj+s8JiUlBceOHcPt27fRpEmTQgVJREREVBYUKPFavXo1vv76a41t165dQ9u2bfUep7rMOHbs2IJFR0RERFSGFCjx8vLyQqtWrdT/Hzt2DA4ODqhXr57W8pIkwdraGt7e3ujXrx9atmxZpGCJiIiISrMij2ps2bKl3kuNlIOjGktQWagDwHoYqizUAWA9DFUW6gCwHoYywToU96jGArV4vezIkSNwdHQschBEREREr4IiJV6tW7curjiIiIiIyrwiJV65JSYmIjQ0FM+ePdM7Z1fuPmJEREREr5IiJ17Hjx/H5MmTcfr06XzLSpKEzMzMop6SiIiIqFQqch+vzp07IyMjA0qlEl5eXnBxcYFCwbW3iYiIiF5WpMRr+vTpyMjIwKBBg7Bo0SI4OTkVV1xEREREZU6REq8LFy6gXLlyWLNmDczMzIorJiIiIqIyqUjXBC0tLVG9enUmXUREREQGKFLi1aRJE4SHh+sdxUhEREREOYqUeE2fPh2JiYn4/vvviyseIiIiojKrSH283Nzc8MMPP+Czzz5DcHAwRo4cCR8fH9ja2uo8xtPTsyinJCIiIiq1irxWoyRJEEJAMmB9pVd5Hi+u1ViCykIdANbDUGWhDgDrYaiyUAeA9TCUCdbBpNZq9PT0NCjhIiIiIqIiJl7h4eHFFAYRERFR2ccp5omIiIhkwsSLiIiISCZFutQYERFR4GM4qpGIiIheVUVKvLy8vArUuf5VHtVIREREVGKjGp8/f464uDgAgIWFBdzc3IpyKiIiIqJSr0RHNSYlJWHlypWYNWsWBg4ciDlz5hTldERERESlWpESr/w4ODjgs88+g7+/P9588034+vpi8ODBJXlKIiIiIpMly6jGzp07o2rVqli8eLEcpyMiIiIySbJNJ1GuXDmEhITIdToiIiIikyNL4hUTE4ObN2/CyspKjtMRERERmaQSTbzi4uKwd+9edOnSBenp6Wjfvn1Jno6IiIjIpBWpc72ZmZlB5YQQqFSpEubNm1eU0xERERGVakVKvIQQevfb2trC29sbXbp0wYQJE1CxYsWinI6IiIioVCtS4pWdnV1ccRARERGVeVwkm4iIiEgmTLyIiIiIZFIsM9enpaVh06ZN2L9/P27fvo3k5GTY29ujZs2a6NSpE/r37w+lUlkcpyIiIiIqtSSRXw/5fFy4cAFvv/02wsPDtXa2lyQJ1apVw5YtW9CgQYOinKpUS0pKgqOjIxITE+Hg4GDYQToWIC9WRXv6DVPS9SgLdQBYD0OVhToArIehykIdANbDUCZYh0J9f+tRpBavBw8eoEOHDkhISEDFihXx3nvvwd/fH66urnj8+DGuX7+OX3/9FaGhoejUqRMuXboEd3f3IgdNREREVBoVKfGaO3cuEhIS0KdPH6xfvx7W1tZ5ykybNg2DBw/GX3/9hblz52LZsmVFOSURERFRqVWkS43e3t6IjY1FdHQ07OzsdJZ79uwZKleuDGdnZ4SGhhb2dKUaLzWWoLJQB4D1MFRZqAPAehiqLNQBYD0MZYJ1KO5LjUUa1fjw4UP4+fnpTboAwM7ODn5+foiOji7K6YiIiIhKtSIlXvb29nj8+LFBZR8/fgxbW9uinI6IiIioVCtS4tWwYUM8ePAAmzdv1ltu06ZNiIyMRKNGjYpyOiIiIqJSrUiJ1yeffAIhBIYOHYrPPvsMYWFhGvvDwsIwfvx4DB8+HJIk4dNPPy1SsERERESlWZHn8ZoyZQrmz58P6f86xCmVSjg7OyM2NhZpaWkAchbTnjJlCubMmVP0iEspdq4vQWWhDgDrYaiyUAeA9TBUWagDwHoYygTrYFKd64GcKSV27tyJZs2aQZIkpKamIjIyEqmpqZAkCS1atMCuXbte6aSLiIiICCiGFq/cnj9/jrt37+LZs2ews7ND9erV2aH+/7DFqwSVhToArIehykIdANbDUGWhDgDrYSgTrIPRZ66/fv067t27BxcXFzRt2lRjn62tLerWraux7dSpU4iJiUH16tVRu3btokVLREREVIoVKPF68eIFOnbsiLi4OBw5csSgY7Kzs/HWW2/Bzc0Nt27d4mLZRERE9MoqUB+vTZs2ITo6GiNHjkTz5s0NOqZ58+Z47733EBkZme+0E0RERERlWYESrx07dhRqWoixY8dCCIG//vqrQMcRERERlSUFSrwuXryIypUrw9fXt0AnqVGjBtzd3XHx4sUCHacSFhaGlStX4r333kPdunVhbm4OSZIwe/bsfI89efIkevbsCWdnZ1hbW6N27dqYNWsWUlNT9R538+ZNDBo0CJUrV4aVlRV8fHwwYcIEPH36tFB1ICIiIipQ4hUXFwd3d/dCncjNzQ1xcXGFOnbx4sV4//338euvv+LKlSvIysoy6LgNGzYgMDAQO3fuhFKphJ+fH+7evYuvvvoKrVq1wosXL7Qed+TIETRs2BAbN25EVlYW/P398ejRIyxYsAANGzY0eJkkIiIiotwKlHhZWVkhJSWlUCdKSUmBpaVloY6tWLEiunXrhq+//hp79+5F37598z0mPDwcI0eORFZWFr799ltERkbiwoULuHPnDmrVqoWzZ89i0qRJeY5LTk5Gv379kJKSgk8//RRRUVE4f/48IiIi0KJFC4SGhmLkyJGFqgcRERG94kQB+Pr6ChsbG5GamlqQw0RqaqqwsbERvr6+BTpOl6FDhwoAYtasWTrLjB49WgAQHTt2zLMvKChIABAWFhbi0aNHGvu+/fZbAUD4+fmJzMxMjX33798X5ubmAoA4f/58gWJOTEwUAERiYqLhB+XMNlKyNzmwDqwH68B6vMp1YD1KdR0K9f2tR4FavAIDA5Gamoo///yzQMnd1q1bkZKSgsDAwAIdV1hCCGzfvh0AtLZONW/eHL6+vsjIyMDff/+tsW/btm0AgGHDhsHMzExjn6enJ9q3bw8ABX4MiIiIiAqUeA0bNgxCCHz++eeIjIw06JiIiAhMmjQJkiRh6NChhQqyoCIiIhAdHQ0AaNGihdYyqu2nT59Wb8vMzMT58+cLfBwRERGRIQqUeDVv3hxvv/02Hj58iCZNmmDr1q3Izs7WWjY7OxtbtmxB06ZN8fjxY/Tt21dnMlPc7ty5AyBnwW43NzetZby9vTXKAjn9wjIyMjT2G3IcERERkSEKvGTQmjVrEBUVheDgYPTv3x/Ozs5o0aIFqlWrBltbWzx//hxhYWEIDg5GTEwMhBBo1qwZ1qxZUwLha5eQkAAAKFeuHCQd6z6VL19eo+zLf6v2G3KcNmlpaUhLS1P/n5SUZEDkREREVJYVOPGytrbG0aNHMWPGDCxduhQxMTHYvn27RoIjhAAA2NnZ4ZNPPsGMGTNgYWFRfFHnQzVHl75RlKqli3KP0sw9t5euY7Udp83cuXMxc+ZMwwImIiKiV0KBEy8AMDc3x+zZszFp0iT8888/CA4ORlRUFJKTk2Fvbw93d3c0b94cXbt2haOjY3HHnC8rKysAQHp6us4yqtYoa2vrPMepjs39v77jtJkyZQrGjx+v/j8pKQkeHh4GRE9ERERlVaESLxUHBwcMGDAAAwYMKK54ioXqcuDTp08hhNB6uVF1qTD3JcXcfyckJKBy5coGHaeNUqnkguBERESkoUCd60uLGjVqAMhpnXr48KHWMqGhoRplAcDLy0t9SVS135DjiIiIiAxRJhMvT09PVKpUCQAQFBSktYxqe5MmTdTbzM3N0aBBgwIfR0RERGSIMpl4SZKE3r17AwBWrVqVZ39wcDBCQkJgYWGBHj16aOzr06cPgJzRmy+vCRkREYGDBw8CgEHLFhERERHlViYTLwCYOHEiLC0tsX//fnz33XfqkZb379/HiBEjAACjRo1St4ypfPDBB6hYsSJu3ryJ8ePHq+f1io+Px8CBA5GZmYkuXbqgYcOG8laIiIiISj1JqDISExYUFISePXuq/3/27BnS0tJgY2OjMbrw4sWLGiMH161bh+HDhyM7Oxvu7u5wcXHBtWvXkJGRgYYNG+LYsWOwtbXNc75Dhw6hW7duSE1NhbOzMzw9PXHz5k28ePECXl5eOHnyZJ6ELT9JSUlwdHREYmIiHBwcDDtIxxxkxUqOp7+k61EW6gCwHoYqC3UAWA9DlYU6AKyHoUywDoX6/tajVLR4ZWRkID4+Xn1TTenw4sULje0vXxocMmQI/vvvP3Tr1g0pKSm4ceMGvL29MWPGDJw4cUJr0gUAb7zxBs6dO4f+/ftDkiRcvXoVrq6uGD9+PC5cuFDgpIuIiIgIKCUtXmUBW7xKUFmoA8B6GKos1AFgPQxVFuoAsB6GMsE6vJItXkRERERlARMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpmU6cRr2LBhkCRJ7y01NVXrsSdPnkTPnj3h7OwMa2tr1K5dG7NmzdJZnoiIiCg/5sYOQA41atSAi4uL1n0KRd7cc8OGDRg6dCiysrLg7u4ODw8PXLt2DV999RV27dqFo0ePwsbGpqTDJiIiojLmlUi8vvjiCwwbNsygsuHh4Rg5ciSysrLw7bffYsKECZAkCffv30enTp1w9uxZTJo0CcuWLSvZoImIiKjMKdOXGgvju+++Q1paGjp27IiJEydCkiQAQNWqVfHbb78BAH755Rc8fvzYmGESERFRKcTEKxchBLZv3w4AGDlyZJ79zZs3h6+vLzIyMvD333/LHR4RERGVcq9E4vXnn3+iV69eaNeuHfr374+lS5ciMTExT7mIiAhER0cDAFq0aKH1vlTbT58+XXIBExERUZn0SvTx+ueffzT+/+OPPzB9+nRs3LgRnTt3Vm+/c+cOAECpVMLNzU3rfXl7e2uUJSIiIjJUmW7x8vHxwTfffIPLly8jKSkJycnJ2L9/P5o0aYKEhAT06tUL586dU5dPSEgAAJQrV07dt+tl5cuX1yirS1paGpKSkjRuRERE9Gor0y1e06ZNy7OtQ4cOaN26NQIDA3HmzBl8/vnnOHToEACo5+iytLTUeZ9KpRIAkJKSovfcc+fOxcyZMwsbOhEREZVBZbrFSxdLS0vMmjULAHD06FF165WVlRUAID09XeexaWlpAABra2u955gyZQoSExPVt8jIyOIInYiIiEqxVzLxAoBmzZoBALKzsxEaGgrg/19GfPr0KYQQWo9TJWmqsroolUo4ODho3IiIiOjV9somXhYWFuq/MzMzAeTMcA/ktGo9fPhQ63GqJE1VloiIiMhQr2zidf36dfXfVapUAQB4enqiUqVKAICgoCCtx6m2N2nSpIQjJCIiorLmlU28FixYAADw9fWFu7s7AECSJPTu3RsAsGrVqjzHBAcHIyQkBBYWFujRo4d8wRIREVGZUGYTrwMHDmDKlCkICwvT2J6YmIhPP/0UmzZtAgB89dVXGvsnTpwIS0tL7N+/H9999526r9f9+/cxYsQIAMCoUaPULWNEREREhpKErl7kpdyOHTvUrVfu7u5wc3NDRkYGbty4gfT0dEiShK+++gozZszIc+y6deswfPhwZGdnw93dHS4uLrh27RoyMjLQsGFDHDt2DLa2tgWKJykpCY6OjkhMTDS8o72OucSKlRxPf0nXoyzUAWA9DFUW6gCwHoYqC3UAWA9DmWAdCvX9rUeZTbwiIyPx888/4+TJk7h79y5iY2MhhEDlypURGBiI0aNH6+2nFRwcjLlz5yI4OBjPnz+Hl5cXBgwYgM8//1w97URBMPEqQWWhDgDrYaiyUAeA9TBUWagDwHoYygTrwMSrlGLiVYLKQh0A1sNQZaEOAOthqLJQB4D1MJQJ1qG4E68y28eLiIiIyNQw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8SIiIiKSCRMvIiIiIpkw8dJjz549aN++PSpUqABbW1s0aNAAS5cuRXZ2trFDIyIiolKIiZcO8+bNw5tvvolDhw6hfPnyqF69Oi5fvoxPP/0UvXv3ZvJFREREBcbES4uTJ0/iiy++gEKhwMaNG3Hv3j1cvnwZFy5cgKurK3bu3IkffvjB2GESERFRKcPES4vZs2dDCIFRo0ZhwIAB6u1169ZVJ1zz5s1DRkaGsUIkIiKiUoiJ10uSkpJw8OBBAMDIkSPz7H/77bfh4OCA+Ph4HDlyRO7wiIiIqBRj4vWSixcvIj09HVZWVmjQoEGe/RYWFggICAAAnD59Wu7wiIiIqBRj4vWSO3fuAAA8PT1hbm6utYy3t7dGWSIiIiJDaM8sXmEJCQkAgPLly+sso9qnKqtNWloa0tLS1P8nJiYCyLmUaVJMLZ7CKAt1AFgPU1IW6gCUjXqUhToArIcpKWAdVN/bQohiOT0Tr5ekpqYCACwtLXWWUSqVAICUlBSdZebOnYuZM2fm2e7h4VHECIuZo6OxIyi6slAHgPUwJWWhDkDZqEdZqAPAepiSQtYhOTkZjsVQfyZeL7GysgIApKen6yyjasmytrbWWWbKlCkYP368+v/s7Gw8efIETk5OkCSpmKL9/5KSkuDh4YHIyEg4ODgU+/3LhfUwHWWhDkDZqEdZqAPAepiSslAHQJ56CCGQnJwMNze3Yrk/Jl4vMeQyoiGXI5VKpbplTKVcuXJFDzAfDg4OpfpNpMJ6mI6yUAegbNSjLNQBYD1MSVmoA1Dy9SiOli4Vdq5/SY0aNQAAERERyMzM1FomNDRUoywRERGRIZh4vaR+/fqwsLBAamoqLly4kGd/RkYGzp49CwBo0qSJ3OERERFRKcbE6yUODg5o3749AGDVqlV59m/duhVJSUlwcnJCmzZtZI5ON6VSienTp+e5vFnasB6moyzUASgb9SgLdQBYD1NSFuoAlM56SKK4xkeWIUFBQQgMDIQkSfj999/VywZdvnwZnTp1wuPHjzF//nxMmjTJyJESERFRacLES4c5c+Zg6tSpAHImTLWzs8O1a9eQnZ2NN998E3///TfMzMyMHCURERGVJky89Ni9ezcWLlyI8+fPIyMjAzVq1MDw4cPx8ccfM+kiIiKiAmPiRURERCQTdq4nIiIikgkTLyIiIiKZMPEiIiIikgmXDCIiIiKTERERUSz34+npWSz3U9zYub4MSEhIwLNnz6DvqTTVFyAR6ff8+XMEBQXh9u3bSE5Ohr29PWrWrIkWLVrA1tbW2OG9UoKCgnDs2DFERUUhNTVVY5Lt8PBwpKeno2bNmkaMMH+JiYkIDQ3N9zujVatWMkalSaFQQJKkIt2HJEk6l/0zNiZepdTt27cxY8YM/Pvvv0hMTNRb1pRfgE+ePMH333+PvXv3qj8MdDGFenz99ddFvg9JkjBt2rRiiKbwjh8/Xiz3Y8wP57IuPT0d06dPx48//ojnz5/n2W9ra4tPPvkE06dPh6WlpREiNNzz58+xa9cuXL58GU+ePEFGRobWcpIkaV0xxNju3r2LQYMG4dy5cwAAIQQkSUJWVpa6zOjRo/Hzzz/j6NGjCAwMNFaoOh0/fhyTJ0/G6dOn8y1r7M9aLy+vIideABAWFlYM0RQ/Jl6l0KVLl9C6dWv1LxYrKys4OztDodDdZc8UX4BhYWEIDAxEdHS03l9euWVnZ5dwVPqpfokV5m2jOu7lD2xjKAu/KItjLj1j10GXrKwsvPnmmzhw4ACEEKhSpQp8fX3h6uqKx48fIyQkBA8ePIAkSejQoQP++ecfk51bcPPmzfjwww+RlJSk3qZ6/+R+DZrKe+Nljx8/RoMGDRAdHY2AgAB069YN69evx7179zRiPXPmDJo2bYpPP/0UixYtMl7AWhw5cgSdO3dGRkYGlEolvLy84OLiovc748iRIzJG+GphH69S6IsvvkBycjLeeOMNLFy4EK+99pqxQyqUzz//HA8fPsTrr7+O2bNnIyAgAC4uLsXyS6ekTJ8+3dghFItWrVqZ9ONsiOL4zWiqvzt//vln7N+/H66urli6dCn69u2bJ0n566+/MGbMGBw4cAC//PILPvzwQyNGrN3JkycxePBgWFtb48svv8Qff/yBu3fvYuXKlYiMjMTly5exa9cuKJVKTJ06FW5ubsYOOY9vvvkG0dHR+Oijj7BkyRJIkoT9+/fj3r17GuUaN24Me3t7BAcHGylS3aZPn46MjAwMGjQIixYtgpOTk7FDeqWxxasUKleuHLKzsxEdHV2q+3g4OTkhJSUF4eHhcHFxMXY4RCajadOmOHv2LM6ePYsGDRroLHfhwgU0atQIjRs3xqlTp2SM0DB9+/bFjh07sGPHDnTv3h2BgYEIDg7WaCkKCQnB22+/jYSEBJw/fx6urq5GjDgvHx8fxMTEIC4uTr0Qs7Z6AED9+vXx6NEjREdHGyNUnezs7GBpaYnY2FiTbRl9lbDFqxTKzs5GrVq1SnXSBQCpqanw9fVl0kX0kps3b8LPz09v0gUADRo0QO3atXHjxg2ZIiuYkydPomLFiujevbvOMr6+vvjrr7/g5+eH6dOnY8WKFTJGmL+oqCjUrl1bnXTpo1QqkZCQIENUBWNpaYnq1auXmaTr6NGj2L9/f54BJ506dULr1q2NHV6+mHiVQvXq1UNoaKixwygyPz8/PHnyxNhhlIhnz56pPxDs7OyMHQ6VMllZWbCwsDCorIWFhdH7PuoSHx+P119/Xf2/ahDA8+fPNX441qxZE/7+/ti7d6/sMebHzs4OsbGxBpWNiIgwyct4TZo0wfnz59X96Eqr8PBwDBw4UD1AIPcFO0mSMH/+fDRr1gy///47vLy8jBRl/jiBaik0ZcoUREdHY/369cYOpUjGjh2L8PBw7N+/39ihFItr165h2LBhcHNzg6OjI6pUqQJHR0e4ublhxIgRuHbtmrFDfKWEhYXhl19+wYQJE/C///0PEyZMwC+//GKSA01e5uPjg2vXriE8PFxvubCwMFy7dg0+Pj7yBFZAqu4EKhUrVgSAPP2jgJxk8/Hjx7LFZqj69evj4cOHuHr1qt5yx44dw6NHj9C0aVOZIjPc9OnTkZiYiO+//97YoRRaQkIC2rZti1OnTsHCwgL9+/fH7NmzsXLlSsyePRv9+/eHhYUFgoOD0a5dO5NseVQTVCqtWLFC2NnZibFjx4qrV6+KFy9eGDukQpkyZYooV66cWLRokUhKSjJ2OIX266+/CqVSKRQKhZAkSetNqVSKX3/91dihCiGEUCgUQqFQiNq1a+fZZujNzMzMiDXQ7cmTJ6J///7CzMxMHaskSRpxDxw4UDx58sTYoeo0a9YsIUmSeP3118Xly5e1lrl06ZJ4/fXXhUKhELNnz5Y5QsM0a9ZMVKhQQf3/vHnzhCRJYtKkSRrlLl26JMzNzUWVKlXkDjFfmzZtEpIkiUaNGomHDx8KIYRo2bKlUCgU6jJ3794V3t7eQqFQiL179xorVJ3u378vli1bJpRKpejVq5fYtWuXuHHjhrh//77Om6n5/PPPhSRJIjAwUERFRWktExUVJQIDA4VCoRCTJ0+WOULDsXN9KVTQ6/SmOmTe29sbAPDgwQN1J9WKFSvq7LsmSZLWX8rGdvr0abRo0QLZ2dno2rUrxowZA39/f/XQ/+vXr2Px4sXYs2cPzMzMEBQUhMaNGxs1ZtUw8lq1auHmzZsa2wrC1C5xpaSkoEWLFrh8+TKEEGjWrFme5+LkyZOQJAn16tVDUFAQrKysjB12Hi9evEDTpk1x7do1SJKEli1bonbt2nBxcUFMTAxu3LiBEydOQAiB119/HSdPnoS1tbWxw85j+vTpmD17Nq5cuQJ/f39ERkaiVq1aSEtLw4ABA9CsWTM8fvwYy5cvR0JCAj755BOTm4oBAN555x38+eefcHR0RKdOnXDy5Ek8ePAAX375Ja5du4Y9e/YgPT0dgwcPxtq1a40dbh65p8Ex5FKjKX5n+Pn5ITw8HPfv39fbL/jx48eoWrUqvLy8EBISImOEBWDUtI8KRVeLir6bKSpoHXL/wjQlffv2FQqFQsydO1dvufnz5wtJksRbb70lU2Svnjlz5ghJkoSfn584e/as1jJnz54VtWvXNug5M6bY2Fj1ayv3eyD332+99ZaIjY01dqg6Xbt2TbRv31789ddf6m1r1qwRlpaWGq2QkiSJZs2aieTkZCNGq1tGRoaYPHmysLKyyvOZpGrN/vzzz0VmZqaxQ9WqatWqwsvLq0A3U2NtbS0aNWpkUNmGDRsKa2vrEo6o8NjiRUZz//79Ah9TtWrVEoikaCpVqgQAePTokd5yQghUrlzZoLJUOPXq1cP169dx69YtdYuqNvfu3YOvry/8/f1x6dIl+QIshLt37+LAgQO4ffs2nj17Bjs7O9SsWRMdO3Y02b5d+QkNDcWWLVsQHh4Oa2trtGzZEr169TL5UXdxcXHYs2cPrl69isTERNjZ2aF27dp488031e9tKhnly5dHxYoVcefOnXzL1qxZE7GxsSbbz4uJF1ERKZVK1KtXz6ClOJo0aYLLly8jNTVVhsgKpl27drCyssKOHTtMfgkaXezs7FCjRg1cvHgx37L169fHnTt39C5TRUSmoXXr1jhx4gQOHDiAdu3a6Sx3+PBhtG/fHq1atcLRo0flC7AAOKqRqIhcXFxw9+7dfPtEZGRk4O7du+qRXabm5MmTiImJKbVJF5DT/1HXOoAvy8jIKFS/NjIcR/Kahr1795rsKg2Geu+99yCEQJ8+fbB06VKN0bJATr/IJUuWqFd5eO+994wUaf7Y4kVURIMHD8bGjRsxduxYLFiwQGe58ePHY/HixRg0aBDWrVsnY4SGqVWrFiwsLEr1l2Xjxo1x/vx5XLhwAXXr1tVZ7tKlS2jQoAECAgIMaqk0lrS0NGzevBn79u3LM1lkx44d0b9/f5McHKBiZmYGf39/DBw4EP369UO1atWMHVKRJCYmIjQ0VL1Ori6mtni8QqGAm5sb3n33XQwdOhR+fn7GDqlQBg0ahE2bNkGSJFhZWcHT01M94CQiIgKpqakQQmDQoEEmPd0SEy8Tp+qnUr16dfV8V/r6rmhjCqMBVU3DVatWxerVqzW2GUqSJBw6dKjYYyuqGzduoGHDhkhPT0f9+vXx0Ucf5RmBtmzZMly6dAmWlpY4f/48ateubeyw8/jiiy/w7bff4saNG6hZs6axwymUpUuXYsyYMXB3d8fy5cu1zpi+c+dOfPzxx4iKisLixYvx8ccfGyHS/AUHB+Pdd9/F/fv3tX7JS5IET09P/P7772jRooURIsyfq6srYmNj1SPpmjZtigEDBuCdd94pVStWHD9+HJMnTzYoSTfFEYF16tTB9evX1c9DQEAAhg0bhv79+6NcuXLGDa6Ali1bhu+++w6RkZF59nl6emLixIn46KOPjBCZ4Zh4mTjVpRBfX1/1siAFvTwiSVKeNcXkVlbqocvOnTsxZMgQJCUlaR2uLYSAg4MD1q9fr3f5FGN68eIFWrVqhefPn2Pz5s16W4xMVWZmJjp16oQjR46oExPVslQxMTG4efMmIiMjIYRAu3btsG/fPpPs0H39+nU0btwYKSkpqFSpEkaNGgU/Pz+4urqq67Fq1So8fPgQ1tbWOH36NF577TVjh51HdnY2Dh06hI0bN2LHjh1ITEyEJElQKBRo164dBg4ciN69e8PBwcHYoep05MgRdO7cGRkZGVAqlfDy8oKLi4vez68jR47IGKFhLly4gDVr1mDz5s2Ii4uDJEmwtLREz549MWzYMHTq1KlUzWp/8+bNPANOSktLHhMvE6ca+WdhYQE3NzeNbQVh7NGAx44dAwDY2NggICBAY1tBmPI6XNHR0fjxxx+1jkDr1KkTPvzwQ5Me+TRixAikpKTgzz//RHZ2Nvz9/eHn56d3XrVVq1bJHGX+UlNTMXXqVKxYsQIvXrzIs9/GxgYffvghZs2aZbKX6Xr37o2///4b7777LlatWqV1+aCMjAyMGjUK69evR69evbBt2zYjRGq49PR07NmzBxs3bsQ///yDlJQUSJIEpVKJrl27on///ujevbtBayLKqVWrVjhx4gQGDRqERYsWmeSSQAWRmZmJ3bt3Y+3atdizZw8yMjIgSRIqVaqEwYMHm+ylyLIw+EeFiRcRAdCcZNEQptwCCQDJyck4ceJEniS4ZcuWsLe3N3Z4ejk5OSErKwuPHj3SmxympqaiUqVKUCgUpWrd0+fPn2P79u3YuHEjDh48iMzMTEiSBHt7ezx9+tTY4Wmws7ODpaUlYmNjTbJ1tCji4+OxceNGrFmzBhcvXlS3eDVq1AjDhw83qUuR1tbW8Pf3x7lz54wdSpEx8SIiACjUjNtDhw4tgUjI3t4etWvXNniKkhs3biA5OVmGyIpfdHQ0Ro4ciX///dckk/kKFSqgevXqOHPmjLFDKTFZWVlYsGABpk6dqu6fpmqNHDBgAKZOnWr0gRFlYfCPChMvInqlJCQkoHz58sYOQ6/GjRsjKioKUVFR+ZZ1c3ODp6cnTp06JUNkxUMIgaNHj2Ljxo3Ytm0bnj59CiEEzM3NkZ6ebuzwNHTp0gXnz5/H48ePS1UfKENcv34da9euxYYNG/Do0SMIIVCxYkUMHDgQjx8/xt9//43U1FTY2Nhg7969CAwMNFqsZWHwjwoTLxM3YsSIIt+HqfbFUdm3bx/+/ffffIdpm+qoRiCnv83q1auxd+9eg+ph7FGmZdWtW7ewb98+NGjQAC1btlRvT09Px4QJE/Dbb78hJSUFXl5e+Omnn9CxY0cjRqvbxo0b8e6772LhwoUYM2aMznJLlizB2LFjsXHjRvTv31/GCAvnzJkz2LRpE7Zs2aL+ogdyEk3VlBOurq5GjlLTqVOn0Lp1a8yePRsTJ040djhFprq8uHbtWly8eBFCCJiZmaFjx44YMWIEevTooe5T+OTJE0yfPh0//vgjmjRpgpMnTxot7rIw+EeFiZeJK44JHk2x+R4AkpKS0KtXLxw7dsygfkWmWo+4uDi0a9cO169fL9X1eNnt27fzzB1l6r80P/30U/z444/YtWsXunbtqt7+xRdfYN68eRpllUolbty4YfRLKLp89913mDZtGrp27YrRo0fDz88PLi4uiI2Nxc2bN7F8+XL8888/mD17NiZMmGDscHW6fv06Nm3ahM2bNyMsLEz9HvHz88OAAQMwcODAAk+RI6eIiAjs2rULn332Gbp06YKRI0fCx8dH56ATIGdaA1OSu0P93r17kZGRASEEatSogWHDhmHYsGF6B/7UqlULkZGRWgeryKWsDP4BmHiZvOJa6d4U++J8+OGH+Pnnn1GhQgW8//77qF+/PpydnfU255viqMZRo0bht99+Q5UqVTBp0iQEBATkO9zc2KNM9fnpp5/w/fffIzw8PM++qlWrYuLEifjwww/lD8wAdevWxd27d5GcnKx+/NPS0uDq6oq0tDTs3LkTTZo0wezZs/H9999j9OjRWLZsmVFjLo4O26Y4dxQAvP7667h+/TqAnMuLHh4e6N+/PwYOHFhqWixyDzox5FKjKT4XLi4uiI+PhxACtra2eOuttzBixAiDLx22adMG//33n1F/MJalwT9MvMhoXF1d8fTpU1y4cAH+/v7GDqfQKlWqhISEBFy/fh3Vq1c3djiFlpWVhXfeeQc7duyAEAJWVlbw8PBQzx2lmhlakiT06NEDf/31l8ktuePi4oIKFSogJCREve3QoUPo0KEDBgwYgA0bNgDIufTo6uqKKlWq4OrVq8YKF0DxtGoDOXNmmRqFQgEnJye89dZbGDhwoFH7CBWWl5dXgft2hYWFlVA0haNQKNCsWTOMGDEC/fr1g52dXYGOv3z5Mp4+fWrUH75lafCPubEDoFfX8+fPUatWrVKddAE5y4jUqlWrVCddALBo0SJs374djo6OmDVrFkaMGAEbGxv1/pSUFKxatQrTp0/Hzp07sXDhQnz22WdGjDivp0+f5rl0+N9//0GSJHTp0kW9zdLSEt7e3rh7967cIeZhiglTcdm1axc6deoEc/PS+1WjreW3tAkJCSlSNwFTaJ001SSqMNjiRUbTqFEjJCYm4s6dO8YOpUjq1KmDjIwMjVaW0qh27dq4desWjh07ptEx/WUnTpxAq1atNFYhMBXOzs6wtLTUGA3YunVrnDhxAmFhYRp9bxo1aoQ7d+4gMTHRGKES0SuKiVcp9PXXXxeovCRJmDZtWglFU3irV6/GyJEjcfjwYbRp08bY4RTa4sWLMX78eJw5cwYNGzY0djiFZm1tDS8vL9y8eTPfsn5+fggPD0dKSooMkRmuU6dOOHjwIHbs2IHu3bvjypUraNCgAapXr54nMa5QoQJcXFxKfcJMlJ+kpCSEh4fDyckJ7u7uGvu2bduGlStX4uHDh2jYsCG+/vprVKlSxUiRGiYyMhL//fcfoqKikJKSgq+++kq9TzVwwJRnt2fiVQrp62T4cl8EVYdQU+1kOGbMGKxfvx4zZ87E8OHDC9z3wBQIITB48GAcO3YMy5YtQ8+ePY0dUqG4u7ujUqVKOH/+fL5lGzRogJiYGDx48ECGyAy3a9cu9OzZExYWFqhTpw5u376N58+fY9GiRfjkk0/U5c6dO4fGjRvjnXfewebNm40YsX6PHz/Gr7/+imPHjiEqKgqpqakaU5Hs2LEDMTExGDJkiNGXP1Itel+1alWsXr1aY5uhTHnKGJXU1FQkJCQgIyNDZxlTG9U4Y8YMzJo1CytXrtSYomjt2rUYMWKExneJh4cHrl69apLrZ8bFxeGjjz7CX3/9pRFz7u+3d999F5s2bTLpH8Kl98L7K2z69Ok69z1//hx3797Fvn37kJWVhdGjR8PR0VHG6Arm22+/RWRkJMaOHYuxY8fC2dlZo19RbqY6/9Ubb7wBAIiJiUGfPn1Qvnx5vcPNTfXLpXPnzli/fj0iIyPh4eGhs1xERASuXbtmkn0uunfvjiVLlmD69Om4cOEClEolJkyYoJF0AcCvv/4KACY7jxeQk1QNGzYMycnJ6i+Zl39Y3bhxA9OmTYOzszN69+5tjDDVjh49CgDw9fXNs81QpjpBaVpaGr799lts2LAh364Rpjiq8cCBAzAzM8M777yjsX3GjBkAgMmTJ6Np06ZYvHgxjh49iuXLl2Py5MlGiFS35ORktG7dGjdv3oSHhwfat2+PAwcO5JlkeNSoUeqJeU018WKLVxn14MEDDBo0CHFxcTh58qRJ/np5/Pgx2rdvjxs3bpTq+a8KOirNVOvx+PFjNG7cGBUqVMCaNWu0dqi9cuUKhg4dioSEBJw5cwYuLi5GiDR/WVlZiIuLg7Ozs9bn5+bNm0hPT0eNGjV0JvrGdOnSJTRp0gRCCIwZMwY9evTA+PHjceHCBY3XTlhYGHx8fDBw4ED8/vvvRoz4/y96b2Njg4CAAI1tBWFqU8a8ePECrVu3xoULF2BhYQFJkpCWlgZ3d3c8evRI/XwolUpUqlQJgOmNanR3d4eFhYXGQIELFy6gUaNGaNeuHQ4ePAggZ3JVd3d31KlTB2fPnjVStNpNmzYNc+bMQd++fbFu3TpYW1sjMDAQwcHBGu+J7Oxs2Nvbo27duggODjZixLqxxauMqlKlCjZv3oyqVati2rRpWLx4sbFDyuPzzz9XT8EwceJE1KtXL995vEzRkSNHjB1Csfj555/Rs2dPrFixAg0aNECzZs3yTNoZHBwMc3Nz/O9//8OKFSvy3Iep9Cc0MzPTOwO6n5+fjNEU3DfffIPMzEz8+uuvGD58OABovZRYrVo1uLq64sqVK3KHmIe2hMnUkqjCWLhwIc6fP4+3334ba9euRYcOHRAcHIzIyEhkZWXh2rVrmD9/PrZs2YKhQ4eqW5FMSXx8POrVq6ex7dixY5AkCb169VJvc3JyQs2aNXH//n15AzTAn3/+CaVSiV9//RXW1tY6yykUClSvXh0REREyRldAgsq0+vXrCw8PD2OHoZWrq6uwtrYWUVFRxg6FhBCSJAmFQiEkScr39nI51f8KhcLY1SgTXF1dhbOzs8a2li1ban18AwICRPny5eUKrUBmzpwpvv76a5GWlmbsUAqtXr16wtLSUjx+/FgIoft5mDdvnlAoFGLTpk1yh5gve3t7UaNGDY1tvXr1EgqFQly+fFlje+PGjYWNjY2c4RnEyspKvP766xrbdD0XTZs2FUqlUq7QCowtXmVcYmIiYmNjjR2GVs+fP4evry/c3NyMHQpBf99BU6Qa3VuxYkWMHj1aY5uhTKWF7mUJCQmoU6eOQWWFEEhLSyvhiApn1qxZqFmzpkk+xoa6e/cuqlWrpr6srmqRz8zM1JifbOLEiViwYAGWLl1qcutm+vr64vz587h9+zZq1qyJhIQEHDhwAE5OTnj99dc1yj58+NAkuxBYWVkhOTnZoLLR0dEm3beZiVcZtnfvXoSFhZnsxJ516tTJ0zGSjKe0JV4zZsyAJEmoVauWOvFSbRP59BnMvQSMKSYFzs7OBl3uycrKwu3bt032x4urq6tJ9qErqNxf4qqR13Fxceo+XUDOJS4vLy/1EkmmZNCgQTh37hw6duyIt956C4cOHUJKSgref/99jXL3799HVFSUSQ468ff3x+nTp3H//n29S65dunQJERER6Ny5s4zRFQwTr1Jo3bp1OvcJIRAbG4uzZ89ix44dkCTJJEefATm/EPv27YstW7bkGW1jqlRDsStXrow5c+ZobDOUKS/eWpqoEsWKFSvm2VbatWzZElu3bsXff/+td3qSNWvWIDk52eRaWFTat2+PLVu2IDEx0aRbIPRRdaJXUS3offr0aY3nJiMjA6GhoSY3ohEAPv74Y/z333/Ytm0bfvjhBwBAkyZN8rxf1q9fDyDneTM17777LoKDg/H+++9j+/btWhP6hIQEjBw5EpIkYciQIUaI0kDGvdJJhaHqS6Pvpup7M2rUKJGVlWXskHVaunSpsLe3F+PHjxfXrl0TKSkpxg5JL9Xj6ufnl2eboTf2g6L8nDlzRigUCuHi4iJ2794thMjbn2Xt2rXC1tZWWFhYiOvXrxsrVL3CwsJE+fLlRe/evcWLFy+MHU6h9OvXT1haWornz58LIYT4+++/hSRJonbt2uLOnTtCCCFSU1PFhx9+KCRJEi1btjRmuHqdP39ebN68WQQFBYns7Ow8+zds2CAWLVokHjx4YITo9MvMzBStWrUSkiQJb29v8fnnn4saNWoIhUIhVq1aJcaNGyecnZ2FJEmiU6dOxg5XL04nUQoNGzZM58g/SZJga2sLb29vdOnSRWNOHVNjZmZWoPKmMD+OaqFWR0dH9WigsrR4q8qpU6dw+fJlPHnyROdEkaZ6ma6sWLhwISZMmAAgZ/Hv1NRUJCUloUWLFrh58yaePHkCAFi2bBk+/PBDY4aq07p16xASEoLvvvsOLi4u6Nu3L/z8/HTOcQfA5FoqNm3ahEGDBmHr1q3o27cvhBBo0aIFTp06BUmSUKFCBSQlJSEzMxOSJGHv3r3o0KGDscMuk5KTk/H+++/jjz/+0OhSkPvvd955B6tWrdL7GjM2Jl5kNAWd/woo2wsKm4Ljx49j5MiRCA0N1VtOmOiKCN7e3mjcuLFBs9EPGDAAZ86cMclJeVX+/fdffPnll7h48WKefa+99hrmz5+vsfi3qXl5lQ1DpooxtddUamoqzp8/Dw8PD/WM9ImJiRg3bhy2bt2K58+fA8jpgzR37lx069bNmOG+Eq5evYrt27fj6tWrSExMhJ2dHWrXro3evXub7KSpuTHxIiIAObOgBwQEICMjA/3798exY8fw4MEDfPHFF4iMjMTly5dx+fJlWFtb48MPP4S9vb3J9alSKBRo2bIljh8/nm/Ztm3b4vjx4yb3Ra9NREREni8ZUx00k5u+1nldVMsNlQZZWVmIjY2FtbV1qenDFh0djStXruhtzQaM2/K4bt06uLq6olOnTkaLoSQx8SIqZikpKbh37x6Sk5Nhb28PHx8fvRP+mYohQ4Zgw4YN6kk7tc0KvX//fowcORLlypXDyZMnTW5tzYIkXs2aNcOFCxdMdioGouIUEhKCjz76KN9lnEyhNVuhUCAwMFBj5YOCtGabOo5qLMVCQkKwb98+hIaG4tmzZzqH0HMUnTz27t2Lb7/9FkFBQRofWqpkYOLEiejatasRI9Tv6NGjcHR01Nv/rGPHjti2bRuaNGmCr7/+Gt9++62MERafuLg43LhxQ+/s9kRAzmjF6Oho2NrawsnJSWe5+Ph4PH/+HG5ubhrze5mCyMhIBAYGIj4+Hi1btsTt27cRGxuLgQMHIjIyEjdv3lS33PXp08fo8WvrzxseHo4qVaoYKaJiZoQO/VRE6enpYtiwYXlGMHIUnfGMGTNG47mwsrISHh4ewsrKSuN5+OSTT4wdqk5KpVLUr19f/X+bNm2EQqHQOhrN19dX+Pj4yBmeVmvWrBFt27ZV3yRJEuXKldPY9vKtadOmwsHBQSgUCjFixAhjV0GrNWvWCIVCIWbOnKm33Ndffy0UCoXYsGGDTJEVXXJysnj48KFITk42digG+fHHH4VCoRALFy7UW27hwoVCoVCIn3/+WZ7ACkA14nLu3LlCiLwjZDMyMsTPP/8sHB0dxRtvvCEyMjKMFaoQQggXFxfh6uoq0tPT1dskSRKBgYFGjKr4mFZaTgb56quvsHbtWlhaWqJPnz6oX79+qVzjUGX9+vXYsGGDehSdrpGLpjCqUZvVq1djyZIlsLCwwCeffILRo0er5/oBchbM/fHHH7Fs2TL8+OOPqFevXoHn/pKDo6OjRktdhQoVAORMqvjy6FhLS0uNBXeNJTw8XOPSiSRJSExMzPdyCgC0a9cO8+bNK7ngikA1auvlCS5fNnLkSMyYMQObN2/GwIEDZYqu4K5evYoffvgB+/btw+PHj9XbVf14xo0bl2cGdVPx559/QqFQYNiwYXrLDRs2DBMnTsTWrVvzfd7ktn//ftjZ2WHcuHFa95ubm+P999+Hp6cnunbtivnz5+PLL7+UOcr/r23btti6dSsCAwPRtm1bdVeNiIgIg1enMOVR1+zjVQp5eHggOjoahw8fRqtWrYwdTqFlZWWhd+/e+Oeff/KdaVzFFEc1NmjQAJcvX8bWrVvRp08fneW2b9+Ovn37on79+jh//ryMERomICAA9+/fR0xMDICcWeBnzZqFefPmYeLEiepy4eHh8PX1haOjo8aXqDHcv39fnQAKIdCuXTvUqVMHS5Ys0VpekiRYW1ujWrVqGhOvmpqqVatCCGHQQr+enp4wMzNDWFiYDJEV3KJFi/D5558jMzNT5/vc3Nwc8+bNw/jx42WOLn/u7u5QKpX5jvQFcvohZWZmmtwCzdbW1qhZsyYuX74MAGjTpg3+++8/vHjxAkqlUqOst7c3rK2tjToD//3799G2bdtC/bjLvSqFqQ6cYYtXKRQfH4+aNWuW6qQLAJYvX47du3ejdevWWLVqFYYOHYrg4GCkp6erR9HNnz8fly9fxtKlS02ylQjI6WtXtWpVvUkXAPTu3RteXl64efOmTJEVTNu2bbFgwQKEh4fDy8sLAwYMwOzZs/Hll18iMTERzZo1w+PHjzFv3jxkZGQYvb+atpFPrVu3xuuvv47WrVsbMbKie/z4MerVq2dQ2cqVK+PKlSslG1Ah/f333+pkqk+fPhg9ejT8/Pzg4uKC2NhY3Lx5Ez/++CO2b9+OiRMnonr16ujRo4eRo9YUHx+PunXrGlTW2dnZJJ8LGxsbjX5bqhGYUVFRGq3zAFCuXDncunVL1vheVrVqVXUf5pCQELx48QIzZ86Ep6cnhg8fbtTYioURL3NSIfn7+wt/f39jh1FkTZo0Eebm5iIsLEwIoXul+U8//VSYmZmJ/fv3yxyhYVxcXESjRo0MKtuoUSPh6upawhEVzqlTp0T16tXF+vXr1du++eabPCslSJIkfHx8xKNHj4wYbU6fj1atWmls8/LyEu+8846RIio+Li4uwt3d3aCy7u7uomLFiiUcUeE0b95cKBQKsWzZMr3lfvzxRyFJkmjRooVMkRnOzc1NODs7G1TW2dnZJN/fdevWFZUqVVL/P3nyZK390WJjY4WNjY0oX7683CHmqyz18WLiVQp9//33QqFQiCtXrhg7lCJxdHQU1atXV/8fGBgoFAqFyMzM1CiXmpoqHB0dRYcOHeQO0SDvvPOOsLa2FvHx8XrLxcXFCWtra9GvXz+ZIisex48fF//73/9Ep06dRK9evcT3338vkpKSjB2WUCgUonnz5hrbysqHc5cuXYRCocj3x8b+/fuFJEkm+96wtbUtUAJpa2tbwhEVXO/evYVCoRAbN27UW27Tpk1CkiTRo0cPmSIz3EcffSTMzMxEdHS0EEKIc+fOCUmShL29vVi5cqW4du2aOHTokGjWrJlQKBTirbfeMnLEea1Zs0bs3bvX2GEUCyZepVBWVpbo1auX8PT0FDt37jR2OIVmbW0tmjRpov6/U6dOQqFQiJiYmDxlAwICRIUKFeQMz2C3b98W5cqVE+3bt9cauxBCxMTEiPbt24vy5cur13ejoinLI5+2bNkiJEkSLi4uIigoSGuZ4OBg4erqKhQKhdi0aZPMERqmQoUKonHjxgaVNdX3uCq5tbOz0zl6dOPGjcLOzk4oFAqTTA72798vzM3NxcqVK9XbRo8erbU128nJiZ9RJYyd60spIQT69u2Lv//+GxUqVICPj4/W1dqBnM6Ghw4dkjnC/NWoUQNZWVnqTqvvv/8+Vq1ahX379qF9+/YaZd3c3JCQkICUlBRjhKq2bt06rdtv376Nb7/9Fubm5ujTp0+efix//fUXsrKyMHHiRNSsWdPk1qMDCjZb9P79+/Ho0SOj1qN///7YunUrAgIC1COfZsyYAU9PT4P7A5ryyKc+ffpgx44dkCQJTZs2RdOmTVGuXDk8ffoUp06dwqlTpyCEQK9evbBt2zZjh6vVm2++iRMnTuDRo0d6JxF+8eIFKleujMDAQOzevVvGCA3z8ccfY/ny5ZAkCW5ubmjcuLH6uTh79iyioqIghMDo0aOxbNkyY4drsHXr1mHjxo0IDw+HtbU1WrZsiUmTJsHDw8PYoZVtRk37qFCSk5NF+/btDZrDy5Tn8erZs6ewsrJSt1hs3LhRSJIkmjdvLp4+faouN3v2bCFJkmjQoIGxQlV7+Rfiy78Wcz/m+rabIm19pnRRzfFlTOHh4aJatWoGvQe0vSdM+b0hRM58fWPHjhWWlpZ54pYkSVhaWorx48eLtLQ0Y4eq0+nTp4VSqRSDBg3SGWd6erp49913hVKpFGfPnpU5QsMtWrRIVKxYUevrydnZWSxZssTYIZYZqs/J2rVr59lm6M3MzMyINdCPoxpLoS+//BKHDh2Ck5MT3n//fdSrV69UzuPVvXt37Ny5EwcPHkSXLl3Qt29fzJw5EydPnkSVKlXg6+uLmJgYPHjwAJIkYcKECcYOGUOGDCl1j3NBCAMbwA0tV5LK+sgnCwsLLFy4EJMmTcKePXtw8+ZNJCUlwd7eHv7+/ujatSsqVapk7DD1Sk1NxdSpU/H111/j0KFDGDlyZJ7W4FWrViE+Ph5fffUVXrx4oXW5J1MYwT1mzBj873//Q1BQUJ7nokWLFnmmZaDCU32+5J4+qKCfOabwGaULLzWWQlWqVEFsbCwuXryI2rVrGzucQktMTMTu3btRt25dvPbaawBylrYYOnSoxgSY5cuXx6xZszB69GgjRfpqKMg6h76+vnj48CGSkpJkiMxwBakDlTyFQqGeVwmA1h8t+vaptpvixMlEhcUWr1IoISEBvr6+pTrpAnLmkhk0aJDGNg8PDxw+fBjR0dG4f/8+rK2t4e/vb/S1w8qiiIiIPBMUJiYm6k1aUlJScOzYMdy+fRtNmjQp4QgLbvXq1Vx/0YS0atWqTLcQm6LimO+Q6/