Computer Hardware Investigation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install ucimlrepo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import Ridge, Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "from scipy.stats import kurtosis, skew\n",
    "from scipy.optimize import curve_fit\n",
    "from ucimlrepo import fetch_ucirepo\n",
    "from IPython.display import display\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computer Hardware Investigation: CPU Performance\n",
    "##### Source: https://archive.ics.uci.edu/dataset/29/computer+hardware\n",
    "###### Citation: Feldmesser,Jacob. (1987). Computer Hardware. UCI Machine Learning Repository. https://doi.org/10.24432/C5830D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'ucimlrepo.dotdict.dotdict'>\n",
      "dict_keys(['ids', 'features', 'targets', 'original', 'headers'])\n"
     ]
    }
   ],
   "source": [
    "df = fetch_ucirepo(id=29) \n",
    "print(type(df.data)) # dictionary\n",
    "print(df.data.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'uci_id': 29, 'name': 'Computer Hardware', 'repository_url': 'https://archive.ics.uci.edu/dataset/29/computer+hardware', 'data_url': 'https://archive.ics.uci.edu/static/public/29/data.csv', 'abstract': 'Relative CPU Performance Data, described in terms of its cycle time, memory size, etc.', 'area': 'Computer Science', 'tasks': ['Regression'], 'characteristics': ['Multivariate'], 'num_instances': 209, 'num_features': 10, 'feature_types': ['Integer'], 'demographics': [], 'target_col': None, 'index_col': None, 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 1987, 'last_updated': 'Fri Oct 27 2023', 'dataset_doi': '10.24432/C5830D', 'creators': ['Jacob Feldmesser'], 'intro_paper': None, 'additional_info': {'summary': 'The estimated relative performance values were estimated by the authors using a linear regression method.  See their article (pp 308-313) for more details on how the relative performance values were set.', 'purpose': None, 'funded_by': None, 'instances_represent': None, 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': '   1. vendor name: 30 \\r\\n      (adviser, amdahl,apollo, basf, bti, burroughs, c.r.d, cambex, cdc, dec, \\r\\n       dg, formation, four-phase, gould, honeywell, hp, ibm, ipl, magnuson, \\r\\n       microdata, nas, ncr, nixdorf, perkin-elmer, prime, siemens, sperry, \\r\\n       sratus, wang)\\r\\n   2. Model Name: many unique symbols\\r\\n   3. MYCT: machine cycle time in nanoseconds (integer)\\r\\n   4. MMIN: minimum main memory in kilobytes (integer)\\r\\n   5. MMAX: maximum main memory in kilobytes (integer)\\r\\n   6. CACH: cache memory in kilobytes (integer)\\r\\n   7. CHMIN: minimum channels in units (integer)\\r\\n   8. CHMAX: maximum channels in units (integer)\\r\\n   9. PRP: published relative performance (integer)\\r\\n  10. ERP: estimated relative performance from the original article (integer)', 'citation': None}}\n"
     ]
    }
   ],
   "source": [
    "# metadata \n",
    "print(df.metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDs:\n",
      "None\n",
      "\n",
      "Features:\n",
      "  VendorName ModelName  MYCT  MMIN   MMAX  CACH  CHMIN  CHMAX  PRP  ERP\n",
      "0    adviser     32/60   125   256   6000   256     16    128  198  199\n",
      "1     amdahl    470v/7    29  8000  32000    32      8     32  269  253\n",
      "2     amdahl   470v/7a    29  8000  32000    32      8     32  220  253\n",
      "\n",
      "Targets:\n",
      "None\n",
      "\n",
      "Original:\n",
      "  VendorName ModelName  MYCT  MMIN   MMAX  CACH  CHMIN  CHMAX  PRP  ERP\n",
      "0    adviser     32/60   125   256   6000   256     16    128  198  199\n",
      "1     amdahl    470v/7    29  8000  32000    32      8     32  269  253\n",
      "2     amdahl   470v/7a    29  8000  32000    32      8     32  220  253\n",
      "\n",
      "Headers:\n",
      "Index(['VendorName', 'ModelName', 'MYCT', 'MMIN', 'MMAX', 'CACH', 'CHMIN',\n",
      "       'CHMAX', 'PRP', 'ERP'],\n",
      "      dtype='object')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"IDs:\\n{df.data.ids}\\n\") # blank\n",
    "print(f\"Features:\\n{df.data.features.head(3)}\\n\") # dataframe\n",
    "print(f\"Targets:\\n{df.data.targets}\\n\") # blank\n",
    "print(f\"Original:\\n{df.data.original.head(3)}\\n\") # dataframe\n",
    "print(f\"Headers:\\n{df.data.headers}\\n\") # column headings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorName</th>\n",
       "      <th>ModelName</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>PRP</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>209 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     VendorName  ModelName  MYCT  MMIN  MMAX  CACH  CHMIN  CHMAX   PRP   ERP\n",
       "0          True       True  True  True  True  True   True   True  True  True\n",
       "1          True       True  True  True  True  True   True   True  True  True\n",
       "2          True       True  True  True  True  True   True   True  True  True\n",
       "3          True       True  True  True  True  True   True   True  True  True\n",
       "4          True       True  True  True  True  True   True   True  True  True\n",
       "..          ...        ...   ...   ...   ...   ...    ...    ...   ...   ...\n",
       "204        True       True  True  True  True  True   True   True  True  True\n",
       "205        True       True  True  True  True  True   True   True  True  True\n",
       "206        True       True  True  True  True  True   True   True  True  True\n",
       "207        True       True  True  True  True  True   True   True  True  True\n",
       "208        True       True  True  True  True  True   True   True  True  True\n",
       "\n",
       "[209 rows x 10 columns]"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.data.features == df.data.original # checking difference between Features and Original dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.data.features.equals(df.data.original) # confirming no difference between Features and Original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorName</th>\n",
       "      <th>ModelName</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>PRP</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adviser</td>\n",
       "      <td>32/60</td>\n",
       "      <td>125</td>\n",
       "      <td>256</td>\n",
       "      <td>6000</td>\n",
       "      <td>256</td>\n",
       "      <td>16</td>\n",
       "      <td>128</td>\n",
       "      <td>198</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>470v/7</td>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>32000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>269</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>470v/7a</td>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>32000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>220</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>470v/7b</td>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>32000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>172</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>amdahl</td>\n",
       "      <td>470v/7c</td>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>16000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>sperry</td>\n",
       "      <td>80/8</td>\n",
       "      <td>124</td>\n",
       "      <td>1000</td>\n",
       "      <td>8000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>42</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>sperry</td>\n",
       "      <td>90/80-model-3</td>\n",
       "      <td>98</td>\n",
       "      <td>1000</td>\n",
       "      <td>8000</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>46</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>sratus</td>\n",
       "      <td>32</td>\n",
       "      <td>125</td>\n",
       "      <td>2000</td>\n",
       "      <td>8000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>52</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>wang</td>\n",
       "      <td>vs-100</td>\n",
       "      <td>480</td>\n",
       "      <td>512</td>\n",
       "      <td>8000</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>wang</td>\n",
       "      <td>vs-90</td>\n",
       "      <td>480</td>\n",
       "      <td>1000</td>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>209 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    VendorName      ModelName  MYCT  MMIN   MMAX  CACH  CHMIN  CHMAX  PRP  ERP\n",
       "0      adviser          32/60   125   256   6000   256     16    128  198  199\n",
       "1       amdahl         470v/7    29  8000  32000    32      8     32  269  253\n",
       "2       amdahl        470v/7a    29  8000  32000    32      8     32  220  253\n",
       "3       amdahl        470v/7b    29  8000  32000    32      8     32  172  253\n",
       "4       amdahl        470v/7c    29  8000  16000    32      8     16  132  132\n",
       "..         ...            ...   ...   ...    ...   ...    ...    ...  ...  ...\n",
       "204     sperry           80/8   124  1000   8000     0      1      8   42   37\n",
       "205     sperry  90/80-model-3    98  1000   8000    32      2      8   46   50\n",
       "206     sratus             32   125  2000   8000     0      2     14   52   41\n",
       "207       wang         vs-100   480   512   8000    32      0      0   67   47\n",
       "208       wang          vs-90   480  1000   4000     0      0      0   45   25\n",
       "\n",
       "[209 rows x 10 columns]"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# since they are the same, update df var to contain the feature dataset\n",
    "df = df.data.features \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 209 entries, 0 to 208\n",
      "Data columns (total 10 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   VendorName  209 non-null    object\n",
      " 1   ModelName   209 non-null    object\n",
      " 2   MYCT        209 non-null    int64 \n",
      " 3   MMIN        209 non-null    int64 \n",
      " 4   MMAX        209 non-null    int64 \n",
      " 5   CACH        209 non-null    int64 \n",
      " 6   CHMIN       209 non-null    int64 \n",
      " 7   CHMAX       209 non-null    int64 \n",
      " 8   PRP         209 non-null    int64 \n",
      " 9   ERP         209 non-null    int64 \n",
      "dtypes: int64(8), object(2)\n",
      "memory usage: 16.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info() # checking for nulls, checking datatypes within each col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(209, 10)"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorName</th>\n",
       "      <th>ModelName</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>PRP</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [VendorName, ModelName, MYCT, MMIN, MMAX, CACH, CHMIN, CHMAX, PRP, ERP]\n",
       "Index: []"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# any duplicated rows?\n",
    "df[df.duplicated()] # there are none"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['adviser' 'amdahl' 'apollo' 'basf' 'bti' 'burroughs' 'c.r.d' 'cdc'\n",
      " 'cambex' 'dec' 'dg' 'formation' 'four-phase' 'gould' 'hp' 'harris'\n",
      " 'honeywell' 'ibm' 'ipl' 'magnuson' 'microdata' 'nas' 'ncr' 'nixdorf'\n",
      " 'perkin-elmer' 'prime' 'siemens' 'sperry' 'sratus' 'wang']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.VendorName.unique())\n",
    "df.VendorName.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"VendorName\"] = df[\"VendorName\"].str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VendorName\n",
       "IBM          32\n",
       "NAS          19\n",
       "NCR          13\n",
       "SPERRY       13\n",
       "HONEYWELL    13\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vendor_counts = df.VendorName.value_counts()\n",
    "vendor_counts.head() # most popular: Vendor Names that occur the most in this dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Column (Variable) Information:\n",
    "1. vendor name: 30 \n",
    "      (adviser, amdahl,apollo, basf, bti, burroughs, c.r.d, cambex, cdc, dec, \n",
    "       dg, formation, four-phase, gould, honeywell, hp, ibm, ipl, magnuson, \n",
    "       microdata, nas, ncr, nixdorf, perkin-elmer, prime, siemens, sperry, \n",
    "       sratus, wang)\n",
    "2. Model Name: many unique symbols\n",
    "3. MYCT: machine cycle time in nanoseconds (integer)\n",
    "4. MMIN: minimum main memory in kilobytes (integer)\n",
    "5. MMAX: maximum main memory in kilobytes (integer)\n",
    "6. CACH: cache memory in kilobytes (integer)\n",
    "7. CHMIN: minimum channels in units (integer)\n",
    "8. CHMAX: maximum channels in units (integer)\n",
    "9. PRP: published relative performance (integer)\n",
    "10. ERP: estimated relative performance from the original article (integer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our target:<br>\n",
    "PRP <br>\n",
    "<br>\n",
    "The predictions (made using linear regression by creators of article):<br>\n",
    "ERP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mostpopular_manufacturers = vendor_counts.head(10)\n",
    "other_count = vendor_counts.iloc[10:].sum()\n",
    "mostpopular_manufacturers['Other'] = other_count\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.pie(mostpopular_manufacturers, labels=mostpopular_manufacturers.index, autopct='%1.0f%%', startangle = 90, colors = sns.color_palette(\"Paired\"))\n",
    "plt.title('Distribution of CPU Manufacturers (Vendor Names)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwgAAAKqCAYAAACepnlGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3QUVRvA4d/uJtn03kNIo4YuSBHpXToqCkhHkSIoCoqACCqIImIBQaQJSBERRFA+pPdq6J2QBEgCCellU3a+PyIbliRAMIWN73POnsPeuTN772V2M+/cMipFURSEEEIIIYQQAlCXdgGEEEIIIYQQTw4JEIQQQgghhBAGEiAIIYQQQgghDCRAEEIIIYQQQhhIgCCEEEIIIYQwkABBCCGEEEIIYSABghBCCCGEEMJAAgQhhBBCCCGEgQQIQgghhBBCCAMJEIT4l5YsWYJKpeLatWtFdsxr166hUqlYsmRJkR1TPJmSk5MZMmQInp6eqFQq3nzzzdIuUqn58MMPUalUxXb8AQMG4O/vX2zHF0KIskICBPFEunLlCkOHDiUwMBBLS0vs7e1p3LgxX331FWlpaaVdvCLz008/MXv27NIuhpEBAwZga2tb4HaVSsXIkSOLtQxz5879zwRH06ZNY8mSJQwbNoxly5bRt2/fAvP6+/ujUql444038mzbuXMnKpWKtWvXFmdx/xOaN2+OSqXK93X+/Pli+cz/0jkvhHjymZV2AYS436ZNm3jxxRfRarX069eP6tWrk5GRwd69exk7dixnzpzh+++/L+1iFomffvqJ06dP57lr7OfnR1paGubm5qVTsFI2d+5cXF1dGTBgQGkXpdht376dhg0bMnny5EfeZ8GCBYwfPx5vb+9iLFnJmzhxIu+9915pFwOAcuXKMX369DzpxdXm/6VzXgjx5JMAQTxRQkNDefnll/Hz82P79u14eXkZto0YMYLLly+zadOmf/05iqKQnp6OlZVVnm3p6elYWFigVpdeB5tKpcLS0rLUPl+UnFu3bhEcHPzI+atVq8aFCxf49NNP+frrr4uxZCUnJSUFGxsbzMzMMDN7Mv4sOTg48Morr5R2Mf6VB/3OCSHEg8gQI/FE+eyzz0hOTmbhwoVGwcFdFSpUYPTo0Yb3WVlZfPTRRwQFBaHVavH39+f9999Hp9MZ7efv70+nTp3YsmUL9erVw8rKivnz5xuGZaxatYqJEyfi4+ODtbU1iYmJABw6dIj27dvj4OCAtbU1zZo1Y9++fQ+tx4YNG+jYsSPe3t5otVqCgoL46KOPyM7ONuRp3rw5mzZtIiwszDB84e746ILmIGzfvp0mTZpgY2ODo6MjXbt25dy5c0Z57o7jvnz5MgMGDMDR0REHBwcGDhxIamrqQ8v+OHQ6HZMnT6ZChQpotVp8fX0ZN25cnv+HxYsX07JlS9zd3dFqtQQHB/Pdd98Z5fH39+fMmTPs2rXL0C7NmzcHcud77N27l1GjRuHm5oajoyNDhw4lIyOD+Ph4+vXrh5OTE05OTowbNw5FUYyOP3PmTJ555hlcXFywsrKibt26+Q7LuTuUasWKFVSuXBlLS0vq1q3L7t27H6lNbt26xeDBg/Hw8MDS0pJatWqxdOlSw/a7515oaCibNm0y1PVhc1n8/f3p168fCxYs4ObNmw/MW9CY+/zG+t+t788//0xwcDBWVlY0atSIU6dOATB//nwqVKiApaUlzZs3z7ecj/J9ufvZZ8+epXfv3jg5OfHss88WWC6A5cuXU79+faytrXFycqJp06b873//M2x/lO9bUSupc76gNslv7lNBv3MA8fHxvPnmm/j6+qLVaqlQoQIzZsxAr9cbHXfVqlXUrVsXOzs77O3tqVGjBl999VURtJgQwpQ8GbdqhPjHxo0bCQwM5Jlnnnmk/EOGDGHp0qW88MILvP322xw6dIjp06dz7tw5fv31V6O8Fy5coFevXgwdOpRXX32VypUrG7Z99NFHWFhY8M4776DT6bCwsGD79u106NCBunXrMnnyZNRqteGP/Z49e6hfv36B5VqyZAm2traMGTMGW1tbtm/fzgcffEBiYiKff/45ABMmTCAhIYHr16/z5ZdfAjxw7P9ff/1Fhw4dCAwM5MMPPyQtLY1vvvmGxo0bc/z48TwXgj179iQgIIDp06dz/PhxfvjhB9zd3ZkxY8YjtW1MTMwj5dPr9XTp0oW9e/fy2muvUbVqVU6dOsWXX37JxYsXWb9+vSHvd999R7Vq1ejSpQtmZmZs3LiR4cOHo9frGTFiBACzZ8/mjTfewNbWlgkTJgDg4eFh9JlvvPEGnp6eTJkyhYMHD/L999/j6OjI/v37KV++PNOmTWPz5s18/vnnVK9enX79+hn2/eqrr+jSpQt9+vQhIyODVatW8eKLL/L777/TsWNHo8/ZtWsXq1evZtSoUWi1WubOnUv79u05fPgw1atXL7BN0tLSaN68OZcvX2bkyJEEBATw888/M2DAAOLj4xk9ejRVq1Zl2bJlvPXWW5QrV463334bADc3t4e2+YQJE/jxxx+LvBdhz549/Pbbb4b/i+nTp9OpUyfGjRvH3LlzGT58OHFxcXz22WcMGjSI7du3G/Yt7PflxRdfpGLFikybNi1PEHevKVOm8OGHH/LMM88wdepULCwsOHToENu3b6dt27bAo33fCis7OzvPd8DS0hJbW9tSOecfVX6/c6mpqTRr1owbN24wdOhQypcvz/79+xk/fjyRkZGGeVBbt26lV69etGrVyvA7ce7cOfbt22d0Y0YI8R+gCPGESEhIUACla9euj5Q/JCREAZQhQ4YYpb/zzjsKoGzfvt2Q5ufnpwDKn3/+aZR3x44dCqAEBgYqqamphnS9Xq9UrFhRadeunaLX6w3pqampSkBAgNKmTRtD2uLFixVACQ0NNcp3v6FDhyrW1tZKenq6Ia1jx46Kn59fnryhoaEKoCxevNiQVrt2bcXd3V2JjY01pJ04cUJRq9VKv379DGmTJ09WAGXQoEFGx+zevbvi4uKS57Pu179/fwV44GvEiBGG/MuWLVPUarWyZ88eo+PMmzdPAZR9+/Y9sF3atWunBAYGGqVVq1ZNadasWZ68d9v6/v+XRo0aKSqVSnn99dcNaVlZWUq5cuXyHOf+MmRkZCjVq1dXWrZsaZR+t65Hjx41pIWFhSmWlpZK9+7d85TtXrNnz1YAZfny5Uaf06hRI8XW1lZJTEw0pPv5+SkdO3Z84PHyyztw4EDF0tJSuXnzpqIouefyzz//bMjfv3//fM+vu+fI/fXVarVG5/H8+fMVQPH09DQq8/jx443O+cJ8X+5+dq9evR5arkuXLilqtVrp3r27kp2dbZT3/s+5X37ft4La437NmjXL97zv37+/oigle87n93+lKPn/7hT0O/fRRx8pNjY2ysWLF43S33vvPUWj0Sjh4eGKoijK6NGjFXt7eyUrKytvowgh/lNkiJF4Ytwd1mNnZ/dI+Tdv3gzAmDFjjNLv3om9f65CQEAA7dq1y/dY/fv3NxqnGxISwqVLl+jduzexsbHExMQQExNDSkoKrVq1Yvfu3Xm65u9177GSkpKIiYmhSZMmpKamPtYqKJGRkYSEhDBgwACcnZ0N6TVr1qRNmzaGtrjX66+/bvS+SZMmxMbGGtr5QSwtLdm6dWu+r/v9/PPPVK1alSpVqhjaKSYmhpYtWwKwY8cOQ9572yUhIYGYmBiaNWvG1atXSUhIeHhD/GPw4MFGwy4aNGiAoigMHjzYkKbRaKhXrx5Xr1412vfeMsTFxZGQkECTJk04fvx4ns9p1KgRdevWNbwvX748Xbt2ZcuWLQ8cvrJ582Y8PT3p1auXIc3c3JxRo0aRnJzMrl27HrmuBZk4cSJZWVl8+umn//pYd7Vq1cqoJ6pBgwYAPP/880bfy7vpd9v2cb4v95+f+Vm/fj16vZ4PPvggz5yge///i/r7BjnDde4/98eNGweUzjn/qPL7nfv5559p0qQJTk5ORuVt3bo12dnZhmFzjo6OpKSk5Ps9F0L8t8gQI/HEsLe3B3L+wD+KsLAw1Go1FSpUMEr39PTE0dGRsLAwo/SAgIACj3X/tkuXLgE5gUNBEhIScHJyynfbmTNnmDhxItu3b89zQf44FwV363LvsKi7qlatypYtWwwTPe8qX768Ub67ZY2LizO0dUE0Gg2tW7d+pLJdunSJc+fOFTg05tatW4Z/79u3j8mTJ3PgwIE88yESEhJwcHB4pM+8v2539/P19c2THhcXZ5T2+++/8/HHHxMSEmI0Xjy/cd4VK1bMk1apUiVSU1O5ffs2np6e+ZYvLCyMihUr5rmorVq1qmH7vxUYGEjfvn35/vvvi2zln8K0K2Bo28f5vjzo+3jXlStXUKvVD53EXdTfNwAbG5sCvwOlcc4/qvza9dKlS5w8efKh5R0+fDhr1qyhQ4cO+Pj40LZtW3r27En79u2LtIxCiCefBAjiiWFvb4+3tzenT58u1H6P+mClB63kcf+2u3c7P//8c2rXrp3vPgXNF4iPj6dZs2bY29szdepUgoKCsLS05Pjx47z77rsP7HkoShqNJt905QHjvR+HXq+nRo0azJo1K9/tdy8ur1y5QqtWrahSpQqzZs3C19cXCwsLNm/ezJdfflmodimobvml31vfPXv20KVLF5o2bcrcuXPx8vLC3NycxYsX89NPPz3y5z8pJkyYwLJly5gxYwbdunXLs72g70ZBvR+FaVfIbdvH+b4U1co6pfF9K8lzvrD/h/m1q16vp02bNoYekPtVqlQJAHd3d0JCQtiyZQt//PEHf/zxB4sXL6Zfv35GE+yFEGWfBAjiidKpUye+//57Dhw4QKNGjR6Y18/PD71ez6VLlwx3ZgGio6OJj4/Hz8/vscsRFBQE5AQtj3on/a6dO3cSGxvLunXraNq0qSE9NDQ0T95HDW7u1uXChQt5tp0/fx5XV1ej3oOSFBQUxIkTJ2jVqtUD67Nx40Z0Oh2//fab0Z3qe4dj3FVcT9P95ZdfsLS0ZMuWLWi1WkP64sWL881/9874vS5evIi1tfUDJxP7+flx8uRJ9Hq9US/C3eEu/+bcvFdQUBCvvPIK8+fPNwz7uZeTkxPx8fF50ouiB+P+csDjfV8edly9Xs/Zs2cLDDwK830rynKV1Dl/t9clPj4eR0dHQ3ph/g+DgoJITk5+pP8bCwsLOnfuTOfOndHr9QwfPpz58+czadKkPL21QoiyS+YgiCfKuHHjsLGxYciQIURHR+fZfuXKFcOSe8899xxAnicR372rd/+KNIVRt25dgoKCmDlzJsnJyXm23759u8B9795tvffOdUZGBnPnzs2T18bG5pGGQHh5eVG7dm2WLl1qdMF3+vRp/ve//xnaojT07NmTGzdusGDBgjzb0tLSSElJAfJvl4SEhHwvzm1sbPK9sP23NBoNKpXK6O7rtWvXjFadudeBAweM5iZERESwYcMG2rZtW+Bddcg5N6Oioli9erUhLSsri2+++QZbW1uaNWv27yvzj4kTJ5KZmclnn32WZ1tQUBAJCQmcPHnSkBYZGZlnha9/6998Xx6kW7duqNVqpk6dmudu+93zqDDft6JSkuf83eDr3uV1U1JSCnVHv2fPnhw4cIAtW7bk2RYfH09WVhYAsbGxRtvUajU1a9YEyLN8qxCibJMeBPFECQoK4qeffuKll16iatWqRk9S3r9/v2GpSIBatWrRv39/vv/+e8Mwg8OHD7N06VK6detGixYtHrscarWaH374gQ4dOlCtWjUGDhyIj48PN27cYMeOHdjb27Nx48Z8933mmWdwcnKif//+jBo1CpVKxbJly/Id2lO3bl1Wr17NmDFjePrpp7G1taVz5875Hvfzzz+nQ4cONGrUiMGDBxuWOXVwcODDDz987Lr+W3379mXNmjW8/vrr7Nixg8aNG5Odnc358+dZs2aNYU32tm3bGu5ODh06lOTkZBYsWIC7uzuRkZFGx6xbty7fffcdH3/8MRUqVMDd3d0wAfTf6NixI7NmzaJ9+/b07t2bW7duMWfOHCpUqGB0EX1X9erVadeundEyp5Cz9OaDvPbaa8yfP58BAwZw7Ngx/P39Wbt2Lfv27WP27NmPPBH/UdztRcjvgvHll1/m3XffpXv37owaNYrU1FS+++47KlWqlO+k7Mf1b74vD1KhQgUmTJjARx99RJMmTejRowdarZYjR47g7e3N9OnTC/V9Kyolec63bduW8uXLM3jwYMaOHYtGo2HRokW4ubkRHh7+SOUdO3Ysv/32G506dWLAgAHUrVuXlJQUTp06xdq1a7l27Rqurq4MGTKEO3fu0LJlS8qVK0dYWBjffPMNtWvXNuqlFUL8B5TS6klCPNDFixeVV199VfH391csLCwUOzs7pXHjxso333xjtGxhZmamMmXKFCUgIEAxNzdXfH19lfHjxxvlUZSCl5LMb2nIe/39999Kjx49FBcXF0Wr1Sp+fn5Kz549lW3bthny5Lfc4L59+5SGDRsqVlZWire3tzJu3Dhly5YtCqDs2LHDkC85OVnp3bu34ujoqACGJRjzW+ZUURTlr7/+Uho3bqxYWVkp9vb2SufOnZWzZ88a5bm7LOLt27eN0vMrZ3769++v2NjYFLid+5Y5VZScJTxnzJihVKtWTdFqtYqTk5NSt25dZcqUKUpCQoIh32+//abUrFlTsbS0VPz9/ZUZM2YoixYtylOuqKgopWPHjoqdnZ0CGJZ/vFuHI0eOPFKd86vLwoULlYoVKyparVapUqWKsnjx4gKX/RwxYoSyfPlyQ/46deoY/f89SHR0tDJw4EDF1dVVsbCwUGrUqJHn/1NRHn+Z03tdunRJ0Wg0+Z7L//vf/5Tq1asrFhYWSuXKlZXly5c/sL73unsefv7550bpBX1vHuX7UtD/1b3b7rdo0SKlTp06hnOrWbNmytatWw3bH/X7VphlTqtVq/bAPCV1ziuKohw7dkxp0KCBYmFhoZQvX16ZNWtWgcucFnQuJSUlKePHj1cqVKigWFhYKK6ursozzzyjzJw5U8nIyFAURVHWrl2rtG3bVnF3dzd81tChQ5XIyMiHtpkQomxRKUox3mYRQggTpVKpGDFiBN9++21pF0UIIYQoUTIHQQghhBBCCGEgAYIQQgghhBDCQAIEIYQQQgghhIEECEIIkQ9FUWT+gRBCiCK3e/duOnfujLe3NyqVqsCltu+1c+dOnnrqKbRaLRUqVGDJkiXFWkYJEIQQQgghhCghKSkp1KpVizlz5jxS/tDQUDp27EiLFi0ICQnhzTffZMiQIfk+26SoyCpGQgghhBBClAKVSsWvv/5Kt27dCszz7rvvsmnTJk6fPm1Ie/nll4mPj+fPP/8slnJJD4IQQgghhBCPSafTkZiYaPQqyqePHzhwgNatWxultWvXjgMHDhTZZ9zviXmS8ibzyqVdBJPz5QsrSrsIJik9OaW0i2BybJwdSrsIJsncwqK0i2BybBxsSrsIJik5Pqm0i2ByzMzNS7sIJmnDd0/m9VppXkcemdCLKVOmGKVNnjyZDz/8sEiOHxUVhYeHh1Gah4cHiYmJpKWlYWVlVSSfc68nJkAQQgghhBDC1IwfP54xY8YYpWm12lIqTdGQAEEIIYQQQojHpNVqizUg8PT0JDo62igtOjoae3v7Yuk9AAkQhBBCCCGEiVOZq0q7CMWmUaNGbN682Sht69atNGrUqNg+UyYpCyGEEEIIUUKSk5MJCQkhJCQEyFnGNCQkhPDwcCBnyFK/fv0M+V9//XWuXr3KuHHjOH/+PHPnzmXNmjW89dZbxVZG6UEQQgghhBAmTW1mOj0IR48epUWLFob3d+cv9O/fnyVLlhAZGWkIFgACAgLYtGkTb731Fl999RXlypXjhx9+oF27dsVWRgkQhBBCCCGEKCHNmzfnQY8hy+8pyc2bN+fvv/8uxlIZkwBBCCGEEEKYNJW5jJovStKaQgghhBBCCAMJEIQQQgghhBAGMsRICCGEEEKYNFOapGwKpAdBCCGEEEIIYSA9CEIIIYQQwqSV5QellQbpQRBCCCGEEEIYSIAghBBCCCGEMJAhRkIIIYQQwqTJJOWiJT0IQgghhBBCCAPpQRBCCCGEECZNJikXLelBEEIIIYQQQhhIgCCEEEIIIYQwkCFGQgghhBDCpMkk5aIlPQhCCCGEEEIIA+lBEEIIIYQQJk2lkR6EolSoHoTAwEBiY2OLqyxCCCGEEEKIUlaoHoRr166RnZ1dXGURQgghhBCi0NTSg1CkZA6CEEIIIYQQwqDQcxC2bNmCg4PDA/N06dLlsQskhBBCCCGEKD2FDhD69+//wO0qlUqGIQkhhBBCiBKjUssQo6JU6CFGUVFR6PX6Al8SHAghhBBCCGG6CtWDoFJJdCaEEEIIIZ4sKo1Mqy1KhWpNRVGKqxxCCCGEEEKIJ0ChAoT+/ftjZWVVXGURQgghhBBClLJCBQiffPIJU6ZMITExMc+2hIQExo4dS3R0dJEVTgghhBBCiIdRa1Sl9iqLCjUH4csvvyQxMRF7e/s82xwcHEhKSmLWrFnMmDGjyApYlJyfrUfg24NxeKo6lt7uHH1+ONG/bXvwPk3rEzzzPWyDK5IeEcnl6d9x/cdfjfL4DetN4JjBaD3dSDx5njNvfkTCkVPFWZVS0f8Fb55r6YqtjRlnLiTz1aIwbkTpCszfubUbndu44eGqBSDsehrL1t3kyIm8ASbAtHcrUr+2Ax98cZn9R+OLowolbnAffzq39cTOxoxT5xKZOfcS1yPTCszfrYMX3Tp44+VhCUBoeCpLVoVx8NgdQx5nR3OGDwri6dpOWFtpCL+Ryo9rwtm1P6bY61NS+nX3pH1zF2ytNZy9lMLXSyO4GZ1RYP5OLV3o2NIVD1cLAMJupLNiQxRHTyYBYGejoW93T56qboe7iwUJSVnsP5bA0nWRpKbpS6ROxa1PFzfaNXHExlrDucupzF0Rxc1bBbdZh2ZOPNfcCQ8XcwDCb+pY+XsMx04nG/KYm6kY3NODpk/bY26m5viZZL5bEUl8UtlZjOLFdg60amCLjZWaC6E6flh3h6iYrALzd2tpT/0a1ni7mZORpXDxmo4Vm+KIvJ27zwfDPKgWZGm039YDSfzwy537D2eSXunqTrsmToZzbc7ymw88155r7sxzzZ0N51rYTR0rN94yOtfaN3WiWQNHKpS3xNpKQ883zpJSRr6bd/Xu5EKbZx2xsVJz/moa3/0UTeTtzALzP9/OmUa1bSnnqUWXqef8lTR+XH+bG9G5+3i6mjPweTeqBllhbqbi+NkUvl99i4Qy9B0VpaNQPQibN2+mX79+BW7v168fv//++78uVHHR2FiTePICp0dNeaT8Vv7lePq3+cTuPMTeel0J/WYpNeZ/jGubZw15vF7sQNXPx3Pp4znsrd+dpJPnabBpIRZuzsVVjVLxUmdPurd356uF4YycdI50XTafvlcJc/OCI+fbdzL4YeUNhk84y/AJZ/n7TBJT36mAXznLPHmf7+BBWZvi0ud5X17o5MPMuZd47Z2/SUvPZtbUGlg8qM1iMpi3NJTBbx5nyFvHOX4yjukTqhFQ3tqQZ+KYKpT3seK9j07Tf+RRdu+PYeq4YCoG2pZEtYpdz+fc6drGjW+WRDB66kXSdXqmvRP0kHMtk0VrbjJy8gXemHyRE2eT+HB0AH4+Oeeas6M5Lo7mLFh1k6ETzjNzQTj1atoxZnD5kqpWsXq+vQudWzkzZ3kkb08LJT1DYeqb5TE3K7jNYuMyWfrLLd78OJQ3PwnlxPkUJo7wpby31pDn1Zc8qF/Tjk/nX+e9z6/h4mjG+8N9S6JKJaJLC3s6PGvPD7/cYcLXUaRnKLz/qjvmD7h1VjXQki37kpj4TRSfzI9Go4EJr3mgtTBu678OJvHalAjDa8XvccVcm5LxQntXOrdyYc7ym4yZdoV0nZ6P3vJ/4LkWE5fJkl+iGP3RFUZ/fIWT55OZNLK80bmmtVBz/HQSazbfLolqlLgebZ3p2MKJ736KZuxn4aTr9Hw4qtwD2616RWs274pn7GdhTP7qOmYaFR++4Ws417QWKj4cVQ5FgUmzI3hvZjhmGhUTh/vwX1xTRqVWldqrLCpUgHDt2jXKly/4D2q5cuW4du3avy1Tsbm9ZTcXJ88mesNfj5Tf77WXSQu9zrlxM0g+f5WwuSuI+mULAaMHGPIEvDmQiIVruL50HcnnrnBq+GSyU9PxHfB8MdWidPTo4M6KXyPZfyye0PA0Zsy9houTOY3rORa4z8HjCRwOSeBGlI4bUToWr7lBWrqeqhWML2SD/Kx4oaMHM+eHFnMtStaLXXz4cU0Yew/FcuVaCh9/eR4XZy1NGroWuM++I7EcPHaH65FpRNxM4/tl10hLzya4cm6vXfUqDvzy+w3OXUriZnQ6S9eEk5ySReUKZSNA6NbOjZUbozjwdyKhEel89n0YLo7mPPNUwQ9oPBSSyJGTSdyMzuBGtI4lv0SRnq6nSlBOYBV2I52Pvr3GoZBEIm9lcOJcMkvWRtKgtj3qMrDwRddWzqzeFMOhE8lcu6Fj1qIbODua0aiOXYH7HD6ZzNHTydy8lcHN6AyWrb9Nuk5P5cCceWbWVmraPOvEwjVRnDyfypXwdGYvuUlwBWtDHlP3XBM71v2VwNEzaYRHZjJnVQxO9mY8Xd26wH2m/3CLXUdTuB6dSVhkJnNXxeLmZEZgOQujfBkZCglJesMrTVc27oB0be3C6t9vcTAkiWvXdXyx6Po/51rekQV3HT6RxNFTuefaj7/eIl2np0pgbjtv+CuWn/+I4fzVgntYTVnnlk78/Ecsh08mE3ZDx+wlUTg7mNGwdsG/21O+vc72g4lERGZw7YaOr36Mwt3FnKDyOTc+qgZZ4e5izlc/RhF2M4Owmxl8tTSKCuUtqVm54HNYiEdRqD+NVlZWDwwArl27VqYmMTs2rE3M9gNGabe37sWpYW0AVObmODxVjZht+3MzKAox2/fj2LBOCZa0eHm5W+DiZMHx07lDg1LSsjl3JYXgio92UapWQfNGTlhq1Zy9lNutrLVQ8/7IQL5ZHE5cQsHd+qbG28MSV2ctR0Jy7xqmpGZz9mIi1asU/If0Xmo1tGrihqWlhjPnc9v+9PkEWjZxx87WDJUqJ4+FhZq/T8UXdTVKnKebBS6O5hw/k3uOpKbpOX81laoVbB7pGGoVNGvgiFar5tzllALz2VhrSE3TozfxUQweruY4O5oTcs64zS5cTaPKI17Iq1XQ9Gl7LC1UnL+SCkAFP0vMzVSEnMttw+tRGdyKzXjk4z7J3J3NcLI349Sl3AvStHSFy+E6KvppH7CnMWvLnD+jyanGJ9KzT9mwYEo5Zr7jRa8Ojg/sOTQVnoZzLfecMJxrQYU51xywtFBz7p9zrazzcDXH2cGME+dz65uarudiaDqVAx79u2Rtdfdcyxk+ZG6mAgUys3KDz4wsBUXJCR7+a2QOQtEq1ByEBg0asGzZMpo2bZrv9h9//JH69esXScGeBFoPV3TRxuO6ddExmDvYobbUYu7kgNrMDN2t2PvyxGJTObAki1qsnBxyxo3efwEfn5CJs6P5A/cN8LXi66lVsDBXk5aezYezrhB+I92wfVhfX85cTGb/sfgiL3dpcnbKuZsYF288vjQuPsOwrSCBfjbM+7wOFhZq0tKyef+TM1yLyP3D8sGMs0wZF8wfKxuTlaUnXafn/WlnuBGZ/oCjmgZnh5yfpPgE43aLT8w0bCuIfzlLZk+q+M+5pmfq16GE38x/joy9rYbeXTz5Y6fpz9twuttmicZjjuOTsnB8SJv5+WiZ+V4AFuYq0nR6Ppl7nYjInLHkTvZmZGbq84wDj0/MNnymKXO00wCQkGRcv4TkbMO2h1GpoH9XJ86HphMRlXvO7jueQkxcFncSs/HzsqB3R0e83c35YqlpD5+5+/8el3jf34LELMPfiYL4+Wj5YnxgzvdTp+fjueFERBY8h60scbLPOZ/i72+3pCzDtodRqWDIi+6cvZxK+M2c7+iF0HTSM/T07+7KsvUxqFTQr5sbGo2qTHxHRekq1Bn0zjvv0KZNGxwcHBg7diweHh4AREdH89lnn7FkyRL+97//PfQ4Op0Onc74hyFT0WOuKgN9/WVAy8bOvDXEz/B+wmeXHvtYETfTGfreWWysNTRt4MS4Yf6MmXqB8BvpNKrrQO1qdrw+/mxRFLtUtWnmztgRlQzvx019/Enq4TdSGTj6KLbWZjRv7MaEtyrzxvgThiBhSJ8A7GzMGD3hBAmJmTRp6MrUccGMeC+Eq2EF3zF/ErVo5MToAeUM7yfNuvrYx7oeqWP4pAtYW2to8rQj77zqx9jpl/IECdaWaj4aE0j4zXSWrY967M8rLc0b2DPiFW/D+ynfhD/2sW5E6Rg19QrWVhqerWvPW4O8ee/za4YgoSx5to4Nr76QOzfs04W3/vUxB3V3xtfTgslzjM+jbYdye3MiojKJS8rmg9c98HAxIzrWdHpKmzdwYGTf3HPtw6/DHvtYN6IyeGPqFWys1DSu68CYQeV497PQMhkkNHvajmG9PQ3vP5p7/V8fc+jLHpT31jJ+Zu73PTE5m88W3OT1Xh50au6EosDuo4lcDk8vc3P6RMkrVIDQokUL5syZw+jRo/nyyy+xt7dHpVKRkJCAubk533zzDS1btnzocaZPn86UKcYThXupnOmjKXhsdmnQRceg9TAuk9bDlcyEJPTpOjJi4tBnZaF1d7kvjwu6KNO9M3ngWDzn7xmacXdyqJODGXfuuSPu6GDOlWsP7iLOyla4GZ3zB+BSaCqVA23o0d6D2QvDqF3NHm8PLRsWGg/HmvxWEKfPJ/P2RxeKqkrFbu/hWM5ePGp4b2GeE+w6OZoTG5d7seXkaMHlq8l59r9XVpZi6A24cCWZqhXteLGLD5/PuYS3pyUvdPah74gjhIbntP3laynUquZAj47ezJz7+MFcaTj4dwIXrtx7ruW0m6ODOXfu6bFytDfnSviDxyZnZSuGlVQuX0ujcoA13dq68fWS3D/OVpZqPnkniLR0PVO+DiXbBBf6OBSSzIWrVwzvDW1mrzHq5XO0MyM04sG9SlnZ/LOKSiZXwtOp6G9Jl1YuzFkeSVxiFubmamys1Ea9CPd/jqk4ejaVS7NyL0bvTg51sFMbrcrkYKvh2s2HB0gDuzvxVLAVH86N5k7Cg0+ky+E5n+tpYgHCoZAkLoTec67902ZO9mbG55q9GVcjHv79jLz7/QxLp5K/FV1bu/DtspvFUPLSdfhkMhfuGY59t90c7c2Iu6enz9HOjNDrDw+QXnvJnaer2zB+VgSx8cbnT8i5VF7/IBQ7Gw16vUJKmp4lnwaxN6bsBfkPoyqjQ31KS6EChJs3bzJ06FA6duzIzz//zOXLl1EUhUqVKvHCCy9Qrly5hx8EGD9+PGPGjDFK2+5ctzBFKRHxB0Nw62A8nMq11TPEHQwBQMnMJOH4GVxbNspdLlWlwqVFI8LmLi/h0hadtHQ9aenGP1qxcRnUqW7PlbCcPwLWVmqqBtmwcWvh7sKp1LkBx6oNkfyx3bjL/YfPq/PdjxEcPB7/+BUoBWlp2dxIM75IiLmjo14tJy6H5lwAW1tpCK5kz/rNhfuDqFLlXgRaanO6o+8fN5+tVzDFhRRyzjXjP2Sx8ZnUCbbl6j8BgbWlmiqB1vy+vXBBt0oF5ma5vZLWlmo+GRtEZqbC5NlXycw0zVtsaTo9abeNT4A78ZnUrmJDaETO99bKUk3lQCv+2FW4lXNUapXh+3k5LJ3MLIVaVW3YfzxnuVgfDwvcXSxMciJpuk4hXWd8cRWXmEWNipaE3cy58WGlVVGhvJatB5IeeKyB3Z2oX92aKd9Fc/vOwy/4/b3/GXJoYktPpun0pN23fOmd+ExqVbXh6j/B591zbfPOwi3hmvP9NMEfrUeQplNIu2/50jsJWdSsbG0ICKws1VQKsOTPPfEPPNZrL7nTsLYtE2ZFcCu24CVRk1Jyzq0ala1xsNNw+OSDb0QJ8TCFChCqVavGnDlz6N27N2+99dZjf6hWq0WrNZ4EVhLDizQ21thUyF2FyTqgHPa1qpBxJ4H0iEgqfzwGSx8PTgx8F4Cw71fhN7wPVaaPJWLJL7i2aIjXix040mWo4RihsxdTa9EM4o+dJuHISfxH9cfMxoqIpeuKvT4lad0ft+jTzYsbUelE3cpgwIvexMZlsu+e5xV8NqES+47EseF/ORf9g1/24XBIArdiMrC20tCysTO1qtrx3qc5d7njErLyvRN5KzaDqNumf/fj599u0P+l8kTcTCMyOp0hr/gTe0fHnoO5F7qzP67J7gMxrNuUEzQM7RfAwWN3iL6djrWVGW2auVOnhiNjJucMWQq7nkrEzVTGjqjInEVXSUjKpGlDV56u7cS4qadLpZ5Fbf2W2/Tq4sGNaB1RtzPo38OL2PhM9h9PMOT5dFwQ+48n8NtfOW058EUvjpxM5HZsJlaWalo0cqJmFVsmzMy5+2ltqWba2CC0WjWfzQ/F2kqD9T9z+BISs9CbZqxgsGHbHV7q6MaNWxlEx2TySlc37sRnceDv3AvdT8b4ceDvRH7fkRM09O/uztHTydy+k9Nmzes7UKOSNR/MzhnCkJqmZ+veOIb09CApJZvUND2v9/Lk3OVULphggJCfzXuS6N7KgcjbWdy6k8VL7R2JS8ziyOncntGJQ905cjqNLfty2nJwD2ca17Hh88W3SNPpcbDL+duVmqaQmaXg4WJG4zo2/H0ujeTUbMp7WdCvixNnr6QTHlnwBZ6p2PBXLC93dOdmdAZRMRn07ebxz7mWu5DCJ2/7c+B4Ir/vyAka+vfw4OippNxzrYEjNSrbMGn2NcM+TvZmODmY4eWeE0z5l7MkLV3PrTuZJKeYVmCVn43b4+j5nAuRt3O+o707u3InIYuDIbkX8lNHl+NgSDKbd8UDMPRld5o+bc+0eTdI0+lx/Ge+Qmqanox/bnC0amRPRFQGiUnZVA60YsiL7vy2Pc7oWQn/FaqysCTdE6RQAcInn3zC0KFD+fXXX5k/fz7Ozqa11r9D3eo02rbM8D545vsARPy4jpODx6P1csPK18uwPe3adY50GUrwF+Pxf6Mf6dejODV0IjFb9xryRP78BxZuzlSaPCrnQWknznG40xAy7pu4bOpWb4zCUqvmrSH+2FprOH0hmfc+vWh0F9bbQ4uDXe5ENUd7M94dHoCzozkpqdmEhqfx3qeXOH4q/wellTUrfonA0lLDuJGVsLUx49TZBN6efMrwww7g42mFo31umzk5mDPxrSq4OFuQkpLFlWspjJl8iqP/rIaUna0w9sPTvD4ggBmTqmNlpeFGZBqfzD5v9DA1U7Zm8y0stWpGD/DF1lrDmUspTJhpfMffy12LvW3uz5ejnRljX/XD2dGM1LRsQiPSmTDzimE1pAr+1oZVkJZ8Hmz0ef3ePku0iXfH//JnLJYWat7o642NtZqzl1L54Ktwo9VNPN3MjdrMwV7DmEHeODuYkZKm59r1dD6YHW60Qs2C1dHoFXh/mG/OQ5jOJDN3RWSJ1q04/bYjEa2FitdecMHaSs2F0HSmL7hF5j33LTxczLGzye1RbftMztKxHw73NDrW3FUx7DqaQlaWQo2KljzXxA6thZrY+CwOn0pl3V8JlAVr/4zBUqvmjX7e2FhrOHsplUmzrxmda15uFtjbGX8/3x5czuhcmzT7GiFnc8+1Ds2d6dPF3fD+s3dzFvr4ctF1/tofX/wVK2br/ncHSwsVw3t7YmOt5tyVNKZ8c/2+76gF9ra5k5afa+YEwLQxxsvLf7U0ku0Hc/6O+nhY0LerG7Y2Gm7FZvLzn7H8tq1sPHNDlC6VohRuKktoaCiDBw/m7NmzLFiwgM6dOxdJQTaZVy6S4/yXfPnCitIugklKTzatibxPAhvngp9BIApmbvHgFatEXjYOj7acrTCWHP/gYVEiLzPzB6+8JPK34bsn83rtWIvGpfbZdXfsK7XPLi6FXgcrICCA7du38+2339KjRw+qVq2KmZnxYY4fP15kBRRCCCGEEOJByuoTjUvLYy2UGxYWxrp163BycqJr1655AgQhhBBCCCGEaSr0lf2CBQt4++23ad26NWfOnMHNza04yiWEEEIIIcQjKatPNC4thQoQ2rdvz6FDh/j222/p169fcZVJCCGEEEIIUUoKFSBkZ2dz+vRpfHx8iqs8QgghhBBCFIrMQShahQoQfHx8mDRp0kPzLVq06LELJIQQQgghhCg9hQoQfvzxR/z8/KhTpw6FXB1VCCGEEEIIYQIKFSAMGzaMlStXEhoaysCBA3nllVdM7mFpQgghhBCibJEnKRetQrXmnDlziIyMZNy4cWzcuBFfX1969uzJli1bpEdBCCGEEEKIMqDQ4ZZWq6VXr15s3bqVs2fPUq1aNYYPH46/vz/JycnFUUYhhBBCCCEKpFKrSu1VFv2r/hi1Wo1KpUJRFLKzs4uqTEIIIYQQQohSUugAQafTsXLlStq0aUOlSpU4deoU3377LeHh4dja2hZHGYUQQgghhBAlpFCTlIcPH86qVavw9fVl0KBBrFy5EldX1+IqmxBCCCGEEA8lT1IuWoUKEObNm0f58uUJDAxk165d7Nq1K99869atK5LCCSGEEEIIIUpWoQKEfv36oVJJhCaEEEIIIZ4cZXWycGkpVICwZMmSYiqGEEIIIYQQ4klQqABBCCGEEEKIJ408KK1oSWsKIYQQQgghDCRAEEIIIYQQQhjIECMhhBBCCGHSZJJy0ZIeBCGEEEIIIYSB9CAIIYQQQgiTJj0IRUt6EIQQQgghhBAGEiAIIYQQQgghDGSIkRBCCCGEMGkyxKhoSQ+CEEIIIYQQwkB6EIQQQgghhEmTJykXLWlNIYQQQgghhIH0IAghhBBCCJOm1sgchKIkPQhCCCGEEEIIAwkQhBBCCCGEEAYyxEgIIYQQQpg0Wea0aEkPghBCCCGEEMLgielB+PKFFaVdBJPz1to+pV0Ek/T0Ow1Kuwgmx75Fi9IugklSzJ6Yn1iTcbV869Iugkly+GpqaRfB5GSl6Uq7CCZqQ2kXIF+yzGnRktYUQgghhBBCGEiAIIQQQgghhDCQ/m8hhBBCCGHSZJJy0ZIeBCGEEEIIIYSB9CAIIYQQQgiTJj0IRUt6EIQQQgghhBAG0oMghBBCCCFMmixzWrSkNYUQQgghhChBc+bMwd/fH0tLSxo0aMDhw4cfmH/27NlUrlwZKysrfH19eeutt0hPTy+28kmAIIQQQgghRAlZvXo1Y8aMYfLkyRw/fpxatWrRrl07bt26lW/+n376iffee4/Jkydz7tw5Fi5cyOrVq3n//feLrYwSIAghhBBCCJOmUqtK7VVYs2bN4tVXX2XgwIEEBwczb948rK2tWbRoUb759+/fT+PGjenduzf+/v60bduWXr16PbTX4d+QAEEIIYQQQogSkJGRwbFjx2jdurUhTa1W07p1aw4cOJDvPs888wzHjh0zBARXr15l8+bNPPfcc8VWTpmkLIQQQgghTFppTlLW6XTodDqjNK1Wi1arzZM3JiaG7OxsPDw8jNI9PDw4f/58vsfv3bs3MTExPPvssyiKQlZWFq+//roMMRJCCCGEEOJJNH36dBwcHIxe06dPL7Lj79y5k2nTpjF37lyOHz/OunXr2LRpEx999FGRfcb9pAdBCCGEEEKIxzR+/HjGjBljlJZf7wGAq6srGo2G6Ohoo/To6Gg8PT3z3WfSpEn07duXIUOGAFCjRg1SUlJ47bXXmDBhAupi6D2RHgQhhBBCCGHaVKpSe2m1Wuzt7Y1eBQUIFhYW1K1bl23bthnS9Ho927Zto1GjRvnuk5qamicI0Gg0ACiKUkQNaEx6EIQQQgghhCghY8aMoX///tSrV4/69esze/ZsUlJSGDhwIAD9+vXDx8fHMEypc+fOzJo1izp16tCgQQMuX77MpEmT6Ny5syFQKGoSIAghhBBCCJP2OMuNlpaXXnqJ27dv88EHHxAVFUXt2rX5888/DROXw8PDjXoMJk6ciEqlYuLEidy4cQM3Nzc6d+7MJ598UmxllABBCCGEEEKIEjRy5EhGjhyZ77adO3cavTczM2Py5MlMnjy5BEqWQ+YgCCGEEEIIIQykB0EIIYQQQpi00nwOQlkkrSmEEEIIIYQwkB4EIYQQQghh0kxpkrIpkB4EIYQQQgghhEGhehDUajUq1YMjNJVKRVZW1r8qlBBCCCGEEI9K5iAUrUIFCL/++muB2w4cOMDXX3+NXq//14USQgghhBBClI5CBQhdu3bNk3bhwgXee+89Nm7cSJ8+fZg6dWqRFU4IIYQQQghRsh67P+bmzZu8+uqr1KhRg6ysLEJCQli6dCl+fn5FWT4hhBBCCCEeSKVWldqrLCp0gJCQkMC7775LhQoVOHPmDNu2bWPjxo1Ur169OMonhBBCCCGEKEGFGmL02WefMWPGDDw9PVm5cmW+Q46EEEIIIYQoSWX1Tn5pKVSA8N5772FlZUWFChVYunQpS5cuzTffunXriqRwQgghhBBCiJJVqAChX79+D13mVAghhBBCCGG6ChUgLFmypJiKIYQQQgghxGOS5yAUqUIFCGVF/xe8ea6lK7Y2Zpy5kMxXi8K4EaUrMH/n1m50buOGh6sWgLDraSxbd5MjJxLzzT/t3YrUr+3AB19cZv/R+OKoQolxfrYegW8PxuGp6lh6u3P0+eFE/7btwfs0rU/wzPewDa5IekQkl6d/x/UfjZ+h4TesN4FjBqP1dCPx5HnOvPkRCUdOFWdVSpxVw1ZYN30Ota0DWVERJP22jKzrV/PN6/jqeCwCq+ZJ150PIWHpLMN7jZs3tu17Yh5YBZVaQ9atGyQs/wZ9Qmyx1aOkrdp9jKXbDhGTmEIlH3fee6ENNfy988274eBJPlix2SjNwkzDkS/HGt5/t3kPfx47R1R8EuYaNcG+nozs3IyaBRzTFK3aeZSlWw8Qm5hMpXIevPtSO2r4++Sbd8OBE0z+caNRmoWZhsPfjAcgMzubOb/tZO/py1yPicfOSkuDKgGM6tYSd0e7Yq9LSfrj919Z/8sq4uPu4B9QgSGvj6Ji5bzfQ4Ctf/7Ozu1bCL8WCkBQhUr06f+qUf4eHZvnu2+/Qa/T7fmXi7z8pcGmSTvsWnVGY+9I5o0w4tYuIjPsSr553UZNRluxWp70tDPHiZ33KQBqOwccuvbBskpNVFY2ZFw+R/zaRWTdjirWepQkuxbP4dCuGxoHJzIirhG78nsyQi8VmN++dWfsmndA4+yKPjmJlGP7if/lR5SsTAAcOjyP9VONMPcqh5KhQ3flPHfW/khW9I2SqpL4DyhUgNCjR49Hyvckz0F4qbMn3du789l314i8rWPgi958+l4lBo09TWamku8+t+9k8MPKG9yISgegbVNXpr5TgdfHnyXserpR3uc7eKDkfxiTpLGxJvHkBSKW/EK9tXMemt/KvxxP/zaf8O9XEdLvHVxaNqLG/I9Jj7xNzNa9AHi92IGqn4/n9IjJxB8+QcCo/jTYtJCd1dqTcftOcVepRGhrNMC2Y2+S1i8hM+IK1o3b4ThoLLFfjENJScqTP2H516g0uV9HlbUtzqM+RnfqsCFN4+yO0+sTSTuyi5S/fkXRpaHx8EHJyiiROpWEP4+dY+av25n4Ujtq+HmzYucRhs1dzYZJr+FiZ5PvPraWWjZMetXwXoXxMEg/d2fGv9iWcq6OpGdmsnzHEYbNWc3GD4bibGddrPUpCVuOnuGLX7YyoVcHagT4sGL7YYZ/vZINHw7D2b7gNlv/4TDD+3tHjqZnZHIuPIpXn2tCZR8PElPT+Ozn//Hmd2v4afzg4q5Oidm7ezuLF8xl6MgxVKpcld/Xr2XqpLF88/0yHB2d8uQ/fSqEZ5u2osrQaphbWPDr2pVMmfQOX81dgourGwALl/1itM/xY4eZ+9VnNHymaYnUqbhZPdUIx+79iFu9gIywS9g274jb8AlEffQm+uS8N8xifphp9LumtrHD473PSfv7gCHN5dWxkJ1FzPefo6SnYtuiE64jJxH9yRiUjIJv3JkK66efxbnnIGKXf4fu6kXsW3fG480PuTFxOPqkhDz5beo3xen5fsQs/gbdlfOYeXjjOmg0KApxaxYBYFm5Okk7NqO7dgnUGpx69MVzzIfcmDSyTLTZ45Ih8EWrUP0xDg4Oj/R6kvXo4M6KXyPZfyye0PA0Zsy9houTOY3rORa4z8HjCRwOSeBGlI4bUToWr7lBWrqeqhVsjfIF+VnxQkcPZs4PLeZalJzbW3ZzcfJsojf89Uj5/V57mbTQ65wbN4Pk81cJm7uCqF+2EDB6gCFPwJsDiVi4hutL15F87gqnhk8mOzUd3wHPF1MtSp51k/akHdlJ+rE9ZN+6SdL6JSgZOqzqNcs3v5KWgj45wfCyqFgdJTOD9HsCBJu2L5Bx4QQpf64mKzKM7Du3yDj3d74Bh6latuMwPRrVolvDmgR5uTLxpfZYWpiz/sDJAvdRqcDV3tbwcrnvovi5etVoWMWfcq6OVPBy453urUhO13Hp5q3irk6JWLbtED0a16HbM7UJ8nJjYq/n/mmzkIJ3UoGrg63h5WKf+1tmZ2XJ/NF9aFc3GH9PF2oGluO9l9pzNjySyDt5L2hM1cZff6ZN+460atMB3/L+DB05Bq2lJdv/tznf/G+NnUiHTt0ICKpIOV8/ho8ai6JXOHniuCGPk7OL0evIwb1Ur1kHT6+y0Vtl16ITKQe2kXpoJ1lRN4hfvQAlIwObRi3yza+kpqBPSjC8LKvURMnQkfb3QQDM3LzQBlQibvUPZIZfIetWJPFrfkBlboFV3cYlWbVi49CmK0l7/kfyvm1kRkYQu/w7lAwdds+2zje/tkIV0i+fI+XwbrJib5F+NoSUw7vRBlQ05ImePYXk/dvJvBlB5vVrxCz6CjMXdyz8gkqqWuI/oFA9CIsXLy6ucpQIL3cLXJwsOH46905HSlo2566kEFzRlp0H4h56DLUKmjZ0wlKr5uylZEO61kLN+yMD+WZxOHEJWcVSflPg2LA2MdsPGKXd3rqX4C/eB0Blbo7DU9W4MmN+bgZFIWb7fhwb1inJohYfjQYzb39Sdt4zjENRyLhyFvPyFR7pEFb1mqI7eRAy/+kdUKmwqFKL1N2bcRg4FnNvP7LjbpOycyMZZ48/+GAmIjMrm3MRUQxu08iQplaraFjZn5PXCu46T9Vl0P6DuegVhaq+HrzRuRkVvNwK/Ixf9odgZ6Wlko97kdehpGVmZXMuPJJB7XIvptRqFQ2q+HPyasFtlqbLoMOEr/9pMy9Gdm1BBe/82wwgOS0dlSoneCgLMjMzuXL5Aj169jakqdVqatauy4XzZx/pGBk6HdnZWdjZ5T/sKj7uDseOHOSNMeOLpMylTqPB3DeQxK3rc9MUhfQLp7Dwr/RIh7Bp1JLU4/tz73Kb5VyC3B06c/eYSlYm2qAqpB7YXkSFLyUaMyz8gkjYvDY3TVFIP3cCbWDlfHfRXT6PbcNmWARUJCP0EmauHljVqEvKgZ0FfozaOqcnVJ+SXGCe/wKVzEEoUv+pOQhODuYAeS7g4xMycXY0f+C+Ab5WfD21ChbmatLSs/lw1hXCb+QOLxrW15czF5PZfyy+yMttSrQeruiiY4zSdNExmDvYobbUYu7kgNrMDN2t2PvyxGJTObAki1ps1NZ2qDSaPF3u+qQEzNy8Hrq/WblAzDx9SfxlYe4xbexRa62wadaJ5P+tJeXP1VhUqolDn1HE/zCdzNALRV6PkhaXkkq2XsnTA+BiZ0NodP5zLPw9XJjS+zkq+riTnKZj6fZD9J+1nHXvD8bDyd6Qb9fpy7y7eAPpmZm42tsyb8TLONma/vCiuOQC2szelmsPaLMP+3Y2tNmPfx1kwOdL+OWDoUZtdpcuM4uvft1O+3rVsLXSFks9SlpSYgJ6vR5HR2ejdEdHJ25EhD/SMX5cPB8nZ1dq1q6b7/Yd27ZgZWVNw2ea/OvyPgnUNvY5v2uJ8Ubp+qR4zD0e3kNi7heEuXd57vz0nSEtK/omWXdu49C5N3GrvkfJSMeuRSfMnFzJsncs4hqUPI1tTptl39dm2YnxmHuWy3eflMO7UdvZ4/XudECFysyMxJ1/GAcZ91KpcH5pCOmXzpJ589HOXSEeRaEChEGDBj1SvkWLFj1wu06nQ6czHienz85ArbEoTHEeqmVjZ94a4md4P+GzgicFPUzEzXSGvncWG2sNTRs4MW6YP2OmXiD8RjqN6jpQu5odr49/tDtPQjyIVb2mZEWGG09o/mdspe7scdL2bQEgKzIc8/IVsGrQskwECI+jVoAPtQJyJ+PWCvSh+8cL+HlfCCM75Y77frpieda8N4j45FR+2X+CsYvWs/ydfgXOayjLagWWo1Zg7sVJraBy9Jgyj7V7jjOiS3OjvJnZ2Yxb8AsKMKHXcyVb0CfYujUr2Ld7O1M/nY2FRf5B0/atm2nSvHWB2/9rbBq2JONGmPGEZn02sT/MxKn3MHw+W4ySnY3uwinSzhz/z44nt6xcHcfnXiB2xXx0Vy9i7u6F88tDyO7Uk4Tf1+TJ79xnKBY+5YmcUUZ6qsQTo9DLnPr5+VGnTh2UfzETd/r06UyZMsUoLaDaqwTWeO2xj5mfA8fiOX85xfDe3DznB8fJwYw78bldmo4O5ly5lvrAY2VlK9yMzglqLoWmUjnQhh7tPZi9MIza1ezx9tCyYaHxEJnJbwVx+nwyb3/037l400XHoPVwNUrTeriSmZCEPl1HRkwc+qwstO4u9+VxQRdl3PNgqvSpSSjZ2ahtje/Gqu0c8p2UZsTcAm2thqRsNZ7on3PMLLJuGQ8bybp9E3O/R+vef9I52VijUauITUwxSo9NSsG1gMm29zPXaKhSzoOI28bDBa21FpR3s6C8mxM1A3zoPHU+6w+cZHDbRgUcyTQ42RbQZonJuNrbFrCXMXONhsq+nkTct0BATnCwjsg7CXz/5itlpvcAwM7eAbVaTXy8cZ3j4+NwdHIuYK8c639Zxbq1P/HhJ1/gH5D/mO+zp09y43oEY96dXGRlLm36lMSc37X77uyr7Rzz3CG/n8pCi3XdxiRuWp1nW2ZEKLdmjENlaYXKzAx9chLub39CRnj+K76ZkuzknDbT3NdmGntHshPyH9Ls2LU3yQd2krxnKwCZN8JQabW49B1BwqafuXcVFOfer2Fd82miPhtPdlzZWcnuccmTlItWoQZsDRs2jISEBEJDQ2nRogULFy7k119/zfN6mPHjx5OQkGD08g8e8Lh1KFBaup6b0TrDK+x6OrFxGdSpnnvhZm2lpmqQjdF8gkehUucGHKs2RPLau2cY+l7uC+C7HyP4fF7ZmbD8KOIPhuDSsqFRmmurZ4g7GAKAkplJwvEzuLa858JMpcKlRSPiD/5dgiUtRtnZZN28hkXQPcv7qVRYBAWTGX75gbta1qiPSmNGesj+vMe8HppniJKZqyf6+LLxh8HcTENVX08OXbxmSNPrFQ5dDKNmAUt23i9br+fSzdu4Ojz44livKGRkmf5cIXMzDVXLe3H4Qu7vjF6vcPjCNWoGPnqbXb5xC1eH3LH0d4OD8Ft3mDe6D45lYDjWvczNzQmqUJmTIbnzd/R6PSdDjlG5SnCB+/26diVrVy1j0tTPqFCxSoH5tv1vE0EVKhEQ+GhzjkxCdjaZEVexrFQ9N02lQlupOhnXLj5wV6s6DVGZmZF6ZE+BeZT0NPTJSZi5eWJePoi0U0eKquSlJzuLjLArWFatmZumUmFZpSa6q/nfOFRptaDojRP1d9/nXgA7934N6zoNiZo5kayYsrHggniyFCpAmDNnDpGRkYwbN46NGzfi6+tLz5492bJlS6F6FLRaLfb29kavoh5eVJB1f9yiTzcvGtV1IMDXineHBRAbl8m+e55X8NmESnRtmzthb/DLPtSoYouHqwUBvlYMftmHWlXt2LYv5+5TXEIW166nG70AbsVmEHXbtJeg1NhYY1+rCva1cv4YWgeUw75WFSx9cy5UK388hlqLZxjyh32/CusAX6pMH4tN5UD8Xu+N14sdCP1qiSFP6OzF+A7uiU/fbthWCaT6nA8xs7EiYumTuzxuYaXu+ROrp5th+dSzaNy8sevaH5WFlrRjuwGwe/E1bNq9mGc/y3rN0J09jpKaN2BN2b0ZbY0GWD7dHI2LO1aNWmNRpQ5pBx/8XApT0rdFfdbtP8Fvh05xNSqGj9dsIU2XQbeGOX9gJ/y4ka9+22nIP++Pvew/F8r1mHjORUTx/tKNRMYl0qNRLSBnAvPXv+3iZOgNbt5J4Gx4FB+s2MSt+CTa1Cn4As+U9G3VgHV7/+a3Aye4GhnDJys3k6bLpOs/bTBxyQa+Xp872XP+pt3sP3uF67fjOBceyYTF64m8k0D3xrWBnOBg7Pe/cDb8JtMGdUOvV4hJSCYmIZnMrOzSqGKx6Nz9Rf7a8js7/vqT6+FhzJ/zJbr0dFq26QDAV19MY/mS7w351/38EyuXLWLEm+Nwd/ck7k4scXdiSUsz7n1OTU1h/95dtG7XsUTrUxKSdvyOzTOtsK7fDDMPHxx7DkGt1ZJycCcATn1HYN+5V579bBq1JO3kEfT5/K5Z1W6ItkIwGhd3LGvUw3XERNJOHkF3vuCVy0xJwtYN2DVti80zLTD3KofLK6+j0lqStC9nZUDXQW/i2KOvIX/aiSPYNe+AzdNNMHN1xzK4Fo7d+pB28oghcHDuMxTbhs24veALlPQ0NPaOaOwdUZmXzHXUE0utLr1XGVToScparZZevXrRq1cvwsLCWLJkCcOHDycrK4szZ85ga/to3dqlZfXGKCy1at4a4o+ttYbTF5J579OLRs9A8PbQ4mCXO2nZ0d6Md4cH4OxoTkpqNqHhabz36SWOn8r/QWlliUPd6jTatszwPnhmzmpEET+u4+Tg8Wi93LDyzb2rnXbtOke6DCX4i/H4v9GP9OtRnBo60fAMBIDIn//Aws2ZSpNH5Two7cQ5DncaQsatsnEnHEB36hDJtnbYtO6B2s6BrMhw4hd/jvLPxGWNowv3PzBD4+qJRUBl4hbOyO+QZJw9RtL6JVg374Sm8ytk3Y4kYcU3ZIY9+O6dKWlftypxyanM3bSHmKQUKvu4M3f4S4ZJuFFxiajvGZuclJrO1JV/EJOUgr2VJcG+nix96xWCvHKGuWnUakKjY/nt8CniU9JwtLaimp8ni998pcCVjkxNu3rViEtO5bvfdxGTmELlch7MfaOXYenSyDsJRuO5E1PT+WjFJmISU7C3tqRqeS+Wjh1A0D/tcSs+iZ0nc86plz5ZYPRZC956hacr+ZdMxYrZs01bkpgQz8rli4mPu0NAYAUmTf3MMMQo5na00bm2ZfMGsrIy+Xya8bChnr3783KfgYb3e3dtR0Hh2WatSqYiJSjt+AHibe2x79gTjZ0jmTeuETN3mmHopJmTa57fNTN3L7RBVbn97Uf5HlPj4IRDj35o7BzJTowj9fBuEv8sYEKuCUo9spc7tvY4de2Nxt6JjIhQomdPQZ/4T5u5uBr1GMT/vgZFUXDs3geNozP6pERSTxwh/tflhjz2LXLmA3mNm2b0WTGLviJ5v4mv/CSeGCrlX0wmiIiIYPHixSxZsoSMjAzOnz//2AFC615HH7cY/1lvre1T2kUwSU+/06C0i2By7Fvkv865eDDF7D+1UFyRuFo+//XhxYM5fDW6tItgcrLS/rsPFfs3/H/YUNpFyFfs1KKdx1oYLh98//BMJqbQ/SI6nY6VK1fSpk0bKlWqxKlTp/j2228JDw9/4nsPhBBCCCFE2aNSq0rtVRYV6vbW8OHDWbVqFb6+vgwaNIiVK1fi6ur68B2FEEIIIYQQJqFQAcK8efMoX748gYGB7Nq1i127duWbb926sjPZVAghhBBCPNlUqrI5Wbi0FCpA6Nev33/24SVCCCGEEEL8FxT6QWlCCCGEEEI8UcroXIDSUqgAYdCgQQ/No1KpWLhw4WMXSAghhBBCCFF6Ct2D4OfnR506dQr1YDQhhBBCCCGEaShUgDBs2DBWrlxJaGgoAwcO5JVXXsHZ2bm4yiaEEEIIIcRDqcroE41LS6Fac86cOURGRjJu3Dg2btyIr68vPXv2ZMuWLdKjIIQQQgghRBlQ6HBLq9XSq1cvtm7dytmzZ6lWrRrDhw/H39+f5OTk4iijEEIIIYQQBZIHpRWtf9Ufo1arUalUKIpCdnZ2UZVJCCGEEEIIUUoKHSDodDpWrlxJmzZtqFSpEqdOneLbb78lPDwcW1vb4iijEEIIIYQQooQUapLy8OHDWbVqFb6+vgwaNIiVK1fi6upaXGUTQgghhBDi4eRJykWqUAHCvHnzKF++PIGBgezatYtdu3blm2/dunVFUjghhBBCCCFEySpUgNCvXz9UqrI5GUMIIYQQQpimsjpZuLQU+kFpQgghhBBCiLKrUAGCEEIIIYQQTxx5UFqRktYUQgghhBBCGEiAIIQQQgghhDCQIUZCCCGEEMKkySI6RUt6EIQQQgghhBAG0oMghBBCCCFMm0xSLlLSmkIIIYQQQggDCRCEEEIIIYQQBjLESAghhBBCmDR5knLRkh4EIYQQQgghhIH0IAghhBBCCNOmknveRUlaUwghhBBCCGEgPQhCCCGEEMK0yRyEIiU9CEIIIYQQQggDCRCEEEIIIYQQBjLESAghhBBCmDSVTFIuUtKaQgghhBBCCIMnpgchPTmltItgcp5+p0FpF8EkHZl5qLSLYHJaNW9W2kUwSarU5NIugskJCt1S2kUwSaE340q7CCYnLT6ttItgkvxLuwAFkUnKRUp6EIQQQgghhBAGEiAIIYQQQgghDJ6YIUZCCCGEEEI8DpVa7nkXJWlNIYQQQgghhIH0IAghhBBCCNOmkknKRUl6EIQQQgghhBAG0oMghBBCCCFMm8xBKFLSmkIIIYQQQggDCRCEEEIIIYQQBjLESAghhBBCmDaZpFykpAdBCCGEEEIIYSA9CEIIIYQQwqTJg9KKlrSmEEIIIYQQwkACBCGEEEIIIYSBDDESQgghhBCmTSX3vIuStKYQQgghhBDCQHoQhBBCCCGEaVPLMqdFSXoQhBBCCCGEEAYSIAghhBBCCCEMChUg3Lp166F59uzZ89iFEUIIIYQQorBUKnWpvcqiQtWqevXqrF27Nt9taWlpjBo1ilatWhVJwYQQQgghhCiL5syZg7+/P5aWljRo0IDDhw8/MH98fDwjRozAy8sLrVZLpUqV2Lx5c7GVr1ABwrvvvku/fv3o1asXcXFxhvQ9e/ZQo0YN/vzzT3bs2FHkhRRCCCGEEKJAalXpvQpp9erVjBkzhsmTJ3P8+HFq1apFu3btChypk5GRQZs2bbh27Rpr167lwoULLFiwAB8fn3/bagUqVIDw9ttvc/ToUS5fvky1atVYu3Yto0ePpmXLljz33HOcOHGCxo0bF1dZhRBCCCGEMGmzZs3i1VdfZeDAgQQHBzNv3jysra1ZtGhRvvkXLVrEnTt3WL9+PY0bN8bf359mzZpRq1atYitjoZc5DQ4O5uDBg/Tp04eXXnoJa2tr/vrrL5o1a1Yc5RNCCCGEEOLBSnEugE6nQ6fTGaVptVq0Wm2evBkZGRw7dozx48cb0tRqNa1bt+bAgQP5Hv+3336jUaNGjBgxgg0bNuDm5kbv3r1599130Wg0RVuZu2Uq7A6ZmZlMmjSJdevW8dJLL2Fubs60adO4fv16cZRPCCGEEEKIJ9b06dNxcHAwek2fPj3fvDExMWRnZ+Ph4WGU7uHhQVRUVL77XL16lbVr15Kdnc3mzZuZNGkSX3zxBR9//HGR1+WuQgUIISEhPPXUU6xatYotW7bw008/cerUKTQaDdWrV2fhwoXFVU4hhBBCCCGeOOPHjychIcHodW8Pwb+l1+txd3fn+++/p27durz00ktMmDCBefPmFdln3K9QAUKDBg1o1KgRJ0+epEWLFgD4+PiwefNmZs6cyZgxY3juueeKpaBCCCGEEELkS6UqtZdWq8Xe3t7old/wIgBXV1c0Gg3R0dFG6dHR0Xh6eua7j5eXF5UqVTIaTlS1alWioqLIyMgouja8R6EChPXr1/P9999ja2ubZ9uQIUM4efIkmZmZRVY4IYQQQgghygoLCwvq1q3Ltm3bDGl6vZ5t27bRqFGjfPdp3Lgxly9fRq/XG9IuXryIl5cXFhYWxVLOQgUIHTp0eOB2Pz8/Zs2a9a8KJIQQQgghRKGo1aX3KqQxY8awYMECli5dyrlz5xg2bBgpKSkMHDgQgH79+hkNURo2bBh37txh9OjRXLx4kU2bNjFt2jRGjBhRZM13v0KvYpSfpKQkVq5cyQ8//MCxY8fIzs4uisMKIYQQQghRprz00kvcvn2bDz74gKioKGrXrs2ff/5pmLgcHh6O+p7Aw9fXly1btvDWW29Rs2ZNfHx8GD16NO+++26xlfFfBQi7d+9m4cKF/PLLL3h7e9OjRw/mzJlTVGUTQgghhBCizBk5ciQjR47Md9vOnTvzpDVq1IiDBw8Wc6lyFTpAiIqKYsmSJSxcuJDExER69uyJTqdj/fr1BAcHF0cZhRBCCCGEKFgpPgehLCpUgNC5c2d2795Nx44dmT17Nu3bt0ej0RTrMkvFYXAffzq39cTOxoxT5xKZOfcS1yPTCszfrYMX3Tp44+VhCUBoeCpLVoVx8NgdQx5nR3OGDwri6dpOWFtpCL+Ryo9rwtm1P6bY61PcrBq2wrrpc6htHciKiiDpt2VkXb+ab17HV8djEVg1T7rufAgJS3Pnp2jcvLFt3xPzwCqo1Bqybt0gYfk36BNii60eJcn52XoEvj0Yh6eqY+ntztHnhxP927YH79O0PsEz38M2uCLpEZFcnv4d13/81SiP37DeBI4ZjNbTjcST5znz5kckHDlVnFUpcat2H2fp9sPEJKZQyced915oTQ0/r3zzbjh0ig9W/GGUZmGm4cist/PN/9HqLazdd4Kx3VvySot6RV720rJq7wmW7jxKTFIqlbxdea97C2qUz381jA2Hz/DB6q1GaRZmGo7MeMMo7Wr0HWb/vpdjV6+TpdcT5OHCF/074uVkX2z1KGmrdh1j6bZDxCQm55xrL7alhr93vnk3HDzJB8s3GaVZmGk4Mnuc4f13m/bw5/GzRMUlYa7REFzek5Gdm1LT36dY61GSnNp3wbnri5g5OqO7doWohXNIv3yh4Pwdu+PUrjPmru5kJyWQeGAPt1csRLlnQRMzZxfcXxmCzVP1UVtoyYi6SeScmaRfuVgSVSp2rl174NGzF+bOzqRduULEN1+SeuFc/pk1Gjx798WlbQfMXV1Jj4jg5oLvSDxy6PGPKcRjKFSA8McffzBq1CiGDRtGxYoVi6tMxarP87680MmHT2afJzI6nSF9/Jk1tQavDD9CRqaS7z63YzKYtzSU6zfTUKmgQysPpk+oxqA3jxEangrAxDFVsLUx472PTpOQmEmbZu5MHRfMkDHHuXQ1uSSrWKS0NRpg27E3SeuXkBlxBevG7XAcNJbYL8ahpCTlyZ+w/GtUmtzTSmVti/Ooj9GdOmxI0zi74/T6RNKO7CLlr19RdGloPHxQsopnqa7SoLGxJvHkBSKW/EK9tQ8fdmflX46nf5tP+PerCOn3Di4tG1Fj/sekR94mZuteALxe7EDVz8dzesRk4g+fIGBUfxpsWsjOau3JuH3nIZ9gGv48fo6Zv+5g4kttqeHnxYpdRxk2dw0bJg7Bxc4m331sLS3YMHGI4b0KVb75tp24yKlrkbg55F2FzZT9+fcFZv62m4kvtKRGeU9W7PmbYd//yoZ3++NiZ53vPraWFmx4t7/hveq+JouIiWfAt2voXr8aw9o1xNbSgitRsViYFcm0tSfCn8fOMvPXbUx8qT01/L1ZseMIw+asZsMHrz3gXNOy4YPXDO/vP9f83J0Z/2Jbyrk6kp6ZxfLtRxj27Wo2Tn4d5wL+L0yJ3TPNcB8wlKj5X5N26RzOnXpQftJ0rrwxiOzE+Dz57Z9tgfsrQ4icM5O0C2ex8C6H18ixgMKtJfMBUNvY4vfJbFJPnyDi4/fJTkzAwsuH7OS8f19MkVPzlpR7fSThs2eSev4s7j16UmHGLM4O6EVWfHye/N6DXsO5dVvCv5hBekQ49vXqEzhlGhdGvU7a5UuPdcz/DHX+v/3i8RSqP2bv3r0kJSVRt25dGjRowLfffktMjGndIX+xiw8/rglj76FYrlxL4eMvz+PirKVJQ9cC99l3JJaDx+5wPTKNiJtpfL/sGmnp2QRXzr2TVr2KA7/8foNzl5K4GZ3O0jXhJKdkUbmCaV+MWDdpT9qRnaQf20P2rZskrV+CkqHDql6zfPMraSnokxMML4uK1VEyM0i/J0CwafsCGRdOkPLnarIiw8i+c4uMc3/nG3CYqttbdnNx8myiN/z1SPn9XnuZtNDrnBs3g+TzVwmbu4KoX7YQMHqAIU/AmwOJWLiG60vXkXzuCqeGTyY7NR3fAc8XUy1K3rIdR+nxTE26NaxBkJcrE3u2w9LCnPUHC+4lUalUuNrbGl4u9nkv7qLjk/h07V9M69cJc03Z6oZetvs4PRpWp1v9agR5ujDx+VZYmpux/vCZAvdRAa72NobX/RfE3/yxn2er+vNW5yZULeeOr6sjzasHFRhwmKJl2w/T45ladGtUM+dce7k9lhZmrD9wssB9VCoeeK4993Q1GlYJoJyrExW83HinRyuS03VcunmruKtTIlw6P0/8X3+QsGMLGdfDiZr/FXqdDsdW7fLNb1WlGmnnz5C4dweZt6NJOXGMxL07sKpQJfeY3V8iK+Z2To/B5Qtk3ooi5cQxMqMjS6paxcr9hZeJ2byRO1s2kx52jfDZn6PXpePSvlO++Z1btyPqp2UkHj5IRuRNYjauJ+HQATxefPmxjynE4yjUX8qGDRuyYMECIiMjGTp0KKtWrcLb2xu9Xs/WrVtJSnqyL/C8PSxxddZyJCTOkJaSms3Zi4lUr/Jo3eZqNbRq4oalpYYz5xMN6afPJ9CyiTt2tmaoVDl5LCzU/H0qvqirUXI0Gsy8/cm4fM+FhqKQceUs5uUrPNIhrOo1RXfyIGT+0zugUmFRpRZZMVE4DByL64RvcRo+GYvgp4qhAqbDsWFtYrYfMEq7vXUvTg1rA6AyN8fhqWrEbNufm0FRiNm+H8eGdUqwpMUnMyubcxFRNKzsb0hTq1U0rOzHydCbBe6Xqsug/eR5tP3gO0Z/v47LkcY3LfR6hQnLNjGgVX0qeBV8I8AUZWZlc+76LRpW9DWkqdUqGlYqz8mwgi+wUjMyaf/xQtpO/YHRi37jclTu0D69XmHPuVD83Jx4ff46mk+eT5+vVrL91OVirUtJyj3XAgxpOeeaPydDbxS4X6oug/aT5tB24reMnr+Wy5G3H/gZv+wLwc5KSyUf9yItf6kwM8MyqBIpJ4/npikKKSePY1Up//mHaefPYBlUEcsKlQEw9/DE9qn6JB/PvWFkV68RaVcu4vP2JCouWkPA59/h2PrBS6qbCpWZGdaVKpF0/GhuoqKQdPwoNsHV8t1HbWGOkqEzSlMydNhUr/nYx/zPUKlL71UGPVatbGxsGDRoEHv37uXUqVO8/fbbfPrpp7i7u9OlS5eiLmORcXbKeZhEXLzxw9zi4jMM2woS6GfD/9Y8y/Z1TXlneCXe/+QM1yJSDds/mHEWM42KP1Y2Zse6JowdUYn3p53hRmR60VekhKit7VBpNOiTE43S9UkJqO0cHrq/WblAzDx9STuyK/eYNvaotVbYNOtExsWTxC/6DN2ZYzj0GYV5QOUir4Op0Hq4oos2vrDVRcdg7mCH2lKLhasTajMzdLdi78sTi9azbFz0xqWkkq1X8tyldrGzISYpJd99/N2dmdK7A7Nf7c60vh3RKwr9v1xOdFzuzYrFfx1Co1bTu1ndYi1/aYhLScu/zWytH9BmTkx5qQ2zB3ZmWp/2OW32zWqi43Pa7E5yKqm6TBZtP0LjKv7Me607LatXYMzS3zl65Xqx16kkxCUXcK7Z2xCTmP+QUH93Z6b06cjsoS8wrX+XnHb7YhnRcca/j7tOXaLhmJk8/dZnLNtxmHkjX8bJ1vR7XszsHFBpNGTHxxmlZyfEYebolO8+iXt3cHvVUvw//pIqq/+gwtxlpJ45Qey6lYY85h5eOLXrTEbkDcI/Gk/c/zbiMWgEDs3bFGt9SoKZgwMqjRlZccZDQLPi7mDu7JLvPolHDuP+wstofcqBSoVd3Xo4PtvMkP9xjinE4/jXYU/lypX57LPPuH79OitXrnz4DoBOpyMxMdHopc8u+vHnbZq58781zxpeZmaPPz4t/EYqA0cfZejbx1n/x00mvFUZf9/cH/0hfQKwszFj9IQTDHnrOKvXX2fquGAC/fIfy/pfYFWvKVmR4cYTmv8Z7Kw7e5y0fVvIigwnddfvZJwPwapBy1IqqTBVtQJ86Fy/OlXKeVCvYnlmDemGk601P+8PAeBseBQrdh3jo1c6oLp/oP1/VC1/bzrXC6aKjzv1gsoxa0AnnGys+PlAzjAuvZIzF6tFtSD6NnuKKj7uDG71NE2rBvLz/oKH35R1tQLL0blBjdxz7dUeONla8fO+v43yPV3JjzXjB/HjmH40Dg5k7KL1xBYQrJV11tVq4tqjF1ELviF07DCuz/gQ26ca4PpCH0MelUpF+tVL3P5pEbrQK8Rv3Uz8X5txbPvfHC5zfc5X6G5EELx4BXW27MD3jTHEbtkMSv5zJIUoLkU240yj0dCtWze6dev20LzTp09nypQpRmm+FftTvvLAoioOAHsPx3L2Ym43nIV5Tjzk5GhObFxuQOLkaMHlh0wkzspSDL0BF64kU7WiHS928eHzOZfw9rTkhc4+9B1xxDBp+fK1FGpVc6BHR29mzr1UpPUqKfrUJJTsbNS2xsOv1HYO6JMSHryzuQXaWg1J2boun2NmkXXLuBs/6/ZNzP0qFUm5TZEuOgath3FPgNbDlcyEJPTpOjJi4tBnZaF1d7kvjwu6KNOaB1QQJxtrNGoVsUmpRumxSSm4FjBp9H7mGg1VynkQcTvnLufxK9e5k5xC+8m5K61l6xW+WL+DFbuO8seHrxddBUqBk41V/m2WnFq4NvNxJyIm3nBMM7WaQA9no3wBHk6EPGColylxsi3gXEtMwdX+0eaNmWs0VPH1NJxrd1lrLSjv5kx5N6gZ4EPnKfNYv/8Eg9s9U2TlLw1ZSQko2dlo7ust0Dg4kXVfr8Jdbi8PIGH3X8Rvy1lpTBd+DZWlJV6vv0nMLz+BopAVf4eM6+FG++luhGPXsEnxVKQEZSUkoGRnYeZk/F0yc3Im807+K/ZlJcRz9YP3UZlbYOZgT2ZMDN6vDkMXefOxj/mfITeBilShAoRBgwY9NI9KpWLhwoUPzDN+/HjGjBljlNb+5UMF5H58aWnZ3EgzfqpzzB0d9Wo5cTk0546OtZWG4Er2rN9cuD98KhWY/xNwWGo1AOj1xnmy9YppT6rPzibr5jUsgqqRcfafcacqFRZBwaQdePDkW8sa9VFpzEgP2W+8ITubrOuhmLkZL1tp5uqJPv6/++MWfzAEtw5NjdJcWz1D3MEQAJTMTBKOn8G1ZaPc5VJVKlxaNCJs7vISLm3xMDfTUNXXk0MXw2hZM2eVNL1e4dCFMF5u+mhzVLL1ei7dvM2zwYEAdKpfjQaV/YzyDPvuZzo9XY1uDaoXbQVKgbmZhqrl3Dl0KYKWNXLmBen1CocuRfBy41qPdIxsvZ5LkTE8W9XfcMxqvh5cu+/CN+x2fJlZ4tRwrl24RstaOTcm9HqFQxfDeLnpow1FyznXbvFscNAD8+kVhYys7AfmMQlZWaRfuYhNjTokH/7nd12lwqZmHeL+2JDvLiqtFkV/353vu38oVSpQFFLPn8HCu5xRFguvcmTeji7qGpQ4JSuL1IsXsatTl4R9e3ISVSrs6tTl9vp1D943M4PMmBjQaHBs0oz4Xdv/9TGFKIxCBQhLlizBz8+POnXqoPyL7i6tVotWqzVKU2sePAegqPz82w36v1SeiJtpOcucvuJP7B0dew7m3oWd/XFNdh+IYd2mnKBhaL8ADh67Q/TtdKytzGjTzJ06NRwZMzmnSz7seioRN1MZO6IicxZdJSEpk6YNXXm6thPjpp4ukXoVl9Q9f2L/4qtk3QglM+Iq1o3borLQknZsNwB2L76GPjGOlC0/G+1nWa8ZurPHUVLz9syk7N6MQ68RWIZeIPPqWSwq1cSiSh3iF0wvkTqVBI2NNTYVyhveWweUw75WFTLuJJAeEUnlj8dg6ePBiYE5j0kP+34VfsP7UGX6WCKW/IJri4Z4vdiBI12GGo4ROnsxtRbNIP7YaRKOnMR/VH/MbKyIWFp2/ij0bVGPScs3U83Xk+p+XizfeZS0jEy6NagBwIRlm3B3sGV0l5xVtOb9sY+a/t6Ud3MiKS2dJdsOExmXSI9GORP6HG2scLSxMvoMc40aVzsb/D3Kxnjdvk2fYtKq/1HN14Pq5T1Zvvt4TpvVz5k4OuGnLbg72DC647MAzPvfQWr6eVHe1ZGkNB1LdhzNabN7Aqb+Leoybtlm6gb68HQFX/adv8bus1f5YdgLpVLH4tC3ZX0mLfudauU9qe7vzfIdR0jTZdKtYc65M+HHjbg72DG6a3MA5v2x955zTceSvw4SeSeRHs/UBnImMP+wZT/Na1TE1cGW+ORUVu0+zq34JNo8VaWAUpiW2I2/4P3GONKvXCTt0gWcO3VHrbUkfvsWALzeGEfWnRhur1gEQPLRgzh3fh5d6GXSLp3HwtMbt5f7k3z0oCFQuLPxF/ynfYVLj14k7t+FVYXKOLV5jsh5s0urmkXq1tpV+L07gdSL50k9fw6353uitrQidkvOMzX83p1IZsxtbi7MWfbVukowFq6upF65jLmrK179BqFSqYle9dMjH/M/S102JwuXlkIFCMOGDWPlypWEhoYycOBAXnnlFZydnR++4xNkxS8RWFpqGDeyErY2Zpw6m8Dbk08ZPQPBx9MKR3tzw3snB3MmvlUFF2cLUlKyuHIthTGTT3H0n9WQsrMVxn54mtcHBDBjUnWsrDTciEzjk9nnjR6mZop0pw6RbGuHTeseqO0cyIoMJ37x5yj/TFzWOLrkGRupcfXEIqAycQtn5HvMjLPHSFq/BOvmndB0foWs25EkrPiGzLCy8VAcAIe61Wm0bZnhffDM9wGI+HEdJwePR+vlhpVvbi9K2rXrHOkylOAvxuP/Rj/Sr0dxauhEwzMQACJ//gMLN2cqTR6V86C0E+c43GkIGbfKTs9L+6eqEpecxtzNe4lJTKFyOXfmDnvRsJxkVFwi6nu6kZPS0pm6agsxiSnYW1sS7OvB0jf7EFTGVit6kPZ1KhOXksbcLQeISUylso8rc1/tZli6NCo+0agnMylNx9Sf/yImMRV7ay3B5dxZ+sZLBHnmBkytalRg4vOtWLT9CDN+3Ym/uxNf9O/EU4Fl54Ff7esGE5ecytxNe4hJSqGyjztzR/TMPdfu3HeupaYz9ac/iElKwd7KkuDyniwd09dwrmnUakKjY/nt0CniU9JwtLaimp8Xi996hQpebqVSx6KWtH8XtxwccXu5PxpHJ3ShVwj/+H2yE+IBMHd1N/p7ELN2BSgKbr0GYObsSnZiAklHD3L7p0WGPOlXLnL9sw9x6zMY1xdfIfNWFNGLvyNxz/aSrl6xiNu5HTMHR7wGDMHcyZm0K5e5/N7bZMXlXD9YuHuAkjv8QG1hgdegV9F6eaNPSyPh0EHCPv2I7JTkRz6mEEVBpRSyK0Cn07Fu3ToWLVrE/v376dixI4MHD6Zt27b/ahLgs513PTyTMLKu+oOHcon8HZlZ9MPZyrpWv497eCaRV2bZefhfidFaPTyPyCN03orSLoLJSYtPK+0imKSntu19eKZSkP77d6X22ZadhpXaZxeXQvfHaLVaevXqxdatWzl79izVqlVj+PDh+Pv7k5xsuk8MFkIIIYQQJkqlKr1XGfSvBmyp1WpUKhWKopCdXQYmYQkhhBBCCPEfV+gAQafTsXLlStq0aUOlSpU4deoU3377LeHh4djaPtrycEIIIYQQQhQZeZJykSrUJOXhw4ezatUqfH19GTRoECtXrsTV9b8zGVAIIYQQQoiyrlABwrx58yhfvjyBgYHs2rWLXbvyn1i8bl3ZWXZRCCGEEEI84WSZ0yJVqAChX79+/2qlIiGEEEIIIcSTrdAPShNCCCGEEEKUXYUKEAYNGvTQPCqVioULZX1+IYQQQghRQmSES5EqdA+Cn58fderUoZDPVxNCCCGEEEKYgEIFCMOGDWPlypWEhoYycOBAXnnlFZydnYurbEIIIYQQQjxcGV1utLQUqjXnzJlDZGQk48aNY+PGjfj6+tKzZ0+2bNkiPQpCCCGEEEKUAYUOt7RaLb169WLr1q2cPXuWatWqMXz4cPz9/UlOTi6OMgohhBBCCCFKSKGGGN1PrVajUqlQFIXs7OyiKpMQQgghhBCPTiYpF6lC9yDodDpWrlxJmzZtqFSpEqdOneLbb78lPDwcW1vb4iijEEIIIYQQooQUqgdh+PDhrFq1Cl9fXwYNGsTKlStxdXUtrrIJIYQQQgjxcPIk5SJVqABh3rx5lC9fnsDAQHbt2sWuXbvyzbdu3boiKZwQQgghhBCiZBUqQOjXrx8qGeMlhBBCCCGeIIpcnxapQj8oTQghhBBCCFF2yYAtIYQQQgghhMG/WuZUCCGEEEKIUidPUi5S0ppCCCGEEEIIA+lBEEIIIYQQpk16EIqUtKYQQgghhBDCQAIEIYQQQgghhIEMMRJCCCGEECZNnoNQtKQHQQghhBBCCGEgPQhCCCGEEMK0ySTlIiWtKYQQQgghhDCQHgQhhBBCCGHaZA5CkZIeBCGEEEIIIYSBBAhCCCGEEEIIAxliJIQQQgghTJta7nkXJWlNIYQQQgghhMET04Ng4+xQ2kUwOfYtWpR2EUxSq+bNSrsIJmdbp89KuwgmqdXq4aVdBJOjd3Iv7SKYJN829Uq7CCZHbW5e2kUQRUgelFa0pAdBCCGEEEIIYSABghBCCCGEEMLgiRliJIQQQgghxGORJykXKWlNIYQQQgghhIH0IAghhBBCCJOmSA9CkZLWFEIIIYQQQhhID4IQQgghhDBtssxpkZIeBCGEEEIIIYSBBAhCCCGEEEIIAxliJIQQQgghTJpMUi5a0ppCCCGEEEIIA+lBEEIIIYQQpk0mKRcp6UEQQgghhBBCGEiAIIQQQgghhDCQIUZCCCGEEMK0ySTlIiWtKYQQQgghhDCQHgQhhBBCCGHSFJmkXKSkB0EIIYQQQghhIAGCEEIIIYQQwkCGGAkhhBBCCNMmk5SLlLSmEEIIIYQQwqBQPQh16tRB9QiTQI4fP/7YBRJCCCGEEKIwFGSSclEqVIDQrVs3w78VRWH69Om8/vrrODs7F3W5hBBCCCGEEKWgUAHC5MmTjd5/8cUXjB49msDAwCItlBBCCCGEEI9KkTkIRUpaUwghhBBCCGEgAYIQQgghhBDCQJY5FUIIIYQQpk2GGBWpQgUIX3/9tdH7rKwslixZgqurq1H6qFGj/n3JhBBCCCGEECWuUAHCl19+afTe09OTZcuWGaWpVCoJEIQQQgghRIlRHmEZfvHoChUghIaGFlc5hBBCCCGEEE8AGbAlhBBCCCGEMChUgLB9+3aCg4NJTEzMsy0hIYFq1aqxe/fuIiucEEIIIYQQD6Oo1KX2KosKNcRo9uzZvPrqq9jb2+fZ5uDgwNChQ/nyyy9p2rRpkRWwOPTr7kn75i7YWms4eymFr5dGcDM6o8D8nVq60LGlKx6uFgCE3UhnxYYojp5MAsDORkPf7p48Vd0OdxcLEpKy2H8sgaXrIklN05dInYrbqt3HWLrtEDGJKVTycee9F9pQw98737wbDp7kgxWbjdIszDQc+XKs4f13m/fw57FzRMUnYa5RE+zrycjOzahZwDFN0ardx1m6/fA9bdaaGn5e+ebdcOgUH6z4wyjNwkzDkVlv55v/o9VbWLvvBGO7t+SVFvWKvOylwfnZegS+PRiHp6pj6e3O0eeHE/3btgfv07Q+wTPfwza4IukRkVye/h3Xf/zVKI/fsN4EjhmM1tONxJPnOfPmRyQcOVWcVSlxqw6cYunuEGKSU6nk6cJ7XZpQw9cj37wbjp3ng7XbjdIszDQc+Wio4X2qLpPZfx5gx9lQElLT8XG2p9czNejZoHqx1qMkrd52gKV/7iE2IZlKvp6826cz1QN9H7rfn4dOMH7+aprXqcqXb/Q1pMcmJPHV2i0cOH2J5LR0nqrkz7g+nfHzcH3A0UzPmpAr/HjsErEp6VR0c2Bci1pU93QuMH9SegZz9p9l+6UbJOoy8bKz5u3mNXk2wNOQ51ZyGl/vOc3+a9GkZ2ZRztGWD9vWJdjTqSSqVOxWH7/E0sPniU1Jp5K7I++2forqXi4F5k9Kz+DbPafYfvE6CekZeNlb807LOjQJyvn7OG/vaebvP2O0j7+zHb8Oea5Y6yGK1pw5c/j888+JioqiVq1afPPNN9SvX/+h+61atYpevXrRtWtX1q9fX2zlK1SAcOLECWbMmFHg9rZt2zJz5sx/Xaji1PM5d7q2cWPmgjCiYjLo38OLae8E8er758nMVPLd5/adTBatucmNaB0qVLR51okPRwcw4oOLhN1Ix9nRHBdHcxasukn4zXTcXSwYNaAcLk7mfPzttZKtYDH489g5Zv66nYkvtaOGnzcrdh5h2NzVbJj0Gi52NvnuY2upZcOkVw3vVRhPHvJzd2b8i20p5+pIemYmy3ccYdic1Wz8YCjOdtbFWp+S8Ofxc8z8dQcTX2pLDT8vVuw6yrC5a9gwccgD2syCDROHGN7f32Z3bTtxkVPXInFzsC2WspcWjY01iScvELHkF+qtnfPQ/Fb+5Xj6t/mEf7+KkH7v4NKyETXmf0x65G1itu4FwOvFDlT9fDynR0wm/vAJAkb1p8Gmheys1p6M23eKu0ol4s+Tl5i5aR8TuzWjhq8HK/adZNii39nwdi9cbPP/LtlqLdjwdm/D+/vPtJmb9nH4ynWmvdQabyc7DlyKYNqG3bjb2dA8OKAYa1Mythw+yRerNzOhbzeqB5bjp637GT5rMeunjcHZvuDv1c2YOL5c8wd1KvkbpSuKwlvfLsdMo2H2qL7YWGpZ/r+9vD5zEes+fhMrrUUx16hk/O/CdWbtPsX7rWpT3dOZn45fZuS6fawb0AZna8s8+TOz9Qxftxcnay2fdWqIu60lkUmp2GnNDXkS0zMYtHoX9cq58nX3Z3Cy0hIen4ydpXme45miLefC+WJHCBPa1qW6lws/Hb3I8DW7WD/kOZxt8muzbF5fsxNna0s+7/oM7nbW3ExIydMeQa72zOvZ3PBeoy6bd7ELxYQmKa9evZoxY8Ywb948GjRowOzZs2nXrh0XLlzA3d29wP2uXbvGO++8Q5MmTYq9jIU6o6KjozE3L/hLa2Zmxu3bt/91oYpTt3ZurNwYxYG/EwmNSOez78NwcTTnmaccCtznUEgiR04mcTM6gxvROpb8EkV6up4qQTl/fMNupPPRt9c4FJJI5K0MTpxLZsnaSBrUtqcsfGeX7ThMj0a16NawJkFerkx8qT2WFuasP3CywH1UKnC1tzW8XOyNL4qfq1eNhlX8KefqSAUvN97p3orkdB2Xbt4q7uqUiGU7jtLjmZp0a1gjp816tstps4MF37lWqVQPbDOA6PgkPl37F9P6dcJcUwZOrnvc3rKbi5NnE73hr0fK7/fay6SFXufcuBkkn79K2NwVRP2yhYDRAwx5At4cSMTCNVxfuo7kc1c4NXwy2anp+A54vphqUfKW7TlBj6eD6VavKkEezkzs1gxLCzPWHz1f4D4qFbjaWRteLvcF5SHhUXR+qgpPB/rg42TPC/WrUcnTldPXy8b3c/mWvfRo+jRdm9QlyMeDCf26Ymlhwfo9xwrcJ1uv5/3vV/N619aUczO+Yx4eHcupKxFM6NuVagHl8Pdy4/2+XdFlZPLHoRPFXZ0Ss/z4JbpX96dLNX8CXex5v3UdLM00bDgdlm/+DaevkZCeyRedG1HbxwVvBxvqlnOjkpujIc+SIxfxsLXiw3b1qO7pjI+DDY38PPB1LBs3QJYfvUCPmoF0rRFIkKsDE9rVw9LcjPWn8l/0Zf3JUBLTM5jV/Vlql3PD28GGeuXdqexu3JuiUatxtbUyvJystSVRHVFEZs2axauvvsrAgQMJDg5m3rx5WFtbs2jRogL3yc7Opk+fPkyZMoXAwMBiL2OhrjB8fHw4ffp0gdtPnjyJl1f+QyieBJ5uFrg4mnP8TLIhLTVNz/mrqVStkP9d3fupVdCsgSNarZpzl1MKzGdjrSE1TY/exEcYZWZlcy4iioaV/Q1parWKhpX9OXntRoH7peoyaP/BXNpOmsPo79dyObLgwDEzK5tf9odgZ6Wlkk/BkbOpKLjN/DgZerPA/VJ1GbSfPI+2H3zH6O/XcTkyxmi7Xq8wYdkmBrSqTwWvsjVs4XE4NqxNzPYDRmm3t+7FqWFtAFTm5jg8VY2YbftzMygKMdv349iwTgmWtPhkZmVz7uZtGlYoZ0hTq1U0DCrHyfCoAvdLzcik/YwfafvpUkb/uJnL0ca9KbXLe7LrXCjRCckoisLhKzcIi4mnUcWHD8F50mVmZXEu7CYNgisY0tRqNQ2Cgzh5JbzA/b7/bTvOdrZ0b5p3SF9GVhYAFua5nfJqtRoLMzNCLuV/8WxqMrP1nI+Op3753N9otUpF/fLunIrMvzdu99VIano5M2N7CG3mb6Lnj3+x6PB5svWKUZ5gDyfG/X6I1vM20Xv5NtYVcPFsajKzszkXFUcD/9zhfmqVigZ+Hpy8GZPvPruu3KSmtyufbj1Gq2/X88KiP1h44CzZ911MhMcl0WbOBjrN/533Nx4gMrHg65H/itKcg6DT6UhMTDR66XS6fMuZkZHBsWPHaN26tSFNrVbTunVrDhw4kO8+AFOnTsXd3Z3BgwcXedvlp1ABwnPPPcekSZNIT0/Psy0tLY3JkyfTqVOnIitcUXN2yPnxjk/INEqPT8w0bCuIfzlL1s+vwe8LazGqvy9Tvw4l/Gb+//n2thp6d/Hkj535/wCYkriUVLL1Sp672S52NsQU8IPk7+HClN7PMfu155nWrzN6RaH/rOVExxlPbt91+jIN3/6Cp8d8zrIdR5g34mWcChgSYUoMbXbfXVkXOxtikgpoM3dnpvTuwOxXuzOtb8ecNvtyOdFxSYY8i/86hEatpnezusVaflOh9XBFF238HdNFx2DuYIfaUouFqxNqMzN0t2LvyxOL1rNsBFhxqek555rt/eeaFTFJqfnu4+/qyJTnWzC773NM69k651z7bh3RCbk3Tt7r0oRAd2fafvoj9SbOZ/jijbzftQl1A0x/jlBcUirZen2eoUQu9rbEJiTlu8/fF6+xfs9RJg3onu92f083PF0c+WbtFhJT0sjMymLx5l1ExyUQE5//MU1NfJqObEXB5b471S7WWmJS814TAFxPSGHbpRvoFYWvuz3DkAZVWH7sMgsP5fZu3UhIYe3Jq5R3tOHbHo15oWYgM3ecYOMZ0w+s4lIzyFaUPMOvXGwsiU3Jv81uxCfz14UIshWFb15oyquNqrHsyAV+OHDWkKe6twtTOzRgzovNeL9tXW4kpDDop+2k6DLzPaYoftOnT8fBwcHoNX369HzzxsTEkJ2djYeH8TwxDw8PoqLyv7Gzd+9eFi5cyIIFC4q87AUp1ByEiRMnsm7dOipVqsTIkSOpXLkyAOfPn2fOnDlkZ2czYcKEhx5Hp9Pliaz02RmoNUU7TrNFIydGD8i9szZp1tXHPtb1SB3DJ13A2lpDk6cdeedVP8ZOv5QnSLC2VPPRmEDCb6azbH3Bd/DKsloBPtQK8Ml9H+hD948X8PO+EEZ2yp3A/nTF8qx5bxDxyan8sv8EYxetZ/k7/Qoco1+W5dtmnyzk5/0hjOzYhLPhUazYdYxV4/qhMqFxluLJU8vPk1p+nkbvu89ayc+HzjCybQMAVu4/ycmIaL7q9xzejrYcC41k2oY9uNnb0LCC6fciFEZKmo6JP/zMpP7dcSrgt8ncTMMXI/owZfE6mr3xEZp/eiQa16iEkv/Utv8ERQEnay0TWj+FRq2iqocTt5LT+PHoRV5rVBUAvaIQ7OHEyGdzJsBXcXfkcmwiv5wKpXM1v9IsfqnQ/xNQTGpXD41aTbCnc06bHT7P0MY5bfRsYO5IjUo4UsPLhefm/c7/LkTQvWbxDz0ReY0fP54xY8YYpWm1RTPsKykpib59+7JgwQJcXUvu5lahAgQPDw/279/PsGHDGD9+PMo/v3wqlYp27doxZ86cPBFRfqZPn86UKVOM0gJrDqVC7dcLU5yHOvh3Aheu5N6xNTfP6TBxdDDnTkKWId3R3pwr4WkPPFZWtsLNWzkrHV2+lkblAGu6tXXj6yXXDXmsLNV88k4Qael6pnwdSnZ2UdamdDjZWKNRq4i9r7cgNikF13zGyOfHXKOhSjkPIm7HGaVbay0o72ZBeTcnagb40HnqfNYfOMngto2KrPylwdBm993BjU1KwfURg5/72+z4levcSU6h/eR5hjzZeoUv1u9gxa6j/PFh0X53TIEuOgbtfSvEaD1cyUxIQp+uIyMmDn1WFlp3l/vyuKCLMv3ePQAna8uccy35/nMtDddHnOxvrtFQxduNiNgEANIzs/j6f4f48pX2NK3iD0AlL1cuRMawdHeIyQcITnbWaNRq7iQmG6XHJibj4mCXJ//127HcjInjza+XGdL0//ztqzdkIr9OewtfdxeC/X1YPeUNklLTyczKwtnelr4fzSXY3yfPMU2Ro5UWjUpFbKrxTbHYVB2u+UxQBnC1scRMrUKjzr2pEeBsR2yqjsxsPeYaNa42lgS4GLd7gLMd2y8VPITVVDhZW6BRqbhzXw9LbEo6LvlMUAZwtbHCTKM2mnQc4GJPTEo6mdnZmGs0efaxs7SgvLMtEXHJebb9lygFLOxRErRa7SMHBK6urmg0GqKjo43So6Oj8fT0zJP/ypUrXLt2jc6dOxvS9P8MOTMzM+PChQsEBQX9i9Lnr9CzHP38/Ni8eTMxMTEcOnSIgwcPEhMTw+bNmwkICCD7Ea6Kx48fT0JCgtErsMagx6rAg6Sl67l5K8PwCruRTmx8JnWCc7uWrS3VVAm0fuB8gvyoVGBultt81pZqpo0NIjNLYfLsqwWuiGRqzM00VPX15NDFa4Y0vV7h0MUwaj7iH75svZ5LN2/j+pBVd/SKYhjLa8py2yy3i1yvVzh0IYyajzhEw9Bm/wyD6FS/Gj+/O5DV4wYYXm4OtvRvVZ/vhr1YLPV40sUfDMGlZUOjNNdWzxB3MAQAJTOThONncG15T8CpUuHSohHxB/8uwZIWH3MzDVW93Th0JfdiSq9XOHTlOjXL5/1Dk59svZ5L0bGG4DUrW09Wth71fT1VarXKcGFsyszNzKjq582hc5cNaXq9nsPnrlAzqHye/P5ebvw8dRSrPhxpeDWrXYWnqwSw6sOReDobL3BhZ22Js70tYdExnL12g+Z1gou9TiXBXKOmiocjRyJyJ6rrFYUjEbeo4ZX/Mqe1vJ2JSEgxOm/C4pJxtbE0LLJQy9uFsDvGF7bhccl42Zv+cFNzjYaqnk4cCsu9ENQrCofDoqnpnf+d4NrlXImISzJqs/A7Sf+0Wd7gAHLmFF2PT8HVNv+gQzxZLCwsqFu3Ltu25S7jrdfr2bZtG40a5b1BWqVKFU6dOkVISIjh1aVLF1q0aEFISAi+vsVz06ZQPQj3cnJy4umnnza8v3jxIj/88APLli0jMjLygfvmF2kV9fCigqzfcpteXTy4Ea0j6nbOMqex8ZnsP55gyPPpuCD2H0/gt79y7jIOfNGLIycTuR2biZWlmhaNnKhZxZYJM68AucGBVqvms/mhWFtpsLbKOVZCYhZ6E/+b2rdFfSYt/51q5b2o7ufF8p1HSdNl0K1hTQAm/LgRd0c7RndpDsC8P/ZS09+H8m5OJKWls+SvQ0TGJdKjUS0gZzLuD1sO0LxGBVwdbIlPTmPVnmPcik+iTZ0qpVXNItW3RT0mLd9MNV/P3DbLyKRbgxoATFi2CXcHW0Z3aQbAvD/2UdPfO7fNth3+p81y2tjRxgpHGyujzzDXqHG1s8Hfo+D1tE2JxsYamwq5F2jWAeWwr1WFjDsJpEdEUvnjMVj6eHBi4LsAhH2/Cr/hfagyfSwRS37BtUVDvF7swJEuuev5h85eTK1FM4g/dpqEIyfxH9UfMxsrIpauK/H6FZe+TWox6eftVPNxo7qvO8v3nSQtI4tudXO+SxPW/IW7vQ2j2+f84Zm37Qg1fT0o7+pAUloGS3b/TWRcEj2ezhnyYWtpQb0Ab2b9cQCtuRlejnYcC73J78cv8E7HxqVWz6L0Srtn+eCHtQT7l6N6QDl+2rqPNF0GXZ99CoCJC37G3cmeUS+0Q2tuToVyxsGW3T8/8Pembz1yCic7GzydHbl0I4rPf/qd5k8F06h6xZKrWDF75amKTN5ylKruTlT3dOKnvy+TlplNl3+GAn3w51HcbC1545/hQi/UCmTNiavM3HmCl2oHER6XzOIjF3i5du7dzj5PVWDg6l0sOnyeNpXKcToqjnWnQpnQumwsJPBKvcp8sPkQwZ7O/yxzeoG0zCy61shZLnjipoO421ozqlnOb/2LtSuw+vglPtt2nF5PVSI8LomFB8/Sq24lwzFn7QihaZA33g423EpOY97e06hVKtpXzRvg/peY0gPLxowZQ//+/alXrx7169dn9uzZpKSkMHDgQAD69euHj48P06dPx9LSkurVjZ9B4+joCJAnvSg9doAAkJqayurVq1m0aBEHDhygXr16ecZgPWnWbL6FpVbN6AG+2FprOHMphQkzje/4e7lrsbfNbRpHOzPGvuqHs6MZqWnZhEakM2HmFcNqSBX8rQ2rIC353PhuUb+3zxIdU/BD2ExB+7pViUtOZe6mPcQkpVDZx525w18yTFyOiks0utuYlJrO1JV/EJOUgr2VJcG+nix96xWC/ll5R6NWExody2+HTxGfkoajtRXV/DxZ/OYrVPByK5U6FrX2T1UlLjmNuZv3EpOYQuVy7swd9mLBbZaWztRVW4hJTMHe2pJgXw+WvtnH0Gb/BQ51q9NoW+4wjuCZ7wMQ8eM6Tg4ej9bLDSvf3LG3adeuc6TLUIK/GI//G/1Ivx7FqaETDc9AAIj8+Q8s3JypNHlUzoPSTpzjcKchZNw3cdmUta9ZkbjkdOb+dZiYpFQqe7kyd2AnwyT5qPjk+841HVN/3UlMUir2VlqCfdxYOqwHQR65d4Fn9GrLV1sOMn71XySmpuPlZMfItg14sUG1Eq9fcWhXvyZxSSl8t/4vYhOSqOzrxZy3BhqGGEX9v737DmvqeuMA/k0ChL33EEQFRHDvvarWVUfVuq1oHVVrba1aV+3QWrXVto62bq21bjus/qx77wGKewAywx4hJCS/P7DBSFCDCTH4/TxPnqc5OffynlMi971n3LQMCIW6TVdIycjGos17kJqVA1dHO3RtUgfvdW9jiPCNpkOwL9KlMqw4dR2peTIEuTngh57N1NNlErPzNLai97Szxo89m2HRkat4Z8MBuNlaoX+dKhhaP1hdp4anMxZ2a4wfj1/DL6dvwNvBBh+1ronOFeRit2P1SkiXyrD8eBRSc/MR7O6IpX1aFfdZVp7G99PT3hpL+7TCooOX0HfNXrjbWWFAvSAMa1R88ywpOw/T/jyFzPwCOFmJUdvXFesHtdf6LAp6NfXr1w8pKSmYNWsWEhMTUbt2bezdu1c9TT8mJgZCI++TL1CpdB8zPn36NFauXImtW7eiUqVKiI6OxqFDh17qwQ0dh14u87Gvq90DK8Y0iXKnMvG9Z43gQNdvjB2CSWr3+1hjh2BylB6+z69EJSgjzxs7BJMjfMZznah01hGfGzsErVKunTHaz3ar0choP9tQdEpPFi1ahBo1auDtt9+Gk5MTjh49isjISAgEAri4VIxpDkRERERkYgQC470qIJ2mGE2ZMgVTpkzB559/DlEpi2WIiIiIiMh06TSC8MUXX2Dr1q2oXLkypkyZ8synKhMRERERlQcVhEZ7VUQ6tWratGm4desWNmzYgMTERDRq1Ai1atWCSqVCenr6809ARERERESvtDKlPa1atcK6deuQmJiIsWPHol69emjVqhWaNm2Kb7/9Vt8xEhERERGVSiUQGO1VEb3UuIidnR1GjRqFM2fO4PLly2jUqBG+/vprfcVGRERERETlTKcE4eDBgwgNDUVWVlaJz/z8/LBv3z5s2rRJb8EREREREVH50ilBWLx4MUaOHAl7e/sSnzk4OGD06NFYunSp3oIjIiIiInoelUBotFdFpFOrrly5gk6dOpX6eYcOHXDhwoWXDoqIiIiIiIxDp+cgJCUlwfwZTx40MzNDSkrKSwdFRERERPSiVKiYi4WNRacRBB8fn2c+++Dq1avw8vJ66aCIiIiIiMg4dEoQOnfujJkzZyI/P7/EZ1KpFLNnz0bXrl31FhwREREREZUvnaYYzZgxAzt27EBQUBDGjRuH4OBgAMCNGzewdOlSFBYWYvr06QYJlIiIiIhIm4q6WNhYdEoQPDw8cPLkSYwZMwbTpk2DSqUCAAgEAnTs2BFLly6Fh4eHQQIlIiIiIiLD0ylBAAB/f3/s2bMH6enpuHPnDlQqFapVqwYnJydDxEdERERE9EwV9YnGxqJzgvAfJycnNGjQQJ+xEBERERGRkZU5QSAiIiIiehVwm1P94ooOIiIiIiJSY4JARERERERqnGJERERERCaN25zqF3uTiIiIiIjUOIJARERERCaNi5T1iyMIRERERESkxgSBiIiIiIjUOMWIiIiIiEwaFynrF3uTiIiIiIjUOIJARERERCaNi5T1iyMIRERERESkxhEEIiIiIjJpXIOgX+xNIiIiIiJSY4JARERERERqnGJERERERCaNi5T1iyMIRERERESk9sqMIJhbWBg7BJOjMntl/veZFEFejrFDMDntfh9r7BBM0oF+y4wdgslpt/sjY4dgkoQOjsYOgcioVAKOIOgTRxCIiIiIiEiNCQIREREREalxjgoRERERmTSVilOM9IkjCEREREREpMYRBCIiIiIyaSre89Yr9iYREREREalxBIGIiIiITBoflKZfHEEgIiIiIiI1JghERERERKTGKUZEREREZNI4xUi/OIJARERERERqHEEgIiIiIpPGEQT94ggCERERERGpMUEgIiIiIiI1TjEiIiIiIpPGKUb6xREEIiIiIiJS4wgCEREREZk0lYojCPqkU4IQExPzQvUqVapUpmCIiIiIiMi4dEoQAgICIBCUzNBUKpW6XCAQQKFQ6Cc6IiIiIiIqVzolCJcuXdJarlKpsHnzZnz//fewtbXVS2BERERERC+Ci5T1S6cEoVatWiXK/v33X0ydOhW3bt3CJ598go8++khvwRERERERUfkq8yLlixcvYsqUKTh27BhGjBiBPXv2wN3dXZ+xERERERE9F0cQ9EvnbU7v3r2Lfv36oWHDhnBzc8P169fx448/MjkgIiIiIqoAdEoQxo4di9DQUGRmZuL8+fPYtGkTAgMDDRUbEREREdFzqSAw2qsi0mmK0YoVK2BpaYnk5GQMHz681HoXL1586cCIiIiIiKj86ZQgzJ4921BxEBERERHRK4AJAhERERGZND5JWb90XqRMREREREQVl04jCHXq1NH6JOWncQ0CEREREZUXZQVdLGwsOiUIPXr0MFAYRERERET0KuAaBCIiIiIiUivzk5SJiIiIiF4FFfV5BMaiU4LQtm3bF6p38ODBMgVTXgZ2d0PHFo6wsRYh+k4elv2aiPjkglLrv9nKCZ1bO8HDxRwAEBMvw29/SXAhKkddx9xMgIi+HmjZwB7mZkJcvJaD5b8mICO70ODtKQ+bD5/Huv2nkJqVgyBfD0zp1xHhAT5a6+4+dQWz1/+pUWZhJsLZH6YBAOSFhVj6x2Ecj7qDOEkG7KzEaBRSGRN6tIW7o53B21JeNh+/gnWHz0OSnYcgb1dM7dkG4ZU8tdbdffYaZv2+X6PMwkyEc/PHa5TdS0rD4r+O48K9OCiUSlTxcMGioV3g5WRvsHaUp82nIrHu6GVIcvIQ5OmCqd1bINzPQ2vd3RduYNY2zX9rLMxEOPfFKPX7PJkci/eewqHr95GZlw8fZ3v0bxqOvo3CDNqO8uTcvD4CP4qAQ90wWHq743zvsUj648Czj2nZEKELp8I2tBryYxNwZ95yxK3fqVHHf8wABE6KgNjTDVlXb+DaxC+QeS7SkE0pd5uPX8K6g+chyc5FkLcbpvZqi3B/L611d5+Nwqzf9mmUWZiJcG7BRPX7mZv24o9z1zTqNA0JwPJRvfUeu7FsPnsd605EQZIjRZCnE6a+2QThvm6l1s+SyvDjwQs4EP0QmVIZvBxs8UmnRmgR5AcAuPAgEWtPRiI6XoKUHCm+69cObav7l1dzygX7jEyRTgnC4cOH4e/vjy5dusDc3NxQMRlU704u6NbOGd+tfoQkiRyDerjj84mVMGbWXcgVKq3HpKbLsW57clESIQDaNXHAjPf98MEX9xATLwMAjOzngfrhdvj6pzjk5ikxZoAnPh3rh0/mPyjH1hnGvvPXsGj7fkzv/ybCK/vg14NnMfb737D7szFwtrfReoytpRi7Phujfv/k2vb8AjmiYxIxsnMLBPt4ICtPim+2/g8Tl2/BpmkRhm5Oudh76SYW/nEUM95ui/BKnvj12CWM+Xkndk8ZChc7a63H2FpaYPeUoer3T+8HECvJwLAft6BnwxoY07ExbC0tcDcxFRZmFWMgcO/V21j49wnM6NEK4X4e+PXEVYxZ/Rd2f9QfLral9JnYArs/GqB+//T9o4V/n8DZu3GY2689vJ3scOp2LObuPgp3Oxu0Dq1swNaUH5GNNbKu3kTs2u2ov23pc+tbBfiiwR8/Iebnzbg85GO4tG2C8J++RH5CCiT7jwMAvPq8ieoLpiHq/dnIOHsFlScMRaO/V+FwjU4oSEkzdJPKxd5LN7Bw1xHM6NMe4f5e+PXIBYz5aTt2Txv+7O/otOKHhGrbs6NZSAA+799J/d7CTKT32I1lb9Q9LNx3FjO6NkW4jxt+PX0NYzbuw+5xveFia1WivlxRiNEb9sHZxhIL+7aFu501EjJzYGdpoa4jlcsR7OGMHnWqYdLvr/bNxbJgn5UfbnOqXzpdWcyfPx9r1qzB1q1bMXDgQAwfPhxhYaZ1J+6tds74/W8Jzlwpuvv/7epH2LgoCE3q2OHouSytx5y9mqPxfsOuFHRu7YzgQCvExMtgbSXEG82dsPCXOFy9kQcAWLw2Hiu+qIrgQCvcvCc1bKMMbMOBM+jVrA56NK0NAJjRvzOORd7BrlOXMbxjM+0HCQBXB1utH9lZWeKnDwZqlE3t1wmD5q9GQlomvJwd9Bm+UWw4ehG9GoehR8MaAIAZvdvh6PX72HX2GiLaNdB6jACAaykJFwD88M9JNK8egA+7tVCX+bk66jNso9pw7Ap6NQhFj/rVAQAzerTC0ZsPsev8DUS0rqv1GIEAcC3lYg4ALsckolvdEDQILBrterthDWw7cx1RcckVJkFI2XcUKfuOvnB9//fegfR+HKI/mQ8AyLlxD85N66HyB8PUCULlie8idtUWxK3bAQCIHDsb7m+2ht+w3ri74Bf9N8IINhy+gF5NwtHj8WjSjD5v4Gj0few6E4mI9o20HiOA4JnfUaAoIXheHVO14VQUetUNRo86QQCAGV2b4ejtOOy6dAsRLWqVqL/z0m1kSmVYF9EV5qKiXdV9nDRHiZtX80Pzan6GD95I2GdkqnR6DsLkyZNx/fp17Nq1C9nZ2WjWrBkaNmyIFStWICtL+8X1q8TD1RzOjua4HF18wZ8nVeLmPSlCAktm8toIBUDLBvawtBDgxt2iZKCqvyXMzQS4HJ2rrheXWIDk1IIXPu+rSq4oRHRMAhqFFF9MCYUCNAoJwNV7j0o9TiorwJvTv0fHT5dg4vItuBOf8syfkyPNh0BQlDyYOrmiENFxyWj8xD/gQqEAjYMq4erDhFKPyyuQo9OXq9Dh85X4YPUfuJOYqv5MqVThWPR9+Ls5YfRPO9B69k8YuOQ3HIy8Y9C2lBe5ohDR8SloXNVXXSYUCtC4ii+uxiSWelxegRyd5q9Hh6/X4YP1e3AnSfPudu1KnjgSfR9JmTlQqVQ4e/cRHkoy0OQ1/uPq2Lg2JAdPaZSl7D8Op8a1AQACc3M41K0ByYGTxRVUKkgOnoRj4zrlGKnhFH1Hk9A4qJK6TCgUoHG1531HC9Dp85/RYc5P+GDVLtxJkJSoc/5OHFrPXIbuc1fjy63/IiPXtG8Q/afoO5qKxoHe6jKhUIDGgd64Gqf93/cjN2NQ09cd8/4+iTYLNqHX0h1YefQKCpXK8grbqNhn5UsFgdFeFVGZHpTWpEkT/PLLL0hISMD777+P1atXw9vb+5VPEpwcigZMMrI01wVkZCvg6PDswRR/HzG2/hCCncurY+wgL3y1LA6xCUXrFpzszSCXK5Er1fwCZ2QVqn+mqUrPyUOhUgWXp+6IudjbQpKVo/WYAA8XfDa4G74b3RdfDesBpUqFYQvWIild+++HTK7Akp0H0al+DdhaifXehvKWnist6rOn7my72FpDkp2r9ZgAdyfM6fcGFr/bDXMHdoJSpcLQH35HUkY2ACAtJw95MjlWHzyHZiEBWPFeT7QNq4pJ6/7C+btxBm+ToaXn5Rf12VNTiVzsrCDJztN6TICrI+b0boPFgztjbt/2RX22fAeSMot/L6d2b4FAd2d0+Ho96s/4CWPX/IlP32qBepW9tZ7zdSD2cIUsSfPCVpYkgbmDHYSWYli4OkFoZgZZcupTdVIh9nQtz1ANpvg7+tS/a3bWkGSV9h11xpx3OmJxRA/MHdgZSqUKQ7//Tf0dBYrWG3w5sBN+GdMHE7u1wIW7sRj7844KcXGXnidDoUpVYlqMi40VJDnav6Nx6dn49/oDFKpUWDqwA95rVRvrT0Xhl6NXyiNko2OfkSl7qavXixcv4siRI4iOjkZYWNgLr0uQyWSQyWQaZYWFBRCJLEo5omxaN7LH+4OKLwTm/BBT5nM9SpRhwud3YW0lQvN69vhwuDemLnigThKoWK1AX9QKLL4TXKuKL3rNWYFtxy7i/e6tNerKCwvxyS/boQIwvX/n8g30FVIrwBu1AryfeO+FnvPXY+upSIx7symUqqL1MW1qVMHgVkXTbUJ83HHlQQK2nryK+lV8tZ63Iqvl74la/p4a73t++xu2nrmGcR2Kpoj8dvIqrsYmYcmQzvB2tMWF+wmYu/sY3Oxt0Ljq6zuKQLor8R2t7I2eX6/F1pNXMa5z0VTLN+uGqD+v5u2GIC83dPlqFc7fiUWjoNdvEalSpYKzjSVmdWsGkVCIUG9XJGflYd3JSIxuXTFGo/SNfUavCp0ThPj4eKxduxZr165FVlYWBg0ahDNnziA0NPSFzzFv3jzMmTNHo6xanbEIqve+ruE805nLObh57676vbl50YCJo70I6ZkKdbmjnRnux+Y/81yKQiAhRQ5Ajrsx+agWYInu7VywdGMC0rMUMDcXwsZKqDGK8PTPMUVOttYQCQVIfequWmpWDlztta8xeJq5SIRgP0/EPrW4sSg52IGEtEz8PHFQhRg9AAAnG6uiPnvqzndqTh5c7V5sbrK5SIQQH3fESjLU5zQTChHo4axRr7KHEy7fj9dL3MbkZG1Z1GdP3VVLzZY+c43Bk8xFIoR4uyE2NRMAkC9X4Pv/ncF3gzqhZUgAACDIyxU3EyRYd/Tya5sgyJIkEHtojgSIPVwhz8yGMl+GAkk6lAoFxO4uT9VxgSyx5JQaU1T8HX3q37XsvBdeP1D8HU0vtY6vqyOcbKwQI8kw+QTByVoMkUCA1BzNKVOpuVK4lrKJgJudNcyEAoiExZMVAt0cIMmRQq4ohHkFWsCtDfusfHGRsn7pNMWoc+fOqFKlCs6cOYMFCxYgLi4OCxcu1Ck5AIBp06YhMzNT41Wl9kidzvEipDIlElLk6ldMvAxpGXLUDin+A2BlKURwoBVu6LiQWCAUwNy86JfxzsN8yBUq1KpefF4fDwu4u1jofN5XjbmZCNUreeHszfvqMqVShbM3H6BmoPZtTp9WqFTizqNkuDoUL7T6LzmISU7Dig8GwrGUfyxNkbmZCNV93XHmdqy6TKlU4cztWNQsZQvFpxUqlbidIFFfrJibiVDDzwMPUjQvRh6mZFSILU7NzUSo7u2GM3eL17UolSqcuRuHmqVsDfu0QqUSt5NS1UmYolAJRaESwqe2mhEKBeoRmddRxunLcGnbWKPMtV1TpJ++DABQyeXIvHgNrm2bFFcQCODSpgkyTl8qx0gNp+g76oEzt4pHlYu+ozE6fkdTnnmjJCkjGxl5UrhVgEXLRd9RF5x54oaEUqnCmXvxqFnKlp21/dwRm5YNpbL4+/YwNQtutlavxYUu+4xMmU4jCHv37oWXlxdiYmIwZ86cEqMA/7l48eIzzyMWiyEWa94t1vf0otLsPpCGfl3c8Ci5oGib07fckJahwKlLxfNIv5rkj1OXsvDXoaKLsaE93XE+KgcpaXJYWQrRuqEDwoOsMWtx0R+XPKkS+4+nY0RfD2TnFiJPqsTo/p6IvpNn8jsYAcDgdo0wc90fCK3khbAAH/x68AykMjnealK0A8OMtbvh7miHCT2KnpPx099HEV7ZB5XcnJEtzce6/aeQkJaJns1qAyhKDib/vB3RsQn4fuw7UCpVkDyeN+5gUzH+ERzcsi5mbv4favh5IKySJzYevQhpgRw9GhYl09M37YO7gw0+6NIcALDif6dR098LlVwdkS2VYe2h80hIz0KvJ/brH9qmHj7ZsAf1An3QoKofTtx4gKPX72HlmLeN0kZ9G9yiFmZuPYgaPm4I83PHxhNXIS1QoEe9omkb07f8C3d7G3zQqejCdcWBc6jp54FKrg7IlhZg7dFLSEjPRq8GRbsg2VpaoH5lb3z7zymIzc3g5WiHC/fj8dfFm/i4Sym7b5kgkY01bKoWL7a1ruwL+1ohKEjLRH5sAoK/nARLHw9ceXcKAODhz5vhP3YgQuZNRuza7XBt0xhefd7Eue7Fz4+4v3gNaq2ej4wLUcg8dxUBE4bCzMYKsY93NaoIBreuh5mb9qKGnyfC/D2x8cjj7+jj79z0X/+Bu4MtPuhatGvYin2nnvqOniv6fWscDgDIkxVgxb5TaF+zGlzsbRAnycB3fx6Fn6sTmj4ewTJ1g5uEYebOY6jh7YowHzdsPH0NUrlCvUPP9B1Hir6j7esDAPo2CMHms9GYv/c0+jcMRUxaFlYeu4IBjYpvKubJ5IhJK16f9igjGzcSUuFgJYaX44uNUr/K2Gflp6IuFjYWnRKE2bNnGyqOcrN9byosLYQYP9gbNtZCXL+dh1lLYjSegeDpZg572+KucbAXYdJwbzg7mCFXqsSDuHzMWhyjsWvRL78nQakCPh3jB3MzAS5ey8GyX0vfDcOUdKxfA+k5eVj+1xFIsnIR7OuBZeP7w+XxnbOEtEwInrhLm5WXjy9+/RuSrFzYW1uieiUvrJs8DFW8iu6YJGdk4/DVWwCAfl9pbpn4y4eD0CAooHwaZkCd6gQjPVeKZftOQZKVh2AfVywb2UO9KDIxIwvCJ/4ty5bK8PnWfyHJyoO9tRihvu5YN74fqngWT/NoF14VM3q3w+qD5zB/52EEuDth0dCuqPuCIzmvuk41qyE9Jx/L/j0LSXYegr1csezdrurF3okZORqjAdlSGT7feRiS7DzYW4kR6uOGdWN6ocoT07Dm9++AJftOY9rv/yIrLx9eTnYY16ER+jSqUe7tMxSHemFocmCD+n3owk8BALHrd+BqxDSIvdxg5Vd8V1z6IA7nuo9C6KJpCBg/BPlxiYgcNUO9xSkAJGz9BxZuzgiaPaHoQWlXonG26wgUPLVw2ZR1qhOC9Bwplu098fg76oZlo3oXf0fTszR/3/Ly8fmW/z3xHfXAugnvqL+jQoEAt+JT8Me5a8iWyuBub4smwf54v3OzCvOskk5hgUjPzceyQxchyZEi2NMZywZ1UC/CTczM1egzTwdbLB/cEQv2nkGf5bvgbm+NgY1q4N3m4eo61+IlGLHuH/X7hfvOAgC616qKL3q2LKeWGQ77jEyVQKV6Ncbau468buwQTM7W/heMHYJJEuRp332JnqGAi/HL4kC/ZcYOweS02/2RsUMwTZmlr4Ug0ifL/lOMHYJW525mGO1nNwh2NNrPNpSKcVuDiIiIiF5bXKSsXzolCHXq1NGYSlKa561BICIiIiJ6XS1duhQLFixAYmIiatWqhR9++AENGzbUWveXX37B+vXrERUVBQCoV68e5s6dW2p9fdApQejRo4f6v1UqFebNm4fRo0fD2dm59IOIiIiIiAzIlB5H+Pvvv2PSpElYsWIFGjVqhMWLF6Njx464efMm3N3dS9Q/fPgw+vfvj6ZNm8LS0hLz589Hhw4dcO3aNfj4GGYd4kutQbCzs8OVK1cQGBj40oFwDYLuuAahbLgGoQy4BqFMuAZBd1yDUEZcg0Dl5FVdg3D6RqbRfnbjEAed6jdq1AgNGjTAjz/+CABQKpXw8/PD+PHjMXXq1OceX1hYCCcnJ/z4448YMmRImWJ+Hp2eg0BERERE9KpRqQRGe+mioKAAFy5cQPv27dVlQqEQ7du3x6lTp17oHHl5eZDL5QadwcNFykREREREZSSTySCTyTTKtD3zCwAkEgkKCwvh4eGhUe7h4YEbN2680M+bMmUKvL29NZIMfeMIAhERERFRGc2bNw8ODg4ar3nz5hnkZ3399dfYvHkzdu7cCUtLS4P8DEDHEYTvv/9e471CocDatWvh6uqqUT5hwoSXj4yIiIiI6AUY80nK06ZNw6RJkzTKtI0eAICrqytEIhGSkpI0ypOSkuDp6fnMn7Nw4UJ8/fXX+Pfff1GzZs2XC/o5dEoQvvvuO433np6e2LBhg0aZQCBggkBEREREr4XSphNpY2FhgXr16uHAgQPq3UGVSiUOHDiAcePGlXrcN998g6+++gr79u1D/fr19RH2M+mUINy/f99QcRARERERlYkpPSht0qRJGDp0KOrXr4+GDRti8eLFyM3NxbvvvgsAGDJkCHx8fNTTlObPn49Zs2Zh06ZNCAgIQGJiIgDA1tYWtra2BolRpzUIBw8eRGhoKLKyskp8lpmZiRo1auDYsWN6C46IiIiIqCLp168fFi5ciFmzZqF27dq4fPky9u7dq164HBMTg4SEBHX95cuXo6CgAG+//Ta8vLzUr4ULFxosRp1GEBYvXoyRI0fC3t6+xGcODg4YNWoUvv32W7Ro0UJvARIRERERVSTjxo0rdUrR4cOHNd4/ePDA8AE9RacRhCtXrqBTp06lft6hQwdcuMCHdxERERFR+VFBYLRXRaRTgpCUlARzc/NSPzczM0NKSspLB0VERERERMahU4Lg4+ODqKioUj+/evUqvLy8XjooIiIiIqIXpVQZ71UR6ZQgdO7cGTNnzkR+fn6Jz6RSKWbPno2uXbvqLTgiIiIiIipfOi1SnjFjBnbs2IGgoCCMGzcOwcHBAIAbN25g6dKlKCwsxPTp0w0SKBERERGRNhV1LYCx6JQgeHh44OTJkxgzZgymTZsGlapoXEUgEKBjx45YunSpeosmIiIiIiIyPTolCADg7++PPXv2ID09HXfu3IFKpUK1atXg5ORkiPiIiIiIiKgc6Zwg/MfJyQkNGjTQZyxERERERDozpScpmwKdFikTEREREVHFVuYRBCIiIiKiV4Gqgm43aiwcQSAiIiIiIjUmCEREREREpMYpRkRERERk0pR8DoJecQSBiIiIiIjUOIJARERERCaN25zqF0cQiIiIiIhIjSMIRERERGTSuM2pfnEEgYiIiIiI1JggEBERERGRGqcYEREREZFJU3GbU73iCAIREREREam9MiMINg42xg7B5Nyr1N7YIZikKvf3GTsEk6N0cjd2CCap3e6PjB2CyTnw1iJjh2CS2m2MMHYIpkcoMnYEpEdKLlLWK44gEBERERGRGhMEIiIiIiJSe2WmGBERERERlQWfpKxfHEEgIiIiIiI1jiAQERERkUnjk5T1iyMIRERERESkxhEEIiIiIjJpSj4oTa84gkBERERERGpMEIiIiIiISI1TjIiIiIjIpHGRsn5xBIGIiIiIiNQ4gkBEREREJo0PStMvjiAQEREREZEaEwQiIiIiIlLTKUG4fv36c+ts3LixzMEQEREREelKqTLeqyLSKUGoV68eFi5cCJWWpeJJSUno3r07xowZo7fgiIiIiIiofOmUIGzcuBHffPMNWrZsibt372qUh4aGIiMjA5cuXdJ7kEREREREpVGpjPeqiHRKEHr37o2oqCi4urqiVq1aWLhwId566y289957mD59Oo4cOYKqVasaKlYiIiIiIjIwnbc5dXd3x86dOzFw4EB88sknsLGxwZkzZxAeHm6I+IiIiIiIqBzpvItReno6BgwYgF27dmHq1Klwd3dH//79cfHiRUPER0RERET0TCoIjPaqiHRKEP766y+Ehobi7t27uHDhAubOnYurV6+iRYsWaNKkCWbOnAmFQmGoWImIiIiIyMB0XoMwfvx4nDp1CiEhIQAAGxsbLF++HH/99RfWr1+P+vXrGyRQIiIiIiJtuM2pfum0BuHcuXOoWbOm1s/eeOMNREZG4sMPP9RLYEREREREVP50ShBKSw7+Y2tri549e75UQEREREREuqio240ai867GGlz584drF69GmvXrkVKSgrkcrk+TktEREREROVM512M/iOVSrF+/Xq0bNkSwcHBOHnyJGbNmoW4uDh9xkdEREREROVI5xGEc+fOYeXKldi8eTOqVKmCgQMH4uTJk1i2bBlCQ0MNESMRERERUak4xUi/dF6DkJWVhQEDBuDkyZOoUaMGAGDq1KkGCY6IiIiIiMqXTgnCzZs30a9fP7Rp04ajBURERET0SlCqKuYDy4xFpzUI9+7dQ3BwMMaMGQNfX198/PHHuHTpEgQC/k8hIiIiIqoIdEoQfHx8MH36dNy5cwcbNmxAYmIimjVrBoVCgbVr1+LWrVuGipOIiIiIiMpBmXcxatu2LTZu3IiEhAT8+OOPOHjwIEJCQp77rAQiIiIiIn1SqYz3qohe+jkIDg4OGDt2LMaOHYvLly9j9erV+ojLoPp0dEC7RrawsRLi5n0ZVu5IQ6JEUWr9Hm3t0TDcGt5u5ihQqHDrgQy//p2OhJTiY2aN8UCNKpYax+0/lY2V29MM1o7y9M9fO7Fr+2ZkpKchoHJVjBg9AdWCq2utu3/vXzh8cB9iHtwHAFSpGoSBQ0dq1O/VpbXWY4cMH40evd/Re/zGsPnIBaw7cAaSrBwE+bhjap8OCA/w1lp39+mrmLXxb40yCzMRzi3+RP1++d/HsPfidSSmZ8NcJEJoJU+M69YSNQN8DNqO8vT7gVNYt/cYUjNzEOTniSkDuyEs0O+5x+09cwXTfvodretUx3fjB6vLUzOzsWTbPpyKuo0caT7qBgXgk4Hd4O/hashmlLvNxy9h3cHzkGTnIsjbDVN7tUW4v5fWurvPRmHWb/s0yizMRDi3YKL6/cxNe/HHuWsadZqGBGD5qN56j90YnJvXR+BHEXCoGwZLb3ec7z0WSX8cePYxLRsidOFU2IZWQ35sAu7MW4649Ts16viPGYDASREQe7oh6+oNXJv4BTLPRRqyKeVu85nrWHciEpIcKYI8nDG1SxOE+7qVWj9LKsOPBy7gwPUHyJTK4OVoi0/ebIwWQUXf6wsPErD2eCSiE1KRkp2H7/q3Q9vqAeXUmvKx+fQ1rDt+pajPPJ0xtWszhPu6a627++JNzNpxRKPMwkyEc59FqN+n5uRh8b6zOHUnDtn5MtQN8MLULs3g7+pg0HbQ60UvD0r7T+3atfH999/r85R6172NPd5sbo9lmyVITlOgb0dHfDrSHR8tiIe8lByheqAl9p3Ixt3YAoiEwDudHTH9PQ98tCAesoLi1PHf09nYsi9D/b6goGKklcePHsSaX5Zh1LhJCAqujr92bcPnMyfjh583wNHRqUT9qMjLaN6yHUJG1YC5hQV2bvsNc2Z+jCXL1sLFtegPyaoN2zWOuXjhLJYt+QaNm7YslzYZ2t4L17Fw5wHM6NcJ4QHe+PXQOYxZ+jt2z3oPLnY2Wo+xtRRj96z31O8F0Fzb4+/ujGl9OsDX1RH5cgU2HjyHMT/+jj9nj4aznbVB21Me9p29ikW/78H0wT0QFuiLTftPYuy3a7Br7iQ429uWely8JB3fbfkHdYICNMpVKhU+/HEjzEQiLJ4wGDaWYmz833GMXrgaO76cCCuxhYFbVD72XrqBhbuOYEaf9gj398KvRy5gzE/bsXvacLiU8ntha2mB3dOGq99rW0bWLCQAn/fvpH5vYSbSe+zGIrKxRtbVm4hdux31ty19bn2rAF80+OMnxPy8GZeHfAyXtk0Q/tOXyE9IgWT/cQCAV583UX3BNES9PxsZZ6+g8oShaPT3Khyu0QkFKRXjRtHeyHtYuPcMZnRrhnBfN/x66hrGrN+L3RPehoutVYn6ckUhRq/bC2cbSyzs1w7u9tZIyMiBnVXxd09aoECwpzN61A3CpM3PTtJM0d7Iu1j4zynM6N4C4X7u+PVkJMas3YPdE/tp7TMAsBWbY/fEfur3T34/VSoVJv76P5gJhVg8sANsxRZYf+IqRq35Gzs+6ANrC3NDN+mVVVHv5BuLTglC27Ztn1tHIBDgwIFX90veuYUddvybifPXpACApZsl+Hm2HxqEWePk5Tytx8xbmazxftnmVKyc44dAXwtE35OpywsKVMjMVhoueCP5c+dWvNGpC9q98SYAYNS4Sbhw/jQO/m8PevUdWKL+h5NnaLwfO2EyTp84iqtXLqJNu44AACdnF406504fR1jNOvD00n6H3dRsOHgWvZrWQo8mRVPuZrzTCUev3cGuU1cR0aGJ1mMEAsD1GRfCnRvU0Hj/ca922HnqCm7HJ6NRcIDeYjeWjfuOo1fLBnirRT0AwPQhb+HY1ZvYdewChndppfWYQqUSn/78O0a/1R6Xbj9Adp5U/VlMUioi78Zi2xcfoIqPBwDg08Fvof2H8/DPmSvo1bKB4RtVDjYcvoBeTcLRo1EYAGBGnzdwNPo+dp2JRET7RlqPEUAAV3vtiep/LMxEz61jqlL2HUXKvqMvXN//vXcgvR+H6E/mAwBybtyDc9N6qPzBMHWCUHniu4hdtQVx63YAACLHzob7m63hN6w37i74Rf+NMIINJ6PQq14wetQNAgDM6NYMR2/FYtfFW4hoWatE/Z2XbiFTKsO6kd1gLiqa0ezjZKdRp3mQH5oHPX+U0FRtOHEVveqHoEe9YADAjO4tcPRmDHZduImIVrW1HiMQCOBaSnL/MDUTV2OTsX3826jq4aw+Z9v5G7D36l30qh9ikHbQ60enBOHw4cPw9/dHly5dYG5uelmqu7MZnOzNEHm7+CJCmq/CnRgZqvmLS00QnmZtWfQPXU6eZjLQvK4NmtezQWZ2IS5ck2L7v5kokJt2SiuXy3H3zk306jtAXSYUClGzdj3cvHH9hc5RIJOhsFABOzs7rZ9npKfhwrnTGD9pml5iNja5ohDRsYmI6NBUXSYUCtA4OABX7z8q9bg8WQE6zVwKpUqF6n6eGN+9Fap6aR+6lysKsf3EZdhZiRHko32o2pTIFQpEP4zH8CemngmFQjQKrYKrd2NKPe7nPw7C2c4WPVvWx6XbDzQ+K1AUDQlamBf/MycUCmFhZobLtx9WiARBrihEdFwSIto3VJcJhQI0rlYJVx8mlHpcXkEBOn3+c9Hvmq8HxndujqpemtOuzt+JQ+uZy2BvZYmG1SphXOdmcLTRfsezonNsXBuSg6c0ylL2H0fook8BAAJzczjUrYG7838qrqBSQXLwJBwb1ynPUA1GrihEdIIEES2L1xkKhQI0ruKNq3HJWo85ciMGNf3cMe+vkzh04yGcbCzRObwK3m1REyJhmZdAmgy5ohDR8RJEtCz+HSjqMx9cjU0q9bi8Ajk6LdhU9P30dsX4NxqokwG5oui6Q2z25L9rAliIRLj0MPG1ThCUpn259crRKUGYP38+1qxZg61bt2LgwIEYPnw4wsLCDBWb3jnaFQ2RP32XPzOnUP3Z8wgEwNC3nHDjfj5iE+Xq8hMXcyFJVyAtqxD+XhYY0MUR3u7mWLQuRX8NMILsrEwolUo4OjprlDs6OuFRbOkXbk9av+YnODm7ombtelo/P3RgH6ysrNG4aYuXjvdVkJ6Th0KlqsT0Dhd7G9xPStV6TIC7M+YM7IJqPu7Ikcqw7sAZDF20ATumj4CHk7263pHI25iyZjfy5XK42ttixbh34GRr+tOL0rPzUKhUlphK5GJviwcJ2r9Dl249wK5j57H5s/FaPw/wdIOniyN+2LYPM4b2hJXYHBv/dwJJ6ZmQZGTrvQ3GkJ4rffy7pnmn38XOGveTtU9rCXB3xpx3OqKat1vR79qh8xj6/W/YMWUYPByLkvimIQFoV7MqfJwdEJuagR/+Po6xP+/Ahg/6vxYXdk8Te7hCliTRKJMlSWDuYAehpRjmTg4QmplBlpz6VJ1U2AQHlmeoBpOel1/0u/ZUkuhiY4X7KZlaj4lLz0b8/QR0rlkFSwd3RExqFub+dRIKpRKj29Qtj7CNSt1nT00lcrG1wn1JhtZjAlwdMadnK1TzdEZOfgHWHb+KoT/vxo4JfeDhYIsAN0d4Odji+/1nMfOtFrAyN8OGk5FIyspFSvaL3eQkehE6JQiTJ0/G5MmTcerUKaxevRrNmjVDcHAwhg8fjgEDBsDe3v75JwEgk8kgk8k0ygoVMojMxLqE81zN69hg5NvFF7Zfr9J+l0MXw3s6w8/TArOXJmqUHziTo/7v2EQ50rMLMWu0BzxczJCUWvoC6Ipux5ZfceLoQXz+9WJYWGj//3tw/x60aN2+1M9fB7UCfVEr0PeJ9z7o+cXP2HriEsZ1LZ5e0yDIH1umDUdGjhTbT17G5NW7sPHjoaWua6iocqUyzFi5FTOH9oRTKW03NxNh0fsDMWfNDrQa/wVEj0ckmoUHvdZzVWsFeKPWE4vla1X2Rs+v12LryasY17kZAODNusV3Iat5uyHIyw1dvlqF83di0SjIv9xjJtOkVKngbGOJWd2bQSQUItTbFclZuVh3IvK1SBDKolYlD9Sq5PHEe0/0XLIFW89FY1z7BjAXCfHtgDfw2c6jaPHVOoiEAjSq4oPmQX6v9b9rpH9lWqTcpEkTNGnSBEuWLMHWrVuxdOlSfPzxx4iPj3+hJGHevHmYM2eORllokw8Q1vTDsoRTqvPX83D72+JExNysaKWPg50QGdmF6nIHWxEexBc893zv9nRC3VArfLYsCWmZhc+seyem6Od6mniCYGfvAKFQiIwMzbuRGRnpcHRyLuWoIru2b8aObZvw2VeLEFC5itY616Ou4lFcLCZNma23mI3NydYaIqEAqU/dzUnNyn3mGoMnmYtECPHzRGxKuka5tdgCldycUckNqFnZB93mrMCuk1cQ0bFpKWcyDU521hAJhUjLytEoT83KgYtDyalpcSmpiJekY+L3G9Rlysd/HeuPmIGdcz+En7sLQgN88Puc8cjOy4dcoYCzvS0Gf7EMoRVk5ycnG6vHv2u5GuWp2XkvvH7AXCRCiI87YiXppdbxdXWEk40VYiQZr2WCIEuSQPzUzldiD1fIM7OhzJehQJIOpUIBsbvLU3VcIEvUHHkwVU7WlkW/a7lSjfLUXClc7bRPPXOztYaZSKgx6hTo5ghJjhRyRSHMK9DCd23UfZbzVJ/lSOH6giO/5iIhQrxcEJuapS4L9XHDlnG9kZ1fAHlhIZxtrDBwxU7U8Cl9N6nXgYpPUtarlxorvnjxIo4cOYLo6GiEhYW98LqEadOmITMzU+NVveHYlwlFq3yZCkmpCvUrLkmO9CwFwqsVb0dqJRagaiUxbj+UPeNMRclBwzBrfLEiCSlpz7/gD/Au2qUhPfvZicSrztzcHFWqBuPq5YvqMqVSiauXLyA4JLTU43Zu+w3bNm/AzM+/QdVqpc+JPPC/v1GlahAqB1bVa9zGZG4mQnU/T5y5+UBdplSqcObWQ9Ss/GIXpoVKJW7HJz83oVCqVChQmPbvGACYm5mhur83zkTfUZcplUqcjb6LmlUqlagf4OWGrZ9PwObPxqlfrWqHoEFIZWz+bBw8nTW3+7OztoSzvS0eJklw/cEjtK5T+u+uKTE3E6G6rwfO3Cqe7qdUqnDmdgxqlrLN6dMKlUrcTkh55u9aUkY2MvKkcKugi5afJ+P0Zbi0baxR5tquKdJPXwYAqORyZF68Bte2T2xAIBDApU0TZJy+VI6RGo65mQjVvVxx5l7x2halUoUz9+JRs5QtO2tX8kBsWhaUT0wOf5iaCTc76wqfHACP+8zbFWfuFa89U/eZn8czjixWqFTidlKa1kXLdpYWcLaxwkNJJq4/kqB1SIC+QifSfQQhPj4ea9euxdq1a5GVlYVBgwbhzJkzCA198T+4YrEYYrHmdBKRWel3r/Rpz7Fs9GzngIQUBZLTFOjXyRHpWQqciyq+2ztjlDvORUmx70TRPOWIXs5oVscGC9YkQypTwsGuKK/Kk6ogV6jg4WKGZnVscClaipy8QlTyssCQ7k64fjcfMQlyrXGYkm49++CHb+eharVgVAuqjj93b4MsPx9tH+9qtGTRXLi4uGLQsKItOnds3YTNG9fgw09mwN3dE+lpRfNyLa2sYGVV/I9cXl4uTh4/gmEjxpR/owxscNuGmLnhL9So5ImwAG9sPHQOUpkcPRoXLfCbvv5PuDvY4YO3WgMAVvxzHDUDvFHJzQnZUhnW/nsaCWlZ6NW0NoCiBcwr951E6/BqcHWwRUZOHjYfvYjkjGy8UbdiLEob1LE5Zq3chtAAX4RV9sWm/ScglRXgreZFUxFm/LIV7k72mPB2R4jNzVHV11PjeDvroruYT5bvPxcJJzsbeDo74vajRCzY9Bda1w1Fk7Bq5dcwAxvcuh5mbtqLGn6eCPP3xMYjFyEtkKt3NZr+6z9wd7DFB12L1vis2HcKNf29UMnVseh37dA5JKRno1fjcABFv2sr9p1C+5rV4GJvgzhJBr778yj8XJ3QtIJcgIhsrGFTtTjxtK7sC/taIShIy0R+bAKCv5wESx8PXHl3CgDg4c+b4T92IELmTUbs2u1wbdMYXn3exLnuo9TnuL94DWqtno+MC1HIPHcVAROGwszGCrGPdzWqCAY3DcPMnUdRw9sVYb5u2HgqCtIChXpXo+nbj8Dd3hofvFG0AUDfhiHYfPY65v9zGv0bhSImNRMrj17BgMbFO7LlyeSISSu+O/4oPQc3ElLhYCWGl+OLjbi+ygY3q4mZ2w+jhrdbUZ+djCz6ftZ73GfbDsHd3gYfdCjaaGDFwQuo6eeBSi72yM4vwNpjV5CQkaOx+Ph/UffgZG0JL0db3E5Kwzd/n0Sb6v5oWs1XawyvC06x0i+dEoTOnTvj0KFD6NChAxYsWIAuXbrAzEyvj1IwuD8OZUFsIcB7b7vA2kqIm/fzMe+XZI1nIHi4mMPOpnhEoUPToikOn43VvCBZtlmCI+dzoVCoEF7NEp1b2EFsIURqhgJnI/Ow41/tC7dMTfOWbZGVmYHfNq5BRnoaKgdWxczPv1FPMZKkJEH4xEbN+/bshkIhx4K5mtOG+g4YincGvqt+f/zIQaigQvNW7cqnIeWoU71QpOfkYdnfxyDJzkWwjzuWvd8XLo/vwCamZWn0WXZePj7f9A8k2bmwt7JEaCVPrJs0GFUe7ywjEgpxPykVf5yJREauFI7WVqjh74U1Hw4qdacjU9OxYU2kZ+di+a5/kZqZjWA/Lyz98F31FKPEtAwIhboNIadkZGPR5j1IzcqBq6Mdujapg/e6tzFE+EbTqU4I0nOkWLb3BCRZeQj2ccOyUb3V61IS07X8rm35HyRZebC3FiPU1wPrJryDKp5F02OEAgFuxafgj3PXkC2Vwd3eFk2C/fF+52awMLF/70vjUC8MTQ4UT08LXVi0G1Hs+h24GjENYi83WPkVj8BIH8ThXPdRCF00DQHjhyA/LhGRo2aotzgFgISt/8DCzRlBsycUPSjtSjTOdh2BgmTtGxOYok7hgUjPy8eygxcgyZEi2NMFywZ3VC/CTczM0fhd83SwxfLBHbFg7xn0WbYT7nbWGNi4Bt5tUbwT0rV4CUas2aN+v3DvGQBA99rV8EUv038uTqfwKkjPlWLZgfOQ5OQh2MsFy4Z2hsvjKUaJGZp9lp0vw+e7jkKSkwd7KzFCvV2x7r23UMW9+JlDKdl5WLjnFFJzpXCztUbXOtUwqjXXdJB+CVSqF8+5hEIhvLy84O7uDoG2J+s8dvHixVI/K02/jx/qfMzrbtboivGgp/JW5f6+51ciDUpr7VvU0rMJMyvOxWF5OfDWImOHYJLabYx4fiXSJKz405wMwbLPR8YOQav1R55fx1CGaH9Uj0nT6XbQ7NkVZyEpEREREVUMfA6CfjFBICIiIiIitYoxoZSIiIiIXltcpKxfOiUIderUeebag/+UZQ0CEREREREZn04JQo8ePdT/rVKpMG/ePIwePRrOzs9+YBYRERERkaFwBEG/XmoNwqJFi/DBBx8gMDBQr0EREREREZFxvNSTlImIiIiIqGLhImUiIiIiMmnc5lS/OIJARERERERqOo0gfP/99xrvFQoF1q5dC1dXV43yCRMmvHxkREREREQvgIuU9UunBOG7777TeO/p6YkNGzZolAkEAiYIREREREQmSqcE4f79+4aKg4iIiIiIXgE6rUE4ePAgQkNDkZWVVeKzzMxM1KhRA8eOHdNbcEREREREz6NUGu9VEemUICxevBgjR46Evb19ic8cHBwwatQofPvtt3oLjoiIiIiIypdOCcKVK1fQqVOnUj/v0KEDLly48NJBERERERG9KJXKeK+yWLp0KQICAmBpaYlGjRrh7Nmzz6y/detWhISEwNLSEuHh4dizZ0/ZfvAL0ilBSEpKgrm5eamfm5mZISUl5aWDIiIiIiKqiH7//XdMmjQJs2fPxsWLF1GrVi107NgRycnJWuufPHkS/fv3R0REBC5duoQePXqgR48eiIqKMliMOiUIPj4+zwzm6tWr8PLyeumgiIiIiIhelCmNIHz77bcYOXIk3n33XYSGhmLFihWwtrbG6tWrtdZfsmQJOnXqhMmTJ6N69er44osvULduXfz4448v2Wul0ylB6Ny5M2bOnIn8/PwSn0mlUsyePRtdu3bVW3BERERERK8ymUyGrKwsjZdMJtNat6CgABcuXED79u3VZUKhEO3bt8epU6e0HnPq1CmN+gDQsWPHUuvrg04JwowZM5CWloagoCB888032L17N3bv3o358+cjODgYaWlpmD59uqFiJSIiIiJ6pcybNw8ODg4ar3nz5mmtK5FIUFhYCA8PD41yDw8PJCYmaj0mMTFRp/r6oNNzEDw8PHDy5EmMGTMG06ZNg+rxuIpAIEDHjh2xdOnSEg0gIiIiIjIkpRGfpDxt2jRMmjRJo0wsFhspGv3QKUEAAH9/f+zZswfp6em4c+cOVCoVqlWrBicnJ0PER0RERET0yhKLxS+cELi6ukIkEiEpKUmjPCkpCZ6enlqP8fT01Km+Pug0xehJTk5OaNCgARo2bMjkgIiIiIiMRqVSGe2lCwsLC9SrVw8HDhxQlymVShw4cABNmjTRekyTJk006gPA/v37S62vDzqPIBARERERUdlMmjQJQ4cORf369dGwYUMsXrwYubm5ePfddwEAQ4YMgY+Pj3odwwcffIBWrVph0aJF6NKlCzZv3ozz58/j559/NliMTBCIiIiIiMpJv379kJKSglmzZiExMRG1a9fG3r171et4Y2JiIBQWT/Jp2rQpNm3ahBkzZuDTTz9FtWrVsGvXLoSFhRksRiYIRERERGTSyvpEY2MZN24cxo0bp/Wzw4cPlyjr06cP+vTpY+CoipV5DQIREREREVU8HEEgIiIiIpOmVBo7goqFIwhERERERKTGEQQiIiIiMmmmtgbhVccRBCIiIiIiUmOCQEREREREapxiREREREQmTckpRnrFEQQiIiIiIlJ7ZUYQcjKyjR2CyXFY8rmxQzBJ9+PTjR2CyfF7o76xQzBJQgdHY4dgctptjDB2CCbpwKBVxg7B5Fj5iI0dgklq2+cjY4egFRcp6xdHEIiIiIiISI0JAhERERERqb0yU4yIiIiIiMpCZdRVygIj/mzD4AgCERERERGpcQSBiIiIiEwatznVL44gEBERERGRGkcQiIiIiMikcZtT/eIIAhERERERqTFBICIiIiIiNU4xIiIiIiKTpuQqZb3iCAIREREREalxBIGIiIiITBoXKesXRxCIiIiIiEiNCQIREREREalxihERERERmTROMdIvjiAQEREREZEaRxCIiIiIyKQpOYSgVxxBICIiIiIiNSYIRERERESkxilGRERERGTSVEpjR1CxcASBiIiIiIjUOIJARERERCZNxUXKesURBCIiIiIiUuMIAhERERGZNCXXIOgVRxCIiIiIiEiNCQIREREREalxihERERERmTQuUtYvjiAQEREREZGaTgmCUqnE/Pnz0axZMzRo0ABTp06FVCo1VGxERERERM+lVBnvVRHplCB89dVX+PTTT2FrawsfHx8sWbIE77//vqFiIyIiIiKicqZTgrB+/XosW7YM+/btw65du/Dnn3/i119/hZJ7SxERERERVQg6LVKOiYlB586d1e/bt28PgUCA+Ph4+Pr66j04IiIiIqLnUVXUuT5GolOCoFAoYGlpqVFmbm4OuVyu16AMbdBb7ujYwgk21iJE38nD0o3xiE8uKLV+59bO6NzaGR4u5gCAh/Ey/PZnMi5E5ajrdGrphFaNHFG1kiWsrUToO/46cqUVY2TFpkVH2LXrBpG9I+SPHiJ922rIH97VWtdtwmyIq9UoUS69dhGpK74GAAjtHODw1kBYhtSEwMoGBXeikbFtNRQpiQZtR3lz6tQdzm/1gZmjM2QP7iJx1VLk37lZev0uPeHUsRvMXd1RmJ2JrFPHkPLrKqie+H6ZObvAfdAI2NRtCKGFGAWJ8UhYuhD5d2+VR5MMbsvlu1h/4TZSc/NRzc0Bn7SphTBP51LrZ+cXYOnJ6zh4+xGyZHJ42Vnjo9Y10byyp7pOco4U3x+LwskHSciXK+DraIvPOtRDqKdTeTSpXGw+ex3rTkRBkiNFkKcTpr7ZBOG+bqXWz5LK8OPBCzgQ/RCZUhm8HGzxSadGaBHkBwC48CARa09GIjpegpQcKb7r1w5tq/uXV3PKxeYz17HuRGRRn3k4Y2qXF+izAxdw4PqDoj5ztMUnbzZ+os8SsPZ4JKITUpGSnYfv+rdD2+oB5dSa8uHcvD4CP4qAQ90wWHq743zvsUj648Czj2nZEKELp8I2tBryYxNwZ95yxK3fqVHHf8wABE6KgNjTDVlXb+DaxC+QeS7SkE0pVz6D+6HSqGGwcHNFTvQt3Jo9D9lXorTWFZiZwX9sBLx6d4eFpzvy7j3A3a8XI+3ICXWdJsf/gZWvT4lj49Zvxq1Zcw3WDnq96JQgqFQqDBs2DGKxWF2Wn5+P0aNHw8bGRl22Y8cO/UWoZ293ckW3di74bnUcEiUFGPyWB774MACjZ96GXKE9+5Sky7F2eyLikwoAAdC+qSNmjquECZ/fRUy8DAAgthDiYlQ2LkZlY1hvT63nMUVWdZvAsecQpP/+Cwoe3oZt6y5wGzsdiV9MhDInq0R9ycqFEIiKf62ENnbwmLoA0kun1GUuIycDhQpIfl4AVX4ebNt0heu4mUj6ahJUBbJyaZeh2TVtBfdho5D40/eQ3o6Gc9deqDRzHu6OH47CrIwS9e2bt4H7oBFIWLoQ0pvXYeHtC69xkwGokLz2JwCA0MYW/l8tRl7UFcR++SkKszJh4eWDwpzs8m2cgfzvZhy+PRqJT9vVRpinMzZdvINxO05gx7A34GxtWaK+vFCJsTuOw8lajG+6Noa7rSUSsvNgJzZX18nKL8Dw34+gvq8rvu/ZFE5WYsRk5MDO0rzE+UzV3qh7WLjvLGZ0bYpwHzf8evoaxmzch93jesPF1qpEfbmiEKM37IOzjSUW9m0LdztrJGTmwM7SQl1HKpcj2MMZPepUw6TfD5Znc8rF3sh7WLj3DGZ0a4ZwXzf8euoaxqzfi90T3i69z9btLeqzfu3gbm+NhIwc2Fk90WcFCgR7OqNH3SBM2vzsi2ZTJbKxRtbVm4hdux31ty19bn2rAF80+OMnxPy8GZeHfAyXtk0Q/tOXyE9IgWT/cQCAV583UX3BNES9PxsZZ6+g8oShaPT3Khyu0QkFKWmGbpLBuXftiGozJuPmjC+QeSkSfsMHofb6FTjdtjvkqSXbF/jxOHj26IIbU+cg9+59uLRqhvCfvsOF3kOQc+0GAOB89wEQiIpniNsEVUWdX39B8p7/lVu7XkXc5VS/dEoQhg4dWqJs0KBBegumPLzV3gW//5WM05eLLqoWrY7Dr9+GoEkdexw9l6n1mLNXNC/A1u9MRufWzggJtFYnCLv/TQUAhAfblDjelNm16YrcUweQd+YwACDj919gVaMubJq0Qfb+3SXqq/Jy8eR31LpeM6gKZJBeOg0AMHPzgrhyEBK/mgRFYlzRObeshNdXP8OqXjPknaoYFyMu3Xoj499/kHloHwAg8aclsK3bCI7tOiJ15+8l6luF1ID0xjVkHT8EAJCnJCHr+CFYVQspPmfPflBIUpCwdKG6TJ5ccUZdNl68jZ5hAeheIwAA8Gn7Ojh+PxG7ox7i3YbBJervjnqAzHw5VvdrDfPHfyy9HTS/f2vP3YKHrRU+61hfXebjULG+oxtORaFX3WD0qBMEAJjRtRmO3o7Drku3ENGiVon6Oy/dRqZUhnURXdX95uNkp1GneTU/NK/mZ/jgjWTDySj0qheMHnUf91m3Zjh6Kxa7Lt5CREttfXarqM9Gdiu9z4L80Dyo4vYZAKTsO4qUfUdfuL7/e+9Aej8O0Z/MBwDk3LgH56b1UPmDYeoEofLEdxG7agvi1hXdWIwcOxvub7aG37DeuLvgF/03opz5jRiC+M3bkbC16O/lzelfwLVtC3j37YGHy1eXqO/Zsyse/PgLUg8X9c+jjVvg1KwxKo0YgusffgoAkKelaxzjPyYCeQ9ikHH6vIFbQ68TnRKENWvWGCqOcuHpag5nR3Ncjs5Vl+VJlbh5T4qQKlalJghPEgqA5vUdYGkhRPTdPEOGa3wiEcz9ApG1f1dxmUqF/JuRsAgIeqFT2DRpi7yLJ4tHBsyKfuVUiiempalUUCnkEFcJqRgJgpkZLKsEQbJzc3GZSoXcqxdhFRSq9RDpjWtwaNkOllWDkX/nJsw9PGFbtyEyj/yrrmNXvwlyLp+Hz0czYV0jHIrUVKTv+wMZ//5j6BYZnLxQiRtJGXi3QXEiIBQI0LCSOyITtN9FPHovATW9nDH/4GUcuZcAJysxOoX4Ymj9YIiEAnWdJv4e+OSvM7gYJ4G7rSXerhWIXuGVy6VdhiZXFCI6PhURzYsvaoVCARoHeuNqXIrWY47cjEFNX3fM+/skDt2MgZO1JTqHV8G7zcMhElb8R+PIFYWITpAgomVNdZlQKEDjKt64Gpes9ZgjN2JQ088d8/46iUM3HsLJ5nGftaj5WvRZWTk2rg3JwVMaZSn7jyN0UdGFrsDcHA51a+Du/J+KK6hUkBw8CcfGdcozVIMQmJvBLqw6Hi5bWVyoUiHtxBnY1y2ZiAKA0MICSpnmlGdlfj4cGmjvD4G5GTx6dEHsyg16i9tUKbkGQa/0/iTlbdu24e2339b3afXCyaGouelZCo3yjCwFnByePeXA30eMRdMCYWEuhFSmxJfLYhCbUDGmw5RGaGMPgUgE5VNTYpTZGTD38H7u8eb+VWDuXQlpm5aryxRJ8VCkpcCh2wCkb/4ZqoJ82LXpCjMnVyjsHfXcAuMws3OAQCRCYYbmXZ7CzHSIfbTfYcw6fggiewcEfPkdIBBAYGaG9H1/InXHb+o65h5ecOrYDWl/bodkxyZYVQ2Gx/D3oVIokHl4v0HbZGgZUhkKVSq4WIs1yl2sxXiQrn0KVVxmLhJiU/BmiB++79EUsRm5+PrgZSgKVXivSXUAwKPMXGy7eg8D61bF8IbBuJ6YjoWHrsBcKES3GqY/pz4973G/PTUtxsXGCvclGVqPiUvPRvz9BHSuGYilAzsgJi0Lc/8+BYVSidGtTf+i7HnS8/JRqFTBxUZLn6Vov0lU3GdVsHRwR8SkZmHuXyeL+qxN3fII2ySJPVwhS5JolMmSJDB3sIPQUgxzJwcIzcwgS059qk4qbIIDyzNUgzB3coLQzAwFEs32FaSkwrqK9psUqUdPwm/EYGScvQDpw1g4NWsEt07tIBCKtNZ369AWZvZ2SNhWckSf6GXonCAoFArcuHEDFhYWCAoqvou8e/duzJo1Czdu3HhugiCTySCTaV5cFxYWQCSyKOWIsmndyAHjBhdfyH72/cMyn+tRYgHGf34XNlZCNKvngEnDfTHlm/sVPkl4GTaN26Lg0UPNBc3KQqSuXAinAWPg880aqAoLIbsZCem1ixAIBMYL1sisa9SEa6/+SPzlB0hvR8PC0wcew8fC9e2BkGz7FQAgEAggvXsLKZuKhqVl9+9C7BcAxw5dTT5BKAuVCnCyFmN6+7oQCQWo7uGE5Bwp1p+/pU4QlCoVQj2cMK55GAAgxN0Rd1KzsD3yfoVIEMpCqVLB2cYSs7o1g0goRKi3K5Kz8rDuZORrkSCUhbrPuj/ZZ7lYdyKSCQLp1e058xHy9Ww0PrAbKpUK0odxSNi6G159e2it79WvJ9IOn0BBsvYRQ6Ky0ilBiIqKQteuXREbGwsAeOutt7B8+XL07dsXUVFRGDlyJP7+++/nnmfevHmYM2eORlnVOmMQVHesLuE815nL2bh5v/ji1Nys6ALUyd4M6ZnFowiO9ma4F/vsJ0IrClVIeLzT0Z2H+QgKsMJb7V3w44Z4vcb8KlHmZkFVWAjhU3f2hXaOWhfaPklgIYZ1vWbI+rvkfHt57H0kz/8EAksrCMzMoMzJhvtHX6Eg5p4eozceRXYmVIWFEDlq7pIjcnCC4qlRhf+4vTMMmUf/RcaBoulCspgHEFhawmv0REi2bwJUKigy0lAQF6NxnOxRDOwatzBMQ8qRo5UYIoEAqXmaCXdqngyuWhYoA4CrjSXMhAL1dCIAqOxsh9Q8GeSFSpiLhHC1sURlF8254pWd7XDw9iP9N8IInKwf91uO5r9fqblSuNpaaz3Gzc76cb8VT40JdHOAJEcKuaIQ5mba71RWFE7WlhAJBUjN1dJndiUXKAOAm601zETCp/rM8bXps7KSJUkg9nDVKBN7uEKemQ1lvgwFknQoFQqI3V2equMCWaLmyIMpkqcXtc/CVbN9Fm4uKEjR3j55Wjoi35sIodgCZo6OKEhKRpWpEyGNiStR19LHC87NGiNy9IcGid/UqLhKWa90mjw5ZcoUVK1aFbt378Y777yDXbt2oXXr1ujWrRvi4uLw9ddfv9DzEKZNm4bMzEyNV5VaI8rciNJIZUokJBeoXzHxMqRlyFGrevEiRStLIYIDrXDj7rMThKcJBMUJR4VVWAh57D1YBoUVlwkEEAeFoeDBs7fVtKrTGAIzM+SdO1ZqHVW+FMqcbJi5ecK8UhVII8/pK3LjUiiQf/cWbMKfuBsrEMCmZh1Ib13XeohALC65h/N/DyB8PLKSd+MaLLw1v18WXr6QpyTpLXRjMRcJEeLhiHOxxXPAlSoVzsUmI9xL+zantbydEZuZC+UTfxQepufA1cZSvZC0lrcLHqblaBwXk54DL3vtF8+mxtxMhOreLjhzv/hGhVKpwpl78ahZypadtf3cEZuWrTFf92FqFtxsrV6LC11zMxGqe7nizL0EdVlxn7lrPaZ2JQ/EpmU91WeZcLOzfi36rKwyTl+GS9vGGmWu7Zoi/fRlAIBKLkfmxWtwbdukuIJAAJc2TZBx+lI5RmoYKrkC2VHRcGraqLhQIIBT00bIunjlmccqZQUoSEqGwMwMbp3aQ7L/cIk6Xn16oCA1DakHS/87S1RWOiUI586dw8KFC9G1a1csW7YMAPDpp5/i448/hpWV9jsv2ojFYtjb22u89D29qDS7/03FO13c0aiWHfx9xPgowhdpGQqculS8ZedXHwWga5vii5KhvTxQo5o13F3M4e8jxtBeHggPtsGhMxnqOk72Zgj0s4SXe1E7AnwtEehnCVsb0/7jkX3oL9g0bQfrhq1g5uEDx74jIBSLkXv6MADAafD7sO/Wv8RxNk3aQnr1HJR5OSU+s6rdGOKqoRC5uMMyvD5c358B6dVzkN24aujmlJvUP7fDsX1nOLR+AxY+leD53gQIxZbIOFi0q5HX+E/gNnC4un7O+dNw6tgV9s1aw9zdEzY168LtnaHIOX9anSik/bkdVkHV4dKrP8w9vWHfvA2c3uiM9L1/GKWN+jaobjXsjHyAP689xP3ULMw7cAlSeSG6P54KNGvvefxwvHjv8LdrBSIrvwALD1/Bw/RsHLuXgDXnbqJvreK5ywPrVkVkYhpWn72B2Iwc/HMjFjsi76NPLdOf3/yfwU3CsOPCLfxx+TbupWTgy79PQipXqHc1mr7jCJb8W7y7Sd8GIciUyjB/72k8kGTi6K1YrDx2Bf0aVlfXyZPJcSMhFTcSiuZOP8rIxo2EVCRklPw+m6LBTcOw48JN/HHpcZ/9dQLSAoV6V6Pp249gyf7iGxZ9Gz7us38e99nNGKw8+pw+S8+pUH0GFG1zal8rBPa1inZXs67sC/taIbD08wIABH85CbXWzFfXf/jzZlhX9kPIvMmwCQ6E/+gB8OrzJu4vWauuc3/xGvhF9IXP4B6wDQlE2NLPYGZjhdh1r+526bqIXbke3v17w7N3d1hXqYzgr2ZAZG2F+K27AADVF32FwE8mqOvb1w6HW8d2sPTzgUODuqi1bjkEQiFifnpqkxiBAF5vv4XE7X9AVVhYji16damUxntVRDpNMZJIJPD2LprT7+DgABsbGzRu3Pg5R71atu2VwFIsxPgh3rCxFuH67TzMXPxA4xkIXm4WsLcr7hpHOzN8FOELZwcz5EqVeBCXj5mLH+Dy9eLdkN5s7YyB3YvvPn0zpegC5LvVcfj3ZIbhG2Yg0ounkGFrD/sufSGyc4T80QNIls2FMrtoMZ+Zk2uJzYfN3L0grlIdKT9+ofWcIgcnOPQaApGdIwqz0pF39iiy9m4zeFvKU/bJI0h2cITbO0MhcnSC7P5dxHz5KQozMwAA5q7uGv0m2fYroFLBrf8wmDm7ojArE9nnT6vXGwBA/t1biPvmM7gNjIBrn0GQJyciac1yZB2rADs/AegQ7It0qQwrTl1Hap4MQW4O+KFnM7jYFE0xSszOw5PLVDztrPFjz2ZYdOQq3tlwAG62VuhfpwqG1i/eCamGpzMWdmuMH49fwy+nb8DbwQYfta6JztUrlXfzDKZTWCDSc/Ox7NBFSHKkCPZ0xrJBHdQLlxMzcyF8ouM8HWyxfHBHLNh7Bn2W74K7vTUGNqqBd5uHq+tci5dgxLri3bEW7jsLAOheqyq+6NmynFpmOJ3CA5Gel49lBy887jMXLBvc8Yk+yym9z5bthLudNQY2roF3WxTvhHQtXoIRa/ao3y/cewYA0L12NXzRy/T7DAAc6oWhyYHi3XJCFxbtRhS7fgeuRkyD2MsNVo+TBQCQPojDue6jELpoGgLGD0F+XCIiR81Qb3EKAAlb/4GFmzOCZk8oelDalWic7ToCBU8tXDZVyX/tg7mzEwI/HAsLN1dkR9/ElaFjIJcU7c5m6eOpcYUpFFsg8ONxsKzki8LcPKQeOo7rH34KRZbmZg3OzRvD0tcb8Vt2lWdz6DUiUOkwaUskEuHWrVtwc3ODSqWCn58fjh8/joCAAI169vb2OgfSZYT2pwpS6X6y+tzYIZik7Hjt6wCodH5v1H9+JSrBzMHR2CGYHmEFn7ppIAcGrTJ2CCbHykf8/EpUQtsHr+Zo/ycrdJsqrk/fjH7xWTSmQucnKT+5c5FKpUKdOnU03gsEAhRyuIuIiIiIyomSi5T1SqcE4dChQ4aKg4iIiIiIXgE6JQjNmzfHwoUL8ccff6CgoADt2rXD7NmzdVqgTERERESkT9zmVL902sVo7ty5+PTTT2FrawsfHx8sWbIE77//vqFiIyIiIiKicqbTCML69euxbNkyjBo1CgDw77//okuXLli5ciWEQp1yDSIiIiIivVA+/Swheik6XdXHxMSgc+fO6vft27eHQCBAfHzFfZowEREREdHrRKcEQaFQwNLSUqPM3Nwccrlcr0EREREREZFx6LzN6bBhwyAWF+8dnJ+fj9GjR8PGxkZdtmNHxXgCIhERERG9+rhGWb90ShCGDh1aomzQoEF6C4aIiIiIiIxLpwRhzZo1hoqDiIiIiKhMVFykrFfceoiIiIiIiNSYIBARERERkZpOU4yIiIiIiF41Sq5S1iuOIBARERERkRpHEIiIiIjIpHGRsn5xBIGIiIiIiNQ4gkBEREREJo0jCPrFEQQiIiIiIlJjgkBERERERGqcYkREREREJo0zjPSLIwhERERERKTGEQQiIiIiMmlcpKxfHEEgIiIiIiI1JghERERERKTGKUZEREREZNJUKk4x0ieOIBARERERkRoTBCIiIiIyaUqlymgvQ0lLS8PAgQNhb28PR0dHREREICcn55n1x48fj+DgYFhZWaFSpUqYMGECMjMzdf7ZTBCIiIiIiF4xAwcOxLVr17B//3789ddfOHr0KN57771S68fHxyM+Ph4LFy5EVFQU1q5di7179yIiIkLnn801CERERERk0iraGoTo6Gjs3bsX586dQ/369QEAP/zwAzp37oyFCxfC29u7xDFhYWHYvn27+n2VKlXw1VdfYdCgQVAoFDAze/HLfo4gEBERERGVkUwmQ1ZWlsZLJpO91DlPnToFR0dHdXIAAO3bt4dQKMSZM2de+DyZmZmwt7fXKTkAmCAQEREREZXZvHnz4ODgoPGaN2/eS50zMTER7u7uGmVmZmZwdnZGYmLiC51DIpHgiy++eOa0pNIwQSAiIiIik6ZSqoz2mjZtGjIzMzVe06ZN0xrn1KlTIRAInvm6cePGS/dHVlYWunTpgtDQUHz22Wc6H881CEREREREZSQWiyEWi1+o7kcffYRhw4Y9s05gYCA8PT2RnJysUa5QKJCWlgZPT89nHp+dnY1OnTrBzs4OO3fuhLm5+QvF9qRXJkEwK0PwrzuF9OXmt72upBlSY4dgcoT8flJ5EYqMHYFJsvJ5sYsTKiZ9xL+hFYnKgNuN6pObmxvc3NyeW69JkybIyMjAhQsXUK9ePQDAwYMHoVQq0ahRo1KPy8rKQseOHSEWi/HHH3/A0tKyTHFyihERERER0SukevXq6NSpE0aOHImzZ8/ixIkTGDduHN555x31DkaPHj1CSEgIzp49C6AoOejQoQNyc3OxatUqZGVlITExEYmJiSgsLNTp578yIwhERERERFTk119/xbhx49CuXTsIhUL07t0b33//vfpzuVyOmzdvIi8vDwBw8eJF9Q5HVatW1TjX/fv3ERAQ8MI/mwkCEREREZk0ZQV7DgIAODs7Y9OmTaV+HhAQoPH8h9atW+vteRCcYkRERERERGocQSAiIiIik2Yqi5RNBUcQiIiIiIhIjSMIRERERGTS9DX3nopwBIGIiIiIiNSYIBARERERkRqnGBERERGRSVNykbJecQSBiIiIiIjUOIJARERERCaN25zqF0cQiIiIiIhIjQkCERERERGpcYoREREREZk0PgdBvziCQEREREREahxBICIiIiKTplIqjR1ChcIRBCIiIiIiUmOCQEREREREapxiREREREQmjU9S1i+OIBARERERkRpHEIiIiIjIpHGbU/3iCAIREREREalxBIGIiIiITJqKaxD0iiMIRERERESkxgSBiIiIiIjUOMWIiIiIiEwapxjpl84jCCqVCrdv38a1a9egUCgMERMRERERERmJTgnC/fv3UbNmTYSEhKBmzZqoUqUKzp8/b6jYiIiIiIieS6lSGu1VEemUIEyePBkKhQIbN27Etm3b4Ovri1GjRhkqNiIiIiIiKmc6rUE4fvw4tm3bhubNmwMAGjduDF9fX+Tm5sLGxsYgARIRERERUfnRKUFITk5GtWrV1O+9vLxgZWWF5ORkVK5cWe/BERERERE9Dxcp65dOCYJAIEBOTg6srKzUZUKhENnZ2cjKylKX2dvb6y9CAxjQ1QVvNHeEjZUQN+5JsXxTEhJS5KXW793RGU1q28LXUwyZXIkbd6VYvysFj5KKj/F0Nce7vd1QvYoVzM0EuHg9Fz//nozM7MLyaJJB2bXpDIeOPSBycEJB7AOk/vYzCu7fLrW+fftusGv9JkTOrlDmZCP3wklkbF8PlaKovxze7A3ruk1g7uULVYEMsrs3kLZtPRRJj8qrSeXC9a1e8OjbH+bOzpDevYvYH75D3s1o7ZVFIngOGAyXDm/C3NUV+bGxiP9lObLOnSn7OU3Q7xdvY93ZG0jNzUeQuyOmtK+LMC+XUutn5xfgx2OROHgrDpn5BfCyt8bHbeugRRVvAMCK41H46eQ1jWMCnO2wc0Rng7ajvG0+ex3rTkRBkiNFkKcTpr7ZBOG+bqXWz5LK8OPBCzgQ/RCZUhm8HGzxSadGaBHkBwC48CARa09GIjpegpQcKb7r1w5tq/uXV3PKxebT17Du+JXHfeaMqV2bIdzXXWvd3RdvYtaOIxplFmYinPssQv0+NScPi/edxak7ccjOl6FugBemdmkGf1cHg7ajvPkM7odKo4bBws0VOdG3cGv2PGRfidJaV2BmBv+xEfDq3R0Wnu7Iu/cAd79ejLQjJ9R1mhz/B1a+PiWOjVu/GbdmzTVYO8qLc/P6CPwoAg51w2Dp7Y7zvcci6Y8Dzz6mZUOELpwK29BqyI9NwJ15yxG3fqdGHf8xAxA4KQJiTzdkXb2BaxO/QOa5SEM2hV4zOiUIKpUKQUFBJcrq1Kmj/m+BQIDCwlf3orhXB2d0aeOEJesSkZQqx8BuLvhsgi/GzXkAuUJ79hlWzRp7jmTg9sN8iIQCDH7LFZ+N98O4z+9DVqCC2EKAzyb44kGcDDMXxwIABnRzxYyxPvjkmxioTDiptW7QHM59hyN143LI7t2Cfftu8Jj4GR7NGAtldmaJ+jYNW8Kp9xBI1vwA2d0bMPPwhuvwDwCVCulbVgMALIPDkH1oD2QPbgNCEZx6DYbnpM/waOY4qApk5dxCw3Bq3Ra+o8chZvFC5N24DvdefVF1/re4Pqw/FBkZJep7D38Pzu07IGbRfOTHxsC+fkMEzpmLmxNGQ3rndpnOaWr2Rcdg0aHLmN6hHsK8XLDp/C2M3XIEu0Z0hrONZYn68sJCjN5yGM7WlljwVlO421kjPjMXdpbmGvWquNpjRd/W6vciYcV6/MveqHtYuO8sZnRtinAfN/x6+hrGbNyH3eN6w8XWqkR9uaIQozfsg7ONJRb2bQt3O2skZObAztJCXUcqlyPYwxk96lTDpN8PlmdzysXeyLtY+M8pzOjeAuF+7vj1ZCTGrN2D3RP7ae0zALAVm2P3xH7q9wJB8WcqlQoTf/0fzIRCLB7YAbZiC6w/cRWj1vyNHR/0gbWFuZYzmh73rh1RbcZk3JzxBTIvRcJv+CDUXr8Cp9t2hzw1rUT9wI/HwbNHF9yYOge5d+/DpVUzhP/0HS70HoKcazcAAOe7D4BAVPydtAmqijq//oLkPf8rt3YZksjGGllXbyJ27XbU37b0ufWtAnzR4I+fEPPzZlwe8jFc2jZB+E9fIj8hBZL9xwEAXn3eRPUF0xD1/mxknL2CyhOGotHfq3C4RicUpJT8//C64AiCfumUIBw6dMhQcZSbbm2dsPWfVJy9mgMAWLw2Eeu+qYLGtW1x7Hy21mPm/Bin8X7J+kRsWFAVVSpZ4vodKapXsYK7izk+nPsQ0vyi1exL1iXi10VVUTPYGldu5Bm2UQbk8MZbyD72P+ScKLrjkbpxOaxq1odd8/bI/Gd7ifriqiHIvxON3LNHAQCK1GTknj0KceXixDJp8RyNYySrl6DS4g2w8K8C2e3rBmxN+XF/+x1I9vyJtH17AAAxixfAvnETuHTqiqTNG0vUd27fEYmb1iPr7GkAgOTPXbCrWx8efd7Bg3lflOmcpmbj+ZvoVTMQb4UHAgCmd6yPY/cSsCvyPoY3rl6i/q6r95GVX4C1A9vD/PEFhrdDybVQIqEQrqVc9FUEG05FoVfdYPSoU/Qdm9G1GY7ejsOuS7cQ0aJWifo7L91GplSGdRFd1f3m42SnUad5NT80r+Zn+OCNZMOJq+hVPwQ96gUDAGZ0b4GjN2Ow68JNRLSqrfUYgUAAVztrrZ89TM3E1dhkbB//Nqp6OKvP2Xb+Buy9ehe96ocYpB3lzW/EEMRv3o6ErbsBADenfwHXti3g3bcHHi5fXaK+Z8+uePDjL0g9XHRh+2jjFjg1a4xKI4bg+oefAgDkaekax/iPiUDegxhknK4YOySm7DuKlH1HX7i+/3vvQHo/DtGfzAcA5Ny4B+em9VD5g2HqBKHyxHcRu2oL4tbtAABEjp0N9zdbw29Yb9xd8Iv+G0GvJZ0ShFatWhkqjnLh4WoOZwczjQv2vHwlbt3PR3Blq1IThKdZWxX9Uc3JKxopMTcTACpojEAUKFRQqYDqVaxMN0EQmcHCvwoy92wrLlOpkB99BeLAYK2HyO7cgG3jVrCoXA0F92/DzNUDVuH1kHvqcKk/Rmhd9EdXmZujz+iNRmBmBuugICT+tqG4UKVC9sXzsAmtofUYoYV5idETVYEMNmE1y3xOUyIvLER0YrpGIiAUCNDI3wNX4yVajzlyNx41vV3x9f4LOHznEZysxXizuj+GNQrRGCWISc/GG0t3Q2wmQk1vF4xvVRNe9hVjUwW5ohDR8amIaF6cCAiFAjQO9MbVuBStxxy5GYOavu6Y9/dJHLoZAydrS3QOr4J3m4dXuNEVbYr6TIKIlnXUZUKhAI2r+OBqbFKpx+UVyNFpwSYoVSpU93bF+DcaqJMBuaLoxpDYrPhPqlAogIVIhEsPEytEgiAwN4NdWHU8XLayuFClQtqJM7CvWzIRBQChhQWUsgKNMmV+Phwa1NFaX2BuBo8eXRC7coPWz18Hjo1rQ3LwlEZZyv7jCF1UlFAJzM3hULcG7s7/qbiCSgXJwZNwbKy9X18XKlOervEK0utfg4sXL6Jr1676PKVeOdmLAAAZWZoPeMvIVqg/ex6BABjRxx3X7+QhJr7oH76b9/ORX6DE0J6usDAXQGwhwLu93CASCeDkYLoPqxbZ2kMgEqEwK0OjvDArAyIHJ63H5J49ivTdv8Fryjz4r9gO369/Rv7NKM0k40kCAZz7jUD+7euQx8fouQXGYebgAIHIDIp0zaFeRXoazJ21z6fPOncW7m+/A7GPLyAQwK5efTg2b6WuX5ZzmpL0vAIUqlRwttacSuRiY4nU3HytxzzKyMG/N2NRqFLhh7dbYmSTGthw7iZWnioehQrzdsHnbzbC0j6t8GmHeniUmYvhmw4iV1b6miNTkp4nQ6FKVWJajIuNFSQ52m9MxKVn49/rD1CoUmHpwA54r1VtrD8VhV+OXimPkI0uPS8fhUotfWZbep8FuDpiTs9WWDyoA+b2aQOlSoWhP+9GUmbRTY0AN0d4Odji+/1nkSWVQa4oxOqjl5GUlYuUbBO9QfQUcycnCM3MUCBJ1SgvSEmFhZur1mNSj56E34jBsAqoBAgEcGreGG6d2kHspn19jFuHtjCzt0PCtt16j99UiD1cIUvSvCkiS5LA3MEOQksxLFyL/j/IklOfqpMKsaf2/w9EZaHz1eu+ffuwf/9+WFhYYMSIEQgMDMSNGzcwdepU/Pnnn+jYseNzzyGTySCTad4tLSwsgEhkUcoRZdOqgR3GDPBUv/9iWdwzar+YUe94oJK3GNMWFl/MZuUU4ptf4jG6vwe6tnaCSgUcPZ+FOzH5Jr3+oCwsg8Pg2PltpP76E2T3bsHc3QvO74xAYde+yPxrS4n6zgNHwcKnEhLmTzNCtK+OuKVLUOmjTxC65lcAKsji45G6bw9cOnUxdmivLOXjhGJmx/oQCYUI9XRGco4U68/ewKhmYQCA5oFe6vpBcES4lws6r/gL/7sZi541A40VulEpVSo421hiVrdmRf3m7YrkrDysOxmJ0a1f7zuQpalVyQO1Knk88d4TPZdswdZz0RjXvgHMRUJ8O+ANfLbzKFp8tQ4ioQCNqvigeZDfa/c34Em358xHyNez0fjAbqhUKkgfxiFh62549e2htb5Xv55IO3wCBcnaR7+IqPzolCCsWrUKI0eOhLOzM9LT07Fy5Up8++23GD9+PPr164eoqChUr15yrvDT5s2bhzlzNOehB9V7HyENxusW/XOcvZqDmw8eqN+bmxWtKnO0N0N6VvFCakc7M9yPe/7i2Pf6uaNBmA2mfRuL1AzNUYjL0XkYPes+7GxEUCpVyJUqsfbrKjguKSjlbK++wpwsqAoLIbJ31CgX2TuiMDNd6zGObw1AzqnDyDm2HwAgf/QQArEYLoPfR+bfW/HkX0vnAe/BumYDJH4zDYXpqVrPZ4oUmZlQFSpg5uSsUW7m5Ax5mvZ2KjIzcG/WpxCYW8DMwR5yiQTeI8dAlhBf5nOaEidrC4gEAqTlaY4WpObmw0XLAmUAcLWxgplIqDEtprKLPSS5+ZAXFsJcVHJU0M7SApWcbRGbXjGmszlZiyESCJCaI9UoT82VwtVW+3x5NztrmAkFGv0W6OYASY4UckUhzM1ebDTVVDlZW0Ik1NJnOaX32dPMRUKEeLkgNrV4975QHzdsGdcb2fkFkBcWwtnGCgNX7EQNn9J3kzIl8vR0KBUKWLhqjlhauLmgIEX7NEB5Wjoi35sIodgCZo6OKEhKRpWpEyGNKXmzztLHC87NGiNy9IcGid9UyJIkEHtojgSIPVwhz8yGMl+GAknR/wexu8tTdVwgS9T+/+F1oVRWzCcaG4tOU4yWLFmC+fPnQyKRYMuWLZBIJFi2bBkiIyOxYsWKF0oOAGDatGnIzMzUeFWrq/8nMktlKiSmyNWv2IQCpGUqUDO4+I+AlaUQQZUtcfO+9BlnKkoOGte2xYzFsUhOLX16QnZuIXKlSoQHW8PBTqReDG2SChUoeHgXltVrFpcJBLAMqQnZvZtaDxGIxcDTjx1Xf2mLt/1wHvAerOs0RuLCGVBIkvUcuHGpFArk3boFuzr1igsFAtjVqYfc69dKPxCASl4AuUQCiERwbNEKmSePvfQ5TYG5SITqnk4487B4DrhSpcLZh0mo6a192Ly2ryti07OhfCLpjEnLhquNpdbkACiaRx6XkQtXW+1Jh6kxNxOhurcLztyPV5cplSqcuRePmqVsc1rbzx2xadlQPrHjx8PULLjZWlX45AD4r89cceZe8bbK6j7z83jGkcUKlUrcTkrTumjZztICzjZWeCjJxPVHErQOCdBX6EalkiuQHRUNp6aNigsFAjg1bYSsi8+enqaUFaAgKRkCMzO4dWoPyf7DJep49emBgtQ0pB48pufITUvG6ctwadtYo8y1XVOkn74MAFDJ5ci8eA2ubZsUVxAI4NKmCTJOXyrHSKmi02kE4e7du+jTpw8AoFevXjAzM8OCBQvg6+ur0w8Vi8UQi8UaZfqeXlSaPw+mo29nFySkFCBJIseAbq5Iy1Tg9OXiC/nPP/DF6cs52HMkAwAw6h13tGxgj7krHkEqU8Lx8XqFPKkSBfKiP7LtmtgjNrEAWdmFCA60wog+7vjjYLrGsxJMUeb+3XAb/gFkD++g4P5t2LfvBoHYEtkn/gUAuA6fCEVGKjJ2FC0qk145B/s33kJBzH3I7t+EmbsXHHsMhPTqOXXi4DxwFGwbtUTSj3OhypeqRyiU0jyo5KY74vKk5G2b4T9lOvJu3UDejWi49e4LoaUVUvf9DQDwnzIDckkK4lcVLTSzDgmFhasr8u7egbmrK7yGDIdAIETS5k0vfE5TN6h+MGbtOYNQT+fH25zehFSuwFvhRQ9hnPH3abjbWmNCq6KEtU/tqvj94m18c+Ai+tcNQkx6Nladvo7+9Yp3zPr20GW0rOINbwcbJOdIseJ4FIQCATpVr2SUNhrC4CZhmLnzGGp4uyLMxw0bT1+DVK5Q72o0fccRuNvb4IP29QEAfRuEYPPZaMzfexr9G4YiJi0LK49dwYBGoepz5snkiEkrvjv+KCMbNxJS4WAlhpejbfk20AAGN6uJmdsPo4a3G8J83bDxZCSkBXL0ePy7M33boaI+69AQALDi4AXU9PNAJRd7ZOcXYO2xK0jIyNFYfPy/qHtwsraEl6Mtbiel4Zu/T6JNdX80rabb38dXWezK9ai+6EtkR15H1uVI+EUMgsjaCvFbdwEAqi/6CrKkJNz75nsAgH3tcIg93JF9/QbEnh6oPHEMBEIhYn5ao3ligQBeb7+FxO1/QPUKb5NeFiIba9hULf73xrqyL+xrhaAgLRP5sQkI/nISLH08cOXdKQCAhz9vhv/YgQiZNxmxa7fDtU1jePV5E+e6F99Evb94DWqtno+MC1HIPHcVAROGwszGCrGPdzV6XXGbU/3SKUGQSqWwfrzjjEAggFgshpeX13OOerXs+F8aLC0EGDvAEzbWQkTflWLOD3EaOxB5ulnA3rb4TlrnVkULcudO0ryoWLIuAQdPF/0R9fGwwOC33GBrI0Jyqhxb96bijwPap+GYkrxzx5Fmaw+ntwZAZO+Egtj7SFo8B8qsomcgmLm4aowYZPy1BSqVCo49B0Lk6AxldhbyrpxDxs7ibTjt2xQ9pMrrE82H4EhWL0HOyYqx53r64YMwc3CE17ARMHdyhvTuHdyZ+hEU6UW/ExbuHhr9JrSwgNfwkRB7eUMplSLzzGk8/PoLFD6xs9PzzmnqOlavhHSpDMuPRyE1Nx/B7o5Y2qeVeopRYlYehE9sPu9pb42lfVph0cFL6LtmL9ztrDCgXhCGNSq+aEvKzsO0P08hM78ATlZi1PZ1xfpB7UsshjZlncICkZ6bj2WHLkKSI0WwpzOWDeqgXoSbmJmr2W8Otlg+uCMW7D2DPst3wd3eGgMb1cC7zcPVda7FSzBi3T/q9wv3nQUAdK9VFV/0bFlOLTOcTuFVkJ4rxbID5yHJyUOwlwuWDe0Ml8dTjBIzcjT6LDtfhs93HYUkJw/2VmKEerti3XtvoYp78WYNKdl5WLjnFFJzpXCztUbXOtUwqnXdcm+bISX/tQ/mzk4I/HAsLNxckR19E1eGjoFcUrR5gqWPp+a/a2ILBH48DpaVfFGYm4fUQ8dx/cNPocjS3DHQuXljWPp6I37LrvJsTrlwqBeGJgeKd2UKXVi0G1Hs+h24GjENYi83WPkVX0dJH8ThXPdRCF00DQHjhyA/LhGRo2aotzgFgISt/8DCzRlBsycUPSjtSjTOdh2BgmTTn25Krw6BSod9oYRCIb788kvY2hbdQZoyZQomT54MV1fNKQATJkzQOZC3xmifskKlWyL/xNghmKS0+/xHVFchA9oaOwSTJLSuGNuplisz0935zZhOTl5n7BBMjvRRxXgwZ3nrIn81r9e6jjTec5T++iX0+ZVMjE7/EleqVAm//FL8EA5PT09s2KC5X7FAIChTgkBEREREVBaqp9c/0kvRKUF48MSOQEREREREVPHotItR586dkZmZqX7/9ddfIyMjQ/0+NTUVoaEVb5iFiIiIiF5dKqXKaK+KSKcEYe/evRoPOJs7dy7S0oqf7KpQKHDz5qs5N42IiIiIiJ7vpVaD6bC+mYiIiIjIICrqnXxj0WkEgYiIiIiIKjadEgSBQADBE3tD/1dGREREREQVg05TjFQqFYYNG6Z+CnJ+fj5Gjx4NG5uivb6fXJ9ARERERFQelNzmVK90ShCGDh2q8X7QoEEl6gwZMuTlIiIiIiIiIqPRKUFYs2aNoeIgIiIiIioTLlLWLy5SJiIiIiIiNSYIRERERESk9lLPQSAiIiIiMjaVkouU9YkjCEREREREpMYRBCIiIiIyaVykrF8cQSAiIiIiIjWOIBARERGRSVPxQWl6xREEIiIiIiJSY4JARERERERqnGJERERERCZNyUXKesURBCIiIiIiUuMIAhERERGZND4oTb84gkBERERERGpMEIiIiIiISI1TjIiIiIjIpPFJyvrFEQQiIiIiIlLjCAIRERERmTQ+SVm/OIJARERERERqHEEgIiIiIpPGNQj6xREEIiIiIiJSY4JARERERERqnGJERERERCaNT1LWL44gEBERERGRmkClUnFVRylkMhnmzZuHadOmQSwWGzsck8F+0x37rGzYb7pjn5UN+0137LOyYb/Rq4AJwjNkZWXBwcEBmZmZsLe3N3Y4JoP9pjv2Wdmw33THPisb9pvu2Gdlw36jVwGnGBERERERkRoTBCIiIiIiUmOCQEREREREakwQnkEsFmP27NlcJKQj9pvu2Gdlw37THfusbNhvumOflQ37jV4FXKRMRERERERqHEEgIiIiIiI1JghERERERKTGBIGIiIiIiNSYIBARERERkVqFThCGDRsGgUCA0aNHl/js/fffh0AgwNChQ9G+fXt07NixRJ1ly5bB0dERcXFxAIBDhw6hc+fOcHFxgbW1NUJDQ/HRRx/h0aNH6p9V2isgIMDQzdWLF+mzYcOG6Vz3v/o9evQo8bO+/vprjWN37doFgUCgl/YYkiH76j+nTp2CSCRCly5dSny2Z88eWFhY4OLFixrlixYtgqurKxITE8vWsHKUmJiI8ePHIzAwEGKxGH5+fujWrRsOHDigUW/evHkQiURYsGBBmc8TEBCAxYsXlzj2s88+Q+3atfXZLL17XvtetG2fffYZBAIBOnXqVKLuggULIBAI0Lp16+ce//Tv8eXLlyEQCPDgwYOXaaZeGKuv/hMXFwcLCwuEhYWV+OzKlSuwsLDAH3/8oVG+fft2WFpaIioqSrfGGsmTf+8sLCxQtWpVfP7551AoFDh8+LDG3z43Nzd07twZkZGRL3yOiqi0a4T/fr8CAgLUZdbW1ggPD8fKlSs1zvF033p4eKB37964d++eMZpEFVyFThAAwM/PD5s3b4ZUKlWX5efnY9OmTahUqRIEAgHWrFmDM2fO4KefflLXuX//Pj755BP88MMP8PX1xU8//YT27dvD09MT27dvx/Xr17FixQpkZmZi0aJFWLJkCRISEtQvAFizZo36/blz58q97WX1vD4ra11tLC0tMX/+fKSnp+uvAeXI0H21atUqjB8/HkePHkV8fLzGZ507d8aQIUMwZMgQyGQyAMD169cxY8YMLF26FJ6envpqpkE8ePAA9erVw8GDB7FgwQJERkZi7969aNOmDd5//32NuqtXr8Ynn3yC1atXv9R5TJG+2+fl5YVDhw6pb3z8Z/Xq1S/8nV21ahVu376t8882tFehr9auXYu+ffsiKysLZ86c0fisVq1amDVrFt577z2kpqYCAJKTkzF69GjMmTNHa1LxqurUqRMSEhJw+/ZtfPTRR/jss880EvibN28iISEB+/btg0wmQ5cuXVBQUKDTOSqa/9r75Ou3335Tf/75558jISEBUVFRGDRoEEaOHIl//vmnxHlu3ryJ+Ph4bN26FdeuXUO3bt1QWFhYnk2h10CFTxDq1q0LPz8/7NixQ122Y8cOVKpUCXXq1AFQdOG2ZMkSfPzxx7h//z5UKhUiIiLQoUMHDB48GHFxcZgwYQImTJiA1atXo3Xr1ggICEDLli2xcuVKzJo1Cw4ODvD09FS/AMDR0VH93s3NzSjtL4sX6bOy1NXmv6Rr3rx5+mtAOTJkX+Xk5OD333/HmDFj0KVLF6xdu7ZEne+++w45OTmYPXs2FAoFhg4dim7duqFfv376a6SBjB07FgKBAGfPnkXv3r0RFBSEGjVqYNKkSTh9+rS63pEjRyCVSvH5558jKysLJ0+eLNN5TJW+2+fu7o4OHTpg3bp16rKTJ09CIpFoHal6WnBwMNq0aYPp06fr/LMNzdh9pVKpsGbNGgwePBgDBgzAqlWrStSZNm0aKlWqpE5YRo0ahWrVquHjjz/WOT5jEovF8PT0hL+/P8aMGYP27dtrjIy4u7vD09MTdevWxcSJExEbG4sbN27odI6K5r/2PvlycnJSf25nZwdPT08EBgZiypQpcHZ2xv79+0ucx93dHV5eXmjZsiVmzZqF69ev486dO+XZFHoNVPgEAQCGDx+ONWvWqN+vXr0a7777rkadoUOHol27dhg+fDh+/PFHREVFqUcUtm7dioKCAnzyySdaz+/o6Giw2I3lRfqsLHWfJhKJMHfuXPzwww8l7tKZCkP11ZYtWxASEoLg4GAMGjQIq1evxtOPLbGzs8Pq1auxaNEiDBw4ELGxsVi+fLkeWmVYaWlp2Lt3L95//33Y2NiU+PzJ79SqVavQv39/mJubo3///hoXXbqcxxQZqn3Dhw/XSDhXr16NgQMHwsLC4oWO//rrr7F9+3acP3++TD/fEF6Fvjp06BDy8vLQvn17DBo0CJs3b0Zubq5GHZFIhHXr1mH37t0YMGAA9u3bh7Vr10IkEpUpvleFlZVViRECAMjMzMTmzZsB4Lm/X6Wd43WjVCqxfft2pKenv1CfAWC/kd69FgnCoEGDcPz4cTx8+BAPHz7EiRMnMGjQoBL1fv75Z0RFRWHixIn4+eef1Xf9b9++DXt7e3h5eZV36Ebzon2ma11tevbsidq1a2P27Nn6Cr9cGaqvVq1apf6sU6dOyMzMxJEjR0rUa9u2Ld5++21s2bIF33//PVxcXPTXOAO5c+cOVCoVQkJCnlkvKysL27ZtU/fDoEGDsGXLFuTk5Oh0nv9MmTIFtra2Gq+5c+e+XGMMSJf26dK2rl27IisrC0ePHkVubi62bNmC4cOHv3BcdevWRd++fTFlypQXPsbQXoW+WrVqFd555x2IRCKEhYUhMDAQW7duLVGvevXqmDhxIn777Td89tlnCAoK0q2xrxCVSoV///0X+/btQ9u2bdXlvr6+sLW1haOjIzZt2oTu3buX+v+mtHNUNH/99dczf+/++70Ui8V4++234eTkhBEjRpR6voSEeX4M8AAABrVJREFUBCxcuBA+Pj4IDg4ujybQa8TM2AGUBzc3N/UUDZVKhS5dusDV1bVEPXd3d4waNQq7du3SWEyrUqlMYtGsPr1on+latzTz589H27ZtTW6YHTBMX928eRNnz57Fzp07AQBmZmbo168fVq1aVWJh5KNHj7B3715YW1vj2LFj6Nu3r97bqG8v+gD33377DVWqVEGtWrUAALVr14a/vz9+//13REREvPB5/jN58uQSi8G///57HD16VKfzlBdd2qdL28zNzTFo0CCsWbMG9+7dQ1BQEGrWrKlTbF9++SWqV6+O//3vf3B3d9fpWEMwdl9lZGRgx44dOH78uLps0KBBWLVqVYmf9d/0wf++s6WNTr/K/rvYlcvlUCqVGDBgAD777DP1ertjx47B2toap0+fxty5c7FixYoXPkdF1aZNmxIjvM7Ozur//u/3MiEhAZMnT8bYsWNRtWrVEufx9fWFSqVCXl4eatWqhe3bt7/w6B/Ri3otEgSgaJh43LhxAIClS5eWWs/MzAxmZprdEhQUhMzMTCQkJLxWowgv2me61tWmZcuW6NixI6ZNm6Z1N59Xnb77atWqVVAoFPD29laXqVQqiMVi/Pjjj3BwcFCXjxw5EvXq1cP06dPxxhtv4O2330arVq1etkkGVa1aNQgEghJzkp+2atUqXLt2TeM7qVQqsXr1akRERLzwef7j6upa4g/uk3+gXzW6tE/Xtg0fPhyNGjVCVFSUTqMH/6lSpQpGjhyJqVOnap1rX96M3VebNm1Cfn4+GjVqpC5TqVRQKpW4deuWxijB5MmTYWlpiZMnT6Jx48ZYv349hgwZ8ty4XyX/XexaWFjA29u7xN/NypUrw9HREcHBwUhOTka/fv1KJGDPO0dFY2Njo/WC/z///V5WrVoVW7duRXh4OOrXr4/Q0FCNeseOHYO9vT3c3d1hZ2dn6LDpNfVaTDECiqZoFBQUQC6Xa93S9FnefvttWFhY4JtvvtH6eUZGhh4ifPXo0mcv07//+frrr/Hnn3/i1KlTZTremPTZVwqFAuvXr8eiRYtw+fJl9evKlSvw9vbW2PVi5cqVOH78OFatWoU2bdpgzJgxGD58eIl5z68aZ2dndOzYEUuXLtUaa0ZGBiIjI3H+/HkcPnxYox8OHz6MU6dO4caNGy90HlNmyPbVqFEDNWrUQFRUFAYMGFCmc8yaNQu3bt1SzzE3JmP31apVq/DRRx+V+M62aNFCY/et/fv3Y+XKlVi3bh1q1aqFL7/8EhMnTlTvfmcq/rvYrVSp0nMv7N9//31ERUWpR0TLco7XjZ+fH/r164dp06aV+Kxy5cqoUqUKkwMyqNcmQRCJRIiOjsb169d1Xgzm5+eH7777DkuWLEFERASOHDminj8+atQofPHFFwaK2rh06bOX6d//hIeHY+DAgfj+++/LdLwx6bOv/vrrL6SnpyMiIgJhYWEar969e6vv1j58+BCTJk3CwoUL4e/vD6BoqpZAIMDUqVP130g9W7p0KQoLC9GwYUNs374dt2/fRnR0NL7//ns0adIEq1atQsOGDdGyZUuNPmjZsiUaNGig7ofnncfUGbJ9Bw8eREJCQpkX8Hp4eGDSpEmvzHfWWH11+fJlXLx4ESNGjCjxne3fvz/WrVsHhUKBrKwsREREYPLkyWjQoAEA4MMPP0RoaCjee++9l4rvVWZtbY2RI0di9uzZOk8LrEhkMhkSExM1XhKJpNT6H3zwAf78889XajMAen28NgkCANjb28Pe3r5Mx44dOxb/+9//8OjRI/Ts2RMhISEYMWIE7O3tTXLe/IvSpc9epn//8/nnn0OpVL7UOYxFX321atUqtG/fXmMa0X969+6N8+fP48qVK4iIiECTJk00Liysra2xdu1aLF++XOuC5ldJYGAgLl68iDZt2uCjjz5CWFgY3njjDRw4cABLlizBxo0b0bt3b63H9u7dG+vXr4dcLn/meUxhR6fnMWT7bGxsXnqnp48//hi2trYvdQ59MVZfrVq1CqGhoVoX4fbs2RPJycnYs2cPJk6cCAcHB4159kKhEGvWrMHBgwexfv36l4rxVTZu3DhER0drXbT9uti7dy+8vLw0Xs2bNy+1fmhoKDp06IBZs2aVY5RERQSq1zmdJyIiIiIiDa/VCAIRERERET0bEwQiIiIiIlJjgkBERERERGpMEIiIiIiISI0JAhERERERqTFBICIiIiIiNSYIRERERESkxgSBiIiIiIjUmCAQEREREZEaEwQiIiIiIlJjgkBERERERGpMEIiIiIiISO3/OMZ2gdzr6F8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = df.drop(columns=[\"VendorName\", \"ModelName\"]).corr()\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f')\n",
    "plt.title('Correlation Heatmap of Numerical Features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pertaining to the heatmap above, keep in mind: target feature is PRP, and ERP is a linear regression prediction of PRP.<br>\n",
    "Hence, there would be multicollinearity (i.e., linear dependency) between PRP and ERP.<br>\n",
    "Intuitively, there would also be multicollinearity between MMIN and MMAX, and CHMIN and CHMAX.<br>\n",
