pymaceuticals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations and Insights "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Capomulin and Ramicane treatment regimens had the most mice in comparison to the other regimens, as seen in the bar graphs. \n",
    "2. Looking at the box plot, Capomulin and Ramicane had the smallest tumors by volume. \n",
    "3. The gender distribution was equal between male and female, however the role of gender in tumor growth and treatment remains unknown. I would look at the tumor volume per regimen grouped by gender. There may be a medication more suitable for one gender over the other, or each gender may process and react differently. \n",
    "4.  By examinging multiple randomly selected test subjects over the course of the the study, Capomulin was successful in shrinking the tumors. The drug began taking effect between day 5 and day 10 when the tumors started to decreased. However, it appears the treatment lost effectiveness around day 35 and the tumors started to increase in volume. Future studies should consider incremently increasing the dosage over time to ensure complete ablation of the tumors.  \n",
    "5. There is a positive correlation of 0.84 between the weight of the mouse and volume of the tumors as demostrated by the linear regression model. This correlation was expected since any increase in volume will influence the mass of the object, thus influencing the weight of the mouse. This is not indicative to the efficiency of the drug regimens. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "import numpy as np\n",
    "\n",
    "# Study data files\n",
    "mouse_metadata_path = \"data/Mouse_metadata.csv\"\n",
    "study_results_path = \"data/Study_results.csv\"\n",
    "\n",
    "# Read the mouse data and the study results\n",
    "mouse_metadata = pd.read_csv(mouse_metadata_path)\n",
    "study_results = pd.read_csv(study_results_path)\n",
    "\n",
    "# Combine the data into a single dataset\n",
    "combined_df = pd.merge(mouse_metadata, study_results, on=\"Mouse ID\", how=\"left\")\n",
    "combined_df.to_csv(\"combined_df.csv\", index=False)\n",
    "# Display the data table for preview\n",
    "combined_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of mice.\n",
    "mice=len(mouse_metadata[\"Mouse ID\"].unique())\n",
    "mice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Issue: How do we know there's a duplicate in the first place when the instructions tell us 249 mice were used in the study and 249 mice are shown in the mouse_metadata and study_results. Simply looking at the number of mice won't work! The mice themselves aren't being duplicated nor was the same Mouse ID used for two mice since the weights and ages are the same. The data records are being duplicated. \n",
    "Thus, the number of timepoints used (10) and the value counts of each Mouse ID is needed. Then, anything over the max number of time_points would be a duplicate, that is if all the mice reached the 45 day mark, which some do not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "g989    13\n",
       "m546    10\n",
       "m601    10\n",
       "o287    10\n",
       "k862    10\n",
       "        ..\n",
       "t573     1\n",
       "v199     1\n",
       "o848     1\n",
       "l872     1\n",
       "u153     1\n",
       "Name: Mouse ID, Length: 249, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "study_record = study_results[\"Mouse ID\"].value_counts()\n",
    "study_record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "g989    5\n",
       "Name: Mouse ID, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the duplicate mice by ID number that shows up for Mouse ID and Timepoint.\n",
    "duplicates = study_results.loc[study_results.duplicated(subset=[\"Mouse ID\", \"Timepoint\"]), \"Mouse ID\"].value_counts()\n",
    "duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "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>908</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>48.786801</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>g989</td>\n",
       "      <td>Propriva</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>47.570392</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </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",
       "    <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",
       "    </tr>\n",
       "    <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optional: Get all the data for the duplicate mouse ID. \n",
    "duplicated_mouse_data=combined_df.loc[combined_df[\"Mouse ID\"] == \"g989\"]\n",
    "duplicated_mouse_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a clean DataFrame by dropping the duplicate mouse by its ID.\n",
    "combined_df.drop(combined_df.loc[combined_df[\"Mouse ID\"]== \"g989\"].index, inplace=True)\n",
    "combined_df.to_csv(\"cleaned_combined_df.csv\", index=False)\n",
    "# Checking the number of mice in the clean DataFrame.\n",
    "mice_check = len(combined_df[\"Mouse ID\"].unique())\n",
    "mice_check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Mean</th>        <th class=\"col_heading level0 col1\" >Median</th>        <th class=\"col_heading level0 col2\" >Variance</th>        <th class=\"col_heading level0 col3\" >Standard Variation</th>        <th class=\"col_heading level0 col4\" >SEM</th>    </tr>    <tr>        <th class=\"index_name level0\" >Drug Regimen</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60level0_row0\" class=\"row_heading level0 row0\" >Capomulin</th>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row0_col0\" class=\"data row0 col0\" >40.68</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row0_col1\" class=\"data row0 col1\" >41.56</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row0_col2\" class=\"data row0 col2\" >24.95</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row0_col3\" class=\"data row0 col3\" >4.99</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row0_col4\" class=\"data row0 col4\" >0.33</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60level0_row1\" class=\"row_heading level0 row1\" >Ceftamin</th>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row1_col0\" class=\"data row1 col0\" >52.59</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row1_col1\" class=\"data row1 col1\" >51.78</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row1_col2\" class=\"data row1 col2\" >39.29</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row1_col3\" class=\"data row1 col3\" >6.27</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60level0_row2\" class=\"row_heading level0 row2\" >Infubinol</th>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row2_col0\" class=\"data row2 col0\" >52.88</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row2_col1\" class=\"data row2 col1\" >51.82</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row2_col2\" class=\"data row2 col2\" >43.13</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row2_col3\" class=\"data row2 col3\" >6.57</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row2_col4\" class=\"data row2 col4\" >0.49</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60level0_row3\" class=\"row_heading level0 row3\" >Ketapril</th>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row3_col0\" class=\"data row3 col0\" >55.24</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row3_col1\" class=\"data row3 col1\" >53.70</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row3_col2\" class=\"data row3 col2\" >68.55</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row3_col3\" class=\"data row3 col3\" >8.28</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row3_col4\" class=\"data row3 col4\" >0.60</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60level0_row4\" class=\"row_heading level0 row4\" >Naftisol</th>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row4_col0\" class=\"data row4 col0\" >54.33</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row4_col1\" class=\"data row4 col1\" >52.51</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row4_col2\" class=\"data row4 col2\" >66.17</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row4_col3\" class=\"data row4 col3\" >8.13</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row4_col4\" class=\"data row4 col4\" >0.60</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60level0_row5\" class=\"row_heading level0 row5\" >Placebo</th>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row5_col0\" class=\"data row5 col0\" >54.03</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row5_col1\" class=\"data row5 col1\" >52.29</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row5_col2\" class=\"data row5 col2\" >61.17</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row5_col3\" class=\"data row5 col3\" >7.82</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row5_col4\" class=\"data row5 col4\" >0.58</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60level0_row6\" class=\"row_heading level0 row6\" >Propriva</th>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row6_col0\" class=\"data row6 col0\" >52.32</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row6_col1\" class=\"data row6 col1\" >50.45</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row6_col2\" class=\"data row6 col2\" >43.85</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row6_col3\" class=\"data row6 col3\" >6.62</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row6_col4\" class=\"data row6 col4\" >0.54</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60level0_row7\" class=\"row_heading level0 row7\" >Ramicane</th>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row7_col0\" class=\"data row7 col0\" >40.22</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row7_col1\" class=\"data row7 col1\" >40.67</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row7_col2\" class=\"data row7 col2\" >23.49</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row7_col3\" class=\"data row7 col3\" >4.85</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row7_col4\" class=\"data row7 col4\" >0.32</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60level0_row8\" class=\"row_heading level0 row8\" >Stelasyn</th>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row8_col0\" class=\"data row8 col0\" >54.23</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row8_col1\" class=\"data row8 col1\" >52.43</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row8_col2\" class=\"data row8 col2\" >59.45</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row8_col3\" class=\"data row8 col3\" >7.71</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row8_col4\" class=\"data row8 col4\" >0.57</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60level0_row9\" class=\"row_heading level0 row9\" >Zoniferol</th>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row9_col0\" class=\"data row9 col0\" >53.24</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row9_col1\" class=\"data row9 col1\" >51.82</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row9_col2\" class=\"data row9 col2\" >48.53</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row9_col3\" class=\"data row9 col3\" >6.97</td>\n",