uWLLZ4lUINGjTAs2fPcPv2bWOHQqXYzJkzNZbfEP8323N+VB8ZmzZtQr9+/UosPvr/UlNTkZCQgIyMDJ1lPD09ZYyobFLNOG9hYYHKlStrbCsIYz4XCoWiyPdhyrO+lwVsRiiFRo8ejffffx9Hjx5FmzZtjB1OoXl7e6Nx48bYvHlzvmUHDBiAM2fO4N69ezJEVjC6RjrqYwqjGr28vDT6zhw7dgwODg46Z0xXLbfj7e2Nfv36oWXLljJFWnilOWFJS0vDt99+iw0bNuDOnTt6y/JyXPHw8vKCJEnw9fVVL5mj2mYoYz8Xq1evNtq5yTBs8SqlJkyYgN9++w0zZ87E8OHDYWdnZ+yQCqwg/XHatm2L48ePm+SvMFU/FkMIE15DrKz0jyoLCcuLFy/QunVrXLhwARYWFpAkCWlpaXB3d8ejR4/Urx+lUqnuYG+qazWWJqoky8fHBwcPHtTYVhB8LkrGkydP8P3332Pv3r0IDQ3Fs2fPdJY11fc2wBavUkm1ttazZ88wduxYjB07Fs7Oznrn8TLFlqKCSE1NNdl+XvpGOj5//hx3797F5cuXYWFhgbfeegsWFhYyR2iYI0eOqNdwK61eTlgsLS3zTVhM0cKFC3H+/Hm8/fbbWLt2LTp06IDg4GBERkYiKysL165dw/z587FlyxYMHToUM2bMMHbIej1+/Bg//fQT9u/fj9u3byM5ORn29vaoWbMmOnfujA8++AAuLi7GDlProsyFWaiZil9YWBgCAwMRHR1t0IhFk25Tknv+Ciq6wsxZZIoMnWU8NjZWODg4CA8PDxmiKhlnz54V/v7+ok2bNhozrVPxUs359s4774iUlBSN9T8zMzPFpUuXxIABA4SZmZmYPn26cYPVo169esLS0lI8fvxYCKF7HdN58+aZ9Mz1QgixZ88eUb58eZ3zDioUClGhQgWTnPG9LEpNTRVBQUFiy5YtYu3atcYOx2Bvv/22kCRJ1K1bV+zatUs8evRIZGdnGzusQuGlxlLo/v37BT6matWqJRBJwaxduxZr165V/3/06FE4Ojqifv36Oo9JSUnBjRs38OzZMwwbNqxUj7S5c+cO/Pz88OWXX2LmzJnGDkenq1evYsmSJTh27BiioqKQlpam0WT/888/4/79+5g8eTIcHByMGGle9evXx40bNxAZGQkXFxcEBgYiODg4z6Xd+fPn44svvsCGDRvQv39/I0Wrm729Pdzd3RESEgIgZ3RgUFAQ0tLSNFp+s7OzUalSJdSoUQNBQUHGClenkJAQNGjQAKmpqfD398enn34Kf39/uLq64vHjx7h+/TqWLFmC69evw8rKChcuXICvr6+xwy6T0tLS8NVXX2HFihUal+hyvzdGjhyJvXv34siRI6hVq5YxwtTJyckJKSkpCA8PN4nW0SIxduZHr44ZM2ZonT3ckNsbb7yhcx3E0qROnTrCx8fH2GHotHTpUmFhYaG3xXTx4sVCoVCI33//3UhR6mZnZydq1aql/l+18HpGRoZGuaysLOHs7JxnkW1TYWdnp7HGoWrRbNUix7kFBAQIR0dHGaMz3JAhQ4QkSeLjjz/WW+6TTz4RkiSJoUOHyhNYETx58kRERESI+/fv67yZmrS0NNGqVSuhUCiEvb29aNeunXBxccnz3t69e7eQJEnMnj3bSJHqZmNjI+rXr2/sMIoFEy+STXh4uDh69Kg4evSoOHLkiJAkSbz++uvqbS/fjh07Js6cOSNiY2ONHXqx8fX1FVZWVsYOQ6vDhw8LhUIhHB0dxbJly0RERIRo0aJFng/n6OhoIUmS6Nu3r5Ei1a2sJCy1atUSnp6e6v8/+ugjoVAoxI4dOzTKpaenCycnJ2Frayt3iAapUqWKqFChQr6X19PT00X58uVFlSpVZIqsYG7cuCH69+8vypUrVyqXqvnuu+/US4Kp3gvaLl+npaUJpVJp8NJhcmrYsKGoVq2ascMoFqbZW5kMFhYWhgMHDuTpsNqhQwdUq1bN2OGpaVuAuXXr1nj99dfRunVrI0Ymn7Nnz+LOnTtwd3c3dihaLViwAACwYcMGvPnmmwC0zyZeqVIleHh44MaNG7LGZwhVJ3oV1UCU06dPo2fPnurtGRkZCA0NNdlRT/Xq1cP27dvx4sUL2NjYoGPHjli+fDm++OIL+Pv7o3r16khLS8O4cePw5MkTtGjRwtghaxUTE4N69erlO6DEwsICNWrUwOXLl2WKzHCnT59G+/bt8eLFCwghYG1tXeqWaNuwYQMsLCywadMmvYNKLC0tUb169UJ1ZylpY8eOxZAhQ7B//3507NjR2OEUjbEzPyqcJ0+eiP79+wszMzONxZhz/+oaOHCgePLkibFDFUJoX4DZy8tLvPPOO0aKqPgcO3ZM5+3o0aNi69atYtKkSeoOxhMmTDB2yFo5OTkJNzc3jW26OnU3adJEODg4yBWawfr16ycsLS3F8+fPhRBC/P3330KSJFG7dm1x584dIURO5+IPP/xQSJIkWrZsacxwdVItGP/nn38KIYTIzs4WzZo1U7/HK1asKCwtLdXv9f379xs5Yu1cXV1FpUqV8i2XnZ0tKlWqJFxdXWWIqmBatWolJEkSPXr0ELdu3TJ2OIViY2Mj/P39Nbbpem83bdrUZFvlp0yZIsqVKycWLVokkpKSjB1OoTHxKoVevHgh6tevr062mjdvLt577z0xdepU8d5774nmzZurP6AbNGggUlJSjB2yUCgUefrTGDqq0dTlTnh13VT9pTp06KBOCkyNUqkUDRo00Nim68O5fv36Jnl5q6wkLCkpKeLEiRMa/YWePn0qhg8fLuzs7NSvp9dee03s2rXLiJHq17t3b6FQKMSCBQv0lluwYIGQJEn06dNHpsgMZ2NjI8qXL1+qRyM7ODiImjVramzT9d6uUaOGcHJykis0g1WrVk1Uq1ZNWFhYqD9XXVxc1Ntfvnl7exs7ZJ2YeJVCc+bMEZIkCT8/P3H27FmtZc6ePStq164tFAqFmDt3rswR5uXi4iJcXV01PrzKSuLVunVr0aZNG623tm3bim7duolPP/3U5IfLe3l55enzpO3DOSUlRVhbW4vatWvLGJ1hykrCok9mZqaIjo4WT58+NXYo+QoKClJ/Sfbp00ccPnxYPQ3Ao0ePxOHDh9XJmZmZmQgODjZ2yHm4uLiIgIAAY4dRJI0aNRKWlpYaA5S0vbfv3r0rJEkSbdq0kTvEfJWVaZSEYB+vUmnLli0wMzPD7t271X1YXtaoUSPs3LkTvr6+2Lx5MyZPnixzlJratm2LrVu3IjAwEG3btoW1tTWAnHXQcq8XqI8kSZg2bVpJhlkoR48eNXYIxaJt27ZYu3YtfvvtN70L7S5evBipqaka/fVMhZWVVZ7+To6Ojvjtt9+wcuVKxMbGwtraulRPFGtmZmbSk7/m1rx5cyxbtgxjxozBjh07sGPHjjxlhBAwNzfHkiVL0KxZM/mDzEfLli1x9OhRZGZmmuwkzvl56623cP78eYwbNw7r1q3Tup5jeno6PvzwQ0iSZJJTrJSp1QCMnflRwdna2op69eoZVLZevXomcUkoPDxcVKtWrcC/WnJPO2EKv2Datm0rxowZo7Ht2LFj4tKlS8YJqBiFhIQIpVIpbGxsxLJly0RycrLGr+KEhAQxc+ZMYW5uLuzs7ERERISRI6bS4tKlS2LQoEHCxcVF473t4uIiBg8ebNLvnytXrgg7OzsxceJEY4dSaC9evFBfAWnatKn46aefRJ06dYRCoRCHDx8WixcvFrVr1xaSJImGDRuW6suqpQEnUC2FHB0d4eHhgWvXruVb9rXXXkNERASSkpJkiEy/9PR07Nu3DyEhIXjx4gVmzpwJT09PDB8+3OD7mD59eglGmD9t6xkqFAoEBgbi2LFjRoyseKiWoElPT4eZmRnMzMyQnp4Od3d3REdHIzs7G5aWlti0aRN69epl7HALtUD5y4y9YHlx1AEwfj0MlZiYiGfPnsHOzq7UtDyeOHECQ4YMQYUKFTB8+HD4+PjoXKINgMbi86bi4cOHePvtt3Hy5EmtIzKFEGjatCm2bdtWalpUSysmXqVQ48aNcf78eVy4cAF169bVWe7SpUto0KABAgICcPr0aRkjNExpXJRZqVSiTp06OHfunHpbaawHAERHR6Ny5cp5tl+9ehUzZszA3r17kZqaqt5uYWGBTp06YdasWahbty7GjRuHhQsXyhlyHgVZoFwXYy9YXhx1AIxfD20UCgUqVKiAqKgoKJVKY4dTaEePHsW4ceNw5cqVfMua8uLMQgj8888/2LZtG65evYrExETY2dmhdu3a6NOnD3r37l0qpskoLdMo6VI6L1i/4gYPHoxz586hW7duWL58Obp3756nzM6dO/Hxxx9DkiQMHjzYCFHmb/Xq1XB1dTV2GAVSrVo1XL9+HRcvXtS71FFp0LZtWxw/fjzP8ht16tTBX3/9hYyMDNy+fVv94VyjRg1137xRo0Zh9erVRk+82rVrV+AvioyMDAQFBSErK8skvmT0LbJe2tnZ2cHHx6dUJ10HDx7Em2++iczMTCiVSlSrVq3UzeOlIkkSunXrhm7duhk7lEJJSEjA6NGjsXXrVvUi2EII9XMhSRL69euHZcuWoXz58sYMVS+2eJVCmZmZ6NSpE44cOQJJkuDp6QlfX1+4uLggJiYGN2/eRGRkJIQQaNeuHfbt2wczMzNjh10mfPPNN5g6dSoUCgWcnZ1hbW2N8PBwWFlZGdw8L0kS7t27V8KR5k+hUMDPzw9HjhwxeO2zrKwsDB48GJs3b4a5uTnS09NLOMrik5WVhTVr1mD27NmIiIiAEAL169fH+fPnjR1amdW4cWPExMQgPDzc2KEUWvPmzXHq1CmMHj0a33zzjcmtT2oIbRNY67J//348evTI5C5dp6SkoEWLFrh8+TKEEGjWrFmedT9Vl1Hr1auHoKAgWFlZGTts7YzSs4yKLCUlRXz22WfC1tZWa4d0W1tbMWHCBJOYwys/V65cEaNGjRI1atQQNjY2eZbcWLFihZgyZYpITEw0UoT/X1ZWlvjiiy+Ek5NToQYKmMogASFyluCQJEnUqVPHoGWZUlNTRY8ePYQkScLS0lJs3bpVhiiLLisrS6xZs0b4+Py/9u4+rub7/x/4492pTtcpijqiq0MxipsUinxuimzCXPMhjBtmGxszZj5jmWbGXGy0ub7eZDKj7ZN8YpTYhjZFuqCU0jmVLlQndV6/P/w6X8c5XZyu3ufkeb/d3G68X+/X+zzfHZ2evd/P9/PlonhQo2/fvuynn37iOzQVT58+ZadPn2abNm1imzZtYpGRkaysrIzvsJrs+++/ZxzHsbNnz/IdSpOZmZkxW1tbvsNoFnUNrOvi7++vNZ9RL9LFNkp1ocRLx5WUlLCoqCi2detWtn79erZ161YWFRWlM119dXlR5vz8fPbgwQPGcRwbOHAge/DgQaP/aIOioiLWv39/xnEc8/DwYAUFBXXuW15ezgICAhjHcczY2JhFRUW1YaRNI5fL2eHDh1mPHj0UCVfv3r3ZiRMn+A5NrbNnzzIbGxuV5rsdO3ZkkZGRfIfXZIsWLWLm5uZs69at9f4f01adO3fW+T5emvRMHDZsmFYmXh4eHkxfX5+lp6fXu19aWhrT19dnHh4ebRNYE1DiRXjTHhZlZky3G8EWFhYyT09PxnEc69evn9olpkpKSpivry/jOI6Zm5uz2NjYtg9UQ8ePH2fu7u6KhMvNzY0dO3aMyeVyvkNTKykpiRkZGTGO45iRkRHr06cPe+211xTbhEIhS0xM5DtMjdV2EdfX19fZbuMzZsxg5ubmOn3lUZPPqJ49ezJzc/NWjkhzuthGqS5UXE940x4WZQYAuVzOdwhNZmVlhQsXLmD48OG4desWAgMDERMTo3jMv7CwEIGBgbhx4wY6dOiAX3/9Fd7e3jxHXbeIiAisW7cOd+7cAWMMYrEYa9aswfTp09U2jdQWmzdvhkwmQ0BAgKIeBwDy8vIwc+ZMXLhwAVu2bMGBAwf4DVRD6mq7JBIJJBKJ2v21sWB9w4YNiI6OxoIFC7Bnzx7trRt6QVZWlsrXvri4uN4nrysqKnDp0iXcu3dPK7/HBQIBnj171qh9nz17ptXf71Rcr8MeP36M8PBw/Pe//1V5rHbkyJFYsGCBVvdj6dSpE4RCIXJychTb/Pz8EB8fr/JovI+PD+7cuYPi4uK2DvOVUFBQgOHDh+P27dvw8vLC+fPn8fTpUwQEBCA5ORk2NjaIjo6ut30Jn06dOoV169bh9u3bYIzB2dkZa9aswcyZM7X6A7iWq6srcnJykJWVBRsbG6Wx/Px8dOvWDXZ2djrXvTszM1PjOd27d2+FSJru0KFDyMjIQFhYGGxsbDBt2rQG+3jxXZi+bt06pRVB2AtP/tWnNh04fvw4pkyZ0mrxNUV7aaMEUOKls3766SfMmzcPJSUlUPcWchwHc3Nz7N69G5MmTeIhwoYZGRmhd+/eSk+V1ZV49e/fH/fu3UNZWVlbh9lo9+7dw6+//oqMjAyUlZWpfV+A5+/N3r172zi6hkkkEgwfPhx37tzBgAEDUFhYiPT0dIhEIsTExKBnz558h6jizJkzWLt2reJJJ0dHR6xevRqzZ8/WqSd5TUxM4OrqWmefqD59+iAtLQ0VFRVtHFnTlJeX4/z580hNTQXwPLEMCAiAqakpz5FprrbPWu33c2MSGL57qh08eFDp6uilS5dgaWkJT09PtftzHAdjY2M4OztjypQp8PX1bZtANbBjxw4sWbIEIpGowTZKOTk52LZtG9555x0eIm0YJV466MqVK/D394dcLkf//v3x9ttvw93dHZ07d1a0k/j2229x48YNCAQCXLx4UWX9Om3g5OSEoqIiPHnyRLFNXeJVWVkJa2trRQ8tbVNTU4O3334be/bsAYA6E65aHMfx/sFcF4lEAn9/f9y5cwcA4OzsjAsXLmjdVYhatT8UBQIBpk2bhrlz58LAwECjYwwePLiVomu8hprw1vULiTY6d+4c5syZg4KCAqXtVlZW2LNnj1aseKCJ2bNna3wLdP/+/a0UTdPoapPnF7WnNkpU46WDPvvsMzDG8NFHHyEsLExpzNnZGT4+PpgzZw4+/vhjfPHFF1i3bh2io6N5irZu7WFRZgAICwvD7t27IRAIMHbsWHh5ecHW1lbrb3HVtVTNrFmzsGbNGnAch7feeqvepZD4vqVSq6amBkeOHMGRI0c0mqfNXcZ1UXJyMiZOnAiZTAahUAixWAzGGNLS0lBYWIipU6fi+vXr6Nu3L9+hNpqu1dWpExsbqzPLM9VFX18f586dwyeffILw8HBkZmaq3Mo2MTHBokWLEBoaqrVJF0BXvHSSpaUlhEIh8vLy6v3hXlNTAzs7O8hkMq2sjUpJSYGHhwcEAgG+/PJLhISEICgoSPGb/ZMnT7B9+3aEhobCyMgIycnJcHBw4DtsFWKxGBkZGThz5oziIQFd0NylarQhaXF0dGx2QbY21E3p6emhR48e+Pjjj9WOf/7550hLS8OBAwfqvKKqDUnwW2+9hf3799f7kMCsWbPaRTKj6+7du4fNmzfj+vXrqKqqglgsxty5cxEcHMx3aA0qLS3FlStXFOUnZmZm6NGjB3x9fWFubs53eA2ixEsHdejQAT179mxU4aC3tzdSUlKUbudpk8YuyvzDDz9g7NixfIerlpGREUQikVZ0o9dEe0la2oP2kAQD7fchAV0THR2Nf//73/D29sYvv/yiMn7p0iW8/vrrqKioUErkOY7Dhx9+iC+++KItw33l0K1GHeTp6YmkpCTU1NTUezm1uroaGRkZWr2m4OTJk+Hu7q6yKHN2djYMDAwQFBSkWJRZW3Xu3BkdOnTgOwyN6fIyLu1Nt27dtLKVgqYePXoEsVisknQBgK2tLcRiMdLS0niIrGXk5ubi77//RmFhYb2tDfi++hgTE4OCggJMnjxZZayqqgohISEoLy+Hqakp3n77bTg7OyMuLg5Hjx7Fpk2bEBwcrBW1j02VmJiI4uJiDB06lO9Q1Gu7lmGkpfz2229MT0+Pffjhh/Xut2LFCqanp8eio6PbKLLmqaqqYrdv32ZxcXEsMTGRlZeX8x1SoyxZsoQZGhqyvLw8vkMhhFcNNer09fXVyq7oDblz5w7717/+pbKqwMt/tGVJsMGDBzOBQKB2mbUff/yRcRzHBAIBi4uLUxpbs2YN4ziOzZkzp61CrRPHccza2pr9/PPPGs/19fVVWXpOm9AVLx3k7u6ODRs2YM2aNbhw4QIWLVoEd3d32NraQiKR4M6dO9i5cydu376NsLAw9OzZE1lZWSrH6datW5vHnpSUhPT0dNja2sLHx0dpzMDAAL1791balpCQgPz8fLi6uqJXr15tGWqjffbZZzh//jymTZuGo0ePws7Oju+QCCEt5OHDh/Dz80NBQQF8fX1x7949SCQSTJ8+HQ8fPsSdO3cgkUhgbGyMN998E/r6/P9Yzc7OhouLi9oFvX/77TcAgL+/v8pVrWXLluHLL79EfHx8m8TZkKKiIowfPx5Lly7Fxo0bNfraMi2uouL/fwjRWG1tDmMMt27dwoIFC9TuxxjDqlWrsGrVKpUxPmpCysvLERgYCKlUitjY2EbNkcvlmDhxIuzt7ZGSkgKhUNjKUWpu69atGDlyJL799luIxWKMGjUKLi4udfYs4jgOa9asaeMoCWkb+fn5dT4xm5+fDwA4fPiwVj8k8KKwsDAUFBRgw4YNWLlyJfz8/CCRSHD48GEAz0s69u3bhxUrViA3N1eR2PBJIpHUWZ5x9epVcByH0aNHq4xZWlqie/fuSk2t+WRqaory8nJs3boVV69exY8//qiVD1hpiorrdVBLFEUDbV8YvXfvXsyfPx8LFy7Ezp07Gz1v8eLFCA8Px759+xASEtKKETbNyw0W61K7jzb38SKkOdrLQwIvcnV1RX5+PiQSCYRCYZ091X777TeMHj0aoaGhWL16NU/RPmdmZobOnTurPPBTUlICKysrAMDvv/+utr+jj48PEhMTeW/WW9t7bNWqVZg5cyYKCwthZWWFgwcP4o033qh3rrb3vaMrXjpIV4uiT58+DY7j8N5772k0b+nSpdi1axd++uknrUy8Pv30U75DIEQrtJeHBF6Uk5ODHj16KK621z7QVNurrNaoUaPg6OiIY8eO8Z54OTk5ISUlBdnZ2ejatatie0xMDBhjEAqFGDBggNq5EolEq5aaCwoKws2bNzFlyhQkJCRg7NixWLZsGcLCwrS6V1d9KPEibebmzZuws7ODm5ubRvPEYjFEIhFu3rzZSpE1DyVehDynq78U1sfExESptqi2EWlOTg6cnZ2V9u3QoQNSUlLaND51AgICkJSUhMWLF+PHH3+EkZERSkpKEBYWBo7jMGLECLVlG4WFhbh//77WrXTi4OCAy5cv46OPPsLXX3+NzZs3Iy4uDidOnIBIJOI7PI1pd2tt0q5IpdImf5PY29tDKpW2cESEEFI/BwcHPHr0SPHv2od8YmJilPaTSqVaU4f6/vvvw9zcHGfPnoWdnR28vb3h6OiIGzduAACWL1+udt6pU6cAQOsSL+D5lcavvvoKp0+fhpWVFa5evQpPT09ERUXxHZrG6IpXO1BZWYmioqJ6+8rw8QTjy4yMjJpcN1BRUQFDQ8MWjqhp6ioc1oS2FRATQtTz9fVFeHg48vLy0KVLF0ycOBEbN27E8uXLoaenh0GDBuHx48f45JNPUFlZqbZova05ODggMjISU6ZMQUFBAf744w8Az+umQkNDMWzYMLXzvvnmG3Ach6CgoLYMVyNjxozBjRs3MHnyZFy/fh3BwcFYvnw5NmzYoPXLtNWi4nodJZPJ8OWXX+Lo0aNITU2td19tKVh1d3dHVlYWCgsLNfqtUCaTwdraGt26dVMs3syn9lhATAhR7/z58xg9ejR27dqFefPmAXj+wM+uXbuUPgcYY7C2tkZCQgJcXV35CldJaWkpoqKikJGRAQsLCwQGBkIsFqvdt6CgAEeOHAHHcVi8eDHv9VMNLexdXV2NFStWYOvWreA4DkOGDMEPP/wAe3t7Kq4nLa+8vBzDhg3DjRs3YGBgAENDQ8hkMohEIuTl5Sn+swmFQq0qkvTz88PevXtx8uRJzJgxo9HzIiIiUFFRAT8/v1aMrvHaYwExIUS9gIAAlbsJ3377Lby9vXHs2DE8ePAAxsbG8PX1xYoVK7Sq3YG5uTmmTJnSqH07duyIJUuWtHJELUdfXx9btmzBsGHDMGfOHFy5cgX9+vXDwYMH+Q6tYfz0bSXNsX79esZxHJs8eTKrqKhQ6gZdXV3Nbt26xaZNm8YEAgH79NNP+Q32BXFxcYzjOCYSiVhWVlaj5mRmZjI7Ozump6fHrly50soREkJI08lkMrZr1y6+w2gXGloF4UX3799nXl5eio78pqamWrGCQF1044YoUXLy5EkYGBhgx44dMDIyUhoTCATw8PDAsWPH8PnnnyM0NBQ//PADT5EqGzx4MCZNmoRHjx7B29sbERERkMvlaveVy+U4ceIEfHx88PjxY0yYMEErCz4JIaS8vBybN2+Gk5MTFi9ezHc4rxxHR0fExcXh3XffhVwuR3l5Od8h1YtqvHSQubk5RCIR7t69CwAYOnQo4uLiIJPJlB57lsvl6NKlC8RiMeLi4vgKV0lFRQUCAgIQHx8PjuNgY2ODIUOGwMnJCaampnj69Cnu37+P+Ph45OfngzGGQYMG4fz58zAxMeE7fELIK6K4uBjR0dF48OABTExM4OnpqfLLX1lZGbZs2YLt27ejqKgIjDHY29sjOzubp6jbD39/f3h4eGDbtm0azTt16hTmzp2L0tJSra3xoluNOsjMzIwNHDhQ8e+goCCmp6fHcnNzVfb18vJilpaWbRhdw549e8ZWr17NLCwsGMdxioVlX1xoluM4Zm5uzj7++GNWVVXFd8iEkFfIkSNHmKWlpcoi2EOHDmVPnjxhjDF29OhR1qlTJ8VnVq9evdi+ffvo84o0iK546SA3NzdUVFQgMzMTAPDOO+9g165dOHXqFMaOHavY79mzZ7Czs0NlZSXKysr4CrdOJSUlOHfuHOLj45GTk4PS0lLF1bzBgwdj9OjRimaFhBDSFv78808MGjQINTU1MDU1RY8ePVBeXo709HTU1NRg6tSpGDBgAJYvXw7GGHx8fLBy5UoEBwfzHTrREZR46aCpU6ciMjISRUVFMDExwZkzZzBu3Di4u7vj559/hqurK2QyGd5//32Eh4djyJAhuHz5Mt9hE0KI1ps5cyaOHj2KSZMmYc+ePTA3NwcApKamYvz48UhJSYGBgQHMzMywe/dupV92CWkMSrx00PHjxzFjxgxERERgwoQJYIxhyJAhSEhIAMdxsLa2RklJCaqrq8FxHH799VcEBATwHTYhhGg9Z2dnPH78GI8ePVK54h4TE4PAwEBwHIcrV65g0KBBPEVJdBklXjqosrISf/31FxwcHBQd6YuLi/H+++8jIiICT58+BQD07t0bYWFhDa7kTggh5DljY2P06NEDiYmJKmOlpaWwtLSEi4tLg42rCakLJV7tTE1NDSQSCYyNjak+ihBCNNRQx3Q9PT0q3yDNQp3rdURSUhLS09Nha2sLHx+fOvcTCATo0qULEhISkJ+fD1dXV8WiroQQQpqPVq4gzUGJlw4oLy9HYGAgpFIpYmNjGzVHLpdj4sSJsLe3R0pKikZrIxJCyKssPz8fhw4davL4rFmzWiMs0k7QrUYdsHfvXsyfPx8LFy7Ezp07Gz1v8eLFCA8Px759+xASEtKKERJCSPugp6fXrCtaHMehurq6BSMi7Q0lXjpgzJgxiIqKQlJSEtzc3Bo9LzU1FT179sQbb7yBM2fOtGKEhBDSPjg6Ojb7VuL9+/dbKBrSHlHipQO6du0KAE1ahsLBwQEA8PDhwxaNiRBCCCGao0WydYBUKoVIJGrSXHt7e0il0haOiBBCCCFNQYmXDjAyMkJFRUWT5lZUVMDQ0LCFIyKEEEJIU1DipQPs7OyQnp4OmUym0TyZTIb09HTY29u3UmSEEEII0QQlXjrAz88PlZWVOHnypEbzIiIiUFFRAT8/v1aKjBBCCCGaoOJ6HRAfHw9fX1/Y29vj6tWrioL5+mRlZcHHxwePHz/G77//jiFDhrRBpIQQQgipD13x0gGDBw/GpEmT8OjRI3h7eyMiIgJyuVztvnK5HCdOnFAkXRMmTKCkixBCCNESdMVLR1RUVCAgIADx8fHgOA42NjYYMmQInJycYGpqiqdPn+L+/fuIj49Hfn4+GGMYNGgQzp8/DxMTE77DJ4QQQggo8dIp1dXVWLt2LXbs2IHS0lIAymuG1b6VZmZmePfdd7F27VoYGBjwEishhBBCVFHipYNKSkpw7tw5xMfHIycnB6WlpTA3N4dIJMLgwYMxevRoWFpa8h0mIYQQQl5CiRchhBBCSBuh4npCCCGEkDZCiRchhBBCSBuhxIsQQgghpI1Q4kUIITri4sWL4DgO/v7+fIdCCGkiSrwIIUocHR3BcZzij56eHiwsLODg4ICAgAB88sknSE5O5jvMFlWb0Kg77/79++M///kPnjx5wneYhJB2QJ/vAAgh2kksFsPW1hYAUFlZCalUipiYGMTExODzzz/HhAkT8N1336Fjx448R9qyald6YIwhOzsbt27dws2bN3H48GHExcXxuui8iYkJevbsiW7duvEWAyGkeaidBCFEiaOjIzIzM7F//37Mnj1baUwqleLo0aNYv349pFIp3NzckJCQoPN94y5evIjhw4cD+L9GxLWuX7+OcePGITc3F9OnT8fRo0f5CJEQ0k7QrUZCSKN16tQJS5YswZ9//gk7OzvcvXsXS5cu5TusVjVw4ECEhoYCAM6cOYOamhqeIyKE6DJKvAghGuvevTt27twJADhy5AgePnyoNF5bJ/bgwQPExsYiKCgInTp1AsdxuHjxIgDA399f6d8vmz17NjiOw4EDB1TGqqqqsGHDBvTs2RNGRkYQiURYuHAhJBIJ1q5dC47jsHbt2hY7Xy8vLwBAWVkZpFKpynh5eTk2btyIAQMGwMLCAiYmJvD09MSmTZsgk8nUHpMxhvDwcHh4eMDY2BidO3fG9OnTkZGRgQMHDoDjOJUrjnUV1z948AAcx8HR0REAsGfPHvTr1w8mJiYQiUR47733FMuM1dTUYPPmzejduzeMjY3RtWtXrFy5ElVVVXWe/927dzF37lw4OjpCKBSiY8eOeP311/G///1P7f4vvv8JCQkICgqClZUVTE1N4efnV+c8Ql4FlHgRQpokODgY9vb2qK6uRnR0tNp9jh8/jhEjRuDatWtwdnZG165dm/261dXVCA4OxurVq3Hv3j04OjrC1tYWe/fuxcCBA1FYWNjs13hZeXm54u8vLzqfk5MDLy8vrFy5EomJiejcuTMcHR2RlJSEFStWYMSIEaioqFA55rx587Bo0SL8/fffsLOzg4ODAyIjIzFgwACkp6c3OdZly5Zh/vz5KC0thYuLC/Lz87Fjxw6MGzcOcrkcEydOxPLly8EYQ/fu3fHo0SNs3LgR8+fPV3u8EydOwMPDA/v370dhYSF69eoFQ0NDREVFYcSIEdixY0edsZw9exZDhw7FH3/8ARcXFxgYGODKlSsYOXJknQk3Ie0eI4SQF3Tv3p0BYPv3729w3wkTJjAAbMGCBWqPIRAI2Lp169izZ88YY4zJ5XJWWVnJGGNs2LBhDACLjY1Ve+yQkBC1cWzatIkBYNbW1iwuLk6xPSsri/Xr148ZGBgwAOzTTz9t9DnHxsYyAKyuj8T//Oc/DABzdnZW2l5TU8MGDx7MALCpU6eyvLw8xdjDhw+Zn58fA8CWL1+uNC8iIoIBYEKhkJ0+fVqxvaCggAUGBirOISQkRG2cw4YNU9p+//59BoDp6+szS0tLFhMToxj7559/WMeOHRkANm7cONa1a1d28+ZNpWMaGhoyACwpKUnpuImJiUwoFDIjIyP2/fffs5qaGsXYmTNnmIWFBRMIBOzWrVtK82rffwMDAxYWFsaqq6sZY4xVVVWxGTNmMADM29tb7deakPaOEi9CiBJNEq+lS5cyAGz8+PFqjzFmzJg65zYl8aqpqWEikYgBYEeOHFGZk5qaygQCQYskXnK5nD18+JBt3ryZCYVCBoDt3r1bad6ZM2cYAObl5aVILl/06NEjZmZmxszMzFh5ebliu4+PDwPA1q9frzKnsLCQdejQoUmJFwD29ddfqxxz1apVivHIyEiV8alTpzIAbMuWLUrb33zzTQaAbdu2TWUOY4zt2LGDAWBz585V2l7f+y+RSBRfz8LCQrXHJaQ9o1uNhJAmMzU1BQBF/dDLZs2a1aKvl5ycjJycHJiammLSpEkq466urvDz82vWa7zYx8vBwQHLli2DhYUFduzYgXnz5inte+rUKQDP69H09VW789jZ2cHLywtlZWX466+/ADz/Wl27dg0AMGfOHJU5VlZWGDduXJPjnzt3rso2T09PAIC1tbXaY/fr1w8AkJGRodhWVVWFqKgoCAQClVqzWsHBwQCAS5cuqR1/+esFPH9Ao7YW7cXXI+RVQX28CCFNVlZWBgCwsLBQO+7u7t6ir5eamgoAcHNzg6Ghodp9+vbt26z6odo+XlVVVUhLS0NRUREsLS3VJnT//PMPAGDXrl04duyY2uPdu3cPwPNaMABIS0sDYwwdO3assydY3759mxS7jY2N2vfCxsYGAODi4lLnPOD/3s/auCsrK2FoaIjRo0erncf+f+uN2nN7WV2vZ2tri5SUFKXXI+RVQYkXIaTJsrKyAEDRaPVltVfEWsrTp08BAObm5nXuU99YY1y5ckXx99qnJ9etW4dRo0bhn3/+QadOnRTjxcXFAIDbt283eNzaAvvWPIeXC/9rcRzXqHH2Qg+z2nOrqqpCXFxcva9bWVmpdntd77+enp7K6xHyqqBbjYSQJpHL5bh69SqA572uNKXuh/2LahOUF9X+IK/vSkldtz2bwtDQEGvXrsXYsWORl5eHlStXKo2bmZkBAM6fPw/2vGa2zj+1t+va+hyaqvbcRCJRg+dGCRQhjUeJFyGkSU6fPo28vDwYGBggMDBQ4/m1CYhEIlE7npaWprKtR48eAJ73lXr27JnaebW3/1pSWFgY9PT0cODAAaW4evXqBaBxV7xqubq6Qk9PD1KpFLm5uWr3aY1z0JRYLIaBgQFyc3NbpUUHIa8qSrwIIRrLzMzEO++8A+B5Ab1IJNL4GM7OzgCAP/74Q2Xszz//RGJiosp2d3d3iEQilJWV4eTJkyrjGRkZuHz5ssaxNMTd3R3BwcGoqanBxo0bFdvffPNNAMB3331X5+22l5mbm8Pb2xsA1DaHLS4uxunTp5sdc3OZmJhg5MiRkMvl2L59O9/hENJuUOJFCGk0qVSK7du3Y8CAAcjNzUWvXr2wZcuWJh0rKCgIALB7925cv35dsT01NRUhISFqnxLU09NTLFH03nvvISEhQTGWnZ2NyZMnK25htrSPPvoIAHDo0CFkZ2cDAMaPHw8fHx/cvXsXY8aMUblKJ5PJcO7cOZUnDT/44AMAQGhoKH755RfF9qKiIkydOlVris5DQ0MhFAqxfv16fPHFFyqNYHNzc7Ft2zaEh4fzFCEhuocSL0KIWhs2bICvry98fX3h5eUFJycn2NjYYMmSJZBKpZg0aRIuX75c5xONDRk1ahRGjBiB4uJiDBo0CG5ubujTpw/c3NzQqVMnte0iAGDp0qUIDAyEVCrFoEGD4O7ujv79+8PJyQkFBQVYuHAhAEAgEDT53NXx8fGBn58fqqqq8NVXXwF4ngieOnUK/fr1Q0xMDMRiMcRiMXx8fNC7d29YWFjgjTfeQFRUlNKxJk6ciLlz56KiogLBwcFwcXGBl5cXRCIRrl27hhUrVrTKOWjK09MTx48fh1AoxKpVq2BtbY1+/frB29sb3bp1g729PZYuXYoHDx7wGichuoQSL0KIWqmpqYiLi0NcXBzu3r2L6upqjBgxAqtXr0ZycjJOnDgBa2vrJh+f4zhERkbigw8+gL29Pe7fv4+nT59i1apViI6OhoGBgdp5+vr6+OWXX7B+/XqIxWJkZGQgLy8PISEhuHbtGoRCIYDmP92oTu1Vr927dytq0+zs7HD16lXs3LkTQ4cORUFBAW7evInS0lIMHDgQ69atQ2xsrMqxdu/ejZ07d6JPnz7IyclBZmYmxowZg+vXr8POzq7VzkFT48ePR3JyMpYsWQJHR0ekpKQgOTkZJiYmGD9+PA4ePKjy0AEhpG4co8dRCCHtyJgxY3D27FlERkY2qxEpn95991188803+PrrrxW3Vgkh7QMlXoSQdiM7Oxuurq6orq5GdnY2unTpwndIGisrK4NYLEZeXh6uX78OLy8vvkMihLQgutVICNE569evV3Sxr5WSkoKxY8dCJpNh7NixWp90bd26Fbdu3VLalpOTgwkTJiAvLw/9+/enpIuQdoiueBFCdI6joyMyMzMV6/4VFxcrEjFnZ2f8/vvvTWpx0Zb8/f1x6dIlWFpawtnZGTKZDHfv3oVcLkenTp0QGxuL1157je8wCSEtjBIvQojO2bt3LyIiInD79m0UFBRAT08PTk5OGDduHJYtWwYrKyu+Q2xQZGQkDh06hBs3bkAikYAxBgcHBwQFBWHFihVanzgSQpqGEi9CCCGEkDZCNV6EEEIIIW2EEi9CCCGEkDZCiRchhBBCSBuhxIsQQgghpI1Q4kUIIYQQ0kYo8SKEEEIIaSOUeBFCCCGEtBFKvAghhBBC2gglXoQQQgghbeT/AfLDTk1QEgb6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of rows (Mouse ID/Timepoints) for each drug regimen using Pandas.\n",
    "\n",
    "drug_group = clean_combined_data.groupby(\"Drug Regimen\")\n",
    "#c = pd.DataFrame(drug_group[\"Drug Regimen\"].count())\n",
    "\n",
    "count_chart = drug_group_count.plot(kind=\"bar\", title=\"Count of Data Points by Drug Regimen\", color=\"red\")\n",
    "\n",
    "count_chart.set_xlabel(\"Drug Regimen\")\n",
    "count_chart.set_ylabel(\"Count\")\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Count of Data Points by Drug Regimen')"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of rows (Mouse ID/Timepoints) for each drug regimen using pyplot.\n",
    "timepoints = clean_combined_data.groupby([\"Drug Regimen\"])[\"Timepoint\"].count()\n",
    "x = timepoints.index.get_level_values(0)\n",
    "y = np.array(timepoints)\n",
    "#print(y)\n",
    "\n",
    "count_chart2=timepoints.plot(kind=\"bar\", figsize=(6,5), color=\"red\")\n",
    "count_chart2.set_xlabel(\"Drug Regimen\")\n",
    "count_chart2.set_ylabel(\"Count\")\n",
    "count_chart2.set_title(\"Count of Data Points by Drug Regimen\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_group = clean_combined_data.groupby(\"Sex\")\n",
    "gender_group_count = pd.DataFrame(gender_group[\"Sex\"].count())\n",
    "#gender_group_count.head()\n",
    "\n",
    "# Generate a pie plot showing the distribution of female versus male mice using Pandas\n",
    "\n",
    "gender_group_count.plot(kind='pie', y='Sex', \\\n",
    "                        title=\"Distribution of female versus male mice\",startangle=140,\\\n",
    "                        autopct='%1.1f%%', fontsize=16, colors =[\"blue\",\"orange\"],legend =False)\n",
    "plt.axis(\"equal\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a pie plot showing the distribution of female versus male mice using pyplot\n",
    "\n",
    "#gender_group_count = pd.DataFrame(gender_group[\"Sex\"].count())\n",
    "genders = list(gender_group_count.index.values)\n",
    "gender_counts = gender_group_count[\"Sex\"]\n",
    "colors=[\"blue\",\"orange\"]\n",
    "\n",
    "plt.pie(gender_counts, labels=genders, colors=colors,\n",
    "        autopct=\"%1.1f%%\", startangle=140)\n",
    "plt.rcParams['font.size'] = 16\n",
    "plt.title(\"Distribution of female versus male mice\")\n",
    "plt.ylabel(\"Sex\")\n",
    "plt.axis(\"equal\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quartiles, Outliers and Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {
    "scrolled": true
   },
   "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>Drug Regimen</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_months</th>\n",
       "      <th>Weight (g)</th>\n",
       "      <th>Tumor Volume (mm3)</th>\n",
       "      <th>Metastatic Sites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a203</td>\n",
       "      <td>45</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>67.973419</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a251</td>\n",
       "      <td>45</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>65.525743</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a262</td>\n",
       "      <td>45</td>\n",
       "      <td>Placebo</td>\n",
       "      <td>Female</td>\n",
       "      <td>17</td>\n",
       "      <td>29</td>\n",
       "      <td>70.717621</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a275</td>\n",
       "      <td>45</td>\n",
       "      <td>Ceftamin</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>62.999356</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a366</td>\n",
       "      <td>30</td>\n",
       "      <td>Stelasyn</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>63.440686</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>z435</td>\n",
       "      <td>10</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>48.710661</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>z578</td>\n",
       "      <td>45</td>\n",
       "      <td>Ramicane</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>30.638696</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>z581</td>\n",
       "      <td>45</td>\n",
       "      <td>Infubinol</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>62.754451</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>z795</td>\n",
       "      <td>45</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>65.741070</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>z969</td>\n",
       "      <td>45</td>\n",
       "      <td>Naftisol</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>73.867845</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mouse ID  Timepoint Drug Regimen     Sex  Age_months  Weight (g)  \\\n",
       "0       a203         45    Infubinol  Female          20          23   \n",
       "1       a251         45    Infubinol  Female          21          25   \n",
       "2       a262         45      Placebo  Female          17          29   \n",