    "We will choose to visualize the following pairs of variables due to the pairs having a positive and negative correlation respectively:<br> \n",
    " 1) MMAX and PRP\n",
    " 2) MMAX and MYCT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def exponential_func(x, a, b):\n",
    "    \"\"\"Creating a function that will compute output values\n",
    "    for a mathematical exponential function, as both the \n",
    "    pairs of variables we chose to visualize appear to have \n",
    "    exponential patterns.\"\"\"\n",
    "    return a*b**x # formula for exponential function, y = a*b^x\n",
    "\n",
    "popt, _ = curve_fit(exponential_func, df['MMAX'], df['PRP'])  # fitting an exponential curve to the scatter points, due to visible trend\n",
    "x_values = np.linspace(df['MMAX'].min(), df['MMAX'].max(), 100) # x values for trendline\n",
    "y_values = exponential_func(x_values, *popt)   # y values for trendline, using exponential_func we created\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.scatter(df['MMAX'], df['PRP'], label='Data Points')\n",
    "plt.plot(x_values, y_values, color='red', label='Fitted Exponential Trendline')\n",
    "plt.title('Published Relative Performance vs. Maximum Main Memory')\n",
    "plt.xlabel('Maximum Main Memory (KB)')\n",
    "plt.ylabel('Published Relative Performance')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "popt, _ = curve_fit(exponential_func, df['MMAX'], df['MYCT'])  # fitting an exponential curve to the scatter points, due to visible trend\n",
    "x_values = np.linspace(df['MMAX'].min(), df['MMAX'].max(), 100) # x values for trendline\n",
    "y_values = exponential_func(x_values, *popt)   # y values for trendline, using exponential_func we created\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.scatter(df['MMAX'], df['MYCT'], label='Data Points')\n",
    "plt.plot(x_values, y_values, color='red', label='Fitted Exponential Trendline')\n",
    "plt.title('Machine Cycle Time vs. Maximum Main Memory')\n",
    "plt.xlabel('Maximum Main Memory (KB)')\n",
    "plt.ylabel('Machine Cycle Time (ns)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorName</th>\n",
       "      <th>ModelName</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>PRP</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>HONEYWELL</td>\n",
       "      <td>dps:6/35</td>\n",
       "      <td>330</td>\n",
       "      <td>1000</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>HONEYWELL</td>\n",
       "      <td>dps:6/92</td>\n",
       "      <td>300</td>\n",
       "      <td>1000</td>\n",
       "      <td>4000</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>64</td>\n",
       "      <td>38</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>HONEYWELL</td>\n",
       "      <td>dps:6/96</td>\n",
       "      <td>300</td>\n",
       "      <td>1000</td>\n",
       "      <td>16000</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>38</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>HONEYWELL</td>\n",
       "      <td>dps:7/35</td>\n",
       "      <td>330</td>\n",
       "      <td>1000</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>HONEYWELL</td>\n",
       "      <td>dps:7/45</td>\n",
       "      <td>330</td>\n",
       "      <td>1000</td>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>SPERRY</td>\n",
       "      <td>80/4</td>\n",
       "      <td>180</td>\n",
       "      <td>512</td>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>SPERRY</td>\n",
       "      <td>80/5</td>\n",
       "      <td>180</td>\n",
       "      <td>262</td>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>SPERRY</td>\n",
       "      <td>80/6</td>\n",
       "      <td>180</td>\n",
       "      <td>512</td>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>SPERRY</td>\n",
       "      <td>80/8</td>\n",
       "      <td>124</td>\n",
       "      <td>1000</td>\n",
       "      <td>8000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>42</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>SPERRY</td>\n",
       "      <td>90/80-model-3</td>\n",
       "      <td>98</td>\n",
       "      <td>1000</td>\n",
       "      <td>8000</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>46</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    VendorName      ModelName  MYCT  MMIN   MMAX  CACH  CHMIN  CHMAX  PRP  ERP\n",
       "80   HONEYWELL       dps:6/35   330  1000   3000     0      2      4   16   23\n",
       "81   HONEYWELL       dps:6/92   300  1000   4000     8      3     64   38   30\n",
       "82   HONEYWELL       dps:6/96   300  1000  16000     8      2    112   38   73\n",
       "83   HONEYWELL       dps:7/35   330  1000   2000     0      1      2   16   20\n",
       "84   HONEYWELL       dps:7/45   330  1000   4000     0      3      6   22   25\n",
       "..         ...            ...   ...   ...    ...   ...    ...    ...  ...  ...\n",
       "201     SPERRY           80/4   180   512   4000     0      1      3   14   24\n",
       "202     SPERRY           80/5   180   262   4000     0      1      3   18   24\n",
       "203     SPERRY           80/6   180   512   4000     0      1      3   21   24\n",
       "204     SPERRY           80/8   124  1000   8000     0      1      8   42   37\n",
       "205     SPERRY  90/80-model-3    98  1000   8000    32      2      8   46   50\n",
       "\n",
       "[90 rows x 10 columns]"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CPU manufacturers with most rows in this dataset\n",
    "vendors_popular5 = df.VendorName.value_counts().nlargest(5).index # same as slicing: df.VendorName.value_counts()[:5].index\n",
    "df_vendors_pop5 = df[df.VendorName.isin(vendors_popular5)]\n",
    "df_vendors_pop5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplots comparing the PRP of the 5 most popular CPU manufacturers in this dataset\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.boxplot(x='VendorName', y='PRP', data=df_vendors_pop5, hue=\"VendorName\", palette=\"deep\")\n",
    "plt.title('PRP Distributions for Most Popular Vendor Names')\n",
    "plt.xlabel('Vendor Name')\n",
    "plt.ylabel('Published Relative Performance')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['VendorName', 'ModelName', 'MYCT', 'MMIN', 'MMAX', 'CACH', 'CHMIN',\n",
       "       'CHMAX', 'PRP', 'ERP'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, shifting from investigating PRP of the most popular manufacturers, we want to investigate the PRP of the best performing manufacturers.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorName</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>PRP</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADVISER</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>199.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>13333.333333</td>\n",
       "      <td>37333.333333</td>\n",
       "      <td>56.888889</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>33.777778</td>\n",
       "      <td>416.333333</td>\n",
       "      <td>436.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>APOLLO</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>3250.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>23.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BASF</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>93.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BTI</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>8032.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>39.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BURROUGHS</td>\n",
       "      <td>137.750000</td>\n",
       "      <td>2129.500000</td>\n",
       "      <td>4862.500000</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>38.375000</td>\n",
       "      <td>49.750000</td>\n",
       "      <td>44.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C.R.D</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>4500.000000</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>42.666667</td>\n",
       "      <td>25.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAMBEX</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>42.800000</td>\n",
       "      <td>58.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CDC</td>\n",
       "      <td>46.666667</td>\n",
       "      <td>2426.666667</td>\n",
       "      <td>9126.666667</td>\n",
       "      <td>45.111111</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>17.555556</td>\n",
       "      <td>130.111111</td>\n",
       "      <td>80.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DEC</td>\n",
       "      <td>401.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>6918.666667</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>47.333333</td>\n",
       "      <td>35.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DG</td>\n",
       "      <td>311.428571</td>\n",
       "      <td>805.714286</td>\n",
       "      <td>8285.714286</td>\n",
       "      <td>9.142857</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.857143</td>\n",
       "      <td>54.142857</td>\n",
       "      <td>41.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FORMATION</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>16.800000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FOUR-PHASE</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GOULD</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>13333.333333</td>\n",
       "      <td>85.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>182.333333</td>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HARRIS</td>\n",
       "      <td>282.857143</td>\n",
       "      <td>630.857143</td>\n",
       "      <td>6752.571429</td>\n",
       "      <td>4.285714</td>\n",
       "      <td>3.857143</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>49.714286</td>\n",
       "      <td>35.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HONEYWELL</td>\n",
       "      <td>208.461538</td>\n",
       "      <td>1615.384615</td>\n",
       "      <td>11461.538462</td>\n",
       "      <td>12.307692</td>\n",
       "      <td>1.846154</td>\n",
       "      <td>32.615385</td>\n",
       "      <td>60.461538</td>\n",
       "      <td>68.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HP</td>\n",
       "      <td>104.285714</td>\n",
       "      <td>1252.571429</td>\n",
       "      <td>4142.857143</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>22.714286</td>\n",
       "      <td>36.428571</td>\n",
       "      <td>31.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>IBM</td>\n",
       "      <td>444.375000</td>\n",
       "      <td>3014.750000</td>\n",
       "      <td>9531.625000</td>\n",
       "      <td>10.718750</td>\n",
       "      <td>3.156250</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>85.093750</td>\n",
       "      <td>71.781250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>IPL</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>11333.333333</td>\n",
       "      <td>18.666667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>68.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MAGNUSON</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>39.666667</td>\n",
       "      <td>64.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MICRODATA</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NAS</td>\n",
       "      <td>64.421053</td>\n",
       "      <td>5368.421053</td>\n",
       "      <td>17894.736842</td>\n",
       "      <td>55.052632</td>\n",
       "      <td>5.684211</td>\n",
       "      <td>13.473684</td>\n",
       "      <td>176.894737</td>\n",
       "      <td>159.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NCR</td>\n",
       "      <td>55.538462</td>\n",
       "      <td>3615.384615</td>\n",
       "      <td>9923.076923</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>4.923077</td>\n",
       "      <td>12.923077</td>\n",
       "      <td>63.307692</td>\n",
       "      <td>84.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NIXDORF</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1333.333333</td>\n",
       "      <td>4666.666667</td>\n",
       "      <td>21.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PERKIN-ELMER</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>674.666667</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>41.666667</td>\n",
       "      <td>37.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PRIME</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>1004.800000</td>\n",
       "      <td>5200.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>53.800000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SIEMENS</td>\n",
       "      <td>92.750000</td>\n",
       "      <td>4252.000000</td>\n",
       "      <td>13750.000000</td>\n",
       "      <td>34.666667</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>13.833333</td>\n",
       "      <td>121.833333</td>\n",
       "      <td>116.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SPERRY</td>\n",
       "      <td>101.384615</td>\n",
       "      <td>2426.769231</td>\n",
       "      <td>22769.230769</td>\n",
       "      <td>44.923077</td>\n",
       "      <td>13.230769</td>\n",
       "      <td>53.384615</td>\n",
       "      <td>254.923077</td>\n",
       "      <td>249.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SRATUS</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>WANG</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      VendorName        MYCT          MMIN          MMAX        CACH  \\\n",
       "0        ADVISER  125.000000    256.000000   6000.000000  256.000000   \n",
       "1         AMDAHL   26.000000  13333.333333  37333.333333   56.888889   \n",
       "2         APOLLO  400.000000    756.000000   3250.000000    2.000000   \n",
       "3           BASF   55.000000   3000.000000  12000.000000   65.000000   \n",
       "4            BTI  275.000000    288.000000   8032.000000    0.000000   \n",
       "5      BURROUGHS  137.750000   2129.500000   4862.500000   18.750000   \n",
       "6          C.R.D  320.000000    320.000000   4500.000000    2.666667   \n",
       "7         CAMBEX   50.000000   1600.000000  12000.000000    8.000000   \n",
       "8            CDC   46.666667   2426.666667   9126.666667   45.111111   \n",
       "9            DEC  401.000000    756.000000   6918.666667    6.333333   \n",
       "10            DG  311.428571    805.714286   8285.714286    9.142857   \n",
       "11     FORMATION  800.000000    256.000000   8000.000000    0.000000   \n",
       "12    FOUR-PHASE  125.000000    512.000000   1000.000000    0.000000   \n",
       "13         GOULD   75.000000   2000.000000  13333.333333   85.333333   \n",
       "14        HARRIS  282.857143    630.857143   6752.571429    4.285714   \n",
       "15     HONEYWELL  208.461538   1615.384615  11461.538462   12.307692   \n",
       "16            HP  104.285714   1252.571429   4142.857143    3.428571   \n",
       "17           IBM  444.375000   3014.750000   9531.625000   10.718750   \n",
       "18           IPL   50.000000   3000.000000  11333.333333   18.666667   \n",
       "19      MAGNUSON   75.000000   1500.000000  12000.000000   18.000000   \n",
       "20     MICRODATA  150.000000    512.000000   4000.000000    0.000000   \n",
       "21           NAS   64.421053   5368.421053  17894.736842   55.052632   \n",
       "22           NCR   55.538462   3615.384615   9923.076923   32.000000   \n",
       "23       NIXDORF  200.000000   1333.333333   4666.666667   21.333333   \n",
       "24  PERKIN-ELMER  250.000000    674.666667   8000.000000    0.333333   \n",
       "25         PRIME  160.000000   1004.800000   5200.000000   12.000000   \n",
       "26       SIEMENS   92.750000   4252.000000  13750.000000   34.666667   \n",
       "27        SPERRY  101.384615   2426.769231  22769.230769   44.923077   \n",
       "28        SRATUS  125.000000   2000.000000   8000.000000    0.000000   \n",
       "29          WANG  480.000000    756.000000   6000.000000   16.000000   \n",
       "\n",
       "        CHMIN       CHMAX         PRP         ERP  \n",
       "0   16.000000  128.000000  198.000000  199.000000  \n",
       "1   13.333333   33.777778  416.333333  436.666667  \n",
       "2    1.000000    4.000000   39.000000   23.500000  \n",
       "3    1.000000    8.000000  115.000000   93.500000  \n",
       "4    2.500000   18.000000   22.500000   39.500000  \n",
       "5    5.750000   38.375000   49.750000   44.625000  \n",
       "6    1.000000    5.833333   42.666667   25.666667  \n",
       "7    3.000000    5.400000   42.800000   58.600000  \n",
       "8    8.333333   17.555556  130.111111   80.222222  \n",
       "9    1.666667    6.500000   47.333333   35.833333  \n",
       "10   1.000000    1.857143   54.142857   41.285714  \n",
       "11   1.000000    4.000000   16.800000   34.000000  \n",
       "12   8.000000   20.000000   36.000000   19.000000  \n",
       "13   1.000000   38.000000  182.333333  115.000000  \n",
       "14   3.857143   25.000000   49.714286   35.428571  \n",
       "15   1.846154   32.615385   60.461538   68.153846  \n",
       "16   3.000000   22.714286   36.428571   31.428571  \n",
       "17   3.156250    6.750000   85.093750   71.781250  \n",
       "18   1.666667    6.666667   69.000000   68.333333  \n",
       "19   3.666667   11.000000   39.666667   64.166667  \n",
       "20   8.000000  128.000000   30.000000   33.000000  \n",
       "21   5.684211   13.473684  176.894737  159.105263  \n",
       "22   4.923077   12.923077   63.307692   84.769231  \n",
       "23   1.000000    3.666667   32.000000   37.666667  \n",
       "24   2.000000    7.333333   41.666667   37.333333  \n",
       "25   1.400000   10.800000   53.800000   34.000000  \n",
       "26   7.250000   13.833333  121.833333  116.333333  \n",
       "27  13.230769   53.384615  254.923077  249.692308  \n",
       "28   2.000000   14.000000   52.000000   41.000000  \n",
       "29   0.000000    0.000000   56.000000   36.000000  "
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_VendorAvg = df.drop(columns=[\"ModelName\"]).groupby(\"VendorName\").mean().reset_index()\n",
    "df_VendorAvg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorName</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>PRP</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>13333.333333</td>\n",
       "      <td>37333.333333</td>\n",
       "      <td>56.888889</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>33.777778</td>\n",
       "      <td>416.333333</td>\n",
       "      <td>436.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SPERRY</td>\n",
       "      <td>101.384615</td>\n",
       "      <td>2426.769231</td>\n",
       "      <td>22769.230769</td>\n",
       "      <td>44.923077</td>\n",
       "      <td>13.230769</td>\n",
       "      <td>53.384615</td>\n",
       "      <td>254.923077</td>\n",
       "      <td>249.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADVISER</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>199.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GOULD</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>13333.333333</td>\n",
       "      <td>85.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>182.333333</td>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NAS</td>\n",
       "      <td>64.421053</td>\n",
       "      <td>5368.421053</td>\n",
       "      <td>17894.736842</td>\n",
       "      <td>55.052632</td>\n",
       "      <td>5.684211</td>\n",
       "      <td>13.473684</td>\n",
       "      <td>176.894737</td>\n",
       "      <td>159.105263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  VendorName        MYCT          MMIN          MMAX        CACH      CHMIN  \\\n",
       "0     AMDAHL   26.000000  13333.333333  37333.333333   56.888889  13.333333   \n",
       "1     SPERRY  101.384615   2426.769231  22769.230769   44.923077  13.230769   \n",
       "2    ADVISER  125.000000    256.000000   6000.000000  256.000000  16.000000   \n",
       "3      GOULD   75.000000   2000.000000  13333.333333   85.333333   1.000000   \n",
       "4        NAS   64.421053   5368.421053  17894.736842   55.052632   5.684211   \n",
       "\n",
       "        CHMAX         PRP         ERP  \n",
       "0   33.777778  416.333333  436.666667  \n",
       "1   53.384615  254.923077  249.692308  \n",
       "2  128.000000  198.000000  199.000000  \n",
       "3   38.000000  182.333333  115.000000  \n",
       "4   13.473684  176.894737  159.105263  "
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_VendorAvg.sort_values(by=\"PRP\", ascending=False, inplace=True)\n",