       "                        <td id=\"T_efa6923e_61ef_11eb_8c17_9cb6d0b98b60row9_col4\" class=\"data row9 col4\" >0.52</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1aaba970e20>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a summary statistics table of mean, median, variance, standard deviation, and standard error of mean(SEM) of the tumor volume for each regimen.\n",
    "# This method is the most straighforward, creating multiple series and putting them all together at the end.\n",
    "\n",
    "mean = combined_df.groupby(\"Drug Regimen\").mean()[\"Tumor Volume (mm3)\"]\n",
    "median = combined_df.groupby(\"Drug Regimen\").median()[\"Tumor Volume (mm3)\"]\n",
    "variance = combined_df.groupby(\"Drug Regimen\").var()[\"Tumor Volume (mm3)\"]\n",
    "std_var = combined_df.groupby(\"Drug Regimen\").std()[\"Tumor Volume (mm3)\"]\n",
    "sem = combined_df.groupby(\"Drug Regimen\").sem()[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "summary_by_series_df = pd.DataFrame ({\n",
    "\"Mean\": mean,\n",
    "\"Median\": median,\n",
    "\"Variance\": variance,\n",
    "\"Standard Variation\": std_var,\n",
    "\"SEM\": sem})\n",
    "\n",
    "summary_by_series_df.style.format({\n",
    "\"Mean\": \"{:,.2f}\",\n",
    "\"Median\": \"{:,.2f}\",\n",
    "\"Variance\": \"{:,.2f}\",\n",
    "\"Standard Variation\": \"{:,.2f}\",\n",
    "\"SEM\": \"{:,.2f}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >mean</th>        <th class=\"col_heading level0 col1\" >median</th>        <th class=\"col_heading level0 col2\" >var</th>        <th class=\"col_heading level0 col3\" >std</th>        <th class=\"col_heading level0 col4\" >sem</th>    </tr>    <tr>        <th class=\"index_name level0\" >Drug Regimen</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60level0_row0\" class=\"row_heading level0 row0\" >Capomulin</th>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row0_col0\" class=\"data row0 col0\" >40.68</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row0_col1\" class=\"data row0 col1\" >41.56</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row0_col2\" class=\"data row0 col2\" >24.95</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row0_col3\" class=\"data row0 col3\" >4.99</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row0_col4\" class=\"data row0 col4\" >0.33</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60level0_row1\" class=\"row_heading level0 row1\" >Ceftamin</th>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row1_col0\" class=\"data row1 col0\" >52.59</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row1_col1\" class=\"data row1 col1\" >51.78</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row1_col2\" class=\"data row1 col2\" >39.29</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row1_col3\" class=\"data row1 col3\" >6.27</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60level0_row2\" class=\"row_heading level0 row2\" >Infubinol</th>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row2_col0\" class=\"data row2 col0\" >52.88</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row2_col1\" class=\"data row2 col1\" >51.82</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row2_col2\" class=\"data row2 col2\" >43.13</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row2_col3\" class=\"data row2 col3\" >6.57</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row2_col4\" class=\"data row2 col4\" >0.49</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60level0_row3\" class=\"row_heading level0 row3\" >Ketapril</th>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row3_col0\" class=\"data row3 col0\" >55.24</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row3_col1\" class=\"data row3 col1\" >53.70</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row3_col2\" class=\"data row3 col2\" >68.55</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row3_col3\" class=\"data row3 col3\" >8.28</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row3_col4\" class=\"data row3 col4\" >0.60</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60level0_row4\" class=\"row_heading level0 row4\" >Naftisol</th>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row4_col0\" class=\"data row4 col0\" >54.33</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row4_col1\" class=\"data row4 col1\" >52.51</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row4_col2\" class=\"data row4 col2\" >66.17</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row4_col3\" class=\"data row4 col3\" >8.13</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row4_col4\" class=\"data row4 col4\" >0.60</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60level0_row5\" class=\"row_heading level0 row5\" >Placebo</th>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row5_col0\" class=\"data row5 col0\" >54.03</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row5_col1\" class=\"data row5 col1\" >52.29</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row5_col2\" class=\"data row5 col2\" >61.17</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row5_col3\" class=\"data row5 col3\" >7.82</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row5_col4\" class=\"data row5 col4\" >0.58</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60level0_row6\" class=\"row_heading level0 row6\" >Propriva</th>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row6_col0\" class=\"data row6 col0\" >52.32</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row6_col1\" class=\"data row6 col1\" >50.45</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row6_col2\" class=\"data row6 col2\" >43.85</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row6_col3\" class=\"data row6 col3\" >6.62</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row6_col4\" class=\"data row6 col4\" >0.54</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60level0_row7\" class=\"row_heading level0 row7\" >Ramicane</th>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row7_col0\" class=\"data row7 col0\" >40.22</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row7_col1\" class=\"data row7 col1\" >40.67</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row7_col2\" class=\"data row7 col2\" >23.49</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row7_col3\" class=\"data row7 col3\" >4.85</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row7_col4\" class=\"data row7 col4\" >0.32</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60level0_row8\" class=\"row_heading level0 row8\" >Stelasyn</th>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row8_col0\" class=\"data row8 col0\" >54.23</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row8_col1\" class=\"data row8 col1\" >52.43</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row8_col2\" class=\"data row8 col2\" >59.45</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row8_col3\" class=\"data row8 col3\" >7.71</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row8_col4\" class=\"data row8 col4\" >0.57</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60level0_row9\" class=\"row_heading level0 row9\" >Zoniferol</th>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row9_col0\" class=\"data row9 col0\" >53.24</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row9_col1\" class=\"data row9 col1\" >51.82</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row9_col2\" class=\"data row9 col2\" >48.53</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row9_col3\" class=\"data row9 col3\" >6.97</td>\n",
       "                        <td id=\"T_f1383866_61ef_11eb_8a42_9cb6d0b98b60row9_col4\" class=\"data row9 col4\" >0.52</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1aab5efe460>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen.\n",
    "# This method produces everything in a single groupby function\n",
    "group = combined_df.groupby(\"Drug Regimen\")\n",
    "\n",
    "summary_single_group_df = pd.DataFrame(group.agg([\"mean\", \"median\", \"var\", \"std\", \"sem\"])[\"Tumor Volume (mm3)\"])\n",
    "\n",
    "stylish_summary = summary_single_group_df.style.format({\n",
    "\"mean\": \"{:,.2f}\",\n",
    "\"median\": \"{:,.2f}\",\n",
    "\"var\": \"{:,.2f}\",\n",
    "\"std\": \"{:,.2f}\",\n",
    "\"sem\": \"{:,.2f}\"})\n",
    "\n",
    "stylish_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar and Pie Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of mice for each treatment throughout the course of the study using pandas. \n",
    "mice_by_treatment = combined_df.groupby([\"Drug Regimen\"]).count()[\"Mouse ID\"] \n",
    "\n",
    "plot_pandas = mice_by_treatment.plot.bar(figsize=(20,10), color=\"cadetblue\", alpha=1, align=\"center\")\n",