       "3       a275         45     Ceftamin  Female          20          28   \n",
       "4       a366         30     Stelasyn  Female          16          29   \n",
       "..       ...        ...          ...     ...         ...         ...   \n",
       "243     z435         10     Propriva  Female          12          26   \n",
       "244     z578         45     Ramicane    Male          11          16   \n",
       "245     z581         45    Infubinol  Female          24          25   \n",
       "246     z795         45     Naftisol  Female          13          29   \n",
       "247     z969         45     Naftisol    Male           9          30   \n",
       "\n",
       "     Tumor Volume (mm3)  Metastatic Sites  \n",
       "0             67.973419                 2  \n",
       "1             65.525743                 1  \n",
       "2             70.717621                 4  \n",
       "3             62.999356                 3  \n",
       "4             63.440686                 1  \n",
       "..                  ...               ...  \n",
       "243           48.710661                 0  \n",
       "244           30.638696                 0  \n",
       "245           62.754451                 3  \n",
       "246           65.741070                 3  \n",
       "247           73.867845                 4  \n",
       "\n",
       "[248 rows x 8 columns]"
      ]
     },
     "execution_count": 372,
     "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) timepoint for each mouse\n",
    "group_by_mouse_id = clean_combined_data.groupby(\"Mouse ID\")\n",
    "max_timepoint = group_by_mouse_id[\"Timepoint\"].max()\n",
    "max_timepoint = max_timepoint.reset_index()\n",
    "#max_timepoint\n",
    "\n",
    "# Merge this group df with the original DataFrame to get the tumor volume at the last timepoint\n",
    "max_timepoint_volume = max_timepoint.merge(clean_combined_data, on=[\"Mouse ID\", \"Timepoint\"], how=\"left\")\n",
    "max_timepoint_volume\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "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></th>\n",
       "      <th>Last_tumor_size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug Regimen</th>\n",
       "      <th>Mouse ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Capomulin</th>\n",
       "      <th>b128</th>\n",
       "      <td>38.983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b742</th>\n",
       "      <td>38.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f966</th>\n",
       "      <td>30.486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g288</th>\n",
       "      <td>37.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g316</th>\n",
       "      <td>40.159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Zoniferol</th>\n",
       "      <th>q633</th>\n",
       "      <td>70.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s337</th>\n",
       "      <td>62.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w140</th>\n",
       "      <td>47.718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w575</th>\n",
       "      <td>68.401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x613</th>\n",
       "      <td>48.078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Last_tumor_size\n",
       "Drug Regimen Mouse ID                 \n",
       "Capomulin    b128               38.983\n",
       "             b742               38.940\n",
       "             f966               30.486\n",
       "             g288               37.074\n",
       "             g316               40.159\n",
       "...                                ...\n",
       "Zoniferol    q633               70.828\n",
       "             s337               62.110\n",
       "             w140               47.718\n",
       "             w575               68.401\n",
       "             x613               48.078\n",
       "\n",
       "[248 rows x 1 columns]"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "four_last_tumor = max_timepoint_volume.groupby(['Drug Regimen','Mouse ID']).agg(\\\n",
    "        # Get the last value of the 'Tumor Volume (mm3)' column using a lambda function selecting the element in -1 position\n",
    "        Last_tumor_size=('Tumor Volume (mm3)',lambda x: x.iloc[-1])).round(3)\n",
    "\n",
    "# View the groupby dataframe\n",
    "four_last_tumor\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "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>Drug Regimen</th>\n",
       "      <th>Capomulin</th>\n",
       "      <th>Ceftamin</th>\n",
       "      <th>Infubinol</th>\n",
       "      <th>Ketapril</th>\n",
       "      <th>Naftisol</th>\n",
       "      <th>Placebo</th>\n",
       "      <th>Propriva</th>\n",
       "      <th>Ramicane</th>\n",
       "      <th>Stelasyn</th>\n",
       "      <th>Zoniferol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mouse ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a203</th>\n",
       "      <th>Last_tumor_size</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.973</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a251</th>\n",
       "      <th>Last_tumor_size</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.526</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a262</th>\n",
       "      <th>Last_tumor_size</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.718</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a275</th>\n",
       "      <th>Last_tumor_size</th>\n",
       "      <td>NaN</td>\n",
       "      <td>62.999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a366</th>\n",
       "      <th>Last_tumor_size</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.441</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Drug Regimen              Capomulin  Ceftamin  Infubinol  Ketapril  Naftisol  \\\n",
       "Mouse ID                                                                       \n",
       "a203     Last_tumor_size        NaN       NaN     67.973       NaN       NaN   \n",
       "a251     Last_tumor_size        NaN       NaN     65.526       NaN       NaN   \n",
       "a262     Last_tumor_size        NaN       NaN        NaN       NaN       NaN   \n",
       "a275     Last_tumor_size        NaN    62.999        NaN       NaN       NaN   \n",
       "a366     Last_tumor_size        NaN       NaN        NaN       NaN       NaN   \n",
       "\n",
       "Drug Regimen              Placebo  Propriva  Ramicane  Stelasyn  Zoniferol  \n",
       "Mouse ID                                                                    \n",
       "a203     Last_tumor_size      NaN       NaN       NaN       NaN        NaN  \n",
       "a251     Last_tumor_size      NaN       NaN       NaN       NaN        NaN  \n",
       "a262     Last_tumor_size   70.718       NaN       NaN       NaN        NaN  \n",
       "a275     Last_tumor_size      NaN       NaN       NaN       NaN        NaN  \n",
       "a366     Last_tumor_size      NaN       NaN       NaN    63.441        NaN  "
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reshape dataframe with Drug Regimen as columns by using unstack, also move Last_tumor_size to index using stack\n",