    "df_top_5_avg = df_VendorAvg[:5].reset_index(drop=True)\n",
    "df_top_5_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AMDAHL', 'SPERRY', 'ADVISER', 'GOULD', 'NAS']\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>VendorName</th>\n",
       "      <th>ModelName</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>PRP</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADVISER</td>\n",
       "      <td>32/60</td>\n",
       "      <td>125</td>\n",
       "      <td>256</td>\n",
       "      <td>6000</td>\n",
       "      <td>256</td>\n",
       "      <td>16</td>\n",
       "      <td>128</td>\n",
       "      <td>198</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>470v/7</td>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>32000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>269</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>470v/7a</td>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>32000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>220</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  VendorName ModelName  MYCT  MMIN   MMAX  CACH  CHMIN  CHMAX  PRP  ERP\n",
       "0    ADVISER     32/60   125   256   6000   256     16    128  198  199\n",
       "1     AMDAHL    470v/7    29  8000  32000    32      8     32  269  253\n",
       "2     AMDAHL   470v/7a    29  8000  32000    32      8     32  220  253"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top5_VendorNames = df_top_5_avg.VendorName.to_list()\n",
    "print(top5_VendorNames)\n",
    "df_top5 = df[df.VendorName.isin(top5_VendorNames)]\n",
    "df_top5.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplots comparing the PRP of the 5 best performing CPU manufacturers in this dataset\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.boxplot(x='VendorName', y='PRP', data=df_top5, hue=\"VendorName\", palette=\"mako\")\n",
    "plt.title('PRP Distributions for Best Performing Vendor Names')\n",
    "plt.xlabel('Vendor Name')\n",
    "plt.ylabel('Published Relative Performance')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that NAS and SPERRY are the only 2 Vendor Names that are in the top 5 for both most popular and best performing Vendor Names. <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>PRP</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>209.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>209.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>203.822967</td>\n",
       "      <td>2867.980861</td>\n",
       "      <td>11796.153110</td>\n",
       "      <td>25.205742</td>\n",
       "      <td>4.698565</td>\n",
       "      <td>18.267943</td>\n",
       "      <td>105.622010</td>\n",
       "      <td>99.330144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>260.262926</td>\n",
       "      <td>3878.742758</td>\n",
       "      <td>11726.564377</td>\n",
       "      <td>40.628722</td>\n",
       "      <td>6.816274</td>\n",
       "      <td>25.997318</td>\n",
       "      <td>160.830733</td>\n",
       "      <td>154.757102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>225.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>16000.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1500.000000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>64000.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>1150.000000</td>\n",
       "      <td>1238.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MYCT          MMIN          MMAX        CACH       CHMIN  \\\n",
       "count   209.000000    209.000000    209.000000  209.000000  209.000000   \n",
       "mean    203.822967   2867.980861  11796.153110   25.205742    4.698565   \n",
       "std     260.262926   3878.742758  11726.564377   40.628722    6.816274   \n",
       "min      17.000000     64.000000     64.000000    0.000000    0.000000   \n",
       "25%      50.000000    768.000000   4000.000000    0.000000    1.000000   \n",
       "50%     110.000000   2000.000000   8000.000000    8.000000    2.000000   \n",
       "75%     225.000000   4000.000000  16000.000000   32.000000    6.000000   \n",
       "max    1500.000000  32000.000000  64000.000000  256.000000   52.000000   \n",
       "\n",
       "            CHMAX          PRP          ERP  \n",
       "count  209.000000   209.000000   209.000000  \n",
       "mean    18.267943   105.622010    99.330144  \n",
       "std     25.997318   160.830733   154.757102  \n",
       "min      0.000000     6.000000    15.000000  \n",
       "25%      5.000000    27.000000    28.000000  \n",
       "50%      8.000000    50.000000    45.000000  \n",
       "75%     24.000000   113.000000   101.000000  \n",
       "max    176.000000  1150.000000  1238.000000  "
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Numerical Columns</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Median</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Range</th>\n",
       "      <th>IQR</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Skew Direction</th>\n",
       "      <th>Skew Value</th>\n",
       "      <th>Kurtosis</th>\n",
       "      <th>Coeff of Var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MYCT</td>\n",
       "      <td>203.822967</td>\n",
       "      <td>110.0</td>\n",
       "      <td>50</td>\n",
       "      <td>1483</td>\n",
       "      <td>175.0</td>\n",
       "      <td>6.773679e+04</td>\n",
       "      <td>Right</td>\n",
       "      <td>2.525857</td>\n",
       "      <td>6.866358</td>\n",
       "      <td>127.690677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MMIN</td>\n",
       "      <td>2867.980861</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>31936</td>\n",
       "      <td>3232.0</td>\n",
       "      <td>1.504465e+07</td>\n",
       "      <td>NA</td>\n",
       "      <td>3.490649</td>\n",
       "      <td>17.166146</td>\n",
       "      <td>135.242979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MMAX</td>\n",
       "      <td>11796.153110</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>8000</td>\n",
       "      <td>63936</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1.375123e+08</td>\n",
       "      <td>NA</td>\n",
       "      <td>2.125268</td>\n",
       "      <td>5.733634</td>\n",
       "      <td>99.410073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CACH</td>\n",
       "      <td>25.205742</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.650693e+03</td>\n",
       "      <td>Right</td>\n",
       "      <td>2.804463</td>\n",
       "      <td>10.005560</td>\n",
       "      <td>161.188361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHMIN</td>\n",
       "      <td>4.698565</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.646158e+01</td>\n",
       "      <td>Right</td>\n",
       "      <td>3.998371</td>\n",
       "      <td>21.906383</td>\n",
       "      <td>145.071401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CHMAX</td>\n",
       "      <td>18.267943</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6</td>\n",
       "      <td>176</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.758606e+02</td>\n",
       "      <td>Right</td>\n",
       "      <td>3.570046</td>\n",
       "      <td>15.481950</td>\n",
       "      <td>142.311145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PRP</td>\n",
       "      <td>105.622010</td>\n",
       "      <td>50.0</td>\n",
       "      <td>32</td>\n",
       "      <td>1144</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2.586652e+04</td>\n",
       "      <td>Right</td>\n",
       "      <td>3.864820</td>\n",
       "      <td>18.766111</td>\n",
       "      <td>152.270094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ERP</td>\n",
       "      <td>99.330144</td>\n",
       "      <td>45.0</td>\n",
       "      <td>28</td>\n",
       "      <td>1223</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2.394976e+04</td>\n",
       "      <td>Right</td>\n",
       "      <td>4.273072</td>\n",
       "      <td>22.513998</td>\n",
       "      <td>155.800744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Numerical Columns          Mean  Median  Mode  Range      IQR      Variance  \\\n",
       "0              MYCT    203.822967   110.0    50   1483    175.0  6.773679e+04   \n",
       "1              MMIN   2867.980861  2000.0  2000  31936   3232.0  1.504465e+07   \n",
       "2              MMAX  11796.153110  8000.0  8000  63936  12000.0  1.375123e+08   \n",
       "3              CACH     25.205742     8.0     0    256     32.0  1.650693e+03   \n",
       "4             CHMIN      4.698565     2.0     1     52      5.0  4.646158e+01   \n",
       "5             CHMAX     18.267943     8.0     6    176     19.0  6.758606e+02   \n",
       "6               PRP    105.622010    50.0    32   1144     86.0  2.586652e+04   \n",
       "7               ERP     99.330144    45.0    28   1223     73.0  2.394976e+04   \n",
       "\n",
       "  Skew Direction  Skew Value   Kurtosis  Coeff of Var  \n",
       "0          Right    2.525857   6.866358    127.690677  \n",
       "1             NA    3.490649  17.166146    135.242979  \n",
       "2             NA    2.125268   5.733634     99.410073  \n",
       "3          Right    2.804463  10.005560    161.188361  \n",
       "4          Right    3.998371  21.906383    145.071401  \n",
       "5          Right    3.570046  15.481950    142.311145  \n",
       "6          Right    3.864820  18.766111    152.270094  \n",
       "7          Right    4.273072  22.513998    155.800744  "
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def stats(t): # takes a df, returns detailed statistical info about column distributions\n",
    "    numeric_col_list = t.select_dtypes(include=[\"number\"]).columns\n",
    "    mean_list, median_list, mode_list, range_list, iqr_list, variance_list, is_skew_list, skew_val_list, kurt_list, cv_list = [], [], [], [], [], [], [], [], [], []\n",
    "    \n",
    "    for i in numeric_col_list:\n",
    "        mean_list.append(np.mean(t[i]))\n",
    "        median_list.append(np.median(t[i]))\n",
    "        mode_list.append(t[i].mode()[0])\n",
    "        range_list.append(t[i].max()-t[i].min())\n",
    "        iqr_list.append(t[i].quantile([0.75])[0.75]-t[i].quantile([0.25])[0.25])\n",
    "        variance_list.append(t[i].var())\n",
    "        if np.mean(t[i])>np.median(t[i]) and np.median(t[i])>t[i].mode()[0]:\n",
    "            is_skew_list.append(\"Right\") # right skewed\n",
    "        elif np.mean(t[i])<np.median(t[i]) and np.median(t[i])<t[i].mode()[0]:\n",
    "            is_skew_list.append(\"Left\") # left skewed\n",
    "        else:\n",
    "            is_skew_list.append(\"NA\") # not skewed\n",
    "        skew_val_list.append(skew(t[i]))  # skewness/asymmetry of distribution\n",
    "        kurt_list.append(kurtosis(t[i]))  # tailedness of distribution\n",
    "        cv_list.append(t[i].std()/t[i].mean()*100)  # percent: std_dev/mean\n",
    "\n",
    "    dictionary = {\"Numerical Columns\": numeric_col_list, \n",
    "                  \"Mean\": mean_list, \n",
    "                  \"Median\": median_list, \n",
    "                  \"Mode\": mode_list,\n",
    "                  \"Range\": range_list,\n",
    "                  \"IQR\": iqr_list,\n",
    "                  \"Variance\": variance_list,\n",
    "                  \"Skew Direction\": is_skew_list,\n",
    "                  \"Skew Value\": skew_val_list,\n",
    "                  \"Kurtosis\": kurt_list,\n",
    "                  \"Coeff of Var\": cv_list}\n",
    "    return pd.DataFrame(dictionary)\n",
    "        \n",
    "df_stats = stats(df)\n",
    "df_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x1200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we will make histograms showing distributions of all numerical variables\n",
    "numeric_cols = df.select_dtypes(include=[\"number\"]).columns\n",
    "\n",
    "plt.figure(figsize=(16, 12))\n",
    "for i, col in enumerate(numeric_cols):\n",
    "    plt.subplot(len(numeric_cols)//2, 2, i+1) # rows, cols, index\n",
    "    sns.histplot(df[col], kde=True, bins=50, color='skyblue')\n",
    "    plt.axvline(df[col].mean(), color='red', linestyle='dashed', linewidth=2) # average (mean) of each distribution is marked by red line\n",
    "    plt.title(f'Distribution of {col}')\n",
    "    plt.xlabel(col)\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Numerical Columns</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Median</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Range</th>\n",
       "      <th>IQR</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Skew Direction</th>\n",
       "      <th>Skew Value</th>\n",
       "      <th>Kurtosis</th>\n",
       "      <th>Coeff of Var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MMAX</td>\n",
       "      <td>11796.153110</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>8000</td>\n",
       "      <td>63936</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1.375123e+08</td>\n",
       "      <td>NA</td>\n",
       "      <td>2.125268</td>\n",
       "      <td>5.733634</td>\n",
       "      <td>99.410073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MYCT</td>\n",
       "      <td>203.822967</td>\n",
       "      <td>110.0</td>\n",
       "      <td>50</td>\n",
       "      <td>1483</td>\n",
       "      <td>175.0</td>\n",
       "      <td>6.773679e+04</td>\n",
       "      <td>Right</td>\n",
       "      <td>2.525857</td>\n",
       "      <td>6.866358</td>\n",
       "      <td>127.690677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MMIN</td>\n",
       "      <td>2867.980861</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>31936</td>\n",
       "      <td>3232.0</td>\n",
       "      <td>1.504465e+07</td>\n",
       "      <td>NA</td>\n",
       "      <td>3.490649</td>\n",
       "      <td>17.166146</td>\n",
       "      <td>135.242979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CHMAX</td>\n",
       "      <td>18.267943</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6</td>\n",
       "      <td>176</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.758606e+02</td>\n",
       "      <td>Right</td>\n",
       "      <td>3.570046</td>\n",
       "      <td>15.481950</td>\n",
       "      <td>142.311145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHMIN</td>\n",
       "      <td>4.698565</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.646158e+01</td>\n",
       "      <td>Right</td>\n",
       "      <td>3.998371</td>\n",
       "      <td>21.906383</td>\n",
       "      <td>145.071401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PRP</td>\n",
       "      <td>105.622010</td>\n",
       "      <td>50.0</td>\n",
       "      <td>32</td>\n",
       "      <td>1144</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2.586652e+04</td>\n",
       "      <td>Right</td>\n",
       "      <td>3.864820</td>\n",
       "      <td>18.766111</td>\n",
       "      <td>152.270094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ERP</td>\n",
       "      <td>99.330144</td>\n",
       "      <td>45.0</td>\n",
       "      <td>28</td>\n",
       "      <td>1223</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2.394976e+04</td>\n",
       "      <td>Right</td>\n",
       "      <td>4.273072</td>\n",
       "      <td>22.513998</td>\n",
       "      <td>155.800744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CACH</td>\n",
       "      <td>25.205742</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.650693e+03</td>\n",
       "      <td>Right</td>\n",
       "      <td>2.804463</td>\n",
       "      <td>10.005560</td>\n",
       "      <td>161.188361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Numerical Columns          Mean  Median  Mode  Range      IQR      Variance  \\\n",
       "2              MMAX  11796.153110  8000.0  8000  63936  12000.0  1.375123e+08   \n",
       "0              MYCT    203.822967   110.0    50   1483    175.0  6.773679e+04   \n",
       "1              MMIN   2867.980861  2000.0  2000  31936   3232.0  1.504465e+07   \n",
       "5             CHMAX     18.267943     8.0     6    176     19.0  6.758606e+02   \n",
       "4             CHMIN      4.698565     2.0     1     52      5.0  4.646158e+01   \n",
       "6               PRP    105.622010    50.0    32   1144     86.0  2.586652e+04   \n",
       "7               ERP     99.330144    45.0    28   1223     73.0  2.394976e+04   \n",
       "3              CACH     25.205742     8.0     0    256     32.0  1.650693e+03   \n",
       "\n",
       "  Skew Direction  Skew Value   Kurtosis  Coeff of Var  \n",
       "2             NA    2.125268   5.733634     99.410073  \n",
       "0          Right    2.525857   6.866358    127.690677  \n",
       "1             NA    3.490649  17.166146    135.242979  \n",
       "5          Right    3.570046  15.481950    142.311145  \n",
       "4          Right    3.998371  21.906383    145.071401  \n",
       "6          Right    3.864820  18.766111    152.270094  \n",
       "7          Right    4.273072  22.513998    155.800744  \n",
       "3          Right    2.804463  10.005560    161.188361  "
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cv_sorted = df_stats.sort_values(by=\"Coeff of Var\")\n",
    "df_cv_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigating which features have the greatest amount of dispersion from the mean of their respective distributions\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(x=\"Numerical Columns\", y=\"Coeff of Var\", data=df_cv_sorted, hue=\"Numerical Columns\", legend=False, palette=\"magma\")\n",
    "plt.title('Coefficient of Variation for Numerical Features', fontsize=16, color='navy')\n",
    "plt.xticks(rotation=45, fontsize=12, color='navy')\n",
    "plt.ylabel('CV (%)', fontsize=14, color='navy')\n",
    "plt.xlabel('Features', fontsize=14, color='navy')\n",
    "plt.grid(True, axis='y', linestyle='--', alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorName</th>\n",
       "      <th>ModelName</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>PRP</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADVISER</td>\n",
       "      <td>32/60</td>\n",
       "      <td>125</td>\n",
       "      <td>256</td>\n",
       "      <td>6000</td>\n",
       "      <td>256</td>\n",
       "      <td>16</td>\n",
       "      <td>128</td>\n",
       "      <td>198</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>470v/7</td>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>32000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>269</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>470v/7a</td>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>32000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>220</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>470v/7b</td>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>32000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>172</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>470v/7c</td>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>16000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  VendorName ModelName  MYCT  MMIN   MMAX  CACH  CHMIN  CHMAX  PRP  ERP\n",
       "0    ADVISER     32/60   125   256   6000   256     16    128  198  199\n",
       "1     AMDAHL    470v/7    29  8000  32000    32      8     32  269  253\n",
       "2     AMDAHL   470v/7a    29  8000  32000    32      8     32  220  253\n",
       "3     AMDAHL   470v/7b    29  8000  32000    32      8     32  172  253\n",
       "4     AMDAHL   470v/7c    29  8000  16000    32      8     16  132  132"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorName</th>\n",
       "      <th>PerformanceType</th>\n",
       "      <th>PerformanceValue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADVISER</td>\n",
       "      <td>PRP</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>PRP</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>PRP</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>PRP</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>PRP</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>SPERRY</td>\n",
       "      <td>ERP</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>SPERRY</td>\n",
       "      <td>ERP</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>SPERRY</td>\n",
       "      <td>ERP</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>SPERRY</td>\n",
       "      <td>ERP</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>SPERRY</td>\n",
       "      <td>ERP</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorName PerformanceType  PerformanceValue\n",
       "0     ADVISER             PRP               198\n",
       "1      AMDAHL             PRP               269\n",
       "2      AMDAHL             PRP               220\n",
       "3      AMDAHL             PRP               172\n",
       "4      AMDAHL             PRP               132\n",
       "..        ...             ...               ...\n",
       "85     SPERRY             ERP                24\n",
       "86     SPERRY             ERP                24\n",
       "87     SPERRY             ERP                24\n",
       "88     SPERRY             ERP                37\n",
       "89     SPERRY             ERP                50\n",
       "\n",
       "[90 rows x 3 columns]"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigate the discrepancies between ERP (estimated performance achieved by linear regression) and PRP (published performance, our target variable)\n",
    "# Looking at our top 5 best performing Vendor Names\n",
    "df_melted = pd.melt(df_top5, id_vars=['VendorName'], value_vars=['PRP', 'ERP'], \n",
    "                    var_name='PerformanceType', value_name='PerformanceValue')\n",
    "df_melted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplots comparing the ERP with the PRP for the 5 best performing CPU manufacturers in this dataset\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.boxplot(x='VendorName', y='PerformanceValue', hue='PerformanceType', data=df_melted, palette='Purples')\n",
    "plt.title('PRP and ERP Distributions for Best Performing Vendor Names')\n",
    "plt.xlabel('Vendor Name')\n",
    "plt.ylabel('Performance Value')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The medians of the ERP predictions achieved by linear regression (done by the creators of the article from which this dataset is sourced) <br>\n",
    "are quite good, except for GOULD, which is significantly off. The upper and lower quartiles being misaligned show some discrepancy <br>\n",
    "between the ERP predictions and the PRP values (groud truth).\n",
    "The predictions appear reasonably good on average."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now, we will build our predictive models using Ridge Regression, Lasso Regression, Decision Trees, and Random Forests.\n",
    "\n",
    "Without implementing the models yet, we already know that Ridge Regression should be a good choice for this dataset <br>\n",
    "due to the multicollinearity present, which Ridge Regression is robust to and can handle well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorName</th>\n",
       "      <th>ModelName</th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>PRP</th>\n",
       "      <th>ERP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADVISER</td>\n",
       "      <td>32/60</td>\n",
       "      <td>125</td>\n",
       "      <td>256</td>\n",
       "      <td>6000</td>\n",
       "      <td>256</td>\n",
       "      <td>16</td>\n",
       "      <td>128</td>\n",
       "      <td>198</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>470v/7</td>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>32000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>269</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>470v/7a</td>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>32000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>220</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>470v/7b</td>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>32000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>172</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMDAHL</td>\n",
       "      <td>470v/7c</td>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>16000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>SPERRY</td>\n",
       "      <td>80/8</td>\n",
       "      <td>124</td>\n",
       "      <td>1000</td>\n",
       "      <td>8000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>42</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>SPERRY</td>\n",
       "      <td>90/80-model-3</td>\n",
       "      <td>98</td>\n",
       "      <td>1000</td>\n",
       "      <td>8000</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>46</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>SRATUS</td>\n",
       "      <td>32</td>\n",
       "      <td>125</td>\n",
       "      <td>2000</td>\n",
       "      <td>8000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>52</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>WANG</td>\n",
       "      <td>vs-100</td>\n",
       "      <td>480</td>\n",
       "      <td>512</td>\n",
       "      <td>8000</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>WANG</td>\n",
       "      <td>vs-90</td>\n",
       "      <td>480</td>\n",
       "      <td>1000</td>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>209 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    VendorName      ModelName  MYCT  MMIN   MMAX  CACH  CHMIN  CHMAX  PRP  ERP\n",
       "0      ADVISER          32/60   125   256   6000   256     16    128  198  199\n",
       "1       AMDAHL         470v/7    29  8000  32000    32      8     32  269  253\n",
       "2       AMDAHL        470v/7a    29  8000  32000    32      8     32  220  253\n",
       "3       AMDAHL        470v/7b    29  8000  32000    32      8     32  172  253\n",
       "4       AMDAHL        470v/7c    29  8000  16000    32      8     16  132  132\n",
       "..         ...            ...   ...   ...    ...   ...    ...    ...  ...  ...\n",
       "204     SPERRY           80/8   124  1000   8000     0      1      8   42   37\n",
       "205     SPERRY  90/80-model-3    98  1000   8000    32      2      8   46   50\n",
       "206     SRATUS             32   125  2000   8000     0      2     14   52   41\n",
       "207       WANG         vs-100   480   512   8000    32      0      0   67   47\n",
       "208       WANG          vs-90   480  1000   4000     0      0      0   45   25\n",
       "\n",
       "[209 rows x 10 columns]"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>125</td>\n",
       "      <td>256</td>\n",
       "      <td>6000</td>\n",
       "      <td>256</td>\n",
       "      <td>16</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>32000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>32000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>32000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>8000</td>\n",
       "      <td>16000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>124</td>\n",
       "      <td>1000</td>\n",
       "      <td>8000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>98</td>\n",
       "      <td>1000</td>\n",
       "      <td>8000</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>125</td>\n",
       "      <td>2000</td>\n",
       "      <td>8000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>480</td>\n",
       "      <td>512</td>\n",
       "      <td>8000</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>480</td>\n",
       "      <td>1000</td>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>209 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     MYCT  MMIN   MMAX  CACH  CHMIN  CHMAX\n",
       "0     125   256   6000   256     16    128\n",
       "1      29  8000  32000    32      8     32\n",
       "2      29  8000  32000    32      8     32\n",
       "3      29  8000  32000    32      8     32\n",
       "4      29  8000  16000    32      8     16\n",
       "..    ...   ...    ...   ...    ...    ...\n",
       "204   124  1000   8000     0      1      8\n",
       "205    98  1000   8000    32      2      8\n",
       "206   125  2000   8000     0      2     14\n",
       "207   480   512   8000    32      0      0\n",
       "208   480  1000   4000     0      0      0\n",
       "\n",
       "[209 rows x 6 columns]"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(columns=[\"VendorName\", \"ModelName\", \"PRP\", \"ERP\"]) # our features/predictors/variables\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      198\n",
       "1      269\n",
       "2      220\n",
       "3      172\n",
       "4      132\n",
       "      ... \n",
       "204     42\n",
       "205     46\n",
       "206     52\n",
       "207     67\n",
       "208     45\n",
       "Name: PRP, Length: 209, dtype: int64"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['PRP']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.30358614, -0.67502598, -0.49546221,  5.69420785,  1.66198873,\n",
       "         4.23103334],\n",
       "       [-0.67332952,  1.32629082,  1.72704937,  0.16762947,  0.48550899,\n",
       "         0.52947875],\n",
       "       [-0.67332952,  1.32629082,  1.72704937,  0.16762947,  0.48550899,\n",
       "         0.52947875],\n",
       "       ...,\n",
       "       [-0.30358614, -0.2243162 , -0.32449978, -0.62188173, -0.39685082,\n",
       "        -0.16456273],\n",
       "       [ 1.06369405, -0.60886675, -0.32449978,  0.16762947, -0.69097076,\n",
       "        -0.70437278],\n",
       "       [ 1.06369405, -0.48275071, -0.66642464, -0.62188173, -0.69097076,\n",
       "        -0.70437278]])"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((167, 6), (42, 6))"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
    "X_train.shape, X_test.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is 1.0:\n",
      "Training MSE:\t3118.006\n",
      "Training R^2:\t0.838\n",
      "Test MSE:\t5674.654\n",
      "Test R^2:\t0.889\n",
      "\n",
      "alpha is 1.5:\n",
      "Training MSE:\t3118.430\n",
      "Training R^2:\t0.838\n",
      "Test MSE:\t5695.815\n",
      "Test R^2:\t0.888\n",
      "\n",
      "alpha is 2.0:\n",
      "Training MSE:\t3119.014\n",
      "Training R^2:\t0.838\n",
      "Test MSE:\t5717.207\n",
      "Test R^2:\t0.888\n",
      "\n",
      "alpha is 2.5:\n",
      "Training MSE:\t3119.751\n",
      "Training R^2:\t0.838\n",
      "Test MSE:\t5738.817\n",
      "Test R^2:\t0.887\n",
      "\n",
      "alpha is 3.0:\n",
      "Training MSE:\t3120.637\n",
      "Training R^2:\t0.838\n",
      "Test MSE:\t5760.635\n",
      "Test R^2:\t0.887\n",
      "\n",
      "alpha is 3.5:\n",
      "Training MSE:\t3121.667\n",
      "Training R^2:\t0.838\n",
      "Test MSE:\t5782.651\n",
      "Test R^2:\t0.886\n",
      "\n",
      "alpha is 4.0:\n",
      "Training MSE:\t3122.835\n",
      "Training R^2:\t0.838\n",
      "Test MSE:\t5804.856\n",
      "Test R^2:\t0.886\n",
      "\n",
      "alpha is 4.5:\n",
      "Training MSE:\t3124.136\n",
      "Training R^2:\t0.837\n",
      "Test MSE:\t5827.240\n",
      "Test R^2:\t0.886\n",
      "\n",
      "alpha is 5.0:\n",
      "Training MSE:\t3125.567\n",
      "Training R^2:\t0.837\n",
      "Test MSE:\t5849.795\n",
      "Test R^2:\t0.885\n",
      "\n",
      "alpha is 5.5:\n",
      "Training MSE:\t3127.123\n",
      "Training R^2:\t0.837\n",
      "Test MSE:\t5872.513\n",
      "Test R^2:\t0.885\n",
      "\n",
      "alpha is 6.0:\n",
      "Training MSE:\t3128.800\n",
      "Training R^2:\t0.837\n",
      "Test MSE:\t5895.385\n",
      "Test R^2:\t0.884\n",
      "\n",
      "alpha is 6.5:\n",
      "Training MSE:\t3130.594\n",
      "Training R^2:\t0.837\n",
      "Test MSE:\t5918.405\n",
      "Test R^2:\t0.884\n",
      "\n",
      "alpha is 7.0:\n",
      "Training MSE:\t3132.501\n",
      "Training R^2:\t0.837\n",
      "Test MSE:\t5941.565\n",
      "Test R^2:\t0.883\n",
      "\n",
      "alpha is 7.5:\n",
      "Training MSE:\t3134.518\n",
      "Training R^2:\t0.837\n",
      "Test MSE:\t5964.858\n",
      "Test R^2:\t0.883\n",
      "\n",
      "alpha is 8.0:\n",
      "Training MSE:\t3136.641\n",
      "Training R^2:\t0.837\n",
      "Test MSE:\t5988.279\n",
      "Test R^2:\t0.882\n",
      "\n",
      "alpha is 8.5:\n",
      "Training MSE:\t3138.867\n",
      "Training R^2:\t0.837\n",
      "Test MSE:\t6011.821\n",
      "Test R^2:\t0.882\n",
      "\n",
      "alpha is 9.0:\n",
      "Training MSE:\t3141.192\n",
      "Training R^2:\t0.837\n",
      "Test MSE:\t6035.477\n",
      "Test R^2:\t0.881\n",
      "\n",
      "alpha is 9.5:\n",
      "Training MSE:\t3143.613\n",
      "Training R^2:\t0.836\n",
      "Test MSE:\t6059.244\n",
      "Test R^2:\t0.881\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# implementing Ridge Regression with various values for alpha (regularization strength)\n",
    "# searching for the alpha value that gives best R-squared value (closest to 1)\n",
    "for i in np.arange(1,10,0.5):\n",
    "  ridge_reg = Ridge(alpha=i)  # the hyperparameter alpha is tuned to i\n",
    "  ridge_reg.fit(X_train, y_train)\n",
    "  y_train_pred = ridge_reg.predict(X_train)\n",
    "  y_test_pred = ridge_reg.predict(X_test)\n",
    "\n",
    "  train_mse = mean_squared_error(y_train, y_train_pred)\n",
    "  test_mse = mean_squared_error(y_test, y_test_pred)\n",
    "  train_r2 = r2_score(y_train, y_train_pred)\n",
    "  test_r2 = r2_score(y_test, y_test_pred)\n",
    "\n",
    "  print(f\"alpha is {i}:\")\n",
    "  print(f\"Training MSE:\\t{train_mse:.3f}\\nTraining R^2:\\t{train_r2:.3f}\")\n",
    "  print(f\"Test MSE:\\t{test_mse:.3f}\\nTest R^2:\\t{test_r2:.3f}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MSE:\t3118.006\n",
      "Training R^2:\t0.838\n",
      "Test MSE:\t5674.654\n",
      "Test R^2:\t0.889\n"
     ]
    }
   ],
   "source": [
    "ridge_reg = Ridge(alpha=1)  # the chosen value for the hyperparameter alpha: 1\n",
    "ridge_reg.fit(X_train, y_train)\n",
    "y_train_pred = ridge_reg.predict(X_train)\n",
    "y_test_pred = ridge_reg.predict(X_test)\n",
    "\n",
    "train_mse = mean_squared_error(y_train, y_train_pred)\n",
    "test_mse = mean_squared_error(y_test, y_test_pred)\n",
    "train_r2 = r2_score(y_train, y_train_pred)\n",
    "test_r2 = r2_score(y_test, y_test_pred)\n",
    "\n",
    "print(f\"Training MSE:\\t{train_mse:.3f}\\nTraining R^2:\\t{train_r2:.3f}\")\n",
    "print(f\"Test MSE:\\t{test_mse:.3f}\\nTest R^2:\\t{test_r2:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRP Predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-17.732598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>867.314682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>278.188780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>109.703372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>130.446898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>45.938254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>37.275343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>105.593884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25.658656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>115.677503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4.298633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>188.519669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>112.328363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>82.635727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>50.918490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>147.990913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>29.112142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.477724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>35.592725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-10.963331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PRP Predictions\n",
       "7        -17.732598\n",
       "8        867.314682\n",
       "9        278.188780\n",
       "10       109.703372\n",
       "11       130.446898\n",
       "12        45.938254\n",
       "13        37.275343\n",
       "14       105.593884\n",
       "15        25.658656\n",
       "16       115.677503\n",
       "17         4.298633\n",
       "18       188.519669\n",
       "19       112.328363\n",
       "20        82.635727\n",
       "21        50.918490\n",
       "22       147.990913\n",
       "23        29.112142\n",
       "24         3.477724\n",
       "25        35.592725\n",
       "26       -10.963331"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_y_pred = pd.DataFrame({\"PRP Predictions\": y_test_pred})\n",
    "df_y_pred[7:27] # we see there are some negative values predicted for PRP, but this is not possible, so we will process the predictions to disallow negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MSE:\t3051.405\n",
      "Training R^2:\t0.841\n",
      "Test MSE:\t5595.763\n",
      "Test R^2:\t0.890\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = np.maximum(y_train_pred, 0) # ensure we achieve non-negative predictions\n",
    "y_test_pred = np.maximum(y_test_pred, 0)\n",
    "\n",
    "train_mse = mean_squared_error(y_train, y_train_pred)\n",
    "test_mse = mean_squared_error(y_test, y_test_pred)\n",
    "train_r2 = r2_score(y_train, y_train_pred)\n",
    "test_r2 = r2_score(y_test, y_test_pred)\n",
    "\n",
    "print(f\"Training MSE:\\t{train_mse:.3f}\\nTraining R^2:\\t{train_r2:.3f}\")\n",
    "print(f\"Test MSE:\\t{test_mse:.3f}\\nTest R^2:\\t{test_r2:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MSE:\t3063.610\n",
      "Training R^2:\t0.841\n",
      "Test MSE:\t5733.517\n",
      "Test R^2:\t0.887\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso(alpha=1)  # alpha is regularization strength\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = lasso.predict(X_train)\n",
    "y_test_pred = lasso.predict(X_test)\n",
    "y_train_pred = np.maximum(y_train_pred, 0) # ensure non-negative predictions\n",
    "y_test_pred = np.maximum(y_test_pred, 0)\n",
    "\n",
    "train_mse = mean_squared_error(y_train, y_train_pred)\n",
    "test_mse = mean_squared_error(y_test, y_test_pred)\n",
    "train_r2 = r2_score(y_train, y_train_pred)\n",
    "test_r2 = r2_score(y_test, y_test_pred)\n",
    "\n",
    "print(f\"Training MSE:\\t{train_mse:.3f}\\nTraining R^2:\\t{train_r2:.3f}\")\n",
    "print(f\"Test MSE:\\t{test_mse:.3f}\\nTest R^2:\\t{test_r2:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MSE:\t84.288\n",
      "Training R^2:\t0.996\n",
      "Test MSE:\t9364.792\n",
      "Test R^2:\t0.816\n"
     ]
    }
   ],
   "source": [
    "tree_model = DecisionTreeRegressor(random_state=42)\n",
    "tree_model.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = tree_model.predict(X_train)\n",
    "y_test_pred = tree_model.predict(X_test)\n",
    "\n",
    "train_r2 = r2_score(y_train, y_train_pred)\n",
    "test_r2 = r2_score(y_test, y_test_pred)\n",
    "train_mse = mean_squared_error(y_train, y_train_pred)\n",
    "test_mse = mean_squared_error(y_test, y_test_pred)\n",
    "\n",
    "print(f\"Training MSE:\\t{train_mse:.3f}\\nTraining R^2:\\t{train_r2:.3f}\")\n",
    "print(f\"Test MSE:\\t{test_mse:.3f}\\nTest R^2:\\t{test_r2:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MSE:\t881.808\n",
      "Training R^2:\t0.954\n",
      "Test MSE:\t6603.908\n",
      "Test R^2:\t0.870\n"
     ]
    }
   ],
   "source": [
    "rf_model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = rf_model.predict(X_train)\n",
    "y_test_pred = rf_model.predict(X_test)\n",
    "\n",
    "train_r2 = r2_score(y_train, y_train_pred)\n",
    "test_r2 = r2_score(y_test, y_test_pred)\n",
    "train_mse = mean_squared_error(y_train, y_train_pred)\n",
    "test_mse = mean_squared_error(y_test, y_test_pred)\n",
    "\n",
    "print(f\"Training MSE:\\t{train_mse:.3f}\\nTraining R^2:\\t{train_r2:.3f}\")\n",
    "print(f\"Test MSE:\\t{test_mse:.3f}\\nTest R^2:\\t{test_r2:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Our Best Model:\n",
    "### Ridge Regression with Processed Results Disallowing Negative Prediction Values for PRP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model Outcomes:\n",
      "\n",
      "Training MSE:\t3051.405\n",
      "Training R^2:\t0.841\n",
      "Test MSE:\t5595.763\n",
      "Test R^2:\t0.890\n"
     ]
    }
   ],
   "source": [
    "ridge_reg = Ridge(alpha=1)  # the chosen value for the hyperparameter alpha: 1\n",
    "ridge_reg.fit(X_train, y_train)\n",
    "y_train_pred = np.maximum(ridge_reg.predict(X_train), 0) # np.maximum will ensure we don't get negatives\n",
    "y_test_pred = np.maximum(ridge_reg.predict(X_test), 0)\n",
    "\n",
    "train_mse = mean_squared_error(y_train, y_train_pred)\n",
    "test_mse = mean_squared_error(y_test, y_test_pred)\n",
    "train_r2 = r2_score(y_train, y_train_pred)\n",
    "test_r2 = r2_score(y_test, y_test_pred)\n",
    "\n",
    "print(\"Final Model Outcomes:\\n\")\n",
    "print(f\"Training MSE:\\t{train_mse:.3f}\\nTraining R^2:\\t{train_r2:.3f}\")\n",
    "print(f\"Test MSE:\\t{test_mse:.3f}\\nTest R^2:\\t{test_r2:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}