    "plt.xticks(rotation=\"horizontal\", fontsize = 14)\n",
    "plt.xlabel(\"Drug Regimen\", fontsize = 16)\n",
    "plt.ylabel(\"Number of Mice\", fontsize = 16)\n",
    "plt.title(\"Number of Mice per Treatment\", fontsize = 20)\n",
    "\n",
    "plt.savefig(\"Visualizations/Bar_plot[Pandas]--Number of Mice per Treatment\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot showing the total number of mice for each treatment throughout the course of the study using pyplot.\n",
    "the_mice =(combined_df.groupby([\"Drug Regimen\"])[\"Mouse ID\"].count()).tolist()\n",
    "\n",
    "x_axis = np.arange(len(mice_by_treatment))\n",
    "fig1, ax1 = plt.subplots(figsize=(20, 10))\n",
    "plt.bar(x_axis, the_mice, color=\"cadetblue\", alpha=1, align=\"center\")\n",
    "tick_labels = [value for value in x_axis]\n",
    "plt.xticks(tick_labels, [\"Capomulin\", \"Ceftamin\", \"Infubinol\", \"Ketapril\", \"Naftisol\", \"Placebo\", \"Propriva\", \"Ramicane\", \"Stelasyn\", \"Zoniferol\"],  rotation=\"horizontal\", fontsize = 14)\n",
    "plt.xlabel(\"Drug Regimen\", fontsize = 16)\n",
    "plt.ylabel(\"Number of Mice\", fontsize = 16)\n",
    "plt.title(\"Number of Mice per Treatment\", fontsize = 20)\n",
    "\n",
    "plt.savefig(\"Visualizations/Bar_plot[PyPlot]--Number of Mice per Treatment\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a pie plot showing the distribution of female versus male mice using pandas\n",
    "#Set up Dataframe for Gender of mice\n",
    "gender = pd.DataFrame(combined_df.groupby(\"Mouse ID\" )[\"Sex\"].unique())\n",
    "gender.reset_index(inplace = True) \n",
    "#Find values\n",
    "m_v_f = pd.DataFrame(gender[\"Sex\"].value_counts())\n",
    "m_v_f.reset_index(inplace = True)\n",
    "\n",
    "#Change column headers to reflect data \"axes\"\n",
    "m_v_f.columns = [\"Sex\", \"Mouse Count\"]\n",
    "m_v_f[\"Sex\"] = m_v_f[\"Sex\"].str[0]\n",
    "panda_pie = m_v_f.plot(kind = \"pie\", y = \"Mouse Count\", autopct = '%.2f%%', labels = [\"Male\", \"Female\"], legend = False, startangle = 300,\n",
    "title = (\"Distribution of Female vs. Male Mice\"), shadow = False, figsize = (15, 10), colors = [\"cadetblue\", \"salmon\"], textprops= {\"fontsize\":14})\n",
    "panda_pie.title.set_size(20)\n",
    "panda_pie.set_ylabel(' ')\n",
    "panda_pie\n",
    "\n",
    "plt.savefig(\"Visualizations/Pie_plot[Pandas]--Gender Distribution of Test Subjects.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 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",
    "# Create Labels for the sections of the pie\n",
    "gender_labels = m_v_f[\"Sex\"]\n",
    "gender_sizes = m_v_f[\"Mouse Count\"]\n",
    "\n",
    "#Create the pie chart based upon the values \n",
    "plt.fig1, ax1 = plt.subplots(figsize = (15, 10))\n",
    "plt.pie(gender_sizes, labels = gender_labels , colors = [\"cadetblue\", \"salmon\"], autopct = \"%.2f%%\", shadow=False, startangle=300, textprops= {\"fontsize\":14})\n",
    "plt.title(\"Gender Distribution of Test Subjects\", fontsize = 20)\n",
    "\n",
    "plt.savefig(\"Visualizations/Pie_plot[PyPlot]--Gender Distribution of Test Subjects\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quartiles, Outliers and Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the final tumor volume of each mouse across four of the treatment regimens:Capomulin, Ramicane, Infubinol, and Ceftamin\n",
    "\n",
    "#Locate/Seperate data by drug regimen and create dataframe for each to work with\n",
    "Capomulin_df = combined_df.loc[combined_df[\"Drug Regimen\"] == \"Capomulin\",:]\n",
    "Ramicane_df = combined_df.loc[combined_df[\"Drug Regimen\"] == \"Ramicane\", :]\n",
    "Infubinol_df = combined_df.loc[combined_df[\"Drug Regimen\"] == \"Infubinol\", :]\n",
    "Ceftamin_df = combined_df.loc[combined_df[\"Drug Regimen\"] == \"Ceftamin\", :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Capomulin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lower quartile for Capomulin tumors: 32.38\n",
      "The upper quartile for Capomulin tumors: 40.16\n",
      "The interquartile range for Capomulin tumors: 7.78\n",
      "The median for Capomulin tumors: 38.13 \n",
      "Values below 20.71 could be outliers.\n",
      "Values above 51.83 could be outliers.\n"
     ]
    }
   ],
   "source": [
    "# Get last (greatest) timepoint for each mouse\n",
    "Capomulin_lastTP = Capomulin_df.groupby(\"Mouse ID\").max()[\"Timepoint\"]\n",
    "\n",
    "# Calculate final tumor volume of each mouse:  \n",
    "Capomulin_final_vol = pd.DataFrame(Capomulin_lastTP)\n",
    "\n",
    "# Merge this grouped dataframef with the original dataframe to get tumor volume at the last timepoint\n",
    "Capomulin_merge = pd.merge(Capomulin_final_vol, combined_df, on=(\"Mouse ID\",\"Timepoint\"), how=\"left\")\n",
    "Capomulin_tumors = Capomulin_merge[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "# Quartiles & Interquartile Range:\n",
    "quartiles = Capomulin_tumors.quantile([0.25,0.5,0.75])\n",
    "lowerqr = round(quartiles[0.25], 2)\n",
    "median =  round(quartiles[0.5], 2)\n",
    "upperqr = round(quartiles[0.75], 2)\n",
    "interqr = round(upperqr-lowerqr, 2)\n",
    "\n",
    "# Outliers:\n",
    "lower_bound = round(lowerqr - (1.5 * interqr), 2)\n",
    "upper_bound = round(upperqr + (1.5 * interqr), 2)\n",
    "\n",
    "# Output:\n",
    "print(f\"The lower quartile for Capomulin tumors: {lowerqr}\")\n",
    "print(f\"The upper quartile for Capomulin tumors: {upperqr}\")\n",
    "print(f\"The interquartile range for Capomulin tumors: {interqr}\")\n",
    "print(f\"The median for Capomulin tumors: {median} \")\n",
    "print(f\"Values below {lower_bound} could be outliers.\")\n",
    "print(f\"Values above {upper_bound} could be outliers.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ramicane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lower quartile of Ramicane tumors is: 31.56\n",
      "The upper quartile of Ramicane tumors is: 40.66\n",
      "The interquartile range of Ramicane tumors is: 9.1\n",
      "The median of Ramicane tumors is: 36.56\n",
      "Values below 17.91 could be outliers.\n",
      "Values above 54.31 could be outliers.\n"
     ]
    }
   ],
   "source": [
    "# Get last (greatest) timepoint for each mouse:\n",
    "Ramicane_lastTP = Ramicane_df.groupby(\"Mouse ID\").max()[\"Timepoint\"]\n",
    "\n",
    "# Calculate final tumor volume of each mouse:  \n",
    "Ramicane_final_vol = pd.DataFrame(Ramicane_lastTP)\n",
    "Ramicane_merge = pd.merge(Ramicane_final_vol, combined_df, on=(\"Mouse ID\",\"Timepoint\"), how=\"left\")\n",
    "Ramicane_tumors = Ramicane_merge[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "# Quartiles & Interquartile Range:\n",
    "quartiles = Ramicane_tumors.quantile([0.25,0.5,0.75])\n",
    "lowerqr = round(quartiles[0.25], 2)\n",
    "median = round(quartiles[0.5], 2)\n",
    "upperqr = round(quartiles[0.75], 2)\n",
    "interqr = round(upperqr - lowerqr, 2)\n",
    "\n",
    "#Outliers:\n",
    "lower_bound = round(lowerqr - (1.5 * interqr), 2)\n",
    "upper_bound = round(upperqr + (1.5 * interqr), 2)\n",
    "\n",
    "#Output:\n",
    "print(f\"The lower quartile of Ramicane tumors is: {lowerqr}\")\n",
    "print(f\"The upper quartile of Ramicane tumors is: {upperqr}\")\n",
    "print(f\"The interquartile range of Ramicane tumors is: {interqr}\")\n",
    "print(f\"The median of Ramicane tumors is: {median}\")\n",
    "print(f\"Values below {lower_bound} could be outliers.\")\n",
    "print(f\"Values above {upper_bound} could be outliers.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Infubinal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lower quartile of Infubinol tumors is: 54.05\n",
      "The upper quartile of Infubinol tumors is: 65.53\n",
      "The interquartile range of Infubinol tumors is: 11.48\n",
      "The median of Infubinol tumors is: 60.17\n",
      "Values below 36.83 could be outliers.\n",
      "Values above 82.75 could be outliers.\n"
     ]
    }
   ],
   "source": [
    "# Get last (greatest) timepoint for each mouse:\n",
    "Infubinol_lastTP = Infubinol_df.groupby(\"Mouse ID\").max()[\"Timepoint\"]\n",
    "\n",
    "# Calculate final tumor volume of each mouse:  \n",
    "Infubinol_final_vol = pd.DataFrame(Infubinol_lastTP)\n",
    "Infubinol_merge = pd.merge(Infubinol_final_vol, combined_df, on=(\"Mouse ID\",\"Timepoint\"), how=\"left\")\n",
    "Infubinol_tumors = Infubinol_merge[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "# Quartiles & Interquartile Range:\n",
    "quartiles = Infubinol_tumors.quantile([0.25,0.5,0.75])\n",
    "lowerqr = round(quartiles[0.25],2)\n",
    "median = round(quartiles[0.5], 2)\n",
    "upperqr = round(quartiles[0.75], 2)\n",
    "interqr = round(upperqr - lowerqr, 2)\n",
    "\n",
    "# Outliers:\n",
    "lower_bound = round(lowerqr - (1.5 * interqr), 2)\n",
    "upper_bound = round(upperqr + (1.5 * interqr), 2)\n",
    "\n",
    "# Output:\n",
    "print(f\"The lower quartile of Infubinol tumors is: {lowerqr}\")\n",
    "print(f\"The upper quartile of Infubinol tumors is: {upperqr}\")\n",
    "print(f\"The interquartile range of Infubinol tumors is: {interqr}\")\n",
    "print(f\"The median of Infubinol tumors is: {median}\")\n",
    "print(f\"Values below {lower_bound} could be outliers.\")\n",
    "print(f\"Values above {upper_bound} could be outliers.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ceftamin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lower quartile of treatment is: 48.72\n",
      "The upper quartile of temperatures is: 64.3\n",
      "The interquartile range of temperatures is: 15.58\n",
      "The the median of temperatures is: 48.72\n",
      "Values below 25.35 could be outliers.\n",
      "Values above 87.67 could be outliers.\n"
     ]
    }
   ],
   "source": [
    "# Get last (greatest) timepoint for each mouse:\n",
    "Ceftamin_lastTP = Ceftamin_df.groupby(\"Mouse ID\").max()[\"Timepoint\"]\n",
    "\n",