    "four_last_tumor_unstack = four_last_tumor.stack(level=0).unstack(level=0)\n",
    "# View the result to confirm transpose\n",
    "four_last_tumor_unstack.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------\n",
      "Capomulin IQR data is:\n",
      "The lower quartile of Capomulin is: 32.38.\n",
      "The upper quartile of Capomulin is: 40.16.\n",
      "The interquartile range of Capomulin is: 7.78.\n",
      "The the median of Capomulin is: 38.12.\n",
      "Values below 20.71 for Capomulin could be outliers.\n",
      "Values above 51.83 for Capomulin could be outliers.\n",
      "--------------------------------------------------------\n",
      "Ramicane IQR data is:\n",
      "The lower quartile of Ramicane is: 31.56.\n",
      "The upper quartile of Ramicane is: 40.66.\n",
      "The interquartile range of Ramicane is: 9.1.\n",
      "The the median of Ramicane is: 36.56.\n",
      "Values below 17.91 for Ramicane could be outliers.\n",
      "Values above 54.31 for Ramicane could be outliers.\n",
      "--------------------------------------------------------\n",
      "Infubinol IQR data is:\n",
      "The lower quartile of Infubinol is: 54.05.\n",
      "The upper quartile of Infubinol is: 65.53.\n",
      "The interquartile range of Infubinol is: 11.48.\n",
      "The the median of Infubinol is: 60.16.\n",
      "Values below 36.83 for Infubinol could be outliers.\n",
      "Values above 82.75 for Infubinol could be outliers.\n",
      "--------------------------------------------------------\n",
      "Ceftamin IQR data is:\n",
      "The lower quartile of Ceftamin is: 48.72.\n",
      "The upper quartile of Ceftamin is: 64.3.\n",
      "The interquartile range of Ceftamin is: 15.58.\n",
      "The the median of Ceftamin is: 59.85.\n",
      "Values below 25.35 for Ceftamin could be outliers.\n",
      "Values above 87.67 for Ceftamin could be outliers.\n",
      "--------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#Calculate the IQR and quantitatively determine if there are any potential outliers.\n",
    "\n",
    "# Set counter to 0 which is used to only print dividing line at top of output\n",
    "counter = 0\n",
    "\n",
    "# Do quartile calculations for each drug\n",
    "for drug in four_drug_list:\n",
    "    quartiles = four_last_tumor_unstack[drug].quantile([.25,.5,.75]).round(2)\n",
    "    lowerq = quartiles[0.25].round(2)\n",
    "    upperq = quartiles[0.75].round(2)\n",
    "    iqr = round(upperq-lowerq,2)\n",
    "    median = quartiles[0.5].round(2)\n",
    "#    skew = round(((upperq-median)-(median-lowerq))//(upperq-lowerq),2)\n",
    "    lower_bound = round(lowerq - (1.5*iqr),2)\n",
    "    upper_bound = round(upperq + (1.5*iqr),2)\n",
    "    \n",
    "    # Check if it is the first time through the for loop, if so then print dividing line at top only\n",
    "    if counter == 0:\n",
    "        print(f\"--------------------------------------------------------\")\n",
    "    print(f\"{drug} IQR data is:\")\n",
    "    print(f\"The lower quartile of {drug} is: {lowerq}.\")\n",
    "    print(f\"The upper quartile of {drug} is: {upperq}.\")\n",
    "    print(f\"The interquartile range of {drug} is: {iqr}.\")\n",
    "    print(f\"The the median of {drug} is: {quartiles[0.5]}.\")\n",
    "#    print(f\"The datapoints of {drug} have a skew of: {skew}.\")\n",
    "    print(f\"Values below {lower_bound} for {drug} could be outliers.\")\n",
    "    print(f\"Values above {upper_bound} for {drug} could be outliers.\")\n",
    "    print(f\"--------------------------------------------------------\")\n",
    "    counter += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 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",
    "fig1, ax1 = plt.subplots(figsize=(9,7))\n",
    "x = [1, 2, 3, 4]\n",
    "ax1.set_title(\"Tumor Volume by Drug Regimen\")\n",
    "ax1.set_ylabel(\"Tumor Volume (mm3)\")\n",
    "ax1.set_xlabel(\"Drug Regimen\")\n",
    "ax1.boxplot(tumor_volume, notch=0, sym=\"gD\")\n",
    "plt.xticks(x,drug_regimens)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line and Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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 single mouse treated with Capomulin\n",
    "capomulin_mouse = clean_combined_data.loc[clean_combined_data[\"Mouse ID\"] == \"i738\"]\n",
    "#capomulin_mouse\n",
    "\n",
    "capomulin_time = capomulin_mouse[\"Timepoint\"]\n",
    "capomulin_volume = capomulin_mouse[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "x_axis = np.arange(0,50,0.5)\n",
    "\n",
    "plt.plot(capomulin_time, capomulin_volume)\n",
    "\n",
    "plt.xlabel(\"Timepoint\")\n",
    "plt.ylabel(\"Tumor Volume (mm3)\")\n",
    "plt.title(\"Tumor Volume by Timepoint for Capomulin Mouse i738\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a scatter plot of mouse weight vs. the average observed tumor volume for the entire Capomulin regimen\n",
    "\n",
    "capomulin_data = combined_data.loc[combined_data[\"Drug Regimen\"] == \"Capomulin\"]\n",
    "#capomulin_data\n",
    "\n",
    "stats_by_mouse_id = capomulin_data.groupby([\"Mouse ID\"]).agg(\n",
    "    mouse_weight=(\"Weight (g)\", np.mean),\n",
    "    tumor_vol_mean=(\"Tumor Volume (mm3)\", np.mean)).round(3)\n",
    "\n",
    "#stats_by_mouse_id\n",
    "\n",
    "plt.scatter(\n",
    "    stats_by_mouse_id[\"mouse_weight\"],\n",
    "    stats_by_mouse_id[\"tumor_vol_mean\"],\n",
    "    marker=\"o\",\n",
    "    facecolors=\"blue\",\n",
    "    edgecolors=\"black\",\n",
    "    s=stats_by_mouse_id[\"tumor_vol_mean\"],\n",
    "    alpha=.75)\n",
    "\n",
    "plt.title(\"Mouse Weight vs Average Tumor Volume\")\n",
    "plt.xlabel(\"Mouse weight (g)\")\n",
    "plt.ylabel(\"Tumor Volume (mm3)\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between both factors is 0.84.\n",
      "The equation of the regression line is: y = 0.95x + 21.55.\n",
      "The r-squared value is: 0.8419461020261081.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation coefficient and a linear regression model \n",
    "# for mouse weight and average observed tumor volume for the entire Capomulin regimen\n",
    "\n",
    "correlation = st.pearsonr(stats_by_mouse_id[\"mouse_weight\"],stats_by_mouse_id[\"tumor_vol_mean\"])\n",
    "\n",
    "x_values = stats_by_mouse_id[\"mouse_weight\"]\n",
    "y_values = stats_by_mouse_id[\"tumor_vol_mean\"]\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "\n",
    "line_eq = f\"y = {str(round(slope,2))}x + {str(round(intercept,2))}\"\n",
    "\n",
    "#scatter plot \n",
    "\n",
    "plt.scatter(x_values, y_values)\n",
    "plt.plot(x_values, regress_values, \"r-\")\n",
    "plt.annotate(line_eq,(17,37), fontsize=15, color=\"black\")\n",
    "plt.title(\"Mouse Weight vs Average Tumor Volume\")\n",
    "plt.xlabel(\"Mouse weight (g)\")\n",
    "plt.ylabel(\"Tumor Volume (mm3)\")\n",
    "\n",
    "print(f\"The correlation between both factors is {round(correlation[0],2)}.\")\n",
    "print(f\"The equation of the regression line is: {line_eq}.\")\n",
    "print(f\"The r-squared value is: {rvalue}.\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#print(regress_values)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}