    "# Calculate final tumor volume of each mouse:\n",
    "Ceftamin_final_vol = pd.DataFrame(Ceftamin_lastTP)\n",
    "Ceftamin_merge = pd.merge(Ceftamin_final_vol, combined_df, on=(\"Mouse ID\",\"Timepoint\"), how=\"left\")\n",
    "Ceftamin_tumors = Ceftamin_merge[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "# Quartiles & Interquartile Range:\n",
    "quartiles = Ceftamin_tumors.quantile([0.25,0.5,0.75])\n",
    "lowerqr = round(quartiles[0.25], 2)\n",
    "median = round(quartiles[0.25], 2)\n",
    "upperqr = round(quartiles[0.75], 2)\n",
    "interqr = round((upperqr - lowerqr), 2)\n",
    "\n",
    "# Outliers:\n",
    "lower_bound = round(lowerqr - (1.5 * interqr), 2)\n",
    "upper_bound = round(upperqr + (1.5 * interqr), 2)\n",
    "\n",
    "#Output:\n",
    "print(f\"The lower quartile of treatment is: {lowerqr}\")\n",
    "print(f\"The upper quartile of temperatures is: {upperqr}\")\n",
    "print(f\"The interquartile range of temperatures is: {interqr}\")\n",
    "print(f\"The the median of temperatures is: {median}\")\n",
    "print(f\"Values below {lower_bound} could be outliers.\")\n",
    "print(f\"Values above {upper_bound} could be outliers.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Put treatments into a list for for loop (and later for plot labels)\n",
    "treatment = [\"Capomulin\", \"Ramicane\", \"Infubinol\", \"Ceftamin\"]\n",
    "# Create empty list to fill with tumor vol data (for plotting)\n",
    "vol_data = [Capomulin_tumors, Ramicane_tumors, Infubinol_tumors, Ceftamin_tumors]\n",
    "\n",
    "# Generate a box plot of the final tumor volume of each mouse across four regimens of interest\n",
    "fig1, ax1 = plt.subplots(figsize=(15, 10))\n",
    "ax1.set_title(\"Final Tumor Volume vs. Treatment\", fontsize = 20)\n",
    "ax1.set_ylabel(\"Tumor Volume (mm3)\", fontsize = 16)\n",
    "ax1.set_xlabel(\"Treatment\", fontsize = 16)\n",
    "bp=ax1.boxplot(vol_data, labels = treatment, widths = 0.4, patch_artist=True, vert=True)\n",
    "\n",
    "for box in bp[\"boxes\"]:\n",
    "    box.set(color=\"cadetblue\", linewidth=2)\n",
    "    box.set(facecolor = \"cadetblue\")\n",
    "for median in bp[\"medians\"]: \n",
    "    median.set(color =\"salmon\", linewidth = 2)\n",
    "for whisker in bp[\"whiskers\"]: \n",
    "    whisker.set(color =\"cadetblue\", linewidth = 2, linestyle =\":\")\n",
    "for cap in bp[\"caps\"]: \n",
    "    cap.set(color =\"cadetblue\", linewidth = 2) \n",
    "plt.xticks([1, 2, 3, 4], [\"Capomulin\", \"Ramicane\", \"Infubinol\", \"Ceftamin\"], fontsize = 14)\n",
    "\n",
    "plt.savefig(\"Visualizations/Box_plot--Final Tumor Volume vs. Treatment.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line and Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random sample selection: b742\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>290</th>\n",
       "      <td>b742</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>b742</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>41.534097</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>b742</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>41.999228</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>b742</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>39.524843</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>b742</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>37.950355</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>b742</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>38.617684</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>b742</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>39.167949</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>b742</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>39.847576</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>b742</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>40</td>\n",
       "      <td>38.255627</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>b742</td>\n",
       "      <td>Capomulin</td>\n",
       "      <td>Male</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>45</td>\n",
       "      <td>38.939633</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mouse ID Drug Regimen   Sex  Age_months  Weight (g)  Timepoint  \\\n",
       "290     b742    Capomulin  Male           7          21          0   \n",
       "291     b742    Capomulin  Male           7          21          5   \n",
       "292     b742    Capomulin  Male           7          21         10   \n",
       "293     b742    Capomulin  Male           7          21         15   \n",
       "294     b742    Capomulin  Male           7          21         20   \n",
       "295     b742    Capomulin  Male           7          21         25   \n",
       "296     b742    Capomulin  Male           7          21         30   \n",
       "297     b742    Capomulin  Male           7          21         35   \n",
       "298     b742    Capomulin  Male           7          21         40   \n",
       "299     b742    Capomulin  Male           7          21         45   \n",
       "\n",
       "     Tumor Volume (mm3)  Metastatic Sites  \n",
       "290           45.000000                 0  \n",
       "291           41.534097                 0  \n",
       "292           41.999228                 0  \n",
       "293           39.524843                 0  \n",
       "294           37.950355                 0  \n",
       "295           38.617684                 0  \n",
       "296           39.167949                 0  \n",
       "297           39.847576                 0  \n",
       "298           38.255627                 0  \n",
       "299           38.939633                 0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a line plot of time point/x-axis and versus tumor volume/y-axis for a mouse treated with Capomulin\n",
    "\n",
    "#Randomly select mouse -- manually selecting a mouse would show bias towards mice exhibiting the greatest decrease in tumor volume. \n",
    "#I have avoided such bias by randomly selecting a sample from the mice treated with Capomulin based on the Mouse ID. \n",
    "sample_selection = Capomulin_df.sample(n=1)\n",
    "random_mouse = sample_selection[\"Mouse ID\"].values[0]\n",
    "print(f\"Random sample selection: {random_mouse}\")\n",
    "\n",
    "#Make Dataframe of \"random_mouse\" data \n",
    "sample_mouse = pd.DataFrame(Capomulin_df.loc[Capomulin_df[\"Mouse ID\"] == random_mouse,:])\n",
    "sample_mouse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#line_plot\n",
    "x_axis = sample_mouse[\"Timepoint\"]\n",
    "y_axis = sample_mouse[\"Tumor Volume (mm3)\"]\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize = (15, 10))\n",
    "plt.plot(x_axis, y_axis, linewidth = 3, color = \"cadetblue\")\n",
    "plt.title(\"Tumor Volume vs. Time of Randomly Selected Mouse\", fontsize =20)\n",
    "plt.xlabel(\"Timepoint (days)\", fontsize = 14)\n",
    "plt.ylabel(\"Tumor Volume (mm3)\", fontsize = 14)\n",
    "\n",
    "plt.savefig(\"Visualizations/Line_plot--Tumor Volumn vs. Time of Random Mouse.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a scatter plot of mouse weight versus average tumor volume for the Capomulin regimen\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize = (15, 10))\n",
    "scatter_plt = Capomulin_df.groupby([\"Mouse ID\"]).mean()\n",
    "plt.scatter(scatter_plt[\"Weight (g)\"], scatter_plt[\"Tumor Volume (mm3)\"], s = 175, color = \"cadetblue\")\n",
    "plt.title(\"Mouse Weight vs. Average Tumor Volume for Capomulin Regimen\", fontsize =20)\n",
    "plt.xlabel(\"Weight (grams)\", fontsize = 14)\n",
    "plt.ylabel(\"Average Tumor Volume (mm3)\", fontsize = 14)\n",
    "\n",
    "plt.savefig(\"Visualizations/Scatter_plot--Mouse Weight vs. Average Tumor Volume for Capomulin.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between the mouse weight and the average tumor volume is 0.84\n",
      "The slope of the linear regression model is: 0.95\n",
      "The intercept of the linear regression model is: 21.55\n"
     ]
    },
    {
     "data": {
      "image/png": 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IL1inyncJOlyb79SuMbPZzrnvhL/vJP/lvJv8jGfF8ID8fjjYzObKJ3EPJ78MnXNPhRc975UfF5Fp6YTkxDCfUW75xj0k9/U/0wvCi69cF+Xr0jc454Lwgiy95WpY0hKlQ+XH7jTKd4V8USljmsJ4D5YfW/FRbZ7E5lUzu0X+DvV/hhtL2KL6Ufm/O309srny47I+bWbnpNx1nxPGfpzCJE+bE9T0MUXJ9/SMPKFkek+fzxLz/8i32G2U75b4tHyXwYR8d6uD5FtXkrIeDynb90rbtov891q+z8fr5Fu9yynTrHtBAWWpY1NHcn7K+D4V4Mfyx/VxZnZ+mJCcKH/z56aUC+mSnzsLsNV+TDkvZJv1MNCW+zj5WRij3MfVSMd1RfleJhOf4SQ335T01TCe38snSsnWqOMlvTHL47J9jpOx75j2czj7YUifqZRjIdd471LZ6jsrlIy3KN9bqG9010S9+an8F9KP5FsNrs9dfdMXwxuylO+RVk/yF61S5psoGS9onHNPOec+Ln9BMUW+O1pM0rfN7KS011jinLNc//L8TalSu2xOkE8gH0yr0yVpetjNL1OSl4xrUp648k1O8O/w5+vTC8LX3mqW0TJI77J5hHxcc9PvvjvnXnF+1rrR8heIJ8uPj/qS/CQsI/FpSdvIH0+v2ZYLc78k32K1i1ImyAi7fK2S9GEza0lJUF/S5qQvKfme7pjnPX0o7XHJ7pSZXCJ/l3+Kc+4o59xpzrmZzi9J0J2hftbjIcf29ZJeyff5cM49m+U5hyrXZ13afMFaKsM5PyUNuRVH2jSRz93y+/9DYXfGE+T3xY1Fim2rl1X2fVzKJFHaHN+v8xxTbyrS60TxXi6W7/XRbhlmP87G/JIKX5G0XNJY59yxzrmznHMXhZ/jrbpxpsj2OU7+vevTfhbjuBmu5MywBX+HA5WIJA91JRyn80v5O5gbtOU4pEySrXydWcqT21Onp0+OtxqdoX7OmTidc4Fz7jHn3FXyF+BSuHZf2PKzQtI423rZg+FKJmsHa+vxeEkPStpBfna/fSX93Tn395TyP4c/D9TIJPd1pvFD71Txeh4kWxqGfKfUOfeoNidKOyp7S1j641Y7534s31r1H/kJB0Yi2Wp6m3zLSvq/X6bVS7pFvrXs4/Iz9u2qDAmqiveepnqLfBfkp1I3hklCpvc86/EQdoHN9Pn6s6SdzGzcCGMtVNbPuvk140p9QTic81MxJG+OnSzfqv1GSfemJc+b3r8sF8vJpQoKie0VZd7HDSr9eKq/yre6vDO8MVIqxdxfOYXjwJLjPy/IV982L82zt/x1473pXV3DMYV753iad6fPDBvqDH8m//5u+SV89gt7LKQr2n7IYdjf4UAlIclDPTpffia4w9K/qDJ4VP5L591m9tHUgvD36ZJWastxQ8nxEZ9Jqz9BfjyS0rZPNb8wbbrkttRJYb4p30pzk2VY88vMdhrCcgUKx+H9Vb7b24nyM5AtS6uWbNm7OPyZPsHJzfIXQReG3U7TY4qZX0w6n+Q4k/PCBCr5+Cb5MYvFkuyml20JgHxukW9F+4L8Mg3L0rv8mtmbsiQaO8knWemLDr/Z/DqDeS8izexd8uPonnLOzXDOnZz+Tz6Je1ZSp5l1pDw8uRTEp7V54oI5GV7mu/Jj276V9vhkDE3p054X4BlJYyxljb1wfM+F8hPXpEtOgvHlcMxX6mOuUOYk/Vvhzxss81p+oyzHmnzD8Ff5FscPh60cydfZVr5beKkN5/xUDL+Tn7TjMG1OErYY2+z8hDr3yZ9bvpoW2zT5iXVekR/rms9CSXua2aFp289X9u6BReH80iTXybcgfSd8b7dgfm27fBNL5XudYu6vQpwv/x5+0vxabZn+rl3N7DuS/jfc9Ez4892pEziZX4LgBuW+ETdG/pyZ+vwflr/xtVrh0gfhmOVb5bu/XpxW/83yLYkD2pyklkK27/D3avPNV6DiMSYPdcc59w9tntwhX11nZsfJf/n+wszulL+wGyvfwvaqpE+nDSi/U7615xPh3c0F8gnFh8Oyj6W9zAxJXzSzh+S/7F6R9Gb5lrM+pazh5py7KZws4wuSnjaz34d/y86S3iR/UXez/DpGhXpAfrD7BEnz0gfHO+dWmllvWJ6sn1r+r/CC8teS/mxmD8i3OCbCv3t/+a6D2RZQTj7PQ2Z2vfzSDivM7FfyX+Yfku+a06vN3eNG4gH5sWY3mNkv5VvW1jnnvlvg438if/ExS34sR6ZWvEmSfm1mj8l3beqV7wr74fAxV6XVf0D+YvVN2nwhlc0p4c8bs1UIJ8i4WX6c52cUjq1zzq0xswflx1gGkv6SaUyqc+6vZnai/HpnK8zsHvlkoVH+PT1QfhziPnliTfUt+Snrl6S8twfIJ3j/T/59To3hafNr3l0uaamZ/UL+ODhE/nhfKmli2mMeMLOz5ZPAVWZ2t/x6k6+T378HySc8W63JNxzOuQEz+7Z8orPEzH4t/716iPx73pvr8UV4/eGcn4rxuoPhBCwz5Wf4fF5+Aql0n5NPRK8OE7TF8q0jx8h/lk8o4Eab5MeYHibpzvA4eDl83TfJdyfvHMnfU4BL5D/Tn5PvovoH+bFou8snLwdIOk+5J5YqRLH2V17OuX+GScsd8mtIHmdm98l/nzTJ99rolL8plexN8ryZ/Vw+6XvCzO6V75J8iPxY2yeUvWX1HknXmNn75T+7b5Eff75R0klpx+jZ8ueYL5mf+OlB+V4HH5O0vfxSDKm9SYrtZvlz5jnmF0t/Un4CnvfLf89ttU4kUJFcBazjwD/+leqfhrAWkDKsk5dSNlb+zuFz8henz8kv0jw2y3ONll9g92X5VptF8l9onUpbV0t+kdgfyH/xJeuvlv+i2WpNtvAxR8gvfvuC/Din5+XvPl4qaZ8h7qPk+lZbrWOUUudn2rwe0+5Z6uwl3wK0Sv6L+9/yF5w/lXRUWt05yrBWnXzvgq+Fj+uTv0j+nvyFxKvyM6Sl1j8+23uW8v53Zdj+dfn1xfrCOs8McZ/dHz5uQNLrM5S3yycnj4bvTZ/8XfPfSXp/hvrPZNofGertKN/NuE8Z1pzLcAwOhsdIU8r2Y1Pe79PyPMeE8L16NnzNl+WT1h9JOjitbpeyrC2V9n49Ef4NL8lfME3Q5nXIOjM85lPyXbk2yieWP5PUGsaxLsvrvFt+Qeje8PPxYvi635QfEzjU80hnjmPJ5C9Knw5fKzkF/nYawtpZaa9zUYayvcKyORnKCj4/5drXQ9wnyePLSbo8R702+fPbs+H+eUk+sXhHluMj2745Uj7p2SjfGv9z+cR9jrKvk7fVvgrLM76XKZ/Frc4H4fv8KfkbMi+Hf8ta+ZsG5yplLccCPgO5zllF2V9DeB+bJJ0kP87yufA1X5VfIuc7kiak1d9O0mXy31Eb5WeD/J78jbwupZ0DUo9p+Rt+98t/N7wqvxj4Vn9X+LgW+Zthq+TPPevkb2YcWqzPTa5jQb63xN1hnP/R5llAM+7zTMfNUOoW+/PJP/455/wikwBQycyvybVSfj08usvUOTPbQX62viecc/vnqw+gPMKu+g9KmuX85CwAIsKYPAAVw8zekD443/z6gMkuq8Uaj4IqYGa7pY9TDCeluEa++y/HAwAAGTAmD0Al+ar8WMYu+a5Db5AfP9Yu39Xx/8oXGsrgI5IuNrP75buF7Sw/7rRDvvvldWWMDQCAikWSB6CS3Cc/wcGh8hf0gXw3ze9IutbRv7zeLJAf8zRdm9dJ/Lv8mKCrnJ8KHgAApGFMHgAAAADUkKpsydt1113dXnvtVe4wAAAAAKAsHnvssZecc7tlKqvKJG+vvfbS4sWLyx0GAAAAAJSFmT2brYzZNQEAAACghpDkAQAAAEANIckDAAAAgBpCkgcAAAAANYQkDwAAAABqCEkeAAAAANQQkjwAAAAAqCEkeQAAAABQQ0jyAAAAAKCGkOQBAAAAQA0hyQMAAACAGhJ5kmdmDWa2xMzuStn2ZTPrNrMVZvaNqGMCAAAAgFoRL8NrnirpKUk7SJKZvUfShyVNdM71mdnuZYgJAAAAAGpCpC15ZtYu6YOSbkzZ/HlJVzrn+iTJOfdClDEBAAAAQC2JurvmtZLOlJRI2dYh6UAzW2BmD5nZOzI90MxOMbPFZrb4xRdfjCJWAAAAAKg6kSV5ZnaEpBecc4+lFcUl7STpnZLOkHS7mVn6451z1zvnpjjnpuy2226lDxgAAAAAqlCUY/IOkHSkmX1A0jaSdjCzn0nqkTTPOeckLTSzhKRdJdFcBwAAACByCee0fG2vlvX0aENfn0Y1N2tie7vGt7UqtnV7VMWJLMlzzp0j6RxJMrNOSac75441s89JOlhSl5l1SGqS9FJUcQEAAABAUld3t+Y9/oT6g0Abg2DT9vmrVqupMa6jJ09W59iOMkaYXzlm10x3k6SbzGy5pH5Jx4WtegAAAAAQmbkLFqpr5Sr1pyR3SRvDpG/uwkXqXbdOM6ZNLUOEhSlLkuec65LUFf6/X9Kx5YgDAAAAACTfgpctwUvVHwTqWrlKrS0tFduiF/li6AAAAABQSRLObeqiWYj+INC8JUuUqNAOiCR5AAAAAOra8rW9BSd4Sf0DgVb09pYoopEhyQMAAABQ15b19GwxyUohNgaBlq7pKVFEI0OSBwAAAKCubejrG+bj+oscSXGQ5AEAAACoa6Oam4f5uKYiR1IcJHkAAAAA6trE9nZtEx/awgPN8bgmjW4vUUQjQ5IHAAAAoK6Nb2tV01CTvMa4xrW2liiikSHJAwAAAFDXYmY6+m37FZzoNcXjOnryZMXMShzZ8JDkAQAAAKh7nWPHqrNjTN5ErykeV2fHmIpdCF2ShtYmCQAAAAA1asa0qWptadG8JUvUPxBssaxCczyu5kbfglfJCZ5EkgcAAAAAm3SO7dD0jjFa0durpWt6tKGvX6OamzRpdLvGtbZWbBfNVCR5AAAAAJAiZqYJbW2a0NZW7lCGhTF5AAAAAFBDSPIAAAAAoIaQ5AEAAABADSHJAwAAAIAaQpIHAAAAADWEJA8AAAAAaghJHgAAAADUEJI8AAAAAKghJHkAAAAAUENI8gAAAACghpDkAQAAAEANiZc7AAAAAACoNINdv1fioXslSfGZV8usetrHqidSAAAAACixxNPdGph12qYET5KCi88oY0RDR0seAAAAgLrn1r+i4NpLM5bZhMkRRzMyJHkAAAAA6pYbDBRcelb2CjvupPjRx0YXUBGQ5AEAAACoSwOzTstZHr/galms+ka4keQBAAAAqCvB7bfIPbUsa3n89Itko7aPMKLiIskDAAAAUBcGF/9Jid/+Mmt5wwlfUmzPN0UYUWmQ5AEAAACoaYneNRq84dqs5bFDj1TD/gdFGFFpkeQBAAAAqEnutf8q+MYFWctt7w7FP/XZCCOKBkkeAAAAgJriXCLv2naNF14TUTTRI8kDAAAAUDMGvn2ZtO7lrOXx866UxRsjjCh6JHkAAAAAqt7gPXcqseDhrOXxr5wr22mXCCMqH5I8AAAAAFUr8de/aPAXc7KWN3z8BMX2GR9dQBWAJA8AAABA1XEvv6Tguiuylsfe1amGQz4UYUSVgyQPAAAAQNVwAwMKLj87e4Wdd1Xjl8+JLqAKRJIHAAAAoCoMzDotZ3l85myZWUTRVC6SPAAAAAAVLbjl+3LPPJ21PH7WpbJtto0wospGkgcAAADUuIRzWr62V8t6erShr0+jmps1sb1d49taFavglq/BRx9U4v67spbHT/mabI/2CCOqDiR5AAAAQA3r6u7WvMefUH8QaGMQbNo+f9VqNTXGdfTkyeoc21HGCLeWePZpDc75ftbyhg8do9jb3hlhRNWFJA8AAACoUXMXLFTXylXqT0nukjaGSd/chYvUu26dZkybWoYIt+T+828F18zKWm7j9lP8o5+KMKLqRJIHAAAA1KCu7u6sCV6q/iBQ18pVam1pKVuLnksMKrjkzJx1Gi+8JqJoqh9JHgAAAFBjEs5t6qJZiP4g0LwlSzS9Y0zkY/QGLj9HGujPWh4//xuyhoYII6p+JHkAAABAjVm+trfgBC+pfyDQit5eTWhrK1FUWwru/LncE4uylse/Nuel/Z8AACAASURBVFO2w46RxFJrSPIAAACAGrOsp2eLSVYKsTEItHRNT8mTvMTSxRq847as5Q2f+qxie1fWRDDVhiQPAAAAqDEb+vqG+bjs3SZHyv3zOQU/nJ21PPaew9Uw/ZCSvX49IckDAACoANW6jhkq06jm5mE+rqnIkUiub6OCK8/LWm5teyp+8qlFf916RpIHAABQZtW4jhkq28T2ds1ftXpIXTab43FNGl28hcWdcwouPj1nnfjM2TJuYhQdSR4AAEAZVds6ZqgO49ta1RSPDy3Ja4xrXGtrUV5/4IfXSP/szVoeP+dyWdPwWhuRH0keAABAmVTTOmaoLjEzHf22/TR34eKCZtlsivsW45F2DR78w++UeOT+rOXxL5wp2+31I3oN5EeSBwAAUAbVtI4ZqlPn2LHqXbc+742EpnhcnR1jRnQDIbH6rxq89Yas5Q0fOVax8ZOH/fwYGpI8AACAMqiGdcxQ/WZMm6rWlhbNW7JE/QNbjvlsjsfVPMIxn279KwquvTRreezt+6vhiI8O67kxfCR5AAAAZVDJ65ihtnSO7dD0jjFa0durpWt6tKGvX6OamzRpdLvGtQ5v9lYXBAouOyt7he1ep8YzZo0gaowESR4AAEAZVOI6ZtWEJSeGJmamCW1tRblBMDDrtJzl8QuulsViI34dDB9JHgAAKAkuwnOrpHXMqg1LTpRH8POb5LpXZC2PnzFLtt3rIowI2ZDkAQCAouMiPL9KWMesGrHkRPQGF/1Ribt/lbW84cQvKzZ6r+gCQl4keQAAoKi4CC9Mudcxq0YsORGtxNp/aPDGb2ctjx1+lBqmHRhhRCgUnWUBAEDRDPUivKt7ZUSRVZ7kOmZN8cLuuRdrHbNqNdwlJxLOlTiy2uP+u0EDs07LmuDZW/ZR44XXkOBVMFryAABAUbDu29BFuY5ZtWPJidJzLqHg4jNy1mm88JqIosFIkOQBAICi4CJ8eEq9jlmtYMmJ0hr45izp1X9nLY+fd6Us3hhhRBgJkjwAAFAUXIQPXynWMas1LDlRGoN3z1Ni0aNZy+Onnidr2TnCiFAMJHkAAKAouAgfmWKuY1aLWHKiuBJPLtPg/92StbzhEycp1vHWCCNCMZHkAQCAouAiHKXEkhPF4f71ooLvXpm1PHbAwWp43wcjjAilQJIHAACKgotwlBJLToyM2/iagqvOz15h193V+MWzogsIJUWSBwAAioKLcJRScsmJuQsXFzTBT70vOZHknFNw8ek568RnzpbV+X6qNSR5AACgKLgIR6mx5MTQDMw6LWd5/KxLZdtsG1E0iBJJHgAAKBouwlFqLDmRX3Dbj+VWPpm1vOHYUxR789gII0LUSPIAAEBRcRGOUmPJicwSSxdr8I7bspbHph2ohsOPijAilIs558odw5BNmTLFLV68uNxhAACAHBLOcREORCDfjJmKN6rxvBzlqEpm9phzbkqmMlryAABASbDuG1BabjBQcGnuGTEbL7wmomhQSUjyAAAAgCqTd1KV86+SNXCpX6945wEAAIAqMXD5OdJAf9by+JfOlu2yW4QRoRKR5AEAAAAVbvCeO5VY8HDW8oYPf1yx/aZGGBEqGUkeAAAAUKEST3dr8GfXZy23MfsqPuPkCCNCNSDJAwAAACqM2/CqgtkX5azDpCrIhiQPAAAAqBDOOQUXn56zTnzmbBnLkCAHkjwAAACgAuSdMfOsS2XbbBtRNKhmkSd5ZtYgabGktc65I1K2ny7pakm7OedeijouAAAAoByCG78tt/YfWcsbTvySYqPfFGFEqHblaMk7VdJTknZIbjCz0ZIOkZT96AYAAABqyOCCR5S4546s5bHOw9Vw0CHRBYSaEWmSZ2btkj4o6TJJX08p+pakMyXdGWU8AAAAQNTc870KfpRj0pSWndV46nnRBYSaE3VL3rXyydz2yQ1mdqR8182luQaQmtkpkk6RpD333LPEYQIAAADF5fr7FFxxbs46zJiJYogsyTOzIyS94Jx7zMw6w23bSTpP0qH5Hu+cu17S9ZI0ZcoUV8JQAQAAgKLKO6nKBVfLYrGIokGti7Il7wBJR5rZByRtIz8m76eS3iQp2YrXLulxM5vqnHs+wtgAAACAosub3H31fNmOO0UUDepFZEmec+4cSedIUtiSd7pz7iOpdczsGUlTmF0TAAAA1Sz49Vy5ZY9lLW/42PGK7TshwohQT1gnDwAAACiSxFPLNHj7LVnLbdIUxY/6RIQRoR6VJclzznVJ6sqwfa+oYwEAAABGyq1/RcG1l+asw6QqiAoteQAAAMAwucSggkvOzFmH5A5RI8kDAAAAhiHvpCrnXiFrbIooGmAzkjwAAABgCAauvVRa/0rW8vjnTpO9vjXCiIAtkeQBAAAABRjs+r0SD92btTx2+FFqmHZghBEBmZHkAQAAADkk/vF3Dd783azl1v5GxU/6SoQRAbmR5AEAAAAZuI2vKbjq/Jx1mFQFlYgkDwAAAEjhnFNw8ek568RnzpaZRRQRMDQkeQAAAEAo74yZp8+SjXpdRNEAw0OSBwAAgLoX/Ox6uae7s5Y3fOqziu3dEWFEwPCR5AEAAKBuJZYs0OBvbs9aHnvnQWo47MgIIwJGjiQPAAAAdce99IKC712VvULzNmo8+7LoAgKKiCQPAAAAdcMFgYLLzspZhxkzUe1I8gAAAFAX8k6qcv5VsgYuj1H9OIoBAABQ0wYuPUsaDLKWx798jmznXSOMCCgtkjwAAADUpMG75ymx6NGs5Q1HfUKxSVMijAiIBkkeAAAAakpi9V81eOsNWctt7DjF//fECCNCtUk4p+Vre7Wsp0cb+vo0qrlZE9vbNb6tVTGzcoeXF0keAAAAaoL7z6sKrrkoZx0mVUE+Xd3dmvf4E+oPAm0MNnfznb9qtZoa4zp68mR1jq3sNRNJ8gAAAFDVnEsouPiMnHVI7lCIuQsWqmvlKvUHW4/h3BgmfXMXLlLvunWaMW1qGSIsDEkeAAAAqlbeGTPPvkzWvE1E0aCadXV3Z03wUvUHgbpWrlJrS0vFtuiR5AEAAKDq5EvuGk76imLtb4woGlS7hHObumgWoj8ING/JEk3vGFORY/RI8gAAAFA1Br51ifTvdVnLYwe/Xw0Hvi/CiFALlq/tLTjBS+ofCLSit1cT2tpKFNXwkeQBAICSqPbZ6VBZEksWaPA3t2evsMtuavzS2dEFhJqyrKdni0lWCrExCLR0TQ9JHgAAqA+1MDsdKgMzZiIKG/r6hvm4/iJHUhwkeQAAoKhqZXY6lF/eSVXO/4asoSGiaFDLRjU3D/NxTUWOpDhI8gAAQNHU0ux0KJ+8k6qcfKpibXtGFA3qwcT2ds1ftXpIXTab43FNGt1ewqiGjyQPAAAURa3NTofo5UvubPI0xY/8WETRoJ6Mb2tVUzw+tCSvMa5xra0ljGr4SPIAAEBR1NrsdIhOcPscuaf+krMO4+5QSjEzHf22/TR34eKCzmNNcT+2uFJvUJHkAQCAoqi12elQeolnn9bgnO/nrENyh6h0jh2r3nXr83Y5b4rH1dkxpqK7mpPkAQCAoqi12elQOi4YUHBZ7uUOSO5QDjOmTVVrS4vmLVmi/oEtZwdujsfVXCWzA5PkAQCAoqi12elQGnlnzDzzEtm220UUDbC1zrEdmt4xRit6e7V0TY829PVrVHOTJo1u17jW6ljnkyQPAAAURa3NTofiyjtj5keOVWz85IiiAXKLmWlCW1vVdiUnyQMAAEVRa7PToTjyJXfafQ81fv70aIIB6gRJHgAAKIpam50OIzP48P1KPPi7nHUYdweUBkkeAAAomlqanQ7D415+ScF1V+SsQ3IHlBZJHgAAKKpamZ0OQ+OcU3Bx7m6X8ZmzZbTcAiVHkgcAAIquFmanQ+Hyzpj5xbNku+4eUTQASPIAAEBJVPvsdMgvX3IXO+hQNXQeFlE0AJJI8gAAADAkwQ3XyvWuyVmHcXdA+ZDkAQAAoCCJp5Zp8PZbctYhuQPKjyQPAAAAObmNrym46vycdUjugMpBkgcAAICs8k6qcu6VssbGiKLZLOGclq/t1bKeHm3o69Oo5mZNbG/X+DYm9gFI8gAAALCVfMldw6c+q9je5VkGo6u7W/Mef0L9wZZLdMxftVpNLNEBkOQBAABgs3zJnY0dp/j/nhhRNFubu2ChulauUn9Kcpe0MUz65i5cpN516zRj2tQyRAiUH0keAAAANHj3PCUWPZqzTrnH3XV1d2dN8FL1B4G6Vq5Sa0sLLXqoSyR5AAAAdcw916Pg+m/lrFPu5E7yY/CSXTQL0R8EmrdkiaZ3jGGMHuoOSR4AAEAdcolBBZecmbNOJSR3ScvX9hac4CX1DwRa0durCW1tJYoKqEwkeQAAAHUm74yZX58p237HiKIpzLKeni0mWSnExiDQ0jU9JHmoOyR5AAAAdSJfchf74EfUMOVdEUUzNBv6+ob5uP4iRwJUPpI8AACAGjdw9UzpvxuyV9h2OzWeeUl0AQ3DqObmYT6uqciRAJWPJA8AAKBGDS7+kxK//WXOOpU07i6Xie3tmr9q9ZC6bDbH45o0ur2EUQGViSQPAACgxrhX/63gm7Ny1qmW5C5pfFurmuLxoSV5jXGNa20tYVRAZSLJAwAAqCF5J1W54BuyWENE0RRPzExHv20/zV24uKBZNpvicR09eTLLJ6AukeQBAADUgLzJ3Slfk+1R3V0XO8eOVe+69XkXRG+Kx9XZMYaF0FG3SPIAoE4lnNPytb1a1tOjDX19GtXcrInt7Rrf1sqdb6CK5J0xc8q71PDBj0QUTenNmDZVrS0tmrdkifoHgi26bzbH42pu9C14JHioZyR5AFCHurq7Ne/xJ9QfbHmBNH/VajVxgQRUheDnN8l1r8hZp9rG3RWqc2yHpneM0YreXi1d06MNff0a1dykSaPbNa6VG1UASR4A1Jm5CxZm7eq0MUz65i5cpN516zRj2tQyRAggl8TfV2nwJz/MWadWk7tUMTNNaGtjoXMgA5I8AKgjXd3deceySFJ/EKhr5Sq1trTQogdUCDcwoODys3PWqYfkDkB+JHkAUCcSzm3qolmI/iDQvCVLNL1jDF2fgDLLO6nKWZfKttk2omgAVLqCkjwz20bS/pL2krStpBclPe6ce7p0oQEAimn52t6CE7yk/oFAK3p76Q4FlEm+5K7hY8cptu/EiKIBUC1yJnlmdoCkUyV9SFKjpPWSXpO0s6RmM/ubpOsl/dA592qJYwUAjMCynp4hLSIs+TF6S9f0kOQBEcuX3Nke7Yqf8rWIogFQbbImeWZ2p6QpkuZKOlTSYufcaynle0s6UNInJH3dzD7tnLuvxPECAIZpQ1/fMB/XX+RIAGQzeN//U+KPXTnrMO4OQD65WvLulXSMcy7jt7tz7m+S/ibpFjMbJ6m1BPEBAIpkVHPzMB/XVORIAKRzL/5Twfe/kbMOyR2AQmVN8pxz3yv0SZxzKyTlXqgFAFBWE9vbNX/V6iF12WyOxzVpdHsJowLqm3NOwcWn56wTnzlbxuRHAIZgWLNrmllMUoNzbqDI8QAASmR8W6ua4vGhJXmNcY1rpaMGUAp5Z8z84lmyXXePKBoAtSSWq9DMGs3sSjN71MwuC7edLWmDpA1mdquZDa//DwAgUjEzHf22/dQUL+z+XlM8rqMnT2b5BKDIBmadljPBi737vWq88BoSPADDlu+b/iJJx0u6VdJRZraLpA9KOllSg6TLJX1V0lWlCxEAUCydY8eqd936vAuiN8Xj6uwYw0LoQBENXHuptP6VnHUYdwegGPIlef8r6WTn3F1m9gNJ3ZJmOOd+IUlmtlHSBSLJA4CqMWPaVLW2tGjekiXqHwi26L7ZHI+rudG34JHgAcWRWLpYg3fclrMOyR2AYsqX5LVKWipJzrnVZtaf/D20SNIbSxQbAKBEOsd2aHrHGK3o7dXSNT3a0NevUc1NmjS6XeNaW+miCRSB++8GBVfPzFmH5A5AKeRL8tZLapG0Jvz9cUmpi543SXIliAsAUGIxM01oa2Ohc6AE8k6qcu6VssbGiKIBUG/yJXlPSpos6S+S5Jw7IK18gqTVJYgLAACg6uRL7ho+9VnF9qYrNIDSypfkfVFSxsXQQ9tIurp44QAAAFSffMmd7d2h+Kc+G1E0AOpdziTPOfdUnvKfFTccAACA6hH86mdyy5fkrMO4OwBRG9Ji6Ga2s6Tdlba+nnPuyWIGBQAAUMkSa57R4E3X5axDcgegXApK8sxssqSb5cfgbVEkP/FKQ5HjAgAAqDhucFDBpWfmrENyB6DcCm3Ju0nSWkmnSvqnmFETAADUmbwzZn79Qtn2O0QUDQBkV2iSN0bSMc45ZtIEgBqRcE7L1/ZqWU+PNvT1aVRzsya2t2t8G+vkAanyJXex9/+PGqa+O6JoACC/QpO8+ZL2FcslAEBN6Oru1rzHn1B/EGhjEGzaPn/VajU1xnX05MnqHMs076hv+ZI7NTSo8fxvRBMMAAxBoUneSZJuNLO9JS2XNJBa6Jx7uNiBAQBKY+6ChepauUr9Kcld0sYw6Zu7cJF6163TjGlTyxAhUF6Df3xQifvuylmnFOPuaF0HUCxD6a65n6TDMpQx8QoAVImu7u6sCV6q/iBQ18pVam1poUUPdcOtf0XBtZfmrFOqSVVoXQdQTIUmeT+S9ICkKzTCiVfMrEHSYklrnXNHmNnVkj4kv+j605JOcM6tG+7zAwAySzi36SKyEP1BoHlLlmh6xxhaEVDz8k6qcsHVslgsZ53honUdQLEVmuS1S/qAc+7pIrzmqZKekpScfuo+Sec45wIzu0rSOZLOKsLrAABSLF/bW3CCl9Q/EGhFb68mtLWVKCqgvPImd5/5qqx1dMlen9Z1AKVQ6C2p+yS9faQvZmbtkj4o6cbkNufcvc655Jntz/IJJQCgyJb19GzRDawQG4NAS9f0lCgioHwGZp2WM8GzyVPVeOE1JU3whtu6nnCsZAUgt0Jb8u6RdI2ZTZT0F2098cq8Ap/nWklnSto+S/mJkn6RqcDMTpF0iiTtueeeBb4cACBpQ1/fMB/XX+RIgPIJfvwduZ5nc9aJajFzWtcBlEqhSd73w5/nZigraOIVMztC0gvOucfMrDND+XmSAkm3Znq8c+56SddL0pQpU7iFBQBDNKq5eZiPaypyJED0Et0rNPjzm3LWiSq5SxpJ6zpJHoBcCkrynHPFGGl8gKQjzewDkraRtIOZ/cw5d6yZHSfpCEnvdY4+CABQChPb2zV/1eohXVQ2x+OaNJpe9Kherr9PwRWZ7lFvFnVyl0TrOoBSKbQlb8Scc+fIT6qisCXv9DDBO1x+opWDnHP/jSoeAKg349ta1RSPDy3Ja4xrXGtrCaMCSifvpCpnXSrbZtuIotkaresASqXgJM/MJkt6j6TdlTZhi3PuzBHE8F1JzZLuMz9F95+dc58bwfMBADKImenot+2nuQsXFzQOqCnu1+Zi+QRUm3zJXcMxn1bsrZMiiiY7WtcBlEpBSZ6ZnSnpSknPaut18obcvdI51yWpK/z/W4b6eADA8HSOHavedevzTtneFI+rs2MMU7WjquRL7rTb69X4hZHcly4uWtcBlEqhLXlfk/R559yPShkMAKD0ZkybqtaWFs1bskT9A8EWF5jN8biaG30LHgkeqsXgPXcoseCRnHXKNe4uF1rXAZRKoUleTNIDpQwEABCdzrEdmt4xRit6e7V0TY829PVrVHOTJo1u17jWVi4iURXcC88p+MHsnHUqMblLRes6gFIoNMn7gaQTJJ1XwlgAABGKmWlCWxtTsaPqOJdQcPEZOevEZ86WVcnNClrXARRboUneLEl3m9kTyrwY+onFDgwAACBd3hkzv3yObOddI4qmeGhdB1BMhSZ5l0k6VNLjknbSMCZbAQAAGK58yV3soEPV0HlYRNGUBq3rAIql0CTvC5JmOOd+UcpgAABA7Ug4p+Vre7Wsp0cb+vo0qrlZE9vbNb6t8JapgdkXSRtezVmn0sfdAUDUCk3yXpO0pJSBAACA2tHV3a15jz+h/mDLMWbzV61WUwFjzBKPL9Dg/7s952uQ3AFAZoUmed+S9FUz+6Jzjq6aAAAgq7kLFmadLXJjmPTNXbhIvevWaca0qVuUuw3/UTD7wpzPT3IHALkVmuQdKGm6pA+a2ZPaeuKVI4sdGAAAqD5d3d15lwOQpP4gUNfKVWptadnUopd3UpXzrpLFC710AYD6VeiZ8iVJ80oZCAAAqG4J5zZ10SxEfxBo3pIlOuDnP8pZr+G4zyu211uKESIA1IWCkjzn3AmlDgQAAFS35Wt7C07wJOmGfyzLWW5j9lV8xskjDQsA6g59HgAAQFEs6+nZYpKVbD734rN6+2vrc9Zh3B0ADF/WJM/M7pd0kXNufq4nMLMWSV+U9G/n3HVFjg8AAFSJDX19OcvHbNygM194OmcdkjsAGLlcLXk/k3Sbmb0m6TeSFkt6TtJG+QXR3yrp3ZIOl3SHpDNLGyoAAKhko5qbM25vcAn9cM3ynI8luQOA4sma5Dnn5pjZrZKOkfQJSSdJ2jFZLOlJSb+XNNk5113qQAEAQGWb2N6u+atWb9FlM9+4u3P2nKDjDj1EE0odHGpOwjktX9urZT092tDXp1HNzZrY3q7xba2KmZU7PKCsco7Jc84NSJob/pOZ7ShpW0n/CssAAAAkSePbWtUUj2tjEORN7n66U5se3n4X7bDtNhrX2hpRhKgVXd3dm2ZyTb2pMH/VajU1xnX05MmbluYA6tGQJl5xzq2XlHukNAAAqEsxM12zcmHOOv+1mE4dPV6S1BT3F+O0umAo5i5YmHUtxo1h0jd34SL1rlunGdOmliFCoPyYXRMAAIzY4MP3K/Hg73LW+cyeE7f4/Q07bE9rC4akq7s7a4KXqj8I1LVylVpbWjjGUJdI8gAAwLC5V/6l4DuX56yTntwlPf/vV9XVvZKLcBQk4dymLpqF6A8CzVuyRNM7xtBajLpDkgcAAIbMOafg4tNz1jll9AS5HBfXXIRjKJav7S04wUvqHwi0ordXE9raShQVUJlI8gAAwJAMzDotZ/msN4xRT9O2BT0XF+Eo1LKeni0mWSnExiDQ0jU9HF+oO0NK8sxsiqQ3S7rLObfBzEZJ6nPODe0TBwAAqk6+5G716DfrKhs1pOfkIhyF2tDXN8zH9Rc5EqDyFZTkmdnr5RdEf4f8GnljJP1N0jflF0c/tVQBAgCA8hq4/lvScz056zReeI0efuhh6W9/H/LzcxGOQoxqbh7m45qKHAlQ+QptyfuWpOcl7SLpHynb/0/SdcUOCgAAlF/iqb9o8PY5Oes0XnjNpv9zEY5SmtjervmrVg+py2ZzPK5Jo9tLGBVQmQpN8t4r6b3OuVdsy4HRT0vas+hRAQCAsnF9GxVceV7OOqnJXRIX4Sil8W2taorHh3Z8NcY1rrW1hFEBlanQJG9bSZn6Uuwm310TAADUgHzj7uJnXyZr3iZjGRfhKKWYmY5+236au3BxQbNsNsXjOnryZGZuRV0qNMl7WNLxks4Nf3dm1iDpLEkPlCAuAAAQoXzJXcPHT1Bsn/E563ARjlLrHDtWvevW510QvSkeV2fHGNZgRN0qNMk7U9JDZvYOSc2SrpE0TtKOkg4oUWwAAKDE8iV3ekOrGj+bp04KLsJRajOmTVVrS4vmLVmi/oFgi5bj5nhczY3+5gHHFuqZOecKq2j2Bkmfl/R2STFJj0v6nnPuudKFl9mUKVPc4sWLo35ZAABqxuBdv1TisT/lrJNp3F2hurpXchGOkko4pxW9vVq6pkcb+vo1qrlJk0a3a1xrK63DqAtm9phzbkrGskKTvEpCkgcAwPC453sV/Ch38jaS5C4VF+EAUDq5kryCF0M3syZJ4yXtLt+St4lz7u4RRQgAAErKJRIKLjkjZ534zNmyIiZfMTNNaGtjoXMAiFihi6EfIumn8gleOiepoZhBAQCA4sk7Y+ap58lado4oGgBAqRXakvc9SXdJukTSP+UTOwAAUMHyJXexg9+vhgPfF1E0AICoFJrk7SHpcufcs6UMBgAAjNzAledJfbmXsS3WuDsAQOUpNMm7S9K7JP2thLEAAIARGFz8JyV++8ucdUjuAKD2FZrkfU7SrWb2dknLJQ2kFjrnflLswAAAQGHcf15VcM1FOeuQ3AFA/Sg0yTtM0nslfUDSf7XlmDwniSQPAIAyyDupyvlXyRoKnkwbAFADCj3rz5b0XUkXOec2lDAeAABQgHzJXcMJX1Rsz70jigYAUEkKTfJaJP2QBA8AgPLKl9zZvhMU/9jx0QQDAKhIhSZ5v5L0PklPlzAWAACQRTD3RrlVT+Wsw7g7AIBUeJL3N0mXmdl0Scu09cQr3yx2YAAAQEr8fbUGf/KDnHVI7gAAqQpN8k6U9Kr8MgrvSitzkkjyAAAoIhcECi47K2cdkjsAQCYFJXnOuTeVOhAAAODlnTHz9FmyUa+LKBoAQLVhTmUAACpE3hkzj/yYYpOnRRQNAKBaFZTkmdl3cpU7575SnHAAAKg/+ZI7bfc6NZ4xK5pgAABVr9CWvAlpvzdK2id8/ONFjQgAgDox+OA9Sjx8X846jLsDAAxVoWPy3pO+zcy2kfRjSY8UOygAAGqZe/klBdddkbMOyR0AYLiGPSbPObfRzC6T9HtJPyxeSAAA1CbnnIKLT89ZJz7zapnFIooIAFCLRjrxym6SmN4LAIA88s6Y+fkzZLu/IaJoAAC1rNCJV76evknSHpI+KenuYgcFAECtyJfcxaYdqIbDj4ooGgBAPSi0Je/Lab8nJL0o6WZJuQcVAABQhwa+f7X04vM56zDuDgBQCiyGDgBAESVWLNXgL3+Ssw7JHQCglFgMHQCAInAbX1Nw1fk565DcAQCikDXJy7cAeioWQwcA1LO8k6qcc7msqTmiaAAA9S5XS176AujZuGIELpWIlAAAIABJREFUAgBAtcmX3DV84iTFOt4aUTQAAHhZk7xMC6ADAID8yZ21v1Hxk+jkAgAojyGPyTOz10lyzrkNJYgHAICKNfiH3ynxyP056zDuDgBQbgUneWb2RUlnSWoLf++RdJVz7vslig0AgIrgXnhewQ+uzlmH5A4AUCkKXQz9XEnnSJotaX64+UBJV5rZDs65K0sUHwAAZeMSCQWXnJGzDskdAKDSFNqS9zlJpzjnbkvZ9oCZrZJ0uSSSPABATck7Y+bXZ8q23zGiaAAAKFyhSd7ukhZl2L5Q0uuLFw4AAOWVd8bMD31MsbdNiygaAACGrtAkb6WkGZIuTts+Q1J3USMCAKAMBn4wW3rhuewVWnZW46nnRRcQAADDVGiSd5Gk281suqRH5dfGe7ekgyQdU5rQAAAovcSyxzT467k56zDuDgBQTXImeWb2FufcaufcPDObJulrko6QZJKelDTVObckgjgBACgqt+FVBbMvylmH5A4AUI3yteStNLOHJd0g6VfOuWMjiAkAgJLKO6nKBd+QxRoiigYAgOLKl+QdIekESTdJ+q6Z3SrpRufcEyWPDACAIsub3J3yNdke7RFFAwBAaeRM8pxzd0u628x2lfRp+YTv82a2VL51b65zbn3pwwQAYPjyJXexd05Xw2EfjigaAABKq6CJV5xzL0n6pqRvhmPzTpJfG2+2mf3KOffpEsYIAMCwBHf+Qu6JhTnrMO4OAFBrCp1dcxPn3AIzWyjpAUnfl/RJ+VY+AAAqQmLNMxq86bqcdUjuAAC1akhJnpm9RdKJ8knd6yXdK+nGEsQFAMCQuSBQcNlZOeuQ3AEAal3eJM/MtpX0Mfnk7t2SnpH0Q0k3O+fWljQ6AAAKlHdSlbMulW2zbUTRAABQPvnWybte0sclNUm6Q9KhzrkHoggMAIBC5EvuGj5+gmL7jI8oGqRKOKfla3u1rKdHG/r6NKq5WRPb2zW+rVUxs3KHBwA1K19L3jRJF0j6qXPulQjiAQCgIANXnCv192Uttze+WfHjvxBhREjV1d2teY8/of4g0MYg2LR9/qrVamqM6+jJk9U5tqOMEQJA7cq3hMKkqAIBAKAQg396SIl7f5OzDuPuymvugoXqWrlK/SnJXdLGMOmbu3CRetet04xpU8sQIQDUtiHPrgkAQDm4V/6l4DuX56xDcld+Xd3dWRO8VP1BoK6Vq9Ta0kKLHgAUGUkeAKCiOecUXHx6zjrxmbNljPEqu4Rzm7poFqI/CDRvyRJN7xjDGD0AKCKSPABAxco7Y+ZXzpXttEtE0SCf5Wt7C07wkvoHAq3o7dWEtrYSRQUA9aeQJRTikk6RdIdzrrf0IQEA6l2+5C72viPUcMB7IooG/7+9Ow+Pqr73OP75zkwSEBfcsIQguBBaQTYp6HWLS92rLa16Rau0FmvVXlzQarVqqVoX3Fo3qqWueN1waV2qVqNyqyK7oLKoKCFuVLGCkjAz3/vHDJjESWYSZs4seb+eJ0+Y8/udM9/Jc8R8OL8lU/Pq6potspKJNdGo5i6rI+QBQBalDXnuHjWzqyQ9HkA9AIBOLHrXJPk7i1rvEA6r7IIrgysI7bK6ofXVTts+rzHLlQBA55bpcM1XJA2T9N6GvqGZhSXNkLTc3Q8zsy0k3SeprxIbrR/Fdg0A0LnEF72h2L1/abMPi6oUvm4VFR08rzzLlQBA55ZpyLtV0tVm1kfSTEmrmza6+6x2vOc4SW9K2jT5+lxJ/3T3y83s3OTrX7fjekDJYiNhlDpvWKPo5ee32YdwVzwGVVVp2uIl7RqyWRGJaHDvqhxWBQCdT6Yhb0ry+zUp2lxSOJOLmFmVpEMlXSrpzOThIyTVJP98h6RaEfIANhJGyUu7qMr5l8siZQFVg2wY2KtS5ZFI+0JeWUQDKitzWBUAdD6ZhrztsvR+10k6R9ImTY5t4+4fSJK7f2BmPVKdaGYnKbEAjLbddtsslQMUJjYSRilLF+7CY05VqM/2wRSDrAqZadSwIZoyfUZGq2yWRxL/YMXIBADIroxCnrtnYy7eYZI+dveZZlbT3vPd/c+S/ixJw4cP9w2tByhUbCSMUpUu3NmgXRT54eiAqkGu1PTvr/qVn6f9e6w8ElFNdT/+/gKAHMh4nzwzGyRpvKSdlBii+Yakie7+eoaX2F3S4WZ2iKQukjY1s7slfWRmPZNP8XpK+rhdnwAoIWwkjFIUe/bviv/f8232Yd5daRk9coQqu3fX1Nmz1bi2+ZDzikhEFQw5B4CcyijkmdnhkqZKeknSk8nDe0iaZWaj3P1v6a7h7udJOi95vRpJ4939uOT2DCdIujz5/dH2fgigVLCRMEqJf/SBordMbLMP4a501fSv1l7V/bSgvl5zl9VpdUOjulWUa3DvKg2oZPEoAMilTJ/kXSLpUne/qOlBM5uQbEsb8tpwuaT7zexESe9LOnIDrgUUNTYSRinweEzR35/TZh/CXecQMtPOvXrx9xMABCzTkFct6a4Ux+9SYiGVdnH3WiVW0ZS7/1vSfu29BlCK2EgYxS7tiplnXSzbeJM2+wAAgA2Tacj7WNIukpa0OL6LpI+yWhHQibGRMIpV2hUzj/hvhYZ8N6BqAADo3NqzGfokM9tR0r+UWHhlDyUWYrkqR7UBnQ4bCaPYrL3hcunfn7TeYcutVXbaucEVBAAA2jUnb5WksyT9PnmsXtJFkv6Yg7qATomNhFEs4nNeU+zR/22zD/PuAADIj0z3yXNJ10q61sw2SR77IpeFAZ0RGwmj0PmqLxS9+uI2+xDuAADIr4z3yVuHcAfkFhsJo1ClXVTlt1fJQqGAqgEAAK3JdJ+8zSVdLGkfST0kNfu/uLv3yHplQCfGRsIoJGnD3cnjZdv0DKgaAACQTqZP8u6UNEDSHUqspuk5qwiAJDYSRv6lC3eh3fdReP/DAqoGAABkKtOQVyNpb3eflcNaALTARsLIh+jDU+TzZrbZh3l3AAAUrkxD3ttqMUQTAFBa4u+9o9jtN7bZh3AHAEDhyzTkjZP0BzMbL2m+u8dyWBMAIEAeXavopW3vZUe4AwCgeGQa8pZI6ippliRZi7lA7h7OblkAgCCkXVTl3EtlFV0CqgYAAGRDpiHvXkmbSfofsfAKABS9dOEuPPrnCvX7TkDVAACAbMo05A2XNMLd5+eyGABAbq295Bwp1vqIe9uhWpHjfhFgRQAAINsyDXlvSNo0l4UAAHIn9n/PKf7s4232Yd4dAAClIdOQd4Gka8zsAkmvS1rbtNHdP812YQCADeefrlD0T39osw/hDgCA0pJpyHsi+f1pNZ+PZ8nXLLwCAAXE3RWdML7NPpELJ35jIS0AAFD8Mg15++S0CgApxd01f3m95tXVaXVDg7pVVGhQVZUG9qpUiF/O0Yq0K2aOO1/WfYuAqgEAAEHLKOS5+wu5LgRAc7ULF2rqrDlqjEa1Jhpdf3za4iUqL4to1NChqulfnccKUWjShbvQgUcovOteAVUDAADyJaOQZ2bD2mp391nZKQeAJE15dbpqFy1WY5Nwt86aZOibMv011a9cqdEjR+ShQhSS6B03yZe+3XqHii4qO/fS4AoCAAB5lelwzRlKzL1rOj6s6dw85uQBWVK7cGGrAa+pxmhUtYsWq7J7d57odVLxt+Yrdt9f2+zDoioAAHQ+mYa87Vq8LpM0VNL5ks7LakVAJxZ3Xz9EMxON0aimzp6tvar7MUevE/E1Xyl6xQVt9iHcAQDQeWU6J++9FIeXmNnnki6S9GRWqwI6qfnL6zMOeOs0ro1qQX29du7VK0dVoZCkXVTl/CtkkUz//Q4AAJSiDf1N4F1JQ7JRCABpXl1ds0VWMrEmGtXcZXWEvBKXLtyFf3aaQr1bDroAAACdUaYLr7Rca9sk9ZR0saSFWa4J6LRWNzR08LzGLFeCQpEu3NmQEYoccXRA1QAAgGLQZsgzs8mSTpe0Qs0XWpESQW+ZJH67ALKkW0VFB88rz3IlyLfYPx5T/JW2d69h3h0AAEgl3ZO8EySdq29uhh6X9ImkJe7evrFlAFo1qKpK0xYvadeQzYpIRIN7V+WwKgTJP6hT9M/XttmHcJcfcXfNX16veXV1Wt3QoG4VFRpUVaWBvSpZ+AgAUFDShTyT2AwdCMrAXpUqj0TaF/LKIhpQWZnDqhAEj8cU/f05bfYh3OVP7cKF61e+bfrf57TFS1ReFtGooUPZygQAUDAymZPXcpgmgBwJmWnUsCGaMn1GRqtslkcSv1zyFKG4pV0xc/zvZN02DqgatDTl1emt7l25Jhn6pkx/TfUrV2r0yBF5qBAAgOYyCXkfWppfIN2dzdCBLKnp31/1Kz9PuyF6eSSimup+PD0oYmlXzBx1rEI7DwuoGqRSu3Bh2v8WpcSelbWLFquye3f+mwQA5F0mIe8kSStzXQiAr40eOUKV3btr6uzZalzbfHhYRSSiCoaHFbW1118qrfy09Q7b9FTZyeNzXgdzzNoWd18/RDMTjdGops6erb2q+/HzAwDkVSYh72/u/nHOKwHQTE3/au1V3U8L6us1d1mdVjc0qltFuQb3rtKASn4JL0bxWa8q9rf72+wT1Lw75pilN395fcYBb53GtVEtqK9n30oAQF6lC3nMxwPyKGSmnXv14hfGIudf/EfRa37XZp8gF1Vhjllm5tXVtWsRJCnx85u7rI7/ZgEAeZXR6poAgPZzd0UntD3sMvLbq2ShUEAVMcesPVY3NHTwvMYsVwIAQPu0GfLcPbjfPACghKRdMfOUs2VbfyugahKYY9Y+3SoqOnheeZYrAQCgfTKZkwcAyFC6cBfac3+F9z04oGqaY45Z+wyqqtK0xUvat29lJKLBvatyWBUAAOkR8gAgC6IP3iVfMKfNPvnezJw5Zu0zsFelyiOR9oW8sogGVFbmsCoAANIj5AHABoi/u0SxO29us0++w906zDFrn5CZRg0boinTZ2T0BLQ8kliVtDMObQUAFBZCHgB0gK9tVPSy89rsUyjhbh3mmLVfTf/+ql/5edrFasojEdVU9+u0i9QAAApLxiHPzHaW9AtJO0j6mbt/YGY/kPSeu8/OVYEAUGjSLqpy3mWy8o4FqlxijlnHjB45QpXdu2vq7NlqXNt8X8GKSEQV7CsIACgwGYU8MztA0mOSnpS0r6SuyaYdJI2R9INcFAcAhSRduAsfO1ahHb8dUDXtxxyzjqvpX629qvtpQX295i6r0+qGRnWrKNfg3lUaUFnJEE0AQEHJ9Ene7yWd6e43mdkXTY7XSmr7tx4AKHLpwp1V76TIMScGVE3HMcdsw4TMtHOvXp1yERoAQHHJNOQNkPREiuOfStoie+UAQOGIvfSs4s892WafQpt3lw5zzAAAKH2ZhrzPJPWStLTF8WGS6rJZEADkm//7E0VvuLzNPsUW7ppijhkAAKUt05A3RdJVZnaUJJcUMbO9JU2U9NdcFQcAQXKPKzrh7Db7RC6cKCuB4YvMMQMAoHRlGvIukHS7pPckmaQ3kt+nSLo0J5UBQIDSrph5+gWyzTYPqJpgMMcMAIDSlFHIc/e1ko41swslDZUUkjTb3RfnsjgAyLV04S508A8VHrFHQNUAAABsuHZthu7ub0t6O0e1AEBgopP/JF+2tPUOG22ssrN/F1g9AAAA2ZLpPnmTW2lySWskLZF0n7vXZ6swAMiF+BvzFHvgjjb7FPOiKgAAAJk+ydta0p6S4pLmJ48NVGJe3kxJoyRNMLM93X1O1qsEgA3kX32p6JW/bbMP4Q4AAJSCTEPe/0laJelEd/9SksxsI0m3Spor6RBJd0q6WtJ+OagTADos7aIqF1whC7dr9DoAAEDByvS3mnGS9l0X8CTJ3b80s0sl/dPdrzSzKyQ9m4siAaAj0oW78In/o1BVn4CqAQAACEamIW9jST0lvdni+LeSbZL0n3ZcDwByJu2KmbvspvBhPw6oGgAAgGBlGsoelvQXMztH0mtKLLgyQtKVkqYm+4yQtCjrFQJAhmJPPqz49Glt9mHeHQAAKHWZhryTJV0j6e4m50QlTZY0Pvn6TUljs1od0MnF3TV/eb3m1dVpdUODulVUaFBVlQb2qlTILN/lFQyvX6borde12YdwBwAAOotMN0P/UtLJZnaWpB2UWFVzibuvbtKHVTWBLKpduFBTZ81RYzSqNdHo+uPTFi9ReVlEo4YOVU3/6jxWmH8eiyl6yTlt9iHcAQCAzqa9m6GvljQvR7UASJry6nTVLlqsxibhbp01ydA3Zfprql+5UqNHjshDhfmXdsXMsyfINuoWUDUAAACFI+OQZ2b7SDpG0raSypu2ufu+Wa4L6LRqFy5sNeA11RiNqnbRYlV2796pnuilXTHzR8cpNHBoQNUAAAAUnlAmncxsjKQnJW0iqUbSJ5I2lzRM0hs5qg3odOLu64doZqIxGtXU2bMVd89xZfm39poJbQY867Wtyi66moAHAAA6vUyf5I2XdJq732ZmX0g6z93fMbMblNgkHUAWzF9en3HAW6dxbVQL6uu1c69eOaoqv2IzXlb88Qfb7MO8OwAAgK9lGvK219cbnTfo673xbpBUK+nc7JYFdE7z6uqaLbKSiTXRqOYuqyu5kOf/+VzRaye02YdwBwAA8E2Zhrx/KzFUU5KWSxqoxAIsW0rqmoO6gE5pdUNDB89rzHIl+ePuik4Y32afyIVXySyj0eYAAACdTqYh7yVJB0h6XdL9kv5oZt+TtJ+kZ3JUG9DpdKuo6OB55ek7FYG0K2ae+mvZVj0CqgYAAKA4ZRryTpPUJfnnPyixEfruSgS+S3JQF9ApDaqq0rTFS9o1ZLMiEtHg3lU5rCr30oW7UM2BCu99QDDFAAAAFLm0Ic/MIpL+W9IjkuTucUlX5LguoFMa2KtS5ZFI+0JeWUQDKitzWFXuRO+/Xf7m6232Yd4dAABA+6QNee4eNbOrJD0eQD1ApxYy06hhQzRl+oyMVtksj0Q0auhQhcwCqC574u8sUuyuSW32IdwBAAB0TKbDNV+RtIuk93JYCwBJNf37q37l52k3RC+PRFRT3a+oNkL3xgZF//CbNvsQ7gAAADZMpiHvVkkTzWxbSTMlrW7a6O6zsl0Y0JmNHjlCld27a+rs2WpcG202fLMiElFFWeIJXjEFvLSLqpx3may8YwvPAAAA4Gvm7uk7mcXbaHZ3D2evpPSGDx/uM2bMCPItgbyIu2tBfb3mLqvT6oZGdaso1+DeVRpQWVk0QzTThbvwT36h0PbFE1YBAAAKgZnNdPfhqdoyfZK3XRbrAZChkJl27tWrKDc6Txfu7DuDFDnqhICqAQAA6DwyCnnuzlw8ABmJ1f5D8ReebrMP8+4AAAByJ9MneTKzgyWdKml7SQe6+zIz+7mkd939n7kqEEBx8BUfKXrjlW32IdwBAADkXkYhz8yOlXSLpNsk7SepLNkUlnSOJEIe0Em5xxWdcHabfSIXTpQVyRxCAACAYpfpk7xzJI119/9NPr1b5xVJE7JfFoBikHbFzDMulG26WUDVAAAAQMo85PWT9HKK46skbZq9cgAUg3ThLnTojxUevltA1QAAAKCpTENevaRqfXMz9L0kvZ3VigAUrOit18nrl7XeYdPNVHbGhcEVBAAAgG/INOT9WdIfmwzV7G1me0q6UtLFuSgMQOGIz5+t2EN3t9mHRVUAAAAKQ6ZbKFxpZptJekZSF0nPS2qQNNHdb8xhfQDyyL9crehVbT+ZI9wBAAAUloy3UHD3883sUkk7SQpJesPdV+WsMgB5lXZRlQuulIXDAVUDAACATGW6hcI4SVPc/RNJM3JbEoB8ShfuwmNPV6iyd0DVAAAAoL0yfZJ3lqSrzOyfku6S9Ii7f9meNzKzLpJelFSRfN8H3f0iMxuixB58XSRFJZ3i7tPbc20AGy7tipkj91T4oB8EVA0AAAA6KtOQ10dSjaTRkm6QNMnMHpF0t6Rn3D2ewTUaJO3r7qvMrEzSNDN7Uol99n7n7k+a2SFKLOZS076PAaCjYn9/UPGZqXZI+Rrz7gAAAIpHpguvuBKLrTxvZqdKOkyJwPewpJWSKjO8xro5fGXJL09+rdtrbzMltmsAkGPx5e8rdtv1bfYh3AEAABSfjBdeWcfdG83sZUnbSRogqX+m55pZWNJMSTtKutHdXzWz0yX9w8wmKrGgy3+1cu5Jkk6SpG233ba9ZQNI8lhU0Ut+3WYfwh0AAEDxyjjkmdmmkn4k6VhJeyuxCfoUJYZsZsTdY5KGmFl3SQ+b2UAlgtsZ7v6QmR0l6S+S9k9x7p+V2K9Pw4cP90zfE8DX0q6Yec7vZV03CqgaAAAA5EKmq2s+KOkQSV9Iuk/SbzZkcRR3X2lmtZIOknSCpHHJpgck3dbR6wJILe2KmUeeoNBOgwKqBgAAALmU6ZO8Rkk/lvSP5NO49cxsf3d/Nt0FzGxrSWuTAa+rEk/rrlBiDt7ekmol7StpceblA2jL2okXSatb387Stt1OkZ+eFmBFAAAAyLVMF14Z3fS1mfWS9FNJJ0raVlImOyL3lHRHcl5eSNL97v53M1sp6Xozi0hao+S8OwAdF3v1JcWfeqTNPsy7AwAAKE3tmZMXlnS4pLGSvidpnqSblRhimZa7z5M0NMXxaZJ2ybQOAK3zzz9T9LpL2uxDuAMAAChtaUOemfWX9HNJx0tarcRiK9+T9BN3fyO35QHIhLsrOmF8m30iF14ls1BAFQEAACBf2gx5ZvaSpIGSHpR0lLu/kDze9vrrAAKTdsXM086Vbbl1QNUAAAAg39I9ydtN0o2SbnX3+QHUAyBD6cJdaL9DFN5jv4CqAQAAQKFIF/KGKzEH7yUzWyrpTkn35rooAK2L3vsX+aI2RkqbqezCicEVBAAAgILSZshz9zmSTjWzsyQdqcRqmlcqsTrmoWb2gbt/lvsyAcSXvKXYPbe22YdFVQAAAJDpFgprJN0l6S4z21GJhVjOkHSJmT3n7gfnsEagU/N4XNHfn91mH8IdAAAA1sl4C4V13H2JpHPN7HxJh0n6WdarAiBJitX+Q/EXnm61PfKbP8jKygOsCAAAAIWuw+upu3vM3R919yOyWRAAKf7GXK393VmtBrzwCb9U2UVXd/qAd9NNN2m77bZTly5dtMsuu+ill15Ke87999+vIUOGaKONNlKfPn101VVXNWuvra2VmX3j66233srVx8jIrbfeqj333FNbbLGFunfvrn322UfTpk1r1ufFF1/U4Ycfrl69esnMdPvtt6e97tKlS1N+3qeeemp9n0L9mQAAgNTa/SQPQO74B3WK/vnaVtttx28rcuzYACsqXPfdd5/GjRunm266SXvssYduuukmHXzwwXrjjTe07bbbpjznySef1OjRo/XHP/5RBx10kN58802NHTtWXbt21Wmnndas74IFC7TFFlusf7311tndhqKmpkZjxozRmDFjMupfW1uro48+Wrvvvrs22mgjXXvttTrwwAM1Z84c9evXT5K0atUqDRw4UMcff7yOP/74dtXz1FNPafDgwetfN/3s6+T6ZwIAALKDnZGBAuBf/Edrf3dWqwEv/NPTVHbR1QUZ8O68805tueWWamhoaHb82GOP1eGHH56z973mmms0ZswYjR07Vt/5znf0pz/9ST179tTNN9/c6jl33XWXvv/97+uUU07R9ttvr0MPPVTnnXeerrjiCrl7s749evTQt771rfVf4XBYkrRmzRoNHDhQP/vZ1yPV6+vrtdVWW2nixNytanrPPffotNNO09ChQ9W/f3/dfPPN2mSTTZo9cTvkkEN02WWX6cc//rFCofb99b7llls2+7zl5d98StzazwQAABQWQh6QRx5dq7WTrlb0mt+lbA8f8d8qu+hqhbbdLuDKMnfkkUcqHo/r0UcfXX/s888/18MPP6wTTzyx1fMOPvhgbbzxxm1+taaxsVEzZ87UAQcc0Oz4AQccoH/961+tntfQ0KAuXbo0O9a1a1fV1dXpvffea3Z8+PDh6tmzp/bbbz89//zz64936dJFU6ZM0ZQpU/TAAw/I3XX88cdr8ODBOuustvcuzKbGxkatWbNGm2++eVauN2rUKPXo0UO77767HnzwwZR9WvuZAACAwsJwTSAP3F2xx+6Tz3ktZXtot70VPiB3T8GyqWvXrjr22GM1efJkHXXUUZKkKVOmaNNNN9Whhx7a6nm33Xabvvrqqw6954oVKxSLxbTNNts0O77NNtvo2WefbfW8Aw88UOPGjdPTTz+t/fffX0uWLNHVVydWJv3ggw/Ut2/f9U8Dv/vd76qxsVF33XWX9ttvP9XW1mqvvfaSJA0aNEiXX365TjrpJL388suaPXu25s2bJzPr0OfpiAsuuEAbb7zxBj8t3XjjjTVx4kTtvvvuikQieuyxx3T00Ufrjjvu0HHHHSdJGf1MAABA4SDkAQGLvfqS4k89krLN+u6o8HEnyYpsGNzYsWM1bNgw1dXVqaqqSpMnT9YJJ5ygSKT1v2J69eq1we/bMlS5e5tBa+zYsXr77bd1xBFHaO3atdp00001btw4XXzxxeuHHvbv31/9+/dff85uu+2mpUuXauLEic0Czbhx4/TYY4/p2muv1f3335/285x88sm6++6717/+6quv9MorrzSbC9jWfMKmrr/+ek2aNEnPPvusNt1007T927LVVls1ewI5fPhwrVixQldeeeX6kJfpzwQAABQGhmsCAYm/vTCxYmaqgNelqyLn/F6RE35ZdAFPkgYPHqxhw4bp9ttv1/z58zVjxoxmc9ZS2ZDhmltttZXC4bA+/PDDZsc//vjjbzzda8rMdMUVV2jVqlV677339OGHH2rEiBGSpL59+7Z63siRI7V48eJmx1asWKE333xT4XBYS5YsafOzStKECRM0Z86c9V/Dhw//xrHKysq017n++ut1wQUX6Iknnlhfe7al+rwd6QMAAPKDJ3lAjvmKjxW98YpmjZVMAAAfeElEQVRW2yOnnCPbuvVgUizGjh2rK6+8UitWrNDuu+/e7MlPKhsyXLO8vFy77LKLnnnmGR155JHrjz/zzDP60Y9+lPb8cDi8/snbvffeq9122009evRotf+cOXPUs2fPZsd+/vOfa4cddtANN9ygY445RgcccIB22WWXVq/Ro0ePZu/RtWtX9ejRQzvuuGPaete55pprdOGFF+qJJ57QHnvskfF57ZXq83akDwAAyA9CHpAj/tWXiv7xMmlN6iATPnasQjt+O+CqcueYY47RmWeeqZtvvlm33HJL2v4bOlzzzDPP1E9+8hONGDFCu+++u2655RbV19fr5JNPXt/nvPPO0/Tp0/XPf/5TUuLp2wMPPKCamho1NDTor3/9qx544AG98MIL68+57rrr1LdvXw0YMECNjY26++679cgjj+ihhx5a3+eWW25RbW2t5syZo+22205jxozR6NGjNXv2bG200UYb9Llac9VVV+n888/X3Xffrerq6vVPMbt27arNNttMUmILhXVPFePxuN5//33NmTNHW2yxxfphoC1/JnfccYfKyso0dOhQhUIh/e1vf9ONN96oK674+h8mMvmZAACAwkHIA7LM4zHF7r5V/m7qoWyhA49QeNfSm8e0ySab6KijjtIDDzywfgGWXDr66KP173//W5dccok++OADDRw4UE888YT69Omzvs8HH3ygt99+u9l5d955p84++2y5u3bbbTfV1tY2G/bY2Nio8ePHa/ny5eratasGDBigxx9/XIcccogkaeHChTrrrLN08803a7vtEqueXnfddRo2bJjOOOMMTZo0KSef98Ybb9TatWt19NFHNzt+wgknrN/0fMaMGdpnn33Wt1100UW66KKLmvVJ9TO55JJL9N577ykcDqu6ulqTJ09ePx9PSv8zAQAAhcVa7g1VDIYPH+4zZszIdxnAN8Se+Zvi/6pN2WZDvqvw4UcHugJj0A4++GBVVVXp1ltvzXcpAAAAJc3MZrr78FRtPMkDsiA+d4Zij9yburFHT0V+Pk5WVhZsUQH69NNP9eyzz+rpp5/W3Llz810OAABAp0bIAzZAfNlSxSb/qdX2yJkXyjbZLMCK8mPYsGH69NNPddlll2ngwIH5LgcAAKBTI+QBHeCff6bodZe02h4Ze7qssneAFeXX0qVL810CAAAAkgh5QDt4Y4Oik66RPl2Rsj38458oNGBIwFUBAAAAXyPkARlwjyv20N3yBannm4X2+p7C+xwUcFUAAADANxHygDRi055T/J+Pp2yz6p0UPvqnslAo4KoAAACA1Ah5QCviCxco9r+TUzdusqkip/5aVtEl2KIAAACANAh5QAv+0QeK3jKx1fbIr86TbbFVgBUBAAAAmSPkAUm+epWi106QYrGU7eETfqlQ3x0DrgoAAABoH0IeOj2PRhW7/Ub58vdTtocP+7FCu+wWcFUAAABAxxDy0Gm5u+JPTFV8xr9StoeG/5dCh4ySmQVcGQAAANBxhDx0SvGZLyv29wdTtllVH4VPOEUW4T8PAAAAFB9+i0WnEl+6RLE7bk7dGA4rcsaFsm4bB1sUAAAAkEWEPHQK/ukKRf/0h1bbIyePl23TM8CKAAAAgNwg5KGkecMaRW+8QvriPynbw//9U4X6Dwy4KgAAACB3CHkoSR6PK/a/k+WL30zZHtrvUIX32DfgqgAAAIDcI+Sh5MSef0rxF59J2WYDhij8o2NlFgq4KgAAACAYhDyUjPiCOYo9eFfqxi22UuQXZ8rKK4ItCgAAAAgYIQ9Fz+uXKXrrda22R06/QLbZ5gFWBAAAAOQPIQ9Fy7/4XNFrJrTaHv7ZrxTq3Te4ggAAAIACQMhD0fG1axW97Xrp4w9Stod/cIxCg4cHXBUAAABQGAh5KBrurtij/yufOyNle+i/ahT+3vcDrgoAAAAoLIQ8FIXYKy8q/o9HU7bZ9v0UPnasLBQOuCoAAACg8BDyUNDiS95S7J5bUzd23UiRX50n67pRsEUBAAAABYyQh4LkKz5S9MYrW22PnHqObKttAqwIAAAAKA6EPBQU/+pLRa+/VGpYk7I9fNxJCu3QP+CqAAAAgOJByENB8FhMsbsnyZe+nbI9dNAPFB65Z8BVAQAAAMWHkIe8iz39mOIvv5CyzYaMUPjwo2RmAVcFAAAAFCdCHvImPneGYo/cm7rxW5WKnPg/skhZsEUBAAAARY6Qh8DFl72r2OQbWm2PnHmRbJNNA6wIAAAAKB2EPATGP/9M0esuabU9ctIZsp5VAVYEAAAAlB5CHnLOGxsUveVq6bN/p2wPH3m8QjsNDrgqAAAAoDQR8pAz7nHFHrxL/sa8lO2hvQ9QuObAgKsCAAAAShshDzkRe+lZxZ97MmWb9R+g8FFjZKFQwFUBAAAApY+Qh6yKvzVfsfv+mrpx080UOeUcWUWXYIsqYtF4XI/Pe10zlr6nrxob1bW8XMP79tGhg3ZWhJAMAACAFAh5yAr/sF7RSVe32h75n9/INt8ywIqK36QXXtQr77wrb3pw9Wot++wzPTJ7jnbdYXv9Yi82iAcAAEBzhDxsEF/9haLXTJDi8ZTt4TGnKNRnh2CLKgEXPvqY3v/0s1bbXdLLb7+j5Z99pglHHB5cYQAAACh4hDx0iEejiv31Bnn9spTt4e8fqdCwXQOuqjRMeuHFNgNeU+9/+pkmvfgST/QAAACwHiEP7eLuij/+kOIzX07ZHhqxh0IH/UBmFnBlpSEaj+uVd95t1zmvvP2OTtxjd+boAQAAQBIhD+0Qm/EvxR9/KGWb9e6r8Am/lIW5pTbE4/Nebz4HLwMu6cnX5+v7gwfloiQAAAAUGX4jR1rxdxcrductqRsjZYqcfoGs28bBFlWiZix9r0PnTX93KSEPAAAAkgh5aIN/ukLRP/2h1fbIL8fLevQMsKLS91VjY6DnAQAAoPQQ8vANvuYrRW+4Qlr9Rcr28DEnKlS9U8BVdQ5dy8ul1as7dh4AAAAgQh6a8HhMsXsny5e8lbI9tP9hCu++T8BVdS7D+/bRss8yW1mzqRHb9c16LQAAAChOhDxIkmLPPan4S8+mbLOdhyr8w9EyY/XGXDt00M56ZPacdi2+YpIO3nlgrkoCAABAkSHkdXLx+bMVe+ju1I1b9VDkpDNkZQwFDEokFNKu22+nl9uxjcKuO2zP9gkAAABYj5DXScWXv6/Ybde32h45/QLZZpsHWBHW+cXee2n5ypUZbYi+7RabsxE6AAAAmiHkdTL+n88VvXZCq+3hE/9Hoao+AVaEVCYccbgmvfiSXnn7nZRDN02JJ3gEPAAAALRk7u3dejn/hg8f7jNmzMh3GUXF1zYqeuv10icfpmwP/3C0QoN2CbgqpBONx/Xk6/M1/d2l+qqxUV3LyzViu746eOeBDNEEAADoxMxsprsPT9XGk7wNFHfX/OX1mldXp9UNDepWUaFBVVUa2KtSIbN8lyd3V+yRe+XzZqZsD+2+j8L7HxZwVchUJBTS9wcPYqNzAAAAZIyQtwFqFy7U1Flz1BiNak00uv74tMVLVF4W0aihQ1XTvzpv9cVefkHxpx9L2WY7VCs8+ueyUDjgqgAAAADkEiGvg6a8Ol21ixarsUm4W2dNMvRNmf6a6leu1OiRIwKtLb74TcWm3Ja6caNuivzqPFmXroHWBAAAACAYhLwOqF24sNWA11RjNKraRYtV2b17IE/0/JOPFL3pylbbI6edK9ty65zXAQAAACB/CHntFHdfP0QzE43RqKbOnq29qvvlbI6ef7la0esvlRobUraHf/ILhbbP37BRAAAAAMEh5LXT/OX1GQe8dRrXRrWgvl479+qV1Vo8FlPsrlvk772Tsj108A8VHrFHVt8TAAAAQGEj5LXTvLq6ZousZGJNNKq5y+qyFvLcXfF/PKb4qy+mbLdhuyp82I9lBbC6JwAAAIBgEfLaaXVD6iGR6c9rzMr7x+dMV+zR+1I39qxS5GenySJlWXkvAAAAAMWHkNdO3SoqOnhe+Qa9b/z9dxT7642ttJoiZ10o23jTDXoPAAAAAMWPkNdOg6qqNG3xknYN2ayIRDS4d1WH3s9XfppYVKUVkZPOkPXs2LUBAAAAlB5CXjsN7FWp8kikfSGvLKIBlZXteh9vWKPozROlzz9L2R4+6gSFvjOoXdcEAAAAUPoIee0UMtOoYUM0ZfqMjFbZLI9ENGro0Iy3T3CPK/bAnfI3X0/9/jUHKbz399pTMgAAAIBOJLCQZ2ZdJL0oqSL5vg+6+0XJtl9JOk1SVNLj7n5OUHV1RE3//qpf+XnaDdHLIxHVVPfLeCP02IvPKP78Uynb7Ns7K3zk8bJQqEM1AwAAAOgcgnyS1yBpX3dfZWZlkqaZ2ZOSuko6QtIgd28wsx4B1tRho0eOUGX37po6e7Ya10abDd+siERUUZZ4gpdJwIu/OU+x++9I3bjZ5or8crysoku2SgcAAABQwgILee7uklYlX5Ylv1zSLyVd7u4NyX4fB1XThqrpX629qvtpQX295i6r0+qGRnWrKNfg3lUaUFmZdoimf7hc0UnXtNoeGXe+rPsW2S4bAAAAQAkLdE6emYUlzZS0o6Qb3f1VM6uWtKeZXSppjaTx7v5ainNPknSSJG277bYBVt22kJl27tWrXRud+6ovFL36d0pk3G8KjzlVoT7bZ6dAAAAAAJ1KoCHP3WOShphZd0kPm9nAZA2bS9pV0ncl3W9m2yef/DU998+S/ixJw4cPT52OCpxH1yo2+Qb5B3Up28OHH6XQ0JEBVwUAAACglORldU13X2lmtZIOklQnaWoy1E03s7ikrSR9ko/acsHdFfv7g/JZr6RsD43cS6EDD5dluAInAAAAALQmyNU1t5a0NhnwukraX9IVSszT21dSbXLoZrmkFUHVlWux1/5P8SempmyzPtsr/JOTZeFwwFUBAAAAKFVBPsnrKemO5Ly8kKT73f3vZlYuabKZzZfUKOmElkM1i1H8nUWK3TUpdWNZuSKnny/baONgiwIAAABQ8oJcXXOepKEpjjdKOi6oOnLN//2Jojdc3mp75JSzZVt/K8CKAAAAAHQmeZmTV4p8zVeK/uly6ctVKdvDx5yoUPVOAVcFAAAAoLMh5GWBr16l6MSLUraFvneYwv+1T8AVAQAAAOisCHlZEH/z9W8cs0G7KPyDY1gxEwAAAECgCHlZENqhWvGtt5E++UjaehtFxp4uKyvPd1kAAAAAOiFCXhbY5luq7JRz8l0GAAAAACiU7wIAAAAAANlDyAMAAACAEkLIAwAAAIASQsgDAAAAgBJCyAMAAACAEkLIAwAAAIASQsgDAAAAgBJCyAMAAACAEkLIAwAAAIASQsgDAAAAgBJCyAMAAACAEkLIAwAAAIASQsgDAAAAgBJCyAMAAACAEkLIAwAAAIASQsgDAAAAgBJCyAMAAACAEkLIAwAAAIASQsgDAAAAgBJCyAMAAACAEkLIAwAAAIASQsgDAAAAgBJCyAMAAACAEkLIAwAAAIASQsgDAAAAgBJCyAMAAACAEkLIAwAAAIASQsgDAAAAgBJCyAMAAACAEkLIAwAAAIASQsgDAAAAgBJCyAMAAACAEhLJdwEAWhd31/zl9ZpXV6fVDQ3qVlGhQVVVGtirUiGzfJcHAACAAkTIAwpU7cKFmjprjhqjUa2JRtcfn7Z4icrLIho1dKhq+lfnsUIAAAAUIkIeUICmvDpdtYsWq7FJuFtnTTL0TZn+mupXrtTokSPyUCEAAAAKFXPygAJTu3BhqwGvqcZoVLWLFqt24aKAKgMAAEAxIOQBBSTuvn6IZiYao1FNnT1bcfccVwYAAIBiQcgDCsj85fUZB7x1GtdGtaC+PkcVAQAAoNgQ8oACMq+urtkiK5lYE41q7rK6HFUEAACAYkPIAwrI6oaGDp7XmOVKAAAAUKwIeUAB6VZR0cHzyrNcCQAAAIoVIQ8oIIOqqtQl0r6dTSoiEQ3uXZWjigAAAFBsCHlAARnYq1Ll7Q15ZRENqKzMUUUAAAAoNoQ8oICEzDRq2JCMg155JKJRQ4cqZJbjygAAAFAsCHlAganp31811f3SBr3ySEQ11f1U0786oMoAAABQDNo3LgxAIEaPHKHK7t310KxZWtO4Vmvj8fVtZaGQupSX6UfDhhHwAAAA8A08yQMKlstkUsuhmGaJ4wAAAEAKPMkDCtCUV6erdtFiNabYGH1tLKa1sZimTH9N9StXavTIEXmoEAAAAIWKJ3lAgalduLDVgNdUYzSq2kWLVbtwUUCVAQAAoBgQ8oACEnfX1Flz0ga8dRqjUU2dPVtx9xxXBgAAgGJByAMKyPzl9RkHvHUa10a1oL4+RxUBAACg2BDygAIyr65Oa9oZ8tZEo5q7rC5HFQEAAKDYEPKAArK6oaGD5zVmuRIAAAAUK0IeUEC6VVR08LzyLFcCAACAYkXIAwrIoKoqdYm0b2eTikhEg3tX5agiAAAAFBtCHlBABvaqVHl7Q15ZRAMqK3NUEQAAAIoNIQ8oICEzjRo2JOOgVx6JaNTQoQqZ5bgyAAAAFAtCHlBgavr3V011v7RBrzwSUU11P9X0rw6oMgAAABSD9o0LAxCI0SNHqLJ7d02dPVuNa6PNtlWoiERUUZZ4gkfAAwAAQEuEPKBA1fSv1l7V/bSgvl5zl9VpdUOjulWUa3DvKg2orGSIJgAAAFIi5AEFLGSmnXv10s69euW7FAAAABQJ5uQBAAAAQAkh5AEAAABACSHkAQAAAEAJIeQBAAAAQAkh5AEAAABACSHkAQAAAEAJIeQBAAAAQAkh5AEAAABACSHkAQAAAEAJIeQBAAAAQAkh5AEAAABACSHkAQAAAEAJIeQBAAAAQAkJLOSZWRczm25mc81sgZn9rkX7eDNzM9sqqJoAAAAAoNREAnyvBkn7uvsqMyuTNM3MnnT3V8yst6TvSXo/wHoAAAAAoOQE9iTPE1YlX5Ylvzz5+lpJ5zR5DQAAAADogCCf5MnMwpJmStpR0o3u/qqZHS5pubvPNbO2zj1J0knJl6vMbGHOC0aubSVpRb6LQMni/kIucX8h17jHkEvcX6WhT2sN5h78wzMz6y7pYUnjJN0q6QB3/9zMlkoa7u7cdJ2Amc1w9+H5rgOlifsLucT9hVzjHkMucX+VvrysrunuKyXVSjpC0naS5iYDXpWkWWb2rXzUBQAAAADFLsjVNbdOPsGTmXWVtL+k2e7ew937untfSXWShrn7h0HVBQAAAAClJMg5eT0l3ZGclxeSdL+7/z3A90fh+XO+C0BJ4/5CLnF/Ide4x5BL3F8lLi9z8gAAAAAAuZGXOXkAAAAAgNwg5AEAAABACSHkIRBmNtnMPjaz+S2O/8rMFprZAjO7Ml/1obilur/MbIiZvWJmc8xshpmNyGeNKF5m1tvMnjezN5N/V41LHt/CzJ4xs8XJ75vnu1YUnzbur6vM7C0zm2dmD69bvA5oj9burybt483MzWyrfNWI3GBOHgJhZntJWiXpTncfmDy2j6TzJR3q7g1m1sPdP85nnShOrdxfT0u61t2fNLNDJJ3j7jV5LBNFysx6Surp7rPMbBNJMyX9QNIYSZ+6++Vmdq6kzd3913ksFUWojfurStJz7h41syskifsL7dXa/eXub5hZb0m3Sfq2pF3Yp7q08CQPgXD3FyV92uLwLyVd7u4NyT4EPHRIK/eXS9o0+efNJNUHWhRKhrt/4O6zkn/+QtKbknopsdfrHcludyjxiznQLq3dX+7+tLtHk91eUSL0Ae3Sxt9fknStpHOU+P8lSgwhD/lULWlPM3vVzF4ws+/muyCUlNMlXWVmyyRNlHRenutBCTCzvpKGSnpV0jbu/oGU+EVKUo/8VYZS0OL+aupnkp4Muh6Ulqb3l5kdLmm5u8/Na1HIGUIe8ikiaXNJu0o6W9L9Zmb5LQkl5JeSznD33pLOkPSXPNeDImdmG0t6SNLp7v6ffNeD0tLa/WVm50uKSronX7Wh+DW9v5S4n86XdGFei0JOEfKQT3WSpnrCdElxSUz8RbacIGlq8s8PSGLhFXSYmZUp8QvSPe6+7r76KDnfZd28F4aco0Naub9kZidIOkzSsc4iCuigFPfXDpK2kzTXzJYqMRR4lpl9K39VItsIecinRyTtK0lmVi2pXBKTfpEt9ZL2Tv55X0mL81gLilhyhMFfJL3p7tc0aXpMiX9MUPL7o0HXhuLX2v1lZgdJ+rWkw939y3zVh+KW6v5y99fdvYe793X3vkr8o/swd/8wj6Uiy1hdE4Ews3sl1SjxpO4jSRdJukvSZElDJDVKGu/uz+WrRhSvVu6vhZKuV2JY8BpJp7j7zHzViOJlZntIeknS60qMOJCk3ygxb+p+SdtKel/Ske7ecgEgoE1t3F9/lFQh6d/JY6+4+8nBV4hi1tr95e5PNOmzVNJwVtcsLYQ8AAAAACghDNcEAAAAgBJCyAMAAACAEkLIAwAAAIASQsgDAAAAgBJCyAMAAACAEkLIAwAUJTNzM/txO/rXJM/ZKos13G5mF2brekEzs4lm9sd81wEAyC5CHgAgZ8zsZDNbbWblTY6Vm9mXZvZ6i779kiFs3wwv31PS37Jc7xgzW5Vh350lHSHpumzWELArJI0xs+3zXQgAIHsIeQCAXHpO0kaSRjQ5NlLS55KqzWzrJsdrJDVI+lcmF3b3D929IUt1dsSvJD3k7v/ZkIs0DcBBc/dPJD0t6Zf5qgEAkH2EPABAzrj7Ikn1kvZpcngfSc9KmqFEsGt6/GV3X2MJ55jZ22b2lZm9bmbHNb12y+GaZjbSzGaZ2Rozm21mhyT7NH0PSRpsZq8mnybOMLNhyfNrJP1VUrfkeW5mF6f6XGYWlnSUpMdaHN/GzB5L1vyemf3UzOY3vU7yuqea2VQzWy3pMjMLm9lfzOzd5LmLk58/1OS8283s72b2azP70Mw+N7PLzSxkZheb2cfJ479uUdMvzGxR8ufyiZn9w8wiTbo8JumYVJ8TAFCcCHkAgFx7Xt8MebXJr6bHa5J9JekSSSdKOlXSTpL+IGmSmR2a6g3MbGNJf5f0lqRdJJ0j6apW6vmDpHMlDZP0b0n3mJkp8QTxdElfKjEUtKekia1cY5CkzZQIqk3dIamPpH2VGMp5XPJ1SxdJekLSzpJuVOL/x8uVCI7fkXS+pN9I+mmL8/aStJ0SP6uTk5/zCUkVkvaQdLGky81sF0kys+HJ6/9OUn9J+0t6qsU1p0vqZWY7tPJZAQBFJpK+CwAAG+R5STeYWYUkk7SrpJ9LWibpekkys28rEaqeM7Nuks6UdIC7v5S8xrtmNkKJ0Pd4ivc4VlJY0onu/pWkBWZ2qaR7UvT9rbs/n3zfCZKmSerl7nVm9rkkd/cP03ymPpJc0vp+ZtZf0oGSdnP3V5LHxkhamuL8+9z9thbHmi7gsjT5hPEYSX9pcvxzSae6e0zSW2Z2lqRKdz8o2b7IzM5VIjzPlLStpNWSHnP3LyS9J2lui/etT37vK+nttj82AKAYEPIAALn2vKQuknZTIuStcPe3zexDSTuY2beUCCVfSnpV0pBk/6fMzJtcp0ypA5MkfVvS/GTAW+fVVvrOa/LndQGnh6S6jD+R1FXSWnePt6ghriZP99x9mZnVtzxZ33wCKDM7WYnw2yd5/TIlQllTbyQD3jofSVrZos9HSnweSXomeY13zewfSsy/m5oMfOus+5l1TVEnAKAIEfIAADnl7u+Y2XtKDDE0JYZpyt1Xm9nM5PEaSdPcfW2TeWjfl/R+i8utbeVtTIkna5loeo1157R3+sIKSeVmtpG7f9mkhkytbvrCzI5WYpXO8UoMG/2PEk8tf9jivJaf31s5FpIkd/8i+URwL0nfk3SeEnMAv+vu68LnFsnvn7SjfgBAAWNOHgAgCOvm5a2bj7dOrRLz12qUWIlTkt5QYpXNPu6+pMVXyydb67wpaWcza/o0akQrfdvSqMSwz3TmJL/v1KKGkBJzAiVJZlYlqTKD6+0h6VV3v8HdZ7n7EklZmSPn7lF3f87dz1NiLmE3SYc16TJQiaD4eqrzAQDFh5AHAAjC80rMxRup5iHvBUn/rcTwwuelxNMnJRY8mWhmPzOzHc1sSHLPvZNauf49kmKSbjWzncxsfyUWLpEyf8InJYaDdjGz75nZVma2UapOya0HZikRztYdWyjpH5JuMbNdzWyIEqt1fplBDYskDTOzg5P7Bf5W0t7tqDslMzvMzMaZ2VAz6yNptKRNlAik6+wp6aUmTyQBAEWOkAcACMLzksolfezuTRf3mKbEXLD/KLFQyDq/VWKlyPGSFigxt+xHkt5NdXF3X6XE8M4BkmYrsbLmxcnmNZkW6e7/knSLpHuVGL54Thvd/6zEgi9NjVFibl+tElsT3CPp4wxqmCTpfklTJL2mxCIoV2dadxtWSvqBEltWvKXEz/PnTRa0kRKLu9yahfcCABQIc2/PP3ACAFAczOwISQ9L6uHuK3Jw/QolgtPxLUJT0z5bKbG4yzHu/lC2a9hQyS0prpI0yN2j+a4HAJAdLLwCACgJZnaCpHeU2JphoBILmfwtFwFPkty9Ifme6xYukZntq8RwyNeVGIJ6qRKLtLTcm65QdJP0UwIeAJQWQh4AoFRso8Sm3z2V2L/ucUm/zuUbuvuLLQ6VKbGR+/b6ekuIvdx9dctzC4G735/vGgAA2cdwTQAAAAAoISy8AgAAAAAlhJAHAAAAACWEkAcAAAAAJYSQBwAAAAAlhJAHAAAAACXk/wFs4zW9JmiPKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation coefficient and linear regression model for mouse weight and average tumor volume for the Capomulin regimen\n",
    "\n",
    "#correlation coefficient\n",
    "corr_co = round(st.pearsonr(scatter_plt[\"Weight (g)\"], scatter_plt[\"Tumor Volume (mm3)\"])[0],2)\n",
    "print(f\"The correlation between the mouse weight and the average tumor volume is {corr_co}\")\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = st.linregress(scatter_plt['Weight (g)'], scatter_plt['Tumor Volume (mm3)'])\n",
    "plt_slope = round(slope, 2)\n",
    "plt_intercept = round(intercept, 2)\n",
    "\n",
    "print(f\"The slope of the linear regression model is: {plt_slope}\")\n",
    "print(f\"The intercept of the linear regression model is: {plt_intercept}\")\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize = (15, 10))\n",
    "y_values = scatter_plt[\"Weight (g)\"] * plt_slope + plt_intercept\n",
    "plt.scatter(scatter_plt[\"Weight (g)\"], scatter_plt[\"Tumor Volume (mm3)\"], s = 175, color = \"cadetblue\")\n",
    "plt.plot(scatter_plt['Weight (g)'], y_values, linewidth = 3, color = \"salmon\")\n",
    "plt.title(\"Mouse Weight vs. Average Tumor Volume for Capomulin\", fontsize = 20)\n",
    "plt.xlabel(\"Weight (grams)\", fontsize = 14)\n",
    "plt.ylabel(\"Average Tumor Volume (mm3)\", fontsize = 14)\n",
    "equation = \"y = \" + str(plt_slope) + \"x\" \" + \" + str(plt_intercept)\n",
    "plt.text(17.5, 37.5, equation, fontsize = 14)\n",
    "\n",
    "plt.savefig(\"Visualizations/Linear_Regression_Model.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* https://appdividend.com/2020/03/07/python-pandas-find-duplicate-rows-in-dataframe-based-on-all-or-selected-columns/#:~:text=duplicated()%20is%20an%20inbuilt,columns%2C%20then%20use%20the%20pandas.\n",
    "* https://thispointer.com/pandas-find-duplicate-rows-in-a-dataframe-based-on-all-or-selected-columns-using-dataframe-duplicated-in-python/\n",
    "* https://stackoverflow.com/questions/18172851/deleting-dataframe-row-in-pandas-based-on-column-value\n",
    "* https://stackoverflow.com/questions/35582531/pandas-subplot-title-size-in-ipython-notebook\n",
    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.SeriesGroupBy.aggregate.html\n",
    "* https://matplotlib.org/gallery/statistics/boxplot_demo.html\n",
    "* https://matplotlib.org/2.0.2/api/colors_api.html\n",
    "* https://www.geeksforgeeks.org/box-plot-in-python-using-matplotlib/#:~:text=Creating%20Box%20Plot&text=pyplot%20module%20of%20matplotlib%20library,we%20can%20create%20box%20plots.&text=The%20data%20values%20given%20to,box%20plot%20by%20using%20numpy.\n",
    "*https://www.geeksforgeeks.org/python-pandas-dataframe-get_value/\n",
    "*https://pythonspot.com/matplotlib-scatterplot/\n",
    "*https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corr.html\n",
    "*https://towardsdatascience.com/simple-and-multiple-linear-regression-with-python-c9ab422ec29c\n",
    "*https://moonbooks.org/Articles/How-to-calculate-the-slope-and-the-intercept-of-a-straight-line-with-python-/\n",
    "*https://www.mathworks.com/matlabcentral/answers/64754-how-can-i-include-an-equation-in-a-scatter-plot-with-linear-regression-line\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}